Openhack Lite Azure Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use this notebook to talk through the process I have had to go through to get up and running on amls\n",
    "\n",
    "* I have created a resource group\n",
    "* I have created an AMLS workspace in the portal\n",
    "* I have created a blob storage and placed it in the same resource group\n",
    "* Using storage explorer I uploaded some data into this container i.e. the kaggle malware challenge data\n",
    "* I have installed az shell on this machine\n",
    "* I have installed azureml, azureml.core using pip on this machine\n",
    "* installed AzureML data prep SDK with `pip install --upgrade azureml-dataprep`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Experiment, Run, Datastore\n",
    "import math, random, pickle, json, os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load an azure machine learning \"workspace\" from a config.json file in the same folder, which is a file looking like this;\n",
    "\n",
    "`\n",
    "{\n",
    "    \"subscription_id\": \"a16914ac-dfcd-49d8-9a99-2be8cf06cbee\",\n",
    "    \"resource_group\": \"openhack-lite\",\n",
    "    \"workspace_name\": \"amls-oh-lite\"\n",
    "}\n",
    "`\n",
    "\n",
    "As you can see it definesd the subscription id, resource group and amls workspace name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found the config file in: C:\\git\\oh-lite\\config.json\n",
      "Performing interactive authentication. Please follow the instructions on the terminal.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note, we have launched a browser for you to login. For old experience with device code, use \"az login --use-device-code\"\n",
      "You have logged in. Now let us find all the subscriptions to which you have access...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interactive authentication successfully completed.\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.from_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the first time around, we need to \"register\" a \"data store\" in azure machine learning. We can use blob, filestore, ADSL etc (see https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data)\n",
    "\n",
    "Looks like you can access blobs using SAS or key, and even create the container(?) if it doesn't already exist. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = Datastore.register_azure_blob_container(workspace=ws, \n",
    "                                             datastore_name='ohlitestorage', \n",
    "                                             container_name='data',\n",
    "                                             account_name='ohlitestorage', \n",
    "                                             account_key=os.getenv('OHLITESTORAGEKEY'),\n",
    "                                             create_if_not_exists=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second time around, you would access this data store using its name i.e. this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = Datastore.get(ws, datastore_name='ohlitestorage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can enumerate all the data stored registered in this amls workspace;\n",
    "\n",
    "There are a couple of default data stores which seem to have been pre-registered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workspaceblobstore AzureBlob\n",
      "workspacefilestore AzureFile\n",
      "ohlitestorage AzureBlob\n"
     ]
    }
   ],
   "source": [
    "#list all datastores registered in current workspace\n",
    "datastores = ws.datastores\n",
    "for name, ds in datastores.items():\n",
    "    print(name, ds.datastore_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also set a default data store for this workspace, so let's set our one to be the default. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws.set_default_datastore('ohlitestorage')"
   ]
  },
  {
   "attachments": {
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QQAABBBBAAAEEEEAAAQQQQAABBBBAAIGKECCAXhHKHAMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgdAIE0EM3ZDQYAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCIECKBXhDLHQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAidAAH00A0ZDUYAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoCIEqRUVFRbkcaOXKlW73+vXr51IN+yKAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEIhAUHFrZqAHMhxUggACCCCAAAIIIIAAAggggAACCCCAAAIIIBA1AQLoURtR+oMAAggggAACCCCAAAIIIIAAAggggAACCCAQiAAB9EAYqQQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgagIE0KM2ovQHAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBABAuiBMFIJAggggAACCCCAAAIIIIAAAggggAACCCCAQNQECKBHbUTpDwIIIIAAAggggAACCCCAAAIIIIAAAggggEAgAgTQA2GkEgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGoCRBAj9qI0h8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBQAQIoAfCSCUIIIAAAggggAACCCCAAAIIIIAAAggggAACURMggB61EaU/CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAoEIEEAPhJFKEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKImQAA9aiNKfxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQCESCAHggjlSCAAAIIIIAAAggggAACCCCAAAIIIIAAAghETYAAetRGlP4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIBCJAAD0QRipBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJoAAfSojSj9QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhEgAB6IIxUggACCCCAAAIIIIAAAggggAACCCCAAAIIIBA1AQLoURtR+oMAAggggAACCCCAAAIIIIAAAggggAACCCAQiAAB9EAYqQQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgagIE0KM2ovQHAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBABAuiBMFIJAggggAACCCCAAAIIIIAAAggggAACCCCAQNQECKBHbUTpDwIIIIAAAggggAACCCCAAAIIIIAAAggggEAgAgTQA2GkEgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGoCRBAj9qI0h8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBQAQIoAfCSCUIIIAAAggggAACCCCAAAIIIIAAAggggAACURMggB61EaU/CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAoEIEEAPhJFKEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKImQAA9aiNKfxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQCEagaSC1mtnLlyqCqoh4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCpdgBnolT4ENAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgHwUCm4Fev379fOwfbUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoMIGgMqYwA73APjh0FwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA9AQLo6TmxFQIIIIAAAggggAACCCCAAAIIIIAAAggggECBCRBAL7ABp7sIIIAAAggggAACCCCAAAIIIIAAAggggAAC6QkQQE/Pia0QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECkyAAHqBDTjdRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhPgAB6ek5shQACCCCAAAIIIIAAAggggAACCCCAAAIIIFBgAgTQC2zA6S4CCCCAAAIIIIAAAggggAACCCCAAAIIIIBAegIE0NNzYisEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBAhMggF5gA053EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNITIICenhNbIYAAAggggAACCCCAAAIIIIAAAggggAACCBSYAAH0AhtwuosAAv+fvTuBl3O6Hz9+QhJpxRLahipaVERrqUaKNqrRxh5bCA262fc1REg0QfKzxxJLCEEEtcZOlZaitLVUkfoVsadEIhKRhft/fU5/5/7nzp2ZO9u9uTP3c16vyb135nnOnOf9zDyZ+T7f53sUUEABBRRQQAEFFFBAAQUUUEABBRRQoDgBA+jFObmUAgoooIACCiiggAIKKKCAAgoooIACCiigQAcTMIDewXa4m6uAAgoooIACCiiggAIKKKCAAgoooIACCihQnIAB9OKcXEoBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCggwkYQO9gO9zNVUABBRRQQAEFFFBAAQUUUEABBRRQQAEFFChOwAB6cU4upYACCiiggAIKKKCAAgoooIACCiiggAIKKNDBBAygd7Ad7uYqoIACCiiggAIKKKCAAgoooIACCiiggAIKFCdgAL04J5dSQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6GACBtA72A53cxVQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQWKEzCAXpyTSymggAIKKKCAAgoooIACCiiggAIKKKCAAgp0MAED6B1sh7u5CiiggAIKKKCAAgoooIACCiiggAIKKKCAAsUJGEAvzsmlFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBTqYgAH0DrbD3VwFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECB4gQMoBfn5FIKKKCAAgoooIACCiiggAIKKKCAAgoooIACHUzAAHoH2+FurgIKKKCAAgoooIACCiiggAIKKKCAAgoooEBxAp2LW8ylFFBAAQUUUEABBRRQQAEFal2goaEhzJ49O3z22Wfh888/r/XNcfwKVE1g6aWXDt26dQsrrrhi6NSpU9X6tSMFFFBAgdoXMIBe+/vQLVBAAQUUUEABBRRQQAEFihKYMWNGWLx4cVHLupACHUmAE0rz5s0LCxcuDD179uxIm+62KqCAAgq0IGAAvQUgH1ZAAQUUUEABBRRQQAEF6kFgzpw5MXjepUuX0L1799C1a9d62Cy3QYGqCBA4nzt3bli0aFHgvbL88stXpV87UUABBRSofQFroNf+PnQLFFBAAQUUUEABBRRQQIEWBT799NO4jMHzFqlcoAMKcEKJ9wZt/vz5HVDATVZAAQUUyCdgAD2fjPcroIACCiiggAIKKKCAAnUkkEq3kIFuU0CB5gLpvUEWuk0BBRRQQIEkYAA9SfhTAQUUUEABBRRQQAEFFOgAAk6Q2AF2sptYloDvjbLYXEkBBRSoewED6HW/i91ABRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgXIEDKCXo+Y6CiiggAIKKKCAAgoooIACCiiggAIKKKCAAnUvYAC97nexG6iAAgoooIACCiiggAIKKKCAAgoooIACCihQjoAB9HLUXEcBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCg7gUMoNf9LnYDFVBAAQUUUEABBRRQQAEFFFBAAQUUUEABBcoRMIBejprrKKCAAgoooIACCiiggAIKKKCAAgoooIACCtS9gAH0ut/FbbeBixcvDpdeemn4+te/HkaMGBHmz5/f5MknT54cOnXqFLbddtswc+bMJo/5hwIKKKCAAgoooIACCiiggAIKKKCAAgoo0N4EOmQA/Z///GdYf/31YzD3qKOOCgsWLGhv+2WJjufPf/5ztCHY3dJt9OjRjWP9+OOPw5133hnee++9MHXq1PDmm282PtYWvzCWQuPdZJNNwt577x0uv/zyMH369PDFF1+0xbB8DgUUUECBNhLI9f+AJ23bCN+nUUABBRRQQAEFFFBAAQXqVKBDBtAffPDB8PLLL8dd+sc//jG88cYbdbp723azVlhhhTBgwICw6qqrhoEDB4Y11lijbQfQwrM9++yz4cYbbwwHH3xw+OY3vxm23377wP5vaGhoYc3yHyYrnxMShx9+eHjqqafK76gV1mzPY2uFzbVLBRToQAK9evWK/x/xf9Lqq68ellqqQ37c6UB73E1VQAEFFFBAAQUUUEABBVpPoHPrdd0+e541a1Z47LHHGgf3/PPPh8cffzzwZdvWXOC+++4Lm266afMH/u+eZZZZpvGxzp07h2OPPTbeGu9cAr9svfXW4bLLLgs9evSIz/7WW2+F2bNnh3nz5sVg9m233RamTZsWHnjggfDEE0+EU045JXAlQua2VGvY//73v8Nhhx0WeJ2R/d6eWnseW3tyciwKKFB7AkOGDAmnnnpq7Q3cESuggAIKKKCAAgoooIACCrQ7gQ4XQP/73/8efv/734ctttgirLnmmmHKlCnh/vvvD4MGDQpkUNuaCiy33HJh5ZVXbnpnO/+LQD7B8zTu9JNh77DDDuH0008Pf/jDH8LJJ58cnnnmmXDiiSfGLPTjjjsusK5NAQUUUEABBRRQQAEFFFCgdQS4ApjSnyQ4DR48uOB3MJa99dZb4/xaJCT5fa119om9KqCAAgoUFuhQ1zR//vnnsWTHJ598EjbYYIOw1157BQLElNggI9nWMQS4lP+nP/1puOuuu8LOO+8cN5rJT//yl790DAC3UgEFFFBAAQUUUKCuBfLNCVHXG+3G1YQAAfE77rgjnHDCCeHII48MDz/8cMFx/+lPfwrDhg0LRx99dLjllltatfxm9kAmT57cbI6tjTfe2NhBNpR/K6CAAh1AoEMF0JnUkvrnNOqiUprkBz/4QZz08u677w4E2G0dR6Bnz55hzJgx8TXApKI333yzE8p2nN3vliqggAIKKKCAAnUvkD0nRN1vsBvY7gUopzp06NDA9y9+brXVVgXHvNlmm4X9998/kARHebbMcqwFV6zCg927d2+cU+UnP/lJWGmllarQq10ooIACCtSiQIcKoDOJJFnGBM2/973vhVVWWSVst912cb/xH/GMGTPy7sNcWRydOnVqdkaa+5ZffvnGCSNnzpwZtt1227gcZ7ALtfQcTHI5f/78Joums9/0RZ8fffRROPPMM+NkmDznWWed1WR5/vjiiy/Ck08+GT9wMGkmy6233nqx3jc1uVtj8sxC29BsgHnu4FK+G264Ie4bLBk3H6zOPffc8MEHH+RZq7y78dhzzz3jyvkmlE2TbVInffPNN2/c55tsskksB8OJmczGvmMfJm+saT/60Y8a182VuVDq82Q+J78zDl4HWPHc3Bgj46ZUDa3csaXnSs9BBn/aN7y2uPSSUkgLFixIizb7mf3aoDY9mSSU2KEvMkqyW1u+FrKf278VUEABBRRQQIFaF2BOCOb94TZhwoRa3xzH38YCfLan9CXzXPFdrNL2xhtvhBEjRsTgOd+XDj/88BbnoWKeKpZjeYLulOF8+eWXKx1KUetztXJ6//zud78rODdYUR26kAIKKKBAzQp0mILPBOIo2UGj/vlqq60WA4wENVddddXw6KOPxqD3brvtVvHOPPDAA2OAvuKO8nTw/vvvh+HDh8e6cWmR7MAlAXbO0I8fPz4tEn9Sqobb1VdfHQPwfBBpT3XkqFHPpXyU1clsBLe5XXfddYFyKwSyq9EIMhMM/va3vx0n+sQmc0LZd999NxxxxBGBiUezGydkuBH4rXRMlTwPJ0puvPHGOE72e2ZLY/zKV75S0Qc+Au9MzEr9+Ozn4INsyuDnyo5x48bFEzWZ48j+/aWXXoqTq2aWzcl+Dbf1ayF7jP6tgAIKKKCAAgoooEBHFpg7d24477zz4lXcI0eOrIiC7xMXXHBBeOKJJ2LCD/NQLbvsskX1yXIs/8orr8Tv7WeffXbsiyQcmwIKKKCAAm0h0GEy0P/973/HoCw1z5lIsmvXrtG3d+/eMYDKH5R34T/2XO2YY44JH374Yd7baaedFlfj0i6W5Ux5azSCjAQyGSdZxZSdIZOcYHlqnCxIwXMuMyMrmWxf7n/11VfD8ccfHxfl7H2urN/UT1v/JJPg0EMPjfuJ8jqU1SFYO2fOnBg833HHHWOQG1+2o1qNkylrrbVW7I7XSWbjUkHu69evX7j22mujI/dxu++++8JGG20Ux3T++eeHWbNmxVW7desW/ud//ie+Vp5++ulYb58HWD69hh555JGw9tprNz5VOc+TVn788cfjPl20aFH8iQ37evbs2eG1116LHy7Th9NyxkZm/IUXXhgzT9gf++23X9xm+ud5eG3xYZhJeXkP/fKXvyy4fz7++ONwzjnnRHNsOQHAa5gMqdSW1GshPb8/FVBAAQUUUEABBRRQoHoCDz30UEyG4vs49c/5DlZKY3nWY/3rr78+kBHeGldUlzIml1VAAQUU6DgCHSIDnf9Yf//738egHtnG66+/fuMe5j/gH//4x/E/cyYoIeD4ne98p/Hx9Av1z7jlamRLk9FNJvspp5xS8oeBXH3mu49MeYLnPB/B/+zGtl555ZUx85yA5sSJEwNBfTKtaeuss06s+73CCivEIPtVV10Vt5+xL8lG8JmsBjKSuVTukksuaeK45ZZbhnXXXTeWo7nnnnvi/mL5pZdeuuJhf/nLX46lcOiILIvM1qVLlziu7bffvtlJEcrpMCHpoEGDYvkSsqp/+MMfRmt8aQTMWYbGa41yJblaOc9DPwsXLoyT8Lz33nsxc58M8cyTN4yDEi6p8ToodWx4n3HGGXH8nCj4xS9+0eSqBfx4Dq7mOOyww+I+vPzyy+M6mWNJYyBbnn3MMtShz25L8rWQPRb/VkABBRRQQAEFFFBAgcoE/vOf/4RJkybFJKQDDjggfm8op0e+E/Jd5OKLL47fB7feeuvG73Hl9Oc6CiiggAIKFCvQITLQqRnOGW8awXJqn2c2An9kEpP1miYZzXy80O/UTecSMkpYEETkP/XWbnzoyBU853nJBr799tvjEA466KC4vSl4nsZFyRayfakFz4mFF198MT3U7Gdm3W76ybzlquPdrIMi7+BSPmpoE8inxl6ujAT2269//evYY0s164t82hYXIzN91113bRKUzlyJWvo4kkFOTb9yW7nPwxUIn376aXxayrTkCliXOybWI1ucDA+27+c//3m85Sv5Q7313/zmN/Hp7r333vC///u/OZ+aEwmUxckVPGeF9vpayLkx3qmAAgoooIACCiiggAIFBUg4o5wq3/W4WvVLX/pSweXzPch3nb333jv2w3cGruq1KaCAAgoo0BYCHSKAziSO/KfNf9jUaM4OKDMJIoF1GoHZVIqjpR1AaYtrrrkm1iIno5YPA/mCiy31Vezj1OqmvEm+9txzz8VyJyxHqZp8GdoELzfYYIPYzV//+td83bXJ/WRRP/zwwzFIyxUCBKXzNa4eYNx/+9vf4qSZ+ZZrq/spjcLrh5Y9mWg1x5Dvedi/ZIDTOEmUXYKm0jEQBOe9Q9B7jz32KPhhl/cVJ5DSyah8J2a+//3v5z0BVMuvhUqtXV8BBRRQQAEFFKhFAUr6MVcRE8ZTcpGkF8plcuUlV2wyJxMJTS01vlsRFGWOJj7v0wc3+uM7HPMkVbOMYxoPcyD16dMnXknJlb5cEdu/f/84/kMOOSR88MEHsVQIwdrs+1MfmT+5IpjEmjPPPDOWgUzb0bdv30DZT5K28pUemTJlStxmHPOZcT+P0y9XiWa31AdXhrI97B+u/OR7FuuwX/hcz5XNlFLMbMkijZnvzynB7Le//W1cPz2W+ZPnzNd4fq5opf3sZz9rcjV4vnUK3c/3QfqhUSKz2O/uhfr0MQUUUEABBVoSqPsSLgTk+A+bDNptttmmyQSRCYcz2QTQqfNMRjaTF3I5WEuNGcn50EKpFOqx5cuobamfUh7nufggk6/985//jA9R7mT11VfPt1jMVP7qV78aH+dDIU58CMpufCjJF7CnNAmB1UobH6r+9a9/xW7IrC/UJxPF8KHvH//4R+BSwGo0tj19QM13AoQPudT8JqDMB3cC1bxO+ADMB00aH/orbaU+D/ts9913j5OccvKHkyZkd++7776hGpPqsL2Uh2HiXcr/tNT4gpMmZE37NHsd+llxxRWz745/L+nXQs5BeacCCiiggAIKKKBAXgHmwiG4mt34/sX3JW6XXnppDKQzr1CuxveRoUOHhsmTJzd7mDl4CPZy22uvveJnzWYLVeEOrigmSefoo48OL7zwQuyRcpe9evWKAfYDDzwwXnXMA9zP517Kd2YmLDFfFeU0R4wYEb9/Zg6LRCduF110UTjuuOPivFnlZmJn9lvodybdZIzpamyWZb/ceeedcb9QNnPw4MHNEswK9VnqY5RIffLJJ+NqfMdOczOV2k9anvXph/mpONnx+uuvhx49eqSH/amAAgoooECrCNR9AP2dd96JmQzocdY91X/O1txss83ibOB8MCODYquttmryYSh7ebIKRo0aFT+AkG1BgLEtGh/QUk3tXM+XgricNGAC0WIaJUAoBZKrEczOV7c71/Ll3EfmQxo3E5tyK6bx4a8ajcD4u+++G7tKk4mmfhkbH/rZ1wSoW6tV8jy89phYlg/7BPMPP/zw+KGdwDoTrq633nplfyhO+4XXQb45ADJNCOi39HrhNZx9FUjqY0m/FtI4/KmAAgoooIACCihQnACf67galyxxriQl4YfvKyS7ULbjvPPOi59RychmfqbsZCCCzkwwT/Cc7y+Uc9xpp53C1772tTgA+iFISnA7XXlZ3MhKW+r999+Pc0V169Ytfh/kczVBc67G5HsACUpMXMm8Wccff3x46qmnAsH9lJTE52YSsoYPHx6fmMD0/vvvHzOu2UaCveeee27g6l8y0Uni4vN7ZgC+tBEXXppx892W5J999tknfi8g6M84eH5OElBLnHKU3/rWt2Jna6+9dgy285mcRnY33y34PnTSSSc1mVsp89kL7ReC+IyBJJvvfve7mauV/Tv90B/98rqglKRNAQUUUECB1hSo+wA6/9nzIYHG5YDcWmpcpvarX/2q8YNE9vJcCseHHz6UUI+cD0b5AoLZ6/p3+xMgU5rLRfkQlllbnpMKV1xxRTjxxBPjiRKyTwYOHBg233zz8I1vfCOWbiFgzONcFlluq/R5eO1xeezTTz8dv3iQ4UOG/oQJE+KND/jDhg0r+oRKudvhegoooIACCiiggAIdT4DPwrmCwCRVkMjB52ZKXVKykatlswPozOFEAhONTHaC1pnfreiHz+jbb799q+ISUCZphAnv+bxP8gjfD8jWZsyU7mQOKILhLEfWfGYAnSxrAtI0EqwIjmfOT4QD80tRWmXq1Kkxg3q77baruKRJPpSXXnopjpMTF1yhmsZC+Rd+J5uf7w+UXUwBdK7Gzc7mTlfosk5LiTK5xpKuSl1jjTWazUWWa/li7sMyBdDpP9/V1MX05TIKKKCAAgoUI1DXAXTKQTz++OPFODRZhoD7s88+2/hBIvNBSmwwSSeziHO2ngzfSi9Dy+y/Wr9zAoAPbmRQtNT4UFTMci31U43HCVjvtttuRXVVKNOhqA5CiHUBb7311rj4D3/4w0DWRWpkS3CJJZnuBMiZ8T198EzLcDKl0lat56FkC3UaOanDa5hLMqlHSEbPZ599Fs4666yCNcwLbQcGc+fObfFDM5k3aVLT9GG7UL+FHmvr10KhsfiYAgoooIACCiigQG6BXMHztCSBcOa/2XDDDWPGM0Hn7EYySbrqMfuxtv6boDJBbhqJMqnECvenK44pRZgCzml8BHAJilP6kCuZyfjO/t7AspT85ITDM888E0tBUlqFmt6t1cgaJ5Cf/bn8O9/5Tsza5sQFgXYy/luj8V0p80rfYq5oLWYcfHddbbXV4qJ8T+H1w/6yKaCAAgoo0FoCdR1AJ8PhgQceiHY33HBDvKywECT/+RIQv+OOO+LlhtRMzw6OE+zk0jwaE9mQVVGo8WGFDAUaAch8jQ9d1ZgAhUsLaWRyFFNOI9942vJ+jFPgmr9KGzsAACAASURBVJI7XLqZmXXSmmPhagNeG+wjZnTP3N9cDkiNc0r/7Lrrrjk/BBOYfvvttysaYrWfh9ccJwOYDIkJmCiJw/uAwDpfXkpp7BdsyGhnO7kkt1D78MMPG+vZ88G81LYkXwuljtXlFVBAAQUUUEABBSoXoFQL2cRkgI8cOTImfpAMVI35fEodHZ/5U9A8rUv2+aBBg3J+F0jL8BmY+uY0Auhf//rX00PNfvJ9jWA8STxkTxNkzn7OZiuVcQd1wsn8zw6e0xXB/RTgb82TF5nJNYXKOJa6eQTQKUdD4zsKhtVIrip1HC6vgAIKKNBxBJaq100lU5zs8zQBIh9SuOSs0O2b3/xm2HLLLSMJ5VmYKDKzzZkzJ4wdOzZm95KNzAeslgK9fDBJ9fvIas+XsZxZqz3zOUv9nXpwXOKYsuhLXX9JLM8HoFS3jpqCKUuhNcfC64PyPkcddVTMMOdSUSaSzWxMJETjw16+uvPU3WMy0Upaaz0Prz1eowTRGScniEptTPhJMJ51ufKCS1bzNUyZhJfXHldn8LyltiXxWih1jC6vgAIKKKCAAgoo0FTgzTffjFc9Uh6EwCaJPOnGZ8I0KWfTtf77FyVD+ExOogclUU444YSY4b3HHnvEcirUQG+rlisIS43zloL5fE+cOXNmHCbZ6YW+IxIspwQJje8B6erNam9joe8w1X6ufP2RJJauOiD7ns/61Wj45