diff --git a/ForMoSA/adapt/adapt_grid.py b/ForMoSA/adapt/adapt_grid.py index b35cc36..320b842 100755 --- a/ForMoSA/adapt/adapt_grid.py +++ b/ForMoSA/adapt/adapt_grid.py @@ -1,31 +1,123 @@ from __future__ import print_function, division import numpy as np import xarray as xr -import time import os, sys +import ctypes +import multiprocessing as mp + +from tqdm import tqdm +from multiprocessing.pool import ThreadPool sys.path.insert(0, os.path.abspath('../')) -from adapt.extraction_functions import adapt_model, decoupe +from adapt.extraction_functions import adapt_model # ---------------------------------------------------------------------------------------------------------------------- +def array_to_numpy(shared_array, shape, dtype): + ''' + Return a numpy array from a shared array + + Args: + shared_array (mp.RawArray): Raw shared array + shape (tuple): Shape of the array + dtype (numpy dtype): Data type of the array + Returns + numpy_array (np.ndarray): Numpy array mapped to shared array + + Author: Arthur Vigan + ''' + if shared_array is None: + return None + + numpy_array = np.frombuffer(shared_array, dtype=dtype) + if shape is not None: + numpy_array.shape = shape + + return numpy_array + + +def tpool_adapt_init(grid_input_shape_i, grid_input_data_i, grid_spectro_shape_i, grid_spectro_data_i, grid_photo_shape_i, grid_photo_data_i): + ''' + Thread pool init function for the parallelisation process of adapt_model() + + This function initializes the global variables stored as shared arrays + ''' + + # global variables + global grid_input_shape, grid_input_data, grid_spectro_shape, grid_spectro_data, grid_photo_shape, grid_photo_data + + grid_input_shape = grid_input_shape_i + grid_input_data = grid_input_data_i + grid_spectro_shape = grid_spectro_shape_i + grid_spectro_data = grid_spectro_data_i + grid_photo_shape = grid_photo_shape_i + grid_photo_data = grid_photo_data_i + +# global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins + +def tpool_adapt(idx, global_params, wav_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, obs_name, indobs, keys, titles, values): + ''' + Worker function for the parallelisation process of adapt_model() + + Args: + idx (tuple): Index of the current model + global_params (object): Class containing each parameter + wav_mod_nativ (array): Wavelength of the input models + res_mod_obs (array): Spectral resolution of the model interpolated at wav_obs_spectro + wav_obs_spectro (array): Merged wavelength array of the data + res_obs_spectro (array): Merged resolution array of the data + obs_photo_ins (array): List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] + obs_name (str): Name of the current observation looping + indobs (int): Index of the current observation looping + keys (list): Attribute keys + titles (list): Attribute titles + values (dict): Values for each attribute + Returns: + None + + Author: Arthur Vigan + ''' + + # global variables + global grid_input_shape, grid_input_data, grid_spectro_shape, grid_spectro_data, grid_photo_shape, grid_photo_data + + grid_input = array_to_numpy(grid_input_data, grid_input_shape, float) + grid_spectro = array_to_numpy(grid_spectro_data, grid_spectro_shape, float) + grid_photo = array_to_numpy(grid_photo_data, grid_photo_shape, float) + + model_to_adapt = grid_input[(..., ) + idx] + nan_mod = np.isnan(model_to_adapt) + if np.any(nan_mod): + msg = 'Extraction of model failed : ' + for i, (key, title) in enumerate(zip(keys, titles)): + msg += f'{title}={values[key][idx[i]]}, ' + print(msg) + else: + mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, obs_name=obs_name, indobs=indobs) + grid_spectro[(..., ) + idx] = mod_spectro + grid_photo[(..., ) + idx] = mod_photo -def adapt_grid(global_params, wav_obs_spectro, wav_obs_photo, res_mod_obs_merge, obs_name='', indobs=0): + +def adapt_grid(global_params, res_mod_obs, wav_obs_spectro, res_obs_spectro, wav_obs_photo, obs_photo_ins, obs_name='', indobs=0): """ Adapt the synthetic spectra of a grid to make them comparable with the data. - + Args: global_params (object): Class containing each parameter - wav_obs_spectro (array): Merged wavelength grid of the data + res_mod_obs (array): Spectral resolution of the model interpolated at wav_obs_spectro + wav_obs_spectro (array): Merged wavelength array of the data + res_obs_spectro (array): Merged resolution array of the data wav_obs_photo (array): Wavelengths of the photometry points + obs_photo_ins (array): List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] obs_name (str): Name of the current observation looping indobs (int): Index of the current observation looping + parallel (bool): Specify if parallelisation is used for adaptation Returns: None - Author: Simon Petrus, Matthieu Ravet and Paulina Palma-Bifani + Author: Simon Petrus, Matthieu Ravet, Paulina Palma-Bifani and Arthur Vigan """ ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4") @@ -34,261 +126,93 @@ def adapt_grid(global_params, wav_obs_spectro, wav_obs_photo, res_mod_obs_merge, attr = ds.attrs grid_np = grid.to_numpy() - - if len(attr['par']) == 2: - grid_spectro_np = np.full((len(wav_obs_spectro), - len(grid["par1"].values), - len(grid["par2"].values)), np.nan) - grid_photo_np = np.full((len(wav_obs_photo), - len(grid["par1"].values), - len(grid["par2"].values)), np.nan) - tot_par = len(grid["par1"].values) * len(grid["par2"].values) - if len(attr['par']) == 3: - grid_spectro_np = np.full((len(wav_obs_spectro), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values)), np.nan) - grid_photo_np = np.full((len(wav_obs_photo), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values)), np.nan) - tot_par = len(grid["par1"].values) * len(grid["par2"].values) * len(grid["par3"].values) - if len(attr['par']) == 4: - grid_spectro_np = np.full((len(wav_obs_spectro), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values), - len(grid["par4"].values)), np.nan) - grid_photo_np = np.full((len(wav_obs_photo), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values), - len(grid["par4"].values)), np.nan) - tot_par = len(grid["par1"].values) * len(grid["par2"].values) * len(grid["par3"].values) * len(grid["par4"].values) - if len(attr['par']) == 5: - grid_spectro_np = np.full((len(wav_obs_spectro), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values), - len(grid["par4"].values), - len(grid["par5"].values)), np.nan) - grid_photo_np = np.full((len(wav_obs_photo), - len(grid["par1"].values), - len(grid["par2"].values), - len(grid["par3"].values), - len(grid["par4"].values), - len(grid["par5"].values)), np.nan) - tot_par = len(grid["par1"].values) * len(grid["par2"].values) * len(grid["par3"].values) * len(grid["par4"].values) * len(grid["par5"].values) - i_tot = 1 - follow_print_title = '' - for par_t in attr['title']: - follow_print_title += par_t + ' \t- \t' - for p1_i, p1 in enumerate(grid['par1'].values): - for p2_i, p2 in enumerate(grid['par2'].values): - if len(attr['par']) > 2: - for p3_i, p3 in enumerate(grid['par3'].values): - if len(attr['par']) > 3: - for p4_i, p4 in enumerate(grid['par4'].values): - if len(attr['par']) > 4: - for p5_i, p5 in enumerate(grid['par5'].values): - time1 = time.time() - model_to_adapt = grid_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] - nan_mod_ind = ~np.isnan(model_to_adapt) - if len(np.where(nan_mod_ind is False)[0]) == 0: - mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt, - res_mod_obs_merge, obs_name=obs_name, indobs=indobs) - grid_spectro_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = mod_spectro - grid_photo_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = mod_photo - else: - - print('The extraction of the model : '+attr['title'][0]+'=' + str(p1) + - ', '+attr['title'][1]+'=' + str(p2) + - ', '+attr['title'][2]+'=' + str(p3) + - ', '+attr['title'][3]+'=' + str(p4) + - ', '+attr['title'][4]+'=' + str(p5) + - ' failed') - print(str(p1_i + 1) + '/' + str(len(grid['par1'].values)) + ' \t- \t' + - str(p2_i + 1) + '/' + str(len(grid['par2'].values)) + ' \t- \t' + - str(p3_i + 1) + '/' + str(len(grid['par3'].values)) + ' \t- \t' + - str(p4_i + 1) + '/' + str(len(grid['par4'].values)) + ' \t- \t' + - str(p5_i + 1) + '/' + str(len(grid['par5'].values)) + ' \t- \t' + - ' Estimated time : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[0])) + - 'h : ' + str(int(decoupe((tot_par - i_tot) * (time.time() - time1))[1])) + - 'm : ' + str(int(decoupe((tot_par - i_tot) * (time.time() - time1))[2])) + - 's') - line_up = '\033[1A' - line_clear = '\x1b[2K' - print(line_up, end=line_clear) - i_tot += 1 - else: - time1 = time.time() - model_to_adapt = grid_np[:, p1_i, p2_i, p3_i, p4_i] - nan_mod_ind = ~np.isnan(model_to_adapt) - if len(np.where(nan_mod_ind is False)[0]) == 0: - mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt, - res_mod_obs_merge, obs_name=obs_name, indobs=indobs) - - grid_spectro_np[:, p1_i, p2_i, p3_i, p4_i] = mod_spectro - grid_photo_np[:, p1_i, p2_i, p3_i, p4_i] = mod_photo - else: - - print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) + - ', ' + attr['title'][1] + '=' + str(p2) + - ', ' + attr['title'][2] + '=' + str(p3) + - ', ' + attr['title'][3] + '=' + str(p4) + - ' failed') - print(str(p1_i + 1) + '/' + str(len(grid['par1'].values)) + ' \t- \t' + - str(p2_i + 1) + '/' + str(len(grid['par2'].values)) + ' \t- \t' + - str(p3_i + 1) + '/' + str(len(grid['par3'].values)) + ' \t- \t' + - str(p4_i + 1) + '/' + str(len(grid['par4'].values)) + ' \t- \t' + - ' Estimated time : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[0])) - + 'h : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[1])) - + 'm : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[2])) - + 's') - line_up = '\033[1A' - line_clear = '\x1b[2K' - print(line_up, end=line_clear) - i_tot += 1 - else: - time1 = time.time() - model_to_adapt = grid_np[:, p1_i, p2_i, p3_i] - nan_mod_ind = ~np.isnan(model_to_adapt) - if len(np.where(nan_mod_ind is False)[0]) == 0: - mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt, - res_mod_obs_merge, obs_name=obs_name, indobs=indobs) - - grid_spectro_np[:, p1_i, p2_i, p3_i] = mod_spectro - grid_photo_np[:, p1_i, p2_i, p3_i] = mod_photo - else: - - print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) + - ', ' + attr['title'][1] + '=' + str(p2) + - ', ' + attr['title'][2] + '=' + str(p3) + - ' failed') - print(str(p1_i + 1) + '/' + str(len(grid['par1'].values)) + ' \t- \t' + - str(p2_i + 1) + '/' + str(len(grid['par2'].values)) + ' \t- \t' + - str(p3_i + 1) + '/' + str(len(grid['par3'].values)) + ' \t- \t' + - ' Estimated time : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[0])) - + 'h : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[1])) - + 'm : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[2])) - + 's') - line_up = '\033[1A' - line_clear = '\x1b[2K' - print(line_up, end=line_clear) - i_tot += 1 - else: - time1 = time.time() - model_to_adapt = grid_np[:, p1_i, p2_i] - nan_mod_ind = ~np.isnan(model_to_adapt) - if len(np.where(nan_mod_ind is False)[0]) == 0: - mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt, - res_mod_obs_merge, obs_name=obs_name, indobs=indobs) - grid_spectro_np[:, p1_i, p2_i] = mod_spectro - grid_photo_np[:, p1_i, p2_i] = mod_photo - else: - - print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) + - ', ' + attr['title'][1] + '=' + str(p2) + - ' failed') - print(str(p1_i + 1) + '/' + str(len(grid['par1'].values)) + ' \t- \t' + - str(p2_i + 1) + '/' + str(len(grid['par2'].values)) + ' \t- \t' + - ' Estimated time : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[0])) - + 'h : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[1])) - + 'm : ' + str(int(decoupe((tot_par - i_tot) * - (time.time() - time1))[2])) - + 's') - line_up = '\033[1A' - line_clear = '\x1b[2K' - print(line_up, end=line_clear) - i_tot += 1 - - if len(attr['par']) == 2: - ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_spectro_np)), - coords={"wavelength": wav_obs_spectro, - "par1": grid["par1"].values, - "par2": grid["par2"].values}, - attrs=attr) - ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_photo_np)), - coords={"wavelength": wav_obs_photo, - "par1": grid["par1"].values, - "par2": grid["par2"].values}, - attrs=attr) - if len(attr['par']) == 3: - ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_spectro_np)), - coords={"wavelength": wav_obs_spectro, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values}, - attrs=attr) - ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_photo_np)), - coords={"wavelength": wav_obs_photo, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values}, - attrs=attr) - if len(attr['par']) == 4: - ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], grid_spectro_np)), - coords={"wavelength": wav_obs_spectro, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values, - "par4": grid["par4"].values}, - attrs=attr) - ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], - grid_photo_np)), - coords={"wavelength": wav_obs_photo, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values, - "par4": grid["par4"].values}, - attrs=attr) - - if len(attr['par']) == 5: - ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], grid_spectro_np)), - coords={"wavelength": wav_obs_spectro, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values, - "par4": grid["par4"].values, - "par5": grid["par5"].values}, - attrs=attr) - ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], - grid_photo_np)), - coords={"wavelength": wav_obs_photo, - "par1": grid["par1"].values, - "par2": grid["par2"].values, - "par3": grid["par3"].values, - "par4": grid["par4"].values, - "par5": grid["par5"].values}, - attrs=attr) + # create arrays without any assumptions on the number of parameters + shape_spectro = [len(wav_obs_spectro)] + shape_photo = [len(wav_obs_photo)] + values = {} + for key in attr['key']: + shape_spectro.append(len(grid[key].values)) + shape_photo.append(len(grid[key].values)) + values[key] = grid[key].values + + print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') + + # + # Shared arrays of image intensities at all wavelengths + # + + grid_input_shape = grid_np.shape + grid_input_data = mp.RawArray(ctypes.c_double, int(np.prod(grid_input_shape))) + grid_input_np = array_to_numpy(grid_input_data, grid_input_shape, float) + grid_input_np[:] = grid_np + del grid_np + + grid_spectro_shape = shape_spectro + grid_spectro_data = mp.RawArray(ctypes.c_double, int(np.prod(grid_spectro_shape))) + grid_spectro_np = array_to_numpy(grid_spectro_data, grid_spectro_shape, float) + grid_spectro_np[:] = np.nan + + grid_photo_shape = shape_photo + grid_photo_data = mp.RawArray(ctypes.c_double, int(np.prod(grid_photo_shape))) + grid_photo_np = array_to_numpy(grid_photo_data, grid_photo_shape, float) + grid_photo_np[:] = np.nan + + # + # parallel grid adaptation + # + shape = grid_input_shape[1:] + pbar = tqdm(total=np.prod(shape), leave=False) + + def update(*a): + pbar.update() + + if global_params.parallel: + ncpu = mp.cpu_count() + with ThreadPool(processes=ncpu, initializer=tpool_adapt_init, initargs=(grid_input_shape, grid_input_data, grid_spectro_shape, grid_spectro_data, grid_photo_shape, grid_photo_data)) as pool: + for idx in np.ndindex(shape): + pool.apply_async(tpool_adapt, args=(idx, global_params, wav_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, obs_name, indobs, attr['key'], attr['title'], values), callback=update) + + pool.close() + pool.join() + else: + tpool_adapt_init(grid_input_shape, grid_input_data, grid_spectro_shape, grid_spectro_data, grid_photo_shape, grid_photo_data) + + for idx in np.ndindex(shape): + tpool_adapt(idx, global_params, wav_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, obs_name, indobs, attr['key'], attr['title'], values) + update() + + # create final datasets + vars = ["wavelength"] + for key in attr['key']: + vars.append(key) + + coords_spectro = {"wavelength": wav_obs_spectro} + coords_photo = {"wavelength": wav_obs_photo} + for key in attr['key']: + coords_spectro[key] = grid[key].values + coords_photo[key] = grid[key].values + + ds_spectro_new = xr.Dataset(data_vars=dict(grid=(vars, grid_spectro_np)), coords=coords_spectro, attrs=attr) + ds_photo_new = xr.Dataset(data_vars=dict(grid=(vars, grid_photo_np)), coords=coords_photo, attrs=attr) + print() print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') print('-> The possible holes in the grid are interpolated: ') print() - for key_ind, key in enumerate(attr['key']): - print(str(key_ind+1) + '/' + str(len(attr['key']))) - ds_spectro_new = ds_spectro_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None, - max_gap=None) - ds_photo_new = ds_photo_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None, - max_gap=None) - + nkey = len(attr['key']) + for idx, (key, title) in enumerate(zip(attr['key'], attr['title'])): + print(f'{idx+1}/{nkey} - {title}') + ds_spectro_new = ds_spectro_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None, max_gap=None) + ds_photo_new = ds_photo_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None, max_gap=None) + ds_spectro_new.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_spectro_{global_params.grid_name}_{obs_name}_nonan.nc'), - format='NETCDF4', - engine='netcdf4', - mode='w') + format='NETCDF4', + engine='netcdf4', + mode='w') ds_photo_new.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_photo_{global_params.grid_name}_{obs_name}_nonan.nc'), - format='NETCDF4', - engine='netcdf4', - mode='w') + format='NETCDF4', + engine='netcdf4', + mode='w') print('The possible holes have been interpolated!') diff --git a/ForMoSA/adapt/adapt_obs_mod.py b/ForMoSA/adapt/adapt_obs_mod.py index ba0002b..5ddf0e6 100755 --- a/ForMoSA/adapt/adapt_obs_mod.py +++ b/ForMoSA/adapt/adapt_obs_mod.py @@ -8,7 +8,6 @@ from adapt.extraction_functions import extract_observation from adapt.adapt_grid import adapt_grid -from main_utilities import diag_mat import glob # ---------------------------------------------------------------------------------------------------------------------- @@ -33,10 +32,15 @@ def launch_adapt(global_params, justobs='no'): res_mod_nativ = attr['res'] ds.close() + # Check if the spectrum is Nyquist-sampled, else set the resolution to R = wav / 2 Deltawav + dwav = np.abs(wav_mod_nativ - np.roll(wav_mod_nativ, 1)) + dwav[0] = dwav[1] + res_Nyquist = wav_mod_nativ / (2 * dwav) + res_mod_nativ[np.where(res_mod_nativ > res_Nyquist)] = res_Nyquist[np.where(res_mod_nativ > res_Nyquist)] + # Extract the data from the observation files main_obs_path = global_params.main_observation_path - for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))): global_params.observation_path = obs @@ -48,93 +52,37 @@ def launch_adapt(global_params, justobs='no'): print(obs_name + ' will have a R=' + global_params.continuum_sub[indobs] + ' continuum removed using a ' + global_params.wav_for_continuum[indobs] + ' wavelength range') print() - obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ, 'yes', + obs_spectro, obs_photo, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ, 'yes', obs_name=obs_name, indobs=indobs) else: - obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ, + obs_spectro, obs_photo, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ, obs_name=obs_name, indobs=indobs) - - # Merging of each sub-spectrum and interpolating the grid - for c, cut in enumerate(obs_spectro): - - if len(cut[0]) > 0: - # Interpolate the resolution onto the wavelength of the data - ind_mod_obs = np.where((wav_mod_nativ <= cut[0][-1]) & (wav_mod_nativ > cut[0][0])) - wav_mod_cut = wav_mod_nativ[ind_mod_obs] - res_mod_cut = res_mod_nativ[ind_mod_obs] - interp_mod_to_obs = interp1d(wav_mod_cut, res_mod_cut, fill_value='extrapolate') - res_mod_cut = interp_mod_to_obs(cut[0]) - - if c == 0: - wav_obs_extract = obs_spectro[c][0] - flx_obs_extract = obs_spectro[c][1] - err_obs_extract = obs_spectro[c][2] - res_obs_extract = obs_spectro[c][3] - cov_obs_extract = obs_opt[c][0] - transm_obs_extract = obs_opt[c][1] - star_flx_obs_extract = obs_opt[c][2] - system_obs_extract = obs_opt[c][3] - # Save the interpolated resolution of the grid - res_mod_obs_merge = [res_mod_cut] - - else: - wav_obs_extract = np.concatenate((wav_obs_extract, obs_spectro[c][0])) - flx_obs_extract = np.concatenate((flx_obs_extract, obs_spectro[c][1])) - err_obs_extract = np.concatenate((err_obs_extract, obs_spectro[c][2])) - res_obs_extract = np.concatenate((res_obs_extract, obs_spectro[c][3])) - if len(cov_obs_extract) != 0: - cov_obs_extract = diag_mat([cov_obs_extract, obs_opt[c][0]]) - if len(transm_obs_extract) != 0: - transm_obs_extract = np.concatenate((transm_obs_extract, obs_opt[c][1])) - if len(star_flx_obs_extract) != 0: - star_flx_obs_extract = np.concatenate((star_flx_obs_extract, obs_opt[c][2]), axis=0) - if len(system_obs_extract) != 0: - system_obs_extract = np.concatenate((system_obs_extract, obs_opt[c][3]), axis=0) - # Save the interpolated resolution of the grid - res_mod_obs_merge.append(res_mod_cut) - - - - # Compute the inverse of the merged covariance