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    "# <span style=\"color:blue\">First steps with `exo_k`</span>\n",
    "\n",
    "*Author: Jeremy Leconte (CNRS/LAB/Univ. Bordeaux)*\n",
    "\n",
    "The goal of `exo_k` is to provide a library to:\n",
    "\n",
    "* Interpolate efficiently and easily in correlated-k and cross section tables.\n",
    "* Convert easily correlated-k and cross section tables from one format to another\n",
    "  (hdf5, LMDZ GCM, Exomol, Nemesis, PetitCode, TauREx, ExoREM, ARCIS, etc.).\n",
    "* Adapt precomputed correlated-k tables to your needs by changing:\n",
    "  * the spectral and quadrature (g) grids,\n",
    "  * the pressure/temperature grid.\n",
    "* Create tables for a mix of gases using tables for individual gases.\n",
    "* Create your own tables from high-resolution spectra.\n",
    "* Use your data in an integrated radiative transfer framework to simulate planetary atmospheres.\n",
    "   \n",
    "This first tutorial will show you how to deal with radiative data.\n",
    "The modeling of planetary atmospheres is detailed later on.\n",
    "\n",
    "If you are reading this online, the executable notebook can be found there:\n",
    "[https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public/-/blob/public/tutorials/tutorial-exo_k.ipynb](https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public/-/blob/public/tutorials/tutorial-exo_k.ipynb)"
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   "source": [
    "## Initialization"
   ]
  },
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   "source": [
    "First, let's make some common initializations.   \n",
    "We also import our library: `exo_k`.\n",
    "\n",
    "For this to work, you need to have installed the library by either typing\n",
    "```\n",
    "pip install exo_k\n",
    "```\n",
    "anywhere or\n",
    "```\n",
    "pip install -e .\n",
    "```\n",
    "at the root of exo_k directory (the location where you first saw this tutorial)."
   ]
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   "source": [
    "import exo_k as xk\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import astropy.units as u\n",
    "import time,sys,os"
   ]
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   "source": [
    "# Uncomment the line below if you want to enable interactive plots\n",
    "#%matplotlib notebook\n",
    "plt.rcParams[\"figure.figsize\"] = (7,4)\n",
    "from matplotlib import cycler\n",
    "colors = cycler('color',[plt.cm.inferno(i) for i in np.linspace(0.1,1,5)])\n",
    "plt.rc('axes', axisbelow=True, grid=True, labelcolor='dimgray', labelweight='bold', prop_cycle=colors)\n",
    "plt.rc('grid', linestyle='solid')\n",
    "plt.rc('xtick', direction='in', color='dimgray')\n",
    "plt.rc('ytick', direction='in', color='dimgray')\n",
    "plt.rc('lines', linewidth=1.5)"
   ]
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   "source": [
    ".. important::\n",
    "    Many global options can be changed after having imported the library using a syntax of the type\n",
    "    `xk.Settings().method_name(args)`. Many examples will be provided along the way, but you\n",
    "    can have a look at :class:`exo_k.settings.Settings` for all the possible options. "
   ]
  },
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   },
   "source": [
    "## Documentation"
   ]
  },
  {
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   "source": [
    "The documentation in html form can be found at http://perso.astrophy.u-bordeaux.fr/~jleconte/exo_k-doc/index.html\n",
    "\n",
    "It is searchable, and has a general index to find any function.\n",
    "\n",
    "To access it through a python interface, just run the following line for any class, function, etc.:"
   ]
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     "text": [
      "Help on method bin_down in module exo_k.ktable:\n",
      "\n",
      "bin_down(wnedges=None, weights=None, ggrid=None, remove_zeros=False, num=300, use_rebin=False, write=0) method of exo_k.ktable.Ktable instance\n",
      "    Method to bin down a kcoeff table to a new grid of wavenumbers (inplace).\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "        wnedges: array\n",
      "            Edges of the new bins of wavenumbers (cm-1)\n",
      "            onto which the kcoeff should be binned down.\n",
      "            if you want Nwnew bin in the end, wnedges.size must be Nwnew+1\n",
      "            wnedges[0] should be greater than self.wnedges[0] (JL20 not sure anymore)\n",
      "            wnedges[-1] should be lower than self.wnedges[-1]\n",
      "        weights: array, optional\n",
      "            Desired weights for the resulting Ktable.\n",
      "        ggrid: array, optional\n",
      "            Desired g-points for the resulting Ktable.\n",
      "            Must be consistent with provided weights.\n",
      "            If not given, they are taken at the midpoints of the array\n",
      "            given by the cumulative sum of the weights\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(xk.Ktable().bin_down)"
   ]
  },
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   "source": [
    "# <span style=\"color:blue\">Dealing with `Ktable()` objects</span>"
   ]
  },
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   "source": [
    "## Loading and saving a `Ktable()` object"
   ]
  },
  {
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   "source": [
    "For this tutorial to work, you should launch this notebook from a base_directory that contains a `data/corrk/` directory where your correlated k files are storred. A directory with some sample files can be downloaded at the following url:\n",
    "\n",
    "[https://mycore.core-cloud.net/index.php/s/w2cHuigAiwcfBVW](https://mycore.core-cloud.net/index.php/s/w2cHuigAiwcfBVW)\n",
    "\n",
    "Many thanks to K. Chubb and the Exomol project for providing these data. "
   ]
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   "source": [
    "datapath = '../data/'"
   ]
  },
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   "source": [
    "### Loading a `Ktable` from a file"
   ]
  },
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   "source": [
    "One of the main objects we will deal with is the `Ktable()` object. This contains a big matrix with k-coefficients for a species or a mix of species along with all the needed supporting information such as the Pressure, Temperature, wavenumber, and g grids onto which the k-coefficients have been computed.  \n",
    "\n",
    "To instantiate a `Ktable()` object from most currently supported formats, just run the following line. If you are specifying the file through the `filename` keyword, you have to give the full path to the file. The format should be recognized from the extension (see http://perso.astrophy.u-bordeaux.fr/~jleconte/exo_k-doc/units.html\n",
    "for supported formats). "
   ]
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   "source": [
    ".. seealso::\n",
    "    :func:`exo_k.ktable.Ktable` for details on arguments and options."
   ]
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     "text": [
      "\n",
      "        file         : ../data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n",
      "        molecule     : H2O\n",
      "        p grid       : [1.00000000e-05 2.15443469e-05 4.64158883e-05 1.00000000e-04\n",
      " 2.15443469e-04 4.64158883e-04 1.00000000e-03 2.15443469e-03\n",
      " 4.64158883e-03 1.00000000e-02 2.15443469e-02 4.64158883e-02\n",
      " 1.00000000e-01 2.15443469e-01 4.64158883e-01 1.00000000e+00\n",
      " 2.15443469e+00 4.64158883e+00 1.00000000e+01 2.15443469e+01\n",
      " 4.64158883e+01 1.00000000e+02]\n",
      "        p unit       : bar\n",
      "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
      " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
      " 3000. 3200. 3400.]\n",
      "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
      " 33167.31250795 33277.87021631]\n",
      "        wn unit      : cm^-1\n",
      "        kdata unit   : cm^2/molecule\n",
      "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
      " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
      " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
      " 0.02030071 0.008807  ]\n",
      "        data oredered following p, t, wn, g\n",
      "        shape        : [  22   27 1538   20]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "h2o_ktab=xk.Ktable(filename=datapath + 'corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5')\n",
    "print(h2o_ktab)"
   ]
  },
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   "source": [
    "### Managing Units"
   ]
  },
  {
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   "source": [
    "#### Tracking units\n",
    "\n",
    "Except for self-defining formats (such as hdf5), most formats handled by `exo_k` do not carry the information on units with them (Exo_transmit, HITRAN .cia, etc.).\n",
    "\n",
    "However, previous experience has shown us that tracking units is essential to avoid errors and working with SI units is recommended when possible. \n",
    "\n",
    "For this reason, the units for pressure, kdata, and wavenumbers are kept as attributes of any Ktable object. They can be seen by printing the object (as above).\n",
    "If the input format is self-defining (e.g. hdf5) the units are read in the file. For all the other formats, the units are assumed to always be the same and have been inferred from reading the codes using these formats and their documentation (see http://perso.astrophy.u-bordeaux.fr/~jleconte/exo_k-doc/units.html for some examples and a more general discussion)."
   ]
  },
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    "tags": []
   },
   "source": [
    "#### Converting to new units"
   ]
  },
  {
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   "source": [
    "To control the unit we want to work with, we just need to specify what units we want with the `p_unit` and `kdata_unit` keywords.\n",
    "\n",
    "These keywords accept string input with units recognized by the `astropy.units` library such as 'Pa', 'mbar', and 'bar' for pressure and e.g. 'm^2/molecule' and 'cm^2/molecule' for cross sections. Although '/molecule' is not a unit recognized by `astropy.units`, it is automatically appended to the unit string provided by the user when necessary as a reminder that opacities per 'moles' or 'kg' are not supported."
   ]
  },
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   "source": [
    "To use SI units, just reload your ktable as shown below. Note that you can also force SI units to be used with the global option to be set once and for all.\n",
    "```python\n",
    "xk.Settings().set_mks(True)\n",
    "```"
   ]
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      "\n",
      "        file         : ../data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n",
      "        molecule     : H2O\n",
      "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
      " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
      " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
      " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
      " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
      " 4.64158883e+06 1.00000000e+07]\n",
      "        p unit       : Pa\n",
      "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
      " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
      " 3000. 3200. 3400.]\n",
      "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
      " 33167.31250795 33277.87021631]\n",
      "        wn unit      : cm^-1\n",
      "        kdata unit   : m^2/molecule\n",
      "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
      " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
      " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
      " 0.02030071 0.008807  ]\n",
      "        data oredered following p, t, wn, g\n",
      "        shape        : [  22   27 1538   20]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "h2o_ktab_SI=xk.Ktable(filename=datapath + 'corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5',\n",
    "                         kdata_unit='m^2', p_unit='Pa')\n",
    "# or simply\n",
    "xk.Settings().set_mks(True)\n",
    "h2o_ktab_SI=xk.Ktable(filename=datapath + 'corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5')\n",
    "print(h2o_ktab_SI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d5ae84",
   "metadata": {
    "papermill": {
     "duration": 0.133092,
     "end_time": "2022-09-02T07:49:16.930749",
     "exception": false,
     "start_time": "2022-09-02T07:49:16.797657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now both the pressure unit and the pressure grid have changed!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1059de",
   "metadata": {
    "papermill": {
     "duration": 0.077108,
     "end_time": "2022-09-02T07:49:17.114082",
     "exception": false,
     "start_time": "2022-09-02T07:49:17.036974",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Overriding default units in a file\n",
    "\n",
    "If for some reason you know that the units used in a given input file are different from the default ones assumed for this format,\n",
    "you can always override the default units by explicitly stating what are the pressure (`file_p_unit` keyword) and k-coefficient (`file_kdata_unit` keyword) units.\n",
    "\n",
    "Let's say, for example, that you know the pressures in the above file where in fact given in mbar, you could specify it using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "033e8ec6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:17.279058Z",
     "iopub.status.busy": "2022-09-02T07:49:17.278169Z",
     "iopub.status.idle": "2022-09-02T07:49:18.514866Z",
     "shell.execute_reply": "2022-09-02T07:49:18.513611Z"
    },
    "papermill": {
     "duration": 1.346817,
     "end_time": "2022-09-02T07:49:18.517665",
     "exception": false,
     "start_time": "2022-09-02T07:49:17.170848",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Be careful, you are assuming that p_unit is mbar\n",
      "but the input file says that it is bar. The former will be used\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : ../data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e-03 2.15443469e-03 4.64158883e-03 1.00000000e-02\n",
       " 2.15443469e-02 4.64158883e-02 1.00000000e-01 2.15443469e-01\n",
       " 4.64158883e-01 1.00000000e+00 2.15443469e+00 4.64158883e+00\n",
       " 1.00000000e+01 2.15443469e+01 4.64158883e+01 1.00000000e+02\n",
       " 2.15443469e+02 4.64158883e+02 1.00000000e+03 2.15443469e+03\n",
       " 4.64158883e+03 1.00000000e+04]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [  22   27 1538   20]\n",
       "        "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.Ktable(filename=datapath + 'corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5', file_p_unit='mbar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39d485e",
   "metadata": {
    "papermill": {
     "duration": 0.054247,
     "end_time": "2022-09-02T07:49:18.626842",
     "exception": false,
     "start_time": "2022-09-02T07:49:18.572595",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Notice how the code pressure unit as changed, but not the actual values in the pressure grid. This is normal, you did not ask for a conversion, you just specified what units you were expecting from the file. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574b8028",
   "metadata": {
    "papermill": {
     "duration": 0.053474,
     "end_time": "2022-09-02T07:49:18.734872",
     "exception": false,
     "start_time": "2022-09-02T07:49:18.681398",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Setting up the search path and searching files using regular expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e846e81e",
   "metadata": {
    "papermill": {
     "duration": 0.05108,
     "end_time": "2022-09-02T07:49:18.839115",
     "exception": false,
     "start_time": "2022-09-02T07:49:18.788035",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "At some point you might get tired of always typing the whole path to your files. To avoid that, we can tell `exo_k` where to look for files. By default, it searches the local directory where the library has been imported (\".\"), as can be seen by running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38ee1dfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:18.942671Z",
     "iopub.status.busy": "2022-09-02T07:49:18.942314Z",
     "iopub.status.idle": "2022-09-02T07:49:18.949459Z",
     "shell.execute_reply": "2022-09-02T07:49:18.948412Z"
    },
    "papermill": {
     "duration": 0.060435,
     "end_time": "2022-09-02T07:49:18.951958",
     "exception": false,
     "start_time": "2022-09-02T07:49:18.891523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ktable': ['/builds/jleconte/exo_k/tutorials'],\n",
       " 'xtable': ['/builds/jleconte/exo_k/tutorials'],\n",
       " 'cia': ['/builds/jleconte/exo_k/tutorials'],\n",
       " 'aerosol': ['/builds/jleconte/exo_k/tutorials']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.Settings().reset_search_path()\n",
    "xk.Settings().search_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b596b1e",
   "metadata": {
    "papermill": {
     "duration": 0.054578,
     "end_time": "2022-09-02T07:49:19.058905",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.004327",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This also shows that `exo_k` keeps track of four different path types in this dictionary:\n",
    "\n",
    "  * `'ktable'`: for k-coefficient tables (i.e. `Ktable` objects)\n",
    "  * `'xtable'`: for cross-section tables (i.e. `Xtable` objects, see below)\n",
    "  * `'cia'`: for Collision Induced Absorption coefficients (i.e. `Cia_table` objects, see below)\n",
    "  * `'aerosol'`: for aerosol optical properties (i.e. `Atable` objects).\n",
    "    Those are still experimental and will not be detailed here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14732bf5",
   "metadata": {
    "papermill": {
     "duration": 0.054791,
     "end_time": "2022-09-02T07:49:19.168478",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.113687",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A new path can be added to the search path as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84aba69f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:19.276123Z",
     "iopub.status.busy": "2022-09-02T07:49:19.275733Z",
     "iopub.status.idle": "2022-09-02T07:49:19.282075Z",
     "shell.execute_reply": "2022-09-02T07:49:19.280674Z"
    },
    "init_cell": true,
    "papermill": {
     "duration": 0.063022,
     "end_time": "2022-09-02T07:49:19.284840",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.221818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xk.Settings().add_search_path(datapath + 'xsec', path_type='xtable')\n",
    "xk.Settings().add_search_path(datapath + 'corrk', path_type='ktable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "110c9852",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:19.392748Z",
     "iopub.status.busy": "2022-09-02T07:49:19.392386Z",
     "iopub.status.idle": "2022-09-02T07:49:19.400008Z",
     "shell.execute_reply": "2022-09-02T07:49:19.398876Z"
    },
    "papermill": {
     "duration": 0.064477,
     "end_time": "2022-09-02T07:49:19.402508",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.338031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ktable': ['/builds/jleconte/exo_k/tutorials',\n",
       "  '/builds/jleconte/exo_k/data/corrk'],\n",
       " 'xtable': ['/builds/jleconte/exo_k/tutorials',\n",
       "  '/builds/jleconte/exo_k/data/xsec'],\n",
       " 'cia': ['/builds/jleconte/exo_k/tutorials'],\n",
       " 'aerosol': ['/builds/jleconte/exo_k/tutorials']}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.Settings().search_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d9d59c",
   "metadata": {
    "papermill": {
     "duration": 0.050012,
     "end_time": "2022-09-02T07:49:19.503729",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.453717",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you do not want to add a path, but to reset the path (in order for exo_k not to see some files in the local directory for example), you can do it as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8aa0128c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:19.603264Z",
     "iopub.status.busy": "2022-09-02T07:49:19.602897Z",
     "iopub.status.idle": "2022-09-02T07:49:19.610735Z",
     "shell.execute_reply": "2022-09-02T07:49:19.609636Z"
    },
    "papermill": {
     "duration": 0.060697,
     "end_time": "2022-09-02T07:49:19.613137",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.552440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/builds/jleconte/exo_k/data/corrk']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.Settings().set_search_path(datapath + 'corrk', path_type='ktable')\n",
    "xk.Settings().search_path['ktable']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4f9d0a",
   "metadata": {
    "papermill": {
     "duration": 0.049402,
     "end_time": "2022-09-02T07:49:19.712593",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.663191",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Notice that these methods can take as many directories as you want following one of the syntaxes below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03e98bd5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:19.812730Z",
     "iopub.status.busy": "2022-09-02T07:49:19.812362Z",
     "iopub.status.idle": "2022-09-02T07:49:19.821333Z",
     "shell.execute_reply": "2022-09-02T07:49:19.820241Z"
    },
    "papermill": {
     "duration": 0.061921,
     "end_time": "2022-09-02T07:49:19.823699",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.761778",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/builds/jleconte/exo_k/tutorials', '/builds/jleconte/exo_k/data/corrk']\n",
      "['/builds/jleconte/exo_k/tutorials', '/builds/jleconte/exo_k/data/corrk']\n",
      "['/builds/jleconte/exo_k/tutorials', '/builds/jleconte/exo_k/data/corrk']\n"
     ]
    }
   ],
   "source": [
    "xk.Settings().set_search_path('.',datapath + 'corrk', path_type='ktable')\n",
    "print(xk.Settings().search_path['ktable'])\n",
    "xk.Settings().set_search_path(*['.',datapath + 'corrk'], path_type='ktable')\n",
    "print(xk.Settings().search_path['ktable'])\n",
    "xk.Settings().set_search_path(*('.',datapath + 'corrk'), path_type='ktable')\n",
    "print(xk.Settings().search_path['ktable'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3026f1db",
   "metadata": {
    "papermill": {
     "duration": 0.049033,
     "end_time": "2022-09-02T07:49:19.924087",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.875054",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Once we have done that, we can only give part of the name of the file, possibly in bits and pieces, as long as it is specific enough to identify a unique file."
   ]
  },
  {
   "cell_type": "raw",
   "id": "1a79e9d7",
   "metadata": {
    "papermill": {
     "duration": 0.052406,
     "end_time": "2022-09-02T07:49:20.025981",
     "exception": false,
     "start_time": "2022-09-02T07:49:19.973575",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    The pattern matching is done using regular expressions (python re module).\n",
    "    Some special characters can have a special meaning. A dot, for example, stands for any single character. \".*\" stands for any number of any characters.\n",
    "    If you want to use these as usual characters, do not\n",
    "    forget to put an escaping backslash in front of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "671b805b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:20.251821Z",
     "iopub.status.busy": "2022-09-02T07:49:20.251369Z",
     "iopub.status.idle": "2022-09-02T07:49:25.271335Z",
     "shell.execute_reply": "2022-09-02T07:49:25.270081Z"
    },
    "papermill": {
     "duration": 5.192238,
     "end_time": "2022-09-02T07:49:25.273938",
     "exception": false,
     "start_time": "2022-09-02T07:49:20.081700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file found: /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file found: /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file found: /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file found: /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n",
      "There is no Ktable for H2.\n",
      "    Notice how the previous statement was looking for 'H2',\n",
      "    but settled for H2O because the string 'H2O' contains 'H2'.\n",
      "    If you want to avoid that, really specify a molecule with the mol keyword.\n"
     ]
    }
   ],
   "source": [
    "h2o_ktab_SI=xk.Ktable('H2O_R300_0.3-50mu.ktable.TauREx')\n",
    "print('file found:',h2o_ktab_SI.filename)\n",
    "\n",
    "h2o_ktab_SI=xk.Ktable('H2O_R300_0\\\\.3\\\\-50mu\\\\.ktable\\\\.TauREx')\n",
    "print('file found:',h2o_ktab_SI.filename)\n",
    "\n",
    "h2o_ktab_SI=xk.Ktable('H2O','R300.*tauREx')\n",
    "print('file found:',h2o_ktab_SI.filename)\n",
    "\n",
    "h2o_ktab_SI=xk.Ktable('H2','R300','tauREx')\n",
    "print('file found:',h2o_ktab_SI.filename)\n",
    "\n",
    "try:\n",
    "    h2o_ktab_SI=xk.Ktable('R300','tauREx',mol='H2')\n",
    "except:\n",
    "    print(\"\"\"There is no Ktable for H2.\n",
    "    Notice how the previous statement was looking for 'H2',\n",
    "    but settled for H2O because the string 'H2O' contains 'H2'.\n",
    "    If you want to avoid that, really specify a molecule with the mol keyword.\"\"\")\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "55ac2516",
   "metadata": {
    "papermill": {
     "duration": 0.071478,
     "end_time": "2022-09-02T07:49:25.404658",
     "exception": false,
     "start_time": "2022-09-02T07:49:25.333180",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. tip::\n",
    "  At any moment, you can override the global path by specifying a local search path:\n",
    "  ```\n",
    "  h2o_ktab_SI=xk.Ktable('H2','R300','tauREx', search_path=datapath + 'corrk')\n",
    "  ```\n",
    "  \n",
    "  This can be particularly usefull if you have different files with the same names in different directories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5e798b",
   "metadata": {
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     "duration": 0.07781,
     "end_time": "2022-09-02T07:49:25.550605",
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    "tags": []
   },
   "source": [
    "By default, the filters you provide are compared to the file names in a case insensitive way to enable more flexibility. This can be changed with\n",
    "\n",
    "```python\n",
    "xk.Settings().set_case_sensitive(True)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c290054",
   "metadata": {
    "papermill": {
     "duration": 0.067429,
     "end_time": "2022-09-02T07:49:25.672270",
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     "start_time": "2022-09-02T07:49:25.604841",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Writing a Ktable into a file "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89e7f77",
   "metadata": {
    "papermill": {
     "duration": 0.05808,
     "end_time": "2022-09-02T07:49:25.789186",
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     "start_time": "2022-09-02T07:49:25.731106",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To save any `Ktable()` object to a file, just use the relevant `write_format('path_to_write')` method.\n",
    "At the time of writing of this tutorial, the available writing methods are:\n",
    "\n",
    "  * `write_hdf5()`: General self defining format, which makes it the most optimal for `exo_k` (based on the Exomol Format).\n",
    "    This method accepts the `exomol_units=True` option to force the output units to be the same as the native Exomol ones (i.e. directly compatible with the TauREX and petitRADTRANS codes).\n",
    "  * `write_LMDZ()`: Format used by the LMDZ Generic Global Climate Model (see LMDZ section below).\n",
    "  * `write_nemesis()`: Format used by the Nemesis retrieval code (binary; .kta).\n",
    "  * `write_arcis()`: Format used by the ARCIS forward model and retrieval code (fits; .fits).\n",
    "  * `write_pickle()`: General python/pickle format. \n",
    "\n",
    "When needed, the data are converted to the right units before writing. \n",
    "\n",
    "Notice that the full path (relative or absolute) must be provided, except for the file extension that will be added automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28e338b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:25.902897Z",
     "iopub.status.busy": "2022-09-02T07:49:25.902532Z",
     "iopub.status.idle": "2022-09-02T07:49:39.311259Z",
     "shell.execute_reply": "2022-09-02T07:49:39.309206Z"
    },
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     "end_time": "2022-09-02T07:49:39.313761",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "h2o_ktab_SI=xk.Ktable('H2O_R300_0.3-50mu.ktable.TauREx', kdata_unit='m^2', p_unit='Pa')\n",
    "h2o_ktab_SI.write_hdf5(datapath + 'corrk/H2O_R300_0.3-50mu.ktable.SI')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24114810",
   "metadata": {
    "papermill": {
     "duration": 0.058535,
     "end_time": "2022-09-02T07:49:39.424361",
     "exception": false,
     "start_time": "2022-09-02T07:49:39.365826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This new file has saved the unit information. So now, it can be loaded with only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "977232d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:39.535958Z",
     "iopub.status.busy": "2022-09-02T07:49:39.535492Z",
     "iopub.status.idle": "2022-09-02T07:49:40.897827Z",
     "shell.execute_reply": "2022-09-02T07:49:40.896324Z"
    },
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     "duration": 1.421938,
     "end_time": "2022-09-02T07:49:40.902368",
     "exception": false,
     "start_time": "2022-09-02T07:49:39.480430",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "h2o_ktab_SI=xk.Ktable('H2O_R300_0.3-50mu.ktable.SI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4ca32d7",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
       " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
       " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
       " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
       " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
       " 4.64158883e+06 1.00000000e+07]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [  22   27 1538   20]\n",
       "        "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_ktab_SI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc9b7a4",
   "metadata": {
    "papermill": {
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     "end_time": "2022-09-02T07:49:41.162720",
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     "start_time": "2022-09-02T07:49:41.106331",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### What is inside a Ktable object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9163a057",
   "metadata": {
    "papermill": {
     "duration": 0.06277,
     "end_time": "2022-09-02T07:49:41.293737",
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     "start_time": "2022-09-02T07:49:41.230967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To see what is inside a `Ktable()` object, you can list the attributes using `__dict__`"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6f9ba317",
   "metadata": {
    "papermill": {
     "duration": 0.06295,
     "end_time": "2022-09-02T07:49:41.420362",
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    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    Although you can access and see these attributes, they should NOT be changed manually\n",
    "    as changing one of them usually implies that others need to be changed too. There is a\n",
    "    method to change all the attributes that can possibly be changed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a058892f",
   "metadata": {
    "papermill": {
     "duration": 0.056656,
     "end_time": "2022-09-02T07:49:41.535589",
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     "start_time": "2022-09-02T07:49:41.478933",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The most useful attributes are the following:  \n",
    "\n",
    " * `mol`, Name of the mol described  \n",
    " * `Ng`, Number of g points  \n",
    " * `filename`,  Name of the file used to load the Ktable  \n",
    " * `ggrid`, Abscissa of the g grid  \n",
    " * `weights`, quadrature weights corresponding to the g points\n",
    " * `kdata`, Numpy array with the coeficients\n",
    " * `kdata_unit`, Units of the kdata  \n",
    " * `pgrid`, pressure grid  \n",
    " * `logpgrid`, Log 10 of the pressure grid  \n",
    " * `Np`, Number of pressure points  \n",
    " * `p_unit`, Units of the pressure grid  \n",
    " * `tgrid`, Temperature grid  \n",
    " * `Nt`, Number of Temperature points  \n",
    " * `wns`, Central points of the wavenumber bins (in cm^-1)  \n",
    " * `wnedges`, Edges of the wavenumber bins (in cm^-1)  \n",
    " * `wls`, Wavelengths (10000./wns in microns)  \n",
    " * `wledges`, Wavelengths (10000./wnedges in microns)  \n",
    " * `Nw`, Number of wavenumber points  \n",
    " * `shape`, shape of the kdata array  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4d9b444",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O\n",
      "[1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
      " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
      " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
      " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
      " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
      " 4.64158883e+06 1.00000000e+07]\n"
     ]
    }
   ],
   "source": [
    "print(h2o_ktab_SI.mol)\n",
    "print(h2o_ktab_SI.pgrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a22e613",
   "metadata": {
    "papermill": {
     "duration": 0.05769,
     "end_time": "2022-09-02T07:49:41.824513",
     "exception": false,
     "start_time": "2022-09-02T07:49:41.766823",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "the data themselves can also be accessed directly as follows (but they cannot be modified this way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45e52a9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:41.955253Z",
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     "shell.execute_reply": "2022-09-02T07:49:41.961357Z"
    },
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     "duration": 0.078528,
     "end_time": "2022-09-02T07:49:41.965685",
     "exception": false,
     "start_time": "2022-09-02T07:49:41.887157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.97818148e-30 1.99009429e-30 2.01152500e-30 2.04241642e-30\n",
      " 2.08267365e-30 2.13212071e-30 2.19045642e-30 2.25722151e-30\n",
      " 2.33171732e-30 2.41298132e-30 2.49969054e-30 2.59013679e-30\n",
      " 2.68222162e-30 2.77340390e-30 2.86082684e-30 2.94136768e-30\n",
      " 3.01184614e-30 3.06921780e-30 3.11082349e-30 3.13465184e-30]\n",
      "[1.97818148e-30 1.99009429e-30 2.01152500e-30 2.04241642e-30\n",
      " 2.08267365e-30 2.13212071e-30 2.19045642e-30 2.25722151e-30\n",
      " 2.33171732e-30 2.41298132e-30 2.49969054e-30 2.59013679e-30\n",
      " 2.68222162e-30 2.77340390e-30 2.86082684e-30 2.94136768e-30\n",
      " 3.01184614e-30 3.06921780e-30 3.11082349e-30 3.13465184e-30]\n"
     ]
    }
   ],
   "source": [
    "print(h2o_ktab_SI.kdata[0,0,0,:])\n",
    "print(h2o_ktab_SI[0,0,0,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeb2554",
   "metadata": {
    "papermill": {
     "duration": 0.06442,
     "end_time": "2022-09-02T07:49:42.118193",
     "exception": false,
     "start_time": "2022-09-02T07:49:42.053773",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Handling zeros"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548f4ffd",
   "metadata": {
    "papermill": {
     "duration": 0.069674,
     "end_time": "2022-09-02T07:49:42.250888",
     "exception": false,
     "start_time": "2022-09-02T07:49:42.181214",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "At wavelengths where data are not available, the ktable can be filled with zeros that can become a problem for some codes (e.g. because of log interpolation).  \n",
    "\n",
    "If you want to avoid that, we have created the `remove_zeros=True` option that will replace zeros with a number 10 orders of magnitude smaller than the smallest number in the table. \n",
    "```python\n",
    "h2o_ktab_SI=xk.Ktable(filename, remove_zeros=True, **kwargs)\n",
    "```\n",
    "You can also call manually the `remove_zeros()` method on a `Ktable()`object. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a112a3",
   "metadata": {
    "papermill": {
     "duration": 0.077557,
     "end_time": "2022-09-02T07:49:42.396694",
     "exception": false,
     "start_time": "2022-09-02T07:49:42.319137",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Copying a Ktable object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3473bc2a",
   "metadata": {
    "papermill": {
     "duration": 0.073028,
     "end_time": "2022-09-02T07:49:42.541891",
     "exception": false,
     "start_time": "2022-09-02T07:49:42.468863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If for some reason, as we will see below, you need to copy a Ktable object before modifying it inplace (to change resolution for example), you can use the `copy` method. This is similar to the deep copy in `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab41d515",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:42.704451Z",
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     "shell.execute_reply": "2022-09-02T07:49:42.769137Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
       " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
       " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
       " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
       " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
       " 4.64158883e+06 1.00000000e+07]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [  22   27 1538   20]\n",
       "        "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_h2o_ktab=h2o_ktab_SI.copy()\n",
    "new_h2o_ktab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc340937",
   "metadata": {
    "papermill": {
     "duration": 0.065287,
     "end_time": "2022-09-02T07:49:42.905963",
     "exception": false,
     "start_time": "2022-09-02T07:49:42.840676",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Sometimes one needs to copy only the structure and metadata, but not the big `self.kdata` table. Then, you can use:\n",
    "```\n",
    "new_h2o_ktab=h2o_ktab_SI.copy(cp_kdata=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5022a81a",
   "metadata": {
    "papermill": {
     "duration": 0.069312,
     "end_time": "2022-09-02T07:49:43.040412",
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     "start_time": "2022-09-02T07:49:42.971100",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plotting opacities and k-distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2635090f",
   "metadata": {
    "papermill": {
     "duration": 0.066627,
     "end_time": "2022-09-02T07:49:43.175879",
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     "start_time": "2022-09-02T07:49:43.109252",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To help you vizualize opacities, the  \n",
    "```python\n",
    "plot_spectrum(self,ax,p=1.e-5,t=200.,g=0.,xscale=None,x=1.,**kwarg)\n",
    "```\n",
    "method is there to make it easy to plot at the requested (p,T,g) point. You can also normalize the cross section \n",
    "with the volume mixing ratio you want for the species with the `x=vmr` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4459ee2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:43.309052Z",
     "iopub.status.busy": "2022-09-02T07:49:43.308605Z",
     "iopub.status.idle": "2022-09-02T07:49:46.636390Z",
     "shell.execute_reply": "2022-09-02T07:49:46.635137Z"
    },
    "papermill": {
     "duration": 3.397431,
     "end_time": "2022-09-02T07:49:46.639033",
     "exception": false,
     "start_time": "2022-09-02T07:49:43.241602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XrdgEQGiY7+IHNptWgF6Y4dacFhYWV0sAXL1yMwDtOrRULB7BwYHo0SQ2b9zByGF3kNqyGUBAAmCx6zcRGe19/8eLv0ogPlVPgiA4RVG8v6qTC4LwA5AOJAmCkAk8K4riZ4Ig3APMRvKn+VwUxc3VHvlx4k9Td6IxNIAGJ4u9ew5y+YX3kHngSI2Od6gm/flzl+v28RQAZZOOGouH6Wjicx9is7sFRfUL78fv/wQgyFw3AuCZPvcZ1D12u4N5c5aS1CCei865nQkvS9o6WfgD2LzJv0uPyWRik46VqqiohKtHP6hpczgciiBS7CEwqlFr3zyFD7XpVz0GXxTkawVARxUCoN218FNrIfX4Z85S3XZTkPdY1q3eWuU47R4LzkNZbotEUWEJyxav44arHiVj+Tc0S27kd2yZ+w8D0qJZFqBDQqQF6crlG9m0fju33H65z+NLiiXhXBa0y1znsNsdjH/kLe4fd6PXGGQBUE8APl78ia73+NnnRDKP+EUUxWt8tM8CZlV1/JnGwIEDueOOO3jiiSdwOBzMmDGD22+/3dAAGpwUnE4n/bpceVzn8DSvBMIP38z0apO1AzLvvP416ef2VbYvGXG31zF1qAE05j6DGlNZWcn7b3/HpAkfKW0L/lnp1e+c/jf6PU9QkImIiHCvdrWWUKa4qFQJaCjW2S9TrhK+PDWAelRU+DbrFhRoTcCTJnzEDWNH07V7O93+sua/qLCEi869nW1b97Az62+vfrLJ05PiolLXmCoY2v9GHn/mdkpdAm1O9jGf47RatQJgcXEJ0TERFBWWUlRYwh03P0NhQTGbN+6sUgA8lCUJ8HabXdHMhoRK2kfZZO1PAPQUVGUhfMnCNXz12W8c2H+IH357Uzte13U8F8m1gT8BcKiffQY1oE+fPowaNYquXbvSqFEjunTpQmxsYOHwqampFBYWYrPZmDZtGnPmzKFjx45VH2hgoMN/W3Zz/12BRYv6w2EPTABcs2qL8ln9YpSp1BEkfQWVyJjrSAOIMfcZVBObzc7SRWvp0Kk1XVqNJMrDXKenvaoSk0nXtHk0J8+rTf2s+BMAPX0Aq0K9wPNMf1LooQH8+vNp/Pj9LPYdzQAkn7WU5o2Jio6koqKCrVt2A3AsN59Vyzd5Xeu6Kx6hbbtUrFZ9s6jsE5x3rJBtW/fwyD2v0KlrGwBysr3/JjJ2u4cAWFRKQmIsRYWlFBYWKz7KgWR2yTqY4zpnhSIAbly33WdAjieef3P5XuU0WXrmcfn79NRk1gb+KoH8W+tXM+CRRx7hueeeo7S0lCFDhtCrV6+Ajtu7d2/dDsyg3rBnVyZn972+Vs5VUakvADaK0qaOuCD9Nr/nsesIklUJl3UlABpzn0F1WL5kPVO+m8X3X/3BjWMvAdzaquPBZDJRqaOB27c3y6tNbX71d22ND2AAAqDDUcGB/YfI3H+YJx95i4hICzPnfQzoR+XK43A6naT3u4HuvTow+9/PeP2lz5VUNnLwhJqKigrm/rWEuX8tYdRl5+qORRa4ZE1icEiwIhRlH8n1eQ+eglNJcSkt0hqxb88h9uzKVNqr+nvYbHZF02i32xWB9Mjho5qAHJvNTmiovk+i1aq9hnxNXwE/4BYAA/EXrC6BVALRm4WdoigG7vlooHD77bezZcsWysvLuemmm+jZs+fJHpJBPSO9X+0If1C130/A59HR9ulNhmqCzMeTkq9qjLnPoCqsVhujzrtL2f7682m6/fQEuao4mpPH4Sx3bffwCAtlpeXs33fIq69aA+grytbpdGp9AFWaNk8/XZkKRwX9ulzl5erhdDp1tVXBwWb27Mpk9p+SsCf76K1c7k5lc/Sot7ZOLYj50nQVuwSuUldFkeBgs+InnHs0X+MHqUYdBOJwOCgvt5GQKFneNm5wm5urMokfOXxU+TvZbXYKPUzgMqUlZT4FQE8hUxbq5Gvbbd5zXl5uodTHh2b0eAhkItuK5PcCEIeUzHR3rY+knvD999+f7CEY1FMcDgfN4obU6jnlCf54KfWIXLRYQrFVMeEF1X02fmPuM/CisrISh6OC1k2G+zRXelKThdIzj03WbMtRtgV53tWs1BG4ekLES899yE8//MXE193BI2phxJcvr8NRobtvzMXjdXqDOTiYkcPu8DJTq/MN5h0r9Dpurirww1f5OVnjJmsezcFm5V4rKyvZvTNTNzm8zeb+jmTtaEKi5C+5cZ1bAKzKJC77JlosodjtFRT6CLYpLS33mWBaLWQ2adqAcpdGUBZq9dxecl0Cc1XzYU2o0oYiimJnURS7uP6lAE8gJUg1MKgXvPjsBzSKGkCjqAEcUq3IZ/3xL089+jaPPfCau236vzz2wGuaFe2pQnL8qVuA/YAruk7GEh5GVma2j94S5jrWABpzX+0gPzvvvvkNDoeDu8Y+x0Xn3k5paTkLM1Zx05jH/CYvPtV45L5XSEk4O2DhD2oWLOWJLBjpacbVgoPnuBbMX8Xk178m62C2xjx8LLeAxQukCoOygOrpu+jQGbc/TVlwsNlL+LNabZiD/T+rH7831T0unSwBAIUFJZSXWzUaQKvVpqRU8cyT99MPf9IoaoBG4JTNqXHx0QQFBbF5ozvKuioT68RnPqBl6xQGpffGbrMrGklPjhw+qpTS80QtZHbv2YHyMpumXU/7KddOPlkm4Ms8+vdGKmZuYFDn+POnOBEMH3QLG9ZtU7a7tx2tlC675ZonlPauPdqzZ3cm777xDQBffvqbV4mzk8k5Z93k08xzKhIWFkZBvrem4ERizH1aZCGmOoL3l5/8qnx+8ZkPaJrciF9/nAPAkN7XKoL/XzMWnlLPix75eYW0SxlRo2NrYgL2RVXuEp5aJLV/mjp1y7j7XwVg3fbfiXHlwnv48bH06tNJMWt7jjsqOoIWSb5jpIJ1fhsF+cWEVJErLz+/iMTEOHJz8zmgY+KOjYumIL+Ilcs2KvcXEhyM3e6gU+c2HNh3mC2bdjL6crf/4FOPvg1I1TtkZAE4PCKM6JhICvKLaNgokewjuX4F24qKCnbtPMBDj93Cnt2Z2Ox2Cgv1TcBXj36Qgvwith34y0sTKGtdF63+ns8//hXrYim9lSzU2jwCVpxOJ4UFkqBprYMgkEC8qH9GKoj+E/ADcDmwrNZHYmCApMrPPZrP+rX/8eP3f5KScDaNogbUygq6uhzNydMIf/546O5JivAns3b1Fh+9Tywrl23UrHSPl559OtXauXwRZgn1m4ICYPy4t+p6GMbc56KszErT2ME0jR1creMee/B1zbY6hYmn1lcvqOBU4b8tu2ss/AEsXbyu1saiZybUmoB9Cwp6ARglxaWKBtAcbKbfgG7Kvhef+UDTt6rUS8E6tYWLCot122WcTiflZVZSUpsA+lHO6ef2xWw2s3jBGuU3JJmA7URFR9CxcyulSodMvstUXlTo/s3JGsDwiDBF6B0ytDfgPwjkWG4BTqeThMRYQkODcdgdimDmiSxkq03E2//b6/I/lK6R3LwJFkuootWTfTM9A9/sdofy7rOW23A6ndx63ZOsXFo775ZABMDnVf+eBW4ELq2VqxsYePD4g2/QMfVCzhs8lntvf0Fp/+SDn074WHylSblqVJVp4AAYcfZtAeXaqkvKyqyMHHZHrZ4zMtI7N1lt47A7qkwDU1XpqVrAmPtcpDZwa318+WgFwl8zFvrc98oLH9f4vHXJmlVbai1qvjZQawAvv/o8wNME7Pv7OaxjardabYoA6KnB8zTN+wp8kAnWMfUWFhYryZIBEhPjlM9Op1MRcpo1852DLz4+hoaNEjh8+KiyUAgJCcZqtRMSEsx5Fwxi5fKNugJuvsqSIAuP4eFhDBwiBWD27N0JiyXU51xdUlxKtiv6NzEpjuDgYGw2h6YWsh5FLg3h3j0HGdz7Wl567iNF4LNYQrFYwigrs3L16AeUsnrqZ8tqtWm0odZyGxvWbWPG7xkUF/uu9lIdAhEAXwZeF0VxgiiKLwK/4XaMNjhBTJo0idatW9OuXTtmz559sodTZ3z12W+67c8+fuIrosz9S98k9e8/K7nnf88HdI7XJn5am0OqFk6nkycfebPqjtXEX9LV2uIUMVcbc58OWZk1qxoDMM9HlQeAD9+dUuPz1gWVlZUczcmrMoXRiUZt7h0wWBJiKhwVZB/J5fChHOx2O916tNc9dt3qrSQ1iNe0lRSXKVomPQGuOuhV5yksLNFE58q5+0Ayrcqat6bJDX2e12QyERMbRUFekWIuNQebsdvthIaF0qd/ZyorKxHf+Z6iwhKNH6Ta7C2bgC3hYbz0+kM8Ov42rrxmBJbwMN0gEKfTScvGw7jteinoJTEpntDQEBx2B4cO5nj119y3S1iW/Syn/zqP8nIrYWGhmEwmwiyhgFR/ectmqSpMSYlbsBt73ZMM6DFG2bbabPwxbT5ms5ne/Tr4vXagBCIArgTUVZZfdLUZnCC2bNnClClT2Lx5M3/99ReCIJwUk2hdU9Xq8kRS1d/3px/+Cug8/205sUGjB/Yfwmq1sXnjDu685Vm+/+qPWr/GibgndbDNScSY+3TwFf3oib/cbKca8nMDsG3rHnZs28v/bniKTmkXneSReeOwV9CqTXNGX34uqWlSAER5uY0urS5m2MBbsNkcnDWou6aSjszWzbto0DBB01bsYQKuDp51dG062seigmKCVRrAGNUxNptDMX82adrA53VMJhNxcTHk5xcprgMhLk1caGgwkZFS8Mp7b37LPbc/z0FVact8tQCoMgFHRkXw8BNjiYmNwmIJ09UAyr/h3TsPAJDYII7gkGAKC4uV4AxfFLmEzUX/rgYk03ZBfrFS0cPiEgDBXSKutKRM0eZ6KiCs5Tb+nbeCfgO6ER1TO2XhAhEAWwMbVNsbgVa1cvV6yAsvvEC7du0YNGgQ11xzDa+//nqVx/z++++MGTOGsLAw0tLSaN26NStWrDgBoz2x7Nt70O/+vbtPTGRtSXGpxvzsjxA/vi0Ac2cvZe7sE+PcXl5upXfHy3lQeIlzzrqJaT/PPSHXPYMx5j4d9Go563HHTc9U+9w92l1S7WOOl7Iy6bl56O5JAAzpcx2Del3LjN8zavU6l111Xq2cx253uHLemZX5Z88uSUDJyT6GzWojNCyUNu1SdY9PTIrTbBcXuQXA6moAmzbTau30chAWFpYQbHbPk5GqmrZ2m13RvCU1iPe6/jnD+yufY+OjWbpoLe+//R0glcqz2+yEhIYQHuEuk7Zz2z4O7HebTtVpc+Rk0uHhbuELJI2gpw/g3bdN4POPftG0JSXFST6AAaT1kYNENm/YQbhLw/jnjAVYLNJYw8LcY1D7PfpShFitNo7l5pPSvHGV1w6UQPIAZgK3CYKwFDAB/wP8v6lPA6Y/+ytZm2v3Npp2asaoCZf53L9y5Up++eUX1q9fj91up2fPnvTq1YvXXnuN7777zqv/kCFDeOeddzh48CD9+7sfhOTkZA4ePO2/Ai/yjvn3qXj2yXf5asordT6OK0fdz+oVmwPqe/cD1/H2a1/57fP6pM8Zdv4Av31qA3kC+2XqnDq/Vj3hjJz7jpdAfACdTidLFq2t9rmzDvpP/VMXyAmNf54yu9aEND2ioyOr7hQADruDyopKzGazorH7b+seZb/d7iA0JJhiH0FU8R6RqSUlpUoQSXUFwOSUxmxTXVtPeCksLNZYVNSCj93uUP7+4eEW4hNiNS4mcvCIKchEXFy0xjXEZrdLWSJCQrxqJquTZatNwPLiJTJK299iCaO8zMYn4o/s2Z3JMy/ezc9TvF2tEhLjCAkJLCvFtJ/n8ubLX7Brx36uuvYC5s1eytGcPFqkNZWuqartq77nosJiLyEdpDyAhYUlXql6jodABMAPgdcA+Y1oAh6ptRHUIxYvXszo0aOxWCxYLBYuvvhiAMaNG8e4ceNO8uhOPuvW+I9s8udAXhvs3Z3Je299F7DwB1J0V1WsXbXFZ5b62mDv7kxSWjSpMmjCoNoYc58OgfzOnn/q/Rqf/1BWjl9z4PHicDjIPHBEZT51a37uv/P462P7wuKhdaopdoeDiopKzCoN4Ocf/azpExIaookMVhPpIUAUF5Uq32l1c2vKFTX8UVxYovnNhIS650G73R0ZGx4RRnx8jFYAdI1H9gFUI5tNQ8NCiFAFpjnxEABVQunRnDxiYqO87tMSHsbM6RnMnJ4BwAPjbva6j9i4aEJCgjVWHzl1jR5//7lY+RwXH8OwEQOY+t0sXQ2gOnilpLScJQu1i6eo6AjKSq0UFZYQE6P9OxwPVb6RRFF8QxCEHGCkq+kPURS/8XfM6YA/Td2JpioNYLNmzThw4IDSnpmZSbNmzU7kEE8Ib7/29Um9/j23v8DKZRur7qhiyaI1AfXb/t8+OnauXethRUUFg3tfx64d+wFY859+AI1BzThT577jZep3s7jwYv9JxcXJNa949M/fy7jupotrfLw/vvr0Nx51JW4fc8NFTP5gvKakWV0GOHkGX9QUyQRcgTkoyGd6ldDQEJ9R8p7aMrUPoKwBXL7hR/p1varKsVgsYcz852MuOud23f2RURF8LP5I48ZJmrHJ2Gx2ykqtyrniErTaSbVGMi4uWrNPDpgIDQ0hXKVNczqdZGUeIT4hhrxjhZp8otlHjulW6SjxqJ9cpJPjLy5eur5agE1qGF+lLyBI2t9YlwArp9KRBUFP9uw8wK3XayutxMVFc+yYlIrGUxA+HgKtpj4b+B54APhXEIRo/91rD0EQLhEE4RNBEKYKgnCeq62DIAgfCoLwsyAId1V1jlOFgQMH8scff1BeXk5xcTEzZswAJA3gunXrvP69844U+Tpq1CimTJmC1Wplz5497Nixg759vR18T3d69OpAQkIsL7wSWJqV2sZfstKhw/rptgeZ3I/QsxPv8X18/xtqPjAPSopL2bVjPznZxxThD+omU7yBMfd5UteaeNkXr7bYtzeL/DxJCHhUVbVnyjczAd+1c2ubjp1bB9z34cfH0rtfZ919DlfalOBgs1fals6uCNsgcxAVPgRAdUoWkEykFRUuDaBL4GqRFpiCISEx1m9aqBhXsuVt/0lm4hEjBxOiEgC/+PgXLr3gbkCqdRzvKQC6BNygoCBiVQJg6zbNFaEtJCRYqwF0OikoKKZJU8k/UR0EcuTIURISvAVAz/Q4ekmeY2Ol66sF2GtvHOnVT4+o6AiauPwlZZO02gQM7qCQ1Su9LVCxcTGKn6Bn4M3xUKUAKAjCMGAnUlLU9q7/Pwzk5IIgfC4IQrYgCJs82kcIgrBNEISdgiA87u8coihOE0Xxf8CdwNWutq2iKN4JXAUMDGQspwJ9+vRh1KhRdO3alQsuuIAuXboQG1u1Cr1Tp05cddVVdOzYkREjRvD+++/XeRmsk8Gyxevo2bcTjZv4Nv8cPI4UFP4oyC/y67PUoZNv7Z08+Qj3X8ui1b41H+okuMfDnWOfY0CPMZoi5+CdtNXg+DDmPgmHTukxfyythu/fkrX6qV9q052hb+cr6NvlSt19113+sKKBOhk88sRY3fbk5o2ZOc+dF1EtdMh584LMZq8gtBapkuBWVFDs0wSsTuacnNKI3KP5Kg2gy+cuwDrbeoEbasIjLMrnTl3a8NWUVwhT3csH7/yg6RsXp68BNJnQCIBR0RFKbeCwsFDN38fpdFJcWEJcfAyhoSGa8ne+NIBFHpHter6MsuAl/41CQoK5454xfDVV65cul6ZTExUdQTNXmhvZD9GzwpWsIdarrx6r8n+sLV9SCMwH8HVgC1IZJJCy4geqovkSeA9QbHuCIJiB95FKKmUCKwVBmA6YAc+l31hRFGWv4Kdcx8nnGQXcBZxWJplHHnmE5557jtLSUoYMGUKvXr0COm78+PGMH69fgPtMQDZrBAUFKS+c0ZefS2VFJX9Mm6/083xQa4Oc7GN0bul7Jde5axufUV+VzkpWbf5FSS/Qpl0q/yz7mnP63+jVd96cZYy67JwajdHpdLJ18246dm7F6hWSTOGZ40/2XzlVuSG2F1usR1hdfurVSfaBMfeBkqRWZsjQPn77XzLi7irP+e3Pr9GpSxuaNmvI2Duu8PJh27RhOz16daz+YNE+KzIF+UX8+4935oS5s5dy3c2janSdmoxLTe9+nTVm4YcfH0vGP8tZvWKzl1D1zIt3K6XNHI4KKioqJQ2ghwDYuKlkas3LK/SZykotACYmxWsEQE/tYFXExcf49W1Wm6HjHWE8mnw/jca6NZshIcGKsB9uCfMyb15yxTCm/TyXa2+8WONvHRkV4S4JF6q9vtPppKiohGYpjYiItGiClo5mH6NXAJWM1K4A3Xt1YN3qrYrvnSy4WcLDMJlMimkXYMKke7nhltG0bDxMc77o6EhFIylHPXsurJIaxJN54IhuBSm1+Vv6G9WO1joQAbA18ALuSTAPiAvk5KIoLhAEIdWjuS+wUxTF3QCCIEwBRouiOAm3r42CIAgmpISsf4qiqDhciaI4HZguCMJMJBONhtzcXI3A1LJlS3r27ElRkf9I07pm7NixbNu2jfLycq699lratGlTq2MqLy8nIyND01ZcXOzVVtcU5BcTGxe4r4K8SmvSLJaNGyUB59ixY9zz8JVcf9twrh75JACffPgdF19WvXJUVbFx3U6f+6JjInj6pVtYt3q77n6bzc7GTesA2LNXv4/M/258ipiEl/z28fVdzZm1jM/E6dx292jFYVjtZHw6EGQKorOlSa0KgHX8uz7t5j69ea9VK33tdVXzQnFxGeHhoZpSWgAL5q+s1t+9/6DOLFukUYSyb/8eQix2tu/YQvc+qfCR9pgRZ9/G1Bk1e1Zm/LaIbz6bxdMTb1XKgQGMu18/g4C6pnddsmGD1r/Ybreyc5d77tm3b6/yLti2bRsZGeE8MeFmFvyzlpyj7rJ5sttHVtZBVq7UCrVl5a7j/9uJ06lvAs7MPMDQ83pTmF9MRUUle/bsZ9XKVQBs2rQJU3DgwsW+/XvA7Lt/aYn7txOTL2kVsze7/dkTkmI4ckgSttatW8OxPK0pNudoFt/8OoHD2fvZscNdltNqcydM3rlzp+Z3UFZaRnlZOfGJkZg9ElOXlVkpKyv2+u107JLGlo3uaObFi6S/63ufj+O3H6V+peXSMbtc31lQkDT/7Nzmvp+8gqOsXOW90NizdxdhkW6BPCMjg5UrtIKeKcipjNGT0jL3Imznzu00TUmolbkvEAFwJzDa9Xk4cBkQWIFUfZoBB1TbmYC+g5XEvcAwIFYQhNaiKH4oCEK6axxhwCy9gxITE5k4URvRtXXrVqKjT5gLjy4//VS3Jc0sFgs9evTQtGVkZJCenl6n15WprKxEnPw9LzwtMmPuR/Tp3yWg4+S8TT16dqOyUnoQklOaKeOOi48mP6+Io9nFtX4vsnCph9MJ6enppKenc/d9Y0mOH6LZbzabqzWeqvr6+q7kMX76/u8BX6u2mPr7W1w9+kG/feSC6sdDmEmajh58eiwvPx9YWbA6/l2fdnOf3rznC3/zgs1mJyXhbG64ZTQPPnaz135fx2Uf8Q6iaNigIQfzF9Aszv3s9OjRQ9HE7NubpXuu431WXhj/mabdYrF49T2RdO6s9enr2q0jHTt2BKRnOjU1lf17jwL76dKlszLvPPAwzPlzMaA1l6emtmDw4MGAW7Ad98RdHMkq5KU3HmLis/reCi1SUxn/3J0A3Hv7CyxZuIYuXboC0KtXLwYM1r4/Jrx8n89KTAMG9CetVTJSsLw3Yaq/eaglFMpg5EXnsqUwmxVLNyjCH8DQc4aSfbiMX374R2kbOGgAKc2lGsEmZySy0jstrQWrlkmm0m7dump+B9lHJF+5Cy9OJyvzGLlHC4g0hWJ1OnBQSafOHYiKitIcc1bGAGxWO9N/+4eH7p5EVKTkmnXeecPYuGY/81hJ27atSU9P5+jhcuBXoqOlczRI2glI7jc9e/YgPX0Qsxd8ht3mUMpwDhjQn/4DuyNVlZR+2/36ncXObYeVfK19+3dn7Sppejn/okFERkbw649SSq/2HdqQMVdKKH12+mAOHdlXK3NfIALgU0i+LybgMcDGCayHKYriO8A7Hm0ZQEZNzud0OgP2bzjdOBXKZz392GQ+ddXtHTnsDi6+ZCifflv1C6m0xJ0LSnaSVTs4r/lvGi0bnVvrDuhVmZTVme31kj5ffd2F1bpeZWVllQXVlWvb7Iw67y6eeLZ2a/lWl6Agb7OQ2WzWmJhCQwOZSvwzJlZ68SQkVO0Xe4I4o+a+6jDTlQT5my9+556HtHVwh1/g2/UwJ8dbAKysrPQyE8ovddDWtq0pM6bN5/OPf+HXWe/57KPOV6dHk6YNql2B5u0PnmTnjv289+a31ToOpL+jOqm22WzGhMn1WTtHRER4C69ms9mrX1KDeL7/9Q0A3z6AqvdffEIMx44V+i0Fd/Ntl/LPnKW079iSj96bSkqLJjzw+NUsnLeJ3v06a/LseeJUmYBNIdJYK+0VPDXhLkad545hOv+iQcTERmrMqaD1kwtXRc1GqZJJh4bp5+WLiolU/m5XxHYj11HCjOItuimGwsJCCQsLVaKJjxw+islkIjomUonWlU3Ach5Aud2iSuciB3J076kt1Sbn7nv93cdo36GldD/hYXz05fOKANi+o1tT37JVCg89dosiAMZ6mIAP1ZIrfJVvIlEUZwBdgHtc/7qIovjncVzzIJCi2k7mBCVXtVgs5ObmnhKCUm3jdDrJzc096avc776crtn+Y9r8gP7eNpsUwRpmCaVla+nn0auv21dDHWlWW8EUAK2bDtdtf/oFwTUubdLbW++8QrN9823VkweaxAwK2I9xz65M1q7awlUX125UdHUXQHovhUef0tZHzTzgf0aSX2yBEBvnX0sfhInulqaYq3HOmnAmzX3VZfMmt2nSU0Dz53qg5/sqa/TVNGzkLkfmy7/2onP1U4vocev141m8YA2//fR3wMd4UpM6uNfcMJK+/bsG1Fc9D7714YOcf+EgzTUt4WEEBckCoHYsETqRtmVlVi8BUL1IrajU/7tecoXbPy0qKpLSkjKuGvWAdF2dv4HFEsaP0yfTtXs7aSzhFpJTGvLRl88TFhbq1wdQPfXLdYJL80o0vos9+3Ti66mvYjabaecSjmTU5w5TlU5T5zL0DKaQiY6OZNKbD3OtK6VQYrAUPKF+r3giB61kH84lKjqCoKAgKl03IQua8t84TM7npxqXOrefmihX4MYNt4z2aRVr2SpZ+RwXH0NEpDQWEybKV+cQYjK77usEJIIWBMEzUZ7shNBFEIQuoij+WsNrrgTaCIKQhjT5jQGureG5qkVycjKZmZnk5JwSdUZrHYvFQnJyctUdA8Bud/DC0+8z5dtZXHXtBbz46gMBHafnvzDt57lceqW+oCVT7kphEhYWyuD03ixe8wOt2jTX7bt71wG6dGsX0HhqQr+zunH3A9fxifgjjzx5q2bfFWNG8NmHbof1QNMlqGnddDhHin2Xhzt8KIfVKzbTtn1qtc8dCNVdAHm+ZP74+0P69O/CpAmS41ZycCxWp4OcCt+CbVA1hDW9l52aDmGN6GZpRkUdLeTOxLkvUBYvWMNlF97D9arACNnZ/vFnbg/YNC8THGzmrvuu8dvHV766Vcs3kTFvOenn6lvJHXYHv/w4h/NGuDWSK5Zu0O0bCEFmfX3InxmfckH6bbr7IPD0S+rnrmEjKfhD/Vu3WMKUxZmnIKanASwsKCIuPoZvf36N3n27eKVQqdCpBNK2XSqxJgsrpyyjz5j+XhUx/AnBsjAW4+Hb7e8Y9Xdrdkr3VpRTRLLKYqCOCu7ZuyP/LPuaEUNuxWazawRadd48dSSsr3Kc0dGR9OnXhR49OvDk7w8r7e07tiRnof66S/47Hz58VNH4eVZJkYNO5MTeoaEqwTRKf+4KJHJX/S5JSIhV/t4NzZHkLzlIcnAse+zHNKX0jhd/dpufkZJqe2JytVe5XBIE4QcgHUgSBCETeFYUxc8EQbgHKb+WGfhcFMXASy8cByEhIaSlpZ2IS532rFq+kY/emwrAJ+KPXHPDRXTq0sbvMXJuKs8V/aw/FlQpAMqmVnk117ptC599hw28hYP5C467soZay6Hmwy8nYDKZWL9jutc+9SSe2rKZz9Xn97++wbWXPay7ryruv3MiGfNWMO2vmldTqE08X4x9z9JqPM6NagvAV/krGRHVnh4X9WDSVCm9Q4I5gkRzBHtteehhJojr43qxsGS30mbfI+VsMwEWUwhlTq0WNtiVezGo7lw5zri5L1Auu1DKZfn7r/OUNtk82K6De+4syC+qUlMLcDDf22Uj3EOYad+xpVcfmR3b9vkUANeu2s7rE7/VpFL5/ONfdPsGQnxCLPv2ePsjNmioTeC8Yed0urZ2C8iy9aIq1HOH7AairuoQZgnFFOTDBKyzKJKDc4aP0DfJV7oEwFffHkf6sH68/9Z33HbXlXx46TuUF5XTelBbjSkVJCEnd+9R3hv9Fp988Bxte7XW7AO8zLSysNohrBFZ9gIKKt1BIe9+/DRXjLxP6lcp3VtxTpHi8gPeFolOnVsrmlC1plCtaZNzKoaYzMy8+TsSPg6TtHWVlQwY1IMli9YqefZsJW6lxHMv3cO639aw9ocVtAhPIa2fNkAqPFz6be7Zlcn5Fw0C3Brq4JBg7GU2Kkul+SgszGUCVo3LU4MpE0j5tkaNE5XPamE+Kki6TniQ9K6pTRc2f2/Q59GfBANGFEXdpZ8oirPwEbxhcGqQeeCwZvufv5dVKQDmZB/D4ajg5tsu5ctP3VUp5LQl/vj7r6ojWt/75Gnu+d8LAHRMvYjtmd61GquDr+z1ngXO1agncb16jTLnnneW32t/8fEv3HL75Zq2gT3G0Lp9MyUR6NbNu/UOPW4eHX8br078NOD+/lb4vft11oRFNAqOJmu2W7C+OFoyt+y35yttJqCXJYWdtqOKENfV4vYJ+/elvwDoaUmms6UJUwrWYnU6CMFMJZWKLjH9nDpLhl7v5774hFjFVUFJt6F6Gb/4zAe89s6jmmPyjhVqtn2ZwzwTB5tMJh567BbefOULr75vvvwFl111nuZZE259jn17s+g7oD0Ae/fUjhX9029epHdH6ZkUP38OYexzgLeGqZGqqgXARaOHsmD+KqZ+J32tI0YO5q8ZC9mydxYdU1U+wqpflCzoqZP6ymlFwPuZ09MAynnwfNGxS2vmzVlKzz4daZHalFcnj8PpdFJeJAlfu5ft9NJYmc1mVv20gpLcYsIOOzTCuZwwOyZWK/gHB5sJwkTf8OZYwxxMr/iPUleVjsHpvZW/hywAFuUUahb4nhrg3ct2css55/PBrD8U3+L8rDyNpnD4iAGMuuxcEmyhVC46yoKP5rN5z0xsNhtvvfIlSxatVcZgVeV5vOu+a3n7/Fc5tPkgn93wIc+un0iIqkRfeIRby/jY09L7QXaBCDabmfLAd2ycuY4gTIrgpxZMfSkE1M9C4ZECohvGKN91m7Yt2LF9n8Y3PF7lBx1llsbUv1cXWiTUruXLpw+gKIrPiaI4QRTFCcCLwI+ufy+62gzOYDydoZctXlflMVkHpWPO9ngxH8w8UqXZcfqvUuRXw0aJPvtccoVbi+jP8TgQKisrNSWgAkV9Hw0aJvjp6Z/HH3rDq23njv389cdSZSJ44mHvPlXx7sdPM+qyc/32SWoQzzWuDPbvf/Zslef0JQDOX/YNP/z6pu4+T9Qm4KbBsXSyNGZ0TGcuiJKcpR0eKStahybRPETSvISZzPTp34Vr43pydZPe4DpX3wHdArp2dTHmPjTPhsPuHSAwQ5WbU2b50vWabatVXzOmpw3xpdU4dqyApx97W9P2y9Q5rFq+SdES6fkYVpflG3/SBKa0VrmfVGVpCA8P452PnlKO+ejL51m15RevBaJa2JLvV20atISFKs++599DVwNYhQD4+NP/Y9b8TzTuMhU2t3XGVmrzMieaTCY2zJATeWv/rnI+SE8NYHCwmRCXVj7MHMysfz5myNA+fP6dlMYn1BU0YXJduii7iMSkON58X0q/41mxZMUPy7CtzuVQ4SKCg4MpOFzAS32fY/mnizTj/OTrFzh/oKQdjm8WT0JiLI2bNKCl63uQf2e2Eu3vsORYCcHhwdhKbWRtyWL1zyt57+I3KcopVDSA4P4N9Owj5aPs1KU1G2euA6BxcLRikpaFPrVvpcyvs97jocduUbazdx3hxV7PsPQr973MXvg52w78pTlOTwPYvWNbvpzystc1jodAKoF0BLYCm1z/tgiC0L5WR2FwynHksDalx/q1VWe/OHRQylub3Lyx1z7PYApPzj63LxGR4X793jxX4sfj7+N5fwGjmhNvF64O6JDX3nmUhx/3zvjfKGoAjaIGcMfNz3D4kFvgll9sNaFLt7ZV9jEFmXjp9Yf45OsXueLq8zX7EhPjvPq3bO1+Gd4U14ffn/mFssIymsTHE6GaMGWBTQ9fPoCyObdtR61rxsCINOWIlBZNmDFX8jcMKUNptxbXbQmv+jz3qcujyRpAtSB0TBW9KiO7jMh88Plzuuce5+FXq8ZTuwaSwNcoagA3XPWopla3LCTVZCGnJjExjlQPX161IODLx8yT3/4S+eG3N7FYwjTCpMyQoX2UerIyaqFQHQTiqRULDw+jWXIjHnvqf0qbtdzGq4Nf5ItbPvG6Vu7eo1RYK7ySHtvKbJrPkZFaAXDPit3k7JLmcbWwCO6/d2OPKNqgoCCCXQEK5hAzHTq14qc/JnPR6HTAHTwR5Lql4qNFVFZWKlpNT1/FssIyHFY7hYcljfKx/VJuwD2Ld3jdZ9ZmSfurjni+7c4reP+zZ7nq2gsAsJZo/dJLjpXQoJv0jtq7ajdTH/iW/Wv3cWhLlsY9QdbaXTFmBCs2/cxZg3oQlSQJvykhcYrmz2Qy8d/+vxB1FtMDh/Tksafd39nRPdI8v2HGOqUtMjLcqzqJWgMY7RIAi3KKKD5aRFlhGbVFIPko3gOaAD+4/jVxtRmcwciFtkEyaeT7ySwvI2sN9cLs1T4fepSVlmt8IAJh8utfVau/Gl9m6fc+eTqg43v368zAIT0D6nvj2Eu45saLfO6f9vNcnnvS/Uh5mtKqQyApZtq1TyMiwuJVlWT5hh/Zsm8W3XpoZZzIyHCOFC/hpdcfAmDx5wt4tuPjTOo3gQq7+zcRbnK/KL/4fhJzF7tNemp/vUod66qeY7OcbuHqgWdrNK9yRPG/H/7jdUwtU2/nPvXzKvu4VSUI7fMwxXrmk5O5YswIn+e4cexonxrsObMWKXnVQPIBBFi6OPDSc3roWSfkCEzAq9qGLxo2SuCc4f01bQtXufN0m0wmth2YrQkAU7/4e/XppAhZnlpNk8nEmv9+46HHb+FQ4SLuvHcMH3/1PEf35LD1b+1c9vvTv/DKoBf48SHvspS2UrcA6Ci3K5U/TCYTrds0J9LpvteMD+ZRVuDOuHDD2NE8/YKAcL82bunItkOMSJH8goN0SpTKfz+TTbqnyopKSvNKFa1mpcd7Rb5m7l7pfZJ/UPIfDlWZZ2VK8yQtaFGO2yJkNpu54urzlblQfc+2MhsOq52Y5jFEN4ph5gvuvKplhWUawV/GZDLRIrUptlIrxUclLWhMSDjD23Vj1sTplBWWEZ8Q47c8a2VlJTm7sylxHW/TCZZUozEBqwTAN4e/wrMdH1f8O4+XQATA3sAToiheL4ri9cCTSBntDc5g1Kvqdu3TsNsdHD501M8RcCgrm9DQEBKT4li5+Rcyln/DFWMkDVNpqf9VS3mZVRPl5Qv1QzZ39tIq+/vi1uv1y+pVZT6VXxbVdcT1rHHpSZFO8fGa4Ok8rubeh29g+YYf6edhOm2e2lT64LqnOQs/141SvuV2z+BYNAJghUqwu3DU2SQnuhcCag1gpc4L16ljxpO1IbtmbGHmi94BOSeAejv3qTVQcoR+cEgwoy71XcrQs063njbPF3KgRWJSHMPO9+8/K7MoYx2AptpHbRER4dbM6Qm+2w78xX/7//Jq96SqSP6wsFAOFy3mSPES4uJjFKHFn8tMUFAQEybdR5t2+ude/MUCAPav2eu1Ty142Mpsivat31ldWbx2Csd2e1Ti2JWtfA4NDeGeB6/X+LntW72XN4e9QlSB9KwGmb3nRYfdIUVPlTtIain5VxdlF/rUAJYXSO+Ko3uPsn/NXjbPkQTcYJ1co2Uuf8aSXN/zp03lA1jsEhRDo8O4XrxZe93CMq8AJTWy9g6gT7eObPx2JRkfzGNS/wlsnr3Rq3/x0SJlfswQ5/HakIls+/c/AKzFVvKz8jQaWTVyPsJ5C78g0ixpGotyCpXx7529y+c4q0MgAuAxYJggCC0FQWiJlBH/+FL+G5zylJe7H5q2rug/vQg5NQf2H6ZJswYEBQXRvEUTOnRqpQRDVGWmKS+3apxpfeGZcFgui1Yd/BWr9+W4LiO/GAMRAJdv+FGJ5I2OidQ4fHtyPMKsGrPZTGKifiLlpybcRWpL7zRBvftKFQo8nfPVrPllJWt/Xe3VvmOh2zWgYQOtT+THY9xRzGoB0KmjAfQsW7XfnqcxOS74yK3t62LxNq/VEcbch7Q4A0kQ0ssT54vqLJJuvPUS3v/sWW689ZJqJ1c/XtS/x2bJjQBtpLJaAJQXRnHxMV5pV/yRouMWI6P+O8kf9fwaS/NL+fWJHxWhQb34UhMaIc1hBYfymfrgd5p99jK75nOnLm0wmUw88OhNAOTuO0qTjs24Z7pU+ackz7ef4ZHth3l/9FuaNr1UOjarnVBTMDghuauUBjM/K0/RAHqZgF0awJxd2bw36i02/CHN1+VF3kqEcpc5tPio70WA2gSclyklKg+JDiWtXysenv8EV799nXTdwjIlsEP+HaiRzc0teqdRcLhAOW95YRm/Py2lBnM6neRlHsNhc/B896f45fGpFBzKZ/3vUiXHXYslrfXR3Tm81Pc5PrlG5Js7vqDCXoGt1MrgNlqTfaPYeHBCXLN4t/AaGUZSZ9+BitUhEAHwU2AUsMP1byTg7XRgcEahFtjaulaacrk2X2QdzCY5RTvRyRNpaRUC4Py5y7VZQ30wYdJ9mm1x8nfVzmsXSLH6qggksXFqy2TOGuQ2gwWSC+p4MQWZePrFuxVzbSC8+f4T/Pz9G2z+xbdgPOX+b5n6gHe1g3/edSfeve8+d8UIp9NJ7l63NsFsck81eulbKiu032GPPh2JjYny6neCMeY+3MEcwcFmnwFBx5tcXzbb+TOj1RXqsU//+wM++Pw5TWqP4027sWj198xZ+AWzX5upCCA+Men7AALMffsvln2zmFU/Lgfw6QsW3cAtmK7+SVuXVq0Ns5XZiE+I4XDRYoYOk0zXeZnHSEhJIDJRevb8ada2zvXOYKQnAFptNsJNktYwra8UVXx0T46SpsXzXuX7Ui/6AA5uzKRZsLS43bNiF2WFZUpEc2leKSXH9MeqTgNz7IC0fguNlr7fRm0a0/PyPgSZgygvLMNkMvHDb2/yZ4b3Y35g3T4s0RZa9m9FcY6k3btk4hV0G9UDa4mV8qJy3rnoDSb1n6C4p6yaupyJfZ7l0FZJeVJyTBKoIxMjiWsax75Ve9g4cx1H9+Yw9YHvaJkTwcL5X+N0OsnanMmx/dJ4W/RKVcZxnXgTMc1rp1pSIJVAXgRuA34DfgVuFUXRf5Vug9MedULnNJfWqKoyScdyC7yCCORV3vq1//k8TnYy16sj6olsUpZ5763vaBw9MOAXUFV+jFXhNgFX/1j5ReIrVUB10dVWOp1ERoZ7VSzxR3h4GLMe+IlZE6f7TMrri0qV83W56oVUVqB9OamrdrQO9TYNegZ0NG4UuPmwrjDmPgl5MRgcrNUAfvy+O+gjJ7vqZ/dk86Sr/q0/klMac9lV59VqrrU27VKxZpcwb/Icvr/bv9+yPxOwHM0aZA7i2P5cPr5a3x3VXu474E6rAdSaH51OJ3kHjhGvEgCLVQJgZWUlTqeTI9ulFGFFR719lYN0FgjWchuWIFdS49aNCI8N5+ieHMzyvVY6KTxSwIJP5mMrtWp89gCadGxG90t6AdA2tAGRQaF8cNk7TH/mF8qLyugwvDNOp5Nl3+inElOngZEFqtBot7uRyWTCEhNOWWEZTqeTocP66bov7F+7j+RuzYlp5Ba+4pMTaNy+KWUFZcx+dSZZmzIBb+FVTZeLuvPMuomcdeMgpa3wcAEbZ0lR9M4SO1v+3sTb57/GZ9dL9Zy7XexWJCQ0r56vvD8CKkoqiuLnwK3AHcB0QRBqnv/C4LSgrLScps0a8sgTY4mOiSQhIZaszGy/x+TlFpDgYX4sKZLU+Y/c+4reIQAkx0tF4rMO+j8/+K4SEegL6NIR9+i2j7nhIuYs/LzK4xNcAm6nrv5zIuohT+o//TG52sfq0aadd7Lsmipi5Ig/p0oT1zWsKR3CGlHuJ9rWrqqVrNZIPNf5CU0/tQawlY4AKK/kZTbP3khRds2DYWqL+jb3yclv1Zq+xx58HZBelE2buk1P773lNi/ef6e23vfHX71QK+OpqipMdWjcJMlvmqm6xGFzaP73RWhhJS1C4sla7K5b7HQ6ydpyUBFkgkODWfTZvxz+T98i47D6FgDVGkC1MAiwa8kObKU2mrRvSlhkGMFhwRoN4OQRr/NYygO8cc4kDq/M4ti+XBKaJ9K8Z6rSRy+DgbXcrQGMahBNUmoDcnZlKy4xrdu24OvbPmPGhGlegV0PzXucB+c8ypWvX0NYVBgmE7RwZRs4uieHClsFLXq2IKVbc3Ys3K5/z6o0MLJfZFis1t88PCacsoIyPr76fT5Rua7IbPprAwc3ZtJmUFtiGro1rPHN4pXtbf/+R9POyQy5Y6jXAlhNWh/JpSqlh3v+3rnYPfa8zGPsXaHNAdtheGflc0Jy7U1BgaSBuVkQhFwkf5gc17+q39QGpzVlpeX07teFceOlEkhxCTEU+glUqKioIC+vUBO9BNC5irQk1dXI+VqZd2l1cZXHfvrBT165ygB+mfkukz8Y7xX9qke7DmnMmPcRz710b9WD9YFnItWa8s1Pr3lpRKurwZv10nQeTXbXGt46TzLrfHrdB/QIb0bf8OY80/4xn8dbi90vFH/pCdJC/b94/ZmafKF+8dQF9XHukwU8vfq8RUUlioAIcOSw28T/z9/LNH1HX+4dTLVu++8sXTfVq/1EcfnV5/H+p8/Sv47yR6qpsFfwaPL9zH9/LuD2Xwsym9m3ei9ludp65v/9s4UlXy0kcZOd9MjWbPtG8hmzllh5LOUB3j7vVcWPzBRkInef74A8Tw2gOkWKrF0Lj43wCkDYOm8zwWEhdL+kFyaTiciEKMWsmrvvKIe2uKO810xezubZG4luEM0NH7tTXKk1gDsX76AopxCrzY7FVcUiOimalO4t2LloO2s+W8LU39/inY/GKyZS2aVk8P/SGTh2CI3aSi5FIZYQmvdoQZgphCRzpOZeLNHhpPRowe5lO3k0+X7W/rZKGcPvT//CnNfdedd3LNxOYoskQiK11hNLbDjrp69h15Id7NRJN7P2t9XENolj8O1DiVZpAOOaJRDtEgCP7s4mKa0BXUfqR7/L9B4jmdtbDWjD9R/eDMD89+YSHitlQsg7cIyDGw8QrxL0zMFmktKkoDp14urjJRAN4BtIZYsWAwtc/7zr+xicUZSXW4lQhd3v3nnAb6H1gvxinE4n8R4awOYt/DvsqyP4lGjUGiJX0PDF+HFv6banVDFGT/r061IjM+5lV0mJrJslH78Db88+nWjarCHvf6rNPVVdATBDnKfZ/vq2z8jPymf7v75N9mrUglu5n1Wvntm33bkdAxylPrdPEY7r+ACod3Ofv6TKZnOQpu5pdWnStAEtW6cE3L9ho8QqTbEDBum/bGNiozQpkbZnziY4OJghQ3vz+5wPlD5Qc625P4pyJO31vx/+Q2VlJZ+MEQEICg7i/dFvsfAx7XP3+Y0fMW38z5q2ssIysndqI6tBEvDUi63gMHeQitPpxGF1YA51C2JqbZQs9EUmRHoJikf3HCUpNYkQizS3RSZGUXKshMqKSsU86UlUUjQRsW4trRyYYi+38/HV7/HptR/gsDsIN4UQFBxEeFwELc+Syq8t/CSD9HP7EWoOxl5uJzgsWDl+6N3DGP385ZrvPzIhihZNGtGri7RQl4VGS7SFlO5ubdqiT/9VPssR0Q1aNcQSI40zpbt3jfnwaIsmE4GnCb4gK4+GrRsSHBpMctcUhtwxlBGPj8QSbSGmkVsjmJTagJTuzTXCG8BlL1/NuAXjefifJ7BESX7xJpNJIywOe/B8Elskkbs/l8yNmbQb2oHOI7rS7/oBANz35yM8uaJ289AHkuAoG3hXFEWxVq9scFJZvGAN7731Ld/+/Jqu43VZqVU3J1JJcaluzrY8V2LYBD+RcZWVlV556gb3cueUeudD/dQsnvy96AsOZeVQkF/Evbe7TU0fvjuFT76uvunpeBIvV4fxE+7i/kduUl48/vh9tsjo830LOIPP7qV8nr3gM5YtXoc4+QeSVQloz7twEHNmLeKXme9qBFbZpCtPRJ681Nd/dZA2Q9qxY4EU/ZuYmqQEe2zL2FrFXWmJSapaE5raJ429KyVzWNNOzZRIvAE3DdbNC1bL1Ku5b9Yf//LVZ7/53G+xhGnqAdcle7L/ISjIROeWI/32Gzy0N917dUCcrM15FxIcTExspLLA9Kxb/OMfk2nTtgWjht/JU8/7fs5GjBwMwJdTXqZFajPdPsVHiwiPi9D4RxYeluZDS7QFh9Vt9q10SAs0R7kDp9PJ9/d87TOh+bMdH1de/mpsZTbKC8toPbAN0Q1jWPvbask/r9KJs8KJ0+kk/a5zWfPLKvIyj1F4OB9nZSXhMRFK3ryE5ols//c/Hk2+nxe3v0poRBi5e3No2Mod/RqZEElJbjHipZN1U8oAxDSKISQ8lB6X9pI0m64IXtnX7tDWLMR5z/LJ2I+IsoZgMpnoeF4XgsxBVFZIPoVybr2+Y85i6deLGHLHUKJ05obIxCic5RVQrl3kWmLCSe3jLllndgnE6gjm3lf1Y84bkiaw1YA2lKHVfpo83gHlhWWEx0ZweNshirILycvKo126VLUoODSYkU9fovSNVpmEk9KSMJlMXPnGNRzdc5RfH59KZGIU/a47q8rFTOsBbdg6d7MS9dyiVxq9rujjvs8oi885u6YEIgAKwM+CIPQFZKccpyiK9/s5xuAUxmq1KUXf//5rCSMuGuzVp7SsXDcnUtvk83ULvMuVARJ0KknIFBeVegk/ubn5yuf+A7sHMHro2r0dXbu3o7BAazac/us8nwKgXNdU5nDRYhpHS0XUreWBFXM/XoKCggIS/oAqoyHvfuA65XP3nh3o3rMDd96rLT/7zY+v6h4rm3RfzayZL6Is/AGaSN/qcM27N+hqGWMaxVJ4xF1losD1Ih1w02DOf+winu34OKCfSqYOqFdz3y3XPOF3f6s2zTGZTIyfcCcTn/1QafdV8u14kHPEBZLYXC+BvCnIhNlVneL9T5/x2n/2UOnFumrLrz7Pq86FecHIIbp9KuwVPN/9KXpf3Y+r3nAvZvOz8gFJEHKoNG0H1u1TPj+W8oDPa8ss/9Y7H6e9zI612Eqzzsk0bNMYWE2hq1zaBY9LAnN4XARjv76DN86ZxNrfVrPqx+WUHCuh9cA2WGLCiU9xa6hydufQtFMz8g4co93QDkp7ZEIUmev3+/VnkzVv17x7I7Nems7CTzMoKyjVmGF3TdtEWuMmiv+hJECNZvpzv1GaX6qkcGl3TgdGPnuJbr4/kLSN5UXlWEushMeGK+MKjw0nIi4C4bf7+eHebziy7TBOp1MRWs8fdyFn33UOYVFhZG06SJ8x/VmwcIHm3MldJR/CXlf2ZfVPKyjNK+XncVM0ms+4pvqVjuSAGYBG7aQFeOuBbWk9sC3dR/fEVmoNKKioUfsmNGrTmJ2LJH/ATud3qfKY4yUQAXAiEA/cqGpzAmfkJFgf2L/Xnc/vpqsf80r663Q6KSst103MrOcbBO7qFZ4+gCBFmZaVWSksLPYrAFU38k6drV9m0b+rGaTSjsm88LRbidN/QDfNtaprAlZTmlfCf/O38tuTP9L+3E5c9/5NAR3XvmNL/tuy2+d+c7CZv/79lJ3b92G12nn4Hm0NSE+NRiDkZ+Vp7luua1mbWKItXgEdeoRGhlHh8DZXC9Pupyi7kPdHvw1AVEIUt3zxPxJaJBEaHsro5y/n92d+oe3ZJ6QimzH3qZA15d17uk33K5dv5FiutixcVRVDKhwVzH51JhniPG799k5Fs6LH40/fXmVN7FvvvJKQkBAaN5G0L7dc+wQmk0lZRA0c4j0fHA9lhWUUHi6gUdvGikl11VQpjdVlk64mOCyYw9vcARrPdXkyoPP2uqIPWZsPcmhrFmn9WhHTKIb10yVtkDnErJhHbaVWyovKsESHE+4yv8qa8T9fngFASFiI4jOmDqzYuXgHSS0baiJZj+07SkJKAvZyO7GN45T2qMQov8IfoHkOw2MjqLBV8J3wlWZxJ/v1qb9n2Y+u6EiBEmkclRTtU/gDSSAFKWq41xV9WfSZZOpN7iqZdFP7tGToPcP49fEf2Tp3M1+6SuT1uLQ3QUFBDLjJW9Ehc94jFzLswRHsWLiN1T+tYNZL09k4az3BYcGKBje+mb4AGBQUxITNk8jemU1yF62LgyXagiXav9bu4flPUJpXQlBQEMMePJ/NszfQbmjHKo+rDQLxAWwLfAOcBwx1/fOdDt7glCfLI53L0Zw8zbac+T9cZWJbvdX3Shn8m4Dlot9bNmmzl1fXX82T4OBgtuydRdfu7mLn336pXzFCbdqSfYBkqkr+7I+vb/+cKfd9g7XYqjhpB8K/K75l7O2XA2jqe8qYzUH06NWRK6+5gOtvHlXj8al5qe9zTOzjNu9+c8cXfnrXjPA4rXtAx/M66/bzVV4rISWRUJWTc2lBKY3bN1XaBo4dwku736DTeXW/OsaY+zTIi4chQ3srbfP/Xq5JBwP6ASBqlny5UPE9XTfNO7m4mrF3XM6Og3MAKTJZdj/pe1ZXpU9ISDC33nkFF41Op2efjspYzz5H0vIFUmGoOnx2/Ye8cc4knE6nJpXKqh9XsGORpB0/5idIwxdhkRalxFe3i3vQ/wZ3wE1s0zjls63URnlROWHRFhq7NE57lmvn1uCwYMwhZi5+7lKv64RGhBIR635Oc3ZnU3hEWsCrzZlqzRbgJZDc8dO9GkEysYXk5+vLfzi6oXvRKkfOFmYXKmXc5Dq7vkhKc/sRN++ZSlJaA3pe1lvxWQTJbArw5S2fYA41c+mkKwNKm2IOMRNiCSEiXgowkTV/jy58mjaDpUBGz7lNTXhshCZXX3Vo1KYxaX0lv8jIhCgeX/osl750ZY3OVV0C0QB+DTQFdgP5dToaHQRBuAS4CIgBPhNFcY4gCOnAC8BmYIooihknelynM4c9BMB9e7NIauBe3ciZ/9U+gMkpjXn48bG88fLn2Gx2ryCIYy5Trp4GUE4NszBjFeddMFBpn/bzXOXzpDcertG9JCbF8cfcD2mRNBSA3376mw+/qF1HWX/sXrqzxsdGRksTijp5anCwGYejwmdJt427/qjx9U4EUYnR5B1wp+Qp9VFJIDQyFHOwdI9Xvn4NPz3yg7JP7YB9nUe5JtAvCVVHGHOfCj1z7MzpGX412Xr88Zx7MWYO4LuUfevUbhEtUpuyYukGr77yT8dkMvH6u49x38M3VKtiRyDIpkVbqc0rlcq2+Vv54qaPAzpPeGw4va/qx8JPMgAIiwpT0sREJEQqAkVSy4ZYosM4tk/yq1vxwzKclU4sURYad2iKKcjEjkXaFCgRcZIg0/PyPpq/N0i+fQkt3ELRxlnrlZQkaoEuQWUm7nvtWZxzz3BeHvC80qYOfgCtgAaSm8cP936jbKv99OJTpOvvXraLrE2ZhISHEp3k/3tK7uYO3kjp3pxxC7x9xhu1a4wlJpzywjK6XdyTs1RCdCBExLuFvCYdmxHXNI4rXr+GmS/8TuuB/jNa1BZ6ybTrikBm0geQzB5qkdQZyLGCIHyOlD0/WxTFzqr2EcBkpAi7T0VRfNnHKRBFcRowTRCEeOB1YI7r+sWABcgM4B7qFQ6Hg53b99O+Y0vd/Z7+cOp0DgBlZZIJz9MHsEWaFKWblXnEq6TYsWOFmM1mXRNvz95SeRvP6Ff1dfXqzAZKba/wA2X7At+Rsk6nkzmvz6LP1f19rkCdLg2oXhBKUJC+D2CgecxydmcTHhOuOFOv+XVVFUf4RvaL8aRZl2QObszEEhNOZHwkufuOEtcsngPr9hHdMIaLn72UkmPFShCHmgYtG9K0czKrf15JXLLWtCKbXJp1TVFKR50kHqCezH3q0o/VobrCnychYdJCctfSneTsOkL/6wf67BtkDlISsPsqSadO1B4WFuqzXq4/jh3IxRxsJrZJnKa9OLeYBSpzamleiVcqlSVf+g4S73heZ7a46toCRMRHajR75hCzomWLiIsgODSYJ1dMICwqjK9v+0zpJ2sdO43ogiXKQpMOTZUExAA3f/k/2p3dQTmPTOcRXUlq2YCz7zqXiLgIHl/yDMu+XcyCj+dTcEiy4KiFOnVk7UVPjSY8JpwXt79KflY+86f/Q4OW2vk8oYVbAOwwrBPdL+nFxpnrCY0MY+Os9RqftrimcXQ6vwv/vCNpd4fcMVQTzayHOgAisUWSbp+goCBS+6Tx37wtpKgExkCJjHdXa7p3hlRNKb5ZAtd/eEu1z3U6EIgAuABq7HH9JfAe0koaAEEQzMD7SHU1M4GVgiBMR5oQJ3kcP1YURTnv1lOu4wAWiqL4ryAIjYA3geswULjjpmeY8XsGXbu3001uXFKi9evYvGEHF158trItZ/6XS/XIpLgiTPfvO+wlAOblFhCfEKPrxycLkurqIgBFhe5cWLWZeT9QhgztQz+VKckfmRsOsOCjfxjzzg3KCu2PCb4jJo9sP8y8yXPY/u82ZSLx5LKrzuO9t77jwovP1jjVAz41gIHy2pCJhEaE8sz6iWS8P5e5b8+u8bnOe+QCRQBs2jmZrE2Z9LqiD7muSL+bPruNOa/PInffURq1bcyeFdGMePQiuo/uSYWjguydRxh2//lEN4xRcg5GJUUzcOwQmvdIpUWvVC54fCRxLh+bYJdJx/MFcxKoN3PfyHPvqFb/iMhwSku8/cOioiO4454xmjaHzcH3d3/FeQ9foDjJy8gawI+ufBdAVwC0WEIxm808P+k+JjwlVb/w9XzIAmAgwSO+ePksScv1auZkKisq2b9mL/mH8tkyZ5PGZF2aX0qFn8TOEfGRGg34WTcO0giAlRWVGqEmrlk8Kd1bcHBjpmLWjHMJiOfcO5zg0GBCLCFs+msDbc9urzwfaX1bKT6Az218STFjgnZevVa8SaM9T2ieSGRiFJWOSg6s3Ys5xExCinuBmZiaRNPOyVTYHYpgGhoRRsPWjWjU09tv2hJl4YaPbsHpdNJ6YFtMJhM3fnorAGMmX+/Vv+dlvdk8eyMAFz4ZmJvL40ueISjY/3d72aSr+P3pX+h6cfeAzqlGzsUX3SjmRFoaTho+71AQhKFIk016TU8uiuICQRBSPZr7AjtFUdztus4UYLQoipOQVsye4zABLwN/iqK4xnVe2XksDzg56p9TmBm/ZwCwYd02L/8+wGvi3qcKCgG3BjDCIw1MSgspKadeTeC8YwUk6Jh/we0UrjbZOJ1O3nhZEk5rw8ft3Y+fVlLCZB3MpmkzfeHhm59eUz5XpyLH17d9Sn5WPvtW7+WJZZIfnToJssz393xNSW4RObukd/eBdft4qd9zPDTvca8Q/k5d2ngF4Mh41lx94tk76NWnk25fNU6nk2XfSiWRbKU2nmozruqb88E1791IctcU4psl0OuKPqz+eSUJyQlkbcokIj5SCfYIMpuUqh0RcRE8s/ZF5RzmYDOXTnQr0NKFcxWn76CgIMXMNfSe4Uqfph2bcd0HN9P+nOPLE1hT6uPct3G9fhUFX0yYdC/j7vOOMt91aK5X28GNB9j05wY2/bmBiTte0+xb8NE/LPx4vvu83cZz74yHNIKI2Wwmq0DSrCkCoA8NedNmDfmfcFWN5pTMDQeUiFSQKjJM6u92J/FMPD55hPZePGnQqiH7Vrm138GhwbTolcq+1XsBqS5so7ZuQarLRd3pcUkvUvu01JhLAdoMbkebwe1Y9u0SNv21gcgEt5DXakBrJd9dmJ/AAT2BRg6sWPr1YpJaNsQc4v67mkwmHvirevNHl4u6B9y33dAONO+ZyvnjLgzY7BmIP19c03hu+uy2gMehJsgcxC1f3U7TTslVdz4D8CfivgM0FwRhHvAnMFMUxSw//QOlGXBAtZ0J9PPT/15gGBArCEJrURQ/FAThMuB8IA5ple1Fbm4u48e7fQRatmxJq1atjnPopx//u+kJbrv7YjIyMpS2bdu0mc537dyj3b9VSlWwfcc2IjLcCpCKigqCgoJYtGAZzVpoo1B3795HkNmpOY8n//y9TNk/76+VSvvFV/T3e1wgOCrdKWFuufZRnphws7J9YJ87mWpBUXa1r+WsdCppHfIyjzF//nxMJhMlBd7+bXpO7fkH85j1/UwS2gZehmrlqpUcOOg2r3Xv04IKiqsc+755e9j8xbqAr6Mmrk0C+TuOEdE4kvZjOlMQV0TB/i2wfwsxgxJouLsxjUcnU5HkJLizhcY9UrDHVrC3eD9H90h+pXuz9uHwM8aIATFEEFP1dxANS1curdF91AKn7dxXnXmvuNj/78kUZFKS40ZGWjR9rfYir/49erfTPd/ad93uA/Pnee9X+3yW5Bbz65s/03p0O69+4K5qcfiItAjds2eP1zWHXdidw9n7OZy9X/ccuue1VTB7rDaAbM6PWq25r1x4vojvn6QRADds3kDbOzqTkpPGovHzie+YyO7CPXS9vSeJnRqwfM1yqWMC/Pvvv7rntMfbaNijMTGDEpT7dlqcdL61OyERoSxc5G2C7vPoACodlbrfzZFM92LekhIe8NxY1W8nUDo/1IODFYc4mKFf1q6u8Dt+Mxz5LwcCy4V/Uqitv79PAVAUxS6CICQDF7j+vS4Iwj6kCXEWsEgUxerV8aoBoii+gzQhq9t+RSrO7pPExEQmTpzor0u9IDo6lqioKNLT05W26T9rX6xBQSGa/UFOyV+sf/++Xrn5kpt/QIUjSNMf4Bn7J6S1bu7V7om8/9P3ZwLQuWubKo8JhOwjuYBU6WPd6u2acz72gHulfuVV3lFxvrCWWHE6ncx96y9N++CBgwkODeZvx4yAz9WrV08lQi0QBgwYUGUVFZl/3p1D5voDXP/RLTx+vW+zdFV0PrsLQ74bSkR8pCYSV+aCKy6UPqir7rkULX8yDYArHryq1pOVnmhO57mvOvNeRkaG32fPEhaquG2UlJRr+jZrshfQRtNfe8Nor/OV5pcya7n7N9mvV1/mMtPvuFq1aulzXOZgSbucnJwCLCctLa1G88fvz/yCs8LJJROvoLKiksdbPOjVZ9PHa6t1zjZD2pHapyUrvltCweECho4ayrr33Avdnj17KRrvkKhQzr3gXCmZ+dDqjX34Red5N/o7R7rvXfui97L6DamM373fPhiwK05Vv51THWP8En6N3KIoZgKfAJ8IghAMDEKaEN8FmiOtQqvLQUDt2Z3sajOoA0KCvb/ikuIyWrZOYfIH4/nw3Sls26p15PYVBALQtVtb1q3RVnyorKxk5479DL/QtwO3jNVqIywslM0bJC2kr7q4//2zhdmvzWTwbem0GdKO6Ab+I8T8BUd8+WnNhKKn2z2q216aV0JMo1ivUkr+MAXokyRPwNXxAfzrFemFunLq8oCPAcknqEmHprwxVHI/G/HYSF3BLxDimsWTfzDvtBf+ZIy5D0JVAmAwQTyafD8XPT2as+84Rze4wrOaQml+Kc911iaXXqUTTOR1Hj+//ZiYKIqLSglQTtGlwl7B4s8lk2lcs3h6XKqfJ1DPxUOPW766nS9u+piUbs0Z/uAIhgrDKDpaSJgqjdZNn91G857uoIrwxIgTUcmmSuSgj5b9W58UP2yDk0vAXo6iKDqADNe/xwRB0K+LUzUrgTaCIKQhTX5jgGv9H2JQU3bu2OfVVlJSRmRkOH3P6sqvP85hySJt/rqiIsm0qRdd27JNc/6csRCn06lMGGVlVioqKoiPrzrdwoqlGxic3pusg5KP3MTXvFfeAN8JX2IttjLl/m9p0SuVu3/X76fmgpFD+HOGNLGrx1fbvNjrmWpX0ahqKL/PFsnMPMIDd0ram0AFwJzd2crnXx6dEtAxPS7tRecRXel8oTYhdk2FP5Ai5uR8Xmca9XXus9vdAQ6P3HUDh37YwswXfufsO/RTIXpWrynLL/XqM/tV/9o/8BYk1fwy811mz1roriFegxCdGS9MUz7Peml6tfO3PTB7HG+fL1kV7vnjIVK6N+f6D29RolyDw4KJb6atBXsiqjrUhPjkBP73g+Dlc2hQP/AXBJIIvAp0Av4FJoiiWCoIwkhgsiiKVTrUCYLwA5ICOkkQhEzgWVEUPxME4R5gNlL02+eiKG4+/lsx0MNXEEhEhJRBvnHTBuQdK6S0tFwpv/TaxE8BSG3p/Z6Li4umoqKC4qJSomMkR+Tvv5Jy00VGhnv196SyslKThqZhI+1E+frQl+hyYXdsJe70CvtW72Xe5NkMvXe43+i+h5+4RREAMw8cVqKW6wI5K39q35ZcJ97MxN7e5abUvDvyTV7a/YbPyDLZ1C4LgL7SwHgi1xwNhLCoMKzFVpK7Ndc4a/e8rDetBrYJ+Dx6RDeIqVJLe7pgzH0S6mCxQz9sUT7by+2a5Lsyl145XLNtMvsW5EY+cwkznp+mu2/mC78z5Pahugu4lq1TuOu+a5k04aOqhu+TXYu1PtBfjv0k4GNbntVaEyDQ3JU/r+vI7rr9B44dQsPWjXT3nSq0Gazvb2lw5uNPA/gRICdn6wuku3JYRQKpgZxcFMVrfLTPQvKlMahjjuUWKNnlZUpKyhRtnZykuSC/SBEA9+6WrFJ6FTIaN5XKC+3csY8evaQozacefRvwn3phwcrvGNLnOjas28ba1W4Tsuc1snccYd5k75Qls1+bRUr3Fn5LgHXs3Fr5/NMPf/HQY7comkaAtFaBR3bNfPF3v/tnTZIcxtsP7UBs41jum/UI9nIbP9z7DfkHvYVuAGtxOcEJgdUC9owC9kV1qqn0uLQ3y75ZrIn0Axjzzg0Bn6OeYMx9fljzy0r6XTfAq91i0T7LenVsZdoOaceYd27gz5emK/We1TgrnX4FyOPB85nxV+4srmkc1hIrZQVlBIcFc+dP91brWqOfv7xGYzQwOBH4szOdC3yPlIX+KqA98DdSbUyD0wSn00lRkdYUU1ZSrmjrYl0+eAX5kklFLTDpMWhITwBWLZfyWdlsbj84z4ARNXKFkBef+YCDB9xRuaFhbk2COhpQD7mYuC/UJqhXXviEfXuz6NHuEqUtUGFp9c8rNPUz9Vj4cQYAQS5BLblrCml9WzHuX+/s9DL//bOVbRlbfe5XE6gJWI7SDITU3lIQSrPOJzW58umAMff5wddzpNbYOZ1O5r/vnRJGJijYTM/LejNu4VO6+zM+mKdE3ld1repSqVOD2hfBYSFKShH1QjrkONwlDAxOFap6y/wrimKxKIo/Axci1cZ8ve6HZVAbDHeVXSvIL9a0l5SUEiELgHGSRko2y+535QS875Ebdc+Z1CAek8lE3jEp79tdt0g58RIT42jXwXeUq7oc09efT1M+qzWAB9b5T9vw9f+8k1p7MjjdXaf03Te+0ex75IlbqzweYOoD33m1JaU1ILWP9/2V5Gr/tiGWEB6cox88MvWBb/ns+g9193kSsABYEbgA2PPyPoxf9XyNa1bWM+r93DfQtdjz5N8PpMVRh06+LeFq31Q9ZD+/0PBQrnzDW1n618sz+Oq2TwMdarWojta8srJSSYKsftaeXvsCz299pdbHZmBwIvH3ltmCVIcSAFEUFyMlKzVChU5RSopLmTt7CeHhYdx57xjuuleaWAs98tWVqjSA0TGSALjwXyn1yyFXneDLr9JJNYCkZYuNiyLvmGS2mT1rkdR/zPl+xxYSEkz/Ad389pETGPujrNC3uQbg7gfcPvXffKE141517QVVnl+Pe2c8xKMLn+K6D7zLAa3+ZaVXW5OONY0RQHm6gsz+TcBOpxOn01mtlxlAbGP9ZN0GGoy5D61LhZpjrgowu3ce0N0PUGnXZsk5937t/OBQRdAnd9HXSJf7Mc1WF/l5gcAWTbd8dTsApXmlSn1ctYXCEmVRBEMDg9MVfwLgw8CvrhQIgJTdHikdwti6HphB9Zn8+tdcd/kjlJVZCQ0LJamBZLHyFABLSsrcGkBX7d5XXpAcoQ8fkgTAJi5fPz3iE2LJy5M0gHKkYCAaq159O2u273lIWx4ozqP2ph4/P/KD3/1Dh/Wv8hzVRa6JGaTjk5TWV18LMujWs3XbwR1A4g9/Ji6n08ljKQ/w9W2f+TUBh4SH0qxrChc9NZrLXr66ymsaKBhzHxAW5h3ooeb2ewL7TaV0b+H17ISp67qm6td1Lckr0W2vLjm7s3ks5QGWfSMtMCsdVT9/Kd2aE2IJYahwLlFJ0Qy54xxu+LjefPUG9QR/iaCXAct02tcC1cuQaVAnOJ1OVq3YRJ9+UoqB/1T5/JYvXs+9D0oCVk52vtLucDiwWm2KBrB1W0m4uWhUOiBpAMMjLMTE+g5WiIuPUTSAMoEUXX/w0Zt5/223efX6m7TlmkrySgiNDCOuSRzZOyU/wbimcaT1a8Xa36QKGxtnraeystJvwMnV113I1O+0fvYffjHBR28tnn6G17zrDpCISvLOWXjxs5fonmfUhMsY8dhFPNXW2xz8Qo+neG6TZ+lXiedevp3d27O9HOoBdizaRlRCFAdddT83z95I76v0C0nc8NEtdL6wG85KZ8BllgwkjLlPYv++w373N26sL7gB7FRF2g578HzS+rXi7zfdCdXVJb1CI8J4NXMyT6Q9pFkclReWsWf5LtL6eS+y7rx3DCuWr+HWO6/Qvf6e5bswhwbTvEcLXhsiRdb/9uRPnHXjICr9LJqeWT+RQ1sOEpUUzfNbX1ECpkY+PdrnMQYGpytVvhkEQXjgBIzDoAa8MelzRp57Bz9PlaJm1fUxe/XtRExsFAkJsWQfPqa0l5a46vxGulfg/Qd0UwS6QwdzaNqsoV8NVFZmNhnzVjBvjruiyLU3epUy9UJOGyNTXq4VtuQEy+3PlWreRiVF8eSKCVzz7o0MvWeY0k9dUF0PvaH3q8L8LLNr6U7NdpcLu6vOa+LWb+7U7I+Ii/B5rtCIMMavep7Bt6dr2kt18qPJtG6bzEuvP+T193c6nXwyRkS8bDLZ290v5rU6pedAqslpMpkM4e84qC9zX0WFvkZs+q/z/B4XprNIkVGXREzt0zKgBOHPrJ/oFeW/f613HlOQFqH3jRtDbJx+IvkPLn+H9y5+06t95dRlOKy+E7hHJUYpaVE8o+UNDM40fL4dBEEwCYLwHmDkiDjJrF/7HzOmzfdqf+2lzwC4+1ZJu6U2wz7x7B0ANGiYQEGBO1ChtFTyq4mMdAsuzVIasWTRWpxOJ4cO5dC4ie+VPcCRw0cBWLp4HSAlMa5JVF6LNK2vXMmxEiITIolPlkzX6lx17Yd2VD5/fdtnfs/rWXXj+Vfup2mzhlWOp8Jewc5F25XtgWOHEBymVZLHNdMGglYVDRjbOJYLnxhFryv7el3LU9jUY8+KXRQcylc0I9Ziq8YXacMf3gqp+/58pMrzGvimvs19vnz5xt4upTAJ8uH6GBLifjbWbdf624ZFuZPIh8dUnR9U7nfjJ1ozqznEzL7VeykvLvd7rK3Uypa/pYWhv2wCPz38g5L2pc0Qbf67e2c8FNA4DQzOFPypB/4ArgACL55qUCecN3gst14/XvHP84Va2xMaKvnvJDWM10QBlxRLk59aA9i9ZwcA/v5rCYezcvz6/wG8JT4JwNZNkgDTvVcHzf6CwwV8/b/PKCvw1nR9/+sbyucIVam54txidi7arrkHk+rFk9avFc9ukEw5VQUytG7TXPncq28n7ri7al8lp9PJE2kPsfCTDKVNL3u/Z26yQARfc4iZ4Q+N0LQ9kfYQH135LgfWe0c+VzgqsJZI2tEPLnuHV4dMxGF1V2UI1vHN6jZaitg89/7zfDrVGwRMvZr7zhuiHx0fvrWMJHMkZtVrIqmltJCqrKikiWpRpZ4zygpK2bHQvZCqDqERYfS8vI+yPf3ZX3l/9Fv8cM/Xuv3loLDZr83iy1s+Ye/K3ZpnxR+XTboKgPbndOTa929SfH0NDOoL/hJBXwB8Koqi/9wcBieMbm1Gc6RYP7lqZWWlVykmkDSAu1Tl4JQ6v+Fu4euqay/g6ccms3XzLg5l5Wgmdj369JOCOebOXkpMbJRXybiZL0xj058b6HJRd3pcoq2zee55Z/HCK/fTSiWkAWycuQ6QfHe6qjR/aiITouh5eR92L/OvObvrvmto3qIpNrudK672H50sYy+zabaf3/qKbpSfvSzw+r9qzD4SO5fqOLp/d9eXbPpzA5P2vKmMreSYW4jXS5R98TOXcN37N9VobAZe1Ku5T13xQyYEM8Ubc7ikYTfCLm1B8ffSMxeVFMXR3dmUFZZx9tA+PPz4WEZeMlRz7OxX3f63k/Z6m2GrwqkT2Z612btkcva6wzx7/ePc+fO9SmRyweECJWoXIHvXEa/jZBJbJFW7pKOBwZmEPw3gtcANgiDoe9kanFQ8zRyrV2zWrR6R1ECrAZSLu4ertG9x8TE0bpLEqhWbcDgqaNBQKs+2+ueV7F62k0eT72fWxOlK/5QW7hJrXbq19RrXut+l2sK+anrefvfVnHveWZo2u8ovJzRSMqtaYrwFsIZtGpF/MI8VPyzlj+d/000ObTabGXXZOQELf+BdDcBXioeE5omKljI81rf/nye+zFK2UhvlxeVUVlQya+J0SrNL2PTnBgDKi9xjmvu2t9Anc9ZNg4hqoO8LZVAj6u3cFxtk4aa4PqSGSq4O9jI79953nbJfFq7+/UDyD3z0qdvo2FkbpFGS555v1AufvtdIz3xc0zi/Y9AL0nDYHNjLbDhsDpZ8tZBdS3Zw7D/JFWXvqj3YXAs4kwlmv+auN/z62S9pzmOJtnDB4yO5/kPvlE4GBvUNf1HAUwVBOAB8C/x84oZk4At1ZGh5uVZjtXvXAV1zZIOGCZSVWSkrsxIeHka5SwD01Nq1aZfK6hWSD01sbBTZO48w9YFvlf0ZH8zjwvGjvI6VzcdKP9HtOP7jg9/xvfAVV711Hb09fOA8UWsve17eh5JjJQy8ebBXv24je/DXyzP4edwUACLiIjn3Pv2chYGSteUgf70yI6C+4THhvLzvLXL3HiXcTwCIJxYfflDf3C4lt27YupES+Syz4GO332dUon5U9iUvXsEAnb+TQc2pz3PfxdFSANaACHfSc3lx1qxLMj0v682GP9aSIc7jwidH6Z5j/XT9QOkrXhvDOfcO9/ksyHQ4pyPrXYtImZLcYsa3GUejto054gqCajVa8uH76+UZxLpSSDlsFZoUM56MfOYSRRA1MKjv+A0RFEVxCVJZJINTAIcqf1VZqdYpuqKiUinLdr+qioeszTuaI0UCXzHyPt1zt2mXSu7RfACioyMpyi706mMv9zZ/qv34nE4nf076wz1ely/Ojw96V9bwRNZ2db+kF+ZgM+l3nasbYOGZM0xvTIGwceY6xbH87fNe5b957mL3gZiFElOT/EYAe2KJsvh98XkKfwDz33OX0vJVmi6le3PddoPjo77OfWaT9ytBfo7Pufc8r8o3arI2Z7L0m0XKtmeQBUga9KqeG7UPoCdHVBHw9mL3IrjgUD4AFTaHku9PzSUTJWVuM8M/1sBAococEaIo7jkRAzHwTfuOLQFJACwuknzGrFatBrC4qASb1U67Dmk8+Zw7VUmjxlK+rcOHjmr6d+7aRrPdtp3bATq1VbISUae5Rm6R8lm+xoDB7nJRe1fs9jpGpqoovtmvSX5Dl718ld9+nuj5C1XFke2H+eaOL3hj6CQeTb5fs88zZUttcu17N9K4XZNaucYFT1zMizteMxzX6xBj7pMoPCyliAoOCyYkwnfU+9vnv8ZvT/ykbF/2UvWeZTV9xvTn7DvP8dtn/zzvr2fT7I26ffvfMJAXd7xGs87JNR6TgcGZhr8gEAAEQWgOPACkAbKdzimKopEZ8wRRodL8HTmcS1R0pLcAWFyK1WbT1NYFlICOw1k5Sr6vq6+70CvRszqRc2paM7765F2vcUzqN4Hn/3sFS5SF+x+5UaNpBBQ/HD0KjxQElAssLDKsyj5qrMXePoBVIfsNyloDNSMerTqfYU1pf05H2p/TEafTycKPM47rXEHmIEKNgvR1ijH3SdhKpec6xBIScIQtQEKLxKo7+eDK16UyljsWbtMNAPHFVp2FK0BQkPG8GBh4EkiW2N+RJsHRSPUw5X8GJwhHRQWJSXGAJAACWF0+gI899T8sllCKi0qxWe2EeqQIadpUEgCzsnKY+t2fAKxbvdXrGm3bpyqfIyPDdVOgAOTomCoD4cg2/1UFZKqbT3DJlwt5b9RbAfdf8cNS3h2pH5l478yHCbH4L39VG5hMJoRpD9To2M4XdJXOoVOWzqDWMeY+wOpKHh8cFkJEvDuZ+/Lv3Yngdy3Z4XVcTXKDenLb9wKtB7apuqMOTyx79rivb2BwJlOlBhBp9TsZeBeouohiLSMIwiVIhdljgM9EUZwjCMJg4Dqk8XcURXHAiR7XicDhcLB44VoqKippltyI3KP5ZB+RBECbTRIAO3RuRVR0JIUFxTjsDkKCPRIXx0vRodN+nssNt0iKi6dfFLyu1aBhAkOH9eOKMVLkbGVFJXHN4sk/mKfpV+HwbXItOead0uS6D27mu7u+5JvbP/frW2eJtvgsa+bJPX88pMnyv3/NXj64/B3u+kXfv1Emc+MBJXhEj0BqEdcWqb3TOPf+83VTuvhDjsL0VwrPoNYw5j7AWiQLgMG06JWqtP/y6BT6XSsFVHx01Xt1cu2oxChu/uJ/uiUVqyI+OaEORmRgcOYQiAD4PDACaA6oIwP0a/SoEAThc6QVc7Yoip1V7SOQJlYzUr6tl32dQxTFacA0QRDigdeBOaIoLgQWuibIlQHcw2nJx+//