diff --git a/doc/examples/00_pedon_structure.ipynb b/doc/examples/00_pedon_structure.ipynb new file mode 100644 index 0000000..4584649 --- /dev/null +++ b/doc/examples/00_pedon_structure.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Package Structure**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import inspect\n", + "import pedon as pe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pedon Structure\n", + "\n", + "Pedon knows three classes;\n", + "- Soil\n", + "- SoilModel\n", + "- SoilSample\n", + "\n", + "The Soil class encapsulates the SoilModel and SoilSample class. The soil class has the following attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': str,\n", + " 'model': pedon.soilmodel.SoilModel | None,\n", + " 'sample': pedon.soil.SoilSample | None,\n", + " 'source': str | None,\n", + " 'description': str | None}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# main class\n", + "# containing both SoilModel and SoilSample\n", + "pe.Soil.__annotations__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The name of the soil, the soil type (e.g. sand or clay) are strings. The source is the origin of the soil, and description contains extra information. The SoilModel is the relation between the pressure head, hydraulic conducitivity and water content. Possible SoilModels are:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Brooks',\n", + " 'Fredlund',\n", + " 'Gardner',\n", + " 'Genuchten',\n", + " 'Panday',\n", + " 'Protocol',\n", + " 'SoilModel']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# classes for soil models\n", + "soilmodels = [\n", + " cls_name\n", + " for cls_name, cls_obj in inspect.getmembers(sys.modules[\"pedon.soilmodel\"])\n", + " if inspect.isclass(cls_obj)\n", + "]\n", + "soilmodels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SoilSample atttribute can contain measurements of the sand percentage `sand_p`, silt percentage `silt_p`, bulkd density `rho` or measurements of the pressure head `h` and the resulting hydraulic conductivity `k` or water content `theta`. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sand_p': float | None,\n", + " 'silt_p': float | None,\n", + " 'clay_p': float | None,\n", + " 'rho': float | None,\n", + " 'th33': float | None,\n", + " 'th1500': float | None,\n", + " 'om_p': float | None,\n", + " 'm50': float | None,\n", + " 'h': float | numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] | None,\n", + " 'k': float | numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] | None,\n", + " 'theta': float | numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] | None}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# class for sample measurements\n", + "pe.SoilSample.__annotations__" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pydon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/01_soil_models.ipynb b/doc/examples/01_soil_models.ipynb new file mode 100644 index 0000000..85f7990 --- /dev/null +++ b/doc/examples/01_soil_models.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Soil Models**\n", + "\n", + "Soil Models are method to describe the relation between the pressure head, water content and the hydraulic conductivity. This notebooks shows two soil models present in the pedon library." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pedon as pe\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pedon does not assume units for most soil types but it is good convention to use cm as the length unit. Let's create two soil models, one using the Mualem-van Genuchten equation, and one using the Brooks-Corey Equation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# shared properties\n", + "k_s = 100 # saturated conductivity [cm/d]\n", + "theta_r = 0.03 # residual water content [-]\n", + "theta_s = 0.42 # saturated water content [-]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Mualem-van Genuchten\n", + "alpha = 0.04 # shape parameter [1/cm]\n", + "n = 1.4 # shape parameter [-]\n", + "\n", + "gen = pe.Genuchten(k_s=k_s, theta_r=theta_r, theta_s=theta_s, alpha=alpha, n=n)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Brooks-Corey\n", + "h_b = 10 # bubbling pressure [cm]\n", + "l = 1.1 # connectivity parameter [-]\n", + "\n", + "bro = pe.Brooks(k_s=k_s, theta_r=theta_r, theta_s=theta_s, h_b=h_b, l=l)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Quick plot method\n", + "ax = gen.plot()\n", + "bro.plot(ax=ax)\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# More extensive plot method\n", + "f, axs = plt.subplots(1, 2, figsize=(8, 6), sharey=True)\n", + "\n", + "pe.plot_swrc(gen, ax=axs[0])\n", + "pe.plot_swrc(bro, ax=axs[0])\n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_ylabel(\"\\N{GREEK SMALL LETTER PSI} [cm]\")\n", + "axs[0].set_title(\"Soil Water Retention Curve\")\n", + "axs[0].set_xlabel(\"\\N{GREEK SMALL LETTER THETA} [-]\")\n", + "\n", + "pe.plot_hcf(gen, ax=axs[1])\n", + "pe.plot_hcf(bro, ax=axs[1])\n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_ylabel(\"\\N{GREEK CAPITAL LETTER PSI} [cm]\")\n", + "axs[1].set_title(\"Hydraulic Conductivity Function\")\n", + "axs[1].set_xlabel(\"Ks [cm/d]\")\n", + "axs[1].set_xscale(\"log\")\n", + "axs[1].set_xlim(1e-10, 1e3)\n", + "\n", + "f.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other soil models that are available are Panday (combination of Genuchten and Brooks-Corey), Fredlund and Gardner. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pydon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/02_datasets.ipynb b/doc/examples/02_datasets.ipynb new file mode 100644 index 0000000..266171f --- /dev/null +++ b/doc/examples/02_datasets.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Soil Parameter Datasets**\n", + "\n", + "Generally either the Brooks-Corey or Mualem-van Genuchten soil models are used. Pedon has some built-in datasets with parameter sets that can be used for both soil models." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pedon as pe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Sand',\n", + " 'Loamy Sand',\n", + " 'Sandy Loam',\n", + " 'Loam',\n", + " 'Silt',\n", + " 'Silt Loam',\n", + " 'Sandy Clay Loam',\n", + " 'Clay Loam',\n", + " 'Silty Clay Loam',\n", + " 'Sandy Clay',\n", + " 'Silty Clay',\n", + " 'Clay',\n", + " 'B01',\n", + " 'B02',\n", + " 'B03',\n", + " 'B04',\n", + " 'B05',\n", + " 'B06',\n", + " 'B07',\n", + " 'B08',\n", + " 'B09',\n", + " 'B10',\n", + " 'B11',\n", + " 'B12',\n", + " 'B13',\n", + " 'B14',\n", + " 'B15',\n", + " 'B16',\n", + " 'B17',\n", + " 'B18',\n", + " 'O01',\n", + " 'O02',\n", + " 'O03',\n", + " 'O04',\n", + " 'O05',\n", + " 'O06',\n", + " 'O07',\n", + " 'O08',\n", + " 'O09',\n", + " 'O10',\n", + " 'O11',\n", + " 'O12',\n", + " 'O13',\n", + " 'O14',\n", + " 'O15',\n", + " 'O16',\n", + " 'O17',\n", + " 'O18',\n", + " 'Medium Sand',\n", + " 'Del Monte Sand',\n", + " 'Fresno Medium Sand',\n", + " 'Unconsolidated Sand',\n", + " 'Fine Sand',\n", + " 'Columbia Sandy Loam',\n", + " 'Touchet Silt Loam',\n", + " 'Hygiene Sandstone',\n", + " 'Adelanto Loam',\n", + " 'Limon Silt',\n", + " 'Yolo Light Clay']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# list all soil types for van genuchten\n", + "pe.Soil.list_names(pe.Genuchten)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Soil(name='Sand', model=Genuchten(k_s=712.8, theta_r=0.045, theta_s=0.43, alpha=0.145, n=2.68, l=0.5), sample=None, source='HYDRUS', description=nan)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the HYDRUS sand\n", + "soil = pe.Soil(\n", + " name=\"Sand\",\n", + ").from_name(sm=pe.Genuchten, source=\"HYDRUS\")\n", + "soil" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we now have a different class; the soil class. This class has some other attributes such as the name. If the name is in the dataset (`pe.Soil.list_names(pe.Genuchten)`), the `from_name()` can retrieve the soil model. Note that we have to parse the soil model `sm` as an attribute since some soil models are available both as a Genuchten and Brooks dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Soil(name='O18', model=Genuchten(k_s=35.95, theta_r=0.01, theta_s=0.58, alpha=0.0127, n=1.32, l=-0.786), sample=SoilSample(sand_p=None, silt_p=0.0, clay_p=0.0, rho=1.1, th33=None, th1500=None, om_p=22.5, m50=nan), source='Staring_2001', description='moerige tussenlaag')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get from the Staring series\n", + "pe.Soil(\"O18\").from_staring(year=\"2001\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Limon Silt')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get for both genuchten and brooks\n", + "ls_gen = pe.Soil(\"Limon Silt\").from_name(sm=pe.Genuchten)\n", + "ls_bro = pe.Soil(\"Limon Silt\").from_name(sm=pe.Brooks)\n", + "\n", + "ax = ls_gen.model.plot()\n", + "ls_bro.model.plot(ax=ax)\n", + "ax.legend()\n", + "ax.set_title(ls_gen.name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pydon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/03_pedotransfer_functions.ipynb b/doc/examples/03_pedotransfer_functions.ipynb new file mode 100644 index 0000000..664209e --- /dev/null +++ b/doc/examples/03_pedotransfer_functions.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Pedotransfer Function**\n", + "\n", + "Pedotransfer functions crate a relation for the soil model based on some measurements of a soil sample. There are different pedotransfers functions available that give a relation for different soil models, e.g. Genuchten or Brooks(-Corey). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pedon as pe\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create soil sample\n", + "sand_p = 50 # sand [%]\n", + "silt_p = 10 # silt [%]\n", + "clay_p = 30 # clay [%]\n", + "rho = 1.5 # bulk density [g/cm3]\n", + "om_p = 10 # organic matter [%]\n", + "m50 = 150 # median sand fraction [um]\n", + "ts = False # topsoil boolean\n", + "\n", + "ss = pe.SoilSample(\n", + " sand_p=sand_p, silt_p=silt_p, clay_p=clay_p, rho=rho, om_p=om_p, m50=m50\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# wosten pedotransfer function (van Genuchten)\n", + "wos = ss.wosten(ts=ts)\n", + "\n", + "# wosten pedotransfer function for sand (van Genuchten)\n", + "woss = ss.wosten_sand(ts=ts)\n", + "\n", + "# wosten pedotransfer function for clay (van Genuchten)\n", + "wosc = ss.wosten_clay()\n", + "\n", + "# cosby pedotransfer function (Brook-Corey)\n", + "cosb = ss.cosby()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aacZF7Nq1CwMDAwYOHKi5r1KpfOMEHS/vm+7lejY2Npbw8HDq16+PLMucP38+T+eclf379xMdHc0333yTqY//67rDpMtpPZHT97mKFStq6itIa8UtX758hrpr165d1KtXL0MrpYODg6ZrWW5s3bqVatWqZWr9hTdfj6w0bdoUe3t7Nm3apNn24sUL9u/fT6dOnXIVY7ly5ahbty7r1q3TbHv+/Dm7d++ma9eu2Yqzbt26mZ7/3bt3z1U82XmstH1dd+3ahVKpZNiwYRm2f/XVV8iyzO7duzNs1/Z7sT6JrlBFXO3atdm2bRtJSUlcuHCBP/74gzlz5vDpp58SGBhIxYoVCQoKQqFQaLoopXNycsLa2vq1/c5zo2HDhixYsIDw8HCOHz+OUqmkXr16QFpz48KFC0lMTMw0vgLS+rF+9913HDp0SPOGki4yMvKtZd+6dYtr165l2cXn1XEQJUuWzNG5eXh4aGaNuHPnDlOnTiUsLCzDG+WtW7eQZZmyZcu+9hjZGRyW0/Nwc3PLtI+NjU2G8QI5ERQURN26dTNtr1Chgub2l7tsvFp+eneiN5VvaWkJpCWQuvK6x/ezzz5j5MiRbNq0iW+//RZZltmyZQstW7bUxHTr1i2ALLuYQNrzMf08BeF16tSpQ61atTJtt7GxydQ9pHv37mzatIkjR47QuHFjDhw4QEhICN26ddPsExQUhLOzs6bLZLry5cu/tnwDAwNKlCiRaXtwcDATJkzgzz//zPQazU49mxvpiXxOp9nNaT2R0/e57NSdWdWHWV337Lhz5w4dOnTI9f1fZWBgQIcOHVi/fj2JiYkYGRmxbds2kpOTc51YQNrzcsiQIQQFBeHu7s6WLVtITk7O8Lx8E3t7e5o1a5br8l+WncdK29c1KCgIFxeXTInty++FOY2xsBCJhQCkzeZUu3ZtateuTbly5ejVqxdbtmxh4sSJmn1yk7XnRHpicezYMY4fP06VKlU0b4T169cnMTGR06dPc/ToUQwMDDRJR0REBN7e3lhaWvL9999TunRpjI2NOXfuHF9//XW2viFWq9VUqVKF2bNnv/Z2V1fXDP/ntI+smZlZhkqyQYMG1KxZk2+//Zb58+drYpAkid27d792VpRXPxS8Tk7PI6vZV+RXBpfpSm7Kt7S0xMXFhcuXL2erjKyety8P7H/V6x5fFxcXGjVqxObNm/n222/5999/CQ4O5scff9Tsk/5cmzlzZqbpCNNl53EUhOxq0aIFxYoVY+3atTRu3Ji1a9fi5OSUpw9lRkZGmaaRTk1NpXnz5jx//pyvv/4aT09PzMzMePToET179sxQz6YP4n3Vm15z2pbTeiJddt/n8qvuzI9r9vnnn7N48WJ2795Nu3bt2Lx5M56enllODJDdY44YMYJ169bx7bffsnbtWmrVqpWnpCpdTut0fb/PZUdhiDG7RGIhZJL+TdmTJ0+AtEHearWaW7duabJtSBuoGxERgbu7u1bKfXkA94kTJ2jQoIHmNhcXF9zd3Tl27BjHjh2jRo0amkFcAQEBPHv2jG3bttG4cWPNfe7du5epjKwqpNKlS3PhwgXef/99nSdQkNbM+cUXX7B48WJGjRqFm5sbpUuXRpZlSpYsSbly5d54//w8j5wcx93dnRs3bmTant4dTVvPlY8++oglS5Zw4sQJ3nvvvTfum9468OoMOLlpaevUqRODBg3ixo0bbNq0CVNT0wxrqaQ3ZVtaWmrt2zZBeBOlUkmXLl1YuXIlP/74I9u3b6dv374ZPqi4u7tz8OBBYmJiMiS2r3utZuXSpUvcvHmTVatWZeiSsn///kz72tjYvLYLR25ec+mvqcuXL2dqTXibnNQTunifc3d317Rivux1193GxiZTHZWUlKR5H05XunTptyZLOa37GzdujLOzM5s2baJhw4YcOnSIcePGvfV+byrH1taW1q1bs27dOrp27cqxY8e0tqjry3X6y5Of5KX3hLavq7u7OwcOHCA6OjpDq4W23wsLIjHGogjz9/d/bTac3tc0/ZuFVq1aAWSqFNK/FW/durVW4nFxcaFkyZIcPHiQM2fOZJptoX79+mzfvp0bN25k6AaV/gb68rkkJSWxcOHCTGWYmZm9tsm+Y8eOPHr0iKVLl2a6LT4+ntjY2FyfV1bGjBlDcnKy5jq2b98epVLJ5MmTMz0usizz7Nkzzf/5eR7p/bSzs/J2q1atOHXqFCdOnNBsi42NZcmSJXh4eORoXMqbjBkzBjMzM3x9fQkJCcl0+507d5g3bx6Q9iHf3t6ew4cPZ9jndc+Pt+nQoQNKpZINGzawZcsWPvroowzz+3t5eVG6dGlmzZpFTExMpvuHhYXluExBeJtu3brx4sUL+vfvT0xMTKZZd1q1akVKSgq//vqrZltqaioLFizIdhmvq2dlWda8zl5WunRprl+/nuH5fuHChVxNufzBBx9gYWHB9OnTM626/LZvc3NST+jifa5Vq1b8+++/nDp1SrMtLCwsw9iDdKVLl85URy1ZsiTTt/AdOnTQdFt+Vfr1yEmdDaBQKPj000/566+/WLNmDSkpKdnqBvW2crp168bVq1cZPXo0SqUyyxmmcio92Xz5esXGxrJq1apcH1Pb17VVq1akpqbyyy+/ZNg+Z84cJEmiZcuWuY61oBMtFkXY0KFDiYuL45NPPsHT05OkpCSOHz/Opk2b8PDw0MxtXa1aNXr06MGSJUs03Y5OnTrFqlWraNeuHT4+PlqLqWHDhpqp615usYC0xGLDhg2a/V7ebmNjQ48ePRg2bBiSJLFmzZrXvul4eXmxadMmRo4cSe3atTE3N6dNmzZ069aNzZs3M2DAAPz9/WnQoAGpqalcv36dzZs3s3fv3tf2ec6LihUr0qpVK5YtW8b48eMpXbo0U6ZMYezYsdy/f5927dphYWHBvXv3+OOPP+jXr59mTvH8PA8vLy8gbbrezz//HJVKRZs2bV67YNY333zDhg0baNmyJcOGDcPW1pZVq1Zx7949tm7dqrVVukuXLs369evp1KkTFSpUyLCi7vHjx9myZUuGud99fX2ZMWMGvr6+1KpVi8OHD3Pz5s0cl+vo6IiPjw+zZ88mOjo605uvQqFg2bJltGzZkkqVKtGrVy+KFy/Oo0eP8Pf3x9LSkr/++iuvpy8IGdSoUYPKlStrJm+oWbNmhtvbtGlDgwYN+Oabb7h//z4VK1Zk27ZtORoX4enpSenSpRk1ahSPHj3C0tKSrVu3vrYPeO/evZk9ezYtWrSgT58+hIaGsmjRIipVqpRpDNzbWFpaMmfOHHx9faldu7ZmfZkLFy4QFxf3xg+TOakndPE+N2bMGNasWcOHH37Il19+qZlu1t3dnYsXL2bY19fXlwEDBtChQweaN2/OhQsX2Lt3r2aK73SjR4/m999/57PPPqN37954eXnx/Plz/vzzTxYtWkS1atUoXbo01tbWLFq0CAsLC8zMzKhbt+4bxwZ26tSJBQsWMHHiRKpUqZKh1SYr6e8Nw4YNo0WLFpmSh9atW2NnZ6cZi/bq+li59cEHH+Dm5kafPn00Sctvv/2Gg4MDwcHBuTqmtq9rmzZt8PHxYdy4cdy/f59q1aqxb98+duzYwfDhwzMM1H7n5OcUVELBsnv3brl3796yp6enbG5uLhsaGsplypSRhw4dKoeEhGTYNzk5WZ48ebJcsmRJWaVSya6urvLYsWMzTOUqy7mfbjbd4sWLZUAuXrx4ptvOnTunmXbu1fiOHTsm16tXTzYxMZFdXFw0U+cCsr+/v2a/mJgYuUuXLrK1tbUMZJgeLikpSf7xxx/lSpUqyUZGRrKNjY3s5eUlT548WY6MjNTsB8iDBw/O1vnIcto1eXmKyJcFBARkmmZw69atcsOGDWUzMzPZzMxM9vT0lAcPHizfuHEjX87j1ekNZVmWf/jhB7l48eKyQqHIMJ3f6/a9c+eO/Omnn8rW1taysbGxXKdOHfnvv//OsE/6dLNbtmzJsD2nz5ebN2/Kffv2lT08PGRDQ0PZwsJCbtCggbxgwYIMz824uDi5T58+spWVlWxhYSF37NhRDg0NzXK62bCwsCzLXLp0qQzIFhYWmaa/THf+/Hm5ffv2sp2dnWxkZCS7u7vLHTt2lA8ePJit8xKKpvTpMk+fPv3a299Ul/z0008yIE+bNu21tz979kzu1q2bbGlpKVtZWcndunWTz58//9rpZs3MzF57jKtXr8rNmjWTzc3NZXt7e7lv376aaTFffc2uXbtWLlWqlGxoaChXr15d3rt3b66mm033559/yvXr15dNTExkS0tLuU6dOvKGDRteG+ersltPZPd9zt3dPcNU4eleN83uxYsXZW9vb9nY2FguXry4/MMPP8jLly/PdI6pqany119/Ldvb28umpqZyixYt5Nu3b7+2jn327Jk8ZMgQuXjx4rKhoaFcokQJuUePHnJ4eLhmnx07dsgVK1aUDQwMMjw+r3sMZDltSlVXV9fXTpEqy6+vm1NSUuShQ4fKDg4OsiRJr50CdtCgQTIgr1+/PtNtWcnq+r7s7Nmzct26dWVDQ0PZzc1Nnj17dpbTzWb3sdL2dY2OjpZHjBghu7i4yCqVSi5btqw8c+bMTNPt5uS9uDCQZLkQjgwRBEEQBEFj3rx5jBgxgvv37792hhlB0IcRI0awfPlynj59mqvF7YTCRyQWgiAIglCIybJMtWrVsLOzy9Z6N4KQHxISEnB1deWjjz5ixYoV+g5HyCdFYozFvXv36N27NyEhISiVSv7999/X9g8XBEEQhMIiNjaWP//8E39/fy5dusSOHTv0HZIgEBoayoEDB/j999959uwZX375pb5DEvJRkUgsevbsyZQpU2jUqBHPnz/HyMhI3yEJgiAIQp6EhYXRpUsXrK2t+fbbb/n444/1HZIgcPXqVbp27YqjoyPz58/Pck0f4d30zneFunLlCl9++SUHDhzQdyiCIAiCIAiC8M4q8OtYHD58mDZt2uDi4oIkSWzfvj3TPn5+fnh4eGBsbEzdunUzzBl969YtzVScNWvWZNq0afkYvSAIgiAIgiAUDQU+sYiNjaVatWr4+fm99vb0ufwnTpzIuXPnqFatGi1atCA0NBSAlJQUjhw5wsKFCzlx4gT79+9/7UqhgiAIgiAIgiDkXqHqCiVJEn/88Qft2rXTbKtbty61a9fWrG6oVqtxdXVl6NChfPPNN5w4cYJJkyaxd+9eAGbOnAmkLYbyOomJiSQmJmr+V6vVPH/+HDs7uxwt5y4IglBUyLJMdHQ0Li4uWlsEURdE/S4IgpBzOanjC/Xg7aSkJM6ePcvYsWM12xQKBc2aNePEiRMA1K5dm9DQUF68eIGVlRWHDx+mf//+WR5z+vTpTJ48WeexC4IgvGsePHhAiRIl9B1GlkT9LgiCkHvZqeMLdWIRHh5OamoqxYoVy7C9WLFiXL9+HQADAwOmTZtG48aNkWWZDz74gI8++ijLY44dO5aRI0dq/o+MjMTNzY2bN29ia2ur2S5d2oLB3jGoSzYhtcO7PT9z0v0gHn7+ObJCQfEd2zF2cNB5mcmpalrMO8qLuBQWfF6VhmXsdV5mdiQnJ+Pv74+Pjw8qlUqzfcONDSy8uBADhQHzvedTxb6KHqPUvayuQ1EjrkOa58+fU65cOSwsLPQdyhu9qX43Cwvj6YiRpD5/jkHx4jjPm4uqRAlSUtUM3XiRf+89x9nKiLW9a2NrZqjHs3i9d+m5KM6lYBLnUjDlx7lER0dTsmTJbNXxhTqxyK6WLVvSsmXLbO1rZGT02ulobW1tsbOz+/8GayswksBECS9vfxfZ2RFftSqJV65g+O9J7Hr2yJdiP6lbjpXH73PoXhxt6xaMa5ycnIypqSl2dnYZXsCD3xvMvaR77Avaxw8XfmBTm03YmxSMZEgXsroORY24DhkV9O5Eb6zfy5bFfstmgvv4kvzgAVGDh+C2dAnGFSqw1Lcxbf2Ocv9ZHON232dtn7oYGhSsLl/v0nNRnEvBJM6lYMqPc0k/bnbq+IJVM+aQvb09SqWSkJCQDNtDQkJwcnLK07H9/PyoWLEitWvXfv0OCmXab3VqnsopLCzapLXyRP/9d76V2ba6CwD7roQQm5iSb+XmhiRJfN/ge0pZlSI0PpQv/b8kMTXx7XcUBCHfZVW/G7q54bF+HUaenqSGhxPUrTuxJ09hZapiWY9amBsZcOrecyb9dUVPkQuCIBRshTqxMDQ0xMvLi4MHD2q2qdVqDh48yHvvvZenYw8ePJirV69y+vTp1+8g/XfpZHWeyikszD/8EFmhIPHyZRLv3suXMqu7WlPS3oz45FR2XXqSL2XmhZnKjPlN52NhaMHFsIt8f+J7CtHcCIJQZLypfjdwcMB9zWpMa9VCHRPDg759idq/nzKOFszvXB1JgvUng1nzb5AeIhcEQSjYCnxiERMTQ2BgIIGBgQDcu3ePwMBAgoODARg5ciRLly5l1apVXLt2jYEDBxIbG0uvXr10G1gRa7EwsLMjtlxZACL/3JEvZUqSRIeaxQH4/ezDfCkzr9wt3ZnlPQulpOTPO3+y+upqfYckCEIOKS0scF2+DPNm7yMnJfHoy+G82LKFpp7FGNPCE4DJf17hxJ1neo5UEAShYCnwYyzOnDmDj4+P5v/0gXc9evRg5cqVdOrUibCwMCZMmMDTp0+pXr06e/bsyTSgO6f8/Pzw8/MjNTWLxEH6L7GQi0ZiARBdoybm128Q9edfOAwbhpQP00q2r1mCn/ff5OS95wQ/i8PNzlTnZeZVfZf6jK49mhmnZjD77GxKWZWiUYlG+g5LEIT/vLV+BxRGRpSYO5enkycTseV3no6fQOqzZ/Tv14/rT6PYEfiYQevO8ueQhrjaFvx6SSi6UlNTSU5OzvNxkpOTMTAwICEh4Y2vncJAnEtGKpUKpVKplXgKfGLRpEmTt3YnGTJkCEOGDNFquYMHD2bw4MFERUVhZWWVeYci1mIBEFOpIpKZGcmPHxN/7hymtWrpvEwXaxMalrHnyK1wfj/3kJHNy+m8TG3o4tmFWy9usfXWVsYcHsO61usoZVVK32EJgkA26vf/SAYGOH3/PUo7O54tWkzY3HmkPHvOjFGjuRcey8WHkfRdfYatA+tjZlTg306FIkaWZZ4+fUpERITWjufk5MSDBw8K/EQNbyPOJTNra2ucnJzyfD1ETZhbRbDFQlapMG/enOjt24nYvj1fEguAT71KcORWOFvPPmT4+2VRKAp+JSBJEuPqjuNe5D3OhZ5j2KFhrGu1DiujrD/ECIJQ8EiShOPw4RjY2hEybRov1qwh9flzFo2dQNvFp7j+NJqRmwP5tatXoaibhKIjPalwdHTE1NQ0zx8Y1Wo1MTExmJubF+iFMLNDnMv/ybJMXFwcoaGhADg7O+cpHpFYZOGtTeVFsMUCwLLtx0Rv3070rt2ox45FYWam8zJbVHLCwsiARxHx/Hv3GfULyJoWb6NSqpjdZDadd3YmKCqI0f+MZmGztLUuBEHQn+x0hXqVbfduKG1seDx2LFE7d2IWEcHiMZP5fPVF9l4JYe7BW4WmRVV496WmpmqSCjstTYmvVqtJSkrC2Nj4nfgwLs7l/0xMTAAIDQ3F0dExT92iCvfV1CExK9TrGXt5oXJ3Qx0XR9TefflTpkrJR9XSpp4tLIO409mZ2LGg6QJMDEw48eQEs87M0ndIglDkvbV+z4JVm49w/XUhkokJsceOYTN+JD82dwNg/sFbhWL2OqFoSB9TYWoqxv8I2ZP+XMnreByRWORWEW2xkCQJ6/YdAIjYtjXfyv2sVtoS8rsuPyE6Ie+D0PJTedvyTGs4DYB119ax/tp6PUckCEJumTdqhPvKFSitrEi4eJGqP45mSCVzAL7afIErjyP1HKEg/F9hHz8g5B9tPVdEYpFb6WMs1AV74TZdsGrXFhQK4s+cJfFe/qxpUcPVmlIOZiQkqwvlt4LN3JvxZc0vAfjx9I/88+AfPUckCEJumVSrhvv6dRg4O5N07x5tl03kE5tE4pNT6bf6LOExYnFMQRCKJpFYZOGtK28r/1s2vQgmFqpixTBr1BCAyG1/5EuZkiTxmZcrUPi6Q6XrU7kPHcp2QC2rGX14NFefXdV3SIJQJL21fs8Go9Kl8Vi/DsPSpUkJCaH/1h9pmhrCo4h4Bq09R1JK0eomKwiCACKxyNJb++Aqim5iAWDdIa07VOT27cgp+XMNPqlRHIUEp++/4F54bL6UqU2SJDGu3jjec36P+JR4hhwcwtPYp/oOSxCKnNyOsXiVytkZ97VrMKlWDTkqitH75tPo+U1O3X/OxD+vvHWqdEEQMlq0aBEWFhakvPS5IiYmBpVKRZMmTTLsGxAQgCRJ3LlzJ9fl3b9/H0mSNIswC3knEovcSm+xSC1c/f21xaJJE5S2tqSEhRFz9Gi+lOlkZUyjsg4AbC2krRYqhYqfm/xMGesyhMWHMejgIGKSYvQdliAIuWRgY4Pbit8wa9wIEhMZe2w5TR+cZcOpYNb+G6Tv8AShUPHx8SEmJoYzZ85oth05cgQnJydOnjxJQkKCZru/vz9ubm6ULl1aH6EKWRCJRW5pEosk/cahJ5KhIVYffwxA5Nb8H8T9+9mHpKQWzq4GFoYW+L3vh72JPbde3GLU4VGkFNGWL0F4FyhMTXH188Py4zZIqamMPruBdrcPM/mvqxy/E67v8ASh0ChfvjzOzs4EBARotgUEBNC2bVtKlizJv//+m2G7j48PiYmJDBs2DEdHR4yNjWnYsGGG1sgXL17QtWtXHBwcMDExoWzZsqxYsQJAk5TUqFEDSZIytIosW7aMChUqYGxsjKenJwsXLtTclt7SsW3bNnx8fDA1NaVatWqcOHFCR1em8BCJRRbePsbCMO13Ef5AaNX+EwCi/QNIefYsX8psVqEYNqYqnkYl8M/NsHwpUxdczF34pekvmBiYcOzRMaadnCa6TQhCPtHGGItXSSoVLjNmYNujBwD9L/9Jt0t/M3jtWR48j9NaOYKQW7IsE5eUkqef+KTUXN0vJ+9vPj4++Pv7a/739/enSZMmeHt7a7bHx8dz8uRJfHx8GDNmDFu3bmXVqlWcO3eOMmXK0KJFC54/fw7A+PHjuXr1Krt37+batWv8+uuv2NunrYeVnqgcOHCAJ0+esG3bNgDWrVvHhAkTmDp1KteuXWPatGmMHz+eVatWZYh13LhxjBo1isDAQMqVK0fnzp0zdOMqisRKXVkYPHgwgwcPJioqCiur16yWnL7IWRFtsQAwLlcO46pVSbh4kcgdf2LXu5fuy1QpaV+zBMuP3mPDqQe8X6GYzsvUlUr2lZjRaAbD/Yez5eYWSliUoHfl3voOSxDeeW+t33NJUihw/OZrlPZ2hP08m463/LFKjKWfuSFbhjTC3Ei85Qr6E5+cSsUJe/VS9tXvW2BqmL3nv4+PD8OHDyclJYX4+HjOnz+Pt7c3ycnJLFq0CIATJ06QmJhIkyZN6Nu3LytXrqRly5YALF26lP3797N8+XJGjx5NcHAwNWrUoFatWgB4eHigVquJiorCwSGte7WdnR1OTk6aGCZOnMjPP/9M+/btAShZsiRXr15l8eLF9PjvywOAUaNG0bp1awAmT55MpUqVuH37Np6ennm8YoWXaLHIrfQWiyI6xiJd+iDuiC1b8u0b98510maH8r8RytPIhLfsXbA1dWvK6NqjAZhzdg5/3flLzxEJgpAXkiRh37cvzlOngEJBi+BTfPaXH6PXnkKtFq2SgvA2TZo0ITY2ltOnT3PkyBHKlSuHg4MD3t7emnEWAQEBlCpVisjISJKTk2nQoIHm/iqVijp16nDt2jUABg4cyMaNG6levTpjxozh+PHjbyw/NjaWO3fu0KdPH8zNzTU/U6ZMyTRQvGrVqpq/nZ2dgbTVq4sy8fVJbqWPsZBTQa2GQr4kfG5Ztm5N6I8/knTvHnGnTmNWt47OyyzjaEFtDxtO33/BljMPGPp+WZ2XqUvdKnbjaexTVl9dzYRjE7A1tqVB8QZvv6MgCAWWdYcOKK2teTBiJO89vYL5b1P4xX4Kw9rW1HdoQhFlolJy9fsWub6/Wq0mOioaC0sLFDn8zGOiUmZ73zJlylCiRAn8/f158eIF3t7eALi4uODq6srx48fx9/enadOm2Tpey5YtCQoKYteuXezfv5/333+fQYMGMX78+NfuHxOTNqHK0qVLqVu3bobblMqM56FSqTR/py8wp1YXzvGf2lI0Pw1rg+KlnExddFstlOZmWH7cBoAXGzfkW7md67gBsPH0g3fiW8Cvan1Fq5KtSJFTGBEwgsvhl/UdkiAIeWTx/vt4LF9GqqkZVZ7do8zUUez5R7y2Bf2QJAlTQ4M8/ZgYKnN1v5yu6uzj40NAQAABAQEZBlQ3btyY3bt3c+rUKXx8fChdujSGhoYcO3ZMs09ycjKnT5+mYsWKmm0ODg706NGDtWvXMnfuXJYuXQqAoWFa75PU1FTNvsWKFcPFxYW7d+9SpkyZDD8lS5bMzaUvUkRikYVsD96GIt8dyubzzwGI3n+AlPD8mQGlVRVnLI0NeBQRz5HbhX/WFYWkYEqDKdRzrkd8SjyDDw4mKEpMVSkIuqCLwdtZMa1dmzIb1hFvYU3JqCcYj+jPlVOXdF6uIBRmPj4+HD16lMDAQE2LBYC3tzeLFy8mKSkJHx8fzMzMGDhwIKNHj2bPnj1cvXqVvn37EhcXR58+fQCYMGECO3bs4Pbt21y5coW///6bChUqAODo6IiJiQl79uwhJCSEyMhIIG28xPTp05k/fz43b97k0qVLrFixgtmzZ+f/xShkRGKRhbcuoKT8f/NXUR7ADWDs6YlJ9eqQkkLE1m35U+Z/g7gBNp4KzpcydU2lVDHXZy4VbCvwPOE5/ff3Jzy+8CdNglDQaGuBvOwyLl+eCr9v4rl1MYrFPSemX2+enD6fL2ULQmHk4+NDfHw8ZcqUoVix/0/S4u3tTXR0tGZaWoAZM2bQoUMHunXrRs2aNbl9+zZ79+7FxsYGSGuVGDt2LFWrVqVx48YolUrWr18PgIGBAfPnz2fx4sW4uLjQtm1bAHx9fVm2bBkrVqygSpUqeHt7s3LlStFikQ0iscitDF2hivbUYgDWn3cCIGLzZuSXmhR16fP/BnHvvxpCWHRivpSpa2YqMxY2W0gJ8xI8innEoANiAT1BeBeYuLtRYctGgu1csUyIIaR3LyKOvXkQqSAUVR4eHsiyrBmAnc7d3R1Zlrl+/bpmm7GxMfPnzycsLIyEhASOHj2aoTXyu+++4+rVq8TFxfHs2TO2b9+eIUHw9fUlODiY1NTUDOtndOnShfPnz5OYmMjz58/5559/+OSTTzLEV716dc3+1tbWyLKcaYXwokYkFrklSaAo2qtvv8zyww9RWFmR/OgRsS/1ddQlTydLarhZk6KW+b2QrsT9OvYm9ixuvhhbY1uuPb/G8IDhJBXxVjFBeBfYujpRau0qLjmWxSg5kYf9+hO5Z4++wxIEQdAakVjkhWbKWfGhT2FsjHW7dgC82LAx38rtXDttEPem08HvxCDudG6WbixsthBTA1NOPjnJuKPjSFXnT0uQIAi6U7akMw5+CzniUhVlagqPRozkxYb8m/hCEARBl0RikRfK/7pDia5QAFh3SusOFfPPPyQ/fpwvZX5UzRlzIwPuP4vj37v5s/p3fqlkV4k5PnMwUBiw5/4eppycIlbnFoR3gE+VEqjH/8BOj/eQZJmnk78n7Bc/8foWBKHQE4lFXogWiwyMSpXEtG5dUKuJ+P33fCnT1NCAttVdAFj/jgzifll9l/pMbzQdhaTg95u/M+fsHPHhQxDeAf2blOVhjyGsLd8cgPBffiHkhx/ybYyaIAiCLojEIgvZmo5QjLHIxOa/QdwvtmxBTsqfhCt9TYu9V56+M4O4X/ahx4dMfG8iACuurGDppaV6jkgQCrf8nG42K5IkMePTalxq3hG/qp+gRuLF+g08GjUKdT7VnYIgCNomEossZGs6QqVILF5l8f77KB3sSQ0LJ2r//nwps3JxK2q4WZOcKrPp9LvXagHQvmx7RtUaBcCC8wtYd22dniMShMIrv6ebzYqxSsmSbrU4Wa0pM2p3JVWhJHr3Hh70709qTKxeYxMEQcgNkVjkhSaxEN8upZMMDTUL5r1YvSbfyu3+njsA604Gk5Kqzrdy81OPSj0YUG0AADNOzWDH7R16jkgQhLxysjJmcTcvTrrV5Lt6fUgxNCbuxL8E9+hByvPn+g5PEAQhR0RikRcGxmm/U9+97jd5YdOpE6hUxF+4QPzFi/lSZsvKztiaGfIkMoED10LzpUx9GFRtEF9U+AKACccncCDogJ4jEgQhr2q62TCtfRUCHcsx8r3+pFpYkXDlCkGdu5D08JG+wxMEQcg2kVjkhYFR2u/kBP3GUcAY2Ntj1aolAM/Xrs2XMo1VSjrVTlswb+2/QflSpj5IksTo2qNpV6YdalnN6MOjOf5ILLIlCIXdp14l8G1Ykls2rgxrMAi5mBNJQUEEde5Mwo2b+g5PEAQhW0RikRcGJmm/U0Ri8SqbL9K+VY/avYeUsLB8KbNLHTckCY7eDudO2Lu7WrVCUjDpvUk0d29OijqF4QHDORdyTt9hCYKQR9+09KRRWXvuGtsxuvFQlKXLkBIWRlC3bsSdPavv8ARBKOQ8PDyYO3euTssQiUVeqP7rCiUSi0xMqlTBpHp1SE7mxabN+VKmq60p73s6Au92qwWAUqHkx0Y/0qB4A+JT4hl4YCAXwi7oOyxBEPLAQKngl841KWlvxpVkI6Y0/xLjGjVQR0UR3LsP0Yf89R2iIOjUokWLsLCwICXl/+uDxcTEoFKpaNKkSYZ9AwICkCSJO3fu5KnM+/fvI0kSgYGBeTpOdl24cIGPP/4YR0dHjI2N8fDwoFOnToSGvhvduEVikRcGIrF4E9vu3QB4sXFjvk09+0W9tEHcv599SFzSu71woUqpYk6TOdRxqkNcShwD9g/gcvhlfYclCEIeWJmqWNq9FhZGBhx+msjytiMxa9IEOTGRh0OHErHtD32HKAg64+PjQ0xMDGfOnNFsO3LkCE5OTpw8eZKEhP9/3vL398fNzY3SpUvrI9RcCQsL4/3338fW1pa9e/dy7do1VqxYgYuLC7Gx78ZMcCKxyIv0xEKMsXgti+bNMXB0JDU8nKg9e/KlzMZlHXC3MyU6IYUdgfmz+rc+mRiYsKDpAryKeRGTHEO//f24+uyqvsMSBCEPyjiaM79zDSQJ1gaGcLjrV1i1awepqTz59lueLV+u7xAFQSfKly+Ps7MzAQEBmm0BAQG0bduWkiVL8u+//2bY7uPjA0BiYiLDhg3TtAI0bNgww3TSL168oGvXrjg4OGBiYkL58uVZty5t2vaSJUsCUKNGDSRJytAysmzZMipUqICxsTGenp4sXLhQc1t6S8e2bdvw8fHB1NSUatWqceLEiSzP79ixY0RGRrJs2TJq1KhByZIl8fHxYc6cOZo4UlNT6dOnDyVLltTEOm/evAzH6dmzJ+3atWPWrFkUL16cUqVKMWTIEJKT/7/8QWhoKG3atMHExISSJUtqzlfXRGKRF6LF4o0klQqbLp0BeL56Tb6sGK1QSHxRN63VYvWJoCKxSrWpyhS/9/2o7lCd6KRo+u3vx43nN/QdliAIeeDj6cjXH3oCMHH3Te77jsS2T28AQmfOIuSnmcjqd3NqbUFHZBmSYvP2kxyXu/vl4L3Yx8cHf///d/vz9/enSZMmeHt7a7bHx8dz8uRJTWIxZswYtm7dyqpVqzh37hxlypShRYsWPP9vyubx48dz9epVdu/ezbVr1/Dz88PW1haAU6dOAXDgwAGePHnCtm3bAFi3bh0TJkxg6tSpXLt2jWnTpjF+/HhWrVqVId5x48YxatQoAgMDKVeuHJ07d87QletlTk5OpKSk8Mcff2T5+UStVlOiRAm2bNnC1atXmTBhAt9++y2bN2fsVu7v78+dO3c4ePAgCxcuZNWqVaxcuVJze8+ePXnw4AH+/v78/vvvLFy4MF+6WxnovIRCys/PDz8/P1JTU7PeSYyxeCvrjh0JX/grCZcvE38+ENOaNXRe5me1SjBr3w2uPYniXPALvNxtdV6mvpmpzPi12a/039+fi+EX6buvL7+1+I0yNmX0HZogFDjZqt8LgP6NS3H9SRTbAx8zeP15dgwejKOtHaEzZ/L8t99IffYM5yk/IKlU+g5VKAyS42CaS67vrgCsc3vnbx+DoVm2dvXx8WH48OGkpKQQHx/P+fPn8fb2Jjk5mUWLFgFw4sQJEhMT8fHxITY2ll9//ZWVK1fSsmXajJRLly5l//79LF++nNGjRxMcHEyNGjWoVasWAG5ubkRFRQHg4OAAgJ2dHU5OTpo4Jk6cyM8//0z79u2BtJaNq1evsnjxYnr06KHZb9SoUbRu3RqAyZMnU6lSJW7fvo2np2emc6tXrx7ffvstXbp0YcCAAdSpU4emTZvSvXt3ihUrBoBKpWLy5Mma+5QsWZITJ06wefNmOnbsqNluY2PDL7/8giRJuLi40KpVKw4ePEjfvn25efMmu3fv5tSpU9SuXRuA5cuXU6FChWw9BnkhWiyykK2VWUWLxVsZ2Npi+XEbAJ6vWJEvZVqbGvJxtbTKc+Xxd3sQ98vMDc35tfmvVLKrxIvEF/ju8+Vu5F19hyUIBU5BWXn7bSRJYkaHqlQtYcWLuGT6rj6D0RfdcZ4+HZRKInfs4OGQoajj4/UdqiBoTZMmTYiNjeX06dMcOXKEcuXK4eDggLe3t2acRUBAAKVKlcLNzY07d+6QnJxMgwYNNMdQqVTUqVOHa9euATBw4EA2btxI9erVGTNmDMePv3ma9tjYWO7cuUOfPn0wNzfX/EyZMiXTYPGqVatq/nZ2dgZ4Y8vA1KlTefr0KYsWLaJSpUosWrQIT09PLl26pNnHz88PLy8vHBwcMDc3Z8mSJQQHB2c4TqVKlVAqlRnKTi/32rVrGBgY4OXlpbnd09MTa2vrN563NogWi7wQYyyyxa5nTyJ/30r0gQMkBQVh6O6u8zJ71Pdgy9mH7L70hCetPHG2MtF5mQWBpaEli5svxnefL9efX8d3ry8rPlyBu6Xur7kgCNpnrFKypFst2vxylBsh0YzcFMiiL9qitLbi0YiRxPzzD8G9++C0YL6+QxUKOpVpWstBLqnVaqKio7G0sEChyOH30irTbO9apkwZSpQogb+/Py9evMDb2xsAFxcXXF1dOX78OP7+/jRt2jTbx2zZsiVBQUHs2rWL/fv307x5c3x9fTONXUgXE5M2Zf3SpUupW7duhtte/jAPaUlMOkmSgLRr9SZ2dnZ89tlnfPbZZ0ybNo0aNWowa9YsVq1axcaNGxk1ahQ///wz7733HhYWFsycOZOTJ09mWW562W8rNz+IFou8EC0W2WJUpgzm3t4gyzx/pW+irlQubkWdkrakqGXWnCg6rRYAVkZWLG2+lLI2ZQmLD6P33t4ERwW//Y6CIBRITlbGLO7mhaFSwb6rIcw9cBMLHx/cfvsNhaUl8efP86hnTwwiIvUdqlCQSVJad6S8/KhMc3e//z5wZ5ePjw8BAQEEBARkGEzduHFjTRef9PEVpUuXxtDQkGPHjmn2S05O5vTp01SsWFGzzcHBgR49erB27Vpmz56tGSthaGgIkKFrZLFixXBxceHu3buUKVMmw0/6IGttMTQ0pHTp0ppZoY4dO0b9+vUZNGgQNWrUoEyZMjmeUtfT05OUlBTOvrT+zY0bN4iIiNBm6K8lEou8EGMsss22d9qgw4htf5Dy4kW+lNm7QdqLf8OpYOKTCnZfam2zNrZmafOllLYqTWhcKL329OJe5D19hyUIQi7VdLNhWvsqAMw/dJudF59gWrMG7mvXYODoSNLtO7j++itJ98TrXCj8fHx8OHr0KIGBgZoWCwBvb28WL15MUlKSJrEwMzNj4MCBjB49mj179nD16lX69u1LXFwcffr0AWDChAns2LGD27dvc+XKFXbu3Em5cuUAcHR0xMTEhD179hASEkJkZFqCPnnyZKZPn878+fO5efMmly5dYsWKFcyePTvX5/X333/zxRdf8Pfff3Pz5k1u3LjBrFmz2LVrF23btgWgbNmynDlzhr1793Lz5k3Gjx+f426b5cuX58MPP6R///6cPHmSs2fP4uvri4mJ7ntviMQiL0SLRbaZ1qmNcaVKyAkJvFi/Pl/KbF6xGCVsTHgRl8z2wEf5UmZBYmdix7IWyyhjXYbQ+FB67+3NnYi8LSQkCIL+fOpVAt+GaV+YjNpygSuPIzEuVw6PDetRebijiojgYY+exL/UV1sQCiMfHx/i4+MpU6aMZlAzpCUW0dHRmmlp082YMYMOHTrQrVs3atasye3bt9m7dy82NjZAWqvA2LFjqVq1Ko0bN0apVLL8v2mbDQwMmD9/PosXL8bFxUXzAd/X15dly5axYsUKqlSpgre3NytXrsxTi0XFihUxNTXlq6++onr16tSrV4/NmzezbNkyunVLW/urf//+tG/fnk6dOlG3bl2ePXvGoEGDclxW+voY3t7etG/fnn79+uHo6Jjr2LNLkovCfJx5EBUVhZWVFeHh4djZ2WW88dwa+HMIlG0BXfNndWl9SU5OZteuXbRq1SpTv77sity5k8dfjUJpa0uZQwdRGBtrOcrMlh25y5Sd1yhXzJy9wxtr+j/mljauQ357nvCcvvv6cvPFTWyNbVn6wVLK2ZTL0zEL43XQBXEd0jx79gx7e3siIyOxtLTUdzjZ9sb6vYBKSVXTa+VpjtwKp7i1CTuGNMDe3IiEkBCudf0C44cPkUxNKbFgPuYvDWYtTN6l15W+ziUhIYF79+5RsmRJjLX0XqtWq4mKisLS0jLnYywKGHEumb3pOZNeV2anji/cV1PfNC0WYkaO7LBs0QIDF2dSnz8ncsef+VJmx9qumBkquRkSw7Hbz/KlzILG1tiW5R8sp4JtBZ4nPKfP3j5cf35d32EJgpALBkoFv3SuSUl7Mx5FxDNw7VmSUtQobW150K8vJvXqIcfF8WDAQCJ37tR3uIIgFDEiscgLw/9mOUiK028chYRkYIDdf3M/P1+xIl8Wd7I0VvFZLVcAfjtWdPseWxtbs/SDpVS2q0xEYgR99vbhyrMr+g5LEIRcsDJVsbR7LSyMDDh9/wUT/7yMLMvIRka4+P2CZauWkJzM41Gjeb5mrb7DFQShCBGJRV4Ymqf9TorVbxyFiFWHT1FYWJB0/z4xAQH5UmaP+h5IEhy6HsrdsJh8KbMgsjKyYskHS6jqUJWopCj67u3LpTDRF1sQCqMyjubM71wDSYINpx6w7tQDACRDQ1xmzcKma1eQZUKmTiVs/vwsV/kVBEHQJpFY5IUmsSi6H1ZzSmluhs3nnQB4tmRpvrzZlbQ3o2n5tAFLq47f13l5BZmFoQWLmy2mpmNNopOj6be/H4GhgfoOSxCEXPDxdOTrD9NW952y6wY3I9PGkEkKBcW+G4f9sKEAhC/8lacTJyEX8JXGBUEo/IpEYuHh4UHVqlWpXr26ZnoyrTASiUVu2HTrhmRoSHxgIHH5tPJt7/9mUtly9iGR8cn5UmZBZW5ozq/NfqW2U21ikmPov78/Z0POvv2OgiAUOP0bl6JddRdS1TIrbioIfp7WNVeSJBwGDcJp0iSQJCI2b+bR8BGoExP1G7AgCO+0IpFYABw/fpzAwED8/f21d1BDs7TfoitUjqgcHbHq0B6AZ4sW50uZ9UvbUb6YBXFJqWw6LRaLM1WZ4ve+H3Wd6xKXEsfAAwM5+eTk2+8oCEKBIkkSMzpUpUpxS+JSJAauCyQmMUVzu83nnSg+dy6SSkX0/v086NuP1BjxZZggCLpRZBILnUjvCpWaBClJ+o2lkLHr4wtKJbHHj+fLnOuSJNGrgQcAK4/dJzlV/8ve65uJgQm/NP2FBi4NiE+JZ9CBQQQ8CNB3WIIg5JCxSsnCLtWxVMncDI1hxKZA1Or/dzO1bPEBrkuXojAzI+7UKYK6dyclPFyPEQuC8K4q8InF4cOHadOmDS4uLkiSxPbt2zPt4+fnh4eHB8bGxtStW5dTp05luF2SJLy9valduzbr1q3TXnDpLRYgukPlkGGJ4lh99BEA4Yvzp9WiXY3i2Jsb8Tgygb8vPs6XMgs6YwNj5jedT1PXpiSpkxjhP4Ld93brOyxBEHLIydKYPuVTUSkl9l8NYe6BmxluN6tXF7fVq1Da2ZF49Rr3u3Ql6cEDPUUrCMK7qsAnFrGxsVSrVg0/P7/X3r5p0yZGjhzJxIkTOXfuHNWqVaNFixaEhoZq9jl69Chnz57lzz//ZNq0aVy8eFE7wSlVoDRK+1skFjlm168vSBIxBw6SeOuWzsszVik1rRaL/7krZkn5j6HSkJ+b/MxHpT4iRU7h68Nf8/vN3/UdliAIOeRhAVPaVgRg/qHb7Lz4JMPtJpUq4bF+HaoSJUgODuZ+ly4kXBdr2giCoD0G+g7gbVq2bEnLli2zvH327Nn07duXXr16AbBo0SJ27tzJb7/9xjfffANA8eLFAXB2dqZVq1acO3eOqlWrvvZ4iYmJJL40uC0qKgpIWz0zOTnzoF8DI3OkuESS4yLB7N0dFJx+7q+7BrmlcHPDrNn7xO4/QNjiJRSbPk1rx85KJy8X/Pxvc/1pNP7Xn9KojH2O7q+L61BQTKo7CROlCVtubWHyiclEJ0TzRYUvXrvvu3wdckJchzSF5fxzWr8XJunxt6nsyI2nMfx2PIhRWwIpYW1IRef/r5QrubhQfPUqHg8YSNLNmwR90Q3n+fMxqV1LX6Fn8i69rvR1LsnJyciyjFqtRq2lNaPSv4xLP25hVtDPJSAggPfff59nz55hbW39xn21dS5qtRpZlklOTkapVGa4LSfP3wKfWLxJUlISZ8+eZezYsZptCoWCZs2aceLECSCtxUOtVmNhYUFMTAyHDh2iY8eOWR5z+vTpTJ48OdN2f39/TE1NM21vliJhBpwI2M8Ls7t5P6kCbv/+/Vo9nlGFCrjvP0DUrl1crFiBZDs7rR7/dWrbKvjnqYIZ288QXTF3L0JtX4eCoqpcladGTzmSeITZ52cTeC0QHyMfJEl67f7v6nXIqaJ+HeLiCscioTmt3wuj/fv3U1kGTysF1yOh57ITfFU1FQtVxv0UXTrjsmo1pvfu8aBfP5506UxspUr6CToL79LrKr/PxcDAACcnJ2JiYkhK0u4Y0OjoaK0e72W//fYbEydO5N69exgYpH1EjYmJoWTJktStW5e///5bs+/Ro0dp06YN586do2TJkrkqLzo6muDgYKpVq8bhw4epUqWKVs7jbS5evMjs2bM5fvw4UVFRFC9enIYNGzJ06FDKlCmjqVOjo6NRKLLXuSivj0tSUhLx8fEcPnyYlJSUDLflpI4v1IlFeHg4qampFCtWLMP2YsWKcf2/5t2QkBA++eQTAFJTU+nbty+1a9fO8phjx45l5MiRmv+joqJwdXXFx8cHu9d86DV4NANCw6lfqypySW9tnFaBlJyczP79+2nevDkqlertd8iBx2fPEXfsGNXu3cexWzetHvt1qkXE8/6co9yMVOBevT6VXCzffqf/6PI6FBSt5Fb8dvU3/C74cSjhEC4eLoyoMSJDclEUrkN2iOuQ5tmzZ/oOIVtyWr8XJq8+Fxs3TebTxSe5/yyO7WH2rOpZC0ODjB9Q1K1bEzLma2L9/Sm+dh0OE8Zj1aGDns7g/96l15W+ziUhIYEHDx5gbm6OsbGxVo4pyzLR0dFYWFhk+WVTXrVs2ZKvvvqKmzdvUq9ePQCOHTuGk5MTZ8+exdDQUHM+p06dws3NjWrVquW4nJfPxdw8bSIeMzMzLC2z/3kgt/7++28+++wzPvjgA9auXUvp0qUJDQ3l999/56effmLjxo2aLzosLCzeGpO2HpeEhARMTExo3LhxpudMeutudhTqxCI7SpUqxYULF7K9v5GREUZGRvj5+eHn50fqfwsKqVSq11cK/80MZZCaAIW8AsyOLK9DHjgMGkjQsWNEb9+O45DBqF5JFLXNw0FF6yrO/HnhMStOBDPv8xo5PoYurkNBMqD6ACyMLJhxagZrr68lPjWe8fXGo1RkbB59169DdhX161BYzj3H9XshlH4u9ioVy3rU5hO/Y5wJimDK7htM+6RKxg8eKhWuC+bzZOJEIrduI2zSZIiMwq5fX519cMyJd/FxyS+pqalIkoRCocj2N95vk97NJv24ulChQgWcnZ05fPgw9evXB9Im8Wnbti2HDh3i1KlTNGnSBIB//vkHHx8fFAoFiYmJjB49mo0bNxIVFUWtWrWYM2eO5ovkFy9eMGTIEPbt20dMTAwlSpRg+PDhDBw4kNKlSwPg5eUFgLe3NwEBAQAsW7aMn3/+mXv37uHh4cGwYcMYNGgQAPfv36dkyZJs3bqVBQsWcPLkScqWLcuiRYt47733Xnt+cXFx9OnTh1atWvHHH39otpcuXZr33nuPiIiIDI9Z+t/Pnj1jyJAhHD58mBcvXlC6dGm+/fZbOnfujFqtZuPGjYwbN47Hjx9jZGSkOW67du2wsLBgzZo1b732CoUCSZJe+1zNyXO3wA/efhN7e3uUSiUhISEZtoeEhODk5JSnYw8ePJirV69y+m0LuBlZpP1O1F3T4LvO1MsLk1peyMnJPFu6LF/K7Ne4FAB/X3zCwxeFoxtHfutaoSs/NPgBhaRg662tjD0ylmR14e/zLAjZrt8LuTKO5szvXANJgg2nHrDm36BM+0gGBjhPmYJdv34AhM2ZQ8j06cgFsN+5kDeyLBOXHJenn/iU+FzdLyeTpfj4+GRYc8zf358mTZrg7e2t2R4fH8/Jkyc1ix6PGTOGrVu3smrVKs6dO0eZMmVo0aIFz58/B2D8+PFcvXqV3bt3c+3aNfz8/LC1tQXQzCR64MABnjx5wrZt2wBYt24dEyZMYOrUqVy7do1p06Yxfvx4Vq1alSHecePGMWrUKAIDAylXrhydO3fO1JUo3d69ewkPD2fMmDGvvT2r8RQJCQl4eXmxc+dOLl++TL9+/ejWrZsm9rZt25Kamsqff/6puU9oaCg7d+6kd+/eWV9sHSjULRaGhoZ4eXlx8OBB2rVrB6Rl1AcPHmTIkCH5E4SxVdrvhIj8Ke8d5TB4MMG9ehOxeTN2fX113mpRubgVDcrYcez2M5YfvcfENgWrb3FB0a5MO0wNTPn6yNfsvr+b2JRYZnnPwqBwVx2CUGT4eDry9YeezNh9ncl/XaWMgzn1X5m0QpIkHEeOwMDOlpDpM3ixeg2pz1/gMm0qkqGhniIXtC0+JZ666+vqpeyTXU5iqsreOCYfHx+GDx9OSkoK8fHxnD9/Hm9vb5KTk1m0aBEAJ06cIDExER8fH2JjY/n1119ZuXKlZrKfpUuXsn//fpYvX87o0aMJDg6mRo0a1KqVNkmBm5ubpnuPg4MDAHZ2dhm+lJ44cSI///wz7dunLehbsmRJrl69yuLFi+nRo4dmv1GjRtG6dWsAJk+eTKVKlbh9+zaenp6Zzu3WfzNgvu62NylevDijRo3S/D906FD27t3L5s2bqVWrFiYmJnTu3JkVK1bw2WefAbB27Vrc3Nw0LTz5pcC3WMTExBAYGEhgYCAA9+7dIzAwkODgtNWTR44cydKlS1m1ahXXrl1j4MCBxMbGamaJyi0/Pz8qVqz4xvEYAJhYp/2Oj8hTeUWdab16aa0WSUk8W7wkX8rs1zit+XPT6QdExolv4rPygccHzPeZj7HSmMMPD9N/f3+ikrLf31IQCpps1+/viP6NS9GuugupaplB688R/Oz1rbS2PXrgMvMnMDAg6u+/eTBoMOpCMjBfeHc0adKE2NhYTp8+zZEjRyhXrhwODg54e3tz8uRJEhISCAgIoFSpUri5uXHnzh2Sk5Np0KCB5hgqlYo6depw7do1AAYOHMjGjRupXr06Y8aM4fjx42+MITY2ljt37tCnTx/Mzc01P1OmTOHOnTsZ9n15llFnZ2eADEsevCy309ynpqbyww8/UKVKFWxtbTE3N2fv3r2az8IAvr6+7Nu3j0ePHgGwcuVKevbsme/dGgv8145nzpzRNHUBmoF3PXr0YOXKlXTq1ImwsDAmTJjA06dPqV69Onv27Mk0oDunBg8ezODBg4mKisLKyirrHY2t036LFos8kSQJhyFDCe7Zk4gtW9JaLf57gepK47L2eDpZcP1pNGtPBjHYp4xOyyvMGpVoxJIPljD44GDOh56nz/4+dJD1P8hTEHIj2/X7O0KSJGZ0qMrd8FguPoyk7+ozbB1UH3OjzB8BrNq0QWltzcNhXxJ79ChBvXrhumgRBjY2eohc0CYTAxNOdjmZ6/ur1WrNIOGcjrEwMTDJ9r5lypShRIkS+Pv78+LFC7y90ybGcXFxwdXVlePHj+Pv70/Tpk2zfcyWLVsSFBTErl27NIPpfX19mTdv3mv3j4lJW5ts6dKl1K2bsZXn1alYXx5/kP4hPqtpX8uVKwfA9evXsxyH8TozZ85k3rx5zJ07lypVqmBmZsbw4cMzzPhVo0YNqlWrxurVq/nggw+4cuUKO3fuzHYZ2lLgWyyaNGmCLMuZflauXKnZZ8iQIQQFBZGYmMjJkyczPQl0SrRYaI1ZvbqY1q6NnJxM+BLdt1pIkkR/77SxFiuO3SchOVXnZRZmNRxrsOrDVTiaOHIn8g6LoxdzP+q+vsMSBCEbjFVKlnSrhYOFETdCohmxKRC1+vXfnpo3aoT7it9QWlmRcOEiQV90I/nJk9fuKxQekiRhqjLN04+JgUmu7pfTb819fHwICAggICAgQ1eexo0bs3v3bk6dOqX50rl06dIYGhpy7NgxzX7JycmcPn2aihUrarY5ODjQo0cP1q5dy+zZszVjJQz/6+6XPpkDpM0u6uLiwt27dylTpkyGn9xObQvwwQcfYG9vz08//fTa2yMiIl67/dixY7Rt25YvvviCatWqUapUKW7evJlpP19fX1auXMmKFSto1qwZrq6uuY41twp8YqEv2W4qFy0WWmU/NG1sTMTvW0l+/Fjn5X1U1YXi1iaExySy+cwDnZdX2JW1KcuaVmtwt3AnUo6k9/7eXA6/rO+wBCFHilpXqHROVsYs7uaFoVLB/qshzD2Q+YNJOpPq1XFfvw4DJyeS7tzhfucuJN6+nY/RCkWZj48PR48eJTAwUNNiAWkzNi1evJikpCRNYmFmZsbAgQMZPXo0e/bs4erVq/Tt21czAxPAhAkT2LFjB7dv39Z8k5/eeuDo6IiJiQl79uwhJCSEyMhIIG28xPTp05k/fz43b97k0qVLrFixgtmzZ+f6vMzMzFi2bBk7d+7k448/5sCBA9y/f58zZ84wZswYBgwY8Nr7lS1blv3793P8+HGuXbtG//79M01cBNClSxcePnzI0qVL833QdjqRWGQh27OGpLdYJETqPKaiwKxOHUzr1oXkZMLzYayFSqnQtFos/ucuyaliJpS3cTF34bfmv1FcWZyIxAh67+3N8cdv7q8qCAVJUZkV6nVqutkwrX3aImDzD91m58WsWyKMSpfGY8N6DEuVIuXpU4K6fkH8f+MdBUGXfHx8iI+Pp0yZMhm6tnt7exMdHU358uU14xkAZsyYQYcOHejWrRs1a9bk9u3b7N27F5v/uvAZGhoyduxYqlatSuPGjVEqlSxfvhxIW0xw/vz5LF68GBcXF9q2bQukffu/bNkyVqxYQZUqVfD29mblypV5arGAtBmcjh8/jkqlokuXLnh6etK5c2ciIyOZMmXKa+/z3XffUbNmTVq0aEGTJk1wcnLSTFr0MisrKzp06IC5uflrb88PBX6MRYGX3mIhukJpjcPQIQSdPEnE1q3Y9e2LYYniOi2vYy1X5h+8zaOIeLaff8RntfK/6bCwsTG2obd5b/aZ7OPk05MMPjiYaQ2n0bJkS32HJgjCW3zqVYLrT6JYdvQeX20JxN3OlMrFXz/WROXsjPu6tTwYMCCtW1Sv3pSYNxfzxo3zOWqhKPHw8HjtQGd3d/fXbjc2Nmb+/PnMnz//tcf77rvv+O677zT/q9XqDIu++fr64uvrm+l+Xbp0oUuXLtmO0draOlsDtGvVqsXWrVuzvD19GEA6W1tbtm/f/tp9Xx3P8ejRI7p27ZphPYv8JFos8krTYhGhzyjeKaa1amH6Xj1ISeHZ4kU6L89YpaRvo7RvIH4NuENqFv2OhYyMJCPme8/nQ48PSVGn8PXhr1l3bZ2+wxIEIRu+aelJ43IOJCSr6bf6DOExiVnua2Bjg/uKFZg1bIgcH8+DQYOJ/OuvfIxWEIS3efHiBX/88QcBAQEMHjxYb3GIxCILOR5jIVostMph6FAAIv7YTtID3Y996FrPHSsTFXfDY9lz+anOy3tXqJQqfmz8I509OyMjM+PUDH45/0uup9QThPxQVMdYvMxAqWDB5zUoaW/G48gEBq49S1JK1l1BFaamuC70w/KjjyAlhcejx/D8lYXCBEHQHy8vL3r27MmPP/5I+fLl9RaHSCyykO0+uOkL5KXEQ3KC7gMrIkxr1sSsQQNISSFswQKdl2duZEDP+h4A/OJ/W3wwzgGFpGBsnbEMqZ428H7xxcV8/+/3pKrFLFtCwVSUx1i8zMpUxdLutbAwMuD0/RdM/PPyG+s+ydAQl59+xKZ7NwBCps8g9OfZor4UhALg7t27REZGZlhITx9EYpFXxlag+G+oSvxz/cbyjnEYMQKAqL/+JuHGDZ2X16uBB2aGSq49iSLgRpjOy3uXSJJE/2r9GV9vPApJwe83f2fUP6NITM26e4UgCPpXxtGc+Z1rIEmw4dQD1vwb9Mb9JYWCYmPHaurnZ0uX8uS775BTUvIjXEEQCjiRWOSVJIFZ2nLwxLx+pUUhd0wqV8Ki5Ycgy4TNmavz8qxNDfminjsgWi1yq2P5jvzs/TMqhYoDwQcYeGAg0UnR+g5LEIQ38PF05JsPPQGY/NdVjt8Of+P+kiRh378fTj98DwoFkVu38fDL4agTRKu9IBR1IrHQBjP7tN+xb66MhZxzGDYMlEpiAgKIO3tW5+X1aVQSQwMFZ4Ne8O9d0QKVG83cm7Go2SLMVGacfnqa3nt7Ex4vXhuCUJD1a1yKT2oUJ1UtM2j9OYKfxb31PjaffUaJ+fOQDA2JOXiQYF9fUl+aaUcQhKJHJBZZyNHgvvQWi1jRfUbbjEqWxLpDB4B86cvraGFMp/+mm10YIBaDyq06znVY0WIFtsa2XH9+ne67u/MgWixAKBQMYvB2ZpIkMb19FaqVsCIiLhnf1aeJSXx79yaLZs1wXbYUhbk58WfOEtStO8mhovVeEIoqkVhkIUeD+zSJhahMdcF+8CAkIyPiz50j5p9/dF5ef+9SGCgkjtwKJ/BBhM7Le1dVsKvAmpZrKGFeggfRD+i2qxvXn1/Xd1iCIAZvZ8FYpWRxt1o4WhhxMySGEZsCUWdj+m2zOnVwX7sGpb09iTduENSlK0n37+s+YEEQChyRWGiDaLHQKVWxYth2+wKAsNlzkNW6XR27hI0p7WqkLcq34OAtnZb1rnOzdGNNqzWUtynPs4Rn9NrTi9NPxYc5QSionKyMWdzNC0MDBfuvhjDnwM1s3c/Y0xOPDetRubmR/PAh97t+QfyVKzqOVhCEgkYkFtqgSSxEP3JdsevbF4WlJYk3bxK1c6fOyxviUwalQuLg9VAuiFaLPLE3sWfFhyvwKuZFTHIMA/YP4GDQQX2HJQhCFmq42TD9kyoALDh0m78vPs7W/QxdXfFYvw6jChVIffaM4O49iP33X12GKgi5snLlStzd3fUdxjtJJBbaIGaF0jmllRV2vr4AhM2bj5yUpNPyPOzNaFc9rdVinmi1yDMLQwsWN19MU9emJKmTGPnPSLbe3KrvsARByEIHrxL0bVQSgFFbLnD5UWS27mdgb4/76lWY1qmDOjaWB337EbV3ny5DFd5RT58+ZejQoZQqVQojIyNcXV1p06YNBw+KL6YKMpFYZEEM3i54bLt9gYGDA8kPH/Ji4yadlze0aVqrxaHroWKshRYYKY34ucnPdCjbAbWsZtKJSSy5uERM6yvkOzF4O3u+aVmBxuUcSEhW02/1GcKis7cujdLCAtelS7Bo3hw5OZlHw4fnS50tvDvu37+Pl5cXhw4dYubMmVy6dIk9e/bg4+PD4MGD9R2e8AYischCzgZvp083KxILXVKYmGD/X4USvnChzqc19LA345P/xlrMy2Y/Y+HNDBQGTHxvIn2r9AVgwfkFzDg1A7Ws23EzgvAyMXg7e5QKiQWda1DK3ozHkQkMXHuWpJTsvVYVRkYUnzsH644dQZZ5OmkSYQsXii8ShGwZNGgQkiRx6tQpOnToQLly5ahUqRIjR47k3/+61wUHB9O2bVvMzc2xtLSkY8eOhISEaI5x4cIFfHx8sLCwwNLSEi8vL86cOZOhnO3bt1O2bFmMjY1p0aIFDx6kzV54//59FApFpv3nzp2Lu7s7ah2P9SzMRGKhDZYuab9jQiBVrD6qS9afdsCwTGlSIyIIX7RY5+Wlt1r43wgTrRZaIkkSw2oO45s63wCw/vp6vjnyDcmpyXqOTBCEV1mZqFjaoxYWRgacCXrBxD8vZzs5kJRKnCZPwn7QQADC5y8g5IcpOp+AQ3g9WZZRx8Xl7Sc+Plf3y0lC+fz5c/bs2cPgwYMxMzPLdLu1tTVqtZq2bdvy/Plz/vnnH/bv38/du3fp1KmTZr+uXbtSokQJTp8+zdmzZ/nmm29QqVSa2+Pj45k+fTqrV6/m2LFjRERE8PnnnwPg4eFBs2bNWLFiRYayV6xYQc+ePVEoxMfnrBjoO4B3gpkDKAxAnQIxT8GqhL4jemdJBgYUGzOGB/3682LNGmw6f46hq6vOynO3M6N9jeJsOfuQuQdusvSLGjorq6jpWqEr1kbWfHf0O3bf201kYiRzmszBVGWq79AEQXhJaQdz5neuQe9Vp9lw6gEVnC3p/p5Htu4rSRIOw4ahtLUjZOpUXqxfT2rEC5xnzEBhaKjbwIUM5Ph4btT0yvNxQt6+Syblz51FMs1e3X779m1kWcbT0zPLfQ4ePMilS5e4d+8erv99Bli9ejWVKlXi9OnT1K5dm+DgYEaPHq05TtmyZTMcIzk5mfnz5/Pee+8BsGrVKipUqMCpU6eoU6cOvr6+DBgwgNmzZ2NkZMS5c+e4dOkSO3bsyMUVKDpEyqUNCiVY/NdqEflIv7EUAWaNGmFWvz5ycjKhP8/WeXlD/mu1CBCtFlrXulRrfnn/F0wMTDj++Di++3x5kfBC32EJgvAKH09Hvvkw7QPa5L+ucvx2zmZBtP2iKy6zZoJKRdSu3TwcMIDUmFhdhCoUctlp3bh27Rqurq6apAKgYsWKWFtbc+3aNQBGjhyJr68vzZo1Y8aMGdy5cyfDMQwMDDKMs/L09Mxw/3bt2qFUKvnjjz+AtJmkfHx88PDwyOspvtNEi4W2WBWHyGCIegjU1Xc07zRJknD8egz32n1C9J49xJ3rjmlN3bUkvNxq8Yv/Xdrb66yoIqlB8QYs/2A5Aw8O5FL4JXrs6cGS5ktwMnPSd2iCILykX+NSXH8azR/nHzFo/Tn+HNwQN7vstzBatW6N0sqah8OGEXv8BME9e+K6ZDEGtrY6jFpIJ5mYUP7c2VzfX61WExUdjaWFRY67AkkmJtnet2zZskiSxPXreVtQddKkSXTp0oWdO3eye/duJk6cyMaNG/nkk0+ydX9DQ0O6d+/OihUraN++PevXr2fevHl5iqkoEC0W2mKZNshXtFjkD+Py5bH+tAMAIT/O0PmAwKFNy6JUSPxzK5z70Totqkiq4lCF1R+upphpMe5F3qPb7m7cjbyr77AEQXiJJElMb1+FaiWsiIhLxnf1aWISczau0LxhA9xXrkBpbU3C5csEdelK8iPxvpkfJElCYWqatx8Tk1zdT5KkbMdpa2tLixYt8PPzIzY2c6tWREQEFSpU4MGDB5rB1gBXr14lIiKCihUraraVK1eOESNGsG/fPtq3b59hzERKSkqGwdk3btzQHDudr68vBw4cYOHChaSkpNC+fftsn0dRJRKLLOR4OsL0AdxR2VtISMg7h2HDkExNSbhwkahdu3RalpudKR1qpiWPux+Il40ulLIuxZqWa/Cw9OBp7FN67O7BpbBL+g5LeAeJ6WZzz1ilZHG3WjhaGHEzJIYRmwJRq3P2xY5J1aq4r1+PgYszSffvc79zFxJuipn3hP/z8/MjNTWVOnXqsHXrVm7dusW1a9c0YyKaNWtGlSpV6Nq1K+fOnePUqVN0794db29vatWqRXx8PEOGDCEgIICgoCCOHTvG6dOnMyQNKpWKL7/8kpMnT3L27Fl69uxJvXr1qFOnjmafChUqUK9ePb7++ms6d+6MSQ5aXooq8QkpCzmejjB9wHbUQ90FJWRg4OCAnW8fAMJ+no06MXtzrOfWEJ+yGCgkrkcqOHX/uU7LKqqczZ1Z3XI1le0qE5EYQZ99fTj++Li+wxLeMWK62bxxsjJmcTcvDA0U7L8awpxcTMdtVKokHhs2YFS2DCmhoQR90Y24c+d0EK1QGJUqVYpz587h4+PDV199ReXKlWnevDkHDx7k119/RZIkduzYgY2NDY0bN6ZZs2aUKlWKTZvS1ktRKpU8e/aM7t27U65cOTp27EjLli2ZPHmypgwTExNGjx5Nly5daNCgAebm5pr7v6xPnz4kJSXRu3fvfDv/wkwkFtoiukLphV2vXhgUK0by48c8X7lKp2W52ZnSsVba4zznwG0xH7uO2BjbsLzFct5zfo/4lHgGHxzMnvt79B2WIAgvqeFmw/RPqgCw4NBt/r6Y89Z6VbFiuK9Zg0mNGqijogju1Ztof39thyoUUs7Ozvzyyy/cv3+fxMREHj58yI4dO2jSpAkAbm5u7Nixg5iYGKKioti8eTPFihUD0sZHbNiwgeDgYBITE3n06BELFizA2NgYgJ49exIUFET79u25c+cOCQkJ7N+/Hzc3t0xxPHr0iCpVqogWzmwSiYW2WKUnFqLFIj8pTExw/GokAOGLF5MckpuJ8LJvkHcpVJLMmaAIAm6IBRF1xVRlyi/v/0ILjxakqFMY888YNl7fqO+wBEF4SQevEvRtVBKAUVsucPlRZI6PobS2xu235Zh7eyMnJvJwyFCixHSeQgEQExPD5cuX+eWXXxg6dKi+wyk0RGKhLTYeab9jQyFJTKGXnyzbtMGkenXkuDhCZ/2s07KKWRrTyDmtpWLm3hs57lssZJ+h0pAfG/1Ip/KdkJGZenIqvwb+KlqKBKEA+aZlBRqXcyAhWU2/1WcIi855l1SFiQklflmAVdu2kJpK6HfjsfnnsA6iFYTsGzJkCF5eXjRp0kR0g8oBkVhoi4lN2g/A83v6jaWIkSSJYt99B5JE1F9/6byfbjMXNWZGSq4+iWLX5Sc6LauoUyqUjKs7joHV0lbuXXhhIdNPTUcti5V7BaEgUCokFnSuQSl7Mx5HJjBw7VmSUnL++pRUKpynT8O2Vy8AHHbtInz2bPFFgqA3K1euJDExkU2bNqFUKvUdTqEhEgttsi2V9vu5mCYzv5lUroT1p58C8PSHKcipqTory0wFfRp4ADB7301SUsWHXF2SJIlB1Qcxts5YJCQ2XN/AN0e+IVmdrO/QBEEArExULO1RCwsjA84EvWDCjsu5SggkhYJiX4/BbuQIACJWrOTJ2G+RU3I2pa0gCPojEgttEomFXjmMGI7CwoLEa9eI2PK7TsvqVd8dWzND7obHsu2cGLCfH7pU6MKMRjMwkAzYfW83IwNGkpiq25nABEHIntIO5szvUgNJgo2nH7D6RFCuj2XTqxdPP/sUlEoit2/n4ZChqOPjtRht0SFafITs0tZzRSQW2iQSC70ysLXF4b8BVmFz55IaEaGzssyNDBjUpDQAcw/cJDFFdy0kwv+1KtWKeU3nYagwJOBBAEMODiEuOU7fYQmCAPiUd+SbDz0B+P7vqxy/HZ7rY0XVqoXz3DlIRkbEBAQQ3MeX1MicDw4vqlQqFQBxcaJ+FLIn/bmS/tzJLQNtBCP8RyQWemfTpTMRWzaTeOs2YQt+wWn8dzor64t67iw7co/HkQmsPxlMrwYldVaW8H+NSzTm12a/MuTQEP598i8DDgzA730/LAwt9B2aIBR5/RqX4vrTaP44/4hB68/x5+CGuNmZ5upYZk2a4Pbbch4MHET8uXMEfdEN12VLUf03paiQNaVSibW1NaGhoQCY5nD169dRq9UkJSWRkJCAQlG4v5cW5/J/siwTFxdHaGgo1tbWeR5PIhKLLPj5+WlWfsw2TWIhBm/ri2RgQLFx4wju2YsXGzZg3bEjxuXL6aQsY5WSYe+X5ds/LuHnf5uOtVwxMxIvqfxQx7kOS5ovYdCBQZwPPY/vPl8WNVuEjbGNvkMTCoFc1e9CtkiSxPT2VbgbFsOFh5H4rj7NtkENMM9l3Wjq5YX7mjU88PUl8dYtgjp3wXXZMoxKiS9y3sbJyQlAk1zklSzLxMfHY2JikuckRd/EuWRmbW2tec7khfgUlIXBgwczePBgoqKisLKyyt6dbNO6xhD1EJLiwDB339IIeWNWrx4WLVoQvXcvT3/4Hvc1a3RWcXxWqwSLD98h6Fkcy4/eY9j7ZXVSjpBZdcfqLG+xnP77+3P12VV67+3NkuZLcDB10HdoQgGXq/pdyDZjlZLF3Wrx8S9HuRkSw4hNgSz+wguFInf1sHH5crhv2MCDPn1ICgoiqGtXXJcswaRKZS1H/m6RJAlnZ2ccHR1JTs77ZBfJyckcPnyYxo0b57m7jL6Jc8lIpVJpbeYrkVhok5kdmNpDXDiE3wCXGvqOqMgq9vUYYg4fJv7MWSL/2I51+090Uo5KqeCrD8ozbMN5Fv9zhy513bA3N9JJWUJmFewqsPLDlfTd15fbEbfpuacnSz9Yiou5i75DE4QizcnKmMXdvOi05F/2Xw1hzoGbfPVB+Vwfz7BEcdzXr+NBv/4kXLlCcI8elPhlAWb162sx6neTUqnUyodGpVJJSkoKxsbGhf7DuDgX3SncHcsKIscKab9Dr+k3jiJO5eKCw5DBAIT+9BMpL17orKyPqjhTtYQVsUmpzD94S2flCK9XyroUK1uupLh5cYKjg+mxpwfBUcH6DksQirwabjZM/6QKAAsO3ebvi4/zdDwDOzvcVq3C9L16qOPiCO4/gKhdu7QRqiAIWiISC21zrJj2O/SqfuMQsO3eHaOyZUmNiCD0Z92tyK1QSHzTMm0mlPUng7kbFqOzsoTXc7VwZeWHK/Gw9OBp7FN67+0tkgtBKAA6eJWgb6O08RCjtlzg8qO8zeykNDfDdfFiLFp+CMnJPPpqFM/XrdNGqIIgaIFILLQtvcUiRCQW+iapVDhNngRA5O9bdboid/3S9jT1dCRFLTNz7w2dlSNkzcnMiRUfrqCUVSlC4kJEciEIBcQ3LSvgXc6BhGQ1/VafITwmb+vPKAwNKT5rFjZdOoMsE/LDFMLmLxBrNghCASASC23TtFiIrlAFgWnNmlh92gGApxMnIWthAFtWvv7QE4UEuy8/5WyQ7rpeCVmzN7FneYvlmuSi195eIrkQBD1TKiTmf16DkvZmPI5MYODasySlqPN0TEmppNj48dgPGQJA+MKFPJ08GVnM9CUIeiUSC21Lb7GIfgzx4sNlQeD41Vcora1JvHWL56tX66yc8k4WfOblCsD0XdfEt2d6kp5clLYqTWhcqEguBKEAsDJVsbR7LSyMDDh9/wUT/7yS5zpSkiQchgzGaeIEkCQiNm7i0civUCclaSlqQRBySiQW2mZsCVZpHy5Fd6iCwcDGBsfRowEI+8WP5Md5G0D4JiOal8NYpeBM0Av2XQ3RWTnCm9mb2LOsxbIMyUVQVJC+wxKEIq2MoznzO9dAkmDDqWDW/qud16RN584UnzMbSaUieu9eHvTtR2qMGOsmCPogEgtdcKqa9vtJoF7DEP7P6pN2mNTyQo6P5+mUqTorx8nKGN+GaQsl/rj7OsmpeWvuF3Lv1eTCd58vT2Ke6DssQSjSfDwdGdMibbKLyX9d5cSdZ1o5ruWHH+K6dAkKU1PiTp4kqHt3UsLDtXJsQRCyr8gkFnFxcbi7uzNq1CjdF1b8v/UrHulusLCQM5JCgfPEiWBgQMyhQ0Tt26ezsvp7l8LWzJC74bFsOv1AZ+UIb5eeXKTPFuW7z5fwePFhQxD0aYB3KdpWdyFFLTNo3VkePI/TynHN6tXDbfVqlLa2JF69xv2uXUl6+FArxxYEIXuKTGIxdepU6tWrlz+FudRM+/1YJBYFiVHZstj59gHg6Q8/kBqZt2kPs2JhrOLL/1bgnnvgJjGJKTopR8geexN7ln6wVLPORd99fYlM1M1jLwjC20mSxI8dqlKluBUv4pLpu/oMsVqqJ00qV8Jj/TpUxYuTHBTM/c6dSbghZuoThPxSJBKLW7ducf36dVq2bJk/BaavuP38rhjAXcDYDxyIYcmSpIaFEzJzps7K6VzHDQ87U8JjklgUcEdn5QjZ42TmxNLmS3EwceB2xG0G7B9ATJLogy0I+mKsUrKkuxf25kZcfxrNyM2BqNXamfDC0MMD9/XrMSpXjtSwcIK+6Ebc6dNaObYgCG9W4BOLw4cP06ZNG1xcXJAkie3bt2fax8/PDw8PD4yNjalbty6nTp3KcPuoUaOYPn16PkUMmNqCTdqCQDw+n3/lCm+lMDLCecoPQNraFrEnTuikHEMDBWNbpc0QtuTIXR6+0E5Tv5B7rpauLGm+BGsjay4/u8yQQ0OIT4nXd1iCUGQ5W5mwuJsXhkoFe6+EMO/gLa0dW1XMEfe1azDx8kIdHU1wH1+iDx7U2vEFQXi9Ap9YxMbGUq1aNfz8/F57+6ZNmxg5ciQTJ07k3LlzVKtWjRYtWhAaGgrAjh07KFeuHOXKlcvPsKH4f92hHp3N33KFtzL18sKmSxcAnkyYiDpeNx8uP6hYjPdK2ZGUombG7us6KUPImTI2ZVjUfBHmKnPOhpxl1D+jSFGLrmqCoC9e7jZM+aQyAPMO3mL3Je1NsKC0tMRt+TLMmzZFTkri4dBhRGzdqrXjC4KQmYG+A3ibli1bvrEL0+zZs+nbty+9evUCYNGiRezcuZPffvuNb775hn///ZeNGzeyZcsWYmJiSE5OxtLSkgkTJrz2eImJiSQm/n9V0KioKACSk5NJzsHiagrnmigvb0V9/wSp7+luUbb8kn7uObkGBZnNsKFEHzpE8oMHhMydi302B/Xn9DqM/bAcbX89wd8Xn/BFnVC83G1yHXNBUpifD+UsyzHPex6D/Adx+OFhvj/+Pd/V+Q5JknJ8rMJ8HbSpsJy/tur3gqgwPxc/qebElUcRrDoRzMjNgazv7QVo6VyUSor9PAtp8vdEb9/Ok3HfkRQahnWf3rl6zedUYX5cXiXOpWDKj3PJybEluRCt4iVJEn/88Qft2rUDICkpCVNTU37//XfNNoAePXoQERHBjh07Mtx/5cqVXL58mVmzZmVZxqRJk5g8eXKm7evXr8fU1DTbsVrGBeFzYzzJCmN2V/0VWVJm+75C/jC7fp3iK1YiSxLBgweR6Oqqk3I23lFwIlSBq5nMyCqpKHT/XiZkw7Xka6yPXY+MzPvG7+Nj7KPvkAqtuLg4unTpQmRkJJaWlvoOJ0vaqt8F7UuVYdE1BTcjFdgayXxVJRVzlRYLkGXs9+zBNuAfAF40bEhY61agKPAdNwRB73JSxxf4Fos3CQ8PJzU1lWLFimXYXqxYMa5fz13Xk7FjxzJy5EjN/1FRUbi6uuLj44OdnV32D6RORZ49E1ViFK1qlEBOH9BdSCUnJ7N//36aN2+OSqXN2l6PWrXiaWgYMTt3UnbvPlw3bUR6y7nl5jrUiUmk2dyjPIhNJdmlOp/UcNFG9Hr1LjwfWtEKt1tuTD89nYMJB2lYrSFtS7fN0THeheugDc+eaWctAl3TWv1eAL0Lz8VGPsl0WPwvwc/jWXFTwdahPpgaG2mvgNatebFqNc9mzcLm6FHcrK1x/H7yW+v9vHgXHpd04lwKpvw4l/TW3ewo1IlFTvXs2fOt+xgZGWFklLkiU6lUOXzAVOBeH27uweDRSXCvk4P7Flw5vw4Fm/N347h7/DhJt24RuXIlDoMGZet+ObkOzjYqhjYty4zd1/n5wC1aVyuOmdG78dIr7M+HLhW7EJYQxrJLy5hyagrOFs40KN4gx8cp7NchrwrLuWuvfi+4CvO5OFipWNajNp/4HeN2FPy0/y5T21fVahmOvn0wcrDn8bjviP77b9RRkZSYOxeFjlusCvPj8ipxLgWTLs8lJ8ct1G2A9vb2KJVKQkJCMmwPCQnByckpT8f28/OjYsWK1K5dO/cHca+f9jvoeJ5iEXTHwMaGYuPGARD+6yKdzXfeq4EHbramhEQlsvgfMf1sQTKsxjDalGpDqpzKqH9GcTfirr5DEnRMK/W7oBPlilnw86dVkJBZd+oB608Ga70Mq7ZtcfX7BcnYmNjDRwju1ZuUF2JqeEHQhkKdWBgaGuLl5cXBl6aQU6vVHDx4kPfeey9Pxx48eDBXr17ldF7mvnZvmPY76Dio1XmKR9Ady9atMG/2PiQn8/jrb5CTkrRehpGBkm9beQKw+PBdHkWIaU4LCkmSmFx/Ml7FvIhJjmHooaFiAb133Kv1u+LfhZAgHvOC4v0KjrRyTXvPnLDjMqfuPdd6Gebe3rit+A2FlRXxFy4Q9EU3kp9ob0YqQSiqCnxiERMTQ2BgIIGBgQDcu3ePwMBAgoPTvsUYOXIkS5cuZdWqVVy7do2BAwcSGxurmSVKr5yrgaEFJETAE7GeRUElSRLOkyahtLYm8fp1whct0kk5LSo5UbekLYkpan4U088WKCqlitlNZuNi5kJwdLCYhraIUR6dCXMqw4FJEBOq73AEoHlxmVaVi5Gilhm49qxO1gIyrVEDj7VrMChWjKQ7d7jfuQuJd0SLsiDkRYFPLM6cOUONGjWoUSNt8PPIkSOpUaOGZrrYTp06MWvWLCZMmED16tUJDAxkz549mQZ055RWmsqVBlDKO+3vWwfyFI+gWwb29jhNmgRA+OIlxF+6pPUyJEli/EcVkST488JjztzX/rdwQu7ZGtsyv+l8TAxM+PfJv8w8rbuV2QX9erV+l23LQmIUHJ0Dc6vAzq/gRZCeoyzaJAmmf1KJis6WPItNot/qs8QlaT/ZNypbFo8N6zEsWZKUp08J6tKV+AsXtF6OIBQVBT6xaNKkCbIsZ/pZuXKlZp8hQ4YQFBREYmIiJ0+epG7dunkuVytdoQDKfpD2+9a+PMck6Jblhy2wbNUKUlN5/M1Y1C/Nd68tlYtb0alW2rS2E3ZcIVVdaGZ7LhLK25ZnesPpAKy/vp7tt7frNyBBJ16t31N67YHP10PxWpCSAKeXwfwasK0/hF7Tc7RFl6mhAUt71MLOzJCrT6IYveUiupghX+Xigvv6dRhXrUpqZCRBPXsRc+So1ssRhKKgwCcWhV6ZZmm/H52F2MIxJWNRVmz8dygd7Em6c4ewefN1UsboFuWxNDbg6pMo1p/S/sBEIW/ed3+fQdXTZgeb+u9Ubr24peeIBJ2TFODZGnwPQI+/oJQPyKlwcSMsrAcbusDDM/qOskgqbm3Cr194oVJK7Lz0BD//2zopx8DGBvcVv2HWoAFyfDwPBg4k8q+/dVKWILzLRGKRBa3NGmJVHBwrATLcOaSV2ATdMbCxwXny9wA8X7GCuLNntV6GnbkRo1qUB2DW3hs8j9X+YHEhb/pX7U99l/okpCYwMmAkccna798t6E+W9bskQcnG0H079PWHCm0ACW7shGXvw6o2cMcfCs+6su+EOiVtmfxxZQBm7bvJ/qshb7lH7ijMzHD9dSGWrVtDSgqPR4/m+erVOilLEN5VIrHIgta6QgGUbZ72++buvB9L0DmLpj5YffIJyDKPx36LOk77Hyq71HGjgrMlkfHJzNwrBnIXNApJwfRG03E0deR+1H0mn5isky4Ygn5kq34vXhM6rYXBp6B6V1AYwL3DsKYdLPWBq3+K2f7yUZe6bnSr5w7A8I3nuRkSrZNyJENDXGb+hE23bgCETJtO6Jy54vUvCNkkEov8UKFN2u+beyFZTDNaGBT7diwGzs4kBwcTOmuW1o9voFTwfdtKAGw8/YALDyK0XoaQN7bGtsxsPBOlpGTXvV38fut3fYck6INDOWi3EIYFQt0BYGACj8/D5m6wsC6cXwepyfqOskiY0KYi9UrZEpuUSt/VZ4iI001rr6RQUOzbsTgMHw7As8WLeTphAnKKmClOEN5GJBb5obgXWJaApBi4ffDt+wt6p7SwwHnKDwC8WL+BmH/+0XoZtT1s+aRGcWQZJvx5BbUYyF3g1CxWk2E1hwEw8/RMgqLETEFFlrUrtPwRRlyGRqPAyArCb8KOQWkDvU8uhiTRZU6XVEoFC7t6UcLGhKBncQxZf56UVN20GkmShP2A/jh9PxkUCiK2/M7D4cN1MqmHILxLRGKRBa2uzCpJULFt2t9Xd+T9eEK+MG/QQNMc/vjbcaQ80/7g+7EtPTE3MuDCgwh+P/tQ68cX8q5npZ7UcapDfEo8446OI1Wdqu+QhDzKU/1uZg/vj09LMJpNBjNHiHwAu8ekTVV7eCbER2g9ZiGNrZkhS7vXwtRQydHb4UzdpdtZu2w6dqT4vLlIhobEHDjIA9++pEbrphuWILwLRGKRBa2OsYD/JxY3dkNygnaOKeic46ivMCpbltRnz3gy7jut97N1tDRmeLOyAPy45zqRcaJLRUGjkBRMaTAFc5U5F8IusOLKCn2HJOSRVup3Y0toOByGX4LWP4O1O8SFw6EpaYvt7Z8A0boZZFzUVXC2ZHbHagCsOHafzWce6LQ8y+bNcV26FIW5OXGnTxPUrTspYWE6LVMQCiuRWOSXErXBwgWSouGO6A5VWCiMjHCZNRNJpSImIICozZu1XkaP+h6UcTTnWWwSs/ff0PrxhbxzNnfmmzrfAOAX6MeN5+JxEv6jMobavjD0HLRfCo4V0+r5Y/PSWjD+Hgkv7us7ynfOh5Wd+fL9tC9lvvvjMmeDXui0PLO6dXBfsxqlvT2J169zv0tXkoLFdOGC8CqRWOQXhQIqt0/7O3C9fmMRcsS4fHkcR30FQPjMWRiGhGr1+Cqlgskfpw3kXvNvEFcfR2n1+IJ2fFz6Y3xcfUhRp/Ddse9IUYuBnMJLlAZQtSMMOAadN0KJOpCaCGeWw/yasLUvhFzVd5TvlC/fL0uLSsVISlXTf81ZnkTqdnIU4woV8Fi/DpWrK8kPHnC/cxcSrorHVBBeJhKLLGh1jEW66l3Sft/cA7Hh2juuoHM23bqlLZyUmIjTxg3ISdqdjaRBGXtaV3FGLcP4HZfFQO4CSJIkJrw3AUtDS64/v87G6xv1HZKQSzqp39MpFFC+JfTZBz13Qun30xbbu7QZfn0P1n8OD05pv9wiSKGQmN2xOp5OFoTHJNJv9VkSknU7BsrQzQ2P9eswqlCB1GfPCOrWndiT4vEUhHQisciC1sdYABSrBM7VQZ0CF7XfpUbQHUmhwHn6NBTW1hg/fsKzX37RehnffVQBM0MlZ4NesEnHfYaF3LE3sefLml8C8EvgL4TGabf1SsgfOqnfXyVJ4NEQum2DfgH/jbOT0tYzWt4cVrROmyVQrI+QJ2ZGBiztXgsbUxWXHkXy9daLOl9zwsDBAffVqzCtXRt1bCwPfH2J2rdPp2UKQmEhEov8VuOLtN+B68QbSiGjcnTEcdIkACJWriL2xAmtHt/ZyoQRzcsBMGP3dcJjxLSGBdGn5T6lqkNVYpNj+fncz/oORygMXGpAx9Uw5HTae4BCBUFHYW17WOINV7aDmG0s11xtTfHrWhOlQmJH4GMWH76r8zKVFha4LluKRfNmyMnJPBo+ghebxBeGgiASi/xWuQMojSDkMjw8o+9ohBwyf78pEXXqgCzzaMwYrU9B27O+BxX/W5F72k7dTqMo5I5CUjC+3ngUkoL9wfu5lXxL3yEJhYV9WWjrB18GQr1BoDKFJxdgSw/wqwPn1kCKbhZ9e9fVL23PxDYVgbQZ9vyv6741UWFkRPG5c7H+7DNQq3k6cSLhv/4qVukWijSRWOQ3U9u05ALg1GL9xiLkSlibjzAsXZrUsHAej/kaWa29BZoMlAqmta+CJMG28484fluMxSmIPG096eKZNmZqT/wesbaFkDNWJeDD6TD8MjQeA8ZW8Ow2/DkE5leHEwshKVbfURY63eq507mOK7IMwzac53ZojM7LlJRKnL6fjN2A/gCEzZtPyJSpWn1fEITCRCQWWdDp4L46fdN+X9ku5jkvhGRDQ4rNmolkbEzssWM8W75cq8ev7mrNF3XdAfhu+2USU8SH1oJoQLUBWKgsCFGH8Pe9v/UdjpADOq3fc8LMDpqOgxFXoPkPYO4EUY9g79i0tTD++QnidTuN6rtEkiQmf1yZ2h42RCem0G/1GSLjdb82kCRJOA4fTrFx4wB4sW4dj0eN1vokH4JQGIjEIgs6HdxXvGbauhbqZDi7UvvHF3TOqEwZnL5LexMJmzuPuHPntXr80R+Wx8HCiLvhsSwK0H1/YSHnrIys6FO5DwCLLi0iIUUsfFlY5Mvg7ZwwsoAGw+DLC/DRXLApCfHPwX9qWoKx7zuIfqrvKAsFQwMFv37hhYuVMXfDYxm24Typ+TTLnm23L3CZNQsMDIjatYsHAwehjovLl7IFoaAQiYW+1ElrNuXMcrESdyFl1aEDlh99BKmpPPrqK1IjIrR2bEtjFeM/Susv7Bdwm3vholtEQdSpXCesJCtC4kJYd22dvsMRCjuVMdTqBUPOQIflUKwyJMXA8QVpi+399SU8F180vI29uRFLutfCWKXgn5th/Ljner6VbfVRa1x//RXJ1JTYY8d41McXRayov4WiQyQW+lKxLViWgJiQtBmihEJHkiScJk1C5e5GypMnPP52nFYH7bWp6kyjsvYkpagZv/2yGBBYABkpjWhm0gyA5ZeXE5Ok+z7dQhGgNIAqn8KAo9BlM7jWg9SktBbuBV7we294elnfURZolYtbMfPTagAsOXyXbece5lvZ5o0a4r7iN5TW1iRevozbr4tIfvw438oXBH0SiYW+GBimNX0DHJsLqbrvBypon9LcjOKzZyOpVMQcOsSLNWu1dmxJkpjSrjKGBgqO3g7nzwvijakgqqaqRknLkkQnRbP5pphuUtAiSYJyLaDPXui1G8o0A1kNl7fCogawriME/6vvKAusNtVcGOxTGoBvtl0i8EFEvpVtUq0a7uvXYeDkhGFYGA+7dSfxlphBTnj3icRCn2p2BzMHiAiGS7/rOxohl0wqVcLx668BCJk5k/hL2vsm0d3OjKE+ZQD44e+rRMaJBLSgUUgKelbsCcDqK6vFWAtBN9zrwxdbof9hqPQJIMGtvfBbC/itJdLtA2JtpNf4qnl5mlVwJClFTf81ZwiNyr/Xp1GpUpRYs5pER0dSQ0O5/0U3rY/HE4SCRiQW+qQygfcGp/19dLZYIKkQs+naBYvmzSA5mUfDh2t1vEU/71KUdjAjPCaJabvE2hYF0YceH+Js5syzhGfsuL1D3+EI7zLnavDZShh6Nu3LKYUKgo9jsOlzvG+MR7q6XbyXvEShkJjTqTplHc0JiUqk35qzJCTn3/UxcHLiwYD+GFWtijoykuDevYkOCMi38gUhv4nEIgv5Nh1hrT5gbA3hN+HCRt2WJeiMJEk4T52KytWV5EePeDRmjNbmMTcyUDKjQ1UANp15wDGxtkWBo1Ko6FW5FwArrqwgRZ2i54iENykw083mhV1p+HgBDL8I7w1BVplhHR+MwR++8EttOLsKUhL1HWWBYGGsYmn3WliZqAh8EMG4P/J3zJrazIziS5dg1rgRckICDwcPIWL79nwrXxDyk0gsspBv0xEaW0Kjr9L+9p8KyfG6LU/QGaWlJSXmz0MyMiL28BHCFy3S2rFre9jSrV7a2hZjt10iPkl8I1nQfFLmE2yMbHgU84jDDw/rOxzhDQrcdLN5YekCLaaSMuQ8153aIRtbw/M78NcwmFcdjv8CiWJSAQ97M37pUgOFBFvPPWT50Xv5Wr7C1BRXPz8sP24Dqak8+WYsz35bka8xCEJ+EIlFQVCnX9oMUVGP4NRSfUcj5IFxhQo4TZoEQPiCX4g5ekxrxx7zYXmcrYwJfh7H7P03tHZcQTuMDYz5pOwnAGy6sUnP0QhFjqktN5zbkzI0ED6YChbOEP0Y9o2DuZUhYAbEPdd3lHrVqKwD41qnTeM9bdc1Dt8My9fyJZUKlxkzsO3ZE4DQn34idNYsMeOf8E4RiUVBoDJOW30V4MjPYqXVQs76k3ZYd+wIsszjUaO0Ns2ghbGKqZ9UBmD50XtcyMcZToTs+azcZ0hIHH98nKCoIH2HIxRFhuZQf0jaYntt5oNtqbT3lIDpaYvt7R0HUUV3hrneDTz41KsEahmGrD+X72sESQoFjl+PwXFUWk+FZ8uW82Tcd8gpovuk8G4QiUVBUbUTOFaEhIi0b5aEQq3YuG8xrlSJ1IgIHg4fgTopSSvHbepZjI+ruaCW4eutF0lO1c44DkE7SliUoGHxhgBsubFFz9EIRZqBEXj1SFts79PfoFgVSI6FE7/AvGrw51B4dkffUeY7SZKY+kllarhZE5WQQt/VZ4hOyN/Z9iRJws7XF+epU0ChIHLbNh4OHYY6XnSFFgo/kVgUFAoltJia9vepJfDkon7jEfJEYWRE8XnzUFhZkXDxIqEztJcsTmxTERtTFdefRrP4n6L3waCg61S+EwA77uwgWaxPI+ibQgmVO8CAI9D1d3Crn7bY3rnV8Est2NKryL3fGBkoWfyFF06WxtwOjWH4xkBS1fnfHcn6f+zdd3hT1RvA8e/N6N60dFHasndbmU4oioiKIqIoiIiAgqAi4sAB8nPgQEW0CIIgU3HgQkARypC9iixBdlktlA660yS/Py6tIhQ60t4kfT/Pkydpmt773tv2JO8957zn3nup88nHKK6uZCcmcmzQYMyZmdUehxC2JImFPanfGZr3VBdA+mUk2KiqkNCGS51wwt97FxSF9PlfkvnTTzbZbi0vV8Z0V8cJT1p+gAOpMjHTnlwffj0BbgFkFGSw/tR6rcMRQqUo0LALPLoEHv0VGnZV32t2L4SpN8LcXnB0ndZRVpvaPm5M7dcaF4OO5X+l8v5v2sxb8+7cmbqfT0fn7U3e1q0cfagfppRUTWIRwhYksbA3Xd8CF284vhm2z9Y6GlFJXjfdRODQoQCcenUMebt222S7PWLD6dQ4iEKzhdEL/8SiwdU2cXkGnYFu0d0AWHRokcbRCHEZdTtA369hyFpo0QsUHRxYBjO7weddYf9vNWKxvZgIP969UMp78sqD/LRDm7knHm3aEDl3DoagIAr+/pujDz5IweHqrVolhK1IYmFvfEIh/iX18bKxkC1XLhxd4LAn8Ox4E9aCAo4/+SRFaWmV3qaiKLzRowUeLno2H0ln3kaZKGxPbo++HYCVySvJNeVqG4wQpQlpAb0+V+dhtH4E9C6QvAHm3wdTboRd3zn9Yns94sJ5/KZ6ADz/7Q52ndBmKJJb48ZEfjkfY2RdTCdPcrTvQza7ECVEdZLEwh61ewxCWqoTuRc9UyOuHDkzRa8nfMIEXKKiKDp1iuNPP43VBpO56/h78HzXxgC8veQvTmTIxD970TKwJRHeEeQV5ZGYnKh1OEJcWa360P0jePpPuO5JMHpCyk749lF1HoaTL7b3/G1N6NQ4iHyThcGzt3DmvDbH6lKnDlHz5+PWrBnmc+c49vDD5KyrOcPThHOQxKIUmq7MqjdAj09BZ4S/FsGfX1d/DMKm9N7e1JmcgM7Li7wtWzk9frxNttvv2ihaR/qTU2jmxe/+lHrodkJRFG6Lug2AFcdWaByN+C+nWHm7KviEwq1vwDO7oNNocPeHc4f+WWxvfQIUVm951uqg1yl89EAc9YI8OZWZz9C5Wyks0maOo6FWLerOnoVHhw5YcnM59vgQspYs0SQWISpCEotSaL4ya0hL6PSC+njxc5B5Qps4hM241qtH2IXJ3BlffkX615VPGPU6hfd6tcLVoGPN32f5clOyDSIVttAxoiMAa0+ulepQdkbz9t3eeQRApxdhxK6LF9v79SV1LYyV7zjdYnu+7kamPdwGb1cDW46mM/Yn7YYh6b28iPhsKt5du4LJxImRz3Ju/nzN4hGiPCSxsGfXPwNh10BBJnz/uNOPda0JvOPjCXrqSQBOv/4Gudu2V3qb9YK8eO7CkKg3f9nD8XQZ028PWga2JMAtgBxTDltStmgdjhDl5/rvxfY+Av9oyDsHK9+CiS3ht1fh/Gmto7SZ+kFeTHowDkWBLzcdY+4G7eau6VxcCP/gffwefACsVlL+9zpnPv5EeqWF3ZPEwp7pDdDzM3W865E1sPo9rSMSNlBryBC8b70VTCaOP/0UppSUSm9zwPXRtCkZErVT3nzsgE7R0bGO2mux+vhqjaMRohIMrurk7uFb4N7PIbgFFGbDukkwsZU6F/Ccc1Qxim9Su+RCzWs/7WbTYe16ZhS9npAxYwgcNgyAswkJnP7f/7Ca5SKjsF+SWNi7wIZw54fq45Vvw6FV2sYjKk1RFMLGv4Vro0aYz5zl+PAnseTnV2qbep3Cu71a4WbU8ceBs8zfdMxG0YrKKE4s/jjxh8aRCGEDegO07AVD/oA+X0NEezAXwJYZ8HFr+G4wpOzROspKG9qxPne2CqXIYmXo3K2aFsZQFIWgJ4cTPObVkmG0J54dhcUGBUCEqAqSWDiCmN4Q9xBghe8GOVXXc02l8/SkTsIn6H19yd+5k1MvvVzpXgZ1SFQTAN76ZS/J52RIlNbahrZFp+g4knWE1FwpHS2chKJAo67qQnuPLIb6N4PVDDu/hk+vhS8fhGTHnb+iKOqFmqahPqTlFPL4nC3kFWrbSxDQpw/hH7wPRiPnly4l+fHHMWc730R64fgksXAU3d6DoKaQkwpf9QVT5a5wC+25REQQ/tFHYDCQtXgxZxMmV3qbA66Lom3UhSFRC6VKlNZ8XHxoEqAme5tPO+4HLSEuS1Eg6nrotxAeWwXN7gYU2LcYPr8FvrgTDiY6ZMl0DxcDn/VrTYCnC7tOZNlFe+rTrRt1p05B5+FB7voNHHv4YZusiySELUli4ShcPOCBeeDmBye2wM9PO2RjLS7m2aE9IWPHAHD2k0/IXPRLpban0ym81ysGN6OOtQfSmLdRhkRprV1IO0ASC+HkwmLh/tkwfDPEPgQ6gzo3cE4PmBYPe38GizYlXCsqIsCDhD7XoNcp/Jh0kmlrDmkdEp7XXUfdWbPQ+/uTv2cPR/r0ofD4ca3DEqKEJBaOpFZ9uH8WKHr48ytY97HWEQkb8L/vPgIefRSAUy+9RF5SUqW2FxXoyfPFQ6IWy5AorbUNUddK2HR6k8aRCFENAhtCjwR4KgnaDwGDO5zcDgsegskdIOlLcKDyy9fWr8WYO5sB6kKkq/af0TgicG/Zgsj58zCGhWE6eoyjD/Yhf98+rcMSApDEwvHU6wS3va0+XjYG9vyoaTjCNmo/OxKvzp2xFhaSPGw4phOVW7fkkeuiaBcVQG6hmee//ROLRXq3tBJXOw6A5PPJpOenaxyNENXELwK6vaMutnfjKHD1hbP74IchMOka2DQNTNpNii6Ph6+NpHebCCxWeHL+No6c1X5ug2t0NJFffolrw4YUnTnD0Yf6kbtFyloL7Uli4YjaDYY2AymZzH14jdYRiUpS9HrC33sX1yZNMKelkTxkKObs7ApvT/evKlHrD6Uxe/0R2wUrysXbxZsonygAdqdpt+iWEJrwDISbX4VndsItr4FnEGQeg8Wj1LUw/vgQ8rO0jvKKFEXhfz2ac01dP7Lyixg8ewvZBUVah4UxuDaRc+fgfs01WM6f59jAQZxfsULrsEQN5/SJRUZGBm3atCE2NpYWLVowbdo0rUOqPEWB29+DJneCuRC+6gOnd2odlagknacnEZ9ORh8USMHff3Pi2WcrVa88KtCT0d2aAjB+yV8cSK14oiIqp0VgCwB2npX/U1FDufnCDc/AiJ1w+wTwrQs5Z+D319TVvJe/DjlntY6yVK4GPVMeak2wjyt/p2bzzIIku+gJ1vv6Uvfz6Xh16oS1oIDjTz5FxncLtQ5L1GBOn1h4e3uzevVqkpKS2LhxI2+99RZpzlBFQaeHe6dD3eugIAvm3us0CxTVZMbQUCImT0ZxdSVn1WpS3nmnUtvr1yGSGxsGUlBkYeTXSZjMjjV50lkUJxa7zu7SOBIhNGZ0V3vdn9oGPaZAYGMoyIQ1E9QEY8kLkFW5oaBVpbaPG1Meao2LXseyPSl8tPxvrUMCQOfuTp1PPsb3nnvAbObUyy+TNn265lWsRM3k9ImFXq/Hw8MDgIKCAqxWq/P8sxnd4cH5ULsZZKeopf0kuXB47i1bEnYhoUifPYdzs2ZVeFvFVaJ83Y38eTyTj1ccsFWYohz+nVg4TfsjRGXojRD7IDyxAXrPg7BroCgPNk7BkNCa2KPTIM0+Prj/W1xdf97q2RKAj5b/zdJdpzSOSKUYDIS+9Sa1Bg0EIHXC+6S+8y5WB6vEJRyf3ScWq1evpnv37oSFhaEoCj/88MMlr0lISCAqKgo3Nzfat2/Ppk0XV1/JyMggJiaGOnXq8NxzzxEYGFhN0VcDd3/o9z3UagBZx2FWd0g/qnVUopJ8butK7VHPApDy9jtkLf21wtsK8XXj9R7qB9uExAMkJWfYIkRRDk0CmqBTdJzLP8eZPO2ryghhN3Q6aHonDF4B/X6A6JtQLEVEnluDYcp18PXDcDJJ6ygv0qt1HQZcHwXAyK93sO/0eW0DukBRFGqPGkXt558H4NwXX3Bq9GisJsepwiUcn90nFjk5OcTExJCQkHDZ7y9YsICRI0cyduxYtm3bRkxMDF27diU19Z9Vbv38/NixYweHDx9m/vz5pKSkVFf41cM7BPovUpOLzGS150KSC4cXMHAg/n36gNXKyeefJ3fr1gpv666YMO6KCcNssfLMgiRyC7WfeFiTuOpdqetdF4BDmdrXwhfC7igK1I+H/j9T9MivnPK9BgWrWvnws44wpyccWWs36ze9fHtTrqtfi9xCM4NnbyEjt1DrkErUenQAoW+PB72ezB9/Inn4cCy5UnZcVA+D1gFcTbdu3ejWrVup3//ggw8YPHgwAwYMAGDKlCn88ssvzJgxgxdffPGi1wYHBxMTE8OaNWvo1avXZbdXUFBAQUFByddZWWq1CpPJhMmes373QOj7PYa5d6OcO4R15u0U9fkGajW0yeaLj92uz0E1qO7zEPD8cxSeOkVOYiLJTwyjzuzZuNSLrtC2xtzRmI2H0zh8Noc3F+3hte5NKxyX/D2oynMeon2iOZJ1hL/T/qZ1YOuqDq1aOcrfgcO272XgTP+Tptqt2FRvBLfGRuC6OQFl9/coB5fDweVY6rTDct0IrA26qMmIhibe35KeUzZy7Fwuw+ZtY3q/OAz6i6/XavV78bzjDkK9vTn97ChyVq3myCMDCJucgN7Xt8LbdKq/MTmWCu2jLBSrAw34VRSF77//nh49egBQWFiIh4cH3377bclzAP379ycjI4Mff/yRlJQUPDw88Pb2JjMzk+uvv54vv/ySli1bXnYfr732GuPGjbvk+fnz55fM1bBnboXnuP7A23gVnKbA4M2G+s+S4VFP67BEJSiFhdT5bBruycmY/P05NuwJzN7eFdrWXxkKn+7VAzCkqZmmfg7z7+/wluUtY1XBKtq5tOMuj7u0DsemcnNz6dOnD5mZmfj4+GgdTqkcvX2vqTwKUmmQupi6aWvQW9UPOJluEfwd0p0Tfu1A0W7wxYkcmLhLT6FFIT7UQo8o+5rT4HbkKOFfzESfl09B7dqcGDiQIr+KJxeiZipPG+/QicXJkycJDw9n3bp1XHvttSWve/7551m1ahUbN25k06ZNPPbYYyWTtocNG8bjjz9e6j4ud0UrIiKCU6dOUatWrSo7NpvKOYt+wQPoTiVhdfHE3Gs21uiOldqkyWRi2bJldOnSBaPRaKNAHY9W58F87hzH+z2M6dgxXJs1I3zmDHQV/CD0v1/+Ys6GY9T2dmXR8Gvx93Ap9zbk70FVnvOw+PBiXln/CnFBcXze5fNqirB6pKWlERoaaveJhVO076Vwpv/JUo/l/Gl0m6ag2zYTpVBdpM7qH4352iextuwNBldN4l2y6zRPLfgTgPfubUGP2LCS79nD76Xg7785OWQo5tRUDCEhhE2dWqGeb3s4FluRYymfrKwsAgMDy9TG2/1QqMpq164dSUlJZX69q6srrq6uJCQkkJCQgPnCOgJGo9Fx/vj8QuGRRfBVX5TDqzAseBDumQIt7q30ph3qPFSh6j4PxuBg6k77jCMPPEjBnj2kPPecWpbWUP5/4Zdub8bag2kcOpPDuF/28cmDcSgVHFIgfw+qspyHRrUaAXAo6xAGg6HC59weOcrfgFO071fh1McSEAG3vQkdR6krd2/4FCX9MIbFI2HNe3DtcGj9CLh6VWucd8VFsD81l08SD/Dyj3toHOpLqzp+Vz6WamRs1ozoL+dzbOAgCo8c4UT//kR8NhX3Vq0qtj1n/htzYFV5LOXZrt1P3r6SwMBA9Hr9JZOxU1JSCAkJqdS2hw0bxp49e9i8eXOltqMZV2/o+w00u1tdRO/bR2HlO3Yz8U2Un0tkJBFTPkVxcyNn9RpOvfZahUqXurvomdg7FoNO4Zc/T/HTjpNVEK34ryjfKAAyCzLJKMjQNJaazuHb95rO3R86Pg/P7IKu48E7DM6fgt9ehoktYOXbkHuuWkMa2aURtzStTWGRhcfnbOXM+YKr/1A1MoaHEzl/Hm4tW2LOyODoIwPI/mOt1mEJJ+TQiYWLiwutW7dm+fLlJc9ZLBaWL19+0dCoGsvgCr1mQodh6tcr31ITDFOetnGJCnOPiSH8g/dBpyPz2+84M/GjCm2nVR0/nuysTux/9YddnMyQv4mq5m5wJ8g9CICT2ZLMCVFpLp5w7RPwdBLc9TEE1IO8dFg5Hia2hN9egfOnqyUUnU7hw96x1A/y5FRmPkPnbqWwyL7mWxgCAoj8Yiae112HNTeX5KFDyVz0i9ZhCSdj94lFdnY2SUlJJcOZDh8+TFJSEseOHQNg5MiRTJs2jVmzZrF3716GDh1KTk5OSZWoikpISKBZs2a0bdu2soegLZ0ebntLbXR1Bti9EGZ2gyz7WNRHlJ93586EvDYWgLSpU0n74osKbWdYfH1iIvzIyi/iuW93YLFIb1ZVC/NSx16fyLbPlYVrCqdp34XK4ArXPAzDt6gX04JbQmE2rPtYTTB+HlEti8d6uxmZ9nAbvN0MbDmaztifdlf5PstL5+lJxJRP8bm9G5hMnBw1inOz52gdlnAidp9YbNmyhbi4OOLi4gA1kYiLi2PMmDEA9O7dmwkTJjBmzBhiY2NJSkpi6dKlBAcHV2q/TtdVfs3D8PCP4B4AJ7fDZ53g6DqtoxIV5H///QQ98wwAqW+/Q8ZlFo68GoNex4f3x+Bu1LP2QBrT/5D1FapauFc4ID0WWnO69l2odHpo0ROGrIG+30Lda9WhwFtnwsfXwHeDIKVqP+zXC/Ji0gNxKAp8uekY8zclV+n+KkJxcSFswgT8+/YFIOWtt0idOLFCQ2uF+C+7Tyw6depUUtHp37cv/nWVdvjw4Rw9epSCggI2btxI+/bttQvYnkXdoK5uGtQEsk+rC+n9MREs9tVdK8qm1mODCXjkEQBOvfwK51cklnsb9YK8GNO9GQDv/bqPXScybRmi+I/ixOJ49nGNIxHCiSkKNOwCjy6FAUugQRewWmDnN/DpdTD/AUiuuqQyvkltnu/aBIDXf/mLg1lVtqsKU3Q6gl95maCnnwIgbcpUTo8Zi/VCQQMhKsruEwthYwHRMGg5tLwfrGb4fSx89WC1T3QTlacoCrWffw7fHj3AbObEM8+Qu2VLubfzQNsIbmsegsls5akvt8uq3FWoeCiU9FgIUU0ir4OHvoXHV0PzewAF9i+Bz29RL64dXFElRU2GdKxH95gwiixWZuzTcyoz3+b7qCxFUQgcOpSQ114DnY6Mb77hxIgRWArsa+K5cCySWJTCqcfgunpBz8/gzomgd4X9S2Fqxyq9giOqhqLTEfrG63jFx2MtKCB5yFDy//qrfNtQFN6+tyUhPm4cOpvD/37eU0XRCkks7INTt+/i8kJj4L4v1HkYcf1AZ4Qja2DOPTCtM+xdZNPee0VRePfeVjQN8Sa7SGH4l0nkm+yzN8D/gd6ET/wQxWjk/LLfSR40GPP581qHJRyUJBalcPoxuIoCbQbAoGXgHw2Zx2BGV0h8C8yOv8R9TaIYDIR/+AHubVpjyc7m2KDBFF4oblBWfh4ufNA7BkWBrzYns2SnTO6vCsEe6tyvM3lnNI6kZnP69l2ULrAB3P2JWkmq/VAwuMPJbbCgL0y5Hv78Bsy26bV1d9GT0CcGD4OVP09k8eoPu+x2HoPPrbcSMW0aOk9Pcjdv5ujD/Sk6I+2UKD9JLGq60Bh4fBW06KUOjVr1Dky/Bc7s0zoyUQ46NzciPv0U1yZNMJ89y7FHB2JKSS3XNq6rH8iQjvUBeHHhTilBWwUC3QMByCrMotBcqHE0QtRgvnWg29vqWhg3PguuPpC6BxYOgk/awNYvoKjyQ4Ii/D3o39CCToFvth5n7sbyXfSpTp4d2hM5Zzb6WrUo2LuXI336lvsilRCSWJSiRnWVu/lCr8+h1wxw84NTSTD1JtgwRSZ2OxC9tzd1p0/DWLcupuPHOTZgAEVpaeXaxsgujYip40tmnolnFiRhlhK0NuXj4oNBp66WnpZXvt+NsJ0a1b6LK/MMhJvHwIid0PkVtXJi+mH4+Wn4KBY2fAqFuZXaRRM/K6NuVdcNGvfTbjYfsd85jW7NmhE1fx7GOnUwJSdzpE9f8vfu1Tos4UAksShFjewqb3EvPLEe6neGonxY+gLMvgvSDmodmSgjQ2AgdWfMwBAaSuGhQxwbOAhzRkaZf96o1/HRA3F4uOjZePgcU1bJ796WFEUp6bU4m3dW42hqrhrZvosrc/eDm567sJr3W+AdCudPwtIX1dW8V0+A/IpXzRt0fRR3tgqlyGJl6NxtnLbDydzFXCIjiZw/r6QH/Gi/h8nZuEnrsISDkMRCXMwnDB5aCLdPUMeeHlkDk69Ft/ZDFItUC3IELnXCiZw5A31QIAV//cWxwY9hzs4u889HBXoy7q7mAHy4bD9JyRlVFGnNVLz6tiQWQtghF0+4dhg8vUMtcOIfBblpsOJ1+LAlLH8dcsrf26goCu/2akWTEG/OZhcwdN5WCorsczI3gLF2bSJnz8KjTRss2dkkDx5M9vLlWoclHIAkFuJSigLtBqu9F/XiwVyAfuWbdNr3KspxuWrhCFyiooicMQO9nx/5O3eS/PgQLLll787v1boOd1y4uvb0V9vJLpCk0lZqudcC4Gy+JBZC2C2Dq1rgZPhWuOczdf2ngkxYM0HtwVj6EmSVr7qbh4uBqf1a4+tuZPuxDF77yb4r8Ol9fIiYPg2vW27GWljI6ZHP4rNJPgOIK5PEQpQuIBr6fQ89p2H1qIVP/gn0s+6ARc9AXobW0YmrcG3YkLozPkfn7U3e1q0kDxtW5vrkiqLwVo+WhPu5czQtl7E/Vu1qtTWJDIUSwoHoDRDTG4auh95zITQWTLmwIQE+ilHnYpw7XObNRdbyZNKD/1qZ244nc4NaGKTOxIn49roXLBZCvlvIuWnT7La6ldCeJBalkMl9FygKtLqfosfXczTgRhSssGUGfHyNem+x365coU7EqzvtM3QeHuSu38CJp57GWli2akS+HkY+7B2LToHvth3npx2y9oIt+Ln6AZBZIKuca0Xad1FuOh007Q6PrYSHvoO614G5UK0e9XFrWPgYpJZtDaGOjYJ4rmtjAMb+tIutR9OrLm4bUAwGQl9/Hf/BgwA4N+ljUt4aj1WKu4jLkMSiFDK57z88AkiKHExR3+8hsLE65nTRM2r1qMOrtY5OXIF7bCx1pnyK4uZG9qpVnBj1HNaisg1tahcdwPD4BgC8/P1OjqdXrjqKUCtDAWQVZGkcSc0l7buoMEWBBrfAo0tgwBL1sdUMfy6Aye1hwUNwcvtVNzO0Y31ubxmCyWxl6NytpGbZ72RuUHuxaz31FKnd7wQgfc4cTj73fJkvVImaQxILUS7WqBth6Fq47R21TG3KLpjVXW1M049oHZ4ohWe7dtT55BN1ZdXffuPk6JewmsvW2/TUzQ2Jq+vH+fwiRnyVRJFZrlJVRkliUSiJhRAOLfI6tffisZVqbwbA3p/hs04wpyccXVfqjyqKwnu9YmgU7EXq+QKemLeNwiL7b1szbriB4PHjwWAg65dfSB76BJacHK3DEnZEEgtRfnojdBgCT26HtoNA0amN6SftYNkYyLXfGt01mdcN1xP+0UfqG8LPP3Ny9OgyJRcGvY6Pesfh5Wpgy9F0EhKlBG1leLt4A3C+8LzGkQghbCIsTp1/8cRGaPUAKHo4uBxmdkM/+05qZ/0Jl5mT4OlqYGq/Nni7qW3r64vsezJ3Me877yDi08ko7u7krF3L0QGPUpRu38O5RPWRxEJUnGctuON9GPIHRHcEcwGs/UhdVGj1e1BQ9hKnonp4d44n/P331eTip585+WLZkou6tTx4vYdagnbSir/ZdiyjiiN1Xj6u0mMhhFOq3QR6ToUnt0LrAaB3QZe8gWsPTkA/4xbY89Mli85GB3ry0QOxKArM2XCUrzcnaxR8+XjdeCORX8xE7+tL/p9/crRPX0wnZR6ekMSiVDK5rxyCm8PDP8KDCyC4hVqSb8UbMCkWNk6ForJVIhLVw6frrYR/8P4/PRcvvFim5OKeuDr0iA3DbLHy7Dd/kicVaCtE5lhoT9p3UaUCoqH7RHh6B+Z2QyjSuaA7vQO+7gefXgs7FoD5nwa0c5NgRt7SCIBXftjlMGsHucfEEDl/HoaQEAoPH+ZIn74UHDigdVhCY5JYlEIm95WTokDj2+DxNdBzOvhHQ84ZWPI8fNwGts+7qCEV2vK59VbCP/xATS4WLVKTizJM6P5fjxbU8XfneEY+3xyW5qMiZI6F9qR9F9XCJwxLlzdY1vxDzNePBFdfOPMXfP/YP5UVL1x4GxbfgFubBVNotjBkzlbOnHeMC3Ku9esT9eV8XOrXp+j0aY70fYjc7VefvC6cl3wyELal00Gr+2D4ZrjjA/AKgcxj8OMT8Ekb2DYbiqSKhD3w6dKl3MmFj5uRjx6IQ69T2HpWx49J0vVdXsVDofLN+RSa5X9BCGdXaPDG0ukleGYn3DwGPAIh46haWfGjWNgwBZ05n/fvj6F+kCens/IZNm8bJgcplGEMDSVy7hzcYlphyczk2IBHyV4t1SJrKkksRNXQG6HtQHhqO3T5H7gHQPph+OlJmBQHm6aBKU/rKGs8ny5dqDPxw5IKHyeff+GqyUXrSH+GdaoHwNhFezmWJiVoy8PL6IWCAkivhRA1ipsv3PgsjNipVlb0DoPzJ2HpCzCxFd5bP2XaA03xcjWw6cg53lq8V+uIy8zg70/kzJl43ngj1vx8kp8YRuZPP2kdltCAJBaiarl4wPVPwzO74NY3wSsYso7D4lEwsRWsnSSTvDXmfcst1PloIhiNZC1eXKbkYuhN0dTztpJTYOapr7Y7zJU1e6BTdLgZ3ADIk+RaiJrHxUOtrPh0ktqz71sXclJh2avUm9uB71uux4tcZq494lALk+o8PIiYnIBP9+5QVMTJ518g7YsvtA5LVDNJLET1cPGE64bD03/C7RPAN6KkIWViC1j5NmSf0TrKGsv75psvSi5OPPccVpOp1Ncb9Dr6NTTj7WYgKTmDScv/rr5gnYC7wR2A3CLp7RGixjK4XujZ3wZ3faLOTcw7R8NdH7LF8xlGGL7lzW/XsT/FcUpTK0YjYe+8TUD/hwFIffsdUt9/H+tlyu0K5ySJhaheRjdoNxie3AZ3J0BAPchLh5Xj4cPm8ONwSHWc7l9n4t25c0lycX7JUo4/PQJLQekTCANc4X/dmwKQkHiAjYfSqilSx1ecWOQVSY+FEDWe3gjX9IPhW+CezyCwEW7m84wwLOR33XA2Tx9B9rnTWkdZZopOR+0XXyRo5EgA0qZN59Qrr5SpQIhwfJJYlELKEVYxgwvEPaQ2pL1mQNg16joY2+fA5A7qqqUHll92USFRdbw7dybik49RXFzIXrGC40OfwJJX+offO1uFcu81dbBY4ZkFSWTmlt7LIf4hiYW2pH0XdklvgJje8MQGuO8LigKb4q3k0df0LcaPY7D++jKcT9E6yjJRFIXAxwYT+sbroNOR+d1Cjj/1NJb8fK1DE1VMEotSSDnCaqLTQ4t7YfAKePRXaNpdXcn74HKY2xMmX6tWkjJJY1RdvDp2JOKzqSgeHuSsW8exwYMxZ5c+D2bc3c2JrOXBycx8Xvphp3R5l4GHwQOA/CL5u9aCtO/Crun00PweDE+s4/DNn7HLGo2rNR9l/SfwUStY/DxkntA6yjLx69WLOpM+KrlYdWzQIMxZUrTCmUliIeyDokDdDtB7rjpMqv1QcPGCM3vVSlIfNIXfXoG0g1pHWiN4duhA3c+no/P2Jm/LVo49MgBzRsZlX+vlauCjB+Iw6BR++fMU3249Xr3BOqCSydvSYyGEKI1OR/SNvUm67QceKXyObZYGUJQPm6aqC9AuegYyjmkd5VV533KL+n7i5UXelq0cfagfptRUrcMSVUQSC2F/AqKh29vwzG7o8ro60TvvHKz7WF1UaHYP2PuzLLhXxTzi4oic9QV6f3/yd+3i6MP9KTp79rKvjY3w45ku6sqxY3/azZGzOdUZqsORoVBCiLLq2yGSWrHd6Vk4jqG6MRSEdwBzobrA3qQ4+HGY3V9082jblsi5c9AHBVKwfz9HH+xD4ZEjWoclqoAkFsJ+ufvB9U/BU0nwwJfQoAugwKFEWPAQTGypVpPKOqVxoM7LrVkzIufMxhAUpL4ZPNQP06nLn+8hHevTPjqA3EIzT0sJ2iuSxEIIUVaKovBGjxY0DfVlSW4THjCNwdRvEdTrBJYi2D5XXYB24WNwZr/W4ZbKrUkToubPxxhZF9OJExzp05e83bu1DkvYmCQWwv7pDdDkdnjoW3XBvetHgEctdWGh4mpSCx6Cv5eBxax1tE7HtUEDIufOwRAWSuGRIxzt+xCm5ORLXqfXKXzYOxYfNwM7jmcy8Xf7fYPTWnFikW+WORZCiKtzd9Ez5aFr8HEzsP1YBm/s8oeHf4SBy6DhrWC1wJ8LIKEdfPMIpOzROuTLcomIIGrePFybNcV87hzHHu5PzoYNWoclbEgSC+FYAqKhyzgYuRfu/RzqXgdWszo0al4vNcn4/TU4e0DrSJ2KS2QkUXPn4hIZienkSY73fwSXlEurk4T5uTO+ZysAJq88yAYpQXtZJetYmGQdCyFE2UTW8uTD3rEAzFp/lB+2n4CIdtD3G3hsJTS5E7DC7u/h0+vUBMMOy7cbAgOJnD0bj/btseTkkDz4MbKW/qp1WMJGJLEQjsngCi17waNLYOh6aD8E3APg/Cn440P4pDV8fitsnQX5UoHCFoxhYUTOnYNrw4aYz5whYspU8nfsuOR1d7QK5f42dbBKCdpSFU/elh4LIUR53Nw0mKc6NwDgxYV/svfUhfe3sDh4YB4MWQvN7qYkwZh8LXwzAFL/0i7oy9B7eRHx2VS8b70Vq8nEiWeeIf3LL7UOS9iAJBbC8QU3g27vwLN/wf2zoWFXtWRt8kb4+Sl4vzF8PwQOrwGLjPuvDENQEHVnz8K1VSv0ubmcGDyY7DVrLnnd2O7NiQ705FRmPi99LyVo/8uoMwJgMkvSJYQon6dvacSNDQPJN1kYOncrWfn/akdCWqjvg0PXQdO7UBOMher6UN8+alcJhs7VlfAPP8Cvd2+wWjk97n+c+SRB3i8cnCQWwnkYXNUrNX2/VodK3TIOAhuBKRd2fAmz7oSPYtShUnY6/tQRGPz9CZ/2GTmNGmHNyyd56BNkLvrlotd4uhqY2DtWLUG78xTfSAnai5QkFhZJLIQQ5aPXKUx6II5wP3eOpOXy4nd/XvphPLg59J6j9mAUJxi7vruQYAyEM/s0if2/FL2ekNfGEvjEEwCc/eQTUl5/HatZ5ks6KkksSiErszo47xC4YQQM2wQDf4fWj4CrD2QeU4dKfXotTL4O1nzgEHXA7Y3Ow4MT/R/Gq1s3KCri5KhRnJsz96LXxET4MfJWtQTtaz/t5rCUoC1h1EtioSVp34Wj8/d04ZM+cRj1Cot3nmbWuiOXf2FIiwsJxh/qArRYYde3kNAevhtkF1WkFEUh6KknCX7lFVAU0ud/yYlRo7AUFmodmqgASSxKISuzOglFgYi20P0jGLUf7pulTnDTu0Dqblg+Ti1b+3lX2DwdcmSycZkZDAS/PR7/vn0BSHnzTc5MmnTRlbPHb6pPh3pqCdoRUoK2hPRYaEvad+EM4ur6M7pbUwDeXLyXHckZpb84pKW6AO3ja/6Z5L3zG5jcHr4bDGf/rpaYryTgob6Evz8BjEbOL1lK8uOPY86WC1KORhILUXMY3aF5D3WC26j9cNfHEH0ToEDyBvjlWXi/Ecy7D3Z8BfmZWkds9xSdjuBXXibwqScBODv5U06/Nq6kG7u4BK2vu5EdxzP56Hft37zsQXFiUWSRRR6FEBU34PoobmsegslsZdj8bVcvlhHaSn0PfHy1mmBYLbDza7VM7cLHNK+o6HP77URM+RTFw4Pc9Rs41r8/RefOaRqTKB9JLETN5O4P1zwM/X+GkXvg1jchNFZdbOjv3+D7x+G9BjC/NyTNh7wMrSO2W4qiEPTEE4S8NhYUhYwFCzjxzEgsBQUAhPq6M75nSwAmrzzAtmPpWoZrF2TythDCFhRF4Z1eragb4MHx9DxGfbujbJOfQ2P+STAa3/GvdTDawsLHNV3J2+v664mc9QV6f3/yd+9WV+k+fkKzeET5SGIhhE8YXDccHl8Fw7dAxxfUSd/mQti/FH4YqiYZ8+5D2TEfY1G21hHbJf8HHiD8ww/UbuzffuPYwIGYMzIAuL1lKPfEhWOxwsgFSeQW1uwr9TLHQghhK77uRib3vQYXvY5le1L4/I/DZf/h0Bh4cD48tgoa334hwfgKPmkLPz0JmdoU3nBv2ZLIefPUhVmPHuXogw+Sv0/7+SDi6iSxEOLfAhtC/EswfDM8sQE6jYagpmAxwd+/YVj0FLftfBL9l/fDttkyJ+M/fG67jbrTPkPn5UXelq0c6fsQphPqlabX7mpOqK8bR9JyGb/YfkoeakHmWAghbKlFuC+vdm8GwNtL/mLr0XL2DIfFwoNfqgvtNbxVXXh222yYFAdLXoDsVJvHfDWu9aKJ+vJLXBs2oOjMGY7260fu1q3VHocoH0kshChN7abQ6UUYtkGtLhX/CtbazdFhRndohXo1Z0JDmHUXbJwKGclaR2wXPDt0UK80BQdTePAghx94gPw9e/B1NzLhvhgA5mw4yqr9ZzSOVDuSWAghbO2h9nW5s1UoRRYrT87fRnpOBaoqhcWpK3k/+itE3aj23G+cAh/FoEt8vdp77I3BwUTOmYN7XByWrCyOPTqQ8ysSqzUGUT6SWAhRFkGNoeNzFA1exe9N38Hc6WUIaaVe1Tm8CpY8DxNbwJQbYeU7cHon1OBFftwaNyJqwVcXVuk+y9GH+pG95g+ubxDII9dFAfDcNzvIyK2Z5QRljoUQwtYURWF8z5ZEB3pyMjOfkV8nYbFU8H2obgd1DmK/HyC8NZhy0a/7iC67n0W3ZgIUnLdp7Fei9/Oj7ozP8erYEWtBAceffJKMhd9X2/5F+UhiIUQ55biFYrn+GRiyBp7ark78jrxeXe379J+w8i2YcgN81AqWjlZX/DbXvDkFxpAQIufPw6NDByy5uSQPGULGd9/xwm1NqBfkSer5Al79cbfWYWpC5lgIIaqCt5uRhD7X4GrQkbjvDFNWV2IStqJA/XgYtBwe+BJr7eYYLXnoV7+tLja77mMw5dku+CvQubtT55OP8e3RA8xmTr30Emmff14t+xblI4mFEJURUE+d+D1gMYz6G+6erFbYMLirC+9tmKyu+D2hAXw/BPb8VK1XerSm9/am7mdT8eneXX0zePkVsqd+yof3xaDXKfy84yQ/7TipdZjVToZCCSGqSrMwH8bd1RyA93/bz5YjlSzXqijQ5HaKBiWyJeoJrAH1ITcNfntFnYOxeToUVX3vs2I0Ejr+LQIefRSA1PcmkPLue1gtsj6SPXH6xCI5OZlOnTrRrFkzWrVqxTfffKN1SMJZeQZCXF+1wsbzh+CB+RDbF9wDIC8ddnwJX/eDd6Jh9t2wfrKmJf2qi+LiQti771DrsccAOJuQQNDUCTx5UxQAr3y/k9OZ+RpGWP0ksRBCVKXebSPoERuG2WLl6a+Srr6+RVkoOk74d6Do8bVw1yfgGwHnT6lrQE1uD7sWVvkQYEVRCH7+OWo/NwqAczNmcGr0S1hN0pbaC6dPLAwGAxMnTmTPnj389ttvjBgxgpwcWclRVDEXD2hyB/SYrPZkPLIYOgxTezgsJji0En4dDR9fAx+3hqUvwaFV1XLVRwuKolB75DPqWhc6HZnfLaTHV+/RPtBAVn4Roxf+Wbba605ChkIJIaqSoii83qMFkbU8OJGRx4u2bGN1BrimHzy5FW6fAJ614dwh+HYATIuHw6tts58rqDVwIKFvvQV6PZk//sjx4U9iyaueYVniypw+sQgNDSU2NhaAkJAQAgMDOSerOIrqpDdA1PVw21vqnIzhW6HrWxDdUW2g0w7AhgSYfRe8Ww8W9IPtczUp71fV/B94gDoJn6B4eJC3YQOv/TaRunnnSNx3hh+Sas4CSAbFAMjkbSFE1fF2MzLpgTgMOoUlu07z1WYbVy40uEK7wer7WqeXwMULTm6HWd1h7r1qEZMq5NfzHup8/DGKqyvZq1Zx7NF/1k4S2rH7xGL16tV0796dsLAwFEXhhx9+uOQ1CQkJREVF4ebmRvv27dm0adNlt7V161bMZjMRERFVHLUQVxDYAK4dBv1/gucPw/2zIfYh8AyCwvOw9yf4cZhayvazeFj5NpzYBk4yjtQ7Pp6oeXMxhITAsSN8/MfHND97iNd+2kPq+ZoxJEqv6AGwUnN6aYQQ1S8mwo/nb2sMwLifd/N3ShXM8XP1gk4vqAlGu8fUC2YHflerJC58HNKP2n6fF3h3jqfuzBnofHzI276do/36YTp9usr2J67O7hOLnJwcYmJiSEhIuOz3FyxYwMiRIxk7dizbtm0jJiaGrl27kpp68dXec+fO8fDDD/PZZ59VR9hClI2bDzS7G3okwLP7YfAK6PgihMaq3z+5DVaOV7uXP2iiTgD/82vIduw1INyaNiXq6wW4tWiBS8553l73Ga33bWBsDakSpVPUptdsNWsciRDC2Q26oR43Ngwk32ThyS+3k2+qonbHqzbc/p667lPznoD1wirebdThvrlVM1rE45priJw7B0Pt2hT8fYAjffpQcKgcq48LmzJoHcDVdOvWjW7dupX6/Q8++IDBgwczYMAAAKZMmcIvv/zCjBkzePHFFwEoKCigR48evPjii1x33XVX3F9BQQEFBQUlX2dlZQFgMpkw1eDJQcXHXpPPAVTDeajdSr3dMArOn0Y5uBzdgd9QDq9EyU5RJ4Dv+BIAa0grLPXisdaLx1qnHehdqiamy7DJefD3J2zG56S89BI5vy/nuW1f8mV2Kj81DaRbqzAbRVq1KnoeLGa198lisTjF/5SjHIMzt+/O1EbLsdjeO/c0p3vCev46fZ43Fu1m7J1Ny72NMh+LT13o8RlKuyHoVvwP3dE/YEMC1u1zsNwwEkubweowKhvSR0cTPnsWJ4cMwXTkKEf69iVscgJuLVpU7lgcQHUcS3m2rVgdaMakoih8//339OjRA4DCwkI8PDz49ttvS54D6N+/PxkZGfz4449YrVb69OlD48aNee211666j9dee41x48Zd8vz8+fPx8PCw0ZEIUT46i4mAnL+pnbWT2ud34pt37KLvF+lcOevVlFSflqR6tyTHNVgtEegILBYCf/2NgJUrAVhXJwb3gb3w9DBqG1cVSjOn8eH5D3HFlVf9XtU6nErLzc2lT58+ZGZm4uPjo3U4pZL2XdRke9MVpvylDsMc1NhMy4Bq+PhntRJ0fifNT35d8r6V7VKbPeG9OeXbxubvU/rsbMJnfoHb8eNYXFw42a8fuY0a2nQfNVF52niHTixOnjxJeHg469at49prry153fPPP8+qVavYuHEjf/zxBzfddBOtWrUq+f6cOXNo2bLlZfdxuStaERERnDp1ilq1alXNgTkAk8nEsmXL6NKlC0aj837guxq7OQ/ZKSiHV6E7lKj2ZuRcPDTK6lv3Qm9GZ6xRN6pDrmyoKs5D2ncLOfO/1zFYzKSE1af9vGkYAgNtsu2qUtHzcCL7BN1/6o67wZ2196+twgirR1paGqGhoXafWDhz+243bZMNyLFUnbeX7uPztUfxczfy07BrCfV1K/PPVupYLGaUP79Cv/JNlBx1qLql7nWYb3kdQmPKt62r7Sonh1MjniFvwwYwGAh+6028/zPyxd5+L5VRHceSlZVFYGBgmdp4ux8KVVk33HADlnJMenV1dcXV9dIuOqPR6PB/fLYg50Gl+XnwrwP+feGavuqk7pSdcHAFHFgOxzagZB5Dv30WbJ8Fih7qtIUGN0P9zhAWBzq9TcKw5XkIeaA3KV5BZI1+juCTBznYqzf1p36Ke4vmNtl+VSrveXAxqsPWLFaLU/w/Ocox1IT2XY7FPtnLsbzQrRmbjmSw80QmLyzczbxB7dHpytdrULFjMULbR6BVL1g7EdZ9jO7YOnQzblHXe7r5VfAOKec2S+HnR93PpnLyhRc4v2QpKS+8CJlZBPR76NKo7OT3YgtVeSzl2a7dT96+ksDAQPR6PSkpKRc9n5KSQkhI5f5AExISaNasGW3btq3UdoSocjqdesXnhmfgkUXwwhHo8zW0exxqNQCrGZI3QOKbMP1meK8+fPMIbJsNmfZV4jXmzs6sH/kOyV5B6NPOcLRvXzJ/+UXrsGyuePK2xeoclb4cjbTvoqZyMeiY9GAc7kY96w+lMWNtNU9ydvWCzq/A8C3Q8j7ACklzYdI1sOo9MNmmMqDOxYXwCRPw79MHrFZS3nyTM5Mm1aj1krTi0ImFi4sLrVu3Zvny5SXPWSwWli9fftHQqIoYNmwYe/bsYfPmzZUNU4jq5eoFjbrC7e+qCxg9/SfcORGadgdXX3UV8N3fw09PwofNIKE9LB0Nf/8OhblaR8+gPp0Yf+fzbApugrWggJPPjiL1/Q+wmp2ngpIkFtqS9l3UZNGBnrxyYfL2u7/uY9/pKihBezV+EXDvdBj4u9qjbsqBxDdgcgfY/6tNdqHo9QS/+gqBTw4H4OzkTzk99jWnei+xR3afWGRnZ5OUlERSUhIAhw8fJikpiWPH1ElAI0eOZNq0acyaNYu9e/cydOhQcnJySqpECVHj+UdCmwHQey48fwge/Q06vqA25ooOzvwFGybDvHvhnSiYfTesnQQpu0GDqztergZe6NWacR0e5dtG8QCkTZvG8SeGYc7OrvZ4qoIkFkIILfVpV5f4xkEUFlkYsSCJgiKNPmxHtIWBy+Dez8E7FNIPw/z7Yf4DcK7yvSmKohA0bBghr40FRSHj6685MeIZLP+aayVsy+4Tiy1bthAXF0dcXBygJhJxcXGMGTMGgN69ezNhwgTGjBlDbGwsSUlJLF26lODg4ErtV7rKhVPSG6Bue4h/CQb9Ds8dhPu+gGseBp86YC6AQyth2avw6XXwfhP4fijs/BZyzlZbmF2bh9CxSTCfN7uD729/rGRl1SO9H6DwyJFqi6OqFCcWVqzSNa8Bad9FTacoCu/0aoW/h5G9p7KY+PvfWgYDLXvB8M1w3VPqAnv7l6i96YnjwZRX6V34P/AA4R9+iGI0cn7ZMk4NfQJdfs1YkLW6lWny9qRJk8q94QEDBuDt7V3un/uvTp06XfWNd/jw4QwfPrzS+/q3YcOGMWzYMLKysvD19bXptoWwGx4B0Pwe9Wa1wtm/4eBydRL4kT8g+zTsmK/eUNS5HA1uRom8CcVSVGVhKYrC/+5uwS0frOIzGhH36gc0+Ph/FB48yOH7exP+wQd43XB9le2/qun+dU3HYrWUrMQtqoe070JAbW83xvdsyZC525iy6iCdm9SmbVSAdgG5esOtr0PcQ7D4OTi8Cla9ra7d1O0daFz6mmZl4XNbV/R+vhx/Yhh5mzdT58QJim7qiDHURpPGBVDGxGLEiBHUqVMHvb5sb37JycnceeedNkkshBDVRFEgqJF66zBUnUSXvEFNMg4mqpWnTiXBqSQMa97ndp0butwF0LCLWm2qVn2bhhMR4MFTNzfkvV/38epfFn6f9yUZo0aSl5RE8mOPUfu55wh4pD+Ko6zX8S863X8SCySxEEJUv9tahNKrdR2+3XqcZxYkseTpG/F207hKUlBjePhH2PODumJ3xlH48gFofAfcMQF8Kr6AqmeHDtSdPZvkxwbjdvIkJx5+mLozPsclIsJ28ddwZS43u2XLFmrXrl2m1zpDQpGQkEBCQgJmmeQjaiqjG9TrpN4Azp9WE4yDK7AeXIEh9yz8/at6A/CPUhOM+jdD9I3gVvkrwYNvrMd3245z6EwOn+7M4IXZszg9bhyZ3y0k9Z13yN+zh9D/jUPn7l7pfVWni3oskHkW1U3adyH+MbZ7M9YfTON4eh6vL9rDu71su65EhSiK2pPeoAusfg/WfwL7foHDq+GWsdBmoFoRsQLcWzQnfPZsDvZ7GJKTOdKnD3WnTcOtSRMbH0TNVKbfytixY/Hy8irzRl966SUCAjTsTrMBqRoixH94h0Dsg3DvNIpG7GFl4/9hjn8Vom4EnRHSj8CWGbCgL7wTDTNuU8sHntiqrrVRAS4GHa/coVYvmfnHEZLPmwh94w2CX3oJ9Hqyfv6ZIw88SOHRozY80KpXPMcCwGyRD7fVTdp3If7h7Wbkg/tjUBT4estxlu9NufoPVRdXL+gyDh5fDeFtoPA8LB4FM7pC6t4Kb9YlMpJjTwzFpVEjzGfOcvShfuRs2mTDwGuuMicWHh4eZd7o6NGj8fPzq2hMQgh7p+jI9IjCct3T/6yd8eACaPfYP2tnHFuvlg+c1hkmNIDvBkHSl3C+fG9a8Y1rc1OjIArNFt5avBdFUQh4uB+RX8xEHxhIwb59HO51H+cTE6vmWKvAvxMLKzJ5Wwihrfb1ajHohmgAXvp+J5l5Jo0j+o/g5jDwN+j2Hrh4wfFNMOVGWPEmFBVWaJNmHx/CZ87AvU1rLNnZJA8azPnff7dx4DWP3VeFEkI4AFcvaHwb3P7ehbUzdsCdH0KTO8HVB3LTYOc38MMQeL8RTLkBfn8NDq+56puCoii8ckdT9DqFX3ensO6gWp3Ko21bor/7DvfYWCznz3N86BOcmfQx1gr2jlSnf0/WNlulx0IIob1nb21MvUBPUrIKeGPRHq3DuZROD+0fg2EbofHtYDHB6ndhWjyc3lmhTep9fKg7fTpenTtjLSzk+FNPk/HttzYOvGYpd2KRlpbGsGHDaNasGYGBgQQEBFx0cxZSjlCISvCPgjaPwgPz1LUzBiyBG5+F0Fj1+6d3wh8fwqw74d1o+LIPbJ6uDqe6jEbB3vRtXxeA//28B7NFvcpvDK5N5OxZ6uqqwNnJk0keMgRzRkbVHl8l/XvCuZSbrX7SvgtxKTejnnd7tUJR4Jutx1m5L1XrkC7Ptw48MF8tle5RC1J2wWedYNW7YC5/T4vOzY06kz7C996eYLFw6pVXOTv1M2mbK6jMk7eL9evXjwMHDjBw4ECCg4MdsiJLWUg5QiFsRG+EyOvU281jIPsMHFzxT1nb3LPqpLx9v6ivD6gPDW5Rb1HXg4snAM/c0ogftp/gr9Pn+WH7Ce5tXQcAxcWFkDGv4h7TilNjxpKzeg2He91HnY8n4da0qVZHfUXSY6Etad+FuLw2UQE8cl0UM9ceYfTCnfz2zE3aV4m6nOLJ3ZE3wKIR8NciSHwT9i2GHlOgdvkmYisGA6FvvIEhoBZp06Zx5sMPKUo7S/CLL6JUcJJ4TVXuxGLNmjX88ccfxMTYQdUAIYTj8QqCmN7qzWKB0zvUBOPAckjeCOcOwqaDsGkq6F3UhKT+zfg3uIUhHevx7q/7+fD3/XSPCcPF8E+D73v33bg2asTxJ5/CdPw4Rx54kND/jcP37rs1PNjL+/cFGVl9WwhhT57r2pjle1M5di6Xtxb/xfieLbUOqXReQdB7rjrUdvEoOLkdpt4EXf4H7R9XE5AyUhSF2s+ORF8rgNS33yF99hzM59IJe+tNFBeXKjwI51LuNKxJkybk5VV+FUQhhECng7A4uGkUPLoEXjisvkm0fgR8I8Bc+K+VwK9lyPYeTPCYRYPMdXy9fv8lm3Nr2pTob7/B86YbsRYUcPKFFzn12mtYCgqq/dCupngCtyQWQgh74uFi4J17WwHw5aZj/PH3WY0jugpFgVb3wxMboeGtYC6ApS+oa1/kpJV7c7UeeYSwd98Bg4GsRYtIfmIYltzcKgjcOZU7sZg8eTIvv/wyq1atIi0tjaysrItuzkLG4AqhATdfaNodun8EI3bCsM3Qdbw6LMrghi7rOL0sv/KFy3v0Wn4j5rn3webPIfN4ySb0fn5ETJlC4BNPgKKQ8dUCjjxofyVpJbHQjrTvQlzZtfVr0a9DJAAvLvyTvEIHGLLpEwp9vlYrR+ldYf9SmHK9uvZFOfnedRcRkxNQ3NzI+eMPjg4YQFF6ehUE7XzKnVj4+fmRlZVF586dqV27Nv7+/vj7++Pn54e/v39VxKgJqXMuhMaKVwK/9gl46Dt4/jA8uADzNQNIUQJxoxD9gd/gl5HwYXP49Hr4fRwc24iClaCnniTis8/Q+/tTsGcvh+/tRdavv2l9VCWKF8mTxKL6SfsuxNW92K0J4X7uHE/P46Plf2sdTtkoilo5avByCGwE50/BrLtg+etgLirXprxuuom6M2eg8/Ulf8efHH2oH6ZTp6oocOdR7sSib9++GI1G5s+fz/Lly1mxYgUrVqwgMTGRFStWVEWMQggBLh7Q+Db0d01kffdV3FbwNh/xIObwdqDo1Mogf3wAM26F9xrAwsfwCjhD9II5uLdW65SfePppTr/5FtbCitU9tyW9Tp3ALYmFEMIeeboaGHdXcwCmrznEvtPnNY6oHEJawmMr4ZqHASusmQBze5Z7aJRHXBxR8+ZiCAmh8OBBjjzYh4IDB6okZGdR7snbu3btYvv27TRu3Lgq4hFCiKvqHhvOx4lN+fBMXVwbNWFIX3848Lva9X3gd8g7B38ugD8XYNS7EnlbJ86EdSDt5w2kz5lDXlIS4R9+iEudcM2OQUGdVCiJhRDCXt3SLJjbmoewdPdpXvlpD/21azLLz8UT7voYojvCT0/B4VXwWUfoPQeCWpR5M64NGhA1fx7HBg2m8NAhjvZ9iIipU3CPja262B1YuXss2rRpQ3JyclXEIoQQZaLXKQzt1ACA6WsOk2/0VSfv9ZoBzx2CRxbDdU9CQD0wF6Ac/JXangupc1M6Ojcd+Tt3cvieHpzXsJfVWUt1CyGcy9i7muHpoicpOZP1qQ7YbrXsBYN+V98PMpPh864oO74s1yaMYWFEzpuLW0wrzJmZHB3wKNmryz93oyYod2Lx5JNP8vTTT/PFF1+wdetW/vzzz4tuQghRHe6ODSPcz52z2QV8veVfFzv0BnX9i1vfgCe3wRMbIP4VCI3FOyyPel1O4V6rEMv5bI4/MYyU4fdiPb4dZDEkIYS4RKivO6O6qqNUfj6q48x5+6uyd1XBzWBwIjS6DcwFGBY9Scvjc8BS9knpBn9/ImfMwPOGG7Dm5ZH8xDAyf/65CoN2TOVOLHr37s3evXt59NFHadu2LbGxscTFxZXcOwupGiKEfTPqdQzpWA+AqasOYTJfZkiRokDtptDxOXh8FYzYhfHet4h8tAkBjXMAOPf7Ho727kXhG7Hq5O/TOyXJcHLSvgtRPg9fG0WLMB/yzApvLtmndTgV4+4HD3wJnV4CoN6ZZei/fRgKc8q8CZ2nJxGTE/C54w4oKuLkc89zbtasKgrYMZU7sTh8+PAlt0OHDpXcOwupGiKE/buvTQSBXq6cyMjjp6STV/8BvwjoMARl4C8Ef5lEnWfvQ+eqJy/NhcNf55M1fzJMuQE+aQsr3oTUvVV/EKLaSfsuRPnodQqv39UMBSu/7DzNmr/PaB1Sxeh00OkFinrOwKwY0f39K8y8Hc6fLvMmFBcXwt57F/9+/QBIGf82qR98iFUuSAEVSCwiIyOveBNCiOriZtQz4PooAGauO1y+ht0jAO/B/yP6l19xj2mFxaTjxLoATm4OwJJyAFa/C5M7QEIHWPUupB2smoMQQggH0CLchxtD1Db2tZ92X76X2EFYm97F2oYvYvWoBaeSYPot5bqQpOh0BL80mqARIwBI++wzTr36Ktai8pW0dUblTizGjx/PjBkzLnl+xowZvPPOOzYJSgghyurBdnVxNejYdSKLbcfKv4CRS51wIufNo9bQIaAoZB504/C6FuT7dASdEc7shcQ34eNrYHoXdUG+3HNVcCRCCGHfukVY8PcwcvBMDrPWHdE6nEpJ92xI0SNLIaC+Oql7xm1wYmuZf15RFAKHPE7I6/8DnY7Mb7/j+NMjsOTnV2HU9q/cicXUqVNp0qTJJc83b96cKVOm2CQoIYQoqwBPF+6ODQPgi3UVW11bMRio/fTT1J05E0Pt2hSeTOPIzCOcC38T612fQP3O6loZxzepC/K93xgWPISybzGKRa5QCSFqBg8DjOrSEICPfv/bMSdy/5t/tFoxqk5byM+AWXfD0fXl28R99xH+0UQUFxeyly/n2KBBmLOyqiZeB1DuxOL06dOEhoZe8nxQUBCnZEVCIYQG+l8XBcCSnadIyar41SLPDu2J/vEHvDp3xmoykfLeRI5/to6iOz6HkXvVSlPBLcBcCHt/xvDtw3Td9RS6X0dDym4bHY0QQtive68Jp2W4L+cLinjv17+0DqfyPAKg3/cQdSMUnlcX0ju0qlyb8OnShYjp09B5eZG3ZStH+z2MKTW1igK2b+VOLCIiIli7du0lz69du5awsDCbBGUPpGqIEI6jeZgvbaP8KbJYmbfxWKW2ZfD3p07CJwS/+op6BWrlSg7f3YOc3UfUtTGGroUhf8C1w7F61sbVnI1+yzT49Dp1qNT2eVCYa5sDE1VC2nchKk6vU3jtrmYAfL3lOEnJGdoGZAuu3tDna6h/M5hyYd596mKr5eDZrh2Rc2ajDwykYN8+jvbpS+HRivWiO7JyJxaDBw9mxIgRzJw5k6NHj3L06FFmzJjBM888w+DBg6siRk1I1RAhHEtxr8XXm5MxWypXnUNRFAL69iXqm69xqV+fojNnODbgUVLffx9rYSGEtISub1L01J+srz8KS5O7QGdQh0r9+AS83wR+GQWnd9ngyIStSfsuROW0jgygZ5y6DPdrP+12jopILh7w4JfQ+A4wF8BXD8GxDeXahFvTpkTNn4exbl1Mx49zpE9f8nbXrN7scicWzz33HAMHDuSJJ56gXr161KtXjyeffJKnnnqK0aNHV0WMQghxVV2aBePnYeR0Vj5rD5y1yTbdGjcm+puv8bvvPrBaSZs2ncO9H6Dg77/VF+gMpPq0wnzvDHhmD9w8FvyjoCATNk+DKdfD511hz4+lLsRkxQnekIUQNc6L3Zrg4aInKTmDX3Y6yVB4gyvc9wU06AJFeTDvfjhVvsWfXerWJWreXFybNsWclsaxh/uTs6F8CYojK3dioSgK77zzDmfOnGHDhg3s2LGDc+fOMWbMmKqITwghysTVoOfuGHU45rdbj9tsuzoPD0Jf/x/hkz5C7+dHwd69HL63F+dmz8Zq+Ve5Re9guHEkPLkd+v0AzXqovRjJG+Drh2FSLKz7BPIzAVBQbBajEEJUt9o+bjx+U30A3l26j8Iixy0/exGDC9w/G+peq14kmtsTzh4o3yaCgoicPQuPdu2w5OSQPPgxsn79rYoCti/lTiyKeXl50bZtW1q0aIGrq6stYxJCiArp1ToCgF93nyYzz2TTbfvceivRP/2I5403Yi0sJOWt8ZwcMhRDZubFL9TpoH483D8LRuyCG0eBewBkHIPfXoYPmsGSF8DqJG/CQogaa9CN0QR5u3LsXC5zNzjRfAIXD3jwK3XYa84ZmHsP5JSvJ1zv7U3EtM/w7tIFq8nEiREjSP9qQRUFbD/KlFj07NmTrHKUzurbty+pNXQ2vBBCOy3CfWgc7E1BkYVf/rR917yxdm0iPptK8JhXUdzcyFu/nsgPJ5Jd2pUon1C4+VUYuQe6fwRBTaAwGzZOUe8B0is32VwIIbTi6WrgmVsaAfDxir9tfkFHU+5+8ND36vDWjGNqz3NRYbk2oXN1JXzih/jdfz9YrZx+7TXOTJ7sHHNSSlGmxOLHH3/kzJkzZGVlXfWWmZnJzz//THZ2dlXHLoQQF1EUhXtbqxMKF26z3XCo/+4joE8fohd+h2uzZujz8jg9ahQnX3gB8/nzl/8hozu0fgSe2AAPLYR68f98b25PWPgYnNlfJfEKIURVur9NHRrU9iI918SUVQe1Dse2vILgwQXg4g1H18LiZ6GcSYGi1xMy7jV1EVbg7KSPSXn9jYuH0jqRMiUWVquVRo0a4e/vf9VbQEAAOTk5VR23EEJcVvcL8yy2HksntRJrWlyNa7161Jk7h7TOndVVV3/8icN39yD3SpWGFAUa3AwP/wBGT/U5qwX+XAAJ7eDbRyHNyd6YhRBOzaDX8eJt6sLJM/44zMmMPI0jsrHaTaDXDECBbbNh49Ryb0JRFGo//TTBL78MQPr8+ZwcNQpLYfl6QByBoSwvSkxMLPeGw8PDy/0zQghRWaG+7sRG+JGUnMGvu0/T79qoKtuXYjSS1vVWWg14hNSXXsaUnMzRh/sT8OgAgp56Ct2V5p/p9GAGes+FLXNg3y+w6zvY8xO0HQg3PQ+etaosdiGEsJWbm9amXXQAmw6f4+MVfzO+ZyutQ7KtRrfCra/Db6+oc+Ui2kJ463JvJqDfQ+j9/Tk5ejRZi5dgzsggfNLH6L08qyBobZQpsejYsWNVxyGEEDbTrUUISckZLK3ixKKYe2ws0d9/T8r4t8j8biHnPp9B9spVhL09HveWLa/8w8HN4MH5aknD5ePURZk2ToGkL9UqU+2HgNGtyo9BCCEqSlEUnu/amF5T1vPNluMM7diAurU8tA7Ltq4dDsc3q+XDv30UHl8Dbj7l3ozvnXeg9/Pj+FNPkbNuPcceeYSIz6ZiCAiogqCrX4WrQjk7WZlVCMfVrUUoABsOnSM9p3q6mvVenoS9+SZ1JiegDwyk8OBBjjzwIKkTJ5atuzu0FTz0nVqqNqSlWubw97HwSVvY/2uVx1+TSPsuhO21iQrgpkZBFFmsTFrxt9bh2J6iQPdJ4FsX0o/AohHlnm9RzOuG64n8YiZ6Pz/yd+3iaJ++mE6csGm4WpHEohSyMqsQjqtuLQ+ahfpgtlhZtielWvft3bkz9X7+CZ877gCzmbQpUznS6z7y9+wp2wbqx8Njq6HHFPAJh8xjMP9++Lo/ZDnJIlQak/ZdiKoxsotaIWrhtuMcPuuE823d/aDX56Do1aGrf1a8fKx7q1ZEzp+HISyUwiNHOPJgH/L3O34RD0kshBBO6dbmwQCs3F/9pa8N/v6Evz+B8I8+Qh8QQMH+/Ry+vzdnPknAaipDOUadDmIfhOGb4bon1TexPT+oE7w3TQMnrSYihHBssRF+3NykNhYrfPS7439IvqyIdhA/Wn289EXIPlPhTbnWq0fU/Pm4NKhPUWoqRx/qR+62bTYKVBuSWAghnNJNjYIA+OPvs5gt2tQM9+l6K/UW/Yx3165QVMTZTz7h8P29yd+3r+Q1V6xn7uIJt74Bj61UJwoWZMHiUWqJWum9EELYoWcu9Fr8uOMkB1JLKcHt6K4foQ5ZzUuHJc9XalPGkBCi5s7FPTYWS1YWxx4dyPmVK20SphbKnVjMnj37otvcuXP59ddfyc3NrYr4hBCiQmLq+OHjZiArv4gdxzM0i8MQEED4xA8J/+B99L6+FOzdy+Fe93Hnmnx0ZU14QlvBwGXQ7T0wesChRPj0OvhrcdUGL4QQ5dQi3JeuzYOxWuHjFQe0Dqdq6I1w18eg6GD3Qti3pHKb8/Oj7swZeHa8CWt+PseHDSfj+x9sE2s1K3di8fTTT190e+KJJ+jWrRvR0dHs3r27KmIUQohy0+sUbmgYCMDq/RXvqrYFRVHwuf126i36Ga+bbwaTiZ6J+bwxy4w17VzZNqLTQ/vH4LFVENIK8s7BVw/CopFQVFC1ByCEEOXw1M0NAfh5x0mOpTnpheewOLVSFKi9FqbKrZukc3cn4pNP8L37LjCbOTV6NOlfVXwOh1bKnVikp6dfdMvKyuLUqVPccMMNjBgxogpCFEKIirmpoTocSuvEopghKIg6n3xM2LvvkOuq0OA0mFesLd9GghrBoN/huqfUr7d8DrPuqtQ4XyGEsKXmYb50bBSExQpTVzvxop+dXgTvMMg4Bhs/rfTmFKOR0PHj8X+4HwBp06ZVepvVrdyJxZ9//nnJLSUlhUcffZRVq1axadOmkueFEEJLN16YZ7HjeCY5BUUaR6NSFAXfu+5iZ4MLywiZzeXfiMFVXayp73fg6gvJG+CzTupaGEIIYQee6FQfgG+2Hif1fOWu5tstF0+4eYz6ePX7NrnAo+h0+PXsCYCl0PF6o8u0QN6/xcbGoijKZSccKopChw4dSh6bK/KGKYQQNhLu5064nzsnMvJISs7g+gaBWodkWw1vgcEr4MvekHYAZnSF++eozwshhIbaRQfQOtKfrUfT+fyPw4zu1lTrkKpGq96waSqc3A4rx8OdH2gdkabK3WNx+PBhDh06xOHDhy+6JSYmotfr2bNnT8lrhBBCa60j/QHYciRd40iqSGADGLQc6t8Mplz48gHY85PWUQkhajhFUUp6LeZtOEZmXhlKbTsinQ5ufVN9vG02ZCRrG4/Gyp1Y+Pv7X3Tz9fUlPT2dN954g65du9KkSRMiIyOJjIysiniFEKJc2kZdSCyOlnGStCNy94M+C6B5T7CY4JtHYMdXWkclhKjhOjepTZMQb7ILipi74ajW4VSdqOsh+ia1/f3jQ62j0VS5Ews/P7+LEotatWpxzTXXkJWVRUJCQlXEWGn33HMP/v7+9OrVS+tQhBDVrE1UAADbjqZTZHbiheX0Rrh3OsQ+BFYzfD8Edn+vdVRCiBpMURQe71gPgNnrj2By5ja44wvq/fY5kHlC21g0VO7EIjExkRUrVpTcVq1axZEjR9i4cSN169atihgr7emnn2b27NlahyGE0ECjYG+8XQ3kFJrZl+KkizUV0+nV2uqtBwBWWPgYHF6tdVRCiBrsjpZhBHm7kpJVwOKdTrywZ9QNEHk9mAthvX1eaK8O5U4sOnbseNHthhtusNuEolinTp3w9vbWOgwhhAb0OoUW4b4A7D6RpXE01UCngzveh6Z3qW9wX/WF07u0jkoIUUO5GHQ81F4dHj9z7RFtg6lq149Q77fPgYJsTUPRSrkTi+q2evVqunfvTlhYGIqi8MMPP1zymoSEBKKionBzc6N9+/Zs2rSp+gMVQtitFuE+AOw+malxJNVEp4ee0yDyBijIggV9IS9D66iEEDVU3w51cdHrSErOYNsxJy2kAdDgFgior7a7O77UOhpN2H1ikZOTQ0xMTKnzNxYsWMDIkSMZO3Ys27ZtIyYmhq5du5KamlrNkQoh7FXzMLXHYtdJ++uxsHJp6W6bMLpB7zngVxfSj8APQ8HixOObhRB2K9DLlbtiwwCY8cdhjaOpQjodtH9cfbxxKlxmaQZnV+51LKpbt27d6NatW6nf/+CDDxg8eDADBgwAYMqUKfzyyy/MmDGDF198sdz7KygooKDgnwVJsrLUDyImkwmTyUlLpZVB8bHX5HMAch6KOdp5aFzbA4C9p7LILyhEr1Nssl1bnAeLxVJ159HoDT1nYJh1B8q+xZjXTsLSYZjNd+MofwfO3L472v/klcix2KfKHku/9nX4dutxluw6zbGz5wn1dbNleOVSpb+X5vdhWD4OJe1vig7/gTWiQ7k3UVR0YUFX69VjrI6/sfJs2+4TiyspLCxk69atjB49uuQ5nU7HLbfcwvr16yu0zfHjxzNu3LhLnk9MTMTDw6PCsTqLZcuWaR2CXZDzoHKU82CxgotOT26hmdnfLyHY3bbbr8h5sFy4knXgwEH+WrzYtgH9R2RYH2KTZ8KKN1h90o1st1Cbbj83N9em26sqNaF9d5T/ybKQY7FPlTmW+t56Dp6Ht75KpFuE9lfzq+r3Eut1DZHn1nD8l/fYUXdguX/e5eRJolAvhiwu4/tDVf6NlaeNd+jE4uzZs5jNZoKDgy96Pjg4mL/++qvk61tuuYUdO3aQk5NDnTp1+Oabb7j22msvu83Ro0czcuTIkq+zsrKIiIggPj6eWrVqVc2BOACTycSyZcvo0qULRqNR63A0I+dB5YjnYebxDfx5PIuwJq3p2jz46j9QBpU5D/NmvwpAgwb1qXf77TaJp1TWbli+Oor+0Ariz3+HucfP6jwMG0lLS7PZtqqSM7fvjvg/WRo5Fvtki2MpCj/Fs9/uJCnLgw+73ohBr82I/Kr+vShHfWHuGiKztxHeZQ4Yy3fhomDfPpI/moSrqyu3X+X9oTr+xop7d8vCoROLsvr999/L/FpXV1dcXV0ved5oNDp8o2ALch5Uch5UjnQeGtT25s/jWRxNz7d5zBU6DxdGY+l0uuo5h3dNgsnXoju+Cd3O+dDmUZtt2lH+BmpC+y7HYp/kWFR3xITzxuK/OJ1VwLrDGdzc1DYXeSqqyn4v9W4Cv7ooGccwHvgNWt1Xrh83Gy58PFfK3r5W5d9YebZr95O3ryQwMBC9Xk9KSspFz6ekpBASElKpbSckJNCsWTPatm1bqe0IIexD/SAvAA6m1swSgPhFQOdX1MeJb0G+/U1kry7SvguhDTejnnuvqQPA/I3HNI6mCul0EPOg+njn19rGUs0cOrFwcXGhdevWLF++vOQ5i8XC8uXLSx3qVFbDhg1jz549bN68ubJhCiHsQHFiceBMDU0sQO2lCKgPOWdg7USto9GMtO9CaOfB9uraZ4n7UjmZkadxNFWoeU/1/tDKGnUhx+4Ti+zsbJKSkkhKSgLg8OHDJCUlceyYmumOHDmSadOmMWvWLPbu3cvQoUPJyckpqRJVUXJFSwjn0qC2J6D2WFhrYAlAAAwu0OV/6uMNUyD3nLbxaETadyG0Uz/Ii/bRAVissGBzstbhVJ2gxuqFHHMhHHCeyftXY/eJxZYtW4iLiyMuLg5QE4m4uDjGjBkDQO/evZkwYQJjxowhNjaWpKQkli5desmE7vKSK1pCOJe6AZ7odQo5hWZSsgqu/gPOqskdENIKTDlqnfUaSNp3IbTV50Kvxbdbj2OxOOmFHkWBpneqj//6RdtYqpHdJxadOnXCarVecvviiy9KXjN8+HCOHj1KQUEBGzdupH379toFLISwSy4GHRH+ap3ZI2k5GkejIUWBG55RH2+cAoU1+FwIITTRtXkI3q4GTmTksfmIE/ecNrmQWOz/DYpqxgUtu08shBDCVsIvJBYn0p14XG9ZNLsb/KMgPwN2f691NEKIGsbNqOf2lup6Ogu3ndA4mioU3gY8g6DwPByvGT2kkliUQsbgCuF8wv0uJBZ2NGFQk0EAOj20fkR9vPULLSLQlLTvQmjvnmvCAVi88xT5JrPG0VQRnQ6iO6qPD63UNJTqIolFKWQMrhDOJ9xPXaTIPnosFG13H9sXdAb1KlrqX1d/vROR9l0I7bWLCiDcz53zBUUs25Ny9R9wVPXj1XtJLIQQwrmUDIWyox4LzXjVhga3qI/3/KhtLEKIGkenU7gnTu21WLjtuMbRVKHiHosTWyE/U9tYqoEkFqWQrnIhnI89DoXSVLO71fsallhI+y6EfSgeDrX677OcOe+kk5v9IqBWA7Ba4Og6raOpcpJYlEK6yoVwPnX+1WPhtCUOy6NxN3U4VOpuOPu31tFUG2nfhbAP9YO8iInww2yx8sufJ7UOp+rU7aDeJ2/SNo5qIImFEKLGCPF1A6CwyMK53EKNo7ED7v4QdaP6+O/ftI1FCFEjdW+lVodavOu0xpFUoTrt1PsaUBlKEgshRI1h1Ovw9zACcDbbSbvdy6t+Z/X+8Gpt4xBC1EjdLpSd3XzkHKlZ+RpHU0UiLiQWJ7aCuUjbWKqYJBZCiBol0MsVgLPnpccCgOib1Psja53+DU8IYX/C/dyJjfDDaoWlu5201yKwMbj6gilXHXrqxCSxKIVM7hPCOZUkFtJjoQppCW5+6gJOp5K0jqZaSPsuhH2540KvxS9/ntI4kiqi00Gd1upjJ59nIYlFKWRynxDOKdBbEouL6PQQ0V59fGKbtrFUE2nfhbAv3VqGALDpyDlSzzvpcKiwOPX+9E5t46hiklgIIWqUQC8XAM5IYvGPsFj1vob0WAgh7Esdfw9iLgyH+tVZJ3EHt1DvU3ZpG0cVk8RCCFGjyByLywiNVe9PJmkZhRCiBrvjQq/FEmdNLEJaqvepe8Fi1jaWKiSJhRCiRgmykzkWiqZ7/4/iHoszf4FJFg8UQlS/Ls0uDIc6fI6sfJPG0VSBgHpgcFcncJ87rHU0VUYSi1LI5D4hnFOtC0OhzuVIj0UJ71DwCASrGc7s0zqaKiftuxD2JzrQk3pBnhRZrKzZf1brcGxPp4faTdXHTjwcShKLUsjkPiGck6+7uo7FeWe8IlZRigK1GqiPzx3UNpZqIO27EPbp5ia1AVi+N0XjSKpIcHP1XhILIYRwDj4XEovMPEksLlKrvnqfdkjbOIQQNdbNTYMBSNyXitli1TiaKlC7mXp/dr+2cVQhSSyEEDWKj5uaWGTlF2G1OuEbV0UF1FPva0CPhRDCPrWJ9MfHzUB6rontx9K1Dsf2asAFHEkshBA1io+7AQCzxUpuofNW5ii3kjc8SSyEENow6HV0aqwOh/p9b6rG0VSBgAvt7LmD4KQXtiSxEELUKO5GPQadWpPJKSuPVFTJG57zXkkTQti/m5uqicWKv5xwnoV/JCh6tTLUeedcZVwSCyFEjaIoSsk8i6y8Io2jsSM+4ep97lkokopZQghtdGpUG71OYX9KNsfTc7UOx7b0RvCrqz520t5hSSxKIeUIhXBePm7qcCjpsfgXjwDQqQkXOU44BOFfpH0Xwn75ehiJjfAD4I+/nbDsbK1/DYdyQpJYlELKEQrhvHxLeiwksSihKOClVmThvBMOQfgXad+FsG/XNwgEYM0BJ0wsApx7PpskFkKIGsfrQo/F+Xx7GAplRxP4vC8kFtmntY1DCFGj3dhQTSzWHTiLxdnKzgZEq/cZR7WNo4pIYiGEqHHcjWpikWeSqlAX8QpR789LYiGE0E5shB9ermrZ2d0ns7QOx7aK57NlntA2jioiiYUQosZxd9EDSLnZ/yrpsXDuoVBCCPtm1OvoUC8AgDUHzmgcjY35Xkgssk5qG0cVkcRCCFHjeBjVxCJfeiwu5qEOPyDPCRemEkI4lBsuzLNwugncPnXU++zTYLaH4bi2JYmFEKLGKe6xyJMei4u5+ar3eRmahiGEEDc0DAJgy5F052qrPYPUCnxWi1OuZSGJhRCixnEzylCoy3LzUe/zM7WNQwhR49UP8iTU141Cs4UtR89pHY7t6HTgE6o+znK+eRaSWAghahyP4h4LGQp1seIeC0kshBAaUxSFDvVqAbD5sBMlFvDPcChJLIQQwvG5G4uHQjnf+NZKkcRCCGFH2kWrE7g3Olti4eu8laEksSiFrMwqhPNylx6Ly6shiYW070I4huLEYntyBgVFTtRee18YCiVzLGoOWZlVCOflLnMsLq8kscjQNIyqJu27EI6hXqAngV4uFBZZ2HnciS54eKoT08lxsopXSGIhhKiBiudYSLnZ/3DxUu9NuWB1stVuhRAOR1EU2kY54XCo4sQiVxILIYRweG52tECeXX1+N7j987goX7s4hBDiguLhUJucMbHIcbLF/5DEQghRA7ka1KbPZLZoGIWi4b5LYXT/57EpT7s4hBDiguIei61H0zFb7OlKTCV4XliMVIZCCSGE4zPqixMLJ3mTshW9ERS1N4eiAm1jEUIIoGmoD96uBrILith7KkvrcGzj3z0WdtVtXXmSWAghapzixKKwSMseCztV3GtRJD0WQgjt6XUKraP8Adh8xEmGQxX3WFiKnK5YhiQWQogax6hXhyEVWSSxuETxPAuTzLEQQtiHuAg1sUhKztA2EFsxuIKrj/rYyYZDSWIhhKhxXGQoVOmKEwvpsRBC2InYun6AEyUW8K95Fs41gVsSCyFEjWMoTixkKNSljNJjIYSwL7F1/AA4mpbLuZxCbYOxFY9a6n2ukwzvuqBGJBaLFi2icePGNGzYkOnTp2sdjhBCY8VDoQo1rQplpwzFcywksRBC2AdfDyP1Aj0B2OEsvRYlC5I60cJ/1IDEoqioiJEjR7JixQq2b9/Oe++9R1pamtZhCSE0VDwUqshZShfaUnGPhSQWQgg7EhvhB8B2SSzsmtMnFps2baJ58+aEh4fj5eVFt27d+O2337QOSwihoeKqUGaL1XnqottKyeRtmWMhhLAfTjfPQhILbaxevZru3bsTFhaGoij88MMPl7wmISGBqKgo3NzcaN++PZs2bSr53smTJwkPDy/5Ojw8nBMnTlRH6EIIO2XQ/7M4nbaL5NkhnUG9t2i/KrkQQhQrrgy1IzkDqzOs/SCJhTZycnKIiYkhISHhst9fsGABI0eOZOzYsWzbto2YmBi6du1KampqNUcqhHAUxT0WIInFJUoSiyJt4xBCiH9pEuqNq0FHZp6JI2m5WodTeZJYaKNbt2688cYb3HPPPZf9/gcffMDgwYMZMGAAzZo1Y8qUKXh4eDBjxgwAwsLCLuqhOHHiBGFhYdUSuxDCPv07sSjSvOSs1vv/D92Flbet0mMhhLAfRr2OFuHqh/Gk5HSNo7EBJ00sDFoHUBmFhYVs3bqV0aNHlzyn0+m45ZZbWL9+PQDt2rVj165dnDhxAl9fX5YsWcKrr75a6jYLCgooKCgo+TorS10+3mQyYTKZquhI7F/xsdfkcwByHoo5w3nQ6xTMFiu5BYV4uShX/4HLsMV5sFgsdnUe9ejQAWZTAZYyxmVP8V+JM7fvzvA/WUyOxT7Zw7G0DPNm69F0ko6lc2eL4Apvxx6ORTF6YQAseemY/xNHUdGFHmPr1WOsjmMpz7YdOrE4e/YsZrOZ4OCL/7iCg4P566+/ADAYDLz//vvEx8djsVh4/vnnqVWrVqnbHD9+POPGjbvk+cTERDw8PGx7AA5o2bJlWodgF+Q8qBz5POiseswo/Pb7cgJcK7etipwHy4VVvw8ePMj+xYsrF4ANtU09Qxiwa+efHDldtrhycx1jWEJNaN8d+X/yv+RY7JOWx2I6owB6/th1lMUcqvT2tDyWoKx9XAecP3Oclf95D3A5eZIo1Ishi8v4/lCVx1KeNt6hE4uyuuuuu7jrrrvK9NrRo0czcuTIkq+zsrKIiIggPj7+igmJszOZTCxbtowuXbpgNBq1Dkczch5UznAeXt62AlNBETfc1JGoWp4V2kZlzsP8OWMBqF+/Pg1uv71C+68K+u8XQsZmWjRrQrO2ZYvLUUp4O3P77gz/k8XkWOyTPRxL/dPnmXdgPSmFRm677VZ0uor3Nmt9LMqJYDg4AR8XK7f/5z2gYN8+kj+ahKur6yXf+6/qOJbi3t2ycOjEIjAwEL1eT0pKykXPp6SkEBISUqFturq64urqSkJCAgkJCZjN6jhjo9Ho8I2CLch5UMl5UDnyeXAx6KAArIq+0sdQofNw4f1Qp9PZ1znUu6h3WNGXMS67iv8KakL7Lsdin+RYbKNxmB8uBh3ZBUWczjYRWcGLQsU0/b14qRczlPysS2IwGy58PFfK3r5W5bGUZ7t2P3n7SlxcXGjdujXLly8vec5isbB8+XKuvfbaSm172LBh7Nmzh82bN1c2TCGEHdIp6id7WcfiP2rA5G1p34VwTEa9jsbB3gDsPln2q+h2qXjydkEmWJynOqHdJxbZ2dkkJSWRlJQEwOHDh0lKSuLYsWMAjBw5kmnTpjFr1iz27t3L0KFDycnJYcCAARpGLYSwd8U96BZnqIduS8WJhZSbFULYoeZhPgDsPung1ZRc/tXbYnKMeWplYfdDobZs2UJ8fHzJ18XjY/v3788XX3xB7969OXPmDGPGjOH06dPExsaydOnSSyZ0l9d/u8qFEM5FfyGzkLziP5TixMJ5rqD9l7TvQjiufxILB++xMLj/89iUC65e2sViQ3afWHTq1OmqKywOHz6c4cOH23S/w4YNY9iwYWRlZeHr62vTbQshtCdDoUpRAxbIk/ZdCMfVLEz9n3X4xEKnA6OHmlQU5mgdjc3Y/VAoIYSoCroLrZ8MhfoPGQolhLBjTUO9URQ4c76A1PP5WodTOcYLZa6daCiUJBalSEhIoFmzZrRt21brUIQQVaC4x0ISi/8o7rFw4snb0r4L4bg8XAzUC1TnJzh8r0VJYpGnbRw2JIlFKaRqiBDO7Z/EQts47C6tUYq7cpy3x0LadyEcW9NQdZ7F/tPnNY6kklwuJBYyFEoIIRxbSVUozTMLO0stSuZYOO/kbSGEY2t0oeTsvhQHTyxkKJQQQjiHksnb9vbBXms1YPK2EMKxFScWf6dkaxxJJRWXnJUeC+cnY3CFcG7FiYXkFf9RAyZvS/suhGNrFKyWZv079bz2vc6VUVqPxYX3J0ckiUUpZAyuEM5Np5Nys5fnuG9oZSXtuxCOLbKWJy4GHfkmC8npDjyMqGSOhQMfw39IYiGEqJFk5W0hhHBMep1C/SC112K/Iw+HMl4YCmWSoVBCCOHQZCiUEEI4rsbBxYmFA0/glh6LmkPG4Arh3GQoVM0l7bsQjq/hhQncDp1YyDoWNYeMwRXCuclQqJpL2nchHF+jksTCgYdCuchQKCGEcAp6WXlbCCEcVuMLicXB1GyKzA667o7BVb035Wsbhw1JYiGEqJHsZeVtIYQQ5VfH3x13o55Cs4Wj5xx0joL+QmJhLtQ2DhuSxEIIUSMpdjMUSuv9l8Ze4xJCCHWeXIPaF9azcNThUAYX9V4SCyGEcGx6jSdvK/a6XoQDL8wkhKhZ6gepcxQOnXXQxKK4x6KoQNs4bEgSi1JI1RAhnJuUm625pH0XwjlEB6o9FofPOOjk5+I5FmZJLJyeVA0RwrkVX5iXcrM1j7TvQjiH6As9FofPOmhioTeq90UyFEoIIRxa8VAo7edYCCGEqIh6gY6eWEiPhRBCOAWdlJsVQgiHFnUhsUjLKSQz16RxNBVQMnnbAWMvhSQWQogaScrNCiGEY/NyNVDbW73qfzjNAXstZPK2EEI4B53Msbgy6ckRQjiAesWVoc44YGUombwthBDOQaqqlkZOjBDCcZRUhnLEeRb6C0OhZPK285NyhEII4ZykfRfCeRRP4D7kiImF9FjUHFKOUAghnJO070I4j+jiylCOuJaF9FgIIYQQQghhH/69loXV0eaGFScW0mMhhBBCCCGEtiL8PdDrFPJMZk5n5WsdTvkUD4WyFIHFom0sNiKJhRBCCCGEcEguBh11/N0BOJqWq3E05VTcYwFgdo7hUJJYCCFqNAfrOBdCCPEfEf4eACSfc7DEorjHApxmOJQkFkKIGkmRsqpXISmXEMIxRAQ4aGLx7x4LJ5nALYmFEEKIf8gCH0IIBxMRoA6FSk7P0ziSclIU0BnUx5YibWOxEUkshBBCCCGEw6p7ocfimKP1WAAoevVeEgvnJgsoCSGEc5L2XQjn4rBzLEB6LGoKWUBJCCGck7TvQjiX4h6L1PMF5JvMGkdTTsWJhVXKzQohhBBCCKEpPw8jXq7qB/Tj6Q7Wa6GToVBCCCGEEELYBUVR/lUZysEmcMtQKCGEcHwlxY+s2pZVlaKuQghReXUvVIZyuAnc0mMhhBDC6WmccAkhRHk47ARu6bEQQgjhvGQdCyGE46lby0FLzpb0WMjkbSGEEEIIITRX0mPhaIvkSY+FEEIIIYQQ9qN49e3jDtdjIYmFEEIIIYQQdiPUV00szhcUcT7fpHE05SCJhRBCCCGEEPbD09WAr7sRgFOZ+RpHUw7KhY/iFgdb2K8UNSKxuOeee/D396dXr15ahyKEsBPF5Wal9pEQQjiHUF83AE5kONA8i5KVty+TWDjgG1SNSCyefvppZs+erXUYQgjxD7svvuSA72hCiBotzE8dDnUqw4F6LGQolOPp1KkT3t7eWochhBD2z+4THiGEuLwwP7XH4lSmA/ZYSGJhG6tXr6Z79+6EhYWhKAo//PDDJa9JSEggKioKNzc32rdvz6ZNm6o/UCGEEEIIYbeKJ3A71lAoWXnbpnJycoiJiSEhIeGy31+wYAEjR45k7NixbNu2jZiYGLp27UpqamrJa2JjY2nRosUlt5MnT1bXYQghhBBCCA2V9Fg41FCo4sTCOSZvG7QOoFu3bnTr1q3U73/wwQcMHjyYAQMGADBlyhR++eUXZsyYwYsvvghAUlKSzeIpKCigoKCg5OusrCwATCYTJpMDlS+zseJjr8nnAOQ8FHOG82CxqHMIzGZzhY+jMueheAZDZfZfFXRmC3rAYrFgLmNc9hT/lThz++4M/5PF5FjskyMcS5CnWhXqZEbeFeO0p2PRo0cHFJkKsF6Ip8h0offCar1qjNVxLOXZtuaJxZUUFhaydetWRo8eXfKcTqfjlltuYf369VWyz/HjxzNu3LhLnk9MTMTDw6NK9ulIli1bpnUIdkHOg8qRz8PpUzpAx+7du1mctqtS26rIebCYLQAcPnSIg4sXV2r/ttTo9H6aAseSk9lRxrhycx1jQaqa0L478v/kf8mx2Cd7Ppa0fAADJ9Jz+OWXxSXV/0pjD8fS/mwaIcDOHds5dtwHAJdTp4hCvRiyuIztcFUeS3naeLtOLM6ePYvZbCY4OPii54ODg/nrr7/KvJ1bbrmFHTt2kJOTQ506dfjmm2+49tprL/va0aNHM3LkyJKvs7KyiIiIID4+nlq1alXsQJyAyWRi2bJldOnSBaPRqHU4mpHzoHKG87A0awdJ51Jo1qw5t3eoW6FtVOY8fDnvNQCi69Wj8e23V2j/VUH3x19wCupGRBBexrjS0tKqOCrbcOb23Rn+J4vJsdgnRziWwiILryf9TpFVoUPHm6nl5XrZ19nTsei/+QqykmjZvBktrlHb3IJ9+0me+BGurq7cfpV2uDqOpbh3tyzsOrGwld9//73Mr3V1dcXV9dI/RKPRqPkfnz2Q86CS86By5POg06lTzPR6faWPoWLnQb2Uptfp7Osc6tXzotPp0JUxLruK/wpqQvsux2Kf5Fiqh9EIgV6unDlfwJkcMyH+V47TLo7FoO7foFjVAwDMxgsfzxWlzPFV5bGUZ7uaT96+ksDAQPR6PSkpKRc9n5KSQkhISJXuOyEhgWbNmtG2bdsq3Y8QQtglq/OuYyHtuxDOq3gtC4epDFVSbtY5Jm/bdWLh4uJC69atWb58eclzFouF5cuXlzqUyVaGDRvGnj172Lx5c5XuRwgh7IvzL2Qh7bsQzivM18HWslCcq9ys5kOhsrOzOXDgQMnXhw8fJikpiYCAAOrWrcvIkSPp378/bdq0oV27dkycOJGcnJySKlFVJSEhgYSEBMxm58gghRBCqKR9F8J5Fa9lcSrTQUrOOtkCeZonFlu2bCE+Pr7k6+KJdf379+eLL76gd+/enDlzhjFjxnD69GliY2NZunTpJRO6bW3YsGEMGzaMrKwsfH19q3RfQgghqo+070I4r+K1LE46ylAopXjwkHMMP9U8sejUqRPWq4zlHT58OMOHD6+miIQQQgghhCOq7aMmFqnnC67ySjtRPPrUatE0DFux6zkWQghRZZx/KoEQQtQ4wd5q5bfULAcZClXcYyGJhXOTqiFC1AxX6zEVzkfadyGc1797LByifS9JLLQNw1YksSiFVA0RQlQHJ3kvcSjSvgvhvGpf6LHILTSTXeAIE6IvdJ9Lj4UQQgjnJSmPEMLxeLoa8HZVpxA7xDwLJ5u8LYlFKaSrXAhRIynOP/lE2nchnFuQj9prkeII8ywU6bGoEaSrXAghnJO070I4t2DvC/Msshyox0ISCyGEEEIIIexL7Qs9FqnnHaHHojixcI6hUJqvY+EMLBYLhYWFWodRpUwmEwaDgfz8/Bq9Wm1lzoOLiws6neTy9sL5B/wIIUTNFHyhMlSKI/RYONnkbUksKqmwsJDDhw9jsTjHH0RprFYrISEhJCcno9SAMdilqcx50Ol0REdH4+LiUkXRiYpwjmtEQgghihVXhnKoyduSWDi3hIQEEhISrnhV2mq1curUKfR6PREREU59NdpisZCdnY2Xl5dTH+fVVPQ8WCwWTp48yalTp6hbt26NTs6E0FpZ2nchhOOqXdJj4QhDoUqW3tY0DFuRxKIUw4YNY9iwYWRlZeHr63vZ1xQVFZGbm0tYWBgeHh7VHGH1Kh7u5ebmVuMTi4qeh6CgIE6ePElRURFGo7GKIhTCRpzjPe6yytK+CyEcV/Hq22ccosfCuYZC1dxPiDZQfLVLhraIsij+O5GrpMK+SW+aEMKxOVaPhXNN3pbEwgZkWIsoC/k7EUIIIaqeY62+XdxjIYmFEEKISrI685gjIYTQgKerAa8Lq2/bfa+Fk03elsSiFLIyqxDOTXqQai5p34VwfiWVoey95GxxYuEkF5kksSiFs67MOmXKFLy9vSkq+qdrMDs7G6PRSKdOnS567cqVK1EUhYMHD1Z4f0eOHEFRFJKSkiq8DSGqkpP0PotycNb2XQjxj0AvNbFIy7H3xEImbwsHFh8fT3Z2Nlu2bCl5bs2aNYSEhLBx40by8//pMkxMTKRu3brUr19fi1CFEEIIISok0FstmJKWbecLGMtQKOHIGjduTGhoKCtXrix5buXKldx9991ER0ezYcOGi56Pj4+noKCAp59+moYNG+Lh4cENN9xw0ZW+9PR0+vbtS1BQEO7u7jRs2JCZM2cCEB0dDUBcXByKolzUKzJ9+nSaNm2Km5sbTZo0YfLkySXfK+7pWLhwIfHx8Xh4eBATE8P69eur6MwIIYQQwlnU8rzQY5Ft5z0WMnlblMZqtZJbWKTJzVqOP8j4+HgSExNLvk5MTKRTp0507Nix5Pm8vDw2btxIfHw8zz//PAsXLmTy5Mls2bKFBg0a0LVrV86dOwfAq6++yp49e1iyZAl79+7l008/JTAwEIBNmzYB8Pvvv3Pq1CkWLlwIwLx58xgzZgxvvvkme/fu5a233uLVV19l1qxZF8X68ssvM2rUKJKSkmjUqBEPPvjgRcO4hBBVxTne5IQQNVMtL7XH4oz0WFQrWSDPhvJMZpqN+VWTfe/5X1c8XMr264yPj2fEiBEUFRWRl5fH9u3b6dixIyaTiSlTpgCwfv16CgoK6NSpE4MHD2bGjBl06dIFHx8fpk2bxrJly/j888957rnnOHbsGHFxcbRp0waAqKiokn0FBQUBUKtWLUJCQkqeHzt2LO+//z49e/YE1J6NPXv2MHXqVPr371/yulGjRnHHHXcAMG7cOJo3b86BAwdo0qRJxU+WEKJ0MqldCOEEank5SI+FJBbC0XXq1ImcnBw2b95Meno6jRo1IigoiI4dOzJgwADy8/NZuXIl9erVIzMzE5PJxPXXX1/y80ajkXbt2rF3714Ahg4dyr333su2bdu49dZb6dGjB9ddd12p+8/JyeHgwYMMHDiQwYMHlzxfVFR0ySq4rVq1KnkcGhoKQGpqqiQWotLk47MQQjivQM8Lcyxy7L3HoviBc/QSS2JRioSEBBISEsq1SrK7Uc+e/3WtwqiuvO+yatCgAXXq1CExMZH09HQ6duwIQFhYGBEREaxbt47ExEQ6d+5cpu1169aNo0ePsnjxYpYtW8bNN9/MsGHDmDBhwmVfn52dDcC0adNo3779Rd/T6y8+DqPRWPK4uDyoxeIcWb0QQhsVad+FEI4l0Ft6LLQgcyxKUZFyhIqi4OFi0ORW3pr88fHxrFy5kpUrV140ofqmm25iyZIlbNq0ifj4eOrXr4+Liwtr164teY3JZGLz5s00a9as5LmgoCD69+/P3LlzmThxIp999hkALi7qFYN/v4EHBwcTFhbGGe88tQAAN59JREFUoUOHaNCgwUW34sneQlQX57hGJMpDys0K4fxqXeixOGvvcyxKJm9rG4WtSI9FDRUfH8+wYcMwmUwlPRYAHTt2ZPjw4RQWFhIfH4+npydDhw7lhRdeKKneNGHCBHJzcxk4cCAAY8aMoXXr1jRv3pyCggIWLVpE06ZNAahduzbu7u4sXbqUOnXq4Obmhq+vL+PGjeOpp57C19eX2267jYKCArZs2UJ6ejojR47U5JwIIYQQwjkUz7HILigi32TGrRwjO6qV9FgIZxAfH09eXh4NGjQgODi45PmOHTty/vz5krK0AG+//TY9e/ZkyJAhtGnThgMHDvDrr7/i7+8PqL0So0ePplWrVtx0003o9Xq++uorAAwGA5MmTWLq1KmEhYVx9913AzBo0CCmT5/OzJkzadmyJR07duSLL76QHgshhBBCVJqPmwEXvfox167nWThZYiE9FjVUVFTUZUvURkZGXvK8m5sbH330Ea+//jo+Pj7odBfno6+88gqvvPJKqfsaNGgQgwYNuuT5Pn360KdPnzLH5+fnV66yukLYs3/m68nftBBC2JqiKNTycuFUZj5p2QWE+7lrHdLllQxld473AumxEEIIcSlJeIQQDq54LYuz9jyB+7I9Fo5bt1ASCyFEjSTLNZRGTowQwjkUr75t3xO4iydvO8dQKEkshBBCCCGE0ynusUiz58SipMfCOXqJJbEQQgghhBBOJ9ARVt92ssnbklgIIWo0KQgghBDOKdAh5ljIUKgaISEhgWbNmtG2bVutQxFCCGFD0r4LUTMUz7Gw73KzUhWqRpCVWYUQwjlJ+y5EzRDg5QCrb8scCyGEEM7POd7khBA1V4CHmlhk5NpxYlFSFco52lxJLIQQNZIUVS2F1OEVQjgJ/wuJRbo9JxYyeVsIUZqoqCgmTpyodRhCCCFEjefnaQQg32Qh32TWOJpSyORt4cimTJmCt7c3RUVFJc9lZ2djNBrp1KnTRa9duXIliqJw8ODBSu3zyJEjKIpCUlJSpbZTVjt27OCuu+6idu3auLm5ERUVRe/evUlNTa2W/QtRNtIzIIQQVcnb1YBBp7a1dttrIT0WwpHFx8eTnZ3Nli1bSp5bs2YNISEhbNy4kfz8/JLnExMTqVu3LvXr19ci1Ao5c+YMN998MwEBAfz666/s3buXmTNnEhYWRk5OjtbhCSGEEKKaKIqCn4faa5GeY9I4mtI410UmSSxqmMaNGxMaGsrKlStLnlu5ciV333030dHRbNiw4aLn4+PjASgoKOCFF14gJCQENzc3brjhhosqqqSnp9O3b1+CgoJwd3enYcOGzJw5E4Do6GgA4uLiUBTlop6R6dOn07RpU9zc3GjSpAmTJ08u+V5xT8fChQuJj4/Hw8ODmJgY1q9fX+rxrV27lszMTKZPn05cXBzR0dHEx8fz4YcflsRhNpsZOHAg0dHRuLu707hxYz766KOLtvPII4/Qo0cPJkyYQGhoKLVq1WLYsGGYTP80TKmpqXTv3h13d3eio6OZN29eWX8NQgghhKgGDjHPwokYtA7AqVitYMrVZt9GjzJPuoyPjycxMZEXX3wRUHsmnn/+ecxmM4mJiXTq1Im8vDw2btzIo48+CsALL7zAzz//zMyZM4mOjubdd9+la9euHDhwgICAAF599VX27NnDkiVLCAwM5MCBA+Tl5QGwadMm2rVrx++//07z5s1xcVH/yefNm8eYMWP45JNPiIuLY/v27QwePBhPT0/69+9fEu/LL7/MhAkTaNiwIS+//DIPPvggBw4cwGC49M83JCSEoqIivv/+e3r16oVymXNisVioU6cO33zzDbVq1WLdunU89thjhIaGcv/995e8LjExkdDQUBITEzlw4AC9e/emVatW9O7dG1CTj5MnT5KYmIjRaOSpp56S4VZCCCGEHZHEonpJYmFLplx4K0ybfb90Elw8y/TS+Ph4RowYQVFREXl5eWzfvp2OHTtiMpmYMmUKAOvXr6egoID4+HhycnKYMmUKCQkJdOvWDZ1Ox7Rp01i2bBmff/45zz33HMeOHSMuLo42bdoA6iTmYkFBQQDUqlWLkJCQkufHjh3L+++/T8+ePQG1Z2PPnj1MnTr1osRi1KhR3HHHHQCMGzeO5s2bc+DAAZo0aXLJsXXo0IGXXnqJPn36MGTIENq1a0fnzp15+OGHCQ4OBsBoNDJu3LiSn4mOjmb9+vV8/fXXFyUW/v7+fPLJJ+j1epo0acIdd9zBihUr6N27N/v372fJkiVs2rSpZJGtzz//nKZNm5bpdyCEEEKIqlcyFCrXXodCFZNysw4hOTmZTp060axZM1q1asU333yjdUia69SpEzk5OWzevJk1a9bQqFEjgoKC6NixY8k8i5UrV1KvXj3q1q3LwYMHMZlMtG/fvmQbRqORdu3asXfvXgCGDh3KV199RWxsLM8//zzr1q27Ygw5OTkcPHiQgQMH4uXlVXJ74403Lpks3qpVq5LHoaGhAFfsGXjzzTc5ffo0U6ZMoXnz5kyZMoUmTZqwc+fOktckJCTQunVrgoKC8PLy4rPPPuPYsWMXbad58+bo9fqL9n3mzBkA9u7di8FgoHXr1iXfb9KkCX5+flc8bmE/LtebJf7FSWqqCyFqtuIeiwx7XX3byd6LnL7HwmAwMHHiRGJjYzl9+jStW7fm9ttvx9OzbFf3y8XoofYcaMHoUeaXNmjQgDp16pCYmEh6ejodO3YEICwsjIiICNatW0diYiKdO3cu8za7devG0aNHWbx4McuWLePmm29m2LBhTJgw4bKvz87OBmDatGkXJSzARR/mQU1iihV/GLRYrlw9oVatWtx3333cd999vPXWW8TFxTFhwgRmzZrFV199xahRo3j//fe59tpr8fb25r333mPjxo2l7rd431fbrxCOz7ne5IQQNZu/Z/FQKHvvsXAOTp9YhIaGllzlDgkJITAwkHPnzlVNYqEoZR6OpLX4+HhWrlxJeno6zz33XMnzN910U8kQn6FDhwJQv359XFxc2LhxIy1atADAZDKxefNmRowYUfKzQUFB9O/fn/79+3PjjTfy3HPPMWHChJI5FWbzPzWkg4ODCQsL49ChQ/Tt27dKj9XFxYX69euXVIVau3Yt1113HU888UTJa8pbUrdJkyYUFRWxdevWkqFQ+/btIyMjw2ZxCyGEEKJy/EuGQtlpj4WT0Xwo1OrVq+nevTthYWEoisIPP/xwyWsSEhKIiorCzc2N9u3bs2nTpgrta+vWrZjNZiIiIioZteOLj4/njz/+ICkpqaTHAqBjx45MnTqVwsLCkopQnp6eDBkyhLFjx7J06VL27NnD4MGDyc3NZeDAgQCMGTOGH3/8kQMHDrB7924WLVpUMt+gdu3auLu7s3TpUlJSUsjMzATU+RLjx49n0qRJ7N+/n507dzJz5kw++OCDCh/XokWLeOihh1i0aBH79+9n3759TJgwgcWLF3P33XcD0LBhQ7Zs2cKvv/7K/v37efXVVy+qcFUWjRs35rbbbuPxxx9n48aNbN26lUGDBuHu7l7h2IU2ZMSPEEI4L4eZvO0kb0aaJxY5OTnExMSQkJBw2e8vWLCAkSNHMnbsWLZt20ZMTAxdu3a9aIx9bGwsLVq0uOR28uQ/w5LOnTvHww8/zGeffVblx+QI4uPjycvLo0GDBiWTmkFNLM6fP19SlrbY+PHj6d69O/379+eaa67hwIED/Prrr/j7+wNqr8Do0aNp1aoVN910E3q9nq+++gpQh6NNmjSJqVOnEhYWVvIBf9CgQUyfPp2ZM2fSsmVLOnbsyBdffFFSFrYimjVrhoeHB88++yyxsbF06NCBr7/+munTp9OvXz8AHn/8cXr27Env3r1p3749aWlpF/VelFXx+hgdO3akZ8+ePPbYY9SuXbvCsYsaRkYcCSFElbP/ydvO9Wag+VCobt260a1bt1K//8EHHzB48GAGDBgAqCtH//LLL8yYMaOkXOrVVnQuKCigR48evPjii1x33XU2i92RRUVFYb1MdhwZGXnZ593c3HjnnXf49NNP0ekuzUdfeeUVXnnllVL3N2jQIAYNGnTJ83369KFPnz5ljtHPz++y8RWrV6/eVZNHV1dXZs6cWbLORrHx48eXPP7iiy8u+bmJEydisVjIysoC1KF1ixYtuug1xcmLEEIIIbRXPMciw957LJyE5onFlRQWFrJ161ZGjx5d8pxOp+OWW2654iJp/2a1WnnkkUfo3LlzmT70FRQUUFBQUPJ18YdIk8l00eJoxc9ZrVYsFovTT+ot/jBffLw1VWXOg8ViwWq1YjKZLpmg7miK/xf++z/hSIp/f2aLucLHUZnzUJwfm80V339V0FnM6AGL1YK5jHHZU/xXUp723dE4w/9kMTkW++Sox+Ltol4MTc8pvOQY7OFYFLMZA2CxWkva3KKiC3Fd+MxwJdVxLOXZtl0nFmfPnsVsNl80VAfUib9//fVXmbaxdu1aFixYQKtWrUrmb8yZM4eWLVte9vXjx4+/aI2DYomJiXh4XFx5yWAwEBISQnZ2Nv9v786jojjTNYA/TbM0yOYKoiwqoBARUQSNC5IQGeNFjdlGnAhGmTEuEy/ROJkYiblXnaAmxIiYqxNIJjHqZNzGdZABNWpcgmSMYhQDShBwCftOd90/CBUJoN30SvH8zqlz6Krqqvf7urvot7+l6uu7RiZcUVFh7BBMQkfqob6+HjU1NThx4gQaGxv1EJXhpaamGjuEDrtdYAbADNnZ2ThUdkWrY3WkHponM8jLy0PuoUNanV+XBhVfxVAABT8WIFPNuKqrjXRjUA1pcn3vrDrzZ/LXWBbT1NnKUtkAAOYor23EPw8egvyBnkemUBbX+99iBIB7d+/gzM/XXMvCInig6ceQQ2peh/VZFk2u8SadWOjCuHHjNPpl+Y033kBsbKz4uLy8HK6urggNDUXPnj1b7FtbW4v8/HzY2tpCoVDoLGZTJAgCKioqYGdn16Xn/9emHmpra2FtbY0JEyZ0+vdLQ0MDUlNT8dRTT7WalrezSP/yEs7fK4SPjw+eHuvRoWNoUw87tr8DoKnLn8/TT3fo/Ppg9vUPwG2gXz8XOKsZ1/379/UclW5ocn3vbKTwmWzGspimzlqWRqUKb144BgB4PORJ9LS1MqmyyC5VAreAXr174+mfr7l1164hPyEBVlZW4rr2GKIsza276jDpxKJXr16Qy+UoLi5usb64uLjFHZx1ycrKClZWVkhMTERiYqL4q6KFhUWrF0ypVEImk8HMzKzNcQdS0pycNZe3q9KmHszMzCCTydp8L3VWnbksza+f3EyudRm0qQczMzPTqkN5078FMzMzmKkZl0nF/xCaXN87K5bFNLEsxmNhAdgrmlosKhsEOD8Qu0mU5eeu0WYymXjNVZr/HNPP3xnUoc+yaHJck/6GaGlpiZEjRyItLU1cp1KpkJaWhjFjxuj13AsXLsSVK1c0noaUiIhMG6/vRF1LD94kz2CM3mJRWVmJnJwc8XFubi6ysrLQo0cPuLm5ITY2FlFRUQgMDERQUBASEhJQVVUlzhJFRKQNAdKYO5yIiNrmaGMJ3K9GSZUJj4eVyH0sjJ5YXLhwQbwRGwCx/2tUVBRSUlLw4osv4u7du1i5ciWKioowfPhwHDlypNWAbl37dVM5ERFJA6/vRF2Lg3VTV56yGlNssZDWuFWjJxYTJ0586H0JAGDRokVYtGiRgSJqsnDhQixcuBDl5eVwcHAw6LmJiEh/eH0n6lqaE4vyWmnMyGjKTHqMBRGR3kjrRyIiImqHvXXT7+im2WIhLUws2pGYmAhfX1+MGjXK2KGQHmRkZEAmk6G0tNTYoRCZJon0920Lr+9EXYu94ucWC5NOLKRxzWVi0Q6pzhqyZcsW2NnZtbhBW2VlJSwsLDBx4sQW+zZ/+b5x44ZW58zLy4NMJkNWVpZWx9HExYsX8fzzz8PJyQkKhQJeXl6IiYnBtWvXDBYDUeck/aYcqV7fiahtYlcoU0wsJHZvMCYWXUxoaCgqKytx4cIFcd3Jkyfh7OyMs2fPora2Vlyfnp4ONzc3DBo0yBihdtiBAwcwevRo1NXV4fPPP0d2djY+++wzODg44K233jJ2eERERGRA9uIYCxNMLCSGiUUXM3jwYPTt2xcZGRniuoyMDEybNg0DBgzA119/3WJ984xddXV1WL58OZydnaFQKDBu3LgWv/aVlJRg1qxZ6N27N6ytreHl5YXk5GQAwIABAwAAAQEBkMlkLVpGtm3bBh8fHygUCgwZMgSbN28WtzW3dOzevRuhoaGwsbGBv78/zpw50275qqurMWfOHDz99NPYv38/wsLCMGDAAAQHB2P9+vX46KOP2nze/fv3MXPmTPTr1w82Njbw8/PDF198IW7/9NNP0bNnT9TV1bV43vTp0/HSSy+1Gw+ZPuP1+JHWr1RERKbKtGeFkhYmFu3oSB9cQRBQ3VBtlOVRM2s9KDQ0FOnp6eLj9PR0TJw4ESEhIeL6mpoanD17Vkwsli9fjn/+859ITk5GZmYmPD09ER4ejp9++gkA8NZbb+HKlSs4fPgwsrOzkZSUhF69egEAzp07BwA4duwYCgsLsXv3bgDA559/jpUrV2L16tXIzs7GmjVr8NZbb+GTTz5pEe+bb76JpUuXIisrC97e3pg5c2aLrlwPOnr0KO7du4fXX3+9ze2Ojo5trq+trcXIkSNx8OBBfPfdd/j973+Pl156SYz9+eefh1KpxP79+8Xn3LlzBwcPHsTLL7/cfmUTkcnhGAuiruWXMRYmPCuURMa1GX26WVPVkekIaxprELw9WM+Rte1s5FnYWNiotW9oaCiWLFmCxsZG1NTU4OLFiwgJCUFDQwO2bNkCADhz5gzq6uoQGhqKqqoqbNmyBYmJiZg8eTLMzMywdetWpKam4q9//SuWLVuGW7duISAgAIGBgQAADw8P8Xy9e/cGAPTs2RPOzs7i+ri4OGzYsAEzZswA0NSyceXKFXz00UeIiooS91u6dCmmTJkCAFi1ahUee+wx5OTkYMiQIa3Kdv36dQBoc9vD9OvXD0uXLhUfL168GEePHsWuXbsQFBQEa2trREZGIiUlBeHh4QCAzz77DG5ubq3GphCRaeN0s0RdS/OsUKbZFUparddMLLqgiRMnoqqqCufPn0dJSQm8vb3Ru3dvhISEYM6cOaitrUVGRgYGDhwINzc3/Oc//0FDQwOCg39JmiwsLBAUFITs7GwAwCuvvIJnn30WmZmZmDRpEqZPn47HH3+83Riqqqpw48YNzJ07FzExMeL6xsbGVv/ohw0bJv7dt29fAE2tBW0lD5q03DxIqVRizZo12LVrFwoKClBfX4+6ujrY2PySrMXExGDUqFG4ffs27O3tkZKSgujoaMgkNvCqq5BJ7GJORERtY1cow2FioUPW5tY4G3nWaOdWl6enJ/r374/09HSUlJQgJCQEAODi4gJXV1ecPn0a6enpeOKJJ9Q+5uTJk3Hz5k0cOnQIqampePLJJ7Fw4UKsX7++zf0rKysBAFu3bm2RsACAXC5v8djCwkL8u/lLvEqlavO43t7eAICrV69izJgxase/bt06fPDBB0hISICfnx+6deuGJUuWoL6+XtwnICAA/v7+2LFjByIiInD58mUcPHhQ7XMQERGR4TV3haquV6JB2fb3B+NjVyhJS0xMRGJiIpRKpdrPkclkandHMrbQ0FBkZGSgpKQEy5YtE9dPmDABhw8fxrlz5/DKK68AAAYNGgRLS0ucPXsWQ4cOBQA0NDTg/PnzWLJkifjc3r17IyoqClFRURg/fjyWLVuG9evXw9LSEgBa1KWTkxNcXFzwww8/YNasWTor16RJk9CrVy/Ex8djz549rbaXlpa2Oc7i1KlTmDZtGn73u98BaEpcrl27Bl9f3xb7vfzyy0hISMD9+/cRFhYGV1dXncVOZFqk8U+uLR25vhNR52Wn+OXrbnlNA+ytTGiIscR6PZhQzZoWqc9zHhoaiq+++gpZWVliiwUAhISE4KOPPkJ9fb04cLtbt26YP38+4uLicOTIEVy5cgUxMTGorq7G3LlzAQArV67Evn37kJOTg8uXL+PAgQPw8fEBAPTp0wfW1tY4cuQIiouLUVZWBqBpvMTatWuxceNGXLt2DZcuXUJycjLee++9DperW7du2LZtGw4ePIipU6fi2LFjyMvLw4ULF/D6669j/vz5bT7Py8sLqampOH36NLKzs/GHP/wBxcXFrfaLjIzE7du3sW3bNg7aJmmS2D+5tkj9+k5ELZnLzWBr1TzOwoQHcEsAE4suKjQ0FDU1NfD09ISTk5O4PiQkBBUVFeK0tM3Wrl2LiIgIREVFYcSIEcjJycHRo0fRvXt3AIClpSXeeOMNDBs2DBMmTIBcLseOHTsAAObm5ti4cSM++ugjuLi4YNq0aQCAefPmYdu2bUhOToafnx9CQkKQkpIiTk/bUdOmTcPp06dhYWGByMhIDBkyBDNnzkRZWRn+93//t83nrFixAiNGjEB4eDgmTpwIZ2dnTJ8+vdV+Dg4OiIiIgK2tbZvbqfOR7u/yRETUzP7nVguOs9AvdoXqojw8PNoc6Ozu7t7meoVCgXfffRdJSUkwM2udj65YsQIrVqxo93zz5s3DvHnzWq2PjIxEZGSk2jE6OjqqNUA7MDAQ//jHP9rdPnHixBbH6dGjB/bu3fvI4wJAYWEhIiMjYWVlpdb+REREZFz21ha4XVZrmnffBjjdLFFXU1JSgn//+9/46quvxGl5iYiIyPTx7tuGwcSCSE0BAQEoKSnB22+/jcGDBxs7HNKSqQwlENgZi4hI7zjlrGEwsWgHZw2hX8vLy4NKpUJ5ebmxQyEiLfD6TtT1dIq7b0sAB2+3g7OGEBFJE6/vRF1P89232WKhX0wsiIioNYkMJCQiAn7pCmVyYyxMpV+ujjCxIKIujd+ff01a/+SIiIAHu0KZWGIhMUwsiIiIiEjS7Dl42yCYWBARERGRpIldoUw1sZBI8zkTCyLqktjhh4io67D7+c7bFXWmNiuUtP4bMbFoR2JiInx9fTFq1Chjh9IppaSkwNHR0dhhEBG1wus7Uddja9WUWFTWmlpiIS1MLNrRFaYjLCoqwuLFizFw4EBYWVnB1dUVERERSEtLM3ZoRER60xWu70TUUnOLRaXJtVhIC2+Q10Xl5eVh7NixcHR0xLp16+Dn54eGhgYcPXoUCxcuxNWrV40dIhEZlTT6+xIRAb+0WFTXK6FUmeL1zRRj0hxbLLqoBQsWQCaT4dy5c3j22Wfh7e2Nxx57DLGxsfj6668BALdu3cK0adNga2sLR0dHzJkzB8XFxeIxvv32W4SGhsLOzg729vYYOXIkLly40OI8e/fuhZeXFxQKBcLDw5Gfnw+gKbExMzNrtX9CQgLc3d2hUqn0XANE1CaJzalORAQAtopffkuvrjehVguJXXPZYqFDgiBAqKkxyrll1taQqfnm/Omnn3DkyBGsXr0a3bp1a7Xd0dERKpVKTCqOHz+O+vp6LFiwADNnzkRGRgYAYNasWQgICEBSUhLkcjmysrJgYWEhHqe6uhqrV6/Gp59+CktLSyxYsAC//e1vcerUKXh4eCAsLAzJyckIDAwUn5OcnIzo6GiYmTHnJcMQjPQrkbT+lRARmTYrczks5WaoV6pQWac0djiSxcRCh4SaGnw/YqRRzj048xvIbGzU2jcnJweCIGDIkCHt7pOWloZLly4hNzcXrq6uUKlUSEpKwpgxY3D+/HmMGjUKt27dwrJly8TjeHl5tThGQ0MDNm3ahODgYADAJ598Ah8fH5w7dw5BQUGYN28e5s+fj/feew9WVlbIzMzEpUuXsG/fvg7WAhEREVHbulnJUV+t4gBuPeLPwl2QoMZcydnZ2XB1dYWrq6u4bsiQIXB0dER2djYAIDY2FvPmzUNYWBj+8pe/4MaNGy2OYW5u3mLWlV8/f/r06ZDL5dizZw+AppmkQkND4eHhoW0RiR5JYq3PRET0CLamPIBbIvexYIuFDsmsrTE48xujnVtdXl5ekMlkWg/QfvvttxEZGYmDBw/i8OHDiIuLw44dO/DMM8+o9XxLS0vMnj0bycnJmDFjBrZv344PPvhAq5iIiIiI2mJrZQGgxsQSC2n9ysUWCx2SyWQws7ExyqLu+AoA6NGjB8LDw5GYmIiqqqpW20tLS+Hj44P8/HxxsDUAXL16FaWlpfD19RXXeXt747//+7/xr3/9CzNmzEBycrK4rbGxscXg7O+//148drN58+bh2LFj2Lx5MxobGzFjxgy1y0FERESkLjsrE26xkAgmFu2Q+g2UEhMToVQqERQUhH/84x+4fv06srOzsXHjRowZMwZhYWHw8/PDrFmzkJmZiXPnzuGVV15BSEgIAgMDUVNTg0WLFiEjIwM3b97EqVOncP78+RZJg4WFBRYvXoyzZ8/im2++QXR0NEaPHo2goCBxHx8fH4wePRrLly/HzJkzYa1BywsRUUdI/fpORG0z6a5QEsHEoh1Sv4HSwIEDkZmZidDQULz22msYOnQonnrqKaSlpSEpKQkymQz79u1D9+7dMWHCBEyaNAkeHh744osvAAByuRz379/H7Nmz4e3tjRdeeAGTJ0/GqlWrxHPY2Nhg+fLliIyMxNixY2Fra4udO3e2imXu3Lmor6/Hyy+/bLDyE9EjSKS/b1ukfn0noraJd982yVmhpHHN5RiLLqxv377YtGkTNm3a1OZ2Nzc3cYYmlUqF8vJy2NvbA2gaH9GcZLQlOjoa0dHRAPDI7k0FBQXw8/Pjr4dkFBL+/txB0urvS0TUTGyxqG2Ek5FjEUlsJhG2WJDRVFZW4rvvvsOmTZuwePFiY4dDREREEsYxFvrHxIKMZtGiRRg5ciQmTpzIblBkcDL+Mk9E1KXYMrHQOyYWZDQpKSmoq6vDzp07IZfLjR0OERERSVhzV6gqUxxjIZF+uUwsiIiMgQ0mREQGZZotFtL6Z8DEQgfUuZM1Ed8nRERExmPH6Wb1jomFFpq779TX1xs5EuoMmt8n7PZFRERkeE133mZioU+cblYL5ubmsLGxwd27d2FhYQEzM+nmaSqVCvX19aitrZV0OR+lo/WgUqlw9+5d2NjYwNycHzvqDNjCRkTSYto3yJPGNZffcLQgk8nQt29f5Obm4ubNm8YOR68EQUBNTQ2sra0hk9icy5rQph7MzMzg5ubWpeuPOgG+P4lIomytmnoMmFRiIbFrruQTi9LSUoSFhaGxsRGNjY149dVXERMTo7PjW1pawsvLS/LdoRoaGnDixAlMmDABFhYWxg7HaLSpB0tLyy7d2mNqJHYtJyKiR/ilK5RSKpMwmRzJJxZ2dnY4ceIEbGxsUFVVhaFDh2LGjBno2bOnzs5hZmYGhUKhs+OZIrlcjsbGRigUii6dWLAeiIiIOqfmrlBKlYAGlZGDkSjJ/3wql8thY2MDAKirq4MgCJydh4iIiKiLsbGQi63VtaZ2KwuJfDc1emJx4sQJREREwMXFBTKZDHv37m21T2JiIjw8PKBQKBAcHIxz585pdI7S0lL4+/ujf//+WLZsGXr16qWj6ImIiIioMzAzk8HWsqnVwnQSC2n1yzV6YlFVVQV/f38kJia2uX3nzp2IjY1FXFwcMjMz4e/vj/DwcNy5c0fcZ/jw4Rg6dGir5fbt2wAAR0dHfPvtt8jNzcX27dtRXFxskLIRERERkelo7g5lOomFtBh9jMXkyZMxefLkdre/9957iImJwZw5cwAAW7ZswcGDB/Hxxx/jT3/6EwAgKytLrXM5OTnB398fJ0+exHPPPdfmPnV1dairqxMfl5WVAQB++ukntc4hVQ0NDaiursb9+/e79NgC1kMTKdRDTWU5VHXVqCwrw/379zt0DG3qoaZeiUqlEnVVlR0+vz6YlVdCXidAVVkLpZpxNV8fTb2bqZSv71L4TDZjWUyTVMpioayFqq4aZZVKkyiLrKwC5nUCVNX14jW3rrQElUol5PX1j/z/YIjXpaKiAoCa13jBhAAQ9uzZIz6uq6sT5HJ5i3WCIAizZ88Wpk6dqtYxi4qKhPLyckEQBKG0tFR47LHHhP/85z/t7h8XFyegaTJhLly4cOGiwXLjxg2Nr/uGxOs7Fy5cuHR8yc/Pf+R11ugtFg9z7949KJVKODk5tVjv5OSEq1evqnWMmzdv4ve//704aHvx4sXw8/Nrd/833ngDsbGx4uPS0lK4u7vj1q1bcHBw6FhBJKC8vByurq7Iz8+Hvb29scMxGtZDE9ZDE9ZDk7KyMri5uaFHjx7GDuWhpHx9l9J7kWUxTSyLaTJEWQRBQEVFBVxcXB65r0knFroQFBSkdlcpALCysoKVlVWr9Q4ODp3+zacL9vb2rAewHpqxHpqwHpqY+n1ausL1XUrvRZbFNLEspknfZVH3xxeT/i/Qq1cvyOXyVoOti4uL4ezsbKSoiIiIiIjo10w6sbC0tMTIkSORlpYmrlOpVEhLS8OYMWOMGBkRERERET3I6F2hKisrkZOTIz7Ozc1FVlYWevToATc3N8TGxiIqKgqBgYEICgpCQkICqqqqxFmi9M3KygpxcXFtNp93JayHJqyHJqyHJqyHJp21Hjpr3G1hWUwTy2KaWBb9kQmCcecHzMjIQGhoaKv1UVFRSElJAQBs2rQJ69atQ1FREYYPH46NGzciODjYwJESEREREVF7jJ5YEBERERFR52fSYyyIiIiIiKhzYGJBRERERERaY2JBRERERERaY2IBIDExER4eHlAoFAgODsa5c+ceuv/f//53DBkyBAqFAn5+fjh06JCBItUvTerh8uXLePbZZ+Hh4QGZTIaEhATDBapnmtTD1q1bMX78eHTv3h3du3dHWFjYI98/nYUm9bB7924EBgbC0dER3bp1w/Dhw/G3v/3NgNHqj6bXh2Y7duyATCbD9OnT9RuggWhSDykpKZDJZC0WhUJhwGh145lnnkH37t3x3HPPtdp24MABDB48GF5eXti2bZsRouu4999/H4899hh8fX3xxz/+EZ15qGVubi5CQ0Ph6+sLPz8/VFVVGTskrVRXV8Pd3R1Lly41digdlp+fj4kTJ8LX1xfDhg3D3//+d2OHpJHO/Nl+kNFeB6GL27Fjh2BpaSl8/PHHwuXLl4WYmBjB0dFRKC4ubnP/U6dOCXK5XIiPjxeuXLkirFixQrCwsBAuXbpk4Mh1S9N6OHfunLB06VLhiy++EJydnYX333/fsAHriab1EBkZKSQmJgoXL14UsrOzhejoaMHBwUH48ccfDRy5bmlaD+np6cLu3buFK1euCDk5OUJCQoIgl8uFI0eOGDhy3dK0Hprl5uYK/fr1E8aPHy9MmzbNMMHqkab1kJycLNjb2wuFhYXiUlRUZOCotZeeni7s379fePbZZ1usb2hoELy8vIQff/xRqKioELy9vYV79+4ZKUrN3LlzRxg4cKBQU1MjNDY2Co8//rhw+vRpY4fVYRMmTBBOnDghCIIg3L9/X2hoaDByRNr585//LLzwwgvCa6+9ZuxQOuz27dvCxYsXBUEQhMLCQsHFxUWorKw0blBq6syf7V8z1uvQ5ROLoKAgYeHCheJjpVIpuLi4CGvXrm1z/xdeeEGYMmVKi3XBwcHCH/7wB73GqW+a1sOD3N3dJZNYaFMPgiAIjY2Ngp2dnfDJJ5/oK0SD0LYeBEEQAgIChBUrVugjPIPpSD00f1nbtm2bEBUVJYnEQtN6SE5OFhwcHAwUnX6lp6e3SixOnTolTJ8+XXz86quvCtu3bzd0aB1y584dwc3NTSgpKRFqamqEUaNGCTk5OcYOq0O+++474cknnzR2GDpz7do1YcaMGUJycnKnTix+bdiwYcKtW7eMHYZaOvNn+1EM9Tp06a5Q9fX1+OabbxAWFiauMzMzQ1hYGM6cOdPmc86cOdNifwAIDw9vd//OoCP1IEW6qIfq6mo0NDSgR48e+gpT77StB0EQkJaWhu+//x4TJkzQZ6h61dF6eOedd9CnTx/MnTvXEGHqXUfrobKyEu7u7nB1dcW0adNw+fJlncZ14sQJREREwMXFBTKZDHv37m21T0e7sT3K7du30a9fP/Fxv379UFBQoJNj67tcvXv3xtKlS+Hm5gYXFxeEhYVh0KBBOon91/RdluvXr8PW1hYREREYMWIE1qxZo8PoWzLE+23p0qVYu3atjiJunyE/O9988w2USiVcXV21jFo92pZNn59tTenydTLk69ClE4t79+5BqVTCycmpxXonJycUFRW1+ZyioiKN9u8MOlIPUqSLeli+fLn4z7qz6mg9lJWVwdbWFpaWlpgyZQo+/PBDPPXUU/oOV286Ug9fffUV/vrXv2Lr1q2GCNEgOlIPgwcPxscff4x9+/bhs88+g0qlwuOPP44ff/xRZ3FVVVXB398fiYmJbW7fuXMnYmNjERcXh8zMTPj7+yM8PBx37twR9xk+fDiGDh3aarl9+7bO4tSUvstVUlKCAwcOIC8vDwUFBTh9+jROnDjRKcvS2NiIkydPYvPmzThz5gxSU1ORmpraKcuyb98+eHt7w9vbWy/xG7IszX766SfMnj0b//d//6f3MjXTRdlMha7KYvDXQe9tIiasoKBAANCqf+myZcuEoKCgNp9jYWHRqlksMTFR6NOnj97i1LeO1MODpNIVStt6WLt2rdC9e3fh22+/1VeIBtHRelAqlcL169eFixcvCuvXrxccHByE9PR0PUerP5rWQ3l5ueDh4SEcOnRIXCeFrlDafi4EQRDq6+uFQYMG6a1rHABhz549LdbpojufIKjfFerzzz/XPPBH0Ee5du3aJSxYsEB8HB8fL7z77rs6ifdh9FGW06dPC5MmTRIfx8fHC/Hx8TqJ92H0UZY//elPQv/+/QV3d3ehZ8+egr29vbBq1Spdht0mfX12amtrhfHjxwuffvqprkLVWEfKZqjPtqY6+joZ43Xo0i0WvXr1glwuR3FxcYv1xcXFcHZ2bvM5zs7OGu3fGXSkHqRIm3pYv349/vKXv+Bf//oXhg0bps8w9a6j9WBmZgZPT08MHz4cr732Gp577jmDNOvri6b1cOPGDeTl5SEiIgLm5uYwNzfHp59+iv3798Pc3Bw3btwwVOg6pYvrg4WFBQICApCTk6OPEFvRd/fOoKAgfPfddygoKEBlZSUOHz6M8PBwrY/7KLool6urK06fPo3a2loolUpkZGRg8ODB+gq5Xbooy6hRo3Dnzh2UlJRApVLhxIkT8PHx0VfI7dJFWdauXYv8/Hzk5eVh/fr1iImJwcqVK/UVcrt0URZBEBAdHY0nnngCL730kr5C1Zg6ZTPWZ1tT6pTFWK9Dl04sLC0tMXLkSKSlpYnrVCoV0tLSMGbMmDafM2bMmBb7A0Bqamq7+3cGHakHKepoPcTHx+N//ud/cOTIEQQGBhoiVL3S1ftBpVKhrq5OHyEahKb1MGTIEFy6dAlZWVniMnXqVISGhiIrK8tgfYx1TRfvB6VSiUuXLqFv3776CrMFXXXvDAsLw/PPP49Dhw6hf//+4j9sc3NzbNiwAaGhoWIi3bNnT52WoS26KNfo0aPx9NNPIyAgAMOGDcOgQYMwdepUfYT7ULooi7m5OdasWYMJEyZg2LBh8PLywn/913/pI9yHklJ3Yl2U5dSpU9i5cyf27t2L4cOHY/jw4bh06ZI+wtWIOmUz1mdbU+qUxVivg7nez2DiYmNjERUVhcDAQAQFBSEhIQFVVVWYM2cOAGD27Nno16+f+Mvrq6++ipCQEGzYsAFTpkzBjh07cOHCBYP2IdQHTeuhvr4eV65cEf8uKChAVlYWbG1t4enpabRyaEvTenj33XexcuVKbN++HR4eHuIH2tbWFra2tkYrh7Y0rYe1a9ciMDAQgwYNQl1dHQ4dOoS//e1vSEpKMmYxtKZJPSgUCgwdOrTF8x0dHQGg1frORtP3wzvvvIPRo0fD09MTpaWlWLduHW7evIl58+YZsxgaO3bsWLvbpk6dapQv5LqwevVqrF692thh6MTkyZMxefJkY4ehU9HR0cYOQSvjxo2DSqUydhgd1pk/2w8y1uvQ5ROLF198EXfv3sXKlStRVFSE4cOH48iRI2IWeOvWLZiZ/dKw8/jjj2P79u1YsWIF/vznP8PLywt79+7t9F8cNK2H27dvIyAgQHy8fv16rF+/HiEhIcjIyDB0+DqjaT0kJSWhvr6+1Q204uLi8PbbbxsydJ3StB6qqqqwYMEC/Pjjj7C2tsaQIUPw2Wef4cUXXzRWEXRC03qQKk3roaSkBDExMSgqKkL37t0xcuRInD59Gr6+vgaJV6rdO6VULpbFNEmpLL8mpbKZdFkMNpqDiIhID9DOwMZFixaJj5VKpdCvXz+NB28bk5TKxbKYJimV5dekVLbOVJYu32JBRESdT2VlZYvB4Lm5ucjKykKPHj3g5ub2yO5bpkpK5WJZWBZDk1LZOm1ZjJrWEBERdUB6eroAoNUSFRUl7vPhhx8Kbm5ugqWlpRAUFCR8/fXXxgtYTVIqF8vCshialMrWWcsiEwRB0HPuQkREREREEif9UYdERERERKR3TCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyITEBSUhLc3NzQrVs3zJgxA3fv3n3o/h4eHpDJZJDJZCgtLW13v5SUFHG/JUuW6DZoIiIiogcwsSAyst27d2PZsmX48MMPceHCBVRUVOC555575PPeeecdFBYWwsHBod19XnzxRRQWFmLMmDG6DJmIiIioFd55m8jIVq9ejUWLFmHatGkAgE8++QSurq746quvMG7cuHafZ2dnB2dn54ce29raGtbW1rC0tNRpzERERES/xhYLIiMqKSlBZmYmpkyZIq5zcXHB0KFDcezYMSNGRkRERKQZJhZERvTDDz8AADw9PVus9/LyErcREVHnlpGRIY53mz59urHDAQDIZDLs3bu3xWOZTAZHR0ejxUSdHxMLIiOqrq4G0JRI2Nraisu+ffvEbUREZFjR0dGtEoAvv/wSCoUCGzZs6PBxv//+e6SkpGgXnJpWrVqF3/3ud2rvX1hYiISEBP0FRF0CEwsiI7KxsQHQ9GtWVlaWuEyaNEncpq4HE5P58+frI1wioi5p27ZtmDVrFpKSkvDaa691+Dh9+vQxWIvAvn37MHXqVLX3d3Z2fuhkIETqYGJBZEQDBw4EANjb28PT01NcamtrxW3qejAxeeedd/QRLhFRlxMfH4/Fixdjx44dmDNnjrj+yy+/hJ+fH6ytrdGzZ0+EhYWhqqpKo2OrVCrEx8fD09MTVlZWcHNzw+rVqwEAeXl5kMlk2LVrF8aPHw9ra2uMGjUK165dw/nz5xEYGAhbW1tMnjy51RTl+fn5uHz5Mn7zm98AAK5fv44JEyZAoVDA19cXqampWtYKUds4KxSREXXv3h0jR47EyZMn4e3tDQCorKzEmTNnNE4Ofj1Og4iItLN8+XJs3rwZBw4cwJNPPimuLywsxMyZMxEfH49nnnkGFRUVOHnyJARB0Oj4b7zxBrZu3Yr3338f48aNQ2FhIa5evdpin7i4OCQkJMDNzQ0vv/wyIiMjYWdnhw8++AA2NjZ44YUXsHLlSiQlJYnP2b9/PyZOnAh7e3uoVCrMmDEDTk5OOHv2LMrKynhfI9IbJhZERvbmm2/ilVdegaurKwYMGIAVK1YgODgYY8eONXZoRERd1uHDh7Fv3z6kpaXhiSeeaLGtsLAQjY2NmDFjBtzd3QEAfn5+Gh2/oqICH3zwATZt2oSoqCgAwKBBg1pNM7506VKEh4cDAF599VXMnDkTaWlp4v+IuXPnthq3sW/fPnEK82PHjuHq1as4evQoXFxcAABr1qzB5MmTNYqXSB3sCkVkZM888wzefvttzJ07F/7+/mhoaMCuXbuMHRYRUZc2bNgweHh4IC4uDpWVlS22+fv748knn4Sfnx+ef/55bN26FSUlJRodPzs7G3V1dS1aQtqLo5mTkxOAlkmMk5MT7ty5Iz4uLy/H8ePHxfEV2dnZcHV1FZMKALxpKukNEwsiEzB//nzk5+ejuroau3fvRu/evY0dEhFRl9avXz9kZGSgoKAAv/nNb1BRUSFuk8vlSE1NxeHDh+Hr64sPP/wQgwcPRm5urtrHt7a2Vms/CwsL8W+ZTNbmOpVKJT5ujsnV1VXtWIh0hYkFUSe1fPly2NraoqysrN19Pv/8c9ja2uLkyZMGjIyISBrc3d1x/PhxFBUVtUouZDIZxo4di1WrVuHixYuwtLTEnj171D62l5cXrK2tkZaWptOYH+wGBQA+Pj7Iz89HYWGhuO7rr7/W6TmJmnGMBVEndPz4cTQ0NAAA7Ozs2t1v6tSpCA4OBgDe9IiIqANcXV2RkZGB0NBQhIeH48iRI8jOzkZaWhomTZqEPn364OzZs7h79y58fHzUPq5CocDy5cvx+uuvw9LSEmPHjsXdu3dx+fJlzJ07t0OxNjY24vDhw1i6dKm4LiwsDN7e3oiKisK6detQXl6ON998s0PHJ3oUJhZEnVDzYMFHsbOze2jiQUREj9a/f/8WycWWLVtw4sQJJCQkoLy8HO7u7tiwYYPGA6LfeustmJubY+XKlbh9+zb69u2r1X2Ijh8/DltbW4wYMUJcZ2Zmhj179mDu3LkICgqCh4cHNm7cKE5FS6RLMkHTudGIiIiISG3NSUlJSYleW4//+Mc/orGxEZs3b+7Q81NSUrBkyRKUlpbqNjDqMthiQURERGQA/fv3R0REBL744gu9HH/o0KEdnvHJ1tYWjY2NUCgUOo6KuhK2WBARERHpUU1NDQoKCgA0fYF3dnY2ckSt5eTkAGia8WrAgAFGjoY6KyYWRERERESkNU43S0REREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWmNiQUREREREWvt/bSCVDppamSoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# More extensive plot method\n", + "f, axs = plt.subplots(1, 2, figsize=(8, 6), sharey=True)\n", + "\n", + "pe.plot_swrc(wos, ax=axs[0], label=\"Wosten\")\n", + "pe.plot_swrc(woss, ax=axs[0], label=\"Wosten Sand\")\n", + "pe.plot_swrc(wosc, ax=axs[0], label=\"Wosten Clay\")\n", + "pe.plot_swrc(cosb, ax=axs[0], label=\"Cosby\")\n", + "\n", + "axs[0].legend()\n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_ylabel(\"\\N{GREEK SMALL LETTER PSI} [cm]\")\n", + "axs[0].set_title(\"Soil Water Retention Curve\")\n", + "axs[0].set_xlabel(\"\\N{GREEK SMALL LETTER THETA} [-]\")\n", + "axs[0].set_xlim(0, 0.5)\n", + "\n", + "\n", + "pe.plot_hcf(wos, ax=axs[1], label=\"Wosten\")\n", + "pe.plot_hcf(woss, ax=axs[1], label=\"Wosten Sand\")\n", + "pe.plot_hcf(wosc, ax=axs[1], label=\"Wosten Clay\")\n", + "pe.plot_hcf(cosb, ax=axs[1], label=\"Cosby\")\n", + "\n", + "axs[1].legend()\n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_title(\"Hydraulic Conductivity Function\")\n", + "axs[1].set_xlabel(\"Ks [cm/d]\")\n", + "axs[1].set_xscale(\"log\")\n", + "axs[1].set_xlim(1e-10, 1e3)\n", + "\n", + "f.