matrix (note: inv(C1, C2) = (in(C1), in(C2)) if C1 and C2 are block matrix on the diagonal) - # if necessary - if len(cov_obs_extract) != 0: - inv_cov_obs_extract = np.linalg.inv(cov_obs_extract) - else: - inv_cov_obs_extract = np.asarray([]) - - # Check-ups and warnings for negative values in the diagonal of the covariance matrix - if len(cov_obs_extract) != 0 and any(np.diag(cov_obs_extract) < 0): - print() - print("WARNING: Negative value(s) is(are) present on the diagonal of the covariance matrix.") - print("Operation aborted.") - print() - exit() - - else: - wav_obs_extract, flx_obs_extract, err_obs_extract, res_obs_extract = [], [], [], [] - inv_cov_obs_extract, transm_obs_extract, star_flx_obs_extract, system_obs_extract = [], [], [], [] - res_mod_obs_merge = res_mod_nativ - - # Compile everything and changing data type to object to allow for different array sizes - obs_spectro_merge = np.asarray([wav_obs_extract, flx_obs_extract, err_obs_extract, res_obs_extract]) - obs_spectro = np.asarray(obs_spectro, dtype=object) - obs_spectro_ins = np.asarray(obs_spectro_ins, dtype=object) - obs_photo = np.asarray(obs_photo, dtype=object) - obs_photo_ins = np.asarray(obs_photo_ins, dtype=object) - obs_opt_merge = np.asarray([inv_cov_obs_extract, transm_obs_extract, star_flx_obs_extract, system_obs_extract], dtype=object) - - + # Interpolate the resolution onto the wavelength of the data + if len(obs_spectro[0]) != 0: + mask_mod_obs = (wav_mod_nativ <= obs_spectro[0][-1]) & (wav_mod_nativ > obs_spectro[0][0]) + wav_mod_cut = wav_mod_nativ[mask_mod_obs] + res_mod_cut = res_mod_nativ[mask_mod_obs] + interp_mod_to_obs = interp1d(wav_mod_cut, res_mod_cut, fill_value='extrapolate') + res_mod_obs = interp_mod_to_obs(obs_spectro[0]) + else: + res_mod_obs = np.asarray([]) + # Check-ups and warnings for negative values in the diagonal of the covariance matrix + if len(obs_opt[0]) != 0 and any(np.diag(obs_opt[0]) < 0): + print() + print("WARNING: Negative value(s) is(are) present on the diagonal of the covariance matrix.") + print("Operation aborted.") + print() + exit() + # Save the new data spectrum np.savez(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'), - obs_spectro_merge=obs_spectro_merge, obs_spectro=obs_spectro, - obs_spectro_ins=obs_spectro_ins, obs_photo=obs_photo, obs_photo_ins=obs_photo_ins, - obs_opt_merge=obs_opt_merge) # Optional arrays kept separatly + obs_opt=obs_opt) # Optional arrays kept separatly # Adaptation of the model grid if justobs == 'no': @@ -155,9 +103,8 @@ def launch_adapt(global_params, justobs='no'): print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') print(f"-> Sarting the adaptation of {obs_name}") - adapt_grid(global_params, obs_spectro_merge[0], obs_photo[0], res_mod_obs_merge, obs_name=obs_name, indobs=indobs) + adapt_grid(global_params, res_mod_obs, obs_spectro[0], obs_spectro[3], obs_photo[0], obs_photo_ins, obs_name=obs_name, indobs=indobs) - # ---------------------------------------------------------------------------------------------------------------------- diff --git a/ForMoSA/adapt/extraction_functions.py b/ForMoSA/adapt/extraction_functions.py index fff7527..5c1b011 100755 --- a/ForMoSA/adapt/extraction_functions.py +++ b/ForMoSA/adapt/extraction_functions.py @@ -4,7 +4,6 @@ from scipy.ndimage import gaussian_filter from scipy.interpolate import interp1d from spectres import spectres -import os # ---------------------------------------------------------------------------------------------------------------------- @@ -65,37 +64,46 @@ def extract_observation(global_params, wav_mod_nativ, res_mod_nativ, cont='no', indobs (int): Index of the current observation looping Returns: - - obs_spectro (n-array) : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]] - - obs_photo (array) : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0] - - obs_spectro_ins(array) : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]] - - obs_photo_ins (array) : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] - - obs_opt (n-array) : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]] + - obs_spectro (array) : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [wav, flx, err, reso] + - obs_photo (array) : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0] + - obs_photo_ins (array) : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] + - obs_opt (array) : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [cov, tran, star, system] Author: Simon Petrus, Matthieu Ravet """ # Extract the wavelengths, flux, errors, spectral resolution, and instrument/filter names from the observation file. - - obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = adapt_observation_range(global_params, obs_name=obs_name, indobs=indobs) + obs_spectro, obs_photo, obs_photo_ins, obs_opt = adapt_observation_range(global_params, obs_name=obs_name, indobs=indobs) # Reduce the spectral resolution for each sub-spectrum. - for c, cut in enumerate(obs_spectro): - if len(cut[0]) != 0: + for range_ind, rangee in enumerate(global_params.wav_for_adapt[indobs].split('/')): + rangee = rangee.split(',') + mask_spectro_cut = (float(rangee[0]) <= obs_spectro[0]) & (obs_spectro[0] <= float(rangee[1])) + if len(obs_spectro[0][mask_spectro_cut]) != 0: # Interpolate the resolution of the model onto the wavelength of the data to properly decrease the resolution if necessary - ind_mod_obs = np.where((wav_mod_nativ <= cut[0][-1]) & (wav_mod_nativ > cut[0][0])) - wav_mod_obs = wav_mod_nativ[ind_mod_obs] - res_mod_obs = res_mod_nativ[ind_mod_obs] + mask_mod_obs = (wav_mod_nativ <= obs_spectro[0][mask_spectro_cut][-1]) & (wav_mod_nativ > obs_spectro[0][mask_spectro_cut][0]) + wav_mod_obs = wav_mod_nativ[mask_mod_obs] + res_mod_obs = res_mod_nativ[mask_mod_obs] interp_mod_to_obs = interp1d(wav_mod_obs, res_mod_obs, fill_value='extrapolate') - res_mod_obs = interp_mod_to_obs(cut[0]) + res_mod_obs = interp_mod_to_obs(obs_spectro[0][mask_spectro_cut]) # If we want to decrease the resolution of the data: (if by_sample, the data don't need to be adapted) if global_params.adapt_method[indobs] == 'by_reso': - obs_spectro[c][1] = resolution_decreasing(global_params, cut[0], cut[1], cut[3], wav_mod_nativ, [], res_mod_obs, - 'obs', indobs=indobs) - if cont == 'yes': + obs_spectro[1][mask_spectro_cut] = resolution_decreasing(global_params, + obs_spectro[0][mask_spectro_cut], + obs_spectro[1][mask_spectro_cut], + obs_spectro[3][mask_spectro_cut], + wav_mod_nativ, + [], + res_mod_obs, + 'obs', indobs=indobs) # If we want to estimate and substract the continuum of the data: - obs_spectro[c][1] -= continuum_estimate(global_params, cut[0], cut[1], cut[3], indobs=indobs) + if cont == 'yes': + obs_spectro[1][mask_spectro_cut] -= continuum_estimate(global_params, + obs_spectro[0][mask_spectro_cut], + obs_spectro[1][mask_spectro_cut], + obs_spectro[3][mask_spectro_cut], indobs=indobs) - return obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt + return obs_spectro, obs_photo, obs_photo_ins, obs_opt # ---------------------------------------------------------------------------------------------------------------------- @@ -111,11 +119,10 @@ def adapt_observation_range(global_params, obs_name='', indobs=0): indobs (int): Index of the current observation looping Returns: - - obs_spectro (n-array) : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]] + - obs_spectro (array) : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [wav, flx, err, reso] - obs_photo (array) : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0] - - obs_spectro_ins(array) : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]] - obs_photo_ins (array) : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] - - obs_opt (n-array) : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]] + - obs_opt (array) : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [cov, tran, star, system] Author: Simon Petrus, Matthieu Ravet and Allan Denis """ @@ -133,62 +140,56 @@ def adapt_observation_range(global_params, obs_name='', indobs=0): except: cov = hdul[1].data['COV'] err = np.sqrt(np.diag(np.abs(cov))) - try: + try: # Check for transmission transm = hdul[1].data['TRANSM'] except: transm = np.asarray([]) - try: + try: # Check for star flux star_flx = hdul[1].data['STAR_FLX1'][:,np.newaxis] is_star = True except: star_flx = np.asarray([]) is_star = False - try: - star_flx = hdul[1].data['STAR FLX'][:,np.newaxis] - except: - pass + if is_star: + i = 2 + while True: # In case there is multiple star flux (usually shifted to account for the PSF) + try: + star_flx = np.concatenate((star_flx, hdul[1].data['STAR_FLX' + str(i)][:,np.newaxis]),axis=1) + i += 1 + except: + break try: is_system = True system = hdul[1].data['SYSTEMATICS1'][:,np.newaxis] except: is_system = False system = np.asarray([]) - if is_system: i = 2 - while True: # In case there is multiple systematics + while True: # In case there is multiple systematics try: system = np.concatenate((system, hdul[1].data['SYSTEMATICS' + str(i)][:,np.newaxis]),axis=1) i += 1 except: break - - if is_star: - i = 2 - while True: - try: - star_flx = np.concatenate((star_flx, hdul[1].data['STAR_FLX' + str(i)][:,np.newaxis]),axis=1) - i += 1 - except: - break - + # Only take the covariance if you use the chi2_covariance likelihood function (will need to be change when new likelihood functions using the # covariance matrix will come) if global_params.logL_type[indobs] != 'chi2_covariance': cov = np.asarray([]) # Filter the NaN and inf values + nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(err)) & (np.isfinite(flx)) & (np.isfinite(err)) + if len(cov) != 0: + nan_mod_ind = (nan_mod_ind) & np.all(~np.isnan(cov), axis=0) & np.all(~np.isnan(cov), axis=1) & np.all(np.isfinite(cov), axis=0) & np.all(np.isfinite(cov), axis=1) if len(transm) != 0: - nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(transm)) & (~np.isnan(err)) & (np.isfinite(flx)) & (np.isfinite(transm)) & (np.isfinite(err)) - else: - nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(err)) & (np.isfinite(flx)) & (np.isfinite(err)) + nan_mod_ind = (nan_mod_ind) & (~np.isnan(transm)) & (np.isfinite(transm)) if len(star_flx) != 0: for i in range(len(star_flx[0])): nan_mod_ind = (nan_mod_ind) & (~np.isnan(star_flx.T[i])) & (np.isfinite(star_flx.T[i])) if len(system) != 0: for i in range(len(system[0])): - nan_mod_ind = (nan_mod_ind) & (~np.isnan(system.T[i])) & (np.isfinite(system.T[i])) - + nan_mod_ind = (nan_mod_ind) & (~np.isnan(system.T[i])) & (np.isfinite(system.T[i])) wav = wav[nan_mod_ind] flx = flx[nan_mod_ind] res = res[nan_mod_ind] @@ -202,71 +203,69 @@ def adapt_observation_range(global_params, obs_name='', indobs=0): star_flx = np.delete(star_flx, np.where(~nan_mod_ind), axis=0) if len(system) != 0: system = np.delete(system, np.where(~nan_mod_ind), axis=0) + + # Check if the spectrum is Nyquist-sampled, else set the resolution to R = wav / 2 Deltawav + dwav = np.abs(wav - np.roll(wav, 1)) + dwav[0] = dwav[1] + res_Nyquist = wav / (2 * dwav) + res[np.where(res > res_Nyquist)] = res_Nyquist[np.where(res > res_Nyquist)] - # Select the wavelength range(s) for the extraction - if global_params.wav_for_adapt == '': - wav_for_adapt_tab = [str(min(wav)) + ',' + str(max(wav))] + # - - - - - - - - - + + # Separate photometry and spectroscopy + cuts + mask_photo = (res == 0.0) + + # Photometry part + obs_photo = np.asarray([wav[mask_photo], + flx[mask_photo], + err[mask_photo], + res[mask_photo]]) + obs_photo_ins = np.asarray(ins[mask_photo]) + + # Spectroscopy part + wav_spectro = wav[~mask_photo] + flx_spectro = flx[~mask_photo] + err_spectro = err[~mask_photo] + res_spectro = res[~mask_photo] + mask_spectro = np.zeros(len(wav_spectro), dtype=bool) + for range_ind, rangee in enumerate(global_params.wav_for_adapt[indobs].split('/')): + rangee = rangee.split(',') + mask_spectro += (float(rangee[0]) <= wav_spectro) & (wav_spectro <= float(rangee[1])) + obs_spectro = np.asarray([wav_spectro[mask_spectro], + flx_spectro[mask_spectro], + err_spectro[mask_spectro], + res_spectro[mask_spectro]]) + + # Optional arrays + if len(cov) != 0: # Check if the covariance exists + cov_spectro = cov[np.ix_(~mask_photo,~mask_photo)] + inv_cov_spectro = np.linalg.inv(cov_spectro[np.ix_(mask_spectro,mask_spectro)]) # Save only the inverse covariance to speed up the inversion + else: + inv_cov_spectro = np.asarray([]) + if len(transm) != 0: + transm_spectro = transm[~mask_photo][mask_spectro] else: - wav_for_adapt_tab = global_params.wav_for_adapt[indobs].split('/') - - # Photometry part of the data (OUT OF THE WINDOW LOOP) - ind_photometry = np.where(res == 0.0) - obs_photo = np.asarray([wav[ind_photometry], flx[ind_photometry], err[ind_photometry], - res[ind_photometry]]) - obs_photo_ins = np.asarray(ins[ind_photometry]) - - # Initiate spectroscopy data numpy arrays - obs_spectro = np.empty(len(wav_for_adapt_tab), dtype=object) - obs_opt = np.empty(len(wav_for_adapt_tab), dtype=object) - obs_spectro_ins = np.empty(len(wav_for_adapt_tab), dtype=object) + transm_spectro = np.asarray([]) + if len(star_flx) != 0: + star_flx_spectro = star_flx[~mask_photo][mask_spectro] + else: + star_flx_spectro = np.asarray([]) + if len(system) != 0: + system_spectro = system[~mask_photo][mask_spectro] + else: + system_spectro = np.asarray([]) + obs_opt = np.asarray([inv_cov_spectro, + transm_spectro, + star_flx_spectro, + system_spectro], dtype=object) - - for range_ind, rangee in enumerate(wav_for_adapt_tab): - rangee = rangee.split(',') - ind = np.where((float(rangee[0]) <= wav) & (wav <= float(rangee[1]))) - ind_photometry = np.where(res[ind] == 0.0) - - # Spectroscopy part of the data - wav_spectro = np.delete(wav[ind], ind_photometry) - flx_spectro = np.delete(flx[ind], ind_photometry) - err_spectro = np.delete(err[ind], ind_photometry) - res_spectro = np.delete(res[ind], ind_photometry) - ins_spectro = np.delete(ins[ind], ind_photometry) - if len(cov) != 0: # Check if the covariance exists - cov_spectro = cov[np.ix_(ind[0],ind[0])] - cov_spectro = np.delete(cov_spectro, ind_photometry, axis=0) - cov_spectro = np.delete(cov_spectro, ind_photometry, axis=1) - else: - cov_spectro = np.asarray([]) - - if len(transm) != 0: - transm_spectro = np.delete(transm[ind], ind_photometry) - else: - transm_spectro = np.asarray([]) - - if len(star_flx) != 0: - star_flx_spectro = np.delete(star_flx[ind,:], ind_photometry, axis=0)[0] - else: - star_flx_spectro = np.asarray([]) - - if len(system) != 0: - system_spectro = np.delete(system[ind,:], ind_photometry, axis=0)[0] - else: - system_spectro = np.asarray([]) - - - # Merge spectroscopic data - obs_spectro[range_ind] = [wav_spectro, flx_spectro, err_spectro, res_spectro] - obs_opt[range_ind] = [cov_spectro, transm_spectro, star_flx_spectro, system_spectro] - obs_spectro_ins[range_ind] = ins_spectro - - return obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt + return obs_spectro, obs_photo, obs_photo_ins, obs_opt # ---------------------------------------------------------------------------------------------------------------------- -def adapt_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name='', indobs=0): +def adapt_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, obs_name='', indobs=0): """ Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are calculated too. Then each sub-spectrum are merged. @@ -274,72 +273,84 @@ def adapt_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, Args: global_params (object): Class containing each parameter used in ForMoSA wav_mod_nativ (array): Wavelength grid of the model - wave_reso_tab (array): Wavelength grid of the model at specified resolution flx_mod_nativ (array): Flux of the model - res_mod_nativ (array): Spectral resolution of the model as a function of the wavelength grid + res_mod_obs (array): Spectral resolution of the model interpolated at wav_obs_spectro + wav_obs_spectro (array): Wavelength grid of the spectroscopic data + res_obs_spectro (array): Spectral resolution grid of the spectroscopic data + obs_photo_ins (array): List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n] + wav_obs obs_name (str): Name of the current observation looping indobs (int): Index of the current observation looping Returns: - - mod_spectro (array) : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid - - mod_photo (array) : List containing the photometry ('0' replace the spectral resolution here). + - mod_spectro (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid + - mod_photo (array): List containing the photometry ('0' replace the spectral resolution here). - Author: Simon Petrus + Author: Simon Petrus, Matthieu Ravet """ # Estimate and subtract the continuum (if needed) if global_params.continuum_sub[indobs] != 'NA': - mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='yes', obs_name=obs_name, indobs=indobs) + mod_spectro, mod_photo = extract_model(global_params, + wav_mod_nativ, + flx_mod_nativ, + res_mod_obs, + wav_obs_spectro, + res_obs_spectro, + obs_photo_ins, + cont='yes', obs_name=obs_name, indobs=indobs) else: - mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name=obs_name, indobs=indobs) + mod_spectro, mod_photo = extract_model(global_params, + wav_mod_nativ, + flx_mod_nativ, + res_mod_obs, + wav_obs_spectro, + res_obs_spectro, + obs_photo_ins, + obs_name=obs_name, indobs=indobs) return mod_spectro, mod_photo # ---------------------------------------------------------------------------------------------------------------------- -def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='no', obs_name='', indobs=0): +def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs, wav_obs_spectro, res_obs_spectro, obs_photo_ins, cont='no', obs_name='', indobs=0): """ Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are calculated too. Args: - global_params (object): Class containing each parameter used in ForMoSA - wav_mod_nativ (array): Wavelength grid of the model - flx_mod_nativ (array): Flux of the model - res_mod_nativ (array): Spectral resolution of the model as a function of the wavelength grid - cont (str): Boolean string. If the function is used to estimate the continuum cont='yes' - obs_name (str): Name of the current observation looping - indobs (int): Index of the current observation looping + global_params (object): Class containing each parameter used in ForMoSA + wav_mod_nativ (array): Wavelength grid of the model + flx_mod_nativ (array): Flux of the model + res_obs_mod (array): Spectral resolution of the model interpolated at wav_obs_spectro + wav_obs_spectro (array): Wavelength grid of the spectroscopic data + res_obs_spectro (array): Spectral resolution grid of the spectroscopic data + cont (str): Boolean string. If the function is used to estimate the continuum cont='yes' + obs_name (str): Name of the current observation looping + indobs (int): Index of the current observation looping Returns: - - mod_spectro (array) : List containing the sub-spectra defined by the parameter "wav_for_adapt". - - mod (array) : List containing the photometry ('0' replace the spectral resolution here). + - mod_spectro (array): List containing the sub-spectra defined by the parameter "wav_for_adapt". + - mod (array): List containing the photometry ('0' replace the spectral resolution here). - Author: Simon Petrus + Author: Simon Petrus, Matthieu Ravet """ - # Take back the extracted data. - spectrum_obs = np.load(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True) - obs_spectro = spectrum_obs['obs_spectro'] - obs_photo_ins = spectrum_obs['obs_photo_ins'] - mod_spectro, mod_photo = [], [] + # Create final models + mod_spectro, mod_photo = np.empty(len(wav_obs_spectro), dtype=float), np.empty(len(obs_photo_ins), dtype=float) # Reduce the spectral resolution for each sub-spectrum. - for c, cut in enumerate(obs_spectro): - if len(cut[0]) != 0: + for range_ind, rangee in enumerate(global_params.wav_for_adapt[indobs].split('/')): + rangee = rangee.split(',') + mask_spectro_cut = (float(rangee[0]) <= wav_obs_spectro) & (wav_obs_spectro <= float(rangee[1])) + if len(wav_obs_spectro[mask_spectro_cut]) != 0: # If we want to decrease the resolution of the data: if global_params.adapt_method[indobs] == 'by_reso': - mod_cut_flx = resolution_decreasing(global_params, cut[0], [], cut[3], wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge[c], + mod_spectro[mask_spectro_cut] = resolution_decreasing(global_params, wav_obs_spectro[mask_spectro_cut], [], res_obs_spectro[mask_spectro_cut], wav_mod_nativ, flx_mod_nativ, res_mod_obs[mask_spectro_cut], 'mod', indobs=indobs) else: - mod_cut_flx = spectres(cut[0], wav_mod_nativ, flx_mod_nativ) + mod_spectro[mask_spectro_cut] = spectres(wav_obs_spectro[mask_spectro_cut], wav_mod_nativ, flx_mod_nativ) # If we want to estimate the continuum of the data: if cont == 'yes': - continuum = continuum_estimate(global_params, cut[0], mod_cut_flx, res_mod_obs_merge[c], indobs=indobs) - mod_cut_flx -= continuum - - # Concatenate to speed up the code - if c==0: - mod_spectro = mod_cut_flx - else: - mod_spectro = np.concatenate((mod_spectro, mod_cut_flx)) + continuum = continuum_estimate(global_params, wav_obs_spectro[mask_spectro_cut], mod_spectro[mask_spectro_cut], res_mod_obs[mask_spectro_cut], indobs=indobs) + mod_spectro[mask_spectro_cut] -= continuum # Calculate each photometry point. @@ -356,14 +367,14 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge flx_filt = np.sum(flx_mod_nativ[ind] * y_filt[ind] * (wav_mod_nativ[ind][1] - wav_mod_nativ[ind][0])) y_filt_tot = np.sum(y_filt[ind] * (wav_mod_nativ[ind][1] - wav_mod_nativ[ind][0])) flx_filt = flx_filt / y_filt_tot - mod_photo.append(flx_filt) + mod_photo[pho_ind] = flx_filt return mod_spectro, mod_photo # ---------------------------------------------------------------------------------------------------------------------- -def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_sigma=1): +def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_sigma=3): # num_sigma = 3 is a good compromise between sampling enough the gaussian and fast interpolation """ Simulate the observations of a model. Convolves the model with a variable Gaussian LSF, sampled at each desired spectral channel. @@ -375,7 +386,7 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si model_fluxes (array): the fluxes of the model num_sigma (float): number of +/- sigmas to evaluate the LSF to. Returns: - - output_model (array) : the fluxes in each of the wavelength channels + - output_model (array): the fluxes in each of the wavelength channels Author: Jason Wang """ @@ -392,8 +403,9 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si if np.sum(lsf) != 0: - - model_interp = interp1d(model_wvs, model_fluxes, kind='cubic', bounds_error=False) + left_fill = model_fluxes[model_in_range][0] + right_fill = model_fluxes[model_in_range][-1] + model_interp = interp1d(model_wvs, model_fluxes, kind='cubic', bounds_error=False, fill_value=(left_fill,right_fill)) filter_model = model_interp(filter_wv_coords) output_model = np.nansum(filter_model * lsf, axis=1) / np.sum(lsf, axis=1) @@ -415,28 +427,28 @@ def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nati function 'convolve_and_sample'. Args: - global_params (object): Class containing each parameter used in ForMoSA - wav_obs (array): Wavelength grid of the data - flx_obs (array): Flux of the data - res_obs (array): Spectral resolution of the data - wav_mod_nativ (array): Wavelength grid of the model - flx_mod_nativ (array): Flux of the model - res_mod_obs (array): Spectral resolution of the model as a function of the wavelength grid of the data - obs_or_mod (str): Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod' - indobs (int): Index of the current observation looping + global_params (object): Class containing each parameter used in ForMoSA + wav_obs (array): Wavelength grid of the data + flx_obs (array): Flux of the data + res_obs (array): Spectral resolution of the data + wav_mod_nativ (array): Wavelength grid of the model + flx_mod_nativ (array): Flux of the model + res_mod_obs (array): Spectral resolution of the model as a function of the wavelength grid of the data + obs_or_mod (str): Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod' + indobs (int): Index of the current observation looping Returns: - - flx_obs_final (array) : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid + - flx_obs_final (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid - Author: Simon Petrus, Matthieu Ravet + Author: Simon Petrus """ # Estimate of the FWHM of the data as a function of the wavelength - fwhm_obs = 2 * wav_obs / res_obs + fwhm_obs = wav_obs / res_obs # Estimate of the FWHM of the model as a function of the wavelength - fwhm_mod = 2 * wav_obs / res_mod_obs + fwhm_mod = wav_obs / res_mod_obs # Estimate of the FWHM of the custom resolution (if defined) as a function of the wavelength if global_params.custom_reso[indobs] != 'NA': - fwhm_custom = 2 * wav_obs / float(global_params.custom_reso[indobs]) + fwhm_custom = wav_obs / float(global_params.custom_reso[indobs]) else: fwhm_custom = wav_obs * np.nan @@ -471,15 +483,14 @@ def continuum_estimate(global_params, wav, flx, res, indobs=0): res (int): Spectral resolution of the spectrum for which you want to estimate the continuum indobs (int): Index of the current observation looping Returns: - - continuum (array) : Estimated continuum of the spectrum re-sampled on the data wavelength grid + - continuum (array): Estimated continuum of the spectrum re-sampled on the data wavelength grid Author: Simon Petrus, Matthieu Ravet """ # Redifined a spectrum only composed by the wavelength ranges used to estimate the continuum - wav_for_continuum = global_params.wav_for_continuum[indobs].split('/') - for wav_for_cont_cut_ind, wav_for_cont_cut in enumerate(wav_for_continuum): + for wav_for_cont_cut_ind, wav_for_cont_cut in enumerate(global_params.wav_for_continuum[indobs].split('/')): wav_for_cont_cut = wav_for_cont_cut.split(',') ind_cont_cut = np.where((float(wav_for_cont_cut[0]) <= wav) & (wav <= float(wav_for_cont_cut[1]))) if wav_for_cont_cut_ind == 0: @@ -497,15 +508,13 @@ def continuum_estimate(global_params, wav, flx, res, indobs=0): wav_median = np.median(wav) dwav_median = np.median(np.abs(wav - np.roll(wav, 1))) # Estimated the median wavelength separation instead of taking wav_median - (wav_median+1) that could be on a border - fwhm = 2 * wav_median / np.median(res) - fwhm_continuum = 2 * wav_median / float(global_params.continuum_sub[indobs]) + fwhm = wav_median / np.median(res) + fwhm_continuum = wav_median / float(global_params.continuum_sub[indobs]) fwhm_conv = np.sqrt(fwhm_continuum**2 - fwhm**2) sigma = fwhm_conv / (dwav_median * 2.355) continuum = gaussian_filter(flx, sigma) - # import scipy.signal as sg - # continuum = sg.savgol_filter(flx, 3001, 2) return continuum diff --git a/ForMoSA/main.py b/ForMoSA/main.py index dc7e844..6cd8367 100755 --- a/ForMoSA/main.py +++ b/ForMoSA/main.py @@ -4,12 +4,12 @@ Here we open the config file and extract all the needed information. Easy to understand and simple access for the new users. -@authors: S. Petrus & P. Palma-Bifani +@authors: S. Petrus & P. Palma-Bifani ''' # ---------------------------------------------------------------------------------------------------------------------- ## IMPORTS import os -os.environ["OMP_NUM_THREADS"] = "1" +# os.environ["OMP_NUM_THREADS"] = "1" import sys # Import ForMoSA @@ -20,47 +20,48 @@ from adapt.adapt_obs_mod import launch_adapt from nested_sampling.nested_sampling import launch_nested_sampling -# ---------------------------------------------------------------------------------------------------------------------- -## USER configuration path -print() -print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') -print('-> Configuration of environment') -if len(sys.argv) == 1: - print('Where is your configuration file?') - config_file_path = input() -else: - config_file_path = sys.argv[1] -print() +if __name__ == '__main__': + # ---------------------------------------------------------------------------------------------------------------------- + ## USER configuration path + print() + print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') + print('-> Configuration of environment') + if len(sys.argv) == 1: + print('Where is your configuration file?') + config_file_path = input() + else: + config_file_path = sys.argv[1] + print() -# ---------------------------------------------------------------------------------------------------------------------- -## CONFIG_FILE reading and defining global parameters -global_params = GlobFile(config_file_path) # To access any param.: global_params.parameter_name + # ---------------------------------------------------------------------------------------------------------------------- + ## CONFIG_FILE reading and defining global parameters + global_params = GlobFile(config_file_path) # To access any param.: global_params.parameter_name -# ---------------------------------------------------------------------------------------------------------------------- -## Run ForMoSA -print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') -print('-> Initializing ForMoSA') -print() + # ---------------------------------------------------------------------------------------------------------------------- + ## Run ForMoSA + print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') + print('-> Initializing ForMoSA') + print() -if len(sys.argv) == 1: - y_n_par = yesno('Do you want to adapt the grid to your data? (y/n)') -else: - y_n_par = sys.argv[2] + if len(sys.argv) == 1: + y_n_par = yesno('Do you want to adapt the grid to your data? (y/n)') + else: + y_n_par = sys.argv[2] -if y_n_par == 'y': - launch_adapt(global_params, justobs='no') -else: - launch_adapt(global_params, justobs='yes') + if y_n_par == 'y': + launch_adapt(global_params, justobs='no') + else: + launch_adapt(global_params, justobs='yes') -print() -print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') -print('-> Nested sampling') -print() -# Run S5 for Nested Sampling -launch_nested_sampling(global_params) + print() + print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') + print('-> Nested sampling') + print() + # Run S5 for Nested Sampling + launch_nested_sampling(global_params) -print() -print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') -print('-> Voilà, on est prêt') -print() \ No newline at end of file + print() + print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -') + print('-> Voilà, on est prêt') + print() \ No newline at end of file diff --git a/ForMoSA/main_utilities.py b/ForMoSA/main_utilities.py index 7562bc2..e1f9792 100755 --- a/ForMoSA/main_utilities.py +++ b/ForMoSA/main_utilities.py @@ -21,37 +21,12 @@ def yesno(text): return yesno() # ---------------------------------------------------------------------------------------------------------------------- -def diag_mat(rem=[], result=np.empty((0, 0))): - ''' - Function to concatenate and align iterativly block matrices (usefull during the extraction and the inversion). - - Args: - rem (list): matrices to be add iterativly (use diag([mat1, mat2])) - result (array): final array with each sub-matrices aligned allong the diagonal - Returns: - diag_mat (matrix): Generated diagonal matrix - (If rem input is empty, it wull return an empy array) - - Author : Ishigoya, Stack-overflow : https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array - ''' - if not rem: - return result - m = rem.pop(0) - result = np.block( - [ - [result, np.zeros((result.shape[0], m.shape[1]))], - [np.zeros((m.shape[0], result.shape[1])), m], - ] - ) - return diag_mat(rem, result) - -# ---------------------------------------------------------------------------------------------------------------------- class GlobFile: ''' Class that import all the parameters from the config file and make them GLOBAL FORMOSA VARIABLES. - + Author: Paulina Palma-Bifani ''' @@ -76,7 +51,7 @@ def __init__(self, config_file_path): model_name = model_name[0] self.model_name = model_name - if type(config['config_adapt']['wav_for_adapt']) != list: # Create lists if only one obs in the loop + if type(config['config_adapt']['wav_for_adapt']) != list: # Create lists if only one obs in the loop # [config_adapt] (5) self.wav_for_adapt = [config['config_adapt']['wav_for_adapt']] self.adapt_method = [config['config_adapt']['adapt_method']] @@ -101,6 +76,12 @@ def __init__(self, config_file_path): self.logL_type = config['config_inversion']['logL_type'] self.wav_fit = config['config_inversion']['wav_fit'] + # parallelisation of adapt + try: + self.parallel = config['config_adapt']['parallel'] + except KeyError: + self.parallel = False + self.ns_algo = config['config_inversion']['ns_algo'] self.npoint = config['config_inversion']['npoint'] @@ -121,7 +102,7 @@ def __init__(self, config_file_path): self.bb_R = config['config_parameter']['bb_R'] self.ck = None - + # [config_nestle] (5, some mutually exclusive) (n_ prefix for params) self.n_method = config['config_nestle']['method'] self.n_maxiter = eval(config['config_nestle']['maxiter']) @@ -152,7 +133,7 @@ def __init__(self, config_file_path): # self.p_init_MPI = config['config_pymultinest']['init_MPI'] # self.p_dump_callback = config['config_pymultinest']['dump_callback'] # self.p_use_MPI = config['config_pymultinest']['use_MPI'] - + # [config_dinesty] & [config_ultranest] CHECK THIS # ## create OUTPUTS Sub-Directories: interpolated grids and results diff --git a/ForMoSA/nested_sampling/nested_modif_spec.py b/ForMoSA/nested_sampling/nested_modif_spec.py index 1f6dc42..da17b52 100755 --- a/ForMoSA/nested_sampling/nested_modif_spec.py +++ b/ForMoSA/nested_sampling/nested_modif_spec.py @@ -161,7 +161,7 @@ def calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro, flx_obs_photo, er if analytic == 'no': r_picked *= u.Rjup d_picked *= u.pc - ck = alpha * (r_picked.value/d_picked.value)**2 + ck = alpha * (r_picked.to(u.m).value/d_picked.to(u.m).value)**2 # Calculation of the dilution factor ck analytically else: if len(flx_obs_spectro) != 0: @@ -448,7 +448,7 @@ def modif_spec(global_params, theta, theta_index, else: # If you want 1 common rv for all observations if global_params.rv != "NA": if global_params.rv[0] == "constant": - alpha_picked = float(global_params.rv[1]) + rv_picked = float(global_params.rv[1]) else: ind_theta_rv = np.where(theta_index == 'rv') rv_picked = theta[ind_theta_rv[0][0]] diff --git a/ForMoSA/nested_sampling/nested_sampling.py b/ForMoSA/nested_sampling/nested_sampling.py index 72f3f32..a73e65b 100755 --- a/ForMoSA/nested_sampling/nested_sampling.py +++ b/ForMoSA/nested_sampling/nested_sampling.py @@ -11,7 +11,6 @@ from nested_sampling.nested_modif_spec import modif_spec from nested_sampling.nested_prior_function import uniform_prior, gaussian_prior from nested_sampling.nested_logL_functions import * -from main_utilities import diag_mat def import_obsmod(global_params): @@ -19,11 +18,11 @@ def import_obsmod(global_params): Function to import spectra (model and data) before the inversion Args: - global_params (object): Class containing every input from the .ini file. + global_params (object): Class containing every input from the .ini file. Returns: - main_file (list(array)): Return a list of lists with the wavelengths, flux, errors, covariance matrix, - transmission, star flux, systematics and the grids for both spectroscopic and photometric data. + transmission, star flux, systematics, grid indices and the grids for both spectroscopic and photometric data. Authors: Simon Petrus, Matthieu Ravet and Allan Denis """ @@ -37,14 +36,14 @@ def import_obsmod(global_params): obs_name = os.path.splitext(os.path.basename(global_params.observation_path))[0] spectrum_obs = np.load(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True) - wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float) - flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float) - err_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float) + wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][0], dtype=float) + flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][1], dtype=float) + err_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][2], dtype=float) # Optional arrays - inv_cov_obs = np.asarray(spectrum_obs['obs_opt_merge'][0], dtype=float) - transm_obs = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float) - star_flx_obs = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float) - system_obs = np.asarray(spectrum_obs['obs_opt_merge'][3], dtype=float) + inv_cov_obs = np.asarray(spectrum_obs['obs_opt'][0], dtype=float) + transm_obs = np.asarray(spectrum_obs['obs_opt'][1], dtype=float) + star_flx_obs = np.asarray(spectrum_obs['obs_opt'][2], dtype=float) + system_obs = np.asarray(spectrum_obs['obs_opt'][3], dtype=float) if 'obs_photo' in spectrum_obs.keys(): wav_obs_photo = np.asarray(spectrum_obs['obs_photo'][0], dtype=float) @@ -64,9 +63,58 @@ def import_obsmod(global_params): ds = xr.open_dataset(path_grid_photo, decode_cf=False, engine='netcdf4') grid_photo = ds['grid'] ds.close() - - main_file.append([[wav_obs_spectro, wav_obs_photo], [flx_obs_spectro, flx_obs_photo], [err_obs_spectro, err_obs_photo], inv_cov_obs, transm_obs, star_flx_obs, system_obs, grid_spectro, grid_photo]) + # Initiate indices tables for each sub-spectrum + mask_mod_spectro = np.zeros(len(grid_spectro['wavelength']), dtype=bool) + mask_mod_photo = np.zeros(len(grid_photo['wavelength']), dtype=bool) + mask_obs_spectro = np.zeros(len(wav_obs_spectro), dtype=bool) + mask_obs_photo = np.zeros(len(wav_obs_photo), dtype=bool) + for ns_u_ind, ns_u in enumerate(global_params.wav_fit[indobs].split('/')): + min_ns_u = float(ns_u.split(',')[0]) + max_ns_u = float(ns_u.split(',')[1]) + # Indices of each model and data + mask_mod_spectro += (grid_spectro['wavelength'] >= min_ns_u) & (grid_spectro['wavelength'] <= max_ns_u) + mask_mod_photo += (grid_photo['wavelength'] >= min_ns_u) & (grid_photo['wavelength'] <= max_ns_u) + mask_obs_spectro += (wav_obs_spectro >= min_ns_u) & (wav_obs_spectro <= max_ns_u) + mask_obs_photo += (wav_obs_photo >= min_ns_u) & (wav_obs_photo <= max_ns_u) + + # Cutting the data to a wavelength grid defined by the parameter 'wav_fit' + wav_obs_spectro_ns_u = wav_obs_spectro[mask_obs_spectro] + flx_obs_spectro_ns_u = flx_obs_spectro[mask_obs_spectro] + err_obs_spectro_ns_u = err_obs_spectro[mask_obs_spectro] + if len(inv_cov_obs) != 0: # Add covariance in the loop (if necessary) + inv_cov_obs_ns_u = inv_cov_obs[np.ix_(mask_obs_spectro, mask_obs_spectro)] + else: + inv_cov_obs_ns_u = np.asarray([]) + if len(transm_obs) != 0: # Add the transmission (if necessary) + transm_obs_ns_u = transm_obs[mask_obs_spectro] + else: + transm_obs_ns_u = np.asarray([]) + if len(star_flx_obs) != 0: # Add star flux (if necessary) + star_flx_obs_ns_u = star_flx_obs[mask_obs_spectro] + else: + star_flx_obs_ns_u = np.asarray([]) + if len(system_obs) != 0: # Add systematics model (if necessary) + system_obs_ns_u = system_obs[mask_obs_spectro] + else: + system_obs_ns_u = np.asarray([]) + wav_obs_photo_ns_u = wav_obs_photo[mask_obs_photo] + flx_obs_photo_ns_u = flx_obs_photo[mask_obs_photo] + err_obs_photo_ns_u = err_obs_photo[mask_obs_photo] + + # Cutting of the grid on the wavelength grid defined by the parameter 'wav_fit' + grid_spectro_ns_u = grid_spectro.sel(wavelength=grid_spectro['wavelength'][mask_mod_spectro]) + grid_photo_ns_u = grid_photo.sel(wavelength=grid_photo['wavelength'][mask_mod_photo]) + + main_file.append([[wav_obs_spectro_ns_u, wav_obs_photo_ns_u], + [flx_obs_spectro_ns_u, flx_obs_photo_ns_u], + [err_obs_spectro_ns_u, err_obs_photo_ns_u], + inv_cov_obs_ns_u, + transm_obs_ns_u, + star_flx_obs_ns_u, + system_obs_ns_u, + grid_spectro_ns_u, + grid_photo_ns_u]) return main_file @@ -98,129 +146,68 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'): for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))): # Recovery of spectroscopy and photometry data - wav_obs_spectro = main_file[indobs][0][0] - wav_obs_photo = main_file[indobs][0][1] - flx_obs_spectro = main_file[indobs][1][0] - flx_obs_photo = main_file[indobs][1][1] - err_obs_spectro = main_file[indobs][2][0] - err_obs_photo = main_file[indobs][2][1] - inv_cov_obs = main_file[indobs][3] - transm_obs = main_file[indobs][4] - star_flx_obs = main_file[indobs][5] - system_obs = main_file[indobs][6] - + wav_obs_spectro_ns_u = main_file[indobs][0][0] + wav_obs_photo_ns_u = main_file[indobs][0][1] + flx_obs_spectro_ns_u = main_file[indobs][1][0] + flx_obs_photo_ns_u = main_file[indobs][1][1] + err_obs_spectro_ns_u = main_file[indobs][2][0] + err_obs_photo_ns_u = main_file[indobs][2][1] + inv_cov_obs_ns_u = main_file[indobs][3] + transm_obs_ns_u = main_file[indobs][4] + star_flx_obs_ns_u = main_file[indobs][5] + system_obs_ns_u = main_file[indobs][6] # Recovery of the spectroscopy and photometry model - grid_spectro = main_file[indobs][7] - grid_photo = main_file[indobs][8] - - # Calculation of the likelihood for each sub-spectrum defined by the parameter 'wav_fit' - for ns_u_ind, ns_u in enumerate(global_params.wav_fit[indobs].split('/')): - - min_ns_u = float(ns_u.split(',')[0]) - max_ns_u = float(ns_u.split(',')[1]) - ind_grid_spectro_sel = np.where((grid_spectro['wavelength'] >= min_ns_u) & (grid_spectro['wavelength'] <= max_ns_u)) - ind_grid_photo_sel = np.where((grid_photo['wavelength'] >= min_ns_u) & (grid_photo['wavelength'] <= max_ns_u)) - - # Cutting of the grid on the wavelength grid defined by the parameter 'wav_fit' - grid_spectro_cut = grid_spectro.sel(wavelength=grid_spectro['wavelength'][ind_grid_spectro_sel]) - grid_photo_cut = grid_photo.sel(wavelength=grid_photo['wavelength'][ind_grid_photo_sel]) - - # Interpolation of the grid at the theta parameters set - if global_params.par3 == 'NA': - if len(grid_spectro_cut['wavelength']) != 0: - flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_spectro_cut = np.asarray([]) - if len(grid_photo_cut['wavelength']) != 0: - flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_photo_cut = np.asarray([]) - elif global_params.par4 == 'NA': - if