CLiF_Criteria_v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import os\n",
    "from astropy.io import fits\n",
    "from astropy import modeling\n",
    "import scipy.integrate as integrate\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Calculate the standard deviation of CL windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CL_computation:\n",
    "    def __init__(self,lam=[],width=[],std_dev=pd.DataFrame(),\\\n",
    "                 area=pd.DataFrame()):\n",
    "        self.lam=lam\n",
    "        self.width=width\n",
    "        self.std_dev=std_dev\n",
    "        self.area=area\n",
    "        #self.flux_re=flux_re\n",
    "        \n",
    "    def wave_win(self,v):\n",
    "        c=299792.458\n",
    "        width=[]\n",
    "        for j in range(len(self.lam)):\n",
    "            width.append((v/c)*self.lam[j])\n",
    "        print('widths are prepared:%s'%width)\n",
    "        self.width=width\n",
    "        return width\n",
    "    \n",
    "    def CL_comp_std_sn(self,lam_no_z,flux,mask,sn):\n",
    "        da=[]\n",
    "        for i in range(len(self.lam)):\n",
    "            ind=np.where((lam_no_z>(self.lam[i]-self.width[i]/2))&(lam_no_z<(self.lam[i]+self.width[i]/2))&(mask==0),True,False)\n",
    "            wave_window=lam_no_z[ind]\n",
    "            #print(wave_window)\n",
    "            flux_window=flux[ind]\n",
    "            if len(wave_window)<3 or len(flux_window)<3:\n",
    "                continue\n",
    "            #\n",
    "            window_min_flux=flux_window[np.where(wave_window==min(wave_window))]\n",
    "            window_max_flux=flux_window[np.where(wave_window==max(wave_window))]\n",
    "            k=(window_max_flux-window_min_flux)/(max(wave_window)-min(wave_window))\n",
    "            b=window_min_flux-k*min(wave_window)\n",
    "            window_number=len(wave_window)\n",
    "            window_length=max(wave_window)-min(wave_window)\n",
    "            def pseudo_con(x):\n",
    "                y=k*x+b\n",
    "                return y\n",
    "            pseudo_range=pseudo_con(np.arange(min(wave_window),max(wave_window),window_length/window_number))\n",
    "            flux_re=flux_window-pseudo_range[0:len(flux_window)]\n",
    "            if sn==True:\n",
    "                std=np.std(flux_re[flux_re>0])\n",
    "                sn=np.mean(flux_window)/np.std(flux_window)\n",
    "                da.append([self.lam[i],std,sn])\n",
    "            else:\n",
    "                std=np.std(flux_re[flux_re>0])\n",
    "                sn=-99\n",
    "                da.append([self.lam[i],std,sn])\n",
    "        da=pd.DataFrame(da,columns=['wavename','stddev','SNR'])\n",
    "        self.std_dev=da\n",
    "        #self.flux_re=flux_re\n",
    "        return(da)\n",
    "        \n",
    "    def CL_comp_area(self,lam_no_z,flux,mask,fit):\n",
    "        dat=[]\n",
    "        for i in range(len(self.lam)):\n",
    "            #if self.lam[i]-self.width[i]/2>min(self.wave_window) and self.lam[i]+self.width[i]/2<max(self.wave_window):\n",
    "            #ind1=np.where((lam_no_z>self.lam[i]-5)&(lam_no_z<self.lam[i]+5)&(mask==0),True,False)\n",
    "            #else:\n",
    "            ind1=np.where((lam_no_z>(self.lam[i]-self.width[i]/2))&(lam_no_z<(self.lam[i]+self.width[i]/2))&(mask==0),True,False)\n",
    "            wave_window=lam_no_z[ind1]\n",
    "            flux_window=flux[ind1]\n",
    "            if len(wave_window)<3 or len(flux_window)<3 or len(wave_window)-len(flux_window)!=0:\n",
    "                continue\n",
    "            #\n",
    "            window_min_flux=flux_window[np.where(wave_window==min(wave_window))]\n",
    "            window_max_flux=flux_window[np.where(wave_window==max(wave_window))]\n",
    "            k=(window_max_flux-window_min_flux)/(max(wave_window)-min(wave_window))\n",
    "            b=window_min_flux-k*min(wave_window)\n",
    "            window_number=len(wave_window)\n",
    "            window_length=max(wave_window)-min(wave_window)\n",
    "            def pseudo_con(x):\n",
    "                y=k*x+b\n",
    "                return y\n",
    "            pseudo_range=pseudo_con(np.arange(min(wave_window),max(wave_window),window_length/window_number))\n",
    "            flux_re=flux_window-pseudo_range[0:len(flux_window)]\n",
    "            if fit==True:\n",
    "                flux_center=np.max(flux_window)\n",
    "                wave_center=wave_window[np.where(flux_window==flux_center)]\n",
    "                g_init = modeling.models.Gaussian1D(amplitude=flux_center, mean=wave_center, stddev=10.)\n",
    "                fit_g = modeling.fitting.LevMarLSQFitter()\n",
    "                g = fit_g(g_init, wave_window, flux_window)\n",
    "                flux_fitting=g(wave_window)\n",
    "                #\n",
    "                def f_test(n):\n",
    "                    m=g(n)-pseudo_con(n)\n",
    "                    return(m)\n",
    "                area=integrate.quad(f_test,min(wave_window),max(wave_window))[0]\n",
    "                area_err=integrate.quad(f_test,min(wave_window),max(wave_window))[1]\n",
    "                dat.append([self.lam[i],area,area_err])\n",
    "            else:\n",
    "                area=np.sum(flux_re)\n",
    "                dat.append([self.lam[i],area,0.0])\n",
    "            #print(\"%s,%f,%f\"%(i,SNR,area))\n",
    "        cat=pd.DataFrame(dat,columns=['wavename','ar','ar_err'])\n",
    "        self.area=cat\n",
    "        return(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "path='/media/richard/Backup Plus/sdss_16_pair/'\n",
    "path_catalog='/media/richard/Backup Plus/314kpair_DR16_Z_1.5_Nrich.csv'\n",
    "#data=pd.read_csv(path_catalog,low_memory=False,chunksize=1000,skiprows=134000,names=pd.read_csv(path_catalog).columns)\n",
    "data=pd.read_csv(path_catalog,low_memory=False,chunksize=1000)\n",
    "chunks=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "text": [
      "widths are prepared:[82.72389560914172, 99.1352490928908, 116.74743331935323]\n"
     ]
    },
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      " 32%|███▏      | 323/1000 [00:31<00:39, 17.20it/s]/usr/lib/python3/dist-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
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    {
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     "output_type": "stream",
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      "100%|██████████| 958/958 [03:27<00:00,  4.61it/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237957\n"
     ]
    }
   ],
   "source": [
    "#cl_all=[3425.8,3758.9,3839.4,4412.5,5159.0,5302.9,5335.2,5426.6,5720.7,6087.0,6374.5,7005.8,7611.0,7891.8]\n",
    "N=[1240,1486,1750]\n",
    "CL=CL_computation(N,[])\n",
    "WIDTH=CL.wave_win(10000)\n",
    "f=open('/media/richard/Backup Plus/error_nrich.txt','a')\n",
    "for chunk in data:\n",
    "    for i in tqdm(chunk.index):\n",
    "        hdu= fits.open(path+chunk.specname[i])\n",
    "        try:\n",
    "            spectra=hdu[1].data\n",
    "            spectra_2=hdu[2].data\n",
    "            Lam_no_z=(10**spectra['loglam'])/(spectra_2.Z+1)\n",
    "            Flux=spectra['flux']\n",
    "            MASK=spectra['and_mask']\n",
    "            ca=CL.CL_comp_std_sn(Lam_no_z,Flux,MASK,True)\n",
    "            #ca_area=CL.CL_comp_area(Lam_no_z,Flux,MASK,False)\n",
    "            #ind_sn=np.where((Lam_no_z>6005)&(Lam_no_z<6035),True,False)\n",
    "            #sn_con=Flux[ind_sn]\n",
    "            for x in range(len(ca)):\n",
    "                chunk.loc[chunk.specname==chunk.specname[i],'STD%d'%CL.std_dev['wavename'][x]]=CL.std_dev['stddev'][x]\n",
    "                chunk.loc[chunk.specname==chunk.specname[i],'SNR%d'%CL.std_dev['wavename'][x]]=CL.std_dev['SNR'][x]\n",
    "            #for xx in range(len(ca_area)):    \n",
    "                #chunk.loc[chunk.specname==chunk.specname[i],'AREA%d'%CL.area['wavename'][xx]]=CL.area['ar'][xx]\n",
    "                #chunk.loc[chunk.specname==chunk.specname[i],'SN_6020']=np.mean(sn_con)/np.std(sn_con)\n",
    "        except TypeError:\n",
    "            print('wrong:%s'%chunk.specname[i])\n",
    "            f.write('%s'%chunk.specname[i])\n",
    "            pass\n",
    "        except Exception:\n",
    "            print('wrong:%s'%chunk.specname[i])\n",
    "            f.write('%s'%chunk.specname[i])\n",
    "            pass\n",
    "    chunks.append(chunk)\n",
    "    print(max(chunk.index))\n",
    "data2=pd.concat(chunks)\n",
    "data2=data2.replace([np.inf, -np.inf],-999)\n",
    "data2=data2.fillna(-999)\n",
    "data2.to_csv('/media/richard/Backup Plus/314kpair_DR16_Z_1.5_Nrich.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavename</th>\n",
       "      <th>stddev</th>\n",
       "      <th>SNR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1240</td>\n",
       "      <td>1.531635</td>\n",
       "      <td>0.645584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1486</td>\n",
       "      <td>0.464944</td>\n",
       "      <td>-99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1750</td>\n",
       "      <td>0.140363</td>\n",
       "      <td>-99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wavename    stddev        SNR\n",
       "0      1240  1.531635   0.645584\n",
       "1      1486  0.464944 -99.000000\n",
       "2      1750  0.140363 -99.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data2=pd.concat(chunks)\n",
    "data2=data2.replace([np.inf, -np.inf],-999)\n",
    "data2=data2.fillna(-999)\n",
    "data2.to_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12_134.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Count the CLs of which std >1,3,5,10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STD3426', 'STD3758', 'STD3839', 'STD4414', 'STD5160', 'STD5304',\n",
       "       'STD5335', 'STD5426', 'STD5720', 'STD6087', 'STD6375', 'STD7006',\n",
       "       'STD7612', 'STD7894'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=pd.read_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12.csv',low_memory=False)\n",
    "data2.columns[114:128]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 218174/218174 [32:12<00:00, 112.91it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(data2.index):\n",
    "    cut=data2.iloc[data2.index==data2.index[i],114:128]\n",
    "    count_10=0\n",
    "    count_5=0\n",
    "    count_3=0\n",
    "    count_2=0\n",
    "    count_1=0\n",
    "    for j in range(0,14):\n",
    "        if float(cut.iloc[:,j])>=10 and float(cut.iloc[:,j])!=-999:\n",
    "            count_10+=1\n",
    "        if float(cut.iloc[:,j])>=5 and float(cut.iloc[:,j])!=-999:\n",
    "            count_5+=1\n",
    "        if float(cut.iloc[:,j]>=3) and float(cut.iloc[:,j])!=-999:\n",
    "            count_3+=1\n",
    "        if float(cut.iloc[:,j]>=2) and float(cut.iloc[:,j])!=-999:\n",
    "            count_2+=1\n",
    "        if float(cut.iloc[:,j]>=1) and float(cut.iloc[:,j])!=-999:\n",
    "            count_1+=1\n",
    "    data2.loc[data2.index==data2.index[i],'CL_10_COUNTS']=count_10\n",
    "    data2.loc[data2.index==data2.index[i],'CL_5_COUNTS']=count_5\n",
    "    data2.loc[data2.index==data2.index[i],'CL_3_COUNTS']=count_3\n",
    "    data2.loc[data2.index==data2.index[i],'CL_2_COUNTS']=count_2\n",
    "    data2.loc[data2.index==data2.index[i],'CL_1_COUNTS']=count_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44832"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(data2[(data2.CL_5_COUNTS>1)&(data2.SN_6020>10)])\n",
    "#data2.loc[data2.GroupID==69592]\n",
    "len(data2[(data2.CL_1_COUNTS>2)&(data2.SN_6020>=15)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2[0:134205].to_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12_134205.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. add the column of SUBCLASS and the BPT classification from PORTSMOUTH Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 134205/218174 [2:12:48<4:30:48,  5.17it/s]WARNING: File may have been truncated: actual file length (163543) is smaller than the expected size (164160) [astropy.io.fits.file]\n",