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pydon", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/04_curve_fitting.ipynb b/doc/examples/04_curve_fitting.ipynb new file mode 100644 index 0000000..ac8af89 --- /dev/null +++ b/doc/examples/04_curve_fitting.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Curve Fitting**\n", + "\n", + "It can be usefull to go from one soil model to the other. When the soil parameters are known the soil water retention curve and hydraulic conductivity function can be fitted." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pedon as pe\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_compare(soilsample: pe.SoilSample, soilmodel: pe.soilmodel.SoilModel):\n", + " f, ax = plt.subplots(1, 2, sharey=True, figsize=(4.2, 4.5))\n", + " ax[0].scatter(soilsample.theta, soilsample.h, c=\"k\", s=10, label=\"Soil Sample\")\n", + " _ = pe.soilmodel.plot_swrc(\n", + " soilmodel, ax=ax[0], label=f\"Fitted Soil Model {soilmodel.__class__.__name__}\"\n", + " )\n", + " ax[0].set_yscale(\"log\")\n", + " ax[0].set_xlim(0, 0.5)\n", + " ax[0].set_yticks(soilsample.h)\n", + " ax[0].set_xticks(np.linspace(0, 0.5, 6))\n", + "\n", + " ax[1].scatter(soilsample.k, soilsample.h, c=\"k\", s=10)\n", + " _ = pe.soilmodel.plot_hcf(soilmodel, ax=ax[1])\n", + "\n", + " ax[1].set_yscale(\"log\")\n", + " ax[1].set_xscale(\"log\")\n", + "\n", + " k_left = 10 ** (np.floor(np.log10(min(soilsample.k))) - 1)\n", + " k_right = 10 ** (np.ceil(np.log10(max(soilsample.k))) + 1)\n", + " ax[1].set_xlim(k_left, k_right)\n", + " ax[0].set_ylabel(r\"|$\\psi$| [cm]\")\n", + " ax[0].set_xlabel(r\"$\\theta$ [-]\")\n", + " ax[1].set_xlabel(r\"$K_s$ [cm/d]\")\n", + " ncol = 3\n", + " ax[0].legend(\n", + " loc=(-0.02, 1),\n", + " fontsize=6,\n", + " frameon=False,\n", + " ncol=ncol,\n", + " columnspacing=0.8,\n", + " handlelength=2.5,\n", + " )\n", + "\n", + " f.align_xlabels()\n", + " # plt.close(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Genuchten(k_s=712.8, theta_r=0.045, theta_s=0.43, alpha=0.145, n=2.68, l=0.5)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sn = \"Sand\"\n", + "soil = pe.Soil(sn).from_name(pe.Genuchten, \"HYDRUS\")\n", + "soilm_genuchten = getattr(soil, \"model\")\n", + "soilm_genuchten" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "h = np.logspace(-4, 6, num=11)\n", + "k = soilm_genuchten.k(h)\n", + "theta = soilm_genuchten.theta(h)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SoilSample(sand_p=None, silt_p=None, clay_p=None, rho=None, th33=None, th1500=None, om_p=None, m50=None)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "soilsample = pe.SoilSample(h=h, k=k, theta=theta)\n", + "soilsample" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Panday(k_s=572.4421544309812, alpha=0.1461730507877064, beta=2.6528189773626414, brook=3.7982278651292423, sr=0.10452908337641295)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "soilm_panday = soilsample.fit(\n", + " pe.Panday,\n", + ")\n", + "soilm_panday" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yRowYwYULF5gxYwZZWVlERESwYcOGagMJTPXH/m8hnI0pbfiF+9rx07EcMnOKmPXFft4aEWH7AEWt53RnOLGxsSiKUu2xbNkywz4TJ07k5MmTlJSUkJaWZpWuLDnDEc7OlDbs5ebC3x8OR6uBNbvO8PXec3aIUNR2Tpdw1GLpNRwh1GZqG+7SrAFPx7YC4G9r9nK+4KotwxN1gCQcI8kZjnB25rThZ/q1pkNIPS4VlzH9870mLd0txB9JwhFC3JKbi5a3RkTg5qLl+0Pn+fS/p9UOSTgxSThGki414ezMbcNtAn2Zek9bAF758gCncottEZ6oAyThGEm61ISzs6QNP96zBfoWDSkurWDyqgwqKqVrTZhOEo4QokZarYb5D4Xj4+7Cf09cYsmPv6odknBCknCEEEYJbejFjLj2ACzYeISD5wpUjkg4G0k4RpJrOMLZWaMNPxTVlP53BlJaUcnzKzMoKZcboYXxJOEYSa7hCGdnjTas0WiY82An/L3dOJR1mYXfHbVihKK2k4QjhDBJgI87rw/rBMC7Pxwn/cRFlSMSzkISjhDCZAM7BDE8qimVCiR+tpuiknK1QxJOQBKOEMIsM+La06S+J6cuFvPqVwfVDkc4AUk4RpJBA8LZWbsN1/NwZf5D4QB8sv0Umw+dt8pxRe0lCcdIMmhAODtbtOGYlv483rMFANP+bw8Xi0qtdmxR+0jCEUJYZOrAtrRq7MOFyyW8uFYm+BS3JglHCGERD1cdbz0cgYtWw9d7s1iXcVbtkISDkoQjhLBYp6Z+PNOvNQAvrdvHufwrKkckHJEkHCGEVTwd25Lw0PpcvlrO1FV7qJQJPsUfSMIRQliFi07LWw+H4+Gq5adjOXyYelLtkISDkYRjJBkWLZydPdrwHY18+Ot9dwLwxjcHOX6h0GZlCecjCcdIMixaODt7teFH9c3p1TqAq2WVJH62m/KKSpuWJ5yHJBwhhFVptRrmDe9MPQ8Xdp/O419bjqsdknAQknCEEFYX7OfJK/EdAfhnylH2/pavckTCEUjCEULYxJDwEO7vHEx5pcLzn2VwtUzWzqnrJOEIIWxCo9Hw6gMdaeTrzrHzhczbcFjtkITK6mTCCQsLo3PnzkRERNC3b1+1wxGi1mrg7ca84Z0BWPpzJtuO56gckVBTnUw4ANu2bSMjI4PNmzerHYoQtVrfto0ZqW8GwJTPdlNwtUzliIRa6mzCsbfS0hLS0tLUDkMIVfztvjtp7u/F2fyrTFr6Ax9++KF8Huogp0s4W7duJS4ujpCQEDQaDWvXrq22T3JyMmFhYXh4eKDX69m+fXuV1zUaDX369KFr1658/PHHNo3373+fD0BhYRHdu3cnKSnJpuUJ4Yi83V34+0PhaBSFH06V8NQr78jnoQ5yUTsAUxUVFREeHs7YsWMZNmxYtddXrlxJYmIiixcvRq/Xs3DhQgYOHMjhw4dp3LgxAD/99BNNmjTh3Llz9O/fn06dOtG5c+eblldSUkJJSYnheUFBAQBlZWWUld2+ayA9PZ2PPvoI/1Fd0Wg0eHp6smjRIuLj44mOjja6ztfLqak8a1Oj3LpUV3uVZ0kbtqayc4cp3LEW7+ihBAyaRG5uplmfhyrHVOn3ZgvOWhdT4tUoTrx4hUajYc2aNcTHxxu26fV6unbtyttvvw1AZWUloaGhTJo0ienTp1c7xtSpU+nQoQOPPfbYTcuYNWsWs2fPrrZ9xYoVeHl51Rhj9hV4PcMFL53CG91kWKioqri4mJEjR5Kfn0+9evVsUoalbdiayithwV4dZ4o1dGxQyRNtK9Fo7BqCsDJT2nCtSjilpaV4eXmxevXqKkkoISGBvLw81q1bR1FREZWVlfj6+lJYWEifPn1YvHjxLeeXutm3w9DQUM6dO4e/v/9t40tPT+ee4aPwH/UPKq8WkvPeGABSUlJMPsPZtGkTAwYMwNXV1ej3WUqNcutSXQFyc3MJDg62acKxpA1bU3p6Ov369UPn34yGf5qDRudKwXf/4qtFL1p0hqPG780WnLUuBQUFBAQEGNWGna5L7XZycnKoqKggMDCwyvbAwEAOHToEQHZ2NkOHDgWgoqKCcePG3XYyQ3d3d9zd3UlOTiY5OZmKimtnKa6urjU2ipiYGB599FG+ARRF4cqVKyQlJRETE2NW/Ywp0xbUKLeu1NUeZVnShq0pJiaGSZMmMW/ePLRbP6RB37E07P8kTduGWxyHWu3FFpytLqbEWqsSjjHuuOMOdu/ebfL7JkyYwIQJEygoKMDPz8/o902ePIVvFvyAj483qamp6PV6k8sWwhrMbcPWNHfuXIYNG8ahw0f4JNuFQ7kwedVuPhnXHZ1W+tZqO6cbpXY7AQEB6HQ6srOzq2zPzs4mKCjIomNbOrW7m5u7JBuhKkdZYkOv15MwehTvje2Ft5uO7ZkXWfpTpqoxCfuoVQnHzc2NqKgoUlJSDNsqKytJSUkxuxvrOlmeQDg7R2vDzfy9eGlwewDe/PYwh7MuqxyRsDWnSziFhYVkZGSQkZEBQGZmJhkZGZw6dQqAxMRElixZwvLlyzl48CDjx4+nqKiIMWPGWFSuo3w7FMJcjtiGR3QNpV+7xpRWVPL8ygxKy2XtnNrM6RJOeno6kZGRREZGAtcSTGRkJDNmzABgxIgRzJ8/nxkzZhAREUFGRgYbNmyoNpDAVI727VAIUzliG9ZoNLzxYCcaeLly4FwB/0g5onZIwoacbtBAbGwsNY3knjhxIhMnTrRTREIISzT29eD1oZ0Y//FO3tlynLvbBRLVvIHaYQkbcLozHLU4YneEEKZw5DY8qFMwwyKbUKnA5M8yKC4tVzskYQOScIzkiN0RQpjC0dvwzCEdCPbz4ERuMa9/fVDtcIQNSMIRQjgEP09X5j8UDsBHqafYcvi8yhEJa5OEYyRH7o4QwhjO0IbvahXAYz3CAJi2eg95xaXqBiSsShKOkRy9O0KImjhLG54+qB0tG3lz/nIJL63br3Y4wook4QghHIqHq44FD0eg02pYv/ssX+w+q3ZIwkok4QghHE54aH0m3d0KgJfW7iMr/6rKEQlrkIRjJGfo/xbidpytDU/o24rOTf3Iv1LGtP/bU+P9d8LxScIxkrP0fwtxK87Whl11WhY8HIG7i5atRy7wUdoptUMSFpKEI4RwWK0a+zB9UDsAXv/qIJk5RSpHJCwhCcfGZPlcISyTEBPGXa38uVJWQeJnGZRXyASfzkoSjpEs7f+W/mehNme7hnOdVqvhzeHh+Hq4sOtUHot/OK52SMJMknCM5Gz930L8kTO34ZD6nswe0gGAhd8dZd+ZfJUjEuaQhCOEcApDI5swqGMQ5ZUKz6/M4GpZhdohCRNJwhFCOAWNRsNrQzsR4OPO0fOF/H3jYbVDEiaShCOEcBoNvd2Y+2AnAP79UyZpmRdVjkiYQhKOEMKp9LszkD91DUVRIOnzfVyVpXOchiQcIYTTeXFwe0IbenIm7yqfn5A/Y85CflNGctYhpUJcV5vasI+7C39/KAKNBtIuaPnuoKyd4wwk4RjJmYeUCgG1rw13a9GQx+8KA+Bv6/aTU1iibkCiRpJwhBBO67l+rQj2VLhYVMZfP98rN1g7OEk4NiYz2whhO+4uWka1rsBVp2HjgWz+b+cZtUMStyEJRwjh1Jp4w7O/r50z64v9/HapWOWIxK1IwhFCOL0neoYR1bwBhSXlTFm1m8pK6VpzRHU24RQXF9O8eXOmTJlil/Kk+QthOzqthgUPh+PlpiP114ss/TlT7ZDETdTZhPPaa6/RvXt3tcMQQlhJc39v/nb/nQDM+/YwR7MvqxyR+KM6mXCOHj3KoUOHGDRokNqhCCGsaGS3ZsS2bURpeSXPf5ZBabmsneNInC7hbN26lbi4OEJCQtBoNKxdu7baPsnJyYSFheHh4YFer2f79u1VXp8yZQpvvPGGnSK+pqy0lLS0NLuWKURdo9FomPdgZ+p7ubLvTAEvfLiFDz/8UD57DsLpEk5RURHh4eEkJyff9PWVK1eSmJjIzJkz2blzJ+Hh4QwcOJDz56/dibxu3TratGlDmzZt7BLv/PnzgWvXjLp3705SUpJdyhWirmpcz4NX4zsCsPpgIU8kvSqfPQfhonYApho0aNBtu8IWLFjAuHHjGDNmDACLFy/mq6++YunSpUyfPp3U1FQ+/fRTVq1aRWFhIWVlZdSrV48ZM2bc9HglJSWUlPzvDuaCggIAysrKKCsru22s6enpfPjhh/iP7goaDZ6enixatIj4+Hiio6ONrvP1cmoqz9rUKLcu1dVe5VnShh3drX5v/kUnuXroRzza9aJR3BQufjLVrM+ePanVBi1lSrwaxYlvzdVoNKxZs4b4+HgASktL8fLyYvXq1YZtAAkJCeTl5bFu3boq71+2bBn79u0znIXczKxZs5g9e3a17StWrMDLy6vGGM9fgdcyXPDUKczpJgtGiaqKi4sZOXIk+fn51KtXzyZlWNqGnVVxOczZrSO/VEOvoEqGt5DrObZgSht2ujOc28nJyaGiooLAwMAq2wMDAzl06JBZx3zhhRdITEw0PC8oKCA0NJS+ffvi7+9/2/emp6fz6IOP4j/6nxQVF/PII48BkJKSYvIZzqZNmxgwYACurq5m1cMcapRbl+oKkJuba/MyLGnDju5Wv7f09HT69euHa2hnGgx9iR+ztHzxzmtsWPaWQ5/hqNEGLXX9jNkYtSrhmOqxxx6rcR93d3fc3d1JTk4mOTmZioprZymurq41NoqYmBhGjXqUrwEUhStXrpCUlERMTIxZ8RpTpi2oUW5dqas9yrKkDTuLP9YlJiaGSZMmMW/ePHQ7vqRe1GBChk6jfXi0w9fZ2X4vpsTqdIMGbicgIACdTkd2dnaV7dnZ2QQFBVl0bHNn2p06dRoAXl5epKamMmfOHIviEMJctW226JrMnTuX1NRU3ny0B8E+Oq7gzowv9qkdVp1WqxKOm5sbUVFRpKSkGLZVVlaSkpJi9lnFdZauJeLq5oZer7coBiEsUZvWwzGWXq/n8YRR/Gu0Hp1Ww7qMs3y556zaYdVZTpdwCgsLycjIICMjA4DMzEwyMjI4deoUAImJiSxZsoTly5dz8OBBxo8fT1FRkWHUmrnq2rdDUfvU5TYc2awBE2JbAvDi2n2cL7iqckR1k9MlnPT0dCIjI4mMjASuJZjIyEjDsOYRI0Ywf/58ZsyYQUREBBkZGWzYsKHaQAJT1cVvh6J2qetteFK/1nRsUo+84jKm/d8eWTtHBU6XcGJjY1EUpdpj2bJlhn0mTpzIyZMnKSkpIS0tzSpdWXX526GoHep6G3bVaXnr4QjcXLRsOXyBFdtPqR1SneN0CUctFn87lC9TQmV1/QwHoHWgL9MGtgXg1S8PciKnSOWI6hZJOEaq698OhfOTNnzN2Lta0P2Ohlwpq2Dyqt1UyNo5diMJRwhRp2i1GuY/FI6Puws7Tl7i3a3H1Q6pzpCEYyTpjhDOTtrw/zRt4MXMuPYAvLXpCPvP5qscUd0gCcdI0h0hnJ204aqGRzXlnvaBlFUoJK7cTUm5zHVoa5JwbEyjdgBCiJvSaDS8PqwTAT5uHM6+zIJNR9QOqdaThCOEqLMCfNx5fWgnAN7b+ivbMy+qHFHtJgnHSNL/LZydtOGbu6dDEA9FNUVRYPKqDApLytUOqdaShGMk6f8Wzk7a8K3NiGtPk/qenL54hVe/PKB2OLWWJBwhRJ3n6+HK3x8OR6OBT/97mpSD2TW/SZhMEo4QQgDd7/DniZ4tAEj6v73kFpbU8A5hKkk4Qgjxu8n3tKV1Yx9yCkv425p9MsGnlUnCMZJccBXOTtpwzTxcdbw1IgIXrYYN+7NYs+uM2iHVKpJwjGTpBVf5niTUJoMGjNOxiR/P9W8NwMx1+zmbd0XliGoPSThCCPEHf+nTkshm9blcUs6UVbuplAk+rUISjhBC/IGLTsuChyPwdNWx7Xguy385oXZItYIkHBvTyNw2QjilFgHe/PW+dgDM+eYQx85fVjki5ycJRwghbuHR7s3p3aYRJeWVJH62m7KKSrVDcmqScIQQ4hY0Gg3zHuyMn6cre37LJ3nzMbVDcmqScIwkQ0qFs5M2bJ4gPw9eie8IwKLvj7H7dJ66ATkxSThGkiGlwtlJGzbfkPAQBncOpqJS4fnPMrhaJmvnmEMSjhBCGOHV+I409nXn1wtFzPnmkNrhOCVJOEIIYYT6Xm7MG94ZgGXbTvDzsRyVI3I+knCEEMJIsW0b82d9MwCmrNpN/pUylSNyLpJwhBDCBH+7/06a+3txLv8qs7/Yr3Y4TqXOJZy8vDyio6OJiIigY8eOLFmyRO2QhBBOxMvNhQUPR6DVwOe7zvDN3nNqh+Q06lzC8fX1ZevWrWRkZJCWlsbrr79Obm6uzcuVac6FqD2imjdgfGxLAP66Zi/nL19VOSLnUOcSjk6nw8vLC4CSkhIURbFpMtD+PrdNWXk5aWlpNitHCGFfz/ZrQ/vgelwqLuMv/97KBx98KJ/xGjhdwtm6dStxcXGEhISg0WhYu3ZttX2Sk5MJCwvDw8MDvV7P9u3bq7yel5dHeHg4TZs2ZerUqQQEBNgs3rlz5wBwtaSE7t27k5SUZLOyhBD24+ai5a0REWiVSnZmlzHh7x/JZ7wGLmoHYKqioiLCw8MZO3Ysw4YNq/b6ypUrSUxMZPHixej1ehYuXMjAgQM5fPgwjRs3BqB+/frs3r2b7Oxshg0bxvDhwwkMDLxpeSUlJZSU/G+p2YKCAgDKysooK7v9CJX09HSWLVtGwBg9Go0WT09PFi1aRHx8PNHR0UbX+Xo5NZVnbWqUW5fqaq/yLGnDjk6t39t1FzP3kf/TR/j2Gk2DfuNQsg+b9RkH9etiLlPi1ShOfHFBo9GwZs0a4uPjDdv0ej1du3bl7bffBqCyspLQ0FAmTZrE9OnTqx3j6aef5u6772b48OE3LWPWrFnMnj272vYVK1YYuuZu51IJzNrpgotG4e/d5e5kUVVxcTEjR44kPz+fevXq2aQMS9uwuL1KBd7er+P4ZQ13+CpM6lCBtg7NEm9KG65VCae0tBQvLy9Wr15dJQklJCSQl5fHunXryM7OxsvLC19fX/Lz87nrrrv45JNP6NSp003LuNm3w9DQUM6dO4e/v/9t40tPT2fAAw8RMPZdlIpyLiQ/AkBKSorJZzibNm1iwIABuLq6Gv0+S6lRbl2qK0Bubi7BwcE2TTiWtGFHp9bv7br09HT69euHtl5jGo6cj9bNk8KfPmT9m8+adYajZl3MVVBQQEBAgFFt2Om61G4nJyeHioqKat1jgYGBHDp0bSqKkydP8uSTTxoGC0yaNOmWyQbA3d0dd3d3kpOTSU5OpqLi2lmKq6trjY0iJiaGsWPG8MXvz69cuUJSUhIxMTFm1c+YMm1BjXLrSl3tUZYlbdhZqFWXmJgYJk2axLx589CkvIf/oGep1/PP1A/rYHY8zvZ7MSXWWpVwjNGtWzcyMjJMft+ECROYMGECBQUF+Pn5Gf2+F//2N754PQWtTkdqaip6vd7ksoWwBnPbsLi9uXPnMmzYMA4fPsK6i27syCrl+ZUZrJt4F+4uOrXDcyhON0rtdgICAtDpdGRnZ1fZnp2dTVBQkEXHNndqd831JT81Gkk2QlWyPIHt6PV6Ro8exeLHe9PQ241DWZdZ+N1RtcNyOLUq4bi5uREVFUVKSophW2VlJSkpKWZ3Y11n7tTu1/ON814pE7WFLE9ge4183Xl96LUu+nd/OE76iYsqR+RYnC7hFBYWkpGRYegWy8zMJCMjg1OnTgGQmJjIkiVLWL58OQcPHmT8+PEUFRUxZswYi8o199vh9Rs/QWYbEOqSMxz7uLdjEMO6NKFSgcTPdlNUUq52SA7D6RJOeno6kZGRREZGAtcSTGRkJDNmzABgxIgRzJ8/nxkzZhAREUFGRgYbNmy45X02xjL7DOeG/1dKvhEqkjMc+5k1pAMhfh6culjMa18fVDsch+F0gwZiY2NrPFOYOHEiEydOtFNEt1f9DKcODdAXoo6q5+HK/IfCGfnvNFaknWLAnYH0bddY7bBU53RnOGoxuzvihvwiZzhCTdKlZl89WgUw9q4WAEz7vz1cKipVOSL1ScIxkqWDBgAUJOMI9UiXmv1Nu7ctrRr7cOFyCS+u3Vfnr+NKwrGxql1qKgYihLA7D1cdbz0cgYtWw1d7z/HF7rNqh6QqSThGMvs+nBv+LwlHqEm61NTRqakfk+5uDcBLa/dxLv+KyhGpRxKOkcztjqhyhiNdakJF0qWmnqf7tiS8qR8FV8uZtnoPlXX0gq4kHBu78RpORR1tZELUda46LQtGRODuouXHozl8lHZS7ZBUIQnHxlxumKdcEo4QdVfLRj68MKgdAK9/fZDjFwpVjsj+JOEYydz+b90NCae0otLaYQlhNLmGo77RMWH0bBXA1bJKEj/bTXkd+5sgCcdI5g+L1uCmu/ZjLq+QMxyhHrmGoz6tVsO84Z3x9XBh9+k8/rXluNoh2ZUkHDtw0V07y5GEI4QIqe/JKw90BOCfKUfZ+1u+yhHZjyQcO7h+Haessm6dPgshbu6BiBDu6xREeaXC859lcLWsbiw/LwnHSJb0f7tKl5pwAHINx3FoNBpeje9EI193jp0v5M1vD6sdkl1IwjGSJf3f17vUyurYBULhWOQajmNp6O3G3AevrZ3zn58ySf219q+dIwnHDly0137MknCEEDe6u10gj3RrBkDS5/u4UsuXzpGEYwduLr93qcl9OEKIP3jx/jtp1tCLs/lX+fxE7f6TXLtr5yBcf+9SKy2XMxwhRFXe7i78/eFwNBrYfkHLxgPZaodkM5Jw7MDL7do6d8WldWMkihDCNF3DGjKuZxgAL31xgJzCEnUDshFJOHbg5aYDoLi0lnfQCiHM9szdrQjxUrhYVMb0/9tbK9fOkYRjJEuGlMoZjnAEMizasbm7aHm0VQWuOg3fHcxm1Y7f1A7J6iThGMmSIaXe7tfOcIpK5AxHqEeGRTu+Jt7w7N2tAHh5/QFOXyxWOSLrkoRjB3KGI4Qw1hM9w4hu3oDCknImr9pdq9bOkYRjB/+7hiMJRwhxezqthr8/HI6Xm47tmRf5z0+ZaodkNZJw7MDX49oZTsHVMpUjEUI4g+b+3rw0uD0Ab357mMNZl1WOyDok4dhBQ283AC4WlqociRDCWfypayh3t2tMaUUliZ9l1Ir7+FxM2fmLL74wuYABAwbg6elp8vts5fTp04waNYrz58/j4uLCSy+9xEMPPWTTMg0Jp1gSjhDCOBqNhjnDOnHPwq3sP1vAou+PMvmetmqHZRGTEk58fLxJB9doNBw9epQ77rjDpPfZkouLCwsXLiQiIoKsrCyioqK477778Pb2tlmZDb1+TzhFknCEEMZrXM+D1+I7MWHFTpI3H6Nvu8Z0adZA7bDMZnKXWlZWFpWVlUY9vLy8bBGzRYKDg4mIiAAgKCiIgIAALl607SytDX0k4QghzHN/52AeiAihUoHJn+3mihMPPjIp4SQkJJjUPfboo49Sr149k4O6na1btxIXF0dISAgajYa1a9dW2yc5OZmwsDA8PDzQ6/Vs3779psfasWMHFRUVhIaGWjXGP2rs6wFcSzg/bku1aVlCiNrn5SEdCaznTmZOEY8tXEtaWpraIZnFpITz/vvv4+vra/T+77zzDgEBASYHdTtFRUWEh4eTnJx809dXrlxJYmIiM2fOZOfOnYSHhzNw4EDOnz9fZb+LFy8yevRo3nvvPavGdzNzXn6JytIrANw9+EGSkpJsXqYQovbw83KlXX46AGkXPYj901+c8u+ISddwHMGgQYMYNGjQLV9fsGAB48aNY8yYMQAsXryYr776iqVLlzJ9+nQASkpKiI+PZ/r06fTo0eO25ZWUlFBS8r+J9AoKCgAoKyujrKzmYc7p6em8vWgRDf/8d7T+zfBu3IxFixYRHx9PdHR0je+/XtaN/9qLGuXWpbraqzxL27AjU+v3Zgu3q0t6ejqrFr2MT+zjeHW+l4D7n+ft9yab9HfEVkz52WsUC2aIu3r1Knv27OH8+fNUVlYdsjdkyBBzD2s0jUbDmjVrDIMZSktL8fLyYvXq1VUGOCQkJJCXl8e6detQFIWRI0fStm1bZs2aVWMZs2bNYvbs2dW2r1ixwqRrVO8d0rL/kpaH76jgrsDac+ewsExxcTEjR44kPz/f6t3P11mrDQv1lVTAvD06cq5qiA6oZFRr9YdKm9KGzU44GzZsYPTo0eTk5FQ/qEZDRYXtL2z9MeGcPXuWJk2asG3bNmJiYgz7TZs2jR9++IG0tDR++uknevfuTefOnQ2vf/jhh3Tq1OmmZdzs22FoaCjnzp3D39+/xhjT09Pp168fPr3H4BVxH8U711P40wekpKSYdIazadMmBgwYgKurq1HvsQY1yq1LdQXIzc0lODjYpgnH0jbsyNT6vdnC7epy/e8IgEtQGxoMfwWNVstzXX2YMOT2vTS2VlBQQEBAgFFt2OwutUmTJvHQQw8xY8YMAgMDzT2M3fXs2bPa2djtuLu74+7uTnJyMsnJyYZE6urqalQDj4mJYdKkSfxrwy68IkDTMJRnnnmmSkI0lrFlWpsa5daVutqjLEvbsDOo7XW5/ndk3rx5kLkbXeoq/HqMYPn+EkbeU2EYmKQGU37uZs80kJ2dTWJiokMlm4CAAHQ6HdnZVVfMy87OJigoyKJjWzLT7ty5c3lv7kwAGreJ5I033rAoFiHMIbNFO7e5c+eSmprKBx98wJdzn+bO4HpcKi7jr587z9o5Ziec4cOHs2XLFiuGYjk3NzeioqJISUkxbKusrCQlJcWsM4obWbqWyNB+3dFpNVwuVcgquGpRLEKYQ9bDcX56vZ5Ro0ZxV0x33hoRjptOy3cHz7Mq3TnWzjG7S+3tt9/moYce4scff6RTp07VTqueeeYZi4O7mcLCQo4dO2Z4npmZSUZGBg0bNqRZs2YkJiaSkJBAdHQ03bp1Y+HChRQVFRlGrZlrwoQJTJgwgYKCAvz8/Ex+v4erjnZBvuw/W8D2zIs8ENHEoniEMJWlbVg4lnZB9Zh8Txve+OYQs9fvJ6alP6ENHXsQiNkJ55NPPmHjxo14eHiwZcsWNBqN4TWNRmOzhJOenk7fvn0NzxMTE4FrI9GWLVvGiBEjuHDhAjNmzCArK4uIiAg2bNhgcdffH/u/zdGjpT/7zxaw7ViuJBxhd9Zow8KxPNHrDr47mM1/T1xi8qrdfDquO1qtpuY3qsTsLrW//e1vzJ49m/z8fE6cOEFmZqbh8euvv1ozxipiY2NRFKXaY9myZYZ9Jk6cyMmTJykpKSEtLQ29Xm9xudbo/+7R6tpNsD8fz3GaPldRe8g1nNpHp9Uw/6H/rZ2z9GfHXjvH7IRTWlrKiBEj0GrrxgoH1uj/7hbWEDcXLb9dusLBc7VjfQvhPOQaTu3U3N+bF++/tnbOvG8PcyTbcf+2mJ0tEhISWLlypTVjcWjW+Hbo7e5C37aNAFi/56y1QhPCKHKGU3s90i2U2LaNKC2v5PmVjrt2jtnXcCoqKpg3bx7ffvstnTt3rjZoYMGCBRYHVxvFhYfw7f5s1u46w+QBbXDR1Y0zRCGE7Wg0GuY92Nmwds7b3x8l0QHXzjH7r93evXuJjIxEq9Wyb98+du3aVeVR21irO6L/nYE09HbjXP5Vvt2fXfMbhLAS6VKr3RrX8+DV+I4AJG85TsbpPHUDugmzz3A2b95szTgcnrWGlHq46nhU34x/fn+Mf//0K/d1Cqoywk8IW5Fh0bXf4M4hbNyfzRe7z5L4WQZfTeqFp5tO7bAMzD7DeeONN1i6dGm17UuXLmXu3LkWBVXbPdq9Oe4uWnadymPTATnLEUJYz8sPdCCwnju/Xihi7oZDaodThdkJ591336Vdu3bVtnfo0IHFixdbFFRt17ieB4/3bAHAnG8OUVbhmBf4hBDOp76XG/OGhwOwbNsJfj5WfYJltZidcLKysggODq62vVGjRpw7d86ioByRtfu/x8e2xN/bjV9zinj3h+NWOaYQtyPXcOqOPm0a8Wj3ZgBMWbWb/CuOsV6Q2QknNDSUn3/+udr2n3/+mZCQEIuCckTWHlLq6+HKi4PvBOAfKUfZdybfKscV4lZkWHTd8tf77qS5vxfn8q8ye/1+tcMBLEg448aN47nnnuP999/n5MmTnDx5kqVLl/L8888zbtw4a8ZYa8VHNGFgh0DKKhTGf7yDS0WlaockhKglvNxcWPBwOBoNfL7zDN85wPVis0epTZ06ldzcXJ5++mlKS6/9ofTw8CApKYkXXnjBagHWZhqNhjnDOnPw3M+culjM0x/vZNnYrri7OM6oEiGE84pq3pBxve7gva2/8sKavUSHNaC+l5tq8Zh9hqPRaJg7dy4XLlwgNTWV3bt3c/HiRWbMmGHN+Gq9Bt5uLBkdjbebjl9+zWXCxzsd9i5hIYTzSRzQhpaNvLlwuYTZ6w+oGovFt7n7+PjQtWtXOnbsiLu7uzVicki2vODaNsiXJaOjcXe5trbFhBU7uVIqM/oK65JBA3WTh6uO+Q+Fo9XAml1n+HZ/lmqxmJRw9uzZY9LyzPv376e8vNzkoByRrS+49mgVwHujo3HTadl0IJtHlqSSW1hS8xuFMJIMGqi7Ips14MneLQH425q9XFTperFJCScyMpLc3Fyj94+JieHUqVMmB1VX9WnTiI+e0OPn6UrG6TyGvP0zO09dUjssIUQt8Fz/1rRu7ENOYSkzv1Bn1JpJgwYUReGll17Cy8u4VeWuDyYQxuvWoiGfP92Dscv+y8ncYh5e/AvP9WtFE1k+Rwhhgetda8Pe2cb63We5r2MQgzpVv5fSlkxKOL179+bw4cNG7x8TE4Onp6fJQdV1LRv58OWknvx1zT7W7z7L/E1HCfPR0TrqMh1DG6odnhDCSYWH1md8n5a8vfkYL67dR7cWDfH3sd+1d5MSzpYtW2wUhvgjXw9X/vmnCHq28ufl9Qc4UVhB/DupPN6rBZPubo2Pu9kj2oUQddikfq347mA2h7IuM+OL/SSP7GK3smUxFgem0WgY0bUZ3zxzF50bVlJeqfDuD7/SZ95mlv2cKcOnhRAmc3e51rWm02r4as85u94QKgnHSGoOKQ328+DxtpUsHhlBmL8XuUWlzFp/gH4LtrB6x2+SeIRRZFi0uK5jEz+e6HVtAuGX1u3j8lX7zLUmCcdIjjCktN+djdmU2IdX4zvSyNed0xevMGXVbvq8uZklW3+1W6MRzskR2rBwHM/1a0OzhtfmWpv/rfHX5i0hCcfJuOq0PNq9OT9MjWX6oHY08nXnXP5VXvv6ID3mfM/rXx8kM6dI7TCFEA7O003H60M7AfBB6kl2nLT9LRiScJyUl5sLf+nTkp+S+jL3wU7c0ciby1fLeW/rr/Sdv4WRS1JZv/usdLcJIW6pZ+sAHuzSFEWBFz7fY/O/FzLUycm5u+gY0bUZD0WF8v2h83ycdpItRy6w7Xgu247n4u/tRlx4CPGRTQhv6ifLWQshqnjx/jvZcvg8R7ILWfpzJn/p09JmZZmUcFq0aGHWH6znnnuOZ555xuT3CeNptRr6tw+kf/tAfrtUzGf/Pc3K9NNkF5SwbNsJlm07QZi/F0MimhAfEcIdjXzUDlkI4QAaeLvxwn13MmXVbhalHGVYZBMa1/OwSVkmJZxly5aZVUhYWJhZ77OVoUOHsmXLFvr168fq1avVDsfqmjbwIvGetjzTrzVbj15gXcZZNu7P5kRuMf9MOco/U47SPrgeAzsEMbBjIG0DfeXMR4g6bFhkEz5KPUnG6TzmbDjEgocjbFKOSQmnT58+hv9v3LiRiIgIGjdubPWgbO3ZZ59l7NixLF++XO1QbMpFp+XudoHc3S6QopJyNh3IZl3GGbYezeHAuQIOnCvgre+OEObvxcAOQdzTIYiI0ProtJJ8hKhLtFoNs4d04IHkn/l85xke7d6cLs0aWL0cs6/hxMfHU1JSQmBgIBEREURGRjJgwABiY2OtGJ5txMbG1rlZE7zdXYiPbEJ8ZBMuFpXy3cFsNu7PYuvRHE7kFvPu1l95d+uv1Pdy5a5WAfRqFUD3FvWrHCMtLY0jR47Qpk0b9Hq9OhURQthEeGh9Hopqyqodv/HM+1uZPzCQ7t27W7UMs0epXb58md27d/Pmm2/SsWNH0tLSuPfee+nbty9FRbYblrt161bi4uIICQlBo9Gwdu3aavskJycTFhaGh4cHer2e7du32yweZ9TQ242Ho0P5d0JXdr00gOSRXRgSHoKvuwt5xWV8tecc0z/fS+zff+S1XTpe/vIgjybNJaZ3X0aPHk337t1JSkpSuxpCCCsrTV9FZekVfrviwt2jnrX659zsM5zc3Fw6duxIx44d+fOf/wzA+fPnGTZsGK+88gpz5syxWpA3KioqIjw8nLFjxzJs2LBqr69cuZLExEQWL16MXq9n4cKFDBw4kMOHD5vV/VdSUkJJyf/WpSkoKACgrKyMsjL73Gh5vRxblOemhXvuDOCeOwMor6hkz5kCfjqWw8/HL7L7dD7nr8KHaadB05HQZz6hLOsopad2886qDcQNeQB9N+vetW7LujpyubbkCG3YVtT6vdmC2nVJT09nyaIFeMc8gnfXYTToPZpFi6YRHx9PdHT0Ld9nSrwaRVHMmvheq9USFBREeHi44REREUFRURH33Xcf58+fN+ewJtFoNKxZs4b4+HjDNr1eT9euXXn77bcBqKysJDQ0lEmTJjF9+nTDflu2bOHtt9+ucdDArFmzmD17drXtK1asMHqZBmd1pRyOFmg4lKfhcL6GnKtVr+146hRa+ym0q6/Q1k8hwDYDW2q14uJiRo4cSX5+PvXq1bNJGXW5DQvTFZXBy7t0XK3Q8FjrCiIDbp8iTGnDZiec48ePs3v3bvbs2cPu3bvZvXs3J06cwM3NjbKyMh555BH0ej0RERH06tXLnCJq9MeEU1paipeXF6tXr66ShBISEsjLy2PdunWGbcYmnJt9OwwNDeXcuXP4+/tbtT63UlZWxqZNmxgwYACurq52KfPGcgMCAhgwYADaeo1xC+2MW/Nw3Jp2ROtRdWh1s4ae3NXSn56t/OneoiH1PE2PVe262rvc3NxcgoODbZpwHKEN24pavzdbULsu6enp9OvXDwCvbsPx6T6C8pxTfDY2/Lbz7xUUFBAQEGBUGza7S61ly5a0bNmySrdWQUEBmzdvZujQoSiKwvLly0lKSqK4uNjcYkySk5NDRUUFgYGBVbYHBgZy6NAhw/P+/fuze/duioqKaNq0KatWrSImJuamx3R3d8fd3Z3k5GSSk5OpqKgAwNXV1e6NQo0yAbp168akSZOYN28eRdknIX09U5OSeOTpJH48msNPR3PYeeoSpy5e4dTF3/jkv7+h1UBEaH16tW5Er9YBhIfWx1Vn/CVDtepq73LtUZYjtWFbkbpYLiYmxvA5v5r6OV5dhuAS0IzKRm1uG48psZqdcBo2bEhERIShO61Tp074+Pjw9ddf07JlSz7++GMAQ+N2JN99953J75kwYQITJkygoKAAPz8/G0Tl2ObOncuwYcOqjVKLbNaAZ/q1prCknNTjufx49AI/Hsvh1wtF7DyVx85Tefwj5Si+7i50b+lP79YB9GzdiDB/L7n3x87qehsWNbvxc/7TlQZ8++tVlv6cSc/WAVY5vtkJZ+nSpYautHXr1nHixAkAvLy8+Oyzzwz76XQ6i4M0VkBAADqdjuzsqus7ZGdnExQUZNGx//jtsC7S6/W3HA7t4+5imOkA4EzeFX46eoGtR3P4+VgOecVlbDqQzabf195o2sCTXq0D6NW6ET1a+lPfy81u9airpA0LY1z/nPfMKeLb+Vv4/tB5Tl8sJrSh5df7LLoP58brJJcvX+bcuXM0adIEb29viwMzh5ubG1FRUaSkpBhiq6ysJCUlhYkTJ1p0bPl2aJom9T0Z0bUZI7o2o6JSYf/ZfH48msOPRy+w4+Qlfrt0hU+2n+aT7afRaqBT0/r0bh3AXXc0oMKsq4qiJtKGhSlaBHhzVyt/fj6Wy7qMM0y8u7XFx7Ta5J2+vr74+vpa63C3VFhYyLFjxwzPMzMzycjIoGHDhjRr1ozExEQSEhKIjo6mW7duLFy4kKKiIsaMGWNRufLt0Hw6rYbOTevTuWl9JvRtRVFJOdszL7L16AV+OprD0fOF7D6dx+7TeSz6Hjx1Or4r3E3fdoH0adPIZvM61TXShoWpHohows/Hclmz6wwT+rayuBvc6SbvTE9Pp2/fvobniYmJwLWRaMuWLWPEiBFcuHCBGTNmkJWVRUREBBs2bKg2kMBU8u3QerzdXejbrjF92127L+pc/hV+OprD1qM5bD1ynvwr5Xy9L5uv913rfmsfXI/Yto2IbduYLs3q42LC4APxP9KGhanu7RjES2v3cfxCEQfPXaZ9iGUjKZ1u8s7Y2FhqGsk9ceJEi7vQhP0E+3nyUHQoD0WHcrWklMWrvqEsoA0/Hstlz2/5hnnf/rXlOH6ervS7szH3dgiid5tGeLja7xqhEHVNPQ9XerYKIOXQeX44csG+CefGyTvrGumOsA+dVkMLX7ivXyum3nsnOYUlbD1ygS2HL7D16AXyisv4fOcZPt95Bk9XHX3aNGJgx0D63RlIPY/aMSzWVqQNC3P0an0t4fx49ALjYy1bK0cWYDOSdEeoI8DHnWFdmjKsS1MqKhXST1xkw/4sNu7P5kzeFTbsz2LD/izcXbT0bx/I0Igm9GnbyKR7fuoKacPCHL3bNAIg/cQlrpRW4Olmfq+CJBzhNHRaDfo7/NHf4c+Mwe3Zf7aADfuy+GbfOY5fKOKrPef4as85Gnq7Edc5mIeiQ+nYRP6wCmGJFgHeBPi4kVNYysGsAouWLZCvgUZKTk6mffv2t53iQdiPRqOhYxM/pgxsy3eJffhyUk/G3tWCAB83LhaVsvyXkwxe9BPD/vUz6zLO2HytdmcgbViY4/pnDWDfmXyLjiUJx0gTJkzgwIED/Pe//1U7FPEH1z8QM+Lak/pCP94f05XBnYNx1WnYeSqPZz/NoOfc7/nHd0fJKy5VO1zVSBsW5ur8e8LZ+5tlCUe61ESt4qLT0rdtY/q2bcz5gqus2H6Kj9NOcf5yCW99d4QlP/7KYz3CeKJXC5ndQAgjtQm6do9lZo5la53JGY6otRrX8+C5/m34Oelu/vGnCNoF+VJYUs7bm4/R580tvP9zJmUV0tUmRE2a/T6tzamLlk3ELAnHSNL/7bzcXLQ8ENGEr5/pxeJHo2gb6Ev+lTJmrz/Aff/4kV2nLqkdol1IGxbmup5wzl8u4Uqp+cPqJeEYSfq/nZ9Wq+HejkF8/WwvXh/aCX9vN46eL+TBd7Yxf+PRWj+Hm7RhYS4/T1d83a9dgTmTZ/5ZjiQcUefotBpG6pvxXWIfhkU2oVKBd3/MJHm/jpzCkpoPIEQdo9FoCPB1ByC30PyBN5JwRJ3VwNuNBSMi+Nefu+DtruP4ZQ0Pv7fd4gujQtRG9b2uzeRxqbjM7GNIwjGS9H/XXvd1CmbNX7rj765w+tIV/vTeL/x2yT6r1NqTtGFhiQa/j+q05NYCSThGkv7v2q1FgDfPdaygVSNvsgtKGL10OxeLatc9O9KGhSXkDEcIK6rnBksTogjx8+DXC0VM/iyjxpnJhagr6nv+foZzRc5whLCKYD8Plo7pipuLls2HL/BR2im1QxLCIXj9PmlnSZn5965JwhHiD9oF1SPp3nYAzNtwqE5PhyPEde4u19JFiQXzEkrCEeImHusRRrsgXy5fLedfW46rHY4QqnN3vZ5w5MZPIaxKp9UYznI+Tj1JYUm5yhEJoS53l9+71OQMx/ZkSGndE9u2EXc08qaotIJ1GWfUDsdi0oaFJQxdanINx/ZkSGndo9Fo+FPXUAC+2ZulcjSWkzYsLCFdakLY2ID2QQCkZeZy+ar59x8I4ewMXWpyhiOEbbQI8KZFgDdlFQqpv15UOxwhVKPTagCosODeNEk4QtSga9i1Ndz3/JanbiBCODlJOELUoFPT+gDstnB5XSHqujqZcL788kvatm1L69at+fe//612OMLB3fn78rrHzxeqHIkQzs1F7QDsrby8nMTERDZv3oyfnx9RUVEMHToUf39/tUMTDqqZ/7XVDs/lX6G0vBI3lzr5PU0Ii9W5T8727dvp0KEDTZo0wcfHh0GDBrFx40a1wxIOrJGPO56uOioVOJt3Re1whHBaTpdwtm7dSlxcHCEhIWg0GtauXVttn+TkZMLCwvDw8ECv17N9+3bDa2fPnqVJkyaG502aNOHMGee/qU/YjkajocG1xQ7ZnLZL3WCEUNn58+dJS0sz671Ol3CKiooIDw8nOTn5pq+vXLmSxMREZs6cyc6dOwkPD2fgwIGcP3/ezpGK2iIpKYlfD+0FYELiNJKSklSOSAj7+/CDDwA4euwY3bt3N+tz4HTXcAYNGsSgQYNu+fqCBQsYN24cY8aMAWDx4sV89dVXLF26lOnTpxMSElLljObMmTN069btlscrKSmhpOR/69wXFBQAUFZWRlmZfW4EvF6OvcpTs1xHq2t6ejqLFi3Cb/A0ADzqBbBo0SLi4+OJjo62Wrm25Aht2FbUai+24Mh1SU9P58uvvqT+4A7otFo8PT0Nn4M2bdoYfRyN4sQrTGk0GtasWUN8fDwApaWleHl5sXr1asM2gISEBPLy8li3bh3l5eXceeedbNmyxTBoYNu2bbccNDBr1ixmz55dbfuKFSvw8vKyRbWEA1pxTEvaBS2Dm1UwoIn1PjLFxcWMHDmS/Px86tWrZ7Xj3kjasLCGvRc1/PuwjjAfhec7/W96G1PacK1KONevz2zbto2YmBjDftOmTeOHH34w9Dt+8cUXTJkyhcrKSqZNm8aTTz55yzJu9u0wNDSUc+fO2W1kW1lZGZs2bWLAgAG4urrapUy1ynW0uqanp9OvXz98eo7Cq8sQinaso+jnj0hJSbHKGU5ubi7BwcE2TTiO0IZtRa32YguOXJf09HQGjUui/uAkys4d5tKqFwFISUmhTZs2BAQEGNWGna5LzRqGDBnCkCFDjNrX3d0dd3d3kpOTSU5OpqLiWmZ3dXW1e6NQo0y1ynWUusbExDBp0iTe/eUsXkAFWp555pkqX2gsLc/WHKkN24rUxbZiYmIYfP9gfgIqKiu5cuUKSUlJxMTEGLpojeF0gwZuJyAgAJ1OR3Z2dpXt2dnZBAUFWXRsmWm37po7dy5PPTEWgPhhw5kzZ47KEZlH2rCwxOjRowFo3aoVqampZn0OalXCcXNzIyoqipSUFMO2yspKUlJSLP5GKmuJ1G2twpoD4NcwQOVIzCdtWFhDQKNG6PV6s97rdAmnsLCQjIwMMjIyAMjMzCQjI4NTp04BkJiYyJIlS1i+fDkHDx5k/PjxFBUVGUatmUu+HdZt1lgLRG3ShoUlNBqNxcdwums46enp9O3b1/A8MTERuDYSbdmyZYwYMYILFy4wY8YMsrKyiIiIYMOGDQQGBlpU7h/7v0XdYo3VDtUmbViozekSTmxsLDUNrJs4cSITJ060arkTJkxgwoQJFBQU4OfnZ9VjC8dnjfXc1SZtWKjN6brU1CL933Xb9TOcq2XOe3YgbVioTRKOkaT/u27T/r7aYaXz3rYmbVioThKOEEIIu5CEYyTpjhDOTtqwsAZLTvIl4RhJuiOEs5M2LCxh+aBoSThCCCHsRBKOEEIIu5CEYyTp/xYAzjtGTdqwUJ8kHCNJ/3fdZo3+a7VJGxZqk4QjhBDCLiThCCGEMJol3cqScIQQQtTICpNFS8IxllxwFc5O2rBQmyQcI8kFV+HspA0LtUnCEcII1xefcuK5O4VQnSQcIYQQdiEJRwghhF1IwhFCCGE8C/qVJeEIIYSokQyLtiMZUiqcnbRhoTZJOEaSIaV12/Uvd848SE3asFCbJBwhhBB2IQlHCCGEXUjCEUIIYReScIQQQhhNZos20dChQ2nQoAHDhw9XOxQhhHAKGissQ1gnE86zzz7LBx98oHYYwolY4x4EIeq6OplwYmNj8fX1VTsM4YRycnJIS0tTOwwhVJObk2v2Z8DhEs7WrVuJi4sjJCQEjUbD2rVrq+2TnJxMWFgYHh4e6PV6tm/fbv9ARZ2ybNkyAH49/ivdu3cnKSlJ3YCEsLOl7y8F4NdM8z8DLtYOylJFRUWEh4czduxYhg0bVu31lStXkpiYyOLFi9Hr9SxcuJCBAwdy+PBhGjduDEBERATl5eXV3rtx40ZCQkJMiqekpISSkhLD84KCAgDKysooKysz6Vjmul6OvcpTs1xHrGt6ejrr16+n/pCOaLVaPD09WbRoEfHx8URHR1ulXFtyhDZsK2q1F1tw5Lqkp6ez/ov11H+gU7XPQJs2bYw+jkZRHHeFD41Gw5o1a4iPjzds0+v1dO3albfffhuAyspKQkNDmTRpEtOnTzf62Fu2bOHtt99m9erVt91v1qxZzJ49u9r2FStW4OXlZXR5wrntu6RhySEdzbwVJneusNpxi4uLGTlyJPn5+dSrV89qx72RtGFhDfsvaXjvkI5Qb4UpN3wGTGnDTpVwSktL8fLyYvXq1VWSUEJCAnl5eaxbt87oYxubcG727TA0NJRz587h7+9vUn3MVVZWxqZNmxgwYACurq52KVOtch2xrunp6Qx6fCr1h7xAWdYxLn32AgApKSkWn+Hk5uYSHBxs04TjCG3YVtRqL7bgyHVJT09n0Ngp1H/gr5RlH+fSymtf7lNSUmjTpg0BAQFGtWGH61K7nZycHCoqKggMDKyyPTAwkEOHDhl9nP79+7N7926Kiopo2rQpq1atIiYm5qb7uru74+7uTnJyMsnJyVRUXMvsrq6udm8UapSpVrmOVNeYmBji4uL4kWtn1FeuXCEpKemWbcbU8mzNkdqwrUhdbCsmJoYhQ+LYSvXPwPUuWmM4VcKxlu+++87k90yYMIEJEyZQUFCAn5+fDaISjmzMmMf4cVk6d7S8g7Wpqej1erVDMpm0YWGJMWPHsvX9/9KiRQuzPwMON0rtdgICAtDpdGRnZ1fZnp2dTVBQkE3LlqndBYC/v79TJhuQNiysw5LPgFMlHDc3N6KiokhJSTFsq6ysJCUlxSrdG7cjU7sLZydtWKjN4brUCgsLOXbsmOF5ZmYmGRkZNGzYkGbNmpGYmEhCQgLR0dF069aNhQsXUlRUxJgxY2wa1x/7v4VwNtKGhdocLuGkp6fTt29fw/PExETg2ki0ZcuWMWLECC5cuMCMGTPIysoiIiKCDRs2VBtIYG3S/y2cnbRhoTaHSzixsbHUNFJ74sSJTJw40U4RCSGEuE6xYL5op7qGoya54Fq3WWOmXLVJGxaWsMYnQBKOkeSCqwBw3NukayZtWKhNEo4QQgi7kIRjJOmOEM5O2rBQmyQcI0l3hHB20oaF2iThCCGEMJol1zEl4QhhDOcfpCaERTRWWGddEo6RpP9bODtpw0JtknCMJP3fAiy76U1t0oaF2iThCCGEsAtJOEIIIexCEo6RpP9bODtpw0JtknCMJP3fdVttGKQmbVhYgwyLFkIIYVMyeacQdubMk3cKoTZJOEIIIexCEo4QQgi7kIQjhBDCLiThGEmGlApnJ21YqE0SjpFkSGndZo2JC9UmbVhYgyXjZiThCCGEqJE1vnNJwhHCBDIsWgjzScIRQghhF5JwhBBC2EWdSzinT58mNjaW9u3b07lzZ1atWqV2SEIIUSe4qB2Avbm4uLBw4UIiIiLIysoiKiqK++67D29vb7VDEw7M+ceoCaG+OpdwgoODCQ4OBiAoKIiAgAAuXrwoCUcIIYygWDByxuG61LZu3UpcXBwhISFoNBrWrl1bbZ/k5GTCwsLw8PBAr9ezfft2s8rasWMHFRUVhIaGWhi1qCsuXrpIWlqa2mEIYXea38/zL126ZPZnwOESTlFREeHh4SQnJ9/09ZUrV5KYmMjMmTPZuXMn4eHhDBw4kPPnzxv2iYiIoGPHjtUeZ8+eNexz8eJFRo8ezXvvvWfzOgnn95///AeAUydP0b17d5KSklSOSAj7WvLvJcC16+DmfgYcrktt0KBBDBo06JavL1iwgHHjxjFmzBgAFi9ezFdffcXSpUuZPn06ABkZGbcto6SkhPj4eKZPn06PHj1q3LekpMTwvKCgAICysjLKysqMqZLFrpdjr/LULNcR65qens66dWupH98JjVaLp6cnixYtIj4+nujoaKuUa0uO0IZtRa32YguOXJf09HTWrl1Lg6Gd0Wg0VT4Dbdq0Mfo4GsWSDjkb02g0rFmzhvj4eABKS0vx8vJi9erVhm0ACQkJ5OXlsW7duhqPqSgKI0eOpG3btsyaNavG/WfNmsXs2bOrbV+xYgVeXl7GVkU4uUN5Gt45qKOJl8K08AqrHbe4uJiRI0eSn59PvXr1rHbcG0kbFtZwOE/Dvw7qCPFSSLrhM2BKG3aqhHP27FmaNGnCtm3biImJMew3bdo0fvjhB6P6FX/66Sd69+5N586dDds+/PBDOnXqdNP9b/btMDQ0lHPnzuHv729mzUxTVlbGpk2bGDBgAK6urnYpU61yHbGu6enp3PvY8zQY+hJlF05w6ZOpAKSkpFh8hpObm0twcLBNE44jtGFbUau92IIj1+XaZ+A5GgydUe0z0KZNGwICAoxqww7XpWZrPXv2pLKy0uj93d3dcXd3Jzk5meTkZCoqrmV2V1dXuzcKNcpUq1xHqmtMTAzx8Q/wA6BUVnLlyhWSkpKqfOmxpDxbc6Q2bCtSF9u69hmIv/YZUJQqn4HrXbTGcLhBA7cTEBCATqcjOzu7yvbs7GyCgoJsWrbMtFu3PfHEEwA0a96M1NRU5syZo3JEppM2LCwx7olxAISGhpr9GXCqhOPm5kZUVBQpKSmGbZWVlaSkpFjl2+btyFoiAqB+/Qbo9Xq1wzCLtGFhieuzRTdoYP5nwOESTmFhIRkZGYaRZpmZmWRkZHDq1CkAEhMTWbJkCcuXL+fgwYOMHz+eoqIiw6g1W5Fvh8LZSRsWanO4azjp6en07dvX8DwxMRG4NhJt2bJljBgxggsXLjBjxgyysrKIiIhgw4YNBAYG2jSuP/Z/C+FspA0LtTlcwomNja1x6oSJEycyceJEO0V0zYQJE5gwYQIFBQX4+fnZtWyhPk0tmE1N2rBQm8N1qTkq6f8Wzk7asFCbJBwjSf+3cHbShoXaJOEIIYQwmiVTBUjCMZJ0RwhnJ21YWMIaVzEl4RhJuiOEs5M2LNQmCUcIIYRdSMIRwgga5x8VLYTqJOEYSfq/hbOTNizUJgnHSNL/LZydtGGhNkk4QgghjKZg/rhoSThCmMBxlysUwsascB1TEo4QQgi7kIRjJLngWrfVhkFq0oaF2iThGEkuuApnJ21YqE0SjhBCCLuQhCOEEMIuJOEIYQJLhoQKURvIbNFCCCFsyhqr3krCEcIYtWGYmhAqk4RjJBlSKpydtGGhNkk4RpIhpcLZSRsWapOEI4QQwi4k4QghhLALSThCmEAm7xR1nSUfAUk4QhjBGkNChXBm1lj1ts4lnLy8PKKjo4mIiKBjx44sWbJE7ZCEEKJOcFE7AHvz9fVl69ateHl5UVRURMeOHRk2bBj+/v5qhyaEELVanTvD0el0eHl5AVBSUoKiKCjSMS+MlJefR1pamtphCKGa/Px8sz8DDpdwtm7dSlxcHCEhIWg0GtauXVttn+TkZMLCwvDw8ECv17N9+3aTysjLyyM8PJymTZsydepUAgICrBS9qK3ee+9dAM6cOUP37t1JSkpSOSIh7Ovddy3/DDhcl1pRURHh4eGMHTuWYcOGVXt95cqVJCYmsnjxYvR6PQsXLmTgwIEcPnyYxo0bAxAREUF5eXm1927cuJGQkBDq16/P7t27yc7OZtiwYQwfPpzAwMCbxlNSUkJJSYnheX5+PgAXL160RnWNUlZWRnFxMbm5ubi6utbqch2xrrt27eL/Vq+i/pDWUF6Kh4cH//znPxkwYACRkZEWlXu9HdnyLNsR2rCtqNVebMGR61L1M1BS5TPQqlUrwMg2rDgwQFmzZk2Vbd26dVMmTJhgeF5RUaGEhIQob7zxhllljB8/Xlm1atUtX585c6bCtZGA8pCHzR7Hjx83q/0aQ9qwPOzxOH36dI1tUaMojnsBQ6PRsGbNGuLj4wEoLS3Fy8uL1atXG7YBJCQkkJeXx7p162o8ZnZ2Nl5eXvj6+pKfn89dd93FJ598QqdOnW66/x+/Hebl5dG8eXNOnTqFn5+fRfUzVkFBAaGhoZw+fZp69erZpUy1yq1LdYVrZxvNmjXj0qVL1K9f3yZlOEIbthW1fm+24Kx1URSFy5cvExISglZ7+6s0Dteldjs5OTlUVFRU6/4KDAzk0KFDRh3j5MmTPPnkk4bBApMmTbplsgFwd3fH3d292nY/Pz+7N4p69eqp0hDVKLcu1RWo8YNqCUdqw7ai1u/NFpyxLsZ+cXGqhGMN3bp1IyMjQ+0whBCiznG4UWq3ExAQgE6nIzs7u8r27OxsgoKCVIpKCCGEMZwq4bi5uREVFUVKSophW2VlJSkpKcTExNglBnd3d2bOnHnTLoraVKZa5daluqpVrlp1tQWpi3NxuEEDhYWFHDt2DIDIyEgWLFhA3759adiwIc2aNWPlypUkJCTw7rvv0q1bNxYuXMhnn33GoUOHbjm0WQghhPocLuFs2bKFvn37VtuekJDAsmXLAHj77bd58803ycrKIiIign/+85/o9Xo7RyqEEMIUDpdwhBBC1E5OdQ1HCCGE85KEI4QQwi4k4QghhLALSTg3Yeps1KtWraJdu3Z4eHjQqVMnvv76a5uWuX//fh588EHCwsLQaDQsXLjQ5PLMKXfJkiX06tWLBg0a0KBBA/r372/yTN2mlvn5558THR1N/fr18fb2JiIigg8//NDkMk0t90affvopGo2mynRKtihz2bJlaDSaKg8PDw+Ty7RETQsUDh06lAYNGjB8+HC7xmWu28U7f/58OnToQMeOHfnoo49UiM48hw8fJiIiwvDw9PS86az6DslWEwY6q08//VRxc3NTli5dquzfv18ZN26cUr9+fSU7O/um+//888+KTqdT5s2bpxw4cEB58cUXFVdXV2Xv3r02K3P79u3KlClTlE8++UQJCgpS3nrrLXOqanK5I0eOVJKTk5Vdu3YpBw8eVB577DHFz89P+e2332xW5ubNm5XPP/9cOXDggHLs2DFl4cKFik6nUzZs2GDTul6XmZmpNGnSROnVq5fywAMP2LTM999/X6lXr55y7tw5wyMrK8ukMi1VXl6uFBUVKYqiKIWFhUpYWJiSk5NjeH3z5s3KF198oTz44IN2jctct4p3z549SmRkpHLlyhWluLhY0ev1yqVLl9QJ0gKXL19W/P39lcLCQrVDMYoknD8wdTbqhx9+WLn//vurbNPr9cpTTz1lszJv1Lx5c7MTjqUzb5eXlyu+vr7K8uXL7VamoihKZGSk8uKLLxq9v7nllpeXKz169FD+/e9/KwkJCSYnHFPLfP/99xU/Pz+TyrCl3NxcpXnz5sqFCxeqbN+8ebPTJBxFuXm8K1euVJ5++mnD8yeffFL55JNP7B2axT7++GPl4YcfVjsMo0mX2g1KS0vZsWMH/fv3N2zTarX079+fX3755abv+eWXX6rsDzBw4MBb7m+NMq3BGuUWFxdTVlZGw4YN7VKmoiikpKRw+PBhevfubVSZlpT78ssv07hxYx5//HGjy7K0zMLCQpo3b05oaCgPPPAA+/fvr/J6bVqg0B51uZWOHTuyZcsW8vLyuHTpElu2bOHMmTNWObY96/XZZ58xYsQICyO2H0k4N7jdbNRZWVk3fU9WVpZJ+1ujTGuwRrlJSUmEhIRUS7jWLjM/Px8fHx/c3Ny4//77WbRoEQMGDDCqTHPL/emnn/jPf/5T7RqGLcts27YtS5cuZd26dXz00UdUVlbSo0cPfvvtN8M+1xcoTE5Ovukxri9QOHPmTHbu3El4eDgDBw7k/Pnzhn2uX5/54+Ps2bMAhgUKMzMzWbFiRbW5C63FHnW5lfbt2/PMM89w9913M2zYMLp3745Op3OqehUUFLBt2zbuu+8+q8RtD3VutmhhHXPmzOHTTz9ly5YtNr+w7evrS0ZGBoWFhaSkpJCYmMgdd9xBbGysTcq7fPkyo0aNYsmSJXZdfjwmJqbKnIA9evTgzjvv5N133+WVV14BYNCgQQwaNOiWx1iwYAHjxo1jzJgxACxevJivvvqKpUuXMn36dACjZ0sPDAwkPDycH3/80SaDBOxZl5t56qmneOqppwB44oknaN26tdnHupG96rVu3Truueceuw8ssYSc4dzAnNmog4KCLJq9Wq0ZsC0pd/78+cyZM4eNGzfSuXNnm5ep1Wpp1aoVERERTJ48meHDh/PGG2/YrNzjx49z4sQJ4uLicHFxwcXFhQ8++IAvvvgCFxcXjh8/bvUyb8bV1ZXIyEjD3II1sUY3aXZ2NpcvXwaunVlu3bqVtm3bGvVea7JHV/P1M4rDhw+zfft2Bg4caJXj3o416+Vs3WkgCacKc2ajjomJqbI/wKZNm4yevVqtGbDNLXfevHm88sorbNiwgejoaLuU+UeVlZVVVrC0drnt2rVj7969ZGRkGB5Dhgyhb9++ZGRkEBoaavUyb6aiooK9e/cSHBxs1P7W6CY9efIkvXr1Ijw8nF69elVboLB///489NBDfP311zRt2tRm1xmt1dV8u3gfeOAB2rdvz6OPPsr777+Pi4vtO3ysVa/8/Hy7JUmrUnvUgqP59NNPFXd3d2XZsmXKgQMHlCeffFKpX7++YXjqqFGjlOnTpxv2//nnnxUXFxdl/vz5ysGDB5WZM2eaNSzalDJLSkqUXbt2Kbt27VKCg4OVKVOmKLt27VKOHj1q07rOmTNHcXNzU1avXl1l6O7ly5dtVubrr7+ubNy4UTl+/Lhy4MABZf78+YqLi4uyZMkSm9b1j8wZpWZqmbNnz1a+/fZb5fjx48qOHTuUP/3pT4qHh4eyf//+mx4fUNasWWN4fubMGQVQtm3bVmW/qVOnKt26dTMpdnurTXW5UW2tl7nkGs4fjBgxggsXLjBjxgzDbNQbNmwwfCM5depUleWAe/TowYoVK3jxxRf561//SuvWrVm7di0dO3a0WZlnz54lMjLS8Hz+/PnMnz+fPn36sGXLFpuV+84771BaWlqtP3/mzJnMmjXLJmUWFRXx9NNP89tvv+Hp6Um7du346KOPTO5KMLVcazC1zEuXLjFu3DiysrJo0KABUVFRbNu2jfbt2xtVXm1aoLA21eVGtbVeRlM74wkhzMMfvj0ryrV7fyZOnGh4XlFRoTRp0sSk+5zUUJvqcqPaWi9zyRmOEE7kxgUKATIzM8nIyDAsUJiYmEhCQgLR0dGGBQqLiooMI6IcSW2qy41qa72sQu2MJ4Qw3ubNmxWg2iMhIcGwz6JFi5RmzZopbm5uSrdu3ZTU1FT1Ar6N2lSXG9XWelmDLMAmhBDCLmRYtBBCCLuQhCOEEMIuJOEIIYSwC0k4Qggh7EISjhBCCLuQhCOEEMIuJOEIIYSwC0k4Qggh7EISjhBCCLuQhCOEEMIuJOEIoymKwoIFC2jRogVeXl7Ex8eTn59/031jY2PRaDRoNJoal8t97LHHDPuuXbvW+oELIRyCJBxhtKlTp/LOO++wfPlyfvzxR3bs2HHbdXDGjRvHuXPnalwb6B//+Afnzp2zcrRCVGXKlyB7iY2N5bnnnjM8r+1fviThCKOkpaWxYMECVq5cSe/evYmKimLcuHF8/fXXt3yPl5cXQUFBNS7d6+fnVzcWnxJm69OnD2PHjq2ybeHChXh7e/POO+8YfRxjvwRZy5gxY3jxxReN3r+2f/mS9XCEUebPn0+/fv3o0qWLYVtgYCA5OTkqRiXqAkVR2LVrFw899BAAxcXFjBs3js2bN7Np0yZ69Ohh9LGufwmyh4qKCr788ku++uoro9/j5+eHn5+fDaNSl5zhiBqVlJTw1VdfMXTo0Crbr169Wqs/HMIxHD16lMuXL9OlSxcyMzPp0aMHmZmZ7Nixw6RkcyuVlZXMmzePVq1a4e7uTrNmzXjttdcMr8fGxjJp0iSee+45GjRoQGBgIEuWLDEsmubr60urVq345ptvqhx327ZtuLq60rVrV+DacumjR4/Gx8eH4OBg/v73v1scu7ORhCNqtHPnTq5cucLkyZPx8fExPKZNm0abNm3UDk/Ucjt27ECn05GdnU10dDR6vZ4tW7YQHBxsleO/8MILzJkzh5deeokDBw6wYsUKAgMDq+yzfPlyAgIC2L59O5MmTWL8+PE89NBD9OjRg507d3LPPfcwatQoiouLDe/54osviIuLQ6PRANeugf7www+sW7eOjRs3smXLFnbu3GmVOjgNddd/E85g2bJlire3t3L06NEqjzZt2igvv/zyTd/Tp08f5dlnn62y7aOPPlK8vb0Nj61bt1Z5nZus/y7ElClTFJ1Op2i1WiU5Odns49ysTRYUFCju7u7KkiVLbvu+nj17Gp6Xl5cr3t7eyqhRowzbzp07pwDKL7/8YtjWunVr5csvv1QURVEuX76suLm5KZ999pnh9dzcXMXT07NaTIpSez8Lcg1H1KigoICAgABatWpl2Hby5EmOHj3Kgw8+aPRxhgwZgl6vNzxv0qSJVeMUtdPOnTvp378/+/btY8eOHVY99sGDBykpKaFfv3633a9z586G/+t0Ovz9/enUqZNh2/UzovPnzxuOe/bsWcNxjx8/TmlpaZX237BhQ9q2bWu1ujgD6VITNQoICCA/Px/lhtXIX3vtNe677z7at29v9HGu93Vff3h6etoiXFHL7Ny5k0GDBrFu3To++eQT3nzzzWr7LFmyhC5dutCxY0dGjBhh9LGNbYOurq5Vnms0mirbrnebVVZWAte60wYMGICHh4fRsdQFknBEje6++26uXr3KnDlzyMzM5NVXX2X9+vUmDUcVwhy//voreXl5dOnShaioKN5//31eeOEF1q1bZ9jn0qVLJCcn89///pd9+/bx7rvvGn381q1b4+npSUpKilXjXrduHQ888IDhecuWLXF1dSUtLa1K3EeOHLFquY5OEo6oUWBgIMuWLeOdd96hQ4cOpKam8tNPPxEaGqp2aKKW27FjBxqNhoiICABGjBjBX//6V/785z8bbt50cXHh0qVLTJs2jf3791O/fn2jj+/h4UFSUhLTpk3jgw8+4Pjx46SmpvKf//zH7JjPnz9Peno6gwcPNmzz8fHh8ccfZ+rUqXz//ffs27ePxx57DK22bv0Jlms4wigjRowwqatCCGvYuXMnrVu3xtfX17Bt9uzZHDhwgCFDhrB9+3aCgoLYt28fa9eu5eGHH+a1114jPj7e6DJeeuklXFxcmDFjBmfPniU4OJi//OUvZse8fv16unXrRkBAQJXtb775JoWFhcTFxeHr68vkyZNvOTVUraX2qAVRO/Xp00dxdXVVvL29lT179tx236eeekrx9vautSNzhG0dOXLE8P/x48crn3766U33u9koNVuIi4tT5s6da9ExautnoW6dzwm7+fjjjzlw4AAZGRk1jsR5+eWXycjI4OjRowwYMMBOEYra4tVXX6Vt27ZERkai0WgMMxLczL/+9S98fHzYu3evzeLp2bMnjzzyiFnv/ctf/oKPj4+VI3IcGkW5YeiREELUUmfOnOHKlSsANGvWDDc3N5Ujqu78+fMUFBQAEBwcjLe3t8oRWZckHCGEEHYhXWpCCCHsQhKOEEIIu5CEI4QQwi4k4QghhLALSThCCCHsQhKOEEIIu5CEI4QQwi4k4QghhLALSThCCCHs4v8BdklLEkSvK2AAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(soilsample, soilm_panday)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit method finds the optimal curve through both the soil water retention curve and hydraulic conductivity function at the same time using the least squares algorithm. All parameters are subject to the optimization algorithm.\n", + "\n", + "The fit method uses the optimization algorithm from the 1991 [RETC Code for Quantifying the Hydraulic Functions of Unsaturated Soils](https://www.pc-progress.com/Documents/programs/retc.pdf) by M.Th. van Genuchten, F.J. Leij and S.R. Yates.\n", + "\n", + "The objective function $O(b)$ minimized is:\n", + "\n", + "$ O(b) = \\sum^N_{i=1}(w_i(\\theta_{o,i}-\\theta_i))^2 + \\sum^M_{i=N+1}(w_iW_1W_2(Y_{o,i}-Y_i))^2$\n", + "\n", + "Using the SciPy least-squares algorithm. \n", + "\n", + "With $N$ the number of $\\theta$ data points and $M$ is the number of $K$ and $\\theta$ data points\n", + "\n", + "$w_i$ are the individual weight factors per measurements (by default 1 for each measurement)\n", + "\n", + "$W_1$ is the weight factor for the hydraulic conductivity function with respect to the soil water retention (default is 0.1)\n", + "\n", + "$W_2$ is the proportional weight factor for two different data types and elimination factor for different units. By default the formulation for \n", + "$W_2 = \\frac{(M-N)\\sum^N_{i=1}w_i\\theta_{o,i}}{N\\sum^M_{i=N+1}w_i|Y_{o,i}|}$.\n", + "\n", + "$Y$ is indicates the for the logaritmic transform of such that $Y = log_{10}K$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be favorable to only optimize the relative hydraulic conductivity function, and leave parameter k_s untouched. That kan be achieved by providing `k_s` to the fit method." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Panday(k_s=712.7999894094452, alpha=0.14656691408919914, beta=2.643965735144585, brook=3.8329491617518454, sr=0.10448779206704849)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "soilm_panday = soilsample.fit(pe.Panday, k_s=max(soilsample.k))\n", + "soilm_panday" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to provide bounds for the parameter space. By default, the bounds argument is `None` which takes the stored parameter bounds per soil model:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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p_inip_minp_maxswrc
k_s50.000.00100100000.0False
theta_r0.020.000010.2True
theta_s0.400.200000.5True
alpha0.020.001000.3True
beta2.301.0000012.0True
brook10.001.0000050.0False
\n", + "
" + ], + "text/plain": [ + " p_ini p_min p_max swrc\n", + "k_s 50.00 0.00100 100000.0 False\n", + "theta_r 0.02 0.00001 0.2 True\n", + "theta_s 0.40 0.20000 0.5 True\n", + "alpha 0.02 0.00100 0.3 True\n", + "beta 2.30 1.00000 12.0 True\n", + "brook 10.00 1.00000 50.0 False" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "panday_bounds = pe._params.pPanday.copy()\n", + "panday_bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Panday(k_s=650.0027068392608, alpha=0.14640200236757875, beta=2.6476559118830254, brook=3.8183761786812824, sr=0.10450508901642255)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "panday_bounds.loc[\"k_s\", \"p_min\"] = 650\n", + "panday_bounds.loc[\"k_s\", \"p_ini\"] = max(soilsample.k)\n", + "soilm_panday = soilsample.fit(pe.Panday, pbounds=panday_bounds)\n", + "soilm_panday" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other available option are to print the optimization result." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SciPy Optimization Result\n", + " message: `gtol` termination condition is satisfied.\n", + " success: True\n", + " status: 1\n", + " fun: [ 2.206e-05 2.206e-05 ... -9.025e-05 -2.615e-04]\n", + " x: [ 5.724e+02 4.495e-02 4.300e-01 1.462e-01 2.653e+00\n", + " 3.798e+00]\n", + " cost: 5.483425529890278e-07\n", + " jac: [[ 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00]\n", + " [ 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00]\n", + " ...\n", + " [ 1.944e-06 0.000e+00 ... -4.053e-02 -1.764e-02]\n", + " [ 1.944e-06 0.000e+00 ... -5.026e-02 -2.187e-02]]\n", + " grad: [ 1.672e-14 3.403e-09 -3.531e-10 5.768e-09 1.080e-09\n", + " 5.588e-11]\n", + " optimality: 1.7851416034930158e-09\n", + " active_mask: [0 0 0 0 0 0]\n", + " nfev: 10\n", + " njev: 10\n" + ] + } + ], + "source": [ + "soilm_panday = soilsample.fit(pe.Panday, silent=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To parse other parameters for W1, W2, and individual weights there are other keyword arguements that for the fit method." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pedonenv-8qwidcX8-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index 16fa028..38cef1d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ description = "Python package for (unsaturated) soil properties including pedotr readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" -dependencies = ["numpy", "matplotlib", "pandas", "scipy", "openpyxl"] +dependencies = ["numpy", "matplotlib", "pandas", "scipy"] classifiers = [ 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.10', @@ -33,6 +33,33 @@ pytesting = ["pytest>=7", "pytest-cov", "pytest-sugar"] coveraging = ["coverage"] dev = ["pedon[linting,formatting,typing,pytesting]", "tox"] +[tool.poetry] +name = "pedonenv" +version = "0.0.0" +description = "Development Virtual Environment" +authors = ["poetry"] + +[tool.poetry.dependencies] +python = "^3.10" +numpy = "^1.3" +scipy = "^1.6" +pandas = "^2.0" +matplotlib = "^3.6" +openpyxl = "^3.0" +flake8 = "^6.1.0" +ruff = "^0.1.9" +black = { extras = ["jupyter"], version = "^23.12.1" } +isort = "^5.13.2" +mypy = "^1.8.0" +pandas-stubs = "^2.1.4.231218" +pytest = "^7.4.3" +pytest-cov = "^4.1.0" +pytest-sugar = "^0.9.7" +coverage = "^7.3.4" +tox = "^4.11.4" +setuptools = "^69.0.3" +ipykernel = "^6.28.0" + [tool.setuptools] include-package-data = true @@ -43,7 +70,7 @@ version = { attr = "pedon._version.