{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\"Dask\n", "\n", "\n", "# Dask DataFrame - parallelized pandas\n", "\n", "Looks and feels like the pandas API, but for parallel and distributed workflows. \n", "\n", "At its core, the `dask.dataframe` module implements a \"blocked parallel\" `DataFrame` object that looks and feels like the pandas API, but for parallel and distributed workflows. One Dask `DataFrame` is comprised of many in-memory pandas `DataFrame`s separated along the index. One operation on a Dask `DataFrame` triggers many pandas operations on the constituent pandas `DataFrame`s in a way that is mindful of potential parallelism and memory constraints.\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\"Dask\n", "\n", "**Related Documentation**\n", "\n", "* [DataFrame documentation](https://docs.dask.org/en/latest/dataframe.html)\n", "* [DataFrame screencast](https://youtu.be/AT2XtFehFSQ)\n", "* [DataFrame API](https://docs.dask.org/en/latest/dataframe-api.html)\n", "* [DataFrame examples](https://examples.dask.org/dataframe.html)\n", "* [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)\n", "\n", "## When to use `dask.dataframe`\n", "\n", "pandas is great for tabular datasets that fit in memory. A general rule of thumb for pandas is:\n", "\n", "> \"Have 5 to 10 times as much RAM as the size of your dataset\"\n", ">\n", "> ~ Wes McKinney (2017) in [10 things I hate about pandas](https://wesmckinney.com/blog/apache-arrow-pandas-internals/)\n", "\n", "Here \"size of dataset\" means dataset size on _the disk_.\n", "\n", "Dask becomes useful when the datasets exceed the above rule.\n", "\n", "In this notebook, you will be working with the New York City Airline data. This dataset is only ~200MB, so that you can download it in a reasonable time, but `dask.dataframe` will scale to datasets **much** larger than memory.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the datasets you will be using in this notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:02.899546Z", "iopub.status.busy": "2023-06-29T15:00:02.899215Z", "iopub.status.idle": "2023-06-29T15:00:03.409016Z", "shell.execute_reply": "2023-06-29T15:00:03.408354Z" } }, "outputs": [], "source": [ "%run prep.py -d flights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up your local cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a local Dask cluster and connect it to the client. Don't worry about this bit of code for now, you will learn more in the Distributed notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:03.412481Z", "iopub.status.busy": "2023-06-29T15:00:03.412011Z", "iopub.status.idle": "2023-06-29T15:00:05.553696Z", "shell.execute_reply": "2023-06-29T15:00:05.553098Z" } }, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-a09c1408-168d-11ee-8f9e-6045bd777373

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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7b025946

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\n", " Dashboard: http://127.0.0.1:8787/status\n", " \n", " Workers: 4\n", "
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Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-25b46997-aa0a-463b-b69f-8d3753677e0b

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\n", " Comm: tcp://127.0.0.1:37169\n", " \n", " Workers: 4\n", "
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Workers

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Worker: 0

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\n", " Nanny: tcp://127.0.0.1:40919\n", "
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Worker: 1

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\n", " Comm: tcp://127.0.0.1:41081\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:44109\n", "
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Worker: 2

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\n", " Nanny: tcp://127.0.0.1:40083\n", "
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Worker: 3

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\n", " Nanny: tcp://127.0.0.1:46061\n", "
\n", " Local directory: /tmp/dask-worker-space/worker-6uui7hwh\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "\n", "client = Client(n_workers=4)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dask Diagnostic Dashboard\n", "\n", "Dask Distributed provides a useful Dashboard to visualize the state of your cluster and computations.\n", "\n", "If you're on **JupyterLab or Binder**, you can use the [Dask JupyterLab extension](https://github.com/dask/dask-labextension) (which should be already installed in your environment) to open the dashboard plots:\n", "* Click on the Dask logo in the left sidebar\n", "* Click on the magnifying glass icon, which will automatically connect to the active dashboard (if that doesn't work, you can type/paste the dashboard link http://127.0.0.1:8787 in the field)\n", "* Click on **\"Task Stream\"**, **\"Progress Bar\"**, and **\"Worker Memory\"**, which will open these plots in new tabs\n", "* Re-organize the tabs to suit your workflow!\n", "\n", "Alternatively, click on the dashboard link displayed in the Client details above: http://127.0.0.1:8787/status. It will open a new browser tab with the Dashboard." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Reading and working with datasets\n", "\n", "Let's read an extract of flights in the USA across several years. This data is specific to flights out of the three airports in the New York City area." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:05.557006Z", "iopub.status.busy": "2023-06-29T15:00:05.556187Z", "iopub.status.idle": "2023-06-29T15:00:05.560305Z", "shell.execute_reply": "2023-06-29T15:00:05.559714Z" } }, "outputs": [], "source": [ "import os\n", "import dask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By convention, we import the module `dask.dataframe` as `dd`, and call the corresponding `DataFrame` object `ddf`.\n", "\n", "**Note**: The term \"Dask DataFrame\" is slightly overloaded. Depending on the context, it can refer to the module or the DataFrame object. To avoid confusion, throughout this notebook:\n", "- `dask.dataframe` (note the all lowercase) refers to the API, and\n", "- `DataFrame` (note the CamelCase) refers to the object.\n", "\n", "The following filename includes a glob pattern `*`, so all files in the path matching that pattern will be read into the same `DataFrame`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:05.563197Z", "iopub.status.busy": "2023-06-29T15:00:05.562977Z", "iopub.status.idle": "2023-06-29T15:00:05.702114Z", "shell.execute_reply": "2023-06-29T15:00:05.700974Z" } }, "outputs": [ { "data": { "text/html": [ "
Dask DataFrame Structure:
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTimeCRSElapsedTimeAirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
npartitions=10
datetime64[ns]int64float64int64float64int64objectint64float64float64int64float64float64float64objectobjectfloat64float64float64int64int64
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Dask Name: read-csv, 1 graph layer
" ], "text/plain": [ "Dask DataFrame Structure:\n", " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled Diverted\n", "npartitions=10 \n", " datetime64[ns] int64 float64 int64 float64 int64 object int64 float64 float64 int64 float64 float64 float64 object object float64 float64 float64 int64 int64\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: read-csv, 1 graph layer" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask.dataframe as dd\n", "\n", "ddf = dd.read_csv(\n", " os.path.join(\"data\", \"nycflights\", \"*.csv\"), parse_dates={\"Date\": [0, 1, 2]}\n", ")\n", "ddf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dask has not loaded the data yet, it has:\n", "- investigated the input path and found that there are ten matching files\n", "- intelligently created a set of jobs for each chunk -- one per original CSV file in this case" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the representation of the `DataFrame` object contains no data - Dask has just done enough to read the start of the first file, and infer the column names and dtypes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lazy Evaluation\n", "\n", "Most Dask Collections, including Dask `DataFrame` are evaluated lazily, which means Dask constructs the logic (called task graph) of your computation immediately but \"evaluates\" them only when necessary. You can view this task graph using `.visualize()`.\n", "\n", "You will learn more about this in the Delayed notebook, but for now, note that we need to call `.compute()` to trigger actual computations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:05.705250Z", "iopub.status.busy": "2023-06-29T15:00:05.704810Z", "iopub.status.idle": "2023-06-29T15:00:05.840705Z", "shell.execute_reply": "2023-06-29T15:00:05.839756Z" } }, "outputs": [ { "data": { "image/png": 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LTU1VKpUbNmww2RXLy8vd3Nw+/PBDk10RHtezZ8/hw4eb8oroOUfoOSnoOTXoOSnoOSnoOTXoOSm17PlzTs3u379vY2OzbNky8QaDOiguLm7cuPGCBQtMdsVBgwb179/fZJcDA2PHjvX399fpdKa53MqVK9Vq9d27d01zOTCwdetWuVx+5coV01wOPecLPacGPScFPScFPacGPScFPSellj1/zqnZJ5980rRp09LSUvEGg7r55z//6erqWlZWZoJrpaSkyGSymJgYE1wLapSUlMQYO3DggAmupdPpOnXqNGnSJBNcC2qk0Wi8vb3ffPNN01wOPecOPScFPScFPacGPScFPScFPaemNj1/1qlZZWWlq6vrJ598IvZgUAe3b9822aOCp0+f3rFjR5P9IgVq1KdPH9M8Kjg+Pp4xduHCBRNcC57mhx9+sLW1LSwslPpC6Lk5QM+pQc9JQc9JQc+pQc9JQc9JqU3P5ezpdu/enZOT88Ybbzzjz4DUWrRoMXjw4FWrVkl9oUePHm3atGnatGkymUzqa8EzTJs2be/evXfv3pX6QitXrgwKCurSpYvUF4JnePXVVxljmzZtkvpC6Lk5QM+pQc9JQc9JQc+pQc9JQc9JqU3Pn3VqtnLlyoiIiNatW4s9GNTN1KlTDx8+fOPGDUmvsnnz5oqKigkTJkh6FXiuUaNGNW7ceO3atZJeJS8vb9euXVOnTpX0KvBcjo6Oo0aNWrZsmdQXQs/NBHpOCnpOCnpODXpOCnpOCnpOzXN7/tRTs6ysrPj4+ClTpkgzGNRBeHh4ixYtoqKiJL3K6tWrR4wY0bRpU0mvAs+lVqvHjx+/evVqSa+yfv16a2vrMWPGSHoVqI2pU6deuHDhwoUL0l0CPTcf6Dkp6Dk16Dkp6Dkp6Dk16Dkpz+35U0/NoqOjHRwcIiMjpRkM6kChUIwbN07Sx4hmZWUlJibiF1lmYvz48devXz937px0l9i8efPLL79sa2sr3SWgloKDgz09PaOjo6W7BHpuPtBzatBzUtBzUtBzatBzUtBzUp7b82edmr388stWVlbSDAZ1M2bMmFu3bv3xxx8SrR8dHe3o6BgWFibR+lAnPXv2bNu2rXSZzsrKOnXqFH6RZSZkMtmoUaM2b94sCIJEl0DPzQp6Tgp6Tgp6Tg16Tgp6Tgp6Ts2ze17zqVlGRkZSUtLo0aOlHAzqoHv37l5eXlu3bpVo/W3btg0fPhx3WvMxYsQI6bY7Ojq6cePG/fv3l2h9qCt9piX67SV6bm7Qc2rQc1LQc1LQc2rQc1LQc1Ke3fOaT81iYmJsbW3/9re/STkY1IFMJhs6dOi+ffukWDw3N/f06dPDhw+XYnGon2HDhqWmpl67dk2Kxfft2xcZGYkfwsxH9+7dW7RoERMTI8Xi6Lm5Qc+pQc9JQc9JQc+pQc9JQc9JeXbPaz41i42NHTBggFqtlnIwqJuIiIiUlJTMzEzRV46Li1MqlbjTmpVevXo1adIkNjZW9JVLSkoSExMjIiJEXxnqTSaTDRo0SIrtZui5WULPSUHPSUHPqUHPSUHPSUHPqXlGz2s4NauoqDh06FB4eLj0g0EdhIaG2tra7t+/X/SV4+LievfubW9vL/rKUG8KhSIsLEyKTB84cKCqqmrgwIGirwzGiIyMPHXqVH5+vrjLoufmCT0nBT2nBj0nBT0nBT2nBj0n5Rk9r+HU7MyZMyUlJXjhSXNjbW0dEhJy6NAh0Vc+dOgQttsMhYWFHTt2rKqqStxlDx061LVrV7yFubnp37+/IAhHjx4Vd1n03Dyh59Sg56Sg56Sg59Sg56Sg56Q8o+c1nJodO3bMzc2tbdu20g8GddO7d+/jx4+Lu2ZaWtrdu3d79+4t7rJgvN69e5eWlor+CpTHjx/Hdpuhxo0b+/r6in4HR8/NFnpOCnpOCnpODXpOCnpOCnpOzdN6XsOpWWJiIu605ik4ODgjIyMrK0vENRMTE1UqVbdu3URcE0TRrl27pk2bipvpR48eXbhwISgoSMQ1QSzBwcHHjh0Td0303Gyh56Sg59Sg56Sg56Sg59Sg56Q8rec1nJqdOnWqV69eJpkK6qZHjx5KpfLkyZMirnnq1KkXXnjBxsZGxDVBFDKZ7MUXXxR3u8+ePVtVVfXiiy+KuCaIJSgo6I8//qisrBRxTfTcbKHnpKDn1KDnpKDnpKDn1KDnpDyt54anZvfu3bt///4LL7xgqsGgDuzs7Hx8fJKTk0Vc8/z58wEBASIuCCLq2rXrhQsXRFzw/PnzTZo08fDwEHFNEEtAQEBFRYWIb2eOnpsz9Jwa9JwU9JwU9Jwa9JwU9JyUp/Xc8NRM/yc6d+5sormgjvz8/FJSUsRaTRCElJQUPz8/sRYEcfn5+d28ebO0tFSsBVNSUvz9/cVaDcTVvn17KysrEX/sRs/NHHpOCnpOCnpODXpOCnpOCnpOTY09Nzw1S0lJcXd3x/t3mK3OnTuL+F05KyursLAQ35XNVufOnbVa7ZUrV8RaED+EmTOVStW+fXsR7+DouZlDz0lBz0lBz6lBz0lBz0lBz6mpseeGp2Y3btzo0KGDqUaCOuvYsWN6erpYz6y+fv06Yww7bra8vb2VSqV+m0Rx/fp1bLc569Chw40bN8RaDT03c+g5Keg5Neg5Keg5Keg5Neg5KTX23PDULC0tzcvLy4RTQd14enrqdLrMzExRVktPT7ezs8NRt9lSKpUtW7ZMT08XZbWioqKCggJPT09RVgMpeHp6pqWlibUaem7m0HNS0HNq0HNS0HNS0HNq0HNSauy54alZeno67rTmTH8fE+t+m56ejjutmfPy8hJru/XrYMfNmZeX161bt8RaDT03c+g5Neg5Keg5Keg5Neg5Keg5KTX23PDULCsrq3Xr1qYbSkpr1qyRyWTbtm3jPYiYnJ2d7e3tMzIyRFktMzPTYrabWeiOe3l5ibXdWVlZjDGLeYMei9xuT0/PoqKioqIiUVZDz80cev4MFrnj6PnTWOR2o+dPY5HbjZ4/g0XuOHr+NBa53ej501jkdtfY87+cmhUXF5eXlzdr1sy0g0HdNGvWLCcnR5SlHjx44OrqKspSIBFxt9ve3t7W1laU1UAK+vzm5uYavxR63iCg56Sg56Sg59Sg56Sg56Sg59Q8eQf/y6mZ/j8FFxcXkw4FdeTi4pKXlyfKUnl5edhuM+fs7CzWdufm5mK7zZx+g0T5royeNwjoOSnoOSnoOTXoOSnoOSnoOTVP9vwvp2b6z2EXzZyLi4sod1rGWG5urrOzsyhLgURE3G78EGb+9Bskys9h6HmDgJ6Tgp6Tgp5Tg56Tgp6Tgp5T8+Qd/C+nZg8fPmSM2dvbSzdBXFycTCb7/vvvExISQkND7e3tu3fvzhgTBOGXX34JCgqyt7e3sbHx9/dfunSpIAjVX3jkyJEJEyZ4e3ur1eqmTZu+9NJLx48ff3zlwsLC2bNnu7u729jYdO/efe/evbUcSRCENWvW9OnTp3Hjxvb29oGBgStXrtRoNIwxrVa7ZMmSbt26OTk5NW7cuHv37t9++21paSljLCEhQSaTvf322war7dixQyaTLVy40Ji/pWdzcHDQ75TxSkpKHBwcRFnqabDjRnJ0dCwtLdVqtcYvVVJSIum9m2G7jWZnZ6dQKIqLi41fCj03/+1m6PmfiOw4eq5HZLvRcz0i283Q8z8R2XH0XI/IdqPnekS2m9XYc+ExsbGxjLGHDx8KktFfYvTo0UqlUj9AQECATqebMGHCk+NOnTpV/1X37t178rNKpTIhIUH/B8rKyrp27fr4Z2Uy2dixYxljW7dufcY8Op1O/8cM/P7774IgfPDBB09+asmSJfqvbdeuXZMmTcrLyx9fMDIyUqFQ3LlzR/y/uz+99tprgwcPFmUpe3v7n3/+WZSlngY7biR9j0pLS41favLkyeHh4cav8wzYbuPZ2NhERUUZvw563iC2Gz0XKO04ei5Q2m4BPSe23ei5QGnH0XOB0nYL6Dmx7X6y5385Ndu1axdjzGAscel3kTE2efLka9euaTQaQRDWrl3LGPPz84uJicnLyyspKUlISPD392eMJSYmCoKQnZ0dFha2Z8+erKysysrK+/fvR0dH29nZRURE6Jf96quvGGPt27ePj49/+PDhrVu3Zs6cqb/Qs3dx1apVjDFnZ+dly5ZlZmaWlJQkJSVNmTLl8OHDgiD4+PjY2dlt3769sLDw0aNH58+ff//991evXq3/2i+++IIxtnnz5urVbt++rVAoXnrpJWn+8v7PtGnTBgwYIMpSarV67dq1oiz1NNhxI+3fv58xVlBQYPxSEydOHDp0qPHrPAO223iNGzdesWKF8eug5w1iu9FzgdKOo+cCpe0W0HNi242eC5R2HD0XKG23gJ4T2+4ne/6XU7OtW7cyxrRarXQT6HexV69eOp2u+sZ+/fopFIq7d+8+/icvXbrEGPvwww/1H545c2b06NHNmzevPjRljLVs2VL/2Z49e8pksosXLz6+Qv/+/Z+7iyEhIezPk84n9evXz8fHp6qqqsbPZmdnK5XKsLCw6ls+++wzxtiuXbuecUXjzZ49u0+fPqIsJZfLH/+vUArYcSMdPnyYMZadnW38UmPHjh05cqTx6zwDttt4zZo1q/4FizHQcwPmud3ouUBpx9FzgdJ2C+g5se1GzwVKO46eC5S2W0DPiW33kz3/y+uaKRQKxphOp2MSGzBggEwmq/7w0qVLWq22VatWSqVSoVDI5XK5XO7r68sYy8zMZIwlJiYGBQVt3br17t27+mfA6pWVlen/4ebNmy1atNB/SbXw8PDqf87NzZU9pvoV+K5everk5DRgwIAa5/zuu+90Op23t/f06dN/+umnc+fOPf5ZV1fXIUOGxMfHZ2RkMMYEQVi9erWbm9vgwYON+Lt5Po1Go98p48nlclGekP9c2PF602/Q4/GqN7lcboJ7N8N2G0ej0Yiy3ei5AbPdbvSczo6j54zSdjP0nN52o+d0dhw9Z5S2m6Hn9LbboOd/OTWzsrJijFVWVko6BGPM4H1h9P/daLVarVZbfSaq/5R+mC+++KKysnLevHk3b94sKyvT/5n27dtLPae/v//Vq1fXrl3r5eV19OjR8PBwX1/flJSU6j8wZcoU/eYxxg4fPpyamvr666+Lco96hsrKSrVaLcpSarXaBNvNsONGqKioYIyJsuPYbgNmuN1MvDs4em7Asreb4Q7+BDPccfRcOma43Qw9l4xlbzfDHfwJZrjj6Ll0zHC7GXoumQaz3Y8/8EzEZ2g/jf4Rg999993jN/bu3dvW1rawsPBpX+Xr6+vq6vr4LTdv3pTL5c7OzvoPjXzEYHx8fG2Gf/ToUcuWLQMDA6tv0Wg0LVu29PDw0Gq148ePZ4xdv369NksZY8KECcOGDRNlKScnp+XLl4uy1NNgx42kf/Z7ZWWl8UtNmzbt8Qe4SgHbbTyVSrVhwwbj10HPn81Mths9FyjtOHouUNpuAT0ntt3ouUBpx9FzgdJ2C+g5se1+sud/eayZra0tY0z/Tp+m9MYbb5SWlg4YMGDv3r05OTmVlZUZGRn79u0bOXLkgQMHGGMeHh4PHjz48ccfi4qKioqKYmJiIiMjH39ko/7Z4CNHjjx48GBJSUlaWtqsWbP0X/tsr732GmPs73//+8qVK2/fvv3o0aOzZ89OmzYtISGBMRYUFLRs2bLLly+XlZUVFRXFxcXl5eXdunWr+ssVCsXrr7+emZkZHR29Y8eO0NBQHx8f8f+C/qqsrMzGxkaUpWxsbKofdWlK2PHaKysrUyqVKpXK+KVsbGxMf+9m2O660Gg0Go1GlDs4em7+283Qc8YYpR1Hzxml7UbPGaXtZug5Y4zSjqPnjNJ2o+eM0nazGnv++BHa5cuXGWPJycnSndvVePap0+lef/31GieOjY0V/jzOf1xAQEDnzp2rzz7r/U6oWq121KhRT15X/3p1NT4O86233np8hbS0NJlM5ujoyBiT+v1u9Pr06TN79mxRlvLz8/vkk09EWeppsONG+uGHHwwO/utt/vz5HTp0EGWpp8F2G0n/rs/V7/FsDPTc/LdbQM8FQaC04+i5QGm70XOB0nYL6LkgCJR2HD0XKG03ei5Q2m6hpp7/5dTswYMHjLFDhw5JN0GNu6i3ZcuWAQMGODk5WVlZtWnTZvjw4Tt37qx+P4UNGzZ06dLFxsbG3d19+vTp+fn53bp1q95FQRAKCgpmzpzp6upqbW0dEBCwa9cu/dNfn72LgiDodLoVK1b06tXLzs7OwcGhR48eq1at0l/3/Pnzs2bN6tSpk42NjYuLS3Bw8KpVq558z4uwsDDGmKOjY2lpqVF/O7XTqVOnefPmibJU375933zzTVGWehrsuJHmzZvXqVMnUZZaunSpi4uLKEs9DbbbSBcvXmSMXbp0yfil0HPz324BPf8TkR1Hz/WIbDd6rkdkuwX0/E9Edhw91yOy3ei5HpHtFmrquUz483XgGGMajcbKyio6OrrG40AwE25ubnPnzn3rrbeMX2r06NEymSw6Otr4pUAic+bMuXDhwpEjR4xfasuWLRMmTKisrJTL5c//08DDkSNHQkND79275+bmZuRS6HmDgJ6Tgp6Tgp5Tg56Tgp6Tgp5T82TP/3LnVCqVrq6ut2/fNvlgUFsVFRU5OTktWrQQZbXmzZvfuXNHlKVAIllZWSJut1ar1T/GGMxTVlaWSqVq2rSp8Uuh5+YPPacGPScFPScFPacGPScFPSelxp4bHml7enqmp6ebbiioo4yMDJ1O5+XlJcpqrVu3TktLE2UpkEhaWpqnp6coS+nXwR3cnKWlpXl4eCgUClFWQ8/NHHpODXpOCnpOCnpODXpOCnpOSo09x6lZA6P/JirWd2UvL6/s7Ozy8nJRVgMppKeni7XdLVq0UKvV+DnMnKWlpYm13Qw9N3voK3M2ngAAHaRJREFUOTXoOSnoOSnoOTXoOSnoOSk19tzw1KxNmzY3b9403VBQR6mpqU5OTk5OTqKs1qZNG0EQUlNTRVkNRPfgwYPi4uI2bdqIsppcLm/dujW225ylpqaKtd0MPTd76Dkp6Dk16Dkp6Dkp6Dk16DkpNfbc8NSsc+fO165dq6ioMOFgUAcpKSmdO3cWa7UOHTqoVKqUlBSxFgRx6bfGz89PrAU7d+6M7TZnFy9eFHe70XNzhp6Tgp5Tg56Tgp6Tgp5Tg56TUmPPDU/NunTpotForl69aqqpoG6Sk5NFvNOq1ep27doh02YrJSXFxcXF1dVVrAX9/PySk5PFWg3EdefOnby8PBHv4Oi5mUPPSUHPSUHPqUHPSUHPSUHPqamx54anZu3bt1er1bjfmidBEC5fvizi77IYMm3eLl682KVLFxEX7NKlS2pq6qNHj0RcE8Ry8eJFxpiId3D03Jyh59Sg56Sg56Sg59Sg56Sg56Q8reeGp2ZKpdLf3//UqVOmGgzq4OrVq4WFhYGBgSKu2b1795MnTwqCIOKaIJaTJ0+Kvt06ne7MmTMirgliOXnyZJs2bZydncVaED03Z+g5Neg5Keg5Keg5Neg5Keg5KU/rueGpGWMsODg4MTHRJFNB3Rw7dszOzq5r164irhkcHJybm4uXJDRDBQUFV65cCQ4OFnFNDw+PVq1aHT9+XMQ1QSzHjx8Xd7sZem7G0HNS0HNq0HNS0HNS0HNq0HNSntbzmk/NkpOTi4uLTTIY1MHx48d79eqlVCpFXLNbt262tra435qhxMREQRB69eol7rJBQUHYbjOk1WpPnTolxXdl9Nw8oeekoOekoOfUoOekoOekoOfUPK3nNZ+a6XS6Y8eOmWQwqIOEhITevXuLu6ZKpQoMDDx8+LC4y4LxEhISOnbs2LRpU3GXDQkJOXbsmEajEXdZMNLZs2eLi4tFv4Oj52YLPScFPScFPacGPScFPScFPafmaT2v4dTMzc3N398/Li5O+qmgDq5cuZKenj5o0CDRVx44cGBcXBxeOsHcxMTEhIeHi77swIEDi4qKTpw4IfrKYIzY2NiWLVv6+vqKuyx6bp7Qc2rQc1LQc1LQc2rQc1LQc1Ke0fMaTs0YY+Hh4fv27ZN4Kqib2NhYJyenHj16iL5yREREdnb2+fPnRV8Z6u327duXLl2S4ruyj4+Pt7d3bGys6CuDMWJjYyMiIqRYGT03Q+g5Keg5Neg5Keg5Keg5Neg5Kc/oec2nZhEREbdu3bp27ZrEg0EdxMTEDBw4UKFQiL5y165d3d3dcb81K/v27bOzswsJCZFicWTa3OTk5Jw5c0aKH8IYem6W0HNS0HNS0HNq0HNS0HNS0HNqntHzmk/NgoODXV1dt27dKvFgUFs5OTkJCQkjR46UYnGZTDZ8+HBst1mJjo6OjIy0traWYvERI0YkJydfuXJFisWhHrZt26ZWq6V4fgdDz80Pek4Nek4Kek4Kek4Nek4Kek7Ks3te86mZQqEYOXJkdHS0lINBHWzbts3KyioyMlKi9ceOHZucnHz58mWJ1oc6ycnJOXLkyNixYyVaPzQ0tHnz5tu2bZNofair6Ojol156yc7OTorF0XNzg56Tgp5Tg56Tgp6Tgp5Tg56T8uye13xqxhgbM2ZMSkoKTrvNRHR09JAhQyS60zLGQkJC3N3dcdptJrZu3WptbS3Rs+gZY3K5/OWXX96yZYtE60OdZGdnHz16dPTo0dJdAj03K+g5Keg5Keg5Neg5Keg5Keg5Nc/u+VNPzUJCQlq3bv3LL79INhjU1q1btxISEiZOnCjdJeRy+fjx49esWaPT6aS7CtTS6tWrX375ZVtbW+kuMXHixEuXLp0+fVq6S0AtRUVFOTg4SPebaoaemxP0nBr0nBT0nBT0nBr0nBT0nJTn9vypp2ZyuXzy5Mlr1qypqKiQZjaorVWrVrm5uUn3mw29qVOnZmRkxMfHS3oVeK7k5OQzZ85MnTpV0qv06tXL399/5cqVkl4FnksQhJ9//nnixIk2NjbSXQU9Nx/oOSnoOSnoOTXoOSnoOSnoOTXP7flTT80YY5MnTy4oKNi9e7cEg0FtaTSaqKioyZMnK5VKSS/Uvn373r17I9PcLV++XL8XUl/ojTfe2Lx588OHD6W+EDxDQkLCjRs3pkyZIvWF0HNzgJ5Tg56Tgp6Tgp5Tg56Tgp6TUpueP+vUrGXLlkOGDPnhhx8kmA1qa+vWrffv3zfBnZYxNmPGjF27dmVkZJjgWlCjgoKCtWvXTp8+XSaTSX2tiRMnCoKARwXztXjx4hdffLFLly5SXwg9NwfoOSnoOTXoOSnoOSnoOTXoOSm16rnwTEeOHGGMJSYmPvuPgXReeOGFcePGmeZaVVVVHh4e7777rmkuB0/6/PPPHRwcCgsLTXO5OXPmeHp6VlVVmeZyYODatWtyuXz79u2muRx6zh16Tgp6Tgp6Tg16Tgp6Tgp6Tk1tev6cUzNBEAIDA0eOHCnSSFA3Bw4cYIydPn3aZFf8+uuv7e3tCwoKTHZFqFZeXu7u7v7BBx+Y7IqpqakKhWLz5s0muyI8bvr06W3bttVoNCa7InrOEXpOCnpODXpOCnpOCnpODXpOSi17/vxTs+joaLlcfvHiRZEGgzro169fv379THnFwsLCxo0bz58/35QXBb2ffvpJrVbfvn3blBcdM2ZMly5dtFqtKS8KgiBkZWVZW1v/9NNPprwoes4Rek4Kek4Kek4Nek4Kek4Kek5NLXv+/FMznU7n7+8/atQoMaaCOtAffB4+fNjE150/f76jo2NeXp6Jr0tcWVlZy5Yt58yZY+LrXrx4US6Xb9myxcTXhenTp3t4eJSXl5vyoug5L+g5Keg5Neg5Keg5Keg5Neg5KbXv+fNPzQRB2LFjh0wmO3funNGDQR0EBweHhYWZ/rpFRUXOzs5z5841/aUp++6772xsbO7evWv6S48fP75jx46mfBwy3Lp1y8rKauXKlaa/NHrOBXpOCnpOCnpODXpOCnpOCnpOTe17XqtTM51O1717dy7fIcj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"text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.visualize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some functions like `len` and `head` also trigger a computation. Specifically, calling `len` will:\n", "- load actual data, (that is, load each file into a pandas DataFrame)\n", "- then apply the corresponding functions to each pandas DataFrame (also known as a partition)\n", "- combine the subtotals to give you the final grand total" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:05.843527Z", "iopub.status.busy": "2023-06-29T15:00:05.843176Z", "iopub.status.idle": "2023-06-29T15:00:06.569204Z", "shell.execute_reply": "2023-06-29T15:00:06.568633Z" } }, "outputs": [ { "data": { "text/plain": [ "9990" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load and count number of rows\n", "len(ddf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the start and end of the data as you would in pandas:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:06.572868Z", "iopub.status.busy": "2023-06-29T15:00:06.572087Z", "iopub.status.idle": "2023-06-29T15:00:06.624094Z", "shell.execute_reply": "2023-06-29T15:00:06.623506Z" } }, "outputs": [ { "data": { "text/html": [ "
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTime...AirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
01990-01-0111621.015401747.01701US33NaN86.0...NaN46.041.0EWRPIT319.0NaNNaN00
11990-01-0221547.015401700.01701US33NaN73.0...NaN-1.07.0EWRPIT319.0NaNNaN00
21990-01-0331546.015401710.01701US33NaN84.0...NaN9.06.0EWRPIT319.0NaNNaN00
31990-01-0441542.015401710.01701US33NaN88.0...NaN9.02.0EWRPIT319.0NaNNaN00
41990-01-0551549.015401706.01701US33NaN77.0...NaN5.09.0EWRPIT319.0NaNNaN00
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" ], "text/plain": [ " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime \\\n", "0 1990-01-01 1 1621.0 1540 1747.0 1701 \n", "1 1990-01-02 2 1547.0 1540 1700.0 1701 \n", "2 1990-01-03 3 1546.0 1540 1710.0 1701 \n", "3 1990-01-04 4 1542.0 1540 1710.0 1701 \n", "4 1990-01-05 5 1549.0 1540 1706.0 1701 \n", "\n", " UniqueCarrier FlightNum TailNum ActualElapsedTime ... AirTime \\\n", "0 US 33 NaN 86.0 ... NaN \n", "1 US 33 NaN 73.0 ... NaN \n", "2 US 33 NaN 84.0 ... NaN \n", "3 US 33 NaN 88.0 ... NaN \n", "4 US 33 NaN 77.0 ... NaN \n", "\n", " ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled \\\n", "0 46.0 41.0 EWR PIT 319.0 NaN NaN 0 \n", "1 -1.0 7.0 EWR PIT 319.0 NaN NaN 0 \n", "2 9.0 6.0 EWR PIT 319.0 NaN NaN 0 \n", "3 9.0 2.0 EWR PIT 319.0 NaN NaN 0 \n", "4 5.0 9.0 EWR PIT 319.0 NaN NaN 0 \n", "\n", " Diverted \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.head()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "raises-exception" ] }, "source": [ "```python\n", "ddf.tail()\n", "\n", "# ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n", "\n", "# +----------------+---------+----------+\n", "# | Column | Found | Expected |\n", "# +----------------+---------+----------+\n", "# | CRSElapsedTime | float64 | int64 |\n", "# | TailNum | object | float64 |\n", "# +----------------+---------+----------+\n", "\n", "# The following columns also raised exceptions on conversion:\n", "\n", "# - TailNum\n", "# ValueError(\"could not convert string to float: 'N54711'\")\n", "\n", "# Usually this is due to dask's dtype inference failing, and\n", "# *may* be fixed by specifying dtypes manually by adding:\n", "\n", "# dtype={'CRSElapsedTime': 'float64',\n", "# 'TailNum': 'object'}\n", "\n", "# to the call to `read_csv`/`read_table`.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike `pandas.read_csv` which reads in the entire file before inferring datatypes, `dask.dataframe.read_csv` only reads in a sample from the beginning of the file (or first file if using a glob). These inferred datatypes are then enforced when reading all partitions.\n", "\n", "In this case, the datatypes inferred in the sample are incorrect. The first `n` rows have no value for `CRSElapsedTime` (which pandas infers as a `float`), and later on turn out to be strings (`object` dtype). Note that Dask gives an informative error message about the mismatch. When this happens you have a few options:\n", "\n", "- Specify dtypes directly using the `dtype` keyword. This is the recommended solution, as it's the least error prone (better to be explicit than implicit) and also the most performant.\n", "- Increase the size of the `sample` keyword (in bytes)\n", "- Use `assume_missing` to make `dask` assume that columns inferred to be `int` (which don't allow missing values) are actually `floats` (which do allow missing values). In our particular case this doesn't apply.\n", "\n", "In our case we'll use the first option and directly specify the `dtypes` of the offending columns. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:06.627289Z", "iopub.status.busy": "2023-06-29T15:00:06.626646Z", "iopub.status.idle": "2023-06-29T15:00:06.637691Z", "shell.execute_reply": "2023-06-29T15:00:06.637201Z" } }, "outputs": [], "source": [ "ddf = dd.read_csv(\n", " os.path.join(\"data\", \"nycflights\", \"*.csv\"),\n", " parse_dates={\"Date\": [0, 1, 2]},\n", " dtype={\"TailNum\": str, \"CRSElapsedTime\": float, \"Cancelled\": bool},\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:06.640370Z", "iopub.status.busy": "2023-06-29T15:00:06.639937Z", "iopub.status.idle": "2023-06-29T15:00:06.690245Z", "shell.execute_reply": "2023-06-29T15:00:06.689585Z" } }, "outputs": [ { "data": { "text/html": [ "
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTime...AirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
9941999-01-251632.0635803.0817CO437N2721391.0...68.0-14.0-3.0EWRRDU416.04.019.0False0
9951999-01-262632.0635751.0817CO437N1621779.0...62.0-26.0-3.0EWRRDU416.03.014.0False0
9961999-01-273631.0635756.0817CO437N1221685.0...66.0-21.0-4.0EWRRDU416.04.015.0False0
9971999-01-284629.0635803.0817CO437N2621094.0...69.0-14.0-6.0EWRRDU416.05.020.0False0
9981999-01-295632.0635802.0817CO437N1222590.0...67.0-15.0-3.0EWRRDU416.05.018.0False0
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5 rows × 21 columns

