diff --git a/docs/user-guide/zonal-average.ipynb b/docs/user-guide/zonal-average.ipynb index 290d073c1..4d4eb2f65 100644 --- a/docs/user-guide/zonal-average.ipynb +++ b/docs/user-guide/zonal-average.ipynb @@ -10,9 +10,23 @@ "This section demonstrates how to perform Zonal Averaging using UXarray, covering both non-conservative and conservative methods.\n" ] }, + { + "cell_type": "markdown", + "id": "191f3851", + "metadata": {}, + "source": [ + "## Notebook Roadmap\n", + "\n", + "- [1. Zonal Mean Basics](#1-zonal-mean-basics) — terminology and the two averaging flavors available in UXarray.\n", + "- [2. Non-Conservative Zonal Averaging](#2-non-conservative-zonal-averaging) — default sampling, intersection-weighted averaging,plotting, and tuning latitude spacing.\n", + "- [3. Conservative Zonal Averaging](#3-conservative-zonal-averaging) — area-weighted bands, conservation checks, and comparisons.\n", + "- [4. Combined Plots](#4-combined-plots) — pair global maps with their zonal means for context.\n", + "- [5. HEALPix Zonal Averaging (Conservative vs Non-Conservative)](#5-healpix-zonal-averaging-conservative-vs-non-conservative) — run the same workflow on a different grid.\n", + "- [6. 2D Zonal Means on NE30 (RELHUM)](#6-2d-zonal-means-on-ne30-relhum) — build latitude–height slices and inspect the differences.\n" + ] + }, { "cell_type": "code", - "execution_count": 1, "id": "185e2061bc4c75b9", "metadata": { "execution": { @@ -20,547 +34,80 @@ "iopub.status.busy": "2025-09-26T15:41:01.836130Z", "iopub.status.idle": "2025-09-26T15:41:05.102704Z", "shell.execute_reply": "2025-09-26T15:41:05.102291Z" + }, + "jupyter": { + "is_executing": true + } + }, + "source": [ + "from pathlib import Path\n", + "\n", + "import hvplot.pandas # noqa: F401 - activate pandas hvplot accessor\n", + "import hvplot.xarray # noqa: F401 - activate xarray hvplot accessor\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "import uxarray as ux\n", + "\n", + "uxds = ux.open_dataset(\n", + " \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\",\n", + " \"../../test/meshfiles/ugrid/outCSne30/outCSne30_vortex.nc\",\n", + ")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "d938d659b89bc9cb", + "metadata": {}, + "source": [ + "## 1. Zonal Mean Basics\n", + "\n", + "### Step 1.1: Understand the two averaging flavors\n", + "\n", + "A zonal average (or zonal mean) is a statistical measure that represents the average of a face-centered variable along lines of constant latitude or over latitudinal bands.\n", + "\n", + "UXarray provides two types of zonal averaging:\n", + "- **Non-conservative**: Calculates the mean by sampling face values at specific latitude lines and weighting each contribution by the length of the line where each face intersects that latitude.\n", + "- **Conservative**: Preserves integral quantities by calculating the mean by sampling face values within latitude bands and weighting contributions by their area overlap with latitude bands.\n", + "\n", + "```{seealso}\n", + "[NCL Zonal Average](https://www.ncl.ucar.edu/Applications/zonal.shtml) — NCL reference with conventional rectilinear grids.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "non_conservative_header", + "metadata": {}, + "source": [ + "## 2. Non-Conservative Zonal Averaging\n", + "\n", + "The non-conservative method samples face values at specific lines of constant latitude and weights each contribution by the length of the intersection between the face and that latitude. This is the default behavior and is suitable for visualization and general analysis where exact conservation is not required.\n", + "\n", + "### Step 2.1: Visualize the global field\n", + "\n", + "The non-conservative method samples face values at specific lines of constant latitude and calculates the average using intersection-line weights. Let's first visualize our data field and then demonstrate zonal averaging:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "global_field_plot", + "metadata": { + "execution": { + "iopub.execute_input": "2025-09-26T15:41:05.104590Z", + "iopub.status.busy": "2025-09-26T15:41:05.104455Z", + "iopub.status.idle": "2025-09-26T15:41:07.888812Z", + "shell.execute_reply": "2025-09-26T15:41:07.888366Z" } }, "outputs": [ { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = false;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === 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(OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" - }, + "data": {}, "metadata": {}, "output_type": "display_data" }, @@ -568,12 +115,12 @@ "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ - "
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\n", "
\n", "" + ], + "text/plain": [ + ":Image [x,y] (x_y psi)" ] }, + "execution_count": 34, "metadata": { "application/vnd.holoviews_exec.v0+json": { - "id": "23d4093b-bded-439e-b138-2526659bd78d" + "id": "f8248ec6-62b8-4594-a002-0b841d96ac54" } }, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the global field\n", + "uxds[\"psi\"].plot(cmap=\"inferno\", periodic_elements=\"split\", title=\"Global Field\")" + ] + }, + { + "cell_type": "markdown", + "id": "0de05a0f", + "metadata": {}, + "source": [ + "### Step 2.2: Compute the default zonal mean\n", + "\n", + "Calling `.zonal_mean()` with no arguments samples every 10° between -90° and 90° and returns one value per latitude line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d342f8f449543994", + "metadata": { + "execution": { + "iopub.execute_input": "2025-09-26T15:41:07.890787Z", + "iopub.status.busy": "2025-09-26T15:41:07.890560Z", + "iopub.status.idle": "2025-09-26T15:41:07.996723Z", + "shell.execute_reply": "2025-09-26T15:41:07.996295Z" + } + }, + "outputs": [ { "data": { "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", + "
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<xarray.DataArray 'psi_zonal_mean' (latitudes: 6)> Size: 48B\n",
-       "array([1.10364701, 1.03868616, 1.00588496, 0.99347061, 0.96125331,\n",
+       "array([1.10364701, 1.03897837, 1.00563134, 0.99327248, 0.96134106,\n",
        "       0.89634629])\n",
        "Coordinates:\n",
        "  * latitudes  (latitudes) float64 48B -75.0 -45.0 -15.0 15.0 45.0 75.0\n",
        "Attributes:\n",
        "    zonal_mean:      True\n",
        "    conservative:    True\n",
-       "    lat_band_edges:  [-90. -60. -30.   0.  30.  60.  90.]
" + " lat_band_edges: [-90. -60. -30. 0. 30. 60. 90.]
" ], "text/plain": [ " Size: 48B\n", - "array([1.10364701, 1.03868616, 1.00588496, 0.99347061, 0.96125331,\n", + "array([1.10364701, 1.03897837, 1.00563134, 0.99327248, 0.96134106,\n", " 0.89634629])\n", "Coordinates:\n", " * latitudes (latitudes) float64 48B -75.0 -45.0 -15.0 15.0 45.0 75.0\n", @@ -2436,7 +1428,7 @@ " lat_band_edges: [-90. -60. -30. 0. 30. 60. 90.]" ] }, - "execution_count": 7, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2453,14 +1445,14 @@ "id": "conservation_verification", "metadata": {}, "source": [ - "### Conservation Verification\n", + "### Step 3.2: Verify conservation\n", "\n", - "A key advantage of conservative zonal averaging is that it preserves global integrals." + "A key advantage of conservative zonal averaging is that it preserves global integrals.\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 41, "id": "conservation_test", "metadata": { "execution": { @@ -2479,14 +1471,6 @@ "Conservative full sphere: 1.000000001829\n", "Conservation error: 2.96e-10\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/_f/dc3kjt3n3ms9ntb5fcj5mplm0000gq/T/ipykernel_18893/1814533177.py:3: DeprecationWarning: zonal_mean returns an xarray.DataArray (no grid topology). Returning UxDataArray is deprecated and will be removed.\n", - " full_sphere_conservative = uxds[\"psi\"].zonal_mean(lat=[-90, 90], conservative=True)\n" - ] } ], "source": [ @@ -2505,17 +1489,17 @@ "id": "signature_behavior", "metadata": {}, "source": [ - "### Understanding the lat Parameter\n", + "### Step 3.3: Understand the `lat` parameter signature\n", "\n", "Both conservative and non-conservative modes can use the same `lat` parameter, but they interpret it differently:\n", "\n", "- **Non-conservative**: Creates sample points at the specified latitudes\n", - "- **Conservative**: Uses the latitudes as band edges, creating bands between adjacent points" + "- **Conservative**: Uses the latitudes as band edges, creating bands between adjacent points\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 42, "id": "signature_demo", "metadata": { "execution": { @@ -2531,23 +1515,13 @@ "output_type": "stream", "text": [ "Non-conservative with lat=(-90, 90, 30):\n", - "Sample points: [-90 -60 -30 0 30 60 90]\n", + "Sample points: [-90. -60. -30. 0. 30. 60. 90.]\n", "Count: 7 points\n", "\n", "Conservative with lat=(-90, 90, 30):\n", "Band centers: [-75. -45. -15. 15. 45. 75.]\n", "Count: 6 bands\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/_f/dc3kjt3n3ms9ntb5fcj5mplm0000gq/T/ipykernel_18893/162242150.py:5: DeprecationWarning: zonal_mean returns an xarray.DataArray (no grid topology). Returning UxDataArray is deprecated and will be removed.\n", - " non_cons_lines = uxds[\"psi\"].zonal_mean(lat=lat_tuple)\n", - "/var/folders/_f/dc3kjt3n3ms9ntb5fcj5mplm0000gq/T/ipykernel_18893/162242150.py:11: DeprecationWarning: zonal_mean returns an xarray.DataArray (no grid topology). Returning UxDataArray is deprecated and will be removed.\n", - " cons_bands = uxds[\"psi\"].zonal_mean(lat=lat_tuple, conservative=True)\n" - ] } ], "source": [ @@ -2572,19 +1546,19 @@ "id": "visual_comparison", "metadata": {}, "source": [ - "### Visual Comparison: Conservative vs Non-Conservative\n", + "### Step 3.4: Visual comparison (lines vs bands)\n", "\n", "The differences between methods reflect their fundamental approaches:\n", "\n", "- **Conservative**: More accurate for physical quantities because it accounts for the actual area of each face within latitude bands\n", "- **Non-conservative**: Faster but approximates by sampling at specific latitude lines\n", "\n", - "The differences you see indicate how much area-weighting matters for your specific data and grid resolution." + "The differences you see indicate how much area-weighting matters for your specific data and grid resolution.\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "id": "comparison_plot", "metadata": { "execution": { @@ -2595,17 +1569,9 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/_f/dc3kjt3n3ms9ntb5fcj5mplm0000gq/T/ipykernel_18893/523619492.py:3: DeprecationWarning: zonal_mean returns an xarray.DataArray (no grid topology). Returning UxDataArray is deprecated and will be removed.\n", - " non_conservative_comparison = uxds[\"psi\"].zonal_mean(lat=band_centers)\n" - ] - }, { "data": { - "image/png": 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", 