      " 66%|██████▌   | 144262/218174 [2:27:14<1:38:34, 12.50it/s]WARNING: File may have been truncated: actual file length (163543) is smaller than the expected size (164160) [astropy.io.fits.file]\n",
      "100%|██████████| 218174/218174 [5:17:06<00:00, 11.47it/s]  \n"
     ]
    }
   ],
   "source": [
    "path='/media/richard/Backup Plus/sdss_16_pair/'\n",
    "data2=pd.read_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12.csv',low_memory=False)\n",
    "#f=open('/media/richard/Backup Plus/e')\n",
    "for i in tqdm(data2.index):\n",
    "    if os.path.exists(path+'%s'%data2.specname[i])==True:\n",
    "        try:\n",
    "            hdu=fits.open(path+'%s'%data2.specname[i])\n",
    "            da=hdu[2].data\n",
    "            data2.loc[data2.index==data2.index[i],'SUBCLASS']=da['SUBCLASS']\n",
    "        except IndexError:\n",
    "            data2.loc[data2.index==data2.index[i],'SUBCLASS']=' '\n",
    "            pass\n",
    "        except Exception:\n",
    "            data2.loc[data2.index==data2.index[i],'SUBCLASS']=' '\n",
    "            pass\n",
    "    else:\n",
    "        print('fits not exist:%s'%data2.specname[i])\n",
    "        #f.write('not exist:%s\\n'%data2.specname[i])\n",
    "#f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data2.to_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12.5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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qfkiPNb6reUQW1tRzIhOp8xaLpa0lM8vChu6ZOhivLGxCS+Rgoy+Rgw09F1nYcOOcRRbWxzwW63WusPHm4D1hjc4jsrDB5xDnCpOF9fG4H3sAlo53aPKgJH+1eOC9Nsknrvz+I5L89eL3f5Pk74895mPm8glJfueQF7Fbxh7bMWO+Lsmvb/ii/JIk7zX22JfmcI8kP7nhHL5g7HHvMN+nTOjx9VZLz+91lvNJ7jX2uI+Zz7VJvjHJG3J34/Vvk5wee2yHjPUHN3xOHLU8eeR5fPxSg3IxyWcluWblNvdM8sVL++Xvktw89j5YGt97JXnFYmyvSfK4JGXp9zcm+aGlv/mlJP904DH2Ur8ycI3vcR4PTfKMQ9b74tb3RUao8z3MYfA639djaY3tdlrj+55HBqjzQ+6L9FTne3hOjFLj+9gXGbjO9zSH5uu8xWJpb4ksbOwxy8ImtkQWNuZ8ZGHDz0MWNswYZWHHr3eQLKzr8WekGt/DPGRhDc4hA9X4IfdFZGGD7oeMUON7mkfzdd5isbS1ZEY52GK8k8rCMoMcbDEPWVgD4zpirLKw8cY6+SwsM8jBFmNsvkfuq35FFjbq+CMLa2YOG2xXFjbivogsbND9EFlYp49fi8UynSWysLHHO/ksLHuWgy3mLAsbbz6ysGHnMPksLBPoj/uqXRmhxvc4F1nYeHMYpc739VhaY7uysJH2Q3qs8T08L2RhDeyHxTqbr/MWi6W9JTPKwoas1R2OWRY2sSVysDHnIwcbfh6ysGHGOIssrMd5OFfYuHPwnrBG5xFZ2FjPCecKk4V1vow+AEuHOzN559zdYNUc8U0AOfiGh8vfdvLiJO8w9thXxvcPkzz3mBexW8Ye4zFj/4ktX5hf2sp+SPIdizHdmuRzF4+ra5OcTfLJR7yYvinJe4899i3m+piJPb7++YaPq68be8zHzOWBK4+l5yV5z7HHdcRYb0jyui2f26vLh404jwfloJmqSV6VEw7Ck3xckrcsbv/aJH+vgX1x/6U69/okH3TMbZ+88rf/nAHG11v9yoA1vq955CAI+tVj1vvi1vdFBqzzPc5hsDrf53NijW13VuOHmkd6rPND74v0UOf7mENGqPE9PrcflIHqfI9zaLrOWyyWNpfIwkZfIgsbffwbzlUWNt5cZGHDz2OwHrnHOTTdI/dZvyILa2H8g9b4HuchC2twDum5xg+9LyILG3Q/ZOAa3+M8mq7zFoulvSUzycEWY5xkFpYZ5GCLecjCGhjbEeOVhY0z1slnYZlBDrYYV9M9cp/1K7KwFsYvC2tkDmtuWxY24r6ILGzQ/RBZWI0szGLZyyWysNGXzCALyx7lYIv5ysLGm4ssbNg5DNoj9zSHpvvjPmtXBq7xfc0lsrAW5jBone/zebHGtmVhI+2H9FTj+5hHZGE1Ph9psVgmvGQmWdjQtbrjscvCJrREDjbmXORgw89jsB65xzk03SP3Wb/iPWGtzEEW1sAc1ty2c4WNuC/iPWGD74vIwprKwkYfgKWjHZmcSvKcpQfafz/h9t+3dNunjz3+pXGVJP85ySOT3DfJ++XgG3wGKco7jv0Tl8Z4Mcm3JnnI4gX8uiTvnuTLcmWjvLz8XANz+OjFWL4zybVH3OYeSf7LIeP/ybHHv+Fc325RhAZr+nYc73XZ7Btxbk/yNmOP+4i5fMjK3/7/S/LWY4/rmPF+4QZ/9+OWl2Xp21tGmMdvLY3ls9a8z/JB5S81sC9uWRrPV59w22uS/OHS7f8uybv3OLbe6lcGrPE9z+Nbkzw2yekk75qDA/3l9b645X2RAet8j3MYrM73+VhaY9ud1fih5pEe6/zQ+yI91PkenxOD1vg+90UGqvM9z+GWpXU0VectFkubS2Rhoy+RhcnC+h2vLGy88crCNuiRe57DLUvjaapH7rN+RRY2+vgzcI3vcR6ysAbnkJ5r/ND7IrKwwfdDBqzxPc/jlqV1NFXnLRZLe0tmkoMtxjbJLCwzyMEW85CFNfj4WoxXFjbeeCefhWUGOdhiTLcsjampHrnP+hVZ2Ojjjyxs4zrf52NpjW3LwkbcF5GFDb4fIgvrvc5bLJb2lsjCRl8ygywse5SDLeYiCxtvLrKw4ecw+SwsDffHfdauDFzje56LLGzcOQxa5/t8LK2xbVnYSPshPdX4Hp8XsrAN6nzPc7hlaR1N1XmLxdLmkplkYUPX6o7HLgtrYD9sMFc52HhzkYONMw9ZmCxs7Hk4V9j4c/CesEYeT4dsRxY2znPCucI2rPM9z+GWpXU0Vec3nsvYA7B0tCOTL1l5cL/PCbd/t5XbP27sORwz1n/bVyHoeJzPX4zvF5KcOeZ2NyT5pSNepB8y4vhP5aDp/fY1bnuPJP97ZeyvS3KPsffDBvP9n4tx/95EHl9fvBjfd489lh3n8bE5+DaTy3/vZ+SIZr+VZamI/2mSz0vynkmuT3Jqjft+/NJcnzziHD5waRxvSnLdmvf7pKX7XUpy/xHn8ISlsbw+yQ1r3OdRK8/v38mAYVdX9WvsGt9XHU7yxJX1vrjlfTF2nd91Di3U+b4eS4dsp9ca38c8MnCd7/F5PVid7+h5/YcZucZ3NI9R63xHc3jC0v0nUectFsv4S2Rhoy+RhcnC+h2vLGy8MY/eJ3cwB1mYLGz0eRyy3kGysC7G30KN33UeLdT5vh5Lh2yntxrfxxwyQo3v8XktCxt+DqPX+I7m8YSl+0+izlsslnGXzDgHW4y3+SwsDfTIHcxh9B554PnKwsaZhyxs+PGP3iN3NI/J9chd1a+x63xfdTiysEHn0UKd7+uxdMh2ZGEj7YvIwsaYw+h1vqN5PGHp/pOo8xaLZdwlsrDRlzTQI+84/tH74xHmLAsbZx6ysOHHP3qP3MEcJtcfd1W7WqjxfdXhyMIGm0Mrdb6vx9Ih25GFjbAfMnCN7+i5/YeRhe1U5zuawxOW7j+JOm+xWMZf0kCf3OPcms/BFuOUhU0oC4scbKx5yMHGmYMsTBY2+jwOWa9zhQ04hxbqfF+PpUO241xhI+2LeE/YWPOQhTWWhZ0Kk1dKuXeSr1u66gW11ucfd59a6wuTPHfpqu9crKdFfzr2AE5SSvnAJO+d5GeSPLrWeuGo29Zab8/BN+j8n0N+/an9jHAtH5eDbyr5FyfdsNb6liTfvHL1Wyc528O4OldK+dwcvDD/Wa587jSplHJdDvbLm3JQyCaplPKwJD+f5F6Lq56f5LG11jvGG9XxSikPSfKgJP8tyYNqrT9Ya/2TWutra62X1ljFpyz9/NReBrmeRyz9/Opa6xvXvN8Ll34uOfiGm8GVUkqSf7V01S2L19KT/K8kL126/EE5+Laeoexcvxqp8X3V4SHr+07baqTO7/r3aqHO977PB6rxnc5jpDrf+b4Yoc7v+rxupcZ3sS/GrvO77oup1nlgRI30yX2ShQ2jhR55ELKwccjCZGHbmnCPLAsbZ72dbqehGi8LW8MANX4OOVgiC2ulxs8hB0tkYcDAGumR+9Z0FtZQj7yrFnrkQcjCxiELGy0La6FH3smEe2RZ2Djr7XQ7DdV5WdgaZGFrk4W1UeNlYZl0nQdG0kiP3DdZWP9a6I8HIwsbhyxMFraNCffHc8nBEllYC3V+DjlYIgu7is9H7kQWdmDsOi8HAwbXUJ/cl6ZzsKSZHnlXrfTIvZODjUMO5vOR25pwjzyXLGzqOdjO22qkzs8hC5tDDpbIwg7lPWFbk4VNt84fyUnZ5+Hzk5xZuvy/1rzfry79fFOSx3Y2om69euwBrOGTkvx1kies88K8KDifleQtK7/6wB7GtpZa6y8leeSahSVJnpnkzpXrXt/tqLpXSnlgkv+Yg7F/RpI3jDuitXx+kndI8kO11r8aezDbWPzdfy53N1+vS/KJaxbRMT0xyf9ba/2cWuubNrljKeXaJP9ocfHlOfygayj3X/r5bCnlrda8X125/NYdjWdTH5vk/7d0+Y/WuVOttebgcbfsKzoa0zq6qF8t1Pi+6vCQ9X3XbbVQ53eaQyN1vtd9PmCN73oeY9T5TucwUp3fdQ6t1Pgu9sXYdX7XOUy1zgPjaqFP7pMsbACN9Mi9k4WNQxYmC9vRVHtkWdg46+16O63UeFnYCQaq8XPIwRJZWNJGjZ9DDpbIwoDhtdAj9631LKyVHnknjfTIvZOFjUMWNmoW1kKPvKup9siysHHW2/V2WqnzsrATyMI2Igtro8bLwg5Mtc4D42mhR+6bLKxnjfTHg5CFjUMWJgvbwVT747nkYIksrIU6P4ccLJGFHcbnI7cnCzswdp2XgwFjaKVP7kvrOVjSRo+8k4Z65F7JwcYhB/P5yB1NtUeeSxY29Rysi221UOfnkIXNIQdLZGFX8Z4w5wrb8n6XTbXOH8lJ2efhs1cuP/fQW13tN1Yuf04HY+nDut/eMKYPSvLNtdbXrnuHxbcTrTbMb9/pqDZUa127gVoUpOVv//mT1g+aSymnkvxIkuuTfEut9XfHHdHJFt+I89VJ3pzk20YezlZKKffMwTdGvc3S1V9Xa33xOCNaz+Lbwh6SgwZ4Gx+Tu+f8tEUzMJay9PM9cvDNUOt4x5XLf9LNcDb28SuX/2aD+/7WyuWPKqXcb8fxrKuL+tVCje+rDg9Z33fdVgt1fue/VwN1vrd9PnCN72weI9b5LucwVp3feg6N1fgu9sXYdX7XOUy1zgPjaqFP7pMsbCAN9Mi9koWNQxaWRBa2q6n2yLKwcdbb9XZaqfGysGMMWOPnkIMlsrBWavwccrBEFgYMr4UeuW+tZ2Gt9Mg7a6BH7pUsbByysCTjZmEt9Mi7mmqPLAsbZ71db6eVOi8LO4YsbGOysDZqvCzswFTrPDCeFnrkvsnCBtBAf9w7Wdg4ZGFJZGG7mGp/PJccLJGFtVDn55CDJbKwK/h85M5kYQfGrvNyMGAMrfTJfWk9B0va6JF31kiP3Bs52DjkYEl8PnJXU+2R55KFTT0H62JbLdT5OWRhc8jBElnYFbwnzLnCIgu7ipOyT1wp5VyS91m5+nlr3v2PVy4/bPHNFa1Z/eaYFv1akv++xf1+deXymzsYy5BuWPr5h0cbxfq+LMnDkvxOkm8deSzremKS+yV5VZLHlFI+dNGUTcnXJnm/pct/mOQ/jzKSDSwOSD5wkwOTFcvfNvZTHQxpFy9cufz1pZRr1rjfRy79/Ce11nXrS9c+bOXy6za472HfSPSRh1zXh53qV0M1vq86PGR933VbLdT5Mfqhrut8n3MYssZ3OY+x6nyXcxirzm89h8ZqfBf7Yuw6v+scplrngZE01Cf3SRbWLllY/2RhI2msT97V2D3yrqbaI8vCxllv19tppcbLwo43VI2fQw6WyMJaqfFzyMESWRgwoIZ65L61noW10iOPQRbWP1nYSBrqk3fVQo+8q6n2yLKwcdbb9XZaqfOysOPJwjYjC2ujxsvCDky1zgMjaKhH7pssrE1Ty8ESWdhYZGGysF1MtT+eSw6WyMJaqPNzyMESWdgqn4/cjSzswNh1Xg4GDKqxPrkvredgSRs98himloXJwcYhB/P5yF1NtUeeSxY29Rysi221UOfnkIXNIQdLZGGrvCdsN7Kw6db5Izkp+/Q94pDrXrLOHWutL8vVD+IP33lE3bs09gBOUmv997XWTV4QLlvdVy/qYjxDKKWcTXLvxcWXJPmeEYdzolLKeyX5liSvT/IZtdY7Rx7SiRaN1r9YXHzHJE9O8ttJbiulPLWU8gmllHuMNsA1lFLeLQff6rPs22qtzT+vk6TWutW3uSyam0cvLr4sybO7GtOWfnnl8vsm+ffH3WHxbUaPX7rqa7se1AbeeeXyvda9Y6315Ules3L1w3Ye0Xp2fZy3UuP7er4O+Tqw07YaqfODvm72VOd7mcMINb6TeYxc57uaw5h1ftfndSs1vou/1dh1ftc5TLXOA+NppU/uU/PHzI30yIOShfVPFja+hvrkXY3dI+9qqj2yLGyc9Xa6nYZqvCzsCAPX+DnkYIksrJUaP4ccLJGFAcNqpUfuW9PHzA31yIOShfWvgT55Z7KwJONnYS30yLuaao8sCxtnvZ1up6E6Lws7gixsK7KwNmq8LOzAVOs8MI5WeuS+NX3M3FCPPJip5WCJLGwssrAksrBdTbU/nksOlsjCWqjzc8jBElnYXXw+shOysANj13k5GDC0lvrkvjR/vNxIjzyoqWVhcrBxNNAj76SRHrkLY/fIu5pqjzyXLGzqOdjO22qkzs8hC5tDDpbIwu7iPWHOFbYgC1vhpOzTt/pNARc2fNF4xcrlB+84Hjaz2gj8ziij2M6jF//emeSzdvj2kN6VUq7NwbcWvVWSr6y1/vnIQ1rXF+TgG3FWvVUOvnXlF5O8sJTyyYOOajNfn+SeS5dfkuRpI41lSB+V5PTi56fVWuuYg6m13prkmStXf2kp5ZuOuds35u7G5/tqrT/bx9jW9DYrl6/f8P6vWrn8gB3GMiQ1fvrU+Z5NuMYn6vxUNVXjE3U+063zwPb0ydOmRx7AhPtkPfJ0NdUn65En2yOr8dM25RqfTKTOq/Gj28c6r8Z3b1/rPLAdPfK06ZEHok8e1T72yElDfbIeOcl0e2R1ftrU+QGo8aPbxzrfTI1P1PmFqdZ5YDt65Gmbco/86MW/TffHl+mTR7WPPXLSUJ88gx55X/tjNX761PkBqPGjUuNlYV3Y1zoPbE+fPG165AHokUelR9Yjd2Ffe2Q1fvrU+Z5NuMYn6vxUNVXjE3U+DdZ5J2WfvvdfufyyDe//ypXLmrBhvcPK5Z8fZRQbKqWUJE9cXPyiWuuzxhzPGr4hB8+VX661fu/Yg1lHKeVeufsbcY7zwCQ/XUr5tVLK2/c8rI2UUv5/SR63cvV/rbW+ZYzxDOyxSz8/dbRRXOmLk6wepP+rUspPLB5vdymlfEaSr1lc/C9J/tkA4zvOHSuX77/h/S+sXD596K3ao8ZPnzrfv8nV+ESdn7gWa3yizi+bSp0HtqdPnjY98jAm1yfrkSevxT5Zj3y3qfTIavy0TbLGJ5Or82r8SPa4zqvx3dvXOg9sR488bXrk4eiTR7DHPXLSXp+sR77SVHpkdX7a1PlhqPEj2eM631qNT9T5qdZ5YDt65GmbZI88sf74Mn3yCPa4R07a65On3CPva3+sxk+fOj8MNX4EavxdWqjxiTq/bCp1HtiePnna9MjD0COPQI98Fz3y7va1R1bjp0+d79/kanyizk9cizU+UeeXjV7nrxl7AGyvlHIqV5/Z/+UbruY1K5f/3vYjYgvvsfTzb9danz/aSDbzNTloar6o1vqDYw/mOKWUhyT56iSvTvLZIw9nEw9J8rwkL0zydjlo1u9zzO0/OslzSimPauhx9NlJ7rFy3c8kd31b0Ycm+aAk75LkvjnYR7cm+fla6+oB4mSUUu6R5JMWF1+W5NkjDucutdY/LqV8TpKnJClLv/q0JH+vlPKPaq2vKKV8WpIfzkHT85W11v80wnBXXUzy1kuX32/H9b3tjvfvnRo/G+p8jyZc4xN1fpJ1vtUan6jzK5qv88D29MmzoEfu2YT7ZD3yRLXaJ+uRr9B8j6zGz8JUa3wykTqvxo9u7+q8Gt+bvavzwHb0yLOgRx6APnlUe9cjJ232yXrkqzTfI6vzs6DO90yNH93e1fkWa3yizh+i+ToPbEePPAtT7ZEn0R9fpk8e1d71yEmbffLEe+S964/V+NlQ53umxo9KjW+kxifq/Irm6zywPX3yLOiRe6ZHHpUeWY/clb3rkdX42VDnezThGp+o85Os863W+ESdXzF6nXdS9ml7hyTXrlx324breNPK5ftuPRq28QlLP3/TaKNY0+LbcL42B2O9mOQtpZR71lrfPO7IDldKuXeSH8tBE/CFtdZXjDyktdVab0lyy+XLi7/9uyZ5aJLH5OCxc2rlbu+c5H+XUh7aSBP2+JXLf5aDx8z3J/nHSd7miPt9Tynlu5N8fa31tX0OsCcfmeTs4uen1VrrmINZVmv98VLKjUm+O1c2YQ9O8rullB/JwUHLnyb5rFrrcwcf5OFekIPH92XvV0q5V631DWvef7W2rX47UIvU+HlQ53sy5RqfqPMTrvPN1vhEnV8yhToPbE+fPH165B5NuU/WI0+2R04a7pP1yHeZQo+sxk/fpGp8Mq06r8Y3YR/rvBrfj32s88B29MjTp0fumT55dPvYIyeN9sl65CtMoUdW56dPne+RGt+EfazzTdb4RJ1fuTyFOg9sR488fZPqkafUH1+mTx7dPvbISaN98oR75H3sj9X4eVDne6TGj06Nb6jGJ+r8kinUeWB7+uTp0yP3SI88Oj2yHrkr+9gjq/HzoM73ZMo1PlHnJ1znm63xiTq/ZPQ6v/rkZVoOe/F6/YbrWG3C3qqUct2W42EDpZT3yN3fivOsWusvjzmek5RSbk7ym0m+OQcv3GeS/Jckf1QOvn2mRf8+yd9P8uO11p8aezC7qAf+b631R2utn5iDx87/OOSm1yf5+VLK6WFHeKXF43v1m7tuysG33rxHki9I8vY5+KaT903yH5JcbuSvSfKkJL9ZSnn7Icbbsccu/fzU0UZxhFrr9+bgG4vuXPnVOyT5lzlolB/cUPOVLB2MLNw7ySducP+bVi7ftstgBqLGT5w637vZ1PhEnZ+Qpmt8os4v3LbLYIDm6ZMnTI88iNn0yXrkSWm6T9YjJ5lGj6zGT9jUanwyyTqvxo9oj+u8Gt+PW1Yu70OdB7ajR54wPfJg9Mkj2eMeOWm4T9Yj3+W2XQYzEHV+wtT5QajxI9rjOt9sjU/U+SW37TIYoGl65AmbWo88wf74Mn3ySPa4R04a7pMn2iPfsnJ5H/pjNX7i1PlBqPEjUePv0lSNT9T5hdt2GQzQPH3yhOmRB6FHHoke+S565G7csnJ5H3pkNX7i1PnezabGJ+r8hDRd4xN1fuG2XQbTBSdln7Z7H3Ldpk3YYW7sYB2c7CsX/745yReOOZCjlFLerZTyTaWU5yR5Zg6+kWXVeyS5pZTy6EEHd4JSysOTfFGSlyf5pyMPp3O11hfWWh+X5B/m6mLywCTfNvigrnTzIde9Kskn1FpvrrX+VK31lbXW19dan19r/cokj0jyuqXbPyjJL0/pwLCUcirJJy8uvizJs0cczpFqrT+S5FFJbj/k1++Z5KcX357Tiqfk7gb9sn+2zh1LKffMwQHjsld2MaieqfHTp873ZO41PlHnWzSVGp+o85lGnQe2p0+eNj1yj+beJ+uR2zSVPlmPPIkeWY2ftuZrfDLdOq/Gj17jkz2s82p8r/axzgPb0SNPmx65Z/rk0fvkmw+5btY9cjKNPlmPnGQaPbI6P23qfI/U+NFrfLKHdX4KNT5R5xemUOeB7eiRp635Hnmq/fFl+uTR++SbD7lu1j1yMo0+eYI98j72x2r89KnzPVLj1fgxTKHGJ+p8plHnge3pk6dNj9wjPbIeeQx65N7sY4+sxk+fOt+Tudf4RJ1v0VRqfKLOp4E676Ts03bPQ657w4bruPaQ6zwuelZKeackj19c/JZa65+MOZ5j3DcH35zxh0l+O0k94nbXJfmJUsoHDDSuYy0Kxw8vLj6h1nrbeKPpV631F5N8WJK/WfnV/1NKefcRhnTZ+xxy3SNrrU8/6g611mcl+fKVq983B99wNBUfkeTtFj8/rdZ61HOmBX+Q5E9y9TekJckjkzynlPKuww7pcLXWVyT5wZWrP7yU8rg17v6AHHyT17I/6mRg/VLjJ0yd788+1fhEnW/MlGp8os4D86VPnig9cr/2qU/WIzdnSn2yHrltavxETajGJxOs82p8kvFrfLKfdV6N78me1nlgO3rkidIj90+fnGT8Pnkfe+RkOn2yHrl96vxEqfP9UuOTjF/jk/2s81Op8Yk6P4U6D2xHjzxRE+qRJ9cfX6ZPTjJ+n7yPPXIynT55Mj3ynvbHavyEqfP9UuOTqPFjmUqNT9R5YL70yROlR+6XHjmJHnkseuQe7GmPrMZPmDrfn32q8Yk635gp1fhEnR+VYjtth30LzqUN13FYE7ZpI8fmnpyDJvpZSf7NyGM5Uq31ubXWb6q1PrHW+tAk90/yzbnym0suuy7JfymlrL7QjeG7czDW76q1/trYg+nbooF/VK587t4jyWeMM6Ikyd9fuXwpB98Uc6xa6w/k6uL4xFLKO3Y1sJ59ytLPTx1tFCcopXxwDhqwt0/y3km+IVcfYP39JP+nlPKBAw/vKF+V5AUr131/KeX9j7pDKeVDk/zPQ371B10OrCdq/LSp8/3ZqxqfqPMNmUSNT9T5TKPOA9vTJ0+XHrlfe9Un65GbMok+WY88iR5ZjZ+uSdT4ZLJ1Xo0fv8Yn+1nn1fh+7VudB7ajR54uPXL/9Mnj98n72CMnE+iT9chJptEjq/PTpc73S40fv8Yn+1nnm6/xiTq/MIU6D2xHjzxdk+iRJ9ofX6ZPHr9P3sceOZlAnzzRHnnf+mM1ftrU+X6p8Wr8WJqv8Yk6n2nUeWB7+uTp0iP3S4+sRx6LHrk/+9Yjq/HTps73Z69qfKLON2QSNT5R59NAnXdS9ml77SHXXbfhOg77dp2/22IsrKmU8g+TPCbJK5M8rtb6lpGHtLZa6ytqrV+f5F2TPOOQmzw4yc2DDmpFKeUxST49B9/28dVjjmVItdbfS/L1K1c/aoyxLJxZufyqWusda973ySuX75nkM3cfUr8WBx+ftLj4siTPHnE4R1q8Bj0zyb2SPKLW+n9rrd+UgwZy9eD+bJJfLaU8eOBhXqXW+vokj0jy/KWrb0jyW6WUf1lKeZdSylst/n1CKeVXk/zvJPdbWdWbkvzWMKPeiRo/Uep8f/a1xifq/NimUuMTdT7TqfPA9vTJE6RH7te+9sl65PFNpU/WI0+mR1bjJ2jKNT5pv86r8VcYs8Yne1