yITx0sTa+eKz2bBuGwczJQ2cHAUcFhZKw0aJZGfnYnc4vKJ75VX4+jX/ccFIKVpU7ben7jdlmluTZi2xEp+cwONLnuHxFg+62/348q39dZXyObZJHAWH8mk3tIPP/jKVlZVYi60+U6940ryHt9/bnuW7sJXZ/Jp55BeZL6IbxgR0/dpi0G1nV1sATO3bkiVfLqRZF8OX6QRQ7+a+o44SkoI9Sja63CxCwkIC1uoNHDuk1sZUVbWdqmjQquoqQAYG9ZFABMBYYBjeEXGBFEr8EngPUNK4C4JgBt4HhgOZwEpBEKa7zjfJ4/ixoihmuz4/5TpOzbWAfhr7M4CcbHcN3/jEGOITYjiw7xDgNgGHhYXSoGE82UekvuryTDLnXdSPFUu2cPjQUaJjIomMrLo0k7XESnSDaILMQYx89hJmTJgGwGfXf8jL+9/S1UCFx0rnfXDOo8Q3T6TCXoElykJIeCiN2zb2fS1XebOwAAVAgIf/eYI3ztH+XI7tz6VxuyY+jpAETV9cexKSKEfGR1bdCbj0pSv57cmf6Hf9ALqP6klan5ZK2guDOqXezX15FaXeAqArQj84NJDXhcQ59wyvtTGZTCbOunEgS7/2ju5Vc+mkK8lcf4CVU5Zx67dSkNpzmyZ5lXM0MDCQCOTJeBBpxbsYqFbODVEUFwiCkOrR3BfYKYribgBBEKYAo0VRnISOf40gCCbgZeBPURTXqNqbAwWiKBZ5HgOQm5vL+PHjle2WLVvSqlUrva6nLAcOuGt0LsxYicUSwrZtO8nIyGDdaqnU0uYtm7DZyzl8KJswSygmUyUZGRma80RFWcjPK2Lt6o1Ex0R47dcjPzuPygjXudqY6HBdF7Z+J0XYzfx+BtHJ3tqyfVv3ktAhiW3ZOyDb7RNUWVnBgfX7mff3PN0C6wV78wHIzD0Y0NhkgkLNVNrclrnPbvuQ/k8Nxuxjwl/15lLd9nPeHUF+bGG1rl1btBjekn1/+46eBrA2dzB08vmEJ6q+u211PzaD02/uq868V1xc7PWbr8A7CfOe7VK07co1K7Hs1S4e/5n7D0HB3ovB1VvWwBav5hpja1C1BX5/9gEaXtiYcwaM4BBHOJRRM3/lE4He3/50whj/yaW2xh+IALgcmCmK4tvHfTWJZoC6+ngm4M/5616kVXisIAitRVH80NV+K/CFr4MSExOZOHHi8Y71pJIxx53SYP/ew/Ts04nQEAvp6elk7pUsUmed1Y8dWw6zfesB4uJiady4Eenp6Zrz/DtPencc2JdD2/apXvv1WMw/JLdIVvqmp6fz6HdSypTU+OZ0Se/udcyaF5eR2DzJ6/yzrL8B0KNddxKae0cGyjnuRlx7gVeeP3+k/ZTK17d/RtER6W9RsCef3DnZXPXGtV59ywrLmLXmN93znDfqfE0N4hPJkCFD+HncFFZNXa5pj2saR35WPkBA35dBnXDazX3VmfcyMjK8flt6Bt74yDgOAIOHDiEyPpJ2f7flreGvAODc4qDP9QOYhfvZioiLqP3fbDqkNmzBL49N1TR3v6QX66atBqBX/941Dhg50ej97U8njPGfXGpr/IEIgOHAK4IgXAoUuNpOWCoEURTfAd7RaT8jQrzy8wq56erH+PjrF2jUOInycisb1m6j71ldNSbLHr07EhcXzbFjBfz20988fI/kOhQWFkZsfDSFBcUkJMTqloNLSJS0dTnZxxic3jugcTlsDoI9ImLbDGnHjgXb+OaOL3QDOqwlVr9Z+Pev3acrAOZn5WGJtlRL+ANo0SuVp1e/gNPp5LGUBwBYNXW5rgBYYXOnr3h67Qsc2X4Ec0gQe1bsPmnCH0jBHFe9cS3nPXwBuXuP8sl1IpX2SloPbseqqct9RmMbnBDO+LnPs0ShSUcELCsoBVD8a5t0aKrsm/3qTPrfMFDZ7nVlX86+Q1sbuLbod90Aul7cgz3Ld7Fv9R7mvzeXhq0bKftbnnV6WXgMDE42gQiAcpSZut6UfmHTwDgIqNOxJ7va6iXtUkYAcP+dE5ky7S3uvnUCM37PoHuvDvTp5375f/vTazz92GR27zrAvDluU2Z4RBixsVHY7Q52bN/Hrp0HvK4hC4DgTgxdFQ6bw8tce/64i9ixwLftsbywXDfX36DbzmbRp//y87gf6D7aOwBl6VeLqKyofkJnmUAc0x0qATAyMYrWA6W/SVrfU+OlEdc0nrim8cr2OfcMxxxs5uLnLj2Jo6r3nPFz387t2ngWvSeprLAMk8nk05fOqarde/Vb1+n2qS3CY8LpOLwz+9fsla7tdDLi8ZG06JlqRMYbGFSTQNPA1CYrgTaCIKQhTX5jkBya6zWdXJU5ZvyeAUi5+vr276rsT2oQT1xcNAX5RVitbnek5JTGxMZFK9t6gQ4JibHK5+oIgJ5O3/6SNO9cvJ2yglLyDh7z2idrDvTKttnKbMcl/AWKfO24ZvGn9Isi9fxW7J6xg/iUBC5/5eqTPZz6zhk/96nLS4L+YqqsoIyQcN8RwJWuc6T2bVn7A/RBu/QO/PPu37QZ3I7U3rX9NRkY1A98CoCCIMSJopgviqLPlAdyHz/7fwDSgSRBEDKBZ0VR/EwQhHuA2UjRb5+Lori5pjdwplBa4p2iRI70lc26MbFRFOQXY7W6q1+YTCbi4vynLwmPcAtuDRu7zawOq4OinEKvfFlOp5MKW4WXAOjPRLtp1gYA9q3yLs8kl19KTG3gtU8uM3W83PrNnXx2w4c+9782RPKLGvn0JbVyvbqi3dWduP194aSapes79WnuU1uATUBUkHfKlfLCMq8E6X2vPYsVrkTQm12+yh2HdfY6tq5I69eKC76+xBD+DAyOA38awIOCIPwM/IG0cs1CmiOaAr2BUcBlQJSvE4iieI2P9lnArBqO+YzkWG6+V1vuUSkJs5zaJS4+msrKSvKOSUEPoaHSpBwb5/4KpkzTr3Ih06iRpAFc+vUifntSqtv54o7XNPnzKuzSit7sIQAGhwYz7IHzmfv2bCorKzWaNLkyxZU6/nddLuoOQFof78m6KFu6l0G3ne133FURn+IWYkvzSjQVC9Sc6ulTTCaTIfydfOrl3DckohWNg70Xk2WFZcR45Mh0qrT2cv3fnN3ZnEhMQUY1HAOD48Hfm+YJ4GzgR2A3UA6UAbtcbYNdfQyOA1mIyj2a77WvsFCqrDH9b0mzFevS9BXkS9kfbDa7q91tAm7cpAFOp5Pio9oMEXfeO4aEhFjatk8je+cRRfgDWPTZv5q+sr+cXt6vIJc2cufC7Zr2xZ8vAKDD8E4+73Xl1OWUHCvWtG1x1e7se81ZPo8LBLUz+1e3fabZpzaLqysZGBj4oN7MfU6XS6MJSA3Vr5xR6aj0SsbcpGMzr37t0tvX+vgMDAzqDp8aQDkCzVV6aCBSNnyA/cAiURQXnYDxndFUVFQowsmxXMkU2q59Gtv+k8yoOdnHCI+w0LV7OwDiXIKerAEcdv5ZmnaAlOaNlYjY8SsnKBqvCZPuY8IkqVTa5Jtf04zjr5dnaBK3VvgRABNcmrZPr/tANxK4Kv+63ct20eXCbsq2nALGU8NQXRJbuM3Te5bvoji3mKhESUFTaa97H0ODM4d6Nfe5Fk79w1P9dvMUALuP7sn0Z3/VtAWH1X0dbQMDg9qjyiAQufTQCRhLvcOmikyVzb1Nkxty+PBRCvKLyD6Sq0nrImv6so/kktKiCZ98I/m1pbZ0lwWLinabPif2eZZHMp70um5y1+Yc3Jjpc1wOqzQuTxMwQPdLezHl/m8BSesmO4Y3bt9EI4T54qdHflAEwCPbDyvt4XERVR7rj+DQYF7e95ZStu6TMe/z4N+PAVBwKP+4zm1QP6kPc5+sOW8b5u2fq+bQFm2wclRStFef6lQKMTAwOPkYzkYnEbvLhBsaGkLu0XycTidWq53GTSRBKvdovkYAjIt3T7rtOqQREWHRbMsaQTUHN3kLenLN25d2v0FsYylCWG0mlX0Ag0O9cwqqNXy7lrirfZQVlCml4PSQr9MgTXrRHNpyUFPKLdAao/5Q+84d2prFrqU7ObY/l9fSJUE5EAHVwMBAIqqBe75Rp3rxRXPDvcLA4LTCEABPIrIPX6MmSdjtDoqLSrHZbDRqnERYmGRyUQtcqWnNlMCPiHBtvr0FK7/ju1/eANAkR501cbrXdee+9RcA5hAzha4qGuunr1X2yz6AehpANWqhrayglPBY31q8cQvG07BNI0JdqWSWfeu/rmdNUQt5H135Lq8OfpFKhyTcDrxlsK/DDAwMPIhK8BnjoktohHcEsYGBwamLIQCeAGbPWqQIezJbNu3i4Xulah5NmkpasdyjeVjLbYRZQmncVBJkgoPdQlhkVIRSySM8wnfFDTkiFyTzZ1lumX4/k4mGbSVhMWenu26mvyAQgCtflwIcD6zbD0gaQ1upza8AGBoRRoOWDSk4lE/2ziOawu6XvXyVz+Oqy8Pztb756hyDK35YVmvXMTA4E8g+4p23U0Yvp6g/TuX8mgYGBt4E5LQhCEIy0AIpdxUgFTuvq0GdSaxYuoEbr3oUgCPFS5T2MZc8yJHDRwF3apZ+XSVBqEVqU6Jdvnxmj5Qgvft2Zt6cpX5ThTisDlqe1ZrdS3cCsHzSQi64/AJlf3TDGDoOl3J23T3tQZ7p8Bjlxe48hFbXZ18r+uY9UwGY/fpM0oVzKSuUBEx/JmCABi0bsnn2Rtb8slLT3v/6gT6OqD7+/JDOumlQrV3HoH5wps9911z6kM99uXuP+j02oUUix/bl1vaQDAwMThBVLtkEQXgV2AcsAOar/hkEQGGhO+2JHL0LUFRUonxu0EhbnSPMEkpUlKRN8xQAG7oqeRQXluCLCptDidYFKD2s7VteWKbU7LVEW4iIj2TXkp3K/uIcKYWMnqM3QKO2jQF3GbW8A9JLILqBfn+ZtP5S/zW/rlLa9CKJj5cbPh6r2972bCNNhUHg1Pe5L6aRu4JQ14t7eO2/5p0bT+RwDAwMaplANIC3A3uBOYDDf1cDT2RfPpCid+MTpACM0hK3WbZhI+/8W1HRkgAYHaP1w2noEhbVgqUnDnsF5pBgnt/6Cs90kCJhrSVWwiLDqLBXYC+3Ex7jNiGX5pVQmleiJHf+5o4vpDEk+vYBGnDzYMWkemSHZD5u2inZZ3+AhObS2PMP5vntd7yo08zoXd/AIEDq9dynzq2pZw1o3rPFiRyOgYFBLROIALgJmCqK4rt1PZgzEbVfTEF+EV1aXUz2Ea3ZRNb2yfwydQ6jLz8XkGoAq4mPdyWDLvAtAFa46vhaoi20G9qBbfO38tfLMxj9wuWUF0mCpyXaba7tdWVfVv+0gkNbsmjSsanSHulHADSHmnFY7Sz4eD4rp0qCoBzp64sGLRtqtq//8Ga//Wub2og0NqhX1Ou5T71QG/7gCK/9xvNkYHB6E4gAmA9MFAShLyDPCE5RFO+vs1GdQdhsNuVzXl6hl/AHcO2NI3nq0beV7d9ni/z4w5+AtwDYoKG03bCRb22Ww+pQInitJVLd4MPbDgFQXij594VFuzWAnS/oyuqfVjB5xGsMvGUIIKWA8OdPl7n+AAAznp+mtHkmi/XE02+x60hvs1JtcdZNg1j6lTtf79VvX1dn1zI4Y8nHmPsAvOqFGxgYnP4EErZ1IVLNy+uAe1T/DALAanVH/+bnFXrtf/6V+4lUaQCHDO1D/4HdlSAQTwEwtWUyn37zIu98+JTPa1bYKwgOk4S34BDJd12O7LWVSQKpuvZvp/O6KJ8XfyH5tw+61X9tXrV5qDqcP+5CAJ5c8VyNjg+Ui54ardlO6W6YqwyqTb2f+3y5U3jS49JedTwSAwOD2iYQDeDQOh/FGYxNLQDmF3ntH33ZOZrtq6+TonXNZklwS0j0NqtefOk5Xm0ylRWVVFZUYnYJfu2GdmTn4h1EuCptKClewrRf/eDb01n4cYayHebK1+eLi8aP4v3Rbyvbek7iepx7//mce//5AfU9HkLDQ3ls8dMABAUHEd/M0GAYVJt6P/f5CqiSeWb9RLI2ZdLyrNYnaEQGBga1RZUaQFEU/wWygI6uf1muNoMAUJuA848VavL3devRnsZNpByATZtJ/nEWiyR47dyxD0CpChIomeul3HxRSZL/3uDb0zX7fdX57Xx+V81232u8q4qoadErjes/vEXZDg0/9eqAJrZIIrFFkiH8GdSI+jT32ZwVNTouKjGKtme3N8rAGRichlT51AqCMAr4EQgBTIBNEIQrRVH8o64H57r+JcBFQAzwmSiKcwRB6Ag8B+QC80RR/PlEjKUqNm/aScdOrTTO0eXlKgEwr5DyMquyHaKaNIOCpGPCLJJpdvTlw5g9cxHDzh9AZWUl5YXlihYPpCStk0e8TsfhnTh/3EXStYrLmf/+XADaD+3oOm8QTfonc/g/yQfQ7tJIemoAG7ZppNkOsVQt0HUd2Z0LnxzFrJemExruX2NoYHC6UV/mvmCCCDV5l300MDA4swlk2TYR2AO84dp+0NVW5SQoCMLnwEggWxTFzqr2EcBkpOSqn4qi+LKvc4iiOA2YJghCPPA6UkqGC4B3RVFcKAjCdOCkC4CrV27mwqH/o1uP9sxZ+LnSLpuAo6IjyD6Sq/GdC1FV+cg8IKVSCQmR2i6/6jwuu3I4JpOJaeN/ZslXC5m483VFMCvNK+XQloMc2nKQTX9u4OF/nuCHe75m69zNAMSoInKjU2I4tCyTssIyHFZ9DWCkquxTSrfmAd93n2v6s3XeZtLvPjfgYwwMThPqxdw3NNLbfNvryr5G3kwDgzOcQATANOABURQ/BRAEAeDtAM//JfAe8LXcIAiCGXgfGA5kAitdE5kZmORx/FhRFLNdn59yHQfwDfCsa4V+SiR3yztWAMD6tf9RUlJGZKSUZkU2ATdqnERWVo7mmJBQby1buKrGr6xJXPLVQkAq65aUJpmM7WVuzeKR7YcB2Ld6r9JmDnav6ONaSYEkB9btU47Ti9jtcmE3Ns5az12/Bh7kGBkfyV2/3BdwfwOD04h6Mfc1DdH6Gad0a87VbxlR8wYGZzqBCIC7gYcEQZALQz4I7Ark5KIoLhAEIdWjuS+wUxTF3QCCIEwBRouiOAlpxaxBEAQT8DLwpyiKa1znzQbudk2ovwYylromNs5dBSMn+xiRac0AKC4qBSRfvr27DwIQHRNJUWGJou0D+HH623z7xXT6DfCOugsOC8ZhdfDbkz/yvx/uBsBerq0tXFlRiTlU34wT1UwaW+7eo1TYJV+fmEYxXv2uefdGRr9Q4mUeNjCop9TLue/On++ti9MaGBicYgTypn8ayQ/mEyQ/GDtwxXFcsxlwQLWdCfTz0/9eYBgQKwhCa1EUP3RNrE8CkcBregfl5uYyfvx4Zbtly5a0atXqOIbtn62b9iifM+YvIHVfEwBWrlhHfEI0mBxkZUkL+tg4SQDMyckhIyNDOigIrrt1mHtbRVBYEFhhx8LtzJ72F2FxFgr25Gv6zJo6E1xueOENIjTnsQdLZt/fnvyJxn2kRM8r1q3EFOQjkevW6t27Qe1RXFys+xswOCmcdnNfdea94mL9ZPKLly/RbT/VOJ2fldN57GCM/2RTW+OvUgAURfF3QRC6IJktAP4WRXH7cV85QERRfAd4x6NtL1KZJp8kJiYyceLEOhyZlmCTWwMYRDg/ffsvkz8cz5Sv5hMXH0vbtq1Z/O8GANq0TSNzfzYhIWGkp6dXee6lMf9iKzwGQMfUDqR0b8HeyN0sZj4XPD6SP1+ewYJH59L9kl6s27+ap5ZM0Jh4MzIyCA4LwWG1c3hlFgBDz6n3GS5OSTIyMgL6TRjUPafj3Fedec/XC+R0+f2dzs/K6Tx2MMZ/sqmt8fsUAAVBuAxYBvR3NR1y/d9ZEITOoijW1PxwEEhRbSe72k5rHA53qdBx970KSBG9DruDkOBg4uLdJtfGTSU/Ptk8XBVyNQ+AskKplJuc0LlpZ3f9XYfVToNWDXX9+6774Ca+GvtpoLdjYFBvMeY+AwOD+oA/DeBPwDXAFEBd9sHk2q5p3oCVQBtBENKQJr8xwLU1PNcpg8PhnUfruy//4PyLBhESGqJJ6Ny0qZTzLxAB0Ol0Ul5YRq8r+rD655XMef1P2g5pr/gARiZG0feas1jxw1IKDxcoCaA9UVf7MDAw8Isx9xkYGJzx+BMAnwc2AxNqenJBEH4A0oEkQRAygWdFUfxMEIR7gNlIE+nnoihuruk1ThYZ85bTf2B3JXGzngAI4LBXEBJiVhI+A7RqK6VZsQSQO89abKWyopJGbSWfwv1r9uJ0OrGXSQJgiCWEjud1ZsUPSzm69yjxyfE+z3XppCv57YmfuOCJiwO7SQOD+km9nfsCyf9pYGBwZuBTABRFcQKAIAi9gH9FUdzn2k4E2gVyclEUr/HRPguYVe3RniJs3byLq0c/yNBh/Zgy7S0AHHaHbt95c5bSs08nTUWPdu1TeXT8bYy6zHdJN5myAklLGJkQqbSV5pWw+HOpIIElykKzLimYTCZK80pISvVdOeSsGwZx1g2Dqr5BA4N6TH2Z+0qKy7zaJu58/SSMxMDA4GRQZSk44Auk9AUyw4GFdTOc0wObTdK+zZ+7XGkrt9p8dWfNys00UgmAFksYDz8xljbtUqu8liwAhseGK/U2//tnq5LzL7phDLGNY5UE08FhxgrewKCWOKPnvoqKyqo7GRgYnLH4CwIZBVyC5PciCIJwgWtXD6C87od26hKkSp9itdoICwsl+3AuINX0zTqY7XVMo8bunK0NGrpr02bvPILT6aRRm8Zex1hLrPzznlTaLTw2gstfuZrXhkxk6gPfktgiicqKSoLMWhn+4KYDXucxMDAInPoy93nOHQYGBvULfzNAD+BmJKfns12fbwa6cYokXz5ZWK3uJMxycueiwhIAmqU08ur/0ZfPY7GE0bZdKleMOV+TNPr19Jd4Y6i7CMCR7Yf56eHvqXBUsOaXlWz4Yy0A4XERNGjZUOmXu+8oTTs1U7bv/v0BAJp0aFoLd2hgUK+pF3NfkMlEXFD4yR6GgYHBScJfEMjHwExgBTAeqQ6lE8gTRXGPn+POeGwqAbCsTFIIOCoqCA42ExMTpek76OxeXHLFMAAWrv7e5zkrKysJCgrisxs+JP9gHs17pWErdZuVLdEWr2P2rXZ/DS16pXHb93eR0r1FzW7KwMBApl7MfSaTiVCTO6C58wVdT+JoDAwMTjQ+NYCiKB4SRXEV0BL4URTF1a5yREGCINRrKUOu7wtQWuISAO0OQkKCiY6J1PQN1an3q8eM56cBkH8wD4A/nvtVicgLDgshIcW77Odt392l2W47pD3hMcaK3sDgeKgvc58TpybHTcPW3tYLAwODM5dAnED+RjJ/yNwMzK2LwZwuqE3AU76dyeTXv8ZudxAcEkxsbLSmb0hoYHV1F336r2bbVmqjvFgSLp9c8ZzSfvOX/wOk4I+mnZIxMDCoM874uU8tAsqBZAYGBvWDQATAZsBe1fY+V1u9xaaK+J363Sxeeu5DHHYHwcFmomMlDWD7ji0BuGDkkIDO2bCNtPpuN7SD0rZx1noAwiLclT1a9pMigc+977zjuAMDA4MAOLPnPg95r/05HU/OOAwMDE4KgaindgOPCIJwECkq7mFXW73FarN7tTkcFQQHBys+gG3bp/H7bFFTAs4Tp9OJyWTC6XRiiZZMt2X57uogxTlFAJjD3F+TJdrCq5mTa+U+DAwM/HLGz32VKikwrW+rkzgSAwODE00gGsBXkJKfzgRmuD5P8nvEGY46CERmx7a9hIQEE+PyAbTZbH6FPwB7mU0xu5TkFmMvt1OUU0TXi3sAUHAon6DgIIKCjHQNBgYngTN67jNMvgYG9ZsqJQtRFL9BKmn0hutfuiiK39XxuE4pKisraRQ1gDdf/gLQmoBl1q3ZSnBIMDGuFC96QqIn1hIrAOYQM8W5RYxv/Qh5mcdo1NadE7DSYSRrNTA4GZzpc9/BzBxDCDQwqMcEqlrKB7KADwGnIAgpdTaiU5Afv/8TgFde/ATQNwGXl9uIjo4kKSkOgKKikirPay2WBMCGbRornwGS0hrw1Ornj3fYBgYGx08+Z+jc99/mvSd7CAYGBieRKn0ABUEYA3yL5AOzAXgCKEHKlF8v+GPafADatJUyQMgawPDwMMrK3IJb0+SGNG0mJWsuLiqlKjLEeQDENYvn0JaDSntoRCgxjWIBI1u/gcHJwpj7DAwMzmQCkS4mAPNU2zOBs+pmOKcm3bq3B1Dq+crmXXVFD4Bgs5lWbZpz062X8sZ7j/s953//bGHFD0sB74je9kOlaLyn177Ag3MfO/4bMDAwqAln9NxnWH8NDOo3gUQBNwU+B4a5tu1Avco2bHUlft6/75C0bbVhNpuJjo7k8KGjSr/1a/8jKCiIVyeP0xxflFOIw+ogPjmBrfM2s/3f/1jvKvEGkNzVbVV6dOFTmEOk7PzRDWKIbuA/kMTAwKDOOOPnPktQYInqDQwMzjwCEQA3Aje6Pt8AjADW19mIThHmzVlKRUUl510wUNH47d+bxc9T/sJmsxMaFkJEpPQu6HdWN5YvXU9hYbHXeSorK3mhx9MAvJo5mS9u+hiA8Fjp2DaD2xJkDuLRhU9xdE8OSWkNTsTtGRgYVM0ZPfeVlZVzXlQ7ADqe1/kkj8bAwOBEE4gA+DBSCgQTcBNwDHikLgelRhCEDsD9QBIwTxTFDwRBaIlUozNWFMUravuaFRUVXHvZwwAcKV6iifq9+7bnGXvHFYSFhRAeHgbAFWPOZ/nS9bp+f9aicuVzZYU7olc2v9z2vQBIgR+G8GdgcEpxRs99v03N4Ka4PgBGCUkDg3pIlQKgKIpLBUFojdv3ZYkoivmBnFwQhM+BkUC2KIqdVe0jgMmAGfhUFMWX/Vx/K3CnIAhBwNfAB6Io7gZuFQTh50DGUV3KSss1255Rv3t2HSA83EJ4hAWApIbxPs9VqkrsXFZYRoglBHu5nfLCMuJTEjCZTLU4cgMDg9qiPs19piBjHjIwqG8EGgUcBPwGfAQ8KAjC06IoLgvg/F8C7yFNXvL5zMD7wHAgE1gpCMJ0pAnRM8nqWFEUswVBGAXcBXwTwDWPm1KVAFhebvXK+zd/7nI6dm5NVFSE1KfMSmRUBPc9fIP3uVQC4OH/DmEvdwuTeQeO1fbQDQwMaol6NfcZC1EDg3pHICbg55FSIVwLXAOUI01svas6UBTFBYIgpHo09wV2ulayCIIwBRgtiuIkpBWz3nmmA9MFQZgJfB/AmMnNzWX8+PHKdsuWLWnVKrBSR4cP5Sqf/5g+i8zMLO9Opgo6dE3hj2mQX5jD51OeAiAjI0PTLWfjEeXzR1e+63Uaz/51QXFx8Qm5jsHxY3xXpxSn3dxX03nv8OHDp93v7nR+Vk7nsYMx/pNNbY0/EAEwBan+5WCkiLhVSCaMmtIMOKDazgT6+eosCEI6cBkQBsxytSUCE4EegiA84ZpANSQmJjJx4sQaDXDLpl3K54YNklm+eJNXn7S0Fjz86J3cdc/NRLhMwXqsK1jDSpZo2m789FYqHZWERoTSPr3uC7BnZGSQnp5e59cxOH6M7+qU4rSb+2o671366BWkdGte7eNOJqfzs3I6jx1Oz/Hb7XYyMzMpLy8nNjYWi8X3e/tUx9f4LRYLycnJhIQEFt0fiABYANyMVAfzRSSH6LKAR3qciKKYAWR4tOUCd9b2tUqKSwmzhFJa6r69rZt36faNS5ByAPoT/gBK86WKILLvH0BYVBhtBrWrjSEbGBjUHfVm7gt2pZ4yMDhTyczMJDo6mtTUVIqLi4mOjq76oFOUoqIir/E7nU5yc3PJzMwkLS0toPMEkgj6Q+AcV99fgQFIWfFrykGklbVMsqvtpOJ0OmnZeBh33/Y8pSVuH8CCvCLd/vHx3vn5Kisrldqa+Vl5ZG3OpMzlA6j2/TMCPwwMTgvqxdwHUFFh1Bw3OLMpLy8n8f/tnXecVNX1wL+zHRZYYCkiqFQLggWwoeJq1Gii8NOoURNbNBhvjDGKSRSjREWNmkSNHkvUGCt2RSV2V6yIlSq9d5a6bN+d3x/vzezM7JQ3s292ZnbOlw+fnXffLefNmzlz3rn3nlNa2m5/fz0eD6WlpdTU1MSubBPTABSRSUB3YC8R2QRcDpycqJDATGCIMWaAMaYAOBuY2or+XGHtmo0AvPrie0EewPXrrUDPQ4cNDqpfX9/Qoo+7T7yDO466haamJm49dBJ3//hOZr3xHQXFhVz8ZOBDe/v8ACpKeyJbdB9AU31jqkVQlKTTXo0/H/Fen5MpYAJDH4hIy2jHETDGPAuUAT2MMauBG0XkUWPM5cDbWLvfHhORufEInQyWLVntf33Dn5uX+Xz+iZWx48JLTqPXbqVMeepN3nrjY4qKClv0sf4HK1PIJ49+5C9bN9/aQLLPsftx2ctX8MxvnwjK/KEoSvqSDboPoPc+fVItgqIobYwjAzBRROScCOXTsBc1pwvV1c1u0xXLmnf9+gzD3LxcTj5lDCecNJpHHniB8y8+Lah9Y0PzE/Ta2asJx4BDBzFx5l/dFFtRlDQkk3QfQGFxywdaRVHSg4qKCs444wxmzpzJhRdeyG23tdj3mhBJNQAziZrq2qjnFy9cAUBeXh6/+V1L3V4dEO+vsHPm7i5SFEVRFCV9KCoq4uabb2bOnDnMmdMyKkmiOAkEXQrk2kFJj8NKkP6iiDhfaZgB1NRENwB/dOIRUc/v2rrL/3rWG9+R36GA+uo6Dj5tJGXm+CgtFUVJR7JF9ylKtnHLXx5g4Q8rXO1z/wOGcMsdV8asd/PNN/PUU0/Rs2dP9thjD0aOHMmECdEzTBYXF3PUUUexePFil6S1cOIBfAP4zhjzHPAe4MVaCP0LVyVJMYsWBH8YRh89gs8+/sZ/fHRZ9NivVQEG4K6KSkp2K2HiojvdFVJRlLYkK3Sfoihtw8yZM3nppZf4/vvvqa+vZ8SIEYwcOZI777yTp59+ukX9MWPGcO+99yZNHicG4FDgEeDHwKfAXODMpEmUAubMWsg9dz0RVBZo/EWjZmcNDbX1VG2tCirfvn67a/IpipIS2r3uU5Rs5PqbL0tJHMBPP/2UcePGUVRURFFREaeeeioA11xzDddcc02by+MkDmAOVryqI4H/AZ8B7WqR28YNLXPy/vmG8Yw59hAArr/psoht7yq7lZsOup6V3y4HoO/wfkmRUVGUNqfd6z5FUVLPnXfeyUEHHdTi/xVXXJHUcZ14AL8EbsSa/rgaOBVYnkSZ2pzCwoIWZf/3s+MZb85i/ryljDp0WIvzUye9TOlePdixwfL0fXjfe3g8Hrav2waAefXKZIqsKEryafe6T1GUtuPII4/k0ksv5dprr6WhoYE33niD8ePHp8wD6MQAPBtrzcsiEZlpjNkT+Dy5YrUdO7ZXct8/n2pR3qlzR4o7dQxr/FVtq+KTRz5qUV5QXMAvH7yIz5/4hD1H7JUUeRVFaTPate4r8Gj6N0VpSw455BDGjh3LAQccQO/evRk+fDglJSWO2vbv358dO3ZQV1fHK6+8wrvvvsvQoUNbJY8TA7AH8IGIzDLG/AprJ9yDrRo1jaipqeWDd78AoHOXYnbusDZzdOpcHLGNz8sXSl5BHgMPH8zAwweHPa8oSkbRrnWfnbVSUZQ2ZMKECUyaNImqqirGjBnDyJEjHbVbvny5/3W4XMCJ4MQAfBooN8Z8gLUg2gscAfy01aOnAd1Lm63v7xa8yqA+JwBQVNRyWthH5ebg/MD7HjeUHz6Yx64tuyK0UBQlA2nXuk9RlLZn/PjxzJs3j5qaGi644AJGjBiRMlmcGIB7A/cCx2JFsP8WSO7KxDYkL6/5LejUuZjJd/6B11/5oEVOvV1bd1HczfIKrvouOGTMwaeP4ocP5tFXU7wpSnuiXes+TUmuKG3PM888k2oR/DjZBdwAjMLKa1kOLHHYLiO55LIzee2dB4LKFpTP56/Dr+Or52cAsGXlFjr1bHa/luxWwu/evJpLnvpNm8qqKEpSade6b1C3XqkWQVGUFOLEA/geYIBG4DzgImBRMoVqa154/V6KO3WIeH7VdysB+OjBDyjoWMDGxevp2LUjlZusqeDe+/TxewcVRWk3tGvdd+KQA9i+oGUILEVRsgMnT7PnAacDI0VkHvAacElSpWpjxhw7ipGH7B/xfN0uK03choXreeo3j7N85jIqN1dy7v0XcPBpI9X4U5T2SbvWfY2V9akWQVGUFBLTABSRaqArMNFOiTRIRJylyWgnLP9qaYuyI84/koPGjeCcf52fAokURUk27V33denfNdUiKIqSQmJOARtjrgduCig6wxjTV0RuTZ5YQePvB/weKyTD+yLyQLiyZI3v9XpZ8dVyRv38MHYf2pehJw6joEMBnXq0fRoZRVHajvau+/Y4Zi/WfrqKXz1xqTsCK4qSNG677TYeffRRcnNzuf322znttNNa3aeTNYCXAK9jRcIH+DswHoipBI0xjwGnABtFZFhA+UnAPUAu8IiI3B6pDxGZD/zGGJMDPAE8EK7MwXU4pqmpiZVfL6ffgXuybMYSvF4v/YbvwegLj3ZzGEVR0pt2rfuaGpoAKOxUmGgXiqK0AfPmzWPKlCnMnTuXtWvXctxxxzF27Fhyc1sXzN2JAdgNeFdEFgMYY97F2hXnhMeB+7AUFXb7XOB+4ARgNTDTGDMVSyHeFtL+VyKy0RgzFrgMeDKgnxZlbvHxw+W8ectrQWUjzjjE7WEURUlv2rXuW/TyfADWzVvLgEMHtaYrRcko3r71TTYv2uRqn7vv35exfz09Zr2bb76Zp556ip49e7LHHnswcuRIJkyYELXNa6+9xtlnn01hYSEDBgxg4MCBfPnllxxxxBGtktmJAfgVcKsx5lD7eBww00nnIjLdGNM/pPhQYLGILAUwxkwBxonIbVhPzOH6mQpMNca8CTwTqSyQiooKJk6c6D8eOHAggwY5U3IzXv2sRdkXX33hqG06UllZSXl5earFUByg9yqtyDjdF4/e275sGwCzZsyirn+jk8tKKzL5u5LJskNmyl9SUsLOnVbkDq/XS2Njg6v919XV+fuPxNdff80LL7zAJ598Qn19PUcffTTDhg3jlltu4fnnn29Rf/To0dx5550sW7aMQw45xN//7rvvzuLFixk2rGWq2pqaGsf3xokB+DusaZBf2sdL7LJE6QusCjheDRwWqbIxpgxrJ14hVjDWsGWhlJaWMnny5IQEXPn0EirmND8dnCsXcFBZ6qJ1t5by8nLKyspSLYbiAL1XaUXG6b549N73R3zFmk9WUXbmsexb1rqcoqkgk78rmSw7ZKb88+fP96dPO2niKa6kUouX7777jtNOO42ePXsCMG7cOAoLC5kwYQLXX399xHYFBQV06NDBL7PH4wk6DqSoqIiDDz7YkTxRDUB7ymIw1pOvL3PkAhFx13SOgoiUYwVhjVrmBltXb2HNnNXM+d8s9jt+fy56fLzbQyiKkgFkg+4rGdSdNZ+sovfeu7nRnaIoCXLnnXfy9NNPtygfM2YM9957L3379mXVquZnxzVr1tC3b99WjxvVABSRRmPMo8A1IvJ4q0ezWAME5kzrZ5ellG9e/oopVzQvqRmpa/4UJWvJBt23+BVrDeD29dvp1q97qsRQlKzhyCOP5NJLL+Xaa6+loaGBN954g/Hjx3PNNddwzTXXRGw3duxYzj33XK666irWrl3L0qVLOfTQQyPWd4qTKeCngQuNMTOBdb5CEUk0hPxMYIgxZgCW8jsbODfBvlyhsb4xyPjb+5h92f/HB6RQIkVR0oB2rfv2OKY/S15fSK9BmhJOUdqCQw45hLFjx3LAAQfQu3dvhg8fTklJScx2+++/P2eddRZDhw4lLy+Pu+66q9U7gMGZAXgF1hTIrIAyr5O2xphnsXbN9TDGrAZuFJFHjTGXA29j7X57TETmxiu4W2xfv53Jo24A4PTbz+KwX4zG49Es6YqitG/dVzHfWuecV+jkZ0BRFDeYMGECkyZNoqqqijFjxjBy5EhH7SZOnOjf4BVrs4lTnHzzp9O8BiYuROScCOXTiLB5o60pLG6OgXXouUeo8acoio92rfu2LdkKQG6+GoCK0laMHz+eefPmUVNTwwUXXMCIEanbYBrzmy8iZW0gR8oo6lzE6AuOpmT3ruTkOEmNrChKNtDedd9B5hDWv7+anDzVe4rSVjzzTIuodSkjogFojBkPHCIivw4o8wAPAV+JyMNtIF+b8H+Tz0i1CIqipAnZovt2P6If5177y9gVFaWd4PV62/Usn9cb34RFtEe/q4H1gQUi4sVaDB15u4qiKEpmo7pPUdoZRUVFVFRUxG0kZQper5eKigqKiooct4k2BbwnsDxM+UqCQxkoiqK0J1T3KUo7o1+/fqxevZpNmzZRU1MTl6GUbkSSv6ioiH79+jnuJ5oBuBk4A3g0pPwMwN0keoqiKOmD6j5FaWfk5+czYMAAwMpk4jRbRjrilvzRDMCXgCuMMbOA9+yy44H9gXtbPbLSZixZsiTj0vZkK3qv0oKs0H2Z/lnLZPkzWXZQ+VONW/JHWwM4ESsMwjDgSvv/MOAj+1y7YtasWbErJdjGSb1odSKdc1q+dOnSmOMnm0TeXzf7Scd7Fa5M71Xb3asoZIXuS4fPWmvIZPkzWXZQ+VONW/JHNABFZJcdBuF44E/AH4EfichxIlLlyuhpxOzZs5PWxkm9aHUinYu3PJW4JVOi/aTjvUrH+wTZc68ikW26T1GU7MTTXnfEeDyeTcAKp/ULCwtLamtrt8czhtM2TupFqxPpnNPyDh069Kiurt4cS85kksj762Y/6XivwpXpvUrqvdrL6/X2jFeeTCIevZcOn7XWkMnyZ7LsoPKnmgTkD6v72q0BqCiKoiiKooRHQ8AriqIoiqJkGWoAKoqiKIqiZBmaBVxRFCWLMMacBNwD5AKPiMjtKRYpKsaYx4BTgI0iMswu6w48B/THCtp9lohsTZWM0TDG7AE8AfQGvMDDInJPplyDMaYIa1d8IZbN8KKI3GiMGQBMAUqBr4HzRKQudZJGxhiTC3wFrBGRUzJM9uXATqARaBCRUW59dtQDqCiKkiXYP4T3AycDQ4FzjDFDUytVTB4HTgop+zPwvogMAd63j9OVBuBqERkKHA781n7PM+UaaoHjRORA4CDgJGPM4cDfgH+KyGBgK3Bx6kSMye+B+QHHmSQ7wLEicpCIjLKPXfnsqAcwCzHGFAMC1AHlIvJ0ikVSImCMGYgVe65ERM5ItTxKxnMosFhElgIYY6YA44B5KZUqCiIy3RjTP6R4HFBmv/4vUI4VsiftEJF1WHmkEZGdxpj5QF8y5BrsPNiV9mG+/d8LHAeca5f/F5gEPNDW8sXCGNMP+CkwGbjKGOMhQ2SPgiufHTUA2wnhpkns8nDTPadjufFfN8Y8B6gB2IbEc6/sH+qLjTEvpkZapZ3RF1gVcLwaOCxFsrSG3rZhBbAea3o17bEN2YOBGWTQNdie46+BwVge5CXANhFpsKusxvpspSN3Y8Xy7Gwfl5I5soNlbL9jjPECD4nIw7j02dEp4PbD44RMk0SZ7ulH849AYxvKqFg8jvN7pShKFGwPVdrHMzPGdMJKM3iliOwIPJfu1yAijSJyENZvx6HAvqmVyBnGGN+D9teplqUVHCUiI7B+G35rjBkTeLI1nx01ANsJIjId2BJS7J/usRe4+qZ7VmN9kUE/A21OnPdKUdxkDbBHwHE/uyzT2GCM6QNg/92YYnmiYozJxzL+nhaRl+3ijLoGABHZBnwIHAF0Ncb4ZhHT9XN0JDDW3kgxBWvq9x4yQ3YARGSN/Xcj8ArWb4Urnx398W/fhJvu6Qu8DPzMGPMA8HoqBFNaEPZeGWNKjTEPAgcbY65NjWhKO2ImMMQYM8AYUwCcDUxNsUyJMBW4wH59AfBaCmWJir3m7FFgvoj8I+BURlyDMaanMaar/boDcALWhooPAd+65LSUX0SuFZF+ItIf67P+gYj8ggyQHaz1+saYzr7XwInAHFz67OgawCxERHYBF6VaDiU2IlIB/CbVcijtAxFpMMZcDryNtdb0MRGZm2KxomKMeRZrwXsPY8xq4EbgduB5Y8zFWKnvzkqdhDE5EjgPmG2M+c4uu47MuYY+wH/tZSo5wPMi8oYxZh4wxRhzC/AtlpGbKfyJzJC9N/CKMQYse+0ZEXnLGDMTFz47agC2b9rLdE82oPdKaRNEZBowLdVyOEVEzolw6kdtKkiCiMgngCfC6bS/BhGZhbVxJbR8KdZ0ZEYgIuVYu2UzRnZbzgPDlFfgwmdHDcD2jX+6B8uYOJvmre9KeqH3SlEURWkzPF5v2m48UuIgcJoE2ADcKCKPGmN+grUN3jfdMzllQiqA3itFURQl9agBqCiKoiiKkmXoLmBFURRFUZQsQw1ARVEURVGULEM3gSiKoihZgR1T81JglIh8bYyZhB1WRkSuNcYMA2YDj4rIJS6P7RvrTBFJSmpHY8UL6SUik+zj/sAy4E0ROcVB+7FYMeX2FxHH+aHb4tpCxhsKzAVOE5FXkz1ee0U9gIqiKEq2MMP+e7j997CQv4eH1Ms0DJYhlijXAJ/GY/zZvAicA3zhtEFAJo64seX7FLgq0T4U9QAqiqIo2YPPQDkcK/f2ocBHwChjTA4BBqAx5jzgFmA3YCtWGq7LgR8DbwJXi8g/ArxR/xKRK+yMPeOxdvl/Blxmx3MLws5TOxkYDCwGJojIu8aYMqxMFdOArsAw4GERucZudzVwLVYEgZlYmSAuwoossL9dx2tf14X2cCXGmKl2nTeAX9g5ZAPl2RM4CitIMgH9LAK+x8pf/gLwHlY6tR1YHr9vsLJq3AicCbxojDnJvrb9sNJengmsw/JGfg5UAcOB3saYXwN/BHa338erROQTY8yFwH+wUrgNBfYEJonIPbZ4rwO3GWP6+tKlKfGhHkBFURQlW/gB2A4cZowZAnQH7gU6YxkZhwG7sAyRzcBdwO+B97Ey8pwNvANsojmV2Jn236eMMRcAt2J5EG8HDsAymoIwxuyNlRu4GrgZqMXK+NAnoNoxwPNABTDBGLOnMeZAW6YNttw/Dqj/AFYKSbC8cTcFnBuNZXgtsM8dFea9OdL+OzOkfAgwz257ETAB+BcwELghzLUNAV7FMuiuseXKDahyBPA18BdjzHHAw1jv51VYRt5UY0xpQP1j7Tpe4HY7haFPTk+A3EqcqAGYhRhjHjTGeI0xI+3jSfbxbfbxMPv4kSSM7RvrjNi1Ex7D2GtSfMf97THfcNh+rF1/aJzjJv3aQsYbao/3f20xnqJkOrbX60sso+YnwE6sNW9bsHLcDgW+EpFGoATL0/YAzUHZh4tIA5Zhdrgxpi+WIbhQRL4EfOvsfk6z93CEMaZ7iCgnAAVYBudtwCFAMZZx5GOq7e16xz7eC8uDB/BPEXkIeCzg2mZgGbeIyBQR+SCgrxkichuW0QnQP8zbs6f9d21I+RoRuRErfSBYnlNfjNIBYfo5ESgEbhGR+0Vksp0Nxce3IvInEXkY6x6AFQv1IayUbN1o9sSCFRP1fqz7VoSVHi1QznDXojhADcDsRNfBREfXwShK+8Wn1y4HZtrG3gwsvZETcP5uoBOWMfd7u6zI/vsUlvfpWqwp2qdDxvgFlpF3ApaXriqCLHcE1DuBYJ27xf7bYP8N9KJFCuAbqTxaX6GEpq3bZv+tt/9ut9+zWP1EItTAhGa5w8kfSfZI6fUUh+gawOxE18HoOhhFyVZ8+m8wzdOzM4CTA177KMDSYf8X2IGIfGGMWYJlNIJlEIKlV87A0kdTsKZJy0TkaGuDrp93gTrgdCy91xU4i+bp5EiU23//YD88/irk/Fbw7waeiTW16pSV9t/dsabKE+UdrCnt6+1r7gJ8TPP0dCDTgKuBvxpjBgEXY13DF8CpMcbZ3f67ohWyZjXqAcxOdB2MroNRlGxlRpjX4cr+gPWAdwMQOIXp4xms797nvodbEfkv8GcsffEAlp6ZHtpQRBZiGX+VWA+SfwCWYBtwkRCR77F0z27AZViGFTR76e4BNmI92F8ara8wfGr/HRVnu1AZFwGnYXn67gR+CzRGqPsBlqOgF/APLN09VkQqHAw1Cksfhrs3igM0FVyWYox5B2vK4Uos46sbluK4Bcu4+lhEyowxZ2N9MQONsr+JyJ+NMfdhPQHvAbwFFIjIPsaYF2g2DAMpBa6g2UvWG7gvTL2fYXnMPgSeFZFzA+J3jQFGYE3P/FpEHjHGTAauAy4SkceNMXOw4lh57Gvtj+11E5HRxpg/Y627OV9Engx5X/6EZbTuKyIL7DIv1jqYfgFjXQI8jjUtMUtEDgyMhRVwbZfb61d8/ftk+VZERthld2E9BZ9oez99Y5wC9MTyAN4mItcZY97CMnj3FJFVxph9gfnAn0TkjjDvpaIo7QhjzG+wdEgnLAOrJzBERNa70Pd0IEdEwj0cpxXGmE+ARhE5JtWyZCrqAcxedB2MroNRFCXzOBJrvfHjWLMg49ww/mzuAo6MdwNcW2PLdyTwz1TLksnoGsDsRdfBhEfXwSiKkraIyHlJ7HsqGfBQaW+AS3s50x31AGYvug4mPLoORlEURWn36BpAJSPRdTAWug5GURRFSQT1ACqZiq6D0XUwiqIoSoKoB1BRFEVRFCXLUA+goiiKoihKlqEGoKIoiqIoSpahBqCiKIqiKEqWoQagoiiKoihKlqEGoKIoiqIoSpahBqCiKIqiKEqWoQagoiiKoihKlqEGoKIoiqIoSpahBqCiKIqiKEqWoQagoiiKoihKlqEGoJL2eDyeoz0ez4JUy6EoitLWJEv/qV5V1ABU0gqPx7Pc4/EcH1jm9Xo/9nq9+7jZp6IoSrrRlvqvtf0qmY8agIqiKIqiKFmGGoBK2uPxeMo8Hs/qgOPlHo9ngsfjmeXxeLZ7PJ7nPB5PUQL9Fno8nrs9Hs9a+//dHo+nMOD8Hz0ezzr73CUej8fr8XgGu3VdiqIosUii/ktKv0rmoAagkqmcBZwEDAAOAC5MoI+JwOHAQcCBwKHA9QAej+ck4CrgeGAwUNZKeRVFUdzCDf3Xlv0qaUheqgVIJsaYx4BTgI0iMqyVfR0EPAB0ARqBySLynH3ucuBKYBDQU0Q2t2YsxRH3er3etQAej+d1LCMuXn4B/M7r9W60+/kr8BDwFyxF+B+v1zvXPjfJrq8oaY3qvazADf3Xlv0qaUh79wA+jvU04wZVwPkisr/d593GmK72uU+xPEUrXBpLic36gNdVQCcAj8fzP4/HU2n/j2Ww7U7wPVthl/nOrQo4F/haUdKZx1G9195xQ/857ldpn7RrD6CITDfG9A8sM8YMAu4HemJ9wH8tIj846GthwOu1xpiNdh/bRORbu28XpVcSwev1nhxH9bXAXsBc+3hPuwxgHdAvoO4erZdOUZKP6r3sJU79p2Q57d0DGI6Hgd+JyEhgAiDxdmCMORQoAJa4LJtike/xeIp8/3HnQSWoT4/Hkwc8C1zv8Xh6ejyeHsANwFN2/eeBizwez34ej6cj1rSwomQqqvcyh7bSf0qWk1UfAmNMJ2A08ELAU2uhfe504KYwzdaIyI8D+ugDPAlcICJNyZU4a5kWcvxpEvqcDNyCtbZpll32gl2G1+v9n8fjuRf4EGgCbgbOB2pdkEVR2gzVexlHW+m/91zoV8lgssoAxPJ4bhORg0JPiMjLwMvRGhtjugBvAhNF5IukSJjleL3e/vHW8Xq9k1rR5xX2/3DtbgNuA/B4PPthGYLrYsmnKGmG6r0MIQX6r1+kerH6VTKfrJoCFpEdwDJjzJkAxhiPMeZAJ22NMQXAK8ATIvJiEsVU0gSPx3OaHSuwG/A34HWv19uQarkUJR5U7ymKEg6P1+tNtQxJwxjzLFb8th7ABuBG4AOssAZ9gHxgioiEmwIJ7euXwH9o3jAAcKGIfGeMuQL4I7AbsBGYJiKXuHgpSgrweDxvAUdghb/4CDBer1c9gEpao3pPURQntGsDUFEURVEURWlJVk0BK4qiKIqiKO14E0iPHj28/fv3T7UYacGuXbsoLi5OtRiKA/ReJY+vv/56s9fr7ZlqOZJJPHov0z9rmSx/JssOKn+qiVf+SLqv3RqA/fv356uvvkq1GGlBeXk5ZWVlqRZDcYDeq+Th8XjafcaKePRepn/WMln+TJYdVP5UE6/8kXSfTgEriqIoiqJkGWoAKoqiKIqiZBmuTwEbY4qBU4Cjgf528QqsMBpvisgut8dUFEVJNar7FEXJJFw1AI0x/wB+DRQDDUAF4AFOBC4DKo0x/xaRq6P08RiWEt0oIsOi1DsE+Bw422mA0vr6elavXk1NTY3TS8ooioqK6NevH/n5+akWRVGyitbqvmTqPUVRgn//S0pKmD9/fqpFSphI8sdrA7jtATwLuBt4HfhWROrBH03+IGAscCEQ0QAEHgfuA56IVMEYk4uVmeGdeIRbvXo1nTt3pn///ng8nniapj1er5eKigpWr17NgAEDUi2OomQbrdV9j5MkvacoSvDvf2VlJZ07d061SAmzc+fOFvInYgO4bQDuJSKNoYUiUgd8CXxpjLkxWgciMt0Y0z/GOL8DXgIOiUe4mpqadmn8AXg8HkpLS9m0aVOqRVGymBuvvZdnn3yThavfTrUobU2rdF8y9Z6ixEv5+zP4+bg/8P2i19itT/uInNSef/8hMRvAVQMwUAEaY34KnAA8DIwAZovI9+GUZDwYY/oCpwHHEkURVlRUMHHiRP/xwIEDGTFiBJWVla0ZPu2pqamhvLw8qKyysrJFmZKeZPq9evBfUwDCXsPN1z3C6DEH8qOT2p/9kmzd1xq9N2jQoLB1M/2zlsnyp7vsd976JABP/vcFDjl8aIvzici/fOk67rj5Cf52z+/o3KWjG2LGRUlJif/3v7GxkZ07d7a5DG4RTf5wNkAkkhIH0BhzJfAPwAtMBU4HzsdaD9Na7gb+JCJNxpiIlUpLS5k8eXJQ2fz58zPa7euEoqIiDj744KCyTI95lE20l3sV7hp+Pus65sxays23X9P2ArURSdR9d5Og3otEpn/WMln+dJf9sQemATBs2DDKysa0OJ+I/L9+7C9UbNpOQ21+Sq498Pc/3BRqJhFN/nA2QCSSFQbmSuCFgOP3sJ6E3WAUMMUYsxw4AxBjzP+51HdaUlFRwbHHHkunTp24/PLLUy2OoiiRuZLk6L6s03uKolgkywZIViaQbsD3WIoKoCOQ60bHIuJf3WiMeRx4Q0RedaPvdKWoqIibb76ZOXPmMGfOnFSLoyhKZJKi+7JR7ynti3a69K5NSJYNkCwDcAZW6AOACcBRwKdOGhpjngXKgB7GmNXAjUA+gIg86LqkbczNN9/MU089Rc+ePdljjz0YOXIkEyZMiNqmuLiYo446isWLF7eRlIqiJEhCuq+96z0ls/B6Uy1B+yWdbIBkGYC/B17DioN1ErAIa2okJiJyjtNBROTCBGQD4Po/3s3cWYsSbR6W/Q8Ywi13XBnx/MyZM3nppZf4/vvvqa+vZ8SIEYwcOZI777yTp59+ukX9MWPGcO+997oqo6IoSSUh3ddWek9RFLjlLw+w8Ad3U4PH+v2H9LMBkmIAish8Y8x+wD520YLW7v5tD3z66aeMGzeOoqIiioqKOPXUUwG45ppruOaa9rswXlGyBdV9SntAp2uTQ7rZAG5nAoloqhpjvCLyezfHaw2xLPW2RD2AipLZZJLuU5Rs5/qbL0urXcDtxQMYbXuKF2t6JGs58sgjufTSS7n22mtpaGjgjTfeYPz48eoBVJTMR3WfoihRSTcbwG0D8FiX+2tXHHLIIYwdO5YDDjiA3r17M3z4cEpKShy17d+/Pzt27KCuro5XX32Vd955h6FDWwboVBQlJajuUxQlKm7ZAK+88grvvvtuq20AtzOBfORmf+2RCRMmMGnSJKqqqhgzZgwjR4501G758uXJFUxRlIRR3aco0fGlYPNm+RZjN2wAtwJZJysTSLhFz14RSdau44xh/PjxzJs3j5qaGi644AJGjHArPraiKKlGdZ+ihMdvAKZYjlSTTjZAspTSfJrvc1egL7A0SWNlFM8880yqRVAUJXmo7lMUJSLpZAMkKwzMsMBjY8yfgOHJGEtRFCVdUN2nKEqmkKwp4NNDxhgFnJCMsRRFUdIF1X2KomQKyZoCfpHgqX4P8E6SxlIURUkXVPcpipIRJMsAvIlmJdgILAdeStJYiqIo6YLqPkVRMoKcJPV7O3CXiPxVRG4BXkE3/7SK2267jcGDB7PPPvvw9ttvp1ocRVHCo7pPURTXCbQB3nvvPVf6TJYBOBO4JeD4FrtMSYB58+YxZcoU5s6dy1tvvYUxhsZGTS+qKGmI6j5FiUK2xwFMhFAb4KqrrnLFBkjWFPBg4B8Bx7OBS2M1MsY8BpwCbAzdTWef/wXwJ6x1NTuBy0Tke1ckbiNuvvlmnnrqKXr27Mkee+zByJEjmTBhQtQ2r732GmeffTaFhYUMGDCAwYMH8+WXX3LEEUe0kdSKojhEdZ+ihMEXBzDbccMGGDhwoCs2QLIMwNXAJcaYz7EU1q+BNQ7aPQ7cBzwR4fwy4BgR2WqMORl4GDgsEQGn3vgya+c6Eck5u+/fl7F/PT3i+ZkzZ/LSSy/x/fffU19fz4gRIxg5cmTMRNBr1qzh8MMP95f369ePNWvclV1RFFdIe92nKNnO27e+yeZFm1ztM9bvP7hnA/Tt29cVGyBZBuCDwJ3AXPvYA0Q3cQERmW6M6R/l/GcBh18A/VohY5vz6aefMm7cOIqKiigqKuLUU08FSFkiaEVRXEd1n6JEI4ungNPNBkhWIOi/G2M2YU1pALwuIk+6PMzFwP8inayoqGDixIn+44EDBzJixAh27twJwLETkhOay9d/OGpqaqitrfXXqauro7a2lltuuYXnn3++Rf3Ro0dz55130qNHDxYvXuxvt3z5crp27Rp2rJqaGsrLy4PKKisrW5Qp6Ul7uVfRrqE9XF8kUq37wum9QYMGhe0k0z9rmSx/ustesbkCgDlz5tChU1OL84nIv3HjRgDmz59PeXlRq2WMl5KSEv9v5vF/Oonc3FzXx4j2+w/u2QBr1qyJywaIRDLzU74N7AC+BPKMMZ1FJPq74xBjzLFYSvCoSHVKS0uZPHlyUNn8+fNdSaCcKD/60Y+49NJLmTRpEg0NDbzzzjuMHz+eCRMmcP3110dsd+aZZ3Luuedy7bXXsnbtWpYtW8axxx4b9gNcVFTEwQcfHFRWXl5OWVmZ25ejJIH2cq+iXUN7uL4YpEz3hdN7kcj0z1omy5/usj/2wDQAhg0bRlnZmBbnE5H/+SfLAdhvv/1Scu2Bv/87d+5MiS3glg2wdOnSuGyASCQrE8jxWOEPOgAnYoVGWAT8woW+DwAeAU4WkYrW9teWHHLIIYwdO5YDDjiA3r17M3z4cEpKSmK223///TnrrLMYOnQoeXl53H///Ul5elEUpXWo7lMUJRJu2QB33XWXKzZAssLA3AXMw1r/AvACcExrOzXG7Am8DJwnIgtb218qmDBhAgsXLuTtt99mxYoVjBw50lG7iRMnsmTJEhYsWMDJJ5+cZCkVxTlnnvp7rpvwj9gVswPVfUpac//dT3PUiHNSLUbW4oYNcOKJJ7oiSzLDwNyMlQcTYCvQNVYjY8yzQBnQwxizGrgRyAcQkQeBG4BSQIwxAA0iMip8b+nJ+PHjmTdvHjU1NVxwwQWMGDEi1SIpSquY/uFMpn84k1vvuirVoqQDqvuUtOam6+9PtQhZTTrZAMkyABcD4+zXJwCnAwtiNRKRqI8lInIJcEmrpUshzzzzTKpFUBQleajuU5QoZHsg6HSyAZJlAF6PlRTdgxW8tA44LUljxYXX6223ASmz/YulKGlA2uo+RUkl6fC7255//yF+GyApawBF5A1gOHC5/X+4iEQM2dJWFBUVUVFR0S4NJa/XS0VFBUVFbb+9XlEUi3TVfYoSD+3wJ7Jd//5DYjaAqx5AY0xoGOz19t/hxpjhIvKym+PFS79+/Vi9ejWbNrkbATxdKCoqol8/jQ+rJJ/6+gau+f0dXHPdxakWJS1Id92nZB/PPvkG+fl5nHH2SakWJS0I/P2vqanJaGdJJPnjtQHcngJ+EQhnXnvs8pTGLsnPz2fAgAGpFEFR2gXl78/g2SfeYNMGjUZik9a6T8k+rrzsVoCEDMD2OEsa+PtfXl7uOFZeOuKW/G4bgDcRXgkqiqK0Z1T3KYoD2ukMbEbiqgEoIpN8r40xucDe9uFCEWl0cyxFUZR0QXWf0p5IhpHWnjdfZCpJ2QRijBkKzAfm2P/nGWP2TcZYiqIo6YLqPkVRMoVkZQK5D+gDPGv/72OXKYqitGdU9ykZTzKdde11F24mkqw4gKOAa0XkPgBjzOXArUkaS1EUJV1Q3acoYdAp4PQjWQbgFuB4Y8w0+/gEQLcLKorS3lHdp2Q86qTLDpJlAD6CtSvu1ICyvyRpLEVRlHRBdZ+iKBlBsjKB3IKVt/IV4GXgYhHRaRBFUdo1qvuU9oDO1mYHydoEgog8BlwMXApMNcZ0T9ZYiqIo6YLqPkVRMoGkTAEbYy4E/g50DSj2OhnPGPMYcAqwUUSGhTnvAe4BfgJUAReKyDetl1pRFKV1JKr7VO8p6YSuAcwOkrUG8O9YqY8+BeINgvo4VtiEJyKcPxkYYv8/DHjA/qsoipJqEtV9j6N6T1GUNiRZBuBG4F8iIvE2FJHpxpj+UaqMA54QES/whTGmqzGmj4isS1BWRVEUt0hI96neU9IJjQOYHSTLADTAi8aYQ4EddplXRH7vQt99gVUBx6vtsiBFWFFRwcSJE/3HAwcOZNCgQS4Mn3lUVlZSXl6eajEUB2TKvZo9+wcAKiq2+MsC5Y52DZHOLV28BoCBg/u2Wr4Ukizd57rey5TPWiQyWf62lD3SOB9++GHE2HwVm63IRXPmzKFDp6YW5xORf8OGDQDMn/8D5eXFcbVtLbO+XcRufUrptZu1HDeTPzvgnvzJMgAnA92A8wPKvIAbBqAjSktLmTx5clsNl9aUl5dTVlaWajEUB2TKvaqvyQegtLR5f0Og3GVlZdTW1lGxeRu79+0V1DbS9f38lNEAbKj8zF1h25aU6r549F6mfNYikcnyt6XskcY55phjyMkJvw/0sQesMJbDhg2jrGxMi/M++dev20SXks507FgUU45XpnwCwL777tPm9+3np1xHTk4O63ZYMmTyZwfckz9ZBuDewJP2/3qX+14D7BFw3M8uUxQljfjNhTcy7fWPWLfjk4g/NO2QZOk+1XuKq7gxFXvgkHGMOmwYb77/sAsSJZemppaezGwnWQbgE8DuwFJgm8t9TwUuN8ZMwVoEvV3XwShK+vHWmx8DWbfmJ1m6T/We4ipufS+/mjHHlX6UtidZBuCVWNMeZwaUOQ0D8yxQBvQwxqwGbgTyAUTkQWAaViiExVjhEC5yUW5FUZTWcCUJ6D7Ve0pbk13PZUo4kmUATsdSenEjIufEOO8FfptI34qiKEkmId2nek9pa7LMM6+EwVUD0BhzLPCxiJS52a+iKKll3dpNdCnpRHFxB8dtsukHRnWfkmx27thFdXUtvXq7k1gm2vczmV/dLFILaY/bHsB7gT2NMe8D/wPeFJG1Lo+hKEobc9De4zhoxL4cNGI/jjnuUPILnKuOLDEEVfcpSeWIg37Opo1bgnbJ33Xro/Ts1Z0LLjkt7v5S9b3MEn2QEbhqAIrIcGNMP6yo9ScDdxljVmApxGnAJyISb2YQRVHSgO+++YHvvvmBxx95hadevDPV4qQVqvuUZLNp45YWZXfe+ihAggZg5HO+8IDJMNbUAEwfXF8DKCKrgX8D/zbG5AFHYSnEfwF7EpwjU1GUdk626HvVfUq7w83vrm1VqgGYPiRrEwgAItIAlNv//2SMyegQ/4qiOMfj8WStslfdp6Q7KVsD2JSdOiEdcXsTSClwB7A/8BHwVxGpMsacAtwjItmZi01RspBsMv5U9ymZhpPvp5vfYY96ANMOtz2ADwGn268PBcqMMScBxUB/l8dSFCUDyBKFr7pPySyifC+drAFM9HudJfogI3A7P9OPgGeALsBZwL7Au1i5MRVFUdorqvuUjKK1HkA1ADOfZKwB/EhEKoEXjTHrsHbB3ZWEcRRFyQCySOGr7lMyhmhfSydf2URz67a1Osgi/RM3bnsA5wE/9R2IyKfAKYDH5XEURVHSCdV9SkbhzAOY2LnWjusmiRqq2YDbBuDVwMt2CAQARGQ6VjiEX7k8lqIoSrqguk/JKKIZYk7WACbuAWxbA1A9gJFxOxD0F8AXYcq/Bb51cyxFUTIDr9fb7pWw6j4l08iWNYBNGnYmIm57AAEwxlyZjH4VRVHSGdV9SqbQ+jWA8RlWqQoD094fPluD23EAPVhR748A7k6wj5OAe4Bc4BERuT3k/J7Af7Gi6ucCfxaRaYlLrShKMskG/au6T8k0UuYBTKhV4qgHMDJuewBfB84A4k9MCBhjcoH7sdInDQXOMcYMDal2PfC8iBwMnA1I4uIqiqK4guo+JaNo7RpAr64BzHjcNgBPBl4TkZUJtj8UWCwiS0WkDpgCjAup48WKtQVQAqxNcCxFUZJIoOLNAiWsuk/JKBx5AKO2T3DcNt6V29bjZRJuxwE8F/iPMeZdEXkxgfZ9gVUBx6uBw0LqTALeMcb8DivK/vHhOqqoqGDixIn+44EDBzJoUHZmY6qsrKS8vDzVYigOyJR7NXv2bAAqKrb4ywLlDnw9/aOPyC/Ia3HuN+ffRtnxIzn7/BOD+s6E6w9DWui+ePRepnzWIpHJ8rdG9nDtovUV6dxnn31Kl5JOYc9t3rQZgPnz5lFeXtDifGVlJdOnT3c0vo/169YBsHjxkriuvaa6lgvO/Cu//+PZjB5zgON2Pqqrav2vfeNm8mcH3JPf7V3AzxljVgFPAYkoQSecAzwuIn83xhwBPGmMGSYiQWZ+aWkpkydPTpIImUV5eTllZWWpFkNxQKbcq2HDhgNQWtrdXxYod+Dro8eMoaiooMW5rVuu45Xny3nwsVuD+s6E6w8lXXRfPHovUz5rkchk+Vsje7h20fqKdO6II0bTs1f3sOcetZeW7rvffmHbl5eXc9CBIxyN72Pqi58D1kNJPNc+f+4SAN6aOoPrbrjCcTsfO7ZX+l/7xs3kzw64J7/ru4BF5DOstEiJsAbYI+C4n10WyMXA8/ZYnwNFQI8Ex1MUJclkwfQvoLpPySyifS19awCjVUp0c0XC+iDBkOrZon8SIRmp4BCRZQk2nQkMMcYMwFJ+Z2NNrQSyEkvJPm6M2Q9LCW5KVFZFUaIz67sFLcriVarZooRV9ylus3nTVj756GvH9b1eL42NjU5qhi1tamri7Tc/8fcVbZxEiF93JDSMH90FHJmkGIB2uIIrgQFY4QoAvCISuqg5CBFpMMZcDrxtt3tMROYaY24CvhKRqVgR9/9tjPkD1if4QhHRO6woSeKEoy5qUXb+WX9MgSTpj+o+xW3O//kf+frLuY7rP/PEG1z129ti1otkiD352GuOxok3E4iHxOIAem1D1eNJzAWYLQ+fiZAUAxB4DTgwpMzRXbDjWk0LKbsh4PU84MjWCqgoShuRXQpYdZ/iKqtXboir/qsvvueoXiTDaP26zQF1orWPS6xWt1MD0H2SZQAOwApo+i/AiS9aUZQMRHVrC1T3KSklJ8eZoeTkuxt9CriN4gC2UskkmrM4G0hKKjjgJmB/YE+gNOC/oihZRjbkAg5AdZ/iKvE6vnJzc2NXInNyAfvqJ+4BTKhZVpAsD2AJVoyq0B1xzj6ZiqJkBMk07Jqamih//0uOPf6whJV/ClDdp7hKvF8xj2MPYGs9a5liALrrAZwzayGlPbrRZ/eervabCpJlAP4BWAF8CtQnaQxFUVJMMqdXHv/3K1x79d954LFJnH7WibEbpAeq+5SUkpvrbGIvVR7AeJMB+4ZJ9BnQ7YfUS867njFlh3DHPde42m8qSJYBOAN4U0TuTlL/iqJkCF5vYtMwK1dYmc4CF6VnAKr7lJSS43HTAIx8Lu5dwLYFl+hDY6IeQLfDwFTurKK6usbVPlNFsgzADsDfjDGnAdvtspihEBRFySySOQXcPPVjHW/buoN33/qUM885OWljuoDqPsVV4v2O5eQ4NQBjjxfdAxiXWI76dKO+2+1Dqa+vd2Xm4723P2PAwH4MGrKnC1IlRrIMwNH236MDynQppqJkELO/X8DtN/87ah0nT9duLRY3F/+V99/5nINHDmXw3nsl1GcboLpPaRWrVq7jystu5T/P3BYxV2802moKOFEjKF514BsncQ+gu8tU6usbaWxsfZ+/+NkEADZUftbqvhIlmWFgFEXJYK64dDLz5ixOtRh+xb92zUYAauvSemmd6j6lVdx166N88tHXvDm1nHPOOyXu9k43gbSaBD158T4Q+h4y02UXcEN9Q7sJLeOqAWiM6Soi20RkRaw6bo6rKEp6k4gXMLSJf0o40aSgSUR1n5IskjcF3FoPYGIZPeI2AFvpbXPbWKurq8fbTtLLue0BXGOMeRF4HSu35VqsFM67A6OAscDpQPx+bUVR0g4nynXtmo0MHLxH3H17Q7b/pXksQdV9Spvg9XqjesOcBoJ24hpzugv4hWf/F3Ntrq96/B5A3xRwXM1aDuwCjY2NeL3eduMBdDsQ9LXAMcDzwFKgBqgGlthlR9t1FEXJEs445YqE2rU2/lcbo7pPcYVQeyX08x/LgGrtJpDgSpFPBRpBl//6Zqqqou+MTXQKuLXr7dzcBVxX1wC0XqZ0wVUPoIjcC9xrjDkaK2elb3vLSuATEfnEzfEURUktTnT5po1bWjWG/+evlfHAkonqPiVZhBpMTU1NUY08NzeBRG8fX39+AzDOcRob7YyKaZALuKHeMgCbWhlcOl08iEnZBCIiHwMfJ6NvRVGSz8YNFdTU1Mas542gyEKVbmJrAK02f/nTPYz/7c8zwiOouk9JhB3bK2loaKR7aUlMOyfWV8nTyjWATsPAxG3EJLoJpJXfezeNrXqfAdjkpWLzNob2/wn/uP9afnHBqXH109CQHmnCk7ULOGGMMSdhJVPPBR4RkdvD1DkLmIT1MPG9iJzbpkIqSjtn+KD4FJqbzP5+AcMP3Cfi+ZqaWq7/491ce+OlFBd3aEPJkovqvuxl3z1PprGx0VFIkFgGVG4bbQKJP55fYu2aGltnLIXzVL4z7QtGHDwq7jA79fVWBAJvUxPLl60B4Mn/vJZUA7Curp5bbhD+8MeL6Na9S1zjxMLtNYCtwhiTC9wPnAwMBc4xxgwNqTMEay3NkSKyP3BlW8upKIpFMjZmHH/kRXbf4cd65IEX+Lc8zwP3POP62KlCdV920xhg5MSaWnVvDWBrDcCQY6eetgTXALrlAVy0YAWPylQuOjf+Jbk+D2Br1wD6ppKdMPXl93novue4+Yb7WzVmONLKAAQOBRaLyFIRqQOmAKER9H8N3C8iWwFEZGMby6goik1TEgxAPxF++HxPz+kyjeISqvuUICIZPLENwMiGUvD0bmJyNbcPNoJibbbwjR3vpozWbuIIfb98euOTj76Ou6/6Ot8UcFOrHn7j0V1+fVfvvr5L2hSwMaYfsBfWdAYAIjI9RrO+wKqA49XAYSF19rb7/9Tue5KIvBXaUUVFBRMnTvQfDxw4kEGDBsVzCe2GyspKysvLUy2G4oBMu1fbt2/3vw6U+8MPPwyq99FHHwXVC1Se4a63vLyc1WtWBx3vqqoCYONGy+5ZsWJFWr5XqdR98ei9TPushZLJ8keSvby8nPXr1wMwf/4PlJcXU19fF1Tno/KPKCwqaNHOx7r16yKeC/SGffnlDNauX95ChhUrm0NZLly4MKyclZWVLF86M6hs+scf06lT5CUZ69ZZ17Vy5cq47tv338+zxty5M6H7vXL5ev/r8vJyli5e4z/+8MMP4/Isrl5l6Z6Kii188803AOxMQK5tW3cGyRSN+fN/AGD9+vX+um599pNiABpj7gCuDnMqN0xZvOQBQ4AyoB8w3RgzPDTAamlpKZMnT3ZhuMynvLycsrKyVIuhOCDT7lWXLs1rUgLlDr2GMWPGBJ0L/CEKd71lZWW89dpXQccdOz4IQO/evQHYa6+90u69SrXui0fvZdpnLZRMlj+S7GVlZbwyxdowvu+++1BWVkZe3h1BdY46+ugWa18D+3r3zW+BL8Kea2honnocNeoQ9h06sIUMn5X/4H89ePCQsHKWl5ezZ78hQWVHjj4y6hq1F56yHgL79esX132r2mEZaCVdSxK633NnL/K/Lisro7hotv/46KOPJi/PuRk0186MVFJSwogRIwBLB8Yr17q1m4Jkilp3VSUAffrs5q/r1mc/WR7A8cBy4B3A+WQ3rAECI8b2s8sCWQ3MEJF6YJkxZiGWUpyJoihtSuL5QONff9S8CzihIdsK1X1Kcom1CSRKGJjApm5vAomlCxKPA9jaTSDB49XWNntUGxubiMP+o95OQ9na7CT1cawBTOYqm2QZgHOA50TkX3G2mwkMMcYMwFJ+ZwOhu9xeBc4B/mOM6YE1LbK0deIqitKWOPvxid4mTcPBqO5TWkUyA0EHGmmtNSxCDb7YBmBi47Z+E0jwgL6dvOHOxSIwDExraExk/XIS9F2yDMBtwGRjzKHAVrvMKyK/j9ZIRBqMMZcDb2NNmTwmInONMTcBX4nIVPvcicaYeUAjcI2IVCTpOhRFiUIkZd7Se5dI3+E9gL4fuDRNDbcN1X2KC/gMnpaetsQ9gHHn741nF7DTQNCJpoJLMAd4Sw9ggAEYp3fRvwnE29pNIPF4AJOn55JlAP7E/vuLgDIvEFUJAojINGBaSNkNAa+9wFX2f0VRUkjgU//2bTuj1AwmUKe9+9anHHr4AW6KlUpU9ymu4CRQcziiBYIO8gCGyclRVVXDPXc94WisUI9fsg3ASLz15seMPurgiDH9QtvXBRiA8YZzqW9o3gXsIxHHXH0CO3qTMeORLAPw2CT1qyhKkvEFOI2X115633HdwB+BX55xDcceH7rhNXb8szSdAlbdpzhm44Yt1NUF7/KNnQkkxhRwlA6CYvWF6efj8q+CjqONFK9nsrW5gMNd1vJla7jg53/i5FPG8PiUFnHT7fGCj+vqEzcAG9yaAm5MDw9gUuIAishHwFqsgKZDgbV2maIoac5hw890XtmhcgpVYt/MnBt0vGzpakKJpPjS1PADVPcp8TF80CmM3O/0oLIWH/sWU63W3z26HxO2z+hGW+DrljXjMTa8TaEGoDNjyukQb75WzsIflkftt3LnLgBWLF8bsU50D2B8nri6gE0godcfD4nEME2G3kuKAWiMGQvMBu7Dim4/2xiTutxSiqIkBadrAEMZ92MTdBxOuYVOUfm6TGcDUHWf4haRPuc+g8ZnjEQ6H+tc2+8CDn0RnV/94jqOHnVu1E0gvnN5eZGjLIXKWRewCzjeKAbNHsCmVuUYjieoc8Z5AIHJwDLgUqywCEvsMkVRlBY4MerSdNNHKKr7lKQS83sQdd1enJlA4loD6KyvhDeBhNERPk9aTrTQNyGeukDDOV4jri5gCrg16eDi2QTiIxnPvclaAzgAuFJEHgEwxgDcnaSxFEVJEck0ylpOhflSSbUuBleSUd2nuEKkNXOxvnPR1qfF8gDGM1boKadxAONOBRfF0GpKxANY58YawKZWGoDp4QFMlgG4FLjKGON7h/6A9SSsKEo7wumUUsydi2k8rRsnqvuUpBLLHnBzCjiecWKtiWvtJpBwLjCfJy03J7IB6OYu4MA1gE1NjbZY8euuhDyACYbBiUayDMC/AM8D/wY8QD1wRpLGUhQlzYj3tyWcaovX85EmqO5TEuad/33K5k1W+EifYdEyELQzT1v4c87qhasfaxzngaATnQJuec7nScuN6gEMPg6aAo5zE0gqPIBRd/W0kmTtAn4NGA5cAfwOGC4irydjLEVRUodbNlnYTSARgkk3p4RLP6+h6j6lNZx35jW8/87nQWVuTgEHGo9OvrtR4wDGKVfCHsAohqXPmIse/DrEA9iKKWB/JhCvt3WbQBKYAk77OIDGmNOxslAfbhets/8OM8YME5GX3RxPUZTU4tZ6vLDKLcFAuKlAdZ/SVsTeA+J0E0jrdgGHCuI4DmCcLi1vlE0gvry+0fMfh2YCCc4FHA/1bnkA48gFHG0TTGtxewr4BaxclVMIdlx67OPIflpFUVKK1+tl/tzEU8vG82MRSjvYBay6T3HEzh272LRxa8LtvV6vYyOv5bn4NoFEI95cwM1jOB4CiJ4L2LeeL9omkFBPpS+dG8T/AOszABsbm6JuTomFzwPoRO+1NhdyNNw2AG8C5gJ/dblfRVGSzItT3uLyX98cV5tE01W1wIEDsHkKKb6u2wjVfYojTjrmYhYvWsmZZ53mqH6oxyxWDDqvw00gjsZ2MxcwzqeAA+tEM4B83ryc3CjPV6EewIDMK9Gml8NRb08fe5ua/EGkEzHM4mkbr4zx4KoBKCJ/BTDGjAQ+EpEV9nEpsI+bYymK4i7z5sS/WdUtr5yzNYDpafmB6j7FOYsXrWxV+1geQHc3gTiPAxjqaYvUl5NxA/ueM2sREH4TiM8AzItiAEbbBRzvJpB6l+IA+vrJyYltADb5jcWEh4tIsgJB/wc4NOD4BODjJI2lKEqqCNDlrZmicGIArl+3OWx5mqG6T0kqXm+saV6HRpujTSDRzoU8oLmYCeTxf7/ifz315cg5xp1MAYcOV9+aOID21G1TU1NCnrm6unp+f9lkVi63lgjn5MQ2wRJJG+cUtzeBjAX+D2tCxxhjTrZPHQzUOOzjJOAerDUzj4hI2AzPxpifAS8Ch4jIV+HqKIrinEQMK6dTwMFTOi0VWjzGYzoagKr7lLbCG2MHalQPYKxNIHF8tUINTcebQGKMsXnTVq6b8A9HMvh3AccRCLq2FQagPw5gkzemwRuOD9+bwZQn3/QfO/EAJnMNoNsewIOBC7E+RsfYry8EDgRi7oIzxuRi5c88GSuR+jnGmKFh6nUGfg/McEdsRcluli9bw3NPTYu7XaBydWqYhVO6TjyAaY7qPiUqjz30Ehs3bGl1P5YBGC08S2pyAbsVBqa6OvzzUrhmTnYBt5wCbn0u4MbGxoSmgEP1nKM1gEn0ALptAD6MNf3hASYChwCjgEEicp6D9ocCi0VkqYjUYe2oGxem3s3A33D4ZK0oSnROPuYStmzZHne7RIy0cEo3fBSYjDIAVfcpEVm8cAXXXv13Lr3gL3G1q69vYOuWHUFl3hjep2hfGzcNwLh3ATtM5Ri4Ri9W/7660aZSWwaCDtgFHK8HsN63CcSbkGcu1OPncTAF7BsnGfrQ7U0g64B1xpiBQK6ILAEwxgwyxuzlWxgdhb7AqoDj1cBhgRWMMSOAPUTkTWPMNZE6qqioYOLEif7jgQMHMmjQoPguqJ1QWVlJeXl5qsVQHJCqe5WI8Qewq6rK/3rhooX+19OnTw+q9/HHn/hfl5d/1KKfyl27go7Ly8vZsGFj0LGPDRs2ALB8xfK0+Vyni+6LR+9lul7IJPlXLl8PwKpVa/1lsWT/4YcfuPuulh+bL774gq7dOgeVffjhh35DZO2atUHnAsdZvar5O/XNt99SU78tqO7sOXOCjpcuXRpWzsrKSubNDpbtq6++ZvvOjS3q+ti8uQKA9es3RL1233sVypYtW1q0W7zY2ri2du26iH3OmjXf/7q8vJzNmzbj8Xjwer18/fXXVNc5D8mzetUaAGrrapk/z+p3+/btjj+Hs2cvCDr2NjXFbLt0qRWaa9Xq1f66bn32k5UK7l2sJ1jf486FwNnAkNZ0aozJAf5h9xeV0tJSJk+e3Jrh2g3l5eWUlZWlWgzFAZl2rzp06OB/vfeQvf2vjz766KB6Rx11lP/1kaOPbNFPl87BP2hlZWU890R50LGPnj17ATCgf/90fK9Sqvvi0XuZ9lkLJZPk9+2wLy4u9pfFkn3fffelc+fiFuWHHnooPXt1Dyo75phj/F6wl5/9JOhc4Dg/zGuO83nQgQcx+uiDg+ru3BrsERswYEBYOcvLyxk6NHiFwkEHHcQhhw2PeD0PdZ8KQK9evaJe+zdfzQtbXtKlpEW796Z9B1if+0h9Vlc2e9nKysroWPwkBQV51NbWM3z4ARxdNiqiLKH4dFJubh6DBw+25CppKVckmuqLgo7zC/Jjtv34A+v96N17N39dtz77ydoF3BdYHnC8wi6LxRpgj4DjfnaZj87AMKDcGLMcK+r+VGOM8zuoKIprBE5LfPXlnCg1mwk/BRzHAuf0nhpW3adEJNAAi4XH44m4Nvam6+8PKos2tRt4HC0TyEcffMmK5cHew2ibQlpMSTr8WsZcA1gVfnVDuHWPvingaOvkvCHtGhsaycvPs/tMLA5gomsAW0wBx7EGMN6QNU5IlgdwKTDBGLMGa03M1XZZLGYCQ4wxA7CU39nAub6TIrId6OE7NsaUAxN0J5yipJ4Xnn3LUT2nijPD1gD6UN2nuILX6w0bIL2pycuT/3ktpG7g+WjrAyMbimeNvTK8DBFouQs4VhiY1m0CCbe5xRfUOZpOCR2voaHRHzYm7lRwDc1xABNJg5lY0OimoL9ukiwP4N+wgp++Cbxhv74tViMRaQAuB94G5gPPi8hcY8xNdpgFRVHSCKdGWuwwMM77TnPDUHWf4hpOd8dH240f7AFM3i5g1wzAiB7AyJtAGqJ4x0LbNTY0kp+wB9AyAL0JxgEMvfZwujAUXxzAZBiASfEAisiTxpgVwCl20RsiMj1am4C204BpIWU3RKhb1ho5FUVpHZGNtMhtwirdOMLApLMBqLpPcROnBmDgdyrUMxdsHBL2dSTi2wXsThzA6uraCOOFmwK2PIDRdvOGjtfQ2Eh+foIewIBMIP4MHeHctBEIHc/J+L5xnBiL8ZIsDyDANmAt8CDgNcbsEb26oiip4Pln/pdw20RssXCKPBlBTlPINlT3KcCmjVs44aiLWLM6/M7WaERbA9iyrPl1qGEW0QMYT9TnMIS2jh0HMPRFeCJ5AMOFvvFNyTY0NLQ45yP0/WgIWAMYr1HV2jiAoVk9nAST9mcfyZQpYGPM2cA3wN+B/sANwL+SMZaiKK3jd+NvTrhtIt44J0r3v4+8EvF3Io0dgKr7lCCef+Z/zPpuAY8++GKLc06+O+EMwHA5dyN5+UKPY3npWvYb5VzCHkD3AkE3OpgebTHtGjgF3NjEiuVrGXPIL9i4oSKqXNAcB7ApKA5gzGYt5PUfOzDqfFPNmbQG8K9AYAK/N4EjkjSWoiipIqE1gLEV2R+vvDNi3z4lnKao7lMc4SRzRljjIpwBGOjZc+gBdPLdjS8TiDtrAKviWAPoXx8XZRdwqMFseQCbp4Af/NcUFsxfxqsvvhdVLmj2ADY1NfkfZON5IA31AAYazTU14ae+/UZuU+ZMAe8OfBBwXA90iFBXUZQMJZF1euGmPWJNdU19uVmdvPu/T+MRsa1R3af4iceACqWpyev6JpBIuYAT+R4nvgs4arW41gD6d8jGkRu5oaEhYBdwI7sqrWD2nbu0jLkYSrQsIu/879MW4XlCCZ2q9hmRK5avZa8ex/L0f19v0aaxMXmbQJJlAM4GzrdfnwdcD3yfpLEURUmQXbuqW9U+kenYRBTZr8+/vkVZmq4bVN2n4PV6Wb9uU9Q6jz30Usw+nG8CCR/rr2W98LuAEwlp0tLQdNYu1ljx7AL2ecdCPWtBcoVcc0PgFLDXy84dVhaiTp06RhecZg8gNG8I8XHemddw/91PR20fbvmL1+tl8UIrq8rrr3zQ4rzv2kLjGbpBsgzAq4HdsKIYXQDkAxOSNJaiKAly+AFntap9YmsA23UcQNV9ClOeepMDh4zjWzurRTjj5fo/3h21j0gGYDgDL8iY80aeAo60VjCS0eiuB9D+G2PzSU1ED2CYKeDG2FPAoRtkGkLWAFbaHsCCwoKockHzphNoNgDjmZoNZ6g2NjZGfZht8scBzJApYBH5HBiMFQrhFGCwiMxIxliKoiSOk4