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3xJ/h+H6+Fi5ciI+PD3Xq1LG7z7Jly5IhQ4Y4hzM/zCeffMK6desYPHgw1atXj3V+1apVZMuWjfbt29vKXF1d6dWrF2FhYbGmlrRt25aMGTPaHsf8brr/91FS/PzPmTOHcuXKUaBAAcD8nDZq1MhuuHmLFi1wcXGxe3/379/PwYMH7dp24cKFVKlShYwZM9q9v7Vr1yY6OprNmzfbXbtly5a2KQUx4vv5WLNmDa6urnTv3t1W5uTkxFtvvRXve69duzb58+e3PS5ZsiTe3t629zw6OppffvmFZs2akSNHDlu9AgUK0KBBg3hfR0TSDiXeIpImeXt7A3Dz5s141T99+jROTk62/0DGyJYtG76+vpw+fdquPHfu3LFeI2PGjHZzPUeOHMn169cpVKgQQUFBDBgwgL1799rOHz9+HMMwGDJkCH5+fnbHsGHDAHOe5/3y5s0b67r169fHx8fH7j+28+fPp3Tp0hQqVMhWFhISQu/evcmaNSuenp74+fnZXu/GjRuPfY/iUqpUKYoUKRLr2lmyZLElXpcvX+b69etMmzYt1n3GzOt98D4f1LZtW7Zt22b7ImLTpk0EBwfb/ce9R48eFCpUiAYNGpArVy66du0a55zLx6latSo1atR46FzvmM9C4cKFY50rWrQoV65cITw83K78wc9LTJIU83kJCQnh4sWLtiOmPRLyOU6KuNq2bUvlypV57bXXyJo1K+3atWPBggV2SfixY8c4cOBArLaN+ezF5zPs4uJCy5YtWb58uW1e+JIlS7h7926sLapmzZpFyZIlbesm+Pn5sXLlyif+DMfc5+M+X8eOHePGjRv4+/vHutewsLDHfoZjbNu2jWHDhlGlShXbz/mDTp8+TcGCBXFysv9vWtGiRW3n7/e4doTE//m/fv06q1atolq1ahw/ftx2VK5cmZ07d9q2ucuSJQu1atViwYIFtufOnz8fFxcXWrRoYSs7duwYa9asifXe1q5dG4jf5wji9/k4ffo02bNnt9u9AIj1+/9RHvdvQHBwMLdv347zNRNyHRFJO7RMo4ikSd7e3uTIkcO2sFh8xXfBmwd7nWMY9+3QWLVqVU6cOMHy5ctZu3YtM2bM4PPPP2fKlCm89tprtuSlf//+1KtXL87Xe/A/aPf3WsVwd3enWbNmLF26lK+++opLly6xbds2Pv74Y7t6bdq0Yfv27QwYMIDSpUuTIUMGrFYr9evXf2hvZny0bduWjz76iCtXruDl5cWKFSto3769bSXgmNd+5ZVXHrraccmSJR97jUGDBrFw4ULeeecdFixYgI+PD/Xr17fV8ff3Z/fu3fz888+sXr2a1atX891339GxY8c4F4B7lGHDhlG9enWmTp1q6/V9Go/7vLRo0cKuF7NTp07MnDmTIkWKALBv3z67ERaJ5XFxeXp6snnzZjZu3MjKlStZs2YN8+fPp2bNmqxduxZnZ2esVitBQUF89tlncb5WQECA3eO4PsMA7dq1Y+rUqaxevZpmzZqxYMECihQpYrfS9//+9z86d+5Ms2bNGDBgAP7+/jg7OzN69GhOnDjxJG8BEL/Pl9Vqxd/f3643934P9r7GJSQkhPbt2+Pt7c3cuXMf+v4nVHx+HyX2z//ChQuJiIhg/PjxjB8/Ptb5OXPm2BZVa9euHV26dGH37t2ULl2aBQsWUKtWLbJkyWKrb7VaqVOnDgMHDozzevd/iQhxf46S6vMRl/i85yIi91PiLSJpVuPGjZk2bRo7duygUqVKj6ybJ08erFYrx44ds/Uqgbkq7/Xr18mTJ88TxRCzWnOXLl0ICwujatWqDB8+nNdee418+fIB5hDSmF6dJ9W2bVtmzZrF+vXrOXToEIZh2PXWXbt2jfXr1zNixAiGDh1qK49rGHxCV9tt27YtI0aMYPHixWTNmpXQ0FDatWtnO+/n54eXlxfR0dFPfJ958+alQoUKzJ8/n549e7JkyRKaNWsWa3szNzc3mjRpQpMmTbBarfTo0YOpU6cyZMiQBPUyVatWjerVqzNmzBi79wuwfRaOHDkS63mHDx8mS5YsCd7mbfz48Xa9kzFDU5s0acLo0aP53//+99jEOyniAnMIbq1atahVqxafffYZH3/8MYMHD2bjxo224bZ79uyhVq1aT7VSc9WqVcmePTvz58/nxRdfZMOGDbG2Slu0aBH58uVjyZIldtd6sOc4oXHE5/OVP39+fvnlFypXrvzQLw8ep3Pnzvz7778sX76cXLlyPbRenjx52Lt3L1ar1a7X+/Dhw7bzCZGQn//4mjNnDiVKlIiz137q1KnMnTvXlng3a9aM119/3TYy5ujRowwaNMjuOfnz5ycsLOypfhfG9/ORJ08eNm7cyK1bt+x6vY8fP/7E136Qv78/Hh4ecb5mYl5HRFIPDTUXkTRr4MCBpE+fntdee41Lly7FOn/ixAnbVlMNGzYEYMKECXZ1YnrxGjVqlODrP7hNT4YMGShQoIBtKK2/v7+tV/XChQuxnn/58uV4X6t27dpkypSJ+fPnM3/+fCpUqGA3FDOmd+bB3pgH7xewJWfXr1+P17WLFi1KUFCQ7drZs2enatWqdtdu2bIlixcvjnMEQnzvs23btvz22298++23XLlyJdYQ5AffbycnJ1tP+oPbWsVHzFzvadOm2ZVnz56d0qVLM2vWLLv3aP/+/axdu9b2WUqIsmXLUrt2bdsRMwe6UqVK1K9fnxkzZrBs2bJYz4uMjLRttZUUcYWEhMQqK126NHDvPW3Tpg3nzp1j+vTpserevn071vD2h3FycqJVq1b8+OOPzJ49m6ioqFhtHNfn+Pfff2fHjh129WKSqfh+huHxn682bdoQHR3NqFGjYj03KirqsdeaMGECP/74I2+//TYvvfTSI+s2bNiQixcv2k3hiIqK4ssvvyRDhgxUq1Yt3vcFCfv5j49///2XzZs306ZNG1q1ahXr6NKlC8ePH+f3338HzLUC6tWrx4IFC5g3bx5ubm40a9bM7jXbtGnDjh07+Pnnn2Nd7/r160RFRT3Rfcb1+ahXrx537961+8xarVYmT54c7/cgPrHUrl2bZcuWcf78eVv58ePHWb16daJdR0RSD/V4i0ialT9/fubOnUvbtm0pWrQoHTt2pESJEkRGRrJ9+3YWLlxo25O6VKlSdOrUiWnTpnH9+nWqVavGH3/8waxZs2jWrFmc+/w+TrFixahevTply5YlU6ZM7Ny5k0WLFtGzZ09bncmTJ/Piiy8SFBRE9+7dyZcvH5cuXWLHjh2cPXuWPXv2xOtarq6utGjRgnnz5hEeHh5rb2dvb2+qVq3K2LFjuXv3Ljlz5mTt2rWcPHky1muVLVsWgMGDB9OuXTtcXV1p0qTJI3tL27Zty9ChQ/Hw8KBbt26x5qZ+8sknbNy4kYoVK9K9e3eKFStGSEgIf//9N7/88kucCd6D2rRpQ//+/enfvz+ZMmWK1TP22muvERISQs2aNcmVKxenT5/myy+/pHTp0najGOKrWrVqVKtWLc490j/99FMaNGhApUqV6NatG7dv3+bLL7/Ex8fHtl90Yvn++++pW7cuLVq0oEmTJtSqVYv06dNz7Ngx5s2bx4ULF2ztndhxjRw5ks2bN9OoUSPy5MlDcHAwX331Fbly5eLFF18E4NVXX2XBggW88cYbbNy4kcqVKxMdHc3hw4dZsGABP//8M+XKlYvX9dq2bcuXX37JsGHDCAoKitVujRs3ZsmSJTRv3pxGjRpx8uRJpkyZQrFixQgLC7PV8/T0pFixYsyfP59ChQqRKVMmSpQoQYkSJR567cd9vqpVq8brr7/O6NGj2b17N3Xr1sXV1ZVjx46xcOFCJk6caLfn9/327t3Lu+++S4YMGShVqhT/+9//4qxXsmRJSpYsyf/93/8xdepUOnfuzF9//UVgYCCLFi1i27ZtTJgwId6LRsZIyM9/fMydOxfDMB76BULDhg1xcXFhzpw5VKxYETDb9pVXXuGrr76iXr16saZwDBgwgBUrVtC4cWM6d+5M2bJlCQ8PZ9++fSxatIhTp07ZDU2PS3w/H82aNaNChQr069eP48ePU6RIEVasWGH7PZRYe2wPHz6ctWvXUrlyZd58802io6OZNGkSJUqUYPfu3YlyDRFJRVJiKXURkeR09OhRo3v37kZgYKDh5uZmeHl5GZUrVza+/PJLu2287t69a4wYMcLImzev4erqagQEBBiDBg2yq2MY5pZacW0TVq1aNaNatWq2xx9++KFRoUIFw9fX1/D09DSKFClifPTRR7H2+z1x4oTRsWNHI1u2bIarq6uRM2dOo3HjxsaiRYtsdeLa0upB69atMwDDYrEY//77b6zzZ8+eNZo3b274+voaPj4+RuvWrY3z58/Hue3SqFGjjJw5cxpOTk52Wws9uJ1YjGPHjhmAARhbt26NM75Lly4Zb731lhEQEGC4uroa2bJlM2rVqmVMmzbtoff0oMqVK8e57ZthmFsb1a1b1/D39zfc3NyM3LlzG6+//nqs/Znjwn3bid1v48aNtvt68L3/5ZdfjMqVKxuenp6Gt7e30aRJE+PgwYN2dWK2InpwO66EbNlkGOZWUOPGjTPKly9vZMiQwXBzczMKFixovP3223ZbGiV2XOvXrzeaNm1q5MiRw3BzczNy5MhhtG/fPtaWT5GRkcaYMWOM4sWLG+7u7kbGjBmNsmXLGiNGjDBu3Lhhq/ew9zmG1Wo1AgICDMD48MMP4zz/8ccfG3ny5DHc3d2NMmXKGD/99JPRqVMnI0+ePHZ1t2/fbpQtW9Zwc3Oz+4zHtT1UjEd9vmJMmzbNKFu2rOHp6Wl4eXkZQUFBxsCBA43z588/9Dkx7+vjjvt/Di9dumR06dLFyJIli+Hm5mYEBQXZbXNlGPe21vr0009jXfPB14vvz398PptBQUFG7ty5H3reMAyjevXqhr+/v3H37l3DMMytxjw9PWNtk3a/mzdvGoMGDTIKFChguLm5GVmyZDFeeOEFY9y4cbbfm4+654R8Pi5fvmy8/PLLhpeXl+Hj42N07tzZ2LZtmwEY8+bNs9V72HZicX2O4/r9uH79eqNMmTKGm5ubkT9/fmPGjBlGv379DA8Pj0e+fyKS9lgMQ6tAiIiIiMizbdmyZTRv3pytW7dSuXLlJLtOs2bNHrnVpIikTZrjLSIiIiLPlAe3CoyOjubLL7/E29ub5557Lsmuc+zYMVatWhXn/u0ikrZpjreIiIiIPFPefvttbt++TaVKlYiIiGDJkiVs376djz/++IlXrY9Lvnz56Ny5M/ny5eP06dN8/fXXuLm5PXTbNBFJuzTUXERERESeKXPnzmX8+PEcP36cO3fuUKBAAd588027xS8TQ5cuXdi4cSMXL17E3d2dSpUq8fHHHydqr7qIpA5KvEVERERERESSkOZ4i4iIiIiIiCQhJd4iIiIiIiIiSUiLq8XBarVy/vx5vLy8sFgsKR2OiIiIiIiIOBjDMLh58yY5cuTAyenRfdpKvONw/vx5AgICUjoMERERERERcXD//vsvuXLlemQdJd5x8PLyAsw30NvbO4WjeTpWq5XLly/j5+f32G9hJHmoTRyP2sTxqE0cj9rE8ahNHI/axPGoTRxPWmqT0NBQAgICbPnjoyjxjkPM8HJvb+80kXjfuXMHb2/vVP/BTivUJo5HbeJ41CaOR23ieNQmjkdt4njUJo4nLbZJfKYnp407FREREREREXFQSrxFREREREREkpASbxEREREREZEkpDneIiIiIiKpWHR0NHfv3k3pMByS1Wrl7t273LlzJ83MJ07tUlObuLq64uzsnCivpcRbRERERCQVMgyDixcvcv369ZQOxWEZhoHVauXmzZvxWgBLkl5qaxNfX1+yZcv21LEq8RYRERERSYVikm5/f3/SpUuXKpKY5GYYBlFRUbi4uOj9cRCppU0Mw+DWrVsEBwcDkD179qd6PSXeIiIiIiKpTHR0tC3pzpw5c0qH47BSS5L3LElNbeLp6QlAcHAw/v7+TzXs3LEH1YuIiIiISCwxc7rTpUuXwpGIpG0xP2NPu46CEm8RERERkVTK0XsMRVK7xPoZU+ItIiIiIiIikoSUeIuIiIiIPIvu3IHZs6FlS6he3fxz9myzXJKdxWJh2bJlSX6d9evXU7RoUaKjowEYPnw4pUuXTvLrxmXTpk1YLJZ4rcx/8OBBcuXKRXh4eNIHlgSUeIuIiIiIPGtWrIAcOaBjR1i2DH791fyzY0ez/Mcfk+zSFy9e5O233yZfvny4u7sTEBBAkyZNWL9+fZJd05E8LNG9cOECDRo0SPLrDxw4kA8++CDR9qdOLsWKFeP555/ns88+S+lQnogSbxERERGRZ8mKFdCsGcT0Mlqt9n9evw5Nm5r1EtmpU6coW7YsGzZs4NNPP2Xfvn2sWbOGGjVq8NZbbyX69RLT0y6u9TjZsmXD3d09Sa+xdetWTpw4QcuWLZP0OkmlS5cufP3110RFRaV0KAmmxFtEREREJDWzWuHy5fgd//5r9moDGEbcrxdT3rGjWT8+rxuTtD9Gjx49sFgs/PHHH7Rs2ZJChQpRvHhx+vbty2+//Ward+bMGZo2bUqGDBnw9vamTZs2XLp0yXY+ptd49uzZBAYG4uPjQ7t27bh586atzqJFiyhZsiTe3t5kyZKF2rVr2w1TnjFjBkWLFsXDw4MiRYrw1Vdf2c6dOnUKi8XC/PnzqVatGh4eHnz99dd4enqyevVqu3taunQpXl5e3Lp1C4B3332XQoUKkS5dOvLly8eQIUNsSfvMmTMZMWIEe/bswWKxYLFYmDlzJmA/1PyFF17g3XfftbvO5cuXcXV1ZfPmzQBERETQv39/cubMSfr06alYsSKbNm165Ps/b9486tSpg4eHR6xzU6dOJSAggHTp0tGmTRtu3LhhO/fnn39Sp04dsmTJgo+PD9WqVePvv/+2e77FYmHGjBk0b96cdOnSUbBgQVY88OXNqlWrKFy4MN7e3tSsWZNTp07ZnT99+jRNmjQhY8aMpE+fnuLFi7Nq1Srb+Tp16hASEsKvv/76yPt0REq85clpXpCIiIhIyrt6Ffz943fkzg03bjw86Y5hGGa93Lnj97pXrz42zJCQENasWcNbb71F+vTpY5339fUFwGq10rRpU1uCtW7dOv755x/atm1rV//EiRMsW7aMn376iZ9++olff/2VTz75BDCHbbdv354uXbqwd+9eNm7cSIsWLTD+u+85c+YwdOhQPvroIw4dOsTHH3/MkCFDmDVrlt013nvvPXr37s2hQ4do3bo1jRs3Zu7cuXZ15syZQ7NmzWzbTnl5eTFz5kwOHjzIxIkTmT59Op9//jkAbdu2pV+/fhQvXpwLFy5w4cKFWPcF0KFDB+bNm2eLF2D+/PnkyJGDKlWqANCzZ0927NjBvHnz2Lt3L61bt6Z+/focO3bsoW2wZcsWypUrF6v8+PHjLFiwgB9//JE1a9awa9cuevToYTt/8+ZNOnXqxNatW/ntt98oWLAgDRs2tPuiA2DEiBG0adOGvXv30rBhQzp06EBISAgA//77Ly1atKBx48b8+eefdOvWjffee8/u+W+99RYRERFs3ryZffv2MWbMGDJkyGA77+bmRunSpdmyZctD79FhGRLLjRs3DMC4ceNGSofy1KKjo40LFy4Y0dHRifvCy5cbRsaMhgGG4eRk/2fGjIaxYkXiXi8NSbI2kSemNnE8ahPHozZxPGoTx5OcbXL79m3j4MGDxu3btw0jONj8P1hKHsHBj435999/NwBjyZIlj6y3du1aw9nZ2Thz5oyt7MCBAwZg/PHHH4ZhGMawYcOMdOnSGaGhobY6AwYMMCpWrGgYhmH89ddfBmCcPHnSiIyMNKxWq9018ufPb8ydO9eubNSoUUalSpUMwzCMkydPGoAxYcIEuzpLly41MmTIYISHhxuGYeYNHh4exurVqx96P59++qlRtmxZ2+Nhw4YZpUqVilUPMJYuXWoYhmEEBwcbLi4uxubNm23nK1WqZLz77ruGYRjG6dOnDWdnZ+PcuXN2r1GrVi1j0KBBD43Fx8fH+P777+3Khg0bZjg7Oxtnz561la1evdpwcnIyLly4EOfrREdHG15eXsaPP/5oF/8HH3xgexwWFmYAtvdm0KBBRrFixQyr1Wprk3fffdcAjGvXrhmGYRhBQUHG8OHDHxq/YRhG8+bNjc6dOz+yTmKy+1l7QELyRvV4S8Kl4LwgEREREUmdjMf1sv/n0KFDBAQEEBAQYCsrVqwYvr6+HDp0yFYWGBiIl5eX7XH27NkJDg4GoFSpUtSqVYuSJUvSrl07pk+fzrVr1wAIDw/nxIkTdOvWjQwZMtiODz/8kBMnTtjF8mDvcMOGDXF1dbUNoV68eDHe3t7Url3bVmf+/PlUrlyZbNmykSFDBj744APOnDkTr3uP4efnR926dZkzZw4AJ0+eZMeOHXTo0AGAffv2ER0dTaFChezu4ddff411D/e7fft2nMPMc+fOTc6cOW2PK1WqhNVq5ciRIwBcunSJ7t27U7BgQXx8fPD29iYsLCzWfZUsWdL29/Tp0+Pt7W1rk0OHDlGxYkW7+pUqVbJ73KtXLz788EMqV67MsGHD2Lt3b6xYPT09bcP6UxMl3pIwd+5A587m3x83L6hzZw07FxEREREAChYsiMVi4fDhw4nyeq6urnaPLRYL1v86gpydnVm3bh2rVq2iaNGiTJo0icKFC3Py5EnCwsIAmD59Ort377Yd+/fvt5tnDsQaEu/m5karVq1sw83nzp1L27ZtcXFxAbAlxw0bNuSnn35i165dDB48mMjIyATfX4cOHVi0aBF3795l7ty5BAUFERQUBEBYWBjOzs789ddfdvdw6NAhJk6c+NDXzJIli+0LiITo1KkTu3fvZuLEiWzfvp3du3eTOXPmWPf1qDaJj9dee41//vmHV199lX379lGuXDm+/PJLuzohISH4+fkl+B5SmhJvSZiFC+HatfjNC7p2DRYtSp64RERERJ5VmTNDcHD8jkmTEvbakyfH73UzZ37sS2XKlIl69eoxefLkOPdijtnLuWjRovz777/8+++/tnMHDx7k+vXrFCtWLN6hWywWW8/p33//jZubG0uXLiVr1qzkyJGDf/75hwIFCtgdefPmfezrdujQgTVr1nDgwAE2bNhg64UG2L59O3ny5GHw4MGUK1eOggULcvr0abvnu7m52fbQfpSmTZty584d1qxZw9y5c+2uU6ZMGaKjowkODo51D9myZXvoa5YpU4aDBw/GKj9z5gznz5+3Pf7tt99wcnKicOHCAGzbto1evXrRsGFDihcvjru7O1euXHnsPdyvaNGi/PHHH3ZlD37RARAQEMAbb7zBkiVL6NevH9OnT7c7v3//fsqUKZOgazsCl5QOQFKZZcvAySl+K1c6OcHSpfDKK0keloiIiMgzy8kJ4tsD2K0bDBliTg18VEeKxQK+vtC1K8QxNPlJTZ48mcqVK1OhQgVGjhxJyZIliYqKYt26dXz99dccOnSI2rVrExQURIcOHZgwYQJRUVH06NGDatWqxbkwWFx+//131q9fT506dciUKRN//fUXly9fpmjRooC5CFivXr3w8fGhfv36REREsHPnTq5du0bfvn0f+dpVq1YlW7ZsdOjQgbx589oNny5YsCBnzpxh3rx5lC9fnpUrV7J06VK75wcGBnLy5El2795Nrly58PLyinMbsfTp09OsWTOGDBnCoUOHaN++ve1coUKF6NChAx07dmT8+PGUKVOGy5cvs379ekqWLEmjRo3ijL1evXqxFpAD8PDwoFOnTowbN47Q0FB69epFmzZtbEl8wYIFmT17NuXKlSM0NJQBAwbg6en5yPfpQW+88Qbjx49nwIABdO7cmT179thWdI/xzjvv0KBBAwoVKsS1a9fYuHGjrc3AXG3+3LlzdkP7Uwv1eEvCXL0a7+0isFrhv1UMRURERMQBeHhATOJlscRdJ6Z81qxETboB8uXLx99//02NGjXo168fJUqUoE6dOqxfv56vv/76v8tbWL58ORkzZqRq1arUrl2bfPnyMX/+/Hhfx9vbm82bN9OoUSOKFy/OkCFDGD9+PA0aNADMIc0zZszgu+++IygoiGrVqjFz5sx49XhbLBbat2/Pnj177HqhAV566SX69OlDz549KV26NNu3b2fIkCF2dVq2bEn9+vWpUaMGfn5+/PDDDw+9VocOHdizZw9VqlQhd+7cdue+++47OnbsSL9+/ShcuDDNmjXjzz//jFXvwdc7cOCAbe52jAIFCtCiRQsaNmxI3bp1KVmypN32at988w3Xrl3jueee49VXX6VXr174+/s/9r26X+7cuVm8eDHLly+nXLlyTJ06lY8//tiuTnR0NG+99RZFixalfv36FCpUyC6OH374gbp165InT54EXdsRWIz4rnLwDAkNDcXHx4cbN27g7e2d0uE8FavVSnBwMP7+/jg5JcL3LC1bmr3e8e3xbtYMFi9++uumIYneJvLU1CaOR23ieNQmjkdt4niSs03u3LnDyZMnyZs3b5yLZT3WihXmejzXrt0bzRjzZ8aMZtLdpEmix53cDMMgKioKFxcXLA/7ouEZM2DAAEJDQ5k6dWqKXP9J2yQyMpKCBQsyd+5cKleunIQR2nvUz1pC8kb9lpaEadYsYT3ezZsnaTgiIiIi8gReegnOn4fZs83/31Wvbv45e7ZZngaSbonb4MGDyZMnT4IWPXMEZ86c4f3330/WpDsxaY63JEzr1tC79+PnBcU4dy7JQxIRERGRJ+DhYa7Fo/V4nim+vr68//77KR1GgsUsHpdapWiP9+bNm2nSpAk5cuTAYrGwbNmyR9a/cOECL7/8MoUKFcLJyYl33nknznoLFy6kSJEieHh4EBQUxKpVqxI/+GdVfOYF3e+992DYsPgl6SIiIiIiImlQiibe4eHhlCpVismTJ8erfkREBH5+fnzwwQeUKlUqzjrbt2+nffv2dOvWjV27dtGsWTOaNWvG/v37EzP0Z9rl55tw4uu1nPAqzQnyccJSwP7P/47LZDGfMHIk9O2r5FtERERERJ5JKTrUvEGDBraVBeMjMDDQtiH8t99+G2ediRMnUr9+fQYMGADAqFGjWLduHZMmTWLKlClPH/Qz7vJlePlluHq1NuTdaQ45v34doqPA2cXcdiIyEi6cJzNXmEsH/LgCEyZAaChMmwbOzil7EyIiIiIiIskozc3x3rFjR6y99+rVq/fIYewRERFERETYHoeGhgLmypSpbdGBB1mtVgzDSLT7uH4drlyx4OEBHh4Wc9VLMsaqd8cdrp6CULzNxBvg228xQkMxZs8GN7dEiSc1Suw2kaenNnE8ahPHozZxPGoTx5OcbRJzrZhDHi7m/dH75DhSU5vE/IzFlRsm5Gc9zSXeFy9eJGvWrHZlWbNm5eLFiw99zujRoxkxYkSs8suXL3Pnzp1EjzE5Wa1Wbty4gWEYibKtxdWrzkRF+eLsbMXV9eE/KHdzZiQ6yhnjvDPc93m0LFpEZEgI12bMAE/Pp44nNUrsNpGnpzZxPGoTx6M2cTxqE8eTnG1y9+5drFYrUVFRREVFJem1UjPDMIiOjgbQdmIOIrW1SVRUFFarlatXr+Lq6mp37ubNm/F+nTSXeD+JQYMG2fWSh4aGEhAQgJ+fX5rYx9tiseDn55co/wDcvAkuLhZcXR/daX33LjhlyYLx/iSMPi9huW9EgfuGDWTt0gVj2TJI5e/vk0jsNpGnpzZxPGoTx6M2cTxqE8eTnG1y584dbt68iYuLCy4u+i/94zyYMEnKSy1t4uLigpOTE5kzZ461j/eDjx/5OokdWErLli0bly5dsiu7dOkS2bJle+hz3N3dcXd3j1Xu5OSUJv4hs1gsiXYvTk7mYuYxR4zQUHBxgXTpYq5pHk5162JZuRKaNoXw8Hsx/forljp1YM0ayJz5qeNKbRKzTSRxqE0cj9rE8ahNHI/axPEkV5s4OTlhsVhsh8TNMAzb+6P3yTGktjaJ+RmL6+c6IT/nae63dKVKlVi/fr1d2bp166hUqVIKRZT23b4Nx47BoUNw7VocFWrVgnXrzIXX7rdzJ1SrBhcuJEeYIiIiIoK5WO6JE48/Ll9O6UglOXTu3JlmzZol+XUiIyMpUKAA27dvB+DUqVNYLBZ2794NwKZNm7BYLFy/fj3JY4mJJzAwkJ07dybL9VK0xzssLIzjx4/bHp88eZLdu3eTKVMmcufOzaBBgzh37hzff/+9rU5Mw4SFhXH58mV2796Nm5sbxYoVA6B3795Uq1aN8ePH06hRI+bNm8fOnTuZNm1ast7bs+T8efhvmgbHj0POnODj80ClSpVg0yaoWxeCg++VHzgAVarAL79AYGAyRSwiIiLybLq3Q83j62bODHPngp9f4l2/c+fOzJo1i9GjR/Pee+/ZypctW0bz5s1TxWJbqdWpU6fImzcvu3btonTp0rbyiRMnJsv7PmXKFPLmzcsLL7wQ57oEL7zwAhcuXMAnViKRNNzc3Ojfvz/vvvturI7bpJCiPd47d+6kTJkylClTBoC+fftSpkwZhg4dCsCFCxc4c+aM3XNi6v/111/MnTuXMmXK0LBhQ9v5F154gblz5zJt2jRKlSrFokWLWLZsGSVKlEi+G3vG5M17b7S4YcDZs3DmTBzbdpcqBVu2QECAffmJE/Dii3D4cLLEKyIiIvKsCg01k253d3Mw4sMOd3ez3n+b/SQqDw8PxowZw7U4h0rKgyIjI5P09X18fPB9cGRqIjMMg0mTJtGtW7eH1nFzcyNbtmzJOvy8Q4cObN26lQMHDiT5tVI08a5evbrdNggxx8yZMwGYOXMmmzZtsntOXPVPnTplV6d169YcOXKEiIgI9u/fb5eYS+JzcoL8+SFXrntl167BuXPm9mN2ChWCrVuhQAH78nPnoGpV2LUrqcMVEREReeZ5ekL69A8/knLzmdq1a5MtWzZGjx79yHqLFy+mePHiuLu7ExgYyPjx4+3OBwYG8vHHH9O1a1e8vLzInTt3vEa5HjhwgMaNG+Pt7Y2XlxdVqlThxIkTgLlA3siRI8mVKxfu7u6ULl2aNWvW2J4bMzx6yZIl1KhRg3Tp0lGqVCl27Nhhq3P69GmaNGlCxowZSZ8+PcWLF2fVqlW28/v376dBgwZkyJCBrFmz8uqrr3LlyhXb+erVq9OzZ0/eeecdsmTJQr169Xj55Zdp27at3X3cvXuXLFmy2EYHr1mzhhdffBFfX18yZ85M48aNbfcFkDdvXsDsyLRYLFSvXh2wH2o+bdo0cuTIEWubrKZNm9K1a1fb4+XLl/Pcc8/h4eFBvnz5GDFixCNX1//rr784ceIEjRo1emidB4eaz5w5E19fX37++WeKFi1KhgwZqF+/PhcemKY6Y8YMihYtioeHB0WKFOGrr76ynYuMjKRnz55kz54dDw8P8uTJY/e5y5gxI5UrV2bevHkPjSuxpLk53pI8bt8210q7//DxMZNvq9U8IiLgo4/M4ed2cuc2e76DguzLL1+GGjVg27Zkuw8RERERSV7Ozs58/PHHfPnll5w9ezbOOn/99Rdt2rShXbt27Nu3j+HDhzNkyBBbB12M8ePHU65cOXbt2kWPHj148803OXLkyEOvfe7cOapWrYq7uzsbNmzgr7/+omvXrrakceLEiYwfP55x48axd+9e6tWrx0svvcSxY8fsXmfw4MH079+f3bt3U6hQIdq3b297jbfeeouIiAg2b97Mvn37GDNmDBkyZADg+vXr1KxZkzJlyrBz507WrFnDpUuXaNOmjd3rz5o1Czc3N7Zt28aUKVPo0KEDP/74I2FhYbY6P//8M7du3aJ58+YAhIeH07dvX3bu3Mn69etxcnKiefPmtiT6jz/+AOCXX37hwoULLFmyJNb707p1a65evcrGjRttZSEhIaxZs4YOHToAsGXLFjp27Ejv3r05ePAgU6dOZebMmXz00UcPfd+3bNlCoUKF8PLyemiduNy6dYtx48Yxe/ZsNm/ezJkzZ+jfv7/t/Jw5cxg6dCgfffQRhw4d4uOPP2bIkCHMmjULgC+++IIVK1awYMECjhw5wpw5cwh8YHprhQoV2LJlS4LiehJpblVzSVre3uaw8qtXzcQ6Lv7+cPGiucp5WBi8+y706wcvvHBfpWzZzDnfDRrAf78EALhxw5wHvmwZ1KmThHciIiIikvYsW2YeDxMaCqdPm6MTH5yJefQo3Lpl/j0qCiIjYcCA2Lu/NmtmHk+jefPmlC5dmmHDhvHNN9/EOv/ZZ59Rq1YthgwZAkChQoU4ePAgn376KZ07d7bVa9iwIT169ADg3Xff5fPPP2fjxo0ULlw4zutOnjwZHx8f5s2bZ9vOqlChQrbz48aN491336Vdu3YAjBkzho0bNzJhwgQmT55sq9e/f39b7+2IESMoXrw4x48fp0iRIpw5c4aWLVsS9F8nU758+WzPmzRpEmXKlOHjjz+2lX377bcEBARw9OhRWywFCxZk7Nixtjr58+cnffr