len(grid_spectro_cut['wavelength']) != 0: - flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_spectro_cut = np.asarray([]) - if len(grid_photo_cut['wavelength']) != 0: - flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_photo_cut = np.asarray([]) - elif global_params.par5 == 'NA': - if len(grid_spectro_cut['wavelength']) != 0: - flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_spectro_cut = np.asarray([]) - if len(grid_photo_cut['wavelength']) != 0: - flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_photo_cut = np.asarray([]) + grid_spectro_ns_u = main_file[indobs][7] + grid_photo_ns_u = main_file[indobs][8] + + # Interpolation of the grid at the theta parameters set + if global_params.par3 == 'NA': + if len(grid_spectro_ns_u['wavelength']) != 0: + flx_mod_spectro_ns_u = np.asarray(grid_spectro_ns_u.interp(par1=theta[0], par2=theta[1], + method="linear", kwargs={"fill_value": "extrapolate"})) else: - if len(grid_spectro_cut['wavelength']) != 0: - flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], - par5=theta[4], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_spectro_cut = np.asarray([]) - if len(grid_photo_cut['wavelength']) != 0: - flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], - par5=theta[4], - method="linear", kwargs={"fill_value": "extrapolate"})) - else: - flx_mod_photo_cut = np.asarray([]) - - - # Re-merging of the data and interpolated synthetic spectrum to a wavelength grid defined by the parameter 'wav_fit' - ind_spectro = np.where((wav_obs_spectro >= min_ns_u) & (wav_obs_spectro <= max_ns_u)) - ind_photo = np.where((wav_obs_photo >= min_ns_u) & (wav_obs_photo <= max_ns_u)) - if ns_u_ind == 0: - wav_obs_spectro_ns_u = wav_obs_spectro[ind_spectro] - flx_obs_spectro_ns_u = flx_obs_spectro[ind_spectro] - err_obs_spectro_ns_u = err_obs_spectro[ind_spectro] - flx_mod_spectro_ns_u = flx_mod_spectro_cut - if len(inv_cov_obs) != 0: # Add covariance in the loop (if necessary) - inv_cov_obs_ns_u = inv_cov_obs[np.ix_(ind_spectro[0],ind_spectro[0])] - else: - inv_cov_obs_ns_u = np.asarray([]) - if len(transm_obs) != 0: # Add the transmission (if necessary) - transm_obs_ns_u = transm_obs[ind_spectro] - else: - transm_obs_ns_u = np.asarray([]) - if len(star_flx_obs) != 0: # Add star flux (if necessary) - star_flx_obs_ns_u = star_flx_obs[ind_spectro] - else: - star_flx_obs_ns_u = np.asarray([]) - if len(system_obs) != 0: # Add systematics model (if necessary) - system_obs_ns_u = system_obs[ind_spectro] - else: - system_obs_ns_u = np.asarray([]) - wav_obs_photo_ns_u = wav_obs_photo[ind_photo] - flx_obs_photo_ns_u = flx_obs_photo[ind_photo] - err_obs_photo_ns_u = err_obs_photo[ind_photo] - flx_mod_photo_ns_u = flx_mod_photo_cut + flx_mod_spectro_ns_u = np.asarray([]) + if len(grid_photo_ns_u['wavelength']) != 0: + flx_mod_photo_ns_u = np.asarray(grid_photo_ns_u.interp(par1=theta[0], par2=theta[1], + method="linear", kwargs={"fill_value": "extrapolate"})) else: - wav_obs_spectro_ns_u = np.concatenate((wav_obs_spectro_ns_u, wav_obs_spectro[ind_spectro])) - flx_obs_spectro_ns_u = np.concatenate((flx_obs_spectro_ns_u, flx_obs_spectro[ind_spectro])) - err_obs_spectro_ns_u = np.concatenate((err_obs_spectro_ns_u, err_obs_spectro[ind_spectro])) - flx_mod_spectro_ns_u = np.concatenate((flx_mod_spectro_ns_u, flx_mod_spectro_cut)) - if len(inv_cov_obs_ns_u) != 0: # Merge the covariance matrices (if necessary) - inv_cov_obs_ns_u = diag_mat([inv_cov_obs_ns_u, inv_cov_obs[np.ix_(ind_spectro[0],ind_spectro[0])]]) - if len(transm_obs_ns_u) != 0: # Merge the transmissions (if necessary) - transm_obs_ns_u = np.concatenate((transm_obs_ns_u, transm_obs[ind_spectro])) - if len(star_flx_obs_ns_u) != 0: # Merge star fluxes (if necessary) - star_flx_obs_ns_u = np.concatenate((star_flx_obs_ns_u, star_flx_obs[ind_grid_spectro_sel]),axis=0) - if len(system_obs) != 0: # Merge systematics model (if necessary) - system_obs_ns_u = np.concatenate((system_obs_ns_u, system_obs[ind_grid_spectro_sel]), axis=0) - wav_obs_photo_ns_u = np.concatenate((wav_obs_photo_ns_u, wav_obs_photo[ind_photo])) - flx_obs_photo_ns_u = np.concatenate((flx_obs_photo_ns_u, flx_obs_photo[ind_photo])) - err_obs_photo_ns_u = np.concatenate((err_obs_photo_ns_u, err_obs_photo[ind_photo])) - flx_mod_photo_ns_u = np.concatenate((flx_mod_photo_ns_u, flx_mod_photo_cut)) - + flx_mod_photo_ns_u = np.asarray([]) + elif global_params.par4 == 'NA': + if len(grid_spectro_ns_u['wavelength']) != 0: + flx_mod_spectro_ns_u = np.asarray(grid_spectro_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_spectro_ns_u = np.asarray([]) + if len(grid_photo_ns_u['wavelength']) != 0: + flx_mod_photo_ns_u = np.asarray(grid_photo_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_photo_ns_u = np.asarray([]) + elif global_params.par5 == 'NA': + if len(grid_spectro_ns_u['wavelength']) != 0: + flx_mod_spectro_ns_u = np.asarray(grid_spectro_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_spectro_ns_u = np.asarray([]) + if len(grid_photo_ns_u['wavelength']) != 0: + flx_mod_photo_ns_u = np.asarray(grid_photo_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_photo_ns_u = np.asarray([]) + else: + if len(grid_spectro_ns_u['wavelength']) != 0: + flx_mod_spectro_ns_u = np.asarray(grid_spectro_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], + par5=theta[4], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_spectro_ns_u = np.asarray([]) + if len(grid_photo_ns_u['wavelength']) != 0: + flx_mod_photo_ns_u = np.asarray(grid_photo_ns_u.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3], + par5=theta[4], + method="linear", kwargs={"fill_value": "extrapolate"})) + else: + flx_mod_photo_ns_u = np.asarray([]) # Modification of the synthetic spectrum with the extra-grid parameters modif_spec_LL = modif_spec(global_params, theta, theta_index, diff --git a/ForMoSA/plotting/plotting_class.py b/ForMoSA/plotting/plotting_class.py index 55ca48e..d34e0cf 100644 --- a/ForMoSA/plotting/plotting_class.py +++ b/ForMoSA/plotting/plotting_class.py @@ -3,15 +3,13 @@ import os, glob, sys import numpy as np import matplotlib.pyplot as plt -from matplotlib.backends.backend_pdf import PdfPages from scipy.interpolate import interp1d import corner import xarray as xr import pickle -from tqdm import tqdm +import astropy.constants as cst sys.path.insert(0, os.path.abspath('../')) -import scipy.signal as sg # Import ForMoSA from main_utilities import GlobFile @@ -273,7 +271,7 @@ def _get_posteriors(self): tot_list_param_title.append(extra_parameters[2][1] + fr'$_{indobs}$' + ' ' + extra_parameters[2][2]) theta_index.append(f'alpha_{indobs}') else: # If you want 1 common alpha for all observations - if self.global_params.alpha != 'NA' and self.global_params.alpha != 'constant': + if self.global_params.alpha != 'NA' and self.global_params.alpha[0] != 'constant': tot_list_param_title.append(extra_parameters[2][1] + ' ' + extra_parameters[2][2]) theta_index.append('alpha') if len(self.global_params.rv) > 3: # If you want separate rv for each observations @@ -283,7 +281,7 @@ def _get_posteriors(self): tot_list_param_title.append(extra_parameters[3][1] + fr'$_{indobs}$' + ' ' + extra_parameters[3][2]) theta_index.append(f'rv_{indobs}') else: # If you want 1 common rv for all observations - if self.global_params.rv != 'NA' and self.global_params.rv != 'constant': + if self.global_params.rv != 'NA' and self.global_params.rv[0] != 'constant': tot_list_param_title.append(extra_parameters[3][1] + ' ' + extra_parameters[3][2]) theta_index.append('rv') if len(self.global_params.vsini) > 4: # If you want separate vsini for each observations @@ -293,7 +291,7 @@ def _get_posteriors(self): tot_list_param_title.append(extra_parameters[5][1] + fr'$_{indobs}$' + ' ' + extra_parameters[5][2]) theta_index.append(f'vsini_{indobs}') else: # If you want 1 common vsini for all observations - if self.global_params.vsini != 'NA' and self.global_params.vsini != 'constant': + if self.global_params.vsini != 'NA' and self.global_params.vsini[0] != 'constant': tot_list_param_title.append(extra_parameters[5][1] + ' ' + extra_parameters[5][2]) theta_index.append('vsini') if len(self.global_params.ld) > 3: # If you want separate ld for each observations @@ -303,7 +301,7 @@ def _get_posteriors(self): tot_list_param_title.append(extra_parameters[6][1] + fr'$_{indobs}$' + ' ' + extra_parameters[6][2]) theta_index.append(f'ld_{indobs}') else: # If you want 1 common vsini for all observations - if self.global_params.ld != 'NA' and self.global_params.ld != 'constant': + if self.global_params.ld != 'NA' and self.global_params.ld[0] != 'constant': tot_list_param_title.append(extra_parameters[6][1] + ' ' + extra_parameters[6][2]) theta_index.append('ld') @@ -330,10 +328,8 @@ def _get_posteriors(self): ind_theta_r = np.where(self.theta_index == 'r') r_picked = results[ind_theta_r[0]] - lum = np.log10(4 * np.pi * (r_picked * 69911000.) ** 2 * results[0] ** 4 * 5.670e-8 / 3.83e26) - #print(lum) + lum = np.log10(4 * np.pi * (r_picked * cst.R_jup.value) ** 2 * results[0] ** 4 * cst.sigma_sb.value / cst.L_sun.value) results = np.concatenate((results, np.asarray(lum))) - #print(results) posterior_to_plot.append(results) self.posterior_to_plot = np.array(posterior_to_plot) @@ -355,9 +351,10 @@ def plot_corner(self, levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16, print('ForMoSA - Corner plot') self._get_posteriors() + fig = corner.corner(self.posterior_to_plot[burn_in:], - #weights=self.weights[burn_in:], + weights=self.weights[burn_in:], labels=self.posteriors_names, range=[0.999999 for p in self.posteriors_names], levels=levels_sig, @@ -484,12 +481,12 @@ def _get_spectra(self,theta,return_model=False): obs_name = os.path.splitext(os.path.basename(self.global_params.observation_path))[0] spectrum_obs = np.load(os.path.join(self.global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True) - wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float) - flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float) - err_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float) - transm_obs = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float) - star_flx_obs = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float) - system_obs = np.asarray(spectrum_obs['obs_opt_merge'][3], dtype=float) + wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][0], dtype=float) + flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][1], dtype=float) + err_obs_spectro = np.asarray(spectrum_obs['obs_spectro'][2], dtype=float) + transm_obs = np.asarray(spectrum_obs['obs_opt'][1], dtype=float) + star_flx_obs = np.asarray(spectrum_obs['obs_opt'][2], dtype=float) + system_obs = np.asarray(spectrum_obs['obs_opt'][3], dtype=float) if 'obs_photo' in spectrum_obs.keys(): wav_obs_photo = np.asarray(spectrum_obs['obs_photo'][0], dtype=float) flx_obs_photo = np.asarray(spectrum_obs['obs_photo'][1], dtype=float) diff --git a/RELEASE_NOTES.txt b/RELEASE_NOTES.txt index 59f2c0a..313c614 100644 --- a/RELEASE_NOTES.txt +++ b/RELEASE_NOTES.txt @@ -389,5 +389,54 @@ Comments: - New approach in the adaptation of the data in the case where the user defined separated windows in 'wave_for_adapt' (eg : '1.4, 1.5 / 1.6, 1.7 / 1.8, 1.9 / ... '). Instead of making a loop on each sub-interval, we now combine the data in one array and do the adaptation on one array - New approach in the lsq function, we can now use the lsq in each sub wavelength separatly (eg. '1.4, 1.5', '1.6, 1.7', '1.8, 1.9' ...) and merge the results into a single array to compute the loglikelihood on one array only. This gives the advantage of limiting flux error calibration that can arrise between order for data such as CRIRES, HiRISE ... and being fast at the same time. Previously, the use had to define separated windows in 'wave_fit' (eg : '1.4, 1.5', '1.6, 1.7', '1.8, 1.9' ...) and one loglikelihood one computed for each of these windows. Now the user has to define separated windows in the format '1.4, 1.5 / 1.6, 1.7 / 1.8, 1.9 / ...' -Tests tha have been done to checkup the changes: - - High resolution datasets (HiRISE, CRIRES) \ No newline at end of file +Tests that have been done to checkup the changes: + - High resolution datasets (HiRISE, CRIRES) + + +- - - + +26/09/2024 + +Matthieu Ravet + +Comments: + - Updating / correting the readthedocs + - Correction of the rv input loop if you want to set it to constant during the ns + - Correction of the plotting function (for constant extra grid and for the custom resolution grid) + + +Tests that have been done to checkup the changes: + - Test with constant priors on the rv + + +_ _ _ + +25/10/2024 + +Matthieu Ravet + +Comments: + Huge optimisation and correction update with multiple changes + + In extraction_functions / adapt_obs_mod and adapt_grid : + - Updating the import name of STAR FLX to STAR_FLX1 to match what is done for SYSTEM1 (i.e. when only one is used) + - Updating the extraction of the covariance matrix (to extract off-diagonal terms if multiple wav_for_adapt) + - Separated spectral obs are not stored separatly anymore (not needed) + - Spectral instrument are not stored anymore (not needed) + - Removing the step np.load() during the adapation of the grid to speed up the code + - Correcting the convolution function : removing factor 2 in fwhm = 2 * wav / res and reseting num_sigma = 1 + - Updating function names in adapt_grid + + In nested_sampling + - Extraction the loop on each spectral window out of the inversion loop to speed up the code + - Updating the extraction of the covariance matrix (to extract off-diagonal terms if multiple wav_fit) + + In plotting_class + - Updating the function according to the previous changes + - Updating to use astropy units to compute the Luminosity + +Tests that have been done to checkup the changes: + - Run with photometry / LRS / MRS and HRS + - Run with/without MOSAIC + - Run with/without multiple wav_for_adapt and multiple wav_fit + - Run with/without continuum \ No newline at end of file diff --git a/docs/ForMoSA.png b/docs/ForMoSA.png index d48bd9c..ab8d329 100644 Binary files a/docs/ForMoSA.png and b/docs/ForMoSA.png differ diff --git a/docs/ForMoSA_old.png b/docs/ForMoSA_old.png new file mode 100644 index 0000000..d48bd9c Binary files /dev/null and b/docs/ForMoSA_old.png differ diff --git a/docs/_build/doctrees/adapt.doctree b/docs/_build/doctrees/adapt.doctree index 3835c3f..eda8918 100644 Binary files a/docs/_build/doctrees/adapt.doctree and b/docs/_build/doctrees/adapt.doctree differ diff --git a/docs/_build/doctrees/api.doctree b/docs/_build/doctrees/api.doctree index 2578554..8a920a0 100644 Binary files a/docs/_build/doctrees/api.doctree and b/docs/_build/doctrees/api.doctree differ diff --git a/docs/_build/doctrees/demo.doctree b/docs/_build/doctrees/demo.doctree index 08a80d7..d27d523 100644 Binary files a/docs/_build/doctrees/demo.doctree and b/docs/_build/doctrees/demo.doctree differ diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle index 4c9a2ea..0208007 100644 Binary files a/docs/_build/doctrees/environment.pickle and b/docs/_build/doctrees/environment.pickle differ diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree index 931e2ec..318e95c 100644 Binary files a/docs/_build/doctrees/index.doctree and b/docs/_build/doctrees/index.doctree differ diff --git a/docs/_build/doctrees/installation.doctree b/docs/_build/doctrees/installation.doctree index 63aba2f..7679ef4 100644 Binary files a/docs/_build/doctrees/installation.doctree and b/docs/_build/doctrees/installation.doctree differ diff --git a/docs/_build/doctrees/main_utilities.doctree b/docs/_build/doctrees/main_utilities.doctree index c3b8f74..3e5a8d6 100644 Binary files a/docs/_build/doctrees/main_utilities.doctree and b/docs/_build/doctrees/main_utilities.doctree differ diff --git a/docs/_build/doctrees/nbsphinx/tutorials/config_file.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/config_file.ipynb new file mode 100644 index 0000000..0d5a1ae --- /dev/null +++ b/docs/_build/doctrees/nbsphinx/tutorials/config_file.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a ``config.ini``\n", + "\n", + "This section will help you set up your configuration file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One observation\n", + "\n", + "Let's say you obtain a spectrum of your favorite exoplanet with the instrument [Gemini/GPI](https://arxiv.org/pdf/1403.7520) operating between 0.9-2.4 μm at a spectral resolution of R~45.\n", + "\n", + "You have converted your data into ``GPI_data.fits`` and chosen a grid; ``EXOREM_native.nc`` for instance\n", + "\n", + "Then the config file should look like :" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2024-09-19T13:22:40.326157Z", + "iopub.status.busy": "2024-09-19T13:22:40.325846Z", + "iopub.status.idle": "2024-09-19T13:22:40.338541Z", + "shell.execute_reply": "2024-09-19T13:22:40.338033Z" + } + }, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (2101482320.py, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[1], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m observation_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" + ] + } + ], + "source": [ + "[config_path]\n", + " # Path to the observed spectrum file\n", + " observation_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits'\n", + "\n", + " # Path to store your interpolated grid\n", + " adapt_store_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/'\n", + "\n", + " # Path where you wish to store your results.\n", + " result_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/outputs/'\n", + "\n", + " # Path of your initial grid of models\n", + " model_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/model.nc'\n", + "\n", + "\n", + "[config_adapt]\n", + " # Wavelength range used for the extraction of data and adaptation of the grid (separated windows can be defined).\n", + " # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " wav_for_adapt = '0.9,2.4'\n", + "\n", + " # Method used to adapt the synthetic spectra to the data.\n", + " # example : 'by_reso' : A Gaussian law is convolved to the spectrum to decrease the resolution as a function of the\n", + " # wavelength.\n", + " # 'by_sample' : The spectrum is directly re-sampled to the wavelength grid of the data, using the module\n", + " # python spectres.\n", + " # Format : 'by_reso' or 'by_adapt'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " adapt_method = 'by_reso'\n", + "\n", + " # Custom target resolution to reach (optional). The final resolution will be the lowest between this custom resolution,\n", + " # the resolution of the data, and the resolution of the models, for each wavelength.\n", + " # Format : float or 'NA'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " custom_reso = 'NA'\n", + "\n", + " # Continuum subtraction. If a float is given, the value will give the approximated spectral resolution of the continuum or the size of the Sav_Gol\n", + " # windows if the option continuum_sub_method is set to \"Sav-Gol\". Format : 'float' or 'NA'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " continuum_sub = 'NA'\n", + "\n", + " # Wavelength range used for the estimate of the continuum (separated windows can be defined).\n", + " # Format : 'window1_max / window2_min,window2_max / ... / windown_min'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " wav_for_continuum = '0.9,2.4'\n", + "\n", + " # Whether to use least square to estimate planetary and stellar contributions\n", + " # Format : 'True' or 'False'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " use_lsqr = 'False'\n", + "\n", + "\n", + "[config_inversion]\n", + "\n", + " # Method to calculate the loglikelihood function used in the nested sampling procedure\n", + " # Please refer to the documentation on 'nested_logL_functions.py' for more information\n", + " # Format : 'chi2_classic' or 'chi2_covariance' or 'chi2_extended' or 'chi2_extended_covariance' or 'CCF_Brogi' or 'CCF_Zucker' or 'CCF_custom' \n", + " # WARNING : need to define separatly when MOSAIC \n", + " logL_type = 'chi2_classic'\n", + "\n", + " # Wavelength range used for the fit during the nested sampling procedure (separated windows can be defined).\n", + " # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'\n", + " # WARNING : need to define separatly when MOSAIC \n", + " wav_fit = '0.9,2.4'\n", + "\n", + " # Nested sampling algorithm used.\n", + " # Format : 'nestle' or 'pymultinest' or 'ultranest' or 'dynesty' (only nestle and pymultinest at the moment)\n", + " ns_algo = 'nestle'\n", + "\n", + " # Number of living points during the nested sampling procedure.\n", + " # Format : 'integer'\n", + " npoint = '50'\n", + "\n", + "\n", + "[config_parameter]\n", + " # Definition of the prior function of each parameter explored by the grid. Please refer to the documentation to check\n", + " # the parameter space explore by each grid.