__version__" } where = ["src"] [tool.setuptools.package-data] -"pedon.datasets" = ["*.xlsx"] +"pedon.datasets" = ["*.csv"] [tool.black] line-length = 88 diff --git a/src/pedon/_params.py b/src/pedon/_params.py index cd1eefb..ce44925 100644 --- a/src/pedon/_params.py +++ b/src/pedon/_params.py @@ -16,30 +16,19 @@ "theta_s": 0.2, "alpha": 0.001, "n": 1.000001, - "l": -7, + "l": -7.0, }, "p_max": { "k_s": 100000.0, "theta_r": 0.2, "theta_s": 0.9, "alpha": 0.20, - "n": 12, - "l": 8, - }, - "swrc": { - "k_s": False, - "theta_r": True, - "theta_s": True, - "alpha": True, - "n": True, - "l": False, + "n": 12.0, + "l": 8.0, }, }, + dtype=float, ) -pGenuchten.loc[:, ["p_ini", "p_min", "p_max"]] = pGenuchten.loc[ - :, ["p_ini", "p_min", "p_max"] -].astype(float) -pGenuchten.loc[:, "swrc"] = pGenuchten.loc[:, "swrc"].astype(bool) pBrooks = DataFrame( data={ @@ -51,21 +40,16 @@ "h_b": 0.0001, "l": 0.1, }, - "p_max": {"k_s": 100000.0, "theta_r": 0.2, "theta_s": 0.5, "h_b": 100, "l": 5}, - "swrc": { - "k_s": False, - "theta_r": True, - "theta_s": True, - "h_b": True, - "l": True, + "p_max": { + "k_s": 100000.0, + "theta_r": 0.2, + "theta_s": 0.5, + "h_b": 100.0, + "l": 5.0, }, }, + dtype=float, ) -pBrooks.loc[:, ["p_ini", "p_min", "p_max"]] = pBrooks.loc[ - :, ["p_ini", "p_min", "p_max"] -].astype(float) -pBrooks.loc[:, "swrc"] = pBrooks.loc[:, "swrc"].astype(bool) - pPanday = DataFrame( data={ "p_ini": { @@ -89,23 +73,12 @@ "theta_r": 0.2, "theta_s": 0.5, "alpha": 0.30, - "beta": 12, + "beta": 12.0, "brook": 50.0, }, - "swrc": { - "k_s": False, - "theta_r": True, - "theta_s": True, - "alpha": True, - "beta": True, - "brook": False, - }, }, + dtype=float, ) -pPanday.loc[:, ["p_ini", "p_min", "p_max"]] = pPanday.loc[ - :, ["p_ini", "p_min", "p_max"] -].astype(float) -pPanday.loc[:, "swrc"] = pPanday.loc[:, "swrc"].astype(bool) def get_params(sm_name: str) -> DataFrame: diff --git a/src/pedon/datasets/Brooks.csv b/src/pedon/datasets/Brooks.csv deleted file mode 100644 index 2deed9c..0000000 --- a/src/pedon/datasets/Brooks.csv +++ /dev/null @@ -1,12 +0,0 @@ -name,source,description,soil type,k_s,theta_r,theta_s,h_b,l -VS2D_Del Monte Sand,VS2D,20 mesh,Sand,700000,0.011,0.36,0.00112,2.5 -VS2D_Fresno Medium Sand,VS2D,,Sand,40000,0,0.375,0.00149,0.84 -VS2D_Unconsolidated Sand,VS2D,,Sand,850,0.09,0.424,0.00114,4.4 -VS2D_Sand,VS2D,,Sand,820,0,0.435,0.00196,0.84 -VS2D_Fine Sand,VS2D,G.E. 13 - alpha 0.0104 in VS2DRTI,Sand,210,0.063,0.377,0.0082,3.7 -VS2D_Columbia Sandy Loam,VS2D,,Sandy Loam,70,0.11,0.496,0.0085,1.6 -VS2D_Touchet Silt Loam,VS2D,G.E. 3,Silt Loam,22,0.095,0.43,0.0145,1.7 -VS2D_Hygiene Sandstone,VS2D,,Sand,15,0.13,0.25,0.0106,2.9 -VS2D_Adelanto Loam,VS2D,,Loam,3.9,0.13,0.42,0.0141,0.51 -VS2D_Limon Silt,VS2D,imbibition data,Silt,1.3,0,0.449,0.00338,0.22 -VS2D_Yolo Light Clay,VS2D,alpha 0.0249 in VS2DRTI,Clay,1.1,0.055,0.495,0.00181,0.25 diff --git a/src/pedon/datasets/Genuchten.csv b/src/pedon/datasets/Genuchten.csv deleted file mode 100644 index d505ace..0000000 --- a/src/pedon/datasets/Genuchten.csv +++ /dev/null @@ -1,63 +0,0 @@ -name,source,description,soil type,k_s,theta_r,theta_s,alpha,n,l -HYDRUS_Sand,HYDRUS,,Sand,712.8,0.045,0.43,0.145,2.68,0.5 -HYDRUS_Loamy Sand,HYDRUS,,Loamy Sand,350.2,0.057,0.41,0.125,2.28,0.5 -HYDRUS_Sandy Loam,HYDRUS,,Sandy Loam,106.1,0.065,0.41,0.075,1.89,0.5 -HYDRUS_Loam,HYDRUS,,Loam,24.96,0.078,0.43,0.036,1.56,0.5 -HYDRUS_Silt,HYDRUS,,Silt,6,0.034,0.46,0.016,1.37,0.5 -HYDRUS_Silt Loam,HYDRUS,,Silt Loam,10.8,0.067,0.45,0.02,1.41,0.5 -HYDRUS_Sandy Clay Loam,HYDRUS,,Sandy Clay Loam,31.44,0.1,0.39,0.059,1.48,0.5 -HYDRUS_Clay Loam,HYDRUS,,Clay Loam,6.24,0.095,0.41,0.019,1.31,0.5 -HYDRUS_Silty Clay Loam,HYDRUS,,Silty Clay Loam,1.68,0.089,0.43,0.01,1.23,0.5 -HYDRUS_Sandy Clay,HYDRUS,,Sandy Clay,2.88,0.1,0.38,0.027,1.23,0.5 -HYDRUS_Silty Clay,HYDRUS,,Silty Clay,0.48,0.07,0.36,0.005,1.09,0.5 -HYDRUS_Clay,HYDRUS,,Clay,4.8,0.068,0.38,0.008,1.09,0.5 -B01,Staring,leemarm zeer fijn tot matig fijn zand,Sand,31.23,0.02,0.427,0.0217,1.735,0.981 -B02,Staring,zwak lemig zeer fijn tot matig fijn zand,Sand,83.24,0.02,0.434,0.0216,1.349,7.202 -B03,Staring,sterk lemig zeer fijn tot matig fijn zand,Sand,19.08,0.02,0.443,0.015,1.505,0.139 -B04,Staring,zeer sterk lemig zeer fijn tot matig fijn zand,Sand,34.88,0.02,0.462,0.0149,1.397,0.295 -B05,Staring,grof zand,Sand,63.65,0.01,0.381,0.0428,1.808,0.024 -B06,Staring,keileem,Sand,104.1,0.01,0.385,0.0209,1.242,-1.2 -B07,Staring,zeer lichte zavel,Sand,14.58,0,0.401,0.0183,1.248,0.952 -B08,Staring,matig lichte zavel,Sand,3,0.01,0.433,0.0105,1.278,-1.919 -B09,Staring,zware zavel,Sand,1.75,0,0.43,0.007,1.267,-2.387 -B10,Staring,lichte klei,Clay,3.83,0.01,0.448,0.0128,1.135,4.581 -B11,Staring,matig zware klei,Clay,6.31,0.01,0.591,0.0216,1.107,-5.549 -B12,Staring,zeer zware klei,Clay,2.25,0.01,0.53,0.0166,1.091,-4.494 -B13,Staring,zandige leem,Sandy Loam,29.83,0.01,0.416,0.0084,1.437,-1.357 -B14,Staring,siltige leem,Silt Loam,0.9,0.01,0.417,0.0054,1.302,-0.335 -B15,Staring,venig zand,Peat,87.45,0.01,0.528,0.0237,1.282,-1.478 -B16,Staring,zandig veen en veen,Peat,12.36,0.01,0.786,0.0211,1.279,-1.221 -B17,Staring,venige klei,Peat,4.48,0,0.719,0.0191,1.137,0 -B18,Staring,kleiig veen,Peat,13.14,0,0.765,0.0205,1.151,0 -O01,Staring,leemarm zeer fijn tot matig fijn zand,Sand,22.32,0.01,0.366,0.016,2.163,2.868 -O02,Staring,zwak lemig zeer fijn tot matig fijn zand,Sand,22.76,0.02,0.387,0.0161,1.524,2.44 -O03,Staring,sterk lemig zeer fijn tot matig fijn zand,Sand,12.37,0.01,0.34,0.0172,1.703,0 -O04,Staring,zeer sterk lemig zeer fijn tot matig fijn zand,Sand,25.81,0.01,0.364,0.0136,1.488,2.179 -O05,Staring,grof zand,Sand,17.42,0.01,0.337,0.0303,2.888,0.074 -O06,Staring,keileem,Sand,32.83,0.01,0.333,0.016,1.289,-1.01 -O07,Staring,beekleem,Sand,37.55,0.01,0.513,0.012,1.153,-2.013 -O08,Staring,zeer lichte zavel,Sand,8.64,0,0.454,0.0113,1.346,-0.904 -O09,Staring,matig lichte zavel,Sand,3.77,0,0.458,0.0097,1.376,-1.013 -O10,Staring,zware zavel,Sand,2.3,0.01,0.472,0.01,1.246,-0.793 -O11,Staring,lichte klei,Clay,2.12,0,0.444,0.0143,1.126,2.357 -O12,Staring,matig zware klei,Clay,1.08,0.01,0.561,0.0088,1.158,-3.172 -O13,Staring,zeer zware klei,Clay,9.69,0.01,0.573,0.0279,1.08,-6.091 -O14,Staring,zandige leem,Sandy Loam,2.5,0.01,0.394,0.0033,1.617,0.514 -O15,Staring,siltige leem,Silt Loam,2.79,0.01,0.41,0.0078,1.287,0 -O16,Staring,oligotroof veen,Peat,1.46,0,0.889,0.0097,1.364,-0.665 -O17,Staring,mesotroof en eutroof veen,Peat,3.4,0.01,0.849,0.0119,1.272,-1.249 -O18,Staring,moerige tussenlaag,Peat,35.95,0.01,0.58,0.0127,1.316,-0.786 -VS2D_Medium Sand,VS2D,,Medium Sand,40000,0.02,0.375,0.0431,3.1,0.5 -VS2D_Sandy Loam,VS2D,,Sandy Loam,70,0.15,0.496,0.00847,4.8,0.5 -VS2D_Silt Loam,VS2D,,Silt Loam,22.5,0.17,0.43,0.00505,7.0,0.5 -VS2D_Del Monte Sand,VS2D,20 mesh,Sand,700000,0.036,0.36,0.00142,6.3,0.5 -VS2D_Fresno Medium Sand,VS2D,,Sand,40000,0.02,0.375,0.00232,3.1,0.5 -VS2D_Unconsolidated Sand,VS2D,,Sand,850,0.051,0.424,0.00134,9,0.5 -VS2D_Sand,VS2D,,Sand,820,0.069,0.435,0.00326,3.9,0.5 -VS2D_Fine Sand,VS2D,"G.E. 13, alpha 0.0104 in VS2DRTI",Sand,210,0.072,0.377,0.0096,6.9,0.5 -VS2D_Columbia Sandy Loam,VS2D,,Sandy Loam,70,0.15,0.496,0.0118,4.8,0.5 -VS2D_Touchet Silt Loam,VS2D,G.E. 3,Silt Loam,22,0.17,0.43,0.0198,7,0.5 -VS2D_Hygiene Sandstone,VS2D,,Sand,15,0.15,0.25,0.0126,10.6,0.5 -VS2D_Adelanto Loam,VS2D,,Loam,3.9,0.16,0.42,0.0274,2.06,0.5 -VS2D_Limon Silt,VS2D,imbibition data,Silt,1.3,0.001,0.449,0.00651,1.3,0.5 -VS2D_Yolo Light Clay,VS2D,alpha 0.0249 in VS2DRTI,Clay,1.1,0.175,0.495,0.00401,1.6,0.5 diff --git a/src/pedon/datasets/Soil_Parameters.xlsx b/src/pedon/datasets/Soil_Parameters.xlsx deleted file mode 100644 index 002f3cc..0000000 Binary files a/src/pedon/datasets/Soil_Parameters.xlsx and /dev/null differ diff --git a/src/pedon/datasets/Staring_2001.xlsx b/src/pedon/datasets/Staring_2001.xlsx deleted file mode 100644 index 40d9550..0000000 Binary files a/src/pedon/datasets/Staring_2001.xlsx and /dev/null differ diff --git a/src/pedon/datasets/Staring_2018.xlsx b/src/pedon/datasets/Staring_2018.xlsx deleted file mode 100644 index 3b202c1..0000000 Binary files a/src/pedon/datasets/Staring_2018.xlsx and /dev/null differ diff --git a/src/pedon/datasets/soilsamples.csv b/src/pedon/datasets/soilsamples.csv new file mode 100644 index 0000000..501dd30 --- /dev/null +++ b/src/pedon/datasets/soilsamples.csv @@ -0,0 +1,110 @@ +name;source;soilmodel;description;soil type;k_s;theta_r;theta_s;alpha;n;l;h_b;silt_p;clay_p;om_p;m50;rho;silt_p min;silt_p max;clay_p min;clay_p max;om_p min;om_p max;m50 min;m50 max;rho min;rho max +Sand;HYDRUS;Genuchten;;Sand;712.8;0.045;0.43;0.145;2.68;0.5;;;;;;;;;;;;;;;; +Loamy Sand;HYDRUS;Genuchten;;Loamy Sand;350.2;0.057;0.41;0.125;2.28;0.5;;;;;;;;;;;;;;;; +Sandy Loam;HYDRUS;Genuchten;;Sandy Loam;106.1;0.065;0.41;0.075;1.89;0.5;;;;;;;;;;;;;;;; +Loam;HYDRUS;Genuchten;;Loam;24.96;0.078;0.43;0.036;1.56;0.5;;;;;;;;;;;;;;;; +Silt;HYDRUS;Genuchten;;Silt;6;0.034;0.46;0.016;1.37;0.5;;;;;;;;;;;;;;;; +Silt Loam;HYDRUS;Genuchten;;Silt Loam;10.8;0.067;0.45;0.02;1.41;0.5;;;;;;;;;;;;;;;; +Sandy Clay Loam;HYDRUS;Genuchten;;Sandy Clay Loam;31.44;0.1;0.39;0.059;1.48;0.5;;;;;;;;;;;;;;;; +Clay Loam;HYDRUS;Genuchten;;Clay Loam;6.24;0.095;0.41;0.019;1.31;0.5;;;;;;;;;;;;;;;; +Silty Clay Loam;HYDRUS;Genuchten;;Silty Clay Loam;1.68;0.089;0.43;0.01;1.23;0.5;;;;;;;;;;;;;;;; +Sandy Clay;HYDRUS;Genuchten;;Sandy Clay;2.88;0.1;0.38;0.027;1.23;0.5;;;;;;;;;;;;;;;; +Silty Clay;HYDRUS;Genuchten;;Silty Clay;0.48;0.07;0.36;0.005;1.09;0.5;;;;;;;;;;;;;;;; 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Loam;29.83;0.01;0.416;0.0084;1.44;-1.357;;67.50;0;4.50;;1.30;;;50;100;0;15;;;; +B14;Staring_2001;Genuchten;siltige leem;Silt Loam;0.9;0.01;0.417;0.0054;1.30;-0.335;;90;0;3;;1.35;50;85;;;0;15;;;; +B15;Staring_2001;Genuchten;venig zand;Peat;87.45;0.01;0.528;0.0237;1.28;-1.478;;0;4;18.50;;1.15;85;100;;;0;15;;;; +B16;Staring_2001;Genuchten;zandig veen en veen;Peat;12.36;0.01;0.786;0.0211;1.28;-1.221;;0;4;54;;0.60;;;0;8;15;25;;;; +B17;Staring_2001;Genuchten;venige klei;Peat;4.48;0;0.719;0.0191;1.14;0;;0;55;25;;1.05;;;0;8;25;100;;;; +B18;Staring_2001;Genuchten;kleiig veen;Peat;13.14;0;0.765;0.0205;1.15;0;;0;45;47.50;;0.60;;;8;100;16;45;;;; +O01;Staring_2001;Genuchten;leemarm zeer fijn tot matig fijn zand;Sand;22.32;0.01;0.366;0.016;2.16;2.868;;5.50;0;1.50;0.0155;1.60;;;8;100;25;70;;;; +O02;Staring_2001;Genuchten;zwak lemig zeer fijn tot matig fijn zand;Sand;22.76;0.02;0.387;0.0161;1.52;2.44;;13;0;2;0.0140;1.55;0;10;;;0;3;0.0105;0.0210;; +O03;Staring_2001;Genuchten;sterk lemig zeer fijn tot matig fijn zand;Sand;12.37;0.01;0.34;0.0172;1.70;0;;26;0;1;0.0143;1.60;10;18;;;0;3;0.0105;0.0210;; +O04;Staring_2001;Genuchten;zeer sterk lemig zeer fijn tot matig fijn zand;Sand;25.81;0.01;0.364;0.0136;1.49;2.179;;41.50;0;1;0.0149;1.55;18;33;;;0;3;0.0105;0.0210;; +O05;Staring_2001;Genuchten;grof zand;Sand;17.42;0.01;0.337;0.0303;2.89;0.074;;0;0;1;0.0310;1.60;33;50;;;0;3;0.0105;0.0210;; +O06;Staring_2001;Genuchten;keileem;Sand;32.83;0.01;0.333;0.016;1.29;-1.01;;22.50;0;4;0.0275;1.35;;;;;0;3;0.0210;0.2000;; +O07;Staring_2001;Genuchten;beekleem;Sand;37.55;0.01;0.513;0.012;1.15;-2.013;;40;0;2;0.0120;1.35;0;50;;;0;3;0.0050;0.2000;; +O08;Staring_2001;Genuchten;zeer lichte zavel;Sand;8.64;0;0.454;0.0113;1.35;-0.904;;0;9.50;1;;1.50;33;50;;;0;3;0.0050;0.0150;; +O09;Staring_2001;Genuchten;matig lichte zavel;Sand;3.77;0;0.458;0.0097;1.38;-1.013;;0;14.50;1;;1.50;;;8;12;0;3;;;; +O10;Staring_2001;Genuchten;zware zavel;Sand;2.3;0.01;0.472;0.01;1.25;-0.793;;0;20;1.50;;1.40;;;12;18;0;3;;;; +O11;Staring_2001;Genuchten;lichte klei;Clay;2.12;0;0.444;0.0143;1.13;2.357;;0;30.50;2;;1.45;;;18;25;0;3;;;; +O12;Staring_2001;Genuchten;matig zware klei;Clay;1.08;0.01;0.561;0.0088;1.16;-3.172;;0;41.50;1.50;;1.25;;;25;35;0;3;;;; +O13;Staring_2001;Genuchten;zeer zware klei;Clay;9.69;0.01;0.573;0.0279;1.08;-6.091;;0;63.50;1.50;;1.20;;;35;50;0;3;;;; +O14;Staring_2001;Genuchten;zandige leem;Sandy Loam;2.5;0.01;0.394;0.0033;1.62;0.514;;67.50;0;1;;1.30;;;50;100;0;3;;;; +O15;Staring_2001;Genuchten;siltige leem;Silt Loam;2.79;0.01;0.41;0.0078;1.29;0;;88.50;0;2;;1.35;50;85;;;0;3;;;; +O16;Staring_2001;Genuchten;oligotroof veen;Peat;1.46;0;0.889;0.0097;1.36;-0.665;;0;0;68;;0.40;85;100;;;0;3;;;; +O17;Staring_2001;Genuchten;mesotroof en eutroof veen;Peat;3.4;0.01;0.849;0.0119;1.27;-1.249;;0;0;70;;0.35;;;;;35;100;;;; +O18;Staring_2001;Genuchten;moerige tussenlaag;Peat;35.95;0.01;0.58;0.0127;1.32;-0.786;;0;0;22.50;;1.10;;;;;35;100;;;; diff --git a/src/pedon/soil.py b/src/pedon/soil.py index b2828ed..4dbe45d 100644 --- a/src/pedon/soil.py +++ b/src/pedon/soil.py @@ -3,8 +3,9 @@ from pathlib import Path from typing import Type -from numpy import append, exp, log, ones -from pandas import DataFrame, read_excel +from numpy import abs as npabs +from numpy import append, array2string, exp, full, log, log10 +from pandas import DataFrame, isna, read_csv from scipy.optimize import least_squares from ._params import get_params @@ -37,91 +38,36 @@ def from_staring(self, name: str, year: str = "2018") -> "SoilSample": f"No Staring series available for year '{year}'" "please use either '2001' or '2018'" ) - path = Path(__file__).parent / f"datasets/Staring_{year}.xlsx" - properties = read_excel(path, sheet_name="properties", index_col=0) - measurements = read_excel(path, sheet_name="measurements", index_col=[0, 1]) - self.h = measurements.columns.astype(float).values - self.k = measurements.loc[name, "k"].astype(float).values - self.theta = measurements.loc[name, "theta"].astype(float).values - - self.silt_p = properties.loc[name, "silt_p"] - self.clay_p = properties.loc[name, "clay_p"] - self.om_p = properties.loc[name, "om_p"] - self.m50 = properties.loc[name, "m50"] + path = Path(__file__).parent / "datasets/soilsamples.csv" + properties = read_csv(path, delimiter=";") + staring_properties = properties[ + properties["source"] == f"Staring_{year}" + ].set_index("name") + + self.silt_p = staring_properties.loc[name, "silt_p"] + self.clay_p = staring_properties.loc[name, "clay_p"] + self.om_p = staring_properties.loc[name, "om_p"] + self.m50 = staring_properties.loc[name, "m50"] if year == "2001": - self.rho = properties.loc[name, "rho"] + self.rho = staring_properties.loc[name, "rho"] return self - def fit_seperate( - self, - sm: Type[SoilModel], - pbounds: DataFrame | None = None, - weights: FloatArray | None = None, - return_res: bool = False, - ) -> SoilModel: - """Fit the soil water retention and conductivity seperate.""" - if pbounds is None: - pbounds = get_params(sm.__name__) - pbounds.loc["k_s", "p_ini"] = max(self.k) - pbounds.loc["theta_s", "p_ini"] = max(self.theta) - pbounds.loc["theta_s", "p_max"] = max(self.theta) + 0.01 - - if weights is None: - weights = ones(self.h.shape) - - sml = sm(**dict(zip(pbounds.index, pbounds.loc[:, "p_ini"]))) - - def fit_swrc(p: FloatArray) -> FloatArray: - for pname, pv in zip(pbounds.index[pbounds.loc[:, "swrc"]], p): - sml.__setattr__(pname, pv) - diff = weights * (sml.theta(h=self.h) - self.theta) - return diff - - def fit_k(p: FloatArray) -> FloatArray: - for pname, pv in zip(pbounds.index[~pbounds.loc[:, "swrc"]], p): - sml.__setattr__(pname, pv) - diff = weights * (log(sml.k(h=self.h)) - log(self.k)) - return diff - - res_swrc = least_squares( - fit_swrc, - x0=pbounds.loc[pbounds.swrc, "p_ini"], - bounds=( - pbounds.loc[pbounds.swrc, "p_min"], - pbounds.loc[pbounds.swrc, "p_max"], - ), - ) - - res_k = least_squares( - fit_k, - x0=pbounds.loc[~pbounds.swrc, "p_ini"], - bounds=( - pbounds.loc[~pbounds.swrc, "p_min"], - pbounds.loc[~pbounds.swrc, "p_max"], - ), - ) - - opt_pars = dict(zip(pbounds.index[pbounds.loc[:, "swrc"]], res_swrc.x)) - opt_pars.update(dict(zip(pbounds.index[~pbounds.loc[:, "swrc"]], res_k.x))) - opt_sm = sm(**opt_pars) - if return_res: - return opt_sm, {"res_swrc": res_swrc, "res_k": res_k} - return opt_sm - def fit( self, sm: Type[SoilModel], pbounds: DataFrame | None = None, - weights: FloatArray | None = None, - W1: float | None = None, + weights: FloatArray | float = 1.0, + W1: float = 0.1, W2: float | None = None, - return_res: bool = False, k_s: float | None = None, + silent: bool = True, ) -> SoilModel: """Same method as RETC""" theta = self.theta + N = len(theta) k = self.k + M = N + len(k) if pbounds is None: pbounds = get_params(sm.__name__) @@ -133,29 +79,28 @@ def fit( pbounds.loc["theta_s", "p_ini"] = max(theta) pbounds.loc["theta_s", "p_max"] = max(theta) + 0.02 - if weights is None: - weights = ones(self.h.shape) - - if W1 is None: - W1 = 0.1 + if isinstance(weights, float): + weights = full(M, weights) if W2 is None: - M = len(k) + len(theta) - N = len(theta) - W2 = (M - N) * sum(weights * theta) / (N * sum(weights * k)) + W2 = ( + (M - N) + * sum(weights[0:N] * theta) + / (N * sum(weights[N:M] * npabs(log10(k)))) + ) - def fit_staring(p: FloatArray) -> FloatArray: + def get_diff(p: FloatArray) -> FloatArray: est_pars = dict(zip(pbounds.index, p)) if k_s is not None: est_pars["k_s"] = k_s sml = sm(**est_pars) theta_diff = sml.theta(h=self.h) - theta - k_diff = log(sml.k(h=self.h)) - log(k) - diff = append(weights * theta_diff, weights * W1 * W2 * k_diff) + k_diff = log10(sml.k(h=self.h)) - log10(k) + diff = append(weights[0:N] * theta_diff, weights[N:M] * W1 * W2 * k_diff) return diff res = least_squares( - fit_staring, + get_diff, x0=pbounds.loc[:, "p_ini"], bounds=( pbounds.loc[:, "p_min"], @@ -165,10 +110,11 @@ def fit_staring(p: FloatArray) -> FloatArray: opt_pars = dict(zip(pbounds.index, res.x)) if k_s is not None: opt_pars["k_s"] = k_s - opt_sm = sm(**opt_pars) - if return_res: - return opt_sm, {"res": res} - return opt_sm + + if not silent: + print("SciPy Optimization Result\n", res) + + return sm(**opt_pars) def wosten(self, ts: bool = False) -> Genuchten: """Wosten et al (1999) - Development and use of a database of hydraulic @@ -214,7 +160,7 @@ def wosten(self, ts: bool = False) -> Genuchten: - 0.1940 * self.om_p + 45.5 * self.rho - 7.24 * self.rho**2 - - 0.0003658 * self.clay_p**2 + + 0.0003658 * self.clay_p**2 + 0.002885 * self.om_p**2 - 12.81 * self.rho**-1 - 0.1524 * self.silt_p**-1 @@ -252,12 +198,12 @@ def wosten(self, ts: bool = False) -> Genuchten: ) theta_r = 0.01 return Genuchten( - k_s=exp(ks_), + k_s=max(exp(ks_), 0), theta_r=theta_r, theta_s=theta_s, alpha=exp(alpha_), - n=exp(n_), - l=exp(l_), + n=exp(n_) + 1, + l=(10 * exp(l_) - 10) / (1 + exp(l_)), ) def wosten_sand(self, ts: bool = False) -> Genuchten: @@ -413,13 +359,14 @@ def cosby(self) -> Brooks: @dataclass class Soil: name: str - type: str | None = None model: SoilModel | None = None sample: SoilSample | None = None source: str | None = None description: str | None = None - def from_name(self, sm: Type[SoilModel] | SoilModel | str) -> "Soil": + def from_name( + self, sm: Type[SoilModel] | SoilModel | str, source: str | None = None + ) -> "Soil": if isinstance(sm, SoilModel): if hasattr(sm, "__name__"): smn = sm.__name__ @@ -429,21 +376,35 @@ def from_name(self, sm: Type[SoilModel] | SoilModel | str) -> "Soil": elif isinstance(sm, str): smn = sm sm = get_soilmodel(smn) - else: raise ValueError( f"Argument must either be Type[SoilModel] | SoilModel | str," f"not {type(sm)}" ) - path = Path(__file__).parent / "datasets/Soil_Parameters.xlsx" - ser = read_excel(path, sheet_name=smn, index_col=0).loc[self.name].to_dict() - # path = Path(__file__).parent / f"datasets/{sm.__name__}.csv" - # ser = read_csv(path, index_col=["name"]).loc[self.name].to_dict() - self.__setattr__("type", ser.pop("soil type")) - self.__setattr__("source", ser.pop("source")) - self.__setattr__("description", ser.pop("description")) - self.__setattr__("model", sm(**ser)) + path = Path(__file__).parent / "datasets/soilsamples.csv" + ser = read_csv(path, delimiter=";", index_col=0) + sersm = ser[ser["soilmodel"] == smn].loc[[self.name], :] + if source is None and len(sersm) > 1: + raise Exception( + f"Multiple sources for soil {self.name}: " + f"{array2string(sersm.loc[:, 'source'].values)}. " + f"Please provide the source using the source argument" + ) + elif (source is not None) and len(sersm) > 1: + sersm = sersm[sersm["source"] == source] + + serd = sersm.squeeze().to_dict() + if isna(serd["description"]): + serd["description"] = serd["soil type"] + self.__setattr__("source", serd.pop("source")) + self.__setattr__("description", serd.pop("description")) + smserd = { + x: serd[x] + for x in sm.__dataclass_fields__.keys() + if sm.__dataclass_fields__[x].init + } + self.__setattr__("model", sm(**smserd)) return self @staticmethod @@ -464,21 +425,16 @@ def list_names(sm: Type[SoilModel] | SoilModel | str) -> list[str]: f"not {type(sm)}" ) - path = Path(__file__).parent / "datasets/Soil_Parameters.xlsx" - names = read_excel(path, sheet_name=smn).loc[:, "name"].to_list() - return names + path = Path(__file__).parent / "datasets/soilsamples.csv" + names = read_csv(path, delimiter=";") + + return names[names["soilmodel"] == smn].loc[:, "name"].unique().tolist() def from_staring(self, year: str = "2018") -> "Soil": if year not in ("2001", 2001, "2018", 2018): raise ValueError(f"Year must either be '2001' or '2018', not {year}") - path = Path(__file__).parent / f"datasets/Staring_{year}.xlsx" - parameters = read_excel(path, sheet_name="parameters", index_col=0) - ser = parameters.loc[self.name].to_dict() - self.__setattr__("type", ser.pop("soil type")) - self.__setattr__("source", ser.pop("source")) - self.__setattr__("description", ser.pop("description")) - sm = Genuchten(**ser) - self.__setattr__("model", sm) + + self.from_name(sm=Genuchten, source=f"Staring_{year}") ss = SoilSample().from_staring(name=self.name, year=year) self.__setattr__("sample", ss) return self diff --git a/src/pedon/soilmodel.py b/src/pedon/soilmodel.py index a57b733..e24e622 100644 --- a/src/pedon/soilmodel.py +++ b/src/pedon/soilmodel.py @@ -49,6 +49,7 @@ class Genuchten: alpha: float n: float l: float = 0.5 # noqa: E741 + m: float = field(init=False, repr=False) def __post_init__(self): self.m = 1 - 1 / self.n @@ -209,7 +210,7 @@ def __post_init__(self): theta_fc = ( self.beta ** -(0.60 * (2 + log10(self.k_s))) * (self.theta_s - self.theta_r) + self.theta_r - ) + ) # assumes k_s is in [cm] self.sy = self.theta_s - theta_fc def theta(self, h: FloatArray) -> FloatArray: diff --git a/tests/test_datasets.py b/tests/test_datasets.py index b8c64d9..40fdb7d 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -10,7 +10,7 @@ def test_sample_staring_2001() -> None: def test_soil_from_name() -> None: - pe.soil.Soil("VS2D_Del Monte Sand").from_name(pe.Brooks) + pe.soil.Soil("Del Monte Sand").from_name(pe.Brooks) def test_soil_from_staring() -> None: diff --git a/tests/test_fit.py b/tests/test_fit.py deleted file mode 100644 index cf444a2..0000000 --- a/tests/test_fit.py +++ /dev/null @@ -1,70 +0,0 @@ -import pytest -from numpy import array - -import pedon as pe - - -@pytest.fixture -def sample() -> pe.soil.SoilSample: - h = array( - [ - -1.0e00, - -1.0e01, - -2.0e01, - -3.1e01, - -5.0e01, - -1.0e02, - -2.5e02, - -5.0e02, - -1.0e03, - -2.5e03, - -5.0e03, - -1.0e04, - -1.6e04, - ] - ) - theta = array( - [ - 0.43, - 0.417, - 0.391, - 0.356, - 0.302, - 0.21, - 0.118, - 0.077, - 0.053, - 0.036, - 0.029, - 0.025, - 0.024, - ] - ) - - k = array( - [ - 2.341e01, - 1.138e01, - 6.040e00, - 3.130e00, - 1.140e00, - 1.600e-01, - 7.500e-03, - 6.500e-04, - 5.400e-05, - 2.000e-06, - 1.600e-07, - 1.400e-08, - 2.600e-09, - ] - ) - - return pe.soil.SoilSample(h=h, theta=theta, k=k) - - -def test_fit(sample: pe.soil.SoilSample) -> None: - sample.fit(pe.soilmodel.Genuchten) - - -def test_fit_seperate(sample: pe.soil.SoilSample) -> None: - sample.fit(pe.soilmodel.Brooks) diff --git a/tests/test_ptf.py b/tests/test_ptf.py index 07c1f5e..86e8740 100644 --- a/tests/test_ptf.py +++ b/tests/test_ptf.py @@ -6,7 +6,7 @@ @pytest.fixture def ss() -> pe.soil.SoilSample: return pe.soil.SoilSample( - sand_p=40, silt_p=10, clay_p=30, rho=1.5, om_p=20, m50=10000 + sand_p=40, silt_p=10, clay_p=30, rho=1.5, om_p=20, m50=150 )