vt+Vo3+7UMBBRRQQIFsgboNoDMJzH333Re3l8Bo+pCSDZD9N5PfEIAm4EgwkKAgjZ9XX311rFVHQJGZ04vJFOBDQsr6vf/++2OdueznJCh5/fXXx8Bj9mOl/k3A82c/+1kMeBLsTzXncvXDhxlmkM8X1M+1Tmvd179//xh0feSRR8K4ceMC5XdyNfYDE+8wSWq5jQ+4F1xwQeBDOR9a+ckH9ZSFkfpN2da8lnJ9cJ8xY0agpiAnafI1PrimrA8mns3VKnkePqgzgWg+L7aVjBgyZ7I/sBYztq985SvxElTGTUkVJk5KkwplbwtXfPB6opHNzwmpclpbvhbKGZ/rKKCAAgoooIACCvxXgAzj6667LvTt2zde9cjVnQTBS22bbbZZLIHCZ3KuRiV5g9rj++23X/wet++++8YgfPpuVmr/rb0820wt8WIaJxYoA9MRWubV2Hx34updmwIKKKCAArUoULcBdD7AEACn/ASXrxEsLKalADTLEoAneEpjksnzzz8//s5M5PRL8DLfjbPtqVEKhqA7wVqyK5iNnqAmHwzJxiD7mYAwE0FW2ggCM1EqGcAEo5n0kqAml7bxfB9//HGcxZ5t6devX6y/V+lzVmN9gq2UxMH17LPPjpPaMNEQQX6yMihvwyQ3LEOAdfbs2Xmflg/yLM++YX1qDDIDPaVa+BBOVsixxx4bP9wfeuihMQBNRkR248QHwe3nn38+1g7k+ZMhmfJMHos3Afh8jX5TXcNrr702bgN9PPbYY7HMDutV+jyUHPrJT34SM8T5YJq8GOOpp54aA/y8DtI40liLGRvL7rLLLgEnxs2XFy6p5XXLa4nn4rVFrXgubSX7nGUrmVi3mq+FtK3+VEABBRRQQAEFFKi+AN83qLFNAHnw4MGB0oSUreS7ULpRCjAlFBUaAWVPzjjjjHgVMLXBmWwzJZrcdNNN8fMuP9tjEJ3vMJShKaYRROYzO431UrJNMevW2jKcLOCECI1a99XadySApauWSZQrJrGt1uwcrwIKKKBA+xKoyxroZHTffffdUZrA9UYbbVS0OgFRMtapc04wkElVuDyMoG4qtUF2bUuN8jE8N40PfgTdqadNMJZgZmbjcSYlJeBJlnqljQz68ePHx2AzpWgIZuZqfJj53ve+1y4mXOEyvD333DMsWrQoBrfZf2kfZo990003Db169cq+u/FvAu8tfYDlUtJhw4bFCUuzM89TR5TD4YM7k7GSTcMts3G1Ah/ysc7X+FC8++67x7r6mX3wmuQLAK0az8NJgnyTrxLgJ5Ce/cGymLExPuqSczUDmTIEyjkRwC1XI3iOV2Yt+VzLFbqvmq+FQs/jYwoooIACCiiggALlCxAIJjGIJAvqfvN5MU3sWH6v//3syfcxblztyfejESNGhGnTpoVLL700bL755o2B9Uqep5rr8jmbz9a0119/PQaK85Vx4btqKmtSTvCXq0EJRtdC4wrYVKv8tddeiydXsr+TlLMdvPYogUqjxE6+73Pl9O06CiiggAIK5BKoywx0SmUwoziNcibUPS+l8aGMiSP5MMhs6nxo4xLCchsfnsjIIKhO5nMK/vKTv8mO5gNiNRs1xZk4cvLkyYFahOnMPz/5m6AvWcQHHXRQ0dn51Rxfrr7IvqC2PLXiCcJyKWdqWDEJDkF1Ml2YILOUxnZzJcCoUaNiFjh1yzkRUujDFuOhVA/7niB0MiS7H7/bbrutxUA9Y2RWe5blCgM+WNMPfaywwgpxEyp5Hl7blFb5n//5n3ilRfrgnrwI2nNZbb4vMy2NLRnT7+mnnx5PKp1yyinxxEt6jJMw3Ed2EQH25JQeL+dna74WyhmP6yiggAIKKKCAAgo0FaCEIEFRGskr+T4DcnVovhKATXts/hfBVub0OfHEE+ODXGWcrhBuvvSSu4crO0liopGFnwLkuUbEdzCubqYxTxdZ2tmNbeSK5eyG5TXXXBNIGGqLRiIZn8tpBK1LDdzzPZgrvGnUya/WvqO8JlfB0vjeU+zV5nEF/1FAAQUUUKAMgbrMQOc/0TQLehkmsV40lw1mNgK7lTQ+PPCh6txzz423XH2RJcwtVxsyZEjgVkojC/jnP/95vJWyHpnz5V5eV41tWGONNWJAlqBsKa3Qc5fST+ayfGikPAq3fI0a5NzyNfrghAy3fK2S58Fr6NCh8Zav/3z3FzO2tG56DXNyg1uprZz9U+5rodSxubwCCiiggAIKKKBAaQIEVlPyBgFjAqyZ2cV8n3jqqadiQkqx9cFzjYB+0nw/ZDPnmugz13pteR8OXJ1K8hJZ+SQCHXPMMY3B5zQWSrcw3xNzKFFmNDNhiGXIpqYRZOdKaL7XppbqzZPp31aN/cnncRrlTflumU4UFDsGrrblBAtzjDGXFVcCV9qwwYh5nrLLVFbat+sroIACCiiQS6AuM9Bzbaj3KaCAAgoooIACCiiggAIKVEege/fuYb311oudMScP5Vb+93//N2ZfczXwb37zm3jlK0HTQu2WW26Jy1599dUxM5sgc5pniqtGKZk4cuTI2AVXF6eM5kJ9LonHuNqU0o204cOHx3mDcODkAtnSBNfJpucKZ0p4Uk6SAHBmW3fddcOWW24Z72K7ufqW9bnSk3mzuHqYkwhtFTSmBEtKJCJgzT5lX2fuI/YVNcnzNQLwqQY+JUu5yruSRgkc5pOikcG/9tprV9Kd6yqggAIKKFCUQF1moBe15S6kgAIKKKCAAgoooIACCihQlgBlM379618HgtxkJ5NZzS2zkUHNlZLMyzRx4sTMhxp/Zw4kSg5yK9Qo50hgOjPLvdDybf1Yjx49wvnnnx8IOhMsZ76jNOdR5lgwOeecc0K/fv0y746/r7766nH+KkpaErDOnuOIgDFlFadMmRIz1Jt10Ap3kFl/xBFHxFKNnAxh3qrsxnxe+eYJo2wlfdx6663x5MG+++4b+vfvn91F0X//61//aixhQ7+pLGbRHbigAgoooIACZQiYgV4GmqsooIACCiigQPsVIHDBvBfcDjjggDBr1qz2O1hHpoACCtSwAAFfAuMXXHBBY+Y05Uw4/jJnEHM9DRo0qGCN6o033jiWsdx+++0Dv2c25g06/PDDA5nLlExMJU4yl2lPv5MdPmHChEA50P3226+xBEu2CT6UR8xu3EeAmmAz2eyprjxZ6cw1dM899wTKbaaJObPXb42/KQvKfEtsE8FvsudLbWTnU7KG7PPrr78+zJkzp9Qu4vJkn7P+yy+/HPujX5sCCiiggAJtIdCpodxi1/83ujR5BzOI2xRQQAEFFFBAgSUlwPwMI0aMaPL0BCkIqJc6oXiTTvxDAQUUqDGBdDwcNWpUkzmW0nc3Jr20KdCWAmTNM68YJxMuvfTSnJnsLY3nkUceCVyJMHfu3LL7aOk5eJwSNbTMGAelapiTjIlQubIgsz59MX26jAIKKKDAkhFIn30yj+nljMQM9HLUXEcBBRRQQAEF2p0AkxWTF5B5u//++w2et7s95YAUUEABBTqawMCBA2MNdbLQL7zwwphFXooBQe1LLrkkTsBKIHuHHXYoZXWXVUABBRRQoCIBA+gV8bmyAgoooIACCiiggAIKKKCAAgoUEqAUzJFHHhn69u0bnn766RhEnzdvXqFVGh9jOSZVZQJW1j/kkEMC/dkUUEABBRRoKwEnEW0raZ9HAQUUUEABBRRQQAEFFGhDAUpYPf744/EZ11hjjTBy5Mg2fPbWfar58+eHTz/9tKIn+fKXv9xuJyWtaMPa6cq9e/cOZ555ZsxEv+qqqwL+lBkqFAxfvHhxrKdPDXzqr7M+/bRFu/POO+Nz81xMdvv8888H6v7bFFBAAQU6noAB9I63z91iBRRQQAEFFFBAAQUU6AAC06ZNC9xozAlRT+2OO+6INbUr2aZJkyaFvffeu5IuXLdEgX79+gVq9B922GHhmmuuCQMGDCj42qTu+bnnnhtrp7Me67dVo9b6gw8+2OTpDKA34fAPBRRQoMMIGEDvMLvaDVVAAQUUUEABBRRQQIGOIMCcENyyW5pIK/t+/1agrQQ6deoUBg8eHPhJI4BeqP30pz+N5V6WWmqpsPvuuzeuV2idaj1GrXVuNgUUUEABBTo1MNNWBS19CKt0NtMKhuCqCiiggAIKKNCBBfgoM3v27PDZZ5+Fzz//vANLuOkK1KfA0ksvHbp16xZWXHHFNg2e1aNm+u7Ws2fPetw8t0mBqggwYSnNGEdVOO1EAQUUWKIC6bNPpcd0M9CX6G70yRVQQAEFFFCgUgG+6FIj1aaAAvUpwIkxJhFcuHBhMPBbn/vYrVJAAQUUUEABBdqzgAH09rx3HJsCCiiggAIKFBSYM2dODJ536dIldO/ePXTt2rXg8j6ogAK1J0DgnFrETOLHe3755ZevvY1wxAoooIACCiiggAI1K7BUzY7cgSuggAIKKKBAhxeYP39+NDB43uFfCgLUsQAnxniP09J7vo43101TQAEFFFBAAQUUaGcCBtDb2Q5xOAoooIACCihQvAAZqTQy0G0KKFC/Auk9nt7z9bulbpkCCiiggAIKKKBAexMwgN7e9ojjUUABBRRQQIGSBTp16lTyOq6ggAK1I+B7vHb2lSNVQAEFFFBAAQXqTcAAer3tUbdHAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoCoCBtCrwmgnCiiggAIKKKCAAgoooIACCiiggAIKKKCAAvUmYAC93vao26OAAgoooIACCiiggAIKKKCAAgoooIACCihQFQED6FVhtBMFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBehMwgF5ve9TtUUABBRRQQAEFFFBAAQVyCCy99NLx3kWLFuV41LsUUCC9N9J7RREFFFBAAQUQMIDu60ABBRRQQAEFFFBAAQUU6AAC3bp1i1v5ySefhIULF3aALXYTFShegPcE7w1aeq8Uv7ZLKqCAAgrUs0Dnet44t00BBRRQQAEFFFBAAQUUUOC/AiuuuGJYsGBBIMt21qxZsiigQA6Bzp07B94rNgUUUEABBZKAGehJwp8KKKCAAgoooIACCiigQB0LdOrUKfTs2TMsu+yywRIVdbyj3bSyBHhP8N7gPcJ7xaaAAgoooEASMAM9SfhTAQUUUEABBRRQQAEFFKhzAQKDPXr0qPOtdPMUUEABBRRQQIHqCZiBXj1Le1JAAQUUUEABBRRQQAEFFFBAAQUUUEABBRSoIwED6HW0M90UBRRQQAEFFFBAAQUqEXj11VfD6NGjw0cffdRiN9OmTQsjRowoatkWO3MBBRRQQAEFFFBAAQXaqYAlXNrpjnFYCiiggAIKKKCAAgq0pQDB86OPPjo89NBDYeHChTE43qVLl5xDeP/998Pw4cPD1KlTw+zZs8OYMWNi7eCcC3unAgoooIACCiiggAI1LGAGeg3vPIeugAIKKKCAAgqUKzB//vxw2GGHha5du8af/F2v7YknnojbybZm3zCop20nK7xPnz5xO6dMmVL0Lp0xY0YYNmxYDJ4PHDgwHHzwwSFf8JxOmWQPuzXXXDNcdtllYfz48WHx4sVFP58LKqCAAgoooIACCihQKwIG0GtlTzlOBRRQQAEFFFBAAQVaQWDBggXhvPPOi9nkffv2jSVcVltttYLPxESUW221VVx2ueWWC2PHjg233HJLaGhoKLieDyqggAIKKKCAAgooUGsCBtBrbY85XgUUUEABBRRQQIGSBLbYYotYkoSyJNw+/vjjcMABB5TURz0vfOedd4Yrr7wyEAgfOXJk6N27d1GbSxB90KBBMRP9k08+iWVcXnzxxaLWdSEFFFBAAQUUUEABBWpFwAB6rewpx6mAAgoooIACCiigQJUF3njjjVh+hQA4JVl+8pOflPQMnTt3DgcddFDMRn/55ZfD+eefH+bMmVNSHy6sgAIKKKCAAgoooEB7FjCA3p73jmNTQAEFFFBAAQUUUKCVBD7//PNw0003BWrEb7jhhmHIkCGBgHipjXIvhxxySFyNbPZHH3201C5cXgEFFFBAAQUUUECBditgAL3d7hoHpoACCiiggAIKKKBA6wm8+eab4a677opPsM8++4R111237CejHvouu+wSyGSfOnVqmDdvXtl9uaICCiiggAIKKKCAAu1JwAB6e9objkUBBRRQQAEFlrjArFmzwr333huGDRsW+vfvH1ZeeeXQtWvXsMoqq4Rdd9013HjjjUUFByljwbKsw7r0wW2DDTaIgUYmbZw7d26rby/juPrqq5uMg/Hstttu4S9/+UuLz7948eKYoXzwwQfHsbMNmOy0006xbvbMmTOb9XH99dfHbd1uu+3CBx980OzxXHeQBU3fa665Zvj73//euMgZZ5wR7+cnjf7OOuuswGSXLM+2UM/8hRdeaPUJLJkgk5InZ555ZujXr198fsbAWE477bRACZNCk2hi+dJLL4VLLrkkELDmtcD6qY+hQ4e22AcGPMdrr70WnzNzHPR33HHHFW1OpvjTTz8da54PGDAgUNO83NajR4/A/qY99NBDcTvL7cv1FFBAAQUUUEABBRRoTwKlX6PZnkbvWBRQQAEFFFBAgSoKzJ8/P5xyyilhwoQJzXr96KOPwj333BNvTEp5+eWXh169ejVbjjueeuqpcPjhh8egbvYC06ZNC9wIpv7iF78I3bt3z16kKn8TZH3wwQfD8ccfH58vs1O25eGHH868K+fv77//fjyRMHny5CaPk2X8wAMPxNu4cePCOeecEzIDsATBaQS7ea6vfvWrTdbP9cc777wT71511VXD8ssv32wRtocg+5FHHtnElf4nTZoUy4ZcddVVYcstt2y2bjXuWLBgQTxhMGLEiJhlndnnc889F7hddNFFMYB9zDHHhC996UuZi8TfCVaTqZ2rpT7YhpNPPjkcddRROcup8Bqlzvi5557bbBzptZWr/+z72Id/+tOf4t0/+tGPwre+9a3sRUr+m/cFpWA4mcG+2nTTTUvuwxUUUEABBRRQQAEFFGhvAmagt7c94ngUUEABBRRQYIkKLL300rEW9LXXXhueeeaZ8N5778UbQfFjjz02LLfccjE4ePbZZ+fMRCdD+aSTTopBxD59+oQpU6bEbGH6IUhMYJG+N99887DUUq33Ueyxxx6LQXyCqtnjeP755+NJAoKd+dqMGTPi+gTPV1pppUDg+Nlnn40WrH/66afH++mfkwVPPvlkY1crrrhi+Pa3vx1ef/31MHv27Mb7C/1CgJpGRjkZ7tntjjvuiCcc6JP9QCb3W2+9Fa644oqYtT59+vRw8cUXB64gqHbjZMeFF14YCIwTeB48eHDMsmZ/kgnOPsaYx8hEHz9+fKC+eK5GtvrIkSPD/fff3/i6yO6DDPcU3M7sg3GwjTwHz0UWO1nkjIMb+3z48OHxNZq5Xq7f2b9kzNM23njjnAH/XOsVuu/rX/96zKpnGU4IMEabAgoooIACCiiggAK1LmAGeq3vQcevgAIKKKCAAlUT6NatW7jgggtyBrYJ6hJwJjuaACYBYwKflM3IbATdCZITQCaQSnAys5GNTbC1NRvBVALcBJV/85vfBMqfEARPjW355je/Gct3kC2c3Qj+XnPNNbGWNdnkBKnJnE4lPli/d+/eYbPNNov98zwTJ04MG220UVh22WWj0Ve+8pXw6quvFh1EpQ8aE1KyH7Ib42QbKH+y5557Nu4jsvgZL5NYUpKGADvlRKrZ2NcErmmjR48ORx99dFhmmWUan+Ib3/hGIIv7sMMOi2acIKGcyfrrr9+4DL/gRZA7OWY+SB8sv++++8aTL4888kj48Y9/HDihkxr3jR07NgbIyfpn2cxJP3ltcfKCuua59mvqh5//+c9/GkvlrLfeepkPlf07J5dSX7w3uDqA+2xtL8DrlJNemW2bbbYJnBDLdYIqczl/V0ABBRRQQAEFFGgq0HppT02fx78UUEABBRRQQIF2L0Bgs1BWOMHKVIIjX3CYLOEl3cheJjOZIPehhx7aJHhezNjefvvtWAeeZffee+9Y7ztX0Jf62wSNadS9JhudxkmGFMTOrIFOljnZ49T8vv322+Oy/PPZZ58FMqJpZDHnKn9CIJaTG2R/Z+4jxkVWNycsyPL/97//3dhvNX5ZuHBhDIrTN/uerO/M4Hl6jp49e4YTTzwxUIKGzG48shvjzuWYluOkxg9+8IP4J8Fnnjs1SreQhU9W98477xx23333JsHztFyxP9MJC04Kfe1rXyt2tRaXW2ONNeIyjJMx25asAGWmKK/EbfXVV2/y3lmyI/PZFVBAAQUUUECB2hEwgF47+8qRKqCAAgoooEANCKy77rqxpAgBdoLXv//972O987YaOkHXNAnnJptsEjPNS31uxk7WNUHrbbfdNm+glmAwNccJGhNgTgF0AuApkEomcmrvvvtuzHrnbzKxU9mWRYsWNWaqp/rpaZ30c4899ggDBw7MGYAmIJ+C7tU+gfHhhx/GciSMgwA6Af58jX1PHXDav/71r6oGkMkYf/HFF2PflP/JVSc+37hy3Z8msOWkUGaWe65lS7mPTHoaY+UkgG3JCgwZMqRxvgLmdkgntpbsqHx2BRRQQAEFFFCgtgQMoNfW/nK0CiiggAIKKNDKAkxWSQYxZVr69+8fyx0QoE23lIGebxiUMaFWNsHnv/71r2H77bePmeDc98c//rGqQdVcY5g3b14sncJjBDO//OUv51qs4H1MHkpjYsmWyj2kcjAsn4LllGAhI5tGDfSUSU1dbCbSpDGJacoW5/GUqZ4vgE6Qt1D2duy0Ff6ZM2dOmDlzZuwZj0JjIIifAshkeH/66afNRsT+ufnmm2MmO+V/0uuKnyussELOCWzphH2SAujVmPCTgDyNUjvVDKpWMxjfDM87FFBAAQUUUEABBRRYAgLWQF8C6D6lAgoooIACCrRPAUpOnH/++eHcc89tzIgudaRk9FKPm+zv8847L5bdIJhK7W5u1PE+8sgjwwEHHBCoWV3t9sUXXzRmvBPILiegmcp7FDO27t2756xzvdZaa8XVyeAm05wTE2Sdk61OcJ1gOjXLqfvNxJ8sRxkWani3p0YWdQpctzQuguCFxs8krJxIoUZ+qY0672lSznJOimQ/H/uNhjv+LZ0oyV7fvxVQQAEFFFBAAQUU6CgCZqB3lD3tdiqggAIKKKBAQQECvDfccEPMPCdQedRRR8VMdLKIyZBON2qLt9TIUmbCyJtuuim8+eabgUklqVtNVjoBWbLbd91119h/S30ticdLqYmdgt+MMwVl+T1lYpNZjl0q30KJkxNOOCFu1oMPPhg+/vjjmJWPOdnQlZYmqbYX+4zAfjEts5Y762VO8MnErtRIJ3hO3fHbbrstZt2n1xU/seDESlu0tI8peUNwvlotnXz57ne/W3Lt/WqNwX4UUEABBRRQQAEFFKimgAH0amralwIKKKCAAgrUrAClRu666644fiaKPPXUU8Paa6/dJAha6sYRSF9llVXCXnvtFX73u9/FgPkpp5wSA+mUMpk4cWJjeZNS+863PFnQKbOdoHW+iRwJ2KY62Nl9pSzq119/vbF8SfYy6W9KnFD/nJayzvmdMRBEpfRIqiPONjNJ5g9/+MOw9dZbx2AytcIJnlN3nfri6blT/0v6J2VZCIbT8OBES75Gpn0qRcMJhFSXneUJnHPyhb7GjBkTdtxxx1iyJV9f2fdnBvJTkDp7Gf5mf+fb55nLpxMcL7zwQkjlXDIfL/d3ThjRyGhvbydDyt0m11NAAQUUUEABBRTo2AIG0Dv2/nfrFVBAAQUUUOD/BAgivv322/EvAuf5gn8EnsttZP0ef/zxgQkxaQSNqYldzUZ5jxQcJThNhnh2I+v4iiuuCFOmTMl+KP693nrrxaxrAtuUXcmXocz9Dz30UAyg9+3bNwbMU4f4EUQloEwN8VS+hUlHKeFChj6B9z//+c/hjTfeiKsRdF9mmWVSF+3iJ2Pt3bt3HMvf/va3xgB5rsERjE7lWci052RGauwLGvXL0wSr6bH0k/3ChKq5GpZpAtOXXnop5z6ZMWNGOP300xtr4OfqJ93Ha5FMeNorr7yS7q7oJ4H7FEDnPdTeToZUtHGurIACCiiggAIKKNBhBQygd9hd74YroIACCiigQKYA9cKXXXbZeBcBX7KJMxu1xW+99dZwxBFHZN5d8u8ESFOGMJM3Zpb5KLmzHCtQ85xgNo1JS8l6zsyaJkh73XXXhbFjx+ZY+793KtriNgAAIABJREFUrbPOOiFNlnrllVfGIHeuhQl+8zjtZz/7WZPAMJaUZCFIPm3atDh5KEFlAquMkeXJqs6cTJRgNfuhPTXGOGDAgDgkrlCYNGlSY435zHESvB43blzcXrLrOUGQ2VJ5G04okLWf3cjUP+6448I111yT/VD8G8uNN944/n7fffeFFJBPC6fSQFOnTk13FfxJMJ5JTGnUoq/GiRy24fnnn499ctKhGrXaC26EDyqggAIKKKCAAgoo0AYCTiLaBsg+hQIKKKCAAgq0fwEClNS6fvLJJ2OQlMku995779ClS5cY/KXcyj333FNwQwiMjxo1Ki5DqRKyjVM2OIFrMpSph05tdAKz2223XWN5kIIdl/ggz73TTjvFkjQjR46MZWJ4LrLsmST1+uuvDwMHDozlVIYPH96sd0qP/PrXvw7UKKdcyJ577hkOP/zwWLedkjQESm+//fZw8cUXx5ru9HXwwQc3mbCUTPJUSoaa7wTSf/GLXzSWLSHASqCZADqTa9IyS8A0G1SZd3DygIA1/qlRqzxdScBPgs+ZgXv2TWb2+Lbbbht23333eAIFL/bj/vvvHydA5UQLJyqYIPavf/1rWHPNNcOwYcPiZKnp+fhJsJp+ccDjpJNOiutjeccdd8TAeaHSLIwH55tvvjk+P1cy0AdXCzzzzDMx85zn57mpt87rrFBjLATkJ0+eHF/zr732WmNAvdB6hR5jYljK9PDe6devX6FFfUwBBRRQQAEFFFBAgZoRMIBeM7vKgSqggAIKKKBAawqQMc3EoQQBCZASKM0OLvfp0ycGLS+99NIY+M01HoK1EyZMyPVQ430rrbRS+O1vfxt22223xvuq+QvlOY4++uiYpUz294EHHtik+1122SWcffbZjeVGmjz4f39ssskm8UTCkUceGT04MZBODmQun/oiezyzEZBebbXV4l0EjQmYU/s8Ncp7kJFO8JhSMTSCz9VuBMtPPvnkvPuEjHJumY2sfcaWGlcKcOKBbSLgzAkQbtmtV69e4ZxzzskZPGbbjznmmGhI2RtumY2ANgFxgs+8DnO1zTffPLA/zjzzzLh+Zh+sT2CeExnU7y+mcRUA++Xll1+Ok5quv/76TU6CFNNHWobXfZpDgH6xsCmggAIKKKCAAgooUA8ClnCph73oNiiggAIKKKBAVQTIEr7ttttiIDKVyyDYTfYxWdsELAmEUoIkVyNLeIcddohBTLKrMwPC9EMpEIKfZOkedNBBVS/fkjkmMoAJaBKMTePo379/zEy++uqrG+/LXCfzdyZAJYj8hz/8IVx++eVxu9gGGv3tu+++4d57743lYFL/mevze2at70033bTJc9I/9dAJGNOYcDT1n91Pe/ibzHtOjPAa2G+//RoDxASut9lmmzB+/PhYLoff2bbsRkb+CSecEPcJr6e0rbzOCPA//vjj8aRKobrhlPshCE8poZ133jlmtNMPmf1cHUEAPnPi0uwxZP9NkDudxOF1T231chsnHe688844pp///OeN5ZDK7c/1FFBAAQUUUEABBRRoLwKdGjKLYpYxqjTZVro8uYwuXEUBBRRQQAEFFChLIH0Oyc5+LqszV1KgAwpwhcKQIUPiVQaHHnpoGDNmTElBeMgoQ0PmOydUyu2jGHrqzNP83tGy1ujRo8OIESPiFQ/FXpHQcq8uoYACCiiggAIK1JZA+r5Y6edHM9Bra787WgUUUEABBRRQQAEFqiaw7rrrhmOPPTb2x+SyZJGXkl+TJqUleM7ktVxZUUoWfNU2xI4UUEABBRRQQAEFFGglAQPorQRrtwoooIACCiiggAIKtHcBys1QDoYMcmrRU8P9lVdeKWrYBNpvueWWMHbs2Fi6hYlmmdTUpoACCiiggAIKKKBAPQk4iWg97U23RQEFFFBAAQVqVmDu3LlhwYIFFY2fetzUYbcpUIoAE+gyYe67774bpk6dGg455JBY976liUAfe+yxOGEpgfczzjgjDBo0KGf991LG4rIKKKCAAgoooIACCrQ3AQPo7W2POB4FFFBAAQUU6JAC48aNi5NIVrLxTOTIxJ82BUoVYB4B6mZTz/yJJ56Ik+ZSQ7tLly45u3rnnXdivfTp06fH7HVqnzPJqU0BBRRQQAEFFFBAgXoT8FNuve1Rt0cBBRRQQAEFFFBAgTIEevfuHSZMmBCmTJkSTjzxxLzBc7pebbXVwnnnnRdLuFBDnSx2mwIKKKCAAgoooIAC9ShgAL0e96rbpIACCiiggAI1J0AJDW42BZakAEH0UaNGFTUElj311FOLWtaFFFBAAQUUUEABBRSoVQEnEa3VPee4FVBAAQUUUEABBRRQQAEFFFBAAQUUUEABBVpVwAB6q/LauQIKKKCAAgoooIACCiiggAIKKKCAAgoooECtChhAr9U957gVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFWlXAAHqr8tq5AgoooIACCiiggAIKKKCAAgoooIACCiigQK0KGECv1T3nuBVQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQVaVcAAeqvy2rkCCiiggAIKKKCAAgoooIACCiiggAIKKKBArQp0rtWBO24FFFBAAQUUUEABBRRQQIH8ApMnTw6PP/54XGCNNdYIZ511VujRo0f+FXxEAQUUUEABBRRQoJmAAfRmJN6hgAIKKKCAAgoooIACCtS+wLRp0wI32jbbbBO++OKL2t8ot0ABBRRQQAEFFGhjgU4NDQ0NlTzn22+/HVf/xje+UUk3rquAAgoooIACCpQskD6H9OzZs+R1XUEBBWpLYMaMGXHAfu+orf3maBVQQAEFFFBAgSUlkL4vVvr50RroS2oP+rwKKKCAAgooULHA0ksvHftYtGhRxX3ZgQIKtF+B9B5P7/n2O1JHpoACCiiggAIKKFBvAgbQ622Puj0KKKCAAgp0IIFu3brFrf3kk0/CwoULO9CWu6kKdBwB3tu8x2npPd9xtt4tVUABBRRQQAEFFFjSAtZAX9J7wOdXQAEFFFBAgbIFVlxxxbBgwYJAduqsWbPK7scVFVCg/Qt07tw58J63KaCAAgoooIACCijQlgJmoLelts+lgAIKKKCAAlUV6NSpU6D++bLLLhss7VBVWjtToN0I8N7mPc57nfe8TQEFFFBAAQUUUECBthQwA70ttX0uBRRQQAEFFKi6AAG1Hj16VL3fXB0y9/rs2bPDZ599Fj7//PNci3ifAgqUIUCQnPIsZJgbJC8D8P9W8RhVvp1rKlALAh4ra2EvOUYFFKhHAQPo9bhX3SYFFFBAAQUUaBWBGTNmhMWLF7dK33aqQEcW4ITUvHnz4lwGZJrbyhPwGFWem2spUCsCHitrZU85TgUUqDcBA+j1tkfdHgUUUEABBRRoFYE5c+bE4HmXLl1C9+7dQ9euXVvleexUgY4owEShc+fOjfMZ8F5bfvnlOyJDRdvsMaoiPldWoCYEPFbWxG5ykAooUIcC1kCvw53qJimggAIKKKBA9QXmz58fOzV4Xn1be1SAE1K8t2jpvaZKaQLJzWNUaW4urUAtCXisrKW95VgVUKCeBAyg19PedFsUUEABBRRQoNUEFi1aFPsmA92mgALVF0jvrfReq/4z1HePyS051vfWunUKdFyB9B5P7/mOK+GWK6CAAm0nYAC97ax9JgUUUEABBRSoAwEnOKyDnegmtEsB31vV2S06VsfRXhRorwK+x9vrnnFcCihQzwIG0Ot577ptCiiggAIKKKCAAgoooIACCiiggAIKKKCAAmULGEAvm84VFVBAAQUUUEABBRRQQAEFFFBAAQUUUEABBepZwAB6Pe9dt00BBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCgbAED6GXTuaICCiiggAIKKKCAAgoooIACCiiggAIKKKBAPQsYQK/nveu2KaCAAgoooIACCiiggAIKKKCAAgoooIACCpQtYAC9bDpXVEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFKhnAQPo9bx33TYFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBsgUMoJdN54oKKKCAAgoooIACCiiggAIKKKCAAgoooIAC9SxgAL2e967bpoACCiiggAIKKKCAAgoooIACCiiggAIKKFC2gAH0sulcUQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUKCeBQyg1/PeddsUUEABBRRQQAEFFMgS+OCDD8LIkSPD9OnTsx5p/ue0adPCiBEjwkcffdT8Qe9RQAEF6ljg1VdfDaNHjy7q+Oexso5fCG6aAgooEEIwgO7LQAEFFFBAAQVqVmDmzJlh2223DZ06dWpy4wtvOY0vwBtvvHGTvuh78uTJ5XTnOgq0OwGC50OHDg1jxoyJgfFPPvkk7xjff//9MHz48DB27Nhw2mmnhXnz5uVd1gcUUECBehIgeH700UfHAPr5558fFi1alHfzPFbmpfEBBRRQoG4EDKDXza50QxRQQAEFFOh4AksttVRYffXVw4ABA+Lte9/7XlUQlltuudCvX7/Gfrt3716Vfu2k9QQ4mbLjjjuGrl27Nrv16dMncHKkXtr8+fPDYYcdFreTn/xdTCMAzsklTgj17ds3BtJ5redrPXv2jM+z5pprhssuuyyMHz8+LF68ON/i3q9AzQqU+56qxQ1+4oknmh0j03GzlONJLWw7x32O/2zflClTih7yjBkzwrBhw8JDDz0UBg4cGA4++ODQpUuXvOt7rMxL4wMKKKBA3QgYQK+bXemGKKCAAgoo0PEEevToESZMmBAeeOCBeDvuuOOqgrDWWms16XfnnXeuSr92osCSEmhoaAgTJ06MgXAC4meeeWb4zne+U3A4XH2x1VZbxaA7gXYy0W+55ZZAXzYFFFCgHgUWLFgQzjvvvDB16tR4opGTjquttlrBTfVYWZDHBxVQQIG6EDCAXhe70Y1QQAEFFFBAAQU6tsDKK68c7r777rBw4cLG26RJkzo2SsbWP/nkk+HCCy+M95x00knxCouMh/P+SmBo0KBBMROdci+UfnnxxRfzLu8DCijQvgW22GKLxmMkx8uPP/44HHDAAe170G04ujvvvDNceeWVgZOGzBXRu3fvop7dY2VRTC6kgAIK1KyAAfSa3XUOXAEFFFBAAQUUUECBlgVmzZoVLrroojhp6D777BP22GOPWOe/5TX/u0Tnzp3DQQcdFLPRX3755UA94Dlz5hS7usspoIACNSHwxhtvxFJVnCyknM1PfvKTksbtsbIkLhdWQAEFakrAAHpN7S4Hq4ACCiiggAIKKKBAaQL3339/uPXWW8Oqq64afvWrX4Xll1++tA5CiCUMDjnkkLgeGZqPPvpoyX24ggIKKNBeBT7//PNw0003BWrEb7jhhmHIkCGBgHipjXIvHitLVXN5BRRQoP0LGEBv//vIESqggAIKKKCAAgooUJYA2ee33357XHf33XePNX3L6iiEmIG+yy67BLIzqQ/MpKQ2BRRQoB4E3nzzzXDXXXfFTeFKnXXXXbfszWLuCI+VZfO5ogIKKNAuBQygt8vd4qAUUEABBRRQoN4Fvvjii1hS4+qrrw77779/DGx27do1cNtggw3CwQcfHJ566qnAci21//znP/Gy82233TZQCzz107dv31iu47rrros1b1vqp5zHp0yZEp9vxx13DDNnzszZBffzOOM644wzmi3DfZmPsfz48ePDT3/603j/KqusEvbdd9/w7LPPtskElrk82SfHHntsi/tk8eLF4aWXXgqXXHJJIAjDepn7Y+jQoYEyKMVMxEmZFF4fu+66a8CAfvi52267hb/85S/NHHPdgdnDDz8c6/myD5ZZZplcixV1H5P2brfddnHZhx56KG5nUSu6UM0KcALm3nvvDcOGDQv9+/dvPL7wOuR1eeONNxZ1IoXXMstmvpZ5PfP+INDIpI1z585tdadK31O8v8lQ5vic3tscc3faaadYNzvXMfD666+P713eOx988EFR28hz4MOEv3//+98b18k+VtLfWWed1fj/B/uFeuYvvPBCUceYxo7L+IVjGCVPmJC4X79+TY5zp512WovHuWodKxnHa6+9FnjOzHGwf5hYvFhzrqp5+umnY83zAQMGlFTmKpvPY2W2iH8roIACtS9Q+jVJtb/NboECCiiggAIKKLDEBV599dV4iTiBjuw2bdq0wG3ixInhiCOOCKNGjQrLLrts9mIxQHLHHXcEgrLTp09v9vhzzz0XuH3ta1+LE0E2W6Ad3sFJg8MPPzwGgNLwPvroo3hpPYE8AtODBw+uKLiR+s3+ycmK2267LQYLsz3TPrn44otj8IxgzUorrZTdRQzAkH2Yq6X9cdVVV4WTTz45HHXUUTlLBBAQevDBB8Pxxx8fXweZfWFBQLyYRkmCRx55JGaMb7PNNrEsQTHrFVqGCQgpb8DrliDfpptuWmhxH6thgfnz54dTTjklTJgwodlW8Dq855574o3XxOWXXx569erVbDnuyPWeTgum9xXB1F/84hehe/fu6aGq/qzGe+r999+Px4bJkyc3GRtXZDzwwAPxNm7cuHDOOeeEzAAsQXAagVzcvvrVrzZZP9cf77zzTrybsku5Si6xPbz/jjzyyGbHykmTJsUSSxxnttxyy1zdV3zfggUL4gmDESNGxONLZofpOMe8CwSwjznmmPClL30pc5H4O8HqSo+VvEaZk+Hcc89tNo702mr2xDnuYB/+6U9/io/86Ec/Ct/61rdyLFXaXR4rS/NyaQUUUKC9C5iB3t73kONTQAEFFFBAgboVIEvtxBNPDATB+bL/3nvvhbfeeiteRr7DDjvE7SYIcfPNN+c0IDMxBc9ZnsvPWT/1Q0YdgS0uRe/UqVPOPtrTndTWTtmTZG//+c9/DgSSCGoTtCXIMWbMmPDiiy9WfdgEpHBmskyC5wTAqBuOZ/Y+ueyyy+JJDYI3uRqZ/yNHjgzUHiczkv3BT7L1+/TpE7eDrM0UsMnu47HHHosnEXhNsDzrpX6ef/75GNDEo6VGtu0//vGPuNjGG28ceL1V2r7+9a/HzFv6IVDGPrHVr8DSSy8dT/Rde+214ZlnnomvZV7PBMW5ImO55ZaLgdyzzz47ZyY6GconnXRSDPJmv5Z5bxMEpu/NN988LLVU6301rfQ9NWPGjPieJHjOiTMCx1zdgQXvydNPPz3ez3uWE4BPPvlk44tixRVXDN/+9rfD66+/HmbPnt14f6FfCFDTyCgnwz278X8GJxzok/3AVS8cp6644oqYtc4xjJN9XEFQ7cbJjgsvvDAGxnn/c0KTK1LYn9nHOU40cjURJ/NytUqOlYyDbeQ5GAf/Z/B/HuPgxj4fPnx4fI3meu7M+9i/XBlE41iZK+CfuXwxv3usLEbJZRRQQIHaETADvXb2lSNVQAEFFFBAgToSIKBClnGuwDbZwhtttFHMdCbrmkDroEGDmgUCfv/738dgLxlzBNq/8Y1vNBHq2bNnIAuuVhoBWQJyBP0p2ZImcKP0CJmYe+21VwxycKLgu9/9bk67crf1X//6V8wcJRBDEJ0AN2NJjX2CM6UsCKBTFmfgwIGxrEVahp+bbbZZDNzk2q/sn/XXXz9uGxncZIf/+Mc/DgQpUyPwQzCOANhvfvObWPImM9OdYNo3v/nNmOme6+qF1A8/KUXz9ttvx7vIDs58nszlSvkdk/XWWy+uQrCMjNpMp1L6ctn2LdCtW7dwwQUX5Axs8zrkJA7Z0QQwCRjzeqBsRmYj6E6QnOMdgVSCk5mNbGwC663ZKn1PEfy95pprYt1/sskJUpM5nd7jWPTu3Tu+93nP8t7l6iGO4Vw5hNFXvvKVwFVHxZ5wog8aE1KyH7Ib732OC1yRs+eeezbuI4LqjJdJLCnzRIC9GifOMp+ffU3gmjZ69Ohw9NFHNykNxXGOY+Vhhx0WzThBQvkajn2ZrdJjJcfPsWPHxuMPWf+Z/2fwPLy2OHnB/xfFHCtTqZx0fMscazm/e6wsR811FFBAgfYr0Hqn+dvvNjsyBRRQQAEFFFBgiQuQbZkCMLkGQ+ZhKo/Bpf8LFy5sthgZePXUCDgQEPrlL3/ZGDxP20fwhaxwGlnVZFdXs5EtTpCFQNiBBx6YMyhMMIxyBGRNEgi77777mu2XlvYrwe8f/OAHcegEn7P3KydLyKJkHIceemjOMjHFbjf1mFPgaK211ip2tRaXW2ONNeIyGOTLwm+xExdo9wIcnwplhXOCK5XgyBccbg/HqErfU5yE4kQmbe+99451tnMdu6m/TdCYRkY22eg0AugpiJ1Zj5ssc7LHqXWeJvpl+c8++yyQEU0jizlXNjTHSk5ukP2duY8YF8cnTliQHf/vf/879lOtfzheMYEwfbPvyfrONa8CJ2+5uooTn2R245HdKjlWctwhC59j0M477xyYIDmdcM1+nmL+TicsOClEybNqNY+V1ZK0HwUUUGDJCxhAX/L7wBEooIACCiiggAJlCZDtSSDl8ccfj7XS//a3vxU16WhZT9YGK+2xxx4xqztXcIogEqVoaGRVZgaiKh0aQRiy32lkTq6zzjp5uyQjlHITNAJDrFutRnAqZUFusskmMdO8kr4//fTTuDqvkWpkn6expCsdKKXDSQCbAvkEeM+StU2AnRNCXDXTlkH1arynGDtZ17yPmKg5X6CW4xY1xwkaE2BOAXSOXSmQSpZ+au+++268koS/KTeSyrYsWrSo8biS6qenddLPQsdKAvIp6F5t6w8//LDxWEkAnQB/vsa+T1dAcYVPNU+2cXVNKuXF8ThXnfh848p1f5rAln3rsTKXkPcpoIACCljCxdeAAgoooIACCiiwhAQImBA4ufHGG2OmcAriFjucrbfeOmY8chl7mtCPUh1k41H2hHIJ+YI9xT5HWy5H4CJX8DyNIWUGEhTKV1M3LVvKTzI+mSCQli/jM/VHcCoFkFmHgFJ2jeJ58+bF/UGmJvWRUyAt9ZHvJ+sRrKPxHF/+8pfzLVrU/ekkAxPiUcqgWq2aAaZqjcl+WkeAuQFeeeWVOIkvmdy8nks5aUQZE67aOPXUU8Nf//rXsP3228eAOsenXXbZJWZLp2Bva2xBNd5T6djA+yj7vZ49Zh7nKhMC6ClYTgkWMrJp1EAnqM9xhOM9E2nSmBiYbHGutOHx9N7NF0Bv6VgZO22Ff7jyhytbaHgUOl6zX9OxkgxvTuhl7+tyj5XskxRAr8aEnwTkaZTaSVcLxDsq/MdjZYWArq6AAgq0IwED6O1oZzgUBRRQQAEFFOg4AgQUTjjhhHgZerlbTUkRJrMjy++ss86KmegEa6nfzY1gOlmf++23X6zFW+7z1Pt6ZFGngFUx25oC+bmWZWJBAobUfS61ffHFF43ZuQTdKg2+dO/ePQ6hlMkLSx2zy9evABnD559/fjj33HNLCppninACj3rcXFFx3nnnxeMdxz5KNXGjjveRRx4ZJw+mZnW1WzXeU6m8RzFj4z1Hpnp2SyWUOOHGiVNOTHDylGx1gusE06lZTgCdiT9ZjjIs1TzxlT2mcv7mWJkC1y2tz0mCQuOv5FjJCdR0IqfSE41sRzpW4o5/SydKWtp2H1dAAQUUqD8BS7jU3z51ixRQQAEFFFCgnQuQxffb3/42BpPSpHRkLBJYIfsw3UaOHNnilhCgoqwApRHIXmYy0f79+8f1CKYfddRRgYnlUk3dFjvsgAsQ8Col6/DNN9+MSpzAyJzgj8kKqftL8JxaurfddlsMzKf9yc+PP/44BgvbgjkFgQg0VTNjPwUUmcg1c4LTttgmn6NtBAjw3nDDDXGCUF4/HEcoWUQWcebrmXr9LTWylJkw8qabbgq8d5hUkrrVvO8IyDIJ6a677hr7b6mvJfF4oRNm2eNJwW/uT0FZfk+Z2Jyowy+Vb+HkJydSaUwqzfGBExeYkw1daWmS2HEV/2GfEdgvpmXWcme9zKuh2tuxMu3jal/d5LGymFeKyyiggAK1IWAAvTb2k6NUQAEFFFBAgToSIBB15513xi066aSTYoCbYGehy+Fb2nwmZCMYf9BBB8XJLak5u//++8fVKCVCmZgl1cgCrTSAS0CPTGoamarVDNxSVoBgOI3AVqFavQS/yFKkUe4lM8OSwDkBRYJFY8aMiWV0VlhhhbhsMf+QsZmycAuNgzGkmr2F+sWIIDctlZMotHyxj6UTCLxm21uAr9htcLnCApQaueuuu+JCTBRJCZa11167SRC0cA/NH+X4xuTIe+21V/jd734XA+annHJKfL9QymTixIkxuNx8zfLvqcZ7Kr3HOf6k8iX5RsTJUU6G0lLWOb/zvua9mMo+pfItTCj8wx/+MFCOi+MHx22C55wMzT6+5HvOtryfYyXHNxoeHJfzNU4Ipyt7OIGQWb6l0mNlZiA/BalzjYNjeaHjeVonneBg0uVUziU9VslPj5WV6LmuAgoo0L4EDKC3r/3haBRQQAEFFFCgAwgQzCRIQuvdu3fOwDlBZyaTK6cRqKIOL1nuBGZo1NclI7C1WgoMZfdPRt8111wTa/xmP1bK3wT0Uo34Pn36VDWAThCYCVlplFFIQY9c4yNo9Mgjj8SHCH5lBpAJftGoyZsmDczuA498+5VSBCmQQ19ks2Y31r/iiivClClTsh9q9jcZrEx6SuNqhEpPYtAPwajkQ0A1BRebPbl31LQAQcS33347bgP7OfN1nrlhnMwpt5H1e/zxxwcmxKQRNKYmdjVbNd5T6623Xsy65phN2ZV87yPuf+ihh2IAvW/fvo0nr9ge/DjhRECZIHwq38Kko5RwIUOfwPuf//zn8MYbb0QCgu7LLLNMNTkq7oux8n8WjUmrU4A8V8cEowmU08i052RGapUeK7FME5i+9NJLOfcJV12dfvrpjfNKpOfO9ZPXIlcN0aj5X43msbIaivahgAIKtB8BA+jtZ184EgUUUEABBRToIAKZl/bnKq1CoIayBpRjqaSRAci1qy67AAAgAElEQVSNRiZ0ly5dKuku57opY5pgCYGMzEaw97rrrgtMclpJ42TCLbfc0ljyhskHK60Pnjke+howYECsR8x2UJs5VyCP+3iMZQgisU7mVQNpvxJUIhM1u3GS4bjjjosnFLIf42/GQeCN9sc//jFms2dmeJbqSVkaJpKlMYFjNTIr2QYmkqRhUI36w7Ez/2lXApQmSldlEPBNx5E0SN6Tt956azjiiCPSXWX95GRSyhDm9ZpZ5qOsDrNWqsZ7ap111glbbbVV7PnKK6+MQe6sp4l/EvzmcdrPfvazJifRsOSEFkFyTmaRcU9QmZMTjJHlyapOk4nSB8HqzBJRseMl/A9j5LhH4wqFSZMmNc7bkDk0/l8bN25c3F5O4nKCILNVeqzEMh3b7rvvvpi5n9l/Kg3E1VfFNILxmSdRcx3/i+kncxmPlZka/q6AAgrUvoCTiNb+PnQLFFBAAQUUUKDGBAiaEHyklMsZZ5wRA9tkM1OW4+677w5XX311ixO1EdSi/AuZzt///vdj1nOq40pAioApwZzHH388lnbZbrvtqhp0TuTrrrtuIIvyT3/6U5NtIXjABITXX399zN5cffXVmwXYUx/pJxmoZCwy4SCZzQSPqZV72WWXxRvLHXjggWGjjTZKqzT+ZJupz5zZ0t8E+zgpgVlqZHamIA73EbimtARj5vm4SoByOCmgTcDr8ssvj3WKCSINGzasMRMz9UkAhscIknEChP3DpIBY3HHHHTFwXqjcAP3wOthpp51icIoa+GT4su8IfifPgQMHxuWGDx+enjrnz8zgIYG5f/7zn/EkQc6Fi7wzlZ5g8sN+/foVuZaL1ZoAAUpqXT/55JMxSMr+3nvvveOxKpVbueeeewpuFu/JUaNGxWV4XXNlRrrCgpNBnIiiHjq10Xnf8DrnZ7Vbpe8pSo/8+te/ju993r977rlnOPzww2PddkrS8P6+/fbbw8UXXxxruvP+PPjgg5scbznepJONHBs4RjA3RSrxxP8HBJp5nzK5Ji2zBEy1TDimcnIP/9S4MildScBPgs+ZgXv2SWb2OHNu7L777vEECscg9iPlwjjWcaKFk3+caOT/IMqKcazk9ZPZKj1WMh6cb7755vj8XMnA8ZarBZ555pmYec7z89z8H8LrrFBjGwnIT548Ob7mOf6ngHqh9Qo95rGykI6PKaCAAjUo0FBhe+uttxq42RRQQAEFFFBAgSUtcP3111OQtWHUqFFlDeWVV15p2GijjeKN3zNb+syzcOHChkpvCxYsaLj88ssblltuuThexpx9++Uvf9lwww03xPsHDBjQ8N577zV5Xv7m/uz1sv/u1atXw80339zAc1Y67lzr0++1116bd1u22GKLhj/84Q8NBxxwQBzryJEjm42D+7LHnf33Siut1DBu3LiGTz75pNn6jGvSpEkt9pHZZ65xzJw5s2Ho0KEF+2EcV111VcOnn37abByMbcSIEXnXZ3+fdNJJcTsYCyYff/xxs34efvjhBvZb5njT77vsskvDq6++2ri9+fpI+2r69OkNW2+9dexrv/32a5g1a1az50vLtvTzww8/bNhnn32q0le+50rvs8z3nr8XJ5Ds8tmWev/f/va3hg033DDn65DXY58+fRpuueWWxtfXo48+2uS1xWs7ve/T6zfXT95TF110Uc73VKljzrd8pe8pjnNsXyEPti29P3ONY/To0Y2WvXv3bnjuuecavej/rLPOanycvrI96TMdKwu97//xj380jpPjYuZYit0nmfsp1zjefPPNhiFDhjQZb+Y6/M4x7K677sr5f081jpUcg8eMGZPz/x6OtaeddlrD+++/3/gazLbIdOF39gf7hbEPHz68Yf78+U3sspcv9LfHyuKOWS6lgAIKtIVA+nxU6XNZwqUGT3o4ZAUUUEABBRSobQHKfpB9SAbnr371q5ilxxb16tUrZjaSzU0WdKrxmmtryZ7++c9/Hvbbb79YXiBzUk2y/shiHj9+fCwDQsmTzFIjufor9z76JSOTcg5kJaZxkJVOCRq2kUnyyNRsqTHuNOkly5IV2L9//3DmmWfGkgeHHHJIq9YE