8. Stackoverflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import functools\n",
    "import operator\n",
    "import os \n",
    "from datetime import date\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from enum import Enum\n",
    "import re\n",
    "from datetime import datetime, timedelta\n",
    "from skimage.segmentation import slic\n",
    "from skimage.color import label2rgb\n",
    "from skimage import measure\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.filters import threshold_otsu\n",
    "import random\n",
    "from collections import Counter\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "300.0 2021-05-11 20:29:22.656\n",
      "2\n",
      "450.0 2021-05-05 12:01:19.699\n",
      "3\n",
      "nan 2021-03-08 13:03:22.653\n",
      "{1: [0, 1, 2, 3], 2: [4, 5, 6, 7, 8], 3: [9, 10]}\n",
      "           score               order_date\n",
      "             max                      max\n",
      "acct_num                                 \n",
      "1          300.0  2021-05-11 20:29:22.656\n",
      "2         9998.0  2021-05-05 12:01:19.699\n",
      "3            NaN  2021-03-08 13:03:22.653\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame({\n",
    "'acct_num': [1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3],\n",
    "'score': [np.nan, np.nan, 300, 250, np.nan, 275, 400, 450, 9998, np.nan, np.nan],\n",
    "'order_date': ['2021-05-11 20:29:22.656','2021-04-15 10:15:20.444',\n",
    "               '2021-03-18 03:29:22.400', '2021-02-21 22:05:22.399',\n",
    "               '2021-05-05 12:01:19.699','2021-04-19 18:16:22.493',\n",
    "               '2021-04-19 05:12:12.837', '2021-04-19 04:22:19.199',\n",
    "               '2021-04-19 03:58:11.121', '2021-02-01 14:29:22.656',\n",
    "               '2021-03-08 13:03:22.653']})\n",
    "##df=df.dropna()\n",
    "##df\n",
    "\n",
    "grouped2=df.groupby('acct_num').groups\n",
    "for group in grouped2:\n",
    "    print(group)\n",
    "    filter=(df['acct_num']==group) & (df['score']!=9998)\n",
    "    max_score=df[filter]['score'].max()\n",
    "    max_order_date=df[filter]['order_date'].max()\n",
    "    print(max_score,max_order_date)\n",
    "print(grouped2)\n",
    "\n",
    "grouped2=df.groupby('acct_num')[['score','order_date']].agg({'max'})\n",
    "print(grouped2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1, 2, 3], [4, 5]], [6]]\n"
     ]
    }
   ],
   "source": [
    "regular_list = []\n",
    "def transform(nested_list):\n",
    "    for ele in nested_list:\n",
    "        if type(ele) is list:\n",
    "            regular_list.append(ele)\n",
    "        else:\n",
    "            regular_list.append([ele])\n",
    "    return regular_list\n",
    "\n",
    "L = [[[1, 2, 3], [4, 5]], 6]\n",
    "\n",
    "result = transform(L)\n",
    "print(result)\n",
    "#print(np.ndarray.flatten(L))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.VirufyAugmenter object at 0x000001645218D148>\n"
     ]
    }
   ],
   "source": [
    "class VirufyAugmenter:\n",
    "    def __init__(self, paths):\n",
    "        assert self.path_exists(paths)\n",
    "        self.paths=paths\n",
    "        \n",
    "    def path_exists(self,paths):\n",
    "        result=False\n",
    "        for path in paths:\n",
    "            result=os.path.isdir(path)\n",
    "            if result==False:\n",
    "                break\n",
    "        return result\n",
    "    \n",
    "paths=[\"c:\\\\\"]\n",
    "obj=VirufyAugmenter(paths)\n",
    "print(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day 45 or Hours 1080 Minutes 17\n"
     ]
    }
   ],
   "source": [
    "start_time=datetime(\n",
    "    year=2021,\n",
    "    month=5,\n",
    "    day=27,\n",
    "    hour=10,\n",
    "    minute=24,\n",
    "    microsecond=0)\n",
    "\n",
    "end_time=datetime.now()\n",
    "delta=(end_time-start_time)\n",
    "\n",
    "seconds_in_day = 24 * 60 * 60\n",
    "seconds_in_hour= 1 * 60 * 60\n",
    "\n",
    "elapsed_seconds=delta.days * seconds_in_day + delta.seconds\n",
    "\n",
    "days= int(elapsed_seconds/seconds_in_day)\n",
    "hours=  int(elapsed_seconds/seconds_in_hour)\n",
    "minutes= int((elapsed_seconds - (hours*seconds_in_hour))/60)\n",
    "\n",
    "print(\"Day {} or Hours {} Minutes {}\".format(days,hours,minutes))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     fruit    colour \n",
      "0    apple       red \n",
      "1   banana    yellow \n",
      "2   banana     green \n",
      "3    apple     green \n",
      "4   cherry       red \n",
      " fruit  {0: ' apple ', 1: ' banana ', 2: ' banana ', 3: ' apple ', 4: ' cherry '}\n",
      " apple \n",
      " banana \n",
      " banana \n",
      " apple \n",
      " cherry \n",
      " colour  {0: 'red ', 1: ' yellow ', 2: ' green ', 3: ' green ', 4: ' red '}\n",
      "red \n",
      " yellow \n",
      " green \n",
      " green \n",
      " red \n",
      "{'red ': ' apple ', ' yellow ': ' banana ', ' green ': ' apple ', ' red ': ' cherry '}\n"
     ]
    }
   ],
   "source": [
    "data=(\n",
    "        (\" apple \",\"red \"),\n",
    "        (\" banana \",\" yellow \"),\n",
    "        (\" banana \",\" green \"),\n",
    "        (\" apple \",\" green \"),\n",
    "        (\" cherry \",\" red \")\n",
    "    )\n",
    "headers= (\" fruit \", \" colour \") \n",
    "\n",
    "df=pd.DataFrame(data,columns=headers)\n",
    "print(df)\n",
    "a_dict=df.to_dict()\n",
    "for key,item in a_dict.items():\n",
    "    print(key,item)\n",
    "    for i in np.arange(len(item)):\n",
    "        print(item[i])\n",
    "        \n",
    "        \n",
    "dct = dict((y, x) for x, y in data)        \n",
    "print(dct)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv('sp500.csv',parse_dates=['Date'],index_col='Date')\n",
    "grouped=df.groupby(pd.Grouper(level=0,freq='D'))['Open'].mean()\n",
    "\n",
    "sns.boxplot(x=grouped.index.year, y=grouped.values,\n",
    "data=grouped)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chain is a function that takes a series of iterables and returns one iterable. It groups all the iterables together and produces a single iterable as output.\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "from itertools import chain\n",
    "print(\"Chain is a function that takes a series of iterables and returns one iterable. It groups all the iterables together and produces a single iterable as output.\")\n",
    "data=[1,2,3,4]\n",
    "cc = chain(*(range(2000*k+10*(k-1), 2010*k) for k in range(1,len(data)//2000)))\n",
    "#data = data.drop(labels=cc, axis=0)\n",
    "print(list(cc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  col1  col2\n",
      "0    A     1\n",
      "1    A     2\n",
      "2    A     3\n",
      "3    B     4\n",
      "4    B     5\n",
      "5    B     6\n",
      "6    C     7\n",
      "7    C     7\n",
      "        0    1    2    3    4    5    6    7\n",
      "col1                                        \n",
      "A     1.0  2.0  3.0  0.0  0.0  0.0  0.0  0.0\n",
      "B     0.0  0.0  0.0  4.0  5.0  6.0  0.0  0.0\n",
      "C     0.0  0.0  0.0  0.0  0.0  0.0  7.0  7.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = {'col1': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C'], 'col2': [1, 2, 3, 4, 5, 6, 7, 7]}\n",
    "df = pd.DataFrame(data=d)\n",
    "print(df)\n",
    "print(df.pivot_table(index='col1',columns=df.index, values='col2').fillna(0))\n",
    "\n",
    "fp=df.pivot_table(index='col1',columns=df.index, values='col2').fillna(0)\n",
    "sns.heatmap(fp,annot=True)\n",
    "plt.title('col1 vs col2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2021-01-03', '2021-01-10', '2021-01-17', '2021-01-24',\n",
      "               '2021-01-31', '2021-02-07', '2021-02-14', '2021-02-21',\n",
      "               '2021-02-28', '2021-03-07', '2021-03-14', '2021-03-21',\n",
      "               '2021-03-28', '2021-04-04', '2021-04-11', '2021-04-18',\n",
      "               '2021-04-25', '2021-05-02', '2021-05-09', '2021-05-16',\n",
      "               '2021-05-23', '2021-05-30', '2021-06-06', '2021-06-13',\n",
      "               '2021-06-20', '2021-06-27', '2021-07-04', '2021-07-11',\n",
      "               '2021-07-18', '2021-07-25', '2021-08-01', '2021-08-08',\n",
      "               '2021-08-15', '2021-08-22', '2021-08-29', '2021-09-05',\n",
      "               '2021-09-12', '2021-09-19', '2021-09-26', '2021-10-03',\n",
      "               '2021-10-10', '2021-10-17', '2021-10-24', '2021-10-31',\n",
      "               '2021-11-07', '2021-11-14', '2021-11-21', '2021-11-28',\n",
      "               '2021-12-05', '2021-12-12', '2021-12-19', '2021-12-26'],\n",
      "              dtype='datetime64[ns]', freq='W-SUN')\n"
     ]
    }
   ],
   "source": [
    "index= pd.date_range(start='2021-1-1',periods=52,freq='W')\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['happy', 'help', 'review', 'enter', 'hello']\n"
     ]
    }
   ],
   "source": [
    "    class Items(Enum):\n",
    "        review = 4\n",
    "        enter = 5\n",
    "        help = 2\n",
    "        hello = 9\n",
    "        happy = 1\n",
    "\n",
    "    values = { 4: \"review\",\n",
    "        5 : \"enter\",\n",
    "        2 : \"help\",\n",
    "        9 : \"hello\",\n",
    "        1 : \"happy\",}\n",
    "    print([values[key] for key in sorted(values.keys())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "class Node(object):\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "\n",
    "#G = nx.Graph()\n",
    "#G.add_nodes_from([Node(1), Node(2), Node(3), Node(4)])\n",
    "#G.add_edge(1, 2, data='some data')\n",
    "#G.add_edge(2, 3, data='some more data')\n",
    "#G.add_edge(2, 4, data='yet more data')#\n",
    "\n",
    "blue_edges = [('B', 'C'), ('B', 'D'), ('B', 'E'), ('E', 'F')]\n",
    "red_edges = [('A', 'B'), ('A', 'C'), ('C', 'E'), ('D', 'E'), ('D', 'F')]\n",
    "\n",
    "G=nx.Graph() #define G\n",
    "G.add_edges_from(blue_edges)\n",
    "G.add_edges_from(red_edges)\n",
    "\n",
    "pos = {'A':(0,0), 'B':(1,1), 'C':(1,-1), 'D':(2,1), 'E':(2,-1), 'F':(3,0)}\n",
    "\n",
    "nx.draw_networkx(G, pos=pos, edgelist = [], node_color = 'k')\n",
    "nx.draw_networkx(G, pos=pos, nodelist = [], edgelist = blue_edges, edge_color = 'b')\n",
    "nx.draw_networkx(G, pos=pos, nodelist = [], edgelist = red_edges, edge_color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['resources_used.cput ', 'resources_used.mem ', 'resources_used.vmem ', 'resources_used.walltime ', 'job_state ', 'queue ', 'comment ']) dict_values([' 46:26:45', ' 5347092kb', ' 7588024kb', ' 01:44:50', ' Q', ' qp32', ' Not Running: Queue not an execution queue.'])\n"
     ]
    }
   ],
   "source": [
    "data=['Job Id: 49361.tyrone-cluster', 'resources_used.cput = 14:32:14', 'resources_used.mem = 13955852kb', 'resources_used.vmem = 14199016kb', 'resources_used.walltime = 05:23:02', 'job_state = R', 'queue = qp32', 'Job Id: 49362.tyrone-cluster', 'job_state = Q', 'queue = batch', 'comment = Not Running: Queue not an execution queue.', 'Job Id: 49395.tyrone-cluster', 'resources_used.cput = 31:20:32', 'resources_used.mem = 19179712kb', 'resources_used.vmem = 158305072kb', 'resources_used.walltime = 01:57:34', 'job_state = R', 'queue = idqueue', 'Job Id: 49396.tyrone-cluster', 'resources_used.cput = 46:26:45', 'resources_used.mem = 5347092kb', 'resources_used.vmem = 7588024kb', 'resources_used.walltime = 01:44:50', 'job_state = R', 'queue = qp32', 'Job Id: 49408.tyrone-cluster', 'job_state = Q', 'queue = qp32']\n",
    "key_value_pairs=[item.split(\"=\") for item in data]\n",
    "#print(key_value_pairs)\n",
    "my_dict={}\n",
    "for item in key_value_pairs:\n",
    "    if len(item)==2:\n",
    "        my_dict[item[0]]=item[1]\n",
    "        \n",
    "print(my_dict.keys(),my_dict.values())        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accounting+finance acc\n",
      "admin / office ofc\n",
      "arch / engineering egr\n",
      "art / media / design med\n",
      "biotech / science sci\n",
      "business / mgmt bus\n"
     ]
    }
   ],
   "source": [
    "EECTION_DICT = {\n",
    "    \"accounting+finance\": \"acc\",\n",
    "    \"admin / office\": \"ofc\",\n",
    "    \"arch / engineering\": \"egr\",\n",
    "    'art / media / design': 'med',\n",
    "    'biotech / science': 'sci',\n",
    "    'business / mgmt': 'bus'}\n",
    "\n",
    "def foo(**args):\n",
    "    for key,item in args.items():\n",
    "        print(key, item)\n",
    "        \n",
    "foo(**EECTION_DICT)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'four', 'two', 'one', 'three', 'five'}\n"
     ]
    }
   ],
   "source": [
    "x = ['one', 'two', 'four']\n",
    "y = ['two', 'three', 'five']\n",
    "z = ['one', 'three', 'four']\n",
    "result=set.union(set(x),set(y))\n",
    "result=set.union(result,set(z))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Population'}, xlabel='Region'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Region=['City1','City2']\n",
    "Men=[10,50]\n",
    "Women=[5,89]\n",
    "df=pd.DataFrame({'Region':Region,'Men':Men,'Women':Women})\n",
    "df.plot.bar(x='Region',y=['Men','Women'], rot=0, title='Population', figsize=(5,4), fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constructor here\n"
     ]
    }
   ],
   "source": [
    "myval=\"constructor: function(some, parameters, here) {\"\n",
    "cons,args=re.search(r\"(\\w*):\\s*function\\((?:(\\w+)(?:,\\s)?)*\\)\", myval).groups()\n",
    "print(cons,args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ nan  10.]\n",
      " [ 10.  40.]\n",
      " [120.  nan]\n",
      " [ 30.  nan]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([\n",
    " [2, 10  ],\n",
    " [3, 120],\n",
    " [4, 30 ]\n",
    "])\n",
    "\n",
    "b = np.array([\n",
    " [1, 10 ],\n",
    " [2, 40]\n",
    "])\n",
    "\n",
    "index_a=a[:,0]\n",
    "value_a=a[:,1]\n",
    "index_b=b[:,0]\n",
    "value_b=b[:,1]\n",
    "df=pd.DataFrame({\"index\":index_a,\"a\":value_a})\n",
    "df2=pd.DataFrame({\"index\":index_b,\"b\":value_b})\n",
    "result=pd.merge(df,df2,on=\"index\",how=\"outer\")\n",
    "result.set_index('index',inplace=True)\n",
    "result.sort_index(inplace=True)\n",
    "print(result.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ret-120',\n",
       " 'ret-115',\n",
       " 'ret-110',\n",
       " 'ret-105',\n",
       " 'ret-100',\n",
       " 'ret-95',\n",
       " 'ret-90',\n",
       " 'ret-85',\n",
       " 'ret-80',\n",
       " 'ret-75',\n",
       " 'ret-70',\n",
       " 'ret-65',\n",
       " 'ret-60',\n",
       " 'ret-55',\n",
       " 'ret-50',\n",
       " 'ret-45',\n",
       " 'ret-40',\n",
       " 'ret-35',\n",
       " 'ret-30',\n",
       " 'ret-25',\n",
       " 'ret-20',\n",
       " 'ret-15',\n",
       " 'ret-10',\n",
       " 'ret-5',\n",
       " 'ret5',\n",
       " 'ret10',\n",
       " 'ret15',\n",
       " 'ret20',\n",
       " 'ret25',\n",
       " 'ret30',\n",
       " 'ret35',\n",
       " 'ret40',\n",
       " 'ret45',\n",
       " 'ret50',\n",
       " 'ret55',\n",
       " 'ret60',\n",
       " 'ret65',\n",
       " 'ret70',\n",
       " 'ret75',\n",
       " 'ret80',\n",
       " 'ret85',\n",
       " 'ret90',\n",
       " 'ret95',\n",
       " 'ret100',\n",
       " 'ret105',\n",
       " 'ret110',\n",
       " 'ret115',\n",
       " 'ret120',\n",
       " 'ret125',\n",
       " 'ret130',\n",
       " 'ret135',\n",
       " 'ret140',\n",
       " 'ret145',\n",
       " 'ret150',\n",
       " 'ret155',\n",
       " 'ret160',\n",
       " 'ret165',\n",
       " 'ret170',\n",
       " 'ret175',\n",
       " 'ret180',\n",
       " 'ret185',\n",
       " 'ret190',\n",
       " 'ret195',\n",
       " 'ret200',\n",
       " 'ret205',\n",
       " 'ret210',\n",
       " 'ret215',\n",
       " 'ret220',\n",
       " 'ret225',\n",
       " 'ret230',\n",
       " 'ret235',\n",
       " 'ret240']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['ret'+str(x) for x in list(range(-120,241,5)) if (x!=0) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index Status\n",
      "0      0     OK\n",
      "1      1     OK\n",
      "2      2     OK\n",
      "3      3     OK\n",
      "4      4     OK\n",
      "5      5     OK\n",
      "6      6   Fail\n",
      "7      7     OK\n",
      "8      8   Fail\n",
      "9      9     OK\n",
      "[6, 8]\n"
     ]
    }
   ],
   "source": [
    "status=['OK'\n",
    ",'OK'\n",
    ",'OK'\n",
    ",'OK'\n",
    ",'OK'\n",
    ",'OK'\n",
    ",'Fail'\n",
    ",'OK'\n",
    ",'Fail'\n",
    ",'OK']\n",
    "df=pd.DataFrame({'Status':status})\n",
    "df.reset_index(inplace=True)\n",
    "print(df)\n",
    "val=[index for index,item in df.iterrows() if item['Status']=='Fail']\n",
    "\n",
    "print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006601400.62797\n"
     ]
    }
   ],
   "source": [
    "mynumber = str(2006601400162797)\n",
    "\n",
    "result=[]\n",
    "[result.append(\".\") if len(result)==10 else result.append(x) for x in mynumber]\n",
    "print(\"\".join(result))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 10), (2, 7), (4, 30), (6, 19), (8, 3)]\n"
     ]
    }
   ],
   "source": [
    "od1 = {0:10, 1:3, 2:7, 3:11, 4:30, 5:15, 6:19, 7:4, 8:3}\n",
    "#print(od1)\n",
    "od2=sorted([(key,item) for key,item in od1.items() if key%2==0])\n",
    "\n",
    "print(od2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "class aDictionary:\n",
    "    def __init__(self, dict):\n",
    "        self.dict=dict\n",
    "    def find(self,searchDict):\n",
    "        key=list(searchDict.keys())[0]\n",
    "        value=list(searchDict.values())[0]\n",
    "        result_list=self.dict[\"nicknames\"]\n",
    "        return(value in result_list)\n",
    "\n",
    "data={\"nicknames\":[\"Johnny\",\"JSwitch\",\"JB\",\"Tc Johnny\",\"Bardy\"]}\n",
    "dict=aDictionary(data)\n",
    "\n",
    "print(dict.find({\"nicknames\":\"JB\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         col1  col2\n",
      "0  a-a1_a2|a3    20\n",
      "1  b-b1_b2|b3    21\n",
      "2  c-c1_c2|c3    19\n",
      "3  d-d1_d2|d3    18\n",
      "  id id1 id2 id3  col2\n",
      "0  a  a1  a2  a3    20\n",
      "1  b  b1  b2  b3    21\n",
      "2  c  c1  c2  c3    19\n",
      "3  d  d1  d2  d3    18\n",
      "   col2 id id1 id2 id3\n",
      "0    20  a  a1  a2  a3\n",
      "1    21  b  b1  b2  b3\n",
      "2    19  c  c1  c2  c3\n",
      "3    18  d  d1  d2  d3\n"
     ]
    }
   ],
   "source": [
    "split_format = 'id-id1_id2|id3'\n",
    "\n",
    "data = {'col1':['a-a1_a2|a3', 'b-b1_b2|b3', 'c-c1_c2|c3', 'd-d1_d2|d3'],\n",
    "        'col2':[20, 21, 19, 18]}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n",
    "\n",
    "symbols=[]\n",
    "for x in split_format:\n",
    "    if x.isalnum()==False:\n",
    "        symbols.append(x)\n",
    "\n",
    "result=[]\n",
    "def parseTree(stringlist,symbols,result):\n",
    "    \n",
    "    #print(\"String list\",stringlist)\n",
    "    \n",
    "    if len(symbols)==0:\n",
    "        [result.append(x) for x in stringlist]\n",
    "        return\n",
    "    token=symbols.pop(0)\n",
    "    elements=[]\n",
    "    for item in stringlist:\n",
    "        elements.append(item.split(token))\n",
    "        \n",
    "    flat_list = [item for sublist in elements for item in sublist]        \n",
    "    parseTree(flat_list,symbols,result)\n",
    "\n",
    "df2=pd.DataFrame(columns=[\"id\",\"id1\",\"id2\",\"id3\"])\n",
    "for key, item in df.iterrows():\n",
    "    symbols2=symbols.copy()\n",
    "    value=item['col1']\n",
    "    parseTree([value],symbols2,result)\n",
    "    #print(result)\n",
    "    a_series = pd. Series(result, index = df2.columns)\n",
    "    df2=df2.append(a_series, ignore_index=True)\n",
    "    result.clear()\n",
    "\n",
    "df2['col2']=df['col2']    \n",
    "print(df2)\n",
    "\n",
    "\n",
    "col_name = re.split('[^0-9a-zA-Z]+',split_format)\n",
    "df[col_name] = df['col1'].str.split('[^0-9a-zA-Z]+',expand=True)\n",
    "del df['col1']\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n",
      "44\n"
     ]
    }
   ],
   "source": [
    "text = 'aa33bbb44'\n",
    "result=re.findall('\\d+',text)\n",
    "for item in result:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'TopicArn': 'arn:aws:sns:us-east-1:700257:test4'}]\n"
     ]
    }
   ],
   "source": [
    "test = [{u'TopicArn': u'arn:aws:sns:us-east-1:700257:test1'},\n",
    "  {u'TopicArn': u'arn:aws:sns:us-east-1:700257:test2'},\n",
    "  {u'TopicArn': u'arn:aws:sns:us-east-1:700257:test3'},\n",
