WDM_Reader_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a WDM and Writing to HDF5\n",
    "+ **Entire WDM is Read and Written to HDF5 File (our prefered database)**\n",
    "+ **You Can View the HDF5 file with HDFView or HDFCompass**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from readWDM import read_WDM\n",
    "\n",
    "from versioninfo import versions\n",
    "versions(['numba', 'numpy', 'pandas', 'tables'])\n",
    "\n",
    "import matplotlib.pyplot as m_plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, this is a copy of the file readWDM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numba import jit\n",
    "\n",
    "# look up attributes NAME, data type (Integer; Real; String) and data length by attribute number\n",
    "attrinfo = {1:('TSTYPE','S',4),     2:('STAID','S',16),    11:('DAREA','R',1),\n",
    "           17:('TCODE','I',1),     27:('TSBYR','I',1),     28:('TSBMO','I',1),\n",
    "           29:('TSBDY','I',1),     30:('TSBHR','I',1),     32:('TFILL', 'R',1),\n",
    "           33:('TSSTEP','I',1),    34:('TGROUP','I',1),    45:('STNAM','S',48),\n",
    "           83:('COMPFG','I',1),    84:('TSFORM','I',1),    85:('VBTIME','I',1), \n",
    "          444:('A444','S',12),    443:('A443','S',12),     22:('DCODE','I',1),        \n",
    "           10:('DESCRP','S', 80),   7:('ELEV','R',1),       8:('LATDEG','R',1),\n",
    "            9:('LNGDEG','R',1),   288:('SCENARIO','S',8), \n",
    "          289:('CONSTITUENT','S',8), 290:('LOCATION','S',8)}\n",
    "\n",
    "freq = {7:'100YS', 6:'YS', 5:'MS', 4:'D', 3:'H', 2:'min', 1:'S'}   # pandas date_range() frequency by TCODE, TGROUP\n",
    "\n",
    "def read_WDM(wdmfile, hdffile):\n",
    "    iarray = np.fromfile(wdmfile, dtype=np.int32)\n",
    "    farray = np.fromfile(wdmfile, dtype=np.float32)\n",
    "\n",
    "    if iarray[0] != -998:\n",
    "        print('Not a WDM file, magic number is not -990. Stopping!')\n",
    "        return\n",
    "    nrecords    = iarray[28]    # first record is File Definition Record\n",
    "    ntimeseries = iarray[31]\n",
    "    \n",
    "    dsnlist = []\n",
    "    for index in range(512, nrecords * 512, 512):\n",
    "        if not (iarray[index]==0 and iarray[index+1]==0 and iarray[index+2]==0 \n",
    "         and iarray[index+3]) and iarray[index+5]==1:\n",
    "            dsnlist.append(index)\n",
    "    if len(dsnlist) != ntimeseries:\n",
    "        print('PROGRAM ERROR, wrong number of DSN records found')\n",
    "    \n",
    "    with pd.HDFStore(hdffile) as store:\n",
    "        for index in dsnlist:\n",
    "            # get layout information for TimeSeries Dataset frame\n",
    "            dsn   = iarray[index+4]\n",
    "            psa   = iarray[index+9]\n",
    "            if psa > 0:\n",
    "                sacnt = iarray[index+psa-1]\n",
    "            pdat  = iarray[index+10]\n",
    "            pdatv = iarray[index+11]\n",
    "            frepos = iarray[index+pdat]\n",
    "\n",
    "            # get attributes\n",
    "            dattr = {'TSBDY':1, 'TSBHR':1, 'TSBMO':1, 'TSBYR':1900, 'TFILL':-999.}   # preset defaults\n",
    "            for i in range(psa+1, psa+1 + 2*sacnt, 2):\n",
    "                id = iarray[index + i]\n",
    "                ptr = iarray[index + i + 1] - 1 + index\n",
    "                if id not in attrinfo:\n",
    "                    print('PROGRAM ERROR: ATTRIBUTE INDEX not found', id, 'Attribute pointer', iarray[index + i+1])\n",
    "                    continue\n",
    "\n",
    "                name, atype, length = attrinfo[id]\n",
    "                if atype == 'I':\n",
    "                    dattr[name] = iarray[ptr]\n",
    "                elif atype == 'R':\n",
    "                    dattr[name] = farray[ptr]\n",
    "                else:\n",
    "                    dattr[name] = ''.join([itostr(iarray[k]) for k in range(ptr, ptr + length//4)]).strip()  \n",
    "\n",
    "            # Get timeseries timebase data\n",
    "            records = []\n",
    "            for i in range(pdat+1, pdatv-1):\n",
    "                a = iarray[index+i]\n",
    "                if a:\n",
    "                    records.append(splitposition(a))\n",
    "            if len(records) == 0:\n",
    "                continue   # WDM preallocated, but nothing saved here yet\n",
    "\n",
    "            srec, soffset = records[0]\n",
    "            start = splitdate(iarray[srec*512 + soffset])\n",
    "\n",
    "            sprec, spoffset = splitposition(frepos)\n",
    "            finalindex = sprec * 512 + spoffset\n",
    "\n",
    "            # calculate number of data points in each group, tindex is final index for storage\n",
    "            tgroup = dattr['TGROUP']\n",
    "            tstep  = dattr['TSSTEP']\n",
    "            tcode  = dattr['TCODE']\n",
    "            cindex = pd.date_range(start=start, periods=len(records)+1, freq=freq[tgroup])\n",
    "            tindex = pd.date_range(start=start, end=cindex[-1], freq=str(tstep) + freq[tcode])\n",
    "            counts = np.diff(np.searchsorted(tindex, cindex))\n",
    "                 \n",
    "            ## Get timeseries data\n",
    "            floats = np.zeros(sum(counts),  dtype=np.float32)   \n",
    "            findex = 0\n",
    "            for (rec,offset),count in zip(records, counts):\n",
    "                findex = getfloats(iarray, farray, floats, findex, rec, offset, count, finalindex)\n",
    "\n",
    "            ## Write to HDF5 file\n",
    "            series = pd.Series(floats[:findex], index=tindex[:findex])\n",
    "            dsname = 'TIMESERIES/TS' + str(dsn)\n",
    "        \n",
    "            store.put(dsname, series)\n",
    "            dattr['start_date']  = str(tindex[0])\n",
    "            dattr['stop_date']   = str(tindex[-1])\n",
