diff --git "a/goofing.ipynb" "b/goofing.ipynb"
new file mode 100644--- /dev/null
+++ "b/goofing.ipynb"
@@ -0,0 +1,37140 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "4937ef6f-0dc7-4dee-8a5b-0501f5066daa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "from datetime import date, timedelta, datetime\n",
+ "import time\n",
+ "import pandas as pd\n",
+ "from os.path import exists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "59d6bfcd-0654-4888-b9ab-83f5f09f3d43",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_assets(total_coins=100):\n",
+ " url = \"https://api.coincap.io/v2/assets\"\n",
+ "\n",
+ " # N.B. here adampt the params dict to only request what you need\n",
+ " payload={'limit': total_coins}\n",
+ " headers = {}\n",
+ "\n",
+ " assets_json = requests.request(\"GET\", url, params=payload, headers=headers).json()\n",
+ " return assets_json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "33610a50-83ad-4ce5-b271-6646b88844b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_symbols(assets_json):\n",
+ " symbols_list = []\n",
+ " names_list = []\n",
+ " ids_list =[]\n",
+ " for dict in assets_json['data']:\n",
+ " symbols_list.append(dict['symbol'])\n",
+ " names_list.append(dict['name'])\n",
+ " ids_list.append(dict['id'])\n",
+ " return symbols_list, names_list, ids_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "c99999c1-28ee-40ad-a6e9-6c96924a7129",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_histories(coin_ids, start, end):\n",
+ " url = \"http://api.coincap.io/v2/assets/{}/history\"\n",
+ "\n",
+ " payload={'interval':'d1', 'start':start, 'end':end}\n",
+ " headers = {}\n",
+ "\n",
+ " histories_dict = {}\n",
+ " for id in coin_ids:\n",
+ " response_histories = requests.request(\"GET\", url.format(id), headers=headers, params=payload)\n",
+ " histories_json = response_histories.json()\n",
+ " histories_dict[id] = histories_json['data']\n",
+ " return histories_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "8e925370-1cad-4778-a493-618fbfc7f2e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert string to datetime.date\n",
+ "def date_conv(date):\n",
+ " return datetime.strptime(date, '%Y-%m-%d').date()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "707b6ab6-d83d-4ec9-8af8-572df067a4e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_unix_dates(today=date.today(), lookback_years = 5):\n",
+ " '''\n",
+ " A function to create start_unix and end_unix times in UNIX time in milliseconds\n",
+ " '''\n",
+ " start_datetime = today-timedelta(365*lookback_years)\n",
+ " start_unix = int(time.mktime(start_datetime.timetuple()) * 1000)\n",
+ " end_unix = int(time.mktime(date.today().timetuple()) * 1000)\n",
+ " return start_unix, end_unix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "cee6d439-fc64-45c6-bea2-3902e9f1ce55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_histories_df(coin_ids, start_unix, end_unix):\n",
+ " '''\n",
+ " A function to create a dataframe of historical prices for all of the \n",
+ " crypto currencies in the coin_ids=ids list, over a period of\n",
+ " lookback_years = 5.\n",
+ " N.B. This code uses the data for bitcoin as the first dataframe on which\n",
+ " other temp_df are outer joined from the right. This is because bitcoin \n",
+ " has the longest history.\n",
+ " '''\n",
+ " print('Downloading data from coincap.io, may take several minutes...')\n",
+ " \n",
+ " # download histories from coincap.io\n",
+ " histories_dict = load_histories(coin_ids, start_unix, end_unix)\n",
+ " \n",
+ " # convert all dates in histories_dict to python datetime.date objects and remove 'time' key\n",
+ " for id in coin_ids:\n",
+ " for dict in histories_dict[id]:\n",
+ " dict.pop('time')\n",
+ " dict['priceUsd']=float(dict['priceUsd'])\n",
+ " dict['date'] = date_conv(dict['date'][0:10])\n",
+ " \n",
+ " # convert histories_dict to pd.DataFrame\n",
+ " histories_df = pd.json_normalize(histories_dict['bitcoin'])\n",
+ " histories_df = histories_df.set_index('date', drop=True)\n",
+ " for id in [x for x in coin_ids if x != \"bitcoin\"]:\n",
+ " temp_df = pd.json_normalize(histories_dict[id])\n",
+ " temp_df = temp_df.set_index('date', drop=True)\n",
+ " histories_df = histories_df.merge(temp_df, how='outer', left_index=True, right_index=True)\n",
+ " \n",
+ " histories_df.columns = coin_ids\n",
+ " return histories_df\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "b4129ae7-034b-4210-98e8-d3bdec5219b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# put cache decorator on this finction\n",
+ "assets_json = create_assets(total_coins=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "7f199fa9-1803-40b9-a7f0-c39dbb2c3735",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# put cache decorator on this finction\n",
+ "symbols, names, coin_ids = gen_symbols(assets_json)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "acdf6c39-b3ff-4978-b3f0-e66290ef759d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "start_unix, end_unix = create_unix_dates(today=date.today(),lookback_years = 5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "8266876b-79bf-42d4-9bab-bfa1b4b64d9b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "histories_dict = load_histories(coin_ids, start_unix, end_unix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "b02e0c4e-16eb-451d-919a-a697623449eb",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'bitcoin': [{'priceUsd': '39147.8638098693520315',\n",
+ " 'time': 1621382400000,\n",
+ " 'date': '2021-05-19T00:00:00.000Z'},\n",
+ " {'priceUsd': '39865.2257138631182931',\n",
+ " 'time': 1621468800000,\n",
+ " 'date': '2021-05-20T00:00:00.000Z'},\n",
+ " {'priceUsd': '39161.3031174275061390',\n",
+ " 'time': 1621555200000,\n",
+ " 'date': '2021-05-21T00:00:00.000Z'},\n",
+ " {'priceUsd': '37543.6829244262777489',\n",
+ " 'time': 1621641600000,\n",
+ " 'date': '2021-05-22T00:00:00.000Z'},\n",
+ " {'priceUsd': '35095.1658253822589551',\n",
+ " 'time': 1621728000000,\n",
+ " 'date': '2021-05-23T00:00:00.000Z'},\n",
+ " {'priceUsd': '37105.8657082232936165',\n",
+ " 'time': 1621814400000,\n",
+ " 'date': '2021-05-24T00:00:00.000Z'},\n",
+ " {'priceUsd': '38419.2573581555758190',\n",
+ " 'time': 1621900800000,\n",
+ " 'date': '2021-05-25T00:00:00.000Z'},\n",
+ " {'priceUsd': '39304.8247191829242468',\n",
+ " 'time': 1621987200000,\n",
+ " 'date': '2021-05-26T00:00:00.000Z'},\n",
+ " {'priceUsd': '38851.8752200497785424',\n",
+ " 'time': 1622073600000,\n",
+ " 'date': '2021-05-27T00:00:00.000Z'},\n",
+ " {'priceUsd': '36666.1779571691927065',\n",
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+ " {'priceUsd': '35301.0491995096393796',\n",
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+ " {'priceUsd': '35514.2038402494780354',\n",
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+ " {'priceUsd': '36175.8345718996082390',\n",
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+ " {'priceUsd': '36628.5155184308958238',\n",
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+ " {'priceUsd': '399.9960539611424351',\n",
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+ " {'priceUsd': '394.6780502294345093',\n",
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+ " {'priceUsd': '386.0425625948540438',\n",
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+ " {'priceUsd': '389.2069802247551525',\n",
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+ " {'priceUsd': '387.3378538798995978',\n",
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+ " 'date': '2022-05-03T00:00:00.000Z'},\n",
+ " {'priceUsd': '391.7769950309583687',\n",
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+ " 'date': '2022-05-04T00:00:00.000Z'},\n",
+ " {'priceUsd': '394.0009321169327402',\n",
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+ " 'date': '2022-05-05T00:00:00.000Z'},\n",
+ " {'priceUsd': '379.1577736002105705',\n",
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+ " 'date': '2022-05-06T00:00:00.000Z'},\n",
+ " {'priceUsd': '376.5399814151459469',\n",
+ " 'time': 1651881600000,\n",
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+ " 'date': '2022-05-08T00:00:00.000Z'},\n",
+ " {'priceUsd': '332.8024852423291161',\n",
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+ " 'date': '2022-05-09T00:00:00.000Z'},\n",
+ " {'priceUsd': '319.4201516525066091',\n",
+ " 'time': 1652140800000,\n",
+ " 'date': '2022-05-10T00:00:00.000Z'},\n",
+ " {'priceUsd': '297.3898655090924890',\n",
+ " 'time': 1652227200000,\n",
+ " 'date': '2022-05-11T00:00:00.000Z'},\n",
+ " {'priceUsd': '261.9241061111955087',\n",
+ " 'time': 1652313600000,\n",
+ " 'date': '2022-05-12T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.7930293608914094',\n",
+ " 'time': 1652400000000,\n",
+ " 'date': '2022-05-13T00:00:00.000Z'},\n",
+ " {'priceUsd': '287.3927157418903443',\n",
+ " 'time': 1652486400000,\n",
+ " 'date': '2022-05-14T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.2107301245594047',\n",
+ " 'time': 1652572800000,\n",
+ " 'date': '2022-05-15T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.8575522579206131',\n",
+ " 'time': 1652659200000,\n",
+ " 'date': '2022-05-16T00:00:00.000Z'},\n",
+ " {'priceUsd': '304.2808838400823271',\n",
+ " 'time': 1652745600000,\n",
+ " 'date': '2022-05-17T00:00:00.000Z'}]}"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "histories_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "326a30bc-a76c-4c39-a265-08e809934106",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from coincap.io, may take several minutes...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_21400/1004101742.py:32: FutureWarning: Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ " histories_df = histories_df.merge(temp_df, how='outer', left_index=True, right_index=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# if histories.csv exists load it into histories_df dataframe\n",
