SP_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import scipy.signal as sps\n",
    "import scipy.stats as stats\n",
    "import pickle\n",
    "# from datetime import datetime as dt\n",
    "import datetime\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import seaborn as sns\n",
    "import csv\n",
    "import os\n",
    "import warnings\n",
    "import h5py as h5\n",
    "import calendar\n",
    "# %matplotlib notebook\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def toYearFraction(date):\n",
    "    '''\n",
    "    Function to convert a datetime to decimal date\n",
    "    '''\n",
    "    def sinceEpoch(date): # returns seconds since epoch\n",
    "        return calendar.timegm(date.timetuple())\n",
    "    s = sinceEpoch\n",
    "\n",
    "    year = date.year\n",
    "    startOfThisYear = datetime(year=year, month=1, day=1)\n",
    "    startOfNextYear = datetime(year=year+1, month=1, day=1)\n",
    "\n",
    "    yearElapsed = s(date) - s(startOfThisYear)\n",
    "    yearDuration = s(startOfNextYear) - s(startOfThisYear)\n",
    "    fraction = yearElapsed/yearDuration\n",
    "\n",
    "    return date.year + fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df = pd.read_pickle('SP_dailyMET_df.pkl')\n",
    "monthly_df = pd.read_pickle('SP_monthlyMET_df.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This function comes up with the number to use for each day of the year (DOY) to scale the t air by.\n",
    "I used the RACMO t2m and tskin fields for South Pole to come up with it. \n",
    "Tsnow[DOY] = tscaler(DOY)*Tair[DOY]\n",
    "'''\n",
    "def tscaler(doy):\n",
    "    a = -5.70493675e-03\n",
    "    b = 1.72479759e-02\n",
    "    c = 3.60427452e+01\n",
    "    d = -9.41404759e-03\n",
    "    return (a * np.sin(b * doy + c) + d) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rhos = 320\n",
    "### MONTHLY\n",
    "monthly_df['bdotIE'] = monthly_df.bdot*rhos/917.0\n",
    "bdIE = monthly_df['bdotIE'].values\n",
    "bdotIE_mean = monthly_df['1982':].bdotIE.mean()\n",
    "bdotIE_std = monthly_df['1982':].bdotIE.std()\n",
    "\n",
    "lower, upper = 0, bdotIE_mean+bdotIE_std*2\n",
    "mu, sigma = bdotIE_mean, bdotIE_std\n",
    "X = stats.truncnorm((lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma)\n",
    "bdot_rand = X.rvs(len(monthly_df))\n",
    "bdIE[np.isnan(bdIE)] = bdot_rand[np.isnan(bdIE)]\n",
    "monthly_df['bdotIE'] = bdIE*12\n",
    "Toffset = -51 - monthly_df.tskin.mean()\n",
    "monthly_df['tskin'] = monthly_df['tskin']+Toffset\n",
    "monthly_df = monthly_df[:'2018']\n",
    "monthly_df.index = monthly_df.index.to_period('M').to_timestamp('M')\n",
    "# monthly_df['decdate'] = (monthly_df.index+pd.Timedelta('14D')).to_series().apply(toYearFraction).values\n",
    "monthly_df['decdate'] = (monthly_df.index).to_series().apply(toYearFraction).values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09741237434050289"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df['bdotIE'].resample('a').mean().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# monthly_df['2017-01':].to_csv('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_monthly_bdot.csv',columns=['decdate','bdotIE'],header=False,index=False)\n",
    "# monthly_df['2017-01':].to_csv('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_monthly_tskin.csv',columns=['decdate','tskin'],header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1958-01-01    -9999\n",
       "1958-01-02    -9999\n",
       "1958-01-03    -9999\n",
       "1958-01-04    -9999\n",
       "1958-01-05    -9999\n",
       "              ...  \n",
       "2019-07-27        0\n",
       "2019-07-28        0\n",
       "2019-07-29        0\n",
       "2019-07-30        0\n",
       "2019-07-31        0\n",
       "Name: pcp, Length: 22492, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.pcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DAILY\n",
    "\n",
    "daily_df2 = daily_df[:'2018'][['Tavg','pcp']].copy()\n",
    "daily_df2.Tavg = (-1*daily_df2.Tavg) + 273.15\n",
    "# daily_Toffset = (273.15-51) - daily_df2.Tavg.mean()\n",
    "# daily_df2.Tavg = (daily_df2.Tavg)+daily_Toffset\n",
    "\n",
    "daily_df2['Tscaler'] = tscaler(daily_df2.index.dayofyear)\n",
    "daily_df2['Tskin']=daily_df2['Tavg']*daily_df2['Tscaler']\n",
    "dd2 = 0.85*(daily_df2.Tskin.mean()-daily_df2.Tskin)/(daily_df2.Tskin.mean())\n",
    "daily_df2['Tskin_damp'] = daily_df2.Tskin.mean()-dd2*daily_df2.Tskin.mean() # Dampen the signal\n",
    "daily_df2['Tskin_boxcar'] = daily_df2.Tskin.rolling(5).mean()\n",
    "\n",
    "\n",
    "for yy in daily_df2.index.year.unique():\n",
    "    for mm in daily_df2.index.month.unique():\n",
    "        mon_pcp = monthly_df.loc[(monthly_df.index.year==yy) & (monthly_df.index.month==mm),'bdotIE'].values[0]/12\n",
    "        dim = len(daily_df2.loc[(daily_df2.index.year==yy) & (daily_df2.index.month==mm),'pcp'])\n",
    "#         daily_df2.loc[(daily_df2.index.year==yy) & (daily_df2.index.month==mm),'pcp'] = mon_pcp/dim\n",
    "        x=np.random.normal(10,5,dim)\n",
    "        y = x/x.sum()\n",
    "        daily_df2.loc[(daily_df2.index.year==yy) & (daily_df2.index.month==mm),'pcp'] = (mon_pcp*y)*365.25\n",
    "daily_df2['decdate'] = daily_df2.index.to_series().apply(toYearFraction).values\n",
    "daily_df2.pcp = pd.to_numeric(daily_df2.pcp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1958-01-01', '1958-01-02', '1958-01-03', '1958-01-04',\n",
       "               '1958-01-05', '1958-01-06', '1958-01-07', '1958-01-08',\n",
       "               '1958-01-09', '1958-01-10',\n",
       "               ...\n",
       "               '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',\n",
       "               '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',\n",
       "               '2018-12-30', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', length=22280, freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df2['snow'] = daily_df2['pcp']*917/rhos/365.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.2203308287417"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.092*917/(daily_df2['snow'].resample('a').sum().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888833259668872"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.092*917/((daily_df2['snow'].resample('a').sum().mean())*340)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-12-31    0.904473\n",
       "2018-12-31    0.988699\n",
       "Freq: A-DEC, Name: snow, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2['2017':].snow.resample('a').sum()*(917/310)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_df2['2017':].snow.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0974123743405029"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(daily_df2['pcp']/365.25).resample('a').sum().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd4 = pd.read_pickle('TempAlter.pkl')\n",
    "daily_df2['Tskin_dd4'] = daily_df2['Tskin']*(1-dd4['mm'].rolling(5).mean())\n",
    "# daily_df2['Tskin_dd'] = daily_df2['Tskin']*(1+dd4['mm']*0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-59.256126482213546"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2[((daily_df2.index.year>1985) & ((daily_df2.index.month==6) | (daily_df2.index.month==7) | (daily_df2.index.month==8)))].Tavg.mean() - 273.15\n",
    "# daily_df2[((daily_df2.index.month==8))].Tavg.mean() - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd4.rolling(10).mean().plot()#['2017-06']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df2['Tskin_dd4_box'] = daily_df2['Tskin_dd4'].rolling(5,win_type='hamming',center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1,a1=plt.subplots(figsize=(10,6))\n",
    "daily_df2[['Tskin','Tskin_dd4','Tskin_dd4_box']]['2017':].plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df2['Tskin_dd4_box'].iloc[-2:] = daily_df2['Tskin_dd4'].iloc[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ddshift = pd.DataFrame({'tskin':daily_df2.Tskin_damp.values},index=daily_df2.index+pd.DateOffset(days=15))\n",
    "# ddshift['decdate'] = ddshift.index.to_series().apply(toYearFraction).values\n",
    "# ddshift.drop((ddshift.loc[ddshift.index > pd.to_datetime('2018-12-31')]).index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_df3 = daily_df2['2017':].copy()\n",
    "daily_df3['pcp'][daily_df3['pcp']<0]=0\n",
    "daily_df3['pcp']['2017']=daily_df3['pcp']['2017']*1.1\n",
    "daily_df3['pcp']['2018':]=daily_df3['pcp']['2018':]*2\n",
    "(daily_df3['pcp']['2017':]/365.25*917/340).cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    0.000263\n",
       "2017-01-02    0.001199\n",
       "2017-01-03    0.001632\n",
       "2017-01-04    0.002119\n",
       "2017-01-05    0.002319\n",
       "                ...   \n",
       "2018-12-27    0.971018\n",
       "2018-12-28    0.973459\n",
       "2018-12-29    0.976716\n",
       "2018-12-30    0.979219\n",
       "2018-12-31    0.979627\n",
       "Name: pcp, Length: 730, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(daily_df3['pcp']/365.25*917/340).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpc = (daily_df3['pcp']['2017':]/365.25*917/340).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f2pS899uiVjqXnbcFKQUbNgzMu/+eCo2Wy+Urge8A76hUKp8ul8sXlcvlX0ttIgAX2ANsS92/FdjX65sFMIzWIR2crON6OkdCo9Esjbg688hgGNY+Mt1IEty3joUzVNsycKLx5d7d4bLs+3ZNrsHRajSatSBeDGMoFwDVDNfs/en/eTy/+Qt5ALYbE5kk9w9/6S4+9tW7uf/RKf7n5t0L7n9RgVUul08GvgL8RqVS+Vx0twA+WC6XR8vlsgX8NvDlSqWyC2hEggzg1cA3e3mjMe32/H27p5bydI1Go0lq24wMhLmc9z0ywf6JGkNFi2I+DHPnTAMnauK6/0gsvordd6jRaI47nLZFLrHAOn3bEKcVw7+r5nCS5J5e7PW+/7iV625ZOG2plxDhVUAe+EC5nDQR/hjwXuB6wAK+VKlUPhs99krgk+VyeQi4FfhQD6+RUGu4mduP7J/hojM2LGUXGo3mBCZQCscJBdZYtFjm6mvvYahkZwSUbcmklc7+w+Fgahq6e5hGc6LQ3iorFlgA/uFdANiyleQ+V8/qk8XoJcn9LcBb5nm4o4R4pVK5A7hsSUeRYqbWegM5y+DQVP1od6XRaE5AXDdAATnboJi3+P9+6Vz+9Zv3MVN1uOSs1mQtZxlJU9f9UfjQXcGacxqNpr9ouj4GLZGVFljBVLheLy/dpCl8rEdKeZNqY/EVyH03XZtNtbY4desghya1wNJoNL0Tz0rz0XLryy/YiowyD7aOlZLtbMtIHKzpuXDc8XwtsDSaE4Wm4zMiW7Xx0gILN1zQlRdu0ipnPNIjF53ZW1St7wRWoBS/8uTTed8bLk86W2s0Gk2vxGkGhVxo0FumZEskrDIhQlPiuD7N6D/QDpZGcyLRdHwGZUpjpASW8poAFKTLdNVhcrbJ3sNVBHDJWRt72n/fCSyAkzYNsHm0yKbRAtNzjm7IqtFoemYqcqOGB1rFindEPca2RlWag9oUI7KK4wbM1lquuXawNJoTh6brkxettCTVnAv/VQF44bgwklMoBTf8fD833nOQc08d5QnnbubFTzlj0f33pcDaMBQuj9wyGi6nPjhZW2hzjUajSZiuhjPPuFErhOkGtinZOByOLdVrfp9fPfIJHM9nNpX3qR0sjebEodEhsCKt4bXuswKHs08a5ks/eIjD0w2uvGgrQgge00OYsOdCo6vJ2FA48zx1S1hZ9JEDs5yy5QSsMqrRaJbMTOJgtQTWS555DhedNtqxStBxA2ZSeZ+udrA0mhMGx2kJLDGwIcnBisODAMqt86SLt7FzzzQATzh3M0Cmpc589J2DZZmSgUJYp2bzaIFS3uTBvdNrfFQajaafqeye5O6HJ7j5vkNMVR1MQ1LMteaPAwWr6ySt6bYcrJxtaAdLozmBaLg+ecLfvxzaTDB7OKx15UYCK1dCOXWecO5mtowVed3zzsMyQ2Flm4vLp75zsEYGckmxUSEEZ2wf5qH9M2t8VBqNpl9xXJ+//sxtye1zTh5huGT31FPQ9fwkpDg2mMPz1SLP0Gg065nZWjgBK+RMmq5P0QhXCJpnXEbzx58mGH8YzNDkkcVhgsl95CzJe3/78sx+cvY6dLBGU7Y+wBnbh9g3Xk2WSab5n5sf5a+u+RnjeqWhRnPCsvvgXOb2zkenuKhwgNlPvJagNtWxfboas4XPkZkmduSc6yR3jeb4QynFD27fy4GJGm/50I/52FfvBsJVhEXDA9PGOvMykCbuQzclDpYojoQ7cJsd+7TN9SiwogT3mDO3D6EIK7q3c0vlEA/smea/r39kdQ5Oo9H0HQ/tC1MI3vmaS4Gw2/zjCAdQ/8DOzid4rZyrvHA5Mt1gsGhhGlKHCDWa45BHDszy6W9V+ONP/BQgSTuKBZawCohcCTl2EsHEHlQ0RsQCSzmdJo5pCBYzyftOYD3zcSdlbp++fQiga5jQihJWda0sjebE5aH9M4wN5Th921Byn5GL6l1FxQLTpIsJFoTD4ek6g0Uby5Q8sHeaP/zIjzMul0ajWd+kVwoDjEVGTtP1KUoP7LBigRzdTjC5L3Gs5AICSwiR5GPNR98JrJHBXOZ2KW+xdazIg3s7BVZcHPDItBZYGs2JyqHJOts3lpCp6eTwaCi2kmXXEcH0Aaqf+cPkdl64HJ5uhAIrmrBNzTnsGa+i0WiOD5ptPQfrzVBwNRyfvHQRVii45Oh2VHUCb3eY0ylKo+ETnO6lohZbSdh3AqsbZ2wf4qH9Mx2zyvikTcw2de6ERnOCMjHTSGrnxYwMhreD2mTmfm9/JXO7IBxcL2CoaGGlVgXdvnN8hY5Wo9GsNu1NmmtNj1vuO8TdD09QMjxE5GAZIzsAcO/7IZAOEXafcFnGwjHCdSOwZqoOX/nRw5kcibjCu1IwOduZhKbRaI5vHNdnpuYyFjnf73ndZfzhyx+DiOrYuHd+C+eu/0m2Dyb2ZJ4f18AZLNmZGll3PniEOx88rEOFGs1xQLrWHUC96XPTvQcZGcyxZUC0HKxNp2W2k5GD1e6Ex+SOBwfrzO3DAPz3DY/w47v2J/c3XT/pLXZY52FpNCcc8cQqzqk4efMAF56+AVI5E82ffCb5OzjyaOb5eREOvINtDtaD+2b44Bfu5N5dWQdMo9GsP2ZqTsd99+2e4oxtQ+A1IRZYpVFKr/jbZBtRGgNAzRMitBaphbUuBNaOTSVOjnqJXX/Xfv7sX27ih3fso+n6bN8YNnE9MqMdLI3mROPITJjE3h4iVG7nhEsp1eFgFSIHa6jYcrCGUi122me+Go1m/dHtdzxXdzl16yA49SRECCAGWy1wRKF7LmfMYsVG+05gBYd3Uf38H6OcemLPm4bkPa+7jJc+7Uwe2jfDo4fmuPqb9+G4QdJbrD3GqtFojn9igTU23Caw0qt+hEQFPqo2hWrOYZWfgnXhLwKQl7GDZSez0XSPsck5PXHTaPqZputz7U8ewQ/mz8NOC6xCqsPDaVsHUW4jK7BESxYJwwTTnt/BWm8hwubtXyeY2sfc1f+Xxnf+KfPYY8/Z1LH9cMnGNASzXSxAjUZzfDMVhQhHB7Krj3HqmGdcRu7JrwUV4M9OEEyE4UHznCvJX/FKsPItB8to8Jz7/4Jzrb2MDub45NufRs42dG6nRtPn/Pf1j/ClHzzEDT8/MO82M6kyDaOpSgWnbspD4CUhwm6IXAlSpV28vffg7b4DgNO3Ltwjue8EFoaV/Ok9dFOm6WKcb5UmZxsMFm1mtYOl0ZwwNByPpuNTa3rYluzIhVCR7S8HQjfKmzmMfyQMDxpjYa09YeXZMhiFBSfvA+AX7AfIWQaGlIwN5rTA0mj6nLoTdnmJF711o+G0OsHE3WI2DOUomeFzhFXIbF/4pbeSu+KV4WN2MRMirF/7N9S/9fd4u27niuDmBY+t/wQW2WWPsVKMeccrH8dTHrMtuZ2zDAYKFnM1LbA0mhOFP/zI9bztn26g1vAyln+McutgF5J8iokffJbgyG5EaSyckQJYOc7fUeKNv3wBxVq4eGa/P5qItVEtsDSavkdGmmGhBb+u2wofxrU2T906lBQiFnbWwTJPvgg7SiMQdjEJESrV2k/92x/E23n9IsfWb9SzBUWD8Ucyt885eYRnpKq95yyDwaLFbF2HCDWaE4WG4zNXd6k1PYptAksFPngOwiogBzaG2++6G++hm5FjrbFDmHlk4HDZeVsIjuxO7k8E1kAosI5MN3C9+WfHGo1m7YhTpoIFFJabqpO5YSiPZUrO2jHcytVsc7Ay5FoOlpo7sqRj6zuBFVRby6Ll8Fb8yX0d22wcbp0MO3Kw2kvhazSa4wulFN+7bS9Hplvtb/YdrnYIrLhEg7ALCNOm9Mq/j3bgY2w4OdlMWLlwiTZhhXcAS/hJE9eNIwWm5pq87Z9u4N++nS1QqtFo+oO4g8N8AksplamfOVi0ec/rLuNZjz8JlThY8wustIMVTIZOt9x8Zm/H1tNWq4nXwNhxPvZlL0NuOJlgan/HJsV8a0DNWZLBgt0RIvzgF+7gA/95+0ofrUajWSXGpxv8+7crXP2t+5L79h+pUci3OVjRYBgPmrI0ilEKa+mlHSysPMptoAIPFTnnlvCS8eXS8qYk7HDjPYdW5D1pNJrlYb4crPYuL7Yl2TpWDMuyROVcxEJJ7vlBVG0a5TbwozIv5qmP7emY+k9gAdZ5TyN3yfOQI9tRs4eSztbdCJPcLWpNL3Mi73zwCD9/eEJXYtZojhP2Rf0B7354InN/u4MVu+BJHzHA3nwqAHKszcFym6haKy3hvJMGuDgq03DSpgHOjJrN+0FAvdlKlNVoNP2BE7lTjXkEVvx4XHU9XX1dOZEbbs8vsKyzfgF8B/e+H+Lt/DFyw6nYFz0bY/t5ix5bXwosObQ5/Hd0BygVdreeh5xlMFAMVx5Wu6wkjOvkaDSa9cvh6Trfv31v18c6crCiPAkxMJbcl9t2Flh55MjW1oZm5GDVppK7ThrLIVJNo//gZY/hd198EUrBHQ8cXoZ3otFolpPYuZpvAuRECe4DhXCcSDdoTkKEC+RgGZvPxNh6Ds2ffIZgch/2Rc9GmDa5y1+x6LH1tcAytp4NgL//vo5tCrnwJNlmWKYB6Fqq4aF9Mx33aTSa9cVHv/xz7nwwFE5xuZYdUReH9hBh7GDJUktgjVz5q5Re/G6EbG0rrBzKaxKkBBZtbnkxb/HYczayfWOJa3+ySzviGk2f0XQjgTWPgxUnuJcKoRGT6R/oLB4iBLAufm74hxCYZ/1CT88B6FzfvMaI3ADCDgdQWRpFDG/F23cvdvwGI5556cl8/YZHKOZNBqITl050t02J4wXsO9y9C7ZGo1k/xAVF87bBi648jVsq4/h+wN7DVYq2Qf1/PoxqVlFeM6xzZRczA6C0C1n3imiATDlYojAEfuckTQrBUy7exue++wCzNTfTSqcb+w5XCZTipE0Dx/iuNRrNYjSjGleNeRwsNxJgA10EVthSS4CV6/bUBPPUS7Aueg7WWZe3JmmLPAf60MFK2/oA5vZz8fdXwqXXKV785NP5yG9sw6h8h8FcOKuM2+UopfD88L754rIajab/abo+H/rinUxXHTaN5Pmj33gcl1+wlTf96kVJ/asRZvAe+Rn+/vsIxh/Ge/SujHs1L1YOAh81exiEQAxsmDffM66d061pbDt/8s838q5P3dT7m9RoNEdNMwoB1pse1/xPhZ17pjKPxzlYO+w5DHxG7rwG585vA1EOlpXPtMfphhCS/BN/HWPT6a37enCw+lBgbcjcNrafB26D4PCu7HZCwF1fp/nTzzK085sASbsczw+SJZtaYGk065cH9kxze5T79IuPPzlszhoRC6zSzEMA5J/9ZgBUbapjotaNeIAMpg8i8kPh7S4OFoQtuWBpzZ+DQIcTNZqVJr7GPzo+x3dv3cvHvnp35nHXC9goZ3jB4U/x/o2fx9p9E81bvxo9WO8oMtoz5sJONvShwLLPfWrmtrHtXAC8ffd22TocwIypsMdYXKqhmaraGsdnNRrN+mPv+Fzy95a2Vln5KA9zYPohRGEY85THJI/FeZwLIczQlQqmDyKKI2BY+Afup/qFP+7ItRo6CoF1eLq++EYajeaYaLphaDBOZh9p60vqegEbjVkAZBBqBBl1eFBOfcEE94UQQoK5cJiw7wRWpk4NIIvDyNHt+F0EVlyFVU0foJgzkxysdD2M+eKyGo2m/9l9qCWw4qT2mOdcdgqPOXMDm+UUcuOpmQR28/THL77z2MGaOYAojSCiGWkwuY/g0IOZTY9GYO3V+Z8azYrTXv9qw1BL9ByeqvOZ6+6nILK/W9WoogKPoDqxYKPnxRCLuFh9J7C6YWw/D//A/Si/TSxFSyxVbYrRIkm7nLRrpR0sjWb9svvgHBedsYGP/sFTGBvKDoRDRZvfe8nFiLnxVmmXqMKyse2cRfct4iRV30MWhzON5t2dP8lsW8yZGFIw00PHiNEoX0svsNFoVp6mG7BppDU2OKmq7Z+69l72H6kxKLLlmlRjFuf2bxAcegjz5AuP/sXXm4PVDWP7eeA5zH3q9fipXCzlNiCq1nxyvpokuadF1XxLNzUazfLwyIGZFQuHHZ6us2Ws0LWhM4QDJW4jEVjF511F6dUfWjRpFcjMXEVxNDMbde/5DtUvvSsp4SCEYKhk9+Rgxb0Mq3Xtnms0K0mgFE3X54kXbOW9b7icLWPFrvWwhmUte4fv4u26DbnpdHKP/9Wjfn1hHQcOlhnlYQE4t19L89av4R98AOU0MDaeBsA2cy4JETqpZZnzlc/XaDTLw59ffQtv/6efMD3XXNb9Oq5Pw/GTBPNuqJmwhU0ssIRdQBaGetq/SC2zFm0OFkBwZDf+/vuT20NFu6dVhH5Udyfd/0yj0Sw/8bU+ZxlsGS2yfUORerN1zc/ZYZ7mSLvAAoLxhzG2Lu50L8jx4GCJ/ADFl/0V1vnPwHvoJpxb/ovaN/4OvAZyZDsAo1Y96XofryoYHrBpOHoWqdGsBrfuXN5K57FbNFScX2AFkcASPSS1tyMHN7X+TjlY6fwtVWs1nx8q2Uz34GDFJWJcX0/uNJqVxPUCDHx2TP4MpQIKOTPjYMU1rzIOVsqpjouZHzXHg8ACMEa2Y53/zNYdbh2UCpdjS5PTRxS1pscP79ifhAiHirbOwdJoVhjTCIeR5XaL43ynwQUcrGA2FHVycOOS9y9yraR5URwGGQ7GcnQHxZf9FUgz2T/AUMlKSsEshB+VZ9AOlkazsviB4hcLd3H6o1/De+CnFGwzY6rEAmtItlIY5EBrrDhWB8vYcNKCj68bgQVgjO3ouE/YBURxmDGryRnbh/jurXsSURU6WL5ub6HRrCCGDHv3uf7yCorYweoWIvQPPYR/eBeqWQ0LBfZQk6Yb5jlPBsICxyqqgSWsHMbIduTw1rAIaUScg7XYeBI3nXd9Pe5oNCuJ7ytKIkxNUM0qhbxBvdm65stI4eRFa3GKiCZjYnhrz+kE82Ff9NwFH19XAgug9Ot/S+6Jv57cFlYeURhG1Wd48sXb2H+kRmX3FBAOzEplVxVAmAT/51ffzMP7dZ9CjeZYUcSOzXI7WPOHCGtf+XNq//VnqOZcxolaKvmnvJbir74HWRxp9SGMxJoY3Jh1sIo2nq/mbSobE4cIPe1gaTQrihcEKKLm7EFAwTYJlEpqYsXXfpvWbzZ2u81jDQ/2wLoTWHJwE+Zpl7busPLI4jCqNs2l5TAPI24KO1wK46Pt1dx3HZjlkQOzfPa6natz0BrNcYwfCQrHXVxQHJ6u85n/vR8/WHzbON9pqNRKPg9mxlGp56q5iWMSWEIaGBtPDfflhjPhuACpHNxIMDtO/bqP4u26PamFtVgeVivJXacnaDQrie+rlsAiSFYb16MwoesGgMIWaYEVagjznCet+PGtO4EF2X6FLQdrmlLexLYkc3WXJ+fu49TZW4FWM0iAgxM1HjkQVnUNdOhQozkmlFKtnKMeQoT/9JW7ue5ne9g7vniNqNmqQyFnYJlhHkXQmKX6+f9H8yf/kWzj77sXkV+mpsp+7GCFAss86QJwG3gP3Uz9ux/vqdhoEMR+ns7B0mhWGs8PUCoUWMpzk+4OscvseAEGAYZIXevtAoVnvxlzW3nFj697cZk+J13jJs7BUvVZUAHDJZvxqQYvKd0EDwG8OuNg/b9P/DT529e9wjSaYyL9G3J7cLAOTISredKLT5RSYW/RNqarTiY8GIw/DIGHe893M9sdi4OVJvcLLwcE5qlhyx3jlMcghregpg+C5zBsh+91doFio15KZC53TppGo8niBwpThGOJcmoUhyMHKyrV4Ho+dvQ4hgW+21OT5uViXTpYaYSVD1cAoVCNWYbb+hBtkHPzNnzWzVg1mmPDTyVyOz2ExOKZZdw39GvXP8xv/vX3uv4Wx6fqbBwJCwn7hx6icf014QNtzvNyCSw5uJHCs34nCREKISn+0lXkn/qboHyGDoWO+EIhQi91PlxPjy8azUri+6qVwN6sUsyF6QTVRqsv8ZlbQkElooT2o27ufBT0JLDK5fKflcvlu6P//qbtsTeVy+Xvp25fUi6XbymXy/eXy+V/LpfLK+OSxYOqaSMKwwCo2nTHiqOzzQOZhrFptMDSaI4NL5UPtVhILJ0cPht1XfjKjx4G4IG90x3bj0/V2RwJrPp3P9YqKrrpDEAgN50OgMgtU4iwC3JoE+Y5T8LYeg7izq9hiGDBEGE6t0w7WBrNyuIHAfmoz6Bq1hgZCK//U7NhPqXr+YwVQ3c8FlgcZXPno2FRgVUul58FPBt4LHAJcGm5XH5x9Nj5wDvannIN8KZKpXIOIIDfWs4Djik85/cxT30sojgS9hEjFFhxnoQvQl13UqHOfdGqwnZ0iFCjOTqUUnz/9r1UGy3RtJjAmphp9QOrRgJr24YiALfeP57ZttpwqTY8No0U8PbdB1Fjd/Osy8k/+f+Qu/JVyFKYiynyy+NgzYcQAvOcK6Exy/aCk4jDIFB85rr7OTjZKmLo+Yrn5O/g14o34ukkd41mRfF8RS5KYFdOjSHvMDlcJiOB5XgBBSMclxIHy1q4OOhy0ouDtR94a6VScSqVigvcC5xSLpdzwMeBd8UblsvlU4FCpVKJE52uBl66vIccYm49m8Jz3oKQRsvBqk9jSIEkwFDhSd8yKKjsnszkRsRoB0ujOTru3TXJv32rwjX/U0nuay+H0k46VB+LlLh/6IP7sg7WoclQUO0wJ6l//X2oxiz2Y55H/mmvx9h4KvYFz0TkQnGGcXQ1sJaCLI4CsCnXpN70uPGeg3zkv+7iulv28LGv3p1s5/sBzyvewVPyFZ3krtGsMKGDFU145o7Q/K8/5dXDP2UicbACcjIcd4wtZyOGtmQ6OKw0i4bvKpVKMnqUy+WzgZcBVwLvBf4FeDi1+XZCQRazH1i41OkyIIqhMg3q03jepkTRAmwfEszscfna9Y/w4iefnnmeXkWo0RwdsZg6Mt1ypRYNEaZW887VXJqOnySMj09mm0WPT4W3N/ut4URuPgMhU0NWlCagnJVpNJ1GlEKBtcFqcKDh8fGvtURVOmToBYp4fpwPFl8pqdFojh7PV5TiEGGUQrDNnOLG2MFyffKxwNp6NrnHvmBVj6/n/KhyuXwBcC3wNuA04JRKpfKH5XL5aanNJJBWLQJY0jRuw4ajyacYpJorkld1nvkLp3L7na36VpsHJY8/bws/u3+cVz///I5nbto0eBSvt7qsh2NcC/R56c5qnJcNR0JR46VcYLXIa9v7w/IoQoAbKIKoxc7p24d4eN8MpcE8xbyF7wd859a9DBQsNnq7iQNwm845H2uktX/niuez974fsOXxT8UaW/w9H8t58UsnsQvYZDs83NZ+q+n6yb5rviJ+dCvjff8d7ffjWyv0eelOv52X0r6ZTJV2gLq9kZmaw4GZJnlnksdO/gCAsY0j5Fb5+HsSWOVy+UrgS8DvVyqVz5XL5X8BLiiXy7cDA8DWcrn8n8DbgW2pp24F9i3lgI4cmTu60F1+iNqRcXY8tsDf/tbjqH3hSwA052bZOlrg1vsO8fDuicxTXC9gfHx26a+1imzaNNj3x7gW6PPSndU6L7OzocCqRzlYpiGoNdwFX/tQtNhk43CeA4ervP3DPwLgnJOGeXjfDPc+MM4pWwbZuWeKnY9O8brnnUfjvm9inPIY8le+mim3CJn9DzLw2n9iyqft/k6O9bwoBRgmJTXLA3umMo/VGx6HDs0ghGB8fA7hFxk1amzlCLfctY960+PcU0eP+rVXCv0b6o4+L93px/MyMVljo3AJhrYhZ0K325YBB8drvPsTP+G3SzdQdMLr/uSch7HMxy+lWNAU6iXJ/WTgK8BvVCqVzwFUKpXXVSqV8yqVyiXA64FbKpXKyyuVyi6gEQkygFcD3zy2t9AbshgWGwXAjcIWQqCcOlvHCgRKJQVGY3SOhEZzdMT9Bx3P59n5O3lKcWfPIcKNwwV2HZyl2vA479RRLjtvC9DKu9p/JPSsyicPE8wcQo5sP6pmzsuJEAJRHGWIWsdjilZtLC8I8KNhtSibvOfqm/mbz962moeq0RxXHJqsse9w93C773nYwsc/+VJKr/4QctPpFESThuPjeKk2OnDU/UqPhV4crKuAPPCBcjmpfPqxSqXysXm2fyXwyXK5PATcCnzomI+yB0RhGP/wLgCUEwosURqLBFaYq/HQvmzvwbgRdLcihxqNZnEcN+D5g7cDMNEcJEzP7E6c5H7qlkHu3TUJwGt/6VwGi2Htmn1HwkH0wEQN05CM5TxqgYcs9Yf7I0ujlKa6l3yZnG0yVLLxfYUtQqFpoVcRajTHyjs+Hq6Z+5d3PKPjMRWZKdLOIwtDYS276iPJ4w3VarMVd2hYTXpJcn8L8JYFHv8+8LTU7TuAy5bh2JaEsfkMvIduwttzd+ukl8YIpvazdSyse7GzzdoPlMLxAnKWsdqHq9Gsa9IlTjwlMUXAmJpc8DkNx8M0BBeeMca3btoNhOFCIQQnbRoIm7RfGbaz2jJagPoU0EowX2tEcYTCxAMAlPJmpkRFI3LnfD/AjISVJbTA0mhWksALf3cybtBulzCD1sKbtMASayCw1n0l9xjr/GcgBjfRvPlLSYhQlEZRTp1CzuT0bUM8vL8z/jpflXeNRjM/6TzJOCRmKBe1wMrcRtMnb5uctWM4uS92j887dZQH9k7jej4HJmpsGSuiqlMAyOLI8r+Bo0CURsl5s4BKmsrGxK05vEBhEDtYLQG20HnRaDRHh/LC0Lw0w9+jyBURTo14rZ2nUuaJabU/fcU5bgSWMG3si55DMP4Q3p6fh/eVRkH54Dtc9YpLuPLCrQDkcHnX8Jc4wzyYzDw1Gk3vdCvSa+EtWLy34XjkbQPbMnj5M87iTb96UfLY+aeN4noB192yh0OTdbZtKBLUQkesXxwsWRrBCFwKwsW2DN7/O1fwp//n8UCrSr3nB0lvtLSD5UR9Gm+69yD3Pzq1ugeu0RynKD8WWJF4yg1A4HPKWOho2amSTekexqvFcSOwAKxzrgDA2xUmlcaVnmMX6zdfcD4feNOV7DAn2GBUeWHhVp3ortEcBWkhFYfEbOElQqIb9abPWdYhql94J89+7BYed06r4N9FZ2zg4jM38IXvP4gfKM4+aQRVjQRWvzhYUbHRYVkjZ0nGhvKMDYV9zeIEft8LsKIcrPTgHk/kPvbVu3nff9y6moet0Ry3tEKEocCKiw//8cvP4+NXPZXLy2s7OTuuBJawi2HxQbcBQiIKUc2LVCFC6/7v8JLiTQAYIuha4V2j0SxMHCIUKAwR/m3jL9h/r+F4PE99l2ByL8Hk3sxjUgpe9vSzkttnnzSMqk4i8oMIY2XamS6V2EkbFjVsMww9FHPhvy0HqyWq0knuOhVBozk2upVvUkHoYBmJwAoXtBl+Hcs0UN78fUNXg+NKYEHLtRLFkVBwka30HNzyeXaY4czYIMBbpo73OsdCcyIRO1hmSkRYwsN15xcSDcdnWIUreZUTljvwDz1I8/ZvALB9Y4nBosXwgE0hZxLMHkYMbFipt7Bk4tWMw7LGQCEc0E1DYkiR5GDFOSGQDRFqgaXRLJ10hMnp1tvTD++LJ2GxwFKNqKyDG1Z0t87vXIG4Ghx3AksMhAJLjmxLQgtBVEK/HYnCXYaGrIem6rzh/T9gz6HuS7g1muONeDZpiNYAmBPegv0Im05rNqlqYc26+v9+BOemzxNMhUUC3/eGJ/KXr788fI2pfcjR7ct+7EdLPJ48/hSLVz0nLFkjhKCQM5MQYRyygHaB5enWXJrjmv+5+dFlzy9M50g3u6QfBFEOFkbkYBWjvsRR/qbymhinXEL+Sa9Z1uPqleNOYMWzTDm8BbnhFER+EG/3Hd23FQrXP/ZBb994Fc8Pkv5pGs3xju8HXJ7bSVG0RJOFl4TKulF0WmUcVD2qSRf1FnTvvx6AQs6kmDdRTh1VnUSO9JHAMm3Ilbhwq8FwyUYFAc0bP8+2XDV534GXPR8xDcfHXSA/TaNZzzQdn899Z+ey5xemnd9mN3c8DsnLMFQfFyQOZsaBUGCtRYHRmP5IblhOogFbDGxASIlx0oV4j96JalYT+zDGYHlysKarUWNJnTCvOUEoTj/Ir5d+wj1OSwDZws/UhmrH8OoQjXVBW9cFb989pKvUxI6WHE133lp7RH4A1azi3PVt3Ad+SjD+ML8qt/Dt5iuBlIMlTXKqNR40HL/7BUKjOQ7YdTAsgVTMLa+kSAssp8vvRwXh703EDpaZQxSGUbOHww3cJsJa/fpXMcedg0U0qAk7LC5qX/BMcBs0fvxvqCArgJZPYIWzVr0iUXPCEDk1G41WbTlbeFQbbtfNlVKYfqsAoKrPoBpzqEb4/GByfyaP0T/0IADGyI5lP/RjQeRKqGaV5k8+SzD+MAAjYpZa5GDFy8bJFRmwFa945tlAGOpoaIGlOU55eH/oSJ+8ef6+fEdDNkS4uIMFIIY2Ecy2HKy1qOAec9wJLPuxL8Q883Kss54IgLHlLKyzr8DfczfKyfYzkiJYFlGUCCy9IlFzghAXF82J9Ko5j9o8DpbrBeTicKKVR9VncO4M25QaJ10Ibh1VmwIgqE7SvOkLyM1nIIY3r9ybOApEbiBx12Js5dCIQ4SRwBJ2ETyXKy/YjCQIe6OlZuN6UYzmeGJX1Oe3mF9eB6uZdrC6LBRRscAyWkVE5eBGgtnD1K/7R3Dq2sFaTmRplMIz35g4WABywymo5hzBRHZpuLlMDtbMXCSw9AxVc4Lgq7ACux3nGVmF0MGqd3ewmq6f5GvJkW34Bx/Auf3a8Klnh/Xr4tIN/sEHwHPIX/GqNSkOuBAiV0LNHcncZ+LhRv1PAzcKWeSKoHzUN/+Svx39TEeIULvdmuOJ2eh3v9zf64bjY+Bj4XVNchdJiLAl7OTgJtTsON5DYTkm7WCtMHLsJAD8/fdl7s8Ld1lEkXawNCcaftSlPnawRL5EboEcrKbrU4gEljF2cpJ7VXzxu0MHCwgmHo3+3QNCIMf6KzwIYQ5WNwrNCQDcaFl4UiLmyG5MEdBwvIzAWo1woecH/OiOfXr1ombFaaQ6GSwndcfjz0b+i78Z/Uz3Mg1BZ4hQbjg5s8la9CCMOaEEVvtqQlMEBF73GfdSSJLc9SohzYlCEA52MioyKuzSgjlYTccnL1yUMDBPf1xyv9xwCrIwhBjeirfn7nDXE3sQQ1vWdGCcj2ShTMohBxBuHc8P8KJSFLHAiqk3sw5WcxXqYn3jp7v412/ex413H1zx19Kc2NSj7/NKOFjDso4U3X8zIhqH0iFCY8vZmW1UsHaRpRNDYOUHMbaVk6TUNHHBw6NFKaUdLM0Jh/Kzg5bIFbGER23eEGFAQTgEZh5j+/mt58lwCDJPvQR/370op4Y/8ShGNCnqN2KBJXKRkxWFMAuiydRsE8+JcrByWYFVa7iZC8RqCKw4Hy4enzSalSIuU7Lc18DmYmUa4hBh2sFq613aHtJfTY6/Mg3zUHje23Du+hbegzcRHNmd3L9n7zj7DlfZvrG0wLPnp+H4iXOl69xoThSUnw0FCruIAOqNRtftm45HQTgoq4AwbfK/+GZkaSR53DrjMtw7v0Xtmx9AzRxCnvuUFTz6oycOEQorR+m1/0hQnaL2hT+mKBwmZpvzOlhuo0rTHePNg9/iUX8DDffSFT9WywzF33IUU9ZoFiIup+AtQ13JNGlHrJvAinOw0g4WgH3J8wlmD4PnYF/83GU9pqVwQjhYECbB5S55AYXnvy1z/8N7J/iTf77xqPc7k5odur4eyDTHB3c/PMGXfvDg/BuoNgcrH05Q3PkEVuRgKSsUHtbpl2JsPjN53Nh8BvZlLyE4+AAA5skXH8vhrxjCjiZiVh5hF5FR5eiicJiaa+K50XjQJrD8Ro2mG3CWdYin5+9dlZpYlhEJLO2sa1YQpVSSg7XcYj793e22ilDE45DMekW5y15K4Zn/l8Jz3oIc2tTxvNXihBFYMcLKZ26nG7IeDWn7XRca1RwvfPALd3DtT3bNG17qyGuwY4HV2c3A84Moyd3tCJ1ldnHhs5O/5djJ8263psR19uL8MLsACArSYWKmmeR0tr/PoFlb9RysloOlxyXNytF0fWLfatkdLLfllLtO51gkAp8AkaQa9Bv9eVQrSZvSTfcLOxqmqw5DosY7hr9KLlpJpNGsd7ZuCAXCvbu6f6dF0JmDBeA7jUyNp10HZvntv/0+P6scoiAdZFtyeGYfpk3hhf+Pwgv+CCHEsb6FFUFuORORGyB36a8AhGUk7AKDhsvUXBPfS9XBSuPUcNJFE7XA0hwnxI3OL8s9xCa/e9/fo8ZtOeKB0zl5E8ojwOi4v1844QRW+8BtHqvAmmtyinmEbcY0g83xY9qXRrMW3PvIBEems6G97RtCR+q+XZPdntKZ5F4YAsAKmhkntxI1f72lMk5BOMgFHCwAc1sZc/t5Szr+1UTmBxn4Px/B2NpaqSRyJYZtj4nZZtIqRw5vyTxPuI1M+HQ1yjSYWmBpVoGG42Hj8srSj/nN3DeWdd8itQgtcDrTD4Ty8YUWWH2HGAorRNtRiPBoKytPVx1GjfBLIHy9Wkezvmi6Pn/7udv5m89mm7QGQfh7mJqb5zvdloMVr9wZkM1MNfd4PxDWnTPyR7eYpJ8RuSIl6TBTdZBBS2DJLWcl2+SFQ7U6l9xOtwBZMaJTr3OwNCtJw/E53QzNBVctr9iRXqq9ltspsGTgobTA6i8Gf/tqis/9A6DlYB1t/tR01WFzLvzgtcDSrDce2hs2XR6fyg5e8UV5XvejPUQYCyzRyFRzj+tiCQJywkPm5g8RrldErkQBh+m5ZssRlybFF7yDwvOuAqAgHOamZ5LnxMvaV5K4wKh2sDQrSb3pcZYV1lp71Btb1sK2wmuFBbsJLAKfQAusPsS0gbB/GoSrAY+mXsxM1WGDGX4JtMDSrDfiEJ4hs6Hz+KI8r/vR4WCNAVCSjUyx0cnZsAhvXPFd2tlFJscDwi6Sp8nUnINJdL4ME2GYGFvPAUL3rjrbaozdaKz8WOEHWmBpVp5602ebEaYSKATeMn7fjFSDeOF1cbCUhxL9W23qxBVYUd2MOMn9r675GX/w4R8veTeNpseIjEKEwbFXhddoVpPdB8OwlR8oZmuti37c8mLewbJjFWERJS0GRDZEmAgsot+GdTw6WEVyNGi6PjnhEkgr6aEoTBsljLBNUNRGB8KyDStN4Pucbh7SAkuzojSiGncQXk+XMySdDhEKr9nxuFA+SmoHq+8QbQJrOso1SeeM9IKvFCUVXqSkFliadUa6d9iBiVrH/fMNlkKl7jfMcPFIboAB2WCui4OVF1Go0DoOHazCMPmgjiSgKFu1vmKUXaQgXHKidV68VRBYm8dv5veHvsWO5gL1zDSaY6Te9JJG7hb+sjpYVpASWH5WYAWBQiofJbWD1X+YocAy2+pgdW0ouQCBHzAQhNa/oQWWZp0RKEUhFw5QEzOtAcz1wolGbw5WGF4UhUFK8zhYicA6DkOEcnATAsWorFIUTZSdTeQXuQGKspmcA+i+Imq5yTfDFiFDvi4fo1k5as2Wg2UKf1kdUyNojUmyTWC5XhCG5HUOVh8iDAIleHbhLh5nt3oULrVhsxU0EpEm1SqsDNJolpEgUBRy4QCVnlx4vkcOdwEHKyWwIjfLKAwykMrBajphg2MhaLk3x2OIcHAjAGNyjoJwkqr2MUZhgAHRSPLQAFSXmj7LjSfCSaSe+GlWklrDoyhXKESY+u4abTnOTc/HEAHK0A5W3yGEwMXAFj7/Z+BHyf3OEuvTlIJW4qqp9ECmWV8EgSJvhwNUeub53OD7/M3YZ5O6Tp1P9Dv+FoVBBmWTatxkOMrp+o1nncOrn3FquM1xGCKUg2Erjg1yjqJwMHJZgSULgwxIJxMiVO5qCKzwc9W5oZqVpFZvJpMHC29Zq7kbgYcrLAIkRpAVWK4bYOIjdIiwP0nX7HjjL18AQHOJ9mZJVZO/TeUddT0tjWYt8JUib0cOVsq9PVc8AsB2daDr8+IcLPP0x2Nd8KzwvnwYIozLNMxWHXI4nL/nS2yUYYkCsUAl9/WKGBhFIRgz5iiKJkZhIPt4foBSW4iw24qo5caPBJb0tcDSrBxevXUNtJc5RCiVSyBMfGlj42RypB3PxxQB9LGD1b9Htgp4KX1pW/FFZmkO1kCU4O4YRSzh4XpBsi+Npt8JAsUOY5KDwss0an3Y38r55m7Olbu6Pk8onwY5Nv3im1r35QfJC4ep6TCBe6bq8ITcQwwcvA3n4O3hRsehgyWkiWMPs6E5R1E6yLYQocgPUqKREViyy4qo5cZDO1ialcdvhALLt4qYTTezcOZYMQIPX1ggBTnhhT1No5xRRztY/c1QviWEckaYqLt0gTVLgMDJj2ELj8Yq9BjTaJYL6bu8tPYZ3jD43UyxXV+Fv4eNYrqrKytUQNA2fIh86NxMHj5CECima06q12e4j+MxRAjg24MMyxo54SFynUnuhggYFjUCI2wSbavGsl6IuhFP9uNafxrNShA0wwlVkBsOc7CWM8kdj0BaBEaOnHAz12fXC7CEj7DsZXu95eaEFljppHRbHl1F90FVpSFLKDOPjb8qFZo1muVie7AXgFPNw5kQoYwuypbwuwoBoXyUaBdYg+Fz/CoHJ2vMVh0GRCoUJo2k/txxh5VnVIYz+Q6BFQnPHbk55OBGFJKCcFd+MhblxplaYGlWEOGG33uVH8IUAe58eZtLRCmFoTyUtAjMUGA1UwKr6fnYwkOYuWV5vZXghBZYeK2kucLUg1yRu78nB+tP//lGPvbVnwMwQJWGMYiwcljCo6YFlmYdcXoQhgADJXDdMJTkBwFWtDLWwk9KNqQRyu/iYIUCa0A0eeTALDNVl01Wqt6Tle9otn7cYOVbBYc7BFZ4XjaLKYzSML6ZpyiaKz8Zi/LkLPxlbV+i0WSIV8QWhgHwneXpUuD5Clv4BNIEM0cOj2ZqEui6YfstYWuB1Z+kiiUO3vhxXl76KU4Ps8q9h6vcdO8hIFo1IW2klQtDhFpgadYRI4S9CKVQ5KK6SZ6nWgJLeF2XXUsVdDpYhVBIjFgOD+ydZqbmsMlsJcAejwnuMcLKhwm3hJXdM49FDhbKR+SHCKwCBeGsvMAKIoElvCUXUNZoesHzg1Yx0FhgucuTX+j5ARYeyrDAypNvc7Ac18PGQ/Zx2sGJLbC64Czy5ZirZxNGJeGFxrBsbOFTa+ocLM36QSiFHw0DxeZhIKzebolUiLBr2DzoaLIaOzWnjwnufWSSqbkmI7TKmIhoAD4eSeeWiVzbKsLCUOrvQZRVpCiclW9ho2KR7Cd9CTWa5aSWquJOMfp9e8vjYMU5VsqwEVaenPCyOViOgxSqr/ubaoHVht9YePn0ocls/RqhApAGhp3HwtM5WJp1hSCgKcOQloxqM3l+kLgxFt0LB0oVoNpDhLkBQHDSUNh256E9ExRp/V7si5+7Qu9i7TFyLXeuw8EaGGv9XRhCWUUKchUEVpSDZaMdLM3KUGt4lESTQJqticUylQWJBRaGhbTzYQ5WKsLkRd0QjD4WWP27vnGN8BepsHxoMttDzIgcLDOXQwktsDTrC6kCXJmDYBYRVUp2vSBZeTafgyWU3+lgSYnIldiUD587HOUk2Ze9BFkawzz98Sv5VtaUzCy6PQdLmmFyv++GbpZdpCj2UV1hgRXXKrOFp3OwNCtCvelRkg0CqwRGtJpvmcqCuFGIEMMmXyrhCpcDqeuv3wwFlpnrX4GlHSyg9KoPknvGGwEI3Ab3PjLBP375ro5BSSnFg3tnktuO6yOiXkimnccSAfXG8tijGs1qIIkEFiCimafnB0l5hflaX8Sh8XZEfoCSCCcpcdK3seFUrLOvOH4T3AEz33KthF3seDy+T+aHELnVChGG+88Jr69ChA/vn2HPobm1PgzNMuB6Qehg2QPIuFzCMjlYnhdgCx9hWtiFEpYI2L1/Knlcuf3vYJ3QAsvYcjYAsjiCjAZA5TT528/dzi2V8Y58q58/PMF3bt2T3K42vMTBMqKVDM3GyrfA0GiWC0GAHwksGbQcLDNZReh1FQJS+aguTVbF8BaY2ge0HCxRGl2RY+8n7EIYIgzMPEJ2OS9R2FDkBxC5EgXh4Lor63bH/SLzwumrEOFffPoW3vUvN63a69UaHvc/OrVqr3ci4QeKgajBuTBDgSWXKwcrnuiZNsIKx6gDh6aSx4MoX1o7WH1K4flvY+A1HwlvRB/g5OR08vhcLSuwHto3gwBe/4LzwsfrLhIV1veJwgJ+qm2ARtPvyChZ3cNMBFa8PBrAECop39D+vG4OlrHlbIKp/RRFg+HIyZIngMCKV0gabVXcY4zt54fb5QeRuRKGUPjOyrbLEdEqQgPVVwJrtfnIf93J+/7j1kz+jmZ58P2AkmyEAiuucRcsz8TBcX0svFC4RYtIZqamkzQc5UQL0nQdrP5EmHayhDpWyDPTLeu63cHaf6TKhuE8p4xfzxPsB5mtOcmFJg4BqObC1vd1tzzKG9///WV8FxrN0SMJQEh8aWFEuROe52GKAE+GM1Lf6VxZG37vO50aY8tZALz9WcNctC0qLNolZHa8IczwAtC+gjAm98Rfp/CiP0aObEVGvQqVs8KTscjBkkIR+CeuuHhoX5jW0S3UrTk2PF9REk3IDbQqqi9TDla11sQQCiuXT1bp5oTHo4fm8IMgSXKPr939iE5yj4gHyCA1q5ytZa3OAxM1tm4oMrzz67xqAO6uvZBhEeZgxUJNNbNJ8O185rqd4esECimP35wUzfpAqgBPGvjCwlCRwIoKBfpmAdNxuhYOjFfPtmNsPh0Mk83NXWzaBD6jx3XuVYIdC6zuYlIYJubWcwAw8yUCIGgsPFYcKyJV588PTlyB5fmhe7fiOW8nIJ7nUpIOjdwA0rQJWL7m4tVqOAHJ5VMCC5f3/cetPL68ie216PejHax1QKSCLVpfjtmUgxUoxYGJGqemSvnMVlshwtjBEm5vg6Yf6B+7Zu2RKBCSIO1guaGgCqzwO91eODBQCqn87rlGZg5j+/l4u24jmDtyQoQHoVUHq72KezfinBG10g2fUwIr8PpPYH34S3d2RAlWgnixktOH52C9E7uwIj+QjAeqh2vb7oOzfOraexbsx1mthikGuUI+CRHmoobpt1TGk3znfm6V05ODVS6X/wx4WXTz2kql8vZyufx/gTcBArgWeHulUlHlcvkS4J+BIeCHwBsrlUrf1y6IbUZbtA41nYM1NdvEcQNOybcKJ87UHIwoxBI7WIa3cJK7IGx76/kKS/uHmjWmFSK0k8lFEIcEI4EVtAmspuOHBf6M7l9g89TH0nz0TtTsONZ5T1uxY+8nliKw4gUx7ed12Y9JtQSF7/ffEHzbzsOcecc+nnf5qavyeq6rJ7XLjWhEAqswiJRGuDRGLX6eP/n1e9g7XuXJF2/nnJNHum5Tr4ZmhWnnkxzHkmz9ZtxGHXIk5kg/sqiDVS6XnwU8G3gscAlwablc/gPgD4HLgIuAK4BfjJ5yDfCmSqVyDqGe+K3lP+wVIFLBOVxsXM4wDzKbElhHZsLQ4SYmkvtmq02kyDpYObVw4mocLumnZdOaExOlFFIolDBQhh22ffID/HgVUCQWgrZVQQ3HxyBAmPMIrJMuSP6WI9tX5uD7jSUIrGTGvUyrreZDpi50qk9zsAp2pwu6UugcrBUgKk4s7SLSiOSEWvy7VmuEgv/eXZPzbtOoR9dSw0IObwVpcrJxJHk8rtvXzzlYvYQI9wNvrVQqTqVScYF7gQA4v1KpVIERYBiYKpfLpwKFSqXy0+i5VwMvXfajXgGEDFdS5YTHHw59k7cMfZtGtZWwfmS6wUY5w9jO/07ua87Nhg6WNMDKEyB7EFjhv1pgadYapYi+vxJlWNgiLMmgogu/jFbEBW5WCNSbYXkSw+h+cRSDm5K/5eiJIbCEXQibWQ9uXHzjSGC1C9flJxUi7BMHy/MD3j38Ra4a+joAeXvlbXxBQA4n02ZFszyoKN/KsGyE0VuIcK7uMjkbOlELCaxmvRUCFKaN3HQaTzupwUufdiYQRpsUolXgtA9Z9NtdqVTujv8ul8tnE4YKr6xUKm65XP4t4P3ATcDtwKWEgixmP3DSUg5ow4buq3BWgyOGxQZjlm3mFADSnWPTprC/WsPfz8X2bkTgMXjJs5i9/TqEM4ckwM7n2Lx5iCMyT95vJs/phpQCP1CMjBTZONJb89uF9ncio89Llq/96EHqd+zj5c8q97S96/kcIsC0bQyzgM04g0MFCnY4CxgcG8PZFSayps/1ZN0jEAG5Qn7ezyCemmw682zMof74nFb6+zL2xg9hlIYR84ROY1xrlBpgC39Fj8kQqWb2Javra632b6jWcKkbNcIzAEPDhRU/hhcXb+Gp+fuYKlzR82vpsaU77eclZ4VjxeiGIQY3DfMokLPkgudvYlcYBRoq2UzOzX+9jBfbDG8YprhpEHn6+Uzf/A1e+uoz+ML3HySHB6bN5s1DXZ/fD/Q8fSiXyxcQ5lq9rVKp7ASoVCqfLJfL/wr8K/Du6PG0NSNIT6N64MiRuTWr2eILm9PMw8ntmfFxDh6cQUrBo/tnONWeRpTG8HY8Dm6/Dnd2EmmEsf3x8Vkcmcd2G4yPz877GpdbFZ4zcBuHxp+I6qHQ4KZNgwvu70RFn5dOPvmVnwPwjMf05ho1HR+JwvMVgQwdrF17p5iLSpU0lY0Ajhyezpzr/QdnGEHhB8z7GeSf9bu49/2AiYaNaK7957Q63xcbmosXGg6i1AOnUV/ZY0qFBacm5zpeay1+QzM1h/Sa0omJ6oofwxNz4crtiUMTjG9a/GKsx5budDsv9blQKM/VfNyojU2z6Sx4/g4eCh8bHchxcLI277Z+bQYsmGkIquOzuKWTwPeYuu82funSLeTvdcDMr+lnJaVY0BTqaRVhuVy+EvgO8I5KpfLpcrl8cnQfUQL754CLgT3AttRTtwL7jvLYV53AsBkSrQEyqE1zS+UQEOZgnWROIjecjMiHittyorY50eoJ18iTZ+HE1ZcUfsKgbOB5/WHZa05cgigHK271ZAufuZqTtLqQ0cKNubnsyth608cWHnKB3AfrjCdQfN5VJ0aJhiUSV7wW3squoBOZEGF/hMfSfS0FasFVZMuFEZ2H9tWwmmNHeXGI0ELIOAdr4c+0EYVqxwZtGo7f9TuglMJyQ+EkiiPha2w4GYD6tz7A8+pf4xdONTGGNizH21gxeklyPxn4CvAblUrlc9Hdw8B/lMvlkXK5LICXAD+uVCq7gEYsvoBXA99c/sNeGZSZCy84ERvsJpXdUwBMTVXZwBTG2MmIQiiwiiqc6cfLUz1ZoLCIwIrNOX+FB1eNZjH8QCU5hGY+jy28cGFHWw5WrVbP9OVsNJrkhIdZWDyhW9OFWGAFK5uDJfowyT1di+qDY/9ObnrXir+eEY3pXlMLrOVGRVXbDdOEuLPDIhGopuNzub2T1078PUXRoNbsNBtcL2BQ1FAQNkgHxNCW5PHgQAWjPokc6CHncQ3pxcG6CsgDHyiXy7eXy+XbgScB7wVuAO4AasDfRdu/Evj7crl8HzAAfGi5D3rFMLM5UWNWk7seOsIffPjHOFMHkATIsR2JgzUsI7crEliBYSU93OZDRQZ5sMJ9yDSaxQgCFZZpkBI7XyAnPGarjSRx1YwElgxcpmZbFycvWpptFtYuX3I9I6SBj0T6K7yKMO1g9Umh0fZin8W53Sv6enWnNc4GK1137EQkWjwhTKtVeHiRVYQN1+cFxdsAON/am6woTNN0fYZEHdds1ddKHLIINXMQ2cuikjWklyT3twBvmefhj3fZ/g7C8g3rDpWuwpwrMezUOXwkXBV4shWGA+XQFoQ0cI0CwyJqZhsXWJNGjwJLaQdLs+YEgY8hQAgDu1DEIyzuV4hmpbGDZeNzZKbB2FBYisCrh86tpR2soybs/bjCIUIVECc8qT5ZRei2pUY0WdkVYI2mRzxtDrq0fNIcG/FkDMNqOViLhAjdRiMpGHqR/SjVRufvwHEDhmUNz25Lqr/ilTi3fR1VD3sGp1cs9yO6knsKGdWy8pVADmxgULRKLmwywniwHNocbmMNMCyzAgthYRAsmKSfOFjL1E5Ao4l5jLWLJ9gP9rx9Ut1bGhhRPlW9VksuxrIwiBKSQVmnWm9dGP0okVuHCI8eX1grLrDSDlYv1bVXg/YwnatWtg5Wvdma8K545fwTkWgyJmQqRKgWDhEacwewhY+SJicZE10dLMfzGZJ1/Fx2UYJ94S9SfMlfJLf73cHSAitFPGNvKgtRGmOTOkJUm5ZNxhzkSknF9iA3wFAcIozrARkGpvAXbIOjQ4SalSBQitcN/oBXDVzf+3NiV0O2emm61ekkyR0zhxrczBZjOpllztVdHngorMTSS1FNTXd82er9uFIIAvxoiO+XOliuk60TqFbYyW+kQ4RdempqjpFIYIUOVmyXLizm4369wdA2BmRzXgdrSNYJcsMdj8nCENa5Tw3/Htl6DAe/8miBlSLOKQkQ2Bc8gwE1yxNzOxkqWjzpdCNxrwDID3U4WEqYGARJc9FutBys/hjwNMcHR9MGJGkALGWyUofadLK8XxgmcnQH24wpqlHPuK/++GHcWrS4w+6tjpumE1+0ej+uFFIF+MTFH/sjB8tvD9Ot8Dlw0/0HVzjn7URExNcxw0QIQaDE4gIrEtlyaBN54VKvdRbnbjpuGEEqdC+rkXvyaym94m+QOkS4frCKocCSKMyTL6YhS5xhHuKiwkGYPoBMrWIgN4AVFfJLQoTSwBTBglXaAx0i1KwAzaOoUh14sb0vkcVwpigas4j4oidNrA0nsUHOUauFbq3j+hRE1KLCLnbuVNMTgbQw1MpOsiQBngjTbPtVYIkVrmbvp0KSytUCa9lRXhgRjMKDASKzerUrUXsdazi8njrVmc5NGnWkUIhc9zFGCJE1PPoULbBS2KUwoS4u1eAZOS7NPcLL1NdR1QmMLWcl24p8S1nHLQKEETpY/gK1XRIHq8/rYN23a5Kr/vH6jMWu6U88P+Cr1z+c3FaL5EAk28VOlTQTB8v2ZlO2v4kxuiMc6OYOAlBteORlKMDmG/w0ixNIC4OVFlgKP17H1CdlGjoSzY9xorn74Cx3PXRk3seDZjX1WjoHa7kRgYcnjKTeXUB3B+t/b36Uq795H/WmB17oWBnDofvk17oJrGibde6Sa4GVwojyUHJm1JDZyGcf33F+8rcopgSWjAYxwwwdrB4Gs34PEX7lxw8zMdPk4f26onG/8+M79/O9W/cmt4MeBZbvtxwsrDy+MMkHVQg8fCURQiIHxsJtalMATM01OWVEAiJpcKxZOsK0MZXXUbZgOZEEBJGD1S9lGvw2x0ocY4jw3f96M3//+TvmfTwtsIQOES47IvCTMDSAQnYILNcL+Ox3dvLDO/axc88U0o1DhKEDFdTnaMd3QpfLyK3vMUYLrBRx0m5cATkwsx+uHGkVqTeKnQ4WkdBaqEp7EJ3ylU7uPFYGixYAszU9KPU7KvV/YMEcwAzxYgwZzkCb5gDFWGBFln9c841mOAg6c9Oc698Hdh4h9PBxtAg7FxV2XbnflyTAl+HvmD4RWCqupn7pS4FjF1iL4rY6c6x0OPJERARem8ASHasI05OIqTkH6TfwMRClUQAMt4vAilcq57SDddyQhDyiC4+KCo+6WJR+4+8ybT/SS9RlKkQI4LvzDxrxV69f6tLMx2AxrE8zW+tvIaiBQs7AToWb/B4Flp+ECKNOBNYgQ7KO8lqDZty1QDpzBEpxmXcLJX8GnMV77mnmx7RyWHjMrLDACvosByvOgzK3n0uNAnKFc1GDVHucFRdzJyAi8AhES2B1y8HyUqvqp6sO0ndwhZ1M3gw324oLIIgS4c28FljHDUnSbvQFUdHtWWMYOZDteWSl8k+SSrNGOFsMFnCn4hysfhdYQ3nJFjnFTFXP+vod31fkRes75/VY80i1CSw/NxQujfbc1qzUKuAjMb0qc3WXfJzgHq861BwVZi7s/biSvy9J2MQb6B8HK3KRLDsMScsVTvRPJ7bLFW5NdCIiVTZEGHQJEXpegEBh4TE918QMmrjSTiJGplelnVhg2fn1nee5aCX3E4pIUJlnPCG6HapnR3TGga1CPvEMYucqDhX6Cyawrw+BddaRH/DMkR/zPzPbgTPW+nA0C2DMHuAvRr+Y3Pa93i6mKikSGA2QuRJF0STwXYLYlRUCxyhi1Kr84La9bJE1nPwGRn/tz5b3TZxgWIUStnCYnls88forP3qIsaE8T3nM9p73r1TYBklFaQv90oswiPtc2vayFFv99dINnGqMA8/o+rjy0gJLO1jLjVRe4pICKCEyTcYBPN/nPSNfZFjW+erUazGDJr7MIaRBQ+Sw/S4OVpSnpXOwjiOElJRe+ffkn/7bAARGWN3ak53tHGQqwbcVIgxni/4Cy4EDsT4EVqlxAABz9gBf/fHDTM7qFTj9SnHy/szthULUaYKk3lX0/c0VKQoH5buZWSm5QUqiwZd/9DCjsoYYOwk5T30aTW/kh8YwRUB9tnMFVTtfu/4Rrv7mfUvav1JgiAAVOVj9Usk9biSOmcOX5jGXqrg89wDbzOl5H49zXT0MLbBWAKn8TIiwWw6W12wkfXvt6j5s5eBH11ZX5DGDLteWKLQrbS2wjitkabQllIzQwYrDehnMXOs5cYjQjFbsLCie1ofAahrhisrJ8UN89ccP87Gv/nyNj6h/2XNorueVeytBg+wg1GufyzgvJ3awZG4AUwTklJMZNAdGxzh9NOyhOSqrFMe2dNudZglYw+HqTHdmYkX27wcqDBFGYxkrHIrrmSjnShgWgTh2gbUokaBzZEELrBVAdAgs2ZGDlZ7wmY0pbBxUtIDMlxZGt9BtVMphva9U1gJrAcY2jgAwOtSZaCeslsCKhZWMQoXBgknukVgL+mTAm4dGJLCKKlzhMT2n8xe68ciBGd71LzfxzZ/uWrNjaC9s26uDFYeNYgc2XrgxKBuZQVPkBylSpyia5ISHHNzQuTPNkhDFcAVVUJ0M/1VqwR6mSyVQCoMgbGECrRWja4yIa1GZ9rIWW5239ltUmsE1CiteOf9ExMBHtSW5t+dg+akWRbY7E+aLWrHAymF1FVhRrmfKyFiPaIG1AENDocgYGejyIZutsKGU4WmUZpTkvsDKmPWS5B4XKBwWobXbDwVH73roCD+6Y99aH0aGI9PhTOuhfYuHelYML9tqwus5RJjNwbKiVlFDso5K5VWIwiCyepg3bg9dTFEaO+ZDPtGRpREAVCSw3vvvP+OPPnZD123fNPhtXlr86ZL27/sBhlBgRONUnyS5C9/FUxIhDZS0MJep2Oq83TOisdg3ixjK67kIr6Y3whBhKgcLiaB9wtcSUKOySk64SZst38hh0SmwhNcMxVo8QVinaIG1EPH3RHSGCNM1gGT0JYiT3Req0p4IrD4Z8OYlOr7NxjT/MPZvnBE8vMgTVp6///wd/OsSc1FWC9HlO7JqtNX3WWgVa3bDqNVT9L21SqHAGhRZB8s85RIATq3fA4CxWS96OFbiVZiqFgqsB/fNcGSm2bVR/NnWQZ6Uv39J4iApdmz0V5kGEbhJ+57YwXJ7XJSxEPOVJhG+S6AEgVUgh4O3QJcNzdIx8JOFFBBe3zpChF5WYOWFi4wEljJz2Lgd323pN3Gx1nZcXQa0wFoAYYWzP1Ho7OidJg6xxEJLLeBgxb0IZ+dqvO593+W+XZPLcajLjwoHvbOsQwA81/7ZWh5N/+I1edPgtxnyViaXptdjSNNrDlbQVqYhl2oVpWRKYJ10Aflnvzm53V6yRLN0hGHhGEVy3mzYPiRi73jnkvWYpfSbVNEkL05yX6wB72ohAxcvcsd9TEx8PvTFO495v92EKYSCzhUmyipSkA71Zn8IzeMFQ7UJLNEZIgwiB6tpDLDJmMEWPkbkliszRw63o6OBDBxcsb7dK9ACa0GMky8md8Uryf3CSxfcTkY5WIYV18FawMFSocA6MhkOpD+5+8ByHOryo7IDUUGufQ7WJjnDKcbhtT6MDKXJnZxtHeTS2o/W7BjiFiC+HQqkXrsEKJVdRWhGraKAjO0PYJ50IQDWuU87pmPVtAgKI4zKGrsPzvKk3H08I/9zHt4/f6i5Wu89nBa3xlFRiFD0iYMlAxc/+m6ZuRyW8Lj7kWOfZHrziE8RCzq7SEG41Fc51eG2neMLfqbrndDBaktyp3uSu1PYQF6E59+MO6GYeXLC7Zg8GIGDJzpX7683tMBaACEk9oW/iFhkJUO8ijAWWgs5WHHc0ZLhl9DtU8u6fUAuiLVPEP2Tka/w1uFvrPVhtLG2OR0TMw3cRoM6OWYuegWwhBBhlINlxK2eUsVz07NSCJNNS6/5MLknverYD1oDgBzeyhZjmnt3TfLS0k38cvFW9h+e38Gq1nuf5CQrmeM6WGrtBNZn/vd+/uAjPw4PJ3DxI2fipG2jFKXLqRuOLpE5vShgPtdWBi4+JjJXIic8GvXVLTfz4S/dxV98+pZVfc3VxMCHTB0siWgL96loAqhKLefbKkUCy8qTEx7NNuFrBA5+l/JI6w0tsJYBabaHCOefJcUJgNvkBP8w9m9sqD2y4sd3VLQJrJxY+yT3vkRl/ll1rvrHG2jWa3hYyNhB7XEBRZDkYLXqYMUo0VmDWOYHW43NNcdMcetpbDDmeOCRg8l9hdr+zDbp3JRatVWQ8fB0fcEFH3GxY2EY+Eog1nAV4XU/25OsQjaUlzhYkgATn18OvrWk/X3rxt188fsPZian89UejB0zmQ9XyDbndPP65cRIFbOFKAeL7iFCc3Bjcl9+IBRY0s5jCEWzmRW+phZYmhjDiEOE4RciWKBqsoy+fGfIcCA9LUoc7j86B+TlSEY9/lj7VUk2Hp60WmVCek5yz5ZpENLEJcp7SNn+mpXB2nhy+Mehncl9I20TrvTquObsVPL3T+4+yL9+875MaOWBPdMcngpX/SaV26URtS9Zu99uUTTZIGfxgwBDuUn7Hjl6EgBnikeXtL/Pf+8BvvHTXZnxyJsnLUNGgs4shgLLqa+uwHpe4TYut3cuvuE6JAgUJn6ykAK6FxqNDQdrpCWw7EhgxasJ3Xq2mrul3KTQ93pGC6xlIMnBiv5lgRBh7GDFye792oC0W87G3BJyQE4U1rLAKMATc/fz2NyusO1IJPAXDlG3aC80CuDIMBzeHiLULD9yLBQYF1p7kvvyzlRmm3QYzK22cnlc10cS0HBav9O/uuZnvP1jP4meF322wsDvUvxxNXnb0Nd518iXqTU8DOUlAss672nsL5zFoWCYnXumeP/nbutpld+QqDEq5/Dc1ngUzONgGYGHL62kBIlXmz8EuxI8p3AXvz7wk1V9zdXC9QNM0SawRGcOVtweyRzclNwnoybyRheBpZQilypGup7RAmsZSFYRmnFbivmFSOxgBVGyu6H6VGB1GZBrjbVPdIcFigquAa0L4NEvJ1ZKsW+B3JuFeEUprI/kCSsR+r2GCGMHy0gNkI4ZJbobWmCtNGJgI66wudBuOTiGnw2VeCmXJqi3BNaWqdt598iXaDZa21t4rfElFSJUyDWtgzVmhN/tat3FTAksIQSeWcTE577dU9zzyCS1xvzf3Z/ec4CJmQZ/MfpF3j3yX3jNVv23+XKwDFwCaWJHOT9eY2653tYJzeRsE9fzsUSQqVWlkMj28Tn6bIxUgWKRC8cZIxcKLK/RElier8gJF7XOi4yCFljLQlxHyLRjB2GBHKzoy2eKKDzTpxXdRbeQwioNTvuPVDkUhTq6MW9RwTVgoXBwr1z3sz38yT/fyAN75++pthhNZWLGxW97DRFGIjqeIAB4ubAkia+HhhVHCEG9sCXp01YVJaSfncT4qbFEOi0Rnm9OMizrNFOOzPvHPsObB78NpL6XMnSw+qFMQ21uDhM3WdkIgDSx8FAzh3he4baMoEzj+QGf+No9vO8/bm3d57TE5Xwrtw3lEQiLXFTjLWisroMVczylV+w6MMtbP3o9u/eFqz9FWmB1afYcO+pmobVKOblm5kOXymu2xvum65MTLlidHVTWG3oUXQ6iooyGFYcIF3CwRPjli5er9q+D1TkgBKsksN75yRt5x8fmt9X7abBqheOO3sF6cM8UO4yJJH/maHB9kXKwejs/SYjQbLlVfj4UWE1fDw2rgRrZAUCzsJGaHMRUWQcr3XZLeqk8lWhi5rSFvM6wxnG9IHHRhTQIEN0nTKtMY2YKCx+Vrs5tWFjCZ2zyLp5TuAtvnhwppRSPtR8mmGvVm6vXWr+XwO/urpvKQxkWVjGqu9Ssdd1uJUg77dUFnLn1xnS1yUY5w/iR0FEVbQ5WOi+11vASw8G0Ox2pXNSeK+1gNetNbOHDOm/0DFpgHRNxkmZcbVYmfb8WChFm3RezX5qwttEtRLha1aDPMfdzmnlo3sf9BeqMrTZqGY6lXL2Ftw9/ndLsI0t6Xjr/y1INjMhBXSgHMI2KVpZJme07CGTyWzQrx6YdocAq7TgL37Ax2/qyZUJfqYmbiMYYt97pyEzMNFpFZA2DQKytg9WMFk44c9OhW5VaHaYMGwsP0QiF1Xy5VL7n8dqBH/G7A60Vh9XUqkp/nu+riYeSFjIXXshxjn4Ss1TSv8/q3OoJu5XGnNzNn458BQ5H3T1SbeMQrUrut+0c500f/CETk+FnG69yTlMaCsebRjUtsMLvtLSLHduvN3SixTFQfOE7CGZaS6xFTwIrO9AtVy+uxfjUtffw84cm+Ps3P6mn7bsKrFXqn/i7Q/8b/fWKro/32sx4VYgXKRxDR4cNblhs1naWFiJMVz/OqwaGaeGzhM8p+p7KVL6VUQwdLMNdm1DKiYa54wKcm7+Edd7T8fd+EUtlHRzf80guSynhHAus9Mw/5siRKQqpKv1hXszaOVgOYbXu5uwUlvDBbF1ohWlhCIXlhu97vt+2HyW/j8jUhXi29Xu59b797GMzT754W6a9ioWPkhbCtHGVgeGtntDxUr/P+swUbB1dtddeSUQz/KyMufDaJ1MCSwmZmAj7Dkxyub2TqekqFADDpPCCd2Suj2ZxkCbg1VqfpRMlvMvc+g8RaoF1DIj8AEaq+rWQEl8JWLBMQ9bBWq5u8ovxyD13s102UOrKnvo7dRuQlyPfaDF6WZU3X82btSAWM8eSFRaLWbHE0ghOqnbMiO1hWqHAoseVqfFSfstqDQPDG8JGzpuKax9SOhEwNp/BwOv/GSFNAiNHgexnl1mwEHQ6WH6XnKL64X3ki1HpDcOMyjSsoYMl8wwGc3hzU5giyDoeUT5WyZ8BI+xbV2t4FPPZS1PQ5XfmzbUqwO/Zc4jvPPxzzj1lhM2jofOhlMIUXpKE7WB15LitJGkX2EmV2FjvBJHQNxpTQGtxF2QruZ9ZvY2nDPyEamCHDb6FxNx+bmZfojCEh0TWWqFfL3KwjNz6d7B0iHCZ8ZHJ4NeNdgfLWiUH6+3D1/LGwe8wNdfbANOeqAir42DVe8hV8PrJwVqGcyLItqzpFbfRWkVV2LAVI75w9ZyDFTlYqQFyeNMWALZsGFzSsWiOnrh4qzLzHY1v046OSIcIo4lZ0CWnyKyOp0pwSII1LtMQl/4Q0UVUpJLcRfSdHRZhfuf0dJU3ffCHfPum3Zl9tJLYW5NDlXI9fmvwe7x/7DM4qVIWfqAyOV+esJDB6gmsdJNjp3r0C1j6jWRi5kQ5WOnQnxBJKSJHheNZSTp4dB/bhJBU5RC5lHvvNcIwrpnXAkvTho8xb4hQKdXhYFnCn7dR6UpwePxIT9t1G5CDVcjBqvdQpybw+sfBStyiY1jZGJ9rJZboYEUD0fSGCyk8+/eSHIeFyoRk8NywXEjKOZObzyR35avJP+nVSzoWzTJg5sgLF89vfZeyDlZniDCIcorSokx4jeS3KqUZtS9ZO4HlRRdaUY9WnZlpgRV+Z+OVlM7cNO8b+SwP3nx9Zh8tB6slsESjU7QEKdHluR6GUImD5WGuat3BtDgOasdPBfl4op33o1BhW4gwXinvp77H8wksgKY1TNFvCWOvGV4DrML6DxFqgbXMLCSwgkCFP/gUeeHSaKzej352/+7FNwIkPo5qiyCvhsCaWXym11c5WHGuyzHkuCQCa4kiLa4DVNt0EbIwBEKGOq9HgSUCFxcjEzIWQmBf8ExkXjtYq46VxxQBjUb38gNpZzwJ4buhMElP0pTvJc6qMMJK7rFLuhbEjsaYG+bsCKt1QZZttY6s2jgF6fIi44eZ++P0hPQvxHQ6myj7qdINiYMU5Rj60sJYVYGVmgg2e1uB/bnr7uedn7hhhY5omYhChAMqSkZPfZ6kmz17qc9CzZ+W4uVHGWI2KTIbRCUbrKi90XpGC6xlxhPmvHH+YB6nyl+FlS0zhBdM98ieRbYMEUrhkF31sRo5WM7c+hJY8Yx4OQTWUh1CLwoRGtHyZyFEKPB7DVv6Lp5Ow+wbZFLVuuXiBqnEdpkRWJGAisaOzG8z8ForRA1jzR2s+IK73ZwCwLBaoqp9ZVm8QnhY1jOuXNrJcyNHLOd1ipa0qPGd6LcZOVi+MFe1LE5mBajb2xh/6n3/xjv4xAod0fIQh5+HRCywWp9nmOQeTRhT18EB0WBeBjYwLOvMzIT7i11Zu6QFlqYNDws5zyxpvgrbqyEYqiKMZ8crPxZD4uOK7OCnVkFgudWWlT5fxfagxzIEq0Kcx3QMixVEMiAtbR9eNBAZqXoxHib0mMgrAy2w+gkRfY5Oqm1IxsFSnQ6W8BrRdikB5XvJWCOkgRLG2gospdgvtyW3ZUZgtTX0TbkeU9Op85A4WCKZ+BX8ToGVTh/wIrElEgfLXlUHK11yQiwisBzX5xP/9i0utHubAK8lce0/K6rpaFjpEKFI0mBE6rNoj9ykkQNhhffa5Hi4j2hci2tkrWe0wFpmfGHOL7DmcShWY1VcPMDafm/LlCUBXrvAWoUQoVtLxeLnEXSq10rlq4BIBNYyhAiXKLD8KERo5FoCyxEW0mtmetjN+7qBi7/EvC/NypE4WKnSCyqT2N76jsXfN+mH34F0xXcVeJBysAJhrGmZBkFAkPqepUVVWmwBGYHVmGrVwks7dG40KdgguuRgOa2xIXGQIgcrkBYmqxkiTH123sICa/+uXfx643MrfUjLQ1sKQtzBBAAhk5Cw6HGiZw2MAGEhWgDcBoECM69zsDRt+NKa14aOZ5kN1RZ6W4XCmUa8WrHHmaxUAZ7Izi5XQ2AF9das1E2VIUjnmPRTiDB2rmSU4zI1Nct3vvAFHLf3c5U4WEs8v4EbXlzNlMByRY6h6i5++NH3MD29cN6HdrD6CzPuy5YKEaYFluySgxX3Lmwv5xDEldwNE4WRfD/XAqEUCsGEGAGyjcTbq3uLlMBKt09RXZLcDaFoqOwYla7o7rc5WIG0VrWws586FsNbIEQGHQVm50snWQu+8I//xNc//9XWHW0T37SD1S6wZtTiKwFzQ2F9MGduKryjOUeDHEKsf3my/t9Bn+ELa97aVrGD1SA7qBxNZfKbv/YFKj+7ueftjTik0ONFXBLgybbZ5SoILJVKBvVSZQjSFxDVRyHC+HzG53fn16/msslr2Xnjj3vehzxKByuIEnqt1HJmT9psNOa4NPcIe267fr6nhq8bePhtLqVm7TCizzEtLOLvvaOMTBjaiASTGYS/kUz43vdbDpZpoKSRfMfWAoFCCUlj43kA5FJ5V+0hQhmkBVYq2T+V5J4uITNhbc48X6XCUnGboTgHSxk5rFV0sIKUg2X6CwusdF9FgOmJSW7+z0/itN2/FjzXvJGnTn25dUe7g9WRgxUKLBk4OG2T9G4UhkOB5UelLKzmFHPy+FhkowXWMhPOkrr/iONB0BFZ4XI0ZQfOPXAt23/20Z63jwfkXkMFkoBAts0OV0FgpXMVnNSFJm23r4bj1yuizcHKB1EV4kVCAmmS2mhLFFgqcrCslIPlp0Sxv/eeBZ9vKJdAagerX8gVozZFXRwsByvz240ncVbkfKR/m0J5yVgjpRmGCNfQwYIAkJz9otdRf+Jvs/38i5NH2h0sI+X6BE5LlLRKj4hMqRsnl62Onk4f8KOJWFznTRnWqtUdTB9LU5kdPSbbaU8TefS/P8a509fz4M19uKKw7TpgpD/DVJK74bv4WBw868WMX/Ab8+6uNBJ+hkE9TA8petM0zOFlPui1QY+uy0xgzG9DB4GPAbgyT7qO51IFw3zJ3wsRC6xeV7tJAgKjbfaxCknuaYctPYNNu3z9lOQeh22M9vO6BIs/EVi91q+KX8KNHKxCy8HyzRzxJH24+siCzzeUR1Ou/4aqxwuFoSEA/FTD48TBws4440b0nbGjC3fgtSoNicCHOEfLCB2sju/nKiKVCpOfDYvNF12ReSwWWI4ysIWfKQTqO2kHu5XkLlFUgxwl2aRpZJ2OrIMVhUnjZuaGHRYeVaqnbhbHSjxOVSlgBwsLrPi3HDMSlbSw+7HYZts4ZeXayzREDpZy8KTFuc/45QV3Z9k5JpWNiKIXA2qOmfzpy3rIa4V2sJYZJe1WvlMbsZBqD70FS0za9pyF7eZumEsUWAYBqq1GjVqNQToVysgOsKlz2k8CK86Fic6vivIG1BJCMknIY4kOYZyvknawlNn6u6RqHPzy39I89EjX5xvK0w5WH1EYCmftqhFeaLzdtyefsSvs5DsGre9bjsjBypRpcJN8PsMwUcLo6CCxmgiCTO5UGjMXjjEzhC3HjCAtkOYPEe4fPJ9bR57NWc/99ewOU2NDHBkw4h6xpo0UKllduNLE43pDFpPPaT7i+l0PDj4egFGixT5rKIyh5QKmSU+CAwVGWyX32GE0ArfnFIQaBaQzi1OboyAcVPH46NuoBdYyE3eH70Zs43tm1jVYag5Wo7r0RrzmUYQIVfvFdxVChKRm6emigUG6KvIqNZ3uhZbAii9g4YVkKS5jXPl4qUnuynNwlZHpJZgWWDnhUhy/m6mvvb/r803loaTOweoXbNumpmxozlE79Cj1b32QHQ98CQBP2BkXKm4Sn4ta62RChIGf/FaFaYAwM+JstREomCdhOXaw6kYosMyMg9X6/adzzCQKaZg89WW/wchIm4OVFljR33G1eKIJo9tY+bqD4bFE7qNRIk9zwTEhiIS0srOO1Vr3XXVrnZP5dMFbDwNDpj5b2So0aiqXoMfxpSGL5NwZ5r785wAYgxuP4aj7By2wlpk4zt/+Y1JKYd76nwAEZnb56VKTtp1qb1WBM68d1SzpNRfDIID2JfyrkYOVCq15marMqXPURzlYMrrQtRysaKa+BAcrufgtVThGldjTCLsz5FcMun9fTLTA6jdq5DHcKocPZOvVebLdwQrwlQgdmWY942AJ5SdiXRpmGCJcxMGq3/FtZj//zmV8Jy3iJPduWJGD5RoDuEpmk9BTKwrj9xOHCLu1lfKUzDhYcQ6UEQmsuEVPOvVgJYkFnmeVMITqCANCayKmosdELlv7Sa2xwHK69LpE+UlnsPYWOOkkdxOvM81kvtcximz19mFVw9Ic9sjmRZ6xPtACa7kxbAyhOlaE+c0axv6fA6CsYxRY9d5qWcWkc5Z6LTgoCcBo+3qshsBKHV/gdIYIoL9WEcauQuwQkuR29O5gJY7nEsMBwvc6e3xZ3XM24gTSNOEAqAVWP9EUBQy3iledytzvG7mWiFcBpgiYU+E40qzNZcs5KC/5rSY5WIsILO/Gz8LU3qTI43IiCFK/iyymYdBUJq49iIeBlVogpNKCJF2igqCrI+ZhIDIhwmgBShTCEtFqN7e5Og5WPBH07dBla8xlJzpBdZK5T/5/uA/dlIhBkR/IbrPGfVfdeutcjV/7TyjfQwQeVRVO5NrLvAghk6KilnJ7FlgNeyT5+4vVJzB2WvkYj7w/0AJrmYn7bMV94mIcJyW42gTWUpPc3ahWlKt6+/h8p/Uj7SVEqFSAFHQ4WKtRBwvlJ32r4jpP0Fnnp1+IL3qGUFHeVXQhWcK5igVWryU0YkTghq1x0sczT3G+6UOdFfxNfOhxANSsDo4sYPt1vNnJzP2BkUtEfDxe1ET4WTvVuaQ1DsRJ7tFKLsMEuXiIsBH1Ha0denR53kgKqRRqnkuNEIL/aD6T/ZuvxMfAnsfBSie5CxThABXyoZln81ezvxo2le4SIowdLDnP2LxSJBPBXCiwmtXsJGdi7y4AGtf9Y/JejTaBpdZYYKVLhszefh3NPfcglE+NAr4SXR0siCYBeCB7G19mNlyU/P3C172OzWPrv4o79LiKsFwu/xnwsujmtZVK5e3lcvm3gd8jnKrfAryhUqk45XL5EuCfgSHgh8AbK5VK/1wRV5roguU2G9ilVn5AWiAIu80GXmoF76jSsyUCal//awrP/X1EW0J6Gi8lsHrJxUjabAiDf517CpN+iT8c/uaqOFhS+TSUTUk0M/Z4Ok9tqedrJTGUn2gq5XmtmXWPCxdUECTh2yUnuQdeh8CKq4HPBTkGZOsCVZ+dJp02mryudrD6CtcsYjuHadayVcqDlIMV5+U0ZQFU6GgHfssxlaqVgyVNA6SRTADmK944GQywzZhiZv9uSieds6zvSRDMGyIE+OWXPZ+NwwWmHjKw0j3rUiUbWiHCqO1Kan+vec2LGCxazF7z7Ux+UOwgxYUwpRW5LqsUIozHKaMQiiankRV2c1UnqYhoNacIlMisCAZgzQVW9ph3TfiIwEdJg1qQwxedDhaEgtjCQ5m9CazTLrwEf7fgPnc7lw+v/wruMYtaIOVy+VnAs4HHApcAl5bL5T8C3gZcAVwc7ed3o6dcA7ypUqmcQ3jp+a3lP+z+Jbah27+YaZfKaFt6u9SQl59qpeHvuxdv330Lb5/uidXDaqL4WJU0uOCpz+ZXf+1ZYcx9NYoVqoBG1GssSOdgpAVqPwmslGD1PbeVg9Vjm4j0e1yqMyeU1zHA2VYouGpkc7HcWnZhRJJUqwVWX+FbJYaYw6wdztyvzFyYxzN7GG867NnmGOFEza1VM+5yLLACJTAMA6LFKgtNTOIk8+bhvcv6fiBOcp+/LMJpW4cYKFiZyYKvRKaXXZzknuwlJbBO3jzAyEAOT5gZgRU7WHGI0IjyvfyjWIV9NCQCKx9+Tu3FRNPulFGfwMXIrAgOt1nbdAivLZyatwVS+QTCoE6+s9VWLLCcemgy9iiwzjhpmD+a/HW+qJ69HIfdN/TiYO0H3lqpVByAcrl8L5AHfqdSqcxE990FnFIul08FCpVK5afRc68G3gP803IfeL8i45UqbQLL91rR6rh33FRQZETWFm2iHMwcwtt7D/Z5TwtvO9kcrH0Hpzn1lPmf77lh9y5XyUVzMeJjhbBR7DMvPQmlFFPIVcvBcqPqv2kHK1P7qo9ChCZeUsPHc50kwbNX0ZyeTfdaQkM5deau/r+cDOwjmwwqo/yHhigALRfEa2TzP+LXFVpg9RXKHoAqbJm7j33eCNvNKYDEoa5+9qpkW88aAA+8RhUZiShHGQhCgeUjkEKADC+Cvusi57ng5QzAB6aWX2DJBZLc06TDTQ1lZXrZxSVi4hpLHQtwAB8j2wc2Ejhm5GAZViywVqk6enQsRtQCKWhr8ZVOeredaVxlZvv6Qc8TtZWi/VyJwEfgE2BQF4VOhyZaUejGecI9CiwpBO/57SeRt4+v3qiLCqxKpXJ3/He5XD6bMFR4ZaVS2Rndtwl4E/BaYDuhIIvZD5y0lAPasGFg8Y36mIHh8PiLecGmTa0QoT9jEcuikeEifz39AprK4l0jX8a2yGzbTvMbf4M3c5htVzwHado8rLI/OksGCz7fmzapA01lYQh/wW0BZo0m40CukEu2PYLEMsSizz1W7hcBnsgRKLBk61gnC6n+ZTIcZFf6WBZDqYBZ4TMVFLBFnaEBCys6Nkss/JnETKsqR6K/DaF6eo47MUssl5Q0M895IB92pt9jn84p7oHkfgsns910MMcRID9QWvPzuFqsh/fZ3H4JM0d+wpBssMVoCeR8qQBZUwtZGoY62MIlVwiFsqtMTHxMUxAg2bRpkFwhnNCNDOUotpU1iM/JgyIUMMX6gWU/TwdQmKa56H53ptzYBjlMvOQ5j9jhhTuewNg5q2N/90e5ZvH9drS7zVtGGR4ZZGJTGCTPm4uPgctxDiwjdOJGN0ava2f3u8dshXVL/gxz5BndmK1gbi4ytq80e43spK+YN3AI8KXJDbkrMAQ8NXV8dlR0tJQXzACFUrHn418Pv8+l0nOVwXK5fAFwLfC2lLjaAXwT+FSlUvl+uVy+kuzyKQFLq3B35MgcQdD7Cqx+o+GFJvbk+BTF8VZF5omJmSRo48zOUM1v5akXboCd0KzVGU9tm2bTpkGaczMYwKG9hzCKwzTbyjTMzVbnfT7AxOFpCkATiyLOgtsCzB4JB/amp5JtAwSus/hzj5nARwmJq0ycei15vempKhuiTeJZ1YofyyKoKMxWV3lGqHP44EQyS/Wa83+maaYPTSezwMBze3rOxN4JYt/Jw8w8pzFyGu+bfiFjJ53OFe5Pwv0qQX16OrPd9IEjSMDxxJqfx9Vg06bBdfE+g9JG/mzqJfz92DXcZVzIJcFdALheZ4jNN8JUg+rUNI45RA5wMBGBj+e4BISfbby+5tDBSUpua8hPn5PY+Sl4MxzcvRdZGFq29yQI8Hy16Pn3U35IExvhNpPn1KtxRCC8Njhe5/48ZWL6rd9QsxaGt6ZmHBx3lpojGQaqUzMLHstyfVfcRgMPg6arGARmp+cy+63OzhHLKQsPTxhUaz7pDF2v0VjT7211epb0N2F2uhbmfgobOXYKXpD9HBw3vNwfPDhJAXC8tR+nVxIpxYKmUE/L0CLh9B3gHZVK5dPRfecCNwCfrlQqfxFtugfYlnrqVmDfURz3umU+GzpdrV26Vf7+zU/iWZedBiyetN2MHk7yaNxsXHyxlSbxKkIXu6cQofIiO1627NpAyVWpKixVmEDpKBOROmeZZeiLtJ1YLeIyEvVoNZfXqCe9CUWPIcJ08n6vIUIvXXS1LVRy3mlj7PdHeebjWzHjurKgLaychAgtvYqwn7jywq285Onn8NaJV3L14UtaD5idc2FZGCRQoNx6koPlYoa17gI/ESzCCJ+7UEFjqTxmg3AKGBzZvUzvJtr3AoVG0wQpB8sROQzVmeQehwi7JesHMtsQm8DDVwIryku0owTyYAVKUXQl8PGVTIqptte0ah+3PaxM4+RaYK9q14pg7ghzn72KYLq14jieRP5PPVzlp3wvHKOFweuefx6//cLzM/uIPxc/SpGRJ3gKQi9J7icDXwF+o1KpfC66bxD4H+BPKpXK38XbViqVXUAjEmQAryZ0uE4Y4saXvtuW5J7Ks5ocPCvcNh40F8kpipM/nagJrGjbt+pSwC7z/CiO7wqrp1WEfixmUgLLRy5YRiCozyzLkmJBuKTbxczmYESvHSgw/P4QWG4kUpzISfCa9eQciR7zxDLJ+z0KrLR4a6+2v3VDiX95xzO45OyNfHL2afz19AupqRzCbRNY0XdGaoHVV1imwXMuO5liqcAvP/mM5P5uuXJ5S+BgoZx6ksfpRb9xofxwUkRaYM1/sTaVxyNeWD27dmDXsr0fWLiSe5p4shCosLBqJp8qEVghShq04wsr06+RqE5cXGk8ly8QKIFyV0dgxat841WMHTWtot9gPQg/W0+YmcbJVZXLnoMVxr3vB6jZw7j3/zi5L7623OacBoQrzGOBJYXo6OmoonOdGAzGid2Kq5d3fxVhUvsHyuWk+Nd/AluAt5bL5bdG932tUqm8C3gl8MlyuTwE3Ap8aHkPub+JV4yottUX8cq8j848i8sj09UyDRpKLlrBO56JxgVGLS+7Ikx4Cw8YcdNTT9gYqEWbnSYtJtIOFnLeVYRKBVT//fcwT30shee8ZcFjWQyhfJSMBFZqcElq/6gcxhonfsbECxkcswRuOGtLHKweB0Y/I7B6i6ZnWgiJ+X/CP3dDF6uJjdV2UYn3IRco76FZG4QQfPDNTwLgbde/mFFZ5QVmS2Dd0jydx+cehpEduMpAeW6SBO4LE6kCUEH4m4VkFWHguXi770T5Dtbpj8+8polHTQwQKJibmmb5AoS9J7knAguJMmzMzMKW8LexkIOlhIkRZB0sT0lkVDMrlzOZVia4q7OKUAQuHga5KHFdtY1bynfxlWBGFSjg4gsTM9U42cPseaK2HLhzYWqIYxST8hF4DZrK5OmPPwV2ggo8JEGHcx6TOFhxZfouzuuJRC9J7m8Bul013zvP9ncAlx3jca1bzKjmiWoLycS1pXwkJ22OGptKgYfscLCC+gzO7deSu+yl4e1ooHRrYe6V5bcJrEUGjNhZ8qQdZsQF/oIzi9htywgsIecVAMFkuK7B23XbgsfRC1IFKGGES67TA1I0g62qHGafhAi9ZgMT8K0BcMMm3LGD1evMM9NjrUcHy3fnd7DSvOIZZ7Fj0wDOt79Dvk2Ex6U7tIPV3xwJBjkSDKJSLVRuc07j36tP4veHtuBionw362CpsFZREF3spBEJl2aV+vc+AID121dnXsdQPvligUbToj63vDkzPTtY0gQ/HCMDaWOmUwSiMTJZRSi7hQhNDM9DuQ3ce76H6VVbIpOwhElTWasisLx992H6DVxMBuIQYSpE69z7fbYf/DEOZlQVfQZfWFimkZRaDaS5qg7WoQOH2ATcv2uCSx8XvxEHR5mcc8rGUGB5HhJ//nEnumYEjg4RwhKS3DW9YRfCCrftOS9xKOiXn3wWZ+0IUxuFEPjK6Ch/0PzRp/Ee+RnmSRfC1iuSWiNe1Bcq57e1yllkwIjzv3yZiwSW15vAMloCSyHmzcHyD+4Mjy9/7B3Q46KEvrCwg3QOVtSZnhwjQX84WL4TCixyA1ALB5W416PsceYZZFqc9CawMvXBFhBYz74sdLBukTnMYCq7j2iGaWiB1df8zq9cyCMHZglKLcc5MAvgCnxf4UftYeJK7r6wwjxLFYS/WVrhReuOL8/7OiYeVj5Ps2Ete46SXKQOVowflWfxlSQw7KShNdDpYHUVWBY2Du69P6B543+yHZhMpYxLIcLkeW/xCZo/uRc5sn1Bp3/e5x5+hPrX38c2YC+bkryqdApF80dXh4tMlE1dDgCHkEJgGjIRWB0hzxXGbIaV5vMidX68Jg4mdlRLDOVjRHmy3QjiZvNRWRhxgocIdaucZSafM6kru4uDFf5s8vlsSMbrUl/Kj5NMo8rDcYgwqM3S+OnnKKjsACgXCxFGAiswctHthX+0icBKzTp9jHkdLGff/QAcWFoP6q5IIgcLCyPdlyw6R02Rx1L9IbC8uGBhPlxeHDgNZOxg9TgwxufaU7LnHKx066NemjW7ZoG8X8s0IA8iB8vs0hxa0z88/tzNvORpZ2