bn1fj+7WGdB7ajR54gPXL/9MlXkIUNaAp9sh45yXR6ZHV+gtT5fqnxV5CFDWgKNT5R5xemUueB7eiRJ2jKPXLr/fFl+uQryMIGNIU+eao98h72x2r8RKnz/VLjr6DGD2gKNT5R5zOdOg9sT588QXrkfumRr6BHHpAeuV972COr8ROlzvdnX2t8os6PbSo1PlHn00idd1L2afvbQ66714bruHHl8mtqrW/ebjicpJRyOsn35eDbS/5RrfWvRx7SVmqt55M8MsnPHfLrTzrkukGUUt4uyfcnuSPJ42utbxxrLCP5ziQvXbr8novGYAz3Wbn8qg3u+/NJ7ly57qN2G84gHprkHRY/P63WuvotM6MrpTwqyU8nuXeSL6y1/vnl39VafzrJxyS5uHK3G5P8YinlnYYa51Fqra/Iwd/5yTl4nicHc/nWJC9K8sbFv9+e5IU5+Maf/7uymmfUWg+rn61R4ydIne+PGp9EnR9T8zU+UecXplLnge3pkydGj9wvfbIeeWTN98l65CTT6ZHV+ImZS41P2qzzanxTNT7Zvzqvxg9gz+o8sB098sTokfunT26qT963HjlpvE/WI99lKj2yOj8x6ny/1Pimanyyf3W+6RqfqPNLplLnge3okSdmLj1yi/3xZfrkpvrkfeuRk8b75Kn3yHvWH6vxE6TO90uNV+NH1nSNT9T5hanUeWB7+uSJ0SP3S4+sRx6ZHrlne9Yjq/ETpM73R41Pos6Pqfkan6jzC03UeSdln7bzSV63ct3qi95J3nbl8ku2Hw7HWRTCH8vBi/Rn1FqfM/KQdlJrvZTkc5PcvvKr9x9hOJf91xx8G8s31Vp/f8RxjKLWemeSH1i66p65+jk+2HBWLq8+To6+Y623JfmdlavfY9cBDeBTln7+qdFGcYRSyvsk+ckk1yb57VrrT67eptb67CQfnuSvVn71djl4fo2u1vraWuuXJXnHHLwGfWeS/57ke5N8RZKPSHK/WusXJrktV78m/b+DDXY3avzEqPO92+san6jzI2u6xifq/JKp1Hlge/rkCdEjD2Kv+2Q98uia7pP1yHeZSo+sxk/I3Gp80mSdV+PbqfHJ/tV5NX4ge1Tnge3okSdEjzwYfXI7ffK+9chJw32yHvkKU+mR1fkJUecHoca3U+OT/avzzdb4RJ1fWcVU6jywHT3yhMytR26wP75Mn9xOn7xvPXLScJ88lx55j/pjNX5i1PlBqPFq/JiarfGJOr9kKnUe2J4+eUL0yIPQI+uRx6RHHsAe9chq/MSo873b6xqfqPMja7rGJ+r8kibqvJOyT9jiWxduXbn6HTdczdusXF799gC6881JPj7JkxbfPjF5tdaLOfgmmmVnxxhLKeVDkjxqcfGbSyl1nSXJM1dW9ZGH3O7mIeeyo19ZuXzPUUaRvHbH+//xyuUzO66vV4sDrE9eXHxpkqYOsEop98hBk3LvxVU/fNRta61/moNvxzm/8qtHlFL+QT8j3Fyt9VW11h+qtX5prfUza63/tNb6HbXW31ocjCTJ/5Nk+Zuh/iLJ04Yf7ebU+ElS53uixl9BnR9Y6zU+UeeX7jaZOg9sT588OXrkHumT76JHHkHrfbIe+S6T6ZHV+MmZXY1P2qnzavxdWqnxyR7VeTV+HHOv88B29MiTo0fumT75Lq30yXvTIydt98l65Gn2yOr85KjzPVLj79JKjU/2qM63XOMTdT4TrfPAdvTIkzO7HrmV/vgyffJdWumT96ZHTtruk+fYI8+9P1bjJ0md75Eafxc1fgQt1/hEnV+622TqPLA9ffLk6JF7pEe+ix55BHrk4c29R1bjJ0md74kafwV1fmCt1/hEnV+6WzN13knZp+/5K5fXbsJKKffMwTcdLGvuhWMOSimfmeRrk3xzrfW7xh5Px/7HyuU3jzKK5LqRttuaFy79fCnJa0Yax0tXLm/amP/lyuVLO4xlCB+a5P6Ln5+2OEhuyacmedDi50tJjj0IrLX+WZJHJnnjyq++sPuh9aOUck2Sz1+5+ttqrW8ZYzxbUuMnQp3vnRp/N3V+eK3X+ESdv2xqdR7Ynj55AvTIg9AnH9Ajj6P1PlmPfGBqPbIaPwEzr/FJG3VejT/QSo1P9qvOq/ENmkmdB7ajR54APfJg9MkHWumT96lHTtruk/XId5taj6zOT4A6Pwg1/kArNT7Zrzrfco1P1PllU6vzwHb0yBMw8x65hf74Mn3ygVb65H3qkZO2++S965Fn0h+r8ROhzg9CjT+gxo+j5RqfqPOXTa3OA9vTJ0+AHnkQeuQDeuRx6JEbM5MeWY2fCHW+d2r83dT54bVe4xN1/rJm6ryTsk/f6jdgPKCUsu5+fUCufgz89u5DYlkp5WOT/Nck319r/fqxx9O1Wusf5Moi/6qxxkKS5PVLP/9FrXW1gA7lBSuXVw/4TrL6rToXdhjLEB679PNTRxvF0T5j6edX1lpPbMxrrb+f5ItXrv6oxbcATcHnJHnnpct/kORHxhnK1tT4CVDnGZg6P7zWa3yizifTrPPA9vTJjdMjMzA98jha75P1yNPskdX4xs29xifqfGNaqfHJftV5Nb5Nc6jzwHb0yI3TIzOCVvrkfeqRk7b7ZD3ygSn2yOp849R5BtZKjU/2q863XOMTdf6yKdZ5YDt65MbNvUfWHzeplT55n3rkpO0+eR975Dn0x2r8BKjzDEyNH0fLNT5R55Np1nlge/rkxumRGZgeeRx65PbMoUdW4ydAnWdg6vzwWq/xiTqfNFbnnZR9+n4pyR1Ll++d5D3XvO+7rVz+myTP7mJQHCilPCTJzyb5mST/dOTh9OnlSz+vflvTIGqtt9Ray6ZLko9aWdWzDrndLSNMaVvLjc7qQdqQfmvl8g2llNXXnOPcsXL5j3ccT98es/j3pWnzG8Y+aOnnv9rgfv8tV35D0fVJTncyoh6VUq5P8g1LV92Z5LNrrXeONKRtqfGNU+eHocZfQZ0fXus1PlHnp1rnge3pkxumRx6OPvkueuRxtN4n65Gn2SOr8Q3boxqfyMJa0UqNT/arzqvxjZlRnQe2o0dumB55WPrku7TSJ+9Tj5y03SfrkafbI6vzDVPnh6PG36WVGp/sV51vucYn6nwy3ToPbEeP3LA96pFHz8ESffKSVvrkfeqRk7b75L3qkWfUH6vxjVPnh6PG30WNH0fLNT5R56da54Ht6ZMbpkcejh75LnrkceiRGzKjHlmNb5w6Pww1/grq/PBar/GJOt9cnXdS9omrtd6e5JdXrv7ANe/+QSuXn1ZrvbT7qEiSUsr75aBJ/o0knzHzv+0bln7+tdFGQZJ8xNLPPzLWIJI8M8nfrlz3sA3uf9+Vy8/abTj9WRxoXf72lafVWuuY4znC9Us/rz2+WutbcnAQecXVnYyoX/8xyf2WLn99rfUPRxrL1tT4tqnzjESdH9BEanyizk+yzgPb0ye3S4/MSPTIA5tIn6xHnmCPrMa3a89qfKLOt6KVGp/sSZ1X45s1izoPbEeP3C49MiNqpU/eix45mUSfrEeeaI+szrdLnWckrdT4ZE/q/ARqfKLOJxOt88B29Mjt2rMeWX/cllb65L3okZNJ9Mn71iPPoj9W49umzjMSNX5gE6jxiTo/yToPbE+f3C49MiPRIw9Mj9ykWfTIanzb1HlGos4PaCI1PlHnm6vzTso+D/9h5fIj1rzfQ5Z+rkm+p5vhUEp5nxx8I8nzkzym1rr6DR/H3ffdSynf0tvg+vFOi3//KlcfFDCsz1n8+/O11t8daxC11jclecrK1R+7wSqWv0HnziQ/sfOg+vPYpZ+fOtoojvfKpZ/vd+StDveipZ/fUGu92MF4elNKeXSSz1266im11n870nC6oMY3SJ1nROr8sKZQ4xN1fsp1HtiePrkxemRGpEce3hT6ZD3ydKnxjdnDGp+o861oosYne1Xn1fjGzLDOA9vRIzdGj6xHHlkTffIe9chJ+32yHnnaPbI63xh1Xp0fURM1PtmrOt96jU/U+anXeWA7euTG7GGPrD9uSxN98h71yEn7ffLe9Mgz7I/V+Aap84xIjR9e6zU+UeenXOeB7emTG6NHZkR65OHpkRsywx5ZjW+QOs+I1PlhTaHGJ+p8c3XeSdlnoNb6rCT/Z+mqR5VSrj3uPqWUeye5eemqn6m1/mkPw+tCGXsAmyilvG+SX0/yf5M8qtb6hhPusnzft0vytBwUnEkopbx37n5B//e11jePOZ59Vkr51CQfleRNSb5s5OEkyXckWX48fGIp5cY17/vhSz8/rdb68s5G1b3LTdhLkzxnzIEc4zeWfr5/KeUBG9z3Xks//3pH4+lFKeVckh9buuqXc2UzNrSd61cjNb6vOjxkfe9sWyPW+VH6oY7r/KR6umOMtS+6rPNdzGHsOj/Efhiixncxj7Hr/CDPiQbrPDCiRvrkPk2qb5KFycLGIgsbjSysEQ32yLKwcdbb23ZGrvGysDbMIQdLZGHr6rvGzyEHS2RhwMAa6ZH7Npm+ad9ysEQW1hJZ2Ghaz8Ja6JEH0WCPLAsbZ729bUcWJguLLGzZ2HVeFnaghTovCwMG1UiP3LfJ9E37loXJwdoiCxuNLKwBDfbHc8nBElnY3Svy+chd6/xkerpjzCELm0MOlsjCLhu7zsvBgME11Cf3ZVI9kyxMFjYWOdhoWs/BkvF75EE02CPPJQubeg7W6bZkYXv/nrBEFnbZ2HV+DjlYIgtbfyPt1fkjOSn7fHxFDr7dJknum+STTrj9P8ndT6rXL+7fqtWG8ppRRrGGUsoH5OAF6oVJPq7W+tpjbltKKdeWUt62lPKgUsq/SPK8JO+ddr8B5DBftfj3N+PblTpRSrmulPLtpZRfKaX8m1LK6TXu8xFJ/tvi4hNrrX/Z7yhPVmt9UZJvX7rqXlmjMSylfEiSc4uLr8vdj7HmlFIenORdFhefVmutx9x8TD+wcvnzN7jvRy/9/F87GEsvSinvmoOG6/rFVU9L8omLb2gaS1f1a+wa31cdHrK+d7Ktkev8WP1Ql3V+Mj3dCXaaRyN1fud90UCd7/XxNGCN72IeY9f53p/bjdZ5YHxj98l9mkzfJAuThXWhkR55Zw30yL2ThbWj0R5ZFjbOenvZTgM1XhbWhjnkYIks7EQD1fg55GCJLAwYx9g9ct8m0Tc10COPRRbWsYb65J000CP3biJZWAs9cu8a7ZFlYeOst5ftNFDnZWFtkIUtNFDnZWEHWqjzsjBgDGP3yH2bRN/UQI88BjlYDxrqk3fSQI/cO1lYGxrtj+eSgyWysCSj1/k55GDJRHq6E8whC5tDDpbIwi4bu87LwYCxtNAn92UyPZMsTBbWhUZ65J010iP3aiI5WDJ+j9y7RnvkuWRhU8/BOtuWLMx7whZkYWmizs8hB0tkYWtptM4fyUnZZ6LW+ttJvmvpqq8tpRy6f0sp90nydUtXfUWt9cU9Dm9X91m5fP2htxpZKeWhOWi+Tif50CR/W0qpRy1JLuXgG0MuJvnDJN+W5KYkf1Br/bMRxn/fUso/Xyzvs+Z9PjnJZyR5WZJPr7W+pddB7o8vzcGB0cOTfE2SPymlPL6UctU3iyya+C9L8qtJ7p3kX9daf3TQ0R7vm5L8ztLlrywH31xyqFLKNUn+89JVX15rfVlfg+vAY5d+fupoozhBrfU3kvz00lVfWkp5r5PuV0q5OckjFxd/pdb6c50PrgOllPdPckuS++cgkPh3ST61gW/j7KR+NVDj+6rDQ9b3nbfVQJ3faQ6N1PlJ9HRr2HUeLdT5rvbFmHW+78fTUDV+53k0UOd73RcN13lgZA30yX2aRN/UQI+86/hb6JE50EKP3BVZWAMa6JF71XCPLAsbZ72db6eRGi8La8MccrBEFraOIWr8HHKwRBYGjKCBHrlvzfdNjfTIu86hhR6ZA630yV2QhY2skR65Vw33yLKwcdbb+XYaqfOysDbIwq4kC9uNLGwNDdd5YEQN9Mh9a75vaqRH3mX8LfTH3K2VPrkLsrCRNdIj96bh/nguOVgiC2uhzs8hB0sm0NOtYQ5Z2BxysEQWlqSJOi8HA0bRSJ/cl0n0TA30yLuOv5UemTZ65K6M3SP3rfkcLGmiR+5Vwz3yXLKwqedgnWyrgTo/hyxsEj3dGmRhd/OesN3Jwk7QcJ0/kpOyz8tX5eABmCTvm+Q7Vm9QSnnrHLxQPGBx1ZNrrd83yOi2924rl9+llPJWo4zkCKWUj8vBtzHct4PVjfXNd0/PQSP/XUmeV0r52eMasVLKP0ny40n+NMlHNX4wPDVnD7n835PcWkr5llLKZ5VSvqCU8l1J/jIHz/WS5PNqrd847FCPtyiAn5SDcSbJdUl+sZTyTqu3XTyvfzzJgxdXPbnWuvptLq253IS9NMlzxhzIGj43yR8vfr5XkmcsGpdDlYNvWrrctP1pkk/rd3ibK6Xco5TyxUl+KwfN19/k4BvJvrrWemnc0SXptn6NWeP7qsOr672hlHK/Dta7zrY2mkMjdX7X/dBCnW++p1vTrvNooc53si9GrvN9P56GqvFdzWPMOt/LvphAnQfaIAsbSSM98q5a6JE50EKP3AlZWFNkYcOThW223r6ysDnkYIksrBVzyMESWdg6hqjxc8jBElkYMJ655mBJ431TQz3yrlrokTnQSp+8M1lYM8bukXsxgR5ZFrbZemVhx5OFtUEWtkQWtjNZ2DEmUOeB8cnCRtJQj7yLFvpj7tZKn7wzWVgzxu6ROzeB/nguOVgiC2uhzs8hB0sa7+nWNIcsbA45WCILW+bzkcC+GrtP7kvzPVMjPfKuWumRaaNH7kQDPXLfppKDJbKwMcwlC5t6DnbYtmRh3hO2C1nYgveEdUIWdoQJ1Pmj1VotM1py8E0Dv5yDbwWoSX4pyT9M8n45ePL9xeL6N+fgG3FGH/Mxc3mrJI/KwbfG1JXlvyd557HHuBjnpy7+nqtj3Ga5lOSdRprHy48Yzy/k4JtvPiDJByZ5fJJfSXJnku9Jcv3Y+2DHed+8MudbGhjTuSRv2OBx85tJ3m/scZ8wp3dO8vtLY76Q5F8m+ZDFY+tzk/zJ4nd3JvmXY495jTm9/9J8vmPs8aw55huTPHNp3Hck+eEk/2hRJx6c5HE5OFh/y+I2v5LkprHHvjKPM0m+cOkx88Yk357kvmOPbTG+XupXBq7xPc7j2iQfkeRFh6z3GUn+fpLSyr7IyHW+q/2QEet8X4+lNbZ788q2bmlhHhmxzvf4vB6szg/xeMoANb6PeWTgOt/j46npOm+xWNpbIgsbY5yysAb2ww7zvnllzrc0MCZZmCysrzHfGFnYEOOThR2/3kGysC7GnwZqfFf7IbKwnWp8h/th1Brf4/NaFjbyHDJCje/x8dR0nbdYLG0tmVEOtphP81lYGuiRO5yLLGzHPrnDMcnCZGF9jPfGzCAHW8yl6R65r/oVWdjg408Ddb6r/RBZ2E41vsP9IAuThfUyh8jCLBbLni6RhY0xxtF75I7msZc52GLuN6/M+5YGxiQLk4X1Md4bM4MsLI33x33VroxQ43uciyxs4H2Qket8X4+lNbZ788q2bhl7DvH5yJ1r/BCPp8jCxnw8NV3nLRZLe0tmlIUNUeM6GqcsrIH9sMO8b16Z8y0NjEkOJgfra8w3RhY2xPhmkYX1OA/nChthP8R7wnau8R3uC1nYnr8nrK95RBbWzDL6ACw97NTkHkmelOT8IQ/4S0l+Lsm5scd5zPi/KsnfLsZ6UuF5U5IXjjzewxrFbZffGnEeD8ndTfpxy0tz8C0s7zr2Y6Wjed+8Mr9bxh7TYlwfmeT/HrMf3pjkF5M8auyxbjCneyX5V0lec8Sc7ly8Pj1o7LGuOZ9/szT2h4w9ng3GXRZN1h+d8Fz//ST/JMmpBsZ8btFg/dhiXJebw1uTfE2S+409xsU4e69fGaDG9zWPHHyz0m1L+++45c1JXp3krceeQ0aq813vh4xQ54d4Tpyw/ZtXtnFLK/PIwHV+iH2Rnuv8kI+n9Fjj+55HBqjzXc8hE6nzFoul7SWysKHH+6I1xrnuIgsbft43r8zvlrHHtBjXoD3yQHOShTWwRBbW5zhlYcevd5AsrMvxZ8Qa3/V+iCxsqxrfxxwyQo0fYl9EFjb6HDJQje96HplInbdYLO0umXgOtpjDZLKwzCQHW8xFFrZln9zTuAbvkweYkyxs/DFPLgdbjHsSPfIQ9SuysEHHH1lY88+JE7Z/88o2bmlhDpGFPajVOSxtSxY24DwykTpvsVjaXSILG3qsL1pjjOsu3hM2ztxvXpnjLWOPaTGuwfvkAeYkCxt/zJPLwqbSHw9RuzJQje9rLpGF7dXnI4d6Xpyw/ZtXtnFLC3OIz0c2O4+lbcnCBppDJlLnLRZL20smnoUNWeM6Gu+L1hjnuossbPh537wyv1vGHtNiXIP2yAPNSQ7WwBJZWJ/jnEUW1tc84lxhG9X5rvdDvCds6xrfxzwiC2tyDkvbcq6wAeeQidT5TZaymBgzVEq5Z5KHJXmfJNcleVmSZ9Za/2rUgdGsUso9cvANJe+V5GwOHjd3Jrk9B43X82utLx5tgHumlHIqyQcned8cfAtIzUHz8idJnltr/bsRh7e1xWvTQ5O8Z5K3SfL6HDT/v1VrffWYY9tEKeXPcvAtXS9N8i51ggW1lHL/JB+a5O2S3DcHTdPfJPntWusrxhzbslLKuST/OAcHHq9O8ldJnlNrvTDqwEakxrMNdb4t6ny75lDjE3Ue2E/6ZDalR26LHrltc+iT9cjTpcazDXW+HXOt8ck86rwaPyx1HuiKHplt6JHbMtc+eQ49cjL9PlmPPG3qPNtQ59sx1xqfzKPOT73GJ+o8sJ/0yGxKf9yeufbJc+iRk+n3yVPpkfXHV1Pj2YY63xY1vm1Tr/GJOg/sJ30ym9Ijt0WP3DY98nD0yFdT49mGOt8Wdb5dc6jxiTo/JidlBwAAAAAAAAAAAAAAAAAAAAAAAAAAAABm7dTYAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6JOTsgMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAs+ak7AAAAAAAAAAAAAAAAAAAAAAAAAAAAADArDkpOwAAAAA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+inPCWhmDXlgDY1hz2a4VNuK6iHPCBl8X0Qtrqhc2egCmjlZkcpDkl5c2tG+/yev/89Jrf3Ts+JfiKkn+Y5InJHlYknfM6R18BknKO8b+wUsxXknyr5M8fvEF/qAkb5XkM3P/Qnl5+r4GxvBXF7F8dZJbb/CaByT5hjPi/66x499wrG+0SEKDFX07xvugbHZHnLuTPHzsuG8wlndd+ex/OMnrjx3XOfF+8gaf+3nT87N095YRxvHzS7F8zJrvWT6o/JEG1sWdS/F8zk1ee0uSX196/Z8neaseY+stf2XAHN/zOP51kicnOUzyFjk90F+e73NbXhcZMM/3OIbB8nyf29Iay+4sxw81jvSY54deF+khz/e4Twya4/tcFxkoz/c8hjuX5tFUnjeZTG1O0QsbfYpemF5Yv/HqhY0Xr17YBjVyz2O4cymepmrkPvNX9MJGjz8D5/gex6EX1uAY0nOOH3pdRC9s8PWQAXN8z+O4c2keTeV5k8nU3pSZ9MEWsU2yF5YZ9MEW49ALa3D7WsSrFzZevJPvhWUGfbBFTHcuxdRUjdxn/ope2OjxRy9s4zzf57a0xrL1wkZcF9ELG3w9RC+s9zxvMpnam6IXNvqUGfTCskd9sMVY9MLGG4te2PBjmHwvLA3Xx33mrgyc43sei17YuGMYNM/3uS2tsWy9sJHWQ3rK8T3uF3phG+T5nsdw59I8msrzJpOpzSkz6YUNnas7jl0vrIH1sMFY9cHGG4s+2Djj0AvTCxt7HK4VNv4YnBPWyPZ0xnL0wsbZJ1wrbMM83/MY7lyaR1N5fuOxjB2AqaMVmXz6ysb99jd5/VuuvP4jxx7DObH+m74SQcdxPnMR3/9KcvGc192W5Edu8CX9+BHjP8hp0fsVa7z2AUl+YSX2lyd5wNjrYYPx/sAi7l+dyPb1jxfxfe3Ysew4jvfP6d1Mrn3eP54bFPutTEtJ/HeT/IMkb5PkoUkO1njvBy6N9atGHMM7L8Xx6iQPWvN9H7L0vqtJHjXiGD52KZZXJLltjfc8cWX//pUM2OzqKn+NneP7ysNJPnFlvs9teV2Mned3HUMLeb6vbemM5fSa4/sYRwbO8z3u14Pl+Y7261/PyDm+o3GMmuc7GsPHLr1/EnneZDKNP0UvbPQpemF6Yf3Gqxc2Xsyj18kdjEEvTC9s9HGcMd9BemFdxN9Cjt91HC3k+b62pTOW01uO72MMGSHH97hf64UNP4bRc3xH4/jYpfdPIs+bTKZxp8y4D7aIt/leWBqokTsYw+g18sDj1QsbZxx6YcPHP3qN3NE4Jlcjd5W/xs7zfeXh6IUNOo4W8nxf29IZy9ELG2ldRC9sjDGMnuc7GsfHLr1/EnneZDKNO0UvbPQpDdTIO8Y/en08wpj1wsYZh17Y8PGPXiN3MIbJ1cdd5a4WcnxfeTh6YYONoZU839e2dMZy9MJGWA8ZOMd3tG//evTCdsrzHY3hY5feP4k8bzKZxp/SQJ3c49ia74Mt4tQLm1AvLPpgY41DH2ycMeiF6YWNPo4z5utaYQOOoYU839e2dMZyXCtspHUR54SNNQ69sMZ6YQdh8kopD0nyL5ae+u1a6zPPe0+t9VlJnrH01Fcv5tOi3x07gJsppbxzkrdL8r1JnlRrPbnRa2utd+f0Djr/+4xff3g/Ea7lr+f0TiX/7GYvrLW+LskXrzz9+kku9RBX50opn5DTL+bfy/33nSaVUh6U0/Xy6pwmskkqpbx3ku9P8uDFU89M8uRa62vHi+p8pZTHJ3lskm9K8tha63+ptf5OrfVltdara8ziby/9/N29BLmeD1j6+cW11let+b5nLf1ccnqHm8GVUkqSf7n01J2L79Kb+aEkz1t6/C45vVvPUHbOX43k+L7y8JD5fadlNZLnd/28Wsjzva/zgXJ8p+MYKc93vi5GyPO77tet5Pgu1sXYeX7XdTHVPA+MqJE6uU96YcNooUYehF7YOPTC9MK2NeEaWS9snPl2upyGcrxe2BoGyPFz6IMlemGt5Pg59MESvTBgYI3UyH1ruhfWUI28qxZq5EHohY1DL2y0XlgLNfJOJlwj64WNM99Ol9NQntcLW4Ne2Nr0wtrI8XphmXSeB0bSSI3cN72w/rVQHw9GL2wcemF6YduYcH08lz5YohfWQp6fQx8s0Qu7jr+P3Ile2Kmx87w+GDC4hurkvjTdB0uaqZF31UqN3Dt9sHHog/n7yG1NuEaeSy9s6n2wnZfVSJ6fQy9sDn2wRC/sTM4J25pe2HTz/A25KPs8/MMkF5ce/9Ca7/vJpZ8vJ3lyZxF168VjB7CGD0nyx0k+dp0v5kXC+Zgkr1v51Tv3ENtaaq0/kuQJayaWJHl6kntWnntFt1F1r5Ty5kn+fU5j/+gkrxw3orX8wySPTPLfaq1/NHYw21h87t+X+4qvlyf54DWT6Jg+Mcn/V2v9+Frrqzd5Yynl1iR/a/HwBTn7oGsoj1r6+VIp5fXWfF9defz6HcWzqfdP8heWHv/GOm+qtdacbnfLPqujmNbRRf5qIcf3lYeHzO+7LquFPL/TGBrJ872u8wFzfNfjGCPPdzqGkfL8rmNoJcd3sS7GzvO7jmGqeR4YVwt1cp/0wgbQSI3cO72wceiF6YXtaKo1sl7YOPPtejmt5Hi9sJsYKMfPoQ+W6IUlbeT4OfTBEr0wYHgt1Mh9a70X1kqNvJNGauTe6YWNQy9s1F5YCzXyrqZaI+uFjTPfrpfTSp7XC7sJvbCN6IW1keP1wk5NNc8D42mhRu6bXljPGqmPB6EXNg69ML2wHUy1Pp5LHyzRC2shz8+hD5bohZ3F30duTy/s1Nh5Xh8MGEMrdXJfWu+DJW3UyDtpqEbulT7YOPTB/H3kjqZaI8+lFzb1PlgXy2ohz8+hFzaHPliiF3Yd54S5VtiW77tmqnn+hlyUfR4+buXxM8581fV+duXxx3cQSx/WvXvDmN4lyRfXWl+27hsWdydaLZjfuNOoNlRrXbuAWiSk5bv//E7rB82llIMk35LkoUm+pNb6f8aN6OYWd8T5nCSvSfJlI4ezlVLKA3N6x6iHLz39L2qtzx0novUs7hb2+JwWwNv4a7lvzE9bFANjKUs/PyCnd4Zax5usPP6dbsLZ2AeuPP6TDd778yuP37eU8ogd41lXF/mrhRzfVx4eMr/vuqwW8vzOn1cDeb63dT5wju9sHCPm+S7HMFae33oMjeX4LtbF2Hl+1zFMNc8D42qhTu6TXthAGqiRe6UXNg69sCR6Ybuaao2sFzbOfLteTis5Xi/sHAPm+Dn0wRK9sFZy/Bz6YIleGDC8FmrkvrXeC2ulRt5ZAzVyr/TCxqEXlmTcXlgLNfKuploj64WNM9+ul9NKntcLO4de2Mb0wtrI8Xphp6aa54HxtFAj900vbAAN1Me90wsbh15YEr2wXUy1Pp5LHyzRC2shz8+hD5bohd2Pv4/cmV7YqbHzvD4YMIZW6uS+tN4HS9qokXfWSI3cG32wceiDJfH3kbuaao08l17Y1PtgXSyrhTw/h17YHPpgiV7Y/TgnzLXCohd2HRdln7hSylGSt195+jfXfPtvrTx+78WdK1qzeueYFv1Ukm/f4n0/ufL4NR3EMqTbln7+5tGiWN9nJnnvJL+S5F+PHMu6PjHJI5K8KMmHlVLebVGUTcnnJXnHpce/nuQ/jhLJBhYHJO+8yYHJiuW7jf2PDkLaxbNWHn9+KeWWNd73Pks//06tdd380rV3X3n88g3ee9Ydid7njOf6sFP+aijH95WHh8zvuy6rhTw/Rj3UdZ7vcwxD5vguxzFWnu9yDGPl+a3H0FiO72JdjJ3ndx3DVPM8MJKG6uQ+6YW1Sy+sf3phI2msTt7V2DXyrqZaI+uFjTPfrpfTSo7XCzvfUDl+Dn2wRC+slRw/hz5YohcGDKihGrlvrffCWqmRx6AX1j+9sJE0VCfvqoUaeVdTrZH1wsaZb9fLaSXP64WdTy9sM3phbeR4vbBTU83zwAgaqpH7phfWpqn1wRK9sLHohemF7WKq9fFc+mCJXlgLeX4OfbBEL2yVv4/cjV7YqbHzvD4YMKjG6uS+tN4HS9qokccwtV6YPtg49MH8feSuplojz6UXNvU+WBfLaiHPz6EXNoc+WKIXtso5YbvRC5tunr8hF2Wfvg8447k/XOeNtdbn5/qN+D13jqh7V8cO4GZqrV9ea93kC+Ga1XX1nC7iGUIp5VKShywe/mGSrxsxnJsqpbxtki9J8ookH11rvWfkkG5qUWj9s8XDN0nyVUl+MclLSynfXUr5oFLKA0YLcA2llLfM6V19ln1ZrbX5/TpJaq1b3c1lUdw8afHw+Ul+qauYtvRjK4/fIcmXn/eGxd2MnrL01Od1HdQG3mzl8YPXfWOt9QVJXrLy9HvvHNF6dt3OW8nxfe2vQ34P7LSsRvL8oN+bPeX5XsYwQo7vZBwj5/muxjBmnt91v24lx3fxWY2d53cdw1TzPDCeVurkPjV/zNxIjTwovbD+6YWNr6E6eVdj18i7mmqNrBc2znw7XU5DOV4v7AYGzvFz6IMlemGt5Pg59MESvTBgWK3UyH1r+pi5oRp5UHph/WugTt6ZXliS8XthLdTIu5pqjawXNs58O11OQ3leL+wG9MK2ohfWRo7XCzs11TwPjKOVGrlvTR8zN1QjD2ZqfbBEL2wsemFJ9MJ2NdX6eC59sEQvrIU8P4c+WKIXdi9/H9kJvbBTY+d5fTBgaC3VyX1p/ni5kRp5UFPrhemDjaOBGnknjdTIXRi7Rt7VVGvkufTCpt4H23lZjeT5OfTC5tAHS/TC7uWcMNcKW9ALW+Gi7NO3eqeAkw2/NF648vhxO8bDZlYLgV8ZJYrtPGnx7z1JPmaHu4f0rpRya07vWvR6ST671vr7I4e0rk/K6R1xVr1eTu+68oNJnlVK+dBBo9rM5yd54NLjP0zytJFiGdL7Jjlc/Py0WmsdM5ha611Jnr7y9GeUUr7onLd9Ye4rfP5zrfV/9hHbmh6+8vihG77/RSuPH71DLEOS46dPnu/ZhHN8Is9PVVM5PpHnM908D2xPnTxtauQBTLhOViNPV1N1shp5sjWyHD9tU87xyUTyvBw/un3M83J89/Y1zwPbUSNPmxp5IOrkUe1jjZw0VCerkZNMt0aW56dNnh+AHD+6fczzzeT4RJ5fmGqeB7ajRp62KdfIT1r823R9fI06eVT7WCMnDdXJM6iR97U+luOnT54fgBw/KjleL6wL+5rnge2pk6dNjTwANfKo1Mhq5C7sa40sx0+fPN+zCef4RJ6fqqZyfCLPp8E876Ls0/eXVx4/f8P3/+nKY0XYsB658vj7R4liQ6WUkuQTFw8/pdb6M2PGs4YvyOm+8mO11v80djDrKKU8OPfdEec8b57ke0opP1VKeeOew9pIKeUvJPnIlaf/a631dWPEM7AnL/383aNFcX//OMnqQfq/LKV852J7u1cp5aOTfO7i4Tck+UcDxHee1648ftSG7z9ZeXx45qvaI8dPnzzfv8nl+ESen7gWc3wizy+bSp4HtqdOnjY18jAmVyerkSevxTpZjXyfqdTIcvy0TTLHJ5PL83L8SPY4z8vx3dvXPA9sR408bWrk4aiTR7DHNXLSXp2sRr6/qdTI8vy0yfPDkONHssd5vrUcn8jzU83zwHbUyNM2yRp5YvXxNerkEexxjZy0VydPuUbe1/pYjp8+eX4YcvwI5Ph7tZDjE3l+2VTyPLA9dfK0qZGHoUYegRr5Xmrk3e1rjSzHT58837/J5fhEnp+4FnN8Is8vGz3P3zJ2AGyvlHKQ66/s/4INZ/OSlcd/cfuI2MJbL/38i7XWZ44WyWY+N6dFzafUWv/L2MGcp5Ty+CSfk+TFST5u5HA28fgkv5nkWUneKKfF+huc8/q/muSXSylPbGg7+rgkD1h57nuTe+9W9G5J3iXJY5I8LKfr6K4k319rXT1AnIxSygOSfMji4fOT/NKI4dyr1vpbpZSPT/LUJGXpVx+R5C+WUv5WrfWFpZSPSPLNOS16PrvW+h9GCHfVlSSvv/T4HXec3xvu+P7eyfGzIc/3aMI5PpHnJ5nnW83xiTy/ovk8D2xPnTwLauSeTbhOViNPVKt1shr5fpqvkeX4WZhqjk8mkufl+NHtXZ6X43uzd3ke2I4aeRbUyANQJ49q72rkpM06WY18neZrZHl+FuT5nsnxo9u7PN9ijk/k+TM0n+eB7aiRZ2GqNfIk6uNr1Mmj2rsaOWmzTp54jbx39bEcPxvyfM/k+FHJ8Y3k+ESeX9F8nge2p06eBTVyz9TIo1Ijq5G7snc1shw/G/J8jyac4xN5fpJ5vtUcn8jzK0bP8y7KPm2PTHLrynMv3XAer155/LCto2EbH7T08xeNFsWaFnfD+bycxnolyetKKQ+stb5m3MjOVkp5SJJvy2kR8Mm11heOHNLaaq13Jrnz2uPFZ/8WSd4jyYfldNs5WHnbmyX5hVLKezRShD1l5fHv5XSb+fokfyfJw2/wvq8rpXxtks+vtb6szwB78j5JLi1+flqttY4ZzLJa63eUUi4k+drcvwh7XJL/U0r5lpwetPxuko+ptT5j8CDP9ts53b6vecdSyoNrra9c8/2ruW317kAtkuPnQZ7vyZRzfCLPTzjPN5vjE3l+yRTyPLA9dfL0qZF7NOU6WY082Ro5abhOViPfawo1shw/fZPK8cm08rwc34R9zPNyfD/2Mc8D21EjT58auWfq5NHtY42cNFonq5HvZwo1sjw/ffJ8j+T4Juxjnm8yxyfy/MrjKeR5YDtq5OmbVI08pfr4GnXy6PaxRk4arZMnXCPvY30sx8+DPN8jOX50cnxDOT6R55dMIc8D21MnT58auUdq5NGpkdXIXdnHGlmOnwd5vidTzvGJPD/hPN9sjk/k+SWj5/nVnZdpOevL6xUbzmO1CHu9UsqDtoyHDZRS3jr33RXnZ2qtPzZmPDdTSrk9yc8l+eKcfnFfTPINSX6jnN59pkVfnuQvJfmOWuv/GDuYXdRTf1Br/dZa6wfndNv572e89KFJvr+UcjhshPe32L5X79x1Oad3vXnrJJ+U5I1zeqeTd0jy75JcK+RvSXJHkp8rpbzxEPF27MlLP3/3aFHcQK31P+X0jkX3rPzqkUn+eU4L5cc1VHwlSwcjCw9J8sEbvP/yyuOX7hLMQOT4iZPnezebHJ/I8xPSdI5P5PmFl+4SDNA8dfKEqZEHMZs6WY08KU3XyWrkJNOokeX4CZtajk8mmefl+BHtcZ6X4/tx58rjfcjzwHbUyBOmRh6MOnkke1wjJw3XyWrke710l2AGIs9PmDw/CDl+RHuc55vN8Yk8v+SluwQDNE2NPGFTq5EnWB9fo04eyR7XyEnDdfJEa+Q7Vx7vQ30sx0+cPD8IOX4kcvy9msrxiTy/8NJdggGap06eMDXyINTII1Ej30uN3I07Vx7vQ40sx0+cPN+72eT4RJ6fkKZzfCLPL7x0l2C64KLs0/aQM57btAg7y4UO5sHNffbi39ck+eQxA7mRUspbllK+qJTyy0mentM7sqx66yR3llKeNGhwN1FKeb8kn5LkBUk+deRwOldrfVat9SOT/M1cn0zePMmXDR7U/d1+xnMvSvJBtdbba63/o9b6p7XWV9Ran1lr/ewkH5Dk5Uuvf2ySH5vSgWEp5SDJhy4ePj/JL40Yzg3VWr8lyROT3H3Gr98myfcs7p7TiqfmvgL9mn+0zhtLKQ/M6QHjsj/tIqieyfHTJ8/3ZO45PpHnWzSVHJ/I85lGnge2p06eNjVyj+ZeJ6uR2zSVOlmNPIkaWY6ftuZzfDLdPC/Hj57jkz3M83J8r/YxzwPbUSNPmxq5Z+rk0evk2894btY1cjKNOlmNnGQaNbI8P23yfI/k+NFzfLKHeX4KOT6R5xemkOeB7aiRp635Gnmq9fE16uTR6+Tbz3hu1jVyMo06eYI18j7Wx3L89MnzPZLj5fgxTCHHJ/J8ppHnge2pk6dNjdwjNbIaeQxq5N7sY40sx0+fPN+Tuef4RJ5v0VRyfCLPp4E876Ls0/bAM5575YbzuPWM52wXPSulvGmSpywefkmt9XfGjOccD8vpnTN+PckvJqk3eN2DknxnKeWdBorrXIvE8c2Lhx9ba33peNH0q9b6g0nePcmfrPzq75dS3mqEkK55+zOee0Kt9Udv9IZa688k+ScrT79DTu9wNBXvleSNFj8/rdZ6o32mBf83ye/k+jukJckTkvxyKeUthg3pbLXWFyb5LytPv2cp5SPXePujc3onr2W/0Ulg/ZLjJ0ye788+5fhEnm/MlHJ8Is8D86VOnig1cr/2qU5WIzdnSnWyGrltcvxETSjHJxPM83J8kvFzfLKfeV6O78me5nlgO2rkiVIj90+dnGT8Onkfa+RkOnWyGrl98vxEyfP9kuOTjJ/jk/3M81PJ8Yk8P4U8D2xHjTxRE6qRJ1cfX6NOTjJ+nbyPNXIynTp5MjXyntbHcvyEyfP9kuOTyPFjmUqOT+R5YL7UyROlRu6XGjmJGnksauQe7GmNLMdPmDzfn33K8Yk835gp5fhEnh+VZDttZ90F5+qG8zirCNu0kGNzX5XTIvpnknzpyLHcUK31GbXWL6q1fmKt9T2SPCrJF+f+dy655kFJvqGUsvpFN4avzWmsX1Nr/amxg+nbooB/Yu6/7z4gyUePE1GS5C+tPL6a0zvFnKvW+o25Pjl+YinlTboKrGd/e+nn7x4tipsopfyVnBZgb5zk7ZJ8Qa4/wPpLSf53KeWdBw7vRv5pkt9eee7rSyl/+UZvKKW8W5IfOONX/7fLwHoix0+bPN+fvcrxiTzfkEnk+ESezzTyPLA9dfJ0qZH7tVd1shq5KZOok9XIk6iR5fjpmkSOTyab5+X48XN8sp95Xo7v177leWA7auTpUiP3T508fp28jzVyMoE6WY2cZBo1sjw/XfJ8v+T48XN8sp95vvkcn8jzC1PI88B21MjTNYkaeaL18TXq5PHr5H2skZMJ1MkTrZH3rT6W46dNnu+XHC/Hj6X5HJ/I85lGnge2p06eLjVyv9TIauSxqJH7s281shw/bfJ8f/YqxyfyfEMmkeMTeT4N5HkXZZ+2l53x3IM2nMdZd9f58y1iYU2llL+Z5MOS/GmSj6y1vm7kkNZWa31hrfXzk7xFkh8/4yWPS3L7oEGtKKV8WJK/m9O7fXzOmLEMqdb6q0k+f+XpJ44Ry8LFlccvqrW+ds33ftXK4wcm+Xu7h9SvxcHHhywePj/JL40Yzg0tvoOenuTBST6g1voHtdYvymkBuXpwfynJT5ZSHjdwmNeptb4iyQckeebS07cl+flSyj8vpTymlPJ6i38/tpTyk0l+IckjVmb16iQ/P0zUO5HjJ0qe78++5vhEnh/bVHJ8Is9nOnke2J46eYLUyP3a1zpZjTy+qdTJauTJ1Mhy/ARNOccn7ed5Of5+xszxyZ7leTm+f3uY54HtqJEnSI3cP3Xy/eiFDWgKdbIaOcl0amR5foLk+X7J8fejFzagKeT4RJ5fmEqeB7ajRp6gKdfIrdfH16iT70cvbEBTqJOnWiPvYX0sx0+UPN8vOf5+5PgBTSHHJ/J8ppPnge2pkydIjdwvNfL9qJEHpEbu1x7WyHL8RMnz/dnXHJ/I82ObSo5P5Pk0kuddlH3a/uyM5x684TwurDx+Sa31NduFw82UUg6T/Oec3r3kb9Va/3jkkLZSaz1O8oQk33fGrz/kjOcGUUp5oyRfn+S1SZ5Sa33VWLGM5KuTPG/p8dssCoMxvMHK4xdt8N7vT3LPynPvu1s4g3iPJI9c/Py0WuvqXWZGV0p5YpLvSfKQJJ9ca/39a7+rtX5Pkr+W5MrK2y4k+cFSypsOFeeN1FpfmNPP+atyup8np2P510mek+RVi3+/IsmzcnrHnz9Ymc2P11rPyp+tkeMnSJ7vjxyfRJ4fU/M5PpHnF6aS54HtqZMnRo3cL3WyGnlkzdfJauQk06mR5fiJmUuOT9rM83J8Uzk+2b88L8cPYM/yPLAdNfLEqJH7p05uqk7etxo5abxOViPfayo1sjw/MfJ8v+T4pnJ8sn95vukcn8jzS6aS54HtqJEnZi41cov18TXq5Kbq5H2rkZPG6+Sp18h7Vh/L8RMkz/dLjpfjR9Z0jk/k+YWp5Hlge+rkiVEj90uNrEYemRq5Z3tWI8vxEyTP90eOTyLPj6n5HJ/I8wtN5HkXZZ+24yQvX3lu9UvvZt5w5fEfbh8O51kkwm/L6Zf0R9daf3nkkHZSa72a5BOS3L3yq788QjjX/Nec3o3li2qtvzZiHKOotd6T5BuXnnpgrt/HBwtn5fHqdnLjN9b60iS/svL0W+8a0AD+9tLP/2O0KG6glPL2Sb4rya1JfrHW+l2rr6m1/lKS90zyRyu/eqOc7l+jq7W+rNb6mUneJKffQV+d5NuT/Kckn5XkvZI8otb6yUlemuu/k/6/wYLdjRw/MfJ87/Y6xyfy/MiazvGJPL9kKnke2J46eULUyIPY6zpZjTy6putkNfK9plIjy/ETMrccnzSZ5+X4dnJ8sn95Xo4fyB7leWA7auQJUSMPRp3cTp28bzVy0nCdrEa+n6nUyPL8hMjzg5Dj28nxyf7l+WZzfCLPr8xiKnke2I4aeULmViM3WB9fo05up07etxo5abhOnkuNvEf1sRw/MfL8IOR4OX5Mzeb4RJ5fMpU8D2xPnTwhauRBqJHVyGNSIw9gj2pkOX5i5Pne7XWOT+T5kTWd4xN5fkkTed5F2SdscdeFu1aefpMNZ/Pwlcerdw+gO1+c5AOT3LG4+8Tk1Vqv5PRONMsujRFLKeVdkzxx8fCLSyl1nSnJ01dm9T5nvO72Iceyo59YefzAUaJIXrbj+39r5fHFHefXq8UB1ocuHj4vSVMHWKWUB+S0SHnI4qlvvtFra62/m9O74xyv/OoDSil/o58IN1drfVGt9b/VWj+j1vr3aq2fWmv9ylrrzy8ORpLk7ydZvjPU/0vytOGj3ZwcP0nyfE/k+PuR5wfWeo5P5Pmlt00mzwPbUydPjhq5R+rke6mRR9B6naxGvtdkamQ5fnJml+OTdvK8HH+vVnJ8skd5Xo4fx9zzPLAdNfLkqJF7pk6+Vyt18t7UyEnbdbIaeZo1sjw/OfJ8j+T4e7WS45M9yvMt5/hEns9E8zywHTXy5MyuRm6lPr5GnXyvVurkvamRk7br5DnWyHOvj+X4SZLneyTH30uOH0HLOT6R55feNpk8D2xPnTw5auQeqZHvpUYegRp5eHOvkeX4SZLneyLH3488P7DWc3wizy+9rZk876Ls0/fMlcdrF2GllAfm9E4Hy5r74piDUsrfS/J5Sb641vo1Y8fTsf++8vg1o0SRPGik5bbmWUs/X03ykpHieN7K400L82evPL66QyxDeLckj1r8/LTFQXJLPjzJYxc/X01y7kFgrfX3kjwhyatWfvXJ3YfWj1LKLUn+4crTX1Zrfd0Y8WxJjp8Ieb53cvx95PnhtZ7jE3n+mqnleWB76uQJUCMPQp18So08jtbrZDXyqanVyHL8BMw8xydt5Hk5/lQrOT7ZrzwvxzdoJnke2I4aeQLUyINRJ59qpU7epxo5abtOViPfZ2o1sjw/AfL8IOT4U63k+GS/8nzLOT6R55dNLc8D21EjT8DMa+QW6uNr1MmnWqmT96lGTtquk/euRp5JfSzHT4Q8Pwg5/pQcP46Wc3wiz18ztTwPbE+dPAFq5EGokU+pkcehRm7MTGpkOX4i5PneyfH3keeH13qOT+T5a5rJ8y7KPn2rd8B4dCll3fX66Fy/Dfzi7iGxrJTy/kn+a5Kvr7V+/tjxdK3W+n9z/yT/orFiIUnyiqWf/1+tdTWBDuW3Vx6vHvDdzOpddU52iGUIT176+btHi+LGPnrp5z+ttd60MK+1/lqSf7zy9Psu7gI0BR+f5M2WHv/fJN8yTihbk+MnQJ5nYPL88FrP8Yk8n0wzzwPbUyc3To3MwNTI42i9TlYjT7NGluMbN/ccn8jzjWklxyf7lefl+DbNIc8D21EjN06NzAhaqZP3qUZO2q6T1cinplgjy/ONk+cZWCs5PtmvPN9yjk/k+WummOeB7aiRGzf3Gll93KRW6uR9qpGTtuvkfayR51Afy/ETIM8zMDl+HC3n+ESeT6aZ54HtqZMbp0ZmYGrkcaiR2zOHGlmOnwB5noHJ88NrPccn8nzSWJ53Ufbp+5Ekr116/JAkb7Pme99y5fGfJPmlLoLiVCnl8Un+Z5LvTfKpI4fTpxcs/bx6t6ZB1FrvrLWWTack77syq58543V3jjCkbS0XOqsHaUP6+ZXHt5VSVr9zzvPalce/tWM8ffuwxb/PS5t3GHuXpZ//aIP3fVPuf4eihyY57CSiHpVSHprkC5aeuifJx9Va7xkppG3J8Y2T54chx9+PPD+81nN8Is9PNc8D21MnN0yNPBx18r3UyONovU5WI0+zRpbjG7ZHOT7RC2tFKzk+2a88L8c3ZkZ5HtiOGrlhauRhqZPv1UqdvE81ctJ2naxGnm6NLM83TJ4fjhx/r1ZyfLJfeb7lHJ/I88l08zywHTVyw/aoRh69D5aok5e0UifvU42ctF0n71WNPKP6WI5vnDw/HDn+XnL8OFrO8Yk8P9U8D2xPndwwNfJw1Mj3UiOPQ43ckBnVyHJ84+T5Ycjx9yPPD6/1HJ/I883leRdln7ha691Jfmzl6Xde8+3vsvL4abXWq7tHRZKUUt4xp0Xyzyb56Jl/tq9c+vmnRouCJHmvpZ+/Zawgkjw9yZ+tPPfeG7z/YSuPf2a3cPqzONC6dveVp9Va65jx3MBDl35eO75a6+tyehB5v6c7iahf/z7JI5Yef36t9ddHimVrcnzb5HlGIs8PaCI5PpHnJ5nnge2pk9ulRmYkauSBTaROViNPsEaW49u1Zzk+kedb0UqOT/Ykz8vxzZpFnge2o0ZulxqZEbVSJ+9FjZxMok5WI0+0Rpbn2yXPM5JWcnyyJ3l+Ajk+keeTieZ5YDtq5HbtWY2sPm5LK3XyXtTIySTq5H2rkWdRH8vxbZPnGYkcP7AJ5PhEnp9knge2p05ulxqZkaiRB6ZGbtIsamQ5vm3yPCOR5wc0kRyfyPPN5XkXZZ+Hf7fy+APWfN/jl36uSb6um3Aopbx9Tu9I8swkH1ZrXb3Dx3nvfatSypf0Flw/3nTx7x/l+oMChvXxi3+/v9b6f8YKotb66iRPXXn6/TeYxfIddO5J8p07B9WfJy/9/N2jRXG+P136+RE3fNXZnrP08ytrrVc6iKc3pZQnJfmEpaeeWmv9NyOF0wU5vkHyPCOS54c1hRyfyPNTzvPA9tTJjVEjMyI18vCmUCerkadLjm/MHub4RJ5vRRM5PtmrPC/HN2aGeR7Yjhq5MWpkNfLImqiT96hGTtqvk9XI066R5fnGyPPy/IiayPHJXuX51nN8Is9PPc8D21EjN2YPa2T1cVuaqJP3qEZO2q+T96ZGnmF9LMc3SJ5nRHL88FrP8Yk8P+U8D2xPndwYNTIjUiMPT43ckBnWyHJ8g+R5RiTPD2sKOT6R55vL8y7KPgO11p9J8r+XnnpiKeXW895TSnlIktuXnvreWuvv9hBeF8rYAWyilPIOSX46yR8keWKt9ZU3ecvye98oydNymnAmoZTydrnvC/3La62vGTOefVZK+fAk75vk1Uk+c+RwkuQrkyxvDx9cSrmw5nvfc+nnp9VaX9BZVN27VoQ9L8kvjxnIOX526edHlVIevcF7H7z08093FE8vSilHSb5t6akfy/2LsaHtnL8ayfF95eEh83tnyxoxz49SD3Wc5ydV051jrHXRZZ7vYgxj5/kh1sMQOb6LcYyd5wfZJxrM88CIGqmT+zSpukkvTC9sLHpho9ELa0SDNbJe2Djz7W05I+d4vbA2zKEPluiFravvHD+HPliiFwYMrJEauW+TqZv2rQ+W6IW1RC9sNK33wlqokQfRYI2sFzbOfHtbjl6YXlj0wpaNnef1wk61kOf1woBBNVIj920yddO+9cL0wdqiFzYavbAGNFgfz6UPluiF3Tcjfx+5a56fTE13jjn0wubQB0v0wq4ZO8/rgwGDa6hO7sukaia9ML2wseiDjab1Plgyfo08iAZr5Ln0wqbeB+t0WXphe39OWKIXds3YeX4OfbBEL2z9hbSX52/IRdnn47NyenebJHlYkg+5yes/KvftVK9YvL9VqwXlLaNEsYZSyjvl9AvqWUn+eq31Zee8tpRSbi2lvGEp5bGllH+W5DeTvF3avQPIWf7p4t+fi7srdaKU8qBSyleUUn6ilPKlpZTDNd7zXkm+afHwE2utz+43ypurtT4nyVcsPfXgrFEYllLeNcnR4uHLc9821pxSyuOSPGbx8Gm11nrOy8f0jSuP/+EG7/2rSz//1w5i6UUp5S1yWnA9dPHU05J88OIOTWPpKn+NneP7ysND5vdOljVynh+rHuoyz0+mpruJncbRSJ7feV00kOd73Z4GzPFdjGPsPN/7vt1ongfGN3ad3KfJ1E16YXphXWikRt5ZAzVy7/TC2tFojawXNs58e1lOAzleL6wNc+iDJXphNzVQjp9DHyzRCwPGMXaN3LdJ1E0N1Mhj0QvrWEN18k4aqJF7N5FeWAs1cu8arZH1wsaZby/LaSDP64W1QS9soYE8rxd2qoU8rxcGjGHsGrlvk6ibGqiRx6AP1oOG6uSdNFAj904vrA2N1sdz6YMlemFJRs/zc+iDJROp6W5iDr2wOfTBEr2wa8bO8/pgwFhaqJP7MpmaSS9ML6wLjdTIO2ukRu7VRPpgyfg1cu8arZHn0gubeh+ss2XphTknbEEvLE3k+Tn0wRK9sLU0mudvyEXZZ6LW+otJvmbpqc8rpZy5fkspb5DkXyw99Vm11uf2GN6u3mDl8UPPfNXISinvkdPi6zDJuyX5s1JKvdGU5GpO7xhyJcmvJ/myJJeT/N9a6++NEP/DSimftpjefs33fGiSj07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      "text/plain": [
       "<Figure size 7500x3000 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''del mpl.font_manager.weight_dict['roman']\n",
    "mpl.font_manager._rebuild()'''\n",
    "m=1\n",
    "plt.figure(dpi=300,figsize=(25,10))\n",
    "for i in range(121,136):\n",
    "    sn=data2['%s'%data2.columns[i]]\n",
    "    sn=sn.astype(float)\n",
    "    sn[np.isinf(sn)]=999\n",
    "    mpl.rcParams['agg.path.chunksize']=1000\n",