    "  {u'TopicArn': u'arn:aws:sns:us-east-1:700257:test4'}]\n",
    "\n",
    "string = 'arn:aws:sns:us-east-1:700257:test4'\n",
    "\n",
    "result=list(d for d in test if string in d.values())\n",
    "print(result)\n",
    "#for item in mylist:\n",
    "#    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0]] [[0, 0, 0, 2, 2], [0, 0, 0, 2, 2, 2], [0, 0, 0, 0, 0, 2, 0]]\n"
     ]
    }
   ],
   "source": [
    "ls = [\n",
    "[1, 1, 0, 2, 2],\n",
    "[1, 1, 1, 2, 2, 2],\n",
    "[0, 1, 0, 0, 0, 2, 0]\n",
    "]\n",
    "\n",
    "ls1,ls2=([[y&b for y in x] for x in ls] for b in (1,2))\n",
    "print(ls1,ls2)\n",
    "#[[y&2 for y in x] for x in ls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'apple': {3, 5}, 'banana': {3, 6}, 'strawberry': {1, 2, 4, 5}}\n"
     ]
    }
   ],
   "source": [
    "dictionary= {\"apple\":[3,5], \"banana\":[3,3,6], \"strawberry\":[1,2,4,5,5]}\n",
    "for key,item in dictionary.items():\n",
    "    dictionary[key]=set(item)\n",
    "\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1234\n",
      "5123\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "a = \"3213>1234<3213>5123<\"\n",
    "\n",
    "p = re.compile('>([0-9]+)<')\n",
    "data=p.findall(a)\n",
    "\n",
    "for item in data:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aaa\n",
      "aab\n",
      "aac\n",
      "aad\n",
      "aae\n",
      "aaf\n",
      "aag\n",
      "aah\n",
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      "acs\n",
      "act\n",
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      "add\n",
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      "adr\n",
      "ads\n",
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      "cgo\n",
      "cgp\n",
      "cgq\n",
      "cgr\n",
      "cgs\n",
      "cgt\n",
      "cgu\n",
      "cgv\n",
      "cgw\n",
      "cgx\n",
      "cgy\n",
      "cgz\n",
      "cha\n",
      "chb\n",
      "chc\n",
      "chd\n",
      "che\n",
      "chf\n",
      "chg\n",
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      "chn\n",
      "cho\n",
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      "chr\n",
      "chs\n",
      "cht\n",
      "chu\n",
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      "chy\n",
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      "cia\n",
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      "cig\n",
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      "cit\n",
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      "cld\n",
      "cle\n",
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      "clg\n",
      "clh\n",
      "cli\n",
      "clj\n",
      "clk\n",
      "cll\n",
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      "cln\n",
      "clo\n",
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      "cls\n",
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      "cmd\n",
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      "cpp\n",
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      "cpu\n",
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      "csv\n",
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      "dim\n",
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      "dir\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lgt\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "rdc\n",
      "rdd\n",
      "rde\n",
      "rdf\n",
      "rdg\n",
      "rdh\n",
      "rdi\n",
      "rdj\n",
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      "rei\n",
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      "rek\n",
      "rel\n",
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      "ren\n",
      "reo\n",
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      "ret\n",
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      "rgb\n",
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      "rgn\n",
      "rgo\n",
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      "rii\n",
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      "ril\n",
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      "rjs\n",
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      "rkf\n",
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      "rkm\n",
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      "rko\n",
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      "rkr\n",
      "rks\n",
      "rkt\n",
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      "rkv\n",
      "rkw\n",
      "rkx\n",
      "rky\n",
      "rkz\n",
      "rla\n",
      "rlb\n",
      "rlc\n",
      "rld\n",
      "rle\n",
      "rlf\n",
      "rlg\n",
      "rlh\n",
      "rli\n",
      "rlj\n",
      "rlk\n",
      "rll\n",
      "rlm\n",
      "rln\n",
      "rlo\n",
      "rlp\n",
      "rlq\n",
      "rlr\n",
      "rls\n",
      "rlt\n",
      "rlu\n",
      "rlv\n",
      "rlw\n",
      "rlx\n",
      "rly\n",
      "rlz\n",
      "rma\n",
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      "rme\n",
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      "rmg\n",
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      "rmo\n",
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      "rms\n",
      "rmt\n",
      "rmu\n",
      "rmv\n",
      "rmw\n",
      "rmx\n",
      "rmy\n",
      "rmz\n",
      "rna\n",
      "rnb\n",
      "rnc\n",
      "rnd\n",
      "rne\n",
      "rnf\n",
      "rng\n",
      "rnh\n",
      "rni\n",
      "rnj\n",
      "rnk\n",
      "rnl\n",
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      "rnn\n",
      "rno\n",
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      "rnu\n",
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      "rny\n",
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      "roe\n",
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      "rog\n",
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      "rol\n",
      "rom\n",
      "ron\n",
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      "rop\n",
      "roq\n",
      "ror\n",
      "ros\n",
      "rot\n",
      "rou\n",
      "rov\n",
      "row\n",
      "rox\n",
      "roy\n",
      "roz\n",
      "rpa\n",
      "rpb\n",
      "rpc\n",
      "rpd\n",
      "rpe\n",
      "rpf\n",
      "rpg\n",
      "rph\n",
      "rpi\n",
      "rpj\n",
      "rpk\n",
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      "rpm\n",
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      "rpp\n",
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      "rpv\n",
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      "rpx\n",
      "rpy\n",
      "rpz\n",
      "rqa\n",
      "rqb\n",
      "rqc\n",
      "rqd\n",
      "rqe\n",
      "rqf\n",
      "rqg\n",
      "rqh\n",
      "rqi\n",
      "rqj\n",
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      "rql\n",
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      "rqo\n",
      "rqp\n",
      "rqq\n",
      "rqr\n",
      "rqs\n",
      "rqt\n",
      "rqu\n",
      "rqv\n",
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      "rra\n",
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      "rru\n",
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      "rsa\n",
      "rsb\n",
      "rsc\n",
      "rsd\n",
      "rse\n",
      "rsf\n",
      "rsg\n",
      "rsh\n",
      "rsi\n",
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      "rsn\n",
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      "rsp\n",
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      "rss\n",
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      "rte\n",
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      "rth\n",
      "rti\n",
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      "rtl\n",
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      "rtn\n",
      "rto\n",
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      "rtq\n",
      "rtr\n",
      "rts\n",
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      "rtu\n",
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      "rtz\n",
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      "rue\n",
      "ruf\n",
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      "rus\n",
      "rut\n",
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      "ruv\n",
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      "ruz\n",
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      "rvm\n",
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      "rvo\n",
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      "rvs\n",
      "rvt\n",
      "rvu\n",
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      "rvy\n",
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      "rwd\n",
      "rwe\n",
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      "rwt\n",
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      "rxa\n",
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      "rxd\n",
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      "rzc\n",
      "rzd\n",
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      "rzh\n",
      "rzi\n",
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      "rzo\n",
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      "rzt\n",
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      "rzy\n",
      "rzz\n",
      "saa\n",
      "sab\n",
      "sac\n",
      "sad\n",
      "sae\n",
      "saf\n",
      "sag\n",
      "sah\n",
      "sai\n",
      "saj\n",
      "sak\n",
      "sal\n",
      "sam\n",
      "san\n",
      "sao\n",
      "sap\n",
      "saq\n",
      "sar\n",
      "sas\n",
      "sat\n",
      "sau\n",
      "sav\n",
      "saw\n",
      "sax\n",
      "say\n",
      "saz\n",
      "sba\n",
      "sbb\n",
      "sbc\n",
      "sbd\n",
      "sbe\n",
      "sbf\n",
      "sbg\n",
      "sbh\n",
      "sbi\n",
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      "sbk\n",
      "sbl\n",
      "sbm\n",
      "sbn\n",
      "sbo\n",
      "sbp\n",
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      "sbs\n",
      "sbt\n",
      "sbu\n",
      "sbv\n",
      "sbw\n",
      "sbx\n",
      "sby\n",
      "sbz\n",
      "sca\n",
      "scb\n",
      "scc\n",
      "scd\n",
      "sce\n",
      "scf\n",
      "scg\n",
      "sch\n",
      "sci\n",
      "scj\n",
      "sck\n",
      "scl\n",
      "scm\n",
      "scn\n",
      "sco\n",
      "scp\n",
      "scq\n",
      "scr\n",
      "scs\n",
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      "scv\n",
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      "scy\n",
      "scz\n",
      "sda\n",
      "sdb\n",
      "sdc\n",
      "sdd\n",
      "sde\n",
      "sdf\n",
      "sdg\n",
      "sdh\n",
      "sdi\n",
      "sdj\n",
      "sdk\n",
      "sdl\n",
      "sdm\n",
      "sdn\n",
      "sdo\n",
      "sdp\n",
      "sdq\n",
      "sdr\n",
      "sds\n",
      "sdt\n",
      "sdu\n",
      "sdv\n",
      "sdw\n",
      "sdx\n",
      "sdy\n",
      "sdz\n",
      "sea\n",
      "seb\n",
      "sec\n",
      "sed\n",
      "see\n",
      "sef\n",
      "seg\n",
      "seh\n",
      "sei\n",
      "sej\n",
      "sek\n",
      "sel\n",
      "sem\n",
      "sen\n",
      "seo\n",
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      "seq\n",
      "ser\n",
      "ses\n",
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      "seu\n",
      "sev\n",
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      "sex\n",
      "sey\n",
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      "sfa\n",
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      "sga\n",
      "sgb\n",
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      "sgg\n",
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      "sgo\n",
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      "sha\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teo\n",
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      "ter\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "\n",
    "dict={}\n",
    "index=0\n",
    "for i in range(26):\n",
    "    for j in range(26):\n",
    "        for k in range(26):\n",
    "            dict[index]=chr(i+97)+chr(j+97)+chr(k+97)\n",
    "            index+=1\n",
    "    \n",
    "for key,item in dict.items():\n",
    "    print(item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "from functools import reduce\n",
    "result=reduce((lambda a,b: a+b),[1,2,3,4])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[128, 32, 10, 1]\n",
      "128 32 32800\n",
      "32800 10 8396810\n",
      "8396810 1 2149583361\n",
      "2149583361\n"
     ]
    }
   ],
   "source": [
    "string=\"128.32.10.1\"\n",
    "ip=[int(x) for x in string.split(\".\")]\n",
    "print(ip)\n",
    "def to_32(x,y):\n",
    "    print(x,y,int(x) * 256 +int(y))\n",
    "    return int(x) * 256 +int(y)\n",
    "\n",
    "result=reduce(\n",
    "    (lambda octet, pwr: to_32(octet,pwr)),ip)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('America', 25, 'm', 'IT')\n",
      "('America', 22, 'm', 'IT')\n",
      "('Japan', 24, 'f', 'IT')\n",
      "('Japan', 23, 'f', 'IT')\n",
      "[entry(country='America', age=25, sex='m', job='IT', count=10000), entry(country='America', age=22, sex='m', job='IT', count=8999), entry(country='Japan', age=24, sex='f', job='IT', count=9999), entry(country='Japan', age=23, sex='f', job='IT', count=9000)]\n",
      "[entry(country='America', age=25, sex='m', job='IT', count=10000), entry(country='America', age=22, sex='m', job='IT', count=8999)]\n"
     ]
    }
   ],
   "source": [
    " dt = {\n",
    "    ('America', 25, 'm', 'IT'): 10000,\n",
    "     ('America', 22, 'm', 'IT'): 8999,\n",
    "     ('Japan',   24, 'f', 'IT'): 9999,\n",
    "     ('Japan',   23, 'f', 'IT'): 9000\n",
    " }\n",
    "    \n",
    "from collections import namedtuple\n",
    "Entry = namedtuple('entry', ('country', 'age', 'sex', 'job', 'count'))\n",
    "\n",
    "for k in dt:\n",
    "    print(k)\n",
    "nt = [Entry(*list(k) + [dt[k]]) for k in dt]\n",
    "print(nt)\n",
    "results = [i for i in nt if (i.country=='America' and i.sex=='m' and i.job=='IT')]\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Nick', 30, 'Doctor'], ['John', 8, 'Student'], ['Paul', 22, 'Car Dealer'], ['Mark', 66, 'Retired']]\n",
      "[['John', 8, 'Student'], ['Paul', 22, 'Car Dealer'], ['Nick', 30, 'Doctor'], ['Mark', 66, 'Retired']]\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "a = []\n",
    "\n",
    "# create the table (name, age, job)\n",
    "a.append([\"Nick\", 30, \"Doctor\"])\n",
    "a.append([\"John\",  8, \"Student\"])\n",
    "a.append([\"Paul\", 22, \"Car Dealer\"])\n",
    "a.append([\"Mark\", 66, \"Retired\"])  \n",
    "\n",
    "print(a)\n",
    "a.sort(key=operator.itemgetter(1))    \n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find factors for a number\n",
      "[(1, 60), (2, 30), (3, 20), (4, 15), (5, 12), (6, 10)]\n",
      "[1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60]\n"
     ]
    }
   ],
   "source": [
    "print(\"find factors for a number\")\n",
    "#number=2234678364\n",
    "number=60\n",
    "tuple_list=[]\n",
    "for i in np.arange(1,number):\n",
    "    if number%i==0:\n",
    "        other=int(number/i)\n",
    "        if any([(x,v) for (x,v) in tuple_list if (i==x) or (i==v)])==True:\n",
    "            break\n",
    "        tuple_list.append((i,other))\n",
    "\n",
    "print(tuple_list)\n",
    "flattened = [item for sublist in tuple_list for item in sublist]              \n",
    "print(sorted(flattened))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Dt1        Dt2\n",
      "0 2010-01-01        NaT\n",
      "1 2010-01-06 2010-01-05\n",
      "2 2010-01-11 2010-01-08\n",
      "3 2010-01-16        NaT\n",
      "4 2010-01-21 2010-01-14\n",
      "5 2010-01-26 2010-01-17\n",
      "6 2010-01-31        NaT\n",
      "7 2010-02-05 2010-01-23\n",
      "8 2010-02-10 2010-01-26\n",
      "9 2010-02-15        NaT\n",
      "0:00:00 10 days, 0:00:00\n",
      "         Dt1        Dt2   Diff\n",
      "1 2010-01-06 2010-01-05 1 days\n",
      "2 2010-01-11 2010-01-08 3 days\n",
      "4 2010-01-21 2010-01-14 7 days\n",
      "5 2010-01-26 2010-01-17 9 days\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Dt1':pd.date_range('2010-1-1', freq='5D', periods=10),\n",
    "                   'Dt2':pd.date_range('2010-1-2', freq='3D', periods=10)})\n",
    "df.iloc[::3, 1] = np.nan\n",
    "\n",
    "print(df)\n",
    "zero_days=timedelta(days=0)\n",
    "ten_days=timedelta(days=10)\n",
    "print(zero_days,ten_days)\n",
    "\n",
    "df['Diff']=np.empty(len(df))\n",
    "df=df.assign(Diff=lambda row:  row['Dt1']-row['Dt2'])\n",
    "mask=(df['Diff'] >=zero_days)&(df['Diff'] <=ten_days)  \n",
    "print(df[mask])\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run fast into a bush run fast into a tree run slow run fast\n",
      "['run', 'fast', 'into', 'a', 'bush', 'run', 'fast', 'into', 'a', 'tree', 'run', 'slow', 'run', 'fast']\n",
      "[[0 0 1 0 0]\n",
      " [0 1 0 0 0]\n",
      " [0 0 0 0 0]\n",
      " [0 0 0 0 0]\n",
      " [1 0 0 0 0]\n",
      " [0 0 1 0 0]\n",
      " [0 1 0 0 0]\n",
      " [0 0 0 0 0]\n",
      " [0 0 0 0 0]\n",
      " [0 0 0 0 1]\n",
      " [0 0 1 0 0]\n",
      " [0 0 0 1 0]\n",
      " [0 0 1 0 0]\n",
      " [0 1 0 0 0]]\n",
      "    bush  fast  run  slow  tree\n",
      "0      0     0    1     0     0\n",
      "1      0     1    0     0     0\n",
      "2      0     0    0     0     0\n",
      "3      0     0    0     0     0\n",
      "4      1     0    0     0     0\n",
      "5      0     0    1     0     0\n",
      "6      0     1    0     0     0\n",
      "7      0     0    0     0     0\n",
      "8      0     0    0     0     0\n",
      "9      0     0    0     0     1\n",
      "10     0     0    1     0     0\n",
      "11     0     0    0     1     0\n",
      "12     0     0    1     0     0\n",
      "13     0     1    0     0     0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import nltk\n",
    "\n",
    "docs = ['run fast into a bush','run fast into a tree','run slow','run fast']\n",
    "str_buffer=\" \".join(docs)\n",
    "print(str_buffer)\n",
    "corpus=nltk.word_tokenize(str_buffer)\n",
    "print(corpus)\n",
    "#print(nltk.sent_tokenize(docs))\n",
    "\n",
    "vectorizer_ng2=CountVectorizer(ngram_range=range(1,3),stop_words='english')\n",
    "bow_matrix=vectorizer_ng2.fit_transform(corpus)\n",
    "\n",
    "print(bow_matrix.toarray())\n",
    "bow_df = pd.DataFrame(bow_matrix.toarray())\n",
    "bow_df.columns = vectorizer_ng2.get_feature_names()\n",
    "print(bow_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 6]\n",
      "[[1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6]]\n"
     ]
    }
   ],
   "source": [
    "data=[[1,2,3,4],[2,3,4,5], [3,4,5,6]]\n",
    "data = [item for sublist in data for item in sublist]\n",
    "\n",
    "print(data)\n",
    "\n",
    "#print(data.sort(key=operator.itemgetter(1)))\n",
    "from operator import itemgetter\n",
    "index=[slice(0,4),slice(4,8),slice(8,13)]\n",
    "result=[]\n",
    "for idx_slice in index:\n",
    "    f = itemgetter(idx_slice)\n",
    "    col1=f(data)\n",
    "    result.append(col1)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "B\n",
      "C\n"
     ]
    }
   ],
   "source": [
    "class Node:\n",
    "    def __init__(self, dataval=None):\n",
    "        self.dataval = dataval\n",
    "        self.nextval = None\n",
    "\n",
    "class LinkedList:\n",
    "    def __init__(self):\n",
    "        self.headval = None\n",
    "\n",
    "    def listprint(self):\n",
    "        printval = self.headval\n",
    "        while printval is not None:\n",
    "            print (printval.dataval)\n",
    "            printval = printval.nextval        \n",
    "        \n",
    "list1 = LinkedList()\n",
    "list1.headval = Node(\"A\")\n",
    "e2 = Node(\"B\")\n",
    "e3 = Node(\"C\")        \n",
    "list1.headval.nextval = e2\n",
    "e2.nextval=e3\n",
    "list1.listprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: skimage.measure.label's indexing starts from 0. In future version it will start from 1. To disable this warning, explicitely set the `start_label` parameter to 1.\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: The new recommended value for bg_label is 0. Until version 0.19, the default bg_label value is -1. From version 0.19, the bg_label default value will be 0. To avoid this warning, please explicitly set bg_label value.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dice_image=plt.imread('femur.jpg')\n",
    "plt.imshow(dice_image)\n",
    "\n",