IUW0vYn/HEMr/9wvO5+KwNeIQuR5KjKOMcrCDJ3YwvcsbEw+E2iKitU4gKPAyhEKZNA7urI65UgHPHN1HNasdjC6GUCmuz9eRgRQWGoxChpULhqJRKJndygTpYNWuEPA7uAzck91ki+5tyhIX0F56Q1h68jdoX3om38/pFj7kbKtUg2RMWpmV2NKJOHsfkbutiAAaYw0i1APKFibGKDlbBnQAglxpfpe/gYiX5wsr3QwE/T4gwMMMFP6IZrU7tsQ7W8YoWWMtMwQ4FVruDFV9IZVtMOsDoiLOr2bBSc61aT7YBKBy8A/fObwFhIb4Y6S0SIoxckiDKt/EXSUaPZ8PpEKFCzJvkXp+ZAqAkjz10JyMHK5DZwSUOgThGAYv+EFix0DGKYcZK4DYTB6vXmWf82TjK7HCwmjd/Ce/A/R3PSSfLLuRgxUza28jTIJhqLehNHKz2woaavmR0qNU+JDcwxOUXbMWQEl9E40cisGykAKncloOVGnNubp4RipSUCEjaOhkWDnZXARKMP0Lzxv+k/r1P4B/YmVlptiCxqO9FYEVtxgIEyrAxhGLun18XjnnR+4vbSnVzsOp26KAHh3cxIccAGBDZ9+IKe9FFMu6RsNiqf/gok/1T46YnLCwz6tkXCaz0whYXk8nSqfzH3BXcOPjsjGMW5tOtjoOlAp98EE3UUwtiQoFlhj0to2NfMEQYteqiEbph7de7Ew0tsJYZy5TUlY1sX6kS/ahkm2XqI5PEaCAzszx4OPqSRtW5g1RD16mg1W5nsQEjdrBUNIB57sI/WuV3DmKBMOZNcm80wgv+gGxmXJKjIc7BCu3xzhwMzyhi4a9O4+lFiBPFrchdUG4jEUm9DoyJcMRAtpXQcG77b+pf+6vO56SXe/cgsI4UTw+Pd+89yX2xSEuvWtL0LxuGWp9TLtXjNLwIt0KEsUgxAjeVg9WajB3yw8mAarTyrJKK4oaFK3MY3RyeuBr83nupfe0vqf7nHyUPqcbc/M5WPGb0ILDitk0BMtOE3Ln3+51jT5fQnZPfmPx93ezZXV/DEzmMXlMMjnIs23Uo1cpMWhiGxE01olbVViNvAYwM5LjJOYtxme3KoKS5eiHCVNg0fe2SgYMn7EQoqcDDJMiIyDRWlIMsou9X+/XuREMLrBWgKXIYbWG7JOxmtgssI+MMqXqr43rOCAcVM3ZFnFYMbjpozWjNYJEcrHiJc+JgLWw7B3FCabuDNU8NrWYzHLAs/MwPNZg9jLfn5wu+VjuSAIQkMCzMTE2baJVUZEEHzdVZar0Q8YUpVyzSVCbKdRKBZfYcIgy3c5WVcbDUAisKM6uRekgiVcUNTAQD+Gk3LPoOtPc+0/QnhZzJXU7YFKNQbH1mvjDDfL/ouxNPokzlouI6WKkxZzwIxZmfGmfiBQ/CtPGk3XURSeK6pEunuA1U4DH377/H3Kd/l2DqQOfzYne+h1wmFaUwGCIAKyv82ydUossFPje6Jfn7Vuf0rq/hGTmsRRbJ7D4YjrP7Dh9dzkP6WH1pI4XATUUqgupE8rglfJ55afi5HpwMhdkHZ57LX029KAz3rpaDlSr1k045MQI3XDgR5WAJ30UKNa+DZRXDnDcZXavi552oaIG1Argy3zELVPM5WKItRJj6osfOk5UIrNasczaVvLloTlIssJJ6LIs4WKqLg7VADpZKHX96Zly/7h+pf+P9BKnBfDEMApQ0wlVEdIYIAzsqg7FaxQIXIBZYxVIRR5ngN5M6Y+ljX4j4fYUFIlPnd4HSHemitZiLD2A7Ng1w2B/AmWkN7MpzwnZE2sFaFwgh+Ne5p/L/Jl9GIdcaQwIRuhzx9ygWWIZykxV0RmrM2XLKqQA4s1PJfXFjemHYeDKH2cXhOXC48zfs7703HK+icSGoTXZsE7vhvThYIsrXMQiSvwGart859nTZ3+hYq81McbB725XAyC06Xk7PhY/P1Y8u/ykOY4avFyXuY0DgopTC3fmT5HFL+Jy+bYhXP6fM6553HgAPe5s5GIwQCDOb6L+CpBc2pK9dhnLwpZU4WCJOR5kn9zNXDB0sywsFluxhfDqe0QJrBXBlvmOWFIsQoyMHK5vcrNIrxOIwTvQjs1I/trrRqlSz2Cyn5Z71JrDwu+RgCTnvKrf04KdSTYXj53sP3bTw66WQUQJlmOTqtUKOsasWCSyvsfYCK/588pGDJbxWDlYep6eGz7G76Ik2gbXAKsRMQdceQoRn7RhmNsjjzk1l9uFhYJp6CFgv+BjUVL5NYFlhODr+bUaO5nwhwvxwmJvUmG31OYxD3cKy8Y08tup0eGSXyVUwdyTbhaJL2D5Ja+iSM9VB5LAbBIiUgzVbczv33WV/Y0M5vlK7lH+bexJPvHAr759+Hn8z/fzs8Rg5cjjUv/eJ+Y8jqWZ6dCHCdLeMeBGKh4n0XYJDD+Ld9/3k8VhAPf2xOzhzR7YPoTKszJi/krj1cDydC3JYqYiIGbjhZNcwCJQg1wg7pzbt4a77KRRyNJRFPiolZOgkd81yEw5S8zhYbYo+EEZGYDVrrVyGJE+mrfr630w/n8Bu9T8y8RbMfYota2nFqwgXvvDHRQvJOFjz18GSKqAaRDO1WmumK0fCrkn+nru7Pq/rviIHSxlWmHsWD6yBT6AEMhfmnjm13u37oDHL7Cdei/vAT3t+Tk/7jUOE+VxYGNVzMFRAQ0X5CrXphZ4e7SQuAtsWIlyo+GxKYIkeBrCTNpeoiiK5xhHm/uMPaN78JYTv4ioDSwusdUfeTtWnkyam8pLfuIp+45ZykhChtFqCbHgsFFhetfU79ZKis3bo8OB1hOSCLt9H5TuZiYDndAktxvvpxcGyYgfLzwisaEfZbbuECDcM5fle4wJ+5pzB48ubedTfyF5/Q2abuBm7t/OGjuen9r7osS5EurZdHPb0ohBhPAGtuOHY2M2hespjtvPSp52JZxQx8TOT7pXCjRY9TAXFjDlg4uIbNjKq2VhohAuwmrmxrvvJ2wb1oNUxJP3dOxHRo+sK4JuF6IfRWSiz08EyMhfWRi3lzEROU/uPcCooJV3LIVq6vIDjEQujJES4SOV4NY+DNb/A8plVYW5Uc641cE9Mh2Kx1xY6SimMeEl3nOQaDy6Bh4/AzEevU+19ubiaDWddzp0Ld21SvkvjR1cT1KZ627Hv4CgDy7JwsJC+g4HPhB8Vm+1hP7G7mNQviu93Fzhn6ervPTgDhpSIqHmvqk7i3PbfSK+Gi6kF1jrij199KS9+yhmZlWZKmuHFLHKKvHwoKAo0kurpaQdrw+ggDWXi1Vqh/CQHy7JQVpTb2dHqq3PMqFbrOPXWdoeOdE56gngs6SlEGIoRSYBhpXMDO1cwiy5lAoZKrTHx5M3dG/A69siixxHrq6So6RJJT45UNAGKVwTGY+F3G2EPP7NLXutrf+lcfunyU/GscDKZjgqsFHFEYDooYhOW/VBKhX8bNoYUBEiKTjSWziOwklX0ESd6nT09uq4AKlqqmqmFFQusthysQGQFlpdK3g78MGZvtTlYrjLI5dq+uAuJmCAIe/hFA+1CTV+h5WClL95KyHmT3AVB2JgUaNZbx//Ao2FOhtPosXJy4pwZicBKxFnUvNYslKLXWUI9nkgoKmfh4/B23Y577/dp3vAZVODTvO2/FxZbnpO4QJ6wkEETg4AjQTi4B9XOnJR2VCKwsiFCfwGBla5wb/T4Cw7sbE5KsTGOq1p92jT9z1k7hnnhFadl7gvrXrXKNDiF1ko6FYmQ9KRuZDAM4QSpMg1xiNCwcmB1GbsA1cX1npyaw0uNO92c8SBxsLqvOksTO+xSZFe3KkGng9Xliy9TwjMWoelwKsC+0Uu5wz0tqTHYnWg/RxkizIjROC1DhG1vYtd7OrUKfD58cxUFVpSHNxOkBLbvIghdOEOKsJYXYRPqdF22NLYlMwLLPMFzsE5s/26FCOzILahNQXEk/DvJwcp+4RqywCn+Hurf+wS5X3gZQdpm97rn8biYFApZgaU8B5Fqp5F5TPkESGS0omPREGE0G5aZVYSyax4GhDPO+EflpnKjhgsGeFCv1RlZ8BUjUoNx7LYlq1uizvR25GC59To9p2fH59Cto1RA8yefxTr3aRhjO/D23oPccDIyPwjRjNV76Cbmorwx/9G7KL7oj7vv13MSF8gXFkZQRQrFRCSwvNlJFh1e4pVF0kIErQF9wVIaqfpgRo+VplV+CFLmaLF5mLll7TinWQuUYWEqH1SArwSqMIyrJJYIUiHC1rdwoGBxWFlYqclGvKpYmnYiPNonI+mwV4znOPiOk8zSu4W1kxChXPx7KlNhQcNuc7DU4g4WwFtfcQmlfHhZ++gfPKXjcdu2OOgNErjz17g6iuLtGdKLfpKV28LEUHUOHp5mFKgG4f33ezu4dJ79BFbc13ZpxV2Phnhi78SizncTUatMGyFEsmjicDCYKRuSRghBU0SpKKozYnOioaevK4BfGAFAzaUcDN8Lv3BtMekb7Ss5KLfg7bwBf8/P8VMDm/K9ZOYYX3s9Jfnb37kSs816VQu1y1EBAQIz+rKnZ1jNW/6L2U+8tm3zzl6ECzlYUvm4Mso1SAmsePLodsnN6Eo6ITa21mOBpQJ8ZBIadd3eV/g0IlfNb9ZQ1Uncn/8v9W99AKUC6tf+DbUv/Em0ZefImlTV74Lw3TBEaEo8aWNHhfqqooSnJM2ZI4seW5I7I9scrAUcSZH6/AzZ2yxbFrNiysAPix9q1jVKhrmKwnfwkeRsK1zRChw2w7pK6UldKW/SVFZmvFBR3qQoDCLtuAxK91ZfGQIv0xezWxgxCHovNCpTrpWRKh8io9Y/abo5WAAXnDbGaVvD73ohZ3Y4WBBGACRq3lp6InGujjZEmNpvnPcqwkLCcXHii87ZyrsmX8Knqk+fdz+xS+RVe8jlPEbisjexqMN3kzCxiPLI4o4iU0GRsaH5HcD4WuAjMXq12I9TTux3v1IUworC3uzh5C4VRC5S2/SoaQ7wLfu54TaekxFY+G5SVDJsCBoODqYpO5I8/QVEjAgCAmRSkyQ9WDq3fq1j+6SSe6oXYRjKnN/BUoaNp2SrfQwks85e+/IlxyWNZOYXW9cEHgESOxKoixVLTTMxOds6jmRVop/UglL16bA0Rbep60LC1XfC1UFC4Ioc+SC8KNk5m+mggDvbQ4gwEVgWUrRKZPgLCEiRcrBkjxcBqxiu+jniD1APopVN4sSeXR4XRGF/GTgECEYHc5RkOGbsts8EwIx+956SWKaBg9Vabg9Qm8BTEqMwiMyFAstpW6UbdPsN+252ItDFbW9N1ha/1KTbNpkpgWXhduR/dkty74VAKdx4YjHPJGa+XNNeSY+vMnIEk3Ia0WteesFJTKsiDX/+82JGAiud17pSJMVm47ZfXuvaE4vEWGA1lbWIwAq/Q54yMI1jtAPXOVpgrQBGcQhPSdy7r8MffwQAEfh4SIy2L5xpSBpB9IN3m0lIrBrYYd2UKN9mLrKUhQDLkBnxA+A15xdYKgofxCsYu1r56XyDeNVhelBcyMEiQBpmVGyzU2AZPS41jhO+kQaqEAoCb2J/sq/QwQrfw1IcrHRdmqRIpwoyBTuDI7vZe6h7rsO8SfoqSGxz1ygkYieXy1FVeYL6bPfnpYkFVpyIHDftXSAHSwYutcBmJsgzN3ru4q8BWMMb+G79fD4++wwOx8UmxYmdH3E8kPQZ9B0CJdk4nOcrtUs54A8zbYT5WIZh8NXa43j/TFiywBM2MtX9QTammAqKmJaJmYtD8Is7WCLwMukGXceV+Dfdg4OVdq2yAsvrDBEeZe7g8y4/NekHO18ZlfTEsJdSK+1kBVaUgyVjgdUkUDA8UJjv6Ql2KXTi3OoqCCyngackZj5apd1o4Ee1seIVnfFY52AxWJx/7PCNVg/dEz3H88R+9ytEsWCHOUkzB6l9+d1AOJPzlcw08wQwpGAmjoJ5DoEbftHDwpUefiSc5iIHSxKEK7/a6h91WyIdI+IcLKMzRJjQpYp4Rx2sRQSWo8xsy4VYYPXYxDiuzyWkgTewnamgQLDnjuhBHx8D244FVu8OVkYgxX8r1erBBgRzEzTmqa0VzB3uej+Bn6zU8oxW0qpZGMBH4C1WbwySHKykaXN0rhbKkxOBx25/A3869TJcq3uyaTuDxRxfrT+eg8FIkoTvawdr3RMXFpV+kwCRlCp47/QvJ+EZwxB8t3Eh+/3QWXeFnWkXI+uhwLIMiZmPnPJGDyFC38s2Hu8WIlxCL8J0VwE71SPTxl02gVXImezYGp6Hbo4btATWKbW7mfvUby39RVJunxl9Br60sWkim3O4mAyWFl9dVyzmqQcWfi8TtWNEuQ2aysSMwrS+00xqYyUuXNwZwM53RGLSBGa4vYfRYSicaGiBtQIMD9gMymxoSQRepOizX7hzTxnhwGQTT0mmpmbAbdJUZthawXdxohBZS2ApDCkSseRGKzu8hVbIRTlYSRJpN0cm1eewFSJMryLs7JUXI1WAMAyaWIiUwIqt9t4drFaI0LIN7nF2IA/cg1IBInKLzFRX914JUm6XEzWmdlwv0/YDp44xXyhzPrETBC2BZbVmpNbACIGSi5bDACB+H/Hq0rin3AIOnVRhDStIXcAWoVRozTgP+1EYQAusdY9ICos6UQjdYCD6rHNWtIqwbczxpZ2p1m40p5kOilimxMpHhXzbW1F1dbBcAn8RB2sJIcK0wLKs1uROojqqyx9tiBDCivVAtqdn+vEeJ4Tzkc7tsqIx9NH8uZgEbBy/GVcZ2KbBEy/Ywu/92sXz7qeYN6mqHEG0itC9/8c4P79u/tdtzNH48b/1XBYng9fEUSZ2LhJYzTr+9f8CpHLjIlFl5BZ23+JSH77qTIk50dAj7AowXMox4ZcYM6qIwU3hnVGIS7R94Z592SnhyoufmdTmqojoi+4pAxl4eNEMsRYJLIFCCJEMMI4ysURb7lY7Uf6XyEe1YRpdZkTphM9uqwil0VHwNMZIOVhmSrTEA5U5z/Pa8aMBWggDyzA4EgyGgs33wnAEstXVfQnNnoOU6GtMHsIEfM/P5K0pp55tWZTdQff7lZ/MzH2ztYJTFodQQuJ7ix+jUgGBEonAUr6HgMyFqx2pfLxIYPW6knwwJbCORCHCYJ52F5r1g4jC/tJvJjkyXlR76uyTwjB7e5jGN+zkd6qUwmpOM/X/b++9wyS77jrvzznn3luh40TNjEajrLKSlSzZQjYytnHAgG2MjQkGYzDLEtawNrzwLmnJG/AusCxLfA0L3iWYaGOwMZiwLAJjnEU5SLIlK03uVOGG8/5xzrn3VtWt6m519UxPz/k8jx51d92qulVT99T3fH8pO8TCTMSZuktyH6oirLjeZJYMtm+ouE4202g0LBUARUP92YYbN8uthJ4C166mX1nmseVB8nYdu693Nfv2XgHA2cYRHj+zn0PiRN7a5Q1fcuPEh2nWAk7pGjUrsLrv+2UAopteUHl87/2/T/zxv0DuO0Z0/XM3dcoi7tHVIbWGdatOfhZlRx8pW/jgenbJiS0uQEfGzfdFNN7B2hYWZiJ+evnFLM8cQ/dsXk+WkurqD9xdN1xCTGAclaRPjzAfDhqvmAqSrjLiSAnzjSrtrtRVDE1MctcajUCFEatZhOpVVKWUB067PlilPK9UREQ6HukYb5qDGgerT4AshwjdBTmhYmfwFOwCrRRRKIsLNDVJrpkokvs3swgOfAn0jLgU6Fy8gun7I7NqUTO2q7ouSuFFrQgRptE8Uqkip2wSWUqKyMWscw8nhgitIwlM7OBfpmlL1/fN11iW5os328CYHc/OJp/fZ5PcAbp987m74QrTDNKFaVzCcblbu+4uI3XCEjM0agFRFJFqQTa8YasQT0IP5mBVuVyuinAjDlZZVA0LrNrQQPutOFjSvmfj8lbXc8ez7vLE6mKdpWQa3rZ6Tx7qDJRgyTZj7m+wwe9MPaSrw8mFNmXcpuypuEZpjx4hNRsiLv/7S5uXFQqbLxpMFljCCqzMywv/DmwHs82QJT3LpzgG/Q46jRFZms8GGyYKJD0dQNJHpH36OjBlvVlC7+GPE2vJ48GlA/dx/Y96ttNSNqkVgs3BUlJwNmuieqNJk1lZYFU4WGYqvOkYnzz8YTp/+Uv0/vkdZCceMgcIRSIi9nYfpv+Rd5s/latxxjgy6amHWfvjn0DHvWJmogyYa4Z5YzudJjaPTCEDe06bcbBKoYCsa3rKGIFVvGfLZ5cGqvMGznFsKEHnIcJy4z0dNRFSbUwE2gaq0opZJzLXE1hIiRBw45XVHZWHWZyr8cI7L+M7XnUL2YxxVb2DdeHjkqhV1svF/kvvvpx6pDi4x3yhSyF49Rdcww+87k6AfHyLjnto2wy3Hy0ghKAWjbZxACrFU9nByjTVTq+7pjfkYCnesXYrP7/8fMJA8UvLX8B7O6bjeV0PhSy3EiK0DlYSj1kz16l6jj/0Ljrv/E+jd1s5ld/fuYlOSIVKsuZ6BWo1EratolEL8hmnGyHfGG/gvR5Gpn2SUg6WazT7l50bkDMmZy2PYKzjYDlBlm6guexux29htwEpBHPNkPaTCbfNgO4s5YnmVYSBJNaKemoEVkwAIkDomPSRj/FAchA9Owel60xYJ8s5WBNHq2hNhkApyZJusFglsNKSoVtRRZh/Gcc9Ou96S/HQdoHWUpHaj1Pv/76N8MbnD83WixEVF2bvvt8mfaxN+ujHyXQTidntXnZwNs8vIUtyB0spRao35mClpz+HXDwyUC3oHEWBJu338tfcW11Bi+qFY2lljQMVfxcUOVhBoxBY9VpoiwLGn2PWXUbUZk3oWMtczPb+9C2Iu1+zTvWSZqZR41e+9XkTjhlECsFrnn+tOdf5fSyfqg8MDPdcmDgHq5Z1OYtxJl9579W88t6rB4578TOP5T9r2wIl63XQq0YUaNu7Lwol3aFqYBi93vpaGQfLfk57Oqzc9GR64yHCKJC8p2tykgIl+Gh8GaezGZ7f+DgAZ7MGC9ImXm+hv5JzsMoJ+oMnPXlt0f0OuruMzrLcmUsevZ/OO/4D9Rd8S75pgsJMCgLJShqBMoOfh1NFqggDSUyESAerm3XSr55B6vJon4LAIotJhaLmmtJagfXR+CjXWpHoUj1G5kQOoWzD69SHCL2DtV2sdGKW7dgBJ7DSMW+3koI+ASLtIzMjsFKhiNIu+tTDfDq+hLBeH7qP+X8hsCZXEWrrYC1lDYL+aA7WgC1uw06y3AcrH10z+Dy6Y8ONUpHI4qLPTj862NZhXM8Z244hWztbChEGKCk5sNfmjKWxfQ0KKaz1XErKzzpLpE98auBxs7NPsPY7/47eX//qQJK6tg6WJBvYweq4O5j0XiLtV+9oRSlEGDaL2WezjRA9YTh2duYxVn/924k//t4iP87lYJ35HJ0/++nK0SQOqbMthUj2LjT5kTMv54HZ257yY3h2BtKKJUVKj43NfdOuKqzfycc5CetS1EJTrMKwa5KleUENGEEldZp/TnuEY3KwRgtmxlEOmzkB8li6mP/t8XShuH0rDlY41MR4mPU2b040xoWrlj76L+auJx/Op05AkSN55eF5VlIbbdiEs5MqU/FZTgUY29k975q/+a91kSVkMigKoWyLhgSVFxzkA5yjyQ5WYNfCzAssL7C2izTTLGd27ETn7EQHSwhBQohMY2PViohMBMymZvE7kc0RNQZnVzmHWQtJrOXEyhFXgdeoBZzNmgTx8kjuTlZOyK7o5J7JodE1Ft2xbphUpCWBtfb272dBL+VhvnEhQmkbYOrO2TzXyT3v3j22nUDcN++fkEgpyBD5wg3Q+9tfZ+0Pf3QgL0LbBSJu/w0yLhakzDpYSmjE8QcA6OrALJalc/yn3hX8yvJz7dsxRhyWHKxmo9jVzTZCMqHGCqz0jOntlTz8Ucgym4NV+mzodHzelzmjp1ymDrB3vkaPKK/I9Fy4lHP/eusPZjL3CYpxOHrlFKkWRLOLAEShsmGp0Rysni4ev6tD09cpTUi1MFWtkwTWBkRFVdisvGY+XhJbcgsCS+UCa0zO5XoOlutd1y8ElpsXKOpzAw7Wgm3HcMd1B0hsA85yX7710KpmWmqUBO84gXV6yZzPk2c3ODmjhMxStFBmHiWQ2DYdiZZ5PlwhsCZXEYb2u2ozQnK34gXWNvHcW4+wbCv/9NqSzcEa/4FLRIDMYlTWJxXmC9o1rjyTzeQfWke87xo+1D/GO9N76OtgxFkaQGdkQtCsByzrptl59gZt57T0he76YA186bvRPCMOlhVYQlGVt9nVbv7hGAFod+B67WyRg2WdszwZNY6RZCYHy83EKi3mmRUs/X/+49KJFYtkfeVz+c+itDhFnzQlz8tZAxF3B1y2j8SX8YjtG6TH9LMyDpb5UmjakRwPxAeYbYYwoW8YeZ8xWQjv8heG1gMO1rAYlugtDUzbO2c+lxtJtPXsbESpZL6/QQdL2DybrN8hXTnJ2azJ/Kz5TEShpKdDRDrsYCUDVWE9HSB1AmlMjCLRElEhTIpGo+t/XseFzU6ks/b/RRh+Gknu49Iqql7HAJUCy0YFgigP+//IN9zFJXvNuh2FisacCcmHmxFYQWQKEkrr1jiBtbRq/s1OnK7u5zcJoRO0DAjsKDJiK7BQebsP930k12nT0KjV6OrACyx8Dta28bUvfhq/I2N4yCQMilLX7yoSESKzJQIdk8rQVAva79XTWZM98yaufTptMgcgA3515bnsm6+bnK0NOFhSCNLQTWhfzsciAAMVb855KVcR5rveodyMzAosIRVNYS5wue9yspNmmGpPh8zSG9/XyS1Wa2fIFo2Qkba6zSWjZtbB0tbBSrUAnZGe/CwibJC5PBIn9mDA4Wp0niTVAiU0KhlsoAiwnNVpJB0IijBfX4d51ec4cSjLDlY94E2nvpoMwU83womzGwdyJWw4Ybgz/4CDpVMo9awyyfVbCBHaQa3BRT4nbDcQ1BpkWiCFprfB8efCOhBxd41k+RRnsiYLszbUKCV9QuSQayt0VrjRQFdHKL0GaUyilQklVTlYFU2LN8tPL72Yy+qrzFOMnhIbSBIfh3Owxrr+6wisuB8jKBLBoSSwbEuZFMmxA7MD99NRE/oQiE0U6Kg6JEWuKzCyOXa4dXvsxm4CygmsIDQzc62DmWiZVyDnx64jsOqRopNFZIEXWF5gbSP1Gdu0r7tmBcKE8QIyROkYpWNSGZHJBDLQCFbFLFcdmefn/uYLeTxd4L9CPvw5CiVxogbaIwwjKMRdWKtBxkhVUDkHS2emN1O5f45rNjdS2uwWGaloYi7KBw5+AVecfCtQOFhJvzpDZGW1QwSkyyeLc7ACyzVTTeMYqTO0VDZEaHKw1t7+A4OvobxglhZJScaKrjEreoTp6O5uWTc4nJ5FlEKEPR2Q2PdsXMNQQSF0mrUg3+E3aoERg+NaKOhCYLnQ50hiajmkmqYDnfslY+YmbhA3R8w7WBc+YaDo6JAZ0acvNpiD1dxHpqHz6ANkyyc5k83moSwwmz01lFhdrowDc30oUnSWkCDNoOmKBp35RmeDAut1L3kal+436+b3fe0zuP8zp3j7Xz3AxzpNnhWdyY+TWwhvO4E1tjBonUajT5xc4RAUax9lgdWHbMxmutaElY33BQRbkNAjz5UDoDsmBysXWJvv4yV1agWWaY+jMrPOv/zea5lrDn6ugnUdrIC/7j0NVTvIMzZ9JrsLv8JuI7PNGl0dEndWCXWP/oQciUyEhFmfgJRMRXSlWWQEmssOL9KsB3wiOcyStqFC++UdBcrkP0zI2RE6Q9sv5Lxb8nBO1N/8AslD/2wfOzWd30tf4q65XNIbdYDA7FA/07gegF/5u5W8cWbP9ekas5i1HzoOQLxyJm8V4ZLrXaVMmsRITIjVhAhFXpKcP399bvA1De1CXYm0qBiOvJzVUVlvoE1DrFXRzHNMwrl5X80l1KiVBJAQJsl9nRAhUuLmGQ7v8HXZDRhyBsxreOqX7p65GlEo847fnguXQMm80CWRG3OwVGOOz6T76bT/Hrl2kiey+SGBFeVfsA5RStwGTA4fCSIzDlYmVHWIMK9I3tiG4PNvOcLVl5q8zKuOzHP95UUbkjAqX2NP/fOvXIh0XOHNuiFC20qltBYmdl7g8ZPLCJ1UVtBJW10XbkYAuRSKksCKP/fxyv53W3KwSNEiIFSmoj20fcf27RkdxRXVJie51yPFX3Rv4sHw2k2fx27DC6xtZLYR0slCks4qzXSZFTl+blwmI0JsTxkV8WD9hvy2q4/M53Fwh7u8olDSJ4AxPZxgsNqt5gadDokGsfIknXf/tH1wO1qn9OmQ9qJKxszrQwU8MncL33HqtSzpJtomvDsHa5zAqkv7StIkd7DyHCznYCXGwXIuT4ZEZIOLiJjdO9DaoNM1z5fUTN7Dmh7/5bOmawRDAitD5A7WOPFqFjLzxVEWWMDE2Y39vnmetV6W52CNhFDKOXFDzy/R6C0kuQdK8oOvu5Pn3X70KT+GZ2cQBjLPA8zWaQDpiELJx/tHyU49gkDzaLJ3UGCp2uhomqEq6ERECEAkfRIUWpi2DcM4IfBUQ4RH9he5p/MzxTXs1oanQhiZMJhbA7t/95v0PviO4oB1HCwnZOKOcZK01gjbwPj4qbNjHSzVMCHDTTtYkKdBAKSf/nvSRz46el6usfNTGPWjKBysGIWTwyosNmGPpkbs1qLJ733droXBRT6HELzA2lZmGyFdHaFXThCSsComCCxVchNURCdc5K/ip/P7q8/g4J4m0RiBFShJotXYJplgq91ygTXGwRo4mSJnyxHYvI20bFWXn0MqolDli33XXuOu8igZI7BqgX0lOi3tdl2Su7mvTvomyd2G4zLEyCL42TMM5KGdXTFC8JPLZlEz44fMe7CaRZxKi9E2K7qORNPsl/IcENx986X21MaFCAsHayTcJsfPbnz8hAm/PPTEapGbNySYxEQHK2Orl+7hfTPUIp8jcaFTi1S+FqzXYTu/T6j4UL/oi/W5dA/zJYGlRUhAgtaarLNE/OD7TTuRUt5fYtu2qNQMpzdVs+MdrKfUmwmol77M58oCawshwiAwOWOuP176uY+Rfu7+/PbhzduIW2QFVuJCdUk/d8ZrUhsxWvF6AyuwnlAHN3yurm1CtmLWpg/0LjenULEOP1UHS2eZ3bQF+feJw4VTAX5u5cX88JlXrLtuNKLqGZgXI15gbSNGYIWoZVPltqrGCyxdsvd1UCMMJL+3fCvv693A4kxELRz8p5qtG/FxZP8MqVAD+UPDuCadAA07a2qco5StnTEOlhYDtr5yvU9WTlXeT0g1IDK69nRiMTnfIbIOlsjSooO8Tb6WoXO/TJYHucAa7IMF8NiqIi7lh7lF0vXRiW0iLsDxbJ7/svSS4r7qCAD1bJXH0wU+0LuCF73wbl569xUkWo4VWKaar/oSmuRgObdRC5l3+B9Och9wJIdCFkpsrYrQs3toREG+qdmMwHoiW8x/74SLgxs4J16yhP4//h7d9/w3DnQ/M1BYkdr1SqVdEkzFWKVzkk+F2PpXzdxMkfuzlcdzYTDXH0+nyUBF4IiDNdRuxQnJ1DlYpVysmkzzqRPD1BoNfursF/EnwYs3fK7CDU62AusP1mw3/v5oJCEXWGPaw4zFrUeyCBE6VEnIrqQBJ7O5kc3+MGFgNufKF9F4gbWdzDZCOjoksH2Y1iaECHXZwQqigQTzhdka0VBFxuWH5viu19zKq7/gmnyszjiELoRAo2ku2M5adagvO/VIESIsfYmHtZqx1deqHSxUQD8uFiZnkbvmo+MElrRhBaHTvJJxOAcrsw6WluMdrFVdG2xtYEWJE1gJKl84Yq3o6GJntlw7RMdWYD2c7OPXVj+fWi1CKWFE2RjxWk5yBzOi5A1fbEO7Qo1f6Nx5CmkdLDXaJ6gcIhxo2WAecyshQs/uoR4VrjHr9CdyuC/Ilee8kf8796KB0BsAbmpDGoML1zMY9kqUEXNB2iMVikwE1Q5WOd/wKXL3jZcAUK8Xa+RWqggDZRK59z3+f03yeJoMJKyPvI6h69gJybRn1tCy2BFZwrieh41I8dl0P2vpxt0319QzWzsDwJKuoxEDoq50ouZ/64z6GcGtLzIgCISpSgdSLSp75dXWKY4RQtCobWwc0G7Hr9LbSD1SdEtf5J0JDlZWGiMjgjphUHw4F2ej3E06eqAIbV1/xV7CQJKKoDL/IX+8UogwsiHCeMzsQh1382HCZZMkCu0w586Z6ueQitWuOYd7bz2SD55N8w7wY0SKXQwkWS6KXL6Gi//rJEaR5ZVIwzlYfa1sVVOpN1Y6KrD6VmD1dZAvIgBzzYgHhLHeZ4RJ7gwDiZLSOFhjFixZSnIHM6Lk7psOuTck7xszgtsxIhCYLvvDOSrlRT4tv3d5BaIP73kGc7DWawDpcPmcq3uu4QNZayD/Cor2KMPO7cBsOTvPMMi6ZCiTUjChinArbRpe/9Lr+e//9vNzkbaaRQObv80SBJKHEjP8KvnMByGNB0XS8OsYzvd0mxyX5F4WO2lsKvIqrk+Xp5lkY9aFClzuK91lejpAI8lUrVJguQ3dhmaglsjXNxWaNc9tjlGVeVTRBlIL6pHyDha+TcO2UotMCTVAiqQXzIw9Ni3dJsJooEeRy4/4oa+/k30Lo2GAdD0HiyJEmM/hivsDvaJy4l5R2VZaxKJQ0dcBMxVzDMEkpq/1zDnces1+sgetwJI10BNG+ZTP2zpFyu6aVVCIM0kxWNmMoSm3VAhJtGnMqrMEIYP8cTs64rFkkVUxS8wpe/zgx362GfHe5Tu4UX2C09lM/noDZRPdxwkssvEhQimRQqN1NjJ81YVzpXDh25BwKEQYlvp1pUlimqnWZxE26fWp5rR4dhdCiPw6ddVx6+HWkM8+sczZlR7HLhnc+OUzQNN4sO9d6TOnwxr0Icx6JEKhZYCs+mKvalq8SZSUqEjm19GqrjG/BYEVKsH/Xr2bW6LPmteYJgOO+MgEhuGKZSdkrMgZEGdpjNBZ5aDjXGAlGw/huZ5TMukQ20KdVNUrBZZya2K6yST3tHCwgHymbKIlUYVIGi64quLFz7yc/RXfVRcbfpXeRqQQeen0WeYmjibJolKX4rCWJ3eGgczF1rFL5pipj5bWaxmgJjhYslRFGER2dxrHlZWHOu7mIcIyUWA6PKu4ugeLUAFXHjav4dDeZm6RZ6pOqsVIg9L83MqLsr3QcwcrF1j2viUHS5XOPUXmbSHyvAorHlMk/2XpxfytuisPEeatLiyzjZAn+k1+c/b1/MHaM/LXmyd8TsrBGvfF4RbYijBhLrDIigrPoR1+Mz6dd7DOkpjV3/5e1n7n+0oOlrffPYaPaTPYWdbHO+RlZhshlx6Y4cOfPMGJs10O7hl0vpyDRRpDv7huXZ87c4xZ1yQZKaEZs1JRHVf0wdr6V01ZYG3l4cx1bTdraWLWwaSfOznrOVi5UxcPhgh7OkBk1sGqChFagRWnGxdYYaklgmvHEctaPiswffyTefV0/h0w4bugktIMWCgJLKpdqGgD/fOef8dRbrlm/+bOYxfiBdY2E1uB9Xi6mHdLriKrFQ6WjBo8/ep95v4b2O1kYrLAEiWnRYZFXlOVcNAlB6tMFBb9dqqQUvGyZ1/Jj73hmVyyt5mHLUQQmFYNYwRWeTFzYUSXg6VC+3zW/XK2u0Ygs3JeEkR1O1g7dQLLPG4QBPSIqEU1E2YEzmRGYK1kNU6ms8zUA7r9lDN6Nu9VJqWwDpYarOjLnzNDivH9qFy+WGVXaHvuihSpbX+vIQcryjo8YYfbuhCh7pz1IULPCH+tb+d7T38Fsjm/4fu0Llvkw586QZppDu0dmnOahwgHHawZSk5NKaUhkeHYJHdhRYjLp9wKrmhkNatvOUSYj/1J42IdtGJyOHdSDye52/MQucAybtJyVrcOVjqQOuBw1XXpZgRWVMvFYN8Kn8eWNd2VZdInH2Dtj34sHxHmUiT0Jh2sfAasdS5dw9pEV4cIx4008oziBdY249ynJ+LmSK7DALVirIIKa1xz1Hy5ztTXj+JmMqjcPTpkqdFo4ARWGlfH6hMjsPSQgxUGKu9pVcb9TcgAJSWH9xmh6BYYpZzAqk6qL+eOCdt7xw0yDgIz44zcwTLvRSakGYBaIu/XMiSw9i+a86lHiqY0Qu2sFVjff+ZV/MjZl1OLFEma0e2XzkWY0ESqZbVIWi/Z3C2wFWFY11JDkuWtHqpyVB63Aisrz0Isj9nxeACQrOka9U203bj+8j35z4f2DQmsfO5ojI47iOYiALMU7rUshSNTEcIYB6t++lOcTGegvnHxN44gNULGOFhP/UteCoGQyjQsLglI50QJnfKh/jHu6xlncPj6d0JSJl2y1dPEn/w7AJayBiJL8k3TMPXcwdp4DlYtUnlKg3Pguzoi7qySPvEpc94d04Mr0GMcuHXIJ2C45tDCTu1AEpTWt5/81mfz5c+9elOPfbHjV+ltZo80i9KpdJaF2fECKyg1dFO1BlIIfvKb7+Zn3/y8dZ9DCzu2YkzVWrlfUxDYflBJXOlgZaUk9zK1UBaDm12xija7NgAxVOXoLHIVBGZ47DiBVZGD5do05A6SGwMknYM1GCLUiHwKfBEiNIvMvj1N+7olM3ZWYm1hH9/0pTdgur9IDiwa9+uzTxTjQepRgJQmB6vKwSqETvWXmpjgYJVDhNLudkWFUHMOVlb+Esirsvwu0mNwhsJmBNYNVxQd0g8POVhCOZc7pt9Zy4V+uUFmfr1hBJaWbg0qxIPOMhqnP80n4sNTadMQ2AHUa1lty3M0g0CSiWBg4+ecKKkzOjrigdj0q8qGrmHnYKm0S+fdP0v2+CcAK/yy2M4oHf23aNYCaqHiNc+7ZsPn6VIzwAis7/+6Z9DVITLp5EPu5fxB4gf+kRpWKG0yyd055C403BO2lY+WqJKDdeNV+/iiZ12+qce+2PECa5v5SO0OHkwP8v7+lSwOl0OXKLdhCGrmuIOLDQ7sWb8ySOdl1eNn5jkhEChBrJUJx1UIh+7q6kDnd4eZeWYW3j4hfdtXatUmXg53Vi6S6gM6OipE0hDlsEIuPKxTpVyuxJDAypBFQqfFvWeueaDL/XAOVqBkPsW+F8xx/bFiB+9GcwDcfNU+vv3Lbs7DJumYECHrlZ+7cGaV6HU5E87BYjRECCasDJD1Cucgr7T0DpbH4gTWRpKPHeXJA82hvE4RFOOtuiurPLRkfn9cHsqPUaXcoFSGZDI0W7LSl3t29nFU2uXTySVbCuk5zh66k3/oXcWfdW/e8mOFyjRHLSeou58lZiyQ61yfpcMOlrmmAx2bsL2lr00197gqQikFP/+me3nOLUc2fJ5RWDhYfQIOLDZY05Fxz6zAQgi6f/5zxZ022abB9UTMQ4TS/NsqkflWC1vEVxFuM936fv7ro6ax3CQHq9ykMww3l6+gS1U/VOQ6lCvwQpt/oF31zBC9tVXQesTBCpTIHayeDghJ0cBaZmf8qWEHy4YkgzDfcVUhdUKqBUroPHQmnIMlBT0UKh1ysIQYEFgak6uQvweQL/QH95rQa1ja8faCuYEvmAMLdeabIUtrMbONkNuuO5DfllE9AiT/IhmXC5WHCNdzsDK0rGg0iukwD6C7ZYHlc7A8gwh7rW3GwQL4he95Pg89MtrXrlxpXBN9ujrkh858GZcdvYSrT7yPVvAoYVQIrMwKLMBshuxmS9veTaezmenUZIQNfnP12UzjoZQSdBJBuLZaPJ51s6TOSBH5Gjic0yTJyLStAq410Ssnzd1RSJ0MTJ3YKrVQsYJzsAKa9YCujgjSLtnZx835DQuqqurwCaSxW3ft88g6pBCJxOdbbRG/Dd5myovepByseq04brhr+3o4gaXHDC+VpT5YynXqTasdLB33Kx0sIQR9m7Df0wExxsFyM/6c65Q/jnu+0ORgybRwsPoffhfLv/g6dNxD6TS3wAsHy7lt5lxF6nKzihBhUM7dEgIRDvXbssLmgM0JCwLJcb1oHj+sDQhaIQQt62gNV/ik44bYrlfNNylE6HKwdJqHE4ZzsJ5Uh/KcC11ysPLdtF/4PJYiRLi5/fKRA7Nce3Rx9PGCYn5okPXp6ZDT2SxZUOP31+7kJ5deRlAfFFhVa5ATWEtZY0s5Uw73GM0N5KWux+WXzJFoxerycv63PAeLjNRmSEK1wMpnm5ZCoolWqCyxc/2mI7CiUOYOViICpBDEsmaaHNv3lyzN81Nh8zlYbui1Gz/kmsjWxCarET0jeIG1zZTnNs1PEFgz9ZCHElPWOjLXbj3cxTW243iWh7JCZYZ5ksZkQ8evZVGe5D7sYAEkNvkx1Ub4xFqxZsOGKhgMM2S5Y2aaraq0yCPqf/jPACMcBGm+gOTCwyW52xChzEOEdmdcER5zuy/XMd6F0mYbNeabIbVQ8T/FK/jRMy8nDOTIzszlFiytDorUdMwQ21w4jVtI7WuoKiTIm6vqzPbSUrmodHxWXmb+nQAqQoTTKHv37A7cZ3nT68YYpL2W0u4KAp1fn+WEZxeSBzOoPpMuMb4ksGz4bEk3puKEuIeYhsD6hpdebxynpCIHy4YIM1u9N5yDJcny1IhspeitF6NQ2gisTEwnOGRChPbfwz5mTw3mzJEmICX/IG7hkWTPpkOEWWzXI7uGx1ZghZsYSu2pxocItxnnYM3PRBMTM2fqIT+x9CICUr57E7kUALjd4xiBJXUx0iVQgkQrwiwm6w8JLB3RSHoIZKWIcT29pNAmzKjJd3KjSe5mNYwCE1pUJQcrz1/SqXGwKAZQp1qMJLnLzNzXhdEyIfOpEOYGkVc+pa6haWZtfKX4tlc+ncWZiJ95dInjWcr1jdFqyMsPzfGvvvRGjl0yO/D3jDEz1tzOdUwulHOksjQvCAcgefjDLHQfNcfkDpYcyMF619otfKD2dGp1WxVUaiqorcM2MlrHc9Eyaz/P0zI13bXkqtNcakA/Ka6DWmjyMCORmrQEFxYsCaxs7SyZtCkCUzg3l8fVrI1ev5sl37yV1iXd76CzlICMxGZIwhiBldVAAf011hau4ocevIsvbHyMkMGmnVulZhs8g63WBFaCPYMHZSkkfXpakSJHG6WuQ5qYBhB5aDgweb+h8AJrq2zoU9BqtX4QeLX99Z3tdvu77d9D4E+BH2m32++zf7sV+GVgHvhr4Jvb7fZF6zU6gXVonWT1mXpAiiJFbaiR2wDlxoBDaK1RIstX38A6WM00JkmGBVaNZtoDXRtp0wCQ2PEYAp1f9A8lB/hsso9mONi1N29sKk1fFaUTdJogVJCLE530UaRFq4e0byoCS+eaIAnsLlPb5x8OXz6p9+Z9dtIzT9jXbeaBSSG4xiaxu4TNcU7iM2+4ZORv44fYrpODJasTZDvvekv+s7AOlhbKvC+Wv+zeQNJPOHZwFvog+qUQYf683sHyGL75ZTfy9x9/YqSf1VPFfdHq7qDAWl4r1oswNC52JFJQEZkTFKViFr12ljichaG5pk8V54JNw8Fy8whVWjhYSWeNcM24bmezUrPk0jWstak9dg4WQD+co0eUO0DA1HKwwlIVYWrz3DrhXigt3WvLywRAL1OmrcymQ4Q2cmAr2bMJE0c8m2PdVbrVar0AeCFwG3ArcEer1XpFq9VqAe8DPm/oLr8BfFu73b4OEMAbpnnCFxouL2LfwjoCq+SqhJt0sFxZNVXz/oacFtedXGTJYH8lYFVHyLRXmYMFxc5GoklsiPD++FJ+aumlI1WEzgGTaFInjFxjPrfDinvIklgTWWKGTFshJKUg1YrAJrRrl8BfEhfvWLuN30+fm4co9Qd+l+SRjxoHq/RYAF07jNrlwn3jF1/PN33pDaPv2cDrCCoFlhM6Ve0VzMnbnl3J0H1L524SYs0g7rKDlSBJM83BvU1iLRFxycHyIULPEHvn63zRsy6fWkJyPgO0a9qWuC/4pbXCnYqCwlkhqOXFNcM5WF05i2BwfXuq9Oz126xtXWAp6+QHWZG6kPbW8mH2RmBV5GDZ628lK3VYt6kT5d5g03KwTM6VFT7u/9Ggy/7w544D0M2Mg1U5smgCeYNn+28oa9MR6p6N5WA9Bryp3W732+12DNwPHAO+AfhPwH3uwFardTnQaLfbf2//9FbgVVM94wsMN3dqz9zkOWHlRWO9aeXDiEkhQj3otARKECMRWZzvXBxrmenjItB5Y9IBAtvzCs2/xEdox0fyRqhqqONvnuRORmZj+m68g1ukXM6Dy/GQWWxnIBaPk5RyGXQw6mB9ND5KX0RFc0QgO/M4ZNbBKgmsjp2V6Bysz7vpMM+6oSg9ryITAbIiBysXp2NysFwbhWEHS8wV4yPc42qpBvoEuZ3zgcUGsVbIksDKpjh6xOOpwl1LomccrFrTOBorZQcrkHlncRFGaOfeDOVgnYprXHpgZqBq96nirt/GFBwsKYQJp5X+lnZXyVaNwLr8qst43jOOAaVrzvwCMOBgPXDCtl0pV39PKckdILWd1Z3AapTaaixndZrSPL8TWJtPcnfnb95XNxXDs3XW/aS22+2PuZ9brda1mFDhPe12+5P2b99ROvwIRpA5HgOObuaE9u2bXf+gC4iOFVjHDs9z4MDGZoUdPrQwsONb736NWbPjmGsqZoeOzfpdVjAzCN3jpAQoHdOsScoDbNZ0DZX1kVKjRTDyvOHMLHRNDtafdm9BCDi8r8ZqN2H/vtmB4z9cu43rup8mPHo9PHAGElicEdQOzLGCSaFqJqfpUOyQlU7JtODggbm8N0+fYtGamTfvoQgCXP5los28rIU9RafombogDASpfSwnqNwCfezIwob/LbIgIozjkeNXkjpPAvVGVPlYjRmzSM3Phewt3b7aX+WTs3dw9uRJrm+eACCIIvYdmMcFV1x49urL9hB/PKBeSsSdn6uxBDQb9bGvYaOv7WLDvy+jVL0n+08ZQR/0jcBa2LcHzkKaFYmPlxyY4yG7MWrOzaKs4J9rynwNWu2u8GRnnptuOjCV9961b9m/pzmVx0tLm7dMQ6D7zIgOXUDM7GVmxqyOczOltbMrWaFooQJwwkbwa406rtenqtWm9nnL7MZShOYxF+brrJ2s06RLoiWzgVnXVmNJGkmkyPLn7p94BJ30qR26auzjn7JfNXv3mXVx394Znvz0HJ+MbuS1Q6/BX0ObY8NbgVardSPwTuC7nLiqQDKUfgxsKuPu5MkVsmzjowR2One1DvC+f3qEa4/Mc/z48vp3AJbOrrG2Yl2MA3Pr3s9NeDl14gydPYPHutLjOCV/nBQF6RpLZ5coZyOtaZMrJbIEraOR503sTkpiQo1CFIM/z55do14yVR4Vh3jjqa/lW5Ka6asCnHrsCQJ1IA9znXz3rwCFwBJZnxTBqZOrrNrctV7pDFf7guPHlyl3UkiRiFSz1is+M8tPPkHc6xMhOX1qhd6arY6xYlcn6Yb/LfqEKFKefOI0omT7d04smfPrZ5WP1e2b13j61Arp8WW01iQP/RNZd5WVRkha6kYfp7B0psOwx9kITBhDJkWH+dOnVlD28auedyOfl4sR/76MMu49WVnp0dASafORRDQDdJififIq25XlTh4i7CUid7BPnzhDx60zvQ7LieLwYn0q7/2tV+3l+sv38Pk3H5rK46Wl8pM1XSNaXWH5icdItGQ1jejY/nNnz6wS2OfLOua6j7ViOaszJ7t07PqV6OLx4lRM7fPmHKyUgOPHlxGZ5i2dL+eqgzVeeOa38hSCvk1yJ+3nz738i28EYO6b3jr28TsraygtWFmNOX58GZ1m/NjZV3B4X5MXl16Dv4ZGkVJMNIU2FGdotVr3AO8Fvqfdbv/ahEMfAQ6Xfj8EPLqR59itXHl4np9/073rhgjLbHYMhCg1BhyhIik6FabjcDZ0vOvUHur+yLBnABUZV2aJGcJAECiZW/9qKKToklqVFHRri+ZUlo1jU+4dA+S5DjLPwSpuc4NHzYnZEGEpgTTRRugFUcQ71m4zt6+dAZ2SDuVgOeabG2/k6hY3Nwg2P2en8saFCPMqQvP+p5/9IN33/DcA1qiT6lIvLykrz3NhJipaNVi0n0Xo2WaCwCSwq9gIe1lr8j1ffTs/8HXPYP+CLeEPFH372VS1OtK6LGm/R7Z8HJ2liCymp0P2zG987ZvEXDPiu77yNhZnp/N45UT05awOcYds9TTLuolUxfiqgSpC+3OGzIfG5zNaVRF1EGo6OVgAmc2x1fb/9Uhxuh/w0FJgNmq2EjLWAakeHyJMHv0X+v/yVyN/10mPPkH+veN6MvbjzVUjekZZ91PQarUuA/4A+Ip2u/0Xk45tt9ufabVa3VardU+73f4/wGuBd03lTD1jKVoUVFURjn4hZyJAZgl6KMndNbYMdL8yB0s25vifK8/meOOK/GJ0uWNqSBQ6gSWlIIsWSJHo5eP5OZUf3VX3KZ2QURtI1o1FsZgqNZrkniCJMP293tO9mS88uoRaOwO6SaZl5aiHRq1aFFWRJ+gnPQTFTqVIcp8ssFy3fJcwDLCa1Wggi+G4Qo0IrP0L9bzRahmXcDs2ud7j2SKBlNbdiU2rk6jJdZctAvDvX38X/SQjUIK+FRYyrOXXWfrwh1m97zepPftrATM+Zq6xuckU54qywFrRdfbHXfTqac7qJlKKvC2MTlN0b9UMhrabQyEVZ7Iml3EqH6mDKr3OKQosrWomJcIKuLodTn/ibJd0VhKkxmnrE9i8smph1HnHTwIQXnUXIirlWSU9ejoYWdPj4QIdz6bZyCr9ZqAOvKXVan3Q/vfNE47/auC/tFqtfwFmgZ+Zwnl6JiBtiwT96McHhq3G7b8h/sif2YMGHSylk6LrucU1xwt1XNkHqx4p3t+/in4wS6DkgIM1LBDc70oKarWQs3qWbMkILEp9Wj6RHuUf9Y3mWEwOVvmxYlksWq4/VvncnC0f2BBFEs2TrZ3JqwjLYu0NX3IDz7rxkk1VW7nEUj3kYOWVRWOETu5guYT+0sDmVV0vFmXzwkbK2I8emCUI5EDYAYzQM0/g+2B5tgfnYAF0dUStlKDeqAUszEREpWNUVCuGPz9+PwCpHYDcI2CuufUKwu2gnIO1ktURSYds+TinslkCKYtNUpay+tvfy+rb3pQ7WCoMeCw1/aiUFTRiwMGa3mvOrBjStsiobv890kwPpBrs2ztPEIaVVc9aZ2C/J5JHPjJ4Y9Knr4M8zOvW9H7iHaytspEk9zcCb5xw+3OHfv8QcNeWz+wi5LUvvI5PfW5p0/cT9Rn+tnsdz374/WQnP4PafwUA3b/6leKgktOipWk9kKWDIUIXagxIoKIPlms5IYQgUAKBKEKEwwKr9GstUpzKZtlvQ4TlIz+YXEkWFLupbKhnTixqeVZf/hzlcKetOgxsLlgSzaGPn0HMHDAly6UTufvGQ9x94+SqwWFSWwGZPvpx1n7339F81Y+j9hwhy0wD0bEOlioWZyjGhgCsZBHzekhgSTGQvHjn9QcJlRgNESau6aoflePZHgIl6NjP3ZqOKodIB0rS14FpNhoGSCmItSR0XcTtNdrV4Y4VWLoksJZ1nSDtold7nEwvNQ6WG9ieZWibe+Wu5zAM+bOlp9MVdT7Qv5KXPPMYrD1cPPiU2jQAnKldxtsev5v6UTNtohwiTUobtSsu3Yf8bNFWZmDWbNxF7j1K9sSnSB/5OOFVxVe0sA7WjN3ANiLnYHmBtVV8nGEH8QW3H+UNXzK5L1MVgZL8U/9KYDAUVUYMhQgDEkj6pLr4ohalMuMqB8uN/ckybRysQObhtmEHRriQQaapRYoT6Qx65USRQ2TppMFgg76hj6TrHg+FwNJDAguKYc59NQtpH5V2ybTccoNDl/8Q3/+X5vns7i8P1Y3JhXJ9rVwOVlYSWJ8+kQ44WGLIwfrer7mdu288RBhUhAhtGbzv5O7ZLkIlTcNKTF5m1WxUIQTHWeSJdIEoUISq1BcL8nYNWtUIg535WR0IEbq+VlpzPJkx47ScY17RBysMQxIU7127nqdfc5BXfcE1eaNOwFQ6T4kwCrmvfy2BbaOwb76oYExLGzURRmihzPgtQPeK7wLd7+RNYMt/BxBpb8jBMu9LuouKzc4XXmDtAkIl6doEdVc1OIIYdLAAiLsDDkm5j0tVo1HXlV5rTagkgRJcfekCVx6epxZV52BpDfVQcTppml1gPHh+a1lIGJZ6XQ05Z2lJYIXhYIjQiEPB/oVGnj/gQopB2iWbQuPFzOVg5SFCNz7DhQU2mIO1eia/bSWrjYYIS79edtDkeik3N7KME1hjntfj2Srlz11HR0RjGh/fl93Mf176YsJAEoWSuBQQcRsKFdUr77sTcOF/GGy7cDIzrV2Ka7hIpcisQxdGxVrp1kUXAYDphgidwHUV2/tKRQPldUSGNTt5wgqsbmmGaX8NbceIlZvBAsjUDPR2a+g0epZ5DP6d3AUEgcxLhXVpbt0A5UaW0vaRijsmx8dOTVdhDayrXJmDFTqBZZ5TADdftY+br9o3cqxzmzKtqUcBZ+2C7cZvONbSgLkwzPvHDA+ZTlQ9HwvhQhV5fhOCF911GS+9+4q8x9Wff+gEXzsLYbpWWQm5WfLmpk4YOuGYjlZnlikWZxciPEtwxe182wduRCMGdp7DOVjuvTPNEIc65OcOlt8bebaHUAli60at6YhmVC2w3HDpMDBOcb/ktmrbsDPYyU0rpcr76bmh9QCnslnmmxGib9ebzqn8Nld5HUWlxtD2/ZFBOV90el+tTuC693uuNOqrvI6oqG4qkt2aU3awemvg8kDjwXxSkfbpU/cCaxvwq/QuIFAyb7HAGIFVrjrTducm4s5ACCqoFTujqipCt1PLtGZhJho70w8KByuzIUIXPsg6gwKrq0OCkrU+7Jy5Kj4w4znKx2gEd994iNlGsftyTl6YdqcisFz3+rxNg93BulyM8Q5WMHCcXjuDaC5ycK/pij0SIizlipV/TodDgS4Hy1cRerYJpSSfTg6an8kqc7CguB6jQBGGciBE6HIOo8bOHbuiS3lSS1lxnmeyJgszUR4ilCsnivv0zEYrKjlY7v0pCywRTM/BGhZYA2kPpdcQhBGxiAhI0WkymC7S7+QuvHOy8ofI+vR0kG/samMEtWfzeKm6CwiVGQiqEXmIcNgGzgZKiAuBlVAWWCU7vyLHx1WvaK15/Uuvn3hO+2y/nEYtINM63xHrztmB47o6JIxCUz0o9IiDJaIGuE7J7sK34iLRKhdWbvFxPWmirINmCl2H8yHWdkC1e3/zHKzJSe5ZaSyQiJr0+gm3X3eAQ2tzYLXwiMASZYFlXk9PB9REks+b9DlYnu0iVJIP9K/kRY2PsCjXiMY4Gi5kHwam0ORgcLq40X7uaztaYBUi6NPJQf5k4Su56orDZH95gvnZiNMrtkpy7WR+XGqv/1o9wq0JbuMpw2IzKKcosGr5+zx6zWvrlPd0QBgqOspWHPZWhhysFbBFTToZElipqSJ0a6lbf8YJa8/G8QJrFxAEAo0gU7VCAAzZwLrkBLkeLTIZdLCisoNV4f64Cy7TMFOfvIC88t6rufLwPDdcsYcPf/pk3pSwnIsERhDVAmmagqJHcrBkrQgxuBwEVxF5Kpthr03MdG0a8qZ/jCbMPxWCqJaLP7BWO5DpyQ5WPrw5TU2JtM5Ahax1Ew4uNpC94tITKhgQVQNtJGwo8Ml0nsuCU2CbCrLJZrQez0aRUvBEtshf6Gfyj6sH+NdjBJa7HqNQIoTg/vgw14ePIQ9cRXb8AWCnhwjN+T+S7CFQkke4hDm9AJxgoRlxxq4zA7NArYMVRiFKxqSZzqury0nu2xkiBHjN86/lsZOrZA/Z3FOtTFGMtAKruzqQg+VmLJqDB78bVGZysMrzZL/rK2/jwOLOzZ+7UPCr9C7A7TyyoF7kYA2FCl0PFSh2bjIZdLCikoNV7j/lKCe5r0cYSJ55g+k5NdsM8/CBXj01cFyfgChU+diKYVEU2CTZRMsi2daKjpPZHIGsdrCqHuupUItUPowaKhysMW0aZLkPlt3Nf/LRZfpJZobVlu83YTDsjDCL4ZOpnbXocrCmOEzW4xkmVJJ3r93Io+leGmNzsFR+bKgkv7T8PD70jB9CNArnOKjtXIHVtNM/39+/iplGSLefsLTapx4papHKN08iKQSJu/6lDFiYNWukc9aDMMQV3k3XwSreZ8cL77yMr3vx0/JKyK4OiQJFrIxjaByrIjnf5cQR1AYcLJ1lKJ0QM7jJu/7yPexf2Ln/dhcKXmDtAtyFl6o6OAEwPNqlJLBciFAlZljozy69kJ9eehFBVDhYbn7gwPO4KpaFze1s9s7V8xDhwE4KAIEQ5O0ihpPr61HA21Y+j/949ovzhSbSZoE4kc7luy4lJd/+ypsRYbEoVCXqb5YoVPTKRq8Vrq7dhBzXaNQ5WFkKtpLwnz9tXnuzFgzkf4gJPXPmpPn3PJHZLy27aG61/YTHMwmlZF44Mi7p2TlYYWhysFLMZsRt5vpaDVQI7zQenL2FP1y7nb/qXs9sPaTXTzm72mfRjjVzmySRlgSJq9JWKh+5Vc8dJpVvWMv5WFul7BQOo0v9xsJA0nchwq4RWKk2xQfZiglz9oJZiHvFJtmKrbRiQ+3ZOjv30+/ZMM7BSlQtD2ENVxO6ajgoSohV1idG8anENN+8p5S4We4/5ZhrRvzrl99Ey47N2CgLMxEJzsEaFljQ6SX5wjQcIqxHivv61wBFGHAmNo9xIpsbmNt427UHeM/+RbBrYFWYc7PUQmWHUTvhOiiwxjpYqpTkbh2sxFb8zNQDVlW5pNs8Rjs+xGpW497S44S2p82pzI7pcbtP36bBs42ESrjLaGzSs9twRaXQ1ScfOUP8mdPcUzdD3Me1eNgJpOEMf9W9CYCZRsippS5Lq/28kacL84tSQ+bCwVIszFjnyr5+N9oqEilyin2wqhwsRyYUaNNOYzaQpEHhYOksIcXk59ZtasYjy4qrw8xs1IIod7O8wNoevIO1C3AXeJ9yDtZQP6ySg1Xu0VIexVLug1XlYAHc+bSDE6sHq5BSUGuYnVW2epq+Vvmg1IN7GrzormNk1sEaDuuVF3eXm7TUMPPEPx0fHOkgPzPbyMVaNhUHS3I6m8l/z3ew61URunyMNEHbykMXBm3Wg4GiA/cY/335hfza6r0Dj/Pn0Qv4087TeTixrTBsiFD6EKFnG3GzRevR6BgnR7lNg3NXPvzpk3nLmJ4OdnSidHlzNlMP6PZTTi/32GdDY3kVYVZ0RM83rirMQ4Qra+aaDALTPyzTEExRYFXlYOXnUwoRhoEkHnCwEhJtXUVb1bmkrcPviqBcb6wp9u3yFHgHaxcw2wg4vK/JQydj9uy1iY1DDUfLZcOilIxZbmQZlhaFKgdrKzRnGtA3OVhrusaPnXkZUmhe8+LLmWtGnHYO1tBiXrV7fmTxGfzvT8yzrBu5q+WYb0Z0dcisSKfmYH24f5SnhY+Z83MO4ToCSylFrKVxnOyxLgzarIXossCaECJcDffwrs6tHJS2+jJ1fbB8iNCzfYQb6IkUBQohTN82IcyIq24/JW2Y+67oemVYa6dQXjtmGyHdfkqcZOyzyd2y6rq0AkuEIS955jHaD5/ltusOAOY9S7QiFTIXqNPg8kNz3HL1Po4dGq2KLgSWmQ+JikzCe3cFnRoHq6tDdMe4/su2Y72Ou4j6bO5gZcF013uPYed++j0bRknJV77gWlazCN1dIX38k3Tf98sDxwwUpo1xsILSDilR073gZmatC5TGrGURfUIzSDZUKCny8NmwKKpX7ICllCzbnZgayoGaa4Z0Mtt0dQoOVi1UfCy+NP897hgB65LcxzlJpvFiYMIL6aCD1agHAwJrkhsV2i+BXAjnswj93sizfbjcxvqEnkhhKK3IEvnvAJ+OL+FMsJ+3rXxe3itrJ1J2sGYbIUma0YtTDiyataUyv9IKLKkiLtnb5Ce+6Vl5SNGECIOB9jHTYLYR8sZX3ZLnfJVxAqtjHSwVKDrUoLdCGvdJ9GB/sqXMOlxubI6tKCyvR57p4QXWLqERBXwquQSZdFn7ox8buX2gDUApAbM8Ub4c40/UdEt0Z+dm859XdSHeaqFCKZGP2ahKch/GhQUFg005wey4XSXhdEKEihPZPL/Oy/jzzo1mxuHaGdN6gfENP6W0Aivp5SFCN5i1EalBgTXhS8gt1K6SUdqKJt9o1LOdbMTBevbNh3nlvVeN3KedHOH35l7LE9nijm5aWXawZhrFprMIEaq8KjAfy+ccrIqQWmhDhAlqJHVh2xBufQgJA4USglVdQ3dXSOKYBDVQWe0EFnEPnSZ0/ujHAciUrxjcDvwqvUuoR4oP9q/gwVu+hdqzXsPMq3+Cn1l6YX57+YIvlxCn5UajQanB5ZgcrKd8fs3iAl4rCyyb4+H6cQ0nuVct0E4sDosrd7xbUKYVIgR4ID7IP/SuRqBJPnVfHiIc5z4pKegRGgcrDxFKnnZs0eRuBBsLETpXsaeL1hqTntfjmQYuxDWuRQPAlYfnecEzLst/L4cDl2xeUlSRN7RTCEqblJl6cQ269gRSFDmhbs0SNre1qlN7oMw6liKn6mBNQtn+fH0dGAdLCdZ0Dd1bJU1iEi3pUZzrGZtPquNu3jLnIXGMs7XD5+R8LzZ8nGGX4JyeM/WjRE+/CzBtDBwDncJLX+7ZGAcrnXJMPggjMm0WrU5p7lc9UmZhynOwhh2s0QVeOAerapxPqDjrxl5MJURoBU4/5XS2yErzUuQD9yFmrzNPEVZb687BapZChFdcuodXvOZ2c0ApBDupKWHeggNJqgXKOVi+0ahnG3EJ1fVNzKUrrx9Lq0Zg7WgHqyT+yk7dvsU6JClKinyyREdHzNJDxGtmHau4ZkMlSVAkWuah/e0mFGbzFqOIAomUgtXMOFhZGJBQ9PHr66CIHiQ9shUjsO7jFsKKSIFn6/hVepdQr9lkx36a/6083X4gRFgSBVl5llV5tymnW1UShkWPmNVscI6XknK8g1WRg1UMQx59nlqkeDAxSaezrI4esElcBU8vNu/r6blryY4/iOyeJdFyrDhyOVgy7eduV71WEmNhuW3G+iFCEPR0iMT139q5X1yeC58rbEL1ZoopyqNcltZMv7YdnYNVWkCaJQdr77xNcheFwHLDoGXSNSHAig1OEJh17FyGCAM7rTrWiiCQ1ELFShqRdVfIkqJNg3sNecPnuJc7WCeTxo52Gi9k/Lu6S3BOT7dvE6qzbCC5sWxZl4crj3Owpl2lFgaSyO62TmdFPlY9MpVI+azC4ST3CSFCUbGI1cNCYB3KntjyeTuBl9okjOP1K0BrmifvNwJqzPukZCnJ3eZglUcRlQWsnCSw7MInYKCjvBdYnu3klqtNW5CHHlva8H1WOkW/qDgxG4GdXEVYFklPO7aHr/+ip/Gdr76lmMknBZl2IcJSj0CtKtMTAiX55/7l/GPvqnMWIgyx670IkULQrFmXqrdKlsS2TYMVWFmUryE67pJZgXUiaezofmUXMt4X3CUoKQkDmTtYvX6aJ1XDoFUflHaVZQer3Gdl2p3Cy4+ddyW35yWEKBqNbiBE6Ba3ucaoy1aLAh5L9wDwOXWUg1s87+EviCeDwyAUYe8MqzQrF1owArVHgMw6aBsiLDtVqvR+TKoIdKJ3/2I9XyihWlx6PNPiWttM+Hm3H93wfc6sGIE13wxzB2tn98Ey11CjZgYdP+fpRwZuF6JIXYh1QKwlociIxzhUoZK8v381AC85xwIrsxu2Zj3gcV0DnSLjFRKCIkRIQNcJxX4HvXIKajOsrsgd7TReyHiBtYuoRyoXWOb/ZhF4PF0YWOjKu6ueLKbdh0ry3s6NLMi1yqZ2W6HsjpVzw3KHSFRXEVYnuZv/75sfTcSvRYoMyfedfhXHjh7gti2et5KSQAmS1DhYnVgjoga6t0JPBzTGCB3nYMm0j7bjbcpCqrxAqwlNCRPbMX7/QoPeaTtuSE92vTyerRIoya/8P1+wKSf7W19xM/045W8/8hhLnzF9l3ayM9KPzbV105V7K293+UwLskOsFbEOCEV/vINVKhI6VyFCYZPcXePiZi1gLTNOeRgvk+p9+cYs1ZIeAZkW6P4aevU0cmYP8RPZjnYaL2S8wNpFGIFldjQuZ+gnz34JZ7Im/2/pAiqLnVNqf/5zEEj+qHMHAC+fcnJqWbCdKnVGz8f85KHKwYXJ9bk6dkkRVnSL/t4KgeX6Zi3rRj5zcavUQkViXahuP4WoAb0V+jpgZsw66pLcZdYnjZ2DVS2wJomlFesEHFis0zvlqiOFn0Xo2XY2myZwR8uE5v/xX57M/zbtjdo0ced2902HKm+XoqgejFG5m5UgKwWUktLkbWk90gB5u/jg4gt48CHJI7VjgHGwXCJ7mHYHktxTJDP1kC4htX7HhAibi6SZ9jlY24QXWLuIehTQ7ZUdLPJwWdkJKucenA3281PfegtSDCa5T2ow+FQoL7Tl1hDF36odLIAffv1d7J0v8pdWOkZ0lP/mGBitM6VdZBQqVrtGJHV6CSKqozE5UeNChEWSe0zmQoSyLLBK4dgJ57lsy933zNXzhTJD+BChZ8fi1o4olDt6I/CcWw5z+aE5rjw8X3m7kiIXK4lWeU5rrBXRmOsvCAT9WI80QN4u4nCe31u7i8vtgOpGLRjoM5gi8zmDKYLZZkQ3DZnvr5lxOvMmLLqTncYLGS+wdhFlB6tcTQiDuRDlEmIZhOyxF6dzvWBz5dkbIQoU7+tez6HZ6ttdiLBqWTp6cPBOp5ZMq4JKB6ssJKe0uJcXn24/RcyZPjkbSXJXOiazc79EKRRYtuTdbvjGK/cyO5RX5oTd4mxRAZQhKysoPZ6dgNvk7PS8HiXlWHEFxsFbsxXPsS4aGCdMmM+oJP04yzvhbzdu7XDv+Uw9ZDUrBFaGygdPZ1oy1wjpLodkvQ66t0oWmhQRL7C2By+wdhG1SOUhpd4EgRUEko/3j7CkGwSL1aHD7XCwfn/tTg43mijZyavyHC5EKEW27mM5EXLp/pmR2wJl7Ps00xOdoc0w1wx5whTcFCFCoEc4NtdCKZPkDpB1V5EM9s4pf/m4xfpNX3HryON8w0uv58/+4WGOXTLHJ1yHei0IdrAz4Lm4cWvNTk5w3whSioEQYS6wbEuEKlzKw7nKwXLP49brcogQIBWKmj3VFGlGiS1FZJ1liLukTmD5EOG24AXWLqIeBZw4Y9ydbpwM3FYO0QVK8gsrLwDgtrKbVVoUqkbUbAW3IC2t9mnUAr7v654xIAKdg6VYX2C9/DlX8synH+HYvmbl7fXIhPSmJbCuO7rIpx4xw5a7/QQRFg7WuDwVN5cMigHRsuRghRUOVhXHLpnjDV9yA587sZonq2YIP+zZs2NxwupCT5yWQuSiSqDzn2PkQA+tMm6Dd66uz+GZkbVI0WWwkXQ3mAMNn032MdcMzfDnlRMIILEjcryDtT1c2FeAZ4ByiHB5NR64rXzBl52qcUmo03aw3A5ptZvQrAUcXGxwWSn058qMNyKwwkBxx9MuGXu7s8unlf/xtGOL+c/dfoqITGiyPyEHC4rKHt0zDU9lKem+VnawNiAEg5IjphFTE48ez7RxX/qfNyZ5/EJBStPaAEzHdNfiYKKDFZy7MTnmHAcFlhSCei0iFsbFyoTiZHSEt5x9CX/evYm5ZkRHR4jOGQBiaQWWd7C2Be9g7SIaUcBKJ+E33/MJ3vtPj4w9rpwfMC4ZczuT3Bv10Y+dFiFoUKQjt20Wt4Oelk1/7dFFokAy14xY7vQhLOdgjb+fE406HxA72HPsnWu3cl34GEc3sCBHQVENFIrU52B5dizPu/0ol+6f5fbr9q9/8A6mPCM1IikcLK3GiqhQVVcYbhdu/S5HHBq1gL6sEaY9tFDUQsW/pKbCc64RDgx/Pt5RQOIdrG3Cy9ZdxLOffphmTU0UVzDsYFUvBtNOci8LrGbVYyuX5L6+g7UeThxOy6avRYr/8ebn8qK7LqMfZ6wl5nHHlWs7MjdvsO9ChMXCFoWSd3efzn9bftGGkvGjUBLY9+bRdNE7WJ4dy2wj5I7WgQs+jG0ajZp1qSbTgST3cW0YwkCcsxYNUPQELIdjm/WAng0TZkINiCfnYDl+7z4z7WK+Od3RaB6Dd7B2EZcdnOXHv+lZPHpijcsPzXFqqcv3/uLfjxwXDOVjVbFdIUIYHKzqECqAZGMhwvWYtoPluPWa/bztzz/Jo6e6XI7p2DXpS0TbEKGwAkuV+l1FmwwR1kLFA4npS/87q8/ie7mwv7w8nguBM9qkMayIWbrWzRKMXzcDJSvnFG4XrliovGlu1gJ6azadQAYDAmthNuLBkoO1piNe8qxjXHpgTHm3Z0t4B2uX0ayHXHN0gTCQY/OrBuYSjlkMGtNOcldlgVUh3pRLct96iHB+xg5mnfKne/9ig2MHZ3nsZMc8PtnkRopB4WAlenDh3WiSe/mYB9LDvPns1/K5dO/UX5vH4xnlwewwv7D8PP5O3ZU7PwHp2Gs2OMchQjfzsTxou1kP6Wdu9JiiZteaKJA0asGAg7Wa1bj+8j3n7HwvNvwyvYsZL7BE6ecxuQRTTnoUQuTPVelgyemFCF3y/HAvsGnQOraHU8s9wNjzkxZTHVgHK+6QIgdy3zab5C6EIAolrlXZTm7g6PHsFqQQfDw+SlSL8hBhKLOxznV4jpPc49QJrMEQYdcJrJKD1aiZn5ezon9gj5CFmdGGzZ7p4AXWLmZjDta5/KI2dvZcMxq5xbUwmIbAuvyQmXX4yPHVLT/WMFdfOo8dYUYgJ4cIZS6wrINVruTcpIMFg32FfA6Wx7P9OKe4WSsajYYTevXtnavljZvPBYWDNRgi7Kbmdy1Vvm40agG1QPKx+ChrM0fo1PYDgoWZ0fXYMx18DtYuZpzAKn+hn8vdlhuYvL+iA7vrci711gXWsUuMwHr0xPQF1jWXLvBH8RFexgf4hL6cl046ODCvU2YxGfWBEGHZgdqoWConsnoHy+PZftx1FoWKHkZgBRME1le+4FqyrS9hGyZxDpaqdrCkELnAqkeKKFKkKD7a+tecXekgH/8csz7BfdvwAmsXM64FQ9l1OZcCy7FvYVRg5T2ipiAc5psRd7QO8Hk3Tr8Pz975OsfFft546mvZVyEUy8io2MkmenxuxkYdrHKyqtdXHs/2U48CVrsJoZJ0bZFKKManHoTneDyQc7CCIQfLjdWKREZmN2ZKCRq2eKkbp5xZS5mfCf1mbRvxAusi4NZrxvejGR6Y/G9ffQudbchdmvScAGv1g7yncxPxoXu4fArP8a2vuHkKj1JNLVTESbZup+ogjEi1QAk9koNVZqMOltuJrle96PF4pkOzHnByyUYDbJ6oEnqde507ivBfOck9YMX17xIx2lVV2zxYKQTdfsrSat/nX20zXmDtcn7uOz9/YsL6ob2D42Zuumrfdp9SZY5CGCje0bmde6OdX9FSjxQrnXjdYbZhKOkT0qBPgqI+xlHc6A7Stbrw+Vcez7nB9ewLlOSEPMDfda/lI/Xbedp5Pi/HV73gWo4emOXGK/bmf2vWwrx/V0iKsAJL2hE+ZuJHytmVfl5x7dkefJL7LqdRCyaGAS/ZWz3PbzupCl06EXgh2NVuFM96Dlat1H09nRAi3CguROjdK4/n3OAqnoNAooKA31q7myW1/ZvQjdKsh7z4mccG1oRmPeD++FIAnqwfy9eNfDB0TdHrp6x2Y2Yb3mPZTrzAusg5lxPvKzu4W8LzkAv2VKnnw2zXcbACmedCTAoRbpRavhPd0sN4PJ4N0rRjvUIl8+vv3FZeb55mLeCh5ADfeeprOFm/Il9bZWmsTref0OkllS1zPNPDv7uec8ZPfes9aKrzF1ySputMvJPJHax1eoVFoZyyg2WezztYHs+5IQ8RBiK/7scNet4pOFGYIQmUyNdUt/7UQkWnn7LWS/JjPduDf3cvUn7wdXdSr+qovo3UJozfcbus7EIQWBt0sKJA5Q5WR4djqzo3inu+CyGM6vHsBpzDo6TMnetgh+dAlkWTUpJMDwqseqQ4u9JH6+qmz57p4d/dixTXjHOnEOYO1jlsIvMUcXMa1+t2H4ZFiPB0Nju9EOHOXt89nl2Dc42TNKMeFflYO5laqBACtDZi8Eq71j/76YcBs3599ollYHLahmfr+HfXsyNwifgXRojQXDa1daoIa4FC26HMp7OZrYcIAx8i9HjOJc5Zj5Msd+B3er6oEIJGFLDWS1BKsH+xwa9+z/Py2+uRYrWbACZJ3rN97OxPiueiIXew0p0vsFyoIFynijAMJE1p5haeqhBYr7z3Km66cm/VXStxC7ybP+bxeLaX0F7rZYGldrjAglL1Y8W5OifOHHdu00QuNjbkYLVarR8EXm1/fWe73f7uVqv1AuAtQAP4rXa7/X322FuBXwbmgb8GvrndbifTPnHP7uJCcrBc2GC9LvhRKJkRRmAZB2vw+JfefQUvvXvjz+vuv/cczjrzeC5mCgcrZaZRs3/b+Q6yy6+tcs3rpVzYZs07WNvJulLcCqkXArcBtwJ3tFqtrwR+FXgZcD1wZ6vVeom9y28A39Zut6/DNJ1+wzact2eX4UqfLwSB5Rp9aj35XKNAMSO6gBVYU1qYv+hZ0+h17/F41sM563GS5c417HyB5dIJqty2ssDyDtb2shGv8zHgTe12u99ut2PgfuA64JPtdvtB6079BvCqVqt1OdBot9t/b+/7VuBV23Denl2GygXWzg9/uRyodfQVYSj5aHwZAEu6ueXqv3tvPcJ3vOoWPu+m6c9Y9Hg8o9x81T6uPDzHy55zVZ57eSGsUW4mYlXPrlopROhzsLaXdUOE7Xb7Y+7nVqt1LSZU+LMY4eV4DDgKHBnz9w2zb9/sZg6/KDhwYGdV/G0He88Yp0cpteHXe77el7lZEypoNMKJ57CaaH5y9W7+of4cpBJTOd+jRxbXPeZi+Lw8Ffz7Mop/T6opvy8/82aTIP7EWRPuV8HG16jzxUzDCKeF+cbIuR7YN5P/fOzSxXXbzQzcd4e/7p3GhqsIW63WjcA7ge8CEoyL5RBAhnHEdMXfN8zJkysXRC+kc8WBA3McP758vk9j21lZNgKr04s39HrP5/vSWesDsLran3gOK8sdUhRPdEOkTM/J+V4sn5fN4t+XUfx7Us249yXpxwCsrU2+7ncC2n6Hdjuj51orxa3Onlnb8GP6z8soUoqJptCGyiFardY9wHuB72m3278GPAIcLh1yCHh0wt89nom4vKYLQVy7EGG2gRwsgE4v2fHNCT0ez2Sc05NcAGvUpEKc6y/fw3OefpjWZYvn+KwuPtZ1sFqt1mXAHwBf0W63/8L++T5zU+sa4EHgq4Bfbbfbn2m1Wt1Wq3VPu93+P8BrgXdtz6l7dhOu2uVCaNNwnV2Y1mux4Ba5OMloTOhi7/F4dj5R3krmAsjBmlDhLITg67/o+nN4NhcvGwkRvhmoA29ptVrub/8DeB3wdnvbnwC/a2/7auCXWq3WPPAB4GemeL6eXUousC6A3eFVR+b5hTffmyeSjqM8q/BC6J3j8XjG49ygC8HBKvfv8pw/NpLk/kbgjWNuvqXi+A8Bd23xvDwXGfsXGgA8/45Lz/OZbIz1xBWYBVlgkhL9/ECP58KmcNl3vmiJSu0lPOcPPyrHsyNo1oOBcQ67ASGEmUcYZ1PrgeXxeM4PC7MRAFdfunCez2R9iv5d6Xk+k4sbL7A8nm0kCpQRWD7J3eO5oNm/0OBHvvGZXLKncb5PZV1ygXUBuG27GZ8Y4vFsIy7RfXhMjsfjufC4dP/MuiOydgL33HSYPXM1nn3z4fUP9mwb3sHyeLYRl6vlQ4Qej+dcsW+hzk996z3n+zQuena+FPd4LmBcsqnvg+XxeDwXF15geTzbiAsRhoG/1Dwej+diwq/6Hs824rq5L9r5hR6Px+O5OPACy+PZRpxz5QWWx+PxXFx4geXxbCOJLZNetD10PB6Px3Nx4AWWx7ONrHYTABbnvIPl8Xg8FxNeYHk820jHCSwfIvR4PJ6LCi+wPJ5tZLUbAz5E6PF4PBcbXmB5PNtIZKfaL3gHy+PxeC4qfCd3j2cbefNrbuX+z5ymZoWWx+PxeC4OvMDyeLaRw/tmOLxv5nyfhsfj8XjOMT5E6PF4PB6PxzNlvMDyeDwej8fjmTJeYHk8Ho/H4/FMGS+wPB6Px+PxeKaMF1gej8fj8Xg8U8YLLI/H4/F4PJ4p4wWWx+PxeDwez5TxAsvj8Xg8Ho9nyniB5fF4PB6PxzNlvMDyeDwej8fjmTJeYHk8Ho/H4/FMGS+wPB6Px+PxeKbMThr2rACkFOf7PHYc/j2pxr8v1fj3pRr/vozi35Nq/PtSjX9fBim9H6rqdqG1PndnM5lnA39zvk/C4/F4PB6PZxM8B/jb4T/uJIFVA+4EHgPS83wuHo/H4/F4PJNQwGHgH4He8I07SWB5PB6Px+Px7Ap8krvH4/F4PB7PlPECy+PxeDwej2fKeIHl8Xg8Ho/HM2W8wPJ4PB6Px+OZMl5geTwej8fj8UwZL7A8Ho/H4/F4powXWB6Px+PxeDxT5ryMymm1Wq8Dnttut193Pp5/J9JqtW4CPgJ8ebvdfvv5Pp+dxnqfmVar9Vbgfe12+63n7qzOD61Waxb4D8CLgFVgCfihdrv93jHHLwBvbbfbrzh3Z3n+8OvLKH59GY9fWwr82jJdvIO1c3g98DvAvzrfJ+LZubRaLQH8MdAHbmi327cA/wb4n61W67lj7rYHuO3cnKFnh+LXF89E/Noyfc7rsOdWq3Uv8GNAE1gEvrPdbv+h3TGcBe4ALgV+uN1u/3/n6zy3m1arFQJfjZln9HetVuvqdrv96Var9RDw28AX2kNf3263/7nVar0POAXcCHxFu93+4Lk/6/ODfe0/1G6339dqta7A7CyvOK8ndW65F7gceF673dYA9jPxo8D3t1qtM8AvYK6pU5jP1c8AR1qt1u9fTDtNv74Y/PqyMfza4teWaXO+HaxvB76x3W7fDnwj8KOl2y7DLAhfCvzn83Bu55KXAp9pt9ufAP4A+KbSbavtdvs24AeAXyv9/cPtdrt1sSx+npw7gfe7BbDEX9vbfhP4kXa7fTPwv4E3Ynahj16EC6BfXwx+ffFsBL+2TJnzLbC+Brip1Wp9P/AmYLZ027vtP/RHgb3n4+TOIV8P/C/7828BX99qtSL7+y8CtNvtPwaOtlqt/fbv953bU/TsEDTVznOEHTzabrffAdBut3++3W5/17k8uR2GX18Mfn3xbAS/tkyZcyawWq3Wc1qt1hH7qwAS4G+Au4B/wlj5onSXLkCFmt5VtFqtg8BLgDdZy/6XMXHtL7OHJKXDJZDanzvn6hzPF2M+M5ricxKelxM7v9wHPMOGfcrcDfwD5v0BoNVq1Vut1lXn8uTOF359qcavL9X4taUSv7ZMmXPpYL0eeLn9+enAA8B1GGv6XcDLMCr5YuO1wHvb7fbRdrt9RbvdvhzzZfDN9vbXALRarVcA97fb7dPn6TzPB1WfmROY3BBKt100tNvtvwE+BvxXtxC2Wq07gO8D/j3wSKvVeqE9/LXAD2O+PM5rvuU5wK8v1fj1pRq/tgzh15bpcy4F1k8AX9dqte4HbgJ+HvgVzD/o/cAc0Gy1WjPn8Jx2Aq8D/vvQ334Os/OuA/e0Wq0PAm8Gvu6cntn5p+oz8x+Bb2m1Wh8AGufz5M4jXwb0gI+2Wq2PAz8NfE273X4fJiz2A/Yz8xXAdwFPAJ9ttVp/eX5O95zg15dqXodfX6rwa0s1fm2ZIkLrXe2QX9BYS/+57Xb7ofN8Kh6PZ5fh1xePZ3s530nuHo/H4/F4PLsO72B5PB6Px+PxTJltT05rtVo/CLza/vrOdrv93a1W6wXAWzBx7t9qt9vfN3SfXwf+ot1uv9VWwby7dPMCcKDdbpdLrj0ez0XIVtcX+/sVwK8D88AZ4Ova7fZnzskL8Hg8u5ZtDRHahe6FmFb6twJ3tFqtrwR+FVPVcz1wZ6vVeok9/kir1fpj4MvdY7Tb7Sfb7fat7Xb7VuB24CEGG+V5PJ6LkGmsL5YfAf6XXWPejqmy83g8ni2x3Q7WY8Cb2u12H8BWbFwHfLLdbj9o//YbwKswpdRfDfwhcHLM4309sNZut9+2zeft8Xh2PtNaXxTGvQKYYZf3gPJ4POeGbRVY7Xb7Y+7nVqt1LcbK/1nMwuh4DDhqj/9P9thnDz9Wq9VSwL/D7Ew9Hs9FzhTXl+/HzOj7N5iu1Xdv42l7PJ6LhHNSRdhqtW4E3oPpm/EApY6wmM652QYe5sWYnelHpn+GHo/nQmUK68uvAd/UbrcvxTTg/P1WqyXWuY/H4/FMZNsFVqvVugd4L/A97Xb714BHgMOlQw4Bj27goV6OGTDp8Xg8wNbXl1ardQB4Wrvd/kOAdrv9dnuf/ePu4/F4PBthu5PcL8NMb/+qdrvtxNF95qbWNTbs91WY/Ij1uBszW8zj8Ximtb6cALqtVus59jHvAZbb7fbx7Ttzj8dzMbDdSe5vxoxjeEur1XJ/+x+Y8Q1vt7f9CfC7G3isqzC7U4/H44EprC/tdlu3Wq0vA3621Wo1gGXgldt4zh6P5yLBNxr1eDwej8fjmTJ+VI7H4/F4PB7PlPECy+PxeDwej2fKeIHl8Xg8Ho/HM2W8wPJ4PB6Px+OZMl5geTwej8fj8UwZL7A8Ho/H4/F4powXWB6Px+PxeDxTxgssj8fj8Xg8ninz/wMOPmPJNjVnzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "f1,a1=plt.subplots(figsize=(10,6))\n",
    "daily_df2['2017':][['Tskin','Tskin_damp']].plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df3.Tskin_dd4_box = daily_df3.Tskin_dd4_box.bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1958-01-01    1958.000000\n",
       "1958-01-02    1958.002740\n",
       "1958-01-03    1958.005479\n",
       "1958-01-04    1958.008219\n",
       "1958-01-05    1958.010959\n",
       "                 ...     \n",
       "2018-12-27    2018.986301\n",
       "2018-12-28    2018.989041\n",
       "2018-12-29    2018.991781\n",
       "2018-12-30    2018.994521\n",
       "2018-12-31    2018.997260\n",
       "Name: decdate, Length: 22280, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2.decdate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1,a1=plt.subplots()\n",
    "daily_df3[80:100].decdate.diff().plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002739726027357392"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df3[80:90].decdate.diff().values[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017.2328767123288"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2[\"2017\":].decdate[85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use daily_df3 for the added accumulation to meet measurements. \n",
    "daily_df3[:'2018-12-24'].to_csv('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_daily_bdot1.csv',columns=['decdate','pcp'],header=False,index=False)\n",
    "daily_df3[:'2018-12-24'].to_csv('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_daily_tskin_dd4_box.csv',columns=['decdate','Tskin_dd4_box'],header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017.0027397260274"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df3.decdate[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2017 = np.cumsum(daily_df2['2017-01-01':'2017-12-31'].pcp/365.25*(917/300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2018 = s2017[-1]+np.cumsum((daily_df2['2018-01-01':'2018-12-31'].pcp*1.8)/365.25*(917/300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "snowseries = pd.concat((s2017,s2018))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc = snowseries.reindex(pd.date_range(snowseries.index[0],snowseries.index[-1],freq='6H')).interpolate('linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01 00:00:00    0.000270\n",
       "2017-01-01 06:00:00    0.000512\n",
       "2017-01-01 12:00:00    0.000753\n",
       "2017-01-01 18:00:00    0.000994\n",
       "2017-01-02 00:00:00    0.001235\n",
       "                         ...   \n",
       "2018-12-23 18:00:00    0.952240\n",
       "2018-12-24 00:00:00    0.953035\n",
       "2018-12-24 06:00:00    0.953746\n",
       "2018-12-24 12:00:00    0.954457\n",
       "2018-12-24 18:00:00    0.955169\n",
       "Freq: 6H, Name: pcp, Length: 2892, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc[:'2018-12-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc[:'2018-12-24'].to_pickle('HeightChange_full.pkl')\n",
    "# (hc['2017-02-05 12:00':'2018-12-24 18:00']).to_pickle('HeightChange.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtimeindex = pd.date_range(snowseries.index[0],snowseries.index[-1],freq='6H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223.88830341113245"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df2.Tavg.mean()\n",
    "# daily_df2['2017-02-05':].Tavg.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Density\n",
    "# '''\n",
    "# getting the diameter wrong by 1mm adds ~2 percent error.\n",
    "# Accumulation at pole is ~0.07 m W.E per year, or 64 kg/m^3 per year.\n",
    "# Adds ~3.6% more stress to the ~4-5 m depth per year.\n",
    "# '''\n",
    "# rho126=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_126.csv',delimiter=',',skip_header=1)\n",
    "# dep126=rho126[:,0]\n",
    "# den126=rho126[:,1]\n",
    "# rho106=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_106.csv',delimiter=',',skip_header=1)\n",
    "# dep106=rho106[:,0]\n",
    "# den106=rho106[:,1]*1000\n",
    "# rhopit=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_pit.csv',delimiter=',',skip_header=1)\n",
    "# deppit=rhopit[:,0]\n",
    "# denpit=rhopit[:,1]\n",
    "# rhonic=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_nicl.csv',delimiter=',',skip_header=1)\n",
    "# depnic=rhonic[:,0]\n",
    "# dennic=rhonic[:,1]\n",
    "\n",
    "# dennic = np.append(290,dennic)\n",
    "# depnic = np.append(0,depnic)\n",
    "\n",
    "# densmooth = dennic.copy()\n",
    "# N=3\n",
    "# idr = int(np.floor(N/2))\n",
    "# densmooth[idr:-idr] = np.convolve(dennic,np.ones((N,))/N, mode='valid')\n",
    "\n",
    "# pitsmooth=denpit.copy()\n",
    "# n2=7\n",
    "# idr2 = int(np.floor(n2/2))\n",
    "# pitsmooth[idr2:-idr2]=np.convolve(denpit,np.ones((n2,))/n2,mode='valid')\n",
    "# pitsmooth[deppit<0.2] = np.mean(pitsmooth[deppit<0.2])\n",
    "# # pitsmooth[0:idr2] = np.mean(pitsmooth[0:idr2])\n",
    "# pitsmooth[-idr2:] = np.mean(pitsmooth[-idr2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "usp_timescale = pd.read_csv('USP50_Timescale.csv')\n",
    "usp_timescale['age'] = 2017 - usp_timescale.Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho126=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_126.csv',delimiter=',',skip_header=1)\n",
    "dep126=rho126[:,0]\n",
    "den126=rho126[:,1]\n",
    "rho106=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_106.csv',delimiter=',',skip_header=1)\n",
    "dep106=rho106[:,0]\n",
    "den106=rho106[:,1]*1000\n",
    "rhopit=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_pit.csv',delimiter=',',skip_header=1)\n",
    "deppit=rhopit[:,0]\n",
    "denpit=rhopit[:,1]\n",
    "rhonic=np.genfromtxt('/Users/maxstev/Documents/Grad_School/Research/SouthPole/DATA/SPdensity_nicl.csv',delimiter=',',skip_header=1)\n",
    "depnic=rhonic[:,0]\n",
    "dennic=rhonic[:,1]\n",
    "\n",
    "dennic = np.append(290,dennic)\n",
    "depnic = np.append(0,depnic)\n",
    "\n",
    "densmooth = dennic.copy()\n",
    "densmooth106 = den106.copy()\n",
    "N=7\n",
    "idr = int(np.floor(N/2))\n",
    "densmooth[idr:-idr] = np.convolve(dennic,np.ones((N,))/N, mode='valid')\n",
    "densmooth106[idr:-idr] = np.convolve(den106,np.ones((N,))/N, mode='valid')\n",
    "\n",
    "pitsmooth=denpit.copy()\n",
    "n2=7\n",
    "idr2 = int(np.floor(n2/2))\n",
    "pitsmooth[idr2:-idr2]=np.convolve(denpit,np.ones((n2,))/n2,mode='valid')\n",
    "pitsmooth[deppit<0.2] = np.mean(pitsmooth[deppit<0.2])\n",
    "# pitsmooth[0:idr2] = np.mean(pitsmooth[0:idr2])\n",
    "pitsmooth[-idr2:] = np.mean(pitsmooth[-idr2:])\n",
    "\n",
    "dep_int=np.arange(0,108.1,0.1)\n",
    "rho_int = np.interp(dep_int,depnic,densmooth)\n",
    "rho106_int = np.interp(dep_int,dep106,densmooth106)\n",
    "rho_int_df = pd.DataFrame({'dep_int':dep_int,'rho_int':rho_int})\n",
    "\n",
    "usp50_ts = pd.read_csv('USP50_Timescale_zero.csv')\n",
    "usp50_ts['tau']=usp50_ts.BP-usp50_ts.BP[0]\n",
    "\n",
    "USPintfun = sp.interpolate.interp1d(depnic,densmooth,fill_value='extrapolate')\n",
    "usp50_ts['density'] = USPintfun(usp50_ts.Depth)\n",
    "usp50_ts['mass'] = usp50_ts.Thickness*usp50_ts.density\n",
    "usp50_ts['bdot_ie'] = usp50_ts.mass/917.0\n",
    "\n",
    "usp50bdot1 = usp50_ts[['Year','bdot_ie']]\n",
    "usp50bdot1.set_index('Year',inplace=True)\n",
    "usp50bdot = usp50bdot1.reindex(index=usp50bdot1.index[::-1])\n",
    "\n",
    "bdot_extend = np.concatenate((usp50bdot.to_numpy().T[0][:-1],np.array([0.1189,0.100,0.1093])))\n",
    "\n",
    "bdot_long=np.tile(bdot_extend,(1,12)).flatten()\n",
    "\n",
    "age_int = np.interp(dep_int,usp50_ts['Depth'].values,usp50_ts['tau'].values)\n",
    "rho_int_df['age'] = age_int\n",
    "rho_int_df['mass'] = rho_int_df['rho_int']*0.1\n",
    "\n",
    "# usp50_ts.loc[1,'bdot_ie'] = 0.15\n",
    "usp50_ts['bdot_mean'] = usp50_ts['bdot_ie'].cumsum()/usp50_ts['tau']\n",
    "usp50_ts.loc[0,'bdot_mean'] = np.mean(usp50_ts.bdot_mean.iloc[1:11].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>BP</th>\n",
       "      <th>Year</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>tau</th>\n",
       "      <th>density</th>\n",
       "      <th>mass</th>\n",
       "      <th>bdot_ie</th>\n",
       "      <th>bdot_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-67</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.577866</td>\n",
       "      <td>-66</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.577866</td>\n",
       "      <td>1</td>\n",
       "      <td>340.964109</td>\n",
       "      <td>197.031472</td>\n",
       "      <td>0.214865</td>\n",
       "      <td>0.214865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.915818</td>\n",
       "      <td>-65</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.337952</td>\n",
       "      <td>2</td>\n",
       "      <td>355.264192</td>\n",
       "      <td>120.062251</td>\n",
       "      <td>0.130929</td>\n",
       "      <td>0.172897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.158721</td>\n",
       "      <td>-64</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.242903</td>\n",
       "      <td>3</td>\n",
       "      <td>359.031127</td>\n",
       "      <td>87.209743</td>\n",
       "      <td>0.095103</td>\n",
       "      <td>0.146966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.433307</td>\n",
       "      <td>-63</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.274586</td>\n",
       "      <td>4</td>\n",
       "      <td>363.289401</td>\n",
       "      <td>99.754188</td>\n",
       "      <td>0.108783</td>\n",
       "      <td>0.137420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>109.344866</td>\n",
       "      <td>950</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.056161</td>\n",
       "      <td>1017</td>\n",
       "      <td>801.103624</td>\n",