+ "if exists('histories.csv'):\n",
+ " histories_df = pd.read_csv('histories.csv', index_col='date')\n",
+ "\n",
+ "# if histories_df exists set the last_date of the history and yesterday's date, \n",
+ "# otherwise set last_date to False\n",
+ "try:\n",
+ " last_date = date_conv(histories_df.index[-1])\n",
+ "except NameError:\n",
+ " last_date=False\n",
+ "\n",
+ "# if last_date is not yesterday's date then redownload the data and save it to csv\n",
+ "yesterday = date.today() - timedelta(1)\n",
+ "if not(last_date == yesterday):\n",
+ " # redownload and save the datafram, print (this maay take up to 2 minutes)\n",
+ " histories_df = create_histories_df(coin_ids, start_unix, end_unix)\n",
+ " histories_df.to_csv('histories.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "f9438317-f856-41f8-91f6-a7d149bd2ddf",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from coincap.io, may take several minutes...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
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+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
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+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "#histories_df = create_histories_df(coin_ids, start_unix, end_unix)\n",
+ "#histories_df.to_csv('histories.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "c33e1cae-80b3-4086-a93e-3dce681f4692",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
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+ "source": [
+ "histories_df.shape"
+ ]
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+ "execution_count": 55,
+ "id": "adb3a3cb-f782-4b94-a83c-922cfadc51fa",
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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = px.line(melt_df, x=melt_df.index, y='value', color='variable')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "id": "b74b2e1f-70a9-49bc-9f36-254ab6c4e245",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "returns_df = histories_df.pct_change(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "id": "3d82c8e7-50b4-4288-bf12-134dd001b32a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " bitcoin | \n",
+ " ethereum | \n",
+ " tether | \n",
+ " usd-coin | \n",
+ " binance-coin | \n",
+ " xrp | \n",
+ " binance-usd | \n",
+ " cardano | \n",
+ " solana | \n",
+ " dogecoin | \n",
+ " ... | \n",
+ " kava | \n",
+ " kadena | \n",
+ " lido-dao | \n",
+ " holo | \n",
+ " gatetoken | \n",
+ " fei-protocol | \n",
+ " kyber-network | \n",
+ " qtum | \n",
+ " bancor | \n",
+ " 1inch | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2017-05-20 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2017-05-21 | \n",
+ " 1.026185 | \n",
+ " 1.067805 | \n",
+ " 1.004166 | \n",
+ " NaN | \n",
+ " 1.000000 | \n",
+ " 1.014179 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 1.492958 | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2017-05-22 | \n",
+ " 1.072226 | \n",
+ " 1.302580 | \n",
+ " 1.004760 | \n",
+ " NaN | \n",
+ " 1.098008 | \n",
+ " 0.912749 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 1.899119 | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2017-05-23 | \n",
+ " 1.102083 | \n",
+ " 1.306241 | \n",
+ " 1.004962 | \n",
+ " NaN | \n",
+ " 1.098008 | \n",
+ " 0.934600 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 2.119522 | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2017-05-24 | \n",
+ " 1.185241 | \n",
+ " 1.488939 | \n",
+ " 1.010544 | \n",
+ " NaN | \n",
+ " 1.224289 | \n",
+ " 0.937986 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 2.241264 | \n",
+ " ... | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 1.288510 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 2022-05-13 | \n",
+ " 15.160872 | \n",
+ " 16.623147 | \n",
+ " 1.010282 | \n",
+ " 0.981754 | \n",
+ " 34185.741652 | \n",
+ " 1.289513 | \n",
+ " 1.001233 | \n",
+ " 24.091683 | \n",
+ " 15.524261 | \n",
+ " 57.259095 | \n",
+ " ... | \n",
+ " 1.965853 | \n",
+ " 4.326262 | \n",
+ " 1.015775 | \n",
+ " 3.077913 | \n",
+ " 10.062238 | \n",
+ " 0.996553 | \n",
+ " 0.932868 | \n",
+ " 0.656371 | \n",
+ " 0.166489 | \n",
+ " 0.144352 | \n",
+ "
\n",
+ " \n",
+ " | 2022-05-14 | \n",
+ " 14.778609 | \n",
+ " 16.186053 | \n",
+ " 1.010443 | \n",
+ " 0.981965 | \n",
+ " 32881.400058 | \n",
+ " 1.252202 | \n",
+ " 1.001094 | \n",
+ " 22.707484 | \n",
+ " 15.216707 | \n",
+ " 55.966711 | \n",
+ " ... | \n",
+ " 2.035888 | \n",
+ " 4.714947 | \n",
+ " 0.908086 | \n",
+ " 2.951196 | \n",
+ " 9.790962 | \n",
+ " 0.988572 | \n",
+ " 0.923299 | \n",
+ " 0.642831 | \n",
+ " 0.161428 | \n",
+ " 0.149041 | \n",
+ "
\n",
+ " \n",
+ " | 2022-05-15 | \n",
+ " 15.137469 | \n",
+ " 16.551304 | \n",
+ " 1.010346 | \n",
+ " 0.981425 | \n",
+ " 34119.119177 | \n",
+ " 1.269642 | \n",
+ " 1.000704 | \n",
+ " 23.832208 | \n",
+ " 16.164686 | \n",
+ " 56.481435 | \n",
+ " ... | \n",
+ " 2.271492 | \n",
+ " 6.203546 | \n",
+ " 0.915617 | \n",
+ " 2.981902 | \n",
+ " 9.871251 | \n",
+ " 0.989930 | \n",
+ " 0.939198 | \n",
+ " 0.665439 | \n",
+ " 0.166412 | \n",
+ " 0.150283 | \n",
+ "
\n",
+ " \n",
+ " | 2022-05-16 | \n",
+ " 15.076566 | \n",
+ " 16.366510 | \n",
+ " 1.011087 | \n",
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sUV2rPerPqbMX8MELc9G8cT19J0wFs85iyUOHsljyEKCPN2expJ8Dy1UsFRUi8vgmZNXpBphCrIOssvVxBKefRE6Njgg/s1Mc45rR6AZkNL5BPxA6WuaVJR3hKkwbSSyFJB9EwvYloncXrpoDc+x/N1P0nikzySf34rFY0ncu+6NYIqKjpy5BWnqWEC0UlkcrRiSEqAwbPw+nzpzHjIm3i31NCZUroe+whzBs8LUuiaXlL3+ENW9+LkLqaF8R7UtasPwtnD570W2xROGCgaZAa1+Vs6N1i4aIiYrQd8JUMOssljx0KIslDwH6eHMWS/o5sDzFUviZHYjbtUaE19HKkSzVN0wS+5XO9lmGiH9+RuyB91ks6TcFKoxlI4klZShpWvOhyKp/rZVzSMoRJGxbBHNMHaS0Hw9LRILP+IDFkr6u8lexRHuD7p+zQuwdotWf79YuFYkWKCFYp+vHYfLoW0qcfdTtxokuiyUSNrSX6YXFD1mdOGvhi/jnzAWHYmnwyIdhsRTis9cXlArDm/3kS9j225/48s1FCA/774EfhQwG0JeFi0sEWCy5hKt0ZRZLHgL08eYslvRzYHmKpejDnyH6yGdicEk9nhQ3jabsi6i6aQYsEfFI6rEIsg6vLOk3ByqKZaOKpaLgCGTX6gISTVRi97+HyGPfiX/bPigwui9YLOnrIX8VS5QFuectk0WWOtqnRPuVZKHQOFNgIB4ceyssFgs++moLvv7+F5HFzpUwvKdXrxX7ieiMpIT4WHFUzeo3PhPJGZRheLTnaQbtfQoy4ZNvfsTbH3+Hxx4agZsHXFNKLFEyCOrf1Z1bY+zwGxAVGY6Df53Eq+99jZeWTEWl2Ch9J0wFs85iyUOHsljyEKCPN2expJ8Dy1Ms2e5Nyq/cBObY2uJGkjbFp3SYIPYuUT1fu6lUeozD8PSbv0rLRhJLUuQXmYIRYDGXeCBQddN0mLKTxXtynnuHkOdXYbHkOUNHFvxVLBGT515Zh+ff+BSrF00RYXKy/LRjHx5f+rrYB0RlQO8rRTa8CSMHY9zwQdbVJ9s9S5RSvMN1o60HyFJacUro8Pvew8JOq+YNUGgpFOcwvfbsDPEerT6dSbpY4kzQe267HpNG/Q8mU6BIU660SW0oVfkTz75p7R+9161TSzzz2MQSq036zpyKYZ3Fkod+ZLHkIUAfb85iST8HlqdYqrzzOYQl7UZBZDUEZZ0Tg6Sn8BSCR2FL9CSexZJ+vq9olo0kluTq0cXODyHi1DYxj0nwZzQaKFZOZaEHBBevnOqeKwrNQKC6wzbdu0DpViyWtCJp344/iyVHZCms7fg/50TK79hoz46QOJuUjMDAQHHOUlmFsuXRuUwUCiiz3DnzPJ0TSmGDVSrHIkTlIbjObPrb5yyWPPQ4iyUPAfp4cxZL+jmwPMWSfMJOIXimnIvWDfE0Wpn9Tm6U9+imUj98qizzypIqTB5XMpJYkqumNI/zKjcpDi0Nj0dm/d4iDI/2K1ESE/r/hW6PuDx28aDhwl4kXTPfq3ueWCy57CqXGrBYcgkXV65gBFgseehQFkseAvTx5iyW9HNgeYolZSIHOnBWvqbRUnIHeo/Fkn6+r2iWjSSWErYtRkjKIdDKUn58U1T9fjpMOclWkUQrpxRuKvfmueoL+V3xdkp9Fkuuesq1+iyWXOPFtSsWARZLHvqTxZKHAH28OYsl/RxYnmKpxpf3iLC7s32WiwHKG0zlKhKLJf18X9EsG0ksyfT3Mm247f48ep/OYaJy5vqXXHKFTIJCjby954nFkkuucrkyiyWXkXGDCkSAxZKHzmSx5CFAH2/OYkk/B5aHWDLlpCDhlyUwZZ0v8WQ94p8fUemP15DaeiRyanURg5aHe7obrqQfOfWWOQxPPStPahpJLClDTCnLozKVuHxAQA8L3BFLtCJFoXxUvH3oLYslT2ao87Yslpwz4hoVlwCLJQ99y2LJQ4A+3pzFkn4OLA+xFH34U0Qf+VwMynYvUmDuJRSGVbIO2DaVuH4k9LPMYkk/tkrLRhJLtiGmNI/j9rwqQvNyal6J1DajYCuo7FEKSj+J8LO/icQQCAwSVWRiFFnf3qG3ehFnsaQX2WK7LJb05cvWjU2AxZKH/mGx5CFAH2/OYkk/B5aHWJLhdjQqZynBA8xZqL7h/hLhevrR0McyiyV9uNpaNZJYKmvVKCT5EAqiqqMwNAa2oXr2KMX++S4ij28UmSFpnxNljySxREUmiZCZI71BmcWSvpRZLOnLl60bmwCLJQ/9w2LJQ4A+3pzFkn4O9LZYIvFTbdNMkR6cVpAuNR+G3OrtHQ7Q3XAl/ai5ZpnFkmu83K1tFLGkdjXUNgmEvXHLOlIQyde00pQX30RkkPRmKB6LJXdnp7p2LJbUceJaFZMAiyUP/cpiyUOAPt6cxZJ+DvS2WKIn6wnbF5cKv3M0QhZL+vm/Ilk2ilhSu89OhtPR4cuUqMFekaF6MlzVNnRPhvt5KxSPxZK+3xgWS/ryZevGJsBiyUP/sFjyEKCPN2expJ8DvS2W5OZ0Z+F3yhHb7v/Qj4Y+lnllSR+utlaNIpbUHqSsPIuJvg/2ipz7ZSWFULM6pSV9Fkta0ixti8WSvnxdsb5zzyHExUahQb2apZpZLIXIyzcjIjzUFZNO6+bm5cMUGIjg4OL9if5WWCx56HEWSx4C9PHmLJb0c6C3xRJl8SLBlNHoBmQ0vkHVwNRshFdlqJwqsVjyDnijiCU5x+U+o7JGH334M0Qf+czhd0GuqpKNrDrXIPLkDyUySLJY8s7c8tZVWCx5i7Tz64yb8QzatWyEe28fUKrytp1/4p6HFuOnT59DpdgoTJ+/Bvfcdj0a1a/l3LCDGndMmI9WzS7DtPuGeWTHVxuzWPLQcyyWPATo481ZLOnnQG+LJTVP021Hy2JJP/9XJMtGEUtqwuuIuzOxpDxPSekn5dlK8lreOpyWV5b0/cawWNKXryvWHYmlzKwcnDiVhCYNayPIZEKL7nfj1Wdm4Iq2TV25RKm6x06eRXh4KKpVqeyRHV9tzGLJQ8+xWPIQoI83Z7GknwO9LZbceRIu26S2uQc5NTvrB0Mny7yypBNYG7NGEUtqxb2zcD3r3qfYuiiIrIrwM7+KEStDWN15+OCJN1gseULPeVsWS84ZeasGiaXwsBCkZ2aDVpLaXt4IC2fdi9o1EvH3iTOYteBFvLNqDpa99CFefvcr1KpeBZViojC4fzcMHdQTv+89jGde+BAH/zqJWtUTcOfNfXBT/6tF2/nPvolfdh1Ag7o1MGHkTehzTQcxrKdWvouG9WuKen8dO40ZC17AgN5X4t11G8Xno4b1x5AbengLgdevw2LJQ+QsljwE6OPNWSzp50BviyW1N5LKEStTjV+8cjryKzfSD4gOllks6QDVjkkjiCVXUt2HJB8U2exszxqTQ5NpwkkcUfa7qptmiI+U4X3OVqe0Js9iSWuiJe35s1g6c64IGRn68rVnvXq1AMREl/6ExNK+g0cxYcRgEWq37KWP0L5VE8ybNhJ/HjqOIWPmYs/Gl0GrQTeOeFiEzjVvVBfVEiujqKgI/W6fLkTPTf274fg/57D7z78wa9Id6Hf7NLRoXA93DbkOv+46gJWvfYIPX3wMzRrVxX2znkWrZg0w5s6B2HvgKIaOexw9u7YVAumfMxcwf9mb+PnzlYiNjvQ+KC9ckcWSh5BZLHkI0Mebs1jSz4HeFktyD8bZPstQFKzuB9+Vc5n0I+W+ZRZL7rNzpaURxJLatOE0LntZ88LO/Y6YQ+uQ0m4cQtKOgVaO5EqSPJdJGXJXlliKOroegeYspDe5yRWETuuyWHKKyKMK/iyW1rxmwY5dhR7xc6fxmLtM6NgusFRT2zC8DT/sxLxnXsfWT1aUEEv2wvCee2Ud3v/se2xZtxwB9KX5t/y0Yx9GT12C79YuRfXE4lC7G+6ahW6dWmHq+KF2xdK+Ta9abXS7cSIenzYSPbq0dWeohm/DYslDF7FY8hCgjzdnsaSfA70pluSNpMzspXZUIoPeoY8Ai9mrZ8qo7Z+zeiyWnBHS5nMjiCVnq0W2I1WmxVceOEvnKhUGRZRIABGSfBjh53YirdkQILA4W5bMLqk8mDYsaRcq71wpPnfloYQaL7BYUkPJ/Tr+LJY+/6YQB494XywNvM6Epo3+EzTSe7Zi6fDRUxg88mFs+vBZXEi+ZF1ZsieWKOEDlUWzx5SYDB9/tQXPvPCBEFyyPLrkVWRkZmPp3PuciqX+d0zHhBE3oX+vTu5PMgO3ZLHkoXNYLHkI0Mebs1jSz4HeFEtqz58pa7TyyfrFzg8hP96zjbT6ES1tmcWSd2gbQSw524dkS0KmBk/q8SSijn8nxA8Vee4SCShHWfVsrxf/y9MIvXjAehmtvysslvSdy/4slvQl67p1W7H01cZfMHXe8/h9w4tiP5EMw5Ni6eWl09C5XXNxoSWr38eWbXvw2esLSlx408+7MGHWMvz82UrExhRHVlAGvGaN6mD2/XeyWCqiAEYuSMvIQl6eGYkJlVyiwWLJJVwVrjKLJf1cqrlYKioEAkqHNNAI5JNzZTYvV0YmUzK7ckaTK/b1qstiSS+yJe36olhSJjyJOLUNJH6omGPqIDj9pPg3CSlLRIJdiLYrWXKlyhIeD1NOsksp+tV4icWSGkru12Gx5D47rVuSWKLEDNPGDxOZ7x5b+jpioiOwcsEDpcLwRk5ehI5tm+Ke2wYgOzsXB46cwKgHn8Ijk4djYJ+uOHs+GT/v2CeSNfQZOhXDbuyJe24fgJ27D2Liw8uxauFkXHNlaxZL/i6WLqakYfikBWLCUaEMIJS7fmCfLuL1xq2/Y9Kc5aXmOin40JBgsFjS+mfAt+yxWNLPX9qKpSJU+3aKSMCQ0n58qU7bCxlyZWTyxpBuBJN6LnKlabnWZbHkHfxGEEuuJlxQpv6OPLbRKpAkMWcPBpRiKbX1CJEEwhIRj7Tmw0C23X0wUZbHWCzpO5dZLOnL1xXrJJboYNrsnFzRjBIwrHhiEqpXjcf+w8dxy+jiBA+0skT3sHOffhUplzIwbvggTBg5GK+t/QaLV71nveTY4Tdg4sib8MO2PXjo8eetduX7VHHi7GVo2ewyjL5jIPYePIahYx+Dcs8SheGRjX49OQzPFV/6TN3zFy/hk2+24oa+XREZHoY3P9yAV9//Rmx+o9SM3239DTMXvCgygihLnZqJYmMbiyWfcbUuHWWxpAtWYVRLseRsT5JMc+zssE5Ho636/XTxxPzCVXNgjq2rHxgNLbNY0hCmA1NGEEtqD6SVw1CKKwrDCzBnlxjhxc5TkR/fpMxRK0Nb05oPEdn1aFXqUqu7UOXHeZrv8WOxpO9cZrGkL193rKemZSA3z2xNyFCWDYulEFQ3Pi7GmpCB3ktOTUelmEiEhARbm9L75y6koHKlGHEPzKWYAO9ZspkJp85eQN9hU/Hmillo17KxEEuPPf1aiU1vyiYslvz7q8RiST//uyKWws9sR3DGmTIzbMkbN+qtvY3lag/rdDRad85p0o+eOsssltRx8rSWEcSSq+ceydBUSh8eknIIlPzEHF3b+u+zfUpHXCg5KbPvZTS6oUT2PLkfSsskDyyWPJ2ljtuzWNKXL1s3NgEWSzb+Wff1Vjy86GUhjipXihZi6f45KzCob1eEhoagQ+sm6Nu9o1jepMJiydgTXO/esVjSj7ArYinxh4cRlHkOZW0alyFB1Ft7Kz/unLFkO3JXb0b1I6feMosl9aw8qWkEseSqmFc+YKCxUwhdbtW2ItGDMsNdWVzkuU7ULrtmV5E9T7aTfclscD1yEy/X5HwyFkuezFDnbVksOWfENSouARZLCt8eOXYKt41/Anfd0lfEdVKh2Mz1m38VB22dSUrG2s824bbBvUR2ECpZuQWGnB3BQcUb2c0F3k93qQWQ0skytbCqvY2wEBPyzBZwmhTt2dLNT2iICbl5FqfGIz4ZB+Rno6BeN+RfcU+p+kFbZBlEAAAgAElEQVTHtyLk15fE+7ndZ6AwsVmJOhFr7xKvs4e87vRaZVUg+3Qduj71wxdKkClQ5Lwwm4t/JyyFRSL8kYu2BGge029xYWH55VMK2/AIAi+dQG7vx1AYV0/VAOX3SsyNmu1gbjoQwYe+Rn6boSiKiHdqQ3yvgsNRGJWIwNQT1u9GyO63EXR4g2hfFB6LnIGOV6mcXogqBADhIUHIyTPm32RVYyjnSo5mZ1BgAEymAOT9+1tRzl1VfXl6IMSFCXhKgMXSvwRPn7uIOyfOR8c2TbFgxr0wmexnzaJc9HOeesW6ee5SZr6nPtClPd3E0x18ro/9sEkY5XdL4Zo7YiKChWCmm0wu2hIIDAhAdHgQ0rLNTg3HfTxC1CmMSEB6z7koCil5qGzIiR8R+dvLok5W+1HIr3uV1WZg1gXErp+GwsgEpPVd7PRaZVUIO/Apwg98gpxmNyK32SC37XizYWhwoBBH2f8KUkqOqjyo0Jt9qcjXig4LQo7ZggJL+f1OxK6fisCsi0jr+xQKI6uowh21bTmCz+4Sdek7Q98dV0qlL+5DQP5/e50yr5wEc/W2wibZluXSgOdKfWdduQ7VJYkfGxmCS1nG/Jvs6njKo76jxyT0AJb+yzboA+KyeFWK4n035TGXKto1WSwBIi/9iMlPoudV7TBn8nBriJ09Z2/9ZS/GTn8av61/AWGhIRyGV9G+ES6Oh8PwXATmQnVXwvBkWmIyby8jncx2R5/T/omMxjdYe+Jp2nBpyNVsYy6g0K0qh+HphraEYSOE4SkPmVU7ajmnqf6l1iNAGfBcKTK8VbaRqcZtQ/y0OHOJw/Bc8YzrdTkMz3Vm3KLiEPB7sXTo739w06g5uL5XZ0wcdRMCA4tXlCLCQxEXG4131m1Ekwa10bxxPaRlZGLq46sRHGTCK89MF/V4z1LF+TK4MxIWS+5QU9fGVbFEIokKZaSzvflS3vTZ7reQ+yc8yYQnfjNO/VRiE7u6UZZvLRZL3uFf3mLJWTbIMilY8hH99zfIrtWlzPOUHBGM2/0SQlKOwBxdC8GZp5DU47+0+ombZ8OUl4qAgnyXhVhgTipiD7yPzPp9YI67THSBxZK+c5nFkr582bqxCfi9WPr6+19EXnnbQucsPTlrNJauWYuX3/3K+nGr5g2weM5Y1KpeHMbAYsnYE1zv3rFY0o+wWrEkbwQpLbE5prYQLbbCR6ZNpt4qz3eRT7gp09e5HgtRFFwyfM+V0bFYcoWWf9UtT7EUlH4SVbYvEam/nZ2NpItXaEMnKRnbUlSI6L++AD3IsF3tddYP+X2mTH0Xr5zKYskZMA0+Z7GkAUQ24bME/F4sqfFcbl4+LiRfQnRkBCrFRpVowmJJDcGKW4fFkn6+dVUsKQ+8VN5EUQ9lpjr6t/IzLQWO8hBOeQOnHx1tLPPKkjYcnVkpL7EkM9JR/9RksHM2Dq0/dzUENvjSMRSGRCPhl8UwZSeL7sjQPl5Z0to7Je2xWNKXL1s3NgEWSx76h8WShwB9vDmLJf0c6I5YuthpKqpumiHOhFGeA6MUSySqZDiQqwd1OhotiyX95oKvWy4vsSRXTpVz3kgs5XeGVoUvdHvEYddCL+xF/K/LStWRq1IslvT1LIslffnqaX3nnkOIi41Cg3o19bxMhbbNYslD97JY8hCgjzdnsaSfA90RSySC5IGX8okz9VDuS6J/K4WUFofRSgLyxlTNjZ9+1FyzzCtLrvFyt3Z5iSUtV07dHbujdnLly/bhhr02ygce9DntUaT9iXKlmMWSHh76zyaLJX35amX9h217sPfAUevxN2R33Ixn0K5lI9x7+wCtLuN3dlgseehyFkseAvTx5iyW9HOgWrFku6Jj7/BNpViiHp+5vvjMJS0Oo5UE5N4poz7Ft+cpFkv6zV+l5fISS1qunOpFSj7cONtnmcM9g7IeJW8JLMiDJSQKVX6mfYbFq8gslvTyULFdFkv68tXK+tsff4dvNv2KN1fMsppkseQ5XRZLHjJkseQhQB9vzmJJPwe6K5bsrRbZpjCmGzMq1TfcL/4vxZOno3EnPbOn1/SkPYslT+ipb1teYknLlVP1o3WtpvLhRuzBj5BTtQ0yG16P6MOfIi++GcwxNRG351XQ/ibbFSjlKjKdk1YtLhxnU3Jc6wDXVkWAxZIqTF6plJObj2UvfYgvv9smsjbfOqgHbup/Dc5fTMUdE55AyqUMXN6kvujL68tnYvKjKxETHYH0jGxQSF6PLm1E9ufaNRJFHXpv8ar3cPTkWfS+uj2GDb4WLZsWtx82fh5G3zEAdGzOgSMn8MT0UQgPDcHCFW9j++8H0LpFA9wyoDv6du8o6s9d8ho6tWuGfj07idebft4lxNui2WPEMT0zFryA/r064a2PvoXZXIApY4YgJCQYa974DKlpGbjz5j4YfcdAr3B05SIsllyhZacuiyUPAfp4cxZL+jnQXbEkn6YrM2zZO+8l0JyFKj/Og5YrQSyW9JsPvmy5vMSSliunevGXgs4cWxvBaf+Iy1DWPgohpJDW3KqtEX3kc/F+WWn/UzpMQF61NiyW9HKSn68sFZ46hsK0VB3p2jdtqlUfAbFxpT4kQULCZfKYW8Qh4o89/RrGDR+EXt3a45kX1uKX3w+IM0OptGvZGBNmLxOC6IF7/4eG9Wth6eq1QtCQUDl5+jz63T4ND44dgm6dWmH9ph34+Ost2Lh2qbDdovvdws7tN/VGjWrx6HlVe4ydtgRtWjQUwubYyXOYOu95bHhvCWpWS8CdExcIoXTb4F6i3bqvt+KND9Zj3StPiPDAoeMeR++rO+CWgd2xZ//fWPnqOjRrVFcIpIICi7D1xRsLUb9Oda/zdnRBFkseuoPFkocAfbw5iyX9HKhWLNnuy5AH0CpvrOQTaLnPoTiUJxd0o6ZMJe7paNSGFHl6Ha3a88qSViQd2ykPseTKfiDvULB/FeUZaPZq0GoSpT1P7ng/8hJblqgiH4zQdz29xVAWSzo60p9XlrKefQTmn7/Xka5905EPPIbgLsWiQxZaVepw3WjMvv9OtL28oXj746+2IOliKpbPmwQ1YXgffbkFb320QQiYVa99gi++24anHx0vbJFgIUHz0UuPo2nDOkIsrV70ILp1Kv7ubf99P0ZNeQqvL5uJyIgw8R6Jt0HXXSUEkhqxtG/Tq0KIZefkomO/sVi7Zi5aNKknbA0e+TCG39IXg/t18zpvRxdkseShO1gseQjQx5uzWNLPge6KJesepriGSGk3FoVhlSBXfGgzeEjKIXFobVBOsuaHyPrCk3ylx1gs6Td/lZa9LZYoZC3inx9F6Fq5nK3kAlaZPty2iRRJ9L7tUQCyrnK/YnKXqaiWdwx5e75AbvV2YtxctCPgz2Ip98NXUbDvd+1gqrQUdvMIBF3erkTtYyfPYsDwmWI1Jiw0xPpZYkIlLJ17nyqxtH7zr1i65gOsf3exCIvbuPV3NGlQu8R1xt01CF07Xi7E0lvPzUbbyxtZhdmcp16xvpaNenRti1HD+rskliyWQrTqNRLvr3nUGjZ4x4T56N+rs3VlSiUq3auxWPIQMYslDwH6eHMWS/o50F2xJLPSUc/kYbO0N4n+nVe5sbiBpLCd4LSTiD7i+oGYjkZsL7mEfoQ8t8xiyXOGaix4WyxV+/YBBOZniq5daj3C0MJB+X2l0NnA/HSYo+vAlHdJfD+pXOw8FfnxTUqhVp4jda7vMlTbvRpIOuAT41Yzb4xUx5/FkpH8kJaRhS4D78MHL8xF88bFqzHK8s66jfhq43YhcGSxTfCgFEtPr16L4/+cxYr5xft3bYutWKJsew89/jy2fbESQSZTqfojJy/C1Z1b4+5brxOf2QvDkytLhYVFaNlzBIslI00wvfrCYkkvsr5hl8WSfn5yVywpb6DkjVbC9sVib1Je5aZiLwTdQAan/wMK2UtrPlTshdCiyPTGRn+aL8fKYkkLrzu34U2xJLMyUq8KQ6ORdM0TDrPMOe+9/jUSt86FObo2UtuMsl4sKOMMErc8UuaqkqwoH1Dk1OqK8FM/Wdv7yndQf7raXIHFkjYctbBCgsRcYMFTc8YioXIsDv19Er/9cRh33dIXv+89jDHTluLrtxfBZApEpZgojJ/5bInU4UqxRPUpdO7JWaPRr1cnpKVn4dstO9GhVRM0rF+z1MoSibVrhzwowuRoDxSVHbsPwVxQgGu7tceq1z/Fr7sOYNm8iTh99gIWrngHmVnZJfYssVjSYhb4mA0WSz7mMI27y2JJY6AKc2rFktzzoEzoUGXrY0IMUSEhRKJICiL6N9WlM1qkcNIqZEd5o6o850k/Sp5ZZrHkGT+1rb0pluQevvy4BkhrcRvMsXXVdtNw9cJPb0defFMRSltWkeOVn9MeRFo9pv2JST0XGW5MvtohFkvG8VzShVTMffo1bNm+x9qpMXcOxKRR/0OBxYIJs54V2euo7PzmBUyZuxLtWzXGPbddL95bv3kHlq5ZK8LwqNCeJxI1tIeISt1aVbF60RTUqVm1lFiiz3ftO4LZT76EE6eSRP2I8DAhtnp1awcKE6TrHT56SrzfvlUjXExJx4cvPoa9B49h6NjH4EgskXCjbHnDbiy5V6u86XMYnoceYLHkIUAfb85iST8HeiKWqFdy/5BM6kCrSabsZGvoXWjyIev+pfz4ppoNxJdC8VgsaeZ2h4a8KZbkwwPbzHHeGWn5XEUmeihq9T+crd0PVb+fLh6GXLhqjk+LxfKhaf+qLJaM5I3ivuTm5YuVoPjKMaVC4mgFKCQ4GOFh/+1rcjSCoqIiJKemIzg4CLHRkaoGS9eg9N/xcTEiYYOynLuQIla97IXqqTJusEosljx0CIslDwH6eHMWS/o50FOxZHsQLZ2tFHFqG+jGKq3ZrQhP+h0hKUeg9QoQiyX95oSvWvamWJKhoEbfq6S1L5WH0vpaOKzWLPSwx2JJD6ps01cIsFjy0FMsljwE6OPNWSzp50C1Yqmsm0OlWJJnKdmG7FDvSUQVBat7kqZmtCyW1FDyrzreFEtyRdXfVlWUYsnXwmF94dvAYskXvMR91IsAiyUPybJY8hCgjzdnsaSfAz0VS/KwS+ohHW55odsjkKmGlb0+c/1Lmg6CxZKmOCuEMW+KJZkmX+uHAEZ3hFIsUV/l99/fVtj08hOLJb3Isl1fIMBiyUMvsVjyEKCPN2expJ8DPRVLcsWJeijPabEVS3LFSctR+FIYFO9ZAgIKchB58gdk1usNBJZOhavF3PCWWPKVg2i1YGprw1Ys2Uv8osd1/cUmiyV/8TSP0x4BFksezgsWSx4C9PHmLJb0c6CnYkneLFEPZRphU9Z5VN08y9rp/PjGuNh5mqaDYLGkKU5djVG4FqWVp2QAKW3HILdGR12u5y2xJMPP9HgIoAsYDY3aiiXKekn7E50luiCBWfn31Qg0Z+HCVY9o2KOKZYrFUsXyJ4/GNQIsllzjVao2iyUPAfp4cxZL+jlQrVgqK+xNKZaUN0yRR78VGbJCUv9Cflwj5McXn0yuVWGxpBVJfe3Im2l5FWXqea2vrKVYCjBniwOW7RW5cipXUrUeh5Ht2YoluT/R2XlLyr2N/rbPyxV/slhyhRbXrWgEWCx56FEWSx4C9PHmLJb0c6CnYkl5M6znjbAtARZL+s0JVZaLihD91+fIq9wE+fFNSjUJzL2EyBObEXF6m1hRov1sweknna5AqLp2GZW0Ekvx25cgNPkgkrovhCWySqmrqRUInozFqG1txZIa4Wh7gHVKhwmgc5q4lCbAYolnhT8TYLHkofdZLHkI0Mebs1jSz4GeiiVl5jtvbvL2pb0SFWnPEu09qrZxGhAQAFp9oUKrCsEZp5FV5xpk1+km3pNn8tC/6QyujMY3gASusxUIT2a6FmJJKf7LOnBVbeiZJ2MxaltbsRScdgJVfpxnTe5ir9+22TGdhewZdeze6BeLJW9Q5msYlQCLJQ89w2LJQ4A+3pzFkn4OVCuWZKpk2/OSlMkcWCzZ91NFEkthSbtFBjQpgmjVSFnkHFCGXaW2HQ1LaAwSti+xJgHRY0ZrIZaqbH1crIBRCB6JQblaGpJ8CFHHvkVgQTbMMbUReWwj0poPFStl/lRsxZKa/VtSONNqEs0ffwxfVDtHWCypJeUb9dZv/hVXtG2GuNho3+hwOfeSxZKHDmCx5CFAH2/OYkk/B2opli52fgj58U3166zCMq8seQVzqYvIVQIKq0u+YjIiT2xC9JHPrOJCpo+vvmGSEBvnei1GYVicNZ28njfKnoolZZa75Pb3iaQUVKjPwRn/WFfSKLGDKTsZ3nw4UD7eLn1VW7FENWQadXvHAyj3NKZ0uA+Vd64Uc+Vsn+VGGZKh+sFiyTjuePndr1CregL6dr9CVafs1W/R/W689dxstL1c2z27qjrkg5VYLHnoNBZLHgL08eYslvRzoBqxJENt7N3kyM+oh7arTvr1GmCxpCfdsm3bC0ELP7MDOYmtUf37h4SgSGl/Hyr/thLKbHFqViA8HZGnYkmumklBR8Iwdv/7VpFk2z9vPhzwlI1W7e2JJSmMbc+cIvFZbdNMwU+GX8qVO39kp8YHLJbUUPJOnUlzlqNpw7oYf9cgVRe0V5/Fkip01kosllzjVao2iyUPAfp4cxZL+jlQjViKPvwpoo98bndzvrwJph5684BOX9o3UpHC8GRIlb0QNHlAqUzmYLs/ydEKhBYz3C2xVFQk9l9RkWPLaDQQGY2Lb5Dohj9h+9Mw5aUht8rlIAElizcfDmjBRwsb9sSSFEC2We7k74ZyNVEmZvHHEEY1/FksqaGkfx0Kn3t40SsICw1GjaoJaHRZLTwxfRTOnLuIhSvexvbfD6B1iwa4ZUB39O3eEWXVJ7F0z23X46cd+3DiVBKGDuqJ8XffiPCwEBQVFWHtZ5vw+gfrkZGZjZv6X41hg3uhWpXK+OvYacx+8iXMmHgb3vxwA85fvCRWqCp6YbHkoYdZLHkI0Mebs1jSz4FqxJLcr3Sx89RSmc+Uma7sheHo1XNfykhWkcSSoyyEthv5bW+IjSaWwk9vR9zul5FV9xpEnNoOBAaKVRB78xxFRQg7v8e6X8vbDwf0+h65ateeWCrrWAF7vxtyjtD+JcqKx6UkAX8WS/tzU3HenOP1KdE8PA6JQeElrpt0IRUPPrYKdWomYnC/boiKDEfD+rUw6O5ZaNOiIe68uQ+OnTyHqfOex4b3liDIZCpVv1mjuiCx1KBuDYwdPggR4aGYOm81ls4dj26dWuHLjdsxd8lreOyhEahfpxqef+NTxEZHYd60kdh74CiGjnscVavE4X/9r0ZYWChGDevvdTbeviCLpX+Jp2VkIS/PjMSESnZ9QOq6wGIptRmOxZK3p6yxrsdiST9/uCKWynqSTns7CiIScKnVCP06amOZxZLXUJe4kFw9spf+2TZFtO1KY1lJQrQaiasrS3Isyus7OmhWmcxEz71XWvHQw449sSQ52opje+F5ciWaQnoz610rzmLLrdpaj676pE1/FktDj36L91P/8rrf3rusN26Na1jqurZhddt/349RU57C68tmIjIiTNQnsTPouqtw2+BeUBOGN33+GiTExWLq+KG4Y8J81K1VFXf8r7ewdeDICSxc8Q62fbESBw6fEGLp169WW6/ldTDlcEG/F0sXU9IwfNICsQxJhZT2vbcPwMA+XcTr7JxcTH9iDb7/aZd43ap5A6x4YhISKseK1yyWymHWGuiSLJb0c4YasaT3ioA7o2Ox5A41z9uUFXIlLcsbZ3tioqwVCM97VWzBVbEkb+aV11eG4Nn2SykG/TWMzJ5YshcSqxRFtskclJkSxd/361/Sagr4vB1/FkuPn92JTRmnve7DR6p3QI/omk7F0sdfbcGcp14playhR9e2YtVHjViav+xNFFgK8eiUu9DtxomICA9DlfiSiwfPPj4BZ5OShVjat+lVBPwbJux1MOVwQb8XSxRv+ck3W3FD366IDA8TMZivvv8NtqxbLmI3X3rnS3zw+Wa8uWK2eD1uxjOoX6e6WI5ksVQOM9Zgl2SxpJ9DnIklZYYwI2WwkpvxfSGcpyKF4TlbHZJ+sScmjCSW5M18YWgssmteidzEFog4tQ2XWt4FBJrK/MJV2/gQ6MBdf9yvRFDsiSV7B9M6O39JeZ4Vi6X/pps/iyX9/sq5Z1mInwZ1xB4jKj9s24OHHn9erPxQ2J1tsa1Pn9smeFCKpZvvfRSD+nYVIX22RYbhsVhyz3cVptWpsxfQd9hUvLliFtq1bAyaNLRJjlabqNBmuSlzV1lVNa8sVRjXuzUQFktuYVPVyJlY8kYWM1Udtalk7wbNHTveaFORxFJZmc+UHAMK8lAUFFoKraP9Tlr4wZWVJXdXJmnemXLTkFOzkxZd9jkb9sSSvX2Lavg6E94+B0eDDrNY0gCiRiZeeOtz7NxzCCvm34+s7FyYTIG4dsiDYg/TA/f+T1xlx+5DMBcU4Npu7WFbv3KlaIdiierTwsGqhZPRvHE9nD53ER9+sRlTxgyx7llisaSRM33VzLqvt+LhRS9j6ycrQBOqY7+xItMICSYq+w8fxy2j5+Lnz1ciNjqSw/B81dEa9ZvFkkYg7ZhxJpacPSHWr2eOLbNY8j750KQ9iN+1GrCY3QqdcpRJT4vRuCKW9O6LFuMxog17Yon6aRueqSZbpbOQTiOOX+8+lRJLFjMCigrtPnzQuy+u2K8RXzJBgittjVr32MmzmDJ3JQ4fPSVC7ygb3a59R0SWOrmlhMLonpw1Gr26tYO9+vZWliyFRXhk8nDk55vxzIsf4o0P1lsRdGzTFK89OwN7Dx7D0LGPcRieUSeHN/p15Ngp3Db+Cdx1S19MGDlYpE+8vMcIoa6vubJ4o+ffx0/jhrtn47v3n0b1qvEixtOIJfDfWNJCSj/LRTcCpsBA0A8MwJy1hxwAkykAljK+YwFJ+2H6biGKEpvB0nuW9pd306JR+2VvOOJ3IgAoFHMYKLAAQWVHerlJROdmGecR9NmDxReJSkDBoGdcvmDgH+sQuPdjFLa8CYWtBrvc3lkD+p2g32L6m+KsmLa9gICjW2G5cjSKLuvmrDp/riAQZAos9TdZ8ixs2heF7e+A9XX7O0Dv2Sumbxcg4PwBWK6diaKqzZmxCHMMEKGO8rci6IsZQM4lFNyy2tB8aE5U1JKcmo6Y6EgEK360KVmZ2VyA+LiYUnuK7NV3xIaSmiWnFF+DtqH4c/H7PUvS+bTMeOfE+SD1vGDGvWJZkwqtLM2fcQ/6XNNBvLZdWTp/Kc+Q8ycyPAiUUjYr12LI/lWUTsXHhCAtK1/cZHLRlgB9BeOiQnAxPd+u4bB/fkTMrleQW7sr0tuO0vbiHlgLvngQcT8/BXN8E6R2ne6BJf2bRoSaQCt4GTkF+l9MpyvIeUDmC2LqIKX7XJevFH70W0TvexfZl/VG5uXDXG7vrEFcVDAycwtgLnAulmJ/XYHQc7uQdsVE5FVr68w0f/4vAbqRpxW8C2kl/ybL72NheDwu9l4MNXzjflqE4ORD7APF7AoLDkTYpSPISzqKgtjaiPvpKfFpyjWPoiC2rmHnYWKl0mG3hu0sd8ywBFgsAeKQrRGTn0TPq9phzuThJTbI0Z6l63pcIQ7vosJ7lgw7l8ulYxyGpx92Z2F4avYe6Ne7si0bdS+VvR5XhD1Lcr9RQVQNZDboCzpw1tWi91xyJQyPQ8Bc9V5x/bLC8Oizqt9PhyknWZyfFLv/XZiykx0mwtB7D5t7IyzfVhSGF/flfUB+domO2EvVX749LXn1ihiGZyS+/tIXvxdLh/7+BzeNmoPre3XGxFE3ITCweEWJDumKi43Gi29/gQ+/+EFkw6P3xk5fytnw/OXboWKcLJZUQHKzijOxFH34M0Qf+QwZjW5ARuMb3LyK9s1YLGnP1JFFKS7sHtiqsit67zMrUyxRWJ5N+l1OLqDSaTbVHIkluU+JMlRSVkQ6S8lRBk3eN2YDtyAfUblnEfPDvP8+oMyMhRZk1b8WlGHSqIXFklE941v98nux9PX3v4iUi7aFzlmizXGUaYQ+37J9j6hyeZP6IgOJPLyWs+H51oTXurcslrQm+p89Z2LJqDc0LJb0mxO2lpXZzmwPmnWlF+UhloLTjqPKT/OR3vhGZDYsjlygoiarnytj85e6jsSSTAYjWTg7uNeoD2LKw5eShbw2CU4SRyGpfyNu94twxrI8+qy8Joul8vZAxbi+34sltW6Um+bkYbSyHYsltQQrZj0WS/r51ZlYMmqoDIsl/eaErWV5E2yJiEdSj0VuX1jvzIrKlSVTbgoStxTvqwowZ8MSHo+knv/13YgHLbsN1osNHYkl6oYMxaN/O1sN0Tss04tY3L4UrcDF/vmuCF9UFnlOmd7fGbc7btOQxZJWJP3bDoslD/3PYslDgD7enMWSfg50JpYq73xOhNQYMWbeV254fX3PklYHAOstcJViSXnoqfz2XLhqDsyxdaF3P/T7tpa/ZWdiSblCcqn1CId726RY8vhg6UKLw4OEhWAuyEFRkPHSW8vwVhLzaS2GodL+9xCYfRFJ3RfCEllFOFz+zplj6iCrXg9k1zZe9kYWS+X/3awIPWCx5KEXWSx5CNDHm7NY0s+BzsSSkTfCs1jSb14oLUvhQUkd6AbYk+LUZ0VFCLDkunVjS2Ip69zfiP35GdDBuGQnN7EVAlCE0PN7xY079d9XntZ7wlmvts7EknJ1yVnIphZhmeGnf0Xc7heEX8tKOhK/fQlCkw8iqfsCWCIT9ULjll3bcNDozGMIyzyFC9X+E0QkKmP3vy9WSKkk9XgSlogEt66nVyMWS3qR9S+7LJY89DeLJQ8B+nhzFkv6OdCZWDLyRninN976YXPJsq+vLGm5b82Rz2jFJ2H7Ypjy0pB0zRMu3xAmhBUg6OvZCMz+L6SJbixNOWR3iTUUTw1SkmcAACAASURBVIubdJcmQAWqrEYs0XEagfkZKAyNcThyLfwgw4QpmcT5qx4pNWeUK4y+kKSm1KG0/xKk7wZ9D2mVl0pOjU5IbXuvYWYWiyXDuMKnO8JiyUP3sVjyEKCPN2expJ8DHYkl5cb+M9e/pF8n3LTMYslNcCqa0dP4IlMoMi/rg4hTP4OebjsLq1JhFo7Et/yM7LgT9pmYvg9BW5+1dkOZjS1h22KEpByyrozRTbYWK2VqxlyR6qgSSyoHrEU4pHLOmGPrISgrCRc7Pwj6NxXpd/o3hbFd6PaIyt7pX83enq2yxBL1RvKSPXP0m0x1Yw59jJzqHZBbrZ3ug2GxVDbiI8dOISMzG+1aNtbUD3SQfF6+WWSQriiFxZKHnmSx5CFAH2/OYkk/BzoSS1rczOjXc9/JaOZrK0vKmzJK6lAYFIng9JOQe3488akM60zuMAl5VVuVMCXFL73pjpCpenYzTL+/JfYlUahdVr1eYh8IFXljSntDsmt1QfSRz50mIPBknBW1rZZiST6McZZivCyWcp5SeyoyTE05d5RiiuoYKYTN3oqtI7FE/Q9JPoS4Pa+IhBAXOz+EuL2vA0WFyK7ZBZn1r0VRcCSC0k+iyvYlVh7uPHhwdf6yWCqb2KrXP8XBv05g+bxJrmJ1WH/bzj9xz0OL8dOnz6FSbJSmtsvLGIslD8mzWPIQoI83Z7GknwMdiSWtNvbr1XtPQgSjjm5AftxlyI9rqFf3rHZ9TSxJYSEHQDejdCOqxY2m8km/Mlua7VNzEjXnuz0ibv7Ulmr7X0fgsa1i9cgSFof8SvVL7H2SmdrkeIwWlqV2nOVZT0uxRONwd3U4KPMs4ncsE6stlCAiP64BYg5+JNAoMx8qkyOQ4PeGcHDmH+pzcMYpIXroe6U8u8yZWCLbMukOfX8ozFBZ6OGGJSxBrKISBxJV3lhRY7HkfbGUmZWDE6eS0KRhbQSZTM6mnU98zmLJQzexWPIQoI83Z7GknwMdiSWjp/Z1VyyZss6j6uZZyI9vjIudp+kH91/LviaW5BNvWzBahGIqxRLZp1UeBAYjt0oLVP5tlbjxDU05LG4iSdSkth4p3lNTqv80DwGXTpS4+VS2o5vUxB8ft6YSp5tUo22UVzPO8qxjFLFUIutey+HIrnO1yHhH6eLlqoslPAFVN80ACYjsml3F4drO0pkr2QbkZ6LIFALQfxoVWk1L2P60WKmlYnt+khqxZHsmk0xsEZ60y7qaRLbpoYFMDKHFgw5HCCqiWJq75DV0atcM/Xp2EkPf9PMufLPpVyyaPUa8fmfdRrz10QZcSE5D3VpVMWHEYHTv0gbZObl4auV7+HLjdoSFBiMiPEwIGkcrS2eTkrH4+fexY/cBBAcH4dpu7TFr0h2g43SeWvkuNvywE9FR4bh5QHeMvmOAEEd/nziDWQtexDur5sBkCsSw8fPQo0sbUZdE1NBBPTH+7hsRHqbd/NXoa1CmGRZLHhJmseQhQB9vzmJJPwc6EkvyptmoT+DdFUtSBLob/uOqN3xJLNFqIoWoURibXIGh8Xp6xpJkFvX314g5+LFIgRx5/HsrSrpppKfhdDObG98c0UfXi9dUnGVVk0ZqfDsJyM92WJ8EE+3Byryst1sZ91z1fUWrr7VYcvc7LH+b0pvehMwG/a2YZcIHmkfZNa9ElR/niZWVtOZDRJIPtYe7kqip+sMjKAoMEuJbK1Et+0erPgVRVXHp8uElbKsRS3LFXw5auTKlPOeKvjex+9eKENSMRgOR0XiQbtNRC7GUfqYIeRm6dbFMwzHVA2AvF8mdExcIoXTb4F6i7bqvt+KND9Zj3StPYNe+I7hjwnwsnTsel9WtgV37/kJBgUXUfWzp6/hh227cd/dgNKxfE6vf+AzBwaYyxZLZXIBBI2YjMSEOo4b1R2FhEV58+wu89dxsTJu3Ggf/OokpY4Yg5VI6Fq54Bw/cezNuv+la/HnoOIaMmYs9G18W4qlF97vRoG4NjB0+SOxjmjpvtehft04lw529T1j9FVksqWdltyaLJQ8B+nhzFkv6OdCRWDLqgbSShtz/QrH7+fFNVUNSZshSeyOu2ridir4ilpQJPWgY9MSabrTkvz1NG25FYykATEElDjCVn8nDOOm1K2nrrftXQiJwtvdyT9zFbR0Q0FosueJjZbfK+m2SGfZIIGU07I/Kv68W85jEUvUN94sHAGf7OJ8f0Yc/FQ8NtJr7gTmpiPxnq1jdolLWSo8asWTKuoCqm2cKO7bjkQ+CZOidUlhpkaClrKmhhVjavsaCUzsKvf796zTGhNodA0td15FYkvuFVi96EFd2aG4NgyPh06b3PZg3bSRu6n+1sKncs7T3wFF8u2Wn9Vr9e3VG6qUMsffoq7cWiRUqWWiFqmO/sVg8Zxz69ype3XryuXfwy+/7hWCzJ5ZIYLW9vJGoO33+GiTExWLq+KFeZ+ruBVksuUvu33YsljwE6OPNWSzp50BHYsndGxn9elvSsgzpclUsKcPMMhoOQE61diiIraNbt31FLClFZGFoLOipPd2UUtHjRsuUmYTIk1sQdWy9lb3yKbkr/pU3yeRHSiHNRR8CWoslV3ysHJGjdsrVFWojQ+/k+85WyumhQbVNM0uEtLkdglpYAAQGIXb/u4g8tlEMwVHyEjViiWxE/f0VgjKTkFu1FXKrtf8PTWEBKu17S2TBy6tyuXjflYOC3Z01Woil/Z8X4sJB74ul5gNNqNI0oNTQHYklEkULn3sH739avDret/sVmDLmFhQVFeG626bh8zcW4rI61cVnSrH0666D+OSbrdZr3TqoJ/46dlqIoB1fry7Rh2Mnz2LA8JklRNQX324TK1dU15lYmr/sTRRYCvHolLvcdavX2xlGLOXk5iMoyITgIN/aDMZiyetz1lAXZLGknzsciSXbAxP164V7lt1d+ZLt5FXVhua410vAV8SSvAHNqt8LWbW6IdCcKUKXqOi5Aic34dsmdZAb2dUINflEPa9OVyS39OzgXHf97A/tjCKWHIXv2X6/5Wql8iGJowcsyiygnmSCDD/9C+J2vyiSSshkDrQCfqn5EBTE2H84o1YsuTrXpGCSyS+CLx1D5V2rYcpORnKnKchLaO6qyRL1tRBLHnVAh8YjJy/C1Z1b4+5brxPWlWF48nJp6Vn448DfWLpmLZo0rIN500ahzbWj8NKSqbiyQ4tSYsleNzf/vBv3zXoWP3y8DAmVY61VyHaXG+7DygUPiL1QVJ57ZR2++n67EFAsljR2eoHFghfe+gLvrvsOKZcy8OSs0RjYpwvGTn8aISHBmqcz1Lj7whyLJT2o+o5NFkv6+aossUR7VijeX6u9KnqMwN09VXLFTNknPTdA+4JYUqZxPtdjYXEWuqJCEbqUV7kRUjpqm/ZWyT5u1wsIP/NrqcQMrvhX3gxmNx2ESw0G6jHd2Cal5w4AqsWF42xKjiY8XBHEygs6epBDczk05Qhi/3xXJHtI7jgJeYnF+zbUXE8ZymcJrywOgi0l2IssQIDjh87y4YPc+6fmt1QvsURj/2/FLQCWyARQOB8VZfZAK+OiQiCgdGhaWU6viGKJVoR+3XUAy+ZNxOmzF8R+ocysbBECt/WXP5CemY2eXduB/oY+vOhlREVF4JHJwzFpznLQGUjT7xsGEjy0ElSjWnyZ99qpaRnoM3QqBvS+EuOGDxILGrQ3ivYm0b6oqMgwPDrlblC9yY+uRJ9rOuLBsUNYLGnyC6QwIlUrxU+S4yljB4klypgx+dHn8PPnKxEbrT49q9b9U2OPxZIaShW3Dosl/XxbllgyeiY8IiJvkJ2F1djSsz17hT6n0JjMBn1REFVDc9i+IJbk3oZSq2wijIhuCkuHqWgFirKYmXLTUBBVrYRJGRaoJoOZXE3IaDcSGdW7aNU1tmNDQGux5M7qsNrzmSijYvjZHciuc411FGrmlPK3zxxTGyTalaFzYuXph9nF77UcbneO2O7/o0pq5rGeYsl2xU0KJRKUUgzS2OL2vCqSqyR1XwBLZKKq70BFFEsUBjdl7kocPnpKZLRr36oRLqak48MXH8P23/Zj4sPLReY7Kl07Xo65D96NGtUSRPKH0VOfFp9RO0ryUCU+1uHCBO2Bmr3oJSRdSBX26ADbN1fMAvXh/jkrROY7KrTCRAse0VER2H/4OG4ZXTLBg3LPEoXhWQqLhIDzlVKuYXi0glS7RiJm338nRk9dgoG9uwixdO5CCnrdMkU4vlmjuoZmyWLJ0O7RvXMslvRDXJZYsndgon69cM+yu4KOwr6KgkIRUJBXIuNbTo0rkNp2tHudcdDK6GKJNp1H/PMjTDkpumfNcgWuK+d8yRWD9E4TkZnQ2pXLcF0XCGgtlhw98KDvd8Q/W5FbpSUyG15v7aUnh2XLVSNHobdKQZVTtY0IRVWeVSSTPzg6v0j+ftKqDaUwLwyJRFrzW8UqjqOip1iCxYzoo18j4p+fxIobMciu3UXsS6R+5dTsjNDze61pzZXJVpxNkYooluSY6V6ZwuNszzKi/UnJqelCEFH2OWUxF1hw/mIqqlWpLNJ6qy1kLyw0BJERYSWanL94CaGhwYZf2FA7zrLqlatY6nnLZIy7axBuofzsdsTSZ68vEOkGjVxYLBnZO/r3jcWSfoztiSXlRn8jHOJY1ujV3PjYtlXeaGXV7SVCzCJPbkXEP1tUpxV21RtGF0tyzxCNS5lgwdVxal3fFf/K1cKU3k8hN6Sy1l1he/8S8IZYIr/Tg4z4nSvEVelGXpm+25V5Yes4NatStuGfMuRPhuoqzwqzt5dPmYHO1e+TrmLpXxihF/YifscKpLQfL84ws02IIcMG6TP6/VdTKrJYUjN+rqMNgXIVSw888hwupWfilaXTxT4lubK0/OWPsObNz/H7hhcRGhKszUh1ssJiSSewPmKWxZJ+jrInluSeHplyV+xfMWBx56ZJuR/hQrfirGlyf5ZeJ90bWSxJ8UgcCkOjkXTNE8X7lQxQXFlBkDe0yf1XIC8g3AC9r5hd0Fos2YbFybTYhSHRCMz/79Ad+i0KT9qN9EYDURQcLlZDHGWVc0RfioMLV82BObZ0VI1taKAUR5n1eyOnRkdU+WmB1by9RBGyvjtnG3lDLNmykeOl3z9TbjLSmw2xrjYl9VykaiKzWFKFiSs5IVCuYunQ3//gplFzRP72jMxstGnRUKQT3LJ9j9hAdu/tAwzvQBZLhneRrh1ksaQfXntiyd2DIvXrpX3LrtxMSwsydE/51NQdO66M1chiSQrFguhaSGk3BgVRxelujVLkqhelbg5OO4kiU1CpfWVWwRcSgYv9n0O+2fvph43CS+9+aC2W5PdR7udRrmrTWOh7Sis1yiLfU7MHyB4P28xwtnWk2JGr6rZ9Uta3DVVTph13J4NkeYglFBYg9OIB5CW2tA7NdjXN2bxiseSMEH+uhkC5iiXqIAkmWkmiHO+06azxZbVw+029xaFZgYH6bdxVA0dNHRZLaihV3DoslvTzrT2xpLxB1e/K2lh2ta9l7XNy1Y4rvTeyWLInHl0Zm9515U0brQJU+Wk+aMXh3LVPl7isFHxFleoiufujLJZ0dIrWYkmGrFHq6oyG/RB1bGMJcURhbLH737fuo1EOTU1K+bJQyNWlzAb9YAmNFckXYg58CFNuKkIu/SVSasuwO1P2BVTdVHwIrCxSsNH/A805yK5xBbLrXiP6Tvvn3D2OoFzEkh1IUjDSahOF61kiEhzOKhZLOn7p/Mh0uYslJWvalBZAv3g+VFgs+ZCzdOgqiyUdoP5r0lYs6b3KovVIpMhR+xS3rA3lep4pZWSxpCY7mNY+c8WeMqQp+sjnoqlt+JQUfIX1uyGl9QgWS64AdrGu1mJJhsVSN2ivDBXKYicLfa8p/I5CxejGPTj9pPWzssLo1AxJJgSRdZO6L0TVzSUFkfI3JezMTgTlXEDMwY/EHqrU1iOsZ5BJG7QSFXX0W5FJzpXkCMr+GkUs0d+BhO2LRSIINftWWSypmXVcxxmBchVLlHIwPSOrzD62bHZZqSwfzgbk7c9ZLHmbuLGux2JJP3/YiiV7e3r0u7rnlmXIYKkbp6KiYuM2D4bKSlXsKPSQwr8q7X0Nqa1HoSC6psudNrJYMnrWQ9k/uemc4NvevFnDqpr0RWqzW1ksuTxD1TfQUyzZ9kJ5LlHkic2gzHTxO5YLwUTz4Wyf5eo7blPTWbhfWfbpPDBzdA1Q2Krt3CQRReKC2lrPKnOxh0YRS9RtV45mYLHkoqO5ul0C5SqWJs5ehu9/2lWma/icJfdnbXREMFBUhIycAveNcEunBFgsOUXkdgVbseRuOm63O+BhQyl+bJ/kJm6ajaDsJOTHNcDFLv89MS5LLMkVDEcbtt3dUG5kseTOOTceusyl5srMYrKh7blacgwFnUfjUrXOLJZcIuxaZa3FkjLBiOxJVr1eCMzPFMkUKMxNWaSv3Q1zk7aUK1r0nhTjhaExKAoMRmrrkciPb+IUTsTJH5BX5XIkbHtKCCUqnvTNSGLJlZBCFktOpwpXUEGgXMXS2aRkZGUXH5ylLLOffAm1ayZi0ewxLuWBVzFezavwypLmSH3KIIsl/dxlK5aMHpZlS0Le9ChPobc9EJKSA8hSligq630pHqm9u9nyjCyWZOZDeyJRv1mn3rLcj6RsYZvSWI7B3HMm0mIasVhSj9flmlqLJXuHtzpKtx2S8hcStj2JjIbXI6PJYJf7LxvYuy59pjac1/bCJPrif3sO+TF1xVllzvb4lNVxI4kl615AFat4LJbcnorcUEGgXMVSWZ7Y+ssfGDt9KbZ/sUqcBmzkwmLJyN7Rv28slvRjbCuWjB6WZY+EbeYm2xtsZYheWeF28ok13ehE//Ul0pvejMzL+lhDbeR13bmZMrJY8oXMh3JfWpEpDAGW3FKiVY4hf+BSpJsqsVjS7+dCRLVWiwvH2ZQcza4SfvpXBBZkI3bfW6rC6wLyM1EUEuXx9RN/mIOgzLNWO8qwP4+Nu2nASGKJhqB2L2dFFEs79xxCXGwUGtQrHXptsRQiL99c6jBaN91u6GbZOXkICQnyynYdQ4qlk6eT0O/26Xhv9aNo2bS+oZ3FYsnQ7tG9cyyW9ENsK5bkxmc1m3r165Vrlm1XR2xDt5SrJmX98ZciUV5ZHoRJ7ytTF7uzAmNUsaTmgE7XPKFP7dCkP2AyZyCnWnskbnlUhDvJTGXKMZhvXoP0HDOLJX3cIKzqIZZkd6OOrkd+bB3kxzfTcQT/mQ5JOYLwszsReXyjeNOVQ1j16qDRxJKj8GQlg4oolsbNeAbtWjaye7zOtp1/4p6HFuOnT59DpVjPhbte88lTuzm5+ehw3WismH8/enZt66k5p+3LVSxdSL6EnNy8Ep3MyMzBO+u+w4YfdmLLuuUIDwtxOgitKhRYLAgMCHQpZTmLJa3o+6YdFkv6+c1WLPnCSoMtDds/6MrQOaorUwwrQ2+UoXlUR25mVtqmfVBRx78VaYRlqmDa25Dc/j5V+xmkLaOKJcnJkz0W+s1M+5blCqDcPyZXEWlVwDLoWRZLOjtET7Gkc9ftmlfuXXL33CYt+200sSQfnjlL0+5vYikzKwcnTiWhScPaXllx0XKOuWKrsLAIB/86gVo1EhHjhQi0chVLZSV4iAgPw6RRN+HOm/u4ws6juqRSbx0zF6PvGIgBva+02tq49XdMmlM6s83vG15EaEgwWCx5hN3nG7NY0s+FSrHkKysNtjRsV8NshY9MCOAoLboyOxbdNNFrmapYZreKOfK5eN/VRA9GE0skkkIv7EOAxSxWzdxNc6zfrCzbslUchccjqeciKBOSBHUby2JJZ6dUNLGkTDDhTBDojFaYN5pYUpsRr6KKJVpISM/MBq0ktb28ERbOuhe1aySCskzPWvAi3lk1R+z5HzZ+Hnp0aSMWIEhEDR3UE+PvvlEsRFxKy8S4mc/gr2OnhY9bNKmHmRNvR5MGtcVrR23pqJ+PvtyCtz/+FqfOXkTThnUwZcwtoi8UJrh41Xs4evIsel/dHsMGX+swSozuv1e99gk2/LBDnLfasU1T0Y/KlWLwyntf4d1PNoIWUnp1a4eZE25HbEyk6N8dE+Zj9v13oFmjunjyuXcQFGTC38fPiOvTmCeOukkw0aKUq1iiA2lTL2WUGEdkRBiaNa7rVUW8ZPX7ePW9r0U/KKmEUix9t/U3zFzwIj588bES/axTM1GcCcViSYtp6Ls2WCzp5zulWJI3ou4mMtCvl44t22Z0k6/NsbURnPaPVdw4yu4kb7qlMKq+4X7rRSUPeWPlatpio4klGYooB+hoQ315+dTRdeWBoubYugiw5It9JySIQzvcwmJJZ4dVNLFEuOSeOE/ObdIKu9HEknyI5OwBkSZiKe00kJOmFUr1dirVAsJiStWnMLx9B49iwojBItRu2UsfoX2rJpg3bST+PHQcQ8bMxZ6NL4v76Bbd70aDujUwdvggsY9p6rzVWDp3PLp1aoW0jCys+3or2l3eCCEhwXjl3a+EwJH3u47afr7hZ8xY8AImjfofrmzfHD/v/BMx0ZG46oqW6Hf7NDw4doi4xvpNO/Dx11uwce3SMs9RnfPUK/hpx15MHHkT6taqKkQYibqDf5/EUyvfw9TxQ1E9sbIYZ41q8Vg+b1KxuOt+N95YPgvtWzUGMSGR9MC9/0PD+rWwdPVadGrXDFPGDFHP20HNchVLmoxAAyOkrnPz83Hb+HmYMnpIKbH02NOvYesnK+xeicWSBg7wYRMslvRzHomlKhe2IWDnG8is3xd08KezP4z69cY9y7ZJKWRYnlwhkmFmjjL90apa4pa5IpNVdp2rIW/IqUdKHtK2K0+hjSSWlBmuzNG1RcIEukn0pWIbZkl9FweCNurEYklnR1ZEsVRt40MIzL3kdiY8LZEbTSzJMEVnobqaiKUtK4Dj27XEqc7W1ROAev9FOslGtnuWaNVo3jOvi/tUe2LpredmixUfKtPnr0FCXKwQIFRoVeePA3/j+Mmz2HvwmBBPf25+zSpGympLqzq1alTBk7NGlxgLrRB98d02PP3oePF+QYEFQ8c9jo9eehyJCXF47f3ihQkqrZo3QNeOLcXeoyemj8Lgft1K2KKVLVqxenTKXeJ9Wry4f84K/PzZSrG6ZCuWlPu4SHC99dEGrHvlCXWsndTyuliic5VOnTmvqvO3DuopQt28VfoOmyqUre3KEjlnUN+uCA0NQYfWTdC3e0frytc5DTPvaDnOqPDic5Yyc/mcJS252tpKiA1DamYeLJZ/DxrV82J+ZjuQxNKuVQj45zfryNNbDEV2/Wt9hkTU4c9A/2U2vkH8V+X76WKfUWr78Yj7bRUKYurg4tWPIHb3qwg/9RPS2oxATq2uDscXt+M5hCbtFnWUPKg92aEEEBd6LVLFKCI0CCZTADKyzcX1aRoHqGrqUaWg9FMIPb8XWQ37We1EHPsOMX++J8ZPHHyx0Apfle/p7Kz/fg8u9HwSsVVrIiPXDLO50BeH5RN9JrGUWCkcSanaZcMr74EHZZxCgDkX5soNy7srCAsxITTEhLTM/HLvC3UgKO0EErbOs/6GltWpapXDPe/vno+Bc/s9t+OqhdY3AdWal2plK5YOHz2FwSMfxqYPnwXlArBdWVIKnvnL3kSBpVAIEAq/GzH5SZF1mkLfKIserRiVJZaUbTv2G4sZE27D/66/ukT/aLWJtq/IUD754bi7BqHxZbXwzAsfWOvTNdu0aIgBw2fiizcWon6d6iVsdbtxolgZkiKKjhu69tYH8fHL84R9R2Jp/eZfsXTNB1j/7mJXqdut73WxNGXuKtAg1BRvH0prTyyR0qb+xkZH4kxSMtZ+tgm3De6F2fffKYZgKTTmTTL94RD3Psbsnhr3+0SdwIBixoxZe3fRFA785AEg86LVuKX3bKCqdzJSaTGigIPfIHDnWyhqeh0KO9wB09rRQH42LDc+A9Mnk4GQCFiGvADTv+O09H8CqFzP4aWlTcTVheX6+SXqmtY9AGRdRGGX0Si6rOQfMXtG6XeCOMufsXxzIUKCA7UYukMbgZufRsCpXSX6GfjtfAQkHVDdd9076e4FUo4j4MwfCNy91upf/p1wF6Zr7QIDAlDIf/Rcg6ayNv1O0O+FYW558rNgWjvG+h0raxgUoVDRiq1Y+mrjL5g673nQXnoSQGrF0qKV7+LAkRN4+elpYn/Tnv1/iwgrNWKJxFmnds2FYFKWp1evxfF/zoosdWoKhQJ2GXgfls2biGu7tS/RhK7R9YqWeGjsreJ9memPRGFiQqWKLZbUwCuvOvbEkm1fPv5qCyi+UsaDchheeXnLGNflMDz9/EB/5Kqun4iiQgvM0bVECBqFnflSUW7yT2s+BLTfSO4rkuF0lPI7YfsSVee40NgDCvIQdfQbZNXrhUKbM11czSJXVhgehf4UBQZpcmaMPX/JvUm05yql/XgkbF8s0m5Tkam3fcnPtn0t3kM2E3nxTZHc+UEkxIRyGJ7ODq2IYXg6I3PJvNHC8Kjzas5a0iQMzyVS+lcmsVSregKmjR8mkjY8tvR1xERHYOWCB5yG4SlXh1a+ug6bft6N55+cLMLlVr72icMwPNu27336PRbMvBdXdmiBnbsPITM7B5UrRePOiQtEeF6/Xp2Qlp6Fb7fsRIdWTdCwfulzoYgWhfRRDgBK1lCvdjV8+d12tGnRAF9//6vY7/TsYxNQtUplPPHsGzh7PgUfvDBX1K/QK0v6TyP3r6BGLG39ZS/GTn8av61/AWGhIZzgwX3cFaIliyX93BhkyUbiN5NUiwj9euK+ZWVcfWrrEai6aQbkAZMyU57t/iX3rwbQ/qZqm2YiwJytSnTYE0vCxsapKAyLRVL3hZ50x27bkORDQhzJQmGDJJTo/8kd7hNhNRWhBGWegyUkSghOFkv6e5TFkr6MjSiW5Dl2jhJgVpNvnwAAIABJREFUVFSxRMkMKHMcFcoGt+KJSaheNR77Dx/HLaNLJniwDcOjiKhHJg8XwoOyUtPqEpVunVqC7nEdrSzJtrTXicTLJ9/8KNpSFutFD48RZx7RosLCFe9Y+0dJG1YvmoI6NavanaQnT5/HrIUvYte+I+LzWtWr4MUlU5FQOQazFr4kxBYVskPjlIfxklh6c8UstGtZnOCBEj3cc9v1ou76zTuwdM1a3w3DsyX104592LH7ILKyS8cZTxlzq1fOWaLzlYoKi0Tc5NjhN2DAtVciODhIdPWddRtFbGTzxvWQlpGJqY+vRnCQCa88M118zitL+v5AG906iyX9PBSacRLxWx63igv9rqSfZaVYSm88UKwgyQ3JpdOID0RG40Eed0Zm3FOTdtueWJKJFqgjZ/ssQ1FwcZpWrYoyFbq0Satt5696BJaIBK0uYyg7LJb0dweLJX0ZG1Esqfmtq4hiSXo6NS0DuXlmkSnOk3Lm3EVUio0W2fJcLfn5ZlxKz0J8XIwI5bP+phcVITk1XdxL0zYWNYXOiMo3F4jVKWWhUL3c3HxUrRKnxowudby+Z0k5ii83bse0eauFIiWFTKqREjrQZjWC9fXbTyEqUoPNeU7Q2dtHJTebkTJ9+d2vrBYoe8fiOWOF8v0/e9cBHlWxtt+zJcluEhJIIAEh9GKh2QAroIAgNq4NC3YFsSL+YsWC7YLitcFVryh4FfsVKyiCCoiNIqg0pUuAJBCS7Cbbzv98czKbs5vtZ09yNpm5z30wuzNzZt6Zc3be833f+1ERZEmXfZkynQqypN9S2fetQe5Pzxsie32is1RLnld1Pg30484V7LhcOO87WTLZkZT1gucRiiypFd3IRdCV16u2WXLUH4IVAanzVFM5jHc/CLIUL2Lx1xdkKX7M4mlhRLLEn1WUmJtUJ0OVpkyW4lk/UVcbAo1Klq687QlGiqbecSUL8Ppy/gy0K8zHMy+/hx9W/4G3XjSGbGx1jYspjGRn2pmmvboIsqRtA6Z6a0GW9FvB7G1fIfu3+Sl9kFYnm6V8O2qypLbgxJsfKRLqsUrqUh+hyBKXO6fvedJcy6EdaL1yBpwF/UHS5FqKOs6gzXcPMxe8eOTOtVy7sdoKsqQ/8oIs6YuxEclScCJoQZb03QPNufdGJUsUI3TdpaMxZtQp6D30KpZxuO8RXZlliVQwQkkJGm2xBFky2oo07HgEWdIP79w/3ob9ry8RizuZfqPQ3jNPLEnEI3vzAj8BoZ5bbPgAlsq/UdVxMGpaH6X9YgD8Bwh7HvYOiSwhHoos8VgqGgy9sSX82yx7mMVBUYnLNc/nRcb+dawfKjxeicdtpZVtRnrpHwyTplwEWdJ/dQVZ0hdjI5IlmjEXygkXtyQsS/rui+bSe6OSpbOvuIfpp1918Uicf91UjBw6ANeMHeUPUJs/6wH0PryLoddCkCVDL4/ugxNkST+ISQSADtepbnXgZIlczchtpCHmw6/595mvRFygUGSpYImSC4oKiS54bflIK9vo74fcXTj5ibb6Lf54F1l/LcTB3pfBk1kI+67vGQYkakEkrLkUQZb0X2lBlvTF2Khkibv1BroM12EhyJK++6K59N6oZGniPc8wnEnu8MXXPwLJGI67YARW/vIbSsrKseT9Z/zJX426IIIsGXVlGmZcgizphzN310p1KWk+DxJ2INLREGQpFpUoWrlgskRKeFze3Gex+eW8iTQ52p+A7M0fw9luIFt0X3oWqtqfEFG9Tm2lUu+UZMVn6bf7ktuzIEvJxTNUb4Is6YuxUckSdxsO54EgyJK++6K59N6oZInkCveVHMSpg/qCFDXun/4qPvnyeyYDeOMV5zDtdqMXQZaMvkL6jk+QJX3w5bE+yMrH36c+oc9FGqhXbqkh1zOy2IR7A5rM4XCSEs0KFEyW1HmaKC9U62WPMOn2/QMnQ5Jl9re6cGW/cGNXW6moHyJclZ1Ob7Kqd+FwEGQpmbs7dF+CLOmLsVHJUjRBG0GW9N0XzaX3RiVLpPFe2LolSy7Fi88nw5RCGZcFWWout0roeQqypM/680O73OUU7Dl8nD4XaaBeuZsIv1xDWMq4pG5kK5aMzAwrLGYJ5VVuWMu3IXvLp8goXo2K7oqMefr+3+Cxt4Y3sw0bPrdYqaGr6HE2qtv0gzsnMD8St1LxurzPBoLdUJcRZEn/5RBkSV+MjUqWIgnaZBSvQqsjUyuRub6rKHpPFIFGJUuUDIuyD4897zSMOm1gzFrsiU5Wj3aCLOmBaur0KciSPmvFXSt8x16G4oLB+lykgXoNJktxCSQkOEaew4mEE1y5nWDf/T0O9L0GMCn541r98jzSyragauhUmFq0waGDB1Gw9D6YXBXs+3Bucn4Sa7XDnd3BH8sUbGEyV+1H7rq5TLyBhCuchf3hKDo1wdmkfjNBlvRfQ0GW9MXYqGRJnZ5h/8kP+EHwJ78e9199gRG9NwsEGpUsrVq3CW+8/yXLtEvl/NGn4vwzTzW8qIN6Zwiy1Czuk7CTFGRJn/X3u64Nuxd70zrrc5EG6jU4biea6EIyhqV2TcnYu5q5/3Erk9ri4zj2WshdToZzx3qQoAYvkQgdiTZUt+kNi7OUSaFTCZY+V8uPNzcxh1DrJ8hSMnZ15D4EWdIXY6OSJZo1F7Sp6jQE1vIdSnzlls+UmEtBlvTdGM2k90YlSxxjyvL72eKVeOt/i5mlqUeX9rh0zDCcO/IkIfCQ4EbMtlsBWUaF05NgD6JZLAgIshQLSvHV4fFKdAD3XfQS9h6ojq8Dg9XmLnE0LC6ZrfcQ1ckaKfktFXeLItCbV/8bVxJpsOfDOfop4Kd5IIJVXdgfVR1OQk2bvtGHKPuY25591wqYHfvBpXtN1eUo+PZ+JjXutbVC2dE3wp3bKXp/TbiGIEv6L64gS/pibGSypI6NVKNAwjTmC57VFxjRe7NAwBBkiSNN8UqvvfM5npr9DvtoxccvGN41T1iWmsV9EnaSgiwlf/35Qb+msD8sp9+R8mSJu8SpCUvyUQvskfvxB1+H4qWytn3FiBEvriPPg3nrt7XiE3fCldczruFxMkhqVKSQ13K1IlfeUMQwrsE2UmVBlvQHXpAlfTE2MlkKdnUmJLhrsBB40HdfNJfeDUGWSCacLEvzP/qaWZYKWrdkliWSEbdazIZeC0GWDL08ug+uSZAlrxswW3XHKtYL8MN3xZEXw95/dJMiS9HU42LFKFq9cGSJXPEyty6G9RC5qih5n5BmB1yOeq500a7Bv1dbsegzbskigQgSdRAFEGRJ/10gyJK+GBuZLIVKUcClxAVZ0ndfNJfeG5UsrVq3GW+8v8gfszTslGNxwVmDMfDoI2A2m1JiDQRZSoll0m2QqU6WuNUjXI6KWIEj17m8n5+H19YSpcfdGmuzkPW44lrZCf+HnC59Up4scTJBkyWCQoRF7+KXXq+9ECWRJRLDCRK5OBYPeRz561+H9e9VrFaiY1OrUVkrdjL3OyZZ3qYPIKXGc1zv9RBkSW+EAUGW9MXYyGRJHSPJUeCqo4Is6bsvmkvvjUqWSA3vt03bMPbc03DOiJPQJj835XAXZCnlliypA051sqR2X9Aiaa2WlNbSDy0OT+K674xnkZffKuXJEpEUevNJpaHEDoJlu8uOmYhWv7zg3/vcwpVTsRmZ3z7JPk80UWwwMeOxUUm90VK8M0GW9F9AQZb0xdjIZEnt6kxuxI52A+AoOoUBIsiSvvuiufTeqGRp6449KDqsIGWsSKE2hSBLzeVWCT3PVCZL9fPgnA3KmRNvCT4sR87tE7l3tbjDvjOeQ35OesqTJbVLHEl5J4JxvGvCDgmfXsuacStS20V1Fj9uSaSktNlLHoTsrMC+kx+AbM1M5FL+azUkIUxooI3USJAl/YEXZElfjI1MliJZ7wVZ0ndfNJfeG5UsNQWQBVlqCquY+BxSmSypc+aQ61Si8TRqywkhmag7F7XlfZF1ouzUqU2OLGkhkvHuUm6h45Ye/jcnT0SMiCxZTDLKK2v8OZjivQ7VV1sWG3KOiYy1MdoIsqQ/6oIs6YuxkclSJOu9IEv67ovm0rsgSxpXWpAljQCmePNUJkvcz5tcw0gdLThXTqxLw3P6ENlKK9vI+ik/4iJGmuIpZFXK3vwxEx2gthX9r24SZElteWtIIsEJDCevLTa8z5LTlhx3CzwtitjSMLJkllBe5Y5nqerVTZY7p6ZBGLixIEv6L44gS/pibGSyFMl6L8iSvvuiufQuyJLGlRZkSSOAKd48lckSV52jAzz5fFMCP54rJ55lUfdjPbTTL0vttzDJPuSum8sC/mWTFa5W3SFbMupdQh2kS2OqKTqpSZAltbtjycDJcOX1igfehOtyAhNJvCNZZImvXaKEO+FJpkhDo5IlqXgH5LxCwJqWIkiGH6YgS/ouoZHJUqQXUoIs6bsvmkvvgixpXGlBljQCmOLNmwpZyihezVzgmIpZQb+4VoVbMDjR4pYm6mTP8H/BtncNiFDxQokCnW2PRnXBMXC16ub/nB/uS4+7BTVt+sBskpoEWaIJ8vghreIX8SxMyzWvwLZ7ZcQ1TRZZ4gHWibpyxjOvVKxrRLJk/WQurJ/Og5xXAOdtM4D8wlSE1j9mQZb0XT4jkyX1C6ng3zBBlvTdF82ld0GWNK60IEsaAUzx5k2FLHGLUCIS4jwWhogRFwjgxId+uNJLNwQkQVWTpr1DFSU2/mOnjqdpSmSJY9SQZMlavh0Z+35FRbczw0p4J4ss0bVaL3sEDSlgkUqPDqORJdOmtciYObkOQlsW3EPPY/+HPRvSwRLIufkJQWzash6mzb/CM/KShNon2kiQpUSRi62dkcmS+oVUsHeEIEuxra+oFRkBQZY07hBBljQCmOLNU5kscUJDrmHppZuQvXlB3IddtXrdnuHP+leTW5fIFS+9bAPMjlJUdhnGRCQy9q5G+v7fYa4+AEf7E1DZ5QxYHPuYvLbaMtGUyFLmtsVsjuVHjDXUjk8WWWKT8nkBk7GTiDcW+EYjS2nvzoLl6w/gGToGUmkxzGtXKNDYsuDt0Yf97T79Arj/cX3ckNknnQc4K+Eadyc8g4bH3T7RBoIsJYpcbO2MTpa4h8OeYc9ATsvyT0qQpdjWV9QSZEnXPSDIkq7wGr7zpkKWLM5S5ioXr5IdtygE59bhn5PLHcVCBcey8DgnWmAiTPQ9EayK7mehosc5bN2bElky6kZOKlky6iQNMK7GJkuWrz8E0jPgOXEk4KiA/b5xjNA4754FuagbyNJk/fqDOtJE3Ld7X1RPmhEXemqLlbfvCagZ/1Bc7bVUFmRJC3rR2xqdLFkO7YLFWVLPjVyQpehrK2pER0BYlqJjFLGGIEsaAUzx5k2FLJk81cyyo5ClK8kxLqaV4ZKtFOdELnfqoiZE9WJZPC7k/vYG7LtWMGuSubqEWZ/UiVEFWYppCTRVEmRJE3wxN25MsqQmMESAfPkFsHy/KCQZYqTpq/dgXreSWZkcT38Y8xypIrdY8UaOR+Y1WCyUIEtxLVXclY1OlsJNSJCluJdaNAiBgCBLGreFIEsaAUzx5qlMlgqW3MUICsXRmJ0lyF85AzBbAK8H1QV9UNZvPGCJrJLF3e1IfpzindSFuegtvQfu7PYoGXhHvYSn6jglyvMUbH0SZEn/m0OQJf0xpis0JlkKJjB8xtW3z4CvR9+QAHBXOsdTH7AYplgLb0dWJXLlY+IR096ItbmmeoIsaYIvamNBlqJCJCo0YQQEWdK4uIIsaQQwxZs3FbJkclexAH11UasKZexdi6y/FqEmrzsqepzrr8Ylo8MJQ0geJ2SLLewqc+EDqhBsfRJkSf+bQ5Al/TFuVLKkcrkjlzjzprUsVsnXoRuq75kVdvIZT0+GafNaOG+fDrlHGHVMnxemP3+Hr3tv1g+3YPk6dEX1bdNhe2wCpNK9kftIIvyCLCURzBBdCbKkL76id2MjIMiSan08Xi9MkgkmU30XpIpKB+j7ljmBb9kEWTL2Btd7dE2FLBFOBUumBMDFCRBZgAqX3A2y/lBR5wpS51iKNwktO0R+P50lsqUSTLgEWdJ79yYvKa3+I03tKzSkZSlt/rMwle6Da9gFjByRPLg6/kja+SekrBbwtWwdFtT02VOZZch1wQQmAkGFSY1/9gZqJk6D98jjYX19OqwrF6Hmhgfh7Xei3wXPfeblcI8eB94HkTSyNFm+eAtpi96Br3U7VN/6RFwWq1hWX5ClWFBKvI4gS4ljJ1qmPgKCLNWuobPahYtueBDXX3YWRg8b5F9Zh7Mad037N75evpp91ueIrnhu2i3Ib5XD/hZkKfVvAi0zaCpkyWvP9