diff --git a/.gitignore b/.gitignore index 4b4b4c46f9..38381e1123 100644 --- a/.gitignore +++ b/.gitignore @@ -29,7 +29,7 @@ __pycache__ .coverage .pytest_cache src - +*.iml *.swp *.map .idea/ @@ -43,3 +43,4 @@ package-lock.json geckodriver.log *.ipynb *.iml +.vscode/settings.json diff --git a/Create and Deploy an Azure ML Web Service.ipynb b/Create and Deploy an Azure ML Web Service.ipynb deleted file mode 100644 index a62ae4c84d..0000000000 --- a/Create and Deploy an Azure ML Web Service.ipynb +++ /dev/null @@ -1,563 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# # Create and deploy an Azure Machine Learning web service\n", - "\n", - "Although web services can be created directly within [Azure ML Studio](https://studio.azureml.net), in some cases it may be more straightforward to develop and deploy a web service entirely within a Python notebook. Python notebooks allow rapid prototyping and allow developers to include commentary in Markdown. This tutorial demonstrates how the [`azureml`](https://github.com/Azure/Azure-MachineLearning-ClientLibrary-Python) package can be used to deploy Azure ML web services directly from within a Python notebook (or other Python environment).\n", - "\n", - "> **Note**: the `azureml` package presently works only with Python 2; be sure to set the notebook's kernel appropriately.\n", - "\n", - "In this notebook:\n", - "- [Prerequisites](#Prerequisites)\n", - " - [Credentials needed to connect to your workspace](#Credentials-needed-to-connect-to-your-workspace)\n", - "- [Explore beaver fever](#explore-beaver-fever)\n", - " - [Loading and exploring the dataset](#Loading-and-exploring-the-dataset)\n", - " - [Transferring data to and from Azure ML Studio](#Transferring-data-to-and-from-Azure-ML-Studio)\n", - "- [Creating the predictive model](#Creating-the-predictive-model)\n", - "- [Deploying the model as a web service](#Deploying-the-model-as-a-web-service)\n", - "- [Consuming the web service](#Consuming-the-web-service)\n", - "\n", - "Refer to the [azureml GitHub repository](https://github.com/Azure/Azure-MachineLearning-ClientLibrary-Python) for more information on its capabilities.\n", - "\n", - "## Prerequisites\n", - "\n", - "This walkthrough assumes that you're familiar with Python and [Azure ML Studio](https://studio.azureml.net). You also need a workspace in Azure ML Studio and its credentials, which is described in the next section.\n", - "\n", - "Your environment also requires the `azureml` package, which is installed by default with Azure Notebooks.\n", - "\n", - "### Create and connect to an Azure ML Studio workspace\n", - "\n", - "The `azureml` package uses your workspace ID and authorization token to connect to the workspace, provide that you're the owner of the workspace (authorized users who are not owners need to ask an owner for these details):\n", - "\n", - "1. Open [Azure ML Studio](https://studio.azureml.net) in a browser and sign in with a Microsoft Account. Azure ML Studio is free and doesn't require an Azure subscription. Once signed in, you're in your \"workspace.\"\n", - "\n", - "1. Select the **Settings** button on the left pane:\n", - "\n", - " ![Settings button](https://github.com/Microsoft/AzureNotebooks/blob/master/Samples/images/azure-ml-studio-settings.png?raw=true)

\n", - "\n", - "1. On the **Name** tab, the **Workspace ID** field contains your workspace ID. Copy that ID into the `workspace_id` value in the code cell that follows.\n", - "\n", - " ![Location of workspace ID](https://github.com/Microsoft/AzureNotebooks/blob/master/Samples/images/azure-ml-studio-workspace-id.png?raw=true)

\n", - "\n", - "1. Select the **Authorization Tokens** tab and copy either token into the `authorization_token` value in the code cell that follows.\n", - "\n", - " ![Location of authorization token](https://github.com/Microsoft/AzureNotebooks/blob/master/Samples/images/azure-ml-studio-tokens.png?raw=true)

\n", - "\n", - "1. Run the code cell; if it runs without error, you're ready to continue." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml import Workspace\n", - "\n", - "# Replace the values with those from your own Azure ML Studio instance; see Prerequisites\n", - "# The workspace_id is a string of hexadecimal characters; the toke is a long string of random characters.\n", - "workspace_id = 'your_workspace_id'\n", - "authorization_token = 'your_auth_token'\n", - "\n", - "ws = Workspace(workspace_id, authorization_token)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore beaver fever\n", - "\n", - "Reynolds (1994) collected data on the body temperatures of female beavers living in Wisconsin. We use this dataset to train a decision tree model which predicts a beaver's body temperature. Then we create a web service based on this model that can be used to predict body temperatures for other beavers.\n", - "\n", - "> P. S. Reynolds (1994). \"Time-series analyses of beaver body temperatures.\" Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. eds (1994) *Case Studies in Biometry*. New York: John Wiley and Sons.\n", - "\n", - "## Loading and exploring the dataset\n", - "\n", - "Our dataset contains the following features:\n", - "\n", - "- `time`: The time of day on which the recording was made\n", - "- `activ`: Binary indicator of whether activity is occurring outside of the beaver lodge\n", - "- `beaver`: The beaver being measured\n", - "- `temp`: The recorded body temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Install the dataset\n", - "!pip install pydataset --disable-pip-version-check -q" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the dataset and plot each of these features on the same axes to see whether any patterns may be present:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "initiated datasets repo at: /home/nbuser/.pydataset/\n" - ] - }, - { - "data": { - "image/png": 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PGPzkkxT8ofbAvGCMgxlFwbPa1vGXTLcbFm67PqtFkoF6xHu5G0cGyE1vH4oyqI/JdRrfJyJJYtMhG4t3OthxOHpfdY0+eKfSwfI9doKnGMX34T5HOIQDvNeFo41Ds0yuGto+a5XDsFgwxk+a86w3JyJxRD3ivVz/VIufXNyGN9ix10VEJFF9sNfOM+vdANgMi/sn+DjX07XzX1oD8O8fp1DrbQ/2qw+0X2H4RPq4rFMun6kbRga4dngAm4FOlhdJIno791KWBR/ts/OnbU52HbEphItIUvlw37EPNdMy+Ki66x9yWxts4RAOsL7WToMXlux28OdtDg58YeapWUOCnOsJYWBR0Mdi/piun1zpsiuEx5OqRoM/b3OyYo89rmaykcQSV/Fr0aJF7Nq1C7/fT0ZGBlOmTGHChAkAbNiwgffee4/GxkYyMzO59NJLGTVq1Am3EwwGKSsrY9OmTTidTqZMmcJFF10Uvn/nzp28+eabHDlyhMLCQq677jqysrJ65DXGi1e3OFm8q/3Y5ls7HDw82cdQXQ1NRJJEVkpkMsp2dz0pZR23jRS7xQubXKw92P5V+u5uix9NaSMn1cJthwcu8GFakJ6WiterpJZM9jQa/PjjFPxm+87X7sYAt4/R0E45c3G1bz1t2jTuv/9+/uVf/oVbbrmFpUuXUl1dTWNjI6+99hqzZs3i4Ycf5oorrmDRokU0NzefcDvvvfce9fX1fOc732HhwoV8+OGHbNu2DYCWlhZefvllZs6cyYMPPkh+fj6vvvpqT77MuLBy/7Ez8IOWwdqD9lOsLSKSWG4u9TMyO0SK3WJIZghfCFbs6drnXHFfi5tL/aQ7LfqlmHx9nI91X/jsbAkYbKqL/Fq1xc/kJhJF5bX2cAgHWLk/rvo1JYHEVRAfMGAADkf7H7NhGBiGQX19PY2NjaSkpFBSUoJhGIwYMQKXy0VDQ8MJt1NeXs7FF19MamoqHo+HCRMmsG7dOgA+++wzPB4PY8aMwel0cskll3DgwAFqa2t77HXGg/6p1nHL6g0XkeTR1w0PTfZx9zg/uxrtLK1y8twGN3/Z3rXANGtIkP+8zMvPZ7QxfqBJdsrxn6Xq+e4N9B0q0RJ3u3BlZWWsW7eOYDBIbm4uJSUlOJ1OPB4PmzdvZsSIEWzZsgW73c7AgQM7PN7r9dLU1ERubm74toEDB7J582YAamtrI+5zuVxkZ2dTW1uLx+OhsbGxQ0+73+8nPT29m14x4Z2Po//3hHvON/l/aw0OthhckGcyc4gNmxH9/TK73Y7TmVyn98eiXj1BtUocqlXnVRyK7AUvr3Uwd1T0uqm/86UQvyk3aPbD5cUmY3PtQORzJlu9kvV9BZ2v1bTBsK8lxEd7beSkWnz9PDOpaiw9J+7eRVdffTWzZ89mz549VFZW4nA4sNlsjBs3jkWLFhEMBrHb7dx00024XK4Oj/f720+Icbvd4dtSUlLw+Xzh+9PS0iIe88X7V69ezfLlyyPunz59OjNmzIjq6zyR7Ozsbn+OozzAk4N77OmSUk/WS7pGtUoc0a7V8Nw23qtqCy8PyXHj8USvY8XjgS+VRG1zCaW3v6/unQ73xroRkvDiLogD2Gw2Bg8eTEVFBStXrsTj8fC3v/2NhQsXkpeXR01NDS+99BK33XYbeXl5EY89Gs59Pl9479Tn84WDucvlCofuo754/4QJExg5cmTE/X6/v1uHrjgcDrKzs2loaCAYPLsLPsQrt9vd4fed6JK1XqpV4ujNtVpdY/DRvvZeyOtHmqSc5lts2gCoGmJjQ62Nwj4WN5W0UlvbGuXWn1qy1StZ31eQ2LXyeDyxboKchbgM4keZpklDQwOhUIjBgwdTUFAAQEFBAQUFBezcubNDEE9NTSUjI4MDBw6QkZEBwP79+8N/oB6Ph/Ly8vD6fr+f+vr68P2ZmZlkZmZGbLO6uppAoPvPhg4Ggz3yPD3J4XAk3Ws6KtnqpVoljt5aqy31Np5Y6caifWjJ/maLe88//bSAt5QCpceWe/pXl6z1Srb3FSRvrSR+xc3Jms3Nzaxfvx6fz4dpmmzfvp0NGzYwZMgQCgoK2L17NzU1NQDU1NRQVVV1wjHiAOPGjWPFihV4vV5qa2tZs2YN48ePB2DUqFEcPHiQTZs2EQgEWL58OQMHDtSepIhInNvWYAuHcIAt9ZrtSUQSW9z0iBuGwapVqygrK8OyLLKysrjyyispLW3vxrjkkkt45ZVXaGlpIS0tjWnTpjF8+HAAKioqeP/99/nmN78JwIwZMygrK+OXv/xleB7xkpL2QXzp6encdNNNvPXWW7z22msUFBRwww03xOZFi4hIpxVnRs5MUdxXM1WISGIzLMvSXEunUV1d3a3bPzorTG1tbdIdEktNTcXr9ca6GVGVrPVSrRJHb67VB3vtfFztoF+KxbxSPxkdz9mPqUAI/rjVyc7DNkb0M7m+JEBGevTqVdVo8OoWF0ETrhkWYHT/nt8ZSdb3FST2eys/Pz/WTZCzEDc94iIiIqcztTDE1MJQrJtxUn/a5uSdyvaJArYftuO2W8w7Nzrb9oXg5ytTaPS3D8/ZedjGf1zcfiVPEUlMCuIiIiJRUtVoO+VyVxxpM8IhHMBvGuxvMZIqiK/YY2fJbicWFqZlkOKwuHFkgJH9NAxJklPcnKwpIiKS6EblRPbWj86JXoDsl2oxMO3Y9jKcFoP6JE9A3dFg4/kNLqqabOxpsrOv2caOw3aeXO2mJblGwIiEqUdcREQkSmYPDeJ2wK7DNkb0CzF9UAiIzhUXHTZ4cJKPsh0OgqbBFcUBMt2nf1yiqGkxImbFOcobNDjcZpDuTJ6ef5GjFMRFRESixDDgssFB6KYrF2enWNw+Jjm7h0f0M0mxW7SFIsN4brrJgDSFcElOCuIiIiIScwPSLB6e3MYHex0YWJgYOG0wa0gAp6aMlySlIC4iIiJxoSjT4tbRydnjL3IiCuIiIiIiUbLxkI2nK9y0BmDWkCBzR2jHQk5Os6aIiIhESSB+pziXHmBa8NRaN0d8BgHToGyHk8/qFLXk5NQjLiIi0kW7jxg8ucZNQ5uNcz0h7j3Pp3HNvVDAbJ/l5YsafR1nghE5SrtpIiIiXfS/G100tLV/pa6vtbOkSv1cvZHbDpPzg+Hl/qkmY/rrMImcnD4pREREuqglYJxyWXqPr431M94TojVoMGFgkAxXrFsk8Uw94iIiIl10efGxXtB0p8WUguAp1pau8AXhYItBME4vKmozYFJ+iBlFwaS64JJ0D/WIi4iIdNFlg4MMzjSpbTUY2c8kJ1UXoOkOu47Y+MVKN80Bg7x0kwcntdFXYVcSmHrERUREoqAk2+SigpBCeDd6ZbOT5s+H/dS02Fi80xnjFol0jYK4iIiIJITjh6ME4nR4ikhnKYiLiIhIQrh2eACnrf2IQ6bLihibL5KINEZcREREEsK5HpP/uLiNg60GRX1MzUgiCc+wLEuD2U6jrq4Om637Dh4YhoHL5cLv95Ns5bDZbJhmch07TNZ6qVaJQ7VKLMlWr+6oVZMPttWDJw0G9Y3KJgHYUgfeAIz2gKsTF1hK5FplZ2fHuglyFtQj3gk+n69bt+90OsnKyqKlpYVAINCtz9XTUlNT8Xq9sW5GVCVrvVSrxKFaJZZkq1e0a1XnNfjxx24afDYMLO4418+0wq5fBOelz5y8U9l+MufQviEemnT6q50mcq0UxBOTxoiLiIhIzLy/106Drz2OWBiU7ej6TCjeIOEQDrDziJ0NhzrRJS7SwxTERUREJGbcx+XjzgwhOR2HAXYjctiMy55cQ54kOSiIi4iIJKEjPli+x87aA+3JNmTC36vtvL/XjjeOJhuZURSkJLt9KEqaw2L+aH+Xt+m0w4Ix/nAYn1oQZHROdMZ+72syWFblYHuDIpR0ncaIi4iIJJkjPnjkwxQOfz7k49KiAIe8Nspr20P5O5UmP7iwrUNvdCykOODhST4O+wwynNZpx3F31sWDQkzM8xIIEbVLzW+tt/H4SjdB08DA4qtj/VxU0PXx7NJ7aXdOREQkyaw9YA+HcIClVY5wCAfY22SLqx5dw4DslOiF8KNSHdEL4QAf7HMQNNuv7Glh8N4e9WdK18TPu1BERESios9x82tbGCdYR2Omz9TxvzP9DqWrFMRFRESSzPkDQ8woCmCjY1B0GBY3jPBTlNl7Q+SmOht/2upk1f4z64K/emiA0TkhDCwG9TG5ZVRyTbUpPU/HVERERJKMYcCCMQFmFgX5Px+khHvEXTaLX8zwkt6Lr0i59oCdX69xhX8nt43yc1lx585eTXXCd7/kw7TA1vEgg8gZU4+4iIhIkirsYzF/dIAMp0W22+Tr4/29OoQDrNpvjxiqs3K/nepmgz9sdvLaZmjrRCZXCJdoUY+4iIhIEps5OMjMwXE0X2GM9U/rOM77xx+n0BpsT9fl+908NKl7r6gtcpR6xEVERKTXuHpogAvzg2S5TcZ5QpzTPxQO4QBb6uNrnnVJbuoRFxERkV7DaYe7xx27aFDlkfY5wY8OV8l2m6TEwfzq0jsoiIuIiEivVdzXYuE5fv6220m6y+DWUh+GxoBLD1EQFxERkV7t4kEhLh4UIjU1Fa+3907rKD1PQVxERESSXiAEf9rmZE+TjdE5If5hqAaCS+wpiIuIiEjSe2WLk3d3OwHYcMhOigNmFCmMS2wpiPcyta0GL33mpCVgMLMoyKT8UKybJCIi0u12HrZ1WJ5RFKPGiHxOQbyXeWK1m+rm9g+jbQ02BqT7GNLXjHGrREREuldJtsnOI/YvLKsjSmJPQTzJfVpj582dTtx2i3kj/eEQDmBhsKfRYEjfGDZQRERioskPv93oorrJht1mYVoGQ7NMbhvtxx0n0/dVHjF46TMXbSGD2UMCXTqKe8PIACkOiz1NNsbkmFw8KETZDgef1DjISbH4yjl+UlOj2HiRToirIL5o0SJ27dqF3+8nIyODKVOmMGHCBCoqKnjjjTfC61mWRTAY5O677yY/P7/Ddn784x9HLAeDQSZOnMjs2bNpaGjgySefxOl0hu+fOnUq06dP774XFiP7mgz+u9yFabXPw/SrNSmUZIfY1tD+CeuyWZRkqzdcRKQ3+u0GF6sORMaAfc020hwWN48KxKhVx4RM+OWqFBr97d9h/1PhorBPGwV9zm5WE4cNris5NiZ8ZY2dRVtdAOxtgv8pd/HIJV1utsgZiasgPm3aNObMmYPD4aC2tpbnn3+evLw8xo4dy9ixY8PrrV27lhUrVpCXl3fC7Xz/+98P/+z3+3n88ccZPXp0xDoPPfQQdnuc7PJ3gz9vc7K0yh4O4QCNfoOHJvl4f6+T5oDBtMIgeRmapklEpDeqbjnxxbVPdntPawkQDuEApmVwoNVGQZ/oDCmpbjaOW46P1y29S1wF8QEDBoR/NgwDwzCor6/v0OtdXl7OuHHjMDox4/6mTZtIT09n8ODBnWpDY2Mjzc3NEbf5/X7S09M79fiz4XA4Iv7vqlU1Bq9v77itIVkmg7Kc3JYNYAH2z/91H7vdHnH0IRlEu17xQrVKHKpVYonXep030KK6uePt5+dyyvb2VK36OWBYlsmOz0+yzHBajOhvw+mMTmAen2fwxg6L0OcdVuflWtjtjrislSSvuPvEKysrY926dQSDQXJzcykpKYm4//Dhw+zevZs5c+Z0anvr1q07YWh/4oknABg2bBiXX355OGivXr2a5cuXR6w7ffp0ZsyYcbYvqdOys7Ojsp2W/W1AW3jZYYObxrqZe46bDLf2+KMlWvWS7qdaJQ7VqvttOxTkseWtNLSanJ/voDDLhtth4AtajPI4mDnc1ant9EStHrvaZNEGH20BmF3qYlBW9DqPPB74aZ8g71f6GZBh47oxbhw2XVJTepZhWVbcjU0wTZM9e/ZQWVnJ1KlTI4aQLF++nJ07d3LHHXecdjuHDx/mySef5L777gt/YPh8Pg4dOkRubi5er5c333wTv9/P7bffDsSuRzw7O5uGhgaCwa7PabqvCf7PCge+UPsHysWDTO4+LzZnh7vdbnw+X0yeu7tEu17xQrVKHKpVYom3ej3wroODrccC5wNfCnJ+buejgGoVnzweT6ybIGch7nrEAWxRdvgDAAAgAElEQVQ2G4MHD6aiooKVK1cyefLk8H3l5eVMmzatU9spLy+nqKgoYq/d7XZTUFAAQEZGBrNnz+bnP/85bW1tpKSkkJmZSWZmZsR2qqurCQS6/8SVYDAYlecZkAL/MjnIyv0Ost0WlxQFWV5p53ebXFhW+5njlw7umQ9Ph8PRI7+7WIhWveKFapU4VKvEEm/1qm+L/OqvbTYJBM78O0G1Eum6uB6nYJomDQ0N4eWqqiqampo6nHh5MkfHkp9KZ8aZJ6KiTIu5IwLMHByk2Q/PrnfhDRq0hQxe3ORkf0tyvm4RETm1C78wBWC602KsR/Npi8RK3PSINzc3s2vXLkaMGIHT6WTnzp1s2LCBuXPnhtdZt24do0aNwu12n3Z7R0P7mDFjIm7fu3cvKSkp9OvXj7a2NhYvXkxxcTEpKSlRf03xoiVghE9Ggfb5w5v8BrnpcTcqSUREutnCc/yMyA7R6De4YGCI/mn6LhCJlbgJ4oZhsGrVKsrKyrAsi6ysLK688kpKS0sBCAQCbNy4kXnz5nV47IoVK6iqqmL+/Pnh28rLy08Y2hsaGliyZAktLS243W6GDh0aEfaT0cB0i9J+ITbXt4+1L84MUZyp+cNFRHojmwFTC3tXL3ggBN4gZJ6+H0+kR8XlyZrxprq6ulu373Q68Xg81NbWdtvYtEAIPqmxY1rwpbwQKT20C5aamorX6+2ZJ+shPVGvWFCtEodqlViSrV6JVquNh2w8tdaNN2gwpn+Ib5/vw3mSyVcSuVYnusChxL+46RHvzVoDUN0Ywt6Nu0ROe+/rAREREfnfDe3nSAFsPGTng30OZhQl12wvkrgUxGNsfa2Np9Y68IWaGJZl558mBkhVVURERKLi6FS+R7Upg0scietZU3qD33/mCn9I7DhsY8UepXAREZFouXrYseEzOakmFxUoiUv8UOqLseBx50wGdA6liIhI1FxeHKQkO0RDm42S7BAZnbtwqEiPUI94jH25JIDNaB8cPiDN4uJC7amLiIhEU3Ffi/MGKoRL/FGPeIxdVBBiZP8gIXcW/YzDODSHjYiIiEivoB7xOJCbAePznTpJU0RERKQXURAXEREREYkBBXERERERkRhQEBcRERERiQEFcRERERGRGFAQFxERERGJAc3TISIicWvjIRtVjTZG9DMZlqUrnsWaZcFHew0C+9sY1QeyNS/3Ka2ssXPIazBuQIj8DM1PLB0piIuISFxavsfO8xvcANgMi+9M8HGOR2E8ll7Y6GTZHgfQRrrTwb9eFMSTpoB5Iq9ucfLWTicAr2+3+OGFbRT00e9KImloioiIxKWP9h3rKzItg49r1HcUax9WH6tBS8Dgfze42FqvKHEiH+2zh3/2hQxW7befYm3prfTuERGRuJSdEtl7mO1Wb2KsHV+DjXV2fvqpm011ihPH6/D3m6K/X+lI7xwREYlLt4zyM7JfiFSHxfkDg1wzLBDrJvV694z3kZ9hYTOO3WZaBqvV29vBV8f6GdTHJNVhMX1QgKmFoVg3SeKQjvOJiEhc6uuGhyb5Yt0M+YLivhaPzQzy1LpUPq4Khm8foHHiHeRnWDw6tS3WzZA4Z1iWpXfPadTV1WGzdd/BA8MwcLlc+P1+kq0cNpsN00yuk6uStV6qVeJQrRJLstXLMAxaQk6e+CjA3iaL8wbCnePBngTH2BO5VtnZ2bFugpwF9Yh3gs/XvT0yTqeTrKwsWlpaCASS69BramoqXq831s2IqmStl2qVOFSrxJJs9XI6nRR4svje5NpwrfxJcuAikWulIJ6YFMRFREQkrnkD8OftThraDC7MD3HeQI23luSgIC4iIiJx7b/L3ZTXtp8Qumq/nYcn+yjJTswhJCJflAQjukRERCSZbW04FlcsDLY1KL5IctBfsoiIiMS1IX0je7+L+6o3XJKDhqaIiIhIXLtnvI9XNrto8BlclB9kdI6CuCQHBXERERGJa31ccNdYf6ybIRJ1GpoiIiIiIhIDCuIiIiIiIjGgoSkJYPkeO2/tdOK2w+1j/JqySURERCQJqEc8zu0+YvC/G1wcbLWxp8nGr1a7CSmHi4iIiCQ8BfE4d8hrw8IILzcHDB5YlsJH++wxbJWIiIiIdJWCeJwryQ6R6bIibmv023h2vYtDXuMkjxIRERGReKcgHucy3fDDi9q4ZFAg4vaQZXCkTUFcREREJFEpiCeA/qkWt40OMKRvKHxbYR+TokwNFhcRkd6nzeeLdRNEokKzpiQIhw2+9yUfH+x1YAJTC4I4NUxcRER6kd17a/inR5+gen8t40aX8PgPv01GelqsmyVy1tQjnkBSHHBZcZArioOkOWPdGhERkZ715G9eonp/LQDlm7bxu0WLY9wika5REBcREZGE0NTcetxyS4xaIhIdCuIiIhJ3WgNwoMXA/HzSqEAI9rcY+IKxbZfE1o3XXIbN1j5RQVqqm2uuuDjGLRLpmrgaI75o0SJ27dqF3+8nIyODKVOmMGHCBCoqKnjjjTfC61mWRTAY5O677yY/P7/Ddp577jn27t2Lzda+n5GZmcm9994bvr+iooIlS5bQ2trK0KFDmTNnDmlpGmMmIhIPKmptPLXWjT9kMCwrxFfP9fPEajcHWm1kOC3+aWIbxX2t029Iks4V0ydTVJBL5Z4azh01jILcAbFukkiXxFUQnzZtGnPmzMHhcFBbW8vzzz9PXl4eY8eOZezYseH11q5dy4oVK8jLyzvptmbPns2ECRM63H7w4EHKysq49dZbycvL44033uDNN9/kxhtv7JbXJCIiZ+bFTS78ofZezx2H7Ty7wcWB1vaOleaAwatbXHz3