0LF26lFdffRWAuXPn8tJLL9mS2ZYtW9rd67fffoufnx8HDx6kRIkS+P03WT9z5swP3fEpY8aMNGjQgLlz51KrVi0AFi1aRJYsWahRo4btft977z06depku79Ro0YxcOBAhg0bFufrnj59mhw5csR57lHu3r3LlClTyJ8/PwA9e/Zk5MiRtvPDhg1j/PjxtGjRAjB79WO+DOjUqRNnzpyhYMGCvPjii1gsFvLkyRPrGjly5OD06dMJji2hlHhLgvj5mYtsPG6+T2gofPON+Yv97l1znlAsmTKZi6o1bQr3favGrVvQuDHMn//0v9VFREREniG3bj164bTwcDOpjlkY934xyXbM36OizGmDd+/GvkZiGDNmDDVr1rTrwYxx6NAhmjZtaldWuXJlJkyYQHR0NM7OzgCULFnSdt5isZAtWzaC/1vIt0GDBraezDx58nDgwAF2795NlSpV4txDOjQ0lPPnz1O5cuVY192zZ49d2f3XzZ49OwDBwcEUKVKEXr168eabb7J27Vpq165Ny5YtbfX37NnDxo0bbT3g9ztx4oQt8S5btqzdORcXF9q0acOcOXN49dVXCQ8PZ/ny5XZDpI8dO8bQoUP5/fffuXLliq2n+8yZMwla76pDhw50796dr776Cnd3d+bMmUO7du1sW2ft2bOHbdu22fVwR0dHc+fOHW7dukW6mP2F73P79u0E7XkdI126dLakG8z3OqZ9w8PDOXHiBN26daN79+62OlFRUbYF2jp37kydOnUoXLgw9evXp3HjxtStW9fuGp6entxKrA/1IyjxlgTz84vf6paffw6TJ0OePPDA7457vLxg5Upo0wZ++uleeWQktGoFM2fCK68kRtgiIiIiaV66dPcWvY2Lq6s5KvG/vNWOiwu4uZl/j9me2Nc3do93HHnVE6latSr16tVj0KBBdr3YCfFgAm2xWGwJ54wZM7h16xZRUVF4/jdp3TORJq/ff92YxcBirvvaa69Rr149Vq5cydq1axk9ejTjx4/n7bffJiwsjCZNmjBmzJhYrxmTwAOkT58+1vkOHTpQrVo1goODWbduHZ6entSvX992vkmTJuTJk4fp06fb5mmXKFEiwYuzNWnSBMMwWLlyJeXLl2fLli18/vnntvNhYWGMGDHC1st8v4cl11myZGHfvn0JigPibt+YFdhjht1Pnz6dihUr2tWL+WLmueee4+TJk6xevZpffvmFNm3aULt2bRYtWmSrGxISYhsNkJSUeEuScXWF3r1jlxuGOQTdNsXD0xOWLIGOHeH+hQ2io+HVV83u8/+GEImIiIjIwz1uGPiJE9C6tZlQP+i+EdeEh5u93Z9+ai6im1Q++eQTSpcuHWtoeNGiRdn2wLo/27Zto1ChQrak6nFy5syJYRhERUXh4mKmPSVLlmTWrFncvXs3VlLn7e1Njhw52LZtG9WqVbO7boUKFRJ0XwEBAbzxxhu88cYbDBo0iOnTp/P222/z3HPPsXjxYgIDA20xxdcLL7xAQEAA8+fPZ/Xq1bRu3dp2D1evXuXIkSNMnz6dKlWqALB161a757v9961KdFzDHe7j4eFBixYtmDNnDsePH6dw4cI899xztvPPPfccR44cocCDiyU/QpkyZfj6668TdduyrFmzkiNHDv755x/b/PO4eHt707ZtW9q2bUurVq2oX78+ISEhZMqUCTAXu4vZZSspKfGWJBXXbgArVsCiRfDBB2D7HevqCv/7H2TIADNm2D/hrbfM5Pu+vR5FREREJPULCgqiQ4cOfPHFF3bl/fr1o3z58owaNYq2bduyY8cOJk2aZLdi9ZPo2bMnX375Je3atWPQoEH4+Pjw22+/UaFCBQoXLsyAAQMYNmwY+fPnp3Tp0nz33Xfs3r2bOXPmxPsa77zzDg0aNKBQoUJcu3aNjRs3UrRoUcBceG369Om0b9+egQMHkilTJo4fP868efOYMWPGY79UePnll5kyZQpHjx61WwAtY8aMZM6cmWnTppE9e3bOnDljt086gL+/P56enqxZs4ZcuXLh4eHx0D2zO3ToQOPGjTlw4ACvPDD6dOjQoTRu3JjcuXPTqlUrnJyc2LNnD/v37+fDDz+M8/Vq1KhBWFgYBw4coHjx4o99D+NrxIgR9OrVCx8fH+rXr09ERAQ7d+7k2rVr9O3bl88++4zs2bNTpkwZnJycWLhwIdmyZbPbPm3Lli2MGjUq0WJ6GK1qLslq925z7vf16zBokP3UbpydYdo0cyW2Bw0aBO+/H8fm4CIiIiKSUHHtUHP/cft28sUycuTIWNtXPffccyxYsIB58+ZRokQJhg4dysiRI594SHqMzJkzs2HDBsLCwqhWrRply5Zl+vTptp7jXr160bdvX/r160dQUBBr1qxhxYoVFCxYMN7XiI6O5q233qJo0aLUr1+fQoUK2b4wiOlRj46Opm7dugQFBfHOO+/g6+trm0P9KB06dODgwYPkzJnTbi66k5MT8+bN46+//qJEiRL06dOHTz/91O65Li4ufPHFF0ydOpUcOXLEmkN/v5o1a5IpUyaOHDnCyy+/bHeuXr16/PTTT6xdu5by5cvz/PPP8/nnn8e5cFmMzJkz07x58wR9gREfr732GjNmzOC7774jKCiIatWqMXPmTNvWaV5eXowdO5Zy5cpRvnx5Tp06xapVq2zv9Y4dO7hx4watWrVK1LjiYjESs78/jQgNDcXHx4cbN27g/eCkllTGarUSHByMv79/vH6Yk1poKIweDfv33ytr1cocZW7rHTcM+PBDGDo09gu89RZ88cW9iUepkKO1iahNHJHaxPGoTRyP2sTxJGeb3Llzh5MnT5I3b94ELVp1+TK8/PKjF2CLkTmzuahuMkx/TTL3DzW3xDUUU5LN3r17qVOnDsePH8fDw8Mh2qRt27aUKlWK999//6F1HvWzlpC8UUPNJVl5e8OoUTB1KqxZY5YtWgRnzkD//uZ0bywWGDLErPzOO/YvMHmymb1/+625AoiIiIiIxFt8d6gB879iqTnpFsdSsmRJxowZw8mTJ21D71NSZGQkQUFB9OnTJ1mup8xFkp2Li7lWWp48MH06WK3mVt4DBpj5dtas/1Xs3dtcga17d7NSjNmzzdXZfvjhIfuUiYiIiMjDxHeHGpHE1rlzZ9sohJTm5ubGBx98kGzX07gkSREWi7lV94gRELNbwunT0KeP/TB0unY1E+wHe7eXLoUmTcxJSCIiIiIiIg5MibekqNKlYfx4yJnTfHzzpjma3G6XgzZtYPlyeHD+0rp1UK+euVKbiIiIiIiIg1LiLSkuZ04z+S5TxpzjPWiQucC5nYYNzUnhts2//7NtG9Ssaa4UIiIiIiIi4oCUeItDSJ8ehg2DsWMhd+6HVKpWDdavh/82u7fZtQuqVoWzZ5M8ThERERFH8uA2XCKSuBLrZ0yLq4nDcHaGwED7sqgoc+h5q1b/DUcvXx5+/RXq1IGLF+9VPHwYqlSBX36B/PmTM2wRERGRZOfm5oaTkxPnz5/Hz88PNze3FN+ayRFpOzHHk1raxDAMIiMjuXz5Mk5OTri5uT3V6ynxFodlGPDVV2YuvWMHvPeeOSecEiVgyxaoXdtckS3GqVNm8r1uHRQvnkJRi4iIiCQ9Jycn8ubNy4ULFzh//nxKh+OwDMPAarXi5OTk0EnesyS1tUm6dOnInTs3Tk5PN1hcibc4rFu34MgR8+/h4eZQ9P/7P3O6t6VAAdi61ez5Pnz43pMuXDCHnf/8M5QrlzKBi4iIiCQDNzc3cufOTVRUFNF2K9NKDKvVytWrV8mcOfNTJ06SOFJTmzg7Oydaz7wSb3FY6dPDp5/CuHHw55/mVt5Tppid3P/3f+CSK5c57LxePdi9+94TQ0LMBdd++slMwkVERETSKIvFgqurK66urikdikOyWq24urri4eHh8Enes+JZbZNn504lVUqXDj74wJzjHWP1ahg61Nx6DH9/2LgRXnjB/ok3b5oJ+erVyRqviIiIiIjIg5R4i8NzcoJOnaBvX3D5b4zGvn3m43//BXx9Ye1ac9j5/e7cgaZNYeHC5A5ZRERERETERom3pBo1asDo0WaeDeai5v36wT//YI5L//FHaNbM/kl370K7dvDdd8kcrYiIiIiIiEmJt6QqRYrA559Dvnzm4/z579v3293d7N1+9VX7J1mt0LUrTJyYrLGKiIiIiIiAEm9JhbJkgTFjoEkTGDTo3vBzwHwwcyb06BH7ie+8A6NGmfuUiYiIiIiIJBMl3pIqeXiYK5t7e9uXnz0LN246waRJ5sbfDxo6FAYOVPItIiIiIiLJRom3pBk3bph7fffpAydPWcwJ4aNHx644bhy88QZov0sREREREUkGSrwlzZg2DYKD4fJls1P7t98we70nT4678quvmouviYiIiIiIJCEl3pJmdOsGhQqZf79zBz76CBYsAOPNHvD99+DsbP+EH36Ali3NyiIiIiIiIklEibekGZkymSPLq1e/VzZ7tjmyPLLtq+aK525u9k/68Udo2BBu3kzWWEVERERE5NmhxFvSFDc36NsXOna8V7Z5szniPKRaczPR9vS0f9LGjVCnDoSEJG+wIiIiIiLyTFDiLWmOxQKtW8MHH5irnwMcO2YuunYsb11Yty72cui//w41asClS8kfsIiIiIiIpGlKvCXNqlgRPv0U/P3NxyEhcPgwULkybNpkbgh+v717oUoVOHMmuUMVEREREZE0TIm3pGmBgfDZZ1CsGNStC40b/3eiTBlzDHrOnPZPOHYMXnwRjh5N7lBFRERERCSNUuItaZ6PD3z4Ibz5pjkM3aZoUaybt0K+fPZP+Pdfs+d7795kjVNERERERNImJd7yTHB1BRcX+7I//4S+XwRyedk2s0v8fsHBUK3af5uBi4iIiIiIPDkl3vJMOnPGnP994gT0GZONQ9O3Qrly9pWuX4fatWHDhhSJUURERERE0gYl3vJMsljA19f8+40b8P6nGVk/ZBNUrWpfMTzc3Of7xx+TO0QREREREUkjlHjLMykgAMaPh5IlzcdRUTBhenq+a78Wa/2G9pUjIqB5c/jhh+QPVEREREREUj0l3vLM8vKCESPMDu0YS1a682GF5dxq3sG+cnQ0dOgA06Ylb5AiIiIiIpLqKfGWZ5qLi7na+ZtvgtN/Pw1//u1C/6zfc7Ftb/vKhgGvvw7jxiV/oCIiIiIikmop8RbB7PUeNQoyZDAf/3vWiSEZPifq7T6xKw8YAEOHmom4iIiIiIjIYyjxFvlPyZLw2WeQK5e5+Nobb1pwmTjeTLIfNGoUvPMOWK3JHqeIiIiIiKQuLo+vIvLsyJ7dHEm+bx+ULQtgMSeCe3tD//72lb/4Am7ehOnTwdk5JcIVEREREZFUQD3eIg9Inx6ef96+zOjbj+VvriGMDPYnvvsO2reHyMjkC1BERERERFIVJd4i8bBsGcz4tx79a/3NOefc9icXLoRmzeDWrZQITUREREREHJwSb5HHuHULliwx/37OsyD9XtjObtfy9pVWr4b69SE0NPkDFBERERERh6bEW+Qx0qWD8eMhMNB8HO6Tk2HlV/Kjeyvs1jXfsgVq1YIrV1IgShERERERcVRKvEXiwd8fxo6FihXNx9ZMfkwrN43JngOI4r6F1XbuhGrV4Pz5lAlUREREREQcjhJvkXjy9ITBg6F16/8KMmbk5/IfMCTdZ4Tida/iwYNQpQqcPJkicYqIiIiIiGNR4i2SABYLdOwI/fqBqyvg7c3+il3pm34q58hxr+I//5jJ96FDKRariIiIiIg4BiXeIk+genUYPRoyZgTSZ8DppSZ45/e3r3TuHFStCn//nRIhioiIiIiIg1DiLfKECheGzz6DkiVhyJgMeG1bYz6435UrUKMGbN2aMkGKiIiIiEiKU+It8hSyZIGPPoKAACBrVti0CZ5/nptkIBJXs1JoKNStC2vXpmSoIiIiIiKSQpR4iySmjBm5u2odo3LPYDAfcQ1fs/z2bWjSBJYuTdHwREREREQk+SnxFklkM+Zl4FCJVhzOWo2+fMY/5DVPREaaS6J//33KBigiIiIiIslKibdIIqtXD7L4O0PZclzJUYqBjGU7lcyT0dE4delCuu++S9kgRUREREQk2SjxFklk+fKZi64VLuoEZcoQkbsQoxnEPNpi/FfH+/334ZNPUjROERERERFJHkq8RZJAxozw8cdQo6YFSgZB/vzMoQOfMoAI3ABwGjwY3nsPDOMxryYiIiIiIqmZEm+RJOLmBn36QOfOFizFikLhImyhCu/xCVfJZFYaMwbeegus1pQNVkREREREkowSb5EkZLFAy5bwwQcWPIIKQvESHKcAP9H4XqWvv4ZOnSAqKuUCFRERERGRJKPEWyQZVKgA48aBf8W8lGgYwMuWH+wr/O9/5ornEREpE6CIiIiIiCQZJd4iySRPHnPRtffmliRs2lcYrq72FZYtM/f6Dg9PkfhERERERCRpKPEWSUY+PuDlBRGNG2MsWwaenpwiD+Ppyx3cYd06qFsXrl9P6VBFRERERCSRKPEWSSn163Nj8S+Mch3JJqozgE8Jxg+2b4caNSA4OKUjFBERERGRRKDEWyQFnQ98gfDqjcHVjVME0pfPOEhR2L0bqlaFs2dTOkQREREREXlKSrxFUlDRojDu20xkb1oe3D24gQ+D+YhfqAVHjsCLL8Lx4ykdpoiIiIiIPAUl3iIpLFcuGP9tJkp1Kwee6YjChYn05hu6Yj19BqpUgf37UzpMERERERF5Qkq8RRyAlxcMn5iRxsOeg/QZAFhGM0YylPCLoVCtGvz5ZwpHKSIiIiIiT0KJt4iDcHGB19/NSI8Zz+HsYybff1GW9/iEqJAbULMm/PprCkcpIiIiIiIJpcRbxME0aOfDyJXl8MriAUA9fsaFaAgLg/r1YdWqFI5QREREREQSQom3iAMqWdmL8b9V5tWSe2nEynsn7tyBpk1hwYKUC05ERERERBJEibeIg8qePx1t/uiPpUULu/LdUcWJavcKfPttCkUmIiIiIiIJocRbxJG5u8P8+dCxIwB/UJ6hjGS4MZSb3XrDhAkpG5+IiIiIiDyWEm8RR+fiAt99x53XezOBdzCwsIdS9GM8Z/uMg5EjwTBSOkoREREREXkIJd4iqYGTEx5ff86Q/wvGhxsAXCA7/RnHX8OWw4ABSr5FRERERByUEm+R1MJioejUd/jsg2vk5SQA4aRnBMNYPv4Yxv+9DtHRKRykiIiIiIg8SIm3SCrjP+ptxk70oBK/AWBgYQavMWmGO1HtX4W7d1M4QhERERERuZ8Sb5FUyKPX/zHo+6K0tSy0la2lLh8sLMmNxh3g9u0UjE5ERERERO6nxFsklbK8+gqvLGnBAJcJuBEJwCkCCVu7DRo2hJs3UzhCEREREREBJd4iqVuzZlRdPYhPPEaQhSu8yxhych42bYLatSEkJKUjFBERERF55inxFkntatem4PopTPUeSBl23yv/4w+iqtXCuHAxxUITEREREREl3iJpwwsv4PbrOvDzsxUZwJf7q/NZ0HdEHjudcrGJiIiIiDzjlHiLpBWlS8PmzZArFwBLac4GarLpagneL7uGkN+PpWx8IiIiIiLPKCXeImlJkSKwZQvkz082LuJOBABHbuagX7WdnPjpUAoHKCIiIiLy7FHiLZLWBAbCli28UOImYxlIFq4AcCUiAwNbHGPrlP0pG5+IiIiIyDNGibdIWpQ9O2zaRL7yWficPhThMACRdy2M6XmGHwbvxzBSOEYRERERkWeEEm+RtCpzZli/Ht9qpfmY96nFerM8Opq5o08ztvNBIiJSNkQRERERkWeBEm+RtMzLC1avxrVhXXozka58iwUDDCtbv/+HWW/9ntIRioiIiIikeUq8RdI6T09YuhRLmzY0ZxlDGIUnt8nOedp/UwemTk3pCEVERERE0jSXlA5ARJKBmxvMnQteXpT/5hvG0R9novHiJrzxBoSGwoABKR2liIiIiEiapMRb5Fnh7AzTp4O3N7k//9z+3MCBXL8UwS8VB9OylQWLJWVCFBERERFJi5R4izxLLBYYPx58fGD4cFvxXVz4eLwbhwIPcPhwMfr1d8LTM+XCFBERERFJSzTHW+RZY7HAsGFmAv6fAxTnCIXh1El+n7aXgf2tBAenYIwiIiIiImmIEm+RZ1XfvubQc4uF0uxhBMNITzic/ZdTS/6mT69oDhxI6SBFRERERFI/Jd4iz7LXXoMffgAXF0qzh/H0Iyfn4OIFQjf8yQeDoli3LqWDFBERERFJ3ZR4izzr2raFZcvAw4OcnGcc/SnDLrh8mahtv/PF+LvMmAHR0SkdqIiIiIhI6qTEW0SgUSNYvRoyZCAD4QxjBE34EUJCYMcOli+MZORIuHs3pQMVEREREUl9lHiLiKl6dVi/HjJmxBkr/8d03mIyzqEhsH072Tyu4+qa0kGKiIiIiKQ+SrxF5J4KFeDXXyFrVgDq8zMf8gEvhq2m+7Ty8M8/KRygiIiIiEjqo8RbROwFBcGWLZA7NwAlOMC7jMXl1HGoUgUOHgTg0qWUDFJEREREJPVI0cR78+bNNGnShBw5cmCxWFi2bNljn7Np0yaee+453N3dKVCgADNnzrQ7P3z4cCwWi91RpEiRpLkBkbSqYEHYuhUKFbIvP38eqlbl5Ip99OwJX38NUVEpE6KIiIiISGqRool3eHg4pUqVYvLkyfGqf/LkSRo1akSNGjXYvXs377zzDq+99ho///yzXb3ixYtz4cIF27F169akCF8kbQsIgM2boVQpu+KIqzf5sNVu7py/yqpVMGwY3LyZQjGKiIiIiKQCLil58QYNGtCgQYN4158yZQp58+Zl/PjxABQtWpStW7fy+eefU69ePVs9FxcXsmXLlujxijxzsmaFjRvNVc937ADAnUheufstX/zmQ1T559m7159+/WDIEDNXFxERERERe6lqjveOHTuoXbu2XVm9evXY8V9CEOPYsWPkyJGDfPny0aFDB86cOZOcYYqkLRkzwtq1UKuWragGmxhtHYjvH+vgwgUuXID+/eGvv1IwThERERERB5WiPd4JdfHiRbL+t9pyjKxZsxIaGsrt27fx9PSkYsWKzJw5k8KFC3PhwgVGjBhBlSpV2L9/P15eXnG+bkREBBEREbbHoaGhAFitVqxWa9LdUDKwWq0YhpHq7yMtSZVtki4drFiBpX17LCtWAFCEI3xmvMOov4bwT6lmhOcKYPhw6NLFoGlTsFhSNuSESJVtksapTRyP2sTxqE0cj9rE8ahNHE9aapOE3EOqSrzj4/6h6yVLlqRixYrkyZOHBQsW0K1btzifM3r0aEaMGBGr/PLly9y5cyfJYk0OVquVGzduYBgGTk6paoBDmpWq22TSJHycnfFcuhQAP64wloF8vucCm+90JTp3AFOmwP79kXTpcivV7PudqtskjVKbOB61ieNRmzgetYnjUZs4nrTUJjcTsNBRqkq8s2XLxqUH9jC6dOkS3t7eeHp6xvkcX19fChUqxPHjxx/6uoMGDaJv3762x6GhoQQEBODn54e3t3fiBJ9CrFYrFosFPz+/VP/BTitSfZssWIDRsyeWqVMB8CCC9/iE3EfO8APDoUABdu50p1WrDKSWDQVSfZukQWoTx6M2cTxqE8ejNnE8ahPHk5baxMPDI951U1XiXalSJVatWmVXtm7dOipVqvTQ54SFhXHixAleffXVh9Zxd3fH3d09VrmTk1Oq/zAAWCyWNHMvaUWqbhMnJ3MfMR8fGDsWAAvQgbnkPnKGCcZXdPusBMWKpa57S9VtkkapTRyP2sTxqE0cj9rE8ahNHE9aaZOExJ+idxoWFsbu3bvZvXs3YG4Xtnv3bttiaIMGDaJjx462+m+88Qb//PMPAwcO5PDhw3z11VcsWLCAPn362Or079+fX3/9lVOnTrF9+3aaN2+Os7Mz7du3T9Z7E0nTLBb45BP46CO74ips5eujtWj4Uw9IA/N2REREREQSQ4r2eO/cuZMaNWrYHscM9+7UqRMzZ87kwoULdiuS582bl5UrV9KnTx8mTpxIrly5mDFjht1WYmfPnqV9+/ZcvXoVPz8/XnzxRX777Tf8/PyS78ZEngUWC7z/Pnh7w9tv24r9uQxTppibe3/3Hbi6smiRmYe3bp26Fl0TEREREUkMKZp4V69eHcMwHnp+5syZcT5n165dD33OvHnzEiM0EYmvnj3Bywu6drXv5Z4zB8LC+L3vfL7/3h3DgNOnoXdvcHNLuXBFRERERJJb6h5ULyKOoVMnWLCAWMuYL1/O+Z4fY9yNAmDzZnjvPQgJSYEYRURERERSiBJvEUkcLVvCjz/CAzsMNN83kvfP9sDDORKAY8egTx/zTxERERGRZ4ESbxFJPPXqwc8/m/O+71Np/3TGHmmGX4bbgNnj/d57sGVLSgQpIiIiIpK8lHiLSOKqUgU2boTMme2K8x5ezed/vkjRnKEAREaau5HNmQOPWOpBRERERCTVU+ItIonvuefMCd05ctgV+5z4m49WlaF2qcu2snnzzMXPRURERETSKiXeIpI0ihUzx5LnzWtX7PrvP/SaUZJutU9jsUD69OYIdRERERGRtCpFtxMTkTQuXz4z+a5TBw4dshVbLl2k2YgyBHy+GedSJciZ895TLl+G0NDHv7S3N/j5JUHMIiIiIiKJTIm3iCStnDnNYef168Nff90rv3aNsj0rmSuhUx0wk+727eHff82e8EfJnBnmzlXyLSIiIiKOT0PNRSTpZckC69fDiy/al4eFQYMGsHIlADduwOHDEBwMt2+Djw/4+sY+3N3h6tX49YyLiIiIiKQ0Jd4ikjx8fMytxh6c0H3nDjRrBvPnc+AA3LwJTk7mlmPnz4OHh9n7ff/xwFbhIiIiIiIOTYm3iCSfdOlg+XJo2dK+PCoK2renxIH5+PmBxWIWX78OBw+avd8iIiIiIqmVEm8RSV7u7uYeYp0725cbBgx+H+87l8iXD1z+W4Hi9m3Yt89cm+3yZYiOTvaIRURERESeihZXE5Hk5+IC33wDXl7w5Zf2586dI4NrOMUK5+Ho3tvcCYsCq5WbN5y4edmd054e5MhpSZm4RURERESegBJvEUkZTk4wcaI59/vDD+3PnTqJx5lDFLdCMFm5QhZu4wkRd7DevIlHRmdue2a2VbdazZcTEREREXFESrxFJOVYLDBqlNnz/e679uesVpyB7FwgOxcIJz1XyEKE4Y774bPczlcW8AVgwQL44w+oVQuqVjVfTkRERETEUSjxFpGUN3AgeHvDm58+tEp6wklPOADhpIPTpyHCE8NwZ8MGuHABjh2DGTOgQgUzCX/uuXtzxUVEREREUor+SyoijuGNN2DzOfgBc1j5I9zG01xlbfVqbuRoZre9WFQUbN9uHj4+UL061KwJ+fIlbfgiIiIiIg+jxFtEHIZ36Fkyc5WrZCYCj0fWzcxVvDcux7dfMyZOhFOnYP162LTJ3IYM4MYNc/ey5cshMNDsWA8ISOKbEBERERF5gBJvEXEYfmEnmcvLhOL92LrehOIXXsL2ODAQunUzdyn7+28zCf/9d7MHHODiRfDzS5q4RUREREQeRYm3iDiOzJnxcwrBz3olfvWPHYM9e6BUKVuRszOUL28eN2/Cli2wYYPZ0+3xQCf61Knmiug1apjD0kVEREREkoISbxFxHM2awZIl8a9/7hyULg0vvQQffGBm2/fx8oKGDc0jpuc7xq1bsHYtREbCypUWsmTxpmFDc1G2zJkREREREUk02vlWRBxH69aQMaO5zVhCrFhhLmVevz5s3RpnlQdXNz9xwv7x+fNOfP+9hS5dYOhQ2LzZTMpFRERERJ6WEm8RcRweHjBrlvn3hCbfAD//DFWqmGPHN2wAw3ho1aAgmD0bevWCEvemimMYsGsXfPopvPoqfPklREQkPBQRERERkRhKvEXEsTRpAsuWga+v+djJyf7PjBlh7lz48EPIlCnu19i0yRwzXrkyrFr10AQ8XTqoUwc+/thg3LhQ2rUzyJr13vlbt+DQIXBzS4wbExEREZFnlRJvEXE8L70E58+bXdLNmpmbcTdrZj4+fx7at4fBg+H0aRg7Fvz9436dHTugUSMoVw6WLjVXUnsIf38rL78M06fD6NFQu7bZAV+zZuzO96+/NldNv3MnsW5YRERERNIyi2E8YizmMyo0NBQfHx9u3LiBt/fjtzVyZFarleDgYPz9/XFy0vcsjkBtkgRu34YZM2DMGHPBtYcpUcJchK1VK3P58/88rE3u3DFz9XTp7r3EmTPw1lvm393dzU71WrXMoetPMjpe4qafE8ejNnE8ahPHozZxPGoTx5OW2iQheWPqvlMREQBPT3j7bXPFtKlTzU2947J/P7RrB8WLw/ffw927j3xZDw/7pBvMvcFjRESYU8kHDzb3EI/pkBcRERERuZ8SbxFJO9zd4f/+D44ehe++g4IF46535Ah06gSFC8O0aQlaPa1VKxg3Dho0gPTp75VfvgwLFsDrr8OAAbBu3VPei4iIiIikGUq8RSTtcXWFzp3NldHmzjV7uONy8iS8/jqWQoVI9+235pD1x7BYzHy9Rw+z0/zdd80p5PePlDp82L5nXERERESebUq8RSTtcnY2F2LbuxcWL4YyZeKsZjl7Fu/Bg7EUKADjx0NYWLxe3s0NXnwRhg2DmTOha1fIk8c8V7u2fd27d82h6KdPP8X9iIiIiEiqpMRbRNI+Jydo0QL++gt++gkqVoyzmuXiRejf35wj/vHHcONGvC+RMSM0b27u+z1hgtkLfr8//zSHovfsCX36wI8/Qmjok9+SiIiIiKQeSrxF5NlhsZjbi+3YYU7Crlo17npXr5orpgUGmt3ZISEJukT+/ODiYl++ceO9vx8/bk4t79TJzO9//x2iohJ+OyIiIiKSOijxFpFnj8VijgX/9Vf49VeMOnXirnf9OowcaY4ff+89CA5+4ku+/ba57lv+/PfKoqLM7wA+/NCckj59Opw69cSXEBEREREHpcRbRJ5tVatirFnD1ZUrMRo3jrtOWJi5R3hgoDlO/FF7hT+Etzc0aWIOQ580yRyWnjHjvfM3bsCKFbBp05PchIiIiIg4MiXeIiLA3eeew1i+HHbtgpYt4650+7aZOefLZy5r/oQrpeXJYy7E9t13MHw4VKliLsQOUKuWfd2QENi6FSIjn+hSIiIiIuIAXB5fRUTkGVK6NCxaBAcOwOjR8MMPYLXa14mMhK+/NseGd+wIgwZBgQIJvpSzM5Qtax7h4WbOHxBgX2f9enPbsvTpzSnptWpBoULmaHkRERERSR3U4y0iEpfixeF//zM35e7aNfZqaWBO0v72W3Nj71degYMHn/hy6dObW5PdzzDMxBvMxHz1anPR9TffNFdIv3LliS8nIiIiIslIibeIyKMULAjffAPHjsEbb5ibdz/IaoU5c6BECWjdGnbvTrTLv/km1KgB7u73ys6dM/cE79oVhgwxV0y/cyfRLikiIiIiiUyJt4hIfAQGmsPL//kHevcGD4/YdQzDHKZepgy89BL88cdTXdJigVKloG9fM9Hu3dvM7e+/3O7d8NlnsGXLU11KRERERJKQEm8RkYTImdNcYO3UKRg4EDJkiLvejz9CxYpQr565OtpT8vQ0d0AbPRpmzIAOHSBbNvOcu3vsYeoXLsDFi099WRERERFJBEq8RUSeRNas5hZjp06Z4719fOKut3atuWx59ermhG3DSJRLt2sH06bBJ5/A66+bifn95s+H7t3Ndd/WrTMXZBcRERGRlKHEW0TkaWTODCNHmluLffih+Tguv/5qdlm/8AKsWpUoCbjFYq4BV6eOffmdO7Btm/n3/fvhiy/Mtd8++8wcmv7gIu0iIiIikrSUeIuIJAYfHxg82OwB//RTs1s6Lr/9Bo0aQblysHRpkmTBViu0bWuOio8RGWkuwjZkCHTrZm5Rdu5col9aREREROKgxFtEJDFlyGDu+XXypNnVfH/2e7+//4YWLczV0+bNg+joRAshXTpo1cpcC27cOGjY0H4q+pUrsHChuUj7P/8k2mVFRERE5CGeOPE+fvw4P//8M7f/mzhoJMKwSRGRNMPTE95+G06cgKlTzVXR47J/P7RvD8WKwaxZcPduooVgsZhbjL/5ptnD/d57UL48OP33mz97dsib1/45V64k6ncAIiIiIsITJN5Xr16ldu3aFCpUiIYNG3LhwgUAunXrRr9+/RI9QBGRVM3dHf7v/+DoUZg5EwoVirve0aPQubOZKU+bBhERiRqGqytUrgxDh5r5fbdu5pbjFot9vTFjoEsX+PZbc9S8iIiIiDy9BCfeffr0wcXFhTNnzpAuXTpbedu2bVmzZk2iBicikma4ukKnTnDwIPzwg7kqWlxOnjSXKS9QAL78MkmWI/f1hWbNYi/Kdu4cHD4M166Z08/fftvcO3zFCrhxI9HDEBEREXlmJDjxXrt2LWPGjCFXrlx25QULFuT06dOJFpiISJrk7GzuBbZ3LyxZAmXKxF3v7Fno1cscCz5uHISFJXloVqu56LqLy72yf/6B6dPN7ww+/BB27ICoqCQPRURERCRNSXDiHR4ebtfTHSMkJAR3d/dECUpEJM1zcoLmzeGvv2DlSnj++bjrXboEAwaYc8Q/+ihJu54DAsx9v7//3lx4rWDBe+eio+H33+Hjj82h6Ik8El5EREQkTUtw4l2lShW+//5722OLxYLVamXs2LHUqFEjUYMTEUnzLBZz2fHt2+GXX6BatbjrXb0KH3wAefKYE7WvXk2ykLy8zB3PPvsMvvoKWraETJnunS9QwJy6fr/IyCQLR0RERCTVc3l8FXtjx46lVq1a7Ny5k8jISAYOHMiBAwcICQlh27ZtSRGjiEjaZ7FArVrmsWWLOa577drY9W7cgFGj4PPPoUcP6NcP/P2TLKyAAHPNt1dfhd27YcMGqFLFvk5UFHTvbo6Kr1ULKlYEN7ckC0lEREQk1Ulwj3eJEiU4evQoL774Ik2bNiU8PJwWLVqwa9cu8ufPnxQxiog8W6pUgZ9/Nsd2N2kSd52wMBg71hyC/s475spoScjZGcqWNUe9Pzgq/u+/ISTEHDU/dix07AiTJ5sLtWmnSREREZEn6PEG8PHxYfDgwYkdi4iI3K9CBXNJ8d27zfndixfHzmRv34aJE+Hrr6FrV3Oz7jx5kjXMiAjw84PLl83H4eGwZo155Mhh9oLXqGHWEREREXkWJTjx3rx58yPPV61a9YmDERGROJQuDQsXmluRffyxuR2Z1WpfJzISpkyBGTPMceGDBtmvjpaEqlSBF1+EffvMoejbtsGdO+a58+dh9mz43//MegMGJEtIIiIiIg4lwYl39erVY5VZLBbb36Ojo58qIBEReYhixcwMdvhwGD3aXH78wb29oqLgu+9g1ixz27LBg83nJTGLBUqWNI833jCT7w0bzF3TwOyov3+BthiGYT5XREREJC1L8Bzva9eu2R3BwcGsWbOG8uXLszauhYBERCRxFSgA33wDx47Bm2/GvZKZ1Qpz50KJEtCqlTlcPZl4eJjDyz/6yAzzlVcge3az7H4hIfD66zBnDly4kGzhiYiIiCS7BCfePj4+dkeWLFmoU6cOY8aMYeDAgUkRo4iIxCUw0Nzv659/zAXWPD1j1zEMc254mTLw0kvwxx/JGqK/P7RtC1OnmuHe79dfzYR73jz4v/+Dd981F3IPD0/WEEVERESSXIIT74fJmjUrR44cSayXExGR+MqZ09xe7NQpM3vNkCHuej/+aO71VbeuuWVZMoprOHlwsH35wYPw5Zfmqujjx8O+fS6xprKLiIiIpEYJTrz37t1rd+zZs4c1a9bwxhtvULp06SQIUURE4sXfHz75xEzAhw4FH5+4661bB1WrQrVq8MsvKbbn1+uvw8yZ5j7hAQH3yiMj4ddfLXz6aQa6dbOwZk2KhCciIiKSaBK8uFrp0qWxWCwYD/xH7fnnn+fbb79NtMBEROQJZc4MI0ZA377mhtqffQZXr8aut3kz1Kljbsz9wQfQsGGyr3SWKRO0bAktWsDx47B+vRlWaKh5Pq6wRURERFKbBCfeJ0+etHvs5OSEn58fHh4eiRaUiIgkAh8feP996NXLnGT96adw6VLser/9Bo0bm/PAP/gAmjUDp0SbiRQvFou5+1nBgtCtG/z+u8GPP97lxAl3qlSxr3v4MCxdai7W9txz4JLgf8lEREREkleC/7uSJ0+epIhDRESSSoYM0K8f9OhhLjM+ZgycPRu73q5dZvdz8eLmNmRt2oCzc7KH6+oKL7wABQqE4+2dnnTp7Hvh162D7dvNw8cHqlc3k/C8eZM9VBEREZF4iVfi/cUXX8T7BXv16vXEwYiISBLy9ISePaF7d3Of79GjzfngDzpwAF5+GYYNM3vMO3Qws+EU8OBgKsOA/fvvPb5xA5YvN4+8ec0EvHr1uKe3X758bwj7o3h7g5/fU4UtIiIiYsdiPDhZOw5549mNYLFY+Oeff546qJQWGhqKj48PN27cwNvbO6XDeSpWq5Xg4GD8/f1xSuahoxI3tYnjeWbb5O5d+OEHc8Pto0cfXi8wEN57z1wFzd09WUJ7VJtERZmd8+vXw++/m4/v5+QEZctC+/bm0HUwk+6XX47fnPHMmc0t0JV823tmf04cmNrE8ahNHI/axPGkpTZJSN4Yrx7vB+d1i4hIGuDqau7d1aEDLFoEH35o350c49QpeOMNGDUKBg40e8zj2jM8mbi4QPny5nHzprkz2vr19747sFrhzz/NBdtihIaaSbe7+6NDv33brBcaqsRbREREEk/q/opBRESenrMztG0Le/bAkiXmimVxOXcOevc2x3R/+imEhSVvnHHw8jIXYx8/Hr76Clq1MldK9/c3p6rfLzzcDNnVFdKnj/tIwe8TREREJA17orVgz549y4oVKzhz5gyRkZF25z777LNECUxERJKZkxM0b26uar56tdnD/dtvsetdumT2fH/yCfTpA2+//fA9w5NRQAB06gSvvmoOLX9wZ7QbN8zR9Zcvm/O4s2SBjBmTfQF3EREReQYlOPFev349L730Evny5ePw4cOUKFGCU6dOYRgGzz2sl0RERFIPi8XsRm7QADZuNBPwTZti1wsJgSFDYNw4M/l+5x1zgnQKc3KCrFnty65fN4eRu7iYC7TduGEeLi7mkPJs2VJs/TgRERF5BiT4e/5BgwbRv39/9u3bh4eHB4sXL+bff/+lWrVqtG7dOiliFBGRlGCxQM2aZvK9ZQvUqxd3vRs3zPnhefKYPeFx7RWewnx9IXduM8G+f324qCi4cMEcZX/6NDwwiEtEREQkUSQ48T506BAdO3YEwMXFhdu3b5MhQwZGjhzJmDFjEj1AERFxAC++CGvWmMuIv/RS3HXCw82533nzmr3f584la4iP4+pq9oSXKgVFi5pDzWOGo1ut5vcFhw/HXiVdRERE5GklOPFOnz69bV539uzZOXHihO3clStXEi8yERFxPBUqmJtm79oFrVvHnkgN5pjuiRMhXz5zNfS49gpPYV5eZnilS0P27Ob6cjHlLk+0+omIiIjIwyU48X7++efZunUrAA0bNqRfv3589NFHdO3aleeffz7RAxQREQdUujQsWGBuP/bKK3GvUBYZCVOnmptpd+0Kx44le5iP4+pqLspWqhTkzBl7brhhwJdfmrdpGCkTo4iIiKR+8U68Q0JCAHPV8ooVKwIwYsQIatWqxfz58wkMDOSbb75JmihFRMQxFSsGs2fDkSNmch1Xd3FUFHz3HRQpYu4ZfuBA8sf5n9u3zRHxDx4REeY88Ac78HfsgLVrYdAgePddc39wJeAiIiKSUPFOvHPkyEG7du04ceIEJUuWBMxh51OmTGHv3r0sXryYPHnyJFmgIiLiwAoUgG++gePHoUcPcHOLXcdqhblzoUQJc8PtXbuSLTxvb3PB9YgIc4Xzhx0REWY9b2/zefcv5n7oEIwcaW5lvmWLeTsiIiIi8RHvmWzTp09n5syZ1K9fn4CAADp37kznzp0JDAxMwvBERCRVyZMHJk+GwYPNbcamTDG7mR+0eLF5NG4MH3wA/42kSip+fmbOHxr6+Lre3mZ9MHu5t2yBhQvhzBmz7ORJGDsWcuQwvz+oUUPzwkVEROTR4t3j/eqrr7J+/XqOHz9Op06dmDVrFgUKFKBOnTrMnz/ftuCaiIgIOXLAZ5+ZC6u9+y5kyBB3vZ9+guefh7p1YfPmJA3Jzw/y53/8EZN0g7noWvXqMGmS+f1AoUL3zp0/D198Ad27J2vnvYiIiKRCCV5cLW/evIwYMYKTJ0+yZs0a/P396dq1K9mzZ6dXr15JEaOIiKRW/v7wySfmJtlDh4KPT9z11q2DatXMY906h5tIbbGYnfLjxplblv834wqAK1fMrclEREREHibBiff9ateuzZw5c/j+++8BmDx5cqIEJSIiaUymTDBihJmAf/SROZE6Lps3Q926WCpXxt1BE/BSpcxbGDfOTMZfeMFcGf1+R46Yc8ZFRERE4CkS79OnTzN8+HDy5s1L27Ztee6555gzZ05ixiYiImmNjw+8/76ZgI8bF3v/rv9Yfv+djB07YilXzpwL7oArmRUubA4/HzDAvjw62ry1bt3MKe7BwSkTn4iIiDiOBCXeERERzJ07l9q1a5M/f36+++47OnbsyPHjx1m3bh3t2rVLqjhFRCQtSZ8e+vUzVyr78kvIlSvOapbdu80VzIKCzNXRoqOTN854eHBhta1b4eJFcxvzlSvh//4PJkyAf/9NkfBERETEAcQ78e7RowfZs2ena9euZM6cmVWrVnHq1ClGjBihlc1FROTJeHpCz55w4gRMmwZ588Zd7+BBcw/wokXNPcHv3k3eOBMgKAiaNwcPD/NxdDSsXw9vvQWjR8OxYykbn4iIiCS/eCfeW7duZdiwYZw7d4758+dTt25dLBZLUsYmIiLPCjc3c3nwo0dh1iyMwoXjrnfsGHTtai4vPnWqufG2g8mUyQzx22+hfft7C7obBmzfDn37muvM7d+fsnGKiIhI8ol34r1371569+5N5octiCMiIvK0XFygY0eMffu4PmUKRokScdc7dQreeMPc/+uLL+DWrWQNMz68vODll80EvGtXMyGPsWsXbNqUYqGJiIhIMnuqVc1FRESShLMzd5o2xdi1C5YuhbJl46537hz07m0OUf/0U7h5M3njjAdPT3Po+fTp5nDzrFnN1dFbtrSvFxXlkGvIiYiISCJQ4i0iIo7LyQmaNYM//4RVq6BSpbjrBQfDwIEQGGhutO2Ae3m5uUH9+uYI+dGjIXt2+/OrV8Obb5rbmEdFpUyMIiIikjSUeIuIiOOzWKBBA9i2zVyprHr1uOuFhMCQIZAnj/nn1avJGmZ8ODtD8eL2ZVFRsGQJnD9vjpzv3h1WrIA7d1ImRhEREUlcSrxFRCT1sFigZk3YuBG2bIF69eKuFxpq9nznyWP2hF+8eO/cnTswe7Y51rt6dfPP2bNTNMsNDYUcOe49vnLFHJrerRssWADh4SkWmoiIiCQCi2EYRkKfdP36df744w+Cg4OxPjAhrWPHjokWXEoJDQ3Fx8eHGzdu4O3tndLhPBWr1UpwcDD+/v44Oel7FkegNnE8ahPHk6A2+fNP+OgjWL784XU8PMwNtUuVgv794do1cxi71Xrvz4wZYdYsaNIkcW8mAY4cgYUL4fff7cs9PaFRI2jaFHx9UyQ0/Zw4ILWJ41GbOB61ieNJS22SkLzRJaEv/uOPP9KhQwfCwsLw9va221LMYrGkicRbRERSkfLlYdky2LPHTMAXLTL37rrfnTvmGO77xXxxHPPn9etmZrtsGbz0UhIHHbfCheGDD+D0afM2fv3VvJXbt83Ha9bAzJng7p4i4YmIiMgTSvBXDP369aNr166EhYVx/fp1rl27ZjtCQkKSIkYREZHHK1XKHJd94AC88orZk50QMcl6584pPrk6Tx7o189ciK1+fXOXNYBq1ZR0i4iIpEYJTrzPnTtHr169SJcuXVLEIyIi8nSKFjXnbB85Yk6SdknA4C7DMIehL1yYdPElQPbs5hZk33xjbknWooX9+du34bPP4PjxlIlPRERE4ifBiXe9evXYuXNnUsQiIiKSeAoUgBkz4MQJyJcvYc/t1g1q1DD3CP/mG3Me+a1bSRNnPGTKBF27gr+/ffmaNeY6c336wNChsH9/7FH2IiIikvISPMe7UaNGDBgwgIMHDxIUFISrq6vd+ZdSaF6ciIhInHLnhoAA+Oef+D/n7l3YtMk8YlgsULCgOaS9ZMl7R5485rlkZhjmHPAYu3aZR9Gi0Lo1lCuXImGJiIhIHBKceHfv3h2AkSNHxjpnsViIjo5++qhEREQSU+bM91Yvf1KGAUePmsf9Q9G9vSEoyEzCY5LyEiXAy+vp434EiwXGjoUNG8yF1y5dMssPHYKRIyFvXjMBr1w54dPdRUREJHElOPF+cPswERERh9esGSxZkjSvHRoK27aZx/3y5bPvGS9VyixLxCzYzc1cfK1OHXNb84UL4cwZ89zJk2Zinj27uZV5gQKJdlkRERFJoAQn3iIiIqlO69bmfO3r1x89CdpiMXuqv/kGDh+GvXvN4+jRhE+e/ucf81i27F5ZunT3esdjjqAgcw/xp+DsDNWrm6ue//GHubj70aPmuWvXIGvWp3p5EREReUpPlHiHh4fz66+/cubMGSIjI+3O9erVK1ECExERSTQeHjBrlrlPt8USdxIdMyH6f/+DJk3sz926ZW5TFpOI791r7ht+7VrC4rh1C37/3Tzulzu3fTJesqQ5nzwhK7L/dwsVK0KFCmaICxaYnewPjnrfu9csz5AhYeGLiIjIk0lw4r1r1y4aNmzIrVu3CA8PJ1OmTFy5coV06dLh7++vxFtERBxTkyZm73PnzmbCHDPnO+ZPX18zOX8w6Qazp7p8efOIYRhw/vy9JDwmIT98GBK63smZM+bx00/3yjw8oHjx2Al5liyPfTmLxRzZXqpU7FDCw+HDD82/N2pkfhfh65uwcEVERCRhEpx49+nThyZNmjBlyhR8fHz47bffcHV15ZVXXqF3795JEaOIiEjieOklM1letAiWLoWQEHOvrubNoVUrM9mNL4sFcuY0jwYN7pVHRJgrnN2fkO/ZA5cvJyzWO3fgr7/M437Zs9sv5FayJBQubE74joOzs/3jlSvN/b/BfBtWrIDataFly9jblYmIiEjiSHDivXv3bqZOnYqTkxPOzs5ERESQL18+xo4dS6dOnWjRokVSxCkiIpI4PDzglVfMIym4u0Pp0uZxv0uXYg9VP3jQ3LosIS5cMI+ff75X5upq7iP2YEKeNWusPcWqVjW/A/jlF4iKgshIWLXK3BO8enXz+4eAgCe5cREREXmYBC+t6urqitN/K7L6+/tz5r/lU318fPj3338T9FqbN2+mSZMm5MiRA4vFwrL7F6B5iE2bNvHcc8/h7u5OgQIFmDlzZqw6kydPJjAwEA8PDypWrMgff/yRoLhEREQSXdas5vLj/fqZQ9p37zbHfe/bB3PmwLvvmj3nOXIk/LXv3jWT+f/9DwYMgHr1zJ7xrFnN7uy+fWHmTNi1i2y+d3jrLXP9uObN73XyW63m1mRvvQWjR8OJE4l58yIiIs+2BPd4lylThj///JOCBQtSrVo1hg4dypUrV5g9ezYlSpRI0GuFh4dTqlQpunbtGq+e8pMnT9KoUSPeeOMN5syZw/r163nttdfInj079erVA2D+/Pn07duXKVOmULFiRSZMmEC9evU4cuQI/hpDJyIijsTV1dzzu0QJePnle+VXrpgJ+f095Pv3m8PPE+LyZVi/3jxiODtD4cJkKlmSriVL0rpNGX66VJ4V2zIRFmbBMGD7dsif3zxERETk6VkMI2H7o+zcuZObN29So0YNgoOD6dixI9u3b6dgwYJ8++23lCpV6skCsVhYunQpzZo1e2idd999l5UrV7J//35bWbt27bh+/Tpr1qwBoGLFipQvX55JkyYB5r7jAQEBvP3227z33nvxiiU0NBQfHx9u3LiBt7f3E92Po7BarQQHB+Pv728bqSApS23ieNQmjkdtEofoaDh2zD4Z37sXTp9OlJe/7ZudNTm7siy6CXcy+PHtuBDSlysK6dMDcOeOlWvXgsmaVW3iKPRz4njUJo5HbeJ40lKbJCRvTHCPd7ly5Wx/9/f3tyW8yWHHjh3Url3brqxevXq88847AERGRvLXX38xaNAg23knJydq167Njh07ki1OERGRROfsDEWKmEebNvfKr1+P3Tu+b585jD0BPK9foPn1j2jEWE4RSPrqx8z54QULQsmSfB/ZkZ3hBXi5WzRV2+TAydny+BcVERER4An38Y6KimLTpk2cOHGCl19+GS8vL86fP4+3tzcZknBT0IsXL5I1a1a7sqxZsxIaGsrt27e5du0a0dHRcdY5fPjwQ183IiKCiIgI2+PQ0FDA/DbGarUm4h0kP6vVimEYqf4+0hK1ieNRmzgetUkCeHtD5crmEcNqhZMnYe9eLPcl45Z4TNx24y6FOGY+MAw4epTQoxdYS1siOM749TCn01paFdxLjcqRuJT+b8uzEiXMWCTZ6OfE8ahNHI/axPGkpTZJyD0kOPE+ffo09evX58yZM0RERFCnTh28vLwYM2YMERERTJkyJaEvmeJGjx7NiBEjYpVfvnyZOwmdT+dgrFYrN27cwDCMVD+UI61QmzgetYnjUZskAi+vWAm5JTwcl8OHcTl4EJdDh3A9cACXQ4dwunnzkS91Ax/ycJqjFALg4t0sTDpYkx8OXqU5S6lHXzyIICp3bqKKFiWqeHHuFi1KVLFiROfJE3tPM0kU+jlxPGoTx6M2cTxpqU1uPubfz/slOPHu3bs35cqVY8+ePWTOnNlW3rx5c7p3757Ql0uQbNmycenSJbuyS5cu4e3tjaenJ87Ozjg7O8dZJ1u2bA993UGDBtG3b1/b49DQUAICAvDz80sTc7wtFgt+fn6p/oOdVqhNHI/axPGoTZJQ3rz2+44bBtYzZ+71isf0kB87huW/b/IDOMs4+rOPIBbQhj2Y67lcJTMzeI35tKUpy2l85ifSn/nZbqszI106szc8KAgjZpuzoCDImDFZbzst0s+J41GbOB61ieNJS23iEbM1SDwkOPHesmUL27dvx83Nza48MDCQc+fOJfTlEqRSpUqsWrXKrmzdunVUqlQJADc3N8qWLcv69etti7RZrVbWr19Pz549H/q67u7uuLu7xyp3cnJK9R8GMBeuSyv3klaoTRyP2sTxqE2SUd685tG06b2yW7fMfcbvmzteYvduSl4bwhEKsZDW/E5FAG7ixf94hY3U4Gve5P7Z35Zbt+CPP+CPP+zKCQi4t994zFGoELg80Sy4Z5Z+ThyP2sTxqE0cT1ppk4TEn+B/3axWK9HR0bHKz549i5eXV4JeKywsjOPHj9senzx5kt27d5MpUyZy587NoEGDOHfuHN9//z0Ab7zxBpMmTWLgwIF07dqVDRs2sGDBAlauXGl7jb59+9KpUyfKlStHhQoVmDBhAuHh4XTp0iWhtyoiIvLsSpcOypUzD8CwWgm+dAn/qCgK79/PB3v3cnrHLyz6PRe/XiqCYUA9fibeS679+6953PdvOO7uULx47ITczy/Rb09ERCQ5JTjxrlu3LhMmTGDatGmA+W1FWFgYw4YNo2HDhgl6rZ07d1KjRg3b45jh3p06dWLmzJlcuHCBM2fO2M7nzZuXlStX0qdPHyZOnEiuXLmYMWOGbQ9vgLZt23L58mWGDh3KxYsXKV26NGvWrIm14JqIiIgkkMUCOXOavdUNGpAH6Ae8fCqCH78Jpn6eVnA4v62HPPiSlbm8TEsWE8DZx79+RAT8/bd53C9bNihVyj4ZL1IEHhh9JyIi4qgSvI/32bNnqVevHoZhcOzYMcqVK8exY8fIkiULmzdvxt/fP6liTTbax1uSktrE8ahNHI/axPE8SZt8PSaUVcsisISGUsn9b1rfmU2BEz9DZOTTB+TiAkWLmkn4/Ul5tmzmFwTPAP2cOB61ieNRmzietNQmSbqPd65cudizZw/z5s1j7969hIWF0a1bNzp06ICnp+cTBy0iIiJpx9278NtBb8gCRhY/tpOf7bSmTPNo2pQ/SfFbf2LZ99/88T17IKHrxERFmfuV79sHc+bcK8+S5V4SHpOQFysGCVgAR0REJLE90QomLi4uvPLKK4kdi4iIiKQRrq4wZQqsWQNLl8K1a2b5rr3O7NpbgKJFC9C6dXvKffxfB/XVq2YSfd9ibuzfD7dvJ+zCV67Ahg3mEcPZ2Vy47f6h6qVKQa5cz0zvuIiIpKx4J96bN2+OV72qVas+cTAiIiKSdnh6QvPm0KiRmQcvWgQxO34eOgQjR0JgIHzwAWTNmhmqVzePGNHRcPy4fTK+dy+cOpWwQKKjzQseOgTz598r9/WNvZBbiRKQPv2T3/SdO7BwISxbZn6ZkDkzNGsGrVur111E5BkW78S7evXqWP77Vvhh08ItFkucK56LiIjIs8vNDerXhzp1YMsWMy+NWTs1PNzMTePk7AyFC5tH69b3ym/ciN07vm8fhIUlLLDr12HzZvOIYbFAgQKxE/LAQHjcXMQVK6BzZ7N738kJrFbzzyVLoHdvmDULmjRJWIwiIpImxDvxzpgxI15eXnTu3JlXX32VLFmyJGVcIiIiksY4O5sd2tWqmVt7L1gANWvG3rr7zz8hKOgRHcQ+PvDii+YRw2o1e8L37LFPyE+cgISsI2sYcOyYeSxefK/cy8sM6v5kPCgIYhbTWbHC7Nm+P577/7x+3dwnfdkyeOml+McjIiJpQrwT7wsXLrB06VK+/fZbxo4dS8OGDenWrRv169e39YSLiIiIPI7FAhUrQoUKsXPic+dg1CjIkMHMUxs1Mv/+WE5OkC+feTRvfq88LAwOHLi3iFtMQn7jRsKCvnkTtm83j/sFBpp7j69f/+gE3zDMG+/cGc6f17BzEZFnTLwTbzc3N9q2bUvbtm05c+YMM2fOpGfPnkRERNCpUydGjBiBy4NfWYuIiIg8hMUSe22zxYvNHPXmTfjf/8zHjRqZSbiv7xNcJEMGM8uvWPFemWHAv//eS8JjEvKjR+/1UMfXqVPxn3NuGOYw9EWLQIvUiog8U55o47TcuXMzdOhQfvnlFwoVKsQnn3xCaGhoYscmIiIiz5imTc3h6DEJ+e3bZp7arRt8/TUEByfCRSwWyJ0bGjeG9983F1w7dMjsHd+5E779Ft55xxwH/9AJ6E/Iyclc5l1ERJ4pCe6ijoiIYPHixXz77bfs2LGDRo0asXLlSjJlypQU8YmIiMgzJE8e6NcPOnQw1yRbt87csjsyElatMrcnq14d2rSBnDkT+eKenlC2rHnEMAy4cCH2yuqHDpmBJZTVaq52LiIiz5R4J95//PEH3333HfPmzSMwMJAuXbqwYMECJdwiIiKS6LJlgx49oF07cz2y1avNnbqsVnNrsrJlkyDxjovFAjlymEf9+vfKIyPN5HvvXnNS+rFj8X/NXbvMcfRt25obnouISJoX78T7+eefJ3fu3PTq1Yuy/30TvHXr1lj1XtJKnSIiIpJIMmWCrl3N3cR++slcPNzLCypXtq935w64u8eeM55k3NygVCnzAOjYMf7PDQ2FV1+FwYOhf3/zBp9m73AREXF4CRpqfubMGUaNGvXQ89rHW0RERJKClxe0b2/u2HXpkrk12f3Gjzd37GrTBsqVS8YEHMxvBXr3NgNIyNZlZ85Ar14wYgS8/Tb07Jn4c8pFRMQhxHtxNavV+thDSbeIiIgkJU9Pcwev+505A7/9BocPw8iRZi67eXPCFyh/Yh4eMGuW+fcnyfivXoXhw80F3955x7whERFJU55oVXMRERERRxEebuasMU6dgk8/hTfegJ9/hrt3kyGIJk3Myegxe545Odn/mTGjeX7ZMqhUKe7XuHULJk6E/PnNoev79ydtzCIikmyUeIuIiEiqVrQoTJoEH3wAhQrdK79wwSx/7TVYvtycB56kXnoJzp+H2bPNMfHVq5t/zp5tljdtah7btpld8g0bxv06UVHmc4KCzC3P4lhTR0REUpcEbycmIiIi4mgsFqhYESpUgH37YMEC2LPHPBcSAjNmmFt0P2KpmsTh4QGvvGIejwq2ShXz2LcPxo6FH36AuKbsrVxpHi+8AO++aybiTuo3ERFJbfSbW0RERNIMiwVKloQPP4Rx48xkPEaDBikX10MFBZm928ePmwuseXrGXW/7drO3PCjInE8eGZm8cYqIyFNR4i3/396dx9lYvnEc/5zZZzCDWWzZ/bKULWoSosiUUihKiiRlq+xZylKJiBSFVLaUylZKorFnK0R2WbOOdcY263l+f9w5HDPKMHPOmZnv+/U6L8793POc65mLcs29iYiIZEtly5rp52PGQJMmqZdWb9sG48ZBTIx74nNSogR8+KHZWG3AAHOOWlq2boXnnjPrwEeNwnb+vCujFBGRG6TCW0RERLK14sXNUdlXbzj+9ddmFveLL8KoUfD3324Jz1lYmNnh/MABE1TRomn3O3gQr+7dCa9eHduAAXD8uCujFBGRdLquwjtfvnzkz5//ul4iIiIinu7MGdiyxfw+JQWio6FTJxgyBHbtcmtoRq5c5mzw3bthyhS47bY0u3mdOYPt7bfNTxc6d4a9e10cqIiIXI/r2lxt1KhRmRyGiIiIiOvkzQuffw5z55rXuXNgWWYp9cqVULUqNG9u6t0bOZo7w/j6wrPPQsuWMG8evPtu2rucX7wIH31k5s4/+ST06gWVK7s+XhERSZPNsizL3UF4mri4OEJCQoiNjSU4ONjd4dwUu91OTEwMEREReGkXVI+gnHge5cTzKCeeJzvn5OJFmD/fHLF96pTztXLlzMzvXLncEdk1rFxpCvDvv//3fg8+aHZCr1PHzT89yDmy89+TrEo58TzZKSfpqRtv6knj4+OJi4tzeomIiIhkJYGBZvO1CRPMdPMCBS5fs9kgKMh9saXpnnvgu++w//knF5s3x/K5xgTG+fPhvvvg7rth9myw210bp4iIOKS78D5//jydO3cmIiKCXLlykS9fPqeXiIiISFbk52cGicePh+7doVgxaNbMebDYsuDXXyEpyX1xOlSoQOwHH2D99Rd07XrtYfm1a6FpU6hQAT77DBISXBuniIikv/Du1asXixYtYuzYsfj7+/Ppp58yaNAgChcuzJQpUzIjRhERERGX8faGunXNMWTVqztf27QJhg6Fdu3gu+8gPt60Hz9u9kH7r1embD5etCiMHGl2Qn/zTbMzelp27IAXXoBSpcwh55qpKCLiMte1udqV5s6dy5QpU6hbty5t2rShdu3alClThuLFizNt2jRatmyZGXGKiIiIuFRay6K/+cb8evIkfPqpOZLsvvvgq6/MTun/JTQUvvwSwsMzNFQjf3544w0zXD9xoimu9+1L3e/wYejZE95+Gzp2NLunXzm/XkREMly6R7xPnTpFqVKlAAgODubUP7uQ1KpVi2XLlmVsdCIiIiIepFUriIy8/P7sWVN0b9hgBpBz5zY7pqf18vc3BXumDzQHBZnF6rt2wbRpUKlS2v1iY835acWLQ4cOZkheREQyRboL71KlSrH3nzMiy5Urxzf//Oh37ty55M2bN0ODExEREfEkZcvC66+baeh1614eFbfb4fRpU+seP25OAcuVy/kVGOjiYH184Omn4Y8/4KefTMBpSUgwx5Ddeqs5imz9eldGKSKSI6S78G7Tpg0bN24EoHfv3nz00UcEBATQtWtXevbsmeEBioiIiHia4sXNjO5PPnE+rctuh5gYSEx0b3xObDaza9zixbB6tdnCPa159Ha7mUtfrRo0aADR0WY3ORERuWnpXuPdtWtXx+/r16/P9u3bWbduHWXKlKHStaYyiYiIiGRDBQvCM8/AzJlmp/PYWDO6nTu3c7+4OEhOdk+MTiIjYdYss9Ha8OEwZUraW7QvXGhe1aqZs8CbNjW7zomIyA256RPLixcvTtOmTVV0i4iISI7l4wOFCkHlylCiROrr+/bB9u1w6BAsXQrnzrk6wquULWt2h9u7F3r0SP2TgkvWrYPmzaFcOTO8f2kbdxERSZd0j3gDREdHEx0dTUxMDHa73ena559/niGBiYiIiGQ1Pj7mdaVz5y7Xq/Hx8MUX8MMPZvD5/vvhjjvcOJhcpIgZ+e7XD8aOhVGjzFz5q/31F7z0EgwYYHZB79ABQkJcHq6ISFaV7hHvQYMG0aBBA6Kjozlx4gSnT592eomIiIjIZQEBUKyY8+ZqSUmwYoU5drtNG/j887RP/nKZvHmhTx8TxNix5qzvtBw9avoVK2amoB854sooRUSyrHSPeI8bN45Jkybx7LPPZkY8IiIiItmKj49ZC54nDxw7BvXqwdatl48VO30aZs82G49/8YU5dsxtAgOhfXto184sXB861JyVdrW4OBg2zIyQt2plzgW/9VaXhysiklWke8Q7MTGRe+65JzNiEREREcmyLl6E8+ev/bp40RTVTz0FkyebY8lq1Lg8Nb1mzdRF965dbtqUzdvbrO1etw4WLDA/LUhLYqJZK16uHDzxBPz2m2vjFBHJItJdeL/wwgt8+eWXmRGLiIiISJYTHAyhoeY47DNnrv1KSDD9goNNsR0ZCX37miL8pZfg4Yed73vhAvTubQaUP/nELLN2+eleNhs88AD88ospqps1S/soMssyI+R33WUWri9YoKPIRESukO6p5vHx8XzyySf88ssvVKpUCV9fX6frI0eOzLDgRERERDxdeDh8+eXlqeP/JjjY9L+67ZFHUvf99VczoJyYCHPnmlexYmbwuW5dsyzbpapXN+d879oF770HkyalfWD54sXmVbUq9OplRsKv3nFORCSHSfd/BTdt2kSVKlUA2Lx5s9M1W1o/ARURERHJ5sLDUxfUN6tYMbj3Xli16vJR2wcOwMSJpuatWtVG1aq+PPig2cDNZf73Pxg/HgYNgg8+gI8/TvunDhs2QIsWZsf07t3NLnJX7jAnIpKD2CxL84CuFhcXR0hICLGxsQQHB7s7nJtit9uJiYkhIiICL6+bPrZdMoBy4nmUE8+jnHge5cR9zp+H5cth0SLYtu1yu2VZJCYmULGiPyNGuHHwIzbWFOLvv292Pb+W8HBzFFnHjpAvn+vicyH9PfE8yonnyU45SU/deFNPevDgQQ4ePHgztxARERGRf5ErFzz4oNlEfPx4ePJJ59H1yMjUYyguPeE1JMRMKd+3DyZMMCPiaTl+3OwoV6yYGQHXvyFFJAdJd+Ftt9t58803CQkJoXjx4hQvXpy8efPy1ltvYbfbMyNGEREREQEKF4ZnnoHPPoO337aoWTORunWd+/z9N7RubWrcxYshPt5Fwfn7wwsvmGH5GTPgzjvT7nfuHIwcac4Kf/5552F8EZFsKt1rvPv168dnn33G0KFDqVmzJgArVqxg4MCBxMfHM3jw4AwPUkREREQus9mgUiUoWPACYWG5na5FR5sNxTduNK+AAKhVy2zKdtttaW9KnqG8veHxx6FpU1P5v/uu2eX8aklJZsH6xInQuDG89hrcfXcmByci4h7pHvGePHkyn376KR06dKBSpUpUqlSJjh07MmHCBCZNmpQJIYqIiIjI9QoPh0KFLr+PjzengfXpYwakp02DI0dcEIjNZo4W+/lnWL/eHGB+rfWcc+aYQ83r1IF583QUmYhkO+kuvE+dOkW5cuVStZcrV45Tp05lSFAiIiIicmMeftisBR82DKKiICjo8rWYGJg+HV58EaZMcWFQVavCV1+Zo8g6drz2NuzLlpkHqFzZ/IQgOdmFQYqIZJ50F96VK1dmzJgxqdrHjBlD5cqVMyQoEREREblxNhuULw+dO8PUqdCzJ9xxh/M086v3QLPbzStTlSoFH30E+/ebY8audRj5n3+axexlysDo0XDhQiYHJiKSudK9xnvYsGE8/PDD/PLLL9SoUQOAVatW8ffffzNv3rwMD1BEREREbpyfnzkP/N574eRJs+x67drUe5+tXQvjxsF995n14LfckolBRUTA22+bdd2ffGKOIjt0KHW//fvhlVfMmeGvvAKdOkFoaCYGJiKSOdI94l2nTh127txJkyZNOHPmDGfOnKFp06bs2LGD2rVrZ0aMIiIiIpIBQkPhiSfMNHSfq4ZffvnFFOYzZkCHDubEr3nz4OzZTAwoTx7zQXv2wOefQxrLGQET2IAB5iiyLl3gwIFMDEpEJOOle8QboHDhwtq9XERERCSbsCyzGbm3N6SkmLadO81rwgSIjDSj4FWrpi7YM4SfH7RpY85B+/57sxP66tWp+124AB98YKart2hhzg+//fZMCEhEJGNd94h3vXr1mDVr1jWvnzhxglKlSmVIUCIiIiLiOjab2fV88mSz8/mV/6RLToZff4U334TnnoPffsvEQLy8zNFiK1fC0qXQsGHa/ZKTzeL1ihWhUSNYsSITgxIRuXnXXXgvXryY5s2bM2DAgDSvp6SksH///gwLTERERERcKyQEHnvMDCp/+KH5fUjI5euxsVCggAsCsdnMovQffzSHkbdsaYbj0/LDD1C7tjmsfO5cF+wQJyKSfula4z127FhGjRpFkyZNOH/+fGbFJCIiIiJuVrKkGf2eNAn694eaNc1O6cWKOfebPRveessMUiclZUIglSrBF1/AX3/Byy9DYGDa/X79FR591IyCT56cScGIiNyYdBXejz32GKtXr2bLli3cfffd7NmzJ7PiEhEREREP4ONjdkDv3dssvb6SZcGCBWZH9CFDzBLtcePMcd2WlcGBlChhhuH37zc/CcifP+1+W7eaOfGlS8OoUXDuXAYHIiKSfune1bx8+fL89ttvFC1alDvvvJNffvklM+ISEREREQ9z5TngAKdPOx+xffasmR3erZs5+WvmTDh1KoODCA83x4vt328K66JF0+7399/QtasZou/fH44fz+BARESuX7oLb4CQkBB+/PFH2rVrR8OGDXn//fczOi4RERER8XD588PEiWbjtTp1zObkl/z9t5mm/txz5iSwo0cz+MNz54ZXX4Xdu83U8ttuS7vf6dNmLnzx4maq+r59GRyIiMh/u+7C23bVjzhtNhtDhw5lypQpvPHGG7zwwgsZHpyIiIiIeDYvL3PMWI8eMGWKqW0rVLh83bJg82YIDs6kAHx9oVUr2LTJbK5Wq1ba/S5ehDFjoEwZs1nbpk2ZFJCISGrXXXhb11io89RTT7FixQr+/PPPDAtKRERERLKeXLmgQQOzFvyTT+CppyAiAu65B4KCnPtOmwbTp0NMTAZ9uJcXPPIILF9ujhdr1Cjtfikp8OWXULmyOa5s6dJMWJAuIuLM53o7Ll68mPzX2MSiSpUqrFu3jh9//DHDAhMRERGRrKtQITOw/PTTzuvAAeLjYc4c8+u0aWYj8nr1zM7pAQEZ8OE1a8L338OWLTB8uPmQ5OTU/X76ybwiI+G118z5aV43tBJTRORfXfd/WerUqYOPz7Xr9NDQUFq1apUhQYmIiIhI9mCzmZHwK23dCgkJl9//+afZJ+3ZZ+H9980s8AwZhL7tNrPQfPdu6NIldSCXrFkDTZua/p9/7hyciEgG0I/0RERERMSl7rgDPvvMFNqFC19uj4+HRYugXz9o2xamToXExAz4wGLFTEV/4IDZCS4sLO1+27ebDy5VCt57z2zTLiKSAVR4i4iIiIjLhYdD8+bm3O9hw+DBB50HpI8fN8u1fX0z8EPz54c33jBHkY0ebc4GT8vhw9CzpynY+/WDY8cyMAgRyYlUeIuIiIiI29hsUL68Ofd7yhTo1QuqVTPt99+f+uzwadNg3Tqw22/iQ4OCoHNn2LXL3LBSpbT7nTkD77xjjiLr2BH27LmJDxWRnOy6N1cTEREREclMfn5Qu7Z5nToFV28vdOiQ2QkdzOD1ffeZTdmKFr3BD/TxMbu/tWgB8+eb7diXLk3dLyEBxo6F8eOhWTOzEVvVqjf4oSKSE2nEW0REREQ8Tv78qc/+Xrbs8u9PnYKZM81AdLdu8MMPN7Ek22aDhx6CJUtg9Wpo0iT1UDuYYfavvzaL1Bs0gOhoHUUmItdFhbeIiIiIZAnNmkHfvub0L2/vy+27dpnB6FatYMgQ+O23m/iQyEiYNctsvf7889deZL5wIdSvD3fdBTNmmPPBRUSuQYW3iIiIiGQJPj5Qowa8/jpMngzt2pkNyC9JToaVK2HevAz4sHLlzNbre/dC9+6QO3fa/X7/3fxEoHx5mDDBbM0uInIVFd4iIiIikuWEhMCjj8IHH5gNyps0gbx5zbV69Zz7pqTA3Llmr7R0K1LEHC124AAMHgwREWn327ULr/btCY+MNNu0x8bewIeJSHalwltEREREsrQSJcys8EmToH9/M/v7SuvXwyefQOvW5hjvX3+FpKR0fki+fGae+759ZqO1K4far+AdE4NXnz7mKLLeveHIkRt4IhHJblR4i4iIiEi24O0Nd95pdke/UnS0+dVuN+u/hw4168HHjoUdO9K5P1pgILRvb75w+vRr724eF2d2SS9RAl580SxEF5EcS4W3iIiIiGRrzz4LzZtDWNjltnPnzFrwHj3MzugzZsCJE+m4qY8PPPmkOVR8wYLU89svSUw0a7/LljVrwX///aaeRUSyJhXeIiIiIpKtFSliiu/PPoO33jLnf185Kn7woNmsbc6cG7i5zQYPPAC//IJ9zRriH3kEK62jyCzLVPd33mmK9AULdBSZSA6iwltEREREcgQvL6hSxZz7PXUqvPIK3H775ev33+/cPzbWnCp23fVx9eqcmTABa+tWs+X61XPeL1m0CKKioFo1cy54cvKNPI6IZCEqvEVEREQkxwkKMgPVQ4aYmeAvvZR6v7RFi+C118y16dMhJuY6b37rrWY3t337zA2Cg9Put2EDPPWUmYY+dixcvHgzjyQiHkyFt4iIiIjkaAULwiOPOLdZlim8wWxMPm0atG0LffqYzdquq0YuVMjs5HbggPm1YMG0++3ZYxaalygB77wDp0/fzOOIiAdS4S0iIiIikobHHzdT069csr15M4waZdaMjxwJGzdex1T0kBAz8r13rxkJ/9//0u4XEwP9+pmjyHr0gEOHnK/Hx5s58o8/DnXrml+nTjXtIuLRVHiLiIiIiFzFZjO17Vtvweefm+PHihS5fD0hARYvhtdfv3xc2X8KCDBrv7dtMxutVa+edr9z52DECChZ0gyzb98O338PhQubQObMgaVLza+tWpn2uXNv7oFFJFOp8BYRERER+RdhYeYksLFj4b33oGFDyJXLXPP1hRo1nPufOmVq52vy9jaj1WvXmqq9QYO0+yUlmaq/fHl47DE4c8a02+3Ov545Y65///0NPqGIZDYfdwcgIiIiIpIV2GxmH7SyZc1A9Nq1Znb4pSL8kunTzfrwyEhzcljVqqbWTvOG999vXhs2wLvvwrffXi6or3atOe2WZe713HNw+LAZWRcRj6IRbxERERGRdPLzg1q1oGlT5/bERFi2zEZSEqxYAYMGQZs2ZuB6//5/uWHVqqZi37kTOnRIf/FsWWZTthkz0v0sIpL5VHiLiIiIiGSQxEQb9epZTieInT4Ns2dD587QpYtZjh0Xd40blC4NH39sjiLr29fMZb9eXl7mg0TE46jwFhERERHJILlzW7RrB5Mnm43XatQAnysWd+7ebTY2b93a1NbXVKAADB5s5qtfL7vdLDAXEY+jNd4iIiIiIhnMx8fUzJGRZnR72TKz7nvXLnM9f34oXtz5a86fh6Ag5+PLiIjguC2COCv3f35msO0c4fnzZ9xDiEiGUeEtIiIiIpKJgoPhkUfM68ABs5F5/vxXFdjA22/D2bNmQ7Y6dUyf4/c/ydOzXuIkYf/5OaHWCb6sF0t4Jj2HiNw4Fd4iIiIiIi5SrJjZbO1qx47B5s3m959/DhMnwh13QPnSj3HCawcB9nMEcvGa971IICcJI87PV4W3iAfSGm8RERERETc7e9Yc132JZcG6dTB+oj/7AstxklAsvMjFhTRfjqK8Y0f47jv3PISIXJMKbxERERERNytTBoYNg3HjoHlzCLtiZrnd24+TuUuw1XYbf1IRu+Of8LbUN0pKhMcfhy+/dEncInJ9NNVcRERERMRDFCkCzz4LzzwDf/5pjvZevx7w84egcPxsZ/GyFTAFtq+f2f08JgaOnLl8k5QUc4Nz5+DFF931KCJyBRXeIiIiIiIexmaDSpUgVy6YN8+8P3fORlh4MIRWd+58yy2wfjscuWINuGXBSy+ZOezdu7s2eBFJRYW3iIiIiIgH8/KCvHlNfX21CxfgzBkbIWXLQfIhOH5Vhx49TPE9YEDqbdRFxGW0xltEREREJAtKSoKdO+HgQdh/wIa9cBHo/HLqjoMGmQLcslwfpIgAKrxFRERERLKkuDhTfAOcOQOHD9s41aoLDB+euvPIkWbqeUqKK0MUkX+o8BYRERER8XAXL8L5886vgAAz/dyywG6HhAQYPBi2P9LDbI9+9dTyCRPMzm2XqnURcRkV3iIiIiIiHio4GEJDTVF95kzql90OERHg7Q1+fmbNd58+sPjWl2DKFHPhSl99BU88AfHxLn8WkZxMm6uJiIiIiHio8HBzJHdc3L/3O3sWJk6EPXsgOdnMLD/wxDM8+3UuvJ5+ChITL3f+/nt45BGYMwdy587U+EXEUOEtIiIiIuLBwsPN67+MGAHjx8P8+eb9jBlwqEYT+nw/F1uTxma++iXR0RAVBT/+aLZMF5FMpanmIiIiIiLZgI8PdOwI7dubI8gAqlQBW1QDWLDAzFu/0sqVcP/9cPzqM8hEJKOp8BYRERERySZsNnj4YXOC2BNPQMOG/1yoVQsWLTILxq+0YQPUqQOHDrk8VpGcRIW3iIiIiEg2U6UKtG59VWO1auydthIKFnRu37YNateGvXtdFZ5IjqPCW0REREQkB1i1Cl4ZcysTOm8kpVhJ54t795pR8W3b3BOcSDanwltEREREJJs7dcrsdA7w/eoIBjXZwPkylZ07HT4M995rpp+LSIZS4S0iIiIiks3lzw8vvHD5WO8Nu0Pofs9KDpWv79zxxAm47z4zPC4iGcYjCu+PPvqIEiVKEBAQQGRkJGvXrr1m36SkJN58801Kly5NQEAAlStXZv6lMxP+MXDgQGw2m9OrXLlymf0YIiIiIiIeKyoK3n4b8uQx7w+dCqJ7uR/44/ZnnDvGxsIDD5gjx0QkQ7i98P7666/p1q0bAwYMYP369VSuXJmoqChiYmLS7P/6668zfvx4Ro8ezdatW2nfvj1NmjRhw1VTYm677TaOHDnieK1YscIVjyMiIiIi4rFuvx3efx+KFTPvzyf5M6DY5/xYsbdzx/Pnzfboc+e6PkiRbMjthffIkSNp164dbdq0oUKFCowbN46goCA+//zzNPtPnTqVvn370rBhQ0qVKkWHDh1o2LAhI0aMcOrn4+NDwYIFHa+wsDBXPI6IiIiIiEcrUACGD4c77zTv7V6+jLvlbcbeNoZkvC93TEiApk1h+nT3BCqSjfi488MTExNZt24dffr0cbR5eXlRv359Vl1jXUlCQgIBAQFObYGBgalGtHft2kXhwoUJCAigRo0aDBkyhGKXfrSXxj0TEhIc7+Pi4gCw2+3Y7fYbejZPYbfbsSwryz9HdqKceB7lxPMoJ55HOfE8yonnyUo5CQiAvn1h6lSYOdMGXl4srdCepiXXU+CHKwbAkpOxnn4aKy7OLBLPYrJSTnKK7JST9DyDWwvvEydOkJKSQoECBZzaCxQowPbt29P8mqioKEaOHMm9995L6dKliY6OZtasWaSkpDj6REZGMmnSJMqWLcuRI0cYNGgQtWvXZvPmzeS5tKjlCkOGDGHQoEGp2o8fP058fPxNPqV72e12YmNjsSwLLy+3T3AQlBNPpJx4HuXE8ygnnkc58TxZMScPPQTBwX5MmhREu5fOYZV7kws9LhB0xSi3zbKwvfQScUePcuHFF90YbfplxZxkd9kpJ2fPnr3uvm4tvG/EBx98QLt27ShXrhw2m43SpUvTpk0bp6npDz30kOP3lSpVIjIykuLFi/PNN9/Qtm3bVPfs06cP3bp1c7yPi4ujaNGihIeHExwcnLkPlMnsdjs2m43w8PAs/wc7u1BOPI9y4nmUE8+jnHge5cTzZNWcNGkC9epBcHB+0zB1KlZYGIwZg+2KfsEDBpAboF8/sNnSuJPnyao5yc6yU06unon9b9xaeIeFheHt7c2xY8ec2o8dO0bBggXT/Jrw8HDmzJlDfHw8J0+epHDhwvTu3ZtSpUpd83Py5s3Lrbfeyl9//ZXmdX9/f/z9/VO1e3l5Zfk/DAA2my3bPEt2oZx4HuXE8ygnnkc58TzKiefJqjnJm/eKN15eWB98yPvbHqJk9AQaM8dRgHsNGADnzsG772aZ4jur5iQ7yy45SU/8bn1SPz8/qlWrRvQVRxXY7Xaio6OpUaPGv35tQEAARYoUITk5mZkzZ/LYY49ds++5c+fYvXs3hQoVyrDYRURERESyqxkzbSwObMjn5YbzAa+SdOV43fDh0LEjZIM1uiKu4vYfMXTr1o0JEyYwefJktm3bRocOHTh//jxt2rQBoFWrVk6br61Zs4ZZs2axZ88eli9fzoMPPojdbqdXr16OPj169GDp0qXs27ePlStX0qRJE7y9vWnRooXLn09EREREJKtJTv7nN2XKEH37q/RjMLFcsQRz3Dho3fqKjiLyb9y+xvvJJ5/k+PHj9O/fn6NHj1KlShXmz5/v2HDtwIEDTkP48fHxvP766+zZs4fcuXPTsGFDpk6dSt4r5sccPHiQFi1acPLkScLDw6lVqxarV68mPDzc1Y8nIiIiIpLltGgBRYuaM78TS5Rkm3cLum4M5w3epCT7TKcvvjDnfX/1FaSxbFNELrNZlmW5OwhPExcXR0hICLGxsdlic7WYmBgiIiKy/BqK7EI58TzKiedRTjyPcuJ5lBPPkx1z8tdf8PbbcPIkcOQwAetX0d0azt2sudypQQOYPRuCgtwW57Vkx5xkddkpJ+mpG7P2k4qIiIiISKYpUwZGjoRbbwUKFSb+ztoM9nqDb2iGY/RuwQKIioLYWDdGKuLZVHiLiIiIiMg15c8PQ4ZA3bpARARE3s1U7zbMpsnlTitWmDPJTp50V5giHk2Ft4iIiIiI/Cs/P+jWDVq1AkJDKfJoNaLyrnXutG4d1KkDR464JUYRT+b2zdVERERERMTz2WzQrBkUKwa33BJBrlM/wQMPwLFjlztt2QK1a0N0NBQv7r5gRTyMRrxFREREROS6RUZCkSJAxYqwbBkULcop8rGSGqbD7t1Qqxbs3OnWOEU8iQpvERERERG5MbfeSmL0ct7JO4wh9GEaT5tN1w4eNCPfmza5O0IRj6DCW0REREREbtjSfcXZceczkCcP03mKofQmHn+IiTFrvtes+e+biGRzKrxFREREROSG1a8PL3QKwFbzHgjJy0ru4TXe5ThhcOaM6bBkibvDFHErFd4iIiIiInLDbDZ47DEY8LYfQfffDflD2UMpuvI+2ygH587BQw/BvHnuDlXEbVR4i4iIiIjITatWDd77wJdCj90F4RHEEkJf3iGa+yE+Hho3hm+/dXeYIm6hwltERERERDJE0aIw4gMfKrWpBgULkYwPo+jCRJ7DSkqCp56CSZPcHaaIy6nwFhERERGRDJMnDwwa7EPDflXhllsASMIXG4DdDm3awJgxbo1RxNVUeIuIiIiISIby8YEOnb3pMLYy1ap70ZbPnDu8/DIMGeKe4ETcQIW3iIiIiIhkioaPeDFgTUO8X+vp1H6eIOjbF/r0ActyU3QirqPCW0REREREMo3NywZDh8LgwQDsoSRt+Yz5RJn2V14xU9BFsjEfdwcgIiIiIiI5QN++xHrn563e/pwnFx/RiQMUo+2Yj/E+dw4mTDBz1EWyIY14i4iIiIiIS+Tp2Z5az5UBs9Uac2nEQAZybtK30KIFJCa6N0CRTKLCW0REREREXMLLC9pOrM0r/fLgY0sB4A+q0IP3ODRjpTnr++JF9wYpkglUeIuIiIiIiEs98HYdBn+Uj2Cv8wAcogjdGcGGn47AQw/B2bNujlAkY6nwFhERERERl6vQoQ7vf3sLJXwOAXCeXAxgEHOX5sGqVx9OnXJzhCIZR4W3iIiIiIi4RUTTWgxbWJXIgI0AWNj4jLYc+u0Q1K0Lx465N0CRDKLCW0RERERE3CawbiT9VjWiWZ6fAWjPOG7hEPz5J9SuDQcOuDlCkZunwltERERERNzKVqUyrX57mZEFhvEgP1++sGuXKb7/+st9wYlkABXeIiIiIiLifmXL8r/VU6FUKafm2Qfu4Le7O8PmzW4KTOTmqfAWERERERHPUKIELF8OFSoAsJIafM7zvHWyI7NqDMda+5t74xO5QSq8RURERETEcxQuDEuXwh13sJa7ALPp2sRzTzDq3lkkRi93c4Ai6afCW0REREREPEtYGCxaxKv3/E5LpjmaFyXcQ78Hf+f0t7+4MTiR9FPhLSIiIiIinickBNuCn3nqgVP0Zih+JAKwPbkM3Z48xJ6P57s5QJHrp8JbREREREQ8U65cMHcuNRtHMIxehHECgBNWfnp1Os/KN35yc4Ai10eFt4iIiIiIeC5/f/jmG0q3rMFIulGWHQAk4MeQt5OZ32mumwMU+W8qvEVERERExLP5+sKUKeR76UneoS/3sRiAPJyl8scvwrBhbg5Q5N/5uDsAERERERGR/+TlBWPH4pc7N11HjKA4+/kfuyjEUXjtNYiLg7feApvN3ZGKpKLCW0REREREsgabDYYPxxYczOMDBjhfGzyYxDMXONjlPUqV0cRe8Sz6EykiIiIiIlmHzQb9+8PIkU7NFvDBR970rL+BZYtT3BObyDWo8BYRERERkayna1f45BPH1PJF3M8y7iVx/2GGt/yDqROTsSw3xyjyDxXeIiIiIiKSNbVrB9Omgbc3dVhKAxaY9iOH+ab3et4ZlEx8vHtDFAEV3iIiIiIikpW1aAGzZuHj501nxtCOCdiwIOYYaz9ex1v9A4mJcXeQktOp8BYRERERkazt0Ufhxx+xBQXxKHMZyEBycR5OnuDo9xvp/nISW7e6O0jJyVR4i4iIiIhI1le/PixcCCEh3MEG3qMHhTiCV2wscQtW069HAgsXujtIyalUeIuIiIiISPZwzz2waBGEhXELhxhBdyqzEeLiSF62kl9/itOGa+IWKrxFRERERCT7uOMOWLoUChcmD+cYyEAe4QduOb+dnjPvxrZnt7sjlBxIhbeIiIiIiGQvFSrA8uVYJUrgQwov8Qkj6E6uA9ugdm0uLfjW6Le4igpvERERERHJfkqVwlq2jOQyZQAI4qJpP3IE7r2XU4s38sorsGmTG2OUHEOFt4iIiIiIZE9FinBq9mysKlWcmhNPxvH2g8vZt/4U/fvDTz+5JzzJOVR4i4iIiIhItmUPC8OKjoYaNRxtKXgTkngcVq8m5ehxPv4Yxo2D5GQ3BirZmgpvERERERHJ3vLmhQUL4P77AQgknjd4iyb2GbB2LRw9yo8/wsCBcPasWyOVbEqFt4iIiIiIZH+5c8OPP0KjRgB4YfE8E+lijcTn99Vw6BAbN0L37nDwoJtjlWxHhbeIiIiIiOQMAQEwcyY89ZSjqR6LeIc+hGxYDPv3c+QI9OgB69a5MU7JdlR4i4iIiIhIzuHrC198AS+84Ggqz3ZG0o2Sf34Hu3dz/jwMGgQHDrgxTslWVHiLiIiIiEjO4u0Nn3wCXbs6miI4zjB6UWPbZ7BzB482sihWzI0xSrbi4+4AREREREREXM5mgxEjIE8eePNNAAJIoA9DWLRzDXW3VgVruOkncpM04i0iIiIiIjmTzWbmlA8ffrkJs+7be9QIeOklSEkBYM0a2LfPPWFK1qfCW0REREREcrYePcxB3lePbk+YAK1a8de2JIYNg549TQEukl4qvEVERERERF56CaZMMeu/r/Tll3zd9GsS41OIj4fBg2HGDLAs94QpWZMKbxEREREREYBnnoFvvwU/P6fmHtvbUnvn55CSjGXB5MkwciQkJropTslyVHiLiIiIiIhc0qQJzJ0LgYGOJn8S6fnXizyz+01ISgJgyRLo2xdOnXJTnJKlqPAWERERERG5UoMGsGABBAc7mmzAkzvfou/edviTAMCOHdC9O+ze7aY4JctQ4S0iIiIiInK1WrVg0SIIDXVqrrFzMsO2NSIs6AIAJ05Ar16wapU7gpSsQoW3iIiIiIhIWqpVg6VLoWBBp+ZSuxfy/pp7KF/4DGBOHMuTxw3xSZahwltERERERORabrsNli+H4sWdmvPu38jgH6pQ7/ZjdOwIt9/upvgkS1DhLSIiIiIi8m/KlDHF9623OjX7Ht7Pq59WpEHEH07tlgWxsS6MTzyeCm8REREREZH/UrQoLFsGlSo5NdtOHIf77nNa5P311/Dyy2bzNRFQ4S0iIiIiInJ9ChSAxYshMtK5/cwZeOABWLSINWtg2jQ4fRr69DHdRVR4i4iIiIiIXK/8+WHhQqhb17n9/Hlo2JDy++c71nsnJcHIkTB5spl+LjmXCm8REREREZH0yJMH5s2Dhg2d2xMSCG7ZiLcqfsODD15unjEDBg+GixddG6Z4DhXeIiIiIiIi6RUYCLNnQ7Nmzu3Jyfg88xQd/T/jpZfA65+Ka80ac953TIzrQxX3U+EtIiIiIiJyI/z84KuvoE0b53bLwtbuBR7Z/QGDBkGuXKZ53z7o2hW2bHF5pOJmKrxFRERERERulLc3fPqp2cb8al26UOWHtxnxnkWRIqYpLg4++URrvnMaFd4iIiIiIiI3w8sLPvgA+vZNfe2NNygyujfvDbeoWtUsD+/TB2w214cp7uPj7gBERERERESyPJvN7KB2qbK+0rBh5I6LY8CHH3HkmBcFC7onRHEfjXiLiIiIiIhklN69YcyY1O3jxuH9fGtuKZjs1JyQAO+9B4cPuyg+cQsV3iIiIiIiIhmpUyeYNOnyluaXfPEFNG9uqm3MOu9Ro2DpUujeHTZudHmk4iIqvEVERERERDJa69bw9dfg6+vcPns2PPooXLjAuXNw4IBpPncO+veHH390faiS+VR4i4iIiIiIZIYnnoDvvoOAAOf2BQsgKoo89liGD4c77zTNdjuMGwdjx0JycurbSdalwltERERERCSzPPQQzJ8PuXM7t69YAfXqEXTxJK+/bmr0S+bNgwED4OxZ14YqmUeFt4iIiIiISGaqUweioyFfPuf2deugTh28jh2hdWvo1g18/jl3atMms+77779dH65kPBXeIiIiIiIime2uu8wuagUKOLdv2QK1a8P+/dx3HwwZAiEh5tKRI6b4vrQOXLIuFd4iIiIiIiKuULEiLFsGRYs6t+/eDbVqwc6dlCsH778PpUqZSxUqwC23uD5UyVgqvEVERERERFzl1lth+XIoU8a5/eBBM/K9aRPh4fDuu9C4MfTsmfpUMsl6lEIRERERERFXKl7cjHzffrtze0yMWQ++Zg0BAdC2LeTK5dxl3z6IjXVZpJJBVHiLiIiIiIi4WqFCsGQJVK/u3H7mDNSvb65d5eRJs9t5166wd68rgpSMosJbRERERETEHUJDzW7ntWs7t587Z44hmzfPqXnsWDh1Co4fh169YPVqF8YqN0WFt4iIiIiIiLsEB5tzvqOinNvj480i72+/dTR17Aj/+9/ly4MHwzffgGW5Lly5MSq8RURERERE3CkoCL77Dpo2dW5PSoKnnoJJkwDInx+GDoV7773cZepUeO89SEx0XbiSfiq8RURERERE3M3fH77+Gp591rndboc2bWDMGAD8/KBHD2jV6nKXZcugd28zDV08kwpvERERERERT+DjY0a3O3RIfe3ll2HIEABsNmjWDPr1g4AAc3nXLrPp2q5drgtXrp8KbxEREREREU/h5QUffWR2T7ta377Qp49jUffdd8OwYRAebi6fOgX797swVrluKrxFREREREQ8ic1mFnO//Xbqa0OHwiuvmCnoQMmS8P77UKGC2Yutfn3XhirXx8fdAYiIiIiIiMhVbDYzlzx3bujSxfnamDHmyLEJE8DHh5AQU6N7e6e+TUpK2u3iWhrxFhERERER8VSvvgqffWamoF9p0iRo0cKxnbmvb+ouy5dDt27m3G9xLxXeIiIiIiIinuz55+Grr8zma1eaMcPML794MdWX7NoFo0bBnj2m+N6+3SWRyjWo8BYREREREfF0zZvDnDnm2LEr/fQTPPQQnD3r1BwQAKGh5vdnzpg92RYtckmkkgYV3iIiIiIiIlnBww+bQjtXLuf2pUvNrmpXHORdtCiMGAGVKpn3yclmE7ZJkxz7sokLqfAWERERERHJKu67D375BfLmdW5fuxbq1oVjxxxNefLAoEHQsOHlbjNnmo3YLlxwSbTyDxXeIiIiIiIiWcndd8OSJZcP8L7kzz+hdm04cMDR5OMDHTqY16XN1377DXr2hKNHXRdyTqfjxERERERERLKaypXNtuX168PBg5fbd+0yxXd0NJQp42hu2BCKFDHHgJ87Z2rz3r3NiWVX74Z+teDg1DW+pI8KbxERERERkayobFlTfNerZ7Yvv+TAAVN8L1wIt9/uaK5c2az7fvNN02XHDnjyyf/+mNBQ+PJLFd83Q1PNRUREREREsqoSJUzxXaGCc/vRo1CnDvz+u1Nz4cKm+H7pJUhIMJuk58177Ze/P5w8CXFxLniWbEyFt4iIiIiISFZWuLDZ2fyOO5zbT52C++83hfkVcuW6PBAeGGje58oFsbGm0L70Plcuc11unkcU3h999BElSpQgICCAyMhI1q5de82+SUlJvPnmm5QuXZqAgAAqV67M/Pnzb+qeIiIiIiIiWVpYmDmou2ZN5/azZyEqCn7++V+//NAhs1R8x45MjDEHc3vh/fXXX9OtWzcGDBjA+vXrqVy5MlFRUcTExKTZ//XXX2f8+PGMHj2arVu30r59e5o0acKGDRtu+J4iIiIiIiJZXkiIKbAfeMC5/eJFaNQIZs1K88uSkyEmBmw2swGbZDy3F94jR46kXbt2tGnThgoVKjBu3DiCgoL4/PPP0+w/depU+vbtS8OGDSlVqhQdOnSgYcOGjBgx4obvKSIiIiIiki3kygVz50Ljxs7tSUnQvDlMnZrqS3x8zBLxkiVTHw8uGcOthXdiYiLr1q2jfv36jjYvLy/q16/PqlWr0vyahIQEAgICnNoCAwNZsWLFDd9TREREREQk2/D3h2++gZYtndtTUqBVKxg7Ns0vCQtzUXw5kFuPEztx4gQpKSkUKFDAqb1AgQJs3749za+Jiopi5MiR3HvvvZQuXZro6GhmzZpFSkrKDd8zISGBhIQEx/u4f7bss9vt2O32G34+T2C327EsK8s/R3ainHge5cTzKCeeRznxPMqJ51FOPE+Ozom3N0yahC1XLmyffOJ8rWNH7L28sax2WBZY1rVvc+m63W6REd/G7JST9DxDljvH+4MPPqBdu3aUK1cOm81G6dKladOmzU1NIx8yZAiDBg1K1X78+HHi4+NvJly3s9vtxMbGYlkWXl5uX1kgKCeeSDnxPMqJ51FOPI9y4nmUE8+jnAADB5LHy4tc48Y5NXsNexcrvCFJQeEk+l678k5KspGc7MXJk2fIkyflpsPJTjk5e/bsdfd1a+EdFhaGt7c3x44dc2o/duwYBQsWTPNrwsPDmTNnDvHx8Zw8eZLChQvTu3dvSpUqdcP37NOnD926dXO8j4uLo2jRooSHhxMcHHwzj+h2drsdm81GeHh4lv+DnV0oJ55HOfE8yonnUU48j3LieZQTz6Oc/GPMGOwFC+I1cKBTs9fxGFK8kkgqX+KaX5qSYtaAh4aGEhFx86Fkp5xcvQT637i18Pbz86NatWpER0fT+J/F/3a7nejoaDp37vyvXxsQEECRIkVISkpi5syZNG/e/Ibv6e/vj7+/f6p2Ly+vLP+HAcBms2WbZ8kulBPPo5x4HuXE8ygnnkc58TzKiedRTv4xYAAEB8M/A47BxBHKCU4eg4QkG+TOYw7yTk42lXbeEMibD7y8CAuDvHltZNS3MLvkJD3xu32qebdu3WjdujXVq1fnrrvuYtSoUZw/f542bdoA0KpVK4oUKcKQIUMAWLNmDYcOHaJKlSocOnSIgQMHYrfb6dWr13XfU0REREREJMfp2hVy54aXXiLcOsGXtCSOYDiFedm8wLKbX2PtcDoEhg8nuEk9wsPdHXzW5vbC+8knn+T48eP079+fo0ePUqVKFebPn+/YHO3AgQNOP0mIj4/n9ddfZ8+ePeTOnZuGDRsydepU8l6x7/1/3VNERERERCRHatfOFN/PPkt4ygnCOXH5mnXVr2dt0P4BKDgHHn3UxYFmLzbL+rc97HKmuLg4QkJCiI2NzRZrvGNiYoiIiMjyUzmyC+XE8ygnnkc58TzKiedRTjyPcuJ5lJN/MWMGNGv23/1sNnO49+HDkI41zdeSnXKSnroxaz+piIiIiIiIpN/Fi9fXz7Lg9GlTqMsNU+EtIiIiIiKS08yZw3XvlublBbNnZ2o42Z0KbxERERERkZzm5Emw26+vr90Op05lbjzZnApvERERERGRnCY0NH0j3vnzZ2482ZwKbxERERERkZymceP0jXg3aZKp4WR3KrxFRERERERymmbNIF8+s2v5v7HZTL8nnnBNXNmUCm8REREREZGcJiAAJk82v79W8X2pffLkDDlKLCdT4S0iIiIiIpITNWpkdjfPm9e8v7Tm+9KvefPCd9+ZfnJTfNwdgIiIiIiIiLjJo4/C4cPmnO7Zs83u5fnzmzXdTzyhke4MosJbREREREQkJwsIgGeeMS/JFJpqLiIiIiIiIpKJVHiLiIiIiIiIZCIV3iIiIiIiIiKZSIW3iIiIiIiISCZS4S0iIiIiIiKSiVR4i4iIiIiIiGQiFd4iIiIiIiIimUiFt4iIiIiIiEgmUuEtIiIiIiIikolUeIuIiIiIiIhkIhXeIiIiIiIiIplIhbeIiIiIiIhIJlLhLSIiIiIiIpKJVHiLiIiIiIiIZCIfdwfgiSzLAiAuLs7Nkdw8u93O2bNnCQgIwMtLP2fxBMqJ51FOPI9y4nmUE8+jnHge5cTzKCeeJzvl5FK9eKl+/DcqvNNw9uxZAIoWLermSERERERERMSTnT17lpCQkH/tY7OupzzPYex2O4cPHyZPnjzYbDZ3h3NT4uLiKFq0KH///TfBwcHuDkdQTjyRcuJ5lBPPo5x4HuXE8ygnnkc58TzZKSeWZXH27FkKFy78n6P3GvFOg5eXF7fccou7w8hQwcHBWf4PdnajnHge5cTzKCeeRznxPMqJ51FOPI9y4nmyS07+a6T7kqw9qV5ERERERETEw6nwFhEREREREclEKryzOX9/fwYMGIC/v7+7Q5F/KCeeRznxPMqJ51FOPI9y4nmUE8+jnHienJoTba4mIiIiIiIikok04i0iIiIiIiKSiVR4i4iIiIiIiGQiFd4iIiIiIiIimUiFdza2c+dOHnvsMcLCwggODqZWrVosXrzYqc+BAwd4+OGHCQoKIiIigp49e5KcnOymiHOGH3/8kcjISAIDA8mXLx+NGzd2uq6cuEdCQgJVqlTBZrPxxx9/OF3btGkTtWvXJiAggKJFizJs2DD3BJkD7Nu3j7Zt21KyZEkCAwMpXbo0AwYMIDEx0amfcuJ6H330ESVKlCAgIIDIyEjWrl3r7pByhCFDhnDnnXeSJ08eIiIiaNy4MTt27HDqEx8fT6dOnQgNDSV37tw8/vjjHDt2zE0R5zxDhw7FZrPRpUsXR5ty4h6HDh3imWeeITQ0lMDAQCpWrMjvv//uuG5ZFv3796dQoUIEBgZSv359du3a5caIs7eUlBTeeOMNp/+nv/XWW1y5xVhOyokK72zskUceITk5mUWLFrFu3ToqV67MI488wtGjRwHzl+Hhhx8mMTGRlStXMnnyZCZNmkT//v3dHHn2NXPmTJ599lnatGnDxo0b+fXXX3n66acd15UT9+nVqxeFCxdO1R4XF0eDBg0oXrw469atY/jw4QwcOJBPPvnEDVFmf9u3b8dutzN+/Hi2bNnC+++/z7hx4+jbt6+jj3Liel9//TXdunVjwIABrF+/nsqVKxMVFUVMTIy7Q8v2li5dSqdOnVi9ejULFy4kKSmJBg0acP78eUefrl27MnfuXL799luWLl3K4cOHadq0qRujzjl+++03xo8fT6VKlZzalRPXO336NDVr1sTX15effvqJrVu3MmLECPLly+foM2zYMD788EPGjRvHmjVryJUrF1FRUcTHx7sx8uzr3XffZezYsYwZM4Zt27bx7rvvMmzYMEaPHu3ok6NyYkm2dPz4cQuwli1b5miLi4uzAGvhwoWWZVnWvHnzLC8vL+vo0aOOPmPHjrWCg4OthIQEl8ec3SUlJVlFihSxPv3002v2UU7cY968eVa5cuWsLVu2WIC1YcMGx7WPP/7Yypcvn9P3/7XXXrPKli3rhkhzpmHDhlklS5Z0vFdOXO+uu+6yOnXq5HifkpJiFS5c2BoyZIgbo8qZYmJiLMBaunSpZVmWdebMGcvX19f69ttvHX22bdtmAdaqVavcFWaOcPbsWet///uftXDhQqtOnTrWq6++almWcuIur732mlWrVq1rXrfb7VbBggWt4cOHO9rOnDlj+fv7W1999ZUrQsxxHn74Yev55593amvatKnVsmVLy7JyXk404p1NhYaGUrZsWaZMmcL58+dJTk5m/PjxREREUK1aNQBWrVpFxYoVKVCggOProqKiiIuLY8uWLe4KPdtav349hw4dwsvLi6pVq1KoUCEeeughNm/e7OijnLjesWPHaNeuHVOnTiUoKCjV9VWrVnHvvffi5+fnaIuKimLHjh2cPn3alaHmWLGxseTPn9/xXjlxrcTERNatW0f9+vUdbV5eXtSvX59Vq1a5MbKcKTY2FsDxd2LdunUkJSU55adcuXIUK1ZM+clknTp14uGHH3b63oNy4i7ff/891atXp1mzZkRERFC1alUmTJjguL53716OHj3qlJeQkBAiIyOVl0xyzz33EB0dzc6dOwHYuHEjK1as4KGHHgJyXk5UeGdTNpuNX375hQ0bNpAnTx4CAgIYOXIk8+fPd0y5OXr0qFOBBzjeX5qOLhlnz549AAwcOJDXX3+dH374gXz58lG3bl1OnToFKCeuZlkWzz33HO3bt6d69epp9lFO3Ouvv/5i9OjRvPTSS4425cS1Tpw4QUpKSprfc32/Xctut9OlSxdq1qzJ7bffDpg/835+fuTNm9epr/KTuaZPn8769esZMmRIqmvKiXvs2bOHsWPH8r///Y+ff/6ZDh068MorrzB58mTg8v8f9N8y1+nduzdPPfUU5cqVw9fXl6pVq9KlSxdatmwJ5LycqPDOYnr37o3NZvvX1/bt27Esi06dOhEREcHy5ctZu3YtjRs3plGjRhw5csTdj5GtXG9O7HY7AP369ePxxx+nWrVqTJw4EZvNxrfffuvmp8herjcno0eP5uzZs/Tp08fdIWd715uTKx06dIgHH3yQZs2a0a5dOzdFLuI5OnXqxObNm5k+fbq7Q8nR/v77b1599VWmTZtGQECAu8ORf9jtdu644w7eeecdqlatyosvvki7du0YN26cu0PLsb755humTZvGl19+yfr165k8eTLvvfee44chOY2PuwOQ9OnevTvPPffcv/YpVaoUixYt4ocffuD06dMEBwcD8PHHH7Nw4UImT55M7969KViwYKpdaS/tuFmwYMFMiT87ut6cXPqBR4UKFRzt/v7+lCpVigMHDgAoJxkkPX9PVq1ahb+/v9O16tWr07JlSyZPnkzBggVT7USrnKTf9ebkksOHD3Pfffdxzz33pNo0TTlxrbCwMLy9vdP8nuv77TqdO3fmhx9+YNmyZdxyyy2O9oIFC5KYmMiZM2ecRliVn8yzbt06YmJiuOOOOxxtKSkpLFu2jDFjxvDzzz8rJ25QqFAhp39jAZQvX56ZM2cCl///cOzYMQoVKuToc+zYMapUqeKyOHOSnj17Oka9ASpWrMj+/fsZMmQIrVu3znE5UeGdxYSHhxMeHv6f/S5cuACYdXhX8vLycoy81qhRg8GDBxMTE0NERAQACxcuJDg4ONV/uOTarjcn1apVw9/fnx07dlCrVi0AkpKS2LdvH8WLFweUk4xyvTn58MMPefvttx3vDx8+TFRUFF9//TWRkZGAyUm/fv1ISkrC19cXMDkpW7as006p8u+uNydgRrrvu+8+x6yQq/87ppy4lp+fH9WqVSM6Otpx/KHdbic6OprOnTu7N7gcwLIsXn75ZWbPns2SJUsoWbKk0/Vq1arh6+tLdHQ0jz/+OAA7duzgwIED1KhRwx0hZ3v16tXjzz//dGpr06YN5cqV47XXXqNo0aLKiRvUrFkz1VF7O3fudPwbq2TJkhQsWJDo6GhHURcXF8eaNWvo0KGDq8PNES5cuJDq/+He3t6OWiTH5cTNm7tJJjl+/LgVGhpqNW3a1Prjjz+sHTt2WD169LB8fX2tP/74w7Isy0pOTrZuv/12q0GDBtYff/xhzZ8/3woPD7f69Onj5uizr1dffdUqUqSI9fPPP1vbt2+32rZta0VERFinTp2yLEs5cbe9e/em2tX8zJkzVoECBaxnn33W2rx5szV9+nQrKCjIGj9+vPsCzcYOHjxolSlTxqpXr5518OBB68iRI47XJcqJ602fPt3y9/e3Jk2aZG3dutV68cUXrbx58zqdwCCZo0OHDlZISIi1ZMkSp78PFy5ccPRp3769VaxYMWvRokXW77//btWoUcOqUaOGG6POea7c1dyylBN3WLt2reXj42MNHjzY2rVrlzVt2jQrKCjI+uKLLxx9hg4dauXNm9f67rvvrE2bNlmPPfaYVbJkSevixYtujDz7at26tVWkSBHrhx9+sPbu3WvNmjXLCgsLs3r16uXok5NyosI7G/vtt9+sBg0aWPnz57fy5Mlj3X333da8efOc+uzbt8966KGHrMDAQCssLMzq3r27lZSU5KaIs7/ExESre/fuVkREhJUnTx6rfv361ubNm536KCfuk1bhbVmWtXHjRqtWrVqWv7+/VaRIEWvo0KHuCTAHmDhxogWk+bqScuJ6o0ePtooVK2b5+flZd911l7V69Wp3h5QjXOvvw8SJEx19Ll68aHXs2NHKly+fFRQUZDVp0sTph1WS+a4uvJUT95g7d651++23W/7+/la5cuWsTz75xOm63W633njjDatAgQKWv7+/Va9ePWvHjh1uijb7i4uLs1599VWrWLFiVkBAgFWqVCmrX79+TseB5qSc2CzLstwx0i4iIiIiIiKSE2hXcxEREREREZFMpMJbREREREREJBOp8BYRERERERHJRCq8RURERERERDKRCm8RERERERGRTKTCW0RERERERCQTqfAWERERERERyUQqvEVEREREREQykQpvERERD1CiRAlGjRqVKfeuW7cuXbp0uen7REdHU758eVJSUq7ZZ+DAgVSpUuWmP8sT9O7dm5dfftndYYiISDagwltERCQdnnvuORo3bnzDXz9p0iTy5s2bqv23337jxRdfdLy32WzMmTPnhj8nM/Tq1YvXX38db29vd4fiEj169GDy5Mns2bPH3aGIiEgWp8JbRETEA4SHhxMUFOTuMK5pxYoV7N69m8cff9zdoWBZFsnJyZn+OWFhYURFRTF27NhM/ywREcneVHiLiIhkoJEjR1KxYkVy5cpF0aJF6dixI+fOnQNgyZIltGnThtjYWGw2GzabjYEDBwLOU81LlCgBQJMmTbDZbI73aY22d+nShbp16zrenz9/nlatWpE7d24KFSrEiBEjUsWYkJBAjx49KFKkCLly5SIyMpIlS5b863NNnz6dBx54gICAAKf2oUOHUqBAAfLkyUPbtm2Jj49P9bWffvop5cuXJyAggHLlyvHxxx87XV+5ciVVqlQhICCA6tWrM2fOHGw2G3/88Yfj+2az2fjpp5+oVq0a/v7+rFixArvdzpAhQyhZsiSBgYFUrlyZGTNmON178+bNPPTQQ+TOnZsCBQrw7LPPcuLECcf1GTNmULFiRQIDAwkNDaV+/fqcP3/ecb1Ro0ZMnz79X783IiIi/0WFt4iISAby8vLiww8/ZMuWLUyePJlFixbRq1cvAO655x5GjRpFcHAwR44c4ciRI/To0SPVPX777TcAJk6cyJEjRxzvr0fPnj1ZunQp3333HQsWLGDJkiWsX7/eqU/nzp1ZtWoV06dPZ9OmTTRr1owHH3yQXbt2XfO+y5cvp3r16k5t33zzDQMHDuSdd97h999/p1ChQqmK6mnTptG/f38GDx7Mtm3beOedd3jjjTeYPHkyAHFxcTRq1IiKFSuyfv163nrrLV577bU0Y+jduzdDhw5l27ZtVKpUiSFDhjBlyhTGjRvHli1b6Nq1K8888wxLly4F4MyZM9x///1UrVqV33//nfnz53Ps2DGaN28OwJEjR2jRogXPP/8827ZtY8mSJTRt2hTLshyfedddd3Hw4EH27dt3fQkQERFJiyUiIiLXrXXr1tZjjz123f2//fZbKzQ01PF+4sSJVkhISKp+xYsXt95//33He8CaPXv2f372q6++atWpU8eyLMs6e/as5efnZ33zzTeO6ydPnrQCAwOtV1991bIsy9q/f7/l7e1tHTp0yOk+9erVs/r06XPN5wgJCbGmTJni1