+cC4nhw17rsTR8he/PHdfcEi2u6iv3NFYgSTbaqTmZaPOTxAFc9QZa07MzY2grLUmw4aa3VYGSJLEl3KOSGFVsWE3KI5HIXam48BxOp2ZGqHf+b6jLhhvMnwH7/5awpJ2K2+y5jliR+Le7+x8jT0PMCxsX71Yqrur0gS8lEs35fgizpi6/o3dgICLIEYMbstzFn/udspZ6894YAsvTKm5/i3Y+XYt5z98KWkYYJU2aic1FbPPJ/VwuyZOy93SCjS2WyxBOl/n3mKwwrtUsc/c3d4nhsExEknlyW51RSxz0R4Yq3pB3YgvwVT6D8yLGo6nRaQHOjkyVpz3aY1yyHZ8TFgMkU79QNUV+QpYZZBt3JkscNyVEB09YNzKLDSRLNLqG4oZJiPxkikYaMlx6EaeeffrCoTyJGvJCqnu3xCYooxLS5zGpE7n5EmDyDRjCCReMi9z/Tzi2sWbLFHwRZ0ncvC7KkL76id2MjIMgSgIPllah2uXDJjY9g0vUXBpCl86+bihGDj8N1l45mK7lw6Y+Y9OCLWL9kDiRJEpYlY+9v3UfXlMgSJz4kA249tANcDlyddDbv5xeZ2xxZkmx71/iTzXLClRDgYfLzGJ0s+d2Mbn4c3iOOTWjqjd1IkKWGWYFkkiVp15/MvY67x6GkGLZnJkM6UAJv7wFKjqQzL4e3R1/I9L9wMUdRpp42dzpTzfMTL1sWfO27slgmTsLoby7kQOQpwN1v0xrYZt4J+bDOkDPsMP35GxuXicZPLn5JzsMkyJK+e1mQJX3xFb0bGwFBllTrM2Lsnbj56jEBZOm4keMx7a5rGGGi8vumbbjg+gex4uMXkJOdKciSsfe37qNrSmSJxw+R+x2p3FEpHjYThV/e7leq425zZHUi0kQKdpWdTkdFj7OTjrXRyRJX/uIxGkkHoAE6FGSpAUBOshpe+gv3wrz+R4VsdO+jECWVlYdmFK/bXUgUSoqRMXeGnxwROfN27IH0OU+wZLOuUYobnvr6VIfinKhIO7Yo1iZVoXGZSveCiBhz57vhQeVbYjoxFMtX78NX1I2p+MkuF6S0OrVOQZZiAFBDFUGWNIAnmqY8AoIsRSBLsizjqCFX4cXHb8epgxSJ1T+37cbZV96Lr95+Cm0L8lBW4TLkJrClWwBZhtPlNeT4msqgcjPTUOF0w+uTU2pKkqsKLT+9CXKaHQfOfIGNvcV3T8JSsgFVx1yDjC1fwly+wz8nX2Y+Dg6fDuue1che+az/8+quw+Doc4kucyehlRZ2Kw5WGusek9b9COmt54G9u9i85f4nQr4pUElQF0B06DQjzcyS/1ZVe3ToXXTJEaB97HB54PFofE5UVcB0i5K0GfmFyr8lxYA9C3BU+j/3PfBvIDN2ye+IK7VxDfD3dsiDhkPKsIF+F8mrghd5/x6YH76BXV++5i7IJ4zwf2e6Zqjy39m5QMt8+Ka+BFZ/yqXKmO1ZkLNzIN9Xq8y3YQ1AwhMFh9UbkvTR65AWvK58Xjtf+exxkM+hvHAK32qZlWbY3+RUvxvSLSZYrSZUOlPrWdEqO3L6i1RfFzH+hkFAkCUVzuEsS49OuRbDT1XcbIItS9UGJSMWs/Jj5vFq/HFumH2YsldJs5rg9viIl6ZUkSr3I/2TOyBn5aNm9NNs7OZty2FZ9x5qhk+D9ZfXYN7xg39OvsOOhuvk28Db8S9cQ++Gr83husyddjDhW+P26dJ/op26Hr0Vvj/W+JtLPfsg7fq7IRW0S7TLRmtHRIkOmfw5QaSfPhMluQikWUwMY5/GB4X3uy/g/vfjAYOTWhfCevcz8Lw2E/Ke7ey/TW3aJncCUXrzbfwV3p+/g3XshID4vZrrz4TsqIT19kdhPuYkfy/ulx6H99sv/H9b73kGkj0Trvuug6moG9Ie+w/7Tq6qgOelJ1gf8vYt7F+JCFYtMTR17Ia0R5W6VNKtZtS4xQtCPRafXl7Rs4F+71Kp0AshUQQCWhEQZEmFYCiyRDFLZww5HtdeciarKWKWtG65ptU+Vd3wWMLYJVPgtedh75An6y2KyVWBnN/+C9vfP7PvSBbcr4C37GFYy3egovvZurjf8cEY0g0vWG3MbAa8yuFMLbOcKru8ubvhkauYefdfkEr2wD1iLKBy60rmGiYrZilt7gxYvl/oj0kilzZyifMXImMxurQlc37h+rJ8swDmP34ByYcHFEcF0l+fzlzypN1/sTnIrQqYzDkVcuczrf8ByGkF0+Z1/qbePoNQM+FhSBSn9dgEpvTHhSKEG56+Kyrc8PTFV/RubAQEWWLWFy9kn4zR4+7G+HFnY/Tpg2C1WtjKvfzfT/DeJ98wNTy7LR3j73paqOEZe0836OiaKlkiENNKNyB/5QyGp5oYSZ4ayJIJMFt1xdqIZMmv8HX0KfD16g/rwvkB8SLJVvjSFWA03zxLJJKQMXtqwNrpSXYTJUtSZTnMPy6GZ+AwthXs941jBMH5yDzI3A1P702iZ/8q1T21gl/wJSk+i/IzeTv1gmRRfpu5AAVfN0GW9FwoQJAlffEVvRsbAUGWAKZuRxYjdflk7uOMFFU5qjH54Vn4dqWiAHRUz8547tFb0SY/l/0t8iwZe4PrPbpUJUucCHF58FA4UTLatotuZV+RVSmRpLNa8DciWcp4ejILeHddcSc8A4eD/83nqeeBWwuW4do2V8sSXzeSwJbtWUwWmwkOBFtAkgR6QmSJq9yReIMtC94efZiKnFpxLknDa9Ru/Kp7NApbFmSvm8U4kZofqfF5ew9EzY31YwIJC1Kk5NLoarIkbVoDc9k+do+KkhwEBFlKDo6il9REQJClGNetvKIKbrfHn4yWNxNkKUYAm2i1pkyWaMm4bHjZsTehuqBfg66iEckSV8Djb/bTXp8Oy8pFcI+4mFmZUu0g2xzJkmnTWmTMnOzPCSSV7GWqbQnlI4rxjkiELPmJeG1i2VQl5NEgImJDEuNUmMLfgNP9cU/S7q2Q89sC6Rkhu+GJcOklha99F+Ts/AM1n77DrG9UKK9T9fVT68Qwog1GfB8WAUGWxOZozggIsqRx9QVZ0ghgijdv6mSpxe/vInPXMuw5/WnA1LCBskYjS1wKOeBQ7ayCeesf8HbqCfsdY9hudjz1AUvKmQqlOZIlddyPe/Q4tkz80E3JVeWibklfunjJUjChs6z8EpY1K+Dr0BWuMy9Lmf0VK5DW/70KuXUhPCeOirUJq0eWJ7JMBRd6aSGVFTM3y1Sz9sYFQANWFmSpAcEWlzIcAoIsaVwSQZY0ApjizVOVLNl3LUfu2jnMWkRWIyMWo5Elf7zSoBFwjZtcDzJuCSBXLnLpSoXSHMlSsHWQ1oknGE52olS+B+IlS6EIXSrsp8YYY9q7s0D3Jr3EsGRnw3HG5ez+89+vqtxPjTG+pnJNQZaaykqKeSSCgCBLiaCmaiPIkkYAU7x5qpMltcqd0ZbCaGTJH690wQSm1hVc+KEtlZLUNjeyxC02wS53dUR4OHMFC1WYmMeW9XBNnBb3rRKSLDkqkPbebGYdofG4zxznV7YLRejivmgzamAq3gm5bQcUtrRhT5mTzZy796Waa6xRl02QJaOujBhXQyAgyJJGlAVZ0ghgijcXZEm/BTQUWSLJ8FolsnBudjzgXE+hgGSj3dzIkvWTuUyemsguuWfx4j9Yd+iK6ntm14dZJRmfiPUpFFkKFgehixJp8vY90W8pcU57I9lL3mT7C1bD426zJBrhePrDJjvvhppYAFlyVKSMK2i7PFtDQSSu04QREGRJ4+IKsqQRwBRvLsiSfgtoFLJkWvUtMl5W1LgivaXmhzOKKwl54NYPqoR7bm5kKZKrpH3CMMBsYXmKHFNfDRAF4CSLExq1FSgW8IPJktp1zHnbDFhXLvLnGOL9BRO6WK7TnOuEkg7nFrpUiiM06hoSWbK8/k94f1rmF9CQ27SD86HXjTpkNi5Blgy9PCkzOEGWNC6VIEsaAUzx5qlKlrI3LUD25gW6J5bVsrxGIUs8+J/mUnP1PfAeNyTstJQDtxlyy9ZwPPAfSNY0LRDo3ra5kaVIh2e1pYeSpPrd8VRWRXUuoHgsTIwslZQBH7zK9ofla8XS4bx3NuT2XZV1dlTA+vWHjDT5uh0F14UTmZqbKLEhEIos8TV13j4dco+GVfOMbdTGrmVesxymHZvg7XU00jt3h3TLOcqAVQqNRsdWkCVj77FUGZ0gSxpXSpAljQCmeHNBlvRbwMYmS5S4NP2dWSyvEnPleep9gJLxRigZj41nOXvYQTgFDmhNjiyVlwItWjHrUHAJF6/E65l/Xgrr4vdh2raBfeQZcg7MPy6BZ/A5CoHp3hfV46f6CU08UuNElqoevhnSRiVfH5WwKm0p5OKk390ff8+hyBLP4RTKSie7aiB5PUBVBcy7/wIsVniPPC7+CzfRFn5FxqD58RcJ3DpqdAuoIEtNdIM28LQEWdIIuCBLGgFM8eaCLOm3gI1NlvhhgGYYq2iD6befkP7uLEh7d7IEp0ZXxUslsiTt3QWk2yDn5tXbdFK1E9i7E7YnFGtMqNw64eKVgjsLSJKq+lK9npwUx0qIW+7+HTXTbvW/kfeMuBiuc6/R7+Zphj2m+5IeAAAgAElEQVSHIks8Fi2Y2KrdKtVWkurbZ8DXo28zRK/+lLlVjsiRedNaJsNOpfruF+Er6l4noKF2O/Z4IMs+Q1nUBVkS2zkZCAiypBFFQZY0ApjizVOVLJFsOMmHlx9xMao6n27IVWhsssTd7+I9QHGSlQr5XQxPlpyVkGpqgMqDsD2tyLXL2S3g69gDNVffy1zXuKKcehP7D8clxUh/bxa83fog7bM3WKxF1PUsKYbtmcn+wyH1GxyrFu8at/joJXi+eJeRbteoSyE1cM4yQ97gSR5UKLJEl+D3seOReUocmtqtsnYMFGdIFmG2b26b0eyT2Ep0D9x/uT9xM+WNs21dhzRPNcq711nf/C7KtizI9kzl/rRno/qeWUle3cS7E2QpcexEyzoEBFnSuBsEWdIIYIo3T3WydLDvVSD5cCOWxiRLAUlB41TS4m+tY7VGNSb2hiZLtaQFbhdgTQsgL4QZkVFf+67ImKnKeVV7aKO34OQeZF67PCLpCYe9tG0DzH+sglRZDsuv38N56z8DDtA8GWqsyoeZd5wH2VEJ5yPzINOBXZSkIxCOLHFLISNC98wCJfglsksE2H38UJgqy+E+Y6yfVNG+cY8cC1RWQC7soH2cPBZt+edwTHoaaN1We5869+C3wqpyyoWSDg+XFFiv5M6JTFuQpURQE22CERBkSeOeEGRJI4Ap3jxVyVL+99ORVrYRJQMnw5XXy5Cr0JhkSUtCS8vKRUh7fTo8YZLXGglsI5OlYGltcq/zde8DlO2FZc1yZgUgskSS7XTAlVvmwVvUC6bSYtABmRdej954u4eeVyeooGEhuPIht2BZfl4KX7oNvt4D6vXqJ975BXA8IqTANcAesWk4skSWJNtjExTSrCLT9dxkS4phJ2sKL7l5cDw+P/HhOipgWfs9rJ/O9RP2AOEQAJalH8Hy8xJUXznFENYs84+LkbZgTp3LncotMVyeJfNPS4AaJySzheUMoxhPFsd0/viQsYMEKLlHolVhg7w4EGQp8S0sWtYhIMiSxt0gyJJGAFO8eaqSpdbfPQzroR3Yf9L9cOd0NOQqNCZZSp89lR3CXVfcCc/A4XHhk0rJMA1HlhwVyJg9FaZdWxV5YpXqltp9LphIBbvW8TfezH1u3GRdDqLcBYneotseV3I2cVevtFemsdgXzylnMSsGkW/f2eNQPVJ1GI9rV4nK0RAIS5aoYUkxMubOUMRayK2S4tpu+2e9XEH8JQm/lhbJcXXMI5FqHvPD94j6WsEkKtpc9fieBG1sj473dx1sGY8lKa06RkxyVLG+aG7eHn1h2rkF3nadkP7BS2HJox7zEmRJD1SbX5+CLGlcc0GWNAKY4s1TlSwVLLkLZkcp9g55Al57viFXoTHJEpeYTsRtKmqCUwOhbTSyFIoEST4v4PXAe+TxfuQ4xuyDcElHvV4m061X8QtBqAgdkxt3VjKCxMjTUx/4kxl773wKNV366DWcZt9vRLJUiw5Z+Sg2yXPamLB4SQdLkfbqYzBv/jUhRUsiBWlvPA3Tjs3sGkxivu8gf2wdJ0b8GeMnZjymKpaVpHuClDlDqD7G0lxdRzqwH5bln4Ms4tx91d13UD2p9VjIErsd77usnsts8JhCkcd4xx1rfUGWYkVK1IuEgCBLGveHIEsaAUzx5qlKltp9ei1Dfs/wf0G2KoG5RiuNRZaCXazixYUHR8cjLR3vNZJV31BkiQLv71AOseQiJZtM8PUeGHaq9GbeVLIH3j6DWB6Yhi4BhK324uwQSG/UySpW+1aduSa1LoTr8f/C5fY19DCbzfViIUuxghGvgIe637AqmrVufrRH3EPHKITalgVPvxOY+1qsEty0761ffwBk5cI55flYpxRQjwid9cNX4BpzXV16BG5xCyPOECtZIiue7fl7IGfnoOaCiUh/YwaQkQWprDjAupb22Tw2byosfuy0MTBt3QDP6f+AbLECGfaE5hbcSJClpMDY7DsRZEnjFhBkSSOAKd48FcmS5K5C20W3QrbasWf4s4ZdgcYiS9wFT0vMEUtOS5aFWV8aFl8amJHIErk9EvbBynNGBpBLiMstWkLOyWOuRqyorE30p3XMVXCOukyQJR0XM5lkibvIxSrgoZ4WtxhVT5wG31GBMWzBllMmJtF3EGwz71SU+KZFiWlTvVCgayYqIhM8Dtqv3qLucJ1/Q9iYvpjJEg2M8leZLYGrTWTx0fFwDTkXnrOvZK6RjDCtWeF/uaC+d5gFjuKe7Nn+fuhFFjKzGVaxFkGWYkVK1IuEgCBLGveHIEsaAUzx5qlIlsyOEhQsmQKvPQ97hzxp2BVoDLLEVaC0SghHcuOTvV5IpgguNGSVyMhMiotNtMU1Elnib+QTPQBGm6te30tEkCxWyDmtkD7vKcgtW8M9cIQ/jokOorZ/vY0KU4YgS3otAokGSEBhSxv2lDk1XyXRuMNoiY8ZQVjyoWJRqa5C9V3PMxl87rrGXPYGhYiRlGXIRLp/+JKJx1DMFSPm4VxQIyDgzzGlIvQ1V9wJb5TYzLjIUjwrwNUCP51XrxXN0zNwmGJJozWuzfXkvP9lyO06xXQVQZZigklUioKAIEsat4ggSxoBTPHmqUiWrOXb0XrZI3C3KML+kx8w7Ao0NFliB6TZD8WWiycKatziEOrwY5t6FaSKg3CNvpy53qgLBVnbH78RnmMGo+bqu9lXpj9+hnxYF8gtWiV9rYxCllhwOeVRiiUPUtJR0KlDUkNb+SV8nQ9Hq779cMjpFmRJJ6jZQTqJZInlYiKX0BjJiOXLd+Dr2BOWtSuYmEdMLnWyT4k7IhL0yVxYP50XNs+T7ZHrgRqHX/2RZPMpxojir+JJfu1XZqyNpfJ27wO5VWsghrxfupElvicosXRlOdKWfAB33xOYIAcnR/5tU0vw4hHEEGRJx5uuGXUtyJLGxRZkSSOAKd48FclSWukG5K+cAVernigZdKdhV6AhyZL/bWuIBKSJABSgxjZphr8L9WGFPgxW21K7x5CFRXJWKYev40+H66q7EhlKxDaGIEvknvPYBEaUYjpkJh0F/TvMb5EuyJLOMCeVLAHwW4fvngW5qFvY0Qcr6FHFqImPQ/TGrUvu866F++TRgE2JJQ1+ZvD+Tbv+ZHFPauIg+3xIf+tZeI85JWQcn/8aZ14O9+hxca2I7mQpeDQqBUN6LpCinvewLn5591gxFmQprmUWlcMgIMiSxq0hyJJGAFO8eSqSJfuu5chdO4clo6WktEYtDUWW1IcRdvAYdbl2qWlVbhd6C8wtSOrgb8Jd/VaYx+yEXI8Y33DHu5ZGIEucICYSHxLvfBurviBL+iOfbLLE96Xz9un1lOHUswkV/+OIM5E19RcsGOI5djBMjkrA7WaS534FudpnAReiobYs9mnQcJg3rWUEKpQbcYD1KlpsVCgyl2ZGRroZBypc+i9mhCvwmFKqwmXYIw1IkKVGXa4mc3FBljQupSBLGgFM8eapSJYyt36FnN/no6rz6Sg/4mLDrkBDkSV+2Em2VUNtreIWJP5ml4gBS6ZK5Izkpsnt575xzLriHnMdpH27Ydq7G778QpaAlT6nfD713nDLMizfL4R74DBIMbjSBC92Y5El04ZVsH6zAJBlhgNzd5o2t17eG8NuzjgHJshSnIAlUD3ZZCmaIh7tW0o4S65wnLAoLnjnwXXBjQnMAEj7z2MsSW29Unt/mPfsAFmPfN17syrqZwwRJLlVYUAuKUri7MsrgLd9F80uxg1uWQqHoKMC6XNnBD4/I6AtyFJCW1E0CkJAkCWNW0KQJY0ApnjzVCRLRJSIMFV0PxsVPc427Ao0BFni7nLsTSxJ5qqUl5IBjPrtNGWst91/OSMGzvFTAxSw+KEnlBJcnTrfcMitCuA+9SwguyUjWBnP/B8L9I7Hh189r8YiS8E5ZlJJAS+RfSHIUiKoxdcm2WSJu9epX6KYf/kGvsIiSFWHkDFzsn+Ant4D4brxEcVldsDpQGaL+Aavqi0V74TtoasD2ke6P4i0pb37Yl18D8X1UKmVr1d3pOU+MwxZognVyrDTf0azLgmylPBWFA3V96UsyySyIkqCCAiylCBwTaRZKpKl/O+nI61sI8qOvQnVBf0MuxKJkiVp91+QPB6mMhVc0l9+GN5ex8Bz8pkB1pywKlQa0VErvMn5hYqSVfe+qJ40w6+ARW4+kYQlgmMiuMSw+q1yLIeGUFNpDLLkd3tUJ3NVuSpqhNyQzQVZ0n9Zkk2WuFsc3W/V4x+C5KhkBEltwSEi5WvTDt5+JzM1xGQV80+LYflpCczrfmBdql15Q15DRR5Y3YHDmLgIjVkq2wfzpjXM6lRz4YSw0uDRxm4osgSAJ4WO5hEgyFK0lRXfx4KAsCzFglKEOoIsaQQwxZunElmi/EqUgLbtolsguR2GTkhL2yIhsqRyZ/N16ArPqefAc+JItsv8MUG1Pv8NkddHfQ0aDxEffvDhP/Y8J0/Yt74VB1hQs+foU1lMAilE0QGByQ87K/0ywiSzWx0moWS426wxyFLGYxOYNYwELHw5rWD5dSVqJjwUkyJXqj4uBFnSf+WSTZb8iniAP69PgDpbA7iORkpBUA/RmmogLV23lANGI0tqMhspP5UgS/rfe83hCoIsaVxlQZY0ApjizVOBLJGgQ+bWxbAe2sEsSRl71xg+x1KiZClYQIFZXJ76gLnXqQOxyZpjXft9AHnRYyv6g7BtWfDlF7D4Bq7ipA7QpmvHou7E3Qb5WBnBGjcZtmcmMxIVr4UsHFmyLnwLvry28B47OKmw8PXRy+0xqYNNYmdGJUsVFUBWlm7n6yQiGL2rpJMlesGyZT2sC14LiAMiok/71z10TD3p/+ijjK+G6bcfIVWWwztASXLdmMVoZImwUMeAklhOqCLIUmPumqZzbUGWNK6lIEsaAUzx5kYmS+klv8PsLEXur6/XQ9noSngJkSWVVYmIR9on89ghh4iRBCkwxqBWOYoIRiwkRcs2DY7PUcuFMxe7T+bBe8QxqLn2vpguo1a1IvcguX1XZmUiSxV30YupIwBqsmRa/wNMFeWQSotZzhc10Yy1v+B65jXLkP7Ko6i5aCLMW/9QrGExEsNEr2nEdkYkS2vWSvjgIzMyMoBBA3wYcqrPiNDFPCY9yBK7OLm4PXQV5Jw8OG+bAblFS0hkwWlmxYhkiT8/mTBOGDl0QZaa2UbVabqCLEUBdvF3q3DL/c/Wq7Vq0ctIT7NCkCWddmaKdGtUspS+fz3yfnzGjyKJOTjan4DszR8j7dB2lPW5Gp6cIkOjHK8bXrBIArdikPKcnFfIrEjkCsfVq9jkdZLkVgOrlrql61ffM1s77hUHFJEHVfGr+nGFvRiu4idL+8uUJJxBJV5LVXD7erLKscRfxDDuVKuSLLL01zYJGzdK6NVTRudOiYcb79kjY9bL1gAYiTD17OnD3r0mrP1VwrDTfOjaJfFrNPQa6UaWaCIklpBh9yeRbei5GeF6RiRLhAtLaP3oeAZRKLEHQZaMsHtSfwyCLEVZw6+++wV3P/Yy3ns50MRbdFgbSJIkyFLq3wOaZmBUssTlwWlyRk8+G24B4iJLqsSmHx45E0XD+uAIaRVTnFMXsiKRFLf51xWQHFVwj7wE7mEXaNoD0RqrBRqiBSNH6yvi9+rkrlEIEw/6TuvcA9Yu3eF4+z9+axJZpygBJFmBtOQ+CnYzpJiqmvNviJizRtP8Ddw4GWTpwAFg1ssWVFcrEx05wscsQvT5wXIJhQU+2GxSPRTo++07JOTkwE+wyKJElqV+fWW0LZTx+UJTvXa5uTIm3eI1MKqBQ9OVLKUMCvoN1KhkiWbMX8qo89ZxJARZ0m9PNKeeBVmKgSw99NRr+O5/z4WsKSxLzel2qT9Xo5IlLg9ek384DvS9Gr6MQCtEKqxaKLJE/vum336Cl+R5a/OMWFYuYsSH3v7+mdYXs/IUi1rnNpWYuPYs/1TlNu3gfKi+S6LeWKiTTbquuBOegcN1uyR7y/r0ZIZFJKuQ2jVQyi+EXFXJ2lRPfJQRJemQIipBhWTJyQJH+VpIZSsWeXU1QfT2Owneom7wnDG22b6ZT5QsEdEhUlNdI+H7HxRCU1gIFBcrW2jIKV6s/tWEgwcVknT7zR6s+dWEbdsk5OYCZBei9rxQ284dfVi91sRIF9Vv2RLYuk3Cyh8kVFdLyMkFtm0H6/PKcR506aTbdk1qx4IsJRXOep0ZmSypVUfdo8cFjF2QJX33RXPpXZClGMjSrfc/h3NGnIj09DQc27cnRgw+DhazmbUUZKm53Cqh52lUspS7dg5I2OFg36tA8UkNWeiA99/5FhR18OHs0YFxEE6nHPLtd6jxuWoA2ZMG89f/RYslb7JDO1eAI/90Kjy2hv67zFyAOd1eRq9+dnz/o5kdBnvV/IDzRx5Cereu7MCfTHnfeDC1T1ACtJ2PzANJiOtZePxSOJc/Lt1NFiTJWQU4KtlwgnM1BUuWU52YYqLUioTd+2qSK9YTp4bsOxGytKcYeOsds58I0XjJEjRyuAdLvzX7yVMs8zi8pw9btysEiReySpF1KlThlieKZ6JYJm7BKikFuneL5YoNX0eQJX0xNzJZ4qqj8mFd4O3eG76i7oCjCnJ6BgrO+Ye+wIjemwUCgixFWeZ1G7Zi4dIfkZOdib/3luKdBUtwyXmn4d5blcNahdNtyI2SZiEyJ8PlSe2gXUOCqxpUZroFTpcXPoOlK7MvfQLm/RvgPPEWeNodHR5Gnw/ypl8h9UpOvqXSMhkvviyh7IByyUsuALp0lvHOBxKqHMDuv4HjjlE+D1d++gX4ZrlS9+Sq93DOoRciboM5LR/Bn2n9IGVm4Y6bZeS1kuBwyHjxFYn1Qdeia8ZSaBkXL5VRUCBh927gpEEysrKUN/PLVgKfLQR6HwEc1g44tr8Mu72+21Oo68jffQF511aYxk6IZRia6/juGAu5pBimGW9CatUGqH25Qx37Hr8d8oY1MF06EdZjTob78dsge72Qpv0HsLeAJNXFqcirlrEAdyrywveVPq+dAunkESHHKFdVQH5yEuTtW9ieMt09U/NcmkIH9jQLajxeeH2xxQDR/n3qOeU+6tZFQeCcM2W0P6xuv63/HVj3G+DxAkNOVva70wm0agmcexaYe57DCeS1VPY/9fl3scTatG8X+Z6g+3j+exK2/KVcm/Y73UtU+P1Lf69aC5wwQLnnXC5g2w4ZRFpsGQgYa0OsISGTmWFFZbUxf5MbAgM9r2E1mWC2SKh2Gc810/fHashPTAo5/dx3lukJi+i7mSAgyFKcC/3BZ9/i/n++irWL/8OsSxUOT5w9NEz19DQT88GocQuypCfimTYLnDUe+AwGc9bnd0CqKkHlyBmQM1uHhMC3fw/w3AOQd2zG1ox+aHnfNOS1r83+ngBoP62S8cVXYAc8mw3+g5stQ8LuPYGHRPr+jNMknKIyetFh7tvvJSz8Sqnb0lOMSSXXwSZX4tvMf8Dmq8T6jJPQtWYNTnG8zyxJr7Wchm6Du6FbFwntCpVDGy80njffVQ6Pxx0NnHICopKbDz+R8e3yusmztsfUjUkNC5Gm3kdS37ERpgQgTbiJ75UnIC/7wt+ekeFr7oJkz4Jv4lnsX2n6W0jPzYHk86C6qhpShh0+yDAh9HyoP+qX2uKkMyAdfRIjRPLSBZCPHQxTVgv43nwe8qL3QK59uGsmTK3bJjyHptTQnmFmz2KvNzxZImKycLHMiMbuPcp9RERp4nWx7a/Nf8o4WJ7c/UhjevUNmd3LVPh9zf+lz+geueMmYM5/4SdX9DmN/fhjkjueiHtCArJtFsP+Jqf6frZYJFjNJjhrjEeW6CUNPdd4Yc8oegYVdUPOLbGpjKb6+ojx64uAIEtx4vvdD+sw/q6n8MvCl5CRnibc8OLEr6lVN6obXrTEs8H5emhdvimaiG7jz2UxDPEUeoM9Z16duxAFht/c+3O8sbQztpoPZ12RO8/FFyovFr75xoyt25UDYH4+cMYwDxwOCZ8tVFznqJDrz8hVk0AuYySKsPG4G7Fho4ROnYA/NkgsDoPiL6geuRiFKjSumc9ZAr6iNldd7va7ApJbILk0FRcrMR6rVfEdNGa12xIfF33+/Q+S3z3qogt8OPJwg7FlEnuojTniADDhhr4nKqqAlJ9p0owA6fBY1tyfSLe2MnPlK92ruOfdNsN/zVCqVLH031TrhHLDo7235ldlz1MM0h8bA0UW6D666nJv3PdjsjEkd8CPPzHjpBN9aNlSxhcL6+5ffo+o/21bIGPPXop/UkYy4To32raNjfBpGbtww9OCXvS2RnbDY78xM26D6c/fUHPz4/B2660k6AUgYpair62oER0BQZaiYPTmh4vRs2sHHNGjE8orKnHnw7NhtZjx6sy7WEsRsxR9kzXlGkYkS5K7Cm0X3arszzNfYW5U1pVKfhs3iQvYM2G/bxwL6P/JNoK5sF1c/iSz1Ky+/E0WnxBroQPfd08ugb1qN3a36I++ozuj7w//BPmQO6UsPH/Yy7C3L8CYXr8gt20WrKu+gbfz4fjq4ElY8o1yOKRDIQWW0+Gqc0cZI4/9G0W/zGXxSabWhaic8mJMogKhxvzregmlpRILeOcHOCJXYy/ygcY+Z57VHyzP23OVsT//At6cb0W3rl6cMdyHjIy6eCs6QPJDI8WRjDnHeG9bObGh+C4mglG6l8VtMfGHCyYwEhouKW2k9ae1pcSc1q8/VCSVawsnTroq/sW6MQ1WL5gshdt7RP6J0NP+6t+HyInBJlI7HHoRwcuSbxVlPSp0H9D9QOWtt02MAKo/03M2gizpiS5gdLJk2r0V0sESeI88LgAIQZb03RfNpXdBlqKs9NP/fgf/eeszf60+R3TF9PvHo31bxbVJkKXmcquEnqcRyZLZUYKCJVPgQwbKSjv6E4GyGZDIgT2THZy5chwpao366lL22dJ+D+P4GwaFXVSK6aFDCS+bPl6Hfp+pfMVrD+P+CqrrqTulA/XmbmPw6U+H+ckKCzg/uRy2xyYoB3sa7gPPorStYp3SWtQWMKYatlYCHfSIrI0cIeOnn03o1cuH44+JjSzyXDWGlVj2eSEV74DcrjPUinwst9S0uYyAJkKW/OvgqIBl5Zfwdj0Kticm1n38yDzFBSZFyo8/m/D9SgkXnu9FW52GHUyWSKqb1O1o7/TvKzOCVNjGuOQo2lIS+duz14QuqtxPPOktfzlBinvFeyX06+ONWeRFfV26xsoflSS6nYq82LDJjAHHe2GvlUsXZCnaKmn73uhkKdzsBFnStu6itYKAIEsx7ITqGhf2lx5EdqYduTmBMR2CLMUAYBOuYiiydLAEaNEKGeu+QKtdH8Bb7kLFd3sY+qR0ZirZC9PmtexvsiK9nTMFRcP7MFc2ntCVYoPW9Z7IgsbPP6/OBYi/CT94UHF9owPP/NdrMHb9tWjl3cvcuky7/mSWBrIwVF//INLfm+2/Hn1GRW5VGPDZnhtn461Pc3H8MV4cb17O1O3IakH1XZOeQn7Xjth7QCXhpXEvcZUvdTdXjfMmnODz6WcV90M6wI0aUfdWXeMwdWmeNncGzOtWovrWJyG378quoYksqUYZnBBYlwlE6JQsGOnpPqxaZWZ5g47uX3cgJ1JLLpY8BxGR5i8WkTJcnbWRuqY1nHCdIqWd7KImS2Q1mvWS4h464XqPbgQt2XOItz/1y4RBA+pyORFBPO9sH7vnvlsusRitY4+pi+UKpZhJa6a2YPGxqF9UCLIU7wrFV1+QpfjwErWbFgKCLGlcT0GWNAKY4s2NQJbIXc366VxmjfH2GQRb9V+w98hANQpRIfdgrnZ7PG3Zm+x27i1MKGF3Tr+AgyG3PPxt6YKXWs1Apbklc6c5a5QXlZWBMUn8YHlO8RM4zrlQiVe5ZxazVFAiUpkH19LbmB1bFJJUVKc3THFI6XOn+61H5CYmle31W8B4/Iu5TVvk56QnlSwFxzH16C7jsrGJu9B98JEJa9bWxZpEkmM2wlaXXTWQan35k0mWqC/L4g/gHXAa5KwcXaZKa6d2heQXIXdO7tLJPyPiM+h4Lw6U0/ooplBy8ezVSw6IN+N7mcgUxQ2Fc6lk+Y5+VaS3ndUSyg8C/fv5/C5n0SZMZOmDT91MUY7cQYlgG32vRJtTLN/zlwlqcsNzQqljAsn6lJ4hsbhEWguKK6QXM1Q+/LjOVZba9O+r5Imiwl13Bw6UkZHuQ/s26Sgprw4gx7GMU9SpjwDt+eJ9JtRUyyho40OXjhZkpJtxoMKVUnAJy1JKLZdhByvIksalEWRJI4Ap3ryxyVKoXDgZPXKQ0SMXf9qGYc6WS+vF5NDb2LEXBrkcUW6cO8b4V4Nc9ObnTMEBSyF7406HElKnO6H7Hny6tT+OrF6Gqw7cz9z6HESU4nW7KilGxtwZfisTI1QkQNCjH1yjLmP9hUpKm4ztUl0DHCiTmQxuy1wZVmviweekgrhvPyX1VA7shFNDxWgkA4tkWZaSMZbgPuiwtn2HxMgJ4UriG2QNUgiOYp0gty5yaaPCE66SWAcXEFGTJ7VYB9U9Y7gHLXPALElqEk0HdxISIVcvdiCvMbF8R8FiH/QdT+oabf6ffWbFyp/rrCdE3K66InGSHu16Rvl+8xYJmzZLOHSI4u0ljBrhwcofTP48aOp4xWhjJszOGFH33AoWlgluT/U7dfRhyODY5NqjXb+5fT/n9TohD5p7u7ZAh8Mk7NuvxG9SIWIbLleXUfASZMkoK5Ha4xBkSeP6CbKkEcAUb96oZElFcMg6I+cVgoL67X3zkNYhC69uuxYrSk9icRF0KKQ34XTw7NUjdGxExtOTA8jLoqwrsCj7SrZCLN+R+3XmZkcufDZfFZP05kIBiS4jz7zOrUlq0qUXWUp0rNHa8RgNwnv0SB/IamX0YkSyRC51a9bUV4eLhOUlF/nQS6WKSLBteD4AACAASURBVPExK39QrBQDj1cOdxs3mZiSIv8s2N0ulIum+ppkdSIXv2qnjOoaiVlqOenZo3i7om0IpXSqR4QuwwaMGu5FixwZpBjH3QKNvkf0GB+52lEhDIj0LFtuYuJl3bv7/GqZ/CVNsIKlejzUduWPJpZw15Yhw+M2we2VA14Qkdsw/b+xyl/bgJoaE7ZtU/Yi33dKnJdC/rt0im901KcEKWH34XBX4xZUEsShFw70LOvcke7FOnXD4LY8UbJR97MgS/HtLVE7NAKCLGncGYIsaQQwxZs3JlnicSIUj+QadydDUjpYivx1z8N6aCfer7oF6NgvLDkKht606lukfbMA7gGnI33eU8xq9I1pJGokO4ZXvq5UVwk4cPlpTUsoy7Aufh/uk0cD6RkBXaUaWaLBq92ObrnRw6TRjVyMRpaC32aTlYcfKIee6kPHIuWQTcT0+x8Va0+yrDRuD7BjJ1BxSMLqNZRPhtzlFKtq8IFbbdWgMXLZb+5Otm07kJsDjBzuYYqL1Me4S2R069b0rUnJ2O88bokst6ZARfWw3atjlqg9kWMiwFR4XCJ9TtZJin8ilz8qRFwPlIO9UCI3TXqZRH+TJSVDUZ9OqFBsGlkkudshdcLdQ4kwff2Nyf9drC6ZNP6Zzyn7iUoyiCC34O4pVl4AqIs6nvPHHy2oqpLQsbMbh8oViy+3ptO8CMfCQiU+MB5F1YTAjaORIEtxgCWqhn++yDLpW4mSKAKCLCWKXNNoF40sUZBzojlGTAf2w+d2AW0Oqw8WWZVq5b+rb58BX4++rA79mOZ8fjfy00uwd8gT8NoTO62nPzsF5j9+CbgusyL1OQG2ZyZDclQl5n4Xx7KnIllKBUlx9RI0BlmiAxnlqaLD6bln14mI8NgjepvNiREdKukXiv4f66E5ji0WtapPBlwuioep76rJLYm8k1B5ufh3PbpKuPpKL1wiSXhUzBOtEErgIZq1MNK1ElW5JKvmhwvqiBC37NO1gt1D1W6IsRAfIuUkyU7tOAnjqQ748/+N+Rb0OVLGgOPrW9O45ejAQSVXHe1ZsrqqCR1Zirp0Ul5MqK2voQQe6Hn34QJLPVdvmkuvHl4cPGRGRroST5aRQdery2tHL0GIVOlt9RNkKdE7SrRTIyAsSxr3gyBLGgFM8ebhyJL6B5PefKsPhcFTNq37AZbff4LrtPPrYn9UZIgn+CQhB6m0mAkomEr3BiQXJZJEBwObyYmbMidCttqxZ/izCaMrHToAy7cfM3U6KmrrlexxQ6qpBjKzE+4/loapSJZoXuQi89pcCzuI0NvWYCEAOqTQW9lwEsrB8uzhsFITM9pjg09VFMbiKQ1FljZsVOS56Y29+mBGhz5SSlMn+dWiThjP3JNRl4gfxUh17uRDzx4+f5JZch0j8keiEWRtuuZSCzySR5ClZIAepo9wanhLlkpY/atCXjihJUIw6HgPi0cjl8rcFl5s22HGhg3KHuV532KNP1Qr+Kmto8EEiH4XuCBMdbXM8rdRDCVXR6TxUTxpYYEvQL2Rx+7R+Ihw8TghHq9H6R8ozo6sZdw6RESE3EapkOvn9u31hVA4lHQf0rNq8CnesC/3IqnhMVn3HxQsg61T0Zack0mySBEmdO/QfUPz4c9IrmhJll61smW0vul7QZZiQUnUiYaAIEvREIryvSBLGgFM8eahyBIdYufMtTBXiQxfJU6q+gAHT7oYZ59jwvIVEr5ZZmZKXQMH+FjcD7cQqcUS0mdPZYldqSjCB30D8yXV4kZWpVXOfljxg5n9wHSw7cDUIx6A156HvUOe1Ixu2rynYP55CZz3vxK/iIPGq6cqWaJpB6uAKXLrPsyZV+eWQ4eEqy6vs6xQOyJSny00s4PLxRd66sW18GSg9MY32GWN2sfydlq9LHqTJUrs+9Enyr2gFkigcZJgQzBxGjVCDog90riFDNM8OM+SYQbWhAYSq3R4KGnyYBjU8YeTbqlznaTPc3IAGTLWrDUzVUSylpDFh4hO546KSyYT0bnAE7NXAbe2qpUC+xzlQ2mZhN1/B8YL0XVuv9nNng2hlCCjLSkRRRK2oecRFSJiPJFwpLaxSocTIST3Q8KFnmNkQSKrEhEhmh9hQy8PyZWPnnWhhFPU4wi22MYr8S/IUrQdIb6PBQFBlmJBKUIdQZY0ApjizUORJTrEbt/mwYCBZpz3yw0sb9A3RRNx1G3nBPibdzVvwWltV6PHTy/CKWUx4kTEyDNwuGLRCU7oastS8iXt/JMJMZR2GIhZWY+xHyCz5MG0I6cg0+yA3eJAdUE/lB17U3LQ9XoBs+L735AllcnSX9skUJC0BJnFR1DhP/p0WKDDA5FbeotKxIGEByjORU0eqA1T9OpEql5yPeU3ak99kswyWW74deiNMr0hjhRwTaTrYLmEo3qZYTFLKK9y67K0akJHBzJ6A15+SGLzoUPr/xaY2WGK3O5iObDpMsgG6FSQJf1BjpUsxToS/sKD7s+OHX1srwbfn+H6ChYcifWaRJpIFp3ubXXh1pccigc6PpCEEYEjNU4SYaBC4y0sIDJXF39EFhkiL2TJSjSPWKxkicYQjpBSGorMzMDE5mQ1Inc9/jwj6xnFjpHFNlhYgluduEU6lgTHgizFuvtEvUgICLKkcX8IsqQRwBRvHkyW6K3a+tkLcVH59AAxBJLinnPYM7BVFqOraw2GV77GkrnyQspzxzq/CPiMRBs8fQchbeHbgMeFF7ddAHObQuZuRT7gPP8I/XBc0H0RBvjeYt15bXkoGXhnwvFKRlmSVCZLagzVb3+5she5+aitTLw+PwRwdxv159w1SP2mlcgHl0bmVin+ppYObB2LvIyM2NLrYufUb7Hp4HTphRL+LvawgHMq/fsqamLkBkPEi4gVuSZRiUfFza8ClwGceIIPp5zoAx1om2MRZEn/VU82WQqOSaMZ8Fgh+pfuE7qfiYiQKASTsV9kxomDvBhwXHzusMHorF4jofSAhF/XKdbXWCTqY7GYaVmFeMiSlusEt6XnDz2n+PzUVnvCnyx4kQigIEvJXI3m25cgSxrXXpAljQCmeHMiS+s31eDFl8w45SQfdqzei6s2X8+sRFTIUkTJYslydH/hx5hQehu6utYqs7Zlwde+K3ztO+Pt9Juw7Zf9mFB2GyNMv3a7AgusV7Bq5Kq1YZMiPxxcuCtX9w3TkVa2kVmUyo+4OOWJEs2zqZAlmsucuWa4XMC4S+tc6+gQQG+rqYR6Y0yHgzW/mgPieYi8kPwwySX37OmrJzmsjmMiUsWTrdI1eEJPHlMQSZCA7zN1ILm6D4qF4AHgwYc0itn6YlFd0Lea0KX47Z7w8AVZShi6mBsmmyzRhT/8iNzEFFcyelFA7mNEjhIV7Yl5MrUVS0sluD0yCgvibZn8+o1FloJnwpUOuZpgJGl5aivIUvL3QnPsUZAljasuyJJGAFO8eVZaOh6Z4QGpC1F8EpGhwzx/wjN0DNwDhkEu6oaMx8Yz17kycyFaeYsZSaq54k54+57gnz39AMx62QJnmRM5vv3YZ+no/44OtVTIYkBvM8k1gVwqOnUEc2tqlVWFwiV3Q3I7sGf4vyBbM1McVWX4TYksaVmQjZsk/He+OS6JbCLWnBQR4aGidiHiClrLlpnx3fcS0tMVBToqtJdpr6lJFR0USUo72D2Iz4vcBUnIQi2HzFXtSPjAqDlYtKxLPG0FWYoHrcTq6kGWEhtJ02xlFLLE0VXL9xNhGjncywRuuEAOz+XVtb29aS6ImFWDIiDIkka4BVnSCGAKN6dA3l9+NmPTnzJaplXiyt0KUWIJVu+ZBdgVtThKFEtKdrxQAln36HH1Zs5jOA6Um9hbzNwcUkqqUxciVa2xF9WXg83c+hVyfp8PV6ueKBmk5FtqCkWQpbpVpCDvdm3luNzYflmtxE0NHazEKbw534RtO0wYc05dAtdIAg8US0BF/Rad3EzJ3YgSbFIi0FDB2USSDu8pByTgbAr7UcscBFnSgl5sbQVZig2nRGsZjSzRPNSEif7mLxbVcuiv/Mua6JRFO4GAHwFBljRuBkGWNAKYQs3JvYgKhe2r82iQstB1ea+hzbK5ClG6bUagclyNE+bffgLMFkj7dsEz7MK4Zk0HVIpRCuX6YXaUoM2yh5lVieKUXHk94+rbyJUFWUr+6gQn+UyWGh6Py4o1uWbyZ2bsHgVZ0n99BFnSF2MjkiWaMb1kXPotuSvXd1Mn8vT8k4Is6bszmkfvgixpXGdBljQCmCLNwwX7njTAjF6Hu1H44LmAsxLqBLF6Tk1yVyHv5xdhrdjJiFJTsyoRdoIs6bmDlL6TRZaor1jzQ+k/K+NdQZAl/ddEkCV9MTYqWeKz/vQLE3bvknDyyTLS0+viOUXMkr77orn0LsiSxpUWZEkjgAZrTq5HJOtMbktk4idz/sofzSyfBRWuftS/r48lBiSBhwO//AjrjDtYLJLj6Q/jnhFZh7z2/JjbEVHKX/kUrId2sDbuFkUoO+bGuPqI+WKNWFGQJf3BTyZZ0n+0qXsFQZb0XztBlvTF2OhkKdzsBVnSd180l94FWdK40oIsaQTQAM3JfE9SzaR4FClBXqiEn0SWKqbeBGnTWvBYJCI/9l0r4M4pAv23ye1AZZcRkC3p/tnS5+llG5G9aQHMzlIm91167ER4WhQF1PFZbUywgdfPKF4N66Gd/jZNQSI83BYQZEn/m0OQJf0xpisIsqQ/zoIs6YuxIEv64it6NzYCgixpXB9BljQC2MjNKR6IZJ154ZYjIk3qYNFRI2T06lCOtPf+zaqadm2B5KhkrmK+/cVIa58JaehQSFYTMvauqTcrR/sTUdH9LEai7LuXw+worVdHttrhzu4AIkicEFElrz2vXv2mkksp0vILsqT/zSHIkv4YC7LUMBgLsqQvzoIs6Yuv6N3YCAiypHF9BFnSCGCE5hS4uW2HGbktvKwWz/Ldr09dFnJylaMcRJTtm+eEIdc5IjpcKrmwUGZJA+lzKhs3mVjGc54/w77/TxydvwVHFFUgN0dmuY+qOvQJlDsuKUbGSw/BUrIVmce2YaTI5/RAsphgslvY3+pC+Y4UopMPsgaR9Si4UJyRo8MJICKVu3YO7LuWB1Qh8kSFYpKoUJ/Vhf3hzm4Pjz2/yUiEh9sCgizpd2/xngVZ0h9jQZYaBmNBlvTFWZAlffEVvRsbAUGWNK6PIEsaAQxqTkIKK35QEgGq88IcWb0M/XNXYY+tB7Zbj0GfQa3w1zYTtv+yF0fVLENLbzGqpSz279/WbiwJLOU0otxGf6b3wwFzof9KLT3FSr4jANTvKY732X+bbGZk9Mhl/+2xtoLH0hKezr1h2boO1r1/wNwiDWkdskJOmCw9lZ2HMXJEZEYdg0QkKHvzAtbOZ8lEVefT4CzoV4/sUCySrdYqRX24czqCPjO5ncza1FTyJ8W6YwRZihWpxOsJspQ4dvG0FG548aCVWF1BlhLDLdZWgizFipSo1xQREGRJ46ommyxRbArlzKED8qEeZycsBZ1ttzJ5qgqnh80wnIgAj4Wx71zhR4KsFrzIVhtq8nqx9uQapi4W+qxiJ3Mdcxb2h6P9oJAHeiJAdl8p0rPtcHjtqKmW4ayWsKdYydlCxZYu40B5YOJMm9mBItsODLYsYETJkqdkZ93hKMJz629Ed/N6jEqbj4LWh9jnstvH/k8WH5/DA9lT+7fDi1K5DatzwFyArq61MLewwmRTLELUL/1rLYwteV1V59NR2el0mJ0lyG2Zi4NuK1zpeRp3kmgejIAgS/rvCUGW9MeYriDIkv44C7KkL8aCLOmLr+jd2AgIsqRxfWIhS9KOLZDzC/xJSsNdkghJ/srpAS5b5KpF5MXkrmLWCiIu3MVL3Y+1fDvSyjbDRgIAtXLS3ta94PXK/r+pPimneW2tYHaW+dXUNEIQsjm5kJW0PR37t5aijWcj8tNLWL2SmkDVNyJPVJy1/3awKQpv9Dlvwy9A1hsqoVzaos3BW+5iJIoKJ0eh2pBLnLtFB6SXbmCElbD0WRTCSJ8T9mqrEQk8lFXUwONVXPxESR4CgiwlD8twPQmypD/GdAVBlvTHWZAlfTEWZElffEXvxkZAkCWN6/P7j05YLUpMidtjR2ZGCfu/pTZGxbx1PdLWLGYHdHfh4fi74w2oRiv/VXOzdiC3eDGz2hAhIiJQ6S2Cw94TbWq+DDk6IjyuvB7wWeyM8KjFAMJNp6o6n40rVNl/sBfIWuK1KMTFUl3KxuKzZrK+yYLkrAY2VvRilhubXMnc20pr8lHiykfP7A04Pn8Z2uVsCIumy2NHWi1O8UBOY/NYW8KT1xHONv3Y2Nr//QLri/o8WNMLzoL+IAuY5HHWYljGxkyxPrQ2hHFwITwc1flMPIHmSUTIlUWubx3iGR5aZqXhkMMNr0+QpbiAi6GyWZKQk2lFWaUrhtqiSiII0AGISOk+2YGqdBcya9Jgr0lLpCvRJgICOXYrHDVeuL0+gZNOCJCPQqvsdJRW1Oh0hebdbbrFhDSrGRVOd0oBccTxtpQarxisMREQZEnrusy9NK4e6IC/vfgktM7dEPIQf7CyCN+snQK31w6r2YF2+avYgZ8O9vaMEnQ/bFFI0kOHf2r7d+nRIILhqMlH65wNrC19Tn9Tf9QHkQf6rLyqTqY6rknEUJmu3S5vFavJxlTei/23vdbCxLvgRJMTKRoX/4zGHK5QP5G+V7fj8+ZkkeMRwzREFYGAQEAgIBAQCAgEUhSB81+xpujIxbCNhIAgS1pXY+6lUKuWUawMuXtVSZ1Qlt0V6RkSZORh0/ZeyE9bjt5tlgVckdzDKitbYfvekxiZIWIRqTBLSeYO5GTtYNYVRpKqilBeqR/x4eOR8wog59UKJTirIDkr2VdSiSKWEK74evTVirJh21stJni8PgoPEyXZCEgA4et2i7fxyYaW92cySSD3paoSZQObbRKstaqRel2zOfbLnhM+H2SxlfVbfglIs5jgEs8LXTCmZ4XJBHg8xvyxK/cpHgg5pkDL+LB7hKVclw3RzDoVZCnGBa+odMDj9aJlTnZAC3XMEicNsj0zZHwS5fSR/96IozOXQ175A1w7FbIh27NQ1aEvqk1ZzL3NlgFkpMvIbakQEU+PPkzNLcNXiaydv8KyZgWksmJIpXsBWxaTuqZryva6sdGPM/bvYTFL6kLt2DVbFcLT7wTAngVvK0X8gIrJ6VBIkKPS/x195mvZBnJRt7BoqQkTjUVyVLE+IrWJEXpDVxMxS/otj4hZ0g9b3rOIWdIfY7qCiFnSH2cRs6QvxiJmSV98Re/GRkCQpSjr43BW465p/8bXy1ezmn2O6Irnpt2C/FY57O9YBB4aYwsEq+E1xhiawzUFWdJvlQVZ0g9bQZb0x1Z9BUGW9MdbkCV9MRZkSV98Re/GRkCQpSjr88qbn+Ldj5di3nP3wpaRhglTZqJzUVs88n9XC7Jk7L3dIKMTZEk/mAVZ0g9bQZb0x1aQpYbFWJAlffEWZElffEXvxkZAkKUo63P+dVMxYvBxuO7S0azmwqU/YtKDL2L9kjmQJElYloy9v3UfnSBL+kEsyJJ+2AqypD+2giw1LMaCLOmLtyBL+uIrejc2AoIsRVmf40aOx7S7rmGEicrvm7bhgusfxIqPX0BOdiZKDxlT1tieYQZkMLlaUfRDIDcrDRUOF4QicPIxpmDinMw0HKgw5j2W/Bk3fI8ZTDocqKoWzwk90ScJ/Koaj2GD4/Wce0P1TWSpVXaaYX+TGwoHva6TbiXpcBMqHEq+wlQpeS2EwEOqrJWRxynIUoTVkWUZRw25Ci8+fjtOHaQouv25bTfOvvJefPX2U2hboCRJFUUgIBAQCAgEBAICAYGAQEAgIBBoeggIshRlTcmy9OiUazH81GNZzWDLUo1BZUotZolZljwiWaqudy1J1VKiSSEdnnyY6U2x1WyCyyP0lpOPrtIjuToSzp4g1Uy9rtdc+7VaJKZMKh7H+u0ASkpLlg+j/ibrN/OG6Zks0CZJgjvFnhVkERNFIKAVAUGWoiBIMUtnDDke115yJqspYpa0brmm1V7ELOm3niJmST9sec9COlx/jOkKQg1Pf5xFzJK+GIuYJX3xFb0bGwFBlqKsz8v//QTvffINU8Oz29Ix/q6nhRqesfd0g45OkCX94BZkST9sBVnSH1v1FQRZ0h9vQZb0xViQJX3xFb0bGwFBlqKsT5WjGpMfnoVvV65lNY/q2RnPPXor2uTnsr9FniVjb3C9RyfIkn4IC7KkH7aCLOmPrSBLDYuxIEv64i3Ikr74it6NjYAgSzGuT3lFFdxujz8ZLW8myFKMADbRaoIs6bewgizph60gS/pjK8hSw2IsyJK+eAuypC++ondjIyDIkrHXR4xOICAQEAgIBAQCAgGBgEBAICAQaCQEBFlqJODFZQUCAgGBgEBAICAQEAgIBAQCAgFjIyDIkrHXR4xOICAQEAgIBAQCAgGBgEBAICAQaCQEBFlqJODpsiQeUVHlQEF+S0jkcJ3E4qx24cDBQyhskweTqX7fPp+MfaUHWAyWxWwOuLLH68X+0nK0ys1Gepo15KhKysqRabfBltG8s2NTLFtNjdsv+BEMVkWlA4Rny5zsejhGWyNqQG2D1yeJ28TwXQl841+irTv2YF/pQQzof3jYxtH2XqTnA31XdvAQrFYLcrIzQ16D6lBSbzMlZ0mRUnrgEHtWhrpXtU4h2j52udw4UF7JniPBvwXR1oo/J0ySKeSzXuvYU6V9LPsy0u9WpDWK5TcxVXBKdJwC30SRE+2aAgKCLDXCKv6xeTvu/+eroH+p2G0ZuO7SM3H12FFRD8Z3PfpvlvOpe+f2YUd+873/wtfLV7PvifCce8bJuGP8hf7633y/lin8OZzV7LOpd1yJC88azP6bpNKfefk9f90Rg4/D1ElXIqeFcijasXsvk0/fvmsv+3vMqFPwwKQrYLUEEq5GgDXgkm/9bzGmPTOPfTZy6ADMeGBCUodEP7rjbnnMj0PXju1w3aWjcdbwE9h1CNu7pv3bvw59juiK56bd4hcIibZGCtb7MPLS/8OX82egXWF+UsefjM4++Oxbto8vOmcoHrh9XDK69Pch8FWg2PjnToy55n7/c+Knz2dHxXnuuwuxdMUavDrzrpB1o+29SM+H73/+Dbfc/5z/2XFcv16YPOEiphLKC5GkB596jf350OSroo63sSt8/vUPeOzZN1B2sIINpaB1S9x36+UYetLRUYcW7XkcbR8TVrPmLsALcz5k16Ln9fOP3Ya+R3Rlf0dbK6pDZOqiGx7E9ZedhdHDBkUdc2NUmPTgC1i49Cd2afXvTbLGEm1fRvrdirZG0X4TkzUHrf3o+TwW+GpdHdE+1REQZKmBV3B/6UEM/sdt7FA96foL0SLbjmU/rsPdj72Mq8eOxIRx50Qc0ZGDr8ScmVNwfP9eYes9/+qHGD74OBQd1gYrf/kdE+95BvNnPYDeh3dhP6ynnHcLbrr6PFw65nR2qLr1/uew8K3paN+2Nd79ZCk6tGuDvkd0w86/9+GaSU/imrFn4sqLzmDXu/7OGcjKtOHRKdeheF8pLrzhIXZQ5iShgeEMezm3x4uaGhdmzHobhyodePrBG5M6tH0lB/G/L77D2SNORKYtA/PeW4Q5b3+Bbz98llnbXnnzU7z78VKWn4v+njBlZkB+rkhrRAMde+Mj+PX3P9mYjUqWLr/5MZQeKGdWyGUfPRfWCpkI8AJfBTV6m0vEe/GyVYz8J4MsaXk+rFz1O/aXHMQpg/qiutqFh2e+zsY464nb2XgpaTeNk4jH+aNPNTxZImJ4490zcdfEsThv5MnwyTLeWbCEvTB67ZkpIDIYqUR7Hkfbx6vXb8ZlNz2Kec/dg969uuDZ/3yATxd/j6/efppZiaI9J2bMfhtz5n/OhvjkvTcYlizR747X68U/rn0A14wdhQvPHpLIYyFsm2j7MtLvVrQ1ivabmNSJaOhMz+exwFfDwoimTQIBQZYaeBmfmv0OFn3zEz5944kAKxK3hPz42Wxk2jOwat0mzHzpPWzYsgPt2+bj8vOHY9vOYvznrc8YqcltkYXzRp2Mi88ZGnUGQy+4ndWjN4/8cLB60ctIq3WxG3XZXYw4XTpmWL2+yHKwe89+9paa3BROOGsi3nj+XvQ/qjur++i/5qF4XxnLPWXEQm+MS8oOMbL089qNePjp17FnXxkb6pAT+uHe2y5nrkRbtu7GlMdeYoeNtz5czL6P50d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+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "melt_df = pd.melt(rebased_df.iloc[:,:5], ignore_index=False)\n",
+ "fig = px.line(melt_df, x=melt_df.index, y='value', color='variable')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "4e33bee8-586e-4260-b9be-a832dc6a3121",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 4.64 ms, sys: 12 µs, total: 4.65 ms\n",
+ "Wall time: 4.69 ms\n"
+ ]
+ },
+ {
+ "data": {
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+ " ...]"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "[date.today() - timedelta(x) for x in range(365 * 5)][::-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "c14f78c9-09bc-4946-be41-07861a0d3f3f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lookback_years = 5\n",
+ "end_date = date.today()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "3228f9e9-6e7d-4c90-97a2-6b0e07ab852b",
+ "metadata": {
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+ "tags": []
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+ },
+ "execution_count": 113,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
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+ "id": "046061cf-2f13-4ca5-8b4e-f680e80c822a",
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+ " -0.061187 | \n",
+ " -0.019975 | \n",
+ " 0.021214 | \n",
+ " -0.070491 | \n",
+ " -0.122613 | \n",
+ " -0.072125 | \n",
+ " -0.145646 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-21 | \n",
+ " -0.017658 | \n",
+ " -0.025315 | \n",
+ " 0.000102 | \n",
+ " 0.000747 | \n",
+ " -0.024327 | \n",
+ " -0.060753 | \n",
+ " 0.000238 | \n",
+ " -0.032925 | \n",
+ " -0.012728 | \n",
+ " -0.006047 | \n",
+ " ... | \n",
+ " 0.022428 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " -0.013591 | \n",
+ " 0.019588 | \n",
+ " -0.006183 | \n",
+ " -0.029013 | \n",
+ " -0.008434 | \n",
+ " -0.038107 | \n",
+ " -0.039256 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-22 | \n",
+ " -0.041307 | \n",
+ " -0.104646 | \n",
+ " -0.000258 | \n",
+ " 0.000169 | \n",
+ " -0.137914 | \n",
+ " -0.155483 | \n",
+ " 0.000257 | \n",
+ " -0.093762 | \n",
+ " -0.141491 | \n",
+ " -0.090236 | \n",
+ " ... | \n",
+ " -0.124879 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " -0.112529 | \n",
+ " -0.187009 | \n",
+ " 0.002130 | \n",
+ " -0.117576 | \n",
+ " -0.168363 | \n",
+ " -0.121015 | \n",
+ " -0.145770 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-23 | \n",
+ " -0.065218 | \n",
+ " -0.104612 | \n",
+ " 0.000092 | \n",
+ " 0.000878 | \n",
+ " -0.155485 | \n",
+ " -0.123637 | \n",
+ " 0.000165 | \n",
+ " -0.124935 | \n",
+ " -0.279095 | \n",
+ " -0.080092 | \n",
+ " ... | \n",
+ " -0.108002 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " -0.130236 | \n",
+ " -0.141261 | \n",
+ " -0.000125 | \n",
+ " -0.156447 | \n",
+ " -0.152963 | \n",
+ " -0.140479 | \n",
+ " -0.167531 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 100 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " bitcoin | \n",
+ " ethereum | \n",
+ " tether | \n",
+ " usd-coin | \n",
+ " binance-coin | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2021-05-19 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-20 | \n",
+ " 1.018324 | \n",
+ " 0.953542 | \n",
+ " 1.000268 | \n",
+ " 0.997696 | \n",
+ " 0.908904 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-21 | \n",
+ " 1.000343 | \n",
+ " 0.929403 | \n",
+ " 1.000370 | \n",
+ " 0.998441 | \n",
+ " 0.886794 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-22 | \n",
+ " 0.959023 | \n",
+ " 0.832145 | \n",
+ " 1.000112 | \n",
+ " 0.998610 | \n",
+ " 0.764492 | \n",
+ "
\n",
+ " \n",
+ " | 2021-05-23 | \n",
+ " 0.896477 | \n",
+ " 0.745093 | \n",
+ " 1.000204 | \n",
+ " 0.999487 | \n",
+ " 0.645625 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "