{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial Look Up Table\n",
    "==================\n",
    "\n",
    "This example is based on the xarray library for an efficient I/O of the Look Up Table (LUT)\n",
    "First, we import some basic python modules and we create the dimensions of out LUT.\n",
    "In this case I am setting up some frequencies, and sizes.\n",
    "I will use only one temperature, but it is possible to put more, just by adding more values ot the array.\n",
    "The scat_angle dimension is required if we want to save the phase function in the LUT.\n",
    "\n",
    "We fix the particle properties. It is possible in general, to have the particle properties as a dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import socket\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import snowScatt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Input parameters\n",
    "\n",
    "Dmax = np.linspace(0.1e-3, 20.0e-3, 1000) # list of sizes\n",
    "sizes = xr.IndexVariable(dims='size', data=Dmax,\n",
    "                         attrs={'long_name':'Size - Maximum dimension',\n",
    "                                'units':'meters'})\n",
    "particle = 'Leinonen15tabA00'\n",
    "filename = 'leinonen_A00_LUT.nc' # output filename\n",
    "\n",
    "frequency =  np.array([5.6e9, 9.6e9, 13.6e9, 35.6e9, 94.0e9]) # frequencies\n",
    "frequency = xr.IndexVariable(dims='frequency', data=frequency,\n",
    "                             attrs={'units':'Hertz'})\n",
    "temperature = xr.IndexVariable(dims='temperature', data=[270.0], # temperatures\n",
    "                               attrs={'units':'Kelvin'})\n",
    "Nangles = 721  # number of angles of the phase function subdivision\n",
    "angles = xr.IndexVariable(dims='scat_angle',\n",
    "                          data=np.linspace(0, np.pi, Nangles),\n",
    "                          attrs={'long_name':'scattering angle',\n",
    "                                 'units':'radians'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the empty variable arrays. Each one is defined by its own set of dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create empty xarray variables\n",
    "dims = ['size', 'frequency', 'temperature']\n",
    "coords = {'size': sizes,\n",
    "          'frequency': frequency,\n",
    "          'temperature': temperature}\n",
    "\n",
    "Cext = xr.DataArray(dims=dims, coords=coords,\n",
    "                    attrs={'long_name':'Extinction cross-section',\n",
    "                           'units':'meters**2'})\n",
    "Cabs = xr.DataArray(dims=dims, coords=coords,\n",
    "                    attrs={'long_name':'Absorption cross-section',\n",
    "                           'units':'meters**2'})\n",
    "Csca = xr.DataArray(dims=dims, coords=coords,\n",
    "                    attrs={'long_name':'Scattering cross-section',\n",
    "                           'units':'meters**2'})\n",
    "Cbck = xr.DataArray(dims=dims, coords=coords,\n",
    "                    attrs={'long_name':'Radar backscattering cross section',\n",
    "                           'units':'meters**2'})\n",
    "asym = xr.DataArray(dims=dims, coords=coords,\n",
    "                    attrs={'long_name':'Asymmetry parameter',\n",
    "                           'units':'dimensionless'})\n",
    "dims = ['size', 'scat_angle', 'frequency', 'temperature']\n",
    "angles = np.linspace(0.0, np.pi, Nangles)\n",
    "coords['scat_angle'] = angles\n",
    "phase = xr.DataArray(dims=dims, coords=coords,\n",
    "                     attrs={'long_name':'Phase function',\n",
    "                            'units':'dimensionless???'})\n",
    "mass = xr.DataArray(dims=['size'],\n",
    "                    coords={'size':sizes},\n",
    "                    attrs={'long_name':'mass',\n",
    "                           'units':'kilograms'})\n",
    "vel = xr.DataArray(np.empty_like(sizes), dims=['size'], coords={'size':sizes},\n",
    "                   attrs={'long_name':'Terminal fallspeed according to Boehm',\n",
    "                          'units':'meters/second'})\n",
    "area = xr.DataArray(np.empty_like(sizes), dims=['size'], coords={'size':sizes},\n",
    "                    attrs={'long_name':'Projected area',\n",
    "                           'units':'meters**2'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finally start the computation and fill the empty arrays.\n",