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" ], "text/plain": [ " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime \\\n", "994 1999-01-25 1 632.0 635 803.0 817 \n", "995 1999-01-26 2 632.0 635 751.0 817 \n", "996 1999-01-27 3 631.0 635 756.0 817 \n", "997 1999-01-28 4 629.0 635 803.0 817 \n", "998 1999-01-29 5 632.0 635 802.0 817 \n", "\n", " UniqueCarrier FlightNum TailNum ActualElapsedTime ... AirTime \\\n", "994 CO 437 N27213 91.0 ... 68.0 \n", "995 CO 437 N16217 79.0 ... 62.0 \n", "996 CO 437 N12216 85.0 ... 66.0 \n", "997 CO 437 N26210 94.0 ... 69.0 \n", "998 CO 437 N12225 90.0 ... 67.0 \n", "\n", " ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled \\\n", "994 -14.0 -3.0 EWR RDU 416.0 4.0 19.0 False \n", "995 -26.0 -3.0 EWR RDU 416.0 3.0 14.0 False \n", "996 -21.0 -4.0 EWR RDU 416.0 4.0 15.0 False \n", "997 -14.0 -6.0 EWR RDU 416.0 5.0 20.0 False \n", "998 -15.0 -3.0 EWR RDU 416.0 5.0 18.0 False \n", "\n", " Diverted \n", "994 0 \n", "995 0 \n", "996 0 \n", "997 0 \n", "998 0 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.tail() # now works" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading from remote storage\n", "\n", "If you're thinking about distributed computing, your data is probably stored remotely on services (like Amazon's S3 or Google's cloud storage) and is in a friendlier format (like Parquet). Dask can read data in various formats directly from these remote locations **lazily** and **in parallel**.\n", "\n", "Here's how you can read the NYC taxi cab data from Amazon S3:\n", "\n", "```python\n", "ddf = dd.read_parquet(\n", " \"s3://nyc-tlc/trip data/yellow_tripdata_2012-*.parquet\",\n", ")\n", "```\n", "\n", "You can also leverage Parquet-specific optimizations like column selection and metadata handling, learn more in [the Dask documentation on working with Parquet files](https://docs.dask.org/en/stable/dataframe-parquet.html)." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Computations with `dask.dataframe`\n", "\n", "Let's compute the maximum of the flight delay.\n", "\n", "With just pandas, we would loop over each file to find the individual maximums, then find the final maximum over all the individual maximums.\n", "\n", "```python\n", "import pandas as pd\n", "\n", "files = os.listdir(os.path.join('data', 'nycflights'))\n", "\n", "maxes = []\n", "\n", "for file in files:\n", " df = pd.read_csv(os.path.join('data', 'nycflights', file))\n", " maxes.append(df.DepDelay.max())\n", " \n", "final_max = max(maxes)\n", "```\n", "\n", "`dask.dataframe` lets us write pandas-like code, that operates on larger-than-memory datasets in parallel." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:06.693134Z", "iopub.status.busy": "2023-06-29T15:00:06.692718Z", "iopub.status.idle": "2023-06-29T15:00:06.785826Z", "shell.execute_reply": "2023-06-29T15:00:06.785053Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 25.2 ms, sys: 29 µs, total: 25.2 ms\n", "Wall time: 84.8 ms\n" ] }, { "data": { "text/plain": [ "409.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "result = ddf.DepDelay.max()\n", "result.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This creates the lazy computation for us and then runs it. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. This means you can handle datasets that are larger than memory but, repeated computations will have to load all of the data in each time. (Run the code above again, is it faster or slower than you would expect?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the underlying task graph using `.visualize()`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:06.789074Z", "iopub.status.busy": "2023-06-29T15:00:06.788465Z", "iopub.status.idle": "2023-06-29T15:00:07.083039Z", "shell.execute_reply": "2023-06-29T15:00:07.080242Z" } }, "outputs": [ { "data": { "image/png": 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q559/fh599NG8+uqrRacAAAAAAE2IUTIAAAAAsEtMnjw5r7/+es4666yiU6BR+8xnPpOWLVtm/PjxRacAAAAAAE2IUTIAAAAAsEvcfffdOfroo3PQQQcVnQKN2u67757TTjstY8aMKToFAAAAAGhCjJIBAAAAgJ1u06ZNuf/++zNkyJCiU6BJGDJkSKZNm5aXX3656BQAAAAAoIkwSgYAAAAAdrrJkyfnzTffzNlnn110CjQJgwcPTqtWrTJ+/PiiUwAAAACAJsIoGQAAAADY6R566KH07t07nTt3LjoFmoTddtstJ598ciZMmFB0CgAAAADQRBglAwAAAAA73e9///sMGjSo6AxoUgYPHpzHHnss69evLzoFAAAAAGgCjJIBAAAAgJ1qyZIleeGFFzJ48OCiU6BJGTx4cDZu3JjHH3+86BQAAAAAoAkwSgYAAAAAdqpJkyalXbt2Ofroo4tOgSbl4x//eA4//PBMmjSp6BQAAAAAoAkwSgYAAAAAdqqJEyfmpJNOSvPmzYtOgSZn0KBBmThxYtEZAAAAAEATYJQMAAAAAOw0mzZtyrRp03LyyScXnQJN0qBBg/Lcc89l5cqVRacAAAAAAI2cUTIAAAAAsNPMmTMn69evz/HHH190CjRJRx99dCoqKjJ9+vSiUwAAAACARs4oGQAAAADYaaZPn56OHTvmkEMOKToFmqSWLVvmiCOOMEoGAAAAAHY6o2QAAAAAYKeZPn16+vXrl7KysqJToMnq16+fUTIAAAAAsNMZJQMAAAAAO83MmTPTr1+/ojOgSevXr18WLFiQtWvXFp0CAAAAADRiRskAAAAAwE6xbNmyvPLKK+nbt2/RKdCk9evXLzU1NXnyySeLTgEAAAAAGjGjZAAAAABgp5gzZ06aNWuW3r17F50CTdo+++yTLl26ZM6cOUWnAAAAAACNmFEyAAAAALBTzJ8/P5/4xCfSsmXLolOgyevZs2cWLFhQdAYAAAAA0IgZJQMAAEsEJfcAACAASURBVAAAO8X8+fPTs2fPojOAJIcffnjmz59fdAYAAAAA0IgZJQMAAAAAO4VRMtQfPXv2zJIlS7Jx48aiUwAAAACARsooGQAAAADY4d55552sWLEihx9+eNEpQP46St68eXP+9Kc/FZ0CAAAAADRSRskAAAAAwA63cOHClEolJyVDPdGtW7dUVlZm4cKFRacAAAAAAI2UUTIAAAAAsMMtW7YsLVq0SOfOnYtOAZI0b948Xbp0ybJly4pOAQAAAAAaKaNkAAAAAGCHW758eTp37pxmzZoVnQL8jwMOOCDLly8vOgMAAAAAaKSMkgEAAACAHW7lypXp2rVr0RnAB3Tt2jUrVqwoOgMAAAAAaKSMkgEAAACAHW758uU54IADis4APsBJyQAAAADAzmSUDAAAAADscCtWrDBKhnqma9eueemll7Jp06aiUwAAAACARsgoGQAAAADYoUqlUl588cV07ty56BTgA7p06ZLq6uq8/PLLRacAAAAAAI2QUTIAAAAAsEO9/fbbqaqqyj777FN0CvABe++9d5Jk9erVBZcAAAAAAI2RUTIAAAAAsEO9P3js0KFDwSXAB73/Z9IoGQAAAADYGYySAQAAAIAdas2aNUmSjh07FlwCfFCbNm1SUVFR+2cUAAAAAGBHMkoGAAAAAHYoJyVD/dW+fXsnJQMAAAAAO4VRMgAAAACwQ61Zsya77bZbWrVqVXRKvffMM8+krKwsw4YNKzqFJqJjx45OSgYAAAAAdgqjZAAAAABgh1q7dm3atm1bdAbwIdq1a5e33nqr6AwAAAAAoBFqXnQAAAAAANC4bNiwIZWVlUVnNAh9+vRJqVQqOoMmpLKyMhs3biw6AwAAAABohJyUDAAAAADsUFVVVamoqCg6A/gQFRUVqaqqKjoDAAAAAGiEjJIBAAAAgB2qoY6Sq6ur8/Of/zxHHnlk2rVrl7Zt26ZPnz65/vrrs379+i3uLZVKGTVqVPr27Zs999wzLVu2TK9evfKLX/xii5OPJ02alLKysvzsZz/L448/ngEDBmTPPfdMnz59kiTPPPNMysrKMmzYsL/r+dvT/FE+2Pjoo4+mb9++2WOPPbL//vvnmmuuqb3v5z//ebp165bddtsthx56aMaOHbvVs6ZOnZovfOELOfjgg1NZWZm99torp59+eqZPn157T01NTQYPHpzy8vJMmDBhi69/7bXXst9++2WvvfbKiy++uE392/KZ73vzzTfzta99LR/72MfSsmXL9OnTJw899FBuvfXWlJWVZdy4cXW6vyGoqKhwUjIAAAAAsFM0LzoAAAAAAGhcGuoo+dvf/nauu+66La7Nnj07s2fPTkVFRS699NIkfx0MX3DBBRk9evQW986fPz+XXnpp5s2bl+HDh2/x3owZM3LFFVdk8+bNSf46zP0o2/P8bW3eFk8++eQWjevXr8+3v/3tVFZWZtWqVfnJT35Se++SJUty3nnn5ZBDDsmnPvWpJMmqVasyYMCALZ65evXqPPTQQ5k0aVKmTJmS/v37p7y8PKNHj07v3r1zwQUXZPbs2enatWuqq6vz+c9/Pq+88komTpyY/fff/282b+tnJsmGDRtywgknZO7cuVv8rM4444wMGTJkq2dv7/0NRWVlpVEyAAAAALBTOCkZAAAAANihNm7cmMrKyqIzttt9992XPfbYI/fcc0/eeuutrFu3LnPnzs23vvWttGrVqva+O+64I6NHj07Pnj3zu9/9LmvWrMm7776bxx9/PL169cqIESMyc+bMLZ49duzYfOlLX8qSJUuyefPmzJkz5yM7tuf529q8LcaMGZNLL700K1asyLvvvptx48alRYsW+Y//+I/ccsstGTlyZF577bWsWbMm3/zmN1NTU5Mbbrih9uvLyspy0kkn5cEHH8yLL76YqqqqvPrqq7n77rtTWVm5xanL7du3z7hx47J+/fqcffbZ2bhxY66++uo88sgjGTZsWE4++eRtat6ez/z5z3+euXPnplu3bpkyZUreeeedLF++PJdeemnGjBmz1bO39/6GoqKiIlVVVUVnAAAAAACNkJOSAQAAAIAdqrq6OuXlDe88hP322y9JcsYZZ6R587/+6rRXr17p1avXFvf95je/SbNmzfL73/8+H//4x2uv9+/fP7/97W/TvXv33H///Tn22GNr3zvmmGMycuTIlJWV/c2O7Xn+tjZvi0GDBm0xMj7rrLNyxhln5J577slPf/rTXHTRRbXvXXvttRk5cmQWLVpUe22fffbJj3/841x77bUZOnRoXnvttdpTl5NkwYIFW3xenz59cuONN+YrX/lKTjrppDzxxBMZPHhwvve9721z8/Z85rhx41JWVpZ77rkn3bt3T5K0atUq//Vf/5XFixfn4Ycf3uLZ23t/Q9G8efMtfkYAAAAAADtKw/s/AwAAAABAvVZRUZFNmzYVnbHdbrjhhtTU1OTggw/O0KFDc/PNN+ePf/zjVvc9++yzqa6uzv7775/mzZunWbNmKS8vT3l5ee149c9//vMWX3PiiSdu0yB5e5+/Lc2rV69OWVnZFq+OHTtu9bkDBgzY6lqXLl2S/HUQ/UHNmjXLvvvum1dffbX22owZM9K3b9+MHTs2L7/88lbD1/fee2+r5w8dOjRnn312pk2blk6dOuWOO+7Y4uf0t9q35zNfeOGF7LvvvrU/ww865ZRTtrq2vfc3FFVVVamoqCg6AwAAAABohIySAQAAAIAdqqKiIlVVVUVnbLdevXpl8eLFuf3229O1a9dMmzYtgwYNSvfu3bc4cbempibJX0+Erq6uTk1NTUqlUkqlUu09//v779ChwzZ3bM/zt7V5W+y2225bXXt/IPxR773fmiTXXHNNqqqq8v3vfz/PP/983nvvvdr2bt26fehnvvrqq5kxY0aSZM2aNVm5cuV2NW/vZ37UMPyDP9u63N8QbNy4MZWVlUVnAAAAAACNUPOiAwAAAACAxqWhjpKTpHnz5unfv3/tycDr169Pt27dctFFF+Wpp55Kkhx66KGZM2dOXn755bRp02aHN2zv8/9Wc8eOHXfJiHbZsmXZZ599MmzYsC2uv/DCC3nuuefSrl27La5XV1fn85//fFatWpWbb745V111Vc4+++zMnj07bdu2TZK/2b49n3nQQQfl6aefzqJFi/LJT35yi/sffvjhrZ69vfc3FFVVVWndunXRGQAAAABAI+SkZAAAAABgh2qoo+S+ffvml7/8ZRYtWpT33nsva9euzaRJk7JmzZosW7as9r6LLroo69evz4knnpiHHnoor7/+eqqqqrJy5cpMmDAhZ511VqZMmfJ3d2zP87e1eVfo3LlzXnvttdx0001Zu3Zt1q5dm9/97nf5zGc+s8WJyu/73ve+l0cffTQ/+MEP8tWvfjW/+c1vsmzZsnzpS1/a5hH19nzmWWedlVKplLPPPjuPPfZY1q1bl5UrV+Yb3/hG/vCHP2z17O29v6FwUjIAAAAAsLM4KRkA/h979x7X1H3/D/wFJNyvCipWERFQEu5BUYlrsRa1S6pW7bfrZWtru64X+9t6X9d2va2bs6vfua3t1vbbtXP7bqXV1cRp633lUoVwNeGmQbwEEAG5X3L7/eE3GSHUK3ACvJ6PB4/4CCcnr9RyciKv8/4QERERERHRsPLy8hqTpeSioiLk5+cP+b2HHnrI/ucf/OAHOHz4MP785z9DqVRedvurdTX7v9LMo+Hhhx/G7t27sXHjRmzcuNF+f0pKCuLj41FfX2+/T6VSYdOmTbj11lvxwgsvALhYAn7yySfx9ttvY9OmTXj++eeH9Tk3btyIv/71rygrK0NmZqb9fjc3N6xfvx7Z2dkQi8XXvP1Y0d/fz1IyERERERERERERjQhOSiYiIiIiIiIiIqJh5evri66uLqFjXLUjR47gscceg0QigY+PD0JDQ5GRkYEPPvgAW7ZssW/n5uaGjz76CP/4xz+wbNkyhISEwNPTE1FRUVi9ejV27NiBZcuWXXOOq9n/lWYeDatWrcJf//pXJCYmwsfHB+Hh4Xj44Yexf/9+hxJsbW0tvv/97yMiIgJ/+ctf4ObmZv/epk2bsGTJEvsU5eF6TgDw8fHBwYMH8fDDD2PKlCnw9vaGTCbDzp07IZFIAAAhISHXvP1Y0d3dDR8fH6FjEBERERERERER0TjkZr3SdfCIiIiIiIiIiIiIrkB2djbuvPNO9Pf3w8PDQ+g4RJdksViQlpaGkpISNDU1YfLkycO6vauJjo7Ghg0b8NOf/lToKERERERERERERDTOcFIyERERERERERERDavQ0FBYLBa0trYKHYXIwVNPPYVt27ahrq4O3d3dKCkpwR133IHi4mLcdNNNTgXjq91+LDh//jxCQ0OFjkFERERERERERETjkEjoAERERERERERERDS+2IqaLD+Sq6mqqsLbb7/tdL+/v/+Q91/t9q7OaDSivb19TJapiYiIiIiIiIiIyPVxUjIRERERERERERENK1sRubm5WeAkRI62bNmC++67D9HR0fDy8kJYWBjWrVuH/Px8JCcnX/f2rq6lpQVWq5UXCxAREREREREREdGI4KRkIiIiIiIiIiIiGlYDJyUTuZKYmBh89NFHI7a9q7P9THJSMhEREREREREREY0ETkomIiIiIiIiIiKiYeXl5YXg4GDU19cLHYWIBmhoaAAATJkyReAkRERERERERERENB6xlExERERERERERETDbtasWairqxM6BhENUFtbCz8/P4SFhQkdhYiIiIiIiIiIiMYhlpKJiIiIiIiIiIho2M2ePRu1tbVCxyCiAU6ePInZs2cLHYOIiIiIiIiIiIjGKZaSiYiIiIiIiIiIaNhFRkbi5MmTQscgogFqa2tZSiYiIiIiIiIiIqIRw1IyERERERERERERDbvIyEhOSiZyMSdPnkRkZOQ1Pba5uRl6vX54AxEREREREREREdG4wlIyERERERERERERDam+vh4mk+maHjt79mw0NTWhq6trmFMR0bW6klKy0WhEWVkZtm3bhueeew633HILwsLCEBoaim3bto1OUCIiIiIiIiIiIhqTREIHICIiIiIiIiIiItf07LPP4h//+AdiYmKQnJyMxMRESKVSSKVSREZGws3N7VsfGxsbC6vVisrKSshkslFMTURDuXDhAurr6xEbG2u/7+zZsygvL0dZWRnKyspQVFSE6upqmM1muLu7w9PTE319fbBarQCAuLg4oeITERERERERERHRGMBSMhEREREREREREQ1JKpXCbDZDp9OhsrIS2dnZMBqNAAAfHx/MnTsXaWlpkEgkSEhIgFQqRXh4OAAgJiYGvr6+KCsrYymZyAWUl5fDarVix44d2Lx5M0pLS9HW1gYA8PT0hNlshtlstm9vsVjQ29vrsA+WkomIiIiIiIiIiOhSWEomIiIiIiIiIiKiIUmlUlgsFgAXC4q2PwNAT08PSkpKcOzYMQCAyWQCAAQEBCA+Ph4pKSm44YYbUF5ePvrBichJWVkZgoKC4OPjg9zcXIcCcn9//2Uf7+HhgZiYmJGMSERERERERERERGOcu9ABiIiIiIiIiIiIyDVJpdLLbmMymeyFZADo6OhAaWkpZsyYgcWLF7OUTOQiysvLkZSUhN///vc4ceIEHnzwQbi5uUEsFl/R46dMmYLOzs4RTklERERERERERERjGUvJRERERERERERENKTp06fDw8PjirYVi8Xw8PDAQw89BL1ej5/+9KdITk5GaWnpCKckoitRVlaGxMREAMCsWbPw/vvv4+jRo1i8eDEAXPJn3c3NDQ0NDQgNDcWkSZOQlpaG73//+9i0aRNUKhX0er3DJHUiIiIiIiIiIiKamERCByAiIiIiIiIiIiJhmUwmnDp1ClqtFjqdzuHWbDZf8rFubm6wWq2IjIzExo0bkZmZicDAQABAYmIimpqaUF9fj/Dw8NF4KUQ0BIvFAq1Wix/84AcO96elpeHQoUPYt28ffvKTn0Cr1QIArFarw3Zubm5ISkrC1q1bHY4R+/btQ319PQDAy8sLc+bMgVQqhUQigVQqRVRUFOLj4+Hl5TU6L5SIiIiIiIiIiIgE5WYd/K+LRERERERERERENC6ZzWbo9XqUl5ejoqIC5eXl0Ol0qKqqQn9/Pzw8PBAVFYWEhATExcUhISEBb7/9No4ePXrJ/YpEIri7u6O/v99+X2hoKGJiYnDkyBFkZ2fj9ttvH+mXR0TfoqysDElJSSgpKUFSUhIaGxtx8uRJ1NbWora2FidPnsTx48dRXl6OpqamIfexfPly7Nmzx+n+1tZW6PV6p4saTp48CYvFArFYjJkzZ9qLyrbbefPmwc/Pb6RfOhEREREREREREY0iTkomIiIiIiIiIiIahwwGg9PU4+LiYnR3dwMAwsPDIZVKkZmZiccffxwSiQSpqanw9fV12M+xY8cuW0o2mUxD3v/oo4+iu7sbubm5LCUTCSgnJwc+Pj646667cOLECfT19QG4OAHZ09MTFosFRqPxkvuQy+VD3h8SEgKZTAaZTOZwf19fH44fP+40Wfm3v/0tent7AfznODRwsnJiYiKmTJkyDK+aiIiIiIiIiIiIRhsnJRMREREREREREY1hQ5WPS0tL0dnZCcC59CeRSJCcnAx/f/8r2v+//vUvfPe7372ibUUiESwWCx577DG8/vrrCAoKwmOPPQaNRoNvvvnmml8jEV2fu+++G83NzXjqqafwox/9CHq9/qr3UVZWhoSEhOvOYjQacfr0afsxyzZluaSkBF1dXQAuFp0HFpVtf549ezbc3NyuOwMRERERERERERGNDJaSiYiIiIiIiIiIxoDW1lZotVpoNBp7+bisrAwdHR0A/lPik8lk9vJxUlISAgICrvq5TCYTdDodCgsLcfDgQWzbtu2S27u7u8NqtWLRokV47733HIqLf/vb33DffffhwoULTlOYiWh0REZGYsOGDXjppZdgNBrxzjvv4Pnnn4fZbL7shGQA8PLysk83HkmDL7LQ6/UoLy9HY2MjACAoKAjR0dEORWWJRIJ58+bBw8NjxPMRERERERERERHRpbGUTERERERERERE5EJs5eOBxbzS0lI0NTUBcJwgartNSkpCWFjYNT2fxWJBVVUVCgsL7V/FxcXo6emBr68vUlJScPz4cbS0tAxZXhSLxQgKCsJvfvMb3HvvvU5TTOvq6hAZGYnDhw/jO9/5zjVlJKJrd/bsWcyYMQMHDhxAZmam/f4zZ87giSeewI4dO+Du7g6LxTLk493c3DBp0iQ8+OCDSE9Px4IFC3DDDTeMVnwAjsdF22RlnU6H2tpaWK1WeHp6Ijo62mmyskQigY+Pz6hmJSIiIiIiIiIimshYSiYiIiIiIiIiIhLAUOXjgRNBhyofJyQkYOrUqdf1vAaDARqNxv6Vl5eHlpYWiMVixMTEQCaT2b8WLFgAT09PfPnll1ixYoXDfsRiMSwWCx577DG8/vrrCAwM/NbnnDVrFjZs2ICXX375urIT0dX761//igceeAAtLS3w8/Nz+r5KpcIjjzyChoYGmM1mp+97enpCKpWiv78fFRUVsFgsuOGGG7BgwQIsWLAA6enpSEtLu6ap7Nerra0Nx48fdygqa7VaVFVVwWw2QyQSISIiwqmofK1T5ImIiIiIiIiIiOjSWEomIiIiIiIiIiIaQRcuXMCJEyeg1Wqh0Wjspbn6+noAQHBwMObMmQOJRAKZTAapVAqpVIrw8PDrfu7BBeQjR46gqakJIpEIsbGxDgXktLQ0eHt7D7mf5uZmhIaGArg4NdVqteKmm27Cu+++i3nz5l02x0MPPQStVou8vLzrfk1EdHXuuece1NfXY//+/d+6TU9PDzZt2oQ333wTAJymou/evRsrVqxAZ2cnSkpKHI4rOp0OABAVFYWMjAz7MWX+/Pnw8vIauRd2Cf39/aipqXGarFxRUYHu7m4AQ1/4MVzHXiIiIiIiIiIioomKpWQiIiIiIiIiIqJhYJvYOXj6sV6vBwAEBQUhOjraqQQXFRU1LM9fX1+PwsJCe1GwoKAAjY2N8PDwwNy5cx0KyKmpqfD19b2q/c+YMQNnz57FtGnT8Lvf/Q7r1q274sd+/vnn+K//+i80NjZi8uTJV/vSiOgaWSwWhIeH4+mnn8Yzzzxz2e1ramrw6KOPYt++ffYLENzc3NDU1PStP7uXmr6emJjoUFSWSCRwc3Mb7pd5xUwmE06dOuU0WbmsrAwdHR0ALpaVB09WlkqliIyMhLu7u2DZiYiIiIiIiIiIxgKWkomIiIiIiIiIiK5Ce3s7ampqnMrHtbW1sFqtCAwMRExMjFOhbfbs2cNWxmtra0N5efmQ00rDw8MdCshyuRwhISHX/Zz33nsvIiIi8MILL8DPz++qHtve3o7Q0FB88sknuPPOO687CxFdmaNHjyI9PR3l5eWIj4+/4sf97//+L5544gmcP38eERERqKuru6rn1ev1yMnJsR+fCgsL0dfXh6CgIMTHx0MulyMjIwMLFy5EWFjY1b6sEdHa2jrkRSW2C0u8vLwwZ84cp4tK4uPjBZsITURERERERERE5GpYSiYiIiIiIiIiIhpCf38/ampq7AU1W/H35MmTsFgs8PT0RHR0tL2gJpPJRmSaZnt7O8rKyhwKyBUVFbBarU4F5MWLF4/YJOLDhw/j0KFD8PX1RVBQEPz8/ODr64uAgAAEBgbC19cXvr6+CA4Ohq+vL7y9vR0ef+ONN2L27Nn485//PCL5iMjZq6++ig8++ACnTp266osi2tvb8dJLL6GlpQV/+ctfriuH0WhEWVmZQ1F58HHMVlSWyWTw8fG5rucbTq2trU6TlQe+F4jFYsycOdOhqCyRSJCSknLVF3AQERERERERERGNdSwlExERERERERHRhDa4fGy7raysHLJ8bLuNi4sb1vIxAHR2dqKkpMShgGzLMbiAnJ6ejilTpgzr819KQUEBFixYALFYDAAwm82wWCzfur2bmxv8/Pzg5+eHgIAA+Pj4oKGhAQ0NDcP+342IhrZo0SLEx8fj/fffv6rHmc1mtLe3o7+/H25ubiNyrLFNfM/NzUVOTg6OHDmCpqYmiEQixMbGOhSVR+J4e736+vpw/Phxp8nKWq0Wvb29AC5Orh88WTkhIQFTp04VOD0REREREREREdHIYCmZiIiIiIiIiIgmBKPRiOrqaqfycVVVFcxmM8RiMWJiYpzKx/PmzYOHh8eI5dFoNPZSni1LcHAwpFKpvZQnl8sRHh4+7BmuhtVqxZQpU3D+/Pmrfuydd96Jp59+GmlpaTh06BBuvPHGEUhIRAOdPn0as2bNwrp16xASEgKTyYSOjg709PSgo6MDXV1d6O3tRWdnJ4xGIzo6OmCxWNDZ2emwnwMHDiAzM3NUMhsMBodjYlFREXp6ehAQEIDExET7RRnf+c53EBkZOSqZrpbRaMTp06edisqlpaX2/7YhISFOk5WlUilmz5591ROtiYiIiIiIiIiIXAlLyURERERERERENK4MLoTZbo8dO4a+vj6IxWLMnDnToXgslUoRHx8PLy+vEclkMplQVVXlMAG5sLAQfX19CAwMREJCgsMUZIlE4pLFtIceegiffPIJ+vv7L7utWCyGSCTCf//3f+OHP/whACAxMRFLlizBH/7wh5GOSjThvfXWW/jFL36B7Oxs3H333Th37txV7yMwMBDNzc0QiUQjkPDyBh47bUXlb5seL5fLERISIkjOK2UwGByKyjqdDuXl5WhsbAQABAUFITo62qGoPJIXxxAREREREREREQ03lpKJiIiIiIiIiGhMMplMOHXqlFP5WKvVore3FyKRCBEREU7lY6lUCm9v7xHLZTabUVlZ6VBA/rZpn65cQB7KF198gTVr1uBy/6To4eGB+Ph4ZGdnIyYmxn7/G2+8ga1bt8JgMAhWciSaKBYsWICEhAR8+OGHOH/+PO6991589dVXsFgsV/R4kUiEu+66Cx9//PEIJ706HR0dKC0ttR9fv/76a5w8eRIeHh6YO3euw/F1wYIF8PT0FDryZbW2tjpNVtbpdKitrYXVaoWnpyeio6OdJitLJBL4+PgIHZ+IiIiIiIiIiMiOpWQiIiIiIiIiInJ5BoMBGo3GoXys0+nQ09MDAPaJmQPLx6NV1jIYDPYJnhqNBsXFxeju7oZYLEZiYiIyMjLsBbm4uDi4u7uPeKaR0N3djV27duHOO+/81lKju7s7LBYLNm7ciLfeesupDHjixAlER0dj//79WLp06WjEJpqQamtrMWfOHOzZswdZWVkAAKvViq1bt+Lpp5+G1WqF2Wy+5D7c3Nzw+eefY82aNaMR+brY3iNsXzk5Obhw4QL8/PyQnJzsUFSWSqVCx71ibW1tOH78uENRWavVoqqqCmaz2X7xzeCiclJSEgICAoSOT0REREREREREExBLyURERERERERE5DJsS9sPLF/ZSr7AxfLx4OJxamoqfH19Ry3fwOJbXl4eWlpaIBaLERMTMyYndF7KmTNnsGvXLqhUKhw4cAC9vb0ICAhAZ2enUzFZLBYjICAA27Ztw8qVK791n6mpqZg/fz7++Mc/jnR8ognr17/+NTZv3gyDwQCxWOzwvcLCQqxduxYGgwEmk+lb9yESidDS0jImy61DTawvKChAf38/goODkZaWZr9gZPHixZg8ebLQka9Kf38/zpw547RSQEVFhf39MiQkxGmlgKioKERFRQmcnoiIiIiIiIiIxjOWkomIiIiIiIiIaNQNVT4uLS1FZ2cngKHLx8nJyfD39x/VjAMLbUeOHEFTUxNEIhFiY2MdCshpaWnw9vYetWwjSavVQq1WQ6VSIS8vD97e3sjIyIBCocDatWuxc+dOPPHEEw5TVt3d3XHTTTdh27ZtCA8Pv+T+N2/ejF/+8pc4e/bsqEyyJpqI4uPjsWTJErz77rtDS6JaWwAAIABJREFUfr+9vR0bNmzAZ599NuT3bRPdIyMjoVAosH79eixevHjMTnoHgK6uLhQXFzsc13U6HYCL7zlyudxeVB6rx3STyYRTp045TVYuLy9He3s7gItl5cGTlaVSKSIjI8f03y8REREREREREbkGlpKJiIiIiIiIiGjEtLa2QqvV2stfWq0WZWVl6OjoAPCfSY4ymUzQZefr6+tRWFjoMFGzsbERHh4emDt3rkMBeTQnM4+G7u5u7N+/H2q1Grt27cLZs2cxdepUZGVlQalUYuXKlQ5l8FOnTmHWrFkALk5SdXNzw+bNm/HEE0/Azc3tss/X2NiImTNn4n/+539wzz33jNjrIpqo8vLykJGRgcLCQshksktu+8knn+CHP/whzGazw9RkDw8PPPnkkzAajfjiiy9QW1uL6dOnQ6lUYvXq1cjMzISXl9dIv5QR19DQgIKCAvuxPz8/H83Nzfbp9wOLyhKJ5IqOca7K9n488IIgvV4PvV4PAPDy8sKcOXOcJitLpdIxWdAmIiIiIiIiIiJhsJRMRERERERERETXbaiyU2lpKZqamgAMvYx8UlISwsLCRj1rW1sbysvLv3Va5sACslwuR0hIyKhnHGl1dXX48ssvoVKpsHfvXhiNRqSkpEChUECpVCI1NfWS5TuJRIKKigrMmTMHn332GZKTk6/q+deuXYvz58/j8OHD1/tSiGiQBx54ABqNBqWlpVe0vVarxe23347a2loYjUYAgJubGwwGA6ZNm2bfZuAEdR8fHyxduhTr16/HbbfdhuDg4BF7PaPNYDAgNzcXOTk59veI3t5eBAYGIiEhwV5UTk9Px5QpU4SOe91aW1sdJivb/lxZWQmLxQKxWIyZM2c6FJUlEglSUlLg5+cndHwiIiIiIiIiInIxLCUTEREREREREdEVG6p8XF5ejsbGRgBDl48TEhIwdepUQfJ2dHSgtLTUoYBcUVEBq9XqVEBevHgxJk+eLEjOkWaxWFBcXAyVSgW1Wo2ioiJ7qVCpVEKhUGD69OlXvL+f/exnaGxsxNatW69pavSePXuwcuVK6HQ6xMXFXfXjiWhoHR0dmD59OjZt2oRHH330qh73ox/9CH/729/g5uaGtLQ0HD16dMhtT58+jd27d0OlUuGrr76C2WzGwoULoVQqcfvttyMmJma4Xo5LMBqNqK6udigqD/U+IpfLsXjx4nEzSb+/vx81NTVOk5W1Wi16e3sBXLyQZ/BkZSHf84mIiIiIiIiISHgsJRMRERERERERkZMLFy7gxIkT0Gq19knCWq0W9fX1AIDg4GDMmTMHEokEMpkMUqkUUqkU4eHhgmXu7OxESUmJQwHZNulxcAF5vEy4vJSuri4cOHDAPt20vr4ekZGRyMrKgkKhQFZWFry8vK5p3729vfD29r7mbBaLBXPmzMG6deuwefPma94PETl677338OSTT+Ls2bPXNOX9o48+wqOPPopXX30Vzz777GW3tx1nsrOzsXPnTrS1tUEikdgvdsjIyLjk1PWxqr29HWVlZfai8tGjR3Hu3DmIRCLExsY6FJVTUlLg7u4udORhYzQacfr0aaeicmlpKTo7OwE4XqBkm6wslUoxe/bscfn/AxERERERERER/QdLyUREREREREREE1hbWxuOHz/uNP1Yr9cDAIKCghAdHe00/TgqKkrQ3LbJlbbycW5uLkpKSmA2mxEcHAypVGovhMnlckHL0qOptrYWe/fudZhgmpycDIVCAaVSCZlMJnREuzfeeAO//e1vUVdXN24mixIJyWq1IiUlBUlJSfj444+veT86nQ5+fn6YNWvWVT3OZDLhm2++QXZ2Nj7//HOcPXsWs2bNwvLly6FQKLBixQqIxeJrzuXqDAaDw3tSXl4euru74e/vj6SkJIeistDvoSPFYDA4FJUHr6ZgO6cYWFSWSCSYN28ePDw8BE5PRERERERERETDgaVkIiIiIiIiIqIJoL29HTU1NU7l49raWlitVgQGBiImJsapfOwKUw1NJhOqqqocJiAXFhair68PgYGBSEhIcJiCLJFIBM88WsxmM0pKSqBSqaBWq6HRaODn54fMzEwolUrcdtttmDZtmtAxh9TU1IRZs2bhN7/5DR555BGh4xCNeV999RWWL1+OoqIipKSkCB0HWq0W2dnZ9mPTpEmTcPPNN0OhUGD16tUIDAwUOuKIGvzelZubi+Li4iGn92dkZGDSpElCRx4xra2tTpOVB56DeHp6YsaMGU7nIHFxcbxohYiIiIiIiIhojGEpmYiIiIiIiIhoHOnv70dNTY29eKzRaKDT6XDy5ElYLBZ4enoiOjraXvqRyWSQSqWIjIx0ieXlzWYzKisrHQrIRUVF6OnpcZo2OdEKyDYtLS3Yv3+/vYjc2tqKqKgoLFu2DAqFAsuXL4enp6fQMa/Iww8/jH379qG6uppTMomuU1ZWFqxWK/bu3St0FCe1tbXYuXMn1Go1Dh06BJFIBLlcDoVCgfXr12P69OlCRxwVnZ2dKCkpcSgq21YmiIqKQkZGhv39bf78+fDy8hI48ciyrdYwsKis1WpRVVUFs9kMkUiEiIgIp8nKSUlJCAgIEDo+ERERERERERENgaVkIiIiIiIiIqIxaHD52HZbWVk5ZPnYdhsXF+cS5WMbg8GA3Nxc5OTkQKPRoLi4GN3d3RCLxUhMTHQoaLla9tGk1+vtJeTDhw/DYrFg4cKFUCqVUCqVkEgkQke8JtXV1YiLi8Nnn32GNWvWCB2HaMwqLy9HUlIS/vWvf2HFihVCx7mk5uZm7Nq1C2q1Grt370Z3dzdSUlKgUChwxx13jNnj2bUyGAwOF+Lk5eWhpaVlyPfBiXIhjtFoxOnTp51Wd6ioqEB3dzcAICQkxGmyclRUFKKiogROT0REREREREQ0sbGUTERERERERETkwoxGI6qrq53Kx7YpgmKxGDExMU7l43nz5rnc5NlLFa9iYmIcJiAvWLBgzEz7HQlmsxn5+flQq9XYuXMnKioqMGnSJNx8881QKBS47bbbEBwcLHTMYXHbbbehpaUFOTk5QkchGrO+//3vo7i4GGVlZWOqtNrT04N9+/bZj3UNDQ2IioqyT1BevHjxhLwYRa/X2y/W0Wg0KCwsRF9fH4KCgjB//nx7UXnhwoUICwsTOu6oMhgMTudE5eXlaG9vB3CxrDx4srIrrQhBRERERERERDTesZRMREREREREROQChpoKqNFoHMrHM2fOdCjZyGQylywfA84F5CNHjqCpqQkikQixsbEOBeS0tDR4e3sLHVlwzc3NOHDgAFQqFXbu3Im2tjZ7OU+pVOLGG2+EWCwWOuaw+/e//40bb7wRX3/9NeRyudBxiMacuro6xMbG4r333sP9998vdJxrNvBijH/+85+oqqpCaGgoVq5cifXr1yMrKwteXl5CxxSE0WhEWVmZQ1G5oqICVqsV4eHhkMlkkMvl9rKyj4+P0JFHXWtrq9NkZb1eD71eDwBDriARFRUFqVTKcxAiIiIiIiIiomHEUjIRERERERER0SgymUw4deqUU3FGq9Wit7cXIpEIERERThP+XLk0U19fj8LCQntRqqCgAI2NjfDw8MDcuXMdCsipqanw9fUVOrLL0Ov1UKlUUKvVOHToEAAgPT0dSqUSq1atwrx584QNOEoyMzPh7u6O/fv3Cx2FaMx58MEHsX//flRVVY2rCfNarRZqtRoqlQp5eXnw8fHB0qVLoVQqsXr1akyZMkXoiIJqa2tDQUGBvag81MU/tqJyXFzchJ0SfOHCBZw4ccKhqKzValFZWQmLxeJ00ZdtynJycjL8/f2Fjk9ERERERERENOawlExERERERERENEJs04IHlo91Oh16enoAwD7dcGD5WCKRuPSEw7a2NpSXlztMQdbpdAD+83psX3K5HCEhIQIndi29vb3IycnBvn37sGPHDlRXVyM0NBSZmZlQKBRYtWoVgoKChI456nJycrBkyRIcOHAAmZmZQschGjOOHz8OiUSC999/Hz/4wQ+EjjNizp07hz179iA7OxtfffUVzGYzFi5cCKVSidtvvx0xMTFCR3QJtvOO3Nxc5OTkoKioCD09PQgICEBiYqL9vXnJkiWYNm2a0HEF1d/fj5qaGqfJyraLxICL5zUDi8pSqRQJCQmYOnWqwOmJiIiIiIiIiFwXS8lERERERERERNfJYDA4TT0uLi5Gd3c3gP+UWgYWj8fCxOCOjg6UlpY6FJAHLxdv+1q0aBFCQ0OFjuySmpqasHv3bqjVanz55Zdob2+HRCKBUqnEsmXLcNNNN0EkEgkdU3C33HILuru7kZubK3QUojHj3nvvxZEjR6DT6SbMcaSrqwsHDhxAdnY2du7ciba2NvsxVaFQICMjA25ubkLHdAkmkwlVVVUORWXbhODB7+NLlixBcHCw0JEFN3hFC1tRubS0FJ2dnQCAkJAQp8nKUqkUs2fP5v97RERERERERDThsZRMRERERERERHSFhiofl5SUoKurC8DQ5eOUlBT4+fkJnPzyOjs7UVJS4lBA/rbiUnp6OqZMmSJ0ZJem1WqhVquhUqmQn58PT09PyOVyKBQKrFmzBhEREUJHdDkFBQVIT0/H7t27sXz5cqHjELm86upqSKVSfPLJJ/je974ndBxBmM1m5OfnIzs7G9u3b8eZM2cwa9YsLF++HAqFAitWrIBYLBY6pksZeMFRbm4uvv76azQ0NMDDwwNz5851eL9fsGABPD09hY7sMmzngbaisk6nw7Fjx9DQ0AAACAoKQnR0tENRWSKRYN68efDw8BA4PRERERERERHR6GApmYiIiIiIiIhokNbWVmi1Wmg0Gnv5uKysDB0dHQD+MyFPJpPZCydJSUkICAgQOPmVMRqNqK6utpePc3NzUVJSArPZjODgYEilUvsS73K5HOHh4UJHdnk9PT3Izc2FSqXCjh07cPr0aYSFhWHFihVQKpVYsWLFmPn/Q0gKhQL19fUoKCiAu7u70HGIXNratWtRVVWFsrIy/rz8H61Wi+zsbKjVamg0GoSEhGDZsmVQKBRYvXo1AgMDhY7okgwGg8NFSTk5Obhw4QL8/PyQnJzsUFSWSqVCx3U5ra2tDkVl221tbS2sVivEYjFmzpzpUFSWSqWIi4tz+VUziIiIiIiIiIiuFkvJRERERERERDRh2crHAwskpaWlaGpqAuC4PLftNikpCWFhYQInv3IDl263fRUWFqKvrw+BgYFISEhwKBtJJBIuPX6Fzp07hz179kCtVmP37t3o7OyERCKBUqmEQqFARkYG/1tepdLSUqSlpWHLli14/PHHhY5D5LL27t2LrKwsbN++HWvWrBE6jkuqra3Fzp07oVarcejQIYhEIvvE+vXr12P69OlCR3RZZrMZlZWVDucOBQUF6O/vx7Rp05CWlmY/b1i8eDEmT54sdGSX1N7ejpqaGqfCclVVFcxmM0QiESIiIpwmKycmJrJAT0RERERERERjFkvJRERERERERDTuDVU+Li8vR2NjI4Chy8cJCQmYOnWqwMmvzlAloqKiIvT09MDf3x9JSUksIF8nrVYLtVoNlUqFvLw8eHt7IyMjAwqFAmvXrsWMGTOEjjgmmEwmnDhxAuXl5dBqtTh27BiKiopQV1eHmJgYtLS0oLq6GkFBQUJHJXI5ZrMZMpkMvb29qKmpQXh4OFJSUpCQkID4+Hj7BFZPT0+ho7qM5uZm7Nq1y34RSXd3N1JSUqBQKHDHHXdAIpEIHdHldXV1obi42OEcQ6fTAQDCw8Mhl8uRkZEBmUyGtLQ0eHt7C5zYdRmNRpw+fdrp3LSiogLd3d0Ahj43jYqKQlRUlMDpiYiIiIiIiIgujaVkIiIiIiIiIho3Lly4gBMnTkCr1drLMlqtFvX19QCA4OBgzJkzBxKJxL4EuVQqRXh4uMDJr43BYEBubi5ycnKg0WhQXFyM7u5uiMVixMTEOBSE4uLi4O7uLnTkMae7uxv79++HWq3Grl27cPbsWUydOhVZWVlQKpVYuXIl/P39hY7psqxWK2pra6HVaqHValFeXo7S0lJUV1fDaDTCzc0Nnp6eMBqNsFgsuOWWW7Bt2zZIJBI8+OCD+NWvfiX0SyByOe+99x6eeOIJlJeXY+vWrXjnnXcAACKRCGazGVarFR4eHpg5cyZSUlKQmJiI+Ph4xMfHIzo6GiKRSOBXIKyenh7s27cParUaO3fuRENDA6KiouwTlBcvXsz3yyvU0NCAgoICe0k5Pz8fzc3NQ56H8EKoK2MwGByKyrb3zvb2dgAXy8oDJyvb/szzPCIiIiIiIiJyFSwlExEREREREdGY09bWhuPHjztNmNPr9QCAoKAgREdHDzlhbqwyGAwO0wnz8vLQ0tJiL/4MnIC8YMECTsi8DnV1dfjyyy+hUqmwd+9eGI1G+0RNpVKJ1NRUFqsuoa6uDq+++ipKSkpQUVGB3t5eAICXlxdMJhPMZrPTY8RiMSQSCXJycuDv74+tW7fimWeewbFjxxATEzPaL4HIZbW3tyM2NhZ33XUX3n77bZjNZqxduxa7du2CyWRy2t7d3R0eHh4wmUywWq0QiUSYM2cOUlNT8eSTTyItLU2AV+E6LBYL8vLyoFar8cUXX6CyshKhoaFYuXIllEolbr31Vvj5+Qkdc0wZfMGURqNBb28vAgMDkZCQYC8qp6enY8qUKULHHTOGWvVDr9fbz309PT0RHR3tdN4rlUo5tZqIiIiIiIiIRhVLyURERERERETkstrb21FTU+NUwqitrYXVakVgYCBiYmKcysezZ88e06XRwQXkI0eOoKmpCSKRCLGxsQ4FZC6Rfv0sFguKi4uhUqmgVqtRVFQEHx8fLF26FEqlEgqFAtOnTxc65phhsViQkJCAiooKXMk/PYpEIkyfPh1Hjx7F1KlTAVxc2j45ORmRkZHYtWvXSEcmGjMef/xxfPrpp6iurkZwcDAAoLe3F5mZmdBoNDAajVe0n6CgINTV1SEoKGgk4445er0eKpUK2dnZyMvLc3gvWL16NUu018BoNKK6utqhqGx7fwgPD7efz8jlcixevBi+vr5CRx5TBq4SYisqa7VaVFZWwmKxQCQSISIiwmmycnJyMld6ICIiIiIiIqIRwVIyEREREREREQmuv78fNTU19uKxRqNxKB8Pnv4mk8nGRfkYAOrr61FYWGgvIBcUFKCxsREeHh6YO3euQwE5NTWVZZ1h0tXVhQMHDkCtVkOlUqG+vh6RkZHIysqCQqFAVlYWvLy8hI45Zh08eBBLly697HYeHh4ICAjAkSNHEBsb6/C9/Px8yOVy/OUvf8Fdd901UlGJxowjR44gIyMDf/7zn3HPPfc4fK+trQ2LFi3C8ePHL1tMdnd3x29/+1s8/vjjIxl3zDt37hz27NmD7OxsfPXVVzCbzVi4cCGUSiXWrFnjdMyiK9fe3o6ysjJ7Ufno0aM4d+6c08VXcrkcKSkpcHd3FzrymDPw3NpWVLadZ9tWMAgPD3coKkulUsTHx2PatGkCpyciIiIiIiKisYylZCIiIiIiIiIaNYPLx7Zb2zS3oZaelkgkiIuLGxeFlLa2NpSXlztMQdbpdADgMC3QVsQJCQkROPH4Ultbi71790KlUtkLZsnJyVAoFFAqlZDJZEJHHFeysrJw6NChby1Iurm5wdPTEwcPHsSiRYuG3OaRRx7BZ599Bp1Oh7CwsJGMS+TS+vv7kZqaiqlTp2Lfvn1DXpBz9uxZpKWl4fz58zCZTEPux93dHTNmzEBNTQ08PT1HOva4YbuQJTs7Gzt37kRbWxskEol9mn5GRsaYv0hKaANXicjNzUVeXh66u7vh7++PpKQkh/OjqKgooeOOWSaTCadOnXKarFxaWorOzk4AQEhIiENRebysREJEREREREREo4OlZCIiIiIiIiIadralugeXj6uqqmA2myEWixETE+NUPp43bx48PDyEjj8sOjo6UFpa6lBAHmq5cplMhkWLFiE0NFToyOOO2WxGSUkJVCoV1Go1NBoN/Pz8kJmZCaVSidtuu43TAEdQRUUF4uPjYbFYhvy+u7s7tm/fjlWrVn3rPtrb2yGVSrF06VJ8/PHHIxWVyOW98sor2Lx5M8rLyy9ZyNRqtVi0aBG6urq+9Wdv+/btWLNmzUhFHffMZjPy8/ORnZ2N7du348yZM4iIiMCKFSugUCiwfPlyFr6HgclkQlVVlUNRubi4GBaLxek8KiMjA5MmTRI68phnMBicJisfO3YMDQ0NAIDAwEDExMQ4FZbH0/k7EREREREREV0/lpKJiIiIiIiI6JoZjUacPn3aoXis0WgcysczZ850KC7IZLJxV17o7OxESUmJQwHZNv15cHEmPT0dU6ZMETryuNXS0oL9+/fbi8itra2IiorCsmXLWBYbZcePH0dWVhZOnz7tNLXVzc0NH3zwAR544IHL7mfnzp1YtWoVdu/ejRUrVoxUXCKXpdPpkJqail/+8pf4yU9+ctntDx8+jFtuuQUmkwkD//lfJBLB398fe/bsQXp6+khGnlC0Wi2ys7PtF7+EhITY33NWr16NwMBAoSOOG4PPt3Jzc6HX6wEAUVFRyMjIsJ9vzZ8/H15eXgInHh9aW1sdisq229raWlit1iHP96VSKeLi4uDr6yt0fCIiIiIiIiIaZSwlExEREREREdFlDV7qeeBtb28vRCIRIiIinMoIUqkU3t7eQscfVrYp0AMLMSUlJTCbzQgODoZUKrUvLy6XyxEeHi505HFPr9fbS8iHDx+GxWLBwoULoVQqoVQqIZFIhI44oTQ3N+O1117De++9h8jISNTV1aGvr8/+fXd3d7z66qt48cUXr3if3/ve9/D111+jtLQUkydPHonYRC6pv78fixYtglgsRm5u7hVf0PPpp5/izjvvxOB//k9MTER5eTnuvPNOvPnmm4iMjByB1BPXyZMn8cUXX9jfjzw8PCCXy6FQKLB+/XpMnz5d6IjjjsFgcLgoLC8vDy0tLRCLxUhMTHQoKkskEri5uQkdedxob29HTU2NU2HZdnEiAISHhzutjJKYmMiyPhEREREREdE4xlIyEREREREREdmZzWbU1dU5lY91Oh16enrg4eGBWbNmOZWPJRIJfHx8hI4/7AYvHa7RaFBYWIi+vj4EBgYiISHBYQoyyy6jw2w2Iz8/H2q1Gjt37kRFRQUmTZqEm2++GQqFArfddhuCg4OFjjnh9Pf3491338Urr7wCkUiEF198EY899hjeeOMNvPHGGzCbzfDw8MB9992HDz744Kr2feHCBSQlJUEmk2H79u0j9AqIXM+zzz6Ld955B0VFRYiNjb2qx77zzjt47LHHAABisRi33347/v73v2Pfvn348Y9/jJqaGtx33334xS9+gdDQ0JGIP6E1Nzdj165dUKvV2LNnD7q6upCSkgKFQoE77riDF8yMIL1ej5ycHKdzt6CgIMyfP99eVF64cCHCwsKEjjvuDLWSik6nQ0VFBbq7uwEAISEhTp8noqKiEBUVJXB6IiIiIiIiIrpeLCUTERERERERTVAGg8Fp6nFxcbG9LDDUZLPU1NRxuwyz2WxGZWWlQwG5qKgIPT098Pf3txciWUAWRnNzMw4cOACVSoWdO3eira0NUVFRUCgUUCqVuPHGGyEWi4WOOSFZrVZ89tlneP7559HQ0ICNGzfihRdesE9B7OzsxOzZs3H+/Hl897vfxRdffHHF014H+vrrr5GZmYk//vGP2LBhw3C/DCKXc/jwYdx88814//33cf/991/TPp566im8/fbbEIvFqKqqwuzZswFcLA1+9NFHePnll2E0GvHss8/ixz/+Mby8vIbzJdD/6enpQW5uLlQqFT799FM0NDQ4vIfddNNNEIlEQscct4xGI8rKyhyKyhUVFbBarQgPD7evcGErK4/HC+1cxeDPH3q9HqWlpWhqagIABAcHY86cOQ5FZYlEgri4OLi7uwucnoiIiIiIiIiuBEvJREREREREROPcUOXjkpISdHV1ARi6fJySkgI/Pz+Bk48sg8GA3Nxce0HFVsgWi8WIiYlxKKewCCEMvV4PlUoFtVqNQ4cOAQDS09OhVCqxatUqzJs3T9iAhG+++QZPP/008vPzsXbtWmzevBmzZs1y2u7999/Hhx9+iIMHD15X2eu5557DH/7wB2g0GsydO/d6ohO5tNbWViQnJyMtLQ2ff/75Ne/HYrHge9/7HmbOnIm33nrL6fudnZ1466238Otf/xphYWF4/fXXce+99/KimxFksVhQXFwMlUqFf/zjH6isrERoaChWrlwJpVKJW2+9ddyfg7mCtrY2FBQU2M8Djxw5gqamJohEIsTGxjoUlXkeOPJaW1udJivr9XrU1tbCarXC09MT0dHRDkVlqVQKqVQKb29voeMTERERERER0QAsJRMRERERERGNE7Zf5ms0Gvsv9MvKytDR0QHgP8sky2Qye/k4KSkJAQEBAicfeQaDwWECcl5eHlpaWuwF5IETkBcsWABPT0+hI09Ivb29yMnJwb59+7Bjxw5UV1cjNDQUmZmZUCgUWLVqFYKCgoSOSQCqq6vx4osvIjs7G8uWLcPmzZuRnJz8rdubzWa0tbVh0qRJ1/W8/f39WLRoEdzc3JCTk8MiEo1LVqsVq1evRlFREUpLS4fl56a3t9c+vXwoZ86cweuvv44PP/wQMpkMb731FpYsWXJdz0tXxnYBTnZ2NvLy8uDt7Y2bb77ZfgHO1KlThY44YdjOF20XrdlWzAgICEBiYqK9qLxkyRJMmzZN6LgTwoULF3DixAmHorJWq0VlZSUsFgtEIhEiIiKcJisnJyfD399f6PhEREREREREExJLyURERERERERjzFCTxAYue2wrHw+cfpyUlISwsDCBk4+OwQXkoSbf2b7S0tJYahRYU1MTdu/eDbVajS+//BLt7e2QSCRQKpVYtmzXFvXVAAAgAElEQVQZl7R3Mc3Nzdi8eTO2bNmCOXPm4NVXX8X69etHNUNNTQ3mz5+PdevW4YMPPhjV5yYaDW+++SZeeeUVHDhwAHK5fFSfW6PR4JlnnsHBgwehUCiwZcsWREdHj2qGicz2npidnY29e/fCZDJh4cKFUCqVWLNmDWJjY4WOOKGYTCZUVVU5FJVtZdjw8HCHc8olS5YgODhY6MgTRn9/P2pqahyKyjqdDjqdDj09PQD+sxrMwMnK8fHxLJQTERERERERjTCWkomIiIiIiIhc1FDl4/LycjQ2NgIYunyckJAwoSbq1dfXo7Cw0F5ALigoQGNjIzw8PDB37lyHskhqaip8fX2FjkwAtFot1Go1VCoV8vPz4enpCblcDoVCgTVr1iAiIkLoiDRIf38/3n33Xfz85z+Hn58ffv7zn2PDhg3w8PAQJI9arcaqVavw7rvv4oc//KEgGYhGwv79+7F8+XJs2bIFGzduFCzHvn378OSTT6KyshL3338/Xn/9dUyZMkWwPBNRd3c39u/fj+zsbKhUKly4cMF+0Y5CoUBGRgbc3NyEjjnhdHR0oLS01F5U/vrrr9HQ0DDkuSdX3xh9JpMJp06dcigqa7ValJaWorOzE8DFz1ADi8q229mzZ/NnioiIiIiIiGgYsJRMREREREREJLDW1lb7L841Gg10Oh2OHTuGhoYGAI6/OJfJZBN2yldbWxvKy8sdpiDrdDoAcJpWJ5fLERISInBisunp6UFubi5UKhV27NiB06dPIywsDCtWrIBSqcSKFSsQEBAgdEwagtVqxWeffYZnn30WTU1NePzxx/Gzn/3MJf6+XnrpJWzatAkHDx5ERkaG0HGIrltdXR3S0tJwyy234G9/+5vQcWCxWLBt2zY899xz6Ovrw3PPPYf/9//+H1cYEIDZbEZ+fj6ys7Oxfft2nDlzBhEREVixYgUUCgWWL1/O8quABq/SkZOTgwsXLsDPzw/JyckO56hSqVTouBOWwWBwmqw88DNXYGAgYmJinArL8+bNE+wiLCIiIiIiIqKxiKVkIiIiIiIiolHS1taG48ePO00/1uv1AICgoCBER0c7Te2KiooSOPnoGziFzvZVUVEBq9XqVEBetGgRQkNDhY5Mg5w7dw579uyBWq3G7t270dnZyQmPY8yBAwfwzDPPoKSkBHfffTc2bdqE8PBwoWPZWSwWfPe734VWq0VBQcGEmhJP4093dzeWLFkCo9GI/Px8+Pn5CR3JrqurC7///e/xi1/8AsHBwXjxxRfx4IMPwt3dXehoE5ZWq0V2djbUajU0Gg1CQkKwbNkyKBQKrF69GoGBgUJHnNDMZjMqKysdzmMLCgrQ39+PadOmIS0tzX4eu3jxYkyePFnoyBPawAtEB35GO3nyJCwWC8RiMWbOnOn0GS0uLo6rsBARERERERENgaVkIiIiIiIiomHW3t6Ompoap19s19bWwmq12qdwccngizo7O1FSUuJQ3KisrITFYnEqIKenp3P5eBem1WqhVquhUqmQl5cHb29vZGRkQKFQYO3atZgxY4bQEekKVFZW4uWXX0Z2djaWLVuG3/zmN0hMTBQ61pBaWlqQnp6OSZMm4eDBgywH0ZhksViwbt06/Pvf/8Y333yD6OhooSMN6ezZs3jttdfw4YcfIiUlBW+99RZuvPFGoWNNeCdPnsQXX3wBtVqNw4cPw93dHUuWLIFCocC6detwww03CB2RcLHcX1xc/K0rfsjlcmRkZEAmkyEtLY0TyV1AX18fjh8/7vB5TqvVoqqqCmazGcDFv7vBF5MmJSUhLCxM4PREREREREREwmEpmYiIiIiIiOga9ff3o6amxv4Lalu5wFY+9vT0RHR0tP0X1bYlmydq+RgAjEYjqqur7WWM3NxclJSUwGw2Izg4GFKpFDKZDHK5HHK53KWmspKz7u5u7N+/H2q1Grt27cLZs2cxdepUZGVlQalUYuXKlfD39xc6Jl2h8+fP4/XXX8c777yDuXPn4te//jVuvfVWoWNd1okTJ7Bo0SKkp6fjn//8J5dYpzHnySefxB/+8Afs3r0bS5cuFTrOZel0Ojz77LPYtWsXli1bhi1btiA+Pl7oWISLF2rs378fKpUK//znP9HR0QGJRIL169dDqVRCJpMJHZEGaGhoQEFBgf28OD8/H83NzRCLxYiJiXEoKkskkgn7+cHVGI1GnD592ukC1MrKSnR1dQEAQkJChlz9ZiKugENEREREREQTD0vJRERERERERJcxuHxsu7VN8x1cPrbdxsXFTeilzU0mE6qqqhwmwhUWFqKvrw+BgYFISEhwmILMssXYUFdXhy+//BIqlQp79+6F0WhESkoKFAoFlEolUlNT+fc4xnR3d+N3v/sd3nzzTQQEBODll1/Ghg0bxlS5NycnB7fccgseeeQRvP3220LHIbpif/rTn/CjH/0IH3/8Me69916h41yVffv24emnn4ZOp8P999+P1157DVOnThU6Fv2f3t5e5OTkQKVSITs7G/X19YiKirK/X990000QiURCx6RBDAYDcnNzkZOTYz9/7u3ttZ8724rKXD3ENRkMBofPjHq9HmVlZTh37hwAIDg4GHPmzHEoKvNzIxEREREREY03LCUTERERERER/R/bFN9vW6LXNrVscPl43rx5Y6q8NxLMZjMqKysdCshFRUXo6emBv78/kpKSWEAeoywWC4qLi6FSqaBWq1FUVAQfHx8sXboUSqUSCoUC06dPFzomXQOLxYLPP/8czzzzDM6fP4+nn34azz33HHx8fISOdk3+/ve/46677sLWrVvx+OOPCx2H6LJUKhXWrFmD1157DS+88ILQca6JxWLBtm3b8Pzzz6OjowNPPfXUmD6OjFcD38s//fRTVFRUIDQ0FCtXruTKBi7O9vlkYFG5oqICVqsV4eHh9nNruVyOxYsXw9fXV+jINITW1laHorLtz0OtsGMrKts+a/J4SkRERERERGMNS8lEREREREQ04Qy15K5Go3EoH8+cOdPhl8EymYzl4wEMBgM0Go29IFFcXIzu7u4hl5vm5K+xp6urCwcOHIBarYZKpUJ9fT0iIyORlZUFhUKBrKwseHl5CR2TrsO+ffvwzDPP4NixY3jggQfw6quvYtq0aULHum6/+tWv8LOf/QyffPIJ7r77bqHjEH2rw4cPY+XKlbjnnnvwpz/9Seg4123gxPXAwEC89NJLY27i+kSi1+vtE5Tz8vLg7e2Nm2++GUqlEqtWreLEaxfX3t6OsrIy+3n40aNHce7cOYhEIsTGxjoUlVNSUnge7sIuXLiAEydOOBWWbZ9LRSIRIiIinCYrJycn80ICIiIiIiIiclksJRMREREREdG4ZTKZcOrUKYfyse22t7fX6Ze8tlupVApvb2+h47sMWwHZ9pWXl4eWlhZ7AXngBOQFCxbA09NT6Mh0DWpra7F3716oVCp89dVXMJvNSE5Oti/zLpPJhI5I/5+9e4+Lus73B/5KZkC5o6LSKsKAioNyVdEYU5K8tDOaK9Puno67W/qrdrXyhlqi5S1vVHazvXg5u+vWFpbpTF4SXLe4ZMjd4R4oJCQgI/fL3H5/7GGOw1BZAl8ur+fjwQND0lepON/P5/V5f7pBXl4eNm7cCLVajcjISLz++uuYMmWK0LG61ebNmxEbG4v33nsPjz32mNBxiKx89dVXiIyMxMMPP4wPPvgAIpFI6EjdpqamBjt37sShQ4cQEBCA2NhYRERECB2Lvkd1dTXOnj2LuLg4XLhwAXq9HjNnzoRCocDSpUsxceJEoSPSXbjz9XpSUhKSk5PR3NxsdWOJTCaDRCIROi79gPb2dhQVFVlNVs7NzUVLSwsAwMPDw2qy8pQpUwbEITMiIiIiIiLq31hKJiIiIiIion7PYDDg+vXrVuXjjk1bGxsbjB8/3qp8zOtwrXUuIF++fBnV1dVWk9dCQ0Mxbdo0lrf7MYPBgMzMTKhUKqjVaqSlpcHBwQERERFQKBRYvHgxSw0DSEVFBbZv344jR44gKCgIsbGxmDt3rtCxeoTJZMLq1atx+PBhfPzxx/j5z38udCQis5ycHERERCA0NBSnT58esFPn8/PzsW3bNsTFxSEyMhKvvvoqAgIChI5FP6C5uRkJCQmIi4uDSqXC7du3IZVKoVAoIJfLER4ejvvuu0/omHQX9Ho9CgoKLIrKGRkZMBqN8PDwsHhNHx4ejuHDhwsdme5Cx6HbO4vKGo0G2dnZaGhoAAC4ublZFJU73nt7e/PPLxEREREREfUKlpKJiIiIiIioX6moqLCaepyRkYHm5mYA/zcx6s5N2JCQENjb2wucvO+prKzElStXzGWF1NRU3Lx5EzY2Npg0aZJFWYH/DweG2tpaJCQkmIvIWq0WEokEkZGRkMvlWLBgASddDzBNTU14++23sXv3bri6uiImJgYrV64c8Fe5G41GPPnkk/jwww9x9uxZzJkzR+hIRCgoKMCcOXPg7+8PtVo9KA5GJSQkIDo6GllZWXj88cexf/9+HnjpJwwGA1JSUhAXF4eTJ0+ivLwcnp6eWLhwIV8z9FONjY3IzMw0v/ZPTExEaWkpAEAikSA8PNz82n/69OkD9tDEQNXxnNy5sFxZWQkAsLOzg4+Pj9Wzsp+fH2xsbAROT0RERERERAMJS8lERERERETUJ3VVPs7MzERTUxOArsvHwcHBcHBwEDh531RXV4ecnByLKci5ubkAYDUtTSaTwc3NTeDE1F1KSkrMJeR///vfMBqN5mvZFQoFpFKp0BGpBxiNRhw/fhybN29GQ0MD1q9fj82bNw+q6eYGgwG//vWvcebMGZw8eRIPP/yw0JFoELt69SoefvhheHt747PPPoOjo6PQkXqN0WjERx99hI0bN6K6uhqrV6/Gli1b4OTkJHQ0+hE0Gg3i4uKgVquRnp4OV1dX86GmJUuWwMXFReiI9BN0viUlOTkZtbW1EIvFCAgIsCgqS6VSTtrth7RarVVROTc3F9euXYPRaIRYLMa4ceOsJiv7+fnx2ZqIiIiIiIh+EpaSiYiIiIiISFBdlY/vvH62q/JxYGAgiyzfo6GhAVlZWRYFg7y8PJhMJqsC8qxZszBy5EihI1M36phsqFarcfr0aeTl5WH48OGYN28e5HI5Fi9eDFdXV6FjUg+Kj4/H+vXrkZeXhyeeeAI7d+7EqFGjhI4lCIPBgBUrVuD999/H+++/j1/84hdCR6JBKC0tDQsXLoRUKoVKpYKzs7PQkQTR3NyMt956C3v27IGjoyO2bduGFStWcEJnP3Tt2jV89tlnUKlUOH/+PEwmE8LCwqBUKhEVFYWf/exnQkeke1BSUoLExETzc8SVK1fQ1tYGFxcXTJ8+3VxUnjlzJtzd3YWOSz9RW1sbiouLLZ7FS0pKcPXqVbS1tQGwfhaXSCQICAgYtK8riYiIiIiI6O6wlExERERERES9QqvVWk1nysrKQnV1NQDAzc3NajpTYGAgN7p/QOdrmNPS0pCfnw+j0WhVQA4LC+MG8gB169YtXLx4ESqVCqdPn0ZdXR0kEgnkcjkUCgXmzJkDsVgsdEzqYRqNBhs3bsSZM2cgl8vx2muvYcKECULHEpzJZMLzzz+PQ4cO4ejRo/jNb34jdCQaRBITE/Hzn/8c4eHh+OijjzBs2DChIwnu1q1b2LFjBw4dOoSJEydi3759kMvlQsein6i2thYJCQlQqVT45JNP0NDQAKlUCqVSCYVCgdDQUKEj0j3S6XTIzs62KCp3PvAok8nMZWV+nevfdDodysvLrZ7d8/PzzbcWdfXsLpFI4O3tzWnaRERERERExFIyERERERERda+uysc5OTm4efMmgK43MKdOnYrRo0cLnLzv0+l0KCwsNJcBkpKSkJmZCYPBAFdXV/j7+5tLATKZDB4eHkJHph5UUlIClUoFtVqNS5cuAQDCwsKgUCiwZMkS+Pn5CRuQes2NGzewY8cOHDlyBCEhIYiNjcWDDz4odKw+xWQyYf369XjzzTfxpz/9CStWrBA6Eg0C586dw7Jly7Bo0SK89957sLW1FTpSn1JQUICtW7ciLi4OkZGRiI2NRWBgoNCx6B60trYiMTERKpUKcXFxqKyshLe3NxQKBRQKBebOnQuRSCR0TOoGdXV1SE1NNReVL1++jOrqaohEIkycONGiqDx58mQMGTJE6MjUDTrfclRSUoLs7GxUVVUBAFxdXeHj42NRVJZKpfw9QERERERENMiwlExEREREREQ/iVarRUlJCTQaDdLS0pCbm4urV6/i22+/BfCf8nHHJmRoaCj8/f0xZcoUjBkzRuDk/YNer0dBQYHFBOSOq5OdnZ0xdepUiynIUqmUU6kGuI6iT3x8PE6ePInCwkKMHDkSERERkMvlWLJkCVxcXISOSb2oqakJBw4cwP79++Hu7o6dO3di+fLl/FrwPV566SXs3LkTMTEx2L59O/9fUY85cuQIfv/73+O//uu/cPjwYRYxv0dKSgo2bNiAL7/8Eo8//jj27t2L+++/X+hYdI+MRiMyMjKgUqnw4YcfIi8vDyNGjMAjjzwChUKBRYsWwdHRUeiY1I0qKirMBycTExORnp6OlpYWODk5ISAgwFxUnj17Np8JB5g7DyZ3rBHk5uaitLQUJpMJtra28PX1tSgqdxxS5mRtIiIiIiKigYelZCIiIiIiIvpedXV1KC4utpp+XFJSAgBwcXGBr69vl9e30t0xGAzIz8+3KCB3bOI7OjoiMDCQBeRBqrq6GmfPnoVarcb58+dRX18PqVQKhUKByMhIThwcpPR6PY4ePYpt27ahvb0dmzZtwvPPP4+hQ4cKHa1fOHbsGJ5++mk8+uij+Nvf/sb/b9StTCYTtm/fju3bt+O5557DwYMH+Xf2XTCZTDhx4gQ2bdqEmzdv4tlnn8WLL74IZ2dnoaNRN+m44SEuLg4pKSmws7PDvHnzzDc88NaUgefOQ5YdReX8/HwYjUZ4eHhYPN/Mnj0brq6uQkembtZ5LaGjsFxQUACDwQCRSARPT0+ronJgYCCcnJyEjk9EREREREQ/EUvJREREREREBACor69HUVGRVfm4Y7qRs7MzJkyYYFU+9vb2ZtnmR+o8RSwjIwPNzc0Qi8WYMGGC+arj0NBQXnU7CGk0GqjVaqhUKqSkpMDW1hYymQxyuRxLly6Fp6en0BFJQPHx8Vi7di0KCgrwxBNPYNeuXXB3dxc6Vr8THx+PqKgo+Pv749SpUxg5cqTQkWgAaGtrw4oVK/DBBx/gnXfewVNPPSV0pH6nvb0d7777Ll5++WWIRCLExMRg1apVPIAzwHQcuoqLi8OFCxeg1+sxc+ZMKBQKPProo5g0aZLQEamHNDQ0ICsry/ws9MUXX+Dbb7+FjY0NJk2aZFFUnjFjBmxtbYWOTD2gvb0dRUVFVpOVc3Nz0dLSAgDw8PCwmqzs7+8PDw8PgdMTERERERHRD2EpmYiIiIiIaJC5cwNQo9EgLS3tO69WlUqlCA0NZfn4HnQUkDvekpOTUVtbC5FIhIkTJ3LjndDS0oKkpCSoVCqcPHkS5eXlcHd3x8KFC6FQKLBw4UJOCiNcuXIF0dHRuHTpEuRyOQ4ePAgfHx+hY/Vr2dnZkMvlsLe3xyeffAI/Pz+hI1E/VllZCaVSCY1GgxMnTmDevHlCR+rXamtrsX//fhw8eBDe3t7YsWMHlEql0LGoBzQ3NyMhIQFxcXFQqVS4ffu2+VYIuVyO8PBwPoMMcJ2flxITE3H79m04ODggKCjI4nnJ399f6LjUg/R6PcrKyiyKyhqNBtnZ2WhoaAAAuLm5WU1W9vf3h5eXFw/zEhERERER9REsJRMREREREQ1QncvHHe87rsztXD7ueM/JvD9d5w31y5cvo7q6ussC8rRp0zB06FChI5NAqqqqcO7cOajVapw9exaNjY0s4FCXysvLsWvXLhw+fBjTp09HbGwsZDKZ0LEGjIqKCixbtgwajQbHjh3DsmXLhI5E/VBiYiIee+wxODk54eTJk5BKpUJHGjCKioqwZcsWnDhxAg899BAOHDiA4OBgoWNRDzEYDEhJSUFcXJz5oNa4ceOwaNEiyOVyLFiwgAf4BgGDwYD8/HyL56rU1FS0t7djzJgxmDZtmvmZ6oEHHsCIESOEjky9QKvVWt3qpNFoUFlZCQCws7ODj4+P1fqGn58fbGxsBE5PREREREQ0uLCUTERERERE1M/pdDoUFhZabc4VFBTAYDBALBZjwoQJ3JzrZpWVlbhy5YrFRvnNmzcBABKJBOHh4ebN8pCQENjb2wucmISm0WigVquhUqmQnJyMoUOHIjw8HHK5HMuWLcPYsWOFjkh9SGNjI2JjY7Fv3z6MGzcOu3fvRlRUFMvqPUCv1yMmJgb79+/Hs88+i9jYWIjFYqFjUT/x5z//Gc8++yzmz5+Pv//973B1dRU60oB0+fJlbNiwAUlJSYiKisL+/fvh5eUldCzqYZ1fO9nb2yMiIgJKpRJLliyBi4uL0BGplzQ1NSEjI8OiqJybmwsA8PDwgEwmMz9/8fDn4KLVaq0mK+fm5uLatWswGo0Qi8UYN26c1WRlPz8/ODg4CB2fiIiIiIhoQGIpmYiIiIiIqJ/Q6XQoLy+32GxLS0uzKB933mwLDQ1l+bgb1NXVIScn5zs3we+cgCyTyeDm5iZwYuoLOq4jV6vV+PTTT3Hjxg2MHj0a8+fPh0KhwKJFi+Do6Ch0TOpjdDodjh07hq1bt0Kv12Pjxo1Ys2YN7OzshI424P3P//wPfv/73yM8PBzvvfceRo0aJXQk6sMaGxvxzDPP4J///Ce2b9+OF198kYcGeoFKpcLatWtRUVGB5557Di+88AKLqYPE9evXcf78eahUKpw/fx4mkwlhYWFQKpWIiorCz372M6EjUi/rfEg0JSUFt27dMh/KvbOoLJVK+TV6kGlra0NxcbFFUbmkpARXr15FW1sbgP88y99ZVJZIJAgICOBrQCIiIiIionvEUjIREREREVEfo9frUVZW1uXVpK2trRCJRPD09LSa9OPv78+JUN2goaEBWVlZFgXkvLw8mEwmqwLyrFmzMHLkSKEjUx9yZ2HmwoUL0Ol0CA4Ohlwuh0KhQEhICAsR9J1UKhXWrVuHsrIyPPPMM9i+fTunrvay9PR0REVFobm5GceOHcOiRYuEjkR9UGpqKh5//HFotVocP34cCxYsEDrSoNJxeCMmJgZGoxHR0dFYu3YtbG1thY5GvaS2thYJCQlQqVT45JNP0NDQAKlUCqVSCYVCgdDQUKEjkkAqKiqQlJSExMRE87Nca2srnJ2dMXXqVHNROSwsjMXTQaqrw965ubnIz89HU1MTAMDNzc2iqNzxbW9vbz7LERERERER3QWWkomIiIiIiARiMBhw/fp1q82w3NxctLS0wMbGBuPHj7cqH0ulUgwbNkzo+ANCY2MjMjMzLQrI+fn5MBqNVgVkblxTV4xGIzIyMqBSqaBWq5Geno5hw4bhoYcegkKhgFwux/333y90TOrjUlNTsWHDBnzxxReIiorC3r17IZFIhI41aNXX1yM6Ohp/+ctf8P/+3//D66+/Dnt7e6FjUR9gMpnw5ptvYuPGjZg9ezb++te/cjqrgLRaLfbt24c33ngD48ePx86dO6FUKoWORb2stbUViYmJUKlUiIuLQ2VlJby9vaFQKKBQKDB37lyIRCKhY5JAdDodCgsLLYrKXR04lclkeOCBB/j3/SBXUVFhNVk5OzsbVVVVAABXV1f4+PhYFZZ5OxUREREREZEllpKJiIiIiIh6QefNLY1Gg4yMDDQ3NwOwvjZUKpUiJCSEm6LdqGNDuqN8nJSUhMzMTBgMBri6usLf39+8IS2TyeDh4SF0ZOqjmpqacPHiRajVaqhUKlRWVsLLywvz58+HXC7H/PnzYWdnJ3RM6gfKysoQExOD48ePY+bMmYiNjcUDDzwgdCz6X//4xz+watUqjBs3Du+99x6mTp0qdCQSUFlZGX7zm9/gyy+/xO7du7Fu3TpOS+wj+LWUOtx5WOzDDz9EXl4eRowYgUceeQQKhQKLFi2Co6Oj0DFJYPX19cjOzjYXlb/66itUVVVBJBJh4sSJFkXl4OBgDBkyROjIJDCtVmtRVO74dmlpKUwmE2xtbeHr62s1WZkHyomIiIiIaLBiKZmIiIiIiKgbdVU+zszMNF8D2lX5ODg4GA4ODgInH1j0ej0KCgosJiBfuXIFbW1t5qt775yCLJVKWSyi71VaWooLFy5ApVLhs88+g8FgQFBQEORyOa8Jpx+N0z37j7KyMixfvhwpKSlYt24dtm/fzkMHg4zJZMJf/vIXREdH4/7778d7772H4OBgoWNRFzh1njorKSkxT1BOSUmBnZ0d5s2bB4VCgcWLF2PMmDFCR6Q+oqKiwuLwanJyMpqbm+Ho6IjAwEDzc+Ps2bPh7e0tdFzqI+rq6lBcXGxRVNZoNCgoKIDBYIBIJIKnp6dVUTkwMBBOTk5CxyciIiIiIuoxLCUTERERERH9BF2Vj7OystDY2Aig6/JxUFAQJ3P1AIPBgPz8fIsCcnp6OlpaWqw2kVlAprtlMBiQmZkJlUoFtVqNtLQ0ODg4ICIigkUW+sl0Oh2OHTuGmJgYGI1GREdHY+3atbC1tRU6Gn0Po9GIw4cPY/369fDw8MCf//xnzJ07V+hY1AuKi4vx1FNP4YsvvsAf/vAHvPLKKzxI1g+oVCqsW7cOZWVleOaZZ7B9+3a4uroKHYsEVl1djbNnzyIuLg4XLlyAXq/HzJkzoVAo8Oijj2LSpElCR6Q+pPMh16SkJGRkZMBoNMLDw8Pi+TI8PBzDhw8XOjL1Ie3t7SgqKrKarJybm4uWlhYA/7dmdGdh2d/fnzc2ERERERHRgMBSMhERERER0fe485rOjvdZWVmorq4GALi5uVkUj/39/REYGAh3d3eBk2IhopIAACAASURBVA9cHVOsOq7bzcjIQHNzM8RiMSZMmACZTIbw8HCEhoZi8uTJvG6X7lptbS0SEhLMRWStVguJRILIyEjI5XIsWLCA5VH6yVQqFdauXYuKigo899xzeOGFF+Di4iJ0LPoRrl27hmeeeQYXLlzAH/7wB+zevRvOzs5Cx6Ie0N7ejj179mDPnj2YOnUq/vKXvyAoKEjoWPQjdBwC2bp1K/R6PTZu3Ig1a9Zw0jkBAJqbm5GQkIC4uDioVCrcvn0bUqkUCoUCcrkc4eHhPMRIVhobG5GZmWkuKicmJqK0tBQAIJFIzM+goaGhmD59Or/ekBW9Xo+ysjKrycrZ2dloaGgA8J81ps6Tlf39/eHl5cW1DSIiIiIi6jdYSiYiIiIiIkLX5ePs7GxUVVUB6Lp8HBAQgFGjRgmcfGC78xrdtLQ0JCcno7a2FiKRCBMnTrSYUDVjxgwWRulH67jWW61W49///jeMRqN5ap5CoYBUKhU6IvVzly9fxoYNG5CUlISoqCjs378fXl5eQseiexAXF4dVq1bBZDIhJiYGq1evho2NjdCxqJvEx8fj+eefR2lpKV566SVs2LCBv779WGNjI2JjY7Fv3z6MGzcOu3fvRlRUFAunZGYwGJCSkgK1Wo2PP/4YRUVFGDVqFBYsWAClUslDafS9Oj+vJiUlQavVQiwWIyAgwKKozBt76Pt0tSal0WhQWVkJALCzs4OPj4/FmpREIsGUKVNYgCciIiIioj6HpWQiIiIiIhpUutrouXr1Kr799lsAXZePp0yZgjFjxgicfODrvKF7+fJlVFdXd1lAnjZtGoYOHSp0ZOqH7iyenD59Gnl5eRg+fDjmzZsHuVyOxYsX84p36hZFRUXYsmULTpw4gYceeggHDhxAcHCw0LGom9TU1GDbtm3mCboHDx5EeHi40LHoHmg0Gqxduxbx8fFQKpXYv38/xo8fL3Qs6ibl5eXYtWsXDh8+jOnTpyM2NhYymUzoWNQHaTQaqNVqqFQqJCcnw97eHhEREVAqlViyZAlvOaAfVFJSgsTERPNz7ZUrV9DW1gYXFxdMnz7dXFSeOXMmb1iiH6TVaq0mK+fm5uLatWswGo0Qi8UYN26c1TqWn58fHBwchI5PRERERESDFEvJREREREQ0INXV1aG4uNhq46akpAQA4OLiAl9fX6uNG4lEInDywaGyshJXrlwxb9Smpqbi5s2bAKyvvg0JCYG9vb3Aiak/u3XrFi5evAiVSoXTp0+jrq4OEokEcrkcCoUCc+bMgVgsFjomDRC1tbXYv38/Dh48CG9vb+zYsQNKpVLoWNRDrl69ijVr1uDixYv45S9/iT179nASdj9TXV2NHTt24I9//CMCAgJw8OBBzJ49W+hY1EOuXLmC6OhoXLp0CXK5HAcPHoSPj4/QsaiPun79Os6fPw+VSoXz58+bb9RQKpVYtmwZxo4dK3RE6gd0Oh2ys7Mtisp5eXkwmUzw8PBAaGgoZDKZ+Rl42LBhQkemfqCtrQ3FxcVW610ajQatra0AAA8PD6v1Lt74RUREREREvYGlZCIiIiIi6tfq6+tRVFRkVT4uLS2FyWSCs7MzJkyYYFU+9vb25tWpvaSurg45OTkWU5Bzc3MBwLwJ2/Emk8ng5uYmcGIaCEpKSqBSqaBWq3Hp0iUAQFhYGBQKBZYsWQI/Pz9hA9KA097ejnfffRcvv/wyRCIRYmJisGrVKohEIqGjUS84deoUNmzYgLKyMjz55JPYsmULy2p9XG1tLV599VW8+eabcHR0xK5du/DEE09gyJAhQkejXhAfH4+1a9eioKAATzzxBHbt2sWJpfS9amtrkZCQAJVKhVOnTqG+vh5SqRRKpRIKhQKhoaFCR6R+pK6uDqmpqeai8pdffomamhqLW4I6isqTJ0/m301013Q6HcrLy62KypmZmWhqagJgeUOYRCLhOhkREREREXU7lpKJiIiIiKhf6DwFpqPY2lE+trOzg4+Pj7l4HBoayk0VATQ0NCArK8uigNx5ClTH26xZszBy5EihI9MA0draisTERMTHx+PkyZMoLCzEyJEjERERAblczuu2qceYTCacOHECmzZtws2bN/Hss8/ixRdfhLOzs9DRqJfpdDq8//772LFjB8rLy7F8+XLs2LED999/v9DR6A6NjY145513sHv3btja2mL16tVYt24d/8wOQnq9HkePHsW2bdvQ3t6OTZs24fnnn8fQoUOFjkZ9XMfrTpVKhRMnTqCiogLe3t5QKBRQKBSYO3cuDyXRj1ZRUYGkpCRzUTk9PR0tLS1wcnJCQECAuag8e/ZsjBkzRui41A9VVFRYTVbOyckx31rVcaPYnUVlqVQKPz8/2NjYCJyeiIiIiIj6E5aSiYiIiIioT2lvb0dRUZHFRolGo0F+fj6MRiNsbW3h6+trMfVYKpVyepAAGhsbkZmZaVFA7vh16lxADgsL4xWh1O2qq6tx9uxZqNVqnD9/3jyxTqFQIDIykoUQ6nEpKSnYsGEDvvzySzz++OPYu3cvC6iE9vZ2bN68GUePHoVOp8PKlSuxZs0aeHt7Cx1tUKuqqsI777yDt99+G0ajEW5ubti+fTuWL18udDQSWFNTEw4cOID9+/fD3d0dO3fuxPLly3mwke6K0WhERkYGVCoV4uLikJubixEjRuCRRx6BQqHAokWL4OjoKHRM6of0ej0KCgqQlpZmLit/1/P27Nmz4erqKnRk6qe0Wq3VZOU7hwDcuQ7XubA8bNgwoeMTEREREVEfxFIyEREREREJQqfTobCw0Kp8XFBQAIPBALFYjAkTJliVjzmhRRgdv14d5eOkpCRkZmbCYDDA1dUV/v7+5slNMpkMHh4eQkemAUqj0UCtVkOlUiElJQW2traQyWSQy+VYunQpPD09hY5Ig0BBQQG2bt2KuLg4REZGIjY2FoGBgULHoj7is88+w6OPPopDhw7h9u3beP3113Hjxg0sW7YMGzZswPTp04WOOKgUFhbitddew9/+9jc4ODhg1apVeP7557Fu3Tr8/e9/x9GjR/Gb3/xG6JjUB9y4cQM7duzAkSNHEBISgtjYWDz44INCx6J+pqSkxFxQTklJgZ2dHebNmweFQoHFixdzwi3dkztvJkpKSsLnn3+OmzdvwsbGBpMmTbIoKs+YMQO2trZCR6Z+rK6uDsXFxRZF5TvX7UQiETw9Pa2KyoGBgXBychI6PhERERERCYilZCIiIiIi6lE6nQ7l5eUWGxhpaWkW5eNx48ZZbGCEhoayfCygOycydbxduXIFbW1tcHZ2xtSpUy02O6VSKSfJUY9paWlBUlISVCoVTp48ifLycri7u2PhwoVQKBRYuHAhNzyp19y6dQs7duzAoUOHMHHiROzbtw9yuVzoWNSHnD59GlFRUdDpdPjyyy8RFhYGvV6PuLg4xMbGIj09HXPmzMFzzz0HhUIBsVgsdOQByWQy4eLFi3j77bdx+vRpSCQSrFu3Dr/97W9hb28PANi9ezdiYmJw33334a233sKqVasETk19hUajwcaNG3HmzBnI5XK89tprmDBhgtCxqB+qqanBmTNnoFarcebMGbS0tGDWrFlQKBRYsmQJ/Pz8hI5IA0BFRYXF4eGUlBQ0NTXBwcEBQUFBFs/u/v7+QselAeDOG87uLCzn5eWhubkZAODm5maxzufv7w9/f38eYCciIiIiGiRYSiYiIiIiom6h1+tRVlZmUT7ueN/a2mqeoNLVpsTQoUOFjj9oGQwG5OfnWxSQ09PT0dLSAkdHRwQGBrKATL2uqqoK586dg1qtxtmzZ9HY2AipVAqFQgG5XI7w8HD+PqRe1dzcjLfeegt79uyBo6Mjtm3bhhUrVvDwDFn45z//if/+7/+G0WiEyWRCVVUV3N3dLT7n4sWLeO2113D27FmMGjUKv/3tb7FixQoWHrtJZWUljh07hqNHj+Lrr7+GTCbD2rVr8eijj2LIkCEWn/vBBx/g17/+NTqWxzdt2oS9e/cKEZv6qPj4eKxfvx55eXl44oknsHPnTowaNUroWNRPNTc3IyEhAWq1Gp988gmqqqogkUggl8uhVCr5+pa6TVfP+KmpqWhvb4eHh4fF8/0DDzyAESNGCB2ZBoiOdcHOk5Wzs7PR0NAA4D9l5c6Tlf39/eHl5WX1Wo2IiIiIiPovlpKJiIiIiOhHMRgMuH79ulX5ODc3Fy0tLbCxscH48eOtNhikUimGDRsmdPxBr2OKUlJSEhITE5GRkYHm5maIxWJMmDABMpkM4eHhCA0NxeTJk7kpRL1Go9FArVZDpVIhOTkZQ4cORXh4OORyOZYtW4axY8cKHZEGIaPRiI8++ggbN25EdXU1Vq9ejS1btnA6N1k5fvw4fvvb38JkMsFkMsHe3h5NTU3f+fnXr1/H0aNHcfToUdy4cQNz587Fk08+iSVLlvD314/U1taGc+fO4dixY/j000/h4uKC5cuXY+XKld87ETI9PR2hoaHmf77vvvsQHR2NvXv3shhIZkajEcePH8fmzZvR0NCA9evXY/PmzTxUSffEYDAgJSUFarUaH3/8MYqKijBq1CgsWLAASqUSCxYsgK2trdAxaQBpampCRkaGRVE5NzcXACCRSMxrAKGhoZg2bRq/xlG302q1VuuIJSUlKCkpAQDY2dnBx8fHYh1RIpFgypQpsLOzEzg9ERERERH9WCwlExERERHRd6qoqLCaetxRYgUADw8Pq+JxSEiI+VpsEtad17impaUhOTkZtbW1EIlEmDhxosWEpBkzZnDjm3rVndPiPv30U9y4cQOjR4/G/PnzoVAosGjRIjg6OgodkwaxhIQEREdHIysrC48//jj279+PMWPGCB2L+qB3330Xq1atwp3LrFKpFBqN5gf/XYPBgHPnzuHw4cP49NNPYWNjg0ceeQSPPfYY5HI5HBwcejJ6v9Xe3o4LFy7gww8/xKlTp9DQ0ICIiAisXLkSS5cuvavySn19PVxcXCw+NmTIEKxcuRLvvvsuD2aRhaamJrz99tvYvXs3XFxcsHXrVqxcuZK/T6hbdD6cZ29vj4iICCiVSixZssTqaxVRd6isrMSVK1fM6wUpKSm4detWlweWeWMS9RStVms1WTk3NxfXrl2D0WiEWCzGuHHjLIrKUqkUwcHBfJ1MRERERNSHsZRMRERERERdlo8zMzPNE/66Kh9zA6Bv6VxAvnz5Mqqrq7ssIHPyEQnl+vXrOH/+PFQqFS5cuACdTofg4GDI5XIoFAqEhIRws5sEl5+fj23btiEuLg6RkZF49dVXERAQIHQs6qP27duHzZs3W3zsvvvuw9KlS/HRRx/9qB+rtrYWJ0+exIcffoiLFy/C1tYWP//5z6FQKLBgwQKMGjWqO6P3O3V1dYiPj8enn36KTz75BLdv38asWbPw2GOPQalU4v777//RP+bw4cOh1WotPjZkyBAolUr8/e9/h1gs7q74NEBUVFRg+/btOHLkCIKCghAbG4u5c+cKHYsGkDtfL58/fx5GoxEzZ86EUqnk7SHU40pKSpCYmGixttDa2gpnZ2dMnTrVXFQOCwsb9K9LqGe1tbWhuLjYarKyRqNBa2srAOu1SolEgqlTp2L06NECpyciIiIiIpaSiYiIiIgGka7Kx1lZWWhsbATQdfk4KCiI00r7mM4TjVJTU3Hz5k0A1levcnI1CcloNCIjIwMqlQpqtRrp6ekYNmwYHnroISgUCsjl8p9UIiPqCTU1Ndi5cycOHTqEgIAAxMbGIiIiQuhY1Ift3bsXL7zwgtXH7ezssGbNGuzdu/cn/9g1NTX46KOPcOLECXzxxRfQ6XQICQnBwoULsWjRIoSFhcHGxuZe4vd5JpMJmZmZOHfuHM6dO4fk5GSYTCbMmDEDy5Ytg1KphKen5z39HDNmzEBqaqrVx0UiER5++GF8/PHHPMhFXcrLy8PGjRuhVqsRGRmJ1157DVOnThU6Fg0wWq0W8fHxUKlUOHXqFOrr6yGVSqFUKqFQKBAaGip0RBrgdDodCgsLkZSUZC4r5+XlwWQywcPDA6GhoeaiMtceqDfodDqUl5dbFZXvXNt0c3Ozmqzs7+8Pb29vHoImIiIiIuolLCUTEREREQ1AWq3W6urDzMxM1NTUALBcoO94HxQUhJEjRwqcnDqrq6tDTk6OxaSi3NxcADBvAna8yWQyuLm5CZyYBrumpiZcvHjRfAV1ZWUlvLy8MH/+fMjlcsyfPx92dnZCxyQya25uxltvvYVXXnkFzs7O2Lp1K1asWDHgC5/005lMJqxfvx4HDx5EV0urIpEI77zzDp566qlu+fmam5uRnJyM+Ph4nDp1Cvn5+XB0dERgYKC5CDR79my4urp2y88nFL1ej6ysLCQmJiIpKQmXLl1CdXU13N3dMXfuXERGRmLx4sUYM2ZMt/2cv/vd7/CPf/wDer3e6vvEYjHCwsJw5swZODk5ddvPSQNLfHw8oqOjcfXqVTz55JPYvn17t/4eJerQ2tqKxMREqFQqnDhxAhUVFfDy8sLixYuhUCgwZ84cTnenXlFfX4/s7GxzUfmrr75CVVWV1S1NMpkMwcHBGDJkiNCRaZDoGMTQUVTOzc1FTk6O+RC/i4sLfH19LYrKUqkUfn5+fPYjIiIiIupmLCUTEREREfVjXZWPs7OzUVVVBaDr8nFAQACv2eyjGhoakJWVZVFA7jyFqONt1qxZLJFTn1FaWooLFy5ApVLhs88+g8FgQFBQEORyOae4UZ9lNBpx/PhxbN68GQ0NDVi/fj02bdqEYcOGCR2N+jCj0YinnnoKx44dg9Fo/M7Pi4+Px7x583okQ0FBAS5evIjk5GQkJibi2rVrEIlECAwMREhICAICAjB16lQEBAT02cNKDQ0N0Gg0yM7ORnZ2NjIzM81XxHt4eOCBBx6ATCbDnDlzEBQU1GNT7Xbt2oVdu3ahra2ty+8Xi8WYMmUKLly4gBEjRvRIBur/jEYjPvroI0RHR6OmpgarV69GTEwMb5uhHnPnbSRxcXHIzc3FiBEj8Mgjj0ChUGDhwoU8TEG9qqKiAmlpaeaickZGBpqbm82HqDrWMWbPng1vb2+h49Igc+fa6Z2F5dLSUphMJtja2sLX19dqsrJUKuWzIRERERHRT8RSMhERERFRP9BV+fjq1av49ttvAXRdPp4yZQqndPVhjY2N5gJOx1t+fj6MRqNVATksLIxFcupTDAYDMjMzoVKpoFarkZaWBgcHB0REREChUHT7JEui7hYfH48NGzYgNzcXTzzxBHbs2IHRo0cLHYv6OL1ej+XLlyMuLg4Gg+F7P7ekpKTXSjcVFRVITExESkoKMjMzkZ2djdraWgDA2LFjMWXKFEgkEnh5ecHb2xteXl7w8vLq8cNNdXV1uHbtGq5du4bS0lLzm0ajMZdAnJycMGXKFAQGBmLWrFkIDw+Hj49Pj+a60wcffIBf//rXXU687nDfffdhypQpiI+P5+sx+l53Tt53cnLCtm3bOHmfekVJSYn5dfmlS5cgFosRHh4OuVyOX/7yl3xdTr1Or9ejoKDAvNaRlJSEjIyMLtc7eOMTCaWurg7FxcUWRWWNRoOCggIYDAaIRCJ4enpaFZUDAwN58IOIiIiI6AewlExERERE1Ifcvn0bX3/9tVUBuaSkBADg6uoKHx8fqwKyRCIRODl9H51Oh8LCQosNuczMTBgMBri6usLf39+8GSeTyeDh4SF0ZCIrtbW1SEhIMBcetFotJBIJIiMjIZfLsWDBAtja2godk+h75ebmYuPGjfj0008RGRmJ119/HVOmTBE6FvUTJ06cwOOPPw6j0Qi9Xv+dn2djY4O2tjZBi4jffPMNcnJykJ2dbZ4EV1paioqKCvOEZ3t7e4wYMQIjRozAqFGjzN92cHCAk5MTRCIRhgwZAhcXF4sfu6GhAXq9HiaTCbdv30Zraytu3bqFmpoa3Lp1C9XV1aipqUFjY6P53xkzZoy5ED158mTzJGdvb+8em4J8N9LS0jBt2rTv/H6RSAR7e3ts2bIFq1evhr29fS+mo/6qpqYGO3fuxKFDhzBx4kQcOHAAjzzyiNCxaJCoqanBmTNnoFarcebMGbS0tCA4OBhyuRy/+tWv4OfnJ3REGqQ6H8xOTExEaWkpbGxsMGnSJIui8vTp02FnZyd0ZBqk2tvb8c0331itzebl5aG5uRlA18MhJBIJ12eJiIiIiP4XS8lERERERAKor69HUVGR1QJ3x9Q4Z2dnTJgwwWqBW+jiBv2wzhOB0tLScOXKFbS1tcHZ2RlTp0612GyTSqX8NaU+686pa//+979hNBoxc+ZMKBQKKBQKSKVSoSMS3ZUbN25gx44dOHLkCIKDgxEbG4s5c+YIHYv6oW+++Qb79+/HH//4RwD/OXjUmaenJ65fv97b0e5Ke3s7ysrKUFpaihs3buDWrVvmInHHt5uamtDY2AidTgej0Yi6ujqLH8PR0RFisRg2NjZwdnbG0KFDzYXmESNGwN3dHSNGjICHh4e5iNxXr76ur6+3Kl0DwJAhQyASibB27Vps3rwZrq6uAqSj/i4/Px/btm1DXFwcIiMj8eqrryIgIEDoWDSINDc3IyEhAWq1Gp988gmqqqogkUggl8uhVCoRHh7OZ1ESVEVFhcXaSVJSErRaLcRiMQICAhAeHs61E+oz9Ho9ysrKrCYr5+TkoL6+HsB/ysqdJyv7+/vDy8sLQ4YMEfi/gIiIiIio97CUTERERETUg9ra2lBcXIy0tLQuy8d2dnbw8fFBaGgoy8f9kMFgQH5+vsUmWnp6OlpaWuDo6IjAwEAWkKlfMRgMSElJgVqtxunTp5GXl4fhw4dj3rx5kMvlWLx4MYtZ1K80NTXh7bffxu7du+Hq6oqYmBisXLmSG8J0z0pLSzFz5kzU1NTAxsbGopwcGRmJCxcuCJiOfozhw4dDq9UCAMRiMYYMGYLx48ebX+eJRCKBE1J/d/HiRURHRyMzMxOPP/449u3bx5tRqNfd+Tr/5MmTKCwshLu7OxYuXAilUon58+dzMi0Jrqs1lo5D3i4uLpg+fbq5qDxr1iyMHDlS6MhEAACtVtvlrXcdN991rP92nqzs7++PoUOHCpyeiIiIiKj7sZRMRERERNQN2tvbUVRUZLH4rNFokJ+fD6PRCFtbW/j6+losPkulUkyePJnFqH6kY4pPUlISEhMTkZGRgebmZojFYkyYMAEymcy8QcZfW+ovbt26hYsXL0KlUuH06dOoq6szT1BTKBSYM2cOxGKx0DGJfhSj0Yjjx49j06ZNaGtrw6ZNm/D8889zw5e6zd/+9jc8+eSTOHv2LP7617/i/fffh0gkgsFgwNNPP4133nlH6Ih0l2bMmIHU1FSIRCI8/fTT2LJlCxobGyGVSnH06FEsX75c6Ig0AJhMJpw4cQKbNm1CVVUVVq9ejS1btsDJyUnoaDRIaTQaqNVqqFQqJCcnY9iwYXjooYegVCp5EJH6FJ1Oh+zsbCQmJpqLynl5eTCZTPDw8LBYhwkNDe2ztzPQ4KTVai0mK3d8u2O9WCwWY9y4cRZFZalUiuDgYDg4OAgdn4iIiIjoJ2MpmYiIiIjoR9DpdCgsLLQqHxcUFMBgMJjLqZ3Lx35+frCxsRE6Pv0Ina8RTU5ORm1tLUQiESZOnGgxAXnGjBmwtbUVOjLRXSspKYFKpYJarcalS5cAAGFhYVAoFFiyZAn8/PyEDUh0D+Lj47Fu3Trk5+fjiSeewM6dOzFq1CihY9EAYjAYIJVKIZPJcOTIEQBAXl4eXnrpJZw4cQKxsbFYt26dwCnpbq1cuRIGgwEvv/wyxo8fb/74k08+iX/9618oKCjg6zzqNu3t7Xj33Xfx0ksvwcHBAS+99BJWrFjBZ0USVFlZGc6dOweVSoXPPvsMBoMBM2fOhFKpxLJlyzB27FihIxJZqKurQ2pqqrmo/OWXX6KmpsZivaajrMwD49QXdTXcoqOw3NraCgDw8PCwmqw8depUjB49WuD0REREREQ/jKVkIiIiIqIu6HQ6lJeXWxSP09LSLMrHd06ykEqlCA0NZfm4n+pcQL58+TKqq6u7LCBPmzaNkzap32ltbUViYiLi4+PN1zWPHDkSERERkMvlWLJkCVxcXISOSXRP0tLSEB0djX/961+Qy+V4/fXX4evrK3QsGoAOHz6MP/zhD8jPz4dEIrH4vpycHNx3332YMmWKQOnox2pvb++ydHz9+nVMmjQJb7zxBp5++mkBktFAduvWLRw4cACvv/46fHx8sH37diiVSqFjEUGr1SI+Ph4qlQqnTp1CfX09pFIplEolFAoFQkNDhY5I1KWKigrzrVZpaWlIT09HS0sLnJycEBAQYC4qP/jggyx1Up/VeT26o6iclZWFxsZGAICbm5vVZGV/f394e3vjvvvuE/i/gIiIiIjoP1hKJiIiIqJBTa/Xo6yszKJ83PG+tbUVIpEInp6eFuVjf39/+Pv7s5jaT2m1WqSlpZk3qlJTU3Hz5k0AgEQisbj2MyQkBPb29gInJvppqqurcfbsWajVapw/f95cKFAoFIiMjMTcuXMhEomEjkl0z7755hvs3LkTR44cQWhoKGJjYzF79myhY9EApdPpMGnSJMyfPx9//OMfhY5DPWzVqlX45JNPUFxczOvgqUcUFhYiJiYGcXFxiIyMxIEDBxAUFCR0LCIA/3ewUaVS4cSJE6ioqICXlxcWL14MhUKBOXPmQCwWCx2TqEt6vR4FBQVIS0szl5Xz8/NhNBrh4eFhXveRyWSYNWsWHBwchI5M9L0qKiosisq5ubnIyckxr2m6uLjA19fXoqjM2/uIiIiISCgsJRMRERHRoPBd5ePc3Fy0tLR8Z/lYKpWygNCP1dXVIScnx2IKcm5uLgBYbEJ1bES5ubkJnJjo3mg0GqjVaqhUKqSkpMDW1hYymQxyuRxLly6Fp6en0BGJuk1jYyNiM8MIBwAAIABJREFUY2Oxf/9+uLu7Y+fOnVi+fDmnQ1GPOnToENauXYuCggJ4eXkJHYd6WGVlJXx9fbF7926sWbNG6Dg0gH355ZfYsGEDUlJSsGzZMhw4cADjx48XOhaRmdFoREZGBlQqFdRqNdLS0jB8+HDMmzfP/Kzh5OQkdEyi79XQ0ICsrCxzUfnzzz/HzZs3YWNjg0mTJlmsEc2YMaPLmxSI+hqtVms1WTk3NxelpaUwmUywtbXF2LFjrda8J0+ezEEMRERERNRjWEomIiIiogGnY3LEnQXkjIwMNDc3A/hPGbVz8ZgTcfu/OzeXOt7y8vJgMpmsCsizZs3CyJEjhY5MdM9aWlqQlJQElUqFkydPory8HO7u7li4cCEUCgUWLlzIcgANODqdDseOHcO2bdug0+mwceNGrFmzBnZ2dkJHowGutbUVEyZMwNKlS/Hmm28KHYd6SXR0NP7617/i66+/5t+p1KNMJhNOnDiBzZs349tvv8Wzzz6LF198Ec7OzkJHI7JSUlJiLihfunQJIpHIfBjyscceg4eHh9ARie5KRUWFeQ0pKSkJycnJaG5uhoODA4KCgizWkvz9/YWOS3TX6urqUFxcbFFU1mg0KCgogMFgMA/o6DxZOTAwkK95iYiIiOiesZRMRERERP1WV+XjzMxMNDU1Aei6fBwcHMwrGQeAxsZGZGZmWhSQu7qGMzQ0FGFhYRg1apTQkYm6TVVVFc6dOwe1Wo2zZ8+isbERUqkUCoUCcrkc4eHhnBRLA1Z8fDzWrFmDoqIi/O53v8Pu3bt5yIR6zRtvvIHNmzejuLgYP/vZz4SOQ72kpqYGPj4+2Lx5M1544QWh49Ag0N7ejnfffRcvv/wyRCIRYmJisGrVKohEIqGjEXWppqYGZ86cgVqtxpkzZ9DS0oLg4GDI5XL88pe/xOTJk4WOSHTXDAYD8vPzLdabUlNT0d7ebrXe9MADD2DEiBFCRyb6UXQ6HcrLy61uE8zLyzMP9HBzc7OarCyRSCCRSAROT0RERET9BUvJRERERNTndVU+zsrKQmNjI4Cuy8dBQUFwdHQUODl1B51Oh8LCQovJNZmZmTAYDHB1dYW/v795Q2j27Nnw9vYWOjJRt9NoNFCr1VCpVEhOTsbQoUMRHh4OuVyOZcuWYezYsUJHJOpRqampiI6Oxueff46oqCjs3buXG6LUq1paWuDr64tf/epXePXVV4WOQ71s27ZteOutt1BSUgI3Nzeh49AgUVtbi/379+PgwYPw8vLCzp07oVQqhY5F9L1aWloQHx8PtVqNU6dO4ebNm5BIJJDL5VAqlTxASf1SU1MTMjIyLIrKubm5AACJRILw8HDzutS0adMwdOhQgRMT/TRdrcHn5OSgvr4ewH/Kyp0nK/v7+8PLywtDhgwROD0RERER9SUsJRMRERFRn6HVaq2mNGRmZqKmpgZA11MagoKCOCFxANHr9SgoKLDY6Lly5Qra2trg7OyMqVOnWkylkUql3NCkAam5uRkJCQlQq9X49NNPcePGDYwePRrz58+HQqHAokWLePCCBoXy8nLs2rULhw8fxowZMxAbG4vw8HChY9EgdODAAWzfvh1ff/01Ro8eLXQc6mV1dXWQSCRYtWoVduzYIXQcGmSuX7+OrVu34vjx44iIiMCBAwcQEhIidCyiH2QwGJCSkgK1Wo2TJ0+isLAQ7u7uWLhwIZRKJebPnw87OzuhYxL9JJWVlbhy5Yp57SolJQW3bt2CWCzGhAkTIJPJzGVlrl1Rf9fVmn1JSQlKSkoAALa2tvD19bWarOzv78+SPhEREdEgxVIyEREREfW6rhYys7OzUVVVBaDr8nFAQABGjRolcHLqTl1diZmeno6WlhY4OjoiMDDQooA8efJkTt2gAe369es4f/48VCoVLly4AJ1OZ772WKFQICQkhBuZNGhotVrs27cPb7zxBjw9PbFr1y5ERUXxzwAJoqmpCRKJBE8++ST27NkjdBwSyJ49e/DKK6/g66+/5nMJCeKrr77Chg0bkJiYiKioKOzbt4+3xFC/0vn2l2HDhuGhhx6CUqnE4sWL4erqKnREontSUlKCxMREi3Wu1tZW8yH7jqJyWFgYX0vQgHD79m18/fXXFkVljUaD/Px8GI1GiMVijBs3zqKozBsOiYiIiAYHlpKJiIiIqMd0VT6+evUqvv32WwBdl4+nTJmCMWPGCJycekJFRQXS0tKQlJSExMREZGZmoqmpqcspMiwg02BgNBqRkZEBlUoFtVqN9PR088a8QqGAXC7H/fffL3RMol6l0+lw7NgxbN26FQaDAdHR0VizZg2n6JGgXnnlFezZswclJSVwd3cXOg4JpKmpCT4+Pli+fDkOHDggdBwaxFQqFdatW4eysjI888wz2LFjB1xcXISORfSjlJWV4dy5c1CpVPjss89gMBgwc+ZMKJVK/OIXv8C4ceOEjkh0z3Q6HQoLC83rYGlpacjLy4PJZIKHhwdCQ0Mt1sKGDRsmdGSibtHe3o6ioiKrycoajQatra0AAA8PD4uisr+/P6ZOncpbaYiIiIgGCJaSiYiIiOiedZ6KcOdiIwC4urrCx8fHqoAskUgETk49paOA3PGWnJyM2tpaiEQiTJw40WIC8owZM2Brayt0ZKJe0dTUhIsXL5onhFVWVsLLywvz58+HXC7nFcY0qLFkRX1RXV0dJBIJVq1ahR07dggdhwR28OBBvPDCCygqKsLYsWOFjkODWMchnpiYGBiNRkRHR2Pt2rV8rqJ+SavVIj4+HiqVCqdOnUJ9fT2kUimUSiVvjKEBp76+HtnZ2eai8ldffYWqqiqr9TKZTIbg4GAe2KcBRa/Xo6yszKqonJWVhcbGRgCWQ0zuLCx7e3vz7wIiIiKifoSlZCIiIiK6a/X19SgqKrIqH5eWlsJkMsHZ2RkTJkywKh9z0XBg61xAvnz5Mqqrq7ssIE+bNg1Dhw4VOjJRryotLcWFCxcspoAFBQVBLpdDoVAgNDRU6IhEguJ19NSXvfzyyzh48CBKS0vh5uYmdBwSWGtrKyZOnIjFixfj7bffFjoOEbRaLfbt24c33ngDnp6e2LVrF6Kiovj8Tf1WW1sbvvjiC6hUKnz00Ue4ceOGxSHOhQsXQiwWCx2TqFt1vlksPT0dLS0tcHR0RGBgoHlNbfbs2XxOogGroqLCoqjc+cZFFxcX+Pr6WhSVpVIp/Pz8YGNjI3B6IiIiIuqMpWQiIiIistLW1obi4mKkpaV1WT62s7ODj48PQkNDWT4eZLRaLdLS0szXTl65csW8OCyRSMxXToaGhiIkJAT29vYCJybqfQaDAZmZmVCpVFCr1UhLS4ODgwMiIiKgUCiwePFijBkzRuiYRIIrKytDTEwMjh8/joiICBw4cAAhISFCxyIyu337Nry9vbF+/XrExMQIHYf6iD/96U949tlnkZ+fz5tfqM8oLy/Hli1bcPz4cYSFhSE2Nhbh4eFCxyK6ZxqNBnFxcebnquHDh2PevHmQy+VYunQpnJychI5I1O30ej0KCgrMh/+TkpKQkZEBo9EIDw8Pi8P/MpmMB+doQNNqtRZF5c77FGKxGOPGjbMakjJ58mSuSxMREREJiKVkIiIiokGsvb0dRUVFFgt6Go0G+fn5MBqNsLW1ha+vr8WCnlQqxeTJk3l94CBQV1eHnJwciynIubm5AMBNEKJOamtrkZCQYC4ia7VaSCQSREZGQi6XY8GCBbxOm+h/1dbWYv/+/Th48CC8vLywc+dOKJVKoWMRWdmyZQv+9Kc/oaSkBM7OzkLHoT5Cp9Nh8uTJePDBB3H06FGh4xBZSE1NRXR0ND7//HNERUVh7969LM/TgFFSUmJ+3rp06RJEIhFkMhnkcjkee+wxeHh4CB2RqMc0NjYiMzPTvD6XmJiI0tJS2NjYYNKkSRZrdNOnT4ednZ3QkYl6VMeNjp0LywUFBTAYDBCJRPD09LSarBwQEMBnOyIiIqJewFIyERER0SCg0+lQWFhoVT7+/+zdeVTU9f4/8KcyAyK74gI3SQdRHBTB0VAYd1KsGbWFuveWdi2/VtbNytQWl1BbNC0rq3tNr3WzdSyVGcMFlwrEhZFFh8UFXBJMxJF9nZnfH/1mLsNgmSlvlufjHI7eGc/pOeeKfD7v9/PzelsX6aRSKYKCghzKxzz+rP0oKytDRkaGXQE5OzsbFovFoYA8YsQI+Pr6io5MJFzDTfEffvgBZrMZw4cPh1qthlqthlwuFx2RqEWpra3FRx99hFdffRUSiQQLFy7EU089BYlEIjoakYPLly9DJpPhlVdewYIFC0THoRbmv//9Lx599FEcP34cwcHBouMQOUhMTMSzzz6LkydP4oknnkBcXBy8vb1FxyK6aS5fvozvv/8eOp0OCQkJqKysRHh4OFQqFR588EEMGDBAdESiW66goMBuHS85ORlGoxFSqRShoaF2p5nJ5XKebkftQl1dHc6fP+8wWTk7OxuVlZUAAB8fH4fJyjKZjA9yEREREd1ELCUTERERtSENF92sU20bl4+tx5kpFAqWj9upxtNV9Hq9bTp24wJyREQEunfvLjoyUYtgMpmQkpICnU6H+Ph4ZGdn2x0fPHnyZJY9iJpgsViwefNmvPjii7h48SL++c9/4uWXX+Z0ImrR5s2bh08++QR5eXk8Gp4cmEwmhIaGIjQ0FF9++aXoOERNqqurw8aNG7F48WLU1dVh/vz5ePbZZzk9k9qcqqoqJCYmQqfTYdu2bfjll18gk8mgUqkQGxuLyMhInvZF7YLJZEJOTo7det+RI0dQW1sLb29vDB061FZU5sABao8KCgochrYcO3YMpaWlAABvb28EBgbaFZV5aiQRERHRjWEpmYiIiKgVqq+vx7lz5xye+D9+/Dhqampsx5M1fuJ/4MCB3IBsZ6xTshtOTUlPT4fJZIK3tzdCQkJsBeSRI0eiT58+oiMTtSjFxcXYu3cvtFot4uPjUVJSYtvgVqvVGD16NKRSqeiYRC3WwYMH8cILLyAlJQX33Xcf3nrrLdx+++2iYxH9posXLyIwMBDLly/Hc889JzoOtVCbN2/GAw88gLS0NAwePFh0HKJrKi8vx6pVq7By5Up069YNy5Ytw7Rp0zgxk9qkhg+Sbt26Fbm5uejWrRtiYmIQGxuLCRMmcF2M2pW6ujpkZmYiKSmpyZPRlEql3URlV1dX0ZGJmp3RaHTYZ8nLy0NeXh4AwNnZGX379nWYrBwSEoJOnToJTk9ERETUMrGUTERERNSCXat8nJWVhaqqqmuWj+VyOReR26H6+nrk5ubaTURJTU1FTU0NPD09MWjQILspyDy6kahpeXl50Gq10Ol02L9/PwAgIiICarUaU6ZM4THtRNfhxIkTWLhwITQaDaKjo/HWW28hLCxMdCyi6/Lss8/im2++walTp9C5c2fRcaiFslgsUCgUCAgIwNatW0XHIfpdP//8M5YtW4b169dj6NChWLVqFUaOHCk6FtEtZTAYoNPpoNVqceDAAbi6umLcuHGIjY3lSTfUbl29ehWpqam2ovLBgwdx+fJlSCQS9OvXDwqFwlZW5oRYas+uXr2K06dP2xWVDQaD7cTBxnsz1snKYWFhcHd3Fx2fiIiISCiWkomIiIhaiKaOD0tLS0NlZSUAwM/Pz6F4PGTIEBYl2qmmjmQ8evQoqqqq4O7ujsGDB9sVkLmJQHRt1dXVSEpKQmJiIrZs2YITJ07A19cXY8eOhUqlwpQpU+Dl5SU6JlGrUFxcjLfeegvvvPMOAgMDERcXh9jYWNGxiK5bYWEhAgMD8dZbb+Gpp54SHYdauO3bt0OlUiElJQXDhw8XHYfouuj1esybNw/79u2DSqXCO++8g759+4qORXTLnTt3Djt27IBWq8WuXbtgMpkwfPhwxMbG4t5770WvXr1ERyQSpqCgAMnJyXYTlaurq+2GHCiVSowaNQo9evQQHZdIqNraWpw8edKuqGzdz6murgbwv70ca1HZeoplz549BacnIiIiah4sJRMRERE1s6bKx+np6aioqADQdPk4PDwcbm5ugpOTSAUFBdDr9bYNAuvfGalUiqCgILvjFllAJvp9RUVFSEhIgE6nw86dO1FaWgq5XA61Wo3o6GiMGTMGEolEdEyiVqO2thYfffQRlixZAjc3NyxZsgSPPfYYnJycREcj+kNmz54NnU6HkydP8nh3ui6jRo2Cq6srdu7cKToK0R+SmJiI559/Hjk5OZgxYwaWLVuG7t27i45F1CyMRiMSExOh1WoRHx+PkpIS2/2gSqVCVFQUT5aidq3haWzWtUjrdFg/Pz/bEASlUonIyEgOzSCC46mX1sJyRkYGysvLAQA+Pj52RWXrr3369OHPHSIiImpTWEomIiIiukWaKh83XIBqqnzMo70I+F8B2fp14MABXLlyxe4YRevXsGHDWJghuk4Nj+5NSUmBs7MzlEolVCoV7rnnHgQEBIiOSNTqWCwWbN68GQsWLMClS5fw9NNP45VXXoGHh4foaER/2Llz59CvXz+89957mDVrlug41Er8+OOPGD16NPbt24cxY8aIjkP0h5jNZmzatAkLFixATU0NFixYgDlz5qBTp06ioxE1m/r6ehw8eBAajQbffvstLly4gNtvvx0TJ06ESqVCTEwMpFKp6JhEwpWWliIzM9NWVP7xxx/xyy+/wMnJCf3797dbr4yIiOD3DVED1r2ihpOVjx8/josXLwIAPD09ERQU5FBYDg4O5sPeRERE1CqxlExERET0JxmNRrvicVZWFtLT03H58mUAvz793vjJ97CwMPj6+gpOTi1B4wLyoUOHUFRU1GQBeejQodwcJvoDqqqqkJycDK1Wiy1btuD8+fPo1q0bYmJioFarERMTw+Ik0Z+wd+9ezJs3D+np6XjooYewYsUK+Pn5iY5FdMNmzpyJxMREnDhxAs7OzqLjUCsyfvx41NTUICkpSXQUohtSUVGBtWvX4rXXXoO3tzcWLlyImTNn8gQeapcMBgM0Gg10Oh30ej26dOmC8ePH2x5m5T0k0f80XNdMTk7GgQMHUFlZCXd3dwwePNhuXTMkJER0XKIWx2g02hWVrb/m5+fDYrFAKpWiV69eDvtLAwYM4IRyIiIiatFYSiYiIiK6Tk2VjzMzM3Hp0iUATZePQ0NDefwp2RiNRuj1eiQlJUGv1yM1NdU2DUEmkyEqKsq2UD9kyBAuLBLdgEuXLmHHjh3Q6XRISEhAeXk5j+EluslycnKwePFiaDQaREdHY/Xq1QgNDRUdi+hPOXPmDPr3749///vf+Mc//iE6DrUyR44cQUREBBISEjBx4kTRcYhu2IULF7B06VJs2LAB4eHhWLVqFUaPHi06FpEw+fn5iI+Ph06nw/79+yGRSGyn7cTGxsLf3190RKIWxWQyIScnx24Aw+HDh1FXVwc/Pz+7knJkZCS6du0qOjJRi1RaWoqTJ086FJZzc3NhMpkANH0SZ2hoKDw9PQWnJyIiImIpmYiIiMhBU+XjhkdpNVU+HjhwIHr27Ck4ObUkJSUlOHbsmN0ifFZWFgA4LMIrlUr4+PgITkzUehkMBuh0Omi1Whw4cACdOnVCVFQUVCoV7rvvPtx2222iIxK1CZcvX8ayZcvw4Ycfol+/fli5ciXuvvtu0bGIbopHHnkEBw4cQHZ2NiQSieg41AqpVCpcvHgRR44c4QNQ1OplZWVh/vz52L59O6Kjo/HOO+9g4MCBomMRCVVcXIzt27fbHoCtrKxEeHg4VCoVHnzwQQwYMEB0RKIWqby8HOnp6U2ukTYe0jBs2DC4uLgITkzUctXV1eH8+fMO+1fZ2dmorKwE0PT+lUwmg0wmE5yeiIiI2hOWkomIiKjdunr1Kk6fPu2wgJOXlwcA8Pb2RmBgYJMLOEQNlZWVISMjw25xPTs7GxaLxaGAPGLECPj6+oqOTNSqVVZWYs+ePdDpdNi+fTsuXLiAHj16YMKECVCr1Zg0aRLc3d1FxyRqMyorK/H+++/j9ddfh4eHBxYvXozHHnsMTk5OoqMR3RQnT56EXC7HJ598goceekh0HGqljh07hrCwMGzevBn33HOP6DhEN0ViYiJeeOEFZGVlYcaMGVi6dCl69OghOhaRcFVVVUhMTIROp0N8fDwuXrwImUxmm6AcGRmJjh07io5J1GIVFhYiNTXVto6akpKC4uJiSKVSBAUFQalU2srKcrmcD3wRXYeCggKHfa6MjAwUFRUBcNzvkslkkMvlGDBgAH9mERER0U3HUjIRERG1edajrhqXj/Pz82GxWODp6YmgoCCH8nGfPn244EkOKioqkJaWZldAzsnJgdlsdigg33HHHdywJbpJzp49i507d0Kr1WL37t2oq6uzTaVSq9UYMmQI/80musnMZjO+/fZbzJs3D5cvX8bTTz+NhQsXsvRPbc7f/vY3HD16FFlZWSzb058SGxuL7OxsZGZmcmOf2gyz2YxNmzbhxRdfRFlZGebOnYsFCxbA1dVVdDSiFsFkMiElJQU6nQ5bt25Fbm4uunXrhpiYGMTGxmLChAmc/Ep0HfLy8pCUlGRbb01NTUVNTQ28vLwwcOBAW1E5IiIC3bt3Fx2XqNVo6mTQvLw82/6Ys7Mz+vbta1dUDgkJQUhICDp16iQ6PhEREbVSLCUTERFRm1FTU4NTp07ZjoBrXD52cXFBYGAgFAoFy8d0Xerq6nDixAnbYnhycjLS09NhMpng7e2NkJAQWwF55MiR6NOnj+jIRG2G2WxGWloatFotdDodjh49CldXV4wbNw5qtRoqlQr+/v6iYxK1WYmJiZg3bx6OHz+ORx99FHFxcejZs6foWEQ3ncFgQGhoKL766ivExsaKjkOt3IkTJxASEoJPP/0Uf//730XHIbqpGp6c4OnpiUWLFvHkBKImGAwG6HQ6aLVaHDhwwHYfGxsbi8mTJ8Pb21t0RKJWwboum5ycbCsrNz6ZruFEZT4sQ/THND5JNC8vDwaDwTaARSKRICAgwGGyclhYGB9WJyIiot/FUjIRERG1OrW1tTh58qRd8bjhYknDJ7sbTj/mMVT0W+rr65Gbm2s3Adk6kcPT0xODBg2ym4LMowOJbr6Kigrs3bvXtoFbWFiI3r17Y8KECVCpVJwwRdQMsrOzsWTJEmg0GkRHR+Ptt9/GoEGDRMciumXuv/9+5ObmIiMjg/cKdFM88sgjSElJQVZWFiQSieg4RDfd5cuXsWzZMnz44YcIDQ3FqlWrMHbsWNGxiFqkS5cuYceOHdBoNNi1axdMJhOGDx8OtVqNe++9F0FBQaIjErUqJSUlOHbsmK2ofPjwYVy6dAkSiQT9+vWzrdsqlUqEh4fz+p7oBjTcf7MWlbOyspCVlYWqqioAgJ+fn8Nk5YEDB/JhdiIiIrJhKZmIiIhaLOs0hGuVj6VSKYKCglg+pj/MZDIhJyfHroB89OhRVFVVwd3dHYMHD7YrIPPvFNGtk5+fj927d0Or1do2acPCwqBSqaBWq6FQKERHJGoXCgoKEBcXhw0bNiAsLAyrVq3CmDFjRMciuqWOHTuGsLAwfPvtt5g6daroONRGnDlzBv3798eHH36Ixx57THQcolsmJycHixcv5oNMRNfJ+hCuRqNBfHw8SkpKIJfLbScBRUVF8eF3ohtQUFBgO+EuKSnJtsbr4eGB0NBQnnJHdJPU19fj3LlzdkVlg8GAjIwMlJeXAwB8fHzsiso8rZSIiKj9YimZiIiIhKurq8P58+dhMBig1+ttixm5ubkwmUyQSqXo1asX5HI5FAqFbTEjODiYx4TSdWm8OJ2eno6Kigpbsb3hUX8sIBPdWiaTCenp6dBqtdDpdNDr9XBzc8PYsWOhVqsxefJkTtUgakYVFRVYu3YtXnvtNXh5eWHRokWYOXMmfxZSuzB58mRcuHABqamp3CClm+qJJ55AQkICTpw4wVMeqM3bs2cP5s2bh4yMDDz00ENYuXIlr+eJfkd9fT0OHjwIjUaDb7/9FhcuXMDtt9+OiRMnQqVSISYmBlKpVHRMolap8Wl4ycnJSEtLg9lshp+fn90gCqVSCR8fH9GRiVq9goICh8nKx48fx8WLFwEAnp6eCAoKcigsc4+PiIio7WIpmYiIiJqN9Unqhk9RWxcnampqIJFIEBAQ4PAU9cCBA7mRS9fNWkC2fqWkpKC4uNjhGD+FQoFhw4bx7xZRM7hy5Qr27NljKyIbjUbIZDJER0dDpVJh4sSJcHZ2Fh2TqF0xm83YtGkTXnzxRZSVlWHu3Ll48cUX0alTJ9HRiJqFXq/HsGHDoNPpcNddd4mOQ21MQUEB+vbti5UrV+Lpp58WHYfoljObzfj2228xf/58FBUV4emnn8Yrr7wCDw8P0dGIWgWDwQCNRmN7cLdLly4YP348VCoVpk6dCk9PT9ERiVq1srIyZGRk2NaLk5KSkJ+fDycnJ/Tv399uvfiOO+7gGhXRTWI0Gh0mK2dlZeHMmTO201CtA4ka7gkOGDAAnTt3Fh2fiIiI/gSWkomIiOimu1b5OCsrC1VVVdcsH8vlcri6uoqOT61I4wLyoUOHUFRU1GQBeejQoSxaETWjvLw8Wwn5hx9+gNlsxvDhw6FWq6FWqyGXy0VHJGq3EhMTMXfuXGRnZ2PGjBlYtmwZunfvLjoWUbOaNGkSiouLcejQIU5Jplviueeew1dffYXTp09zQ53ajcrKSrz//vt444034O7ujsWLF+Oxxx7jBDyiPyA/Px/x8fHQ6XTYv38/JBIJlEolVCoVYmNj4e/vLzoiUZvQeF05OTkZRqMRUqkUoaGhtlP1FAoF5HI57xmIbqKamhqcOnXKbv+w4empAODn52e3fyiTyTB48GB069ZNcHoiIiK6HiwlExER0Z9iPZbJYDBAr9cjKysL2dnZqKysBGC/cKBQKPiUM90wo9Fom2Sh1+uRmppqO/5LJpPZLRQPGTLZ+B1pAAAgAElEQVSEf8eImpnJZEJKSgp0Oh3i4+ORnZ1tN91p8uTJ8Pb2Fh2TqF0zGAyYP38+vv/+e0RHR2PNmjUICQkRHYuo2R04cABRUVHYvXs3oqOjRcehNury5cuQyWRYuHAh5s+fLzoOUbMqLi7G0qVL8eGHH6Jfv35YuXIl7r77btGxiFqd4uJibN++HTqdDgkJCaisrER4eDhUKhUeeOABPuxLdBOZTCbk5OTYFZWPHDmC2tpaeHt7Y+jQobb15xEjRsDX11d0ZKI2p66uDufPn3cYeJSTk4OKigoAgI+Pj8PAI5lMBplMJjg9ERERNcRSMhEREV2XhuVj66/p6em2hYDGTy3L5XKEh4fDzc1NcHJqjUpKSnDs2DG7ReCsrCwAv/5dazgBWalUwsfHR3BiovapuLgYe/fuhVarRXx8PEpKSiCTyaBSqaBWqzF69GhIpVLRMYnavQsXLmDp0qXYsGEDhgwZglWrVmHUqFGiYxEJM378eNTU1CApKUl0FGrjXn75Zaxbtw55eXnw9PQUHYeo2eXm5mLRokXQaDSIjo7GqlWrMHjwYNGxiFqlqqoqJCYm2h4Evnjxou3+OzY2FpGRkejYsaPomERtSl1dHTIzM21DMvR6PbKzs2GxWODn5welUmkrKvOUPqJbq/EeZV5eHjIzM3Hp0iUAgLe3NwIDA+2KynK5HAMGDODPRyIiIgFYSiYiIiI7TZWPMzIyUF5eDqDp8nFYWBjc3d0FJ6fWqqysDBkZGXYF5IaLuw0LyJxCQSReXl4etFqt7ShZAIiIiIBarcaUKVMQHBwsNiAR2VRUVGDt2rVYvnw5unTpgmXLlmHatGk8dpbataSkJIwcORL79u3DmDFjRMehNu7q1auQyWSYM2cOlixZIjoOkTApKSl44YUXcPDgQTz00ENYsWIF/Pz8RMciarUanlS0detW5ObmwtfXF5MmTYJarcZdd93FQRFEt8jVq1eRmppqKyofPHgQly9fhkQiQb9+/WxDNKKioliGJGoGRqPRrqhs/X1+fj4sFgucnZ3Rt29fu6KydW/T1dVVdHwiIqI2i6VkIiKidqrhjbr11/T0dFy+fBlA00cghYWFsRBKf0pFRQXS0tLsCsg5OTkwm80OBeQ77rgDPXr0EB2ZqN2rrq5GUlISEhMTsWXLFpw4cQK+vr4YO3YsVCoVpkyZAi8vL9ExiaiB+vp6/Oc//8GSJUtQU1ODBQsWYM6cOZzaRARgzJgxkEgkSExMFB2F2olly5Zh9erVOH36NLp27So6DpEwFosFmzdvxoIFC/DLL7/gn//8J15++WVOESe6CawPD2s0Ghw4cACurq4YN24c1Go1pk6diu7du4uOSNSmFRQUIDk52W6icnV1NTw9PTFo0CBbUXnUqFFc7yZqJlevXsXp06cdCsu5ubkwmUyQSCQICAhwmKzMIUxEREQ3B0vJREREbVxT5eOGRxo1VT4ODQ3lYjX9aXV1dThx4oRdAfnIkSOora2Ft7c3QkJCbAXkkSNHok+fPqIjE9H/V1RUhISEBOh0OuzcuROlpaWQy+VQq9WIjo62FbqIqOVJTEzEc889h9zcXMyYMQPLly9Ht27dRMciahF27dqFiRMn4scff8TIkSNFx6F2ory8HIGBgZgxYwbefPNN0XGIhKutrcVHH32EV199FRKJBAsXLsRTTz3F+wuim+TSpUvYsWMHNBoNdu3aBZPJhOHDh0OtVuPee+9FUFCQ6IhEbV59fT1yc3Oh1+ttZeWmBnMolUpERkaic+fOoiMTtRu1tbU4efKkw2TlrKwsVFVVAfjfibENJysPHDgQPXv2FJyeiIio9WApmYiIqI1oqnx8/PhxXLx4EUDT5WPeRNPN0nCh1fqVmpqKmpoau4kQ1i+5XM6j44laGIPBAJ1OB61Wi5SUFDg7O0OpVEKlUuGee+5BQECA6IhE9Bv0ej1eeOEF7N+/HyqVCmvWrEFgYKDoWEQtyogRI+Dj44Pvv/9edBRqZ1atWoXFixfj1KlT8Pf3Fx2HqEW4cuUKVq5ciTVr1qBPnz5YunQpYmNjRccialMqKiqwd+9eaDQaxMfHo6SkxPbAsUqlQlRUFNfniJpJaWkpMjMzbUXlH3/8Eb/88gucnJzQv39/u7XziIgISKVS0ZGJ2pX6+nqcO3fOrqhsMBiQmZmJsrIyAL/uszYsKlt/7dOnD3+eEhERNcJSMhERUSvT+MihhkcPAYC3tzcCAwMdboplMpng5NRWmEwm5OTk2BWQjx49iqqqKri7u2Pw4MF2i6gDBgxAx44dRccmokaqqqqQnJwMrVaLLVu24Pz58+jWrRtiYmKgVqsRExMDDw8P0TGJ6HecP38ey5cvx/r16zFs2DCsWrUKSqVSdCyiFmf79u1QqVQ4dOgQ7rjjDtFxqJ2prq5GUFAQ7r33Xrz77rui4xC1KCdPnsQrr7yCzZs3Y9y4cXjrrbcQHh4uOhZRm2MymZCSkgKNRoPvvvsOP//8M26//XZMnDgRKpUKEydOhLOzs+iYRO1KQUGBbX09OTkZBw4cQGVlZZNr7CEhIaLjErVbBQUFDpOVDQYDCgsLAQAuLi4IDAy025OVy+UIDg6Gk5OT4PRERERisJRMRETUQpWUlODUqVMO5eP8/HxYLBZ4eXmhb9++fCKXbjnr4qj1qLn09HRUVFRAKpUiKCgISqUSUVFRLCATtQLWY1x1Oh0SEhJQXl7OKUlErVR5eTlWrVqFFStWoFevXnjttddw//3383uYqAkWiwURERHw8/PDtm3bRMehdurDDz/Ec889h9zcXPTu3Vt0HKIW59ChQ3jhhRdw4MAB3HfffVi5ciW/V4huIYPBAI1GA51OB71eDx8fH0RHR0OlUmHq1Knw9PQUHZGo3WlqGMjhw4dRV1cHPz8/u5JyZGQkunbtKjoyUbtmNBodispZWVk4c+YMzGYzpFIpevXq5bCPGxwcDDc3N9HxiYiIbimWkomIiASrqanBqVOnoNfrmywfW5+wtT4Nz/Ix3UoNpzPo9XqkpKSguLgYEokE/fr1s1v4HDZsGFxcXERHJqLfYTAYoNPpoNVqceDAAXTq1AlRUVFQqVS47777cNttt4mOSER/QF1dHTZu3IhFixahvr4e8+fPx7PPPsufyUS/YcuWLbjvvvtw9OhRhIWFiY5D7VRdXR2Cg4Mxfvx4rFu3TnQcohbJYrFg8+bNeOmll1BQUIBnnnkGL730Ery8vERHI2rT8vPzER8fD51Ohx9++AFOTk5QKpVQqVSIjY2Fv7+/6IhE7VZ5eTnS09Pt1uyzsrIAADKZzDYshOv1RC2Hdd+38Wm3x48fR01NDQDAz8/P4bTb0NBQdO/eXXB6IiKim4OlZCIiomZSW1uLkydP2t2EGgwG5OTkwGw2w9nZGX379nU43oeTZ+lWaVxAPnToEIqKiposIA8dOhSdOnUSHZmIrkNlZSX27NkDnU6H7du348KFC+jRowcmTJgAtVqNSZMmwd3dXXRMIroBWq0Wc+fOxdmzZ/HEE08gLi4O3t7eomMRtWgWiwXh4eEICgqCRqMRHYfauY0bN+L//u//YDAY0L9/f9FxiFos60NYCxcuhNlsxrx58/Dcc8/B2dlZdDSiNq+4uBjbt2+3nbBUWVmJ8PBwqFQqPPDAA5DL5aIjErV7hYWFSE1NdRgs0tTJhnK5nMNtiFqIuro6nD9/3mGyck5ODioqKgAAPj4+DpOVZTIZB1UREVGrw1IyERHRTVZXV4cTJ05cs3xsXRhi+Ziak9FohF6vR1JSEvR6PVJTU3Hx4kUAjhMVhgwZgs6dOwtOTER/xNmzZ7Fz505otVrs3r0bdXV1tk1DtVqNIUOGcNGSqBU7cuQIXnjhBfz000+4//778eabb0Imk4mORdQqfPPNN/jb3/6GtLQ0hIaGio5D7ZzJZMKgQYOgUCjw2WefiY5D1OIZjUasWLEC7777Lm6//XYsW7YMsbGxomMRtRtVVVVITEyETqdDfHw8Ll68CJlMZltrGDNmDCQSieiYRAQgLy/PtvZvXf+vqamBl5cXBg4caCsqR0REcBIrUQtUUFDgMFk5MzMTly5dAgB4e3sjMDDQrqjMvWUiImrJWEomIiK6QQ2faLUemWUwGJCbmwuTyQSpVIpevXpBLpdDoVDYysfBwcFwcnISHZ/asJKSEhw7dqzJI938/PzsJiArlUr4+PgITkxEf5TZbEZaWhq0Wi10Oh2OHj0KV1dXjBs3Dmq1GiqViserErUB586dw8KFC7Fp0yZERERg9erViIyMFB2LqNWwFkDDwsLwxRdfiI5DBAD46quv8NBDD7EoT/QHNLwmGj58OFatWsVrIqJm1nAd4uuvv0ZOTg58fX0xadIkqNVq3HXXXXBzcxMdk4j+P+vwnOTkZFtZOTs7GxaLxbZH0HCisqurq+jIRNQEo9FoV1S2/j4/Px8Wi8XuFF5rUdm6H83vayIiEomlZCISrrCwEDt27BAdo8X7+9//DhcXF9Ex2qX6+nqcO3fO4Tid48ePo6amBhKJBAEBAQ7H6QwcOPA3/z9LSUlBTk5OM36S1sfPzw8xMTGiY7RoZWVlyMjIsCsgN15ctH6NGDECvr6+oiOTYF9++SWqq6tFx2jRIiMjW+Rx2hUVFdi7dy90Oh20Wi0KCwvRu3dvTJgwASqVChMmTOC1ArVIvN6/Pg2v9xtOBQwICMDy5cs5FZCEaO3fvz///DNWrFiBV155BT179rxl/x3er7c9t/J+3WKxIC4uDpGRkZgwYcIt+W80B96vkwiNT49YsWIF+vTpY3uf97u/r6Xe71Lrk5eXB61WC41GgwMHDtg9KD1lyhT06NFDdEQArf96trnwerZ9sA4zsRaVDx8+jEuXLkEikaBfv352g0zCw8M5fbUB7uf9Pt4fNK+SkhKcOnXKobBsHZxl3btuXFQePHgwPDw8RMenRni9cn14vULUurCUTETC7d27F+PHj0fnzp1FR2mRzGYzqqurUVRUxDLhLXat8rHBYEB1dfU1y8c3+rTpk08+iXXr1qFTp0634NO0frW1tVAqldi3b5/oKC1GRUUF0tLS7ArIOTk5MJvNDgXkO+64o8Us/lPL0rVrV5SXl/N4zWuorKzEhx9+iCeffFJ0FABAfn4+du/eDa1Wi127dsFkMiEsLMx2VKpCoRAdkeh38Xr/tzW83vfy8sLGjRuxcOFCmM1mzJs3D8899xycnZ1Fx6R2it+/v433620X79d/G+/XSTStVovnn38e586dwxNPPIG4uDh4e3vzfvd3tLT7XWo7ioqKkJCQAI1Gg927d6O+vh7Dhw+HWq3GPffcg379+gnLxuvZ38brWSooKIBer7cVlY8ePYqqqip4eHggNDTUtt8wcuRIuweB2hveH/w23h+0HLW1tTh58qTDZOWsrCxUVVUB+LVA3niyckhICPz8/ASnb794vfLbeL1C1DpxZYaIWoyzZ8/yIqIJ1otQurkKCgpshWO9Xo+srCxkZ2ejsrISwP9uyKKiovDMM88gJCQEAwYMuOk3A6NGjeJN+jU8+eST7frJc+vxag0LyEeOHEFtbS28vb0REhKC6OhoLFiwoN0vCNIft2bNGm5CXkPXrl2F/vdNJhPS09Oh1Wqh0+mg1+vh5uaGsWPH4v3338fkyZNv6aRHoluJ1/tNs17v79y5E0uWLEFBQQGeeeYZvPTSS/Dy8hIdjwgAv3+vhffrbRvv16+tvd+vk3hqtRoxMTHYuHEjFi1ahE2bNmH+/PkAeL/7W0Tf71Lb1a1bN0yfPh3Tp09HZWUl9uzZA41GgzfeeAMvvvgi5HI51Go1VCoVoqKi0KFDh2bPyOvZpvF6lvz9/eHv7w+1Wg3g1+E9ubm5tj2J5ORkrF27tsnBKEqlEj4+PoI/QfPh/cG18f6g5XB2draVjBuyDuZqWFQ2GAz48ssvUVZWBgDw8fFxmKwcEhKC3r17c3J6M+H1StN4vULUOrGUTEREbVrD8rH11/T0dFRUVACwLx/PmjULcrkc4eHhcHNzE5yc2pPGC316vR6pqamoqamBp6cnBg0aBIVCgVmzZkGhUEAulwtZvCeiW+PKlSvYs2ePrYhsNBohk8kQHR2NJUuWYOLEiZySStQOTJ8+HdOmTcPy5ctx2223iY5DRERE1KJJpVLMmjULDzzwAF5//XUsWbKEE5KJWoDOnTtDrVZDrVbDZDIhJSUFGo0Gn3/+OVasWIGAgADExMRApVJxvYOoBZJIJLZC4/Tp0wEAZWVlyMjIsO1d/Pe//0VcXBycnJzQv39/hxMc+X1N1PJIJBLIZDLbvkNDRqPR4RThxMREFBYWAgBcXFwQGBjocIpw//79ef1NRETXxJ8QRETUJjRVPs7IyEB5eTmA/5WPFQoFpk+fDrlcjrCwMLi7uwtOTu2NyWRCTk6OXQHZeiSau7s7Bg8ebFdAHjBgAJ9AJmqD8vLybCXkH374AWazGcOHD8eCBQugVqshl8tFRySiZnbgwAFERESIjkFERETUqnh7e2PlypWYPXs2FAqF6DhE1ICTkxOUSiWUSiXeffddGAwGaDQa6HQ6rFu3Dj4+PoiOjoZKpcLUqVPh6ekpOjIRNcHDw8P2vWxVUFBgt8cRFxcHo9EIqVSK0NBQREVF2YrKHLJC1LL5+Pg4fI8Dv5aVG09W/uyzz3DmzBmYzWZIpVL06tXLYbJycHAwh38RERFLyURE1Lo0flpTr9cjMzPT7mgZ601PbGwsFAoFBg8eDA8PD8HJqb2yLs4lJycjKSnJNqlbKpUiKCjIVpSPiopiAZmoDbNOB9LpdIiPj0d2dja6dOmC8ePHY/369Zg8eTK8vb1FxyQigQIDA0VHICIiImq1evfuLToCEf0O6/TVV199FWfOnMG2bdug0+kwc+ZMPP7441AqlVCpVIiNjYW/v7/ouET0G/z9/eHv7w+1Wg2g6WEs//rXv1BbWwtvb28MHTrUVlQeMWIEfH19BX8CIvo9Pj4+tocLGqqpqcGpU6ccJiuvWbMGNTU1AP43LMy6Zy+TyRAaGoru3buL+ChERCQAS8lERNQiNXVUTGZmJi5dugTAsXwcEhLCmxkSrvF0gJSUFBQXF0MikaBfv35QKBS2svywYcPg4uIiOjIR3ULFxcXYu3cvtFot4uPjUVJSAplMBpVKhffeew+jR4+GVCoVHZOIiIiIiIiIqFn17t0bc+bMwZw5c3DlyhXodDrodDosWrQIzz//PMLDw6FSqaBWqzkFnagVcHJysj14MH36dABARUUF0tLSbPslGo0GS5cuhcVigZ+fH5RKpa2oPHToUHTq1EnwpyCi6+Hi4mL7fo+NjbW9XldXh/Pnz9vt7ycnJ2PDhg2oqKgAYL+/L5PJbL/v06cPJ6oTEbUxLCUTEZFQTZWPjx8/josXLwKwvzlRqVQICQnBwIED0bNnT8HJqb1rXEA+dOgQioqK7ArIixYt4oIaUTuTl5cHrVYLnU6H/fv3AwAiIiLw0ksvYcqUKQgODhYbkIiIiIiIiIioBenSpQumT5+O6dOno6qqCsnJydBqtVi3bh3i4uJsD3ir1WqMGTMGEgm3t4laAzc3NyiVSiiVSttrV69eRWpqKpKSkqDX67F8+XJcvnzZbl/FWlbmyZJErYtUKoVMJoNMJrNNUbcqKCiw6wLk5eVh69attmFk3t7eCAwMdCgsBwcHw8nJScTHISKiP4l3bURE1CyuXr2K06dPOxSQ8/LyANjfbERHR9vddBCJZjQaodfrbQtlqamptuK8TCZDVFQUXnnlFSgUCgwZMgSdO3cWnJiImkt1dTWSkpKQmJiILVu24MSJE/D19cXYsWOxYcMGTJkyBV5eXqJjEhERERERERG1eK6uroiOjkZ0dDTeeecdpKWlQavV4uuvv8Z7770HX19fTJo0CWq1GnfddRfc3NxERyaiP8Db29v2PW5VUFCA5ORk2/6LRqNBdXU1PD09MWjQIFtRedSoUejRo4fA9ER0o/z9/eHv72/3vQ/YDy/Ly8uDwWDAunXrkJ+fD4vFAmdnZ/Tt29dhsrJcLoerq6ugT0NERNeDpWQiIrqpSkpKcOrUKYfysfXmwcvLC3379oVcLsesWbN4LAu1OCUlJTh27JjdFOSsrCwAgJ+fHxQKBR5//HEoFApERUWhS5cughMTUXMrKipCQkICdDoddu7cidLSUsjlctxzzz2Ijo7m1B4iIiIiIiIioj+pY8eOUCgUUCgUePXVV22nU2k0Gjz44IPo1KkTxo8fD7VajSlTprCsSNRK+fv7IzY2FrGxsQCA+vp65Obm2orKiYmJWLt2Lcxms22PxlpUjoyM5JAYolbMx8fHYaI68L++gbWonJWVBa1Wi1WrVsFkMkEikSAgIMChqDx48GB4eHgI+jRERNQQd8qJiOiG1NTU4NSpU7bCZuPysYuLCwIDA6FQKFg+pharrKwMGRkZdgXk7OxsWCwW2+JWbGwsFAoFRowYAV9fX9GRiUgQg8EAnU4HrVaLlJQUODs7Q6lUYunSpbjnnnsQEBAgOiIRERERERERUZslk8kwZ84czJkzx/bAuEajwTPPPIPZs2dj+PDhUKvVuOeee9CvXz/RcYnoBkkkEoSEhCAkJASzZs0CAJSWliIzMxN6vR7Jycn417/+hbi4ODg5OaF///52ReWwsDA4OTkJ/hRE9Gd4eXnZvq+tDywAQG1tLU6ePGk3WTk5ORkff/wxqqqqAPxadG5YVLb+e+Ln5yfq4xARtUssJRMR0W9qeHHfcPpxTk4OzGaz3bEp06ZNs13gDxgwAB07dhQdn8imoqICaWlpdgVk69/jxgXkO+64g5M1iNq5qqoqJCcnQ6vVYsuWLTh//jy6deuGmJgYzJkzBzExMXzinoiIiIiIiIhIgG7dumH69OmYPn06KisrsWfPHmg0Grz55pt48cUXIZfLoVar4e/vLzoqEd0Enp6etmmqc+bMAQAUFBTY9nqSk5Px8ssvo7KyEu7u7hg8eLCt0KhQKBASEiL4ExDRzeDs7GwrGTdUX1+Pc+fO2U1WNhgM+PLLL1FWVgbg17Jy48nKISEh6N27NzsNRES3AEvJREQEAKirq8OJEyeuWT6WSqUICgpCSEgIYmNjWT6mFs3697lhAfnIkSOora2Fl5cXBg4ciOjoaCxYsABKpRIymUx0ZCJqQb788kvMnTsX1dXVUCgUeOyxx6BSqTBkyBBO+yciIiIiIiIiakE6d+4MtVoNtVoNk8mElJQUaDQafPHFFygqKhIdj4huEX9/f/j7+0OtVgMATCYTcnJy7IrKH3zwAUwmk20wjfUrKioKXbp0EfwJiOhmkUgkkMlkkMlkiI6OtnvPaDTadR+ysrKQmJiIwsJCALCd/tywqCyTyTBw4EC4uLiI+DhERG0CS8lERO1MXV0dzp8/D4PBAL1eb7sAz83NhclkglQqRa9evSCXy+3Kx8HBwTzuiFqk+vp65Obm2hWQU1NTUVNTA09PTwwaNAgKhQKzZs2CQqGAXC5nqZCIftPtt9+O6dOn4+677+aRXkRERERERERErYSTk5NtmuqaNWvw+eefY9q0aaJjEVEzcHJysk1QnT59OgCgvLwc6enptr0jjUaDuLg4AIBMJkNUVJStqDxs2DAWEInaIB8fH9u1QUNGo9FhsvJnn32GM2fO2Aa2WTsTDQvLwcHBcHNzE/RpiIhaD5aSiYjaKOsxJY2f/Dt+/DhqamogkUgQEBBgO8ZswYIFCAkJ4VN/1KI1ftJdr9fj6NGjqKqqsjuSy1pA5iRvIroRkZGRmDlzpugYRERERERERER0gzp06AB/f3/RMYhIIHd3d4cyYmFhIVJTU217TEuXLsWVK1cglUoRGhpqV1TmkBuitsvHx8f2vd5QTU0NTp065TBZ+d1330V1dTUAwM/Pz2GycmhoKLp37y7ioxARtUgsJRMRtXLXKh8bDAZUV1fblY+jo6PxzDPP2J4U7tSpk+j4RL+psrISmzZtQmpqKlJTU5GWlobKykp07twZYWFhGDp0KB5//HEMHToU/fv3ZwGZiIiIiIiIiIiIiIiImuTn5we1Wg21Wg0AMJvNyMnJweHDh3H48GEkJSXho48+Ql1dHXx9fXHHHXegtLQUEgmrNUTtgYuLi61LERsba3u94WnUWVlZyMvLg16vx4YNG1BRUQHg16Jzw6Ky9fd9+vThAw5E1O7wyomIqBUpKCiwFY71ej2ysrKQnZ2NyspKAP97Ki8qKspWPh4wYAA6d+4sODnRjcnJycGMGTPQr18/KBQKxMbG8hgtIiIiIiIiIiIiIiIi+tM6duwIuVwOuVyOf/zjHwCAqqoqpKWl2YrKP//8M7p06SI2KBEJJZVKIZPJIJPJbA81WDXscFgLy9u2bcMvv/wCAPDy8kLfvn3tispyuRzBwcFwcnIS8XGIiG45lpKJiFqJ2267DTU1NejYsSN69+6NkJAQ3HnnnXj22WdtN8ucfExtTUhICPbv3w9nZ2fRUYiIiIiIiIiIiIiIiKiNc3V1RWRkJCIjIwEATz75JHJycgSnIqKWyt/fH/7+/oiOjrZ7vbCwENnZ2cjJyUFWVhZycnKwbt06FBYWAvj135rg4GD4+PiIiE1EdEvxjHMiov8vKSkJd955J7y8vODh4YHRo0dj165domPZrFmzBkeOHEFpaSlOnz6N+Ph4vPHGG3j44YcxZMgQFpLphpw9exZr167FxIkT4eLigg4dOmDHjh2iY9m4uLiwkEzUxlRUVODzzz/H5MmT0bt3b7i4uMDf3x8PPvgg0tLSRMcjojbqp59+wsyZMzFgwAC4urqiS5cuuPPOO6HT6URHI6I/6JFHHkGHDh3QoUMHlJeXi45DdMu4u7vb/q43/vrXv/4lOh4R/QaLxYL//Oc/iIyMhJeXF7p16wa1Wo2ffgzQdxYAACAASURBVPpJdDQiamZr1qy55s9z69fKlStFxySiVsBsNuPTTz/FiBEj4OvrCx8fHwwdOhQffPAB6urqRMcjoib4+flh3LhxmD17NtauXYvExEQUFBTAaDQiJSUFa9euRXR0NMxms+ioqK2txeuvv46QkBB06tQJXbp0wZQpU5Ceni46GhG1UpyUTEQEYOfOnbj77rthMplsr/3444/46aef8PXXXyM2NlZgul/df//98PX1FR2D2piIiAjb0TFERM3hjTfewGuvvWb3WmFhIb755hts2bIF8fHxiImJEZSOiNqiU6dOYdSoUXavVVdXIzExEYmJiVi9ejWef/55QemI6I/Ys2cPPvvsM3Tu3BmVlZWi4xARETmoq6vDAw88gK1bt9q9rtPpkJCQgPr6ekHJiKilGjdunOgIRNQKTJs2DV988YXda3q9Hnq9HjqdDt9//z06dOggKB0R/RHe3t4YPnw4hg8fDgDYu3cv9u/fLyxPfX097r77biQmJtpeq6mpQXx8PHbu3Indu3dj5MiRwvIRUevESclE1O7V1tbi8ccfh8lkwvPPP4+ioiIYjUYsW7YMFosFs2fP5vQlarN69+6Np556Cjt27MD//d//iY5DRO2Ah4cHHn74YWi1WuTl5aGyshJpaWm48847UVdXh6eeekp0RCJqYzp27IiJEyfi888/x4kTJ1BVVYX8/HzMnz8fAPDyyy+jtLRUcEoi+j3V1dV44oknMH36dAQFBYmOQ9QsoqKiYLFYHL6eeOIJ0dGI6BqWLl2KrVu3onv37vjss89w5coVVFZWYteuXRgzZozoeETUzJ599tkmf5aXl5fDw8MDAwcOxNChQ0XHJKIWLi0tDV988QVcXFzwySef4MqVKygpKcHmzZvh6emJHTt22JUJiYj+iP/+979ITEzEX/7yF+h0OpSWluLChQtYvHgxampqMHPmTLvhfkRE14OTkomo3UtMTMTZs2cxatQorF692vb6woULkZaWhu+++w7btm3DQw89JDAl0a1x8OBB2++1Wq3AJETUXixYsMDhtbCwMGzduhW33XYb8vLyUFxcjK5duwpIR0RtkUwmw44dO+xe6927N1asWIFDhw7hhx9+QHZ2NiIiIgQlJKLrERcXh9LSUrz99tucJkdERC2S0WjE6tWr4eTkhISEBAwZMsT23p133ok777xTYDoiakm++uorlJWV4dFHHxUdhYhaAYPBAACYMWMGHnnkEdvr9913H44dO4a4uDgYDAZeaxDRDYmPjwcAfPDBB7j77rsB/DpgKC4uDpmZmdi6dSv27duH6OhokTGJqJXhpGQiavd+/PFHAGiydPzwww8DAH744YdmzURERNTedO7cGQEBAZBIJHBzcxMdh4jaCalUCgDo3r274CRE9FuOHTuG1atX47333kOXLl1ExyEiImrS9u3bUVVVhcmTJ9sVkomIGvv4448hlUpte1BERL+lZ8+ev/tn/Pz8miEJEbVFv/zyC4BfBwg1Zn1t//79zRmJiNoAlpKJqN07deoUAGDgwIEO74WGhtr9GSIiIro1cnNzcezYMUyePBmdOnUSHYeI2jCz2YwLFy5g+fLlSExMRExMDPr06SM6FhFdg9lsxqxZszBx4kQ8+OCDouMQNaucnBz07dsXzs7O8Pf3x1//+lekpaWJjkVE16DX6wEAMTEx+OqrryCXy+Hi4oI+ffrgueeew9WrVwUnJKKW4Pjx4zh06BBUKhW6desmOg4RtQJjx45FSEgINm7ciE8//RRXr15FaWkpvv32W7zzzjsICAiAWq0WHZOIWilfX18AQHp6usN71tdOnjzZrJmIqPWTiA5ARCRaaWkpADQ5bcn6WklJSbNmIiIiak8qKirw97//HV5eXli9erXoOETURuXk5GDAgAG2/92pUyfMnj0bK1asEJiKiH7Phx9+CIPBgKysLNFRiJpdcXExiouLAQCFhYX4+uuv8d133+Grr77CvffeKzgdETVWVFQEADh48CA2btxoe/3MmTNYs2YNdu/ejZSUFHh4eIiKSEQtwMcffwwAePTRRwUnIaLWwsnJCXv27MHcuXPx6KOPwmw2296bOnUq3nnnHXTu3FlgQiJqzWJiYqDT6fD0009DIpFg9OjRKC0txb///W9s27YNAPiAJRH9YZyUTETtnsViuaH3iIiI6M+rqKjAlClTkJOTg61bt6J3796iIxFRO1FdXY2UlBRkZmaKjkJE13DhwgW88sorePPNN3HbbbeJjkPUrMaPHw+tVovCwkKUlpbi8OHDuP/++1FXV4eZM2eirKxMdEQiasRaEPrkk08wZ84cnD17FuXl5UhMTERQUBAMBgPefvttwSmJSKTq6mps2rQJfn5+mDRpkug4RNSKHD16FGlpaXaFZADIzMzEoUOHBKUiorZg5syZGDZsGH7++WeoVCp4eHjgL3/5C5YuXYpp06YBADp2ZL2QiP4Y/qtBRO2el5cXAODKlSsO7xmNRrs/Q0RERDeP0WjEnXfeiYMHD+L777/HqFGjREciojYsODgYFosF9fX1uHDhAj7++GOcPn0a0dHRyM/PFx2PiJrw9NNPY9CgQXjyySdFRyFqdtu2bYNKpULPnj3h4eGBYcOG4ZtvvsHYsWNhNBqxb98+0RGJqBHrGnJUVBTWrFmDgIAAuLm5Yfz48fj0008BAN9//73IiEQk2LfffosrV65g2rRpcHJyEh2HiFqJw4cPQ6VSoaqqCt9//z2MRiNKS0uxd+9euLm54a9//SsSEhJExySiVsrFxQX79u3Diy++CJlMBmdnZ/Tp0wdr166FWq0GAHTr1k1wSiJqbVhKJqJ2r2/fvgCA48ePO7xnnZpm/TNERER0cxQWFmL06NE4duwYEhISMHr0aNGRiKidcHJygr+/P2bOnInXXnsNVVVV0Gg0omMRUSNXrlzB1q1bkZycjI4dO6JDhw62r4yMDACAh4cHOnTogPr6esFpiZpHhw4doFQqAQAXL14UnIaIGuvXrx8AICwszOG98PBwAEBRUVGzZiKilmX9+vUAgEcffVRwEiJqTTZs2ACz2Yz3338fkyZNgre3Nzw8PDB27Fhs3LgRALBu3TrBKYmoNXNzc8Mbb7yB06dPo6amBnl5eXjqqaewa9cuAIBCoRCckIhaG5aSiajds05l/Pzzzx3e27Rpk92fISIioj/v9OnTiIqKwtmzZ7Fz506MHDlSdCQiaqdqamoAAKWlpYKTEFFjjY+kJSLAYrEgKSkJANCzZ0/BaYiosTFjxgAA0tPTHd5LS0sDAPTo0aM5IxFRC3Ly5En88MMPiIyMRP/+/UXHIaJWpKnTfq2s987FxcXNFYeI2olTp05h06ZNcHJywr333is6DhG1MiwlE1G7Fx0djYCAAPz444+YO3cuLl++jJKSEixfvhzfffcdfH19MXXqVNExiYiI2oTjx49DqVTCaDRi9+7diIyMFB2JiNq4119/HQsWLMCRI0dQXFyM6upq5Ofn47333sPixYsBgA9HELVAvr6+sFgsTX4NHjwYAFBWVgaLxQKJRCI4LdHNtWLFCrzwwgs4fPgwiouLUV5ejtTUVDz44IPYt28fvLy8bOVHImo5wsPDMWzYMCQlJeHZZ5/FuXPnUFFRgb179+KRRx4BAEyePFlwSiISZf369bBYLJgxY4boKETUylhPXPjnP/+JhIQElJSUoLy8HHv37rX9m2L9M0REN2Lq1KnQ6XQoLi5GWVkZtmzZgvHjx6OqqgpPPPEEevXqJToiEbUyXLEnonbP2dkZ//73v6FSqfD222/j7bfftr3XoUMHfPDBB3B3dxeYkOjWefjhhx2mhE+aNMn2e41Gg/vvv7+5YxFRG7ZmzRrbUdMRERFN/pm0tLQmj7slIroRV65cwerVq7Fy5com33/ooYcwceLEZk5FRER0bUajEatXr8bq1asd3pNIJFi3bh08PT0FJCOi3/Pxxx9j5MiRePfdd/Huu+/avTd06FDMmTNHUDIiEqm+vh6ffvop3Nzc8OCDD4qOQ0StzJNPPokNGzYgLy8Pd911l8P7PXv2xPz58wUkI6K24uDBg9i2bZvD6+PHj8dbb70lIBERtXaclExEBCAmJgb79+/H+PHj4eHhATc3N4wcORI7duzAAw88IDoeERERERHdoEWLFuGDDz7A6NGj0b17d0ilUvTo0QOTJk3Cl19+ic8++0x0RCIiIjsvv/wy1q5di5EjR8LX1xdSqRQBAQF4+OGHcfjwYa5VEbVggwcPxpEjRxAbG4uuXbtCKpWib9++ePnll7F//3507txZdEQiEiA+Ph6//PIL7r//fnh4eIiOQ0StjI+PDw4fPoy5c+eif//+cHFxgbOzMwIDAzF79mwcPXoUf/nLX0THJKJWTKvV4t5770X37t3h5uYGhUKB999/Hzt27ICrq6voeETUCnFSMhHR/6dUKpGYmCg6BlGz2rRpEzZt2iQ6BhG1I+vXr8f69etFxyCidsTLywuzZ8/G7NmzRUchov/H3r3H5Xz//wN/XNfVSaQojBXChKKYbNSVkLOPWT60OYwwhzE2scJs5tgc5mwyzOFjW2GMOc2xRYU5FBJzzjGSMqWuruv1+8NP39lyrOt6XYfH/XZzu21ddV0Pbtf17v14v5/v17uEHD9+XHYEIr0qW7Yshg4diqFDh8qOQkSvwMPDAzExMbJjEJERCQ4OhhBCdgwiMmHOzs6YOXMmZs6cKTsKEZkhX19frF+/XnYMIjIjXCmZiIiIiIiIiIiIiIiIiIiIiIiIiIiIioVDyURERERERERERERERERERERERERERFQsHEomIiIiIiIiIiIiIiIiIiIiIiIiIiKiYuFQMhERERERERERERERERERERERERERERULh5KJiIiIiIiIiIiIiIiIiIiIiIiIiIioWDiUTERERERERERERERERERERERERERERMXCoWQiIiIiIiIiIiIiIiIiIiIiIiIiIiIqFg4lExERERERERERERERERERERERERERUbFwKJmIiIiIiIiIiIiIiIiIiIiIiIiIiIiKhUPJREREREREREREREREREREREREREREVCwcSiYiIiIiIiIiIiIiIiIiIiIiIiIiIqJi4VAyERERERERERERERERERERERERERERFQuHkomIiIiIiIiIiIiIiIiIiIiIiIiIiKhYOJRMRERERERERERERERERERERERERERExcKhZCIiIiIiIiIiIiIiIiIiIiIiIiIiIioWK9kBiIge+/HHH1GmTBnZMYxOamqq7AikRzdv3sT3338vO4ZROnPmjOwIRGYrISEBdnZ2smMYpby8PNkRiMwW9/eLxv19MgX8/BaNn1/zxr7+dOzrZMzYd5+OfZcsGfdni8b9WaIXx37wdOwHRCWD+ytF4/4KkWniUDIRSadSqWBvb4+IiIiX/lkhBBQKhR5SGRd7e3uL+HtaGltbW1y5cgXDhg3T6+vodDoolaZ5c4TmzZvLjkBkdkqXLo3169dj/fr1en0dU972WFtby45AZFaKs7//sky1H3B/n4yVIT+/RTGF/Ql+fs2Tofp6cWm1WqhUKimvzb5OxshQfVcfDPU7j32XLI3s/dmSos/f+dyfJXo+U+kHL0Jf+xzsB0Svzlz2Vx7Tx3aG+ytEpkchhBCyQxARvQyNRoOVK1diypQp2LFjB2rXri07EpHRSkpKwuDBg5GQkCA7ChFZkNmzZ+POnTuYMmWK7ChEZCFycnLQqVMnzJ49G97e3rLjEFEJiIiIQOnSpTF+/HjZUYiMklarRaVKlZCcnIwqVarIjkNExfTmm29ixYoVqF+/vuwoRGSEQkNDUVBQgO+++46rwRNRsYSFhSEnJwdz5syBra2t7DhEZIb69euHcuXKYdq0abCxsZEdh4gkMe6lRoiI/kar1WL16tV444038OGHH8LLy4sDyUTPkJSUhObNm8PKijdGICLDmT17NkaOHMnBCCIymNzcXLRv3x579+7ltofITBw7dgwzZ87E9evXZUchMlq3bt1CRkYGRowYITsKEZUAKysrNG/eHCdPnpQdhYiMUOPGjfG///0ParUat27dkh2HiExYYGAgFi9ejLfeegsXL16UHYeIzFBQUBC++eYbNGnSBOfPn5cdh4gk4VAyERk9nU6HmJgY1KlTB3379sWVK1cAAKNHj5acjMh4paamolWrVsjKykK1atVkxyEiC/Hdd98hLCwMAODq6io5DRFZgvz8fAQHB+P333+HtbU1XFxcZEciomIqKChA3759odVqce3aNdlxiIzW48/HunXrsHHjRslpiKi4qlWrhszMTAQEBODEiROy4xCRkWncuDEA4OjRo2jYsCGSkpIkJyIiU6VWq6FUKpGcnAxvb29s3rxZdiQiMjNBQUFQKBRITk5GgwYN8OOPP8qOREQScCiZiIzarl270LBhQ7z33nu4ePEidDodFAoFvL29ERAQIDsekVFKSUmBn58fsrKyYG1tjcqVK8uOREQW4Ntvv8WgQYMghAAAbnuISO/y8vLQqVMn7Nq1CwBQoUIFKBQKyamIqLi++eYbnDp1CgBw6dIluWGIjNiNGzcAAAqFAoMGDUJWVpbkRERUHG5ubrC2tsb9+/fRvHnzwt+FREQA4O3tDSsrK+h0OqSnp+Ptt9/mRUlE9EqcnJzg6ekJIQQePHiAzp07Y/jw4dBoNLKjEZGZqFixIurUqQMhBHJyctCjRw/06tULDx48kB2NiAyIQ8lEZJR27dqFRo0aoXXr1jh16hSEENBqtQAAIQTGjh0rOSGRcTp79iwCAwORnZ2NgoICAFytlIj0b/ny5Rg6dGjhQDLAbQ8R6Vd+fj66du2KvXv3Fu7zvP7665JTEVFxXbx4EV9++WVh/388dElE/3bt2jVYW1tDCIHMzEyEh4fLjkRExeDq6gqFQoGCggLcv38farUaJ0+elB2LiIyEnZ0d6tSpAwDQarXIy8tDcHAwJkyYIDcYEZmkNm3awMbGBjqdDgCwaNEiNG/eHNevX5ecjIjMRceOHWFjY1P4/9HR0fD29ubdHogsCIeSicio7N+/H/7+/mjdunXhbeoen4x8rHLlyggODpYRj8io/fnnn/D390dmZmbhcE5BQQEHdIhIr77//nsMGDDgiYFkpVKJSpUqSUxFROZMo9Gga9eu2LFjR+E+j0KhQPXq1eUGI6JiEUKgX79+TxwDuHv37r+OCRDRI9evX4dS+ejwvkajwZIlS7Bnzx7JqYjoVbm6uhauUPh4MDkgIIArJhNRoWbNmsHa2hrAo31nIQQmTZqEkJAQ5ObmSk5HRKakefPmyM/PL/x/rVaLP/74A56enti5c6fEZERkLlq3bv3EdqagoACXL1+Gr68v5s6dKzEZERkKh5KJyCgkJCSgZcuWUKvVOHjwIAAUDhj8nZWVFUaPHg0rKytDRyQyapcvX0ZgYCDu3bv3xGdHCIEqVapITEZE5mzlypXo37//EwPJAODs7AyVSiUpFRGZM61Wi169emHbtm1P7PNYW1tzhXYiE7ds2TLExsY+cctYnU6HW7duSUxFZLyuX7/+xNC+UqnEgAED8PDhQ4mpiOhVubq6PtGtOZhMRP/UuHHjwlVNH9PpdPj5558REBDA/WYiemEBAQGFFzg+ptFokJ2djXbt2mHChAn/2t4QEb2MgICAJ1ZKBh51HI1Gg08//RTvvPMOMjMzJaUjIkPgUDIRSffHH3+gefPm2LdvH4Cih5Efs7W1Rf/+/Q2UjMg0XLlyBX5+frh9+/YTJ/Af40rJRKQPMTEx6Nev378GkgFud4hIP7RaLXr27In169cXuXIqL8QiMl03b97EyJEji9yv4O1jiYqWlpb2xDE0rVaLK1euYNKkSRJTEdGrKuoCu4KCAmRnZyMgIAApKSkSUhGRMfH19S2yCxcUFCApKQne3t44evSohGREZGocHR3h6en5r6/rdDrodDpMmjQJrVq1Qnp6uoR0RGQO7Ozs0LRp039dAAE8WlRt69at8PT0xIEDBySkIyJD4FAyEUnXuHFjbNiwAdbW1s9cVdHa2hofffQRHBwcDJiOyLilpaXBz88P6enpRQ4kKxQKvPbaaxKSEZE5W7duHd5///0iB4cUCgWqVasmIRURmTOtVovevXtj3bp1RZ6E1Wg0vCCCyIQNGTLkqau7ciiZqGhXrlz519e0Wi2+/vprHDt2TEIiIiqOKlWqFHnC/vFgslqt5mAykYXz8vL614qDj2k0Gty5cwd+fn7YuHGjgZMRkSlq06bNU7cpOp0OBw4cgLe3NxISEgycjIjMRfv27Z86/1NQUID09HQ0b94c06dPL/J8IxGZNg4lE5FR6NixI3bu3AlbW9un7pjodDp8/PHHBk5GZLyuXr0Kf39/3Lp1q8iBZABwcnKCra2tgZMRkTlbv349QkJCIIQo8iCBlZVVkSs8ERG9Kp1Ohz59+iAmJqbIgWTg0eoKHEomMk2bN2/Gxo0bi+w0VlZWHEomeoobN24U+XWFQoEPPvjgmXciIyLjo1Kp4OzsXORjHEwmIuDRvnH9+vWf+rhWq0VeXh6Cg4Mxffp0AyYjIlMUGBiI/Pz8pz6u0Whw69YtNG/eHJs3bzZgMiIyF61bt37qDAPwaN9Fp9MhPDwcy5YtM2AyIjIEDiUTkdEICAjA3r17i1wRwtraGiEhIXBzc5OQjMj4XLt2DWq1Gjdu3HjmzjxXSSaikvTzzz8/cyAZAJRKJapUqWLgZERkrnQ6HUJDQ/HTTz89dSD5scqVKxsoFRGVlKysLAwYMKDI4wDAowEtDiUT/VteXh7u379f5GMFBQU4ffo05s6da+BURFRcz7rA9/FgcmBgIP78808DpiIiY9K0adOnrmwKPBpctrOzg0qlem6HJiLLplarn9rFgUfbEysrK0ycOBHt27c3YDIiMhc+Pj5wcnJ66uM2NjZwcnLCzz//jAEDBhgwGREZAoeSichoCCGwZMkSKJVKODo6wsrKqvAxjUaDUaNGSUxHZFzmzp2Ly5cvP/f7qlataoA0RGQJsrOz8dVXX0Gr1T7zYKVGo+FqpURUYvbt24cNGza80Pfygggi0xMWFoa7d+9Cp9MV+bhWq+VQMlERrl+//sxbm2q1WowbNw7nz583YCoiKi53d3coFIqnPq5QKJCRkYElS5YYMBURGZPGjRsXeTeEx3cgbd26NVJTUxEWFvbUu5ISEQGAo6MjPD09i3xMoVDA29sbSUlJiIiIeOKcPRHRi1IqlWjTps1TtyFvv/02Tp06hXfffdfAyYjIEDiUTERGIywsDKtXr8b69euRmJgIZ2dnWFtbQ6VSISAgAA0bNpQdkchoTJ8+HWfPnsXgwYNhbW1d5M68tbU1qlWrJiEdEZmjsmXL4vjx49i0aRN8fHwAoMiTGzqdjkPJRFRiWrZsiWvXrmHWrFmoUKEClEplkYMaDg4OKF26tISERPSq9u7di+XLlxc5VPFYQUEB0tLSDJiKyDTcuHHjud+Tn5+P/v37P3N4mYiMi5ubG6ytrf/19ccrFYaEhCAlJQUzZsyQkI6IjIGvr++/LuhTKpWwsrJCUFAQtmzZwoVKiOiFtW3b9onV162trQvPzXft2hV169aVmI6IzEGbNm2e2HextraGvb09XF1dodVqUbFiRYnpiEifOJRMREZhzJgxmDdvHlavXo2OHTuiTp06SEhIQKVKlaDVahEeHi47IpHRqVWrFubNm4eDBw8C+L8TFI8plUquGEhEJUqhUOA///kP/vjjD4wZMwZCCCgUin+dNOVQMhGVJAcHB4wYMQJpaWnw9PSEnZ0dFArFE6u2v/baaxITEtGrSEtLQ8uWLWFvbw/g0UmJou7GcOXKFUNHIzJ6169fL/IiHSsrq8KvV69eHbVr10Z6erqh4xHRK/pnl348GKTT6RAbG4vVq1fDw8NDUjoiMgZ16tRBqVKlADz6vW9vb4+ZM2di9erV2L17N2JjYyUnJCJTEhgYiPz8fACPjv03btwYKSkpmDhxIr766iukpKRITkhEpq5169aFQ8kKhQJ+fn5ITU3F5s2bcfjwYcydO1dyQiLSFw4lE5F0kyZNwtdff42oqCh079698Ovu7u5ITExE165d0b59e4kJiYzbunXrUL58eVy4cAHh4eEoU6YMrKysoNFoOJRMRHqh0+nwyy+/oG/fvjh06BA6duwIhUJRuHIytz1EpA+pqak4efIk1q5di1WrVqF27doAHh3MdHV1lZyOiF7WBx98gF27diErKwuHDx9Gjx49IISAk5MTgEdDFkqlEjdv3pSclMj4XL9+HQAKLw5UKpVwcXFBuXLlsH79eqSnp+PChQtYsmQJKlWqJDMqEb0ENzc3aDQaqFQqlC5dGuHh4UhLS0ONGjWwatUq2fGIyAgolUr4+PhAoVCge/fuOH/+PD799FN069YN7dq1w7Bhw555JxIior/z9/eHUqlEqVKlMH/+fBw4cAC1atXC6NGjUb9+ffTv3x9arVZ2TCIyYVWrVkWNGjVga2uL+fPnY8+ePXBzc4OPjw/Gjx+PsWPH4uTJk7JjEpEecCiZiKSaP38+vvzySyxcuBD9+/f/1+Ovv/461q1bV+TqL0QEZGdnY9GiRRgxYgTc3NwwefJkXLt2DVOmTIGLiwtXKyUivdiwYQNSU1MRFhaGxo0bY8OGDUhJSUGvXr3g5OQER0dH2RGJyAxNnToVDRo0QIcOHdCrVy+kpKTgl19+QePGjVGtWjXZ8YjoFVlZWaFx48YoW7YsGjVqhLt37yIlJQULFy7E+++/D0dHR+Tl5cmOSWRU7t69i2bNmiE8PBzbt2/HvXv3sGTJEmRkZKB58+aoUKGC7IhE9ApcXV3h7OyMSZMm4fr165g0aRIqVaqEsLAwrFixAteuXZMdkYiMQN++fREbG4s1UOAUJAAAIABJREFUa9Y8cdegefPm4c8//8TixYslpiMiU+Lo6IiwsDCkpKRg6NChhefjrayssGzZMhw9ehTz58+XnJKITN2YMWNw4sSJJ7YzABAREQEfHx988MEH0Gg0EhMSkT4ohBBCdggiskwrV65EaGgopk2bhvDwcNlxiEzS119/jalTp+Ly5cuFK4o99vDhQ2g0Gjg4OEhKR0TmytfXF9WqVcO6dev+9djt27c5BEFEJe78+fPw8PDADz/88MTdVR7jtofI9DVs2BCBgYGYPXu27ChEJunOnTuoWLEiNm/ejI4dO8qOQ0SvICcnBwqFAqVKlXri63l5eahZsyZCQkIwa9YsSemIyBSMGTMGixYtQmpqKipXriw7DhGZuAkTJmDGjBlISkpCrVq1ZMchIjN05swZNGrUCJ999hm+/PJL2XGIqARxKJmIpFi/fj1CQkIwbtw4fPXVV7LjEJmkvLw8uLu744MPPkBkZKTsOERkIXbu3Ik2bdrg8OHDaNy4sew4RGQhBg4ciN27d+Ps2bNQqVSy4xBRCcvOzkb58uURExOD4OBg2XGITFa9evXQuXNnHiMgMkOzZs3Cl19+iYsXL/JiPCJ6qpycHNSrVw8tW7bE8uXLZcchIhNXUFCAJk2aoGzZsti7dy/vbExEejFv3jyEhYUhPj4evr6+suMQUQlRyg5ARJZn06ZNeP/99/HRRx9xIJmoGL7//nvcvXsXw4cPlx2FiCxIZGQkWrduzYFkIjKYmzdvYvXq1YiIiOBAMpGZ2r9/P3Q6Hfz9/WVHITJparUacXFxsmMQkR4MHjwY9vb2WLBggewoRGTE7O3tMWPGDKxYsYL7BERUbFZWVli+fDni4+OxePFi2XGIyEx9/PHHaNWqFfr06YPc3FzZcYiohHAomYgMavfu3QgJCUHPnj0xd+5c2XGITJZWq8WsWbMQGhqKKlWqyI5DRBbi8OHD2LNnDyIiImRHISILMnPmTDg5OaF3796yoxCRnsTFxcHDwwMVK1aUHYXIpKnVahw+fBg5OTmyoxBRCStdujSGDRuGuXPnIisrS3YcIjJi3bp1Q9u2bTFixAhotVrZcYjIxPn4+CAsLAyjR4/GhQsXZMchIjOkUCiwdOlS3Lx5E+PHj5cdh4hKCIeSichgEhMT0aVLF3Tq1AlLly7lLV6IiiE6OhoXL15EWFiY7ChEZEGmTp0KX19ftGzZUnYUIrIQmZmZWLJkCUaNGgU7OzvZcYhIT+Li4qBWq2XHIDJ5gYGB0Gg0OHTokOwoRKQHI0aMAACuVEhEzzVv3jykpKRgyZIlsqMQkRmYMGECqlevjg8//BBCCNlxiMgMubq6YtasWZg9ezb27dsnOw4RlQAOJRORQSQlJaFDhw5o2bIlfvjhB952maiYZs6ciZCQENSqVUt2FCKyEKmpqdi0aRPGjh0rOwoRWZAFCxbAysoKAwcOlB2FiPQkLy8PR44c4VAyUQlwdXVF1apVebt2IjPl6OiIQYMGYfbs2bytMRE90xtvvIHhw4dj3LhxuH37tuw4RGTibG1tsWzZMsTGxuL777+XHYeIzFRoaCi6dOmC0NBQ3L9/X3YcIiomDiUTkd6dPXsWbdu2RaNGjRAdHQ1ra2vZkYhM2pYtW3Ds2DGMGjVKdhQisiCRkZGoXbs2OnfuLDsKEVmInJwczJ8/H8OGDYODg4PsOESkJ4mJiXj48CGHkolKiFqt5lAykRkbOXIksrOzsWzZMtlRiMjIffHFF7C3t+cCA0RUIt566y0MHz4cI0eOxNWrV2XHISIztXjxYuTm5vJu0URmgEPJRKRX58+fR4sWLVCjRg1s3LiRt1wmKgGRkZHo2LEjGjZsKDsKEVmItLQ0/Pjjj4iIiIBSyQpBRIbx3Xff4cGDB/j4449lRyEiPYqLi4OrqyuqV68uOwqRWVCr1YiPj4dGo5EdhYj0oFKlSujXrx+mT5+O/Px82XGIyIiVKVMGM2fOxPLly5GYmCg7DhGZgcmTJ6NixYoYPHiw7ChEZKYqVKiAqKgoLF26FFu2bJEdh4iKQSGEELJDEJF5unbtGtRqNcqWLYu9e/eiXLlysiMRmby4uDgEBAQgLi4O/v7+suMQkYUYPnw4Nm7ciHPnzsHGxkZ2HCKyABqNBm+88Qa6dOmCOXPmyI5DRHrUtm1buLi4YM2aNbKjEJmF06dPo169ejh06BB8fX1lxyEiPUhLS0OtWrUQFRWFvn37yo5DREauVatWyMrKwsGDB6FSqWTHISITFx8fD7VajVWrVqFnz56y4xCRmerVqxf27NmDEydOwNnZWXYcInoFXOaMiPTi9u3baN26NWxsbLBjxw4OJBOVkMjISLz99tscSCYig8nIyMDy5csxatQoDiQTkcGsWbMG169fx6effio7ChHpkVarRUJCAtRqtewoRGajTp06qFixIuLi4mRHISI9cXNzQ48ePTBlyhRotVrZcYjIyM2fPx/JyclYunSp7ChEZAaaNWuGwYMHY8SIEbh165bsOERkphYuXAgrKysMGzZMdhQiekUcSiaiEpeVlYV27dohPz8fe/bsQaVKlWRHIjILycnJ2LZtG8aPHy87ChFZkDlz5sDOzg79+vWTHYWILIQQAjNnzkTPnj1RrVo12XGISI+OHz+O+/fvcyiZqAQpFAo0a9aMQ8lEZi4iIgIXLlzAzz//LDsKERm5evXq4eOPP8bYsWNx584d2XGIyAxMnz4djo6OGDhwoOwoRGSmHB0dsXz5ckRHRyM6Olp2HCJ6BRxKJqIS9eDBA3Tq1Anp6enYuXMnqlSpIjsSkdmIjIxE/fr10b59e9lRiMhCPHjwAN9++y0++eQTlClTRnYcIrIQGzZsQEpKCkaNGiU7ChHp2e+//47y5cujbt26sqMQmRW1Wo39+/dDCCE7ChHpiYeHB7p27YopU6bws05EzzVhwgTY2dlh3LhxsqMQkRkoXbo0vvvuO2zevBnr1q2THYeIzFRQUBAGDRqEwYMH4+rVq7LjENFL4lAyEZWY3NxcdOrUCWfOnMFvv/0Gd3d32ZGIzMaFCxewdu1aREREQKFQyI5DRBZi0aJFePjwIYYMGSI7ChFZkJkzZ+Ldd9+Fp6en7ChEpGdxcXFQq9VQKnmIkqgkBQQE4M6dOzh9+rTsKESkR+PGjSu8sxoR0bM4ODhg+vTpWLp0KQ4ePCg7DhGZgZYtW6Jfv34YMmQI0tPTZcchIjM1c+ZMVKhQAf379+fFmEQmhkf8iahEaDQadOvWDcePH8eOHTu4yhFRCZs+fTqqVq2Kbt26yY5CRBYiLy8Pc+bMwUcffQRnZ2fZcYjIQuzevRsJCQlcJZnIAgghcODAAajVatlRiMyOj48PHBwcEBcXJzsKEemRt7c32rdvj0mTJsmOQkQmoGfPnggICMDQoUOh0+lkxyEiMzBr1iyUKlUKI0eOlB2FiMxU6dKlsWLFCuzevRvLli2THYeIXgKHkomo2LRaLXr37o19+/bh119/RcOGDWVHIjIrt27dwqpVq/DZZ5/ByspKdhwishArV65ERkYGPvnkE9lRiMiCTJs2Da1atULTpk1lRyEiPTtz5gzS09M5lEykB1ZWVnj77bc5lExkAcaPH4/ExETExsbKjkJEJmDBggVITk7G8uXLZUchIjPg6OiIxYsXY82aNdiwYYPsOERkppo1a4awsDB88sknOHfunOw4RPSCFILrmxNRMQghMHDgQKxZswZbt25FYGCg7EhEZic8PBwrV67ExYsXUapUKdlxiMgCaLVa1K1bFy1atEBUVJTsOERkIQ4fPowmTZpg586dCAoKkh2HiPRsyZIlGDlyJDIzM2FtbS07DpHZmTRpEpYsWYK0tDTZUYhIzwIDA2Fra4sdO3bIjkJEJuCTTz7B6tWrcebMGbi4uMiOQ0RmoHfv3ti9ezdOnTqFcuXKyY5DRGYoLy8PTZo0gYODA2JjY6FSqWRHIqLn4ErJRFQsYWFhWLVqFdauXcuBZCI9yMrKQlRUFEaOHMmBZCIymLVr1+LChQsYNWqU7ChEZEEiIyPh6+vLgWQiCxEXF4emTZtyIJlITwICAnD16lVcvnxZdhQi0rMxY8bgt99+w+HDh2VHISITMHHiRNjZ2eGLL76QHYWIzMScOXMghOD5BCLSG1tbW6xcuRKHDx/GvHnzZMchohfAoWQiemVjxozBvHnzsHr1anTs2FF2HCKztGDBAgDAoEGDJCchIksyY8YMdOvWDW+88YbsKERkIc6cOYONGzciIiJCdhQiMpC4uDio1WrZMYjMVpMmTWBra4u4uDjZUYhIz9q2bQtfX19MmzZNdhQiMgFly5ZFZGQkoqKicOjQIdlxiMgMODs7IyoqCsuXL8f27dtlxyEiM+Xj44Px48djzJgxOHnypOw4RPQcCiGEkB2CiEzP5MmT8cUXX+C7775D//79ZcchMksPHz6Eu7s7+vfvj8mTJ8uOQ0QWYtu2bejQoQOOHDmCRo0ayY5DRBaiX79+iI+PR0pKCpRKXj9NZO6uXr0KNzc37NmzBy1atJAdh8hs+fn5wcvLC1FRUbKjEJGe/fzzz/jvf/+L5ORkeHl5yY5DREZOCIEWLVogJycHiYmJ7OFEVCK6d++O+Ph4nDx5Ek5OTrLjEJEZKigogL+/PzQaDRITE3kHNiIjxoZBRC9twYIF+OKLL7Bw4UIOJBPp0dKlS3Hv3j0MGzZMdhQisiDTpk1D+/btOZBMRAZz9epVrFmzBuHh4TwRSmQhYmNjYW1tjbfeekt2FCKzplaruVIykYV499134enpienTp8uOQkQmQKFQYMGCBTh27BhWrlwpOw4RmYlFixZBo9Fg7NixsqMQkZmysrLCypUrcfr0aUydOlV2HCJ6Bp7tI6KXsnLlSowYMQLTpk3DkCFDZMchMlsajQazZs3CgAED8Nprr8mOQ0QW4uDBg4iLi8OYMWNkRyEiCzJz5kxUrFgRPXv2lB2FiAwkLi4OjRs3hr29vewoRGZNrVYjNTUV6enpsqMQkZ4pFAp89tln+OGHH3Du3DnZcYjIBHh5eWHIkCGIiIhAZmam7DhEZAZcXFwwe/ZsLF68GDt37pQdh4jMlIeHB6ZNm4bJkyfj8OHDsuMQ0VMohBBCdggiMg3r16/He++9hzFjxmDixImy4xCZtVWrVmHAgAE4e/YsqlevLjsOEVmI//znP7h9+zYSExNlRyEiC5GRkYHq1atj8uTJGDFihOw4RGQgnp6e6NSpE77++mvZUYjMWlZWFpydnbF27Vq8++67suMQkZ5ptVrUqVMHQUFB+Pbbb2XHISITkJ2djTp16qBr166YP3++7DhEZCaCg4Nx7NgxnDhxAmXKlJEdh4jMkBAC7du3x5UrV3DkyBGUKlVKdiQi+geulExEL2THjh3o0aMHhgwZwoFkIj0TQmDGjBl4//33OZBMRAaTkpKCrVu34vPPP5cdhYgsyLx582BjY4P+/fvLjkJEBnL37l2kpqZCrVbLjkJk9hwdHeHl5YW4uDjZUYjIAFQqFcLCwvD999/j2rVrsuMQkQkoW7Yspk6dim+//RbHjx+XHYeIzMTChQuRlZWF8ePHy45CRGZKoVBg6dKluHnzJrc1REaKQ8lE9Fx79uxBly5d0KNHD8ydO1d2HCKzt2nTJpw6dQqjR4+WHYWILMiUKVNQp04ddOjQQXYUIrIQDx48wKJFizBixAiumkJkQX7//XcIIdCsWTPZUYgsglqt5lAykQUJDQ2Fs7MzZs+eLTsKEZmIPn36wM/PD0OHDgVvsExEJaFy5cqYNWsW5s2bxy5CRHrj6uqKWbNmYfbs2YiNjZUdh4j+gUPJRPRMiYmJeOedd9CxY0csXboUCoVCdiQiszd9+nR07twZXl5esqMQkYW4ePEiYmJiMHbsWCiVrAhEZBiLFy9Gbm4uPvroI9lRiMiA4uLiUL9+fZQvX152FCKLoFarcezYMdy/f192FCIyAFtbW4wcORKLFy/GnTt3ZMchIhOgUCgwZ84cHDx4EKtXr5Ydh4jMRGhoKNq0aYMBAwYgNzdXdhwiMlOhoaHo0qULQkNDedyDyMhw4oCIniopKQkdOnRAy5Yt8eOPP0KlUsmORGT29u7di/j4eHz22WeyoxCRBZkxYwbc3NwQEhIiOwoRWQiNRoO5c+di8ODBcHFxkR2HiAwoLi4OarVadgwiixEQEACtVouEhATZUYjIQAYPHoxSpUph/vz5sqMQkYlo2LAhBg0ahM8++wz37t2THYeIzMTSpUuRnp6Or776SnYUIjJjixcvxoMHDzBq1CjZUYjobziUTERFOnv2LNq2bYtGjRohOjoa1tbWsiMRWYTIyEi0aNGCtzImIoO5desWVqxYgdGjR8PKykp2HCKyECtXrsTNmzcxYsQI2VGIyIAePHiA48ePcyiZyIBee+011KpVi7dNJrIgpUuXxrBhwzB37lxkZWXJjkNEJmLy5MnQ6XSYMGGC7ChEZCZef/11TJs2DTNnzsThw4dlxyEiM1WhQgVERUXhu+++w5YtW2THIaL/j0PJRPQv58+fR4sWLeDu7o6NGzfCzs5OdiQii3D8+HHs3LkTERERsqMQkQWZPXs2ypYti759+8qOQkQWQqvVYsaMGejTpw/c3NxkxyEiA4qPj4dGo4Gfn5/sKEQWRa1WcyiZyMJ88sknAICoqCjJSYjIVJQrVw7Tpk3DggULkJSUJDsOEZmJQYMGoWXLlujTpw8ePnwoOw4RmakuXbqgR48e+PDDD5GRkSE7DhGBQ8lE9A/Xrl1D69atUaFCBWzduhVlypSRHYnIYkyZMgXe3t5o3bq17ChEZCGys7MRFRWFTz75BKVKlZIdh4gsxPr163H+/HmMHj1adhQiMrC4uDjUrFkTrq6usqMQWRS1Wo3ExEQOARBZEEdHRwwaNAjffPMNcnNzZcchIhMRGhoKX19fDBs2DEII2XGIyAwoFAosWbIEaWlpmDZtmuw4RGTGFi5cCCsrKwwbNkx2FCICh5KJ6G9u376N1q1bw8bGBjt27EC5cuVkRyKyGOfPn8eGDRswduxYKBQK2XGIyEIsXLgQOp0OgwcPlh2FiCzI9OnT0bVrV9SuXVt2FCIysLi4OKjVatkxiCxOQEAA8vLycOTIEdlRiMiARo4ciezsbCxfvlx2FCIyEUqlEgsXLkRCQgJ++OEH2XGIyExUr14dkydPxtSpU9lJiEhvHB0dsXz5ckRHRyM6Olp2HCKLpxC8zNFi5efnIyMjAxkZGcjLy0NmZiYAIC8vDzk5OQCA+/fvo1SpUrCysoJSqYSjoyMAoFSpUrCzs0O5cuXg4uKCsmXLSvt7UMnIyspCy5YtkZWVhd9//x1VqlSRHYnM1F9//fXEtufBgweFX9doNBBCIDs7u3B7Y2dnV7h6Z9myZWFnZwdnZ2c4OzvDzs5O2t+jpA0YMAD79u3DmTNnoFKpZMchMjuPtzv3799/Yntz7949AI/2f7RaLezt7QE82t6oVCqoVCqULVsW9vb2hdsec/mMPnz4EDVq1EDfvn0xdepU2XGIzA77VtF27NiBdu3a4Y8//sCbb74pOw6R2THmvqXRaODk5IT58+ejX79+JfrcRPR8r7/+Oj7++GNERESU+HOzbxEZr2HDhmHTpk04d+4cbGxsZMcpMexbRPo1ePBgbNy4EWfOnCn87JBx9y0iY6fT6RAYGIjs7GwcPnwY1tbWsiOZDPYtopczZMgQ/PTTTzhx4gTv1vaK2LeoJHAo2QwJIXDjxg1cunQJFy9exKVLl3Dp0iVcvXoVGRkZuH37duFOy/OULl0aubm50Ol0z/w+a2vrwh0ZZ2dnVKpUCdWrV0f16tXh7u5e+N+8LbhxysnJQdu2bXHp0iX8/vvvcHd3lx2JTFB+fj4uX75cuM15/Of69euFOyyPd1qex8HB4YW3UY+3OxUqVECVKlX+td15/fXXjb5gXbt2DTVq1MCiRYvQv39/2XGITEpWVtYT25zH+z537tx5YtvzIvsyKpXqhW5nXL58+Sf2ex5vb/7+x8XFpaT+inqzcOFCjBo1ChcuXEDlypVlxyEyGexbxRMYGAg7Ozts375ddhQik2IOfSshIQHNmjXD2bNn8cYbb7zQzxBRyQkJCcFff/2FLVu2vPDPsG8Rmb60tDTUqlULUVFR6Nu3r+w4z8W+RWQc7t69Cw8PD3zwwQeYNWuW7Dh6Zw59i8gUnD17Fj4+Phg3bhzGjRsnO4507FtE+vHgwQM0bNgQ7u7u2L59O+9S/TfsW2RIHEo2cZcuXcKJEyeQnJyM5ORknDx5EufPny8sRdbW1nBzc4O7uzvc3Nzg4uLyxJ/HH/pSpUoVXrVga2tbeBXV3+l0OmRlZQH4v6sfMjMzcefOnX/tGN24caNwA/b4igkAeO2111C3bl3Ur18fDRo0QIMGDeDp6Vnk65Fh5ObmomPHjjh58iRiY2NRt25d2ZHIyOXn5+P06dOF256kpCScPn0a165dK9zhcHBwKNx5cHV1fWIn4/EfFxcXlCpVqnDnokyZMkVeFfvw4UPk5uYCeHS1eU5OzhPbm4yMDNy5cwe3b9/G1atXcenSJVy+fLmweFlbW6NatWrw8vJCgwYNUL9+fXh7e6NmzZpQKpUG+ld7trCwMERHR+PChQtmtWIJUUm6d+8ekpOTn9j2nD179l/7GY+3PRUrVizc1jw+sOvs7AwHBwc4ODjAysoKCoUCTk5ORb5ednY2tFpt4dXmj7c9j7c3f9/+PN7nuX79+hPbwTfeeOOJfZ4GDRqgYsWKBvn3eh6tVgsPDw+0a9cOCxYskB2HyGixb5WsQ4cO4a233sK+ffvQvHlz2XGIjJI5962vv/4ac+bMwY0bN/T5T0hET7FgwQJ8/vnnyMjI+NdwC/sWkXkLDQ1FQkICUlJSjOZ4KMC+RWTslixZgqFDh+LIkSNo0KCB7Dglwpz7FpGpmD59Or744gv88ccf8PLykh3HINi3iAwvPj4eAQEBWLx4MQYMGCA7jhTsWyQbh5JNyM2bNxEfH48DBw7g0KFDOHHiROGH2t3dHfXr14eXlxc8PDyM6grKv1/hdeHCBaSkpCApKQmnTp1CTk4OVCoVatasiYYNG6JZs2bw8/ODt7c3rKyspOa2BBqNBsHBwYiLi8OePXvQqFEj2ZHIyAghkJKSUrjtOXr0KFJTU6HRaGBjY4N69eoVbntq1KhhNFdQ/vMKr/PnzxfucJ0/f77wFjaenp7w9fVFs2bN4O/vj2rVqhk86927d1G9enVMmDABI0eONPjrExmj3NxcHD58GAcOHEBCQgKSkpJw5coVAEC5cuUKD8DWrVv3iasoZV9B+c8VNc6cOVNY9G7dugUAqFSpEry9vfH222+jWbNmaNq0qZTb1vzvf/9D3759cebMGdSsWdPgr09kjNi39O+dd97BzZs3cfDgQYO/NpExsrS+NXToUNjb2yMmJkZqfiJLlZycDG9vbyQmJiIvL499i8iCnDlzBvXq1cNPP/2Ebt26ScnAvkVkenQ6HZo1awYbGxvExsaa3CqDlta3ZJzfInoVOp0OarUaBQUFiI+Pl/67vqTx/BaR8fjss8+waNEiHD9+HLVq1ZIdR6/Yt8gYcSjZiKWmpuL333/HgQMHEB8fj3PnzkGlUsHLywtNmzaFt7c36tevj/r165vkL3udTofz588X7swcPXoU8fHxuHv3LsqUKYMmTZrA398ffn5+8Pf359UPJUyr1aJnz5749ddfsX37dvj7+8uOREZAo9EgISEB+/fvR3x8POLj45GZmYkyZcrgrbfegq+vb2FZ8vDwKPLKb2OXm5uLU6dOISkpCSdOnMChQ4dw5MgR5Ofn4/XXX4efnx/8/PygVqvh4+Oj9wNtX331FebNm4dLly7BwcFBr69FZKwyMjIQGxuL/fv3IyEhAUeOHIFGoyn8TDZs2LBw2+Pm5iY77itJT08v3OdJSkpCQkIC/vzzT6hUKnh6ekKtVqNp06Zo0aIFqlSpotcsQgg0aNAAPj4+WL16tV5fi8iYsW8Ztm+dPn0aXl5e2LBhAzp37qzX1yIyVpbetypVqgQfHx906NDBYH2LiP6vb8XFxWHHjh04d+4c+xaRBerWrRv+/PNPHDt2zCC/f9m3eH6LzMORI0fQpEkTrFmzBu+9957sOM9k6X1Lxvktold1+vRpNGrUCJMmTcKoUaNkxykWnt9i3yLjlZeXhyZNmsDBwQGxsbHSB3BLEvsW+5Yp4FCyEcnJyUF8fDx27dqFX375BampqShdujR8fHwKP0x+fn4oX7687Kh6deHCBezfvx8HDhzA/v37cfr0adja2sLf3x9BQUEICgrCm2++KTumSRNCYODAgVizZg22bt2KwMBA2ZFIolu3bmHHjh349ddfsXPnTty7dw+VK1fGm2++WbjtadKkCWxsbGRH1RuNRoPk5OTCbc/evXtx584dVKhQAYGBgQgKCkLnzp3x2muvlejrPnjwANWrV8fQoUMxYcKEEn1uImOm0+lw7Ngx7Nq1C7t27UJsbCx0Oh08PDwKtztvvvkmPD09ZUfVq1u3bhUeOH683/Pw4UPUqFEDnTp1wn/+8x+o1WrY2tqW6Otu2rQJXbp0QXJyssXcHo0IYN96TFbf6t27N44cOYKTJ0/yFqNkUdi35PUtIkvFvvWIrL5FZKySkpLQsGFD/Prrr+jQoUOJPz/71iM8v0Xm6MMPP8Svv/6KM2fOGN1QC/sW+xaZrkmTJmHKlCk4duwY6tatKzvOC2PfeoR9i0zF8ePH8dZbbyEyMhKffvqp7DivjH3rEfYt08KhZMkuX76MdevWYevWrdi/fz8KCgrw5ptvol27dmjfvj2aNGliVldrvIobN25gx44d2L59O3bu3Im7d++ievXqaNeuHbp06YJWrVpxafaXFBaZDXHDAAAgAElEQVQWhvnz52PDhg3o2LGj7DhkYEIIxMfHY8OGDdi2bRtSUlJQunRptGzZEu3bt0fbtm1Ro0YN2TGlEkIgKSkJ27dvx/bt23HgwAEIIeDr64sOHTogODi4RMrk7Nmz8fnnn+Py5cvSbwlGpG9//fUXfv31V2zevBm//fYb7ty5Azc3N7Rr1w7t2rVDUFCQ0R3UNrTc3FzExsZi+/bt2LZtG86ePQsHBwcEBQWhY8eO6NKlC5ydnYv9Ov7+/nB2dsYvv/xSAqmJjBv71vMZom+lpaWhZs2aWLZsGXr37l1CyYmME/vW8xmqbxFZEvat5zNU3yIyZh06dEBmZiYSEhJK5PnYt56P57fIHNy9exceHh4IDQ3F9OnTpWZh33o+9i0yFQUFBXj77bdhY2OD/fv3G/UiBuxbz8e+RcZs8uTJmDx5Mv744w+TWqyJfev52LeMG4eSJbh69SrWrl2LmJgYHDx4EOXKlSvcaLRt2xYVKlSQHdFoabVaHDx4ENu2bcO2bdtw5MgRODs7Izg4GCEhIQgMDLT4je7zjBkzBjNmzMCaNWsQEhIiOw4ZiBAChw4dQnR0NNatW4e0tDTUqVMHHTt2RLt27Xil4nNkZ2dj165d2LZtG7Zu3Yrr16/Dy8sL3bt3R/fu3eHh4fHSz6nRaFCrVi0EBwdj9uzZekhNJF9ubi62bNmC6OhobN26Ffn5+QgICCjc7zGl4ifDhQsXCg/g7N69GwUFBQgKCkL37t3RpUsXODk5vfRzxsbGIjAwEPv374efn58eUhPJx7716vTVt4YNG4ZNmzbh/PnzJnl7VKLnYd8qHn30LSJLwL5VPProW0TGLjExEU2bNkVsbCwCAgJe6TnYt14dz2+RKfv2228xfPhwHD16FPXr1zfoa7NvFQ/7FhmzpKQk+Pr6YtasWfj4449lx3kC+1bxsG+RMSkoKIC/vz80Gg0SExON+hwF+9arY98yPhxKNpC7d+/ihx9+QHR0NOLj41G2bFm88847CAkJQVBQkFFv9IzZxYsXERMTg5iYGBw9ehQVK1ZE165d0atXLzRr1kx2PKMzZcoUjB8/Ht999x369+8vOw4ZwMmTJ7Fq1SqsXbsWly5dQu3atQsPNBj6wJG50Ol0OHDgAKKjo7F+/XrcvHkTPj4+6N69O3r37g1XV9cXep7ly5dj8ODBOHfuHKpWrarn1ESGU1BQgG3btuHHH3/E5s2bkZubi8DAQHTv3h3BwcFcFfwV3b9/H5s3b0Z0dDR27NgBAGjTpg3ee+89BAcHw87O7oWep127dnj48CH27dunx7REhse+pR8l0bfS09NRvXp1zJgxA0OHDtVzYiLDYt8qeSXVt4jMFfuWfpRU3yIyBc2bN0epUqWwffv2F/4Z9i394PktMiU6nQ5NmzaFg4MDdu3aZZDXZN8qeexbZIzGjx+Pb775BsnJyahZs6bULOxb+sG+RcbgzJkzaNiwISIiIvDFF1/IjvME9i39YN8yAoL0RqfTib1794oePXoIOzs74eDgIHr16iU2bdokHj58KDue2Tl79qyYPHmyqF+/vgAg6tWrJ2bPni3u3LkjO5pRmD9/vgAgvvnmG9lRSM/++usvsWzZMtG0aVMBQLi7u4uIiAhx7Ngx2dHMTkFBgdizZ48YPHiwcHFxESqVSnTq1Els3LhRaDSap/6cVqsV9erVE/369TNgWiL9unDhghg3bpyoUqWKUCqVonnz5mLhwoXi5s2bsqOZnXv37omVK1eKjh07Cmtra1GuXDkxfPhwkZyc/MyfO378uFAoFGL79u0GSkqkX+xbhvWqfWvs2LGiYsWKIicnx0BJifSLfctwXrVvEZkj9i3DedW+RWQqtm/fLgCIQ4cOPfP72LcMi+e3yBQcOnRIKJVKERMTo7fXYN8yHPYtMhZ5eXnCy8tLtGjRQuh0OikZ2LcMh32LZJozZ46wsrJ6bhcyBPYtw2LfkoNDyXpw9+5dERUVJby8vAQA8eabb4qoqChx//592dEsxsmTJ0V4eLgoX768sLW1Fd26dRM7d+6UtiMr24oVK4RSqRTTpk2THYX06O/vexsbG4t/3xtaXl6e2LRpk+jWrZtQqVTitddeE+Hh4eLcuXP/+t5169YJpVIpTp06JSEpUcl5mfc96cfNmzfFnDlzhKen53P3O7t37y68vb35e4FMHvuWfC/at7KysoSTk5OYMmWKpKREJYd9Sy7ud5Il4vtevpfpW0SmpHHjxiI4OLjIx9i35OP5LTJmoaGhwtXVtcS3CexbcnG/k2Q7ePCgUKlUIioqymCvyfe9fOxbZGg6nU60adNG1K1bV9oiKuxb8rFvGQ6HkktQUlKSGDhwoLCzsxOOjo5i4MCB4ujRo7JjWbTc3FwRExMjgoKCBABRq1YtMWfOHItapWv9+vXCyspKjB8/XnYU0gOtVis2bdok/Pz8BADh4eEhIiMjRXp6uuxoFi0tLU1ERkaKatWqCaVSKYKCgsSmTZsKH2/cuLHo2rWrxIRExZOVlSXmzJkjXF1dC9/jMTExIj8/X3Y0i/bHH3+IgQMHCnt7e+Hg4CCGDx8uLl++LIQQ4ty5c0KlUono6GjJKYleHfuW8Xle34qMjBRly5YVmZmZkpMSvRr2LeP0vL5FZOrYt4zTs/oWkalZv369UCgU4sSJE4VfY98yPjy/Rcbozp07wtnZWURERBT7udi3jBP7FskyevRoUbZsWb3vY7NvGSf2LTKUtLQ0Ua5cOREWFmbQ12XfMj7sW/rHoeRi0ul0YseOHaJNmzZCoVAIT09PsXTpUr5JjVBycrIIDQ0Vtra2olKlSmLixIni9u3bsmPp1fbt24Wtra34+OOPZUehEvbXX3+JefPmiZo1awqlUim6dOkiYmNjZceif9BoNGL9+vWFB9V8fX3FuHHjBABx+PBh2fGIXtq5c+fEsGHDROnSpYWTk5MIDw8XV65ckR2L/iEjI0NERkaKKlWqCGtra9GrVy/x7rvvipo1a4qCggLZ8YheCvuW6fhn3/riiy9EpUqVRHh4uOxoRC+Nfcs0FNW3oqOjeathMlnsW6ahqL7FW8qTqdHpdMLLy0v07t2bfctEWOL5LTJe8+fPFzY2NuL06dOv9PPsW6aBfYsM7eHDh6Ju3boiKChIL6tVsm+ZBvYtMoTly5cLpVIp9u3bp9fX4fkt08G+pR8cSn5FGo1GrFy5UtSvX18AEK1atRJbt27lct4m4MaNG2Ls2LGifPnywt7eXgwZMkRcvHhRdqwSt3v3bmFnZyf69OnD96UZSU9PF2PGjHni/Xv27FnZsegFJCQkiP/+979CpVKJcuXK8SorMimHDh0SXbt2FUqlUri7u4s5c+aI7Oxs2bHoOfLy8sSKFStEgwYNBADRoEED8dtvv8mORfRC2LdM1+O+Va5cOWFtbS369Oljln2LzBP7lun6e9+qXr06+xaZFPYt0/TPvtWqVSv2LTIZGo1GjBw5Uri5ubFvmRhLOb9Fxq2goED4+PiIoKCgl/o59i3Txb5FhhIfHy9UKpVYsWJFiT0n+5ZpYt8ifQsODhbu7u562R7w/JbpYt8qWRxKfklarVasWbNG1K5dW1hZWfHKHBP2119/ifnz5wt3d3dhY2MjBg8eLNLS0mTHKhEJCQmiTJkyomvXrlwR0UxkZGSIsWPHijJlyoiKFSuKSZMm8cocE3X+/HkxbNgwYW9vL6pUqSLmz58vHj58KDsWUZGOHz8uOnfuLBQKReFKCPy9YnoeX4nbunVrAUD4+/uLvXv3yo5FVCT2LfNhzn2LzA/7lvlg3yJTwr5lHti3yJSwb5kP9i2S7cCBA0KhUIh169Y993vZt8wH+xYZwvDhw4Wjo6O4evVqsZ6Hfcs8sG+RvqSnp4uKFSuKgQMHlthzsm+ZD/atksGh5Bek0+nEpk2bhI+Pj1AqlaJbt268etNM5Ofni5UrV4qaNWsKGxsbMXDgQHHt2jXZsV5ZUlKSKF++vOjcubPIz8+XHYeK6f79+yIyMlI4OTkJZ2dn8eWXX4qsrCzZsagE3L59W4SHh4tSpUoJNzc3MWfOHB68IaORkpIievfuLZRKpahfv76IiYnh1ZtmIiEhQXTq1EkAEH5+fjx4Q0aDfct8mVvfIvPCvmW+2LfImLFvmS/2LTJW7Fvmi32LZPrggw+Em5ub+Ouvv4p8nH3LfLFvkT49ePBA1KxZU3Tq1OmVfp59y3yxb1FJ27Bhg1AoFGLLli3Feh72LfPFvlU8HEp+Adu2bRMNGzYUSqVShISEiNOnT8uORHqQl5cnFixYIKpUqSLs7e3F6NGjRWZmpuxYL+XMmTOiUqVKomXLliI3N1d2HCqG3NxcERkZKcqXLy+cnJzExIkTeSsZM3X16lUxZMgQYWNjI2rUqCFWr17NckzSXLx4UfTs2bPwYM2GDRv4fjRT+/btE2q1WgAQ7du3F8ePH5cdiSwY+5ZlMIe+ReaDfctysG+RMWHfshzsW2RM2LcsA/sWyXDz5k3h5OQkxo4d+8TX2bcsB/sW6cuePXuEQqEQP/744wv/DPuW5WDfopLUo0cPUblyZXHnzp1X+nn2LcvAvvVqOJT8DKmpqaJDhw4CgOjSpYtITk6WHYkMICcnR3zzzTfCxcVFuLi4iG+//dYkbuVx+fJlUbVqVfH222+L+/fvy45DxbBu3Trh7u4uSpcuLT7//HP+MrMQFy9eFP369RMqlUo0adJEJCQkyI5EFuT+/fti3Lhxws7OTnh4eIiffvpJaLVa2bHIAHbs2CF8fX2FSqUSAwcOFOnp6bIjkQVh37JMptq3yHywb1km9i2SiX3LcrFvkUzsW5aJfYsMbc6cOcLGxkakpqYKIdi3LBX7FunDoEGDhIuLi7h58+Yzv499y3Kxb1FJyMzMFG5ubuK99957qZ9j37JM7Fsvh0PJRcjMzBTh4eHC1tZW1K1bV2zfvl12JJLg7++DOnXqiG3btsmO9FRXr14V7u7uwtvbW9y9e1d2HHpFKSkpom3btkKhUIhu3bqJS5cuyY5EEqSkpIh27drxfUAGodPpRExMjKhatapwcnISkZGRvM2aBXr8PqhWrZooU6aM+PLLL/k+IL1i3yIhTKtvkXlg3yIh2LfIsNi3SAj2LTI89i0Sgn2LDEej0YgGDRqIgIAA9i1i36ISlZWVJapWrSpCQkKKfJx9i4Rg36KS8dtvvwmFQiF++umn534v+xYJwb71ojiU/DdarVZ8++23wsXFRVSoUEEsXryYE+30xBUu7777rrhw4YLsSE9IT08X9erVE7Vr137ulYJknG7fvi0+/PBDoVKpxFtvvSUSExNlRyIj8PcVBSZOnChyc3NlRyIzk5CQIJo0aSJUKpUYNGgQryCmJ1YU+H/s3XmYzXXj//HXjGWsIZSflCVClrEbZoys2alsEXfK2kZKtJfcpUVKyi4U2catsoyxi7ENwoxoobK0YLJrxjif3x99R4nhzJxzPu/POef5uK77uu5rjPN5cZn3fZ7n/pyZO+64w1q8eLHpSQgw9Bauxum9Bf9Hb+Fq6C34Gr2Ff6O34Gv0Fq6G3oKvHT161OrQoYMVGhpKb+ESegvesnz5ciskJMSKiYm57OP0Fv6N3oKn+vfvbxUsWNA6ePDgVX+d3sLV0FvXxk3J/ycxMdGqV6+elSNHDmvw4MH8OBlcYenSpVaFChWsvHnzWu+++64j/gfmxIkTVo0aNazbb7/dOnz4sOk5yIJPP/3UKlq0qHXLLbdYM2bMsFwul+lJcJDz589br7/+upUvXz6rQoUK1rp160xPQgA4ffq09fjjj1uhoaFWo0aNrJ07d5qeBIc5cOCA1bFjR0uS1aVLF970BK+gt3A9Tuwt+D96C9dCb8EX6C1cD70FX6C3cD30FnyB3sK10FvwlgcffNAqVqyYdfz4cXoL10VvIavOnDljlStXzmrevPkVz2noLVwPvXV1QX9TcmpqqjVy5EgrLCzMqlatmpWQkGB6EhzMSf9ezp49a0VFRVm33HIL77bwQ4cPH7Y6dOhghYSEWH379rVOnjxpehIc7PDhw9Y999xjhYSEWD169LCOHz9uehL81JIlS6ySJUtaBQsWtCZMmMALxbgm/r3AG5z0/BnOx78XeAu9hcygt+AtPH9GZvDvBd7A82dkBv9e4C30FjKD3oKnkpOTrf/3//6f1apVK54/w230FrJiw4YNVrZs2azJkydblsXzZ2QO/16uFKogtnHjRlWvXl2vvvqqXn31VSUkJKhmzZqmZ8HBcuTIoaFDh2r37t0qWLCgIiIiNHDgQJ07d87WHefPn1ebNm20b98+LV++XKVLl7b1+sg6y7I0ceJEVahQQYmJiVq5cqUmTJigG264wfQ0OFjx4sW1YMECzZkzR8uWLVPlypUVExNjehb8yB9//KF+/fqpVatWqlKlipKSktS3b1+FhISYngYHa9mypfbs2aN+/frpkUce0V133aVvv/3W9Cz4EXoLmeWU3oL/oreQFfQWPEVvISvoLXiK3kJm0VvwFL2FrKC34A116tTRkiVL6C24jd5CVtSvX1+DBw/WwIEDNX/+fHoLmUJvXSkob0pOSUnRkCFDFBUVpVtuuUVJSUkaOnSosmXLZnoa/ES5cuW0cuVKjR49Wh9//LFq1qyp7du323LtCxcuqHPnztq+fbtiY2NVsWJFW64Lzx04cEANGjTQY489pkGDBikxMVGNGjUyPQt+pFOnTtqzZ4+aNm2qjh07qnv37jp58qTpWXC4hQsXqnz58lq6dKm+/PJLffnllypevLjpWfATefLk0ciRI7V+/XolJyerevXqGjdunCzLMj0NDkZvwVMmewv+i96Cp+gtZAW9BU/QW8gKegueoreQFfQWPEVvISvSe2v79u30FjKN3kJWvPDCCwoPD1eXLl3oLWQJvfW3oLsp+ZtvvlFERIQmTJigSZMmadmyZXyXWWRJaGioHnvsMSUlJal48eKqV6+e3nrrLblcLp9d8+LFi+rZs6dWr16tRYsWqUaNGj67Frzr008/VbVq1XTq1CklJCRo+PDhCgsLMz0Lfqhw4cKaMWOGlixZolWrVik8PFzr1683PQsOdPbsWfXr10/33HOP2rVrp6SkJLVp08b0LPipiIgIbdu2TYMHD9bjjz+u9u3b6+jRo6ZnwYHoLXiLid6C/6K34C30FtxFb8Gb6C24i96Ct9BbyAx6C95Cb8Fd9Ba8id6Cu7755hs1bNhQu3fvprfgEXrrL0F1U/KMGTNUu3Zt5ciRQ9u2bdNDDz1kehICwK233qoVK1borbfe0osvvqimTZvq0KFDXr+OZVnq37+/Pv/8cy1atEhRUVFevwa879SpU+rRo4d69uyprl27atOmTapatarpWQgALVu21O7duxUeHq6GDRtq4MCBSk1NNT0LDrFt2zbVrFlTc+bM0axZszR58mTlz5/f9Cz4uZw5c+q1117T+vXrtWfPHlWuXFmLFy82PQsOQm/BF+zqLfgnegu+Qm/hWugt+AK9heuht+AL9Bauhd6Cr9BbuBZ6C75Ab+F66C34QrD3VlDclPz777+rbdu2euihhzR48GDFx8erXLlypmchgISEhGjgwIHauHGjfvnlF4WHhysmJsar13j66ac1ffp0zZs3T3fddZdXHxu+8dVXX6lq1apauXKlli5dqgkTJihPnjymZyGAFClSRAsXLtTYsWM1efJkRUdH64cffjA9Cwa5XC6NHDlS9erVU4kSJZSUlKT777/f9CwEmPR3lTdp0kRt27bVoEGDlJKSYnoWDKK34Gt29Bb8D70FX6O38G/0FuxAb+Hf6C34Gr2Fq6G34Gv0Fv6N3oId6C38G70FXwvm3gr4m5I3bdqkmjVrKjExUWvXrtXw4cOVPXt207MQoGrUqKFt27apU6dO6tixo5588klduHDB48d97rnn9P777+uTTz5R69atvbAUvmRZlt555x01btxY4eHh2rlzp+6++27TsxCgQkJCNGDAACUkJCg1NVU1a9bUF198YXoWDPjjjz/Utm1bvfzyy3r99dcVFxenW265xfQsBKgCBQpo1qxZmjFjhj7++GM1aNBAP//8s+lZMIDegp181VvwL/QW7ERvIR29BTvRW0hHb8FO9BYkegv2oreQjt6CnegtpKO3YKdg7K2Avil54sSJatiwoapUqaLt27crMjLS9CQEgTx58mj8+PH67LPPNGnSJDVu3Fi//PJLlh/vv//9r0aOHKnx48erS5cuXlwKXzhz5oy6du2qYcOGacSIEVq4cKGKFi1qehaCQMWKFbVx40Z16dJFHTp00LBhw3Tx4kXTs2CTnTt3qnbt2tq5c6fWrFmjp59+WqGhAf00Dw7xwAMPaNu2bfrzzz9Vo0YNxcXFmZ4EG9FbMMHbvQX/Qm/BFHoruNFbMIXeCm70Fkygt4IbvQVT6K3gRm/BFHoruNFbMCHYeisg/9c8JSVFffr0Uf/+/fXkk09q0aJFKlSokOlZCDJdu3ZVQkKCjh8/rmrVqmn16tWZfoyxY8fqhRde0DvvvKPevXv7YCW86bvvvlO9evW0atUqLV26VEOHDlVISIjpWQgiYWFhmjBhgqZNm6YxY8aoWbNm+v33303Pgo/NmjVLkZGRKl68uBISElSvXj3TkxBkypYtq82bN6tNmzZq1aqVXnnlFblcLtOz4EP0FpzAG70F/0JvwTR6KzjRWzCN3go+9BacgN4KPvQWTKO3ghO9BdPoreBDb8EJgqW3Au6m5J9++kmRkZGaP3++vvjiC40cOZJ3UsGYChUqKD4+XnXq1FGLFi304Ycfuv17p0+froEDB+qNN97Q4MGDfbgS3vD555+rVq1ayp07t7Zv365mzZqZnoQg1rNnT61Zs0bff/+9IiIitGPHDtOT4ANpaWl64okn1L17d/Xr10+rVq1SsWLFTM9CkMqdO7emTZumt956S//973/VqVMnnT592vQs+AC9BSfxpLfgX+gtOAm9FRzoLTgJvRU86C04Cb0VPOgtOAm9FRzoLTgJvRU86C04STD0VkB9dSUkJCgiIkIpKSnaunWr2rRpY3oSoIIFC+qLL77Q888/ryeeeEKPP/74dX/kzIIFC9S7d28999xzGjZsmE1LkVWjRo3Svffeq86dO+urr77SrbfeanoSoDp16mjbtm0qVaqUoqOjtWjRItOT4EWnTp1S69atNXXqVM2ePVujRo1S9uzZTc8CNHjwYK1YsULr169XdHS0Dh8+bHoSvIjeghNlpbfgX+gtOBG9FdjoLTgVvRXY6C04Eb0V+OgtOBG9FdjoLTgVvRXY6C04UaD3VsDclBwbG6vGjRurSpUqWr9+vcqWLWt6EnBJSEiIXnrpJc2bN09TpkxR69atM3x3VVxcnLp166b+/fvrtddes3kpMuPixYt6/PHHNWTIEL344ouaNGmSwsLCTM8CLilatKji4uL0wAMPqEOHDho7dqzpSfCCI0eO6K677tKuXbu0Zs0adenSxfQk4DINGzbU5s2blZKSojp16mj79u2mJ8EL6C04WWZ6C/6D3oLT0VuBid6C09FbgYnegpPRW4GJ3oLT0VuBid6C09FbgYnegpMFcm8FxE3JEydOVNu2bdWpUyctXrxYBQoUMD0JuKp7771Xq1ev1o4dOxQVFaVDhw5d9uurVq1S+/bt1bVrV73//vuGVsIdZ8+e1T333KPJkydr1qxZeuWVV0xPAq4qe/bsGjdunEaNGqWBAwdq4MCBcrlcpmchi3bv3q2IiAilpqZq06ZNqlWrlulJwFWVKlVKGzZsUPny5dWwYUMtXrzY9CR4gN6Cv7heb8F/0FvwF/RWYKG34C/orcBCb8Ff0FuBg96Cv6C3Agu9BX9BbwUWegv+IhB7y69vSna5XBo0aJD69++vESNGaMqUKcqRI4fpWcA11a1bV/Hx8frzzz8VFRWlpKQkSdKmTZvUoUMHtWrVSpMnT1ZoqF9/eQa0I0eOKDo6Wps3b9aaNWvUtWtX05OA6xo4cKA++eQTTZgwQZ07d9b58+dNT0ImLVmyRPXr11f58uW1YcMGlSxZ0vQk4JoKFSqk2NhY3XvvverQoYMmTZpkehIyid6CP8qot+A/6C34I3rL/9Fb8Df0lv+jt+CP6C3/R2/BH9Fb/o/egr+ht/wfvQV/FGi95bd3PaalpemBBx7QhAkTNHv2bA0dOtT0JMBtt99+u+Lj43XbbbcpOjpas2bNUuvWrVW/fn3NmjVL2bNnNz0RGThw4ICioqJ07tw5bdy4UXXr1jU9CXBbt27dFBcXp9WrV6tly5YB82MfgsGsWbPUvn17derUSUuWLOFdnPAbOXPm1LRp0/T888+rX79+euONN0xPgpvoLfizf/fWli1bTE+Cm+gt+DN6y3/RW/BX9Jb/orfgz+gt/0VvwZ/RW/6L3oK/orf8F70FfxZIvRViWZZlekRmpaam6v7779eyZcv0v//9T82aNTM9CciSlJQUde3aVV9++aWqVq2q+Ph45cqVy/QsZGDfvn1q2rSpihYtqmXLlqlo0aKmJwFZ8s0336hZs2YqVqyYYmNjVaRIEdOTcA2TJk1S//799dRTT+nNN99USEiI6UlAlkyYMEGPPPKIhgwZopEjR5qeg2ugtxAo0ntr+fLl+vzzz9WkSRPTk3AN9BYCBb3lX+gtBAp6y3/QWwgU9JZ/obcQKOgt/0JvIVDQW/6D3kKgCITe8rubks+dO6d7771XmzZtuvRjHgB/duHCBXXq1EnLly9XTEyMWrRoYXoSrmLPnj1q2rSpSpQoodjYWN14442mJwEeOXDggJo2bapcuXJp+fLlKl68uOlJuIqPPvpIjz/+OJGLgDFr1iz95z//UZ8+fTR27FiFhvrtD24JWPQWAs2FCxfUvXt3LV68mN5yMHoLgYbe8g/0FgINvQCL8w4AACAASURBVOV89BYCDb3lH+gtBBp6yz/QWwg09Jbz0VsINP7eW351Sp49e1Zt27bVli1bFBcXxwGCgJAjRw7FxMSoS5cuat++vWJiYkxPwr9s27ZN0dHRKleunFauXMkLNggIpUuX1urVq3XhwgU1btxYBw8eND0J//Lmm2/q0Ucf1auvvsoLNggY3bp1U0xMjKZOnar//Oc/SktLMz0J/0BvIRDlyJFDn332Gb3lYPQWAhG95Xz0FgIRveVs9BYCEb3lfPQWAhG95Xz0FgIRveVs9BYCkb/3lt/clHz69Gk1bdpUe/bs0bp161SnTh3TkwCvyZYtmyZPnqyHH35YXbt21bx580xPwv/ZtGmTGjdurLp16yo2Nlb58+c3PQnwmttuu01r165V9uzZ1bBhQ164cZBXXnlFzz77rD788EO98MILpucAXtWuXTvFxMRo/vz5evDBB+VyuUxPgugtBDZ6y7noLQQyesu56C0EMnrLmegtBDJ6y7noLQQyesu56C0EMnrLmegtBDJ/7q0Qy7Is0yOu59y5c2rZsqW+/fZbrV27VnfccYfpSYBPWJalQYMGady4cVqwYIHatGljelJQ+/rrr9WoUSNFRUUpJiZGOXPmND0J8Iljx46pUaNGSk1N1bp163TzzTebnhTU3nrrLQ0bNkwTJ05U7969Tc8BfGb58uVq166devTooQkTJigkJMT0pKBFbyFY0FvOQm8hWNBbzkJvIVjQW85BbyFY0FvOQm8hWNBbzkJvIVjQW85BbyFY+GNvOf6m5NTUVN1zzz2Kj4/XqlWrVL16ddOTAJ+yLEv9+/fXtGnT9Pnnn6tFixamJwWlb7/9VtHR0apcubIWLVqkXLlymZ4E+NTvv/+uhg0bKkeOHFq9erUKFy5selJQ+uijj/Too4/q7bff1tNPP216DuBzy5YtU/v27dW3b1+NGTPG9JygRG8h2NBbzkBvIdjQW85AbyHY0Fvm0VsINvSWM9BbCDb0ljPQWwg29JZ59BaCjb/1lqNvSr548aLuv/9+LVu2TCtXrlStWrVMTwJs4XK51KNHDy1cuFCxsbFq0KCB6UlB5YcfflB0dLRKliypuLg45cuXz/QkwBaHDh1SgwYNVLBgQa1evVoFCxY0PSmozJgxQ7169dKIESP07LPPmp4D2GbBggXq0qWLnn32WQ0fPtz0nKBCbyFY0Vtm0VsIVvSWWfQWghW9ZQ69hWBFb5lFbyFY0Vtm0VsIVvSWOfQWgpU/9ZZjb0pO/0v8/PPPtWzZMkVGRpqeBNjqwoUL6tSpk9asWaNVq1apRo0apicFhYMHDyo6OlpFihTRihUrVKBAAdOTAFt9//33io6OVtmyZRUbG6s8efKYnhQU5s6dq27dumnYsGEaMWKE6TmA7T7++GM9/PDDeuONNzR06FDTc4ICvYVgR2+ZQW8h2NFbZtBbCHb0lv3oLQQ7essMegvBjt4yg95CsKO37EdvIdj5S2859qbkJ598UuPHj9eiRYvUpEkT03MAI1JSUtS2bVvt2rVLmzZtUqlSpUxPCmgnT55UZGSkQkJCtGbNGn68D4JWUlKS7rrrLkVGRiomJkbZsmUzPSmgrVmzRnfffbcGDBig9957z/QcwJixY8fqiSee0LRp09SzZ0/TcwIevQXQW3ajt4C/0Fv2oreAv9Bb9qK3AHrLbvQW8Bd6y170FvAXeste9BbgH73lyJuSJ06cqP79+2vGjBl64IEHTM8BjDp9+rSio6OVkpKiDRs2qFChQqYnBaQLFy6odevWSkpK0saNG3XbbbeZngQYtWXLFjVq1Eh9+/bV6NGjTc8JWHv37lX9+vXVtGlTzZ49W6GhoaYnAUY9++yzGjVqlJYsWaKmTZuanhOw6C3gb/SWPegt4HL0lj3oLeBy9JY96C3gb/SWPegt4HL0lj3oLeBy9JY96C3gb07vLcfdlLxkyRK1b99er732moYNG2Z6DuAIR44cUUREhEqXLq24uDiFhYWZnhRw+vfvr5kzZ+qrr75StWrVTM8BHGHevHnq2rWrRo8erSeeeML0nIBz7Ngx1atXT4UKFdKaNWv4UWKAJMuy1KNHD3355Zdav369qlSpYnpSwKG3gCvRW75HbwFXord8i94CrkRv+R69BVyJ3vI9egu4Er3lW/QWcCV6y/foLeBKTu4tR92UvH37djVs2FCdO3fWlClTTM8BHCUpKUmRkZFq2bKlZs2apZCQENOTAsbw4cM1fPhwLViwQO3atTM9B3CUkSNH6vnnn9f8+fN1zz33mJ4TMM6fP68mTZro119/1aZNm3TTTTeZngQ4xp9//qmmTZvq8OHD2rhxo4oVK2Z6UsCgt4CM0Vu+Q28BGaO3fIPeAjJGb/kOvQVkjN7yHXoLyBi95Rv0FpAxest36C0gY07tLcfclPzzzz8rIiJCVapU0aJFi5QjRw7TkwDHiY2NVdu2bfXCCy/o5ZdfNj0nIMycOVM9evTQhx9+qAEDBpieAzhS3759NWvWLK1du1Y1a9Y0PcfvuVwudezYUevWrVN8fLzuuOMO05MAx/nnd1pYu3atcufObXqS36O3gOujt7yP3gKuj97yLnoLuD56y/voLeD66C3vo7eA66O3vIveAq6P3vI+egu4Pif2liNuSv7zzz/VoEEDnT9/XvHx8brhhhtMTwIca9y4cXr00Ue1cOFC3vXsoR07digyMlL9+/fXu+++a3oO4FhpaWlq1aqV9u3bp4SEBBUtWtT0JL82fPhwjRgxQitXrlSDBg1MzwEc67vvvlPdunXVtm1bTZ8+3fQcv0ZvAe6jt7yH3gLcQ295F70FuIfe8h56C3AfveU99BbgHnrLu+gtwD30lvfQW4D7nNZbjrgpuXfv3po7d642b96sihUrmp4DOF6fPn00Z84cvmY88Mcff6hWrVoqWbKk4uLilD17dtOTAEdLTk5W7dq1VapUKS1btoyvmSxavny5WrZsqffff1+PPvqo6TmA4/E14x30FpA59Jbn6C0gc+gt7+C5I5A5fM14B70FZA695Tl6C8gcess7eO4IZA5fM95BbwGZ46TeMn5T8sSJE9W/f3/Nnz9f9957r8kpgN9ISUlRgwYNdPr0aW3evJl3A2WSy+VSmzZtlJiYqG3btvGuWMBNO3fuVP369fXYY4/pzTffND3H7/z000+qWbOmWrRooU8//dT0HMBv8N0XPENvAZlHb3mG3gKyht7yDL0FZA295Rl6C8g8essz9BaQNfSWZ+gtIGvoLc/QW0DmOam3jN6UvGXLFkVHR+uZZ57R8OHDTc0A/NLBgwdVs2ZNNW7cWLNnzzY9x6+88MILeuedd7Ru3TrVqVPH9BzAr3z66afq2bOn5syZo06dOpme4zfOnz+vqKgoXbx4UfHx8cqTJ4/pSYDfsCxL9913nzZu3Kht27apePHipif5DXoLyDp6K+voLSDr6K2sobeArKO3so7eArKO3so6egvIOnora+gtIOvorayjt4Csc0pvGbsp+dixY6pRo4YqVaqkxYsXKzQ01MQMwK+tWLFCLVq00DvvvKNBgwaZnuMXvvzyS7Vv314TJkxQnz59TM8B/NKAAQM0c+ZMbd26VeXLlzc9xy/07NlTixcvVkJCgkqXLm16DuB3Tpw4odq1a6t48eJauXIlP2LPDfQW4Dl6K/PoLcBz9Fbm0VuAZ+itzKO3AM/RW5lHbwGeo7cyj94CPENvZR69BXjOCb1l7KbkDh06aMeOHdqxY4duvPFGExOAgPD666/r1Vdf1aZNm1S9enXTcxztyJEjCg8PV9u2bTV16lTTcwC/lZqaqqioKKWlpWnTpk3KmTOn6UmOlv7u+0WLFqlVq1am5wB+a9euXapbt66GDRuml19+2fQcx6O3AO+gt9xHbwHeQW9lDr0FeAe9lTn0FuAd9Jb76C3AO+itzKG3AO+gtzKH3gK8w3RvGbkpefz48Xr00Ue1YsUKNWrUyO7LAwHF5XKpWbNm+uWXX5SQkMCPTMmAZVlq3bq1vv32W+3YsUP58+c3PQnwaz/88IOqV6+uAQMG6M033zQ9x7EOHjyo8PBw9ezZU++9957pOYDfGzNmjJ566imtW7dO9erVMz3HsegtwHvoLffQW4B30VvuobcA76K33ENvAd5Db7mH3gK8i95yD70FeBe95R56C/Ae071l+03J33//vapXr66BAwdqxIgRdl4aCFiHDh1SeHi4unbtqg8//ND0HEcaNWqUhg0bpvXr16tu3bqm5wABYcqUKerbt6/i4uLUpEkT03Mcx+VyqXHjxjp69KgSEhKUO3du05MAv2dZltq1a6ekpCR9/fXXuuGGG0xPchx6C/A+euv66C3A++ita6O3AO+jt66P3gK8j966PnoL8D5669roLcD76K3ro7cA7zPZW7belHzhwgVFRUXp4sWLio+P58dhAF4UExOjTp06aeHChWrXrp3pOY6SmJio2rVr68UXX9Rzzz1neg4QULp27ar169dr586dKly4sOk5jjJ8+HC9/vrr2rx5s8LDw03PAQLG77//rvDwcN19992aNm2a6TmOQm8BvkNvZYzeAnyH3soYvQX4Br2VMXoL8B16K2P0FuA79FbG6C3AN+itjNFbgO+Y6i1bb0oeNmyYPvzwQ23fvl3lypWz67JA0OjVq5cWL16sXbt2qVixYqbnOML58+dVu3ZtFS5cWKtWrVK2bNlMTwICSnJyssLDw1W3bl3Nnz/f9BzH2Lx5s6KiovTOO+9o4MCBpucAAWfx4sVq27atPvvsM3Xp0sX0HMegtwDforeuRG8BvkVvXR29BfgWvXV19BbgW/TWlegtwLforaujtwDforeujt4CfMtEb9l2U/KWLVtUv359jRs3Tn369LHjkkDQOX36tKpVq6bq1asTT/9n2LBhGjdunHbt2qWSJUuangMEpFWrVqlp06bE0/9JSUlR9erVVaJECS1btkwhISGmJwEB6ZFHHtG8efOUlJSkm266yfQc4+gtwPforSvRW4Dv0VuXo7cAe9Bbl6O3AN+jt65EbwG+R29djt4C7EFvXY7eAnzPRG/ZclNyWlqa6tSpowIFCmjVqlU8eQF8aPXq1WrSpIliYmJ0zz33mJ5j1M6dO1W7dm2NGTNG/fv3Nz0HCGh9+/bV//73P33zzTcqUqSI6TlGvfTSSxo1apR2796tMmXKmJ4DBKyzZ8+qSpUqqlevnmbOnGl6jlH0FmAfeutv9BZgH3rrb/QWYA9662/0FmAfeutv9BZgH3rrb/QWYA9662/0FmAfu3vLlpuSX3vtNb3xxhvatWuXypYt6+vLAUHvwQcfVFxcnJKSklSoUCHTc4xIS0tTRESEcubMqfXr1ys0NNT0JCCgnTx5UpUqVVLz5s01depU03OMSUxMVM2aNfXWW2/xY60AGyxdulStWrXS559/rnbt2pmeYwy9BdiL3qK3ALvRW3+htwB70Vt/obcAe9Fb9BZgN3rrL/QWYC966y/0FmAvO3vL5zcl79u3T9WqVdPw4cM1ZMgQX14KwP9JTk7WnXfeqQ4dOmj8+PGm5xjx1ltv6aWXXtL27dt15513mp4DBIWYmBh17NhRcXFxatasmek5tnO5XGrQoIHS0tIUHx+vbNmymZ4EBIX7779f8fHxSkxMVP78+U3PsR29BdiP3qK3ABPoLXoLMIHeorcAu9Fb9BZgAr1FbwEm0Fv0FmA3O3vLpzclu1wuNWzYUKdPn9bWrVuVI0cOX10KwL989tln6t69u1asWKHGjRubnmOrAwcOqEqVKnr22Wf1/PPPm54DBJV7771XO3bsUGJiovLmzWt6jq3ee+89DRkyRFu3blW1atVMzwGCxrFjx3TnnXfq/vvv1/vvv296jq3oLcAceoveAkygt+gtwG70Fr0FmEBv0VuACfQWvQXYjd6itwAT7Ootn96UPHnyZA0YMIAnL4Ahbdq00f79+7Vz586g+h/xFi1a6MiRI9q2bVtQ/bkBJzhy5IjuvPNOPfLII3r99ddNz7HNkSNHVL58eT355JMaPny46TlA0Jk+fboeeughbdmyRTVr1jQ9xzb0FmAWvUVvAXajt+gtwAR6i94CTKC36C3AbvQWvQWYQG/RW4AJdvSWz25KPn36tO644w517tw56N7RATjFgQMHdOedd+qNN97QoEGDTM+xxZdffql27dpp7dq1io6ONj0HCErvvfeehg4dqsTERJUrV870HFv07NlTa9as0d69e5UnTx7Tc4CgFB0drbS0NG3YsEEhISGm5/gcvQWYR2/RW4AJ9Ba9BZhAbwGwG71FbwEm0Fv0FmACvQXAbnb0ls9uSh4yZIimTp2qb7/9VoULF/bFJQC44bnnntO4ceP03XffqUiRIqbn+FRqaqqqVKmi6tWra/bs2abnAEErLS1N1apVU/ny5RUTE2N6js9t27ZNderU0Zw5c9SxY0fTc4CgtWPHDtWqVUszZ85U165dTc/xOXoLcAZ6C4Dd6C0AJtBbAEygtwDYjd4CYAK9BcAEX/eWT25K/uGHH1SpUiW9++67euSRR7z98AAy4cyZMypfvrzat2+vjz76yPQcnxo1apRefPFF7dmzR6VKlTI9BwhqK1asULNmzRQXF6dmzZqZnuMzlmUpOjpalmXpq6++Cop3rwJO9tBDD2n58uXau3ev8ubNa3qOz9BbgHPQWwBMoLcAmEBvAbAbvQXABHoLgAn0FgC7+bq3fHJTcvv27fX9999r586dyp49u7cfHkAmTZs2Tb1799b27dtVtWpV03N84ujRo7rjjjv0+OOPa/jw4abnAJDUunVr/fTTT/r6668D9vnAzJkz1bNnT23ZskU1a9Y0PQcIer/99pvKly+vwYMH66WXXjI9x2foLcBZ6C0AJtBbAOxGbwEwgd4CYAK9BcBu9BYAE3zZW16/KXnlypVq2rRpwL9zDPAnLpdLERERuuGGG7RixQrTc3yif//+WrRokfbt2xfQ7xwD/MnevXtVtWpVjR07Vn379jU9x+vOnTun8uXLq0WLFpo0aZLpOQD+z5tvvqnhw4dr3759KlGihOk5XkdvAc5DbwEwgd4CYAK9BcBu9BYAE+gtACbQWwDs5sve8upNyZZlqW7durrpppu0aNEibz0sAC9Yv369GjRooOXLl6tp06am53jV999/r4oVK2rSpEl68MEHTc8B8A9PPPGEFixYoO+++065c+c2Pcer3nnnHb366qv6/vvvdfPNN5ueA+D/pKSkqEKFCrr77rs1fvx403O8it4CnIveAmACvQXAbvQWABPoLQAm0FsA7EZvATDBV73l1ZuSv/jiC3Xo0EGbN29W7dq1vfWwALykRYsWOnXqlOLj401P8aoePXpo8+bN2rNnDz/iAXCYX3/9Vbfffrv++9//atCgQabneM2ZM2d0++236+GHH9brr79ueg6Af5kyZYoGDBigvXv3qkyZMqbneA29BTgbvQXAbvQWABPoLQAm0FsA7EZvATCB3gJggi96y2s3JVuWpRo1aqhMmTKKiYnxxkMC8LJt27apdu3aWrx4sVq2bGl6jld8++23qlSpkj755BN17drV9BwAVzFkyBBNnz5d+/fvV758+UzP8YoRI0bo7bff1oEDB3TjjTeangPgXy5evKhKlSqpfv36mjp1quk5XkFvAc5HbwEwgd4CYDd6C4AJ9BYAE+gtAHajtwCY4Ive8tpNyfPmzVPXrl21fft2hYeHe+MhAfhAu3btdOTIEW3dulUhISGm53isc+fO2rNnj3bt2qXQ0FDTcwBcxbFjx1SmTBm98MILeuaZZ0zP8djJkydVpkwZPf7443rllVdMzwGQgU8++US9evVSUlKSypcvb3qOx+gtwD/QWwDsRm8BMIHeAmACvQXAbvQWABPoLQAmeLu3vHJTssvlUrVq1VSpUiV99tlnHo8C4Du7d+9WtWrVtGDBArVv3970HI8kJiYqPDxc8+bN07333mt6DoBreP755zVhwgTt379fN9xwg+k5HnnppZf0wQcfaP/+/SpUqJDpOQAycPHiRVWpUkU1atTQp59+anqOR+gtwH/QWwBMoLcA2I3eAmACvQXABHoLgN3oLQAmeLu3vHJT8uzZs/XAAw8oMTFRFSpU8HgUAN/q2LGjfvjhB23fvt2v301+33336cCBA9q2bZtf/zmAYJCcnKwyZcromWee0XPPPWd6TpadOHFCJUuW1NChQ/36zwEEizlz5qhbt25KSkry606htwD/Qm8BsBu9BcAEeguACfQWALvRWwBMoLcAmODN3vLKTcl16tTR7bffzrsaAD+xa9cuVatWTbGxsWrevLnpOVny3XffqUKFCpo7d67uu+8+03MAuOG5557TlClT9NNPPylXrlym52TJyJEj9cYbb+jnn39WgQIFTM8BcB2WZalSpUqKiorSxIkTTc/JMnoL8C/0FgAT6C0AdqO3AJhAbwEwgd4CYDd6C4AJ3uytUE/HrF69Wlu3btWgQYM8fSgANqlataoaN26sUaNGmZ6SZaNHj1bJkiXVoUMH01MAuGngwIE6deqUZs6caXpKlly4cEEffvih+vXrxws2gJ8ICQnRE088oRkzZujXX381PSdL6C3A/9BbAEygtwDYjd4CYAK9BcAEeguA3egtACZ4s7c8vil51KhRio6OVt26dT0eA8A+Tz31lOLi4vT111+bnpJpycnJmjFjhgYPHqxs2bKZngPATTfffLPuv/9+vf3223K5XKbnZNpnn32m3377TY899pjpKQAy4cEHH1TBggU1btw401OyhN4C/BO9BcBu9BYAE+gtACbQWwDsRm8BMIHeAmCCt3rLo5uS9+3bp6VLl+qpp57yaAQA+7Vs2VLh4eF67733TE/JtI8++kg5c+bUgw8+aHoKgEwaPHiwvv32Wy1dutT0lEwbPXq0unTpottuu830FACZkCtXLvXv319jx47V2bNnTc/JFHoL8F/0FgAT6C0AdqO3AJhAbwEwgd4CYDd6C4AJ3uotj25KHjVqlMqWLas2bdp4NAKAGQMHDtSsWbN08OBB01PclpKSoo8++kgDBgxQvnz5TM8BkEmVK1dWixYt/O7H6y1fvlxff/21Bg8ebHoKgCx49NFHdf78eU2fPt30lEyhtwD/Rm8BsBu9BcAEeguACfQWALvRWwBMoLcAmOCN3sryTcnJycn65JNP9OSTTyo01KN7mwEY0q1bNxUpUkQTJkwwPcVtc+bM0fHjx/Xoo4+angIgi5566imtXr1aO3fuND3FbWPGjFGjRo1UvXp101MAZEHRokXVs2dPjRkzRpZlmZ7jFnoL8H/0FgAT6C0AdqO3AJhAbwEwgd4CYDd6C4AJ3uitLH/1z5gxQ9mzZ1f37t2zfHEAZoWFhemhhx7SlClTdOHCBdNz3DJx4kR16NBBxYsXNz0FQBY1adJEFStW1OTJk01PccuhQ4e0dOlSPfLII6anAPDAI488on379umrr74yPcUt9Bbg/+gtACbQWwBMoLcA2I3eAmACvQXABHoLgN280VtZvil58uTJ6tatm/Lnz5/VhwDgAA8//LB+//13LV261PSU69q7d6/i4+PVp08f01MAeKhXr1765JNPdO7cOdNTrmvKlCkqVKiQ2rZta3oKAA9UrVpVtWvX1qRJk0xPcQu9BQQGeguACfQWALvRWwBMoLcAmEBvAbAbvQXABE97K0s3JcfHxyspKYlwAgJA6dKl1aRJE794AjNhwgSVKlVKjRs3Nj0FgId69eqlP//8UzExMaanXJPL5dLHH3+sXr16KSwszPQcAB7q3bu35s+fr+TkZNNTroneAgIHvQXABHoLgAn0FgC70VsATKC3AJhAbwGwm6e9laWbkidNmqSqVauqVq1aWbooAGfp3bu3li5dqoMHD5qekqHU1FTNnDlTvXv3Vmholr/JOwCHKFKkiNq0aeP4H3G1bNky/fTTT+rVq5fpKQC8oFu3bsqRI4dmzpxpeso10VtAYKG3ANiN3gJgAr0FwAR6C4Dd6C0AJtBbAEzwpLcyXT4nT57UvHnz1K9fv0xfDIAzdejQQYULF9b06dNNT8lQTEyM/vjjDz344IOmpwDwkj59+mjdunX65ptvTE/J0KRJk9SwYUNVrFjR9BQAXpAvXz517tzZ0d9Bh94CAg+9BcAEeguA3egtACbQWwBMoLcA2I3eAmCCJ72V6ZuS586dK5fLpe7du2f6YgCcKWfOnOrRo4c+/vhjWZZles5Vffzxx2rdurWKFy9uegoAL2nWrJlKlizp2BeMjx07pkWLFql3796mpwDwot69e2v37t3atm2b6SlXRW8BgYfeAmACvQXABHoLgN3oLQAm0FsATKC3ANjNk97K9E3Js2fPVps2bVSgQIHM/lYADtatWzft379fCQkJpqdc4ejRo1q9ejVPXoAAExoaqq5du2ru3LmOfME4JiZGOXLkUIcOHUxPAeBFERERKlu2rObMmWN6ylXRW0BgorcA2I3eAmACvQXABHoLgN3oLQAm0FsATMhqb2XqpuSjR49q3bp16tKlS6YuAsD5atSooXLlyjnyCcz8+fOVM2dOtWrVyvQUAF7WpUsXHThwQFu3bjU95Qpz5sxRmzZtlC9fPtNTAHhZp06dNHv2bMe9YExvAYGL3gJgAr0FwAR6C4Dd6C0AJtBbAEygtwDYLau9lambkufOnauwsDC1aNEiUxcB4B86d+7syHd0zp07V+3atVPevHlNTwHgZdWrV1e5cuU0d+5c01Mu89tvv2ndunXq3Lmz6SkAfKBz5846ePCgNm/ebHrKZegtILDRWwDsRm8BMIHeAmACvQXAbvQWABPoLQAmZKW3Mn1TMuEEBK70JzCbNm0yPeWSX3/9VV999RXhBAQwJ75gPH/+fOXOnZvvYAEEqGrVqqlixYqOe8GY3gICG70FORytWwAAIABJREFUwAR6C4Dd6C0AJtBbAEygtwDYjd4CYEJWesvtm5J/+eUXbdiwgXACAljVqlVVsWJFR/2Iq3nz5ilPnjy8owoIYOlPYDZu3Gh6yiVz5sxRu3btlDt3btNTAPhIx44dNW/ePLlcLtNTJNFbQDCgtwCYQG8BMIHeAmA3eguACfQWABPoLQB2y0pvuX1T8oIFCwgnIAh06tRJMTExjnlHZ0xMjNq2bUs4AQGsatWqqlChgubPn296iqS/voPFhg0b1KlTJ9NTAPhQ586ddejQIcd8Bx16CwgO9BYAu9FbAEygtwCYQG8BsBu9BcAEeguACZntLbdvSl6yZImaN2+uXLlyZXkcAOdr27atDh06pN27d5ueolOnTik+Pl7t2rUzPQWAj7Vt21ZLly41PUOSFBsbqxw5cqhp06ampwDwocqVK6tMmTKKjY01PUUSvQUEC3oLgAn0FgC70VsATKC3AJhAbwGwG70FwITM9pZbNyX/+eefWrNmDe9qAIJAjRo1dNNNNzniCczy5cvlcrkIJyAItGjRQnv37tX+/ftNT1FsbKwaNmyofPnymZ4CwMfuvvtuR7xgTG8BwYPeAmACvQXABHoLgN3oLQAm0FsATKC3ANgts73l1k3Ja9eu1blz59S8eXOPxgFwvtDQUDVv3twRT2CWLl2qOnXqqHDhwqanAPCxqKgo5c+fX8uWLTO64+LFi1qxYgXhBASJFi1aaPv27frtt9+M7qC3gOBBbwEwgd4CYAK9BcBu9BYAE+gtACbQWwDsltnecuum5NjYWFWpUkW33XabR+MA+IeWLVtqw4YNOnnypNEdcXFxatmypdENAOyRM2dONW7c2Ph3sdi8ebOOHz/O2QMEiSZNmihHjhyKi4szuoPeAoILvQXAbvQWABPoLQAm0FsA7EZvATCB3gJgQmZ6y62bkpcuXcqTFyCING/eXC6XS6tXrza2Yffu3Tp48CBnDxBEWrZsqZUrVyolJcXYhtjYWJUqVUoVKlQwtgGAffLmzauoqCjjLxjTW0BwobcAmEBvAbAbvQXABHoLgAn0FgC70VsATMhMb133puQff/xR+/bt48c8AEGkSJEiqlWrltEfcRUbG6ubbrpJNWrUMLYBgL1atGihs2fPav369cY2LFu2jOc8QJBp2bKl4uLi5HK5jFyf3gKCD70FwAR6C4AJ9BYAu9FbAEygtwCYQG8BsFtmeuu6NyWvXbtWYWFhql+/vlfGAfAPjRs31rp164xdf+3atbrrrrsUGurWN3QHEABKliyp22+/3djZc+bMGW3fvl1NmjQxcn0AZjRu3FjHjh3Tnj17jFyf3gKCE70FwG70FgAT6C0AJtBbAOxGbwEwgd4CYIK7vXXdGtqwYYNq166tsLAwrwwD4B8iIyO1b98+HT161PZrW5aljRs3KjIy0vZrAzArKipKGzZsMHLtTZs2KS0tTfXq1TNyfQBmVK1aVQUKFDB29tBbQHCitwCYQG8BsBu9BcAEeguACfQWALvRWwBMcLe33LopmXACgk9kZKRCQkK0ceNG26/9zTffKDk5mbMHCEKRkZGXXjyx24YNG1S6dGndcssttl8bgDnZsmVTnTp1jL5ow3MeIPjQWwBMoLcA2I3eAmACvQXABHoLgN3oLQAmuNtb17wp+cSJE9q7dy+HCBCEChYsqIoVKxp5ArNhwwblzZtX4eHhtl8bgFmRkZE6e/asdu7cafu14+Pjec4DBKnIyEitX7/e9uvSW0DworcAmEBvATCB3gJgN3oLgAn0FgAT6C0AdnO3t655U3J8fLwsy1JERIRXxwHwD5GRkUZetImPj1fdunWVPXt2268NwKyKFSvqxhtvtP3scblc2rx5M+EEBKnIyEgdOHBAhw8ftvW69BYQ3OgtAHajtwCYQG8BMIHeAmA3eguACfQWABPc6a1r3pS8YcMGlS9fXkWLFvXqMAD+ITIyUgkJCfrzzz9tvS4/5gEIXiEhIapfv77tL9rs3r1bJ0+e5OwBglRERISyZ89u+4/1pLeA4EZvAbAbvQXABHoLgAn0FgC70VsATKC3AJjgTm9d86bkrVu3qm7dul4fBsA/REREKCUlRbt377btmidOnND333/P2QMEsYiICG3dutXWa27dulX58uVTpUqVbL0uAGdI//rfsmWLrdelt4DgRm8BMIHeAmA3eguACfQWABPoLQB2o7cAmOBOb13zpuRdu3YpPDzc68MA+IeyZcsqb9682rVrl23X3LVrlyzL4uwBgljVqlX1448/6tSpU7Zdc9euXapSpYpCQ6/51AhAAKtataqt/0eVRG8BwY7eAmACvQXABHoLgN3oLQAm0FsATKC3ANjNnd7K8JnJ0aNH9dtvv6lq1ao+GQfA+UJDQ1WpUiVbn8Ds2rVLN954o0qUKGHbNQE4S5UqVWRZlu1nD895gOBWpUoVW/+PKnoLAL0FwAR6C4AJ9BYAu9FbAEygtwCYQG8BsJs7vZXhTclff/21pL8OLwDBy+4nMLt37+bJCxDkSpYsqYIFC9r6ok1iYiLPeYAgV7VqVR05ckRHjx615Xr0FgCJ3gJgP3oLgAn0FgAT6C0AdqO3AJhAbwEw4Xq9leFNybt27VKxYsV00003+WQYAP9g94s26T9iBkDwCgkJsfW7WBw8eFDHjx/nBWMgyKU//0hMTLTlevQWAIneAmA/eguACfQWABPoLQB2o7cAmEBvATAhyzcl825OANJf76o6fvy4jhw54vNruVwu3s0JQJK9LxinX6dy5cq2XA+AMxUvXlxFixa17eyhtwBI9BYAM+gtAHajtwCYQG8BMIHeAmA3eguACdfrrQxvSk5MTOTJCwBb31X1448/6syZM7xoA0BVqlSx7d2ciYmJuvXWW1WoUCFbrgfAuSpXrmzr2UNvAaC3AJhAbwEwgd4CYDd6C4AJ9BYAE+gtAHa7Xm9leFPyDz/8oHLlyvlmFQC/UaRIERUsWFD79+/3+bV++OEHSeLsAaCyZcvqxIkTSk5O9vm1eM4DIF3ZsmVtec4jcfYA+Au9BcAEeguACfQWALvRWwBMoLcAmEBvAbDb9Xrrqjcl//HHHzpx4oRKlSrly20A/ESpUqV04MABn1/nwIEDyp8/vwoXLuzzawFwttKlS0uSLWfPjz/+yHMeAJLse85DbwH4J3oLgN3oLQAm0FsATKC3ANiN3gJgAr0FwIRrnT1XvSn5xx9/lPT3EyYAwa106dKXzgVf+umnn3jyAkCSVLJkSYWGhvKiDQBblS5dWgcPHlRaWppPr0NvAfgneguA3egtACbQWwBMoLcA2I3eAmACvQXAhGv1VoY3JYeEhKhkyZK+3AXAT5QuXdq2d5Lz5AWAJOXMmVPFixf3+QvGLpdLP//8My/aAJD017s509LSdPjwYZ9eh94C8E/0FgC70VsATKC3AJhAbwGwG70FwAR6C4AJ1+qtq96UfODAARUrVky5cuXy6TAA/sGuH/XAuzkB/JMdZ8+RI0eUkpLCC8YAJNn3o/XoLQD/RG8BMIHeAmA3eguACfQWABPoLQB2o7cAmHCt5zxXvSn5p59+4skLgEtKly6tY8eO6fTp0z69Di/aAPgnO360Xvrjc/YAkKSbb75ZefLk8fmLNvQWgH+itwCYQG8BsBu9BcAEeguACfQWALvRWwBMuFZvXfWm5EOHDqlEiRI+HwbAP9x2222S/jobfCUtLU2//vqrbr31Vp9dA4B/ue2223Tw4EGfXuPw4cPKli2bihcv7tPrAPAPISEhuvXWW336nEeitwBcjt4CYAK9BcBu9BYAE+gtACbQWwDsRm8BMOFavXXVm5KPHj2qm266yberAPiNIkWKSJKOHz/us2scP35clmWpaNGiPrsGAP9SpEgRHTt2zKfXOHr0qAoXLqzQ0Ks+JQIQhOw6e+gtAOnoLQAm0FsATKC3ANiN3gJgAr0FwAR6C4DdrtVbV32Gcvz4cRUuXNi3qwD4DbtetPnntQCgSJEiPj13JJ7zALhS4cKFOXsA2IreAmACvQXABHoLgN3oLQAm0FsATKC3ANiNm5IBeCRnzpzKnz+/T99Vlf7YnD0A0hUuXFipqak6ffq0z65x/PhxXiwGcBleMAZgN3oLgAn0FgAT6C0AdqO3AJhAbwEwgd4CYLdr9dYVNyVblqXk5GSewAC4jK+fwBw/flwhISG68cYbfXYNAP7Fju9icezYMZ7zALiMr3+8Fb0F4GroLQB2o7cAmEBvATCB3gJgN3oLgAn0FgATMuqtK25KPnnypC5cuMAhAuAyvv5RD8eOHdMNN9ygnDlz+uwaAPxL+nMRX8YT7+YE8G++fs5DbwG4GnoLgN3oLQAm0FsATKC3ANiN3gJgAr0FwISMzp4rbkpOTk6WJN7NmUUJCQkKCQnRK6+8YnpKQIuNjVVISIjee+8901MkOW+PL/j6XVXJycmEUxZx7tjDaV/nTtvjC+lngi/jKTk5mec8WcTZYw+nfa07bY8v+PpFG3rLM5w99nDa17rT9vgCveVcnDv2cNrXudP2+AK95WycPfZw2te60/b4Ar3lbJw99nDa17rT9vgCveVcnDv2cNrXudP2+AK95WycPfZw2te60/b4Ar3lbJw99nDa17rT9vhCRr11xU3JZ86ckSTlz5/f96sA+I18+fLp7NmzPnv8M2fOcO4AuEy+fPkkyadnz+nTpzl7AFwmX758l5rIF+gtAFdDbwGwG70FwAR6C4AJ9BYAu9FbAEygtwCYkFFvZf/3B1JSUiSJHzGTRbVq1ZJlWaZnAF6XM2dOnTt3zmePn5qayrmTRZw7CFShoaHKnj37pecmvsDZk3WcPQhUYWFhcrlcSktLU/bsV+SSx+gtz3D2IFDRW87FuYNARW85G2cPAhW95WycPQhU9JZzce4gUNFbzsbZg0BFbzkbZw8CVUa9dcV3Sk5NTb30GwAgXVhY2KXzwRdSUlI4dwBcISwszKcv2nD2APi39DPBV2cPvQXgaugtACbQWwDsRm8BMIHeAmACvQXAbvQWABMy6q0rbkpOP5zCwsJ8v8rHLl68qA8++EA1a9ZUoUKFVLBgQdWqVUvvvvvuFXdoW5alqVOnqn79+sqfP79y586t8PBwffjhh5e9UyE2NlYhISF67733tHbtWjVs2FD58+dXrVq1JEkJCQkKCQnRK6+8kqXHz8zmjPxz4+rVq1W/fn3lzZtXt956q0aOHHnp8z744AOVL19euXLlUoUKFTRv3rwrHmvdunXq3r27ypYtq7CwMBUtWlRt27bVhg0bLn2Oy+VSy5YtFRoaqsWLF1/2+3///XeVKFFCRYsW1cGDB93ab1mWpk2bpujoaBUsWFD58+dX7dq1NWnSJKWlpV3x+Zs2bdJdd92lvHnzqnDhwvrPf/6j5OTkyz5n8uTJCgkJ0fz586/4/em/tnDhwqv+Hbrz+BnZvXu3SpYsqWLFimnz5s1u/R6nypkzJ+/mdAPnDudOOs4d78iZM6dPXzBOTU3lOQ9njyTOHs6ev6WfCb46e+gtzp5/4uzh7ElHb7mHc4dzJx3njnfQW+7h7OHsScfZ4zl6y32cPZw96Th7PEdvuYdzh3MnHeeOd9Bb7uHs4exJx9njOXrLfZw9nD3pOHs8l2FvWf+ydOlSS5J16tSpf/+S3xkyZIgl6ar/+eCDDy59nsvlsrp3757h5/bp0+fS56b//XTq1MnKnj37pc+pXr26ZVmWtXXrVkuS9fLLL2fp8d3dfC3pG7t06XLZxvT/vPvuu9YzzzxzxcdDQ0OtHTt2XHqcX375JcMt2bNnt9auXXvpc48fP26VLFnSKlSokLV//37LsiwrLS3Naty4sRUaGmotW7bMre0ul8vq0qVLhtddvnz5ZX/Gbt26WWFhYVd8XnR09GWPO2nSJEuSNW/evCuumf5r//vf/674O3T38dM/f/To0Zc+tnjxYit//vxW5cqVrR9//NGtP7+TPfHEE1ZUVJTPHv+hhx6yWrRo4bPHtwvnDudOOs4d7yhWrJg1ZswYnz1+/vz5rcmTJ/vs8e3C2cPZk46zx3Pr1q2zJFm//PKLTx6f3uLsScfZw9nzT/SWezh3OHfSce54B73lHs4ezp50nD2eo7fcx9nD2ZOOs8dz9JZ7OHc4d9Jx7ngHveUezh7OnnScPZ6jt9zH2cPZk46zx3MZ9VaG3yk5EN7RuXDhQuXNm1cxMTE6ceKEzp49q6+//lpPP/208uXLd+nzPv30U82cOVNVqlTRkiVLdPz4cZ05c0Zr165VeHi4Jk2apI0bN1722PPmzVPPnj21b98+paWlafv27RnuyMzju7vZHXPmzNFjjz2mH3/8UWfOnNH8+fOVI0cOvfrqqxo3bpwmT56s33//XcePH9dTTz0ll8ul0aNHX/r9ISEhatasmb788ksdPHhQqamp+u233zR37lyFhYVd9i6JG2+8UfPnz9e5c+fUsWNHpaSk6MUXX9SqVav0yiuvqHnz5m5tnjp1qubMmaPChQtr/Pjx+vnnn3XmzBlt3bpVvXv3Vo4cOS77/FmzZqlXr1767rvvdO7cOa1fv1633Xab1q1bp507d2bq7+tqsvr4Y8aMUbt27RQVFaX4+HiVLFnS4y2m8U5y93DucO54inPncpw97uHs4ezxFGfP39Lf4e2rs4fe4uxJx9nD2fNPPOdxD+cO546nOHcux9njHs4ezh5Pcfb8jd5yH2cPZ4+nOHv+xnMe93DucO54inPncpw97uHs4ezxFGfP3+gt93H2cPZ4irPnb25/p+R58+ZZkqyLFy/acre0LzVq1MgqV66cdeHChet+XrZs2awjR45c8WtJSUmWJGvo0KGWZf19B3tERITlcrmu+PyrvbMhM4/v7uZrSd94tXfl3nfffZYka9SoUZd9PC0tzSpQoIBVq1atyz6ekJBgderUySpevPgV75IoUaLEFY8/fvx4S5LVoEEDKyQkxGrZsuVV/54y0qBBg8vewXC9P2Pz5s2v+LWxY8dakqzp06df+lhW39ng7uOnf/7bb79tDRgwwJJkPfbYY1ZaWppbf25/8Pzzz1vh4eE+e/zOnTtb9913n88e3y6cO5w76Th3vKNcuXLWiBEjfPb4oaGh1uzZs332+Hbh7OHsScfZ47kdO3ZYkqx9+/b55PHprctx9nD2uPP4wXD20Fvu4dzh3EnHueMd9JZ7OHs4e9Jx9niO3nIfZw9nTzrOHs/RW+7h3OHcSce54x30lns4ezh70nH2eI7ech9nD2dPOs4ez2XUW9n1L9myZZMkuVwuhYZe8Y2U/cro0aN13333qWzZsrr77rsVHh6uevXqqXr16pd9XlJSki5evKhbb71VkmRZlizLuvTfJennn3++7Pc0bdpUISEhbu3IzOO7s/nYsWMqWrToZdcoXLiwjh07dtnHGjZseMWW9Dvso6OjL/t4tmzZdMstt+i333679LH4+Hg1atRIqampV/1znT9//oqP9evXTytWrND8+fN1yy236NNPP73s7+l62/fu3atChQqpadOmV73mv911111XfKxMmTKSpNOnT7v1GN58/Ndee02nTp3Ss88+q9dff93j6ztJWlqasme/4sjwmmzZsmX4b82fcO5w7niKc+dyvj57QkNDdfHiRZ89vl04ezh7PMXZ87f0M8FXZw+9xdmTjrOHs+ef6C33cO5w7niKc+dy9JZ7OHs4ezzF2fM3est9nD2cPf+fvfsMj6pM2Dh+DwFCkyIIuO7SMYYWwFACGEMnSO9I0QWUtbCsYkFl16CsguLSbDRBikgTAU1CkRIIoQQIhBJQCVgAMUhRCC2Z9wNvEIXAJDNznin/33XthxWcc+O1efb845mMszh7fkdvOYZzh3PHWZw7f0RvOYazh7PHWZw9v6O3HMfZw9njLM6e32V3z3PDKZH1Y9bd+VESVgkJCVFKSopmzZqlihUrasOGDWrTpo2qV6+u5OTka78vMzNT0tUDOiMjQ5mZmX/4Qpd0wxdSyZIlHd6Rk9d3dLMjChQocMNfy/qCzu7XsrZK0ujRo3Xp0iW9+uqr+uabb5Senn5te1BQ0E2v+dNPP2nTpk2SpJMnT+rIkSM52pxTBQsWvOGvZf0Zr//nm/V/iNf/+bLc7DDM6etnadeune6880599NFHLvlx757k4sWLbv0YhsDAQJ/4pg3nDudOFs4d17h48eK1j5pxh8DAQO55OHs4e3Lw+ll8+ezJOhPcdfbQW5w9WTh7OHuuR285hnOHcycL545r0FuO4ezh7MnC2eM8estxnD2cPVk4e5xHbzmGc4dzJwvnjmvQW47h7OHsycLZ4zx6y3GcPZw9WTh7nJddb93wUHLW4eQL8SRdfQdIeHi4hg8frnnz5ik1NVVnz57VwIEDr/2e++67T4UKFdLp06evfXH/+T+LFi3K9Yacvv7tNpcqVeqGv//P72pwhUOHDqlMmTKKiopS5cqVVaBAAdlsNn377bf6+uuvb/j9GRkZ6t27t44fP673339f+fLlU7du3XT69Olrv+d22++77z6dOnVKX331lUv/LKVLl5Ykpaam3vBra9ascdl16tWrp7i4OAUEBOjBBx/Uxo0bXfbapl26dMmt4ZQ/f37OHc4dzp1c8OVzR+LsyQnOntzj7Mk5Xz573P1NG3qLsycLZ0/O+fLZwz2P4zh3co9zJ+d8+dyROHtygrMn9zh7cs6Xzx56K2c4e3KPsyfnfPns4Z7HcZw7uce5k3O+fO5InD05wdmTe5w9OefLZw+9lTOcPbnH2ZNzvnz2ZHfPk+1PSvaFQ6RRo0b68MMPtW/fPqWnp+vMmTOKjY3VyZMndejQoWu/b+DAgTp//rxatGihL774Qj///LMuXbqkI0eO6Msvv1TXrl2d+h91Tl7f0c1WKFeunE6cOKF3331XZ86c0ZkzZxQdHa22bdve9B0CI0aM0Nq1a/Xaa6/piSee0IwZM3To0CH1799fdvuN7wK4mUceeUSS1Lt3b02dOlU//PCDzp07p+3bt+vxxx/X+vXrc/VnqVatmiRp/PjxWrdundLT03X48GENGzZMn3/+ea5eMzvVq1dXfHy87rrrLrVq1UrR0dEufX1TrHg3J+cO5w7nTu746rkjcfY4irPHOZw9ueOrZ0/WmeDud5Jz9nD2cPbkjq+ePdzzOIZzxzmcO7njq+eOxNnjKM4e53D25I6vnj30luM4e5zD2ZM7vnr2cM/jGM4d53Du5I6vnjsSZ4+jOHucw9mTO7569tBbjuPscQ5nT+746tmT7T2P/U82b95sl2T/7rvv/vxLXicwMNAu6ab/+ec//3nt92VmZtofffTRbH+vJHtMTIzdbrfbY2Ji7JLs48aNu+k1t23bZpdkf/XVV3P1+o5uvpVbbRw2bJhdkj05OfmGX6tevbr9nnvuufbfP//885vuqFOnjr1GjRr2kiVLXvu9y5Yts9tsNnvbtm3tmZmZ1/76s88+a5dkf/PNNx3anpGRYe/WrVu2/wxWrVp12z9j1q9NmjTpD3+9S5cuN7xe3rx57f369bNLsi9ZssShf4Y3e/2b/f7jx4/ba9eubc+XL5997ty5Dv35PVm/fv3sHTp0cNvrDxs2zN6gQQO3vb5VOHc4d67HueO8wMBA++zZs932+uXKlbOPHTvWba9vFc4ezp7rcfY4Z+nSpXZJ9gsXLrjl9ektzp4snD2cPdejtxzDucO5cz3OHefRW47h7OHsuR5nj3PoLcdx9nD2XI+zxzn0lmM4dzh3rse54zx6yzGcPZw91+PscQ695TjOHs6e63H2OCe73sr2JyVn/Vh3b7ZlyxY99dRTqlatmgoWLKhSpUqpcePGmjZtmsaNG3ft99lsNs2YMUPz589XixYtVKJECeXPn1+VKlVSp06dtGTJErVo0SLXO3Ly+o5utkLHjh01d+5c1apVSwULFtTdd9+twYMH66uvvvrDE+6pqanq37+/ypUrp9mzZ8tms137tTFjxuiBBx649q6H28mTJ48WLFigKVOmqGHDhipcuLCKFi2q+vXra9q0aYqIiMj1n2f69OkaMGCASpYsqQIFCigsLEyrV69WeHh4rl/zVsqUKaN169apYcOG6tu3r9577z23XMcqFy9evHY+uIOvvJuTc8c5nDvO8bVzR3L/x1sFBgZyz8PZw9njJF87e7LuR9x130NvcfZk4exxjq+dPfSWYzh3nMO54xxfO3ckestRnD3O4exxjq+dPfSW4zh7nMPZ4xxfO3voLcdw7jiHc8c5vnbuSPSWozh7nMPZ4xxfO3voLcdx9jiHs8c5vnb2ZNdbNrv9jz8HOyUlRcHBwdq9e7dq1qxp2UAAnq1jx44qWrSoZs+e7ZbXf/311/XJJ59o//79bnl9AN7nwoULKliwoJYtW6b27du75Rq1atVSp06d9Nprr7nl9QF4nzlz5uixxx5Tenq6W16f3gJwM/QWAKvRWwBMoLcAmEBvAbAavQXABHoLgAnZ9dYNPyn5zjvvlCT98ssv1iwD4BVOnjypkiVLuu3177zzTs4dAH9w8uRJSeLsAWCptLS0a03kDvQWgJuhtwBYjd4CYAK9BcAEeguA1egtACbQWwBMyK63bvpQss1mu3ajBADS1RsYd4ZTyZIldfLkSWVmZrrtGgC8ixXftClVqpTS0tLc9voAvM/JkydVqlQpt70+vQXgZugtAFajtwCYQG8BMIHeAmA1eguACfQWABOy660bHkrOmzevihUrxg0MgD9w9w1MqVKllJGRoTNnzrjtGgC8S9a9iDvPnqxvGANAFnf/9Bx6C8DN0FsArEZvATCB3gJgAr0FwGr0FgAT6C0AJmTXWzc8lCxdvYHhEAGQJTMzU6dOnXL7O8klcfYAuCYtLU158uRR8eLF3XYN7nkA/Jm7f3qOxNkD4I/oLQAm0FsATKC3AFiN3gJgAr0FwASopVQjAAAgAElEQVR6C4DVbtVbN30ouVSpUryrCsA1p06dUkZGhiXftOHsAZDl5MmTKlGihAICAtx2DcIJwJ+lpaW59SdYSPQWgD+itwCYQG8BMIHeAmA1eguACfQWABPoLQBWu1VvZfuTkjlEAGSx4iNmsl6beAKQxYp3cxJOAP7M3R9vJdFbAP6I3gJgAr0FwAR6C4DV6C0AJtBbAEygtwBY7Va9ddOHksuWLatjx465dxUAr3H8+HFJUunSpd12jUKFCumOO+7g7AFwzfHjx1WmTBm3XqNMmTJKT0/X6dOn3XodAN7DirOH3gJwPXoLgAn0FgAT6C0AVqO3AJhAbwEwgd4CYLVb9dZNH0ouX768Dh8+7NZRALxHamqqChYsqLJly7r1OhUqVNCRI0fceg0A3iM1NVUVK1Z06zUqVKggSdz3AJAknTt3TidOnLh2NrgLvQXgevQWABPoLQBWo7cAmEBvATCB3gJgNXoLgAm36q2bPpScFU6ZmZluHwfA86Wmpqp8+fKy2WxuvU6FChWUmprq1msA8B6pqaluD6cKFSrIZrMRTwAk6dp9iBXfMKa3AGShtwCYQG8BsBq9BcAEeguACfQWAKvRWwBMuFVv3fSh5IoVK+rixYv8yHUAkq6+w9LdNy/S1RsYwgmAJNntdn333XduP3sKFCigMmXK8A1jAJJ+/6ZN+fLl3XodegvA9egtAFajtwCYQG8BMIHeAmA1eguACfQWABNu1VvZ/qTkrL8RAKz4iBmJd5ID+N1PP/2k8+fPW3L2VKxYkXseAJKu9s9dd92lO+64w63XobcAXI/eAmA1eguACfQWABPoLQBWo7cAmEBvATDhVr1104eS77nnHuXPn594AiDJmo+Yka7ewBw/flwXLlxw+7UAeLasexCrzh7CCYBk3b+oorcAXI/eAmA1eguACfQWABPoLQBWo7cAmEBvATDhVr1104eS8+TJo3LlynGIANDFixd19OhRS25gKlWqJLvdztkDQKmpqcqXL5/uuecet1+rYsWKOnTokNuvA8DzWfUvqugtAFnoLQAm0FsATKC3AFiN3gJgAr0FwAR6C4DVbtdbN30oWZKqVaumvXv3um0YAO+wb98+ZWZmqlq1am6/VlBQkAICAjh7AGjPnj0KCgpS3rx53X6t4OBgHTx4UJcuXXL7tQB4tj179lhyzyPRWwCuorcAmEBvATCB3gJgNXoLgAn0FgAT6C0AVrtdb2X7UHKtWrW0e/dutw0D4B2Sk5MVGBioe++91+3XKliwoKpUqaLk5GS3XwuAZ9u9e7dq1qxpybVq1aqlS5cu6cCBA5ZcD4BnOnfunA4dOqRatWpZcj16C4BEbwEwg94CYDV6C4AJ9BYAE+gtAFajtwCYcLveyvah5Jo1a+rgwYNKT0932zgAni85OVnVqlWz5N2cEjcwAK5KTk627Js2wcHByp8/P2cP4Of27t2rzMxMy84eeguARG8BMIPeAmA1eguACfQWABPoLQBWo7cAmHC73rrlT0rOyMjQ/v373TYOgOezMpykqzcwhBPg386cOaPvv//esndz5suXT/fddx8/xQLwc7t371bhwoVVqVIlS65HbwGQ6C0A1qO3AJhAbwEwgd4CYDV6C4AJ9BYAE27XW9k+lFy1alUVKlSIeAL8nJUfMSNdvYFJTU3V2bNnLbsmAM+SnJwsu91u+dnDN20A/5acnKwaNWooT55sE8ml6C0AEr0FwHr0FgAT6C0AJtBbAKxGbwEwgd4CYMLteivbEykgIEDVqlXjBgbwYydPntSxY8csezendDWc7Ha79u7da9k1AXiW3bt3q3jx4vrb3/5m2TX5KRYAkpOTLb3nobcA0FsATKC3AJhAbwGwGr0FwAR6C4AJ9BYAqznSW7d8m0Tt2rWVmJjo8mEAvEPW139ISIhl16xQoYJKlCih7du3W3ZNAJ5lx44dCgkJkc1ms+yaderU0Q8//KDjx49bdk0AniMzM1M7d+609J5HorcAf0dvATCB3gJgNXoLgAn0FgAT6C0AVqO3AJjgSG/d8qHkxo0ba9u2bbp06ZJrlwHwChs3blTVqlVVpkwZy65ps9kUFham+Ph4y64JwLNs2LBBTZo0sfSaDRs2VEBAgDZt2mTpdQF4hj179uj06dOWnz30FuDf6C0AJtBbAKxGbwEwgd4CYAK9BcBq9BYAExzprds+lJyenq6dO3e6fBwAzxcfH2/5zYt09ezZsGGD5dcFYF5aWpq+/vprNW7c2NLr3nHHHapZsybfMAb8VHx8vIoWLaoaNWpYel16C/Bv9BYAq9FbAEygtwCYQG8BsBq9BcAEeguACY701i0fSq5atarKli3LDQzgh65cuaJt27ZZHk7S1RuYH3/8Ud9//73l1wZg1saNG2Wz2dSwYUPLr924cWPueQA/FR8fr7CwMAUEBFh6XXoL8F/0FgAT6C0AJtBbAKxGbwEwgd4CYAK9BcBqjvbWLR9Klq5+3AOHCOB/du7cqd9++83IN23q16+v/Pnzc/YAfig+Pl7Vq1dXiRIlLL9248aNtWPHDp0/f97yawMwa+PGjUbueSR6C/BX9BYAE+gtACbQWwCsRm8BMIHeAmACvQXAao721m0fSuZdVYB/io+PV6lSpRQUFGT5tQsWLKjatWtz9gB+yNTH6klX73kuX76sxMREI9cHYMbRo0d15MgRY9+0obcA/0RvATCB3gJgNXoLgAn0FgAT6C0AVqO3AJjgaG859FDyTz/9pIMHD7psHADPt2HDBjVq1Eg2m83I9Zs0aaK4uDgj1wZgxvnz57Vjxw41atTIyPXLlSunv/3tb5w9gJ+Ji4tTvnz5VL9+fSPXp7cA/0RvAbAavQXABHoLgAn0FgCr0VsATKC3AJjgaG/d9qHk+vXr684771RsbKzLxgHwbFeuXNFXX32lli1bGtvQokULJScn6+jRo8Y2ALDWmjVrdOnSJTVv3tzYhpYtW2rFihXGrg/AerGxsWrUqJGKFCli5Pr0FuB/6C0AJtBbAEygtwBYjd4CYAK9BcAEeguA1XLSW7d9KDkgIEDNmjXjEAH8yKZNm3TmzBm1adPG2IaIiAgVLFiQeAL8SGxsrOrUqaO7777b2IY2bdooISFBv/zyi7ENAKxjt9u1cuVKo/c89Bbgf+gtACbQWwCsRm8BMIHeAmACvQXAavQWABNy0lu3fShZunoDs27dOp0/f97pcQA8X0xMjKpWraoqVaoY21CwYEGFh4crJibG2AYA1oqNjVVkZKTRDS1btpTNZtNXX31ldAcAa+zcuVPHjh0z+k0bid4C/A29BcAEeguA1egtACbQWwBMoLcAWI3eAmBCTnrLoYeS27ZtqwsXLiguLs7pcQA8X2xsrPGbF+nqDcyqVat05coV01MAuNnBgwf17bffGj97ihcvrgYNGvCOTsBPxMTEqGzZsgoJCTG6g94C/Au9BcBq9BYAE+gtACbQWwCsRm8BMIHeAmBCTnrLoYeS7777btWsWZMbGMAPHD9+XLt27TIeTtLVb9qcPn1aW7ZsMT0FgJvFxsaqaNGiatCggekpatOmjWJiYmS3201PAeBmWT/BwmazGd1BbwH+g94CYAK9BcAEeguA1egtACbQWwBMoLcAWC2nveXQQ8mSFBkZqejo6FwPA+AdoqOjFRgYqIiICNNTFBQUpEqVKunLL780PQWAm8XExKhVq1bKly+f6SmKjIzUsWPHlJSUZHoKADc6deqUNm/erNatW5ueIoneAvwFvQXABHoLgNXoLQAm0FsATKC3AFiN3gJgQk57y+GHkjt27Kivv/5au3btyu02AF5g4cKFat26tQoVKmR6iiSpU6dOWrBggekZANzo1KlTWrNmjTp16mR6iiSpbt26Kl++vBYtWmR6CgA3WrJkiQICAjzip+dI9BbgL+gtAFajtwCYQG8BMIHeAmA1eguACfQWABNy2lsOP5TcsGFDVahQgXgCfFhWOPXo0cP0lGt69Oihb7/9Vjt37jQ9BYCbLF68WHny5FH79u1NT5Ek2Ww2devWTZ9++qnpKQDcaMGCBYqMjFSxYsVMT5FEbwH+gN4CYAK9BcAEeguA1egtACbQWwBMoLcAWC03veXwQ8nX38DY7fZcDQTg2bLCqV27dqanXNOgQQNVrlyZGxjAhy1YsEBt27ZV0aJFTU+5pmfPnjp06JB27NhhegoAN0hLS9OaNWvUs2dP01OuobcA30dvATCB3gJgNXoLgAn0FgAT6C0AVqO3AJiQm95y+KFk6eo7OrmBAXzXggUL9NBDD3lUOElS165dNX/+fG5gAB+UlpamtWvXelQ4SVK9evVUuXJlzZ8/3/QUAG7w2WefKV++fB71L6okegvwdfQWAKvRWwBMoLcAmEBvAbAavQXABHoLgAm56a0cPZRcr149ValShXd0Aj4oK5w86aOtsvTo0UOpqalKTEw0PQWAiy1evFj58+fXQw89ZHrKDXhHJ+C7ssKpSJEipqf8Ab0F+C56C4AJ9BYAE+gtAFajtwCYQG8BMIHeAmC13PZWjh5Klq7ewCxYsIAbGMDHLFq0SIGBgR4ZTvfff7+qVKnCOzoBH5QVToULFzY95QY9e/bUd999p61bt5qeAsCFfvrpJ61fv17du3c3PeWm6C3AN9FbAEygtwBYjd4CYAK9BcAEeguA1egtACbktrdy/FBy3759dfjwYa1ZsyanfysADzZjxgx17tzZI8NJkvr06aM5c+bo8uXLpqcAcJHU1FStW7dOffv2NT3lpurUqaPq1avro48+Mj0FgAvNnj1bRYoU8biPtspCbwG+id4CYDV6C4AJ9BYAE+gtAFajtwCYQG8BMCG3vZXjh5KrV6+usLAwTZs2Lad/KwAPlZycrK1bt2rQoEGmp2Rr0KBBSktL0/Lly01PAeAi06dPV+nSpdW2bVvTU7I1aNAgffLJJ/r1119NTwHgIh999JH69u2rggULmp5yU/QW4HvoLQAm0FsATKC3AFiN3gJgAr0FwAR6C4DVnOmtHD+ULEmPPfaYPvvsM/3888+5+dsBeJgpU6bo3nvvVXh4uOkp2frrX/+qVq1aaerUqaanAHCBK1euaObMmRo4cKDy5s1rek62+vXrpytXrmjhwoWmpwBwgbi4OO3fv18DBgwwPeWW6C3At9BbAKxGbwEwgd4CYAK9BcBq9BYAE+gtACY401u5eii5V69eKlSokObMmZObvx2AB0lPT9fcuXM1cOBA2Ww203Nu6bHHHtPKlSt15MgR01MAOCk6OlpHjx7V3//+d9NTbqlkyZLq3Lkz3zAGfMTUqVNVr1491alTx/SUW6K3AN9BbwEwgd4CYAK9BcBq9BYAE+gtACbQWwCs5mxv5eqh5IIFC6pXr16aMmWK7HZ7bl4CgIdYtGiRfv31V/Xr18/0lNtq3769ypYtq48++sj0FABOmjZtmpo3b67KlSubnnJbjz32mDZv3qxdu3aZngLACWfOnNFnn33m0R/nmYXeAnwHvQXABHoLgNXoLQAm0FsATKC3AFiN3gJggrO9lauHkqWrNzApKSnauHFjbl8CgAeYOnWqOnTooLvvvtv0lNvKmzev+vfvr48++khXrlwxPQdALv3www+KiYnxinCSpIiICFWpUkXTpk0zPQWAE2bNmiWbzaZevXqZnuIQegvwDfQWAKvRWwBMoLcAmEBvAbAavQXABHoLgAnO9lauH0quW7euGjZsqHHjxuX2JQAYlpiYqA0bNuipp54yPcVhgwcP1vHjx7Vw4ULTUwDk0sSJE1W6dGl17tzZ9BSH2Gw2Pfnkk5o5c6ZOnTpleg6AXMjIyNCECRPUr18/FS1a1PQch9BbgPejtwCYQG8BsBq9BcAEeguACfQWAKvRWwBMcEVv5fqhZEkaNmyYli5dqpSUFGdeBoAhY8eOVUhIiJo2bWp6isMqVKigLl266J133jE9BUAu/Prrr5o6daqGDBmi/Pnzm57jsEGDBilv3ryaPHmy6SkAcmHJkiVKTU3V0KFDTU/JEXoL8G70FgCr0VsATKC3AJhAbwGwGr0FwAR6C4AJrugtpx5K7ty5sypWrKhJkyY58zIADDh8+LAWL16sF154QTabzfScHHn++ee1fft2rV271vQUADk0ZcoUXb58WY899pjpKTlyxx136LHHHtOkSZN06dIl03MA5ND//vc/dezYUffdd5/pKTlCbwHei94CYAK9BcAEeguA1egtACbQWwBMoLcAWM1VveXUQ8kBAQEaOnSoZs6cqbS0NGdeCoDFxo8fr7Jly6p79+6mp+RYaGioHnjgAd5NDniZK1euaOLEiXrsscdUsmRJ03NybOjQoUpLS9O8efNMTwGQAxs3blRCQoKGDRtmekqO0VuA96K3AFiN3gJgAr0FwAR6C4DV6C0AJtBbAExwVW859VCyJA0cOFAFCxbUBx984OxLAbDIqVOnNH36dD3zzDPKly+f6Tm5MmzYMEVHR2vfvn2mpwBw0Pz58/Xjjz9qyJAhpqfkyj333KMePXpo7NixstvtpucAcNA777yjevXqqXHjxqan5Aq9BXgfeguACfQWABPoLQBWo7cAmEBvATCB3gJgNVf2ltMPJRcqVEiPP/643nvvPZ0/f97ZlwNggffff18BAQEaNGiQ6Sm51r59e1WtWlVjx441PQWAA+x2u8aOHatu3bqpUqVKpufk2rPPPqs9e/YoNjbW9BQADkhJSdGyZcv04osvmp6Sa/QW4H3oLQBWo7cAmEBvATCB3gJgNXoLgAn0FgATXNlbNrsL3gqVlpamSpUq6T//+Y+ee+45p0cBcJ8zZ86oUqVKevrppzVy5EjTc5zy8ccfa+DAgdq7d6+CgoJMzwFwC4sXL1b37t21c+dOhYSEmJ7jlHbt2un48ePatm2bbDab6TkAbqFXr17as2ePdu/erTx5nH4/pjH0FuA96C0AJtBbAEygtwBYjd4CYAK9BcAEeguA1VzdWy55KFmSXnrpJU2bNk2HDh3SHXfc4YqXBOAGUVFRGj9+vFJTU1WiRAnTc5ySkZGhGjVqqF69epo1a5bpOQCykZmZqTp16ig4OFiffvqp6TlO27Fjh0JDQ7V06VK1b9/e9BwA2di7d69q1aql+fPnq1u3bqbnOI3eArwDvQXAavQWABPoLQAm0FsArEZvATCB3gJggqt7y2UPJZ8+fVoVK1bU888/r5dfftkVLwnAxbK+TocNG6YRI0aYnuMS8+bNU79+/ZScnKzg4GDTcwDchC9+nXbp0kXffPONkpKSvPrdqYAv87WvU3oL8Hz0FgATfPHr1Nfu4wBf5Gtfp/QW4PnoLQAm+OLXqa/dxwG+yNe+TuktwPO5o7dc9lCyJL366quaOHGiDh065PXvUAV80csvv6wpU6bo0KFDKlq0qOk5LuFr71AFfE3WT3wIDQ3V7NmzTc9xmax3qC5YsEBdu3Y1PQfAn2T9xIfPP/9cHTp0MD3HZegtwLPRWwCsRm8BMIHeAmACvQXAavQWABPoLQAmuKO3XPpQ8pkzZ1SpUiUNGTJEUVFRrnpZAC6QlpamSpUqacSIEXrhhRdMz3GpxYsXq3v37tq5c6dCQkJMzwFwnY8//lgDBw7U3r17FRQUZHqOS/Xq1Ut79uzR7t27feJdqoAvadeunY4fP65t27bJZrOZnuMy9BbguegtACbQWwBMoLcAWI3eAmACvQXABHoLgNXc1VsufShZkt588029+eabOnDggO6++25XvjQAJwwZMkSLFi3SN998o8KFC5ue41J2u12hoaEqXbq0YmJiTM8B8P/S09MVHBysFi1aaNq0aabnuNyBAwdUo0YNTZ48WQMGDDA9B8D/W79+vSIiIhQTE6M2bdqYnuNy9BbgmegtAFajtwCYQG8BMIHeAmA1eguACfQWABPc1Vsufyj5woULCg4OVkREhGbMmOHKlwaQS/v27VNISIhPh0V8fLweeOABffHFF2rbtq3pOQAkvfbaa3r77bd14MAB/eUvfzE9xy2GDBmiBQsW6ODBgypWrJjpOYDfy8zMVP369VWiRAmtWrXK9By3oLcAz0NvATCB3gJgNXoLgAn0FgAT6C0AVqO3AJjgzt5y+UPJkrRgwQL17t1bCQkJql+/vqtfHkAOtWnTRidOnFBiYqJPfwRL9+7dlZSUpD179igwMND0HMCv/fjjjwoKCtKIESM0fPhw03Pc5tSpU6pataoGDRqk0aNHm54D+L3Jkyfr6aefVlJSkqpXr256jtvQW4BnobcAWI3eAmACvQXABHoLgNXoLQAm0FsATHBnb7nloWRJevDBB3X58mXFx8fLZrO54xIAHLB06VJ16tRJ69evV3h4uOk5bvX999/rvvvu06hRo/TMM8+YngP4tT59+mjz5s3au3evChQoYHqOW02aNEnPPfec9uzZo6pVq5qeA/its2fPKigoSL169dK4ceNMz3E7egvwDPQWABPoLQBWo7cAmEBvATCB3gJgNXoLgAnu7i23PZS8c+dOhYaGavbs2Xr44YfdcQkAt3Hp0iXVrFlTdevW1bx580zPscQrr7yid999VwcOHFDZsmVNzwH8UkJCgho3bqzFixerc+fOpue43ZUrV1SnTh1VqVJFS5YsMT0H8FvDhg3TzJkzdfDgQZUsWdL0HLejtwDz6C16CzCB3gJgAr0FwGr0Fr0FmEBvATCB3gJgNSt6y20PJUvSoEGDtHLlSu3bt09FihRx12UAZGPMmDEaOXKkUlJSVK5cOdNzLPHbb78pKChIDz30kKZMmWJ6DuB3MjIyFBYWpiJFimjNmjWm51hmxYoVatOmjVauXKmWLVuangP4nZSUFIWEhGjcuHF68sknTc+xDL0FmEVv0VuA1egtegswgd6itwAT6C16C7AavUVvASbQW/QWYIIVveXWh5JPnDih4OBg9e3bVxMmTHDXZQDcxLfffqtatWrppZde0ogRI0zPsdS8efPUp08frVu3zuc/0gvwNBMmTNALL7yg7du3q0aNGqbnWKpr165KSkpScnKyChUqZHoO4DcyMzMVERGhc+fOacuWLcqbN6/pSZahtwBz6C16CzCB3qK3AKvRW/QWYAK9RW8BJtBb9BZgNXqL3gJMsKq33PpQsiTNnDlTAwcO1IYNG9SoUSN3XgrAdVq1aqXjx48rMTFR+fPnNz3Hch07dlRKSop27dqlAgUKmJ4D+IXvvvtO1atX17PPPquRI0eanmO5Y8eOqXr16ho0aJDeeust03MAv/Hhhx9qyJAh2rx5s+6//37TcyxHbwFm0Fv0FmA1eoveAkygt+gtwAR6i94CrEZv0VuACfQWvQWYYFVvuf2hZElq3bq1vvvuOyUlJSkwMNDdlwP83owZMzRo0CC//j/v7777TjVq1NDQoUP1+uuvm54D+IUOHTro4MGDSkpK8ttvlk6ZMkVPPvmktmzZ4pfxCFjt2LFjqlatmgYPHqzRo0ebnmMMvQVYi96itwAT6C16C7AavXUVvQVYi96itwAT6C16C7AavXUVvQVYy8resuSh5MOHD6tmzZp64YUX9O9//9vdlwP8Wlpa2rWPORg3bpzpOUZNmjRJzz77rLZu3ao6deqYngP4tLlz56p///5av369mjRpYnqOMXa7XS1bttTPP/+sxMRE5cuXz/QkwKd16dLl2sfKFS5c2PQcY+gtwDr01u/oLcA69NZV9BZgLXrrKnoLsA699Tt6C7AOvXUVvQVYi966it4CrGN1b1nyULIkvfPOO3rllVe0c+dOBQcHW3FJwC/16tVLmzdv1p49e1SkSBHTc4zKzMzUAw88oCtXrmjTpk0KCAgwPQnwSWlpaapWrZq6deum999/3/Qc4w4ePKiQkBC9+uqrGj58uOk5gM9avHixunfvrtWrV6tZs2am5xhHbwHWoLd+R28B1qC3/ojeAqxBb/0RvQVYg976Hb0FWIPe+iN6C7AGvfVH9BZgDat7y7KHkjMyMtSoUSNlZGRo06ZNyp8/vxWXBfzKvHnz1KdPH0VHR6tNmzam53iEffv26f7779fLL7/MO6sAN+nSpYsSExOVnJysYsWKmZ7jEcaMGaNXX31VW7ZsUUhIiOk5gM85duyYQkJC1KFDB02bNs30HI9AbwHuR2/diN4C3I/euhG9BbgXvXUjegtwP3rrRvQW4H701o3oLcC96K0b0VuA+5noLcseSpakb7/9VnXq1NGTTz6p0aNHW3VZwC/88MMPqlWrlvr27auJEyeanuNRJkyYoOeee05xcXEKCwszPQfwKVOmTNETTzyhVatW8U7O62RmZqpFixb66aeflJiYqIIFC5qeBPgMu92udu3aKSUlRUlJSbrjjjtMT/IY9BbgPvRW9ugtwH3orZujtwD3obeyR28B7kNvZY/eAtyH3ro5egtwH3ore/QW4D6mesvSh5Iladq0aRo8eDA3d4ALZWZmqnnz5jpx4gRxcBN2u13t27fX/v37tXPnThUtWtT0JMAnfPPNN6pbt66efvppvfHGG6bneJwffvhBISEhevjhhzVp0iTTcwCfMW7cOL3wwgvasGGDGjZsaHqOx6G3ANejt26N3gLcg966NXoLcA9669boLcD16K1bo7cA96C3bo3eAtyD3ro1egtwPZO9ZflDyZLUs2dPxcfHa9euXSpZsqTVlwd8zuuvv67//ve/fIzKLZw4cUK1atVSZGSkZsyYYXoO4PUuX76sJk2a8DEqt7F48WJ1795dS5cuVfv27U3PAbze3r17FRoaqhEjRuiVV14xPcdj0VuAa9Fbt0dvAa5FbzmG3gJci95yDL0FuBa9dXv0FuBa9JZj6C3Ategtx9BbgGuZ7C0jDyWfPHlSISEhCgsL08KFC62+POBTtmzZoiZNmujtt9/Wv/71L9NzPNry5cvVsWNHzZs3Tz179jQ9B/Bqw4cP17vvvqsdOyOk9HIAACAASURBVHbo3nvvNT3Hoz3yyCOKjY3V7t27VaZMGdNzAK+Vnp6uevXq6c4779TatWsVEBBgepLHorcA16G3HEdvAa5DbzmO3gJcg95yHL0FuA695Th6C3Adestx9BbgGvSW4+gtwHVM95aRh5Il6auvvlKrVq00fvx4DRkyxMQEwOudPHlS999/v4KDgxUdHS2bzWZ6ksd76qmnNGfOHG3dulVBQUGm5wBe6YsvvlDHjh01ZcoUDRw40PQcj3f27FnVqVNH5cuX18qVK5U3b17TkwCvNGDAAC1ZskRJSUkqX7686Tkej94CnEdv5Ry9BTiP3soZegtwDXorZ+gtwHn0Vs7RW4Dz6K2cobcA16C3cobeApznCb1l7KFkSRo1apRee+01rV69WuHh4aZmAF4pMzNTDz30kPbt26fExETdddddpid5hcuXL6tp06ZKS0vT1q1bVbRoUdOTAK9y+PBhhYaGKjIyUrNnzzY9x2vs3r1bYWFhevLJJ/X222+bngN4nQ8++EBPPfWUFi9erM6dO5ue4zXoLSD36K3cobcA59BbuUNvAc6ht3KH3gJyj97KHXoLcA69lTv0FuAceit36C0g9zylt4w+lGy329W9e3dt3LhR27dv1z333GNqCuB1XnrpJY0fP15xcXGqV6+e6Tle5fvvv1doaKgaNWqkzz77jHfgAw5KT09X48aNlZmZqU2bNqlQoUKmJ3mVuXPnql+/fvr000/Vo0cP03MAr7FlyxY9+OCDGj58uKKiokzP8Sr0FpB79Fbu0VtA7tBbzqG3gNyht3KP3gJyj97KPXoLyB16yzn0FpA79Fbu0VtA7nlKbxl9KFmSfv31VzVo0EDFixfXunXrlD9/fpNzAK+wbNkyderUSVOnTuWjZXJp7dq1atWqld58800999xzpucAXuHRRx/V8uXLtW3bNlWqVMn0HK/09NNP6+OPP9bmzZtVvXp103MAj3fixIlrHy0TExOjgIAA05O8Dr0F5By95Tx6C8g5est59BaQM/SW8+gtIOfoLefRW0DO0VvOo7eAnKG3nEdvATnnSb1l/KFkSdqzZ48aNmyoAQMGaOLEiabnAB7twIEDql+/vnr27KkpU6aYnuPV3n77bb388stauXKlmjZtanoO4NEmTpyoZ555RtHR0WrdurXpOV7r0qVLatq0qU6ePKktW7aoWLFipicBHuvKlStq2bKlvvvuOyUmJqpEiRKmJ3ktegtwHL3lOvQW4Dh6yzXoLcBx9Jbr0FuA4+gt16G3AMfRW65BbwGOo7dch94CHOdpveURDyVL0vz589W7d2+9//77+sc//mF6DuCR0tLS1KhRI5UoUUJxcXEKDAw0Pcmr2e129ezZU2vWrNGmTZt07733mp4EeKSYmBh16NBBI0eO1Msvv2x6jtc7evSoQkNDVbNmTX355ZfKmzev6UmARxo8eLDmzJmj+Ph41a5d2/Qcr0dvAbdHb7kWvQU4ht5yLXoLcAy95Vr0FnB79JZr0VuAY+gt16K3AMfQW65FbwG354m95TEPJUvSa6+9ptdee02fffaZOnToYHoO4FEuXLigFi1a6Mcff1RCQoLKli1repJPSE9PV/PmzXX8+HFt3rxZpUuXNj0J8Ch79uxRkyZNFBkZqU8++UQ2m830JJ+wY8cOhYeHq1evXpo2bZrpOYDHGT16tF555RUtWrRInTt3Nj3HZ9BbQPboLfegt4Bbo7fcg94Cbo3ecg96C8geveUe9BZwa/SWe9BbwK3RW+5BbwHZ89Te8qiHku12uwYMGKBFixZpw4YNvGME+H92u139+vXT8uXLtXHjRtWsWdP0JJ+SlpamsLAwlShRQuvWrVOhQoVMTwI8wtGjR9WwYUNVrFhRK1eu9Ih3U/mSL7/8Uh07dtSoUaM0fPhw03MAj7Fw4UL16tVL//vf/zR06FDTc3wKvQXcHL3lXvQWcHP0lnvRW8DN0VvuQ28BN0dvuRe9BdwcveVe9BZwc/SW+9BbwM15cm951EPJknT58mU99NBD2rt3rxISElSuXDnTkwDjXnrpJb3zzjuKjo5WixYtTM/xSSkpKWrUqJGaN2+u+fPnK0+ePKYnAUb9+uuvCg8P18WLFxUfH68SJUqYnuSTJk+erCeeeEKzZs1S3759Tc8BjNu6dauaNm2qfv366cMPPzQ9xyfRW8CN6C33o7eAP6K3rEFvAX9Eb7kfvQXciN5yP3oL+CN6yxr0FvBH9Jb70VvAjTy5tzzuoWRJOnPmjBo3bqyAgADFxcWpWLFipicBxmTd0M+cOVP9+/c3PcenrV+/Xq1atdKQIUM0duxY03MAYy5fvqwOHTooKSlJCQkJqlChgulJPu2ZZ57Rhx9+qBUrVig8PNz0HMCYgwcPqnHjxmrcuLEWL16sgIAA05N8Fr0F/I7esg69BVxFb1mL3gKuoresQ28Bv6O3rENvAVfRW9ait4Cr6C3r0FvA7zy9tzzyrZLFihXTl19+qZMnT6pt27Y6d+6c6UmAEfPmzdNTTz2lkSNHeuQB4msefPBBzZgxQ+PGjdOoUaNMzwGMyMjIUN++fRUfH6/ly5fzDRsLvPPOO2rXrp3at2+vxMRE03MAI44cOaIWLVqoSpUq+uSTT/iGjZvRW8BV9Ja16C2A3jKB3gLoLavRW8BV9Ja16C2A3jKB3gLoLavRW8BV3tBbHvmTkrN8/fXXCg8PV7Vq1fTll1+qQIECpicBllm2bJm6deumJ598UuPHjzc9x6989NFHGjRokMaMGaPnn3/e9BzAMna7XYMHD9acOXMUHR2tiIgI05P8xqVLl9SlSxfFx8drzZo1qlOnjulJgGVOnDih8PBwBQYGau3atbrzzjtNT/Ib9Bb8Gb1lDr0Ff0VvmUNvwZ/RW+bQW/Bn9JY59Bb8Fb1lDr0Ff0ZvmUNvwZ95S2959EPJkrR79241bdpUoaGhWrZsmQIDA01PAtxu9erVat++vXr37q3p06fLZrOZnuR3JkyYoGeeeUYffPCBBg8ebHoOYIlhw4Zp0qRJWrJkiR566CHTc/xOenq62rZtq3379mndunUKDg42PQlwu7S0NEVEROjSpUuKi4tT2bJlTU/yO/QW/BG9ZR69BX9Eb5lFb8Ef0Vvm0VvwR/SWefQW/BG9ZRa9BX9Eb5lHb8EfeVNvefxDyZK0ZcsWtWzZUi1bttT8+fOVN29e05MAt0lISFCrVq3UqVMnffzxx8qTJ4/pSX4rKipKr7/+umbNmqU+ffqYngO41UsvvaS3335bc+fOVc+ePU3P8Vvnzp1TmzZtdPjwYcXFxalixYqmJwFuc/bsWTVv3lwnTpxQXFycypcvb3qS36K34E/oLc9Bb8Gf0Fuegd6CP6G3PAe9BX9Cb3kOegv+hN7yDPQW/Am95TnoLfgTb+str3goWZLWrVuntm3bqlOnTpo1axYHCXzShg0b1K5dOzVv3lwLFizgf+ce4Pnnn9f48eM1a9Ys9e7d2/QcwC1GjBihN998UzNmzFD//v1Nz/F7p06dUtOmTXX27Fl99dVXfOMGPunUqVOKjIzUDz/8oLi4OFWqVMn0JL9Hb8Ef0Fueh96CP6C3PAu9BX9Ab3keegv+gN7yPPQW/AG95VnoLfgDesvz0FvwB97YW579yPR1IiIitGzZMi1btkzdu3fXxYsXTU8CXGrlypVq06aNmjdvrk8//dQrDhB/8NZbb2no0KHq16+fpk2bZnoO4FJ2u13/+te/NHr0aE2bNo1v2HiIEiVKaNWqVSpevLjCw8N14MAB05MAlzpx4oSaNm2qo0ePas2aNXzDxkPQW/B19JZnorfgy+gtz0RvwdfRW56J3oKvo7c8E70FX0ZveSZ6C76O3vJM9BZ8nbf2ltc8lCxJLVq00Nq1axUXF6fIyEj99ttvpicBLvHFF1+oY8eO6tixoxYsWKD8+fObnoT/Z7PZNHbsWP33v//V448/rnHjxpmeBLhERkaGHn/8cb3//vv65JNP9Pe//930JFznrrvu0po1a1SuXDmFh4dr165dpicBLnHs2DE1b95cZ86c0dq1a3XvvfeanoTr0FvwVfSW56K34KvoLc9Gb8FX0Vuejd6Cr6K3PBe9BV9Fb3k2egu+it7ybPQWfJU395ZXPZQsSfXq1dOqVau0Z88etW3bVmfPnjU9CXDKp59+qi5duuiRRx7RnDlzvOYdDf7mxRdf1MSJEzVs2DANHz7c9BzAKRkZGRowYIBmz56tBQsWqEePHqYn4SaKFy+ulStXKiQkRBEREdq8ebPpSYBTDh8+rAceeEAZGRnauHGjKleubHoSboLegq+ht7wDvQVfQm95B3oLvobe8g70FnwNveUd6C34EnrLO9Bb8DX0lnegt+BrvL23vO6hZEmqW7eu1q5dq2+++UYtW7bUzz//bHoSkCsffvih+vTpo3/+85/64IMPlCePV35J+o2nn35a7733nt5++229+OKLstvtpicBOXb+/Hl16dJFn332maKjo9WpUyfTk3ALhQsX1tKlS9WoUSO1bt1aa9asMT0JyJU9e/aoSZMmKlasmOLi4nTPPfeYnoRboLfgK+gt70JvwRfQW96F3oKvoLe8C70FX0FveRd6C76A3vIu9BZ8Bb3lXegt+Apf6C3vW/z/qlevrvXr1ystLU1hYWE6cOCA6UmAw+x2u1588UU9+eSTGjlypMaOHSubzWZ6FhzwxBNPaObMmRo/frwefvhhXbx40fQkwGE//fSTmjZtqvj4eK1cuVLNmjUzPQkOKFiwoJYsWaK2bdsqMjJSs2bNMj0JyJHVq1erSZMmqlKlitasWaNSpUqZngQH0FvwZvSW96K34M3oLe9Eb8Hb0Vveid6CN6O3vBe9BW9Gb3knegvejt7yTvQWvJkv9ZbXPpQsSVWrVtXWrVt19913KywsTOvWrTM9Cbitixcvqk+fPho/frw+/vhjjRgxwvQk5FC/fv0UExOj2NhYRURE8O4qeIVvvvlGDzzwgNLS0hQfH6+wsDDTk5AD+fPn1yeffKKXXnpJjzzyiIYOHcpPs4BXmDlzptq2batWrVopNjZWxYoVMz0JOUBvwRvRW96P3oI3ore8G70Fb0VveTd6C96I3vJ+9Ba8Eb3l3egteCt6y7vRW/BGvtZbXv1QsiSVLFlSq1evVuvWrdW6dWvNmTPH9CQgW7/88otatmyp2NhYrVixQv369TM9CbnUrFkzxcfH6/jx47y7Ch4v65s0JUuWVEJCgoKCgkxPQi7YbDZFRUVp+vTp+uCDD9SrVy9duHDB9Czgpux2u6KiovT3v/9dTzzxhD799FMVKFDA9CzkAr0Fb0Jv+Q56C96E3vIN9Ba8Cb3lO+gteBN6y3fQW/Am9JZvoLfgTegt30FvwZv4Ym95/UPJkhQYGKi5c+dqyJAh6t+/v1577TXeXQWPk5KSogYNGuiHH35QfHy8IiIiTE+Ck6pVq6aEhASVKFFCTZo00YYNG0xPAm4wc+ZMNWvWTE2bNtXatWtVunRp05PgpAEDBmj58uWKiYlRZGSk0tLSTE8C/iA9PV19+vTRG2+8oenTp2vChAnKk8cnssNv0VvwBvSW76G34A3oLd9Db8HT0Vu+h96CN6C3fA+9BW9Ab/keeguejt7yPfQWvIGv9pbPnJ558uTR2LFj9d5772nUqFHq3Lmzzp49a3oWIElavHix6tevr1KlSikhIUHBwcGmJ8FFypYtq3Xr1umBBx5Q8+bNNXHiRNOTAEnSpUuX9NRTT2nAgAF65plnNH/+fN7F6UNat26tjRs36tChQwoNDdX27dtNTwIkSampqWrcuLFiY2P15ZdfasCAAaYnwUXoLXgyest30VvwVPSWb6O34KnoLd9Fb8GT0Vu+i96Cp6K3fBu9BU9Fb/kueguezJd7y2ceSs7yxBNPaM2aNdq6datCQ0OVnJxsehL8WEZGhqKiotS9e3f17t1b69evV5kyZUzPgosVLlxYixcv1uuvv65nn31WDz/8sM6dO2d6FvzY0aNH1bRpU82aNUuffvqpRo8eLZvNZnoWXKxWrVrasWOHgoKC1LhxY02dOtX0JPi5mJgYhYaGKjMzU9u2bVPLli1NT4Ib0FvwJPSWf6C34GnoLf9Ab8HT0Fv+gd6CJ6G3/AO9BU9Db/kHegueht7yD/QWPIk/9JbPPZQsSU2aNFFSUpL++te/qlGjRpo/f77pSfBDaWlpioyM1JgxYzRlyhRNnjxZ+fPnNz0LbmKz2fTiiy9q9erV+uqrrxQaGqqUlBTTs+CHNmzYoNDQUKWlpSkhIUE9evQwPQluVLJkSUVHR2v48OH6xz/+of79+ys9Pd30LPgZu92uMWPGqF27dmrTpo02bdqkypUrm54FN6K34AnoLf9Cb8FT0Fv+hd6CJ6C3/A+9BU9Ab/kXeguegt7yL/QWPAG95X/oLXgCf+ktn3woWZJKly6tFStW6NFHH1Xv3r317LPP6uLFi6ZnwU9s2rRJdevW1ddff634+HgNGjTI9CRYJCIiQlu2bFGhQoUUFhamzz//3PQk+Am73a6xY8eqWbNmatiwobZt26YaNWqYngULBAQEKCoqSosWLdLSpUvVtGlTpaammp4FP5GWlqYOHTroP//5jyZMmKC5c+eqUKFCpmfBAvQWTKK3/Be9BVPoLf9Fb8Ekest/0Vswid7yX/QWTKG3/Be9BZPoLf9Fb8Ekf+otn30oWZLy5cunSZMmadasWZo2bZoaNmyo/fv3m54FH3blyhVFRUXpwQcfVK1atZSYmKi6deuangWLVahQQfHx8erWrZs6d+6swYMH83FXcKujR4+qVatWevnll/Xf//5XixcvVtGiRU3PgsU6d+6srVu36vz586pdu7bmzJljehJ83KpVq1SrVi3t3r1ba9eu1dNPP216EixGb8Fq9BYkegvWo7cg0VuwHr0FegtWo7cg0VuwHr0Fid6C9egt0Fuwmj/2lk8/lJylb9++2r17t4oUKaK6detqwoQJstvtpmfBxxw5ckTNmjXTmDFjNHbsWC1fvlwlS5Y0PQuGFChQQFOnTtVnn32mxYsXq2bNmkpISDA9Cz7o888/V0hIiL799lutW7dOL7zwgmw2m+lZMCQoKEjbtm3TE088oUceeUTdu3fXL7/8YnoWfMzly5cVFRWlNm3aqFGjRtq5c6caNWpkehYMordgBXoL16O3YBV6C9ejt2AFegt/Rm/BCvQWrkdvwSr0Fq5Hb8EK9Bb+jN6CFfy1t/zioWTp6js7165dq6ioKD3//PNq06aNjh07ZnoWfMTChQtVu3Zt/fLLL9q8ebOGDh1KNEHS1Xd27t27V/fdd5/Cw8MVFRWljIwM07PgA9LT0zV06FB17txZkZGR2r17N9EESVJgYKBGjx6tFStWaNOmTapTp47Wr19vehZ8xP79+9WgQQP973//0wcffKBFixbpzjvvND0LHoDegjvRW8gOvQV3obeQHXoL7kRvITv0FtyJ3kJ26C24C72F7NBbcCd6C9mht+BO/txbfvNQsiTlzZtXL774otasWaODBw+qTp06mj9/vulZ8GLHjh1T165d1bNnTz366KNKTExUSEiI6VnwMGXKlNEXX3yhN954Q2+++aYiIiKUkpJieha82Nq1axUSEqI5c+Zo0aJFmjVrlooUKWJ6FjxMixYtlJSUpNq1a6t58+YaNmwYH7WHXLty5YrGjBmj+++/X4GBgdq5c6cef/xx07PgYegtuBq9BUfQW3A1eguOoLfgSvQWHEFvwdXoLTiC3oKr0VtwBL0FV6K34Ah6C65Gb/nZQ8lZmjRpoqSkJHXs2FG9e/dW+/bt9f3335ueBS9it9s1depUVatWTUlJSVq5cqXGjRunAgUKmJ4GD5UnTx49//zz2rp1q9LT01W7dm29/vrrunz5sulp8CKnTp3SoEGD1Lx5cwUHB2v37t3q2rWr6VnwYHfddZeWLl2qyZMna8aMGapZs6ZWrlxpeha8zPbt21W/fn1FRUVpxIgR2rBhgypXrmx6FjwYvQVn0VvIKXoLrkBvIafoLbgCvYWcorfgLHoLOUVvwRXoLeQUvQVXoLeQU/QWnEVv/c4vH0qWpGLFimny5MmKi4vTN998o/vuu09jxozhY2dwW99++61atmypf/zjH+rRo4d27dqlFi1amJ4FLxESEqLNmzdrzJgxeuutt1S3bl1t3rzZ9Cx4geXLl6tWrVr64osvNHPmTC1dulT33HOP6VnwEgMHDtT+/fvVpEkTtW7dWj169NDPP/9sehY8XHp6uqKiohQWFqbChQtrx44devnll5U3b17T0+AF6C3kFr0FZ9BbyC16C86gt5Ab9BacQW8ht+gtOIPeQm7RW3AGvYXcoLfgDHoLuUVv/ZHfPpScpUmTJtq5c6eGDh2qf//73woPD9euXbtMz4IHunDhgkaNGqUaNWro5MmT2rp1qyZPnsxHyiDH8ubNq6FDhyopKUllypRR48aNNXToUJ0+fdr0NHigw4cPq2PHjurYsaNatWql/fv3q3///qZnwQuVKVNGs2bN0ueff65NmzapRo0amjVrlux2u+lp8EDR0dGqUaOGJkyYoHfffVdxcXEKDg42PQteiN6Co+gtuAq9hZygt+Aq9BZygt6Cq9BbcBS9BVeht5AT9BZchd5CTtBbcBV6C46it27O7x9KlqQCBQrojTfe0LZt25SRkaH7779fgwcP5h1WuGbRokWqVq2aRo8erZEjR2rbtm26//77Tc+Cl6tcubJWrVql6dOna968eapatao+/PBD3mEFSdJvv/2mV155RcHBwTpw4IBWr16t6dOnq0SJEqanwct17NhRe/fuVdeuXTVgwACFhYVpy5YtpmfBQ6SkpKht27Z66KGHVLduXe3du1ePP/64bDab6WnwYvQWbofegjvQW7gVegvuQm/hVugtuAO9hduht+AO9BZuhd6Cu9BbuBV6C+5Ab+F26K3s8VDydUJCQpSQkKB58+ZpxYoVqlSpkqKionTx4kXT02DI/v371aZNG/Xo0UOhoaHau3evXnjhBT7WAS5js9n06KOP6uDBgxo4cKD+9a9/qUaNGoqNjTU9DYbY7XYtXLhQ1atX1/vvv6+oqCjt2rVLzZo1Mz0NPqRYsWJ6//33lZycrOLFiyssLEw9evTQkSNHTE+DIadPn9bw4cNVu3ZtHT58WLGxsVq4cKH+8pe/mJ4GH0Jv4c/oLbgbvYU/o7dgBXoLf0ZvwQr0Fv6M3oK70Vv4M3oLVqC38Gf0FqxAb+HP6K3b46HkP7HZbOrevbv27t2roUOH6q233lJISIiWLVtmehosdPz4cQ0ePFg1a9bU6dOnlZCQoAULFqh8+fKmp8FHFS9eXKNHj9auXbtUuXJlRUZGqmvXrvr6669NT4OF4uPjFRYWpt69eysyMlIHDx7Uiy++qMDAQNPT4KOCg4OvxXliYqKqV6+uUaNG6dy5c6anwSKXL1/WBx98oKpVq+qjjz7ShAkTlJycrNatW5ueBh9Fb0Git2A9egsSvQXr0Vugt2A1egsSvQXr0VuQ6C1Yj94CvQWr0VuQ6K2c4KHkbBQuXFijRo3Svn37VKtWLXXq1EkNGjTQihUrTE+DG/3888967rnnVLlyZUVHR2vGjBlKSEhQgwYNTE+DnwgKCtIXX3yhmJgYpaSkqFq1ahowYIBSU1NNT4Mbbdu2TZGRkWrSpIkKFy6s7du368MPP9Rdd91lehr8RNeuXbVv3z79H3t3Hmd1fdj7/z3MsCggRnDfgGqMAm7RqIBLRCsYNakxGBuXJtHUpWnTpre5N23jTU3ba241Jo19tFWjyCKCK1JBBRUFVFxQNhGRJS5IYNhhYAY4vz/yc26m4JaB8x3OPJ+PBw/lnImPN0YP39fXzznzt3/7t/npT3+anj175uabb05dXV3R09hJNm/enLvuuitHHHFEvve97+Xyyy/PvHnz8qd/+qeprq4ueh6tgN5qnfQWRdNbrZPeomh6q/XRWxRNb7VOeoui6a3WSW9RNL3V+ugtiqa3Wie99ek5lPwxunfvnlGjRuW1117LoYcemkGDBqVv376ZMGFC0dPYgVasWJH//b//dw477LAMGTIkP/rRjzJv3rxcdtllqaqqKnoerdDAgQMzc+bMjBgxIlOmTMkRRxyRyy+/PAsWLCh6GjvQrFmzMnjw4Jx00klZtWpVxowZk4kTJ+aYY44pehqtUIcOHfK//tf/ysKFC3PNNdfkxz/+cQ499NDceOON2bhxY9Hz2EG2bt2a0aNHp3fv3vnOd76T/v37Z86cObnpppuy5557Fj2PVkhvtQ56i5ZGb7UOeouWRG+1DnqLlkZvtQ56i5ZGb7UOeouWRG+1DnqLlkZvtQ56qxlKfCrPPfdc6ayzziolKZ155pmliRMnFj2JZvjNb35T+tGPflTaY489St26dSvdeOONpfXr1xc9C5poaGgo3X777aVDDz201L59+9J3v/vd0qJFi4qeRTO8/PLLpa9+9aulqqqq0uc///nS2LFji54E23j//fdL3/ve90odOnQoHXzwwaV/+7d/K23YsKHoWfyeGhoaSiNGjCgdddRRperq6tLll19emj9/ftGzYBt6q7LoLXYFeqvy6C12BXqrsugtdhV6q7LoLXYFeqvy6C12BXqrsugtdhV6q7LoreZzKPn3NGnSpNIZZ5xRSlI6/vjjS8OHDy81NDQUPYtPaN68eaWrr766tNtuu5W6du1auuGGG0pr1qwpehZ8pE2bNpVuvfXW0sEHH1yqqakpff3rXy+99NJLRc/iE9q6dWvp0UcfLZ155pmlJKVjjz229OCDD5a2bt1a9DT4SO+8807p2muvLXXo0KG09957l66//vrSb37zm6Jn8QmtWbOm9LOf/ax06KGHlqqrq0uXXHJJ6fXXXy96FnwsvbVrr7jerAAAIABJREFU01vsivTWrk1vsavSW7s2vcWuSm/t2vQWuyK9tWvTW+yq9NauTW+xq9Jbuza9teM4lNxMr7zySumyyy4r1dTUlPbff//S9ddfX1q5cmXRs/gQL730Uumyyy4rVVdXl3r06FG65ZZbSuvWrSt6FnwqW7ZsKY0ZM6Z00kknlZKU+vXrVxo1alRp8+bNRU9jOzZt2lQaMmRIqXfv3o3/f40ZM8bNGnY5v/nNb0rXX399qVu3bqX27duXLrvsstLs2bOLnsWHeP/990vXX399aa+99ip16tSp9J3vfKf0xhtvFD0LPjW9tWvRW1QCvbVr0VtUCr21a9FbVAq9tWvRW1QCvbVr0VtUCr21a9FbVAq9tWvRWzteValUKoVmW7RoUX7+85/njjvuSJs2bXLppZfmqquuyjHHHFP0tFZv3bp1GTlyZG677bZMmzYtJ598cv76r/86f/RHf5Q2bdoUPQ+aZeLEibnpppsyfvz4HHHEEbnqqqty+eWXp1u3bkVPa/UWLFiQO+64I3feeWdqa2tzySWX5Pvf/3769OlT9DRolg0bNmTIkCH52c9+lrfeeisDBw7MlVdemfPOOy9t27Ytel6rViqV8tRTT+W2227LAw88kK5du+a73/1urr766nzmM58peh40i95qufQWlUxvtVx6i0qlt1ouvUUl01stl96ikumtlktvUan0Vsult6hkeqvl0ls7l0PJO9iqVaty++235/bbb88bb7yRE088MVdeeWUuueSSdO7cueh5rcq0adNy++23Z+TIkWloaMiFF16Ya6+9Nv369St6Guxws2fPzq233poRI0akrq4uX/nKV3LVVVflzDPP9JtlGW3atCkPPfRQbrvttjz11FPZf//9881vfjPXXHNNDjjggKLnwQ61devWjBkzJv/+7/+eJ554Ivvss0/+5E/+JN/+9rdz2GGHFT2vVVmyZEnuuuuu3HHHHXnrrbdyyimn5Dvf+U4uueSStG/fvuh5sEPprZZDb9Ga6K2WQW/RmuitlkNv0ZrorZZDb9Ga6K2WQW/RmuitlkNv0ZrorZZDb5WHQ8k7SalUyrPPPpvbbrst9913X2pqajJ48OB84xvfyOmnn57q6uqiJ1akd999N6NHj86dd96ZGTNmpHfv3rnqqqty6aWXZq+99ip6Hux0GzZsyOjRo3PbbbdlypQp6dmzZ/7kT/4kX//613P44YcXPa9iTZs2Lffcc0+GDRuWVatW5dxzz82VV16Zc8891+s9rcLixYvzq1/9Kr/61a/y7rvv5otf/GKuuOKKfPnLX06XLl2KnleR6urq8uijj2bo0KH5r//6r3Tp0qXxnbW9evUqeh7sdHqrGHqL1k5vFUNv0drprfLTW7R2eqsYeovWTm8VQ2/R2umt8tNbtHZ6qxh6q/wcSi6DlStXZtiwYbnzzjszffr07LvvvrnooosyePDg9O/f37s8m+n999/Pfffdl1GjRmXKlCnp3LlzLrroolx55ZU5+eSTi54HhZkzZ05uv/32jBgxIkuXLs1xxx2XwYMHZ/DgwenZs2fR83Z5r7zySkaNGpVRo0Zl4cKFOfzww3PFFVfkm9/8pneN02pt2bIl48ePz+23355x48YlSc4555wMHjw4F1xwgXd5NtOmTZsyfvz4jBo1KmPGjEldXV3OOOOMfPvb386FF17oXeO0Wnpr59JbsH16a+fSW7AtvbVz6S3YPr21c+kt2D69tXPpLdiW3tq59BZsn97aufRWsRxKLrN58+Zl1KhRuffeezNr1qwccMABueiii3LBBRekf//+frP9hBYuXJhx48blvvvuyzPPPJPdd989559/fgYPHpyBAwf6+wi/Y8uWLZk0aVLuvffePPDAA1m+fHlOPPHEfO1rX8u5557rHYef0JYtWzJt2rSMHTs2o0aNyvz589OjR4/GG2HHH3980ROhRVm1alUefvjh3HvvvZkwYUJqamoyaNCgXHjhhfnDP/zD7L333kVP3CWsWbMmEyZMyMMPP5yHH344a9euTb9+/XLxxRfnq1/9avbbb7+iJ0KLord2DL0Fn5ze2jH0Fnw6emvH0Fvw6eitHUNvwSent3YMvQWfjt7aMfQWfDp6a8fQWy2HQ8kFmjNnTkaNGpX77rsvs2fPTqdOnXLmmWdm0KBBGThwYLp37170xBZj48aNmTRpUsaPH59x48bljTfeSKdOnXLuuedm8ODBOffcc7PbbrsVPRNavM2bN2fixImN70Jcvnx5DjnkkAwcODADBw7MWWed5Z2ev2Pp0qUZP358xo8fn8cffzwrVqxI9+7d89WvfjWDBw/OF77whaInwi5hxYoVefDBB3Pvvfdm0qRJ2bx5c0444YQMHDgwgwYNyoknnuhb0fz/SqVSZsyY0fjaM2XKlGzZsiUnn3xyBg8enIsuuigHHnhg0TNhl6C3Pjm9BTuG3vp09BbsGHrrk9NbsOPorU9Ob8GOobc+Hb0FO4be+uT0Fuw4euuT01stl0PJLcTixYszbty4jB8/PhMnTsy6devyuc99LgMGDEjfvn1z6qmn5uCDDy56ZtnU1dXlpZdeyuTJk/Pss89m0qRJ2bBhQ/r06dMYl/3790+7du2Kngq7rC1btuSll15qfO158cUXU11dnX79+uX0009P3759c8opp7SqmzhLly7N1KlTM3ny5Dz99NOZPn162rVrl9NOO60xLo888siiZ8Iube3atZk4cWLjTYnFixena9euGTBgQPr3759+/frlmGOOaTU3cUqlUl5//fVMmTIlkydPzoQJE/Lee+9ln332yTnnnJNBgwbl7LPPTrdu3YqeCrs0vdWU3oKdT29tS2/Bzqe3mtJbUB56qym9BTuf3tqW3oKdT281pbegPPRWU3pr1+FQcgtUX1+fyZMn57HHHsszzzyTl19+OQ0NDTnooIMaL2ZOPvnk9O7dOx06dCh67g7x9ttv5+WXX87kyZMzderUvPzyy6mvr8+BBx6Y/v3756yzzsrAgQNz0EEHFT0VKtby5cvzxBNP5LHHHsuUKVMyf/78VFdXp0+fPunfv3/69u2bL3zhC+nRo0fatGlT9Nxma2hoyNy5czNt2rTG15558+aluro6vXr1arxRc8YZZ6Rjx45Fz4WK9frrr2fcuHF58sknM3Xq1KxcuTKdOnXKySefnH79+qVv3745/vjjK+amxerVqzNjxoxMmTIlU6ZMydSpU7NixYp06tQpX/jCF3LmmWdm4MCBOe644yritRZaIr2lt6AIektvQRH0lt6CctNbeguKoLf0FhRBb+ktKDe9pbd2JQ4l7wI2bNiQF198sfFfsKlTp2bVqlWprq7O4Ycfnj59+uToo49Onz590qdPn3Tv3r3F/ia/evXqzJ49OzNnzsyMGTMya9aszJgxI6tWrUqbNm3Sq1evxhfKfv36+ch5KND777/f+K7qqVOn5pVXXklDQ0M6deqUXr16NXnd6d27d4sOqrfffjuzZ8/OjBkzMnPmzMycOTOvv/566uvr07Fjx3zhC19ovDHVt2/f7LHHHkVPhlZp69atje+q/uDHW2+9lSTZf//9t7nmOfLII1tsUNXX1+fNN9/MrFmz8tprr2XWrFmZOXNmFi1alCQ54IADGq93+vXrl2OPPTY1NTXFjoZWSm8BRdBbQLnpLb0FRdBbQBH0FlBuektvQRH0Fi2ZQ8m7oK1bt+bNN99sEh8zZszIwoULUyqV0q5duxxyyCHp3r17evTo0fjHQw89NF27dm38UVVVtUN3bdiwIbW1tamtrc17772XRYsWZeHChY1/XLhwYVasWJEk2WOPPdK7d+8mL4BHH310unTpskM3ATvOhg0bGn/j/+C157XXXmvy7/V/f93p0aNHDjjggOy9997p2rVrOnXqtMN3rVq1KsuWLUttbW3eeeedbV53Fi1alI0bNyZJDjrooG0uvI466iihBC3Y0qVLM2PGjCavPbNnz86mTZuS/PZmzu++7nTv3j3du3fPvvvu23jN0759+x26afPmzY3XPMuWLdvmNWfhwoV59913s3Xr1tTU1OSII45I7969c8wxxzS+Bh1yyCE7dBOw4+gtoAh6CyiC3gLKTW8BRdBbQBH0FlBueouWxKHkCrJ27drMnj078+fP3+Zf4HfeeScNDQ2NX1tVVZVu3bo1vqDstttu2WOPPVJdXZ3q6urGd1G2b98+9fX1KZVKqa+vz/r165Mk69atS319feOLxvLly1NXV9dkz1577bXNRVSPHj1y5JFHpnv37jv8RQwoxrvvvps5c+ZsEyyLFi3K0qVLm3xt+/btG193unXrlrZt2zZePOy2226N7wjt0KFD442W9evXN74OrVq1Kps2bWp87amtrc3mzZsb//pt2rTJgQce2OSmUffu3dOzZ8/07t07e+21V5n+rgA70+bNmzNv3rzMmzdvuzdq161b1+TrO3funK5du2bvvfdOly5d0qFDh+y2225J0nj988HN2w9eU1auXJkk2bRpUzZs2JB169Zl+fLlWbZsWVavXt3kr9+hQ4dtbhr16NEjhx12WHr16pV27drt7L8lQBnoLaAIegsoN70FFEFvAUXQW0C56S2gCHqLIjiU3Eps3rw577//fpN3Pf1u9NTV1WXdunVpaGjIli1bsmbNmiTJG2+8kR49eqRdu3Zp165dOnbsmOS3Fzg1NTVN3inxuz/233//dO7cuchfMtAC1NXV5b333mu82Pjd150PbrisWrWq8Ws/uFEze/bs9OrVK0nSsWPHtGvXLm3atEmXLl2a3Pj54McH71Tfb7/9xBHQeHPlv7/mfHDD5XfDaM2aNdmyZUtWrFiR9evX5+CDD06SfOYzn0mSxhs8nTp1avJ688HN57322iv7779/Yb9WoGXQW0AR9BZQBL0FlJveAoqgt4Ai6C2g3PQWO4tDyXyoUqmUnj175oYbbsill15a9ByglXjxxRdz2mmn5f333/ctGICy+cEPfpA5c+bkkUceKXoK0EroLaAIegsogt4Cyk1vAUXQW0AR9BZQbnqLT6JN0QNouZ577rksWrQow4YNK3oK0IqMGDEiGzduzAMPPFD0FKCVKJVKGTp0aMaNG5fa2tqi5wCthN4CiqC3gHLTW0AR9BZQBL0FlJveAoqgt/gkHErmQ40cOTJVVVWZMGFCli9fXvQcoBXYunVrhg8fniQZOnRowWuA1mLy5MlZsmRJSqWSG8ZA2egtoNz0FlAEvQUUQW8B5aa3gCLoLaAIeotPwqFktmvLli0ZPnx4SqVSqqqqMnr06KInAa3AU089lWXLliVJJk2alPfff7/gRUBrMGLEiLRr1y5Jcvfddxe8BmgN9BZQBL0FFEFvAeWmt4Ai6C2gCHoLKDe9xSflUDLbNWHChKxYsSLJb9/Z6QIGKIcRI0akbdu2SZI2bdq4gAF2uoaGhtxzzz2pr6/P1q1bM2XKlLz77rtFzwIqnN4CiqC3gHLTW0AR9BZQBL0FlJveAoqgt/ikHEpmu343nLZu3ZoXXnghixYtKnYUUNHq6+szevToNDQ0JPntO6xcwAA72+OPP57Vq1c3/rympib33ntvgYuA1kBvAeWmt4Ai6C2gCHoLKDe9BRRBbwFF0Ft8Ug4ls42NGzfm/vvvbwyn5LcXMKNGjSpwFVDpHn300axbt67x56VSKS+99FLmz59f4Cqg0v1uOCXJ5s2bM2TIkAIXAZVObwFF0FtAEfQWUG56CyiC3gKKoLeActNbfBoOJbONsWPHZsOGDU0ea2hocAED7FTDhw9PTU1Nk8fatm3rAgbYaTZs2JAHH3ywSTiVSqXMmDEj8+bNK3AZUMn0FlAEvQWUm94CiqC3gCLoLaDc9BZQBL3Fp+FQMtsYNmxYqqurt3l8zpw5mT17dgGLgEq3fv36jB07tkk4Jb+9gLnzzjsLWgVUujFjxmTjxo3bPN6uXbuMHDmygEVAa6C3gHLTW0AR9BZQBL0FlJveAoqgt4Ai6C0+DYeSaWLNmjUZN25cNm/evM1zbdu2zb333lvAKqDSPfjgg6mvr9/uc/Pnz8/MmTPLvAhoDYYPH77dcKqvr/eOTmCn0FtAEfQWUAS9BZSb3gKKoLeAIugtoNz0Fp+WQ8k08cADD2z3BST57Ts677rrrpRKpTKvAirdsGHDUlVVtd3n2rZtm3vuuafMi4BKt3Llyjz22GMfet2zYMGCTJ8+vcyrgEqnt4Ai6C2g3PQWUAS9BRRBbwHlpreAIugtPi2Hkmli6NChHxpOSfL222/nxRdfLOMioNItX748EyZMyJYtW7b7vAsYYGe47777PvR1J/ntt7hywxjY0fQWUG56CyiC3gKKoLeActNbQBH0FlAEvcWn5VAyjZYtW5ZJkya5gAHKavTo0R/7NUuWLMlzzz1XhjVAazF06NCPfL6+vj5Dhw7N1q1by7QIqHR6CyiC3gKKoLeActNbQBH0FlAEvQWUm97i9+FQMo3uvffej3wBSX57ATN8+PCP/TqAT2rYsGEfG0VVVVUuYIAd5r333suUKVM+9rXn/fffz+TJk8u0Cqh0egsogt4Cyk1vAUXQW0AR9BZQbnoLKILe4vdRU/QAWo533nknZ5xxRpNvITN16tR07949BxxwQONj1dXVefPNN/O5z32uiJlABVm7dm06duyY0047rfGxVatW5bXXXkvfvn3Ttm3bxsdXrFhRxESgAr366qs5/fTTm9y0efPNN7Nhw4Ycc8wxjY+1adMmc+fObfIaBfD70ltAuektoAh6CyiC3gLKTW8BRdB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", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# notice the parallelism\n", "result.visualize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "In this section you will do a few `dask.dataframe` computations. If you are comfortable with pandas then these should be familiar. You will have to think about when to call `.compute()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. How many rows are in our dataset?\n", "\n", "_Hint_: how would you check how many items are in a list?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.086080Z", "iopub.status.busy": "2023-06-29T15:00:07.085562Z", "iopub.status.idle": "2023-06-29T15:00:07.088628Z", "shell.execute_reply": "2023-06-29T15:00:07.088036Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.091840Z", "iopub.status.busy": "2023-06-29T15:00:07.091525Z", "iopub.status.idle": "2023-06-29T15:00:07.175605Z", "shell.execute_reply": "2023-06-29T15:00:07.175030Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "9990" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ddf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. In total, how many non-canceled flights were taken?\n", "\n", "_Hint_: use [boolean indexing](https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.178548Z", "iopub.status.busy": "2023-06-29T15:00:07.177935Z", "iopub.status.idle": "2023-06-29T15:00:07.180865Z", "shell.execute_reply": "2023-06-29T15:00:07.180316Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.183297Z", "iopub.status.busy": "2023-06-29T15:00:07.182880Z", "iopub.status.idle": "2023-06-29T15:00:07.277436Z", "shell.execute_reply": "2023-06-29T15:00:07.276873Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "9383" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ddf[~ddf.Cancelled])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. In total, how many non-canceled flights were taken from each airport?\n", "\n", "*Hint*: use [groupby](https://pandas.pydata.org/pandas-docs/stable/groupby.html)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.280614Z", "iopub.status.busy": "2023-06-29T15:00:07.279995Z", "iopub.status.idle": "2023-06-29T15:00:07.282827Z", "shell.execute_reply": "2023-06-29T15:00:07.282351Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.285293Z", "iopub.status.busy": "2023-06-29T15:00:07.284723Z", "iopub.status.idle": "2023-06-29T15:00:07.402955Z", "shell.execute_reply": "2023-06-29T15:00:07.402299Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Origin\n", "EWR 4132\n", "JFK 1085\n", "LGA 4166\n", "Name: Origin, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf[~ddf.Cancelled].groupby(\"Origin\").Origin.count().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. What was the average departure delay from each airport?" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.406284Z", "iopub.status.busy": "2023-06-29T15:00:07.406032Z", "iopub.status.idle": "2023-06-29T15:00:07.409683Z", "shell.execute_reply": "2023-06-29T15:00:07.409091Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.412888Z", "iopub.status.busy": "2023-06-29T15:00:07.412466Z", "iopub.status.idle": "2023-06-29T15:00:07.589597Z", "shell.execute_reply": "2023-06-29T15:00:07.589036Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Origin\n", "EWR 12.500968\n", "JFK 17.053456\n", "LGA 10.169227\n", "Name: DepDelay, dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.groupby(\"Origin\").DepDelay.mean().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. What day of the week has the worst average departure delay?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.593697Z", "iopub.status.busy": "2023-06-29T15:00:07.592667Z", "iopub.status.idle": "2023-06-29T15:00:07.596838Z", "shell.execute_reply": "2023-06-29T15:00:07.596200Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.600463Z", "iopub.status.busy": "2023-06-29T15:00:07.599339Z", "iopub.status.idle": "2023-06-29T15:00:07.765134Z", "shell.execute_reply": "2023-06-29T15:00:07.764382Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.groupby(\"DayOfWeek\").DepDelay.mean().idxmax().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Let's say the distance column is erroneous and you need to add 1 to all values, how would you do this?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.768020Z", "iopub.status.busy": "2023-06-29T15:00:07.767790Z", "iopub.status.idle": "2023-06-29T15:00:07.770513Z", "shell.execute_reply": "2023-06-29T15:00:07.769998Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.773400Z", "iopub.status.busy": "2023-06-29T15:00:07.772993Z", "iopub.status.idle": "2023-06-29T15:00:07.945472Z", "shell.execute_reply": "2023-06-29T15:00:07.944782Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/share/miniconda3/envs/dask-tutorial/lib/python3.10/site-packages/dask/dataframe/core.py:4139: UserWarning: \n", "You did not provide metadata, so Dask is running your function on a small dataset to guess output types. It is possible that Dask will guess incorrectly.\n", "To provide an explicit output types or to silence this message, please provide the `meta=` keyword, as described in the map or apply function that you are using.\n", " Before: .apply(func)\n", " After: .apply(func, meta=('Distance', 'float64'))\n", "\n", " warnings.warn(meta_warning(meta))\n" ] }, { "data": { "text/plain": [ "0 320.0\n", "1 320.0\n", "2 320.0\n", "3 320.0\n", "4 320.0\n", " ... \n", "994 417.0\n", "995 417.0\n", "996 417.0\n", "997 417.0\n", "998 417.0\n", "Name: Distance, Length: 9990, dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf[\"Distance\"].apply(\n", " lambda x: x + 1\n", ").compute() # don't worry about the warning, we'll discuss in the next sections\n", "\n", "# OR\n", "\n", "(ddf[\"Distance\"] + 1).compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sharing Intermediate Results\n", "\n", "When computing all of the above, we sometimes did the same operation more than once. For most operations, `dask.dataframe` stores the arguments, allowing duplicate computations to be shared and only computed once.\n", "\n", "For example, let's compute the mean and standard deviation for departure delay of all non-canceled flights. Since Dask operations are lazy, those values aren't the final results yet. They're just the steps required to get the result.\n", "\n", "If you compute them with two calls to compute, there is no sharing of intermediate computations." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.948809Z", "iopub.status.busy": "2023-06-29T15:00:07.948353Z", "iopub.status.idle": "2023-06-29T15:00:07.960086Z", "shell.execute_reply": "2023-06-29T15:00:07.959505Z" } }, "outputs": [], "source": [ "non_canceled = ddf[~ddf.Cancelled]\n", "mean_delay = non_canceled.DepDelay.mean()\n", "std_delay = non_canceled.DepDelay.std()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:07.963415Z", "iopub.status.busy": "2023-06-29T15:00:07.962831Z", "iopub.status.idle": "2023-06-29T15:00:08.206174Z", "shell.execute_reply": "2023-06-29T15:00:08.205516Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 78.7 ms, sys: 4.18 ms, total: 82.9 ms\n", "Wall time: 237 ms\n" ] } ], "source": [ "%%time\n", "\n", "mean_delay_res = mean_delay.compute()\n", "std_delay_res = std_delay.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `dask.compute`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But let's try by passing both to a single `compute` call." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:08.209154Z", "iopub.status.busy": "2023-06-29T15:00:08.208789Z", "iopub.status.idle": "2023-06-29T15:00:08.362424Z", "shell.execute_reply": "2023-06-29T15:00:08.361820Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 54.1 ms, sys: 0 ns, total: 54.1 ms\n", "Wall time: 147 ms\n" ] } ], "source": [ "%%time\n", "\n", "mean_delay_res, std_delay_res = dask.compute(mean_delay, std_delay)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `dask.compute` takes roughly 1/2 the time. This is because the task graphs for both results are merged when calling `dask.compute`, allowing shared operations to only be done once instead of twice. In particular, using `dask.compute` only does the following once:\n", "\n", "- the calls to `read_csv`\n", "- the filter (`df[~df.Cancelled]`)\n", "- some of the necessary reductions (`sum`, `count`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see what the merged task graphs between multiple results look like (and what's shared), you can use the `dask.visualize` function (you might want to use `filename='graph.pdf'` to save the graph to disk so that you can zoom in more easily):" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:08.365885Z", "iopub.status.busy": "2023-06-29T15:00:08.365136Z", "iopub.status.idle": "2023-06-29T15:00:08.881629Z", "shell.execute_reply": "2023-06-29T15:00:08.880965Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f6e07ecb46384b39830ea4dc8bf99527", "version_major": 2, "version_minor": 0 }, "text/plain": [ "CytoscapeWidget(cytoscape_layout={'name': 'dagre', 'rankDir': 'BT', 'nodeSep': 10, 'edgeSep': 10, 'spacingFact…" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dask.visualize(mean_delay, std_delay, engine=\"cytoscape\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### `.persist()`\n", "\n", "While using a distributed scheduler (you will learn more about schedulers in the upcoming notebooks), you can keep some _data that you want to use often_ in the _distributed memory_. \n", "\n", "`persist` generates \"Futures\" (more on this later as well) and stores them in the same structure as your output. You can use `persist` with any data or computation that fits in memory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to analyze data only for non-canceled flights departing from JFK airport, you can either have two compute calls like in the previous section:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.103173Z", "iopub.status.busy": "2023-06-29T15:00:09.102573Z", "iopub.status.idle": "2023-06-29T15:00:09.109200Z", "shell.execute_reply": "2023-06-29T15:00:09.108567Z" } }, "outputs": [], "source": [ "non_cancelled = ddf[~ddf.Cancelled]\n", "ddf_jfk = non_cancelled[non_cancelled.Origin == \"JFK\"]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.111964Z", "iopub.status.busy": "2023-06-29T15:00:09.111408Z", "iopub.status.idle": "2023-06-29T15:00:09.363448Z", "shell.execute_reply": "2023-06-29T15:00:09.362393Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 74.6 ms, sys: 0 ns, total: 74.6 ms\n", "Wall time: 244 ms\n" ] }, { "data": { "text/plain": [ "18503.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "ddf_jfk.DepDelay.mean().compute()\n", "ddf_jfk.DepDelay.sum().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, consider persisting that subset of data in memory.\n", "\n", "See the \"Graph\" dashboard plot, the red squares indicate persisted data stored as Futures in memory. You will also notice an increase in Worker Memory (another dashboard plot) consumption." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.366454Z", "iopub.status.busy": "2023-06-29T15:00:09.366223Z", "iopub.status.idle": "2023-06-29T15:00:09.380168Z", "shell.execute_reply": "2023-06-29T15:00:09.377928Z" } }, "outputs": [], "source": [ "ddf_jfk = ddf_jfk.persist() # returns back control immediately" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.395360Z", "iopub.status.busy": "2023-06-29T15:00:09.395015Z", "iopub.status.idle": "2023-06-29T15:00:09.611744Z", "shell.execute_reply": "2023-06-29T15:00:09.611136Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 83.8 ms, sys: 4.65 ms, total: 88.4 ms\n", "Wall time: 208 ms\n" ] }, { "data": { "text/plain": [ "36.85799641792652" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "ddf_jfk.DepDelay.mean().compute()\n", "ddf_jfk.DepDelay.std().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyses on this persisted data is faster because we are not repeating the loading and selecting (non-canceled, JFK departure) operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom code with Dask DataFrame\n", "\n", "`dask.dataframe` only covers a small but well-used portion of the pandas API.\n", "\n", "This limitation is for two reasons:\n", "\n", "1. The Pandas API is *huge*\n", "2. Some operations are genuinely hard to do in parallel, e.g, sorting.\n", "\n", "Additionally, some important operations like `set_index` work, but are slower than in pandas because they include substantial shuffling of data, and may write out to disk.\n", "\n", "**What if you want to use some custom functions that aren't (or can't be) implemented for Dask DataFrame yet?**\n", "\n", "You can open an issue on the [Dask issue tracker](https://github.com/dask/dask/issues) to check how feasible the function could be to implement, and you can consider contributing this function to Dask.\n", "\n", "In case it's a custom function or tricky to implement, `dask.dataframe` provides a few methods to make applying custom functions to Dask DataFrames easier:\n", "\n", "- [`map_partitions`](https://docs.dask.org/en/latest/generated/dask.dataframe.DataFrame.map_partitions.html): to run a function on each partition (each pandas DataFrame) of the Dask DataFrame\n", "- [`map_overlap`](https://docs.dask.org/en/latest/generated/dask.dataframe.rolling.map_overlap.html): to run a function on each partition (each pandas DataFrame) of the Dask DataFrame, with some rows shared between neighboring partitions\n", "- [`reduction`](https://docs.dask.org/en/latest/generated/dask.dataframe.Series.reduction.html): for custom row-wise reduction operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a quick look at the `map_partitions()` function:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.614841Z", "iopub.status.busy": "2023-06-29T15:00:09.614610Z", "iopub.status.idle": "2023-06-29T15:00:09.620341Z", "shell.execute_reply": "2023-06-29T15:00:09.619808Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method map_partitions in module dask.dataframe.core:\n", "\n", "map_partitions(func, *args, **kwargs) method of dask.dataframe.core.DataFrame instance\n", " Apply Python function on each DataFrame partition.\n", " \n", " Note that the index and divisions are assumed to remain unchanged.\n", " \n", " Parameters\n", " ----------\n", " func : function\n", " The function applied to each partition. If this function accepts\n", " the special ``partition_info`` keyword argument, it will receive\n", " information on the partition's relative location within the\n", " dataframe.\n", " args, kwargs :\n", " Positional and keyword arguments to pass to the function.\n", " Positional arguments are computed on a per-partition basis, while\n", " keyword arguments are shared across all partitions. The partition\n", " itself will be the first positional argument, with all other\n", " arguments passed *after*. Arguments can be ``Scalar``, ``Delayed``,\n", " or regular Python objects. DataFrame-like args (both dask and\n", " pandas) will be repartitioned to align (if necessary) before\n", " applying the function; see ``align_dataframes`` to control this\n", " behavior.\n", " enforce_metadata : bool, default True\n", " Whether to enforce at runtime that the structure of the DataFrame\n", " produced by ``func`` actually matches the structure of ``meta``.\n", " This will rename and reorder columns for each partition,\n", " and will raise an error if this doesn't work,\n", " but it won't raise if dtypes don't match.\n", " transform_divisions : bool, default True\n", " Whether to apply the function onto the divisions and apply those\n", " transformed divisions to the output.\n", " align_dataframes : bool, default True\n", " Whether to repartition DataFrame- or Series-like args\n", " (both dask and pandas) so their divisions align before applying\n", " the function. This requires all inputs to have known divisions.\n", " Single-partition inputs will be split into multiple partitions.\n", " \n", " If False, all inputs must have either the same number of partitions\n", " or a single partition. Single-partition inputs will be broadcast to\n", " every partition of multi-partition inputs.\n", " meta : pd.DataFrame, pd.Series, dict, iterable, tuple, optional\n", " An empty ``pd.DataFrame`` or ``pd.Series`` that matches the dtypes\n", " and column names of the output. This metadata is necessary for\n", " many algorithms in dask dataframe to work. For ease of use, some\n", " alternative inputs are also available. Instead of a ``DataFrame``,\n", " a ``dict`` of ``{name: dtype}`` or iterable of ``(name, dtype)``\n", " can be provided (note that the order of the names should match the\n", " order of the columns). Instead of a series, a tuple of ``(name,\n", " dtype)`` can be used. If not provided, dask will try to infer the\n", " metadata. This may lead to unexpected results, so providing\n", " ``meta`` is recommended. For more information, see\n", " ``dask.dataframe.utils.make_meta``.\n", " \n", " Examples\n", " --------\n", " Given a DataFrame, Series, or Index, such as:\n", " \n", " >>> import pandas as pd\n", " >>> import dask.dataframe as dd\n", " >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5],\n", " ... 'y': [1., 2., 3., 4., 5.]})\n", " >>> ddf = dd.from_pandas(df, npartitions=2)\n", " \n", " One can use ``map_partitions`` to apply a function on each partition.\n", " Extra arguments and keywords can optionally be provided, and will be\n", " passed to the function after the partition.\n", " \n", " Here we apply a function with arguments and keywords to a DataFrame,\n", " resulting in a Series:\n", " \n", " >>> def myadd(df, a, b=1):\n", " ... return df.x + df.y + a + b\n", " >>> res = ddf.map_partitions(myadd, 1, b=2)\n", " >>> res.dtype\n", " dtype('float64')\n", " \n", " Here we apply a function to a Series resulting in a Series:\n", " \n", " >>> res = ddf.x.map_partitions(lambda x: len(x)) # ddf.x is a Dask Series Structure\n", " >>> res.dtype\n", " dtype('int64')\n", " \n", " By default, dask tries to infer the output metadata by running your\n", " provided function on some fake data. This works well in many cases, but\n", " can sometimes be expensive, or even fail. To avoid this, you can\n", " manually specify the output metadata with the ``meta`` keyword. This\n", " can be specified in many forms, for more information see\n", " ``dask.dataframe.utils.make_meta``.\n", " \n", " Here we specify the output is a Series with no name, and dtype\n", " ``float64``:\n", " \n", " >>> res = ddf.map_partitions(myadd, 1, b=2, meta=(None, 'f8'))\n", " \n", " Here we map a function that takes in a DataFrame, and returns a\n", " DataFrame with a new column:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y))\n", " >>> res.dtypes\n", " x int64\n", " y float64\n", " z float64\n", " dtype: object\n", " \n", " As before, the output metadata can also be specified manually. This\n", " time we pass in a ``dict``, as the output is a DataFrame:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y),\n", " ... meta={'x': 'i8', 'y': 'f8', 'z': 'f8'})\n", " \n", " In the case where the metadata doesn't change, you can also pass in\n", " the object itself directly:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.head(), meta=ddf)\n", " \n", " Also note that the index and divisions are assumed to remain unchanged.\n", " If the function you're mapping changes the index/divisions, you'll need\n", " to clear them afterwards:\n", " \n", " >>> ddf.map_partitions(func).clear_divisions() # doctest: +SKIP\n", " \n", " Your map function gets information about where it is in the dataframe by\n", " accepting a special ``partition_info`` keyword argument.\n", " \n", " >>> def func(partition, partition_info=None):\n", " ... pass\n", " \n", " This will receive the following information:\n", " \n", " >>> partition_info # doctest: +SKIP\n", " {'number': 1, 'division': 3}\n", " \n", " For each argument and keyword arguments that are dask dataframes you will\n", " receive the number (n) which represents the nth partition of the dataframe\n", " and the division (the first index value in the partition). If divisions\n", " are not known (for instance if the index is not sorted) then you will get\n", " None as the division.\n", "\n" ] } ], "source": [ "help(ddf.map_partitions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"Distance\" column in `ddf` is currently in miles. Let's say we want to convert the units to kilometers and we have a general helper function as shown below. In this case, we can use `map_partitions` to apply this function across each of the internal pandas `DataFrame`s in parallel. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.623484Z", "iopub.status.busy": "2023-06-29T15:00:09.623134Z", "iopub.status.idle": "2023-06-29T15:00:09.629443Z", "shell.execute_reply": "2023-06-29T15:00:09.628866Z" } }, "outputs": [], "source": [ "def my_custom_converter(df, multiplier=1):\n", " return df * multiplier\n", "\n", "\n", "meta = pd.Series(name=\"Distance\", dtype=\"float64\")\n", "\n", "distance_km = ddf.Distance.map_partitions(\n", " my_custom_converter, multiplier=0.6, meta=meta\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.632375Z", "iopub.status.busy": "2023-06-29T15:00:09.631792Z", "iopub.status.idle": "2023-06-29T15:00:09.870184Z", "shell.execute_reply": "2023-06-29T15:00:09.869482Z" } }, "outputs": [ { "data": { "image/png": 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f5p5CQsuWLYtNN900DjjggJgwYULuOVALelpNegrtp6fVpKfQfnpaTXoK7aen1aSn0H56Wk16Cu2np9Wkp9B+elpNegrtp6fVpKfQfnpaTXpaCt7sVFT33HNPzJkzJz772c/mnkJiXbt2jU996lMxceLEWLJkSe45UHl6Wl16Cu2lp9Wlp9BeelpdegrtpafVpafQXnpaXXoK7aWn1aWn0F56Wl16Cu2lp9Wlp9BeelpdeloOjp0K6rzzzothw4bFyJEjc0+hBT784Q/H0qVLY+rUqbmnQOXpabXpKbSPnlabnkL76Gm16Sm0j55Wm55C++hptekptI+eVpueQvvoabXpKbSPnlabnkL76Gm16WnxOXYqoBdeeCEmTZoUxxxzTHR0dOSeQwv07ds3DjrooDj//PNzT4FK09Pq01NoDz2tPj2F9tDT6tNTaA89rT49hfbQ0+rTU2gPPa0+PYX20NPq01NoDz2tPj2F9tDT6tPT4nPsVEBXXnllLF68OD7wgQ/knkILffjDH445c+bEvHnzck+BytLTetBTaD09rQc9hdbT03rQU2g9Pa0HPYXW09N60FNoPT2tBz2F1tPTetBTaD09rQc9hdbT03rQ02Jz7FRAl1xySYwaNSr69++fewotNHr06OjTp49X30EL6Wk96Cm0np7Wg55C6+lpPegptJ6e1oOeQuvpaT3oKbSentaDnkLr6Wk96Cm0np7Wg55C6+lpPehpsTl2KpjFixfHjBkz4tBDD809hRbr1q1bjBs3LqZMmZJ7ClSSntaHnkJr6Wl96Cm0lp7Wh55Ca+lpfegptJae1oeeQmvpaX3oKbSWntaHnkJr6Wl96Cm0lp7Wh54Wm2Ongrn22mtj6dKlMW7cuNxTaIODDz445s6dG0888UTuKVA5elovegqto6f1oqfQOnpaL3oKraOn9aKn0Dp6Wi96Cq2jp/Wip9A6elovegqto6f1oqfQOnpaL3paXI6dCmbGjBkxYsSI2GCDDXJPoQ1GjRoVPXr0iFmzZuWeApWjp/Wip9A6elovegqto6f1oqfQOnpaL3oKraOn9aKn0Dp6Wi96Cq2jp/Wip9A6elovegqto6f1oqfF5dipYGbOnBljx47NPYM26dmzZ4wcOTJmzJiRewpUjp7Wi55C6+hpvegptI6e1oueQuvoab3oKbSOntaLnkLr6Gm96Cm0jp7Wi55C6+hpvegptI6e1oueFpdjpwKZN29ezJs3L0aPHp17Cm00evTouOGGG6LRaOSeApWhp/Wkp5CentaTnkJ6elpPegrp6Wk96Smkp6f1pKeQnp7Wk55CenpaT3oK6elpPekppKen9aSnxeTYqUBmz54da665Zuy44465p9BGe+yxRzz99NPx8MMP554ClaGn9aSnkJ6e1pOeQnp6Wk96CunpaT3pKaSnp/Wkp5CentaTnkJ6elpPegrp6Wk96Smkp6f1pKfF5NipQObMmRPbb7999OjRI/cU2mjEiBGx1lprxS9/+cvcU6Ay9LSe9BTS09N60lNIT0/rSU8hPT2tJz2F9PS0nvQU0tPTetJTSE9P60lPIT09rSc9hfT0tJ70tJgcOxXIHXfcETvvvHPuGbRZt27dYvvtt4/bb7899xSoDD2tJz2F9PS0nvQU0tPTetJTSE9P60lPIT09rSc9hfT0tJ70FNLT03rSU0hPT+tJTyE9Pa0nPS0mx04FsXz58rj//vtj6623zj2FDLbaaqu49957c8+AStDTetNTSEdP601PIR09rTc9hXT0tN70FNLR03rTU0hHT+tNTyEdPa03PYV09LTe9BTS0dN609PicexUEA8//HAsWbJEHGtqq622ivvuuy8ajUbuKVB6elpvegrp6Gm96Smko6f1pqeQjp7Wm55COnpab3oK6ehpvekppKOn9aankI6e1pueQjp6Wm96WjyOnQriN7/5TXR0dMQWW2yRewoZDB8+PJ599tl4/PHHc0+B0tPTetNTSEdP601PIR09rTc9hXT0tN70FNLR03rTU0hHT+tNTyEdPa03PYV09LTe9BTS0dN609PicexUEI899li8/e1vj7XWWiv3FDIYMmRIRETMmzcv7xCoAD2tNz2FdPS03vQU0tHTetNTSEdP601PIR09rTc9hXT0tN70FNLR03rTU0hHT+tNTyEdPa03PS0ex04FMW/evBg0aFDuGW11xx13REdHR4wfPz73lOze8Y53RI8ePeKxxx7LPQVKT0/rTU8hHT2tNz2FdPS03vQU0tHTetNTSEdP601PIR09rTc9hXT0tN70FNLR03rTU0hHT+tNT4vHsVNBzJ8/PwYOHJh7RnKzZ8+Ojo6O+PKXv9yS56uio6MjNtpoI5egkICert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnkI6ert7zVaGnxdM19wD+5umnn45dd90194y22mGHHaLRaOSeURj9+/ePBQsW5J4Bpaen6CmkoafoKaShp+gppKGn6CmkoafoKaShp+gppKGn6CmkoafoKaShp+hpsXizU0EsXLgw+vbtm3sGGfXt2zcWLlyYewaUnp6ip5CGnqKnkIaeoqeQhp6ip5CGnqKnkIaeoqeQhp6ip5CGnqKnkIaeoqfF4tipIBYtWpQ1js8880wcd9xxseGGG0bPnj1jhx12iCuuuCIuuuii6OjoiGnTpr3m+UajET/4wQ9it912i7XXXjt69uwZ22yzTXz7299ecd355S9/Od797ndHRMSpp54aHR0dK35FRNxxxx3R0dER48ePb+r5Zr83IuLqq6+Ojo6OOOecc+L666+P3XbbLXr16hUbbbRRnHnmmSue+9a3vhXDhg2LHj16xOabbx5Tp05N/5vbpL59+8aiRYuyfT9UhZ7qqZ5CGnqqp3oKaeipnuoppKGneqqnkIae6qmeQhp6qqd6CmnoqZ7qKaShp3qqp5CGnuqpnhZL19wD+JvFixdH7969s3z30qVL4z3veU/cfffdKz6bO3duHHjggXHYYYe97vlGoxEf+MAHYtKkSa/5/N57741PfepTcc8998R5552XfOfqfO+tt94aJ554YixbtiwiIpYsWRL/+Z//GWuuuWb86U9/iq997Wsrnn3ooYfiiCOOiE033TS23Xbb5Ps707t373j++efb/r1QNXraOT0FmqGnndNToBl62jk9BZqhp53TU6AZeto5PQWaoaed01OgGXraOT0FmqGnndNToBl62jk9pa0aFEJHR0dj8uTJWb77a1/7WiMiGsOGDWtce+21jeeee67x2GOPNT796U83IqIREY2pU6eueH7ixImNiGhstdVWjauuuqqxcOHCxuLFixs33nhjY5tttmlERGPOnDmNRqPRuPnmmxsR0fjSl770uu+9/fbbGxHR+OIXv7jiszd7flW+d8aMGSu2H3/88Y158+Y1Fi9e3Jg2bVqjW7dujXXXXbex9tprNy644ILG008/3Vi4cGHj85//fCMiGh/84AcT/w4358QTT2zsuOOOWb4bqkRPG50+r6dAM/S00enzego0Q08bnT6vp0Az9LTR6fN6CjRDTxudPq+nQDP0tNHp83oKNENPG50+r6dAM/S00enzego0Q08bnT6vp7TRHWsE2b388svRaDSie/fuWb5/2rRp0dHREZdeemm85z3vid69e8egQYNiwoQJsc8++7zu+QsvvDC6dOkSM2fOjLFjx0afPn2iV69eMXLkyPjJT34SERHTp09PvnN1vne//faLb37zmzFw4MDo1atXvO9974sDDzww/vrXv8b48ePj6KOPjvXXXz/69OkTX/3qV2PdddeNBx54IPn2ZnTv3j1efPHFLN8NVaGnzdFToDN62hw9BTqjp83RU6AzetocPQU6o6fN0VOgM3raHD0FOqOnzdFToDN62hw9BTqjp83RU9qpa+4BRCxfvjwiItZYI8/t2SOPPBIDBgyId73rXa/72b777hvXXHPNaz67//77Y/ny5bHRRhtFRESj0YhGo7HiryMifv/73yffuTrfu+eee77u7zNw4MCIiBg5cuRrPu/SpUsMGDAgnnrqqeTbm9GlS5cVfxaA1aOnzdFToDN62hw9BTqjp83RU6AzetocPQU6o6fN0VOgM3raHD0FOqOnzdFToDN62hw9BTqjp83RU9rJsVMB/P0C9OWXX862oaOjY6Wf/z06r/bKK69ERLzpv8gvvfRSmmFv8Xt79Ojxumf+/s/6Rj/7+/e020svvRRrrrlmlu+GqtDT5ugp0Bk9bY6eAp3R0+boKdAZPW2OngKd0dPm6CnQGT1tjp4CndHT5ugp0Bk9bY6eAp3R0+boKe2U5/SQ11hjjTWia9euLQlKM4YOHRp//OMfV/q6t3++Ao2I2HzzzWOttdaKv/zlLyuuMf/517Rp0yLiH9ety5Yta2rLmz2/Kt9bRi+++KI4wlukp/+gp3oKb4We/oOe6im8FXr6D3qqp/BW6Ok/6Kmewluhp/+gp3oKb4We/oOe6im8FXr6D3qqp/BW6Ok/6Kmewluhp/+gp3paFI6dCqJHjx6xdOnSLN/9vve9LxqNRhxyyCFxww03xPPPPx/z58+Pz33uczFr1qzXPX/00UfHkiVLYvTo0XHFFVfEggUL4qWXXor58+fHlVdeGe973/vi2muvjYiIPn36RETEzTffHAsXLux0y5s9vyrfW0ZLly5d6XUqsGr0NDp9Xk+BZuhpdPq8ngLN0NPo9Hk9BZqhp9Hp83oKNENPo9Pn9RRohp5Gp8/rKdAMPY1On9dToBl6Gp0+r6dAM/Q0On1eT2mnrrkH8DfrrbdeLFq0KMt3f/rTn45JkybFvffeG6NGjVrxeUdHRxx66KExderU6Nat24rPP/ShD8WNN94YF110URxwwAEr/Xsee+yxERGx6aabxoABA+K6666Lfv36rfh5o/H61+l19vyqfG8ZLVq0aMV/HIDVp6fR6fN6CjRDT6PT5/UUaIaeRqfP6ynQDD2NTp/XU6AZehqdPq+nQDP0NDp9Xk+BZuhpdPq8ngLN0NPo9Hk9BZqhp9Hp83pKO3mzU0H07du3qUvJVujZs2dcf/318fGPfzw22GCD6NGjR4wYMSIuv/zy2HLLLSPib/H+u46Ojrjwwgtj8uTJMXr06FhvvfWie/fuMWTIkBg3blxcdtllMXr06IiI6NKlS0ybNi322GOP6NWrV6db3uz5VfneMlq4cGH07ds39wwoPT2NTp/XU6AZehqdPq+nQDP0NDp9Xk+BZuhpdPq8ngLN0NPo9Hk9BZqhp9Hp83oKNENPo9Pn9RRohp5Gp8/rKdAMPY1On9dT2qmj8UYnebTVPvvsE4MHD47zzjsv95QVXnnlldhhhx3i7rvvjgULFvgXt8W23377GDNmTJx55pm5p0Cp6Sl6CmnoKXoKaegpegpp6Cl6CmnoKXoKaegpegpp6Cl6CmnoKXoKaegpelooc73ZqSDe8Y53xB//+Mds3//5z38+fvzjH8f8+fNjyZIlcffdd8dhhx0Wd911V+y1117C2AZ/+MMf4p3vfGfuGVB6eoqeQhp6ip5CGnqKnkIaeoqeQhp6ip5CGnqKnkIaeoqeQhp6ip5CGnqKnhZL19wD+JtBgwbF7bffnu37H3rooTj77LNf93nv3r1X+jlpPffcc/HnP/85Bg0alHsKlJ6e1pueQjp6Wm96Cunoab3pKaSjp/Wmp5COntabnkI6elpvegrp6Gm96Smko6f1pqeQjp7Wm54Wjzc7FcSgQYNi3rx50Wg0snz/N7/5zfjwhz8cm2yySay55pqx/vrrxyGHHBK33HJLbLvttlk21cm8efMiImLw4MF5h0AF6Gm96Smko6f1pqeQjp7Wm55COnpab3oK6ehpvekppKOn9aankI6e1pueQjp6Wm96Cunoab3pafF4s1NBbLbZZvHCCy/E73//+xg4cGDbv3/TTTeNCy+8sO3fy9889NBD0aVLlxgyZEjuKVB6elpvegrp6Gm96Smko6f1pqeQjp7Wm55COnpab3oK6ehpvekppKOn9aankI6e1pueQjp6Wm96Wjze7FQQW221VXR0dMS9996bewoZ3HvvvbHJJptEz549c0+B0tPTetNTSEdP601PIR09rTc9hXT0tN70FNLR03rTU0hHT+tNTyEdPa03PYV09LTe9BTS0dN609PicexUEOuss04MHDgw7rvvvtxTyOC+++6LrbfeOvcMqAQ9rTc9hXT0tN70FNLR03rTU0hHT+tNTyEdPa03PYV09LTe9BTS0dN601NIR0/rTU8hHT2tNz0tHsdOBbLtttvGnXfemXsGGdx1113iCAnpaX3pKaSlp/Wlp5CWntaXnkJaelpfegpp6Wl96Smkpaf1paeQlp7Wl55CWnpaX3oKaelpfelp8Th2KpBdd901Zs+enXsGbfb444/H/PnzY4899sg9BSpDT+tJTyE9Pa0nPYX09LSe9BTS09N60lNIT0/rSU8hPT2tJz2F9PS0nvQU0tPTetJTSE9P60lPi8mxU4Hsvvvu8dRTT8Wjjz6aewptdNNNN0W3bt1ip512yj0FKkNP60lPIT09rWevvXkAACAASURBVCc9hfT0tJ70FNLT03rSU0hPT+tJTyE9Pa0nPYX09LSe9BTS09N60lNIT0/rSU+LybFTgYwYMSJ69OgRN910U+4ptNFNN90U22+/fay11lq5p0Bl6Gk96Smkp6f1pKeQnp7Wk55CenpaT3oK6elpPekppKen9aSnkJ6e1pOeQnp6Wk96CunpaT3paTE5diqQHj16xMiRI+Pqq6/OPYU2mjVrVuy77765Z0Cl6Gk96Smkp6f1pKeQnp7Wk55CenpaT3oK6elpPekppKen9aSnkJ6e1pOeQnp6Wk96CunpaT3paTE5diqYsWPHxqxZs2LZsmW5p9AGv/nNb+LRRx+N/fbbL/cUqBw9rRc9hdbR03rRU2gdPa0XPYXW0dN60VNoHT2tFz2F1tHTetFTaB09rRc9hdbR03rRU2gdPa0XPS0ux04FM3bs2HjmmWfil7/8Ze4ptMGVV14Zffr0iZ122in3FKgcPa0XPYXW0dN60VNoHT2tFz2F1tHTetFTaB09rRc9hdbR03rRU2gdPa0XPYXW0dN60VNoHT2tFz0tLsdOBTNs2LAYPnx4TJ06NfcU2mDy5Mnx3ve+N7p06ZJ7ClSOntaLnkLr6Gm96Cm0jp7Wi55C6+hpvegptI6e1oueQuvoab3oKbSOntaLnkLr6Gm96Cm0jp7Wi54Wl2OnAjr88MNjypQpXn1XcY8++mjMnTs3Dj/88NxToLL0tB70FFpPT+tBT6H19LQe9BRaT0/rQU+h9fS0HvQUWk9P60FPofX0tB70FFpPT+tBT6H19LQe9LTYHDsV0OGHHx4LFiyI6667LvcUWujiiy+Ofv36xahRo3JPgcrS03rQU2g9Pa0HPYXW09N60FNoPT2tBz2F1tPTetBTaD09rQc9hdbT03rQU2g9Pa0HPYXW09N60NNic+xUQJtuumnsvvvuccEFF+SeQos0Go248MIL4wMf+EB07do19xyoLD2tPj2F9tDT6tNTaA89rT49hfbQ0+rTU2gPPa0+PYX20NPq01NoDz2tPj2F9tDT6tNTaA89rT49LT7HTgV17LHHxvTp0+Ppp5/OPYUW+MUvfhGPPPJIHH300bmnQOXpabXpKbSPnlabnkL76Gm16Sm0j55Wm55C++hptekptI+eVpueQvvoabXpKbSPnlabnkL76Gm16WnxOXYqqEMPPTR69erlGrSivvvd78buu+8eW265Ze4pUHl6Wm16Cu2jp9Wmp9A+elptegrto6fVpqfQPnpabXoK7aOn1aan0D56Wm16Cu2jp9Wmp9A+elptelp8jp0Kaq211opjjz02JkyYEEuXLs09h4QefvjhmD59enzuc5/LPQVqQU+rS0+hvfS0uvQU2ktPq0tPob30tLr0FNpLT6tLT6G99LS69BTaS0+rS0+hvfS0uvQU2ktPq0tPy8GxU4F95jOfiWeeeSYuueSS3FNI6Oyzz46BAwfGuHHjck+B2tDTatJTaD89rSY9hfbT02rSU2g/Pa0mPYX209Nq0lNoPz2tJj2F9tPTatJTaD89rSY9hfbT02rS03Jw7FRgAwYMiCOOOCLOPPPMWL58ee45JPD444/HRRddFCeccEJ06dIl9xyoDT2tHj2FPPS0evQU8tDT6tFTyENPq0dPIQ89rR49hTz0tHr0FPLQ0+rRU8hDT6tHTyEPPa0ePS0Px04Fd+qpp8YjjzwSkyZNyj2FBE4//fTo06dPfOQjH8k9BWpHT6tFTyEfPa0WPYV89LRa9BTy0dNq0VPIR0+rRU8hHz2tFj2FfPS0WvQU8tHTatFTyEdPq0VPy6Oj0Wg0co/gzR199NFx4403xm9+85vo1q1b7jmspvnz58dmm20WEyZMiI9//OO550At6Wk16Cnkp6fVoKeQn55Wg55CfnpaDXoK+elpNegp5Ken1aCnkJ+eVoOeQn56Wg16CvnpaTXoaanM7TJ+/PjxuVfw5rbZZps444wzYp111olddtkl9xxW03HHHReLFy+OCy64wCvvIBM9rQY9hfz0tBr0FPLT02rQU8hPT6tBTyE/Pa0GPYX89LQa9BTy09Nq0FPIT0+rQU8hPz2tBj0tlSfXyL2Azg0aNCg+97nPxWmnnRYLFizIPYfVcMstt8Qll1wSZ511VnTv3j33HKgtPS0/PYVi0NPy01MoBj0tPz2FYtDT8tNTKAY9LT89hWLQ0/LTUygGPS0/PYVi0NPy01MoBj0tPz0tn45Go9HIPYLOLV68ODbbbLM44IAD4nvf+17uOayC5cuXx8477xzrrbdeXHPNNbnnQO3paXnpKRSLnpaXnkKx6Gl56SkUi56Wl55CsehpeekpFIuelpeeQrHoaXnpKRSLnpaXnkKx6Gl56WkpzfVmp5Lo3bt3nHXWWXHBBRfEzTffnHsOq2DChAnx61//OiZMmJB7ChB6WmZ6CsWip+Wlp1AselpeegrFoqflpadQLHpaXnoKxaKn5aWnUCx6Wl56CsWip+Wlp1AselpeelpO3uxUMgcddFA8+OCDcc8990SPHj1yz6ET8+fPj+HDh8cJJ5wQX/ziF3PPAV5FT8tFT6G49LRc9BSKS0/LRU+huPS0XPQUiktPy0VPobj0tFz0FIpLT8tFT6G49LRc9BSKS0/LRU9La65jp5L5/e9/H8OHD4+PfvSjcc455+Sew5tYtmxZ7L333rFw4cK48847o3v37rknAa+ip+Whp1BseloeegrFpqfloadQbHpaHnoKxaan5aGnUGx6Wh56CsWmp+Whp1BseloeegrFpqfloaelNrfL+PHjx+deQfPWXXfdGDJkSJx00kmx9dZbxxZbbJF7Em/gf/7nf2LatGlx1VVXxYABA3LPAf6JnpaHnkKx6Wl56CkUm56Wh55CselpeegpFJueloeeQrHpaXnoKRSbnpaHnkKx6Wl56CkUm56Wh56W2pPe7FRSxx57bEybNi3uvPPOGDx4cO45/JPrrrsuxowZE9/97nfj2GOPzT0HeBN6Wmx6CuWhp8Wmp1AeelpsegrloafFpqdQHnpabHoK5aGnxaanUB56Wmx6CuWhp8Wmp1Aeelpselp6cx07ldTSpUtj1113jW7dusXs2bO9Uq1Annrqqdh2221j5MiRMXny5NxzgE7oaXHpKZSLnhaXnkK56Glx6SmUi54Wl55CuehpcekplIueFpeeQrnoaXHpKZSLnhaXnkK56Glx6WklzF0j9wJWT48ePWLKlCnx4IMPxhe+8IXcc/j/XnnllXj/+98fvXv3jvPPPz/3HKAJelpMegrlo6fFpKdQPnpaTHoK5aOnxaSnUD56Wkx6CuWjp8Wkp1A+elpMegrlo6fFpKdQPnpaTHpaHV3Gjx8/PvcIVk/fvn3jne98Z5x88smx0UYbxXbbbZd7Uu0df/zx8bOf/SxmzpwZAwcOzD0HaJKeFo+eQjnpafHoKZSTnhaPnkI56Wnx6CmUk54Wj55COelp8egplJOeFo+eQjnpafHoKZSTnhaPnlbGk46dSm6bbbaJZcuWxcknnxzDhw+PLbbYIvek2jrnnHPiS1/6Ulx00UUxZsyY3HOAVaSnxaGnUG56Whx6CuWmp8Whp1BuelocegrlpqfFoadQbnpaHHoK5aanxaGnUG56Whx6CuWmp8Whp5XyZEej0WjkXsFb02g04mMf+1hMmjQprrnmmth9991zT6qdadOmxeGHHx5nnHFGnHTSSbnnAKtJT/PTU6gGPc1PT6Ea9DQ/PYVq0NP89BSqQU/z01OoBj3NT0+hGvQ0Pz2FatDT/PQUqkFP89PTypnr2Kkili9fHoccckjceOONcdNNN8Xw4cNzT6qNGTNmxLhx4+Lf//3f49xzz809B3iL9DQfPYVq0dN89BSqRU/z0VOoFj3NR0+hWvQ0Hz2FatHTfPQUqkVP89FTqBY9zUdPoVr0NB89rSTHTlWydOnS2HfffeORRx6Jm266KYYMGZJ7UuVdd911sf/++8cRRxwR3//+96OjoyP3JCABPW0/PYVq0tP201OoJj1tPz2FatLT9tNTqCY9bT89hWrS0/bTU6gmPW0/PYVq0tP201OoJj1tPz2trLlr5F5AOj169Ijp06dH//79Y+TIkfHggw/mnlRpV155Zey///4xbty4OP/884URKkRP20tPobr0tL30FKpLT9tLT6G69LS99BSqS0/bS0+huvS0vfQUqktP20tPobr0tL30FKpLT9tLT6vNsVPFvO1tb4trr702Bg4cGHvuuWfcdddduSdV0uTJk+Pggw+OI488Mn70ox9Fly5dck8CEtPT9tBTqD49bQ89herT0/bQU6g+PW0PPYXq09P20FOoPj1tDz2F6tPT9tBTqD49bQ89herT0/bQ0+pz7FRBb3vb22LWrFmx9dZbx5577hkzZszIPalSvv71r8eRRx4Zn/zkJ+P8888XRqgwPW0tPYX60NPW0lOoDz1tLT2F+tDT1tJTqA89bS09hfrQ09bSU6gPPW0tPYX60NPW0lOoDz1tLT2tB8dOFdWrV6+48sor4+CDD44DDzwwvvOd7+SeVHovv/xyfOITn4hTTjklzjrrrDj77LO96g5qQE/T01OoJz1NT0+hnvQ0PT2FetLT9PSU/8fenUdbXdf7H39vzmFQJhHEISVIul4VUVOXAwIqoKCIXjJpFWoat0wzK++1bv0q69pgV0xz6KamqSBKYYAIKDiAOGEIMaUSghUaswwyKJ79+8MrQhyP5+CH8/3usx+Ptc5auvd2+WItfcoS3udLedLT9PQUypOepqenUJ70ND09hfKkp+npKZQnPU1PT8tLxVVXXXVV1iPYNSoqKuKss86KQqEQV155ZSxYsCBOPfXUaNKkSdbTSs6rr74ap59+ekyePDnuu+++uPDCC7OeBNQjPU1HT6G86Wk6egrlTU/T0VMob3qajp5CedPTdPQUypuepqOnUN70NB09hfKmp+noKZQ3PU1HT8vO64VisVjMegW73qOPPhqDBw+OVq1axciRI+Pwww/PelLJGDt2bFx44YWx9957x8iRI6NLly5ZTwIypKc7T0+BbenpztNTYFt6uvP0FNiWnu48PQW2pac7T0+BbenpztNTYFt6uvP0FNiWnu48PQW2pac7T0/L0oxGWS+gfvTq1StmzZoVHTp0iOOOOy5uuOGGrCfl3pYtW+Lb3/52nH322XHGGWfE888/L4yAnu4EPQWqo6d1p6dAdfS07vQUqI6e1p2eAtXR07rTU6A6elp3egpUR0/rTk+B6uhp3ekpUB09rTs9LW8VV1111VVZj6B+tGjRIj73uc/FW2+9Fd/73vfiz3/+c/Ts2TOaN2+e9bTcmTdvXgwYMCDGjx8fd9xxR/zgBz/wuEBgKz2tPT0FaqKntaenQE30tPb0FKiJntaengI10dPa01OgJnpae3oK1ERPa09PgZroae3pKVATPa09PS17r3uyU5mprKyMq6++OiZOnBhPP/10HHLIIXHPPfdkPSs3Nm/eHD/4wQ/iU5/6VBQKhfjjH/8Yn//857OeBeSQntZMT4Ha0tOa6SlQW3paMz0FaktPa6anQG3pac30FKgtPa2ZngK1pac101OgtvS0ZnoK1Jae1kxPeY9jpzLVp0+fmDdvXnz2s5+NL3zhC3HKKafEn/70p6xnZWrcuHHRtWvXGDp0aFxzzTXx9NNPx0EHHZT1LCDn9HRHegrsDD3dkZ4CO0NPd6SnwM7Q0x3pKbAz9HRHegrsDD3dkZ4CO0NPd6SnwM7Q0x3pKbAz9HRHesq2HDuVsZYtW8aNN94YTz/9dGzYsCGOOuqo+PKXvxyvvfZa1tPq1ezZs6Nv375x5plnxuGHHx7z5s2Lr3/961FRUZH1NKBE6Om79BT4qPT0XXoKfFR6+i49BT4qPX2XngIflZ6+S0+Bj0pP36WnwEelp+/SU+Cj0tN36SnwUenpu/SU6jh2Io499th45pln4s4774zx48dH586d44orrohly5ZlPW2Xmj9/fgwaNCiOPPLIWLFiRUyZMiVGjhwZH//4x7OeBpQoPdVTIA091VMgDT3VUyANPdVTIA091VMgDT3VUyANPdVTIA091VMgDT3VU3bk2ImIiCgUCnHeeefFwoUL4/rrr4/77rsvOnToEOeff368+OKLWc9LasaMGXH++edH165dY+7cuXHnnXfG9OnTo0ePHllPAxoAPdVTIA091VMgDT3VUyANPdVTIA091VMgDT3VUyANPdVTIA091VMgDT3VU7bn2IntNGnSJL70pS/FggUL4uc//3lMmzYtDj300Bg4cGBMnDgxqqqqsp64UzZu3Bh33313nHDCCXH00UfHSy+9FCNGjIg5c+bE+eefH40a+VcBSEtPAdLQU4A09BQgDT0FSENPAdLQU4A09BQgDT0FSENP4V2FYrFYzHoE+fXOO+/EqFGj4uabb44nn3wyDjjggPjCF74QgwYNikMOOSTreTWqqqqKp556KkaMGBEjRoyIDRs2xIABA+JrX/tadO/ePet5QJnRU4A09BQgDT0FSENPAdLQU4A09BQgDT0FSENPAdLQU8rUDMdO1NrLL78ct99+e9x7772xZMmS6NKlS3z605+O008/PY4++uhcXFNu2rQppk6dGg899FCMGjVq684LLrggzj///Gjfvn3WEwH0FCARPQVIQ08B0tBTgDT0FCANPQVIQ08B0tBTgDT0lDLi2Im6e+/C8v7774+xY8fG3/72t2jXrl307t07unXrFt27d48uXbpERUXFLt+yadOm+OMf/xjTpk2LqVOnxpQpU2LDhg1x2GGHxb/927/FueeeG4ceeugu3wGwM/QUIA09BUhDTwHS0FOANPQUIA09BUhDTwHS0FOANPSUMuDYiY9u3rx5MWHChHj88cfjqaeeijVr1kSLFi2iS5cu0bVr1zj00EPjwAMPjE6dOkWnTp1it912q/Pf44033ohFixbF4sWL4+WXX445c+bE3Llz489//nO89dZbsd9++0X37t2jd+/e0bdv39h///13wY8UYNfSU4A09BQgDT0FSENPAdLQU4A09BQgDT0FSENPAdLQUxogx06kVVVVFfPmzYtnn302Zs+evTViK1eu3PqZVq1aRbt27aJdu3bRqlWraNSoUbRu3Tre+0fxjTfeiGKxGCtXrtz6tWHDhoiIKBQKsf/++28Nb9euXeP444+PTp06ZfLjBdhV9BQgDT0FSENPAdLQU4A09BQgDT0FSENPAdLQU4A09JQGwrET9WPt2rWxePHiWLx4cSxbtixWrFgRK1eujLVr10ZVVVWsWbMmXnzxxTj44INjjz32iEKhEHvuuWe0bds22rZtG/vss0907NgxOnbsGM2aNcv6hwOQGT0FSENPAdLQU4A09BQgDT0FSENPAdLQU4A09BQgDT2lxDh2Ih9mzZoVPXr0iKVLl+7UY/EAeJeeAqShpwBp6ClAGnoKkIaeAqShpwBp6ClAGnoKkIaekjMzGmW9ACIiRowYEevWrYsHH3ww6ykAJU1PAdLQU4A09BQgDT0FSENPAdLQU4A09BQgDT0FSENPyRvHTmSuWCzG3XffHRERw4YNy3gNQOnSU4A09BQgDT0FSENPAdLQU4A09BQgDT0FSENPAdLQU/KoUCwWi1mPoLxNnTo1evbsGRERlZWVsXTp0thzzz0zXgVQevQUIA09BUhDTwHS0FOANPQUIA09BUhDTwHS0FOANPSUHJrhyU5kbsSIEdGkSZOIePcq9A9/+EPGiwBKk54CpKGnAGnoKUAaegqQhp4CpKGnAGnoKUAaegqQhp6SR57sRKa2bNkS7du3j9WrV0dERKNGjaJ79+7xxBNPZDsMoMToKUAaegqQhp4CpKGnAGnoKUAaegqQhp4CpKGnAGnoKTnlyU5k65FHHtkaxoiIqqqqmDp1aixZsiTDVQClR08B0tBTgDT0FCANPQVIQ08B0tBTgDT0FCANPQVIQ0/JK8dOZOree++Nxo0bb/daRUVF/P73v89oEUBp0lOANPQUIA09BUhDTwHS0FOANPQUIA09BUhDTwHS0FPyqlAsFotZj6A8bdq0Kdq2bRsbNmzY7vVCoRBHHHFEvPDCCxktAygtegqQhp4CpKGnAGnoKUAaegqQhp4CpKGnAGnoKUAaekqOzfBkJzIzZsyY2Lhx4w6vF4vFmDlzZixYsCCDVQClR08B0tBTgDT0FCANPQVIQ08B0tBTgDT0FCANPQVIQ0/JM8dOZGb48OFRUVFR7XuNGzeO+++/v54XAZQmPQVIQ08B0tBTgDT0FCANPQVIQ08B0tBTgDT0FCANPSXPCsVisZj1CMrPG2+8Ee3bt4+33377Az/ziU98IhYuXFi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"text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_km.visualize()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.873317Z", "iopub.status.busy": "2023-06-29T15:00:09.872606Z", "iopub.status.idle": "2023-06-29T15:00:09.905815Z", "shell.execute_reply": "2023-06-29T15:00:09.904884Z" } }, "outputs": [ { "data": { "text/plain": [ "0 191.4\n", "1 191.4\n", "2 191.4\n", "3 191.4\n", "4 191.4\n", "Name: Distance, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_km.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is `meta`?\n", "\n", "Since Dask operates lazily, it doesn't always have enough information to infer the output structure (which includes datatypes) of certain operations.\n", "\n", "`meta` is a _suggestion_ to Dask about the output of your computation. Importantly, `meta` _never infers with the output structure_. Dask uses this `meta` until it can determine the actual output structure.\n", "\n", "Even though there are many ways to define `meta`, we suggest using a small pandas Series or DataFrame that matches the structure of your final output." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Close you local Dask Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's good practice to always close any Dask cluster you create:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2023-06-29T15:00:09.908790Z", "iopub.status.busy": "2023-06-29T15:00:09.908557Z", "iopub.status.idle": "2023-06-29T15:00:10.411236Z", "shell.execute_reply": "2023-06-29T15:00:10.410600Z" } }, "outputs": [], "source": [ "client.shutdown()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { 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