From 4bd3fe3c5962dd128f67adf57442600514b32bf2 Mon Sep 17 00:00:00 2001 From: jnsbck <65561470+jnsbck@users.noreply.github.com> Date: Mon, 7 Oct 2024 16:47:14 +0200 Subject: [PATCH] more options for plotting (#437) * enh: add more options for plotting * fix: add more doc to vis * fix: address comments * fix: fix copy paste error * enh: update morph notebook * fix: fix warning div 0 for 0 len comps * enh: more tutorial changes and small fix in plot_utils * fix: blacked --- .../tutorials/08_importing_morphologies.ipynb | 110 +++++-- jaxley/modules/base.py | 51 ++- jaxley/utils/plot_utils.py | 308 +++++++++++++++--- tests/test_plotting_api.py | 26 +- 4 files changed, 423 insertions(+), 72 deletions(-) diff --git a/docs/tutorials/08_importing_morphologies.ipynb b/docs/tutorials/08_importing_morphologies.ipynb index fd0fd72f..587b0839 100644 --- a/docs/tutorials/08_importing_morphologies.ipynb +++ b/docs/tutorials/08_importing_morphologies.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -243,7 +243,7 @@ "[1256 rows x 8 columns]" ] }, - "execution_count": 85, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -466,7 +466,7 @@ "[314 rows x 8 columns]" ] }, - "execution_count": 86, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -480,9 +480,16 @@ "cell.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once imported the compartmentalized morphology can be viewed using `vis`. " + ] + }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -508,14 +515,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "While we only use two compartments to approximate each branch in this example, we can see the morphology is still plotted in great detail. This is because we always plot the full `.swc` reconstruction irrespective of the number of compartments used. This is stored in the `cell.xyzr` attribute in a per branch fashion.\n", - "\n", - "To highlight each branch seperately, we can iterate over them." + "`vis` can be called on any `jx.Module` and every `View` of the module. This means we can also for example use `vis` to highlight each branch. This can be done by iterating over each branch index and calling `cell.branch(i).vis()`. Within the loop." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -540,6 +545,77 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While we only use two compartments to approximate each branch in this example, we can see the morphology is still plotted in great detail. This is because we always plot the full `.swc` reconstruction irrespective of the number of compartments used. The morphology lives seperately in the `cell.xyzr` attribute in a per branch fashion. \n", + "\n", + "In addition to plotting the full morphology of the cell using points `vis(type=\"scatter\")` or lines `vis(type=\"line\")`, `Jaxley` also supports plotting a detailed morphological `vis(type=\"morph\")` or approximate compartmental reconstruction `vis(type=\"comp\")` that correctly considers the thickness of the neurite. These can either be projected onto 2D or also rendered in 3D. For details see the documentation of `vis`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the cell\n", + "fig, ax = plt.subplots(1, 4, figsize=(10, 3), layout=\"constrained\", sharex=True, sharey=True)\n", + "cell.vis(ax=ax[0], type=\"morph\", dims=[0,1])\n", + "cell.vis(ax=ax[1], type=\"comp\", dims=[0,1])\n", + "cell.vis(ax=ax[2], type=\"scatter\", dims=[0,1], morph_plot_kwargs={\"s\": 1})\n", + "cell.vis(ax=ax[3], type=\"line\", dims=[0,1])\n", + "fig.suptitle(\"Comparison of plot types\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# set to interactive mode\n", + "# %matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YsMHs5yaRSIxWDPXq1cPjx4+xc+dOKggAaGIhH/n5+Rg9ejQA0AQ1iURi06GnprCFAdkWrkGAH0EULMDQlwCYXylkZWUhKyuLNjExtW9BQQEUCgWCgoIgl8utft3Pnz+n/5fJZHB3d+d0/Cpu3fnCxMuUUKnVaiQlJSE1NdXsOQ0xt4oiJbGrVKmC2NhYep+9vLwwdepU9OvXD23btqWmPwBYtmwZli1bZnSeY8eOIS0tDWKxGPv27cOVK1fQoUMHxMfHY/fu3Ub7E9NgUbCFGaktXAPDMIIo2CiCKFgAaVsolf5zu0QikdEMlHzJY2NjaeE7sp3PfJSZmYnAwMBSa+5x8+ZNAHpR6tOnD6Kjo42uwdpdvcxx/fp1+Pr6onLlyiZF0FxHL8O/3Nxc2j/h/fffx/Pnz3HhwgXMnDkTAwcOBKAffJydnTmiYAqdTodHjx6BYRjMnTsXKpUKR44cMbl/9erVce7cuSKtoAoKClBQUIAnT56UaNVVUvNfeQ/IOp3O5hz0QpVUPYIoFALbl2BY0oKUUGBvA8ApfAfw29IZhkF+fj4qV65catceEREBAHBycrKJPIU7d+5g1qxZAIArV66gZcuWRvsUpaPXzz//TM0Q8+bNM2n+8fLyQlpaGgB9tVRnZ2ekpKSgUqVKSE5OBgB8/vnnaNeuHX2NoeD7+PigcePG+Oabb+h1f/zxx2jVqlWRVlJarZYmPxZm/its5VZc8x/pK3Hv3r1iC1FJzX+C+ch2EUShEFJSUhAREYE2bdpwtpua/QOAp6cnHBwc6HY+81FeXh6cnJxgZ2dXSlcO/P333wD0g6ItiAK7zEf79u3RsmVLrFy5kiOgRYGYdOrWrWvWH8A2o8nlcty+fRsXLlyg9ZAAcHwLAODo6Eidyc+fP0eVKlXoc6NHj0Zubi46deqEjh074scff8Rbb71l0TXHxsZCq9Wibt26Fu1vDktWVHz7xMXFgWEYeHh48L6GZOEXx4RoiVCJRCJoNBqIxWJqrrOWABXltQL8CKJgBvID0Wq1RrMavkFWpVIBAAICAszuq1arUVBQAF9f31K6cuCnn37CmTNnAID2byhv6tWrh4ULF2Lp0qVgGAa3b99Gp06d4O7ujoEDB2LhwoUIDAy06Fj5+fl48eIFAFAHMB/Hjx/n3PusrCw4OzujY8eOnP0uXbrEeezi4oKcnBy88847HEEAQENX27dvj0ePHqFr165ISUkxK0wEawpzcc1/pNlQtWrVrHYtBEvNf1FRUZDL5fD19bXIj0WEqrj+Lz4MRSIkJMSiz/C/jiAKZiARR3w/ZL7Zf1xcHACYLWlB9pPL5dSXYO2l9GeffcaJyhk2bJjNJK99+OGHmDdvHr7++mv88MMPyMzMRGZmJrZv347t27cjMDAQw4YNw0cffYSgoCCTx1q3bh29b5MmTTK5H59grFu3jgomwdCMl5+fDwCoVKkS73FXrlyJ27dvAwA++eSTIg0mtiDQpXUNlpr/YmNj4eTkZFFZEGtQmKBoNBo4OTmVybXYOsIaygQMw0ClUtE+v4YYDrJarRaxsbEQi83XRGIYBrGxsXB1dS0Vc86bN2/www8/AAD8/Pywf/9+LFiwwCYGIoJYLMaiRYuQmJiICxcuoEuXLtTxHBcXh9WrV6NOnTpwc3ND06ZNsWDBAqN7SkxH9erVM+m0/vTTT40q2QLA119/TZ3wBEP/Bsl/4OvWdv/+fSxevJiev6TZ0WVNeZsRyTWU5XdSLBZDKpVCLpfD3t4ejo6OcHZ2houLC9zc3ODh4cEJJKnICHfBBOxWm2S1YK53QkJCAuzs7Ojqgg1736SkJEilUjg4OBg5qq3BkCFDwDAMZDIZ7t+/D1dXVwC2V+Zi7ty5SElJwc8//4yTJ0+CYRgcOnQIa9euxf3796FWq6FUKvH06VM8ffoUDx48wO+//w5AX9OINM9h+wXYaDQakxnKfIMiyecgqNVqAOCdyY4ePRoMw0Aul+P8+fMWvnvT5y4PynuSIDiabRdhpcADwzBQKpUch5S54ncMoy98R+rgmNpXp9MhJibGZOmL4pKdnY1WrVrB1dUVDx48AAB8+eWXVBDINZQ3RBR27NiBtWvXYs+ePUhNTQWgF86hQ4fi6tWryMnJwZ07dzBz5kzUqlULAHD69GmMGzcOmZmZWLt2LQ1pnDBhAu+5Zs+eXaRr69OnD+cxiT4y9Cfs378fT548AQA0a9aMc48tpbw/C1sYkG3hGgT4EUSBB9Jq05wosAf1lJQUAHr7s6moJFL6QqFQwM/Pz2rRQAzDoHXr1oiMjKSO7nr16mHGjBlG11DeKwXCRx99RP/v7e3Nu0/Dhg2xdOlSREZGokaNGgD0JiM/Pz988803AID69eubNB39+OOPvNv5TASurq6c4xB/AgCOX+OLL77A6NGj6ed248YNIzH5N2ALAzLD2F7ymq1dT3khiIIB7CY67GQWvoGezP7Zhe/4VgBkW0xMDAIDAyGRSHh9D8Xh7bffpqaUESNG4Pfff0d4eLjRfrbwhReJRNizZw997OLiUuhrxGIxrly5gmrVqtH3QMxuhhFEhOTkZJOCyzbZkePVrFmTbrt06RKaNGlCH5OVwpAhQ6gYsVmxYkWh78HWsAUTli0IkwA/gigYoFKpoNFoaJhfYSuFjIwMFBQU0DBUUysFlUqFjIwMGuVijZXCnDlzcO7cOQDA0KFDsXXrVvTs2ZM38qO08xQYhsFnn32G7t27m92H3e/g/ffft+jYnp6eePr0KXJyctCrVy+6nd31jA27F7Y5yKBE+ivs3r0bPXv2RExMDACgadOmkMvl6NGjB44fP8557dixY5Gamlrkxj1lPSA3b94cnp6euHjxImd7eQ/ItpjRLKBH+FRYGK4SAJhdKRgWvmNvN9w3JycHlSpVoslqJR2kT506hXXr1gEAGjdubFQO2pDSFoWDBw9iw4YNuHLlCn766SfefbZu3Yq8vDz62JST2BRSqRRPnz6lj2NiYtC3b99i914mnykxZ7Hvj5OTE8aMGYOmTZvi8uXLdHvNmjURGxuLjRs3cpLxbJE3b97g0aNHyM/PR+/evTF16lQAtjFLt4VrEOBHEAUWxJfATgYiAsE30Gs0GmRmZnJi3PnMRzqdDvn5+RynZUkH6dWrV9P/JycnU4etKYp7PhLDXRjDhg2jtv8ZM2YgMTGR87xKpcK+ffvoY7lczjHTWEJmZiZev34N4J9Q0fPnz6Nq1ap0dk/+tRQXFxf4+/tj//79GD9+PN2el5eHTz75hCNCMpkMd+7cMekHsZTSHAwfP36MGTNmQKFQ4NChQ5zntmzZgjdv3gjmIx5s4Z7YCoIo/D86nQ7Z2dm8DjBTZbLVajX8/f05Tkq+fXNyciCXyzk2dLZPoThfyEWLFtH/JyYmGiVjGVJUUUhMTISvry8cHR3h6uqK+/fvF/qaU6dOQSKRQKPRGLW8HDVqFA3zBLh2fEtZs2YNAP29e/LkCQYNGgRALzgxMTGIi4tD/fr1zR7D0NFMCuexBYENOzt9zJgxJc54Le7gExYWhmvXrpndJzc3F82aNcMPP/yA3r17U5MRO3M+KSkJgG2Yj8r7GgyxtespLwRR+H/UajXu3LnDMW8Q+Gb/JI/BMGTRcPBlGAbZ2dmcWkh8+xWVdu3accoUsB24fBTlfGFhYRg9ejQth8AwDLZs2VLo66pUqYLly5cD0Ndd+uqrrwDonbeG1UbN+R5MsX//fgB6c5m9vT1d0Tk4OKBx48Zo0aIFR3gIbNs1e9UzaNAgLF++HL169eJ93eLFi+k9trOzw3fffVfkay4pGo0GK1asQK9evdClSxcqYoaoVCo0bdqUs+3Ro0cAuDWnWrRoYRMDsi1cgwA/gihA/wVVqVQmB06+2X9KSgrEYrFRSQvDqKKkpCSIxWKj8tjWsPGTKqiAcVVPQyw9365du9CtWzf89ddfAPSNZLZu3Upn6YXxySef0D4SK1asgEKhwLBhwwCAcw/Gjh1r0fEI6enpePPmDYB/ZvWnTp0CAHTt2hUtWrSgDXwMMSVA9+/fR+XKlel7ZRMYGIhBgwbR2fmUKVOslvFq6WD48OFDVKpUCYsXL6ZiNmTIEM4++fn5WLFiBerUqUPLiANAhw4d6KqArG6cnJysmh9TEgRRsF0EUYB+NqZWq03+YAy3q9VqpKam8n6p2YMvCUP18PAo9JjFwd7eng5UXbt2NbuvpaJgWCRNq9Xi888/L9KAePDgQVoaulmzZtQRTCJ17O3tUbt2bYuPB/zjQ5FIJBg1ahQuXLhAS1g8fvyY+hr4MBW6GhUVRY/JRiQS4cyZM1S4HB0dsWTJkiJdb0m5ffs2QkNDac5EgwYNsHfvXk77z9zcXCoaCQkJnNcXFBTQiQIRBXZ2dnkPyLaYpyCgp8KLAok4IiFyltQ5iouLMxl5wh7sSZ9mNzc3s/WQ0tLSkJCQgKSkJKSkpCAtLQ0ZGRnIyspCTk4OcnNzkZ+fD4VCQUNmGYZBeno6nUF+8803WLx4sclIHHI+U8KQnZ2NL774AnFxcThw4ADnuaSkJDRu3Nho4DFFcHAwXS2QHIoBAwbQ3gXFKRv966+/AtCbjqRSKTVTsc9hKrvY09OTd3utWrXw3XffISsrC+7u7nS7TqfDyJEjab5Hv3790Lt3b7z33nvo2rUr5syZQxMFi4olwnzp0iV06tSJdvs7ceIEwsPDaRvQ9PR0uLu7w9vbm37+VatWxdq1a+kx1q9fD0Dv0CcrBpIdbguzdFu4BjZCg51/qPC1j8gqgSSUFbZS0Gq1iImJQY0aNWi/AjZsAWEnq5kqfZGbm4uIiAi4uroWufyvSCRCcHAwXr16hby8PKxYsQIrVqzAqFGjMGrUKN7a8Q8ePOCtLT9q1CiTSW86nQ5///03goODYW9vj2nTpqFhw4Zo3749PD09eTt/sVcWYrEYu3btooMzO9fAEtLT02nXOBI+atj/QCaT0eYxbKpWrUrNTmxWrVqFRYsWYebMmTh9+rSRmN69e5e+DyJIhKtXr+Lp06e4du0aevXqVWg4sKXk5+fjyy+/xPfff0/rV509exZNmzbFxx9/jEaNGmHSpEl49eoVLdgHAHXq1EFkZCQAfRFANqRNKaDPWQBsY0AW8hRslwotCsSXAIAOaoWtFBITEyGXy+Hu7m5yX51Oh7y8PGRkZKBu3brIyMjgFQViXvL390ejRo0KvVa+uvG3b99GWFgYFixYgIcPHwIAXr16herVq3P2U6lUSE1NhYODA+c4ZNXRrl07XlEwvG6FQsHJ4pVIJGjYsCE+/PBDVKtWDQkJCfDx8aGmGUBvKti5cyed1bZs2RJ3796lYvLixQt4eXnh2rVrWL58OWrWrIlt27bB3d0dYrEYK1eupJ+Rj48PPv/8c071U5FIhN27d1PfBRuZTMZZVRDat29PTTPsyK2AgADEx8fTx+SaSbFDwvnz56HRaHDw4MEiiwJ7QM7OzsbGjRuxd+9evHjxgt5vqVQKmUyGrl27cvxF9evX55TwuHr1Kh3s+VaJzZo1o59F586di3SdpYktCJMAPxVaFNitNgFjJzGBrBRIslq1atWoHdpU9dSYmBiarGYqy5lhGCQlJVnUtctcnfpevXrB3d0dnTp1AgBs2LDBqLqnSqXCixcvUL16dd6mLI0bN8aKFSug0WioaaxDhw5wdXVFQkICFQy5XA6xWExnqlqtFpGRkfj000/h5+eH7OxsTu0gQJ+5fPXqVQB6+3ajRo1oiZA1a9YYJbs9fvwYX331FRYuXAiGYWh1VIZhMHjwYM79rly5Mn788UfegR/QrzIMkUql+PLLL3n3ZwsCoWrVqnSl4ufnhzNnzqBx48YA9J8LCTogf2TVydcF7NWrV/j888/x8OFD3ognsvpLTk7mLft98uRJej+GDRuGevXqYfv27UhPT+dd5R48eJD+n3S4s4UB2RauQYCfCi0KxJdAvpymzEdkAE9JSQHDMPD19aUzSIZhjJLd1Go1EhMT0aJFC7qNr0yGWq2Gq6urRTWACoPMHp2dnbF//37s3bsX7dq1w9atW+k1AIXbtKVSKb1eYj8/cOAARo0aBQAIDw9HrVq1oNFo0KtXL1y9epUe0zBhjbBt2zaaP1C7dm1OGQpT2c/NmjVDSEgIUlNTqS+CwH4Pnp6e2Ldvn8kYfr6IJI1Gg5MnT5q6BRSRSIS33nqL9l7w8/PDnj17OFE+APD06VOLWlMuX77cqI8DOU9QUBB69+6NQYMG4fTp0ybDX0kWOwAcOHAABw8etKiGllwup8mZWq2Wtxx8WWJromALEVm2QoUVBdI0XSKRcEpamDMfxcXFcQrfAfw1kXJycuDm5kZn3KbEhuQ5WOMLSRKVWrVqhfDwcLx+/ZpzXEtFgb3PF198gZkzZ9JZdZUqVaizUiqV4ty5c0hPT0eDBg3o4MueVRMYhqF2fbYJ49atWyav4aeffkJQUBAtBW6K+/fvW5RYVxwmTZpEGxZVq1YNERERsLe35zTV0el0aN68uVFoMnmOCMSCBQuoINjZ2WHEiBHo2rUrqlWrRluQkn137NhhdCyJRMIbdky+rzKZDBqNxuTnq1KpOC1HMzIy8PjxY7r6LOs/YtJkl6gnqymB8qXCioJSqQTDMEYOUVOO5ry8POTn53MK3wHGNZEAfQYz20fAt1Ig9t9Hjx6hfv360Ol0cHd3R0BAAIKDg+Hv7w9fX18EBgaiSpUqaNKkCSdCxvBYZDb94YcfUpNBWloa5xoAy0Sha9euOH/+PL0msiqKiYnB8OHDUa1aNUyfPh3e3t7w9PTE+PHjaaE7Q0FwcXHBpUuX6IBGspAB01nEgL50x/jx48tskPD29jYqFUIEoXr16oiIiKCZ64aJgjdu3ECXLl2MjikS6XsoX7hwgc7w69Wrh23btpkspHfs2DHekiXs8NL58+fj2bNnuHz5Mvr3749FixbR74ZGo0FkZCSePHmCtLQ0ZGVlITs7G05OTujZsyd0Oh1u374NPz8/Tn9k9h9ZSRQW+MAwDDWBWbIv+0+j0VBnvuE9s6b4sI9nyqxH9jNVhr2iUSFFge1LYA86plYKYrEYaWlpCAwMpCJiaqWQn58PsVjMCYPkOy4ZxEeOHEkdmMnJyUhOTuYkpbGv4bfffuOt379582a6z9GjR6nNmR02WxRR+PTTT2lHMcO6R6SezrFjxyyaoQcEBFBHrJOTE7y8vAAAkZGRePbsGe9rDhw4gDlz5hitdkoTU7WjatWqhXv37nEmD4bRTLdv3+YVBUBvUiMJZ46Ojti6davJqBuGYfDhhx/yPmdnZ4d169bhgw8+MBu1I5VK0bx5c+p8NoR8D2QyWYlLdly6dAm9evVC+/btCy2zYsjZs2fRqlUrODs7F1lQzP0RC0BRX+fm5obQ0NAS3Y//ChVSFHbs2IFq1apRmz/BnJknPz+fU/iOb6Wg0+mQk5MDJycnI7FhHzcnJwf5+flYv349LSXx6aefIiEhAS9evEBCQgLy8vKgVCqpHZhhGIwaNQoJCQlGiWS//fYbAH2TH/L/atWqcTJ1LRWFN2/e0Hh4grOzM5ycnGi8OwBOORBSNK5SpUpG9v9nz54hJycHgD50kpx/4sSJJq9hwIABGDBgAN5//30cPnzY7PWWJnXq1EF4eDjnfj99+pTO2olJZ/HixTh79iwCAwORnp4OT09PhIaGYuPGjXj+/Dl9bX5+Ptq2bYvvvvvOqBjg8+fPMWzYMF7n8vDhw/Hdd9+ZXCkWFWsJ7aBBg6DT6ThVZItyDXwh0+WFJX6ZikKFFIVff/0VPXr0MGrWbmqloFAo4OLiQsteEwxFJCMjA1qt1mgGZrhfTEwMsrKycOHCBQBA7969eRu4EG7cuIFOnTohJycHY8eO5dicGYahdnf2QL1x40beDFZzA4JKpUKbNm2or2XQoEH47bffkJeXh3v37mHNmjXUpMJehbx69QoAjO4PgUT0dOnSBTqdDk+fPjXKM+Dj+vXrRttM2datjUgkMhIEgOsY9/f3p07nsLAwzn6GCYBsvvnmG7Rp0wYhISF49913cfLkSZOVaN3c3PDzzz8X923wYi0nr2GUWVGvwRbEQMCYCvmpyOVyTsw5gS8kVaFQQKFQ8GbLGq4AYmJiTO5HjqtSqZCcnIxZs2YB0BdzKyzOvXXr1hgwYAAA/WCze/du+hw7H4DMyAGgf//+OHDgAOf9FFbqolOnTtQP8fPPP9PG9zqdDlu2bKENfQBuljBZQRTW14DM+tntOA0hx01ISODNoC4LQQD075kU9GPDNpN88skn2LJlC9555x0EBwfDx8fHoh4LKSkpCA0Nhb29Pf744w+zpclLq2dDSUWBvYLjC3EuDFuKPjKX6V8RqbCiwFemgG/QjI2NhZ2dnVFBO7I/GXRJspq7uztvRBLZFhcXh4MHD9LwzY0bN1pk2921axeNcBk3bhxcXFzQsmVLXLx4EQ0aNDDan5ibHB0d4e7ujurVq2P16tUmv/yTJk2ijr8pU6bgvffeg6OjI/3h+vn5cTK4iW9gxIgR1GTEFiXAuLzEy5cv8fjxY96wTEJ6ejo+++wz3jaX1ipIZymGs32GYTgi3KtXL4wcORJ79+7FkydPMG3aNI75RyaTYdu2bWjTpg3dZmo1ZYqS2v35KOmAvGjRIk7XvGnTphX5/ED5118S4KdCioKdnZ3JEsvsmbVGo0FcXBxcXFzMJrUB+lWCr68vZDKZyTpHDMMgMjKSRq/Url3byH5vCrlcjt9++43aldVqNR48eIBPPvmk0NcqFArEx8fj0qVLvE7sLVu24JdffgEAtG3bFqtWrQIATg6CYbbz/fv38erVK+rD4IMvcWzcuHGFXu+GDRuwbds2o+2WNPsxh2H58sIwTGQ7c+YMZ0AjRf1iYmLQuHFjLFiwgO7r5OSEqKgovP/++5wJBd8KlU1ISAjGjx+PoKAgAOANdS1PLl26xGmpKpPJsGzZsiIdQxAF26ZCioI58xF7Jk0K39nb25tNalOpVEhMTESVKlVMdmljGAbJyclYtGgRDYUlGbuWLl27dOmCxMREPH/+HKGhoXSQI3XzAb2d23B2yU6OIx3LCLdu3aIzPX9/f5w+fZo+d+LECQD6wZQUuCNER0ejXr16Fl03G75+FXwUt+CcOfhqI5nDcFbP9uW4ubkhPT0d48aNQ926dY3qYB07dgyVKlUCwF+FtXr16kYVaaVSKS5duoTvv/+ein9piEJRVwobN25EaGgofH190bNnT85zrVq1Ktb5AUEUbJUKKwqmzEdkls8wDGJiYhAUFGQ2qU2n0yEuLg5ubm549uwZvv32W5MrhR9++IGWeF60aBFcXFyKZcusUqUKbty4wTvI5efnIzU1FXv37qUmHrZZ55dffqGhpKmpqejZsycYhoG9vT2uX7/OMdHcuHGDnm/69Ok03NYSnJycivSeHBwcitV4x9rY29vT+0bKQhBIqQ5A/5lWrlwZu3fvhlarpXHwgL62E9tktHr1aqP79vLlS6Ny30OGDKGx8sQMVdT7aAmWikJ+fj7atWuHmTNn4t69ezRSjk1RixuS8wOwKUezUCX1H2znUylDSMq/IeyVQmJiIqRSKby8vMwmtWk0GsTGxiI7Oxtdu3bF9u3bMXHiRI7pRCwWIz8/Hxs2bAAA1KhRg9NEvTiYel1WVhZcXFwwffp0dOjQAXPmzOGYTVauXIm33noL9vb2qFy5MgoKCiASiXDy5EmjekkknJIkWpmqDcWHpSsCQD+TvnnzptEstKzp2rUrLl68SJ3t7OS6/Px8TrhtRkYGLXHSu3dvDBgwAFqtlhbnA/Shwq1bt0bTpk2NQpfZBAcHY+zYsbQkiUajob4Lw2RAa2DJdy4xMRHVqlXDnTt3AOgz1T/44APs3r2b+nvEYjGnv4OlkHshDMK2SYUMSbWzs+OtiUNWBKTwXdWqVekMwpRPISMjA3K5HC1atECtWrXw7NkzPHnyBDVr1sRXX32FKVOmQCQSYdmyZdBoNBCJRDh06JDJ5DdLsbe3h0KhQKdOneDi4oJjx47RQVun0yEpKQlHjhxB8+bNaXIRyeg0FMTly5dzZraA/odLhI0kZj158qRY1yqVSuHq6srrYxCLxTh//jxq167NscmXNZUrV8b58+dx+/ZtAHqh6t+/PwC9IBiaegD9+7pz5w515ANAnz59sHr1auzatYs3ZFMkEsHOzg6+vr6oU6cOJk6caJSQSOpMAUU3eVmKuQFZoVCgZcuWyM7OBgDMmDHDyG/Qrl07iMVio4mEJQjmI9tGWCmwICuC1NRUaDQa+oU3F8qZkpKCKlWqQCKR4Pbt2/joo48gEomg0Wgwf/581KpVC1988QUtaz1p0iRUr17dbJkMS7h27Ro++ugjnDp1ijpEa9asifj4eGzcuBE+Pj4A9A5iEu3j6+vLKUYH6Ac2wxr85HXkPZPBkS+bujCioqIQGxvLEQRiqxeJRNi3bx8101haw8hUtm5JIPkGZCCsW7cuNBoNVq9ejUqVKtHtbJo2bYqrV69i5MiR1E8UFRWFzZs38wpC//79cf/+fYSFheHZs2f4448/eO8pyRoHuP4ia8FnPiJFAseOHYsaNWogJSUFALBp0yZeR3Lz5s1Nluqw5PyAIAq2SoVdKfA5msmKIDo6mha+A0ybTUgNF19fX7pt9uzZ6NChA5YsWYKnT58iKSmJmo18fHw4P7CS9GmuX78+radDykW0a9cOnp6eiIyMpD9qANQWzFcW2lQc/PHjxwHoVySenp44deqURZ3X7Ozs0KJFC1y9ehWOjo5wcXExKg1O7v26deto/gVguankypUrpR6V8+jRI5Od3Ah37tzB3bt36Xdj0KBBnFLVNWvWhJOTEyIjIyESifDjjz/yrpYMadq0KSIiIjBhwoRSC0kF9EUUv//+e9y5cwcpKSlG38XPPvsMo0ePLrXzC6Jgm1TIlYJMJjO5UlCpVMjNzaWVK8l2vsFbpVLBw8ODE10iFotRqVIl3Lp1C/v37+cIxu7duznOtZKIAiE1NZU6Jf/3v//h3XffpVm3fE7KoKAgTllmU6JAnKrkPhAfSGG5AitWrKB1hEQiESpVqsTbP/njjz/GhAkT6OMbN25YtGpq1KgRZs6cWeh+pQ0JM2VfM1sQevXqhYiICGpy69atG7y9vS06dlhYGGJjY2lLTWuSnJyMBQsWoHr16ujduzdOnjyJ5ORk+j10c3ND69atsWXLFnz99ddWPz9gW4lrAsZUSFEwl6egVCo5he8A/vIXubm50Gg0RvVo2AN9nz59cPnyZSxatAhLly41Wm6zm/cUl71799JjzZ8/H3/88QcAoEmTJnj16hXnx2dvb4+rV69i8uTJdICKjY1F1apV8f777+PUqVN0XxJiGRUVhUWLFiEmJgYAsHPnTvTo0YP3WkQiEcaMGUMLxhk6mzdv3kxFkd1OkmEYzorBHA8ePOB0HjN1HcUhODiYd+BmH69KlSq4c+cOb5c6Njt37sTixYtplBspD2LJ9UmlUosFxBIYhsHy5cvRrFkzBAUF4fbt23S15u7ujj59+mDz5s1ITU1FUlIS/vrrL4wcOdJq5zdEEAXbRjAfsVAqldBoNKhSpQpnO99KISYmBnZ2drwx6ERAdDodYmNj8d577+H58+cm8xeeP3+OuLg4GtZIyv2aK/VL/khzGYZhqJO0Xbt2+Pnnn5GTk4MWLVrQ7QqFArNnz8batWtx9OhRDB8+HG/evEFycjIOHz6Mw4cP49ChQ+jbty8nM5ckK1WuXBnvvPMObty4wSn3IJVKodFoUKNGDUilUt4Z/7Bhw9C2bVs0a9YMd+7cwY4dO/D222+jV69emDx5Mm+4IzmuJbBXf8UV2aVLl2L48OEAAFdXV+pHYB8vPj4eb731ltlVTZMmTeDs7EzFKzQ0lH6nyrKcwvnz5/Hjjz/i3LlzHBGWSCQYMGAAvvjiC5qAV5bYqijY4jWVBxVSFGQyGW+eQlpaGqRSqVHSkuFKQaVSISkpCa6urkaDA1tASGtKPz8/Tv9d9nHVajXi4uLQrFkzyOXyIpf8nTZtGk6dOkV/9P369cO8efOQnJwMhmEwfvx4KgqAfmURGxuLWbNmYf369bhx4wb++OMP6tC8ePEiZDIZvvnmG6Ns6Xbt2mHAgAFGZabJwO3s7IwWLVrQezt8+HAsWrSIClhKSgo+/fRTfPjhh1Cr1Rg0aBDGjx9Ps6kNKUoG85IlSzB37txC93NwcOCN6LG3t6elG1xcXDB58mTeFp+W1F7asmULVq9eTZ3NpLR5aXHs2DGsX78ePj4+yM3NRXp6Ol6+fGnkv6hfvz569+6Ndu3aITQ01GpVV4uKUAzPtqmQosBnPlIqlTS81BBD239cXBzc3d0hlUpNzv4B/WoiICCAtrjkS2pLSUmBq6tricwF165dw5gxYzBkyBB89tlnnOdatWqFPn364NNPP6UlKS5fvgxPT0+kpKTAzc0Nhw8fRp06dQDoC+Ft3rwZNWvWxMiRI3H06FGa/LZ//36z18EuodGzZ09MnToVaWlptPR3QUEBPDw8sHnzZkyaNAlKpdJkO86iwjZ9mUIqlXIEgd0ljoiqXC7HsGHDeGsvAfoMYzLYe3h48IY2t2rVin4vgoKCjGbj1pyRnj59Gu+++67JFYi7uztCQ0OxatUq1KhRAwBw4cKFcp0VMwwjzMptmAorCobmo9jYWLi6unKW2QR29JFWq0VsbCzq16+PxMREk9nLSqUSKSkptAyAKadyamoqatWqVaIldf369Wn2MR/e3t7YvXs3oqKiaMnqo0eP0ufZpS3IoPnkyZNi5yU4ODhgxYoVqFu3Lmf7y5cvkZOTg65du6Jz584ICQkptLIqGycnJ96kOE9Pz2K15OTrKe3k5ESTyPgggtC7d2+MGDGCmpvYsD/n9u3b4/Hjx9QUSETkzZs3Re4iRo5Bcmfu3buHd955h57P398fDg4OdJLx8ccf84a8lrf5przPL2CeCikKhnkKpPBdjRo1aG8ANmyTUFJSEuRyOTw9PZGUlGSyImpsbCy8vLxo6KSpCCaGYWhOQUk4fPgwQkNDjWobsVmwYAEGDx5sdL1sk0ibNm3Qrl07/PHHH3j58iUcHR2h1WrpasGSfgYFBQVo2rQp1q9fT+Pce/XqxRFGf39/PH36FJMnT0aHDh0sqrRpKkv6vffeg1wux9q1a82+nm2OIpFmhhjO/H18fCASiTjZzBKJBD/88ANdlbEnDY6Ojvj2228REBCAgIAAODo6cjqCaTQaMAyDtLQ0i7qImaqNlZKSgo8//hharRYSiQSbN29GtWrVjITk7t27Rts0Gg2io6Ph4OBARcYS/5W5VpZFwdZEQSibzaXCigJ7pRAfH09j6k3VOCI/zpiYGFr4jm+gF4lE0Gq1iI+P55S05lspaDQaeHt7l9i+umrVKixYsADOzs4m20oC+hVDQEAAzVcIDg7GhQsXEBoaisTERLi6utLGP1999RUuXbqEQYMG0dlx48aNcebMGQQFBRVa7RMA9UnMnDmTt0aOu7s7rRi7fv16Tllqvn1NrSpatmyJJUuWFHo9bAoLf/Xx8cHy5csxYsQIo2zmt99+G/n5+Thy5IjRsSZPnmy2EuzTp0/BMAzq169v8bWSCDUiEqmpqWjatCnUajXEYjEOHTqEli1bmhQWdr9lIujFaVlJ/gwpSmAEyahXq9V4/Pixxauj0hIoAWMqpCgQnwL5scXExKB27dqQSCQmaxzpdDqkp6dDpVLR3ANTfgJA78z28PDgbGfvm5+fD61Wy9mnuJC8A75WjmweP37MSWB79eoV3nnnHZogpVKp0KhRI2i1WiQnJxsd7/79+/D39y/yzIoMRObyMti5DIbO4JkzZyI6OpqTB8BmwYIFiIuLK9I1mSMkJITT9c3wPuTl5aFhw4ZG78XR0dEicSrqwEXMRWKxGIcPH8aECRPoNe3evbvIReliYmJQq1atYjXwIb8Zw9VMUf5yc3Pp6oRUGba2QPGJiqkVkUgkQtWqVUslUfDfSIUVBWI6SEpKgkQigbe3NwoKCsyuFGJiYlC5cmUahmpqpQDowzfZP37DfePi4iCVSq3SOKZmzZqcCCM+oqKi6MxdIpEgMDAQ0dHRnLaYCoWC01OYD0sFwd3dnZqd+MJN2Wg0Go5Jys3NjYqCo6Mjpk2bxlt7iGBNQRCLxfj999/p4+zsbCNR4GtS37ZtW+zbt89q12HIq1ev8O6779LWq4B+MmBpPw42JTGXsAWquKSmpiIzM5MGNxQXQ4EqrlCRKrfCKkNPhRQFUjqbFL4zNAcZ2jyJ3T0zM5Oz7OdbKRDbO6mlz7evVqtFQkIC7Ozs6LaSfCE7d+6M27dvm5z5paeno1WrVtTccPHiRbz11lv4+uuv8d1330GhUECn09GVErkmT09PNGnSBA8fPuSUzbCEzMxM+p6I/8bUSsEwc5ZUKQWANWvWYMKECTRixVQvDGvRpUsXzmdXmNgCep/G9u3bLTp+UQdkjUaDjz76CHv37qWvDQwMxK5du4yKGFpKedv0rXV+awgUIERDGVIhg4VJnkJ6ejrUajWn8B1g/MMlfgJfX19OyCqfKJD6QIZfVPaAmJSUBDs7O05Ia0lmb2zzjCEKhQIhISF0trthwwZai2jBggXIyMjAli1b6HHID+TLL79EfHw8/vzzT8TExJjMYjYHeU+5ubno3bu3SaepYfgnERGJRIJu3brR6KjRo0cXuuooKYYRU507d6Z5K82aNcOOHTsQHR2NNm3aQCKR4OzZsxYJwunTp7F8+XIsXrwYkyZNQosWLRAcHAxPT084OjrCyckJLi4u8Pb2RrVq1RASEoIOHTrAx8cHe/bsgU6ng1wux7JlyxAVFVVsQSCUtygIeQq2S4VcKdjb20OlUiE6OtrIHAToZw7sLy1x1vFlOrNFQaFQ0FmuqaQ2EplUuXJlTs2ZkkCiagx/6AzD4K233kJSUhIAfQnkrl27cvZJTEzE0qVL6WORSITu3bujd+/enP3OnTvHe25zWcfsENKLFy9i165d6Nu3r9F+plYQM2bMwOjRo+mAuG7duhK35CwMwxXR5s2b6crkxx9/RJMmTQCAOuQtYdy4cbTHgjlIhFJubq5RuGy/fv2wc+dOqxQCLO+VgjAzt20qpFwT81FOTo5R4TvAeNZOBlVD84zhYEbCUPmOQVYV2dnZKCgogK+vr8kw1aJiaqXQo0cPWsNo5syZGDp0KOd8v/32G2rVqsUJw9XpdDhz5gxatWqFuXPnIjc3FxMnTjQZrcM3SBOHnWEIabVq1XjfL7uK6syZMxEREYHmzZtj5MiRuHLlCgB9yXG5XF6k7ODirG7i4uJw7do17NixA4sXL8aiRYsA6FcQRBCKColSkkqlcHd3R9WqVdG6dWv069cP48aNo5+bo6MjvLy84OPjg0qVKqFSpUqoW7cuNm/ejE2bNsHR0bHEomgL4ZflLUp82Nr1lCcVcqVgZ2dHcxPYTdX5ehxotVoqCny+BvIjI2GoDRs2RGpqKq8okFWCv78/zXK2xo+Uzy8xatQohIWFAdDXHVq6dCkiIiKoz2DUqFE0mkcsFuPbb7+FSCTC0aNHERYWBp1Oh7Vr1xYa+8+HQqFApUqVOLH9APDrr79i/vz5RvuTXhNDhw6lq5arV6+ibdu2APTRSGT77NmzzZ6bXQOJzyFcGGFhYbSpEJvVq1cbbUtNTcXEiRMRGBiIgQMHomPHjkZmkUuXLlFxvHHjBo14IWaqzz77jH4H8vPzjfowJCcn46OPPqKPRSKRVRrvlLf5SBiEbZcKKQokWqVRo0ac7Xw+BZKsptFojL7MbJ9CUlIS7O3t4eHhYTJUVa1WIyUlBS1btjR6fUkgKwWlUoljx45h4cKFePr0KQD9gP/bb7/h119/hYODA3x8fFBQUEAHbB8fH9r5DNDH2T98+BDjxo3D/fv3ja7P19eXiqS5RDZDQQD0JqjLly8jMzOTDp6bN2+m/g52s5/w8HBaiXTWrFmQSqW4ffu20fm8vLw4jmm+6reFUVhC3qBBg3iF4p133sGtW7fo+xCJRPDy8kJwcDBq1aqFCRMm0GKCHh4eaNiwIR4/fsz5Dp08eRKAvgBf8+bNoVarkZycjIyMDOTl5UGpVHKujXxnimuTt4VeBoIo2DYVUhSIY9XQR2DYepNEJwUGBvJWOWX7CQpLahOLxcjMzISbmxvtc2DtlUJBQQGGDh3K+xygn4mSstYA0LdvXxw8eNBogGnYsCFu3LgBhUKB77//HleuXEF8fDy6du2K6dOnc8JDi/oeVCoVYmJiEBQUxOloZm9vz+moRhLAXFxcaKE7vtn66NGjOf0hioq7uzumT5+OxYsXm9yHr1Q3wzC4e/cuZxvp2peamorbt29j79699N7ylZvQaDTUdPf555+bzOpmGAYJCQm4ffs2qlevXiInrSAKAoVRIUXh7bffxvbt28225ARAo5N8fX3x/PlzMAzDKZVNBCQzMxNKpZKT1MY3UGZmZqJevXqc17Ojj4r7Qxk4cCB+/vlnjgBIpVIMGjQI1atXR82aNSGXy3Hy5Em8fPkSBQUF+PTTTwttum5vb49Zs2Zh1qxZdNuJEyfo/9kz2GbNmuHatWuIjIzE6NGjzdZNyszMxPXr1zktLklLTkBvciGv/+qrr6hDn12vCdCbipYsWYLt27dzVguW4uPjQ/tEfPnll/T+scXex8eHt5ro4cOHqX3/9evXcHR0xOHDh/Hbb78hOTmZ1mIix9y3bx8ePHgAJycn+Pv7o0mTJoiOjqaf/9ixY01ep1gsRmBgIMf/9W/GFh3NtnY95UmFFIV+/foBgNmWnABodBJJMDPlJ4iJiUFgYCAVDD6zEBEg4ogGuNFLJflSxsfHQyqV0oS8KlWq4PLly0Z1kBo0aAA7OzvUqlWr2Ofq27cvFAoFDh8+jO3bt+PFixcQiUQ4fPgwAH0vgXv37sHBwcHkCmL//v20lShh0qRJ9P/Ehu7l5UW3k7BMNh06dIBYLMb06dOxYMEC3nOZKpUNgAo0OztWIpEgJiYGdevWRXZ2Njp06MD7WrJ68PX1pSHNo0ePpu0rP//8c6xfv55+7gzDcIr2kfsFAAEBAcXKLi4OwkpBoDAqpChIJBJIJBLegmhklpibm4usrCw0aNCA1wFN9tVqtcjKyuKUR+YzHykUCri6unKW/lFRUTh+/DgYRt/0XaFQIC4uDsnJyUhPT0dBQQEnY1On00Emk8HBwQESiYTmORD7vUgkwvTp03kbrZPnrRV98s4775jNpv3yyy9p5I4ha9asMdpGQlWPHTtGS16sXLmSPs/XWpREF5HZPvDPeyShsuacsrm5uRgxYgT+97//0W1arRZnzpyhqxh2y1A2JKmNTDAMWbJkCW2n2blzZ7i6uiIhIQFxcXHIzc2FUqmkgjFjxgyT12htbEUUhDwF26VCigLALXXBhszy4+Li4OfnR5PV+AZUkUgEhUIBHx8fTt0Uw5XCli1bMHPmTPj7+6NDhw5wdnbG/fv3LcqWNUSpVPKucKpWrYoTJ06YXQVYUxQKg28QZ0PCM0kBv1evXqFWrVq0FEdAQABtegPoRSgxMZHOygFgzpw5uHDhAif0lLw/c6GbZIV29+5d3L1718gpTkRVJpOhY8eOdHtcXBwOHDiAiIgI+t0xFQ21d+9eOuj/8ssv9LofPXoEmUxWLh3PACEkVaBwKqQoiEQik93XxGJ9n+akpCQaJUReY7hS0Ol0UKvVqFy5stHx2T++L7/8EoA+2/nAgQOcfSUSCTw9PWmxLg8PD/j7+yMoKAiVK1eGk5MT7Ozs4ODgADs7O8TExCAhIQEKhQJKpRIKhQKtWrUy6pJm6n2X1aDw/fffA9DndhjWDrKzs4NOp8OkSZNoAblGjRpxro+vab27uzu6deuGBw8eIDU1FVqtFqdPn8bjx48xb9482imtbdu2uHr1Kn1d3bp1aTQWwF3xSSQSGroL6H0bJIHNw8MD48aNw61bt/DmzRsqxmRA8/LyQlBQEH1tQkIC1q1bh99//52udipXrswRMlsYlIHyXynYkijYymdiK1RIUQBMrxTEYjFSU1Ph4eHBme3ymYRIfR83NzejY7DzFyZPnozvvvuO7qtSqWBvb48ePXrg448/RvXq1fHixQv6WnMlhENDQ3mfT0tLK7TUcFmJQlRUFB0U16xZg2fPnmHVqlX0eTK4GlYUJddWp04dk2aZ48ePA9CvBD788EMcPHgQMTExVBCcnZ3h4OBA9xeJRDh06BCnjHlQUBDevHkDLy8v5OTk0O9BtWrVcOHCBWrfT05ONspEZoevKpVKODk5UTMiXxjyvHnzjN5DeQ/ItnANtiQKgOBoZlOmonD58mWsXLkS4eHhSEhIwJEjRzBw4ED6vE6nw+LFi7FlyxZkZmaibdu2+PHHHzkmkfT0dEydOhXHjh2DWCzG4MGDsW7duiI76mQymcnCaqmpqbw5DOwfvU6noz2dDb9Q7H0TExPRtWtXvPPOO5BIJKhZsybdLyoqCmq1GjExMdBoNPD19eWt4mhJaWG+ypB8AiASiRAfH2+1OvZ8+5HmM/b29hg0aBC12fv5+fF2OwOA7t27Y+zYsbC3t+fNCTBEKpVi165daNmyJebMmcNZYUycOJHuN3z4cKPQ43v37kEsFuPtt9/G5cuX6faQkBAEBgYaDe4ymQxyuRxyuZwWPAT+KanNjsKSSqVo1KgRhg8fjnHjxtlcOWZbmBXboigI/EOZikJeXh6aNGmCMWPG8Dopv/32W6xfvx47duxAcHAwFi5ciJ49e+Lx48f0xzV8+HAkJCTg7NmzUKvV+PDDDzFhwgTs3bu3SNdi2H2NoNVqIZVKjfocGK4UMjIyoNFoeB1mZEau0+kQFxeHwMBA5Ofn884kGYZBcnIyQkJCOJFJ1sCw7j0RoerVqxdJXMifWq0utCyxRqOhdZJatGiBq1ev0lUDX0IbQS6Xw8fHB2KxmA7algjU22+/DT8/P8yaNQvNmjXDW2+9xVkBrlixwshkxzAM7O3tMXfuXI4okHIUhpCmMIZlO6pXrw6lUol27dqhT58+8PPzQ/v27f8VTlRhpSBgijIVhd69exsVWiOQsgoLFizAgAEDAAA7d+6Er68vjh49ivfeew9PnjzBqVOncPv2bbRo0QKA3nbdp08frFq1CgEBARZdh0gk4jUf6XQ6qFQq+Pn5mZ39A/qIFx8fH974eOLIzMrKgkKhgJ+fH16/fm3k/CQlC+zs7Hhj4UsK8VNIJBI629XpdHB1dbX6uQhbtmzhOFirVKmC3bt30zwPQO+EFolEHF/D4MGDUatWLYsEinQNI4UKq1evjoMHD4JhGM4gP3v2bERGRmLHjh2ca2T7EKpWrYro6Gij9yGRSFCrVi34+vpCJpPR3henTp2CRqNB165dsWbNGiORYq/C+MRMrVZDIpFAqVQa9VwuC2zBfGSLeQoC/2AzPoVXr14hMTER3bp1o9vc3NzQqlUrXL9+He+99x6uX78Od3d3KggA0K1bN4jFYty8eRODBg2y+Hx85qP09HTodDpeUxR7pZCfn4/09HQ0btyYt/0lWSnExcXB398fEomE154vFouRn5+PatWqlcmPpCx8CiTctEaNGtRs07BhQ06dpWfPnsHb25vmdxAfizVo3bo1Tpw4gRo1auCrr74CAM6qVC6Xo23btpDL5WAYBi1atOAVBa1Wi6dPnyIqKgoeHh5o0KABAgICqLCPGTOGt6WlJSspAJzMcnJfSsOUZ/gcmQjl5eUZPV9W4mRrK4WyFOV/AzYjCsTWTLKCCb6+vvS5xMREo+Y1UqkUnp6eJm3VpuAzH0VHR8Pe3p73C8JeKcTGxsLHx4fTJMdwX1LDplWrVkavJ6hUKk4/h9KmtEQhPT0dUVFRuHnzJl6+fAmAG3vft29fWkZi/Pjx8Pb2BvBPmRFr290PHTrEeaxQKOj/3dzcIJPJaI+Ebdu2wd3dHffu3UNUVBQnyxoA/RyTk5Nx8eJFAHpn9uDBg4t1bQ8ePIC9vb1ZE54l4sJ+XqPRWHwc4v+4evUq73e3NH1N5K+goAAikQg5OTlmgyIEygebEYWyxs7OjrNSIMlq7u7uJn8sOp0OGo0GCQkJaNq0qZGfgb1vRkYG3N3daf17vgE5OzsbdnZ2nEqtpYk1RWHChAnYvXs3772ys7PDhx9+SB83bNgQ586dM9t7obQwzAUxzJ+wt7fHDz/8QB83b94cjx49AqB3WmdlZSEsLAy3b99Geno6XFxcTCYHWgKZJROzXlmTl5eHq1evokePHtTvVRQBMve8OXFi70d+d6mpqfQ5Q0gNscJEiKxwLNnP3PPW6JX+X8FmRIHMlpOSkjjlGZKSktC0aVO6j6GzUqPRID09vcizbdJTgRAdHQ0/Pz9aDdUQMtNPTEyEo6MjXF1doVAoeL/QAJCVlcWpc2QoIBqNBjk5OWVW3gCwrijwOc7FYjGcnZ2xfv16I2dru3btkJqaarYmUmlAyk4QXFxczN6D8+fPo1q1alAoFJg9ezaePn3Kqf30b4dtuiFmk7J2jBsm8BFxskSELBEorVZbaMQe+zj29vZo3759md4DW8ZmRCE4OBh+fn44f/48FYHs7GzcvHmT1r8JDQ1FZmYmwsPDaUXNCxcugGEYaqaxFLb5SKlUIjk5GS1btsTr169NrhQYhkFsbCz1AZAfk6GNlMyGiZkEMDYfJScnQyaTlekP0pqiMH/+fPTr1w++vr6oWbMmAgMDbS7q5pNPPkFUVBRnm2FOiSHu7u44efIkunbtShMDnz9/zmnDWlLKO/KnvOErQV8e4kQwVza9IlKmopCbm0uTtAC9czkiIgKenp6oWrUqPv30U3z99deoVasWDUkNCAiguQz16tVDr169MH78eGzatAlqtRpTpkzBe++9Z3HkEYHdAD4uLo4mq5kyCREbqEajoX4Ndk0ktimgoKAALi4uRl989nHJOYm9uyx+rNYUhfr166N+/frldv7CSE9Px08//WS03dPTs9DXtmnTBhs3bsTHH3+MpKQkdOnShROx9G+nvO31tuZoFuBSptJ8584dhISEICQkBIDeGRkSEkILp82ePRtTp07FhAkT0LJlS+Tm5uLUqVMcR+SePXtQt25ddO3aFX369EG7du14f/yFQcxHWq0WcXFx1OlpqvEN8ROwZ8TslQIhLy8PKpUKLi4uRq8n+2VnZyM/Px9ubm5lOnMry0G5PGEYhpPBDIBWurU0F2TMmDE0Ce7OnTs4deqUVa6tvO+/LQzItnANAqYp05VCp06dzP4oRCIRvvrqKxpKyIenp2eRE9X4sLOzg1qtRmJiIuzs7KijydRKQavVIj8/n7Mi4evUFhcXBwcHB6OlMFtsSLE9mUxWoUShLM5/6dIljBgxAhkZGXSbk5MTbXPp4+Nj8bHWrl2Ln3/+GWq1mtPH+t9MeYsSYHt5CkJIKhfbMgKXIcR8xO6YBnB7HLBRKpVwdnamoYyAcU9nEpnk7OxsdAwyIGo0GiQlJSEgIKDMB+n/uigkJiaiZ8+etKAdoWXLlvS8bD+PJZDX/Vd8CrZwfmGlYNtUaFHIzs6mNYcIfOYjjUYDhUJhZBIyXCkkJSXBwcGBZg6zISuQxMREODs7U5+Dqeil0qC8RaE0UalUJnMHxowZQ1dufG1VzWFtUSjv+28LA7ItXIOAaSq0KKSmpqJy5cocUw+f+SghIQFSqZSzSgC4PZ11Oh1iY2NRuXJlSCQS3t4LDMPQWkhkm7BSKDlhYWFwdXVFeHg47/NDhgyhIctHjx4tVq5EWeWSlDa2MCDbUpMdEg4r8A+28cmUA2lpaThz5oxRBjVfNdSYmBhekxDwj4iw+zTzrQBEIhE0Gg2USiUneqkiiYI1uHLlCsaNG4fw8HAcO3YMly5dQp8+fcy+RqPR0LDmlJQUtGvXzuIV2n/RfFTe2IIwCZjGZvIUypr79++jUqVKnNr7gPFKIS0tDQzDwNHR0WxSW2F1jkjpC7IP37lKm/IWhZKen/gMGIahfQ4sOebff/+NWbNmISYmBj/99BP+/vtvuLi4wNnZGTNmzMCcOXNMvrY0RKE8sYUB2RauQcA0FXKlEB8fj2fPnvG2ROSrhkpMTKZWCkqlEikpKdQsxLcvyaIk+/Cdq7Qpb1EoKenp6Ub3i88UYTjgkK5r69evR/fu3QGA9tZeu3atRee2lvmovO+/LQzItnANAqapkKLg5eWFkSNHGq0SAO7sPS8vD1lZWQgICDCb1JaSkgIPDw+zdY7S09MhEok4tXfY+/3bktfK4/z169fH0KFDjbazhcLT09NIJJ4/f07/P2XKFISFhaFz585Gr+Xjv7ZSsAVsTRSEkFQuFVIU7Ozs0KBBA97Oa4bVUCtVqgS5XG52Vk8c1gRDAdHpdEhNTeWd0VY081FxYRgGn3zyCX777Tez+6WnpxdatqB+/fq0LpWl12QYZFASyrvMRXkPgLaWpyDApcL6FEjymiHE9KNWq5GQkEBrLJkaUMmPjJ0payggmZmZ0Gq1Rj8EtnhER0cjPz/fZEVHU1UezW03nAGVtygAxVsRaTQa1KtXDzExMQD0s/ZWrVrhypUrFr2eXWCRQD4fSwcnIfrov3UNAqapsKIgk8mMOq8B/wzUCQkJcHFxobkJpnwKpBZSYXWOvL29jRrykP3y8/Px/PlzBAcHU9+DYQMXvuqQpPMY+zm+90P+AH1/gKtXr5ZIdCxp5sJXG7+4ojR16lQqCF26dMFvv/2GK1eumBUFiURCVwyGPZpJRc6iYK2Vwr9RlEvjGmwlJFXAmAorCnztOAH9wKXVahEbG4uaNWvS7Xw+hdzcXDAMY7afs0qlQkpKCho3bmxU9pusKOLj4+Hj48Pr+C4K7BLEfCKSmZmJqKgoi9te8pUftqSsMd89JSsXrVaLy5cvWyxEKSkp2L59OwCgY8eOGDt2LDp06MDb8Y5gb2+PiIgI1K1bFwA4pj0iUMTPQPxAfLDzGazdCKg8Ke9ZurBSsG0EUTBALBZbVPoa0K8A+Mpfs/eNj4+nzXb4wlQBfailYQG34sAuQUwKwLFhGAZisdioex2bWrVqQa1WY8CAAVi3bl2Rr8GwNj5bLPLy8hAZGYkGDRqYFBfD7Z9++il0Oh2kUil8fHwwfPjwQq/h448/xtmzZ+njZ8+e4fXr13S1N3ToUNpFLS4uDrt370adOnWMxIntcyIJjMVdRRFRBASfgi1cg4BpKqwo8LXjBP7JJwgODjab6azRaJCYmAgHBweTJS10Oh3i4+NRs2ZNajph/yDY1VYtKelcUgoz3zAMQ800mzdvpo3pi3oOU7XxyTZLK5VGRkbSLmhz5syxSKRCQkKwbNkynD59mm7r0aMHFZxr167hwoULnNdMmDAB+/btQ0hICEecSBE9QB/CmpOTY3a1ZLidbxKg0+mQlpaGly9fFtlPZA1/ky0MyLZwDQKmsUlR2LhxI1auXInExEQ0adIE33//Pd566y2rnoPdT4EN6aZm2J/BcKVAOrBJpVKTxe9IXL23tzcVIMPOVwBoFnRpY4kofPTRR9i8eTMAfXx/UXsmFHb+orB69WoAehPPwoULsXTp0kJfc+TIEQD6mT2g9y2wTT9818AwDIYNG4bZs2dzKvSy+343bdq0yMLNJxwPHjyAu7s7KlWqZLFpTqvVmmx1acrXZEqYCOfPn+ddyZRUdCzZ1xajj2ztesoTmxOFAwcOYMaMGdi0aRNatWqFtWvXomfPnnj27JlZs0dRIdFHhrMWEjpqGG1imFMQFxeHqlWrIjk52WydI39/f46jl70vESVfX1+byFOQSqVYt24dtm7dCq1Wi48//hh//fWXVa/B0vf5/Plz/PrrrwD0rTy3bt1q0ev8/PyQkJCAmzdvAoBRH2RDk+G8efOwfPlyAMC3336LH374AUuWLMGkSZM4k4bi+BTYnztBKpXCwcGhzHoCs015Wq0WiYmJiIuLQ6NGjYq06mEHQBQmRHzBEIbcuXOnyCsia6yU2AIoCAE/NicKq1evxvjx42nj902bNuHEiRPYtm0b5s6da7Xz2NnZGa0U1Go1TTIzhMxwANA6R5UqVUJKSgqv+Uir1SItLQ21atUCwN97ISkpCUDZJUZZGv0zatQobNu2DTdu3MDHH3/MaWxf0vNbSvPmzem1njlzxsjkYwrDwVulUsHBwQEymQxSqdRIFFatWoWvvvoKa9euRXp6OnJzczF9+nQcOHCA07zp35q8Rmb/EokEMpkMcrkcUqkUrq6uZXYNhj6mq1evol69enByciqSuLCFqaiCxhcAQUSiTp06qFGjRpndD1vHpkRBpVIhPDwc8+bNo9vEYjG6deuG69evW/VcfD6FuLg4ODs7Iy8vz2h/tk8hNjYWAQEBdMbBZz7SaDTw9PSkWdOGvRdIGW3y/7LAUlH44Ycf8PDhQ9y6dQvbtm1D48aNaReykp4fKNymzDCMUSXT4lQ2Jeh0OqhUKt7AArVaDXd3d8THxyMyMpL2+r5x4wZ27txJ9+Nz3Bf3WioabGEC9PfA0dGxzIXJMHybCMi/VfBLC5sKFk5NTYVWqzWqXOrr68ux71oDwzwFYu7x8/MzGVbJMAyUSiVSU1M5dY74fugajcaozhHwz6CQnp5O/893vtLC0kHpwoUL1K8yc+ZMxMXFleZlcRCLxThy5Eixfqzr16/HkydPEB8fb+QPadq0KecxGejJ5KBJkyY4e/YszUlYtWpVMa7etrEFJ2955CmQ4AeZTAY7Ozs4ODjAyckJzs7OgigYYFOiUJbY29tzRCE1NZVmJvPVWCeDf1xcnNEKwHBQz8nJAQCjLGfgn0E5Pj4e/v7+ZZplbErA+JBKpbh27RqkUim0Wi06derEO9MuCnwmNFOkpaUV63wTJkxAcHAwPD09cffuXVy/fp2KM3E+A/oWnWTmyj5P+/bt8erVK6NZ7MyZMzk1lEqCEJJa/tcgYBqbEgVvb29IJBJqayckJSXRJinWQi6XcwYDdjVUwHjgIolX8fHxnBUA30CbkpICsVjM+eITxxZ7tVEeLTmLci4/Pz+sWLECgP7+NG7cuMTCYAnp6ekWm6sKm3GGhIRg+vTpAMBp03nx4kX6+Ri+J09PTxw/fpyzbePGjWjUqBE8PT0xevRoTJs2DWPGjMG7776L3r17o23btggJCcHGjRvNXk95m4/K+/zkGgRRsF1sShTkcjmaN2+O8+fP020Mw+D8+fMIDQ216rnY0Uc5OTnIycmhkUKA8Y+HOI/FYrHZOkcFBQXIzs7mPScRgMTERLi7u8PBwaFIs/eSYuq9mWPy5Mn45JNPAACvX7+Gh4cHatasiW7dumHLli1FOr+lK4W+fftaLD6Gtv7c3Fyjffr37w9A/12qV68epk2bhsaNG9P7wZev8tZbb+H8+fNwcXGBm5sbNTHk5+dj//792Lx5M/bu3Ys//vgDFy9eRHh4OJ48ecLxh9kq5T0g25ooCJFIXGxKFABgxowZ2LJlC3bs2IEnT55g0qRJyMvLo9FI1oLYjZVKJWJiYuDv78/JTuZzHmu1WlSuXJnzBTIc1EkGMx8kgik+Pp7a69lJbWVFUc/17bffYsaMGQBAS4CEhYVh6tSp6N27d5F9IubOv379ety7d8/iYxmKx+3bt432Yfuo+vXrh4ULFwL4Z3BcvXo1WrdubfTatm3bYuXKlfDy8uIVDqlUCl9fX9SuXRsNGzak12NqUkCoyOYjwwROAdvD5kRh2LBhWLVqFRYtWoSmTZsiIiICp06dMnI+lxQy88vLy0NycjKtj2MYJURQKBQAjCtuss0/DMMgISHBrLM6OzsbGo0GPj4+dFtZOZpL4txbtmwZ7t69i/nz56NPnz70Ply8eBFOTk4ICgoqdDAvbCDYsWOH2S5ophg8eDDNK+G7BpIEJxKJ0KJFC/p5ETNgfn4+IiIi0KtXL/o5A0C3bt0wceJEvHz5EjqdDjKZDPXr16erE41Gg6SkJCgUCjRp0oS+ztLw2fLAVgZkW7gGAX5sThQAfSOUN2/eQKlU4ubNmzRM0JqQlcKbN2/g5uZGm9+YMnEQPwdfUhsZ1EniG0lM4vNLpKSkwM/Pjw7Q5VHOurgiVL9+fSxatAiHDx/Gq1ev8N577wHQv8+kpCTMnDnT7OvNmY/Onj2Ljz76qMj3wt/fH7t27aKO4SdPnhjt88svvwAAGjZsyCmAd/fuXWzatAn9+vUDoJ8gvP3223j16hVq166NsLAwznHCwsJw9+5dpKenY8qUKTTYIDo6Gnv27KH7TZw4EUePHuW93vK26dvK+QVRsF1sUhTKAiIKsbGxnNLKbIcwQaPR0KqcpqKSAH2eQ2BgoEkTFABkZGRwSmiUdfSRNdm+fTvi4+NpqOf9+/eLdZz8/HwMGjTI5PPmrvuvv/6CWCymK6+7d+9ynn/z5g2t5zRz5kzOYCSVSjF69Gj89ttv1LF9+fJl1KtXD9HR0QCA6tWr0/3T0tIA6FeZzs7OcHR0xIQJE9CuXTs4OzvT/TIzM/Hee++he/fuvD6O8qY8B2Tym7AlUbCla7EFKqwokMSzjIwMowJthn6ChIQEOsM0VdIiPz8fWVlZNMyUb1+StMNuycnOlC4rrHk+T09P2uc4NzeXDsB8mLovnTp1MkpOc3NzQ506dVCtWjWT1zts2DAEBQUBAN59910AwKNHjzgmpG+++QaAfhJA9uFj7dq1qFq1Kn0sk8mwadMmzJ8/n157p06dAOijo7799lukpaVhy5Yt+PTTT5GamoquXbsC+EfErly5gsDAQHz55ZeoXLkyPvvsMwD/ZM6XB+VtPiKffVnnKQhYToX/ZCIjI41+JOyVAslNILN7UyuF+Ph4eHt7Qy6X80b56HQ6muVseC52TaXSpLR+iK1bt6ZmtaJGJH3//fdGKwx3d3dIpVJa8powePBgzn4dO3ak/587dy6drY8dO5ZuZxcfLOz9nz17Fm5ubvD398f9+/cxevRo/PnnnwD0eS2Ojo4ICgrChAkTON+Pd999F7du3UKHDh0AcEVXqVRi+fLlSE1NxYYNG9CrVy/Uq1cPAQEBRb5X/wUE85HtU2FF4c6dO9DpdBgyZIjRc+yVQkZGBtRqNS3GZyoqKSEhgTou+ZzV2dnZ0Ol0cHNzM3p9WZa5MLwua1GnTh0A+kglUx3RDFcKb968waxZs4z269GjBzXVELy9vXHo0CHOtu7du9P/i8VifPHFFwCAx48f49KlSwBAcxQUCgXef/99/P7777zXlpycjCpVqiApKQmvXr1CcHAwACA8PByAPtQY0PuWTpw4wXktwzDYvn07DX01B7sc97x589CrVy8EBgbC3d0d48ePL/T1JcVWVgqCKNguNlX7qCw5fPgw7O3tedssslcKpNIpiTjhWykolUrIZDIaispnJomPj4dMJjP6MRABSk9PR2JiosWVIIvyuCx+gCtXrkTv3r0B6E057OxhQ8h9YQ/qbDIzM422+fr6GnVb+9///odWrVph8eLFcHV1xZQpU7Bs2TKkp6dj2rRpiIiIQK1atRAQEID4+HgcPnwYADBt2jTaZhUA9u7dizFjxkAul2P58uWYPHkyfY5t6jO8fjbbtm3D8ePH4eLiQjPaCyM3N5dThXbXrl24du0aLl26BGdnZ9y9exf37t1DXFwcXFxcIJVK4ejoiP/973/F7r8hiIIxtnQttkCFFYVly5Zh586dJruvMQwDhUKB1NRUtG7d2qQ9XCQSQaFQoFq1ahxTBXsFQEIXHRwceFcaDMPg5cuXcHJygpOTk1FFSEtKFZuroU9MJ6Ssw82bN2mBsqKWJTb1uFmzZmjZsiVu376NjIwMxMTEYNy4cZg4cSJ1IrN/fFOmTKHOXDYymYyTvAjoB2bSbIfNnTt3cOfOHWzduhU7duzAwIED0bBhQ1y+fBlZWVl0v6lTp3KSyn7++WdaLhsADYNVqVSYOXMmVq1ahV69eqFBgwa4fv06jh8/jpCQENSrV8/oGtiw2606ODhg8uTJ2LJlC+daDJHL5ejXrx9SUlJw5coVREVFcdqH8jFnzhwsWbKEroKKghB9JFAYFVYURCIRzWo2hO0n8PLyMlvnSKVSQaPR8OYvkH2TkpLg5OTEm71MRCU3NxctWrSwSnEuc+WEr1+/jpo1a0Iul5sVFlPNXcy95v3336cJYHXr1oVWq8WNGzfg6OiI5ORkbNq0CWPGjMHXX3/N6Y/g5uZGB85KlSrR4nsikQgzZ840aXuvU6cO/v77byiVSrz33nto1aoVdTJ36dIFGo2GChabEydOUFE4cOAALX8RFBSEN2/eICEhgYaxzp8/32Qymkwm4/3+AHpzE7ugXqdOnehK5+HDh3S7SqXCd999B39/f6xbtw7z5s3jfMekUinkcjlN+lIoFNBoNJg3bx7mzZuHNWvWYNKkSbzXYIryXikIgmDbVHhR4Ou+Rkpfx8fHc2aHfKKQmZkJmUxmtikPyWA21ZAnMzMTPj4+VqvWyNfchSCRSODm5sYJobQW7B+7VqsFoHe0urq64v3330deXh6ePXvGCdPs1q0b8vLyaGl0Ug1XLBbj6tWr2Lp1K+9MOyAgAD/88AOioqLw2WefITs7mzbWAfShpEePHgXDMPjggw84r33x4gUuXboEmUxGcyv8/Pywb98+hIWFYevWrfj7778B6AftoUOHGvk4gH/KYxiKw5IlS3DgwAHO4N+sWTMsW7YMN27cwMKFCzl+l8aNG+PJkyeYNm0aunbtinXr1mHEiBFo27atURmPhIQE9O3bF48fPwagrwDQuXNn1K1b1+j6DPnxxx+xbNkyLFmyBI0aNSp0/9LAFruuCXCpsI5mwLh8NkEsFiMrKwsSiYRjuzWc6Wu1WioKfMcgdZXy8vJoy00+8xEJZS3vpX1JITNq9o++a9euGDRoEO1RwRaEhQsX