    "While we compute we also plot some basic variable to have a quick look at what we are computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6NKji77DOytPkcVJVv/VpJCYkrYw5zGOzI0g4lcFTV7Rl5HlN7WrDhAxVZe5f+3j2m2hOZWTx2IDWjLqgmU8aMvQ2T5PHShF5GeczHWmnR6rqXz6JygS9lPRMnl+8mc//2EObupX4dGQP2tar7O+wjCkyccdTeHLeJlZtPcI5Tarx0rWdaFG7or/D8pinyeN0/xrd3cYpcJF3wzGhYOPeEzz05QZ2Hk3mrvOb8shlrQP2vq4x3uZwKJ/9sZuXvo1BgQmD23NLr+JXvudpbSurjmsKLTPLwTs/7mDKD9uoU6ksn9/Zkz7Nrb9wEzp2HElizJwI1uw6zgWtavHCNR2K7bNLZ00eInKzqn4mIg/nNF1VX/NNWCbY7D6azINfbmD9nhMM6VKfZ4d0sO5gTcjIyHIwbVUsU37YRrnSJXnl+s5c261BsX52Ka8rj9ONB/muRxET1FSVL9fs5dlvoilVQnjzxq4M7lzf32EZU2Q27TvJ6DkRRO1P4IqOdRk/uD21KxX/rgPOmjxU9T0RKQkkqOrrRRSTCRLxSWmMmRPJ95sP0ad5DV65vjP1q5bzd1jGFInUjCze/GEb762KpVr5Mrx7czcGdqjn77C8Js8yD1XNEpHBgCUP47EVMYd4fHYECamZjB3Uljv6WhVcEzrW7jrG43MiiD2SzPXnNGTsoHZUKR9ct2k9rW31q4hMBb4Ekk+PtKq6JrvUjCyeX7yZT3/fTZu6lfj8zl60rmt3PU1oSErL5OWlMXzy+24aVC0X1B2VeZo8+rj+Pus2zqrqmr/ZcjCRB2auZ8uhRO48rymPDWxN2VJWBddTInI+MBzn97KdqvbJYxETQH7aeoQn50ay/+Qpbu0dzmMDWlMhgBoy9DarqmsKTVX59PfdPLd4M5XDSvPxHT3oF6RnW7kRkenAlcBhVe3gNn4gMAUoCXygqi/mtg5VXQ2sFpGrgTU+Dtl4yYmUdJ79Jpq5f+2jea0KzL6nN+c0CbyGDL3N04YR6wAvAPVV9XJXT4K9VfVDn0ZnAt6x5HQen72R7zcf5sLWtXjl+s7UrFjW32H5w0fAVOCT0yNclU3eBi4F4oA1IrIQZyKZlG35O1T1sOv/m4A7fR2wKbwlkQd4ZsEmTqRkcH//Ftx/UYuQeeDV02uqj4AZwFOu4a04yz8seYSwn7fF8/BXGziRksG4q9pxW5/wYl1vvTBUdZWIhGcb3QPYrqqxACIyCxiiqpNwXqX8g4g0xtmWXIIPwzWFdDghlWcWRLE06iAdGlTm4zt60L5+YDdk6G2eJo+aqvqViDwBoKqZImKt64ao9EwHr363hWmrYmleqyIf3d6DdvWtXaocNAD2ug3H8f9N/eRmJM4TtVyJyChgFEDjxo0LE5/Jp9NdBzz3TTSpmQ5GD2zDXec3pVQxaMjQ2zxNHskiUgNnITki0gs46bOo3IhIM5xXPFVU9TrXuKuBQUBt4G1VXV4UsRjYGZ/Mf2etJyLuJDf1bMzTg9pRrkxoXKYXQE6XYXq2BVR1XF4rVdVpwDSA7t27n3V9xnv2HkvhyXmRrN4WT4/w6rx4bUea1So+DRl6m6fJ42FgIdBcRH4BagHX57WQlwoRY4GRIjLbbdx8YL6IVANeASx5+JiqMntdHOMWRlGmVAnevfkcBnao6++wAl0c0MhtuCGw30+xmALKciif/LaLl5dtQYCJV3dgeI/GIf/ckqfJIwroB7TGeTa1BfDkOu0jvFeImJOxrnUZHzp5KoOx8zexaON+ejWrzus3dKFeleB8Uvzrr79m4MCBVKpUieeee46//vqLsWPH0q1bt4Ksbg3QUkSaAvuAYTgLw00xsf1wIo/PjuCvPSe4sHUtnr+mIw2slQTA8+Txm6p2w5lEABCRv4CzfqO8VYiYnThLZV8EvrUHFX1r3e5jPDBzAwcTUnlsQGvu6deckkF8xjVx4kSuv/56fv75Z5YtW8ajjz7Kvffeyx9//HHW5URkJnAhUFNE4oBxqvqhiNwPLMN5cjRdVaPOshoTIDKyHLz30w7e/GE75cuW5PUbOnN1l+LdkKG35dWqbl2chX7lRKQr/38PtzJQ0HaE81WI6CpreR7oKiJPuJLMf4BLgCoi0kJV381hOStULITMLAdvr9zBlB+20rBaeWbf05uujav5OyyfK1nSWX6zePFi7r33XoYMGcL48ePzXE5Vb8xl/BJgiTdjNL4VGXeSx2ZvJOZgIoM61WPC4PahWv38rPK68hgA3IbzXu2r/H/ySACeLOA281WIqKpHgXuyjXsTePNsG7FCxYLbd+IUD85az5pdx7mmawOeHdKeSmHB1S5Pbho0aMDdd9/N999/z+jRo0lLS8PhcPg7LFMEUjOyeOP7bby/OpYaFcrw3i3nMKC9levlJq9WdT8GPhaRx1V1svs0133cgrBCxAC2OOIAT8yNwKHw+g2duaZrQ3+HVKS++uorli5dyqOPPkrVqlU5cOAAL7/8sr/DMj72R+xRxsyNZGd8MsPObcQTV7S1/mby4GmZxzBgcrZxs4FzCrBNK0QMQCnpmYxfGMVXa+Po0qgqU4Z1oUmNCnkvGGQOHDjAoEGDKFu2LD/++CMRERGMGDHC32EZH0lMzeClpTF89vseGlUvx+d39qRvC+vd0hN5lXm0AdrjLFsY6japMpBnbyZWiFg8bNp3kgdmrmfn0WTu79+C/17SktIh+NATwLXXXsvatWvZvn07I0eOZPDgwdx0000sWWLFFsFmZcxhnpoXyYGEVEae15RHLmtF+TLB25Cht+X1TrXGWQOqKnCV2/hE4K68Vm6FiIHN4VA+/Hknk5fFUKNCWb64sxe9m9fwd1h+VaJECUqVKsXcuXN58MEH+c9//kPXrl39HZbxomPJ6Uz8Jpp56/fRsnZF5tzbh24hUBnE2/Iq81gALBCR3qr6WxHFZIrA4cRUHvlqI6u3xTOgfR1eHNqJahXK+DssvytdujQzZ87kk08+YdGiRQBkZGT4OSrjDarK4sgDjFsQxclTGTxwcUvu69/cug0oIE+v0daLyH04b2GduV2lqnf4JCrjUytiDvHY1xEkp2fywjUdubFHI6u/7jJjxgzeffddnnrqKZo2bcrOnTu5+eab/R2WKaRDCamMnb+J76IP0alhFT67sydt61l7bIXhafL4FIjBWXX3WZwd1mz2VVDGN1Izsnjx2xg++nUXbepWYtaNvWhZx3r5c9euXTvefPP/a4E3bdqUMWPG+DEiUxiqypdr9vL8ks2kZzp46oq23N43PCQbMvQ2T5NHC1W9XkSGqOrHIvIFzgJvU0xsO5TIf2auJ+ZgIrf3DWf0wDYh0+9Afmzbto0nnniC6OhoUlNTz4yPjY31Y1SmIPYcTWHM3Ah+3XGUnk2r89K1nQivGXo1CH3F0+Rx+qbvCRHpABwEwn0SkfEqVeXzP/Yw8ZtoKpYtxYzbzqV/m9r+Ditg3X777UyYMIGHHnqIlStXMmPGDFTtGdPiJMuhzPhlJ68s30KpEiV44ZqODDu3Ucg3ZOhtniaPaa4WbMfibF23IvCMz6IyXnE8OZ3RcyJYHn2I81vW5NV/daZ2pTxrWIe0U6dOcfHFF6OqNGnShPHjx3P++eczYcIEf4dmPLD1kLMhww17T3BRm9o8f02HoG3E09887cP8A9e/q4BmvgvHeMuvO+J56MsNHEtOZ+ygttzRt6mdeXkgLCwMh8NBy5YtmTp1Kg0aNODw4bM17GwCQXqmg//9uIOpK7dRKaw0U4Z1YXDn+lYRxIc8KjUSkRdEpKrbcDURec53YZmCyshyMHlpDMM/+IMKZUsx7999ufP8ZpY4PPTGG2+QkpLCm2++ybp16/jss8/45JNP8l7Q+M3GvScYPPVnXv9+K1d0rMd3D13AEGsB1+c8vW11uaqeaQhRVY+LyBU4b2OZALH7aDIPzNrAxr0nGHZuI565qp09MZtPIsItt9zC7t27zzzfcddddxEREeHnyEx2p9KzeP37rXywOpbalcL4YER3LmlXx99hhQxPf1lKikhZVU0DEJFygLVRHEDm/hXH0/M3UbKE8M7wblzRsZ6/QyqWhg8fzssvv0zHjh0pUcKqcwaq33YcZczcCHYfTeGmno0Zc3kbKodIy8+BwtPk8Rnwg4jMwNl8+h3Axz6LyngsMTWDp+dvYv6G/fQIr87rw7pYT2eFUKtWLQYPHuzvMEwuElIzmLQkhpl/7qFJjfJ8cVdP+jS3hgz9wdMC88kiEoGzAyYBJqqqPefhZ3/tOc5/Z61n/4lUHr60Fff1bxHUvfwVhQkTJnDnnXdy8cUXU7bs/19cDx069CxLmaLww+ZDPDVvE4cTUxl1QTMeuqQV5crYs0r+4lHyEJEKwHJVXSoirYHWIlJaVa3RHz/Icij/+3E7r3+/jXpVwvjq7l6c06S6v8MKCjNmzCAmJoaMjIwzt61ExJKHHx1NSmPComgWbtxP6zqVePeWc+jSqGreCxqf8vS21SrgfNezHt8Da4EbcDZTYorQ/hOneOjLDfyx8xhXda7P89d0sHu9XrRx40YiIyP9HYbB+YDrwo37mbAomsTUDB66pBX3XticMqWsLCoQeJo8RFVTRGQk8JbrNtZ6XwZm/mnppgOMnhNJZpaDV6/vzNBuVh3R23r16kV0dDTt2rXzdygh7cDJU4ydt4kfYg7TpVFVJl/XiVbWDltA8Th5iEhvnFcaI/O5rCmklPRMJn4Tzcw/99KpYRXeHNbV2ujxkZ9//pmPP/6Ypk2bUrZsWVQVEbGqukXE4VBmrtnDpCUxZDocjB3Ultv7NrWyvADkaQL4L/AEME9Vo0SkGbDSd2GZ06L2O3v5i41P5t4Lm/PQJa3sst2Hli5d6u8QQtau+GTGzI3g99hj9GlegxeHdqJxjfL+DsvkwtPksUdVz9RfVNVYEfnURzEZnGdgM37dxUvfxlC1fGk+G2l9KxeFJk2a+DuEkJOZ5WD6Lzt5dflWypQswYtDO3LDudbHTKDzNHnMEZHBqroPQET6AVOBjj6LLIQdSUzj0a838tPWI1zStg6Tr+tEdevlzwShmIMJjJ4dwca4k1zStg7PXd2BulWs8c7iwNPkcQ8wX0SuAroBLwBX+CyqEPbjlsM8+vVGElMzmXh1B27u2djOwEzQScvM4u2VO3hn5XaqlCvN1Ju6MqhjPTvWixFPHxJcIyIPAMuBVOBSVT3i08hCTFpmFpOXbuHDn3fSuk4lPr+zF63rWu0SE3z+2nOc0bMj2HY4iWu6NuCZK9tRza6si52zJg8RWYSzOZLTygMngQ9FBPdyEFNw2w8n8cDM9UQfSODW3k144oq21sufCTop6Zm8unwr03/ZSd3KYdYxWTGX15XHK0USRYhSVWat2cuERVGUL1OKD2/tzsVtrVVQE3x+2R7PmLkR7D12ipt7NWb0wDZUsodbi7WzJg9V/QlARJoCB1Q11TVcDiiSXzlXteCngCqqep1r3IXARCAKmKWqPxZFLN50IiWdJ+ZG8u2mg5zXoiav/asztStbQaEJLidPZTBpyWZmrdlL05oV+HJUL3o2q+HvsIwXeFpg/jXQx204yzXu3LMtJCLTgSuBw6rawW38QGAKUBL4QFVfzG0dqhoLjBSR2e6jgSQgDIjzcB8Cxu+xR3noyw0cSUzjicvbcJd11mQAEWkHjAeOAj+o6uyzLxHYlkcdZOz8TRxNTueefs158JKWdjs2iHiaPEqpavrpAVVNFxFPSrg+wlml90xXbCJSEngbuBTnD/8aEVmIM5FMyrb8HaqaUx+gq1X1JxGpA7xGMWljKyPLwZs/bGPqyu2E16jA3H/3oVNDa+AtGHjjRAm4HGfzP6td34limTzik9IYvzCKbyIO0LZeZT689Vw6Nqzi77CMl3maPI64nvNYCCAiQ4D4vBZS1VUiEp5tdA9gu+uKAhGZBQxR1Uk4v3x5UlWH69/jFJNOqfYeS+GBWetZv+cE15/TkPGD21OhrLXwEkQ+opAnSsCnwDgRGQwUu3s7qsr8DfuYsCialLQsHr2sFXf3a07pktYiQjDKz3Men4vIVJz9eewFRhRwmw1cy58WB/TMbWYRqQE8D3QVkSdUdZKIDAUGAFVxfmFzWm4UMAqgceoSjNQAACAASURBVOPGBQzVOxZs2MfYeZtA4K0bu3JV5/p+jcd4nxdPlO5zJZ25vorVF/adOMVT8yL5ccsRujV2NmTYorZVNQ9mnj7nsQPoJSIVcbawm1iIbeZ0c19zGHd620dxJi/3cXPJ48ulqtOAaQDdu3fPdf2+lJSWyTPzNzF3/T7OaVKNN27oQqPq1lZPCMnviVI48CRQAXj5LPMFzImRw6F8/uceXlyyGYfCuKvaMaJ3uDVkGAI8vm8iIoOA9kDY6adAVfXZAmwzDmjkNtwQ2F+A9QS0DXtP8N9Z69l7LIX/XtyS/1zUglJ2+R5q8nuitAtXUjibQDgxAog9ksSYOZH8uesY57WoyaShHe3kKIR42pPguzgfEOwPfABcB/xZwG2uAVq6qv/uA4YBNxVwXQEny6G8t2oHry3fSp3KYXx5d2/ODbde/kJUUJ4oZWY5+ODnnbz+3VbKlirB5Os6cf05Da1pkRDj6ZVHH1XtJCIRqjpBRF7Fg3uyIjITuBCoKSJxwDhV/VBE7geW4Sw4nK6qUQWMP6AcPJnKQ19u4LfYowzqWI8XrulIlfL2IFQIC7oTpej9CTw+ZyOb9iUwoH0dJg7pYM8nhShPk8cp198UEamPsx5607wWUtUbcxm/BFji4baLheVRB3l8TgTpmQ47EwtBwX6ilJqRxdQV23n3px1ULV+G/w3vxuUd6/k7LONHniaPb0SkKjAZWOca94FvQipeTqVn8fySaD77fQ8dGlTmzWFdaVaror/DMkUsmE+U1u0+xuOzI9hxJJlruzXk6SvbUrW8NWQY6jxNHq8A9wLnA78Bq4H/+Sqo4mLzgQQemLmebYeTGHVBMx69rLX18meCRnJaJi8v28LHv+2ifpVyfHxHD/q1quXvsEyA8DR5fAwkAm+6hm/E+TDUv3wRVKBTVT7+dRcvfBtDlXKl+XRkD85vaV8qEzxWbzvCE3Mj2XfiFCN6NeGxgW2oaA+1GjeeHg2tVbWz2/BKEdnoi4AC3dGkNB6bHcGKmMNc1KY2L1/XiRoVi8VD7sbk6WRKBs8tjubrdXE0q1WBr6y2oMmFp8ljvYj0UtXfAUSkJ/CL78IKTKu2HuGRrzdy8lQG469qx619wq1Q3ASNpZsO8PSCKI4lp/PvC5vzwMXWkKHJXV6dQUXifKipNDBCRPa4hpsA0b4PLzCkZzp4ZfkWpq2KpWXtinxyRw/a1qvs77CM8YrDiamMWxDFt5sO0q5eZWbcdi4dGlhDhubs8rry8KihwmAWeySJB2atZ9O+BG7u1Zixg9rZ2ZgJCqrKnL/2MfGbaE5lZPHYgNaMuqCZNWRoPJJXZ1C7iyqQQKOqfL02jnELoyhbugTTbjmHy9rX9XdYxnhF3PEUnpy3iVVbj9C9STVevLYTLWpbFXPjOas+kYOTpzJ4cl4kiyMO0LtZDV6/oQt1q9hTtKb4cziUT3/fzUtLYxDg2SHtublnE+uMzOSbJY9s1uw6xoOzNnAoIZXHB7bm7guaWwuhJihsP5zEmDkRrN19nAta1eKFazrQsJo1ZGgKxpKHS2aWg7dWbOetFdtoVL08s+/tQ5dG1sufKf4yshxMWxXLlO+3Ua5MSV69vjNDuzWwmoKmUCx54Lz/++CsDazdfZyh3Rrw7JAO9kCUCQqb9p3k8dkRRB9I4IqOdZkwuAO1KtlzSabwQv4XctHG/Tw5LxIUpgzrwpAuDfwdkjGFlpqRxZQftjFtVSzVK5Th3Zu7MbCDNWRovCekk8dT8yL5/I89dG1clTeHdbWObExQ2HsshVun/0lsfDLXn9OQsYPaWdcAxutCOnm0rF2R/1zUggcubml1203QqFsljBa1KzJhSHtrc834TEgnj9v65tkliTHFTumSJZg2oru/wzBBzk63jTHG5JslD2OMMfkmqurvGHxORI4AJ4CTrlFV3P6vCcR7YTPu6yzMvLlNy2l89nHuw7n9Hwr76z5cFPvbRFX9UrgQYMd2XvN5+lnnZzgY9jencYHyXc792FbVkHgB03L5f62311+YeXObltP47OPOso8htb/uw/7Y36J+Bcpnndd8nn7W+RkOhv3N5z769bvs/gql21aLcvnfF+svzLy5TctpfPZxue1jqO1vXtssCF+8h94SKJ91XvN5+lnnZzgY9jencYH6XT4jJG5bnY2IrFXVkKmaYvsbOkJt321/i1YoXXnkZpq/Ayhitr+hI9T23fa3CIX8lYcxxpj8sysPY4wx+WbJwxhjTL5Z8jDGGJNvljyMMcbkmyWPPIhIBRFZJyJX+jsWXxORtiLyrojMFpF7/R2Pr4nI1SLyvogsEJHL/B1PUbLjOrgVxbEdtMlDRKaLyGER2ZRt/EAR2SIi20VkjAerGg185Zsovccb+6uqm1X1HuBfQEDXl/fS/s5X1buA24AbfBiu19hxfWZ8UB7XUHyO7aCtqisiFwBJwCeq2sE1riSwFbgUiAPWADcCJYFJ2VZxB9AJZ/sxYUC8qn5TNNHnnzf2V1UPi8hgYAwwVVW/KKr488tb++ta7lXgc1X9q4jCLzA7roP7uIZidGx7o22UQH0B4cAmt+HewDK34SeAJ86y/PPAG8ByYAFQwt/75Mv9zbauxf7enyL4fAV4CbjE3/tSxPttx3WAv4rDsR1qnUE1APa6DccBPXObWVWfAhCR23CeoTl8Gp335Wt/ReRCYChQFlji08h8I1/7C/wHuASoIiItVPVdXwbnQ3ZcB/dxDQF4bIda8pAcxuV5305VP/J+KEUiX/urqj8CP/oqmCKQ3/19E3jTd+EUGTuug/u4hgA8toO2wDwXcUAjt+GGwH4/xVIUbH+De39PC7X9DrX9hQDc51BLHmuAliLSVETKAMOAhX6OyZdsf4N7f08Ltf0Otf2FANznoE0eIjIT+A1oLSJxIjJSVTOB+4FlwGbgK1WN8mec3mL7G9z7e1qo7Xeo7S8Un30O2qq6xhhjfCckCsxr1qyp4eHh/g7DBKl169bFq5/6MLdj2/jS2Y7tkEge4eHhrF271t9hmCAlIrv9tW07to0vne3YDtoyD2OMMb5jycMYD+yKT/Z3CMZ43an0LPYeSynQsiFx28qYgth7LIXZ6+L4JmI/O44ks+KRfjSrVdHfYRnjFb/uiGfMnEgqlyvFwvvOo0SJnJ5DzJ0lD2PcZDmU5VEH+fyPPfyyIx6A3s1qcFufcGpUKOvn6IwpvITUDCYt2czMP/cSXqM8Ywe1y3fiAEsexgCQmpHFvPX7mLYqlp3xyTSoWo4HL27Fdd0b0qBqOX+HZ4xXfB99iKfmR3IkMY27L2jGg5e0olyZkgValyUPE9LSMrP44o89vPPjDo4kptGpYRXeGd6NAe3rUrIAZ2PGBKKjSWmMXxTNoo37aVO3Eu+P6E6nhlULtU5LHiYkZTmUeev38fp3W9l34hS9m9Vgyg1d6N28BiKWNExwUFUWbtzP+IVRJKVl8vClrbinX3PKlCp8XSlLHiakqCo/bD7M5GUxbD2URKeGVXjp2k6c17Kmv0Mzxqv2nzjF2PmbWBFzmK6Nq/LStZ1oVaeS19ZvycOEjO2Hk3j2m2hWbT1Cs5oVeGd4Ny7vUNeuNExQcTiUL/7cw4vfxpDlUJ65sh239gn3+m1YSx4m6CWmZvDWiu1M/3kn5cqU5Jkr23FL7yaULmmPOZngsjM+mTFzIvhj5zH6tqjBpGs60bhGeZ9sy5KHCVqqyty/9jHp2xiOJqdxQ/dGPDqgNTUrWpVbE1wysxx8+PNOXvtuK2VKlWDytZ24vntDn15V+yV5iMhAYArOzts/UNUXs01/DBjuGiwFtAVqqeoxEdkFJAJZQKaqdi+ywE2xsTM+mSfnRvJb7FG6Nq7Kh7d2p3OjwtUuMSYQbT6QwOg5EUTEneTSdnV47uoO1Kkc5vPtFnnyEJGSwNvApTh7x1ojIgtVNfr0PKr6MvCya/6rgIdU9ZjbavqranwRhm2KiYwsB9NWxTLlh22ULVWCSUM7ckP3RgV6CMqYQJaWmcXbK7bzzo87qFq+NG/f1I0rOhZdGZ4/rjx6ANtVNRZARGYBQ4DoXOa/EZhZRLGZYmz9nuM8MTeSmIOJXNGxLuOvak/tIjgDM6ao/bXnOKNnR7DtcBJDuzbg6SvbUa1CmSKNwR/JowGw1204DuiZ04wiUh4YiLMHrdMUWC4iCrynqtNyWXYUMAqgcePGXgjbBKqU9EwmL93Cx7/tok6lMN4f0Z1L29Xxd1j5JiIXAhOBKGCWqv7o14BMwElJz+SVZVuZ8etO6lUOY8bt59K/dW2/xOKP5JHTNVVu3RleBfyS7ZZVX1XdLyK1ge9EJEZVV/1jhc6kMg2ge/fu1l1ikFq3+xiPfLWRXUdTGNG7CY8NaE2lsNJFHoeITAeuBA6rage38Wct38tGgSQgDOdJlTFn/LwtnjFzI4g7fooRvZvw+MA2VCzrvzpP/thyHNDIbbghsD+XeYeR7ZaVqu53/T0sIvNw3gb7R/IwwS0tM4vXv9vGtFU7qFelHDPv6kXv5jX8GdJHwFTgk9Mjcivfw5lIJmVb/g5gtar+JCJ1gNf4/0ojJoSdPJXB84uj+WptHE1rVuCru3vTo2l1f4fll+SxBmgpIk2BfTgTxE3ZZxKRKkA/4Ga3cRWAEqqa6Pr/MuDZIonaBIxN+07y8Fcb2HooiRt7NOKpQe38egYGoKqrRCQ82+gcy/dUdRLOq5TcHAdyrU9st2RDx7Kogzw9fxNHk9O598Lm/PfiloSVLlhDht5W5N84Vc0UkfuBZTjPwKarapSI3OOa/q5r1muA5arq3gtPHWCeqzZBKeALVV1adNEbf8rMcvD2yh28tWIb1SuUYcZt59K/jX/u93rI4/I9ABEZCgwAquK8ismR3ZINfkcS0xi/MIrFkQdoW68yH956Lh0bVvF3WH/jl9M1VV0CLMk27t1swx/hvBXgPi4W6Ozj8EwA2nsshQdmrWf9nhMM6VKfCYPbU7V80dYuKYD8lO+hqnOBub4LxwQ6VWeDnc9+E01KWhaPDWjNqAuaBWRrCPaEuQl4CzbsY+y8TQC8eWNXBneu7+eIPJaf8j0T4vadOMWTcyP5aesRzmlSjZeu7UiL2t5ryNDbLHmYgJWUlsm4BVHM+SuObo2rMmVYVxpV9007PT7iUfmeCW0Oh/LZH7t56dsYFBh/VTtG9A4P+AdbLXmYgBQRd4IHZq5nz7EUHrioBQ9c3JJSAXjpfpqIzAQuBGqKSBwwTlU/zKl8z49hmgCz40gSY+ZEsGbXcc5vWZMXrulYbE6QLHmYgOJwKO+vjuXlZVuoVaksM+/qRc9mfq2C6xFVvTGX8f8o3zMmM8vBtNWxvPH9NsJKleDl6zpx3Tm+bcjQ2yx5mIBxIiWdh7/ayIqYwwxsX5cXr+1YHArFjcmXqP0nGT0ngk37EhjYvi7PXt2e2pWKXzM6ljxMQNi49wT//vwvDiemMmFwe0b0blKszsKMyUtqRhZvrdjGuz/FUq18Gf43vBuXd6zn77AKzJKH8StV5dPfdzPxm2hqVwrj63v60MWaTjdBZu2uYzw+J4LYI8lcd05Dxg5qW+yvqi15GL9JSsvkibmRLNq4n/6ta/Hav7oUecugxvhSclomLy9zNtpZv0o5PrmjBxe0quXvsLzCkofxi62HErnns3Xsik/msQGtubdf84CvmmhMfqzaeoQn5kay/+Qpbu0dzmMDWlPBz83oeFPw7IkpNhZs2MeYOZFUKFuKz+/0e4OGxnjViZR0nlu8mdnr4mhWqwJf392b7uH+b8jQ2yx5mCKT5VAmL4vhvZ9i6dG0OlNv7GqdNZmg8m3kAZ5eEMXxlHTu69+c/1wUOA0ZeluhkoeIvALMsAefTF4SUjP478z1rNxyhJt7NWbcVe0Dsr0eYwricGIq4xZE8e2mg7SvX5mP7ziX9vUDqyFDbyvslUcMME1ESgEzgJmqerLwYZlgsjM+mTs/XsPuoylMvLoDt/Rq4u+QjPEKVWX2ujgmfhNNaqaDxwe25q7zA7MhQ28rVPJQ1Q+AD0SkNXA7ECEivwDvq+pKbwRoirfV245w3+d/UbKE8OnInla+YYLG3mMpPDkvktXb4jk3vBovXtuJ5rUq+jusIlPoMg9Xb2ltXK94YCPwsIjcrarDCrt+UzypKh/9uovnFm+mRa2KfHBr92LTZo8xZ+NwKJ/8tovJy7YgwMQh7Rnes0nI1RYsbJnHazj7GV8BvKCqf7omvSQiWwobnCme0jKzeGZ+FF+u3ctl7erw2g1d/N7TnzHesP1wIqPnRLJu93H6tarF89d0oGG10DwpKuw3ehMwVlVTcpjWo5DrNsXQkcQ07v1sHWt3H+eBi1rw4CWtQu6MzASfjCwH01bFMuX7bZQvW5LX/tWZa7o2COkmdAqbPIar6nT3ESLyg6pebAXnoWfTvpOM+mQtx1LSmXpTV67sVGw6bTImV5v2neSx2RFsPpDAoI71GD+4PbUq5drFfMgoUPIQkTCgPM6+C6rx/91tVgbsFyMELY44wCNfb6B6+TLMvqcPHRoEdzVFE/xSM7J44/ttvL86luoVyvDuzecwsENdf4cVMAp65XE38CDORPGX2/gE4O3CBmWKD4dDef37rby1YjvnNKnGuzefU6zPylJTU/nwww+JiooiNTX1zPjp06efZSkTbP7ceYwxcyKIjU/mhu6NePKKtlQpX9rfYQWUAlVGVtUpqtoUeFRVm7q9Oqvq1LyWF5GBIrJFRLaLyJgcpl8oIidFZIPr9Yyny5qik5SWyd2freOtFdu5oXsjvrirZ7FOHAC33HILBw8eZNmyZfTr14+4uDgqVQrcfqSNdyWlZfL0/E38673fSM9y8NnInrx0XSdLHDko6G2ri1R1BbBPRIZmn66qc8+ybEmcVyeXAnHAGhFZqKrR2WZdrapXFnBZ42N7j6Vw58dr2X4kifFXtePWPuFBUXi4fft2vv76axYsWMCtt97KTTfdxIABA/wdlikCK7cc5qm5kRxISOWOvk15dEArypexWoK5Keg70w9n9dyrcpimQK7JA2ctrO2qGgsgIrOAIYAnCaAwyxov+W3HUf79+TocCh/f3oPzWtb0d0heU7q08wyzatWqbNq0ibp167Jr1y7/BmV86nhyOhO/iWbu+n20qF2R2ff04Zwm1fwdVsArUPJQ1XGuv7cXYPEGwF634TigZw7z9RaRjcB+nLfHovKxLCIyChgF0Lhx4wKEaXLy6e+7mbAwivCaFXh/RHea1qzg75C8atSoURw/fpyJEycyePBgkpKSePbZZ/0dlvEBVWVx5AHGLYji5KkMHrioBfdd1IKypYKzIUNvK+xDgi8Ak1X1hGu4GvCIqo4922I5jNNsw38BTVQ1SUSuAOYDLT1c1jlSdRowDaB79+45zmM8l5HlYPzCKD7/Yw8XtanNG8O6UDks+O4D33nnnQD069eP2NhYP0djfOVQQipPz9/E8uhDdGxQhU9H9qRd/cr+DqtYKWzrXZefThwAqnocuCKPZeKARm7DDXFeXZyhqgmqmuT6fwlQWkRqerKs8b5jyenc/MEffP7HHu7p15z3R3QPysQBcOjQIUaOHMnll18OQHR0NB9++KGfozLeoqp8uWYPl7z2Ez9tPcITl7dh3r/7WOIogMImj5IicqZ6jYiUA/KqbrMGaCkiTUWkDDAMWOg+g4jUFVfpq4j0cMV51JNljXdtPpDA4Kk/s37vCd64oQtjLm9DySB+Yvy2225jwIAB7N/vPCdp1aoVb7zxhp+jMt6w52gKN3/4B6PnRNK2XmWWPngBd/drTqkQaAHXFwpbleAz4AcRmYHz9tEdwMdnW0BVM0XkfmAZUBKYrqpRInKPa/q7wHXAvSKSCZwChqmqAjkuW8h9MLlYFnWQh77cQKWwUnx9d286N6rq75B8Lj4+nn/9619MmjQJgFKlSlGypN0DL86yHM5GOl9ZtoWSJYTnru7ATT0aW7M5hVTYJtkni0gEcIlr1ERVXebBckuAJdnGvev2/1Qgx+dFclrWeJeqMnXFdl79biudG1Vl2i3nUCdEevyrUKECR48ePVPt+Pfff6dKFXtavrjadiiRx+dEsH7PCfq3rsXz13SkftVy/g4rKHijEvN6oDTOK4/1Xlif8aOU9Ewe+zqCxZEHGNq1AS8M7Ri03Wjm5LXXXmPw4MHs2LGDvn37cuTIEWbPnu3vsEw+pWc6ePenHby1YhsVy5bijRu6MKRL/aB4FilQFLa21b+Al4EfcdaEektEHlNV+7YVQ/tOnOKuj9ey+WACT17RhrvObxZSXzaHw0Fqaio//fQTW7ZsQVVp3br1mWc/TPGwce8JRs+JIOZgIld1rs+4q9pRs2LxbvkgEBX2yuMp4FxVPQwgIrWA7wFLHsXMml3HuOfTdaRnOph+67n0b1Pb3yEVuRIlSvDII4/w22+/0b59e3+HY/LpVHoWb3y/lfdXx1KrUlneH9GdS9vV8XdYQauwyaPE6cThcpTC1+AyRezzP3YzfmEUDauV5/0R3WlRO3S60szusssuY86cOQwdOjSkrrqKu99jjzJmTgS7jqZwY49GjLm8LVXK2RWjLxU2eSwVkWXATNfwDVhhdrGRnulgwiLng3/9WtXizRu7hvwX7rXXXiM5OZlSpUoRFhaGqiIiJCQk+Ds0k4PE1Axe/DaGz//YQ+Pq5fnizp70aRE8zeUEssLWtnpMRK4F+uIs85imqvO8EpnxqfikNP792V/8uesY9/RrzmMDWgf18xueSkxM5NixY2zbtu1vTbKbwLMi5hBPzdvEoYRU7jyvKY9c1ppyZUKncoe/Fbq2larOAeZ4IRZTRNx7/JsyrAtDujTwd0gB44MPPmDKlCnExcXRpUsXfv/9d/r06cMPP/zg79CMy9GkNJ79JpoFG/bTqk5F3hneh66NrSHDolbQJtkTyblNKQFUVe1Z/wC1YMM+Hp8dQc2KZa3HvxxMmTKFNWvW0KtXL1auXElMTAzjxo0rkm2LyPnAcJzfy3aq2qdINlxMqCqLIg4wfmEUiakZ/PfiltzXvwVlSlkxqz8UtFVd6x2nmMlyKJOXxvDeqlh6hFfnnZu7WfXFHISFhREW5nwgMi0tjTZt2rBly5Y8lxOR6cCVwGFV7eA2fiAwBWeLCB+o6ou5rUNVVwOrReRqnE3xGJeDJ1MZOz+S7zcfpnPDKrx0XU/a1LVzVH8q9G0rETkPaKmqM1yNF1ZS1Z2FD814y7HkdP47az2rt8Vzc6/GPHNleztby0XDhg05ceIEV199NZdeeinVqlWjfv36niz6Ec5WET45PSK3zstwJpJJ2Za/w63m4k3AnYXbk+Cgqsxas5cXFm8mw+Fg7KC23N63qZXPBYDCPiQ4DugOtAZmAGVwtnfVt/ChGW/YsPcE//5sHfFJ6Uwa2pEbe1jfJmczb56zvsf48ePp378/J0+eZODAgXkup6qrRCQ82+gcOy9T1Uk4r1L+QUQaAydVNdfqXaHSV82u+GSemBvJb7FH6d2sBi9e25EmNYKr/5jirLBXHtcAXXH2v4Gq7hcRu6UVAFSVz/7Yw7OLoqhTOYw59/ahY0Mr38iPfv36FXYVHnde5mYkzhOxXAV7XzVZDmX6zzt59bstlC5RgklDOzLs3Eb23E2AKWzySFdVFREFEBE7LQgAKemZPDVvE/PW76N/61q8fkMXqpYv4++wQpHHnZedmejqpTNUbTmYyOOzN7Ix7iSXtK3Nc1d3pG6V0GiUs7gpbPL4SkTeA6qKyF04m2R/v/BhmYKKPZLEvZ/9xdbDiTxyaSvu69/Cmp72H+u8zEPpmQ7eXrmdd37cTqWw0rx5Y1eu6lTPrjYCWGGThwNYDSQArYBnVPW7QkdlCmRxxAFGz4mgdEnhkzt6cH7LWv4OKdSd6bwM2Iez87Kb/BtS4Nmw9wSPz97I1kNJDOlSn3FXtad6BbtSDnSFTR6VcN6jPQbMAiIKHZHJt1PpWTz7TRQz/9xLl0ZVeWd4N+uzoIiJyEzgQqCmiMQB41T1Q+u8LHen0rN4dfkWpv+yk9qVwvjw1u5c3NYaMiwuCts8yQRggoh0wtmu1U8iEqeql+SxqPGSmIMJ3P/FenYcSeLfFzbnoUtbUdq61SxyqnpjLuOt87Ic/Lo9njFzI9lzLIXhPRsz5vI2VAoL7XbVihtvdAYFcBg4iLNV3dBry9sPVJXPft/NxMWbqVKuNJ+N7ElfaxDOBLiTpzKYtGQzs9bsJbxGeWaN6kWvZjX8HZYpgMI+53EvziuOWjj78LhLVaO9EZjJ3YmUdEbPiWBZ1CH6t67FK9d3poY9LW4C3HfRhxg7P5IjiWncfUEzHryklTVkWIwV9sqjCfCgqm7wRjAmb6u2HuHx2REcTU5j7KC23NG3qdWmMgEtPimN8Quj+CbiAG3qVuL9Ed3p1LCqv8MyhVTYMo8xBVkur/Z+RGQ4MNo1mATcq6obXdN2AYlAFpCpqt0LFn3xkpKeyQtLNvPZ73toWbsiH9za3Ro1NAFNVVmwYT8TFkWRlJbJw5e24p5+za1pnCDhrTIPj+XW3k+22107gX6qelxELsf5NK37k7n9VTW+yIL2s7W7jvHI1xvZcyyFu8539lsQVtou903g2n/iFGPnb2JFzGG6NKrK5Os60aqONT4RTIo8eZBLez/AmeShqr+6zf87zoerQk5aZhavfbeVaatiaVC1HLPu6kVPK1w0AczhUL74cw8vfhtDlkN5+sp23NYn3BoyDEL+SB75be9nJPCt27ACy11NorznaufnH4p743F/7TnOmDkRbD2UxI09GvHUoHZULOuPj8sYz+yMT2b0nAj+3HmMvi1qMOmaTjSuUd7fYRkf8cevkcft/YhIf5zJ4zy30X1dDTDWBr4TkRhVXfWPFRbTxuOS0jJ5eWkMn/y+m7qVw5hx27n0b2O1NssYvQAACJVJREFUn03gysxy8MHPO3n9u62UKVWCydd24vruDa1pkSDnj+ThUXs/rgcPPwAuV9Wjp8er6n7X38MiMg/nbbB/JI/i6PvoQzy9YBMHE1K5tXc4jw5obVcbJqBF709g9JwIIved5NJ2dXju6g7UqWwNGYYCf/wy5dnej6tPg7nALaq61W18BaCEqia6/r8MeLbIIveRwwmpTFgUzeLIA7SuU4m3h3ejm/XJbAJYWmYWU1ds538/7qBq+dK8fVM3ruhY1642QkiRJw9VzcypvR8Rucc1/V3gGaAG8I7rYDxdJbcOMM81rhTwhaouLep98JbMLAef/7GHV5dvITXTwaOXtWLUBVaV0QS2dbuPM3pOBNsPJzG0awOevrId1awhw5Djl3siObX340oap/+/kxy64XTV0Ors8wCLwK874pmwMJothxLp26IGE4d0oFmtiv4Oy5hcJadl8sryLXz06y7qVQ5jxu3n0r+1lceFKruhXsTijqcwaUkMiyMP0LBaOd69uRsD2tvlvglsq7cd4Ym5kcQdP8WI3k14fGAbK48LcfbpF5HUjCze+ymW//20HYCHL23FqAua2cN+JqCdTMng+SXRfLU2jqY1K/DV3b3p0bS