FajRg2rY8eOTm2RkZFW5cqVHe9Lly5tffnll0593nrrLatGjRqWZVnW2LFjrdDQUOvixYuO6xMmTLAAa8OGDZZlWdbixYstwJozZ46jT3x8vBUUFGStXLnS6d5t27a1WrRo4ficBg0aOF3/+++/LcDasWOHtW7dOguw9u3bd83njo2NtQBryZIl1+wjIiLyX3zcWfSLiIhkN7/88gtDhgxh+/btxMXFkZycTHx8PBcuXMj0Ndy7d+8mMTGRyMhIR1v+/PkpW7as4/2ff/5JSkoKt956q9PXJiQkEBoaes17X7x4MdU0823bttG+fXuntho1arB48WLATHvfvXs3bdu2pV27do4+ycnJhISEALBjxw4qVarkdO+77rorzRiuHHH/66+/uHDhAg888IBTn8TERKpWrQrAxo0bWbx4Mblz5051r927d9OgQQPq1atHxYoViYqKokGDBjzxxBPky5fP0S8wMBCACxcuXOM7IyIi8t9UeIuIiGSQffv28cgjj9ChQwcGDx5M/vz5WbFiBW3btiUxMfGmC28vLy+nadAASUlJ6brHuXPn8Pb2Zt26dal2J0+rQL0kLCyM06dPp/uzACZMmOD0wwDghnZGz5UrV6p7//jjjxQpUsSpn7+/v6NPo0aNePfdd1Pdq1ChQnh7e7Nw4UJWrlzJggULGD16NP369WPNmjWULFkSgFOnTgFm8zsREZEbpcJbREQkg6xbtw673c6IESPw8jLbqHzzzTdOffz8/P71HOxLfH19U/ULDw9n8+bNTm1//PEHvr6+AJQuXRpfX1/WrFlDsWLFADh9+jQ7d+6kTp06AFStWpWUlBRiYmKoXbv2dT9b1apV2bp1q1Nb+fLlWbNmDa1atXK0rV692vH7AgUKULhwYfbs2UPLli3TvG/ZsmX54osvSEhIcBTM17OmvUKFCvj7+3PgwAHHs13tjjvuYObMmZQoUQIfn7T/yWOz2ahZsyY1a9akf//+FC9enNmzZ9OtWzfAbM7m6+vLbbfd9p8xiYiIXIsKbxERkXSKjY117Lh9SWhoKGXKlCEpKYnRo0fTqFEjfv31V8aNG+fUr0SJEpw7d47o6GgqV65MUFBQmiPhJUqUIDo6mpo1a+Lv70++fPm4//77GT58OFOmTKFGjRp88cUXbN682TG1Onfu3LRt25aePXsSGhpKREQE/fr1c/wQAODWW2+lZcuWtGrVihEjRlC1alWOHz9OdHQ0lSpV4uGHH07zmaOiohwbol3y6quv8txzz1G9enVq1qzJtGnT2LJlC6VKlXL0GTRoEK+88gohISE8+OCDJCQk8Pvvv3P69Gm6devG008/Tb9+/XjxxRfp3bs3Bw4c4L333gNMUXwtefLkoUePHnTt2hW73U6tWrWIjY3l119/JTg4mNatW9OpUycmTJhAixYt6NWrF/nz5+evv/5i+vTpfPrpp/z+++9ER0fToEEDIiIiWLNmDcePH6d8+fKOz1m+fDm1a9d2TDkXERG5Ie5eZC4iIpKVtG7d2gJSvdq2bWtZlmWNHDnSKlSokBUYGGhFRUVZU6ZMsQDr9OnTjnu0b9/eCg0NtQBrwIABlmWl3lzt+++/t8qUKWP5+PhYxYsXd7T379/fKlCggBUSEmJ17drV6ty5s2NzNcsyG6w988wzVlBQkFWgQAFr2LBhVp06dRybq1mWZSUmJlr9+/e3SpQoYfn6+lqFChWymjRpYm3atOmaz33y5EkrICDA2r59u1P74MGDrbCwMCt37txW69atrV69ejltrmZZljVt2jSrSpUqlp+fn5UvXz7r3nvvtWbNmuW4/uuvv1qVKlWy/Pz8rGrVqllffvmlBTg+69Lmald+Dy3Lsux2uzVq1CirbNmylq+vrxUeHm5FRUVZS5cudfTZuXOn1aRJEytv3rxWYGCgVa5cOatLly6W3W63tm7dakVFRVnh4eGWv7+/deutt1qjR492+oyyZctaX3311TW/LyIiItfDZllXLRYTERERSUPPnj2Ji4tj/Pjxmfo506ZNc5x37s6R5p9++onu3buzadOma05VFxERuR46x1tERESuS79+/ShevDh2uz1D7ztlyhRWrFjB3r17mTNnDq+99hrNmzd3+/Tu8+fPM3HiRBXdIiJy0zTiLSIiIm41bNgwPv74Y44ePUqhQoVo3LgxgwcPzvTj10RERFxFhbeIiIiIiIhIJtJUcxEREREREZFMpMJbREREREREJBOp8BYRERERERHJRCq8RURERERERDKRCm8RERERERGRTKTCW0RERERERCQTqfAWERERERERyUQqvEVEREREREQykQpvERERERERkUz0f5NsMJwDS6uyAAAAAElFTkSuQmCC", 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" ] @@ -2658,7 +1624,7 @@ "id": "understanding_differences", "metadata": {}, "source": [ - "### Understanding the Differences\n", + "### Step 3.5: Quantify the differences\n", "\n", "The differences between conservative and non-conservative results depend on several factors:\n", "\n", @@ -2673,12 +1639,12 @@ "**When differences matter most:**\n", "- Variable resolution grids (where face sizes vary significantly)\n", "- Physical conservation requirements\n", - "- Quantitative analysis and budget calculations" + "- Quantitative analysis and budget calculations\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 44, "id": "difference_analysis", "metadata": { "execution": { @@ -2693,9 +1659,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Maximum absolute difference: 0.010437\n", - "Mean absolute difference: 0.006088\n", - "Relative difference (max): 1.044%\n" + "Maximum absolute difference: 0.010691\n", + "Mean absolute difference: 0.006063\n", + "Relative difference (max): 1.069%\n" ] } ], @@ -2717,14 +1683,16 @@ "id": "4b91ebb8f733a318", "metadata": {}, "source": [ - "## Combined Plots\n", + "## 4. Combined Plots\n", "\n", - "It is often desired to plot the zonal average along side other plots, such as color or contour plots. " + "### Step 4.1: Pair a global map with its zonal mean\n", + "\n", + "It is often desired to plot the zonal average alongside other plots, such as color or contour plots, so you can see both the spatial distribution and its latitude summary in one glance.\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 52, "id": "cb2255761173d53e", "metadata": { "execution": { @@ -2736,55 +1704,129 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Global Field:\n" - ] + "data": {}, + "metadata": {}, + "output_type": "display_data" }, { "data": { - "image/png": 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", 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" + ":Layout\n", + " .Image.I :Image [x,y] (x_y psi)\n", + " .Overlay.I :Overlay\n", + " .Curve.I :Curve [zonal_mean] (latitudes)\n", + " .Scatter.I :Scatter [zonal_mean] (latitudes)" ] }, - "metadata": {}, - "output_type": "display_data" + "execution_count": 52, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "7922485d-7955-42d6-8d67-38dae0e029d9" + } + }, + "output_type": "execute_result" } ], "source": [ - "# Display the global field\n", - "print(\"Global Field:\")\n", - "uxds[\"psi\"].plot(cmap=\"inferno\", periodic_elements=\"split\", title=\"Global Field\")\n", - "\n", - "# Create zonal average plot\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", - "\n", - "ax.plot(\n", - " zonal_mean_psi.values,\n", - " zonal_mean_psi.coords[\"latitudes\"],\n", - " \"o-\",\n", - " linewidth=2,\n", - " markersize=6,\n", + "# Pair the map and the zonal profile using hvplot\n", + "zonal_df = zonal_mean_psi.to_dataframe(name=\"zonal_mean\").reset_index()\n", + "map_panel = (\n", + " uxds[\"psi\"]\n", + " .plot(cmap=\"inferno\", periodic_elements=\"split\")\n", + " .opts(title=\"Global Field\", colorbar=True, width=525, height=400)\n", + ")\n", + "profile_line = zonal_df.hvplot(\n", + " x=\"zonal_mean\",\n", + " y=\"latitudes\",\n", + " line_width=2,\n", + " xlabel=\"Zonal Mean Value\",\n", + " ylabel=\"Latitude (degrees)\",\n", + " title=\"Latitude Profile\",\n", + " ylim=(-90, 90),\n", + " width=400,\n", + " height=400,\n", + ")\n", + "profile_points = zonal_df.hvplot.scatter(\n", + " x=\"zonal_mean\",\n", + " y=\"latitudes\",\n", " color=\"blue\",\n", - " label=\"Zonal Mean\",\n", + " size=6,\n", ")\n", - "\n", - "ax.set_xlabel(\"Zonal Mean Value\")\n", - "ax.set_ylabel(\"Latitude (degrees)\")\n", - "ax.set_title(\"Zonal Average\")\n", - "ax.grid(True, alpha=0.3)\n", - "ax.set_ylim(-90, 90)\n", - "ax.legend()\n", - "\n", - "# Add reference latitude lines\n", - "sample_bands = [-60, -30, 0, 30, 60]\n", - "for lat_band in sample_bands:\n", - " ax.axhline(y=lat_band, color=\"red\", linestyle=\"--\", alpha=0.5, linewidth=1)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" + "profile_panel = (profile_line * profile_points).opts(show_grid=True)\n", + "(map_panel + profile_panel).cols(2)" ] }, { @@ -2792,14 +1834,16 @@ "id": "4f923644", "metadata": {}, "source": [ - "## HEALPix Zonal Averaging: Conservative vs Non-Conservative Methods\n", + "## 5. HEALPix Zonal Averaging (Conservative vs Non-Conservative)\n", + "\n", + "### Step 5.1: Compare conservative and non-conservative averages on HEALPix\n", "\n", "This example demonstrates the key differences between conservative (area-weighted) and non-conservative (point-sampling) zonal averaging on a HEALPix grid.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "52d24b6b", "metadata": { "execution": { @@ -2809,23 +1853,56 @@ "shell.execute_reply": "2025-09-26T15:41:08.652469Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================================================\n", + "ZONAL AVERAGING ON HEALPix GRID: Conservative vs Non-Conservative\n", + "=================================================================\n", + "\n", + "Test function: sin(latitude)\n", + "Analysis band: 32°N to 52°N\n", + "Center latitude: 42°N\n", + "Grid resolution: HEALPix zoom level 3 (~1.8° spacing)\n", + "\n", + "-----------------------------------------------------------------\n", + "RESULTS:\n", + "-----------------------------------------------------------------\n", + "Conservative (band average): 0.6528\n", + " → Theoretical value: 0.6590\n", + " → Physical meaning: Area-weighted average over 20° band\n", + " → Use case: Flux calculations, energy budgets\n", + "\n", + "Non-conservative (point value): 0.6687\n", + " → Theoretical value: 0.6691\n", + " → Physical meaning: Value at exactly 42.0°N\n", + " → Use case: Station comparisons, spot measurements\n", + "\n", + "-----------------------------------------------------------------\n", + "KEY INSIGHTS:\n", + "-----------------------------------------------------------------\n", + "• Difference between methods: 0.0159 (2.4%)\n", + "• Conservative < Non-conservative because sin(lat) increases toward\n", + " the pole, and southern portion of band has lower values\n", + "• Both methods are 'correct' - choose based on your application:\n", + " - Conservative: preserves integrated quantities\n", + " - Non-conservative: provides local values\n" + ] + } + ], "source": [ "# HEALPix Zonal Averaging: Conservative vs Non-Conservative Methods\n", "# This example demonstrates the key differences between conservative (area-weighted)\n", "# and non-conservative (point-sampling) zonal averaging on a HEALPix grid.\n", - "\n", - "import numpy as np\n", - "\n", - "import uxarray as ux\n", - "\n", "# Create HEALPix grid with synthetic data\n", "# Using sin(latitude) as test function - varies smoothly with latitude\n", "uxgrid = ux.Grid.from_healpix(zoom=3, pixels_only=False)\n", "data = np.sin(np.deg2rad(uxgrid.face_lat.values))\n", "uxda = ux.UxDataArray(data, uxgrid=uxgrid, dims=[\"n_face\"], name=\"val\")\n", "\n", - "# Define analysis region: 20\u00b0 latitude band centered at 42\u00b0N\n", + "# Define analysis region: 20° latitude band centered at 42°N\n", "band_edges = np.array([32.0, 52.0]) # Southern USA to Canada\n", "lat_center = 42.0 # Approximate latitude of Chicago/Boston\n", "\n", @@ -2842,7 +1919,7 @@ "z_noncons = uxda.zonal_mean(lat=lat_center)\n", "\n", "# Calculate theoretical values for validation\n", - "theoretical_point = np.sin(np.deg2rad(lat_center)) # Exact at 42\u00b0N\n", + "theoretical_point = np.sin(np.deg2rad(lat_center)) # Exact at 42°N\n", "# For band average: integrate sin(lat)*cos(lat) / integrate cos(lat)\n", "lat_s, lat_n = np.deg2rad(band_edges)\n", "theoretical_band = (\n", @@ -2854,36 +1931,2862 @@ "print(\"ZONAL AVERAGING ON HEALPix GRID: Conservative vs Non-Conservative\")\n", "print(\"=\" * 65)\n", "print(\"\\nTest function: sin(latitude)\")\n", - "print(f\"Analysis band: {band_edges[0]:.0f}\u00b0N to {band_edges[1]:.0f}\u00b0N\")\n", - "print(f\"Center latitude: {lat_center:.0f}\u00b0N\")\n", - "print(\"Grid resolution: HEALPix zoom level 3 (~1.8\u00b0 spacing)\")\n", + "print(f\"Analysis band: {band_edges[0]:.0f}°N to {band_edges[1]:.0f}°N\")\n", + "print(f\"Center latitude: {lat_center:.0f}°N\")\n", + "print(\"Grid resolution: HEALPix zoom level 3 (~1.8° spacing)\")\n", "\n", "print(\"\\n\" + \"-\" * 65)\n", "print(\"RESULTS:\")\n", "print(\"-\" * 65)\n", "print(f\"Conservative (band average): {float(z_cons.values[0]):.4f}\")\n", - "print(f\" \u2192 Theoretical value: {theoretical_band:.4f}\")\n", - "print(\" \u2192 Physical meaning: Area-weighted average over 20\u00b0 band\")\n", - "print(\" \u2192 Use case: Flux calculations, energy budgets\\n\")\n", + "print(f\" → Theoretical value: {theoretical_band:.4f}\")\n", + "print(\" → Physical meaning: Area-weighted average over 20° band\")\n", + "print(\" → Use case: Flux calculations, energy budgets\\n\")\n", "\n", "print(f\"Non-conservative (point value): {float(z_noncons.values[0]):.4f}\")\n", - "print(f\" \u2192 Theoretical value: {theoretical_point:.4f}\")\n", - "print(f\" \u2192 Physical meaning: Value at exactly {lat_center}\u00b0N\")\n", - "print(\" \u2192 Use case: Station comparisons, spot measurements\")\n", + "print(f\" → Theoretical value: {theoretical_point:.4f}\")\n", + "print(f\" → Physical meaning: Value at exactly {lat_center}°N\")\n", + "print(\" → Use case: Station comparisons, spot measurements\")\n", "\n", "print(\"\\n\" + \"-\" * 65)\n", "print(\"KEY INSIGHTS:\")\n", "print(\"-\" * 65)\n", "difference = float(z_noncons.values[0]) - float(z_cons.values[0])\n", "print(\n", - " f\"\u2022 Difference between methods: {difference:.4f} ({difference / float(z_noncons.values[0]) * 100:.1f}%)\"\n", + " f\"• Difference between methods: {difference:.4f} ({difference / float(z_noncons.values[0]) * 100:.1f}%)\"\n", ")\n", - "print(\"\u2022 Conservative < Non-conservative because sin(lat) increases toward\")\n", + "print(\"• Conservative < Non-conservative because sin(lat) increases toward\")\n", "print(\" the pole, and southern portion of band has lower values\")\n", - "print(\"\u2022 Both methods are 'correct' - choose based on your application:\")\n", + "print(\"• Both methods are 'correct' - choose based on your application:\")\n", "print(\" - Conservative: preserves integrated quantities\")\n", "print(\" - Non-conservative: provides local values\")" ] + }, + { + "cell_type": "markdown", + "id": "9bae1538", + "metadata": {}, + "source": [ + "## 6. 2D Zonal Means on NE30 (RELHUM)\n", + "\n", + "Everything above uses a single-level field. For the NE30 multi-level RELHUM data, we can see how a zonal mean collapses every longitude in each latitude ring—averaging the native face values with intersection-line (non-conservative) or area (conservative) weights—so the result becomes a latitude–height diagnostic, unlike a cross section that interpolates to create a 2D output over a single path. Each step below walks through the process with extra explanations for first-time users.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "zonal_vs_cross_section_note", + "metadata": {}, + "source": [ + "```{tip}\n", + "`zonal_mean()` works directly on the native cell values to build latitude-only diagnostics. When you instead need interpolation along a specified great-circle or constant-lon/lat path, use `.cross_section()` to construct that transect and see the [Cross-Sections](./cross-sections.ipynb) guide for path-based examples.\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4fcf1856", + "metadata": {}, + "source": [ + "### Step 6.1: Load the NE30 grid and prepare the field\n", + "\n", + "We point UXarray to the grid and data files under `test/meshfiles/scrip/ne30pg2/`, open the `RELHUM` variable, and drop the leading time dimension so the array is only `(level, face)`." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1a6cbff1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Attributes:\n",