    "            dattr['freq']        = str(tstep) + freq[tcode]\n",
    "            dattr['dsn']         = dsn\n",
    "            store.get_storer(dsname).attrs.metadata = dattr\n",
    "            \n",
    "            \n",
    "def todatetime(y=1900, m=1, d=1, h=0):\n",
    "    '''takes yr,mo,dy,hr information then returns its datetime64'''\n",
    "    return pd.datetime(y,m,d,23) + pd.Timedelta(1,'h') if h == 24 else pd.datetime(y, m, d, h)\n",
    "    \n",
    "def splitdate(x):\n",
    "    '''splits WDM int32 DATWRD into year, month, day, hour -> then returns its datetime64'''  \n",
    "    return todatetime(x >> 14, x >> 10 & 0xF, x >> 5 & 0x1F, x & 0x1F) # args: year, month, day, hour\n",
    "\n",
    "def splitcontrol(x):\n",
    "    ''' splits int32 into (qual, compcode, units, tstep, nvalues)'''\n",
    "    return(x & 0x1F, x >> 5 & 0x3, x >> 7 & 0x7, x >> 10 & 0x3F, x >> 16)\n",
    "\n",
    "def splitposition(x):\n",
    "    ''' splits int32 into (record, offset), converting to Pyton zero based indexing'''\n",
    "    return((x>>9) - 1, (x&0x1FF) - 1)\n",
    "\n",
    "def itostr(i):\n",
    "    return chr(i & 0xFF) + chr(i>>8 & 0xFF) + chr(i>>16 & 0xFF) + chr(i>>24 & 0xFF) \n",
    "\n",
    "@jit(nopython=True, cache=True)\n",
    "def getfloats(iarray, farray, floats, findex, rec, offset, count, finalindex):\n",
    "    index = rec * 512 + offset + 1\n",
    "    stop = (rec + 1) * 512\n",
    "    cntr = 0\n",
    "    while cntr < count and findex < len(floats):\n",
    "        if index >= stop:\n",
    "            rec = iarray[rec * 512 + 3] - 1  # 3 is forward data pointer, -1 is python indexing\n",
    "            index = rec * 512 + 4            # 4 is index of start of new data\n",
    "            stop =  (rec+1) * 512\n",
    "            \n",
    "        x = iarray[index]                    # control word, don't need most of it here\n",
    "        nval = x >> 16\n",
    "        \n",
    "        index += 1\n",
    "        if x >> 5 & 0x3:    # comp from control word, x\n",
    "            for k in range(nval):\n",
    "                if findex >= len(floats):\n",
    "                    return findex\n",
    "                floats[findex] = farray[index]\n",
    "                findex += 1\n",
    "            index += 1            \n",
    "        else:\n",
    "            for k in range(nval):\n",
    "                if findex >= len(floats):\n",
    "                    return findex\n",
    "                floats[findex] = farray[index+k]\n",
    "                findex += 1\n",
    "            index += nval\n",
    "        cntr += nval     \n",
    "    return findex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdmfile = 'test10.wdm'\n",
    "hdffile = 'test10.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_WDM(wdmfile, hdffile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that metadata was extracted and placed with the corresponding data in the HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 {'TSBDY': 1, 'TSBHR': 1, 'TSBMO': 1, 'TSBYR': 1975, 'TFILL': -999.0, 'TSTYPE': 'FLOW', 'STAID': 'DAILY FLOW', 'STNAM': 'IOWA RIVER MARSHALLTOWN', 'TCODE': 4, 'TGROUP': 6, 'TSFORM': 1, 'VBTIME': 1, 'COMPFG': 1, 'TSSTEP': 1, 'A444': '200507251408', 'start_date': '1976-01-01 00:00:00', 'stop_date': '1977-01-01 00:00:00', 'freq': '1D', 'dsn': 113}\n"
     ]
    }
   ],
   "source": [
    "with pd.HDFStore(hdffile) as store:\n",
    "    d = store.get_storer('TIMESERIES/TS113').attrs.metadata\n",
    "    \n",
    "print(len(d), d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TESTING\n",
    "Read a Selected Timeseries from HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     366.000000\n",
       "mean      599.021851\n",
       "std      1049.336792\n",
       "min        18.000000\n",
       "25%        70.000000\n",
       "50%       120.000000\n",
       "75%       806.000000\n",
       "max      7920.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_hdf(hdffile, 'TIMESERIES/TS113')\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Flow at RCHRES ###')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIytO0jWuJTNJUkJ4BQAAKCSCK4CRle/jWi6lwZWqKwAAQDERXAGMrFqSDmcqRWlwZZ0rAABAMRFcAYysfDucvFU4doIrAABAERFcAYysWpyo3FpxjQmuAAAARURwBTCy8uFMzTWuyZCvCAAAAN0QXAGMrHQf10gRrcIAAACFRnAFMLJqcaJKZCoznAkAAKDQCK4ARla+HU6+xrXOGlcAAIBCIrgCGFn1pH04U0KrMAAAQCERXAGMrHo2nKlRcaVVGAAAoJAIrgBGVrqPa6RylP6nkDWuAAAAxURwBTCyanGiSslUyv5LSHAFAAAoJoIrgJGVbodjKlFxBQAAKDSCK4CR5O6Kk7xVmDWuAAAARUZwBTCS8pBajkxRYx/XZJiXBAAAgB4IrgBGUr5na7nUrLjG5FYAAIBCIrgCGEm1rLqaDmfKW4VJrgAAAEVEcAUwkhoV15Z9XBnOBAAAUEwEVwAjqZ71BZdLEcEVAACg4AiuAEZS63CmMsEVAACg0AiuAEZS63CmyNgOBwAAoMgIrgBGUutwpnIpDa4JwRUAAKCQCK4ARlJzOFNzOxwqrgAAAMVEcAUwkmqN4UzWaBVmjSsAAEAx9RVczeygmf2Omf2lmT1hZt9pZvNm9oiZPZl9nMvONTP7gJmdMrPHzOy1LY9zb3b+k2Z27179UACwnbhtOFPUdgwAAADF0m/F9Z9J+oS7f6ukV0l6QtJ7JX3S3U9K+mR2W5LeJulk9u9+SR+UJDObl/SApNdLep2kB/KwCwA3Wj1p2Q6nRMUVAACgyLYNrmY2K+lvSHpQktx9w90vSbpH0kPZaQ9Jenv2+T2SfsNTn5F00MxulvQWSY+4+5K7X5T0iKS37upPAwB9qmVrXCuRqcRUYQAAgELrp+J6h6RFSb9uZl80s18zsylJR939BUnKPh7Jzj8m6XTL15/JjvU6DgA3XOt2OKV8H1cnuAIAABRRP8G1LOm1kj7o7q+RdE3NtuBurMsx3+J4+xeb3W9mj5rZo4uLi31cHgAMrpY0hzPlU4XjbGATAAAAiqWf4HpG0hl3/2x2+3eUBtkXsxZgZR/PtZx/ouXrj0s6u8XxNu7+IXe/y93vWlhYGORnAYC+xXFzOFO+xpVWYQAAgGLaNri6+zclnTazl2WH3iDpa5IelpRPBr5X0seyzx+W9EPZdOG7JV3OWon/UNKbzWwuG8r05uwYANxwjeFMUdRY45rQKgwAAFBI5T7P+/uSftPMqpKelvRupaH3o2Z2n6TnJL0zO/fjkr5P0ilJK9m5cvclM/tZSZ/PzvsZd1/alZ8CAAbUGM5UssYaVyquAAAAxdRXcHX3L0m6q8tdb+hyrkt6T4/H+bCkDw9ygQCwF1q3w2mucSW4AgAAFFG/+7gCwL5Sa13jylRhAACAQiO4AhhJcZJvh2MyM0XWPAYAAIBiIbgCGEn1uDmcKf/IGlcAAIBiIrgCgXr8+ct67MylYV9GsFqHM0lSFFFxBQAAKCqCKxCof/rvn9D7/uCJYV9GsFqHM0lpxZXgCgAAUEz9bocDoGBWN2KRs3audTiTJJUiI7gCAAAUFMEVCNRGtkYTO9MYztQSXPMqLAAAAIqF4AoEaqOeKDIb9mUEKx/OVGqruA7zigAAANALwRUIVC12lVilvmO1xFXJtsKR0sprTMUVAACgkHjZCwRqo56wJvM61OOksRWOJEVmbIcDAABQUFRcgUCt1xPRKbxztdhVLjV/geWSKSG4AgAAFBIVVyBQG/WYiut1iBNvDGaS8uFM/D4BAACKiOAKBKoWe2NLFwyuniSNPVwlqWRshwMAAFBUBFcgUBtxwjCh61CLXZWOiivBFQAAoJgIrkCA4sQVJ646Fdcdq8ftFddyieAKAABQVARXIEC1bMNR1mTuXC1pH85UYqowAABAYRFcgQCt1/PgSqvwTsXx5uFMiRNcAQAAiojgCgRoo07F9XrVE1epZR/XchTRej0i/uSvFvX8pdVhXwYAABgAwRUIUN4q7C7WZe5Q4q6WJa6KIn6Xo+JHP/JFffBTp4Z9GQAAYAAEVyBAecVVol14p+LEVbJmq3A5ihTTKjwSNuqJTi9RcQUAICQEVyBAG3FLcKW9dUcSd0Uda1xpvR4NibvO0ioMAEBQCK5AgNorroStnUjcFVnnPq5Ur0dBkkjPX1qVU2EHACAYBFcgQO0VV8LWTnS2CqfBdYgXhBsmcdfKRqxLK7VhXwoAAOgTwRUIUGvFlYFCO5Mk6UCmXJmK68jI1zIzWRgAgHAQXIEA0Sp8/TpbhSPWuI4Ed1feIUxwBQAgHARXIEA1hjNdt9hdpah1qrApSVxx4rrnn/+ZPvH4N4d4ddgrre9NPH+R4AoAQCgIrkCA2A7n+iXJ5uFM9cR1aWVDXz59SY8/f3mIV4e90tpaT8UVAIBwEFyBALUNZ6K9dUcSl1oKrtkaV9fStQ1J0spGPKQrw15KWiYJU3EFACAcBFcgQG0VV1qFdyRO2luF84rrhSy4rtbqw7o07KHW4Hr2MsEVAIBQEFyBALVXXGkV3olu+7gmietiHlypuO5LrHEFACBMBFcgQEwVvn6dwbUcRW0VV1qF96d8jev8VFUXrm3wBgUAAIEguAIBolX4+nW2CkfWvsZ1tUag2Y88axU+MT8piQFNAACEguAKBKhGq/B1SzzduzVXLnUEVypx+1JecT0xNyFJOktwBQAgCARXIEBUXK9f2ircvF1iqvBIyDvrb6XiCgBAUAiuQIDWWyquMWtcdyROXKXW4UxmqicJrcL7XD5V+OYD4ypFxoAmAAACQXAFAlSrN8Nqa9sw+pck3tYqXIpMiatlOBPb4exHeXCtlCLdNDtOqzAAAIEguAIB2oib1UAqrjuTuNpahcvZjcWr65JY47pf5c+XKDIdOzihMwRXAACCQHAFAtS6xrVGcN2R2DumCmefL13LgiutwvtSVnBVyUxTYyUq6wAABILgCgSo1jKQKWaq8I4kSec+runniUszY2XVYqcNex9qVlzTdmGGmwEAEAaCKxCgjXqi8Ur69K3xwntHko6Ka+vnx7KtUpgsvP/ka1wjM1VKEW9OAAAQCIIrEKD1eqLJalkSa1x3Ku6ouLYG1+NZcF2jXXjfaQ2u5ZKpzvMHAIAg9BVczewbZvYVM/uSmT2aHZs3s0fM7Mns41x23MzsA2Z2ysweM7PXtjzOvdn5T5rZvXvzIwH730acaKJSkiTVqRjtSDqcaXOrsCQdO9i94vrVs5f1xl/+tC6v1m7MRWLX5Tm1FJnKEa3CAACEYpCK63/q7q9297uy2++V9El3Pynpk9ltSXqbpJPZv/slfVBKg66kByS9XtLrJD2Qh10Ag6nVE01Ws+BKxWhH0lbh5u1S1LxxfG5S0uYtcb529opOnVtmC5WANda4mlQpGa3CAAAE4npahe+R9FD2+UOS3t5y/Dc89RlJB83sZklvkfSIuy+5+0VJj0h663V8f2BkbcSJJsfSVmEqRjuzuVW4eV++xrVzS5y1bJoza1/DRaswAABh6je4uqT/YGZfMLP7s2NH3f0FSco+HsmOH5N0uuVrz2THeh0HMKCNeqLJChXX65G4N7bAkZoV12op0uHpMUmbt8RZz26z9jVc+RDuyNJWYSquAACEodzned/t7mfN7IikR8zsL7c417oc8y2Ot39xGozvl6Rbb721z8sDRkstTjRRZY3r9Ug83cszl69xnZ+qNtqwOyureWCl4hquvOJaioxWYQAAAtJXxdXdz2Yfz0n6PaVrVF/MWoCVfTyXnX5G0omWLz8u6ewWxzu/14fc/S53v2thYWGwnwYYERv1luBKxXVH0lbh5u2oJbjmv9tNrcK1NOR0VmIRjjgLrmZSmX1cAQAIxrbB1cymzGwm/1zSmyU9LulhSflk4HslfSz7/GFJP5RNF75b0uWslfgPJb3ZzOayoUxvzo4BGNB6PdFYOVIpMrbD2YEkH9ATbV1x7QyoecV1jYprsLyt4hqpnnjjGAAAKK5+WoWPSvo9S1vqypL+H3f/hJl9XtJHzew+Sc9Jemd2/sclfZ+kU5JWJL1bktx9ycx+VtLns/N+xt2Xdu0nAUZILU5ULaXBtZbQ6jioRrtol31c56eqmqyk/2nc1Cpcz1uF26cNIxxxyxrXSvY3ryeuSqnbahYAAFAU2wZXd39a0qu6HL8g6Q1djruk9/R4rA9L+vDglwmg1UacqFqOVIlMMa2OA8vbRduGM1kzuI5X02aU1Y6A2mwV5s2CULVPFU7/zvXYlc06AwAABXU92+EAGJKNerPiyhrXwbVOls2VSs3gmv9uew1nYo1ruJKOfVwl0bUAAEAACK5AgDbqiSrlKFujx4vuQTUnyzaPta5xNTNNVkpd1rhmFVdahYOVv89TiqzxN2dAEwAAxUdwBQKTJK564s2KKy+6Bxa3tIvm8lbhQ1NVSdJEtbRpqvB6nYpr6JpThVtbhXnzBwCAoiO4AoHZyF5kVxsVV4LroJrtos3gemJ+Uoenq/rWm2clpcG1Z6vwBkEnVJ37uEpSjecQAACF189UYQAFkgfXfDscqkWDa20XzZ2Yn9Sj//hNjdsTlW7BNR/ORKtwqFrXuJYjKq4AAISCiisQmFo9fZFdKUUqlxjOtBNxS3jpZbJaalRYc82KK63CoYqT1qnCWcWV4AoAQOERXIHAtLYKl1njuiNJl+1wOqWtwh3b4TT2cSW4hip/nycyUyVb41rjOQQAQOERXIHAbGQV12opUjlijetONNY52hbBtVLu2SrcWYlFONrXuDb3cQUAAMVGcAUCk7c1Vsp5qzBtjoOKuwxn6jRZ7bYdDlOFQ9eotpuarcI8hwAAKDyCKxCY9baKqzVCGPqX55StWoUnu22Hk1VcaRUOV+NNi8hUiai4AgAQCoIrEJi8VXisnLYKM1hmcM120d7njFfag2uceGN9Ma3C4fKWNa55xZWpwgAAFB/BFQhMY41r1ipMxXVwsffXKrxSi+XZuev1ZlhlqnC48udLydjHFQCAkBBcgcDkE1ArpXQfVyaiDi7pc41rnHjj95sPZpodL7cFWoQlr7Yb+7gCABAUgisQmI04rfZVy5EqpYiK6w7kv7LSltvhlCU1q6t5e/DcVFXuzbXGCEvrVOHmPq48hwAAKDqCKxCY1u1w0oorAWpQzanCvc+ZqJQkSSu1dC/XRnCdrLbdRli67ePKZG4AAIqP4AoEZiOrDlXL6Ro9Kq6DS/pc4yo1JwjnrcJzk5W24whLc6qwVI7yiivBFQCAoiO4AoFpVlxLKkWR6gTXgbW2i/YykQXXRqtwvb3iyl6uYfKWNy3yiiutwgAAFB/BFQhM21ThyGhz3IG4j+FMeatwHlBb17hKTBYOVftUYfZxBQAgFARXIDAbWeWvUrI0uPKie2CNVuEtKq6drcLrHa3CVFzD1Fjj2jKciTd/AAAoPoIrEJhaY41ruo8rrcKDa0wV3qri2mgVTocz5UH14CQV15A11zdLlYhWYQAAQlEe9gUAGMxG3NoqzHY4O9HPVOHJfDucjlbh+axVmOFMYWpd35z//dnHFQCA4iO4AoFZZzuc65Yk27cKN7bD6ZgqfDBrFWY7nDDlT5fITJb1HNG1AABA8RFcgcDU4kSVksmM7XB2qtEqPMhU4RpThfeD1q2Q2A4HAIBwsMYVCMxGPVE1m4ZaiiKGM+1A7P20CndUXOu0Cu8HSUubeJS1C/McAgCg+AiuQGA26omq5fSpWymxHc5OJH1sh1MpRaqUrGWNa/p7PjBBq3DIOqvt5VKkGs8hAAAKj+AKBCZtFc4rrqbEm0EM/Wkd0LOV8Uqp0Sq8Xos1Vo40Vo4UGVOFQ5VX2y1706ISmWp1nj8AABQdwRUITHvFNf3IcJnBxH1UXKW0XXgl2w5nrRZrvFKSmWmyWqZVOFDu3vaGRbkU0bUAAEAACK5AYNbjZnDNX4DzwnswrQN6tjJZLWs1axFeqyUar6S/9/FKieFMgYoTb1vbXClF7OMKAEAACK5AYFqHM5UbwZUX3oPoZ6qwlAbUlfWs4lpPK66SNFGNWOMaqMTb37ColIx9XAEACADBFQhMraXi2giuVIwGEifbTxWWpLnJii6t1iRlrcLlNLhOVspapufFAAAgAElEQVSNFmKEJXFvC67lkvHGDwAAASC4AoFpq7g21rhSMRpEo1V4m+R6aHpMF5bXJXW0CldLjRZihCVJ2te4VqKIfVwBAAgAwRUITOtwJiquO9OYKrzNGtdDU1VdWN6QlFZcx/JW4UqkNYYzBSl2V+ufvVwynj8AAASA4AoEpnU7nLziGtPqOJC8wLbdcKZDU1VdXa9rrRZrrZ401rhOVstaqdEqHCL39rXN5YipwgAAhIDgCgRmvUvFlVbHweT73kbb/Bfw0PSYJGnp2obWa7HGs9/7RMv+rghLOlW4fTgTU4UBACg+gisQmI3W4Uyl9AU4FdfBNFqFt13jWpWUBte1WqyJalpxHSe4BiveNJyJiisAACEguAKBqcWJxkqdFVeC6yDiPvdxPZwF1/PL6+lwpnyqcJV9XEPl3r6Pazky1eo8fwAAKDqCKxCYjXpzjWspYo3rTjRahbdd45q2Cl9Y3sj2cc1ahQmuwYo7pwqXItWouAIAUHgEVyAwbVOFs1ZhXngPJs/5/bYKX7i2nu7jWmm2Cq/VkkYARjgS16Y1rkwVBgCg+AiuQGC6bYdDxXUwcaPiuvV502NlVcuRzi9vaK2WNLbDmczWuq7VqbqGJkm8bShXucQ+rgAAhIDgCgSmlnij0lrOXoHzwnsw+XCmaJvkamY6PFXV2UurktRsFc4CLAOawpP45qnCdd74AQCg8AiuQGDixBuVVqYK70xjqvA2a1yldEuc5/Pgmg1nyqcLrxBcgxN7+9+9HEWq88YPAACF13dwNbOSmX3RzP5ddvt2M/usmT1pZr9tZtXs+Fh2+1R2/20tj/HT2fGvm9lbdvuHAUZBnHjjhXceYFmjN5g8p2w3nEmS5tsqrllwzT6uMaApOIm7Wv/sZfZxBQAgCINUXH9M0hMtt39B0vvd/aSki5Luy47fJ+miu98p6f3ZeTKzl0t6l6RXSHqrpF81s9L1XT4wWhrTcKP2VmFaHQfTbBXe/txD01Wdu7ouqUurMME1OEnnVOGIfVwBAAhBX8HVzI5L+n5Jv5bdNknfK+l3slMekvT27PN7stvK7n9Ddv49kj7i7uvu/oykU5Jetxs/BDAq4o4W12arMC+8B5G/AdBPq/Dh6TFlv/ZGxXWSVuFgda5xLTNVGACAIPRbcf0VSf9QUv7q+JCkS+5ez26fkXQs+/yYpNOSlN1/OTu/cbzL1wDoQ7yp4ppth8ML74HkbwD00yp8aKra+DyvuI5XqbiGKk46t8NhqjAAACHYNria2d+WdM7dv9B6uMupvs19W31N6/e738weNbNHFxcXt7s8YKQ0hgo1hjOlT2GGMw2ms+V6K4emxxqfN4Yz5WtcqbgGx71jO5yINa4AAISgn4rrd0v6u2b2DUkfUdoi/CuSDppZOTvnuKSz2ednJJ2QpOz+A5KWWo93+ZoGd/+Qu9/l7nctLCwM/AMB+1nc0eLarLhSMRpE4mpb57iVQ9PNimvnPq60Cocndm+fKlxijSsAACHYNri6+0+7+3F3v03pcKU/cvf/UtIfS3pHdtq9kj6Wff5wdlvZ/X/k7p4df1c2dfh2SSclfW7XfhJgBOSvryO2w7kusbv6zK06PNVScc1ahafG0vfsltfrXb8GxZV4e6W9mk0Vduc5BABAkZW3P6Wnn5L0ETP7OUlflPRgdvxBSf/SzE4prbS+S5Lc/atm9lFJX5NUl/Qed6dcAQygOZwpvZ1XDWsE14Ekife1vlVqr7jmw5lmxtP/dF5dq+3+xWFPdf7tW9vt8zeCAABA8QwUXN39U5I+lX3+tLpMBXb3NUnv7PH175P0vkEvEkCq0SqcBdZKtlgvplV4IIl7363C81Obg+tYuaRqOdLVNSquoUk2tQpneyEnrjIbtAEAUFiD7OMKYMia+4+mL7ZLLS+60b/OybJbGa+UNJO1Bo+Xm//JnB2v6ArBNThx4mr90+dv/rBOHACAYiO4AgHpHM6Uv+gmuA4mGWCNqyTNZ+3CecVVkmbHy7pCq3BwvGMwV6PiymRhAAAKjeAKBKRzH9f8BXidatFABmkVlpp7ubYG15nxMq3CAUoHc21e41pjsjAAAIVGcAUC0tjHtWM7HCqug4kHGM4kpXu5VkrWFnZnxisMZwpQ4t42VbgSUXEFACAEBFcgIJ3DmaLIFBkvugfVGV62c3h6TBOV9sk9VFzDlE4Vbt7OK648hwAAKLbr2Q4HwA3WOZxJSl94U3EdTJKobbLsdu7767fpe04ebjuWBlcqrqFJvP1vX8nWuG7Qbg8AQKERXIGA5K+t27bziIw1rgOKBxzOdOeRGd15ZKbt2Ox4hYprgNKpwq3Pn3zAGc8hAACKjFZhICDNVuHmsVJkVFwHlCSDtQp3MzNe0cpGzJsGgUkHczVvV5gqDABAEAiuQEAarcJtrY4R1aIBDTpVuJuZ8bRhhaprWJKOqcKVEvu4AgAQAoIrEJDO4Uz55zEV14HEroGmCndDcA1T4p1rxJnMDQBACAiuQEDiLsOZKpGpRpvjQDony+7EzHhFknSFAU1BSTq2QsrXuFJxBQCg2AiuQEAaFdeWF96lEhXXQe1Gq/DsBBXXECXuKrX86VnjCgBAGAiuQEDygFpuq7hGVIsGFHdU3XZiNqu4siVOWOKONa6NfVxZJw4AQKERXIGAJMnmVmHWuA6uc0DPTrDGNUxJ0rHGNfucdnsAAIqN4AoEJF/jWoraK0a86B5MnOzGVGEqriFKOvbwzacK0yoMAECxEVyBgOSV1fbhMqaYNseBJK5dGM6UVlyvUHENSuebFvlUYdrtAQAoNoIrEJCka8XV2MpjQIl7W7voTlRKkcYrERXXwCQumbWvEZcIrgAAFB3BFQhI/tq61FFxpc1xMHHibb/DnZodr7DGNTDpVOGW4FpmH1cAAEJAcAUC0mgVbnnmlqOI4UwD2o3hTFLaLkxwDUvnGtd8H9c6FVcAAAqN4AoEpFercI01rgNJJ8te/+PMjFd0hVbhoMRJe5t4pcRUYQAAQkBwBQKSV1Y7W4WpuA4m9uufKixRcQ2Ru9jHFQCAABFcgYDkFdf2fVzZDmdQu9UqnK5xpeIakk1ThdnHFQCAIBBcgYB0q7hWSmyHM6gk2aXgOlFmO5zAJO4y9nEFACA4BFcgII3g2lZxZarwoHavVZiKa2g6pwqXIpMZrcIAABQdwRUISLdW4UopYiuPASWJtAu5VTNjZa3VEvYADUjSscZVSvdypVUYAIBiI7gCAem2j2tacSU4DWI3t8ORxICmgHROFZayydw8hwAAKDSCKxCQ2Dfv41opGRXXAXUO6NmpmfGKJNEuHAjPnz8df/oyb/4AAFB4BFcgIEmX4UyliOA6KCquo6nbcDNJqpYj1XgOAQBQaARXICDdhjOVo4hq0YAS16Z20Z2YnUgrrleouAYhz6abWoV5DgEAUHgEVyAg3YYzlam4DixOXKXdGM6UVVyvrFJxDUHj+WOb17gymRsAgGIjuAIB6dbqWGKN68B2q1V4ljWuQcmDa6nj//kqJVqFAQAoOoIrEJDYN7cKj5Ui1eKksf4V20u6TJbdCda4hiV/42dTxZXhTAAAFB7BFQhI0uWF98x4Re7StQ3CU79i900DenZieozgGpLGGtdNrcLs4woAQNERXIGANPZxjVqDa7bOkvDUt3Q40/U/TrkUaapaolU4EM03ftqPp1tKUXEFAKDICK5AQOIu+1Dmk20JT/1Lkt1Z4ypJByYqurTK7z4ESZdWeylvFabiCgBAkRFcgYDESaLIJLPNFVfaVfsXu28KLzt1eGZM55fXd+WxsLfyN36sS6vwBmtcAQAoNIIrEJA4SfecbDWTTba9QtWvb7tZcV2YHtPiVYJrCLLcuulNi0qJ4UwAABQdwRUISOK+aW3mLBXXgSW+eUDPTh2ZHdM5gmsQ4p5rXCO2lAIAoOAIrkBA4mTzNNxGxZU1rn2LE9+0l+dOLUyP6cLyeiMUobgS77UdDlOFAQAoOoIrEJC4y/6jrHEdXOK72Co8M6bEpaVrG7vyeNg7+eDgzr89rcIAABQfwRUISNJlqNB4paRqOaLiOoC05Xr3gqsk1rkGoOdUYVqFAQAovG2Dq5mNm9nnzOzLZvZVM/sn2fHbzeyzZvakmf22mVWz42PZ7VPZ/be1PNZPZ8e/bmZv2asfCtivurUKS+k61yurVFz71ev3uBN5cD13dW1XHg97pzlVuP14JTLVqLgCAFBo/VRc1yV9r7u/StKrJb3VzO6W9AuS3u/uJyVdlHRfdv59ki66+52S3p+dJzN7uaR3SXqFpLdK+lUzK+3mDwPsd70qhbPjFfZxHUA6nGl3HmthelwSFdcQeM+KK/u4AgBQdNsGV08tZzcr2T+X9L2Sfic7/pCkt2ef35PdVnb/GyzdNO8eSR9x93V3f0bSKUmv25WfAhgRvSqFM+Nl1rj2Kckny+52qzB7uRZe3GONa9oqTMUVAIAi62uNq5mVzOxLks5JekTSU5IuuXv+SvmMpGPZ58cknZak7P7Lkg61Hu/yNQD6ECebq0VSOlmYNa79ydtFd6tVeKJa0sxYmYprAJrb4XQMZ4pMG3WCKwAARdZXcHX32N1fLem40irpt3U7LfvY7dWgb3G8jZndb2aPmtmji4uL/VweMDK67eMqSbMTVFz71dgSZbd6hZVWXQmuxdfcDqf9OMOZAAAovoGmCrv7JUmfknS3pINmVs7uOi7pbPb5GUknJCm7/4CkpdbjXb6m9Xt8yN3vcve7FhYWBrk8YN/r2So8VtGVVSqu/ei1Jcr1ODwzpnME18LrNVW4UopY4woAQMH1M1V4wcwOZp9PSHqjpCck/bGkd2Sn3SvpY9nnD2e3ld3/R55OxHhY0ruyqcO3Szop6XO79YMAoyDuNZyJimvfGq3Cu7gZ2MLMmM4TXAsvL6p228e1xhpXAAAKrbz9KbpZ0kPZBOBI0kfd/d+Z2dckfcTMfk7SFyU9mJ3/oKR/aWanlFZa3yVJ7v5VM/uopK9Jqkt6j7vHu/vjAPtb0nM4U0WrtVi1OFFlNxPZPtRsF93FVuHpMf0JwbXw4h6DuaqlSO7SRj1RtczzBwCAIto2uLr7Y5Je0+X40+oyFdjd1yS9s8djvU/S+wa/TABS1ircdThT+lS+ulbX/FT1Rl9WUJIeA3qux5HZMV1dr2t1I9ZElV2+isp7rHHNnz/L63XNl3n+AABQRLy1DAQkce8auGbHK5LEXq59yKtu3d4A2KmF6WxLHKquhdb423c8h2Z4/gAAUHgEVyAg/VRcsbXGOsddniosSYvLa7v2mNh9+d/eNgXX9PlzZZXnDwAARUVwBQISe/fAlVeMmCy8vV5bolyPRnCl4lpovaYKz05QcQUAoOgIrkBA0uFMm4/PTmQVIyqu2+rVLno9CK5h6PWmRaPiSnAFAKCwCK5AQHq1CudrXHnhvb1GeNnFkuuhqTFFJvZyLbheU4Wbzx/e+AEAoKgIrkBA4m2HM/HCezv5dp27OVW4FJkOTY9RcS0477GPK88fAACKj+AKBCTpUXGdbgxnouK6nbixznF3H3eB4Fp4vdrEpxvDmXj+AABQVARXICCxdw+upcg0VS0xFbUPzXWOuzidSek611FqFf7Nzz6rn/7dx4Z9GQNptom3Hy9FpumxMhVXAAAKjOAKBCRJurcKS+lkVCqu20uSvQmutxwc19lLq7v6mEX2Z6fO66OPntFaLR72pfRtqzctZsbLPH8AACgwgisQkHqPVmEpf+FNxWg7cY8tUa7XiflJXbi2oWvro/E3WNmIFSeux5+/POxL6Vu+j2uvvZAZbgYAQHERXIGA9JoqLKUDZnjhvb29GM4kSSfmJiVJpy+u7OrjFtXKRlpp/dLpS0O+kv41pgp321JqvMIbPwAAFBjBFQhI4t5z/1Eqrv3ptZfn9bp1Pg2uz10YjeC6mgXXL58JqeK6daswb/wAAFBcBFcgIFtVXGeouPalMVl2D1qFJen0xdFY57qykb5J8uWAKq5bBdd0jThv/AAAUFQEVyAgiUtRr1bhCSqu/WhOlt3d4Do3WdH0WFmnl0ar4vrc0oqWrm0M+Wr6k7eJ91rjyvMHAIDiIrgCAYkTV6lH3poZT6cKexbM0N1ebYdjZjo+NzEywXWlFutlR2ckSV8+E0bVNR/M1e1PPzNe0ZVVnj8AABQVwRUISJx4z0rhzHhZtdi1Vktu8FWFJc6rbrscXKV0netzoxJcN2L9tdvnFFk47cK+xUTp2fGK6gnPHwAAiorgCgRkq+FMs+MVSWIvym00W4V3/7FPzE/q9MWVfV+1ixPXRj3R4ekxnTwyE8xk4XiLidIz42VJYp04AAAFRXAFArL1cKb8hTfr9LaSJHvTKiylFde1WqLF5fVdf+wiyQczTVZLetWJA/ry6UtBhPXtpgpLvPEDAEBREVyBgCTeu1X4wERacb28GsagnGGJt2gXvV4n5ickSaeX9vdk4Xww00S1rFceO6CLKzW9eKX4YX2rrZBms+cPb/wAAFBMBFcgIOlwpu6Ba36qKklaukbFaCtZwXXPKq6SdObi/l7nupIF18lKSTfNjkuSXryyNsxL6kuyxVZIs3nHwirPHwAAiojgCgRkq1bhuck0uF4MZGuSYWm2Cu/+Yx+fS4PrcxdGJLhWSzqaBddzV4tfcY2zNy2s2z6ujTXiVFwBACgigisQkMR7VwoPTafB9QLBdUvxFlW36zVeKenIzJhO7/OK62otDXcT1ZKOzI5Jks5dLX7FdaupwjMEVwAACo3gCgQkrbh2v2+yWtZ4JdLSteJXvoZpr/ZxzZ0YgS1xmhXXsg5Pj8lMQaxxjbeotjNVGACAYiO4AgGJtxjOJEmHpsZY47qNvQ6ut85P7vvhTK2twpVSpPnJqhYDqLjGW/ztJ6sllSJjqjAAAAVFcAUCkmwxnEmS5qYqVFy3ke/luRetwpJ0Ym5CL1xe1UY92ZPHL4K1Wj5VuCRJOjI7rnMBVFx9i8FcZqaZ8bKurNIqDABAERFcgYDE3ns4kyTNT41piTWuW0oa6xz35vHvPDqjxKVT55b35hsUQGvFVZKOzIyFMZxpm/XNs+MVKq4AABQUwRUIhLvLtxjOJEmHpqpaWiG4biUPrt0my+6GV9wyK0l6/OzlPXn8Imhuh5OuCz0yMxbGdjhb7OMqpetcGc4EAEAxEVyBQPQzDXdusqqlZYLrVhq/xz0KrrcfmtJUtaSvnb2yJ49fBKsbzanCknR0dlznl9cbv9uiShKXWe83LWbGywxnAgCgoAiuQCDiLbbyyB2aruraRtxYg4jN8my1V2tco8j0bTfP6vHn93fFtRyZquX0/0KOzI4pcelCwddXb7WdlJS3ClNxBQCgiAiuQCCSbNbPVi+856fSvVwv0i7cU5LkrcJ79z1eeeyAvvbClcb32m9WNuJGtVVKW4UlFX5AU+xbDzebIbgCAFBYBFcgEHEfQ4XmJtPgeoF24Z76qVxfr5ffMquVjVjPXLi2Z99jmFY3Yk1UWoLr7Lgk6VzBt8RJ3Ld8wyKdKkyrMAAARURwBQKRrx/ccjjTdBpcmSzcW2Oq8B6WXPMBTV/dp+tcV2pxY6KwFE7FNUm2nso9O1HR8kZ931bKAQAIGcEVCETSx3AmWoW312wV3rvgevLIjKqlSF/dp+tcVzfqmqiWG7cX8uBa8C1xtl/jWpa7dHWddmEAAIqG4AoEop8W13lahbfVz3Tm61UtR/qWm6b3b8V1o73iOlYuaW6yUvgtceLEe26FI6XDmSSxlysAAAVEcAUC0U/gOjBRUSkyWoW30JgqvJfTmSS98pYDevzsZbnvv7bTzuAqSUdmxgtfcXV3RVs8f2bG0yoyA5oAACgegisQiH72H40i09xkRUu0CveUr3G1Pf6v3ytumdWllZrOXi52FXInOoczSemWOEUPrttNFT4wkVZcL61QcQUAoGgIrkAgGsOZtmlxnZ+qaolW4Z76eQNgN7w8G9D0xD5sF16p1TdVXBdmxnSu4K3CiW+9tvnQdLpW9/xysQM4AACjiOAKBKLfabhzk1VahbfQaBXewzWukvTShWlJ0jf24ZY4qxtx23AmSTo6O67Fq+uFnsibThXuff/hbCo3wRUAgOIhuAKB6Heo0KHpKq3CW2i0Cu9tbtXByarmJit6+vz+C67d17iOqZ54oSdaJ9u0Cs9NVlWKjOAKAEABEVyBQOSBq69WYSquPd2oVmFJuv3wlJ5Z3F/B1d21Wus+nEkq9pY4cbJ1q3AUmQ5NVXX+Ks8fAACKhuAKBCJO0o/bBa75yaourmw0AhraJX1sK7Rbbj88rWf2WcV1vZ7IXZroCK5HZ9P1oUXeEsfdt/27H54eo+IKAEABbRtczeyEmf2xmT1hZl81sx/Ljs+b2SNm9mT2cS47bmb2ATM7ZWaPmdlrWx7r3uz8J83s3r37sYD9p9kqvPV581NVuUuXV5mM2k2+BnOryttuuWNhSt+8sqZr6/tne5WVjViSNNkxVfjmgxOSpLOXihtcY996H1dJOjwzpkWCKwAAhdNPxbUu6Sfd/dsk3S3pPWb2cknvlfRJdz8p6ZPZbUl6m6ST2b/7JX1QSoOupAckvV7S6yQ9kIddANtrtApvV3HNJqMuXePFdzdxH1W33XLboSlJmwc0ffwrL+gnfvtLN+QadtvKRhrCJzuGM900O65yZDpzcWUYl9WXxLdvtT88XdX5Arc7AwAwqrYNru7+grv/Rfb5VUlPSDom6R5JD2WnPSTp7dnn90j6DU99RtJBM7tZ0lskPeLuS+5+UdIjkt66qz8NsI/1O5xpfjKdjHqBLXG6SvzGrG+V0jWukja1C//BYy/o97/0fKEn8PaymlVcO1uFS5HploMTOn1xdRiX1Zck8W3f+FmYGdP55Q25h/e3AQBgPxtojauZ3SbpNZI+K+mou78gpeFW0pHstGOSTrd82ZnsWK/jAPoQDzCcSVKhp7sOU5L4nk8Uzt12eFKSNg1oempxWUmg7dx5q/BER6uwJB2fm+hacb28UtNXzlze82vbznZThSVpYXpMG3GiK2v7p70bAID9oO/gambTkv6NpB939ytbndrlmG9xvPP73G9mj5rZo4uLi/1eHrDvJX1Owz3U2IuS4NpNnNy4VuHJalk3Hxhvq7gmiTduh7htUWONa3VzcD0xN6nTS5srrr/2p0/rHf/Xn6ueTxgbkriPNy0OZ632DGgCAKBY+gquZlZRGlp/091/Nzv8YtYCrOzjuez4GUknWr78uKSzWxxv4+4fcve73P2uhYWFQX4WYF/rex/XqaoqJdPZS8Vt2RymG9kqLKXtwq17uT5/aVXr9TTAhbht0WotrUR2tgpLacX1/PK61mpx2/EzF9OfedhvpiS+/fMnD66LrHMFAKBQ+pkqbJIelPSEu/9yy10PS8onA98r6WMtx38omy58t6TLWSvxH0p6s5nNZUOZ3pwdA9CHuM/hTOVSpONzk3r2QnGH5AxT4jeuVVjKguvicmPNZGuIDTG4Niuu5U33nZhPW6M724XzEPjNIW+Vk3h/a1wlKq4AABRNPxXX75b0g5K+18y+lP37Pkk/L+lNZvakpDdltyXp45KelnRK0v8t6b+XJHdfkvSzkj6f/fuZ7BiAPiT5Pq59tLm+5NCknl3aX/uH7pYb2SospcH1ylpdF1fS9axPLy437rsYdHDtXnGVtGlA07mraWD95uUCBNc+pgpLYrIwAAAFs/kt8w7u/qfqvj5Vkt7Q5XyX9J4ej/VhSR8e5AIBpPKK63b7uErSS+Yn9YVvXJS735D9SkOS3MDtcKR0L1dJeub8suan5vX04jVNVEparcVBrnHtNVVYaqm4LrVXXM9lIfDFIVdc42T7fVznJqsqRTb0tmYAANBuoKnCAIYnH860XaujJL3k0JSurjerfGhKbnCYv/3wtCTpmfNpmHtqcVkvu2lGE5WSlgIMR1tVXBemx1QtRzrTUnFdr8e6lP3vcNitwt7H+uYoMs1PVWkVBgCgYAiuQCD6Hc4kpa3CkvSNC7QLd4qT7bdE2U3H5yY0USnpC89elCQ9vXhNdyxMaX6qGmjFNR3ONF7eHFyjyHT84IROt6xxba1cvjjkVuG4j31cpXRAE8OZAAAoFoIrEIh+hzNJacVVkp4dYnB97sKKnnzx6tC+fy/9TJbdTZVSpLe84qj+4LGzunhtQ9+8sqaXLkxrfqoa7BrXiUqp51rRY3MTbRXXcy1V1mFXXGN3RX38v97CzBgVVwAACobgCgQiGaDiemJ+QmYa6mThf/Jvv6p7/vmf6StnLg/tGrpJ+tjLc7f9Z99xXFfW6vrQf3xaknTH4SnNTVXDnCpci7u2CedOzE/qdMsa13x9663zk0MPrt7HVGEpHdDEGlcAAIqF4AoEojmcafsX3mPlkm45MDHU4HpxZUMrG7He/S8+p+cKtDVPfIOHM0nSd730sG6aHdev/9kzkqSXHpnWoWBbheOug5lyx+cmdHGlpuX1tKU4b7n9T44fKESrcD9/+4XpMS0urze2MAIAAMNHcAUCEQ8wnEnKtsQZYqvw8npdrzw2q3ri+uGHPt+oGA9b0seAnt1Wikxvf80xrdUSRZb+beYmq7p4LbzhWSsb9a0rrnPp+urns3bhc1fXZSa94pZZXduIdXVteD9z4uprMNfh6TFt1BNdzcI3AAAYPoIrEIhkgIqrlAfX4VU6l9fq+tabZvWTb/oWnTq3rOcvrW7/RTfAMFqFJekd33FMknR8blJj5ZLmpypaXq9rvR7f+Iu5Dqu1RBPV3jupNfZyzdqFF6+u6dBUVccOpseHuZdr4q5SH3/7wzPpXq4MaAIAoDgIrkAg4iT9WO47uE7pwrWNoVW4ltfrmh4r6+W3HJAk/VVBBjX12y662+48MqO775jXa249KEmam0rDUWhV19WNuiYrW69xlaQz2Twje2IAACAASURBVGThc1fWtTAzrptmxyUNd0BT0uca14Xp9FrPE1wBACgMgisQiDhJk2uvaa6dXpIFiGFUXd29EVxPHk33Mf16QYJrv+FlL/zGD79e/8c7XyVJOpQF19AGNK1ss8b10FRV02NlPbWYtqkvLq/ryMyYbjqQBdchVlzjpL/nT15xZUATAADFQXAFApFXXPtdn9ncEufGB9e1WqLEpenxsmbHK7rlwLj+6psE12o5UrmU/md3bjLM4LrdcCYz07cfP6Avnk73rU0rrmM6mlVcXxxixTWdKrz9eYenxySlbc4AAKAYCK5AIBr7uPb5rL31UFZxXbrxA5qurqftr1Nj6VrIb7lpRl9/cfmGX0c3w2oV7jSfV1wDmyx8Za2m2fHKlue89tY5PfHCVV1br+t8VnEdr5R0cLIy1Fbhfv/285NVTVRKem6pGOuyAQAAwRUIRmMf1z6rhdNjZR2eHtM3zt/44HptPR04NJMF15cdndFTi8uq52XjIUq8/3brvTTfWOMaTnB1d11aqWlucpvg+pKDihPXp/9qUfXEdWQmrWDeNDuub14e3rrRxL2vqcJRZLr98JSePl+MN1sAAADBFQhGvh3OINXCOw5P6ZkhBNfltXQbkeksuJ48OqONeqJnl4a/n2vSZ7voXjswUZFZWK3CV9frqieug9sE19ecmJMkfeLxb0qSjmRtwjcdGB9qq/AgWyHdsTClpxeHt50UAABoR3AFApE0WoX7T10vPTKlU+dufNVoOdv/cqql4iqpEOtc48Rv+D6u3ZRLkQ5MVIIKrpdX0hbwg9n63F7mpqq64/CU/vgvz0mSFlorrkOfKtzfuXcsTOv0xRWt1cLarggAgP2K4AoEIh6wVViSXrowrYsrNV1YvrHtmXlwnRlPg+udR6ZlVozJwnHihWgVltJ24ZDWuF7MrnVum+AqSa+5dU5Xs/8d5K3CR2fHdX55XbUhtYwP8rd/6cKU3Icz3AwAAGxGcAUCkQ9nGqRV+M4j6VY0T93glsdrHRXXiWpJL5mf1JMFGNDkrkK0CkvpEKCQ1rhebFRct24VltJ1rrkjM81WYXdpcUj7o6Z/+36Da/rceXpx+P+bBQAABFcgGPlwpkG2cslffN/oduG80pavcZXSda6FqLh6MaYKS2lLbUitwpcaFdc+guut6TrXmbFyY/ucYwcnJGkoA8OkwdrEbz+cbif19JCuFQAAtCO4AoFo7OM6QOg6dnBC45VIT93gqtG1LsH1ZUdn9Mz5a1qvD3fNYJwMbx/XTvOToQXX/ta4StK3HJ3RVLXUWN8qSa86flBm0heevbhn17iVxPtvFZ4aK+um2XE9NYQ14gAAYDOCKxCIxj6uA2SuKDLdcXj6hldcl9fqKkWm8UrzPzHfctOM4sSH3i7sXqDgOl3VxZUNefa3Lbo8uB6Y2L7iWopM33NyQS+7aaZx7MBkRS87OqPPDzO4DvCnf+mRKT1FxRUAgEIguAKBSJL0RXc/+1C2uvPI9A2vuC6v1zVVLbVd6923z6scmR7+8tkbdh2nzl3VDz74Wa1s1BvHitQqPD9ZVS32Rmt10V1c2dDM/9/efcfHddV5H/+caepdlixbkqtcktiOW+I0x6mQEEICBEgCCSEQet19KM/CA7uwuyzLwi5kAxvYkEIIAQIpkEJ6SOLEvSXutlxl9TYjTT/PHzMjy01WmdGM7O/79ZqXNHdm7j0zOpLu757f+Z0sF27n4P51/OTG+fzkxvlHbFs0uYQ1e9r7io2Npqgd6nJS+exq9o6ZCwsiIiKnMgWuImPEcAOu6RX5HOjopTc4eim63kCYguwjR+UqCrO5fHYlf1i9f9TShVfWt/O37S3UtxyuDBuJDm2ecCqV5MVSblvSVKxoqDp6ghTnnXy0NcHjchwT5C6eXIo3EGbLoa5kN++khpomPnVcHt3+MM2jXJVbREREjqXAVWSMiA5zbua0cflYy6iOuvoCYfKynMdsv+ncWtp8QZ7edGjU2gHQ0Xt4HqkdYrpoKs2tLgLg1R0taW7J4HT0hijOOfn81oEsnBQr2rSqfvTThaNDTBOf2ldZWOnCIiIi6abAVWSMiESHP+IKoxu4egPhIwozJVw4vZza0lwefHPvqLUDoDM+NxOG/zmmwozKAmZU5vPn9Q3pbsqgtPeEBrUUzkAmFudQVZTNyvq2JLVq8BLp9oM1bVy8srACVxERkbRT4CoyRkTs4Jfy6G9yeS4Ow6hWR+32h/vWcO3P4TDceE4tK3a3saMp9UvjJEZcO3v7Ba5DqCw7Gt49dwIr6tto6OxNd1NOqqMnSMkgKgoPxBjDosmlrKxvG/W5o0Od4zqhKFaVW2u5ioiIpJ8CV5ExIhodXsCV5XJSW5rLzlEcNfIFwhRkHxu4AtywqBqPy8GPnt2W8nZ4A7G5tB39AldrM2eOK8A18yYA8JcNmT/q2pGEEVeAxZNLaOwKsL99dIP1iLVDKm6WqMqdCesPi4iInO4UuIqMESOphju9