5vnImKUOOhmmqUQAY2F70jj23XffnKUmyDI94YQTYvZ4pgX9nHzyyfFqAOrSt1Q3nMxuyiMcddRRja8NHMii5OoEnIptZH6yf2hkyaaaxMWun7kcE6Qy+S1OvPZSiY/MZfy9fgTIwL3tttvi1RTpvcD7m9c2V5ZQ75v3Nlc65GpkCe+www4xG5vs6szXLf1QCiS9p7jao9rlWzLHVOl7iuMcJVf+8Ic/xCtR2K50rGO7OCbce++9sWRV5nZmjiFzXoRNN920iQf9c4xJmdocB1P/mX20l985nk+YMCG+Bvg/iP+7aBwbttlmm8b/e/g91/891ThW8no55phj4v89O++8c3xuzNL/rWSkZ05c2pId27DbbrvFxXjdZ5cka2n9zMc9VmZq+LsCCihQHwKdiMBXsilpcpl0OV4lfbmuAgoooIACCihQiQCXX++zzz4xCHrqqaeW3BW1aQcPHhzXo6xACgpwR/rMQ11aW3UFKGNDYPmAAw4I55xzTklBj+qOpD57o/wLJx8IynMyhfIKqZxEsVtMmQrKUhAkPPTQQ8OYMWNaZT+lOQH8blHsnvn/y3mM+v8W/qZAOQJ8BhgyZEgsC1Pucc5jZTnyrqOAAgq0nkD6fFTpZ0sz0FtvH9mzAgoooIACCiiggAJLXIDa+GTUk92aarFn1kBuaYBp8lKC59SEJ1u4lMzOlvr3cQUUUKA9CDCnx7HHHhuHwgTYXHFTSr6hx8r2sBcdgwIKKNA6AgbQW8fVXhVQQAEFFFBAAQUUaDcClLAgiE5jssPHHnusqLERPLrlllvC2LFjY4kE+mCiPpsCCihQbwKUm6EcDFfbMPE0V0S98sorRW2mx8qimFxIAQUUqFmBzjU7cgeugAIKKKCAAh1eYNasWWHo0KHhzTffjBYffPBBVUxee+21WE4kZdlyKff3v//9qvTdHjqZO3duWLBgQUVDodYtNY5ttSFAvWBex2+99Vasr3/kkUfGOsUE1gs1Au2UQyKYRKmdQYMG5axpXKgPH1OgVAGPUaWKuXy1BJjbYfjw4eHdd98NU6dOjeWvLr/88iYl3XI9l8fKXCrep4ACCtSPgAH0+tmXbokCCiiggAIdTuCLL76IAcEHH3ywqttOsDAzQ5dJ0uqpjRs3LtYcr2SbmCSNSfVstSOQHRi68sor40SpnAzJ1d55551Y63z69OkxI5MAfGtO9JhrDN7XMQU8RnXM/d5etpq5TkaPHh2oZ87Ey0yaO2LEiNClS5ecQ/RYmZPFOxVQQIG6EjCAXle7041RQAEFFFCgYwmsvPLK4f7776/aRjNp6HPPPZezvzQBTc4HvVOBGhEgMMQkouuss0445phjYlmWfENfbbXVwnnnnRdLuFAXmAC8TQEFFOgIAr179w4TJkwIU6ZMCSeeeGLe4DkWHis7wivCbVRAgY4u0KmhlFkxcmilL5OVzmaao2vvUkABBRRQQAEF2o1A+sxDANKmgAKtIzBjxozYsd8tSvf1GFW6mWsoUKsCHitrdc85bgUUaGuB9Pmo0s+WTiLa1nvO51NAAQUUUEABBRRQQAEFFFBAAQUUUEABBRSoCQED6DWxmxykAgoooIACCiiggAIKKKCAAgoooIACCiigQFsLGEBva3GfTwEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUKAmBAyg18RucpAKKKCAAgoooIACCiiggAIKKKCAAgoooIACbS1gAL2txX0+BRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgZoQMIBeE7vJQSqggAIKKKCAAgoooIACCiiggAIKKKCAAgq0tYAB9LYW9/kUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFakLAAHpN7CYHqYACCiiggAIKKKCAAgoooIACCiiggAIKKNDWAgbQ21rc51NAAQUUUECBmhRYeuml47gXLVpUk+N30Aq0d4H03krvtfY+3vY2vuSWHNvb+ByPAgpURyC9x9N7vjq92osCCiigQCEBA+iFdHxMAQUUUEABBRT4P4Fu3brF3z755JOwcOFCXRRQoIoCvKd4b9HSe62K3XeIrpKbx6gOsbvdyA4q4LGyg+54N1sBBZa4QOclPgIHoIACCiiggAIK1IDAiiuuGBYsWBDI/Jo1a1YNjNghKlB7Ap07dw6812ylC3iMKt3MNRSoVQGPlbW65xy3AgrUqoAZ6LW65xy3AgoooIACCrSpQKdOnULPnj3DsssuG7xsuk3pfbIOIMB7ivcW7zHea7bSBTxGlW7mGgrUmoDHylrbY45XAQXqRcAM9HrZk26HAgoooIACCrS6AAGqHj16tPrz+AQKKKBAOQJteYxqaGgIs2fPDp999ln4/PPPyxmu6yigQA4BguSUZOKqEk8o5gDyLgUUUGAJCBhAXwLoPqUCCiiggAIKKKCAAgooUMsCM2bMCIsXL67lTXDsCrRLAU5IzZs3L863wlU5NgUUUECBJS9gAH3J7wNHoIACCiiggAIKKKCAAgrUjMCcOXNi8LxLly6he/fuoWvXrjUzdgeqQHsXYKLQuXPnxjlXeK8tv/zy7X3Ijk8BBRSoewFroNf9LnYDFVBAAQUUUEABBRRQQIHqCcyfPz92ZvC8eqb2pEAS4IQU7y1aeq+lx/ypgAIKKLBkBAygLxl3n1UBBRRQQAEFFFBAAQUUqEmBRYsWxXGTgW5TQIHqC6T3VnqvVf8Z7FEBBRRQoBQBA+ilaLmsAgoooIACCiiggAIKKKBAFHCCQ18ICrSOgO+t1nG1VwUUUKBcAQPo5cq5ngIKKKCAAgoooIACCiiggAIKKKCAAgoooEBdCxhAr+vd68YpoIACCiiggAIKKKCAAgoooIACCiiggAIKlCtgAL1cOddTQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUqGsBA+h1vXvdOAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIFyBQyglyvnegoooIACCiiggAIKKKCAAgoooIACCiiggAJ1LWAAva53rxungAIKKKCAAgoooIACCiiggAIKKKCAAgooUK6AAfRy5VxPAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoK4FDKDX9e514xRQQAEFFKhvgZkzZ4Ztt902dOrUqclt9OjR9b3hbp0CCiiggAIKKKCAAgoooECbCHRuk2fxSRRQQAEFFFBAgVYQWGqppcLqq68eBgwYEHv/4IMPwrPPPtsKz2SXCiiggAIKKKCAAgoooIACHVHAAHpH3OtuswIKKKCAAnUi0KNHjzBhwoTGrZk8eXLYZ599Gv/2FwUUUEABBRRQQAEFFFBAAQUqEbCESyV6rquAAgoooIACCiiggAIKKKCAAjUpwJVrI0eODNOnT29x/NOmTQsjRowIH330UYvLuoACCiigQH0JGECvr/3p1iiggAIKKKCAAgoooEAHFKj2nBAECzfeeOMm80sw3wRX+tgUqAcBgudDhw4NY8aMiYHxTz75JO9mvf/++2H48OFh7Nix4bTTTgvz5s3Lu6wPKKCAAgrUn4AB9Prbp26RAgoooIACCiiggAIKdDCBzDkhmBfie9/7XlUElltuudCvX7841wT9du/evSr92knrCXAyZccddwxdu3ZtduvTp0/g5Ei9tPnz54fDDjssbic/+buYRgCcCcc5IdS3b98YSOe1nq/17NkzPs+aa64ZLrvssjB+/PiwePHifIt7vwIKKKBAnQlYA73Odqibo4ACCiiggAIKKKCAAh1PoLXmhFhrrbXiXBO9evVqRH377bcbf/cXBWpNoKGhIUycODEGwgmIn3nmmeE73/lOwc3g6outttoqBt0J1JOJziTmgwcPjldpFFzZBxVQQAEFal7ADPSa34VugAIKKKCAAgoooIACCiiggAL/FVh55ZXD3XffHRYuXNh4mzRpkjz/J/Dkk0+GCy+8MP510kknxSssisEhiD5o0KCYiU65F0q/vPjii8Ws6jIKKKCAAjUuYAC9xnegw1dAAQUUUEABBRRQQAEFFFBAgZYFZs2aFS666KI4aeg+++wT9thjj5IyyDt37hwOOuigmI3+8ssvh/PPPz/MmTOn5Sd2CQUUUECBmhYwgF7Tu8/BK6CAAgoooIACCiiggAIKKKBAMQL3339/uPXWW8Oqq64afvWrX4Xll1++mNWaLLPaaquFQw45JN535513hkcffbTJ4/6hgAIKKFB/AgbQ62+fukUKKKCAAgoooIACCiiggAIKKJAhQPb57bffHu/Zfffd4+ShGQ+X9Cv10HfZZZdAKZepU6cGJiW1KaCAAgrUr4AB9Prdt26ZAgoooIACCiiggAIKKNDuBb744otYUuPqq68O+++/fwxsdu3aNXDbYIMNwsEHHxyeeuqpwHIttf/85z9h/PjxYdtttw3UAk/99O3bN5bruO6662Jd8Jb6KefxKVOmxOfbcccdw8yZM3N2wf08zrjOOOOMZstwX+ZjLM/2/PSnP433r7LKKmHfffcNzz77bGAyzNZuuTzZJ8cee2yL+2Tx4sXhpZdeCpdcckmgXArrZe6PoUOHBsqgFLMdlEnh9bHrrrsGDOiHn7vttlv4y1/+UhQDZg8//HBYbrnl4j5YZpllilov10JM2rvddtvFhx566KG4nbmW8z4FFFBAgfoQ6Fwfm+FWKKCAAgoooIACCiiggAIK1KLAq6++GoYMGRJeeOGFZsOfNm1a4DZx4sRwxBFHhFGjRoVll1222XIEYe+4445AUHb69OnNHn/uuecCt6997WtxIshmC7TDOzhpcPjhhzdx+eijj8JNN90U7r333hiYHjx4cEk1vIvdTE5W3HbbbWHYsGHNPNM+ufjii+PJjdNOOy2stNJKzbp++umnY63wZg+EEPcF++Oqq64KJ598cjjqqKMC9cWzG/v1wQcfDMcff3x8HWQ+jgUB8WLa559/Hh555JGYMb7NNtuEDTfcsJjVCi6zxRb/j707gbtq2v84vhCKa8hwiYgMT2VIqRuFqG4ylajMiUopfylXXaVQVMaEqyLXUOFGEqmkNBqihCKJonnUoNHQ+b++69517Gc/+5znPGd6nnPOZ71ejzPtvdda773Pyf7ttX+rtt2OjtuPPvrI1KxZM+ryfIgAAgggkLkCjEDP3H1HyxFAAAEEEEAAAQQQQACBrBDQiN5u3brZILgCtKtWrTLLli0z77zzjrnkkktsHzX548iRIwP7+/nnn4eD51pe62l9tx3lqR4yZIg5+eSTUxJwDmxUAm8qt3bbtm1t8Fyjtz/88EOzYsUKG9RW8FepQ/r162fmz5+fQC3BqypoLWdNlqmLEQ0bNrR5w+Xp3yeDBw+2FzV27NgRuDGN/L/33nuNco8vXrzY7g89arR+jRo1bD/69u1rpk+fHrj+jBkz7EUEHRNaXuu57Xz55ZfmueeeiykYrhHs8+bNs3WcccYZRsdbouWoo46yo+q1HV0M0D6hIIAAAghkp0DBS7zZ2U96hQACCCCAAAIIIIAAAgggUAIFTjrpJDvKeI899ijQOo0Wrlq1qh3prFHXCrQ2a9bMpuHwLjxp0iQb7D3nnHOMAu3ly5f3fmyOOOIIoxHDmVIUkFWqEQX9lbLFjc5W+hdNgHn11Vfb9Ce6UHDqqacm9aLAd999Zx599FEbEFYQXQFutcUV7RM5a3S6AuhKi9O4cWNTr149t4h9POuss4wC4EH7VfunSpUqtm8awa3R4XXr1jV77bVXeBu6YPDAAw/Y/dq6dWub8sY70l0peo477jijke5Bdy+EN2SMUSqa5cuX27fy8vLy1eNdrijPZVKpUiW7ioL6GhHvdSrKtlgWAQQQQKBkCzACvWTvH1qHAAIIIIAAAggggAACCGS1wJ577hkYZHWdVq5rlx5j3bp1gTnMlW87m4oCscod3qpVq3Dw3PVPgWeNClfRqGqNrk5m0WhxBaQrV65sbrnllsCgsNLodO7c2ear18jr8ePHF9gvhe1XBb9r1aplm67g86+//pqvG7pYojsH1I4OHToEponJt0KUF8ol74LsFStWjLJk0T469thj7QoyiDQKv2hbZGkEEEAAgZIoQAC9JO4V2oQAAggggAACCCCAAAIIIBCzgCaoVNB55syZNlf6nDlzYpp0NOYK0rxg8+bN7ajuoNHbZcqUsalo1KQlS5YYXVRIVlEgWKPfVTTK/MQTT4y46aOPPtqcffbZ9nNNBprMFCYKpistj0r16tXtSPOIDYnhg+3bt9uldIx4R7nHsGrURdydDkqlo4sAFAQQQACB7BQghUt27ld6hQACCCCAAAIIIIAAAghkjMCuXbtsuo/XXnvNjhR2QdxYO1C/fn3TsWNH079/f/Puu+/aP6XquPLKK43SnijvtUuDEus2i3M5BXmDgueuTZoMVUUj7zVBZrLKzp07zerVq+3mlONbwfpIZZ999gmnytE669evN0qr4i3btm2z++Ltt982ylmuXOaxFK2nyWVVFKTeb7/9Ylkt4jLuIsPxxx9vDj744IjLFfWDZAbji1o3yyOAAAIIpE+AEejps6YmBBBAAAEEEEAAAQQQQAABn4AmqlSe74svvti8/PLL4RHQvsWivlRKkV69ehkFajVyWkXBWuXvVu7zatWqmUGDBhkFZimRBTSK2gWbIy/15ycukP/nO38+mzt3rp0AVpOgalLSWIPn2sLu3bvtxQE9L126dMKjxv/yl7/YhmnE/qZNm/5sJM8QQAABBBCIQYAAegxILIIAAggggAACCCCAAAIIIJB8AeXvvv/++81bb71lKlSoYJ599lmzatUqoxHpSuPh/u69995CK9cI80aNGhlNKKrRy5pM1E1sqeBtp06dzI033mjWrFlT6LZydQGlOClbtmzM3V+6dKldVhcwFOh2RROAduvWzXz00Ufm9NNPN2+++aYNzLv9qcfNmzebtm3bulVS+uhGxivNTDJH7Ovij4omcvVOcJrSzrBxBBBAAIG0CxBATzs5FSKAAAIIIIAAAggggAACCEhAubPHjBljMf75z3/aALeCndHSlxQmp8krFYxv166dndzyu+++M23atLGraYS60sQUV9HI6kQDuKFQyOY+Vx8OP/zwpAZulbJFwXCVlStXRp0YU0FwpW1RUboXb2oUBc41AagC8v369bNpdA466CC7bCz/UXoY9U0lWjvUhq1btxa6SQW3FeRWWbx4caHLx7qAu4CgY/bAAw+MdTWWQwABBBDIMAEC6Bm2w2guAggggAACCCCAAAIIIJAtAgpmusknK1euHBg4V9D5t99+i6vLCsQfd9xxdpS78qSr/PDDD0a5vlNVXD5w//aVr/zFF180kydP9n9UpNdKQeJyxNeoUSOpAXQFgTUhq8qsWbOMCxAHNVDpUKZMmWI/qlWrVr4Asi5aqCjn+LHHHhu0uk3REmm/Kue5m6BT29q4cWOBbchTdyy8+uqrBT7zv3HYYYcZTXqqorsREr2Ioe3s2LEj7HPCCSfku4Dgr5/XCCCAAAKZLUAAPbP3H61HAAEEEEAAAQQQQAABBDJWwOWmVgeCUqsouH7ffffZdCyJdFIpYfSnopHQe++9dyKbC1zXjZj+6quvzDfffJNvGQV7hw0bZic5zfdBEV/oYsIbb7wRTnlz+eWXJ5wf3NsETYrZsGFDU65cOTuZ67/+9a/AvPHKJa/P1Fdd+NA63rsG3H5VPnWl6fEXXWS488477QUF/2d6rXb87W9/sx9NmzbNjmbXyHtXiuqptDSaSFZl9uzZZu3atW5TcT+qD5oYVUUGiU50GndDWBEBBBBAIOUCpVJeAxUggAACCCCAAAIIIIAAAgggECCgkbsKPiqVy4MPPmgD2xrNrLQcY8eONS+88IKZP39+wJp/vrVhwwaj9C8a6XzmmWfaUc9uckuNElbAdOjQoWbmzJk2tctFF12U1KCza8nJJ59szjvvPDN9+vR8fVGgdcCAAWb48OHmpJNOMsccc0yBALvbhntUDnelQalevbod2azgsfKKDx482P5puVtuucVUrVrVrRJ+VJ+3b98efq0n7rUC8LooITNX9t13X+MC3npPgeurr77atln16S4BpcNxAe1PP/3UDBkyxEycONGmaLn77rvtPnTb06NGsSt9i/LZ6wKI9k+VKlWMLJTvXiPxXf5w73re5zoOLrvsMvPOO+8Y5cBXuhbtOwW/nWfjxo2NluvRo4d31QLPvQF53QHw9ddf24sEBRYswhu6C0AWuthw7rnnFmFNFkUAAQQQyDQBAuiZtsdoLwIIIIAAAggggAACCCCQJQIKnt9xxx3mH//4hx3NfMUVVxToWatWrewI52uvvbbAZ+4NBWpfeukl9zLwMS8vz/Tp08ecffbZgZ8n+qYC48q1Pnfu3MC+1K5d2zzwwAM25Yh/hLq/buUP119QUT7vgQMHmptvvtlo4lR/UYBak6UGFV2MUDu8RcFpbwBaAfV77rnHXsx4+OGHbaBcwXJ/UTseeeQR06xZs3yjz7VcnTp1TOfOnU3v3r3N+++/b/+86yu4rqC6gs+a3DWo6CKIjg2lcFHaFV0w8BaNvlf9utAQS1G6G6XxUQBdefB1HLh877Gs711Go+oV2Ff5+9//bnRsURBAAAEEsleg4L+22dtXeoYAAggggAACWSagnKhdu3YN5yDVreIUBBBAAIHMEVDaDwV7FUjXaPMPPvjAjkxWQFKByRYtWtiRz5988knETmn0tILrRx55pP33QGlFfv75Z7u8JhM9/fTT7cjlpk2bGk32mKqivqi9aodGaCs/uNqhUenNmzc3119/vU3z4fKGR2uH2q0gsxt9r+c1a9Y0DRo0sHVEyisebZtF+Uz1Kfh95ZVX2tQzGr3v8q6rP40aNYraDjIqPaYAACAASURBVAXh77rrLttmjTZ3FkqjcvHFF5urrrrKVKpUqdAJXTWyW4FqpYvRhQGNWq9Xr57RRRWNTlcAPNYAuoL12j8KoI8ePdq2QcdYPEUXNzT5rZx07MUbiI+nbtZBAAEEEEi/wB4hbyKxOOpfvny5XctN8BHHJlgFAQQQQAABBBCIS0C3oF933XXmvffey7e+Tvp79uyZ7z1eIIAAArkkMGLECBuwjff3UCN+FeRU+c9//pNvhK07BzziiCNyiTQtfVUam/vvv9+0bdvWPProo6ZMmTJpqTdXKlH6l1tvvdUG5TWCXYF5l7s+VgOloWnfvr0ZN26c6dChg+nXr19K9pObE4BYS6x7huUQQACBggLu/1kS/S1lBHpBW95BAAEEEEAAgQwR0EjCCRMmZEhraSYCCCCAAAIIFKeA0sLcdtttNi++RrRrVL9SzQSlwglqp5u8VMFz5YRXbngucgRJ8R4CCCCQXQJ7Zld36A0CCCCAAAIIIIAAAggggAACCCAQLKC0MAqiqzz99NNmxowZwQv63tXN+2+88Ybp37+/Td2ibSgNDQUBBBBAIPsFGIGe/fuYHiKAAAIIIIAAAggggECWC6RqTojFixfbdCJulK1SVpx55plZo7l161aza9euhPqjPNj77LNPQttg5fQJaLS5juNly5aZwYMHm9tvv90888wzRoH1aEWBdqWH++WXX4xS7QRNnhptfT5DAAEEEMhcAQLombvvaDkCCCCAAAIIIIAAAgggYAV2795tA4ITJ05MqoiChd4Rui1btkzq9ot7YwMHDrQ5xxNphyaUrF27diKbYN00C2jSzx49epiVK1eat99+2wwdOtRoglNdDAkqK1assLnONYmp8p8rAB9r2peg7fEeAggggEBmCRBAz6z9RWsRQAABBBBAAAEEEEAAgQICyZ4TIi8vz3zxxRcF6tEbbkKuwA95E4EMEdAkuJpE9MQTT7R50CMFz9Wdo48+2jz++OM2hUuXLl2MAvAUBBBAAIHcEdgjpEReCRT3P0+JzmaaQBNYFQEEEEAAAQQQQAABBBBAIE0C7hxQAUgKAgikRmDNmjV2w8RaUuPLVhFAIDcE3P+zJPpbyiSiuXG80EsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBIgoQQC8iGIsjgAACCCCAAAIIIIAAAggggAACCCCAAAII5IYAAfTc2M/0EgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCIAgTQiwjG4ggggAACCCCAAAIIIIAAAggggAACCCCAAAK5IUAAPTf2M71EAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKKIAAfQigrE4AggggAACCCCAAAIIIIAAAggggAACCCCAQG4IEEDPjf1MLxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKKEAAvYhgLI4AAggggAACCCCAAAIIIIAAAggggAACCCCQGwIE0HNjP9NLBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSIKEEAvIhiLI4AAAggggAACCCCAAAK5LLDXXnvZ7v/222+5zEDfEUiZgPtuue9ayipiwwgggAACMQkQQI+JiYUQQAABBBBAAAEEEEAAAQQkULp0aQvxyy+/mF9//RUUBBBIooC+U/puqbjvWhI3z6YQQAABBOIQKBXHOqyCAAIIIIAAAggggAACCCCQowIHH3yw2bVrl9Eo2Y0bN+aoAt1GILUCpUqVMvquURBAAAEEil+AEejFvw9oAQIIIIAAAggggAACCCCQMQJ77LGHOeKII8z+++9vSDGRMbuNhmaIgL5T+m7pO6bvGgUBBBBAoPgFGIFe/PuAFiCAAAIIIIAAAggggAACGSWgwF7ZsmUzqs00FgEEEEAAAQQQiEeAEejxqLEOAggggAACCCCAAAIIIIAAAggggAACCCCAQNYLEEDP+l1MBxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiESCAHo8a6yCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkvQAB9KzfxXQQAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIB4BAujxqLEOAggggAACCCCAAAIIIIAAAggggAACCCCAQNY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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can download, upload data and even mount it depending on the execution context, presumably the aim with azmls is to support hetrogeneous computation contexts.\n",
    "\n",
    "When we mount, it returns an environmental variable (?) giving the mount path. Mount only works in the azureml hosted compute environment\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thinking out loud, the only reason to use this data store feature in AML therefore is when doing things in a pipeline or azureml compute, useless for local computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try doing some stuff to our train.csv from the kaggle dataset with the azure data prep SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a feature to \"auto read\" the file and figure out the encoding etc. Of particular note is this executed much faster than Pandas takes to load the file but I assume that it hasn't actually loaded the entire file as this is some kind of streaming data library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "dflow = dprep.auto_read_file(path=\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 22] Invalid argument",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-69-2627c16b39ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdflow_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\dataflow.py\u001b[0m in \u001b[0;36mrandom_split\u001b[1;34m(self, percentage, seed)\u001b[0m\n\u001b[0;32m    912\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    913\u001b[0m         \u001b[0mdflow_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataflow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 914\u001b[1;33m         \u001b[0mreference\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_activity_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdflow_1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    915\u001b[0m         \u001b[0mreference\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreferenced_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdflow_1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_steps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m         dflow_2 = dflow_2.add_step('Microsoft.DPrep.ReferenceAndInverseSplitBlock', {\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\references.py\u001b[0m in \u001b[0;36mmake_activity_reference\u001b[1;34m(reference)\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mdataflow\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataflow\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreference\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataflow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_to_anonymous_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreference\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreference\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExternalReference\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m         return ActivityReference(reference_container_path=reference._package_path,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\references.py\u001b[0m in \u001b[0;36m_to_anonymous_reference\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msteps_to_block_datas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     return get_engine_api().create_anonymous_reference(\n\u001b[1;32m---> 12\u001b[1;33m         CreateAnonymousReferenceMessageArguments(blocks))\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\_aml_helper.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(op_code, message)\u001b[0m\n\u001b[0;32m     36\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchanged\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m                 \u001b[0mengine_api_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_environment_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchanged\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msend_message_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\api.py\u001b[0m in \u001b[0;36mcreate_anonymous_reference\u001b[1;34m(self, message_args)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mupdate_aml_env_vars\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_engine_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcreate_anonymous_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage_args\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCreateAnonymousReferenceMessageArguments\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mActivityReference\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Engine.CreateAnonymousReference'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mActivityReference\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\engine.py\u001b[0m in \u001b[0;36msend_message\u001b[1;34m(self, op_code, message)\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;34m\"opCode\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mop_code\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m             \u001b[1;34m\"data\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m         }, cls=CustomEncoder))\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\engine.py\u001b[0m in \u001b[0;36m_write_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument"