S5o1o7cqHV5M6fDiWDdDJCriamjKgAEDcDja9w0Mw8AwDOrr6zusV15ezrhx4zCMM59Hu6KighEjRlBcXIzb7WbmzJl89tln+DQVkohIXAgeNzNr6DTLIiKJKq56xAHKyspYt24dwWCQ3NxcSkpKIu4/fPgwu3fvZs6cOafczpIlS3j33Xfp378/M2fOZMiQIQDU1tYyaNCg8Hr9+vXDbrdTV1dHfn4+jY2NNDc3R2zL7/eTnp4epVfY0dGdj6P/JxO73Y7TmVxTvCRrvVSrxJHstbqhNMRvyg1My2BgusXt55r88lMbjX4Dl93iy6VWQr3+ZKtXsr6vIPlqJfEv7t5FV199NbNnz2bPnj1UVlZ2eKOXl5dTVFREdnb2Sbdx+eWX4/F4sNvtbNiwgZdeeol77rmHfv364ff7cbvdEeunpKSEe8RXr17N8uXLI+6fPn06M2bMiNIrPLlTvSaJP6pX4lCtEkd2djZzJ8Dk4SEOtZqU5DhIcxmMGWyyqz5EfqadARlxdTC319L7SqTr4i6IA9hsNgYPHkxFRQUrV65k8uTJ4fvKy8uZNm3aKR9fWFgY/nn8+PGsX7+ebdu2MWnSJFwuV4dhKD6fLxzOJ0yYwMiRIyPu9/v91NbWdvVlnZTD4SA7O5uGhgaCweSaEsDtdifdsJ9krZdqlTh6Q61cQL4DWo7A0QnqCpyAF2q9MWzoWUi2eiXr+woSu1YejyfWTZCzEJdB/CjTNGloaAgvV1VV0dTUxOjRo89oO4ZhYFntZ9h7PB4OHDgQvq++vp5gMEhOTg7QPsNKZmZmxOOrq6sJBAJn+zI6LRgM9sjz9CSHw5F0r+moZKuXapU4VKvEkqz1Uq1Eui5uju81Nzezfv16fD4fpmmyfft2NmzYEB7bDbBu3TpGjRrVYWjJF3m9XrZv304gECAUClFRUcHu3bsZPnw4AGPHjmXLli3s3r0bv9/PsmXLTrtNEREREZFoi5seccMwWLVqFWVlZViWRVZWFldeeSWlpaUABAIBNm7cyLx58zo8dsWKFVRVVTF//nxM02Tp0qUcOnQIwzDo378/N998M/379wfaZ2a5+uqrWbRoEV6vNzyPuIiIiIhITzKso2M25KSqq6u7dftOpxOPx8NHWw6xq8FkeFaIQZkWdV6D9bU2slMsxg1IzPm6UlNT8XoTbEDnaRytV21tbVIdwlStEodqlViSrV6qVXw60QUOJf7FTY94b7dsh5+ffmDHwoHdsLhrrJ/fb3LRHGifK/3qYQHmjkiuDzwRERGR3ixuxoj3dou3+LFoD90hy+DtXY5wCAdYVqV9JhEREZFkoiAeJ/qmRF4ltI8rcsRQpksjiERERESSiYJ4nLh7UipD+prYDIvSfiG+Ps7PlIIgdsMiJ9Xkq2P9sW6iiEi38gct/rrDxp+3OahtNU7/ABGRBKfxDnHCk27j36aH8PsDGJ9//3x1rJ+7ziW8LCKSzB55t4XV++yAnaVVFo9O8ZKVEutWiYh0H/WIx5njQ7dCuIj0Bt4grN537CqNTX6DLfX2GLZIRKT7KYiLiEjMue2Qddy5Mv3TdG6MiHReZWUl55xzTqybcUYUxEVEJOZsBvzo8nQGZ1p4Uk1uG+1nWFZiXj9BRHqvYDB4+pW+QEFcRETiQukABz++JMhjl7Rx2eAz+zITEYH2IDx//nxGjx7NDTfcQGtrK6tXr+aSSy7h/PPPZ9asWdTU1ADw9NNPM3HiRMaOHcvcuXNpbW3lyJEjFBcXY5rtHQHNzc0UFRURDAbZsWMHV155JRMmTGDatGls3rwZgIULF/LAAw8wY8YMHn744TNqr4J4HHqvys5Ta1z8eZuDoDqERERERDply5YtfPOb32TTpk3079+fX//619x777388Y9/ZM2aNSxcuJDvf//7AFx//fWsXLmSiooKxowZwzPPPEPfvn0ZN24cy5cvB+Avf/kLs2fPxuFwcPfdd/PUU0+xevVqHnvsMf7xH/8x/Lxbt25lyZIlPP7442fUXs2aEmc+2Gvnfze6AVh1ANqCBjeP0hU1RURERE6nqKiICy+8EIBbb72VX/7yl2zYsIHLLrsMgFAoRF5eHgAbN27k+9//PocPH6a5uZlZs2YBMG/ePF5++WVmzJjByy+/zP33309zczMfffQRc+fODT+Xz+cL/3zjjTdis515/7aCeJzZ2mA/blkHLURERETO1pgxY/j444873P6Vr3yFP//5z4wbN44XXniBJUuWAHDttdfy8MMPU19fT0VFBdOnT6e5uZmsrCzWrVt3wudIT08/q7Yp5cWZIX3NUy6LiIiIyIlVVVWFQ/fvf/97Jk+eTG1tbfi2QCDAxo0bAWhqaiIvL49gMMiLL74Y3kZGRgZf+tKXuO+++7jmmmuw2WxkZmYyZMgQXn31VQAsy6K8vLzL7VUQjzMzioLcNNLPmJwQs4oD3FyqYSkiIiIinVFaWspzzz3H2LFjqa2tDY8Pf/DBBxk3bhzjx4/no48+AuDf/u3fmDRpEpdddhmlpaUR25k3bx4vvvgi8+bNC9/24osv8swzzzBu3DjGjBnD66+/3uX2GpZlaaLW06iuru7W7TudTjweD7W1tQQCyRW8U1NT8Xq9sW5GVCVrvVSrxKFaJZZkq5dqFZ/y8/Nj3QQ5C+oRFxERERGJAQVxEREREZEYUBAXEREREYkBjRHvhLq6urOaG7KzDMPA5XLh9/tJtnLYbLbw1amSRbLWS7VKHKpVYkm2eqlW8Sk7OzvWTZCzoHnEO+GLE7Z3B6fTSVZWFi0tLTrxJQEka71Uq8ShWiWWZKuXahWfFMQT0xl18z777LNcfvnljBkzhssvv5xnnnkm6faGRURERER6QqeD+Pe+9z1++tOfcv311/P4448zd+5cfvazn/Hggw92Z/tERERERE7rzjvvZMCAAZxzzjknvN+yLO677z6GDx/O2LFjWbNmTQ+3sKNOD015/vnnWbNmDYWFheHbrrrqKs4//3wee+yxbmmciIiIiEhnLFy4kG9961ssWLDghPcvXryYnTt3sn37dlauXMk3vvENPvnkkx5uZaROB/E+ffrQp0+fDrdlZmZGvVEiIiIikniKL7iy25+jctVfT3j7xRdfTGVl5Ukf9/rrr3P77bcDMHHiRJqbm6mpqSEvL687mtkpnR6acv/993P99dfzt7/9jc8++4x33nmHG2+8ke985zvs3Lkz/E9EREREJN7s27ePgoKC8HJhYSH79u2LYYvOoEf829/+NgDLli2LuH3JkiXcd999QPuURqFQKIrNExERERHpuhNNMGIYRgxackyng3iizqspIiIiInJ8D/jevXvJz8+PYYt0ZU0RERER6QWuvfZaXnjhBQBWrlxJenp6TMeHwxn0iFdVVfGjH/2ItWvX0tzcHHHf1q1bo94wEREREUksJzuRsifccsstvPfeexw6dIjCwkJ+9KMfhS86dc899zB79mzeeusthg0bRlpaGs8991zM2npUp4P4jTfeSGlpKY8++iipqand2SYRERERkTPy0ksvnfJ+wzB46qmneqg1ndPpIL5582Y+/vhjbDaNZhERERER6apOp+prrrmG5cuXd2dbRERERER6jU73iP/qV7/ioosuYtiwYQwcODDivmeffTbqDRMRERERSWadDuJ33HEHdrudUaNGaYy4iIiIiEgXdTqIL126lOrq6g6XuRcRETmRrfU26toMRueE6Os++XpBE9ZVG2S2+Bmmfh4R6UU6HcTHjh1LXV2dgriIiJzWmzsc/HGrC4C+bosfXthGTmrHq9qZFjyxys3GOjvQyjn97XznggC22F7sTkSkR3Q6iM+cOZMrrriCO+64o8MY8TvvvDPqDRMRkcT1TqUz/PMRn8Hfq+1cNSzYYb29TcbnIbzdhkM29jYZFGV2DO0iIqeydetW7rrrLhoaGrDZbPzwhz/kxhtvjFjH5/OxYMEC1qxZQ05ODn/4wx8oLi6OTYM5gyD+wQcfUFBQwDvvvBNxu2EYCuIiIhIhzWnR6De+sHyS9RxgYGHRvq6BRWqnv5lERI5xu908/fTTlJaWcuDAAc4//3yuuOIK+vbtG17nmWeeoX///mzbto1XX32VBx98kJdffjlmbe70x92yZcu6sx0iIpJE7jzXz6/XuGnyG5w/MMi0wo694QD90yzmlQZ4dYsTMLh5tIknTb3hIonqgm/8ptufY9X//eoJbx88eHD454EDB9K/f38OHjwYEcRff/11Hn30UQCuv/56vvWtb2FZFoYRm/FwZ9TvUFdXx1tvvcX+/fv57ne/S3V1NaZpUlhYGJXGLFq0iF27duH3+8nIyGDKlClMmDCBiooK3njjjfB6lmURDAa5++67yc/Pj9hGMBjkzTffZOfOnXi9Xvr168ell15KSUkJAA0NDTz55JM4nce6Z6ZOncr06dOj8hpERARKsk2enOnFb4Lbfup1Zw0JcuVwA4+nPw11h/j8itQiImdt5cqVhEIhhg0bFnH7vn37KCgoAMBut5OVlUVdXR39+/ePRTM7H8SXL1/O3LlzueCCC/jwww/57ne/y7Zt2/jZz34WEZK7Ytq0acyZMweHw0FtbS3PP/88eXl5jB07lrFjx4bXW7t2LStWrCAvL6/DNkzTJDMzk4ULF9K3b9/woYdvfOMbZGdnh9d76KGHsNtP8+0gIiJnzTBOH8KPctjAoTM0pYesXLeRNRu2MGJoETMuuiDWzZEoO3jwILfffjvPPPNMhyvCW1bHI26x6g2HMwji999/Py+//DKXXnppONBOmjSJTz/9NGqNGTBgQPhnwzAwDIP6+voOvd7l5eWMGzfuhL84l8vFjBkzwssjR44kKyuLmpqaiCAuIiIivc+Kv6/l4Z/8ZziQ3f+1W7jpmstj3CqJltbWVq699lp+8IMfMGXKlA73FxYWsm/fPgoLCwmFQhw+fJh+/frFoKXtOh3EKysrufTSS4Fjew4ul4tg8MTj/s5WWVkZ69atIxgMkpubGx5SctThw4fZvXs3c+bM6dT2mpubqaurw+PxRNz+xBNPADBs2DAuv/xy0tPTAWhsbKS5uTliXb/fH76/Ozgcjoj/k4ndbo8YBpQMkrVeyV6rJh/8dacNXxDsNghZMHWQSXHf02wkDiV7rZJNstUr0Wu14pM1Eb2iyz9ew23XzwY6V6vlf1/DyrUbGDF0MNfO0rDWeBIKhbjlllu47rrrmD9//gnXufbaa/ntb3/LpEmTeO2115g+fXpi9IiPHj2at99+m1mzZoVve/fddzn33HOj2qCrr76a2bNns2fPHiorKzu80cvLyykqKupU73YoFGLRokWMHz8+HMTT0tL42te+Rm5uLl6vlzfffJPXXnuN22+/HYDVq1ezfPnyiO1Mnz49ope9u6jHPrGoXomjT98sHlrWRGWDGXH7sio7/3VdHwr7aphavND7KnEkaq2GDy2GJR+Gl4cWD+rQWXcyb737Pv/8o1+Gl72+AN+665ZoNzGhnexEyp6wePFiysrK2L17N3/4wx8AeP7553nttde44IILuPbaa7nrrru4/fbbGT58OP369QuvFyudDuK/+MUvuOqqq7jqqqvwer18/etf54033uD111+PeqNsNhuDBw+moqKClStXMnny5PB95eXlTJs27bTbME2T1157DbvdzuzZs8O3u93u8CD9jIwMZs+ezc9//nPa2tpISUlhwoQJjBw5MmJbfr+f2traKL26jhwOB9nZ2TQ0NET9CEOsud1ufD5frJsRVclar2St1RErk+c/baSyoWOPhy8IH25tYGZxYs3Skay1Ssb3FSRfvRKlVoePNPHcK2/Q0urlxqsuZeTwYgBuunomO3buZmX5JkYOG8w3FswNf8efrlZvvhvZUff2ex8y79rLuu01nInO7kwks6uvvppQKNTh9vHjx4d/TklJ4dVXX+3JZp1Sp4P4+++/T0VFBb/73e+48847GTRoEJ9++imvvPIKEydO7JbGmaZJQ0NDeLmqqoqmpiZGjx59ysdZlsVf/vIXWlpauO222055UubxhyMyMzPJzMyMuK26uppAD5zGHwwGe+R5epLD4Ui613RUstUrGWt1uA0eXt5Mk+/khx09KUECAfOk98ejZKzVUcn2voLkrVe81+pb3/8pW3bsBuDdFZ/wwq8fJdeTg80w+P63I69/cvR1nK5Wg/IjL2hYlJ8b178DiX+dDuKPPvoo//zP/8z3vve9iNv//d//nQceeKDLDWlubmbXrl2MGDECp9PJzp072bBhA3Pnzg2vs27dOkaNGoXb7T7ltsrKyqitrWXBggUdxnrt3buXlJQU+vXrR1tbG4sXL6a4uJiUlJQuvwYRiS+VRwyafJG93QPSTFyf75vPGBSkNCexQriInF5jU3M4hAO0tHrZtHUXuZ6cLm331i9fSV3DEVat28TwIYP49ldv7mpTpZc7bRBfunQp0D7eetmyZREnOOzcuZM+ffpEpSGGYbBq1SrKysqwLIusrCyuvPJKSktLgfa91Y0bNzJv3rwOj12xYgVVVVXMnz+fw4cPs3r1aux2Oz/72c/C61xzzTWMHTuWhoYGlixZQktLC263m6FDh0aEfRFJHnkZFk47BD4/UtnXbfHvU9twaki4SFLLSE9joCeHA7V1QPtJmEMGdZzy+Ew57Ha+87Vbu7wdkaMM60QTKn7BkCFDgPZhIUVFRcceaBjk5uby0EMPce2113ZvK2Osurq6W7fvdDrxeDzU1tYm3SGu1NRUvF5vrJsRVclar2StVWVbFi+sasJhs5g30s+gzMQaD34iyVqrZHxfQfLVK1FqVbmnml8/+zKt3jZunnMF0y+ccNJ1/756Pa+WvUtmnwzunv9l8gbE5uIuXXH8VM+SGE7bI75r1y4AFixYwG9/+9tub5CISDRNHOSkOCUU14FBRKKveFA+P//X75x2vV1V+/jej39FMNh+6Gzz9kpe+q8fd3fzRIAzGCOuEC4iIiKJpq7hCE88/XsOHKpn1vTJzL3q0vB9r7zxN/745pJwCAfYvbeGllYv6Wmpndr+8o9X84fX3yEtLZV775xHcWHXh8BI75GYs/GLiIiIdMK//uy/WbN+MwAbNu8gd0B/pkwcx/K/r+GJp1/qsP7w4sJOh/Adu/fyg8f+X3jKvF1V+1j09GMxvUCMJBZbrBsgIiIi0l22V+6JWN5RuTfi/6NcTgdzr7qUX/7onzq97co91RHzVu8/WEdTS2sXWiu9jYK4iIiIJK2J445de8Rus3H+ue2zsZ1/bik227Ge60suuoAf3P9VcrL7dnrbo0cMJS312PTHpcMHk5mRHoVWS29hf+SRRx6JdSPiXVNTU7du3263k56eTmtrK6aZXHMaO53OuL7y2tlI1nqpVolDtUosyVavRKvVlAvGYbfbKMj1cM+CueEgnjsgh9ElQ3E6HUybdB7fXHgTKSnuM6pVn/Q0Jp1/DpZlMbB/Pw4cqmfRW0vw9O/X42PFozWdtPSs005fKJq+sCuSbdouSN56qVaJQ7VKLMlWL9Wqo/qGI1z/te/h97f/PlxOB6/+z0/x5GRHu4knpekLE5NO1hQRkTNiWvDCRief1jjon2pxz3gfeRntfTqf1dl4br0Lb9Bg9tAA/zA0eXqCRU7mUMORcAgH8AeC1NYf7tEgLolJY8RFROSMfLDXznt7nLQGDaqabPymwgVAyIT/XOOm1mujOWDwyhYX2xv0NSPJr3hQHkMHF4SXBxfmMayo4BSPEGmnHnERETkjDW3GCZd9IWgNnvg+kWTmcjr5r/94kD//dTmmZfLlK2fgdrti3SxJAArikhBM0yQQCOqDTc6IPwSmToOJunM9IRbvsvCF2kP2hQXt07elOWH8gCDrDrZ/tfRLMSnNCZ10OyLJJLNPBgtuvCrWzZAEoyAuce/TtRv54WP/l6aWVmZdciE/vP8ubDYd7paTMy34n3IXn9Q4yHA1cu8EgxFZsW5V4mvywy9Xudl1xE7/VJNL84IUZ5pMzDsWtr95np8P94VoCxpMygvSR/vOIiInpTQjce/fnvhN+AIJb7/3MUs/XBXjFkm8+6Tazic17f0MzX6L/15rj3GLzp43CM3+7n+eRh/4Tv54A/sAACAASURBVHNe5V+2O9l1pP13echr40CLERHCARw2mD4oxKwhQbJSTrQVERE5Sj3iEveOv0pZU3NLjFoiieL4ccotCTrD2rIqB7/b5MS0DGYWBbh9TPRfyBePHjhsFned62dy/omHkxz/ez1+WUREzox6xCXu3XztFeGfcwfkMP3CCTFsjSSCC3KD9Es5dqGRfxgW/xcdOV5rgHAIB1ha5WRblGcgCZnw3p5jRw+CpsFzG1yYJxlWf8mgIC5b+512w+KywZqaUESkK9QjLnHvngVzuWDcaOoPH2HSeefQNzMj1k2SONfXDY9MaWNzg4vBA/tS4DxMol13JGgSDuFHtUUx93oD8NinbiobI4ftBELtveS2E3R2l2Sb/GhqG7sO2yjsYzIoUyfCioh0hYK4JIQLxo2KdRMkwfRxwUWFFh6Pk9raWLfmzGW6YfqgAMv3OAEoyQ4xKic6PfveACza6uwQwgH+YWgQxyk63nPTLXLTjw1dMS3Y22SQ6mifpcYwID9DAV1EpDMUxEVE4tTCcwJcmB/CH4JROeYpA3Jn1TQbPPapm8O+yI31dZt86zw/w7M7H/aDZvssKpvq7IAFtHejd9d4dhGRZKMg3glut7tbp8szDIPW1lacTicOR3KVxGazkZqaGutmRFWy1ku1ik/jT3Bxvq7U6u3/z959BzZVrg8c/2Z3712gg1FaNhVBVhkiCHgRURFF3Ljw6nVct+L6eV2o173Ri1tUoIiiTBFUQNuyoUBbSiktbaE7aZr8/ogGQlvoSJqT8nz+oSfJec+TPpzTJ2/e877b4KjRcdyJRmVlVl8VfWIMLWrr94OwveTvto63uTJPx+QkLTH+zW+rI+SqKR3t3JJcCeE8HesMchGj0ejS9nU6HUFBQVRVVVHnaQNZT8Pb25uamhp3h+FUHTVfkivP0ZZc1dfrOfHS3zWwntn9TET4WmlpkyaTBmi8eK+traVG2/whKh01V9Dxzi3JlTIFBwe7OwTRCjJrihBCnEEmda0jyGAbfuKrs3JFrzoifFs3prtfRD297CtnHm9jTJc6omWcuEc5VHSEtb/+SUGhB95Q4SbllVWs+z2D3fvy3B2K8GDSIy6EEGeQKF8r/zeilsPVKsJ9rPjqWt+WVg13DjKSX6HCRwvGettsK1KEe5YtO7K549EXqKk1YtDrmTf3XwzoneTusBTtSOlRbrjnKQ4Xl6BSqbhz9uVMmzTW3WEJDyQ94kIIcYbx1kF8YNuK8L+pVdAlwEqYj5VYf6sU4R7os0XLqam1DcE0mkx8+u0Pp3z9psztLPp+FWVHy9sjPEX6ftUGDheXAGC1Wpn/RbqbIxKeSnrEhRBCiDOYt5fhlNsneufjb/jg8yUARIaH8u7zDxEaHOjS+JTI20t/0nbLbnQW4m/SIy6EEEKcwa67fAqdYyIBiIkKZ/bMqU2+9vPFy+0/Hy4uYdUvm1wenxJNHjeSQf1SAPD18ebft8xyc0TCU0mPuBBCCHEGi44I45PXnuJoeQVBAf5oNE330fn5+lBdc3wmMX8/n/YIUXEMeh0vP3E3pUfL8fP1Rq9zwjgvcUaSHnEhhFA4qxXWH9SwbJ+Ww1WNrD0vFGHV+k0sWLiMvTn57g6lxTQaNaHBgacswgEeuv16Avx8ARg/6hzOHTG4PcJTrJCgACnCRZtIj7gQQijc/7bpWPXXUvfpe608PRYCpBtFUd795Fve/2wxAO99uog3n7mfpK5xbo7K+c7ql8yPn79OYGAQFRXlHW4ecSHam1zKhRBC4dYXHO8zqTar2HzIjcGIRn2/eoP9Z6PJ1KHHTqvVarzk5kQhnEIKcSGEULhgL8cpAUNlBW7FiQh1XNUwPExWORRCnJ4U4kIIoXA39zcS62fBV2dlfEIdg2PdHZE42QO3XUPPbvH4+/rQv1cPcg4UsHLdRneHJYRQOBkjLoQQCtclwMqTI2pPeEQu3UrTKSaS9+c9whdLfuSldz4lY9tuFi5dyQO1tVxywXnuDk8IoVByNRdCCIXYU6bm53wtKqxYrGDQwsQEMyHeslqlp/hlY5bD9vpNWVKICyGaJIW4EEIowMEKFc/9bqDO4jg94ZZiDU8Or0WncVNgokXiO0ezMWObfTuuU7QboxFCKJ0U4kIIoQDZR9UNinCAomo1R2pURPtJr7gnuOnKaVRV17Bj93769erBNdMvcHdIQggFk0JcCCEUoJOfFRVWrDgW4346a4NZU4RyeXsZeOj269wdhhDCQ0ghLoQQCtA12ML1fU2sPqDFagWLFby1cHGSCS+5UguFqamt5fk3/8e+nHzOOasvl02RcfBCtIZc3oUQQiGGxtYzNLbe3WEIcVqPPvs6XyxeDsDGzO0E+PkycewwN0clhOeRQlwIIdxs6V4tfxZpiPCxcnmyCT9929vMr1Dx+U49xnrbzCv9I6XAF44qKqt5+d1Pyc0/xNBB/Vo0nj1r+26H7R179kshLkQrKKoQX7hwIfv378dkMuHn58ewYcNITU0lKyuLJUuW2F9ntVoxm83Mnj2bmJiYBu1UV1ezePFi9u7di4+PD2PHjqVv377257OyslixYgXV1dUkJiYyZcoUfHx82uU9CiHEidYf1PDVblvlvfcoGOvhtoGmNrVptsALGw0cNdrWbHv1qJonR9QS5StjzcVxz73xET/9/DsA23bvIzwkiMnjRjRr30EDerMzO8e+3TeluytCFKLDU1QhPmLECKZMmYJWq6W4uJj58+cTHR1N3759HQrpP//8k7Vr1xId3fi0UN999x0ajYa7776bwsJCPvnkE6KiooiIiKCoqIj09HQuv/xyoqOjWbJkCUuXLuWSSy5pr7cphOggTPWwYLuevWVqEoMsXNnLhL6F0wzmVzgucHygvO0LHlfVYS/CAeqtKgoq1UT5Sq+4s+UcKOC5N/7HsYpKLp40lgsnjALAYrHw2vwv2bA5i67xnbn7xpkEBvidsq1ao5Hn31zA9t376JPcnTtnX4FBr3NZ7Htz8h229+QcaPa+D995I3qtln25+ZyT2odxIwc7OzwhzgiKKsQjIiLsP6tUKlQqFaWlpQ16vTMzM+nXrx8qVcOpvkwmE9u3b+eWW27BYDAQFxdHUlISmZmZjBs3jqysLHr06EF8fDwAY8aM4dVXX8VoNGIwGCgvL6eysrJBm76+vs5/w3/RarUO/3YkGo0Gnc51f0jcoaPmS3LVcl/tVvNzvq3yLqhSE+Cl4vJelha10SdSxff7j8+W0ifCeto8nC5XIVrbapx55bY2fXRWuoeq0enaXuS7iqeeV/9+8r/kHyoC4NnXP6JHYhz9evXg80XL+fTbHwDIOXAIlUrF0/fPOWVbr7z/Od+t+MW+T0hQAHOume6y2M8e2Jv9Bwrs20MG9mnWNUCr1aLX6Zhz7XTMZrPL4nOHjngdFMqmuCteeno6GRkZmM1moqKi6N7d8euuo0ePkpuby5QpUxrdv6SkBLVaTVhYmP2xyMhIcnNzASguLqZz587250JCQtBoNJSUlBATE8PmzZtZs2aNQ5tpaWmMHj3aWW+xScHBwS4/hnAeyZfncFWuSuoqgeOFyBGTgRUFWr7baUQF1FvBW6fipiHepMY2/sd9bDj4+NWxPreO2EA1F/cxoFXbCuhfN2fx6LOvU1NrZM61lzFq2CDumvs82fvzGDV0EE/cOwettvEu+OcmW/g800iN2co/UgwkhnjGikCedF7Vmc32IvxvpccqCQ8P53BJmcPj+YeKCA8PP2V7BUUlDtuHikpPu09r7MrO4d+Pz6O4pIwRgwcSFRHGmOFnc/7Y4S1qx5NyJYRSKa4Qnzx5MhMnTuTAgQPk5OQ06B3JzMykS5cuTV4ATCYTBoPB4TEvLy+MRmOznk9NTSUpKalBm8XFxW16X6ei1WoJDg6mrKysw/UuGAwG+++2o+io+ZJctVxKkIpf845fo4K0Rj7YdPJxrDz+YyWvjDfj3cQVt5sPdEu2/VxWUgFArdHEDXfNpaKyGoD7nnqJgX2S2ZS5HYBPv1lGRGgQM6dNbDK+i7r+9UN9FS68hDmFp55X55zVlw2bbMva+3h70T0+luLiYlL79GTBV0uxWm3j8s8Z2Oe0f0cGD+jFmvWb7Ntn9092yd+e2Xc/Rm7+IQAKDhfz0uN3c1bfpGYfy1Nz1RyefB10xYc24XqKK8QB1Go1cXFxZGVlsXHjRoYMGWJ/LjMzkxEjmr6ZRK/XNziJ/h520pznAwICCAgIcHi+oKCAurq6Nr2n5jCbze1ynPak1Wo73Hv6W0fLl+Sq5UbEgl5VT/ZRNV2DLNSYVUDDnufaehWlVWYifJp/s2Rp2VF7EQ5gsVgpOGaiy9TH0Qd3oirvT3ILtne4nCn9vPp80XLmf5GOl5eBe2+9iqfuvZnPF/3IsYpKzh8zlMjwEOrq6ji7fwovPHIHv/6xhe6JcUwcM/S07+vC8Wn4eBnYtnsffZO7MXb42Q32+fm3P3n+zQXUmc3ccPmFTD2/5d/WFhQ6FtwHDh6irn9Ki9tReq5aoyNfB4UyKbIQ/5vFYqGs7PjXe3l5eVRUVJCS0vQFIzQ0FIvFQklJCaGhoQAUFhbaPymGh4dz+PBh++tLS0sxm8321wrlsVqtjd4PIIQSDI6pZ3CM7SbIomoV3lrrXwX5cQmB9YR5t2zGkrCQIPqldCdz+x4AIsKC6Tz2Jo7qOwEQ0H0Ygf5xzW7PagWV6sR/5bxqqV17c3n5vc8AOFZRyUPPvM7S/73MVZdObvT1Q1L7MCS1D97e3tTU1DTrGOelDeG8tCGNPldZVc0jz72F0WSbVef5NxcwoHcS8Z0bzh72t8byPHrYWSxf8ytg68UfPKB3s2ITQjifYgrxyspK9u/fT48ePdDpdOzbt4+tW7cybdo0+2syMjJITk5uMLTkRHq9nuTkZFatWsU//vEPCgsL2bVrF9ddZ1tyuG/fvrz77rvk5uYSHR3NqlWrTtumcI+ffv6d517/CFOdmetmTGHmtPPdHZIQpxThY+Xhc2r59ZAWndqK2QJ6DYzubEbdwppXpVIxb+6dfLNsFbVGE5PPHc5rO2M4WnH8NT7BkcCpe+/2HlXz+p96jtaq8NJBbR1Qms3ub56ie5connnwNsJDZaxvcxwpdRz3XV1TS1V1jUtnNjlReUWVvQgHW5F9pPRYo4W41WrlhbcWsHj5WoIC/Hjs7psY0Ns27PKhO66jT3I3yo6WM3b4IDrFRLZL/EKIhhRTiKtUKjZt2kR6ejpWq5WgoCAmTJhAz549Aairq2Pbtm1Mn97wDvK1a9eSl5fHzJkzAZg0aRKLFi3iueeew9vbm0mTJtlnZImIiGDy5MksXLiQmpoa+zziQlkqq6p58qV3MdXZxh++/uGXDBnYm24JnU+zpxDuFe1nZWp353y17e1l4PKpE+zbQ2PN5O20zTmuUVkZHH366QjfydRTWmubLaX677BCuhPUdzI7N33Fa/O/ZO5ds50Sb0fXN7kHMZFhFBw+AsDgAb0JCQo4zV7OExURSv9ePcjYZltMp0tsFCk9Ehp97dpf/+Dr71YBcKT0GHNfeJtFH7wAgFajYdrEMU6NrbqmFh9vL6e2KcSZQDGFuK+vL9dcc02Tz+t0Ou6///5Gnxs5cqTDto+PDzNmzGiyrZPnJRfKU1Vday/C/1Z2rNxN0QihDOMTzET5Wig2GugWUEt84OmHu5SbGu+K13jbCsiyo3JeNZe/nw9vP/cQy9f8ireXgfPHDG3X46vVal6ceyffrfwFU52Z80ef02TxW3aswmH76EnbzlJSdow7577Inv15xHeOZt7cO4kKl6GeQjSXcieVFWe0iLBgzjnr+IelrnGd6JPczY0RiY6s3gJHa8HSgmHc5lbs4wz9Iixc0INmFeEAo7s0nNXCYjZRvnstarWKf4wf2cheoikhQQFcNuU8poxPw2Sqo7yi8vQ7NYOpro6SsmP2WVaaYjDomXr+aKb/YxwB/k0vEDT87P6EhQTat6eMT3NKnCd795Nv2bM/D7DNff7Gh1+55DhCdFSK6REX4kQqlYpnHpjDyl82YTSaGDN8EF4yjl+4QFG1iud/N1BcoybCx8I9g4yEnWZ2k31H1by4yUBlnYqEwHruHmTER6FrgFySVEf34HrKatX46qxU1akwVOVTOPUceiROp3fPrqdvRDSwYOEy3vjoK6xWKzOnnc8tV7V+deYtO7O554mXKa+ooldSV1567E58fbzbFF9YSBDvz3uUdb9nEBwUQNqQgW1qrymVVdWn3BZCnJr0iIsWKTtWTn7BYSyWlq0e2BparZbz0oZwwXkj2/xHSYimfLNbR3GN7VJYVK3mm+zTV9Sf7NBRWWcb8rH/mIblOcru00j0Lqeb9iCpEXWM7mJmaHIUF00cLUX4CUqPlpN/qOi0PdIAxSVl9iIcbEV59v7mLw9/shff/pjyiioAtu3ay1fpK6irM5Obf4iq6ubNttKYsJAgLpwwymVFOMBFE8eg/+tmVa1Ww7RJY112LCE6ImX/9RCK8t2KX3j61fnU19czZGBvnn3onx63HLUQJzOedL+j6fT3P2Ksdxx3bapX7jSAq9dvZu4Lb2GqM9MvpQcvPXYnBoPe3WEpyuLla3nu9Y+ot1gYNqgfTz8wB62m6ZVIjUZTg4K91mhq4tWnV1PruG9ZeTlX/+sx9ucdxM/Xm+cevp1+KT1a3b4rDeidxIcvzWVndg7dE7uQ2CXW3SEJ4VGkR1wBikvK2LJjj8O0VErz91RY9fW2KuXXP7ayav