    "    mpl.rcParams['font.serif']='Times New Roman'\n",
    "    mpl.rc('font',family='Times New Roman')\n",
    "    ax=plt.subplot(3,5,m)\n",
    "    civ=ax.hist(x=sn,bins=5000,color='black',alpha=1,rwidth=1,linewidth=1,histtype='step')\n",
    "    ax.set_xlabel(r'%s'%str(data2.columns[i]),fontsize=12)\n",
    "    ax.set_xlim(0,15)\n",
    "    ax.set_ylim(0,100)\n",
    "    ax.set_xticks(np.arange(0,20,1))\n",
    "    plt.xticks(fontsize=12)\n",
    "    ax.spines['bottom'].set_linewidth(1)\n",
    "    ax.spines['top'].set_linewidth(1)\n",
    "    ax.spines['left'].set_linewidth(1)\n",
    "    ax.spines['right'].set_linewidth(1)\n",
    "    #ax.set_yticks(np.arange(0,10000,1000))\n",
    "    ax.axvline(x=10,ls='--',color='gray')\n",
    "    ax.axvline(x=5,ls='--',color='gray')\n",
    "    m+=1\n",
    "'''\n",
    "ax1=plt.subplot(3,5,15)\n",
    "ax1.hist(x=data2.CL_5_COUNTS,bins='auto',color='red',alpha=1,rwidth=0.5,linewidth=1,histtype='step')\n",
    "ax1.set_xlabel(r'CL_std-deviation>5',fontsize=12)\n",
    "ax1.set_xlim(0,5)\n",
    "ax1.set_ylim(0,10000)\n",
    "ax1.spines['bottom'].set_linewidth(1)\n",
    "ax1.spines['top'].set_linewidth(1)\n",
    "ax1.spines['left'].set_linewidth(1)\n",
    "ax1.spines['right'].set_linewidth(1)\n",
    "ax1.axvline(x=1,ls='--',color='gray')\n",
    "ax1.axvline(x=1,ls='--',color='gray')\n",
    "'''\n",
    "\n",
    "'''    \n",
    "sn=data2['STD3426']\n",
    "sn=sn.astype(float)\n",
    "sn[np.isinf(sn)]=999\n",
    "mpl.rcParams['agg.path.chunksize']=1000\n",
    "mpl.rcParams['font.serif']='Times New Roman'\n",
    "mpl.rc('font',family='Times New Roman')\n",
    "ax=plt.subplot(3,4,8)\n",
    "civ=ax.hist(x=sn,bins=6000,color='black',alpha=1,rwidth=1,linewidth=1,histtype='step')\n",
    "ax.set_xlabel((r'STD3426'),fontsize=10)\n",
    "ax.set_xlim(0,10)\n",
    "ax.set_xticks(np.arange(0,10,1))\n",
    "plt.xticks(fontsize=8)\n",
    "ax.spines['bottom'].set_linewidth(1)\n",
    "ax.spines['top'].set_linewidth(1)\n",
    "ax.spines['left'].set_linewidth(1)\n",
    "ax.spines['right'].set_linewidth(1)\n",
    "ax.set_ylim(0,4000)\n",
    "#ax.set_yticks(np.arange(0,10000,1000))\n",
    "ax.axvline(x=10,ls='--',color='gray')\n",
    "'''\n",
    "'''\n",
    "sn=data2['STD7006']\n",
    "sn=sn.astype(float)\n",
    "sn[np.isinf(sn)]=999\n",
    "mpl.rcParams['agg.path.chunksize']=1000\n",
    "mpl.rcParams['font.serif']='Times New Roman'\n",
    "mpl.rc('font',family='Times New Roman')\n",
    "ax=plt.subplot(3,4,9)\n",
    "civ=ax.hist(x=sn,bins='auto',color='black',alpha=1,rwidth=1,linewidth=1,histtype='step')\n",
    "ax.set_xlabel((r'STD7006'),fontsize=10)\n",
    "ax.set_xlim(0,40)\n",
    "ax.set_xticks(np.arange(0,40,5))\n",
    "plt.xticks(fontsize=8)\n",
    "ax.spines['bottom'].set_linewidth(1)\n",
    "ax.spines['top'].set_linewidth(1)\n",
    "ax.spines['left'].set_linewidth(1)\n",
    "ax.spines['right'].set_linewidth(1)\n",
    "ax.set_ylim(0,15000)\n",
    "#ax.set_yticks(np.arange(0,10000,1000))\n",
    "ax.axvline(x=10,ls='--',color='gray')\n",
    "\n",
    "sn=data2['STD7612']\n",
    "sn=sn.astype(float)\n",
    "sn[np.isinf(sn)]=999\n",
    "mpl.rcParams['agg.path.chunksize']=1000\n",
    "mpl.rcParams['font.serif']='Times New Roman'\n",
    "mpl.rc('font',family='Times New Roman')\n",
    "ax=plt.subplot(3,4,10)\n",
    "civ=ax.hist(x=sn,bins=1000,color='black',alpha=1,rwidth=1,linewidth=1,histtype='step')\n",
    "ax.set_xlabel((r'STD7612'),fontsize=10)\n",
    "ax.set_xlim(0,30)\n",
    "ax.set_xticks(np.arange(0,30,5))\n",
    "plt.xticks(fontsize=8)\n",
    "ax.spines['bottom'].set_linewidth(1)\n",
    "ax.spines['top'].set_linewidth(1)\n",
    "ax.spines['left'].set_linewidth(1)\n",
    "ax.spines['right'].set_linewidth(1)\n",
    "ax.set_ylim(0,30000)\n",
    "#ax.set_yticks(np.arange(0,10000,1000))\n",
    "ax.axvline(x=10,ls='--',color='gray')\n",
    "'''\n",
    "'''\n",
    "sn=data2['STD7894']\n",
    "sn=sn.astype(float)\n",
    "sn[np.isinf(sn)]=999\n",
    "mpl.rcParams['agg.path.chunksize']=1000\n",
    "mpl.rcParams['font.serif']='Times New Roman'\n",
    "mpl.rc('font',family='Times New Roman')\n",
    "ax=plt.subplot(3,4,11)\n",
    "civ=ax.hist(x=sn,bins=1000,color='black',alpha=1,rwidth=1,linewidth=1,histtype='step')\n",
    "ax.set_xlabel((r'STD7894'),fontsize=10)\n",
    "ax.set_xlim(0,30)\n",
    "ax.set_xticks(np.arange(0,30,5))\n",
    "plt.xticks(fontsize=8)\n",
    "ax.spines['bottom'].set_linewidth(1)\n",
    "ax.spines['top'].set_linewidth(1)\n",
    "ax.spines['left'].set_linewidth(1)\n",
    "ax.spines['right'].set_linewidth(1)\n",
    "ax.set_ylim(0,15000)\n",
    "#ax.set_yticks(np.arange(0,10000,1000))\n",
    "ax.axvline(x=10,ls='--',color='gray')\n",
    "'''\n",
    "plt.subplots_adjust(wspace=0.3,hspace=0.3)\n",
    "plt.savefig('/media/richard/Backup Plus/std_star_Fo_FHIL_distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. test of Gaussian fit from Astropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38.41657684397117, 44.170557486139295, 53.07671882792995, 53.38693343646423, 54.29756341635519, 60.912139424134544, 63.79413320664658, 34.28371770446607, 70.1085015287476, 76.17269677945, 78.99464902482637]\n",
      "flux_re:[ 4.32282832e-07 -2.85194043e-02  1.02160360e+00  9.36426930e-01\n",
      "  2.76932324e-01  8.16872952e-01  1.23840156e+00  1.03654665e+00\n",
      "  1.09551594e+00  1.58510216e-01  3.00809662e-01  1.70533569e+00\n",
      "  1.13586222e+00  3.39254993e-01  1.29731887e+00  9.54547130e-01\n",
      "  8.71046062e-01  9.16786937e-01 -5.08022248e-01 -1.09319356e-01\n",
      "  1.19619494e-01 -1.32017217e-01  7.06765159e-02  4.13733942e-01\n",
      "  1.24996115e+00  1.50186353e+00  1.54856046e-01 -7.07872755e-01\n",
      " -2.19856356e-01  5.68538843e-01 -3.20501621e-02 -9.22925708e-01\n",
      "  4.10347337e-02 -8.90871803e-02  4.21484093e-01  2.99995793e-01\n",
      " -5.85015070e-03]\n",
      "SNR:0.487975\n",
      "flux_mean:0.701612\n",
      "flux_std:0.487975\n",
      "(28.176487731727086, 3.0854710437496605e-07)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = np.where((lam_no_z>(WAVE[i]-WIDTH[i]/2))&(lam_no_z<(WAVE[i]+WIDTH[i]/2))&(spectra['and_mask']==0),True,False)\n",
    "wave_window=lam_no_z[ind]\n",
    "flux=spectra['flux'][ind]\n",
    "if WAVE[i]-5>min(wave_window) and WAVE[i]+5<max(wave_window):\n",
    "    ind1=np.where((lam_no_z>WAVE[i]-5)&(lam_no_z<WAVE[i]+5)&(spectra['and_mask']==0))\n",
    "else:\n",
    "    ind1=ind\n",
    "flux_center=np.max(spectra['flux'][ind1])\n",
    "wave_center=wave_window[np.where(flux==flux_center)]\n",
    "#\n",
    "window_min_flux=flux[np.where(wave_window==min(wave_window))]\n",
    "window_max_flux=flux[np.where(wave_window==max(wave_window))]\n",
    "k=(window_max_flux-window_min_flux)/(max(wave_window)-min(wave_window))\n",
    "b=window_min_flux-k*min(wave_window)\n",
    "window_number=len(wave_window)\n",
    "window_length=max(wave_window)-min(wave_window)\n",
    "\n",
    "def pseudo_con(x):\n",
    "    y=k*x+b\n",
    "    return y\n",
    "pseudo_range=pseudo_con(np.arange(min(wave_window),max(wave_window),window_length/window_number))\n",
    "flux_re=flux-pseudo_range[0:len(flux)]\n",
    "SNR=np.std(flux_re[flux_re>0])\n",
    "#\n",
    "g_init = modeling.models.Gaussian1D(amplitude=flux_center, mean=wave_center, stddev=10.)\n",
    "fit_g = modeling.fitting.LevMarLSQFitter()\n",
    "g = fit_g(g_init, wave_window, flux)\n",
    "flux_fitting=g(wave_window)\n",
    "def f_test(n):\n",
    "    m=g(n)-pseudo_con(n)\n",
    "    return(m)\n",
    "area=integrate.quad(f_test,min(wave_window),max(wave_window))\n",
    "#snr=area/np.std(flux_re)\n",
    "print(\"flux_re:%s\\nSNR:%f\\nflux_mean:%f\\nflux_std:%f\"%(flux_re,SNR,np.mean(flux_re[flux_re>0]),np.std(flux_re[flux_re>0])))\n",
    "plt.plot(wave_window,flux,color='gray')\n",
    "plt.plot(wave_window,pseudo_range,color='orange')\n",
    "#plt.plot(wave_window,flux_re,color='red')\n",
    "plt.plot(wave_window,g(wave_window),color='yellow')\n",
    "print(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for chunk in data:\n",
    "    for i in tqdm(chunk.index):\n",
    "        spectra = fits.open(path+chunk.specname[i])[1].data\n",
    "        lam_no_z=(10**spectra['loglam'])/(chunk.Z[i]+1)\n",
    "        for x in range(len(WAVE)):\n",
    "            ind = np.where((lam_no_z>(WAVE[x]-WIDTH[x]/2))&(lam_no_z<(WAVE[x]+WIDTH[x]/2))&(spectra['and_mask']==0),True,False)\n",
    "            flux = spectra['flux'][ind]\n",
    "            if len(flux)<=2:\n",
    "                chunk.loc[chunk.specname==chunk.specname[i],'SN%d'%WAVE[x]]=-999\n",
    "                continue\n",
    "            wave_window=lam_no_z[ind]\n",
    "            #,\n",
    "            window_min_flux=flux[np.where(wave_window==min(wave_window))]\n",
    "            window_max_flux=flux[np.where(wave_window==max(wave_window))]\n",
    "            k=(window_max_flux-window_min_flux)/(max(wave_window)-min(wave_window))\n",
    "            b=window_min_flux-k*min(wave_window)\n",
    "            pseudo_con_line=k*x+b\n",
    "            window_number=len(wave_window)\n",
    "            window_length=max(wave_window)-min(wave_window)\n",
    "            pseudo_con=k*(np.arange(min(wave_window),max(wave_window),window_length/window_number))+b\n",
    "            #,\n",
    "            flux_re=flux-pseudo_con[0:len(flux)]\n",
    "            chunk.loc[chunk.specname==chunk.specname[i],'SN%d'%WAVE[x]]=np.mean(flux_re)/np.std(flux_re)\n",
    "            #,\n",
    "            #g_init = modeling.models.Gaussian1D(amplitude=1., mean=0, stddev=1.),\n",
    "            #fit_g = modeling.fitting.LevMarLSQFitter(),\n",
    "            #g = fit_g(g_init, wave_window, flux),\n",
    "            #flux_fitting=g(wave_window),\n",
    "            #area=integrate.quad(g-pseudo_con_line,min(wave_window),max(wave_window)),\n",
    "            #snr=area/np.std(flux_re),\n",
    "            #print(\"%f,%f,%f,%f,%f\"%(np.median(flux_re),np.mean(flux_re),np.std(flux_re),np.mean(flux_re)/np.std(flux_re),np.mean(flux)/np.std(flux_re)))\n",
    "    chunks.append(chunk)\n",
    "    print(max(chunk.index))\n",
    "data2=pd.concat(chunks)\n",
    "data2=data2.replace([np.inf, -np.inf],-999)\n",
    "data2=data2.fillna(-999)\n",
    "data2.to_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v8_star_fo_in_pair.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.064793"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6881793e50>]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcSUlEQVR4nO3df5RcZZ3n8feHNGmSGJKQ9JC2E0nU+AN/oKFFwNXjkeGXeoyzow6cOWvGyZ44I7Mr45x1YDwrq3PmqLu4jpwzy0zWIGFlEYy6iRxGYCKKiEA6oJAfJukQQzokpEkC+UWnf333j3o6VDfd6U5Vp6qo5/M6p0/d+9ynbj1PVdf93PvcW1WKCMzMLD+nVbsBZmZWHQ4AM7NMOQDMzDLlADAzy5QDwMwsUw3VbsCJzJo1K+bNm1ftZpiZvaqsW7fu+YhoGq1eTQfAvHnzaGtrq3YzzMxeVSTtGEs9DwGZmWXKAWBmlikHgJlZphwAZmaZcgCYmWXKAWBmlikHgJlZphwAlrwE3Ab468HNcuEAMAAi/guwGPi3ajfFzCpk1ACQdIukvZLWD7PsbySFpFlpXpJuktQu6UlJC4vqLpa0Nf0tHt9uWLk6O59Mt9uq3BIzq5SxHAHcClwxtFDSXOAy4Jmi4iuBBelvKXBzqnsWcAPwXuAC4AZJM8ppuI2v7u5jAHR1dVW5JWZWKaMGQEQ8COwfZtG3gC8yeNB4EXBbFDwCTJfUDFwO3B8R+yPiAHA/w4SKmZlVTknnACQtAnZFxG+HLGoBdhbNd6SykcqHW/dSSW2S2jo7O0tpnpXAPw1tlp+TDgBJk4G/A748/s2BiFgWEa0R0drUNOq3mdo4k1TtJphZhZRyBPAGYD7wW0m/B+YAj0uaDewC5hbVnZPKRiq3GhM+FDDLxkkHQEQ8FRF/EBHzImIeheGchRGxB1gNfDpdDXQh8GJE7AbuBS6TNCOd/L0slVmN8I6/WX7GchnoHcCvgTdL6pC05ATV7wGeBtqB/w18DiAi9gN/D6xNf19NZVYjvONvlp9RfxEsIq4eZfm8oukArhmh3i3ALSfZPqswnwMwy4c/CWwASD4EMMuNA8DMLFMOAAMgwkM/ZrlxAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyABjgr4Iwy5EDwMwsUw4AA/xVEGY5cgCYmWXKAWCAzwGY5cgBYGaWKQeAAT4HYJajsfwm8C2S9kpaX1T2PyT9TtKTkn4saXrRsusltUvaLOnyovIrUlm7pOvGvSdWJg8BmeVmLEcAtwJXDCm7H3h7RLwT2AJcDyDpXOAq4G3pPv9L0gRJE4B/Aq4EzgWuTnWtxvg3gc3yMWoARMSDwP4hZfdFRG+afQSYk6YXAd+PiGMRsR1oBy5If+0R8XREdAPfT3WtZhQ2/BE+EjDLxXicA/hz4F/TdAuws2hZRyobqfwVJC2V1CaprbOzcxyaZ2ZmwykrACR9CegFbh+f5kBELIuI1ohobWpqGq/V2qi852+Wm4ZS7yjpz4CPApfEy+MGu4C5RdXmpDJOUG41xOcAzPJR0hGApCuALwIfi4ijRYtWA1dJapQ0H1gAPAasBRZImi9pIoUTxavLa7qNL2/4zXIz6hGApDuADwKzJHUAN1C46qcRuD/tMT4SEX8RERsk3QVspDA0dE1E9KX1/BVwLzABuCUiNpyC/ljJPARklptRAyAirh6mePkJ6v8D8A/DlN8D3HNSrbOK80VAZvnwJ4FtEJ8CMMuHA8ASb/nNcuMAsMRjP2a5cQCYmWXKAWCJh4DMcuMAsMRDQGa5cQDYIL4M1CwfDgAbxJeBmuXDAWCJt/xmuXEAWOKxH7PcOABsEJ8DMMuHA8AG8TkAs3w4AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAsCF8GZBZLkYNAEm3SNoraX1R2VmS7pe0Nd3OSOWSdJOkdklPSlpYdJ/Fqf5WSYtPTXesfP4ggFkuxnIEcCtwxZCy64A1EbEAWJPmAa4EFqS/pcDNUAgMCj8m/17gAuCGgdAwM7PqGDUAIuJBYP+Q4kXAijS9Avh4UfltUfAIMF1SM3A5cH9E7I+IA8D9vDJUzMysgko9B3B2ROxO03uAs9N0C7CzqF5HKhup/BUkLZXUJqmts7OzxOZZ6XwOwCwXZZ8EjohgHAeOI2JZRLRGRGtTU9N4rdbGzOcAzHJRagA8l4Z2SLd7U/kuYG5RvTmpbKRyMzOrklIDYDUwcCXPYmBVUfmn09VAFwIvpqGie4HLJM1IJ38vS2VWczwEZJaLhtEqSLoD+CAwS1IHhat5vg7cJWkJsAP4VKp+D/BhoB04CnwGICL2S/p7YG2q99WIGHpi2WqCh4DMcjFqAETE1SMsumSYugFcM8J6bgFuOanWmZnZKeNPApuZZcoBYEP4HIBZLhwANoTPAZjlwgFgZpYpB4AN4SEgs1w4AMzMMuUAsCF8DsAsFw4AM7NMOQBsCJ8DMMuFA8CSGHJrZvXOAWAARBT2/CUfAZjlwgFgAEiFPf/C1zmZWQ4cAJZoyK2Z1TsHgCU+B2CWGweADeJzAGb5cADYEA4As1w4AMzMMuUAsCF8DsAsF2UFgKS/lrRB0npJd0g6Q9J8SY9Kapd0p6SJqW5jmm9Py+eNSw/MzKwkJQeApBbgPwOtEfF2YAJwFfAN4FsR8UbgALAk3WUJcCCVfyvVs5rjcwBmuSh3CKgBmCSpAZgM7AY+BKxMy1cAH0/Ti9I8afkl8iUnNchDQGa5KDkAImIXcCPwDIUN/4vAOuCFiOhN1TqAljTdAuxM9+1N9WeW+vhmZlaecoaAZlDYq58PvBaYAlxRboMkLZXUJqmts7Oz3NXZSfNBmVkuyhkC+kNge0R0RkQP8CPgfcD0NCQEMAfYlaZ3AXMB0vJpwL6hK42IZRHRGhGtTU1NZTTPzMxOpJwAeAa4UNLkNJZ/CbAReAD4RKqzGFiVplenedLyn4W/eawG+SUxy0U55wAepXAy93HgqbSuZcDfAl+Q1E5hjH95ustyYGYq/wJwXRntNjOzMjWMXmVkEXEDcMOQ4qeBC4ap2wV8spzHs0rwOQCzXPiTwGZmmXIAmJllygFgZpYpB4CZWaYcADaELwM1y4UDwMwsUw4AG8KXgZrlwgFgZpYpB4CZWaYcADaIv57JLB8OADOzTDkAbBD/SJtZPhwANoiHgMzy4QAwM8uUA8AG8RCQWT4cADZIRH+1m2BmFeIAsMR7/ma5cQBYMnDy10FwqkUE69evp6+vr9pNscw5AMwqbOvWrfzwhz/k5z//ebWbYpkrKwAkTZe0UtLvJG2SdJGksyTdL2lrup2R6krSTZLaJT0paeH4dMHGh/f8K+Xo0aMAHDx4sMotsdyVewTwbeCnEfEW4DxgE3AdsCYiFgBr0jzAlcCC9LcUuLnMx7Zx5ev/K8VXWlmtKDkAJE0DPgAsB4iI7oh4AVgErEjVVgAfT9OLgNui4BFguqTmUh/fzMzKU84RwHygE/iupCckfUfSFODsiNid6uwBzk7TLcDOovt3pLJBJC2V1CaprbOzs4zm2cnxXqlZbsoJgAZgIXBzRLwbOMLLwz0AROF7BU5qbCEilkVEa0S0NjU1ldE8OzkeAqqUgSEgf+2GVVs5AdABdETEo2l+JYVAeG5gaCfd7k3LdwFzi+4/J5WZmVkVlBwAEbEH2CnpzanoEmAjsBpYnMoWA6vS9Grg0+lqoAuBF4uGiqzqPARklpuGMu//n4DbJU0EngY+QyFU7pK0BNgBfCrVvQf4MNAOHE11rWZ4OKLSPARk1VZWAETEb4DWYRZdMkzdAK4p5/HM6oEvA7Va4U8CW+KNklluHABmVeIhIKs2B4BZhXkIyGqFA8DMLFMOADOzTDkALPF4dKX4k8BWKxwAZmaZcgBY4hOTZrlxAGQsIli3bh3d3d3VbkqWPARk1VbuV0HYq9i2bdu4++672b17N+ed542RWW58BJCxgT3/gZ8otMry5wGs2hwAloYivDGqNA8BWbU5ADLmPVCzvDkAzKrEAWzV5gAwD0VUiZ93qzYHQMa8B1odft6tVjgALPHeaKV4z99qhQPAzCxTZQeApAmSnpB0d5qfL+lRSe2S7ky/F4ykxjTfnpbPK/exbTx5WKJSPARktWI8jgA+D2wqmv8G8K2IeCNwAFiSypcAB1L5t1I9s+x4CMhqRVkBIGkO8BHgO2lewIeAlanKCuDjaXpRmictv0TeFaoJhQ2SN0pmuSn3COAfgS8C/Wl+JvBCRPSm+Q6gJU23ADsB0vIXU/1BJC2V1CaprbOzs8zm2Yk4f6vDz7vVipIDQNJHgb0RsW4c20NELIuI1ohobWpqGs9V2wl5o2SWm3K+DfR9wMckfRg4AzgT+DYwXVJD2sufA+xK9XcBc4EOSQ3ANGBfGY9v48pDQGa5KfkIICKuj4g5ETEPuAr4WUT8KfAA8IlUbTGwKk2vTvOk5T8Lnw0zM6uaU/E5gL8FviCpncIY//JUvhyYmcq/AFx3Ch7bSuYhILPcjMsPwkTEz4Gfp+mngQuGqdMFfHI8Hs/GV/GBmA/KzPLhTwJnbPDVKN7wm+XGAWBmlqm6DoDu7m4eeOAB+vr6qt2UmuZfBDPLU10HwC9/+UsefPBBHn/88Wo3pSZ5CMgsb3UdAD09PQD09vaOUtPMLD91HQBWCh8JmOWirgPA37lyMvxcmeWmrgPAxsbfBmqWJwdAxoY/QnIQmOUiiwDwp1vHwkNAZrmp6wAY2PD7XMBYOCTNclPXAWAnz0dLZvnIIgB8BAAPPfQQmzZtGnG5nyKz/NR1AHhv9mVr1qzhrrvuGnZZRBDhBDDLTV0HgJ2YvwrCLG9ZBICHgEbnp8gsP3UdAB4CGpvi50nyc2aWi7oOADux4iOjgXMADs1Tz8+x1YqSA0DSXEkPSNooaYOkz6fysyTdL2lrup2RyiXpJkntkp6UtHC8OmHl856/WX7KOQLoBf4mIs4FLgSukXQuhR97XxMRC4A1vPzj71cCC9LfUuDmMh57TPxBsFI4CMxyUXIARMTuiHg8TR8CNgEtwCJgRaq2Avh4ml4E3BYFjwDTJTWX+vg2vnwZaOV4CMhqxbicA5A0D3g38ChwdkTsTov2AGen6RZgZ9HdOlLZ0HUtldQmqa2zs3M8mmdj4CEgs/yUHQCSXgP8ELg2Ig4WL4sSvmc4IpZFRGtEtDY1NZXVNg8BjY33SM3yVFYASDqdwsb/9oj4USp+bmBoJ93uTeW7gLlFd5+TyqxKht/wOwxONQeu1YpyrgISsBzYFBH/s2jRamBxml4MrCoq/3S6GuhC4MWioSKrMp8DMMtPQxn3fR/wH4CnJP0mlf0d8HXgLklLgB3Ap9Kye4APA+3AUeAzZTz2SfEQ0Oh8DsAsPyUHQEQ8xMi/InLJMPUDuKbUxyuFD7VPzM9Pdfh5t1rhTwLbEN44meUiiwDwENCJeY/ULE91HQC1tmHbv38/a9asqZl2DdeOWmlbPRt4jv1cW7XVdQDUmjvvvJOHHnqI/fv3V7spJ+CNUqX4yNSqLesAiAhuv/12nn766Yo8Xn9//6DbWuFfBKss7/lbrajrABjtjXbs2DHa29tH/KnE8XbaaYWnu1YCwL8DUB3+hLrViroOgAG1ssc18IavlfZYdfj1t1qRdQBU641YKxuA4drhndLK8RGAVVsWATCSSg/FVOsNP5bAGahSK+FUz/wcW63IIgBq7QigVngsujr8vFutqOsAGO1664EjgJMNgq6uLtra2koOkEoHjwOwtvh5t1pRzpfBveqVOgS0evVqNm3aRHNzMy0tr/hNmxFV6yTwaAFQaNdAHW+cTjUfAVitqOsjgAGjHQF0d3dz4MCBMa/v8OHDAPT29p5UO2o1AKyy/LxbrajrABjrEBDATTfdNOh+GzZsoKen54TrLXUPrhY/B+A9f7P81HUAjGakYNizZw8rV67kvvvuO+H9Sw2AWjsCkIQ0EEoOglNtrDsQJ9oJMRsPdR0Ao53kHWlP/MiRIwAjfmdPqUcAA/UrfQQwtsDxhr9SxvJ6dHR0sHLlSlatWjVqXbNS1XUAdHd3AyO/4fr6+oYtH9hAT5gwYdjl5X6bY1tb2/GQqYSxnQOonwDo7u5m27Zt1W7GiAae92PHjo34P9jV1QUUjgKee+65irXN8pJFAOzbt49nn32Wzs5O1q5dS1dXF7feeitbtmwZVH9gozxw2D3w3T1DDbyBR3rzjmQgWDZu3MgPfvCDk7pvOUYLwNNOO+34EFA9nKBctWoV3/ve93jxxRer3ZRhDTzvmzdvHvH/oPgocc+ePRVpl+Wn4peBSroC+DYwAfhORHx9vB/j8OHDfPOb3zw+/8QTT/DEE08cn7/nnnsA2LFjx6D73XjjjYPmN2/ezFe+8pURH2fFihUjLnvTm97Eli1bOPfcc3nHO95BY2PjoDfyjh072LFjB8eOHeOll15izpw5rFmzBoDzzjuPpqYm+vv7mT59Op2dnUyZMoXGxkb6+vqYPHky+/btIyLo7e1lypQpdHd3c+jQIebMmXP8yKWnpwdJr7ha6ejRo0yaNOn4hqi/v//49OHDh4BCEIzHZYovvfQS3/3ud1myZAmNjY1lr28sdu/eDRT2oqdNmzZq/V//+tfcd999fPnLXx5znw8dOsSRI0eYPXv2SbeveMdh8+bNw9Ypfs1OdkdjrB5++GGeeuopPvvZz56S9eeov78/nVN7dVziq0ru8UmaAGwBLgU6gLXA1RGxcbj6ra2t0dbWdtKP093dzde+9rVympqdP/mTO3jLWzZz990fYd2691S7OSWR9IojmJaWFhoaGujs7Bw05DJz5kz27ds3qO706dOZNWsW7e3tALznPe+hu7ubnp4e5s6dS19fH4cOHaK/v5+1a9cCMGPGDN75znfy7LPP0tLSQnNzM1A4qpowYQI9PT10dXUhiYMHDzJlypRXjOt/5CMfob+/n4kTJ9LW1saBAwdoaWlh69atAMyfP5+LL76Y7u5uZs+ezQsvvEBXVxfNzc309PRw+umn09vbe/y2u7ubhoYGGhsbOXz4MIcOHWLKlCn84he/oKenh/PPP5+pU6dy2223AXDttdeyf/9+mpub6ezs5KyzzuLYsWO88MILTJ06lcmTJ7Nx40ba2tq4+OKLOf3002lububMM89EEn19fWzfvp2WlhYOHz7MjBkzOHr0KF1dXWzZsoXzzz+fvr4+Dhw4wNy5c9m+fTtnn302kydPZseOHbz2ta/l0KFDHD58mOeff553vetdvPTSS0ydOvUVr3Fvb+/xHZziy6p7eno4dOgQZ555Jnv37uXAgQO87W1v4+DBg3R3d7N7926am5s5ePAgs2bNYurUqSc8wh9Yd29vLz09PTQ2NtLV1cXkyZPZuXMnP/nJT47v1Gzfvh1JnHPOOXz1q1+ltbWVvr4+Fi5cSHd3NzNmzGDixIk8++yzLFiwAIBt27bR1NREY2Mju3btYv78+Rw7dowJEyZw+umnj/q/PhpJ6yKiddR6FQ6Ai4D/FhGXp/nrASJi2K11qQEAPSxf/hcAnHnmmRw8eHAMbSvhYUoYN6/Vx/mjP/oR06Yd5LHHLuCpp94+sJaTflwzGx9vetNC3v/+z5V037EGQKWHgFqAnUXzHcB7x/9hXmDJklvGf7UZuOCCx7jggseq3Qyz7HV0/JSIvzylw0k191UQkpYCSwFe97rXlbiWacC9pTx65vcJoLdoOg+/+tWv2L59OxdddBFveMMbjl88MHHixCq3zEpx5MgR+vv7Bw0hPfzww8yePZt58+aVPUYfETzzzDPHh3B6eno444wzOHLkCD/+8Y+PD9lBYahn9uzZx4cOW1paTnh+ra+vL/1CX9DSMvOUn0uodADsAuYWzc9JZcdFxDJgGRSGgEp7mInAZaXd1bLz3HNH2LZtAu94x/uA8/B2/9VtypRXll188eXjtn4Jzjnn5fmBq8WnTIE//uMrOeOMM45vuN/4xsKy17xm8P1HMsKV56dMpS8DXQsskDRf0kTgKmB1hdtgNkhDQ2E/aKTPfZiN1aRJk141VwBBhY8AIqJX0l9RGJ+ZANwSERsq2QazoS699FImTZrEW9/61mo3xayiKn4OICLuAe6p9OOajWTSpElceuml1W6GWcXV9SeBzcxsZA4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy1RFvw76ZEnqBHaMWvHUmgU8X+U2VEoufc2ln+C+1qOx9POciGgabUU1HQC1QFLbWL5Xux7k0tdc+gnuaz0az356CMjMLFMOADOzTDkARres2g2ooFz6mks/wX2tR+PWT58DMDPLlI8AzMwy5QAwM8tUtgEgaYKkJyTdnebnS3pUUrukO9NPViKpMc23p+XzitZxfSrfLGn8fnR0HEn6vaSnJP1GUlsqO0vS/ZK2ptsZqVySbkp9elLSwqL1LE71t0paXK3+nIik6ZJWSvqdpE2SLqq3vkp6c3otB/4OSrq23vo5QNJfS9ogab2kOySdUY/vVUmfT33cIOnaVHbqX9OBX6DP7Q/4AvB/gbvT/F3AVWn6n4G/TNOfA/45TV8F3JmmzwV+CzQC84FtwIRq92uYfv4emDWk7L8D16Xp64BvpOkPA/8KCLgQeDSVnwU8nW5npOkZ1e7bMH1dAfzHND0RmF6vfU1tnQDsAc6px34CLcB2YFKavwv4s3p7rwJvB9YDkyn8SuO/AW+sxGta9c5X6QmfA6wBPgTcnZ7I54GGtPwi4N40fS9wUZpuSPUEXA9cX7TO4/Vq6Y/hA2Az0Jymm4HNafpfgKuH1gOuBv6lqHxQvVr4A6aljYXqva9FbbsM+FW99pNCAOxMG7SG9F69vN7eq8AngeVF8/8V+GIlXtNch4D+kcIT3J/mZwIvRERvmu+g8M8HL/8Tkpa/mOofLx/mPrUkgPskrZO0NJWdHRG70/Qe4Ow0PVKfXg19nQ90At9NQ3vfkTSF+uzrgKuAO9J03fUzInYBNwLPALspvPfWUX/v1fXA+yXNlDSZwh7+XCrwmmYXAJI+CuyNiHXVbkuF/LuIWAhcCVwj6QPFC6Owq1AP1wI3AAuBmyPi3cARCofNx9VRX0nj3h8DfjB0Wb30M415L6IQ7q8FpgBXVLVRp0BEbAK+AdwH/BT4DdA3pM4peU2zCwDgfcDHJP0e+D6FYaBvA9MlNaQ6c4BdaXoXhTQmLZ8G7CsuH+Y+NSPtRRERe4EfAxcAz0lqBki3e1P1kfr0auhrB9AREY+m+ZUUAqEe+wqFQH88Ip5L8/XYzz8EtkdEZ0T0AD+i8P6tu/dqRCyPiPMj4gPAAWALFXhNswuAiLg+IuZExDwKh9A/i4g/BR4APpGqLQZWpenVaZ60/GcpjVcDV6UrD+YDC4DHKtSNMZE0RdLUgWkKY8brGdynoX39dLrK4ELgxXQIei9wmaQZaa/sslRWMyJiD7BT0ptT0SXARuqwr8nVvDz8A/XZz2eACyVNliRefk3r8b36B+n2dcC/p3CByql/Tat9AqTKJ18+yMtXAb2ewj9FO4XD6sZUfkaab0/LX190/y9RuKJgM3BltfszTP9eT+Hqh98CG4AvpfKZFE6Cb6VwxcFZqVzAP6U+PQW0Fq3rz9Nz0A58ptp9G6G/7wLagCeB/0fhSoi66yuFoZB9wLSisrrrZ2rjV4DfUdhx+T8UruSpx/fqLymE22+BSyr1mvqrIMzMMpXdEJCZmRU4AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPL1P8HvlwYsRBVfM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=lam_no_z\n",
    "f=spectra['flux']\n",
    "ind=np.where((l>5006)&(l<5008))\n",
    "g_init = modeling.models.Gaussian1D(amplitude=np.mean(f[ind]), mean=5007, stddev=5.)\n",
    "fit_g = modeling.fitting.LevMarLSQFitter()\n",
    "g = fit_g(g_init, l, f)\n",
    "flux_fitting=g(l)\n",
    "plt.plot(l,f,color='gray')\n",
    "#plt.plot(wave_window,flux_re,color='red')\n",
    "plt.plot(l,g(l),color='yellow')\n",
    "plt.xlim(5000,5020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6884c5a160>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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76ZvSuNevNs5TkUITvVIqZtUkbYAEOzxydhJr3P14+tPdgP9knJOTU2+eibpq1unUIZ4l/e7E2Ow8dm7jdivaOE9FCk30SqmYlpOTg9Pp5OYJ8WQmVzLn0PmU71wPNE7Gdbvi2Wy+T4911zl1/Fiec53PL0bEMbL78eV1aFwVSTTRK6Vi3mMP5ZJ7eiJLq0fw4Zp9gDRKxg274vlqfd9wnTNP6M7L9umU0oH/vKArxhicTidz587V+/MqYmiiV0rFpLql8x1v/JHUDvCY62rKNnzmMxn76ooHnn71/hJ4Ypydk4cP4DmmcV5mFe6ibykoKNAkryJK+5umVikV82pK52VlZXRwwHVDyviodDD7kvpSvnsTvqbM8He/3u12+71fDzAtO4NbvjuPG1I+JH7po5DzWtA+h1LBoCV6pVTMqVs6v35MHOnJNp52XMX+/A98Jnnw33iuuUZ1kwamkZDcmY86TYfNi6B4Y9uCVyrINNErpWJOTencYYPfnZLAVwc6s0KGsnv5fL/rNOyKB4E1qouz25g6sieP/HgK4kiEr55s+wdQKog00SulYk5NKfzSoQ6yuth4qnIqFXu3kNHJ/93Kmq54TqezxY3qpmX3Zk9VRwr6TIPVr8BRHYVTRQ5N9EqpmFNTOr9lQjybDxo+63IBVVu+arZ0npOTQ0FBAW63u0WN6sY7u9KrcyL/W3U+uCrg22eC8TGUCgpN9EqpmFC3lX1ubi73Xj+N05wOnt83BMHGg7+cFvTW8DX7dDjs7Fm+gFe3xVPV/1xY8Ty4qoK6L6VaSxO9Uirq+ZqOtvuOBVSbONaPv48Ts7pxy79dHdJ97vn6XVxieL54CBzdC5s+COr+lGotTfRKqajXsA98p3i4apjh7YJkvis2XDQ6I+T7rNy3laqSnfx5bVdKHV1Y+vhMnbZWRQRN9EqpqNewD3zOqDg6xhue3p+N3WaYOqJn7Xt1q/jbkoR99bsvXb+M6u5D+OtXZZyWUYWz8/FpazXZK6tooldKRb2Gfd1nZsezeq+Lrc5LOHVgGqkdEwDfVfytTcK++teXrl+KMTZePDwWEfj12Hig/qx3SoWbJnqlVNSr2wd+aJqNk/rYeWVnOnRMZ1r28Wp7X8PctjYJ++p3H1/+ExV7t3Co389YuKWaGdlx2Lzj8xQWFmpVvrKEJnqlVNSr2wf+38bEU+WGA6fPJsFh47zhPWqX8zfMbWvmjvfX7z5h7/ckZAxh3uZkeqfYODPr+Lz2ba1FUKo1NNErpWJCTk4OBVu3cNd5fbGfMJUVlX0464TudEqMq12mtcPcNrnPOv3uwXOfHuDjhDM4WC5clx3XaD2tylfhpIleKRVVGjamu/nmm2v/nnm6E47uY0PPi9h/tKJetT20fpjbQOOaNWsW+4s2U77jBxwnnMVrP1RxydA4khvn+lbVIijVGprolVJRw1djuqeeeqr273N7/ERJmTD1iRW4K8q46eLT61WRt2WY2+bUvf9fun4p8WlOXi5MpWO84edDG2f61tYiKNVSmuiVUlHD35zxAB0cMP0EB29sqMadeSJlm76icNuWRvfDWzvMbXPqltDLNnyOuF2s7no2235yM3NMQr1lg1WLoFQgNNErpaJGU9XdFw1x0DHe8FbJAGyJHSldvxQI3/3wuiV097HDlBesInno6bxflMSZWQ7GDukb9FoEpQKhiV4pFTWaqu6+akQcu4+4WZU2BVfZIcoLV9e+F4774Q3v/5euW4qjS08Sz7kLG25WvHR/0GsRlAqEZYneGNPXGPOJMWadMeYHY8ztPpaZbIw5ZIxZ5X3cb0WsSqnI4KsxHUDnBDh/oIPX1gsJA0+mbMPn4HbVvh+O++EN7/+nle/CYYSdfaZA2mBY+3bQRuVTqiWsLNFXA3eKyDDgZOAWY8wwH8t9JiKjvY+HwhuiUiqS+GpMd9NNN/HrU3uS4DAsqh6HLS6xtosbhPd+eL37/1s2cO7wXiz4fi+uYZcghV9w/2+DMyqfUi1hWaIXkT0i8p33+RFgPdDbqniUUtGhYWO6//mf/+E/Zk6CLpn0uuo/6Bznpqe9NCLuh0/LzmD/0QpWpZyFQbigX3W997U/vQqHiLhHb4zJAsYAX/t4e6IxZrUxZqExZnh4I1NKRbxjB2Hbp5QPvJBlm/dz5cSBFBRsj4j74ftWfQxVx/jZ/S+zeq+LK4Y7Gi2j/elVqFme6I0xHYE3gTtE5HCDt78DnCKSDfwdeMfPNmYZY/KNMfnFxcUhjVcpFWE2LQJ3FUsdp1DtlkaD5FglLy+PW26cxdENX5I0+BTe3OjilL52uiebestpf3oVapYmemNMHJ4knycibzV8X0QOi8hR7/P3gThjTJqP5eaKyHgRGZ+enh7yuJVSEWT9fOiUwfOF3eiflszwjBSrIwKO9/kvXb8UW2JHPjg6BJsxXDT4eKle+9OrcLCy1b0BngXWi8jjfpbp6V0OY8yJeOItCV+USqmIVnEUtiymdMD5LC84yEXZGXhPGZarqZIvL1yNq+wQ2zPOY9tPbi4+wRER7QdU+9H4hlH4TAKuBb43xqzyvnYvkAkgIk8DlwE3GWOqgWPAL0RELIhVKRWJNn8I1eV8YjsZEZg2OjKq7cFTJV9YWAhuF2UbPid55Nn8a93/cPP4ONzH9kFCJ6tDVO2Ela3uPxcRIyKj6nSfe19EnvYmeUTkCREZLiLZInKyiHxpVbxKqQi04T1ISuPZwh4Mz0hhQHpHqyOqVbfPf+n6ZdjiEllcNYIEO7BlibXBqXbF8sZ4SinVKq4q2PIRR5xns3LX0YhphFejbp//yl3roewn3JPvgqRUzwWKUmGiiV4pFZ2KvoLyQyxjAgAXRliih7p9/l3MmjKOLcc6UDHgPE9PAVeV1eGpdkITvVIqOm1ciNgTeHpnJhOyutK7SwerI2rStOwMqlzC1/EToeIQFHxudUiqndBEr5SKPiKwcSGlvSfxfXF1xFXb+zI8I4X+ack8szsL4pK0+l6FjSZ6pVT0Kd4IP23nC/sE7DbD1JG9rI6oWcYYLsrO4LOCo5Q7J3sSvXYiUmGgiV4pFX02LQTg6T0DOXVgGqkdEywOKDDTRmcgAl8nnAJHdsPulVaHpNoBTfRKqeizcSGlqSNZeTA5KqrtawxI78jwjBTm7h0Exq7V9yosNNErpaJL6X7Y8Q0LD/eH6kqumDQkquZ2n5adwRe7XBzrPZGDX+v89Cr0NNErpSJSXp6fJLhpESA8WzyM0q3f4q4oi6q53Wu6Af552SG6VO6Bg0U6P70KKU30SqmIk5eXx6xZsygsLGycBDe+z+5j8axPHEHZuqW160TL3O6fvvcWVbvXs6TDuQBMHXR8JPJo+QwqumiiV0pFnJqZ3+oqKyvjofvvha2f8MHhfrgrjnFsW369ZaJhbvfc3FwOr/2EnV3HseWQrV6ih+j4DCq6aKJXSkUcf8lugH03VJWytOP5lG36CqmurPd+NMztXlRURNmGzxG3m8WHszirn4PEOrk+Gj6Dii6a6JVSEcdfsvvFmK5U25NYbh9N9bbl9d6LlrndMzMzcR87THnBKpYln0NSnGFylifTR8tnUNFFE71SKuLUnfmtrjN6V5AvQ0hO7sjfcm/B6XRG3dzuNZ+tdP1SViSdQmmVYeogB6mpqVHzGVR0sXI+eqWU8qkm2d1