XP0UgkDmCGGnaOUN2nAHzx6XdAc+aPeDdfQOQ1gLE8gNE8c5HqRTIu4osDGOMaHa4BrI+wozecB6w5Rp6zOIBer5fGhoaANYCNfgMwmhHpl62uef1xYEgYp4QzAKPdu8A2GTMFbO+EOxn4ECui/QvGmMOTMZaiKKkjkbVDU19+P+K54D4SEimlqO5TADx2Xt4mh0ZPOOKbAnYWBzDSFHAk4yKa1C3jADpfA3ji0b9iz9KysPUa6sMbgAlPAQddf/AUcGNjE5X2FHDolG446uubx/FvCAkxPqPd63BGZuB7H65tJu4CvgnrKfhc4BysvJX3JWksRVFSRCJGmpPddlbfGWgBqu5TaM756vNMJeQBbPLGjPnnI5oR4SQXcCIx5qKNE61+U1MT33/7A7V2EOdQIhlzPmO6rq6e3p1G8+C/nnUUIy94fWRTUBzApsYmdu60DcAosQR9BHoA/WOH5hqO4o0M7wFsihpKJpm5gJNlAO6Blf/yUOAx4CpgaJLGUhQlRSTTSMtQA1B1n0KOx/YA2saBk4C/oXi93rCGYzhPWFPAOrQWU6VByy+awhVH9N5F380ffO7Cs//MvX9/InJ9u3okD5+PSFOxPhl3VVob1+6+87/+XbXRUsEFeTrtvvMTnQIOXAPY4AsKHfzeBXoJQwknZyzj278LOIHPUCySZQBuBy4EfoIVFNUDtG67oaIoaYfzNYABr5MkS5qguk8hx54CXrViHZD4GsBw037h1sLFmkYMd87RFHCcYWwm3/hg5Pr4PHjRDcBInji/MR3GoIvmdQus75vmzc/z5QJu3gXsaAq4rp6Cgnz/63BjB3oJQwln/DY1NfmNxnBvd8atAcRKgXSc3f/LwGhgVpLGUhQlRUT8gYi6e9DhLuDMNBVV9yn+KWAfiXyWvd7w35VwhkqgcRA9FVx8m0Cif4/juyavfwo3/NSvj0hTwD4vqs/r58HjbBdwgJw+4zNcKrhYnkmwvHsFhbYBaNcPNcijGZLhdwE3RTUafR7CjJkCFpFJQHdgLxHZBFyOtTBaUZQ0ofz91m9OTWSatrYm+g9Aa/pONar7spumpib+/Ie7WLhgeVB5IiE8rCnglgZgOAMq0ABquQkkWL7A/sOVxyNfIvXrI3gAa2pqqaurj7IJxG4fcD7eTSA+49E3BRx43dH68NVtbGyk0A4X01Af3vtYHyVTUfhNII3NxmS4XcBJDASd53qPNiKyLeB1pdN2xpiTgHuAXOAREbk95PxVwCVAA7AJ+JWIrHBDZkXJJn4+7g+t78Thb0CgYlu7ZqPDNokIlHpU92UvC39Yzn/+/XKL8kSmgJuamsJO99bWtjQwAo2QhDaBRIwDGF2+RIi0+WOvHscycPAe7N63V9Txwm3EiGYcBcpZZ7f1eQAD38vQLB2h+Iw0nwHoXwPYFLoG0JkHMC8vl4aGRrxer99oDLsLOAMzgSSEMSYXuB/riXkocI4xJnQB9bfAKBE5AHgRuKNtpVQUxUcqvXRpmgkkIVT3tQ9868lCSTQMTNgp4DDThc6ngMMbEYnEAYxlMEXqK5qBtHTxqiibQFq2d5ILOPASfN5FXyo43waQWHIFti0sKgg6XrdmIyvttZ4Q2cMJwZtAiu3MI42NTVHXRTpZ55goSfMAJsihwGIRWQpgjJkCjAPm+SqIyIcB9b8AftmmEipKO+CFZ//nSj+R1jaF/nB88tHX8fedqS7AxFDdl8Fs2riFB//1LIcecWDY84mk8fIS3jNXG8MADP3aRAzCHvAykU0gsQymb7+eR/8B/ejWvQter5ftW3cCzV64iP1G3ARiyVgXZgo42g7ZYA+gbwrYMn0qdzYbgLGmgH1t/R5AW46amjoO2f9nzfJHeV8CDdVOnTqyfdtOKzRNtDb+NYAZMgVsjCkFckVkozHmOKwE6S+KSPgkf830BVYFHK8GDotS/2LAnV8yRckiLv/1za70E+mHY/myNUHH4XL5xiITDUDVfdnJtVf9nddf/ZAz1m0Oez7RNXZh1wCGmUINDgMTeSxvK+IAVmzeRmNjE716dwfCe7oCvfInHXMJQ4cN5sMvnuCh+59j8aKVtvzRDcCIm0BseX3Gksfj8RuL8a8BtEyfql0BBmAMg9Zn2BWFeABb1ouyCzjAuC0u7gD4PIDR2iQvDEyyPIBvAN8ZY54D3sN61jgZ+IVbAxhjfgmMAo4Jd76iooKJEyf6jwcOHMigQYPcGj6jqKyspLy8PNViKA7ItHsVaffaj0ZfGFc/O3bsaFFWsTl6mrply5el43uVUt0Xj97LtM9aKOkk/5o1awFYsWJV2PM7d+6Mu8/FixazefP6FuVzZs9tUfbllzPZvMWSYevWrUHnPvnkEzp1tqYbZ8+e09zPnDkUl1iv164Jb7iuXLnK/x7//JTrAHjujVuprKxk8eIlYduUl5f7Dcp5cxZTXl7OlKem+s9XVVUF1YVgA27btu1h+62traW8vJyF821Dsr7O75Wrr6+P+FlYuGiR//WMGV8C0NhktVu2tHkJ7ZIlS6N+nio2W3JV11hRnSoqtoSt98UXX/rvRSgrVqz0v/bJ8Pnnn/PDDwsA696FyuD77Oyq3OU/59ZnP1kG4FDgEeDHwKfAXOBMB+3WYAVS9dHPLgvCGHM8MBE4RkTCZo4uLS1l8uTJcYrdPikvL6esrCzVYigOyLR7lZOb60o/JSVdWpSVlnaP2mZA/wHp+F6lVPfFo/cy7bMWSjrJ/+/73gCgZ4+eYc937Fgcd58DBw70e9sC6d9/QIuygw46mBGjrCWjXUqmBJ0bPfpIupdalt6OLc1epKFDh/rfv4U/LA8rQ79+fVu8x2VlZZSXl7P77ru3qO/xeCgrK6OqqiaofreuLwDWGIGOrDFjxpCTk8PWLc0PgEWFRWFlycvLo6ysjMK8bwEoyC+grrY5FEukz8IPc5qN6OHDhgNQXNwRj8dDhw7N96Vv35bXGohvVqNXr54smLeCDh06hq13wPADOPSIA8Ke+9+rM/2vd+vTiyWLVnPIqENYv9raK1ZS0rWFDEWFVmzFgoIC/zm3PvvJMgBzsBTYkVjTFKuB8xy0mwkMMcYMwFJ+Z2OlVPJjjDkYeAg4SUScbSdUFMXPtNc/cq2vZMSm8pGBM8Cgui/ruPvO//oNqEgZKRKfAm75JQg3XRgUzy5kOjRyLuDw7YPbws1/uZ8Xp7zd4ly0tW7RQj2F7uItKMjxp2ODaJlAfJtA7PMeZztkvWHWAObk5pCbm9OqTSCR6jvdBezzyjY1BcQBbOMwMMnaBfwlcCOWEnwXKzfm8liNRKQBK27W28B84HkRmWuMuckYM9audifQCSvJ+nfGmKkRulMUJQwXnXOta30lEt6inaO6L4uoqanltr8+xMrl1pRfJOPFzV3A4cKoBI4buiwj0iYQp3EA7/vn06wPs7YxWsqzFrEKA9YG1oUJ4xK4GSPWJpDANXbN+XiboqSzC5TZapubk0NOTk7wuFGux5LbNgALLAMw0r2Otjs6aBdwceAu4PoW5334DL9MigN4Ntaal0UiMtMYsyfwuZOGIjINmBZSdkPA6+PdFFRRlMRxa6NGOEMyVt9pGgZGdV8WEboRItKPf0K7gCN5AMPGAWw2DkI9UEGGXpwGYNRdwGE8kR6Ph6amJnZsdxb+0udV2xXgiXMaBsb3nnboUEh1dS21tfV06FAYpl3L9ybX9gDu2tWcpTFWLmDfvfV7ACPc62gbOgLH8G0CaWpq8huyYb27SQwDkywPYA/gAxF50xjzK2A/rCkORVHaEW49lYbbURfLAEzTXcKq+7KI0B/sjz4If6sTmQJesngVb705vUV5uF2mgd/D0O9SUOSXCJlAEgoDE8EAuu7qf3DUyHPDngvFZ/gETgFH2tEbGgjaN36Pnt0A2La15UYyCMkFbLfNyc0hJzeXqspmAzDS9fjPh04BRzD0nE4BF9nGalNTswcw3M7qjAsDAzwNlBtjPsBaEO0FjgB+mqTxFEVxwKKQFFWtxa2n0nBPvmlq4MVCdV+WMHf2oqCpzWg0JfBZfvaJN8KWh8sEEhgGpi6MAbJp4xZmfbcgSi7g+AzA99+ayfQwxq7HQ9hMKJHepnBTwDU1Yfd1+o1Xn4Hla1vasxurVq5n65Yd9Nm95SacwGsO9QD61gAWFOTHDgNj66iiwhhrAB0EdQYotHMKB+YCDhfix+d5bPJmjgG4N3AvcCzWlMa3wBVJGktRFIc4fTJva8Ip01iBWdN0Clh1X5Zw3BEXOK7r5sNMrCngloaMl5OOuYTVqzbwj/uvDShtJl4P5cP3vRJX/UjfVZ9xE/hgGmgMBhI6BezziPXsZe2U3rolfPiYIA9gffAmEN96ys5dimPqm40brLAvJV07B/UVSjRPYuAYBfZawqamJn+bujDeXd+9yaRNIA1YcarKgHJgSRLHUhTFAZHSVKUD4aZTKnemr7xRUN3XzqmtrWPTxvAx4CKRaN7csOOHbrAg9hrA1as2WG0DdueGCwT9m9+dHdI2PtnCGXrR08lZBtH0D5u9iZEMq6YIHsBYU8Dh1gDm5eaSk9P8tezcpTimB7D8gxl0KenEAQftEzR+KJGmhiF4k0dBkAfQjmcYxnvoJN9xoiRLMb0HGOAArKfg/YFFUVsoSpry4Xtf0LvTaHp3Gh2kTJqamli+dDXvvf2Zv6y6upaNG6IHME4Vg3c/IdUiRGTVypYBb7c7XEieZqjuC+GEoy6id6fRzP7eCnZbuXMXzz75Bl6vl9raOtav25RiCePj4l9OZNjAU+Jq4+Zu+fC5gAN2AYd4kQINsOrqmsATAe0tvXb8j0cHd+yC57Kmpi6yB9A2uhb8sIyCgvyo/TQ0NFJXV+9v47tmvwcwxAD83xvTGdL3xKCYhEFrAG0DMC8vlw4dimKGgZn+4Vccfcwo8m05EwkD45sCHjCoH/vsZ8VzDFwDGD7Ej28NYOZsAjkPOB0YKSLzgNeAS5I0lqIklbP/7yr/67enfeJ/vc8eJ3HYAWfxi59N8Jftt+dJDB90Kh998GWbytgeiZUyKk1R3RfCrO8sw+/4Iy9i0YLlDOpzAldediufffwtx4++kAOHjOP7b39IsZTOefd/n8bdJlp6tngJOwUcGAYmJJzJpOvu87+uqW5eX7dy+TrunPwIXq/Xb0zl5uZy37//0ix3GAMw0KAKJZyhV7lzV5QpYGvcXZXVfkMuHD5jbfXK9S2MJJ8HMHQK+IY/38uO7ZWssb2f0HINIECHjkXk5+dFjOEIlpG2ZtUGhuyzl79dJI/hvNmL+evE+8K+dw0NDYw6bBhffP988y7gQA9gGO+tz2jNGA+giFQDXYGJdkqkQSLyTTLGUpRk8t9Hgte6XHj2n/2vA0MdeL1evv16HtW2gj1r7JVtIp9T7rnriVSLEBc5OTkx1+Tk5KbfzKrqvmBmfPZ90PHXM5vTmG1Yv5mF9tqvE4/+VVuKlRC1tXWMGZVYRj83PYC1UbxE0NIweeWFd/2vqwI8gH+75d/cddtjLFuy2r9JJTc3hzPPOTnq+AN6HReXvIEhXkKpb2igvr6Burp6f7aScAwc1A+wsnGE6oXOnYspLCxoMQXsGzfQ6A02AK0sRn379SY3LzfqFPDOHbtoamqiW/cu5OR4gvoK5T//fhm555mwsRMbGhrJs8f1GbVBawBDNoFUV9dSX99Afn5e5hiAxpjrgcewUiCdCTxmjLkuGWMp7ZvXXnqfdWtTM0Xk9Xr545V3Oqq7W+cjOemY9HX03DrpQdf6KrR3wSWTwqKCmDuMc9PQAGzPuu/7b39oYdDFYuyJlwUdB/54X/arSS5I1XZ8NWMOC35YllDb0OnJ1hBoJPiySQR+V6LFoava1dJ719jY6J9eDFwXB/FvXgnrAaysirgLuLGhkSo7Fl9pj64R+x0w2MqSuGzJ6hbXl5+fS2mPrmxYH7z0xuep3LGj+UHdvwkkJ8f/ADlo8J7k5+dFnbr1eRe7dS/xv0ex3pvt21rmf25saCQvP88vA4TsAg5ZA+gzakt7dKWpqcn1yAjJ0qCXAK9j7YjbGytB+vgkjaW0QxYtWM4jD7zA+Av+wkF7j4u5PiMZhFuXFg8zZ8x2SZLW8U4CU1bRiPak7haFhfkxDcBbbngg6XIkQLvUfV6vlxOP/lULgy5e8vIi546OFP4jHZg5Yzan/+TyhNtXR5k2jZdAI8FcYe3qb94p2ojX6/UbhqFsXN/SK1VdXev3LoV61R9/JL7dvuGItKsXLIPMF4y5V+/SiPV69y6lQ8ciVixf08JTl5uby9Bhg/xLDXz43vPAmRqfoRU4BTxgcD/y8vKiBoL2GfBdu3XB43FmNgVOSVfu3GVN5zY0+L8DvvGbmrx+o7axsTFI7231G4Dd7LpNXHTOtXz5WbMnvTUkywDsBrwrIotFZDFWSqRuSRpLaUc89tBL9O40mqNGnsvEa/7pL//8k+/aXJYn//NaXOWhnPKjS90UJ2HOO/MaV/tri/RvuyqrY04BpyntUvft1vlI/+vWeCGWLl4V8dw709x9UHGL6uparrj0llSL4ae2ttlQ9tjTkT4Dzveg3K1bl7Bt16/fTHGnjkGevsqdVf72rfWqh/P0VVZWRdQZjQ2N/qnaPffqE7Hf3Nxceu9WyqaNW1t4AHNzczloxH4s/GF52MgB4TyAubm57D9sCAA9e3YjLy83avgWnyeue/cSx+/R1i1Wmw3rNzOozwk8+uCLlgcwL3gK+NGHXghycAS+3ral2QMIMHf2Yqa9/hGVAQGsW0OyDMCvgFuNMU8YY54AJqPR8BUHXHv138OWn3lq24dSuzfCurkJv/sbc2YtdNTH++84ygKWNF5+/h3X+3QzpEUkBu+9V1qHrYlCu9d9rVmSEW0t6q/Pvz7hfpPJwfuMi2q4tjU11c1TwD6jyW8A2t7BSNOpG9Ztpnv3LkHfYctAS8wAjLZxw9//zqogr92e/Xf3vw70AAaWh5KT46FHz25s2rilxYNhXl4uBxy8L16vlx/mW1P0gde3Y1uYKeDcHCbf9QeOOmYkPxlbZm0CiTDLdN2Ef/D6qx8C0LVbZ/8awFhssT2APufFw/IcDQ2N5PoMQPu9fuuNj5k/d0mzjPY9fHvaJ5xzmrUB0Xc/33vLijhx4IghjmSIRbIMwN8Bm4Bf2v832mWKEpFUTPMmyo9GX+io3n3/eCq5gkSgurqWH+YtTco6q3hjoCVCoELMMNq97gu3tqk9UVNTS21tHd99M5999vix35OTLvhCufz4p0cx+ugRgDV1+Oc/3MX1f7RmTU4784Swa3VXrVxP1xDvYOXOXf44gKFrAGPRe7fgadtwzuHKyqqgAMc9ezU7xBsaG9lle7P67N4r4jgej4eePbuzedPWFr8TuXm59NujNwA/PW48Tzz2qj/uIUTyAObQZ/eevPTmv9hzrz7k5eeGnXHYuGELjz74Ik8//jpgTQE7fY+2+g3Aby05tldSX9dAXm7wGkAIjtHq83Cef9Yf/YGq/QbgO5+x79CBlPZwZxmO6wagMSYXGAyMA4bb//cTkfluj6W0L1atWBf1/OpVrVuT55StW3bwJ4ebP2JtiPjsk2/5Yd5SN8SKSV1dPb07jeauWx9lQK/jOObQX7bJuG6Tphk+YpItum9LRfiMC6FcN+EfcfcduOyjrfB9b+64xQqJctwRF7BnaRk/HnMx27a6Z+z64r61Fl8w51GHDvNPJ65Yvpb//Ptlpjw1DYAOHYq44urzwrbv1j3YeKisbJ4CjtcADF23F+4hvrKyKijAcffSrv7XjQ2NVFVZBmDnLi3XLf7p+l8D1lR3j57dmD93CQ/f/1xQnby8XHrv1sN/LPc8w7w5i/3HgYadb6zQtah5udYmkMbGRn92kpuuv5//PhKc1s7aBex8Crhy5y5mfD7Lf7zgh2V+D2Akb2u4TTw+A/CbmfM47IgDHY3vBNcNQBFpBB4FRonIXPt/5rh2lJTx7TfRfyfPHveHpI6/fdtOnnt6GvvtdbLjxc+/NmfGrHPMob9MWl7b+voGfpi3lLq6ejZv2grAnbc+mql5dIGMzQGcNbrPiQfw9Vc+4NEHX4y770ceeMG1/NLhaGxspKamlgXzl/l/6LdUbAPg77c/xvjz/8KSRSuTMvZPTj3GlX58YU1ycpqDGf/r708G1cnLzwvyfAUSuolrV2VVUBzAePClRfMRbnnIrp1Vfk8WBE9P19c3+D2AxcUdW/SH/SyYk5Pjj/cXSl5ebotr8hmAocbaD/OW0rVbZ7qUFAf3kZ/LgvnLGDbgFA7Z/wzWr9vE/Xc/zV23Peav07lLMXl5eUEbZTp3Ce7HR1FRAfff/TSD+pzA/LlL+O2Vv/B7KZvXAAY/6PoCYa9ft6lFXMxOnSzj2Ov1MnTYoLBjJkKycgE/DVxojJkJ+N06IhJz7sgYcxJwD5ALPCIit4ecLwSeAEYCFcDPRWS5e6IrqeK6COv/fCxauIKmpqa4n1KdsHjhCn58zMVRd6yF46XnnK2xGz7oVGYtntoq2X3XvnjhCt6c+hHnXTSO/fZqjtl1571/TLjvtqCDJ586bwONZKaB55B2p/tCDfIb/nwPPxkb2ZhZtXIdl5yX+Hq+3UuOZkPlZ7ErRqGxsdFvzLz8/DsUFRVy0ilHc8ZPr+Aze0oO4NGnJjPAjjEHMPWVD1o1bjSihTmJB18sv8BYduGIlEmnS5dOQcebN2+jl+1B83mlJt16eVAA6Ujk5+dFrduxuAN3TH6k+diTT2mAB7D8vRk89vBLABR36kDXbp2DHjB8swG+NYDhyA1J62bFZJ3PgEH9qKutZ83q5ungOd8vYuShw1rMMsybbRmMvnV75e+3DOS/uz1FHWi4HTRiPz4u/6pF3Zqa4Hh+I0YNpVfv7tx47b9YsXwN0HIz3aFHHMAnH33Nv/7+FG9OLW9xjT72238Q1XVbW4yZCMlaA3gFcDQwC2s9zCastTBRsadQ7gdOBoYC5xhjhoZUuxjYKiKDgX8Cf3NR7naP1+ulwQ6+WV/fQENDg3/reeD/jRu28NhDLzH+gr/wn4dfYsmilWzbuoMd2yup3LmLXbuqqa621sr4+tm4YQtP/uc1f9q03p1Gc9pJv2Xa6x+xfdvOsO3q6uqp3LmLWd8tYNvWnQw7YAi33HFlRPn7dDmKidf8k2VLV7NzhyVLZWUVu3ZVU1VVQ3V1jX8NT11dPfX108zfNQAAFjpJREFU9f5rrKmppbKyiorN21i7ZiNTX36fM0/5Pb07jebIEedENf6uvCZ84vf6ugZ+e6UViuGzb6cw5dXw016bNm6hT5ej6N1pNC899zbvv/M5s79fwJJFK1m2ZBU7tlcyZ9ZC1q3dzNzZi3l72icsXriC3p1G+2X0tT9yxDncOunBIOMP4Jor7ohx9y08eMjB4/+bg4dcPOT5S1v+yyWHXDx0zikkn1yKPHkUeawWffK60D23I7l4GJDfnU45BeSTQ4Enl9LcYnrmdiIHD2eVHMSPO+1LLjn0yC2mR24xHT0F9M0roYMnn30LenFMx0H+4w6efPrllVDsKaCjJ5/d8jrTJaeQDp58DizcnU65hY6ut43JWN3X1NTEiuVr+f1vbmHRguX+7/DaNcHir1q5nvlzl7B9204aGhqorq5l545dbNq4haF7/YRRQ38Wc6zBQ/YE4NlXwn9fencazb57nsS0qR+xYP4yZn+/gNWr1rN1yw6WLVnND/OWsnnTNl5+/h3WrN7A5BsfoHen0Tx8/3P07jSa3UuO9st/2a8mcdG519Kny1FBxh9Yad2OOyL8d9sJHvB/hzxhvlM5Af/79OzR4ht2/oXjwn7fXvuf8PDjNzFi1P548DBwYD9y8FDoyaOmqpY8cqirrGXb6i3+72EOHq68+nwKPXlsXLieXet30tFTQJEnj3xybJlyKNrhZXBBDwo9eXTpXMz9/3iaGybcTR45VG2uZNmXSxizz3A65RT49UI+OeSTS679vc63y0bsvy/nnvVTinMK6JPXhY6egqBrLszP978+oucgziw5iK5bc+jgySePHP7z8Mt+ufLI4YjDDqRzTiG9cztR5MnjoKF7U+wp4OABg9m9e3cKPXnke3L583W/JpccuuZ0YP2XK9iwaL11jhxWLV3L+9M+o2zMIXQpLqbAk8shHfZkYH4p1DUxdLc9WP3xSua9N4eV3yxn6ReLKfV0JN/Wcbl4+MNvbvXLvf/+g8nFw4H7783q2atY980qOnryKfLkceffrubG6y6jyJNHgSeXgXv05Q+/P58iTx7FngIKPLl08OQzsG8fyg4fQZEnj4ofNrBp6UY2L9tEp5xCOnryySeHQf360qNzF96eOj3oPe+a04FtM9bQLacDnXMK6Z5fTF1ly3zQCX1+kzHdYowph5aP+SJybIx2RwCTROTH9vG1drvbAuq8bdf53BiTB6wHeopI0HijRo3yfvVVS8s8lEXfLeHfp9wbs56iKGmGB25f8U/HXlWPx/O11+sdlUyRUq37nOo9gCvLrqFgsTs/JIqitB17n7Efl9z9G8f1I+m+pEwBi0hZgk37AoH77VcDh0WqIyINxpjtQCkQFOGyoqKCiRMn+o8HDhzIoEEt5853VOykbnDyMxukksDpELDc6h5P9DzfOTkeOnXuSENDIx06FlK1q4aG+ga83uYpIa/Xa/fhxev1kp+fT5eSYnr06sqO7bv8ax3Wrt6Ex+MJ284ay1rLUtqzhG7dW8av8nnrvV4vO3dUMfOLeZT26EqHjoVB01OBffpfe5t/jXNyPHg8HnJzc2hsbLIX6VZT3KmIhvpGevbuzp79e1NcXBT1/Vy3toKiogK6dusUdcNCbU09dXX1FBYV4G3ysm7tZjZu2EKXkk5s31aJ1+ultqaOXrt1Jycnh6pdNXhyoGPHImvRtMdDXW09Vbuq2bp1J6U9SqiqrCG/II9Ona1YXoVF+VRX1VJQmE+XLsXs3q8HOTk5bN60jaKiAmpr68nPz2X79l3s3q8nuyqr6da9M01eLzXVdRQU5lFdVUtVVS0dCgso7JBPTk4uGzZsoWPHIooK8+nYqQOV23ZRWVVNp7wiinsUk5OXQ2NjIzkeD401jdQ3NbBx0zZ269Wd/MJ8aurrKCjIp3p7NdWbqyjs1ZFdO6vp2KUD3sYmChpyyC3KY9O27XTv1Jn8zvnUb68jN8dDcZ/OrF2xic4lHanaXEWn3TpRkJ9P1bpKinp1ZMP6LXTw5NNn/92YPn161HvV1qRa9znVewC9hvdkI8EhXZqamli7ejN7DdiNhoZG27j20qFjEXsN6ENTYxPv/m8Ge++7J42NTXYd63vV0NDIvDlWGI7+A/ow8rB9g4Lm7qqsZtvWnfTp2yOM0e6lYvMOOnfpSGNDI4sXrKKhoZHa2nqKigrYsH4Le+zVm7y8XOrq6mlsbKKgMA+8vilAD1XVdWzeuJX8gjy6duvCzh27yMnx0LVbZ7xeK2NEYWEBu3bVkJ+fS15eLh6Ph169u1FQmE9Obg51tfXs2lVN126d2bh+C8WdOtC5SzGbNmzB4/FQ1KGQTp07UlNdi9drvS+eXA+NjU14mrzU1TdYWWyamsjPy2VXZTW7dtVQ0rUTRR0L2b61krzcHHJzPRQUWh7srVt24vV66V7ahabGRtatraDvHi13xFbtqqFjlyK2V1TiyfXQpWsxeYX5NNY24PVNJ3rBk+uhY4+ONDY0UVtVR54nh8b6RpoamvB4PBSWFFJU2pHty7bibfSyacNWOnfpSE1dPb36dqewWxFN9U00VNVTu7PW+r3I8QBetm7eSffeJdTW1FJQUEBehzy8jV52bt9FXlMOXo+1nq6+vpH8vFw8OR7q6xtYs3Ijgw7Yk91H9mXLggrqK+uo3lnD+tWb6bVbNwqKCixd6oGcvBwKuxZSVVFFXkE+ObkecovyqN9Vz+YNW8nxeCjp0onqqlqKuhXRrV9Xdq2rtK6xvpGmJku3dexYCPZ0bceeHamvqqexron8Dnnk9ykkryGXhuoGcvJzaaiuZ9c6a8q8YtN2cvNyKCkptn58vNbO6y6lnSnoXEBBl0JWzl5Dt26dyc3PxZOXw86KneTl5lJQVIAX2zNckENdZR1NeMkvyCe3IJfG2gaKunegoare/8NWX11PfXUdBR0Lqa6qYde2KrqXdoEcD9VVteR1zKPP8D5snLeR3NwcijoXkdMtl/Ly8rDf63hw1QA0xowHDhGRXweUeYCHgK9E5GE3x4tGaWkpkydPdlR37M9OTbI0qaW8vJyysrJUi6E4QO9VZpIuui8evQdQ9q+yuMe4jMQzYrhJJn9XMll2UPlTjVvyu70G8GqsaQk/9vTEOsBJOoI1wB4Bx/3ssrB17GmQEqwF0YqiKKlCdZ+iKBmF21PAewLLw5SvJFi5RWImMMQYMwBL2Z0NnBtSZypwAfA5cAbwQej6P0VRlDZGdZ+iKBmF2x7AzViKKZQzgJj5g+yYWZcDbwPzgedFZK4x5iZjzFi72qNAqTFmMXAV8GdXJFcURUkc1X2KomQUbnsAXwKuMMbMAt6zy44H9gccbbUVkWnAtJCyGwJe1wCxo+8qfpYsWZLR6x2yCb1XGUvG6b5M/6xlsvyZLDuo/KnGLfnd9gBOBKYDw4Ar7f/DgI/sc2nLrFmzktbGSb1odSKdc1q+dGnbpCKLRiLvr5v9pOO9Clem96rt7pXLZJzuS4fPWmvIZPkzWXZQ+VONW/K7agCKyC47DMLxwJ+APwI/EpHjRCS+FAttzOzZs5PWxkm9aHUinYu3PJW4JVOi/aTjvUrH+wTZc6/cJJN1n6Io2UlSAkGnAx6PZxOwwmn9wsLCktraWmdZzuNs46RetDqRzjkt79ChQ4/q6urNofXakkTeXzf7Scd7Fa5M71VS79VeXq+3Z7zyZBLx6L10+Ky1hkyWP5NlB5U/1SQgf1jd124NQEVRFEVRFCU8ycoFrCiKoiiKoqQpagAqiqIoiqJkGUnJBawoiqKkJ8aYk4B7gFzgERG5PcUiRcUY8xhwCrBRRIbZZd2B54D+WAG4zxKRramSMRrGmD2AJ4DeWKnJHxaRezLlGowxRVg73AuxbIYXReRGO2j5FKx81F8D54lIXeokjYwxJhf4ClgjIqdkmOzLgZ1AI9AgIqPc+uyoB1BRFCVLsH8I7wdOBoYC5xhjhqZWqpg8DpwUUvZn4H0RGQK8T3oHxW4ArhaRocDhwG/t9zxTrqEWOE5EDgQOAk4yxhwO/A34p4gMBrYCF6dOxJj8HivAuo9Mkh3gWBE5SERG2ceufHbUA5iFGGOKAQHqgHIReTrFIikRMMYMxIojVyIi4TJNKEo8HAosFpGlAMaYKcA4YF5KpYqCiEw3xvQPKR4HlNmv/wuUY4XfSTtEZB1WTmhEZKcxZj7Qlwy5BjvdYKV9mG//9wLH0Zyu8L/AJOCBtpYvFsaYfsBPgcnAVcYYDxkiexRc+eyoAdhOCDdNYpeHm+45HcuN/7ox5jlADcA2JJ57Zf9QX2yMeTE10irtjL7AqoDj1cBhKZKlNfS2DSuA9VjTq2mPbcgeDMwgg67B9hx/DQzG8iAvAbbZKQzB+hz1TZF4sbgbKy5nZ/u4lMyRHSxj+x1jjBd4SEQexqXPjk4Btx8eJ2SaJMp0Tz+afwQa21BGxeJxnN8rRVGiYHuo0j6emTGmE1bKwCtFZEfguXS/BhFpFJGDsH47DgX2Ta1EzjDG+B60v061LK3gKBEZgfXb8FtjzJjAk6357KgB2E4QkenAlpBi/3SPvcDVN92zGuuLDPoZaHPivFeK4iZrgD0CjvvZZZnGBmNMHwD778YUyxMVY0w+lvH3tIi8bBdn1DUAiMg24EPgCKCrMcY3i5iun6MjgbH2RoopWFO/95AZsgMgImvsvxuBV7B+K1z57OiPf/sm3HRPX+Bl4GfGmAeA11MhmNKCsPfKGFNqjHkQONgYc21qRFPaETOBIcaYAcaYAuBsYGqKZUqEqcAF9usLgNdSKEtU7DVnjwLzReQfAacy4hqMMT2NMV3t1x2AE7A2VHwI+NYlp6X8InKtiPQTkf5Yn/UPROQXZIDsYK3XN8Z09r0GTgTm4NJnR9cAZiEisgu4KNVyKLERkQrgN6mWQ2kfiEiDMeZy4G2staaPicjcFIsVFWPMs1gL3nsYY1YDNwK3A88bYy7GSn13VuokjMmRwHnAbGPMd3bZdWTONfQB/msvU8kBnheRN4wx84ApxphbgG+xjNxM4U9khuy9gVeMMWDZa8+IyFvGmJm48NlRA7B9016me7IBvVdKmyAi04BpqZbDKSJyToRTP2pTQRJERD4BPBFOp/01iMgsrI0roeVLsaYjMwIRKcfaLZsxsttyHhimvAIXPjtqALZv/NM9WMbE2TRvfVfSC71XiqIoSpvh8XrTduOREgeB0yTABuBGEXnUGPMTrG3wvumeySkTUgH0XimKoiipRw1ARVEURVGULEN3ASuKoiiKomQZagAqiqIoiqJkGboJRFEURckK7JialwKjRORrY8wk7LAyInKtMWYYMBt4VEQucXls31hnikhSUjsaK15ILxGZZB/3B5YBb4rIKQ7aj8WKKbe/iDjOD90W1xYy3lBgLnCaiLya7PHaK+oBVBRFUbKFGfbfw+2/h4X8PTykXqZhsAyxRLkG+DQe48/mReAc4AunDQIyccSNLd+nwFWJ9qGoB1BRFEXJHnwGyuFYubcPBT4CRhljcggwAI0x5wG3ALsBW7HScF0O/Bh4E7haRP4R4I36l4hcYWfsGY+1y/8z4DI7nlsQdp7aycBgYDEwQUTeNcaUYWWqmAZ0BYYBD4vINXa7q4FrsSIIzMTKBHERVmSB/e06Xvu6LrSHKzHGTLXrvAH8ws4hGyjPnsBRWEGSCehnEfA9Vv7yF4D3sNKp7cDy+H2DlVXjRuBM4EVjzEn2te2HlfbyTGAdljfyc6AKGA70Nsb8GvgjsLv9Pl4lIp8YYy4E/oOVwm0osCcwSUTuscV7HbjNGNPXly5NiQ/1ACqKoijZwg/AduAwY8wQoDtwL9AZy8g4DNiFZYhsBu4Cfg+8j5WR52zgHWATzanEzrT/PmWMuQC4FcuDeDtwAJbRFIQxZm+s3MDVwM1ALVbGhz4B1Y4BngcqgAnGmD2NMQfaMm2w5f5xQP0HsFJIguWNuyng3Ggsw2uBfe6oMO/NkfbfmSHlQ4B5dtuLgAnAv4CBwA1hrm0I8CqWQXeNLVduQJUjgK+BvxhjjgMexno/r8Iy8qYaY0oD6h9r1/ECt9spDH1yegLkVuJEDcAsxBjzoDHGa4wZaR9Pso9vs4+H2cePJGFs31hnxK6d8BjGXpPiO+5vj/mGw/Zj7fpD4xw36dcWMt5Qe7z/a4vxFCXTsb1eX2IZNT8BdmKteduCleN2KPCViDQCJVietgdoDso+XEQasAyzw40xfbEMwYUi8iXgW2f3c5q9hyOMMd1DRDkBKMAyOG8DDgGKsYwjH1Ntb9c79vFeWB48gH+KyEPAYwHXNgPLuEVEpojIBwF9zRCR27CMToD+Yd6ePe2/a0PK14jIjVjpA8HynPpilA4I08+JQCFwi4jcLyKT7WwoPr4VkT+JyMNY9wCsWKgPYaVk60azJxasmKj3Y923Iqz0aIFyhrsWxQFqAGYnug4mOroORlHaLz69djkw0zb2ZmDpjZyA83cDnbCMud/bZUX236ewvE/XYk3RPh0yxi+wjLwTsLx0VRFkuSOg3gkE69wt9t8G+2+gFy1SAN9I5dH6CiU0bd02+2+9/Xe7/Z7F6icSoQYmNMsdTv5IskdKr6c4RNcAZie6DkbXwShKtuLTf4Npnp6dAZwc8NpHAZYO+7/ADkTkC2PMEiyjESyDECy9cgaWPpqCNU1aJiJHWxt0/bwL1AGnY+m9rsBZNE8nR6Lc/vsH++HxVyHnt4J/N/BMrKlVp6y0/+6ONVWeKO9gTWlfb19zF+BjmqenA5kGXA381RgzCLgY6xq+AE6NMc7u9t8VrZA1q1EPYHai62B0HYyiZCszwrwOV/YHrAe8G4DAKUwfz2B99z73PdyKyH+BP2Ppiwew9Mz00IYishDL+KvEepD8A7AE24CLhIh8j6V7dgMuwzKsoNlLdw+wEevB/tJofYXhU/vvqDjbhcq4CDgNy9N3J/BboDFC3Q+wHAW9gH9g6e6xIlLhYKhRWPow3L1RHKCp4LIUY8w7WFMOV2IZX92wFMctWMbVxyJSZow5G+uLGWiU/U1E/myMuQ/rCXgP4C2gQET2Mca8QLNhGEgpcAXNXrLewH1h6v0My2P2IfCsiJwbEL9rDDACa3rm1yLyiDFmMnAdcJGIPG6MmYMVx8pjX2t/bK+biIw2xvwZa93N+SLyZMj78icso3VfEVlgl3mx1sH0CxjrEuBxrGmJWSJyYGAsrIBru9xev+Lr3yfLtyIywi67C+sp+ETb++kb4xSgJ5YH8DYRuc4Y8xaWwbuniKwyxuwLzAf+JCJ3hHkvFUVpRxhjfoOlQzphGVg9gSEist6FvqcDOSIS7uE4rTDGfAI0isgxqZYlU1EPYPai62B0HYyiKJnHkVjrjR/HmgUZ54bxZ3MXcGS8G+DaGlu+I4F/plqWTEbXAGYvug4mPLoORlGUtEVEzkti31PJgIdKewNc2suZ7qgHMHvRdTDh0XUwiqIoSrtH1wAqGYmug7HQdTCKoihKIqgHUMlUdB2MroNRFEVREkQ9gIqiKIqiKFmGegAVRVEURVGyDDUAFUVRFEVRsgw1ABVFURRFUbIMNQAVRVEURVGyDDUAFUVRFEVRsoz/BxLA7n8OuXJGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x468 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "p_plot=1.e5\n",
    "t_plot=1000.\n",
    "fig,axs=plt.subplots(2,2,sharex=False,sharey=False,figsize=(9,6.5))\n",
    "h2o_ktab_SI.plot_spectrum(axs[0,0],p=p_plot,t=t_plot,g=1.,yscale='log',xscale='log',label='g=1')\n",
    "h2o_ktab_SI.plot_spectrum(axs[0,0],p=p_plot,t=t_plot,g=0.,yscale='log',xscale='log',label='g=0')\n",
    "axs[0, 0].set_title('Log-Log')\n",
    "h2o_ktab_SI.plot_spectrum(axs[1,0],p=p_plot,t=t_plot,g=1.,xscale='log',label='g=1')\n",
    "h2o_ktab_SI.plot_spectrum(axs[1,0],p=p_plot,t=t_plot,g=0.,xscale='log',label='g=0')\n",
    "axs[1, 0].set_title('Lin-Log')\n",
    "h2o_ktab_SI.plot_spectrum(axs[0,1],p=p_plot,t=t_plot,g=1.,yscale='log',label='g=1')\n",
    "h2o_ktab_SI.plot_spectrum(axs[0,1],p=p_plot,t=t_plot,g=0.,yscale='log',label='g=0')\n",
    "axs[0, 1].set_title('Log-Lin')\n",
    "h2o_ktab_SI.plot_spectrum(axs[1,1],p=p_plot,t=t_plot,g=1.,label='g=1')\n",
    "h2o_ktab_SI.plot_spectrum(axs[1,1],p=p_plot,t=t_plot,g=0.,label='g=0')\n",
    "axs[1, 1].set_title('Lin-Lin')\n",
    "for axs in axs:\n",
    "    for ax in axs:\n",
    "        ax.legend()\n",
    "#ax.set_ylim(bottom=1.e-37)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14616bd",
   "metadata": {
    "papermill": {
     "duration": 0.063959,
     "end_time": "2022-09-02T07:49:46.767827",
     "exception": false,
     "start_time": "2022-09-02T07:49:46.703868",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The g distribution is shown with `plot_distrib`. Notice that when you ask for a log scale in g, we switch to 1-g as it is the most relevant quantity to look at in log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b13feed7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:46.943306Z",
     "iopub.status.busy": "2022-09-02T07:49:46.942694Z",
     "iopub.status.idle": "2022-09-02T07:49:50.469880Z",
     "shell.execute_reply": "2022-09-02T07:49:50.467968Z"
    },
    "papermill": {
     "duration": 3.609975,
     "end_time": "2022-09-02T07:49:50.472732",
     "exception": false,
     "start_time": "2022-09-02T07:49:46.862757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x468 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_plot=1.e5\n",
    "t_plot=1000.\n",
    "fig,axs=plt.subplots(2,2,sharex=False,sharey=False,figsize=(9,6.5))\n",
    "h2o_ktab_SI.plot_distrib(axs[0,0],p=p_plot,t=t_plot,wl=1.0,yscale='log',xscale='log',label=r'$\\lambda=1\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[0,0],p=p_plot,t=t_plot,wl=10.,yscale='log',xscale='log',label=r'$\\lambda=10\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[1,0],p=p_plot,t=t_plot,wl=1.0,xscale='log',label=r'$\\lambda=1\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[1,0],p=p_plot,t=t_plot,wl=10.,xscale='log',label=r'$\\lambda=10\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[0,1],p=p_plot,t=t_plot,wl=1.0,yscale='log',label=r'$\\lambda=1\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[0,1],p=p_plot,t=t_plot,wl=10.,yscale='log',label=r'$\\lambda=10\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[1,1],p=p_plot,t=t_plot,wl=1.0,label=r'$\\lambda=1\\mu$m')\n",
    "h2o_ktab_SI.plot_distrib(axs[1,1],p=p_plot,t=t_plot,wl=10.,label=r'$\\lambda=10\\mu$m')\n",
    "for axs in axs:\n",
    "    for ax in axs:\n",
    "        ax.legend()\n",
    "#ax.set_ylim(bottom=1.e-37)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c91aba6",
   "metadata": {
    "papermill": {
     "duration": 0.077691,
     "end_time": "2022-09-02T07:49:50.618607",
     "exception": false,
     "start_time": "2022-09-02T07:49:50.540916",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Interpolating and remapping tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac62265b",
   "metadata": {
    "papermill": {
     "duration": 0.070695,
     "end_time": "2022-09-02T07:49:50.763951",
     "exception": false,
     "start_time": "2022-09-02T07:49:50.693256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Basic interpolation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae51f0b",
   "metadata": {
    "papermill": {
     "duration": 0.067625,
     "end_time": "2022-09-02T07:49:50.899603",
     "exception": false,
     "start_time": "2022-09-02T07:49:50.831978",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To get the value of the cross section of the molecule or mix described by our `Ktable()` object, we can use:\n",
    "\n",
    "```python\n",
    "self.interpolate_kdata(logp_array=None,t_array=None,log_interp=None,wngrid_limit=None)\n",
    "```\n",
    "This returns the cross section of the molecule along a LogP-T profile specified by `logp_array` and `t_array`. This returns an array of shape `(logp_array.size, self.Nw, self.Ng)`,  i.e (number of PT points, number of wavenumber points, number of g-points). `logp_array`and `t_array`must have the same size.  \n",
    "\n",
    "It also works if `logp_array` and/or `t_array` are `floats`\n",
    "    \n",
    ".. important:: Notice that the input is always in Log10(Pressure) because this is the quantity used for the interpolation"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b8ea3aef",
   "metadata": {
    "papermill": {
     "duration": 0.074921,
     "end_time": "2022-09-02T07:49:51.037823",
     "exception": false,
     "start_time": "2022-09-02T07:49:50.962902",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. seealso::\n",
    "    :func:`exo_k.data_table.Data_table.interpolate_kdata` for details on arguments and options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5328f3cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:51.190578Z",
     "iopub.status.busy": "2022-09-02T07:49:51.190063Z",
     "iopub.status.idle": "2022-09-02T07:49:51.203658Z",
     "shell.execute_reply": "2022-09-02T07:49:51.202485Z"
    },
    "papermill": {
     "duration": 0.089952,
     "end_time": "2022-09-02T07:49:51.207216",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.117264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1538, 20)\n",
      "[[[1.57577243e-26 1.70593813e-26 1.71642784e-26 ... 4.95008363e-22\n",
      "   8.13668554e-22 9.35286142e-22]\n",
      "  [2.90167053e-26 4.18911348e-26 4.71088691e-26 ... 8.99173698e-23\n",
      "   4.25319413e-22 6.23065168e-22]\n",
      "  [5.81193968e-27 5.82883586e-27 5.92632233e-27 ... 1.21096603e-25\n",
      "   3.52299381e-25 4.86654868e-25]\n",
      "  ...\n",
      "  [2.43561538e-34 3.12899011e-34 3.55619421e-34 ... 2.18003828e-33\n",
      "   2.92876500e-33 3.94600721e-33]\n",
      "  [2.71942214e-34 3.29638704e-34 3.74713705e-34 ... 2.21029744e-33\n",
      "   3.13286094e-33 5.08112258e-33]\n",
      "  [2.86054904e-34 3.25393178e-34 3.70676091e-34 ... 2.45139953e-33\n",
      "   3.46404319e-33 7.47015700e-33]]]\n"
     ]
    }
   ],
   "source": [
    "logp=3.\n",
    "Temp=1000.\n",
    "tmp_opa=h2o_ktab_SI.interpolate_kdata(logp,Temp)\n",
    "print(tmp_opa.shape)\n",
    "print(tmp_opa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c561a5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:51.371149Z",
     "iopub.status.busy": "2022-09-02T07:49:51.370032Z",
     "iopub.status.idle": "2022-09-02T07:49:51.402407Z",
     "shell.execute_reply": "2022-09-02T07:49:51.400910Z"
    },
    "papermill": {
     "duration": 0.124463,
     "end_time": "2022-09-02T07:49:51.407088",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.282625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6, 1538, 20)\n"
     ]
    }
   ],
   "source": [
    "logp_array=np.linspace(1.,6.,6)\n",
    "t_array=[1000. for logp in logp_array]\n",
    "tmp_opa=h2o_ktab_SI.interpolate_kdata(logp_array,t_array)\n",
    "print(tmp_opa.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470760bb",
   "metadata": {
    "papermill": {
     "duration": 0.075063,
     "end_time": "2022-09-02T07:49:51.555149",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.480086",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Interpolation options"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a06e79",
   "metadata": {
    "papermill": {
     "duration": 0.064376,
     "end_time": "2022-09-02T07:49:51.684177",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.619801",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The default interpolation scheme interpolates `log(kdata)`. This can be changed directly using the `log_interp=False` keyword. But the default behavior can also be changed using `Settings().set_log_interp`. The keyword always supercedes the default behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bf17e95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:51.822595Z",
     "iopub.status.busy": "2022-09-02T07:49:51.821983Z",
     "iopub.status.idle": "2022-09-02T07:49:51.831750Z",
     "shell.execute_reply": "2022-09-02T07:49:51.829990Z"
    },
    "papermill": {
     "duration": 0.081792,
     "end_time": "2022-09-02T07:49:51.835952",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.754160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "logp=3.\n",
    "Temp=1000.\n",
    "tmp_opa_lin1=h2o_ktab_SI.interpolate_kdata(logp,Temp,log_interp=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "183bd1b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:51.991540Z",
     "iopub.status.busy": "2022-09-02T07:49:51.990996Z",
     "iopub.status.idle": "2022-09-02T07:49:52.005383Z",
     "shell.execute_reply": "2022-09-02T07:49:52.003976Z"
    },
    "papermill": {
     "duration": 0.100518,
     "end_time": "2022-09-02T07:49:52.008997",
     "exception": false,
     "start_time": "2022-09-02T07:49:51.908479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xk.Settings().set_log_interp(False)\n",
    "logp=3.\n",
    "Temp=1000.\n",
    "tmp_opa_lin2=h2o_ktab_SI.interpolate_kdata(logp,Temp)\n",
    "np.sum(tmp_opa_lin2-tmp_opa_lin1)\n",
    "xk.Settings().set_log_interp(True) ## let's go back to default behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a187bca7",
   "metadata": {
    "papermill": {
     "duration": 0.081373,
     "end_time": "2022-09-02T07:49:52.166810",
     "exception": false,
     "start_time": "2022-09-02T07:49:52.085437",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Linear interpolation is of course faster, but is believed to be less accurate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1332c89c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:52.298182Z",
     "iopub.status.busy": "2022-09-02T07:49:52.297259Z",
     "iopub.status.idle": "2022-09-02T07:49:52.787565Z",
     "shell.execute_reply": "2022-09-02T07:49:52.782695Z"
    },
    "papermill": {
     "duration": 0.560337,
     "end_time": "2022-09-02T07:49:52.791463",
     "exception": false,
     "start_time": "2022-09-02T07:49:52.231126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.95 ms ± 110 µs per loop (mean ± std. dev. of 2 runs, 100 loops each)\n",
      "411 µs ± 12.8 µs per loop (mean ± std. dev. of 2 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "logp=3.\n",
    "Temp=1000.\n",
    "%timeit -n 100 -r 2 h2o_ktab_SI.interpolate_kdata(logp,Temp,log_interp=True)\n",
    "%timeit -n 100 -r 2 h2o_ktab_SI.interpolate_kdata(logp,Temp,log_interp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f17f0e9",
   "metadata": {
    "papermill": {
     "duration": 0.070183,
     "end_time": "2022-09-02T07:49:52.946462",
     "exception": false,
     "start_time": "2022-09-02T07:49:52.876279",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If we just want a part of the spectrum, we can specify a **wavenumber** range (in cm^-1) to cover as follows.   \n",
    "You can see that it goes much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9967b895",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:53.086701Z",
     "iopub.status.busy": "2022-09-02T07:49:53.085843Z",
     "iopub.status.idle": "2022-09-02T07:49:53.189708Z",
     "shell.execute_reply": "2022-09-02T07:49:53.188424Z"
    },
    "papermill": {
     "duration": 0.180921,
     "end_time": "2022-09-02T07:49:53.194191",
     "exception": false,
     "start_time": "2022-09-02T07:49:53.013270",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 µs ± 963 ns per loop (mean ± std. dev. of 2 runs, 100 loops each)\n",
      "179 µs ± 3.06 µs per loop (mean ± std. dev. of 2 runs, 100 loops each)\n",
      "(1, 54, 20)\n",
      "[[[4.07753036e-29 6.85197398e-29 1.02732246e-28 ... 2.41869028e-25\n",
      "   4.85016488e-25 1.14633633e-24]\n",
      "  [5.57875921e-29 7.97288442e-29 1.21104087e-28 ... 3.42969506e-25\n",
      "   1.22902497e-24 2.53733317e-24]\n",
      "  [6.17042832e-29 9.10883868e-29 1.31081205e-28 ... 3.53333315e-25\n",
      "   8.73232110e-25 3.80127738e-24]\n",
      "  ...\n",
      "  [2.02444510e-30 3.93145431e-30 5.47901786e-30 ... 2.32477558e-27\n",
      "   7.86972907e-27 3.78365657e-26]\n",
      "  [2.33493343e-30 3.54354098e-30 4.84518937e-30 ... 2.32501304e-27\n",
      "   6.12582068e-27 2.35847913e-26]\n",
      "  [2.50675050e-30 3.68390571e-30 4.92245284e-30 ... 2.94029197e-27\n",
      "   1.10535798e-26 4.63705729e-26]]]\n"
     ]
    }
   ],
   "source": [
    "logp=3.\n",
    "Temp=1000.\n",
    "wn_range=[6000.,5000.] # does not need to be sorted\n",
    "%timeit -n 100 -r 2 h2o_ktab_SI.interpolate_kdata(logp,Temp,wngrid_limit=wn_range,log_interp=True)\n",
    "%timeit -n 100 -r 2 h2o_ktab_SI.interpolate_kdata(logp,Temp,wngrid_limit=wn_range,log_interp=False)\n",
    "tmp_opa=h2o_ktab_SI.interpolate_kdata(logp,Temp,wngrid_limit=wn_range)\n",
    "print(tmp_opa.shape)\n",
    "print(tmp_opa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d7e569",
   "metadata": {
    "papermill": {
     "duration": 0.076534,
     "end_time": "2022-09-02T07:49:53.349845",
     "exception": false,
     "start_time": "2022-09-02T07:49:53.273311",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### (Log P, T) grid Remapping "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e585381",
   "metadata": {
    "papermill": {
     "duration": 0.06472,
     "end_time": "2022-09-02T07:49:53.490546",
     "exception": false,
     "start_time": "2022-09-02T07:49:53.425826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If for computational efficiency reason we just want to use a subset of the (Log P, T) grid, we can remap the table. This will modify the Table in place. So we might want to make a copy before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3546a59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:53.651467Z",
     "iopub.status.busy": "2022-09-02T07:49:53.650732Z",
     "iopub.status.idle": "2022-09-02T07:49:53.904803Z",
     "shell.execute_reply": "2022-09-02T07:49:53.903672Z"
    },
    "papermill": {
     "duration": 0.332363,
     "end_time": "2022-09-02T07:49:53.907663",
     "exception": false,
     "start_time": "2022-09-02T07:49:53.575300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04 1.e+05]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 200.  300.  400.  500.  600.  700.  800.  900. 1000.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [   6    9 1538   20]\n",
       "        "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Small_Grid_h2o_ktab=h2o_ktab_SI.copy()\n",
    "logp_array=np.arange(6.)\n",
    "t_array=np.linspace(200.,1000.,9)\n",
    "\n",
    "Small_Grid_h2o_ktab.remap_logPT(logp_array=logp_array,t_array=t_array)\n",
    "Small_Grid_h2o_ktab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c3f69b",
   "metadata": {
    "papermill": {
     "duration": 0.08784,
     "end_time": "2022-09-02T07:49:54.078215",
     "exception": false,
     "start_time": "2022-09-02T07:49:53.990375",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Remapping in g-space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c12c60e0",
   "metadata": {
    "papermill": {
     "duration": 0.084773,
     "end_time": "2022-09-02T07:49:54.232748",
     "exception": false,
     "start_time": "2022-09-02T07:49:54.147975",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Sometimes, we also want to reduce the number of g-points, or combine two tables with different g-quadratures. For this we can use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d9d65b66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:54.376922Z",
     "iopub.status.busy": "2022-09-02T07:49:54.376338Z",
     "iopub.status.idle": "2022-09-02T07:49:54.386651Z",
     "shell.execute_reply": "2022-09-02T07:49:54.385588Z"
    },
    "papermill": {
     "duration": 0.085002,
     "end_time": "2022-09-02T07:49:54.389349",
     "exception": false,
     "start_time": "2022-09-02T07:49:54.304347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01985507, 0.10166676, 0.2372338 , 0.40828268, 0.59171732,\n",
       "       0.7627662 , 0.89833324, 0.98014493])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple function to compute a set of gauss legendre points and weights\n",
    "from exo_k.util.interp import gauss_legendre\n",
    "weights, ggrid, gedges=gauss_legendre(8)\n",
    "ggrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b81535f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:49:54.535079Z",
     "iopub.status.busy": "2022-09-02T07:49:54.534190Z",
     "iopub.status.idle": "2022-09-02T07:49:59.760698Z",
     "shell.execute_reply": "2022-09-02T07:49:59.759080Z"
    },
    "papermill": {
     "duration": 5.305011,
     "end_time": "2022-09-02T07:49:59.764609",
     "exception": false,
     "start_time": "2022-09-02T07:49:54.459598",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
       " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
       " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
       " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
       " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
       " 4.64158883e+06 1.00000000e+07]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.05061427 0.11119052 0.15685332 0.18134189 0.18134189 0.15685332\n",
       " 0.11119052 0.05061427]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [  22   27 1538    8]\n",
       "        "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_ktab_8g=h2o_ktab_SI.copy()\n",
    "h2o_ktab_8g.remap_g(ggrid=ggrid, weights=weights)\n",
    "h2o_ktab_8g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1497aea",
   "metadata": {
    "papermill": {
     "duration": 0.076859,
     "end_time": "2022-09-02T07:49:59.909326",
     "exception": false,
     "start_time": "2022-09-02T07:49:59.832467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The table now has only 8 g points. You can of course specify custom g-points and weights if you want."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea5fa36",
   "metadata": {
    "papermill": {
     "duration": 0.077815,
     "end_time": "2022-09-02T07:50:00.066067",
     "exception": false,
     "start_time": "2022-09-02T07:49:59.988252",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Extending the spectral range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504d5ef8",
   "metadata": {
    "papermill": {
     "duration": 0.072484,
     "end_time": "2022-09-02T07:50:00.234336",
     "exception": false,
     "start_time": "2022-09-02T07:50:00.161852",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we will see afterwards, we need tables to have the same spectral grid to be able to combine them. If they cover the same spectral range, but with different resolution, the solution is to bin them down as explained a few sections down. If, however, we want to combine molecules that are absorbing in rather different part of the spectrum, we may often encounter tables that cover only the spectral region where each molecule has a significant absorption. \n",
    "\n",
    "To circumvent this issue, `exo_k` allows you to extend the spectral range of your table adding a null opacity (or a very small one if one wants to use log interpolation afterwards) in the new spectral bins. This can be done as follows. It changes the table in place. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21790266",
   "metadata": {
    "papermill": {
     "duration": 0.081795,
     "end_time": "2022-09-02T07:50:00.430158",
     "exception": false,
     "start_time": "2022-09-02T07:50:00.348363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are 3 options to specify the left and/or right extension of the spectral grid:\n",
    "\n",
    "  * The `wngrid_left/right` keyword allows you to specify the center of the new bins.\n",
    "    The edges are computed automatically. This option is most useful with\n",
    "    `Xtables` for which you most want to control the center of the bins.\n",
    "  * The `wnedges_left/right` keyword allows you to specify the the edges of the new spectral bin.\n",
    "    Bin centers are computed automatically. This option is most useful with\n",
    "    `Ktables` for which you most want to control the edges of the bins.\n",
    "    Since we are re-using the last wnedges on the left and right of the current\n",
    "    grid, the user only needs to specify `N` new values to create `N`new bins. \n",
    "  * You can specify both at the same time."
