diff --git a/Cheatsheet and useful functions.ipynb b/Cheatsheet and useful functions.ipynb
index 2be14ea..67a8bf7 100644
--- a/Cheatsheet and useful functions.ipynb	
+++ b/Cheatsheet and useful functions.ipynb	
@@ -971,9 +971,7 @@
     "    return r\n",
     "except:\n",
     "    print ('No field star found near %s' %cl_name)\n",
-    "    return [[0.,0.,0.,0.]]\n",
-    "\n",
-    "\n"
+    "    return [[0.,0.,0.,0.]]"
    ]
   },
   {
@@ -1074,7 +1072,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.9.12"
   },
   "varInspector": {
    "cols": {
diff --git a/creating_model_files.ipynb b/creating_model_files.ipynb
new file mode 100644
index 0000000..0d82abb
--- /dev/null
+++ b/creating_model_files.ipynb
@@ -0,0 +1,3924 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1ea42bb7-785c-437d-b8de-aa215351f6be",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Imports and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "14da6f65-fafa-4b8c-b536-fd1028279d87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import xarray as xr\n",
+    "from scipy import interpolate\n",
+    "from astropy import constants as const\n",
+    "from astropy import units as u\n",
+    "pd.options.mode.chained_assignment = None  # default='warn'\n",
+    "\n",
+    "\n",
+    "\n",
+    "DIR_MODELS = 'models/'\n",
+    "\n",
+    "\n",
+    "def print_progress(current_iteration, total_iterations=100, step=10, message='Progress'): \n",
+    "    '''\n",
+    "    Print progress\n",
+    "    '''\n",
+    "    if (current_iteration%step==0):\n",
+    "        print ('\\r %s:  %d/%d  (%d%%)' %(message, current_iteration+1, total_iterations, 100*(current_iteration+1)/total_iterations), end='')\n",
+    "        \n",
+    "        \n",
+    "def reprint(message):\n",
+    "    print ('\\r %s' %(message), end='')\n",
+    "\n",
+    "    \n",
+    "def get_kurucz_spectral_library_params(dir_model_files, plot=False, overwrite=False):\n",
+    "    '''\n",
+    "    Reads and stores all the parameter values from all the files in 'dir_model_files'.\n",
+    "    '''\n",
+    "    output_name = DIR_MODELS + 'kurucz_spectral_library_params.csv'\n",
+    "\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            model_params = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "            \n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        # Get the list of all files and directories\n",
+    "        file_list = os.listdir(dir_model_files)\n",
+    "\n",
+    "        model_params              = pd.DataFrame()\n",
+    "        model_params['file_name'] = file_list\n",
+    "        \n",
+    "        str_params                = model_params['file_name'].str.findall('[0-9]+|m|p')\n",
+    "        str_params_T              = np.array(list(map(list, zip(*str_params))))\n",
+    "        \n",
+    "        model_params['Te']        = str_params_T[2].astype(int)\n",
+    "        model_params['logg']      = str_params_T[3].astype(int)/10.\n",
+    "\n",
+    "        # Create numerical value of metallicity (p05 --> +0.5, m02-->-0.2)\n",
+    "        sign               = str_params_T[0]\n",
+    "        conditions         = [sign == 'm', sign == 'p']\n",
+    "        values             = [-1*(str_params_T[1].astype(int))/10.0, (str_params_T[1].astype(int))/10.0]\n",
+    "        model_params['MH'] = np.select(conditions, values)\n",
+    "\n",
+    "        # Saving numerical alpha values (denoted by 'a' before 't' in the file_name)\n",
+    "        for i, file in enumerate(file_list):\n",
+    "            model_params.loc[i, 'alpha']   = file[4]\n",
+    "        model_params['alpha'] = model_params['alpha'].str.replace('t','0.0')\n",
+    "        model_params['alpha'] = model_params['alpha'].str.replace('a','0.4')\n",
+    "        model_params['alpha'] = model_params['alpha'].astype(float)\n",
+    "        \n",
+    "        # Sort the parameters in acending order (with give order)\n",
+    "        model_params = model_params.sort_values(by=['alpha','Te', 'logg', 'MH'])\n",
+    "        model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "        print('Saving %s'%output_name)\n",
+    "        model_params.to_csv(output_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        sns.pairplot(model_params, corner=True, diag_kind=\"hist\")\n",
+    "\n",
+    "    return model_params\n",
+    "\n",
+    "\n",
+    "def get_wavelength_params(model_params, dir_model_files, output_name = 'models/wave_params.csv', plot=True, overwrite=False):\n",
+    "    '''\n",
+    "    Reads and stores limits of the wavelengths and step sizes for all files in 'model_params'.\n",
+    "    '''\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            df_wave_params = pd.read_csv(output_name)\n",
+    "            \n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "    \n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        df_wave_params              = pd.DataFrame(columns=['file_name','log_wave_min','log_wave_max',\n",
+    "                                                            'diff_log_wave_min','diff_log_wave_med','diff_log_wave_max'])\n",
+    "        df_wave_params['file_name'] = model_params['file_name']\n",
+    "\n",
+    "        len_model_params = len(model_params) \n",
+    "        for idx in range(len_model_params):\n",
+    "            print_progress(idx, total_iterations=len_model_params, step=10)\n",
+    "\n",
+    "            data_flux         = pd.read_csv(dir_model_files + model_params.file_name[idx],\n",
+    "                                            comment='#',engine='python', header=None, delim_whitespace= True,skipinitialspace=True)\n",
+    "            data_flux.columns = ['Wavelength', 'Flux']\n",
+    "\n",
+    "            data_flux['log_Wavelength']      = np.log10(data_flux['Wavelength'])\n",
+    "            data_flux['diff_log_Wavelength'] = data_flux.log_Wavelength.diff()\n",
+    "\n",
+    "            df_wave_params.loc[idx, 'log_wave_min']        = np.nanmin(data_flux['log_Wavelength'])\n",
+    "            df_wave_params.loc[idx, 'log_wave_max']        = np.nanmax(data_flux['log_Wavelength'])\n",
+    "            df_wave_params.loc[idx, 'diff_log_wave_min']   = np.nanmin(data_flux['diff_log_Wavelength'])\n",
+    "            df_wave_params.loc[idx, 'diff_log_wave_med']   = np.nanmedian(data_flux['diff_log_Wavelength'])\n",
+    "            df_wave_params.loc[idx, 'diff_log_wave_max']   = np.nanmax(data_flux['diff_log_Wavelength'])\n",
+    "\n",
+    "        print('Saving %s'%output_name)\n",
+    "        df_wave_params.to_csv(output_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        sns.pairplot(df_wave_params, corner=True, diag_kind=\"hist\")\n",
+    "\n",
+    "    return df_wave_params\n",
+    "\n",
+    "\n",
+    "def combine_kurucz_spectral_library(model_params, dir_model_files, log_wave_list, debug = False, overwrite=False):\n",
+    "    output_name='models/kurucz_spectral_library.nc'\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            with xr.open_dataarray(output_name) as da_model:\n",
+    "                pass\n",
+    "            \n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        flux_model       = pd.DataFrame()\n",
+    "        len_model_params = len(model_params) \n",
+    "\n",
+    "        Te_list          = model_params.Te.unique()\n",
+    "        logg_list        = model_params.logg.unique()\n",
+    "        MH_list          = model_params.MH.unique()\n",
+    "        alpha_list       = model_params.alpha.unique()\n",
+    "\n",
+    "        wave_list = 10**log_wave_list\n",
+    "\n",
+    "        for idx in range(len_model_params):\n",
+    "            print_progress(idx, total_iterations=len_model_params, step=10)\n",
+    "            data_flux         = pd.read_csv(dir_model_files + model_params.file_name[idx],comment='#',engine='python', header=None, delim_whitespace= True,skipinitialspace=True)\n",
+    "            data_flux.columns = ['Wavelength', 'Flux']\n",
+    "\n",
+    "            # interpolate to predetermined wavelengths\n",
+    "            interpolater = interpolate.interp1d(data_flux.Wavelength, data_flux.Flux)\n",
+    "            flux_interp  = interpolater(wave_list)\n",
+    "\n",
+    "            if debug:\n",
+    "                print(data_flux.Wavelength.min())\n",
+    "                plt.plot(data_flux.Wavelength, data_flux.Flux, 'o', wave_list,flux_interp, '-')\n",
+    "                plt.xlim(2200,2600)\n",
+    "                plt.ylim(10e-4, 5)\n",
+    "                plt.xscale('log')\n",
+    "                plt.yscale('log')\n",
+    "                plt.grid()\n",
+    "\n",
+    "            if idx==0:\n",
+    "                nd_arr = np.empty((len(wave_list), len(Te_list), len(logg_list), len(MH_list), len(alpha_list)))\n",
+    "                nd_arr.fill(np.nan)\n",
+    "\n",
+    "                da_model = xr.DataArray(nd_arr, coords={'Wavelength': wave_list,\n",
+    "                                                        'Te'        : Te_list,\n",
+    "                                                        'logg'      : logg_list,\n",
+    "                                                        'MH'        : MH_list,\n",
+    "                                                        'alpha'     : alpha_list})\n",
+    "\n",
+    "            # add flux to given model\n",
+    "            da_model.loc[dict(Te   = model_params.Te[idx],\n",
+    "                              logg = model_params.logg[idx],\n",
+    "                              MH   = model_params.MH[idx],\n",
+    "                              alpha= model_params.alpha[idx])] = flux_interp\n",
+    "\n",
+    "        da_model.attrs['unit']      = 'erg/s/cm2/A'\n",
+    "        da_model.attrs['long_name'] = 'Flux'\n",
+    "\n",
+    "        print('Saving %s'%output_name)\n",
+    "        da_model.to_netcdf(file_name)\n",
+    "    return  da_model\n",
+    "\n",
+    "\n",
+    "def get_kurucz_synthetic_photometry_params(dir_model_files, plot=False, overwrite=False):\n",
+    "    output_name = DIR_MODELS + 'kurucz_synthetic_photometry_params.csv'\n",
+    "    \n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            model_params = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        # Get the list of all files and directories\n",
+    "        file_list = os.listdir(dir_model_files)\n",
+    "\n",
+    "        model_params              = pd.DataFrame()\n",
+    "        model_params['file_name'] = file_list\n",
+    "        str_params                = model_params['file_name'].str.findall('[0-9]+|m|p')\n",
+    "        str_params_T              = np.array(list(map(list, zip(*str_params))))\n",
+    "\n",
+    "        model_params['Te']        = str_params_T[3].astype(int)\n",
+    "        model_params['logg']      = str_params_T[4].astype(int)/10.\n",
+    "\n",
+    "        # create numerical value of metallicity (p05 --> +0.5, m02-->-0.2)\n",
+    "        sign               = str_params_T[1]\n",
+    "        conditions         = [sign == 'm', sign == 'p']\n",
+    "        values             = [-1*(str_params_T[2].astype(int))/10.0, (str_params_T[2].astype(int))/10.0]\n",
+    "        model_params['MH'] = np.select(conditions, values)\n",
+    "\n",
+    "        # Saving numerical alpha values (denoted by 'a' before 't' in the file_name)\n",
+    "        for i, file in enumerate(file_list):\n",
+    "            model_params.loc[i, 'alpha']   = file[18]\n",
+    "        model_params['alpha'] = model_params['alpha'].str.replace('t','0.0')\n",
+    "        model_params['alpha'] = model_params['alpha'].str.replace('a','0.4')\n",
+    "        model_params['alpha'] = model_params['alpha'].astype(float)\n",
+    "\n",
+    "        # sort the parameters in acending order (with give order)\n",
+    "        model_params = model_params.sort_values(by=['alpha','Te', 'logg', 'MH'])\n",
+    "        model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "        print('Saving %s'%output_name)\n",
+    "        model_params.to_csv(output_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        sns.pairplot(model_params, corner=True, diag_kind=\"hist\")\n",
+    "\n",
+    "    return model_params\n",
+    "\n",
+    "\n",
+    "def combine_kurucz_synthetic_photometry(model_params, dir_model_files, keep_common_filters=False, overwrite=False):\n",
+    "    output_name='models/kurucz_synthetic_photometry.nc'\n",
+    "\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            with xr.open_dataarray(output_name) as da_model:\n",
+    "                pass\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        flux_model       = pd.DataFrame()\n",
+    "        len_model_params = len(model_params) \n",
+    "        Te_list          = model_params.Te.unique()\n",
+    "        logg_list        = model_params.logg.unique()\n",
+    "        MH_list          = model_params.MH.unique()\n",
+    "        alpha_list       = model_params.alpha.unique()\n",
+    "\n",
+    "        file_name = \"models/kurucz_synthetic_photometry.nc\"\n",
+    "        print('Saving %s'%file_name)\n",
+    "\n",
+    "        for idx in range(len_model_params):\n",
+    "            print_progress(idx, total_iterations=len_model_params, step=10)\n",
+    "            data_flux         = pd.read_csv(dir_model_files + model_params.file_name[idx],comment='#',engine='python', header=None, delim_whitespace= True,skipinitialspace=True)\n",
+    "            data_flux.columns = ['FilterID', 'Wavelength', 'Flux']\n",
+    "            data_flux         = data_flux.set_index('FilterID')\n",
+    "\n",
+    "            if idx==0:\n",
+    "                data_flux   = data_flux.sort_values(by=['Wavelength'])\n",
+    "                data_flux   = data_flux[data_flux['Wavelength']>0]\n",
+    "                data_flux_0 = data_flux.copy()\n",
+    "                filter_list = data_flux_0.index\n",
+    "                wave_list   = data_flux_0['Wavelength']\n",
+    "\n",
+    "                nd_arr = np.empty((len(filter_list), len(Te_list), len(logg_list), len(MH_list), len(alpha_list)))\n",
+    "                nd_arr.fill(np.nan)\n",
+    "\n",
+    "                da_model = xr.DataArray(nd_arr, coords={'FilterID': filter_list,\n",
+    "                                                        'Te'      : Te_list,\n",
+    "                                                        'logg'    : logg_list,\n",
+    "                                                        'MH'      : MH_list,\n",
+    "                                                        'alpha'   : alpha_list})\n",
+    "                da_model.attrs[\"Wavelengths\"] = wave_list.values\n",
+    "\n",
+    "            if not keep_common_filters:\n",
+    "                if len(data_flux)!=len(data_flux_0):\n",
+    "                    raise Exception('ERROR: Model files have different number of filters.\\n           Use \"keep_common_filters=True\" to use the exact filters available in the first file.\\n           (sometimes, some filters are missing from the specific temperatures/logg...)')\n",
+    "\n",
+    "            # keep the same filter order as the first file    \n",
+    "            data_flux_0['Flux_new'] = data_flux['Flux']\n",
+    "\n",
+    "            # add flux to given model\n",
+    "            da_model.loc[dict(Te   = model_params.Te[idx],\n",
+    "                              logg = model_params.logg[idx],\n",
+    "                              MH   = model_params.MH[idx],\n",
+    "                              alpha= model_params.alpha[idx])] = data_flux_0['Flux_new']\n",
+    "\n",
+    "        da_model.attrs['unit']      = 'erg/s/cm2/A'\n",
+    "        da_model.attrs['long_name'] = 'Flux'\n",
+    "\n",
+    "        da_model.to_netcdf(output_name)\n",
+    "    return  da_model\n",
+    "\n",
+    "\n",
+    "def get_koester_spectral_library_params(dir_model_files, plot=False, overwrite=False):\n",
+    "    output_name = DIR_MODELS + 'koester_spectral_library_params.csv'\n",
+    "\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            model_params = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "            \n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        # Get the list of all files and directories\n",
+    "        file_list = os.listdir(dir_model_files)\n",
+    "\n",
+    "        model_params              = pd.DataFrame()\n",
+    "        model_params['file_name'] = file_list\n",
+    "        \n",