    "segments= slic(dice_image,n_segments=10)\n",
    "\n",
    "#segmented_image=label2rgb(segments,coins_image,kind='avg')\n",
    "segmented_image=label2rgb(segments,dice_image)\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(6,4))\n",
    "ax.imshow(segmented_image)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.   0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1  0.11 0.12 0.13\n",
      " 0.14 0.15 0.16 0.17 0.18 0.19 0.2  0.21 0.22 0.23 0.24 0.25 0.26 0.27\n",
      " 0.28 0.29 0.3  0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4  0.41\n",
      " 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5  0.51 0.52 0.53 0.54 0.55\n",
      " 0.56 0.57 0.58 0.59 0.6  0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69\n",
      " 0.7  0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8  0.81 0.82 0.83\n",
      " 0.84 0.85 0.86 0.87 0.88 0.89 0.9  0.91 0.92 0.93 0.94 0.95 0.96 0.97\n",
      " 0.98 0.99 1.   1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=np.arange(0,110,1)\n",
    "Y=np.arange(0,1.1,.01)\n",
    "print(Y)\n",
    "plt.scatter(X,Y)\n",
    "\n",
    "Cosine = np.cos(X) + 0.5\n",
    "plt.plot(X, Cosine)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2bob receive value 1 result=2\n",
      "1dan receive value 1 result=1\n",
      "2bob receive value 3 result=6\n",
      "1dan receive value 3 result=9\n",
      "2bob receive value 5 result=10\n",
      "1dan receive value 5 result=25\n",
      "2bob receive value 7 result=14\n",
      "1dan receive value 7 result=49\n",
      "2bob receive value 9 result=18\n",
      "1dan receive value 9 result=81\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import asyncio\n",
    "\n",
    "class SubscriberDan:\n",
    "    name=\"\"\n",
    "    def __init__(self,name):\n",
    "        self.name=name\n",
    "        \n",
    "    async def notificationMethod(self,value):\n",
    "        print(\"1{} receive value {} result={}\".format(self.name,value, self.do_something(value)))\n",
    "        \n",
    "    def do_something(self, value):\n",
    "        time.sleep(1)\n",
    "        return (value*value)\n",
    "\n",
    "class SubscriberBob:\n",
    "    name=\"\"\n",
    "    def __init__(self,name):\n",
    "        self.name=name\n",
    "        \n",
    "    async def notificationMethod(self,value):\n",
    "        print(\"2{} receive value {} result={}\".format(self.name, value, self.do_somethingbob(value)))\n",
    "        \n",
    "    def do_somethingbob(self,value):\n",
    "        time.sleep(2)\n",
    "        return (value+value)        \n",
    "    \n",
    "class MainWorker:\n",
    "    \n",
    "    subscribers=[]\n",
    "    def addSubscriber(self,subscriber):\n",
    "        self.subscribers.append(subscriber)\n",
    "        \n",
    "    def do_somework(self):\n",
    "        i=0\n",
    "        while True:\n",
    "            print(i)\n",
    "            i=i+1\n",
    "            if i%2==True:\n",
    "                self.changeOccurred(self,i)\n",
    "            if i==10:\n",
    "                break\n",
    "                \n",
    "    def changeOccurred(self,value):\n",
    "        for subscriber in self.subscribers:\n",
    "            #asyncio.ensure_future(subscriber.notificationMethod(value))\n",
    "            asyncio.Task(subscriber.notificationMethod(value))\n",
    "            \n",
    "main=MainWorker\n",
    "subscriberbob= SubscriberBob(\"bob\")\n",
    "subscriberdan= SubscriberDan(\"dan\")\n",
    "\n",
    "main.addSubscriber(main,subscriberbob)\n",
    "main.addSubscriber(main,subscriberdan)\n",
    "main.do_somework(main)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index     Col1     Col2     Col3\n",
      "0      0  value11  value21  value31\n",
      "1      1  value12  value22  value32\n",
      "2      2  value13  value23  value33\n",
      "       Col1     Col2     Col3\n",
      "0   value11  value21  value31\n",
      "1   value11  value21  value32\n",
      "2   value11  value21  value33\n",
      "3   value11  value22  value31\n",
      "4   value11  value22  value32\n",
      "5   value11  value22  value33\n",
      "6   value11  value23  value31\n",
      "7   value11  value23  value32\n",
      "8   value11  value23  value33\n",
      "9   value12  value21  value31\n",
      "10  value12  value21  value32\n",
      "11  value12  value21  value33\n",
      "12  value12  value22  value31\n",
      "13  value12  value22  value32\n",
      "14  value12  value22  value33\n",
      "15  value12  value23  value31\n",
      "16  value12  value23  value32\n",
      "17  value12  value23  value33\n",
      "18  value13  value21  value31\n",
      "19  value13  value21  value32\n",
      "20  value13  value21  value33\n",
      "21  value13  value22  value31\n",
      "22  value13  value22  value32\n",
      "23  value13  value22  value33\n",
      "24  value13  value23  value31\n",
      "25  value13  value23  value32\n",
      "26  value13  value23  value33\n"
     ]
    }
   ],
   "source": [
    "data={'Col1': ['value11', 'value12', 'value13'],\n",
    "'Col2': ['value21', 'value22','value23'] ,\n",
    "'Col3': ['value31', 'value32', 'value33']\n",
    "}\n",
    "df=pd.DataFrame(data)\n",
    "df.reset_index(inplace=True)\n",
    "print(df)\n",
    "\n",
    "col1=pd.DataFrame(df['Col1'])\n",
    "col2=pd.DataFrame(df['Col2'])\n",
    "col3=pd.DataFrame(df['Col3'])\n",
    "\n",
    "col1['key'] = 1\n",
    "col2['key'] = 1\n",
    "col3['key'] = 1\n",
    "\n",
    "col1=pd.merge(col1, col2, on ='key')\n",
    "col1=pd.merge(col1, col3, on ='key').drop(\"key\", 1)\n",
    "\n",
    "#col1.to_frame().merge(col2.to_frame(), how='cross',left_index=True, right_index=True)\n",
    "#col1.merge(col3, how='cross',left_index=True, right_index=True)\n",
    "\n",
    "print(col1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'home': 'mary', 'cowname': 'betsy', 'date': '10-jan-2013', 'theSentence': 'There was a cow at home.'}\n",
      "{'theSentence': 'There was a cow at home.', ' home': 'mary', ' cowname': 'betsy', ' date': '10-jan-2013'}\n"
     ]
    }
   ],
   "source": [
    "s = \"There was a cow at home. home=mary cowname=betsy date=10-jan-2013\"\n",
    "\n",
    "d = {}\n",
    "\n",
    "def add(m):\n",
    "    d[m.group(1)] = m.group(2)\n",
    "\n",
    "s = re.sub(r'(\\w+)=(\\S+)', add, s)\n",
    "d['theSentence'] = s.strip()\n",
    "\n",
    "print(d)\n",
    "\n",
    "#\\S one character that is not whitespace\n",
    "\n",
    "#patternRegex = re.compile(r'[\\w\\s]+\\.{1}')\n",
    "#patternRegex = re.compile(r'[\\w\\s]+\\.{1}')\n",
    "#patternRegex = re.compile(r'([\\w+\\s]+\\.{1})|((\\s+\\w+)=(\\S+))')\n",
    "#all_matches = patternRegex.finditer(s)\n",
    "s = \"There was a cow at home. home=mary cowname=betsy date=10-jan-2013\"\n",
    "all_matches = re.findall(r'([\\w+\\s]+\\.{1})|((\\s+\\w+)=(\\S+))',s)\n",
    "d={}\n",
    "for i in np.arange(len(all_matches)):\n",
    "    #print(all_matches[i])\n",
    "    if all_matches[i][0] != \"\":\n",
    "        d[\"theSentence\"]=all_matches[i][0]\n",
    "    else:\n",
    "        d[all_matches[i][2]]=all_matches[i][3]\n",
    "       \n",
    "print(d)\n",
    "#print(all_matches.group(4))\n",
    "#for match in all_matches:\n",
    "#    print(match)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTCGCGGACGGTTCATCAGCCCTAGCCGGTTAAGAACTATCGAGCCACCCTAAGAACGGTCCATATTTGGAGTGTTACAACTTTGGATCTTCTACGTTGC\n"
     ]
    }
   ],
   "source": [
    "dna = ['A','G','C','T']\n",
    "def generateDNA(N):\n",
    "    result= [random.choice(dna) for i in np.arange(N)]\n",
    "    return(\"\".join(result))\n",
    "\n",
    "print(generateDNA(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested_list = [[['list 1'],['list 5']],['list2'],['list3'],['list4'],{'hello':'world'}]\n",
    "\n",
    "def countLists(nested_list):\n",
    "    #print(nested_list)\n",
    "    if len(nested_list)==1 & isinstance(nested_list,list):\n",
    "        #print(\"return\")\n",
    "        return 1\n",
    "    \n",
    "    count=0\n",
    "    for alist in nested_list:\n",
    "            if isinstance(alist, list):\n",
    "                retCount=countLists(alist)\n",
    "                count+=retCount\n",
    "                #print(alist)\n",
    "    return count\n",
    "            \n",
    "countLists(nested_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I swear', 'I googled', 'first']\n"
     ]
    }
   ],
   "source": [
    "my_list = ['I', 'swear', 'I', 'googled', 'first']\n",
    "\n",
    "def two_words(my_list):\n",
    "    i=0\n",
    "    words=[]\n",
    "    for word in my_list:\n",
    "        \n",
    "        words.append(word)\n",
    "        if i%2:\n",
    "            #print(word)\n",
    "            yield (\" \".join(words))\n",
    "            words.clear()\n",
    "            # Yield the line of data\n",
    "        i+=1\n",
    "    if len(words)>0:\n",
    "        yield (\" \".join(words))\n",
    "        \n",
    "print(list(two_words(my_list)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             city  temperature    measure   counts\n",
      "0        Houston          69.7  degrees F  2144491\n",
      "2          Miami          77.3  degrees F   404048\n",
      "1        Hialeah          77.9  degrees F   217141\n",
      "4  North Westside         77.3  degrees F   101285\n",
      "3      Carol City         77.3  degrees F    59443\n"
     ]
    }
   ],
   "source": [
    "data=[\"Houston 69.7 degrees F 2,144,491\",\n",
    "\"Hialeah 77.9 degrees F 217,141\",\n",
    "\"Miami 77.3 degrees F 404,048\",\n",
    "\"Carol City 77.3 degrees F 59,443\",\n",
    "\"North Westside 77.3 degrees F 101,285\"]\n",
    "\n",
    "city=[]\n",
    "temperature=[]\n",
    "measure=[]\n",
    "counts=[]\n",
    "\n",
    "for s in data:\n",
    "    #print(s)\n",
    "    all_matches = re.findall(r'([a-zA-Z]+\\s*[a-zA-Z]*)|([0-9.,]+)',s)\n",
    "    city.append(all_matches[0][0])\n",
    "    temperature.append(all_matches[1][1])\n",
    "    measure.append(all_matches[2][0])\n",
    "    counts.append(all_matches[3][1])\n",
    "    \n",
    "\n",
    "    \n",
    "df=pd.DataFrame({'city':city,'temperature':temperature,'measure':measure,'counts':counts})\n",
    "df['temperature']=df['temperature'].astype('float')\n",
    "df['counts']=df['counts'].apply(lambda row: int(row.replace(',','')))\n",
    "print(df.sort_values(by='counts', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ['\"16','bob','2440', '-2', '34\"']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous Node is None\n",
      "CurrentNode one\n",
      "\n",
      "\n",
      "Previous Node one\n",
      "CurrentNode two\n",
      "\n",
      "\n",
      "Previous Node two\n",
      "CurrentNode three\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class Node:\n",
    "    def __init__(self, dataval=None):\n",
    "        self.dataval = dataval\n",
    "        self.nextval = None\n",
    "        self.previousval=None\n",
    "\n",
    "class LinkedList:\n",
    "    def __init__(self):\n",
    "        self.headval = None\n",
    "\n",
    "    def listprint(self):\n",
    "        node = self.headval\n",
    "        while node is not None:\n",
    "            if node.previousval!=None:\n",
    "                print(\"Previous Node\",node.previousval.dataval)\n",
    "            else:\n",
    "                print(\"Previous Node is None\")\n",
    "            print(\"CurrentNode\", node.dataval)\n",
    "            print(\"\\n\")\n",
    "            node = node.nextval        \n",
    "        \n",
    "        \n",
    "list1 = LinkedList()\n",
    "some_list = [\"one\", \"two\", \"three\"]\n",
    "for item in some_list:\n",
    "    if list1.headval==None:\n",
    "        currentNode=Node(item)\n",
    "        list1.headval = currentNode\n",
    "        currentNode.previousval=None\n",
    "        previousNode=currentNode\n",
    "    else:\n",
    "        currentNode=Node(item)\n",
    "        currentNode.previousval=previousNode\n",
    "        previousNode.nextval=currentNode\n",
    "        previousNode=currentNode\n",
    "        \n",
    "list1.listprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'day_name': 'Wednesday', 'day': 28, 'month': 7, 'year': 2021}, {'day_name': 'Thursday', 'day': 29, 'month': 7, 'year': 2021}, {'day_name': 'Friday', 'day': 30, 'month': 7, 'year': 2021}, {'day_name': 'Saturday', 'day': 31, 'month': 7, 'year': 2021}, {'day_name': 'Sunday', 'day': 1, 'month': 8, 'year': 2021}, {'day_name': 'Monday', 'day': 2, 'month': 8, 'year': 2021}, {'day_name': 'Tuesday', 'day': 3, 'month': 8, 'year': 2021}, {'day_name': 'Wednesday', 'day': 4, 'month': 8, 'year': 2021}, {'day_name': 'Thursday', 'day': 5, 'month': 8, 'year': 2021}, {'day_name': 'Friday', 'day': 6, 'month': 8, 'year': 2021}, {'day_name': 'Saturday', 'day': 7, 'month': 8, 'year': 2021}, {'day_name': 'Sunday', 'day': 8, 'month': 8, 'year': 2021}, {'day_name': 'Monday', 'day': 9, 'month': 8, 'year': 2021}, {'day_name': 'Tuesday', 'day': 10, 'month': 8, 'year': 2021}]\n"
     ]
    }
   ],
   "source": [
    "import calendar \n",
    "from datetime import datetime  \n",
    "from datetime import timedelta  \n",
    "\n",
    "def generateDays(start_date,weeks):\n",
    "    days=7*weeks\n",
    "    week = []\n",
    "    for day in np.arange(days):\n",
    "        a_date = pd.to_datetime(start_date + timedelta(days=int(day)))\n",
    "        day_num  = calendar.weekday(a_date.year,a_date.month,a_date.day)\n",
    "        day_name = calendar.day_name[day_num]\n",
    "        daydate = { \"day_name\":day_name,\n",
    "                \"day\":a_date.day,\n",
    "                \"month\":a_date.month,\n",
    "                \"year\":a_date.year,\n",
    "               }\n",
    "        week.append(daydate)\n",
    "    return week    \n",
    "\n",
    "\n",
    "print(generateDays(date.today(),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hello my', 'my name', 'name is', 'is bob']\n"
     ]
    }
   ],
   "source": [
    "tuples = [('hello', 'my'), ('my', 'name'), ('name', 'is'), ('is', 'bob')]\n",
    "result=[]\n",
    "[result.append(k+\" \"+v) for k,v in tuples]\n",
    "    \n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index  a  b\n",
      "0      0  1  1\n",
      "1      1  2  2\n",
      "2      2  3  7\n",
      "3      3  4  4\n",
      "4      4  5  5\n",
      "5      5  5  5\n",
      "6      6  6  9\n",
      "7      7  7  7\n",
      "8      8  8  8\n",
      "9      9  1  0\n",
      "   variable  value\n",
      "0     index      0\n",
      "1     index      1\n",
      "2     index      2\n",
      "3     index      3\n",
      "4     index      4\n",
      "5     index      5\n",
      "6     index      6\n",
      "7     index      7\n",
      "8     index      8\n",
      "9     index      9\n",
      "10        a      1\n",
      "11        a      2\n",
      "12        a      3\n",
      "13        a      4\n",
      "14        a      5\n",
      "15        a      5\n",
      "16        a      6\n",
      "17        a      7\n",
      "18        a      8\n",
      "19        a      1\n",
      "20        b      1\n",
      "21        b      2\n",
      "22        b      7\n",
      "23        b      4\n",
      "24        b      5\n",
      "25        b      5\n",
      "26        b      9\n",
      "27        b      7\n",
      "28        b      8\n",
      "29        b      0\n",
      "          mean  count  sum\n",
      "variable                  \n",
      "a          4.2     10   42\n",
      "b          4.8     10   48\n",
      "index      4.5     10   45\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame({\"a\":[1,2,3,4,5,5,6,7,8,1],\"b\":[1,2,7,4,5,5,9,7,8,0]})\n",
    "df.reset_index(inplace=True)\n",
    "print(df)\n",
    "df=df.melt()\n",
    "print(df)\n",
    "grouped=df.groupby(['variable'])['value'].agg({'count','sum','mean'})\n",
    "print(grouped)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('the well', 'the', 'well')\n",
      "('she fell', 'she', 'fell')\n",
      "('the shelves', 'the', 'shelves')\n",
      "('she fell', 'she', 'fell')\n",
      "['thewell', 'shefell', 'theshelves', 'shefell']\n"
     ]
    }
   ],
   "source": [
    "paragraph=\"\"\"either the well was very deep, or she fell very slowly, for she had\n",
    "plenty of time as she went down to look about her and to wonder what was\n",
    "going to happen next. first, she tried to look down and make out what\n",
    "she was coming to, but it was too dark to see anything; then she\n",
    "looked at the sides of the well, and noticed that they were filled with\n",
    "cupboards and book-shelves; here and there she saw maps and pictures\n",
    "hung upon pegs. she took down a jar from one of the shelves as\n",
    "she passed; it was labelled 'orange marmalade', but to her great\n",
    "disappointment it was empty: she did not like to drop the jar for fear\n",
    "of killing somebody, so managed to put it into one of the cupboards as\n",
    "she fell past it.\"\"\"\n",
    "\n",
    "sentences=paragraph.split(\".\")\n",
    "\n",
    "pattern=\"\\w+\\s+((\\whe)\\s+(\\w+el\\w+)){1}\\s+\\w+\"\n",
    "temp=[]\n",
    "for sentence in sentences:\n",
    "    result=re.findall(pattern,sentence)\n",
    "    for item in result:\n",
    "        print(item)\n",
    "        temp.append(\"\".join(item[0]).replace(' ',''))\n",
    "print(temp)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['robotic@934']\n"
     ]
    }
   ],
   "source": [
    "whitelist = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','1','2','3','4','5','6','7','8','9','0']\n",
    "\n",
    "pattern= r\"^[a-z,A-Z,0-9\\s]+$\"\n",
    "\n",
    "myval=\"robotic@934\"\n",
    "\n",
    "#cons,args=re.search(pattern,input).groups()\n",
    "all_matches=re.findall(pattern, myval)\n",
    "print(all_matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              id\n",
      "domain          \n",
      "facebook.com   1\n",
      "google.com     1\n",
      "twitter.com    2\n",
      "vk.com         3\n"
     ]
    }
   ],
   "source": [
    "ID=[ \n",
    "123, \n",
    "123, \n",
    "123, \n",
    "456, \n",
    "456, \n",
    "456, \n",
    "456, \n",
    "789, \n",
    "789] \n",
    "\n",
    "domain=['vk.com',\n",
    " 'vk.com',\n",
    " 'twitter.com',\n",
    " 'vk.com',\n",
    " 'facebook.com',\n",
    " 'vk.com',\n",
    " 'google.com',\n",
    " 'twitter.com',\n",
    " 'vk.com'\n",
    "]\n",
    "df=pd.DataFrame({'id':ID,'domain':domain})\n",
    "fp=pd.pivot_table(data=df,index='domain',aggfunc=pd.Series.nunique)\n",
    "print(fp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    f\n",
      "1    f\n",
      "2    f\n",
      "Name: data, dtype: object\n",
      "0    fl\n",
      "1    fl\n",
      "2    fl\n",
      "Name: data, dtype: object\n",
      "0    flo\n",
      "1    flo\n",
      "2    fli\n",
      "Name: data, dtype: object\n",
      "fl\n"
     ]
    }
   ],
   "source": [
    "data=['flower','flow','flight']\n",
    "min_length=min([len(x) for x in data])\n",
    "df=pd.DataFrame(data,columns=['data'])    \n",
    "\n",
    "results=[]    \n",
    "for i in range(1,min_length):\n",
    "    alist=[]\n",
    "    alist=(df.iloc[0:min_length]['data'].str[0:i])\n",
    "    print(alist)\n",
    "    \n",
    "    if len(set(alist))==1:\n",
    "           results.append(set(alist))\n",
    "print(max([x.pop() for x in results],key=len))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Num        Date  Occurence\n",
      "ID  Step                            \n",
      "4   A      38  2018-08-02          3\n",
      "    Bar    38  2018-12-02          3\n",
      "    F      38  2019-03-02          3\n",
      "88  F     320  2017-03-02          2\n",
      "    Bar   320  2018-03-02          2\n",
      "323 F     433  2020-03-04          2\n",
      "    A     432  2020-02-03          2\n",
      "If 'Num' is different for the same 'ID', then delete the rows of this ID.\n",
      "         Num        Date  Occurence\n",
      "ID Step                            \n",
      "4  A      38  2018-08-02          3\n",
      "   Bar    38  2018-12-02          3\n",
      "   F      38  2019-03-02          3\n",
      "88 F     320  2017-03-02          2\n",
      "   Bar   320  2018-03-02          2\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame({'ID': {0: 4, 1: 4, 2: 4, 3: 88, 4: 88, 5: 323, 6: 323},\n",
    "         'Step': {0: 'A', 1: 'Bar', 2: 'F', 3: 'F', 4: 'Bar', 5: 'F', 6: 'A'},\n",
    "         'Num': {0: 38, 1: 38, 2: 38, 3: 320, 4: 320, 5: 433, 6: 432},\n",