   ]
  },
  {
   "cell_type": "raw",
   "id": "d5b934d7",
   "metadata": {
    "papermill": {
     "duration": 0.068789,
     "end_time": "2022-09-02T07:50:00.589143",
     "exception": false,
     "start_time": "2022-09-02T07:50:00.520354",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    The user must make sure that the new left and right wavenumber grids do not overlap with the current one.\n",
    "    If both wnedges and wngrids are specified, the user should also make sure that there is one \n",
    "    (and only one) wavenumber point\n",
    "    between each pair of wnegdes points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "65ccf47b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:00.740658Z",
     "iopub.status.busy": "2022-09-02T07:50:00.740094Z",
     "iopub.status.idle": "2022-09-02T07:50:03.191512Z",
     "shell.execute_reply": "2022-09-02T07:50:03.190374Z"
    },
    "papermill": {
     "duration": 2.524213,
     "end_time": "2022-09-02T07:50:03.194742",
     "exception": false,
     "start_time": "2022-09-02T07:50:00.670529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old wn grid:  [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
      " 33167.31250795 33277.87021631]\n",
      "New wn grid:  [1.00000000e+01 1.94736842e+01 2.89473684e+01 ... 4.82526316e+04\n",
      " 4.91263158e+04 5.00000000e+04]\n",
      "Old wn edges:  [  199.57138937   200.23662733   200.90408276 ... 33112.21730444\n",
      " 33222.59136213 33333.33333333]\n",
      "New wn edges:  [5.00000000e+00 1.44736842e+01 2.39473684e+01 ... 4.83026316e+04\n",
      " 4.91763158e+04 5.00500000e+04]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "large_spectral_range_h2o=h2o_ktab_SI.copy()\n",
    "new_wn_points_left=np.linspace(10,190,20)\n",
    "new_wn_edges_left=np.linspace(5,185,20)\n",
    "new_wn_points_right=np.linspace(33400,50000,20)\n",
    "new_wn_edges_right=np.linspace(33450,50050,20)\n",
    "\n",
    "# specifying only wngrid\n",
    "#large_spectral_range_h2o.extend_spectral_range(wngrid_left=new_wn_points_left, wngrid_right=new_wn_points_right, remove_zeros=True)\n",
    "large_spectral_range_h2o.extend_spectral_range(wngrid_left=new_wn_points_left, wnedges_left=new_wn_edges_left, \n",
    "                                               wngrid_right=new_wn_points_right, wnedges_right=new_wn_edges_right, remove_zeros=True)\n",
    "#large_spectral_range_h2o.extend_spectral_range( wnedges_left=new_wn_edges_left,wnedges_right=new_wn_edges_right, remove_zeros=True)\n",
    "print('Old wn grid: ',h2o_ktab_SI.wns)\n",
    "print('New wn grid: ',large_spectral_range_h2o.wns)\n",
    "print('Old wn edges: ',h2o_ktab_SI.wnedges)\n",
    "print('New wn edges: ',large_spectral_range_h2o.wnedges)\n",
    "\n",
    "p_plot=1.e5\n",
    "t_plot=1000.\n",
    "fig,ax=plt.subplots(1,1,sharex=False,sharey=False,figsize=(6,4.5))\n",
    "large_spectral_range_h2o.plot_spectrum(ax,p=p_plot,t=t_plot,g=1.,yscale='log',xscale='log',color='black',linestyle='dashed',label='after extension')\n",
    "h2o_ktab_SI.plot_spectrum(ax,p=p_plot,t=t_plot,g=1.,label='before extension')\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21aeae8c",
   "metadata": {
    "papermill": {
     "duration": 0.098047,
     "end_time": "2022-09-02T07:50:03.368645",
     "exception": false,
     "start_time": "2022-09-02T07:50:03.270598",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Clipping data to a given spectral range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d57ed0",
   "metadata": {
    "papermill": {
     "duration": 0.082762,
     "end_time": "2022-09-02T07:50:03.552021",
     "exception": false,
     "start_time": "2022-09-02T07:50:03.469259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If for effciency reasons you want to only deal with a small part of the spectral domain, you can use the `Ktable.clip_spectral_range(wl_range= , wn_range= )` method with either a wavelength range (`wl_range`in microns) or a wavenumber range (`wn_range` in cm^-1).\n",
    "Data outside this range will be removed from the table. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c765c15",
   "metadata": {
    "papermill": {
     "duration": 0.070352,
     "end_time": "2022-09-02T07:50:03.702833",
     "exception": false,
     "start_time": "2022-09-02T07:50:03.632481",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Binning down a `Ktable`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b7c154",
   "metadata": {
    "papermill": {
     "duration": 0.071193,
     "end_time": "2022-09-02T07:50:03.843336",
     "exception": false,
     "start_time": "2022-09-02T07:50:03.772143",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We now get to one of the core features of `exo_k`: we want to create a new `Ktable()` object with just the spectral resolution we need in the model we want to run (whether it is a Global Climate Model, a 1D climate model, or a simple forward model for a retrieval).  \n",
    "\n",
    "You first need to provide a grid, `wnedges`, containing the edges of the new wavenumber bins. So the final Ktable will have a spectral dimension with size `wnedges.size-1`.\n",
    "\n",
    "You can of course provide any custom grid you want. But if you want a grid at fixed resolution you can use the following\n",
    "```python\n",
    "xk.wavenumber_grid_R(min_wavenumber, max_wavenumber, R)\n",
    "```\n",
    "where $R=\\frac{\\nu}{\\delta \\nu}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fa683e40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:04.017228Z",
     "iopub.status.busy": "2022-09-02T07:50:04.016349Z",
     "iopub.status.idle": "2022-09-02T07:50:04.028175Z",
     "shell.execute_reply": "2022-09-02T07:50:04.026863Z"
    },
    "papermill": {
     "duration": 0.116828,
     "end_time": "2022-09-02T07:50:04.037987",
     "exception": false,
     "start_time": "2022-09-02T07:50:03.921159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  200.        ,   221.03418362,   244.28055163,   269.97176152,\n",
       "         298.36493953,   329.74425414,   364.42376008,   402.75054149,\n",
       "         445.1081857 ,   491.92062223,   543.65636569,   600.83320479,\n",
       "         664.02338455,   733.85933352,   811.03999337,   896.33781407,\n",
       "         990.60648488,  1094.78947835,  1209.92949288,  1337.17888846,\n",
       "        1477.81121979,  1633.23398251,  1805.00269989,  1994.83649096,\n",
       "        2204.63527613,  2436.49879214,  2692.747607  ,  2975.94634497,\n",
       "        3288.92935422,  3634.82907389,  4017.10738464,  4439.59025629,\n",
       "        4906.50603942,  5422.52778413,  5992.82000948,  6623.09039174,\n",
       "        7319.64688874,  8089.46087201,  8940.23689866,  9880.48982111,\n",
       "       10000.        ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R=10\n",
    "wavenumber_grid=xk.wavenumber_grid_R(200.,10000.,R)\n",
    "wavenumber_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccf4292",
   "metadata": {
    "papermill": {
     "duration": 0.071831,
     "end_time": "2022-09-02T07:50:04.240158",
     "exception": false,
     "start_time": "2022-09-02T07:50:04.168327",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then we just need to use the `bin_down(wnedges)` method. However, bin_down modifies the `Ktable()` in place. So again, if we want to keep the original one in memory, we just copy it or use `bin_down_cp`.  \n",
    "\n",
    "Optional keywords can be `weights=` and `ggrid=` where you can provide the weights you want (and the corresponding g points). In general, for an LMDZ user, just input the values inside g.dat (except for the final zero) in a `w_array`, and use `weights=w_array`. If you want gauss legendre weights, you can have a look at `exo_k.gauss_legendre` in the index."
   ]
  },
  {
   "cell_type": "raw",
   "id": "1e193b26",
   "metadata": {
    "papermill": {
     "duration": 0.07314,
     "end_time": "2022-09-02T07:50:04.383561",
     "exception": false,
     "start_time": "2022-09-02T07:50:04.310421",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. seealso::\n",
    "    :func:`exo_k.ktable.Ktable.bin_down` :func:`~exo_k.data_table.Data_table.bin_down_cp`\n",
    "    for details on arguments and options."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2184068a",
   "metadata": {
    "papermill": {
     "duration": 0.075006,
     "end_time": "2022-09-02T07:50:04.531264",
     "exception": false,
     "start_time": "2022-09-02T07:50:04.456258",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It should only take a few seconds. (However, the bigger the final resolution wanted, the longer it takes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d028b36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:04.698224Z",
     "iopub.status.busy": "2022-09-02T07:50:04.697492Z",
     "iopub.status.idle": "2022-09-02T07:50:20.787497Z",
     "shell.execute_reply": "2022-09-02T07:50:20.786194Z"
    },
    "papermill": {
     "duration": 16.164757,
     "end_time": "2022-09-02T07:50:20.790303",
     "exception": false,
     "start_time": "2022-09-02T07:50:04.625546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computation time= 16.077264547348022 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
       " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
       " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
       " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
       " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
       " 4.64158883e+06 1.00000000e+07]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [ 210.51709181  232.65736762  257.12615657  284.16835052  314.05459683\n",
       "  347.08400711  383.58715079  423.9293636   468.51440396  517.78849396\n",
       "  572.24478524  632.42829467  698.94135904  772.44966345  853.68890372\n",
       "  943.47214947 1042.69798161 1152.35948561 1273.55419067 1407.49505412\n",
       " 1555.52260115 1719.1183412  1899.91959542 2099.73588355 2320.56703413\n",
       " 2564.62319957 2834.34697599 3132.4378496  3461.87921405 3825.96822926\n",
       " 4228.34882046 4673.04814786 5164.51691178 5707.67389681 6307.95520061\n",
       " 6971.36864024 7704.55388037 8514.84888534 9410.36335988 9940.24491055]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [22 27 40 20]\n",
       "        "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=time.time()\n",
    "LowRes_h2o_ktab=h2o_ktab_SI.bin_down_cp(wavenumber_grid)\n",
    "end=time.time()\n",
    "print('computation time=',end-start,'s')\n",
    "LowRes_h2o_ktab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cba89627",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:20.928239Z",
     "iopub.status.busy": "2022-09-02T07:50:20.927144Z",
     "iopub.status.idle": "2022-09-02T07:50:21.975550Z",
     "shell.execute_reply": "2022-09-02T07:50:21.974336Z"
    },
    "papermill": {
     "duration": 1.121623,
     "end_time": "2022-09-02T07:50:21.978992",
     "exception": false,
     "start_time": "2022-09-02T07:50:20.857369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_plot=1.e0\n",
    "t_plot=300.\n",
    "fig,axs=plt.subplots(1,2,sharex=False,sharey=False)\n",
    "h2o_ktab_SI.plot_spectrum(axs[0],p=p_plot,t=t_plot,g=1.,yscale='log',xscale='log',label='Original data')\n",
    "LowRes_h2o_ktab.plot_spectrum(axs[0],p=p_plot,t=t_plot,g=1.,label='Binned data')\n",
    "h2o_ktab_SI.plot_spectrum(axs[1],p=p_plot,t=t_plot,g=1.,yscale='log',xscale='log',label='Original data')\n",
    "LowRes_h2o_ktab.plot_spectrum(axs[1],p=p_plot,t=t_plot,g=1.,label='Binned data')\n",
    "axs[1].set_xlim(left=1.,right=6.)\n",
    "axs[1].set_xscale('linear')\n",
    "for ax in axs:\n",
    "    ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf096b2",
   "metadata": {
    "papermill": {
     "duration": 0.067832,
     "end_time": "2022-09-02T07:50:22.115103",
     "exception": false,
     "start_time": "2022-09-02T07:50:22.047271",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Rescaling and mixing `Ktable`s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c20b26a",
   "metadata": {
    "papermill": {
     "duration": 0.067816,
     "end_time": "2022-09-02T07:50:22.251953",
     "exception": false,
     "start_time": "2022-09-02T07:50:22.184137",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The `Ktable` we have seen up to now contain opacity data (in terms of cross section in surface per molecule) for pure species. To create a mix, you can dilute your tables with (multiply by) the Volume mixing ratio of the relevant species and add them."
   ]
  },
  {
   "cell_type": "raw",
   "id": "42f684fe",
   "metadata": {
    "papermill": {
     "duration": 0.069101,
     "end_time": "2022-09-02T07:50:22.389695",
     "exception": false,
     "start_time": "2022-09-02T07:50:22.320594",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "    Although we are using the '+' sign,\n",
    "    the tables are actually combined using the Random overlap method."
   ]
  },
  {
   "cell_type": "raw",
   "id": "ae72099b",
   "metadata": {
    "papermill": {
     "duration": 0.068678,
     "end_time": "2022-09-02T07:50:22.526324",
     "exception": false,
     "start_time": "2022-09-02T07:50:22.457646",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    `Ktable`s should have the same dimensions and grids to be combined.\n",
    "    For efficiency, the code only checks whether the dimensions are the same\n",
    "    (and raises a `TypeError` if they are not).\n",
    "    The user should make sure the grids are the same as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b17eb531",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:22.707748Z",
     "iopub.status.busy": "2022-09-02T07:50:22.707355Z",
     "iopub.status.idle": "2022-09-02T07:50:29.440796Z",
     "shell.execute_reply": "2022-09-02T07:50:29.439705Z"
    },
    "papermill": {
     "duration": 6.826265,
     "end_time": "2022-09-02T07:50:29.443305",
     "exception": false,
     "start_time": "2022-09-02T07:50:22.617040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mixing time= 2.384451389312744 s\n"
     ]
    }
   ],
   "source": [
    "h2o=xk.Ktable('H2O_R300_0.3-50mu.ktable.SI', remove_zeros=True)\n",
    "co2=xk.Ktable('CO2_R300_0.3-50mu.ktable.SI', remove_zeros=True)\n",
    "for ktab in [co2,h2o]:\n",
    "    ktab.remap_logPT(logp_array=np.arange(0,5),t_array=np.arange(200.,500.,50.))\n",
    "start=time.time()\n",
    "mix=0.01*h2o+co2*0.02\n",
    "print('Mixing time=',time.time()-start,'s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab2a54ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:29.586257Z",
     "iopub.status.busy": "2022-09-02T07:50:29.585319Z",
     "iopub.status.idle": "2022-09-02T07:50:30.435246Z",
     "shell.execute_reply": "2022-09-02T07:50:30.434030Z"
    },
    "papermill": {
     "duration": 0.925808,
     "end_time": "2022-09-02T07:50:30.437832",
     "exception": false,
     "start_time": "2022-09-02T07:50:29.512024",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "p_plot=1.e0\n",
    "t_plot=300.\n",
    "fig,ax=plt.subplots(1,1,sharex=False,sharey=False)\n",
    "h2o.plot_spectrum(ax,p=p_plot,t=t_plot,g=1.,x=0.01,yscale='log',xscale='log',label='H2O')\n",
    "co2.plot_spectrum(ax,p=p_plot,t=t_plot,g=1.,x=0.01,label='CO2')\n",
    "mix.plot_spectrum(ax,p=p_plot,t=t_plot,g=1.,yscale='log',xscale='log',label='mix')\n",
    "ax.set_ylim(bottom=1.e-36)\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "557b03f8",
   "metadata": {
    "papermill": {
     "duration": 0.080429,
     "end_time": "2022-09-02T07:50:30.588129",
     "exception": false,
     "start_time": "2022-09-02T07:50:30.507700",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    " For the moment, the `Ktable` for the mix inherits attributes (such as the name of the molecule)\n",
    " from the most leftward `Ktable` in the operation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54082271",
   "metadata": {
    "papermill": {
     "duration": 0.0727,
     "end_time": "2022-09-02T07:50:30.730850",
     "exception": false,
     "start_time": "2022-09-02T07:50:30.658150",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The renormalization operation also accepts an array of volume mixing ratio, but only if it has the dimensions `(Number of P points, number of T points)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e57bcbc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:30.873347Z",
     "iopub.status.busy": "2022-09-02T07:50:30.872855Z",
     "iopub.status.idle": "2022-09-02T07:50:32.919939Z",
     "shell.execute_reply": "2022-09-02T07:50:32.918726Z"
    },
    "papermill": {
     "duration": 2.122631,
     "end_time": "2022-09-02T07:50:32.923588",
     "exception": false,
     "start_time": "2022-09-02T07:50:30.800957",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_h2o=np.array([0.01+0.02*logP+0.00001*T for logP in h2o.logpgrid for T in h2o.tgrid]).reshape((h2o.Np,h2o.Nt))\n",
    "mix=x_h2o*h2o+co2*0.02"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59afe758",
   "metadata": {
    "papermill": {
     "duration": 0.071064,
     "end_time": "2022-09-02T07:50:33.071281",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.000217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">Dealing with mixes: The `Kdatabase()` object </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c397dc",
   "metadata": {
    "papermill": {
     "duration": 0.077008,
     "end_time": "2022-09-02T07:50:33.220278",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.143270",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading a `Kdatabase()` object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92cf90d",
   "metadata": {
    "papermill": {
     "duration": 0.069942,
     "end_time": "2022-09-02T07:50:33.360239",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.290297",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Global loading from files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2353536",
   "metadata": {
    "papermill": {
     "duration": 0.072233,
     "end_time": "2022-09-02T07:50:33.504408",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.432175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A `Kdatabase()` object mainly contains a dictionary of `Ktable()` objects identified by the name of their molecule (in the attribute `self.ktables`, but they can be accessed with `self['mol_name']`). In addition, most of the attributes of the `Ktable()` are reloaded as attributes of `Kdatabase()`.\n",
    "\n",
    "To load a database, just specify a list of molecules and a string filter that is common to all your filenames."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7957d830",
   "metadata": {
    "papermill": {
     "duration": 0.070831,
     "end_time": "2022-09-02T07:50:33.646005",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.575174",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For details on the available arguments and options, check-out :func:`exo_k.kdatabase.Kdatabase`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "36d4e973",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:33.809602Z",
     "iopub.status.busy": "2022-09-02T07:50:33.808763Z",
     "iopub.status.idle": "2022-09-02T07:50:38.087665Z",
     "shell.execute_reply": "2022-09-02T07:50:38.086511Z"
    },
    "papermill": {
     "duration": 4.365017,
     "end_time": "2022-09-02T07:50:38.090187",
     "exception": false,
     "start_time": "2022-09-02T07:50:33.725170",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The available molecules are: \n",
       "H2O->/builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "CO2->/builds/jleconte/exo_k/data/corrk/CO2_R300_0.3-50mu.ktable.SI.h5\n",
       "All tables share a common spectral grid\n",
       "All tables share a common logP-T grid\n",
       "All tables share a common p unit\n",
       "All tables share a common kdata unit"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecs=['H2O','CO2']\n",
    "filt='R300_0.3-50mu.ktable.SI'\n",
    "database=xk.Kdatabase(molecs, filt)\n",
    "database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc41dd8",
   "metadata": {
    "papermill": {
     "duration": 0.067341,
     "end_time": "2022-09-02T07:50:38.225362",
     "exception": false,
     "start_time": "2022-09-02T07:50:38.158021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It will look for files starting with the molecule names, followed by `_`, `.`, or `-`, and containing the filters.\n",
    "\n",
    "If you want the moleculer name to be followed by something else than these characters, then just change the setting by using the `xk.Settings().set_delimiters` method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be71b5b",
   "metadata": {
    "papermill": {
     "duration": 0.06402,
     "end_time": "2022-09-02T07:50:38.352697",
     "exception": false,
     "start_time": "2022-09-02T07:50:38.288677",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Of course, this is unpractical when you do not have a homogeneous set of Ktables to start with. Then you can just feed a dictionary of molecules with the associated files path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "288b28a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:38.485960Z",
     "iopub.status.busy": "2022-09-02T07:50:38.485582Z",
     "iopub.status.idle": "2022-09-02T07:50:42.488461Z",
     "shell.execute_reply": "2022-09-02T07:50:42.487299Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The available molecules are: \n",
       "H2O->../data/corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5\n",
       "CO2->../data/corrk/CO2_R300_0.3-50mu.ktable.SI.h5\n",
       "All tables share a common spectral grid\n",
       "All tables share a common logP-T grid\n",
       "All tables share a common p unit\n",
       "All tables share a common kdata unit"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database=xk.Kdatabase({'H2O':datapath + 'corrk/H2O_R300_0.3-50mu.ktable.TauREx.h5',\n",
    "                       'CO2':datapath + 'corrk/CO2_R300_0.3-50mu.ktable.SI.h5'},\n",
    "                       p_unit='Pa', kdata_unit='m^2/molecule')\n",
    "database"
   ]
  },
  {
   "cell_type": "raw",
   "id": "baed1549",
   "metadata": {
    "papermill": {
     "duration": 0.067106,
     "end_time": "2022-09-02T07:50:42.626867",
     "exception": false,
     "start_time": "2022-09-02T07:50:42.559761",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "  The latest release of Exomol data is using a new convention to write molecule names in filenames to include isotopologues.\n",
    "  For example, the name of a file describing the water molecule now starts with '1H2-16O' instead of 'H2O'.\n",
    "  To use these, use the new name in the method call (`database=xk.Kdatabase(['1H2-16O','12C-16O2'], filt)`).\n",
    "  However, be aware that inside the database object, individual `Ktable`s are recognized and called by the name\n",
    "  of the molecule that is stored *inside* the file ('H2O' and 'CO2' in our case; see the accessing individual `Ktable`s section below).\n",
    "  \n",
    "  For FITS and Nemesis formats that do not yet include the molecule name inside the file, we recommend that you use the second loading method:\n",
    "  `database=xk.Kdatabase({'H2O': datapath + 'corrk/1H2-16O__POKAZATEL__R1000_0.3-50mu.ktable.ARCiS.fits', etc.})`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1398c4",
   "metadata": {
    "papermill": {
     "duration": 0.065176,
     "end_time": "2022-09-02T07:50:42.757784",
     "exception": false,
     "start_time": "2022-09-02T07:50:42.692608",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Adding individual, already-loaded `Ktable`s "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2460acb0",
   "metadata": {
    "papermill": {
     "duration": 0.071283,
     "end_time": "2022-09-02T07:50:42.899168",
     "exception": false,
     "start_time": "2022-09-02T07:50:42.827885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Sometimes, you want to modify/remap/bin_down your `Ktable`s before loading it into a `Kdatabase`. However, you do not necessarily want to have to store these intermediate steps into a file to read it into a database as described above.\n",
    "\n",
    "To do this, you can add one or several `Ktable` using the `add_ktables(*ktables)` method.\n",
    "If you want to start from scratch, you'll need to create an empty `Kdatabase` as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3ef9303c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:43.041689Z",
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     "shell.execute_reply": "2022-09-02T07:50:47.165671Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The available molecules are: \n",
      "All tables share a common spectral grid\n",
      "All tables share a common logP-T grid\n",
      "All tables share a common p unit\n",
      "All tables share a common kdata unit\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Careful, not all tables have the same logPT grid.\n",
      "                        You'll need to use remap_logPT\n",
      "The available molecules are: \n",
      "H2O->/builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
      "CO2->/builds/jleconte/exo_k/data/corrk/CO2_R300_0.3-50mu.ktable.SI.h5\n",
      "All tables share a common spectral grid\n",
      "All tables do NOT have common logP-T grid\n",
      "You will need to run remap_logPT to perform some operations\n",
      "All tables share a common p unit\n",
      "All tables share a common kdata unit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "database=xk.Kdatabase(None) # create empty database\n",
    "print(database)\n",
    "\n",
    "h2o=xk.Ktable('H2O_R300_0.3-50mu.ktable.SI', remove_zeros=True)\n",
    "co2=xk.Ktable('CO2_R300_0.3-50mu.ktable.SI', remove_zeros=True)\n",
    "co2.remap_logPT(logp_array=np.arange(0,5),t_array=np.arange(200.,500.,50.))\n",
    "\n",
    "database.add_ktables(h2o, co2)\n",
    "print(database)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d973706b",
   "metadata": {
    "papermill": {
     "duration": 0.074101,
     "end_time": "2022-09-02T07:50:47.318089",
     "exception": false,
     "start_time": "2022-09-02T07:50:47.243988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Dealing with inhomogeneous databases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3f5f19",
   "metadata": {
    "papermill": {
     "duration": 0.07171,
     "end_time": "2022-09-02T07:50:47.466814",
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     "status": "completed"
    },
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   },
   "source": [
    "As we are loading `Ktables` from various possible sources, that we may have modified however we like, we are bound to end up with a inhomogeneous database because all the `Ktable`:\n",
    "\n",
    "  * do not have the same datatype.\n",
    "  * do not have the same weights and g grid,\n",
    "  * do not have the same logP-T grid,\n",
    "  * do not have the same spectral grid,\n",
    "  * do not have the same units\n",
    "\n",
    "For the moment, a `Kdatabase` needs to be homogeneous to be used to create mixes or compute forward models (see below).\n",
    "\n",
    "The 1st issue is due to the fact that `exo_k` can handle other types of data (e.g. `Xtable` that are regular cross section tables; see dedicated section below). Although these data types can be handled in a database, a `Kdatabase` can only deal with a single type at a time. Loading different types will result in an error.  \n",
    "\n",
    "The 2nd issue is delicate because there are no easy way to accurately re-interpolate a `Ktable` on a different g grid. So for the moment, there is no way to remedy this. The user needs to provide `Ktable` with the same g grid and weights. Loading tables with different g grids will result in an error. \n",
    "  \n",
    " The remaining issues are easily handled by following the suggestion in the warning message (see above):\n",
    " \n",
    "   * Using `Kdatabase.remap_logPT(logp_array= , t_array= )` will homogenize the log P-T grid by\n",
    "     interpolating (in place) all the `Ktables` to a common grid.\n",
    "   * Using `Kdatabase.bin_down(wnedges= , **kwargs)` will homogenize the spectral grid by\n",
    "     binning down (in place) all the `Ktables` to a common grid.\n",
    "   * If you are using cross sections, the spectral grid can be homogenized using\n",
    "     `Kdatabase.sample(wngrid= )` instead of bin_down (see below).\n",
    "   * All the `Ktable`s can be converted to the same units using `Kdatabase.convert_p_unit()` and\n",
    "     `Kdatabase.convert_kdata_unit()`, or directly `Kdatabase.convert_to_mks()`\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "899a7631",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:47.659875Z",
     "iopub.status.busy": "2022-09-02T07:50:47.659473Z",
     "iopub.status.idle": "2022-09-02T07:50:47.786493Z",
     "shell.execute_reply": "2022-09-02T07:50:47.785384Z"
    },
    "papermill": {
     "duration": 0.202344,
     "end_time": "2022-09-02T07:50:47.788915",
     "exception": false,
     "start_time": "2022-09-02T07:50:47.586571",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The available molecules are: \n",
      "H2O->/builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
      "CO2->/builds/jleconte/exo_k/data/corrk/CO2_R300_0.3-50mu.ktable.SI.h5\n",
      "All tables share a common spectral grid\n",
      "All tables do NOT have common logP-T grid\n",
      "You will need to run remap_logPT to perform some operations\n",
      "All tables share a common p unit\n",
      "All tables share a common kdata unit\n",
      "\n",
      "remapping...\n",
      "The available molecules are: \n",
      "H2O->/builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
      "CO2->/builds/jleconte/exo_k/data/corrk/CO2_R300_0.3-50mu.ktable.SI.h5\n",
      "All tables share a common spectral grid\n",
      "All tables share a common logP-T grid\n",
      "All tables share a common p unit\n",
      "All tables share a common kdata unit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(database)\n",
    "print('remapping...')\n",
    "database.remap_logPT(logp_array=np.arange(0,5),t_array=np.arange(200.,500.,50.))\n",
    "print(database)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1189a722",
   "metadata": {
    "papermill": {
     "duration": 0.069013,
     "end_time": "2022-09-02T07:50:47.928899",
     "exception": false,
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     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "  A `Ktable` is not deep copied when loaded into a database! Modifying the database inplace thus modifies the original table as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "89d7e204",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:48.064688Z",
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     "shell.execute_reply": "2022-09-02T07:50:48.072013Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [200. 250. 300. 350. 400. 450.]\n",
       "        wn grid      : [  199.90345491   200.56979976   201.23836576 ... 33057.12210094\n",
       " 33167.31250795 33277.87021631]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
       " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
       " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
       " 0.02030071 0.008807  ]\n",
       "        data oredered following p, t, wn, g\n",
       "        shape        : [   5    6 1538   20]\n",
       "        "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See how the values in the P and T grids have changed\n",
    "# compared to their values in the previous section.\n",
    "h2o"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65514fc4",
   "metadata": {
    "papermill": {
     "duration": 0.071169,
     "end_time": "2022-09-02T07:50:48.216061",
     "exception": false,
     "start_time": "2022-09-02T07:50:48.144892",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Accessing individual `Ktables`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a50f2d2",
   "metadata": {
    "papermill": {
     "duration": 0.07478,
     "end_time": "2022-09-02T07:50:48.369840",
     "exception": false,
     "start_time": "2022-09-02T07:50:48.295060",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A `Kdatabase` is essentially a dictionary and individual `Ktable` objects can be accessed using the molecule name as a key. When adding `Ktable`s one by one, the molecule name used as a key is inherited from the `Ktable.mol` attribute. If you are loading a new `Ktable` with a molecule name that is already in the database, the latter will be replaced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a9d2fe64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:50:48.517749Z",
     "iopub.status.busy": "2022-09-02T07:50:48.517332Z",
     "iopub.status.idle": "2022-09-02T07:50:48.524691Z",
     "shell.execute_reply": "2022-09-02T07:50:48.523564Z"
    },
    "papermill": {
     "duration": 0.085427,
     "end_time": "2022-09-02T07:50:48.527483",
     "exception": false,
     "start_time": "2022-09-02T07:50:48.442056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/builds/jleconte/exo_k/data/corrk/H2O_R300_0.3-50mu.ktable.SI.h5'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database['H2O'].filename"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f0f19999",
   "metadata": {
    "papermill": {
     "duration": 0.066615,
     "end_time": "2022-09-02T07:50:48.667608",
     "exception": false,
     "start_time": "2022-09-02T07:50:48.600993",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    You **cannot** load a new table this way!\n",
    "\n",
    "    ```python\n",
    "    database['SiO']=SiO_ktable\n",
    "    TypeError: 'Kdatabase' object does not support item assignment\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ae3c1a",
   "metadata": {
    "papermill": {
     "duration": 0.072205,
     "end_time": "2022-09-02T07:50:48.806324",
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     "start_time": "2022-09-02T07:50:48.734119",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Create a `Ktable()` for a mix of gases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ed1da2",
   "metadata": {
    "papermill": {
     "duration": 0.065122,
     "end_time": "2022-09-02T07:50:48.939371",
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     "start_time": "2022-09-02T07:50:48.874249",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "One of the main interests of databases is to actually be able to create new correlated-k tables for mixtures of gases (the other being running a full atmospheric model as we will see at the end). \n",
    "\n",
    "Just create a dictionary with the names of the molecules and the corresponding volume mixing ratios. Notice that the sum of the mixing ratios does not need to be equal to one. In that case, it is assumed that the rest of the gas is composed of radiatively inactive species. Then, the opacity given is in m^2 per total number of molecules (couting the inactive ones).\n",
    "\n",
    "If a molecule is not in the database, it will just be considered radiatively inactive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "35bbff70",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computation time  42.86336851119995 s\n"
     ]
    }
   ],
   "source": [
    "molecs=['H2O','CO2']\n",
    "suffix='R300_0.3-50mu.ktable.SI'\n",
    "database=xk.Kdatabase(molecs,suffix)\n",
    "\n",
    "composition={'H2O':0.001,'CO2':400.e-6,'N2':'background'}\n",
    "\n",
    "start=time.time()\n",
    "mixH2O_CO2=database.create_mix_ktable(composition)\n",
    "\n",
    "mixH2O_CO2.write_hdf5(datapath + 'corrk/mix_H2O_CO2')\n",
    "\n",
    "end=time.time()\n",
    "print(\"computation time \",end - start,'s')        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104f4789",
   "metadata": {
    "papermill": {
     "duration": 0.070258,
     "end_time": "2022-09-02T07:51:35.967788",
     "exception": false,
     "start_time": "2022-09-02T07:51:35.897530",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here, the `'N2':'background'`in the composition dictionary tells the code that N2 is the background 'filler' gas. So whatever the vmr given before, N2 will be adjusted to get a total vmr of 1. Because N2 is not in the database (because it does not have any strong lines), its opacity will however not be considered.\n",
    "\n",
    "(Why doing this you might ask? Well, here including N2 does not change anything as it is considered transparent anyway. This will become important however when we will deal with atmospheric models where including N2 will allow us to have the right mean molecular weight for the atmosphere). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b797a904",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:36.110380Z",
     "iopub.status.busy": "2022-09-02T07:51:36.109302Z",
     "iopub.status.idle": "2022-09-02T07:51:37.209882Z",
     "shell.execute_reply": "2022-09-02T07:51:37.208757Z"
    },
    "papermill": {
     "duration": 1.17397,
     "end_time": "2022-09-02T07:51:37.212939",
     "exception": false,
     "start_time": "2022-09-02T07:51:36.038969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z21wnFIl9Ej2vq/L9WlF0oMNup6qeNzVQZ0qiZaLaNcZRpXa8/fzMy1h+/OzOO46BgUGXEYkH9SSqUKw7F/d/wIxIdi6K4vvAMmCUKIoFoiheJUmSE7UZ4g+oQrQfSZLUPhG4PzA7lqzks7/fE3YdZ2srm6+8hvx/SSx8/MWw65qV/WjV7lMH5Z/5NnTIgHbv7odLr+34WPwwOVpQ7FEc+dTtCInJ9MmMR5ZlMmvLsPp3HTQwMOiVRJpm/hCqBh+omXNJkexckqQLQyz/Fvg2kn0Y6Nl91z0MskBdWQXx6alB19m32VuYGvfD13D79SH3tz8369Jd3rkrxc9AmTZtaPf++ld1bC4sGCbZiWxVEzyj+6ZjdjTx8ZV/p/fnthkYGLiJxIPKBeZor2cBtwFGqX0Pkao9Uux654OQ69SWlEe8P+t+iILIrd46KH8D5U9DUnBj2lWYcCLYVAPlNNup21PEoMI8z+ef3RTeCzUwMOh5IjFQdwOHoYb47gAGa8sMehDnN1+H/KyhLLA1eShsQc6ApvrI6oF8220ghzdQx37ybsRj6gyi7Cb6TcgCoHxfHQ3FFVhd3p5Vgzau7NbxGBgYtJ82DZQkSV+jyhrdoP2MlySp86WnDSLClZDY5jr1JZGlVMtOJ2YhMMZXmpsX0fa+WnyDJnmbdpTt2hvR9l2F4nKBo5X4AekACDYbJgx1CAODA42Qc1B+faAA3IUq40VRHK/VIhl0NxFkxzWVlodsfeGL4gjeBbe1NjI9Pl8PSlC8xmrti6+REtEeuobWWtUDbG5SjZJgs2NmP5JBDAwMeoRwSRL+faDcCNrynmiS+oentbHJ0wHX2dqKxWYLWOeww0ZTvHJxm/tyNjYBEH/2WewojWPb+/OZMXAfyfbI/rVR8VHUaq8rdpV4mhKmrO3aNu5tUbNVbfFRll/NUECw27GaDANlYHCgEc5A+faBMuglCC6XJ+G/sbqOhIzA5AP/FuUupxOzJfBfXVKgOsVb95azd1MddrPqcey56x7S5rcdxU3ql4hbybemuKodfwWUuwTSzIGn16qZsznsx68DlMfbQ/7dqi6dJTYWgJLiMoZ2eG8GBgY9RUgDJUnS/e7XmgL5SO3tdkmSjIB+D2HxMT5mJfi/Yfe2PHz9qvz1W8g6LLA7Skt9o/q7vJm9a4qJtUYHrBMWxbcOqn0eis1mA1dwFYuWugaiktojlBQca4LaUDGtJm//8ukNDAx6hEiUJMaiFtRu1H42i6I4uqsHZhCcfLO3260lxHzUvjxVOSo3Q01cMIXwRloaVAMlWKwAOFyRKl+pFG7yacWlFfwqSmROt8US2kOq/LXt8GQkNGvjcfXSQEBzVXVPD8HAoFcTyR3pBaAf8L72048QCuIGXY/sY2xqQqSTy04nDlkhftQIABwtwVueb3r5DQBcdepMku9t3OVUa5wcFZUozuDdcn3FYt0eVE1xZBmEQkJoDyn3/U8i2kcwqnZ4Mwgfevg1br/pCbbV9U4D1RqiC7GBgYFKJAZqCvBPSZIukSTpEuAu1J5OBj2AzewNVe3Zujv4Sk4HDgX6DFC7NUXbgkdys6qLAJC1JoWK4t332pNOY9XM2aw/72JWn3Ra0O11BkrzoHZe8ueQY3f4SJ6PuuWGkOu1NHdcwHbDwy95Xpc5Wnj79c/5sbh35vOYgyS4GBgcTKz439d8eUfHW8FEInVUCZwgiqJbmmgWEHklqEGnMnb0EGrXb0CQZZqqq4OuoziduBAYOjKLXUByYlzQ9SxaDVRqegrQ2O5AmOKj45d56MCQ67VOUdt91Q0dSUruFoCwSd91NZG3nfdH8BlTvaZu0SQ7iexU715s0UF7bRr0Unb87yeqt+Ry+L3X9fRQDhhML79I+1U5fbaPYJ1XgdOBHdrPqcAr+3FMgwjYtWIdPz8ZKBovOx0IWnZac3Vd0G1tioJTwZMJ52wNHuJzM/iYyQgmQedB+aMEme/yLdQNJ3XkSlcLZuPivEkYtSHGDl5V4o7g8vG+Gl3q+Jo7oKzeHZit1p4egkE7qH35aUwLjcYL3UkkShL/B1wNfIbaJPAqSZIe7uqB/dHZc9udJH/3BbLfzXXf7n3UtKjGIHrNiqDbjhvalz6jh7FhixoC3LJ2a9hjjbjsLGZce3xYr8YVpKg3NjnW87pyT+i5p9gY1TC1tHj3IYeY1wKwuxzUrV2Po7L9PZyU+lrP6wbNZja2oRPYU5hM7UtKMegdRJoIZLD/RHSFSJL0OnAVcA3wpSiKPSkU8IcgQZs2WfOJXvS9oa6B6lb1hhtTWsSuFesCtpUdDmyxMdji1NCea2VwQ+amtaGVgnV7Cee7NDcE9mpK7p+kHg+BxorQYTmbVf1jfEN3jfWN2DIygq7fxyyz/dY72Hr9zWHHHYyomEAzW+GUMe4pBp1F1cJfe3oIfxgiSTO/XBTFCtS5qDLtpzT8Vgadxsv6fk4mRcFl885d7LlzbsAmhXuKKCyuIEqrA7KtXsm6e5/SrdO405tgse7L1eQuUVt0uELcyIOFCd3enYLgSZIIRo0WzktK8WbuNdQ2MPS+u0g69mh2pgWfv2otjfw0q6uooqG6lurCooDPmmQnH20fSUljO+u8DAyCULFue08PIYDmqjpWzZzNb3Of6/Zjb1+8gnXfzO+SfUfiQT2FKmu0BFik/RiPEJ1EY20dn1x3R8TdZM0oyGYze7R6qIQgIqi1lTWUV9YSnZzkWda0JEe3TsGipZ7Xvt5Foyt4MkFNYWCvpvw1eYCWYa7IuEL8DaXFak6Nr382Yc6JxI4exbD75pLWNy3odu1h+3kXsfXs84mzBJ7SCtAkO9pd52VgEIySHcVtr9TNNJVqLXZWd/+tue6+e3E+/WSX7DuS1KZS4F+SJEldMoI/OMvmPUPW9vWs/uuNTH/Hm3vSZI8huqUxYH0TCpjMYUNWJkVGMZmJTk7EnYrQGKMPp+3asYd07bVvNl5JTDLxrYHzScU/zmfwYeP0CxX39iYERaFJ0/YLQPO09hWUMhwomj6DyT7NFrOGDaJq49rQf1A7iAmRUR5tsiKHSQIxMIgUxdn75jStWimJKUwB/P4iyzLL3vqY6ZeeFVQ6rSuI5JFSBB4SRfFNURSf13663488SIm3q5lc1sIC3fKUEUOCrm8zm7DFRIUNqZlRUEwmkvqme5b1P+Eo3TqNdV7j587GO+Sk8Zz9xatB9znkqMMDlrknixVBQBCgLK8gYB2A+HjV2xt95cW4FIUJF5zhN+DQp+Har34K+Vl7aXQaWXMG+485PnjZRk/i0oq+5XZKjrWHBc+8QtS7b/D1naFz5BSXi/Jvf2izgWmkRGKg5gHJwGV4e0KFrrI0aBehTieXHNxFSkmK57Bph3r+cS1B1jMrCpjNxCd7e0fFpcXr1okf5FOdoO0iW5xJ6Y7g8z7rvv0lcKFmJGNT40nLSqHy9tuDbhsfpxqoCafMZOqC78kcr1fK2pmbH3Q7ANezT4f8rD0sadzNe4WhU9sNDNqiiSiA/RIy7ioaq9QWM61NwVvodAa2JvUYiSE0NAFy3/uYPU89y8aXXu+UY0bip40E3tF+uu6v/4OSn18SVGl7x+ZdDAbkZH1nJ8XlRLCYGTqkP85du7AESVW2mMBktdJc5w25NTfokxxifvgGgF3WeI6eNowz5p1DjjSfkh0lzAziaFSt3xS40G0bzeawYrHOEH2n3FTXNHR5/6jc1nL6YgVi2lzXwCAYijaLKvdCiSp3naIlhGpMZ2CzqTeG6OiokOs0VlYDULlb37RUluUOlVVEssXbgB3YBWzw+THoDEJMJpk076SqTN/GoqmukS1b8xhw9hwAYgb0C9g2NTGWacdMprqo2rOsdLs+uy1KU0JPyD6WfmP6c+Sfj8Eea0cO4ZpbsgYHLEvIUL2ylkYHtcXVAZ8D5EYnM/aqS4N+5qazn0jXVsOTOwPn7yrbMJQGBmER1Ntlcv/9V9rvbNxG0xbbdeokjY1qElRDfeC1BeCsq6OqSq1DrKmqpWmXN1M4XN1jOCIxUH8HzkVVkTDSzDuZqOjgemxmzUAF3LplFzW1jaSdfCKJ06dijtF7BHJrK666eqxpqZh9Jkzta4KngdpsVurL69i3UZ0/kp3BPSG5NfDmntRf9e6cTgVHY3C3/9wv39XNhQXjxHl3hv28LRpr9aG7b0ubWFAeON7WLq6FKm5HlmCVy0jYOPBQ/2cpAyLpV929yK2aARC67ryq1eoYy0orWPbOJ3x43l90n2/7+21UVasGasCe7Wz+i+j5rLWD+pqRXFGLgvwYaeadhEkI/i9wGyiLoL+rmgWvx7F7TzFFe/WeUU2Jmm66e18Z8en6J72GkkBlhsFZ/Vn92UqeO/kJHC1OZKfM0HfeZJj0L9161tzA2g+XZswUwYQQYjZNiOCCScgIn2bellRT4aYduvd1vVQ5wpfMc+b09BAM2on7DG+tD5Gt2oO4PZTG6uDeTWcwclQWAGnpKdjefJXhFQW6a7M5b0/IFrfWDhrOkAFLURSPA36VJCm7Q3vuJERRPAM4BUgAXpMk6UdRFAcBz6MWD2+XJOnRHhzifiGHCG+ZtdCfb5+9lro6zIKATVtWWdNAQqP+hKytUEOClbWNRMXrY8WttfXE9tHP9iiK4jmpzFYzsstFcv8+FG3VK0MMNAV6JAXr9gDgbHVhUZSgQhSRGKi2WDd7DoXTjuW0h/+pW95U38DmOeeQN3A4WT7LnbIJgtSHdT2R/61RYeL4Br2TpIEpOPPryV20hUFX9/Ro9CSkx1FB185BmdzZtj7TEgGeUYgpC6GDepjh/prngUGiKM4HvgO+kSSpsD07F0XxdVRx2VJJksb5LD8ZeA41gvVqOAMjSdLnwOeiKCYDTwI/AuOBjyVJelcUxQ/bM6bexuBDRuLYvAbQC64mxUdDUyMWwTvBuO/TrwAYuFfzZixm1TD40LBYLcC1tzbTVKt/0rPaA41hSVE5ikVNm5128ZFMnHMYAGZb2ynZ3jRzE0IXNwXs/9siwM9AaeGErIJc3XJZMdMT+TztKbPat6+UwFk9g96M1W7BiSol1tuwRam3ciFMyUZHKd9TyPw77idj5FASgPq6RpK0z1ob9cX55hCJEPXVtSQltn/uLlzL9/GiKA4EZms/T4qiuAfVWH0LLI6g9fubqM0N33Yv0NrHv4jatqMAWCGK4peoxuoRv+2vlCTJPd91t7YdwHLgY1EUr0TNLjxg6dsnGXeStavB6w3ZLSZcgFkQwOUCk4kmuyrVUzYgS13JYsXid1Ms/+57YgFXUTGlO/TqD1ZtZV+xS6vNQqtTfZ85IRNbjDrJaotpWxbIvR+T1UKU3L0X7YdnX0nc2NH0DfJZfQ8VUlqtVvV/FQGlpe0XwjXoWZprtAe+XqiOv/s9VWW9K+qgNj/5HMPL8qmoU6MzzT5eU6tfcb4Soj6zsrSCpMGhW/KEIqw/KElSAWprjVdEUbQAR6Maq38Bg8BjSENtv0gUxSy/xVOBXEmSdgGIovgBMEeSpEdQvS0doigKwKPAd5IkrdYWXwHcp+3/Y+AN/+0qKiqYO9erUzd06FCGDRsWbrjdTnN1Hco3P+HWePjig0+w908jJyeH2MYmzwThol9+AZuNwrUbGAWUZQ0mJyeHuuYm+qGQk5Pj2Wd8s2rkqhoaWb1qFYN8jrfkl18xDegDLhfuqqi8vDxqW9SnoO8/+Z7WmhbSxmXgaGoOSP32PQ5AXX4B/QFrahSE8K39twnFzpgUJjaGv2l/8p83Pd/P8OoiWBqouwfQ0ENzUPGJMVAZmWRVRUVlxN9N2/vq/ed6MOrr6zvtO+gWSqqJN0FjXV23jDvi70eWiV++EAB76/6PrWJ7HlEpiSTl78UxdjS15eXqQ69mmOOjvV7Sqt9/w1ejJj0teBnHyiXL2NtQFfSzcEQcsJQkyQnkaD93iKLY0T5UA8DjNIDqRU0Ls/6NwAlAoiiKwyVJehn4HrhfFMWLgLxgG6WmpjJvXsc7OXYHn848g8F4n0YGxibSFBfHsccey6oHH6XFZMGuuDhs/AQS+vVh1UOPATBqQH+mZmdT/fZnxDZWMjk727MP9zrWuHgmTpxIpU8AdHjGYAZlT6e2rBJ3WkFiXDLHXHYqE4+YRPHWQla9tpxHdj+Ny+lk7ZN6wZBsn+MArP59F8pGSExJobkwuIqE/zahaPxiAawPb6CyPvyQvHPP5dRrT/X8ncFo6KEn3Pj4eFojbBEy9bLzGR/hd9MWB8K5HoycnJyIz4/ewKIH1cShmCh7t4w70u/HVd/AWp/3w1zRZM4Md0sNz6qHZnteR339PfXJfQAQzKq5SC3c5/k84xV9Qa49J3j+3LDMQbr7VKSES5JIBR4HDgEWAg9IktQoiuKpwHOSJHXLI5okSc+jzof5LtsInNMdx+9s5NZWWgqLiM4arDNOAJbX/wP33IGjuQWTIFDvcGG3wOILr+RPOd941jNrcebMPepc1KrPvmfymSfr9pU1fBAokFuVyPBktaW7s0XNuKlZu96znkuW6Tu6P31H9+eHJ77xpJlHorWV1C+JKqCuooH9FRGKjoksaaChoITv7n+K4I06VFp7KAJTU1NPqMCoMyoaS3MTHH8CjsFDmDz7+G4dm0FnoIa00wb1rm5DzVpxrJuohtC92ULRUF1LbFLwOaJ+VepUQbPWz83ian9NU3Ntx7pkh5tR+zdqKG0qcBvwi5aoEAu6pKn2sg/I9Hk/UFt2UCLLMi6fIrUVt85l81XX0lxdE3KblnpNtkRQkxr6mPV33LKiMp458XHP+/p9gaEuWZaRZYVVpX28yzQDVfyq96nHZrVQVVDJ7t92IphMKIriaaMx+JabPOsJ0YG3Xk8dVIiIWsNJARHbkGREqGhuW/47Gb/+HHadcOkad2zqujTcysrQ/9PqZLUWrKK+memXnNVlYzDoehL79q5C3ardenV1R5h7SzC+ue9Jtp59PjuWrAy7nms/MnJb6jomMxbOQM0E3kNN7z4PGA38hKrLtz+sAEaIojhEFEUbcAFw0PZR/t/FImtPOs1z07ds3gjAgvtDy9M31akGKtYn6a5+8xZqNUNw2MVnU1NYRZFDzb4zRwV6H6XFFfQd3Q+7T0zYpT0BNVRVe5b16ZvKyv/9zktnP49g0qRcNC8q7RSvVybEBMaWHdr+FFNwbys5KzPo8qBEeO5HOfcvGUNxpLa9UgcJ9ydUa0WO9SGq8A26D0WWO9QVV9C2aa3tPf/D5soadrygn4IvWZsbYu3gtK5aBcC6l17j+3nPh1yvX2agao0/FU5o9avttPTrx4Qph7RrTG7ayklcKElSvSRJHwN/QtXli7jxhyiK7wPLgFGiKBaIoniVNpd1A/ADsAX4SJKkIEJvBwfDS9VaoZUffa1bHrVvT8ht3Jkxvqnbu9ZspsZkZbc5loyhg0gf3oei6BGA6gX5M2D2LGKTYxl6yUTPMpfmQeGjCiHLiqd2waJ1vlWCZAKVllQELCtYq+ptVVU2BP07fr7vS6oLq0P9mTp828GHI20/yzxalK7TUfM3UNETJwR+2gszwP5orJ51CnmPPN72in7Ea9Je+Wt2t7Fm97Hs2rtJqNwJQMNQteNA9Y7Q4svBSNUaiQ4pyiN9wXch1zMHuc/4kxJjw5aU5Hk/5rOPmPDu66QeOb1dY3IT7oibUQtkXwGQJGmJNv8U+i/wQ5KkC0Ms/xY1Vf2Pw9LFcMHpnreJlWXIsbGYGgJv7nFRVkoBR98BUKyebGUlFZhlmVatsHfPyt0k2VsgC0YM6w9AyU6v0Zs050Tqy+so/WqH57+c3F9VN7f53EntgzJRKtR6okNPm0T/cQMxWQPrpdIsQeLUmmGrbnIyIEi9sSLD9oVbmHrhEeG/GyAvrzCiuqBoZf8y9Fr3c/twuD1QNw1Tj8K0dp32oSFt1Btwe06V83PIvFHEEh/fxhZezFrphxCm1U23U1freXnYAzey4qpbEZpqw2wQiBLhQ1N5WVXQsg6A5sRkomqqEFpbMfeLx1WlJgvFJET+/QYjnAd1K/Cpll4OqGnjqKnmV+7XUf9I2FStPWHnjoCPKpOCa9RFaRp6U/6qF1nNSEtk8HBv4rhT1kJyTWqyRVKsGuozHTsDgH0bC6jY6Z0wjY5Vx+JuOtt06CRGzTzaU/ydOjiNUdljdBp+vqz51P/ZRNswNnR/HGsIrcGeois9KIu/KoivwdIMlBKijYpB96D4SPPsfuSJdm3bUq+VEMiRP+TUV9bw0WU30lDdPqMRKXFp3msvpm8qxCVgdUVW6uCmpiqy+aHoMNZCifLOUdfLnfcwFvKQkiQtlyTpbS0k57t8jSRJb3XaCA5yYqep6Z4Fg0bqljfHxlNdXu15vwc7ewTVwNRVqZOcDc0+oTiXixirhT4Dvc8wLk26YN03OdRt2+m5+AYfrR7T/2ZYu6eI1vIKjwCtLdWdjaQgCAKVeyvY8M1aHM3Bw232WP08lNuwldhD33QdTeF19Nx0l4PRlR5Uhp+MlG97gT791CSQzMw+GPQcik/CUmtJ+zSvm7W5p7Y8qLK8Aj4848/UVVTx/Z0PMWxfLr9ffX37BxsBthjvA6DJZMKcmIRNcOgSs9oiPT0povXMzaHn3oqqvFl6+6qDh/w7Qpu6GKIo/r3TjvYHZN0G1XNy90SSLWpC9j4HCD43S9lk9uhV5W1XQ3W79xaRr6gnoKOhkZbGRho01WKzzczoU1RZosQNK9gu3sCeHFXmqLC0GiBgIrjs9VfZcP4lVMeqob7Rl5wHwMQzJnPpK1eyfdE23rnmjZCCk1Z/+SPtQv1l+eag67c4zRHFrQFi42IjWq8tdteE30+93DFV5UiIPy5b995XdmbopLEADB6nf1Ax6F58ZYqa80LPAwfD8wzVhoFacPsDDK8rZcEjLzBkpzq9nlBV3q5jRYq/9p4tNQWTADvei3wGRYgwYSSuMXSquBztfXgV4vYvrOdLSAMliqIgiuILQPhmPgbhcevVaYkJqcccqb6PiUOQFUpcJlKfeBzZbPIomLc2qEkSlqgojn5NVXeKs5poqqlj02Z1gtYaZSPKT9uq5Ce162251pMlVDgpqUH10BKy1HBhnxF9GXfyoZi12F+onlDDR+iz8lIGqV6BTQluFKpaoph05uSgn/kTHd05fWy+Lw9fb9EZHlTR9Bn0f0kKWG6brP9bBZ9spsyrryDrn7eRceps/80MuhFPolCHUK+npD5t3IDdQs+t+lCb3AUJMg1+CUrDZqkPrQ1vvRTxPqoq2peWHozY/t7ITvSggbTICrmJ+x8tCOdBfYVaDHvmfh/lD0rprr3EVKnZb+7wW/5uTXHB6UCQZVrMFrIOG489JoYoLTmhoUydN0rom0HqIDW1c/jAdFXZXPNimmubWPb2Epw+Tz+xheoTYYXW/TbS+Y7SnSVs+2ULJreBCtETSvEzXAl9VAOpKCY2VajhraWF+lRUU4TilekZnVP8uKi6YwWB7eHUeXeSkqp6oZjN1GjRlLQhegM+KKu/57VgNpN6wvEIHegqatB5uJo7bqDcHlRUXPiHqX5R6v+4z6bVuuUfX3p9pxuppho12uFQ1HuHPTUpou2Ktu1k1czZ/Pbfz2huat+cFQTWGmYeOtrzOqZPOqPefp2z3v93u/frT7irZTbwhSRJe8OsYxCGNVdcS3+TdvfSQgslharxEZwuoqxmLFoSxaFTxtJfm8No+vFHANKGDkIwmzHHxuKorsZqEjBZ1fUnnD6JxH5JQVUTpt1wFQBZU4cy+vixzN8bvh5p5Ye/8eZVr3iMia/gZLHLO/G/e4c+JNLaoIbLBAU2lqfx4baR5Nd5ny4Fk8Caz8IX/7lxODpnbiiqiw2AcsKJALpEkuMXfsfk+d8FZCxFqo5h0H24HPvjQak4GsL3g4otDJ7mPaw4j+b6zpufAW+0w6GoD65NzV7TEc4Ylv6mXpf1P/yIpQMK6JnX6psVJo0Z5Xkdl5ZKamZ/rPb9j4qEG9lFwKWiKB6QkkK9gTSL92QZf8gQwKcviuxk+PBMRo0bri63Wj3x8RjNk0obrModljU6WPX9IgBMdtVAWaOsCAIo5sA5nowhqmpwbHIsJ/z9JMqb2lAmV9S+TSbtpuvyMRan5Hjrt/a+oH8iKlivXohWk7pdVJ9YhAQruSkD2BI3BkVWqNgTWD8VjKYOPMX583u5jZLWtieHX93Rdoy8MSb4Oq6soYBX9iWUenS9S6FBy66sNfWuTMY/Ml/cfF+Ht42OV2+4Nfs6rkTv356is5DNqoFyCd77gTNEl2sAu/ZgbLFZiO1AeL3ULxEiY6KnmxIjpk1q9/5CES6L70NUkdb2V7QZBGDXnlLcGUBDB/VFcTo9Aoxbt+2lplxV+x2RfSS2fn09enjVZhsx2gSlSTuxVn70O9WF1SRlBCojWDXVh6qCSt76y2ttjk1R1Cy+4UeNQPz87zw981G+fujzgPX6mf29HNUAx2g34NioaA47cSInPfckLfFqMoArQs9o376Stldqg/t9UurDsaimbVVlUwhPzK2D2KLNKfq3F0h48CHka69n7JuvEqvN8dVP6bhwp0HnMrax48kKgud3aM9kS86ysPtw7tsXECrfL9xz3NpDYky6V+hHCDPfWlejppY3NrWSWF/d7sNWKQIlLvVaqJo9h+j4OGLHjMaamkLykPARm/bQVruNpaIozuy0o/2BKckvYhheDyo+xs7ebXm44uoYAdQ0NBGrpYbKLS0eQwSgmM2eGoRR4/VZYL7rAeQrNiZrOdt71+yhrqTt+gtFUUAQiE2JIyZZTXhY9O9fOPWeMwDYNXg0Q/dsDbYhAMmmaFCgpriGI684loen3ucjmxThxaiNOTdjEMNLuzaq3KQ4aEvIP9TcmUXzMt1/V7PfvWrEUVN07yf99A2TjXmngwJnqwOB8FlvO75fQLg2D3tu/yeNF53PoKsu75QxJaTFQVGFx0BF+8zlCmHqtcpLKshANZgEL3sMys7+Q4kdksVJl59H2YwjqCooYvLMowEY8dj/oThdCCG6hHeENq8cSZJ6j65HL8bldFJXFjqcVbiniM9uvpcsRXXx6ypraG1solbTZhMsFk/zwU1rtpBf5H3SU0wmogX1osgYoNfxLiqr1o/D52YYaZKE6kFBTVE1y99ZEvD55GsuC7pd40711NjtUD0SZ4vDkwnoPrYrQgMluJ9PO1jHurM2cj31zNERNE5rI/U2Pj2FJpdCw8yTw64XyhMz6H5c+zn/42xRHyBNQuhzY9JxbUv65M0P3pKiI9js6vmVNFCNpNjjveneLeE0A7Xzu39AVERPo0thtzkWe381+elPzz3MKQ/ehsVmo9+oYYzVjBOAOTYWSwe65oajzSIVURQHAX8HhuC1tYokSXM6dSQHOGtPOg2A8V9/xoaLr4Caat3nJtnFoPUrPO8L9xRiRUHRnjYEixWbSaBZlnE0NOLyvUGaLZi1+3dds4NUwGK3cuTlx9C48E1dY0HZZ04qUgmTKedMZeSxoynZXsxnd/0PgFm3etOhk/r3wR0Uc1RXY9W0tuKL1fonU0oUaI7ahze/p9t3fHpkJ2xikjrnk5aWBBUF7dasq3JEbghu+sdl8O9nwq7T0NhMsJkjm+axRsXGcHTO9+0ZokEPU7SrfXVPoQgX4tv99vv4F12UKRbSBe/caF19+CSLSClbu43WPPUhMf3wQwM+L/51FcMuCt5RIDY2ggQes5lj5qtz0I7KKkDBmpLY4fF2hEiu6i9QDdQc1I637h+DIDRU1QQYJwCT31yFBQWTooD2hC1o6eMuhxOTLCObvG5yis880+49Xml9QdAbJMBj8CByD+qZEx/ns7v+50kzv/bjG5l1s9czSMn0pksvPiNQXjGv1Dum+gq9bMqMayPre+TOeIuLjyHYsO/ZGD5sUOSIXErm2SfaFkJpCVEvk5axv2L+Bj1FfZh2KJHgnoOy2YKfiyU79xBbHNg5qNFP7b+pqXOKxStWevu6DbjqzwGfV7/2YkjVdqej7WQi35IIa0oy1pTu74MViYEaAjwHDNdeDwGGduWgDmSifU7eMsV7Ypr8ThSLImNCAc2gxCdrTyY1dVgUGVmb69iWs4WKVV4DYNVu5M4WBwtfXgCK/mLpO0g1Jo5mBxu+XdfmeN11FJV7KzzzLi+f8y9uH3hT0PUTtMO1+qTaxvjUhVjt3lDbkZcf0+bx3bjrimJio3H6ZcYtqmhlVYM3c+p6fXlJxLjrkrZtiSRqHUJ7yZDSO2BpqtlfPTz1ny+EOAkK/npt0OVOq58v3kmyXk6Xd0dCiAajsk/ngvLvf6K1pBTF5SJ+wQ9t7t9h2d82pPtPJAbqQdSuuoOAVJ8fgyCYfG6uJh+BObOfB2UG7BYzMZrEz6Sp4wFI/vermBXZkz6+5adNHs09wDMBOePa47HYrVCljyEPHqlqgn/z0Bds+mEDAw7NZOZNJ4Ycr6/unimESGwwnL7psj5eW3x6PKfedwZnP3Y+eSt389PTkYnfN2kpsdWVNfrwJvB2nv5CKXQGziXERJDKXVIUeQaXu86pWjYRO2YUtVp0e3euURZ4oNIURLDV5QiuOxkM91WoOJ0RFdy6fZSYVH+vu3MslCPEnFrq2Wd719EeJFtratjzxNNsuOjPLLprHqYIxl9e33WyYJESiYFKRE03n4/abND9YxCESp++SclJXqXhAf3SIM2rXp5kgYTYKCYcrjbyaigo9HwWa7eSoJ3UJosJ2ddAaa65yWxCkWVciv5fWLVTvYFW7FFvxiOOGklcamBNzy6rOjanT91QpKoP4JWM2W5Kp06wYNe8qOikGA49ZSKf3PEhhRsLqMiLzCg0anVQzU0tNCv6C3i0PYMhVm94IZgiuU1o27iGCtsFIzZOnWw2x8cz+oVnsVxwIc2ywrCjjZTxA5VgbcedbRTd+mKxe70UX88kGLtNMeyJUq/hcUfrMzsHWDpHTcJZF9xAZYlXe143F6rRF7nBmzARtzJ8Krxn2wPEg7oZ2AO8D7zt82MQhLIir0Kyyaf7q0l2sbNOf4N0NTR46qD25nlj14lRVsZMUqVDDj11os5AxWlG75cXf8blcJEwUN8qvWmLmriQNEC9OHJems8X934SMM6U2ScBelmjvqP7kZoVWet1d5ijyh6D2Wziz6+pleUZw/uw9gtvDC7SLL7SEjWEV1lRQ4NJf2HIioIC7KmNZ1VV8KfPSC75zMGBHUHzGi1sbgoMj5g1EU6rVjR9zF8u5qhfvidtcP+AdQ0ODBy7vaHdHVVqSNnV2J6EBQVF835cfvNIzXu96hFlhx/FOT99glnLbIpKSiQ/wftwamvpnCQJp+ZBxV0dGI4v7avqQtZtUDt478sLnBsLxV5sOBWF+BNDR166i0ikpn8DvpEk6dkuHstBQavPCe9qbPSkPTbWNoBipUVWsPv0Cdq4aSeZQEVFLW5/y1Vfj8mmeiRZhw/lF5/Q24Cx+jqolia9NyGItwJw/I2zyF+3l8KNqvZf3ahDiN/mbVxscdcp+bj6Aug8ntamVmwh+jm5J4pdcRbGjhtGYj/1gk/qn8yX933qWW/dl2u46MU/I0TcT0PBz4FCRsGhuFhe1I+l1uAyMpG08B6c1Z/8PUW6ZRPffhGz2UTFX7zSLRlnn0HiibPYcc31ZMzMjnDcBj1Fa1MzX1x7Gyc8cjfJ/UMLlKat8wZ+mp3qra89zQdlp4yCgICCs8WBe+a1adduNv9F9Kxna1GjAS4t3L92/Q6OufFK8uY9FvGxIqH/qAzK18DwswNLVSfccD5Fd6/CFh/L1htvoWHzloj2WdZvENPm3kafEVkeoYCeJJIRRAOPiaJ4JuBOgzHSzEPgaGzE/fxv9rn5O5qaMNnMlAo2Msxgd6neVb029+Jo1T+Rrd2wg0ygrrQWV6v35ps6boxuveqSevr7zAgOOVJtM548MIVpFx/BZ/9U08bra134BvrMWoZOVJw33dRfyNTlcKn/fT9khwNF8w6XLt/AgnIHu85W205v/H59wPoVeeWkDQnenNFzbLcBU2DoiEE4cnd6jwc4NR8pujaUB9W2gTruhGksXriKzEF9eWdHDAowLyUFFAV3YLbcKTBZvAaACZ9/hDm2c9qAGHQd6954n6EFuaya9xSZ044m6/TjsCeFl7Nyz+sqrZHPsyiyjFMxYTOBq9m7XWlunm69kVMn6N63OmWSjz4S37VWzZzN+PfewtZHX9fYLuqqsSQlYvZvgwOkjxpIEVDwzHPt2uWxD9xBzLDekwMXSYjvSMAKHIORZg5A8Y7dzH/uVc/7r+/xqkE5/Vz/csHG3oxMtQhXkVEEgaYWb+jPnX2TcMhY3XZuEdglb/6qC/G5MZlNHH/jLGw2/Y05TROcXfjyAo9xAthZks5ml7dANVEr6Evok8igSWpihW+MHeC+sXcGnQx2tLRQU6iG5FJNqt/nFoXdtSw3YP1Q/aV8SUlTPbDUtCT6n6l/9lEUGZf2pDsxKnidfmFzoIEaPmKQ7r1bpuiu+6/log9v56ENL/PinGf4wsfjm/TGy57Xlvh4Q338AMCmFc5GVVZS/9ZLLLvq9ja3adFEkGuLI9OKBDXCoGhzvr5tO0r8iuUt49WaJKtWM2ey2zDZbEz84mPdeuUbI/NqQlG+dhuu6OB1Sf7lJxET07seyCJNM/f/6T0mtgfYfP3NJH35CU1aDLjf0l88n9WX6vXgak1WnNGx2EwCgqygCCYafOoiBC18d9JdN7LT7j3ZzJoSsMkkBDVQbnmiVKv+5t+qZd7s/n2nbrnJZqc109uvKEp76lIUhbQh6aQMUt2wMSccQspgr0smOwIN1G/vfY5LM7IuzS7YY0MLTobq0OtLdIzqqkVH20k7eZbus9XN+2iS1X00KOqNwb8jyHtFgRlafft7vbYp08Zh0rINn3nsTb74dD4JiXFEJUTTUtfMjgEjqJh9BhlDBwXsx6Dn+GTWWfw0I3wPrSYtWcCpGY346ryg68VNngQ2O9/syqJJC/FVFUVuoABMUarRsdl96g39wteKdm0Ndqg1gdEN6m+zX1PO3bsK2nVsfxr2lVBVHbyeKYIyp6Ao0W0IS3cz4RoWJgFIkrQn2I/vOn80El1aWC7IBGv6fL+0apOASesqO3hgOoOHZTLm/7yKym4tPZPJRMYp3uJYU5R6wxdMgiccYYqNZd1XawC1CHfB8z+SV6tXaqgrVy8G/zkfk8WE7JKpG6h6S+4eMAXr9rL605VU7lUv1NSsNCp9FMiDFfTFfvhfTxaT20DFpaqe1Ox/nuZZL17rFxWJHt/Awyeqv48OlIppUhw0KK1Uu5oodzZgMpkoiyAys3jhKs/rlb9tJDZWvfi2b8vj1ZdU7zIqLorm+mYuePt5TvzHNW3v1KBbyZKbSGnDGSjaq84rNrVxnpkEAfvATOodNhTtmmrMmR/xWAQBTFbV+LgTIADMdv08bZyWgas0afeHvDzPZw3nXex57WgjE7AtTLITwR5cEaKsqjqifQz4y5VMnv8d+w47ktyYVGKTOleqaH8J96/fJ4rix6iNC1cAhahebn9gCnA6cBYQF3IPBzmNNXXsXRu83bkbRRAQtJPaboKo5AT6HzLSrQyka3LnViEHtZsuqKE8twfVVNfEj//8iAmnTeLU+87g6wc+Z3tVElXNdo4fpD6NudUj3CnjmRMHM/r4MeQu2YHslClpbCIe2LOzgLjfdrL07cUAHHXFsQC6DDxQ23oEI2/hEqLwGih3DVV0YjRnP3Y+tlg779+gJntmHd62w91n+uEkh4jJ97XEU+ysw4SAgkJR7WLuGXs8Z/i0CYgVbB7vys2pc7JxOJ388I36NwYr0I2Kj/IYZ4PeRdXiQF3IYAzaoj20tSFSmr8rH2QBSMJs0lzwTYFzpuFwyWoNY0tdI27TYLFZ8TWN8X51T0KSNzLSb2im59on4sSh4JhxYQrRc6moqJyi2niyEuqCfu6m7wXnAnD6E/fs11i6inAhvn8CM4CPgF1AM9AE7NSWHaOt84dl4a330vLYI2HXGTNuBFOPVtswN9fUUlPbSFSs140eP8XbRyV9gDcDKbWfGp4SfAyU3SRj15IavCKkAmVNXsOmaJlDgmagjvlrNrNuma0aOpdM9MgRAKSMHMZLZz/POs0gjTlBrcdyF6he8vLlpA/LYP3Xa4P+XVG/qk0Vo8yqhXKHCONS4+gzqh91Zd6QWygj50+oCeNMSxJRgoUEcxRDbOpxXt2r76vTqOifRrOGDkBRQPbRTcr5+beAfds1D8qg97Fq8Zp2rd+nNny7ldqKaipr1XBgq6vtujlf3Fmitjj12q0rbrtlS6Gs9XU76wzPsszRw9p13HBYBBnBHoWiKLz39tdsXL+dfQUlfP7xz+Tt3kdpQ0zY7Yc9Nq/TxtJVhPSgJEl6HnheFMVjgKNQlSQA9gKLJUla3A3jQxTFMcBNQBowX5Kkl0RRHArMBRIlSeqxhooD5bbrGWLjY7HHqidKY3UtJU0KU6zeG7bg0y5j4NCBuKdN3W01xhw/lrX/8j5pVRVU4mh2eFK5Bx0+hL0rvJ6BS5uc6TuqH+u/WsMvL/zMuJMP5dwnLwRBrY/aMn8Jk2cdQ9kOWPKG2ghR0EIWbj2+ws2FlO0s5f0b3mbSGd65K38aZQdvvPcIEyaNIzYlliHTh/PhTe+ydYHXsyzeVkTfUYE1SJHQ4jIho3jUJQbNVBs/Zli8WVo7a+0oPll8R8+YzOKFq8jb5a39mHbEBMZNGMH2bXm6/R921hSGHTG8Q2Mz6Fr8Ja/2F7Ms02o3c8hJ49n0w4b2baydf4LFigLIPolOI0YMwt2MpinR6z21jhkH29aRMcprlEyx4Y1GpDiamjEJCuboaFwuFzeLD3PhZaeyOGcl+XuLueOev9Ash1dvT5pyWKeMpStpM9VDkqRfgQ7pw4ui+Dpqxl+pJEnjfJafjKrvZwZelSTp0TDH3wJcK4qiCbVA+CVJknYBV2khyB7D1ty2gSotq6a2WK0tsikKil9W2M68Qg6bqr72ddfdhqvf2AHEDkgG9lHZpH6+d41Xlblyt/6p0d0S4OirZ7Dh23UUbd6H7HR5PByAQ2apGnmCTxzdnUDg9qAWPP9jm38bQI3cwuChA0jJTGXqhUcAeIzTzJtOYv5zP1C0eV+HDdTO6gQUXLi0NPOolMBJ3MIGddmRR09i6eI1jDlkmGf+6fKrz6SstIrX33uYf7/4YcC2I2eoBdFVBZUs+NdPnDHvHF07d4Oew9XqDdlWFZaErXGKBDMKitnSZslDMNyt1ZsaWokCZJ/W8YqPIY0+/wLP6zNfeJiGiiri073XntNHnSH+kw9wXn0hFlvbMl0BaN/NkKNHe+qt3n/7a0aPVcPprS0OllXKnBSiOVWps5MEAbuYrq7EehN4AR/lCVEUzcCLwCygAFghiuKXqMbKP152pSRJpaIong5cB7zTxeNtF00DBxGbnxd2nT17Cknqn0kaYDPhUS9346taXlLhVVvesWsfU6ZOoa60lpr8GhgEslbF/u9z/+VZr75cL9/S/xD1jIyKi2LinMMo2rwPQRDY/PNG1n+5hhnXzaTfGFUNYfGrCwE45i/ZJPVLAlQPq3x3+FCJbvxhyo8OP38a85/7IeKuusEQEDjk0OGs/l2dY6vaps4XORQX7gj1pmrVcP++XJ1PaPCRr3ns2ds8r0ePUb2v5JQEBgxUb3aN1Y1UF1bx5b2fsmt5LoeeOpERx4zq8HgNOg/Zx0Cteehxjn/pqYB11n71U8T7sygyYFJFlv1wtrZStHUnmYeOCdwQULTIRHODU517bXHQmLeHPc+8SFOZ93qZeJY349BkMumME0BMgr4+69Orb+W8t/9Fe1Ga1bC0PSnBY6AEQWD8hJFs3byLkpIKGlwKhfWx9I8L9KRmL/y23cfsCbrUQEmStEgUxSy/xVOBXM0LQhTFD4A5kiQ9Qoj6KkmSvgS+FEXxG+C9YOv0BLUNzQG9XwIwmT0JD+73vth85qPsPmmoZi2Lb92Xqz2yQv5iCdMuPpLf/rtUt8w93/PDE98y/zlVsVgwmVj5wW9s/H49qz9dyeMF+uK9mX870dNJ99ynLuTrBz6PSAkdIMkUOmRh1mSC9sdAAaxftx27linVUqum72UO6Q+oN4YCRzUAx2RP4Zeff2N3bj6HHX4ICQl+/x1tUvqRp25l8lTVoV/35Wo+u+t/9BnZV1tnv4Zq0InIPhmk1trgrTIaq2oJXeCgx2oSEDQPJrGvvn7oq9sfYtCGldTPvYcxxx8ZOBZ3PaDJBLJarL5p7iOYivU9pszWyOZb3djz29+jSnE6KVuqRgjqqpuJ9qlVrCivBuC9t75ilC1dJ1jtptkV5qmyl9ETWhYDAF+9mgIgpAKnKIrZqNmCduBbbVkqMA+YJIriPzXjpqOiooK5c+d63g8dOpRhwzo2QZn33SJcm7cz7FZVhNH9DFRTWU1bgauG1laaS4pJ0t43tjSTk5Pj2ce2nTupzFE9gebqOtzBhx15edTk5LA7N9czodvkVE/+zOOzyF+QF2CcAL5483MSs5LYttbbon3Rr4sor/RmquXk5ACQOCSJmt3VfPLs/xg4Y7CnTXv0hHjtm/auH6ouX0Fh5YoVlJUH1nQs/11NStiyeStNOZELtYL3O67NUNicW0xMUjTHPn4CTquTnJwcapu9nmOdlvZfq93Eli1Zy/BRmbhcrZ6/FaCosByb3Uqrq45du7exa/c29uWrp6IpWf2Ot+ZtY59TL4fU2+nMc707qa+v1/1//HE5vDV+VS456LqF+/IJ5u8GWzc+2oopMx1WNpN0aCrb1iQzKqWKZSecQoOiPgAt+/xrSkyB56qrqYUkQLabwAn7KgpQisvwTcr+uqSFujB/j2ccPq+dsivkdxDq+7H/tADbclW2aeP67WzJ8157DY3e62JnawUWkz6cuePU05CdMo4IxtkbiMhAiaI4EBiMT/d6SZIWddWgfJEkKQfI8VtWAQRvvqKRmprKvHmdk6Wy6iFVQ2tydrbuvd1shjacg7jkZDLHjoEVqjE5fPokxmVne/Yx5YjpDJmsttpwtLSw/l+SeqypUxh25BSsu0xsad3A0sK+FDeoHsHAzIHk64RTvPSxpDM9+yjKvy2mAPXpLPu4bIo/y6cYNWkgW/s7vt39GQAbXlvDcefMZOD4TBa/tpDfH/Hmvxx+wXSyfcbrz6ZakM47w1NjBJCT/CP9xvTn2OxjmM+3DM0awrHaMSPFfby4pHgcyAwaPIBTLzqNnJwcsrOzsca/QqurBZtZxqopmZeVejMHc7fl6/5WNxderM+p2ezYyLqXVjL7+lOJvSeWfmMGBChq9HY681zvTtz/y1AUF5Wyb6PqySsDs4KuW5+Wzraff2DwP29jzyNPeJYfOWkKtkRvBYwsy6x56DH69OsL5DF4yGBq1ywHwKbIJNit0NLM4MzMoMdpqW1g45PPEpecBA0lTJgwjg1fLtCpFJ/1wjzGZx/R5t/94YvvMrxSvRYHDe7P4SG+g1Dfz+rX3vekBE09/3SuPuYKQC2rmDX7KH5bogrEOpFJsOmN7QU3ixxItKkkIYri46hq5ouAX3x+Oso+INPn/UBt2QGHyRW6XFvQwnqC3Y41xnvzTvBrmRyX7H1v9UmSsGtq2u42Fvl1CTi0tFXZJXPRi4EdNMGrTu7bOkMwCZgsZlIGpQaE9zx/i7a+uxAYID4jgTkPnR10fTdzLjhZZ5xADROecvcc7PFR3L/hYY68LPLGhW7KotSbS4LDxmP33sR7nz6t+/zIoyd5MvfKXOpT4948tWXJv998kPETRjJr9lFtHscdEv3ink9oaWiJWH3doOtx+sgJDVwTvEWEe57KnqH3FFZef5fu/Y+PvABA2c8/A2r4eVRKtefzAS1qvdCYQ0LU7GnzPO5LvqGijuhEfXg7EuMEMPVOr/p4XHz7pYUcjd7vpc8kr/+Ylp7M1s27PO/nnHm8Z1pgW2USUY892e5j9TSRSB39FcgD/gNIPj8dZQUwQhTFIaIo2oALgC/3Y3/dhjuTx40lhEBp2imzqdcMxeSjJjHqUK8CeVlFtW5d33buviSNDJ363FjZgMkcfLLErdrgW3skCIKqJBHm5mvSwntuQ3XNRzfwz2X3talC3mdAYEbUISeOZ+ChmZhMJmKSYzvkkSQfqV7sdburGJ7Sh4w++nbTDz52E4qmYxhQoHtGNoIgRKRwnjlxMKmD06jYU85/zn+Rku3FbW5j0D0sW7hS17xSbg6sV8vboUYJCgoraHB4zzNrbaluPXe/tTLFwp/uOp0B4waSXxeoMbD7ueC3Not2PSVnqbV6lXklxKWFF6QNhdVHeaLRp09TpDTXBN/mjVc+RXrOO0V/611X4dS+vu1VyRwy5ZB2H6unicRAbQSelSTpOkmSbnT/RLJzURTfB5YBo0RRLBBF8SpJkpzADcAPwBbgI0mSNoXbT6/Bz0D1TQ0uC1JaVuVpBBiXkojVJ0li5069sygEUSIGiEtQL54hU4dhj7NjjbJ6kg42/7yRd699U7f+iuIMFuYP8GT0DJyg15Q7Ze7pVBdWI535rGfZsdcc73kdn6H+LcdcPQOA2NQ4nj35cd69Tn8cXx7ZGEu9I3yR63ePfsW2X9oviunSsh1dshC00299XSNrmcD6sjSatdbXh08fj91uw2KxsH7tNn7+PnCOzp+o+CjOf9YrP9Nc1zm9egz2H8XpxHc+3xmkg+zejdsBqG9updWndYb/g1Vsppq5OuLOW8gWZ5Ixoi/bq/w73UKUs9Wjsek3GgBMbg1LhxOXT7NPZ3Tk9U0WHwOVv9f7QNRcWcPvM09h3XP/Dbt9nKKGsltlM+eedhNzgrTbAFj662p+sSUB4BA6kMreC4jk0bYamCeK4lTAXT6tSJIU2CXLD0mSLgyx/Ft00/AHBs3FxdjTvA39tPlSAPYeMpnk9auIN8Ou3fvI0E7okso6BF+Fc/+utT49V+QghYmpWWlMPmcqy99Z4vnc1RroCe2qSQJA0a7oSWdMZuHLCzz9oNxZenk+Rb0n3fYnFv1bTbl1930aN3sCt8y/k6dnqqVpyQP1nosvm6Mbuf3kqSE/B1j0n19QZIVRxwVP3w3FYX/7K19t28vW7Q5GWwKfo+be9gymzeXEmlIwsRsZhZGjs1i32pscEmkPqtTBkTVpNOheFJfeQLXU1mFL00cc3ELNcUkJ1PqIKvurg4weOZiK+dB/SCblu8tI6p/Mxa9eQfGDDwYct2znHgZN0HcXcDSpXnp1cS021Cy+poIST2sdRzsS46LjghuzsnXbMSNT//mHcNPFQdfxpVw2seiXFRyTPSXo55s25BI/aRrPv7eOSeNDFET1ciLxoP6Eqrd3Marn4/75w7HlimtYf+lVnve+LaQFs9njTgsmkycnvLC0CqfP1yz4yeCbfOqifNuvu6krrWXPqt1BjZc/qYPTGJk9mtsH3sSPT33HNR9ez99/UOuAdi4NbIPx3SNfkdAnkZPvOMXTQRa8ahIQWui1wimwav0nTJk6LujnbsxWS4fmdaJiY5j18D3IiiloK/rNG3P5vn4r066b4ekV9d83v6K11YHL5WLr3u/ZXvBDRMeKS/eGalZ8sJz8dXvbPV6DLsAl4wLKB6nh7nq/8LgvMYlxmHyus2j0BipWewCrK67n8WP+j13Lc+k3bVLQfbXWBbaGd5dKNNaqhkpxOLG6vN52bUPkcllxPoKsgo9ha8hXvSlLdISJ81pI89eclbrFf73+fACsVgsjWpLoI8Qx6+aTAzY/EIjEQB0X5Of4sFscBOSt3oDLGWgw5Opq7+sWr2dksli8CT0mEybNg7LGxhDlMxEqhBO0DDJnsnXBZvZtKGD40SODbODlzIfPRfzsJp49Ue1N9fMz3/Pk8Y/Q/xC1B9S6r1YHbFNVUEljdSNTLzxCZwTMPmN0at6a4Ndd04GF+8beSdkufazfH7PFHNTjiwRPwkeQEF9UtJ16uYWxZ3ibw51wkjpvJQgCySkJJCQGzjEEw9fTWvflGtb7JIoYdD07l6/mpxmz2bvOT3jZ6cSpCJRnqgXWaz/5JuQ+rHa7px17MIq1m79DqyxvqW/m2yeCP8BsvO/hgGWKS38dKMX6sooGxRJRWxnwdjAAMPukAbZUq4kalpZ6ZKcT1/tfs/ONj0LupylIKxyA/2iKKVu37CY6Xk1g+u29tsPdvZE2DZQkSQtRlczHaj+F2rKDlqJtO6m47XY+uTx8FNPiG/O2mHG5LxCz2dOx1hYXR1SC90YpaK03CiYdSW6SvopKUeDz3KF8kTuUzT9t1H025Txvqdi0SwILCQdOGOTR1XMj+zRNWvOZt/WEO3lAltXW1d/83xe67Xw9qN2/7cTlcHHYD1/p1jGfdJ66bhDvRreezdzhzLikgck8tO1xJs4J1Ax77d153HbXVR5pFwCndpzI28sHx1A3715Kv/qWFAvs+UIf9R/QP43ouBiPOnjGitDyn1GJ8WF7KuduUbPbnFqYw9ni9IS3/ckksJeLu0uAPVF92IyJ1T80rc8byEe3hJ87cuP7kOoQvK/jk73ZsFvf/IKk3E1Uv/tGyP00hoiqjByVBahp5+7Q/db54bsu9FYiSTM/HdiAKln0IrBBFMXTwm91YBNrU0+afhWFYdfzrdK2mkxkDlW9FUEwEWXXWmzEx2L3STOfNFWteZrz5D2c/8nruv0pikKLy0Kzy8IyrQ2G25gUtBF2ikuNC5Bw8b1Pj8r2zgG5LzZFq0Bf9fEK/d/lZ3TcyuR7Rk/0LIvv2zfouv6YLeYOK0mYTCbssXYstsCp0oGZffnHXVfpjNGmDWoYsyMG6tJ/X+F5XVNU3f7BGrSJLMt8eOpFLH5dr4lo0x6I/HUqB/RPJz45gVlzQgdscqOTccgKyf37eHo8BT22Q/VuzJr34k44+loYyfKqCDwf7VpxJ0k4avVtLFpcFrYv3BqwWVv0OcSbJp7Yxxtqtrd4lTNqtwWG5wFdUogvt9x5BXllv3DVtecQm3Jgd0OKJMQ3D9gNXIOacr5TW3bQYtNOYqsz8oZig2tLPIKrh00b7zECUfFxunkm37qncPjP/Sx+zeu0/vbuUv78+tW6z10OV4Cx8G1Xfs4TF5CapSYDuOezlBBCelEJ0Uw606tg7vbEZtzpTd4UtCc/U5AEBl/uXHqvqqTeAWqKa/jqgc8o2hL+QWHwEDVDK1h7+khxtzEBw0B1FU219QxvqsL23zd1y6urtNDW/J90/8PWpma1W22InkcAyckJNEbFoshKWA9K0WSTzJqBcc+5HnfeGVy56IuQ23m2d78wmVAEAbneO0/Vot1GD78gsNlmKGrmqAXj9s0bWP/XG9i5fDVOn2QqV1mJ5/W26/4GwO83/5Pl53vrH9dVeTOAR40ZQv8Bagp8eVkV0do8ljs790Al0pbvT0mS9KokSa8Cz3CQt3yvLNVCPK7In/ydJ5xMmaaDFR0bjSKr2w4Zo/+qSn0EYf2x2r0n3NhZ48Meb9VHv+ve563YFWAsfD2J8l2lVOSpqupvXfUq4BWW9ccea+fCf13maWLormNK1VJ1AUxasocp3Jwa6pNqR0Nu9eV1/PpKDhV7ysOut2zth/y24X8sX/cRPy0OHRIJx+d3q8L4sSmx1JbWGgW7XUBDZTXgI0ejUaFdE3YT1OZ7yzDWrdzE3vxSnLbQBmrYkP701YR/w3lQisOBS1HIGNqHMx8+l35jtYcap6yLcIQiJlFdJ3PiYFwucJWqc1pztzbw/KY4z74i5fi/eZOtHDt3Uj13Lvmve7Ww63Z4s21NWiaFef1arOXqnO++uljmV3qVU3Zs20PhPvWz6Ud552UHT86KeEy9kUjSzHcBt4ii6P72b0b1og5aKkpUAyVHUOjpJqZPOrtr64kToKi4AkHzTmyxaszakZCEtbaa/MIyQuW9ucMOAJPPVdO3RxwzikGTButabABs/F7fCVRRlABjMeV877yVy+fiqa9Qn/5m33kav7zwc8A4ZFlm3uH3ceRlR4dUnhgwYTCzbp1NVHzwltNu1ny+ioJ1ezntvjPDrhcMt6fXZhjRbCZriGpsD53YMSVyt4L7Td/fhjXK2uYxDdpPs0923NaFy70f+Hj60VHeBIKYxnrMyLoOtYrLpZvDkZ1OBKsFk9mE0xb6fya3NONUVI/iiMuO9oSdXU6XLsIBEDsm8Bxyh9otNqvWQFR9P9Y0AovQfgMVDKHe+/DqKC4ieIWkyupKO1U+/ejcnucZ55zA+Ak+6hJD0jn8gums+GB5wD4OBCK5Cu8BhgGvAK8Cw4G7u3JQPY1bYsXShnfgizU6yvMEt2dPkScjz6l5D1VV6sln0cIVW+Zv4qenv9Ptw/ep3X3DTB6Ywrg/TSASTGZBV2908u2neF77Gj/ZZ07IbAv8G53NDupKavn52dBp2pkTBjHr5pOxx4ZPic39dVuHs+I83YFNXS8x7jZIscmxxKbE7XeihUEgDh/porpSr1ds80mrbqjy3qT7mV1kaB2bbaeq097NfkW0m9ZsJXfnPtWAuLz/M3vmQM/rZW9/zPDSvdhNAi0NLezbWICjxcGje59h5t9O1O0vZsRwLAmBYfjmWtUYlOeVYdXaxbtkAYsSh2BXr6E9q3cHbNdRrARmEPvyY21wFfSHHgtM7MoY0Yek/kkRKav0NiLJ4vsCGA/8DbgRGC9J0lfhtzqwcWr6Xu15irZG2XFHGASzGfc91RKjFuW56x0sWhuNjd+u46env9fto9VHYytHUj2bqn2VEU2+pg/rw9wVD3L8DbM8y3yTE3wbFrrndN67/i2i4qI47+mLdPtyz125HC7uGPR39m0MVCpvrmumurC6zXkfa5QNR0v4iy0U7nbt3eHNHK/dqJrrm/nhiW8p2JDfxhYG7cXlY6BMPmULTp8WNPk7g3/vu/apBm39U2+wauZscj9Rrw+51YHDJdPa2IrsUy3bUuv1Lgpylnhe79uQz3MnP0H+2j0BnhPAnoJStm8KTEpwah10GyobPMlHda2aqkSLep2V7QxfchEpwaaGW+v08kZm9MoQ7336FCX1S8noEyidNuOa4zn78QvYsWhbp4yvOwl55YuieJYoiv1FUTwLGAcUaT/jtGUHLc5mdbJScDr5eFZ4sVQ31qhoPM2EzCaqTz+bPFM0tmhNNFYLCbgN1IoPfwvcic8TTkOl+qS47Zet5C7e3ubxzVYzFruF7Yu8xuyJGd5cltjkWP501+m6bWqKa0AQGDxlSMC+AAaMH4giK7oi4fpzLiQ3qS/L313Cw1Pvw9EUPpHEEmWNuD7EH3eWYXcYqBNvnc3jBc/hbHWqXYA3HZD6xb2azExvR9wkq9fbUXz6Pjma9AWv8uAsqvZVIjvU9S1L1Ie6fS/9G4BoVyuOqGg1ScJnDqqp2htOTEr1ekTuS0wQBL645xPWf71Wd7yG5tYALw18uub6ONbNLv0Mie8DZiTs7JsVsExWoDFjROC6d9+ne++U/aScYsPPo712ycu8evFL7RpfbyDclf8/4GjgY+21+8f9/qDF1eq9oQ6RIxNzjEmM9YSiTGYLM2+6mrN/+tTzuft0skaHnrPx9cDbe1OPSYzhx6e+Y/5z3lbt/mEqe7w+HKcoCg0V9bz9l9d0y01mE48XPMeJ//iTup6PgZpx3WWc/8kbEc8PWaMsOJsdHQovDJk6jMfyn22zSLkzidIKG5tqDU2+zkbxKXxPjvZ6ANOmeUVMfQ2US1HYK0Tx7jVvsu9Hr0o3eJMWrIqMYrOjyLI2N6Qi+OT0tWzxiUB4zkOBFR/9xo7F22iu8x5TMZkxBUnfdp+/vtfU/gbMlCB1TILFgmALvEc0btTXRdb4iQjExYfWAvSdr17+7pKQ6/VGwiVJPAhsAh7oprH0OO5w1ZgxWSG6LYUmKjaGoSMG0bp9hy692/O53QauVipXlcIlIXbicxOvLa52L2zz2IMmDSY1K401n+klT/wL631DGmW7Sj1p5iXbgit4u41PMOPivrjaSjO3x0Zhj7PjcriC1jO1RXfPBdnj7AiC4JlzMOg8igq8qdM1ldWQriY/mHzCxG4DJcsyZkFARqBsVympfhl6pmp1jtYiqDd1RVZ0oTHB55zt1+Sd1/J6UGC1W/jt3aXsWbGbGdrnstmMuTVI2NpdOyh4z/fUqGZM6VGY61wdihKkpCS4m0J7sMRE0W/iEGr3bQi7bZWfgRo2YnDIdWsKqz2vP73zI6ZedETQ8GZvJOQoJUl6QFMZ3w28qb1/ALVg96fuGmB34WxtZc2sU/jkypsD0mAjQbBYsGr1U5OnHxrweVyc+sS39su1uuWhPAtnG8WtE3zUFdzzS2Y/SSB/Q+nr7TRVN4asg/Jur94UgukAuoL0nQpGtjiTB7c81iHjVLytiI9v+8CTMNIdmEwm7HF2w4PqAirLKj2vN67yKtzv2u6d8HdqLTVcTer3H+VowWQScCnBz7Moq5m0/ulakbv3/BcIPGdt19+I54FPELBrGai+16BiNmMO8lBYvSMP0LexsZll/v7NPzhOm/e95qP2SZTaowITjFx19dSFKMD1RfYZoyAInrqnYOxZpU/eOFCME0SWxfcG4CtZPQv4tWuG03O0NKoXxtD87eTvCkwKaAtBECgtVidyg9VVuOdTopLUlNQjLjsKW4x+otPmkxF3yEnh66CSB3hTb8fMUhPX/Wt3nM36mHiKj2q3o9nB0OnDsNgtnOST7ac7xsAUjrryWBIyArOaFFlGMAld6uHUFFbz+/vLaKgMFO/sSqLio3RhH4POwTdJwuWjY1m0t8jzeuhgVf6rSmtp3n+fWt8ny8HPMysKo8ePxBplZUN1H/bUhu7RNHLmUfQZ0ZcLnruEjOEZHhkg3x5gaXYLfcyBBqL6X2q33kGTsnTLzRYzPz6pSjQNODTTf7OwxI4ZHXT56y99pntf0Rwu4Vw1sK4wNZst7Zwb602EfKzVJI7OQA0UiaIoztY+mgQcdFevq9X7TyzcW0RHGjDUVNUSCxSXVQds39LUjB1IGaVKBI0/ZRJ9RvVjz6o8WuqaGXXcGJ2XcdTlahfaMTMPYcr501jpl1Sx5A3vM8KQqWoxsLsQ1834Uybq3vcf6y20dbY6+dNdpwckTviSPjSDOQ8GTxIZddxYTwuPcOz+bSdL3vyVOQ+eRXx6+6ra3SHXYCHTruQfC+fqnpQNOgeXw+mp7ZF9rjd8OlPHx6hejaPZa8AEswlXkCJcxeUCWcZks2GLsZP5p0NY/jnk25wck+j1MGx9MmgtKcWemIijop6R2WOIS40jKsH7IPlpSyxRZoFzB6TTUF0WUG/lHYz3ZbPTzKc3eBsErv5kBUf+OfLu0ZP/cglf5xXQb7le2vSoFBu+of2KFjupUeFDiOG8ojMeOpuk/kksf2cJiqzQXN9MVFz4+sXeQrgrfxJwOeo3NUN7fTkwAfg01EYHKg6fxAiXI/J4cumMWeyyqk9tbvXzkspAtYhmLUadlKmaLkWRKd1egnTGs7x26cuAKmDpprZErRJP7JfE8CMDs3rc/WkgdIHgkZfrL5aaYu+4IvF8ZFmmtak1qKpC1pQhHHN1dpv7qCmuYf1Xa/ji3k/bLcIaaSJGZ2OLthl1UB2grqyCD8+9mroy9f+8bdFv/DxjNgUbt1FXVkH1W2971tUbKJenn1Jxnppmnl9WDcCaAaM4/PzpQUN8DTWqRNJvKzbjcrqoWq/OcSkuQWf0bOnpxE9SawkfnDCXByfMpbmuGfFTb81Q6ZDRrI7OIOZwtbeSfzahm91LvKKr1S12qgq8Ycsv7v4kzLcTnFPn3UnSsUfrlq1QMjyvFzomUuXXhmdvg4WUFDWq8dq783ju5blhz9fkgSmcOe9cT0jfFaStT28l3JX/H9TQngDMBQ4HpgDDJEm6tBvG1i0sfu0Dvsk+haZqr2xI+sIfw2yhZ/a9t3Du96okvrvWKVimXp1JfXZc8+EKFEVhe85Wlr6lj5S2+PSU2bl0B6AW7K7zS4X1x618fvr9erWGhgp9aGzTD96JV2uUlZfP/Rcf3PRuyP3mr9nD3SNuC5rmXlda22arDQCLVgi8/qs1fHZX+5I/PYkYIdrbdxUrPlgeUERt0DZLH32e4ZX7WP7ECwCsffE1ki2w4rX3WPzws/Q1ex905AJvvVNmXTnurPPmDz6gftMWZM1AHHLaiZx8+ymc+aZXGshNc52aDq6YzdSW1FC1S40gyK1qxqB7bmnX9jx25OrD9sXbinTvX3nn//js+xdZvVY916t95st8qV6zll/2DmRjeSorWofqHp4URelQtmrebm9JQ7PTxI5y73X7+OqFtPrt87O8RC6+Qo18jBw9hAsuCR6i9+fMh88F9l/xojsJlyRRJEnSSlTdvY8kSVolSdJqwCSKYuiUkQOM2nfeoa9ZpnSzt4jN1MbT84Crrwi63J3aao0OnIOa/K8nWF2STpPTiuySA5THCzfv06WZuxXEd/y6jS1+rTf82bVcLSz0b9Hxnwsl3fvGKm99x5Bpw2iqbqSlPnS01v1UFixJYsELP/HCaU+HHRega4S4LWcLi175hdsH3hSRNyWYTNhi7UH7QXUluUu2Byi8G7RNvJYIlJSoRhT69FWTd4YMHUBCvD4cPKzVqwZu91MK2fa3W6jXdPvsiXFU7CnH2Rh4q2quUM8hi92uS/ixtGrZpw4HztZWMlobEKqrdNsKgtrt2c3v7y8DwKy1Y28J2vYdrGaF0qYYNlWk4nCaArJYO9L7rLzC+3DcIguU13iPbcFMgt37vrbFyqqmEpo1YdlvvsyJ+Dju6+hA0pmMJHbyE2poz83lQKCA2wFOlC2yOYemiy6n74XnBf3MrF1otiBJEgPHjWJHtZrYoLgCn7J8jQd4n3L8n8iGHTlCl0wB3tBg7pIduuVpWfqZsCMu04cSFFkJKyMkhEkzl11yRHNDvhJLAF8/8Dng9RDDcchJ4/m/bY/Td1S/NtftTKISoo008w7g7sdVu3GTtkQrrjWbg+TUebGmpZFwtE+PM0FgwdfqvIxTMPPRLe/xzlVvB2xX+arariamtkpnoBQtHCg7HGz6SY1S+Cc+CILAnpXe7La6UtVIuAvpW+qD1z8KDictsnq9VVTXBoSfKws60EvMp4Hhl4UKLT5ZfPZoK74BhO+LrdTLLWTPVPPWho8YFPFh3NfiQeFB+TAAdGVBe7RlBwWxcWqBm90a2VO6W9k7GKlpqgEKZqB8kWXZc2IPmqQ6o2aLWVcH5QrRjKyptsmT/XfqvWeQOXEwp/mF9tz4G5/kgSme1zuX7kBRlLBGxr19MIMqu+Q2a6AAbDHB01/lNlLce5LohGia65oPSO2ynqSkSA2xJZWqUlp12k2+8uNPKC2rClhf1jLPXA4Hit0bFjdFR3HCcepcUObIrJBZnK3b1KhHY1UNjx31kHe/WkKFq6kFiyXE9SoIumvZfT6ao9Rrt7k0eGlDvRzFokZVK3ttXT6DJ+tVWFo6kP055Y6/eV7/r7SKEof3YbWhqZknt3vvBfNrSpCtAtkzp7J83Uecdmbkzc0TMhIYOn14t0ck9odIDNQu4B+iKJ6sZfLdqi07KEjTigV9e7GEw2yzhfyszwS1In7YoeEVtdWbu3qSuFXK04f30a/jfsrxu0cWbiygvqyOkTNGc+xfj+PGr28hfag6qTpyhj5tdd8Gfdzd16DUldW16UF5CnWD6O3JTjmieoosPxklN+NnB9aKefbtkvnqwc9Y/u5S/iu+6Xm67S6i4qORXTK/vprTrcc90BH86nfcIb5YQf8gVNlHFXJ1K0tUV9awZo03xC43NjF2xEAQBPoO6h8yScZ95g677hrd8mbtfHU2tWAOk2Bj8Wlv4+nfpoUnG157Neg2Ux65g0JnLW9Vr+B/617hT3P1WbCFm8P3LgvGsKkTmTz/O85d0YRdsOH08TcVwIWJ34r6UNViorbFRmurg+KicoYMGxh6p0EYOWM01358I0n9k9o9xp4iEgP1GDAK+Ab4Wnv9SFcOqjtxPyS7CwPbwmIPbaAG3XwjI598lJgB/YN+Pm72oUQlRGMyCUy76AiiE73yJP5pr/3GBN+H203PnBg4DdhWtptvpo/sdDH2xHEMnT4s5PrxafHq8YLMyUXqQYUiXIq6y+Hi1//k8OmdH7LuyzXt1jjbX6IS1Kd5dzjSIDKGDEjXvY/x0YcbM96biTruNLWw1f0cb0IBs4lKi9eLWvzzMppl9ZwN98Tf5FIYOV2v9t+oheCcTc04QtQHJfVP0nlQ7oew6ZepjQSjRwwPul3ioDReeOUe3njvETIH9dPrk6F2Augol44/lmNihmLxuy3Xy83k1SbyY95wzkhQ6yMtB5AXtD9Eomb+DpANPKX9ZEuS9N8uHleXs+6b+fz0xEvs3aNm8+S99lZE2w0aEvqpxRwd7UlnDcZlr1zFg5sfxRZjZ8Kcwzj9AW9orrqwGovNwgRxCuc8cQEn/P0kAMafMoHplx7lWc/d3mLZ24sDpI22Ltise3/oqRMDxpB93UxANTB/uuv0sHUbligrLoeLL+4JTJ89/PxpYWuo3FTmB4/J560I7YT71yAJ3ZzFN+1i73yIEeaLHEeDfh61qtqbCGHzUU1waerlipbubEHtADD5da+YqauxiWaPmn3o/3+0WaC1QR/9KNJCZFariaFZ6oPegGv1HahtsXZifR6S3OFvW3QUst2OIzZ4q/RDhp3ODX95iHETVH3IT+78SPd5R4WRAex2GwoKFkF/W25RXNS69KHDkKHLMOQu2cFjRz/UZofq3kSkj8DVQCHwMqCIoti+kuleiPPpJ0n5/ksULSwRrHo8GFbNg6o763zyJ0xrY209hZv3UbVPTV8VBHTp2wXr96q1TbLCkGleryY+PYHMCd6J0Gs+uoHplx5FY1UDLQ16z+KvH14PwNgTVWWJSWdOCRjDkVeoBqlqX+CcgD/up8Fghm7o9OFM9JFbCrkPn9qubHGm5/XetcH72YBqrH3p7jooX0/zjsy/d+uxD2TkPvpkluJ8r/ZeSbG3iNwdzqvVSjtMAmAykzKgr2cdU2urp5X60VdlA7CqLLjRaPEzUFOOmqgNSKZVy8aLGZKlW6eputEjhgxgjbZ5sttqG1rI3RS8J6v7zKjTUtybavTJFI7mjnv7NpsVRQGnIlNYH8uKCq+X5P+YlJzSsVbuFXnlB5RgbJtXviiKFwCrUb2nLOBe4F9dOyzd8ceIoviyKIofi6J4nc/yWFEUV4qieOr+7D+W9qVc1mpPhdnXX84ZT9/frm2fPfFxHpn2AHWltexalqtLZVZcMk21Tax7eRVPHDuP965XPbribUU6Tyl9WB+KNqt1E/6Rt+FHjUQQvPJD1YWBRsit17fq4995/Nh5fHzbByHHW7JDvcH4z2WBKjZbvLXtJzF3SPK460/gT3edztFXq7Kc4UIh+WvydO+7W0nC3+vbtTxXZ2gNAqktLcf+k1o7JmhlForP/63aXVgu3ohJy5iVWx24GhqwmQQGlKvndJkm62N1tuLQPInDzprC4wXP8Xtt8Mw63ww+18QEflm4CoAtfxXZcJvaW7WwWF/X5F/D9/4Nb/PrKznqPgBChAZdmuTSIePUEKC7B9Rf3hex2C375UHFRNuZfvREXMj8um8AvxZ5s3CbFXW/5z55ISX1SzG3o5mqm9TB6pzgsrcWd3iM3U0kV/4DwHyf998AR0Syc1EUXxdFsVQUxY1+y08WRXGbKIq5oijeGW4fkiRtkSTpWuA84Cifj+4APgq+VeTEtzN8VFHetufRFi6nK2ACXpYVvzoo1RDuXLJDlz4umARa3T2YgswNKYriKcgNFpqLS48neWAKgyZl4WxuDdtwsJ8mjZSlSSn58t3DX/HeDYGpv/64w3W/vPgztw+8iYGHqt6gozn0Dd9XKDe+T0JAqnpX43+Tefmcf/HhzaELmv9oNFTXsuJDfc/S7268K2A9U6w6x+pUFGTNO8kYMQSTlmjkanFQulH1phSt03RBbRIAg50NpGqda4u3FrJz6Q4SrYEtJXbFpOgM1ILFK4gWvOHEWEHdx75te1nz+SrPctkps/G7dbp9rdYeGJ2CXokC1P5Lxc5U4jKSueRyb2i7XiuqlV0yf/vmVo7963EBY4wU2aXQ6nB4vKUBVq8G5uom9SExyUeDs70kD0zxzF2v62CX6+4mkkBmf+B14ATtvQMIn0ft5U1U9XPPnUwURTPwIqrobAGwQhTFL1HnTP2TL66UJKlU0wW8DnhH28csYDPQ7YJSUfHBwwztQXbJnhoj32XB3vvPgQhmAbNV3TZcPfHQI4YHVSsXBAGzzYIiy2oWX5iduGP0C57/EVu0leNv9LbHdrnkiEJv0Qn6U+WDv70DhI/Vu6VYJp4xmXOfvLDbdfHcY45OjPGEcHxVOP7ofH3FjQyvLWX38CyGTFYn7YXaas/nipZwpGhGSQCPgTJbrVi1+ShnUzMrH3qc/kCTVb2UfTX3orVstu8e+4YtP21k+MABgL4r7NDGSt1DloxCS5B5w8ayJn54xvtAFRUfRYGfSr5bXULGhODnQQkCRA/qT+mqXRQVerdzd9q12K30HR08sSlSjr9xFgUFxbBAryQ3cfIYbv7zBSy49+sOdQXwZdzsQ8lfu4f/XvcmE06btF/76g4i+Ws3AJdpry8FTgbWhV7diyRJi0RRzPJbPBXIlSRpF4Aoih8AcyRJegQIGq6TJOlL4EtRFL8B3kNN2ogFxgJNoih+K0mS7g5fUVHB3LlzPe+HDh3KsGHeuZ04QdD1jGmL1X0GY8lIY4jcRFFOTsTbBWPZ0mU4/PT+tmzZQonsDTtUVVaRk5NDXq4aC08emULV9koWLVpEk1OdMN1bkU9jTvD0+OqqKhAEcoKMtbm5iZLiEpqbWyguKQ66TsCYP16Kabw3g7G8tIzWxpaItj3qoWwqt1Ww5V3vTX5v3p6Q2+7doBZQrv18FRU1FYy/ynsh1dfXR3TM/cGlhfMGnZRFwuAkVjyxlPRJfbv8uB2lrXO9s7FXlIEVli9YSNFXn2P/dSmCSf+s+OO33zF0j9oo0CwIlOwrYACweu0aKirLSQYaKqpwNrXQH9hX20hOTo5OtXyvK4Oyz76nrEi9LuorG9Wr3o/ff//d89pkEnAEqdsrV1SjKJgEFFlhw9aN1ASZh83JycGhKDiamz3/b8XpJEFQ2KMZpvk/Lgs4F9ZtWMfy+UuxxtrImNSXDmEFZ4Z67pU564k3RZGWnsTaVVvYOVb9Ln9b9Bt7mvLD7SUsymjvQ+XX739FXL/Q6u+9gUgM1K2o6eUC8GegEvjHfhxzAOD7DRcAIbMNRFHMBs4C7MC3AJIkzdU+uxwo9zdOAKmpqcybN89/MQBbFiylsZ3ZWX0zMzntsbltrxiGb1Fl9KcePpVNiWtprvSmtp900clYbBYWoLa0jo+JIzs7m8W5sJn1jD5iLMtzl5CdnU3J5wXItS7OuSFQ0aLk3H1s+HYdlVvVeZTs7OyAdVbHLyctJY16ax39B/QPuo7/mGt2VunWy31lC83m5rDbesiGVR+v8Biov/94O/3G9A/pvS3ZZWIjawHI/yWPa/5zvac1Qk5OTmTH3A8UReEHvsLeZOfcm85n3YsrGTFhRJcft6OEO9e7gm+ingNXM6NHjsT5r+cAGDVsII4duZ51xg8fje8MZXK0Gp6bfuRR1PcbQMVvS+ibkoD9+GxY8D3H3H8Hw46YzEbFm03bXNtKXB6kpqdSTinBotHm0+dwygWnsvAfaou6xOQEWhtr8e/WGd2sesVDpg5j1/JcDpt0GC0jmln/n9W69bKzs1kz4iNMUVFM0P7f9YWlbAMSMtKBbQwZNtBzLrivj8mTD+Pj2z8gfWgG2TdnR/hN6inaUojFbmF36QJun34nzWWNlGuiueffcxHL+//KyTedut9e1Pd8AcDOd7Zx07f7cyvvetr8SyVJWiaK4nC8805LJUmq7tJR6Y+fA+SE+OzNjuxzz/yFpLe9mg5rJ8yDHHvNcWz6fgO2aBsjZ4zWNeJLHZyGs9XJ0fOOw7Q3UD5/+JEjSB+ijnrItGHEpoYINSpKm6G3WbecjD0+irSh6fQb2zFRENnpapeIq2/NlD1G7VrbUNWAQGBN1KGnTeSLe73zZ201Vuxs3IbTFmNj9287aW1sZdG/f2HRv3/h8YLnunUsvZEBmX1x5eWRGGunXHvQk1v02WvuxoNuxo0aRMOuzSSlJhLj6EMFkBhrZ9SoTPYu+J7MEVnqfnxCfIoi0HdUPwrW7Q34zI15sL4ecNY5M1j81pf4Gyh3Qbo9zk7/cQNJH5pOZX5wQdiYhDhdfVNrnZacYVVvl//3+N89nx3552NY+tavRCfGYI2y4mjqeJLEB397h5RBqfz5tavJsMVTb/aOITE9kVPvOaPD+/bl5h9v55kTH6ff6O6VEOsIkWbxzQZ+AS4A/ieK4vT9OOY+wDdNfaC2rNsYdkjwIrxw9O2T0vZKbXDqPWdwx5J7SOyXxJTzpnHyHV4V4poitQ4qYXASp95zhqdC/bCzD+fWX/7JtoVbWfCC+pQ44phRrPjgN9Z+sTrgGKs+XuHRket/SHDjM/6UiYw8djR/uut0Jp0xOaKxTz7ncN37mTedxKxbZ4dYO5BUn2aJPz39Has/XckD4+/i/vF3BWRU+feNMoVRu+gqHi94jvOeuogcP1FfAzA71Zvwrhdf8cz/VJXqMx+T/PoNmau08gqz2VMH1VzfiKKFuk1WdZ7Rdw5KVtSHBI+iSZCxtCqCLuvylvuv1kkFedbTir23/LyJw86egi3GTlR88Cnsyq251K/fyOb5arZbdJzqvccOUc/hwVneuaYz5p3D4wXPkTG8Dxa7Fed+tLLwVXaZdt2xLGzYSabWwLEzC3Pdxxh9/CGdts+uIpIsvgeB4cBFwIXAdNTEh46yAhghiuIQURRtqEbvy/3YX7uxBWmHERKtIG7A2MCeTO0lb8Uuz83YYjOz+hNv+vimHzfQ0tDCnp926Tp8xiTH0mdEX/as3O3NGHLKNNU04mqjLbx/w0I3JTuKKd1ZgrPVGVSp3JdDtYnUHb9u57O7vEmTw44cwYijw0s6+TJo0mDu+v1+rnz7GrYu2MyuZd5w0FKf5osABevzOf2Bszzv/RNKuhOrn/aif43WH5FqzRgl13oNg78HtfoVfdZjZaF63rsUhSrNI8nbupdNa9W5lWa3tJfVRx9PEXjtkpc9LdWDeVDJWZk01agPZONOPhQzAvHpgZluyQO9y9ylFkn9g2fE2VrU/W3+8HMAT8JEodaCw6256Y/VbvEkTXQEWfYKMP+et508RyUv/OceiuuWdGp/ssWvLQII6OjdG4nkys9E1d6biprNdwtqckKbiKL4PrAMGCWKYoEoildJkuQEbgB+ALagtvLYFG4/nc26l96IeN0+Z5/B8IcfJO347P0+rnTmczxx7DwKNuSzZ1UepbneQsbP535MY3UDm95ax9MnPMpzf3oSgIIN+Sx65Red0fruUTXFt61ztqaoOujy/936Pl/c8wkPTrybrx/6POw+kvonYYuxUVtSw7K3vQV++Wv3ULgpsD4q/L6SGX38WKzRNl3m1QS/gt+N363j6wc/p68WgginF9jV+Bcpl+0sCb7iH4iiOLWeZnd6Jlbtf5Ok6G/Mzq1bde+LC9TzVzCbPJmw5pZmKjVjZ9Z08Rwtvhl56r4HTRrMUVccG9RApQ3q7wkBb/x+PRW7y5j7wI26dcyTDiOhrzdlO1TZwmFnBRa2AzRq7TB2bVSnzhMSg8t0mW2W/fag3GHz+AT1O8oaOqDTm2e666GWvL6o17feiGS2rQa1xcYo4P9Qg7sRCddJknRhiOXfoiU89AQDXPrh58alM7w+uHrxtu17mfnXwGZp+4PscLHs7dDFcoqseNKbdy3P5esHPmfo9OGevk9uyxTuxm2xW3Tq5b6YzAKKS0aR5TZP/pRBqUy75Eh+/U+Obvnncz8mJjmWq969Nuz2brYv2sqrF73EjGuPB0XR6evFJOvrW2SnKqY7/ZKjWPzawm5XkvDl0FMnoVZLqJTvLmPEMZF7jgcjiluFPMzcoKvZL7vU6QSTmmYerQmyHlqUR2FfdQ7JHhONoijIPp1zFc0g7V2zh5iU2KAGyhYbgyL7iAkLAqmp6WwsT2Vcmmr8YgcNhAwfA6V5UP41gKs/Xcn5z13ieT+gvzrn21SthgxL9qpqGE2NLcQnBN46z3vqolBfR0TILu/1eNM/LmPIiFT69mvvbHnb2LXw67acLSx6eYHHQ+2NRHLlvwwcr637KXAksL4rB9XdHP3I3Z7Xucn9Gffx++RlqiG9qqq6UJt1GFlWgoetfK53fzXzy165knk7ngB8VMbD5A48vPMpjrv+hKCfCSYTrU0OWupb2nzi275wa4BxUv8GuV0aeW5j6m7U6Nv/Kn/NXt26LqcLk8XEkZcfw+2/3t2r2q+3tyvwQYmmQh4bpJVKbqo676m06kN+dmRcioLJZCI22Wss+herklcmkwlFVgLmoACWvrGIn576zvOZUxbYXKG1trFZkf3SyuOT4thUkUqjQzUiq1dt1skhucVnTSYTZz16vm5b315JfbQ5Klmbc3MfZl9BcC86Li2euLSOp22f/sBZHHWlqrQiCAKJSftfcxkMt/oF4AmP9lYiEYu9H0gBBkuSVIYanot8drwXIozWt6Wwxaouu1OB8z9+DXtyEi53FlAXqAYrLjmgDcCo7DGeolyT2eS5UNwxbZPFjFVLtY5NUceb2C+pQ8c3mU2ezKi838N3Ttm7Oi/octnVdrag/pje7zEqIdrztwCU+LXflp2y5ynXoPfhnpMZNzZQYaTPDLUppkMLHdVrd/V0k+wxOOHatPjWQbk9Jvd14faoZEVgQ3k6/9oyAJPJ5AnZ9RnVl4wRfYhLVG/sMVbVkPbft1vXgNK3Bcz0S3waJQIuh5PoP6kafaZ01XuRtYc4pztjMYTn+MLpz3D7wJvanNcNxZiZhzB4claHtm0PfX2y92q7uZVNe4kood43rVySpODdww4gdhRW4ZvHl5KWxMoZsxgyw3uyTr72z+y9cy6T/3pZ4A72E9klY/FTR5B9KtdNFrPn/fePfQPoezIlZ6qhuwHjApXVJ845jPx1ewOW+2IyC56LqC1lY3dihj+KK7J+UG7MPmnmty74JwA/PfO9+mTsFwd3e1AGvYuWwiLqWl2ktqrer+J0sCs6maFNVbjMFswup2d+KXrwINiXh+Wsc+GLjwE881UAO/sMJrMoD5vPMkXRe1AmwW2Y1PcOWT0n1pWp2XSHTFCjHAMPzdSl/8fG60PGZpeT3au83XNT/TpNp2alUZGnhu+crS5iZx5P07ffUvHWO2RddpGnb9VFV8whraKSUWOygn4/7oe5loaWAAWVSNj9205iUtSkqK5k6kVHkLdyN6v+9zsNFb37dv6HvAsMr9W76KaoKGbfewujZ3iz54dNP4zjcr7zSLl0BnMeOptBkwYTn5EQ0MZ8ZPYYEvsmMeOpWZx67xwmnK4mDlz3yd+YcPok7D4psQPGZXL01TNC1iG1FRI7/sYTPYkJ8X3CqyK7ldH9UUN87fCg/DwiZ4uTHb+qsjX15fow6qybT+a6T/+GQc9TW1rOJ+Kd1OzcxcZLr2TPVX8hSVbDdxtXb0XWUsbNmnadRcuQjbWpy/sND9GS3GzWGScAFH2mXk2L3e9jgQ+3jWRXTRKp2YNDNr30T4JoFcw64eTG6saQ62/5eWNAlq+sZcu+8drn/PfNr0IKtbozTzvav+y9699i0b9/6dC27UEQBM5/5mJGzxxLXXnnT2F0Jn9IA+XLXuwIHVAG7ghHXXEsN3x1CxnD+zD9kiM56Tav3P+Ma47HbDUT2yeOI/98DHMePBtQi3Ivli7XeSsxidEsffNXti/aFnCMoq2FugLgYAw7cgSjsscAtJkqfvnrfyFFy/rx5axHzuO4G4LPcQUjPsMbm//+sa/5z4UvesKL/pXx8RkJZAzTdxjuSQZN0heD/pF6RH33t7lkbVvH0keeD/isqaGB6Lpqz3sZGDpGlViK1h6eMo8NoSsd5Jrz96DcHpP/ZGuj3MrTn39M8hS1Hqlw8z5eu/RlTzTAHQJ3szV9sC5knO/X6qV0h/eBtbqwGmuU1/upLS0nJTMJgGJHeG/DXXDe2hhZd25/5DY6XHc2GcP7EpcaR2tTKzXFNd123PYQSaFuqiiKGdrr40VRvEQUxW4Xae0K0p9+ipnvB2/t3BVsy9lC0eZ9KIpCVEKMJ2EA1IuzqaaRnV9v17WxWPPZSj6/+2PdflwuGdkpB1VYuOHLW7hvw8Nhx1G0pdCTIt5U2/YkaVqWGov3DbsNnT6cgeMjbwuWPCCFO5fdy/Vf3MyW+Zt1c1/+7TS2zN/Eyv/97r+LHuPwC/R16Ru+WdszA+kBYhvUG1fG7sCHIZNLZoDJm2TjkhVitAy9mhL1IckhK+yyBE722+sDn9xNZhPDZ3k9dvfZfcLNJweui4BJM2YNlfVs+2WLZ57JP4Iw9pJzuPyNv3jeh0sMsEZbscd5DdTiux8GLRzuVBSdioQ/A8YN5OQ7TtF1ym4PihyZAHNnceo9c7j6v9fx6sUvMW/Kvd123PYQybfxNfCApon3M/AW8FpXDqqriRqSRdLRRzJowlgSMtLa3qCTeO2Sl3nmxMfZ8vMm8tfk0VznlYN5/k9P0ljdyLYPNvHapf9m7nBVI2vv6j0BnXPXfq4qSLjj5r7Y/TqFBuP7x75m8asLAdjy08aw6wKc88QFDJo02FMn4nK6WPfVGgo2RC5aqSgKCRmJDJyQqQtNXv/FzZxyt74z76qPV/CLpprRG/DtsAuwb2P76r8OZKJjQs+lZDr1HoUMNGvzia4GNYxmi7KjaF7BDqvXix5kCixoNZlNTLrQ+zDg1Dyo5AHJzLzJq6QvIDAtejAfX/g6dw6+meXvLFWXm/RJFTuq1GzBo044QhfWq/Lr9zXmhEOI0uaMouKiPEYWoM/OLdQWq+HBKMFObFxo49NnZF+Ov/FE4kLJkLWB0s0elCzLPH3Co20mSvUkkRioscBK4CRgCfAKqqL5AUtFcTl7C0rbXrGLkF0yv3+wXL/MKXseGQUBj0qEerHpT1p3woEcoqlaW7gNoyAI3DI/bDsuQA177F2zxzMv9skdH/Lf695kyWsLIz5m+e4y7hp2K/+99k1duDI1Ky3giVN2uHp1Fl8oDbeDETlLzXjbKwQGTSx+noqsqNJDACZHK7KiYLHZGDdRzZoVbN45pT5/uyHwWLJMXb431FTbqq6ft3K3bg5TQUFGQZEVZJfMlp/Vhyz3zd3tQa0pzeCmtTKmqCieOFYV041OjOGwc6bqjrvl500e78vldGH2a6feqCUS2AQLw4aHjho4W5xU5lfQ2tSxOSjZFb79TWezb30+xVuL2l6xB4nEQJlQ9fKOAr4DltIDfZg6E4tJwGIPrOHoLhSXHJA0ILtkb5q51ex9rwQqRrhDbS5nx9JZh05XcxjHnzqR1EGB80v+fHTzfwGoLa5BlmUq96hPoO152nMbnI3fr9dtt+rj3wOap7lccq/L4hv/pwme1+uCaCAerIwclQUEJrkEwwXEJKleS7TJWzcUpTUuPGKWdz4qYeyYgO2dLU6+uufTgOXL313KT0+p3Xr3tFbyae0GZJ+iQXe3Y/+bu4KAotgpK/U+UFz2ypXEp+trlS5+6XLOevQ8hh05ggSfgl43uz94Tz2OAuMmjAz+xwOFmwp49IgH2bl0R8h1wnHJy5dzxGVHd2jbjuCbqGXRJL3qymqpLek981GR3AV+B+5DNVA/oery5XXhmLqcCY8/xJQ7ei5LTJaVAAFUl8PlKZp1Zw3JLhkliIVy1z8l9Am8mCLh6KuOBWD9V2soiuAJyp108b9/vI+zxUnaUPecVORejq/BiUmOJbFvIrNunc3KD39jrU+nU+idHpR/mM8XZ6uTTT8enA0NTdq5p0RS2yOYiE5Qw8tmQcCp2ZCiYk3OKM4b+orymecxTdN3oXXJAntqvUbE4eORjMjMxIXMkcfom+1lDO+jq61zY4+xkZScgD1OfSD993kvBGTZTThtEtMvOYprPrqBQ05Ss3brfYp/M1AjDk5ZobkpdAKEu3TEbTDby4hjRtFnZNemmPtijbYR3yeBiXMO4/6Naq/Yhybdw/9N7j3zUZEYqAtQ9fdOlyRpBWqzwt7dRKQNYkePUus0egjZJXueWNy0NrZ6Qm8eQ+WUsUZZA2oq0rS2G+7f7cW33Xp7Y96yU/a0o29PGwzfyd8r376GuSsfZNbNJ9Pa2BrwxCm7el8d1MgZoxk5Y3TQzxa/upC3rny1zfqzA5E1K1WZTEVuO5yclJqE2WKhVTsv7Fq6tluHb1Oud+7OYvMak3UtmjHSIggf7xjO8iLvjdpXdaS1RH396yL9Q82tv/yTfmMCO9o+8ewd2GxW+oz0eguRnFv1Rx8fsMylKKz4LfSDiDsbtaN6fJt+3ODp6ttdDJ02jLVfrObpmY8CbZeodDeR3AXSgAWSJH0jiuKVwBhURXKDdvLn165mzAmHkD40g9hk/UTq1AunkzVlCNlPn8gFz13KUVcei2ASPC06fEnJTGXmTSeS1D+pQ+PwLZptt4FyyZ7ECt8bR1v4e1sNlfXUFNdQVVBJc12zLpvwohf/zGWvdq7+YWcwMls1UHMeOlu3PFH7P7if0g9kdv6+lq+OO43iHWpha3O9+j9OjGk7qm+yaYKv2nOLLVr9PuyacSsOcb4U7lLTvL3Z5AK+867OIKr9Rc5anVcrhwh3f/j3d3E0O3RhvWCZci6ni8eOeoglb6hK36f83+0B67QqkJAQOgHCbaBcHTRQ/73uTVZ/3L231qh49eG3cm8FjmYHI44dxaDDsrp1DOGIxED9F7hcFMVTgVeBB1Az+QzaySEnjeeKN//KwEPVQtvZd6od7k1mEyf+40801zfjbHKSdfgQ5jx4dsjOmSmDUjnptlN0PZbag2+BbSS9lrKmeiVtSrYVeWqofBWi28JX2v/HJ7/lgUPn8uyJj3mWucVxAWJT4gJ6QvUmxs2eoHvv9iR729NnR1j5xAv0NzlZ9MCT/P7BlyitarZdRor3Bu/qN4Dc+IyAbUsa1XXjU5IAcGg2w1PEmxjifyrrs+8CPg5hoM5+zKuj98KcZ6jY481qvfTfV3het9Q3s32hqq5+wt9PCnpdmcwmKvaU01AZ+qGr2NEQEFr0xbyfHpQiK+3St+wMfB+qXA4XBev2drsXF45IDNRIVHHY41AVyB8Gum8m7yBi/ddr2bl0B85WJwl9E/nu0a8B1Stprm9m7+o8Fs9dQP66fFqbWlEUheXvLuXj2z/o1HH4GqVIbqq+0isvnf0823K2ALRrnsgWbePOZfdy26K57NEkYaw+ck/urMW1X6zm9oE3kSPNj3jf3YVLm1vwrxnZ9stmgIDGiwciibGqpzSsaBfmV17yCMO21nlv3AkpiUELbR3V1QBYotSbXk2V1r9Mm7+Kjg/ufWiKRljtVl0fMDe+N8yxp6jqEWZMVBdWe5oO7lufj6PZm7o++vixDDtSlUKqKa7xGI1scWbwMQiCqoEZZq7trc+eDvkZqAX0pz9wFoMnDwm7XihklxxQE9jV2GO9Bkp2umisbqS1oWOFxl1BJN+GE5gCZKO2Xt8Z4XYGfrx77Rv8+7wXWPnRb+Sv0Vez3zfmTk+a+W//XcrdI26joaKegvV72fJz57bLcofb+ozqG5Hg7ORzpzJk2jBO+PtJuuWL25FmrigKUXFRxKcneNLMLXYr/1x+H9d+fKOneZy7RuXbh7u1h2VEHHuNd16ifHcZi175BcWndUiwJ/0DjQCPWlPybqr1ioo2NrfoDNQ+WfUcMrQW5bWakoJZVtdx60j6G6i1pWnsrE6garWaZWe2mgM6N7txG66kNNVrH2VP5+Gp9+lqCX3D1dZoG5kT1Hnm/5z/gsfLDSX0CpoGZoj/YcnA6Rw6OnQGn/uYR181I+hcWCQoQZKnupqYJG/NpOySiUmOZcwJvafTbiSG5mdABA5F9aAOATqWR2kAqCfC6k/1xbeKonguIm+dk1Yb1cnnrMVu4aTbT+Gcxy/0yLOEo3hrEbt/2+mpg+oo94+/C+mMZz1hDGuUleSBKQydPtzjTdVX9l7xSt/J9TevfIWvH/icPavyPFJI0Ultf5e9neZmfYZbsqYMbvapudu1u9Cj8u9SFJrSVFmq3HTVIFTWaGKyWk1U34Hq5wl+beC3VaWwssTrnbscLnavCF402lTTyMybTuS4G04gc1Bf5CDhQF8DJcsyOS+pXvjACd6EqJb60N6B2WoK2sBPAXLmV9Jc2xy4kR/FWws7lKbt7k3V3c05Y3xkoVxOGavd0uFC464gEgN1KXAWMFmSpM3AF8DVXTqqgxzFpQTNJHJpNwG3hyO7FBRFQehkC2WxWTxtBhwRFBW6W737a5id/fgFER/THUos3lbkCWNY7FZqimtY/u4Sz0UdarK7N+AbDq3SinUFQSBDC4EeDEkSg7P0T/+p+arB8FUiRzCRkqHWz1W7BE8ZhDlenadyaAKy7v9k1nmq9zNphr5A1p+W+mbevPyVoJ/99PT3VOSVk9gviZ+XvMkt/7wiYB3fAnDf/5VvOUY4nbwxJ4zTpXlXjFJllypGqF5dJMbj6RMe61CatiAIXPfp35hy3rR2b7s/ZE4YxMAJavGxIivUFNew4sPfunUM4YikH1QTkATMFUXxQ2CYJEl/nErFTsSdPSSH0Nxy35zd6sqyywVK58ufKIrCb+8t48U5z1Adoi28bn3Ns/volvc8y4ZOH860i0IIgbZBgqagfsxfsynfXcand35Eaa46f9PbEw1Ou/9MAKIS1ewnW7TVk4HY0eyt3oQ9KrCWyB/FJHDIBFVkuE//DMza+TlYK+pNc6mehtuDSjt5FhM+/YDooeHnZtoqWlj7xWpcThdJyQn07R9YYmGxe+c0fc+jutJarn7vOvqM6huyyzTAkKlDdYkWfTPV1PT4eNXLaM916D4ntszfFJEQqyAIDJk6LOz4uoK0Ielc+fa13L/h4Q5nBXclkYjF3g28Dpyr/bwuiuJdXT2wgxF3aEt2yUG9oj4j+3LoNZM99U2KSyE6KabDBbnh+F5L0GivQbjk5SsYfvRITvxH+3tWnnrfGVwk/ZmzHz2fxwueY+LphwWk5vYP0uOqN+H+34w/eQL3rHmIjJF92aP1GvKtLztQKS4K1Hf0Z6ijHsGqGgOTSfB4V1kjswCI1c5zdz8nAEti4DmcPr1fwLK2cBfB5q/V15yd+fC5IW+wdWV1jDx2NLfO/2fIzFhQuyX/8sLPnvdjrr2SxCOmEXWYGm1oj4GSznwWRVF448//YamWuh4Ol9PFqo9/7/YMOkVReHjqfdw//i42fLMWa5S1Vz0kRhLiuxr4CjWbbySqeOxfu3JQByvipzcx5bypQbN8Trj5ZJIHpjDwmEEMP2oEx//tRKISojj1njO48etbOnUcvidge72z+c//SO7i7Qw8NHIlczfH/uU4JmrzWKU7S6gpqsas9Q1yZ1lNOXcqsalxHHvNcSH305O4DenSt34lPj0Bs8XsUXVPH9qxwume5sOzruDTmapnWBKBgQJYtUrN5KytrmPECPXvb9UeupLOPQeAmKTwTfsa89SC71m3aNKeEbQxcZ+vaX7ftW8Gnz+jjxvDuq/W8MMT37a5f1+syUkM/7/7scTHE5McG7IPVDBKthVTq3lOUfFt15C5Wp18+Pf/snX+5naNcX8RBMFj9N+55g1GHTeGjJG9p9VNJAYqGfhJkqRcSZJyUeWOkrt2WAcnmRMHc97TF6sFudfP5JR75gDqCXzirbNpaWihcls5qVnpnHz7KcSmdP1kZSQG6tqPb/RkVxVt3gdAVUFVuE3CsuBfP/LkjIf55v++CFp9f9+6eZx6zxkd3n9XMuYEbzuI2wfeRFVBJQ3uxI5e9OTZFs7aOlrLVAmr4TXFDNbkfIhAMQLAbQ6a6hup+00tLl3xs6oqnjBQ9YwsMdZgm3poKFa/t+mXHgVEZJ88D1dTztXPZ3394OcBjS8925gEdvy6jd/fXxZ23/3G9A+aNHToqRO5f8PDAZ14g5E+zFsftlfL1I0kxOfOLuzuOih/Nn63vs1+ct1JJAZqJfCwKIpvi6L4NjAPQ0miQ6z6+HfWf72W5rpmkvon881DXwDq019NcQ27luWy/KFfKdlWRF1ZLS6Hi5yX5vOhJtbaFURioIZOH86hp+kLFPdHiqhkuyp9Y4myeg2U9hT3yZ0f8tTMRzq8767Gv1vrk8c9wvePfQPAvna0H+lp1l5wGRsuuEy3bNXM2VgbIsuiNFtU4yMAFoeaaOPYo4bd1m9UEyuqtTYVFXnlvHbJy55QqD/u4tiouCguf+MvjDouUEjWjft8NVvNJGfq52v8a5jcaiS7ludqosvhz/Wbf7qD+9vopdYWty2c63n99UOfA7D0zV/DbpO7ZIendU53Z/EBHH6+PjHD1dp7yiUiucvcCJQBl2g/pdoyg3by1QOf8+61b7DoP7+w+7ednuUuh4t5U+71PIlvW7iVhybdQ9HWQoq3FOrW7Wwi9dL6jRnAxDMme97vT2M197bWKCtJA5L5x8K7OORkVaTT0dgaNlzT05TsKNa9982CDFfk2dsQWoKnTA+m7SJN66GHemSNTAJUn6pm6Y26Vs2sc2lZfDYt5Ldn1W625WxhyRvBb9QvznkGUMsfxs4ax1XvXMvjBc/x0LbHPeu4u0+7b+DRiTEccaleL8D/nHRn5OWt2I0iyx12cHcs3sbbV7/m9ZQjpCrCtiz/Of8Fnjv5CUCfidhdnPvURbrvOliDyJ4i7LchiqIZVb18DjBe+xkjSdKWbhibewxjRFF8WRTFj0VRvE5bdoy27FVRFJd211j2F/cFosgya79UEyGzDh/C0VfNALxqCu46KMXdcqMLHqriNG0yXzWHcCT1T2LSmV4DtT8TqW6pJYvdgsVmIWNYH6K0GhmXS8bUjlh/d1O5tyLkZ29f/Ro/am0hDmr69MWkib2aUJh581+YPP87Dp2tFjJbNQ0+AW/7GFCVCoIha8rhjmYHm3/aSKVWrG2PtXuUHxwtTq546686I+Qu5nbPY/p7H69d+rLntQJthmB/fTVHt42byr2VbPx+fcQq5R1JakrUZMN6woMC9bu+6/cHAIjPiG9j7e4jdEoLIEmSSxTF14DbJEl6s707F0XxdeBUoFSSpHE+y08GngPMwKuSJD0aZgxbgGtFUTQBbwMvSZL0K/CrKIpncACGG32ftPuN6U8frS+Le7m3DkpGiSA00RHqNUXyloYWj3EIR3NdMzuX5nrem8NkQ7WF+ynRGmXD2eJkyRuLGDJtGIMmDUZ2yjox295OvzH9KdpS6Hn/8zPfM+uWk3tVJlQ43AWi7aG8oZV0bZ7JFOTvtEaryRHuj9ySWK5QShva5FNTTSNvXvEKw48eSe7i7Zx856keg7Tg+R/JnDCIMTNVlQPfOUt3SMr/O3f3LZt05mQE2vb6K/dWsFeT4dINr51FtH/5QOSp47xh6ozh4ZMOhkwbhuySueLta7okYzdS3H/nLy/8HOCd9hSR3GXcYrErAE8OpCRJkfivbwIvoBoWwOOVvQjMAgqAFaIofolqrPwnH66UJKlUFMXTgeuAd/w+vwjofbLXbeBbjLrs7SUcffUM3XJvHZTqQXXlza6hvD4iA1W0ZR+L/r0AgFPuntNmW/lwJA1Qc2zGzT4UWZb55v++YPadp2oGyrVf4cOuxi2fc+Sfj2HpW79SXVgdsM7H/3ifc5+6qJtH1jEaKiJPdqk8+XQaS8qZdZtIy7r17ALiYgMz9axag0IBNY3Z5KuM4kNUajTNFU2e1jPuJAl30XZdaa1HbRvQtTMJZixCPTRNPmdqyFYpuu2tZppqmmhpaNFp1HnEgCM0UCkDU/jrhzfwn/NfAKDRRwjZn+a6Zk8IP3Vwmu643U291jm4el/HE6A6m0gM1N9QPeT1PsuUSLaVJGmRKIpZfounArmSJO0CEEXxA2COJEmPoHpbwfbzJfClKIrfAO9p2w0CaiRJCpq6U1FRwdy53gnLoUOHMmzYsLaG3KU4HOrcyt49e3VPk/l71Mn1CioZe82hFNaqzwGrVqym1lGLkGAiJyenS8a0ct1K7HltG6iqHerT6OG3HYky2sTiZYs7fEzLRDt/evdMcit2sqNM9cpyt+ci5FhR+pqIi0sM+ffW19d32XcRKX9690xkl4z9cztJY1JoWq6/Aa348DfST+uYHltH6Mi57g7ifPfe/4j0qmi1uEg89Rh+X70K8/ZcYgAXSsD/o7q0kEygxWXmlwW/ULFNTQDI35rPv6+XGHnOGARBoO9R/cn7cictTa3k5OTQXKUWtzY2qN/n3t17iUrWn5vuY/krn6eNz2DZCn2W3mF/n8bqZ3/jzb+8yon/CXpr0bHpV7XX0yviS4y7YqJned42Nelj6bJl2BPab0ASRyeHPGfdmYwA9469g2MePp64AWohe3ef6+7v1BJj7fFrzE0kBmoRbRd5t4cBgG+6UwEQUt9DFMVsVKklO6oWoJurgDdCbZeamsq8efP2a6CdzfjvxrHio98YcfRI1n+zlr2oWU3TTzmCcdPHc/j50/l97e9MHDmRDHs6E06fRMr1bbdk7wjf8hkAJ82JbEI0P2kPy1jEIYccwtjscW1vEAZni5P8dXtIH5pBXFo8P1i+ZEDfAWRnZ0N2dthtc3Jy1PV6GEVR+L7mC2p3VAf9vDvH2JFzfdVDaquTzOTQqdO5aZkMv+AsWl98AZvi4tjjjyV+gqomviRXTRapU8wc5/e3NvYfyJYvv8Q66hCOys7GdaSTdEsq3877kvqCWo6YfSSHnjqRhbf9BMDx188iOzubmqJqFvA9rTVq4kmCLZ6TbzyVpz71BlZ8v9cVQ5ZQvruM9GEZxEbFBn7n2RBTH8WKD5YTXWinck85s/95Wsi/t/TLfVRsLmfv/N3c8NbfPctXlsVQtCCfY449hpikmJDbu3G2ONnw7VoueP5SPvjbO1z2xBUhFSJKdhSzEPV7UFwKfcwZHJ49HeiZc73q0jI2fLuuV1xjEJkXlN0N4wh3/BxUFXX/5fd1+2D2k9SsNE6+/RRAzTBKH5LOl/d/Rp+R/Zh8aCY1xTWUrismZmo0x11/Qg+PVo97XuzNy1/h/o2PRHShhuLbh79k8WsLOeeJC5h64RHEpyd4u/R2cUizs9i6QC2obKio5+irZgQou/v/Hc5WJ29e8Qp1ZXXc/GNgM7yewvbWa6E/NJuYfObJbPjua1p37qTF4fJ4XvYk9VVjQ1PAZjEjhzPyqceIG38IgtmEKdpG9nUz2fjdevauzmP+8z9y6KkTaShSvQf3ue52itxf25Bpw+gzoi/DjhxB9b4qLn75ct1xxp44jua6Zn5/bxllO9UkC/+kH7WFhsKuZTv+v73zjo+iWv//O40kJNSE0EmB0IsiIEUBQYqK2EDlehW46lWPKF9FVKzYrl5743CxXfxZ8CqooKAgKCCCdOlK7zUhQEhPNr8/zsxkdrObupvdwHm/Xnlt5syZmbOzZ+a05/k87F+3r8QGKqlnK9Z8uapYereRPYr5XZVEbmYOM+77hOGTr+OBBQ9z6lCaxwbKVXsyyM/T22ERYWSkniV1X0qF4815E493QwjxTyHE+y5pQUKI94QQlVGSOATYZQiaGWnnPMun/8rKz5aTfvwM0bG1mDNZjWJyM3M4vusY2xdvY80rKzh99DSnDqdx+sgpFrw6j8/v9X98SHsYAE+B5cpKyl7lCGi+TGo3rEP6cRXOYdrId/nPiHcqdf6q4Oifaho28eKWtB3Qvtj+00cM8VuHg6zTmTyWNIHtS/7kyNZDlb5/lWHRWx+QdbZskZCDQlT/NcNohA4ePGbtq9eiaYnHbt50hq8fm4nD4SB1bwpvDPq3tfbYqI0y/27QuSGxiQ1IO3SSwsJComOiuevLcQy8X4V1ady+CenHz9BjVE/u+uo+S7HDZPCEKxgwbhDx3ZQyi7s1ouDgYAodDrWOVEq/x+5GURlMq8U5k7/hjcEv89VDMzzmdVVP93fnzFS9+Hef5zwbtVQhJTXXEwAnpw8pZSHKUGJiJa65GkgWQiQKIWoANwOBF/zHByx47QdmPfI/Frz2A9uX/mml//XLNl7t9y9O7FKCqdlnsvhXj8n8MWcdJ3Yf59Dmg14vS0L3RFr1SS5z/rrN6hN/UQJQ3Fm1vJgV3xT3vP2Tu7hl6hhACeRWxgm4qjBjQLXqk2wtottxFBRwYvdxHm/5EE93mOS0b/YTs6qkjK6s+mIOdefM4vsbxpTruLRTanSbl1vkn5bQtSM741oQ/9gjbo/5ZtJXrPxsOTlnc8hIy+DItsNs/G494KyskLLnBK/2+xf5OfmEhofSsncy/cVAXj74FsmXtGHf2j3MuO8TMtOK+yBtX/oXL/V+ltpxas3GXQMVFBKkjI2g1GCAZr2u4WKosOG79bw/akqZlP8BQlxGQaal5G8fLWXbIufYbsVGUH4yMzfpen1RPK6DG/eXkLNqKOkXawHsdZO+H+cRkEeEEDOAFUAbIcRBIcTtUsp8YBwwH9gGfCml9G5EvgDHUeBgy/xN1naoMZIwK7JpjeTL0BOXjRtEv3vcRxd1R2TtSNoNUmtP5Ymk6w7TLNgcQdWsF0VkbWWt5ch3r/QeaFxyRz/aD+5I77F9nWLqmKz+YiWnDqW57YUu/7hkZQFfkbFZPWZJ+WVzOC00QrVnGTGiCmwNVEhoKDfNmEbHwX1LPEduRk6x33P3CmUYYx9J5ufmk5uZw/pv1liqClD0LCz/uLhRzk+v/wjAaUOR393LvcfNvbjjs3tUOPVS3v1BQUGMeOVmbpGjndLTDpxkx6/by7wQH+zSgSs0/LzWzlpVLFK0a3BDf9f9mIRYHlqitMDNDrM/KWkNKgUYAbhOUo9AKUuUipRylIf0eTgbPJxXFLr4npgOjJYflFFJXfN5E9OfpKzkZeVakX1Nx8iKYipFRBiN0rqv15B1OpM+Y/sqR91qMIKKqhfFmI/utP53JSwilORL21R1sUqk9orSVbXtJCWqabzI/v1h8Xziu3cp87ERtSLITs/m9JFT1vplSRTk5pN1OpcZ933CyNdGWbp35qhm3aw1jHCJP2Zq75mad+5UGGISYolJiGXdrNVlcqfYtnALJ/enOj0flh9UGaffXBsZ8/mOiI4g36XDEhYRRkhYCAV5BbTqk0zbgcWni6sac+1p/ivzqjw+lSslNVCzgPuFEBtRUXUBLkdF1H3b1wU7l3EdGZkvODPdrOAFARS872zqWfatUVaHlZVj6XRlFw78sY+YeGWhuGnuH6TuS6HP2L6Go27gKkl4onW/tmxfUjRt++O/59JhaGeP+dOPn6GWMTUVqATvVIGzr3jy/+DJ/yvXsTXrR5Gdnk1GWga1Gjh/T9OXrEnv5kQEh3NwwwE10rSMJIoaAlOr0V1nzXxO7pwhnBx37RzbfpQj2w4z4tVRZaq3wSHBnD15lsNbDhLXqhGh4aFFQq5lnH4LCgri9k/v5sO/K1UK8/idvxUPRH7yQKo1yt6zeneZGlFfExIaQu/Rl9LxSs/1t6ooqYF6HLgA6AvY7YoXG/s0FcTusDhw/BDiDM2wpJ6tyGvooG6TugQFBeHIdxCT0MDtGkdVY74Mbvj3TZU+V38x0JKwATWNYzbOXW/oVikLQX9hb5xM7GoCrqTuS6myBmrvuk2kTiy/5WC71z0KvJSKPc6X65plA0NZodmlLWjTujVfTZhBQX5B0ZSfrYEKCTMbqOLPgDnSrt2oDg2TGxXbD7Bl/kZ+/PdcOg7tTHB46Q1UUEgw6cfO8OaQV3hk2ZPEJMRa1y7P9FvzLi1oP7gjiT1a0qxzc49h4E/sLJpGK8hV65YNkuLc5q1Krn1hhF+NeUw8NlBSygygvxBiAHARqn+zVkr5S1UV7lxjwqJH2bJgEzEJsWz+wfR7LiSmRSzXv3QTCd0TORl9iho1w7nuxRtp2rEpzS+I92uZTaxowD4QRA0NCyE/T/WA+901wOvnD0TOHDtTJddJT00rd+N0Mh8GLamcpuA9s+5nw5z1NG7flJj4WEa+NoqvJihrNjMoX15GLnGt4rnuxZHUrBtlibHaZ9LiklVj1sIw0LHT4sJ40g6cpFasZ+040zBi8dRFZKdnM8wIceMJeyN0YON+YhJiiYqJolGbxuXSxNy6cAv97h5AYg/lBj3/lblu8xUUOE/5Hdl6OCAaqN2/7+Q/I97hiklXc9m9l7Px+z9I7tvGWiuuKsriB/Uz8HMVlOWcp1ZcbXr+XcW+ad4lnvCa4Sx6awH97h5Iz7/3Ju3QSY6sPER2t2x6/r23n0vrjNlb/eaxr+h1m3d1uoLDQizDiZyMHEJCQyz5m+pC/3sGsnjqIqIb1LJ0DgG3PlKgQnN0HnaBz8t16GfPBhn1XnqJtEcfdUrbGR1LvT6Vr3tR9aPpPeZSa7v7TT2tBuqwYZW6/p3VbA/fwr2zHwCUT5krkbUjadimkdtGqN3lHck5m2OtZbrjsBG/bO3MVdSIrIHSvfaMXQfSHNX3uvWScmvTff/st3QZfiER0RGk7k+1LBft8aIAHMb0XuP2TTmy9RA5Ge5V5qsaMyDp8Z3HOLLtMJ/e/V8uuKYrf5syupQjvUv1egtUcxbLRQSFBNFhcCdiExuweKqy6MnLyuXE7uMc3LCf9e+s4vKRgzi5L4XIujVZMX0ZJ3YfZ/SHd/i17KWZ6FYGtUisRlCvD3yJpF6tuOmNW3x2PV9w5ePDufLx4TzcbLxTurvGCYwYRVVAwfp1Tts76zam1Sk1gknq3oW1Lvlvmu0qd1kxfv/0N7Yt3MKIV24mOz2b926aYu2LilEhXgopJDs9m4MbDxCX3JA6jety/7yHnOI85WTkcJm4nOYXFp9JaDugPY3bNi7R8nPDbPX9U/em0Lh9yb5bAB2GdmbtTEN/uhJTXJlpGaz4eBn71uwhZc8JEi9WI6leRnBGE3NGwpzWNt0X/E2NmuGEhodSq0EtImqrdbFYP4zsAt9c6hxi8dRFzH1uNt898w1bF2620o/+dYR3rnqNfWv3WmnTbnyXpdN+Ie3QSY7vPObmbFVLeHS4FRLA21zzzA2W1H+gi8V6i9QSwnZ4k+yVK522+79cNQIsm+ZtYNvCLWz87g/Lms/EbiR0bPtR3r7yVY7vPEZoeCjNOjd3sorMTMvgi/Gfsnf17mLX2LZwM28MfpnTR08V22cSm1gUGr4sRg4dbYYtZvv023+XMvWGitmFBRlKFn/9oiIUbf5xk9P+Atvaq2t5/U1QcDCL5SLCIlRolSg37hS+5tx/EwQgjgIHfxlSOeB+fUdJtASOFV9IaAgteydTv4X3tQHNuFBAtTEzryz2aUBfkm4scbT413O0fOZJGraMp/7lA4jupOyeEh5+kLqX9fP6dU3DCHfWdVmmurdtgOLIV4obv3/6m1PIcfM8q//n3NDa0zJPeVYLH/lqkadLWazEs84USTfViFR+emkHT3JwY8WiJYdFhDlZILqOnDsO7cyT656jfnP1XAVS58x0TF79xe8AHg09fEng3I3zCFeLJPOFbK/IwaEhPnXULS8Oh4M/Zq8rMWBfRfnrl218+8RMCgsLlZl5AAcsLI2knq38XQQnGj71JPvad6XBxd2oe4laW0qcNJE2b6oIrjFDBpH0mGFE4UWZnTBDJcST+bcrjvwC0k+k8/WjXzqF1TAddfeuKj6Cyjf86TwFQgS1tmMaWkTHlB6Ib/7LRcYMprWho8BBcEjF7k1YRBiOAge9buvjcX+tuNokdE/iqT+et4wqAoEuwy8E4MRuZWmYcbJsElneRDdQfsA+Mopr1dCKAWNGFgWlfVfSg+cPfDWiO7hxP8un/4oj36FeBtV4BJXQI8nfRXCi3YDeXP9OyUrnQcHBNPnHaNpNe9dr1zXVFHIzPYeQb35ZAt1vVsrdBfkOy6w5yMnM3HNnpb8hMtugpeeAgBG1Iji+Q02R3/7p3aWX21b34lo1JD8nn2UfLCm3Lt2wp68FlJxXoaOQtjbHX/t05/71+5j/yjzycvKIjq0VUMZBF9+iGtXIOsoIxTTwqkoC526cR9hf9J2HXWAZILS6JJno7nWo27iuGkE5CmnUprGlWedPgoODCQoKYsD9g71+brOXnJ+bz1WPDw+I71tRmnZs5u8iVIjGt9xceqZyEGa8aHPO5lj/m5hKJE16NiM+sjmrv/hddcbM/pltsBIa5vkV1XFoZ14++JZXyx1sG71npmWw7ps1AGUO926S2D2JC6+7iF63XcKAcZdzYnfRtOX6b9ZafoB/zF7Lsg+W0OcffSHaC1/Ai4SEqfdSboaa6gutpIJMRdANVBUy6fen2bViB2HhYWz+YQOgImrWblSHUe/cSkKPJHJ25BMWWYPhz1xPdGw08Rcl+rnURSjhTe+P6szpoCnD32DUu7cV0yerTvzpIgbablBHtv202UPuc5cbX7+FdgM7ULdpPRq1beKkrDDiZbUulH0yi/A24dz89q00bN2IrNNq/cdpBGW8FDtffWGFyxKbFEfK7uM83Gx8qQ2afSpv+5I/+f6Zbyt0zZQ9J+gwpDMJ3dWIetqNRVaMiRcXjbKXfaCsPIP9LBLrjoatGxMcEszKz5YDyvClUduqfTar71xKNSQ8Kpz2l3ck+dI2XP309Vw27nKWfbiEiOgILryuG448BweW7CXrTBYdhnQKqMYJ1EL2L+8uLD1jOTFHUEf/OsLc52dbWn3VkVO26RsoblZ8PtHpqgssR/M2/dtZ6ebaxob/rGH2U1/T9fpu1G5Yx61yQVBQEFEx0dQzQnVUhPKsH9l1Ax22teLbPri9XNeccd8nfHr3fzmy7TArP19Bfk5Rna5tWMPm2tTRA2lqzyQ0PNRptufM8apxLrejG6gqZP4rc/npjR85sGE/oeGh1ss+Pzefnb/tYPOPG9n0/nrOpqRzYMN+jv55mNlPzuL9UVNKOXP1Jiyi6OHcvuRPtybF1YUdS/9y2v7otmlO24Guv+ctlk//FXndmxzbcZRjO47yROuiCD371+0FlBl3blYuu1bs5GxKOrEJDZi49HHaXNbO6VxPb3iBq54o2cG2JB5c9GjpmQza9G9r/V9ovJybXxBP674VE/7dvuRPZj38BQD1De1Jc03MHDECZRLUrWqUY3MR0TG1KMgvYPuSP31iLOUO3UBVISs+XsZPr/3A15O+ZOP3f1jpZ0+k895N77J9yTYrbeZDM5j/yjzSU9Kt4Hf+JigoyCfOeheNcI5WWqNmDQ85qw/RDdxbjKX7oRfqD/au3s3e1XtYPGUhuZm5Tg6o791c1OE6dfAk00a+w87lOwgND6VBUpzXBVPLEwTQbkWXZ4x6Dvyxj49Gv1eua8YZFoBBttGb6WO149ftgLMBSWVjrFUFi96az2NJE/jglqlsnPtHlVxTN1B+wJHvYNfy7da26UBon+EIDg0sPyhQEU4bJPnekdC151Yd6Ti0M4+vedbfxfA77uqw5T5RWGTt58h3cDb1LEv+87Nl1uwtfn7npzLnzT5bJDWUl1U0LWfGsCor93x9PxN+nuSkoN60YzPCImtgWoPknC1qoPwdSbesmC4ym+ZuqJLr6QbKDxS6PrRu6mZwSGD5QQEc3nLICnXuTUzxUJMaNcM95Ax82g9WDrDXvjCimPLGFZOu9ooafHWiNCV+M7SKI7+A00dOMff52RzbfrTEY8rL1gVlN1Kx562Mqn5U/Wgatm5kma1f8egwWnRNICw8lPxsZRGYk6EaqP7lCBxa1ZgSTa6kH6+aWR3dQPkBh80ht1Zc7aLek20IpUZQgeUHldAjyfJb8SauIqHVeYqvURtl+eQu9lBsYgN6/K2Xte3wYUDKQMHhoYFyOBwkXtHSejkX5BVYfn/e9oMzQ8mXxSTdrHtDHr7K8v+pDKYyRNcRPYiJjyUsIsyaOjT1JwMhtLon7pl1P1c+NpwLrr3IKd3us+lLAs905DzA/uN2u7GHNYJqO7ADja9uQb2m9QkOCaYg30Gzzs2Jrh8YDhLi6/GlZ6oAdgum5L5tiKxT/eJBmVw0sofHXmdE7QinqZzCgsJztotoKowXFjjcdjgc+Q4aXtSETh268O0TMynIL7CeC28riZRHNsx0mo+Jj2XNV6sqfe1OV11Ag6Q4Jf5cJ5LQ8DDLoq9137Yk921TzLAm0OgvBjJz4hdOaWeOneZs6lnysnOp17S+hyMrj26gqpDJm1/k4MYDZKdnsWmemsPNOJlBdP1oxky/k8btmrBhx0ZCw0O54tFhBAUHBZypuS+wO+YOmXhVQJrclpUGSXFO8XyGPHyVJZ/TsleyU15HfkG1WByvCNe/eCOdh11IeHQ4DZMbMW7OA7w7/A0ARn90h4pceySdzCaZjP7wDhq3b2IpLHh7BJWXncf+dXtZ+t4v9P3nZSXmDTPWPz+/92OvXDuqXhTpJ9L5/N6PmfDLJMb8907Co4umsAO9cTJZNWOF03aDlnHMeWoWqftSue/7B3123XO0/xa4NOvcnFZ9WnPdCyO57N7LWfX5CsIia9D+8o7kZeWxd/4usk5nktA96bxonKAo+ipAncZ1/VcQH2CPieSqpl0QYEYw3qZVn2QrvHuLrglWevtBHQkOCWbT++uZ/eRMOgzpRP3mMdaaq7cFU8MijA5QGewQIqLdr38mV9DM/PjOY/z89gJAjc4atm5E3SbKp8s0t6+O/OPju5SwrrEs4Shw8HCz8VYIIW+hG6gqZM7kr/nhxe+s8OC/TFF+UHlZuWz9aTObf9jA1k82cjb1LPvX72PXip3MnPgFU65904+l9j3mC6RByzifhfTwFydsoVJcLbWq81pbaSyeuojXB77E/nV7OfrnYac4WdsWbiE3K5e07akc2XaYrQs3c2L3ceIvSuTxNc96vWP292ljAdyuC7rSqG0T7vj8nmLpFS3ToU0HLCOgGjXD2TJ/E5sME+2UvSkADHpwaIXO7Q96jVaBG1P3pXDqcBpnjfVj08l63gtzvHq96juXUg1ZN3M1macyWTNzFVc9NtxKz0jLZPrY90nqVaSEveit+Zw+eprYxAZklRBO4FygbjPVo7zgmq5+Lon3OVKC1WNZXpjVlUObD3L0ryP89PqPDH7oCqd908e+zyPLngTUFPf0Me8zaMIVDHpgqE86KKbVbFlHZq37tnVat3ph56sVnna0O+CGR4WzfPpScrPy6HTVBZZRiKsfYCDy+Jpnyc/O46uJKiryB7dMddof4iNH43P3CQlgCgsc7F2zB4Chjw6z7Sj6NzgkuLg5+jmKNbUTgN70lcWMoTNw/BArLb5bIq0uae2vIlUpniwV7WE4goKUcn/KnhMseO0HTh0+5dUy/Pr+YnWdckkeqVdjsy7NCYsIq/AL2N4ohoSFOBlJmArp1aHe12lUh5iEWMZ8dKfb/Xb/sbMp6bzQ7SmObD1U6etWixGUECIKWAJMllJ+b2xLIBdYLKX8zK8FLCemtVJ0bDQDxg1ykt83CQ4NCThHXV9hPqj2QHXnCqbBhFNk1yAqFU68OuGpk2VvoEJqhFCQW0Dq3hQWvvEjbfq3o26Tul4rw2HjRdm4Xekh302ufGw4c57+2sngpSKYRjAD7hsEqOlsU2uywBhBmarh1YGIWhHUj4/h5D5nqaOT+1Ks/3cs287po6d5Y/DLJF/amjtn3Fvh6/m0gRJCfAQMA45LKTva0ocCbwEhwAdSypdKOdUjwJe27euBmVLK74QQ/wOqVQNV6KFXWejiB1UQYI66viI8KpyxH//TaSHdE7t27aJ///4+L5O36DL8Quo1q+f03fau3uO/AlUxnvxl7OErgkNDrJe1L9i3di8AmafKHnDvktv7sWTqokqHfjFHUO0HqdefvYEKCQ0hsk4kIR5CigRqXXcnRWU6HYNqxEwqK9Pm66Z7OuC0AiiECAGmAFcA7YFRQoj2QohOQojvXf7ihBCDgK2AXf+kGWDGYK5Qzd64cWOl8nja55pu365h+Fjk2XqPGzdutCKZdhjaidAbalK/eYwKWFhQQGKPJKtyV5SyfNeKHOON+2OmtRvYgah6UW7z2Ld37/aNkGx571FZ709QkHIVsBtI9BnblzbD23ulHGWhqn9LgFqGFmF6ejrhtYq/0PJz8qBmEN1vupiQ0JByBwQsCddyRdVX9crTmp+775Gy5wSnj562XB4qen+SerXi1mljre93JiPdCqV+8S29uWHG35wUKwKxrrse0+nKLsX2f37v/wOUkoqpQwhqus/TNctSDp82UFLKpcBJl+QewE4p5W4pZS7wBXCNlHKTlHKYy99xoD/QE/gbcKcQIhg4iGqkKvwdNm3aVKk8nva5ptu3H1s5mYd/fYL40a0IDQ8lPCqCTZs2EVUviru+HEeXq7sSFB1MSFgIA+4fzKi3b6PP2L5c+fhw18uUi7J814oc44374y6ttG1fUN5rVOb+XPPcDUT0ce987Yvv6o/fcvjk6xn/40TqDowlrmVDHlr8mJXnri/H0aRDU8JGRjHytb8x5qM7VMA+L+FaLlOpZPX/VpYpP4C87k0AWhqGSxW9PzUia7Dy8xXMfX42ADW6RXL/vIkejw3Euu56zMDxQ+h39wBr++Fm461liuGTrycmPtbal3kqk/3r9pbpuXdHkLsYLN5ECJEAfG9O8QkhRgBDpZR3GNu3AhdLKceVcp4xQIptDepdIBtY5m4NKjQ0NL127dpW45WdnX0iKyvLmigNDw+vk5OTU+L4s6Q8nva5prvLZ09z3R8ZGRlrL6c3KMt3rcgx3rg/7tJK2vbF/SmpvJXJX8n7E19YWFgmZd5Aqevu0vRvWf3vT1mPKe89cklzW9+rhZEEgJRyuu3/DGBsSfnz8/PdxzvQaM4xdF3XnKv4w3zkENDctt3MSNNoNBqNxsIfI6jVQLIQIhHVMN2MWl/SaDQajcbCp2tQQogZKCOHWOAY8LSU8kMhxJXAmygz84+klC/4rBAajUajqZb43EhCo9FoNJqKUG2MJM5HqrtiRlUghEgCHgfqSClH+Ls8moqh63rpnI91XTdQVUw51TWqtWJGRSnPPZJS7gZuF0LM9E9pNZ7Qdb10dF0vmeojAnXuMJ0yqmvgBcWMasp0yn6PNIHLdHRdL43p6LruEd1AVTHlUdfAC4oZ1ZFy3iNNgKLreunoul4y501FCHCaUtR7BPWwNgW+Bm4QQkwFvvNHwQIIt/dICBEjhPgPcKEQYpJ/iqYpB7qul46u6wZ6DSqAKYtixvmOlDIVuNvf5dBUDl3XS+d8rOt6BBUYaHWN0tH36NxA/46lo++RgR5BBQZaXaN09D06N9C/Y+noe2SgHXWrGK2uUTr6Hp0b6N+xdPQ9KhndQGk0Go0mINFrUBqNRqMJSHQDpdFoNJqARDdQGo1GowlIdAOl0Wg0moBEN1AajUajCUh0A6XRaDSagEQ76noRQyfrLqCblHKtEGIy8DTwkpRykhCiI7AJ+FBKeYeXr21ea6SU0idy/EIIAcRJKScb2wnAHmCulHJYGY4fDswGOkgpt5bjupPx8XdzuV57YAtwnZTyW19frzqi63qpx+u67gX0CMq7rDQ+exqfF7t89nTJV90QqIenokwEfivPA2swExgF/F7WA4QQFe58GeX7DXiwouc4D9B1vWR0XfcCegTlXcxK1RMVz6UHsAToJoQIxvbQCiFuBZ4HGgFpwDfAOGAIMBeYIKV83dbDeUdKeb+hYvxPlOf5cuAeI5CZE0KIYcALQCtgJ/CQlPInIUR/4BdgHlAX6Ai8J6WcaBw3AZiE8mpfDYxGiXj2BzoYeQqN7zXGuFwdIcQcI8/3wC1SSicPcCFEC+AS4BFbWiGwA9iAionzFbAQFajtDKoXuQ4YgdGrBGYawdxeANqhQhWMBI6gergrgEygE9BQCHEn8DDQxLiPD0oplwkhxgD/RYUyaA+0ACZLKd8yivcd8KIQoqmU8rzUQSsFXdd1Xfc5egTlXf4ETgMXCyGSgfrA20AtVMW4GMhAVZ4U4FVgPLAIpVJ8M7AAOIGqqKAqJMCnQojRwL9QvdKXgM6oiu6EEKI1MAvIAp4DcoBvhBCNbdn6AV8CqcBDQogWQoguRpmOGeUeYss/FSX7D6qH96xtX2/Uw/KXse8SN/emj/G52iU9GdhqHDsWeAh4B0gCnnLz3ZKBb1EP4USjXCG2LL2AtcCTQogBwHuo+/kg6sGcI4SIseW/zMhTCLwkhKhhK2eQrdwaZ3Rd13Xd5+gGyosYPalVqIp4JZCOmoc+CQxCPbhrpJQFQB1U720qRUKQnaSU+aiHqacQoinq4d0upVyFCg0NcBNFPdKuQoj6LkUZBNRAvSReBLoDUagKbTLH6EEtMLbjUb1CgDeklNOAj2zfbSXqhYSU8gsp5c+2c62UUr6IelEAJLi5PS2Mz8Mu6YeklE8D843tKageI0Cim/MMBsKB56WUU6SUL0gpl9n2r5dSPiKlfA/1G4DSN5sGfAjUo6h3D0rnbArqd4sAGrqU0913Oe/RdV3X9apAN1Dex5xzHwesNh7Qlag57WDb/jeBaNQDON5IizA+P0X1aCahpiU+c7nGLagHcxCq55fpoSwv2/INwnk9wIzimW982ntmngQaPaWXdC5Xgly2TxmfecbnaeOelXYeT7i+FKCo3O7K76nsruXUFEfXdV3XfYpeg/I+5tx8K4qmJFYCV9j+N6mBml+/1n4CKeXvQohdqAcd1EMMas57BGqu/AvU1EB/KeWlyujI4icgF7geNSdfF7iRoikUTyw2Ph8wFl7/4bI/DSwLp9Wo6YSyst/4bIKaHqooC1DTOE8Y37k28CtFUzJ25gETgGeEEC2B21Hf4Xfg6lKu08T43FeJsp7r6LruHl3XvYQeQXmflW7+d5f2AGpx9CnAPmw3+RzVs1lhLgxLKT8GHkVNq0xFzYEvdT1QSrkd9cCeRS3CPgDswnjoPCGl3ICaF28E3IN6GKCo5/cWcBw1NXFXSedyw2/GZ7dyHudaxh3Adaje4yvAvUCBh7w/oxbZ44DXUQ/2cCMyaWl0Q/VC3f02GoWu6+7Rdd1L6HAbGieEEHejLISiUQ9FAyBZSnnUC+deCgRLKd0tLAcUQohlQIGUsp+/y6LxDbquKwK5rusRlMaVPihfjOkoC6drvPHAGrwK9DHMiQMWo3x9gDf8XRaNT9F1PcDruh5BaTQajSYg0SMojUaj0QQkuoHSaDQaTUCiGyiNRqPRBCS6gdJoNBpNQKIbKI1Go9EEJP8fROL4ilZIgSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptest=1.e4\n",
    "ttest=300.\n",
    "fig,axs=plt.subplots(1,2,sharex=True,sharey=True)\n",
    "for species,x in composition.items():\n",
    "  if x != 'background':\n",
    "    database[species].plot_spectrum(axs[0],p=ptest,t=ttest,g=1.,x=x,linestyle='dashed',yscale='log')\n",
    "    database[species].plot_spectrum(axs[1],p=ptest,t=ttest,g=0.,x=x,linestyle='dashed',yscale='log')\n",
    "mixH2O_CO2.plot_spectrum(axs[0],p=ptest,t=ttest,g=1.)\n",
    "mixH2O_CO2.plot_spectrum(axs[1],p=ptest,t=ttest,g=0.)\n",
    "axs[0].set_title('g=1')\n",
    "axs[1].set_title('g=0')\n",
    "for ax in axs :\n",
    "    ax.set_xscale('log')\n",
    "    ax.label_outer()\n",
    "    ax.set_ylim(bottom=1.e-40)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba578e9",
   "metadata": {
    "papermill": {
     "duration": 0.070148,
     "end_time": "2022-09-02T07:51:37.355500",
     "exception": false,
     "start_time": "2022-09-02T07:51:37.285352",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Binning down a `Kdatabase()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5398f6",
   "metadata": {
    "papermill": {
     "duration": 0.065282,
     "end_time": "2022-09-02T07:51:37.490116",
     "exception": false,
     "start_time": "2022-09-02T07:51:37.424834",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Just like `Ktable()` objects, you can change the spectral resolution of an entire database with the same `bin_down(wnedges)` method. It will be applied to all ktables in place (`weights` keyword available as well). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7898a9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:37.624528Z",
     "iopub.status.busy": "2022-09-02T07:51:37.623757Z",
     "iopub.status.idle": "2022-09-02T07:51:44.696858Z",
     "shell.execute_reply": "2022-09-02T07:51:44.695376Z"
    },
    "papermill": {
     "duration": 7.145681,
     "end_time": "2022-09-02T07:51:44.700475",
     "exception": false,
     "start_time": "2022-09-02T07:51:37.554794",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "R=10\n",
    "wavenumber_grid=xk.wavenumber_grid_R(200.,10000.,R)\n",
    "database.bin_down(wavenumber_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f341645",
   "metadata": {
    "papermill": {
     "duration": 0.071721,
     "end_time": "2022-09-02T07:51:44.846015",
     "exception": false,
     "start_time": "2022-09-02T07:51:44.774294",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Notice how the resolution has changed below when we run the exact same code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "14b817bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:44.993190Z",
     "iopub.status.busy": "2022-09-02T07:51:44.992781Z",
     "iopub.status.idle": "2022-09-02T07:51:46.221011Z",
     "shell.execute_reply": "2022-09-02T07:51:46.219807Z"
    },
    "papermill": {
     "duration": 1.305221,
     "end_time": "2022-09-02T07:51:46.223746",
     "exception": false,
     "start_time": "2022-09-02T07:51:44.918525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptest=1.e4\n",
    "ttest=300.\n",
    "\n",
    "fig,axs=plt.subplots(1,2,sharex=True,sharey=True)\n",
    "for species,x in composition.items():\n",
    "  if x != 'background':\n",
    "    database[species].plot_spectrum(axs[0],p=ptest,t=ttest,g=1.,x=x,linestyle='dashed',label=species)\n",
    "    database[species].plot_spectrum(axs[1],p=ptest,t=ttest,g=0.,x=x,linestyle='dashed',label=species)\n",
    "mixH2O_CO2.plot_spectrum(axs[0],p=ptest,t=ttest,g=1.,label='mix')\n",
    "mixH2O_CO2.plot_spectrum(axs[1],p=ptest,t=ttest,g=0.,label='mix')\n",
    "axs[0].set_title('g=1')\n",
    "axs[1].set_title('g=0')\n",
    "for ax in axs :\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_yscale('log')\n",
    "    ax.label_outer()\n",
    "    ax.set_ylim(bottom=1.e-40)\n",
    "    ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6920c4af",
   "metadata": {
    "papermill": {
     "duration": 0.074981,
     "end_time": "2022-09-02T07:51:46.375565",
     "exception": false,
     "start_time": "2022-09-02T07:51:46.300584",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">Loading and creating correlated-k tables to use with LMDZ GCM</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4f5f5a",
   "metadata": {
    "papermill": {
     "duration": 0.072174,
     "end_time": "2022-09-02T07:51:46.521734",
     "exception": false,
     "start_time": "2022-09-02T07:51:46.449560",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating new corr-k for LMDZ-GCM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0517d6",
   "metadata": {
    "papermill": {
     "duration": 0.073575,
     "end_time": "2022-09-02T07:51:46.668408",
     "exception": false,
     "start_time": "2022-09-02T07:51:46.594833",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`exo_k` has also been designed to create corr-k tables to be used with the LMDZ gcm.  \n",
    "\n",
    "To produce a LMDZ usable directory with the corr-k and all the supporting files, nothing is simpler. "
   ]
  },
  {
   "cell_type": "raw",
   "id": "5540191d",
   "metadata": {
    "papermill": {
     "duration": 0.072842,
     "end_time": "2022-09-02T07:51:46.814973",
     "exception": false,
     "start_time": "2022-09-02T07:51:46.742131",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "        This section only deals with corr-k tables *without* variable gases.\n",
    "        For these, we need to deal with gas mixes and\n",
    "        `Ktable5D` that are discussed a little further down."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5515aa01",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "First, let's create a grid for our IR and VI channels and create our two ktables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc064841",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:47.107235Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "R=10\n",
    "IR_grid=xk.wavenumber_grid_R(220.,10000.,R)\n",
    "VI_grid=xk.wavenumber_grid_R(5200.,33000.,R)\n",
    "\n",
    "IR_h2o_ktab=h2o_ktab_SI.bin_down_cp(IR_grid)\n",
    "\n",
    "VI_h2o_ktab=h2o_ktab_SI.bin_down_cp(VI_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c23f8b",
   "metadata": {
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     "start_time": "2022-09-02T07:51:52.381445",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Once your low resolution corrk table has been produced, just run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4056e3a6",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-09-02T07:51:53.492176Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "IR_h2o_ktab.write_LMDZ(datapath + 'corrk/lmdz/new_h2o_corrk',band='IR')\n",
    "VI_h2o_ktab.write_LMDZ(datapath + 'corrk/lmdz/new_h2o_corrk',band='VI')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9ce810",
   "metadata": {
    "papermill": {
     "duration": 0.069847,
     "end_time": "2022-09-02T07:51:53.631518",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "**Should you care about units?**  \n",
    "\n",
    "Actually... no! Because we have tracked units from the beginning, and we know LMDZ likes ktables in cm^2/molec and pressures in log mBar, **the conversion has been done automatically** for you.\n",
    "\n",
    "The LMDZ aficionados may have noticed that in the `corrk/new_h2o_corrk` directory, two subdirectories have been created with the names `IR self.Nw` and `VI self.Nw` that contain the corr-k tables whereas we would like a single directory called `Number_of_IR_bins x Number_of_VI_bins`. Then just run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ad491eae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:53.768611Z",
     "iopub.status.busy": "2022-09-02T07:51:53.768234Z",
     "iopub.status.idle": "2022-09-02T07:51:53.774064Z",
     "shell.execute_reply": "2022-09-02T07:51:53.773112Z"
    },
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     "duration": 0.075998,
     "end_time": "2022-09-02T07:51:53.776411",
     "exception": false,
     "start_time": "2022-09-02T07:51:53.700413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Everything went ok. Your ktable is in: ../data/corrk/lmdz/new_h2o_corrk/39x19\n",
      "You'll probably need to add Q.dat before using it though!\n"
     ]
    }
   ],
   "source": [
    "xk.finalize_LMDZ_dir(datapath + 'corrk/lmdz/new_h2o_corrk',IR_h2o_ktab.Nw,VI_h2o_ktab.Nw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6d9cbb",
   "metadata": {
    "papermill": {
     "duration": 0.065771,
     "end_time": "2022-09-02T07:51:53.911326",
     "exception": false,
     "start_time": "2022-09-02T07:51:53.845555",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You just have to create a `Q.dat` file in the directory and you are good to run LMDZ with new corrk. If you do not know what I am talking about, you have not been using the LMDZ GCM long enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb09509c",
   "metadata": {
    "papermill": {
     "duration": 0.068115,
     "end_time": "2022-09-02T07:51:54.044341",
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     "start_time": "2022-09-02T07:51:53.976226",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading existing LMDZ corr-k tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f42d829",
   "metadata": {
    "papermill": {
     "duration": 0.073769,
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    },
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   },
   "source": [
    "To load a LMDZ corr-k table, the initialization method needs some more information:\n",
    "\n",
    "  * The directory to use (`path=`)\n",
    "  * The resolution (`res=`)\n",
    "  * Whether you want to load the 'IR' or 'VI' band (`band=`)\n",
    "  \n",
    "The `mol` keyword can be given to specify which species is described. This will be useful for mixes. \n",
    "\n",
    "Here, if you do not specify units, you will keep the LMDZ ones (cm^2 and mBar). But we advise to use the SI units. In particular because our radiative transfer method use SI units. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bc4d4e7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:54.325330Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        file         : ../data/corrk/lmdz/new_h2o_corrk\n",
      "        molecule     : H2O\n",
      "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
      " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
      " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
      " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
      " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
      " 4.64158883e+06 1.00000000e+07]\n",
      "        p unit       : Pa\n",
      "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
      " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
      " 3000. 3200. 3400.]\n",
      "        wn grid      : [ 231.56880099  255.92310439  282.83877223  312.58518557  345.46005652\n",
      "  381.79240782  421.94586586  466.32229996  515.36584436  569.56734336\n",
      "  629.46926376  695.67112414  768.83549494  849.69462979  939.05779409\n",
      " 1037.81936442 1146.96777977 1267.59543418 1400.90960974 1548.24455953\n",
      " 1711.07486126 1891.03017532 2089.91155497 2309.7094719  2552.62373755\n",
      " 2821.08551953 3117.78167359 3445.68163456 3808.06713546 4208.56505219\n",
      " 4651.18370251 5140.35296264 5680.96860295 6278.44128649 6938.75072067\n",
      " 7668.50550426 8475.00926841 9366.33377387 9917.13029426]\n",
      "        wn unit      : cm^-1\n",
      "        kdata unit   : m^2/molecule\n",
      "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
      " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
      " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
      " 0.02030071 0.008807  ]\n",
      "        data oredered following p, t, wn, g\n",
      "        shape        : [22 27 39 20]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "IR_h2o_ktab=xk.Ktable(path=datapath + 'corrk/lmdz/new_h2o_corrk',res='39x19',band='IR',mol='H2O',\n",
    "                kdata_unit='m^2',p_unit='Pa')\n",
    "print(IR_h2o_ktab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d60798",
   "metadata": {
    "papermill": {
     "duration": 0.071269,
     "end_time": "2022-09-02T07:51:55.212975",
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     "start_time": "2022-09-02T07:51:55.141706",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating a LMDZ corr-k with a variable gas from a `Kdatabase`: the `Ktable5d`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31e14c1",
   "metadata": {
    "papermill": {
     "duration": 0.072759,
     "end_time": "2022-09-02T07:51:55.354081",
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     "start_time": "2022-09-02T07:51:55.281322",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here, we will finally see one of the strengths of `Kdatabase` objects. Because we can create mix of gases and bin down, we have everything we need to create LMDZ like ktables for variable gases.\n",
    "\n",
    "For this, use\n",
    "```python\n",
    "Kdatabase.create_mix_ktable5d(vgas_comp=, bg_comp=, x_array=)\n",
    "```\n",
    "where:\n",
    "\n",
    "  - `vgas_comp` must contain a dictionary of the composition of the variable gas (it can be a single molecule).\n",
    "  - `bg_comp` does the same for the background gas.\n",
    "  - `x_array` is an array of the vol mix ratios of the variable gas that we want to compute in the table. "
   ]
  },
  {
   "cell_type": "raw",
   "id": "36fb338f",
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    "raw_mimetype": "text/restructuredtext",
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   },
   "source": [
    ".. warning::\n",
    "  A correlated-k table with a variable gas has one more dimension than a standard `Ktable`\n",
    "  (for the mixing ratio of the variable species; attribute `self.xgrid`).\n",
    "\n",
    "  Therefore, such tables are handled with a special class: `Ktable5d`. \n",
    "\n",
    "  Most methods available to `Ktable` can be used with `Ktable5d`\n",
    "  (like plotting, writing, interpolating, binning down, remapping, etc.).\n",
    "  Note that for some of these methods, a new argument (x_array)\n",
    "  is sometimes needed. For example, `remap_logPT` can now be used to\n",
    "  remap the table on a new (LogP, T, vmr) grid. \n",
    "  \n",
    "  Because `Ktable5d` already represent a mix and never a pure species,\n",
    "  they cannot be used exactly the same:\n",
    "  \n",
    "    * They cannot be normalized by a table of volume mixing ratios.\n",
    "    * They cannot be combined with other `Ktable5d`.\n",
    "    * They cannot be used in a `Kdatabase` along with other regular `Ktable`s\n",
    "      (Although they can be used alone).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d4c3c18d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:51:55.648766Z",
     "iopub.status.busy": "2022-09-02T07:51:55.648339Z",
     "iopub.status.idle": "2022-09-02T07:52:03.164836Z",
     "shell.execute_reply": "2022-09-02T07:52:03.163549Z"
    },
    "papermill": {
     "duration": 7.5881,
     "end_time": "2022-09-02T07:52:03.167815",
     "exception": false,
     "start_time": "2022-09-02T07:51:55.579715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the output Ktable5d (p,t,x,wn,g): [   6    5    2 1538   20]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computation time  7.507834196090698\n"
     ]
    }
   ],
   "source": [
    "start=time.time()\n",
    "\n",
    "molecs=['H2O','CO2']\n",
    "suffix='R300_0.3-50mu.ktable.SI'\n",
    "\n",
    "database=xk.Kdatabase(molecs,suffix)\n",
    "# we first remap on a smaller logP T grid for speed\n",
    "database.remap_logPT(logp_array=np.arange(6.), t_array=[200.,300.,400.,500.,600.])\n",
    "x_array=[1.e-7,0.1]\n",
    "vgas={'H2O':'background'}\n",
    "bg_gas={'CO2':4.e-4,'N2':'background'}\n",
    "mix_var_gas=database.create_mix_ktable5d(vgas_comp=vgas,bg_comp=bg_gas,x_array=x_array)\n",
    "end=time.time()\n",
    "print(\"computation time \",end - start)        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7100d59b",
   "metadata": {
    "papermill": {
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     "end_time": "2022-09-02T07:52:03.315997",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then we can bin down before writing to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "65016898",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:03.466099Z",
     "iopub.status.busy": "2022-09-02T07:52:03.465633Z",
     "iopub.status.idle": "2022-09-02T07:52:07.425730Z",
     "shell.execute_reply": "2022-09-02T07:52:07.424632Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory was already there: ../data/corrk/lmdz/new_earth_h2o-var\n",
      "Directory was already there: ../data/corrk/lmdz/new_earth_h2o-var\n",
      "Everything went ok. Your ktable is in: ../data/corrk/lmdz/new_earth_h2o-var/39x19\n",
      "You'll probably need to add Q.dat before using it though!\n"
     ]
    }
   ],
   "source": [
    "R=10\n",
    "IR_grid=xk.wavenumber_grid_R(220.,10000.,R)\n",
    "VI_grid=xk.wavenumber_grid_R(5200.,33000.,R)\n",
    "\n",
    "IR_mix_var_gas_ktab=mix_var_gas.copy()\n",
    "IR_mix_var_gas_ktab.bin_down(IR_grid)\n",
    "\n",
    "VI_mix_var_gas_ktab=mix_var_gas.copy()\n",
    "VI_mix_var_gas_ktab.bin_down(VI_grid)\n",
    "\n",
    "IR_mix_var_gas_ktab.write_LMDZ(datapath + 'corrk/lmdz/new_earth_h2o-var',band='IR')\n",
    "VI_mix_var_gas_ktab.write_LMDZ(datapath + 'corrk/lmdz/new_earth_h2o-var',band='VI')\n",
    "\n",
    "xk.finalize_LMDZ_dir(datapath + 'corrk/lmdz/new_earth_h2o-var',IR_mix_var_gas_ktab.Nw,VI_mix_var_gas_ktab.Nw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6737e94b",
   "metadata": {
    "papermill": {
     "duration": 0.065386,
     "end_time": "2022-09-02T07:52:07.593204",
     "exception": false,
     "start_time": "2022-09-02T07:52:07.527818",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can also write your 5d table to our more common hdf5 format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03a30f8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:07.721857Z",
     "iopub.status.busy": "2022-09-02T07:52:07.721415Z",
     "iopub.status.idle": "2022-09-02T07:52:07.759973Z",
     "shell.execute_reply": "2022-09-02T07:52:07.758951Z"
    },
    "papermill": {
     "duration": 0.105829,
     "end_time": "2022-09-02T07:52:07.762610",
     "exception": false,
     "start_time": "2022-09-02T07:52:07.656781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "IR_mix_var_gas_ktab.write_hdf5(datapath + 'corrk/lmdz/new_earth_h2o.h5', kdata_unit='cm^2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb7f8ab",
   "metadata": {
    "papermill": {
     "duration": 0.065204,
     "end_time": "2022-09-02T07:52:07.893840",
     "exception": false,
     "start_time": "2022-09-02T07:52:07.828636",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's plot the result compared to the initial cross sections at two water mixing ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "18109f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:08.045096Z",
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     "shell.execute_reply": "2022-09-02T07:52:09.220104Z"
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     "exception": false,
     "start_time": "2022-09-02T07:52:07.970275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptest=100.\n",
    "ttest=500.\n",
    "fig,axs=plt.subplots(1,2,sharex=True,sharey=True)\n",
    "database['H2O'].plot_spectrum(axs[0],p=ptest,t=ttest,g=1.,x=x_array[0],label='H2O')\n",
    "database['CO2'].plot_spectrum(axs[0],p=ptest,t=ttest,g=1.,x=bg_gas['CO2'],label='CO2')\n",
    "IR_mix_var_gas_ktab.plot_spectrum(axs[0],p=ptest,t=ttest,x=x_array[0],g=1.,label='mix/IR')\n",
    "VI_mix_var_gas_ktab.plot_spectrum(axs[0],p=ptest,t=ttest,x=x_array[0],g=1.,label='mix/VI')\n",
    "database['H2O'].plot_spectrum(axs[1],p=ptest,t=ttest,g=1.,x=x_array[-1],label='H2O')\n",
    "database['CO2'].plot_spectrum(axs[1],p=ptest,t=ttest,g=1.,x=bg_gas['CO2'],label='CO2')\n",
    "IR_mix_var_gas_ktab.plot_spectrum(axs[1],p=ptest,t=ttest,x=x_array[-1],g=1.,label='mix/IR')\n",
    "VI_mix_var_gas_ktab.plot_spectrum(axs[1],p=ptest,t=ttest,x=x_array[-1],g=1.,label='mix/VI')\n",
    "axs[0].set_title('$x_{H2O}=10^{-7}$')\n",
    "axs[1].set_title('$x_{H2O}=10^{-1}$')\n",
    "for ax in axs :\n",
    "    ax.set_ylim(bottom=1.e-40)\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_xscale('log')\n",
    "    ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05759315",
   "metadata": {
    "papermill": {
     "duration": 0.074921,
     "end_time": "2022-09-02T07:52:09.374739",
     "exception": false,
     "start_time": "2022-09-02T07:52:09.299818",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Adding the opacity of a trace species to an existing corr-k table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc49d61",
   "metadata": {
    "papermill": {
     "duration": 0.072044,
     "end_time": "2022-09-02T07:52:09.517984",
     "exception": false,
     "start_time": "2022-09-02T07:52:09.445940",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you want to add the opacity of a given molecule to a corr-k table (whether it has variable gas or not), it is rather simple. Just load your table and add the species you want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c6447900",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:09.671857Z",
     "iopub.status.busy": "2022-09-02T07:52:09.671380Z",
     "iopub.status.idle": "2022-09-02T07:52:12.366694Z",
     "shell.execute_reply": "2022-09-02T07:52:12.365402Z"
    },
    "papermill": {
     "duration": 2.775512,
     "end_time": "2022-09-02T07:52:12.369504",
     "exception": false,
     "start_time": "2022-09-02T07:52:09.593992",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        file         : ../data/corrk/lmdz/new_earth_h2o-var\n",
      "        molecule     : H2O\n",
      "        p grid       : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04 1.e+05]\n",
      "        p unit       : Pa\n",
      "        t grid   (K) : [200. 300. 400. 500. 600.]\n",
      "        wn grid      : [ 231.56880099  255.92310439  282.83877223  312.58518557  345.46005652\n",
      "  381.79240782  421.94586586  466.32229996  515.36584436  569.56734336\n",
      "  629.46926376  695.67112414  768.83549494  849.69462979  939.05779409\n",
      " 1037.81936442 1146.96777977 1267.59543418 1400.90960974 1548.24455953\n",
      " 1711.07486126 1891.03017532 2089.91155497 2309.7094719  2552.62373755\n",
      " 2821.08551953 3117.78167359 3445.68163456 3808.06713546 4208.56505219\n",
      " 4651.18370251 5140.35296264 5680.96860295 6278.44128649 6938.75072067\n",
      " 7668.50550426 8475.00926841 9366.33377387 9917.13029426]\n",
      "        wn unit      : cm^-1\n",
      "        kdata unit   : m^2/molecule\n",
      "        weights      : [0.008807   0.02030071 0.03133602 0.04163837 0.05096506 0.05909727\n",
      " 0.06584432 0.07104805 0.07458649 0.07637669 0.07637669 0.07458649\n",
      " 0.07104805 0.06584432 0.05909727 0.05096506 0.04163837 0.03133602\n",
      " 0.02030071 0.008807  ]\n",
      "        x      (vmr) : [1.e-07 1.e-01]\n",
      "        data oredered following p, t, x, wn, g\n",
      "        shape        : [ 6  5  2 39 20]\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "IR_mix_var_gas_ktab=xk.Ktable5d(path=datapath + 'corrk/lmdz/new_earth_h2o-var',res='39x19',band='IR',\n",
    "                kdata_unit='m^2',p_unit='Pa')\n",
    "co2=xk.Ktable('CO2_*R300_0.3-50mu.ktable.SI', remove_zeros=True)\n",
    "co2.remap_logPT(t_array=IR_mix_var_gas_ktab.tgrid, logp_array=IR_mix_var_gas_ktab.logpgrid)\n",
    "co2.bin_down(wnedges=IR_mix_var_gas_ktab.wnedges)\n",
    "earth_plus_co2=IR_mix_var_gas_ktab+1.e-1*co2\n",
    "print(earth_plus_co2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e6c9789f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:12.520134Z",
     "iopub.status.busy": "2022-09-02T07:52:12.519716Z",
     "iopub.status.idle": "2022-09-02T07:52:13.416028Z",
     "shell.execute_reply": "2022-09-02T07:52:13.414553Z"
    },
    "papermill": {
     "duration": 0.973876,
     "end_time": "2022-09-02T07:52:13.418728",
     "exception": false,
     "start_time": "2022-09-02T07:52:12.444852",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "ptest=100.\n",
    "ttest=500.\n",
    "x_array=IR_mix_var_gas_ktab.xgrid\n",
    "fig,ax=plt.subplots(1,1,sharex=True,sharey=True)\n",
    "IR_mix_var_gas_ktab.plot_spectrum(ax,p=ptest,t=ttest,x=x_array[1],g=1.,label='mix')\n",
    "co2.plot_spectrum(ax,p=ptest,t=ttest,x=0.1,g=1.,label='CO2')\n",
    "earth_plus_co2.plot_spectrum(ax,p=ptest,t=ttest,x=x_array[1],g=1.,label='mix+0.01*CO2')\n",
    "ax.set_ylim(bottom=1.e-40)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3cc8ca80",
   "metadata": {
    "papermill": {
     "duration": 0.074401,
     "end_time": "2022-09-02T07:52:13.569835",
     "exception": false,
     "start_time": "2022-09-02T07:52:13.495434",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "    When starting from a mix with a variable gas:\n",
    "    \n",
    "     * The opacity from the background and variable gases cannot be\n",
    "       isolated.\n",
    "     * The values of the array for the vmr of the variable gas (self.xgrid)\n",
    "       are not modified (diluted) during the mixing operation.\n",
    "\n",
    "    Therefore, this treatment is valid only if the mixing ratio of the other \n",
    "    species is << 1 (you are adding a trace gas).\n",
    "    \n",
    "    For this reason, it is always more accurate to start back\n",
    "    from the data for the pure species when they are available.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bd4da3",
   "metadata": {
    "papermill": {
     "duration": 0.073085,
     "end_time": "2022-09-02T07:52:13.715893",
     "exception": false,
     "start_time": "2022-09-02T07:52:13.642808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">Creating corr-k from `High_resolution` spectra</span>"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7d0bb059",
   "metadata": {
    "papermill": {
     "duration": 0.07246,
     "end_time": "2022-09-02T07:52:13.861079",
     "exception": false,
     "start_time": "2022-09-02T07:52:13.788619",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "    Many cells in this part of the tutorial are not executable because the data needed to run them\n",
    "    are not in the sample `data` directory provided."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612054c2",
   "metadata": {
    "papermill": {
     "duration": 0.072036,
     "end_time": "2022-09-02T07:52:14.007235",
     "exception": false,
     "start_time": "2022-09-02T07:52:13.935199",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## A note on ascii vs hdf5 data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4869960d",
   "metadata": {
    "papermill": {
     "duration": 0.073465,
     "end_time": "2022-09-02T07:52:14.153377",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.079912",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The native ascii format for many high-resolution spectra (e.g. Kspectrum)\n",
    "is highly inefficient and takes a lot of space.\n",
    "Some Kspectrum files take more than a minute to read where the hdf5 equivalent would take a second. \n",
    "Keeping it will be the bottleneck of all \n",
    "the following computation. \n",
    "\n",
    "So we recommend you seriously consider converting your files to hdf5 before anything else.\n",
    "\n",
    "If you have a bunch of file called `k001`, `k002`, `k003`, etc, `k0N` (let's take N=63 for exemple)\n",
    "in `path_in` you might want to use something like\n",
    "\n",
    "```python\n",
    "path_in='/Users/jleconte/root/data2/jaziri/exo_k/spectrum_data/o3_step_band/'\n",
    "path_out='/Users/jleconte/root/data1/leconte/atmosphere/radiative_transfer/hires_spectra/yassin_O3_hdf5/'\n",
    "for i in np.arange(1,64):\n",
    "    file='k'+str(i).zfill(3)\n",
    "    xk.convert_kspectrum_to_hdf5(path_in+file,path_out+file+'.h5', wn_column=None,\n",
    "        kdata_column=None, data_type='abs_coeff',\n",
    "        file_kdata_unit='unspecified', kdata_unit='unspecified', skiprow=0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa09786",
   "metadata": {
    "papermill": {
     "duration": 0.073264,
     "end_time": "2022-09-02T07:52:14.298666",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.225402",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The various available arguments are:\n",
    "\n",
    " * `file_kdata_unit`:\n",
    "       Specifies the unit for the opacity data in the file. \n",
    "       The type of quantity may differ whether we are handling cross sections (surface)\n",
    "       or absorption coefficients (inverse length)\n",
    "  * `data_type`: 'xsec' or 'abs_coeff'.\n",
    "       Whether the data read are cross-sections or absorption coefficients. Make sure you specify this if you changed\n",
    "       the default `kdata_column`.\n",
    " * `wn_column`/`data_column`:\n",
    "       Number of column to be read for wavenumber and kdata in python convention\n",
    "       (0 is first, 1 is second, etc.)\n",
    " * `kdata_unit`:\n",
    "       Unit to convert to.\n",
    " * `mult_factor`:\n",
    "       A multiplicative factor that can be applied to kdata (for example to correct for\n",
    "       any dilution effect, or specific conversion).\n",
    " * `skiprows`:\n",
    "       Number of header lines to skip. For the latest Kspectrum format,\n",
    "       the header is skipped automatically."
