calc_dfm_gridcell_john.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import xray\n",
    "from xarray import ufuncs\n",
    "import os\n",
    "import math \n",
    "import gc \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORT DATA FROM UI METDATA SITE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################# INPUTS #############################\n",
    "#lat = 48.03125\n",
    "lat = 48.021\n",
    "#lon = -121.28125\n",
    "lon = -121.25\n",
    "######################################################################\n",
    "\n",
    "direc = '/raid/gergel/dfm_ui_metdata'\n",
    "tmin_file1 = \"tmmn_%s.nc\" % \"1981\"\n",
    "tmin_file2 = \"tmmn_%s.nc\" % \"1982\"\n",
    "tmin_full1 = xray.open_dataset(os.path.join(direc,tmin_file1)) ## load tmin\n",
    "tmin_full2 = xray.open_dataset(os.path.join(direc,tmin_file2)) ## load tmin\n",
    "tmin_full = xray.concat([tmin_full1, tmin_full2], dim='day')\n",
    "\n",
    "tmax_file1 = \"tmmx_%s.nc\" % \"1981\"\n",
    "tmax_file2 = \"tmmx_%s.nc\" % \"1982\"\n",
    "tmax_full1 = xray.open_dataset(os.path.join(direc,tmax_file1)) ## load tmax\n",
    "tmax_full2 = xray.open_dataset(os.path.join(direc,tmax_file2)) ## load tmax\n",
    "tmax_full = xray.concat([tmax_full1,tmax_full2], dim='day')\n",
    "\n",
    "rhmin_file1 = \"rmin_%s.nc\" % \"1981\"\n",
    "rhmin_file2 = \"rmin_%s.nc\" % \"1982\"\n",
    "rhmin_full1 = xray.open_dataset(os.path.join(direc,rhmin_file1)) ## load rhmin\n",
    "rhmin_full2 = xray.open_dataset(os.path.join(direc,rhmin_file2)) \n",
    "rhmin_full = xray.concat([rhmin_full1,rhmin_full2], dim='day')\n",
    "\n",
    "rhmax_file1 = \"rmax_%s.nc\" % \"1981\"\n",
    "rhmax_file2 = \"rmax_%s.nc\" % \"1982\"\n",
    "rhmax_full1 = xray.open_dataset(os.path.join(direc,rhmax_file1)) ## load rhmin\n",
    "rhmax_full2 = xray.open_dataset(os.path.join(direc,rhmax_file2)) \n",
    "rhmax_full = xray.concat([rhmax_full1,rhmax_full2], dim='day')\n",
    "\n",
    "q_file1 = \"sph_%s.nc\" % \"1981\"\n",
    "q_file2 = \"sph_%s.nc\" % \"1982\"\n",
    "q_full1 = xray.open_dataset(os.path.join(direc,q_file1)) \n",
    "q_full2 = xray.open_dataset(os.path.join(direc,q_file2)) ## load specific humidity\n",
    "q_full = xray.concat([q_full1,q_full2], dim='day')\n",
    "\n",
    "pr_file1 = \"pr_%s.nc\" % \"1981\"\n",
    "pr_file2 = \"pr_%s.nc\" % \"1982\"\n",
    "pr_full1 = xray.open_dataset(os.path.join(direc,pr_file1)) \n",
    "pr_full2 = xray.open_dataset(os.path.join(direc,pr_file2)) ## load precip\n",
    "pr_full = xray.concat([pr_full1,pr_full2], dim='day')\n",
    "\n",
    "## select out gridcell \n",
    "\n",
    "lon_ind = np.argmin(np.abs(pr_full.lon - lon))\n",
    "lat_ind = np.argmin(np.abs(pr_full.lat - lat))\n",
    "\n",
    "## get julian days \n",
    "julians = pd.DatetimeIndex(np.asarray(pr_full.day)).dayofyear\n",
    "\n",
    "# tmin = tmin_full[:,lat_ind,lon_ind]\n",
    "tmin = tmin_full.isel_points(lon=[lon_ind],lat=[lat_ind])['air_temperature'].values[0,:]\n",
    "tmax = tmax_full.isel_points(lon=[lon_ind],lat=[lat_ind])['air_temperature'].values[0,:]\n",
    "q = q_full.isel_points(lon=[lon_ind],lat=[lat_ind])\n",
    "pptamt = pr_full.isel_points(lon=[lon_ind],lat=[lat_ind])\n",
    "rhmin = rhmin_full.isel_points(lon=[lon_ind],lat=[lat_ind])['relative_humidity'].values[0,:]\n",
    "rhmax = rhmax_full.isel_points(lon=[lon_ind],lat=[lat_ind])['relative_humidity'].values[0,:]\n",
    "\n",
    "del tmax_full, q_full,pr_full,rhmin_full,rhmax_full\n",
    "gc.collect() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "100-hr and 1000-hr DFM FUNCTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def calc_fm100_fm1000(pptdur,maxrh,minrh,maxt,mint,lat,tmois,bv,julians,ymc100): \n",
    "    \"\"\"this subroutine computes the average boundary conditions for the past \n",
    "    24 hour and 100-hr-tl fuel moisture. The boundary conditions are weighted averages \n",
    "    of the EQMCs calculated from the temp and RH values. Philab is used to calculate \n",
    "    daylength which is the basis of the weighting function.\"\"\" \n",
    "\n",
    "    emc1 = 0\n",
    "    emc2 = 0\n",
    "    emc = 0\n",
    "    bndry1 = 0\n",
    "    bndry = 0\n",
    "    bvave = 0\n",
    "    daylit = 0\n",
    "    ambvp = 0\n",
    "    fr100 = 0.3156\n",
    "\n",
    "    '''# daylit = calcDaylight(j_date,lat) ########## need to write this function \n",
    "    #######################\n",
    "    phi = lat * 0.01745 ## lat is in degrees \n",
    "    decl = 0.41008*np.sin(np.deg2rad((julians-82) * 0.01745))\n",
    "    daylit = 24 * (1.0 - ((np.arccos(np.tan(np.deg2rad(phi)) * np.tan(np.deg2rad(decl))) / math.pi)))'''\n",
    "    #######################\n",
    "    \n",
    "    ## John's calcDaylight function\n",
    "    if julians > 365:\n",
    "        julians = 365\n",
    "    phi = lat * 0.01745 ## converts latitude to radians\n",
    "    decl = .41008*np.sin((julians-82)*0.01745)\n",
    "    daylit = 24.0*(1-np.arccos(np.tan(phi)*np.tan(decl))/3.14159)\n",
    "    daylit = daylit.real\n",
    "    \n",
    "    #emc1 = np.ndarray(shape=(len(maxrh)),dtype='float')\n",
    "    if minrh <= 10:\n",
    "        emc1= 0.03229 + (0.281073 * minrh) - (0.000578 * minrh * maxt)\n",
    "    elif minrh > 10 and minrh <= 50: \n",
    "        emc1 = 2.22749 + (0.160107 * minrh) - (0.014784 * maxt)\n",
    "    else: \n",
    "        emc1 = 21.0606 + (0.005565 * (minrh**2)) - (0.00035 * minrh * maxt) - (0.483199 * minrh)\n",
    "\n",
    "    #emc2 = np.ndarray(shape=(len(maxrh)),dtype='float')\n",
    "    if maxrh <= 10:\n",
    "        emc2 = 0.03229 + (0.281073 * maxrh) - (0.000578 * maxrh* mint)\n",
    "    elif maxrh > 10 and maxrh <= 50: \n",
    "        emc2 = 2.22749 + (0.160107 * maxrh) - (0.014784 * mint)\n",
    "    else: \n",
    "        emc2 = 21.0606 + (0.005565 * (maxrh**2)) - (0.00035 * maxrh * mint) - (0.483199 * maxrh)\n",
    "\n",
    "    '''phours = np.ndarray(shape=(len(maxrh)),dtype='float')\n",
    "    for day in np.arange(len(minrh)):\n",
    "        if pptamt[day] > 0 and pptdur >= 0 and climcl < 3:\n",
    "            phours[day] = ((pptamt[day]/0.25) + 0.4999) + 0.5 \n",
    "        elif pptamt[day] > 0 and pptdur >= 0 and climcl >= 3:\n",