6v8MyAcCSh49lZjmYu34fb3y3lf0nUxnUqR5PXvF/7d19bFX1Hcfx95cqTw0gS+kE2lFErBVaHbCRLptzGxvI2MIG0TnjIC57MNmyLBkTZSaL2UMaY0YciUQSN8weEhEdU5gYo6Lb2AYlSqsirQ0NhbreCmWUIQr97g8ectf00h56zz3nnvN5JTe559fT0++n93f7vU/9nRqm6nwbEnPPNr/DfVuaOXLife66aQY/+NxMPdiRC9Q8QuLuPPfGv3lg+1u0dvVSVzGBB2+5gfoZ+g9xibfM8bMLGW5t6qRm8ngeXfExaiu0jpr8PzWPEOx8+10ant3Hqwd7uGpSKQ/fPodFs/W+hsSbu/PknkPc/8wbnHz/DKsWVvPtG6/SycVkQGoeedTYfoS1z7fwSks3V44fTcOyWpbNqeAy3fkk5jqO/pd7n2rm5f0Z5k6bSMOyWq4u10KGkpuaxzC5O39rfZd1L7bwj7YjTBx7OWsW13BH/TS9Piyx19fn/O6f7TT8ZR8O/PRL1/GN+iqdI0YGpeZxifr6nBf2dbHuxVZePdhD+bhR/OSLNXx9/kcYO1K/Vom/tzO9rN68l10HjvKpmWX84iu1VH5ICxnK0OivXEC9p06zubGDjTsP0JY5QcXEMfxs6WyWz63QMw0pCh+c6WPDK22sfb6F0ZeN4IHldSyfq4UMJRg1jyFqy/Ty2M52nmjsoPfUaa6vvIJf3Xo9S+qm6A1FKRrNh45x9+a9vH74PyyadSX3L51F+TgtZCjBqXlcxOkzfbzckmHj39vZsT/D5SXGkroprPhEFTdU6hzMUjze++AMv36hhfU72pg4diQP3z6Hm2snR12WFDE1jwG0dh1nU2MHT+05RNfxU5SPG8UPF1zDbfMr9ShNis7uA0f48ea9tGVOsGxOBfctqeGKsVrIUIZHzeOcwz0n2dbUydN7O3ntYA8lI4zPVJezfG4Fn722XCvdStHJPrHYlAlj2Hjnx/n0NZOiLksSItXN451j77GtqZOtTZ00th8FYNaU8axZXMPSj05l0rhREVcocml27M9w75NNHD52khX1VaxaWE2pFjKUPEr1bPrRptf4a2s3NZPHs2phNYtrJzO9rDTqskSG5UD3CVb+5l9MLytl03fqmVelhQwl/1LdPFbffC1jRpYwQ+fRkASpKitlwx3z+OTMMn18XEKT6uYxe6oWe5NkWnDdh6MuQRJO7wKLiEhgah4iIhKYuXvUNYTOzDJAD3Ds3NCErOtlQHcefkz2MYezb66vDTTefyx7O9f1NOTN3i5E3mnuHslnYGM2twfbb6i3dZDtJOQdaCwu9+Xcc9vdU3EBHslxfXe+jz+cfXN9baDx/mMXyZiqvNnbUeQt9CUut/Vg+w31tg6ynYS8ATNGel/OvqTpZaunc1wP4/jD2TfX1wYa7z+WK2Pa8g72My9FGL/DfInLbT3YfkO9rYNsJyHvQGNxvS9fkIqXrS7GzHa7+7yo6ygU5U2PtGVX3sJK0zOPXB6JuoACU970SFt25S2g1D/zEBGR4PTMQ0REAlPzEBGRwNQ8REQkMDWPQZhZqZk1mtmSqGsJm5nVmNl6M3vCzO6Kup6wmdlSM9tgZlvM7AtR11NImtfJVoi5ndjmYWaPmlmXmTX3G19kZm+ZWauZrR7Coe4GHg+nyvzJR153f9PdvwvcAsT6I495yvsnd/8WsBK4NcRy80bz+sJ4Iuc1FM/cTuynrczsRqAXeMzdZ58bKwH2A58HOoBdwG1ACfDLfoe4E6jj7BIAo4Fud3+mMNUHl4+87t5lZl8GVgPr3P0Phao/qHzlPfd9DwK/d/c9BSr/kmleJ3teQxHN7Xz8e3tcL0AV0Jy1XQ9sz9q+B7jnIt//c2At8BywBRgRdaYw8/Y71tao8xTg9jWgAVgQdZYC59a8jvmlGOZ22s7nMRU4mLXdAczPtbO7rwEws5WcfYTWF2p1+Rcor5ndBHwVGAVsC7WycATKC3wfWABMMLOr3X19mMWFSPM62fMaYji309Y8bICxQV+3c/ff5r+UggiU191fAl4Kq5gCCJr3IeCh8MopGM3rZM9riOHcTuwb5jl0AJVZ2xXA4YhqKQTlTXbe89KWO215IYaZ09Y8dgEzzWy6mY0Evgb8OeKawqS8yc57Xtpypy0vxDBzYpuHmf0R2AlUm1mHmX3T3U8D3wO2A28Cj7v761HWmS/Km+y856Utd9ryQvFkTuxHdUVEJDyJfeYhIiLhUfMQEZHA1DxERCQwNQ8REQlMzUNERAJT8xARkcDUPEREJDA1DxERCUzNQ0REAvsffic055t7MgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Compute\n",
    "for fi, freq in enumerate(frequency):\n",
    "    wl = snowScatt._compute._c/freq.values\n",
    "    for ti, temp in enumerate(temperature):\n",
    "        SS_RGA = snowScatt.calcProperties(diameters=Dmax,\n",
    "                                          wavelength=wl,\n",
    "                                          properties=particle,\n",
    "                                          temperature=temp.values,\n",
    "                                          Nangles=Nangles)\n",
    "        ssCext, ssCabs, ssCsca, ssCbck, ssasym, ssphase, mass_p, ssvel, ssarea = SS_RGA\n",
    "        Cext.loc[sizes, freq, temp] = ssCext\n",
    "        Cabs.loc[sizes, freq, temp] = ssCabs\n",
    "        Csca.loc[sizes, freq, temp] = ssCsca\n",
    "        Cbck.loc[sizes, freq, temp] = ssCbck\n",
    "        asym.loc[sizes, freq, temp] = ssasym\n",
    "        phase.loc[sizes, angles, freq, temp] = ssphase\n",
    "        f, axs = plt.subplots(2, 2)\n",
    "        axs[0, 0].loglog(Dmax, ssCbck)\n",
    "        axs[0, 0].set_ylabel('backscattering')\n",
    "        axs[0, 1].loglog(Dmax, mass_p)\n",
    "        axs[0, 1].set_ylabel('mass')\n",
    "        axs[1, 0].semilogx(Dmax, ssvel)\n",
    "        axs[1, 0].set_ylabel('velocity')\n",
    "        axs[1, 1].loglog(Dmax, ssarea)\n",
    "        axs[1, 1].set_ylabel('area')\n",
    "        f.suptitle(str(freq.values*1.0e-9)+'GHz   '+str(temp.values)+' K')\n",
    "        f.savefig(str(freq.values*1.0e-9)+'_'+str(temp.values)+'.png')\n",
    "        \n",
    "\n",
    "# These last two depend only on size, no need to recompute\n",
    "mass.loc[sizes] = mass_p\n",
    "vel.loc[sizes] = ssvel\n",
    "area.loc[sizes] = ssarea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finalize the computation by putting together the variables into one dataset and saving it as a netCDF file. We add some basic and autometic general attributes such as the username, the host machine name, and the date of creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (frequency: 5, scat_angle: 721, size: 1000, temperature: 1)\n",