Ip9to3jyHasqfPzAtTw/iy9cOp0nNx7iubcbU9qOvjmuR6cKZ07cypTyPM6aWMgTGR64RqKWLn9oUGu4nsyiSaUAvLl7dNOFrbUMsiByn3k1xazb10E0DVWQRURE5DAFriJjxEiq4S6YVML2Ji8HO0ZnhMsbCJN/ghFXgDuWTmNmZQHfemxT3zzUVDhuqvAwR65T6Zq5E1i/r4O9rT0nf3KadPaGsBZKkjDiOmt8AROLc/jT2v1JaNngDbWqMMCC2mK6/WF2KF1YREQkrRS4iowR0ejQR4sS3nHmeAD++lbqq/laa/GdoDhTgsfl4F/fN4dDXX5+9NfUpQz3FWc6pqpwpgWuVQD8cZQDuaHo6Il9hslIFXY4DB9aXMNrO1rZ3TJ6KezRYaSJL4hXQV6zZ/SrIIuIiMhhClxFxojhFmeC2JI4dRX5PPNWalNzAXpDEaKWAQNXgAW1Jdx4Ti33L69PWfEbX/A4qcIj+BxTpbokl4vqynl45T7CkWi6m3Nc7fHPMBmpwgAfXFyD02F4aMXoVJhOpPoONXCdWp5Hca6btXs7UtEsERERGSQFriJjxHCLMyW848zxrKhvo80XPPmTR8DrjwWLA6UKJ3z58hlkuRz829NbUtKWnnhxpiNThcm4VGGAm8+tpaHTz0tbm9PdlONKjLiOtKpwQkVhNlfMruQPq/fT7gvy7cc28akHVh/xs0qmaLyC8VCzFowxzK8pZs1ejbiKiIikkwJXkTFiJMWZIBa4RqKW5zandtQ1kZ57shFXgHEFWXx62TSeeauRFSko1OM9TnGmYDiCJ5OqM8VdNruSioIsHnxzT7qbclyJz7A4Z+Spwgk3L6mlzRdk6Q9e5P439vDc5kY++D/LOdTpT9oxEiLxwHUoVYUTFtTG5oinKqgWERGRk1PgKjJGRKIjC1zPmljIxOIcntl0iD2tPp7f3EgoBWmpQwlcAW6/cCrjC7NTMuqaKM7UFQ84/KEIXf4w4wqykn6skXI7HXxwcQ0vbWtmf3vmFWlqT/KIK8AF08qZWVlAWb6H33/yPO697Rz2tfXwvp+9TmdPcoPExJKxw/kdSsxzXbdP6cIiIiLposBVZIyILeMy/MDVGMOVZ1by/JYmLv73l7j9vlX88m+7k9jCmETgmjfIwDXH4+Smc2tZvac9qcFKNGrxBSM4HYbuQJhQJEpTVwCAysLspB0nmT64uAaA/3pue1qXX9nf3sPao1JjO3pCOAwnXJ93OBwOw2Ofu4DnvnIxiyaXcmFdOffffi4HO3v575d2JO04EPv9ARjOtZ95NcU4jAo0iYiIpFPyzkBEJKVGOuIK8LELphCOWGZVFfDE+oP84m+7uPX8SeSeYM3V4eib4zrIwBVgYXxEa+2+dpbNrEhKO3pCsfmt4wuzOdDRS1dviMbuWApqpgau1SW5fOKiqdz9yi5CkSg/eP88PK7UXl+MRi2v7Wyh1Rskai0vbGniyY0NWOCumxZw1ZxYxeOO3iBFOe6kzw/OdjuPuL9wUgnvX1DNva/V85Elk6gpzU3KcRKpwsOpKJ2f5WJGZYHmuYqIiKSRAleRMSKahGVcakpz+e51ZwEwa3wh7/vZ6/z6jT3csXRaMpoIHK7kO5TAtW9Ea29H0gLXRJrwhOJY4NrZG6KxK7MDV4BvXDWLohw3//7MVjp7Q9x9yyLcw10H6SR+t3IfP3t55xFL0hRkufjERVNZtaedLz68jrL8LM6ZUkp7TyipacID+bsrZ/LEhoP829NbuPOmBUnZp41nxQ/3d2jR5BL+tOYA/lDkmGBbREREUk+pwiJjRDJGXPtbOKmEC6eXc/cru+gNRpK236FUFU5IjGgdnZ46onbEA9eJxTkAdPSGaOxLFc68Oa4Jxhg+e8l0vnfdWby4tZmvP7IRa5OfNrxuXwdffWQD+VkufnrjfF78+2W8+PfLePMfLuMbV8/ml7csorokh4/ft5KGzl46eoJJWcN1MMYXZXPHRVP584aGpI1yRvqqCg/vd+idZ1bhC0Z4aWtTUtojIiIiQ6PAVWSMiNjkL+PyhcvqaPEGeXhl8tbS9MaXoBnKiCvECuCs29eRtLmdiRHXiSWxwLWzJ0RTlx+Py0FREivjpsqHl0ziy5fP4JE1+/mPv25L+v7fPtgFwF03L+Dd8yYwpTyPKeV5fWnjJXke7rl1MT3BCD9/aScdPaGkreE6GHdcPI3y/Cz+5S+bkxK4R+3w57gCLJlaSnm+hyfWN4y4LSIiIjJ0ClxFxoho1JLsVVzOmVLK7KpCntx4KGn79AZCuByGrCHOzVxQW0K3P8yOZm+S2pFIFY4HrvFU4fGF2cNaEiUdvnDZdG48p4Y7X9zBUxuTGzBtb+omx+3sG5E+nsnlebx/YTUPrdzHvraeURtxhdiFj7+7cgar9rTzzFsj75+JCyLDvfjjcjq46qwqnt/S2HdRREREREaPAleRMSLZqcIJl8+uYNWeNjriy52MlNcfJi/LNeTgcH5tMZC8yq2++MhvX6pwT5BDXf6MThM+mjGGf7z2LObVFPPVRzawry15y+TsaPJSV5l/0kDus5dMJxq1dPnDozbHNeGGhdXMqMzn+09tIRge2dJNiYH8kcwTf/e8CfhD0ZSvhSwiIiLHUuAqMkZEklCc6XgunVVB1MLL25qTsj9vIDLkNGGAqeV5FOe6kzansSd45IhrR2+Ipq4AFRlcmOl4PC4Hd944H4DPPbR2xAFcwvZGL9Mr8k/6vJrSXN67YCIAxaOcYu1yOvjG1bOpb+3hoRUjS2fvm+M6gt+hRZNKGF+YrXRhERGRNFDgKjJGRFM04jqvupiyPA/Pb05O0RlvIDSswNUYw/yaYtbs7UhSO2KBa2G2m4IsV1+qcGXB2ApcIRY8/sv1c1i/r4OnNo08aOrsDXGoy09dRcGgnv/ZS6aT43YyuTxvxMceqmUzxrFwUgm/em33iOY/J147kms/DofhXXOreGVbc9IyFERERGRwThq4GmPuMcY0GWM29dtWaox51hizPf61JL7dGGN+YozZYYzZYIxZ0O81t8afv90Yc2tq3o7IqStiUxO4OhyGS2ZV8PK2ZsKRkY/m+QKRIVUU7m9BbQk7mry0egNJaEcscM3LclKY4+ZAey++YGRMpQr3d/WcKsrzPTyXhAsMO5pi84jrBjHiCjCpLI+V37yca+ZWjfjYQ2WM4ZbzJlHf2sOrO1qGvZ/oCKsKJ9ywqJpgJMoDy/eMaD8iIiIyNIMZcb0XeOdR274OPG+trQOej98HuAqoi9/uAH4GsUAX+DZwLnAO8O1EsCsigxONpiZVGOCyWRV09oaSMtrZHYjNcR2Od5w1HmPgntd2j7gdierGeR4XxblutjV2A5m9hutAnA7DJTMreGlrE6ERXmDY0RT7LGZUDm7EFWLFktJV1OqdZ42nLM/DA28MP1hMxhxXiK1/fMnMcfzq9fqkLiMlIiIiAztp4GqtfQVoO2rze4D74t/fB1zXb/v9NuYNoNgYUwW8A3jWWttmrW0HnuXYYFhEBpCqEVeAC+vKcTsNz28ZedEZXyBMwTAD1xmVBVw9p4p7X6unzTeyVExfIEyux4nDYSjOdbMnXthorAauAJefUUm3P8zK3Uf/SR6a7Y1est2OvqWCMl2Wy8kHF9fw/OZGDnT0DmsfkRFWFe7v08um0+YL8rtV+0a8LxERERmc4c5xrbTWNgDEv1bEt08E+v8n3x/fdqLtImPaWwc7aegc3on0UEWiIx8tOpGCbDdLppbx+LqDBMIjG0Xy+sPDmuOa8KXL6ugJRbj7lV0jaoev38hvUY6bxFKgYzVVGOCiunI8LseI04W3NXmZNi4/ZRdCUuGmc2sB+M2bwxt17faHAMge4jJNx7N4cgkLJ5Vw9yu7Rjz6LSIiIoOT7OJMxzsLsgNsP3YHxtxhjFlljFnV3JycKqciqfKpX6/m24+9NSrHihVnSt3+P3HRVBo6/Tyy+sCw92GtpcsfomCYc1wB6ioLePfcCdy/vJ6WEcx19QYOB9BFOYeXcRlrVYX7y/W4uGBaGc9vacTa4Rcq2tHYPej5rZmiuiSXK86o5IHle+jsCQ359ev3xdLgz5pYNOK2GGP49MXTONDRy5NJXl9XREREjm+4p8GN8RRg4l8Tl//3AzX9nlcNHBxg+zGstXdbaxdZaxeNGzdumM0TSb1o1HKo08/yXa19aYiplMpUYYiN5p1dU8x/v7hj2EuudPnD9AQjjC8aWXD4hcvq6A1FuP/1+mHvIzbi6gSgODe2jEt+lmtEo8GZ4LLZlexp7ekrsDRU3f4QBzv91A1hfmum+NLlM+gOhPn5KzuH/Nq1+zoYX5jdtzzSSF06q4Kp5Xnc8+ruEV1EEBERkcEZbuD6OJCoDHwr8Fi/7bfEqwsvATrjqcTPAFcaY0riRZmujG8TGbM6e0OEIpZuf5i3D3al/HipLM4EsVGkL15ex4GOXv64Zv+w9nEwPv+wqmhkwcH0inwum1XBr9/ciz80vNRlXyBCnudwqjBAxRhOE064fHYlAI+uG97I+FArCmeS2VWFvGfeBH712m6auvxDeu2ave3Mry1OWlscDsNtF0xm/f7OpC3hJCIiIic2mOVwHgKWAzONMfuNMbcD3weuMMZsB66I3wd4EtgF7AB+AXwGwFrbBnwXWBm//VN8m8iY1T+N9fWdw1+mY7BSPeIKsTUz51YXceeLO4YVMCbm+1YVjzwd92MXTqHNF+SxYQZovuDhVOHieOA6fgynCSeML8rmXXOr+MXfdrO7xTfk1289FKsoPBZHXAG+csVMwhHLT17YPujXNHcH2NfWy4La5Bazf++CagqyXUmpgi0iIiIDG0xV4RuttVXWWre1ttpa+7/W2lZr7WXW2rr417b4c6219rPW2mnW2jnW2lX99nOPtXZ6/ParVL4pkdHQ3B0LXI2B5btaU368SNTiTPFyJMYYvvqOWexv7+XOF3YM+fUHO2KjYBNGOOIKcN7UMmaNL+B/h5mK6QuEyU0ErvFU4bFcUbi/b19zBllOB998dOOQP5u/bGxgYnEOk0pzU9S61Koty+Wmc2t58M29PLp2cBc11u5tB2DBpOSNuALkZbm48Zxant50qC/bQERERFIjhaVeRE5tzfER1yVTyli5uy3l1UWjUZuUpTxO5sK6ct47fyI/f3ln3+jcYDV09uJyGMYVjDwl1xjD7RdOYVujl1d3DH1E2xuIkB+f41p4CqUKQ6zA1FevmsVrO1r50yCDN4j9fF7d0cL7FlaPSl9KlW9cNZslU8r4yu/W8fj645ZLOMKavR24nYYzJ4y8MNPRbjlvEtZa7hvBfGwRERE5OQWuIsOUGHG99uwJ+IIRNuzvTOnxIjb1I64J//Cu2RRku/j6HzcMqfBUQ4efysLspKU0X3v2BCoKsvjhX7cRHWIBLF8g3DfHtTheVbiy4NQYcQW4+Zxa5tUU88Nntg66mNYf1xzAWnjfgrG9GlmOx8n/fnQRiyeX8uWH1/GXDcdW9n160yF+/Gys36zZ284ZE4rIdjuT3pbqklyunlPFg2/upbN36NWORUREZHAUuIoMU4s3iNtpuPKMWLGcN1KcLhyJMmqjZGX5WXzzXWewdm8Hf95w8hGthAMdvVSNsKJwf1kuJ9+4ehbr93Xw25X7Tv6CuEjU0huK9K3jOqksl4WTSlgytSxpbUs3h8PwpcvrONjpP2YecGdPiA/dvZybfvEG3/jjBl7b0YK1lt+v2sc5U0qZVJaXplYnT67HxT0fXczC2hK+8Nu1PNVvWZqXtjbx2d+s4b+e3863H3+Ljfs7mV+T3DTh/j69bBreQJgHlten7BgiIiKnOwWuIsPU3B2gPD+LsvwsZo0vSHmBpqhN7TquR7t+/kTqKvK568Wdgx7tbOj0J225kYTrzp7IuVNK+bent9A6yHVdfcEwQF9xprwsF498+nzOmFCY1Lal27IZ4zijqpCfvbzziJHx/3p+O2/ubqMnGOHJjYe4+Zdv8on7V1Pf2sMNC6vT2OLkystycc9ti5lfU8znH1rL1x/ZwAPL6/nMg2uYWVnAredN4oE39tAbirBgUnILM/V35oQils0cxz2v1dMbHF4VbBERERmYAleRYWrxBvrmcp5dU8yWhqHNBx2q0SjO1J/DYfjsJdPZ2tjNs5sbT/r8xLq2yago3J8xhu9ddxa+QJhvPrppUGmxvkAscM0b42u2nowxhk8vm8auZh9/fesQADuaurl/eT0fWlzLo5+9gDf/72V87IIpPLe5kVyPk6vnVKW30UmWn+XiV7ct5pq5VfxlQwPfeuwtSnI93HvbYr5z7ZncsLAap8OwKIWBK8Bnlk2nzRfk4ZV7U3ocERGR09WpfVYnkkIt3kBfldrqkhxafUF6gxFyPMmfRwexwNDpGN1rTdfMreLHz23jzhd2cOUZlZgBAudWX5BgJJqUisJHq6ss4KvvnMm/PLmFll++wV03LxywANThwDU1P4tMcvWcKv7jr1v5wTNbCUai/GH1fnI8Tv7+yhkAZLud/L93n8FVc8bj75c+fSopyHbznx+aTzgSZdPBLiYUZVMR/938wfvn8pUrZ4x4beGTOWdKKedMLuUHz2xlUnkel8ysSOnxRERETjcacRUZpliqcKzoT3VJbGmRAx09KTteODq6qcIALqeDzyybxsYDnTy3uWnA5/at4ZrEOa793bF0Gj+5cT4bD3Ry3X+/NuDyI95ALF0z/xQM0o7mdBi+c+2Z9ATDfPG36/jb9ha+eFkdZflHBvaLJ5dyUd24NLVydLicDs6uKe4LWiE2Kp3qoDXhzpvmM7ksj4/ft0ojryIiIkmmwFVkGKJRS6sv2DfqN7EkdmK8vz11azlG7Ogsh3O06+dXU1eRz9cf2cChTv8Jn5cIJJM9x7W/a+dN4HefPI+u3hAf/dWKE1ZxPV1ShROWzaxg+dcv4/efOo9/vPZMbj1/crqbdFqqKMzmd586jwuml/O1Rzby8rbmdDdJRETklKHAVWQY2nuCRKKW8vioVvUoBK7RUZ7jmuBxOfjZhxfQG4rwud+sOeF6tQc7YkFtqkZcE+ZWF/M/H1nI7hYfn7h/Ff7QscVw+gJXz+kRuEJsTvLiyaXcev5k3KM9NC998rNc/M+HFzKzsoCvPLyOxq4TX+wRERGRwdPZjcgwtHiDAH0jrhUF2bidhgMDpK+OVMTapK2POlTTKwr4/vvmsmpPO996dNNx13Zt6Owly+WgNM+T8vacP72cH94wj5X1bXz4l2/S0RM84vFEVeHTYY6rZJ4cj5M7b5pPTzDCZx5cw78+tZnb713Jpx5YzTcf3ciq+rZ0N1FERGTMUeAqMgzN3bFlWRIjrk5HbB5dqkZcrbVYC440jLgmXDtvAp+9ZBq/XbmPzz+05piRzoPxpXAGKuCUTO85eyI/vXE+G/Z3csPPlx9x0eB0muMqmamusoB/es+ZrN7Tzj2v7mZ/ey87mr38ac0BbvvVypRe5BIRETkV6axOZBha4uuJ9q9sW12Sw4H21BRnSoxwpmvENeH/vGMWxTke/vnJzXT1ruLe2xbjiqelNnT0pjxN+GjXzJ1AaZ6HT96/mnf95G/85wfPZtnMitNujqtkphsW1XBR3TjK8j196dv72np453++wt/9bh2/+fiStMxbFxERGYs04ioyDEePuAJMLE7diGvEZkbgCvCJpVP5/nvn8OqOFn707La+7Qc7/KNWvbW/86eV89jnLmB8YTa33buSbz26iZ1NXoyB3BQtTSQyWOOLso+Yc1xTmsu3330mb+xq439f3Z3GlomIiIwtGo4QGYYWbwCPy0Fh9uFfoeqSXJq6AwTCEbJcyQ2YovF6SOlMFe7vQ+fUsm5fB3e9tJNzppRy4fRymrr9TCge3RHXhKnj8vnTZy7ge395m4dW7CUcteRnuUYtbVlkKG5YVM1zmxv5/tNbqCzK5tp5E9LdJBERkYynwFVkGJq7A4zLzzoiMEosiXOww8+U8rykHu/wiGtSdzsi37n2TNbt6+BTv15NVVEOUUtaRlwTcjxO/vn6OXzu0unc+3p90i8eiCSLMYYff/Bsbrt3JV/67VrCkSjvXVCd7maJiIhkNAWuIsPQ7A1Q3m9+KxxeEudAe2/yA9f4HNdMGXEFyHY7+cUti/jZyzvp7Akxs7KApTPK090sqopy+MZVs9PdDJEB5WW5uPe2xXz8vlX83e/XE45YPrC4Jt3NEhERyVgKXEWGobk70BeoJkwsTqzlmvwCTdEMKc50tJrSXP7l+jnpbobImJTrcXHPRxfziftX8dVHNhCOWm46tzbdzRIREclIGZR4KDJ2tHiDRxRmAqgqysbpSM1arplUnElEkieRuXDprAr+75828qNntx13nWQREZHTnQJXkSGKRC1tvsARS+EAuJwOxhdmp6SycDQDU4VFJDmy3U5+eUqgfQAAGrNJREFU/uGFvG9BNT95fju33rOib8ktERERiVHgKjJErd4AUcsxI64QK9CUilRhjbiKnNo8Lgc/vGEu33/vHFbUt3HFj17mj2v2Y61GX0VERECBq8iQ7WmLBaa1pbnHPFZdnMOBFIy4JlIHnRpxFTllGWP40Dm1/PnzFzKlPI+v/G49t9yzgn1tyb8YJiIiMtYocBUZot0tPgAmH6dycHVJDoe6/IQi0aQes28dV424ipzyZlQW8IdPnc8/vedM1u7t4Mofv8IvXtlFOMl/V0RERMYSBa4iQ1Tf4sPlMMdUFQaYVVVI1MLK+rakHjMT13EVkdRxOAy3nDeZZ7+ylAuml/PPT27m+rteZ9OBznQ3TUREJC10GiwyRPWtPmpKc3EfJ4q8ZGYFuR4nj687mJRjhSNRNuzvyMh1XEUk9aqKcvjFLQu56+YFHOryc+2dr/KVh9exo8mb7qaJiIiMKgWuIkO0u6WHyWXHzm8FyPE4eceZ43lq0yEC4ciIj3XPa7u59s7X+P5TmwEVZxI5HRljuHpOFc99+WI+dsEUntp0iCt+/DKf+80athzqSnfzRERERoUCV5EhsNayp9V33PmtCdfOm0Bnb4hXtrWM+HjPvd1EttvBc5ubABVnEjmdFeW6+eY1Z/Dq1y7hUxdP48UtTbzzP//GHfevUgqxiIic8hS4igxBU3eAnmCEKQMErhfWlVOS6+bx9SNLF+7sDbF6bzu3XziFb11zBk6HOWbtWBE5/ZTlZ/G1d87ita9fyhcvq+ONXa1c89NXue1XK1iztz3dzRMREUkJV7obIDKW7GqOVxQuO3Hg6nY6eNfcKv6wej++QJi8rOH9mr26vYVI1HLJzAoWTS7lpnNqyfE4h7UvETn1FOd6+PIVM7j9oik8sHwPv/zbLt571+tcOL2cz186nXOnlqW7iSIiIkmjEVeRIahvjQWuA424Alx39kT8oSg/f3nnsI/14tYminLcnF1TDKCgVUSOqzDbzWcvmc6rX7uUf7h6NlsOdfPBu9/gAz9fzt+2N2PjVclFRETGMgWuIkNQ3+LD43QwofjYpXD6WzS5lPcvrObOF3fwyrbmIR8nGrW8tLWZpTPG4dIaOCIyCHlZLj6xdCqvfu0SvvPuM9jb1sNH/ncF19/1Oo+vP0hPMJzuJoqIiAybzohFhmB3i4+a0pxBVff97nvOoq4iny8/vI5Dnf4TPs8XCPOjZ7dR3+Lr2/Z2Qxct3gCXzByXlHaLyOkj2+3koxdM4eWvLuOfrz+LFm+ALzy0loXffY4vPLSW595uJBiOJu14/lCEpu4T/40TERFJBs1xFRmC+lbfSdOEE3I8Tu66eQHv/ulrfOuxTfzilkXHPKfFG+Bj965kw/5OXtraxB8/fT4up4O/vt2IMbB0hgJXERmeLJeTm8+dxI2La1lR38bj6w/y5MYGHl9/kMJsF0tnjOPSWRVcPGMcZflDL/z2zFuHuOfV3azd14G1lgduP5clw5hX++auVsYXZTNpgNoBIiIiClxFBikatexp7WFp3eCDyekVBXz+sun84OmtvLKt+YhAtLHLzwf/ZzkNnX5uOW8S9y/fw72v1zNzfAE/e2kHy2aMo3wYJ5MiIv05HIYlU8tYMrWMf7z2TF7d3sKTGxt4cWszf97QgDEwr7qYpXXlnD+9nPm1xWS5Bp5T//SmBj7z4Boml+Vx63mTeH5LE5/+9Woe/9yF1JQef53ro1lr+dnLO/nB01upLMzisc9eyPii7GS8ZREROQWZTC7asGjRIrtq1ap0N0MEgAMdvVzw/Rf43nVn8eElkwb9ukA4wpU/fgWXw/D0l5bidjqw1nLrr1aycncbv/74OSyoLeHj963itZ0tOI2hpjSXhz95HkU57hS+IxE5nUWjlk0HO3lhSxMvbm1m4/4OohayXA4WTy5l8eRS6irzmTYun8nluWS5nPhDEZ7f3MSXHl7LnIlFPHD7ueRludjV7OW6/36NCcU5PHD7uSdduisatXzrsU08+OZeLp9dyfKdLUwZl8fvP3m+CtGJiJxmjDGrrbXHpiYe/TwFriLHCkWiPPjGHvKyXNywqAaAJ9Yf5PMPreXBj5/LBdPLh7S/5zc3cvt9q/j8pdP50uUzeGjFXr756Ca++54z+ch5kwFo6Ozlih+9QlGOmz9+5nwqCzXyICKjp7M3xIrdbby+s4XlO1vZcqi77zGHgfGF2TR2B4hELWdUFfLQHUuOuLj2yrZmbr9vJdkuJ1+8vI4PL5lEtvvYINRay7cff4v7l+/hkxdP5WvvmMWLW5v4+P2ruGBaOd+77iwml+exq9nLM281MqE4m7nVxUwuy8WYk9cXEBGRsUWBq5y2IlHLm7taeeatQ7y5u402X5CeYIRLZlXwmWXTmF1VOODrN+zv4GuPbGRzQxcAH15Sy/nTyvnyw+uYWJzDE5+/cMhrs1pr+cyDa3hq0yHqKvI50NHLwkkl3P+xc444Eatv8VGQ7RrWfDMRkWTqCYbZ1exjZ7OXnc0+9rb6qC7JZU51ERfVlZPrOfbv4M5mL//0xNu8vK2ZPI+TZTMrOG9aGdMr8plYnEOW28Gvl+/hJy/s4I6lU/nGVbP6/gY+tGIv3/3z2wTDUeZUF7F2b8cR+541voCPXzSVa+dNwONKX23JYDjK7hYfM8cXpK0NIiKnEgWuctoJR6I8uu4gd76wnfrWHrJcDpZMLaMqPmfqifUH8QUjXFRXzs3n1nLZ7ErcRy01c6jTz7IfvkhRjpvvvPtM1u/v7FuLdV5NMb/66GJK8zzDal80anlq0yF+9OxW2nxBnvziRVQVDbysjojIWGOtZfnOVp7Y0MCzbzfS4g0c85wPLKrm394395gR1KZuPz95fjsrdrdxzdwJfGBRDW2+IKv3tPHrN/aytbGbsjwP18+fyLvnTaCuMv+4AfRI2r6yvp31+zrY2tjNgtoSrj17Avnxi5VtviCffGAVK+vb+fCSWr51zRknnQ8sIiIDU+Aqp4WOniCr97Tz3OamvhOkMycU8qmLp3HZ7IojTmg6e0L8+s09PPjGHg52+pk2Lo9f3rr4iCrB33p0Ew+t2MsLf7eM2rJYgZHH1h1g9Z52vn7VrKScIEWilkA4ktSTLRGRTBSNWhq6/Oxs8tLQ2UsoYsnPcnHN3Kohr1FtreWV7S38dsVentvcSCgSO38ZX5jN1HF5TCmP3aaOy6OyMJviXA9FOW7yPM4TphiHIlGsBY/LwZ5WH197ZANv7GoDoCjHTWdviDyPkwvrypk5vpDH1h2godPPO84czxPrDzK3uoh/uHo250wpVRqziMgwKXCVU9buFh9/WnuApzY2sL3JCxBLSZtVwfVnT+Sy2RUDnkBEopZn3z7EN/64kaiFu25ewAXTy9nf3sMlP3yJ9y+s4V/fO2e03o6IiAxRqzfAm7vb2N0SS2Xe3eJjV7OPzt7QMc91OQxFOW6KctwU5rgpznVTkO1mX1sPbzd0EY5EqSrKodUXwO1w8NWrZnH1WeMpzfOwZm8Hv12xl9V72tnd6qM018Pdtyxi4aQS/vrWIb72yAbae0LMripkclkunb0hJhTn8JElk5hXU5yGT0bk1BONWhwOXRg6lSlwlSGJRC3eQBhvIIwvEKbbH6Y3GCFiLdZaLIAFi8VaYjfoe8wmngDkelyU5nn6bscrzjFY1lq6esM0dvtZvrOVP609wLp9HRgDS6aUcWFdbOmGBbUlQz7OvrYebr9vJdubvHxocS3eQJhnNh3ipf+zjAnFSuEVERlLrLW094TY1eyluTtAZ2+Izt4QHfGvnb0hunpDdPSE6PKHGF+YzdzqInI8Lva19eBxOvjSFXUnnMLREwzjdJgjUoN7gxEeW3eAh1buoycQpjDHzZaGLnzBCLPGF3BGVSGTyvLIy3KS5XKQ5XKS5XaQ5XLgcTnIdjvJz3IdvmW7yHGfeIQ48T4PdPSyfl8n7T1BeoJhALLdsWPEvsaOk93va3Gum7J8j1KbJam2NXazfl8HTd0Bmrr8NHYFaPUFcBiDx+XA7XTgdhomFOewcFIJZ00oYkJxzqDmqW860MmdL+zgr28fYsnUMm5YVM3Vc6rUh09BClxHwYNv7iEcsZTnZ1Ge76G8IIviHHdfELjxQCfr9nXg9YePeF2Ox8mMygJmVxUwo7KAguzDVRmtjb222x+7dfaGaPEGaPUFCYWjRK0lErVELX3fx+5bwlFLMBwlGI4SCEcI9H1//G2BUIRgJIovEKE3FEnZ5+RxOQ7/w3Yd/oed5XaS53FSlOMm2+0kEI7gD8Xa2RuM0OIN0tjlJxCO9u1rdlUh18+fwLXzJiZlvT9vIMyPn93Gva/XE4labj1vEv/4nrNGvF8RETk9dftD/GH1fp7f3MTOZi8Nnf4hvd5hIC/LRUE8kM3PclGQ7cbpMLR6Axzo8B933vBgjSvIYtb4As6aWMTFM8axcFLJMfUekiUStbT3BIFY6vVQj2OtpTsQxu2InTv0H3WzNnYh/UQjcZGoxR+K4HSYEV1AHylrLcFIlN5gBF8wQm8wjC8QoScYoTcU+z72WJieYARvIExjp5+GTj8OBxRkucnPdlGQ7cLjctAbjNDtD9PQ2UtjV4Buf4ieYASHib3PHI+DXLeLbI+THLeDXE/sYkjfYx4XLofBGMhyOakpzaG2NI/a0lwKc1w8uvYA97xajzFw5RmVLJ0xjrqKAopyY+eq0ahle5OX13e28OjaA6zf39n3XguzXVQWZlOW78HaWCp+KBI7N93T5sMfip3PmXiV8uqSHGpKcplXU8y5U0uZUp5HlsvJ6j1t/PSFHby0tZmCbBdXn1XF67ta2NfWy+SyXL51zRmcN62MrYe6Wb2nnVd3tLCz2cv8mhIunF7OGRMKmTouT1OyxpCMDVyNMe8E/gtwAr+01n7/RM/N9MD14n9/kT2tPQM+J8ftpCT3yLU4u/1hugOHg9mJxTkYA129IbyBMNFh/EiMAacxscDQ7cTjdJDldvR9zXI5+33vwBMPIj0uB3keJ3lHXfHNy3KR53ER+x8T+wNnAGNM/CuY+Hb63QfwBcO0+YK0+4K0+oJ09YZigXI8eO4LnMNRfIFYcO4PRch2O8mOtzXb7aA8P4vKwmwqCmJfZ46PBfqpsL2xmz+s2c8nl04bdvElERGRoyUuyh7vAnJvMIIvnu10dNZT4qs3EDtnCIWjlOV7qIyPFM+vKaGyKKvv5DwQiuCPX5T2h6L4wxECfV8jtPeEaOkOsKethy2HutjS0E04GptzXFOaS1VRNnlZLtwOExslcxkMhnA0SiRqcTkdfY+5nA7CkSi+YASw5Lhj5wut3iDN3gAt3mDsors3cMQ5TW78YnVBtgtH/AQiP8tFca67bwTOF4jQ4g3EXx8k3G8HiQvh1sbONQxQmpdFca6bYDiKPxS7EB8IRQlGDl/0LsvzUJ6fhcNhcBhwmNhX0+8rxIo89gQj8VuY3lCkr/35WS7K8z3kZ7mOGBG31hKxsYAuMZAQDMf24wvGstfCQzixczkMlYXZVBbGVhfwBsJ444MZgUi075xtfGE2lUXZFOW4yXU7sUBvKBYE9wYjh78PHfV9MEIo/tkc3S6nwxCJWs6cUEhelotV9W19778w24XL6SAQisR/7rFK3zcsquHSWRWML8wecA3mUCTK5oYuthzq5kB7L/vbe9nf3kN9q4/GrsMXYrLdDvyhKKV5Hm6/cAofOW8ShdluolHLS9ua+N5fNrOr2XfEvqeW51FXmc/qPR1HXNRJ9Cm3w1CQ7e53Ich1RIZCtttBQbab0lwPJXkeSvPclOZlUVWUTXl+Fk6lKadcRgauxhgnsA24AtgPrARutNa+fbznZ3rgGola2nzBvj+wLd4AXb2JVCIHs6sKmTW+4JgCFNZaDnb62RL/Bd7W2I3TGAqyXRTG/6AXZsfm4BTmuCjPz6I0z9N3tdFhDE5jcDhiwarTYVQUQkRERAat2x/itR0tvL6zlQPtvTR0+vHHM7ESI2VRa3E7HTiNIRy1hCJRwvHH3E5DjseFMcSDsyhleVmUF2QxLt8Tz0aLZaQ5HIbOnsNp215/GEsse8wbzy5LBFPZbmcsiy0/tq/SXA/h+OhpIB6cOowhL8uJtcTOvfwhPE4HOZ5YmnSOx0m2KzbCGAhFaejy9wXR1h7OWovGA84Eh8OQ54m9Ps/jIsfjxGEMFovXH6bFG8AXODZDzeEwOE0s8HMYgzs+KJDrcZEbDzRz3E7yspzkeFxHHCP3qONluRyjdk7nD0XY397L3jYfe1t7aOj0c/70cpbWlWNMbIR/7d4OdrV4OdDeS9TG3uOZEwpZMrWM6pKcEbfVWsv+9l5W7G7jUJefjp4gE4tz+MDimuOOmAbDUX63ah+t3iCzqgqYM7Gob3qXtZYdTV62NXrZ2eylJx5gB8NRvIHQERmN/lCk72KHPxyl2x/qK/jWn9MROz9PDO4cMdDTd9/ZN+iT2B6J2r4pCp29sVHxxEWaxJz7HLcTt8uBq++CUPyikSP2vctp8MQvFCWec6oG0ZkauJ4HfMda+474/W8AWGv/9XjPz/TAVURERERExrbEVL12X4i2niAt3QEauvwc6uyNZT74j8yKOPx9LL17IMbEMjATgfRIOAx92Q8u5+E5xC6nwe1w9AXAR2ZIGL5yxQzm15aM+PipMtjAdbSTvycC+/rd3w+cO8ptEBERERERAWLp4gXxbMfEcoiDFY1aekKRI9L8HcZQnBurZF6Q5cLhMIQj0b76NYlpcqGIJRSNEo7YeKZD7PtwNEowYgnH7wf7bQ9F+mU/RG1fFkTitaF+28MRG095z9yaRkMx2oHr8ca3j/gkjTF3AHcA1NbWjkabREREREREhszhMH0pwpWFJ36ey+mgJC82j1aGJzVl5E5sP1DT7341cLD/E6y1d1trF1lrF40bN25UGyciIiIiIiKZZ7QD15VAnTFmijHGA3wIeHyU2yAiIiIiIiJjyKimCltrw8aYzwHPEFsO5x5r7Vuj2QYREREREREZW0Z9ZV5r7ZPAk6N9XBERERERERmbRjtVWERERERERGRIFLiKiIiIiIhIRlPgKiIiIiIiIhlNgauIiIiIiIhkNAWuIiIiIiIiktEUuIqIiIiIiEhGU+AqIiIiIiIiGU2Bq4iIiIiIiGQ0Ba4iIiIiIiKS0RS4ioiIiIiISEZT4CoiIiIiIiIZTYGriIiIiIiIZDQFriIiIiIiIpLRFLiKiIiIiIhIRjPW2nS34YSMMc3AnnS34yTKgZZ0N0IyivqEDET9Qwai/iEnoz4iA1H/kIFkav+YZK0dd7InZXTgOhYYY1ZZaxelux2SOdQnZCDqHzIQ9Q85GfURGYj6hwxkrPcPpQqLiIiIiIhIRlPgKiIiIiIiIhlNgevI3Z3uBkjGUZ+Qgah/yEDUP+Rk1EdkIOofMpAx3T80x1VEREREREQymkZcRUREREREJKOddoGrMeYeY0yTMWZTv23zjDHLjTEbjTFPGGMK49tvNsas63eLGmPOjj/mMcbcbYzZZozZYox53wmOtzC+3x3GmJ8YY0x8+w3GmLfi+xyz1b1OFRnUL75rjNkQ3+9fjTETRuP9y4llUN/4jjHmQL99Xz0a718GlkH94+F++603xqwbjfcvg5NB/eS4x5T0SUPf+GdjzD5jjPeo7UuNMWuMMWFjzPtT+Z5l8JLRP4wxBUdtbzHG/OcJjpfZcYu19rS6AUuBBcCmfttWAhfHv/8Y8N3jvG4OsKvf/X8Evhf/3gGUn+B4K4DzAAM8BVwV3z4bmAm8BCxK9+dyut8yqF8U9nvOF4Cfp/uzOd1vGdQ3vgP8fbo/D90ys38c9Zz/AP5fuj8b3TKvnwzmmLqd8n1jCVAFeI/aPhmYC9wPvD/dn4tuye0fRz22Glh6gscyOm457UZcrbWvAG1HbZ4JvBL//lngeFepbgQe6nf/Y8C/xvcZtdYes5ivMaaKWCCy3MZ+6vcD18Vfs9lau3Uk70WSJ4P6RVe/p+YBmoSeZpnSNyQzZVr/iF8d/8BR+5Y0y6B+Mphjyigazb4Rf+wNa23DcbbXW2s3ANGhvQNJpST2DwCMMXVABfC34zyW8XHLaRe4nsAm4Nr49zcANcd5zgeJdwBjTHF823fjaRW/N8ZUHuc1E4H9/e7vj2+TsSEt/SKRxgPcDPy/kb0FSZF0/c34nImlkt9jjCkZ0TuQVErn/5SLgEZr7fbhNl5GTTr6yWCOKemXqr4hp4Yh9Y+j3Ag8HA9Mj5bxcYsC15iPAZ81xqwGCoBg/weNMecCPdbaRH65C6gGXrPWLgCWAz88zn7NcbZpBG3sSEu/sNb+g7W2BngQ+NyI34WkQjr6xs+AacDZQAOxdFDJTOn8n3Lcq+ySkdLRTwY8pmSMVPUNOTUMtX/09yFO/D8i4+MWV7obkAmstVuAKwGMMTOAdx31lKN/yK1AD/Cn+P3fA7cbY5zE8sYBHid2olnd73XVwMGkNl5SJgP6xW+AvwDfHv67kFRIR9+w1jYmNhpjfgH8ORnvRZIvXX87jDEu4L3AwqS8EUmpNP0dOdkxJQOkqm9Ya5XFdQoYRv8g/tx5gMtauzp+f8zFLQpcAWNMhbW2yRjjAL4J/LzfYw5iw/BLE9ustdYY8wSwDHgBuAx421obITYa0n/f3caYJcCbwC3AT1P8diRJ0tEvjDF1/VL8rgW2pOjtyQikqW9U9ZuXdD2xVCHJQGn8n3I5sMVa2z/VSzJUmv6OnPCYkjlS2Tdk7Btq/+jniIycMRm3pLLyUybeiP3AGoAQsdzt24EvAtvit+8Dpt/zlwFvHGc/k4hNjN4APA/UnuB4i4idYO4E7kzsm9iJ534gADQCz6T7szmdbxnULx6Jb98APAFMTPdnc7rfMqhvPABsjL/+caAq3Z+NbpnTP+KP3Qt8Kt2fiW6Z208GOqZup03f+EH8ONH41+/Ety+O3/cRG8F9K92fjW7J6x/xx3YBs05yvIyOWxKNEREREREREclIKs4kIiIiIiIiGU2Bq4iIiIiIiGQ0Ba4iIiIiIiKS0RS4ioiIiIiISEZT4CoiIiIiIiIZTYGriIiIiIiIZDQFriIiIiIiIpLRFLiKiIiIiIhIRvv/l3QuY/9KXZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_plt.figure(figsize=(16,8))\n",
    "m_plt.plot(df1.resample('1D').min())\n",
    "m_plt.title('Flow at RCHRES ###')"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}