    "            phours[day] = ((pptamt[day]/0.05) + 0.4999) + 0.5 \n",
    "        elif pptamt[day]\n",
    "    '''\n",
    "    \n",
    "    '''if pptamt > 0:\n",
    "        pptdur = ((pptamt/0.05) + 0.4999) + 0.5\n",
    "    else:\n",
    "        pptdur = 0'''\n",
    "    \n",
    "    ## qc maxrh \n",
    "    # for day in np.arange(len(emc1)):\n",
    "    if np.isnan(maxrh):\n",
    "        emc1 = np.nan\n",
    "        emc2 = np.nan \n",
    "\n",
    "    emc = (daylit * emc1 + (24.0 - daylit) * emc2) / 24.0 \n",
    "\n",
    "    ## qc precip duration \n",
    "    # for day in np.arange(len(pptdur)):\n",
    "    \n",
    "    # pptdur = pptdur*1.25\n",
    "    \n",
    "    if pptdur < 0:\n",
    "        pptdur = 0\n",
    "    elif pptdur > 8:\n",
    "        pptdur = 8\n",
    "        \n",
    "    bndry1 = ((24 - pptdur) * emc + (0.5 * pptdur + 41) * pptdur) / 24.0 \n",
    "    fm100 = ((bndry1 - ymc100) * fr100) + ymc100 \n",
    "    ## calculate 1000-hr fuel moisture daily using average of boundary conditions for\n",
    "    ## past seven days. starting value set by climate type. \n",
    "\n",
    "    fr1 = 0.3068\n",
    "\n",
    "    bvave = 0\n",
    "\n",
    "    ## accumulate a 6-day total\n",
    "    for i in np.arange(0,6):\n",
    "        bv[:,i] = bv[:,i+1]\n",
    "        bvave = bvave + bv[:,i]\n",
    "\n",
    "    bndry = ((24 - pptdur) * emc + (2.7 * pptdur + 76) * pptdur) / 24.0 \n",
    "    bv[:,6] = bndry\n",
    "\n",
    "    ## add today's boundary from subfm100, divide by 7 days \n",
    "    bvave = (bvave + bndry) / 7.0 \n",
    "\n",
    "    ## calculate today's 1000 hr fuel moisture \n",
    "    # fm1000 = tmois[:,1] + (bvave - tmois[:,1])*fr1 \n",
    "    fm1000 = tmois[:,0] + (bvave - tmois[:,0])*fr1\n",
    "\n",
    "    ## move each days 1000 hr down one, drop the oldest \n",
    "    for i in np.arange(0,6):\n",
    "        tmois[:,i] = tmois[:,i+1] \n",
    "    tmois[:,6] = fm1000 \n",
    "\n",
    "    return(tmois,fm1000,fm100,bv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORT BETA VALUES AND APPLY EMPIRICAL TRANSFORM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "direc = '/raid/gergel'\n",
    "pdur_file = 'pduration.nc'\n",
    "pdur_full = xray.open_dataset(os.path.join(direc,pdur_file)) ## pdur beta parameter for John's transform from Matt Jolly\n",
    "## select out gridcell for pdur b parameter\n",
    "lon_ind_1 = np.argmin(np.abs(pdur_full.lon - lon))\n",
    "lat_ind_1 = np.argmin(np.abs(pdur_full.lat - lat))\n",
    "beta = pdur_full.isel_points(lon=[lon_ind_1],lat=[lat_ind_1])\n",
    "#beta = 5.6\n",
    "\n",
    "pptamt_in = pptamt * 0.0393701 ## convert from mm to inches \n",
    "pptdur_calc_df = pptamt_in['precipitation_amount'].to_series().map(lambda x: int(24 * (1 - (np.exp(-beta['pdur']*x)))) \n",
    "                                                            if (x > 0) else 0) \n",
    "pptdur = xray.DataArray.from_series(pptdur_calc_df).to_dataset()['precipitation_amount'].values[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ITERATE AND CALCULATE 100-HR AND 1000-HR DFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = 1\n",
    "import math \n",
    "\n",
    "tmois=np.zeros(shape=(x,7))\n",
    "bv=np.zeros(shape=(x,7))\n",
    "ymc=np.zeros(shape=(x,1))\n",
    "ndays = len(rhmax)\n",
    "\n",
    "fm1000 = np.ndarray(shape=(x,ndays),dtype='float')\n",
    "fm100 = np.ndarray(shape=(x,ndays),dtype='float')\n",
    "\n",
    "\n",
    "for day in np.arange(ndays):\n",
    "    # pptdur_day = int(24.0 * (1 - math.exp(-1*beta['pdur'].values[0]*pptamt_day))) ## to cross check pptdur calc above\n",
    "    tmois,fm1000[0,day],fm100[0,day],bv = calc_fm100_fm1000(pptdur[day],rhmax[day],rhmin[day],tmax[day],tmin[day],\n",
    "                                                            48,tmois,bv,julians[day],ymc)\n",
    "    ymc=fm100[0,day]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORT UI METDATA SITE DFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "direc = '/raid/gergel/dfm_ui_metdata' \n",
    "file = \"fm100_1981.nc\" \n",
    "ds = xray.open_dataset(os.path.join(direc,file)) \n",
    "\n",
    "file2 = \"fm100_1982.nc\" \n",
    "ds2 = xray.open_dataset(os.path.join(direc,file2))\n",
    "                        \n",
    "## concatenate netcdf files \n",
    "ds_full = xray.concat([ds, ds2], dim='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "tp_start = dt.datetime(1981,10,1)\n",
    "tp_end = dt.datetime(1982,10,1)\n",
    "\n",
    "\n",
    "ui_start = dt.datetime(1980,12,31)\n",
    "ui_end = dt.datetime(1982,12,31)\n",
    "arr_ui = [ui_start + dt.timedelta(days=i) for i in range(0, (ui_end-ui_start).days)]\n",
    "dates_ui = np.asarray(arr_ui)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORT DATA FROM JOHN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "direc = '/raid/gergel'\n",
    "filename = 'diana_fm100_fm1000.csv'\n",
    "df = pd.read_csv(os.path.join(direc,filename))\n",
    "dss = xray.Dataset.from_dataframe(df)\n",
    "# ds_sel = ds.isel_points(lon=[lon_ind],lat=[lat_ind])\n",
    "# ds['year'].values = ds['year'].values.astype(str)\n",
    "# ds_sel = ds.loc[dict(year=slice(1981, 1982))]\n",
    "dss['year'][366+366+275]\n",
    "start = (2*366)+275\n",
    "end = (3*366)+275\n",
    "ds_sel = dss['fm100'][start:end]\n",
    "ds_time = dss['day'][start:end]\n",
    "bool_arr = np.ndarray(shape=(len(ds_sel)),dtype='bool')\n",
    "for i in np.arange(len(ds_sel)):\n",
    "    if ds_time[i] == 60:\n",
    "        bool_arr[i] = False\n",
    "    else:\n",
    "        bool_arr[i] = True\n",
    "dfm_john = ds_sel.squeeze()[bool_arr]\n",
    "dfm1000_john = dss['f1000'][start:end].squeeze()[bool_arr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PLOT AND COMPARE RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving figure to '/raid9/gergel/agg_snowpack/feb1_2'\n"
     ]
    },
    {
     "data": {