       "Coordinates:\n",
       "  * size         (size) float64 0.0001 0.0001199 0.0001398 ... 0.01998 0.02\n",
       "  * frequency    (frequency) float64 5.6e+09 9.6e+09 1.36e+10 3.56e+10 9.4e+10\n",
       "  * temperature  (temperature) float64 270.0\n",
       "  * scat_angle   (scat_angle) float64 0.0 0.004363 0.008727 ... 3.137 3.142\n",
       "Data variables:\n",
       "    Cext         (size, frequency, temperature) float64 3.171e-15 ... 6.53e-07\n",
       "    Cabs         (size, frequency, temperature) float64 3.171e-15 ... 2.978e-08\n",
       "    Csca         (size, frequency, temperature) float64 2.559e-19 ... 6.232e-07\n",
       "    Cbck         (size, frequency, temperature) float64 3.838e-19 ... 7.804e-08\n",
       "    asym         (size, frequency, temperature) float64 1.207e-06 ... 0.8366\n",
       "    phase        (size, scat_angle, frequency, temperature) float64 0.75 ... 0.06262\n",
       "    mass         (size) float64 1.067e-10 1.533e-10 ... 4.19e-06 4.199e-06\n",
       "    vel          (size) float64 0.07778 0.09149 0.1045 ... 0.8484 0.8484 0.8485\n",
       "    area         (size) float64 2.693e-09 3.773e-09 ... 5.031e-05 5.04e-05\n",
       "Attributes:\n",
       "    created_by:           dori\n",
       "    host_machine:         dori-X240-ssd\n",
       "    particle_properties:  Leinonen15tabA00\n",
       "    created_on:           2021-02-01 19:03:44.614662\n",
       "    comment:              this is just a test</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (frequency: 5, scat_angle: 721, size: 1000, temperature: 1)\n",
       "Coordinates:\n",
       "  * size         (size) float64 0.0001 0.0001199 0.0001398 ... 0.01998 0.02\n",
       "  * frequency    (frequency) float64 5.6e+09 9.6e+09 1.36e+10 3.56e+10 9.4e+10\n",
       "  * temperature  (temperature) float64 270.0\n",
       "  * scat_angle   (scat_angle) float64 0.0 0.004363 0.008727 ... 3.137 3.142\n",
       "Data variables:\n",
       "    Cext         (size, frequency, temperature) float64 3.171e-15 ... 6.53e-07\n",
       "    Cabs         (size, frequency, temperature) float64 3.171e-15 ... 2.978e-08\n",
       "    Csca         (size, frequency, temperature) float64 2.559e-19 ... 6.232e-07\n",
       "    Cbck         (size, frequency, temperature) float64 3.838e-19 ... 7.804e-08\n",
       "    asym         (size, frequency, temperature) float64 1.207e-06 ... 0.8366\n",
       "    phase        (size, scat_angle, frequency, temperature) float64 0.75 ... 0.06262\n",
       "    mass         (size) float64 1.067e-10 1.533e-10 ... 4.19e-06 4.199e-06\n",
       "    vel          (size) float64 0.07778 0.09149 0.1045 ... 0.8484 0.8484 0.8485\n",
       "    area         (size) float64 2.693e-09 3.773e-09 ... 5.031e-05 5.04e-05\n",
       "Attributes:\n",
       "    created_by:           dori\n",
       "    host_machine:         dori-X240-ssd\n",
       "    particle_properties:  Leinonen15tabA00\n",
       "    created_on:           2021-02-01 19:03:44.614662\n",
       "    comment:              this is just a test"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Finalize dataset and write netCDF file\n",
    "variables = {'Cext':Cext,\n",
    "             'Cabs':Cabs,\n",
    "             'Csca':Csca,\n",
    "             'Cbck':Cbck,\n",
    "             'asym':asym,\n",
    "             'phase':phase,\n",
    "             'mass':mass,\n",
    "             'vel':vel,\n",
    "             'area':area}\n",
    "\n",
    "global_attributes = {'created_by':os.environ['USER'],\n",
    "                     'host_machine':socket.gethostname(),\n",
    "                     'particle_properties':particle,\n",
    "                     'created_on':str(datetime.now()),\n",
    "                     'comment':'this is just a test'}\n",
    "\n",
    "dataset = xr.Dataset(data_vars=variables,\n",
    "                     coords=coords,\n",
    "                     attrs=global_attributes)\n",
    "\n",
    "dataset.to_netcdf(filename)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a multi-dimensional LUT it is most conveniently stored as netCDF, but we also show how to save a 2D ASCII csv files to make the example more complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>frequency</th>\n",
       "      <th>5.600000e+09</th>\n",
       "      <th>9.600000e+09</th>\n",
       "      <th>1.360000e+10</th>\n",