+++2UlJQAMDjVhjMF/lE2koHdjnDdNTlcd030JcWaz5Y7+2HKq+HLir7MPKWU297bCsZYHJ2KRVqiV0pFpJycHDp2PD6//JSBnnLJMjOOb15/0qqwgiInJ4eCLRs4PzuT/K5T6FRdAvs3Wx2WilGa6JVSEatuo7zzBzrYVN6VgkNQtOJT64IKommjM3i3bITnj80fWhuMilma6JVSEaumUV6iA85w2llmn0Dp+mVkZva1OLLgOOuE7hxO6MmehH6weZHV4agYpYleKRWxahquTc5y0CHOsFTG4t7+Tcy0TE+Ms3Pe8B4srBiJFH4F5YetDknFIE30SqmIlZOTw9y5c7liTFfK3A6+OdiNJx/5Q0y1TJ+WncEHFdkYdxVs+9TqcFQM0kSvlIooDce4B7j2lN58LcO58ednck0UtrRvyqSBaRR0GE6ZLVnv06uQ0ESvlIoIeXl5pKWlcc0119Qb4/5Pd8/CcXAbn7pHM2109ExJG6g4u43zRvVhafVI3Js/BBGrQ1IxRhO9UspyNZPY1PSZr+v0jCoAdqedwoD0jo3ejwXTsnuzxJWN7eg+2LfW6nBUjLEs0Rtj+hpjPjHGrDPG/GCMud3HMsYY8zdjzBZjzBpjzFgrYlVKhZavSWxqTB3akQJ3D8aPnRDmqMJnvLMrG5K9n2/zR9YGo2KOlSX6auBOERkGnAzcYowZ1mCZ84FB3scs4KnwhqiUCgd/k9jE2+FMJyx1j6qdxz0W2WyGU0aPYJ04qd6kiV4Fl2WJXkT2iMh33udHgPVA7waLTQdeFI/lQBdjTK8wh6qUCjF/k9iclmkn2e5ifdwIenfpEOaowmtadgafurKx7fzGbze7hg0V8/LywhylikYRcY/eGJMFjAG+bvBWb2BHnb930vhiQCkV5fxNYnPJhAwqxEH2WVdYEFV4Dc9IYVOnk7FJNWxf2uj9mnYMdRsqzpo1S5O9apblid4Y0xF4E7hDRFo1WoQxZpYxJt8Yk19cXBzcAJVSIVfTX77ubHTz5s3jirHd+FZO4LwxA6wOMeSMMfQbfSaHpQNl6z5o9L6vdgxlZWXk5uaGK0QVpSxN9MaYODxJPk9E3vKxyC6g7liXfbyv1SMic0VkvIiMT09PD02wSqmQysnJoaCgALfbTUFBAVdfcBppx7ZT1O0UUjsmtItq6wvHZvKFewQVa98jK8tZ77P6a8fg73WlaljZ6t4AzwLrReRxP4vNB67ztr4/GTgkInvCFqRSyjJF37wLQOroC9pNtfWA9I6stI+kq/xEctnOep+1W7duPtfx175BqRpWlugnAdcCZxljVnkfU40xNxpjbvQu8z6wDdgC/C9ws0WxKqXCrOyHReyWVE456ZR2VW09/7t9AFwwsmvtazWfvWE7hqSkpJgZ91+FjpWt7j8XESMio0RktPfxvog8LSJPe5cREblFRAaIyEgRybcqXqVU+FRXVtD30Dds6TyRTh3i21W19YqlS9jg7svUYSn1Xj9w4ECjdgxz586NqXH/VWhY3hhPKaUaWvfNYjpyjORhPwP8V0/HYrV1764d+PhQbyZ2O0hy3PHXMzMzG7Vj0CSvAqGJXikVcfaveo8qsTP81IsA393vYqnaum5Dw6NHj/LBxmMkGBfnZnuGDYmlz6rCTxO9UiqiVFS7yNj/OYXJI0ns6LlP7av7XaxUWzdsaFhSUsKn3/xAqSRw4ejuMfVZlTUcVgeglFJ1LV+1ljMoZOugy+u9npOTE5PJzldDwyM/HeDLsvGc3+cg12/bhrFpmUy1nh49SqmIsuPbBQBknjjN4kjCw1+Dwvc2VZAhP7Luh+/CHJGKNZrolVIRo7Simm57lnHEkUpcxiirwwkLfw0K8ws8pfyir+eHMxwVgzTRK6UixpIfdnGK+Z5y55lgjNXhhIW/hoY33f0ge+Iy6bJrKS63WBSdigWa6JVSEWPtN5/QxZSSOvoCq0MJm6YaGm51DGas+wfSB2XH7LC/KvQ00SulIsLBskpSdi3FjQ3bgMlWhxNWvvrH5+Xl8di8xSSYKs6ZMJDCwkKuueYa0tLSNOGrFtFEr5SKCAvX7uU0s4ry7mMgyfe47u1Jbm4uH64/TJnLzpR+AnZPJ6mSkpKYHOdfhY4meqVUWPmbhe7TFevItm2jg3c0vPauqKiIChcs3ZPA5Lh1dOg3tva9WB3nX4WG9qNXSoVNzeAwNf3Ga2ZmO1xlSN65BuLADDrX4igjQ2ZmJoWFhby/9ifO71PG8OzJ5G/5pvb9WBznX4WGluiVUmHjbxa6R1/+iDNsq6nukAa9RlsTXISpaY2/cFMlAD8baMfEJda+H4vj/KvQ0ESvlAobf6XQqp7DOdPxPY5B50A7HgWu7m2N3NxcZsyYwUFbN7YednBm3Do6DDwJ0LHvVcu031+UUirsfJVCHV16cmJvBylyBNpxtX3DMe8LCwt54YUX+Otf/0pV30lMtP1A6vBJOva9ajFN9EqpsPE1OEyXUWcz2b4aMTYYcJZFkVnP322N3NxcTrjgVpJMJWcN6cSqdZs0yasW0USvlAobX4PD9Jt8BRd0WIvpPa5dd6vzd1ujqKgIsk7FbU9gEqtZuHZvmCNT0U4TvVIqrOoODvPB8jVUV5QyoGozDGy/1fbgv3FdZmYmxCdhsiZxbtwa5q/aHebIVLTTRK+Ussz8VbuZ7FiDQWDQOVaHYyl/Y97XNLozA88hS3ayY/sGnnr+ZZ9jESjliyZ6pZQlRIR31+zmspQNkJQGvcZYHZKlmhrzHqit8Tjdtob75r5dr9GejpSnmqKJXilliZU7DrLrQCnjqr6DgWe36251NXyNeV8rbRB0zmRy9VfEDZxYbz0dKU81RX9ZSilLzF+1m7GO7SRUHWz39+cDYgwMOoeJcRvpmDEAR5ee9d7WkfKUP5rolVJhV+1ys2DNHmambwZMu+5W1yIDz6GTw8V420aShp5e7y0dKU/5Y2miN8Y8Z4z50Riz1s/7k40xh4wxq7yP+8Mdo1Iq+JZvO8D+oxWcyiroPQ6SU60OKTr0Ox0Xdk4t+5jkoWfUvqwj5ammBJTojTHDfLw2OQj7fx6Y0swyn4nIaO/joSDsUyllsfmrd9E3oYzOP33frkfDa7GETtj7TeLi1CLi053Ep2fpSHmqWYGW6F8zxtxjPDoYY/4O/KmtOxeRZcCBtm5HKRU9KqpdLFy7lxt6F3i61en9+ZYZeA69XbvIsP3Ewy8ubNxoT6kGAk30JwF9gS+Bb4HdwKRQBdXARGPMamPMQmPM8DDtUykVIks3FnOkvJpz4tZAUipktO9udS3mvTC6vudW3l2zGxGxOCAV6QJN9FXAMaADkAhsFxF3yKI67jvAKSLZwN+Bd3wtZIyZZYzJN8bkFxcXhyEspVRrzV+9m24d7PQo/gIGaLe6Fus+FDplMCVhLTsOHGPljoNWR6QiXKC/sG/xJPoJwGnAVcaY10MWlZeIHBaRo97n7wNxxpg0H8vNFZHxIjI+PT091GEppVqptKKaxev38euBhzBlJXp/vjW83ex6H1hOB4fokLiqWYEm+l+KyP0iUiUie0RkOjA/lIEBGGN6GmOM9/mJeOItCfV+lVKhsXj9Psqr3FyUtA5Pt7qzrQ4pOg08B1NxmF9mFvPe93twuaXeXPY6LK6qyxHgcj8aYxp20lza1p0bY/4PmAykGWN2ArOBOAAReRq4DLjJGFONp0bhF6I3pJSKWvNX7aZX50R67/8Ceo/VbnWt1X8yGDsXd1rPE9u6M+d/X+PR/zerdprbmmFxAW2opwIu0b8HLPD+uwTYBixs685F5CoR6SUicSLSR0SeFZGnvUkeEXlCRIaLSLaInCwiX7Z1n0opaxwsq2TZ5mKuGJqA2ZUPg84D0JJoayR2hr4n0f/gV3RMcPDc4tV+57JXKqBELyIjRWSU999BwInAV6ENTSkVSxau3UuVS7g8ZT0gMHgKeXl5zJo1SydoaY1B52Dbt4ZLBztw984Ge+MKWh0WV0ErR8YTke/wdLlTSqmAzF+1m35pyfT+8VPolAG9ssnNzdWSaGsN+hkAV3XdgC2xIx36jW20iA6LqyDAe/TGmP9X508bMBZPX3qllGrWvsPlLN9ewh2TMzH5n0D2leS9/DKFhYU+l9eSaAB6DIfOmQw++BlJ9kxSRp7NsS3f1L6tw+KqGoGW6DvVeSTguVc/PVRBKaViy4I1exCBy7tth6pSPtmdWNtYzBctiQbAGBhyPrZtn3LF2J50GnIKzv6DfM9lr9q1gEr0IvJgqANRSsWmvLw8Hv70IJXVLpb85RGuGhrPjY++3KjKvoaWRFtgyPnwzT+4Km07z0syf33zE6aP7m11VCrCNJnojTHvAn67s4nItKBHpJSKGXl5edz4u/tInfF3Sj95ljNOOcb7m2DTtv1+19GSaAs4J0FCCoMPfkavzhfz7urdmuhVI82V6P8zLFEopWJSbm4utn6edrsD9y8js7ONBz49ht1ux+VyNVre6XRqkm8JR7xn8JxNH3DhyBt4/qsiDpZV0iUp3urIVARp7h79dhFZ6u8RlgiVUlGrqKiI5KFnUL7jBy7ocwS3CO9trsblcpGUlFRvWa2yb6UhU6H0R67M2E+VS/hg7V6rI1IRprlE/07NE2PMm6ENRSkVazJHnkx8upPS9Uu5aHAcX+908WOp1DYWczqd2nisrQadA8bOgAPL6JeWzPzV2iFK1ddcojd1nvcPZSBKqdhzxnV3Im4XnXd+zoTedt7dVF1bcs/JyaGgoAC3261zqrdFh67gPAWzaSEXZWfw1bYS9h0utzoqFUGaS/Ti57lSStXTcCjbefPy2ObqyuAUN9eO9lTTrzjSXUvuoTDkfPhxHZdkVSHi6c6oVI3mEn22MeawMeYIMMr7/LAx5ogx5nA4AlRKRT5fQ9necv9/sOPAMW44fzx/mnk6dM5k0coiTfKhMOR8ALJKljE8I0Wr71U9TSZ6EbGLSIqIdBIRh/d5zd8p4QpSKRXZfA1la+9/Eriq+NmgZNj2KQyZ4hnkRQVft/6QfgJsfJ9p2Rms3nGQwpJSq6NSEaJVY90rpVRdjYasNTaSTjiVsi3f0mnHp1BdDkN12I2QGnI+FHzBRUM8t0nebVCq11kC2y9N9EqpNms4ZG1i5kgcHbuRVLwO1s2HpDRwnmJRdO3EkAtAXGTsW8aErK71qu91lsD2TRO9UqrN5syZU69ffNLQ03FXlvHwr6fD5g/hhAvAZrcwwnag9zjPrIDr/sW07Aw27TvKhr2eplQ6S2D7poleKdVmOTk5x/vFO+LoNPQ0xnZ3cO1J3aHyKAzTObBCzmaDYdNgy2KmDumE3WaYv8pTqvc3G6DOEtg+aKJXSgVFTb/4Rat3QHwSt/98Eqz7FyR2gX6nWx1eTGp43/3DXUngqiB11ydMGpjGu2t2IyJ+ZwPUWQLbB030Sqmgmr96N12T4ji1XwpsXOgZotUeZ3VYMcfXffdLbn+MMntnWPcO07Iz2HHgGCt3HGx0awV0yOH2RBO9UipoSiuqWbxuH1NH9iKu8HOoOKTV9iHi6757aVkZr60th80f8bNBycQ7bMxftbv+rRUdcrjd0USvlAqaxev3cazKxbTsDFj/L4jvBAPOtDqsmOTv/vrz3/wE1eV0KvqEs4Z0573v9+Byiw453I5poldKBc38Vbvp1TmRCZkpsH4BDP4ZOBKsDism+bu/XkQGJHf3tL4fnUHxkQqWbysJc3QqkmiiV0oFxcGySpZtLubCUb2wFX0Jxw5otX0I+bvv/vAj/w5DL4LNH3LWgI50THDUtr5X7ZOlid4Y85wx5kdjzFo/7xtjzN+MMVuMMWuMMWPDHaNSKjAL1+6lyiVMy+4N6+dDXBIMPMfqsGJWk/fdh02HqjISCz7mvGE9WLh2DxXVLqtDVhaxukT/PDCliffPBwZ5H7OAp8IQk1KqFeav2k2/tGRG9Er2VNsPPAfik5pfUbWa3/vuzkme0Qh/eIeLRmdwuLyaZZv2WxussoyliV5ElgEHmlhkOvCieCwHuhhjeoUnOqVUoPYdLmf59hIuys7AFH4BR/fC8J/XW0bHWg8juwOGXgibFnGqM5muSXE6o107ZnWJvjm9gR11/t7pfU0pZRFfCXvBmj2I4Glt//1rntb23qlTa9bRsdbDbNh0qColbtsSpo7sxeJ1+yirrLY6KmWBSE/0ATHGzDLG5Btj8ouLi60OR6mY5S9h/3PJGob1SmFgV7tnEpth0yGuQ+16Ota6BbJO97S+X/Mq07IzOFbl4qN1+6yOSlkg0hP9LqBvnb/7eF+rR0Tmish4ERmfnp4etuCUam98JezK+BR2HnMwbXQGbPoAKg7DqCvqLaNjrVvA7oCRl8OmRUzoYejVObHR1LWqfYj0RD8fuM7b+v5k4JCI7LE6KKXaK1+JOWmoZxz7i7IzYM1r0KkXZJ1abxkda90i2VeCuwrbure4cFQvlm4q5mBZpdVRqTCzunvd/wFfAUOMMTuNMb80xtxojLnRu8j7wDZgC/C/wM0WhaqUwndiTh56BhRvpXdcmWdK2pGXNZqSVsdaD7+8vDyyJk5j7Y8uVvzzbuL2rKHKJXywdq/Voakws7rV/VUi0ktE4kSkj4g8KyJPi8jT3vdFRG4RkQEiMlJE8q2MV6n2rmHCjktzEp/u5KLsXrDubXBXw6grG62nY62H1/G2FEW8uLqKcd1dzL37atyH9jJ30XdWh6fCLNKr7pVSEaRhws6YOA0bwuzrp3mq7bsPgx4j/K6rY62HR922FC9/X4VbhGuz4zm89mO2HrXz1PMvWxyhCidN9EqpFqlJ2C6Xi76nXsykQemkVe6GHV97GuEZY3WI7V7dthS7jggfb3dxzcg4StctxRgbj778oYXRqXDTRK+UapWVOw6y48Axuh8r4i8zTwZg4qz/0r7xEaBhW4oXV1cxoJuNE5P3UrlvKxU9R1oUmbKCJnqlVKvMX7UbhxH+cd+NXJBZyifbq1m+focOhGOxvLw8jh49Wu+1t9ZXUVopXDsqjtJ1y0jIOIGikjI/W1CxRhO9UqrFXG7hve/3UF20mhHJhzghzc6876sAHQjHSjWN8EpK6k9LW1oFb2+o4srhccTt+BqAd9don/r2QhO9UqrFlm8rofhIBcXffcCvxsZxtFJ47Yeq2vd1IBxr+BrQCMButzPv+2q6djC8df8VjHd21alr2xFN9EqpFpu/ajfJ8Xay3Lv5xYg4XllbxdE647DoQDjW8HeB5Xa7+WBjOaT05vTk7UwfncHGfUfYsPdwmCNUVtBEr5RqkYpqFwvX7uFnw3vy7O3n0THe8Mx3x0vzNQPh6Gx14dfkCIQ2O4y5FrZ+zIV9K7HbjJbq2wlN9EqpFlm2aT+Hy6u5aHQGJ8dt5GB8L/Y6etcbCAfQ2eos0OwIhGOuAWPouvFVJg1M4901uxERCyJV4aSJXinVIvNX76ZrUhyndtwDu1bQ5czfUFBQWG8gHJ2tzhrNjkDYpS8MPBdWzmP6yHR2HDjGyh0HLY1ZhZ4meqVUwMoqq1m8bh9TR/ai4NU/cKwaup19a6OqeZ2tzjrNjkA4/t/g6F6mxq8m3mHT6vt2QBO9UipgH63bx7EqF132fUvG/s/JW1PJT8caV83rbHURbOC5kNKHDque46wh3Xnv+z243Mer77VtRezRRK+UCti7q3fTq3Mix+bnkhxvePLb403t61bN62x1EczugAnXw/alXN2/jOIjFSzf5ul3f3wyHG1bEUs00SulAnKwrJKlm4q5cGQPfjHgKF/uqGbVXne9ZWqq5nW2ugg3dibYEzil5C06Jjhqq++1bUVs0kSvlArIB2v3UuUSrk7dwuBUW73SfI3MzMzaqt9rr70WgJdeeklnq4s0yakw8jIc37/KtCHJLFy7h4pql7atiFGa6JVSAZm/ejf90pLJ2vQcZfbOvLc9vt77SUlJTJ06Vat+o8WJs6CqlOuTP+dweTXLNu1vUdsKvZcfPTTRK6X8qjmZOzql8uWWYk6TlZjtS0k687c8+XTjqvn3339fq36jRcZocE5iwNYXSe9gmL96d8BtK/RefnTRRK+U8qnuyTxpyKlgbJyw8s9UmQQYN9NnNy6t+o0yp/wGc3gXd/Vdz+J1+/j55VcG1LZC7+VHF030Simf6p7Mk4edQbf9K7lswDHm/QB06OpzHe1WF/nqVrn3+9kNHIrvyQVHXudYVTUfrdvXfD98dJyEaKOJXilVq24SKCwsBMDRpScJGUO4vupVbAYeWVzid33tVhfZGla5FxQW8bu3ikj+aT2nlH/O7X95MaDqd72giy6a6JVSQOMkUCNp6Ol04zD/1qeIl7+vwpXS1+82tFtdZPNV5f7iqnJ2HXZzM68jPYdyw22/bTbZ6wVddNFEr5QC/M9lnjz0DK4re5EODuHPX1RRWFjYZCvrQKp+lTV8Va1XuuCxLys5tct+TnZswfQd0+y9dr2giy6a6JVqx3xV1dcVl+YkLT2Nf+v0NW+sq2Z9sQtAW1lHKX9V63NXVLLniJvb+D+Shp4R0L12vaCLHpYmemPMFGPMRmPMFmPM7328P9MYU2yMWeV9/MqKOJWKRf6q6utKHnYGv7IvoHOcizmfVdR7T1tZRx9fVe4A5dXeUn3CFk7tF0/m4BEWRKdCxbJEb4yxA08C5wPDgKuMMcN8LPqqiIz2Pp4Ja5BKRZmWDGLir6q+rr7Dx/BL+/u8uraKNfvcjd7XVtbRpW6VO4Axpva9f+RXsrfUcFfcG1xw4x+tClGFgJUl+hOBLSKyTUQqgVeA6RbGo1RUa+kgJs0l6fiMIdzebTkJVDF7aYXPZbSVdfTJycmpLdnXrck5Vg2P5xtOtq2nh1unro0lVib63sCOOn/v9L7W0KXGmDXGmDeMMf6b+yrVzrV0EBN/SdputwMwePR4rrF/xAtr3Gzc765X+gNtZR3N/NXmvF2YwqHEPlx+8FmK9h+1IDIVCpHeGO9dIEtERgEfAS/4WsgYM8sYk2+MyS8uLg55UNE0xnM0xarapqWDmPjrIuVyucDYeGjkHkSEBz8pBUBEtJV1jPB3TGwt2IFrci5DbUVsXPxsmKNSISMiljyAicCiOn//AfhDE8vbgUPNbXfcuHESSvPmzZOkpCQBah9JSUkyb968kO63NaIpVtV2Tqez3v91zcPpdNYuM2/ePHE6nWKMEafTKTfddFO9v2veP2P8EJHZKfLnywb43I6Kbk0eKy6XbHl4nPz4QD+RiqNWhxozGv72gn0eBvLFX/7090aoH4AD2Ab0A+KB1cDwBsv0qvP858Dy5rYb6kQfyMk0UkRTrKrtmruwC/TCb95LL0r+nQNk7319pWOHeL1AjEHNHQvvv/eWyOwU2f+vP1ocaWwIR6ErIhO9Jy6mApuArUCu97WHgGne538CfvBeBHwCnNDcNkOd6I0xPpOnMSak+22NaIpVtU1NaQEQu91ee0FX90QS6IVfZf5LIrNT5P/9+rKQlT6U9Zqs3Rk8XN764/lS9UCaSMm2Fm1Hj5XGwlHoithEH4qHluiPi+RY9eQQPIGWFgK68Du6XyrmZEr+fePk43V7wvxJlFV8HUOjc+6R0vvTxT3vchG3O+D1tPansXAUujTRB5GvAxuQ1NTUiDu4I/VHGKlxRSt/F3R2u73RvXd/F3417z83rYNU3t9Fpt37pFRWu6z+aCpMfB0bySPOlofuvVlkdorI928EvF6kFCYiiZboIzjR+yt1zps3T1JTUxv9p0VisorEkrOeHILLX2mh4bF50003+bzAqnl9cpZdZHaKPPH7X0j3qbdHxLGiwsPXMWTikyTrzjdk56MniTzaX6S0JKD19PZgY+36Hn0oHsFK9M39x2iyaj09OQSXv2OxqZJ7w5J+p3hk++0dZcv/S5Mh97wpCX1H6LHcjvg9n139sFz18P+K+8FuIm/+OvD19NhppF22ug/VI1iJvrkDWJNV6+nJIbj83U4K9Ng0xsgz0xKl+r5OMvWaX0rvm58XMHostyP+Cja/f/JVcd6zQArf+KO3Cv/NgNZrKolFYi1jLNBE3wrNJfJYSVZW/Oj0Hn3w1f1/rGl1H+ix+evTeonMTpF/P6+TZP7ubel65i+j8lhWbePrXHCsslqG3/+B/P61FSJzzxL5U1+RgzuaXc/f69H224+mixJN9K3QXCJvywEb6MET6oPMyoaF0fQDijYtOjYPbJfyB3vIdzd0km6jzxHnPQskvufAiD75qvD67SsrZeTsD6R83yaRR3qJPHe+SHVlk+v4OwZ9tW2K1IvKaLso0UTfCoH8J7cmWQU8aEkYDrKm7u1G8gGtmhfQsVlVLvKPM0T+va+888//FufMv0jGr/+hF16qno837BPnPQvkwx/2iqx+1VOF/8G9Ta4TaLuRSL7lGW21tproWykUpc5AD55wHGTNtdaO1ANaBYHbLfKvWz0n7XXvyr5DxyTr9wvkLx9utDoyFWEqq10y+sFFcuvL33leeO93Pu/X1xVIT5BIP9eEqh1WqGozm0r0kT6pjaVycnIoKCjA7XZTUFAQlAk8Ap14pKUTlLRGc1OMBrKvmklzjDE4HA6MMTp5TggFbZKir56E716E0+6EoReyYM0eRGBadkZwA1ZRL85uY+rIXixet4+yymo4bw70PQneuRl2rfC5jr9zS2pqaqOJlOLi4jh69GjETbzl7zO0ZWrmlk4lHTT+rgCi9RHqAXPaKpJK9M211m5uX02tr1X/9QXjKj5ot3PWvSsyu7PIq9eKuDyD4kx/4nM5/7+XtTgm1T4s37pfnPcsEOdpl4gxRsYN6SuHH+4n8tgAkQPbGy3f1LFa97eQmpoq8fHxEXnuCMXt01Ce19Gq+8gRSffoa/bT2sF/mrsPF4nVcVYI1v9lUE4SWz8VeShdZO6ZIhWlIiJSuL9UnPcskKc+3dKieFT78eJL86TPzc9L+iX31R53o/skSfmDPUT+Nlbk8N5G6wRycevvmG44qqNVgl3NHspu2ZrogyRY/+mR0uq+rftq7j5cJDawsUKwruLbfJIo+sbTavrJk+uNcvbEx5vFec8C2flTWYviUbGtYZfNLmdeL5m/e1tsiR1rj71LJvQWeaSnyBMnyesvPB30c0gklfCDQUv0EZ7oQ9UKP5ppiT4wwbqKb9NJYtsykTkZIn8dLXK4/mQ15z2+VC79ny9aFIuKbb7Od/E9BojzngXScdR59V7/aO59UvVAqqy5qZP0SDZ+z4++BNo6P1bOJaGsqdVEHwTNnWQjpc9luGsB9B5984J1Fd/qcQ82vC/ycHeRJ06sTfI1x0l8ulOc9yyQW//6Whs+oYo1/o7ZjF//Q7pfOafRb/3n2V3l6B86yZbbOkq/LibgY7ytozpGOn+DBoW71b3liTnYj1Al+lCMlBfs/3ArLjZqPgP4nwe9rduOthqShnH7m0ymtQ3yAm5T4XaLfPmkyANdRJ4+XeTo/tpt1MTT5bRrJfOuf0lyas+o+X5V6Pk733U+9WrJvHu+2JO7NnrvxN522X9XR9l7Z0c5pa894ATdklEdo+Wc0Ja2T62liT4ImkvkLa2eDXZSnjdvXouHPo1kkVJD0lL+4r7pppuCdoIK6KKyolTknVs8/Z3/72qRiqM+18+44RnpfsVDUXucqNDwd4w5uvUR5z0LpNO4aT7fH5Jqk023JkvFHzvJr8fGidOZ6XcfLR0iN1rOCW3tzdRamuiDoLmDrKUl+mA2ymjuwIrGaq9QNloJpXDE3exF5b71ngZ3s1Pk+/++QrKcmfVOpjXrx2cMEec9CyR5xNlRe5yo0GjqnNJr5l+l57X/We+11NTU47VEicgHOUkis1Nk+3+cKVL2U0Db99X9ru5FcbDPmaGqGWiu3UGofmea6IOkqYOjpVebwexm0dyBFenJ0ZdonR0wHHH7+//un5Upsuwvnu5zj/aTJf/4Q5PjjXc9e5Zk3vmWmPikqD1OVOj4qyVMOfFScd6zQByde/hN0P2yMmXl364ReaCryOPDRTYuqrdtf8dwU93qgvXbCkXNQN3P3tS5WEv0UZDom9OSq8TWHOj+NHVwRWLVViCCcfUe6vt5vrYfaNxtic3Xieq8wUlSMmeYp6r+lRyRw3v9xpKamipJycnS+5YXJf3ie6P6OFGh5evcYu+ULs57FkjniVc0f+zu+Fbk7xM8x+VrM0V+KvS73ebOXcEq0Qe71i3QBoWh/p1pom+jtiaMQO9FtfagaOqiIdR9/UOlrd0ZQ30/r6l78YHE3dbYaj77yB52WTgz1XMifXy4yA/v1C7TVAnowadfEec9CyTphFMt+f9V0cFvUrz+r3Le40sD20hVucgnf/b0/HgoXeTD+2TM4L4BJca6yTdYv+lg17o1V6Na9wI7lL8zTfRt0NaDK9B7Uf4a0tVN1v4SWyga9kVCo5e2JPJg1pj40lSpoLmLpJaUKHxuy+0WKfjC08hudorIv/cR+fTR2pHuAtnP3a+vlmH3LZRjldWt/g5U7PP3O7v1r6+J854FsmHP4cA3dnCHyJuzRGZ3lqoH0uTvF3SUQd1sTSbHhsk3GAWQtpTofe2/qdqJcBaUNNG3QVureQJdv7nq9+ZKisEsgQe7aisUmosxmCNuteTHHUipINB1G55k+6YYyZ2cLAfmnOBJ8H92iix5RKTsgN+4fR0zz784T0bO/kDueGVls7Eq5ev4Lz5SLv3/8J489sH6lm/wxw0ib84S1+wuIrNT5NOZSXJtdrwkOsJzzmltQcbfer660VlxvtRE3wZtreZpKuHUTdL+SvR1S6LhOJjmzZsX0NW11VX7rR3XoKXfX1t/3G25j+90ZsrgVJvcPCFOPp3hacUss1Pku5u7inz7bKMSvL/4G+7/wx/2ivOeBfLx+n0t+cqVqueaZ5bLaY9+LG63u3UbOLzX03j0v7NFZqfIwd93klcu7SDXZcdJepIJaS1ia85f/n63dXsctLQQEUwRm+iBKcBGYAvwex/vJwCvet//GshqbpvRUqKvORh8ldRb8mhNi9OWVIf7+syRULXf3P9LoA1kmvv+WvLjjouLk9TU1GYHyvH3+ssv/lNk9yqRFS+IvDlLdvy2Y21yX3dzsuSeFi/9u5o2teB3u91y8ZOfy4RHPpLKalert6PUa98WifOeBfJdoe8apYC5XCLblsnmv0yRvXd1FpmdIq77U2T/v48Qef8ekdWviezf4rll1YxQtKeq0VThwuqCj0iEJnrADmwF+gPxwGpgWINlbgae9j7/BfBqc9uNhnv0dR/NleSbW64lJfrW3tcOdLlwVlUF0vah5kfY1PfaXMyB/rh9Tbfpa12HDTlxaF9Z9L8Pyh1n9pTZZyTIm9ekyYE5Q0Ue7Fab2OXR/jL/ulT59dg46d/VBO17fmflTnHes0Be/bao1dtQSkTk0LFKGZT7vsz+19rgbdTt9lzsLn1M5LnzPRPm1Pwm/pQp8sx5Iu/cLPLZ457plnd9J3Jot0h1VUjbU4mEptFzMDWV6I3n/fAzxkwEHhCRn3n//gOAiPypzjKLvMt8ZYxxAHuBdGki6PHjx0t+fn5QY83LyyM3N5eioiIyMzOZM2cOOTk5Aa9XWFjYpv0nJSUxY8YMXnjhBcrKyuq9Pnfu3IBiAcjKyvIZi9PppKCgAJvNhq+v1gAvvfQiOVd79mN3OPAc4/XZjKGqsrLBqw2W8/lf5+O1Rss1XuaVV17hgQceYMeOHfTt25cHHngAEG677bZ635Np+Hm8LyR1SOJvf/8bV15xhe+4xMW4Mdns272TODvE2w3xdoizgbNPL95+4zVwVUJ1JbfMmkn54f10jDd0ioeO8YaUBEN6kqF78vFHapKtYTSQ0hu6D4WeI72PUeR98BW33/FbSkpK6i3d0v/zGnl5eeTe/yDVP7sXR3UZD5/emWuvadk2lGro9ldW8q9Vu/nVqf24a8oQEhz24O7AVQ3FG2BXPuxeCfs3ex6lP9ZfztgoLhN2HKxmzxE3B44JhyvhcIVg69CFe+57GBJSIK4DOBLAHg+ORHDEgz0BHAmcOvksthftwuUGt3geLhH69Mnk+7VreeW1N7j5lls5Wlrmfe/47lv7uwwmY8wKERnv8z0LE/1lwBQR+ZX372uBk0Tk1jrLrPUus9P791bvMvv9bTeYid7tclH+UK9mlzM+klDj13wn0ObWC2QZ/6/VZzPW/F+3RxXioFQSKZGOlEgKJdKJ/W7PvwfoTHF8b3bSg12kU0VcvXWrqqspLy9vdPFhjCEhMZE4h6NFsdRuzxhscYnsnXc39p8KLD8xqehXXuXi399fz4tfFZLgsOGw+TpjBV8nSnGyh+78RDo/kW4O0qXqR7qbQ3S3HaSzKaMj5XQyx4gzrpDG4hKDG1vtGVjqnXmNz9cFw+bT/0b22b8IWhxNJfqWnTEilDFmFjALIDMzM6jbXt3zEl97bPRKzX/igQMlFBQU4HZL41Tf4AWb3UZqajr7S4px17k8NDYb/fr1Iy01lf0lJWzbvh232318PZuNfv36k5aW1iAGX47H+t3KlVRUNCxxe9az2Wykp6dTXFyM2+2ujdVmtzFgwADS0tJrly/ev5+tW7fictWPacDAAaSndff73fgIyf8yPhbcV1xMYUEB5RUVJCYk4MzKont6j3rLLPt8GYiv/5/64hMSOemkk5rcnxiDy8Sxa18x6zdt5/CxChwJyQwcOpyMvv1wGQdu46DaxPN/77zP3kMVlLoclLocVInt+Cbr7NwR5+Dcc89k6AlD6evjEwM888wzHD18uNHrKSkpXPerX/lZy7+626vct5WKXesAyM3N1USv2iQxzs5D00dwztAeLNtUHOa9D619VgI8+swzHG70uxHSOydz43VXkOA6isNdgcNdiUMqsbursEslDqnC7q7ky6VLqC4vwxjBhmA3gg1I6pDAxJMmYEQwuPnyi8+w4VnGZjwFqJo0b7cb3C6pTe92u42srCxSu3WrjajmvZ69BoTyy2nwNVh3j34isKjO338A/tBgmUXARO9zB7Afby2Ev4eVI+OJBNba21c/eH8NOYJ5PzyQxnbNNSqpe/+7qdnqgt04JdD7b4F8//7G1G7LxDNNDaDT0m0Ge0CPaB1OWKmW8Hd+C3SgmraeY6yeVIwIbYznALYB/TjeGG94g2VuoX5jvNea267Vib6tjb8C3V5rT9KBdp/zt24go761dnrGYFzw+IqxYWv4miTf0pEJA70IasvFTVNdLVt7woiExpOq/QjmBXRL91lzHmvN+cdX7L7W8XcebO15NVgiMtF74mIqsAlP6/tc72sPAdO8zxOB1/F0r/sG6N/cNq1O9IG2XG/r9tpykm7tNptbL9DueXU19QOt+321pGtLICeWQEr+zX22YHcpbOr7i4WRDlXsa80FdHPba03iDfWFbVvGxwiViE30oXhYnejbWn0UyPaCMdNSa7bZ1kFqmhv5rakfib9tt3awikBGzgvks7X1R1z3hBHIMMjB2E+oSlVKtfQCuinBvF3n6/wTbFZfUGuiD7NQ3J8O9kk6mCND1fxoW3rbIpAfaM2PM5jDTwYyEmGgn60tJ49gDeqjVKRo6QV0UwK5uG7qVqSvC+ZQX+haeUGtib4dCtXFQTAG3KnR0rmbfX2mlibgQJNroJ8tFLdQgrkPpcIpmMd0c7fr/F3kt/S37Us01oBpom9nQlmF1NQPoKW3LZo7KQQSc0sTcKAnorrb8dd4r63faTAn3lEqEgTzHn1Lbtc1fNRtgNvSxq1WV8G3lib6GNPc1WYwS58tvbJtyfK+flA1ya+pdevuw9fQs039KAOtWvS1PX+frbVX/81ddAQyRbFSkSZYre79XTTYbE1PbQvU20dLa/1CUXsXDproQyycJ+FArjaDdT85XC3NW3ohEUj3OX9aWqIP5dV/c6Wf5tonaLJXsa41VfSBtgXy95uO1nEnNNGHULhPwoEctMG6Ig3GdoJ9EdTWmFpyjz4cV/+B9JuP1hKGUnWFquaruXNvS8/R0fp700QfQuE+KAK52gzWxUdbr2zDeV+7JVfbTVUttvR+XrDiaep7ivTpMZVqTlvOBYHebmuqC3Nzv5O23A6MFJroQyjc1TyBXlgEIwG09SImFBdBTW0zGJ851Ff/rbnP728frR1DQKlwa8u5wN+6weou15bbgZF0oa2JPoTCXaIP562Ctu6rtRdBLW3ZXzOmfLC+l7Y2KPS339Z+n/7Wa80YAkpZoS0FolCf8wI5h/s6J0Ra2xlN9CFkxX92uBv/tXZfrbkICuT79BWTlffVAv2O2hKjr31Ea6Mh1f609ffZ2vNQIOs19zuKlgttTfQhFknVN/5YEaO/hm9N3Utr7QkhGpJesGO08uJGqZawqkDUliF0a35H/t7397DqnKOJvp2zsorJX/cYf/tvbTKMhqQX7BgjrepQqaaEu7AR6O+ttY1h/T20RK+J3hJWJ8GW7L+1DW/C3XahtdWIoZigKNJrk5SyQqA9lGrOOTU9bhr+jvydkyKtMawm+nbO6mrtluw/kH7uTTV2C3XSa2uy1sSsVHg0V8AI9Lfc1HKR9HvWRN/ORWqJ3t/0q3V/PC3t1x5MkdboTykVuOYSeUt+y5GU0P3RRN/OWX0vt6lSenNxWFUb4e8781fLEEmN/pRSHk0laKtrOoNNE72y/Ip03rzmh3r1xaoSdFO1EFqiVyr6xVrtXFOJ3oZqF3JycigoKMDtdlNQUEBOTk6L1s/LyyMrKwubzUZWVhZ5eXkt3r/b7fb5XlFRkd/15syZQ1JSUr3XkpKSmDNnTov231L+YnK5XJbEo5QKLqvOLZbwdwUQrQ8t0QdfsKr+W3sFbUVtRFOxWl07opQKjlj6LdNEid543o8d48ePl/z8fKvDiClZWVkUFhY2et3pdFJQUBDwdvLy8pg1axZlZWW1ryUlJTF37twW1zCEWjTFqpRSxpgVIjLe13tada+a5a8au6kqd19ycnKYO3cuTqcTYwxOpzNiE2c0xaqUCkxbb0FGKy3Rq2YFq0SvlFJWifVaOi3RqzZpV41WlFIxKTc3t16SBygrKyM3N9eiiMLHkkRvjOlmjPnIGLPZ+29XP8u5jDGrvI/54Y5TeWg1tlIq2gXrFmQ0sqpE/3tgiYgMApZ4//blmIiM9j6mhS+86BGue05t7Z6nlFJWyszMbNHrscSqRD8deMH7/AXgYoviiGo195wKCwsREQoLC5k1a1a7aWCilFKBas+3IK1K9D1EZI/3+V6gh5/lEo0x+caY5caYi8MTWvRoz/eclFKqJdrzLciQtbo3xiwGevp4Kxd4QUS61Fn2JxFpdJ/eGNNbRHYZY/oDHwNni8hWH8vNAmYBZGZmjvPVQjwW2Ww2fP3/GWP8jkKnlFIq9jTV6t4Rqp2KyDlNBLTPGNNLRPYYY3oBP/rZxi7vv9uMMZ8CY4BGiV5E5gJzwdO9LgjhR4XMzEyf3d7awz0npZRSgbGq6n4+MMP7fAbwr4YLGGO6GmMSvM/TgEnAurBFGAXa8z0npZRSgbEq0f8ZONcYsxk4x/s3xpjxxphnvMsMBfKNMauBT4A/i4gm+jra8z0npZRSgdGR8ZRSSqkopyPjKaWUUu2UJnqllFIqhmmiV0oppWKYJnqllFIqhmmiV0oppWKYJnqllFIqhmmiV0oppWKYJnqllFIqhsXcgDnGmGKgfcxqc1wasN/qIKKcfofBod9j2+l32Hbt8Tt0iki6rzdiLtG3R8aYfH8jIqnA6HcYHPo9tp1+h22n32F9WnWvlFJKxTBN9EoppVQM00QfG+ZaHUAM0O8wOPR7bDv9DttOv8M69B69UkopFcO0RK+UUkrFME30McYYc6cxRowxaVbHEm2MMf9hjNlgjFljjHnbGNPF6piihTFmijFmozFmizHm91bHE22MMX2NMZ8YY9YZY34wxtxudUzRyhhjN8asNMYssDqWSKGJPoYYY/oC5wFFVscSpT4CRojIKGAT8AeL44kKxhg78CRwPjAMuMoYM8zaqKJONXCniAwDTgZu0e+w1W4H1lsdRCTRRB9b/gu4G9CGF60gIh+KSLX3z+VAHyvjiSInAltEZJuIVAKvANMtjimqiMgeEfnO+/wInkTV29qooo8xpg9wAfCM1bFEEk30McIYMx3YJSKrrY4lRlwPLLQ6iCjRG9hR5++daJJqNWNMFjAG+NriUKLRf+Mp7LgtjiOiOKwOQAXOGLMY6OnjrVzgXjzV9qoJTX2HIvIv7zK5eKpS88IZm1LGmI7Am8AdInLY6niiiTHmQuBHEVlhjJlscTgRRRN9FBGRc3y9bowZCfQDVhtjwFPl/J0x5kQR2RvGECOev++whjFmJnAhcLZo39NA7QL61vm7j/c11QLGmDg8ST5PRN6yOp4oNAmYZoyZCiQCKcaYeSJyjcVxWU770ccgY0wBMF5E2tukDm1ijJkCPA6cISLFVscTLYwxDjyNF8/Gk+C/Ba4WkR8sDSyKGM8V+gvAARG5w+Jwop63RP87EbnQ4lAigt6jV+q4J4BOwEfGmFXGmKetDigaeBsw3goswtOI7DVN8i02CbgWOMt77K3ylkyVajMt0SullFIxTEv0SimlVAzTRK+UUkrFME30SimlVAzTRK+UUkrFME30SimlVAzTRK9UO2eMcXm7c601xrxujElq4foZxpg3vM9H1+0WZoyZprPZKWUt7V6nVDtnjDkqIh29z/OAFSLyeCu3NRPPYE23BjFEpVQbaIleKVXXZ8BAY0w3Y8w7xpg1xpjlxphRAMaYM+oM6LLSGNPJGJPlrQ2IBx4CrvS+f6UxZqYx5gnvulnGmI+921xijMn0vv68MeZvxpgvjTHbjDGXWfbplYpBmuiVUkDtULbnA98DDwIrRWQUngmTXvQu9jvgFhEZDZwGHKtZ3ztF7f3AqyIyWkRebbCLvwMveLeZB/ytznu9gFPxzDPw5yB/NKXaNU30SqkOxphVQD5QBDyLJ+m+BCAiHwOpxpgU4AvgcWPMb4Au3uFvAzUReNn7/CXvPmq8IyJuEVkH9GjLh1FK1aez1ymljnlL6LW8syA2IiJ/Nsa8B0wFvjDG/AwoD0IMFXV3H4TtKaW8tESvlPLlMyAHamcC2y8ih40xA0TkexF5FM8sdSc0WO8InomBfPkS+IX3eY53H0qpENNEr5Ty5QFgnDFmDZ575jO8r9/hbXi3BqgCFjZY7xNgWE1jvAbv3Qb8m3fda4HbQxa9UqqWdq9TSimlYpiW6JVSSqkYpoleKaWUimGa6JVSSqkYpoleKaWUimGa6JVSSqkYpoleKaWUimGa6JVSSqkYpoleKaWUimH/Hx+FIIozSWD9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "import scipy.integrate as integrate\n",
    "# Generate fake data\n",
    "np.random.seed(0)\n",
    "x = np.linspace(-5., 5., 200)\n",
    "y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)\n",
    "y += np.random.normal(0., 0.2, x.shape)\n",
    "\n",
    "# Fit the data using a box model.\n",
    "# Bounds are not really needed but included here to demonstrate usage.\n",
    "t_init = models.Trapezoid1D(amplitude=1., x_0=0., width=1., slope=0.5,\n",
    "                            bounds={\"x_0\": (-5., 5.)})\n",
    "fit_t = fitting.LevMarLSQFitter()\n",
    "t = fit_t(t_init, x, y)\n",
    "\n",
    "# Fit the data using a Gaussian\n",
    "g_init = models.Gaussian1D(amplitude=5., mean=3, stddev=2.)\n",
    "fit_g = fitting.LevMarLSQFitter()\n",
    "g = fit_g(g_init, x, y)\n",
    "\n",
    "# Plot the data with the best-fit model\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(x, y, 'ko')\n",
    "plt.plot(x, t(x), label='Trapezoid')\n",
    "plt.plot(x, g(x), label='Gaussian')\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('Flux')\n",
    "plt.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.849318113739489, 6.885207176941062e-14)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate.quad(g,0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Gaussian1D\n",
      "Inputs: ('x',)\n",
      "Outputs: ('y',)\n",
      "Model set size: 1\n",
      "Parameters:\n",
      "    amplitude mean stddev\n",
      "    --------- ---- ------\n",
      "          1.0  0.0    1.0\n"
     ]
    }
   ],
   "source": [
    "print(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Add the line area and line ew of HeII 4685"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:3062: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      " 62%|██████▏   | 134205/218174 [1:51:29<4:16:27,  5.46it/s]WARNING: File may have been truncated: actual file length (163543) is smaller than the expected size (164160) [astropy.io.fits.file]\n",
      " 62%|██████▏   | 134207/218174 [1:51:29<4:03:02,  5.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fits destoryed:spec-7340-57196-0668.fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 144261/218174 [2:04:07<1:25:58, 14.33it/s]WARNING: File may have been truncated: actual file length (163543) is smaller than the expected size (164160) [astropy.io.fits.file]\n",
      " 66%|██████▌   | 144263/218174 [2:04:08<1:34:19, 13.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fits destoryed:spec-6737-56365-0212.fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 218174/218174 [4:23:19<00:00, 13.81it/s]  \n"
     ]
    }
   ],
   "source": [
    "dat=pd.read_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12.5.csv')\n",
    "path='/media/richard/Backup Plus/sdss_16_pair/'\n",
    "f=open('/media/richard/Backup Plus/v12_0505.txt','a')\n",
    "for i in tqdm(dat.index):\n",
    "    try:\n",
    "        if os.path.exists(path+'%s'%dat.specname[i])==True:\n",
    "            hdu=fits.open(path+'%s'%dat.specname[i])\n",
    "            data=hdu[3].data\n",
    "            dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685_ERR']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685_ERR']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "        else:\n",
    "            print('%s not exists'%dat.specname[i])\n",
    "            f.write('%s not exists \\n'%dat.specname[i])\n",
    "    except IndexError:\n",
    "        dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685_ERR']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685_ERR']=-999\n",
    "        print('fits destoryed:%s'%dat.specname[i])\n",
    "        f.write('fits destoryed:%s'%dat.specname[i])\n",
    "        pass\n",
    "    except Exception:\n",
    "        pass\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat.to_csv('/media/richard/Backup Plus/314kpair_DR16_final_Z_0.4_v12.5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat=pd.read_csv('/media/richard/Backup Plus/DR16_Z_1.5_final_2.0.csv')\n",
    "path='/media/richard/Backup Plus/sdss_16_pair/'\n",
    "f=open('/media/richard/Backup Plus/error_0511.txt','a')\n",
    "for i in tqdm(dat.index):\n",
    "    try:\n",
    "        if os.path.exists(path+'%s'%dat.specname[i])==True:\n",
    "            hdu=fits.open(path+'%s'%dat.specname[i])\n",
    "            data=hdu[3].data\n",
    "            dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685_ERR']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "            dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685_ERR']=data[data.LINENAME==b'He_II 4685   '].LINEAREA\n",
    "        else:\n",
    "            print('%s not exists'%dat.specname[i])\n",
    "            f.write('%s not exists \\n'%dat.specname[i])\n",
    "    except IndexError:\n",
    "        dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEAREA_HeII4685_ERR']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685']=-999\n",
    "        dat.loc[dat.index==dat.index[i],'LINEEW_HeII4685_ERR']=-999\n",
    "        print('fits destoryed:%s'%dat.specname[i])\n",
    "        f.write('fits destoryed:%s'%dat.specname[i])\n",
    "        pass\n",
    "    except Exception:\n",
    "        pass\n",
    "f.close()"
   ]
  }
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