   ]
  },
  {
   "cell_type": "raw",
   "id": "89c3f3bf",
   "metadata": {
    "papermill": {
     "duration": 0.074014,
     "end_time": "2022-09-02T07:52:14.446873",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.372859",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "    here again, with HDF5 files, we are tracking the units and data type (xsec or abs_coeff).\n",
    "    So conversion to the right units can be made here, or at the k-coeff creation stage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a159db64",
   "metadata": {
    "papermill": {
     "duration": 0.072094,
     "end_time": "2022-09-02T07:52:14.591082",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.518988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For example, if you want to convert your absorption coefficients to a weird unit, you can do\n",
    "```python\n",
    "path_in='/Users/jleconte/root/data2/jaziri/exo_k/spectrum_data/o3_step_band/'\n",
    "path_out='/Users/jleconte/root/data1/leconte/atmosphere/radiative_transfer/hires_spectra/yassin_O3_hdf5/'\n",
    "for i in np.arange(1,64):\n",
    "    file='k'+str(i).zfill(3)\n",
    "    xk.convert_kspectrum_to_hdf5(path_in+file,path_out+file+'.h5', data_type='abs_coeff',\n",
    "        file_kdata_unit='m^-1', kdata_unit='cm^-1')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21039ea9",
   "metadata": {
    "papermill": {
     "duration": 0.072847,
     "end_time": "2022-09-02T07:52:14.735874",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.663027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you want to use the cross-section of a given species in a Ksectrum file (4nd column for example=>index=3) instead of the absorption coefficient and convert them to mks, you can do\n",
    "```python\n",
    "path_in='/Users/jleconte/root/data2/jaziri/exo_k/spectrum_data/o3_step_band/'\n",
    "path_out='/Users/jleconte/root/data1/leconte/atmosphere/radiative_transfer/hires_spectra/yassin_O3_hdf5/'\n",
    "for i in np.arange(1,64):\n",
    "    file='k'+str(i).zfill(3)\n",
    "    xk.convert_kspectrum_to_hdf5(path_in+file,path_out+file+'.h5', wn_column=None,\n",
    "        kdata_column=3, data_type='xsec', skiprow=0,\n",
    "        file_kdata_unit='cm^2/molecule', kdata_unit='m^2/molecule')\n",
    "```\n",
    "Note the last `kdata_unit` is superfluous if you have set `exo_k.Settings().set_mks(True)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1b35d5",
   "metadata": {
    "papermill": {
     "duration": 0.071857,
     "end_time": "2022-09-02T07:52:14.879053",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.807196",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Organizing your spectra into a grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55ac530",
   "metadata": {
    "papermill": {
     "duration": 0.070363,
     "end_time": "2022-09-02T07:52:15.020868",
     "exception": false,
     "start_time": "2022-09-02T07:52:14.950505",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now that you spent a few cpu x hours to compute some high resolution spectra, you would like to create a corr-k matrix with it.  \n",
    "\n",
    "For this you'll need to provide the `logpgrid` and `tgrid` (and potentially `xgrid`) arrays onto which your spectra have been computed."
   ]
  },
  {
   "cell_type": "raw",
   "id": "901c851b",
   "metadata": {
    "papermill": {
     "duration": 0.073484,
     "end_time": "2022-09-02T07:52:15.164268",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.090784",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    By default, log pressures are specified in Pa in logpgrid!!! If you want\n",
    "    to use another unit, do not forget to specify it with the grid_p_unit keyword.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05161967",
   "metadata": {
    "papermill": {
     "duration": 0.071689,
     "end_time": "2022-09-02T07:52:15.310902",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.239213",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You'll also need to provide the corresponding spectra filenames in an array of shape `(logpgrid.size, tgrid.size (, xgrid.size))` with the following order:\n",
    "```python\n",
    "[['spectrum for logP1,T1', 'spectrum for logP1,T2'],\n",
    "       ['spectrum for logP2,T1', 'spectrum for logP2,T2'],\n",
    "       ['spectrum for logP3,T1', 'spectrum for logP3,T2']]\n",
    "```\n",
    "or\n",
    "```python\n",
    "[[['spectrum for logP1,T1,x1', 'spectrum for logP1,T1,x2',\n",
    "         'spectrum for logP1,T1,x3'],\n",
    "        ['spectrum for logP1,T2,x1', 'spectrum for logP1,T2,x2',\n",
    "         'spectrum for logP1,T2,x3']],\n",
    "\n",
    "       [['spectrum for logP2,T1,x1', 'spectrum for logP2,T1,x2',\n",
    "         'spectrum for logP2,T1,x3'],\n",
    "        ['spectrum for logP2,T2,x1', 'spectrum for logP2,T2,x2',\n",
    "         'spectrum for logP2,T2,x3']],\n",
    "\n",
    "       [['spectrum for logP3,T1,x1', 'spectrum for logP3,T1,x2',\n",
    "         'spectrum for logP3,T1,x3'],\n",
    "        ['spectrum for logP3,T2,x1', 'spectrum for logP3,T2,x2',\n",
    "         'spectrum for logP3,T2,x3']]]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25271a9e",
   "metadata": {
    "papermill": {
     "duration": 0.07715,
     "end_time": "2022-09-02T07:52:15.460942",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.383792",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Kspectrum files generated for LMDZ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64f7414",
   "metadata": {
    "papermill": {
     "duration": 0.070827,
     "end_time": "2022-09-02T07:52:15.607963",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.537136",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you are using Kspectrum files generated for LMDZ, your files should be called k001, k002, etc. \n",
    "\n",
    "In the case of 4 pressure points, and 2 Temperature points, you can create the list of files in the right order as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a2928206",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:15.761888Z",
     "iopub.status.busy": "2022-09-02T07:52:15.761411Z",
     "iopub.status.idle": "2022-09-02T07:52:15.769539Z",
     "shell.execute_reply": "2022-09-02T07:52:15.768486Z"
    },
    "papermill": {
     "duration": 0.094446,
     "end_time": "2022-09-02T07:52:15.772063",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.677617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['k001', 'k002'],\n",
       "       ['k003', 'k004'],\n",
       "       ['k005', 'k006'],\n",
       "       ['k007', 'k008']], dtype='<U4')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.create_fname_grid_Kspectrum_LMDZ(4,2,suffix='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbb36ab",
   "metadata": {
    "papermill": {
     "duration": 0.074306,
     "end_time": "2022-09-02T07:52:15.922065",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.847759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you have a variable gas with 3 different volume mixing ratios, then use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b8d498b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:16.069267Z",
     "iopub.status.busy": "2022-09-02T07:52:16.068894Z",
     "iopub.status.idle": "2022-09-02T07:52:16.076076Z",
     "shell.execute_reply": "2022-09-02T07:52:16.075121Z"
    },
    "papermill": {
     "duration": 0.082562,
     "end_time": "2022-09-02T07:52:16.079290",
     "exception": false,
     "start_time": "2022-09-02T07:52:15.996728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[['k001', 'k009', 'k017'],\n",
       "        ['k003', 'k011', 'k019'],\n",
       "        ['k005', 'k013', 'k021'],\n",
       "        ['k007', 'k015', 'k023']],\n",
       "\n",
       "       [['k002', 'k010', 'k018'],\n",
       "        ['k004', 'k012', 'k020'],\n",
       "        ['k006', 'k014', 'k022'],\n",
       "        ['k008', 'k016', 'k024']]], dtype='<U4')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.create_fname_grid_Kspectrum_LMDZ(4, 2, 3, suffix='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e054e21",
   "metadata": {
    "papermill": {
     "duration": 0.070524,
     "end_time": "2022-09-02T07:52:16.222628",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.152104",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And if you have converted them to hdf5, as recommended above, you can then use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6fc4a411",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:16.376353Z",
     "iopub.status.busy": "2022-09-02T07:52:16.375920Z",
     "iopub.status.idle": "2022-09-02T07:52:16.384612Z",
     "shell.execute_reply": "2022-09-02T07:52:16.383659Z"
    },
    "papermill": {
     "duration": 0.087405,
     "end_time": "2022-09-02T07:52:16.387072",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.299667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[['k001.h5', 'k009.h5', 'k017.h5'],\n",
       "        ['k003.h5', 'k011.h5', 'k019.h5'],\n",
       "        ['k005.h5', 'k013.h5', 'k021.h5'],\n",
       "        ['k007.h5', 'k015.h5', 'k023.h5']],\n",
       "\n",
       "       [['k002.h5', 'k010.h5', 'k018.h5'],\n",
       "        ['k004.h5', 'k012.h5', 'k020.h5'],\n",
       "        ['k006.h5', 'k014.h5', 'k022.h5'],\n",
       "        ['k008.h5', 'k016.h5', 'k024.h5']]], dtype='<U7')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk.create_fname_grid_Kspectrum_LMDZ(4, 2, 3, suffix='.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fd2496",
   "metadata": {
    "papermill": {
     "duration": 0.075579,
     "end_time": "2022-09-02T07:52:16.539773",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.464194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Files named with information on P, T (and x) in the title"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a8ece1",
   "metadata": {
    "papermill": {
     "duration": 0.070712,
     "end_time": "2022-09-02T07:52:16.684715",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.614003",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can create your grid of filenames by hand. But if the names are regular enough, the following function may help you."
   ]
  },
  {
   "cell_type": "raw",
   "id": "63ca0360",
   "metadata": {
    "papermill": {
     "duration": 0.070302,
     "end_time": "2022-09-02T07:52:16.825268",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.754966",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. seealso::\n",
    "    :func:`~exo_k.util.filenames.create_fname_grid`\n",
    "    for details on arguments and options."
   ]
  },
  {
   "cell_type": "raw",
   "id": "5879ffa0",
   "metadata": {
    "papermill": {
     "duration": 0.07292,
     "end_time": "2022-09-02T07:52:16.971193",
     "exception": false,
     "start_time": "2022-09-02T07:52:16.898273",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. warning::\n",
    "    in :func:`~exo_k.util.filenames.create_fname_grid`, the walues in `logpgrid`, `tgrid`, and `xgrid`\n",
    "    will be converted to string to be put in the filename. Using integers therefore provides more control\n",
    "    to the way your numbers will look, but floats can be used anyway."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a52a08a5",
   "metadata": {
    "papermill": {
     "duration": 0.073557,
     "end_time": "2022-09-02T07:52:17.117413",
     "exception": false,
     "start_time": "2022-09-02T07:52:17.043856",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "See the 3 examples below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a429db46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:17.264369Z",
     "iopub.status.busy": "2022-09-02T07:52:17.263911Z",
     "iopub.status.idle": "2022-09-02T07:52:17.272663Z",
     "shell.execute_reply": "2022-09-02T07:52:17.271545Z"
    },
    "papermill": {
     "duration": 0.084444,
     "end_time": "2022-09-02T07:52:17.275262",
     "exception": false,
     "start_time": "2022-09-02T07:52:17.190818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['spectrum_CO2_1Pa_100K.h5', 'spectrum_CO2_1Pa_150K.h5',\n",
       "        'spectrum_CO2_1Pa_200K.h5'],\n",
       "       ['spectrum_CO2_10Pa_100K.h5', 'spectrum_CO2_10Pa_150K.h5',\n",
       "        'spectrum_CO2_10Pa_200K.h5']], dtype='<U25')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logpgrid=[1,10]\n",
    "tgrid=[100,150,200]\n",
    "file_grid=xk.create_fname_grid('spectrum_CO2_{p}Pa_{t}K.h5',\n",
    "                  logpgrid=logpgrid, tgrid=tgrid, p_kw='p', t_kw='t')\n",
    "file_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "078f21ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:17.475939Z",
     "iopub.status.busy": "2022-09-02T07:52:17.475537Z",
     "iopub.status.idle": "2022-09-02T07:52:17.486411Z",
     "shell.execute_reply": "2022-09-02T07:52:17.485328Z"
    },
    "papermill": {
     "duration": 0.103775,
     "end_time": "2022-09-02T07:52:17.488992",
     "exception": false,
     "start_time": "2022-09-02T07:52:17.385217",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[['O3-H2O_1e1Pa_100K_vmr0.01.h5', 'O3-H2O_1e1Pa_100K_vmr0.1.h5'],\n",
       "        ['O3-H2O_1e1Pa_150K_vmr0.01.h5', 'O3-H2O_1e1Pa_150K_vmr0.1.h5'],\n",
       "        ['O3-H2O_1e1Pa_200K_vmr0.01.h5', 'O3-H2O_1e1Pa_200K_vmr0.1.h5']],\n",
       "\n",
       "       [['O3-H2O_1e2Pa_100K_vmr0.01.h5', 'O3-H2O_1e2Pa_100K_vmr0.1.h5'],\n",
       "        ['O3-H2O_1e2Pa_150K_vmr0.01.h5', 'O3-H2O_1e2Pa_150K_vmr0.1.h5'],\n",
       "        ['O3-H2O_1e2Pa_200K_vmr0.01.h5', 'O3-H2O_1e2Pa_200K_vmr0.1.h5']]],\n",
       "      dtype='<U28')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logpgrid=[1,2]\n",
    "tgrid=[100,150,200]\n",
    "xgrid=[1.e-2,1.e-1]\n",
    "file_grid=xk.create_fname_grid('O3-H2O_1e{logp}Pa_{t}K_vmr{x}.h5',\n",
    "                  logpgrid=logpgrid, tgrid=tgrid, xgrid=xgrid, p_kw='logp', t_kw='t', x_kw='x')\n",
    "file_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ec129ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:17.645951Z",
     "iopub.status.busy": "2022-09-02T07:52:17.644998Z",
     "iopub.status.idle": "2022-09-02T07:52:17.655416Z",
     "shell.execute_reply": "2022-09-02T07:52:17.654162Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Kspec_1Pa_100.0K.h5', 'Kspec_1Pa_150.0K.h5',\n",
       "        'Kspec_1Pa_200.0K.h5'],\n",
       "       ['Kspec_10Pa_100.0K.h5', 'Kspec_10Pa_150.0K.h5',\n",
       "        'Kspec_10Pa_200.0K.h5'],\n",
       "       ['Kspec_100Pa_100.0K.h5', 'Kspec_100Pa_150.0K.h5',\n",
       "        'Kspec_100Pa_200.0K.h5'],\n",
       "       ['Kspec_1000Pa_100.0K.h5', 'Kspec_1000Pa_150.0K.h5',\n",
       "        'Kspec_1000Pa_200.0K.h5']], dtype='<U22')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logpgrid=[1,10,100,1000]\n",
    "tgrid=np.array([100.,150.,200.])\n",
    "file_grid=xk.create_fname_grid('Kspec_{logp}Pa_{t}K.h5',\n",
    "                  logpgrid=logpgrid, tgrid=tgrid, p_kw='logp', t_kw='t')\n",
    "file_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583e92e9",
   "metadata": {
    "papermill": {
     "duration": 0.076416,
     "end_time": "2022-09-02T07:52:17.812124",
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     "start_time": "2022-09-02T07:52:17.735708",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Actually making the `Ktable`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1821e62d",
   "metadata": {
    "papermill": {
     "duration": 0.078185,
     "end_time": "2022-09-02T07:52:18.002070",
     "exception": false,
     "start_time": "2022-09-02T07:52:17.923885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Once we have our grids of pressure (in Pa), temperature (, vmr), and the grid of corresponding filenames, producing the `Ktable` is done like this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4b6157",
   "metadata": {
    "papermill": {
     "duration": 0.07574,
     "end_time": "2022-09-02T07:52:18.156302",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.080562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "```python\n",
    "temp=np.arange(100.,401.,50)\n",
    "logpres=np.arange(-1,8)\n",
    "file_grid=xk.create_fname_grid_Kspectrum_LMDZ(logpres.size, temp.size, suffix='.h5')\n",
    "path='data/hires/yassin_O3_hdf5/'\n",
    "\n",
    "#that's where you choose your wavenumber bin edges grid\n",
    "wnedges=xk.wavenumber_grid_R(10.,10.1,980)\n",
    "tmp=xk.hires_to_ktable(path=path, filename_grid=file_grid,\n",
    "                    logpgrid=logpres, tgrid=temp, wnedges=wnedges, mol='O3')\n",
    "tmp.remove_zeros() #if you do not want to have zeros in the table for interpolation purposes\n",
    "print(tmp)\n",
    "```\n",
    "With these options, you create a table with 20 gauss point following the Gausse-Legendre quadrature. "
   ]
  },
  {
   "cell_type": "raw",
   "id": "f727d6fa",
   "metadata": {
    "papermill": {
     "duration": 0.078865,
     "end_time": "2022-09-02T07:52:18.314476",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.235611",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. seealso::\n",
    "    :func:`exo_k.util.user_func.hires_to_ktable` is a function provided for the user that uses the \n",
    "    :func:`exo_k.ktable.Ktable.hires_to_ktable` and :func:`exo_k.ktable.Ktable5d.hires_to_ktable` methods.\n",
    "    A list of all the arguments and options can be found there."
   ]
  },
  {
   "cell_type": "raw",
   "id": "3e5b1784",
   "metadata": {
    "papermill": {
     "duration": 0.076838,
     "end_time": "2022-09-02T07:52:18.474402",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.397564",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    ".. important::\n",
    "    Whether data are in cross section or absorption coefficient form in the input data,\n",
    "    they will be converted to cross sections to be put in the Ktable.\n",
    "    As this conversion is done using the pressure grid you provide,\n",
    "    it is important to specify its unit with the grid_p_unit keyword if you are NOT using Pa."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1119271",
   "metadata": {
    "papermill": {
     "duration": 0.074154,
     "end_time": "2022-09-02T07:52:18.624348",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.550194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Hereafter are some variations around the same theme to show some commented examples of use. Pick the one that fits better to your use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d43644d",
   "metadata": {
    "papermill": {
     "duration": 0.072236,
     "end_time": "2022-09-02T07:52:18.768969",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.696733",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "```python\n",
    "temp=np.arange(100.,401.,50)\n",
    "logpres=np.arange(-1,8)\n",
    "path='data/hires/yassin_O3_hdf5/'\n",
    "\n",
    "#that's where you choose your wavenumber bin edges grid\n",
    "wnedges=xk.wavenumber_grid_R(10.,10.1,980)\n",
    "tmp=xk.hires_to_ktable(path=path,\n",
    "                    # because files organized as k001, k002, etc.\n",
    "                    #  is the default, the above result can be\n",
    "                    #  obtained without specifying a filename_grid.\n",
    "                    #  You'll need to provide suffix here, however\n",
    "                    suffix='.h5',\n",
    "                    logpgrid=logpres, tgrid=temp, wnedges=wnedges, mol='O3')\n",
    "tmp.remove_zeros() #if you do not want to have zeros in the table for interpolation purposes\n",
    "print(tmp)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0ae954",
   "metadata": {
    "papermill": {
     "duration": 0.076953,
     "end_time": "2022-09-02T07:52:18.919661",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.842708",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here starting from ascii files and doing directly the conversion from abs_coeff to cross sections.\n",
    "```python\n",
    "temp=np.arange(100.,401.,50)\n",
    "logpres=np.arange(-1,8)\n",
    "path='data/hires/yassin_O3_hdf5/'\n",
    "wnedges=xk.wavenumber_grid_R(10.,10.1,980)\n",
    "tmp=xk.hires_to_ktable(path=path, data_type='abs_coeff',\n",
    "                    logpgrid=logpres, tgrid=temp, wnedges=wnedges, mol='O3',\n",
    "                    file_kdata_unit='m^-1', kdata_unit='m^2/molec')\n",
    "tmp.remove_zeros() #if you do not want to have zeros in the table for interpolation purposes\n",
    "print(tmp)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c036154b",
   "metadata": {
    "papermill": {
     "duration": 0.071707,
     "end_time": "2022-09-02T07:52:19.066393",
     "exception": false,
     "start_time": "2022-09-02T07:52:18.994686",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you want to provide the g weights yourself, run this instead:\n",
    "```python\n",
    "temp=np.arange(100.,401.,50)\n",
    "logpres=np.arange(-1,8)\n",
    "path='data/hires/yassin_O3_hdf5/'\n",
    "wnedges=xk.wavenumber_grid_R(10.,10.1,980)\n",
    "\n",
    "weights=[my g weights]\n",
    "\n",
    "tmp=xk.hires_to_ktable(path=path, suffix='.h5',\n",
    "                    logpgrid=logpres, tgrid=temp, wnedges=wnedges, weights=weights, mol='O3')\n",
    "tmp.remove_zeros() #if you do not want to have zeros in the table for interpolation purposes\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bc23f0",
   "metadata": {
    "papermill": {
     "duration": 0.074763,
     "end_time": "2022-09-02T07:52:19.213718",
     "exception": false,
     "start_time": "2022-09-02T07:52:19.138955",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can now save your brand new Ktable on disk with any of the writing routines (`write_hdf5`, `write_LMDZ`, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0857e2",
   "metadata": {
    "papermill": {
     "duration": 0.076397,
     "end_time": "2022-09-02T07:52:19.366878",
     "exception": false,
     "start_time": "2022-09-02T07:52:19.290481",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Corr-k with a variable gas: `Ktable5d`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75f5b4e",
   "metadata": {
    "papermill": {
     "duration": 0.075701,
     "end_time": "2022-09-02T07:52:19.521159",
     "exception": false,
     "start_time": "2022-09-02T07:52:19.445458",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you have a grid of spectra with a gas that is varying along one dimension,\n",
    "you just have to provide the grid of volume mixing ratios (`xgrid`) along with the other arguments. \n",
    "\n",
    "`hires_to_ktable` will automatically detect the new dimension and create a `Ktable5d` instead of a regular `Ktable` (see 4.3)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb5c9b7",
   "metadata": {
    "papermill": {
     "duration": 0.073211,
     "end_time": "2022-09-02T07:52:19.669055",
     "exception": false,
     "start_time": "2022-09-02T07:52:19.595844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">Dealing with cross sections: `Xtable()`objects </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c06b2e",
   "metadata": {
    "papermill": {
     "duration": 0.076459,
     "end_time": "2022-09-02T07:52:19.819572",
     "exception": false,
     "start_time": "2022-09-02T07:52:19.743113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Cross sections are like corr-k tables, except for the fact that they do not have a \"g-dimension\".\n",
    "\n",
    "Thanks to the way `numpy` handles arrays and to the `python` inheritance system, handling a `Xtable()` is very similar to handling a `Ktable()` and you can do everything that you did before.  \n",
    "\n",
    "It is so similar that you can even load a `Kdatabase()` with `Xtable()` objects. \n",
    "\n",
    "Just remember that the kdata array now has one less dimension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8f1a8670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:19.969060Z",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        file         : /builds/jleconte/exo_k/data/xsec/H2O.R10000.TauREx.pickle\n",
       "        molecule     : H2O\n",
       "        p grid       : [1.00000000e+00 2.15443469e+00 4.64158883e+00 1.00000000e+01\n",
       " 2.15443469e+01 4.64158883e+01 1.00000000e+02 2.15443469e+02\n",
       " 4.64158883e+02 1.00000000e+03 2.15443469e+03 4.64158883e+03\n",
       " 1.00000000e+04 2.15443469e+04 4.64158883e+04 1.00000000e+05\n",
       " 2.15443469e+05 4.64158883e+05 1.00000000e+06 2.15443469e+06\n",
       " 4.64158883e+06 1.00000000e+07]\n",
       "        p unit       : Pa\n",
       "        t grid   (K) : [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000. 1100. 1200.\n",
       " 1300. 1400. 1500. 1600. 1700. 1800. 1900. 2000. 2200. 2400. 2600. 2800.\n",
       " 3000. 3200. 3400.]\n",
       "        wn grid      : [  666.61240713   666.67906837   666.74573628 ... 33326.66766653\n",
       " 33330.0003333  33333.33333333]\n",
       "        wn unit      : cm^-1\n",
       "        kdata unit   : m^2/molecule\n",
       "        data oredered following p, t, wn\n",
       "        shape        : [   22    27 39124]\n",
       "        "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xsecs=xk.Xtable('H2O.R10000.TauREx.pickle', kdata_unit='m^2', p_unit='Pa')\n",
    "xsecs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7fc819",
   "metadata": {
    "papermill": {
     "duration": 0.076685,
     "end_time": "2022-09-02T07:52:21.861940",
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     "start_time": "2022-09-02T07:52:21.785255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Saving to a file is similar to `Ktable`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cf93dfae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:22.013700Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xsecs.write_hdf5(datapath + 'xsec/H2O.R10000.SI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "18dc29f5",
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   "outputs": [
    {
     "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": [
    "ptest=1.e2\n",
    "ttest=300.\n",
    "fig,ax=plt.subplots(1,1,sharex=True,sharey=False)  \n",
    "xsecs.plot_spectrum(ax,p=ptest,t=ttest,yscale='log')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06826f44",
   "metadata": {
    "papermill": {
     "duration": 0.080105,
     "end_time": "2022-09-02T07:52:35.360429",
     "exception": false,
     "start_time": "2022-09-02T07:52:35.280324",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## `Xtable`: sample or bin_down"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5568bdaf",
   "metadata": {
    "papermill": {
     "duration": 0.079711,
     "end_time": "2022-09-02T07:52:35.521524",
     "exception": false,
     "start_time": "2022-09-02T07:52:35.441813",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A unique feature of `Xtable`s over `Ktable`s is that there are two ways of changing the spectral grid:\n",
    "\n",
    "    * `bin_down(wnedges= )`: does an area-conserving binning\n",
    "      inside the bin edges provided as `wnedges`.\n",
    "      The final spectral grid has a dimension `Nw=wnedges.size-1`. Usually not recommanded (See Leconte, A&A, 2020).\n",
    "    * `sample(wngrid= )`: just interpolates the cross sections\n",
    "      on the given grid. The final spectral grid has a dimension `Nw=wngrid.size`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b81b151",
   "metadata": {
    "papermill": {
     "duration": 0.079337,
     "end_time": "2022-09-02T07:52:35.678587",
     "exception": false,
     "start_time": "2022-09-02T07:52:35.599250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading `Xtable` with `kspectrum`-like spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6f8aee",
   "metadata": {
    "papermill": {
     "duration": 0.077038,
     "end_time": "2022-09-02T07:52:35.835089",
     "exception": false,
     "start_time": "2022-09-02T07:52:35.758051",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can also load your spectra to be used as cross sections.  \n",
    "\n",
    "For this you'll need to provide the `logpgrid` and `tgrid` arrays onto which your spectra have been computed, as well as the corresponding spectra filenames in an array of shape `(logpgrid.size,tgrid.size)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9851d9a3",
   "metadata": {
    "papermill": {
     "duration": 0.076185,
     "end_time": "2022-09-02T07:52:35.987798",
     "exception": false,
     "start_time": "2022-09-02T07:52:35.911613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "```python\n",
    "path='data/hires/yassin_O3_hdf5/'\n",
    "temp=np.arange(100.,401.,50)\n",
    "logpgrid=np.arange(-1,8)\n",
    "hires_o3_xsec=xk.hires_to_xtable(path=path,filename_grid=None, suffix='.h5',\n",
    "                             logpgrid=logpgrid,tgrid=tgrid,\n",
    "                             mol='O3',kdata_unit='m^2')\n",
    "hires_o3_xsec.remove_zeros()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b881862",
   "metadata": {
    "papermill": {
     "duration": 0.083902,
     "end_time": "2022-09-02T07:52:36.149051",
     "exception": false,
     "start_time": "2022-09-02T07:52:36.065149",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can then save it to a hdf5 file with `write_hdf5()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26a7d68",
   "metadata": {
    "papermill": {
     "duration": 0.080906,
     "end_time": "2022-09-02T07:52:36.309535",
     "exception": false,
     "start_time": "2022-09-02T07:52:36.228629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating a `Ktable` from an `Xtable`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b34f9a4",
   "metadata": {
    "papermill": {
     "duration": 0.080753,
     "end_time": "2022-09-02T07:52:36.471476",
     "exception": false,
     "start_time": "2022-09-02T07:52:36.390723",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As an `Xtable` represents monochromatic cross-sections, it is only natural that we should be able to produce `Ktable` with them. And we indeed can with the `Ktable.xtable_to_ktable()` method, or directly from the `Ktable()` constructor method with the `xtable=` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7d9ea0b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:36.633789Z",
     "iopub.status.busy": "2022-09-02T07:52:36.633398Z",
     "iopub.status.idle": "2022-09-02T07:52:45.270784Z",
     "shell.execute_reply": "2022-09-02T07:52:45.269738Z"
    },
    "papermill": {
     "duration": 8.720468,
     "end_time": "2022-09-02T07:52:45.273232",
     "exception": false,
     "start_time": "2022-09-02T07:52:36.552764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "R=20.\n",
    "xsecs_h2o=xk.Xtable('H2O.R10000.TauREx.pickle', kdata_unit='m^2', p_unit='Pa')\n",
    "\n",
    "ktab_h2o=xk.Ktable(xtable=xsecs_h2o, wnedges=xk.wavenumber_grid_R(1000.,20000.,R))\n",
    "\n",
    "p_plot=1.e0\n",
    "t_plot=300.\n",
    "fig,ax=plt.subplots(1,1,sharex=False,sharey=False)\n",
    "xsecs_h2o.plot_spectrum(ax, p=p_plot, t=t_plot, yscale='log', xscale='log', label='Original data')\n",
    "ktab_h2o.plot_spectrum(ax, p=p_plot, t=t_plot, g=1., label='corr-k, g=1')\n",
    "ktab_h2o.plot_spectrum(ax, p=p_plot, t=t_plot, g=0., label='corr-k, g=0')\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "be28857c",
   "metadata": {
    "papermill": {
     "duration": 0.083356,
     "end_time": "2022-09-02T07:52:45.438604",
     "exception": false,
     "start_time": "2022-09-02T07:52:45.355248",
     "status": "completed"
    },
    "raw_mimetype": "text/restructuredtext",
    "tags": []
   },
   "source": [
    " .. warning::\n",
    "    Although in theory there is no formal criterion separating a cross section table\n",
    "    and a set of high-resolution spectra, in practice, cross section table often\n",
    "    have a moderate resolution. Its resolution should be high enough to sample individual lines\n",
    "    for a cross-section table to be suitable for the computation\n",
    "    of accurate k-coefficients. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33bc34c3",
   "metadata": {
    "papermill": {
     "duration": 0.077283,
     "end_time": "2022-09-02T07:52:45.594265",
     "exception": false,
     "start_time": "2022-09-02T07:52:45.516982",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">Dealing with continua: `Cia_table()` and `CIAdatabase()` objects </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcaa4010",
   "metadata": {
    "papermill": {
     "duration": 0.077553,
     "end_time": "2022-09-02T07:52:45.749695",
     "exception": false,
     "start_time": "2022-09-02T07:52:45.672142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Cia tables are like cross section tables, except for the fact that they do not have a pressure dimension.\n",
    "\n",
    "They can still be loaded to be converted from one format to another (in particular HITRAN .cia files or water CKD files to HDF5), from one unit to another (our preferred unit is m^5 / molec^2 ).\n",
    "\n",
    "Finally, they will also be usefull to account for continua in opacity calculations and forward models below"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbb6fa0",
   "metadata": {
    "papermill": {
     "duration": 0.077574,
     "end_time": "2022-09-02T07:52:45.906817",
     "exception": false,
     "start_time": "2022-09-02T07:52:45.829243",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "But first, as for `Ktable`s, we need to setup the path to tell `exo_k` where to look for cia tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d10e182e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:46.064467Z",
     "iopub.status.busy": "2022-09-02T07:52:46.063186Z",
     "iopub.status.idle": "2022-09-02T07:52:46.069504Z",
     "shell.execute_reply": "2022-09-02T07:52:46.068358Z"
    },
    "init_cell": true,
    "papermill": {
     "duration": 0.086831,
     "end_time": "2022-09-02T07:52:46.072128",
     "exception": false,
     "start_time": "2022-09-02T07:52:45.985297",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xk.Settings().add_search_path(datapath + 'cia', path_type = 'cia')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2298ee",
   "metadata": {
    "papermill": {
     "duration": 0.079412,
     "end_time": "2022-09-02T07:52:46.233220",
     "exception": false,
     "start_time": "2022-09-02T07:52:46.153808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then you can load a `CIAdatabase` by providing the list of molecules you want to look for. It will try to load CIA data for as many pairs as it can find with these molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ab25352f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:46.392572Z",
     "iopub.status.busy": "2022-09-02T07:52:46.392170Z",
     "iopub.status.idle": "2022-09-02T07:52:47.555686Z",
     "shell.execute_reply": "2022-09-02T07:52:47.554667Z"
    },
    "papermill": {
     "duration": 1.247287,
     "end_time": "2022-09-02T07:52:47.558354",
     "exception": false,
     "start_time": "2022-09-02T07:52:46.311067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The available molecule pairs are: \n",
       "H2-He->/builds/jleconte/exo_k/data/cia/H2-He_2011.hdf5\n",
       "H2-H2->/builds/jleconte/exo_k/data/cia/H2-H2_2011.hdf5\n",
       "N2-H2->/builds/jleconte/exo_k/data/cia/N2-H2_2011.hdf5\n",
       "N2-N2->/builds/jleconte/exo_k/data/cia/N2-N2_2011.hdf5\n",
       "H2O-N2->/builds/jleconte/exo_k/data/cia/H2O-N2_continuumTurbet_MT_CKD3.3.hdf5\n",
       "H2O-H2O->/builds/jleconte/exo_k/data/cia/H2O-H2O_continuumTurbet_MT_CKD3.3.hdf5\n",
       "All tables do NOT have common spectral grid\n",
       "You will need to run sample before using the database"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols=['He','H2','N2','H2O']\n",
    "cia_data=xk.CIAdatabase(molecules=mols)\n",
    "cia_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedbc25a",
   "metadata": {
    "papermill": {
     "duration": 0.08189,
     "end_time": "2022-09-02T07:52:47.722618",
     "exception": false,
     "start_time": "2022-09-02T07:52:47.640728",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can filter the files you want to look for by adding a string filter as the first argument(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6197c6f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:47.881098Z",
     "iopub.status.busy": "2022-09-02T07:52:47.880576Z",
     "iopub.status.idle": "2022-09-02T07:52:49.017851Z",
     "shell.execute_reply": "2022-09-02T07:52:49.016785Z"
    },
    "papermill": {
     "duration": 1.219683,
     "end_time": "2022-09-02T07:52:49.020580",
     "exception": false,
     "start_time": "2022-09-02T07:52:47.800897",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The available molecule pairs are: \n",
       "H2-He->/builds/jleconte/exo_k/data/cia/H2-He_2011.hdf5\n",
       "H2-H2->/builds/jleconte/exo_k/data/cia/H2-H2_2011.hdf5\n",
       "N2-H2->/builds/jleconte/exo_k/data/cia/N2-H2_2011.hdf5\n",
       "N2-N2->/builds/jleconte/exo_k/data/cia/N2-N2_2011.hdf5\n",
       "All tables do NOT have common spectral grid\n",
       "You will need to run sample before using the database"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols=['He','H2','N2','H2O']\n",
    "cia_data=xk.CIAdatabase('2011', molecules=mols)\n",
    "cia_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393f463b",
   "metadata": {
    "papermill": {
     "duration": 0.079569,
     "end_time": "2022-09-02T07:52:49.181051",
     "exception": false,
     "start_time": "2022-09-02T07:52:49.101482",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you only want a subset of these pairs, you can directly specify the pairs you want to look for with the `molecule_pairs` keyword. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f9fd7732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:49.342605Z",
     "iopub.status.busy": "2022-09-02T07:52:49.342154Z",
     "iopub.status.idle": "2022-09-02T07:52:49.855913Z",
     "shell.execute_reply": "2022-09-02T07:52:49.854925Z"
    },
    "papermill": {
     "duration": 0.598576,
     "end_time": "2022-09-02T07:52:49.858451",
     "exception": false,
     "start_time": "2022-09-02T07:52:49.259875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The available molecule pairs are: \n",
       "H2-He->/builds/jleconte/exo_k/data/cia/H2-He_2011.hdf5\n",
       "N2-H2->/builds/jleconte/exo_k/data/cia/N2-H2_2011.hdf5\n",
       "All tables do NOT have common spectral grid\n",
       "You will need to run sample before using the database"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol_pairs=[['He','H2'],['N2','H2']]\n",
    "cia_data=xk.CIAdatabase(molecule_pairs=mol_pairs)\n",
    "cia_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89c34be0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:50.053060Z",
     "iopub.status.busy": "2022-09-02T07:52:50.052601Z",
     "iopub.status.idle": "2022-09-02T07:52:52.041458Z",
     "shell.execute_reply": "2022-09-02T07:52:52.040397Z"
    },
    "papermill": {
     "duration": 2.071979,
     "end_time": "2022-09-02T07:52:52.044836",
     "exception": false,
     "start_time": "2022-09-02T07:52:49.972857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xk.Settings().set_mks(True)\n",
    "mols=['He','H2','N2']\n",
    "cia_data=xk.CIAdatabase(molecules=mols)\n",
    "ttest=300.\n",
    "fig,axs=plt.subplots(2,1,sharex=False,sharey=False)  \n",
    "cia_data['H2']['H2'].plot_spectrum(axs[0],t=ttest,x_axis='wns',yscale='log',label='H2-H2')\n",
    "cia_data['H2']['He'].plot_spectrum(axs[0],t=ttest,x_axis='wns',label='H2-He')\n",
    "cia_data['N2']['H2'].plot_spectrum(axs[1],t=ttest,x_axis='wns',yscale='log',label='N2-H2')\n",
    "cia_data['N2']['N2'].plot_spectrum(axs[1],t=ttest,x_axis='wns',yscale='log',label='N2-N2')\n",
    "for ax in axs:\n",
    "    ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad65ccce",
   "metadata": {
    "papermill": {
     "duration": 0.078659,
     "end_time": "2022-09-02T07:52:52.204025",
     "exception": false,
     "start_time": "2022-09-02T07:52:52.125366",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Because CIA are often on very inhomogeneous grids (as above), you will need to sample them on the grid of your `Ktable` or `Xtable` to use them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "75317cbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:52.359710Z",
     "iopub.status.busy": "2022-09-02T07:52:52.359279Z",
     "iopub.status.idle": "2022-09-02T07:52:52.997519Z",
     "shell.execute_reply": "2022-09-02T07:52:52.996131Z"
    },
    "papermill": {
     "duration": 0.719177,
     "end_time": "2022-09-02T07:52:53.000585",
     "exception": false,
     "start_time": "2022-09-02T07:52:52.281408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_wns_grid=h2o_ktab_SI.wns\n",
    "cia_data.sample(ref_wns_grid)\n",
    "fig,axs=plt.subplots(2,1,sharex=False,sharey=False)  \n",
    "cia_data['H2']['H2'].plot_spectrum(axs[0],t=ttest,x_axis='wns',yscale='log',label='H2-H2')\n",
    "cia_data['H2']['He'].plot_spectrum(axs[0],t=ttest,x_axis='wns',label='H2-He')\n",
    "cia_data['N2']['H2'].plot_spectrum(axs[1],t=ttest,x_axis='wns',yscale='log',label='N2-H2')\n",
    "cia_data['N2']['N2'].plot_spectrum(axs[1],t=ttest,x_axis='wns',yscale='log',label='N2-N2')\n",
    "for ax in axs:\n",
    "    ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e9deee",
   "metadata": {
    "papermill": {
     "duration": 0.078071,
     "end_time": "2022-09-02T07:52:53.161204",
     "exception": false,
     "start_time": "2022-09-02T07:52:53.083133",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The method:\n",
    "```python\n",
    "CIAdatabase.cia_cross_section(pressure,temperature,composition)\n",
    "```\n",
    "can be used to compute the effective CIA cross-section for the whole gas (in m^2 per average molecule)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "44607360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:53.317241Z",
     "iopub.status.busy": "2022-09-02T07:52:53.316869Z",
     "iopub.status.idle": "2022-09-02T07:52:54.237065Z",
     "shell.execute_reply": "2022-09-02T07:52:54.235997Z"
    },
    "papermill": {
     "duration": 1.000763,
     "end_time": "2022-09-02T07:52:54.239671",
     "exception": false,
     "start_time": "2022-09-02T07:52:53.238908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compo={'H2':0.2,'He':0.1,'N2':0.4}\n",
    "raw=cia_data.cia_cross_section([1.,3.],[300.,500.],compo)\n",
    "fig,axs=plt.subplots(2,1,sharex=False,sharey=False,figsize=(6,5))  \n",
    "cia_data['H2']['H2'].plot_spectrum(axs[0],t=ttest,x_axis='wns',yscale='log')\n",
    "cia_data['H2']['He'].plot_spectrum(axs[0],t=ttest,x_axis='wns')\n",
    "cia_data['N2']['H2'].plot_spectrum(axs[0],t=ttest,x_axis='wns',yscale='log')\n",
    "axs[1].plot(cia_data.wns,raw[0])\n",
    "axs[1].set_yscale('log')\n",
    "axs[1].set_ylabel('Cross section (m^2/molec)')\n",
    "axs[1].set_xlabel('Wavenumber')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813b2214",
   "metadata": {
    "papermill": {
     "duration": 0.085571,
     "end_time": "2022-09-02T07:52:54.411434",
     "exception": false,
     "start_time": "2022-09-02T07:52:54.325863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span style=\"color:blue\">How to use `exo_k` as an opacity interpolator inside your code: The `Gas_mix` object</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aece1492",
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   "source": [
    "`Exo_k` can also be used to directly access and interpolate opacity within your code. For this, one possibility is to load and access individual K/X/CIA tables and add all the opacities by yourself. \n",
    "\n",
    "Another is to use the `Gas_mix` object: it contains the information on the pressure/temperature conditions of the gas, its composition, and can be connected to radiative databases to compute the opacity of the gas from all contributions. "
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "## Composition and background gas"
   ]
  },
  {
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   "source": [
    "An empty `Gas_mix` object can be instanciated as follows and filled later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c6655d75",
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    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume mixing ratios in Gas_mix: \n",
      "inactive_gas->1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gas=xk.Gas_mix()\n",
    "print(gas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e30ddbb",
   "metadata": {
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   },
   "source": [
    "The message shows that, at this time, the `Gas_mix` is empty. Effectively, it is filled with 'inactive_gas'.\n",
    "\n",
    "Composition is specified using a dictionary where molecules are the keys and the volume mixing ratios as values. The rest of the `Gas_mix` is filled with inactive gas so that Sum(vmr)=1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ba0c67a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:55.380217Z",
     "iopub.status.busy": "2022-09-02T07:52:55.379640Z",
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    },
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     "end_time": "2022-09-02T07:52:55.388835",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.01\n",
      "inactive_gas->0.99\n",
      "\n",
      "Volume mixing ratios in Gas_mix: \n",
      "N2->0.8\n",
      "inactive_gas->0.19999999999999996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gas.set_composition({'CO2':0.01})\n",
    "print(gas)\n",
    "gas.set_composition({'N2':0.8})  # reset the whole composition\n",
    "print(gas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688bc1ee",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "One of the gases can have a vmr equal to 'background'.\n",
    "In this case, the vmr of this gas will always be updated to be 1.-Sum(other vmrs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8b0a1de6",
   "metadata": {
    "execution": {
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.01\n",
      "inactive_gas->0.0\n",
      "N2->0.99\n",
      "\n",
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.01\n",
      "inactive_gas->0.0\n",
      "N2->0.79\n",
      "O2->0.2\n",
      "\n",
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.01\n",
      "inactive_gas->0.0\n",
      "N2->[0.78  0.789]\n",
      "O2->0.2\n",
      "H2O->[0.01  0.001]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gas=xk.Gas_mix({'CO2':0.01,'N2':'background'}) # can also directly instantiate with a composition\n",
    "print(gas)\n",
    "gas['O2']=0.2  # only adds a gas or changes its vmr.\n",
    "print(gas)\n",
    "gas['H2O']=[0.01, 0.001]  #  Accepts arrays\n",
    "print(gas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7784645",
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     "duration": 0.079361,
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     "start_time": "2022-09-02T07:52:55.820954",
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    },
    "tags": []
   },
   "source": [
    "## Getting the molar mass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f9058d",
   "metadata": {
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     "duration": 0.082132,
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    },
    "tags": []
   },
   "source": [
    "The molar mass (in kg/mol) of your mix can be computed simply using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "107b92a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:52:56.227943Z",
     "iopub.status.busy": "2022-09-02T07:52:56.227371Z",
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     "shell.execute_reply": "2022-09-02T07:52:56.236536Z"
    },
    "papermill": {
     "duration": 0.094998,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.028008920548799998\n",
      "0.06005255999999999\n",
      "0.1649981\n",
      "[0.02800892 0.0270191 ]\n"
     ]
    }
   ],
   "source": [
    "gas.set_composition({'N2':'background','CO2':340.e-6,'H2O':0.001})\n",
    "print(gas.molar_mass())\n",
    "gas.set_composition({'CH3COOH':'background'})\n",
    "print(gas.molar_mass())\n",
    "gas.set_composition({'Mg3SiO4':'background'})\n",
    "print(gas.molar_mass())\n",
    "gas=xk.Gas_mix({'N2':'background','CO2':340.e-6,'H2O':[0.001,0.1]})\n",
    "print(gas.molar_mass())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77353b2",
   "metadata": {
    "papermill": {
     "duration": 0.086045,
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     "start_time": "2022-09-02T07:52:56.323551",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Connecting a gas with a radiative database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351be0a2",
   "metadata": {
    "papermill": {
     "duration": 0.082859,
     "end_time": "2022-09-02T07:52:56.572995",
     "exception": false,
     "start_time": "2022-09-02T07:52:56.490136",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To connect with radiative data, first you need to load the databases you want to use. You can combine a `CIAdatabase` with a `Kdatabase` filled with either `Ktable`s or `Xtable`s (but not both). \n",
    "\n",
    "Then these database can be added to a `Gas_mix`, or you can directly instantiate with these databases. This only needs to be done once."
   ]
  },
  {
   "cell_type": "raw",
   "id": "3de07bae",
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     "duration": 0.084581,
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    "raw_mimetype": "text/restructuredtext",
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   },
   "source": [
    ".. warning::\n",
    "    With `Gas_mix`, radiative databases need to be converted in SI units to be used.\n",
    "    The simplest way to achieve that is to use `exo_k.Settings().set_mks(True)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "426febdf",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-09-02T07:53:01.041516Z"
    },
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    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.00034\n",
      "H2O->[0.001 0.1  ]\n",
      "inactive_gas->0.0\n",
      "N2->[0.99866 0.89966]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "database=xk.Kdatabase(['CO2','H2O'],'R300_0.3-50mu.ktable.SI')\n",
    "\n",
    "gas.set_k_database(database)\n",
    "\n",
    "#Loads CIA and sample them on the right spectral grid.\n",
    "cia_data=xk.CIAdatabase(molecules=['N2','H2O'], mks=True)\n",
    "cia_data.sample(database.wns)\n",
    "\n",
    "gas.set_cia_database(cia_data)\n",
    "print(gas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb334b4",
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     "start_time": "2022-09-02T07:53:01.134025",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Or everything can be done at initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3629953e",
   "metadata": {
    "execution": {
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume mixing ratios in Gas_mix: \n",
      "CO2->0.00034\n",
      "H2O->[0.001 0.1  ]\n",
      "inactive_gas->0.0\n",
      "N2->[0.99866 0.89966]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "database=xk.Kdatabase(['CO2','H2O'],'R300_0.3-50mu.ktable.SI')\n",
    "\n",
    "#Loads CIA and sample them on the right spectral grid.\n",
    "cia_data=xk.CIAdatabase(molecules=['N2','H2O'], mks=True)\n",
    "cia_data.sample(database.wns)\n",
    "\n",
    "gas=xk.Gas_mix(composition={'N2':'background','CO2':340.e-6,'H2O':[0.001,0.1]},\n",
    "               k_database=database, cia_database=cia_data)\n",
    "print(gas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9692a4",
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     "duration": 0.082536,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, the effective cross section on the gas can be computed as many times as necessary by calling the `Gas_mix.cross_section()` method providing log pressure (in Pa), temperature, and composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6648e466",
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    },
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logp_array=6.\n",
    "t_array=300.\n",
    "gas.set_composition({'CO2':0.01,'N2':'background'})\n",
    "# composition can be set once and for all and only logP and T or changed at call time.\n",
    "kdata=gas.cross_section(logp_array=logp_array, t_array=t_array)\n",
    "fig,ax=plt.subplots(1,1,sharey=False,figsize=(8,4))  \n",
    "ax.plot(10000./gas.wns,kdata[0,:,-1])\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel('Cross section (m^2/molec)')\n",
    "ax.set_xlabel('Wavelength (micron)')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "951fdc0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:53:07.244494Z",
     "iopub.status.busy": "2022-09-02T07:53:07.243992Z",
     "iopub.status.idle": "2022-09-02T07:53:07.932667Z",
     "shell.execute_reply": "2022-09-02T07:53:07.931369Z"
    },
    "papermill": {
     "duration": 0.779229,
     "end_time": "2022-09-02T07:53:07.935947",
     "exception": false,
     "start_time": "2022-09-02T07:53:07.156718",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logp_array=2.\n",
    "t_array=300.\n",
    "composition={'N2':'background','CO2':340.e-6,'H2O':1.e-3}\n",
    "kdata=gas.cross_section(composition=composition, # composition can also be specified at call time\n",
    "                    logp_array=logp_array, t_array=t_array)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,sharey=False,figsize=(8,4))  \n",
    "ax.plot(10000./gas.wns,kdata[0,:,-1])\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel('Cross section (m^2/molec)')\n",
    "ax.set_xlabel('Wavelength (micron)')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96261ae",
   "metadata": {
    "papermill": {
     "duration": 0.089837,
     "end_time": "2022-09-02T07:53:08.116423",
     "exception": false,
     "start_time": "2022-09-02T07:53:08.026586",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you need to call this function for many logP-T points, it is more efficient to call the function once, providing logP-T (and even vmr) arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0e1df03c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:53:08.311489Z",
     "iopub.status.busy": "2022-09-02T07:53:08.311042Z",
     "iopub.status.idle": "2022-09-02T07:53:08.691757Z",
     "shell.execute_reply": "2022-09-02T07:53:08.690684Z"
    },
    "papermill": {
     "duration": 0.482297,
     "end_time": "2022-09-02T07:53:08.695083",
     "exception": false,
     "start_time": "2022-09-02T07:53:08.212786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1538, 20)\n"
     ]
    }
   ],
   "source": [
    "logp_array=np.linspace(1.,6.,100)\n",
    "t_array=300.*np.ones(logp_array.size)\n",
    "composition={'N2':'background','CO2':340.e-6,\n",
    "             'H2O':0.001*np.ones(logp_array.size)}\n",
    "kdata=gas.cross_section(composition=composition,\n",
    "                    logp_array=logp_array, t_array=t_array)\n",
    "print(kdata.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a8f53a",
   "metadata": {
    "papermill": {
     "duration": 0.088085,
     "end_time": "2022-09-02T07:53:08.870573",
     "exception": false,
     "start_time": "2022-09-02T07:53:08.782488",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Choosing gas recombination method (when using `Ktable`s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6e11b2",
   "metadata": {
    "papermill": {
     "duration": 0.087342,
     "end_time": "2022-09-02T07:53:09.046015",
     "exception": false,
     "start_time": "2022-09-02T07:53:08.958673",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Two recombination methods are available when several gases are present:\n",
    "\n",
    "  * Simple addition: Fast. It is ok at moderate resolution\n",
    "    (and if you are not looking for ppm level accuracy), but can be inacurrate\n",
    "    if you are using large bins that encompass molecular bands of several molecules.\n",
    "    This is the default mode for `Gas_mix.cross_section()`.\n",
    "  * Random overlap: Very accurate in many situations, but a bit longer,\n",
    "    especially if you have many molecules. This is the default mode for regular `Ktable`\n",
    "    recombinations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512a78d3",
   "metadata": {
    "papermill": {
     "duration": 0.08994,
     "end_time": "2022-09-02T07:53:09.222064",
     "exception": false,
     "start_time": "2022-09-02T07:53:09.132124",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For `Gas_mix.cross_section()`, the behavior can be controlled with the `random_overlap=True/False` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2e345a23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-02T07:53:09.403618Z",
     "iopub.status.busy": "2022-09-02T07:53:09.403188Z",
     "iopub.status.idle": "2022-09-02T07:53:16.892417Z",
     "shell.execute_reply": "2022-09-02T07:53:16.891335Z"
    },
    "papermill": {
     "duration": 7.584014,
     "end_time": "2022-09-02T07:53:16.895409",
     "exception": false,
     "start_time": "2022-09-02T07:53:09.311395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.58 ms ± 198 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.2 ms ± 229 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logp_array=2.\n",
    "t_array=300.\n",
    "gas.set_composition({'N2':'background','CO2':340.e-6,'H2O':0.001})\n",
    "gas.set_logPT(logp_array=logp_array, t_array=t_array)\n",
    "\n",
    "kdata=gas.cross_section()\n",
    "kdata_randoverlap=gas.cross_section(random_overlap=True)\n",
    "\n",
    "%timeit -r 10 -n 10 gas.cross_section()\n",
    "%timeit -r 10 -n 10 gas.cross_section(random_overlap=True)\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots(1,1,sharey=False,figsize=(8,4))  \n",
    "ax.plot(10000./gas.wns,kdata_randoverlap[0,:,-1], label='Rand. Overlap')\n",
    "ax.plot(10000./gas.wns,kdata[0,:,-1], label='addition')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.legend()\n",
    "ax.set_xlabel('Wavelength (micron)')\n",
    "ax.set_ylabel('Cross section (m^2/molec)')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f34509",
   "metadata": {
    "papermill": {
     "duration": 0.090077,
     "end_time": "2022-09-02T07:53:17.077951",
     "exception": false,
     "start_time": "2022-09-02T07:53:16.987874",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can try any molecule with a regular Element Number of atoms notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3417b879",
   "metadata": {
    "papermill": {
     "duration": 0.088171,
     "end_time": "2022-09-02T07:53:17.254472",
     "exception": false,
     "start_time": "2022-09-02T07:53:17.166301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Initialization Cell",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 252.67112,
   "end_time": "2022-09-02T07:53:18.264585",
   "environment_variables": {},
   "exception": null,
   "input_path": "tutorial-exo_k.ipynb",
   "output_path": "../ci_notebooks/tutorial-exo_k.ipynb",
   "parameters": {},
   "start_time": "2022-09-02T07:49:05.593465",
   "version": "2.3.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "256px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}