      "image/png": 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zrPUL3YYNwG23qceveY1aZmdK0durOv6hofjkfNcJ6CFh2105UwDw4Q+r5y68\n0NwJ0n2md/aXXCKnHQKA730v/hgmhcf0UJ9vCehlOlN6mM9lAnqVgkbPuYwb0MIwDOOQIs7UIIB/\nCYJgLwD/AOBTQohXATgdwLVBEOwO4HoAX4zdgt6xXHmlurPcZ5/6xFRZzpSp+nlebEJ9rot2hoRt\ndymmjjlGul0AcOed0Wrc+ucCrTk9ulCI2yem2lMhehK6qwR0V0U784wEtU1A18N8urOUVHjTpzCf\nLqaqnjmBYZiOJLeYCoJgZRAE929f3gTgUQCzAbwHwAXb33YBgPfGbkS/yF50kVr+4Adbwy5V4bsz\nBdglobsQAyZnKhQ1uuNSREztsAPwjnckr5sU5tOnIDKJqUMOSS682Y7OFN1ncc4UHXAxYoQUQt3d\n9u2mrlbdCei6E8ViimGYCnCSMyWEmA9gXwB3AJgWBMEqQAouAPEFcegFes0a4K9/VY+PPbY+MeV7\nzhTQOomtibIS0MO20+OzbZs5Z8qUzxWXy3XggcltocJg9Giga/vpu3Vra64MFcSveQ1w7rlRsW5C\nF1O+lUYoGuaLu0nQRX4oOG1DfdSZCudrdCUgs8LOFMO0L9u2Affc4+UUVYVLIwghxgG4BMBngiDY\nJITQe8+Y7GjgjNWrgTPOAAAsGjkSiwYH5Quvfz0wbx6wZIl6c5VFO2mnNWKEcj1c1pnKW7AzxCbM\nV1ZphFDU6GJX73CDQO4zXZDFtSttn1ABKoRsW/jdw3ypEOpQHHAAcOqpydsGWqugd0oCuh7iC7EJ\n1QWB2ZmqK8zHzhTDtCdBABx6KHDXXcBHPwr8/Oelf+SNN96IG2+80eq9hcSUEKIHUkj9OgiCcP6P\nVUKIaUEQrBJCTAcQmwF6BvCKmMKFF6oXdt5Z/vchzDdjBrB8uVwu2inQjjTvpLkhNmG+Kp0pk5gK\n26ZvIy58FiemJk4ETjmldXoYKqaWLYu+Fs7xB9h/9yRnqu4wXxCUl4Cuj+QLsWn3hg1yQmhAHuew\nxIQvOVO6yGYYppk89pgUUgDwi19UIqYWLVqERYsWvfL4zDPPjH1vUWfqFwAeCYLgXPLcHwCcCODb\nAE4AYJhkbTvhHG3d3WbXxocwHxVTRZ0p2qm4FFMmZ2p4OOrm0Y4xC0kJ6LQ207Zt8WJKr2ZOOzz6\nPUxiqrtbOiemXCfqmoXHKMSFmPLJmerrU/MZjholR3TaYONM6SP5QmzabUo+t123DDjMxzDtiefT\nUhUpjfDozw62AAAgAElEQVRGAB8G8I9CiPuEEPcKId4OKaLeIoRYAuBwAN9K3FB4oTV1XL44UyEu\nnam84iYkLcxH99fIkSq/KCtJpRFsnSnK0BBw003q8d57q2WTmBo7Nj5pnO4DXUw995xaziOm9DpT\ndTtTeYXdmDFq2qItW2RBUh2bMF9WMeVLmG/1aiVCszI0BPzgB8D3v2/ebwzDVIcupjz7TeZ2poIg\nuA1A3ORyb7beUF+f7KhMnYVpvrQq0J2pkKJ32C7FVFqYj7pSdD9mJcmZ0nOMbMTU3XerznvmTFlo\nMyROTMVBhZ4+l6LrMF8RZ6q3V4rZ4WGZfzc4qMpA2JJXTAkh92voPq1fD+y0U/Q9RcJ8vjtTw8Py\nOdNcj2mcfz7wmc/I5R12AI4/vnj7GIbJB73pA6S4otermqm3Ajqg7lp9CvM1wZlKC/O5ElNJzhQN\n861dax5hoYupq69Wy299a9R1KiKmdKgzZZvsX1YCuhDFa03RtmQdvJBWHqGMMJ8vzhSQP9T3iU+o\n5c99Lt82GIZxg0lMeUT9YsrHMF9ZzpTLnKm0MF/VzpTuCIToJ/w116jlt741+ppJsOQVU/T7152A\nDhR3aooIu7TyCHFhviaKKdN56CJvqmgpE4ZhikFv+gAWUy00yZnyNcxXtTOVRUxRQbJ+PXDHHXJZ\nCOAtb4m+1+S4JAkmW4GTR0ytX69qV3V3F9uH+rbznEf0nMwqptKS0NslzBeXt+dCTM2aVXwbDMPk\nh8VUCrbOVF11ppoQ5jN1IPSzXDtTcUU7TdC2PfmkSgbec085YTJlxIjWtuZ1pih5xBQNF40fn1w5\n3QafnakiYT5TjSmgHmcqTtDnEVP6b0o/VxmGqRYWUymEF1pTZ9HTo0ahDQ2pejZlw2G+5HXD40Nz\npuKgnRLdf3FzE+rulA9iqki+VIhLMZU1Zyrts12F+bKu65q4SY3ziKknnog+rtIZZximFT1nqsr0\nAQvqF1PhhdYU5hOi+lDf8HC049ppJ+VKbNuWf5h1WA08pGjYKEuYr4gLpjsyQqih9kmTBofEiam4\n71+nmKLfR3emilKnM6XngumUEebzyZmKez6Jxx+PPvbsws0wHQc7UykkOVNA9WJq82Y1n9zYsdId\ns6m3k0Z/vxrtNmKE/CtCVaURdGi7bcQUPeFtBF4WMWUrcmzfR/cTrZztgzNVJGfKJKa2bZPVhIeG\n7MJ8cWLCRkxV5UxR0URd0zzXDRZTDOMXLKZS6OuT4iWus6i61pSpHTadShouQ3xAdQnoOjZiinZk\ndTtTY8cqJy0N2ibaMds6YEn45EwFAXDIIXIOzBNOiJZLoOHXIs7UiBGqltbgYDUF9ugxmz1bLefJ\nt9TDfJs3A3feKSdj93CSVYZpe7g0QgqbN8u75HAy4d7eaCddtTNlyk1xcZftMvkcqC5nSoeKqbgp\nTWhHRh0e186UjcjJkl9E9xO9C3IhfuusM6WLqaefBhYvlo8vvFA5sZMmRY9vETEFVB/qoxdbOvou\nz/7WnalHHgEOOkiW87j44nztYxgmP+xMpdDXl3zXXbWYKsuZci2mqJDYtKn1brkKMSWEOQmdThHz\n5JPZ2pRFTNk4NFOnpr8nhB4XOtjBxfEqWhrBlTPV1xf/O9Iro6eJqf5+5Tx2dbUeu6qT0KmYmjlT\nLWe9bgRBq5iifOhD2bbHMEwxBgdbTQMWUxqbN5uTz0PYmTLT3d0qqChViCnAHOp79avVKMzly9Vx\ns9kHulBIElOve136d1uwIPl1m89ycbzqDPPprpgpxw5oFZ5pbdYT1/X5H+t0pqiYyupMXXeduVI8\nwzD1YPo9spjS6BRnynXOFJAc6qtTTE2eDMydK5eDQIaVbNuUxZmaPBk48cTE5mYSU3GiyQcx5TIB\n3VRrCsjuTCWF+GzWd40rMfV//6+b9jAM4wY9xAewmGpBd6aSxFQVhTub4kwBySP6qhJTpjDf2LHA\nrruqx2Eyr80+yCKmAOCzn01+feedk1+3+SwfxJTLBHRTjh3QKqbSzvs0MVW1M0UvuLQ+XJabsMce\nA/78Z7mcVKg1zDNjGKZ8WExZoDtTPoX5fM6ZApJH9LkUU7vvrpYPPTT6msmZGjsW2G039TjMm3Lt\nTAHAwoXA0UfHv+7CmXKdgF510U5bZyprmM8XMbVtm8zfcuFM/fa3avmII+Irn9PvzjBMuegj+QAW\nUy1s3ux/mM+FM9XkMN/vfgdMny6FyznnRF8ziakxY6pzpgDg5z8Hzj4buPLK1ryddsiZCoLo8c1a\nRDSvM5U1Z0qnijDfE0/I0aMzZkSTxvMmoK9YoZbf8Y74c+LZZ7O1k2GY/DTAmeqpuwHo60sO81Vd\nZ6qpzlSZYb5995WdR3d3a+jDxpkKxVQZzhQg98OXv6y2S4/R/Pnp64f4mjNFK++PGBFfkiIOfTRf\nnJhqojP1f/4PsGZN6/M0zJfFmaK/o4kTk8XUfvvZb5dhmPywmLJAd6bqDvPRi344Ws7FHXYZYqrK\nBPSemFMlLmdqzhz1OAzzleVMUfROO8t3L1NMpU3pkgR9fx5Xs50T0G+6qfW50aPlb7erS5YMGRyU\nf3HnMEV3ANmZYpj64TCfBWnOVNViytThu7jDpuuVIaaqKo2gExfm23ln5WKF5RHKcqbiyJqoHTfN\nj4vjRd2krNXAiwpxvTSCbZhP/+3ptcx8cKbiRpMKkc/Vps7UhAksphjGBxrgTNUvpnxzpmjHFV5I\nXTtTrnKmaJ2pqkbz6cSF+UaOjJZHWLrUfZ2pNOKSh5MwfZ5PYirPscybgC5EcuV2n8WU/pqtG8jO\nFMP4B9eZssC3OlNlOVNlh/l8cqbC/UWTv597rnpnKo+YMh0bn8RUnrbYJqCb9ldSvqAPYT7TeRSK\nqTyhVXamGMY/TMKpr0/lknpA/WIqS52pusSUrzlTPjhTppyp8PvREVUvvGDXplGjZAV1ANhjj/jJ\nlG143euyr+OjM2Uzp2ESegK6yZmaPDk9xKmf+z44UybCkYV5wnzsTDGMf8TN2lBFMWBL6hdT+sU9\nKcxXRdHOKnKmyiiNoCv3MsSbiSRnioqpFSvs23T55cC3vgVccUVy4UQTl1wiRx3uuCNw1lnZ1o1r\nV91iynWYz+RM6flSIUXEVBXOlCmXokiYz9aZev751hwyhmHKIS6k51Gor/7RfEEAvPiieqzX0GFn\nKh4fnCldTPX0KOFAh6fbOlOArFF12mn52vO+90nXYPLkfK5WFc7UwEC2dYueO3Q/bNtmzj/IKqaC\noH5nanAw3mUDsjtTYfFPQJ7HI0fGi6mBAWD1amDatGxtZhgmOw0QU/U7UwCwcqVarrvOFOdMZUMX\nLPS76WG+qtyyGTPyhwdN7XLhJNaZM9XVFd0f9OYlRE8+N30ebcfGjVLMhO9JciiBcsRU3GTEeZ0p\n3ZUSIvnYVxm6ZJhOhvZvdaUPpOCHmKIXsbqdKVMJAxfOVBmlEXxwpvScKXqi62G+qtpUhHbMmQKi\n+9uUtJnVmUqrfp60bhGuvBI4/njgrrvip3TJm4BuqjKfJKY8ytdgmLaGiqnp083P14wfYopCBQLg\nR5jPtTNVds5UENTnTMWJqeXLVXirqyt7Fe+qMIkVF/uvzpwpm/Wyiqm0EB/g/g5y40bgve8FfvMb\nYNEic74UkD8BXXemABZTDOMDLKYy0tXVetGvUkwFgbnjalrO1LZtannECJmQXRZJYormTD3/vFoe\nPTp7YnlV6J1nb69d5ew06gzzAeliat99zc/Hnfv0IhZXHNW1M7V8uVresiVeTLkI89k4U1UMiGGY\nTmdoSF0/hIje+LkUU/39wL33yoElS5cC554bveakUH8COmX8+OS538oWU3T7I0cqEeJ6ouOyc6aq\nDKcl5UyNHSs7Wn30WJn5UkXR2+aqrT6F+UIWLAA+/nF5DI880rxe3LlvI/DKzm147jnz8y7CfOxM\nMYwf6FEdevPmSkwFAXDwwcDixcDHPgbcdhvw2GPSBV+82GoT/okpnSrFVFwH4Xqi47IroFcpppJy\npgAZ6tPFlK/5UkBr+30QU2U5U9OmAV/6UvJ6ce5SVjHlQnjo2wjnfNQxOVNZw3ycM8UwfkAF07hx\n0X7PlZhasUKJpt/8RkV37r5bOlVd6UE8v8J8er4U4J+Y6uuTKtbVtoswdqxy8rZsUYnFdTpTeudD\nQ30h7ExlW9dFzpTpe+g13dLWo+2wcctc3IRQ9G089ZT5fZMmyf9lO1Mc5mOY8tHFlOvrir4dmiYD\nWOsOv8SUyZmiF8Syh0HGCZ7ubuXABEF2UReXi1UUIcwq3ZcwHxBNQg/pRGeKVhfv788myMtypmwm\ngnblTLn47do4UzNnqmlxiiSgxzlTVICyM8Uw5aOLqTzTRKWRdH2y/J37H+ajF/y4OcVckdRBjB2r\nFGtfXzZBsHWr6jxdJTSHjBunOoFNm+TF3idnyiSmOtGZ6u6Wf0ND8lwYGrI/D8rKmXIlpuLOsd5e\n9Z0HBqSILDKKU7/gPfGEWj7tNGnHH3202q9ZE9BtSiNMn64KhbKYYpjyoTc5ZYmppN+y5e/cL2fK\nFOajd4Jxs927IklMFbEWy8iXCqECNDzpfMuZ0mmSM+XyeOnulC1lOVNFwnw2bdKd06LulH5Ro27T\nm94EnHMOcOCB6rmsF12b0gh0WDaH+RimfJLCfK5uaNpOTJmcqbFj1ai6LVuyT8WRhTRnyvS+rNt1\nLSR8D/OZcqZ8FlNlOVNA/rypsupMZXWmqBiyFXguQ31J65sKh2YN89k4U/R8ZmeKYcqnijBfR4gp\nIaIX/TLdqbKcKZrQlneakzjqdqbSwnyzZ7eu43OYr6ycKcCNmKramaLnFxUbtm1yOfIm6aJmKhxa\ndDoZINmZYjHFMOXDYioHJjEFVBfqSwrHFXGm9PpVLjGVR/BJTO26a+s67ExlE1Nl5UyFo96SoO+h\nc+HZtqnpzpSe10YnNuYwH8OUDxVT48d7m4Dul5gy5UwBUTFVZhJ6050pU5ivbBdIF4f6502d2iqS\n2Zmq3pkyrbfzzunrUTFFb2RsQ49VOFNCmF02F84UAHz+8/L/SSdFxRQ7UwxTPuxM5SDOmfIhzFfE\nmaJiqtOcKSGAhQujz7Ez5UfOlMk11IlzpnzKmZo50zxlkouJjgHgu98