     ]
    }
   ],
   "source": [
    "dflow_sample = dflow.random_split(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a feature to get some summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 22] Invalid argument",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\dataflow.py\u001b[0m in \u001b[0;36mget_profile\u001b[1;34m(self, include_stype_counts, number_of_histogram_bins)\u001b[0m\n\u001b[0;32m    278\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mprofile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m         \"\"\"\n\u001b[1;32m--> 280\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_if_missing_secrets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    281\u001b[0m         return DataProfile._from_execution(self._engine_api,\n\u001b[0;32m    282\u001b[0m                                            \u001b[0mmake_activity_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\dataflow.py\u001b[0m in \u001b[0;36m_raise_if_missing_secrets\u001b[1;34m(self, secrets)\u001b[0m\n\u001b[0;32m   1690\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1691\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_raise_if_missing_secrets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecrets\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1692\u001b[1;33m         \u001b[0mmissing_secrets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_missing_secrets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1693\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_secrets\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1694\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\dataflow.py\u001b[0m in \u001b[0;36mget_missing_secrets\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1466\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mA\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0msecret\u001b[0m \u001b[0mIDs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1467\u001b[0m         \"\"\"\n\u001b[1;32m-> 1468\u001b[1;33m         \u001b[0msecrets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_secrets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGetSecretsMessageArguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps_to_block_datas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1469\u001b[0m         missing_secret_ids = map(\n\u001b[0;32m   1470\u001b[0m             \u001b[1;32mlambda\u001b[0m \u001b[0msecret\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msecret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\_aml_helper.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(op_code, message)\u001b[0m\n\u001b[0;32m     36\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchanged\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m                 \u001b[0mengine_api_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_environment_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchanged\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msend_message_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\api.py\u001b[0m in \u001b[0;36mget_secrets\u001b[1;34m(self, message_args)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mupdate_aml_env_vars\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_engine_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_secrets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage_args\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGetSecretsMessageArguments\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSecretData\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Engine.GetSecrets'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtypedefinitions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSecretData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\engine.py\u001b[0m in \u001b[0;36msend_message\u001b[1;34m(self, op_code, message)\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;34m\"opCode\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mop_code\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m             \u001b[1;34m\"data\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m         }, cls=CustomEncoder))\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\azureml\\dataprep\\api\\engineapi\\engine.py\u001b[0m in \u001b[0;36m_write_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dflow_sample.get_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the docs.. The data prep seems to give us;\n",
    "\n",
    "A nice fluent style API. I assume the justification for this to exist over just using Pandas is that this uses a streaming architecture i.e. you \n",
    "don't need all data to be in memory at the same time, flows can be persisted to files, and support native hetergeneous execution i.e. hopefully on \n",
    "a Spark context it will use Spark\n",
    "\n",
    "I can see from the task manager that this library seems to be running on .NET and not utilizing more than one core at least on my machine context here\n",
    "\n",
    "* Summary statistics\n",
    "* Mutate/split column by example\n",
    "* Assertions and filtering out errors from these assertions\n",
    "* Replace nulls/nas\n",
    "* Rename/Select columns\n",
    "* Head\n",
    "* Convert types\n",
    "* Filtering\n",
    "* Replacements\n",
    "\n",
    "Of course the obvious thing we have to do in machine learning is normalisation and encoding, can't see anything in the tutorials to do that? I can see stuff in the API reference. Of course on this challenge we can't one-hot encode the categories because there are too many.\n",
    "\n",
    "Slightly struggling to think of a reason to use this library over the standard open source libraries. Apart from anything else, there is zero documentation and no examples online whatsoever of people using this. The MinMaxScalerBuilder API seems onerous. Why would I use this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 7 required positional arguments: 'dataflow', 'engine_api', 'column', 'range_min', 'range_max', 'data_min', and 'data_max'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-63-0812338770c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdprep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMinMaxScalerBuilder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: __init__() missing 7 required positional arguments: 'dataflow', 'engine_api', 'column', 'range_min', 'range_max', 'data_min', and 'data_max'"
     ]
    }
   ],
   "source": [
    "dprep.api.builders.MinMaxScalerBuilder(dflow, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = \"my-first-experiment\"\n",
    "run = list(Experiment(workspace = ws, name = experiment_name).get_runs())[0]"
   ]
  }
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