1mlx1rb04+eQcLXdK+8GyHKlXkVziv8B2fYEavsRVVBo2V8+JPv1LgpMQ61CrbPgF6K6M6K3d1wXlvL7Df+Jy5fTfLVq13c0TKUl9vYd7bH1P/17d8v2zMZN1vGafcp1NMJOeOONu+PXhAb5K7Nz57SXPMumQS6r/mtwwNDsRcV8/+vIMAVFbV8Or7X2C1Wsnef0CR18W4TtGMH3WOFOEnKCgsZve+POrrXf/tsfBs0p3pZqvWb2Lu829TZzaTGBfL60/fR4Cfr7vDasBqtdqL8L+5Ymljq9XKI8+9yYp1GwG4ctpEbr7qYqcfR3imT3foWJ5j+xr8nBgzs/u1/cNrUoiFp4bXcqBCTecAS7MW3hkSU09n/1qKq9UkBtUToODbF8xmx/O2roMtSd5WVqwNiqXm/I4eu/tG/nFeGvWWelL7JqPRtL5f6/zRQ0nqGkdBYTG9khL5+OvvHZ431dXx0DNvsGr9JsBWuN905bTGmhIK8Mk33/Pa/C+xWq0M6pfCC4/eId8eiyZJj7ibvT7/K/tFf1/uQRb/sNbNETVOrVYze+ZF9u2e3eIZPewspx9ny45sexEO8L+F31FSdszpxxGep6RGZS/CATYUaNl31DmXsDAfKwMiW7b6Zay/lf6R7VeE5+Qf4o8tO6k1Glu03+yZF9l7WzvHRBIVHuq0mT46Aq1Gww2XX2jf7pXUlZHNGFOtUqk4q18ygwf0PuUwluZK7BLL8LP7ExwYwLRJY4gIsy2ypNdpOXfE2fYiHOCjL5dS2sppJ3fs2U/m9t3SU+sidXVm3vjw+P0DGzO388vGTDdHJZRMPqKJZrt86gTOSe3DsYoqUnokoNe1zzQRyh19K0T7WPjdSua99TFWq5XEuFjmv/w42mYu1XnhhFEM7NOTtb/+wbufLOLep14hNDiQN56+T1ZU/MusSyYxfHB/KiqrSOmeiE7n3j+N0RFh/O+/T5Cdc4DoyDAO/bWA0IlUrbgwznv7Y75KXwHAOal9ePah29vUky+EaDs5A93s1qsvsRe0XeM7KX5O34QusfTv1cNlRXif5G6MPWHs5axLJhESHHiKPcSZItTbyoSE4ytWDo01kxjUul69rTv38svGjDbNSNGe3v7f1/Yetn25B1m2Yl2L9u8SG8Xa3/7EVGf7/ZWUHeOL9J84Wl7Bz7/9aZ8H+kyW2CWWfik93F6E/83fz4cBvZOICretpjnmhG8gr750MsGBLVvRs6TsmL0IB9iweQuZ23c7Ld72sDM7h3W/Z1BRqdwpEnU6Lbdecymqvz4pnT2gF8MG9XNzVELJlHHFOYONGprKoj7zqLeqCPb3aVUvR0eiUql44p6buPrSyRh0OumxEw6m96wjrbOZeottaEhrvPfpIt77dBEA8Z2jefvZB/Hz9XFmmE538vjS1ow3PXn4hMlUx6x/PsqR0qOoVCruvfUq/nGesjsCzlQqlYon/n0z1+QdbPV1UaNRo1KpHGZ70XnQuOXPFi3nv+99Bti+MXjn+YcICWrZh5H2ctmU80gbMpCq6hoSusTKtw7ilOR/hwKEhQTRu2c3+1yswraSphThojFRvtZWF+EWi4WPvlxq3y40+fLRugJKatr+CbikRsX6gxr2Omnc+onuvnkm+r96agf26cnkc0e0uI0511xKgL/tRvC4TtH4+/pwpPQoYLtJ+qMv050XsHA6lUrVputiUIA/N8+62N5Te8G4ER61WvGJ/z8PFR1h+Zpf3RjN6UVHhtEtobMU4eK0POfjsBBCtJFarcbLoKfObCYwZSxRaTew0QLb1ll58JxaYvxaV+AXVKp4aoMX1WYVKqzM6mViVJdmTEjeTKOHnkVq32TKK6qIiQxDr9dRU9Oy2U9SeiTy9bvPUVp2jMjwUL5M/8nheW8vL6fFK5Rp5rTzOX/MUEx1dURHhLk7nBbx8jJA+fGbjL29FDxVkRAtIB/VhBBnhLo6M8tWrWfcyMEY9DqC+060P1dtVvH+2oPs2LP/lG1YLFY+WpPDi8v2kn3o+Gw+6/K1VJttPY1WVPyY27Zvt6pralmyfC3LVv5C3V9zgAf4+dIpOgK1unmXbXN9PcvX/MqiH9ZQXmlbtdHH24tOMZHodFoumjia/r1si8T4+/ly100z2xSzJzKa6vhuxS+k//gzNbUtm43GU4UGB3pEEV5TayT9x5/5bsUvmOrquPfWq/Dxtn1YHDKwNxPHDHNzhEI4h/SICyE6PIvFwt1PvMzGjG0AdI2LJbFTGHlVx1/z66+/88Pb6fznwTkMG9S/0Xbu+TKH0oBeAGRsOMbD5xyjW3QgPjrHnnRfXet61sFWHN5y/3/Yvc92A+X3qzbw4mN3NrsA/9tDz7zO2l//BODTb3/gvRcextfH2/68l8HA60/fR+nRcvx9fRRzk2J7qa+3cOfcefy5dRcA33y/mjf+c1+7zQYlmmaqq+PWB55hZ3YOAEtXrOO/T9zDdwtepqq6psU3qgqhZNIjLtiYsY2PvlpKxjb33kF/sLCIBQuX8d2KX7BYZI5b4RwlZUd546Ov7EU4wN7cgwzz20+wl+3/WdWBLMq2fk+9xUL6j03PSFLie3yZcbV3IOkZtmnlxsWbSQm1DUUJ9rJwZUrrFxravnufvQgH2zzEBYXFLWqj9Gi5vQgHyDtYSMa2XY2+NiQo4IwrwgHyDh6yF+Fgm197zz7lzR6Tf8h2XVy28sy5Lu7am2svwgH+3LqLAwWF6HU6KcJFh3PmXX2Fg2Wr1vPEi+8CoFareOq+W0lrxmIWznao6AjX3fUE5RW2Lso/t+7iwduvbfc4RMdSWVXN1XfMJTf/kMPjKpWK7mEazhtYy5OvfMh3P66xPxd8ipkYrMYKVD4h9u0QH9tMJAYN3HO2EVM96Nu4tktwUIDD7BZarQY/v5bN6uLr7YWXQU+t8fgHAilgHAX4+6HRaOwrBqvVKgID/N0claNDh23XxYq/hhZlbN/D/XOudm9Q7SA4wN/hHNBoNIpccVoIZ5Ae8TPcD6s32H+2WKz86KY70X/dvMVehAP8sGbDKV4tRPNk7chuUIQb9Hr+ee10ErrEAnDLlVPpm9wNjUZDv5QezJ45tcn2pieUYKkuxVpfR0j5NmalxTs839YiHCC+UzS3X38Z3l4G/H19eOj26whqYYFoMOiZe9eNBPr7YdDrueGKqaT0SGx7cB5u97483l7wNfM/X8LX361k1Dmp+Hp74eNt4F+zr6BTdIS7Q3SwflOmvQgHx+u1kuXmH+Kdj7/h88U/2u9xaK68g4V8v3oDaUMG4u1lwNfHmwduu1rWkxAdlvSIn+EiQoMdtsPDQpp4pYvjCHOM4+S4hGiNiNBgh541H28DSz58yWHGhZCgAN585gGsVqt9aremTBwYy8SBYLGYUKsTXBb3pReM49ILxrWpjZFDBjByyAAnReT59ubmc+O//w+jyXHY0IDeSbz2f/e6KapTCw91vB6Hhyj/unio6Aiz73mKiirbojt/bt3Jfx64rVn7Hi4u5YZ7nrJ/+Bh+dn+efeifLotVCCWQQrwZDAZDi2+UagmVSkV1dTU6na5VC3W0xV03z6KkrJxtu/cyoHdPbrv2Mry9vU+/YzOp1erTtrd+YyabsnYyZGAfdmTvJyQokCfvvcWpcTiTO/PlSs3JladJ7pHIQ3dcz2sffIHBoOPB268jJDjI3WG1WUfMlavPq02ZOxoU4WAbBldrqnPp0J3W5mv86KHs3JvLt8tWEhIcyJP33qqIvJ8qV1k7su1FOMDPv2Wg0+sbLCjVmKyd2Q7fAKz7PYOaWhOfLVpOdW0tF08aS3znGOe9kUZ0xHNLKFvHqSJcyGh07bRWOp2OoKAgqqqqqKurO/0OTmTQaXnh0TscHqupcd6y397e3qds7/c/t/GvufPsPZYzLhzPbddOd3oczuTOfLnS6XLliXQ6HdddMY1/nDfSnquO8B47aq5ceV6d/K3b3wL9/dCqVS79fbYlX7OvuJDZV1xo31ZC3k+Vq/AQxw+6EWEh1JlMNCejJ+8bHhrEbQ89w/bdtmlF05ev5X+vPE5YiOs+THvyuRUcrPxvTERDMkZcuNWGzVkOSy6v35TlxmjEmcRsNvPhF+k8+fJ7rNmw2d3hCBcbPfQsrpl+AdERYcRGhRMZHkL3hM48+9A/O9Q3W43Zn3eQ4e9ALwAAIABJREFUZ177kHlvfUxxSZlLjzWwT09uu3Y6MZFhJMbFktw9nhfeXMDh4tJT7rfu9wzSf/qZIQN7ExMZRs9u8Tx0x/X2IhzgWEUl23bvc2n8QrS3jn31EYoX1ynaYTv+pG0hXOXFtz/hm+9XA/Ddil944dE7OCe1r3uDEi51wxVTueGKpm/G7YjKjpVzy/3PcKzCtirl7xlbWfDKEy798DHjwvFMGjuMy299iH25BwHY8McWPn71SQz6hvO0b8rczr1PvWLvlJk0djgP3n4t5vp6QoICKD1aDoBGraZzTKTL4hbCHaQQFy6xfc9+Pv32B3y8vLj60slERza+ktuU8WkUFpWwbmMGXWKj+Pcts9o5UnGm2pi53WF7U+YOKcRFh5O9/4C9CAfIO3iYw0dKiY1y/gwxP6zewJoNfxAbFc5Z/VPsBTRAQWExhw4XNzrGe1PWDodvRjf9dW5qNRpeePRfvPTOJ1TXGJk57XwS/5rtSIiOQgpx4XTFJWXc/vDzVFXbxtlt3rKDz15/qtEeGJVKxU2zpnHTrGntHaY4w3VP6EL+oSL7dreEzm6MRgjX6BwbhV6vw2SyjdIODgwgzAU3LK/7PYPH5r1j384vLMbby0BNre0eq0B/P8KbmA2r+0nnXvfELvafk7rG8cZ/7nd6vEIohRTiCmE0mfjve5+yc08OA/okMeviSS6dqcWVsnMO2ItwsPWEHCk7RlR4qBujEsLRvXOuwtvLwIGCw4wYPIDzRw91d0hCOF1UeCjPPvhPPvwyHb1Ox81XXYzBoHf6cbJ27HHY3r03l+cevp33P1uMRqPhpisvwten8dlIxg4/m+KSo6z6ZRMxUeHccf0Mp8fnDuWVVbw+/0sOFZVw7vBBXHDeSHeHJBRICnGFeP71D/nfV98Btq/MvQ0Gpk85z81RtU5il04OPSGR4aGEBsliDEJZAvx8eeiO69wdhhAud/aAXpw9oJdLj5HS3XHBqJSkRAb26cnAPj2btf9lU87jMg/9m9eUJ196j3W/ZwCwMWMbIcGBDBvUz81RCaWRQlwhMrfvdtjekZ3j0uMdK6/kxXc+4eChIkYNTeWKi853SrtLlq9l8Y8/0yOxCzqdluDAAK6bMQWdTv6rCXE6y1at55tlqwgK8OeO62cQExXu7pCEaJZRQ1P59y2zWLPhDzpFR8hwQ2DHnv0O2zuzc6QQFw1IdaQQZ/fvxe9/bLFv90vp7tLjPfXf9+2f1Lft3kdEWAjjRg5uU5t/bNnJ06/Ot28nd0/gnecf8dg5WYVoT1t2ZvPkS+/Zb1orOFzMgleecHNUQjTfhRNGceGEUe4OQzH6pXRn5S+bANv9UK7+uy48kxTiCvGvG6/EYqlnx+79DOyTxNTzR7v0eNk5Bxy39x9ocyF+cpt7T9oWoj19tmg56T/9THRkOHffOJPI8JDT7+RG+3IPOswcsT+vgPp6CxqNZ94rohTm+npeff9zfs/YRmKXWP5961UE+Pm6OyxxBnjw9uuIjgjjUNERxgw/m7P6pbg7JKFAUogrhEaj4Zrp/2i3lRoH9e/FkuVrAdsn9UH9236BGNA7CZ1WS53ZDCAXHeE2v/6xlf++9xlgK3CLj5Qx/6VH3RzVqfVL6e4wu8XAPj2lCHeCLxb/yBdLfgIg58AhdDodj955g5ujEmcCby8Dt15zqbvDEAonhbjC1NWZefb1j/g9Yxtd42J5+F/XExwY4PTj3H3jTGIjwzlYWMTIIQPaVDSb6+uZ99bH/LIxk65xsXSJjSI6KpxZF09yYsRCNF9u/qGTtgvcFEnzxXeO4ZUn7yH9p3UEB/hz5cUT3R2SRyuvqOTJl9+3z0n9t7yDhW6KSAghGpJCXGE++eZ7lq5YB9jm437x7U94/J6bnH4cnU7LrEucUygvTF/Bt3+tUFhcUkZYaDA3zrzIKW0L0RqD+qdg0OsxmkwADD+7v5sjap4+PbvRp2c3d4fRIbzy/hf2+2BOJDfLCSGURApxhSkoOuK4fbjYTZE0Lb/gMI88/yb5h4oYOXgg3t4Gh+eVGLM4syR2ieX1p+9lxbqNxHWOZfLYYe4OSdHe+3QRX6b/hL+vLw/efi39e/Vwd0htdvJ1qEtsFFdMnSBzOQshFEUGICrM6HNSUatV9u0xwwa5MZrGPf3qfHZm51JZVcN3K38BQHPC4kNKjFmceZK7J/Cv2Vdw46xL0Ot17g7ntE68UbM9bc7awXufLqK8ooqDhUU88J/X3BKHs40Zfvw6pFKpuHnWNCnChRCKIz3iCjMktQ//feIeNmftoGtcJ4c/JkpRXFLmsP310pUkxsVy9oDeJHWN47y0IW6KTAjPY6qrY+7zb/Pzb38SExXOfx6YQ0KX2HY7fnHJUYftY+WVmOrq0OuU/+HlVKZNHENYcCC79uYyoHcSg/q7dkEbIYRoDekRV6CBfXpywxVTFVmEAw2WArcCe3MPUms0SREuBFBrNGKxWJr12oVLV7J6w2bqLRYOFBzmmdc+dHF0js7un0JYyPGVb8cMH3TaIry6ptbVYTlF2jmpzJ55kRThQgjFkh5x0WLXXPYPEuM78eZHX5Gbf3wGgrJj5W6MSgj3s1gsPPnye3y/agO+Pt48cc9NDEntc8p9jh6rcNguO2nb1UKCA3n3+YdZsW4jAf6+TBg1tMnXlh0r567HXmRndi6dYyKZN/dfxEZFtGO0QgjRsUiP+BmkorKa8opKp7SVNmQgN8262D42XKPRcMG4EU5pWwhPtXrDH3y/agMAVdU1zJ33DpVV1afc57xRQ/Dx9rJvu2NlwoiwEGZcOJ5JY4efcu7y+V+kszM7F4ADBYd57YMv2ytEIYTokKRH/AyxYOEy3vjoK6xWK1dOm8jNV13c5jbThgzk7eceZGd2Dik9EknqGueESIXwXCcX3eUVlUy44jZuu2Y606ec1+g+XeM6Mf/FR9mUtYNO0RGKXgirstLx/VWc5kOGEEKIU1NUIb5w4UL279+PyWTCz8+PYcOGkZqaCoDJZGL58uVs27YNi8VCZGQk1157baPtPPXUUw7bZrOZQYMGMXHiRMrKynj55ZfRnTAGcvjw4aSlpbnujbVSfb2FgsPFBPr7EuDv1+p9jaY6exEO8L+F33Fe2hC6xndqc4zJ3RNI7p7Q5naEaK7ikjLM9fVER4S5O5QGRg4ZwEdfplNw+Pg0pBaLlVc++JzzRg1pcnGuTjGRdIqJbK8wW+3C80exav0mao0mNBoNl0w+t91jMNfXU1BYTFCgvyxVL4TweIoqxEeMGMGUKVPQarUUFxczf/58oqOjiYmJYcmSJVgsFubMmYO3tzeFhU2vjvbggw/afzaZTDz33HOkpDj2Mt13331oNBqXvZe2qqk1cscjL7BlZzZ6vY7H7r6RtCEDm7VvrdHIvx6dR+b2Peh1WuZcO73B1Gi1RqMrwhbCpd795Fve/2wxAFMnjOKeW2a5OSJHQQH+vD/vEb5IX8H7ny6yP26xWDH+tXS9J+vTsxsfvfwY2/fsp2tcJ6d8mG+J6ppabn/kBbbt2otBr+fxe25kxOAB7RqDEEI4k6IK8YiI4zf9qFQqVCoVpaWl6PV6du3axZ133omXl20sZUxMTLPa3L59O76+vsTFNW/YRHl5OZWVjuOoTSYTvr6u63nRarUO/wJ88/1qtuzM/uv4dbz49iecO2Jws9pbvHwtmdv32PatM/PRl0sZP+ocflhtG7s69Ky+9EnuccqxoM6i0Wgcvn3oCBrLV0eg9FwdLi61F+FgO0cumjSWnt3im9ynPXNVW2tkX95BIsJCuOGKqWzZkc3GjG0ATDp3OJ1jopx2LHfmKiGuEwlxzi/Am5OrZd+tZNuuvQAYTSZeeudTxgw/2+mxOJvSz62W6qjXQOh4uRLKp7izKD09nYyMDMxmM1FRUXTv3p0dO3YQFBTE6tWryczMxN/fn1GjRjXo5W5MRkYG/fr1Q6VSOTz+0ksvAdC1a1fGjRtnL7Q3b97MmjVrHF6blpbG6NGjnfQOmxYcHGz/2cvL2+E5i9VKeHh4s9ppsK/FypvPPcKGzVnUm+sZOqifor8N8BQn5ku4Xq2pvsFjfv7+zTovXJ2roiOlXPnPR8g5UICXwcAbzz7Ep28+y/pNGeh1Ooak9nXp8TuSU+WqLddF4XxyDRSi7RRXiE+ePJmJEydy4MABcnJy0Gq1lJeXU1RURHJyMnfddRf5+fl8/PHHhIeHn/IifPToUXJzc5kyZYr9MR8fH2644QaioqKoqalh6dKlfP3111x55ZUApKamkpSU5NCOyWSiuNh1y7ZrtVqCg4MpKyvDbDYDMGrIABZ0jmH/gQLUahU3XzmN4uJitu7MxmK10qdntwYfLv42cnA/EuNi2Zd7ELVaxU2zpnHkyBG6x9m+RSgtLXXZezmZwWDA2MGGwTSWr45Aybmqt0BuhZbzL7yEZd/aZuoYPewsYiNCTnlutleu3vzwS3IOFAC2YV//9/I7fPLaUyR37QLg9OuHknPVWs3J1ahzBvDxwhhyDhSgUau58cqLXHptdpaOlq+Oeg0Ez86VfCj1TIorxAHUajVxcXFkZWWxceNGdDodarWakSNHotFoiI+PJyEhgb17957yP15mZiZdunRx+NRuMBiIjbWtWufn58fEiRN54YUXqK2txcvLi4CAAAICHG+oKigooK7O9eM7zWaz/TjeXnrefeFhdmbnEB4SRKeYSB597k37kvKjh57Fk/fe3Ggx7mXQ8+7zD7EzO5fQ4EA6x0S2S/yN0Wq1bju2q52Yr45Aqbmqt8C8TQa2l2ggehoz7x/FyOACeiUlNrsIcHWuGizeY8Wlx1NqrpzhVLny9fbivZOui57we+io+epo10DouLkSyqXoecQtFgtlZWVERrZuNoHMzEz69et3ytc01ausBN5eBgb0TqJTTCR5BwvtRTjAqvWb2L0vr8l9vQwG+vfqQWR4CL9szOSPLTvbI2QhnG5HidpWhP9l49FQ4hO7KurcvXjSWLrE2q5TXgY9t1zd9ulBReNOvC4KIYSnU0yPeGVlJfv376dHjx7odDr27dvH1q1bmTZtGnFxcQQGBrJu3TqGDx/OwYMHycnJYdy4cU22l5eXR0VFBb16OS5tnJ+fj5eXFyEhIdTW1rJs2TLi4+PtN4Eqla6Rm2K02lOP8zbV1XHrA8/ab25S4iwTQpyO9qTuAhVW1MqpwQHb6pQfvvQYOfmHCA8NJiSo8WkKhRBCiBMpphBXqVRs2rSJ9PR0rFYrQUFBTJgwgZ49ewIwY8YMFi9ezLp16wgMDGTq1Kn2YSlr164lLy+PmTNn2tvLzMwkOTkZg8HgcJyysjJWrFhBVVUVBoOBxMREpk2b1n5vtBWqqmvYumsvY4cPYsW6jQBMn3IeXU8zc8GmzB32Ihxss0zMnnkRgQEtm5NcCHdKCrEwOMrIb4UGVFi5NMmEjwInNTAY9LKolRBCiBZRTCHu6+vLNddc0+TzERERXH/99Y0+N3LkyAaPXXDBBY2+tk+fPvTp06d1QbpBdU0tN977f+zLPQjYxobffv1lRISFnHZfby/HDyG2aZkUk3IhmsVoMrJh/tPsL67GWm9iw6BkJtx9o7vDEkIIIdpM0WPEBWzO2mEvwsE2Nty7mcNoBvROYur5tmkXNRoN99x8Jd5eBlau28i336+mtOyYS2IWwpn+3LqLPfvzMFceob6mnB/X/kbp0XJ3hyWEEEK0mXSPKpyfr4/Dtl6vQ69v/vfy99x8JTfOnIpOp8Pby8DTr3zAkh9/BmD+F0t4f96jMp5VKNrJ54BOq8XLoHdTNEIIIYTzSI+4wg3oncTlUyegVqvwMuh5+I7rMLSgEAcI8PfD28uA2Wxm6Yp19seLjpTx6+Ytzg5ZCKc5UHCYzG17GDaoP2q1CoNez4O3X4uPt7Jvrj4T7Ms7yP8WfsdPP//e5raqqmv4bNEPfPDZt1RW1TghOiGE8AzSI+4B5lxzKbNnTkWr0aBWt/6zk1arJcDPl6PllfbHpDdcKFV+wWGuu+txe2E25byR3H3zLDQa6T9wt705+dxwz5PUGk0A7Nqby61XX9KqtmyzOz1jn47108Q43n7uAfSyzLgQ4gwgf9E8hP6vRY3a6ol/30xYSBB6vY4ZF45nSKrn3LgqzizrNmY69I7++PNvUoQrxOoNm+1FOMAPqze0uq39eQUOayLs3pfL/ryCNsUnhBCeQnrEzzCpfZNZPH+eu8MQ4rROnhkoPPT0MwWJ9hF5Um5O3m6JkKAAtFoNZnM9YFsfQb6pE0KcKaR7SQihSGOGncXlUycQ6O9H17hOPH6PcqYstFgsfJW+gnlvfcyvf2x1dzjtbuLYYUw9fzQB/r4kdY3joTuua/Aas9nMJ998z7y3PyZz++4m2woPDebhO64nPDSYyPBQHrvrRsJDg10ZvhBCKIbKarVa3R2E0hUUuPZrUp1OR3h4OMXFxdTV1bn0WO3N29ubmpqOdfNVR82X5Kr5Xnn/cz799gfAthjZvLn/YvCA3k5r/3Q8IVdPvPguy1atB2y93G8+8wAp3ROafH1HPa/AM/LVEpIrZYqJiXF3CKIVpEdcCCFaaP2mLPvPVqv1jOwVP50Tf0dmcz0b/9zmxmiEEEKZpBAXQogWiu8U7bAdFxvlpkiUK75z9Cm3hRBCyM2aQgjRYvfOuQq1Wk3ewUKGDerHlPFp7g5JcR67+0aeff0jio6UMi5tCGnnpLapvYXfrWRTxna6xnfi6ksno9XKny8hhOeTK5kQQrRQUIA/T913i7vDULSIsBCef+QOp7S1ePlaXnhzAQBrfv2Dmlojt1073SltCyGEO0khLoQQQlHKK6t47YMvKCwqYdzIwWTt2OPwfNb2PU3sKYQQnkUKcSGEEIry+Lx37Dd7bszczkUTRzs8n5KU6I6whBDC6aQQF0IIoSg79uQ4bAcHBjDnmkvZmLGdbgmdueGKC90TmBBCOJkU4kIIIRSlX6/urF6/GbDN096/Vw9S+yZz+dQJbo5MCCGcSwpxIYQQrbZk+Vo+X/Ij/r4+3HXjTLoldG51W9t37+P+J1/iWEUFQwb2xtfHm7HDzya1b7ITIxZCCOWQlTWboaSkBLXadVOuq1Qq9Ho9JpOJjpYOtVqNxWJxdxhO1VHzJbnyHErJ1bZde7nytoftv9uo8FCWffJqq9ubcPmtHC4uBWy5W/Dqk6T08Pzx4ErJl7N01PMKPDtXwcHB7g5BtIL0iDeD0Wh0afs6nY6goCCqqqpkuWAP0FHzJbnyHErJ1Z59uQ6FWGFxCaVlR/H2MrS4LaPJbC/CwbZiafb+PBI6wEJASsmXs3TU8wo8O1dSiHsmWVlTCCFEq/Tr1QN/Xx/79sA+PVtVhAP4+XozeGAf+7a/ny/9Urq3OUYhhFAy6REXQgjRKlHhobz5zP2k/7QOP18fLptyXpvae/+lx3nl3QWUV1Qx+dzhRISFOClSIYRQJinEhRBCtFpCl1inrXLp6+PNdTMubDDcwWq1olKpnHIMIYRQEhmaIoQQQpEKi0u46va5jJh6PXMefJbKqmp3hySEEE4lhbgQQtGMZrB0rIkZRDP9973P2LM/D4vFyh9bdjL/i3R3hySEEE4lQ1OEEIpUb4G3MvVsLNTiq7NyywAjKaGeOa2YaJ2jxypOuS2EEJ5OesSFEIr06yENGwttfQVVdSrez9K7OSLR3qaMT7OPDdfrtEw6d7ibIxJCCOeSHnEhhCJV1znenFdtlpv1zjTjR51DbFQE2TkH6JPcja5xndwdkhBCOJUU4kIIRTo72sz3+7WU1tq+uBsf37EWDhHN07tnV3r37OruMIQQwiWkEBdCKFKgAeYOq2X7EQ1BXlaSQmR8uBBCiI5FCnEhhOJU19SSk3+IqPBQBscEuDsc0UL5BYepqqmlW3xnNBq5FUkIIZoihbgQQlEOF5dy8/1PU1hUgreXgWce/Cdn9Ut2d1iimT76cilv/m8hAIMH9Oa5R25Hq9G4OSohhFAm6aoQQijKZ4uXU1hUAkBNrZG3Fnzt5ohEc1itVjZn7uCtBQvtj/3251Z+3bzFjVEJIYSySY+4EEJRTp4bRS1Lmyue1WrlkefeZMW6jQ2ek6XphRCiadIjLoRQlBkXjicmKhwAH28vbpo1zc0RidPJzjnQaBE+9Ky+DBnYxw0RCSGEZ5AecSGEooSHBrPglcfJO3iYyLAQAgP83B2SOA2dtuGfkucfuYMhA3ujVkt/jxBCNEWukEIIxfEyGOiR2EWKcA8R3zmGy6dOsG/PumQSQ8/qK0W4EEKchvSICyGEaLM511zK9H+MA2zfagghhDg9KcSFEEI4hRTgQgjRMooqxBcuXMj+/fsxmUz4+fkxbNgwUlNTATCZTCxfvpxt27ZhsViIjIzk2muvbbSdDz74gPz8fPvXogEBAdx2223257OyslixYgXV1dUkJiYyZcoUfHx8XP8GhRBCCCGE+IuiCvERI0YwZcoUtFotxcXFzJ8/n+joaGJiYliyZAkWi4U5c+bg7e1NYWHhKduaOHGivYg/UVFREenp6Vx++eVER0ezZMkSli5dyiWXXOKqtyWE6MA2Z+1g2659pPRIlIWHhBBCtIiiCvGIiAj7zyqVCpVKRWlpKXq9nl27dnHnnXfi5eUFQExMTKuOkZWVRY8ePYiPjwdgzJgxvPrqqxiNRgwGQ5vfgxDizLFi3e888txbWK1WVCoVc++azbiRg90dlhBCCA+hqEIcID09nYyMDMxmM1FRUXTv3p0dO3YQFBTE6tWryczMxN/fn1GjRpGSktJkOytWrOCnn34iLCyMMWPGkJCQAEBxcTGdO3e2vy4kJASNRkNJSQkxMTGUl5dTWVnp0JbJZMLX19c1bxjQ/jX1l7aRKcA8nUajQafTuTsMp+qo+ZJctdyPa3/HarUCtkVtflz7GxPHDnfJsU4kufIsHS1fkishnEdxZ9HkyZOZOHEiBw4cICcnB61WS3l5OUVFRSQnJ3PXXXeRn5/Pxx9/THh4OOHh4Q3aGDduHOHh4Wg0GrZu3cqnn37KTTfdREhICCaTqUHPt5eXF0ajEYDNmzezZs0ah+fT0tIYPXq06970X4KD5UYnTyL58hyuylV851jW/vrH8e0unRq9Jonmk/PKc0iuhGg7xRXiAGq1mri4OLKysti4cSM6nQ61Ws3IkSPRaDTEx8eTkJDA3r17G/2j16lTJ/vP/fv3Z8uWLezZs4fBgwej1+vtRfffThyWkpqaSlJSksPzJpOJ4uJiF7xTG61WS3BwMGVlZZjNZpcdxx0MBkOD37en66j5kly13NWXTmJf7gGytu+hT3I3rp0+2aXXir9JrjxLR8uX5EqZpBPAMymyEP+bxWKhrKysQWHcUiqVyv71cXh4OIcPH7Y/V1paitlsJjQ0FLDNsBIQEOCwf0FBAXV1dW2KoTnMZnO7HKc9abXaDvee/tbR8iW5ajkvg55nH/qnw2Pt8TuUXHmWjpovyZUQbaeYZc8qKyvZsmULRqMRi8VCdnY2W7duJSEhgbi4OAIDA1m3bh319fXk5eWRk5ND165dG7RTU1NDdnY2dXV11NfXk5WVRW5uLt26dQOgb9++7Nq1i9zcXEwmE6tWrSI5OVlu1BRCCCGEEO1KMT3iKpWKTZs2kZ6ejtVqJSgoiAkTJtCzZ08AZsyYweLFi1m3bh2BgYFMnTrV/jXM2rVrycvLY+bMmVgsFlauXMmRI0dQqVSEhYVx2WWXERYWBthmZpk8eTILFy6kpqbGPo+4EEIIIYQQ7Ull/XvMhmhSQUGBS9vX6XSEh4dTXFzc4b4S8/b2pqamxt1hOFVHzZfkynNIrjxLR8uX5EqZWjuts3AvxQxNEUIIIYQQ4kwihbgQQgghhBBuIIW4EEIIIYQQbiCFuBBCCCGEEG4ghbgQQgghhBBuIIW4EEIIIYQQbiCFuBBCCCGEEG6gmAV9hBBntorKap546V12ZufQr1cPHvznNXjJirdCCCE6MOkRF0Iowmvzv2Dd7xkcKT3Kip9/571PF7s7JCGEEMKlpBAXQijCocNHHLYLi4408UohhBCiY5BCXAihCGOGD7L/rFKpGD30LDdGI4QQQriejBEXQijClPFphAQF2MaIp/Tg7AG93B2SEEII4VJSiAshFGPE4AGMGDzA3WEIIYQQ7UKGpgghhBBCCOEGUogLIYQQQgjhBlKICyGEEEII4QZSiAshhBBCCOEGUogLIYQQQgjhBiqr1Wp1dxBKV1JSglrtus8sKpUKvV6PyWSio6VDrVZjsVjcHYZTddR8Sa48h+TKs3S0fEmulCk4ONjdIYhWkOkLm8FoNLq0fZ1OR1BQEFVVVdTV1bn0WO3N29ubmpoad4fhVB01X5IrzyG58iwdLV+SK2WSQtwzydAUIYTb5RccZlPmdiqrqt0dihBCCNFupEdcCOFWy9f8yhMvvUd9fT1REaG89cwDhIdKz44QQoiOT3rEhRAuUVxSxpoNm8k5UHDK17336SLq6+sBKCwqYfHyte0RnhBCCOF20iMuhHC6vbn53HLff6ioqkaj0fDEPTcxamhqo6/Vah0vQzqdXJaEEEKcGaRHXAjhVLv35fHSO59S8dd47/r6ej7+5vsmX/+vG2bg4+0FQM9u8UybOKZd4hRCCCHcTbqehBBOs2bDZh565g3qT5r+y8fL0OQ+Z/VLYdEHL3C0vIKo8DA0GukfEEIIcWaQQlwI4TRff7eqQREeFhLEP6+77JT7+fp44+vj7crQhBBCCMWRQlwI4TT+fj4O2xeMG8E9t8xCq9G4KSIhhBBCueQ7YCGE09x69aUkdI4BIKVHAjdfdbEU4UIIIUQTpEdcCOE00ZFhfPzakxhNdRj0OneHI4QQQiia9IgLIZxOinAhhBDi9KQQF0IIIYQQwg2kEBdCCCGEEMINpBAXQgghhBDCDaQQF0IIIYT/SuqiAAAMoklEQVQQwg2kEBdCCCGEEMINpBAXQgghhBDCDaQQF0IIIYQQwg2kEBdCCCGEEMINFLWy5sKFC9m/fz8mkwk/Pz+GDRtGamoqACaTieXLl7Nt2zYsFguRkZFce+21Ddowm80sXbqUffv2UVNTQ0hICGPHjqV79+4AlJWV8fLLL6PTHV9wZPjw4aSlpbXPmxRCCCGEEAKFFeIjRoxgypQpaLVaiouLmT9/PtHR0cTExLBkyRIsFgtz5szB29ubwsLCRtuwWCwEBARw9dVXExgYyJ49e/jyyy+5+eabCQ4Otr/uvvvuQ6PRtNdbE0IIIYQQwoGiCvGIiAj7zyqVCpVKRWlpKXq9nl27dnHnnXfi5eUFQExMTKNt6PV6Ro8ebd9OSkoiKCiIQ4cOORTiTSkvL6eystLhMZPJhK+vb2veUrNotVqHfzsSjUbj8O1DR9BR8yW58hySK8/S0fIluRLCeRR3FqWnp5ORkYHZbCYqKoru3buzY8cOgoKCWL16NZmZmfj7+zNq1ChSUlJO215lZSUlJSWEh4c7PP7SSy8B0LVrV8aNG2cvtDdv3syaNWscXpuWluZQ3LtKcz4oCOWQfHkOyZXnkFx5DsmVEG2nslqtVncHcTKLxcKBAwfIyclh+PDh/PLLL6xcuZK0tDRGjBhBfn4+H3/8MbNnz25QYJ+ovr6eBQsWEBISwgUXXACA0WjkyJEjREVFUVNTw9KlSzGZTFx55ZWA+3rEg4ODKSsrw2w2u+w47mAwGDAaje4Ow6k6ar4kV55DcuVZOlq+JFfKdKp6SCiX4nrEAdRqNXFxcWRlZbFx40Z0Oh1qtZqRI0ei0WiIj48nISGBvXv3Nvkfz2Kx8PXXX6PRaJg4caL9cYPBQGxsLAB+fn5MnDiRF154gdraWry8vAgICCAgIMChrYKCAurq6lz3hv9iNpvb5TjtSavVdrj39LeOli/JleeQXHmWjpovyZUQbafo6QstFgtlZWVERka2aD+r1crixYupqqpi+vTpp7wpU6VStTVMIYQQQgghWkwxhXhlZSVbtmzBaDRisVjIzs5m69atJCQkEBcXR2BgIOvWraO+vp68vDxycnLo2rVro22lp6dTXFzMjBkzGtx0kZ+fz5EjR7BYLFRXV7Ns2TLi4+PtN4EKIYQQQgjRHhQzNEWlUrFp0ybS09OxWq0EBQUxYcIEevbsCcCMGTNYvHgx69atIzAwkKlTp9qHpaxdu5a8vDxmzpzJ0aNH2bx5MxqNhueff97e/gUXXEDfvn0pKytjxYoVVFVVYTAYSExMZNq0aW55z0IIIYQQ4sylyJs1zzTl5eVs3ryZ1NTUBuPThfJIvjyH5MpzSK48h+RKCOdRzNCUM1llZSVr1qxpMFuLUCbJl+eQXHkOyZXnkFwJ4TxSiAshhBBCCOEGUogLIYQQQgjhBlKICyGEEEII4QaauXPnznV3EGc6q9WKXq8nPj4eg8Hg7nDEaUi+PIfkynNIrjyH5EoI55FZU4QQQgghhHADxcwjfqaqrq5m8eLF7N27Fx8fH8aOHUvfvn3dHdYZ64MPPiA/Px+12jZqKyAggNtuuw2ArKwsVqxYQXV1NYmJiUyZMgUfHx9A8tgefvvtNzIyMigqKqJ3795MnTrV/ty+fftYunQpx44do1OnTlx44YUEBQUBtmW409PT2b59OzqdjmHDhjF06NBm7Stap6lclZWV8fLLLzsstDZ8+HDS0tIAyZU7mM1mli5dyr59+6ipqSEkJISxY8fSvXt3QM4tIVxNhqa42aJFi1CpVFxzzTV07tyZr7/+mqSkJHx9fd0d2hkpIyOD4cOHM336dEaOHMngwYMBKCoq4rPPPuPSSy9l/PjxZGdns3v3bnr16gVIHttDeXm5/atwi8VCcnIyAFVVVbz//vtMmDCBKVOmUFJSwvr160lNTQVg5cqVFBYWcsMNN5CSksKSJUuIiIggNDT0tPuK1mkqV7W1tfz22288+OCDpKWlMXLkSOLj4+37Sa7an9lspqioiAkTJnDuuecSEBDAV199Re/evbFYLHJuCeFicrOmG5lMJrZv387o0aMxGAzExcWRlJREZmamu0MTJ8nKyqJHjx724mLMmDHs2LEDo9EoeWwnKSkpJCcn4/3/7d3fS1P/A8fx1zxrczHLX4dJpkgLZAQqRRd1kRruIkkIoqLCi10E0T8QQTdBNwWB/0BXI5Qo6YcVlDLsLoJ+UZnVLKUonZOizbXNuc/Fl0bWJ/1+Sn374fN8XG3n7H14zxfv+eJwdubxzNk+NDQk27a1adMmrVq1Si0tLRofH1csFpMkPX78WDt27JDH45Ft29qyZYsePXr0f43F7/lVVgshq+XncrnU2tqqsrIyFRUVqb6+XqWlpfrw4QNrC1gGFHGD4vG4ioqKVFlZWdjm8/n4oDJsYGBAZ86c0fnz5/XmzRtJUiwWU1VVVeE15eXlsixL8XicHA37MRuXy6WysjLFYjGlUil9+fJlzv7vs5lvLJZOV1eXzp07pytXriiZTEoSWa0QiURC8Xhctm2ztoBlwDXiBmUymZ++cV5cXKx0Om1oRggGg7JtW5Zl6enTp+ru7tbRo0fnzaqoqIgcDcpkMoVr9b/59vfPZDKSNCef77OZbywW3+rVq3XkyBFVVVUplUrpxo0b6u3tVWdnJ1mtALlcTpcvX1ZTU5Ns22ZtAcuAM+IGuVyunz6U0uk0t4MyaP369XK73XI6nWpqalJNTY1evXo1b1bkaNZC2Xx7/uO+hcZi8bndblVXV8uyLHm9XrW3tysajerr169kZdjs7Kx6e3tlWZba29slsbaA5UARN6iiokKzs7OKx+OFbR8/fpRt2wZnhe85HA7l83nZtq3x8fHC9qmpKc3MzKiiooIcDfsxm0wmo6mpKdm2LY/HI6/XO2f/99nMNxZLz+FwFB6TlTn5fF7Xrl1TMpnUgQMHZFmWJNYWsBwo4ga5XC4FAgFFIhFlMhmNjY1peHhYjY2Npqf2n5RKpfT69Wtls1nlcjk9efJEo6Oj2rhxoxoaGjQ8PKzR0VFlMhlFIhEFAoHCmSFyXHq5XE7ZbFb5fF75fL6QUyAQ0MTEhJ4/f65sNqvBwUH5fL7CP/zGxkbdvXtXqVRKsVhMDx48UFNTkyQtOBa/51dZvXv3TpOTk5qdndX09LRu3bqluro6FRcXSyIrU/r6+hSLxXTw4ME5t5ZkbQFLjx/0MWx6elpXr17VyMiIPB6P2trauP+0IclkUhcuXNDk5KQcDocqKyu1c+dO+f1+Sf+7c0p/f79SqdTf3kecHJdWJBLR4ODgnG3Nzc1qbW1VNBrVzZs39fnzZ1VXV2vPnj0qKyuTtPC9jucbi9/zq6wqKys1MDCgZDIpt9utDRs2KBgMqqSkRBJZmfDp0yd1dXXJsqzC7ydIUkdHhxoaGlhbwBKjiAMAAAAGcGkKAAAAYABFHAAAADCAIg4AAAAYQBEHAAAADKCIAwAAAAZQxAEAAAADKOIAAACAARRxAAAAwACKOAAAAGAARRwAAAAwgCIOAAAAGEARBwAAAAygiAMAAAAGUMQBAAAAAyjiAAAAgAEUcQAAAMAAijgArGBjY2Pyer3K5XKmpwIAWGQUcQBYYerq6tTf3y9Jqq2tVSKRkGVZhmcFAFhsFHEAAADAAIo4AKwgnZ2dGhsbU0dHh7xer86ePSuHw6GZmRlJUktLi06ePKnt27fL6/Wqo6ND8Xhchw8f1po1a7R161a9ffu2cLwXL14oGAyqvLxc9fX1unjxoqF3BgD4EUUcAFaQcDis2tpaXb9+XYlEQvv37//pNT09PQqHw3r//r2i0ai2bdumUCikqakpBQIBnTp1SpKUTCYVDAZ16NAhTUxMqLu7W8eOHdOzZ8+W+20BAP4GRRwA/mVCoZD8fr/Wrl2rXbt2ye/3q62tTU6nU/v27dPDhw8lSX19faqrq1MoFJLT6dTmzZu1d+9eXbp0yfA7AABIktP0BAAA/4zP5ys89ng8Pz1PJBKSpNHRUd27d0+lpaWF/TMzM+rs7Fy+yQIAfokiDgArjMPhWJTj1NTUqLm5WXfu3FmU4wEAFheXpgDACuPz+TQyMvLHx9m9e7devnypcDisbDarbDar+/fva2hoaBFmCQD4UxRxAFhhTpw4odOnT6u0tPSPrucuKSnR7du31dPTo3Xr1qmqqkrHjx9XOp1exNkCAH6XI5/P501PAgAAAPiv4Yw4AAAAYABFHAAAADCAIg4AAAAYQBEHAAAADKCIAwAAAAZQxAEAAAADKOIAAACAARRxAAAAwACKOAAAAGDAX8G07YNqOKLeAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from pydataset import data\n", - "\n", - "df = data('beav1')\n", - "df['beaver'] = 1\n", - "df = pd.concat([df, data('beav2')], sort=True)\n", - "df.loc[df['beaver'].isnull(), 'beaver'] = 2\n", - "df.drop('day', 1, inplace=True)\n", - "\n", - "%matplotlib inline\n", - "from ggplot import *\n", - "ggplot(aes(x='time', y='temp', color='beaver'), data=df) + geom_point()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the data reveals some conspicuous patterns. Beavers tend to be warmer when there is activity nearby, which tends to be in the afternoon and evening hours:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ggplot(aes(x='time', y='temp', color='activ'), data=df) + geom_point()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It also appears that all else being equal, Beaver 2 is warmer than Beaver 1.\n", - "\n", - "Notice that some trends seem to apply to a subset of the data. For example, if the measurement is from Beaver 2 *and* the beaver was active, then the body temperature is likely to be above 37.5; otherwise, the temperature is likely below 37.5. When trends apply to observation subsets that can be clearly defined using thresholding, decision tree models are good candidates. We therefore implement a decision tree model in the next section of the tutorial.\n", - "\n", - "### Transferring data to and from Azure ML Studio\n", - "\n", - "In this section, you create a predictive model entirely in Azure Notebooks. (You can also use the `azureml` Python program to transfer the data over to Azure ML Studio to create your model there.)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# This code is repeated from the Prerequisites section\n", - "\n", - "from azureml import Workspace\n", - "\n", - "# Replace the values with those from your own Azure ML Studio instance; see Prerequisites\n", - "# The workspace_id is a string of hexadecimal characters; the toke is a long string of random characters.\n", - "workspace_id = 'your_workspace_id'\n", - "authorization_token = 'your_auth_token'\n", - "\n", - "ws = Workspace(workspace_id, authorization_token)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml import DataTypeIds\n", - "\n", - "dataset = ws.datasets.add_from_dataframe(\n", - " dataframe=df,\n", - " data_type_id=DataTypeIds.GenericCSV,\n", - " name='Beaver Body Temperature Data',\n", - " description='From Reynolds 1994 via pydataset'\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After running the code above, you can see the dataset listed in the **Datasets** section of the workspace (you may need to refresh the page):\n", - "\n", - "![Dataset shown in Azure ML Studio](https://github.com/Microsoft/AzureNotebooks/blob/master/Samples/images/azure-ml-studio-dataset.png?raw=true)