4vjx45xQUCIm7dq1Q3BwMHbs2MF7rs8++wypqamYPXs2srOz4ejoyDnvkiVLMGLECI4gENElZdKjoqLo7H3x4sXw9PREv379cPjwYU5joWPHjuHatWuc85OQZoBbOykwMBCNGjXC119/jU2bNtHtUqkU586dQ1ZWFubOnYsuXbrQ53JyclC/fn3cvXsXGo0GU6dOhZeXF54/f45nz57h+fPniIqKwuvXr6FWq/HHH39g4cKFcHd3R+/eveHl5YWbN2+id+/emDZtGuLj46FQKKBSqWjns9u3b2PGjBlISUnB999/z3tPywJbWyn8239zpUGFXSkA/I12AP2glpaWhsDAQKNKp+xBPSkpCXK5nPdLTvZNTEyEr68vpFIpb6SRWq2GSCQqs/aMpVmAjzhY2cf/8MMPcfHiRaN9e/bsiXr16mHGjBlGCWcAsHnzZgwfPtxkc52ePXuiSZMm9HmFQlHo+1KpVHB2dkZubi62bdtGcyr8/f2NamvVrFkTCxcupCsEiUSCmjVr4tmzZxCJRHj48CGkUikGDRqElJQUDBkyBO+//z5CQkKoT+rOnTv0eO3bt0erVq0QEREBPz8/7Ny5Ezt27MDnn38OQC+oBw8epOGuDMPg1q1b+OGHH9C3b19Uq1YN0dHRiI+PR1JSErKysjBhwgR06dIFo0aNoqU1Ll68iM2bN9P8CG9vb6hUKowePZren+joaFy+fLnYQQzFeQ3bzybkKNg2FVoUTJmPiJPZ0E/AV/zOy8sLGRkZRscgoapJSUm0Lg7fgEzMK2U5eyqtZLnQ0FAj4Rs3bhzv+U6fPo3Tp0/zHkcikSAlJcXI4cymQYMGnMeG97Vhw4bo168fBgwYwMks9vX1RW5uLq5evUpfw3Y6s+nfvz+2b98OmUyG58+f05aN3bp1oyGr7IGfjVgs5jienZycaASRs7MzvL29MXPmTHh5edFs6jVr1uCnn36ipdfbt2+PzMxMGhZryF9//YWVK1dy3ju5/3FxcVi3bh2uXr2KXr16cUJhc3NzqVmU+IVGjhyJGzduICQkBB07dkSfPn3g7e1NS70X1u6Sb5shZHWl0Whw6dIli8SnMMHhe450ejPcZuo3ZksrF1vAJkRh6dKlOHHiBCIiIiCXy3lDEqOjozFp0iRcvHgRzs7OGDVqFJYvX86xuf7111+YMWMGHj16hCpVqmDBggUYPXq0yfMa9lQgqFQquLq6mq1zRMxC1apV481OFYvFSE9Ph52dHa3LYzhgKpVKOnstK0pzllajRg28efOGYw5iF5jjQyaTwdvbmxPC2rx5cwwfPtyicw4YMAC///67kSj88MMPeOuttwAAHTp0wMmTJ+k1kgJ3AODj40NrOBkyb948bN++HWq1Gj169KCl04kTujBMheWyByHDkNe8vDwcOnQIgYGBRr8DsVgMuVwOBwcHSKVSTn/wgQMH4ocffsDp06epL+bBgweYM2cONX2NGDECu3fvhk6nQ2xsLEJCQgDoCxFevXoVwD8RXWvXrkVGRkax/Vw6nY5XNLKysvD06VM0atTIokAHhmE4PZlNtdTke2wIn5BIpVK0bdu2WO/xv4pNiAJx5oWGhuLnn382el6r1aJv377w8/PDtWvXkJCQgA8++AAymQzLli0DALx69Qp9+/bFxIkTsWfPHpw/fx7jxo2Dv78/evbsyXteOzs7I1FQq9VQKpU0WY2NYZ2jSpUqQSqVmqyImpaWRhvpkG185idr9f+1lNIsq1GpUiVO6Y7u3bvj7NmzAIxLegQHB+PgwYN08Ca0bNmy0GY17u7u2Lp1K1q0aMGbkMY+5qBBg6gonD17FkFBQTRTmiS88REUFARfX18kJSVRx/PQoUPh7e1t9toI7KY+psSYXIdMJsPw4cORm5uLJk2a0B4WHh4eePXqFSdTnhATE4O9e/eib9++qF+/PoKDg40aVP34448A9OawrVu34sCBA1Cr1Thx4gRCQkKQn5+PadOm0f1dXV2RnZ0NrVYLhUJRbFEgs3USBk1Qq9VGvrrSgERrFbayIT4sgX+wCVH48ssvAegLrPFx5swZPH78GOfOnYOvry+aNm2KJUuWYM6cOfjiiy8gl8uxadMmBAcH47vvvgMA1KtXD2FhYVizZo1JUeBzNCcmJkIqlZpNaiN5B2SmZcqWnZ+fz+kYZ1jSIj4+Ho6Ojpxtpb2ULYulMnvgJ8lZDg4O6Ny5Mx2cAb2Qt2jRgvNasViMn376yeSx69atSx2ycrkcDMPQUFIC21QC6Adyki2s0+ng5eWF169fIzAwkFMSg49BgwZRh7FMJqODrCWwk+1MNTgifg0nJyd6HoZhaIXWIUOGwN7envf4VapUoTkWH3/8sZEgsElLS8OoUaPg6OiIrKwsGu01fPhwKBQKiEQi3Lt3Dy9evMCQIUMgEomMWpJag7JyNBNzEVkNmKKs6479G/hXeHyuX7+ORo0acXoq9OzZE9nZ2TSp6fr16+jWrRvndT179qRffj4MzUfEFuvo6Ghy+UnKRDs6OlJfAJ8oqNVqIxMUe6WRnZ0NlUrFm9BWmpRl/wbgn5nhhQsXMGrUKJP7ERMaMRfw4eHhgbt371IzCqC/p8+ePePsZzijtre358xMW7RogfT0dOzatQujR49GgwYNTOZCzJ07l/5/0qRJnNLbhcEXxmoICcFlZ1hv3ryZOtBJQyBz3Lt3j5q0OnfujJs3b2LXrl1o3rw5NU9lZGTgwIEDNLz3/PnzcHNzo7WdRo4cibp169I+1HwRddbA1qKPAMGnYMi/QhRIBA8b8pj8qEztk52dTevWGGJoPiJ1jtizdzbEeUxKZJNthoMsWU0YdmBj75uQkABfX1+OUJTVDKq0uHXrFmrVqmW0ff/+/QgJCcGAAQPw2WefYcWKFXj+/DlnBldYiWlHR0e8evWqUJ+IqedJeW8PDw84OjoiICAAXbp0wf79+xEVFYXFixfzvs7Pzw8LFizA4MGDacVVS2H7BLZs2UIHevZnQJzR7O8KMZ3VrFmT14zJhmEYDBgwgFbgPXLkCJo0aYKhQ4fi6tWrSEtLw7Fjx/DWW28ZVQMmQRZubm50lULMWUURv6Ig5CnYPqUmCnPnzqVLOFN/T58+La3TW4ThSiE2NhYBAQEmy1mLxWLk5+ejoKCA/lj5IopSU1MhEoloJjSBrCpIVBIps12WsdKluVKwt7fnlM4WiURYtWoVp1Bc586d8eGHH0IqlZoMNyWQe0y+K6bMKK9evaL/b968Oe8+5BoyMjKwZs0aIwe4uVLWCxYswJ49e4rspGebIHft2gUXFxd0794dQUFBcHBwQN26dWkGdnx8PM6fP4/k5GS8ePECADg1mEwxbdo0Kiy//PIL7z3q3r07Ll++jLi4OPz66690e//+/RESEoKTJ0/S90Y+v9IwHQG2uVIQ4FJqojBz5kw8efLE7B+7gYk5/Pz8jOyl5DGx2Zvax9XV1WhwJrDzFBQKBc1NMBXLT/ov+Pn50Vku3yAbFxfH65Mgx01OToa9vT1cXFxKNW+Aj9L8QTZu3BiLFi1Chw4daPTKlClTjM6/ceNGGtIJAL169TIqRQHoTRx79+5FeHi42Rnzb7/9Rv/PTgpjY9i9TyaToVu3btR8Aujt99YUzBUrVnActWRCQP7/+vVrOltPS0tD3759aeSWWCxGmzZtjIoAsomMjKTlQrp06YIBAwYUek3Vq1enAtCjRw9cv36dI6TkN2S4qrAWtpanICSvGVNqjmYfHx/aDaukhIaGYunSpUhOTqaDw9mzZ+Hq6or69evTfdhOTLJPaGioyePK5XL6o4yLi4OXlxfs7e1NrhQAvS+AlIkGuA5EkUiEgoICZGZmwt3dnbekhU6nQ0JCAo1KIqJC/qwRS21u39JemcyfP582puFjzZo1NBoJAHr37o0jR44YmStq166NWrVq8ZqjDCFlss3RsGFDzmO1Wo1z584hPj4eTZo0QWRkJI4fP44qVapg+/btJiu4FoXg4GA8evQIrVq1Qnp6OmQyGcRiMa0pZQ6GYaiQyWQyuLq60lBKOzs7eHh4ICoqCjqdDg4ODhxhNAcp356RkYFLly4ZlesmfhBr90Rnn19YKdg2NiHZ0dHRiIiIQHR0NLRaLSIiIhAREUHtzD169ED9+vUxcuRIREZG4vTp01iwYAEmT55MZ+Skwfrs2bPx9OlT/PDDD/j111/NVpIkPgWGYRAfH0+rU5oaOFUqFezs7Dh5BYY9ChISEuDl5QWJRMJb0kKlUiErK4v+6NgmJYVCQQcMwz+1Ws273ZI/rVZL/wDQbXyF80z9kRA/S/5MsWrVKioI/v7+mDNnDg4dOoRbt24Z3aslS5aYPI4h7AivlStX8u4jl8tx4MABzJw5E/PmzUObNm0A6HsvfP3117SSa1paGvr374/u3btbZdVQpUoVxMfHIz8/Hzk5OTh58iRevnyJ1NRU7N69m35/3Nzc4OLiYhTCCegFLC0tDcnJyUhMTMSbN28QERFBM8h//vlni30AOp2Odqvja59KHNHEZ2ZtBFGwfWwiJHXRokWcGjck1PPixYvo1KkTJBIJjh8/jkmTJiE0NBROTk4YNWoUvvrqK/qa4OBgnDhxAtOnT8e6detQuXJlbN261WQ4KvCPTyE5ORkymYyWmuBbKZAKlXytO8nzRFzq1q2LhIQE3t4L+fn5tEMb8I/5SafT0Zmg4XktwbDLG99zGo0GeXl5sLe358Rns8uCswXRUBzNrWLMOcujo6Px9ttvU1u5nZ0d7t+/TyNjDMNCnZ2d0b9/f/oZFDaITJkyBbt37wZg2qcA6BPdXF1dERISgsWLF8Pd3R0KhQL9+/fHb7/9hnHjxuGDDz5AWloarly5gilTpnBqIJUEw5BUZ2dnDBkyBCNGjACgTyDr1auX0esePnyIP//8Ey9fvoRarYZarUZ2djbS0tKQmZmJfv364Z133inStTRu3BgRERGIjY01eo5k5xtm81sLWxMFW7oWW8EmRGH79u0mcxQIQUFBRuYhQzp16sTpz1sYpHR2bGwsp84Rn58gKysLDMMYzcjYA2p6ejpEIhG8vLyQlJTEO6AXFBTQcgkA11HNN0u09EtryX7p6elwcHAw6v5mjqKamgz3X7RoEdatW0fvZ7Vq1XD8+HHY2dlBo9Hg8ePHNAySMGHCBGg0GotFqUGDBrhy5Qrc3NxQvXp1aDQazuDD/ozIqkyr1aJTp044deoUAH0uw/Dhw/HmzRt0794d169fx7Zt2xAZGYm//vqL+pBWrVqFM2fOYO/evbxJbEUZZNgRV6bMZA0bNjQyfZUEnU6H9u3bY+fOncjPz4dCoeA4pyUSCbRaLY4dO4YFCxZY7bwEW4w+srXrKW9swnxUXshkMuTl5SE3N5djguBz/pL8BcMvENt8FB8fTyOK+ISFhMayY+ZJmCvJAC1NEhMTOe/TEgqLIGMnCbH/wsLC4O7ujjVr1oBhGMhkMixevBjbtm1DcHAwzXY1dES7u7tjyZIltF4NX1E19t+rV69Qv359dO3aFSEhIXBxcYGzszNcXFzQoEEDHDlyhJrASFkR0kvi119/xa5du6j47N69G9OnT8eePXtoiGh4eDhcXFxQv359TJw4kZa9njRpUpFNd0SsyPU8efKEvu8qVaqUqumOoNPpOE53X19fBAUF0Qq377//PgC9E3vbtm1F+q5Ygq2tFASMqdCiYGdnh+zsbPj5+XGSdQwHdJVKhZSUFDg5OfH6CYB/opfIspvPL5GTkwM7OzteE1FpC4JarUZqamqRHYhPnz7F5cuXi2xf79OnDzVR1a9fH1999RX27dvHOc6LFy+MCsrt2bOH/r8wIcrKykK7du0QExPDKRNNePPmDUaOHIlq1aph3bp1yMzMhJOTExwdHakonT59mvOaLVu2oFWrVkYz91evXnFMnMePH+ctsmY4SBuWVlAoFJDJZNBoNNScRr4PbH8S+//m/EmmXmMoSuQxwzCQSCTUf6ZUKmkyJsMw+PHHH+lzU6dOpc5sawgSuT+2IgpFue6KhE2Yj8oLlUqFW7ducbJJAeOVQmJiIlxdXWkHLDZkYEhOToaHhwcNfzU8hlqtRl5eHq/pJjc3F3///bfZ6o9FLW1s+MNLSUmBs7Mzb89hQD/ohYWFITw8HI8fP0ZUVBRSUlLowL5w4UJOdq850tPT6XuXSqV4/PgxbYc5ZswYOkP++OOPjV7bt29f9OrVC4cOHTJ7Do1Gg5YtW9IZ7syZM1GrVi24ubnB3d0dL1++xPLlyxEbG4u0tDQsWLAAS5YsQc2aNdG3b18MGzYMGzZsoL4Iw3vFrltkiufPn6N27doWD3L5+fnQ6XRwdXWFWCym7UhlMhnvpKCkpjv2NvKvUqmEVCpFREQETp48icePH9PPi4jq2bNn0ahRI2g0GnTt2hVPnz61aNJi6Jsy3Ab80y/D0kmGtaLuBCynQovCnTt3IJFIjCIt2CsFUvqievXqyM7ONln8Ljk5mTO7NFxtJCYmwt7e3uiL6+HhQe3nZEbJboFpSZniJ0+eYMeOHejQoQMkEglev36N6Oho5OXlwc7ODk5OTnBwcKDClJeXR+3J5K+wASgsLIyWdC5MkEgxNwBGoZfk8dOnT2llTrFYjJo1a9Kic6dPn8aLFy9Qs2ZNk9fToUMHxMfHA9DnAxiaoTp06IDRo0fj8uXLmDVrFh4+fAilUolHjx7h0aNH+Pbbb+m+9evXh0KhwMuXL9GwYUOkpaVxKpw6Ojri3XffRWxsLM6dOwcAqFy5MmrXrm32nhmSlZVFBQEAdfSayqOxpj8J0H+X8/LyaO7O4MGDMXjwYKP9qlatih9++AETJkxAYmIiOnXqRGsx8R3T3P8N/1WpVDRx0VSAQ3HfnynTLvv8hqs70tu7tBzr/0YqrCgwDINr167xfhnY0UcZGRnQaDTw8fFBbm6uyfhyrVbLcTwafskTEhLg7u7OKbmh1WphZ2eH4ODgYldKffz4Md5++23odDre5vZFQSwWw9HREV5eXqhSpQqaN2+OgwcPIj4+HhkZGXjx4oVJuzchPz8fR44coY89PT052cLTpk1DeHg4jbqxs7PDhQsXoFQqaeKZTqdDkyZNULt2bSxYsABt2rThiM+YMWMQGRkJQN+vYfz48bTGkuGPvkOHDrh58yaePHmCefPm4fXr13jz5g3NZK9Tpw6uX7+Ovn374uXLl8jOzsbQoUOxfv16zntiB0K4uLjQXtRFISsri7NSJMJTGqXTFQoFsrOzOUl/SqUSGo3G5GqRzfDhwxEWFoadO3di6tSpVsmfIddFzHfmsGbUnbnjzZo1C23bthVEgUWFFQWVSoWOHTvi5cuXRs8ZlsgmpS/MlYjw9vbm+ArEYjEVkJycHBQUFKBy5cqcCp4kEqMkGZ7169dH69atcfPmTXrdDg4OqFSpEjw8PGheRF5eHhwdHeHs7ExNLJ6enqhUqRKaNm2KDh068PobLl++jPj4eEilUpOJgGzbOYn3B/SVaqOiojj79u3bFydOnKCmk88++wzOzs6chjqk5/Hff/+NDz74AJUqVcKqVavg7++PDRs20FyHFi1aoF+/frh8+TLnHHyrGa1Wi6lTp8LNzQ1isRiJiYl4+vQpunfvjufPn6NOnToICwtDcnIyDh8+DEAf+TN69GjMmjWLM6g4ODjg3Llz6NWrl9GKydwgmZWVxcniJ9nKhjWySkpmZibq1KmD3Nxc+Pj4oEOHDpg4cSJq165t0YBM+PHHHzFz5kyzK7aiUlBQYFHZbGuvkvjIzc2lrXIF/qHCioK9vT2GDh2KpUuXGj1HBn+lUonU1FS0bt0aAH9UEkl+M8xfYAtIQkKCUe8Fc7kJRYWYNEwRHh4Ob29vmrRUFEi4orlmOZGRkfjggw/w+vVrajOWSCT46aef0L59e86xqlSpQvNLgoKCaPYzKe8gEomQnp6O77//HqtXr0ZqaiqSk5Mxbtw4hISE4ObNmwCARo0a4eLFi3RFZs7cxjAM3rx5AycnJ/j5+YFhGHh7e6NevXr0eXJ+lUqFuLg4APo6TU2aNIGHhwdntZOcnIyRI0fCz88PU6dO5XSBY0dMsVc3IpEIubm5iI6ORkJCAsRiMV0pODg44Pnz5xaZ5sz5k8gqid1pLiUlBYcOHcKhQ4cgk8lQvXp19OvXD++++65Foa7WFARALwqmzGVlDTGHllZJj38rFVYUANOd18jgn5CQwHEe860UEhMTIRKJjJqRsDOVExMT0bhxY6jVaiMba2lHHSmVSmRkZBi1r7QUkpdhShQiIyN5O1cdPnzYqKOZj48Ppk6diry8PADA3r176XMdOnQAoL8vN2/exLRp0zBt2jTs2rULkyZNgkqlooLg7+9PewwDsGi19fTpU9SuXdtk6RUiXuyEuS+++ALdunWjgrBmzRp4e3tj1qxZSExMRGJiIj7//HM0a9YMv/76Kzw8PEz6f0i1Xn9/f/q9IPfBy8sLWq2WTjDM+Y8MHxvy888/4/HjxwD0zYp0Oh0eP36M/Px8qNVqPHv2DM+ePcN3330HiUSCoKAgdO/eHe+//z48PT3NBjkUJlKFzdq1Wi2USqXNiEJqaiqtPiDwDxVeFPhq95Pcgfj4eI7z2HCloNPpG+XIZDLe7GWGYZCamgq5XA43NzdOVE5Z5SYkJyfDzc3NZIXRwiD2Z8Ne1rm5udiwYQPvSmv27NmIjIykM24ikO7u7ti1axcAfekSUs4a4NbamTVrFnVsjhw5Ei1btqR+BDc3N9y6datIHcEUCgUKCgpoxjofhiGolStXxkcffUSTIT/66CNMmDABAPDOO+9g165dmDdvHjIyMnD37l3Url0bDMOgQ4cOqFmzJnbs2IGmTZtS01ZeXh48PDxouCe5LwDw999/o27duha/H4Jh2Ou5c+dw7NgxAPrM7sOHD9PnYmJisHr1akRERCA+Pp6G8L58+RKbN2/G5s2b4enpiZYtW+Ldd99F7dq1Cw1y4IvEMyccZP9nz57RbeTfqKgoGi0ml8thZ2cHe3t7ODg4wNHREY6OjrTPtYuLCxwcHJCeno6VK1dyVklFISUlxWr12f5LVGhRsLe3h1KpNIqdJv4Aw6Wl4UohKyuLJkPx5S+Q1Yafnx/90pIfMVD6qwSgeAlrbIgo5OTkYOnSpbhy5QoePnxIyyEY0rx5cyxevJgT0UXe94MHDwDofQZEHNg0atQIDx48wL1793D79m20bNkSgL7b2rVr15CcnAxPT88iO+XT09Ph4uJi9nWGdu5KlSrRInOdO3fG6tWrOc+PHDkSI0eOxNKlS/HNN9/Qz/Ty5ctUCMLDw7F06VJ8/vnnRk5mQO90X7BgAV6+fInJkycX2oLUEHbiYHp6OkaOHAlAX/b6zJkznImAj48PxowZg9atW8PJyQlv3rzBzp07cfToUTx//hxarRbp6ek4ffo0Tp8+DUBfDmPx4sW85TeAf0yglkTIMYy+P3N+fj7c3NzotsuXL2PVqlWcznlFoUOHDtRRb6nJLTExETt27EB8fDyysrKwePFi2Nvbw97eHgMHDrS4evN/lQotCubMR1qtFpUrVzZyHrNnR/Hx8fDz80NWVhbvSkGj0SAzMxP16tUD8I+oEAdzaYuCQqFAVlYWGjduXOxjkB9cZmYm7YdtSOXKldGkSRN06dIFEyZMQExMDG0w4+HhwREQBwcH/PLLL7wRN5MnT8bEiRMB6Afi9PR0zoqgsIYzpsjIyChyT+Dw8HAA+lLTf/zxh8n9Pv/8czx58oQTccXmm2++wYgRI5CVlYUqVapwnps+fTrOnDmDy5cv08qsAwcOLNJ1Erp27Urbav75559GK0MS4EBMN0FBQVi4cCEWLlwIhmFw5MgR/PLLL7h16xY1a92/fx+DBw+GnZ0dfHx8YGdnh5YtW2LevHmoWbMm/W0Yfo+Tk5OxYcMG3Lp1C++99x5GjBhBw1BdXFxQrVo1bNmyBcuXL+eUu3dyckLHjh2h0Who3xKyylOpVFAqlbT+k1arhaurKwIDA9GkSZMimdzy8vLg5+eHmJgY2NvbIzExkYZmd+zYsVj3/79EhRYFvh7NgN7ZqNPpEBAQwNnOXimo1WokJyejRYsWyM7O5l1KKxQKeHp60kquRFQYhqGzltIkKSkJHh4evL0dLKVr1660VaVcLkdAQACaNWuGunXr4ttvv8W6deswevRozmtIohoADBw4kLaKbNy4MS5evGjSlDV8+HDs3LkT165d46yoSgKpSUVKrBcFd3d3XL9+3ezntG3bNioIhuG3gH423b9/f3z33Xe8jWuOHTuG4OBgOtP/+OOPMWXKFCMBMcenn35Kczy++uorjlmOkJeXBycnJ973IhaLOTkL169fx/z582k1V6VSSXMqoqKisH//fsjlcmoOq127Npo2bYr79+/j7NmztOkPAFpY0NfXF3Xr1kVAQAB+//13KjyA3nQ4b948ozLepUVQUBDatGmDOXPmQKvVWq3o4X8Fmyhz8fr1a4wdOxbBwcFwcHBAjRo1sHjxYqMB+/79+2jfvj2NYmEnIBEOHjyIunXrwt7eHo0aNTJbRM/e3p7Xp0BqyhsOXuzcg6SkJDg7O8PZ2dlkqKpSqeTEP7P3KwvTUVJSUonD7fr374+HDx/ixYsXyMjIwKNHj7Br1y58/vnnSElJMRIEANSuLRKJMGjQIPTo0QPjx4/H9evXzfo2xGIxp/R1cf0gbAoKCqBUKi0K+zS064eFhZnNIQgPD8e0adMAAAEBAXj16hV27tyJ4cOHc173+vVrODk58ZqvpFIpzp49S0utb9iwAXXr1oWfnx8GDx7M6SrHx6lTp6hot23bFjNmzODdLycnx+J8iNDQUFy8eBFv3rzBw4cP8fHHH2PgwIEIDQ2ln4lKpUJSUhLCw8Oxb98+zJkzB3v27OEIAkGn0yExMRF//fUX9u7dSwWhVq1aOHToEF6+fFlmgsAmNTW12KvP/zI2sVJ4+vQpGIbB5s2bUbNmTTx8+BDjx49HXl4eVq1aBUDf3KZHjx7o1q0bNm3ahAcPHmDMmDFwd3enDsBr167hf//7H5YvX45+/fph7969GDhwIO7evcsbfsdnPmIYhveLDfzjPCYOZuI0ZCe7EUiWMF9CW0lzEyyhoKAA2dnZvLPGosLuksaGz9l76tQpmp/RqlUrSKVSHD582GInIClbYS3S09Ph5uZmkQhfunQJY8aMwYkTJ+Dk5IS8vDx07twZ0dHRyM7OpmUg2NVWdTod7O3tERYWBqlUSmfcP/30EyIiIrB161bUrl3bbGXaunXr4vDhw5g9ezYtkZ2Tk4NTp07h1KlTaNq0KTZu3Gj0WaamptICdu7u7jh+/LjJc+Tl5RmVc7GE4OBgox4Vly9fxs2bN3H//n08f/6c2ublcjlatmyJcePGYeDAgRCLxcjNzcVvv/2G33//Hbdu3UJmZibEYjEOHTqEHj16FPl6rElqairatWtXrtdgi9iEKPTq1YvjzKpevTqePXuGH3/8kYrCnj17oFKpsG3bNsjlcjRo0AARERFYvXo1FYV169ahV69emDVrFgB9o5azZ89iw4YNtDE5GyIKbEdzWloaHbD5HNA6nY4mo5nr05yZmUk7bbHR6XRlYjpKTEyEp6dnkaJ0rAG7qdHnn38OHx+fIkWFkFmkterYFMWf4OzsTBvX+Pr64v333zdKvjNELBbj999/512RNW3aFBs2bMCdO3cKLVferVs33L17F4B+hfLTTz/h+PHjUCqViIiIQNu2bSGXy1GnTh3Mnj0b77zzDrp06QKlUgmxWIxTp06Z/axzc3OtlrXboUMHGkJcGM7Ozhg9ejRGjx6NixcvokmTJnB3dy/1778lpKSkCCsFHmxCFPjIysri/JivX7+ODh06cL74PXv2xIoVK5CRkQEPDw9cv37daPncs2dPHD16lPccZCmsVqup3T0uLg6+vr6Ijo42EgUy04+Pj4evry+nTzNbFDQaDbKzs43isUlm7ZUrV4pU3K6oSUwSiYS2/CSDBl8JCGsRFxeHrl27IjY2lnMf5HI5b88Bc5BB2RrXSfwJ7DDQwiDmmipVqtA2n23btkW7du3g4uICmUwGuVwOqVQKmUyGnj17mh1YdDqdUQvXwmjXrh3atWsHhmGwYsUKrF27Frm5uVCpVHjw4AFGjhyJ8ePH09yRpUuXolGjRiaPxzAM8vPzS6WchqWQSq+kL3l5o9PpkJaWJoSk8mCTovDixQt8//33dJUA6Ge+hmYMMjtLTEyEh4cHEhMTjWZsvr6+SExM5D0PERilUgk7OzsUFBQgIyMDtWrVoqLAhkQlJSUl0e5wgHGoalJSEu31zEYqlaJVq1bUfmxpwTuyzVyhPPZjsu/z58+NGthYKiyWCpZIJEKHDh2M7vGXX34JhUIBV1dXGm1lyUBPMnGtMXDk5eVBo9EUqakQiYYhxfpkMhlOnTpV7OvJzc2FTqezqN6QIWKxGPPmzcO8efMQFRWF7du349dff0VsbCwVhE6dOnEKEPKRl5cHsVhsFR9NcSkoKKBCagvodDqkpKQIJS54KFVRmDt3LlasWGF2nydPnnAcfHFxcejVqxeGDh1a6s4nsjogfoX4+Hh4e3vTHw/DMBxbtEgkgkajgYODA8c+a2g+SkhIgKenJycUk4iGg4NDqf8wSGG3Jk2amC0BUZTHRJDY2968eYNly5ZRQSA1i2rXrk3PfeXKFc59KkxsiIiJRCI8ffrU4tUS3zFTU1Ph7u5u8arj+fPn9LtA/CK9e/cukUCR/ISSilyNGjWwZMkSLFmyBOHh4fj888+p6aowcnNzaUBEeWFL5S0A/YpUqVQK5iMeSlUUZs6cyRudwoadKBIfH4/OnTujTZs2+Omnnzj7+fn5cWKagX9mdSQ5y9Q+ppK32KLAMPrOaQ0aNDCqAU8gK4WAgAAjsxIZ9HNzc5Gbm4uqVatywhPLKjcB0K+cqlevzklusjZhYWH44IMP6D1q164dzUL+/vvvYWdnB19fXwQEBBRJfMjxyOBOBKmogsbm3LlzhQqLRqNB//796Wu6du2Kdu3aYdCgQXj16lWxTXl8SWslpXnz5rSNqCUQUShPbE0UUlJSIJVKrV6M8L9AqYqCj4+PxTa7uLg4dO7cGc2bN8cvv/xiNJCFhobi888/h1qtpjPts2fPok6dOrR8QWhoKM6fP49PP/2Uvu7s2bMmq3sSO7tSqaRfEnYpBFMRRYZLTrZPISEhAT4+PkZZzmWVm5Cbm4uCgoIi2/KLyuzZs+l7HjZsGM1I9fDwQKtWrXDp0iU0atQIUqm0SBnI7BVYUfsVEMjq6PLly2jYsCGcnZ0LFZNBgwZRJ7dMJsOqVavAMAxUKhUUCkWxBQnQfz/i4uKKLSwlMe8B+u9EeRd9KygoMOpvXp6QEhe24N+wNWzCpxAXF4dOnTohKCgIq1at4nS9IrP8999/H19++SXGjh2LOXPm4OHDh1i3bh3WrFlD9502bRo6duyI7777Dn379sX+/ftx584do1UHQSQSwc7ODmq1GnFxcQgMDOR0jDL8gaempkIkEhmZf4j5iGEYJCYm0tWGYbepslol+Pj4FLs/gyWkpqbi/v37AID58+dj5syZVPyHDx+O1NRUODs7F8uGPWDAACxbtgxKpRIPHz4sVtN6kUiEvLw86HQ6eHl5mf3hv3jxAhMmTOD0oggMDCyxIDEMA7VajatXryIkJAQymaxIfiTDAnmW+p4M7wNZ3WZlZSE6OrpYQQvFCXwwvOcFBQU25dRNSUmBt7e30K2NB5sQhbNnz+LFixd48eKFUaQIGVjd3Nxw5swZTJ48Gc2bN4e3tzcWLVpEw1EBoE2bNti7dy8WLFiA+fPno1atWjh69KjJgYUM8KTfAHs/Qz+BVqul9e/5jsMwDNLS0iCRSODh4UEdjIB1+iZYAkkSMizuZm3mz58PnU4HqVSKOXPm4KOPPqLvceHChXj58mWxVyoNGzaEg4MDCgoKsGnTJmzYsKFYx0lPT4eHh4fJe37p0iW88847nOqvpBQDKaNdHIiJUCKR0J7cZTVLN+wJzTD68u+3b99GgwYNaPRbYWLEDmiwRJAMe2OT+8AWCJVKhby8PMTHx5faasmSnhaE1NRUmxIpW8ImRIHEMRdG48aNOY5LPoYOHYqhQ4dafG65XI64uDjUqlWLE+5quFIg5iWNRsObv8AwDBISEuDv709t+Ww7uTX6JhRGTk4OVCqV1UxHCQkJ+OKLL/D7778jJycHq1atwkcffUSb0HTr1g2rVq3C/v37AQCtW7eGo6Mj0tLSitW7gdC0aVNcv36dNtMpDubyExiGwYgRI6ggSKVS+Pn5ITs7G9nZ2UUqMWGO0vAnmIMtSASlUgm5XF7qA2BhAQ3h4eEIDg6mK6aSRthZKkiGInL//n3s3bsXSUlJYBgGH374IS2GN2nSpGKvEP9L2IQolCdyuRxJSUno1KkTZ7vhSoHkJrx584Y3f0GtViMtLY3O0on5qKz6JgB6p7qPj49VzjVjxgxs3ryZs+2XX37BqlWraEvR2NhY6vD09fXF8ePHacYqX50fSxk2bBiuX7+O2NjYQns188EwDDIyMky+7ssvv6RBAL///ju6desGlUpFI1FMZXAXlezs7DIVBT7KysnMFiRD8yrxxZEOhtamKBF2Op0O/fv3xx9//AGpVIrg4GCL+5RXFCq8KKjVajx69Mjox8teKeTn5yMrKwt16tShosBGLBYjPz8f7u7uNMKCiEpZ9U0gpqPi1OXng7THZK962HZ3BwcHPHz4EIC+hk1YWBjs7e3x5s2bEttqR44cSYMFOnXqRIuxWUp2djbEYjHvYJiZmYm1a9cC0AcmdOvWDYDel0HqYLGjkEoCX2XUssZWIo/48nashTlBMsTX1xetW7fG/fv30bhxYyxYsKBUrunfTIV2vYtEIlqywnAQIyYhQG9G8fb2piGsfBEm+fn5RsXvyEqhLFYJWVlZ0Gg0VrNfE1+NYbVS0ueZrBbGjh2LiIgIOvBYw1Zrb2+PZs2aAdCbgdavX1+k15MMdz5hGjduHM1ByMvLw5dffol58+bRHggffvhhsZzbhigUCiiVyhKtmKwBqY5anhBRsCWs7VNQKpVo2rQpRCIRIiIiOM9ZUsjTlqjQonD//n2oVCo6W2TDjigiJSPITMdUn2b2l4w9Kyor01GlSpWsNhsbOnQo76Can59Ps447derEGbDz8vJoufCSws5mnzdvnskIMj7S09NNXgPpKwDoP/9vv/2W8x6OHDmC69evY8OGDXj69GkxrlxPVlYWnJ2dSzUKzBJsZaVgS+GogPVFYfbs2Ual9oF/CnkGBQUhPDwcK1euxBdffFGk73NZU6FF4dGjR/D29uYtJMaOKBKLxfD09DSZ1EaiTAyznwllEXVkLkmvOIwfP57Xxmpvb4/27dtj4cKFtEQ2ISUlBR4eHlYZCFu1aoU///yT5i189tlnOHjwYKGvYxgGmZmZJkWhsFlaZmYmunXrhjlz5vBOFiylrJ3MfKhUKqhUKpsQBVtaKZASF9bKZv7zzz9x5swZzkSGwC7k2aBBA7z33nv45JNPjDr52RIV2qfwv//9D+vXrzfZfY200yQRRYBxVJJWq0VOTo6RmYAMqLdv3y60HIOlIXimXpudnQ2GYayWnfnmzRvqUzDkyZMnJn9MqampVq0l06FDB5w7dw6tW7eGVqvF6NGjMX/+fPzyyy8mSx5nZmZCKpWanJlWrlyZhrwWRkZGBk06LCpZWVm8M8eyJDc3F/b29uW+WikoKCj35Dk2Op3Oat/VpKQkjB8/HkePHuX9zllSyNPWqNCiAJhuyUkyndPT0zkVLg1FITk5GXK53GjgEIlEaNGiBQDLoyMME5Ys2Z89m79w4QINxyuJ+LRu3Ro6nQ5yuRyDBw/Gvn376DmCg4NRs2ZNTJw4EaNHj6blwUnr0QYNGljtswH0eQsnTpzARx99hJiYGMTHx2PSpEm037MhJBTVlKP7r7/+4nxWdnZ2aNKkCeLi4hAXF2eVa2YYBtnZ2bQNa3lhC6YjwPZKXGRmZkKj0ZR4paDT6TB69GhMnDgRLVq0wOvXr432saSQp61R4UVBJpNBqVQabReLxXSAYS99DUNVExIS4O7uzjkGCX1zcnIq9X4GpBR3/fr1aUVSSxOO2M8RMfr7779p+eqhQ4di7NixCAkJwa5du/Do0SMwDIMXL17gs88+w5w5c1C1alU0a9YM3bp1g7+/P27evFnilY/h/xs3boxbt26hZ8+euH//PnJycqBWq+nzbAFIT0/nnaFnZmbinXfewc2bN+m2IUOG4Oeff6YzaYZh8P333yMsLAwnT54sdt3/vLw8iESicnfw2oIoaLVaKJVKmxKF5ORk2Nvbm2w6ZGkhzzNnziAnJ4fTfva/QIUXBVLmwhCRSITMzEyj2R5fqGrt2rU5paPLMjchMzMTIpGoyM1sTPHy5Uv6f+IMa9WqFSZPngyNRoNNmzZh8+bNePnyJbRaLV69eoVXr17h0KFDkMvlqF27NgYNGoQRI0bAzs6uUGEqSjnwqlWr4v79+8jPz8dff/1Fr5MtDmq1GkqlEjExMXT7w4cPMWPGDCrcPj4+WLt2LRo2bMjZTywWY9iwYdRX4unpiaysLItLORCysrLg6upa7iUUcnNzyz0kVqFQQCwWl3mzJ3MUVuLC0kKeFy5cwPXr1416oLdo0QLDhw/Hjh07LCrkaWtUeFEwZT4imcuGtlD2SoGEqpIsTUJZ5SYAoD0krDUAxcTEAABvvLdUKsWUKVMwZcoUJCUlYdWqVbhw4QJevnxJnZoPHz7Ew4cP8fXXX6Nq1aoYMGAAFi5caJXok8uXL+P48ePQ6XTo0qULFQ3yb3p6Ol6/fo169erRbZs3b8bKlStpwuHo0aPxySefcFZHhuJDZvjp6emIiIgotLaQoWiQxkZ37961uIRDUc19hX3eOp3OJlYK+fn5cHBwKHeBZEMij0xdk6WFPNevX4+vv/6aPo6Pj0fPnj1x4MABtGrVCoBlhTxtjQovCnZ2dryiQGLM+XwFxDyUmJhI/Q3sOkdA2awSGEbfT5rd8KekxMfHA4DR7McQX19f2rs3IyMDR44cwZMnT3D69Gm8fPkSOp0Ob968wfr167F9+3ZERkaW2IZLonk0Go1ROQdAL5De3t7w8vKCSqXCe++9h9OnTwPQJ9udOHGC/ljN8dZbb+HcuXMoKChA+/btOaHIlpjknjx5Al9fXzg5OfHup9FooFKpzJrzDJ8zpDABIYmTr1+/ptusYc4r6uCuUChsynQE/FMhtaQY1sgiAlyjRg1aw82SQp62hs2Iwttvv42IiAgkJyfDw8MD3bp1w4oVKzj24fv372Py5Mm4ffs2fHx8MHXqVMyePZtznIMHD2LhwoV4/fo1atWqhRUrVqBPnz4mz8u3UlCpVCYbcJAfXHp6OnQ6HW2mY1j8rixEIS0tDVKp1Kqhj8nJyQD0q5358+dznnNwcMCkSZOMaiulpKRALpdj+/btqFu3Lo4ePYqXL19i586duHfvHrKzsxESEoIHDx6UKIeBRFepVCrk5+cbrT7S09NRtWpV7Nq1C9OnT6cRRtWrV8elS5csPnfnzp1ppdb+/fvjxIkTAPhrCxmi0WigVCpRrVq1QoW1KBQl+IA4uvPy8uDs7MxZUZUkkIHcg6KsbLKzswHoq9EWZ1XEFiNrrTbKshieJYU8bQ2bEYXOnTtj/vz58Pf3R1xcHD777DMMGTIE165dA/BPEki3bt2wadMmPHjwAGPGjIG7uzu9wdeuXcP//vc/LF++HP369cPevXsxcOBA3L1712SWKp8oJCQk0D68hpCVQmJiIvz9/ekXlqwQSAhjcRyURSUpKcmqpiMA9EdcUFCAdevWGT1/+fJlo0J1qamp2LBhA/Lz83H37l0MGDAAzZo1Q1hYGLZt24apU6ciMzMTISEhePToUbFNGuxiZXXr1sXevXtpaKparcaTJ0/w0UcfcVqQvvfee9iyZUuRPo82bdqgatWqiI6OLrQAoyFZWVmwt7e3qiAAKPJ3SqVSwcPDo8R1nExFzlkayJCZmUl/Y5YIEfsx3z0oSQj3kydP8OTJE1y8eBHu7u44fvw4LYbXtGnTEpvaqlWrxpvbY0khT1vCZkRh+vTp9P9BQUGYO3cuBg4cSG1x7CQQuVyOBg0aICIiAqtXr6aisG7dOvTq1QuzZs0CACxZsgRnz57Fhg0bsGnTJt7zGoqCTqdDfHw8XfobIhaLoVarkZKSQk0R7OU6UDamI61Wi+TkZDRv3tyqx502bRpu377NiePX6XQ0IqlatWqc/UmG85MnTzjbnz17BgAYM2YMCgoKMHv2bKSmpiI4OBheXl70B+vh4YHWrVtj4MCBaN26tdmBLzQ0FJ988gnWr1+PtLQ09OzZE66urti0aRMOHDjAaU1ZvXp17Nu3r9glK8jqq6iO2uzs7HIvbQFYL/KoKHWF+EhOTkZQUFCRTYekRIylKyNLAhkePXqEP//8E8+ePYOLiwsWLFiAgoICKBQKHDt2DI0bNy7y+/svYjOiwCY9PR179uxBmzZt6BfRkiSQ69evY8aMGZxj9ezZE0ePHjV5Ljs7O9pxC/gnhtnR0ZFX9UlUkqurKzVfsMtkl0XfBEBvOpLL5VYfgNq0acOJQAKA7du3Y/LkyQCAL774gvNcSkoKIiMjaT2hypUrIzY2lhPGO3nyZCiVSixcuBD5+fmcUhMAEB4ejo0bN0IkEsHNzQ3Ozs5wd3fHvHnzMHDgQM6+y5cvR4MGDTB37lxkZGQgOzsb77//Pn3e3t4ey5cvL9HyXKPR0OJ/w4YNK9Jrs7KybKLFY25ubrk3pdfpdMX2KbBbyVor+a5Ro0aYNWsWevfujQ8++ABjx461ynH/a9hUmYs5c+bAyckJXl5eiI6O5sz8SJQNG3YSiLl92OGihshkMs5KIT4+Hn5+fpBIJLwrBZFIhPT0dE7xO7JS0Ol0ZWY6SkxMhJ+fX5lEdWzZsgWAfpUQGBjIeS41NZWGcPr6+tKVS1paGi0yB+hLcf/6668YNGgQ+vXrhzZt2kAsFsPBwYEOGjqdDpmZmYiNjcXDhw8xfPhwfPDBB0afw4gRIxAdHW00i+/Rowfi4uJKbK8dO3YsPSe7tWth6HQ6myhvwTCMTRTCU6vV0Gg0NuVoJiUurOFTeP36NcaOHYvg4GA4ODigRo0aWLx4sZE5WiiIx2Lu3LlU8U39sYuOzZo1C/fu3cOZM2cgkUg4jeFLC3t7e/ohErNQQEAAp50mGxLKyF4Os30KZWU6SklJKbOZIMnUbN++PQD9TDoyMhK7du3C+vXrcffuXQBA7969sX37dipUx48f5xynb9++2L17NxYvXoxr166BYRgUFBQgNTUVcXFx2LhxI8aMGYMBAwbQUOBDhw6hdu3anAzmFy9eoHbt2jR8FgAGDhyIAwcOWKXGzvTp0+Hs7Iy33367SCsxhUIBtVpd7uYjUvW3vIvQFRQUQCaTlXuZDUOsVffo6dOnNOz50aNHWLNmDTZt2sQJ0Pg3FsQr1U/L0iQQgre3N7y9vVG7dm3Uq1cPVapUwY0bNxAaGmpREoipfcwlibBXComJiXBxcYGTkxPEYjHvSkGlUsHV1ZXzRScrhbKKOkpJSYGDg0OZxKBXqlSJmtd27dqFXbt2mdw3KysLq1evpmI6c+ZMzvNxcXEYM2YMwsLC6DYiIO7u7pwOfAzD4H//+x+OHz+OhIQEtG7dGm+99RaaNm2KrVu3cj4bsViMKVOmWC1BqmnTpnj16lWRBYZURi2L74A5cnNz4eTkVO65AbZW3gLQT6jS09OtIgq9evVCr1696OPq1avj2bNn+PHHH2lxPEt8obZGqYqCpUkgfJAfPclCtSQJJDQ0FOfPn+cs+c+ePYvQ0FCT5yF5CsTBTGKPDWscAaAhfYbviTjEkpOTIZPJTEZAGP6/uD/a0og6MoW5wnHETEbu05EjR3DmzBkAQJMmTehKRqVSYdSoUfjjjz+MjjFq1CiTxz5w4AD27t2LTz/9FHl5ebh16xZu3boFQB8g0LVrV/z5559gGAZDhgxBWFiY1bqmFWeWTTKZyxtbSFoDbFMU0tPTwTCM1SqkGpKVlcUJfRYK4hWTmzdv4vbt22jXrh08PDwQFRWFhQsXokaNGnRAtyQJZNq0aejYsSO+++479O3bF/v378edO3fMLtXkcjnUajWys7OhUCjol4WduUxITU2FWCw2CjeUSqXw9vZGcnKy2TA9UxmxlsZtkxloSkoK7XJW1NcXNQFp7969WLRoEf7++28Aekfynj170KBBA9y8eRODBw+GQqGgUVxkVUFmSjExMQgNDUVGRga93+PHj8fGjRsB6H8g5hgyZAhOnDjBCRaQyWR48uQJvvnmG7otOzu7RH2hrUF2dna5V0YF9KJQ3n4NwDZFITU1Fc7OzqXib3nx4gW+//57TgltoSBeMXF0dMThw4exePFi5OXlwd/fH7169cKCBQvoAGxJEkibNm2wd+9eLFiwAPPnz0etWrVw9OhRs2GJcrkcSqWS42AG+FcK8fHxcHBw4E3oqV+/fqGx6ZbEfBcWYpeTkwOpVAq1Wg2FQmHR8fiu19JM1ho1amDfvn1YsGAB/vzzT8TGxmLQoEH49ddfERkZCYXi/9o777gm7v+PvxIhYQkoU9uKi6LiBqWoddUKirY+6oOqtdZVbftFUcBabd3bui2OukD71QfWfqttbR24WwUH4ihS60CxIhAIMYzs3O+P/O6aCUnIOPTzfDx4cLm75C6Q3OveWwoASE1NZf4XtAWRmJiI7777jnHPxcfHY9euXTh16hQjCoMHD671b9atWzcmpsHn8yGTyaBQKNC+fXsd12NoaKhDAvymoAvGnN0ZFdCIgn5CgDOQSCSssJy0ofse1Ya5DfG0R98+ffoUsbGxiI+Px5QpU2xyrs6CFaLQqVMnnDlzps79zCkCiY+PR3x8vNnHpi80paWlzAhIwNBSkEqlEIlECAgI0FlvSW1CfXO+ASA3NxchISEWuUkszfc2trxs2TIEBgZi7969EAqFiIuLY6wqLy8vnS+/Wq3G22+/rfO+k5KS0K9fP/z+++/Ys2cPAE2F9I0bN0xaNNu2bWMEITY2FmvWrMGCBQtw+PBhyOVynSSF6OhoVFVVGRU2R7jZ6GM7O+NHpVKhpqaGuI9MUFpaWmfzSEtjoUVFRRgwYAB69epl4JUgDfEaIDwej2mZoN1KV99SePbsGZo2bQoXFxed9Y6sTVAoFCgvL9eZ72AO5rRnMIetW7ciMjISiYmJTCdSQHO3b6zwx83NDeHh4Vi2bBl69uzJCExhYSEATZFi69atDYRILpcjLy8P6enpAIDw8HAsWrQIlZWVSE5OxqhRo7B8+XKdjKT09HQcPnwYHTt2xKhRo3T60ljiWrNkP+1l2i2hVCodKkb6VFdXw8XFxeYV1ZZSnxoFe1JWVlanpWBJLPTp06cYMGAAIiIikJaWZnAdIA3xGiB8Ph9Pnz41+PBqZx/RE9jatm0LkUik0+eIoijm4mBvSktL0bhxY6emGk6aNAlBQUF4//33mXUpKSm4du0a8zgsLAyHDh1CmzZtjL7G48ePAWgaz2kH/BITE7F3716mEA7QxGt+++03nS9y9+7dMXz4cAQFBTHzogFNkO/ixYu4ePEivL29kZiYiBkzZtTptjt//jy+/PJLqFQqZGZmgsvlWtTSm/4NwGhL7/oKjiXPr6yshJeXl9Mzj6RSKSiKYtUYTsB26aiARhD69++PkJAQrF27FgKBgNlGWwGkIV4D5PHjxygrKzMIEGq7j0QiEVQqFfz9/fH8+XMdsQAcU5sAGC/OcwZ37txhlhs1aoSuXbvquP9OnTplsvmcUqlk+itpB5mFQiH27Nmj45pzdXXFxo0bTd7ZLVu2DF988QWWLl0KHo+HrVu34vHjx5DJZBCLxVi2bBnWrVuHqVOnYtGiRQYpq6WlpRg7dqzO4J3q6mqrWodcunQJbdq0gZ+fn8VxImNtGSyJORmDnjBnicCYI0zmNqyj5zI7M85jDFsVrgGaO/779+/j/v37TFdUGvpzTBriNUAuXLgALy8vg4uYtvuIDkLTXwT6TtaRcxPkcjkqKipsPu7SGo4dO8Ys0xf2jz/+GPPnzwcA7Nq1y6B7LY22eGjneM+aNYuxuo4fP47OnTvX6RcfMmQIunbtih49egAAE+DLycnB9OnTcfPmTaax3zfffIOwsDCMGzcO48ePx5IlS7Br1y6Di6qxKXx1oVAoUF1djSZNmji8UEu7R5BarcatW7fg6+uLoKAgg3kTtQmUJWJkqmGdtmDQ+9KT+GxtEWnvZwllZWUWu19NoV1XUxukIV4DgqIo3L59m+lzpH2nQ1sKSqUSAoGAmbes3ecIcJyVUFJSAh8fH6eb40qlEjdu3GAe040Mvb29ERwcjOLiYixZsgT9+vUzOruAFhRPT0/mvUilUvz4448AwLTAMAd6XKo+ERERuHTpEv7++2/85z//QXZ2NtRqzZyDL7/8UqfitFGjRkhJScGWLVtQXV2NFStWGFRi14VYLIabm5tTpotp9wgCNA0K27RpY3LUpK2gxciUcDx58gRyuRyvvvpqrQJjbvdU7WX992+OqBw6dAgPHjxAdnY2Y0m6ubnB3d0dU6dOtfvfqyHxUosCh8NBeno6Ro8ebXSbWq1GSUkJPD09mbtWWizoALMjRYENrqOSkhKdu2ntC/jJkyfRuXNnUBSFH3/80ago0MVn2t1WZ86cCYVCAQ6HY7avlZ5poW+2a/P666/j1KlTqKqqQmpqKjIyMnD//n3mBqBv377Yt28f/P39UV5ejt27d+P8+fNGZzXUBhv6HQFgRpE6IvNIX4z0efLkCZo0aWLzz2xdYmRquUWLFpDJZJDL5XBzc4NAIIBUKoVEIsGkSZNseo4NnZdaFABNjYSxGc30xf/Zs2c68QZtS8Ea89Ua6HTYTp062f1YdfHDDz8wy/369dPZdvjwYWZZv1stTUFBAQBNsFipVGLq1Kn4/vvvAWjcSeZeRKqrq6FUKs26GHt5eWHOnDmYM2cOM/MhMjJSx+pasWIF0tLSoFarMX/+fKxbt86s8wAMq1idRXV1Nfh8vtXpzrZEIpHUmeVjDXWJkSk++eQTUBSFjIwMfPXVV4zLkWAIu6JATsDUOE46dlBVVaWTraCdleQoK6G0tBS+vr6sSDM8ePAg81j/wr9t2zYAQMeOHY1e3JVKJZ4/fw5Ak8b6yiuv4ODBg0x3WUuahFVUVMDX19fii4OHhwf69Olj4Ibz8vJC7969AaDW/k76sKUzKgAm84gNsLFGQaFQoKKiwmbWy5YtW9CyZUu4ubkhKiqKsYIbOi+9KNBtLvThcDhQqVQICAjQufOiLQVH1SYA/7bJdjbPnz/HzZs3mceDBg1ilnNycpgW5fPmzTP6fO10zS1btuikk8bHx1t0ty0UCm1+d063J6iurkbHjh2NBlP1kUqlUCqVrPBJs6FdNvBvjzC2iUJ5eTkA2CQl9eDBg0hOTsbChQtx/fp1dOnSBTExMcw424YMEYX/b3NhrE02RVE6cxNoHOk6kkgkEIvFdmvgZQnalZn6d8Zz585l1g8fPtzo83/77TdmOTs7m1nmcDhITU01+zwoijIZZK4PHTt2ZMSXdnPVBVs6owKaqmo2iJNEIgGXy3VK4L02BAIBfHx8bGJxr1+/HlOmTMHEiRPRoUMHbN++HR4eHky1fkPmpY8p0G4EpVKp8yGm3Rz6VYccDgcymQwlJSVwcXExO7e7Ph1RmzZtyoovGJ0hBECnN0xNTQ2ysrIAAGPHjjX5fFPmtYuLC/r3749WrVohIiICU6dOrXV6WVVVFdRqtV0ugA8ePEB6ejquXbtmluizxXVEURTTMtvZ0K4jZxfQ6WOrGgW5XI6cnBzmRgjQuJUHDRrEfA8aMi+9KNAXW7rTJ412daI2np6e8PDwgEAgqDMPXBtjHVHNEZOioiL4+vqisLDQ4ipY/YKi+iCRSLBr1y7msfbFf+nSpYz1tHDhQpOvce/ePWZZOzajUCiQl5eHvLw8HD16FFu3bmV6HhlDKBSiSZMmdrPUzM0/BzSiUFsGlKOQy+VQKBSsiCmwMZ4A/NsMr77fibKyMqhUKqNTHrX7cTVUWCkKMpkMUVFRuHnzJnJzc9G1a1dm261bt5CQkICrV68iICAA06dPNyiUOnToEObPn49Hjx4hNDQUq1evxtChQ40eizYlteMKMpkMIpEIAHTqF9RqNTw8PNCpU6c6Mzy0C4pqa41Q23apVMoEwSsqKsxKvdN3g9W3rQK9XFFRgdjYWKSnpyM0NBSVlZXM9r179wIAoqKiar1Tpf+O/v7+ePDgAQ4fPozvvvsOr7zyCvLz83H16lUAYKaumcIe8QRrUKvVqKysZIWlUFVVBXd3d1a4sdgsCraqZn6RYaUozJ49G82bN9cJagL/jrYbNGgQtm/fjtu3b2PSpEnw9fVlysYvXbqEMWPGYOXKlRg2bBgOHDiAESNG4Pr160ZbaNPWgXYGUnFxMXx8fHT6HAH/Nr+ztCOqtTx8+BD+/v7o0qWL2c+prxCZKigSi8UYNWoUhgwZgsaNG+PGjRtQq9W4cuUK42obMWIETp06ZbI196ZNm3D37l3ExcXhzp07aN++PVatWqWzb2VlJSQSCf755x+TQlVRUYFXX30Vcrnc6spWW0ALo7PHXgLsGawDaESBjc3eysrKbBKb8/f3R6NGjSye8thQYJ0oHDt2DCdPnsT//vc/nXYKgHmj7TZt2oTY2Fh8/vnnADSujczMTKSmpmL79u0Gx6Pv+OmCLLo2ISQkBCKRCGq1mrmwOzLADGg+ZNpFXuZgj/NTKpU4d+4cevbsaWAJaFf/Pnz4EB9//LFJwQkJCUGfPn1q7fnD5XLh7u6O0tJSo6+hVCqhUql0qqoB05Wt9lym4wls8J2zTRTYMGxIH4FAYJOLNo/HQ0REBE6fPo0RI0YA0FwbTp8+jWnTptX79Z0Nq0ShpKQEU6ZMwZEjR4zefZkz2i4rK8sgfz4mJkZncpc2HA4HfD6fcR+JxWLI5XKm8Ia2FCyZm2ALqqqqUFNTwwpzt7y8HO7u7kZdQ4sXL8bPP/+MoqIifPvtt3j06BEWL15st0K7goICiMVipnLaksrW2kTGXIvKWJba2bNnLY4VWSNCtU3Oq6qqqtPt5ggoimKt+6isrMxoi3drSE5Oxvjx4xEZGYmePXti48aNqK6uxsSJE23y+s6ENaJAURQmTJiATz/9FJGRkUYDjeaMtjPWSTQoKIjJoTcGPUoS0DS/CwoKYhqb0cFQR9cmlJSUwN/f3+EN1oxRVlZmUpy8vLyQl5eHXr16IT8/HydOnMCZM2eYmIytoYeuW1vZWl+03XNXrlxBSEgIfH196+2eq23ZWNKCfp+fmpoaFBQU4OnTp3axjMxNWlAoFFCpVKwVBVuldo8aNQoCgQALFixAcXExunbtiuPHj7OiFU19sfsVx9zRdidPnkRlZaVOmpcj4HA4cHV1hUwmg1KpRGlpKbp168Z8CbTvEB01N4GiKBQXF6Nt27Z2P5Y55yIQCGq9w+LxeMjOzsbAgQNx/fp1u10Q1Go1RCKRzbpcWgN9oVQoFJBIJAgKCrJ7unBd8yAkEgnu3LmDli1bmowpmZoPUZuYGXvvdVk+KpUKXC4X9+7ds4nFZCvXHP05tmW9z7Rp014Id5E+dhcFc0fbnTlzBllZWQaFJZGRkRg7diz27t1r1mg7U/vU5kukW10IBAK4u7sz+e9cLpdpwAU41nUkk8ns0jvGUp4/fw6KomqtGwA0tQYXLlwAAKMXFFsgEong4uLCilz858+fw93d3SH1I3WNcVUoFPD09DRaaGkt1jaeE4lEcHV1hYuLi0XzIejfxrLn6mvtHD16FBwOB0VFRfj777/B4/Hg5uYGDw8Pm7mTXiTsLgrmjrbbvHkzli1bxjwuKipCTEwMDh48yHTbNGe0XXR0NE6fPo2ZM2cyr5WZmYno6GiTx3Z1dYVcLkdRURGaNWvG3J3QloIj5yYAGldYQEAAK9IL6fGFllhI9rKm6CpmNgR22VK0BtgnyGyte46OvYWGhlp1XG1Lx1Zxop9//hkVFRWQSqVITU2FQqGAVCoFoJmeRtDF+Q7r/0d7pi4A5kPepk0bpjjInNF2M2bMQL9+/bBu3TrExcUhIyMD165dM9lsjQ40V1ZWQqVS6QRIaXMYcJyVQLuOnOki0UYgEFicAWUvhEIha7JaxGIxKwK7AHvaWwCazKP6pOjaI3vu+PHjePLkCdq1a4dbt26xojsAm2lQvY/o0XYFBQWIiIhASkqKwWi7Xr164cCBA9ixYwe6dOmCH374AUeOHDFao0Dj6uqK0tJS+Pv763xg6KZ4jrQSxGIxlEolK1xHEokE1dXVrDgXlUrFmhbVdGdUb29vZ58KAE0jPDalozp7EJQxBAIBmjZtanFb8ZUrV6JHjx5o3LgxAgMDMWLECNy9e1dnH6lUioSEBPj5+cHLywsjR440cGE3JFhjKejTsmVLo+l/5oy2i4+PR3x8vNnH4vF4KC0tNfDJ0paCozuiBgQEOKUYS5+ysjL4+vqyoj+/SCQCn89nRVaLRCJhTWdUtVrNOlFgw/9In9oy6Grj/PnzSEhIQI8ePaBUKvHll19i8ODBuHPnDhPbSkpKwq+//opDhw7Bx8cH06ZNw3vvvYeLFy/a+m04BNaKgiORSCTIzc01OqeZzjpyBBRFoaSkBB06dHDI8eqC7hXDBtjS2gLQxBMaN27MiphPTU0NuFwuK+7O1WpNaxY2VHjrU1paioCAAIvjUcePH9d5nJ6ejsDAQOTk5KBv3754/vw5du/ejQMHDmDgwIEAgLS0NLRv3x7Z2dl44403bPYeHIXzb0dZQGlpKdOyQBtaFBx1105XULPh4qdUKiEUCllRPAewTxTYFGT29PRkRfCd7grg7GFQxrDWUtCHbulCfxZzcnKgUCh0Zou0a9cOLVq0aLAdU196S6GsrAwikQhvvvmmznq6vcVff/2F+/fv15pnbU1VqrFtxcXFCAwMZIXrSCgUmqxidjQKhQKVlZWs6acjFovx2muvOfs0ALCvvYWbmxsrPr/62KIZnlqtxsyZM9G7d28mRllcXAwej2eQsl1XwSybeelF4cqVK2jatKnBP5WiKHTo0IGJKeinwNVVrWptXjaHw0FJSYnZgmNNUZD+nAdjd5lsch2JRCK4u7uzxkUiFotZE2SuqqpijViyNZ4AaG7+QkJC6vUaCQkJ+PPPP/HHH3/Y6KzYyUsvCkOHDkXv3r0Z05dGpVLB1dXV7sFEWizKy8uRn5+PyMhInaIhc3Kz9QuE6npOXe216bYJnp6euH79utWCU9+WCTRsch1VVlbCxcWFNX7zqqoq1lgtbBeFHj16WP38adOm4ejRo7hw4YLO/Izg4GDI5XKIRCKdG8uG3DH1pRcFQDN9TXueAl2R64hAIn2xLC8vR3BwsENcAcaKg7R/V1ZW4sGDB2jRogXTYkF/P+0CIXOsIn0ssXDoMYoPHjyw2nqylUuDTkVlgw9fpVJBIpGwyn3ElnPRx1r3EUVRmD59Og4fPoxz584Z9F6LiIiAq6srTp8+jZEjRwIA7t69i8LCwloLZtkMEQXoNsQDLJubYAvUajVKS0stmptQH+q6SFZUVCAgIACvvPKKTY5nbssEY+voiWI8Hg9SqbRWMautd09drbXNtYSKi4vB5/MhEAjqfI69fetVVVVwdXVlTTGWRCJhTWKCNnTfI2ua1SUkJODAgQP46aef0LhxYyZO4OPjA3d3d/j4+GDy5MlITk5G06ZN4e3tjenTpyM6OrpBZh4BRBQAGBcFR3ypaYRCIbhcbp39hRyFLfyv2tSno2lJSQmEQiHCw8Mtep6+EFkzcMhY357KykooFArcv3/f6Ovov29rXG/mbi8vL4eHhwcUCkWtbbUdBZvdR+Xl5VY1w9u2bRsAoH///jrr09LSmJ5uGzZsAJfLxciRIyGTyRATE4OtW7fW95SdBhEF/NsQD3D83AQATLtvNrgkpFIpa3rzA9bHE+zRWlsul+P8+fPo2bOn0YK+urqZmmPhWBIfUiqVADQFVtrvu77JB5YKF52IIZfLWSkK1dXVqK6uttp9VBdubm7YsmULtmzZYs3psQ4iCtBYCjU1NQAcPzdBrVZDIBCge/fuDjleXdD+e7a4JIRCodXN1WzN8+fP4eHhYbLCu65uprYmJycHgYGBaN68udkuNVPiIpPJzH6OsUQF+obm8uXLJoXGlpl0ltxACQQCcLlc1tzosB0iCtD0PlIoFExmjqPmJgAaV42rqytrUhxtVeRjC6RSKWpqaliTcikWi1lTtAZoYgpt2rRxqBABMJp8UF5ejsLCQnTo0MEsd52xQUO1CZKpltp11f2kpaWhpqYGfD4fCxcuhIeHB9zc3NCiRQuLWuG8TLBOFFq2bInHjx/rrFu5ciXmzJnDPL516xYSEhJw9epVBAQEYPr06Zg9e7bOcw4dOoT58+fj0aNHCA0NxerVqzF06FCjx6TdR46emwBofOZscR2pVCpW3ZlXVFTA29ubFb2XAI2lwJbaDblcDrlc7pTiQm2LiKa8vBxeXl52Sx22Jj4kk8nw2muv4d69e3Bzc0NpaSmkUikkEgmePXtGRMEErBMFAFiyZAmmTJnCPNauFRCLxRg8eDAGDRqE7du34/bt25g0aRJ8fX2ZbqmXLl3CmDFjsHLlSgwbNgwHDhzAiBEjcP36daPdUmlRcHRHVJVKBYFAUK/8aVtSXl4OPp/PiipmgF31CXRn1DZt2jj7VABorAQ3NzfWCKa9g8wcDsfi0bQtWrRAVFQU9u3bB4VCgZ07d9brHFatWoW5c+dixowZ2LhxIwCNNZuSkoKMjAydIHNDHsvJvnp0aEQgODiY+dG+SO3fvx9yuRx79uxBeHg4Ro8ejcTERKxfv57ZZ9OmTYiNjcXnn3+O9u3bY+nSpejevTtSU1ONHk87+8iRVkJZWRn4fD5rcrtp1xEbrBaAXaJQU1MDtVrNis6ogCZ4yhbxBjR/HzYGmQHYZAzn1atX8e233xpMaktKSsIvv/yCQ4cO4fz58ygqKsJ7771Xr2M5G1aKwqpVq+Dn54du3bphzZo1TJYFAGRlZaFv3746gdCYmBjcvXsXFRUVzD7aDarofUw1qKJFwZFWAqDJOgoODmbFRZjO5WaLe0QikUAmk7EmTZfujOqoWFNdsGmwDqC5Y2arKNCzUqylqqoKY8eOxc6dO3XiW3SH1PXr12PgwIGIiIhAWloaLl26hOzsbFuculNgxydci8TERGRkZODs2bP45JNPsGLFCp14AZ2+qQ39mC4sMbWPqQZV2qLgqC+9UqlEWVkZa8zMyspKqNVq1gR1hUIhfHx8WNGeGmBnkJktlgJFUay2FMrLy+v1PUtISEBcXJzBjeaL2CEVcFBMYc6cOVi9enWt++Tn56Ndu3ZITk5m1nXu3Bk8Hg+ffPIJVq5cabeWvBRFoaqqCmKxGF5eXuDz+QZ377a+my8pKYGHhwdrXEcCgQB+fn6suRNmk+sI0NwV6o+MdRb055Utnx06c4+toiAQCNC7d2+rnpuRkYHr16/j6tWrBttexA6pgINEISUlhan+M0Xr1q2Nro+KioJSqcSjR48QFhaG4OBgg1F39GO6AZWpfUw1qMrPz8eVK1eYWcQcDgdubm5wc3NjunPq/6aXTT3W/m1sfUJCAt5++2107NgR7u7uBnfEjnYpCQQCVl30hEKhzdps1Be6HxRbLAWZTAalUskaS0EikYDH47HGqtPH2pjCkydPMGPGDGRmZrKiQ6+jcIgoBAQEWJ37fuPGDXC5XOafGh0dja+++goKhYLJvMjMzERYWBjj+oiOjsbp06cxc+ZM5nUyMzNNNqhKTU3Fhg0bmLx4iUQCiUTCLOuvp3/o9fRvsVhssJ1+HXqZ3lcmkyE7OxtLly4FoKmV0BeY2kRFf3tt64y9lqurKyM8BQUFWL58OQ4dOmTV/8jW1NTUQKlUsiaeQHdGZcudcFVVFTw8PFhzEWaz64iiKKtbXOTk5KC0tFSnsFSlUuHChQtITU3FiRMnXrgOqQDLUlKzsrJw+fJlDBgwAI0bN0ZWVhaSkpLw4YcfMhf8Dz74AIsXL8bkyZPxxRdf4M8//8SmTZuwYcMG5nVmzJiBfv36Yd26dYiLi0NGRgauXbuGHTt2GD0uh8MBj8cDj8ezWxGZdvFNeno6UlNTceHCBQOx0RYZffHRF6WKigoUFRUZFSLtZfr52r15uFwuIxZKpRJSqRT9+/c3ag2ZEilzhEt7vbkuuVOnTkEsFrPGlUVPWmNDQgDArsE6ALuDzHQChTWi8NZbb+H27ds66yZOnIh27drhiy++wGuvvfbCdUgFWCYKfD4fGRkZWLRoEWQyGVq1aoWkpCSdOIOPjw9OnjyJhIQEREREwN/fHwsWLGBqFACgV69eOHDgAObNm4cvv/wSoaGhOHLkiNEaBUehfUHx8/PDrFmz4OnpaVcXgLYQqdVqKBQKHQuIXk5OTkZYWBiGDRtmIEL61lB5eblZIiSRSHSaDAKo0yVH/7548SL8/f2Rl5dnkUuOXufh4WFTl9xff/3FuBbZANtEgc2WQmVlJWQymVWB5saNGxtcMzw9PeHn58esf9E6pAIsE4Xu3bublcrVuXNn/P7777XuEx8fz9qKxXfeecchx9G+CNIVqG5ubjoZRnTLglmzZqFDhw71Op62CNEVqLSY0CJTl0uupqYGv/76K/r168eM4axLhLSXtanLJVfbeu3fc+fOxYQJEzBgwIBaXXI8Hs/uCQqARhTY0ooE0FgKbHH16SMQCODq6mq3eNCL1iEVADiUOW0ACQQHcfPmTbz55psQCoV1VrDqf3Tp1gbGrCGpVIrq6uo6XXLaQiORSPD8+XNkZWWhQ4cOUCgUBtaQ9gAhLpdbZ0zI0riQ/npXV1cMHDiQsXz1XWzOcHH9/vvvCA8PZ1W2GE1WVhbGjRuHp0+fssb9x3ZYZSkQCJ07d0Z+fr5ZLQ30v+SNGjWCh4cHPDw8bNYR8+jRo5g1axby8vIAGFpDplxyxoTHli65Xr16AdC4XOsSHFNZc5bGhdzd3Q3+L3K5HGvXrq13Cwl7wbYq/YYAEQUCq+BwOKxJRQWAjh07Mn1uAF0h4nA44PP54PP5dnOf6IvQDz/8gOXLl+PkyZNWZclVV1dDIBBY7ZKjs7BoweByuXj06BFGjRrFdCA1xwqqzQ1nS5ectWM4X2aIKBAItdCyZUunBpn1Rahp06b46KOP7FYJb6lLLisrC5s3b8a0adOMJijQLrji4mIDATNlDWm75DgcjtnWjzHBuXTpklmDcgj/QkSBQGhADBo0yKDdgi2x1CVXVlaGN954A+PGjbP6mPVxyRmzhuh0bYlEguLiYta0gm8okEAzgUAgGOHp06f44osvcOzYMdTU1KBt27ZIS0tDZGQkAI2ALVy4EDt37oRIJELv3r2xbdu2Bi9C7KgOIhAIBBZRUVGB3r17w9XVFceOHcOdO3ewbt06nXTur7/+Gps3b8b27dtx+fJleHp6IiYmBlKp1IlnXn+IpUAgEAh6zJkzBxcvXjRZD0VRFJo3b46UlBTMmjULgKbyPSgoCOnp6Rg9erQjT9emEEuBQCAQ9Pj5558RGRmJ+Ph4BAYGolu3bjpptwUFBSguLtaJ7/j4+CAqKqpBt80GiCgQCASCAQ8fPmTiAydOnMBnn32GxMRE7N27F8C/s1ssmdvSUCDZRwQCgaCHWq1GZGQkVqxYAQDo1q0b/vzzT2zfvh3jx4938tnZF2IpEAgEgh7NmjUz6AXWvn17FBYWAvh3doslc1saCkQUCAQCQY/evXvj7t27Ouv+/vtvhISEAABatWqF4OBgnD59mtkuFotx+fLlBt02GyDuIwKBQDAgKSkJvXr1wooVK/D+++/jypUr2LFjBzOThcPhYObMmVi2bBlCQ0PRqlUrzJ8/H82bN8eIESOce/L1hKSkEggEghGOHj2KuXPn4t69e2jVqhWSk5MxZcoUZjtdvLZjxw6IRCL06dMHW7duxeuvv+7Es64/RBQIBAKBwEBiCgQCgUBgIKJAIBAIBAYiCgQCgUBgIKJAIBAIBAYiCnZi+fLl6NWrFzw8PExO5SosLERcXBw8PDwQGBiIzz//HEqlUmefc+fOoXv37uDz+Wjbti3S09Ptf/IEAuGlhYiCnZDL5YiPj8dnn31mdLtKpUJcXBzkcjkuXbqEvXv3Ij09HQsWLGD2KSgoQFxcHAYMGIAbN25g5syZ+Pjjj3HixAlHvQ0CgfCyQRHsSlpaGuXj42Ow/rfffqO4XC5VXFzMrNu2bRvl7e1NyWQyiqIoavbs2VR4eLjO80aNGkXFxMTU+7xCQkIoADo/K1eu1Nnn5s2bVJ8+fSg+n0+9+uqr1OrVq+t9XAKBwG6IpeAksrKy0KlTJ50uizExMRCLxcjLy2P20R+9GBMTY7PWvEuWLMGzZ8+Yn+nTpzPbxGIxBg8ejJCQEOTk5GDNmjVYtGgRU9Fpa7Zs2YKWLVvCzc0NUVFRuHLlil2OQyAQaoeIgpMoLi422naX3lbbPmKxGBKJpN7n0LhxYwQHBzM/np6ezLb9+/dDLpdjz549CA8Px+jRo5GYmIj169fX+7j6HDx4EMnJyVi4cCGuX7+OLl26ICYmBqWlpTY/lj6LFi0Ch8PR+WnXrh2zXSqVIiEhAX5+fvDy8sLIkSMNmqARCC8SRBQsYM6cOQYXEP2fv/76y9mnaTarVq2Cn58funXrhjVr1ugEubOystC3b1/weDxmXUxMDO7evYuKigqbnsf69esxZcoUTJw4ER06dMD27dvh4eGBPXv22PQ4pggPD9exmP744w9mW1JSEn755RccOnQI58+fR1FREd577z2HnBeB4AyIKFhASkoK8vPza/1p3bq1Wa8VHBxstO0uva22fby9veHu7l6v95KYmIiMjAycPXsWn3zyCVasWIHZs2cz282xZGyBXC5HTk6OjpuMy+Vi0KBBDptg5eLiomMx+fv7A9CMV9y9ezfWr1+PgQMHIiIiAmlpabh06RKys7NtcuwLFy5g+PDhaN68OTgcDo4cOaKznaIoLFiwAM2aNYO7uzsGDRqEe/fu6ewjFAoxduxYeHt7w9fXF5MnT0ZVVZVNzo/w8kFEwQICAgLQrl27Wn+076xrIzo6Grdv39ZxkWRmZsLb25vp4x4dHa3Tmpfex1RrXkssmeTkZPTv3x+dO3fGp59+inXr1uGbb76BTCaz5k9jNWVlZVCpVE6dYHXv3j00b94crVu3xtixY5me+Tk5OVAoFDqC1a5dO7Ro0cJmglVdXY0uXbpgy5YtRrebMxx+7NixyMvLQ2ZmJo4ePYoLFy5g6tSpVp1PXSI1YcIEg89UbGyszj5EpBo2pHW2nSgsLIRQKERhYSFUKhVu3LgBAGjbti28vLwwePBgdOjQAePGjcPXX3+N4uJizJs3DwkJCeDz+QCATz/9FKmpqZg9ezYmTZqEM2fO4Pvvv8evv/5q9JgpKSmYMGFCredlypKJioqCUqnEo0ePEBYWZpYl8yIQFRWF9PR0hIWF4dmzZ1i8eDHefPNN/PnnnyguLgaPxzOoM7GlYA0ZMgRDhgwxuo2iKGzcuBHz5s3Du+++CwDYt28fgoKCcOTIEYwePRr5+fk4fvw4rl69isjISADAN998g6FDh2Lt2rVo3ry5RedDi9SkSZNMusliY2ORlpbGPKY/rzRjx47Fs2fPkJmZCYVCgYkTJ2Lq1Kk4cOCARedCcBLOTn96URk/frxByicA6uzZs8w+jx49ooYMGUK5u7tT/v7+VEpKCqVQKHRe5+zZs1TXrl0pHo9HtW7dmkpLS7PL+f73v/+luFwuJRQKKYqiqK1bt1JNmjSh5HI5s8/cuXOpsLAwmx5XJpNRjRo1og4fPqyz/qOPPqLeeecdmx7LHCoqKihvb29q165d1P79+ykej2ewT48ePajZs2fb/NgAdP4ODx48oABQubm5Ovv17duXSkxMpCiKonbv3k35+vrqbFcoFFSjRo2oH3/80abnQ1Gaz/W7775r8jl37tyhAFBXr15l1h07dozicDjU06dP63U+BMdA3Ed2Ij09HRRFGfz079+f2SckJAS//fYbampqIBAIsHbtWri46Bpv/fv3R25uLmQyGR48eFCnJWAOWVlZ2LhxI27evImHDx9i//79SEpKwocffogmTZoAAD744APweDxMnjwZeXl5OHjwIDZt2oTk5OR6H18bHo+HiIgIHTeZWq3G6dOnnTLBytfXF6+//jru37+P4OBgyOVyiEQinX0cNXLRnOHwxcXFCAwM1Nnu4uKCpk2b2s39du7cOQQGBiIsLAyfffYZysvLmW1ZWVnw9fVlrBYAGDRoELhcLi5fvmyX8yHYFiIKLyF8Ph8ZGRno168fwsPDsXz5ciQlJenUIPj4+ODkyZMoKChAREQEUlJSsGDBAqt91bWRnJyMnTt3Yu/evcjPz8dnn32G6upqTJw40ebHqouqqio8ePAAzZo1Q0REBFxdXXUE6+7duygsLGzwIxetJTY2Fvv27cPp06exevVqnD9/HkOGDIFKpQLgHJEi2BYSU3gJ6d69u1nZM507d8bvv/9u9/MZNWoUBAIBFixYgOLiYnTt2hXHjx83uEO2B7NmzcLw4cMREhKCoqIiLFy4EI0aNcKYMWPg4+ODyZMnIzk5GU2bNoW3tzemT5+O6OhovPHGG3Y/N+3h8M2aNWPWl5SUoGvXrsw++vUcSqUSQqHQLtbM6NGjmeVOnTqhc+fOaNOmDc6dO4e33nrL5scjOB5iKRBYwbRp0/D48WPIZDJcvnwZUVFRDjnuP//8gzFjxiAsLAzvv/8+/Pz8kJ2djYCAAADAhg0bMGzYMIwcORJ9+/ZFcHAwfvzxR4ecmznD4aOjoyESiZCTk8Psc+bMGajVaof8DVu3bg1/f3/cv38fgONFimAHnBrRIBBeciorK6nc3FwqNzeXAkCtX7+eys3NpR4/fkxRFEWtWrWK8vX1pX766Sfq1q1b1Lvvvku1atWKkkgkzGvExsZS3bp1oy5fvkz98ccfVGhoKDVmzJh6nxuMBJr1efLkCcXhcKiffvqJoqh/A83Xrl1j9jlx4gQJNDcgiCgQCE7k7NmzRrPUxo8fT1EURanVamr+/PlUUFAQxefzqbfeeou6e/euzmuUl5dTY8aMoby8vChvb29q4sSJVGVlpVXnU5tIVVZWUrNmzaKysrKogoIC6tSpU1T37t2p0NBQSiqVMq9hL5EiOAYORVGU08wUAoHAKs6dO4cBAwYYrB8/fjy2bduGESNGIDc3FyKRCM2bN8fgwYOxdOlSnfiPUCjEtGnT8Msvv4DL5WLkyJHYvHkzvLy8HPlWCFZCRIFAIBAIDCTQTCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYCCiQCAQCAQGIgoEAoFAYPg/bbngnORTQy8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot in 3D\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "cell.vis(ax=ax, type=\"line\", dims=[2,0,1])\n", + "ax.view_init(elev=20, azim=5)\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -549,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -592,17 +668,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jnsbck/Uni/PhD/projects/jaxleyverse/jaxley/jaxley/modules/base.py:1528: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " self.pointer.edges = pd.concat(\n" - ] - }, { "data": { "image/png": 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", @@ -662,7 +730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.1" } }, "nbformat": 4, diff --git a/jaxley/modules/base.py b/jaxley/modules/base.py index cf2dfa3f..33188083 100644 --- a/jaxley/modules/base.py +++ b/jaxley/modules/base.py @@ -38,7 +38,7 @@ concat_and_ignore_empty, cumsum_leading_zero, ) -from jaxley.utils.plot_utils import plot_comps, plot_morph +from jaxley.utils.plot_utils import plot_comps, plot_graph, plot_morph from jaxley.utils.solver_utils import convert_to_csc from jaxley.utils.swc import build_radiuses_from_xyzr @@ -141,7 +141,10 @@ def _update_nodes_with_xyz(self): np.sum(np.diff(xyzr[:, :3], axis=0) ** 2, axis=1) ).cumsum() branch_len = np.hstack([np.array([0]), branch_len]) - branch_len = branch_len / branch_len.max() + 2 * i # add padding like above + max_len = branch_len.max() + branch_len = ( + branch_len / (max_len if max_len > 0 else 1) + 2 * i + ) # add padding like above branch_len[np.isnan(branch_len)] = 0 branch_lens.append(branch_len) branch_lens = np.hstack(branch_lens) @@ -1357,11 +1360,27 @@ def vis( ) -> Axes: """Visualize the module. + Modules can be visualized on one of the cardinal planes (xy, xz, yz) or + even in 3D. + + Several options are available: + - `line`: All points from the traced morphology (`xyzr`), are connected + with a line plot. + - `scatter`: All traced points, are plotted as scatter points. + - `comp`: Plots the compartmentalized morphology, including radius + and shape. (shows the true compartment lengths per default, but this can + be changed via the `morph_plot_kwargs`, for details see + `jaxley.utils.plot_utils.plot_comps`). + - `morph`: Reconstructs the 3D shape of the traced morphology. For details see + `jaxley.utils.plot_utils.plot_morph`. Warning: For 3D plots and morphologies + with many traced points this can be very slow. + Args: ax: An axis into which to plot. col: The color for all branches. dims: Which dimensions to plot. 1=x, 2=y, 3=z coordinate. Must be a tuple of two of them. + type: The type of plot. One of ["line", "scatter", "comp", "morph"]. morph_plot_kwargs: Keyword arguments passed to the plotting function. """ return self._vis( @@ -1384,10 +1403,14 @@ def _vis( ) -> Axes: branches_inds = view["branch_index"].to_numpy() - if type == "volume": + if "comp" in type.lower(): return plot_comps( self, view, dims=dims, ax=ax, col=col, **morph_plot_kwargs ) + if "morph" in type.lower(): + return plot_morph( + self, view, dims=dims, ax=ax, col=col, **morph_plot_kwargs + ) coords = [] for branch_ind in branches_inds: @@ -1396,7 +1419,7 @@ def _vis( ), "No coordinates available. Use `vis(detail='point')` or run `.compute_xyz()` before running `.vis()`." coords.append(self.xyzr[branch_ind]) - ax = plot_morph( + ax = plot_graph( coords, dims=dims, col=col, @@ -1420,7 +1443,7 @@ def _scatter(self, ax, col, dims, view, morph_plot_kwargs): coords = self.xyzr[branch_ind] interpolated_xyz = interpolate_xyz(comp_fraction, coords) - ax = plot_morph( + ax = plot_graph( np.asarray([[interpolated_xyz]]), dims=dims, col=col, @@ -1852,11 +1875,25 @@ def vis( ) -> Axes: """Visualize the module. + Modules can be visualized on one of the cardinal planes (xy, xz, yz) or + even in 3D. + + Several options are available: + - `line`: All points from the traced morphology (`xyzr`), are connected + with a line plot. + - `scatter`: All traced points, are plotted as scatter points. + - `comp`: Plots the compartmentalized morphology, including radius + and shape. (shows the true compartment lengths per default, but this can + be changed via the `morph_plot_kwargs`, for details see + `jaxley.utils.plot_utils.plot_comps`). + - `morph`: Reconstructs the 3D shape of the traced morphology. For details see + `jaxley.utils.plot_utils.plot_morph`. Warning: For 3D plots and morphologies + with many traced points this can be very slow. + Args: ax: An axis into which to plot. col: The color for all branches. - type: Whether to plot as points ("scatter"), a line ("line") or the - actual volume of the compartment("volume"). + type: The type of plot. One of ["line", "scatter", "comp", "morph"]. dims: Which dimensions to plot. 1=x, 2=y, 3=z coordinate. Must be a tuple of two of them. morph_plot_kwargs: Keyword arguments passed to the plotting function. diff --git a/jaxley/utils/plot_utils.py b/jaxley/utils/plot_utils.py index efa863c8..a40f8c97 100644 --- a/jaxley/utils/plot_utils.py +++ b/jaxley/utils/plot_utils.py @@ -2,9 +2,11 @@ # licensed under the Apache License Version 2.0, see from typing import Dict, Optional, Tuple, Union +from warnings import warn import matplotlib.pyplot as plt import numpy as np +import pandas as pd from matplotlib.axes import Axes from numpy import ndarray from scipy.spatial import ConvexHull @@ -12,8 +14,8 @@ from jaxley.utils.cell_utils import v_interp -def plot_morph( - xyzr, +def plot_graph( + xyzr: ndarray, dims: Tuple[int] = (0, 1), col: str = "k", ax: Optional[Axes] = None, @@ -23,22 +25,26 @@ def plot_morph( """Plot morphology. Args: + xyzr: The coordinates of the morphology. dims: Which dimensions to plot. 1=x, 2=y, 3=z coordinate. Must be a tuple of - two of them. - type: Either `line` or `scatter`. + two or three of them. col: The color for all branches. + ax: The matplotlib axis to plot on. + type: Either `line` or `scatter`. + morph_plot_kwargs: The plot kwargs for plt.plot or plt.scatter. """ if ax is None: - _, ax = plt.subplots(1, 1, figsize=(3, 3)) + fig = plt.figure(figsize=(3, 3)) + ax = fig.add_subplot(111) if len(dims) < 3 else plt.axes(projection="3d") for coords_of_branch in xyzr: - x1, x2 = coords_of_branch[:, dims].T + points = coords_of_branch[:, dims].T if "line" in type.lower(): - _ = ax.plot(x1, x2, color=col, **morph_plot_kwargs) + _ = ax.plot(*points, color=col, **morph_plot_kwargs) elif "scatter" in type.lower(): - _ = ax.scatter(x1, x2, color=col, **morph_plot_kwargs) + _ = ax.scatter(*points, color=col, **morph_plot_kwargs) else: raise NotImplementedError @@ -91,16 +97,144 @@ def compute_rotation_matrix(axis: ndarray, angle: float) -> ndarray: ) -def plot_cylinder_projection( - orientation: ndarray, +def create_cone_frustum_mesh( length: float, - radius: float, + radius_bottom: float, + radius_top: float, + bottom_dome: bool = False, + top_dome: bool = False, +) -> ndarray: + """Generates mesh points for a cone frustum, with optional domes at either end. + + This is used to render the traced morphology in 3D (and to project it to 2D) + as part of `plot_morph`. Sections between two traced coordinates with two + different radii can be represented by a cone frustum. Additionally, the ends + of the frustum can be capped with hemispheres to ensure that two neighbouring + frustums are connected smoothly (like ball joints). + + Args: + length: The length of the frustum. + radius_bottom: The radius of the bottom of the frustum. + radius_top: The radius of the top of the frustum. + bottom_dome: If True, a dome is added to the bottom of the frustum. + The dome is a hemisphere with radius `radius_bottom`. + top_dome: If True, a dome is added to the top of the frustum. + The dome is a hemisphere with radius `radius_top`. + + Returns: + An array of mesh points. + """ + + resolution = 100 + t = np.linspace(0, 2 * np.pi, resolution) + + # Determine the total height including domes + total_height = length + total_height += radius_bottom if bottom_dome else 0 + total_height += radius_top if top_dome else 0 + + z = np.linspace(0, total_height, resolution) + t_grid, z_coords = np.meshgrid(t, z) + + # Initialize arrays + x_coords = np.zeros_like(t_grid) + y_coords = np.zeros_like(t_grid) + r_coords = np.zeros_like(t_grid) + + # Bottom hemisphere + if bottom_dome: + dome_mask = z_coords < radius_bottom + arg = 1 - z_coords[dome_mask] / radius_bottom + arg[np.isclose(arg, 1, atol=1e-6, rtol=1e-6)] = 1 + arg[np.isclose(arg, -1, atol=1e-6, rtol=1e-6)] = -1 + phi = np.arccos(1 - z_coords[dome_mask] / radius_bottom) + r_coords[dome_mask] = radius_bottom * np.sin(phi) + z_coords[dome_mask] = z_coords[dome_mask] + + # Frustum + frustum_start = radius_bottom if bottom_dome else 0 + frustum_end = total_height - (radius_top if top_dome else 0) + frustum_mask = (z_coords >= frustum_start) & (z_coords <= frustum_end) + z_frustum = z_coords[frustum_mask] - frustum_start + r_coords[frustum_mask] = radius_bottom + (radius_top - radius_bottom) * ( + z_frustum / length + ) + + # Top hemisphere + if top_dome: + dome_mask = z_coords > (total_height - radius_top) + arg = (z_coords[dome_mask] - (total_height - radius_top)) / radius_top + arg[np.isclose(arg, 1, atol=1e-6, rtol=1e-6)] = 1 + arg[np.isclose(arg, -1, atol=1e-6, rtol=1e-6)] = -1 + phi = np.arccos(arg) + r_coords[dome_mask] = radius_top * np.sin(phi) + + x_coords = r_coords * np.cos(t_grid) + y_coords = r_coords * np.sin(t_grid) + + return np.stack([x_coords, y_coords, z_coords]) + + +def create_cylinder_mesh(length: float, radius: float) -> ndarray: + """Generates mesh points for a cylinder. + + This is used to render cylindrical compartments in 3D (and to project it to 2D) + as part of `plot_comps`. + + Args: + length: The length of the cylinder. + radius: The radius of the cylinder. + + Returns: + An array of mesh points. + """ + # Define cylinder + resolution = 100 + t = np.linspace(0, 2 * np.pi, resolution) + z_coords = np.linspace(-length / 2, length / 2, resolution) + t_grid, z_coords = np.meshgrid(t, z_coords) + + x_coords = radius * np.cos(t_grid) + y_coords = radius * np.sin(t_grid) + return np.stack([x_coords, y_coords, z_coords]) + + +def create_sphere_mesh(radius: float) -> np.ndarray: + """Generates mesh points for a sphere. + + This is used to render spherical compartments in 3D (and to project it to 2D) + as part of `plot_comps`. + + Args: + radius: The radius of the sphere. + + Returns: + An array of mesh points. + """ + resolution = 100 + phi = np.linspace(0, np.pi, resolution) + theta = np.linspace(0, 2 * np.pi, resolution) + + # Create a 2D meshgrid for phi and theta + phi_coords, theta_coords = np.meshgrid(phi, theta) + + # Convert spherical coordinates to Cartesian coordinates + x_coords = radius * np.sin(phi_coords) * np.cos(theta_coords) + y_coords = radius * np.sin(phi_coords) * np.sin(theta_coords) + z_coords = radius * np.cos(phi_coords) + + return np.stack([x_coords, y_coords, z_coords]) + + +def plot_mesh( + mesh_points: ndarray, + orientation: ndarray, center: ndarray, dims: Tuple[int], ax: Axes = None, **kwargs, ) -> Axes: - """Plot the 2D projection of a cylinder on a cardinal plane. + """Plot the 2D projection of a volume mesh on a cardinal plane. Project the projection of a cylinder that is oriented in 3D space. - Create cylinder mesh @@ -111,11 +245,11 @@ def plot_cylinder_projection( - fill area inside the outline Args: + mesh_points: coordinates of the xyz mesh that define the volume orientation: orientation vector. The cylinder will be oriented along this vector. - length: The length of the cylinder. - radius: The radius of the cylinder. center: The x,y,z coordinates of the center of the cylinder. - dims: The dimensions to project the cylinder onto, i.e. [0,1] xy-plane. + dims: The dimensions to plot / to project the cylinder onto, + i.e. [0,1] xy-plane or [0,1,2] for 3D. ax: The matplotlib axis to plot on. Returns: @@ -123,7 +257,7 @@ def plot_cylinder_projection( """ if ax is None: fig = plt.figure(figsize=(3, 3)) - ax = fig.add_subplot(111) + ax = fig.add_subplot(111) if len(dims) < 3 else plt.axes(projection="3d") # Normalize axis vector orientation = np.array(orientation) @@ -139,33 +273,31 @@ def plot_cylinder_projection( else: rotation_matrix = compute_rotation_matrix(rotation_axis, rotation_angle) - # Define cylinder - resolution = 100 - t = np.linspace(0, 2 * np.pi, resolution) - z = np.linspace(-length / 2, length / 2, resolution) - T, Z = np.meshgrid(t, z) - - X = radius * np.cos(T) - Y = radius * np.sin(T) - - # Rotate cylinder - points = np.dot(rotation_matrix, np.array([X.flatten(), Y.flatten(), Z.flatten()])) - X = points.reshape(3, -1) + # Rotate mesh + x_mesh, y_mesh, z_mesh = mesh_points + rotated_mesh_points = np.dot( + rotation_matrix, + np.array([x_mesh.flatten(), y_mesh.flatten(), z_mesh.flatten()]), + ) + rotated_mesh_points = rotated_mesh_points.reshape(3, -1) # project onto plane and move - X = X[dims] - X += np.array(center)[dims, np.newaxis] - - # get outline of cylinder mesh - X = extract_outline(X.T).T + rotated_mesh_points = rotated_mesh_points[dims] + rotated_mesh_points += np.array(center)[dims, np.newaxis] - ax.fill(X[0].flatten(), X[1].flatten(), **kwargs) + if len(dims) < 3: + # get outline of cylinder mesh + mesh_outline = extract_outline(rotated_mesh_points.T).T + ax.fill(*mesh_outline.reshape(mesh_outline.shape[0], -1), **kwargs) + else: + # plot 3d mesh + ax.plot_surface(*rotated_mesh_points.reshape(*mesh_points.shape), **kwargs) return ax def plot_comps( module_or_view: Union["jx.Module", "jx.View"], - view: "jx.View", + view: pd.DataFrame, dims: Tuple[int] = (0, 1), col: str = "k", ax: Optional[Axes] = None, @@ -179,7 +311,9 @@ def plot_comps( Args: module_or_view: The module or view to plot. view: The view of the module. - dims: The dimensions to project the cylinder onto, i.e. [0,1] xy-plane. + dims: The dimensions to plot / to project the cylinder onto, + i.e. [0,1] xy-plane or [0,1,2] for 3D. + col: The color for all compartments ax: The matplotlib axis to plot on. comp_plot_kwargs: The plot kwargs for plt.fill. true_comp_length: If True, the length of the compartment is used, i.e. the @@ -194,7 +328,7 @@ def plot_comps( """ if ax is None: fig = plt.figure(figsize=(3, 3)) - ax = fig.add_subplot(111) + ax = fig.add_subplot(111) if len(dims) < 3 else plt.axes(projection="3d") module = ( module_or_view.pointer @@ -211,7 +345,20 @@ def plot_comps( locs = module.xyzr[idx][:, :3] if locs.shape[0] == 1: # assume spherical comp radius = module.xyzr[idx][:, -1] - ax.add_artist(plt.Circle(locs[0, dims], radius, color=col)) + center = module.xyzr[idx][0, :3] + if len(dims) == 3: + xyz = create_sphere_mesh(radius) + ax = plot_mesh( + xyz, + np.array([0, 0, 1]), + center, + np.array(dims), + ax, + color=col, + **comp_plot_kwargs, + ) + else: + ax.add_artist(plt.Circle(locs[0, dims], radius, color=col)) else: lens = np.sqrt(np.nansum(np.diff(locs, axis=0) ** 2, axis=1)) lens = np.cumsum([0] + lens.tolist()) @@ -226,10 +373,10 @@ def plot_comps( center = comp[["x", "y", "z"]] radius = comp["radius"] length = comp["length"] if true_comp_length else l - ax = plot_cylinder_projection( + xyz = create_cylinder_mesh(length, radius) + ax = plot_mesh( + xyz, axis, - length, - radius, center, np.array(dims), ax, @@ -237,3 +384,82 @@ def plot_comps( **comp_plot_kwargs, ) return ax + + +def plot_morph( + module_or_view: Union["jx.Module", "jx.View"], + view: pd.DataFrame, + dims: Tuple[int] = (0, 1), + col: str = "k", + ax: Optional[Axes] = None, + morph_plot_kwargs: Dict = {}, +) -> Axes: + """Plot the detailed morphology. + + Plots the traced morphology it was traced. That means at every point that was + traced a disc of radius `r` is plotted. The outline of the discs are then + connected to form the morphology. This means every trace segement can be + represented by a cone frustum. To prevent breaks in the morphology, each + segement is connected with a ball joint. + + Args: + module_or_view: The module or view to plot. + view: The view dataframe of the module. + dims: The dimensions to plot / to project the cylinder onto, + i.e. [0,1] xy-plane or [0,1,2] for 3D. + col: The color for all branches + ax: The matplotlib axis to plot on. + morph_plot_kwargs: The plot kwargs for plt.fill. + + Returns: + Plot of the detailed morphology.""" + if ax is None: + fig = plt.figure(figsize=(3, 3)) + ax = fig.add_subplot(111) if len(dims) < 3 else plt.axes(projection="3d") + if len(dims) == 3: + warn( + "rendering large morphologies in 3D can take a while. Consider projecting to 2D instead." + ) + + module = ( + module_or_view.pointer + if "pointer" in module_or_view.__dict__ + else module_or_view + ) + assert not np.any(np.isnan(module.xyzr[0][:, :3])), "missing xyz coordinates." + + branches_inds = np.unique(view["branch_index"].to_numpy()) + + for idx in branches_inds: + xyzrs = module.xyzr[idx] + if len(xyzrs) > 1: + for xyzr1, xyzr2 in zip(xyzrs[1:, :], xyzrs[:-1, :]): + dxyz = xyzr2[:3] - xyzr1[:3] + length = np.sqrt(np.sum(dxyz**2)) + points = create_cone_frustum_mesh( + length, xyzr1[-1], xyzr2[-1], bottom_dome=True, top_dome=True + ) + plot_mesh( + points, + dxyz, + xyzr1[:3], + np.array(dims), + color=col, + ax=ax, + **morph_plot_kwargs, + ) + else: + points = create_cone_frustum_mesh( + 0, xyzrs[:, -1], xyzrs[:, -1], bottom_dome=True, top_dome=True + ) + plot_mesh( + points, + np.ones(3), + xyzrs[0, :3], + dims=np.array(dims), + color=col, + ax=ax, + **morph_plot_kwargs, + ) + + return ax diff --git a/tests/test_plotting_api.py b/tests/test_plotting_api.py index e0046c31..21d151fb 100644 --- a/tests/test_plotting_api.py +++ b/tests/test_plotting_api.py @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt import numpy as np +import pytest import jaxley as jx from jaxley.synapses import IonotropicSynapse @@ -165,9 +166,28 @@ def test_volume_plotting(): net = jx.Network([cell] * 4) net.compute_xyz() + morph_cell = jx.read_swc( + os.path.join(os.path.dirname(__file__), "swc_files", "morph.swc"), + nseg=1, + ) + fig, ax = plt.subplots() - for module in [comp, branch, cell, net]: - module.vis(type="volume", ax=ax) + for module in [comp, branch, cell, net, morph_cell]: + module.vis(type="comp", ax=ax) if not isinstance(module, jx.Compartment): - module[0].vis(type="volume", ax=ax) + module[0].vis(type="comp", ax=ax) plt.close(fig) + + # test 3D plotting + for module in [comp, branch, cell, net, morph_cell]: + module.vis(type="comp", dims=[0, 1, 2]) + if not isinstance(module, jx.Compartment): + module[0].vis(type="comp") + plt.close(fig) + + # test morph plotting (does not work if no radii in xyzr) + morph_cell.vis(type="morph") + morph_cell.branch(1).vis( + type="morph", dims=[0, 1, 2] + ) # plotting whole thing takes too long + plt.close()