+       "    zonal_mean:    True\n",
+       "    conservative:  False
" + ], + "text/plain": [ + " Size: 5kB\n", + "array([[1.39507087e-04, 1.46236242e-04, 1.57333357e-04, ...,\n", + " 1.38013987e-04, 1.37959549e-04, 1.37912750e-04],\n", + " [1.46500082e-04, 1.56465467e-04, 1.59396877e-04, ...,\n", + " 1.38380448e-04, 1.37978277e-04, 1.37925847e-04],\n", + " [1.39333293e-04, 1.40268996e-04, 1.41239536e-04, ...,\n", + " 1.41590019e-04, 1.37986484e-04, 1.37942334e-04],\n", + " ...,\n", + " [9.95636673e+01, 9.57479248e+01, 9.42285156e+01, ...,\n", + " 1.00770416e+02, 1.01268219e+02, 1.04364807e+02],\n", + " [9.75083466e+01, 9.47997131e+01, 9.26638336e+01, ...,\n", + " 9.82491531e+01, 9.99979477e+01, 1.02430000e+02],\n", + " [9.58398132e+01, 9.40848160e+01, 9.15409851e+01, ...,\n", + " 9.66513596e+01, 9.93786469e+01, 1.01074776e+02]],\n", + " shape=(72, 18), dtype=float32)\n", + "Coordinates:\n", + " * lev (lev) float64 576B 0.1238 0.1828 0.2699 ... 986.2 993.8 998.5\n", + " * lat (lat) float64 144B -85.0 -75.0 -65.0 -55.0 ... 55.0 65.0 75.0 85.0\n", + "Attributes:\n", + " zonal_mean: True\n", + " conservative: False" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lat_edges_deg = np.arange(-90.0, 90.0 + 10.0, 10.0)\n", + "lat_centers_deg = 0.5 * (lat_edges_deg[:-1] + lat_edges_deg[1:])\n", + "\n", + "\n", + "def stack_zonal_means(data_array, lat_values, *, conservative):\n", + " slices = []\n", + " for lev in range(data_array.sizes[level_dim]):\n", + " zonal_slice = data_array.isel({level_dim: lev}).zonal_mean(\n", + " lat=lat_values, conservative=conservative\n", + " )\n", + " slices.append(zonal_slice)\n", + " zonal = xr.concat(slices, dim=level_dim)\n", + " return zonal.assign_coords({level_dim: levels}).rename({\"latitudes\": \"lat\"})\n", + "\n", + "\n", + "zonal_nc = stack_zonal_means(relhum, lat_centers_deg, conservative=False)\n", + "zonal_c = stack_zonal_means(relhum, lat_edges_deg, conservative=True)\n", + "zonal_diff = zonal_c - zonal_nc\n", + "\n", + "zonal_nc" + ] + }, + { + "cell_type": "markdown", + "id": "bbbfb613", + "metadata": {}, + "source": [ + "### Step 6.3: Visualize the latitude–level slices\n", + "\n", + "Placing the two 2D sections next to each other shows the smoothing introduced by area weighting, while a third panel plotting the signed difference (conservative − non-conservative) highlights where the methods diverge. Because every panel uses the same color scale (and the difference plot uses a diverging palette), the contrast is easy to spot." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "b752c490", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharey=True, constrained_layout=True)\n", + "\n", + "value_min = float(min(zonal_nc.min().values, zonal_c.min().values))\n", + "value_max = float(max(zonal_nc.max().values, zonal_c.max().values))\n", + "\n", + "mesh_nc = axes[0].pcolormesh(\n", + " zonal_nc[\"lat\"],\n", + " levels,\n", + " zonal_nc.transpose(level_dim, \"lat\"),\n", + " shading=\"nearest\",\n", + " cmap=\"viridis\",\n", + " vmin=value_min,\n", + " vmax=value_max,\n", + ")\n", + "axes[0].set_title(\"Non-conservative (centers)\")\n", + "axes[0].set_xlabel(\"Latitude (deg)\")\n", + "axes[0].set_ylabel(level_dim)\n", + "axes[0].set_xlim(-90, 90)\n", + "\n", + "mesh_c = axes[1].pcolormesh(\n", + " zonal_c[\"lat\"],\n", + " levels,\n", + " zonal_c.transpose(level_dim, \"lat\"),\n", + " shading=\"nearest\",\n", + " cmap=\"viridis\",\n", + " vmin=value_min,\n", + " vmax=value_max,\n", + ")\n", + "axes[1].set_title(\"Conservative (bands)\")\n", + "axes[1].set_xlabel(\"Latitude (deg)\")\n", + "axes[1].set_xlim(-90, 90)\n", + "\n", + "diff_max = float(np.nanmax(np.abs(zonal_diff.values)))\n", + "if diff_max == 0:\n", + " diff_max = 1e-6\n", + "mesh_diff = axes[2].pcolormesh(\n", + " zonal_diff[\"lat\"],\n", + " levels,\n", + " zonal_diff.transpose(level_dim, \"lat\"),\n", + " shading=\"nearest\",\n", + " cmap=\"RdBu_r\",\n", + " vmin=-diff_max,\n", + " vmax=diff_max,\n", + ")\n", + "axes[2].set_title(\"Difference (conservative - non)\")\n", + "axes[2].set_xlabel(\"Latitude (deg)\")\n", + "axes[2].set_xlim(-90, 90)\n", + "\n", + "cbar_field = fig.colorbar(\n", + " mesh_nc, ax=[axes[0], axes[1]], orientation=\"vertical\", fraction=0.04, pad=0.02\n", + ")\n", + "cbar_field.set_label(\"RELHUM zonal mean\")\n", + "\n", + "cbar_diff = fig.colorbar(\n", + " mesh_diff, ax=[axes[2]], orientation=\"vertical\", fraction=0.08, pad=0.02\n", + ")\n", + "cbar_diff.set_label(\"RELHUM difference\")" + ] + }, + { + "cell_type": "markdown", + "id": "6530ad41", + "metadata": {}, + "source": [ + "### Step 6.4: Spot-check the difference numerically\n", + "\n", + "Visuals are helpful, but printing quick summary statistics and a few sample latitude bands makes it clear how large the conservative correction is." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "2f6ff242", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max absolute difference: 6.528\n", + "Mean absolute difference: 0.488\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'RELHUM_zonal_mean' (lev: 5)> Size: 20B\n",
+       "array([2.2769236e-06, 3.7543796e-06, 2.1681481e-06, 4.8164511e-06,\n",
+       "       2.9139337e-06], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lev      (lev) float64 40B 0.1238 0.1828 0.2699 0.3986 0.5885
" + ], + "text/plain": [ + " Size: 20B\n", + "array([2.2769236e-06, 3.7543796e-06, 2.1681481e-06, 4.8164511e-06,\n", + " 2.9139337e-06], dtype=float32)\n", + "Coordinates:\n", + " * lev (lev) float64 40B 0.1238 0.1828 0.2699 0.3986 0.5885" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs_diff = np.abs(zonal_diff)\n", + "max_abs = float(abs_diff.max())\n", + "mean_abs = float(abs_diff.mean())\n", + "per_level_max = abs_diff.max(dim=\"lat\")\n", + "\n", + "print(f\"Max absolute difference: {max_abs:.3f}\")\n", + "print(f\"Mean absolute difference: {mean_abs:.3f}\")\n", + "\n", + "preview_levels = per_level_max.isel({level_dim: slice(0, 5)})\n", + "preview_levels" + ] } ], "metadata": { diff --git a/test/core/test_dataarray.py b/test/core/test_dataarray.py index e27fe10e0..49061748f 100644 --- a/test/core/test_dataarray.py +++ b/test/core/test_dataarray.py @@ -93,3 +93,27 @@ def test_geodataframe_caching(gridpath, datasetpath): # override will recompute the grid assert gdf_start is not gdf_end + +def test_isel_invalid_dim(gridpath, datasetpath): + """Tests that isel raises a ValueError with a helpful message when an + invalid dimension is provided.""" + uxds = ux.open_dataset( + gridpath("ugrid", "outCSne30", "outCSne30.ug"), + datasetpath("ugrid", "outCSne30", "outCSne30_var2.nc"), + ) + + # create a UxDataArray with an extra dimension + data = np.random.rand(2, uxds.uxgrid.n_face) + uxda = UxDataArray(data, dims=["time", "n_face"], uxgrid=uxds.uxgrid) + + with pytest.raises( + ValueError, + match=r"Dimensions \{'invalid_dim'\} do not exist\..*Available dimensions: \('time', 'n_face'\)", + ): + uxda.isel(invalid_dim=0) + + with pytest.raises( + ValueError, + match=r"Dimensions \{'level'\} do not exist\..*Available dimensions: \('time', 'n_face'\)", + ): + uxda.isel(level=0) diff --git a/uxarray/core/dataarray.py b/uxarray/core/dataarray.py index ad74be6f1..7ad62050c 100644 --- a/uxarray/core/dataarray.py +++ b/uxarray/core/dataarray.py @@ -514,7 +514,12 @@ def integrate( return uxda def zonal_mean(self, lat=(-90, 90, 10), conservative: bool = False, **kwargs): - """Compute non-conservative or conservative averages along lines of constant latitude or latitude bands. + """Compute non-conservative or conservative averages of a face-centered variable along lines of constant latitude or latitude bands. + + A zonal mean in UXarray operates differently depending on the ``conservative`` flag: + + - **Non-conservative**: Calculates the mean by sampling face values at specific latitude lines and weighting each contribution by the length of the line where each face intersects that latitude. + - **Conservative**: Preserves integral quantities by calculating the mean by sampling face values within latitude bands and weighting contributions by their area overlap with latitude bands. Parameters ---------- @@ -526,7 +531,7 @@ def zonal_mean(self, lat=(-90, 90, 10), conservative: bool = False, **kwargs): - array-like: For non-conservative, latitudes to sample. For conservative, band edges. conservative : bool, default=False If True, performs conservative (area-weighted) zonal averaging over latitude bands. - If False, performs traditional (non-conservative) averaging at latitude lines. + If False, performs non-conservative (intersection-weighted) averaging at latitude lines. Returns ------- @@ -1652,44 +1657,57 @@ def isel( indexers, indexers_kwargs, "isel", ignore_grid ) - # Grid Branch - if not ignore_grid: - if len(grid_dims) == 1: - # pop off the one grid‐dim indexer - grid_dim = grid_dims.pop() - grid_indexer = indexers.pop(grid_dim) + try: + # Grid Branch + if not ignore_grid: + if len(grid_dims) == 1: + # pop off the one grid‐dim indexer + grid_dim = grid_dims.pop() + grid_indexer = indexers.pop(grid_dim) - sliced_grid = self.uxgrid.isel( - **{grid_dim: grid_indexer}, inverse_indices=inverse_indices - ) + sliced_grid = self.uxgrid.isel( + **{grid_dim: grid_indexer}, inverse_indices=inverse_indices + ) + + da = self._slice_from_grid(sliced_grid) - da = self._slice_from_grid(sliced_grid) + # if there are any remaining indexers, apply them + if indexers: + xarr = super(UxDataArray, da).isel( + indexers=indexers, drop=drop, missing_dims=missing_dims + ) + # re‐wrap so the grid sticks around + return type(self)(xarr, uxgrid=sliced_grid) - # if there are any remaining indexers, apply them - if indexers: - xarr = super(UxDataArray, da).isel( - indexers=indexers, drop=drop, missing_dims=missing_dims + # no other dims, return the grid‐sliced da + return da + else: + return type(self)( + super().isel( + indexers=indexers or None, + drop=drop, + missing_dims=missing_dims, + ), + uxgrid=self.uxgrid, ) - # re‐wrap so the grid sticks around - return type(self)(xarr, uxgrid=sliced_grid) - # no other dims, return the grid‐sliced da - return da + return super().isel( + indexers=indexers or None, + drop=drop, + missing_dims=missing_dims, + ) + except ValueError as e: + if "Dimensions" in str(e) and "do not exist" in str(e): + # The error message from xarray is quite good, but we can add to it. + # e.g. "Dimensions {'level'} do not exist. Expected one of ('n_face', 'time', 'lev')" + # Let's just append the available dimensions. + original_error_msg = str(e) + raise ValueError( + f"{original_error_msg}. Available dimensions: {self.dims}" + ) from e else: - return type(self)( - super().isel( - indexers=indexers or None, - drop=drop, - missing_dims=missing_dims, - ), - uxgrid=self.uxgrid, - ) - - return super().isel( - indexers=indexers or None, - drop=drop, - missing_dims=missing_dims, - ) + # re-raise other ValueErrors + raise e @classmethod def from_xarray(cls, da: xr.DataArray, uxgrid: Grid, ugrid_dims: dict = None):