    "         'Date': {0: '2018-08-02',\n",
    "          1: '2018-12-02',\n",
    "          2: '2019-03-02',\n",
    "          3: '2017-03-02',\n",
    "          4: '2018-03-02',\n",
    "          5: '2020-03-04',\n",
    "          6: '2020-02-03'},\n",
    "         'Occurence': {0: 3, 1: 3, 2: 3, 3: 2, 4: 2, 5: 2, 6: 2}})\n",
    "df.set_index(['ID','Step'],inplace=True)\n",
    "print(df)\n",
    "print(\"If 'Num' is different for the same 'ID', then delete the rows of this ID.\")\n",
    "\n",
    "#exclude id with single occurrences\n",
    "grouped=df.groupby([df.index.get_level_values(0)]).size().eq(1)\n",
    "labels=set([x for x,y in (grouped[grouped.values==True].index)])\n",
    "\n",
    "filter=[x for x in df.index.get_level_values(0) if x not in labels]\n",
    "\n",
    "# remove ID where Num is unique\n",
    "grouped=df[df.index.get_level_values(0).isin(filter)].groupby([df.index.get_level_values(0),'Num']).size().eq(1)\n",
    "\n",
    "labels=set([x for x,y in (grouped[grouped.values==True].index)])\n",
    "if len(labels)>0:\n",
    "    df = df.drop(labels=labels, axis=0,level=0)    \n",
    "print(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What ABC is $verb$?\n",
      "What ABC is DEF?\n",
      "What ABC is DEF?\n"
     ]
    }
   ],
   "source": [
    "str = \"What $noun$ is $verb$?\"\n",
    "new_str=(re.sub('(\\$noun\\$)', 'ABC',str))\n",
    "print(new_str)\n",
    "new_str=(re.sub('(\\$verb\\$)', 'DEF', new_str))\n",
    "\n",
    "print(new_str)\n",
    "\n",
    "\n",
    "dict = {\n",
    "    \"(\\$noun\\$)\" : \"ABC\",\n",
    "    \"(\\$verb\\$)\": \"DEF\"\n",
    "  } \n",
    "new_str=str\n",
    "for key,value in dict.items():\n",
    "    new_str=(re.sub(key, value, new_str))\n",
    "print(new_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index   col1  col2\n",
      "0      0      1     3\n",
      "1      1      2     4\n",
      "2      2      3     5\n",
      "3      3      4     6\n",
      "4      4  apple     7\n",
      "5      5  apple     8\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4     True\n",
      "5     True\n",
      "Name: col1, dtype: bool\n",
      "{4, 5}\n",
      "   index col1  col2\n",
      "0      0    1     3\n",
      "1      1    2     4\n",
      "2      2    3     5\n",
      "3      3    4     6\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(data={'col1': [1,2,3,4,'apple','apple'], 'col2': [3,4,5,6,7,8]})\n",
    "df.reset_index(inplace=True)\n",
    "print(df)\n",
    "\n",
    "grouped=df.col1.str.isnumeric().eq(0)\n",
    "print(grouped)\n",
    "labels=set([x for x in (grouped[grouped.values==True].index)])\n",
    "print(labels)\n",
    "if len(labels)>0:\n",
    "    df = df.drop(labels=labels, axis=0)    \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_lists = [['d','b','s'],['e','b','f'],['s','f','l'],['b','l','t']]\n",
    "for i in range(len(list_of_lists)):\n",
    "    for j in range(i+1,len(list_of_lists)):\n",
    "        result=(list_of_lists[i]+list_of_lists[j])\n",
    "        #print(result)\n",
    "        counts=(Counter(result))\n",
    "        matches={x for k,x in counts.items() if x==2}\n",
    "        if len(matches)==0:\n",
    "            print(\"missing a match\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'Tomas', '100', '90', '80') (1, 'Marcos', '88', '99', '111')\n",
      " (2, 'Flavia', '45', '56', '67') (3, 'Ramon', '59', '61', '67')\n",
      " (4, 'Ursula', '73', '79', '83') (5, 'Federico', '89', '97', '101')]\n",
      "0 Tomas\n",
      "1 Marcos\n",
      "2 Flavia\n",
      "3 Ramon\n",
      "4 Ursula\n",
      "5 Federico\n"
     ]
    }
   ],
   "source": [
    "grades =  [\n",
    "   ['Students', 'Test 1', 'Test 2', 'Test 3'],\n",
    "   ['Tomas', '100', '90', '80'],\n",
    "   ['Marcos', '88', '99', '111'],\n",
    "   ['Flavia', '45', '56', '67'],\n",
    "   ['Ramon', '59', '61', '67'],\n",
    "   ['Ursula', '73', '79', '83'],\n",
    "   ['Federico', '89', '97', '101']\n",
    "] \n",
    "#print(grades[0])\n",
    "Columns=grades[0]\n",
    "df=pd.DataFrame(columns=Columns)\n",
    "for i in range(1, len(grades)):\n",
    "    df_length = len(df)\n",
    "    df.loc[df_length] = grades[i]\n",
    "\n",
    "\n",
    "print(df.to_records())\n",
    "\n",
    "dict=df.T.to_dict()\n",
    "for k,v in dict.items():\n",
    "    print(k,v['Students'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index 0 replace -1\n",
      "[[-1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
      "index 1 replace -2\n",
      "[[-1, -2, 3], [4, 5, 6], [7, 8, 9]]\n",
      "index 2 replace -3\n",
      "[[-1, -2, -3], [4, 5, 6], [7, 8, 9]]\n",
      "index 0 replace -4\n",
      "[[-1, -2, -3], [-4, 5, 6], [7, 8, 9]]\n",
      "index 1 replace -5\n",
      "[[-1, -2, -3], [-4, -5, 6], [7, 8, 9]]\n",
      "index 2 replace -6\n",
      "[[-1, -2, -3], [-4, -5, -6], [7, 8, 9]]\n",
      "index 0 replace -7\n",
      "[[-1, -2, -3], [-4, -5, -6], [-7, 8, 9]]\n",
      "index 1 replace -8\n",
      "[[-1, -2, -3], [-4, -5, -6], [-7, -8, 9]]\n",
      "index 2 replace -9\n",
      "[[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]\n",
      "result [[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]]\n"
     ]
    }
   ],
   "source": [
    "my_list = [[1,2,3],[4,5,6],[7,8,9]]\n",
    "dictionary = {1: -1, 2: -2, 3: -3,4:-4,5:-5,6:-6,7:-7,8:-8,9:-9}\n",
    "\n",
    "def replace_all(word_list, dictionary):\n",
    "    for i, j in dictionary.items():\n",
    "        for lst in word_list:\n",
    "            if i in set(lst): \n",
    "                print(\"index\",lst.index(i),\"replace\",j)\n",
    "                lst[lst.index(i)] = j\n",
    "        print(word_list)\n",
    "    return word_list\n",
    "        \n",
    "result=replace_all(my_list, dictionary)\n",
    "print(\"result\",result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A         B      C\n",
      "0  foo  1.624345  False\n",
      "1  bar -0.611756   True\n",
      "2  baz -0.528172  False\n",
      "3  foo -1.072969   True\n",
      "4  bar  0.865408  False\n",
      "5  baz -2.301539   True\n",
      "A\n",
      "foo    0.551377\n",
      "bar    0.253651\n",
      "baz   -2.829710\n",
      "Name: B, dtype: float64\n",
      "Index(['foo', 'bar', 'baz'], dtype='object', name='A')\n",
      "3    0.551377\n",
      "0    0.551377\n",
      "4    0.253651\n",
      "1    0.253651\n",
      "5   -2.829710\n",
      "2   -2.829710\n",
      "Name: B, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "rand = np.random.RandomState(1)\n",
    "df = pd.DataFrame({'A': ['foo', 'bar', 'baz'] * 2,\n",
    "                            'B': rand.randn(6),\n",
    "                            'C': rand.rand(6) > .5})\n",
    "print(df)\n",
    "grouped=df.groupby('A')['B'].sum().sort_values(ascending=False)\n",
    "print(grouped)\n",
    "print(grouped.index.get_level_values(0))\n",
    "\n",
    "grouped=df.groupby('A')['B'].transform('sum').sort_values(ascending=False)\n",
    "print(grouped)\n",
    "\n",
    "#sort1 = df.ix[grouped[['B']].transform(sum).sort('B').index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 'short', 'red', 9), (12, 'tall', 'blue', 1), (4, 'tall', 'blue', 13)]\n"
     ]
    }
   ],
   "source": [
    "data=[[12, 'tall', 'blue', 1],\n",
    "[2, 'short', 'red', 9],\n",
    "[4, 'tall', 'blue', 13]]\n",
    "\n",
    "data=[tuple(x) for x in data]\n",
    "result = sorted(data, key = lambda x: (x[1], x[2]))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "current_dt=datetime.now()\n",
    "feed_dt = datetime.now()-timedelta(days=3)\n",
    "from_dt=datetime(year=feed_dt.year,month=feed_dt.month,day=feed_dt.day)\n",
    "days_elapsed=(current_dt-from_dt).days\n",
    "print(True if days_elapsed <=3 else False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello, world\n"
     ]
    }
   ],
   "source": [
    "from threading import Timer\n",
    "\n",
    "def hello():\n",
    "    print (\"hello, world\")\n",
    "\n",
    "t = Timer(10.0, hello)\n",
    "t.start() # after 30 seconds, \"hello, world\" will be printedfrom threading import Timer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<textarea id=\"Foo\">Bar</textarea>\n",
      "<textarea id=\"Foo\">Bar</textarea>\n"
     ]
    }
   ],
   "source": [
    "s = '<textarea id=\"Foo\"></textarea>'\n",
    "results = re.search(r'(<textarea id=\\\"Foo\\\">)*(</textarea>)*', s)\n",
    "print (results.group(1)+'Bar'+results.group(2))\n",
    "\n",
    "output = re.sub(r'(<textarea.*>).*(</textarea>)', r'\\1Bar\\2', s)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "garp\n"
     ]
    }
   ],
   "source": [
    "string=\"foo,bar,qux,garp,wobble,thud\"\n",
    "results=re.search('(garp)',string)\n",
    "print(results.group(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "factor 12 numerator 13 denominator 15\n",
      "2.884441020371191\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "equation = \"12*math.sqrt(13)/15\"\n",
    "results=re.search(\"([0-9]*).*\\(([0-9]*)\\)\\/([0-9]*)\",equation)\n",
    "factor=results.group(1)\n",
    "numerator=results.group(2)\n",
    "denominator=results.group(3)\n",
    "print(\"factor\",factor,\"numerator\",numerator,\"denominator\",denominator)\n",
    "\n",
    "x=eval(equation)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 35, 12, 99, 53]\n",
      "Min value 7\n"
     ]
    }
   ],
   "source": [
    "from random import seed\n",
    "from random import choice\n",
    "\n",
    "seed(123)\n",
    "sequence = [i for i in range(1,101,1)]\n",
    "#print(sequence)\n",
    "mylst=[]\n",
    "for _ in range(5):\n",
    "    selection = choice(sequence)\n",
    "    mylst.append(selection)\n",
    "\n",
    "print(mylst)    \n",
    "print(\"Min value\",min(mylst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted\n",
      "Undeleted\n",
      "Read\n"
     ]
    }
   ],
   "source": [
    "phrase = \" hello world Deleted,Undeleted,Read in a sentence\"\n",
    "results=re.search(\"(Read|Deleted|Undeleted).*(Read|Deleted|Undeleted).*(Read|Deleted|Undeleted).*\", phrase).groups()\n",
    "for group in results:\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world \n"
     ]
    }
   ],
   "source": [
    "phrase = \" / hello world / \"\n",
    "\n",
    "\n",
    "results=re.search(\"\\s*\\/\\s*([a-z\\s+]*)\\s*\\/\\s*\", phrase)\n",
    "print(results.group(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['mouse'], ['I see '], ['banana'], ['cat'], ['I buy '], ['soap'], ['woman'], ['I like '], ['soap'], ['cat']]\n"
     ]
    }
   ],
   "source": [
    "name = ['I love ','I have ','I hate ','I want ','I buy ','I like ','I see ']\n",
    "second = ['banana','lemon','water','cat','soap','woman','shopping','pen','mouse']\n",
    "\n",
    "#result=list(zip(name,second))\n",
    "#print(result)\n",
    "result=[]\n",
    "#[result.append(random.sample(name,1)[0]+random.sample(second,1)[0]) for i in range(10)]\n",
    "#print(result)\n",
    "for i in range(10):\n",
    "    a_num=random.sample(range(10000),1)\n",
    "    if a_num[0]%2:\n",
    "        result.append(random.sample(name,1))\n",
    "    else:\n",
    "        result.append(random.sample(second,1))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "21\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "A = [(10, 50, 1), (20, 4, 100), (11, 21, 10), (1, 42, 60), (0, 7, 74)]\n",
    "indices = [1, 2]\n",
    "\n",
    "for i in range(3):\n",
    "    col=[]\n",
    "    for index in indices:\n",
    "        col.append(A[index][i])\n",
    "    print(max(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Name': 'ABC', 'Number': '123', 'Elements': [{'Element 1': '1'}, {'Element 1': '2'}, {'Element 1': '3'}, {'Element2': '1'}, {'Element2': '2'}, {'Element2': '3'}]}\n"
     ]
    }
   ],
   "source": [
    "#https://www.geeksforgeeks.org/python-convert-list-tuples-dictionary/\n",
    "    \n",
    "d1 = {'Name':['ABC'], 'Number':['123'], 'Element 1':['1', '2', '3'],\n",
    "      'Element2':['1','2','3']}\n",
    "\n",
    "def Convert(tup, di):\n",
    "    for a, b in tup:\n",
    "        #di.setdefault(a,[]).append(b)\n",
    "        di[a]=b\n",
    "    return di\n",
    "\n",
    "dict={}\n",
    "listElements=[]\n",
    "for key,value in d1.items():\n",
    "    if isinstance(value,list) and len(value)>1:\n",
    "        for item in value:\n",
    "            listElements.append((key,item))\n",
    "    elif isinstance(value,list) and len(value)==1:\n",
    "        dict[key]=value[0]\n",
    "    else:\n",
    "        dict[key]=value\n",
    "        \n",
    "dict['Elements']=[Convert([(x,y)],{})  for x,y in listElements]\n",
    "print(dict)    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': ['el1']}\n",
      "{'name': ['el1']}\n"
     ]
    }
   ],
   "source": [
    "a={'name':[]}\n",
    "a['name'].append('el1')\n",
    "print(a)\n",
    "\n",
    "a={}\n",
    "\n",
    "if 'name' not in a.keys():\n",
    "    a['name']=[]\n",
    "\n",
    "a['name'].append('el1')\n",
    "    \n",
    "print(a)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-9  1  2  3  9]\n",
      "[[15. 20.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.  0.  0.]\n",
      " [15. 20.  0.  0.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.]\n",
      " [15.  0. 20.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.  0.]\n",
      " [ 0. 15.  0. 20.  0. 10. 12. 16.  0. 10.  0.  0.  0.  0.]\n",
      " [ 0. 15. 20.  0.  0. 10.  0. 12. 10.  0.  0.  0.  0.  0.]]\n",
      "0.25 0.0\n",
      "[15. 20.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.  0.  0.] [-9]\n",
      "[15. 20.  0.  0.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.] [-9]\n",
      "[15.  0. 20.  0. 10.  0.  0.  0. 10.  0.  0.  0.  0.  0.] [-9]\n",
      "[ 0. 15.  0. 20.  0. 10. 12. 16.  0. 10.  0.  0.  0.  0.] [-9]\n",
      "[ 0. 15. 20.  0.  0. 10.  0. 12. 10.  0.  0.  0.  0.  0.] [-9]\n",
      "[[0 0]\n",
      " [1 0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -9       0.00      0.00      0.00       0.0\n",
      "           9       0.00      0.00      0.00       1.0\n",
      "\n",
      "    accuracy                           0.00       1.0\n",
      "   macro avg       0.00      0.00      0.00       1.0\n",
      "weighted avg       0.00      0.00      0.00       1.0\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "line1 = '-0.9821 1:15 2:20 4:10 8:10'\n",
    "line2 = '0.1235 1:15 2:20 6:10 10:10'\n",
    "line3 = '0.2132 1:15 3:20 5:10 9:10'\n",
    "line4 = '0.328 2:15 4:20 6:10 7:12 8:16 10:10'\n",
    "line5 = '0.973 2:15 3:20 6:10 8:12 9:10'\n",
    "\n",
    "data=[line1,line2,line3,line4,line5]\n",
    "\n",
    "sparseMatrix = csr_matrix((5, 15), \n",
    "                          dtype = np.float).toarray()\n",
    "row=0\n",
    "for item in data:\n",
    "    for entry in item.split(' '):\n",
    "        if ':' in entry:\n",
    "            index,value = entry.split(':')\n",
    "            sparseMatrix[row,int(index)]=value\n",
    "        else:\n",
    "            sparseMatrix[row,0]=entry\n",
    "    row+=1\n",
    "    \n",
    "  \n",
    "#Print the sparse matrix\n",
    "#print(sparseMatrix)    \n",
    "\n",
    "X=sparseMatrix[:,1:15]\n",
    "y=(sparseMatrix[:,0]*10).astype(int)\n",
    "print(y)\n",
    "print(X)\n",
    "\n",
    "knn=KNeighborsClassifier(algorithm='auto',\n",
    "                         leaf_size=10,\n",
    "                         metric='minkowski',\n",
    "                         metric_params=None,\n",
    "                         n_jobs=1,\n",
    "                         n_neighbors=3,\n",
    "                         p=2,\n",
    "                         weights='uniform')\n",
    "\n",
    "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.2, random_state=21)\n",
    "knn.fit(X_train,y_train)\n",
    "\n",
    "train_accuracy = knn.score(X_train, y_train)\n",
    "test_accuracy=knn.score(X_test,y_test)\n",
    "print(train_accuracy,test_accuracy)\n",
    "\n",
    "for item in X:\n",
    "    prediction=knn.predict([item])\n",
    "    print(item,prediction)\n",
    "\n",
    "y_pred=knn.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([0.58], [0.69])\n"
     ]
    }
   ],
   "source": [
    "stats = {'node100': {'load_1min': '0.58'}, 'node200': {'load_1min': '0.64'}, 'node28': {'load_1min': '0.69'}}\n",
    "alist=[[float(value2) for item2, value2 in value.items()] for key,value in stats.items()]\n",
    "results_val=(min(alist),max(alist))\n",
    "print(results_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0*x+-9.73133548174993**2+2.0+1\n"
     ]
    }
   ],
   "source": [
    "equation=\"p0*x+p1**2+p3+1\"\n",
    "mydict={'p0': -1.0, 'p1': -9.73133548174993, 'p2': 0.0306594941614714, 'p3': 2.0, 'p4': 0.514879452523938}\n",
    "\n",
    "for k,v in mydict.items():\n",
    "    equation=re.sub(str(k),str(v),equation)\n",
    "\n",
    "print(equation)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.988945457, 0.960482604, 0.997873928, 0.984294588]\n"
     ]
    }
   ],
   "source": [
    "arr=np.array([[9.88945457e-01, 1.12012004e-11, 1.10545428e-02],\n",
    "       [3.19212970e-03, 9.60482604e-01, 3.63252661e-02],\n",
    "       [9.97873928e-01, 1.10315885e-12, 2.12607185e-03],\n",
    "       [5.13391890e-07, 9.84294588e-01, 1.57048982e-02]])\n",
    "\n",
    "result=[max(item)  for item in arr]\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22.856503896430368, 4.786269642161807, 12.73001227370828, 4.9396204086790165, 23.80376378587624]\n"
     ]
    }
   ],
   "source": [
    "func = lambda x,y: x**2 + y**2\n",
    "x = [2.9326170613860096, 1.8654993478646646, 2.878679040963291, 1.2534822780679544, \n",
    "2.9724667148405075]\n",
    "y = [3.7757464252380686, 1.1428831196926126, 2.107894507044161, 1.8353208404114492, \n",
    "3.868876505529935]\n",
    "\n",
    "print(list(map(func,x,y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['today is great weather.', 'final dataset.', 'for money.']\n"
     ]
    }
   ],
   "source": [
    "my_list=['Value today is great weather.','Value final dataset.','Value for money.']\n",
    "\n",
    "for i in range(len(my_list)):\n",
    "    my_list[i]=re.sub('^Value ','',my_list[i])\n",
    "\n",
    "print(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1 col2  rnk\n",
      "0    11    a    1\n",
      "1    11    b    2\n",
      "2    11    c    3\n",
      "3    22    a    1\n",
      "4    22    b    2\n",
      "5    33    a    1\n",
      "6    33    b    2\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'col1':[11,11,11,22,22,33,33],\n",
    "    'col2':['a','b','c','a','b','a','b']\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df['rnk']=df.groupby('col1')['col2'].cumcount()+1\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24963541666666667\n",
      "0.24963541666666667\n"
     ]
    }
   ],
   "source": [
    "list_size=307200\n",
    "list1=[random.choice([0,1]) for i in range(list_size)]\n",
    "list2=[random.choice([0,1]) for i in range(list_size)]\n",
    "\n",
    "list3=[]\n",
    "for i in range(list_size):\n",
    "    list3.append(list1[i]&list2[i])\n",
    "\n",
    "#or\n",
    "list4=[x&y for (x,y) in list(zip(list1,list2))]\n",
    "#print(list4)\n",
    "print(sum(list3)/list_size)\n",
    "print(sum(list4)/list_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.5014, 1: 0.4986}\n"
     ]
    }
   ],
   "source": [
    "flips=10000\n",
    "\n",
    "data=[]\n",
    "for i in range(flips):\n",
    "    data.append(random.choice([0,1]))\n",
    "\n",
    "dictProbability={}\n",
    "for key,value in Counter(data).items():\n",
    "    dictProbability[key]=value/flips\n",
    "    \n",
    "print(dictProbability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([17, 17, 17, 17, 17, 17, 17, 17, 17], dtype='int64', name='DateTime') Int64Index([24, 25, 26, 27, 28, 29, 30, 31, 32], dtype='int64', name='DateTime')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "txt = '''Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3\n",
    "16/12/2006;17:24:00;4.216;0.418;234.840;18.400;0.000;1.000;17.000\n",
    "16/12/2006;17:25:00;5.360;0.436;233.630;23.000;0.000;1.000;16.000\n",
    "16/12/2006;17:26:00;5.374;0.498;233.290;23.000;0.000;2.000;17.000\n",
    "16/12/2006;17:27:00;5.388;0.502;233.740;23.000;0.000;1.000;17.000\n",
    "16/12/2006;17:28:00;3.666;0.528;235.680;15.800;0.000;1.000;17.000\n",
    "16/12/2006;17:29:00;3.520;0.522;235.020;15.000;0.000;2.000;17.000\n",
    "16/12/2006;17:30:00;3.702;0.520;235.090;15.800;0.000;1.000;17.000\n",