F1q4FfvGLcpJfGYaJh8VU\nDuoO8yVN+VLEmaIhhqqdqbrFFADstlv0MTtT9Yf5Ro82Dw7Qob+9PGKqCmcqThS6mOg4JBSV9Lty\nmI9hyofFVA5swnzsTEXxzZkydazsTEl8SkDfdVe7yab1Om/Dw9na5NKZyiqmXNSZ0qH7kZ0phimf\npNIIVYgpy+uWX2KqbmeqrJypMhPQ05wp106YTlqdKYCdqRCfxNQuu9it19OjzrEgUILD1i1z6UzF\n/e5snKkiOVMUdqYYplqSnCmuM7Ud/c7YZzHlyplyLW5MzlSZn6eTloAOtIopn52pkSOj52XdYmpg\nABgclMvd3dHCn1kwOVO2mPKmmuZMpYX5tm1Tx6SnJ/6mh3OmGKZaGjKdTL1iShdPPof5XI3mq8KZ\nKvPzdHRBbJoiRRdTZQu8IggRFQAuK6DnEVOuRmbq6+YVU6apVKoazVemM6XPyxcXAuUwH9OOhOF7\nH+HpZCzQxZTPzlSRO9Kq60yV+XlpmEZV6TWA1q6tpi15oce6bmfKRYgPaG9nKi5kmSUBnbYt7qYO\n4DAf036ceab8jX/jG3W3xAwnoFvQJDGVdWQQpeo6U1U6UwBw0EHy/+TJdp20zzlTQFQAsJgyj+ir\nYzRfnBiLu25kSUC3HbTBYT6mnRgYAM44Q96If+UrMi/SN5LE1NatbtrceDGlJ3n6LKayjgyiVD2a\nr8qcKQD47W+Bs84Crr8+KhgoP/uZ/D97NvDhD5ffpiL46kwVyTUbGoo+Nk3zE4fvzlQc+m826aJr\nK6Y4zMe0EzSEBpQ7921edDHV1RW9lmY1N0w0XkzZDkP3QUwVsRarHs1XtTO1YIG8q9l33/j3fOxj\nwLPPAk884b8ztd9+8v+oUa1lHYpALwC2Fy1XOVMLFkQfm8KxcehianBQtV+/sOmU7Ux1JVzCenpU\nDl8QJO9zWzGlJ7X7nGvCMGno/akLYeIaXUwB7kN9jU9A15M845I+fRNTPoX50pypqnOmkpg92+/k\n85Dvfhf45jeBq682T6CblzrDfDNmAD/8IfC2twG33pptXV1M6QIvqV6VK2cqCMwXtcMPT17P9qJr\n6wB2dRW7FjCMT/gupoaHo9eN8HriOnfRgTNlGHpVITZFAwE/xJSvYT5TZ1VnAno7MG0a8MUvut9u\nnWIKAE45Rf5lRR/NlyX06MqZ6u+PhirHjJFlIn760+T1Ro+OFrM1zeEXvka3nbbN8P19ff47rQwT\nh96f0r7KB2h4fswY5ai7dqYaH+bLI6bqmui4yN1omdPJmOpf+epMdTpFSyPUVZ9Ld6ayCDxXzhT9\nzIkTgeefB1asAObPT17PttZUlv3MI/rahyCQju3Xvla8b/nb34B//mfg9tvdtK0K9O/smzNlCvEB\n7nMXHVRAb4YzNW6cfG8QyJ07NJQt58OWJjpTI0fK0MPwsOygBwerT0Bn7KjbmcqLPpovS5tcOVO6\n1U8FXhK2d7B5xRQnoTebq64CPv1pudzfD/zHf+TbThAAb3iDXP7xj+X12LZ/qxPfw3z0mkFvzNiZ\nyklXVzTRuix3qoqcKddiSi8yuXkzO1O+0lQxlZYzlUQZzlSW/UBHDNOJmnWyiCke0dc+nHmmWv7W\nt/JvR+8TfAuXxeG7mIr73bsUU6EJYdOGBJoR5gPKz5saGEgeoVTEmSozzAe0VmfnnCk/qbM0QhGK\nhPnKcqZs2XFHtbxmTfz7OMzXmeglQ/Kin9tNOS98D/PR9tDfpUsxlSaWGiGmXv96tZxm25ctpugO\nGzu2Vej56kwBrXlT7Ez5SZ3TyRQhSUzZuDjhb2nr1vydV15naqed1HKSmMrynTjM1z50upjyPQE9\nzoioWkxZFAatV0ydfrqsTTR1KvDnPye/t0oxZbqY+pozBXCYrym0Q5hPH82X1iZTGDoPPjlTHOZr\nH1zVCWsXMeWbM0X3Y11iamjIqi5gvQnoY8cC994rG2uaIJdStphKS9r2dTQf0Brm4wR0P2nqaD49\nAZ0KGxuBN3as6mw2b26d+cCGvKIyj5hK2z6H+dqHTnemmhrmc/kbtLkh6utLLk6Mup0pQN65pgkp\nwDw/mEvS3Bz6XNb5gNiZYgBZFynEVkz5cCxHjFAXr+FhYPVq9ZqNsHGRN+WTM8VhvvahLDHVlPPC\nd2eqijCfjVtucTzrF1O20ErUL7/sfvu0czMp0K6uVkFlS9nOFO1cNm1K/y5MPeRxpnw5ljTU98IL\natnWmQrJG+Zz4UxREajDYb7ORA/znXMO8IlPyDpmWWiqM1WnmBoakvW9Tjopfn/bhPmK/gZtnakU\n6g3zZcH2DjMvNg7AqFHqfVu32oddqnSmqGs3YkTy3GVMteQRUz44U4AUU6GIyiqm2tmZakqnyZjR\nnanTTpP/16wBLr/cfjtNFVN6mK/KBPQbbgC+/nW5fPPNwAMPtP62fRjNZ/me5vS0VYqpOAcg7wEs\nu0OkF3fq2nG+lF8UFVN1OlNUEFGHx+aGok5nqkmj+W6+GTjiCOA3v3G3TResXQt85jPAV7+aXI+n\nCQSBnGw9TNOIC/NdcUW27TZVTNXpTD3zjFpeuhQ4+eTW9/gwmg+wum6xmAqhnVuSMxXia5iPiinO\nl/KLomG+Oo8nFRD099eJzlRZYb63v11W5D7++OSQZNV85SvAD34AnHUWcMEFdbemGMcdByxcCHzg\nA/Jxpyeg1ymmdIFy4YXAsmXR53wYzWf5HhZTITbuka/OFIupZtDkMF8RMVWnMzV5sqpztXZtvLPi\nw2g+uq2HH3a33aL86Edq+Xvfq68dRRkeBn77W7n8+9/L36Ar8dDEBPQgqHc0n+nGiqYQABzmKwUf\nwnx5nSkWUwzQ7AR0eo699JJa9t2Z6u4GpkxRj+MGr/g2mm/t2nK2W5Qy5kStCv2avXkzsHGjm203\n0ZnavLk1Ab9KMWXa9/p+5NF8JeBDmC/vAayyzhSLKX9pF2cqa1X2Op0pwO7akUVMFSngG4cebipj\nxLILmiym9GP18stWxRitaKKYMtVrrDIB3XRjlbQf6e+ujjpTKTRHTNHSCGvWZKvzZIPtaL4QWwU/\nPBz9wZbhLsQ5U5yA7hdNTkCPEzBVOVNFipfSJPS4XKS8YspV56NfT1audLNd17STmErbxw88IPN4\nbDrrJoopPcQH1B/mq8OZ6jgxNWqUuigPDppPhCKUNZpPD9NkmdzZFg7zNYN2SUCnZB3NlzcsVkRM\n2ThTWUbz0ePgSkzp1xM9d8QXmlxqJauYOvBAmbB+5pnp29ZDVk0QUyZnymcxxTlTDikz1FfWaL6y\nQ3wAi6mm0I7OFHWdbNbNG+YrW0xl2X4VYmrFCjfbdU2TnSm9Q0zbx+Gx/fa307fdRGeqCWKq6qKd\n9Pymy20npvRQn0vKGs1XRc4L7ayoY8diyi+a7EzFJX1nFVO+OlNZ8sDYmWomWZ2pLDRxNJ+PYkp3\n+KoI89H1Z8wwL7edmKrKmbIJ89medFVMOhzX0bGY8ot2dKbGj09f10UCeplianBQlUzo6orOoWii\nk8VUk52pKsVUE5wpU6qMbwnoVYT56PonnyxF1M47AyecoJ4vU0wJIX4uhFglhHiAPPc1IcRzQoh7\nt/+9Pe/2jZQpprImoNseQHoylCVu4sQUJ6D7RZOdqSJiyndnSt92Wl5j3jk6kzB19PqwdR9oJzHl\nMpTaRDHlozOVJ8znUkztv78sHPrEE8CCBer5kiugnw/gbYbn/zMIgtdu//vfAttvpSox5TIBvYow\nHztTzYCeV7ZDsn0sjUCxCfPVnYBO0wNojay8267CmRoYMLe1bjjMZ4bFVHaKjOZzWRpBd7/COW0z\n3gTm/mUEQXArAFNluRKGq22niQnoVYT54jo6FlN+0eQwn0mwjxyZHhID6k9Ap4LP9PlZRvIB1Ygp\nwI8kdF30++iW2aJ3iJ0upkxixmcxVUWYL06w1ZQz9WkhxP1CiJ8JISY63XLdYb48B7DOMB+LKb9o\ntzCfjSulr1uHM5VW58pHZwrwI29KF59VdrauKcuZ6u9vFZ1NSEA3nXNVHd/hYfONTd4wX5G6k3GC\nLeN1qyd/C4z8CMDXgyAIhBBnA/hPAB+Le/MZZ5zxyvKiRYuwaNGi5K3XHeajB/P735fvO+205E6l\nzjAf50z5BXVx+vvlBSApP2doSFXGFqLefBWTmLLJlwKKh/mCoJiYSkuA91VM+eBM6cerncRUFpdt\neDg+xJkm0H3F1MaqEtDjrgO2Yb6uLtn/hjeb27bl7+8SnKkbAdwIAEuWAESvmHAqpoIgoOWFzwPw\nx6T3n5HSuBbqDvPRC21/P3D22fJAfPe78dutIszX2wv09LRO4srOlF90d8u/oSEpEIaG5HGLQz8n\nyyj4aosrZypPmC8UnoAUpFlFZRYxZVPRnZ2pZlJE4GzdGn9uNFVMmY5lVcc3biYE2zBf+Di8Rm7Z\n4kZMac7UIgCLAJl3ecYZODOhgGvRMJ8AyZESQkwnrx0F4KGC249Sd5jPdLC+971ki7GKMB9g/qGz\nmPKPLKE+X/KlALP7aetMFQ3zFXGlgOY6Uz6IqXZypoqE3pK+d1PFVJ1hPrrP6A2lbZgPcFe40ybJ\nveTSCL8FcDuAhUKI5UKIkwCcI4R4QAhxP4A3Afhc3u0bSRuVU4Sso/kojz9ut90yw25xCcKMX+QV\nU3UfyyJhPt2ZyprfUFRMuc6Z0o+hi3lCTZ3YWsP4nlWrgFNOAX74w+KfaYMuPqusQ+SaIgInaV0W\nU9mhxTlpcUy6L4MgeQYRV0nocb//jOkJucN8QRB8yPD0+Xm3Z8WECWpZr5RalKyj+Sj/+7/A7rub\nX6vKmWIx1QyyiClfks+BYmG+nh6V3xAE2fMbioqp3l4VXh0YkH80fy3raL6wsGeYdNzfX/z4mDoD\n03PHHQdce61cfuMbgX33Lfa5abSTM8ViKkpczlRaLqcL6D6bPh149tnW5wcHVV5bd3drSoQLMZUk\n2DwYzVceY8aoJMCtW+1r9diQdTQf5X8TymlV5S6YxBQnoPtHU8N8RZwpff2slnxRMSVEcqgvz/Zd\nh/psxNTq1UpIAcA99xT/3DQ4Zyp93Tgx5cKxLJO472Q70rgIdJ9NmaJubgYGonlQIabfpYtaUwMD\nSrD19EQFW1uLKSGiF3CX7pTNdDJx4uTGG+MPZlVhPs6ZagZNdaZCd4di60wBxaaUKSqm0j7fVzGl\nX8B/97vo46TBC64wOVO+i4Q4qhRTgP/CM659VbSb7rNx48yh+KQQH+DGmUr67dNBP9u2qZHVMTRL\nTAHRUJ9pbqG8FHGmtm4Fnnoq/rW07bqAw3zNQC+PkIRPzpQQrYK9Kc4UkJw3lXU0H1CPM/XrX0cf\nx3XiLjEdqyqcizIokqScR0z5HuqLa18niamkz9CveSnnT/PEVFnOVN7RfCFxd9t1hvlYTPlHU50p\noFVo5HWm6hBTTXSm6HNPPQUsXhx9vQoxZbqu+e64xFGWMxXXDzVJTFHXuW4xFe7PtN9l2c4UwGIq\nFzZhvqQLrY2YqjrMxzlT/pFlklyfnCnAnTPlW5gvawI6UH2Y7+67W1/POzVPFkwdSFNH9FUd5muS\nmJo0SS3XLabqcqY6TkzVGebL40xxmI+hZLkA+FQaASjmTNUd5nPtTOWZpzOJNGfq0UdbX2dnKhtl\niam4a7/vU8rQ40jFVBVimZ6748cXF1MupqkyfUZbi6kqwnxlOVNldohTp7Y+50MHzETJIqZ8C/Pp\ngj2LM1V3mM82Z8onZ8oHMWU6Vu0mpuLK2tisC8TvD5+dKb0kgI/OFIf5SqYsZ6pInSmg/jDfrru2\nPiMCW3YAACAASURBVOdDB8xEyetMcZhP4oszVXWY75FHWl+vIszXTs5UXGf4qlelr5v0W417zWcx\npUdM6O/TFzFVdwI60OZiyqcEdDrxZd1hPhZTzSBLbRTfnKl2DfPlueGpQkwNDMjh2IOD5lkW2JnK\nRtzvbbfd8q8LNNOZ0n9TrsPWlCCQ5YOuvVaV1XAhplzUmUq7tmRw1Jsnpsqqgm7jAuizhs+dq5Zt\n4ua2w67zYLogcAK6f3SqM+Vznak8ofgqxFT4/NNPm0d+spjKRtw+njcv/7pJrzVFTI0alW1gTFau\nuw447DDgLW8BrrpKPudLmI+dqe1UHebTsRFT9KQxJYm7Ytq01u374GYwUZqcgK6fX01yppJypvKI\n1qrEVF9fNF+qSLg0D+0U5ovbx3Pm5F8XiO4PerPvs5jSR7FRIeE6Af2449Tyu98t//sS5uvonKky\nwnzDw9JKD6GFFZN43evUctyFjT5fppgSojXU50MHzETJMgLFplxHlbhypnwO8/ngTNGOY8uWqJja\nf3+1XJcz1cTSCEND8XXd6E1xHLbO1JQpatnn0XxVhvlM+4723SymaqKMBHS900qa5PHSS4F99gHO\nOSc6CsRGTGW5k8+DHupjMeUfTXamXOVM+Rbmy+NKlymm9A6ZJp8fcIBa5tII9iT91ubMAT7xifzr\n0/0xebLdOnVTpZgymRO6M0VvzExhPh8S0FOuW80TU2U4U1k6raOOAu6/H/jXf7XLA6nKmQLYmWoC\nTS6NoLtjWYRN3WE+n52pIIgXU1u2AHfeqR5XLabaJWcq6be2ww7Aj34kE6RvvDH7+knHzld0oVK1\nmNJzidOcKS6NUAJlJKDndQDoBTruwlalmNKdKU5A948mJ6CHs6uHJDm4Oq7CfHkHcST9VusWU/rM\n9fSG8a67gCVL5PLYscDhh6vXOGfKnqTfmhByvx9+ODBjRvb12ZlKxjQht/75dRXt5AT07ZQV5rPF\nN2dqwYLo4ypmlWey0WRnKmXW9ESKhPnyTPeiQy/WSc5UHQnoesdCv+Mvf6mW3/Uu6aKEInbr1miu\nZxl0gjNFiTu/2i1nSnd9XIetKaZ+SP/8tNF8nDNVAr46U6YOIgjqFVOMf2SpjeK7M5UFDvPFo38/\nuq9oiO/975dCKkkYuqYTnClKHjFF98cOO6hln8VUkjPl2lHTw3x69fWRI/OF+aqoM9XWYqoMZypv\np5Umpvr71Z1jT0/5HeK8ecA73iGXP/axcj+LyUeTE9CLiCmf60zVnYCe5EyFjB2rfttF9mUWhofN\n52gTR/PFdYTnnRd9XNSZaqqYKnKzk4YupgYHo2Htnp6omApNkoZVQG9eHKgMZypvOCXtolalKwXI\nu9arrgKWLgV22aX8z2OykzfM54MzpRetzYJPzpRvOVNJHVvIYYep755UM8slcQ5U052pN70J+OhH\n5XE74YTo+zrRmRo1qlyBrof5TAKGw3w1MGaMuqhv2eImZ6CsMF+VZRFCurrkqL4sycFMdTTZmfrc\n59SF8YtfzLZu3WLKNmcqj5gqKi5snCmaGF2VmLKZIqsp0H08dizwkY/Icgi6axJ3wxD3Wx0ejp4/\nNAG9igECedFDaGUWg80rpqoYzZfmTGUYONM8ZyrMGQhDfBs3Rk/gPJSVgF61M8X4T96inT6Iqdmz\ngVtukXWPPvCBbOv6HObzOQE9hCY2VxXmizs/my6m8pxDcZ21LsSLjFqtEn1/lNluXUzRc7ZJztTV\nVyduqnliCpChPpdiypUzFQRRR4jFFKPT5NIIAHDQQfIvK3U7U3G/1SDwL2fKFOajYorDfNkpOiLU\nZv69skWJS5La7Vqg65XnX3pJLYciadQo6QqGTt/AQHr/6VpMpeVMpdC8MB/gPgk9r5iiSeX6CAWA\nxRTTSpNLIxSh7ulkRoxQIZ2hIfWbHxhQ7+npsc8Lq9OZYjGVnaK1yuJ+q3qYqOq5E/NSZc6U/vtY\ns0Yth+e6aZSqXthTR3f5gyB727KMGEyhmWLKdRJ6kUTfpJOwqkmOmebQdGcqL/rQ6ywjA12IKcCc\nN5X3RqppYb4//lE6iv/1X/btalcxxc5Utc6Ufr5QMUWvC/pNAt1/pv6zuzt6XczzO8wS5kuhmWLK\n9ZQyRRJ9kxJb2ZlidPLWmWq6M9XVla9i8eCgGmTS1WU/CbkJU4eRdx/TTsD3MN/wMPDud8uaVaee\nCqxfb9euONHUxNIIRcXU0FDUxQxpqjNVZQK6fr6YwnxA63lN2xEnaoqG+rKURkihmWLK9WTHRTqt\nJEXPYorRaXJphKKYJjNNQ+8Ei4xSTRNTWfZxk8J8f/979PHKldnbRWl3Z+ryy4GpU4Fjj43ua9P+\nqLJek0uqdKaSwnxFnCmguJhiZ8qxM1VWmI/FFKOj//iT4vzt5EwB+XIdXYX4AHOtKR/DfGnOVNaO\n77rroo9t933cnHNNFFP0O9Pz0MR73ysF50UXpXfWurPRxDBf2e3Wfx+rV0c/OyRJTJXlTGUZMZhC\nM8UUdaZsLeskqnCmqqozxfhNd7cKVekjyXTaKQEdyJfr6FJMmRydvPvYpZii648a5d6Z0sVUnjDf\npEnm55vCyy+rZbov4wgd0LRpVvTzc8QIVQpgcDD5910nPuZM6SZJFWIqLQG9u9t6U80UU/Quae3a\n4tsrkujLzhSTFdsLQDsloAP1O1MmEeKDM6W7G/r3HDky+lwWMdXfD9x8c/S5deuyt6vpYor2E1lK\n6WR1poBmhPp0EdHbW44IHB5uLaxtGs0HRM/rl15Sg1Ro23TKDvMBwL/8i/x/7LGJm2qmmKIl+2ky\nW16KOAA8mo/Jim0idjuH+epwpkw5Wz6KKf0ufMqUaK5YFhfhjjtaz7FOd6ZciinT+dmEUJ+p3WUk\noZt+GzYJ6C++qJaT8paKiCm9nJEpzAcA3/ueFOMXXZS4uWaKKWrTuhBTnIDOVIntBaDdEtDrDvOZ\nxJwPCehpzpQelsriTN13X+tzts4U3fdUTDVxNB91pmzCfCFFnSlfR/SlicAyxZRNAnoVYkr/7SfV\nmKPnfwzNFFPUmaJ3HHnhBHSmSmzLI7SzM1V3mM8kpnxxplyKKdP5xc6U/Xppv9U0h6cJzlQoaMoQ\nU6ZzxUZMrVqllpP6TlczK8S5UhlovphiZ4ppGuxM1e9M+ZSAbhPmo2Tp9Ey5L0XFVN6pO+oiCPLn\nTJlGgFJMzlRTw3xltNv026D1umzEVFnOVFryeUaaKaY4zMc0mTwJ6OxM+etMFXVqijhTaaLUJKby\nJKA3uTRCX5/qwONGS8aRludn6pCbEOYztbuqMB+lzjAfO1PgMB/TbGwuAENDajSLEJmG6HpLOyeg\n55kXjK5Pt5vmTE2cqJbTXCZXztT48SqnZHDQXA3cV/KG+ID0c9bUITctzFd1AjqlaJjPlTPVsWJq\n4kT1w96wofgPu6zSCNQW5jpTTIjNBUDvYItU/vaFusN8ac5Ult9+d3dU4Ba5BukXdf3CrguAomIq\nTwK6Xky0SaG+vMnnQDZnqszcI5cEQb05U5S40ghVOFOO6/g1U0x1dUUvMEXdKXohZGeKKZs8Yqod\nqDvM5zJnSn9/kbwpvUPWRxXpncm4cUpcb97cWseH4sqZ0qe58dVxMVGVM9WUBHR6ro4YoW4KfArz\n0YnQyxJTRfp9A80UU4DbJHS6U7NOpMpiismKzQWgyDnpKz6F+YrmTOnvdymmdPTv3dVlPz+pKzGl\n5xqxMyVpYtHOuMRrn8QUpazRfI4H+LSHmHLpTLGYYsrG5g6/HcWU72G+ssVUXx/wzW8C//3f0Ryr\nNDFles421OcqAV0fZdgkMVXEmUo7Z6uq1+SSuPOtqtF8FBsxleRMFdnXjsVUTI32BuByRB+1yePK\n1scRdzA3b47++FhMMSE2nVI7iikfw3xViqnzzgO+/GW53NsLfPKTcjlNTJk6mSJiasMGKebS8vD0\nvJqmhvnYmYoSJyLKSEBPy5lqIzHVHs6Ub2G+yy+XJ0Z499nT0x51ghg3cJjPT2cq62+UdgQ2YurH\nP1bLJ5+slmmHEwq0E0+U/6dOBf7pn1q3VURMDQ3ZdTzt4kzlrTEF5MuZ8r3OVNwNRBPDfGl1wJKg\nvw0H19j2EFO+hfl+8pP49zBMp4op2zwfis8J6LQjsBEXu+wSffzcc/K/yd347/+Wc4HdcYf5zryI\nmALsQn3tkjPlKgHddM42cTqZOEembjFF9zWFnamS8TnMd/XV0ffYJnwynUGniin9Lt+mNlOZzlQQ\nFAvzZXVqdtwx+vjii+V/2gY6TP3YY4EFC8zbKiqmbK5J7TKar8wwX9NH89XtTNFzavRoc+g5SUzR\n3zSLqZz4GOYL73Z1hZ1VoDHtTaeKqd5edfEeGrKrpO1STI0YoT5/eFhuu4iYyioudGETzkKfp3gg\ndfmqcqaaGuYrszRCE6eTqdKZypIz1dVljuLYhvlYTOWE3mH4FOZbv771R3fccfnbxrQfnSqmgOxJ\n6C7FlP75mzZV60zphT3vu0+KOlPOVBp5nCn63W2cqaQE9CaJqTqdKR/DfHHnfBmOWpYwH2DOm+Iw\nX8m4dKZomC9rx9Xbq5ynoSFg6VL1Wnc38PvfRxNPGSarmGonZzNrErprMaWH+ookoGftfHQxNTwc\nrfTc29tasDOOPGJq6lS7dUKScqZ8dFziKDMBvZ1G89WdMwWwmKqFssJ8eTouekCXLFHLhx4KHH10\n+1SwZtxgI6aKCHyfyVprqmxnqkgCetEwH6CS0IFs84PZiiname20k1rmMJ8dI0eqPmFgoFUcNLHO\nlE85UzZiqqwwH1dA347LBPSiIZU4MTV7dv42Me2LzQWAw3ySKp2pqsN8QFRMZfn8qp2ppiagDw9H\nhWNWMSVEsjvFzlQyWXKmgGLOVJHSCB0tpqgztWZNsVnbWUwxVUIvGHEXgE4QU3U7U0XFlAtn6vnn\n1XJeZ8p2OpksztTgoExbAGS6Qk9PM52pl19WfcOECfkiD0nnbDuN5isj14t+Fj1nASlUdRFj6jPT\n5uYLRwBu26bOWRtYTG1n3Dhg0iS53N8PrFqVf1tFSiMA8WJq1qz8bWLal04WU1lrTbVzAjrgRkyV\n4UzpyedAMxPQly9Xy3lvbuPEVBCYBw/4HuarK2dqzpzoa6NGtZZCWLiwdRtJYT4h8otAFlOE+fPV\n8jPP5N+OS2fq8cfVMjtTjIlOFlN1O1N1JqCX5UzZiinqTKWJKVP4qolhPiqm5s3Lt424c5aeOyNG\nSAcPaK4zVfbcfHp/aDrfTWIqyZkC8teaYjFFoAXtnn46/3ZciinaObKYYkywmJLUHeZzmYDuImeq\nSjGV5gqm5QI1xZlatkwtuxZTcfXBdDFVJAWlDGycKVftpvvI5Ezp5BFTeR01nk6G4MqZchnmo7CY\nYkzoYsp00WpXMZUlzKeHUbKIjTjKSkDPUxoBqFZM0QrsecRU08N8c+fm20ZWMdXdrc4l/Rz2gbhz\nXm+3i2OcFubT2W231ueqEFMd70z5GOaj26F3ggwT0turfryDg+bwT7uKqSyj+fR8FNsaTLaf71sC\nepbP179HXOKtC2eqKYnVJlw7U3S/JZ07tE+wndS7KpJEhI1rnoWsYmrSpNYZRNJ+9yymHOBLmM80\nnHPWLDcXf6Y90R0SnXYt2mnrqADuQ3xAa2dRdwI6vfhncaa6u9NDpkNDsjQAIK9FtCxAmQnoQQCc\ndRZwwglR560OqJhy7UwlnTvh4CjAv7lZk9qdZzJy28+aOTP6Wtx1LWtEh8WUA1w4U0FQTpiPR/Ix\nSaTdAbZr0U7aodPK1CbKEFNJzlQdCeiUrGHMNGGqdxZZOsoiYb7bbgO++lXgV78C3vWubMPVXVNV\nArouSrKc51WTJCJciyl6Hunhujjnq4iYyuKmsZgiUDG1bJm6C8sC/aF3deVzk0xiatq07NthOoc0\nMdWuYT7ayaTVOipbTPmQgE6pWkwlJRgXKUb5yCNq+e9/B374w/j3lsmWLWq6nu7uVmfElriq/bbO\nlE21+Sqpy5nSz++47Wc1ItiZcsD48ap4Z38/sGJF9m246LRMYorWdGEYnU4VU7SToXfsps6dCpS0\nJFRbfEtAp5Qtpnp71WcMDiYLwCLOFJ1vEAC+/GU3HXNWnn1WLc+erUoXZKXdnKm6xJT+WXG5ZFnD\nsXnFFE8no1E01OcinMJiislKp4opUydz7rny+YMPjgqAsp2pDRvcJaDbOFP0u5k69rLFFGDfWZoS\n0G0T7leujD7etAm4//7495eFi+RzoHjOlG/OVJVhPn0f0SKdcWHvU05RfeonP5n+GexMOYImoecR\nUy4SfVlMMVnRw006nSCm1q2TofnPflb+v/124Ior1Ou0w3Ylpujnv/xysZupIs7U9Omtr2cVU3Eu\nX0gRMWVKQLdNuDfNRlGHO1OlmNKPHTtTEn1ELt1+HNOmATfeCPz4x8B3vpP+fi7a6QjqTOUZ0cdh\nPqYOOtWZmjBB3Z1u3AgsXhx9/eKL1XIZzhTt5Gin39vbOrVFGllKIwwPR/MzTWIqqzNGc4BoSCvE\nlTOVNcynO1OAFK5V46LGFMDOVBF0wWkjpgBg//2Bk0+2ez87U47wNczHNaaYJLKURmgnMdXVFQ1P\n/e530df/9Cd1ES9bTNHOIquQAbKVRtCPJy2gGZLVmUq79pk6C9vSFCYxRduXNKmsL87Uww+rZRrB\nyEqeOlPsTJk/69RT1eOTTiq+fcCbCujNL2BTtNYUh/mYOuhUZwqQHU14t37RRdHXtm0DLr9c1igq\nQ0yNGiX/9KrURcVUmjOlC5u6xFQRZ0oIeRzC47J1q/naZ3KmqhYUQSDDxiEHHph/W3EuEztT6ej7\n6DOfAZYulefDN79ZfPsAl0ZwRlFnisN8TB1kEVPtVLQTiA+1hVx6qfxfhpjSPz8kj5gaMUKVUhkc\nTB6t54szVSQBXV/eskWGF5cuVc/19Zmd1qrDfM88o0Td+PHAnnvm31Y4YhwAXnpJLbMzlUwQtH7W\niBHAj34kb6JMoe48cJjPEfSCsnx59gJxZYipri5gypR822I6g04t2gmYxQwNpfztb61zg/kopoSw\nD/Xpw7BNN1tZxRRNql6+vLXOnusEdCD6fe+8E9hlF/l39dXyOZM4BqoXFH/7m1o+6KD8ZRGAVpcp\n7GNsnSnfxFRVzpR+Q1jWjCAsphwxZoy6MA0ORue6sqFo9XOgVUzttBNPJcMk08lhPtrRhBx/vHp+\nzRoZsq9STOWdRNk2KVvPzzCNLsvahvHj1U3btm2tQsZ1zhQQ/b7/8R/qPH372+V/U4gPqF5Q0BDf\nG95QbFs9Peawna0z5VuYrypnqqprGIsphxQJ9ZXhTHHyOZNGp5ZGAMxiZuedo3ktd9xRrZjKG5a3\nzZvSj6cpITqPO5Z07XOdMwVEv69enHNoKCroqDtfdZjPpZgCot8lDPU1NcxXlTPlWKzEwmLKIUVq\nTZUhpjhfikmjk50pk5iZN0+GY0LuvLNaMZU3f8NWTOkXbpOYyuOOVS2m6HHQp8y6//6oM/WqV6nl\nKgXF5s1yGhtAhmJf//ri2zQJwywJ6ElT91RNuzlTeetMOa6A3h6ZrUVqTbnITdFPSM6XYtLo1NII\nQLyYojclP/hB9HWXYsr0+5wxI9+2bMN8+vGcNk1eN5IKP9pAw4VZxVTRMJ/uNl13XVRQ7rmncoiq\nFFOrVqn8sTlzoqHNvNAk9PB76wUpKb29Umj39UnHbtOmqBtdJ3XkTHGYryG4CvPlzZnSi/2ZRvcx\nDIWdqSjz5iUPX2+6M6XfBXd1Ra9bQHFnilb8BtJzppI6yzj3gn5fOrINkGKKhvl0Z6oqd4bu6zyh\nUxNZw3yAv+URqnKm6gjzNbE0ghDi50KIVUKIB8hzk4UQ1wghlgghrhZCOLglsIBa5pdeCtx1l/26\nZXRaLKaYNDpZTOkJ6KNHyzzDHXYAdtvNvE7ZYqpsZ8pUINC1mEpypsJOM0+Yj3a49PvqYuqWW6KV\n2OfOVdfCoaH4iW1dU0YnnjXMB/ibN5W0f8aOVebAli3pk3Mn4bMzFQTOi3YWcabOB/A27bnTAVwb\nBMHuAK4H8MUC27eHXlA2bpQx8v/5H7t1yxiCzmKKSaOTxZQuZubNUxfwj3zEvI6vYipvAjrQmjdV\nVEzpKQ5FcqbihAI9DvTaCcjOl97ITptWj6Ao47djqjWVFqJtojMlRPQcKSKAq7qGjR6trh9Jlfkp\nQ0PKKe3uLlY6Yzu5xVQQBLcC0H8d7wFwwfblCwC8N+/2M2EaZnzttXbrllEckV5gGcYEF+1U0N/v\nl78M3HefTGR+4xvV87vuWt7nA27CfLbOVChsXDhT+gwQVOAUyZmyCfOZWL1aLU+f3jqxdBWU0Yn7\n6kytWSOLYNKpc9JIc+5chfqqElNCRA0MGwHo2JUC3OdMTQ2CYBUABEGwEkA1w9pGjQLe/Oboc7Y/\nXFcHnHaOBx+cfztMZ5BWGqGTinZSMSUEsO++0tX44x+luLrgAvlcWZ8PuAnzFXWm8uT3jB+v2j4w\nEA310c6+SM5UXAJ6GnPmREVIuzhTtmKqCmfqox8FPvUpYNEie+GT1m5XYqqqnCkg+3lWQtvKvuVN\nzDg844wzXlletGgRFi1alP+T/vAH4KtfBb77XflYj+fH4arT+vOf5bxDhxzSKuwYRqe3V55vAwPy\nr78/+qNu5zCfnjOlOzQhkycDZ5/t/vN1MTViRP4RuHlLIwBuwnwAsPvuwIoVcnnJEuXimT5Tn7Q3\nCFoH0AD5namQadOk8GqXMF+eBPSyv3sQyBsOQDpUf/oT8MEPJq8zNBQNg5lc76Y5U4A8PsuXy+WX\nX06f2NpSTN1444248cYbrZrgWkytEkJMC4JglRBiOoAXk95MxVRhRo8GPvSh7GLKVTjlkEOAe+/N\nvz7TeYwbpy6yGzdG7347SUzNnVvt5+tiavp0s6CwIW9pBKBVROYdebb77kB4wV+yBHjnO+WyqcPo\n7VUTPQ8NSQFoyvGMEwrUgU8idBvrCPP5koBetjOlb/OBB9LFlD4owXTeN1VMhSSdZ+vXy/Pd8hzR\nTZ4zzzwz9r1Fw3xi+1/IHwCcuH35BABXFtx+NkxWbBrt3GkxfpOUN9XO56X+faquy2YSU3nJWxoB\naJ3sOO/Ald13V8uPP66W4zoMm86yqJgKhWInh/nKdqb00Zu33Za+jo2IaHqYL67v/3//Twrc/faL\nfi9HbStSGuG3AG4HsFAIsVwIcRKAbwF4ixBiCYDDtz+uDpMVm0Y756YwftOpYkrn1a+u9vP0cBrt\nPLJSpDSCELI209FHA1ddld8Zp2JqyRLzZ9IOg+ZNxTkmcaURijhTTRZTecJ8VCzHTQBdBL2u2F13\nRdtkIq3NQHs6U08+CZxyilx+6CHgL39Rr9WdMxUEwYdiXqovYWjsWLlj+vvlxaCvLz3G30mdFuMX\nnSym/vIX4KyzgPe/XyYq10neXCWgWGkEAPjHf5R/RcgqpqZOBZ54Qi6vWgXssUfrNpsc5ivjtzNp\nkhS/QSBDRYOD6cKEhq/DfB6X6GJq2zbg7rujo2B1TLXHdNpNTAWBTNKn0BIedTtTXiJE9lBfOw9B\nZ/yGJgPrw9TbXUy9/e0yLPGZz9TdkmJVsouURnDF/PlqmytWqA4w7jPpyMUwcV0nbjRfVjHVLmG+\n7u5oDtTateliit4kVCGmAODWW5PXMY3w1Gm3MN999wHXXBN9bvFitcxiKoasob5277QYf6ETxdIJ\nYgE+L6ukiDNVpDSCK7q7o3W4wrypvGIqCOKFgml+ua4uYPbs6HNhzlTdzpTLTlzvsNPE1KxZKsF7\nxYpi1cRNmMRU2iCorM5UUi2yNHxxpkxTzNECtyymYjBVqk2Cc6aYukjq1FhMlctrXqOWw9FveaDO\nVNJUFmV18CELF6rlxx6T/+PEFE24N4mpwcH46tAmZ2r06FYxZXKm6hjN5/K3o0c90sTUyJFqXw8P\nAy+84K4tgFlM0aKpJmycKVejEH0RU2lTzLCYiiHrj5fDfExdJIkpKvL5vHTPb34DvOENwMc/Dhx7\nbP7tpBVfDSmrgw/ZZx+1HIY0bJwp3REFkkWCSUyNGdP6ncL9QpOwbQcFFaWsTlyPetgkc5cZ6jM5\nLmn72MaZympIxFGlmEpK70mb/JjFVAxZTwR2AJi6YGeqPvbeW+ZsnXeeDFPlxTYkUrYz9e53q+U/\n/lF2mnnDfFnF1OjRrfP0hdDr8Zo15ve4pqzfDv0uq1fbiSmahE4ngS7K5s3m/i2tz7NxplyJKV9y\nptKcKU+nk6kfDvMxTYHFVPOhZQaSxFTZztR++6nQ2rp1wA035A/zxZVFAOLFFM3/o4wfr5zVvr7o\ntsuirN8O/Y6rV0e/S1zeXVkj+miIjx7PTnWmksQUdaZMx4mdqRj0nTo8DNx+e/yoBA7zMXURJ6aC\nIH3KB8YPbMVU2R2LEMBRR6nHl13mJsyndz5jxrRWzR4zRpa5CJ//8Y+j7XLVOdtSlgs4lUw1u3Kl\n3eeUFeajYmrPPdXnhyWB4sjqTBVxE6sUU/pAh4DMZEedKVPZCBZTMeg/3M9/Xu7AvfeOXlxC2AFg\n6iJOTOkCP+9UJ0z56GIqiJmOtOwwHxAVU1deGd9x7rSTSirXE6mB5PBVV1drpfbRo2Xh1XvukfXD\nPv7x6OtVi6myXEDqTNGQXW9v/G+0rDAfHY02b579PrZxpqghsW5d9MYuC1WG+UaPViNr+/ujAoou\n04EnjtvW/mLq+9+Xy8uWAdde2/p+DvMxdTFxorrz37xZzs8HsMBvEqNGqYvxwEB8GKvsMB8gE+rD\n69+qVbK+TgjtMLq6kstypOUC6aG+cETjfvvJ+mF09B9QfRJ6Wb8f6kxRYZRUp8xVmG/JEuAb31DH\n9NFH1WsLF9qLKRtnqqdH3SQEQf76YFVfx+JCfTTMt9tureuxmIohqc6Uqaged1xMXQhhdqf4gZvH\nxwAAIABJREFUnGwWNkUOq3CmuroAMilr5Hqnf2ZS3lRWMUVrbZmoOgm9ipwpKoySxBQN8+V1poIA\nOOII4CtfAfbfHzjtNOCRR9Tre+6ZT0wltduFAPZFTFFnaurUqJsMsJiKhZ5UdNJPwJzVzzlTTJ2w\nmGo+NnlTVThTQPzUNHrHmZQ3ldeZiqPOnKmynCkqQJNEyU47qdfXrgWuuCL7527cqKb/GR4GzjkH\nuP569foee+QL8yWJCBfHrMowH2DnTI0dC8ycGV2PxVQMSXdBpoJmHOZj6oTFVPOxEVNVHdPDDjM/\nr3cYSSNJy3Sm2iUBnZIkprq6otXpjzwS+OlPs31ukpvX2wssWODemXJxzHx0psaNYzFlDd2hOiYx\nxR0XUyemTo0LdjaLrM5UmXfpr3pVNIQX95lJYiqpNALQOqWMb2KqLBdw5MjWEFH4fBL/+Z/RfXDh\nhdk+N0lMLVworxE+OlM+iil2pjIwcmTraJOQNDHFHRdTNabcFRb4zcInZ0oIszuVlDOVFOYz1eXJ\nGuZrlwR0wFxPK01MvfWt0Yl177kn2wi5pCli9thD/rfdx3U5Uxzmayj0rovy4outz3GYj6kTDvM1\nHyqm6kxADznyyNbn9M+kHcrSpdHXOAE9HlOoL01MATIUN2uWXN68OToaL42kfRaKKR+dqaryBEPi\n2qyH+XR9wBXQE9hzT/PzHOZjfIPFVPOxmVKmyo7lfe9rLU6od5x0Lr/Fi2VicwgnoMeTx5kKOeAA\ntXzXXfafScUUPdcAs5hKEl95nKm8Arjq61hcm9mZKsBee5mf5zAf4xuzZ6vlsKoxi6lmkTXMV7Yz\n1dUF/PCH6vGYMa0d5/z5cqQZIN20JUvUa5yAHk9eZwoADjxQLWcRU7TfOvro6GtlOVNNLI1Aj00Y\nhQoCzpkqRF4xxR0XUzU776yWn3lGhp35nGwWPpVGCNlnHzmJ86tfLYfS6zeKQgAHHaQe33mnWk4T\nU1kT0KvOmSpzX5ucqbh5+XTyiinqshx4oDymgBQPr3qVXPZxNF/VpRHosVm1Sv7fulW5rr298nxg\nMZWBODG1cWNrhWLOmWLqZOxYFeobHJTFAFlMNQufEtApH/848OCDwKc+ZX799a9Xy3fcoZZdh/km\nT1bTraxbF73mloGPOVOALLgZ8sAD5iLSJqiY2mkn4JJLZAHPP/1JfbaPOVNVn/MmMaW7UkBrzpSj\niFR7iqlQrZvQ3SkO8zF1Q+vQPPUUi6mm4VsCui1UTMU5Uzaj+dKcqe5uYNIkuVxkehJbfM2ZmjgR\n2H13uTw0BDz0kN16tM/acUe5jbPOioozW8FKxb5+HClNFFN6mE8P8YXfVz+nkyYoz0B7iqlRo1qt\n6JAkMcUdF1MHVEw9+SSfk03DtwR0Ww44QHXADz4I9PXJ5bQ6U1mdKaDavClfnSlA5TgB8sbJBupM\n0ZAphQpWIF6w0jIYpnpkIfrxipvAO4mqw3xjxqhzs79f/hb15HMTjkaYtqeYAqRSN6GLKQ7zMXWz\nyy5q+cknuWhn0/AtAd2WiRNV5z40pPJ4XCegA/WJKdf7uogzBURzJPOIqXDQgAmbfUzFVFwJIUAK\nk9DB6e83T8WWRh03hXqoz+RM6ZgKseaAxRS7AEzdsDPVbHxMQLfl4IPV8s03y/9ZE9BtnKkqk9DL\n3NcLFrQ6HFnEFL1xshFTg4OqAKUQ8f0aEBUSL7xgfg+tdp/kTAHRYxa3vSR8EFNxztRvfiP/T50a\nn1OYkfYVU2edZX6ec