\n", - "\n", - "It is also straightforward to list the datasets available in the workspace and transfer datasets from the workspace to the notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beaver Body Temperature Data\n" - ] - } - ], - "source": [ - "print('\\n'.join([i.name for i in ws.datasets if not i.is_example])) # only list user-created datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beaver Body Temperature Data\n", - "From Reynolds 1994 via pydataset\n", - "abe2e90f1544425999981e5e637982ea\n", - "GenericCSV\n", - "2018-11-27 17:18:36.802000\n", - "3579\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " activ beaver temp time\n", - "0 0 1.0 36.33 840\n", - "1 0 1.0 36.34 850\n", - "2 0 1.0 36.35 900\n", - "3 0 1.0 36.42 910\n", - "4 0 1.0 36.55 920" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read some more of the metadata\n", - "ds = ws.datasets['Beaver Body Temperature Data']\n", - "print(ds.name)\n", - "print(ds.description)\n", - "print(ds.family_id)\n", - "print(ds.data_type_id)\n", - "print(ds.created_date)\n", - "print(ds.size)\n", - "\n", - "# Read the contents\n", - "df2 = ds.to_dataframe()\n", - "df2.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the predictive model\n", - "\n", - "To assess overfitting, train your decision tree using only a subset of the available data. You can then assess performance using the withheld observation. Below, `sklearn`'s `train_test_split()` function is used to select 70% of the data points for training and 30% for validation." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " df[['activ', 'beaver', 'time']],\n", - " df['temp'],\n", - " test_size=0.3,\n", - " random_state=42\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fit `scikit-learn`'s `DecisionTreeRegressor` model using the training data, then make predictions about the withheld body temperature measurements:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R^2 for true vs. predicted test set temperature measurements: 0.94\n" - ] - } - ], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "from sklearn.metrics import r2_score\n", - "\n", - "regressor = DecisionTreeRegressor(random_state=42)\n", - "regressor.fit(X_train, y_train)\n", - "y_test_predictions = regressor.predict(X_test)\n", - "print('R^2 for true vs. predicted test set temperature measurements: {:0.2f}'.format(r2_score(y_test, y_test_predictions)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It seems like this is a reasonably accurate model!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploying the model as a web service\n", - "\n", - "To deploy your model as a predictive web service, create a wrapper function that takes input data as an argument and calls `predict()` with your trained model and this input data, returning the results." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml import services\n", - "\n", - "@services.publish(workspace_id, authorization_token)\n", - "@services.types(activ=int, beaver=float, time=int)\n", - "@services.returns(float)\n", - "\n", - "# The name of your web service is set to this function's name\n", - "def beaver_body_temp_predictor(activ, beaver, time):\n", - " return regressor.predict([activ, beaver, time])\n", - "\n", - "# Hold onto information about your web service so you can call it within the notebook later\n", - "service_url = beaver_body_temp_predictor.service.url \n", - "api_key = beaver_body_temp_predictor.service.api_key\n", - "help_url = beaver_body_temp_predictor.service.help_url\n", - "service_id = beaver_body_temp_predictor.service.service_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Consuming the web service\n", - "\n", - "While you are in the notebook session in which the web service was created, you can call the predictor directly:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36.78" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beaver_body_temp_predictor.service(0, 1, 1200)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At any later time, you can use the stored API key and service URL to call the service:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# import urllib2\n", - "import json\n", - "\n", - "data = {\"Inputs\": { \n", - " \"input1\": {\n", - " \"ColumnNames\": [ \"beaver\", \"activ\", \"time\"],\n", - " \"Values\": [[\"1\", \"0\", \"1200\"]] \n", - " }\n", - " }, # specified feature values\n", - " \n", - " \"GlobalParameters\": {}\n", - " }\n", - "\n", - "body = json.dumps(data)\n", - "headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)}\n", - "req = urllib2.Request(service_url, body, headers) \n", - "\n", - "try:\n", - " response = urllib2.urlopen(req)\n", - " result = json.loads(response.read()) # load json-formatted string response as dictionary\n", - " print(result['Results']['output1']['value']['Values'][0][0]) # get the returned prediction\n", - " \n", - "except urllib2.HTTPError, error:\n", - " print(\"The request failed with status code: \" + str(error.code))\n", - " print(error.info())\n", - " print(json.loads(error.read())) " - ] - } - ], - "metadata": { - "celltoolbar": "Attachments", - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebook/static/acc_overwrite.less b/notebook/static/acc_overwrite.less index 8acbd9f67b..f3f86e2569 100644 --- a/notebook/static/acc_overwrite.less +++ b/notebook/static/acc_overwrite.less @@ -45,10 +45,15 @@ // To make keyboard focus clearly visible on the Controls(File,Edit,View,Insert,Cell...etc)present on the menubar. //&:hover, //To use browser's hover default value &:focus { - /* -webkit-focus-ring-color = '#5B9DD9' */ - outline: -webkit-focus-ring-color auto 5px; - // color: @navbar-default-link-hover-color; - // background-color: @navbar-default-link-hover-bg; + outline: 1px dotted #212121; + outline: 5px auto -webkit-focus-ring-color; + } } - } +} + +.menu_focus_highlight{ + a:focus { + outline: -webkit-focus-ring-color auto 5px; +} +} \ No newline at end of file diff --git a/notebook/static/base/js/dialog.js b/notebook/static/base/js/dialog.js index 8fe9deb6b9..3bb6f7367a 100644 --- a/notebook/static/base/js/dialog.js +++ b/notebook/static/base/js/dialog.js @@ -65,6 +65,7 @@ define(['jquery', .attr("data-dismiss", "modal") .attr("aria-hidden", "true") .html("×") + .attr("tabindex","0") ).append( options.type ? options.type.addClass('modal-title').text(options.title || "") : $("

").addClass('modal-title').text(options.title || "") @@ -130,6 +131,9 @@ define(['jquery', options.keyboard_manager.enable(); options.keyboard_manager.command_mode(); } + if (options.focus_button) { + $('#notification_trusted').focus(); + } }); if (options.keyboard_manager) { @@ -190,12 +194,19 @@ define(['jquery', .append(textarea) ) ); + + //fix issue#4012 var editor = CodeMirror.fromTextArea(textarea[0], { lineNumbers: true, matchBrackets: true, indentUnit: 2, autoIndent: true, mode: 'application/json', + extraKeys:{ + Tab:false, + Shift:false, + 'Shift-Tab':false + } }); var title_msg; if (options.name === "Notebook") { diff --git a/notebook/static/base/js/notificationarea.js b/notebook/static/base/js/notificationarea.js index c3d8528ee0..77bb6eee90 100644 --- a/notebook/static/base/js/notificationarea.js +++ b/notebook/static/base/js/notificationarea.js @@ -71,8 +71,13 @@ define([ // create the element for the notification widget and add it // to the notification aread element - var div = $('
').attr('id', 'notification_' + name); - $(this.selector).append(div); + if (name === 'trusted') { + var div = $(' -
+ +
-