+    "        str_params                = model_params['file_name'].str.findall('[0-9]+')\n",
+    "        str_params_T              = np.array(list(map(list, zip(*str_params))))\n",
+    "        model_params['Te']        = str_params_T[0].astype(int)\n",
+    "        model_params['logg']      = str_params_T[1].astype(int)/100.\n",
+    "\n",
+    "        # Sort the parameters in acending order (with give order)\n",
+    "        model_params = model_params.sort_values(by=['Te', 'logg'])\n",
+    "        model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "        print('Saving %s'%file_name)\n",
+    "        model_params.to_csv(file_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        sns.pairplot(model_params, corner=True, diag_kind=\"hist\")\n",
+    "\n",
+    "    return model_params\n",
+    "\n",
+    "\n",
+    "def combine_koester_spectral_library(model_params, dir_model_files, log_wave_list, debug = False, overwrite=False):\n",
+    "    output_name='models/koester_spectral_library.nc'\n",
+    "\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            with xr.open_dataarray(output_name) as da_model:\n",
+    "                pass\n",
+    "            \n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        flux_model       = pd.DataFrame()\n",
+    "        len_model_params = len(model_params) \n",
+    "\n",
+    "        Te_list          = model_params.Te.unique()\n",
+    "        logg_list        = model_params.logg.unique()\n",
+    "\n",
+    "        wave_list = 10**log_wave_list\n",
+    "\n",
+    "        for idx in range(len_model_params):\n",
+    "            print_progress(idx, total_iterations=len_model_params, step=10)\n",
+    "            data_flux         = pd.read_csv(dir_model_files + model_params.file_name[idx],comment='#',engine='python', header=None, delim_whitespace= True,skipinitialspace=True)\n",
+    "            data_flux.columns = ['Wavelength', 'Flux']\n",
+    "\n",
+    "            # interpolate to predetermined wavelengths\n",
+    "            interpolater = interpolate.interp1d(data_flux.Wavelength, data_flux.Flux)\n",
+    "            flux_interp  = interpolater(wave_list)\n",
+    "\n",
+    "            if debug:\n",
+    "                print(data_flux.Wavelength.min())\n",
+    "                plt.plot(data_flux.Wavelength, data_flux.Flux, 'o', wave_list,flux_interp, '-')\n",
+    "                plt.xlim(6550,6570)\n",
+    "                plt.ylim(2e6, 4e6)\n",
+    "                plt.xscale('log')\n",
+    "                plt.yscale('log')\n",
+    "                plt.grid()\n",
+    "\n",
+    "            if idx==0:\n",
+    "                nd_arr = np.empty((len(wave_list), len(Te_list), len(logg_list)))\n",
+    "                nd_arr.fill(np.nan)\n",
+    "\n",
+    "                da_model = xr.DataArray(nd_arr, coords={'Wavelength': wave_list,\n",
+    "                                                        'Te'        : Te_list,\n",
+    "                                                        'logg'      : logg_list})\n",
+    "\n",
+    "            # add flux to given model\n",
+    "            da_model.loc[dict(Te   = model_params.Te[idx],\n",
+    "                              logg = model_params.logg[idx])] = flux_interp\n",
+    "\n",
+    "        da_model.attrs['unit']      = 'erg/s/cm2/A'\n",
+    "        da_model.attrs['long_name'] = 'Flux'\n",
+    "\n",
+    "        da_model.to_netcdf(file_name)\n",
+    "    return  da_model\n",
+    "\n",
+    "\n",
+    "def get_koester_synthetic_photometry_params(dir_model_files, plot=False, overwrite=False):\n",
+    "    output_name = DIR_MODELS + 'koester_synthetic_photometry_params.csv'\n",
+    "    \n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            model_params = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "\n",
+    "        # Get the list of all files and directories\n",
+    "        file_list = os.listdir(dir_model_files)\n",
+    "        \n",
+    "        model_params              = pd.DataFrame()\n",
+    "        model_params['file_name'] = file_list\n",
+    "        str_params                = model_params['file_name'].str.findall('[0-9]+')\n",
+    "        str_params_T              = np.array(list(map(list, zip(*str_params))))\n",
+    "\n",
+    "        model_params['Te']        = str_params_T[1].astype(int)\n",
+    "        model_params['logg']      = str_params_T[2].astype(int)/100.\n",
+    "\n",
+    "        # sort the parameters in acending order (with give order)\n",
+    "        model_params = model_params.sort_values(by=['Te', 'logg'])\n",
+    "        model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "        print('Saving %s'%file_name)\n",
+    "        model_params.to_csv(file_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        sns.pairplot(model_params, corner=True, diag_kind=\"hist\")\n",
+    "\n",
+    "    return model_params\n",
+    "\n",
+    "\n",
+    "def combine_koester_synthetic_photometry(model_params, dir_model_files, keep_common_filters=False, overwrite=False):\n",
+    "    output_name='models/koester_synthetic_photometry.nc'\n",
+    "\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            with xr.open_dataarray(output_name) as da_model:\n",
+    "                pass\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        flux_model       = pd.DataFrame()\n",
+    "        len_model_params = len(model_params) \n",
+    "        Te_list          = model_params.Te.unique()\n",
+    "        logg_list        = model_params.logg.unique()\n",
+    "\n",
+    "        for idx in range(len_model_params):\n",
+    "            print_progress(idx, total_iterations=len_model_params, step=10)\n",
+    "            data_flux         = pd.read_csv(dir_model_files + model_params.file_name[idx],comment='#',engine='python', header=None, delim_whitespace= True,skipinitialspace=True)\n",
+    "            data_flux.columns = ['FilterID', 'Wavelength', 'Flux']\n",
+    "            data_flux         = data_flux.set_index('FilterID')\n",
+    "\n",
+    "            if idx==0:\n",
+    "                data_flux   = data_flux.sort_values(by=['Wavelength'])\n",
+    "                data_flux   = data_flux[data_flux['Wavelength']>0]\n",
+    "                data_flux_0 = data_flux.copy()\n",
+    "                filter_list = data_flux_0.index\n",
+    "                wave_list   = data_flux_0['Wavelength']\n",
+    "\n",
+    "                nd_arr = np.empty((len(filter_list), len(Te_list), len(logg_list)))\n",
+    "                nd_arr.fill(np.nan)\n",
+    "\n",
+    "                da_model = xr.DataArray(nd_arr, coords={'FilterID': filter_list,\n",
+    "                                                        'Te'      : Te_list,\n",
+    "                                                        'logg'    : logg_list})\n",
+    "                da_model.attrs[\"Wavelengths\"] = wave_list.values\n",
+    "\n",
+    "            if not keep_common_filters:\n",
+    "                if len(data_flux)!=len(data_flux_0):\n",
+    "                    raise Exception('ERROR: Model files have different number of filters.\\n           Use \"keep_common_filters=True\" to use the exact filters available in the first file.\\n           (sometimes, some filters are missing from the specific temperatures/logg...)')\n",
+    "\n",
+    "            # keep the same filter order as the first file    \n",
+    "            data_flux_0['Flux_new'] = data_flux['Flux']\n",
+    "\n",
+    "            # add flux to given model\n",
+    "            da_model.loc[dict(Te   = model_params.Te[idx],\n",
+    "                              logg = model_params.logg[idx])] = data_flux_0['Flux_new']\n",
+    "\n",
+    "        da_model.attrs['unit']      = 'erg/s/cm2/A'\n",
+    "        da_model.attrs['long_name'] = 'Flux'\n",
+    "\n",
+    "        da_model.to_netcdf(output_name)\n",
+    "    return  da_model\n",
+    "\n",
+    "\n",
+    "def read_parsec_isochrone(file_name, skiprows=13, plot=False):\n",
+    "    '''\n",
+    "    Reads the files downloaded from parsec website\n",
+    "    (http://stev.oapd.inaf.it/cgi-bin/cmd) as pandas dataframe\n",
+    "    '''\n",
+    "    # Reading data\n",
+    "    iso = pd.read_csv(file_name, engine='python',\n",
+    "                      delimiter='\\s+', header=None, comment='#')\n",
+    "    # reading column names\n",
+    "    _df_column_names = pd.read_csv(file_name, engine='python', delim_whitespace=True, skipinitialspace=True,\n",
+    "                                   skiprows=skiprows, nrows=1, header=None)\n",
+    "    iso_column_names = _df_column_names.drop([0], axis=1).values[0]\n",
+    "    # saving the column names to previously read file\n",
+    "    iso.columns = iso_column_names\n",
+    "\n",
+    "    iso['logR'] = np.log10(calc_radius(iso['Mass'].values*u.solMass, iso['logg'].values).value)\n",
+    "\n",
+    "    if plot:\n",
+    "        fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "        plt.title(\"H R diagram\")\n",
+    "        plt.xlabel('logL')\n",
+    "        plt.ylabel('logTe')\n",
+    "        p1 = ax.scatter(iso.logTe, iso.logL, c=iso.logAge, cmap='rainbow', s=1, marker='.')\n",
+    "        ax.set(title = 'HR Diagram', xlabel='log(L/L$_{\\odot}$)', ylabel='log(T)')\n",
+    "        plt.colorbar(p1, label='log(age/yr)')\n",
+    "        ax.invert_xaxis()\n",
+    "\n",
+    "    return iso\n",
+    "\n",
+    "\n",
+    "def get_ZAMS(MH, iso, plot_simple=False, plot_corner=False, save_plot=False):\n",
+    "    '''\n",
+    "    creating a ZAMS by combining main sequences of various ages.\n",
+    "        younger isochrones for high mass stars, older for low mass stars.\n",
+    "    '''\n",
+    "    if MH not in iso.MH.unique():\n",
+    "        # round off the MH to closest stepsize available\n",
+    "        _MH = MH\n",
+    "        MH = find_nearest(np.unique(iso.MH), MH)\n",
+    "        print('WARNING: Metallicity ([M/H]) rounded from %.2f to %.2f' % (_MH, MH))\n",
+    "\n",
+    "    cl_iso_625 = iso[(iso.logAge == 6.25)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "    cl_iso_65  = iso[(iso.logAge == 6.50)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "    cl_iso_675 = iso[(iso.logAge == 6.75)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "    cl_iso_7   = iso[(iso.logAge == 7.00)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "    cl_iso_75  = iso[(iso.logAge == 7.50)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "    cl_iso_8   = iso[(iso.logAge == 8.00)& (iso.label < 2) & (iso.MH==MH)]\n",
+    "\n",
+    "    ZAMS_625   = cl_iso_625[(cl_iso_625.logL < 5.6) & (cl_iso_625.logL >= 3.5)]\n",
+    "    ZAMS_65    = cl_iso_65[  (cl_iso_65.logL < 3.5) &  (cl_iso_65.logL >= 3.0)]\n",
+    "    ZAMS_675   = cl_iso_675[(cl_iso_675.logL < 3.0) & (cl_iso_675.logL >= 2.5)]\n",
+    "    ZAMS_7     = cl_iso_7[    (cl_iso_7.logL < 2.5) &   (cl_iso_7.logL >= 1.4)]\n",
+    "    ZAMS_75    = cl_iso_75[  (cl_iso_75.logL < 1.4) &  (cl_iso_75.logL >= 0.9)]\n",
+    "    ZAMS_8     = cl_iso_8[    (cl_iso_8.logL < 0.9)]\n",
+    "\n",
+    "    ZAMS = pd.concat([ZAMS_625, ZAMS_65, ZAMS_675, ZAMS_7, ZAMS_75, ZAMS_8])\n",
+    "\n",
+    "    if plot_simple:\n",
+    "        '''\n",
+    "        plotting HR diagram and M-L relation showing ZAMS\n",
+    "        '''\n",
+    "        color_list = plt.cm.gist_ncar(np.linspace(0, 1, 7))  # creates array of N colors\n",
+    "\n",
+    "        fig, ax = plt.subplots(figsize=(20, 10), ncols=2)\n",
+    "\n",
+    "        for idx, cl_iso in enumerate([cl_iso_625, cl_iso_65, cl_iso_675, cl_iso_7, cl_iso_75, cl_iso_8]):\n",
+    "            ax[0].scatter(cl_iso.logTe, cl_iso.logL, color=color_list[idx],\n",
+    "                          s=3, label='LogAge ' + str(cl_iso.logAge.iloc[0]))\n",
+    "            ax[1].scatter(cl_iso.Mass, cl_iso.logL, color=color_list[idx], s=3)\n",
+    "\n",
+    "        for idx, cl_ZAMS in enumerate([ZAMS_625, ZAMS_65, ZAMS_675, ZAMS_7, ZAMS_75, ZAMS_8]):\n",
+    "            ax[0].plot(cl_ZAMS.logTe, cl_ZAMS.logL,\n",
+    "                       color=color_list[idx], lw=5)\n",
+    "            ax[1].plot(cl_ZAMS.Mass, cl_ZAMS.logL, color=color_list[idx], lw=5)\n",
+    "\n",
+    "        ax[0].set_ylim(-2, 7)\n",
+    "        ax[0].set_xlim(4.75, 3.6)\n",
+    "        ax[0].legend()\n",
+    "        ax[1].set_xscale('log')\n",
+    "        ax[0].set(title='MH = '+str(MH), xlabel='logTe', ylabel='logL')\n",
+    "        ax[1].set(xlabel='Mass', ylabel='logL')\n",
+    "\n",
+    "        for axes in ax.ravel():\n",
+    "            axes.axhline(0.9, c='0.8')\n",
+    "            axes.axhline(1.4, c='0.8')\n",
+    "            axes.axhline(2.5, c='0.8')\n",
+    "            axes.axhline(3.0, c='0.8')\n",
+    "            axes.axhline(3.5, c='0.8')\n",
+    "            axes.axhline(5.6, c='0.8')\n",
+    "        if save_plot:\n",
+    "            plt.savefig('plots/calibration/simple_ZAMS_MH'+str(MH) +\n",
+    "                        '.jpg', format='jpg', dpi=300, bbox_inches='tight')\n",
+    "            plt.close()\n",
+    "\n",
+    "    if plot_corner:\n",
+    "        '''\n",
+    "        plotting corner plot of important parameters\n",
+    "        '''\n",
+    "        ZAMS_reduced = ZAMS[['logTe', 'logL','Mass', 'logg', 'logR', 'logAge']]\n",
+    "        # Converting age to string which is used as 'label'\n",
+    "        ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+    "        # Converting Mass to logM\n",
+    "        ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n",
+    "        ZAMS_reduced.columns = ['logTe', 'logL','logM', 'logg', 'logR', 'logAge']\n",
+    "\n",
+    "        sns.pairplot(ZAMS_reduced, hue=\"logAge\", corner=True, diag_kind=\"hist\",\n",
+    "                     palette=\"nipy_spectral\", diag_kws={'alpha': 0.55, 'bins': 30})\n",
+    "        plt.title('ZAMS of MH = '+str(MH))\n",
+    "\n",
+    "        if save_plot:\n",
+    "            plt.savefig('plots/calibration/cornerplot_ZAMS_MH'+str(MH) +\n",
+    "                        '.jpg', format='jpg', dpi=300, bbox_inches='tight')\n",
+    "            plt.close()\n",
+    "\n",
+    "    return ZAMS\n",
+    "\n",
+    "\n",
+    "def calc_radius(M,logg):\n",
+    "    '''\n",
+    "    Calculate logg from mass and radius\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mass :  Quantity or list\n",
+    "        mass\n",
+    "    logg : float or list\n",
+    "        logg [log of cm/s2]\n",
+    "    Returns\n",
+    "    -------\n",
+    "    radius : Quantity or list\n",
+    "        radius\n",
+    "    '''    \n",
+    "    g=10**logg * u.cm/u.s**2\n",
+    "    R=((const.G*M)/g)**0.5\n",
+    "    return R.to(u.solRad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa5efab-32ef-45b8-988a-940a59703daa",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Kurucz models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da7146f6-a0b3-40f6-a619-310809b35c55",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Spectral library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41e80f65-f3d6-4bb1-824c-0465193e2c24",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Checking parameter space of the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "86a2e454-e448-46af-bc4d-8644bf3e1d37",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/kurucz_spectral_library_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 14 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir_model_files = 'models/Kurucz_spectra/'\n",
+    "kurucz_spectral_library_params = get_kurucz_spectral_library_params(dir_model_files, plot=True, overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ff593c3-ebce-4e2c-9c0d-250f2c469f01",
+   "metadata": {},
+   "source": [
+    "#### Checking the spacing and limits of the wavelength axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4cd9c860-5d2b-403b-bc29-fb1fbe970350",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: models/kurucz_spectral_library_wave_params.csv file will be overwritten.\n",
+      " Progress:  8091/8092  (99%)Saving models/kurucz_spectral_library_wave_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 900x900 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_params    = kurucz_spectral_library_params\n",
+    "dir_model_files = 'models/Kurucz_spectra/'\n",
+    "\n",
+    "df_wave_params  = get_wavelength_params(model_params[:], dir_model_files, \n",