       "      <td>44.990731</td>\n",
       "      <td>0.049063</td>\n",
       "      <td>0.075722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>109.401027</td>\n",
       "      <td>951</td>\n",
       "      <td>999</td>\n",
       "      <td>0.056161</td>\n",
       "      <td>1018</td>\n",
       "      <td>800.090781</td>\n",
       "      <td>44.933849</td>\n",
       "      <td>0.049001</td>\n",
       "      <td>0.075696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>109.463428</td>\n",
       "      <td>952</td>\n",
       "      <td>998</td>\n",
       "      <td>0.062401</td>\n",
       "      <td>1019</td>\n",
       "      <td>798.965397</td>\n",
       "      <td>49.856274</td>\n",
       "      <td>0.054369</td>\n",
       "      <td>0.075675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>109.513349</td>\n",
       "      <td>953</td>\n",
       "      <td>997</td>\n",
       "      <td>0.049921</td>\n",
       "      <td>1020</td>\n",
       "      <td>798.065092</td>\n",
       "      <td>39.840075</td>\n",
       "      <td>0.043446</td>\n",
       "      <td>0.075643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>109.613191</td>\n",
       "      <td>954</td>\n",
       "      <td>996</td>\n",
       "      <td>0.099842</td>\n",
       "      <td>1021</td>\n",
       "      <td>796.264480</td>\n",
       "      <td>79.500374</td>\n",
       "      <td>0.086696</td>\n",
       "      <td>0.075654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1022 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Depth   BP  Year  Thickness   tau     density        mass  \\\n",
       "0       0.000000  -67  2017   0.000000     0  290.000000    0.000000   \n",
       "1       0.577866  -66  2016   0.577866     1  340.964109  197.031472   \n",
       "2       0.915818  -65  2015   0.337952     2  355.264192  120.062251   \n",
       "3       1.158721  -64  2014   0.242903     3  359.031127   87.209743   \n",
       "4       1.433307  -63  2013   0.274586     4  363.289401   99.754188   \n",
       "...          ...  ...   ...        ...   ...         ...         ...   \n",
       "1017  109.344866  950  1000   0.056161  1017  801.103624   44.990731   \n",
       "1018  109.401027  951   999   0.056161  1018  800.090781   44.933849   \n",
       "1019  109.463428  952   998   0.062401  1019  798.965397   49.856274   \n",
       "1020  109.513349  953   997   0.049921  1020  798.065092   39.840075   \n",
       "1021  109.613191  954   996   0.099842  1021  796.264480   79.500374   \n",
       "\n",
       "       bdot_ie  bdot_mean  \n",
       "0     0.000000   0.135441  \n",
       "1     0.214865   0.214865  \n",
       "2     0.130929   0.172897  \n",
       "3     0.095103   0.146966  \n",
       "4     0.108783   0.137420  \n",
       "...        ...        ...  \n",
       "1017  0.049063   0.075722  \n",
       "1018  0.049001   0.075696  \n",
       "1019  0.054369   0.075675  \n",
       "1020  0.043446   0.075643  \n",
       "1021  0.086696   0.075654  \n",
       "\n",
       "[1022 rows x 9 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usp50_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dft5 = pd.read_pickle('dft5.pkl')\n",
    "dft4 = pd.read_pickle('dft4.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "depth_master = np.append(0,usp_timescale.Depth.values)\n",
    "age_master = np.append(0,usp_timescale.age.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Interpolate the temperature\n",
    "temp_170205 = np.append(dft5.iloc[0].values,np.mean(dft5.iloc[0].values[-10:]))\n",
    "therm_depths_170205 = np.append(dft4.depth[1:].values,depth_master[-1])\n",
    "tfun = sp.interpolate.interp1d(therm_depths_170205, temp_170205, kind='slinear',fill_value = 'extrapolate')\n",
    "t_interp = tfun(depth_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Interpolate the density\n",
    "denfun = sp.interpolate.interp1d(depnic,densmooth,kind='slinear',fill_value='extrapolate')\n",
    "den_interp = denfun(depth_master)\n",
    "den_interp[0:2] = 340"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Interpolate the density\n",
    "denfun = sp.interpolate.interp1d(depnic,densmooth,kind='slinear',fill_value='extrapolate')\n",
    "den_interp = denfun(depth_master)\n",
    "den_interp[0:2] = 340"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "spin_df = pd.DataFrame({'depth':depth_master,'density':den_interp,'temperature':t_interp+273.15,'age':age_master,'bdot_mean':usp50_ts.bdot_mean})       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>density</th>\n",
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       "      <th>1</th>\n",
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       "      <td>340.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>109.344866</td>\n",
       "      <td>801.103624</td>\n",
       "      <td>222.089268</td>\n",
       "      <td>1017</td>\n",
       "      <td>0.075722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>109.401027</td>\n",
       "      <td>800.090781</td>\n",
       "      <td>222.089421</td>\n",
       "      <td>1018</td>\n",
       "      <td>0.075696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>109.463428</td>\n",
       "      <td>798.965397</td>\n",
       "      <td>222.089591</td>\n",
       "      <td>1019</td>\n",
       "      <td>0.075675</td>\n",
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       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>109.513349</td>\n",
       "      <td>798.065092</td>\n",
       "      <td>222.089727</td>\n",
       "      <td>1020</td>\n",
       "      <td>0.075643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>109.613191</td>\n",
       "      <td>796.264480</td>\n",
       "      <td>222.090000</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.075654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1022 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           depth     density  temperature   age  bdot_mean\n",
       "0       0.000000  340.000000   241.100000     0   0.135441\n",
       "1       0.577866  340.000000   237.819683     1   0.214865\n",
       "2       0.915818  355.264192   236.304584     2   0.172897\n",
       "3       1.158721  359.031127   235.056396     3   0.146966\n",
       "4       1.433307  363.289401   233.756789     4   0.137420\n",
       "...          ...         ...          ...   ...        ...\n",
       "1017  109.344866  801.103624   222.089268  1017   0.075722\n",
       "1018  109.401027  800.090781   222.089421  1018   0.075696\n",
       "1019  109.463428  798.965397   222.089591  1019   0.075675\n",
       "1020  109.513349  798.065092   222.089727  1020   0.075643\n",
       "1021  109.613191  796.264480   222.090000  1021   0.075654\n",
       "\n",
       "[1022 rows x 5 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "spin_df.to_csv('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_2017start.csv',index=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>density</th>\n",
       "      <th>temperature</th>\n",
       "      <th>age</th>\n",
       "      <th>bdot_mean</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>801.103624</td>\n",
       "      <td>222.089268</td>\n",
       "      <td>1017</td>\n",
       "      <td>0.075722</td>\n",
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       "    <tr>\n",
       "      <th>1018</th>\n",
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       "      <td>800.090781</td>\n",
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       "      <td>0.075643</td>\n",
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       "      <th>1021</th>\n",
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       "      <td>796.264480</td>\n",
       "      <td>222.090000</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.075654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1022 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           depth     density  temperature   age  bdot_mean\n",
       "0       0.000000  340.000000   241.100000     0   0.135441\n",
       "1       0.577866  340.000000   237.819683     1   0.214865\n",
       "2       0.915818  355.264192   236.304584     2   0.172897\n",
       "3       1.158721  359.031127   235.056396     3   0.146966\n",
       "4       1.433307  363.289401   233.756789     4   0.137420\n",
       "...          ...         ...          ...   ...        ...\n",
       "1017  109.344866  801.103624   222.089268  1017   0.075722\n",
       "1018  109.401027  800.090781   222.089421  1018   0.075696\n",
       "1019  109.463428  798.965397   222.089591  1019   0.075675\n",
       "1020  109.513349  798.065092   222.089727  1020   0.075643\n",
       "1021  109.613191  796.264480   222.090000  1021   0.075654\n",
       "\n",
       "[1022 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11ec97c70>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1,a1 = plt.subplots()\n",
    "a1.plot(temp_170205,therm_depths_170205,label='original')\n",
    "a1.plot(t_interp,depth_master,label='interp')\n",
    "a1.invert_yaxis()\n",
    "a1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11efa1670>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pmMREYmbcTF11FYUfvkFq5QaCPvwDeZ/mEjd2Gun95YNLvInNZiM+PsXoMjzOqFtiexTySqn7gCtcD5dpre9USk0AHgfswCKt9T09rFGIbomIjWP41BtoqJlG0eolpJWvw7r6D+RtGk3/S2YTGhFhdIlCeFy3765xhfkFwE+AEcAopdRMYAEwGRgIjFZKTXRDnUJ0W1hUNEOmXE/wFY9RHDGCrJrPOfTK79i6fDEN9Q1GlyeER/XkFspS4DatdYvWuhXYDuQCO7XWRVrrNmAhMN0NdQrRYxExsQyf9WtqzrqNeksUWfuXUbfwf/l80XMc2H/0+UaE8HXdHq7RWm/75mulVH86hm2eoiP8v1EKpHe7OiE8oM+goTDoD+zbtpmGTW8zsGYtLcvWsS54CH3PnUpyH3lTlfAfPb7wqpQaDCwD7gDa6Dib/4YJ6NLlZNccDG6VkBAYY6+B0ie4p9eEc86Cc86iem8hu999lQHlX8GKrRQkncGoGdcTGhnlhkp7WGOAvKbSp+f09MLrGGAJ8But9atKqXFA58vkyUBJV/YpE5R1T6D0CR7o1Z5A1pRfUlNRRtG7C+lb9im7n9xEVdYF5J47CZvN5r5jdUGgvKbSZ890mqDs6M93d8dKqQzgTWCW1vpV1+INHU+pHKWUBZgFrOjuMYToTVEJSYyYfRuVY27jsDmWPsVL2bfgDr5e+xHtPbi/WQgj9eRM/nYgBHhcqW/fZPIMMIeOs/sQYDnwWg+OIUSv6zdkKI5Bg9m94WOCtr5JZv4L6IIPiDzratJdnwAkhK/oyYXXXwO/PsbTw7u7XyG8gdlspv8Z42k/dSw7Vi0ltmglIR89yqbNZzBw0mxCQ+1GlyjESZFZKIU4DovFxsALphJ+5R85EDGU3JrPKHvp9+zdlmd0aUKcFAl5IU5CeFQ0g2f9L1Wn3IyVNqLWPsHWNxbQ1tZidGlCHJeEvBBdkDXydGJnzmePfSBZFWso+tc9lBUXGV2WEMckIS9EF4VFRjH8mjvZN+AqIh2Hsb77MFvefb1HMwwK4SkS8kJ006Czzyfosgcpt6XRd+9Stv/7IWoqK40uS4jvkZAXogdikpJRc+6jOOMSUlr20rTkHvRnHxldlhDfkpAXoofMZgtDJk7nyIS7qDeFk/r1C3z58pM0NDQZXZoQEvJCuEtKv/5kzplPccxp9G/4ktKF93BwT6HRZYkAJyEvhBvZgoIZMv0WqkfdSDj1mN77Izs3bTC6LBHAJOSF8IDMUWOwTppHoymM+I3PsnHFWzid7pt4T4iTJSEvhIfEp6aTPPMBKoLSUfve4NOXn+VIS5vRZYkAIyEvhAfZIyLJuWYeZbEjGd64nu2Ln5IzetGrJOSF8DCzxUa/qb+kKPZM+jXksW35qxL0otdIyAvRC0wmE4OnXE+RLYfMA++xZeHjNDY0Gl2WCAAS8kL0EovFwuDZd7En/iyym7ZSsvAeyvbuMbos4eck5IXoRRarlaGXX0/VqJuIoAHHij9SsiPf6LKEH5OQF8IAWaPOxHnh72jFStCHj1O8+QujSxJ+SkJeCIMkZ2UTculcaogkcsPTFHyyyuiShB+SkBfCQIkpKSReMY9ySxLJ+QvJX7PS6JKEn5GQF8JgETExZM26hzJLMinb/8O+LRuNLkn4EQl5IbxAcGgYqVPvpJooQtY9S1HeJqNLEn5CQl4ILxERE0PET2+n0RRK1Pq/89nbbxtdkvADEvJCeJHEtDTir7iPamsCiXkvsPmdRUaXJHychLwQXiYsOpbMq+/jYGh/+pWs4OvFT9Pe3m50WcJHScgL4YVswXZO/9XD7AgfReahDegX7qeqrNzosoQPkpAXwktZrVZGzryVvdlTSG4rofmN+8hfv87osoSPkZAXwouZTCYGT5hM+4V30WYJJi3vH3y9apnRZQkf4paQV0r9WSn1guvrCUqpLUqpnUqph92xfyECXUJWf5JmzafUmkbGrsVsfl/uvBEnp8chr5Q6D7jW9bUdWABMBgYCo5VSE3t6DCEEhISFkX3VXA4G9aFv0RK2rlxqdEnCB/Qo5JVSscB84BHXolOBnVrrIq11G7AQmN6zEoUQ3wgKsdN35t2U2DLpU/g6+Ws+MLok4eV6eib/LDAXOOR6nAqUdnq+FEjv4TGEEJ0EhYTQb9ZdlFlSSN7+CkWbZRoEcWzW7m6olLoB2Ke1XqWUmuNabAY6f66ZCXB0Zb9xceHdLemYEhIi3L5PbxQofULg9HrsPiMIv+kBdjxzJ2Eb/kFTnzT6qNxerc2d5PX0nG6HPDADSFFKbQZigXAgE+j8ro1koKQrO62qqsfhcN/nXyYkRFBRUee2/XmrQOkTAqfXE/cZRPglt9P6zsOULf4DR6bdR3RcXK/V5y7yevaM2Ww67slxt4drtNbna62HaK1HAPcCS4GJgFJK5SilLMAsYEV3jyGEOL74tHTaz7qZSBooef0vNDY2G12S8DJuvU9ea90MzAGWAPlAAfCaO48hhPi+9EEjODRkBhnOEvL/+yRtMgWC6KQnwzXf0lq/ALzg+noVMNwd+xVCnJx+Yy5gx6FSVMmHbFu+mOGTrjS6JOEl5B2vQviJnItnsy8om/SSlRwsLja6HOElJOSF8BNms5m0iTfixETlyudpd3TpxjbhpyTkhfAjUUkpVGRdRKZjL9s+fM/ocoQXkJAXws+oCVMoMycSv2spjXW1RpcjDCYhL4SfMVssBI25ljCaKVzxktHlCINJyAvhh/oMHMzusBFkHNpIVdEOo8sRBpKQF8JPZV10NY3OYA6tXkBba4vR5QiDSMgL4adi4+OozJ1MUvtBdvz3bzgc8iapQCQhL4QfG3ruRHTcOWQ0bGPrf56ktbXV6JJEL5OQF8LP/WTKNRTGjSW7IY/tLz5CXV290SWJXiQhL4Sfs5jNDJ96AyXZk8lsK6Jk0XxamhqNLkv0Egl5IQKEmjCF0kFXk9x+kMJXH6WpocHokkQvkJAXIoAMOGsCe7KmkNJSTMnL8ygpLDK6JOFhEvJCBJjhF15K1Sk/J8zZgHXlI3z9yWqjSxIeJCEvRADKHnUG9svup84SQ+b2F8l74984ZEIzvyQhL0SAikpKIePqh9hjH0x2xYdsefmvHDlyxOiyhJtJyAsRwIJCghl81W0UJ46jX9NWdr30MDXVh40uS7iRhLwQAc5sNjPksp9xUF1BWvt+KhY/QOnefUaXJdxEQl4IAUD/cRdTf+YviKQBx/JH2L11i9ElCTeQkBdCfCt96GgsF/0Op9lC1GdPsvOLT40uSfSQhLwQ4nviMvsRdfm91Jiiif/yeXau+9DokkQPSMgLIX4kIj6RxBnzKDcnkrDlRQo/fd/okkQ3ScgLIY4qIiqKtCvncsCcStzXr7Dnk2VGlyS6QUJeCHFM4RER9LnybopMmcRtX8zOlYuNLkl0kYS8EOK4IiLCyLnqLnZZckguWsa2t+Tdsb5EQl4IcULhYXYGz76T3cGD6VP2IXmL/k5rW5vRZYmTICEvhDgpQUFBDJ39v+yJPpWcuo18/dKfONIs0yB4ux6FvFJqklJqo1Jqu1Lqb65lE5RSW5RSO5VSD7unTCGEN7CYLQyZfgv70y8kp1Wz++VHaG5uNroscRzdDnmlVDbwDHAZMAwYqZSaCCwAJgMDgdGuZUIIP2EymRh48Uz2Z19ORnsxO159XMbovVhPzuSnAIu01vu11q3ADKAR2Km1LtJatwELgeluqFMI4WUGTriU4pTz6duyA71ikdHliGPoScjnABal1FKl1GbgF0AqUNppnVIgvQfHEEJ4sYGXzGS3rT+p+99nx/o1RpcjjsLaw23PBs4B6oGlQBPg7LSOCejS33FxceE9KOnoEhIi3L5PbxQofULg9OoLfYbe9Hu2PX0XCXkvsNcezKgJE7q8D1/o0x2M6LMnIX8Q+EBrXQGglHqDjqGZ9k7rJAMlXdlpVVU9DofzxCuepISECCoq6ty2P28VKH1C4PTqO32aSZl+N6X/nU/c+mdZebiREePGn/TWvtNnz3iqT7PZdNyT454M17wDXKiUilZKWYCJwGuAUkrluJbNAlb04BhCCB8QFhVN6oy5HLLEk1nwEuve/8DokoRLt0Nea70BeAxYC+QDxcDTwBxgiWtZAR3BL4Twc6GR0WTMuodaWzyq6BU+fW+l0SUJejZcg9Z6AR23THa2Chjek/0KIXyTLTSS9Cvnsf/Vhxi45z+sX2nm9PPPM7qsgCbveBVCuJUlNIL0K+dRa4snt/Bldm3aYHRJAU1CXgjhdpbQCJKnz6XWFIl94785XFVldEkBS0JeCOERIRFR2M+7mVCa2bfsOaPLCVgS8kIIj0nqN4Di+LFkNW9HfyJ33BhBQl4I4VEDLp5FqSmJ5PyX2fGRfLpUb5OQF0J4VIg9hPSZ89hnySBlx2K2vP48LS0tRpcVMCTkhRAeFxoeTr+r7qbQPoS+lZ9Q/MLdFBdsN7qsgCAhL4ToFXZ7CMNn387BodcS7mwg/OM/s3fLJqPL8nsS8kKIXtX/jPGETX2IWiIIXfc0RXlfGV2SX5OQF0L0usj4eMIn/Y4G7DS+/RglOwqMLslvScgLIQyRkJqC/eI7acGG6cMnKNm9y+iS/JKEvBDCMIkZGSRceS9OTDg+eJwDRcVGl+R3JOSFEIZKz8nBev5vsdFO23t/prrsoNEl+RUJeSGE4RKzc+HcXxFmaqJ66V9oPdJsdEl+Q0JeCOEVkvoPpnzwVSQ4Ktj58qPU1dQYXZJfkJAXQniNQWPHU5x5Kamte6h6dR5F+flGl+TzJOSFEF5l2EWXUzfmVwTRSsQnj3OwSO666QkJeSGE18kY8hPCLr+fVqzUr/onjvY2o0vyWRLyQgivFJWQRHnOZSQ5yshf9orR5fgsCXkhhNcaOv4CioIHkFG6msLP1xhdjk+SkBdCeC2z2Uz/6b+kzJRI5FcvUrJTpj/oKgl5IYRXs4eGEXfpbTQTjGP1U9RUyufFdoWEvBDC68UlJ2E6538Io4kD7/wdp9NpdEk+Q0JeCOET0tQgilMmkNGym4JVbxldjs+QkBdC+IzBF1/BXksfUne/Sd67bxpdjk+QkBdC+Ayb1UrOrLsoDcoke++bbHrrFRm6OQEJeSGETwm2h9L/6rnst+eSW/Y+eW/8W4L+OHoU8kqpq5VS21z//uxaNkEptUUptVMp9bB7yhRCiO9YbEHkzrqTPaFD6Ff5EVtWLDG6JK/V7ZBXSoUCTwLjgOHAWUqpScACYDIwEBitlJrojkKFEKIzi8XKoJm/ZZ+tL5n7lnFgW57RJXmlnpzJW1zbhwE2179aYKfWukhr3QYsBKb3uEohhDgKi8VCxuW/4TARmNY+S21VhdEleZ1uh7zWug6YBxQA+4E9QCpQ2mm1UiC9B/UJIcRxRURFwdm3EMwRyl9/jOaGOqNL8irW7m6olBoGXAdkAjV0nLXnAp2vgJgAR1f2GxcX3t2SjikhIcLt+/RGgdInBE6v0ufJbn8qm1puIG79P9m/6A8MvflhwqOi3FSd+xjxenY75IELgVVa63IApdQLwO1Ae6d1koGSruy0qqoeh8N9V8oTEiKoqPD/3+yB0icETq/SZ9f0GX4G2w7Xk1bwCtv+7w4SptxJRHySGyp0D0+9nmaz6bgnxz0Zk88DJiilwpRSJmASsAFQSqkcpZQFmAWs6MExhBDipA0edz4Hh19PhKOW2iUPULTlK6NLMlxPxuTfB/4DbAK20HHh9X5gDrAEyKdjvP61nhYphBAna8DpY2k65zbasRCz7kk2vfkK7e1dGjX2Kz0ZrkFr/Sjw6A8Wr6LjlkohhDBEHzWAxtT5FL/xFLnl75P/UhH9r/gtIaGhRpfW6+Qdr0IIvxQaEcmA2XdTnHYhfY7spPjlB2iorTW6rF4nIS+E8Fsmk4khl8ykbPBsUp1lFK15x+iSep2EvBDC7+WOPY8Skggv+YK2thajy+lVEvJCiIDQknMOcRxi58I/0nqkyehyeo2EvBAiIAw9dyKF6ZNIPVLInoUPUl120OiSeoWEvBAiYAy/eCp7cmcR21ZO25v3kvfeW35/e6WEvBAioAwffwGOi+/lkDWB7OI32PbSH3E4/DfoJeSFEAEnISOT/j97kD0JZ9O3ZQe7v/jU6JI8RkJeCBGQzGYLA356NVXOKEI2L2JvwXajS/IICXkhRMCy2YIwj70BMw7CP/4TXy5bQrufDd1IyAshAlqfwUOJuuIhKoIy6H/gbbYs+rtffWashLwQIuCFx8TR/9p7KY4+hZy6jWz+YLnRJbmNhLwQQtAxRj9w6s8ps6aSXvgGVaWlJ97IB0jICyGEi8ViI/rCmwmijZI1b/jFrZUS8kII0Ul8Wh+KQxRZNZ+z4/m5FGz83KfH6CXkhRDiBwbMvJ29mZOIdNSQ9uXf2frcfdRUVxtdVrdIyAshxA8EBdkYfOFUEub8hZI+F5Hp2EfhB775IXcS8kIIcQy2YDvqoisptWWQUr2J5uZWo0vqMgl5IYQ4ji0ffkBy6z4O2RKx2XwvMn2vYiGE6CUH9uwjY8crVJvjyJ75OywWi9EldZmEvBBCHENcYjyHTFGEO2qpPlhmdDndIiEvhBDHEBJqJ+qS23Bg5vDqfxldTrdIyAshxHHEpaVTHpSB3dFgdCndIiEvhBAn4LAEEepsoLm+3uhSukxCXgghTiB65IXYTS3sfvdlo0vpMgl5IYQ4gawhw9gVMpS0qvWU79ltdDldIiEvhBAnIWvitbRgo3LVv40upUusJ7OSUioS+Az4qdZ6j1JqAvA4YAcWaa3vca03AngOiATWADdrrds8UbgQQvSmmIR4tkQOJrV2q9GldMkJz+SVUqcBa4Fc12M7sACYDAwERiulJrpWXwjcqrXOBUzAjZ4oWgghjOA0WTDjpOGw70xWdjLDNTcC/wOUuB6fCuzUWhe5