    "16/12/2006;17:31:00;3.700;0.520;235.220;15.800;0.000;1.000;17.000\n",
    "16/12/2006;17:32:00;3.668;0.510;233.990;15.800;0.000;1.000;17.000'''\n",
    "\n",
    "from io import StringIO\n",
    "f = StringIO(txt)\n",
    "df = pd.read_table(f,sep =';' )\n",
    "\n",
    "df['DateTime']=pd.to_datetime(df['Date']+\"T\"+df['Time']+\"Z\")\n",
    "df.set_index('DateTime',inplace=True)\n",
    "\n",
    "#print(df.index.hour,df.index.minute)\n",
    "grouped=df.groupby([df.index.hour,df.index.minute])['Voltage'].mean()\n",
    "grouped.plot()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.plot(df['Time'],df['Voltage'])\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  1  2  3  5  6  7  5  6  7  1  2  3 10 20 30]\n"
     ]
    }
   ],
   "source": [
    "lst = [1,2,3,1,2,3]\n",
    "lst2 = [5,6,7,5,6,7]\n",
    "lst3 = [1,2,3,10,20,30]\n",
    "\n",
    "result=np.concatenate([lst,lst2,lst3],axis=0)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "txt=\"\"\"1/1/2019,7980.185714\n",
    "2/1/2019,9478.297619\n",
    "3/1/2019,9282.166667\n",
    "4/1/2019,6900.833333\n",
    "5/1/2019,5563.716667\"\"\"\n",
    "\n",
    "from io import StringIO\n",
    "f = StringIO(txt)\n",
    "df = pd.read_table(f,sep =',')\n",
    "df.columns=['Date','Sales']\n",
    "df['Date']=pd.to_datetime(df['Date'])\n",
    "df.set_index('Date',inplace=True)\n",
    "plt.title(\"Sales\")\n",
    "plt.plot(df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "txt=\"\"\"T-Rex,12,15432,40,20,33,40000,12800,20,19841,0,0,Primary Hunter\n",
    "Crocodile,4,2400,23,1.6,8,2500,3700,30,881,0,0,Primary Hunter\n",
    "Lion,2.7,416,9.8,3.9,50,7236,650,35,1300,0,0,Primary Hunter\n",
    "Bear,3.6,600,7,3.35,40,20000,975,0,0,0,0,Primary Scavenger\n",
    "Tiger,3,260,12,3,40,7236,1050,37,160,0,0,Primary Hunter\n",
    "Hyena,0.27,160,5,2,37,5000,1100,20,40,0,0,Primary Scavenger\n",
    "Jaguar,2,220,5.5,2.5,40,5000,1350,15,300,0,0,Primary Hunter\n",
    "Cheetah,1.5,154,4.9,2.9,70,2200,475,56,185,0,0,Primary Hunter\n",
    "KomodoDragon,0.4,150,8.5,1,13,1994,240,24,110,0,0,Primary Scavenger\"\"\"\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from io import StringIO\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "f = StringIO(txt)\n",
    "df = pd.read_table(f,sep =',')\n",
    "df.columns=['Name','TeethLength','Weight','Length','Height','Speed','Calorie Intake','Bite Force','Prey Speed','PreySize','EyeSight','Smell','Class']\n",
    "\n",
    "transform_dict = {\"Class\": {\"Primary Hunter\" : 0, \"Primary Scavenger\": 1     }}\n",
    "df.replace(transform_dict, inplace = True)\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "\n",
    "COLUMNS=[column for column in df.columns if column not in ['Class']]\n",
    "X = df[COLUMNS]\n",
    "y = df.iloc[:,-1]\n",
    "X['Name_enc']=encoder.fit_transform(X['Name'])\n",
    "X=X.drop(['Name'],axis=1)\n",
    "\n",
    "logReg = LogisticRegression()\n",
    "\n",
    "scaler=StandardScaler()\n",
    "X=scaler.fit_transform(X)\n",
    "\n",
    "logReg.fit(X,y)\n",
    "\n",
    "y_pred_prob=logReg.predict_proba(X)\n",
    "\n",
    "predictions=logReg.predict(X)\n",
    "\n",
    "sns.countplot(x=predictions, orient='h')\n",
    "plt.show()\n",
    "\n",
    "fpr, tpr, threshholds = roc_curve(y,y_pred_prob[:,1])\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "cm=confusion_matrix(y,predictions)\n",
    "sns.heatmap(cm,annot=True,fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': '13', 'B': '14', 'C': '29', 'M': '99'}\n"
     ]
    }
   ],
   "source": [
    "s = 'A - 13, B - 14, C - 29, M - 99'\n",
    "\n",
    "result=re.findall('\\s*([A-Z\\s+\\-0-9]+)',s)\n",
    "dict={}\n",
    "for item in result:\n",
    "    key,value =item.split(' - ')\n",
    "    dict[key]=value\n",
    "    \n",
    "print(dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group 1 \n",
      "print (\"hi\")\n",
      "group 2 \n",
      "print (\"bye\")\n",
      "group 3 \n",
      "print (\"zap\")\n"
     ]
    }
   ],
   "source": [
    "content=\"\"\"\\\\[-16pt]\n",
    "x = 10\n",
    "print (\"hi\")\n",
    "\\\\\n",
    "thinhline\n",
    "\\\\[-16pt]\n",
    "y = 3\n",
    "print (\"bye\")\n",
    "\\\\\n",
    "thinhline\n",
    "\\\\[-16pt]\n",
    "z = 7\n",
    "print (\"zap\")\n",
    "\\\\\n",
    "thinhline\n",
    "This is random text.\n",
    "\"\"\"\n",
    "#pattern=r\"\"\"(\\\\\\[-16pt]\\n)    \n",
    "#                         (.*?)             \n",
    "#                         (\\n\\\\\\nthinhline) \n",
    "#                      \"\"\"\n",
    "pattern=r\"\"\"(\\\\\\[-16pt])(\\n[a-z]+\\s+\\={1}\\s+[0-9]+)(\\nprint\\s+\\({1}\\\"[a-z]+\\\"\\){1})\\n(\\\\\\nthinhline)\\n\"\"\"\n",
    "result=re.findall(pattern,content)\n",
    "count=1\n",
    "for item in result:\n",
    "    print(\"group\",count,item[2])\n",
    "    count+=1\n",
    "    \n",
    "# result = re.compile(r\"\"\"(\\\\\\[-16pt]\\n)    \n",
    "#                          (.*?)             \n",
    "#                          (\\n\\\\\\nthinhline) \n",
    "#                       \"\"\", re.X | re.DOTALL)\n",
    "\n",
    "# for m in result.finditer(content):\n",
    "#     print(\"Matched:\\n----\\n%s\\n----\\n\" % m.group(2))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    OFFSET                                               TEXT\n",
      "ID                                                           \n",
      "1        1                                 This text is short\n",
      "2        1           This text is super long and got cut b...\n",
      "2     2000                       o it will come out like this\n",
      "3        1                                      I'm short too\n",
      "\n",
      "\n",
      "This text is short\n",
      "\n",
      "\n",
      "This text is super long and got cut by the database s. o it will come out like this\n",
      "\n",
      "\n",
      "I'm short too\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "txt=\"\"\"ID,OFFSET,TEXT\n",
    "1,     1,         This text is short\n",
    "2,     1,         This text is super long and got cut by the database s\n",
    "2,     2000,      o it will come out like this\n",
    "3,     1,         I'm short too\n",
    "\"\"\"\n",
    "\n",
    "from io import StringIO\n",
    "f = StringIO(txt)\n",
    "df = pd.read_table(f,sep =',')\n",
    "#df.columns=['ID','OFFSET','TEXT']\n",
    "df.set_index('ID',inplace=True)\n",
    "\n",
    "\n",
    "for my_tuple in df.groupby(df.index)['TEXT']:\n",
    "    lst=[item.strip() for item in my_tuple[1]]\n",
    "    print(\". \".join(lst))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.5 1.5 7.5]\n",
      "[[1.5 1.5 7.5]]\n"
     ]
    }
   ],
   "source": [
    "row = np.array([0,0,1,2,2,2])\n",
    "col = np.array([0,2,2,0,1,2])\n",
    "data = np.array([1,2,3,4,5,6])\n",
    "\n",
    "B = csr_matrix( (data,(row,col)), shape=(3,3) )\n",
    "\n",
    "sparseMatrix = csr_matrix((3, 3), \n",
    "                          dtype = np.int).toarray()\n",
    "\n",
    "my_tuple=list(zip(row,col))\n",
    "index=0\n",
    "for item in my_tuple:\n",
    "    #print(item)\n",
    "    sparseMatrix[item[0],item[1]]=data[index]\n",
    "    index+=1\n",
    "\n",
    "\n",
    "print(sparseMatrix.sum(axis=1)/2)\n",
    "print((B.sum(axis=1)/2).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SKDL2MgPZ8BisdFA7Wixd6jmvoYDNUpb2DfL+z91CfuwET4To2BLH3raO5DtuQIThxV2k0YzdLmiaCNU5m4kYNDI6nRBPGayCvOzO24tBZgLjWUzgqhNWWoxvsUqQl50XsZZwFGB8gQ7FGZ4DqESgOpJT7TodmzGv2yxqDX0ZXmKRXcnR77Wkr7q+1yJOL5R4VkBdspX9/9HjksFZ9u1fD54BIyivFLtkLrDCIrIzehIygS5ZbAYqPv24lW4hSe0y924XFVjrYm+/KZhrVaiWElrVGjY7u3W6fkCQvKKB8IMnpBGLbsL4S08S3+JReWiIfHIKm2eYRuuCrwGYeR5cE7bJ+6OzdyVt0Zmk3Y6wfYa0K11CcdUDsqwZBSvdtWQ1ZxSttHhdMIK1pKPx3T9hLcIIVOKOr/gpTWP553gzbRNy/bZjPPK2dfzkni8RiYxPbL2cQ9tvZOxP7vlXTaPueQzfYohrLuPQL9d48577WY5L1Pd6lI4ERFM+fsstAIRbIKorzzYOFrCCvGzJyxZhndttPbd4EBYrIC+5RSVy8NqCJPEp+Rk6tGulwFVES5bfufpD6OftecLztuWIoajNVUNTpNvG3PkEAXKw/wlft+UfMu6M66xsOZs4pSPlSo0zIaaiyarueozvvABhC7a1LcIC457TAehAYFTxOEUbduE55OUi16KdBzVSarFifPbH43x6fg/fPXYnDz///byn/wTv7jvFX2z9R77vPR9n+t9fjyxfnPjttyOe1DAIIf5MCDErhHjkjMcGhRCfEUIcKH4OnPHczwohDgoh9gkhXvVMnfhzHlLRfeONdH6lw8u27WcxrVAJUuKbW8SXxqhCOU3FrIUSqiuKmFu47ubCCzAe6JJxC104j0Hk7iecru+boFhgRhB6OSa08HUkn/JMykavwbH3XKDpocDUq8fIjeQrJ7eRVZ3jKUsRduyJh8z4n7uP//SHP0R7/dndViaQSM/Qt18QnPLI+twqN96qURSoRDivwXPXIfLTXAXk6f8LffreyMxVJgBMLWd7ZY6j+RBTcT8nm/1cG55CidPLoCoj/k3f4/zID32Uo//l6n+1xuFiPIa/AL6+8f5ngM9Za3cAnyt+RwixB3gHcFnxmvcKIXqDBc6D9JXXot4zw4nDI3z6i9fwyKKTWb9iYopKLUYlOL6/dm6yyAQmcAvfWyl8aSgec1UJEzmqs0zBFuQgYU4nK11Y4mjLvtTIRGC73bPOKzy2wAPJBNnKE+cNmlsMj50ap3uoji6tJjbc+T4hrGXD791HZdKePd9SwGBfG5VC/36wypKXwOu665HaLXLjW/KSSzJaBbrkDIVRhRFJwetaZA5Cu34JK12ycmLDIuv8ZT69fDlfObaNTrrKxjwbVRnxXfWD/Pu3/QPHf/zqf5VzOJ/UMFhrvwwsft3DbwDeV/z/fcAbz3j8Q9baxFp7BDgI3Pj0nOq3D7xtW5j/t22m7ppg4ydxgiNAqHJqfkI1ShAGgqZdo/nKDGQqkInbaYUW2MAWcbhAxQKZurjcBG4BCXPa4xDGLS6VQN4IOLow6D59/2ytBTM9wy/+yfew5xcuMK26ED/Z9GlNEOSYkiUrF7t/4JP3PfkiMknC/HWG/Ppdpx/zBcPlNs3NgtJiTjTjEY8YksIXtQWHwesKUBYdFclItWognTflrr0gNgWCrALdcUu8I2ZzbYkjyQgnO/0kiyV2Ds2d5S2ciT5Z4gWlg7z7ez7JiR+/9l/d4JxvNMcwZq2dBih+jhaPrwfOTGefLB47B0KIHxZC3COEuCfjX087rPAD9v3IOvpKMSP3G1a2eeSbY5Sw1APHKVDCklVY2+1kBsGKwOuC3xKY0BZ5BLummGQCi0wLV1u4hOTqa1TsHIw8cgsnOuWR76+hA3tOTsDEMRt+9z7y6VPnPf/FH7gJVa9TfuA49Shh+64pkrr7GokoorPuyQ2DUIo3veBuDn6vv7bgjBJMN+pYZSnfc4xt/2eK8pQk29alM2HW8gYyEUV+wXkNKhVgXR4Fy1r50uu4a42HLerSJjdecpTltMRsUsMTGlVPeef4157wPK8OQ/ZEk3zH2++g+/p/XaXMpzv5eD6ZjvP6ltbaP7bWXm+tvd7nIstd3wbIXnQFr3zx/TTikOkXCAZvm8TmkpnFOo00YrpTx1eaZNi4sKFjUbF1nYWWtbupuhLVLj6+Iknnfjr2nywWjPFYK1mCa0QqzVjqR8DUc9Itw2ednxobpf3qKxH+ue3WAAN7O9g0xSyv0B91+Z71d7KyR4MQ2Chg+k3pk+6uNs/5zF/dxA/d9GW8dWMgFUld0mpH7jrynPzwUTb9wzx2MeTyq4/S3R2T1Z1B9FoSmQp0UIQJkQsVTCCQ2nEbdAjdcUNlxzLVUkJuJMeXBvjaiS3snRlHKcPN0cwTnifALVGT6ypH2fEzj2FvvupJj/92wTdqGGaEEOsAip+zxeMngTNF/TYAPVXOAmpkhJP/NmNraY6f2/1JvvDmX+OvLv0AY2PL+EGONpLluEQ7DTBDGXG/ROrTMbKOwITgtSReR+B1CjssOK2JIF2yTuTFa0LWMvYmgHTAoANB0i+oDnVY3BWdpY3YeNFWfu7X3sf8951/hxR3PIiJY2R/H7cO72eLP8+mnTMI5YzB/7jhY4hrdp33tWdi3W/eyf/+0otp3LgRIQXJgCCMMnTJwJCLH8z+w2z7u5SHH93E9duOMXL1DOmWhLxqMJFFVzVZzZDVXGgRD0FrA7S2WBavMngb23Qf66d55wgnm/10pqoEX62RTFVYN9CgKvwnPkkcdXqLP8+G0hJLP9fF2/ivY0bGN2oYPgZ8X/H/7wM+esbj7xBChEKIrcAO4K5v7hS/TSAVJ9+1g1ds28cV0UneWl1gk1dlnVflry97H9uGF1iKS2gjibycoeEm7U3W7YoFOUnFYJR1yUXp6vNSFwzB1eKCOa3HaqVLzK0yBeNhg98U9B/OnWpSmNLcDLJ6upOyvneZX/zZ72f0I4894eXoDSO8vf4gAD+0+SvI7VsQScbtjR2I/Hyc6K+D0Xhtydx3dRBBQDxiWd+3gjcS0750yJ1/nqO+/CA7/jLh7r3byLRi0/giW/dMM3HJHNt2nGLjVdOUdi8T7VkmuGaJ4PIV9GhKuKAYe3+JHX88CRIiL2f4HsmGvzlG337J5QPT5xWhOR8uDzL2tcZ406YHeexnJ1D9fRf1uucyLqZc+UHgDuBSIcRJIcS7gV8BXiGEOAC8ovgda+2jwIeBx4BPAu+x1vaUOAGu38OeNz9Ov9/hJaXWWUmvTV6Vt4zfS64lxkIrCahHMcHOBu0Nq8Qet+ur1FUZrACR4RiBvlv4UIQQAvJK0SeQutd2x9xi3fJ3DaJ/vJv6cU039cnWpYh1o2vnovcepPp/70Qvrzzh5RhfMSg9ajLmpaVjLDxvhHxymjv/9zXYRw9c1C3Z9tcNksUScnzUlVqB8cEG7dEzQhGj8Q5MUdvrMzfTRzMJmG1W6WYenczHWkEpyBBA80Sdyt/U2fU/l9ny/z1A9E93k24YxO5pcuKxcUY+dYT85CQT/3iCUJ47E+NCqMqI3Eg+dvIKdu8+ydS7Lvu2T0Y+KfPRWvtdF3jqZRc4/peBX/5mTurbDbJW49BPSF5aP8Hb6vcTinO1Dl5bOcIferfQ6ETkmUJJyw0Tx3nIX8fygUFKs3ItjwAFf6HIwMvcEZmkFlhYa56yCqyGrG7R/TnrP6mwDz7uGpeUwFhBbaBDd9sgwf5DAHjrxi5qJoO3/wS/unAd399/J5GQzN6a0f9+TVoX2Gt3w10PP+l72PsfZdd/qpBdvQMTGXIrUdKcJfwCIKplV5kJNJ04JJkuI44phu5oY6VAVDxqsWbD0Sny4yfRReu3t3EDh15ZAlps/sdsLaE69+IN/O+RDwLnfg7amvNWKh48sYHy3WUmb11i9I0naE9dT+Vv7zqnzfzbBT3m478AGq/ew9t238el0TRb/fMLoAyrCm/a+CBp4mOM8xwkluvHTrD72mO0t+SkdUf/E7ogNUWuJKlLButbRFY0HRWehdDQ3Z6itrao7guof/bxtZZoq8BXmpFqm6UdPsLzUEOD7PuPm5GVypNek40TlrIysZWEwmP9elfRrp0w7P/B0pO8+jRMu01eUviDMUNRm4Gw48RXzsh7dLcPkwxalKdJOj4qlqy7vYX45weRX32A4NP3Ir90v+vvWDUK27Zw9F2byLfH2P1VonsOghB033gjP//z7ztHdq5lYk7mLe5OLPuz9lmzOQHk4RLrPzVPs1HCl5rO9y2jdl1y0df5XEPPMDzDUP19NL+nwbDf5NZo9gmP/d6++xkfWmGwv4UUsG95lJlunUhlrN8yT+mSFbobc5JBu8ZmlJlwvQBdp7+gS5asXxNvThBXNlg/sYjdX2Xj302fFR5kFcF4rUnNT2huM8hyGVGtOMLUReyC5rJt/PzYF/lK9xKm9emegvKplPp48yndI11STAw2yI1kIOiSDLKWzEQqkgEPHVny1MOminBRoA5Nn36Drztfb/NGjnz3BJUXzGFyweZPdLHacOwXbuLXfvP3eX2lc/o+WM28bnNnUuGRdIhD2SiHs0E+1RlnXjvh2FndZvghizlwlIGvhhyaHaavFLP3J/rxxsee0rU+V9BronqG0Xj5Lt6x7YvcUDp8ljLS+bDOq3LbxKP83fGrWJivIRd8JvtzgmqKOVIh79cMb1jGU5ok80gyjyxTRJ4h6foE5ZQtA8v0h12mWn0sdUpMPzbKtk920YfPJixlFcG6UoOltIQYi2F8BLvSZNd759GdzgXO8DSsJykLRWJ8fAGy8P/9xQ7NlTLjT+EeWQVDUZuqnyCFJe2zCM/D5jmyFJH0S0ykIVHIlqLvkEYvfD3nDpAKcd0eDry5CpvbSGEJjkZYr8vJ92/kvht++6yEY2Iz7k8kE57lVN5P24REIuV45pKfn+9OcEUwzS9PvYa+Lx1GZynjnzjBvh0baVVidm6f5tj3bWfDbyx92zVc9QzDMwhZqzH5mpzEelzqd4End9FfVXuYr9W20mhHRCdCancoJr8joLwiuOQvG8Qb+lja6bOyO6c82ubqDZPUvITMSkaCFvuaY+xfGGFpqg9vWRGtCFqbIgYPDpOfOl23lyl0tU+gNAP1DsmGfqJGCzP15LX9MzHhL+EDVw5Ocbi/D6MUXphfUMnpfPBaGk8a8kIkRlgcIzOOkSNDjlkZGmSgCY/79N8zSX6e9xbX7OLAj3lcs+UAxxsDzC9Xia5Y4b+96wO8IDQocdootEzMI6nPrK6xbMo82lmPLzVVFWOsZNhrsqwr/PLUbdz38T1sXrkPgHxymrG7NzA9WkUPCPpecorlyWvp+8Cd31b5hp5heAbRfeEudm+ZpKpiBuTFxd1XBorXjj5EO7ue6WqVvtuP0RneyvLlGlMNCD51D2Ofgon+fkRfjdk92zi8zqO5xZUm64chHhOoPtdXkZctK9sl9YOjcIZhiJYNnTxgrNSgEqQsXRowtjgEB564bXoNRQ6gaSJiC+8Z+QL/af0PuGYmI5/SXAuVGXZWZ3lkZYLlpITXEmCKJqp6GR3h2I6pojxt0VPnsjLV2CiH31Dn+dv3UlIZss9y67qD/MLonYWX4KLmFdPlI82tHE+HqKqYQdVGCUNiPKaTPjaVFtkaziExxKbEA6fWM35XikmLKobR9H1mH43Nu2lfn+MpQ/MNbfofvwx7zyPnnNdzFT3D8AxBVioc/w7JVu0VX76LS+f4QvGd1QPcXdvKlNyAabVZ99EjJAPbOP6qKlsXtqIPHHbqxktLhEePE/kBY+Oj2DTFttq0X34Z81d4rhoRAVgWrqgy+ngd3XACjfWDbY4tD1D2UnIjaW+Axs46fScjuIihLCaQSCTj3goZgglP0NzZT+3hWfLGyFO7Wdoy6LVp5wFx7jnlKSldfmG8ii4BmURoQXU6P2eilCyXmXr7JQzcMEMn9xkM2rxm8EFeU17BP8NLmNVtbv7KexAnS4xeOcPO/jk2lRYZ9RvUvZhDrRF2VU4RG5+miVjKKth7+ghvf5AzmRl6aYnxu7oc2lQiCTPG+xvse88wu39y4NtGdbqXfHyGkLxgNzsvP4mxgkX95CHEmRhWFb5/5CvUjllMq0U+fYpNn1gmGTQce+s43tbNZx1vs5T8xEn0zCym3aZ213EG9mnCRaf4pEuW9gZBdtX2tZ1eLbZYWS7TyQNKfkbWZ5i/QnDwP+28IB36TIR7J/m5mZu5JWoyoRRVEXLq+RKWG66T8yLeYxUyN1waTVHxUqcTEQFb1+OtX0daV8SjOYQGtCBY+rpYXgiSF+4hf/EKVw5NMRR2GA8afEd5Cf/rGnu/2J1ACouKYebRUe47tYFHG+uYz2o08ohYexyLh1jMXcXivqWNDD+cu+lVX+cB+fcfYvBBSWOpTCA112w/ztS7dvPtMmG7ZxieAchymaNvUKwvr9CIQx5vrTvreW0N03mLJX3hJN8LIskVP/Kw4+cLgX1kP+N3QGdTzsEfnMA+/6oLLr58+hR9n91PZcp1XxrflTYX90R4WzYBYKdnCQ9FlL2UvqCLHEhJx3P+w+s/jhp+Yk0FgPzUDPf8z+uYNylVGaGExKyLMStNdu8+SfqSKy/6fqmVLtpKApXTzXzyqmHyFYPMvHIjjU0KfAuppDQjkcnZ+hHe2CgnXu6zaWCJkaDFzsop3tl/L+F56M6jqsmrdzxGMpaj+3NqUUKkcl5Se4xtpTk6WcCBxghLeZmHm+s5MDVKZd/Cec9ZNxqMfnmW0uGQpbjEdLtOdusK4vrLL/q6n83oGYZnAI3XXMGOXZNkVrJ8YJAvHtix9py2htsTyZ8vX8dvLd7AXzRGmc7PH9f/6aav8nPvfz8n/uvzkf199H9yL32PedhLOhz4gYDmG6+5IOdALy0x/JUpgmWJreRkA5rlXZap16xHXHMZslbF60CsPWLtozyNWlG897FbyGcuboRb7ZOP8Mn2zrXff/r6T6GGBpDC0h2++CjVhj79qsNA0CHN1Zpe5dIVlvY1XUSoUS3JyP0Z8vDU2q4s/IDFl2zF39ZECkskM95ef/C8E7QSm7Ggq+wozfK2G+/me6+/g1vGDrK+tMwWr8X2YJbJuX4OPT7BQ0vraaQlvCMR9uT0Oe+1do8PHmXdPycsPj5EoxNhreDAd1fOEth9rqJnGJ5mqIEBZt+UMFFZYaZTp35IIqcisoIZvj+LeTjeyKHOCHcvbuYjp67j1+Zu4Z86Ecfz1pqC8SpeXDLc/yO/zSWfbDD1vZcx8mCXPPHYvGWO6ddmzH33laih8+/w+bGT9B0yqHJObbxJeUuD5ctypm/tw4wNEi5bjq0McGK5H4DSrGTD4PIF3+8caE1iTu/Mb6we4PGf3sqBmREGPrHvCV54NqwniY1PvRifHSxL1t3eoTwpMbGHbXlUJiWVhyZdDG8tSEV66xWcekVOvRxT9RPe1nfvBcfqrZiUZV1mY7DA1nCOPtVlfbhEWaY0jSK1CtPxkLHgVLPGgflh1n0txzxR6dZogrv2M36HpTNXQWtJeUuDxddc+pwPKXqG4emEEMy/fhfrhlbwhOHA5CjrPjPD8AOWj7UH+HTH5y+WbmYyGSCzkk4WMNOqcawzyEfmb+B351/EHy7v4muxXjMkAKHw+Z2Ju7nzp3+b17/38/QNtJlvVRgYbKFfu8TkO3ehdu8493yMpjyboTsekZ8zUW/g9aVOZfrIJIOPdlherlCNErSWRPOWvqCLXTd0cdd76VauLR1Z+3VUVXjwrb/FNRtOPqW5kGpynr+efx6+0HSTgIF9Bm++RWnWUt3vUz3sMfhYSj59Rin1+j0ceYviks0zVIKUHxr/Ejv9C+dyQiGpyITY+NRUzLWloygMC1mFo/kAHROiqhnB5pbjiRypUbn3AmI1Z8A0m9QfXaS+zyM7VSbZ18fc9eCtn7jo6382omcYnkZ4mzeyclubkpcxE9cY+GqIOXwMgF/Z/2p+6dBrmez2M530Yayk7KcIYWmmEbpoidzXGeP3Tr2Mtx969TkhRih8/sPAMb543Z9zy8ZD9Jdirhidwrx4mWNvHEGNjZ5zTtHj01T3BXRTn3YWIKShPWHRuzbjHZwi3FcizjzK5YSkXzDZ6sOULy5xqMsBX2rtpmNSdDFl5vPdQSQWETx5S/MqbLvD7ce3Mp30kWWK0nxONlIlGRT4TUt1ylB+fGYtAeitn+Do66ps2T7D7r5T/NzWf+JlpScuj/bJEleEU6RWUZNdJvMBYuuTW8X9nS1EMuPNux9gy9AivjL4LYk9YxjPE57/yVOM3tdl4FFB5YQgWJbMvHrTc9pr6BmGpxEn37iBsf4moZfz8P6NjH1hBmss7XWSbupT9lPqfkw7D5iPK2RGUfYzhqI2odQMeB3m4iq3P34JD921nY+3z8/F75MlfnPiK7xrw9dopCW2Di6SXNFh9vXbnXE44wuZT06x8e9P0T3YRyMOCQKNHY+Zvb6GbTaZuD1maaHGpv5lumOWoVKHpV0XV0URdzzIV2+d4PK/+THefPA7eNXe1/L//sb3cd/ndmHTi+9eBMhzxUJSQXc9jCeIR0KSfqctUT/YRk/NoPr78NZPMP+yzWSbE0pexncPfu1JjcIqtnquSjGX15nK+inLlEtKszzSnOBwMsq/G/wqLxw6xOxcnWREM/O2PYhrLnvSCotpNvEfPMzo15aoTeYMPmZYuDFHXnbpU7oHzyb0DMPTBLVzO82rE+Lc4/DCEOOfV+hDxxC+R/24JnusjrUCiWUg6NBMQxY7JYZLLS6tzrC1NM+R7jAP3red2qMBY5fPsiM8v7waOO/h++uz/NTGT9AfdNg4skTjZW2O/LtLzmnu0QePMPFVTRz7+EoTlVOWr8oQ2zYRPHICNROQaYXQcMPAMfLvXLzocqNeWuLSn3+Uxi9uQL2lzcgffo1t/2cG9MUTnJov381v3PBhyl4KicRvZAhbSOIbUMsdbJZiujH5xmGWd0Kt3uWN4w9wU3Tx7c9lGfCC0lFqqkuf6tLSEaHMGAw6dEzAoFK8sLoPmyq8hmTp5pT9PxGy9F3XoeoXnrAFoJdXsI8dpLp/mdqhJv6Cx/HXDz5n27N7huFpgPA8Trx+DJsLVloluKuPwS8cWXN9+24/xsRXc1YSp4dorKTRjUgynz4/ZtBrU1YJc3EVFQta2zSXD05TEU/Ov39BJPndjZ/iF7Z/lDfsfJitLz7KzC3DZ8ueW0v1n48g91foxAHlMGN4/QqzNw+B0YRLgrSgI4/5K/yXSz+NiC5ebs80m/ifuw/TaoO16INHnjhp93WYvVZyQzhLIHNkLPGXuoTzKcGKIGhZkg39eBvWI5SiuaVMOppz7fhJvrt2+KL/xiq2+1VuCCcZ95aRwjDiNbi6epzY+HyqM85VQZdfeNHfIba1sV2F8gzzr4iZecdlT9owZfMcTp7C+opoTtDdFeOtX/eEr3m2omcYngaIy3bQ2pUifEO6HDJ6X0o+4zopbZaTz8xROjjP/HKVrvaZ7tZpLZSJuwHH2wNoBInxqfoJO286yjtuvoPLK1Msm9Ja7P5E6JMlbongp0e+yuvHHsS+ZpHOyy5fG7Wm+vuwE8MEyy7ECLycapiwdLklu3wzlSlLN/PJBjSDXos/PHYrNn3qTUGL33Mdase2p9QzIPyAV736HpQQHG8O4rWFU6xKNeES+E1NMujRvnKC+EV7mL9S0D/W5OfXfYKqPC08q61hXrfZm3Z4IEm4K8k4krVI7LkhzVa/yg5/nrJMmcoGmM9qLGYV/uj4rfxdazMvLR/lc89/Lz/6os/RX+8wNNgieXWDQz+yHXn5rif0AnSjgZpvEi5bWPaZeeXG52SuoUeJ/mYhFdO3DqCWDXY8ITruU3rsGPnq4ii8BlMrY41gulvn2OIAsuGhlSVUOcZKtJUYKxiLXE0+s4qmKWFIuVhndFhV+N76EQZ3tfhv73wD6/JriD77INkV2zj4AwrSHDFfYkkZKlGKqeUs7oroO5Jx5FQ/wWBMJDIaf7eOMDn61O6DtQw+2kJ0Yrx14xz73m1sfv/hC6pNr8LcsJt/O/yHSGCpU3ICNJFLXIYrBi/WICEvSRZ3KS55/lH+cPuH18qSic346+Y6fu/QS1huluirxoReTiMOuWH8BDU/5tV9D/PCaOUsQ7I7KDOuDvKleJRlXcYg+GprK39x/GaW15fZFszyhtpDvGjPfk7pPr7S2Mkn7W4mVwbZYLajH9t/4Ws6eoL+df3EQyUWbswY/eggev78RKlnK3oewzcJefkOWpuNGzvf9Ri9PyM/T5OP7CSYhs9SXCJJfLy2QM0HDIZtjieDPNDYQCsP6fc7+EKjETR0hOEi9BPPQFkGvLmyxC9e81Gmvjel9YZrsJ7Am/UJ5hSlSUV3vsz8TB0ZaBrb3Osq+wKyxKNjQupHL77UeCbs3Q+TT06RbRvnhjc9zMrNm5/0NXnZY1BqBmSJ3SMz6BCSkYjW5jJpVWCFQIfOKFz7msf48I6/XTMK2hp+deEKfvnB25ifq5Onbp+rBgnGSJp5SDsP+bNTL+TXF65d01dYxYAq87pyg+dFR9kUzHPJ4DwL7TL3NTbxD4vX8OszL2d/OsbLS/P84NDtSOnG5mVDlSccfGvznODwDJUpAxI6N277hu7ntxI9w/DNQCpO3DaI15JOEWnOo7x//rydhTbyEZWcOPXRHc/Nhygbbu3fjy80xgrqfkxJZXSMS/wNea3zUnufDEpI3lxZ4ueu+QTTb0xpbgjZ+UdTbP+9Q2z6x0UG71eEkz6m7WOVpbHZBwF+mLMrOMWJ78nxNqyHm650fRlP0RUWtz/A9IsSJ332JAi+9DBveuT7UUJyY99RrAQVOyXrvCyIhzxaE4qNLz3On27+zFm7/h+ubOZP734h+WQZmws8X9NoRyzHJQIvp5MH7F0aY7ZT4/7ljfzS7K3clZwdWigh2R2Uua1yjFcOP4YUlv1Lo8zFVb5yfDu/dP9r+JX5GxhRhls3HMJrW7xmgig9cbdsPj1D7VhM6bjP9M3ec26Cds8wfBNQuy8hHjGUp90sh5H7DebE+fUSV3b3sWfzNKGfg7IYD3bsmmTIazGfVmllIeuiFSKZUS6GVd4QXtj9zKxmSXdY0h1WTJcV0z0rnlZC8q7aKX7q2k8ze0vOynXj0I0xD+9j9GvLyEwQziqCZUlac4Nx+6pdBlXGYy/5Y37oC1/ih9//9xz8wQlUrfaU743N0ovKNdgsJfq9AU7mLaoqXtN77I5K2hsMc9dI+t8wyYcv/b9nGcm/b1f5ta+8GozA1DSqnDutTGVIc4WnDLmRdFIfJQ1SGB5cXM//c+SNfLjVx8n87PzDsKrw7r7j/OrlH2GiuuL0J5Uhny3xkf1X8weLN/C2oTsZfPtJpm7tJ79qOyJ8ggSt0QRHZilPWbINKXLH1qd8D7+VeG6ZsWcTpOLkq4eoHgW/Y/Gbgvq+FUxy7lQtEYasbJUMAtpIsILuJQmvHX+YU1kfEktmFJPdfrpBwFDQ4obSEUbVuXyC43mLz3e28WhnPXfMbsWThg3VZWLtIYXlpYOP85bafoZVBSUkP9R3guzmT/Mb+lWoeBeVLz+OmFnE6wzQGQcRWWTuhrds6VukLASh8DmaDvOh//Vqtn7obvT5WIxPQYjlyVB5cJIpHTKZDmAFrGwJaFyaIysZXinjt3f8NX1n6Fl8qDnA/3P/6xCxwgYGUdLojvsqVwZTWt2QasmpQVkrmG9VyEqKVCtKXsafnLiFX25WuXb8JD8x/hmuDJwX4gvFK8sZL9n+SY7nXf6w/iL+tn01Werx5blLqKmYP93xQX7mba/jvsHdbJvbiN538ILXpWfnqZ2cYLHhMXvzMEN7L049+9mAnmH4BqEu2UJzZ8a6zyuMD+u/2IL9R889rl5n4Y2XUb9lhqqfcCQZhFRyw+WHuDSc4o72DhLjMVJqMdOtMRdX+Z71R3h+lABuh9TWMKs7fLBxJR88ej3d1CfPJXmuKJfdlKWSn3F8foDH50f54sil/Pf1/8juoIwSkv8wcIxrX/mHfG/13YxV91A91kXFoAczBkaarBwcwG9KTjT7mdOCAWl433tvY/Qv//n8Y8TgaTMKAGaojrGSL83sQA9kLN5iUJ5FN33Gxhe51Hfp15aJ+YPly3j/gechpUUOJ5hcoHxDnvqIUNPqhuhc0kl8llTJyd+lHnHiU6/EdHOfTCuyXPHVw9vpap//b+PHzuqx8IViu1/lf47dw3JW5rMP7aGdBuzrjCHr8Fub/pHvv6XEwsHNDBw+fkFZN5ulhPNdqsf6aG22jJTLT6mM+61EL5T4RiAEx948hmp46FBQPZnC/XsxX0ehVSMjTL/rckrvnOZ5o8d4aHoCcXcfqql40cBBTmRDLOVlcivp87usryxzZf8kb6gcXXObH027fN+xl/LKe3+Y//34zQhhUdKgc0UYZkR+jrbCCanWOtSjhIW4wr8/+A4+2QnXyp0viCQPvvgPeNFPf42D7yjRGbeItmK02oKRBJnBqROD/Nbsy1gyXUoLTy3p+c1g8hWDTHhdMiO5dNs0ezZPIyYj/CXFWLlJKHxaJuYnJl/Gnz/+fAIvZ6DWwfNzpGcJwgx8gzWCPFMYKzBGkmsXWuhMkmeKJFdk2hmZKMjImz73HNvEuw98F7fH516vLxT/a+LTDK9bQQhLajymdMiQLHHr8AFWdoCsn79paxVqcp7+gzlZzSK2bnzCY59N6BmGbwDepg1kV7YpnxJ0xgXhkblzmoaEHzDznZfg3zbHjr45vjK1DXFvnWjBcu3z9/Pa6qMcSUbwhWZzeZFmFuELww8O3b4mGnsyb/EfD72NOw5tpdMO8TyNkoY48fGDHCEgyTzSXBHnHoHSlHwXN08u9vFjd7+D9y5vXWvIqsqI/9/YA3ztO3+d1952J7aWc3hmmHq9SzJsCGY9Pn3fFRzMIhpva16UjPzTAZXAoPT46e2f5IXDh5hc6XPj9wQcXh7i0x2ff3PsNr5wcCfGCKRwk6WCwN3zNPEhdyFaEGZIYRHC4qlCKj91BgKcaG1upBtSExp0I+D4/AD/Zf9b+HCr7xzeyLCq8KEr/ox1lQYz3RpT+QBKSF5fe5BsW0y2ZzNqeMgJ0Z4nwZjPzlM52iRYkSxf8dxpx+4Zhm8Ay89bT7YcUpk2DOzL0ZPn9uzLHVtYuSVmS98iM3GNxfka3XFD7W1T/K+NH2XRBMwkdRq5m3LkSc33jX6VywIXS3dMym/Pv4hmEqJ8TRDmeNKQZO7LJ4TFGEGaeVgr6CQBvtJ0Mt+9Rhmy2OM3bn8l7zr6Co5kpxuyRlWF/zzyZXZvmcYeL7M8X8WfaGMCKE16/MbUK/najX/K/NsvXmzlm8HEP03yx8t7eE25xQsr+1leqJLVDPlEQl8U8+vHXsVdh7ZgLaRdn2YnZL5VwZOGgb42QZjhV1P8ckqWKYS0hH7ujECuQFpnSIFu5pNkHnHmEZRTKqNt+qpdjBX81fRN/MnKxnNmSmz3q/zipo8RSM1j3fUcyVps832+c88DTN5awmxZhxoaRO7cdq5xMBq53KIyaVncLZ+SstW3Ej3D8BQhwpDpF0B9v0dpPqd21/FzW4yFYOH6IUaKWQkL3TLbNs7x1lu+xqvHH+Nr8Wbu7m7FWEEoc5azMv9m7MvcUlTiMqv505UdfO7kThYbZaS0+L4rdba7znMQAqS0lKMEJS2dTkimFY1uxNxCjW4rhEwi24q779rJbXf9CB9u9a1l4heNopFEqFhQ3RtgrcDb2qI7kXNwcZhQ+HRf2/gX4frnR47xBx9/FQDXhx38UoYuGzaNL3JpfZa5dgWMQEiLUBbf12gtiYuKg7vnFs8z5IlHEOSEfs5Kp0SeKfxyRrnwJNIinOjEIZVSQn/ZGYEk81iMy/yf48/jv5265RzjsMsPed3ogxzpDvPx9m4yq/nBwdtJ93RIByPIUhACEXzdwpcKU6sQNC3JeIba8NygSPeSj08RYsdW/PEO5a+VCRa6Z0myr8LbupnZ52suK3UYLzV589h9bPQX+PDC85js9tMsR3S1TzMP2VBa4t8Of3nNU0hsxvsam/nHU1fQiQPy2Kdcj1HCooXF87RrxpIGawVp7iGEJYwymrErn5lcIn2DKOWYXICEZLbMf3/w9Xxi41FeO/ggh9PtaCtIBzTWk7x+x8O8rv9+7uls46rSMXyhWNfXQAY+Jn6Gxo+uKjF5PjIDg0VjGexrEw2tcPWQ08zMjUQo4xKtuXIzLJQhSdzXV2tJHvsE9djxMZRmsXG6VyQqpQReTpqHlIKMVhxijMBXBk8amnGIsYLphT6iUsp98xv568p2frjvdOlZCclLywf53OJuIpGxaHJGlOBNux7kU1c8n/W353DkBDb7uk3CaDh4lLq/nVM3V0k3DSKPHHtm7ufTiJ7H8BRx6sWDpAsR4YpGTc6fU6sXfsCJN05w3eWuwecF9QPcVj7BP61czacP7iKUOX1eh1DmvHxoLz85dM+aUWiZmN9a3MOfHbmZo/OD5Lmb0aCkoRMHGCOJggwpnYvsK732sxRktNoR3U4I2u2uUllUX0YwEBONtQn8nLunNvFLe2+jo0N+89IP84qbHiK6bJmX1R/jlgh+cvDwWhvzWyfuxVy982nn+nvbtnDg957H7nsUu+9RXHVXymfe9av4QmGs5UXjh3j5+OPkRrF3ZZx2K8Jqia+cUWx3QrpdtzNrI6mVE6RvyHNJX1+R9beCUpRhcS3dmVYuryAs1SghCrNisniGkoZSkFEuJwSeC9k+PnvFOV7DJq9MahTH0yHuS8Y5lvv85PBXiK9vIwcHMJ3OeSsUNs+RnYRwUTF/2cWP7/tWoucxPAXIcpmVG2LKj0dAhmmeR6vxqp1wyxIDQZd+f4FbS8f4y8ZlfPieG4j6Y27tf5xRr8mltYVijuXpnMKvL1zLZ6Z30eyGZLGHUJag7L7cacdVKZzHAFq7obTVUkIlSJltVNG5BAsy0phMYo2ATKJ9SW3ALRglHQHojoWtvHvgLn5v/Vd5dOSL+MKsncsqfrhviuzPvsBvfey1XPKhFcxD+77pMqUaG2XoA4sc2PS3TOsO9yTjXBWcWisXVmXIy+qP8ZmVyzjcGuLw9DDWCIJySpp7SGmQQpJ1fUSU0+yEhEGOKjypJHMelOe7RK01grgTuHCsSEZWwpRSkJFkHgudCknmEfox6+oNWmlIbiTLSYlH0pAXRGecu5C8c/xr/NeH3sDJ9QO8dfhurgw6/PhVn+cvXvFahu+qIuYW0TNfN4pQKazvESxBZ+K5MZSm5zE8BcjxUdaNLeO3HW336+cbeOvGOf6qOmO1Fu084AcHb6djBZ+a24O34DHe3+Cl5aO8otQ9a7htYjN+Y/FKPnfqUuZWqmSph/INnq8RRQLNL2WQStLUc+50kWDrL7nEWZp6KM8gpMWkCpYD6CoQ4IVOi6Hdjmh3QipRykyzxqfal+ALxdVhyCX+uXvEo6nbMT/9Xb/Kz//tX3Lyp5/3TeUchOex/z9v5082fY7bE8kvTL+K3z32Uj6wcv1aNcAXikv9BTxpOLnSh2n6SOXyB+KMMdhe5IxBlnqEfk5ftUsYZnieRkqLUgZPuXuIhW47oNkq0YkDupmHNhJPGeLMQ2tJqDSjpSa7B2ZIco+FdpmH43PLi68sLTJSa3OoMcw/Ll3N/anhddW9LNySsnjtAGZi5NwEo7EIa6nMGDed/Btgkv5Lo2cYngJWrhlj6vgQ/YcygoUO9gwxElmrcer1W6m/YBZjBdsq80x4grvjTeybGkNYuG3dI2zwqucMn/lst8anp3ez2C6Tp4o88cgTt/izTJHnEqUsItKkKyF5prBWoI1z8X2lEYAFTNtHLfiOWixB+AYhDViBENYpOKc+2gr+buaaNXf5fD0ZY8qwlFf4VPtSbgwtX/rRX+XkzzzvG86s6xdcwd++9TeJbc4XmnswViCF5VBnhJzT93LCc7t2HPuI8umY3VqBtYIwylCeRucKm0u0EWS5WnveGPevHQcoZRDKur4QC3mm6CaBy8sUYUOllNAfdmllIStZhBCWbjfg709dfZb2JrgmtT+/9C/pD7s8sLCeTzevYFB6bN84S16CeLyMGj9bYs9mKUyeonq8iwksYmz4G7p//5LoGYaLhRA0Nyr6HvEpTbaxnkR4pxeTrFborBNsqi/x1ol7ec/gHRzMFJ9YuAI9F1G9bJH39O89522n8xZ/Pv1ClDQksY9p+thYQSbXau957JOlHqVqAr5Zq0i0uyErcYQnDNVKDIVupMzABAaUxRqBNa6l21pB1gxoNyJ8pZlp1dibXniRD6sKb+u7l37V4dE0Z0CW+NKP/Con/3oHK++86WwxmCeBrNVIfm6ZK4OIea1JjEeochY7JdaFK3hnNJeHwqfqJfi+plKPkUpjjDt/91FY8sxDZxKrBSvNMisrZbqdEGsFQri8gtbuHgppwTeYzIVaqySxJHccEGsFi3GZ+W6V4w0nfz/Y12a6UWdad8+5lq1+lR9d/3lCL+eR5gQncsOb191HZ0ygUgPq3GWll1fw5lvIRGD6Lv6+favQMwwXCeH5tDca+g9mdCcqrOysISsuJlf1OmakHx1ZdlZn+a76QTLg4WQDsfaI1rf40FV/dtakZXBU5z9fvo7pdp1UK7ez+xZVy/CqGdZSLAgwuWPzBeUMhHU5Bi1pxwGzLReWKE8jIo0uWYQRUIQWooi1gzADCzYuknF+xqeaVzyhGIzC0jAl7o03c2+qGVYVHrnpA/zj//p12q+8uOEqslbjwB9t5wuX/w0AyybgkcYE98xuJPJzfmjwn887ws8YR/s2RiKlCyOsFcTdAJ0WxxuB7npIZQmjlLF6EynNafJT6mG1QIYacomQjhCljXu9a7pyYVQ79Wl0Ikp+xhVD04zXmny0edl5r+mFUZvdfTO0spA74y3cWj5AvDFDJhq7vHJWwlZ4HrJcRjTbBA3Bys5vg1BCCLFRCPEFIcReIcSjQogfLx4fFEJ8RghxoPg5cMZrflYIcVAIsU8I8apn8gL+pSC3bkRXDMFSSmOTR3dYYrMcVa8z847LOPqmQfouX+AtffcQCY+vdDdzJBmh7KX8/Q1/dF5p8wXT5e9PXEkzDklzj1KUEdQT+uptqpUYUzRcCQHSs+hcFkZCkrUCgjAn8DSySCiWo5SgnGIigw0MKtSstitKaUm6PngWkUs67YhUK744u4Mvxhdu7V6nAjomYCbr477ulrXQY1hVGP/Ph9YmW53/pim46UpOvG8Te2/907XF37YBuZFs61/g/bvff1a+BRyPY29znDTxiJcizEJItxO4smSmityLO1b4BgRIaTBGMr1cJ0nc9eS5Iuu6lvKolOLXEoSw6FSRa0l/qUvk50hpqAYJgacphSnWCo62BlHScPvydk6eZyBQWQa8duABAplzT3MriVW8+IrHWbq0hOjvO8ubEkGAHBvBNJqUT1niwWf/fnwxZ5gD/8lauxu4CXiPEGIP8DPA56y1O4DPFb9TPPcO4DLg1cB7hRDPTUXMMxBv6sdfVHjLHawS1I/nmG4M4yPEQ4J4Xc5L1+9npy84mOU0dYS2kh8d+8IF5x28b+VKFpertJsRK62IOPGR0pLmHu1uQBp7zjhIR/FFWIxWRTJBoLWk3Q0K4+CovsYI97xy+QShLEq5LkO7HEAmsdJiLTQ6Ee004Gcf/07uTc7fCGQwrORlTiYD/PPKdr7QHVkjSX142+cY/9AiK++8CXH95Wv/Vr7nJvb/wY0c/ssr+K9/9X4euekDZ82RvKezjcmVPi6tzrDdP7fX4G9aw9z90HbEdIToKqxvsVqgtdNcCP0cpEX5BhU445cnHnmmiDsB1kK7ESEAL8zxfI0xkjAsqhfGVS+6mY8UltDTdLIAbST9pZhGHNJOA+Y7FRbiCp/vbDnvvbnUX6DsZSymZb7S2cmrBh6huRWyiQHYuvG0cRAC63uIKKQ8pzHPAfLjk5YrrbXTwHTx/6YQYi+wHngD8OLisPcBXwR+unj8Q9baBDgihDgI3Ajc8XSf/L8k5q4NKU9Dd1MfeQnKx9tYo7HlkGAF4krGC6pO7uv27naaJmJrOMcN4bkcAG0Nn+mW+PDR6zBaYDOJtYJyNVmrQhgjsVqCKGJkQCmLMRbpW2yReU+TgHaRTIu7AQiLqDg3ejUmz1IP5WlMLYdUIkvu+bjjvqFp4vNfDr2FD176wXNavTWWpbzM0dYgK0nE73dewkcry/z42Ge5IvD5001fpfMrn6dTGAuDG+4SCY8VkxJby6EMNIKm8fladzufndvFunqD51fObVm+N0n52c+/lWjWQ2SQ1xxXwZbc9ft+TicOsF2FP+SMmc7kWn5lrR3UCnSqiKpJkXdwxlXnjjJtrWCxVXZGBmgmjifSsBHtbkgeZnjSYKzg0e4GstrMOUNyJ7yQsbDBgdYoB7qj9FXbCC3Iqz7LO8r09Ueou/cia1XoJlhAB4Kk3zFov76q9WzCU+IxCCG2ANcAdwJjhdHAWjsthFhNxa4HvnbGy04Wjz1nIaOI9kbNpk8YuiMeVoE8cQpbqZCMlomHYdP4IruCOU5qmM3qlGXKqyoHUeLsHVFbw5+sbOS9+2/BGIkf5mR4eEFOKchIc9fwY4xAa8/RbJVFZ9J5DVZQrsTEsY/WkiDMXYVCSzxfk6YeUSV1iUwt3PsnnqvjlzJE2VIpJXQKluTqwlhsl3nfypW8s/4g685oQZ7RBl9o+oMuifao+TEz3Rr/7/HX87Lhx5nwlziRDfLJmcvYf2IMmj7Ws6As9ZEWl42cwpMaYyVz3SrznTI7Bue5sjZJJLK1/EbXpszpnP965O2UJj1MYFFaOHFYCVo47sbqHBtVddedZcoZUAtCGoQSZJ2goE+7+2i0XDMOUSmlo0O0lvi+ppv4+L4mkAYpDUqa4v5LqqUYbST3LGxieuir54y/C4XPq/oeZjJ+EV0dUJEpCEt0bJm5q0dJ62XCbddiBQzfMYs5NYvxBMlYjixF6G8HwyCEqAJ/A/xHa21DXJgNd74nzmF1CCF+GPhhgIhnd5ZWjo9iQ0OwkjF7jU/fEYNeXMbcfAWz1wTEGzK21Rbol3BfMsix7hCvG7yfDeeZo/iZbok/O3IzkZ/Tjt2OXaokLhTQklxLhABrBMIzeIFeK03mqeMlrOYebMG6jLsBRgtKlRRZ0KZT4STkjK8pFYZCpwoK8ZI8c+XQ3HNciTRX3LuymbJMGfdWGPEadEzIx5dvoJlHDIctltMSI1GLdh4y063xufldlL2UR+bGaR/to3ZUUp3UpDVFYzvEdZ/UKCpeylxcouRla23PU0k//5RfxZ2dFoe7w7TzkFsG9nNyuR+VglWCtN+6L04ROlVKKf3lLmnkekJk0UimLchAozN3f7CAcMzPNPZRnkGnihQYHWwUFQv3NTVaklpBKciIvJzQyzFWUAmcN9LNfJaaZf6htZv39J845/PcEywxHLSRwhm4ZDyHhSXCpREWbsoIqinpUkTf4TryoKa+v8n0SyrwROpPzwJclGEQQvg4o/ABa+3fFg/PCCHWFd7COmCV7nUSOJMZsgE4R+/MWvvHwB8D1MXgs5oOlmwdJjrpk1cs3fU5mz7ZBinojockA5ZoIGZXdZqmsTzY3YQUhpujOeBst3xWt/mfh960RqoBx2QE8IodqxwaVlpucrIX5hitsAakZzC5xHY82qnEK7u26zTxMF0PUZCYVpNwUhpM6BZLrBWlckIiPbCCLPGwHQ8babo2RHcVXjnnWGMAY3dyWW2a491BjrcHCFVO2UuRWFppyEPxBElBEBood2nnAVnmIRPhhFzrEi+2GCWweaGNYMWawlTk57SykE4eEMicL67soJv67Bk9xb7OOMnBOuUM8mGL3dB11xwrvECjrWClG1EKsqJPRJHFHmTSSeYagVfJINBYLdG5PE2KEs7IdFPfMUSFy7NYK7CFkVjqlKiEKbUwoeKnNIo5IEoZPjO3hx/uO3pOODGmSmyMFnm4uZ598TpeeuVeju+6hPqxjPg7umwaWGIy7KOxtZ+BuzzkkZP4S3ue0tyObwUupiohgD8F9lprf+OMpz4GfF/x/+8DPnrG4+8QQoRCiK3ADuDJVUGfxVi8NKT/oMF4gmBRIQ+4nUNmlnwg59LRWXaEMxzN+5hNa7ygfoDh88iy/W3TjYyvRQmdpRKd5RJCuC+eX1B6jXWx9Cp028MasaYxIEo5WOHG1q8y+4QFCzpR+AVb0g9yvECvZeylsERRRrUSu1xGKUf6hlq1iwiMCzuK42oqZixssK68wmDYpuKlTHfra8m63EgCT7Nn4BRKGLLUc56MB51xQXudRNcMnp8z26lxvD3AVKPOUlxy9O12lVQrptt1R2n2M2pewt3zm/Fags6YJR/KnBBNOUVVcvwgJ009p76klRt4LR15SVYyN6g30K40a4W7Z17xfMEH0SsBKytlsq5L8lailFI5wfM1gXL3rS+MGSs1kYWTu9IukeeSx6bG+WBz7JzSri8Um4N5civpmIDvHrmD6ReUie49TLqvzuRKnzOgE67z0rS7eG1Btv4iJ4p/i3AxVYkXAO8CXiqEeKD4dxvwK8ArhBAHgFcUv2OtfRT4MPAY8EngPdbaZ6g975mHyy9A394m7XGPcEmgm00AjC8Y37TIjtosO/w5mqaERnJT6dzuuY5J+fT8HsDtWhR9DGmqiHwXLweeJteKWjlGeZooyhCRdslHYd2XX7n/+74rU2ax54hMRQJOF/F0kjgXOowySqWUXEuyQrvBWogqbqBupl35T/mabuYz1erj4dZ6MqsYC5vUvQRtBTOtKkJYttYXGau5idCNLCJQGmvAKov1IOuzZDXAgO9rQi8nN+6ccq3WmJfTjTpLrfJptiLClW37DXm/RviGbjMiTV3vwyrZy1faidMkPkoZlKcLA+jugc6Uax6LnAHVsYfREj/KwbOYjoeQLs/S7EQkiU+WKbQV9Jdi+sIurTzEk5q46M0QwjEmf3XvK/nvc1ed01z10vJJLq3O0NIhy7pCclUHGyds+FxKY7bKXLNCXrbIeg2rNeEiZPVnd2niYqoSX+X8eQOAl13gNb8M/PI3cV7PGohSiaxqkZ2Ezrhg8HFn49SGCZrrFRtLHXaUXOv10XSYUb/JZu/cD71lM2Y7NVcKa0aIwBBWUqdsXPAQjJZIYdEFy09rSRBlZIlHnnhYLZGBRoaaJPHwfVd6I1+17y7mzjKFNQKlDN12QGwDorKLmaU0gKJejlk2JZfEjD2wsLBQwkaa0XITTxiOtwao+Anz3SqZVgReSihzSl7GsTlHWxkpt5HKogNLMqxdaVFJrHJhQ8VP8YRmpNTisto0z6sc4gvN3XzmxKW04giTKIIgZyEpPKyRBE/ZtaTU6hcvK3Ii1gqS1CPveGS+XjOGQuDYjalC+MZp1eYC6WtnPDxD5htXtVF2Lfk60tcqeCQKqwQnmv340lAPYxZXKni+Xmtcs1bw4b3XMre9ynvX377GyxiSJa4sneBENohG8Kodezl0yTbCf97LxMAVpNU6nXGBnhiCqWnqx3P0eapVzyY8+5kW32KYLROoRIC1ZHVL5UQHNTrC4vMnaG7XrCs1eHH5ABrB4e4Il5dOnLfv4PGs4oRCUrfARbEDGuPUlwJPk2Tudb4yRGGGMY7Sa61AenYthas8p/motXTNVQXHQRTZdM9zik/GCIQEmj5xOyAKXPIvSz06qU9YqBpJ3+3QVlnIJK0sJDGKE0v9PDq9jlQrykVcbxAEMkcp6yZ1h202jCwhB1Nsqdi5BeBbKoEzClU/YWd1lvcM3s2rywlv7r8bIRxdGyNoNyKaaUg5TInKKVEpRUrjBFkySZ4ol09JPVaWy2SJh19N1/IpJheORi7AK+WuHGkchyEqpxgjiGMfqwU2VWssSikNFndbu0ngaNKZR2YkS3EJIS0D1Q61UkIQuFCjUk749KN7+Ez3dCeqEpLt/hwKy7KucE31GFMvHsB0u9Q+9gDDH3mEaMEyf2UVVasRzccX3mqfJegZhidBe0uV0oxAJCkYkI0u+fZ1LO4R9G9ZZmtpnrKwHM6GGQ2a3BDOnvMe2ho+MP98N4JNQFRNCKJ8jbCTa0mzG5HnrqdhtdlJKeOUmIzAGhwHQbqWYj9wCz+KMmSoEYHBj3KsFnRWSuhcOqajsNiCGp1pRRz7LhkY+1jAD3KUZ/BDJ9dOYNaG72apR9oK6Kb+WhKvpDJ21WaYGFgh0wqJZWffLMMDTbxyjlfLignVHivdiL4gZkO0zLsH7ljLu5zK+4hTn3I1cdoRSwHtNCDOXOeo83gkgZ/jhTlCWdLYUZtt5r6yq/0Q1uJCrMw9J5Vz/a0VZK3AsSALYRsEiMQdsyqjERfhlZSWZux6LXxp8KWhr9plrNykFiYo6URdtHUe2s899sazGqzGVEZmFUt5hRGvgX3JEmpoEJskmGaTyqwmqwtQCjXfJA/ls3qmZU+P4UnQ2KKoThrMQA2ZCVhu4CmJMBV2Dc1yfdkJsizkVW6pPn4WB2AVj2cJ98xsdHLvUUKS+cRdt9jCkqPgpqmCVTdZS4xxWXXb9ZCVDCFBlHN0JvF84RJ+FmIrnGKTZ8gz5ZJtuEQmAoIgJy5Jl9Uv3tekCiMlefGaMMrWyD9BJaXsZ3TygLzjQSppzFfoVn3WD63wmoEH2OYtMuY3mM+rrA+WaOmI6Uofaa7IjaRZDRCJZMfQHD+77pNMKEVVnr4vJ7IhhHDekkgFVkAtTJha6nMqz5nLCXRjnzz1XIIVF+eLwCCVJev6qEBTriR0uwEGR48OgpxWx4dEITKBVhYRuu5Tv5Rho6KHohPQP+iozn6ReMy1q6KAG3NX9lMilROpFrPNKrro05BRztKpOnuzjCsDV6UYViWqKuZQPEokMn5u9yf5/Re9jfLfuaFB5WNt2qN1bJ4jmm10IEBIeJam33qG4YkgFc0thuGHMtKhEl4HhBDQifE6MBo1We81eDAdZlFX2OK1gLMNQ2Iz/mDuxTTbEX3VmGqYMJ97RdzrvhRCWNcn4Wm0ESwvV1wFQoLIXYbddN2isJkk91d3RRy3oSjXrTIkvUCv8RSktJSrCXkuSTo+0jeuDGpOex/g5NHKRX+GLzW5Uagl53FoAVkzZLlUIhJZMRB2L7+zeD3vO3YTkedyCbesP0Qzi8jGJJtKS/zM8N1U5bnVmTtXtjoh29RDFKVCKSx9lS4rlNBKkheybTaTZJnEr6TU6l2S1CNpB4i2hx10ClZWC0R/ihe43AuxQhj3WLmSkqZFI1Yh4GKMQAYuiZnnLseRZQrPM2gtWTRl6mFM1U9oZE6sVxtZCORIonJKJ1N8ePkGrhx9GHDViV3hFAe6Y5zIhnhp+SA/+2rDrs/WMM0maqlJea7qpohrjZdYhCw8wWcheobhCSCU2w3CqQbLVw1hPFh54RY3V7FiKcsUXxgOJOMkxqfvPCImH2mNc9fsJqxxNfVu5lMKHH04STzC0KkZA4SemxGBEcjA6Sjo0Kx5nDaRoFwTENIWbD+L9Q1eqAnCbK2ByPNdwqzbDqjUYqR0rrbBVRDKlcSJvhRNSX3VrtN3KFSOJpt9yBxUVyKMIO/PybTiRDZEEk1y3cd+gl0/v596dwqkJK9VeXTXFSztiLjuhx/gF0YeRInonPuxpDvsXRh33ksuUYlA5jDfcgYkz9w9tLGCIlm4mltJi4WMFtiwUGdqho6HrQVppiATCCOwoUYpS5qqok39tCaFtQLP02ut2Z40+JGm1Y4wxXPTzRolP6caJLSK/EeSeQRhzkClS5Z6TMd9Z13bLr/NV70O83mNpvV4yw33cM8Lrif45N3YbkxSl1Su3IlodLHCKYLpRuMb/4I+g+gZhifAqqKvWGrQmhihuzFnekRifYvoS7iyfIIxJTFWsiFYPGuMGsBDacwfHb2FZicqOiMFSe6++FqvshcFBrdjtpOAtHjeGIHNPGQpdyzIULvEWeQUiZRnyDq+ey5wSbpOErlFE7mmKgsubCi8A+EbbC6xGcRFC7hXVDZyLcmNK2kG9QZx5iFX85oasI5M9fH5K5jwltj9a6fIl5ZOX2yng5yZZehLcPyuXbz3g1P8h4Fzy7ZNawqSkcImCpmD9UBbwVClQ6NRchRnaZGhxsSFsbWO0iilcQbMK/pHQu2Sj7iKDQGYjodXzsljD7+UUSnHZFo5L2W181ILN7CmKNm6D9oiis+k1YnoH1pmMOwQ5z6eNK4js9phtNxECstl1bN5e1Xhsy2cZVmXeSDewCvqj/CRt17Hnr0bIUlZuQTmXhQSnawhE+jfuA4efXYahl7y8QmQj9QdV99aOusNm7fNsnHPKUa3LnDZxml2BaeY0Yamjrg6PJsu2zEpv3TyNZxaqq0lGbupTzdx6kl5UWKMuwFJ4pEVBkFK6zoG7enQwCbKVSWExSSFFxM5Ug/SEpQyVBFenPWJWoFXNEwJYfECV31AWacQpQzlKGV0oLnWM6G1YLFbJu4GZFVLMmDI+jSy7QyMsYIh1WbulokLSpSZhx7nNz/7Hefcj+m8xQdXriFNFTpxu3tesejQ0u2E9AUxlVpcdEy6BS6bnmuUwJUs89TDr6XOCBTXJZTFr2REpdTxHQoPw8m/OV/dK2TmhbDksecIXUXXZRL7pIlrex/qb1EKMpebyT1OtvoBmKissKV/kR39c5S9jCsHp3hD7aGzrrEsAzSSpok4kowypNp8x5WPMPeSDRAGZHXLzq2nUDFUpi3xuieeYvWtRM9jeAIkwxGlWYEol2BdTORlrCQRw+U2Lx1+nEt8y0fbm1DCsNM/O8P8weYmHp0Zd1yDotae55I884rWaOH4/MUXNs8lxvgYLcnbbg6jVI62iwHPz/F8V4nQmcvcIywYQdr1UcWINuEZF3MX5J8gyFHCEqc+UhkC5ZqE4m6AzhWtdnTGOSjC0LnPjXJCa1jCiu8IVJ5jXhoraJqI3/zvv89/+a63MHV8F0hLdX/A0MMZlQdOYNOUcKLt7qHN+Gh7mA+dupF9c6N0W6EzdB3laNRVg0icFFvkZQyWu0x2QndtEkxfvmYEsE79OghzOlNVqGVYASaTCGldBUeAqjkjUQlTtHFiNqUwpRwaWsYlXaXnGqxW76lY7VYvOiqBNWn+sp+5Mq3S9PuO3HR5ZZIt3rk9PgrDbFqnqhIyq7i6epxPvOhyqlNjCOPCxeGHM8r75mleOUrgeefOJXkWoGcYngBLO32qUwarJHol4NDJTVgB1WtPckPpMMsm51gyzAsr+89RZ/r9A7cSdwKqtdjVyRsRpXpMuRKTJD4Wu/ZFz1OFkBAEKRRDUzACFeZOYCWX6Fyt6Qoo34mgCmXxShlZO3DNVx0FdWcgVr0BTznX3bVoO8HYVcqwBTzfkGSuUzNLPWrVLoNhh+WgRFtGa2U9W3Ol0IdPrufdx74PqSybRhd56ZV7+c7he7jhVQuUheKvm1tY1BX+ou8fOJRZbu9u4QOTN3HgxBhqJkAJXMt4V6BSgZXOxdFdb22GhM6ku/5qhgDylg9FSGU7Hh0tsIGBVGHQTgk7lc5bEq4l3RhBN/XXmspKfs5Cs0LS8dca01bLoqudl91OiK80g+UuWa4IPRdqdDKfpbhMoj1aWcimyhKByM+rOnVFOMX9nc3MpTUeSjYypFpUBrpk1SrBiqTqJTz2EkVlxzq8rqU+PkZ+cvIZ/R5/I+gZhidAMgDDD2eY/grhrEf1hCXpFyhhGFFdjuVllDBcHjT5+oapkUq70BZ0TMZKf9ftPgVRKM8VUZDS7oRrugvGCEphShK4Rh8p3Zc6DzR5otaSjjIwBQnIrdo8cb0KtgSeZzDC7YCZdh6BtYJotSyaeFSrMWFfh2arRJ66kMLzDMZzjUpzcZVO6mM6HpQMIpaQSEQq0KHFa0pUKjiyFHKkNswDcxNIAf2lLuvKK8x06rz37hdDIb/mL3lUZwV+25Gf4iG3iI1vERpM4Nq0W1nIYqtc0LvdfdSZdFTmVLn38+xawpWC+KW7hYxd4DwBCkp4tkqS0pI8yJzkW6nQjdDu/kovJ018R0kPi6Rw7lGLEtqp7xSehGWxW6YaukTkHStbKG1IoX4uZ2VVbXtfY5SRoMn26gy1UowwFWpHLItJmStvPET7mpD9ByaoH5sgmJw6Zz7Jtxo9w3ABqIEBkrGcrKpY3l7DBBYdCrrjhl19MxzN+jmQjnNN6eh5G6ZeNfYYh7sjnOz0c3yln/5yl27m0YpD8lxSjlJCP19LfDldBfdal3iU5J5LmFmcBLwxjoiji1g7LDm5dOUb8kZQeBvu/ZJ2gC2n6FxiYo9O8VosZLlyJCql0XFA0giR/TFB4MqYrVWBWGkRicJru5YiryvQmSVccpUEoRV5Q9I+GqK6gmYE092NzqvKKNqnwfjgdSw6EKjUEi1asqrACgGB6wFBCxLtuSnUVVl0lQpo+QTDXaeM7TsOA8JC6sIm3fBRbYmuAL6hVErR2uUNpGcd3wPopK5xKvAy55UUQje20LcIi4HBgdIulEqiollLUI8SOkUDma9y5nWFvY1xVsa65yScPRTrgmVK3jgPrqxnazjLNcOT3LFhjNKcIc59AqUZitpEQ13igQpRtYop+m+eLeglHy8A0VeDULN0qYd97SLZcM7ovS2EFuwozbCoq/hCsytYOu/rX159jIqXuNjU064kpsxaQ5AQds1IrCbIpLRO8DXMnWhr0fBkjcAPcmwu3TTnomlKa5dBF9JAUBTEC4VpmzgXWSpnDKx2BkZ5hjTxnXqTFajI9V4IYUni1Yx9UcmINCIViAy8WCBTiOYF0YJFxRaRg9cR+C1Hpsrqhs6EprM1I+13x1iBa8cecMck/YKsUqg9G2c83LgowVyrQpz6ZB0n9moSZwyEsE7hGVzDVkGltoU+hfVwJcpE0Wk79qIp5PeDMCeMXBlXClskfhWep9fyPi6pCpmWpFqRaA+LG0xjrUBJg680jThioVOhFGTMtasczM4tTysh2RbMUvUSYu2zP17HzvIpli/L8RLD1GKdQOYcWBohXo6IljRifOTp/Oo+LegZhgsgH+9HzQfkJbh8xE2zNp7Ebuyy0V9ACkMk0vNyFwAi4STSAQLldqOoiFmDIuZd7apcVWxaHboKILzV4N7lA9LEkY3SxEd5GhlobKF0LKV1tXzravjkApFK0kYhpx44SrTva0ql1LnMArLYwxalz7gbYIpFoI2g0wwxLR+vWPSm6J6UGc7QKOc1yBTyCLKqRa3rsGnXDOMbF8krlrQuMIFrx7YS0hpkdYvxQSUQLoHfFAgtkF1XKi2FKUJaompKVE9Q1Yw8d4zOIMrww3ytUUpVMmQlw1Rzl3MwAhMrkvbpfE/g5egibFh11k2hlwkUYZ1c89w6qU8jjhgrNxkqddY6UAFHXe+4hishLB9rXHPO3AmAG8JZNpaX8KRhMavQ0SFjmxfpDCvyuRL3HtiC+MgQtcd9oqkmLK18w9/TZwq9UOICWN5ZwW8JhIbFpII/77G0y+NF2x4hEhmZ9dgSzJ/jSq6iaXy62qfux3Rzn1bqxEa1kZSihCT3MKtNUdIQ+q6JqtsO1zwC6xXdgLLYMYXFaIHn43bKtWqHQngW4WmMV+gfKuO4C8ISVlLS2CNeCdFVgee55ygqGUa6JGgUZRgrSOIAGj7BiiMgQbGzA3kJdCTIqhYrXajgvAXNSK3ruhK7ZUxZ097kEqwMpNA4LfmW1SR6WeJ1BCpxxkElgnZUYsOlywC0u+GaZqVJi7ACd81CGcKyo3HHrRAKr2i1SmMNLszQknbXybjVKjGh7wbMzCU+SRw4nYq1ZjYJSpNlToB3KSnTF7jXtBKnal0u8hQlP8cCX5vfyt6+e9do0asYViU2hQt0Kz57ylOs6DKDpQ57b8zBN8gln76DXfJSCRP5ML/wdH1tnzb0DMMF0B0RqNjtdI9PjVGZESxepXle/TDLpozCnpcCvYoD6RgArcxNUs61QhuX/dZGkuaKwNOEoZtN6StDfIZQsyq78mS+Wo0oWo69Qu78tC5hQuIruoQYLdyUa3k6kbXaB5HoYghsQf012rnbSNddmbUCZDnBV5r2SgkbGUxXogrpgXjIoksGUUzPNqFBaOEWu28gk8zP17BW0F/qIis5Rrn3r1QT2qksjJfB4JFEhryp8JsSv+k8D5G7xR8VuZcs87DGoq0jMill6Kw4Q6xXG86UAe3oyi5uEQVj0p13lrjmqzR0Ai9pqtY0Gyik4YyRJM2QuORTKrkPYaFdpuRlDJU6LIsSs8tVhiodIk+seVatLOBDSzdy5djZfAZfKF5c3s9KXsYXmrJMCaQm6EtIlyJM2dCZiAhXLHPXVhk1lyP3Hwel0MvLz4pEZM8wnAciDOmOWia+qmmPKTqNgLQftu6eZtxfYTIb4LJwknXq/N7CvG7zUOdKFpIKK2lEN/OJs9O3WklDLcppJwHlYrhqWnQVAuiOayvOMw+EdWSbQgF6taIRRhlp6tHshE7sdPW9Cyl5cLvrat7AFpLyttB68AKNLYRckthxFdLUo1JLEZ4BacmHLLqkEFpgIoNXT8ljD9Fdje0tSItfSdd6ERaXKmgjMKlCNj1Mye3IquQo2qblI6sZpuWjB3J0SSIyD6VwsnNaoYTFk4aMgiGaO+8pzxXS15i2j/Yl1VpMriXdduiqJpHGSon0Cy6HsK5Kodw9AzfVy4sydK6QuJZurEtkmkw6+TtceDbXrlLyM4ZLbTqpq1w0ig7MvlJMXxjz0Mp65ofvOCcBvdkTTARLxMZnzF9morzCPjnq7lc9YWVrlf6DmtYWwdINEeWDl1OdtAzds4Dee+Bbbhx6huE8kP196Kqh+vApukPrGZhYoTMYcFn/NA0dkVlFTcb44vwqPI+kNY53B1lKygRS09DhWm5hdd5kJ3ULNlCaTurTbpawWuBHOWnntCakzSU6cElHp0ngEoOeNIgwK6TmraM6W1em1Lkijz1K9dh5C01H/HGdVo79JyUI5aTcpDL45RTPM7STANNazfSbtZ4E1+F5hjdSlAeVZ1DKkqcSWzAzlxaqLvwpjEKaeHh+TlrI1UtpsbnAekBgiMc1XtuVIuthTNVL2JeOkBQTvpEWE3vowrDhG6yWdGO/kLkz5FIRljPiZoiJ1Vqnpe9rN7rOFISywA2r8YoyppC20NMsEqKF1J44I9/T9XyGKh2MFbQ6ofO4IkHVTzjR7OdwHjD8dammqowY91ZY0FWGVIud5VM8Ul/Hsp8zUW+w79IAvxmQ9ee89LLHGbu2wSONCY6MbWODr7CPHviWEp96ycfzoHPdZvwliW22aGwDJS1GS9aHyxgkNRmz0cvO+9qWiTmeDRJrJ5iaGkWuXdjgKY0uaMWZVk6azUjarQixKtsGiFK+JluGdS3WttAUCAJNver8e0+6pKVSFpMpV6nIFUa7BGSaOCZlUHUUYhG4RZ503YDXNPVoLFZIYx9rJP3lLp1OiGpL59YXcnIETq3a97VrXVYWVdJrsxqEsKhC0dqvpqel6DxXLTFWkGdOuxJlyVs+tqxBOk9CaIGOXAL1VKvGQux236iSOkk2LRAdhW67uZ4qOj1kxhjprpeiLVsVk6l8l2PoFgraLsnrErBGu4Y2AQUZTOOFOWE5czL7qxWLItnYSEN8qan4KaP9LQarHbQVnGrX0UaS2fMnoHf4C0xl/WTW4+roONcOn8BXTl9yy8Y5lq9PqY83+coXruDvPvpCAF7y9rvZ9+467ddfhxoeehq/1U8NPY/h6yEEs9f6DOw1CN8nGctJ52vYTLI5mKcs3SyAAXlu5yDAY5nivvYWVtISce6RaYmnNIGXEyhNq3BFA6/gDMSec7t9jcklaSbxQk0YZWSZIiinzgvQshCMdWpDaTErYk0CrZjqLKVLzCWey0FYKxAUHkXmmpOsEc6ArIqeSMvwQJN6GHPK9qFLhahJpN2k6CL5F3cClK+dhJp2CUvP1y4BKKzr7ozdTAdbhAB4Bh07nUUVOn1IkxXTtLRAVTRpyRlA1ZLMz9WRo5b+UkyaO21HfANd11VJkRuQvsUUas8mdx6C0QLbdRqYfbUOqpgWLn1nuPwzqkC1UkyaK1oqWps7YY2lVE7W5loa4yjs7SRgIOrSzgK0FYyU2yx0y044N0yIRA6c6z1u8EJW8jIfX76S66tH2FGa4Wh9iJGoxYbyMkNRm8dmxrnktw6hZ+fQf7qez/zADdz0qr3s2zrCzPU72fH7x8gnzxFZf8bR8xi+Dt7EOrrrc+pHumRbx5EVx9Wf2LhAv+oQCM2oap4jI76KU3kfJzv9AJT8DClguNxBAO2COGQsSIEbk9YN1lh+bgBtoalQqB07UpMhLFh53ioXwtOue3J1ClPhAq8OfZXKydGXyonTLCx2a+E7A7IqFyd9JwO3GtcrzzUwCe2eX93981Rh2j5ZI4Ri+I1JFd1G5Fqk7ekSoFBuuE001EWVtJOO8wy6qzCxB5njISBcclCUtPMsAouNlavceNmapH6pljjZOGlBO4FXz9OucmMEfilzMzsLQ4Rn6MQhSyuVtRItFANui8ThcqtMJw6Jwgy/GICbdzxay2Xy3A2j8f2cciXGWkEnC8i0ohqk1P2YjbXltYnZ6tyxKYAbSPPy2qM084jpbICKTLiq/yS+1EyEy1xWnyaeqmAWFsFa8hMn2fSLd7D0nnUsTvbzPa/5Egfesxnh/8sLx/Y8hq9D43kbwTPM3Fihtclw1aZDzHer7OibI7Y+EkPbBsC59WttDXN5fa2Vd7FTQmtJWIjDqqJZyVpBbtzQl9XEF4DyXfnM9zV+IeKilC3CEEM7DpDSEgUZ2nNu8up4d5sqKBq2Vl1nUWhMCsGaR2LbHjZ0A29dC7MkCjLGKw0C6TwSArdD6yJWdxLsFm3ACSu6x4QpOhf7U6yRZImHyRQq1MW1Oe8gqBQLs+12cwK7tvuropnJFm+9Ck8Y1tUbHNcDzjsq5ZjYcyFO2b1GFGXXVZWnoOyMpzXOoOpcQgiBp4kT1w9itCSMMrR2A2x8T5OkHoP9LbKqohMHZKmH5zkDHPk5uZasdCPKYUpuJDPdGr7UrK8ss5yWCcWFVZguD5pUVEpmFXN5DV9oRoImO6NpjJWorsTqM15vLeaBx9j1k2U+c9uL8HcIRBRis/PPFn2m0DMMZ0IIFi9ViC50buywZXSRiVKDkajFdbVjjKgGc7pOv+xyPtfRYDmeDtHKnAJxOcic8g+ubyHy8rWOv04xhNXmci0MWO2GzDJvrYqxWqkIfNdQlaZqbUeTyqBTb61TM2s5WrQqpMxUYVySQrdhtX0Z4cRSs3aALOWEvhsqc6I1gM6cMbDSogJTSL5JdLfwkHzjVJiLRN6qxiJKE7eitTkWeVocn7sBN6464HgM0jdY5RqibLiqdC2wJY1IlJvQVXMaFeUwXSspqkqG8U/rM3jF3887HkZZfC9fMwim67m/7WtElK4xHXWRXzBa0e4GjPS3KIWZI3ZJseZdKWlICg+jEqZ40tAfdSl7KZ4wHGsOUPUTruk/wUbvwo73gCzx/PpBHu2s51h3iH6/w4DXQWG5uXSCsatmkOehRJtOh8pH7qQiBOZbUKHohRJnQA0O0NmcEy64BJ4UFoOgpDIikVIRGWWRsM07f7Z4Xnfp6IDMKJqF6nAlSElzt9CbcVhMcXLVAz/M3W4q3Gj7NPZpt5yoSxy7xeyagATd2HdGIXW5hSxTjtgk7ZqoyepOTkGTDgsXGVFoO1iByN3fVp5BVTNKlZRAaQ6vDDPbqDr9yCIUCMLM7bqroY4WrlRpnL6EsW7Rg9NxkOUcVTmdlLVZUULMnFoTuYSuM4Y2dg1hgtVzc5qNWOgulji0OMRsu+pUm1MPqRyPISineIFjM2ZdnzzznKGSZk3OTvl6LVQxRpJk7p6FYea4EJ0QnbpEbaMbnR5s23ZzJii4JlJaqlGCV4QMjSSi4qUYXOfmvvlROjqgVFSnfuD4i3jjgVfxaHp67oQSkluLOSNLaYnpuI9DnRGWtWvZ/tlLPo7YNHHhL+W3qGzZMwxnQNSqiFLO2D0Zw5+OODw9TGYUuVHENuBoPkQgNPULJB7vTkb559mtTK/UyQsPYTV8iPwcTzmV4VKQrQ1PjcLM7abF5KTVLsgoytb0CYWAIHCzEU7Ph3BK0iaT2MSNcIuqKdIza2QfKSxhkDsFaL8IYzy302u9Kk0PC60yzTh0hkbgyE+F4hHWCcWIRGFDQzTexosy4k5QLGqImyFZK8Dmck2mzhksiklZIJV1hkWL00YhOt0YRmCcpkRxnqv3KM088thDF7MyjHYLWinjriN1U8Gzlpv4XY4SV33oS/AHYqRyKtxJ4uEpQ6WcuERn6IRkK+FpF311Tofva8phSiVyz1lciffUUo1DK8PEuc9IpU0pyOjq057jTLdG9t2KH/rpn2Bv2ll7fEyVWBesIIVlOS0x2enj/tZm7ownuCKY5/jrvnXVhwuhZxjOQDYxAC2fyr3HGf7sEexyQFf7ZFayyV/AFzn9snvePnxtDY90N5LmHnkuabcjZho1FttlklzR7ERuqIxxvPwk9VDKxfi2YCyGYUbou3FsldDFv0GQ01ftoqRz60PflTKT2F9ThRa+2y2zzCkjmcy5zK12RKsVkXZcA5FrkTwtTR8EOeUwo7/SZbTWIghcotXm0r2PltjcjZq2yiICsyZga2OFztyMzKiWIEPXv1Grdh3NWLhKSZ4pyIVbwIHBRqfVT23sDFFUciVOIVzuQZZdT8mqloJQjreQtX2ytu9IUkU5149yyIVLTApLNwlOb7KFLHw5zFzoVRgtP8gplxO0lqy0S6y0S2vVCkcjh0Y7Whv80y3ITaUoIytK0KGXM1FdoaRSprUzAh/d+Q/s/w+b6Q5LAnH6On2h2BGeIlIZUlgS7XG8MwDAova58nV7Eddd9gx8o79x9HIMZ6A7FuE1JTbLMFsn6Nu4wnDYoixTyjIhNj4TXg6cO5D0gTTnvpWNCGEZqHWIUyfh1k18x0MoJkMpaegkAUZLEiNcI1RBOtJakitXlkwyj1YzQnmGUpCRFbvkakOP52unm1h0X6KLeY0FJdpqsdaCvTrWTlVclr3bCd2iKsqoAuhkPq2lMjLQeIXEuudrMhM4fcWib6O5WHGLODDYVGF8N4NBFpWOND/NHMyTInkZOWPjTsYiIsdMxDiDkUgfm0tyW9CtM0mzGxL6uZtCPZTT6oZryVTddpqRQrrZnZm0qFJOf61LkjnBF1P0QHRbIYuJa1E3VtDphIVQ7ulMZ1+lS6A0Xfwin+E8kzj1HW3dz/GloeRndDOfbu6zpbbI8dYAifE4lpdZpwwSwSPv+h1WTMqoOpsqvydYoO65UncjiYhURtsEfLx5JetLyxz8H8P0/co1yK8+8C1nPULPYzgLSV0SzQlsu8PsDTVev+Vhqsp9mEfTYZZNmfJ5ypTTeYs/nb+F2U6NwVKHsp+t9fKbwmWv1ruUw5RqlKwlyPLEQ0hDELodTClHygn9HCEsQZSTZ4r/f3vvHW7ZVZ55/tbOe59wU+UoqVTKCIEEiGCCMMEGG+w2GNu06XbP0N1m2nbb0w7jmXa7o91jY8bj7nHjjAMYG2Nokk2UAIECyqEklVSlyuHmc87Oa6/541tnVwmVqm6ZklSC+z6Pnnvr6Jx717337G+v9X1v8F1NP8kBGC3GZKOgDYBRjowcvUCTdOVrNLbx50c1ST/HD2uqTFiEoSfBr412SHMRdvmuZim19G4j5iitcapz4vsox7TmKzgGv1cQBLU4UhlFUyuG85IUpZyGoFvKUSSTXkhnJhVqtFZQORDqlqOBEmm42y9PCpNRVLVrOR9y9/dtalY9EHl2XYlBrqMMi8uJqFhtz6AsfEzmta7Qo1EkxyMgHYVU1iW7MYqRdXuanBixcXKZyX7a9h6KysN1GhK/JPBqlrKI43mX2Kt4fmc/zw9K9tUpr7r3h3isqlh3Cn+OzW7CK/oPM+ln+K7GUw2DJubjB57Hxz5zvexYfvUYSz/6kqf00nwmsVoYTkKdQDhvUEHA8kWwzl8m0z6hU7OkO0SqahtNYyw1GX+0eC17h9NoIzRZ39GEfiWkprCm15U3Q2Vj4UO/lug4BXXpURYegdU41LXL4iBmmNo3sY2wG+ahjB7dEzyEpnLE79CO/bR28IOaICkJY2kcFnkgF569oJcGkobVSQom7RFFDEiEa2Bs0w5AD2z4rte0DVJsQTJatTF5rtvguBK+68a13J1tDoMXaFTlYEqHsvBaUhWhsCiVnWq4oc23SD1MLv2EXlSwsb9MY48EoV+1vQW3W+PbXQBGjiPjpmFZu8L6bBQq0kxOjvBt+O5Y4u4H9QnHpsojKwIy6yBdackTTcKSYR4yGMYsZRHaOPSCgqkkY7mICJyay8JDdJ2ILV7M6zbuYn89wdsfey3DJn/C+8RVDi+MDuArzbbOAju7x7g22suGzoDJh2DwjTU8dmyGDe/ew67/51LKN74InkLS/0xgtTCcBOMqHA3mws2sveoYPScjawLW+ANy4xGp6kn9hVvyPrcuXNA6/iyVMXNph35UEPk1viveC74r1OBBJr0Gz5emILbB1xjxDmjP8EbupN1JOb82jdz1wqQiSQpp2DlCWVbIGz6w/YcksiO+wkUv+5LC1C/b0aPva6Y7Kb2wYJCHVNoVUxKtMNqRIFffEooaoRc7roHCxe2Jp7zjyQVd5Z4kbluMORjGujD7QS1GsvbC96LK7iQUGEWTepgGwqiUqYRWRDMZM5NDSi1kp9BOJMY+jXFcyu8vEKVpU7iMBhHaahuSsBRdh5L8yqwIqLVLp5cTRhVRWJFEJVFQ0e3kkjJeC8dhlIUcWehR1q6dTDR0O8LCXLR5lRuTZS6dPIbnNDxarpOfW7n81PTt/PMb38XwLYZrbvoX7KmGfD0/wVHY7gWsCwZ4jiguL/ZzrugfZnCBYssXcy7+V4cZ/dtNYOD5/+lOdv/mi/A2rH9a3utnwmphsFCex2izQfuQbu1xxdRR0iak4xZEquLOpW08Um540useKTeQa4+ilmi5pTyiqF2GRShmpI4Ik0Dm8uMx49iQ1fEbXF+Lc5F1TnJdmbOPMyGysSehJ8ShflQw2U0lwzKsW/elNA1xlaEfFTQ2t4LoRLyb/KBSdJbziNlhh7p2ySof39XCW8jEfTqJStxODdbkpbHaidZJyShRco7zHVwROuWDUPQJuUe6HIm1Wig9BtdrCMNalI3WkFWVDk3lUuSBOFlZWrf8PqTR57tCxpLjmbBCfV+3zUK/Uwl1O3dJUxkJKyW7DxlhSnhtnvuMlqO2kQsQ+jWdoCQMayFJBTVhWFNUPqNCJi/a9iNq7ZBWAWXjMh2M6HkFtw52tOPJO8oel/3WED03z8W/WXHDp3+WH/nMv2zfK6HyeX68j7pxOVhM8kDV4XjZQ8eG4N696OPHcW68kyt+eT+f+uyL+IU3fpyH37sRd/26c/5+PxNWC4OF8jyqKU0wNOhIuAu58cl0wIFymuN5l8Y8+deVNkEbAtsNS+mkOw2NkS1qpV08t6HSDqNMmIuduBAfBmtA2rE+heI0JDP5MCpFsGRkNwByJ3Q9zagIpLmpHerSpa5cIuvMlBU+R5fEFwHH4Ec1ypFm33j0qbVDVvqM0rC9MIZFSBBWGCW5kGketDuDpnaIuwVOpxbmZCaEpSSSO3dsextep5L0bZD+QyIjV+XKEWMcS6dtOK+uHZiocEMtLEsl4qymcZiI5U5+dKnHkeVeS4qMogrPlYDZypLAlDXN7UxnGGB5GEtzMtR4Nn4uzwJMI5mYnis2e7510B6VAXFQEYdl25A1RqzdkrCUsay1znedhiOjPnfNb2HvcJojeY/fPnYDB+ohr44qHn2nTBvMN+5n0+cVW3ccf8L75apgjkzLkSVvfPpeRh0bmuVh+5z6yFF2/Ls7+Z3feys/ePldPPaeHc/4sWK1MIzhymw9nq0oO/JrSXXITDCkMi6LWcy+YvpJLxvqiIlQMim1pSEXlUdR+e3213Wa1kexaRR6LJu24zht5dLjUeR4hBiFFYktILp2yCsZcfqepm6EeGTsUQSwQbguvi/nZz+piGO5Gzp+Q9Ip8INazEuMPD8KKqajEcsjy80IZTvv2vwJ5QhRqcgD+V7WmMWxP2dt1wS09vZaO4RhTV26ZEsRYSzHH2OgHAVQiyLScU9oQ+Q4JYVyLHtOiwDf02yeWGJDbyBcEKehsOG5nifNy7HHRDYKaErxc3QckYQHVhXaWKp4EpXklScFoQgYjSIJAip9au3iu/K3ct2GiTAndHUrgutFBVNhSujVTEUpsSfjx/es+yLr3RhXOfjLJ6YdSxe53PS8jz7h/bLRTXjTmnvYECwzaGK6XoEJGxHPnIQmz9n8P+7mI1+4nhu+507UtVeci3f5irFaGMZwHJykRkcOyztgezwLwIS1MPJcfUp5baiETjxW5I0xJjZpLTJrg7VE0A5l5dmQVNXuFHxfzsJpGpLbkVte2DesDZcZZSFV5ZKV0lhzvQY/kos+z2R+H4QiPqpto3N5ISEdhjIZiQrK4kQ/wLNd91z7bVMu6JY4kc2tsFZybqTbrTeOwY3E8anIfCEQ2Sbk2JFZOYYsC+So4DXo2rFuTI4dderWqq1JPZqFQHotQBTILml+lNBYQxRHGVynoR+daOjlWUCeBW0WpdjgGyamRoRBbaPuFaFf0YuLtsgVlcdoGFFWnnAlrHQ9ywLywictfapajnuHlvscH3TE9g3x0Hh8aYq5UUJaB3zPuvv4jW0f4+ogwlculdFk6xu8jXLk3Pz6ffzUoRfx4jvfduJ9oRyuCQ+wa7Sex8q1rPGGqLjG6T55ktGMRlzyhwvcObuZfW98ZicVq4XBQnkSK18lDuVMQ+KUDHVI3vgcLUQY9bapW5/0uglPmoNjl6bCbm8NMhrsxoW8QT1NkhQyd69cysojiiUQxrM2bXXlEoYVWRqynEZ4njzXGEuNPin3sg26tQ7QuhRCUl27LA+SJygdm0IKS62F/OS4pt0R1NolrQJCv5bUKnv3rwe2q+8Ym4IlfQJlg2ZV7tCULnUujtOVja8fp1o1pfhDoKAaBOSLkRSZQBN1SsmDsMG1qlY4sYxs+1FBFDzR66LQHsdHXUK3JgkqXOsB0Zq7jjkcbkNReeS2JwOQlz5F5RGGlTA6LZ3bH3Mk4pI4LOkkRatgHRcb6W0YepGsa6aTsr4rEXajKuBF8WNs807wFXzl8uAP/b/s/MQsxfe8iE2dJR78uatI/tsk+qRY60hJmvjO8Cg9N6M3kaH6p77w9YO7Wbp5PcUl2TOqsjxjYVBKRUqpW5VSdyul7ldK/ap9fFop9Vml1CP249RJr/klpdRupdRDSqk3PJ0/wDnDuhmagU/nQI5xDJGqSNySwngUjXSod/rZk1622V+g6xdMx6L/Hyshx0cFbRORdKNkS+rL5KHMPfJMEqTGkWhxUtCJShE4VS79JCcKqzZcJowqoqiiKsXhKYwqexcGPxb5sO/LNtrzdHs2RoGxFul+XNHrZhLnbmnHjT0CRX5NVXr4QY0KG6rF6AR3oB1TOjLGVLRjTcdrWh3DRC+T57uNsCFdg5OI2Uoz8CU4x3o0qlx0F02iCWNJnZoddigqH4M0OEvtEro1/Shv2ZCOJTaNJzheIEYrINwFbaPtwR6vjGI0jMQo1v6sCtrmomOPcb6rKUuhTnfjgsDTTCUZ65IBoStfL3A0Xb/k0oljbLUcl5MRKp/3bbydP/jd36LjlgSPz/H4mxXNSdLsC7yEd2+4kReGR3BomO6kmOgpLvpGs+6Oin4/w9385Ob304WV7BgK4AZjzPOBa4A3KqWuB34R+LwxZifweftvlFJXAO8ArgTeCPx3pZ7CvOA8QrWuB2GDm5YQNvTcjNCpcDDM5l02dZdOSVxZ6y7TGIcG1V7koTUEKSqPWrutBXlR+e0Wu9uTbfH4zlYWcrG7TkMYV7iuYWGQyM7C3t0cpyEJKuKkQI25B7YAlcvihpyEEjJTlUIyijulmLMYJUy+QLd3xPH37gbyBk+LoH0sSEoINfXIJx1J/oXyGmhAxRo1UbY+C+2xwBbBwK9FyGQkB8L1tJVbywixHAaYpQBViqrSsY3JsSahLGWXlFgdw5FBj7TyWS4iRqXfGriMdzdBULe9BqD1V/CDWgJsxmIzSwUXmrQ0GsdHPlm/QxjU1t6/oqxlIgJiwScfa3pBzrQ/Ou37aYff5bsn72fXT2/iy29+L5d8+p+3+glXObw8qkiUIm1C5kYJKn9qWXWye4H1vQH1uonTfs9ziTMWBiMYt0x9+58B3gL8iX38T4C32s/fAnzIGFMYY/YAu4EXn8tFPx1ofGnmpdt6TK0Z4GKYrXrMVR104/DS6cee8rW1kTHlmBhT1S69SC62WkuHPfJrIfjYCy9NhVbtuhJm0hiRFg+yiCIXGnUxlJ5CXsko1LWd8bGlW904RJHddhu5MIvKE98FLTbxZeGhhp5s2wHf1dTabZmUjVFktVB/fVfjeprJbsaGyQGT0yOolKVzW61FKWSlphZilR/UIuQyMjHQWizyfV/LccJICC+1glqMWpXfYFxDEzb4E0UrDNNW7GQaR7b5Xn1SwKzLwLpfjac3T/j7aaftBdSVSzoMqSuXsnYpMsvliES6boximIfoxqFuHIrapahkpxDYRKp1yYBN/WUmgoxN8RJdryB0a3LtUzYeQx1yW7HuCUeEb8ZbO0Puftv7+OvBVVz+6/PcXWxu/5+vXKacmNz4oqRdPk0S1fySNF3Xnlq893RgRT0GpZSrlLoLOAZ81hhzC7DeGHMYwH4cD1s3Aydnwh+wj33z13y3Uup2pdTtFU/ekj3jUODN+jS+YlN/GW33yYuVkFq+K3n4lC9bbBKGlYShRn4t2RFGtW/o0K9ZTGNGedDOybtJjjop06BuHIKgJvDr1tVZl047hktHwr6rtMvSMCbN5c5eWPORIKwIJuR3OBxGEtbSEQKP52vo1fgdUQsmoUTjjUeptXaYHXZkB+Jp+h1xP54bJcIEDBtM7oqnQyl28GObdsfuPALrlVgUvgi5bMDLOCTG9TWOJUbp0sFkMgEilKlBLy7aMarnNSTWxr6ykukkqPCtTsSzTUXf3tl9T1MUkiBuGvm9GUT1qWuHdCmmGfnUmUivtZZeTlF4lJVHVXnSFDaKsnZJC+lJHBxOcHBpgvm8w6gOOZ53ZddXS3GfLxPuTC+gPoVhz8noOhF/+MhLUVnBtDt8wv/LTMlWf55OXNIMT7MDqWsit+Lotf5pv9e5xIoKgzFGG2OuAbYAL1ZKXXWap6tTPPYkVYgx5v3GmOuMMdf5pxAlPdPQgYNTQdlxuKg7S9qEHCt6HE4n8F3NRX5+ytctajleNEYReuL24zkNpRU7jUk5QMv3HwuNfF+i61LL4W+MNND8oMaPaqJuYXsGxjo7i0xa125LR9basanNimrZpkd5mqluSt0IXXnN2mVmJmQkmVl36rz0275B3/of1toh9qsTEwigNzPC7xcilfZsrkSD7R80baEbW9Y79miTpSF+XOF5jZi/OCKFxtKU4+mMzpT0bLQd446j48YahbEa0gC9sMB1Gqt0xIYCWyOcxmnJXnDC5dnUDo6vcToVvnWRKlL5ucfWbbHlngD0IsvHcBtcZeiEwhbdN5pidtjh8KBHVgsR7bGlNXxjcRsH6jPf1Fxl2Pej23lN/MT30GJTc0VwhK2Ti6d9vSkKDgwmaa4c0nadn2aclbrSGLOolPoS0js4qpTaaIw5rJTaiOwmQHYIW0962RbgmXezPEs0gaLxIZ9RXBQfJ298Dqd95kYJl8wcZ+YUiVPaNAx01DbFQq8ms32FNA9QCnp2y15rB9eRsVtZuzS+oixdisZvSUyFNXid6qWt3Fc3jugAwIqyXFxPWH8n8hflYm9doAsPHcqIsCw81vWGDIoQ35e7W2UvqrEWIC0CuRjsXfr4qGO5Ey6Vwhq8OMKitF4PYLfvlii0kAaoeetpuS5vG6Bi0Q66cNGFOEwlvYI8l3CXTliSlT6+r+nYhl9jFIujmKmOCM9yu7spK/F6XNMfcXS+T26ZpUL1dmVUG8r0ZmwHPzGREgcVc8sd0UhENf1O3vaDjFGtj2Zt+SXj4jkdp8ymCU0jrk+mcchrj1HpU1Q+Hb9kbz3BDv/0u4a/eP4fMv+86Ek+oVvsRGMmHHH4dO/NNGX2/rX8r2/8HDdt2vmMmMOuZCqxVik1aT+Pge8GdgEfB95ln/Yu4GP2848D71BKhUqpC4GdwJPnfOcZGl+JPsiAi2G+7qKNxJJNB+kpX5OZkgfSTSzmMXnlsZxHuI5pLcEcp6G2NuRZEZDbtOs0l/Ge68rIzWh5c0r3X+6grqXxOk5DYDMvM+s8BFIksjSkKHyaUsac3cmU3vSIyV5GVvqtBf2xQZdhFrZswuEoaj0lPVcIVrpRLA7jtklZW5m054lZrElqWPKfsPdzrRP13GIX52hIOOcQLDiofTH6aCzb+EbEXir1UIUE5fquPhGiAxSlJ72U0meQCY9Da4ei9lgcJtTa9lkql8ivmYwy+r20VbB2koIgrNqGq+M0wsa0Yb3DXHgcjT1GDDPJnywtXyT2xQcjO2kXpax7l6MQFigyKUqLgMDTTCQZsVcxp0+dRHYyLg8SXh6d2sMDYKFIaPMKnwLb/q6k6+Yce8P2M36/c4GVHCU2Al9USt0D3Ib0GD4B/BrwOqXUI8Dr7L8xxtwPfBh4APgM8B5jTpH8eZ6hcYVB6FTgWJONSktXOnBObeXmK5fQEZem6USUimPOwUQ3s2QdYTpWlWuNWcdbbnEb8jxRJWa536YljfKA5SwSRaCRDAqDdPeTfi5kpbhoPR1xTJuU7VrGYGYJT541W+3GBd2gIPJq4qRglItiE2inHq4ruxnH5mh4vlzE052UrZvmMV2N6dUSAFNKeGxZeJhDEdGc5FCqGvyRHDeo1QldhGswoegu8tJn/cSAXjdjIszx7GSjssa1jT0CLS4nIj/3tGhAeimeq5nL5C5e1yIZ9ywnQ6Trpg3/NQ0M0wjdOMIkjUvLkkRGvtZxylGG0NV2UtG07lGNUfSjnE4sHIY4rCgLj0EmjeZ+kHG0mjxlsO1KcEynFKYiq8/cOwhveZj/9sCrWP/je3Ev3/kP+n5ng5VMJe4xxrzAGHO1MeYqY8y/t4/PGWNea4zZaT/On/Sa/2SM2WGMudQY8+mn8wc4VzCuRLonsw2PZWtxVUM3KKi0y97hzBPm0GOEyue1/QdYnwxwVUPgaqZj2brKGw4RQAFxXLY9hSCoicMTng2mUdTW0Wn8pm0aybssCuE7eI5oE7SW83jgydfoJAW9fkbg1ZLLaBRZGrSORFPdtG3YLRURShmmEyla0520Hat2/ZJ+IirCOKjaXIthGrZeBVNrBuKY1ChU7mIKh2Ykzs3FlKHsGZrQCPe/I+pMPfLQNuvB7QlFuyo9RqWs8eDShPWytD2ZSiY7M92UKC5Z1x8S+1V70fqWXu65Db2ksOYw8nvcOrlIFIhbk8iwZULRNIq88sQ+vhY36yqX3UIUVKSVT92IMYtuHDqB9B3mRwmDIiSwBdi3Iraqckn8iuUy5uNHrmZPfer+05nwQDXBjVlCoc98om8GA9b8acKr1jzMwnsbmle9ACeKpOegFE6SnFMC1KqDk0WVSGRaNFvxeDrNpckRLu0d5XF3Wi7yU/ZU4aXRIgenH+ArSzs5NJqw20/TNiO9nqasXTLL+x+z+iJPphWNZSiOHZCCQDgQjpIjydiVqKy9J9COF0cxRR600uOqFit6VxnipKQsZYeSWo+CvFJ42mlHgJ2gIq+9VrXpOk1rSOI6Df04PxEfbxR57bWu11XmY0oHggbHa9CR7HrqwpU+R+VI8fDkbDZOh3Jtz6HTyZmIZPJhjN099QoCryYvfZKwpLKU8WU7VpyIc8rGYVB55KVPFFREgTRKZ4/3MY1ivzOJ52ocRxigrqftyFaOCK4rDtrG0OZbajv58F3525S1S6Fd+sGJ4BmlDKErjWXHa1gzMSTxSxaLmEERMGjOflqwoFM+PPdaAI4POmw9zdhzjOQzd/OBnW/geT/wIOGvH+NL9zyP7m4fo6AJ4MI/O0i95/GzXsupsFoYLKqOIt9QU0x5LBQJh8tJQkcuorp5an7WhBPzvZ3d7Mo2cjzvspDHrQUYQOhqQlfj2226a4lJIJMM5TQ4vvQSdO0QBbIVrhvxB6isL0Ceyyze8zVl6pNWkegOQkORB6LSTIqWSwFyp8wKnzX9kfU2UASO5sgooapdJjoZk0mGbhwOLE2Q5z4T3ZzIq8nHKVBAE0pRrJSMArtTaWsPZyxt2nEM2k5Q4gm5wMbReuMIOc/T5FmA39UcWuyLn4N1du5GhagXrQ+m72rioEI3UiSXsqhtFvp+3ZKfykbhRRUY0TtMT4zoBBlHF3uYxqGurX7Eft1eN2t3GFXpsbic0EkKXFtAi0rEVOPEsNCGBi3kMi5O7O+4akSLkuYhn1i+hmvW3HdKL9CnwqfSrfz9PVcST+bkB7uYM/QYQKYTW37nDh47cg3HXlcyuX6A2iATq/oLa9AHTtfCPDusFgYL40okW7YmwDeKR9M1TAcpufbxnNNX82k3ZMKT0ZsCSu22RKTapkB3ghKlpJO9VERU2pEtagRZFohDsVHCUbACKeekM79plJiv+pbdF4lQKA5L8lFA0QinoRxJRJwfiyFJaW3rxx4EWe2Lu7FlYC5lEYFnvRGU9MAW0xjf0ySdnOGyNBDHx5Hx72JUybSkrh2SqLRqR0nBwmoOwkh0H8oRPcO42VhbTQeArlz8pGydl5Qy5JXXhutMxDkLo/hETJ9fMxnnrImHLJUxR+cmCKOSwNPt6x07jmzchrqWhmNkj3et6Kp2RGhli2elXTZOLNvMD+n3hGFF4Gm6QXnCs8IxVLXL3CghDirqyuWDD13LdZ3HeFOysiNFZTQfPfYCursChpdBd78Dzcr6FE2eM/HntzB92w5mr19LManoHG1Y/9ld6HMYSrNaGCzCRUM669M5oslrj8Y4zBZdjo+6XLP24GnvBg4OXTfHUyJHzmuPyKsZWIqxa2f8iV8Re1Xr3zBIQ+rKo9FKdA8GjDEySmtUu5UPvIqUUEhFobgh+UFtMxN82bp7Bq0NnvVf8H2RCxt4Aoswq3xqawE3EeZkrk9padv6JPbgeDIAwhkYZiH9JKfWnjAuuwXTnZT5UdIWDcfVVJ5MF1yrF/H82gqXDGXu47jG2tTLiNG1kxilDJ6rGWWhDd0RB2kdSVHqRgUKif0DSOtAgmY7eVv0okCOQ2N2pFKGOKokk6OW48JwFNE0Do7dtfW6WTuJcZUYvoZezagIiIKK0NUMbbRgElQUtWd3H4rIExepbDbh1tEO3pTcv6L32q2F4vGlaTpHGoaXQbB8luavxqAf2s3UI3twAp+mrNArLCwrxWphsPBHBm/kEM3mzGcRdd/hwGCS5VFEvOHMlXipTtru8igPGCF3PDFtMVSNxJwtOHHbdHSsclEr6Qd4tgdRe7p1htZG4bvipZCp0N6l3ScoLJOp7In/jmS7KyQm2r6BCwzysKVfz2cJS0MRSoWBjETTPGDthNiqGU8x2ck4vtilKj2GTtiKr9b1hiznkUwlDPRiIfp04oI0DykLDzc+YccOIsf2gxpdO2IuFYsF3dgPc1xQFCL6wjo5JSdlPzgYhlXQ7jCSsGRkC/Cm/jJZLe7cQioT/UoYikBKNw6T/ZTA1dLfaVRrHKuU4dByn6Zx2Dix/IRWs24cpqKMrPZZXE7QlUOnn7OcRzLWdQ3bgrkVvc8eLFN+/9gbmD08waW7hsxe06X/+KmT08+IRtPkT8/Ab7UwnIRifY1Tyjm45xUUtUcQ1Ez5p+YxjOErl2lvxKiSO0snktCYoj7RmwhdjQkquxWXN0LdOKSlz/htX1Uunagkcipqy5xs7AUg7EZ5nmdpwHJG91sPyDFzcGwRB7Q6DN04TEcj8trDVYaBChnlAVEkqsaR1W749vgzzEKmuimRJ/oCzxKiqlrIW0uLCb2JTIxfSo/U7hDG9OVjs31SE6KcBtcVt6bQ6jqq1G8dncZYHsS4nqaXlNT2bj+OpDdGkVUeE3Eu7FLjtBTmyN7d69pluQzbbT9AHMrfoROWZJVHmoZ0pwvWJQPxXdCiv3CVscVEitR8GgsfpfJaH43DVU80DUjjUmuHtBFiVrR2yOs6u4HTcxq+lDl8cO617B1MM3mXjyoHYBTJ/Yc59UD82cNqYbAIhpp4TYZRCj3ymPRToeCmIc5TpBmfjE3+Ar6rySqRWI+RFT6e1+Ao2QYv2Ui0wCr76lr4AGLBJjLtWgvTLx2FGK0ofJ/aRsmPuf4A3U4mRqe2IBhDuwVPrBntVJQxlyWMyoDJMCOxjdEkLIUI5NfS6DRy9yxrl5GdoCxnEcQ5vi9y5KUqJgjGsvGQuiv8gIEb0jSO8DJsD2LNzICs9Cnt6LUxEuJbl66E0SpDJ5IiUNbC+OwmBVXtCn/EhuFUtrjWNgF7MY9pjKIbFFLAipAkqFC2CGSVT+xXoj3xNMM8ZNk6bOvS5eh8n0EeElkF7Dj3w/NFJ9IJSgZFQFoEbUYoyjBcjnFcQxSXbfMYJNbuis1H2Owmp31/pE3Jrz3+A3T9gr2PrufyL80x3DkhCd+nE1A9S1g1arFI9iwBoHRDdEDm9oGrW1XimXBDfIS18bCNr/dduXuOdQ61dizDUCzaitpr/RIcX2zIJHZOiFWBV4NBxnhhRdQrUDaBGWUIrGGpaxtsee7bi/ZESrMCFvOYxWFCXnnM54k4Qvslvl2ngXYK0Fh7tfF5v8j9Nm+i0TL/H+sKvJ7lOeSheBrYfMe6ceiHeStnFm9Lq6GwrtZhUskkw05fysLDtYzMvPQJfbG0CzyZPijbNDywNMFiFrGchwzLEM9pGNidjqMMi8OY5TxkKY8sMUx6Nb2owPMaepMp/W7WhgaXtUtdeQRhTScu2jV37EjZtwSvuh7ndQqZLfQlCLgXFThOw09s+soZJxINDfNZwsHhBMGsi/EcFi72iI846MFqYThvYfYfFpOPbsDae2oOZpOikKxW9iuachP+t02fpx/m7Rt5HDE/Zju6yrC2NyQJKtLCZymTENWxj0Dg18S++AcUlS/mJUrO6HUlrtF17RIE1gWqdG2Mm2zXC5ul0IlKekHRNuomuynrekMcZRjZRtpkmNlOvki6deMQ+RKN51r/CGXDcx0lvQGlTjA069xrHaVq7ZCOxB27tI5QeSneE2kaWq9L8Wg0tUNp7eYrKwLT9Zh30DDRyegEFUXtcmy+z+IoJvA0a7uSDRF68h/AlokleklB4Gp8p2Gym7G+N5RdSCmalbL0bHBtxYbegNCXxmaWhmSjUHZNoUw1RkXAsmWDhn7V7gymuynr1y+ypj8i8DTLadQWke/e8TDflyyf8f1RmYYtvUUWBgkz9xrqXsjw8pLe/ua8SJ76ZqwWBguTZXAoYnFHRGfPgIOjCTZ0lnE61Sm9Hk+Fl4cNr5h5lNiv6QdC2An9mtCak2aVFINBFuJZ6u3Yhch1hVs5sn6OxvorVPbYoJT4IWjbcJvqpWL3ZouPMbRjOaUMg1LunHXjsDYZtQ7HjYEDixMcHEy0uxdtSU5pEQjBx7IEq1y2+FnuU+R+O0EQYZQRToW1ao/ikn5U4CjD8UGHURagMw+9FLQZlEUe4CclUVIShhVFJUSlpFuQJDa+LY3IKh9XGXrdjH4iMvCeL0ejxoBulPR/LB1dG0XVOPTCAgcpyv1OTjeSrxvYdPJRFUjfwNq7dXo5E71Meg02Ak8pQ6ldaptpUdYei2nc9jkaIz0kVxleum4P/2XjF1fEX6gwbEkW8W/vMfGp+0k3hmzYtMD0HfNnfO2zgdXCYGHqmqkHYP55BlVp5kYJ10wcYHpqxAPLG05ryDGGqxzeOfkNLujPU4z7BEUgZ+3aY5iFrRTaQOsP6QWiVQDL7bdS6nGGou9qJnoZyWQGyrA8ihgVQetEBEKjHjf3jFEspTHzSyIJL7XLqAxYzCLK2qMofIZZ2Ia2jj0Uxw5REx0Z4XmBlvwKT0JdTCM8iWYhFO/GUNKulDJiElN7rbahE5c44Tjp2vpAVo496ljbtsISoBpFZd2mx65M47W4yrCQxzy+ONWG4ywPRbR2cDghrEWbOeE7mtrIzsd1RDp9wjBH9CZJcCJFHERENu6LVHbUOTbvbezauvZrZFZABTAZZfzimpuZOIXq9lSY04rZosv0gzVUFcevdThyeIrmkb0rev0zjdXCcBLWfn0WM1NifJfh4xNMeSN2TM0yqsJTaiVOhW1el1dMPkLkVUxEeZtDOY5o74QlPTuTr2q3vVDyyiPPfRrtkCQF0/2UXicXBeM3TTiKYcjweEcoz0XAYBjTZCcEWiAXVr+bSfpVGbaZjhNxTr+bEfjSnJtM5DmjPGjt2cdZj8oxzC11ZJzZK1pzWBoxQalqMZmdiHM2Ty0JD6EIcJyGNA/kQu9VbcCOH9uIOadpDWlGWSjHCLdhIsmI/FoIRtYUpbTKykq7eE5DLyqslZtMDZKwpB/mJ3ofShiOgauZjlJxvbKUaoCFkdDQxyE9pXXTbhqhm+elT1oELQ/CcQxZ6TPMQ+ulIQzSX9n2cabO0HA8Gbfl27hlzwV07z+G2r6FddccZf3nPcw5JCWdS6wWhpNg9h2Cgc/ogh5r7lTcsbydi5JZJsInm8CeDjvDIwzLkNCtmY5TIr+Wc7kWq7HAFZPRMQMPJNwkCMRaLbTahbI+cbFH9kJ23QbH1+DJ9rwbFcRJQdAvmOxk9CMxph0nT09Yy/W0CFqV5skagPGdcazinE9jKu3guQ1hWBFHctZOopLYWrDRr8DQrn0xjZkddsQuzUrMy0Eg0XXKyEjUEqiqyqWqPBy3YaKX0e+I4a2xjc9OIA5T45Fl4Iqy0rFj1MYofKu2HBuqLOYxunGYTTvUxqHSjrh2W3FS6AvFe8keCYJQpha6dkhtT6EqZa1aOwRezWQnI7Tajtw6ZRW5z/yRCV65+VGuDVcuWKqM5tFiPZ07Y5qjxzn+8rUcmZtg+nNPbRf4bGO1MJyEZjSi/5DLsRd6rLl1ji/uupQLolmundj3lCKqU+ECb4luUJDVPnNpx44lHWnG5SFLWcSoDNqsyUY7jIqAIheH42EWspxF7cVUVt6JC9uGzipHLozJKGMyziWv0p61x0QrrR0cDK7d3o/ygOMLPSrbpwApGCBCpkEqzdC8lHn9eHczyuR7d6NC3KijWlSKNkE7GwUsD2I7zZDdgBtLAhVgaclem7EJJ4yIxhTmunZZHCYU2iX0axuKKxTsJBRDl7XdkRRTO+GJvJqqcRjYi3sslV7bGdENC4alKEMba3pTFl4b6GOM5HSW1uLNNOKLMQ6v6folnaDEs3Z3Wivq1INa8bqJ+87qfXVUZ9y7tIm1dxaojeuYfUXF1Oci9NFjZ37xs4TVwvBN2PDVJYp1NWppyOQtokF4TfeBM3r7nQwHcRVujLLNMsmjHGsbxhTp0BfyUF1Y89LCpdGqDXsZw/NOuBm7noz9lNtQlB6Pz00zN0xaR+rGKOaGCd2oYKKTsVREDPIQz9VMdjLWTQ3oJ3m7ZR7zF8bNS9+mSY1RNw69jpjZVlqEXZ6n8RMRLunKxQs0Sceew7MAUzt4fi3xdpVMHBynaX0ajRFnq7EVnhyz5CL0nca6VYsB7KgM2nCexUymAZ2oJPJrhjaqD6Cwx5VRGaCNI2pI+7pRHtAJSwnDtQ5aIAG7RkuvI0pKXJtYNcoCZtOEQSHHq7EVnAoaJjct85JwZSzHMW7MtnPX3q2E++aZf8l6UIZ1nz5/dwuwWhieBLVrL07hUG+eYeqhkjsG29jgFmdlxnFzvp29C1Ms22ZZYc/QSSQBM6M8aFWDAJ2JnCCqCTvy5gzCishOM6Z6KYFXt3qAOJQLSLmGqvAo7eSgKr3WkkwpaWwGtrE2DtZtrJx7ORWlYhxUrOmKCWljLeS6UYHv11Q2PKey+gnXaeiFJaHNjtTa+im6ppWKgwinwk4pvAC3QblGjiQ22GV8kblOQ1Z5BJ4WktZJfQDHNjPzQkJ8Wkq3I0eLyP4+xmnUY8foMV8kq3wCVxO4mrWdEb24EPZoLUe50SiiqlzCsCbqlBKmY0VkvU6O6xoGo4hhGpFXHouDmKpy6fUzXr9tFz1n5ceItCl5MNuEdzDEdGOOvRgmbw+pjxxd8dd4NrDKfPwmNKMR3ccdFq7osuaWWb627wLyjYr9dcPlK3w/3JNuZbgU44U1E92c3E4fppJMTEEqFxXK1r8oPMJQHKK1zX30PS2xbK5mKY0lVk5JAjZJIfwBIwrM2lq0jy9MY3UCZe2hbVc/8DSOkjuw40tDraxkC51ZLUIvydvCEvk1rm3uOTZuLrXGr02jaBoXoyXGPkhyGwJrvQ1Kr1UlRmFFZbfuulEiaa5lMrGcRnhuI8ncidzVF0axbVRCEMjkwLO9lvEEJVO+jCW1ixsVhHYUOShCQlezsbNMWgcsF5EVw6lWQDa26h9PfMZNz6aRQhTYHIqs8FutRpH7bV8i9GsuiY7gsfKYlKGpxAnqMUi3dDGJZtMnD513FOhvxuqO4RRYe2fO0k4wvotzd49PDJ7HUd1d8a6h5+aYyqG2XW8QDktRC3OwkxQyNrR5jkXun6QPkDvh0ihmKZU7leMKganRVnFpzUs9X+NHtU2xPuFV6LmSbhR5NYM8ZJiHdMOitWB3nAbfl4tgTOzJSp+i8BjZnoNQpqVzjy02mZVNG6PwwhovkvBd1/Y7HCXycFcZyccoPYrct87OYn4bh5V1uxbuxFImd+Wx1sMYCK3bc11Z+br1Zhgbv45ToUB+p6MywHUaRmXAfN4hcDWhW0sKWOUxn8boRrGmO5KUMBt2q2vZCU12M5QCz444a6uRGNOhHZs3cdHkHDcku8/Kd2HQGB5L1+AWcOiVLtF+n/rx/Wd+4bOM1cJwCgT3PU65tqaeitnyhRF/tf8FJE7BsFlZ/sWPT97Ohs0LKLeRO7P1MqwtYUaPz/VIWpLryV1VQml8snQc1ioFxfcl2s61/o1KGbqdvLWjZ/x1nIaZOGVNd9TeST17lyy1CI4CVxIzxs7HgVeT50IB73VkK69tInfg1YRRRb+XEoY1YVi164ojmVgMchFjdQI5JrmebOu7dqwYJyeyLHxfW0GY11q8T8R5mwLe7+TMTIxaW3zPl4IQusLQHPdmxtqP0o40Y1+8FsYalTlrl+Y6hq39JdZ2R5S11xYh39Os7Q2l5xNULXU6L32W85AwKm3YrdP6d144NcfLJx9lm7fyEeVSk/GxwdXcdsslxMdr2JKx5cbsvGQ6fjNWC8MpoOcXSfb6zF4V4+85wtHH1jBoIhabM5OcQLgM79x+K569K9WWvjzMQwp7YWh7Zw19GZuN8oBBKq5BUVyypmcDSKy3QG0vFKVoY90r6648Pkp4rmZYhswOO9SNw3IetV9TAZGNbe9GRUtESvyKyV5mY9nqJzQ9x8WjrMeu14YkrGSKkgo3wnOa1h2pG0pfodIuvbAgCSq2TS3QC4WePdXJ2lxIpWQKcnSpR1l41LXwFRKba9FLcvpJTuxXDIqA5VFEYaczoyKgH+UkfkU/yom9isSvmOmICnYpjakaB90ojox6EhLTKIZp2MbSjUohiC2nEVkR0E1yAttbGZOYqtJrbe8AXhQ/dla7haO64dalC9jwNYNqDOyL8W57aMWvfzaxWhhOhUaz4esFi1fV4Hls+iJ8dP46UuOSNisjpNyQPMRUL5U5vg2lzbOgZRmOWY91I3LksRek40rgSlaJ6q+xhaFnLybZRSiGgwgMLaXaGGHmjR2ZYq+i1g69REhV2ijms4SBzZEYXyCzQ8mQ6ATiwjR2pm6MYjiKJDK+8FoLe90oup285RWMewvHl7vMjxLqWrgKWSVjwqPDLoMiJLdGKbpx5KIOKhkBVi7dTt7uRvLaoxOVUjidRu7ypc9EN6MbFUJzDip8RzMoAmYioXtX2mUpj5gbyR197HlRVEKSkjRvcWAay8pdt2lzRkGanuPfeRRJSLDnNnSDkrXRkE3eyvkshan4UrqTWx66iP5DSxx/QcCGWxua9PQS/vMFq4XhKRDev59k3YiFl2+l9/ASX3h8J8d1hz31yvoMF/shL1//WGvZ5roNE33ZBdS1IyQeG3AyLhRKgeuaNjrOsed2z8qH82Foo+4lOxJoWXzjCHu5w7ssFTJ5mI7TNgTHsf0HbRWUw1zyFeYXOxxb7lJph8HoifmIRisJg7UNy+VB0joj5bl4GuS5T5H6DJZjysynTGVLLvkN4mblKsPssENVuSxajkYY1qydGrC2M2pZIrk1ZnUd+3toxMVq3EAcT1oGpYizDg0nWCoi0srHdeT3FdqjhetImlTiV0wlJ8JrAPqJaClCv6aX5FaHIQpKz8YNxmHJZJzxwun9/Niar51RWn0yHqo0nzh2NZs+I98v29DQv3nvil//bGO1MDwF9NFjOF+b4NiLwFlOcb/eZ1exkdvy7SzoM1d9X7lcFB8nsf6H42i60k4Uylq2z2keWF9Fh24sOROOMmQ2zj2OTzQBvUjyH5WCsFMSd8rWObqu3daBaawTSMKS2LOmMFpMYbJSPCx9t7EjUNqMzLL25EwdS1LTlrULTE6NUAqGmSgRO528HV8am5fhuhLwEiUlcbfA8RtGw6jtYQyLgHQ88bATi7FGIq88jo86IvcGfFeSoMauTq4SF6yFNKasXWYi6Z+MmY+ldls7t/HPaWwBqbWE1h5dkuOE72oKa3A7yJ4Yi7iUxqRpSBiInL2qXTFrqT2mvRHXBeWKjxGFqfjI0rXc++gW+g8tceylU0w+qKjPY0LTN2O1MJwGmz+/RHDBkHrdBJu/tMz/9/Ar2Z2v54FqZanDr04eFkp0UFHYc/T4DTxWUII4PwOkhRi1buwvEwY1ricXh6OMjbuXpt+Yjec4TasUHAuzPO9EYEriV5SNi2N5DQoIvJoZO7FwrQhr7dSAvjVZScKK44Mug2HMUhaJYtKRM/9kx2ovRhHDNGwboY29GMcBO0EopjNixOK2R46xrf7iMCH2ZaIBcjQaZ3GMw4BFm9DI6NZpyLOArAiYzbqklW+fS2t7P47z68UFnitHEKUMk1FGL8lbCfWJo5dqlaTDLKQsXZSdCI0DfifjjDXxkOuSx0jOgrtwX2n4xsI2Zm72cRaHDLfC+ptmnxNNxzFWC8PpcO9D5Ec7HLyhh3t8ieq2KR4breEro0tXNLq8zA+5fs2e1mrNGCgtJ9/zNHEo4Sh+IBMEYxSDNOLA4mQrZy4L8TWoMp8q98jGc3U7XQiiiqZRlKVMP6TL7rKURSznEWkViILQhsUaoxgU4vtYa4es9FkaxcwPE2sBJxd7rytjyLSSY4K2LkxNowjDSjgVNnNz7NXgOsIH8P1atAh2ShBZUxPAaiVkhOs7TZtV6diLUTdKipQ9TvjWel9Zg5TEL4m82h4dDN1AUr19S2gKXN32T1zHMJ8luFZ9OcwlPGZcoCvtkhU+vqfpJgVhKLF5YwfrqTBlR3eW68Lhk/+4T4HCVNw4uoyFPKa3v2J01Qa8kaJ5ZM/ZvPOedawWhtPA1DXrv6pwrl+gmeqx+SsZ9xzdxGPZGg7rMzeiXOXw9onbiYOq7QEEgYzt9Fio5IviMPBrOpEwA8vyREK2MbLVxyhMJfqEPAvIKo9eVNDv5DTWTbrRDoVV/3lu09qU6cahE5ZMRVnriDRO4/asUclYs2CMoh/neK54NxSVhxfWDOxUILEZDZ5fs27NsngTuBLqsjSMJMY+qOgnOY7iRIqT3drHYcX0hGge0spvXaQqLcEvoyykahx8tyG3OwHPaZiZGBEFQhYKHN02Qivt0o8Kds4cx3fF4bsXSINy3HOotUtq07RqaxgTWBKXWOPL8alj7e58vxavS2V408TdK5ZWA9yYJTw42sjhY5Nkazz2fY/D9C6Nqc93StMTsVoYzoDpLx8gCSqGF/Xw734MbplgqYq5o9iwotdf5oe8bMMea8HmtjHrsjsIye0b33N1S3mOooo4lEmFyJQNaAWuwY8ke7LWLrPLHbLSJ4pLkqhoOQRNI+PPtJLmoG6UGKYax5J/5C49FlLFft0eE4YjmfWPz9ieq5nsZdZtWjIfsvxEmM4gFXpxEFZ0k4L1/UErZlJ2uy/PrcWe3jIRgXZKUZYe2Ziy7RhJ2WqPPprYr+gGpewO3FoSvhzx0Sy0eEQOq7D9WavGZdH6UVRazHSHqZjjjL/vGErRmrEoSz6L/JpuULAuHPD8YOW7hbQp+duFa3lsMIO/L2TxEgdvJqd/+8EVf43zBauF4QyoDx5m/q61zF7lwvq1bPxqyh37t/BAtplhc+aAEVc5vG7iPglftTLe2N5Rx3FzoS8Nr1EetI25wjorjSca0WROZyInjsvWk3FM6x0nRXuebv0XXdvECz3psPuubpmXee21Jqtl7bbHCAN0O7k03ywXIbDN0DE5KfBqQuuRePL4NLSU7rE5TVoEVLUcoYZZ2Co+q1pGmZEn7tSu0xCH8vsYh8GMSVuBJ8VyUITMpzFLecRs1mE+S3CUYTIWc1sDDMuQNfGI7RMLTIUpvTgnCEWlmWUBjQ2S6dvE7zHpKgyr1qw2LURsta4zZDLI2Bkfo++srJ8E8HAlbtP7b9vMhls0xdqazpc71Aeee4VhVStxJjSajTdr0n+xyB61jvW3VjT7PR65YB1fSSZ4Y3JmNuQLg1m6YSkpU0YxzEPqWuzRoqAiL31LVKJtTrruCQej8YQiiSSzoa5cGss8DDzNIA1bGbPc6eW1IpyCXlDgO41Imt0aP9IMnNBOLqo2pVtrh/WTywzLgNo6IoWuw6AIWpJPaiPl/XHkW1LQGHAUgNjXRUEFJa31e5o7jMpApil+TVG77JufaovB2B8TkFwNmyfZCVPS0sezlGQJnRWj3en4xGRoKkxJvJLQ0YROjaMa1kVDAqfmUDbBo85MW2wXholtmoq9fFGIdqXbzcmygCQsubR3lJf1dnNDfARXrWxEmTYlt+YXsn80xYUfHeKOSpx8ik2f3E/9HGo6jrG6Y1gBOrftlfm3gs5d+9lwS8PhtM/t6YUrakJu9Lq8dfPdhIHcaUWIpKwfoWYwiigHkvGwMEikQQlyvnYbOnFhKc+ipWi0eEHW2mFxOaG0fYei8CTevXGEZVmI8xFAWonMeliGLBURXb9kTTxkJhnRCaShF/o1R5Z7DPOQxiCS7SJoMy6kISoNwVERcHhugvnFDrXNyxyPY3XjtEVBN4ootM1By92A8S7JY2kUi4uTdsR2zW7347BkMspY2xmxrjOkHwjnYG1H1tv1CxKvZH08YHsyz7Z4gdo4LNchvtJ4Sv4ugaPZ2l9iy8QS/STH88R0phn3Jzo5cVISBxXrpgZcMXWUH56+hX/UXT4rh6YPDrbxV4euZf+nLsDd9TjzL5hi+j5Fve/Ayt9o5xFWdwwrgD56jGLXDppeQzMcMfHVx3nwzZu5pH+M+8uaa8Izq+3ePfkA927YzF1HN9sMRk0vkkRlRxmiaXE4nnOS9uIpK2Hh9eO81Qk4jiHulG1+hDHKKi1lMuAFFd2oYHEU43kijz427FpaM23TsWxcmkqMV5fzkE39ZcJOzfFRR1ysT/Jr8F0td2yrOuxEJ9ifnq/JchkVVpVL4yrKyqMbi/KxqD2METKVQcxjpropCpgbdCRhWslRyLf0Y0cZppOMunEYVQFl7tIJytb1eiLI8FTDUIdAwWKVMNSafcMp6Vl0PNJa1u6phq4vu7oi8NjYWaYxDotR3PYaumHBRJBzQXeOV/d3cW2wcvUkwP1lxl8dvpYDN23lwg/spr7yQuavUOz87ceek7sFWN0xrBhbPl8yfdkc5pJt1EeOsu5zPvNlhy+lKxtddp2I/7Dp0+ycOU4UVszYi2NpFBPHJdOdlGEZtFqFsUU6yM5h3CUfx9MbQ+t+DKJElK16xfygI6Gs1iEqL337GiUXfOmzmMYMy4CBTXXOa5/Spno7VsMx9kUo7E4hDCUPoqiFmyBNzxKlpK/RtfqGbizHi6UsYmko04zhIGrXVNr8iV4i049OVNKNi9bcphcV9PycyK1bP8fQrYm9iqoRa7z5IuHYqMuuhXUcGE2ye3ktx4cd0spnIU9YzGP2zE2zZ2Gao1mPxihmohEb4gGeo9nQWWZn/zhbOotsTJZlNBkd51XRsbPSQyw1Ge898joe2rWZCz94FLKcvd+fMPkQ573nwumwWhhWiPDmB5md63Ho1RM4YcjMjQe4efdFLNQd9tUr49Bv8br8+taPcfnaoyLyMYokKphKMvEkHIknYeTXdIKS4KRcxaoRzkFWioZisic8gzAqcfyGZihn5aLyRbXYzcRKHmxDr3lCJBxIAXAsO9F3dUtBnhsmYqHWOPaiN+SlTy8qmOqlEgprlZGSj2noR3IsGmtAxuseB+Ao10hD1JqvVNqxo0TRZ/QjEUyNlaBLZUyD8Btiv2JdPBC/h9rjaNrj2FDi4IxRDCtxwG6sCeyoCkRrYo9bgyLksaUZDgwneWwww7AKCZya2CnxVENtHAZVyM7wyFkdHyqj+eXDr+YL913Gjg/XNI89zvLrLqearlnzyYdX/HXOR6y4MCilXKXUnUqpT9h/TyulPquUesR+nDrpub+klNqtlHpIKfWGp2PhzzSaNGXjJwKGz8+pXnoF+thxendE7Mum+Z/Dq1bs1XCh3+VnNn6WxWEizcHGoWqc1pm4rCUCfj6NybKA3HbUZSIgpJ+kkxO4msJ2/U0DKpHk5bH7clW7LKeRWKZHwgasCo8sDams6/J4LAjgKmFO+q7NjrABNGNpdFF5lLVc0GUpO4uiFvm05zUcW+zKzsQo0sIXybKrmeqmJGFFr5PTj3OJgbNjUs9p2Dq5yEQo053GKKaijMgT89ZxslXgaI5lvTYbdGR3VoV1mRqvNfBEyp6WYo8v2hPrfOWLRHz/wiRzaYd9g2n2jGaojUPHK9kUL/GScOGs3hMfGa7h0w9dwfoveXhfvQ932xYOfq9m0+dd9OzZ2b+dbzibHcNPAw+e9O9fBD5vjNkJfN7+G6XUFcA7gCuBNwL/XSl1doe28xQTX3gE12vY97oQJ0nY/KmjfOmuy7l7sJX7y5UTWF4UKl5zwSMohEA0t9yRVClHBECSMiXRaaF1WC6qk2zOlHlCxJ1yDTSKPA2oc0nNHidY9aJCUqFyH+WKuYvjNJSFRzYUS/RKuyzmJ0g8a3tDoSzHomgcFWIFL7maLp24bHkXa3vD1tQlCiom45yO9TNIrbWdUiJm8h0hXQWuSLy13ZU0RhF7FYMiZDGP25+xQRG5sps5OhC9w7h4RX7NKBPPx9TmdpS1pE7VjcN0byT2csqQ2oSpuhFSWWZ9F2rj0nFLLojn+Ndrv3hWu4VPphG/vusNrPtUyNRH78WZnmTPj2yCwqH/6ftX/qY6T7GiwqCU2gK8Cfj9kx5+C/An9vM/Ad560uMfMsYUxpg9wG7gxedktc8y9Owcaz8eMfn8WdLrL0bv3svGLzpk2uer2cUrCqUB4Ta8Z+0XWzNYY5QYn9g7aRjWEktvvQ7Hjb2qEs9C4QpYUVJQST6kVphMAmPHBUdbynNVenheI1Jiv6af5HSSAuUIy7Io5IiynEUUlRCuukFhG4dCVJpKMro2yq0TigZifIHpRrFuYsjaZMS23oLc9f0a369bNeOaeMR0NCJ0pXexJhb242zaYamIKLR4VMR+xXIuMu26cTg07HN02CP0a9Z3B/j29zHIQ3G1ahSjofhOyM/iCXciD22SlMtwGLG42BH3aqtmDTxN4NSsDQb8+OStXOifPqn6ZPzB0gb+1Vd/lOCvp5j8m7tospzFV1+Ee90iO/+0oDkPsyjPFivdMbwP+Hng5Hf+emPMYQD7cZ19fDNwsnfVAfvYE6CUerdS6nal1O0VK3NGOh8w+XcPkVcej/8guJftYPKeOe48sIV9xQyPrrDXAMKIfMHaA/YcrvEsCzC31F1HGbJU3ty6EbOXxqoFx1MCz9XtiM8JNV6votPLqWrJuPT92vYhqpOo1orEr1oD1SqT3oRBdBzGwL7ZKY6PumSVT1GJE1JW+Sxmkk85Ducd27PFfi3NvO4x1ofLdPyCxK9Y3xuysTdgXWdI4pVCYzZOOw2I/aotHBNBhu8KY3KYRmLAaqXi03FKLyyYTTvMjRLK2iVNw1aTMdaaVKMAbCEr7RFjzDbt9HKiuJTkKivd7vkFPzJ521kVhT3VkP944/ez/S8cpj70DZqyonn51cz+QEr4yQnU1+9Z8dc6n3HGwqCUejNwzBjzjRV+zVMFMDxpZmOMeb8x5jpjzHU+4Slecn5CLywQfmySF13+GPvftAYOH2PmbxJuOX4BHxtcTWGqFX0dVzn8101f4PsuuI9eXLQXhVJy1i4qjyguCX1JfXY9EVuNO/uOFQmNG3iOzZI8OdpOQbuzCP0a7OhxVAYM04i4UxJ1y5ZpaIy4FtXliZPfKAtIC2FlDpdjRsNIGo9JxkSSEbqaLb1FruwdRuNwOJ8g8SpmohETQcaO/iwAh9O+CLcasXZfLOTCj6wuoW5cKu2QVj7dJGeim0tAbunToFjMYnFbygLK2iMIxJKuKOTzyW6GE2gxvbFN27EuJIpLJq0DddM4BJ6mGxS8a+1XucTvrPhvX5iKf7H7HWz+rCL8/F2YusJcfxWPv0dTLUas+/D9zykF5emwkh3Dy4HvV0rtBT4E3KCU+jPgqFJqI4D9OBabHwC2nvT6LcChc7bi8wBr/+fDkpt47RA1PcXk5x5m//0buHNpG7fYC3ElmHBifnXdnfyLi24CaM1QgfaIUVR+m0vhWd3EWC0oY0bbPLQpVY11Ux47K7lu08bORXFJpV0WBgmhZTw2jULXDnnuEwRiftpknvV9VGKc6ml827doln0O7VnD3HJHpgTaZbmMqIwYt9bGIXRqpoOUqSBjaPkEAPN5h1K7LNh+xjhXYn6UsFiIJ4V7kqktSOhNxyuFXemY9mf0PY3rSh8lseKqMJb+zCgPyIcBozxoaeOZFWxFQcVklPGTW7/Id0Ur7wtp0/ALR17K7F9upff3D2DqGnfNGh79IeGLXPFrR9DLZ069fq7gjIXBGPNLxpgtxpgLkKbiF4wx7wQ+DrzLPu1dwMfs5x8H3qGUCpVSFwI7gVvP+cqfRejZOcwfr+P67Xs59uqNNMtDNtxsWCxjPrN0NUvNyo8UvnL5sd5hvmvDo9SV2xqhnuy9OEZjx47LmSRG6UZs17V22u772LQVZdoIuaKSTMaTbebH0Wuu2xAlpTgiY52R/YZhGrap10Xl0Y0KvECDA6p02lCcyKsptMfBfJJRHbJQSAPPU5pJP6XvFUyHKb2gYCmLODrfZ2B7CLPDjjhAFT5pJQQngMEoYmQNZ/LS52jaJc1D8iygHEpiV2r9IPrWwNa1KlFdu5S2OOdZgGeFZa7T0I0KelHBz2z7HN/fSc+Kr/C7S9v5wl+8mPV/tYtmMMCdmuLYWy4m2j5gy2+61Hv3rfhrPRfwrfAYfg14nVLqEeB19t8YY+4HPgw8AHwGeI8xZ5HW8hxB/6N3cvPNVzD7XSXu5g1M3ryfXfdv5WjR54PLK29EghSHn13zZdZNL4s/gxVbOY7IsoUxqMjSsBVNaesp4DimtV5vGuFAaO2gixOmMACBL4naxoCuXapS3JrGsumxm3QS2gi6RoJmxj4OiZWHq1JhgoakI30EYxTHhx3un9vAoA7peCVLVcR8KVv0TEuGpKc0kbWADy3fYZSGbVELrIfC2GymF8s0ZZQFDHOJqI+TQhK/obV3dx3JqKi1i+82TPZTPL/GDeVYEdrfx/xywjAP+dGtt/K6eOWFW5uGjwz7/Nan3sTWD+1FLyzg9HocfNfldN9+mMm/7KG+dveKv95zBWdVGIwxXzLGvNl+PmeMea0xZqf9OH/S8/6TMWaHMeZSY8ynz/WizweYouCS359jes2AQ2/aQrO4xKW/t8yX9+zggXQTXz/LfupGr8v/dfEnJaQ2kGah5zSiQ7Aei47TkA2kOJiG9sIab/Pr2qUxco7244okKtvtcy8qbH6jL0lXsYwUc7srGI2i1msSsFkWTZsDsZgJ+cqEDSqW7zc/Eht36WUEHEt71MbhWCpMw/3ZFEvW7WpQRSwMElxX0qMbIwEvnicq0UEesjSSvsOa3oheWIjN+6TInsf2dWNEUcX6/kBMbGtxhB7H2Y9t7qSp26At3+OdF9/GP5vYd1Y7hT8dbODnP/mjXPIHc9QHD6H8gPRVl9N74xEO37aR/sfvOrs/9HMEq8zHbwH6wUcIPjiN88ZZ0huuxNz/CJs+ELB7sJZPLl3DrB6d1dd7XZzxg1vvkhg268RUWNfi0K/lYg2ll+DaMBSA0SiiGAnXwFGSC9lNxhbx4nQ8pj73uhnTXYmHHy7FjAYRjXZoalFXjiyhqso9ykJi5n23YZCG6NpBxZogKVvS1djN2bHqzaz2WUhj5vIOc3mHQRkxKCOODbriwGwFZI1R9K0JaycuKCuPOCzFUSqLWm8F1xY8ENq3mOqmOE7DwfkJFtKY6X7aWsO5jqR069ohS0PmFzooZfjJq2/iZ6d34a+QUqNNw+8ubuY/fPoH2fnnQ/Su3ThRxOjNL+DwjxUcPDDNjt96mCY/s/T+uYjVwvAtYvJv72H+4CT7fqDBuXAb0Rfu4eiHtrNrsJ4PLl+x4ikFyKTin07cw4bOMmXttTZjWS5MPm0vEKUMdeHJebr0aLTC8RtrOOu2ydjjUWNtcynGfgdF7bUqRpPK603mnfCHiEqCWHQRwzxkdqFHVXqEkYxDEyui0nZ34TqG0NNtCK1SYqkG0AskvLYxik43b41nxvZ0xhrjNo3CUeKFWdUuc6OE3O4sPE9Tj3x0KWNYz23E1Vk7rUdjWYtNfCcomemmGO1Qj3wc1/DLL/wUPz21+6yKwq8cfz7/7Y/ews4/G2C+IdOG+rrLOPaOjG4n5/L/uvCcZzeeDquF4VtEk6Zc/OcVz9+xn9mXrcdUNes/eD+PfmQnf7HvOv56uDKnpzHWuB2un9wjWoXcoylFHFXXDroS23hjlNi9IeIlL6hbQ9nx3dsYRZ4FzM71yIqA0K/b0NhBGkpsnHYglGBaANPY1CtP43kNnqcx9ntEcWl5C5Yb4YvBSVZIUM7icsIok15A6Aldu7Lp24knMfYdy5B0nYbARtiP/RjGmLIGsbmNw8ssUQvXEPdyylLMW3Wj6CTiDbmcSrNybrHLoYUJhkUgbtqh5l9efRP/uHfkrI4PHxlN8Tcf+S62fWgf5o4HwBjcyQkOvjrh0g3HmPm/E/TDj57V3/W5htXCcA7gfu1eHvrCDo5/V4W780L08jKb/2wXC7et4wMHXso95dltN3+ofw/dqJAOm/VyrNJALmTbPHRcyYmUDEa3VVzGgczxi9ynKV1MJiNQpQxpKc5Mvq/Jcx/lNkRdSXt2OhWuJ4areSn+DmNF5jgNuixPsC6zIrANStVOKKrSs0naMBnnuE7D0bTHYhGzrjOUYmcnJWJRLz0NVxn6nbwNtTFG4XondhN15RJ0RMWpC/lZxzuiscHMeLfTNPIa12948xX38q+mHjmronBTDr98+w+w5p6a+uDhtigcfueVrH/VQY7+wYU4X7nrrP6ez0WsFoZzAFPXXPT7e/E7FY/++Fq8rVvQc/Nc9KdH2fv1rfzG4ddzuF65d+A2r8ubt9wHjsEPa4KwIuiUhN0CUzvo3EXXDo53QjEpuomT1gS4ocbpCDvQsd6GILmSYVjh+E177g+iuo24rysXXbgUo4DRIKIsPfJRgK5kK69t6I2B1oi2kxQknbEVnEiu81rYh1XjEji1GM94QmnOKh/PMjjXdeV3c2y+z/HlLp7lZGgtJrau9ZWoawc3kKYoWG8Ka7QbJ4UUE+ty/eodj/BrG7664uMDwNdzzb++/+2E9yR0b3oEGo07M83Bf3Ill/7ILo7etJmpv7jt24bEdDqsFoZzhPrgIbb9D5fwykUe+6fb8C7cjt69h4s+uMDNN1/BB5ZewDE9WvEY82em7+KGyx8iCqs2nq4qPagclCfhsY4jF7HRSlKr7JSh1g5BUDM1MaLTzeUO7DQkYdlG1juOYaKXMtEfMd0XB+ax87JSBs+Sf4xWNJUDRtFYcRdwQrnoyH8g/hCNUWSW1j1WY27uLFI3rrV0l9yLXijjyEo71EZ6ILpyKEYBZem149RxI7aTSC4mSsazY/XmeKrSBuU6hh3Tc/z7jX93VlkQX80bfuyr/wvDu2cIFg2q38VJEpZeewlr3nyAW+69mAt+58HnnNvzPxSrDk7nEO6NdxJtv56LfuJRHtY7uOCjIfr+h7j0d7bxh/l3M/s9XV7bf4CXhAtnVPJ1nYjf2Pz3vKv4QfYuiKLdcQwm0nT6OZ7TWC9I8Y4cN/TK2mul100ohi+1dqi0g9uyKT3rACU+CtooUTrW0hPoJIW4J5Wx+FBWDn6noi5ddOZhAoXrSd9hvI2vtEtgNQpKGRpHUqaUMtSNy1SY0kwo9sxPA7TeD7ryWFruiN5BgSkd3G5DnJTkpc/8KBF3aUvzDqy4rBOWDLKIMhNLvMk4Z1T6XL15H7+++TOsc1emf9Cm4b8vXsj7/+RNXPKZeWgy9n3/NAffshWn2oL63jkO7VvHFb+6j3rh7GTZz2WsFoZzCWOY/tAdPHDZC3nxmx7g1s7l7PhLl/ruB9nxG8t8Zuml7H/LFHf39zPljXhr9xHWuU/N1Z9yE/7L9o/yM/XbOTA/SRSX1L4QmUaFbO2jpGwnDFXtSlGwprKLS522aViVXkubTsIKz2taAtT4nF5XLr5VTw7yEOU1mMKFRpqdnb4YphqtaBqhUZvGaX0gupMFZW0ZmH5NWtgJhWVDRm5NJxTZ9YK1a/c8TT4MqfDwfI3fKUQVWgSUhY/jaurSww9rkqSQnYXtS4wNZi6cmuMV049yQ+dBrgw8fLUy/UPalLzzsTdx9Hd2sOWTd9OMRjhRROfgFMdfonnJ83fzjce3ccWvHKE+fORbfHM8t7BaGM4xTFFw8Xt3c+sF27no+n0cnN3Oln1T6IUFtv7ufTw8uJL7XreRIvf5xOar+cOLPsKa0xSHK4OYf3fhx/iZ7B0t979pXKpRgHJlS40rx4RBExHF0qTLc7/tO2gt3AHVKHpJZQ1eFJ1Ijg9p6VPXLspODOpGLO4zz5dQW092K2OvSnxankVZKOKwbG3j4qCSbIfaJUsD1k4PmAwzjmddsXyPMvLaZyrJ2tGmmcioa7clYh1f7koSdVSSDUNM6aJdQ6FMO7Jddhsm45wf3no7P9zbxZQT46qVi/H2VEPefPs/Z9NvB/RuvoOmKkEp1EXbWLwMXn7Nw9z88A4u/5Vj1Pufm4au3wpWewxPA/Tx41z871MW8xjv1XMc/4HL8DasRy8vs+4P72DN7yc0RyMenZ3hFw6+4YwhuS+PHH7zig9LPHztEgQ1Ya8g7JTUlSs7hcpyAezFDcJeNMbmZGqFsaSkzDImB2nIIA/bf4ehUJwXBnKHD8Ma129w/YYoEL8Iz5N8DMeOHDtJgetIUI4CGkMrH/cD6TnktQjBxqPLyTCjH+YsDWLmF6VgBH6N72mGRYDnaaaSjF5ckPQKnKimqURC7bhyhLlk5ji/fcmHeM/kfta4nbOaPNxVFLz2Sz/F5vf6ODfdhRkXheuu4pF3zfCqG+7ha7dexmW/sJ/68f1n/oLfhlgtDE8T9IOP0P+PHfpRwYU/8TD7/vEOvO1bMUVB9Pd3svMvRuj7Jvjqvgv51WOvPKPw6pURfPzqP+Qnn38j26cW6CUFdSUXytiKvWkUmZ3/OzZstqpc6tLDaEfGkpY3MO7sS9ycpqklEaoxCt/XDPOQsvTo91JmJofUjcPyICEdhRJaY9OrPbehsroN3TikedimUM30R9aqrSJ0a7p+wUwoRfDIUGjTjvVmcJQRHwnrJ5nXHoMsxHUavEDjBJogqEmikgun5/n1bX/LNeHZy/VvLSp+6CM/zaW/maK+di8Yg/IDmldew8P/OOGG19zFjZ+/mkt/9SH0cyid+lxDmfNg9NJX0+Yl6rXP9jKeFrgXX8iDvzTDyy/fza03Xs6ODy3S3PuwjMIu2cGed6ynuXLI2y69k19Ze9eKxmtpU3JT3uN9+76bx+em2/6B4xh0LQVAa0f4Do6EziZhRW0j4hxLKIqCitDVHB90KAsfz9et7XtpHZqUMmIoi2gQisyn0zvBy6hsI9NRRhqSVucxFl71/IIGoUBvjJfpexmPDNexmMdUjUNoC8NCGrfBN3XjtByKwK/JsoDY2sm9etNu/ve1N7HRW7m5yhgfGkzxH/7kR7jgjx5rewbK8yhvuIa9P2q4fudj3P9Xl7Ppd+/4tqQ6f8789TeMMdet5LmrO4anGXr3Hi77qQd54E8v55KX7mXX/9aluuEanF4P/fCjXPiXx6gPJtw2t51Pp70VjTMTJ+CNScFfX/IR/tfLv4pvxUiBX+NaV+ax76Nrm4yeK4KisTV8mgettiAKKtn2Wxn3wNq1GTupcOz0Ig5LlCu9hpPdeKJIZN7fnCrVGEWDXOh9P8dRDfNVh7pxmIwyCZFxa9YlA6Y7KZ61uq8qtyUs6UamLleuPcJ/ufyj/OcNt5x1UdCm4T/OXsb//ZvvYNv/e++JRqLjUr/iavb/k5qXXfIoD//hZWz8nVu/LYvC2WK1MDwDaEYj1v7u1yn/j3WoULP/JzRz/+gqnE4H/cgetnyx4eF963n/wVfxP9P+ih2nu07ET0/t5q9f9H5ete3RVjcgZiaWtTjeSdjsRnF7djGNROXNDjtkhRw/fF+3xKVhGsnUwSg6YWkTqWU3k6divjL2g1TK0InLNl5u7ECVVT6BU7MxXqbjlYzqkON5l65fEDg1jS0vC0VCVonvZJmLirLR4j0xmWT86+d9nvdv/zSvTypCtXIjHIBhk/PzR67jb3/7Naz7wJ0n/BiVQr3wch79EZe3XnY3u/7ocmb+6NbvGJ7CmbB6lHiG4W3fyoM/v4lgbcr6P42J/160/PXLruTAa0LU5UN+8oqbePfk7rO6CCqj+WQ6wf+z97UcXeoBiPmpp4kj2+1f6mKaExRnpeTu32iHxOoOai3NzLFTkrbcATGPkWmHLly8UOP5thFpzVWVMpJ+bb0jPV9z5cbDbEkWeXBpA8MywHcaJiNJmfKchlElgq/FTOTZZSniMD+ouXrjIX5209/x4vDsisEYwybnFw+/is//z2u58H/sbnsGTpJQveQyHv1xxasvf5i7PvA81r//278onM1RYnVc+Qyjfnw/l/+Xhgf+3UaO/XjGOvN84s/cgfulO7noGz3yF+/kvf/oDcy/rMPPzdxBd4Vpy75yeWtnyGsu/xB/OdjBBx6/nkPppFifhSWZJRspV+7wQqUWB6TGQFGJK5If1Cd4DbUjPIW4aH0UXachUxLOW1t/ySQqqbVYs2utRK1ZO2zZMMePb/wa0+6Q369eyaPVGmKvwsGQ1eJGvTFZZr5I8F3NRT1RKy5VEVf2DvOe6dtOO8o9HQ7XQ/7JI+9gz61b2XxLiT52XNY/M83cmy5l9NZl/vfLbuL9v/d9bPwOKApni9Udw7ME94pLWPhNzdGjk1zyOwXmjgeh0aAU7hWXsOdtM7z9B27k38x8Y8XF4WTM6hGfGF3Iw9kGluuYB5fWMyoDYUxaq7fajj61thOJykG5xmoeapYHCXXmMb1umUq7DBcT3MBOEawBijGInFpJ4O5gFDHZy3jr1nt499Qd7YU9q0f8+vFX8NBgPZ7SNMZhJhyxLZ7n+ck+nhccYaMb4CqFNoZQeWc1gjwZX80b3vmld7P9rxWdB4+h9x/EaI13wTaOvH4TzvfN8ZL1j/PVP7qW9b93u4wrvwNwNjuG1cLwLKL4nhcR/ZtDPPTwZrZ/3BB/eVd7BvYu3M7jb9/MBd+zh1/d/jGuDVfO+/9maNPQYKiMZk+t+eP5l/GlQzsZWN9EjAIbJ1fkPt1uzkScMzdMyNNAhFxBzXApFkVnUrW7jjr3Sfq5jcBzcJ2Gf7rza7xn8tEnTVhm9YgPLD2PB0cb6XsZr5u4n+vC+X/wruBUP+ffjib5t3/8Trb/wYmjA44L113Bnh/octnL9uApzfH3XUTyt7dLMf4OwWpheK5AKZZ+9CVc/J5d3Lz7Iqa+ErLh0/tbpp0TRZQvv5LHftjhF17xKX68v+eshEFPBW0a7q9KPrr0Qm5b2M6xUVeaktZFSX0TTyEvfHxfSy5F47SSaKUMdSVGL34oHIP/ZedXeffE3tOOXYdNjq/cs24kng4LOuUXD7+WW/70BWz60/vRi0sAMv25ege7fyTkR19xM188spPe/5lgbr/vnH3v5wpWC8NzCUqx9GMv4aJ/+RDLVcT+v72QLR96lProMayRAu7Oi3j8h9bzirfeyf+14bNs+QfM8E8FbRp2VQV/PP8yHhqs57G5GRtM47XNR89rWsFVmQvXAUtNdq2rs65d1k4P+MmLvsSP9c4uLfpc4K6i4OcefRvpH2xi8mP30KRConLXr2P2jTuYfU3J9z/vbj5+39Vc9p+X0Q/tfkbXd75gtTA816AUgx9+CS/+N7ezdzjDQ5/fwdbPpzi3PtCef92ZaWa/71LS71/mvVd/mNfE+Vl5DZwO2jTsq1N+b/5l3Hz8Ig7OimDLd7UYslhRVl25beS91g5N5eCFmis3H+bfbfv4P4iJ+K3gcD3kb4aX8xu3vJ4L/xyCL9+HKcSF110zw+ybL4G3zfKuC7/O+z7xZnb++kPoufkzfNVvX6wWhucilKJ5+fM58nMl37XlMf7uy9ew5QsN3bsPiZNQo1FhSP3SK3n0HR6vf+G9/NM1X+FFoTpnd2htGo7plDvKNQx0zPG6z3zdYV82zQML64m8msU0xnMbQk9i6f7lpi/yiig/p8eCM2HY5HwuW8P/cfdbST7dY/0XDkmugzEoz0NduZNDN0zh3TDLqzft5jMfuZ5t77ur3Ul8p2K1MDyH4W3fyoM/t4kXX/sI9xzeBHf22fr5Ic49u+WN7bg4V+zk8GumWXphwQ89/w5+cubLZ5W/eLbQpqEwNQ0NR3VNomDCCfCVe852LStBZTQfHq7j9/e9gsNf28QFH12Gex+SUaPj4k5PUl25jT3fF3L9y3ZRNi6Hfutiuh+/8ztm8nA6rBaG5zicTofDP/F8Xvmu2ygaj7+/70qmb/GZerggeOAA+thxnG4Xc+l2jrysz/J1Od97+f28e+2NXOkHz/gZ/+lGZTRfzCLeu+/1PHr7NjZ8raH3pYfR1jjF6fUoX3QJh18aUl2Z8oOX38Ud81vh36/BufHOZ3n15w9WC8O3AxyX4g0vpPiped65/VY+fewq7n9sMxN3Bmz6whzNw3swdYW3aSPVBeuYvzxm7jrN9113Jz+/7ovnrEH5bGJfPeQ3jr2Gz+65DO+WHhtuywgen0MfOCS7BKXwNm9i/ru2cuRVDW+69m76XsZff/rl7PwfB75jJdNPhdXC8G0Ed2qKue+7jPptc/yj7Xdz0+zF7Ll5G5u+UhPfshscJbz/Xpelazcwe7XD1IuO8cPbvsEbOg9wsR8+o9v9bxWV0dxTan732Gv40o1Xc+FHU/y9R9HzC21jEcfFW7+W6qINHLs2oXrVEj+y8xvsSdfw4Puuov+RO1aPDqfAamH4NoS3YT0HfngHW39gDxvjZT53/+VM3+Izubsk/MZu9OIS7pWXcvD1MzQ+JIcNg+2K6MVzvGfnjXxf91FmnPi8PWakTckHli/kfffdgHNvj61/N8C5/zGakU3zUgonDHFmpskv3cjRF4Vkz8v47kt2sTVa4I8+/2ouff8c+sFHnt0f5DzGamH4doVSuDsu4PG3baT/yqPsmJjja49eyLpPhUx//TBmcRlmJlGjjProcZwopLz+co69MCRfZ+hfPsfbL7iTt/fvZJuXPOtFojAVD5YNvz/7Sm46eBHhxydZe8s8HDrW9g+U5+GuXUN14XoG22MWL3Fwrl7i2k372RIt8uEHrmXLn3lEn7t7dZdwBqwWhm93OC7uRdt4/G0bmXzVERRw5N71rPuGYfLeBZrde9tttxNFqIk+lBUqiRldvZnj1/iEL53j3Rd/hTd0HmK9G5wTRuVToTKaA3XGIZ1wb76VLy1cykKe8Mh9W1j/dZi8fxGVFjT7D2GKQorBmhn01nWkm2PmrvDIr8i4aOMsl08cYd9omvtvuYjtny7wb9n1HT+GXClWC8N3ELztW9n7o1u5/HsfJtc+9+/ayppbXaYeylC6wTs4j0lTmuXhCW/DIICrLubIyyZINxn01py10wMir+aKqSNcHB9jezDLZm+BTV5GpBSJHU16uO1OozKahoZBU3JLMcP+coZBE/HAcBM3PXIxZjnAeA3ussfUA9A5VuOlGn8hF1bnQ3ueYIqiPA9380YWX7yJ49c6mG0Zk/2UV27czYw/4o/vv561H42ZuPExUUueB+/d5xJWC8N3GpRCXXMFj3//BL0XH2ciyhkUIfPLCc3+DvFRRe9AQ3/3EFXWOHPLNHPzGGNw4gjWr0XlBRQlJDH1uj7zV3QYbVY4FfhDaFxINxrq9SW9qZTRIILjIW6p6D8KM/eOcLIK47s4gxxzQOLdVK+LGY5O9AocF+W6qCjE5AUq8FGb1lOt77N8UcziJRBetcjrtj7ElJ/ywUeuxf3qBBu+NsK9Z/eJr7OKs8ZqYfhOhVJ4mzYyfMEWjlzvMvGCWS6bPsp80eHwoMf8wUm8JZfu44r+vppgUOEt5jjzA5r5BUxZSbItoK64mHJth/CuPegFESQpR6E8sfAwdY1p7HvHNE++eyuFE8eoOMKUFc7kBHrNBPmGBB05NB7UkUM+rRhcook3DNk0uYyrGg4sTlI90GfTlyviWx5pBVGr+NawatTynQpjqA8eIjp4iAs+IfqKYxdt5+iLeyzvbFi7c44XX7uPI3mPR+fXkOYB5UKP4Pgk0fw2wgWDlxvcwpCtcVA1zCxtwl0zDVrDYCT+8KZBaetNqTWmLKWPMdlHVTVUNeX2NczvjCknxL6t7EO+vcSNKxynoZsUbJlYotQu3TLk6L3rKW/s0Nl1nK2H9rR9g+8cUfT5hdUdw3cIlB/gbljH4vWbWbjUJdtUE8zkXLBmnqsmD6FxeGy4pjVRybXHXNphbu8UTq4wLvgDhZcqGhdUA24hH/2hIV+jKCcMOpL3k7sx5eL1s2yMl5kORox0SKZ97pvdyPxCB46H9B91mN5VEN1/AD07t+qi9DTjnB8llFJ7gQFSwGtjzHVKqWngL4ELgL3A240xC/b5vwT8M/v8nzLG/N3pvv5qYXiGoRTKdcF1cdfMUF2wjmIqYLTeZbRFkW+o8adytq9dILUmrVOJ5F7oxqEf5pTapWpc1sZDHlucwXc1kVczKEJm906jSoVxDapRBPMOa+7VdPYOcfYcoBllGK2/o0xSzgc8XYXhOmPM7EmP/Vdg3hjza0qpXwSmjDG/oJS6Avgg8GJgE/A54BJjntr6eLUwnH9QnoczMy09haYBxwHPxUQheiqBxqAMUDc4eWkjqDQsLou0+TzYia7iiXimegxvAV5tP/8T4EvAL9jHP2SMKYA9SqndSJH42rfwvVbxDMPU9RmTmMaX/up9/9sPK6W+GeDvlVLfUEq92z623hhzGMB+XGcf3wycrF45YB97ApRS71ZK3a6Uur2i+IetfhWrWMXTgpXuGF5ujDmklFoHfFYptes0z1WneOxJ+0pjzPuB94McJVa4jlWsYhXPAFa0YzDGHLIfjwEfRY4GR5VSGwHsx/G+8wCw9aSXbwEOnasFr2IVq3j6ccbCoJTqKKV648+B1wP3AR8H3mWf9i7gY/bzjwPvUEqFSqkLgZ3Ared64atYxSqePqzkKLEe+KhSavz8vzDGfEYpdRvwYaXUPwP2AW8DMMbcr5T6MPAAUAPvOd1EYhWrWMX5h/OC4KSUOg6MgNkzPfc8wBpW13mu8VxZ63NlnXDqtW43xqxdyYvPi8IAoJS6faUz1mcTq+s893iurPW5sk741td6ftr5rGIVq3hWsVoYVrGKVTwJ51NheP+zvYAVYnWd5x7PlbU+V9YJ3+Jaz5sewypWsYrzB+fTjmEVq1jFeYJnvTAopd6olHpIKbXbqjSf7fX8oVLqmFLqvpMem1ZKfVYp9Yj9OHXS//slu/aHlFJveAbXuVUp9UWl1INKqfuVUj99Pq5VKRUppW5VSt1t1/mr5+M6T/rerlLqTqXUJ87zde5VSt2rlLpLKXX7OV+rMeZZ+w9wgUeBi4AAuBu44lle0yuBFwL3nfTYfwV+0X7+i8Cv28+vsGsOgQvtz+I+Q+vcCLzQft4DHrbrOa/WimhnuvZzH7gFuP58W+dJ6/1Z4C+AT5yvf3v7/fcCa77psXO21md7x/BiYLcx5jFjTAl8CJFtP2swxtwEfHNW+lsQaTn241tPevxDxpjCGLMHGEvMn4l1HjbG3GE/HwAPIirW82qtRjC0//Ttf+Z8WyeAUmoL8Cbg9096+Lxb52lwztb6bBeGFUm0zwN8SxLzpxtKqQuAFyB34/NurXZ7fhcitPusMea8XCfwPuDngeakx87HdcLTYIVwMp5tM9gVSbTPYzzr61dKdYGPAD9jjFm2mpZTPvUUjz0jazWilblGKTWJ6G6uOs3Tn5V1KqXeDBwzxnxDKfXqlbzkFI89k3/7c26FcDKe7R3Dc0WifV5KzJVSPlIU/twY8zfn81oBjDGLiNPXGzn/1vly4PutjeGHgBuUUn92Hq4TePqtEJ7twnAbsFMpdaFSKgDegci2zzecdxJzJVuDPwAeNMa893xdq1Jqrd0poJSKge8Gdp1v6zTG/JIxZosx5gLkffgFY8w7z7d1wjNkhfBMdVFP0139XqSj/ijwy+fBej4IHAYqpNL+M2AG+DzwiP04fdLzf9mu/SHge57Bdb4C2Q7eA9xl//ve822twNXAnXad9wH/1j5+Xq3zm9b8ak5MJc67dSJTvLvtf/ePr5tzudZV5uMqVrGKJ+HZPkqsYhWrOA+xWhhWsYpVPAmrhWEVq1jFk7BaGFaxilU8CauFYRWrWMWTsFoYVrGKVTwJq4VhFatYxZOwWhhWsYpVPAn/P+kqKcUx3k1xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:47: FutureWarning: The behavior of rgb2gray will change in scikit-image 0.19. Currently, rgb2gray allows 2D grayscale image to be passed as inputs and leaves them unmodified as outputs. Starting from version 0.19, 2D arrays will be treated as 1D images with 3 channels.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 511.5, 511.5, -0.5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x230.4 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from skimage.feature import canny\n",
    "from skimage.transform import hough_circle, hough_circle_peaks\n",
    "from skimage.segmentation import watershed, random_walker, active_contour\n",
    "from skimage.morphology import erosion, dilation\n",
    "from skimage.restoration import denoise_bilateral\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.filters import threshold_local\n",
    "\n",
    "image=plt.imread('medical_image.png')\n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "\n",
    "canny_edges=canny(image, sigma=1.5 )\n",
    "hough_radii = np.arange(29, 32, 1)\n",
    "hough_res = hough_circle(canny_edges, hough_radii)\n",
    " #Identifies most prominent circles separated by certain distances in a\n",
    " #Hough space. \n",
    "accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,total_num_peaks=4, min_xdistance=70,min_ydistance=200, threshold=0.25)\n",
    "\n",
    "img_center = (image.shape[0]//2, image.shape[1]//2)\n",
    "\n",
    "dist = []\n",
    "for idx in range(len(cx)):\n",
    "     dist.append(abs(img_center[1]-cx[idx])+abs(img_center[0]-cy[idx]))\n",
    "sortD = np.argsort(dist)\n",
    "Cx = cx[sortD[0]]\n",
    "Cy = cy[sortD[0]]\n",
    "radius = radii[sortD[0]]\n",
    "\n",
    "markers = np.ones(image.shape, dtype=np.uint)\n",
    "markers[image==0] = 0\n",
    "markers[Cy-radius//2:Cy+radius//2, Cx-radius//2:Cx+radius//2] = 2\n",
    "\n",
    "border = 71\n",
    "surround = (\n",
    "   (dilation(markers, np.ones((border, border))) == 2)\n",
    "  ^ (markers==2)\n",
    " )\n",
    "markers[surround] = 0\n",
    "\n",
    "labels = random_walker(image, markers)\n",
    "\n",
    "block_size=35\n",
    "grayscale_image=rgb2gray(image)\n",
    "denoised_image=denoise_bilateral(grayscale_image,multichannel=False)\n",
    "\n",
    "local_thresh= threshold_local(grayscale_image, block_size,offset=.01)\n",
    " #apply the thresholding to the image\n",
    "binary_global = grayscale_image<local_thresh\n",
    "\n",
    "plt.clf()\n",
    "fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4, figsize=(8, 3.2),\n",
    "                                sharex=True, sharey=True)\n",
    "ax1.imshow(canny_edges, cmap='gray')\n",
    "ax1.axis('off')\n",
    "ax2.imshow(markers,cmap='gray')\n",
    "ax2.axis('off')\n",
    "ax3.imshow(labels,cmap='gray')\n",
    "ax3.axis('off')\n",
    "ax4.imshow(binary_global,cmap='gray')\n",
    "ax4.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: skimage.measure.label's indexing starts from 0. In future version it will start from 1. To disable this warning, explicitely set the `start_label` parameter to 1.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:18: FutureWarning: The new recommended value for bg_label is 0. Until version 0.19, the default bg_label value is -1. From version 0.19, the bg_label default value will be 0. To avoid this warning, please explicitly set bg_label value.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "image=plt.imread('medical_image.png')\n",
    "#image = Image.open('medical_image_segment.png')\n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "\n",
    "canny_edges=canny(image, sigma=1.5)\n",
    "\n",
    "plt.imshow(canny_edges, cmap='gray')\n",
    "plt.show()\n",
    "\n",
    "segments= slic(canny_edges,n_segments=30)\n",
    "\n",
    "#segmented_image=label2rgb(segments,coins_image,kind='avg')\n",
    "segmented_image=label2rgb(segments,canny_edges)\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(6,4))\n",
    "ax.imshow(segmented_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   code_desc  product_id\n",
      "0  Product 1          30\n",
      "1  Product 1          70\n",
      "2  Product 2          40\n",
      "3  Product 3          50\n",
      "4  Product 4          60\n",
      "           product_id\n",
      "code_desc            \n",
      "Product 1           2\n",
      "Product 2           1\n",
      "Product 3           1\n",
      "Product 4           1\n",
      "Index(['Product 1', 'Product 2', 'Product 3', 'Product 4'], dtype='object', name='code_desc')\n"
     ]
    }
   ],
   "source": [
    "txt=\"\"\"code_desc,product_id\n",
    "Product 1,                         30\n",
    "Product 1,                         70\n",
    "Product 2,                         40\n",
    "Product 3,                         50\n",
    "Product 4,                         60\"\"\"\n",
    "\n",
    "from io import StringIO\n",
    "f = StringIO(txt)\n",
    "df = pd.read_table(f,sep =',')\n",
    "#df.set_index('product_id',inplace=True)\n",
    "print(df)\n",
    "\n",
    "grouped = df.groupby(['code_desc']).agg({'product_id': pd.Series.nunique })\n",
    "print(grouped)\n",
    "labels=grouped.index\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boise:name:david nishimoto\n",
      "Boise:occupation:programmer\n"
     ]
    }
   ],
   "source": [
    "class aClass:\n",
    "    a=\"\"\n",
    "    def __init__(self,a):\n",
    "        self.a=a\n",
    "    def print_all(self,**kwargs):\n",
    "        for key, value in kwargs.items():\n",
    "            print(self.a+\":\"+key+\":\"+value)\n",
    "          \n",
    "obj=aClass(\"Boise\")\n",
    "obj.print_all(name=\"david nishimoto\", occupation=\"programmer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install opencv-contrib-python\n",
    "\n",
    "import cv2\n",
    "\n",
    "cv2.namedWindow(\"preview\")\n",
    "vc = cv2.VideoCapture(0)\n",
    "\n",
    "if vc.isOpened(): # try to get the first frame\n",
    "    rval, frames = vc.read()\n",
    "    #print(\"return val\",rval,frames)\n",
    "\n",
    "else:\n",
    "    rval = False\n",
    "\n",
    "while rval:\n",
    "    cv2.imshow(\"preview\", frames)\n",
    "    rval, frame = vc.read()\n",
    "    key = cv2.waitKey(20)\n",
    "    if key == 27: # exit on ESC\n",
    "        break\n",
    "cv2.destroyWindow(\"preview\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}