+    "                                        output_name = 'models/kurucz_spectral_library_wave_params.csv', plot=True, overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4b1b482d-5a8c-4d69-a9d8-4dacdd878f2d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          diff_min   diff_med   diff_max   wave_min   wave_max\n",
+      "Actual :  0.000837   0.001817   0.301030   2.607455   6.204120\n",
+      "Rounded:             0.001000              2.608000   6.205000\n",
+      "Wavelength steps:   [2.608 2.609 2.61  ... 6.202 6.203 6.204]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('          diff_min   diff_med   diff_max   wave_min   wave_max')\n",
+    "print('Actual :  %.6f   %.6f   %.6f   %.6f   %.6f'%(np.nanmin(df_wave_params['diff_log_wave_min']),  np.nanmin(df_wave_params['diff_log_wave_med']), np.nanmax(df_wave_params['diff_log_wave_max']),\n",
+    "                                                  np.nanmax(df_wave_params['log_wave_min']),np.nanmin(df_wave_params['log_wave_max'])))\n",
+    "# ~median/4\n",
+    "# log_wave_diff = 0.0005\n",
+    "# log_wave_min  = 2.6075\n",
+    "# log_wave_max  = 6.2040\n",
+    "# ~median/2\n",
+    "log_wave_diff = 0.001\n",
+    "log_wave_min  = 2.608\n",
+    "log_wave_max  = 6.205\n",
+    "print('Rounded:             %.6f              %.6f   %.6f'%(log_wave_diff, log_wave_min,log_wave_max))\n",
+    "\n",
+    "log_wave_list = np.arange(log_wave_min, log_wave_max,log_wave_diff)\n",
+    "print('Wavelength steps:  ', log_wave_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8807d9c8-467c-40af-9b9f-b656ce99d260",
+   "metadata": {},
+   "source": [
+    "### Combining spectra into single file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3df8e168-1d33-4111-a18e-f82ac09c2fb7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/kurucz_spectral_library.nc\n"
+     ]
+    },
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (Wavelength: 3597, Te: 76, logg: 11, MH: 9, alpha: 2)&gt;\n",
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+       "Attributes:\n",
+       "    unit:       erg/s/cm2/A\n",
+       "    long_name:  Flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Wavelength</span>: 3597</li><li><span class='xr-has-index'>Te</span>: 76</li><li><span class='xr-has-index'>logg</span>: 11</li><li><span class='xr-has-index'>MH</span>: 9</li><li><span class='xr-has-index'>alpha</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cc52ab5b-f07f-4d99-8091-601c12a85061' class='xr-array-in' type='checkbox' checked><label for='section-cc52ab5b-f07f-4d99-8091-601c12a85061' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[54127656 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-54508512-f802-4a16-95d4-2c6ee91646ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-54508512-f802-4a16-95d4-2c6ee91646ab' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Wavelength</span></div><div class='xr-var-dims'>(Wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>405.5 406.4 ... 1.596e+06 1.6e+06</div><input id='attrs-676c1be8-04ce-4202-ac97-fa500e03732e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-676c1be8-04ce-4202-ac97-fa500e03732e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2de9b27-1992-4986-ae55-e24a985043f9' class='xr-var-data-in' type='checkbox'><label for='data-f2de9b27-1992-4986-ae55-e24a985043f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4.055085e+02, 4.064433e+02, 4.073803e+02, ..., 1.592209e+06,\n",
+       "       1.595879e+06, 1.599558e+06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Te</span></div><div class='xr-var-dims'>(Te)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3500 3750 4000 ... 49000 50000</div><input id='attrs-ae82dfa9-9c47-4884-be91-1fba42ce4ceb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae82dfa9-9c47-4884-be91-1fba42ce4ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eef083e8-c108-496d-91b0-7676bc52df58' class='xr-var-data-in' type='checkbox'><label for='data-eef083e8-c108-496d-91b0-7676bc52df58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 3500,  3750,  4000,  4250,  4500,  4750,  5000,  5250,  5500,  5750,\n",
+       "        6000,  6250,  6500,  6750,  7000,  7250,  7500,  7750,  8000,  8250,\n",
+       "        8500,  8750,  9000,  9250,  9500,  9750, 10000, 10250, 10500, 10750,\n",
+       "       11000, 11250, 11500, 11750, 12000, 12250, 12500, 12750, 13000, 14000,\n",
+       "       15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000,\n",
+       "       25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000,\n",
+       "       35000, 36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000,\n",
+       "       45000, 46000, 47000, 48000, 49000, 50000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>logg</span></div><div class='xr-var-dims'>(logg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-9e2f9cdf-6ff0-4d74-86b1-25e30645f7a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e2f9cdf-6ff0-4d74-86b1-25e30645f7a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83fae3d7-db8b-4535-acda-2c534deea745' class='xr-var-data-in' type='checkbox'><label for='data-83fae3d7-db8b-4535-acda-2c534deea745' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>MH</span></div><div class='xr-var-dims'>(MH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.5 -2.0 -1.5 ... 0.2 0.5 -4.0</div><input id='attrs-e68858f6-e0fc-4e49-a66d-f7586b989f2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e68858f6-e0fc-4e49-a66d-f7586b989f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e13f2707-283d-4ab8-8da7-ce730ddfd2f2' class='xr-var-data-in' type='checkbox'><label for='data-e13f2707-283d-4ab8-8da7-ce730ddfd2f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2.5, -2. , -1.5, -1. , -0.5,  0. ,  0.2,  0.5, -4. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alpha</span></div><div class='xr-var-dims'>(alpha)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.4</div><input id='attrs-f5530820-55c6-436d-a1ca-498439f3f3fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5530820-55c6-436d-a1ca-498439f3f3fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-615d27e8-ea8e-41fa-bd78-32c9c62c5643' class='xr-var-data-in' type='checkbox'><label for='data-615d27e8-ea8e-41fa-bd78-32c9c62c5643' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9130b8d-f755-48dc-ab25-5856fd6e3d73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9130b8d-f755-48dc-ab25-5856fd6e3d73' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>erg/s/cm2/A</dd><dt><span>long_name :</span></dt><dd>Flux</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (Wavelength: 3597, Te: 76, logg: 11, MH: 9, alpha: 2)>\n",
+       "[54127656 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * Wavelength  (Wavelength) float64 405.5 406.4 407.4 ... 1.596e+06 1.6e+06\n",
+       "  * Te          (Te) int64 3500 3750 4000 4250 4500 ... 47000 48000 49000 50000\n",
+       "  * logg        (logg) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
+       "  * MH          (MH) float64 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.2 0.5 -4.0\n",
+       "  * alpha       (alpha) float64 0.0 0.4\n",
+       "Attributes:\n",
+       "    unit:       erg/s/cm2/A\n",
+       "    long_name:  Flux"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_params = kurucz_spectral_library_params\n",
+    "# model_params = model_params[(model_params.Te<5500) & (model_params.logg>4.50)]\n",
+    "# model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "dir_model_files  = 'models/Kurucz_spectra/'\n",
+    "da_model = combine_kurucz_spectral_library(model_params,dir_model_files, log_wave_list)\n",
+    "da_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "23d33a4d-b553-4ff0-9797-2fa454dbf91a",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "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": [
+    "# Testing if the da_model worked\n",
+    "flux = da_model.sel(Te=3500).sel(logg=0.0).sel(MH=-2.0).sel(alpha=0.0)\n",
+    "plt.scatter(da_model.Wavelength, flux)\n",
+    "plt.xlim(2200,2600); plt.ylim(10e-4, 5); plt.xscale('log'); plt.yscale('log'); plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb2bba8-6ea9-4a58-9a77-10d725084167",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Synthetic photometry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ced125e-eb6f-43da-aab1-c7c850364c14",
+   "metadata": {},
+   "source": [
+    "### Checking the parameter space of the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "dfa2ebf8-38da-42d2-b5d2-93b595c47132",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/kurucz_synthetic_photometry_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file_name</th>\n",
+       "      <th>Te</th>\n",
+       "      <th>logg</th>\n",
+       "      <th>MH</th>\n",
+       "      <th>alpha</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kurucz2003all_fm25t3500g00k2odfnew.fl.phot.dat</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.5</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Kurucz2003all_fm20t3500g00k2odfnew.fl.phot.dat</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Kurucz2003all_fm15t3500g00k2odfnew.fl.phot.dat</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Kurucz2003all_fm10t3500g00k2odfnew.fl.phot.dat</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kurucz2003all_fm05t3500g00k2odfnew.fl.phot.dat</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8087</th>\n",
+       "      <td>Kurucz2003all_fm10at50000g50k2odfnew.fl.phot.dat</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8088</th>\n",
+       "      <td>Kurucz2003all_fm05at50000g50k2odfnew.fl.phot.dat</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8089</th>\n",
+       "      <td>Kurucz2003all_fp00at50000g50k2odfnew.fl.phot.dat</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8090</th>\n",
+       "      <td>Kurucz2003all_fp02at50000g50k2odfnew.fl.phot.dat</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8091</th>\n",
+       "      <td>Kurucz2003all_fp05at50000g50k2odfnew.fl.phot.dat</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8092 rows Ă— 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             file_name     Te  logg   MH  \\\n",
+       "0       Kurucz2003all_fm25t3500g00k2odfnew.fl.phot.dat   3500   0.0 -2.5   \n",
+       "1       Kurucz2003all_fm20t3500g00k2odfnew.fl.phot.dat   3500   0.0 -2.0   \n",
+       "2       Kurucz2003all_fm15t3500g00k2odfnew.fl.phot.dat   3500   0.0 -1.5   \n",
+       "3       Kurucz2003all_fm10t3500g00k2odfnew.fl.phot.dat   3500   0.0 -1.0   \n",
+       "4       Kurucz2003all_fm05t3500g00k2odfnew.fl.phot.dat   3500   0.0 -0.5   \n",
+       "...                                                ...    ...   ...  ...   \n",
+       "8087  Kurucz2003all_fm10at50000g50k2odfnew.fl.phot.dat  50000   5.0 -1.0   \n",
+       "8088  Kurucz2003all_fm05at50000g50k2odfnew.fl.phot.dat  50000   5.0 -0.5   \n",
+       "8089  Kurucz2003all_fp00at50000g50k2odfnew.fl.phot.dat  50000   5.0  0.0   \n",
+       "8090  Kurucz2003all_fp02at50000g50k2odfnew.fl.phot.dat  50000   5.0  0.2   \n",
+       "8091  Kurucz2003all_fp05at50000g50k2odfnew.fl.phot.dat  50000   5.0  0.5   \n",
+       "\n",
+       "      alpha  \n",
+       "0       0.0  \n",
+       "1       0.0  \n",
+       "2       0.0  \n",
+       "3       0.0  \n",
+       "4       0.0  \n",
+       "...     ...  \n",
+       "8087    0.4  \n",
+       "8088    0.4  \n",
+       "8089    0.4  \n",
+       "8090    0.4  \n",
+       "8091    0.4  \n",
+       "\n",
+       "[8092 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 720x720 with 14 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir_model_files  = 'C:/Users/Zert W/Desktop/python/Synthetic_photometry_models/Kurucz_synthetic_photometry/'\n",
+    "kurucz_synthetic_photometry_params = get_kurucz_synthetic_photometry_params(dir_model_files, plot=True)\n",
+    "kurucz_synthetic_photometry_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23fe7ecc-8f3c-4975-a303-d47324e9c923",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Combining synthetic photometry into single file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "21ca8eba-f310-4d17-950a-226f36a02c38",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/kurucz_synthetic_photometry.nc\n"
+     ]
+    },
+    {
+     "data": {
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: 'â–ş';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: 'â–Ľ';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (FilterID: 8147, Te: 76, logg: 11, MH: 9, alpha: 2)&gt;\n",
+       "[122596056 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * FilterID  (FilterID) object &#x27;IUE/IUE.1250-1300&#x27; ... &#x27;QUIJOTE/MFI.11GHz_H3&#x27;\n",
+       "  * Te        (Te) int32 3500 3750 4000 4250 4500 ... 47000 48000 49000 50000\n",
+       "  * logg      (logg) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
+       "  * MH        (MH) float64 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.2 0.5 -4.0\n",
+       "  * alpha     (alpha) float64 0.0 0.4\n",
+       "Attributes:\n",
+       "    Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...\n",
+       "    unit:         erg/s/cm2/A\n",
+       "    long_name:    Flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>FilterID</span>: 8147</li><li><span class='xr-has-index'>Te</span>: 76</li><li><span class='xr-has-index'>logg</span>: 11</li><li><span class='xr-has-index'>MH</span>: 9</li><li><span class='xr-has-index'>alpha</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d9ee379-475c-4343-afdc-d81f053909b7' class='xr-array-in' type='checkbox' checked><label for='section-7d9ee379-475c-4343-afdc-d81f053909b7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[122596056 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-e355099b-4b8a-4a21-8412-2141c53ad729' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e355099b-4b8a-4a21-8412-2141c53ad729' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FilterID</span></div><div class='xr-var-dims'>(FilterID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;IUE/IUE.1250-1300&#x27; ... &#x27;QUIJOTE...</div><input id='attrs-12521ee5-9895-4beb-b90b-e6ff6edd5c66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12521ee5-9895-4beb-b90b-e6ff6edd5c66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e826f0da-1dc2-4f5f-b875-22751e4f0114' class='xr-var-data-in' type='checkbox'><label for='data-e826f0da-1dc2-4f5f-b875-22751e4f0114' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IUE/IUE.1250-1300&#x27;, &#x27;HST/STIS_FUV.F25LYA_G140M&#x27;,\n",
+       "       &#x27;HST/STIS_FUV.F25LYA_G140L&#x27;, ..., &#x27;QUIJOTE/MFI.13GHz_H1&#x27;,\n",
+       "       &#x27;QUIJOTE/MFI.11GHz_H1&#x27;, &#x27;QUIJOTE/MFI.11GHz_H3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Te</span></div><div class='xr-var-dims'>(Te)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3500 3750 4000 ... 49000 50000</div><input id='attrs-13bb3833-3124-49c9-a9a6-f5a901abef94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13bb3833-3124-49c9-a9a6-f5a901abef94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e844e235-b4f4-473b-b3ca-9f215df1981e' class='xr-var-data-in' type='checkbox'><label for='data-e844e235-b4f4-473b-b3ca-9f215df1981e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 3500,  3750,  4000,  4250,  4500,  4750,  5000,  5250,  5500,  5750,\n",
+       "        6000,  6250,  6500,  6750,  7000,  7250,  7500,  7750,  8000,  8250,\n",
+       "        8500,  8750,  9000,  9250,  9500,  9750, 10000, 10250, 10500, 10750,\n",
+       "       11000, 11250, 11500, 11750, 12000, 12250, 12500, 12750, 13000, 14000,\n",
+       "       15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000,\n",
+       "       25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000,\n",
+       "       35000, 36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000,\n",