       "      <th>3.560000e+10</th>\n",
       "      <th>9.400000e+10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>3.838219e-19</td>\n",
       "      <td>3.314802e-18</td>\n",
       "      <td>1.335121e-17</td>\n",
       "      <td>6.267222e-16</td>\n",
       "      <td>3.041995e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00012</th>\n",
       "      <td>7.928839e-19</td>\n",
       "      <td>6.847549e-18</td>\n",
       "      <td>2.758003e-17</td>\n",
       "      <td>1.294523e-15</td>\n",
       "      <td>6.279367e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00014</th>\n",
       "      <td>1.464727e-18</td>\n",
       "      <td>1.264969e-17</td>\n",
       "      <td>5.094896e-17</td>\n",
       "      <td>2.391131e-15</td>\n",
       "      <td>1.158997e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00016</th>\n",
       "      <td>2.493137e-18</td>\n",
       "      <td>2.153109e-17</td>\n",
       "      <td>8.671953e-17</td>\n",
       "      <td>4.069406e-15</td>\n",
       "      <td>1.970754e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00018</th>\n",
       "      <td>3.986183e-18</td>\n",
       "      <td>3.442498e-17</td>\n",
       "      <td>1.386498e-16</td>\n",
       "      <td>6.505376e-15</td>\n",
       "      <td>3.147358e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01992</th>\n",
       "      <td>4.177232e-10</td>\n",
       "      <td>1.826404e-09</td>\n",
       "      <td>2.449269e-09</td>\n",
       "      <td>1.074654e-08</td>\n",
       "      <td>7.664113e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01994</th>\n",
       "      <td>4.192151e-10</td>\n",
       "      <td>1.831346e-09</td>\n",
       "      <td>2.452651e-09</td>\n",
       "      <td>1.077327e-08</td>\n",
       "      <td>7.699276e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01996</th>\n",
       "      <td>4.207107e-10</td>\n",
       "      <td>1.836295e-09</td>\n",
       "      <td>2.456027e-09</td>\n",
       "      <td>1.079988e-08</td>\n",
       "      <td>7.734349e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01998</th>\n",
       "      <td>4.222100e-10</td>\n",
       "      <td>1.841249e-09</td>\n",
       "      <td>2.459398e-09</td>\n",
       "      <td>1.082636e-08</td>\n",
       "      <td>7.769319e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02000</th>\n",
       "      <td>4.237130e-10</td>\n",
       "      <td>1.846209e-09</td>\n",
       "      <td>2.462763e-09</td>\n",
       "      <td>1.085271e-08</td>\n",
       "      <td>7.804132e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "frequency  5.600000e+09  9.600000e+09  1.360000e+10  3.560000e+10  \\\n",
       "size                                                                \n",
       "0.00010    3.838219e-19  3.314802e-18  1.335121e-17  6.267222e-16   \n",
       "0.00012    7.928839e-19  6.847549e-18  2.758003e-17  1.294523e-15   \n",
       "0.00014    1.464727e-18  1.264969e-17  5.094896e-17  2.391131e-15   \n",
       "0.00016    2.493137e-18  2.153109e-17  8.671953e-17  4.069406e-15   \n",
       "0.00018    3.986183e-18  3.442498e-17  1.386498e-16  6.505376e-15   \n",
       "...                 ...           ...           ...           ...   \n",
       "0.01992    4.177232e-10  1.826404e-09  2.449269e-09  1.074654e-08   \n",
       "0.01994    4.192151e-10  1.831346e-09  2.452651e-09  1.077327e-08   \n",
       "0.01996    4.207107e-10  1.836295e-09  2.456027e-09  1.079988e-08   \n",
       "0.01998    4.222100e-10  1.841249e-09  2.459398e-09  1.082636e-08   \n",
       "0.02000    4.237130e-10  1.846209e-09  2.462763e-09  1.085271e-08   \n",
       "\n",
       "frequency  9.400000e+10  \n",
       "size                     \n",
       "0.00010    3.041995e-14  \n",
       "0.00012    6.279367e-14  \n",
       "0.00014    1.158997e-13  \n",
       "0.00016    1.970754e-13  \n",
       "0.00018    3.147358e-13  \n",
       "...                 ...  \n",
       "0.01992    7.664113e-08  \n",
       "0.01994    7.699276e-08  \n",
       "0.01996    7.734349e-08  \n",
       "0.01998    7.769319e-08  \n",
       "0.02000    7.804132e-08  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=dataset.Cbck.squeeze('temperature').to_pandas()\n",
    "table.to_csv('backscattering_table.csv')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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