6YY32Ax1Wx8TUC34U1vUss33ST/t5Mz5Xpf9/QA//AP0efyiim9WKoJWsl7\n8uTkPmrePLUcllmhbNmizs+enuSp14Bovcb77ktvq04dbiwNw+rOFBVTH/6wHOzz9NOttbty0r5i\n6l3vAr79beDTnwZOPVU9z2E+xjf0C6yvLgZjxqe5+bJCxdTf/ibbWbaYKrsKetnC9ZBDoo/LDPPZ\nhvgAWTss5JlnWl/X86W6Urr/171OLV9/vZyg+YEHkteh1HEdo87Uiy8mh/nmzLFzVS1pXytGCODf\n/k0u//jH6nl9Shlf7xiZzmHSJGmpr1kjk3/phZDPSf8ZO1Z2TMPD8u5/YKD1uPl6nZk1S4r5p56S\nbV+82P3cfED7OFNANDQKZBNT8+apc+X55+XvPalOlU3yeQgVUyZnyjb5PISOFvzpT+X/ceOko5MU\nbgzxIcxHj01cAroj2teZolDrj8N8jI/QUB+dt8unjpcxI0Q0VGAqj+BjAnqIHuprpzBfGfualpQA\n1ChIG3p7gblz5XIQyDBTEnpZhCRomM/kTNF8qaTk8xAqpkI2bQLuvjt9XaAeMZUU5ksqBeGAzhBT\npmJeIRzmY3yA2v9PPKGW+ZxsBmmhPp9Dt1RM3XpremkE3YmyEVPtkoAOtDocJuGSBP2tp+VN5Q3z\nuXCm5swxf6ZtMnodoW09zGdTGsERnSGm6IlDTyjAX/ud6SzoHH30bpXPyWaQJKaCwO+btv32U8tP\nPpnuTHV1AUccIZcPP9xvZ6qsff2lL6nlk07Ktm6WEX1Zwnyh4wUAzz7bWrgzq5gSIpo3FbJ8efq6\ngB9hvgqdqc6Ia9EdvHKlvLiFxeo4zMf4wJw5apnm9fnW8TJmaEf33HPAvvvGX2PC532BhoeWL48K\nn7h8oEsvleEeU2drop0S0AHg3/9dlojYcUfgsMOyrZtFTK1bp5bT8pRGjZIiaeVKWTfs+eejxzZr\nmA+IXpdCnn3Wbl3fwnzsTDlg3Di1I7dti945+nzHyHQO1JmisMBvBuHkswBw7rmyE3rb22Sow3f3\ne8IE1VFv2xZ1HuLE1IgRskSAbfim3ZypUaOA00+X8x+mjYrToWG+J59Mfi8Nudo4gEnlEbI6UwBw\n7LGtz9k6U3WH+ZLqTJVAZ4gpwBzqCwKp4EO6u6ttE8OExIkpHztfphUaKrvuOukKXHONnJTW5+Tz\nENoJ05yYLCPVknAxPYktvu/vcH4+AHjsseT3UjGVNOovxFQeYXhYTop89dXqNVsxddhhwH/9V9R9\n8znMN3GiOuabN0ddfk5Ad4RJTOkH2zf7nekcWEw1m9e+1vz8X//qd/J5CBVTFJsO3IZRo5QzMDgI\nbNzoZrs6w8P+3yAvXKjcrKefTh4NmFVMmZypb39b1l2k/Z1tmE8IWavxkkvUc88+my6Gg6AeMSVE\ntK9fskQtszPlCLqDwxF9nC/F+MLUqeYLjq+dLxNljz3MLs6110ZDDTahmjqIE1OunCmgmlBfE26Q\nR41SeVNBEO3wdVw4U3/4Q+v7bJ2pkMmT1SjOTZuiuVwmdEGbNRRaBJqTRkdGszPlCJMzxflSjC90\ndckCijp8XjaDESOieVMhzz0H3HCDemzrCFRN1WKqrCR03/PTQvbaSy0//HD8+1w4U6a6UFkdRyFa\nRwsmUacbu3Ch+Xl2phxhE+ZjmDoxhfr4vGwONG+K8qtfqWWTYPaBdnWmfIXOe/fII/Hvyyqm6Pm1\nYoWsaq+XSNDnFrSFiqm0vKk6j8Nuu5mfT5uLsCAspkI4zMfUjWkYss8dAhNlwQLz8zffrJZnzqym\nLVmh4SGKSzFVReFO35PPQ6gzlSSmtmxRyzZiijqfK1a0FgU94QTgJz+xa6MOvT5lEVNVHweTMzVu\nXDT8VwKdLaY4zMf4BDtTzeZd71LLceKEnSkJO1Nq2WWYb8cdlTGwdi3w4IPqtbe+FfjlL4HXvCZT\nU1+hyWG+172u9LytzhFTeuFOoDk/PKYzMIkpX50MppW995Y1pj7wAZn0a7p4+3o8d9ihdZqYESPc\ndkBViKkmjJwEZHmEcN8uXRp1oChZxVRXV9Q4uPVWtUzn/8xDXmeq6uOwYEHreWuaZ9AxnSOmOMzH\n+I4upnbdFXjHO+ppC5OPU08Ffvc7efdvcqd8daaEaHWnXLpSACegU0aPVsU7h4fjR/RlFVNANNR3\nyy1quaiYSptImVLncejtbQ2521bqL0DniClaZv7FF+XQTXo34PrCwTBZ0XOmzj7b7w6BScbUefnq\nTAHRytyA+2tiWs7U0BDw858DF1yQv6hnU8QUIMtphNAh/JSiYuqBB9RyUTFFz4+0aXDqqH5O0ZPQ\n2ZlyyMiRKpt/eFj+mOlcRVnrbjCMa/bYQ07tAQAHHAAcc0y97WGKYeq8fHWmAODII6OPXXeCaWG+\nCy6Q07OceKJczkNTEtABuzn6ioopioswXyhQ9aladOoWtfq+Kjn5HOgkMQVEBdOKFbIGTIjPFzmm\nMxg3TtYk+s535PQPVRa6Y9yjd15jxiix7CMf/GD0Mb3ZdAEVU6tXt75+9tlq+aST8n1G3Z14FmgH\nHzdHXx4xZXI/hYgfbWpLd3c0dK2PFKTUfRz0kHUF19LOulpTwbR8uZw/KyRuOg+GqZLXvhb4wheA\nnXaquyVMUXQxNWuWnxW5Q8aMkSO+yoJ28vTaC8iwHr25BeJDX0k0JQEdyO5M/f/27jxcjqrM4/j3\nzQJkISFIBAlZZJJAWBISSEBhTFBABkfgcWcRBQYcgSjIMIo4I7iAoDOyiKPIoizK8AgIIjBGIOyB\nkI0QEhLMZZNFFllkieRy5o9zyjrdt/re7lu91b2/z/Pc51ZXVVe9fbqq661zTlVVe/f8rJqpsWPr\n82igamKG1tcQnnBC2kx90UVNWWX/SqbiH7dHH1XNlIg0Tnky1c79pRJnn51ejHPAAfVd9uabp8t+\n4YXSPqtPPll6AAa4/PLa19HqGpFa9JSYrF+fPpZl4MDqL5LKSqbi+1rlUW0y1eqkdvx4X3O2dGnv\nazlrpGQqoZopEamn8g7dRahtnDLFNzGffDL86Ef1XfbAgaUnrfHv74IFXee//PLaO6IXKZmaMCFt\nfnrqKVi3rnR6b5r4IDtpnzOn1uiy9aZmqlXfw5ZbwrRpTasN7t/JVFzVrJopEamn8qvh4rP1drbP\nPnD66Y05wYyvWI1v/JiVTK1dC0uW1Lb8Vjcv1WKDDdLycA46Okqn9zaZyqqZ2nPP2uPLUpRmvhbo\n38mUaqZEpFl6e7l/X1Ipmbr33nR4k03S4auvrm357VAjUovukpPeJlPxbYDA97Wq9NzIWhWlma8F\n+lcytfXWaZXf2rXw8st+ePDg0nugiIjUw6mnpsNf+1rLwmgbWcnUunWweHE6/qyz0uFrrqlt+UU7\niDcimRo4sPT15Mn1uyl13HT9+ONd+7klipbU1kH/SqY22ii7BmrMGF2GLiL1d+KJPjm44gp4//tb\nHU3rZSVT99+fJkGTJsEhh6SPtlm1qvsHAZcr2kG8EclUuWnTev/eckOGpH2yOjsrP1amaN9DHfS/\nDKJoN9ITkeIaPhxOOgkOPrjVkbSHrGTq9tvTcR/4gE+k9tsvHXfdddUvv2gH8e7uNZUnmTr2WP9/\n8GD4xjd6F1slccyrV2fP0+o7oLeAkilQfykRkWbIelhunEzNnu3/x8nUffdVv/yidXyOj0fl99XK\nk0ydfjqcey7Mm9f10Sp57bBDOrx8efY8RUtq66D/Pd1XNVMiIq1RXjP19ttwzz3puCSZip+lFven\n6knRDuKTJ/t+vM75Zr5169KrQPMkUyNGwNy59YszNnVqOhw/+y9WtO+hDlQzBaqZEhFphs02SxOD\nV1/1j0964w3/evx4GDfOD0+Zks735JPZj5/JUrQO6EOHpo8+eeed0tqpevWZqrdak6ki1BDWQf9L\npnbdteuVDaqZEhFpPLPSk9crrkiHk1op8L/RccfpamunilgjMmVKOrxyZTrcrslU3My3cmX2/dOK\nltTWQf9LpsaM8WdD73uffz16NOy9d2tjEhHpL+KmvhtvTIfLr3acMSMdVjLVvHh6MmJE+tDk9ev9\nFZflivg95NT/kimAPfaAu+/2bdRr1sCoUa2OSESkf0ia8sA/oy9R3gUjbzJVlOaloiVT0HNTXxG/\nh5z6ZzIFvrp5661h5MhWRyIi0n9k9VuFtLYjESdTixZVt+wi1ogUPZnKuqJPzXwiIiINlJVMDRhQ\n2vwHsP326YG4owNeeaXnZRfxIB4nU4884m+GCcVJppYt6zq9iEltTkqmRESkebKSqbFjux50N9yw\ndN7ungW3Zo1/PFgRD+Kbbpo+T++tt/xjWpLhRLslU3En9BUruk5/5pl0eMSIxsfTBpRMiYhI88R3\n0E5MmJA9b/wsuLVrs+c54wx/v6Ydd4Q//SkdX5RkCkprp5LH57RzMjVxYtoX6qmnutYaLl2aDse1\nWH2YkikREWmeUaN8bUysvL9UIk6mOjq6Tl+8GL7+dT/81FNw1VXptCJ1fM66q3g7J1ODBsE226Sv\ny/t6JQmhWX2fDdjGlEyJiEhzlTf1VZNMlddMdXbCkUdWXkeRaqbihCOp1WnnZApgu+3S4bip76GH\n0n5fEyfCxhs3N64WUTIlIiLNVY9k6t57S5uTyhUpmdppp3Q46dDd7snU9tunw3EytWRJOhx/rj5O\nyZSIiDRXPZKpuGkpy5AhtcfVKjvs4K9oBFi9Gl5/vVjJVNKsB6UJ7vTpzYunxZRMiYhIc5UnU5U6\noMdJ1mOPpc1HAI8+mg7HD0YG/6SL+PE07W7IkLQPknO+31S7J1OVmvnimiklUyIiIg2yxRalr7fc\nMnu+YcPS2wasX+87mSfiZGruXPjoR/2Dg485xt+VO3lfUcT9ppYta/9kKuuKvs7O0vtOqZlPRESk\nQWbNguHD/fDuu6dNXFkqXdEXJ1PbbgvXXw+vvQbnn9/1asEiiBOPpUvbP5kqv6Lv4Yf9TUffeMO/\n3mKLrklzH6ZkSkREmmvkSLj2Wjj+eLjwwu7nzeo35VxpMpU0G3aXlLW78k7o7Z5MQektHZYtg/vv\nT1/PmtX8eFpoUKMWbGaPAa8A7wBvO+f6V8mKiEhle+3l/3qSlUw9+2xaA5J136oiipv5HnzQ34g0\n0a7J1M47w69+5YcfeMDftT4xc2ZrYmqRhiVT+CRqjnPuLw1ch4iI9GXxHdNXr/b/s2qlim6LLWDz\nzeG55/zVfHGn7na9MjHu+L9wYemNUvtZzVQj60StwcsXEZG+bttt0+Hkdgh9MZmC0tqp+KHN7Voz\nNX26v8s5+Nq0Bx5Ip5VfYdnHNTLZccA8M1toZkc1cD0iItJXxc+tW73aX9XXV5OpSle/tWsyNWJE\naSf0xMSJfaPptQaNbObb3Tn3jJmNxidVK51zd8UznHrqqX8fnjNnDnPmzGlgOCIiUjgjR/pbJzz9\ntK+tWbtWyVQ7mTkTVq0qHddHmvjmz5/P/Pnzq5q3YcmUc+6Z8P95M7sWmAVUTKZEREQyTZnikynw\nTX1xf6K+lExVeihwOydTu+wCl11WOq6PJFPllTynnXZaxXkb0sxnZkPNbHgYHgbsAzzUiHWJiEgf\nF99t++6702Rq0KDKCUgRTZ6cnTjFV8m1m/K+UVttBYcd1ppYWqhRfaY2B+4ysyXAAuC3zrnfN2hd\nIiLSl8X9pi66KB2eMcPfJb2vGDSo9N5N4O+dNaiRPXJymjkTpk71w3vu6a/qGzWqtTG1QEO+Iedc\nB9B/7iMvIiKNE9dMvfRSOrzHHs2PpdF22aX0qrihQ1sXSzUGD/YJ1GOPwaRJ6dV9/YxuXSAiIu0t\nrpmK9cVk6sQTS2/YGd9nq11tsIGPuZ8mUgDmnGvNis1cq9YtIiIF4hyMHg0vvlg6/tln/Y0u+5r1\n6+HKK/3jWY46CnbcsdURCWBmOOcyM0YlUyIi0v6OPx7OOSd9PWlSekd0kSboLplSM5+IiLS/M8+E\nww9PX++/f+tiESmjmikRESkG5+C666Cjwzd/DR/e6oikH1Ezn4iIiEgOauYTERERaRAlUyIiIiI5\nKJkSERERyUHJlIiIiEgOSqZEREREclAyJSIiIpKDkikRERGRHJRMiYiIiOSgZEpEREQkByVTIiIi\nIjkomRIRERHJQcmUiIiISA5KpkRERERyUDIlIiIikoOSKREREZEclEyJiIiI5KBkSkRERCQHJVMi\nIiIiOSiZEhEREclByZSIiIhIDkqmRERERHJQMiUiIiKSg5IpERERkRyUTImIiIjkoGRKREREJAcl\nUyIiIiI5KJkSERERyUHJlIiIiEgOSqZEREREclAyJSIiIpKDkikRERGRHJRMiYiIiOSgZEpEREQk\nByVTIiIiIjkomRIRERHJQcmUiIiISA5KpkRERERyUDIlIiIikoOSKREREZEclEyJiIiI5KBkSkRE\nRCQHJVMiIiIiOSiZEhEREclByZSIiIhIDkqmRERERHJQMiUiIiKSg5IpERERkRyUTImIiIjkoGRK\nREREJAclUyIiIiI5KJkSERERyUHJlIiIiEgOSqZEREREclAyJSIiIpKDkikRERGRHJRMiYiIiOSg\nZEpEREQkByVTIiIiIjkomRIRERHJQcmUiIiISA5KpkRERERyUDIlIiIikoOSKREREZEcGpJMmdm+\nZrbKzFab2VcbsY5Wmz9/fqtD6JWixg3Fjh2KFX+RYi1X1NiLGneiqPEXNW4oduxQ/PhjdU+mzGwA\n8CPgw8D2wEFmtm2919NqRd0Iiho3FDt2KFb8RYq1XFFjL2rciaLGX9S4odixQ/HjjzWiZmoWsMY5\n97hz7m3gSuCABqxHREREpOUakUyNAZ6MXj8VxomIiIj0Oeacq+8CzT4OfNg5d3R4fSgwyzn3pbL5\n6rtiERERkQZyzlnW+EENWNefgHHR663CuKoCEhERESmSRjTzLQQmmtl4M9sA+AxwfQPWIyIiItJy\nda+Zcs51mtlxwO/xydpFzrmV9V6PiIiISDuoe58pERERkf6kx2Y+MxtjZr8JN+BcY2Y/NLNua7TM\n7MtmtlGFaceG5XSa2abR+E3M7BozW2ZmC8xsu2jaCWb2kJk9aGZXhOZDzOwTYXynmc3IWNc7Zvb9\n6PWJZvafPX3mnpjZP5rZIjN728w+VjbtTDNbHmL9VDT+Q+E9S8zsDjPbOozfxszuMbO3zOwrYVyn\nmS0On22JmX3FzHL3MWt03Bnre63NYj44bF/LzOwuM9sxY31J2S8J/8eVzxPNO9vMflvFZ6jnNn+W\nma00s6Vm5szsV9F7BprZ82aWq1ndzC4ys+fM7MGy8VPDd77MzK4zs+Fh/CAz+3mIdYWZfS2MH2Jm\nN4R4l5vZ6RnrOjDsp5PzxByWlfl7YGaDzeziEN8SM5sdTTsojF9qZjcm308o/xVh/DwzGxvivDR6\nb13KOyxrUzO71cxeM7Nzy6Z9OpT5cjM7Ixo/NrxncYjzn8L4aeF7Wh7GfypjffXYN5sacy3xm9lt\nln1MaNS+eLWZjegmnrpt59Eym1L+ZnZK+PzLwvtmFiX2pnLOdfsH3AccFoYNuBA4q4f3dACbVpg2\nDd9BfW08D3AW8B9heBvgD2F4yzDvBuH1/0bxbANMAm4FZmSs603gj8l6gBOB/+zpM1dRJuOAHYCf\nAx+Lxu8H/F8op6HA/cDwMO0RYHIY/iJwcRgeDewMfBv4Shj3arTMzYB5wKntHnfG+l5ts5h3A0aG\n4X2BBXliBmYD11cxXz23+b2AAWF4HfAssGH0mRZXE1MUw8CMcXsAOwEPlo2/H9gjDH8e+FYYPgj4\nZRgegt//x4Xh2WH8IOAO/JW+8TKvBG4HvtmLbWNA2evM3wPgGHx3g2S7fSD57MBzwKjw+kzC70P4\nbjcKw/8a4nwtlG+vy7ubzzIUeD9wNHBuNH5T4HHS37BLgD3D8E+BL4ThKUBHGJ4E/EMYfg/wNDCi\nAftmU2OuJX7gNrKPCY3aF78HnNFNPL3ezltZ/vjfzLuBQdGytyhC7PUq52r/uq2ZMrMPAm865y4F\ncD7aE4AjzGwjMxtgZt+PMsJjzWxu2ABvM7NbypfpnFvmnHsCfxCMbYf/EcQ59wgwwcxGh2kDgWHm\na8SGhsLCOfeIc25NxrIS64ELgC41J+Y7yN8SnXluZWYjzOyxaJ6hZvaEmQ0s+wxPOOceAsrbSLcD\n7nDeG8CD+B9cgHeAkWF4ZPQZnnfOLQqxduGcewG/wR0XYhoQzojuC7EfFcX7VUvPvrvUAjQz7iim\noWb2BzN7IJxt7B/Gjzezh83sgnDWc7OZbdjgmBc4514J4xeQff+zLttSd2UOjDRf+7LKzH6cVQZ1\n3ub/4Jx7J/qcfwY+El4fBMQ1VTPDGdsi8zVxk8L4z5mvWboF+ENGvHcBf8n4KJPCNML7Pp68JcQ6\nMMS6Dn+we9M5d3tY5np84rFVFN8wYHfgyBB7Mn62md2eVa7hTPYHZrYE/0Mfx13p9yAu5+eBl81s\nl2i+jc3M8AePpJxvd869FabH28qN1F7et5vZ1Gi+O62sVtQ594Zz7p5QdrGtgdXOuZfC61soLfek\nNmQTwlXTzrk1zrk/huFn8NvIaEqZldWsmtl5ZnZYGO4ws1PDZ1lmGTUqLYi56vgraeC+uIBo2y4L\ntLvtvFL572e+1muhmZ1jGTXgTSr/9wAvhP0X59xLzrlnQ4wzzGx+iPEmM9s8jL/NzM4Ox6EHLaMm\nq8XbTkP01My3PbAoHuGcew2fOU7EH+THA1OdczsBVzjnzsN/yDnOuQ/VEMsy4GMAZjYLf/awlXPu\naeC/gCfCcl92znU5AFTggPOBQ8xs47Jp5wGXhLh/CZznnHsViJsB/hm42TnXWcNn2Nd888ZmwJ7A\n2DDtKOAmM3sCOBR/JlPdh3CuAxgQdu4j8WWwK/5u80eHxGRf4KPATOfcdPyZVrUaEnfwFnCgc24X\n4IP47zIxEV/uOwCvkO40zYj5X4CbMsYPsbSZ7+owLrPMw7SZwLH4M6WJVtYUWcVnyLPNDwIuxz+y\naUNgKr4mObESX5O0M/BN4Ixo2nR8Td+eNcS7IkmGgU+RHjx+DbwBPAM8BvzAOfdy/EYz2wS/fcYn\nWAfg969HgRfMbHo0rVK5DgPudc5NDz/G1VgG7G++We69+BrVseEAcQywHH9z4SnARRnc7H0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b4866def850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tp_start = dt.datetime(1981,10,1)\n",
    "tp_end = dt.datetime(1982,10,1)\n",
    "date_range = [tp_start + dt.timedelta(days=i) for i in range(0, (tp_end-tp_start).days)]\n",
    "\n",
    "lw = 3.0\n",
    "lt = 10\n",
    "rt = 7\n",
    "\n",
    "fig = plt.figure(figsize=(lt,rt))\n",
    "\n",
    "lon_ind = np.argmin(np.abs(ds.lon - lon))\n",
    "lat_ind = np.argmin(np.abs(ds.lat - lat))\n",
    "\n",
    "dfm = ds_full.isel_points(lon=[lon_ind_1],lat=[lat_ind_1])\n",
    "dfm_sel = dfm.sel(day=slice('1981-10-01', '1982-9-30'))\n",
    "# dfm_sel['dead_fuel_moistue_100hr'].plot(label='UI MetData DFM',color='b',linewidth=lw)\n",
    "\n",
    "# plt.plot(date_range,dfm_john,label='DFM from John',color='g',linewidth=lw)\n",
    "\n",
    "plt.plot(date_range,fm100[0,:][(dates_ui >= tp_start) * (dates_ui < tp_end)],linewidth=lw,color='r',\n",
    "         label='DFM')\n",
    "\n",
    "plt.title('100-hr DFM')\n",
    "plt.legend()\n",
    "\n",
    "## save plot\n",
    "direc = '/raid9/gergel/agg_snowpack/'\n",
    "plotname = 'feb1_2' \n",
    "savepath = os.path.join(direc, plotname)\n",
    "print (\"saving figure to '%s'\" % savepath)\n",
    "plt.savefig(savepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.99377126]\n",
      " [ 0.99377126  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(np.corrcoef(dfm_john,fm100[0,:][(dates_ui >= tp_start) * (dates_ui < tp_end)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         0.9986312]\n",
      " [ 0.9986312  1.       ]]\n",
      "saving figure to '/raid9/gergel/agg_snowpack/feb1_3'\n"
     ]
    },
    {
     "data": {
      "image/png": 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DR8C+CswZTFmb7ppeeMF+vGQJMG8e8N13+jEz2+XUqlXoVFX79vbAJV0kmplK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\n6x4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b4f06e0e250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(lt,rt))\n",
    "\n",
    "# plt.plot(date_range,dfm1000_john,label='DFM from John',color='g',linewidth=lw)\n",
    "\n",
    "plt.plot(date_range,fm1000[0,:][(dates_ui >= tp_start) * (dates_ui < tp_end)],linewidth=lw,color='r',\n",
    "         label='DFM algorithm')\n",
    "plt.title('1000-hr DFM')\n",
    "plt.legend()\n",
    "\n",
    "print(np.corrcoef(dfm1000_john,fm1000[0,:][(dates_ui >= tp_start) * (dates_ui < tp_end)]))\n",
    "\n",
    "## save plot\n",
    "direc = '/raid9/gergel/agg_snowpack/'\n",
    "plotname = 'feb1_3' \n",
    "savepath = os.path.join(direc, plotname)\n",
    "print (\"saving figure to '%s'\" % savepath)\n",
    "plt.savefig(savepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WRITE TO CSV FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = {'rhmax': rhmax,'rhmin': rhmin,'tmax':tmax, 'tmin':tmin, 'precip': pptamt_in['precipitation_amount'].values[0,:],\n",
    "     'julian_day':julians,'fm100':fm100[0,:],'fm1000':fm1000[0,:]}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv\n",
    "df_for_csv.to_csv('/raid/gergel/csv_for_john.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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