+       "       45000, 46000, 47000, 48000, 49000, 50000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>logg</span></div><div class='xr-var-dims'>(logg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... 3.5 4.0 4.5 5.0</div><input id='attrs-f5bff4e5-e3ed-4f9b-9601-6f7beafa810d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5bff4e5-e3ed-4f9b-9601-6f7beafa810d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ab2f6d-0eb1-4bd9-af81-50307383576c' class='xr-var-data-in' type='checkbox'><label for='data-e8ab2f6d-0eb1-4bd9-af81-50307383576c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>MH</span></div><div class='xr-var-dims'>(MH)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.5 -2.0 -1.5 ... 0.2 0.5 -4.0</div><input id='attrs-5df01107-b3b8-408d-ba56-584de3738a7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5df01107-b3b8-408d-ba56-584de3738a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-230995f0-c664-4040-a9b7-96b7e7ca9b10' class='xr-var-data-in' type='checkbox'><label for='data-230995f0-c664-4040-a9b7-96b7e7ca9b10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2.5, -2. , -1.5, -1. , -0.5,  0. ,  0.2,  0.5, -4. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alpha</span></div><div class='xr-var-dims'>(alpha)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.4</div><input id='attrs-a67e49fd-95c8-473c-9d55-502665cd5943' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a67e49fd-95c8-473c-9d55-502665cd5943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f04b45-78d9-493e-8894-d48f9c33c556' class='xr-var-data-in' type='checkbox'><label for='data-97f04b45-78d9-493e-8894-d48f9c33c556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-969a448e-c20c-455a-aad5-fa437eb597be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-969a448e-c20c-455a-aad5-fa437eb597be' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Wavelengths :</span></dt><dd>[1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.86833493e+08\n",
+       " 5.62983439e+08 5.99076004e+08]</dd><dt><span>unit :</span></dt><dd>erg/s/cm2/A</dd><dt><span>long_name :</span></dt><dd>Flux</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (FilterID: 8147, Te: 76, logg: 11, MH: 9, alpha: 2)>\n",
+       "[122596056 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * FilterID  (FilterID) object 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'\n",
+       "  * Te        (Te) int32 3500 3750 4000 4250 4500 ... 47000 48000 49000 50000\n",
+       "  * logg      (logg) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0\n",
+       "  * MH        (MH) float64 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.2 0.5 -4.0\n",
+       "  * alpha     (alpha) float64 0.0 0.4\n",
+       "Attributes:\n",
+       "    Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...\n",
+       "    unit:         erg/s/cm2/A\n",
+       "    long_name:    Flux"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_params = kurucz_synthetic_photometry_params\n",
+    "# model_params = model_params[(model_params.Te<5500) & (model_params.logg>4.50)]\n",
+    "# model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "dir_model_files  = 'C:/Users/Zert W/Desktop/python/Synthetic_photometry_models/Kurucz_synthetic_photometry/'\n",
+    "\n",
+    "da_model = combine_kurucz_synthetic_photometry(model_params,dir_model_files, keep_common_filters=True)\n",
+    "da_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "603497e7-5134-4b34-8afc-906fd3569a0e",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Koester models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e9bfc6-35e7-4b90-81b0-e3646f07043f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Spectral library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d49b1c7-fedc-4204-9e7a-12dcd4be2bd0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Checking the parameter space of the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "a9b2e25e-b7b4-4ff6-afd5-a3d5ea361d54",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/koester_spectral_library_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir_model_files = 'models/Koester_spectra/'\n",
+    "koester_spectral_library_params = get_koester_spectral_library_params(dir_model_files, plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5208016c-5a54-4e16-92a0-7d45652bf51b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Checking the spacing and limits of the wavelength axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c03edbf3-254b-4e78-bc56-d81a62173726",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/koester_spectral_library_wave_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_params = koester_spectral_library_params\n",
+    "dir_model_files = 'models/Koester_spectra/'\n",
+    "\n",
+    "df_wave_params = get_wavelength_params(model_params[:], dir_model_files, \n",
+    "                                       output_name = 'models/koester_spectral_library_wave_params.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "bb1d35e7-bb37-40fd-8e6d-6b61df29f36a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          diff_min   diff_med   diff_max   wave_min   wave_max\n",
+      "Actual :  0.000000   0.000041   0.014512   2.953879   4.477003\n",
+      "Rounded:             0.001000              2.954000   4.477000\n",
+      "Wavelength steps:   [2.954 2.955 2.956 ... 4.474 4.475 4.476]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('          diff_min   diff_med   diff_max   wave_min   wave_max')\n",
+    "print('Actual :  %.6f   %.6f   %.6f   %.6f   %.6f'%(np.nanmin(df_wave_params['diff_log_wave_min']),  np.nanmin(df_wave_params['diff_log_wave_med']), np.nanmax(df_wave_params['diff_log_wave_max']),\n",
+    "                                                  np.nanmax(df_wave_params['log_wave_min']),np.nanmin(df_wave_params['log_wave_max'])))\n",
+    "\n",
+    "log_wave_diff = 0.001\n",
+    "log_wave_min  = 2.954\n",
+    "log_wave_max  = 4.477\n",
+    "print('Rounded:             %.6f              %.6f   %.6f'%(log_wave_diff, log_wave_min,log_wave_max))\n",
+    "\n",
+    "log_wave_list = np.arange(log_wave_min, log_wave_max,log_wave_diff)\n",
+    "print('Wavelength steps:  ', log_wave_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d038c5b6-7d50-4bf5-88ff-13cd7aaa1632",
+   "metadata": {},
+   "source": [
+    "### Combining spectra into single file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c2c6fe51-0c19-4d10-b917-8e53e02d9c95",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/koester_spectral_library.nc\n"
+     ]
+    },
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (Wavelength: 1523, Te: 82, logg: 13)&gt;\n",
+       "[1623518 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * Wavelength  (Wavelength) float64 899.5 901.6 903.6 ... 2.985e+04 2.992e+04\n",
+       "  * Te          (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000\n",
+       "  * logg        (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5\n",
+       "Attributes:\n",
+       "    unit:       erg/s/cm2/A\n",
+       "    long_name:  Flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Wavelength</span>: 1523</li><li><span class='xr-has-index'>Te</span>: 82</li><li><span class='xr-has-index'>logg</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1e424488-0f6b-40ba-93a8-612cf19ace27' class='xr-array-in' type='checkbox' checked><label for='section-1e424488-0f6b-40ba-93a8-612cf19ace27' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1623518 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-85a33463-2f61-46b9-90ed-34033a0e7485' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85a33463-2f61-46b9-90ed-34033a0e7485' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Wavelength</span></div><div class='xr-var-dims'>(Wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>899.5 901.6 ... 2.985e+04 2.992e+04</div><input id='attrs-f2892709-7d85-46cf-8dc7-c6d63972e68f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2892709-7d85-46cf-8dc7-c6d63972e68f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a79ac4d0-8b19-4c8a-a98c-c2504fca8204' class='xr-var-data-in' type='checkbox'><label for='data-a79ac4d0-8b19-4c8a-a98c-c2504fca8204' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  899.497582,   901.571138,   903.649474, ..., 29785.164294,\n",
+       "       29853.826189, 29922.646366])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Te</span></div><div class='xr-var-dims'>(Te)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5000 5250 5500 ... 70000 80000</div><input id='attrs-378d0fbf-c27f-4e46-aa17-bdebbc75e98e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-378d0fbf-c27f-4e46-aa17-bdebbc75e98e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b77bb3a7-999b-4d0d-b77c-fb6dcf2eccdb' class='xr-var-data-in' type='checkbox'><label for='data-b77bb3a7-999b-4d0d-b77c-fb6dcf2eccdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 5000,  5250,  5500,  5750,  6000,  6250,  6500,  6750,  7000,  7250,\n",
+       "        7500,  7750,  8000,  8250,  8500,  8750,  9000,  9250,  9500,  9750,\n",
+       "       10000, 10250, 10500, 10750, 11000, 11250, 11500, 11750, 12000, 12250,\n",
+       "       12500, 12750, 13000, 13250, 13500, 13750, 14000, 14250, 14500, 14750,\n",
+       "       15000, 15250, 15500, 15750, 16000, 16250, 16500, 16750, 17000, 17250,\n",
+       "       17500, 17750, 18000, 18250, 18500, 18750, 19000, 19250, 19500, 19750,\n",
+       "       20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,\n",
+       "       30000, 32000, 34000, 35000, 36000, 38000, 40000, 45000, 50000, 60000,\n",
+       "       70000, 80000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>logg</span></div><div class='xr-var-dims'>(logg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5 6.75 7.0 7.25 ... 9.0 9.25 9.5</div><input id='attrs-fcac86bb-3f6a-4972-841b-4e13e1140f3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcac86bb-3f6a-4972-841b-4e13e1140f3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bad1c356-8aa6-4f64-99b5-1d6ebb4813d1' class='xr-var-data-in' type='checkbox'><label for='data-bad1c356-8aa6-4f64-99b5-1d6ebb4813d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6.5 , 6.75, 7.  , 7.25, 7.5 , 7.75, 8.  , 8.25, 8.5 , 8.75, 9.  , 9.25,\n",
+       "       9.5 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-866b6d04-97aa-4ed1-b6e1-9eb31e3e9e98' class='xr-section-summary-in' type='checkbox'  checked><label for='section-866b6d04-97aa-4ed1-b6e1-9eb31e3e9e98' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>erg/s/cm2/A</dd><dt><span>long_name :</span></dt><dd>Flux</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (Wavelength: 1523, Te: 82, logg: 13)>\n",
+       "[1623518 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * Wavelength  (Wavelength) float64 899.5 901.6 903.6 ... 2.985e+04 2.992e+04\n",
+       "  * Te          (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000\n",
+       "  * logg        (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5\n",
+       "Attributes:\n",
+       "    unit:       erg/s/cm2/A\n",
+       "    long_name:  Flux"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_params = koester_spectral_library_params\n",
+    "# model_params = model_params[(model_params.Te<5500) & (model_params.logg>4.50)]\n",
+    "# model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "dir_model_files  = 'models/Koester_spectra/'\n",
+    "da_model = combine_koester_spectral_library(model_params[:],dir_model_files, log_wave_list)\n",
+    "da_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab3f7685-a0cb-45f3-8015-6177126a294a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Syntehtic photometry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360de664-70c7-443c-93f6-6ef83b90e34e",
+   "metadata": {},
+   "source": [
+    "### Checking the parameter space of the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "ffa24a54-6dff-41bb-9c3b-bfe7ccb4818b",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/koester_synthetic_photometry_params.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dir_model_files  = 'C:/Users/Zert W/Desktop/python/Synthetic_photometry_models/Koester_synthetic_photometry/'\n",
+    "koester_synthetic_photometry_params = get_koester_synthetic_photometry_params(dir_model_files, plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f558a22a-4f06-4b2b-bcfb-f9f87d78b480",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Combining synthetic photometry into single file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "d0466e35-c8e8-44d7-a13a-d0fb9ddd2ec3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
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+      "Reading models/koester_synthetic_photometry.nc\n"
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (FilterID: 8102, Te: 82, logg: 13)&gt;\n",
+       "[8636732 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * FilterID  (FilterID) object &#x27;IUE/IUE.1250-1300&#x27; ... &#x27;QUIJOTE/MFI.11GHz_H3&#x27;\n",
+       "  * Te        (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000\n",
+       "  * logg      (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5\n",
+       "Attributes:\n",
+       "    Wavelengths:  [1.28470318e+03 1.35608150e+03 1.35899193e+03 ... 3.8683349...\n",
+       "    unit:         erg/s/cm2/A\n",
+       "    long_name:    Flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>FilterID</span>: 8102</li><li><span class='xr-has-index'>Te</span>: 82</li><li><span class='xr-has-index'>logg</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0dd16cdd-f958-438e-af70-deeb752db723' class='xr-array-in' type='checkbox' checked><label for='section-0dd16cdd-f958-438e-af70-deeb752db723' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[8636732 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-1ae94241-1d59-4566-aa32-920ab13be4d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ae94241-1d59-4566-aa32-920ab13be4d9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FilterID</span></div><div class='xr-var-dims'>(FilterID)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;IUE/IUE.1250-1300&#x27; ... &#x27;QUIJOTE...</div><input id='attrs-ff067ef4-2fb0-494c-ab86-b1114280c107' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff067ef4-2fb0-494c-ab86-b1114280c107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46977f3-7dec-4569-98f1-d4236ad9819b' class='xr-var-data-in' type='checkbox'><label for='data-e46977f3-7dec-4569-98f1-d4236ad9819b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IUE/IUE.1250-1300&#x27;, &#x27;HST/STIS_FUV.F25LYA_G140M&#x27;,\n",
+       "       &#x27;HST/STIS_FUV.F25LYA_G140L&#x27;, ..., &#x27;QUIJOTE/MFI.13GHz_H1&#x27;,\n",
+       "       &#x27;QUIJOTE/MFI.11GHz_H1&#x27;, &#x27;QUIJOTE/MFI.11GHz_H3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Te</span></div><div class='xr-var-dims'>(Te)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5000 5250 5500 ... 70000 80000</div><input id='attrs-4e2b2aac-88f3-471c-87b0-17add899f5c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e2b2aac-88f3-471c-87b0-17add899f5c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7803b017-1691-4e1f-859e-e2236c9e4150' class='xr-var-data-in' type='checkbox'><label for='data-7803b017-1691-4e1f-859e-e2236c9e4150' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 5000,  5250,  5500,  5750,  6000,  6250,  6500,  6750,  7000,  7250,\n",
+       "        7500,  7750,  8000,  8250,  8500,  8750,  9000,  9250,  9500,  9750,\n",
+       "       10000, 10250, 10500, 10750, 11000, 11250, 11500, 11750, 12000, 12250,\n",
+       "       12500, 12750, 13000, 13250, 13500, 13750, 14000, 14250, 14500, 14750,\n",
+       "       15000, 15250, 15500, 15750, 16000, 16250, 16500, 16750, 17000, 17250,\n",
+       "       17500, 17750, 18000, 18250, 18500, 18750, 19000, 19250, 19500, 19750,\n",
+       "       20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,\n",
+       "       30000, 32000, 34000, 35000, 36000, 38000, 40000, 45000, 50000, 60000,\n",
+       "       70000, 80000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>logg</span></div><div class='xr-var-dims'>(logg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5 6.75 7.0 7.25 ... 9.0 9.25 9.5</div><input id='attrs-78ad337a-59a0-48f9-b7a2-70cbdf53822d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78ad337a-59a0-48f9-b7a2-70cbdf53822d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a010b20-1350-454b-955a-825a9cde8ea8' class='xr-var-data-in' type='checkbox'><label for='data-5a010b20-1350-454b-955a-825a9cde8ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6.5 , 6.75, 7.  , 7.25, 7.5 , 7.75, 8.  , 8.25, 8.5 , 8.75, 9.  , 9.25,\n",
+       "       9.5 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9db0b53b-763a-4ec7-95bf-d21b2a714c5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9db0b53b-763a-4ec7-95bf-d21b2a714c5b' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Wavelengths :</span></dt><dd>[1.28470318e+03 1.35608150e+03 1.35899193e+03 ... 3.86833493e+08\n",
+       " 5.62983439e+08 5.99076004e+08]</dd><dt><span>unit :</span></dt><dd>erg/s/cm2/A</dd><dt><span>long_name :</span></dt><dd>Flux</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (FilterID: 8102, Te: 82, logg: 13)>\n",
+       "[8636732 values with dtype=float64]\n",
+       "Coordinates:\n",
+       "  * FilterID  (FilterID) object 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'\n",
+       "  * Te        (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000\n",
+       "  * logg      (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5\n",
+       "Attributes:\n",
+       "    Wavelengths:  [1.28470318e+03 1.35608150e+03 1.35899193e+03 ... 3.8683349...\n",
+       "    unit:         erg/s/cm2/A\n",
+       "    long_name:    Flux"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_params = koester_synthetic_photometry_params\n",
+    "# model_params = model_params[(model_params.Te<7500) & (model_params.logg<7.50)]\n",
+    "# model_params.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "dir_model_files  = 'C:/Users/Zert W/Desktop/python/Synthetic_photometry_models/Koester_synthetic_photometry/'\n",
+    "\n",
+    "da_model = combine_koester_synthetic_photometry(model_params,dir_model_files, keep_common_filters=True)\n",
+    "da_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab48a121-2b59-4c71-ba23-20df03846cc9",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# master_ZAMS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7fe0b2c7-663e-4537-bef5-a5dc7605a55c",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available metallicities, [M/H] : \n",
+      "         [-2.  -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.  -0.9 -0.8 -0.7\n",
+      " -0.6 -0.5 -0.4 -0.3 -0.2 -0.1  0.   0.1  0.2]\n",
+      "Available log(age/yr) : \n",
+      "         [ 6.    6.25  6.5   6.75  7.    7.25  7.5   7.75  8.    8.25  8.5   8.75\n",
+      "  9.    9.25  9.5   9.75 10.  ]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iso_file_name = DIR_MODELS+'isochrone_GaiaEDR3_logAge6-10_MH-2-0.2.dat'\n",
+    "iso = read_parsec_isochrone(iso_file_name, plot=True)\n",
+    "print('Available metallicities, [M/H] : \\n        ',iso.MH.unique())\n",
+    "print('Available log(age/yr) : \\n        ', iso.logAge.unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a504d828-bc0b-4b93-ac4e-8f86d45958a5",
+   "metadata": {},
+   "source": [
+    "## Plotting ZAMS and testing continuity for all metallicities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "89b6a900-9a20-4c81-ad90-3bfb0b8137ec",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-2.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:101: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['logAge'] = ZAMS_reduced['logAge'].astype(str)\n",
+      "C:\\Users\\Zert W\\AppData\\Local\\Temp\\ipykernel_13536\\1809152771.py:103: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ZAMS_reduced['Mass'] = np.log10(ZAMS_reduced['Mass'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "for idx, MH in enumerate(iso.MH.unique()[:]):\n",
+    "    print(MH)\n",
+    "    ZAMS_temp = get_ZAMS(MH,iso,plot_simple=True, plot_corner=True, save_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b780964-855b-4810-9565-bb2d16d2dbe1",
+   "metadata": {},
+   "source": [
+    "## Saving ZAMS of all metallicities as one file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "67bcaa5c-beaa-4923-9f62-d1f6c67103e2",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true,
+     "source_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/master_ZAMS.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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L186bRTexbTorTwCvAX4FvBoEVVATLqWSph0rxho+lchhgiw15VLSPjU/YLiYp5wNSFyNCdw0CcySQvqmgZN2p8s2k92mMzgIJ50Etdp8VdeyLGv3tutnp/8FOFBE9hURD3gZ8JOHnPMT4B+mZqk/EZg0xmya5WvnzaJuiW9VWAYnvhF+drFQ6VUUuxwqHQnjOkOytMZwJsTRCbXYIQzbtFxN1fGJDWRyLfxWhEQJmTjB5HxkWQEwMNmAnJuuI69F6US36bRa8MMfwitfOa/1tizLWoyMMZGI/DPwK0ADXzXG3CYi/zT1/BeAy4HTgXuBGvDa7b12AaoB2CD+gKd/Shi7D665XiFtULGHxDDmhqxuaWSVoSvX5C56ODwzRJ2ARs4ljFq40VRwFggRpBmlaVhjkwbwjVOJX4aq04+PNxqwefP8VdayLGt3tnPGxLfLGHM5aaDe9rEvbHPbAG+d7WsXig3iU0TgjK8Jf+uHdlZR7TRUiw6VjphmTrFhSw5jFDk/YijK0u9UiUVR9wMms238dkTec2l2ZgjqTWTYpF3qHWGagrXSSvOs19oPnwKhBE48cUHqbVmWtVuyaVdnxY6Jb8MvwJlfE4qbhd41Qs+gom/Qxd3k0Shrak2HcsNltB1SSnya4jDuhkyGGUaKebZ0F4kDl7gjg8n76fh4cWqDlIF8uu+4r/9+fFyA0IcX/T849lj4xjfSHOuWZVmL1S5O9rI3sS3xh3jMy+BVbeGb79boSNCRIDEMOoZKZ4sRL8AghLqN60ZMuBn8uI0KDEm3IlZCv6MJMz7B3ZuR7mzarb65ArWpxC+TzbRlDukM9nItPQY3wD/+I/z2t2kwtyzLsqztsEF8Gke+Cs7YBD/6nDA5piiMORSGEyY6fBzHECdCM+6intc0AockEGpOnYLnExkhjCK8OMH0F9NGd7WVZnHrz8LmKqwbh3qUBvPh6t9/eLMJ3/8+nH02HHroQlTfsixrYcns8p9bNojP6IT3CE98u/CTj8C3bgKjXCBkrC2w0pDPRIw0srSMSzPwONDZQtPxGPOyDExOUg19VEeG0BikKwuby7C5lK4nNwbuG4P6NOPjAFEbrr3WBnHLshavRdQlPhc2iG+HE8CBj1Ec9FXBraVryNe3hYm6k25U1mUo+G1iI1RUQGCqKGBTZwfaGFq+SzcQTlSRgg9JLn3jZCpyOzP8kiYG+vvno4qWZVm7H7uf+KzZrzo7cMCzIKhDbkKRLaUHLcXwbQXuW52n1HAptT0mTUgJn7YoKkHAUEeB0WKeSkeOVi7A+O6DE9qybjpTPe9PnwTG0TA5CZ/9LFx3XdpytyzLWjTETmybJdsS34GwE178BeHrbzdUOhTVDoda3jC6NKI6HrB6Y4FoQIgTwQsjjAh5N4PjJxgR1i7t5eBGE7fahMBF6u00FWu5CeN16M+l68e3Bmot4Djw1rem4+OuCyecAD/9aXrbsizLsqbYID4LjzkTXusovvJWQ+QqIu0Qa8O4ThgeDQmCmHbi0EgcVmYmGfdCVjLGgWYIgDv3W85+riYfuniDE+kYeT1K86lvLKVLz1px2iofqUGt/uCHt1pwzTVw/vlw1lkLUn/Lsqx5ZbvTZ80G8Vk67EVw1FcVzbsEZdJDDIxlYsYmfQI/puArqrFHojW3e8sIaNPrVgi8iInOAn6UIAbcagMZdWBJHlwF7ThdhibApjUP//B6Hf71X2HVKjjjjHmuuWVZ1jzbuk7c2iEbxGdJBF75U+HQr8Fv/lez79UK52aHNcbnyrdPUMpEBG5E1o0IdA0MjLo5iqZJojTVwKeS8VGtCHegmL7hmrG0FR4l6f1qa+YCxDG8/OVw9dXwhCfMX8Uty7Lm3a5Pu7q3sEH8EVAOPP6NwuPfCMYIn1piaD7WsOIvBe7uNBgDrZbC9CT0BQ0mJMOw16S7VWU0l8NtpznWVTsicDT0F9IA/ue1iAECDa6GeIaNUhqNdP34FVekQd+yLMta1GwQf5TiJlQmQQw4baFwd5bRhqbR5+I4UAp99skKBbeBdg2B38Ir5ml6LtVMQFBv0TU8QThRQwI3XTNej2DfDrhzZOYt5q+8Eo44An7+c9hnn/mssmVZ1vwQ0j0lrB2yQfxR0j5k8xCUBa8KQS1dP15Z57ChqlGPGefOqItiT4NYK6JQpxPQjSHfaJK4mjh0YQLoycLgRPpL252B5QVYX5r5w2+/HY4/HtasSWeyW5Zl7W1sd/qs2Kv0KInAKR8S+tdD5yZF5yZFflSn24iP+qzflCWKFVtaOUr4DDs5binuw5pcDyP5LE3XIVGK2Hcx3VlYWoDuELJeuklKRzD9GnJIl6Nt2ABLl8LIyLzW27Isa5fbOjt9LsciYZtxc/D4t4BB8e2PQ6Q1sYJEJ1Q6DJMljyBM6AiaiBh63ToZ1WJj2El3u0KkNVXfY/84IRNFqFaUzlSfaCDtOA3UrkqXnM3UtT48DEcdBWvXgtbzWXXLsqxdyE5smy0bxOdABI57qzBym+YXPxO6Nir61mo2HhizXifU8hHDlQClQGNoOA6eThgKCzRdl3KQoeG5LC+MEQ50khsp4TaTdIy8M4SbN6VLz8bqMxdiwwa4/HJ47nPnr+KWZVnWbsEG8Z3g6Z+GtdcLt2/RKBTtLNQLPhsyMUplUCLEsbBPoUQbxWaniBGhKA1arkcz9Ik8Fz9J8A7qS5eaaQWPWwo3b4ZSM53FPpMvXGCDuGVZew+b7GXWbH/FTuBl4S1/FvZ3wa9AUFLkJjWZTQGTd+YZGg1YM5JnSz3DYCtHQ7nUHJ+2aGqeR81ziZVgAOMoWFFMx8Y7gnS2+srCzOPjAH/5PdzyV2i356nGlmVZu5hSczsWicVT03nwyp8K/aNC9zroXqfoHXTwG4rxjSGttmbNaIEt9RwjSYYxQka8LBN+hvFclrbWVLMBzXyI6chAfx5WdKS51ZcVYFl+5g8ersAxx0BXF3zpS/NWX8uyrF1C5jipbRG14m13+k7Ueyj8yy3CJw/VhBVF31qH8QGX4RUud74oRinDZBiwRoq0s5ohcmT9Fvc4XZxoVtPwXCROZ7EFBmSyDo8ZgI4w7V4XYEPlwa1MtxUnUKnAO94BK1fCM585v5W3LMvaWWza1VmzQXwnKyyDV39X+NprQcdCxxaFXxdKf8ix+XhwdYKSBEcbfB2jgBXOOPd0LeGgic1UV3gcnCRIFOO7Ou1F78nDWC0N1K6GVpLe3lR5eAHqdfjP/7RB3LIsaxGwX3V2gYOeA+esFo7dH9TUMHVxk0vmtiwb7sqxaTTLpskMjdihFjs0cWhphy3ZIuO5Apt7Ohhe1k21O0esFSZ009Z4VyY9PP1gy3w6d9wG1WkCvGVZ1p7CdqfPig3iu4hfgJPeL+QakJkUwrKQmdRIxaM0GDI0keX+4QKTDY9NjTxlfCa9kLbWrOnroxYGDA90UeuZGgtfVkzHxQ/ogpXFdA35TEoTsGIJvPmf4L775qW+lmVZO42Indg2S4unpgtg1Ulw9KnQtUXo3Kjp2aDpGNE0RnxGN/lMVlwabYdN9SxjkmVChYy4WVqOw/19vQx3FGgGHu3Qw/TkYJ9OOLAbch4UgjTD23RfOBsRjFfgixemedZ/+cv5rrplWdbc2Jb4rNgx8V1IBF58kXDkz+HX/6O46VaH7ISw8YCIjTpm2IFcGJHPwuZmlsQT2o4DBlSS0PJc7l25hP0EOgRcY0ApZEXxwXWUjoLxRjpGriQdL9/KGKjV4HnPg+uug2OPXbBrYVmWZe18NojvYiJw8HOEg58jtFrCP7wcqsNQLrpMdEfcP5hjxdIq91DE747wnJiW59AWhRdHFGiwblkfiJALXMLRCtRbaSrW0IWeDKweS++vn5y+EO02nHA8/OGPcPTR81l9y7KsR87OTp81e5XmkecJb36Fpme9oneDQ2a1D3XN6IRPuR4wWCuwqZ1jLA7ZoosMBXkm/ZCm61LJhsS+S+I5mP6pzVKKQfqLXgh2vHVfswUf+MC81dWyLGtOlMztWCRsS3yeHXqgcOTvNdlRIT+uWVdxGFvuUHYTxoMATyWoAFpK0elUIQBtErxiOsEt7zl0aMGJEvBdxFVpt3qjDUUfhrezYcqf/zx/FbUsy3q0ZGE3QBGRLuB7wCpgDfASY8z4Q85ZAXwTGAAS4EJjzOennjsHeCMwPHX6+40xl++KstogPs96D4cuX+gYUlR7DJ3dmkpnzNhdeeqjLuYQAwKeStgQFKl7HlXHZ322m97cJCtKY1SyGQYCjyDnp8vNkDRF6+oxqEVQaU3/4cuWzmtdLcuyHrWFbU2fDVxhjPmEiJw9df+9DzknAt5tjLlRRPLADSLya2PM7VPPn2uM+cyuLqjtTp9nIvCyHwg9W4SOjULnJkX3ZgeJhNpIwN1rOxgqZ5hsetxX76RCQE28NC1rGFIJQ4a6O9IZ64GLyfgP7kNeDGBJDnLuNB8MfPgj815fy7KsPdDzgW9M3f4G8IKHnmCM2WSMuXHqdhm4A1g2XwXcyrbEF8Dy4+Ad1wjnnqDSTU8MkMDQyjatqsNt93bQ212nkAsZr/kc2T3EPkzQUA4110WbhPtXDLB/bAhE0tUURqARIc0o3fFM1aHcTD9QBF71YnjBCxau0pZlWbO18BPb+o0xmyAN1iLSt72TRWQVcDTwp20e/mcR+QfgetIW+/h0r50rG8QXSP8R8KoLFd96m5CbUPTfrxlb6jC0n8P9T60xMRaQ0MbTMZNxyF3Ko1tVyXkN3CSGbMi65b3ku/PI/gMQxfT9cXWaA8bRSHc93dLUUZjDepHODfC24yHfC4edAndtgo2b4cij4RWvgO7uhb4klmVZU3bK5LQeEbl+m/sXGmMufOATRH5DOp79UI9oBrCI5IBLgXcaY0pTD/8v8FHSGUofBT4LvO6RvO9sLXgQFxFN+k1lgzHmOQtdnvl0+Evhw6cLt/8Ytgxqkrrmyi96rL7B52/PrBE9Y4KJakCp5dMT1Bklx6DfSU17LKuM44cBXpzmX89UG7SOWIqb9ZBNJRipphPelEEqbag1YagC990Ln/0ZtOP010t/E/7t3+C3v7XryC3L2j3snJb4iDFmxv/UjDFPm/HjRbaIyJKpVvgSYGiG81zSAP4dY8wPt3nvLduc8yXgZ4+mArOxO4yJv4N0LGFR8vNw9CuF084Whq4WqhlDKyNkhzwqd2dptRVrx3MMN0NqkcO9pocJJ8O9Hf1szhephH7aJb/1S2vWgwN64LiVsCQPjTh9fLQGmyuwvgSt+MEZ7HEC5TKceeZUv75lWdZuYGGXmP0EePXU7VcDP37oCSIiwFeAO4wx//WQ55Zsc/cM4Na5FmgmCxrERWQ58GzgywtZjt2FTP3eSQIqFhhzGb2twIbVHQxVQkqRz5ZWnvvpYkxlua24nNu7l7K5o8CWriJGKxInHWcnSiBJpn6adMb6RAPKM8xcHxyEtWvnq6qWZVm7s08ATxeRe4CnT91HRJaKyNalYscDrwJOEZGbp47Tp577lIj8TURuAU4GztpVBV3o7vTPAe8B8gtcjt3CUa+D294jZMYgWxJyJU0jYyhvDlmTKaBWlSiGLWqJR6QbJAJDQZGljUkqGcX6/h5WtSLCkQpGCYKk3VJbW9hxMvPOZ60mfOrj8Lwz4NSngTvNDHfLsqz5sMDrxI0xo8Cp0zy+ETh96va1zPA/qjHmVbu0gNtYsKskIs8BhowxN+zgvDeJyPUicv3w8PD2Tt3jHfFyOPIJ0LNR0b1WM3C/S9cWBxXB2EjITXd1c+O93QzVQsaTEGNgUocMZ/JUXY/hziK3HbCSSm8u/QPwHSj4adeSlrSrPe9N/2uXGPjCl+DZz4aennSM3LIsa6HYjG2zspAt8eOB5011PwRAQUS+bYx55bYnTc0mvBDg2GOP3asHbZWGl/9IOP5a+OmHFH/9tdBzgGJoX4fBw2HikITxUsCa0SK+m9DyFANOlXVBNytknJqJGc/m8JttDkg2ERiDxEm6q1lPFmnFUGlCPUofM0y11KcKsHW9W6mUbpqydq2dtW5Z1vxb+CVme4wFu0rGmPcZY5YbY1YBLwN++9AAvhiJwKoThWd/QNG5RZGbVAQ1RTDhQEmBgVLdY81ogc3NPGubRSriM+JliREMMNxVZPOSbrYcuJSJY/YhesI+yKG96V7kh/fD4b2wT8eDSWKm02zCu94FIyPzWX3LsizrEbBfdXZT+54MHTHkhyE3JuQnNOGQC2VFteowUg7ZMJ5hLAq5r9lJSUJGvRyxKFqOQzkbUs0GNPIZkkKIGSjCvt3w5H3g8IE0oBcD0oXl04giuPhiWLECvv3t+a28ZVmL3By70m13+vwyxlwFXLXAxditiII3/F4470RFK4TIc4h0whYj1NsKtaLOmAooZltoz2OYLJESWp4idHzCVguVJDhRTBR6eL7zYNf5yg7YXE7zroduOmt9uoGK1tRM9te+FlauhKc8Zf4ugGVZi5cAyrYxZ2O3COLW9PqPgA8PCTd/W/Hj/xDW3+GzZT+H4VUJ9z9e2LS0RehF1LIuriS0fIeyCnElQhcNLddlMtvEJIaBdkwQxbCpBDkPWZrHtOM0TWvGhVp75t3PoghOPRW++EV43S5JOmRZlvX39OJpTc+FDeK7Oe3B414njN0l/PxbQrkXvAbkN3nUO2LWbsyyz7Iqg+QwImScNoqEAadExQ+p+QETYRYlkOnM4S7tJtg8jm8MYgBHINAw2oCxerqufDpRBG99K5xxBnR2zus1sCxrkRGxLfFZskF8D3Hc2+Ca86EwLJR7hNykYmLYpV1xGPMTqlmPrN+mmWiUQCkI8L2I7laVluMwVsjTdh1UIUufBl9JukFK6MC+Xems9NuG4Lr1M7fIlcBvfgMvfvG81t2yLMuanv2qs4coLIdXXiT0rFf0rNV0r1X0rXVwGkLpvizVimbjeIZSw6MVC2ujDoZ0nnV+J6NOhnXd3TQ8dypFq2C0Sie6LS1AZwZ6spDzoTczcyGMAd+ftzpblrWI2Ylts2Jb4nuQg58H77pS+N5bFLde79Ix7NC/PmayJ2aoDmv2d/D3naQeOZQaHoXuNm3tMKEydEYNhooFwlaLfKmKUwwJ4gQJPfCmfg0KPhzQnbbEh2vTF+LpT4f77oO774ZDDoFVq+ar+pZlLRaCHROfJRvE9zADR8E7rhNAYxL4j07NffsY1mzW/GmfFnffX2Dlsiq+pxhqZ8k4Ho4kjLshbtLGjWOGOwu47QiJEvzYwHgVqbcxB/VCpYn0ZNKx8YnGgx8sAgcUYKAzzb/u6HQK/XOeAxddBN4M680ty7IeDTsmPis2iO/B2jVotcFokETovSnD8GNrTJQ9PM+wzs+xb0cJlLBOd0EALXGoOy5OkhDmMmQLNYLuLDpKcBttdFcG/roBVo/BUDXN7JZxoSuE24eh1Jz68Cj9+bOfpVuZfupTC3chLMvau4iQLKIu8bmwQXwP5mah0AVuHYKKUBh1KA0GlCYcOg+oMVwOCb2IgbDGsM6QU02aocuEnyWIIvomJmn5LtJTpHu0hLdpHHydJoMpBGlrvNJK9yZfO/FgAN9WqwVf+pIN4pZlWQvABvE9mAic9hlh6B1Q2SzUCopm6DC8HEbvyFLeV5MLI2IjFP02Ga9NQbfQkrAkmEAVE/wowokT8qUqsasR30GqLXBUGsSbUznWt/6cTqUyn9W2LGsvZ4DEdqfPig3ie7jHvAzekIdL3qsIbxSKQ4plyxxMZ8LdUcLdSZH9VgqeNtyfdLIqmKBDN9kQdGCAXKuJk8SMFXNkKnWcRgszXkM6QtgyldVNAK3TVnozfnghDjkArvgxPOlUyOTm+xJYlrUXst3ps2OD+F7goGcLH3j21l94zdrfwYVnGMKTFL9/wyTrN+cYL/t05xtUsw4HFCZoOBrJQN5rkG01cHNtOvMZlAFXKZzBcWjHkJAmgekKkHoGNpT/vkWuBDobcN4H4bPvgwOeClvG4Mgj4Q1vgL6+BbgilmXtyYwIsd3FbFZsEN8L/fUbUPITVKRZdXWOtcdVyRwYU274OC6MNUMSXyiqLC1PU3JCuupVJoo5apkAr7tAcvAyIhGkHdN363rCX90BsUmD9mQzbZEXPDikN00YMzgCN2+GX96RBvkfXpqOk197LTzmMQt9SSzLsvZKNojvhZIYENARuC2Fv8VnzIGOfap05mFDJY8jhjVeJ93U6FFVxoMsOjF0lSskWrG1Xa8A1Z+Hkw5Idz2L4jSATzYe7FqvteHOkTTIbxXFMDkJb3wj/OEP83wFLMva09nu9NmxQXwvdMTL4U8/U3i1hNyEkB/X1HMOoyMBmSCiM9/m/nIRV8UU3CzHFBNWZ/uItGYyDFkxMkoQRQjgRjFGJM3mpiRdH+7odNY6U0F8rJ4G8un8+c/pDHa7jtyyrNkSMHZi26zYIL4X2u9p8ITnQPkqiHyNAbyGMNIUNipDMlCjq9CiaTRjSchk5Ke/CaGwrDXOff39HLh5M04cU/ccCr6TBvD9umH1SNpdnvMeDNztOJ0qb6aZvq61TdpgWdYjks5Oty3x2bBBfC8kAi/4qvC4Pyiu+qLh9s1CO9Ecvb/H0wfyPOdHk9xxXIPegSaQcH+mg2V+GeMKzcAhZxoMr8xx+MgGdJLQNVml0ZklMAYwyGgVch6m3oZyCwkcyHvpWPlDLe2Aq38Opz5/nq+CZVl7LJvsZdZsEN9LicDKJwv/8OQH/xCMgfMPNTzWKRJP+mw+fQK/M2Ky4SMibGwV6PLqHKKHEBc2FTroaNTY2NtBvy6RaMF3Nbo7h2m2qR+9HP3Lu/GbEVKemuzWjB4sROjC/jn47w9CuwmnvWQBroRlWdbeywbxRWT4NhgegsYh4FYdkltzVPZrMOS1iY2Q89s0Y81q1c2+MsqEn8EowUli/DhhsphDlqXvZYBICfT3cdDXrgZXp13sEw1oJ9AdwtJ82pXerMNXPwP5ZXDJJdBopNuZnnJK+m3DsixrGzbZy+zZIL6INEtgnPQPxGmB19RMjPisG/NoHliid2mNcsPlsK5x1kk3xhG8OGI8kwNj6KjVCVptDFD3PSqZkMPWD0N3BvI+7N8Fa8YfntnNGPjDnfCDEx587lvfhJe8FL76VRvILct6GNudPjs2iC8iS44BvwVhSciPKgojimpBaPuweTDH5rJPJtOmXHE5Zvkwg7qTVuAQmDYD2mdfNfZ3G9C7rTZhsw1HLks3TWnHabrWdvL3HzxYeniSmFodvv99eO1r4SlPmY/qW5a1hzAixGJb4rNhr9Ii4gTw/AuE7mHoXicMrNYsv9slP6pxKgpjhMQoJhsBa0oFNjZy1I1HXflMBHlWF/toiyIhjceZRpPY0ZD1YP9uWFZMf277DTpKYLg6fYFqNfjRj+ah5pZl7WkSJXM6FgsbxBeZI14B7/o/xdOWa46+2+WYK32O/WXIEVdncSqKdkuBwGTdJ0G4v9lBjNDAoer63F/sYSyTZTyTJVZCFLgYpTD5IM2zvrSQZnHz0qVtNKOZu8sFyGTmsfaWZVl7FxvEF6GBI+G1PxA+fb3ioDsUQVUQoOuekGjcpdVSlOse5aZLM3HYFOUYJUuEoqFdEqWJlable0wWc9R6ckSdWUwhxABmSQHzmH7EVemEt8wMozZKwegQHLB/mmP9Fa+ANWvm8UpYlrW7si3x2bFj4otcYRmEk+BVoTCiKXW6TGBgRZ1SwyNOBFdFOJ4hUBGhtAl0RDGuU/U8ytmQWCuymQDdVySJE7pXb0Y8DZvLEJXTbG/9OdjykC1LAw1f/NKDY+UXXQQ//zncfjssXTrv18KyrN2DWeCMbSLSBXwPWAWsAV5ijBmf5rw1QJk0fWVkjDn2kbx+Z7At8UXu5I8IvYNCcZNQGFX0DDq44w4Twz4bh0JqLYexeshE22ckyVIVj3V+N0NunrEwz1g+y2gxz0hXkeHuIsZzIPSgNweFAJbkYb9OGMjBqg7ozaYB/aDuNOPbtpPdjEnzrR96KPzlLwt1SSzLWnBza4XvhJb42cAVxpgDgSum7s/kZGPMUVsD+KN4/ZzYlvgid9iL4Pn3CZd8RqHaoNoOCGwSmDBCve7Q092iWVS085rEF/p1hUknQ6IVBkOHU6fuexRqdYwI7dDDrbWQ5UUYddKJb8sK6QYpa6eWoN06NHOhSqV0xvrNN8PBB8/XpbAsy9rq+cBJU7e/AVwFvHceXz9rNohbnPAegURz6bmCKAEDKoaNQFMZKmGCSIbQi1EYlJ/Qp6qEKCb8LEoJxXqNlqOphT6ZfIhuRajuLJIY6MzASAXKrXQcPEpA7+CbcqMBRxwBX/86vPzl83EZLMvaXciCJ3vpN8ZsAjDGbBKRvhnOM8D/iYgBvmiMufARvn7ObBC3ADjhbBBHuPRTChWBGEgc2CwwKQZHx4xVA+pTLXXlGVBgtMGNI7Qf40Vtmr7LZDELAoEI2oCMVCDrpzufhc7UTxd8BxrRzIVqt+H1r4enPS2d+GZZ1qJggGTuSaB6ROT6be5fuE2QRUR+AwxM87oPPILPON4Ys3EqSP9aRO40xlzzKMv7qNggbj3g+H8R2jX48fkq3ZQMgxGPTQrGDCCwpMswUgswZmqHNAXiCQaQ0CBAgtDyHHqAQCkcY9JAHrrQlYF6BD2ZNDnMpsr2A3mzAc96Grzi1fDc58GBB87PxbAsa0HthHHtkYeMU/8dY8zTZnpORLaIyJKpVvQSYNrxP2PMxqmfQyJyGfAE4BpgVq/fGWwQt/7OSf8uaC1cej5EjkPipt3rmxMYTZeQ098F7VjhSIL2EkQBnkGRPm+ArnKV0e4iXQIhBsd1EEjXjAsPHgCbytCIpy+QAW78G9z4L/Duf4FDDknHyn1/114Iy7IWjBFZ6O70nwCvBj4x9fPHDz1BRLKAMsaUp24/A/jIbF+/s9ggbj3MCe+HxAiXnT/VtZ5AomGLNoxogwgMdNUZaYS0jSLxhUSBeICAEZNuJQhIZwEx4HkOHiYdc98a6UUeDOojNSi1dly4O++EE06ws9cty9qVPgF8X0ReD6wDXgwgIkuBLxtjTgf6gcsk7fZ3gO8aY365vdfvCjaIWw8jAk/9NyFuCD/+GiReOkbuNGGDwHACxhjiTlAYfBWjXBAlGDdtXosBjEFhSHo66FaTYAyeVqQz5wTRAlsTwmgFUoVS8+EbqDzU9dfD8cena8o7Onbx1bAsayHEC7gxkjFmFDh1msc3AqdP3b4POPKRvH5XsEHcmtHJH4XuQ4Vvv02RHxFUDLEHw5EwYoTJks9AT5V2t8JkBeMCyoBr0jXfpGPlvabCaGeBboR2xscpBDhDZfTq4bRH3VEPdq3nPKi20kA+2Zy5cH/8Izz/+XDllemMd8uy9hp2K9LZs0HcmpEIHPkKoe9Q4X9OM3jXuThtyJYUm6qKyrI269uKGIdG5DAZ+tQCTctRNDyHmuNTcTwm/YD+SoWG55Kv1SlqRZQPyYYurBlNA3dXBjqDdKLbSC1dhra9VnmSwDXXwKpVcNVVsN9+83hlLMvatQRjtyieFRvErR1acgz822rhEyugfYuD04yR2GOoLUwsF0a85IGWdxQL9cBjuV+ioT2agUNLuySOQ5frUPdcIsehc7JCsyOLv68g+QqM19K144ELGTdN0doVwmh9+4Vbvx5OOQXuuAPCcF6uh2VZu5jY/cRnywZxa1b8PPzjVcKFz1a016YT3SLH0AwMdW0YwaDEIEWD6xg2So6a65AoQdy0QW0y6axTL46JtaI3STBaCAxpUhhXwfrJtBXuKEhMGtiHatsv3Nq10NsLl14Kz3zmvFwPy7Ks3YEN4tasDRwF71stfGqpQrfTyWtuG7bUFRMt2JwIcaKIDVQdDTnBcQ2iIHZVGsiVIlKKznIV1VWkY7ICCIESmKghUTrpjcBJ07R6Om2dD5bSoD6TajUdI7/jDth333m6IpZl7Qppshc7Jj4bNohbj4gbwBuvES58lkLfLvgVQbcFow2TShhKhMRAf2eD8WZAO1G0fU2iQLmGlnIAgzbmgY0KtrbIQ2PSVrirYN0ELM2nAd2dmvi2pTLzenKAZhMOOgg+8AH40Idm3sfcsqzdnu1Onx0bxK1HrO8x8P41wqeXgxhNO0xTtDptYSwRRqf+9pIYJJcuQXPcLKIgrxWOm2AyQktp2o6GJKF3tESzmMGXtBUukYENk9CXTcfbzdSa8g0laCczFy6K4BOfSMfH37tL9huwLGtXE9kZaVcXBRvErUdFaXjtb4QvPgPM3Rq3AbolRK6hJDCcCMYIRtLsbnXfIQ6ESATtGNqSdq87JmGsWMCI0CslEpW2yE1nhDgKBiemlqAJZKZ+Xcfr219+1mzC2WfDeefBLbdAV9d8XBLLsqx5Z4O49aj1HQ4fWCd86clw0z2ayIfYc3EixZiBMQzGQNIhONow3g5QLjjK0NYax0tzrccixCptaXeUqhhXk1GCyXrp8vGhcpoMZrgK0dRkt9g8uJ58Jhs2wLHHwn33zc8FsSxrpzBAbNeJz4oN4tacKA1nXipsOAwiX6FiB7cOBo/xWNIWOUKSCI1Q08ooYg86dQ1Hx0Re2mXmJgmT2RABnCim1pMnGKuiOqM0VatXTcfH2/FUi9xAxYUt1e0X8P774R/+Af7zP2HZsl1+PSzL2jlsd/rs2CBuzVlhOZx1s/Bfj1MYA7Hn0MoKsZtQ0h7jYhAMjjZ4Tow7lVvdVYZYK1w3gQy0lMaJE6SzQL5cwyghmySY2CDGpGPhywowWoPlHen4uDEwXNt+i/w730lTtN50E6xcOV+XxbKsR2knbUW6KNggbu0UnfvB+1cL/3WoQt0GTh2CcZeNBypG2sKwEZIkTbTWijWJEfAMkVb4TkSi0jFyN4lpeB6JEhQG1Z3D1wrlakQEsl7atQ7ga9hYAkfDxvLMhUsSGB+H1742XUtu861b1u5NBGO702fFBnFrpwm74F13CD/7F8W1vxCclhBlhSgwTHiGScfguWkymIrjgmQwIihJ6FYVlJdFGUNBNRAMBugQIXEdAmPQjk43TWlGUzPWgYF8uj/5QA42V2YunDHw29/CkiXwhS/Aq189X5fFsixrl7FB3Nqpwi548VcFXgpX3KiIAgenJWQnFJvqwuamotFS1IqKvkIDLckDCWG0riBehkQpIhGajkMiQug36YwTcnEJs6QIBiTrpbPWa0EazDeV0rXkQ9V00ttMGg34p3+Co46CI46wm6dY1m7KdqfPjg3i1i7xvC8LE88DfasiOwLuYdDMGUY8w4RjcDS4U7997USIfXBUQuwoGspBjMFLYsbzWRxjqBYyiIBbbuD1JjBUgv58Grx7M2lAz9XTvOubKlBrz1y4RgOOPhry+XQp2tln28QwlrUbsWPis2eDuLVL+Hl445XCmqvgghcpGkVohaAjGGsJo22II8F0G1yVUNUx425IohQ5hHE3zcwWOi6Q/lHXA59uXULHBu0opFSfyq+u0pnrxkCcpC3xLZW0m30mxkCpBB/7WHr7/e/f9RfFsqxZs0F8dmwQt3apVScJJ74QGv+nCMqQH1UEFWEQYQxFFEOrpankHepZh8ivEIngOoaGduilTOC12dxRoH+ixEQxi0oSHF/ja4W0YgjdtCtdTyWFcaeCer2dtsq3p1aDj3407V4/7TTbvW5ZuwEjYnOnz5IN4tYu99wvCEu/Aj/6Tw0mplp0qBUMo9pQcRMCPyEMYirtmCHJEnkaRyV4yk/H1DHkWk2Gi9BZrZIoRTZsYLQiMAkyUk271hOTdqs7ChLSfck7AphsbH8JWqMBL34RrFgJv/tduiOaZVnWHsAGcWuXEwXHvlE48pXwH6sUrRw4bciPKcY3a0ZWOVSWOVS6FAPFdGmZ68V06TpjOkszcFimJyj7ASP5PF3VCj2lCrXQY0kzwhUFPVnoyyODE9CdSTdPGZxMk8GM1NJUrdX2zDuh1eqwejW8+c3wgx/M6/WxLOvhbHf67Nggbs0bN4Q3Xa744gtgcJNifECx8RBN4ggTCtY3HOpNn3IxYCjM0BnUWRZUaStNw3fpNlW6mxXKfoDOJegkYdM+fXSOV/DqLVynjm60oe6mmd2WFdPu9Z4M3DeepmndXJm5VR5F8MMfwllnpTPYDz54Pi+PZVlT0rSrNojPhg3i1rxa+jg4Z63ixq8bvvExTSsrRI6L2xbGVjUYkzTfOhh8HTOkDJGr6NUxdeMw7mVQGBquR9X16KjXaQYeneNlso4ijBKk1gKtYbiSjpNXmmlQ35oQZrQOrRm2NDUGPv95uPBCuOgieN7z5uvSWJa1lR0TnzUbxK15JwqOea3wlwugcaciM+nQuUWxcUzYcLDDcFVTqWgmO12WdNZpZjQtT9PjaBIqJJ7gJRFiDH4SIwJOO8ZvRdR68vjlBjpwkTiBQpDuhGby4Om0Sz0sp7PXqzMsQzMmnfD2mtfAli3guvN5eSzLsmbNBnFrQYjAP/xKqJ0Eq9dodAuqHQ6VUcOon1D3E1q5mGrTRQQSFAlCWQX0qQp5qRO4bXSYpFuaZkMQyNSbJJ5DMF7F6cwgEzUoBmlgbkRTW5q66bh5o5SOkc/UvV4uw0knpePkL3sZOPbPxbLmi7Fj4rNi/1eyFkymB955s3DhUw03b1H0roNEg9MURlsw2hbabWGgB1wd44iP6yfUcEEMnhOhDERKU/V8CoFPZ6UKBlSU4DQjjO8gWR8TuEjGg4kG+A6sm0i3NK20YKwG0/WuRxFcdx3cfDN885vwy1/aJWiWNQ8MkGCD+GzYIG4tKOXAm64Rfvx6uOJKhVsDrw6SwBalmKw6TE569Pf79BRqVDIO3aFDn1tFlAFPKCUB/ZTItluUMiFeO8JvtTF9ebxKE8d3UM0oXU/ejkEyaau8NwuDpbR1PlyduUVeq8G118IvfgHPfva8Xh/LWqzs7PTZsUHcWnBKwwu+JoT/Cr/4viLREDmGyDWMLDWYvFCtObiOj9IQOAlbjKLuehiBHlWjrhwmwixFqTGZzQDgtyOKuorvKLxGhAyVoDcHm8rQFcBYI5253mhDw4dyc+ZAXq/Dv/4r3HJLunnK0qXzd4Esa9GxE9tmywZxa7cgAqd9RjjlHOH/Pmb4xp8cdCQEVWG8qig3BBJwdYKnEwgNGafNuGQQwHgG064RZxR1x6Xme3RWa+goRpIEN+vBkg6k0oDAgfEA/FK6jnxpAVydJoaZaECpOX0h77gDzjkH/uM/4Kc/hZNPns9LZFmW9TA2iFu7FS8Hp75X84f9EjLj0LNeMz6gWf04zUTFpVx1KHW7lAoelaxLf7ZG4gqJEiJP45qYJWaSbKtJNQwImy28doR053FrTZzAQQVuOtomkgb0oWq6T3krgvsn0iVpyQwFbLXS48wzYeNGO0ZuWbuA3QBl9uz/QNZuJ+yEM78g9G7RLL1bURh16BhSqEigoalUPEpVj1rkMdYMGWwU2ZwU2SgdlCSg7ARMBBlaWjORzTBZzDHcU6TcnafWnU8ntjka8n66lVpvNs237jsQOuke5UrY7ryaiQm44IK0dW5Z1s4lEIvM6VgsbBC3dkuPeanwoRvhWWcKh0bQt1YzsMbF3+TSmHSo1TWTVZdK0yVGaMSaauLSFJeyE1DyQ0ayeTZ2dLKxs8hYMc9kLoMooZ31Mf0FKIawvJimbN2/C/pzcHAPZBzYpwgDuTS4T6fZhPe9Dx73ODjlFKjsYKMVy7JmbWtLfC7HXIhIl4j8WkTumfrZOc05B4vIzdscJRF559Rz54jIhm2eO31OBdqOBetOF5EVwDeBAdLOywuNMZ9fqPJYu5/8EuE5H4dnVB3ec7iQG9MM9DtUOn3WPMZlfb/PaG+Lcq9LNa8ZyChcL0YpQ+wqyk5EELdx4xgRIQh9SrmQgjEYrXAdhXRmoNpExqZmp/sO7NeG0Vo6Nh4lae716Sa8Vavpz9//Ht76VvjGN+bz8liWteucDVxhjPmEiJw9df+9255gjLkLOApARDSwAbhsm1PONcZ8ZlcXdCHHxCPg3caYG0UkD9wgIr82xty+gGWydkNeFp79Es3l3zU0AsXqx0UMr3AZD6GcMbh+QGyEStujknMZdjJ0uQ26VI1OKhQ9jzBqMxkG+O2Ilu8S5NsE9SZ+vUVgDIxVoTOEoUo6Y11JmhBm9RjU2ukB0wfzVitN0fqVr9iEMJa1UwjJwnYUPx84aer2N4CreEgQf4hTgdXGmLW7tlgPt2BXyRizyRhz49TtMnAHsGyhymPt3p72CXj+myGoCsUhRe96RVBRSElTLrnUGi4Yod52qEUubeNQw6OuAka9LKOZLJNhhg2dHYxns0zks1RDn8jVGK2gJ5dObuvPQU8W059LM7stL6Td6ssKaVCfSbsNhx0GT30q/OQn83dhLGsvZUTmdAA9InL9NsebHsHH9xtjNkEaq4C+HZz/MuCihzz2zyJyi4h8dbru+J1lt2g2iMgq4GjgTwtcFGs3JQpO+oCwz5MNn/sHReR7uA3F8EpNaUAzXheSSIg6BZ03lNou4iZkVIuSDokCTcZpoYOETr9KvtkgUkKu1qTRlSU0wHInbXH3F5BSA9aMQ18u3URlsglR/Pet8oe65570uOEG+Ld/g7PPntdrZFl7i500O33EGHPsTE+KyG9Ih3Mf6gOP5ENExAOeB7xvm4f/F/goaVU+CnwWeN0jed/ZWvAgLiI54FLgncaY0jTPvwl4E8DKlSvnuXTW7mbfk4V3XwRffZmid61HK3QZW+pz1xM9NhzgMzQQMNwT0NdRpyvTpJ5x6HHr5FUD34vIJw2ycQuaDaphyGhXnly1QdtzcRstdD7Cm6xDqQFZN03LWgzSdeTGpPuSD0Yz70sO6Vj5+98PQ0PwwQ9C5y77Em5Ze61dnXbVGPO0mZ4TkS0issQYs0lElgBD23mrZwE3GmO2bPPeD9wWkS8BP9sZZZ7Ogg46iIhLGsC/Y4z54XTnGGMuNMYca4w5tre3d34LaO2WVp4gnHWdon9CyI0rMpNCbkLjNhVEQrOpqTVdJlse41HIhlaBjUmRigS0xaGmPSbCDC3HYTyXpZzPMNzbwUR3gVY2SFve4dQmKa6G3gx4GqME05OFFYU0MUzozPwXZAyc999w9NHpRiqWZe1JfgK8eur2q4Efb+fcM3lIV/pU4N/qDODWnVq6bSxYEBcRAb4C3GGM+a+FKoe1ZyquhPfcIBx3PPRPKHrWK/rWOzjDDpWyS7XukCSKasuhHmsAGsahIj4TbkjZDRnJ5tjY2ck9/X2M57KMFvPpErSMh+nJQS5Ix8KXFjBH9MO+ncihvdAdpsfSPATb2aY0imHtWli1Kt08xbKsWTFTaVfncszRJ4Cni8g9wNOn7iMiS0Xk8q0niUhm6vmHNkI/JSJ/E5FbgJOBs+ZaoJksZHf68cCrgL+JyM1Tj73fGHP5zC+xrAfll8Krvi28CuHWv7p87FPC/ZsVY0s8aks81h/g0rVE099Zx1UJ4hkC3WZCZYg9jWNixBh6myVy7RaJEibyGTqMwYjgKYGubNqqLtWRrT3oK4rQ1U6Xn7kK7hqdOec6wNgY/L//B9dck64rtyxrhxZyFzNjzCjpjPOHPr4ROH2b+zWge5rzXrVLC7iNBQvixphr2X5OLMuatcccKZz7Ly7/dZqmkjPc80SXv7Ud1owFrOtq0d3dZFl3lZ4ww9KwSl43QQk9VCjqOg3HAWOo5sJ0CVqhjcQJ+Uodr9LArbfSDG8T9am9yMtpt3t3FnrqM68l36peh499DC67bDsnWZYFYMSmXZ0tm7HN2mssORoOO1qQRAiqmtyEgoYiTgQQmpHDeCtkYyvPUJSjbHza6AcyvDVcl9F8jqbrUg99apkAlJC4GrJ+mqq1f6oLvSeDAUzWxXSGaYY3Zzv/6RgDv/11uq2pHSO3rB2KkTkdi8WCz063rJ3p1T8TomdDdS0MLHGp5xMmQ4eSH+O5Lo4TkxihHjv4OqYuLnXlMerniJRDxY8YyhUImw0KzSbZap2sMXhTAVxGKxA4mFYMhytYM4b0ZuHPg+luaJsr0IqnL1y9AR3FtMV+0EHwpS/Bk588r9fHsqy9iw3i1l5FOfDGXwlPuFjx5fMgMxmweV+H8eUu5QEXs0ph+oTevCHQMZMSoCWhqTQN3wWE2BgOTIaJ4oih7iLL4gTVncMr1XE8B6KE2FPoSgsZqaY7mR3el64rdxQMV6Hcenjh2tsE99tvh2c8A/76V9h//3m7Ppa1J0gnti2e1vRc2CBu7ZWOfJnivJcpjDF8/FCHTc2QclfCX56T47aDWyRLmyztr7C8q8rSbIUlYRVfIhKELlOlqdM/jVoYMNGRQ+czqJ4iThRNjZM30VvXkNfb0BHCQAT9QH8W7hmFiSbESZqPvRE9vJDNJvz3f8Pn7ZYBlvVQZhF1ic+FDeLWXk1EOOUt8J1zQSVCUCWdZR5DreExWktoGpeyCfB1TLdXJytNyk6AEyZ0NmqMZ0M6qvV0IttUkhcjkq4lD13oysBYLc25PlzD5HykM4RCkBai1oYtlYdPfIsiuPjb8NGPQKE4r9fFsnZ3O2GZ2B5BRP57FqeVjDH/Nt0TNohbe70n/rNw59WGv9wM3RsctuwTM5HR1AJNJnTIhRGNSKMlXdRSIqTi1FBApBReEDKUL5LEQl+9RLFSIwpcfCVpznVIZ663YszKNvx1E2Z5AdlYTpehedv5z6hVh+MeBzqAQw5JU7UeO2OmSMuy9j7PB/59B+ecDdggbi1OouC1lwrPvgu+9iFN+0aP+yIYVTDuJPhujIghdCKqkYNyPMZUjpbrUnJ8BKjh0h+V6Yjr3L+sn/0GN1PrzhFKFfEdTK0NxhB1Z3FDD7lzKO1qLzXTFngreXhrXEiXrE2sTu/ffjv84hfwwx/CM585/xfKsnYThoVdJz7PzjXGbHcf4+1toPKog7iIfM8Y89JH+3rLmm99Bwvv+a6G4+F3I8J9Y4rBw4R1TcWS/nT83GQNIoZRyZIoBYSIgDYxEqUt82roM9TTgd+OkJ4iRAkdk1VEC95EDXIeFPx0F7RGlP6PdER/Omt9spF2yYduOl7e3GaymzHpErQ3vxlWrwY7scdatGTRBHFjzOfmcs5cWuJPmsNrLWtBiIJ/uUaz31nCZb8WsiXh3scKgw2huVRIYqGd1bR9IXahW9fBpJNsmsqh7Ad01mtMZkI6avX0vxkfTK2BGIOKEyT0oBjCZB26Qhirp/uTb825vtX949MXct062LIZBpZM/7xlLQKLJYjvaEzcGPP27T1vu9OtRUe78OLzFUdfKZz7ViE3orjrOIfhhkNjqabR69DKKdqBJvIVvboGKGrKo+IGU0E9ndzWWa0B0PIc/Gab2HNQOR/pCtOWeN5Ps7pVmlD04ebNaQvckE6Ui5KHFzCJ4ZlPhC9dBH1LYckS8P35vESWtaAMEC+enqgbtrn9YeBDj+TF2w3iInLMTE8B29n5wbJ2fwecLJx/q8P/vkjxq58p7n2iZt3hirUNh2a/pl3UREYR+4ouXWNSZVBuQls0sVZ0SI3I0XSXKpRzIV6rTTvr49aamL48MlROl5dtNVaDnA+rx2C0CgNZ2Fj5+21NhbQ7fu0WeNIJEGbSbvWzz063N108/7FZ1qKw7Xi4iLxzR+PjD7Wjlvhnt/PcnY/kgyxrdyQK3vJDxWnXuHzsbYrCiGL1UQ6DNU19qaLVJUQ5RcNz6HZrxAKxq2kpjUHIN2rEHQW6SmVGuop0jZWo9RYIR8qwtIhsLkE8FaS1gKdhVUe6jenSQprKde1E+rwhbb0reXBCXLWaPvfxj0N3N/zTP83/RbKsBbBYutMfYns7MExru0HcGHPyoy+LZe059nuKcN4Vmv95sXDlRsWdT9YM1TS1pZrWgKaRdWiFmsjTJEpoak0UaCLRNNsNjAi5ap1ECYVSFdMr+BNVnKUdaVa3eivNvz5eh8BJW9QCHNwDgU4zvDkqDeBrJx7+p1yrwVnvhDvvhLe8JU3ball7KYOQ2K09ZmVWY+Ii8sJpHp4E/maMGdq5RbKshZHtEf71Ss3RnxS+/n3FbU8V7jtKsbruUF9Sp9WpaSeKpq/o0Q5tNLEvdIgiUpoEaLkORoSW16CohLjawBeBShMZq0JfDoYqMJBL86wbk2Z7a0+NkxsD0wyTA9Bownnnwf/8D7z73XDOOXas3NprLZaMbSJSJv3rFyAUkdLWpwBjjCls7/Wzndj2etLZ6FdO3T8J+CNwkIh8xBjzrUdacMvaXT3tvYoT36L4t6cqbtjscOeTNIN1h9pSTb1b08wrGr6mx3GIlSb2NQ3HJVGKgq4Ra02XUrQdhy4g9lxCAVyNDJfTXdBqrbRrfWp9OeXmVGpWSVvk0014A0iS9PjEJ+ArX4HrroMDDpjHq2NZ82OxdKcbY/Jzef1sg3gCHGqM2QIgIv3A/wLHAdcANohbexU/D5+6QXPlucIFP1LcdqJmw2Ga6gqXZr+iXnBphC5tT9PWmiaaduAQi6KtG8RKKFTrxFrROVHB9BUJx8ootwMZKqXd5q047Vbvyabfw2vtdCy8O4Sh6o5Hx4aH0yxvX/kKvPrV83FZLMvayUTkeuD3wC+Aq4wxjUfy+tkG8VVbA/iUIeAgY8yYiLQfyQda1p5CBE55l+LApwj//UbhD+sc7nyi5r6qorKkTqtb084o6r5Dn6No4xD7QkM5NHXard7w0vHvTL0JAl65gasEmailLXBnatxPgOFaOrGtGaVBfrwx/cYp24pjeM1r4I9/hHPPhSDY/vmWtQdYZBnbngicAJwGfFhERoFfAb8wxty9oxfPNoj/TkR+Blwydf9FwDUikgUmHnGRLWsPsuJY4dM3OXzv7YrvX6z421M1G49wqa3UVHs1jYJL3df0unUipWh7mqZOu9fzqkbkOHTrMm3XoUMpomCqez1wEVUGz4EtU8vRNpVheQEGS2lWt3ITRmo7bpV/8YtwxRVw/fVQ2O4QmmXtERZLEDfGRMBVUwcisgR4FvAxETkQ+IMx5i0zvX62QfytwAtJvy0I8A3gUmOMAewMdmtReOl/K07b6HLhJzWXXu1w75Ga+/dzqS91qBUdGlmXquvQrR1a2qEROARuhs5mlUiEYr1BW2s6JyskjiIcqaBcjYxU0k1StgbwLVXYp+PBiW+NaPr9ybdlDNxzDwwMpPnXn/rUebkmlrUrGIR4kQTxhzLGbAK+CnxVRBQ7yI46qyBujDEici3QIm0T/HkqgFvWolJcKvzr5zX/YgLe9lyHqx/rs/4xAaP71Vi+1KGccykHARWvRpdUKToNmo6XBnW3SiUMaHou2XqdTq3wyg18raDRRpSCaguUSie+KYGqn3a5F6M0yMc7+LOr1+Gkk9LEMB/+MDg2KaNl7c5E5Cfbe94Y87ztPT/bJWYvAT5N2twX4DwR+VdjzA9mWU7L2quICJ+9yOWTJyuuvSud+HbPwR7jK6os6a5Tyzo0Q4emdsmbBviGWGka7TqJCHXXoe04FPwamYxHMFFDe07aKg8cGK2ls9jHpn6OVMFVMFyFyiymoXziE/C738HPfw75OU1+tawFsViWmJG2tNcDFwF/gkdW8dl+Tf8A8Pita8JFpBf4DWCDuLVo+Xn49+s1t/+f8OGPav52ksPgYxXVlQ6NJZp6pKgEDn1+Oj7e9BwayiERRUs7tF2HRCvaroNRCrfawBfAdxCtwNdpazxwHvwpAmEznQS3PUmSBvFly+Caa+Coo+bjkljWTrNYxsSBAeDpwJnAy4GfAxcZY26bzYtnG8TVQ5K6jIJNp2NZAIc9Q3HBKuHzr1dcvVFz53Eud5dc+pe5NHtcWomm6Wv6HE3kaNo6nb3e1ppYayKlaXkuXVoR+y6hVhA46X9hoQebJtNJbhsEMh6sm3xwEtyOutfLZXjc4+Bd70pb51rPxyWxrDkxQGwWRxA3xsTAL4FfiohPGsyvmsrBct6OXj/bIP5LEfkVaXMf4KXA5Y+mwJa1N+o5SPjo7zS3/0z46H8o/nayw+DjHOr7OFT7HOoFl1rg0O14dIhL5AntqaVokVKEzSaxEvKVOkYp/MkajqPTLG9a0u70ZYUHtzUdqYIjsLm642VoSQKf+Qx8+ctw442w777zc1Esaw4WUUucqeD9bNIAvgr4b+CHs3ntbCe2/auI/D/geNL++guNMZc9qtJa1l7ssOcoPn+Ay/nv1lwxprjrOJd79/WoLPEod7jUcy4tL90FreF6VLWXZnpTmkRp2q5D23MoaoUTuARKwHUQJelStG272f2p7vVyK12GtiMTE7DffnDhhfDGN+7ya2FZ1o6JyDeAx5Ame/mwMebWR/L6WU9dNcZcClz6yIpnWYtP3yHCR36uOfXcgC98yeFvT1GsfbxDeZVLrdehVnBoZFzq2qOpHZJAqGqfglujrRVtrWm5Ll2qROJqfE+jcz7iTELem+pWd9MWetaDdRNpCtctlR13rwO86U3wrW/B5ZdDLrfLr4dlPVIGWUwT214FVIGDgLfLg9sNzz13+jaJ2R/21Gze3LIWs6eeJRzzcof/fkeGn0043Hucw337OzSWOZRbHn1ZjyW+piEuHV49DehK0XQ9an6Dtlbkag0KOu1ed6UIk3VEBCbq6VK00SpIR9oS9zVsLEMz3nHhfvc76OuDq66CJzxhV18Ky3rEFssuZsaYOVV0R1uR2rUpljUH+X7hAxdrjv6gz9e/5XLbiR5rH+Mytm+der+mXnAYCKtTE94UzdCh4NTpUopYqXTim6MpuA5etkXoKMh4iKPS7nQtkJtaSx5O/TnPtnu9Xofjjkt3RPv0p9OuecvaTSQLOLFNRF4MnAMcCjzBGHP9DOedBnwe0MCXjTGfmHq8C/ge6fj2GuAlxpjxGd7jRmPMMTsoz4zn2EwQljUPTv+o4tSK8D9v8vjhoOLuJzmsPkhTX+ZQLXiMhz7LAo+K1Gm4mkgULcel7nlkG3XarkNQb4ASvHI9nfQWVNNZ60OVNIgHDjgaNpTS4L6lOrvCffaz8NOfwtVXpxnfLGuBGVjojG23kmYp/eJMJ4iIBi4gXR42CPxFRH5ijLkdOBu4whjzCRE5e+r+e2d4q0NF5JbtlEWA4kxP2iBuWfPEzwnv/I7Dsrcrvvc9za1PdVl7iM/Qihr7LHVo5B2WZByMFtqeR9kJ6NYVekRAhILrYrQm57v4mSahI+n2plsnvTlqKpjrNPe6PxXQZ9G7zt13p2vKv/tdeOlLd/m1sKzdmTHmDkiTOm3HE4B7jTH3TZ17MfB84PapnydNnfcN0kRpMwXxQ2ZRpBn/im0Qt6x5JAIvOU9xRlP4+juF79yluetEzeqGprFcU49cJgKfJUGVonjEnqKtHCqeT8VvUAoDer0yebdGogQv9HDzARK40BFMpWgN0qA+XElns4/VYbK548IlCbzsZfCf/wk//jHss8+uvyCWNS3B7P7rxJeRZlrbapB0e26A/qkc6BhjNolI30xvYoxZO5dC2CBuWQvA9YU3/I9D51mK7/5Ac9tTXNYcHLBxWY2VSz2qRZelGRfjSNql7rboUWWMCAg0PJeW75INPTJuDV8JjFXTDsjJxtQSNJ3OXndU+nNTece7oQH89a/pWvI3vxnOP9+OlVvzbidtRdoztVf3VhcaYy7cekdEfkOaLe2hPmCM+fEs3n+6As77niI2iFvWAhGBF31O8YKWcMmHhQt/lLbK17UV7eWKZuQyEQYMBBU6pI7xoKUd8l5Awa9R8326XIdIazKBi5f1ccsNZEtpKnhr6AzT5WdD1amd0iqzm71uDPzP/8CPfgR/+AOsXLnLr4dlPcDslIxtI8aYY2f8CGOeNsf3HwRWbHN/ObBx6vYWEVky1QpfAgw97NU7iQ3ilrXAHE848z8cMu9VfOsSzW0nuqw9LGDj8jorllSpdWiWZBxwoOb6TDhN+pTGSDqDvRr4dIQ1shmfvFJ4AkzUEVelrXJIg3rOS5ellWeRe32rjRvTBDHf/z688IW77BpY1h7oL8CBIrIvsAF4GWnuc4CfAK8GPjH1czYt+0fFBnHL2k08/5OKZ9WFn50nXPAjxZ0nKNa3NO1YUY9cxsOAJX6VvKqDB03tkvOb5IM6Tc+ly9EYEYKsh58P01b5psl0FzRPQyFIk8RsKKfL0TaWIZpF718cp5PdfvITeOYz0y8ClrUL7aTu9EdNRM4AzgN6gZ+LyM3GmGeKyFLSpWSnG2MiEfln4FekS8y+us2mJZ8Avi8irwfWAS/eVWW1QdyydiNeKLzwPQ5dn9Vc+G2HO5/ssfpIj8GVDfZZXqHRpekPHZSbtspdN6ZLVRCEWGmqgU+m0cLvbBGOV+kGZKIGoYOUmum2RRkvzb2uVbrV6WRrxwWLInj+89MlaL/4BRx++K6+FNYit5AT26bSij8stbgxZiNw+jb3L2eafUSMMaPAqbuyjFvZIG5Zu6GT3i084XWaH57r87VfKG57imJ9omhFDpPFgLvpZFVukiVhhZrvUXMC+vxJspkmxWY9zXW1BHoHOimOlekbHMEMV6aSxFSgL5vOYl89BvlWmrK1nWy/UO02rF8PT3kK3H8/FGzCRmtXkUW1Acpc2CBuWbupTKfwyo9onnGn4j9e6nLzsSF3Pj5g+OAavUvqRHEHlchnVW6SxBFGdYZl3gSJUuSbDdwkphoGqC4Imy0ynoP2NAQuUmulM+tyXtqtnnGh1ITNlR0XbGwMenrg9a+Hz38ePG/XXwxrUTEsbMa2PYkN4pa1m+s7RPj8XzX3/wUuOD/HL9Y7DB7jUt2vjvQJrUSR9zMs8WsoZYhDIeeF+ElEr0wStNuM9naAo5HODLnNJcxoJV1b7jtp2tacB6M1CHU6Zt6aRav8a19L15Z/ccakVpZl7WI2iFvWHmLfx2s+cSHkjstw/a0edx2X4Y6DMhT2q7Kyv4oUFXEgJFrh6zaZpIXOpMvJMmGbUjZD92SZtnbI+w5Otg55H6k205Z4Vwb6c9CTSQP5WGP7e5U3m+m2po0GfPKTNmWrtVPthCVmi4IN4pa1B3F8+NCNit+c5/DtK4U/xrBRwepIU2u7bM6EDGQzDPgVctLEDWLq2iMXNXHjNokS8r6LEnC6cgSjFXSpno4+ikDGgXYMotK87IMlaMXbT2HxrW/B//0f3HYbdHXN05Ww9naLaCvSObFB3LL2MKLg6e9QPP0diis+7vD1j3vcekqWwSeVqRxQxfQIUV7o811QUPF8fC+mGNVw44SW4xArRaFSI1JCjnR9DIGLVFsPjpP3ZqDgw7pJGG9AMkMkNwa2bIHXvS5dT27HyK05smPis2eDuGXtwU55n1Ata1p/ENpujnV1ly1LQvr6MizrqdKX8VgWVujUTSItONmYhusyGYb0u5Pksi0q+QwdY2XcvgLeZA3xNSSkudgh/R81dNPNVGZqkRuT5ls/5BD44x/Tvcot69EyYrvTZ8kGccvag4nA8/5T8VwjbLzf5z1vd/jT8ZrxIxQCxF2KtnEZdZvk3Qyxqyg7AV4SU/F8lk2M06EUqpiQ13UkinEHiuka8lITCd10XXlPBhzZcdrWtWvhn98K379k3q6BZS1mNohb1l5ARFi2n+aT5yg+9iqHu57oc/cxIbesaNC5ssqKgQorisJ6v4NuXQdJiAPBKRrqgc9wMU+2VqfY3aBrZIKwM4s3XoPxatr67grT7nRHp93rrRkCeZLAJT+A978PPvox0Hper4O1d0i70xe6FHsGG8Qtay+y/FjhM1fDN97n8NM/wd9MwphniEXRjF02B1l6wjrLggqx0iShUPJC3Dii6PkE8Sjj3UX01I5pvgJpRTBeTye8uTrdHW31+PZnrn/2v6BcgfPOm7/KW3uVPWAr0t2CDeKWtZfJ9Qlv/YrmrWh++W8e3/hum1uf0WT9Ez269quiug2I0PIdIiV06AYOCZEo/GJENmxSCQN6JkpkA5eM76I760jOT3Ow+xqiJG2R12cI5K0W/O//wrvfDatWzWv9rT2fndg2ezaIW9Ze7JkfEdbc5tD4syJWmvUln8GBLP19dVb2lukNfVaGJfKqxaiXQ2cTcn4TL4xoO5pCJqDP1fi+g6ckbZUj0IxAC9w9lgb06cQxHHQQXHJJmnfdsqydzgZxy9qLiYJ/ukzx0vXwg696XPJn4ebHCKNicB1DI3ZoGJes06LLzdAKHApJA53EtFU6nq2MoVMEFSU4xkDGQxTpTmjtBNZuZ4y83YYzz4RNm6BYnL+KW3s8mzt9dmwQt6xFoHOF4o0fUrxqwuF9R/ncdUyWu5/Q4N5jKkzsU2VFTwUvl7Bed9Kpa7gqxgsiHJNQCwPKYUChmKVvywSZwIV6G2IDpVY6W3395MzLzxp1+NCH4Nxz0+n0lrUDBpuxbbZsELesRSTogH+5TPjU6xS1gkctn2Vdy2F0MmBDd4O+Qp1V+Un63ArDbp5EFPmoSc1xCNttKoUMutnGX1pMd0SLDbgK4iRdfjbdlGJDOsFtdBS++U0byK0dM2Ints2SDeKWtcgsO1r4/E2a0UHhN9dqzv2V5p4nCqMYfDdmg85T8T0m/YB9PJeM26JLOYTFNjXPI9aKTkfjexrVjtM9ykvNNOHLpsr0LfIkgR/+MM3qdvLJ811law+UJDaIz4YN4pa1SHUvV7z0ZYqB9ZqvnOtz95NzrD66weBBZfZdWUZ1JfhBgU5VR3mGkWybtuOigMjR9BpDUG8jjQhWdqQT3cqt9JhOrQZvfxv87lro6JjHmlrW3ssGccta5J7yL8L4kOI7N2gmuzwGs1nuRlNu+mwq1NknP0nkCU4YU3V9Wkrht1rU8yFudxbdaKc95ErSMfJ1EzDRnP7Dbr0N+vvhb39LZ65b1jTsmPjs2SBuWYucCLzg04rnRsJtfxT+68ea3wwrho9M8N2YUT+DVkLT0fS4VZIQMh0tqp7HgOfQ7Tl4jkYmGrCsAJUWTDZnnujWaqU51sfGbIvcmp6x68RnywZxy7IA0I7w2BMcLjjI4d1Pcbn1pJA7n1Bn02NKHLpqAqcjJnRjxt2IfKZJrBT5ZotMo41XmQrg7Tid5NaK0j3Jt7dhyv77w5o1kM/PZzWtPYSd2DY7NohblvV3sn3wz58QPv4ZzfBylw3dGe7xDPXYpZx3WRUq3KmudSeK8JptvO4mYSMCrZAogdpUUpjB0swfNDYGL3gBXHHFfFXN2kMYxLbEZ0ktdAEsy9r9POYFwrevdvhsv89RV2eorgkZL/mMNDMMtovcJ91sdAuM5IoM9nWzaVkPpf36YHkn9GRhnwJk3XTns+357W/hG9+Yn0pZ1l7IBnHLsqalNDznjQ5fOz3kmZ/pwny/j1tv62ZwIstkElATnxE/x2QQMpHPUi5kiXI+ZlkBBgqwqgOWzKKr/I1vhMHBXV4fa8+SmLkdi4XtTrcsa7sOOFU4+uOKDWtdhtZluc2HWuTS6NJoP0ZlDXXtkK03KRYzZHvz0E6QZgSOSsfJN1dn/oB2G446CoaGQNl2hZVOmYjtOvFZsUHcsqztEoH3XKE44BMO//VHn1tybUodPpvCPEoJkatphg5dnVVaWrPMdejauklKO07XjTsKBsszf8joKLzhDfDVr85fxazdmp3YNjv2a69lWTskAv/vfZrfnx/yznM64bJu1m3KMdn2mCDDuAqZCHNM5jKMdxWIfQfTX4CeDPRm05894fY/5Gtfgw0b5qdClrWXWNAgLiKnichdInKviJy9kGWxLGt2nvN2zYF/CJm4rpvf/W2AO8Y7KBGwOSgwki0wmQkZW9IBWQ/TlYVDemGfDigEEOrtv/lzn5tmdrMWvcTInI7FYsG600VEAxcATwcGgb+IyE+MMbcvVJksy9qx414nPPf3mvWrXe5d5jE4UURpoZ5zaIUa02lQcYwbJ+RchRMlSH8eRmrpGvK1kzO/+c03w7OfDVdeOW/1sXY/BjsmPlsL2RJ/AnCvMeY+Y0wLuBh4/gKWx7KsWfrHr2h++w6fZ/4lz/C6DOWmx2iUZZPqYMjPU8plWbusj5F9+qA7C50hrOqEfTuhO5j5jY2BP/0pDebW4jXHVvhcW+Ii8mIRuU1EEhE5doZzVojIlSJyx9S579jmuXNEZIOI3Dx1nD6nAm3HQgbxZcD6be4PTj1mWdYeoHup5qJ3hzzrvb1s+PFS7h0qUkk8xp0sI5k8lTCkEfhEXVlMTxZWFKE/C0cMQLCdbvVGHc56O1Qq81cZa7diAJPM7ZijW4EXAtds55wIeLcx5lDgicBbReSwbZ4/1xhz1NRx+ZxLNIOFnJ0+3Velh63uE5E3AW8CWLly5a4uk2VZj4CfhyO7Fbfc53PrrT00YocnLTNkgyZ1xyNotSn2FehotEBJ+kef9WFLBdZMQD16+Jsa4KrfwcAA/OpXcPzx81spa9EzxtwBIDJzi94YswnYNHW7LCJ3kDZE53VIeCFb4oPAim3uLwc2PvQkY8yFxphjjTHH9vb2zlvhLMuanbf/SDjhVpfcRo+xks89lV5uipez3u1kY0cnd+2zjKH9ltDevxezXw8EDuzflXatq+10e1ar8LznpevIrUVnT5rYJiKrgKOBP23z8D+LyC0i8lUR6dxVn72QQfwvwIEisq+IeMDLgJ8sYHksy3oU3EA4/+cu1z4lS+F3RYYnAmqxy4RkGA7ytDyXkZ4io0u701b48kLatb6iCIdPBfWZVCtw+c/nrzLW7sFAksicDqBHRK7f5njTth8hIr8RkVunOR7R3CwRyQGXAu80xmzdLOB/gf2Bo0hb65+d6yWZyYJ1pxtjIhH5Z+BXgAa+aoy5baHKY1nW3Bx4sMv5Py1yzscCrv8nh8LRTUacHIVsHQxkaw2aHRn8Sg60RkqNNBlMLYKNJajHD3/TZgte/CJ47/vgAx+AYDuT4qy9xk7aT3zEGDPtpDQAY8zT5voBIuKSBvDvGGN+uM17b9nmnC8BP5vrZ81kQTO2TQ3277IBf8uy5teT/wl6n6e59a4OfuEpTj5Q4QURFSdAxTG5aoPE0/j3DaNWdSGRSfcfr7SgUZ1+69J2DJ/8JFx1FVxzTZp5xrIWmKQD5l8B7jDG/NdDnlsyNWYOcAbpRLldwmZssyxrp/Fzwr+9S7HPLR7VssvqShd/ai3nXqebTYVO1vd10vZdmqt6oDuTZnI7tBeOGoC8N/10V0jHxW+6Ca6+el7rYy0ck8icjrkQkTNEZBB4EvBzEfnV1ONLRWRrw/N44FXAKdMsJfuUiPxNRG4BTgbOmlOBtsPmTrcsa6c66jmaP58W8Jr/heuKLXoydWp+wBYvT0++zJbuJkuGxjFLO6AVI1vKELrQEcC162C4Cq1p1ghVq3DeufDUp9rW+F7OsLA7kRljLgMum+bxjcDpU7evZYavncaYV+3SAm7DtsQty9rpHEfz7bdl+cf3rOT+XyxjqJFh0smyMdfJmr5eNvd2UO/Ow5IiZqCQJoPpCNNAXghmbpH/8CfQ0w3f+9681seaZ0aIk7kdi4VtiVuWtcuc/Cq45A9FflzYn/ITXJxiRKPoUXIDglaEcRROxsO/dwiZqMPh/RBvgrF6mr1tOmPj8LrXQRimS9CsvY6BrTPMrR2wQdyyrF3m8a9QPPEih9VP0Nw92klTHA7Mj7EkV2JJf4nY0XSJ4MYJOjYQmzQ9azGEGzdCe4bUW7UavP/9Nohbi57tTrcsa5cRgfN/7vLOwTyj6zPUI4fJOKAmAaO5PEMdBUqFLO1iBrOsA5bmoT8HnUGaEGZ5YeY3v/12+K9PQjL3HJvW7scYmdOxWNggblnWLveh93gMn7qE/suWMFoNqRuH9WEnWwpFNnZ3Ui5maPXmMQMFzAHd6dalB3SDp2bO6mYM/Mv7oK8PfvObea2PtYuZ9LvZXI7FwnanW5Y1b868v5OPfDJkwyvXkzm8hcrChJ+hFITsn91MoZihc+0IoqaCd70F1TYMzbCG3BgYHYXnPz9tme+zz7zXydr57Jj47NkgblnWvDntPcIX/klz3f3d/CireUzvEAeFYwx2d+AS4/T3cKCBXq2QRhuaMbgaVo/B/RMQzdDEatThgvPhU5+e1/pYu4ix+4nPlu1Otyxr3mS6hA/9P4feezwmyy5bmgXujnrZQoGSF5IAE30dVAshZnknHNwLB/bAqg7Ypwi9memXnyUGzvscPOdp0GrNb6UsawHZIG5Z1rw6/qWau9+R4eCbOxkpezQjTWIU6zJd1F2Pqu+zZZ8+WkuKGFdhlhfhoJ50nHx7m6U0Ivj5FeD78LGPzVt9rJ3PMLfNTxZTV7ztTrcsa965rsNvXlMgSXL886+2MHziWroyeYJcRNn1WaHGyPU3yOQCsvdsQqpZONikY+DlFkw2ph8j3+qDH4QnPQlOPXXe6mTtXGYRTU6bCxvELctaMEopXjs0wHve1sXt/7iOZz/uHvpCny1hkb91L+Wk++5iSTsizIdIIYBmlI6Rr5lIjyiZOT/ni14I994H3d3zWSVrZzA7ZRezRcEGccuyFtTjXiEMfEXx17t6+aobcsiKCfqyVbrcGgf1DRO7mn2aEd4SkCRJF58b0gA+UoNSc/o3nijBwAD89Kdw2mnzWifLmi92TNyyrAWlHPjk5zQH3OBRn3QpN1zqsYtSwrgfMpkNGVnWTasQYDoymIN7MYf3QX8Wsu723zyK4PRnwS9/OT+VsXaKrUvM7Jj4jtmWuGVZC27lMYo/H5PhnO8pvuy3KIQt6r7D/dle/CTBb0XEWtGd8XHGq2jXQTcjJHBguDbz0jNII8Kzn53uR37iifNVJWuOFlPClrmwQdyyrN3GOS8NOPXSZbzj13k2vr1NZ7ETLx9Rdj16sxXaStGnFdKTJ3QVGsAR+OMglNszv3GSwDOeDpf/Ak4+eb6qYz1ahjnvCb5Y2CBuWdZu5cnPE/b/epbf3biE5mM1rU7FskyWDZkWN/buw8DYOI/fcj9LVjXJOBoJXKhFsHoctlRmnrXeaKZj49deC49//LzWyXpkbMa22bNB3LKs3Yp2hQu/oHneB4vcFAv3LymSy7bpzdXpzjY5JJNlee8kiQgHlGqwJI888yC4cjVUmlDZTou81YJnPh1u+qtN0WrtFezENsuydjudyxTfeG3AsV8rMHpnnsmyT8s4JChGvBwt7TBZyDJ48HLayzsxoYs5cgnm5P0g523/zccn4dBD7aYpuzMDcTK3Y7GwQdyyrN3Sficqvnl2wFMuzlLdGFBtuLRiRWwU9+R6Gc3mKOczbD50BZXHr6L5hFWwsgNz4sp0K9PtqdfhFa+ws6d2UzZj2+zZ7nTLsnZby45SXHy+zwdO6+HGU0K2vHEzuVUtVgd9ODnDpJ+hp1LmsHZEdpkiOf0xhH9Zk774zxtgtD7zm48Ow7H7w/LD4AlPgrPOgmx2Xupl7YABEy+eQDwXNohblrVbKywVzr4E3vvWDKMfXsW6fZYy+vr7CA9sU9R1hoMCt3YvY9XwMEdtXseSx7bxsi44Cn6/DsYa079xbOCmNenx08vh3/8dLroIXvrS+ayeNQ3D4uoSnwsbxC3L2u0tO1g47xLh9S8TxtoON/x5BdcPdfPkgzazqjiJ0oaxfEiXqtNSmv1rTdSJ+8LyDrjsNtgyw37k2zIGXv5yOP54WL58PqplWXNmx8Qty9ojdHYqXnuMwquA11BEseL+cgd/nexnQ6vIuJshEWG8O8/mA5YQ9+Ux+3ZiXnkkZlUnaHnwmEmSwBtenQZ0a0HZMfHZsS1xy7L2GM/5uOLqJwmTPQ739TvU6pqufPpcS3sMZwr0mUnKHTnk8BXkV9Zwhsr4zQQzVEZuGwYM3Do884f84ffw1KPg6z+EFSvB3UFqV2vnM3bO4WzZIG5Z1h5DBD51rUd4ivDbkSx3PtdQyrUoBE2asea+oJtIhKrns0JpwkYLM1DEPP0Qgls2QLUN9QjuHU/3H5+OFrjub7D/Aen9xz4WfvYzWLFi/ipqIXNsTS+WvhTbnW5Z1h5FOfDRa1y+/7aAb/ylhyc+exXjv+ljtB6ynk42BF2sy/dw3aoDuPSYx/PHVQdQ7srD/j1wyv7ga9ivE6aLEa7AeCOd9LbVLbfA/vvD4OC81dGyZsu2xC3L2iMtO1IwDfjJZZrsj3u4aWORvz12jJOOWcOqXAkBIi2sWdFH3jQoTFTw6UC27oDW5cPfhtPWOUDBg2YM7WkyvrXbsGoVXHAB/OM/zmc1FycDeo5LzGboZ9nr2CBuWdYeq/dQcBpTS5Icw1gl5PK792Npd4WVHRVyfpuGcTghl2VkaRe960fwDu5H/DHwXdAa1pcefMP1kzN/WBzDO96R/nzDG8DbQWY461ETQNkx8Vmx3emWZe2x/AI85RQhP6YJqoLEKk0UohRj7ZBa4hHomJEwx4beLjbsN0BteRfJyk7M0gLs2wVL89u8od7+Bzab8La3QXc3/OpXu7Zyi5kRVDK3Yy5E5MUicpuIJCJy7HbOWyMifxORm0Xk+m0e7xKRX4vIPVM/O+dUoO2wQdyyrD3aSy8UDrpfWHmbgzehaTQ0rbbGmPQ/ckcSNnkdDBWKbOrsYHSgk/r+fbBvN+agnnR8fEUhbf51hjv+wCSBSgVOPz3do9zaJSSe2zFHtwIvBK6ZxbknG2OOMsZsG+zPBq4wxhwIXDF1f5ew3emWZe3RROCs7yo+8xyf7LBwwwtgNIgoZFokBtqJYr3upBA2SBC0MUQI/cYQigAGyfvpOPndoxAl/P/27jxKsrs67Pj3/t5aa++zakEbsjiyJCQhAgJHIPbVYGMMBDjGCbEdB2zHkTAcJ3bk2Dic2CRgjIE4CBtj7CNLEBCLwAEsE1YjJEC70DJbz3RPd1V17VXv5o9fjWjG3TMl9T59P+e801XVr7vue6Pqq992fxw6wZamx2SZ35u8XIabbrJ9yk8hqnongMjjbtG/HLhq8Ph64EvAtSuNaymWxI0xW96eS4S3fxF+++dD5r6T48404+EgIwp6BAWlFYUccGWyHGTiqOTzLORTngCUBDQJkXIK+QRmGlBtwQ/nYF/15Mm8WoWrr4b77oOzz16Hqz31iUKwNQq2KPB5EVHgz1T1A4PXd6rqQQBVPSgiO9YqAEvixphTwuT5cPHpwr79AZP7Eg4UM+7NHK1dNbIRJcz1CQNo50JG4ha5TpvZHWM4VfLOQT5B0ggKEfRKUE5gZxHuOwrzy9RfP0YV3vIWv57crIpVmNg2uXicGvjAoiSLiHwB2LXEz71DVT8x5HtcqaoHBkn6FhG5S1WH6YJfNZbEjTGnjJ95l/CtVwj1YkgrTTkqcCBQwkBAhCwRdoSODo6JXJ5ovI9oxkQUEhcS4nIOzhjzS8hrLfjHB2EiB199BBY6J26V/+M/QK/nZ7w//m5Yg2+Ju5XvYjZz3Dj1j1HV56z0DVT1wODrYRG5EbgCP44+LSK7B63w3cDhlb7XciyJG2NOGVPnC897uuOmOwKaZaWbi5iPUjIVGq2A/oTg8uBcxpG0RDCo7dmLIkajkFwaEdfbhK0u4gQu2wvtHpwzATd8H442obtME7FS9SVayyX4tV/3u6IFJ5ntbpa10opta01ECoBT1drg8fOA/zL49ieBNwLvHHwdtmX/mFkSN8acUn7uQ8KVtwe8899CP1DujTOqsRKGSj7pkQYZmkAcdiEPnSimGUZU04SRhQalRovC3AL5SgOJAt/6PnMMXnge3PoQ3D+3dIv82GvVGvzedfDwQ/C/P7x+F25WjYi8AngPMAV8WkRuU9Xni8ge4EOq+iJgJ3DjYPJbCPyVqn528CveCfyNiPwi8DDwqrWK1ZK4MeaUs/ci4fLTA/bdC61CnwdjmA8z0jglCXv0FTQFQkcziGlJgHNCPZ/DqbKrXGHn/hlyIrhDVWh1kcmCL9t6bgW+9AC0TrCOKVP48PWQpPC7vws7d67btZ8KRCFY+TKxx01VbwRuXOL1A8CLBo8fAC5e5udngavXMsZjLIkbY05JZ14MYx8Xdo3FtEvCoUA5EilhoIhAJBmFpEvHhWSJEJJR6HYotlu0w5C5yRGIAmTnCNEPjxD1M6TRhSeMwrPO9t3s3Qz+38PQW2aw/M/+DD76UbjjDl+21Qxp5QVbtgtL4saYU9LTf1341B87+jEEHSjPOg7Ohey7QAY93w5EmYwD+iJIAsWwTdcFuCwj7vWolnKUFlp0njCJhgGRE2SuiUzlYboOUQBJCL0l6q0fs7AAT34yPPSQX1NuTk7BbWBLfCuxim3GmFNSnId/cz2UDwnFeUeu5ijMhbjDCUdmU45UUw4uFHlgYZR72hM8KOMcCUscSUscLpW5b8cUMyMlpidHODI5yoOXncs/vfoZcNFeeN5PwO6if6NdxaV3RFusUoEPfnDNr9lsP9YSN8acss5/sfAH34G/+E3HFx+GVlFZGImYz/fZ1xca7ZCJcgstOEphl1zUJwM6aUgSdzlYGmdBYgqtFuc1juBU/X7jfYULdsK5kzCS+j3Kj9SXn7muCn/+IfiN37DlZ0MQNv/s9M3Ckrgx5pRWnBJ++XphJi+MPyiMXgJ3PxVmzoU5gSBQ4qjP0SglkIyGi2i6FnGQkSFkCGd3Z3y3pQidfEJcbyPhoCNzMgfnT/giMQ/OL5/I770Hpkpw42fgGc+wZH4iGzyxbSux7nRjzPYQQNR1jE9HXPQPOS79TJlgf8z0oZRHDhU5WClwqJVnvp8wrykdAjJxRPRBfrSCrD5eoJeP0YkCKqDFBPaU4ZxxOGNk+a71bgazdXjWVX4nNLOsY1uRruTYLqwlbozZFi76OTj8BQgHhdckE0bvzzGLMNt3zM6luGCM03bV2VOuc+7oHHviOqFm1OOYkU4TgGqpSNzL6JRyhCM54v401NrgBLp939W+v7p8i7yfwZ//L3jDG+CKK9bvBmwlq1OxbVuwlrgxZlt4/h8Jp5egMCdETVCBsC9E8xFuOgEgiTNanYhqN+Xu2gQddfQRalGOSpKiwHy5wHy5QC8KyHIJjOXhjFEoJfCEMXjihD9OK/v660tptuCGG9bt2s2py1rixphtIR2B/3iHcNNb4HOfc3RySqMkNAqOViGDHo/uQe6cEopSyVIi16fpIipJnr44egi5dodWGlMK66TlBT9xTRUersBEHro9P9ktDnzf8FLLyB/6IfT7Vpp1GbKNusRXwpK4MWbbEIE95wljfwXuuwFRC1xPaZSFVjWkHSqUoNN19COh2k9QhEyEfiQ0woRKkmdfaYzTqnOc0+rSHs2TiCAjOciAXOSPww3fdT67TDB/ewN8dgLe9ja49lqb6LaIr9hm92MYlsSNMdvK3qf64WsRIcgcOx+OSRqO/ecGHL64x9x8jHMZSdyj0olJ0x41SQkCqKIkrsPObpVmknBgcpSx6gKu2ydqdJBS6gvAOIFnPgG+/gg0u3Ck4d98cYs8y/z68euugzj2y8/Mo6zYy3BsTNwYs63svQLOu9Q/Djv+qxNH0nHk9iV06wGHpnPc/3CJfXNFDrfyNNS3d1SEhB6J9gk0oxtH1HMxnXKObiFBx/OoE7QY+270J0767vUzR2Ay75P78RoN+P3f993xBhhsRZrJio7twlrixphtRQRe/2nhB09S2keETqLolF+XLJngZmOyyTbdKORoLYcizDdjwrE+O8ImIHTF0Q1CtNuhVszjMvVryEspcTEmmq5Bo+NnqJ8+AvMtP4N9prF0UEeP+r3Io2hd74XZ+qwlbozZdoIIxluQNISJ/Y6RaaEw5yhUBpPM+j/60xgGijjHfC9HXSOaElEPE9pBQM8FNJKY6kiRubESzTTGZSAjedg7AmnoW+DjOT+DvRAvHZAqnH0W3HTTml/7ViH9lR3bhSVxY8y2tOscIT/vE3m+6th1f8DEoYB0AWQhoNvxXbLdvkMVuhpQy1JmtEBDYmaTIkdzBSr5HJ0goBOF9KKQLAoHQ98CYzkop7C3DPkIzp9cuksdYN9+eO1r4TOfWac7sImpEPRXdmwXlsSNMdvSyz4ESQNf4rMHglCoBex+MGZsfwRzEfNzEbVGRLvv6GT+z2UfR9XlOBKWWYhSWnHC7EiJHpA5R7vk14ZrKYViArnQT3aLnG+Vnznil54tpdmEt799fW7AJiaDXcxWcmwXlsSNMdvS+Lnw7GuEqA2Fo0JwrJIbQtoKcO2AzmxMpRIxW8lxuJZnvhPRHkxy6+PouoB6kqBAI5fSSiJwAY3JIlqI0cihU4PdzpLQT3YrxrCntHxg99yz1pduTiGWxI0x29azr4O3fhxGDwnlI0JhTkhrDlHfGhQHmTp6fUelkfBgZYRKN2Iuy9FX6EhAXwIqaY56mpA5x+xYiV4a09g1Sv/0cWSi4GurlxMInf/qWL5bvdGA858I3/nOut6LzcZqpw/HkrgxZltLC8J4Fwrzjl33OiYfCchV/Tg5TUe77f9MOlFEhEY/Ybab41BWoieO2ShPNwjpBQFHiwV6YcDsxAjg16JTzvniL6eP+rHx3SXfzT6WLr9Zyj33wlVX+Vnr25GC9GVFx3ZhSdwYs61NnA/a84+zCNKmMDITsOvBkFw1RPtCq+1odwMUyBR8p7swT45uEPpJbkmebhRSKeTIRFARerkYFQZd6QJhAJMF2F2ECyZhIrd8i7zbhb/4i/W5CZuM4Jf8reTYLiyJG2O2tbgAV/1nIW1D2BK/PEmgHwqjsyFuNqAyH1FvBLQ7AQudiGbfT0zLXMBRV6DpQppRQicIaSYJM6UCjTSmm4/JogAdy/vtTEuDIjBxCPnYz1wfS5cOrNmEO+9ct/uwqdjEtqFZEjfGbHtXXgOv+wMYafkEELYgGiwxC1ohNAPmKwmHjyYcms+x0I5oZ45GFpLhUByIUElzgM/TtWKOThzRHC/Sniiiu0aQXWXfKh8ftMDz0Y8mvC3lgx+El78cqtX1uRFmy7EkbowxwKVvEN52gzD5gN+uNFcFFNK6QxqBbx06oZ8FtHoh1W5CTVN6CE0iqkECIjSjmG4QgHPMj5Vo5nxrnDjwLe89ZT82vqPoZ6kXIl8UZqlEnmXw2c/Ca16z3rdjQwl+P/GVHNvFhpRdFZF3AS8FOsD9wC+o6vxGxGKMMcfcco0gIsQtR9xUJh8BCGgW+vTU726WJkI3c/TV0eyHzFCgJ0Liej6Rx4rLMuYLeUYX6mTOkR3bbjRc9LUQ+fFx8K3x/VVfnvX4EuqdDvz938OBA7BnzzrdiQ2mthXpsDaqJX4LcKGqXgTcA/zWBsVhjDGPmrsfRIWkDmPTjuK8Y9eDET/xjYTi/Sn1WkivL4DQ6gnVbgwImQRUJMd0PMKhpMx8Ls9MsciRkRLVgt/4pFNM0Sjw4+G5Qfsp8t3w5CJf3S1c5k9yHMOhQ+t1GzacTWwb3oYkcVX9vOqx+aB8DThtI+IwxpjFzngGRC2/blwyIVdzoNDNC+XZCDkQ0WgEVBsR1VaK4ugrtDRERfze487h0zz0g4B2ErFQSOkWU3pxiE6VoJj63c1yoR8bP7brWW6ZDVCqVXjd6+D667fHbmdq3enD2gxj4m8CrFiwMWbDPfddUJ73rfGgB+p8bu2koE7JEqHRCGm0Iir1iForZLado5UFdDSgi0/gXfGbowD0naNeyDE7XqY9MtiOdLIIp43C3lE4Y1CGdTznZ6ovt+TsrrvgV37F9h03P2bNkriIfEFEvrfE8fJF57wD6AEfPcHvebOIfEtEvnXkyJG1CtcYY8hPwFvuEHZEkJv3rXIA14MMhY7QzxzVWkCjHXOomqerAYfbRe5rjTKrBdoE1KLU73ImjkYSo0A/DMjigF4u+tEGKXEApcQn8PE8nD0O50/4lvlSf50bDXjPe+D229frlmwMW2I2tDVL4qr6HFW9cInjEwAi8kbgJcDrVJfvH1LVD6jq5ap6+dTU1FqFa4wxAEycB//5buHciuB6QvGIUKg6AnUIAg2h2fRj2k6E2XqKDh63NGKaMhWXYzYpMp/L0Q8C5osFMhG6YeBb4/CjrvM4gNHUT3SLA1/NbUfBj50vpd+HZz7zlB4j97PTNy6Ji8irROT7IpKJyOXLnHO+iNy26KiKyK8Nvvc7IrJ/0fdetLKIlrch3eki8gLgWuBlqtrYiBiMMWY57Ro0poWJh/y4eNQUwjYU5wKkGgG+ilun68gQMhUC8W2R0GXUg5SGi8lcQCuMaMYR06Nl9u2YQEXolHNoHP4oaYv45WcCJIH/Gp3gz3OjAe9613rcio2x8WPi3wNeCXxl2RBV71bVS1T1EuAyoAHcuOiUPz72fVW9eaUBLWejxsTfC5SAWwb/l/L+DYrDGGP+mSCGsANhX4hbkKvD6HRAsRIwciSEiqNajWi2A7pdx0InpNnzY+A9DVCFAKURxNSSlE7gJ7610oSjYyU6hZheMpjkFjq/dnw09Ym8EPmtS0dOUFu914OPfAT27Vu/m7KNqOqdqnr3Y/iRq4H7VfWhtYppORuyTlxVz92I9zXGmGGECYyMC3OZkixAfs7RKmTseCRgdjckjZTpC1vMaEKtFrJrSijt6tLqOwIi0qBLD0c1TMn3OzTjhLjZA5RuFNJKU2QM+rmYJFNkru4TdyCQD6GvMNOATh8O15cOcmYGzjoLPv5xeOUr1/X+rLVj3elbyM8DHzvutV8VkTcA3wL+g6rOrcUbb4bZ6cYYs+m84iOQPwr5quAyoTQz6KpVyELBVSOohbTbIZ1+xHwrYb6bY76XMtvPcZScL8Ua5eg4RybOV3ITaOR9F3o/F/ux78nBnuPO+cIvoYOdRXjybijFywfZ68GrXgX796/PTVkvqzOxbfLYpOjB8ebFbzHM5OthiEgMvAz420Uv/ylwDnAJcBD47yu4Gye0IS1xY4zZ7M55Hlz6PLj1m0JpWtGdgqgSN4W2L5GOCKgoWSZ0+wGVVgwpNPoR3SQklS44JQhTgpwy0mown88zVm9QKeUZqTXoJyFBM0Oc+JnqtbbvSp8fTI3fU4KHKtDqLR1olsF118H7T6FRSV2VlviMqi45KQ385OsVv4P3QuCfVHV60e9+9LGIfBD41Cq91z9jSdwYY5bx6r8R7isr032HuIxu6uiFGfVRh+tDv+WgMKjg1nFASEk7RINJbh0XM6cB7ShiIUyoRCmnMUeu3YEc5JttpJSSb3fR0TyS1f3EttEUKoMSrGnoE/mDc7BcKdKvfnWd7sj6ELZUwZbXcFxXuojsVtWDg6evwE+UWxPWnW6MMcsQB8/4TaHQhvy8I24JYcdRmIOJgyHSFmg5Gg3H/IJfD97tO/oqvmIbjp4E1FwKIsSa0QsC5koFGnHE3GiRThrTHCv4WepjOdhdhjTy3ekAozk/SFxKThCowK23+la5WTEReYWI7AOeBnxaRD43eH2PiNy86Lw88Fzg7477Ff9NRO4QkduBZwG/vlaxWkvcGGNO4JnvgAN3wFf/CZI6FI9CFga087Bjf8y0gxopTLWYj2ICMqaKLRq9kAoJE0Hz0UnmGdCMYvLdDtVigb5ziMJUr08vjQhRpNr2s9UnC34J2v6qXzeOQq0D2RJlNW6/HZ5zNUxO+V3PLrxwHe/QGlid7vTH//aqN/Ljy8WOvX4AeNGi5w1gYonzXr+mAS5iLXFjjDmBIILX3iDsbkH5sJCrB7i+UJ4NcJkQdQVpO2oLEe1uSDdzzNYTKt2YSj+llkVkCArUwpTMORbiBAV6QUAWONpxSHs079ePTxV9y/tYNbczRuHi3fDkPfC00/0StKW0O36C25VX+nXkW5hYxbahWRI3xpgh7DlXSBaE0mEYf0RI6kJpVijPhtB0oEK/D/O1mJ4GNHsR1W5CfbDneIWUvgtYCGKaUUwnCGnFPiFXykUyJ3TziZ+tvmvEv2m6qLO03fflWHeX4MyR5ZN5tQpXXOEru21hlsSHY0ncGGOG8Lw/gtIsFCqOfMUx+YijNBdQnPNbljIXMjMb0e2HLDQDGh2fgHsqVPsJXRdRkZRalGMmLlJJc/ScY7ZYQJ1QLRVAhG4h8VuWJuGPj4Mf26ZUgMD9833HF/v+9+HNbz7BCZubbHzFti3Dkrgxxgxh71Pgeb8jxA0ozwiivppbvuYo1EJcK4D5mOnDMXPVhGozpt4OmGundIhoakBHIppEdIMQRFhIcnTiiMOjZdppTCcM6JRSn8gni5AsmuB2rILbaOqfL9cSP+b6630hGHNKs4ltxhgzpJ96O8w9BLd8TijOKr3Y0SwqQU8YORJQ2QFZvk+rHVJvZSykMSpCH2hGwljYRiQj0R7VIKVMi45zxEAjjtGxEqVaA0SQfka42yGZQhjAbB26fb+feK3tu9bnWtBbZkZ6vw/XXAMvfSnk8+t5m1bFduoSXwlriRtjzGPwsvcLl58L5WlH+bAwMu2Y3OfItQKCLkgtotFwZJnfIKXeielljoVeSktj+hIw5wrUooS6i6ikeXpAtZCjG4UsFH0lmV5x0JVeiP0kt4k8lBM4bcRPdttVgr2l5eurAzz8MOzYAV/+8lrfltVlE9uGZi1xY4x5DETgxe8R7n2y0kuFVtnRS0ADIctC5nb1aIcR83EGpOwca1JrJ4S5NgpUsoQx16IuKS5SHAqFErluBxWh2GiRK3YoLzRpl3MkWQYLbSQOfX11HQRRTmDa+UIw+2vLB1yvw7OeBV//OjzlKet0l1ZGNniJ2VZiLXFjjHmMpi6AC57lZ6iPPwzFOUe6IIzPhIzNRAT1kPZCRKUScGQ+pdkNqLVDZlopbY1oq6NFSE8c81GergQ0I18sppnENPMpR0dLtIspjcky7BnxLfJdg5b3+KDu63jOT4CbzJ04YFV4+tPh3nvX+taYdWYtcWOMeRxe92k4ch7c13NEzYzSjNBNhKArjB4JOSpKK1DmxKEqBONKHGbMtBVNlLK0ECkwRp35OM9UZ4GFOKHUadMNHMQh9UJKWRUNA5go+vrq/cy3xnMhhOJb5YIfNz9cX7oYDPjNUi67DO6/H6am1vNWPS7WEh+OtcSNMeZxEAdvvVsYnYGx/RC1HcmCMHZQSFqOtCFQCWm1AhSh3oyYbyY0ehGHW3lqmqNByFHydFzAbJSnGSe0g5CZUpG+CM2cHxdvjvsZ6loarCM/fcR/3TviW+Nnj0M+gqmTTGBrNuF971vrW7NitsRseJbEjTHmcXIh/OubhLGDjqkHYOSwo1ALKB0RRo5GBK0AesLcXEitGaEqVFoxiqPRD6lmOTIXMC8F2kHE0ahANc1RT1MOj5bpBgEzo0U0CmhOltAoRPORb3WnITjxyduJb5nnIz8Jbjm9Hvze78HrX++LwmxiNrFtOJbEjTFmBc56NlxzpzCWd4w/IowedBQqAaXDwsSBEI5EdBohC/WQmUpCpgH1TkC1m9BV4Ug/T18cc6R0gpBakNCIEnpBwMxIiXohx/TUKO00pDlVhsmSHx/fWYRcBFMFv/PZnhIEAnuKvpt9Ob0e/OVfwu7dmzeR2+z0oVkSN8aYFRo9E/7rbXDZMxylGWHigFCqhORqjpHZEJmJqS9EVKoh03MJC52EWjtktp0jw1HLYvouog8sRCl9ccznCqgIlUIeRKiOlNDA+frqE0WYKvmJbhN5v2FKLoKzxn0L/bQRn9BPpNGAN71pPW6PWUM2sc0YY1aBc8IvfxDe3HVM7xO+cZvy1fvgmzXh+6lSn4moC8QxzFYVyhAFGYdbeUihnwmQMSp1jiQldrSrzOXyjDcbzOdzjDaazJcLjAJoRgzIXAPKqS/+srMIB2s+ge+r+nHzI3Wod5cP+oYb4KKL4Gtf21QFYWyJ2fAsiRtjzCoKImHPWcJPnwU/zbHx6Rw/rPV5//0tvtuJmAta1BcAVVy+xVwnQWJouh5QZIw6h5MSO9s15lO/fEwFxoBmuwuqRAttmCwih2u+Jf7IPOwtwyMVOGMEjjb9WPl8E462lg/4jjvg0kvhrrvW9sY8RpbEh2NJ3Bhj1sFZpYA/vKQAFB59ra0Z3203+CF17u8d5XA4C64O5BmXBtNxkZ0sMJcoY0A1y/yab1WYKpM/UoXJAjJTh91lOFCFyTxM1/2s9aNNXxTmREkc4O67/dKzc85Zy1swNGuJD8+SuDHGbJBEHFekRa6gCOx89PU6bQ7oPDWZZzY+RFcO0uhPQw6CTFER3CCRF7SK7gmRA5VBV3oFxjOfwHcWYXoBdhZ8Yj+R73530yRxMzxL4sYYs8kUSDhPdoLsBHc+REAeuv0WR0o/QJtH6OQPIjMPEHQz0rkFdCwPcw3k2K5nucGf90DgQA2eEMJDlaW3MHUOzj57vS7v5BRcb6OD2BosiRtjzBYRBSl78pdCHpgAzgTVPvTayOG74NB9cNfXIL4X9h32M9ipwWllP9ltxzIt8ic9CS65ZH0v5iS2U8GWlbAkbowxW5hIAFEe9l7qj8t+zn+juQB33Ar3/gD+/mbgAThQgSCEQxU4toPp858PH/vYRoW/JBsTH54lcWOMORXlinDFC/zxut/wr1Xm4bOfhFoVLr4cfvISSNONjHJZlsSHY0ncGGO2i5FRePUbNjoKs4osiRtjjNlUrDt9eJbEjTHGbC6WxIdmSdwYY8ymY0l8OJbEjTHGbCrWnT4828XMGGOM2aKsJW6MMWZzsYptQ7OWuDHGmE1F8N3pKzlW9P4i7xKRu0TkdhG5UURGlznvBSJyt4jcJyJvW/T6uIjcIiL3Dr6OrSyi5VkSN8YYs7noxiZx4BbgQlW9CLgH+K3jTxCRAPgT4IXAk4DXiMiTBt9+G/BFVT0P+OLg+ZqwJG6MMcYsoqqfV9VjHfpfA05b4rQrgPtU9QFV7QB/Dbx88L2XA9cPHl8P/PRaxWpj4sYYYzadTTQ7/U3Ax5d4fS/wyKLn+4CnDh7vVNWDAKp6UER2rFVwWyqJf/vb354RkYc2Oo7HaBKY2eggVsiuYeNt9fjBrmEzWO34z1zF3/Wog3z7c7+DTK7w16Qi8q1Fzz+gqh849kREvgDsWuLn3qGqnxic8w6gB3x0ifOW2mZtqY1e19SWSuKqOrXRMTxWIvItVb18o+NYCbuGjbfV4we7hs1gq8Svqi9Yh/d4zom+LyJvBF4CXK2qSyXnfcDpi56fBhwYPJ4Wkd2DVvhu4PBqxLwUGxM3xhhjFhGRFwDXAi9T1cYyp30TOE9EzhKRGPh54JOD730SeOPg8RuBT6xVrJbEjTHGmB/3XqAE3CIit4nI+wFEZI+I3AwwmPj2q8DngDuBv1HV7w9+/p3Ac0XkXuC5g+drYkt1p29RHzj5KZueXcPG2+rxg13DZrDV418XqnruMq8fAF606PnNwM1LnDcLXL1mAS4iS3f1G2OMMWazs+50Y4wxZouyJL6KRCQQke+IyKeW+f5Vg/GV74vIl9c7vpM5WfyDc54iIn0R+dn1jG1YJ7oGEXndoIzi7SLyVRG5eCNiPJmTXIOIyP8clHm8XUQu3YgYlyMiqYh8Q0S+O/jv/HeXOGdERP7PonN+YSNiXcow8Q/O27Sf5WGvYXDupv48m5OzMfHV9Vb8BIfy8d8Y1N59H/ACVX14LRf/r8Cy8cOjZQb/ED+RY7M60TX8EPiXqjonIi/Ejw8+dYnzNtqJruGFwHmD46nAn7K5rqENPFtVF0QkAm4Vkc+o6tcWnfPvgB+o6ktFZAq4W0Q+Oqh6tdFOGv8W+CwP82+wVT7P5iSsJb5KROQ04MXAh5Y55bXA36nqwwCqumbrBh+PIeIH+PfADazhmseVONk1qOpXVXVu8HS5Uoobaoh/h5cDH1Hva8DoYB3qpjCIa2HwNBocx0+8UaAkIgIUgaP4ghobbsj4N/VnechrgE3+eTbDsSS+et4NXANky3z/icCYiHxJRL4tIm9Yt8iG825OEL+I7AVeAbx/HWN6rN7Nif8NFvtF4DNrGs3j825OfA1LlXrcu8YxPSaD4YDb8MnhFlX9+nGnvBe4AF8Y4w7grao6zL/Zuhgi/s3+WT7pNWyRz7MZgiXxVSAiLwEOq+q3T3BaCFyGb2U9H/htEXniesR3MkPG/27gWlXdPBWNFxnyGo6d+yx8Er92zQN7DIa8hk1R6vFEVLWvqpfgezquEJELjzvl+cBtwB7gEuC9IrLkEM5GGCL+TftZPmaIa3g3m/jzbIZnY+Kr40rgZSLyIiAFyiLyl6r6rxadsw+YUdU6UBeRrwAX47e522jDxH858Ne+B5RJ4EUi0lPVm9Y92qUNcw2IyEX4ruoXDtZybibD/ne0XKnHTUVV50XkS8ALgO8t+tYvAO8clLK8T0R+CPwE8I31j3J5J4h/M3+Wf8wJrmGzf57NsFTVjlU8gKuATy3x+gX4fWVDII//QF240fEOG/9x53wY+NmNjvVx/BucAdwHPH2jY1zBNbwYPwwgwL8AvrHRsR4X3xQwOnicA/4BeMlx5/wp8DuDxzuB/cDkRsf+GOLf1J/lYa7huPM39efZjhMf1hJfQyLySwCq+n5VvVNEPgvcjh/v/JCqfu+Ev2CDLY5/o2N5vI67hv8ETADvG7RAeroFNoM47hpuxleMug9o4Fu1m8lu4PrBzGeHL0X5qeOu4TrgwyJyB/5/Rq5V1c2yM9hJ498Cn+Vh/g3MKcIqthljjDFblE1sM8YYY7YoS+LGGGPMFmVJ3BhjjNmiLIkbY4wxW5QlcWOMMWaLsiRuzCoQkYWTn7Xkz9042A3rPhGpDB7fJiJPX+0YjTGnHltiZswqEJEFVS2u4OevAn5TVV+yakEZY0551hI3ZhWJ9y4R+Z6I3CEirx687kTkfYP9nT8lIjcvt4eziEyJyA0i8s3BceX6XoUxZquwim3GrK5X4jf1uBhfk/qbg9raVwJPAH4S2IHfL/zPl/kd/wP4Y1W9VUTOwO/3fMHahm2M2YosiRuzup4BfEz97lDTIvJl4CmD1/9W/Zabh0Tk/57gdzwHeNKgNCz4jVBKqlpby8CNMVuPJXFjVtdSW4We6PWlOOBpqtpchXiMMacwGxM3ZnV9BXi1iAQiMgX8FH6LzVuBnxmMje/E71K2nM8Dv3rsiYhcsnbhGmO2MmuJG7O6bgSeBnwXUOAaVT0kIjcAV+O3rbwH+DpQWeZ3vAX4ExG5Hf8Z/QrwS2sduDFm67ElZsasExEpquqCiEzgW+dXquqhjY7LGLN1WUvcmPXzKREZBWLgOkvgxpiVspa4McYYs0XZxDZjjDFmi7IkbowxxmxRlsSNMcaYLcqSuDHGGLNFWRI3xhhjtihL4sYYY8wW9f8BqQn3dp6S4iEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_master_ZAMS(iso, plot=False, overwrite=False):\n",
+    "    output_name = 'models/master_ZAMS.csv'\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            master_ZAMS = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "        master_ZAMS = pd.DataFrame()\n",
+    "        for idx, MH in enumerate(iso.MH.unique()[:]):\n",
+    "            \n",
+    "            ZAMS_temp = get_ZAMS(MH,iso)\n",
+    "            if idx==0:\n",
+    "                master_ZAMS=ZAMS_temp\n",
+    "            else: \n",
+    "                master_ZAMS=pd.concat([master_ZAMS, ZAMS_temp])\n",
+    "            print(MH, len(ZAMS_temp), len(master_ZAMS))\n",
+    "\n",
+    "        master_ZAMS.to_csv(output_name, index=False)\n",
+    "    if plot:\n",
+    "        fig, ax = plt.subplots(figsize =(8,6))\n",
+    "        p0 = ax.scatter(master_ZAMS.logTe, master_ZAMS.logL, c=master_ZAMS.MH, cmap='rainbow', label='ZAMSs')\n",
+    "        plt.colorbar(p0, label='[M/H]')\n",
+    "        ax.invert_xaxis()\n",
+    "        ax.legend()\n",
+    "        ax.set(xlabel='logTe',ylabel='logL')\n",
+    "    return master_ZAMS\n",
+    "\n",
+    "master_ZAMS = get_master_ZAMS(iso, plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c837264f-f04d-49f6-a29d-631901aa3360",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# master_isochrone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6df47088-c586-4d83-a7a0-e52714d1e42e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available metallicities, [M/H] : \n",
+      "         [-2.  -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.  -0.9 -0.8 -0.7\n",
+      " -0.6 -0.5 -0.4 -0.3 -0.2 -0.1  0.   0.1  0.2]\n",
+      "Available log(age/yr) : \n",
+      "         [ 6.    6.25  6.5   6.75  7.    7.25  7.5   7.75  8.    8.25  8.5   8.75\n",
+      "  9.    9.25  9.5   9.75 10.  ]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iso_file_name = DIR_MODELS+'isochrone_GaiaEDR3_logAge6-10_MH-2-0.2.dat'\n",
+    "iso = read_parsec_isochrone(iso_file_name, plot=False)\n",
+    "print('Available metallicities, [M/H] : \\n        ',iso.MH.unique())\n",
+    "print('Available log(age/yr) : \\n        ', iso.logAge.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "45bdf061-776d-4431-b14a-166513422730",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Zini', 'MH', 'logAge', 'Mini', 'int_IMF', 'Mass', 'logL', 'logTe',\n",
+      "       'logg', 'label', 'McoreTP', 'C_O', 'period0', 'period1', 'period2',\n",
+      "       'period3', 'period4', 'pmode', 'Mloss', 'tau1m', 'X', 'Y', 'Xc', 'Xn',\n",
+      "       'Xo', 'Cexcess', 'Z', 'mbolmag', 'Gmag', 'G_BPmag', 'G_RPmag', 'logR'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "iso_7 = iso[(iso.logAge==7.0) & (iso.MH==0.0) & (iso.label<3.5)]\n",
+    "iso_8 = iso[(iso.logAge==8.0) & (iso.MH==0.0) & (iso.label<3.5)]\n",
+    "iso_9 = iso[(iso.logAge==9.0) & (iso.MH==0.0) & (iso.label<3.5)]\n",
+    "iso_10 = iso[(iso.logAge==10.0) & (iso.MH==0.0) & (iso.label<3.5)]\n",
+    "\n",
+    "master_iso=pd.concat([iso_7,iso_8,iso_9,iso_10])\n",
+    "print(master_iso.columns)\n",
+    "master_iso[['MH', 'logAge', 'Mini', 'Mass', 'logL', 'logTe',\n",
+    "       'logg', 'label', 'mbolmag', 'Gmag', 'G_BPmag', 'G_RPmag', 'logR']].to_csv('models/master_isochrone.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "989f6e46-2774-45a0-babe-90223f22c7ce",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Bergeron WD models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b3716612-bc5c-4858-a9b8-2545bd527e13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_Bergeron_WD_cooling_curve(WD_mass, spectral_type='DA'):\n",
+    "    if spectral_type == 'DA':\n",
+    "        WD_file_name = DIR_models + 'WD_cooling_curves/Table_Mass_'+WD_mass\n",
+    "    if spectral_type == 'DB':\n",
+    "        WD_file_name = DIR_models + 'WD_cooling_curves/Table_Mass_'+WD_mass + '.DB'\n",
+    "\n",
+    "    df = pd.read_csv(WD_file_name, engine='python',\n",
+    "                     delimiter='\\s+', skiprows=1, header=None)\n",
+    "    df.columns = ['Teff', 'logg', 'Mbol', 'BC', 'U', 'B', 'V', 'R', 'I', 'J', 'H', 'Ks', 'Y', 'J', 'H', 'K', 'W1', 'W2', 'W3', 'W4', 'S3.6',\n",
+    "                  'S4.5', 'S5.8', 'S8.0', 'u', 'g', 'r', 'i', 'z', 'g', 'r', 'i', 'z', 'y', 'G2', 'G2_BP', 'G2_RP', 'G3', 'G3_BP', 'G3_RP', 'FUV', 'NUV', 'Age']\n",
+    "    df['logL'] = ((4.8-df['Mbol'])/2.5)\n",
+    "    df['mass'] = float(WD_mass)\n",
+    "    df['spectral_type'] = spectral_type\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "DIR_models= 'models_individual/'\n",
+    "\n",
+    "WD_Bergeron_model = read_Bergeron_WD_cooling_curve('0.5', spectral_type='DA')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "3b22bb24-5b0a-477c-b6c1-25ab89db197b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading models/master_Bergeron_WD.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_Bergeron_model(overwrite=False, plot=False):\n",
+    "    output_name = 'models/master_Bergeron_WD.csv'\n",
+    "    # If the model_params file exists, it will be read\n",
+    "    if os.path.isfile(output_name): \n",
+    "        if not overwrite:\n",
+    "            print('Reading %s'%output_name)\n",
+    "            master_Bergeron_model = pd.read_csv(output_name)\n",
+    "        if overwrite: \n",
+    "            print('WARNING: '+output_name+' file will be overwritten.')\n",
+    "\n",
+    "    if (overwrite) or (not os.path.isfile(output_name)):\n",
+    "\n",
+    "        WD_mass_list = (np.round(np.arange(0.2, 1.3, 0.1),1)).astype(str)\n",
+    "\n",
+    "        master_Bergeron_model = pd.DataFrame()\n",
+    "        for WD_mass in WD_mass_list:\n",
+    "            for spectral_type in ['DA','DB']:\n",
+    "                reprint([WD_mass, spectral_type])\n",
+    "                _model = read_Bergeron_WD_cooling_curve(WD_mass, spectral_type)\n",
+    "                master_Bergeron_model = pd.concat([master_Bergeron_model,_model])\n",
+    "        master_Bergeron_model.reset_index(drop=True, inplace=True)\n",
+    "        \n",
+    "        print('Saving %s'%output_name)\n",
+    "        master_Bergeron_model.to_csv(output_name, index=False)\n",
+    "\n",
+    "    if plot:\n",
+    "        _model = master_Bergeron_model.copy()\n",
+    "        _model['logAge'] = np.round(np.log10(np.select([(_model['Age']<1e5)], [1e5], _model['Age'])))\n",
+    "        _model['logT'] = np.log10(_model['Teff'])\n",
+    "        DA = _model[_model.spectral_type=='DA']\n",
+    "        DB = _model[_model.spectral_type=='DB']\n",
+    "        \n",
+    "        fig, ax = plt.subplots(figsize =(12,6), ncols=2, sharex=True, sharey=True)\n",
+    "        sns.scatterplot(x='logT', y='logL', hue='logAge', size='mass',\n",
+    "                    sizes=(10, 200), alpha=.5, palette=\"Spectral_r\",\n",
+    "                    data=DA, ax=ax[0])\n",
+    "        sns.scatterplot(x='logT', y='logL', hue='logAge', size='mass',\n",
+    "                    sizes=(10, 200), alpha=.5, palette=\"Spectral_r\",\n",
+    "                    data=DB, ax=ax[1])\n",
+    "\n",
+    "        ax[0].invert_xaxis()\n",
+    "        ax[0].set_title('DA')\n",
+    "        ax[1].set_title('DB')\n",
+    "    return master_Bergeron_model\n",
+    "            \n",
+    "master_Bergeron_model = get_Bergeron_model(overwrite=False, plot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/models/master_Bergeron_WD.csv b/models/master_Bergeron_WD.csv
new file mode 100644
index 0000000..7678dd9
--- /dev/null
+++ b/models/master_Bergeron_WD.csv
@@ -0,0 +1,1363 @@
+Teff,logg,Mbol,BC,U,B,V,R,I,J,H,Ks,Y,J,H,K,W1,W2,W3,W4,S3.6,S4.5,S5.8,S8.0,u,g,r,i,z,g,r,i,z,y,G2,G2_BP,G2_RP,G3,G3_BP,G3_RP,FUV,NUV,Age,logL,mass,spectral_type
+1500.0,7.158,19.149,1.009,23.068,20.66,18.139,17.863,19.87,20.125,20.048,22.246,20.089,20.052,19.879,22.217,16.935,16.221,15.34,14.386,16.594,16.208,16.289,15.937,24.093,19.438,17.724,19.998,19.722,19.051,17.8,20.067,19.782,19.607,18.524,18.506,18.681,18.561,18.51,18.744,58.769,34.052,14180000000.0,-5.7396,0.2,DA
+1750.0,7.157,18.478,0.79,22.301,20.108,17.687,17.235,18.501,18.897,19.026,20.683,18.907,18.819,18.882,20.648,16.342,15.701,14.93,14.109,16.045,15.696,15.714,15.416,23.296,18.942,17.145,18.724,18.552,18.576,17.203,18.774,18.561,18.569,17.881,17.986,17.718,17.911,17.99,17.768,56.737,32.976,11660000000.0,-5.4712000000000005,0.2,DA
+2000.0,7.157,17.896,0.561,21.672,19.646,17.335,16.741,17.408,17.859,18.137,19.358,17.91,17.781,18.016,19.317,15.824,15.257,14.549,13.842,15.567,15.257,15.228,14.955,22.641,18.542,16.723,17.701,17.596,18.194,16.758,17.736,17.575,17.703,17.342,17.597,16.943,17.366,17.601,16.98,54.881,31.946,9847000000.0,-5.2384,0.2,DA
+2250.0,7.156,17.383,0.332,21.133,19.249,17.051,16.345,16.553,16.964,17.322,18.161,17.06,16.892,17.223,18.118,15.334,14.839,14.185,13.573,15.117,14.843,14.777,14.526,22.073,18.209,16.409,16.904,16.831,17.878,16.424,16.926,16.794,16.985,16.884,17.295,16.317,16.903,17.299,16.345,53.188,31.066,8347000000.0,-5.033199999999999,0.2,DA
+2500.0,7.155,16.923,0.134,20.614,18.872,16.79,16.0,15.849,16.277,16.716,17.325,16.361,16.211,16.63,17.283,14.972,14.531,13.915,13.365,14.785,14.538,14.451,14.217,21.528,17.888,16.15,16.254,16.188,17.575,16.15,16.267,16.142,16.368,16.47,17.028,15.782,16.485,17.032,15.802,51.696,30.252,6953000000.0,-4.849199999999999,0.2,DA
+2750.0,7.154,16.507,-0.057,20.147,18.537,16.564,15.719,15.291,15.619,16.094,16.537,15.697,15.56,16.021,16.498,14.602,14.22,13.642,13.146,14.444,14.228,14.128,13.912,21.041,17.605,15.942,15.76,15.652,17.309,15.934,15.763,15.609,15.812,16.119,16.802,15.346,16.132,16.807,15.36,50.197,29.467,5510000000.0,-4.6828,0.2,DA
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diff --git a/models/master_ZAMS.csv b/models/master_ZAMS.csv
new file mode 100644
index 0000000..a1195d7
--- /dev/null
+++ b/models/master_ZAMS.csv
@@ -0,0 +1,2683 @@
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diff --git a/models/master_isochrone.csv b/models/master_isochrone.csv
new file mode 100644
index 0000000..d26a8ba
--- /dev/null
+++ b/models/master_isochrone.csv
@@ -0,0 +1,783 @@
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diff --git a/models/readme_models.txt b/models/readme_models.txt
new file mode 100644
index 0000000..1b07536
--- /dev/null
+++ b/models/readme_models.txt
@@ -0,0 +1,96 @@
+master_Bergeron_WD.csv:
+	WD cooling curves (version from August 18th, 2020):
+	http://www.astro.umontreal.ca/~bergeron/CoolingModels
+  	43  0.2 Mo  Pure-hydrogen grid                          Wed Jan 13 09:14:54 2021
+	61  1.3 Mo  Pure-hydrogen grid                          Wed Jan 13 09:14:54 2021
+	Tables are provided with bolometric and absolute magnitudes on various photometric systems for an extensive grid of pure-hydrogen and pure-helium white dwarf model atmospheres. These include, in this particular order, the Johnson-Kron-Cousins UBVRI, Two Micron All Sky Survey (2MASS) JHKs, Mauna Kea Observatory (MKO) YJHK, Wide-field Infrared Survey Explorer (WISE) W1 to W4, Spitzer Space Telescope Infrared Array Camera (IRAC) 3.6, 4.5, 5.8, and 8.0 microns, Sloan Digital Sky Survey (SDSS) ugriz, Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) grizy, Gaia G, GBP, and GRP, and Galaxy Evolution Explorer (Galex) FUV and NUV colors. These calculations can easily be extended to any given photometric system upon request.
+	Stellar masses and cooling ages are also obtained for each model from detailed evolutionary cooling sequences appropriate for these stars. We use our latest generation of evolutionary sequences published in Bédard et al. (2020, ApJ, 901, 93) and provided below. We rely on stellar models with thick (qH?MH/M*=10–4) and thin (qH=10–10) hydrogen layers for the pure-hydrogen and pure-helium model atmospheres, respectively.
+	Details of the color calculations are given in Holberg & Bergeron (2006, AJ, 132, 1221, "Calibration of Synthetic Photometry Using DA White Dwarfs"), which represent an extension of the earlier work by Bergeron, Wesemael, & Beauchamp (1995, PASP, 107, 1047, "Photometric Calibration of Hydrogen- and Helium-Rich White Dwarf Models"). For each set of filters, the files Table_DA and Table_DB correspond to pure-hydrogen and pure-helium model atmospheres, respectively. The DA grid covers a range from Teff = 2500 K to 150,000 K and from log g = 7.0 to 9.0, while the DB grid covers a range from Teff = 3250 K to 150,000 K and from log g = 7.0 to 9.0. At low temperatures (Teff < 5000 K and 8000 K for the DA and DB grids, respectively), we use the models of Blouin et al. (2018, ApJ, 863, 184), which include non-ideal effects. At high temperatures (Teff > 30,000 K and 40,000 K for the DA and DB grids, respectively), we rely on the models of Bédard et al. (2020, ApJ, 901, 93), which include non-LTE effects. At intermediate temperatures, we use the DA models of Tremblay, Bergeron, & Gianninas (2011, ApJ, 730, 128) and the DB models of Bergeron et al. (2011, ApJ, 737, 28). The DA grid incorporates the Lyman alpha profile calculations of Kowalski & Saumon (2006, ApJ, 651, L137). Tables are also provided at given masses for both DA and DB models through the files Table_Mass_*.
+	Acknowledgement: If you are using any of these color tables, please acknowledge this Web site (http://www.astro.umontreal.ca/~bergeron/CoolingModels) and include the relevant references mentioned above.
+
+
+master_isochrone.csv:
+	http://stev.oapd.inaf.it/cgi-bin/cmd
+	# File generated by CMD 3.6 (http://stev.oapd.inaf.it/cmd) on Sat May 21 15:47:49 CEST 2022
+	# isochrones based on PARSEC release v1.2S +  COLIBRI S_37 + S_35 + PR16
+	# Basic references: Bressan et al. (2012), MNRAS, 427, 127 + Chen et al. (2014, 2015), MNRAS, 444, 2525 + MNRAS, 452, 1068 + Tang et al. (2014), MNRAS, 445, 4287 + Marigo et al. (2017), ApJ, 835, 77 + Pastorelli al. (2019), MNRAS, 485, 5666 + Pastorelli al. (2020), MNRAS, in press
+	# Thermal pulse cycles included 
+	# On RGB, assumed Reimers mass loss with efficiency eta=0.2
+	# LPV periods and growth rates added cf. Trabucchi et al. (2019)
+	# but fundamental-mode LPV periods are from Trabucchi et al. (2021)
+	# Photometric system: Gaia EDR3 (all Vegamags, Gaia passbands from ESA/Gaia website)
+	# Using YBC version of bolometric corrections as in Chen et al. (2019)
+	# O-rich circumstellar dpmod60alox40 dust from Groenewegen (2006)
+	# C-rich circumstellar AMCSIC15 dust from Groenewegen (2006)
+	# IMF: Kroupa (2001, 2002) + Kroupa et al. (2013) canonical two-part-power law IMF corrected for unresolved binaries 
+	# Kind of output: isochrone tables
+
+
+master_ZAMS.csv:
+	ZAMS created by stiching following solar metallicity isochrones. (_625, _65 etc. are logAges 6.25, 6.5 etc.)
+    	ZAMS_625   = cl_iso_625[(cl_iso_625.logL < 5.6) & (cl_iso_625.logL >= 3.5)]
+    	ZAMS_65    = cl_iso_65[  (cl_iso_65.logL < 3.5) &  (cl_iso_65.logL >= 3.0)]
+    	ZAMS_675   = cl_iso_675[(cl_iso_675.logL < 3.0) & (cl_iso_675.logL >= 2.5)]
+    	ZAMS_7     = cl_iso_7[    (cl_iso_7.logL < 2.5) &   (cl_iso_7.logL >= 1.4)]
+    	ZAMS_75    = cl_iso_75[  (cl_iso_75.logL < 1.4) &  (cl_iso_75.logL >= 0.9)]
+    	ZAMS_8     = cl_iso_8[    (cl_iso_8.logL < 0.9)]
+
+
+koester_spectral_library.nc:
+	Koester WD spectra interpolated for logWavelength of (2.954, 4.476) with steps of 0.001. Saved as xarray with following structure.
+	<xarray.DataArray (Wavelength: 1523, Te: 82, logg: 13)>
+	[1623518 values with dtype=float64]
+	Coordinates:
+	  * Wavelength  (Wavelength) float64 899.5 901.6 903.6 ... 2.985e+04 2.992e+04
+	  * Te          (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000
+	  * logg        (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5
+	Attributes:
+	    unit:       erg/s/cm2/A
+	    long_name:  Flux
+
+koester_synthetic_photometry.nc:
+	Koester synthetic photometry in 8102 filters
+	<xarray.DataArray (FilterID: 8102, Te: 82, logg: 13)>
+	[8636732 values with dtype=float64]
+	Coordinates:
+	  * FilterID  (FilterID) object 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'
+	  * Te        (Te) int64 5000 5250 5500 5750 6000 ... 50000 60000 70000 80000
+	  * logg      (logg) float64 6.5 6.75 7.0 7.25 7.5 ... 8.5 8.75 9.0 9.25 9.5
+	Attributes:
+	    Wavelengths:  [1.28470318e+03 1.35608150e+03 1.35899193e+03 ... 3.8683349...
+	    unit:         erg/s/cm2/A
+	    long_name:    Flux
+
+
+kurucz_spectral_library.nc:
+	Kurucz spectra interpolated for logWavelength of (2.608 6.204) with steps of 0.001. Saved as xarray with following structure.
+	<xarray.DataArray (Wavelength: 3597, Te: 76, logg: 11, MH: 9, alpha: 2)>
+	[54127656 values with dtype=float64]	Coordinates:
+	  * Wavelength  (Wavelength) float64 405.5 406.4 407.4 ... 1.596e+06 1.6e+06
+	  * Te          (Te) int64 3500 3750 4000 4250 4500 ... 47000 48000 49000 50000
+	  * logg        (logg) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
+	  * MH          (MH) float64 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.2 0.5 -4.0
+	  * alpha       (alpha) float64 0.0 0.4
+	Attributes:
+	    unit:       erg/s/cm2/A
+	    long_name:  Flux
+
+
+kurucz_synthetic_photometry.nc:
+	Kurucz synthetic photometry in 8174 filters
+	<xarray.DataArray (FilterID: 8147, Te: 76, logg: 11, MH: 9, alpha: 2)>
+	[122596056 values with dtype=float64]
+	Coordinates:
+	  * FilterID  (FilterID) object 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'
+	  * Te        (Te) int32 3500 3750 4000 4250 4500 ... 47000 48000 49000 50000
+	  * logg      (logg) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
+	  * MH        (MH) float64 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.2 0.5 -4.0
+	  * alpha     (alpha) float64 0.0 0.4
+	Attributes:
+	    Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...
+	    unit:         erg/s/cm2/A
+	    long_name:    Flux
+
+
+..._params.csv:
+	parameter space for various models for spectra and synthetic photometry.
\ No newline at end of file