ztIXAtOVUpmAXWu93rXeC8B0N9crhBCGiRp6NuCkdMmfaKitNbqck3LCkNda36C1/qTTolSg80emlALpx1kuhBB+IWvwMPbmzCChrZS6/9zBV2+9TGNjk9FlHddJjcn/gBnoPIO+CXAcZ3mXxMWFd6Ok40tIiHD7Pr1RoPQJgdOr9Ol9zr9yGrvz+lOz8iVyylZS/dJn7M29mNOnTMVmsx13WyP67E7I7wdSOj1OpmMo51jLu6Sqqh6Hw32fwpKQEEFFRZ3b9uetAqVPCJxepU/vFZmaTeS197F/60aaN7xG2s7FbH5sJeZzbqFPrjrqNp7q02w2HffkuDu3UG4AlFIqRyllAWYBK7TWxUCzUmqMa73ZwIpu7F8IIXxC+tBTyL7uEQ4OnUOws5mmNS8YXdKPdDnktdbNwBxgCZAPFADffGTKVcBflVIFQDjwpHvKFEII72Q2m+l/xjlUpIwl1VFK/qq3veoDwE96uEZrndXp61XA8KOsk0fH3TdCCBFQ+p0ziX3/3UbG7iVs25dHn0v/h6jYGKPLkne8CiGEO4RFRtF/zsPsSTmfjCO7qX7tQRprDxtdloS8EEK4i8VqYeikq6gcfTPRzhoOLH6UluZGQ2uSkBdCCDfLGXka+3JnkNhWSuHLf6Bib5FhtXTnFkohhBAnMGz8hWxtayG98A2s7z7Ap6uzaEkbSdrw04lJTOy1OuRMXgghPGTo+ZNgyqPsiB2H/UglmUWv43zj9zTW9d77AiTkhRDCg2ISExg17WeMuPtf7I48FZupnYpenJNeQl4IIXqB2Wwm4dSLOOwMJ3bdE+R/vLJ3jtsrRxFCCEFadjYNmWcD0FR9sFeOKRdehRDCw9ra29i44h2aN79LmqOUYlM6Qy++oleOLSEvhBAetmbp24yueOvbxy32ePJXLSMkLpXI1AzikpIIDvJMHEvICyGEh+WMHkPJ56W011YTd2Qv/Rs3Q+Pmjrl78+CgI4wjF91Hbpb7b62UkBdCCA9x1FXSVrSJxEMHONxcivVIKcGm7z72usYZRq0tgaaovuQmRHmkBgl5IYTwkCOfv0bb7vWYQiKwhaew3zwCS2waESl9ic/MJj0y0uM1SMgLIYSHhJxzA84zZ2G2R9I3IYI4Az4cRUJeCCE8xGSxYrJ7/mz9eOQ+eSGE8GMS8kII4cck5IUQwo9JyAshhB+TkBdCCD8mIS+EEH7Mm26htACYzSa379gT+/RGgdInBE6v0qd/8XC+WY72vMnpdLr9oN00FvjE6CKEEMJHnQWs/eFCbwr5YGA0UAq0G1yLEEL4CguQAnwBHPnhk94U8kIIIdxMLrwKIYQfk5AXQgg/JiEvhBB+TEJeCCH8mIS8EEL4MQl5IYTwYxLyQgjhx7xpWoNuUUo9CEwDnMDzWuvHlVITgMcBO7BIa32Pa90RwHNAJLAGuFlr3XbUHXsppdSfgXit9Rx/7VMp9SGQCLS6Fv0ciMDPelVKTQLuA8KA97XWv/a311QpdQNwa6dFfYGXgDfxoz4BlFJXA793PVyhtb7dG15Pnz6TV0qNA84FhgGnAL9USg0HFgCTgYHAaKXURNcmC4Fbtda5gAm4sfer7j6l1HnAta6v7fhhn0opE5ALDNdaj9BajwC24Ge9KqWygWeAy+j4+R3p6smv+tRaP9fpdbwKKAcexc/6VEqFAk8C44DhwFmuX+KG9+nTIa+1/hgY7/oNmEjHXybRwE6tdZFr+UJgulIqE7Brrde7Nn8BmN77VXePUioWmA884lp0Kn7YJ6Bc/31fKZWnlLoV/+x1Ch1ndvu11q3ADKAR/+uzs6eBu4Fs/K9PCx15GgbYXP9q8YI+fTrkAbTWrUqpB4B8YBWQSsf8N98oBdKPs9xXPAvMBQ65HvtrnzF0vI5TgPOAm4E++F+vOYBFKbVUKbUZ+AX++5riGrawa60X44d9aq3rgHlAAbAf2IOX9OnzIQ+gtb4PSAAy6PhTv/OEPCbAQUevR1vu9Vzjmvu01qs6LT5WPz7bJ4DWep3W+hqtdY3WuhJ4HngQ/+vVCkwArgfOAE6j4wzX3/r8xs/pGJsGP/zZVUoNA64DMukI8Xa8JIt8+sKrUmoAEKK13qy1blRKvU7HRdjOs1gmAyV0/HZNOcpyXzADSHGd8cUC4XT8MPlbnyilxgLBnX6hmeg4KzpaT77c60HgA611BYBS6g06/mT3x9c0iI6x6jmuRcfqx5f7vBBYpbUuB1BKvQDcjhe8nr5+Jp8N/FMpFez6QZpMx7CGUkrlKKUswCw6rnQXA81KqTGubWcDKwypuou01udrrYe4Ll7dCywFJuJnfbpEA39SSoUopSLouNB8N/7X6zvAhUqpaFdPE4HX8L8+oePC8g6tdYPr8Qb8r888YIJSKsx188AkvKRPnw55rfVyYBnwFbAJ+Exr/SodZwxL6BinL6Djfx7ouLr/V6VUAR1nw0/2ds3uorVuxg/71Fq/w/df0wVa63X4Wa9a6w3AY3R8yEM+UEzHhck5+FGfLtl0nL0C/vmzq7V+H/gPHT+zW+i48Ho/XtCnzCcvhBB+zKfP5IUQQhyfhLwQQvgxCXkhhPBjEvJCCOHHJOSFEMKPScgLIYQfk5AXQgg/JiEvhBB+7P8Ds7R79lWAwKMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1,a1 = plt.subplots()\n",
    "a1.plot(densmooth,depnic,label='original')\n",
    "a1.plot(den_interp,depth_master,label='interp')\n",
    "a1.invert_yaxis()\n",
    "a1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='0bb0d765-e34b-4488-991c-9935840a592e'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11f11af40>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "f2,a2=plt.subplots(figsize=(9,9))\n",
    "# a2.plot(den126,dep126)\n",
    "a2.plot(den106,dep106,label='106')\n",
    "\n",
    "a2.plot(pitsmooth,deppit,label='pitsmooth')\n",
    "a2.plot(densmooth,depnic,label='smooth')\n",
    "a2.plot(dennic,depnic,label='NICL')\n",
    "# a2.set_ylim(0,4)\n",
    "a2.invert_yaxis()\n",
    "a2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='2989caa3-7d6b-41fb-94bf-89dafb1f9443'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f1621c0>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1,a1 = plt.subplots()\n",
    "a1.plot(monthly_df.index,monthly_df.tskin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "spinyears = 1200\n",
    "dates = monthly_df.index + pd.Timedelta('14D')\n",
    "st_year = dates.year[0] - spinyears\n",
    "date_end = 2018\n",
    "months = dates[0:12].to_pydatetime()\n",
    "helper = np.vectorize(lambda x: x.timetuple().tm_yday)\n",
    "decis = (helper(months)-0.5)/365\n",
    "\n",
    "yrvec = np.arange(st_year,st_year+spinyears)\n",
    "yrtile = np.tile(yrvec[...,None],[1,12])\n",
    "allspintime = yrtile + decis\n",
    "allspintime_vec = np.ndarray.flatten(allspintime)\n",
    "\n",
    "yrvec2 = np.arange(dates.year[0],date_end+1)\n",
    "yrtile2 = np.tile(yrvec2[...,None],[1,12])\n",
    "allspintime2 = yrtile2 + decis\n",
    "allspintime2_vec = np.ndarray.flatten(allspintime2)\n",
    "\n",
    "time_out = np.concatenate((allspintime_vec,allspintime2_vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "tileyears = 25\n",
    "reps = spinyears/tileyears\n",
    "tskin_rep = np.tile(monthly_df.tskin.iloc[0:tileyears*12].values,int(reps))\n",
    "bdot_rep = np.tile(monthly_df.bdotIE.iloc[0:tileyears*12].values,int(reps))\n",
    "tskin_long = np.concatenate((tskin_rep,monthly_df.tskin.values))\n",
    "bdot_long = np.concatenate((bdot_rep,monthly_df.bdotIE.values))\n",
    "bdot_long[bdot_long<0] = 0.005\n",
    "bdot_con = np.mean(bdot_long)*np.ones_like(bdot_long)\n",
    "tskin_out = np.array((time_out,tskin_long))\n",
    "bdot_out = np.array((time_out,bdot_long))\n",
    "bdot_con_out = np.array((time_out,bdot_con))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1958-01-31   -26.556834\n",
       "1958-02-28   -37.256834\n",
       "1958-03-31   -55.656834\n",
       "1958-04-30   -63.756834\n",
       "1958-05-31   -58.456834\n",
       "                ...    \n",
       "1982-08-31   -63.356834\n",
       "1982-09-30   -62.956834\n",
       "1982-10-31   -50.456834\n",
       "1982-11-30   -33.956834\n",
       "1982-12-31   -28.656834\n",
       "Freq: M, Name: tskin, Length: 300, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df.tskin.iloc[0:tileyears*12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='900ecc93-e382-4e75-8dd4-b382e3c0106c'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f1eeb50>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1,a1=plt.subplots()\n",
    "a1.plot((tskin_out[0,:]-2018),tskin_out[1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "a1.plot((bdot_out[0,:]-2018),bdot_out[1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_tskin_long.csv',tskin_out,delimiter=',',fmt='%1.4f')\n",
    "# np.savetxt('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_bdot_long.csv',bdot_out,delimiter=',',fmt='%1.4f')\n",
    "np.savetxt('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_bdot_long_pos.csv',bdot_out,delimiter=',',fmt='%1.4f')\n",
    "np.savetxt('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SouthPole/SP_bdot_long_con.csv',bdot_con_out,delimiter=',',fmt='%1.4f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfm_results=h5.File('/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SP_test/CFMresults.hdf5','r')\n",
    "modeltime = cfm_results['depth'][-24:,0]\n",
    "dep = cfm_results['depth'][-24:,1:]\n",
    "rho = cfm_results['density'][-24:,1:]\n",
    "age =  cfm_results['age'][1:]\n",
    "temp = cfm_results['temperature'][-24:,1:]\n",
    "# rd3[fn]['DIP'] = cfm_results['DIP'][1:,1:]\n",
    "# ddh3[fn] = rd3[fn]['DIP'][:,2]\n",
    "# fcd3[fn] = rd3[fn]['DIP'][:,3]\n",
    "cfm_results.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "f1,a1=plt.subplots(figsize=(11,9))\n",
    "a1.plot(rho[0,:],age)\n",
    "a1.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots(figsize=(11,9))\n",
    "a1.plot(rho[0,:],dep[0,:])\n",
    "a1.plot(den106,dep106)\n",
    "a1.plot(denpit,deppit)\n",
    "a1.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "depths = dft4.depth[1:].values\n",
    "tprof170215 = dft5.iloc[38].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1 = plt.subplots(figsize = (8,8))\n",
    "a1.plot(tprof170215,depths,label = 'data')\n",
    "a1.plot(temp[2,1:]-273.15,dep[2,1:],label = 'model')\n",
    "a1.set_ylim(-1,40)\n",
    "a1.invert_yaxis()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare daily vs monthly model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_d = '/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SP_test_daily/CFMresults.hdf5'\n",
    "fn_m = '/Users/maxstev/Documents/Grad_School/Research/FIRN/CFM/CommunityFirnModel/CFM_main/SP_test_monthly/CFMresults.hdf5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5.File(fn_d,'r')\n",
    "time_d = f['temperature'][1:,0]\n",
    "# i2007 = np.where(time_model>2007.45)[0][0]\n",
    "# temp_2007 = f['temperature'][i2007,1:]\n",
    "dep_d = f['depth'][1:,1:]\n",
    "rho_d = f['density'][1:,1:]\n",
    "DIP_d = f['DIP'][1:,1:]\n",
    "dcon_d = f['Dcon'][1:,1:]\n",
    "f.close()\n",
    "\n",
    "f = h5.File(fn_m,'r')\n",
    "time_m = f['temperature'][1:,0]\n",
    "# i2007 = np.where(time_model>2007.45)[0][0]\n",
    "# temp_2007 = f['temperature'][i2007,1:]\n",
    "dep_m = f['depth'][1:,1:]\n",
    "rho_m = f['density'][1:,1:]\n",
    "DIP_m = f['DIP'][1:,1:]\n",
    "dcon_m = f['Dcon'][1:,1:]\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df2['2017-02-05':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_results=pd.DataFrame(data=DIP_d[:,0:4],columns=['DIP','DH','DHc','comp'],index=daily_df2['2017-02-06':].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_results.index.get_loc('2017-02-28')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "# a1.plot(time_d[1:],DIP_d[1:,3])\n",
    "a1.plot(time_m[:],DIP_m[:,2])\n",
    "a1.plot(time_d[:],(DIP_d[:,2]-DIP_d[22,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "# a1.plot(time_d[1:],DIP_d[1:,3])\n",
    "a1.plot(time_m[:],DIP_m[:,2])\n",
    "a1.plot(time_d[:],(DIP_d[:,2]-DIP_d[22,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "# a1.plot(time_d[1:],DIP_d[1:,3])\n",
    "a1.plot(time_m[1:],DIP_m[1:,3]/np.mean(np.diff(time_m)))\n",
    "a1.plot(time_d[1:],DIP_d[1:,3]/np.mean(np.diff(time_d)))\n",
    "# a1.plot(time_m[:-1],daily_results.comp.resample('M').sum().values[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "# a1.plot(time_d[1:],DIP_d[1:,3])\n",
    "a1.plot(time_m[:-1],np.cumsum(DIP_m[1:,3]))\n",
    "a1.plot(time_m[:-1],np.cumsum(daily_results.comp.resample('M').sum().values[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots()\n",
    "# a1.plot(time_d[1:],DIP_d[1:,3])\n",
    "# a1.plot(time_m[:-1],DIP_m[1:,3])\n",
    "a1.plot(time_m[:-1],(daily_results.comp.resample('M').sum().values[1:]-DIP_m[1:,3])/1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f2,a2=plt.subplots()\n",
    "daily_results[1:].comp.plot(ax=a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf2017 = daily_df2['2017-02-05':]\n",
    "f3,a3 = plt.subplots()\n",
    "ddf2017.Tavg.plot(ax=a3)\n",
    "a3b = a3.twinx()\n",
    "ddf2017.pcp.plot(ax=a3b,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_indices(points,lon,lat,tree=None):\n",
    "    if tree is None:\n",
    "        # lon,lat = lon.T,lat.T\n",
    "        lonlat = np.column_stack((lon.ravel(),lat.ravel()))\n",
    "        tree = cKDTree(lonlat)\n",
    "    dist,idx = tree.query(points,k=[1])\n",
    "    ind = np.column_stack(np.unravel_index(idx,lon.shape))\n",
    "    print(ind)\n",
    "    for i,j in ind:\n",
    "        ii=i\n",
    "        jj=j\n",
    "    # return [(i,j) for i,j in ind]\n",
    "    return ii,jj #, [(i,j) for i,j in ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import cKDTree\n",
    "import netCDF4 as nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fn_t2m = \"/Volumes/Samsung_T1/RACMO/Antarctica/t2m.KNMI-2011.ANT27.ERAINx_RACMO2.4.1.DD.nc\"\n",
    "fn_tskin = \"/Volumes/Samsung_T1/RACMO/Antarctica/tskin.KNMI-2011.ANT27.ERAINx_RACMO2.4.1.DD.nc\"\n",
    "lat_int = -89.54 #south pole\n",
    "lon_int = 137.04\n",
    "nc_t2m = nc.Dataset(fn_t2m, 'r') \n",
    "lat = nc_t2m.variables['lat'][:]\n",
    "lon = nc_t2m.variables['lon'][:]\n",
    "time_t2m = nc_t2m.variables['time'][:]\n",
    "ii=117\n",
    "jj=135\n",
    "t2m_in = nc_t2m.variables['t2m'][:].data[:,0,ii,jj]\n",
    "nc_t2m.close()\n",
    "nc_tskin = nc.Dataset(fn_tskin, 'r') \n",
    "tskin_in = nc_tskin.variables['tskin'][:].data[:,0,ii,jj]\n",
    "nc_tskin.close()\n",
    "# ii,jj = find_indices((lon_int,lat_int),lon,lat)\n",
    "# print(ii,jj)\n",
    "# print(lat[ii,jj])\n",
    "# print(lon[ii,jj])\n",
    "racmodate = [pd.datetime(1950,1,1) + pd.Timedelta(time_t2m.data[ii],unit='d') for ii in range(len(time_t2m.data))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "racT_df = pd.DataFrame({'tskin':tskin_in,'t2m':t2m_in},index=racmodate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "racT_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots(figsize=(18,6))\n",
    "(racT_df['2011-07']['t2m']).plot(ax=a1,label='t2m')\n",
    "(racT_df['2011-07']['t2m']*0.985).plot(ax=a1,label='t2m scale')\n",
    "(racT_df['2011-07']['tskin']).plot(ax=a1,label='tskin')\n",
    "a1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = racT_df.copy()\n",
    "df2['Tdiff'] = df2.tskin-df2.t2m\n",
    "df2['Tper'] = (df2.tskin-df2.t2m)/df2.t2m\n",
    "df2.drop([df2.iloc[424].name,df2.iloc[1885].name],inplace=True) # get rid of the leap days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for jj,yr in enumerate(df2.index.year.unique()):\n",
    "    if jj == 0:\n",
    "        df3 = pd.DataFrame({'tper{}'.format(yr):df2[str(yr)]['Tper'].values},index=df2[str(yr)].index.dayofyear)\n",
    "    else:\n",
    "        df3['tper{}'.format(yr)] = df2[str(yr)]['Tper'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Pmean']=df3.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots(figsize=(8,6))\n",
    "df3['Pmean'].plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.DataFrame({'Tdata':df2.Tper.values})\n",
    "# df4=pd.DataFrame({'Tdata':(df2['Tdiff']-df2['Tdiff'].mean()).values})\n",
    "df4['smooth'] = df4.Tdata.rolling(30,center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "def test_func(x, a, b,c,d):\n",
    "    return a * np.sin(b * x + c) + d\n",
    "params, params_covariance = optimize.curve_fit(test_func, df4.index.values, df4.Tdata.values,p0=[0.1,np.pi*2/365,35,-0.01])\n",
    "\n",
    "def tscaler(doy):\n",
    "    a = -5.70493675e-03\n",
    "    b = 1.72479759e-02\n",
    "    c = 3.60427452e+01\n",
    "    d = -9.41404759e-03\n",
    "    return (a * np.sin(b * doy + c) + d) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tscaler(180)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev=365*5\n",
    "f1,a1=plt.subplots(figsize=(18, 8))\n",
    "a1.plot(df4.index.values[0:ev], df4.Tdata.values[0:ev], label='Data')\n",
    "a1.plot(df4.index.values[0:ev], df4.smooth.values[0:ev], label='Data')\n",
    "a1.plot(df4.index.values[0:ev], test_func(df4.index.values[0:ev], params[0], params[1],params[2],params[3]),label='Fitted function')\n",
    "a1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots(figsize=(18,6))\n",
    "(df2['Tdiff']-df2['Tdiff'].mean()).plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,a1=plt.subplots(figsize=(18,6))\n",
    "(df2['Tper']).plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where((df2.index.month==2)&(df2.index.day==29))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.iloc[424].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop([df2.iloc[424].name,df2.iloc[1885].name],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['2012-02-20':'2012-03-02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t109_df = pd.read_pickle('T109data.pkl')\n",
    "t109_df['K2'] = t109_df['Tbox2']+273.15\n",
    "t109_df['K1'] = t109_df['Tbox1']+273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t109_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "racT_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rac2=racT_df.copy()\n",
    "rac2.index=rac2.index+pd.DateOffset(years=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "f1,a1 = plt.subplots(figsize=(18,8))\n",
    "t109_df.K2.plot(ax=a1,label='T109')\n",
    "t109_df.K1.plot(ax=a1,label='T109_1')\n",
    "daily_df2.Tavg['2017':].plot(ax=a1,label='Tavg')\n",
    "daily_df2.Tskin_damp['2017':].plot(ax=a1,label='damp')\n",
    "rac2.tskin['2017':].plot(ax=a1,label='RACMO')\n",
    "a1.legend()"
   ]
  }
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