\n", + " # Format : \"function\", function_param1, function_param2\n", + " # Example : \"uniform\", min, max\n", + " # \"constant\", value\n", + " # \"gaussian\", mu, sigma\n", + " # 'NA' if the grid cover a lower number of parameters\n", + " par1 = 'uniform', 400, 2000 # Teff\n", + " par2 = 'uniform', 3.0, 5.0 # log(g)\n", + " par3 = 'uniform', -0.5, 1.0 # [M/H]\n", + " par4 = 'uniform', 0.1, 0.8 # C/O\n", + " par5 = 'NA'\n", + "\n", + " # Definition of the prior function of each extra-grid parameter. r is the radius (RJup, >0), d is the distance (pc, >0),\n", + " # alpha is a scaling factor applied to the model flux, rv is the radial velocity (km.s-1), av is the extinction (mag),\n", + " # vsini is the projected rotational velocity (km.s-1, >0), ld is the limb darkening (0-1), bb_T is the black-body temperature (K)\n", + " # and bb_R is the black-body radius (Rjup)\n", + " # Format : \"function\", function_param1, function_param2\n", + " # Example : \"uniform\", min, max\n", + " # \"constant\", value\n", + " # \"gaussian\", mu, sigma\n", + " # 'NA' if you do not want to constrain this parameter\n", + " # WARNING : need to define separatly when MOSAIC \n", + " r = 'NA'\n", + " d = 'NA'\n", + " alpha = 'NA'\n", + " rv = \"uniform\", -50, 50\n", + " av = 'NA'\n", + " vsini = 'NA'\n", + " ld = 'NA'\n", + " bb_T = 'NA'\n", + " bb_R = 'NA'\n", + " \n", + "\n", + "[config_nestle]\n", + " # For details on these parameters, please see: http://kylebarbary.com/nestle/index.html\n", + "\n", + " mechanic = 'static' # Sampler “super-classes” of dynesty\n", + " # e.g. 'static' / 'dynamic' # the number of living point is fixed / variates\n", + "\n", + " method = 'multi' # Reduction of the parameters space\n", + " # e.g. 'single' / 'multi' #single-ellipsoidal / multi-ellipsoidal\n", + "\n", + " maxiter = None # Stopping criterions\n", + " maxcall = None\n", + " dlogz = None\n", + " decline_factor = 0.1\n", + "\n", + " update_interval = None # Divers\n", + " npdim = None\n", + " rstate = None\n", + " callback = None\n", + "\n", + "[config_pymultinest]\n", + " # For details on these parameters, please see: https://github.com/JohannesBuchner/PyMultiNest \n", + "\n", + " n_clustering_params = None\n", + " wrapped_params = None, \n", + "\timportance_nested_sampling = True\n", + "\tmultimodal = True\n", + " const_efficiency_mode = False\n", + "\tevidence_tolerance = 0.5\n", + " sampling_efficiency = 0.8\n", + "\tn_iter_before_update = 100\n", + " null_log_evidence = -1e90\n", + "\tmax_modes = 100\n", + " mode_tolerance = -1e90\n", + "\tseed = -1\n", + " context = 0\n", + " write_output = True\n", + " log_zero = -1e100\n", + "\tmax_iter = 0\n", + " init_MPI = False\n", + " dump_callback = None\n", + " use_MPI = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple observations\n", + "\n", + "Now, if, in addition to your GPI spectrum, you want to add another observation, for example from [JWST/MIRI](https://arxiv.org/pdf/2303.13469) spaning 4.9-27.9 μm at a spectral resolution of R~2000, you should have two data files: ``GPI_data.fits`` and ``MIRI_data.fits``\n", + "\n", + "These two ``.fits`` should be placed in ``~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/``\n", + "\n", + "Then the config file should look like :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-09-19T13:22:40.363077Z", + "iopub.status.busy": "2024-09-19T13:22:40.362891Z", + "iopub.status.idle": "2024-09-19T13:22:40.368019Z", + "shell.execute_reply": "2024-09-19T13:22:40.367620Z" + } + }, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (986878237.py, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[2], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m observation_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/' # /!\\ NOW YOU NEED TO SPECIFY THE FOLDER WHERE THE TWO .FITS ARE /!\\\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" + ] + } + ], + "source": [ + "[config_path]\n", + " # Path to the observed spectrum file\n", + " observation_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/' # /!\\ NOW YOU NEED TO SPECIFY THE FOLDER WHERE THE TWO .FITS ARE /!\\\n", + "\n", + " # Path to store your interpolated grid\n", + " adapt_store_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/'\n", + "\n", + " # Path where you wish to store your results.\n", + " result_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/outputs/'\n", + "\n", + " # Path of your initial grid of models\n", + " model_path = '~/YOUR/PATH/formosa_desk/inversion_targetname/adapted_grid/model.nc'\n", + "\n", + "\n", + "[config_adapt]\n", + " # Wavelength range used for the extraction of data and adaptation of the grid (separated windows can be defined).\n", + " # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " wav_for_adapt = '0.9,2.4', '4.9,27.9'\n", + "\n", + " # Method used to adapt the synthetic spectra to the data.\n", + " # example : 'by_reso' : A Gaussian law is convolved to the spectrum to decrease the resolution as a function of the\n", + " # wavelength.\n", + " # 'by_sample' : The spectrum is directly re-sampled to the wavelength grid of the data, using the module\n", + " # python spectres.\n", + " # Format : 'by_reso' or 'by_adapt'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " adapt_method = 'by_reso', 'by_reso'\n", + "\n", + " # Custom target resolution to reach (optional). The final resolution will be the lowest between this custom resolution,\n", + " # the resolution of the data, and the resolution of the models, for each wavelength.\n", + " # Format : float or 'NA'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " custom_reso = 'NA', 'NA'\n", + "\n", + " # Continuum subtraction. If a float is given, the value will give the approximated spectral resolution of the continuum or the size of the Sav_Gol\n", + " # windows if the option continuum_sub_method is set to \"Sav-Gol\". Format : 'float' or 'NA'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " continuum_sub = 'NA', 'NA'\n", + "\n", + " # Wavelength range used for the estimate of the continuum (separated windows can be defined).\n", + " # Format : 'window1_max / window2_min,window2_max / ... / windown_min'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " wav_for_continuum = '0.9,2.4', '4.9,27.9'\n", + "\n", + " # Whether to use least square to estimate planetary and stellar contributions\n", + " # Format : 'True' or 'False'\n", + " # WARNING : need to define separatly when MOSAIC\n", + " use_lsqr = 'False', 'False'\n", + "\n", + "\n", + "[config_inversion]\n", + "\n", + " # Method to calculate the loglikelihood function used in the nested sampling procedure\n", + " # Please refer to the documentation on 'nested_logL_functions.py' for more information\n", + " # Format : 'chi2_classic' or 'chi2_covariance' or 'chi2_extended' or 'chi2_extended_covariance' or 'CCF_Brogi' or 'CCF_Zucker' or 'CCF_custom' \n", + " # WARNING : need to define separatly when MOSAIC \n", + " logL_type = 'chi2_classic', 'chi2_classic'\n", + "\n", + " # Wavelength range used for the fit during the nested sampling procedure (separated windows can be defined).\n", + " # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'\n", + " # WARNING : need to define separatly when MOSAIC \n", + " wav_fit = '0.9,2.4', '4.9,27.9'\n", + "\n", + " # Nested sampling algorithm used.\n", + " # Format : 'nestle' or 'pymultinest' or 'ultranest' or 'dynesty' (only nestle and pymultinest at the moment)\n", + " ns_algo = 'pymultinest'\n", + "\n", + " # Number of living points during the nested sampling procedure.\n", + " # Format : 'integer'\n", + " npoint = '50'\n", + "\n", + "\n", + "[config_parameter]\n", + " # Definition of the prior function of each parameter explored by the grid. Please refer to the documentation to check\n", + " # the parameter space explore by each grid.\n", + " # Format : \"function\", function_param1, function_param2\n", + " # Example : \"uniform\", min, max\n", + " # \"constant\", value\n", + " # \"gaussian\", mu, sigma\n", + " # 'NA' if the grid cover a lower number of parameters\n", + " par1 = 'uniform', 400, 2000 # Teff\n", + " par2 = 'uniform', 3.0, 5.0 # log(g)\n", + " par3 = 'uniform', -0.5, 1.0 # [M/H]\n", + " par4 = 'uniform', 0.1, 0.8 # C/O\n", + " par5 = 'NA'\n", + "\n", + " # Definition of the prior function of each extra-grid parameter. r is the radius (RJup, >0), d is the distance (pc, >0),\n", + " # alpha is a scaling factor applied to the model flux, rv is the radial velocity (km.s-1), av is the extinction (mag),\n", + " # vsini is the projected rotational velocity (km.s-1, >0), ld is the limb darkening (0-1), bb_T is the black-body temperature (K)\n", + " # and bb_R is the black-body radius (Rjup)\n", + " # Format : \"function\", function_param1, function_param2\n", + " # Example : \"uniform\", min, max\n", + " # \"constant\", value\n", + " # \"gaussian\", mu, sigma\n", + " # 'NA' if you do not want to constrain this parameter\n", + " # WARNING : need to define separatly when MOSAIC \n", + " r = 'NA'\n", + " d = 'NA'\n", + " alpha = 'NA'\n", + " rv = \"uniform\", -50, 50, \"uniform\", -50, 50\n", + " av = 'NA'\n", + " vsini = 'NA', 0, 0, 'NA', \"uniform\", 0, 100, 'Accruate'\n", + " ld = 'NA', 0, 0\n", + " bb_T = 'NA'\n", + " bb_R = 'NA'\n", + " \n", + "\n", + "[config_nestle]\n", + " # For details on these parameters, please see: http://kylebarbary.com/nestle/index.html\n", + "\n", + " mechanic = 'static' # Sampler “super-classes” of dynesty\n", + " # e.g. 'static' / 'dynamic' # the number of living point is fixed / variates\n", + "\n", + " method = 'multi' # Reduction of the parameters space\n", + " # e.g. 'single' / 'multi' #single-ellipsoidal / multi-ellipsoidal\n", + "\n", + " maxiter = None # Stopping criterions\n", + " maxcall = None\n", + " dlogz = None\n", + " decline_factor = 0.1\n", + "\n", + " update_interval = None # Divers\n", + " npdim = None\n", + " rstate = None\n", + " callback = None\n", + "\n", + "[config_pymultinest]\n", + " # For details on these parameters, please see: https://github.com/JohannesBuchner/PyMultiNest \n", + "\n", + " n_clustering_params = None\n", + " wrapped_params = None, \n", + "\timportance_nested_sampling = True\n", + "\tmultimodal = True\n", + " const_efficiency_mode = False\n", + "\tevidence_tolerance = 0.5\n", + " sampling_efficiency = 0.8\n", + "\tn_iter_before_update = 100\n", + " null_log_evidence = -1e90\n", + "\tmax_modes = 100\n", + " mode_tolerance = -1e90\n", + "\tseed = -1\n", + " context = 0\n", + " write_output = True\n", + " log_zero = -1e100\n", + "\tmax_iter = 0\n", + " init_MPI = False\n", + " dump_callback = None\n", + " use_MPI = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra-grid parameters\n", + "\n", + "In ``[config_parameter]``, you need to define both the grid (see grid tutorial) and extra-grid parameters. Let's go through them one by one to see how you can use them.\n", + "\n", + "\n", + "- ``r`` = radius in Rjup\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : You need to define both ``r`` and ``d`` so ForMoSA can compute an analytical scaling factor ``ck=(r/d)²``. This parameter can only be defined once.\n", + "\n", + "\n", + "- ``d`` = distance in pc\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : You need to define both ``r`` and ``d`` so ForMoSA can compute an analytical scaling factor ``ck=(r/d)²``. This parameter can only be defined once.\n", + "\n", + "\n", + "- ``alpha`` = scaling parameter\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : You need to define both ``r`` and ``d`` so ForMoSA can compute an analytical scaling factor ``ck=alpha*(r/d)²``. By default ``alpha=1``. You can define multiple ``alpha`` if you have more than one observation, i.e. ``'prior_1', value1_1, value2_1, 'prior_2', value1_2, value2_2, ...``.\n", + "\n", + "\n", + "- ``rv`` = radial velocity in km.s⁻¹\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : You can define multiple ``rv`` if you have more than one observation, i.e. ``'prior_1', value1_1, value2_1, 'prior_2', value1_2, value2_2, ...``.\n", + "\n", + "\n", + "- ``av`` = extinction in mag\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : This parameter can only be defined once.\n", + "\n", + "\n", + "- ``vsini`` = rotational velocity in km.s⁻¹\n", + " \n", + " format : ``'NA'`` or ``'prior', value1, value2, 'method'``\n", + "\n", + " comments : You need to define both ``vsini`` and ``ld`` so ForMoSA can compute the broadning. You also need to specify a fourth parameter ``'method'``, to choose how you want to compute the broadening. ``'method'='FastRotBroad'`` and ``'method'='RotBroad'`` both uses the classical line broadning function extinction.fm07 in fast and slow modes, respectively. ``'method'='Accurate'`` uses the formula from [Carvalho & Johns-Krull 2023](https://arxiv.org/pdf/2305.09693). You can define multiple ``vsini`` if you have more than one observation, i.e. ``'prior_1', value1_1, value2_1, 'method_1', 'prior_2', value1_2, value2_2, 'method_2', ...``.\n", + "\n", + "\n", + "- ``ld`` = limb-darkening\n", + "\n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : You need to define both ``vsini`` and ``ld`` so ForMoSA can compute the broadning. You can define multiple ``ld`` if you have more than one observation, i.e. ``'prior_1', value1_1, value2_1, 'prior_2', value1_2, value2_2, ...``.\n", + "\n", + "\n", + "- ``bb_T`` = black-body temperature in K\n", + "\n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : This parameter can only be defined once.\n", + "\n", + "\n", + "- ``bb_R`` = black-body radius in Rjup\n", + "\n", + " format : ``'NA'`` or ``'prior', value1, value2``\n", + "\n", + " comments : This parameter can only be defined once." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pRT3_env", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb index bfffa13..b51a0aa 100644 --- a/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb +++ b/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb @@ -4,10 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# demo AB Pic b\n", + "# Demo AB Pic b\n", "\n", "\n", - "This tutorial is intended as a quick start. \n", + "This tutorial is intended as a quick start when using a single observation. \n", "\n", "\n", "We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in [P. Palma-Bifani et al (2023)](https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf).\n", @@ -41,7 +41,7 @@ "source": [ "## 0. Setup\n", "\n", - "You need to create a config file with extension <.ini> and modify the parameters. Learn more about our config files in it's specific tutorial.\n", + "You need to create a config file with extension ``.ini`` and modify the parameters. Learn more about our config files in it's specific tutorial.\n", "\n", "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows" ] diff --git a/docs/_build/doctrees/nbsphinx/tutorials/demobetapic.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/demobetapic.ipynb new file mode 100644 index 0000000..4e5fe3a --- /dev/null +++ b/docs/_build/doctrees/nbsphinx/tutorials/demobetapic.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo β Pic b\n", + "\n", + "\n", + "This tutorial is intended as a quick start when using multiple observations. \n", + "\n", + "\n", + "We will use low resolution Gemini/GPI YJHK-band and medium resolution VTLI/GRAVITY K-band data of β Pic b. These observations and example model were published in [GRAVITY collaboration et al (2020)](https://arxiv.org/pdf/1912.04651).\n", + "\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Generic packages\n", + "import sys, time, os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ForMoSA modules\n", + "sys.path.insert(0, os.path.abspath('/home/mravet/Documents/These/FORMOSA/ForMoSA_test/ForMoSA/'))\n", + "# For the interpolation & sampling\n", + "from main_utilities import GlobFile\n", + "from adapt.adapt_obs_mod import launch_adapt\n", + "from nested_sampling.nested_sampling import launch_nested_sampling\n", + "# For the plots\n", + "from plotting.plotting_class import PlottingForMoSA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Setup\n", + "\n", + "You need to create a config file with extension ``.ini`` and modify the parameters. Learn more about our config files in it's specific tutorial.\n", + "\n", + "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "base_path = '/home/mravet/Documents/These/FORMOSA/OUTPUTS/Channel6/'\n", + "\n", + "# CONFIG_FILE \n", + "# reading and defining global parameters\n", + "config_file_path = base_path + 'config_BetaPicb.ini'\n", + "global_params = GlobFile(config_file_path) \n", + "\n", + "# Optional: Add \"time_now\" and \"save_name\" to avoid overwriting results\n", + "time_now = time.strftime(\"%Y%m%d_%H%M%S\")\n", + "save_name = 'test'\n", + "\n", + "# Create directory to save the outputs \n", + "global_params.result_path = global_params.result_path+ save_name+'_t' + time_now+'/'\n", + "os.makedirs(global_params.result_path)\n", + "\n", + "# Overwrite some parameters\n", + "global_params.config.filename = global_params.result_path + 'config_used.ini'\n", + "global_params.config['config_path']['result_path']=global_params.result_path\n", + "global_params.config.write()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Interpolate the grid\n", + "\n", + "Once everything is setup, we start by adapting the models and observations. \n", + "\n", + "The grid of models is interpolated for this, but you don't need to repeat this step once you've adapted the grid for a specific dataset. \n", + "\n", + "(Answer 'no' only the first time)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Have you already interpolated the grids for this data? \n", + "y_n_par = 'yes'\n", + "#y_n_par = 'no' # Only answer no the first time, then comment to save time\n", + "\n", + "launch_adapt(global_params, justobs=y_n_par)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Lunch Nested Sampling \n", + "\n", + "Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n", + "-> Likelihood functions check-ups\n", + "\n", + "1_GPI_BetaPicb will be computed with chi2_classic\n", + "\n", + "2_GRAVITY_MRS_BetaPicb will be computed with chi2_covariance\n", + "\n", + "Done !\n", + "\n", + "\u001b[Kit= 782 logz=-1201.1251603 \n", + "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n", + "-> Nestle \n", + " \n", + "The code spent 28.971810579299927 sec to run.\n", + "niter: 783\n", + "ncall: 1820\n", + "nsamples: 833\n", + "logz: -1200.705 +/- 0.516\n", + "h: 13.299\n", + "\n", + "\n", + " \n", + "-> Voilà, on est prêt\n" + ] + } + ], + "source": [ + "launch_nested_sampling(global_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Plotting the outcomes\n", + "\n", + "ForMoSA has been designed with a plotting class. Bellow we show 4 main features: \n", + "\n", + "- Plotting corner-plots\n", + "- Plotting spectra and residuals\n", + "- Plotting chains \n", + "- Accessing the different parameters\n", + "\n", + "All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc...\n", + "\n", + "We need to start by initializing the plotting class as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Path to output file created in the first step\n", + "config_file_path_pl = '/home/mravet/Documents/These/FORMOSA/OUTPUTS/Channel6/test_t20240919_145812/'\n", + "\n", + "# Initialize the plotting class and set the color\n", + "plotForMoSA = PlottingForMoSA(config_file_path_pl+'/config_used.ini', 'blue')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PLOT Corner" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ForMoSA - Corner plot\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plotForMoSA.plot_corner(levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16, 0.5, 0.84), burn_in=0)\n", + "#plt.savefig('') \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PLOT Spectrum and Residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ForMoSA - Best fit and residuals plot\n" + ] + }, + { + "data": { + "image/png": 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+FtStWxePHz+GiYkJKlWqJHfT19eHrq4urKysMq3TnJGqqqo4L+9rbG1t8erVK7x69Urcdu/ePURHR4trQgNAlSpVMHHiRJw/fx7du3eHh4cHAKBmzZrZxlGxYkWoqanJLY+RmpoKPz8/uWN/+X5ERUXh0aNH4vvxpa+dM7vXeP369WzPBwBXr17FuHHj0KFDB1SrVg3q6ur48OGDXJus3tOrV69iyJAh6NatG2rUqAEzMzO8ePEix7Gx4omTQMZKmEuXLiEpKQlly5ZFtWrV5PZVrlwZ/fr1AyB8qyrrBbS1tYWamhoA7glkjLGiLCIiAq1atcLOnTsRGBiI58+f48CBA1iyZAm6du0q1/bAgQPYtm0bHj16hNmzZ+PGjRsYO3YsAKEQi7GxMbp27YrLly/j+fPn8Pb2xrhx4/D69WsAwt+RZcuWYfXq1Xj8+DFu3bqFNWvWiMeXJYlhYWFfXavW0dERNWrUQP/+/XHr1i3cuHEDgwYNQvPmzVGvXj0kJiZi7Nix8Pb2xsuXL3H16lX4+fmJCdr06dPh5+eHMWPGIDAwEA8ePMCGDRvw4cMHaGtrY/To0Zg8eTLOnj2Le/fuYcSIEUhISMCwYcPk4pg3bx68vLxw9+5dDBkyBMbGxnB2ds4y5tmzZ2PPnj2YPXs27t+/j6CgICxevDjb1zh+/Hhs27YNHh4e4vsdHBws16Zy5cr4559/cP/+fVy/fh39+/fP1OOY1XtauXJlscjOnTt30K9fP0il0mxjYSWEoiclliQ8OZYVBhMmTCAANGzYsCz3P3r0iJSVlQkAderUiQDQoEGDxP2yojJ6enoFFTJjjLE8kpSURNOmTaO6deuSvr4+aWlpkY2NDf3222+UkJAgtgNA69atozZt2pC6ujpZWVmJRVBk3r59S4MGDSJjY2NSV1enChUq0IgRI+SuczZu3Eg2NjakqqpKZcqUITc3N3Hf8ePHqVKlSqSiopJpiYgvfW2JiOTkZOrTpw9ZWFiQmpoamZub09ixYykxMVF8vre3NzVq1IjU1dXJwMCAnJycKCoqioiIEhMTyc3NTXwd2S0RceLECapWrRqpqalRgwYN5IrpZLVExKFDh6h27dqkpqZGxsbG1L1796/+bP744w8yNjYmHR0dGjx4ME2ZMkXuvbh16xbVq1ePNDQ0qHLlynTgwAGytLSkFStWfPU9ff78ObVs2ZI0NTXJwsKC1q5dS82bN6fx48d/NZ68wNe+hZeEiEhxKWjJEhsbC319fcTExIhD6hgraLa2tnjw4AEOHDiAnj17Ztlm6NChcgUCli1bBnd3dwDCxHQzMzNIJBKkpaX90LwPxhhjhZNEIsGRI0ey7ekqSby9vdGyZUtERUXBwMBA0eEUKXztW3jx1RtjJcjz58/x4MEDKCsrw9HRMdt2M2fOhLKysvhYVhQG+DwclIgQFxeXb7EyxhhjjLH8wUkgYyUEEWHy5MkAgCZNmnz128wKFSpg6NCh4mPZ8hAAoK6uLi7Ay8VhGGOMMcaKHl4igrES4u+//8ahQ4egoqKC5cuXf7P9b7/9hsOHD6Ny5cooVaqUuF0ikUBfXx8fPnzg4jCMMVZM8Wyhz1q0aMHvByt2OAlkrAR48eIF3NzcAABz585F3bp1v/kcS0tLPHnyJMu1jvT09PDhwwfuCWSMMcYYK4I4CWSsmCMiDB48GB8/fkTjxo0xderUHD/X0NAwy+2yeYGcBDLGGGOMFT08J5CxYu7WrVu4dOkSNDU18ffff8sVfPlesgpfPByUMcYYY6zo4SSQsWLu5MmTAID27dujQoUKeXJM7glkjDHGGCu6OAlkrJg7deoUAKBjx455dkxZEsg9gYwxxhhjRU+u5gRKpVL4+Pjg8uXLePnyJRISElC6dGnUqVMHjo6OsLCwyK84GcuxtLQ0qKjwdFcACAsLg5+fHwCgQ4cOeXZc2XBQ7glkjDHGgO3bt2PChAmIjo5WdCjfNGfOHBw9ehQBAQE5av/ixQtYW1vj9u3bcusGs6ItRz2BiYmJmD9/PiwsLNChQwecOXMG0dHRUFZWxpMnTzB79mxYW1ujQ4cOuHbtWn7HzFi2Ll26BH19fWzatEnRoRQKZ86cAQDUq1cPZmZmeXZc7glkjLGi6/379xg9ejTKly8PdXV1mJmZwcnJCVevXi3QOCQSCY4ePVqg58wvLi4uePTokaLDYCzHctRdUqVKFTg4OGDz5s1o06aNuFB0Ri9fvsTu3bvRp08fzJgxAyNGjMjzYBn7Fm9vbyQkJODSpUv45ZdfFB2OwsnmA+blUFCAewIZY6wo69GjB1JSUrBjxw5UqFAB7969g5eXFyIiIhQdWiYpKSlQU1NTdBjfpKmpmeWSSowVVjnqCTx//jz279+PDh06ZJkAAsKaYtOnT8fjx4/RqlWrPA2SsZyKjIwEIPzRKOmSk5Nx/vx5AECnTp3y9NhcGIYxxoqm6OhoXL58GYsXL0bLli1haWmJBg0aYPr06ejSpYvYTiKRYMOGDWjfvj00NTVRoUIFHDx4UO5Yr169Qu/evWFgYAAjIyN07doVL168kGuzbds2VKtWDerq6ihTpgzGjh0LALCysgIAdOvWDRKJRHw8Z84c1K5dG1u2bIG1tTU0NDQAACEhIejatSt0dHSgp6eH3r174927d+J57ty5g5YtW0JXVxd6enqwt7fHzZs3xf1Xr15FixYtoKWlBUNDQzg5OSEqKgqA8Pdy3LhxMDExgYaGBpo0aSJOpQCEL5glEglOnTqFmjVrQkNDAz/99BPu3r0rttm+fTsMDAzkXvuJEydQv359aGhowNjYGN26dcv25yJ73du2bUP58uWho6ODMWPGID09HUuWLIGZmRlMTEzwxx9/yD3vW+8LACxatAimpqbQ1dXFsGHDkJSUlOn8W7Zsga2tLTQ0NFC1alWsX78+21hZ8ZCjJNDW1jbHB1RVVUXFihVz1HbhwoWoX78+dHV1YWJiAmdnZzx8+FDcHxkZCTc3N9jY2EBTUxPly5fHuHHjMl14SiSSTLe9e/fKtfH29kbdunWhrq6OSpUqYfv27ZniWbduHaysrKChoYGGDRvixo0bcvuTkpLg6uqKUqVKQUdHBz169Mj0QWOKJfsPPTU1VcGRKN7ly5cRFxcHU1PTHC0Onxu8RARjjGVGBMTHK+ZGlLMYdXR0oKOjg6NHjyI5OfmrbWfOnIkePXrgzp076N+/P/r06YP79+8DEP7OOjk5QVdXF5cvX8bVq1eho6ODdu3aiV/EbtiwAa6urhg5ciSCgoJw/PhxVKpUCQDEJMvDwwNv376VS7qePHmCQ4cO4fDhwwgICIBUKkXXrl0RGRkJHx8feHp64tmzZ3BxcRGf079/f5QrVw5+fn7w9/fHtGnTxI6LgIAAtG7dGnZ2dvD19cWVK1fQuXNnpKenAwCmTJmCQ4cOYceOHbh16xYqVaoEJycn8YtlmcmTJ2PZsmXw8/ND6dKl0blz52yvN06dOoVu3bqhQ4cOuH37Nry8vNCgQYOvvt9Pnz7FmTNncPbsWezZswdbt25Fx44d8fr1a/j4+GDx4sX47bffcP36dQDI0fuyf/9+zJkzBwsWLMDNmzdRpkyZTAnerl27MGvWLPzxxx+4f/8+FixYgJkzZ2LHjh1fjZcVcZRH4uLiyMfHJ1fPcXJyIg8PD7p79y4FBARQhw4dqHz58hQXF0dEREFBQdS9e3c6fvw4PXnyhLy8vKhy5crUo0cPueMAIA8PD3r79q14S0xMFPc/e/aMtLS0yN3dne7du0dr1qwhZWVlOnv2rNhm7969pKamRtu2baPg4GAaMWIEGRgY0Lt378Q2o0aNIgsLC/Ly8qKbN2/STz/9RI0aNcrx642JiSEAFBMTk6v3ieVcp06dCAC1b99e0aEo3Pjx4wkADR06NM+PffLkSQJA9vb2eX5sxhgrquLiiIR0rOBvny6dcuTgwYNkaGhIGhoa1KhRI5o+fTrduXNHrg0AGjVqlNy2hg0b0ujRo4mI6J9//iEbGxuSSqXi/uTkZNLU1KRz584REZG5uTnNmDEj2zgA0JEjR+S2zZ49m1RVVSk8PFzcdv78eVJWVqaQkBBxW3BwMAGgGzduEBGRrq4ubd++Pcvz9O3blxo3bpzlvri4OFJVVaVdu3aJ21JSUsjc3JyWLFlCREQXL14kALR3716xTUREBGlqatK+ffuIiMjDw4P09fXF/Q4ODtS/f/9sX/uXZs+eTVpaWhQbGytuc3JyIisrK0pPTxe32djY0MKFC4koZ++Lg4MDjRkzRu5cDRs2pFq1aomPK1asSLt375Zr8/vvv5ODgwMRET1//pwA0O3bt3P8emT42rfwyrMlIp48eYKWLVvm6jlnz57FkCFDUK1aNdSqVQvbt29HSEgI/P39AQDVq1fHoUOH0LlzZ1SsWBGtWrXCH3/8gRMnTiAtLU3uWAYGBjAzMxNvsuEDALBx40ZYW1tj2bJlsLW1xdixY9GzZ0+sWLFCbLN8+XKMGDECQ4cOhZ2dHTZu3AgtLS1s27YNgDDsbevWrVi+fDlatWoFe3t7eHh44L///uNiOIWIrCewpA8HJSJxPmBeDwUFuDAMY4wVZT169EBoaCiOHz+Odu3aiaOlvhwl5eDgkOmxrCfwzp07ePLkCXR1dcXeRSMjIyQlJeHp06cIDw9HaGgoWrdunev4LC0tUbp0afHx/fv3YWFhIVeF3s7ODgYGBmI87u7uGD58OBwdHbFo0SI8ffpUbCvrCczK06dPkZqaisaNG4vbVFVV0aBBA/HYWb0fRkZGsLGxydQmJ+fMjpWVFXR1dcXHpqamsLOzg5KSkty28PBwADl7X+7fv4+GDRtm+zri4+Px9OlTDBs2TPw56ujoYP78+XLvISt+CtU6gbJhnkZGRl9to6enl2kJAFdXVxgbG6NBgwbYtm0bKMO4CF9fXzg6Osq1d3Jygq+vLwAhYfD395dro6SkBEdHR7GNv78/UlNT5dpUrVoV5cuXF9swxeM5gYJ79+7h6dOnUFNTQ5s2bfL8+FwYhjHGMtPSAuLiFHPT0spdrBoaGmjTpg1mzpyJ//77D0OGDMHs2bNz/Py4uDjY29sjICBA7vbo0SP069fvh4qkaGtr5/o5c+bMQXBwMDp27IgLFy7Azs4OR44cAQCFFGz5nnN+WXdDIpFkuU0qlf5QbBnFxcUBADZv3iz3c7x79y53chRzOU4CjYyMvnpr1qzZDwUilUoxYcIENG7cGNWrV8+yzYcPH/D7779j5MiRctvnzZuH/fv3w9PTEz169MCYMWOwZs0acX9YWBhMTU3lnmNqaorY2FgkJibiw4cPSE9Pz7JNWFiYeAw1NbVMk34ztvlScnIyYmNj5W4sf3FPoEBWctvR0VHuW8W8wj2BjDGWmUQCaGsr5iaR/FjsdnZ2iI+Pl9v2ZRJw7do1sU5E3bp18fjxY5iYmKBSpUpyN319fejq6sLKygpeXl7ZnlNVVVWcl/c1tra2ePXqFV69eiVuu3fvHqKjo2FnZyduq1KlCiZOnIjz58+je/fu8PDwAADUrFkz2zgqVqwINTU1ueUxUlNT4efnJ3fsL9+PqKgoPHr0KNu6GV87Z17Jyftia2srziGUyfg6TE1NYW5ujmfPnmX6OVpbW+dr/EyxcryidnJyMkaPHo0aNWpkuf/ly5eYO3fudwfi6uqKu3fv4sqVK1nuj42NRceOHWFnZ4c5c+bI7Zs5c6Z4v06dOoiPj8fSpUsxbty4744nLyxcuPCH3hOWO0Qk9gSW9MIwsiTQ2dk5X44v6wlMSkoqMuW7GWOMAREREejVqxd+/vln1KxZE7q6urh58yaWLFmCrl27yrU9cOAA6tWrhyZNmmDXrl24ceMGtm7dCkAoxLJ06VJ07doV8+bNQ7ly5fDy5UscPnwYU6ZMQbly5TBnzhyMGjUKJiYmaN++PT5+/IirV6/Czc0NAMQksXHjxlBXV4ehoWGWMTs6OqJGjRro378/Vq5cibS0NIwZMwbNmzdHvXr1kJiYiMmTJ6Nnz56wtrbG69ev4efnhx49egAApk+fjho1amDMmDEYNWoU1NTUcPHiRfTq1QvGxsYYPXo0Jk+eDCMjI5QvXx5LlixBQkIChg0bJhfHvHnzUKpUKZiammLGjBkwNjbO9u/s7Nmz0bp1a1SsWBF9+vRBWloaTp8+jalTp/7Ijy9X7wsAjB8/HkOGDEG9evXQuHFj7Nq1C8HBwahQoYJ4nLlz52LcuHHQ19dHu3btkJycjJs3byIqKgru7u55Fi8rZHI6ebBRo0a0cuXKbPcHBASQkpLSd01MdHV1pXLlytGzZ8+y3B8bG0sODg7UunVruYIv2ZEVrUhKSiIioqZNm9L48ePl2mzbto309PSISJjIrKysnGly8qBBg6hLly5EROTl5UUAKCoqSq5N+fLlafny5VnGkZSURDExMeLt1atXPDk2H8XHxxMAAkDVq1dXdDgKExISQgBIIpHQ27dv8+UcaWlp4nudcfI+Y4yxwi0pKYmmTZtGdevWJX19fdLS0iIbGxv67bffKCEhQWwHgNatW0dt2rQhdXV1srKyEougyLx9+5YGDRpExsbGpK6uThUqVKARI0bIXeds3LiRbGxsSFVVlcqUKUNubm7ivuPHj1OlSpVIRUWFLC0tiUgokJKxaInMy5cvqUuXLqStrU26urrUq1cvCgsLIyLhOq5Pnz5kYWFBampqZG5uTmPHjpW7ZvT29qZGjRqRuro6GRgYkJOTk3hNl5iYSG5ubuLraNy4sVhYhehzYZgTJ05QtWrVSE1NjRo0aCBXTOfLwjBERIcOHaLatWuTmpoaGRsbU/fu3bP9uWT1ugcPHkxdu3aV29a8eXO5a9qvvS8yf/zxBxkbG5OOjg4NHjyYpkyZkulcu3btEmM1NDSkZs2a0eHDh4mIC8MUVzlOAv/44w+aM2dOtvtDQkJoyJAhuTq5VColV1dXMjc3p0ePHmXZJiYmhn766Sdq3rw5xcfH5+i48+fPJ0NDQ/HxlClTMiUFffv2JScnJ/FxgwYNaOzYseLj9PR0Klu2rFiBKTo6mlRVVengwYNimwcPHhAA8vX1zVFc/EHIX7IkGwBVqVJF0eEozNq1awlArirXfg9tbW0CQE+ePMnX8zDGGCt4yKJyZ0klSwK/7Ahg38bXvoVXjoeD/vrrr1/db2FhIY69zilXV1fs3r0bx44dg66urji3Tl9fH5qamoiNjUXbtm2RkJCAnTt3ys2rK126NJSVlXHixAm8e/cOP/30EzQ0NODp6YkFCxZg0qRJ4nlGjRqFtWvXYsqUKfj5559x4cIF7N+/H6dOnRLbuLu7Y/DgwahXrx4aNGiAlStXIj4+HkOHDhVjGjZsGNzd3WFkZAQ9PT24ubnBwcEBP/30U65eN8sfsvmAQMmeE3js2DEA+TcUVEZfXx/x8fFcHIYxxhhjrIjJcRKYHzZs2AAAaNGihdx2Dw8PDBkyBLdu3RIns8oWF5V5/vw5rKysoKqqinXr1mHixIkgIlSqVElc7kHG2toap06dwsSJE7Fq1SqUK1cOW7ZsgZOTk9jGxcUF79+/x6xZsxAWFobatWvj7NmzcsViVqxYASUlJfTo0QPJyclwcnLKtOAmU5yMi7qW1CQwOjoaFy9eBFAwSWBoaCgXh2GMMcYYK2IkRBnWUsglPT09BAQEyE0uZdmLjY2Fvr6+uMwF+3FLliyBpqYm3NzccOTIEXTv3h2A0FMsW0enJNm1axcGDBgAOzs7BAcH5+u5fvrpJ1y/fh1Hjx7NVEyAMcYYY4yvfQuvH+oJ/IH8kbEfFh4ejqlTp0IikeDnn38u0cNBb926hdWrV2PPnj0A8r8XEPi8TAQPB2WMMcYYK1oUOhyUsR/x+vVrAMKXEa9evSqxw0EPHz4slsEGgEaNGmHixIn5fl7ZN3o8HJQxxhhjrGjJ8WLxWRkwYAB37TKFefPmjXj/1atXJbYnUFbgqFmzZrh27RquXr0KY2PjfD8v9wQyxhhjjBVNP9QTKCvswpgihIaGive/7AlMT0+HVCqFktIPfc9RJPj7+wMAJkyYgIYNGxbYebknkDHGGGOsaPquJNDPzw8XL15EeHg4pFKp3L7ly5fnSWCMfcvXegIBIDU1Ferq6gUdVoFKTEzE3bt3AQD29vYFem7uCWSMMcYYK5pynQQuWLAAv/32G2xsbGBqagqJRCLuy3ifsfz2tZ5AQBgSWtyTwMDAQKSnp6N06dKwsLAo0HPLkkDuCWSMMcYYK1pynQSuWrUK27Ztw5AhQ/IhHMZyLmNPYEhISKaewJIwL1A2FNTe3r7Av4SRDQflnkDGGGOMsaIl10mgkpISGjdunB+xMJYrX/YEfpn0lYQk8ObNmwCAevXqFfi5eTgoY4wxxljRlOuqGRMnTsS6devyIxbGcuXLOYFZDQct7jL2BBY0LgzDGGOMMVY05boncNKkSejYsSMqVqwIOzs7qKqqyu0/fPhwngXHWHaSk5MREREhPo6Pj8/UJjU1tSBDKnCJiYkIDg4GwD2BjDHGGGMs53KdBI4bNw4XL15Ey5YtUapUKS4GwxRCNhRUXV0dOjo6cgmhrq4uPn78WOx7Au/cuYP09HSYmJigbNmyBX5+7glkjDHGGCuacp0E7tixA4cOHULHjh3zIx7GckSWBJqbm0NfX19MArW1taGjo1MikkDZUNB69eop5MuYjNVBiYi/EGKMMcYYKyJyPSfQyMgIFStWzI9YGMsx2XzAsmXLonz58uJ2IyMjqKmpASj+cwJlRWEUMR8Q+JwESqXSLIfjMsYYY4yxwinXSeCcOXMwe/ZsJCQk5Ec8jOVIxp7AjOvjGRoaFtskkIgwfvx4DBw4EA8fPpTrCVQETU1NKCsrA+B5gYwxxhhjRUmuh4OuXr0aT58+hampKaysrDIVhrl161aeBcdYdjL2BJqamorbjYyMEB4eDqD4FYa5cOECVq9eDQDYu3cv0tPTASiuJ1AikUBfXx+RkZGIiYlRyLxExhhjjDGWe7lOAp2dnfMhDMZyJ2NPoLm5ubjd0NAQ0dHRAIpfT+CaNWsAAGXKlMHbt28BAKampnKvv6Dp6ekhMjKSi8MwxhhjjBUhuU4CZ8+enR9xMJYrGXsCy5UrJ243MjIS9xWnJPDFixc4ceIEAKFH8PXr11i8eDGcnZ0VWpCFl4lgjDHGGCt6cp0EZufatWv45ZdfcOfOnbw6JGPZKmlzAjds2ACpVApHR0dUrVoVVatWhaOjo6LDgpGREQDILdHBGGOMMcYKt1wngUpKStn2PFhaWv5wQIx9CxHJ9QSWLVsWEokERFQsq4MmJiZiy5YtAAA3NzcFRyPPzMwMAMThqYwxxhhjrPDLdRJ45MgRucfp6el4+vQpVq1ahcWLF+dZYIxlJzY2VqxOa25uDlVVVZQpUwahoaFyPYHFpTDMnj17EBkZCSsrq0K3PmeZMmUAAGFhYQqOhDHGGGOM5VSuk8CuXbtmub127doYOXIkevXq9cNBMfY1sl5AAwMDaGlpAQCsra0RGhoKExMTsWJtcekJ/OuvvwAAY8aMEZdkKCy4J5DllXfvAD8/4RYdDbRoAbRtC2hrKzoyxhhjrPjJ9TqB2alQoQI+fPiQq+csXLgQ9evXh66uLkxMTODs7IyHDx/KtUlKSoKrqytKlSoFHR0d9OjRA+/evZNrExISgo4dO0JLSwsmJiaYPHky0tLS5Np4e3ujbt26UFdXR6VKlbB9+/ZM8axbtw5WVlbQ0NBAw4YNcePGjVzHwvJfxvmAMgsXLoS7uzvat29frIaDfvz4EX5+fgCAvn37KjiazLgnkP2owECgd2+gY0dgzhzg1Cng6lXgjz+EJHDmTODpU0VHyRhjjBUveZIEJicnY/ny5RgzZkyunufj4wNXV1dcu3YNnp6eSE1NRdu2bREfHy+2mThxIk6cOIEDBw7Ax8cHoaGh6N69u7g/PT0dHTt2REpKCv777z/s2LED27dvx6xZs8Q2z58/R8eOHdGyZUsEBARgwoQJGD58OM6dOye22bdvH9zd3TF79mzcunULtWrVgpOTk7jmXE5iYQVD1uuUMQls2rQpli1bBk1NzWKVBF67dg1SqRRWVlZyVVALC1lPICeB7HtcuACMGgU8ewYoKQHVqwM//wy4ugLW1kByMnDmDDBiBBAZqehoGWOMsWKEcsnAwIAMDQ3lbioqKiSRSDJtz63w8HACQD4+PkREFB0dTaqqqnTgwAGxzf379wkA+fr6EhHR6dOnSUlJicLCwsQ2GzZsID09PUpOTiYioilTplC1atXkzuXi4kJOTk7i4wYNGpCrq6v4OD09nczNzWnhwoU5juVbYmJiCADFxMTkqD3L2oYNGwgAdevWLcv9gwcPJgC0ePHiAo4s782aNYsA0IABAxQdSpaCgoIIABkZGSk6FFaESKVEO3cS1atHZG9PNHEi0Zf/LUqlRHfvEvXuLbSZPl0xsTLGGPt+fO1beOV6TuDKlSvzLgP9gmytMVnZeX9/f6SmpsqVwq9atSrKly8PX19f/PTTT/D19UWNGjVgamoqtnFycsLo0aMRHByMOnXqwNfXN1M5fScnJ0yYMAGA0GPk7++P6dOni/uVlJTg6OgIX1/fHMfypeTkZCQnJ4uPeUHtvJGUlAQA0NDQyHJ/cSoMc+XKFQBAkyZNFBxJ1mTDQSMjI5GcnAx1dXUFR8QKOyJgxQpg927hce/ewKRJQk9gRhIJUK2aMER08GDg/HmgXTugWbMCD5kxxhgrdnKdBA4ePDg/4oBUKsWECRPQuHFjVK9eHYAwxExNTQ0GBgZybU1NTcXhZ2FhYXIJoGy/bN/X2sTGxiIxMRFRUVFIT0/Pss2DBw9yHMuXFi5ciLlz5+bwHWA59a0ksLgUhklNTcW1a9cAFN4k0NDQEKqqqkhNTUV4eLjcmo2MZWXPns8J4IQJQP/+QsKXHVtbYMAA4O+/gUWLgLp1AR2dAgmVMcYYK7byrDDMj3J1dcXdu3exd+9eRYeSZ6ZPn46YmBjx9urVK0WHVCwkJiYC+HZPYFFPAgMCApCQkABDQ0PY2toqOpwsKSkpiV+ecIVQ9i1XrgCywSTu7kJy97UEUGbkSKBcOSA8HFizJl9DZIwxxkqEQpEEjh07FidPnsTFixflil+YmZkhJSUF0dHRcu3fvXsnFqQwMzPLVKFT9vhbbfT09KCpqQljY2MoKytn2SbjMb4Vy5fU1dWhp6cnd2M/LqfDQYt6EigbCtq4cWMofTlWrhDhCqEsJ549A379FZBKgW7dgNwUu9XQAH77Tbh/6BBw507+xMgYY4yVFAq9siQijB07FkeOHMGFCxdgbW0tt9/e3h6qqqrw8vIStz18+BAhISFwcHAAADg4OCAoKEiuiqenpyf09PRgZ2cntsl4DFkb2THU1NRgb28v10YqlcLLy0tsk5NYWMGQJYGamppZ7i9uSWBhHQoqw2sFsm+JihKGfiYkAPb2wJQpOesBzKhePaBLF+H+5s15HiJjjDFWouR6TmBecnV1xe7du3Hs2DHo6uqKPQn6+vrQ1NSEvr4+hg0bBnd3dxgZGUFPTw9ubm5wcHAQC7G0bdsWdnZ2GDhwIJYsWYKwsDD89ttvcHV1FYtUjBo1CmvXrsWUKVPw888/48KFC9i/fz9OnTolxuLu7o7BgwejXr16aNCgAVauXIn4+HgMHTpUjOlbsbCCURIKwxBRkUkCuSeQfY1UCsyYAYSGCkM6lywBPk3bzbXhw4GTJ4Fr14BHj4AqVfI2VsYYY6ykUGgSuGHDBgBAixYt5LZ7eHhgyJAhAIAVK1ZASUkJPXr0QHJyMpycnLB+/XqxrbKyMk6ePInRo0fDwcEB2traGDx4MObNmye2sba2xqlTpzBx4kSsWrUK5cqVw5YtW+Dk5CS2cXFxwfv37zFr1iyEhYWhdu3aOHv2rFyxmG/FwgpGSSgM8/TpU4SHh0NdXR316tVTdDhfxWsFsq/56y/gxg1AUxNYvhzQ1//+Y5mbA23aAOfOATt2CAvKM8YYYyz3cpUEnj59GocPH4aRkRF+/vlnVK1aVdwXFRWFHj164MKFCzk+HhF9s42GhgbWrVuHdevWZdvG0tISp0+f/upxWrRogdu3b3+1zdixYzF27NgfioXlv5IwJ1DWC1i/fv1Cv+wCDwdl2fnvP2DLFuH+jBlAhQo/fsxBg4Qk0NNTWFTe3PzHj8kYY4yVNDmeE7h792506dIFYWFh8PX1RZ06dbBr1y5xf0pKCnx8fPIlSMYyKs5J4Nu3bzFlyhSMGzcOgFAUprDj4aAsK2/ffi7m0quXsMZfXrCxARo2FIaZZvgTxBhjjLFcyHFP4NKlS7F8+XLx4nT//v34+eefkZSUhGHDhuVbgIx9qbjOCbxz5w4aNWqEhIQEAEDNmjUxYcIExQaVA9wTyL6UmgpMmwbExgJ2dsDEiXl7/MGDgevXgaNHgREjgC+Wb2WMMcbYN+S4J/Dx48fo3Lmz+Lh37944ceIEJkyYgI0bN+ZLcIxlpbj2BHp4eCAhIQHVq1fHiRMnEBAQkO3yI4VJxjmBORnizYq/zZuB4GBATw9YvBj49JHMM/XrCz2CycnAgQN5e2zGGGOsJMhxEqinp5dpHb2WLVvi5MmTmDx5MtbwCr6sgHxrsfiiWhjm0qVLAIDffvsNnTp1giS3NfQVRJYEZrWOJit5AgKA7duF+7/9BnwaLZynJBKhNxAA9u0DithHnTHGGFO4HCeBDRo0wJkzZzJtb968OU6cOIGVK1fmZVyMZas4rhMYExODO59WwG7atKmCo8kdDQ0NGHwaj8dDQku2+Hhg1ixhvl7nzkCrVvl3rtatARMTIDoa4OnojDHGWO7kOAmcOHFitj0vLVq0wIkTJzBo0KA8C4yx7BTH4aD//fcfpFIpKlasCPMiWO6Qi8MwAFi6VFgP0NwcmDTp+45x5swZjBs37ptfKCgrC4kmABw//n3nYowxxkqqHCeBzZs3x/Tp07Pd37JlS3h4eORJUIx9TXEsDHP58mUARa8XUIbXCmReXsJC7kpKwLx5gLZ27o9x584ddOvWDWvWrEG9evVw7dq1r7aXJYHXrgFfzFZgjDHG2FfkOAnMSseOHXn4FytwxbEnUDYfsFmzZgqO5PtwhdCS7f37zwu3DxkC1K6d+2PEx8ejT58+SE5OhqqqKkJDQ9G8eXNskS00mIVy5QB7e4BISEAZY4wxljM/lAReunRJLNLBWEH5VhJY1ArDJCYmws/PD0DR7Qnk4aAll1QKzJkjLAdhayss2fA9xo8fjwcPHsDc3ByPHj1Cjx49kJKSghEjRsDd3T3byrNduwr/Hj8uxMIYY4yxb/uhJJAxRShuPYE3btxASkoKypQpg4oVKyo6nO/CPYEl1/79wpp96urA778Dn76DyZV9+/Zh69atkEgk2LlzJ6ysrHDgwAEsWLAAEokEK1aswIYNG7J8bqtWgJYW8OYNcPv2D74YxhhjrIT4oSTQ0tJS7HVhrCCkp6eLc/2KSxKYcT5gUVkW4kvcE1gyPXsGrF4t3J8wAbCyyt3zk5KSsH37dowcORIAMGPGDLRs2RIAIJFIMH36dCxevBiA0FPo7e2d6RgaGkC7dsL9Y8e+40UwxhhjJdAPJYF3796FhYVFXsXC2DfJegGB4lMYpqjPBwS4MExJlJoqrAOYkgI0agT07Jnz54aGhmLmzJkoX748hg4ditjYWDRp0gSzZ8/O1HbSpEno378/0tLS0KtXL7x48SJTmy5dhH+9vIC4uO98QYwxxlgJovI9T0pKSkJgYCDCw8Mh/WISRhfZX2PG8kFuksCi0BOYlpaG//77D0DRnQ8I8HDQksjDA3j0CNDXF9YGzEkn9vXr17Fq1SocOHAAaWlpAAALCwuMHTsWY8aMgYpK5j9JEokEmzdvxoMHD+Dv7w9nZ2fs3bsXNjY2Ys95tWpAhQpCz+T580D37nn6UhljjLFiJ9dJ4NmzZzFo0CB8+PAh0z6JRIL09PQ8CYyxrMiSQBUVlSwvGIGiVRjm2rVriI+Ph4GBAapXr67ocL6bbDhoZGQkEhMToampqeCIWH56/lxIAgFg6lTA2Pjr7W/duoXRo0fjxo0b4ramTZti/Pjx6Nq1a7afZRlNTU0cPXoU9erVw507d2BrawtLS0u0a9cOvXv3RqtWrdClC7BypVAghpNAxhhj7OtyPRzUzc0NvXr1wtu3byGVSuVunACy/PatojBAznsCz58/n+3wsoKyc+dOAEIPupJS0a3TZGRkhNKlSwMAevfuLddjy4oXqRSYP18YDtqkCdCmzdfbnz9/Hs2aNcONGzegpqaGIUOG4NatW7h06RJ69OjxzQRQply5cjh79izatm0LNTU1vHz5Eps2bULr1q3RqVMnVK36DMrKwN27Qo8gY4wxxrKX66vOd+/ewd3dHaampvkRD2NflZskMC0tLduy8gAwefJkHDx4EC4uLgqZP5iUlIR9+/YBAAYPHlzg589LsqqOGhoaOHnyJLp06YKEhARFh8XywZEjwJ07QkXOadO+Pgx0165d6NixI+Lj49G6dWuEhITAw8MDderU+a5z165dG+fOnUNkZCROnTqFkSNHQkVFBadOnULjxrYgugSpNB3Hj3/ni2OMMcZKiFwngT179syyQhtjBSE3SSCQfXGY58+fIzAwEICwRMPcuXPzMMqcOXnyJKKjo2FhYYEWLVoU+PnzWtu2bXHmzBloa2vD09MTHTp0wMePHxUdFstD4eGfq4GOGQN8mgqapWXLlmHAgAFIS0tD3759cfr06Tz78lBbWxsdOnTApk2bEBQUhHbt2iElJQV+fr/h5cuXOHUK+DTlkDHGGGNZyPWcwLVr16JXr164fPkyatSokWmJiHHjxuVZcIx9KbdJYEpKitxjmeOfugpMTEwQHh6OBQsWoE2bNmjevHkeR5y9HTt2AAAGDBhQpIeCZtSiRQucP38e7du3h4+PD5ycnHDmzBno6+srOjSWB5YuBeLjgerVgd69s2+3Zs0aTJo0CQAwceJE/Pnnn/n2O161alWcPn0aR44cQY8evREZ+QShoWa4ckULxeC7FcYYYyxf5DoJ3LNnD86fPw8NDQ14e3vLrWsmkUg4CWT5KidJYMYvJrKbF3js04Ji06ZNQ1BQEDw8PDBgwADcv38fOjo6eRhx1sLDw3HmzBkAwKBBg/L9fAWpUaNG8PLyQtu2beHr64vWrVvj3LlzKFWqlKJDYz/g8mXg4kVAWVlYGiK7nG7v3r0YP348AGDOnDlZLvuQ1yQSCbp3744+fXph796TePPGGsePV+YkkDHGGMtGrr+anTFjBubOnYuYmBi8ePECz58/F2/PeDY+y2eJiYkAvp4EKisri19OZJUERkZGimvzde3aFatXr4alpSVev36NI0eO5EPUme3Zswfp6elo0KABqlatWiDnLEj16tXDxYsXUbp0afj7+6Nly5YIDw9XdFjsOyUlCb2AANC/P1CpUtbtPD09MWjQIBAR3NzcMGvWrIILEsC8efOgpHQaMTExOHv2I7IoYs0YY4wxfEcSmJKSAhcXl2IzfI0VLbKewK8tQSCRSL5aIfT06dNIT09H9erVUaFCBejo6GDIkCEAhF6MgvD3338DKH69gBnVqlUL3t7eKFOmDIKCgtC8eXOEhoYqOiz2HbZuBUJDAVNTYPjwrNtcu3YN3bp1Q2pqKvr06YOVK1fKjRQpCJUrV8awYa0ABOLVqzc4dSr7wlCMMcZYSZbrTG7w4MFiRcMfdenSJXTu3Bnm5uaQSCQ4evSo3H6JRJLlbansK2kAVlZWmfYvWrRI7jiBgYFo2rQpNDQ0YGFhgSVLlmSK5cCBA6hatSo0NDRQo0YNnD59Wm4/EWHWrFkoU6YMNDU14ejoiMePH+fJ+8ByLifDQYHP8wKzKgwjGwratWtXcVufPn0ACOXsIyIi8iTW7Hz48AG3bt0CALi4uOTruRTNzs4OPj4+sLCwwIMHD9CsWTOEhIQoOiyWC8+fA//8I9yfPFmoCpoREWH9+vVo3rw54uPj0aZNG+zYsUNhXxTOmjULKipnEBcXh82b3+ErBYIZY4yxEivXf6XT09OxZMkSNG/eHG5ubnB3d5e75UZ8fDxq1aqFdevWZbn/7du3crdt27ZBIpGgR48ecu3mzZsn187NzU3cFxsbi7Zt28LS0hL+/v5YunQp5syZg7/++kts899//6Fv374YNmwYbt++DWdnZzg7O+Pu3btimyVLlmD16tXYuHEjrl+/Dm1tbTg5OfF6aAUst0nglz2BycnJOHv2LAD5JLBq1aqoXbs20tLScPDgwbwMORN/f38AQJUqVWD8rVW2i4HKlSvj0qVLsLa2xtOnT9GsWTM8ffpU0WGxHCACFi0SKm02awZ8WTcpJiYGLi4ucHV1RUpKCrp27YrDhw9nWYypoJQrVw5jxlQGkIRbtyJw5swbhcXCGGOMFVa5TgKDgoJQp04dKCkp4e7du7h9+7Z4CwgIyNWx2rdvj/nz56Nbt25Z7jczM5O7HTt2DC1btkSFChXk2unq6sq109bWFvft2rULKSkp2LZtG6pVq4Y+ffpg3LhxWL58udhm1apVaNeuHSZPngxbW1v8/vvvqFu3LtauXQtA+KZ75cqV+O2339C1a1fUrFkTf//9N0JDQzP1XrL89aNJ4IULFxAXFwdzc3PY29vL7evbty+A/B8SevPmTQDCvLmSwsrKCpcuXUKVKlXw8uVLNGvWDA8fPlR0WOwbzpwB/P0BdXVg0qTPawI+e/YMCxcuRO3atXHgwAGoqKhg+fLlOHLkSIEUVvqWmTPdoabmg8TERHTsuAX16tXD4sWL8eYNJ4SMMcYY8B1J4MWLF7O9XbhwIT9iBCAsUn/q1CkMGzYs075FixahVKlSqFOnDpYuXYq0DAtE+fr6olmzZnLfTDs5OeHhw4eIiooS2zg6Osod08nJCb6+vgCENeXCwsLk2ujr66Nhw4Zim6wkJycjNjZW7sZ+TE6TQFmF0C+TQNkalx06dMg0XE02NNPHxydfLxZlPYFfJqHFXbly5eDj44Nq1aohNDQUzZo1Q1BQkKLDYtmIjQVWrBDujxgBmJsDR48eRf369VGxYkX8+uuvePHiBcqXL48rV65g4sSJBT4HMDvGxsbYuNEJurq6ANrA3/8Rpk2bBmtra4wYMYKH8jPGGCvxikx1lx07dkBXVxfdu3eX2z5u3Djs3bsXFy9exC+//IIFCxZgypQp4v6wsLBMCxTLHoeFhX21Tcb9GZ+XVZusLFy4EPr6+uLNwsIiNy+ZZeFHewJlQ3zr1q2b6TmWlpZo1KgRiAgHDhzIi3CzVBJ7AmXMzMzg7e2N2rVrIzw8HC1atBDnR7LCZd06ICoKqFBBqAh65coV9OzZEzdv3oSSkhIcHR2xefNmBAUFoWHDhooON5MhQ+qhQwcb1KpVH8OHH0aTJk2QmpqKLVu2oGrVqujfvz/i4uIUHSZjjDGmEEUmCdy2bRv69++f6eLf3d0dLVq0QM2aNTFq1CgsW7YMa9asQXJysoIi/Wz69OmIiYkRb69evVJ0SEXejxaGkfU81ahRI8vnyYaE7tmz54fizE54eDhevXoFiUSCOnXq5Ms5CjtjY2NcuHABDRo0QGRkJFq1aoVr164pOiyWQXAwcPiwcH/aNCAy8h169+6N9PR09OzZE2/fvoWnpyeGDx8OPT09xQabDYkE6N4dUFFRRWysIy5duoyrV6+iU6dOkEql2L17d6YiYowxxlhJUSSSwMuXL+Phw4cYnl1t8gwaNmyItLQ0vHjxAoDQ8/Du3Tu5NrLHZmZmX22TcX/G52XVJivq6urQ09OTu7Efk5N1AoGsewIzJuLVqlXL8nm9evWCiooKbty4gTt37uRFyHIyFoURhqqVTIaGhvD09ESTJk0QExODNm3aiGs3MsVKTwcWLBCKwnTsCNSqlY5+/frh7du3sLW1hYeHB0xMTBQdZo60by/MZ3z6FAgKAho1aoQTJ07gn0/lTv/66y8u7sUYY6xEKhJJ4NatW2Fvb49atWp9s21AQACUlJTEixQHBwdcunRJrkfI09MTNjY2MDQ0FNt4eXnJHcfT0xMODg4AAGtra5iZmcm1iY2NxfXr18U2LP/4+Pjg5MmTAHK2TiCQdRIoGwpatmxZ8Wf/JVNTU7H67Jo1a34s8CzIksCSOBT0S3p6ejh79ixat26NuLg4tGvXDv/++6+iwyrxDhwAHj4E9PSA8eOB2bNn48KFC9DW1sahQ4cKReGXnNLVBdq2Fe4fOfJ5e58+fVC+fHm8f/8+33r9GWOMscIsx0lgfgyvjIuLQ0BAgFhV9Pnz5wgICJBbRyw2NhYHDhzIshfQ19cXK1euxJ07d/Ds2TPs2rULEydOxIABA8SL/H79+kFNTQ3Dhg1DcHAw9u3bh1WrVsktZzF+/HicPXsWy5Ytw4MHDzBnzhzcvHkTY8eOBSCsVzhhwgTMnz8fx48fR1BQEAYNGgRzc3M4Ozvn+fvCPktOTkbHjh3h7OyMqKioHyoMI0sCsxsKKiNbYmTXrl15vmagbD5gSSsKkx1tbW2cOHECHTp0QGJiIjp16oRTp04pOqwS6/17YP164b6rK2HnztX4448/AABbtmyBra2tAqP7PrJp5OfPAx8/CvdVVFTg6uoKAFi9ejWIFxNkjDFW0lAOqaurU4sWLWju3Ll06dIlSklJyelTs3Xx4kUCkOk2ePBgsc2mTZtIU1OToqOjMz3f39+fGjZsSPr6+qShoUG2tra0YMECSkpKkmt3584datKkCamrq1PZsmVp0aJFmY61f/9+qlKlCqmpqVG1atXo1KlTcvulUinNnDmTTE1NSV1dnVq3bk0PHz7M1euNiYkhABQTE5Or55Vkt2/fFn8v7t27R/369SMAtHz58q8+r2XLlgSA9uzZI25zdXUlADRp0qSvPlcqlVKdOnUIQJa/Kz+iXLlyBIAuXbqUp8ct6pKSksjZ2ZkAkKqqKh0+fFjRIZVI06YR2dsT9e6dQO3bdxQ/e25ubooO7btJpUQuLsLr2rfv8/aIiAjS1NQkAOTj46O4ABljrBjja9/CK8dJoIeHBw0ePJgsLS1JIpGQlpYWOTo60oIFC8jX15fS0tLyM85igT8Iubd9+3bxQvTq1avUvXt3AkDr16//6vOcnJwIAP3999/itubNmxMA2rFjxzfP6+HhQQCofPnylJqa+sOvg4goLCyMAJBEIqGPHz/myTGLk5SUFOrTpw8BIGVlZdq9e7eiQypRfH2FRKlKlSgyNPyJAJC6ujqtXr2apFKposP7Ifv2Ca+tVy8hKZQZOXIkAaDu3bsrLjjGGCvG+Nq38MrxcNAhQ4Zg+/btePHiBZ48eYI1a9bA3NwcGzduROPGjWFoaIiOHTv+WLckY18IDAwU70dGRn73EhFEJFYGrV69+jfP26dPHxgbGyMkJATHjx//rti/JJsPWLVq1SI1r6qgqKqqYufOnRg8eDDS09PRv39/bN++XdFhlQgpKcDixUBKSjKePFmAqKhrqFmzJm7evAk3N7dCs/7f9+rQAdDUBJ49A27f/rxdNvT76NGjePnypYKiY4wxxgredxWGqVChAn7++Wfs2LED3t7emD59OiQSCc6ePZvX8bESLmOFztzMCfwyCQwLC0NkZCSUlJRyNK9JQ0MDI0eOBADMnz8/01IT34PnA36bsrIytm3bhl9++QVEhKFDh2Ljxo2KDqvY274dePUKiI5+DKl0HZo1a4br16/n6AuTokBHR0gEAWDfvs/bq1evjtatW0MqlWLdunWKCY6xPEQ8v5UxlkO5TgJDQkKwY8cODB06FNbW1qhZsyauX7+OSZMm4eLFi/kRIyuhiOi7k8AvC8PIegErVar0zcqiMm5ubjA0NMTt27fzZD2xkrxIfG4oKSlhw4YNGD9+PABg9OjRWLlypWKDKsZCQgAPD+Gz8vbtFAAJ+P3337/5GStqevcW/r14EQgP/7x93LhxAITCN7xcBCvK1q5dCwMDA9y4cUPRoTDGioAcJ4E///wzKlSogJo1a2L//v2wsbHB7t27ERUVBU9PT8ycORPNmjXLz1hZCRMWFoYPHz6IjyMjI797ncCcVgbNyMzMDGvXrgUAzJs3D7czjiPLpYiICJw/fx4A0KRJk+8+TkkhkUiwYsUKTJs2DQAwceJELFy4UMFRFT9EwjBQoaP7GtLSzqBZs2bF8v/yihUBe3tAKgUOHfq8vWPHjihXrhyioqJw4sQJxQXI2A+IjIyEm5sbYmNj4eHhoehwGGNFQI6TwO3bt0MqlWLGjBn4/fffMXXqVDg4OEBFRSU/42MlWMb5gIB8T2BO1wmUDePMzXzAjPr27YuePXsiLS0NgwYNwuPHj/Hu3Tu5pSdyYvv27UhOTkbt2rVRt27dXD23IERGAlevAo8eAQkJio5GIJFIsGDBAsydOxcA8Ouvv2L27Nk83CkPeXoC168DEkkKHj0aBgCYOXOmgqPKP7LewCNHhHmQgDAEeeDAgQCAv//+W0GRMfZj1svWdgFgZGSUb+eJj4/HuHHj0KpVKxw/fhynT5/GzZs3+f9lxoqgHCeB9+/fx7Rp0+Dv748OHTrAyMgInTt3xp9//ombN29CKpXmZ5ysBMo4FBT4sTmB39MTCAiJyPr162FiYoK7d++iSpUqMDMzQ6lSpXDr1q0snxMVFYXhw4eLC59LpVJxXtvo0aMLVZGNDx+A5cuBTp2EhcH79QOaNQMGDABCQxUdnfD+z5o1C4sXLwYg9MhOnTqVLzjyQHy88LMHAEPDY0hOfoKffvoJrVu3Vmxg+ah5c8DERPjSw8vr8/ZBgwYBAM6cOYN3794pKDrGvt/hw4fF+3FxcXlyzNGjR6Nz585yw6R37dqFNWvW4OLFi+jatSs6duyI+vXrY8eOHSCh4jwAYXj1ggUL+P9qxgqxHCeBNjY2GDVqFPbu3YuwsDBcvXoVHTp0wI0bN9CpUycYGRmhU6dO+RkrK2FkPYEVKlQA8P3VQdPT0xEcHAwg9z2BAFC6dGns3bsXNjY2YlXPuLg4zJ49O8v2ixYtwtatW9G1a1cEBwfDy8sLT548ga6uLvr165fr8+eXI0eALl2A3buFXpGyZQF9fWHfgwfAyJHA69eKjVFmypQpWLVqFQBg6dKlGD9+PH/x9IM2bBC+BDAxScaVKyMACL2AhelLirymogL06CHcP3Dg8/aqVauiQYMGSE9Px+7duxUTHGPfKTQ0VG66Ql4kgX5+fti4cSNOnjwpN7zUK+O3JxlcvnwZI0aMgJGREXx9fTFixAjMmDFDnAvPGCt8vqs6KADY2dmhe/fu6N69O7p27QoiwpkzZ/IyNlbCyXoCW7RoAeD7C8M8f/4ciYmJUFdXR6VKlb4rlpYtW+LBgwf4+PEjHj58CCUlJZw8eTJTb2VCQgK2bNki3u/evTuWLVsGQOhtKAxLQ0ilwOrVwB9/CMlfzZrAmjXA0aNC78jx44ClJRAWJiSChaVy/rhx47Bp0yZIJBKsWbMGo0aN4kTwOz14AOzfL9zX1FyFxMQY2Nvbo3379ooNrAB06yYkg4GBwL17n7cPHjwYAA8JZUXPl9deHz9+zNXziYD37+W3CdVyWwEYjd9/P4r09HRIpVJ4eXkD6AWgolz7bdu2YevWrYiOVkXnzv8DoAxgIJYufQ3uDGSscMpVEhgeHo79+/dj9OjRsLW1hbm5OYYOHYoHDx5g4sSJuHDhQn7FyUqY5ORkPHjwAMDnJPB7ewJl8wHt7OygrKz8w7FVqVIFvXr1AgAsWLBAbt/u3bsRGRkJS0tLWFhY4NGjRzh37hwAYWiNoqWmAr/+Csiuc0eNArZuBRwcAFkHkLk58NdfQIUKQhVFd3cUmj/iI0eOxPbt26GkpITNmzdjyJAhSEtLU3RYRYpUCixcKPxrbHwLhw9PBSAshVKcewFljIyAtm2F+xlXH+nTpw9UVVUREBCQaT4yY4XZqVOnAADW1tYActYTmJ4OJCcL/86aBbRvD3wabIGIiAjs3v0CwCIAP+Pt29+xefM93Lx5FxERTaGkNA1aWscA/AFgLoDan446CMBJRESsBnANwDicPm0LXj2MscIpx0mgra0typQpg4EDByIoKAg9e/bE+fPnERUVBR8fH8yZMwfNmzfPz1hZCXL//n2kpaXBwMAANWvWBPB9cwJTU1O/ez7g1/z6668AgAMHDuDhw4cAhCUtVq9eDUDotTp48KAYR9OmTVGtWrU8O//3WrMG+PdfQFUVmDcPGD78c/KXUalSwKZNgLq60BP45EnBx5qdQYMGYffu3VBWVsY///yDfv365ck6jiXF8eNAcDCQnBwFT09h8bz58+ejXbt2Co6s4IwcKfQG/vcfIKumL5vnDgA7duxQYHSMZe3x48d4/vy53LaUlBR4enoCAFxcXADYIjBwAMaMEXr7ZYMliIArV4C1awFnZ6BGDWEUSLVqwN69wPv3hPnz41GrVghatNiH1NRZUFLShoqKIQAljB+vjV69AgD0BKANqbQilJR6Ql19AoBNAEYCcMWXl5Xx8XGYNCkF27YJSWdMTO5e8/37wNu3uXsOYyxncpwEOjs748yZM4iKisKVK1fw+++/o3Xr1sVuLSlWODx9+hSA8OWDrNLZ+/fvxUnm39MTmJcLX9esWROdO3cGEYlrCPr4+CAoKAhaWlr4+eef0aBBA2zZsgWWlpZihUtFunxZmP8HAIsWfV48OzuGhkCDBsL9K1fyN7bccnFxwcGDB6GqqooDBw6gZ8+eSE5OVnRYhV5srHARmJSUiKdPJ0EqfYdBgwaJX2qUFOXKfZ4buHr15wtl2ZDQXbt2cQ8zK1RiYmJQpUoVVKhQQe7/usuXLyMuLg6mpqawtW0LYB0iI2vj+nXhi77x44Hp04HKlYF27YCpU4WqwC9fCnO+X70C3rz5iDdvTuPDh/sIDAzH3bsNAOhBU1MV5ubXAIQiNTUJERFmACpBWVkNVapowMamCoyM9CBcSo4AoAQtrY9QVd0DAwNlaGrOA+CDx48jMG4cwcQkDra2aejQQfjyJeN/2TExwpdTGfn4AAMHCq+BMZb3cpwELly4EG3btoWWllZ+xsMYAIjrA5qamsLQ0BAA5OZ/5SYJzI+eQACYMWMGAGH5hwYNGuB///sfAOFC0sDAAAAwcOBAvHjxAi1btszTc+dWeDgwZ45wv18/oUpiTjRuLPx79Wq+hPVDnJ2dcfz4cWhoaOD48ePo2rUrEgrL+haF1KZNQEREGp4/v4jExB1o2rQp/vrrrxIxDPRLw4cDWlrC/MhPS3iiffv2KF26NN69e4ezPIaNFSIZC6xkLAKze/ceAM1hYuKBP/+sC0AXaWkvEBEBPH8OeHgIRaDevgWI0qChkYYKFYCOHYFOnWJhZOSKxEQjpKd3AjBfPK6yciwOHSqLgwfbAjgGogSkp5cFAHTu/B537ghzap88+TwHX09PH0ePtsTBg8uwaFEdODp2BTAJqaljkJjoh9jYBwgLuwdv71Q4OsajTJkVaNLkBTw9ARcXYPDgz5/FN28AWe2158+FYauMsbyVoyRw0aJF4iLd33L9+nVxfDpj30uWBBobG0NbW1v8IyOjrq7+1efL2n/8+BGPHj0CkLc9gQDQsGFDTJ48GcrKyvDz8xOXjBg7dmyenudHSaXAzJnCN602NkBuwpMlgYGBQi9SYdOuXTucOnUKWlpaOHfuHDp27Jhn5dGLmydPhIqY4eHvkZAwB1ZWFjh8+PA3P0vFlaEhMGSIcH/9eqFIkqqqKgYMGABAmH/67NkzxQXIWAb+/rcAjAEwERcv+gMAoqNj8c8/ZQH8idevm+DJE1UAjyCVTkapUsKwfm1twMAAKFMmGUpKXVG6dHV06jQdwcHVsH+/Pl6/Xg+JJB0DBw5EYODvEOYBXkXPnmfh5KSF+vVLo27d1wCikJSUCOAjRo78PElcSwtYuVIflSsr4/RpM7RpI1Sd/uUXYPduF1SuXBnAUUiljQC0AlEPpKX9h7i4p4iKaoqrVz9g4MCP+PQnH6tWCX9rfv0VEP4rJxABEREF9EYzVoLkKAm8d+8eypcvjzFjxuDMmTN4n6GMVFpaGgIDA7F+/Xo0atQILi4u0NXVzbeAWcmQMQmUSCRibyAg9AJ+q+dC1hMYFBSE9PR0GBgYoGzZsnke55IlSxAaGoqVK1eiadOmmDp1Kuzs7PL8PD/i9GnA3x/Q1BQKgnx6a3KkTBmhQIxUCly7ln8x/ohWrVrh/Pnz0NXVhbe3N5ycnBCT24knxRwRsHQpkJaWjqiogwD8MH/+fBgbGys6NIXq1w8oXVpYE/PgQWHbzJkzUb16dbx9+xZt2rRBaGFYMJOVeJ6eUQCGAuiH9etr4+xZoE6dh0hN7QyJRB1aWjowNY0BMBTKyi+xfbvwxc+sWUDv3kClSqsQF3caDx8+xKJFi3AvQ2ncw4cP4++//0aNGjWwfHljNGiwG3/+OU7c7+zcBEAfAAsAjMFPP1WVi23MGCs8elQHjRvLX/vp6Ojg1q1b2Lp1KwYO7Idp01xhZBSJ1NRuIPrcsxke/hR2dlKoqwPv3gGtWglDQ1NTYxAU9B/u3LmDdev25fVbyhijHAoICKDhw4eTgYEBKSkpkaqqKuno6JCSkhIpKSmRvb09bdiwgRITE3N6yBInJiaGAFBMTIyiQyn0BgwYQADozz//JCKiqlWrEgACQAYGBt98/rZt28T2AKhJkyb5HXKhFB9P1LYtkb090Y4d33eMVauE58+cmbex5bXr16+TgYEBAaB69epRRESEokMqNDw9hZ+hlVUoAWXI2tqaUlNTFR1WoXDkiPDetGolfF6IiEJDQ6lixYoEgOzs7OjDhw8KjZExA4NNBPgR4EcSyR0yMSFSUgom4DpVrHiYNm8mun49XPybl56eTr6+vuTh4UHXrl0jJSUlcZ+ampp4v2zZsiSVSr967oCAALG9tbX1D72OS5cuUcWKFUlTU4/Gj99B6upmBIAqVx5F//0XQW3aCJ/HWrVSSFV1IQHbCfCjMmX6/NB5meLwtW/hpZLTZLFWrVrYvHkzNm3ahMDAQLx8+RKJiYkwNjZG7dq1S/w3yixvZewJBJCpJ/Bb1L7o7srr+YBFxdatwjAaCwugT5/vO0aTJsKSElevCj2CSt+9umj+atCgAS5evIg2bdrg5s2baNWqFTw9PVG6dGlFh6ZQcXHAsmUAkRQxMasAvMWUKRugopLj//6Ltc6dhd/vkBChN3DQIKBMmTL4999/0aRJE9y7dw/t27fH5cuXS+zQWVbw4uOFuXBVqwIvXsQjOrr2pz3XQPQT4uKiIZUmQFl5A65dWwpjYyAxUSfD8+PRr18/uWqi/fr1Q+/evWFlZYWyZcti3rx56N+//zdH1tSqVQv//PMPAgMD4ezs/EOvq2nTpniSodx0pUoEN7chePx4Ixo33oRx447j8uUOiIqKRmrqcQDdAQAfP3IRQsbyWq6vApSUlFC7dm3Url07H8JhTPBlEiirEAp8XxKY1/MBi4JXrz5XA3V3z90w0Ixq1gR0dD5XbyvM+XTt2rXh7e0NR0dH3LlzB82bN4eXlxfKlCmj6NAUZvVq2ULQrxAVtQJmZmYYIpsMx6CsDAwdCsydC+zcKRSoUFcHrKys4OnpiSZNmsDPzw9btmyBq6urosNlJQARMGkS4Ocn/H6+fx8DQAkSyRNoa+9DXFxVJCQ8AfAG/fopi38nNTQ0oKysjPT0dLx//14uAbS0tMSqVavkvrCXLWmUE7K5snlt7NjB8PF5gIMHF4GIsGpVZwBTAegCuI569dxx8yaQkKANIiqRRawYyy+F9Dt9VtLJksBSpUoByH1P4JeFZEpiT+CKFcLi8A4OQm/e91JREY4BFL6lIrJSrVo1+Pj4oFy5crh//z6aNWuGV69eKToshbh5Ezh8GAAIERETAaTA3d2dl/b5Qvv2gLk5EBkJHD36ebutrS1+//13AMDvv/+O+Ph4xQTIShR/f+Do0XcICAhGYGASQkM1AQCmpoHo0KEsgN4ApkJbexIWLZonPk8ikUBHR+gNzDjnz8PDA0FBQYV2xNb+/QsxdqwUxsbDP21ZDOA3AFK0aCGsryuVGvEyQIzlMU4CWaGU18NBS1pPoLc3cOmS8C3yxIlZLwifG40aCf/KFtYu7KpUqYJLly7BysoKT548QbNmzTItslzcJSUB8z9VfLexCcbLl0dgYGCAUaNGKTawQkhF5XOl0B07hEqhMsOHD4e1tTXevXuHtWvXKiQ+Vvy9fCmsm/fxIzB6dDrCwl4hPf0KkpLuQlU1BkAsGjYMQ926dQBEAbiAceN6wdzcXO44siRQtj5utWrVMGTIkEJdsE8iAebOlaBOnT+hqiq8Hj09PfTu3RuNGlX51MoYsYWxRDVjRRgngazQSUpKEsv858Vw0LJly8olkcVdQgKwZIlwf9Agobrnj5KN/n74UP4CuTCztrbGpUuXULlyZbx48QJNmzYVlwspCTZuFBaDNjZOx61bQwAAbm5uhfpiUJE6dRIqhYaHAydPft6upqaGuXPnAgAWL16M6OhoxQTIiq2lS9/Azu4+rK1DUb06EBT0EUAqgJkAfkVc3CoAA2BnZ4aaNWuKzxszZkymY8k+37L1ca2srPI9/rxgZASsWaOPWrX8YGd3F3PnxmDfvn0wM1OGkpISABNOAhnLYwpNAi9duoTOnTvD3NwcEokERzOOwwEwZMgQSCQSuVu7du3k2kRGRqJ///7Q09ODgYEBhg0blmmdsMDAQDRt2hQaGhqwsLDAEtkVcgYHDhxA1apVoaGhgRo1auD06dNy+4kIs2bNQpkyZaCpqQlHR0c8fvw4b94IJifi04JAysrK0NfXByDfE6ipqfnNY2RMAktaL+DGjcKFbNmywLBheXPMcuUAfX0hASxKv/YWFhbw8fGBnZ0d3rx5g2bNmiE4OFjRYeW74ODP80FLlfoLDx74w8TEBOPHj1dsYIWYmprwpQkAbN8OpKV93tevXz/Y2dkhKioKf/75p0LiY8XT8+fAihXpSEiIx9u3oQgLe4/09HcAjqNyZQMAngBWAniLihUrwtHREUOGDMGqVatQrly5TMeT9QQWtSQQENaxXbjQHJqa1bBzp/BljIGBcC0A6HMSyFgey3USmHGNwC/Jhh/kVHx8PGrVqoV169Zl26Zdu3Z4+/ateNuzZ4/c/v79+yM4OBienp44efIkLl26hJEjR4r7Y2Nj0bZtW1haWsLf3x9Lly7FnDlz8Ndff4lt/vvvP/Tt2xfDhg3D7du34ezsDGdnZ/E/UUBYD2716tXYuHEjrl+/Dm1tbTg5OSEpKSlXr5l925drBAI/1hNYkuYD3r8P7N0r3J8+HcirqV8SyeeCMLn8mCtcmTJl4O3tjVq1auHdu3do0aIFAgICFB1WvklNBebNEyq5Vq/+Cnv2CMVMNm3aJM6xZVnr1k3okQgNBc6c+bxdWVkZ8z+NrV25ciXevXunoAhZcfPHH8D79ykAfAH8jZSUZ0hLOwxNza04ceIEjh07Jra1traGqqoqPDw8MG7cuCyPV1R7AmUcHYHhn6YGLlwoDI8VkkA9TgIZy2u5XVPC1NSUTp48mWn70qVLSUND47vXqgBAR44ckds2ePBg6tq1a7bPuXfvHgEgPz8/cduZM2dIIpHQmzdviIho/fr1ZGhoSMnJyWKbqVOnko2Njfi4d+/e1LFjR7ljN2zYkH755RciIpJKpWRmZkZLly4V90dHR5O6ujrt2bMnx6+R10rJmX///ZcAULVq1cRtJ06cENcp6tat2zePcfPmTbH99u3b8zPcQiM1lahvX2GNpRkz8v74W7YIx/7117w/dkGIiIig+vXri2tNXr9+XdEh5Yu//hJ+Ti1apJG1dR0CQIMGDVJ0WEXGjh3C+9epE1GGPxsklUrF3x8bGxtyd3en48ePU3R0tOKCZUVOcjLR6NFEgwYJa7hqaxNJJEEE2H36myWs57fj08KuKSkpZGxsTLq6ujlar7J79+5ya+QeOHAgv19SnktPJxo1SvgcDhxIpK19jwA/OnjwuKJDY9+Br30Lr1z3BLq7u6NHjx4YPXo0EhMT8ebNG7Ru3RpLlizBbtn4ozzk7e0NExMT2NjYYPTo0eJQQQDw9fWFgYEB6tWrJ25zdHSEkpISrl+/LrZp1qyZXM+Qk5MTHj58iKioKLGNo6Oj3HmdnJzg6+sLAHj+/DnCwsLk2ujr66Nhw4ZiG5Z3viwKA3BPYE54eACPHgnDNt3d8/74RbUnUMbIyAienp5o1KgRoqOj4ejoiCtFodxpLjx7JqwNCQC6uhvw/PltlCtXDqtWrVJsYEVI797C3MC3b4FDhz5vl0gkWL58OTQ1NfHw4UMsX74cXbp0gYWFBS5fvqy4gFmRcvWqULTrxAmh2rJUKgXRfwDufZr7JgXweUkGVVVVBAcH4+7duznqybexsZF7XKlSpbx+CflOSQmYORPQ0hKGtkulwqVqeHiigiNjrHjJdRI4ZcoU+Pr64vLly6hZsyZq1qwJdXV1BAYGolu3bnkaXLt27fD333/Dy8sLixcvho+PD9q3b4/09HQAQFhYGExMTOSeo6KiAiMjI4SFhYltTE1N5drIHn+rTcb9GZ+XVZusJCcnIzY2Vu7Gvk2W6GdMAr+3OqiSkhJsbW3zOMLC58mTzxf/U6YIQ9rymp2dMCw0NFQopV8U6evr49y5c2jZsiU+fvwIJycnXLhwQdFh5QmpVBgGmpYGlC59HydOuAEAtm7dCgMDA8UGV4RoaAAjRgj3t24VFu2WadKkCZ49e4bdu3dj5MiRsLa2xsePH9GnT5+vTpVgDBCGai9ZIswDTE4GrKyA0aNfAJgEfX19/Pvvv6hbty6uXr36KSEUmJiYoHz58jk6h52dnXhfVVVV7nFRYm4OTJgg/M1JTU0FALx/z0tEMJaXvqswTKVKlVC9enW8ePECsbGxcHFxgZmZWV7Hhj59+qBLly6oUaMGnJ2dcfLkSfj5+cHb2zvPz5UfFi5cCH19ffFmYWGh6JCKhLzoCaxYsSJat24NV1fXHBWSKcrS0oA5c4R/mzcH2rbNn/Po6ADW1sL9DNNlixwdHR2cOnUKTk5OSEhIQMeOHXEm4wSwImrvXuDuXUJU1CucPdscADBu3Di0za9fiGKsa1egfHkgOhr45x/5fWZmZujbty82bdqEwMBAVK1aFaGhoRg4cCCkUqlC4mWF34sXgLMzcOlSCpKTX6BUKTfMmHEMdnYXAXyEnZ0dWrZsCX9/fzSSrcnzHTJ+6WlnZ5dpuaSipFs3oEEDQCIRksAPH4pIaWrGiohcJ4FXr15FzZo18fjxYwQGBmLDhg1wc3ODi4uLOLwyv1SoUAHGxsZ48uQJAOGPcXh4uFybtLQ0REZGikmpmZlZpkn8ssffapNxf8bnZdUmK9OnT0dMTIx4K6kLVudWVklgbnsCVVRU8O+//2L16tV5H2AhQgSsWgU8eADo6QnFYH50TcCvKepDQmU0NTVx7NgxdOnSBUlJSejatWum6sRFycuXwLp1hFevXuPZs/EA3sPd3R0rVqxQdGhFkrIyMHascH/Xrux7vnV0dHDgwAFoamri3LlzWLx4ccEFyYqEuLgEDB4cgQYNAG9vKRITHyI9/RJevVqHYcP6YMaMGQCAjh075sn5qlatKt4vW7ZsnhxTUSQSYNYsQEVFGP11+3baN57BGMuNXCeBrVq1gouLC65duwZbW1sMHz4ct2/fRkhISL7PvXr9+jUiIiJQpkwZAICDgwOio6Ph7+8vtrlw4QKkUikaNmwotrl06ZI4nAAAPD09YWNjIyYWDg4O8PLykjuXp6cnHBwcAAgVuczMzOTaxMbG4vr162KbrKirq0NPT0/uxr4tqyRQTU0N2traAHKWBJYUHh6ArGDur78CGd6yfFFckkBA+HwePHgQvXr1QmpqKnr27Il9+/YpOqxcS0sDZsyQ4sGD5wgPPwHgKP78808sW7ZMbkgZy52WLYFq1YDERGDLluzbVa9eXVxEfubMmfDx8SmgCFlht3nzAxgaHsXff79AbOxHqKjEgugs9PWnASAkJSXh3bt30NfXx1jZtw4/SPZ3EoC4xFJRZmYGlCkj9LDfu5eOT5cHjLG8kNtKMt7e3lluT09Pp3nz5uXqWB8/fqTbt2/T7du3CQAtX76cbt++TS9fvqSPHz/SpEmTyNfXl54/f07//vsv1a1blypXrkxJSUniMdq1a0d16tSh69ev05UrV6hy5crUt29fcX90dDSZmprSwIED6e7du7R3717S0tKiTZs2iW2uXr1KKioq9Oeff9L9+/dp9uzZpKqqSkFBQWKbRYsWkYGBAR07dowCAwOpa9euZG1tTYmJiTl+vVwhKWfatGlDAOiff/6R216uXDkCkOvfs+Lq8GGhepq9PdGuXQVzzidPhPM1aSJUcCsOUlNTaeDAgQSAlJSUilw12fXr08nI6CkBF0hFxZx27typ6JCKDT8/4fe9QQOily+zbyeVSuV+h7p160YXLlwgqVRacMGyQmXnTiJl5QAC/Ai4QYaGO6l+/V8JkNC4ceNo165dBIAkEolYCTSvTJo0ibS1tenRo0e5fm5h/J1t0eI6AX6krb0mXypfs/zF176FV66TwLx08eJFuVLGstvgwYMpISGB2rZtS6VLlyZVVVWytLSkESNGUFhYmNwxIiIiqG/fvqSjo0N6eno0dOhQ+vjxo1ybO3fuUJMmTUhdXZ3Kli1LixYtyhTL/v37qUqVKqSmpkbVqlWjU6dOye2XSqU0c+ZMMjU1JXV1dWrdujU9fPgwV6+XPwg5U6eOUNb+zJkzcttr1qxJAGjJkiUKiqzw8PIiql9fuEBdu7bgzpueTtS0qXDe77i+KLTS09NpxIgR4v9BGb8kKswCAtLJ2PgpAX6kpNSOjh49quiQih03N+H3feLEr7f7+PEjde7cWe5vWfXq1emvv/6i+Pj4ggmWKVR6OtG+fVKyt48nbW0pAbcI2EaApdzvxdWrV0kqldKuXbvoxo0b+RJLTpM5T09P6tmzJzVo0IDKlClDampq1KNHD3rx4kW+xPU9una9QYAfaWquIXt7omK6uk+xxde+hZeEiCg3PYfz5s376v5Zs2bl5nAlSmxsLPT19RETE8NDQ7+ifPnyePXqFfz8/OSW/2jRogV8fHywevVquLm5KTBCxbp5E3BzEyrNOTsDM2bk7zzAL40ZA9y4IQw/7d694M6b34gI48ePx5o1awAAq1atynZB5sIgPp5QvfodvHiRBuAc9u+vgl69eik6rGLn+XPAxUWovrp+vVCo4mvu3buHtWvXYseOHUhISAAgzGkePnw4xowZU+QW72Y59+efwKxZ75CY+AqqquZITT0PZeX/YdmyWThw4ACSkpJgb2+PDRs2FIqh2tevX0ezZs2QkpK54IqGhgamT5+OyZMnf7W42rNnz7Bv3z74+flBRUUF6urq0NDQQIUKFWBvb4+6devKTe34HkOG+GPHDkKpUjdgZTUGlpbCNIgiXPOmROFr38Ir10lgnTp15B6npqbi+fPnUFFRQcWKFXHr1q08DbA44Q9CzmhpaSExMRHPnz+Xu2AaMmQIduzYgX379qF3796KC1CBHjwARo4EEhKEOUuLFglFLArSunXCXMRu3YQEtDghIkybNg1LliwBACxatAhTp05VcFRZa93aCxcu6AMIx+bNcRg+vGR+JgrCn38K1VcrVgR2787ZZy46OhoeHh5Yu3Ytnj17BkBYsqZr165wc3NDixYtICnIb29Yvvr3X6BnT0JsbBCIDkEiuQ6ii6hWrTLuFsJyymFhYahXrx7evHmDdu3aYeTIkbCwsIBUKsW0adNw8eJFAEDp0qXh5OQEJycnNGnSBB8+fMDTp0/x8OFDsWL7t5QrVw42NjaoWrUqrKysEBISguDgYNy7dw86Ojro1q0bevbsifr162f5mZg48Q5WrkyFmdlN1KgxCpGRQLNm5/D+/U7873//Q3JyslgHghU+fO1biOVFd2JMTAx169aN/v7777w4XLHFXeLfFh8fLw6ZiY2Nldv39u1b2rNnDyUnJysoOsV6+ZLI0VEYmjZyJJGi3gZPTyGGgQMVc/78JpVKafbs2eLv4Zw5cwrdPJk1a25/mmvkR7/+eljR4RR7MTFELVsKv/cHD+buuWlpaXT8+HFydHTkoaLFkFRKtH49kakpkbp6LAHHCFASf84uLi6KDjGTlJQUatq0KQEgW1vbTH9rpVIp7du3T5yH/7WbkpISOTo60ooVK2jNmjW0dOlSmjt3Lrm4uFClSpW++fyMN0tLS1qyZEmma6QFCx4Q4Ed6epvpzBmiSpVOZ3ruxo0bxfYBAQEUGBhYIO8l+za+9i28ct0TmJ2goCB07twZL168yIvDFUv8bci3hYSEwNLSEmpqakhKSuJvyj95/x4YNkxYqN3GBvjrLyBDEbgCFRIiDANVUwMuXy74nsiCsmjRIkyfPh0AMHXqVCxcuLBQ/D4+exYFO7sgJCdroX79x7hxo6+iQyoR9u0Dli4FDAyAI0cAXd3cH4OHihYvjx8DCxcCx49LkZgogUTyFPHxQwFcEdvMnz9fXAaisBg3bhzWrFkDPT093LhxAzY2Nlm2S0lJwdWrV3H+/HmcO3cOAQEBMDU1RcWKFVGpUiU0aNAAPXr0gKmpabbniomJQXBwMB4+fIiHDx/ixYsXKFeuHKpVq4Zq1arh1atXOHDgAE6ePIn4+HgAQlXTMWPGwN3dHcbGxti27RWGDXsHVdW7SE4eAiurjggJOS13Hg0NDdy+fRvKysqoUqUKdHV18eHDhyK9TmJxwde+hVheZZOXL18mAwODvDpcscTfhnybv78/ASBzc3NFh1JoxMQQubgIvRDOzkQREYqNJ2NxmKdPFRtLfluxYoX4TfO4ceMU3iOYni6lSpWOEuBHOjqnKCLi47efxPJEaipRz57C7/2yZT92rKioKFq2bBlVqFBBrkeFq4oWDWlpRGPHEhkZEWloJBLgT3p6waSp2ZAAkJqamvhzvXv3rqLDFSUnJ9Po0aPF2I4dO5ar56elpeVTZMIooK1bt5KNjY0YX5UqVSg+Pp7OnIn4NPJhH8XGxpKamjoBoFKlhpGhoYlcT2LDhg3FxyEhIfkWL8s5vvYtvHI9M3n16tVyt1WrVmHatGlwcXFB+/btfzgpZSVbVmsElmRpacDkycCTJ0CpUsJ8PCMjxcakpARUrizcf/hQsbHktwkTJmDDhg0AhP/7Ro8eDalUqrB4xo49hydPykIiSceOHeVhZKSjsFhKGhUVwN1duL9374/97hsYGMDd3R2PHj3C8ePH4ejoCKlUiiNHjqBVq1aoWbMmNm/eLPYWssJlxQpgxw4gKQkAogEEIDa2KxITr8Pa2hp79uxBjRo1cObMGVSrVk2xwX4SFhaGVq1aYcOGDZBIJFi+fDm6dOmSq2Mo5+OwDy0tLfz888+4d+8ejhw5gjJlyuDRo0f4/fffYWEh63Y3wOHDh5GSkoxSpaxQvvxm1Kv3DsHB4bCwaIWXL1/i+vXr4jHDw8PzLV7GioXcZo1WVlZytwoVKlDDhg1p+vTpmcaVM3n8bci3ydZOatWqlaJDKRSWLRN6Hpo2LVxLMixeLMS1cqWiIykYHh4epKSkJC5hk5/fiGcklUrp9u3bNHfuXKpRoz0BPgT4UZ8+Z779ZJYvpk37PCc2L9fKDA4OptGjR5OWlpbYk2FoaEiTJ0+m58+f592J2Hfx9yf6/XciJycibW0iLS2iRo2IBgwYKf68lJWVM61vqyhSqZRCQkLo9OnTtHjxYjI3NycApK+vTydPnlR0eN907Ngx8T29cCGIlJT8CbhOnTp1IQA0YsQosrERfg4WFkQ1aiRTlSp9yNDQUPx5fLnUF1MMvvYtvBS6TmBJwx+Eb1u1ahUBoN69eys6FIU7d+7zYvAXLig6GnlHjghxjR6t6EgKzp49e0hZWVks9pCSkpLv55w4ceKnCxoVAnYQ4EdVqnhTamoeZh8sV96/J2reXPj93707748vGypqbW1dIoaKSqVSGjVqFM2cOVPRoch59+4d+fj4UHp6OqWmEjVpkkKlSiWSlpaUtLSI7OyIPn4kcnBwIAA0dOjQQrO23unTp6lixYqZiqfY2dl91wLyitKjRw8CQPb2P5Gq6h0C/EhLqwwBIHd3LzEJ1NIiql6dyM1NSsHBqdSypQsBoG3btin6JTDia9/CjJPAAsQfhG+bOXMmAaAxY8YoOhSFevKEqHFj4UJzzRpFR5NZcLAQW+vWQnW8kuLw4cOkqqpKAMjZ2ZmSkpLy7VwBAQEkkUg+VZLcTJaW76lx42QKC8u3U7IcOnjwcw99fv08SkpV0cDAQPG1JSYmyu1LTU1VSEzHjh0jJaWuBJygOXMO0d9/Eykr3yfAj9TU9lGlSl7066+LycvLS4w9KChIIbFmFBYWRn379hVjUlFRoWrVqpGLiwstWbKkyI3WevPmDenp6X2aZ3n107xASwKqUa1aCWRvL1Rl1dIiMjb+/KWpqel9AkrTokWLFP0SGPG1b2GmkpMho91zsSL04cOHc9yWsS/J5gSWKlVKwZEoTkqKsBB7UpKwMPWYMYqOKLOKFYW5gdHRQuVSExNFR1QwunXrhqNHj6J79+44evQounXrhkOHDn11MeXvNW3aNBARWrX6DTExw6GuDvzxB/CVQnysgHTrBpw6BQQGAkuWAMuW5f05lJWV0blzZ3Tu3Fmuqujdu3cxcuRITJ06tVhUFX39+rV4/82bN6hYsSIA4Ny5c+jatSt69eqFjRs3QjufyiEnJiZiypQpaNKkCVxcXAAA69efg1T6GwBg8+Z3SE9PRHp6HIADSElZgidPgAULhJtMdhU281toaCguXryIixcv4vDhw4iKioKSkhLGjRuHefPmQfd7ytgWEubm5li8eDFGjx6NlJRwAOUAGEJNbRhUVDTQvDkwejTQu7dQOTsmRpg7D+gAGIJ3794pNH7GCrscFYbR19fP8Y2xHyH7T/trJaeLu40bgadPhQIw8+cLyVZho64OyK47Hz1SaCgFrkOHDjh58iQ0NTVx5swZdOrUSSxtnle8vLxw9uxZKCubIDlZWKaid2+gefM8PQ37TkpKwIwZwvIoPj7Ap3W1842dnR3Wr1+PN2/eYNmyZbC2tkZUVBSWLl2KihUrolu3brh48SIob1Z8KlBPnz4V779+/Rrp6enYuHEjfv/9dyQnJ2Pnzp1Ylh9Z9id//PEH1q5diz59+uD9+/fYu3cvLlwwF/e/eaOL9++ff3p0Burq6pmO0bhxY6iqquZbjFl5+fIlHB0dUbZsWQwYMABbt25FVFQUateujevXr2PFihVFOgGUGTlyJBo3bgwg8tOW8tDQaA1AAldXoFIlYMIEoGxZwMICWLwYUFVVAdANoaGR2R6XMQbkqCewW7duaNeuHa+3wvJdWFgYAMDMzEzBkShGQADwzz/C/RkzFF8J9GuqVgWePROSwCZNFB1NwXJ0dMS5c+fQoUMHXLhwAe3atcOpU6fyZA0kqVSKKVOmAJDA1vYgkpK0ULmycKHDCo+KFYHBg4Ft24T1Axs0yP+1O2VVRcePH4/Tp09j9erV+Pfff3H06FEcPXoU1atXx7hx49C/f39oaWnlbzB55MmTJ+L9169fY+PGjRg7dqxcm2vXruX4eFKpFKdPn0aTJk1gYGDw1bZbt27FH3/8IT42MTEBIAFwNkMrLaSnJwB4iKFD62Py5H1ISEhAamoqdHR0kJCQUOA9sXv27MHo0aMRExMDiUSCunXrolWrVmjVqhUcHR2hopKjS7siQUlJCX/99ReqVTv3aUt3aGnpoXJloEIFYUu/fsDJk8Lfo6tXASOjdLx+rYoXLzIn7Iyxz3LUx9CtWzfExMQAEIaocNldll/evn0LAChTpoyCIyl4CQnA7NkAEdC5c+Hv9alSRfj3wQPFxqEoTZs2haenJ/T19XHlyhW0adMGUVFRP3zcffv24datW1BXHw9l5Z+gqSkMO+Pv4AqfYcOAcuWA8HBg/fqCO69sqKinpyeCg4MxatQoaGlpiUNFy5UrhylTpuDFixcFF9R3+jIJ3LVrV6Y2wcHBOT7eli1b0LlzZ/Ts2fOr7d6+fYvhw4dnsccWgBGABAD3MmzfiUaNHGBrawt7e3v89NNPqF69Oho0aPApecxf6enp+O+//9C/f3/069cPMTExcHBwwOPHj3Hz5k0sWbIE7dq1K1YJoIydnR0sLGTfsFSDpqYGHBw+71dRAaYLAyZw+DCgqZkKAAgNNSzYQBkrYnKUBJYuXVr8Jo6IIJFI8jUoVjIRUYnuCdy+HXjzBjAzA/73P0VH822yJLCkDQfN6KeffsKFCxdQqlQp3LhxA61atcL79++/+3jJycmYMWMGAAeUKjUZKiqqmDEDsLbOu5hZ3lFXF+bvAsD+/UAucpU8Y2dnhw0bNuD169dFcqhoxuGgjx49wu3btzO1CQkJQVhYGPz8/LI9TnJyMogIq1evBiAMqU5LS8u2fcYEeceOHQAAFRV1qKtPAQCYmKRCW3sLgAsAZqNVq9RvJpb5ITAwEAMGDICpqSkaN26M3bt3Q0lJCbNnz8alS5fEOZTFXY0aFuL9mJgYWFrK769TB+jUSbgfEiKsZ/j2bRlxqHZ6ejqOHj2KefPm4c2bNwURMmOFX06qx8yePZskEgkpKSl988ayxxWSvi46OlqsalZcKt/lVEQEUZMmhXM5iOxERX2uxhYXp+hoFCsoKIhMTU3FMuxv3779ruP873//I8CM1NQuU506acTF7YqGmTOFz0HfvkQFtIRktopSVdG0tDRSU1MTY9TU1BTv6+np0YoVK8T17VRUVAgAeXp6ZjpOWFgYGRgYUJ8+fah69eriMRYsWCBXYTQyMpICAwMpJCSE5s+fTwCoUaPG9OCBlExNH5ORUSRpaaWTsXEEXbwYSxcuXKCePXvS69evC/JtEb148YKMjIzE12NgYEAuLi7k6+urkHgUaejQfz9VBxVus2cfo/fv38u1SU4m2raNqHnziE/tjlGdOkm0cSPRX39tFt9HJycn8TlSKdHp0//S8uXLi93yK4UFX/sWXjleIuL+/ft04sQJkkgktH37djp69GiWN5Y9/iB83YMHD8Q//iWNbFH4gQOL1pIL7dsLcd++rehIFO/BgwdUtmxZAkBVqlShV69e5er5R44cIUCVgB1UsWIkDRwoXNSwwi8ykqhlS+GzMH8+0dWrRG/e5O1i8t8jODiYRo0alWcL0Of1RXJYWBiZmJhkWs+uV69eYpu2bdvK7Rs/fnym46xcuVJuwfaM7efPn09ERB8+fKDy5ct/cS4dMjHxI319YZkBHR0iGxui6dPz9GV+l8TERKpXrx4BoDp16pCPj4/ClswoDJYvDybAjzQ0gj4leKUJAFWuXJkmTpwot1xPdLT00+LyfmRr+4Ds7YmsrVvJ/ex9fX3p1atX5OzsT4APAU3p2LFjCnyFxRdf+xZeuV4ncM6cOYXqm8SihD8IX+ft7S1eQJck794ROTgIF5D//afoaHJn4kQh7n37FB1J4fD06VOytLQkAGRtbZ3jC+2nT5+Svr4+ARPJxCSEWrYUkghWdBw79rlnXHZr3JioXz+i334j2rqVyMuL6NkzooK+lo+MjMxyAXpnZ+ccL0D/9OlTsrOzo86dO+f5+piXL1+Wu0CfM2eOuG/RokVy+5ydnTM9f8WKFZkSSdnNzs6OiIj69++fxf42pKoaQlpaRPr6RDVqEA0ZQqSIS5yEhARatWoVXbt2jYiIRowYQQDIyMio0CxCr0jv3hHVrJlMtWunkonJS7KxsZX7WXbp0oWSM3xrpqPzHwF+NGKEN9nZ+X1q15709G4T4ExOTk5kZmYp17vYu7ebAl9h8cXXvoUXLxZfgPiD8FlWFxF79uwhANS8efOCD0iB/vhDuGAcPrxo9QISEW3YIMQ+b56iIyk8Xr58SZUqVSIAVK5cOXr06NFX2ycmJlKdOnUIcCBt7XtUt246+fgUULAsz0ilREePEk2dStS7N1HDhpmTQtmtQQOiHj2IJk8mWreO6PRpovv3iRIS8jfGtLQ0OnbsWK6Hin748IGqVKkith88eHCe9gomJiaShoaGePwDBw6I+6RSKfn4+NDAgQMJAFWqVCnT86dOnSr3etq1a0dRUVHiENK7d++KvaHdu3sQ8AsBbgT4kapqGBkbE3XooLjeW6lUSr179yYAJJFIqF27duL9c+fOFXxAhVTGzxARUUREBO3cuVP83enRo4fYW2pldY4AP2rUaP6n3wt7kkgCSU8vmoDLBJgR0EAuCdTWdpZLJFne4GvfwouTwALEHwTBvn37CADt3r1bbrvs21wXFxcFRVbwwsKEC0J7e6JbtxQdTe55eQmx9++v6EgKlzdv3lDVqlUJAJmZmVFwcHC2bUeNGkWAIamqXqAaNZJp8eICDJTlm7Q0opcviby9iTw8iGbNEoZ7N22afXJob0/UqRORmxvR8uVER44QBQQQ5cefjJwOFU1ISKBGjRqJv8uy4ZZ//vlnnsbTpk0bMY6sPi/v3r0TE6PQ0FAKCQkR9/Xr108uCVy4cCERkZjsduvW7dNUg55Up46UtLVfiRf+hoYRtHgxkYKm/RERifMTlZSUshzKygSTJwufkS5d5LefOXNGnFvat29fSktLo6ZNT336GY8mwJzU1K6SpmYsaWmlfdq+i4DpZGERRqamr0gi8SdgBAUGBirmxRVjfO1beHESWID4gyDo27dvlsN6pkyZQgBowoQJCoqs4O3Y8bkXsCh6/VqI/6efCn6IW2H37t07qlmzJgEgY2NjCggIyNTm8OHDBEgIWEOVK0dT795EeTzSjhUyUqnw5Y+vL9Hu3cJIgOHDiVq3/npy2KYN0ciRRAsXEu3dS3T9ujBE7kc75L42VNTLy4t69OhBsqIkwcHBtGrVKrHN6dOn8+ZNIaJx48aJ509JScmyTenSpcU2mpqa4rzbJk2aZJrvRfTlUFI9KlXqLhkaEqmpfRCTQBubjwoZ/ilz9OhRMcZNmzbRuXPnqHbt2jR8+HBKV/Sk0kImPp5ozRqip08z7zt27JjY8+vu7k4//+z56We8hNTVt1Dt2qnk7Cz8rQIuEOBHamp3xLn4mpp3CVjEPa/5gK99Cy9OAgsQfxAEtWrVIgBkaWkpt33QoEEEgBaVoJKIAwcKF3gZRj8VKenpRM2aCa/h8WNFR1P4REREkL29vdjLcuPGDXHfmzdvPlX+60empq/IwSHrixtWckRFCSMCDh0SikW5ugrDFL+WHDZrRjR4MNGcOcKXSj4+RCEhuR/WmN1QUQCkpqZG3t7eRCQMXZTNV9PT0yM/P788ee2PHz8mLS0tatu2bbZtXFxc5OLasmULEZE4D/fLJPLiRW8CZNU1J5K6ejgZGhJZWLwhYCEBfWn37rA8if97BAUFkY6ODgEgV1dXhcVRXMhGGQGg4cO3iIl++fLh5OAgfC4ePiSqWvU6KSk9IB2dOGrRQko+PkR6eg8J2EceHh6KfhnFDl/7Fl6cBBYg/iAIFxoZ535ERESI+2TDgbZv367ACAvOmzfCRVz9+sISEUXViBHC6zh1StGRFE7R0dHk4OAgXjRfuXKF0tPTqXXr1gTUJi2tIKpbN50OHlR0pKywio8nCg4WPmNr1xK5uxN16yb835FdcujgQNSnj1DpctMmovPnhS9qcjLl6e7du+JQUYlEQrt27ZLbn5ycTM2aNSNAqMY5ZcqUPCkY9/btW4r7ynozERERVLduXfHvh2zoX8aKoKNHjyYiYWTChAnJnxKBUQT4kKZmCnXtSvTmTSKpq6uTgYFBtr2O+eHhw4fk7u5OHTp0oIoVK4rDP1u2bFmgcRRnEydO/PR/bVlSVb1EuroPyN5eShkvKzZtukBAEAExpKx8j6pV20za2vcIuEa//y58Cf3x48cSXY01L/G1b+ElIfr2CrKyxVdzYty4cTlue+nSJSxduhT+/v54+/Ytjhw5AmdnZwBAamoqfvvtN5w+fRrPnj2Dvr4+HB0dsWjRIpibm4vHsLKywsuXL+WOu3DhQkybNk18HBgYCFdXV/j5+aF06dJwc3PDlClT5J5z4MABzJw5Ey9evEDlypWxePFidOjQQdxPRJg9ezY2b96M6OhoNG7cGBs2bEDlypVz/HpjY2Ohr6+PmJgY6Onp5fh5xcmzZ8/kFrf18vJCq1atAAA1a9ZEUFAQzp07h7Zt2yoqxALz99/A6tVAvXrAxo2Kjub7/fknsHcv0L8/MHGioqMpnD5+/IjOnTvDx8cH2tra6NmzJ3bsOA0lpT2wtW2MLl008McfgESi6EhZUZKSArx+DTx/Djx7Jvz74oVwS0nJ+jlKSkC5coCVFWBtLX/T0pJvGxMTg6ioKFhZWWU6TmRkJEaNGoUDBw4AAKytrbFx48Z8/79bKpXi1KlT6NKlC0xNTXHz5k1YWFhAWVkZiYmJUFZWhpKSErZtA5YsAR4+vAkAkEhUUbNmLdy8CaioAB8+fAAAGBsb52u8Ml5eXujevTtiY2PlttetWxfnz59HqVKlCiSO4i4lJQWNGzfGzZs3UbPmAJiYbEfduspYuFD43X/y5Anq1auHmBgA+BVAq0/P1ACQhO7dT2LFip9Rt25d1KtXD2fPngUAxMXFYefOnejVqxf/rHKJr30LsZxkilZWVnI3bW1tkkgkZGhoSIaGhiSRSEhbW5usra1zlYGePn2aZsyY8WleDOjIkSPivujoaHJ0dKR9+/bRgwcPyNfXlxo0aED2srJQn1haWtK8efPo7du34i3jN4kxMTFkampK/fv3p7t379KePXtIU1OTNm3aJLa5evUqKSsr05IlS+jevXv022+/kaqqKgUFBYltFi1aRPr6+nT06FG6c+cOdenShaytrSkxMTHHr5e/DSE6ceKE3LCdpUuXivtk8z3u3LmjwAgLjmwoaFHvAZKVxh81StGRFG7x8fEZ1jxTJWAbWVqGk4tL/leFZCVLerowX/fyZWGI6Ny5wtIHzZt/fWhp+/ZEY8YQLV0q/L/k708UHf31cx0/fpwsLCzE/9NXr16d768vKSlJLASyYcMGwhdVQ+PiiKpVE9b+U1MLI4nkNlWo8JEy/NkvUNu3bxfnqzk4ONDGjRvp4sWLFBoayguU54OnT5+Snp4eAaAxY8aK12nx8fHiPO1GjRpRdHQMLV0aTOXKPRGHjmpqziJLy5Hi73N8fDy9evWKpk+fToA+Vao0iyIiuIcwN/jat/DK9XDQXbt2UePGjenBgwfitgcPHlDTpk1p586d3x/IF0lgVm7cuEEA6OXLl+I2S0tLWrFiRbbPWb9+PRkaGsqV/Z06dSrZ2NiIj3v37k0dO3aUe17Dhg3pl19+ISJhDoSZmZlcwhIdHU3q6uq0Z8+enLw8IuIPAhHRkiVL5JLAfv36ERFRSkqKuC08PFzBUeY/WUGVoj4UlIjowQPhtbRsWfSWuChoiYmJ1KlTZwKmk4HBY2rRQkq5XFOese8mlRK9f09044awtueiRUS//ELUtu3Xk8OuXYW1DvftE+atfvk5//jxI/3yyy9ZfrmXX2TVd2XDUmV/SwIDiRwdhQRQW5towQKi/fuJ5s8nio3N97DkSKVSmjNnjvi+9OnTJ1dfHLPvd+DAgf+zd99hTV1vHMC/CZCwl8ieoqI4q1bEWSettnXUah0VZ6t1W7X6E8Wt1da9Wq2j1j3bunGgVXGLC8WFOFiKsjc5vz+ONwMCggZC4P08Tx6Sm5PkzYWE+95zznvk+93JyYktX76c9e3blwFgtra27LlSOdiYGBmztNzDgMtMJApk+vp28sfOmjWbiUSit7d/ZsBl1qDBZa0sJaKr6Ni37NIvbs/h1KlTsXv3bnh5ecm3eXl5YfHixejevTv69OlT3KcsssTERIhEIlhaWqpsnz9/PmbNmgVXV1f07t0bY8eOhb4+f2shISFo2bIlJBKJvL2fnx9+/vlnvHnzBlZWVggJCcG4ceNUntPPzw/79+8HAERERCAmJgbt2rWT329hYQEfHx+EhITgm2++URtvZmYmMjMz5bfzDgOpiO7evQsA8PHxwcWLF3H9+nUAQFxcHABAT0+vQgy1OH6c/2zYELC21m4sH8rDA9DTA5KSgNhYwN5e2xGVvOxsIDlZcUlKAlJS+M+821VvGyI5+W/UqJEKExMTzJkjgrOztt8NqShEIsDGhl8+/lj1vqQkPow0IkL1EhXFh5w+fw4cPszburoCrVsDbdoA3t6AqakpVq9ejcqVK2P27NmYMGECMjIyEBAQUGLvxdPTE/fu3cOZM2cAAHXrNsbSpcCGDcDr13zI5+efA5Mnl1gIhWKM4ccff8TixYsBAJMmTcKcOXMgFou1E1AF0717d2zcuBEBAQF4/vw5Ro4cCYAfY+zYsQNOTk7ytnZ2IgwY4IHFi3PBmCFycmLl902duhtAVQC2EIaOXrsGtGnzGtu3W1eI/3ek/Cp2EhgdHY2cnJx823NzcxEbG6vmEZqRkZGBn376Cb169VIZUzxq1Cg0aNAA1tbWOH/+PCZPnozo6GgsWrQIABATEwMPDw+V57Kzs5PfZ2VlhZiYGPk25TYxMTHydsqPU9dGnXnz5mHGjBnv+Y51U0ZGBmJiYtTOIQGAsLAwAECfPn1w8eJFhIeHIy0tDdHR0QD4Pq0I/ySFJLB9e+3GoQkSCVClCvDgAXD/vm4kgYwBaWnqk7aiJHMZGR/y6iJYWJhi1CigaVNNvSNCPoy5OVC3Lr8oS04G7twBbt0CbtwArl4Fnj4FNm3iF09PoFcv4LPPRJg1axYMDQ0REBCAqVOn4u7du+jRowfatm0LU1NTjcbr7l4VwCbww5jBWLXqcyQm8hM0FhY8rr59NfqSRZY3AVy5ciV++OEH7QRTgfn7++Obb77Bhg0bMHfuXDx79gwLFy7EJ598kq9t06Z2WLw4CkAVpa2tASxQaWdtbY3Xr1/j6tU4bNpkhZ9+ooncRHcVOwls27Ytvv/+e6xbtw4NGjQAAFy9ehXDhg1T6SnTpOzsbPTo0QOMMaxevVrlPuUevLp160IikeD777/HvHnzIJVKSySeopo8ebJKfElJSXBxcdFiRCVvyJAh2Lp1K/bu3YvOnTur3McYk/cEtmnTBvb29oiJicHNmzflk/QdHBxKPebS9vw5cPcun6TeurW2o9EMLy+eBIaHAy1bls5rKvfGFTeZS0kBZLIPj8HUFDAzU72Ym/Pt5ub5tytf1/LXEyFFYmYGNGnCLwCQmgqcOwecOgX89x/w6BEwezawfDnQpQvQp88UGBoaYvz48di6dSu2bt0KAwMDNG3aFM7OzrCwsICFhQWsra3h4OAAicQdDx5UQZMmdmjTpuATgK9fA9evA82b889OTk5TKA7Y/8PLlx4wNARcXPjnDyi97yJljDGMHz9engD+9ttv+O6770o/EAIAkEqlGDp0KAYOHIgXL17k6xQQNGxoDSAKgCsAKfT0OiA3d5pSCxmcnFLw9ddnsWLFv0hJ+Rlz5lxB375ucHKyLYV3QojmFTsJXL9+Pfz9/dGoUSMYGBgAAHJycuDn54d169ZpPEAhAYyMjMTJkyffWVnIx8cHOTk5ePLkCby8vGBvb5+vh1K4bf+2y6KgNsr3C9uUk5TY2FjUr1+/wFikUqnWE9HSdvXqVchkMgwfPhytW7dW+X1FRUUhKSkJenp6qFatGj766CMcPnwY165dk/8t2etCN9IHEnoBGzUCrKy0G4umVK/Of96/X/THyGS8N67w4ZMFJ3NKI63fm4FB/gStoNt5EztTU57IE1KRmJgAHTrwS3Iy8M8/wI4dfNio0Dvo7f0jpk7tiGfP/sKZMzvw+PEjnD59Ws2zeQDYCeAFTEziER7uDCcnS7WvGxAAhIQAlpZAgwbArl31AfApFvr65nByEiMgAHj5klcrbtuWx1rSTpw4gV27diE9PR3Z2dmIjY3FyZMnAVACWJZIJJICE0AAcHc3hLu7BZ4+TQZwHrm5/CyhoaEIXbua48yZOLx8aYElSxrAy+siwsNfITnZBv/731Fs2vRtKb0LQjSr2Elg5cqVcejQIdy/fx/37t0DANSoUQPVhaNADRISwAcPHuDUqVNFmisWGhoKsVgMW1t+ZsbX1xdTpkxBdna2PNEICgqCl5cXrN4egfv6+uLEiRMYM2aM/HmCgoLg6+sLgJe+tre3x4kTJ+RJX1JSEi5evIhhw4Zp8B3rPiGZfvHiBQICAlSWFxF6AT09PSGRSORJ4PXr1+Hq6gqgYvQECklgCXWca4UwRfjOHeDIkaIldprojROJ1PfGFTWZq2DnaAjRKDMzvjRMr17AmTM8ITx3DggLA8LCagKYg6pV56BVqzcAbsPAIAZALGSyKCQnv8Dz569x40YSUlNtkZoqQ7t2c3HkSCCMjU1w/Tqwdi2ffxgby587N5e/7qNHQHq6M4AwAM9Rv34l9O8P+Pvz75QGDfilJIWGhuKnn37CsWPH1N6/Zs0aSgB1iEgEjBlTDZs3M8hkDJGRYXj9+iiyso7gwYMEvHiRC2A09PXro1mzEahVKw179wJ795pgwwYGsZiGhRLdU+wkUODu7g7GGDw9PeVFWIorJSUFDx8+lN+OiIhAaGiofJhI9+7dce3aNRw4cAC5ubny+XfW1taQSCQICQnBxYsX0bp1a5iZmSEkJARjx45F37595Qle7969MWPGDAwaNAg//fQTbt++jaVLl8qHagDA6NGj0apVK/z666/o1KkTtm/fjitXruD3338HAIhEIowZMwazZ89GtWrV4OHhgalTp8LR0VG+riHhSfvr16/lt1esWIG+ffuicePGABRJoLe3NwDIhxOfPn0arVq1AlD+ewKfPQPu3StfQ0EBQFguMy6On7EvDomk6L1veduZmFBvHCHaJhYDn3zCL2/eAEFB/GTQnTtAfDwQH28FoIXax1arBjx/noWXL3Nw795w1KnzAtWqVcXz52KkpPCD80qVFAmggQG/7uFhiLt374Kxfjhy5BWEc8RiMS9YU1JSUlIwfPhwbN68GYwxGBgYYODAgfD09ISBgQH09fXRsGFD+Ulkojs6dwY2bxZBLBZh//4aWLNmHrZuPY4rVwAjIyP8+OMjNGnSA23bGuHly1Ts338bKSmu2LLlGvr0+QhisRjp6ekQiUQwNDTU9tsh5N2KW040NTWVDRw4kOnp6TE9PT326NEjxhhjI0aMYPPmzSvWc506dUpluQDh4u/vzyIiItTeB4CdOnWKMcbY1atXmY+PD7OwsGCGhoasZs2abO7cuSwjI0PldW7cuMGaN2/OpFIpc3JyYvPnz88Xy86dO1n16tWZRCJhtWrVYgcPHlS5XyaTsalTpzI7OzsmlUpZ27ZtWXh4eLHeb3kvkxsVFcUAMLFYzHr16sUAsF69esnvHzp0KAPAJk+ezBhj7NWrV8zKyooBYAYGBgwAW7FihbbCLxXr1/OS68OGaTsSzVuxgrE+ffh6gRMmMDZzJmOLFzP2xx+8tPzhw4ydPcvYjRuMPX7M2KtXjCmt3EIIKWcyMhgLDWXsr78YmzKFsUGDGOvUiS+NIyw/4evLWJ06jBkbZzMg9u1abTHMwkLGbG0Zc3FhrGpVvu7fL7+oLl2xY0cECwsLK8X3k8HatWsnPxbp1auX/BiIlA+HDvH/VYwxlpuby2bNmsVGjBjBnqlZy6dGjX/fri/oz5ycnFhERASzs7NjJiYmbNWqVaUcedlV3o99dVmxk8BRo0axhg0bsv/++4+ZmJjIvwD379/P6tevr/EAy5Py/kG4fv06A8Ds7OzYiRMnGADm4uIiv79Vq1YMAPvzzz/l2/bs2aOS4O/Zs0cboZea3r35wcvevdqOhBBCtCMnhy/oLqy1Fh3NE8Pq1ROZSHSFAZeZtfUj5uMjY82bK06c5eYy9vXX/Pby5aUdcw7r3r07A8BMTEzYmTNnSjcAUuYMHXrmbRK4hAFgnTt3ZkBDBnRlenq/s/Xr6SwnY+X/2FeXFXsg1f79+7FixQo0b94cIpFiDHStWrXw6NGj4j4dKUeEtf5sbW3RuHFj6Onp4dmzZ3j27BmysrJw5coVALyKq6Bbt24q8ybK83DQp0959czyNhSUEEKKQ09PdSi3vT0wcyZgZWUOT09PADl4/Xo+vLzGYvt2hsmTgYULefslS4AVK4Dhw0svXsYYhg8fjt27d8PAwAD79+9Hixbqh7eSiqNPH++31+oBEOHvv8MArAbwP+TmfoR589KRlqa9+Ah5l2JP5nv58qW86Iqy1NRUlaSQVDxCEmhnZwdTU1PUq1cP165dQ0hICOzs7JCamgpbW1vUqVNH5XGLFi3ClStXEBkZiVq1amkj9FIhFIT5+GNe4Y4QQgj36adA7drAkSOWeP06CZMn/4E//5TBxkYPv/zyi/z4wtGRX0pLZmYmJk2ahN9++w0ikQhbt24tseWwiG7x9a2EypVf4uVLAKgOoCf4OrCWSExMwLNnz3DkiATduhlpN1BCClDsnsBGjRrh4MGD8tvCF/O6detoIrQOYIyV2HMLlUGFkwTNmjUDAJw7dw5Hjx4FALRv3z7fYvAmJia4cOECnj9/DgsLixKLT9vK0wLxhBCiac7OwODBwMSJHbB27VoA/CThjBkz3vt/V1xcHP79918EBASgf//+CAoKUtsuNjYWSUlJKtsOHz6M2rVrY8mSJQB4xc/u3bu/Vxyk/NHTA/r0qfG293omgI4wN7fA1KlPAfyBjIwMDB4cjPDwTI2sS0uIphW7J3Du3Ln47LPPEBYWhpycHCxduhRhYWE4f/58AesAkbIgNDQUnTt3homJCcLCwgpsJ5PJIBKJ3qtXV3k4KAA0bdoUy5cvx/nz5+X/wDt06KD2sQYGBvIlPMqjp0/5Gno0FJQQQt5t4MCBSE5OxpgxYzBjxgzcu3cPv/32W5FPFN68eRPffvstbt68qbJ906ZNaN++PX7++WfUrFkT+/btw2+//SY/fqlatSo++ugjpKam4tChQwD40kVLlixBjx49NPsmic5r2hQ4ftwYQBUAQJs2mRg1qiUiIy9g+fJUvHlTGc2aRaNTp5dYvboWjI2NtRswIUqKnQQ2b94coaGhmD9/PurUqYNjx46hQYMGCAkJyTfMj5QdZmZmePr0KYyMjMAYKzDJ69SpE8LCwnDnzh2YmpoW6zWUh4MCip7A69evQ/b2NFj7CtoNJvQC+vgA5bizkxBCNGb06NEAgB9//BE7duzApUuXsH37dvmyQwUJCgrCV199heTkZABAzZo10aRJE0gkEqxfvx5BQUEICgqChYUFEhMTVR778OFD+dJVenp6GD16NAIDA2Fubl4C75DouqZNAalUAhMTE0ilF7F5cw8YGOhj2bL/wcHhLP73PyA+/hX+/DMN2dkjsXXrH9oOmRA5ESvJ8YFERVJSkvyfTmn/Q0lPT5efgXrz5g0s1UxKe/78OVxcXAAAZ8+elSdxRdWpUyccOnQIf/zxBwYOHAgAcHFxwfPnzwHwgjA3btz4gHehu3r1Ah48AKZO5WsREUIIKZoLFy6gV69eePLkCfT19REQEIDx48fDxMQkX9sNGzbgu+++Q05ODlq1aoWdO3eq1DF4/PgxAgICsG3bNgCAs7MzBg8ejIEDB8LIyAjXr1/H9evX8fLlS/Tv379cz1MnmjFuHBARAaxdK4ONjep0l+++G4m1a9sCcAYwGw8e/IKqVatqJU5t0eaxLylcsecEXrt2Dbdu3ZLf/vvvv9GlSxf873//Q1ZWlkaDI5pjZGQEKysrAMCLFy/Utjl16pT8emRkZLFfI++cQAAqiWRBQ0HLu8hIngDq6fHFlAkhhBRdkyZNcP36dXz99dfIycnB9OnTUa1aNfz+++/IyclBYmIi9u/fjwEDBmDgwIHIyclB7969cfTo0XyF7KpUqYKtW7ciLCwMp0+fxpMnTxAYGAgXFxfY2Nigffv2mDhxIhYuXEgJICmSRYuAvXuRLwEEgFWrFuPHH91haGgEwB916tTHzp07Sz9IQtQodhL4/fff4/79+wD4GbWePXvC2NgYu3btwsSJEzUeINEcx7cl1aKiotTeHxwcLL/+5MkTALyQzMmTJ+VDPQuTd04gwOcFCipqEkhDQQkh5MNYWlpix44d2LZtGzw8PBAdHY3vv/8ezs7OsLa2RteuXbFx40YAwP/+9z9s3rwZUqm0wOerWbMmWrZsCT09vVJ6B6Q8K6iMgr6+PmbOrI969VwhlVZDRsYn6NmzJ86cOVO6ARKiRrGTwPv376N+/foAgF27dqFVq1bYunUrNm7ciD179mg6PqJBeZPAW7du4YsvvpAP0VTuCRSSwDNnzqBt27YYPHhwoc/NGMs3JxCAfC0lIyOjCruuklCMjqqKE0LI+xOJRPjmm29w9+5dLFmyBJUqVUJsbCxkMhmqV6+OH374AUFBQZgzZ06+KtSEaIuxMTB0qBlq164FF5fxAPjxMyHaVuzCMIwxeZGP48eP4/PPPwfA5369evVKs9ERjXJycgKgSAJXrlyJAwcOID4+Htu2bUNERIS8rZAEXrp0CQBw9+7dQp87KSkJmZmZAIDKlSvLt3/00UdYuXIlXF1dYWhoqLH3oiuePAEePqShoIQQoilSqRSjR49G//79ERISAm9vb7i6umo7LEIK1KUL8PvvIkgk9QDUxsmTJ7UdEiHFTwIbNWqE2bNno127djh9+jRWr14NAIiIiFDpASJlT96ewMePHwMAQkJCEBgYCID/c83MzJTPCbx37x4AIDo6utDnFnoBTU1N85VA/uGHHzT0DnTPsWP8p48PQPOhCSFEcywsLPDpp59qOwxC3snSEujQAdi/3wzANISF+SM6OhoODg7aDo1UYMUeL7FkyRJcu3YNI0aMwJQpU+RVjnbv3q0y/4uUPUISKBSGEZJAgK+dBABdu3YFwAvDyGQyhIeHAwBSU1ORkpJS4HOrGwpa0WVnA/v28euffabdWAghhBCiPaNGAfb2+jA2rgVgGHbs2KHtkEgFV+yewLp166pUBxUsXLiQJliXcco9gbm5uWorgH777bfYuXMnMjMzERsbK+8JBHhvYLVq1dQ+t7qiMBXdiRPAy5dApUo0H5AQQgipyKytgenTgZ49K+Pp056YN+9HAPz4adasWXQMTUqdxmZOGxoawsDAQFNPR0qAchL4/Plz5OTkQCKR4NtvvwUAGBgYoFWrVnB2dgYAXL16FfHx8fLHFzYkVN3yEBXd22Wo0L07QB8NQgghpGJr0gTo0cMKBgZSxMUtxtixDTBvng3WrNmi7dBIBVSkJNDa2lpe9MXKygrW1tYFXkjZJRSGiY6OxsOHDwEAbm5umDZtGipVqoQePXrAxMQE7u7uAICjR4+qPL6wJJB6AlXdugXcucOTv6++0nY0hBBCCCkLhg/Xh729/dtbxgBaYuJEfcTHp2ozLFIBFWk46OLFi2FmZgaAzwkkusnOzg4ikQg5OTm4fPkyAL5wbtWqVRETEwN9ff7n4ObmBgA4cuSIyuNjYmIKfG6aE6hK6AX89FM+BIQQQgghxN0dmDzZEuPGnUZGxj8ABiMtrTq++OIWzp1rUuCag4RoWpGSQH9/f7XXiW4xMDCAra0tYmNj8d9//wHgSSAAeQIIQN4TKPQWCmg4aNHExSkWiO/VS7uxEEIIIaRsGTZMirZt6+PFi8q4dCkTkyZJcPGiISZNuog2bRLQpk0bGBgYgDGGhw8fwtPTk9a+JBpXpL+opKSkIl9I2SbMCzx//jwARRKoTEgCBS4uLgBoOGhRMAb8/DMgkwENGgDVq2s7IkIIIYSUNdWrV0fr1q0xcWIH2Nhsh0wmw4IFmfj0037o3bs3AOCPP/5A9erVsWzZMi1HS8qjIiWBlpaWsLKyKvQitCFlm5AEJiQkAChaEti6dWsARUsCK/pw0H//BU6f5nMBJ0zQdjSEEEIIKctEIhH69jUAcBN8juAvOHToFmQyGYYMGQIAGDt2rDZDJOVUkYaDnjp1qqTjIKVEKA4jUJcECnMCBZ988gn+/PPPIs0JrMg9gVFRwC+/8OvDhgEFrKZBCCGEECL31VddsWRJXwB/AqiJtLQpuH79EYBvARwDEIu0tDQYGxtrNU5SvhQpCWzVqlVJx0FKidATKPDw8MjXxsXFBSKRCIwxmJqaomHDhgAK7gnMzs7G69evAZSPJDApCcjKAiwseI8eY0BGBpCZybepm7QdHw8EBABpacBHHwF9+5Z+3IQQQgjRPc2aNcMvv4yEvn4SpkypjNTUmujRQw/AKACNAYxESEgI2rZtq+VISXny3rNM09LScO/ePdy8eVPlUhxnzpzBF198AUdHR4hEIuzfv1/lfsYYpk2bBgcHBxgZGaFdu3Z48OCBSpvXr1+jT58+MDc3h6WlJQYNGoSUlBSVNjdv3kSLFi1gaGgIFxcXLFiwIF8su3btQo0aNWBoaIg6derg0KFDxY5FFygngdbW1rCwsMjXRiKRyHsMa9SoIX/Mq1evkJWVla99VFQUAF54RleXCTlyBBg1CvjsM6BNG17V09cXaNGCr+vTogVf8P2rr4Bly4CLF4GwMODhQ2DNGqBzZ+DmTcDYGJgxA6D524QQQggpCpFIhB9//BGjR38OV1d+Uv3pU6HORhMAR7FlS4jW4iPlU7EPVV++fInPP/8cZmZmqFWrFj766COVS3GkpqaiXr16WLlypdr7FyxYgGXLlmHNmjW4ePEiTExM4Ofnh4yMDHmbPn364M6dOwgKCsKBAwdw5swZfPfdd/L7k5KS0KFDB7i5ueHq1atYuHAhpk+fjt9//13e5vz58+jVqxcGDRqE69evo0uXLujSpQtu375drFh0gXISqG4oqEAYEurl5QVra2sYvF3tXKgCquzRo0fy59O16lWMAb//znvxzp8HXr7k24XevvR0IDdX0f7pU+DPP4Hhw4F+/YBvvgHWreM9hbVrA6tXA3k6WwkhhBBCisTHhx905OTkAEiCVCoFYI2tW5Gvk4OQD8KKqXfv3qxZs2bs8uXLzMTEhB07doxt3ryZeXl5sQMHDhT36eQAsH379slvy2QyZm9vzxYuXCjflpCQwKRSKdu2bRtjjLGwsDAGgF2+fFne5vDhw0wkErEXL14wxhhbtWoVs7KyYpmZmfI2P/30E/Py8pLf7tGjB+vUqZNKPD4+Puz7778vcixFkZiYyACwxMTEIj9G065du8YAMACsR48eBbbz9/dnANjcuXMZY4w5OzszAOzixYv52v72228MAOvYsWOJxV0ScnMZW7CAsYYN+WXxYsZu3GAsJYXfl5jI2NOnjMXGMpaWxrcHBTE2ZQpj3box1rEjY23aMNarF2MnTjAmk2n7HRFCCCFElx0+fJEBqxnQlQF2zMsrgkmlNxmwh/3xxx/aDq/YysKxL1GvSHMClZ08eRJ///03GjVqBLFYDDc3N7Rv3x7m5uaYN28eOnXqpJHkNCIiAjExMWjXrp18m4WFBXx8fBASEoJvvvkGISEhsLS0RKNGjeRt2rVrB7FYjIsXL6Jr164ICQlBy5YtIZFI5G38/Pzw888/482bN7CyskJISAjGjRun8vp+fn7y4alFiUWdzMxMZGZmym+XhSU0lAvDFNYTOGXKFDg6OsorUzk4OOD58+dqi8MI6wlWrVpVw9GWHJkMmDWLV/MEgIkTgR49VNuYm/OLsnbt+IUQQgghRNMaNvQA4CO/HRh4G+PG1UdMjCt27AjHp59Gw9HRQXsBknKj2GP3UlNT5cU/rKys8PLt+Lk6derg2rVrGgtMSDbyLjlgZ2cnvy8mJiZfIRJ9fX1YW1urtFH3HMqvUVAb5fvfFYs68+bNg4WFhfwirLenTTY2NvKF4QtLAqtVq4a5c+fCxsYGAE8CAfXFYYThoLqSBMpkwNy5PAEUi3kymDcBJIQQQggpbTY2Nir1GmrVcoWnpwwAcOxYe7i4zMD58+eRm5uLXOX5KoQUU7GTQC8vL4SHhwMA6tWrh99++w0vXrzAmjVr5IkC4SZPnozExET55dmzZ9oOCWKxWN4bWFgSmFdhSaDQE+jp6amBCEsWY8DChcD+/TwBnD2bF4MhhBBCCNE2kUik0ung4eGBr76Sym/LZIMxYsRKtG3bFpUqVcLy5cu1ESYpB4qdBI4ePVqeCAQGBuLw4cNwdXXFsmXLMHfuXI0FZm9vDyB/IZLY2Fj5ffb29vL16QQ5OTl4/fq1Sht1z6H8GgW1Ub7/XbGoI5VKYW5urnIpC2bNmoWBAweiZcuWRX6M8D7zJoGMMZ0ZDpqZCQQGArt28cIvgYFAhw7ajooQQgghREEoxgcAZmZmGD26MvgQ0fMAgOvXgdOnTyMxMRGjRm3GX39FQcfqFJIyoNhJYN++fdG/f38AQMOGDREZGYnLly/j2bNn6Nmzp8YC8/DwgL29PU6cOCHflpSUhIsXL8LX1xcA4Ovri4SEBFy9elXe5uTJk5DJZPDx8ZG3OXPmDLKzs+VtgoKC4OXlBSsrK3kb5dcR2givU5RYdMm3336LP/74Q+VL5l2EnsC8w19jYmKQlpYGsVgMd3d3TYZZqJwc3qtXVHFxwJAhwKFDvAdw6lRAQ9NXCSGEEEI0Rk9PT+W2WCyGlZUFAKFqfbW3Pz8HsAr/+58eBg7kaxwTUlQfXM/f2NgYDRo0kM8dK46UlBSEhoYiNDQUAC/AEhoaiqdPn0IkEmHMmDGYPXs2/vnnH9y6dQv9+vWDo6MjunTpAgCoWbMmPv30UwwZMgSXLl3CuXPnMGLECHzzzTfypRB69+4NiUSCQYMG4c6dO9ixYweWLl2qUghm9OjROHLkCH799Vfcu3cP06dPx5UrVzBixAgAKFIs5V1Bw0GF+YBubm4qxXc0TSYD/voL6NYNaNmSr93XujVflH3qVGDnTiA8XH1ieOsW8O23fF0/c3Ng5Urgyy9LLFRCCCGEkPc2f/58AMDQoUPl206cOIFevT5+24FRDcCXAKYCABISEnH/PtC0KT/GIaQoRIwVpz+FD//bvXs3Tp06hbi4OMhkMpX79+7dW+TnCg4ORuvWrfNt9/f3x8aNG8EYQ2BgIH7//XckJCSgefPmWLVqFapXry5v+/r1a4wYMQL//vsvxGIxvvrqKyxbtgympqbyNjdv3sTw4cNx+fJl2NjYYOTIkfjpp59UXnPXrl0ICAjAkydPUK1aNSxYsAAdO3ZUed/viuVdkpKSYGFhgcTExDIzNLSoLl++jMaNG8PZ2VllbuPGjRsxYMAAtGvXDkFBQSXy2omJwLRpwLlz725btSpPFDt0ACwtgQMHgDlzgOxsoEoVYPFiQKlAKiGEEEJImfPkyRO4uLio9Aq+eAE0aRKD58+fAwBMTEyQmroZQCiAmbC1tYW7uyuCgvJXN9cWXT72Le+KnQSOHj0av/32G1q3bg07OzuIhFW139qwYYNGAyxPdPmD8OzZM7i6ukJfXx+ZmZnyReEDAgIwZ84cDB06FKtXr9b46yYlAf7+wLNngEQCjBnDewHNzYHXr/n28HDg9m3gyhXFUAixmCeE9+/z2598AsycCRgbazxEQgghhJASJ5MBNWok4sGDBwCAzz57hQYNzmHOnNkA2gOYi3r16mP6dH2UlYFqunzsW94Ve53AzZs3Y+/evSq9ZKT8EwrDCIV3hOG/Jbk8hEwGBATwRM/eHli0CFDueLW05L17rVrx20lJwOHDfOmHe/cUCeCQIfwi/uDBz4QQQggh2iEWAx07irB0aTqApejevRMGDpyFtm3boE2bNgAckZj4K44fr1RmkkBSdhX7sNjCwqJYSwuQ8sHAwEC+bk18fLx8e0kuD7FuHXD+PO8BzJsAqmNuDvTsyecO/vsvMGkSHxv//feUABJCCCFE9y1aZAagDYA9cHd3AwC0bt0aU6dOBXAeCQlvcPMmP5FOSGGKfWg8ffp0zJgxA+np6SURDynDKlWqBECRBDLG5EMSNN0TeOsW8Pvv/PqUKe9OAPNycAC6dwfeFoklhBBCCNF5YrEI+/btwvTp01XqavBChY+QlBSHlBQZIiK0FiLREcUeDtqjRw9s27bt7eRT93zLDFy7dk1jwZGypVKlSnj8+LE8CXz9+jUSExMBFG/h+aLYtIn/7NiRlnIghBBCCBF06dIlX3X6jz76CC4uTnj27CaSk71x86YlSmCQFilHip0E+vv74+rVq+jbt6/awjCk/MrbEyjMB3R0dISxBiuuPH0KnD7Nrw8YoLGnJYQQQggpl0QiETp37owVK27i9esOCAqyxJdfAozlgDF9FGNpaFJBFDsJPHjwII4ePYrmzZuXRDykDMubBJbUfMBt2/h6f82aAR4eGn1qQgghhJByqVevXlixoj9ev/4Wx49Xwtdf38fff09Gw4YbsWmTM2rW1HaEpCwp9pxAFxcXKvFaQeVNAqOiogAArq6uGnuNxETgn3/49b59Nfa0hBBCCCHlWtOmTTF8eAcAkxEbG4O//9aDTDYfly/H4NChLG2HR8qYYieBv/76KyZOnIgnT56UQDikLMubBMbGxgIAbG1tNfYae/YAmZm8EEyjRhp7WkIIIYSQcq9///4ATiM9/RfI5CVC7yItbZEWoyJlUbGHg/bt2xdpaWnw9PSEsbFxvsIwr1+/1lhwpGwpKAm0s7PTyPNnZQE7dvDrffsCNN2UEEIIIaToateuDX19fWRnrwNwE0AL1K17Cy1aTNZ2aKSMKXYSuGTJkhIIg+iCkk4CjxwB4uMBW1ugQweNPCUhhBBCSIVhaGiIWrVq4caNGwAuoV+/Gtiw4TDEtGAyyaNYSWB2djZOnz6NqVOnwoMqdlQ4JZkEMgZs2cKvf/MNoF/s0xOEEEIIIcTJyeltEggMGzaMEkCiVrH+KgwMDLBnz56SioWUcSWZBF64ADx6BBgbA127fvDTEUIIIYRUSHxeIDBy5Eg0adJEu8GQMqvYpwa6dOmC/fv3l0AopKxTTgJlMhlevnwJQDNJ4ObN/GfnzoCZ2Qc/HSGEEEJIhdS9e3c8efIES5cu1XYopAwr9qC7atWqYebMmTh37hwaNmwIExMTlftHjRqlseBI2SIkgZmZmXj27Blyc3MBfHh10Bs3gEuXALEY6NXrg8MkhBBCCKmwRCIR3NzctB0GKeOKnQT+8ccfsLS0xNWrV3H16lWV+0QiESWB5ZiJiQkkEgmysrIQFhYGALC2ts5XIba4Vq/mPzt3BhwdPzRKQgghhBBCSGGKnQRGRESURBxEB4hEIlSqVAnR0dHyJPBDh4JevgxcuQIYGACDBmkiSkIIIYQQQkhhPqhcEGMMjDFNxUJ0gDAkVBNJIGOKXsBu3QB7+w8OjxBCCCGEEPIO75UE/vnnn6hTpw6MjIxgZGSEunXrYrNQ2YOUa0ISePfuXQAflgQePAjcvAlIpcCAARoJjxBCCCGEEPIOxR4OumjRIkydOhUjRoxAs2bNAABnz57F0KFD8erVK4wdO1bjQZKyQ1M9gc+fAwsW8OuDBgE2NhoJjxBCCCGEEPIOxU4Cly9fjtWrV6Nfv37ybV9++SVq1aqF6dOnUxJYzglJYGJiIoD3qwyamwtMnQqkpQEffQS8Xc6GEEIIIYQQUgqKPRw0OjoaTZs2zbe9adOmiI6O1khQytzd3SESifJdhg8fDgD45JNP8t03dOhQled4+vQpOnXqBGNjY9ja2mLChAnIyclRaRMcHIwGDRpAKpWiatWq2LhxY75YVq5cCXd3dxgaGsLHxweXLl3S+Pst64QkUPA+PYFLlwK3bgEmJsDMmXxpCEIIIYQQQkjpKPbhd9WqVbFz585823fs2IFq1appJChlly9fRnR0tPwSFBQEAPj666/lbYYMGaLSZoEwzhBAbm4uOnXqhKysLJw/fx6bNm3Cxo0bMW3aNHmbiIgIdOrUCa1bt0ZoaCjGjBmDwYMH4+jRoyrvb9y4cQgMDMS1a9dQr149+Pn5IS4uTuPvuSz70CTwr7+ArVv59YAAwMFBU5ERQgghhBBCiqLYw0FnzJiBnj174syZM/I5gefOncOJEyfUJocfqnLlyiq358+fD09PT7Rq1Uq+zdjYGPYFlJY8duwYwsLCcPz4cdjZ2aF+/fqYNWsWfvrpJ0yfPh0SiQRr1qyBh4cHfv31VwBAzZo1cfbsWSxevBh+fn4A+FzIIUOGYMDbCiZr1qzBwYMHsX79ekyaNEnj77us+pAkcP9+YMkSfn3UKKB9e83FRQghhBBCCCmaYvcEfvXVV7h48SJsbGywf/9+7N+/HzY2Nrh06RK6du1aEjHKZWVl4a+//sLAgQMhEonk27ds2QIbGxvUrl0bkydPRlpamvy+kJAQ1KlTRyVZ8fPzQ1JSEu7cuSNv065dO5XX8vPzQ0hIiPx1r169qtJGLBajXbt28jYVxfskgQkJwOTJwOzZ/Hbv3sC335ZAcIQQQgghhJB3KnZPIAA0bNgQf/31l6Zjeaf9+/cjISEB/ZUqifTu3Rtubm5wdHTEzZs38dNPPyE8PBx79+4FAMTExORLVITbMTExhbZJSkpCeno63rx5g9zcXLVt7t27V2C8mZmZyMzMlN9OSkoq/psuY/ImgUZGdti1iy/38Po1YGnJLxYWgJER8PgxcPcukJnJ5/4NHswvSjk8IYQQQgghpBS9VxKoLX/88Qc+++wzODo6yrd999138ut16tSBg4MD2rZti0ePHsHT01MbYcrNmzcPM2bM0GoMmqacBBobd0K3bobIyFDcHxWl/nGensD06UDNmiUbHyGEEEIIIaRwRU4CxWKxyhBMdUQiUb6qm5oSGRmJ48ePy3v4CuLj4wMAePjwITw9PWFvb5+vimdsbCwAyOcR2tvby7cptzE3N4eRkRH09PSgp6entk1BcxEBYPLkyRg3bpz8dlJSElxcXN7xTss2RRLYDrm5c5GRAVSpAnTpAnh7A0lJfPhnYiKQkgK4ufHEz82NqoASQgghhBBSFhQ5Cdy3b1+B94WEhGDZsmWQyWQaCUqdDRs2wNbWFp06dSq0XWhoKADA4W3ZSV9fX8yZMwdxcXHyNe2CgoJgbm4Ob29veZtDhw6pPE9QUBB8fX0BABKJBA0bNsSJEyfQpUsXAIBMJsOJEycwYsSIAmORSqWQSqXFfq9lmZWVFYDWAObAwECCjh15Dx8leIQQQgghhOiGIieBnTt3zrctPDwckyZNwr///os+ffpg5syZGg1OIJPJsGHDBvj7+0NfXxHyo0ePsHXrVnTs2BGVKlXCzZs3MXbsWLRs2RJ169YFAHTo0AHe3t749ttvsWDBAsTExCAgIADDhw+XJ2hDhw7FihUrMHHiRAwcOBAnT57Ezp07cfDgQflrjRs3Dv7+/mjUqBEaN26MJUuWIDU1VV4ttKKIj9eHnt4s5OaK4eoaiunTa1ACSAghhBBCiA55rzmBUVFRCAwMxKZNm+Dn54fQ0FDUrl1b07HJHT9+HE+fPsXAgQNVtkskEhw/flyekLm4uOCrr75CQECAvI2enh4OHDiAYcOGwdfXFyYmJvD391dJWD08PHDw4EGMHTsWS5cuhbOzM9atWydfHgIAevbsiZcvX2LatGmIiYlB/fr1ceTIkfdaLF1XMQbMmgXo65sjN/cKWrW6CrH4G22HRQghhBBCCCkGEWOMFbVxYmIi5s6di+XLl6N+/fr4+eef0aJFi5KMr1xJSkqChYUFEhMTYW5uru1wim3fPmDOHCA8/CZSUr7A9OkDERgYqO2wCCGEEEJIGaTrx77lWZEH8i1YsABVqlTBgQMHsG3bNpw/f54SwAokLg5YvJhfr1YtCMBTlSqthBBCCCGEEN1Q5J5AsVgMIyMjtGvXDnp6egW2e1f1zopMl8+GTJoEHD8O1K4NfPfdBWzevAlz5859WyiGEEIIIYQQVbp87FveFXlOYL9+/d65RAQpn86f5wmgWAz8739A9epN0LRpE22HRQghhBBCCHkPRU4CN27cWIJhkLIqMxP4+Wd+/ZtvgOrVtRsPIYQQQggh5MNQcX9SIJkMmDoVePECsLUFhg7VdkSEEEIIIYSQD0VJICnQ4sXAyZOAgQEwezZgbKztiAghhBBCCCEfipJAotbWrcC2bfz6jBlAgwbajYcQQgghhBCiGZQEknyCgoBFi/j10aOBDh20Gw8hhBBCCCFEcygJJCquXwemTePXe/YE+vbVbjyEEEIIIYQQzaIkkMjFxwMTJgDZ2UDr1sCPPwK0KgghhBBCCCHlCyWBBADAGDBrFpCQwJeBmD2brwtICCGEEEIIKV/oMJ8AAPbtA86eBSQSngxKpdqOiBBCCCGEEFISKAkkiIhQFIIZMQLw9NRuPIQQQgghhJCSQ0lgBZeaCowfD2RkAI0bA998o+2ICCGEEEIIISWJksAKTCYDAgOByEjA1pbmARJCCCGEEFIR0CF/BbZ5MxAcDBgYAAsXAtbW2o6IEEIIIYQQUtIoCaygbt4EVq7k1ydMAGrV0m48hBBCCCGEkNJBSWAFlJQE/O9/fDionx/Qtau2IyKEEEIIIYSUFkoCK5jcXD4PMCYGcHHhySAtCE8IIYQQQkjFQUlgBcIY8PPPwH//8fUA580DTEy0HRUhhBBCCCGkNFESWIGsXw/s3ct7/mbPBmrU0HZEhBBCCCGEkNJWppPA6dOnQyQSqVxqKGUuGRkZGD58OCpVqgRTU1N89dVXiI2NVXmOp0+folOnTjA2NoatrS0mTJiAnJwclTbBwcFo0KABpFIpqlatio0bN+aLZeXKlXB3d4ehoSF8fHxw6dKlEnnPJeXUKWD1an59wgSgTRvtxkMIIYQQQgjRjjKdBAJArVq1EB0dLb+cPXtWft/YsWPx77//YteuXTh9+jSioqLQrVs3+f25ubno1KkTsrKycP78eWzatAkbN27EtGnT5G0iIiLQqVMntG7dGqGhoRgzZgwGDx6Mo0ePytvs2LED48aNQ2BgIK5du4Z69erBz88PcXFxpbMTNMDHB2jaFOjfH+jRQ9vREEIIIYQQQrRFxBhj2g6iINOnT8f+/fsRGhqa777ExERUrlwZW7duRffu3QEA9+7dQ82aNRESEoImTZrg8OHD+PzzzxEVFQU7OzsAwJo1a/DTTz/h5cuXkEgk+Omnn3Dw4EHcvn1b/tzffPMNEhIScOTIEQCAj48PPv74Y6xYsQIAIJPJ4OLigpEjR2LSpElFfj9JSUmwsLBAYmIizM3N33e3vLfcXL4YPBWCIYQQQgghJU3bx76kYGW+J/DBgwdwdHRElSpV0KdPHzx9+hQAcPXqVWRnZ6Ndu3bytjVq1ICrqytCQkIAACEhIahTp448AQQAPz8/JCUl4c6dO/I2ys8htBGeIysrC1evXlVpIxaL0a5dO3mbgmRmZiIpKUnlok16epQAEkIIIYQQUtGV6STQx8cHGzduxJEjR7B69WpERESgRYsWSE5ORkxMDCQSCSwtLVUeY2dnh5iYGABATEyMSgIo3C/cV1ibpKQkpKen49WrV8jNzVXbRniOgsybNw8WFhbyi4uLS7H3ASGEEEIIIYRokr62AyjMZ599Jr9et25d+Pj4wM3NDTt37oSRkZEWIyuayZMnY9y4cfLbSUlJlAgSQgghhBBCtKpM9wTmZWlpierVq+Phw4ewt7dHVlYWEhISVNrExsbC3t4eAGBvb5+vWqhw+11tzM3NYWRkBBsbG+jp6altIzxHQaRSKczNzVUuhBBCCCGEEKJNOpUEpqSk4NGjR3BwcEDDhg1hYGCAEydOyO8PDw/H06dP4evrCwDw9fXFrVu3VKp4BgUFwdzcHN7e3vI2ys8htBGeQyKRoGHDhiptZDIZTpw4IW9DCCGEEEIIIbqiTCeB48ePx+nTp/HkyROcP38eXbt2hZ6eHnr16gULCwsMGjQI48aNw6lTp3D16lUMGDAAvr6+aNKkCQCgQ4cO8Pb2xrfffosbN27g6NGjCAgIwPDhwyGVSgEAQ4cOxePHjzFx4kTcu3cPq1atws6dOzF27Fh5HOPGjcPatWuxadMm3L17F8OGDUNqaioGDBiglf1CCCGEEEIIIe+rTM8JfP78OXr16oX4+HhUrlwZzZs3x4ULF1C5cmUAwOLFiyEWi/HVV18hMzMTfn5+WLVqlfzxenp6OHDgAIYNGwZfX1+YmJjA398fM2fOlLfx8PDAwYMHMXbsWCxduhTOzs5Yt24d/Pz85G169uyJly9fYtq0aYiJiUH9+vVx5MiRfMViCCGEEEIIIaSsK9PrBJY3tFYKIYQQQgipKOjYt+wq08NBCSGEEEIIIYRoVpkeDlreCJ2u2l40nhBCCCGEkJImHPPSwMOyh5LAUpScnAwAtFYgIYQQQgipMJKTk2FhYaHtMIgSmhNYimQyGaKiomBmZgaRSKTtcAghhBBCCCkxjDEkJyfD0dERYjHNQitLKAkkhBBCCCGEkAqEUnJCCCGEEEIIqUAoCSSEEEIIIYSQCoSSwCKaPn06RCKRyqVGjRraDosQQgghhBBCioWqgxZDrVq1cPz4cfltfX3afYQQQgghhBDdQllMMejr68Pe3l7bYRBCCCGEEELIe6MksBgePHgAR0dHGBoawtfXF/PmzYOrq2uB7TMzM5GZmSm/LZPJ8Pr1a1SqVImWiCCEEEIIIeUaLRFRdtESEUV0+PBhpKSkwMvLC9HR0ZgxYwZevHiB27dvw8zMTO1jpk+fjhkzZpRypIQQQgghhJQdz549g7Ozs7bDIEooCXxPCQkJcHNzw6JFizBo0CC1bfL2BCYmJsLV1RXPnj2Dubl5aYVKCCGEEEJIqUtKSoKLiwsSEhJgYWGh7XCIEhoO+p4sLS1RvXp1PHz4sMA2UqkUUqk033Zzc3NKAgkhhBBCSIVA06DKHhqc+55SUlLw6NEjODg4aDsUQgghhBBCCCkySgKLaPz48Th9+jSePHmC8+fPo2vXrtDT00OvXr20HRohhBBCCCGEFBkNBy2i58+fo1evXoiPj0flypXRvHlzXLhwAZUrV9Z2aIQQQgghhJQL06er/iQlgwrDlKKkpCRYWFggMTGR5gQSQgghhJByjY59yy4aDkoIIYQQQgghFYhODgfNzs5GTEwM0tLSULlyZVhbW2s7JEIIIYQQQgjRCTrTE5icnIzVq1ejVatWMDc3h7u7O2rWrInKlSvDzc0NQ4YMweXLl7UdJiGEEEIIIYSUaTqRBC5atAju7u7YsGED2rVrh/379yM0NBT3799HSEgIAgMDkZOTgw4dOuDTTz/FgwcPtB0yIYQQQgghhJRJOlEYplevXggICECtWrUKbZeZmYkNGzZAIpFg4MCBpRRd0dHkWEIIIYQQUlHQsW/ZpRNJoLKUlBSYmppqO4z3Qh8EQgghhBBSUdCxb9mlE8NBlVlYWGDPnj3aDoOQErNx40ZYWlpqO4wimT59OurXr1/k9k+ePIFIJEJoaGixXkeX9gkhhBBCdNMnn3yCMWPGlMprFfcYStN0LglkjOG3335Ds2bN0Lx5c4wZM4YKwmjRy5cvMWzYMLi6ukIqlcLe3h5+fn44d+5cqcYhEomwf//+Un3NktKzZ0/cv39f22HoPHd3dyxZskTbYRBCCCGE5DN+/HicOHFCfrt///7o0qVLqb2+Ti4Rcf36dXzzzTcwMTHB1atX0aJFC4wYMQK//PKLtkOrcL766itkZWVh06ZNqFKlCmJjY3HixAnEx8drO7R8srKyIJFItB3GOxkZGcHIyEjbYRBCCCGEkBJiamqq1SluOtcTCABbt27F8uXLMX/+fAQFBeHSpUvYs2cPFi9erO3QKpSEhAT8999/+Pnnn9G6dWu4ubmhcePGmDx5Mr788kt5O5FIhNWrV+Ozzz6DkZERqlSpgt27d6s817Nnz9CjRw9YWlrC2toanTt3xpMnT1TarF+/HrVq1YJUKoWDgwNGjBgBgPf4AEDXrl0hEonkt4Vu9nXr1sHDwwOGhoYAgKdPn6Jz584wNTWFubk5evTogdjYWPnr3LhxA61bt4aZmRnMzc3RsGFDXLlyRX7/uXPn8Mknn8DY2BhWVlbw8/PDmzdvAPDiRKNGjYKtrS0MDQ3RvHlzlZ7q4OBgiEQiHDx4EHXr1oWhoSGaNGmC27dvy9uoG/r477//4uOPP4ahoSFsbGzQtWvXAn8vwvtev349XF1dYWpqih9++AG5ublYsGAB7O3tYWtrizlz5qg87l37BQDmz58POzs7mJmZYdCgQcjIyMj3+uvWrUPNmjVhaGiIGjVqYNWqVQXGWpCNGzfC1dUVxsbG6Nq1a76TCo8ePULnzp1hZ2cHU1NTfPzxxzh+/Lj8/k8++QSRkZEYO3YsRCIRRCIRACA+Ph69evWCk5MTjI2NUadOHWzbtq3Y8RFCCCFEt6WmpqJfv34wNTWFg4MDfv31V5X7MzMzMX78eDg5OcHExAQ+Pj4IDg6W3y8crx09ehQ1a9aEqakpPv30U0RHR8vbBAcHo3HjxjAxMYGlpSWaNWuGyMhIAKrDQadPn45Nmzbh77//lh+3BAcHo02bNvLjXcHLly8hkUhUehHfC9MxlSpVYnfv3s23/cCBA6xatWpaiKjoEhMTGQCWmJj4zrYyGWMpKdq5yGRFez/Z2dnM1NSUjRkzhmVkZBTYDgCrVKkSW7t2LQsPD2cBAQFMT0+PhYWFMcYYy8rKYjVr1mQDBw5kN2/eZGFhYax3797My8uLZWZmMsYYW7VqFTM0NGRLlixh4eHh7NKlS2zx4sWMMcbi4uIYALZhwwYWHR3N4uLiGGOMBQYGMhMTE/bpp5+ya9eusRs3brDc3FxWv3591rx5c3blyhV24cIF1rBhQ9aqVSt5vLVq1WJ9+/Zld+/eZffv32c7d+5koaGhjDHGrl+/zqRSKRs2bBgLDQ1lt2/fZsuXL2cvX75kjDE2atQo5ujoyA4dOsTu3LnD/P39mZWVFYuPj2eMMXbq1CkGgNWsWZMdO3aM3bx5k33++efM3d2dZWVlMcYY27BhA7OwsJDHc+DAAaanp8emTZvGwsLCWGhoKJs7d26B+zswMJCZmpqy7t27szt37rB//vmHSSQS5ufnx0aOHMnu3bvH1q9fzwCwCxcuMMZYkfbLjh07mFQqZevWrWP37t1jU6ZMYWZmZqxevXryNn/99RdzcHBge/bsYY8fP2Z79uxh1tbWbOPGjYwxxiIiIhgAdv369QLjv3DhAhOLxeznn39m4eHhbOnSpczS0lJln4SGhrI1a9awW7dusfv377OAgABmaGjIIiMjGWOMxcfHM2dnZzZz5kwWHR3NoqOjGWOMPX/+nC1cuJBdv36dPXr0iC1btozp6emxixcvFhgPIYTokkuXGPv5Z8YK+bdMSKkpzrGvIDCQX0rasGHDmKurKzt+/Lj8eMzMzIyNHj2aMcbY4MGDWdOmTdmZM2fYw4cP2cKFC5lUKmX3799njPHjNQMDA9auXTt2+fJldvXqVVazZk3Wu3dvxhg/TrawsGDjx49nDx8+ZGFhYWzjxo3yY5XAwED5MVRycjLr0aMH+/TTT+XHLZmZmWzLli3MyspK5Th70aJFzN3dncmKesBeAJ1LAtu2bcsmTpyYb/u9e/eYVCrVQkRFV5wPQkoKY4B2LikpRX9Pu3fvZlZWVszQ0JA1bdqUTZ48md24cUOlDQA2dOhQlW0+Pj5s2LBhjDHGNm/ezLy8vFT+mDMzM5mRkRE7evQoY4wxR0dHNmXKlALjAMD27dunsi0wMJAZGBjIk0LGGDt27BjT09NjT58+lW+7c+cOA8AuXbrEGGPMzMxMnrTk1atXL9asWTO196WkpDADAwO2ZcsW+basrCzm6OjIFixYwBhTJIHbt2+Xt4mPj2dGRkZsx44djLH8SaCvry/r06dPge89r8DAQGZsbMySkpLk2/z8/Ji7uzvLzc2Vb/Py8mLz5s1jjBVtv/j6+rIffvhB5bV8fHxUkkBPT0+2detWlTazZs1ivr6+jLGiJYG9evViHTt2VNnWs2dPlX2iTq1atdjy5cvlt93c3OQnCgrTqVMn9uOPP76zHSGE6IKGDfll3TptR0LI+yWBpSE5OZlJJBK2c+dO+TbheGz06NEsMjKS6enpsRcvXqg8rm3btmzy5MmMMX68BoA9fPhQfv/KlSuZnZ2d/PkAsODgYLUxKCeBjDHm7+/POnfurNImPT2dWVlZyY8RGWOsbt26bPr06e/1vpXp3HDQ2bNnY9myZfj2228REhKC1NRUxMXFYe7cufDw8NB2eBXOV199haioKPzzzz/49NNPERwcjAYNGmDjxo0q7Xx9ffPdvnv3LgA+/PLhw4cwMzOTj4+2trZGRkYGHj16hLi4OERFRaFt27bFjs/NzQ2VK1eW37579y5cXFzg4uIi3+bt7Q1LS0t5POPGjcPgwYPRrl07zJ8/H48ePZK3DQ0NLTCOR48eITs7G82aNZNvMzAwQOPGjeXPrW5/WFtbw8vLK1+borxmQdzd3WFmZia/bWdnB29vb4jFYpVtcXFxAIq2X+7evQsfH58C30dqaioePXqEQYMGyX+PpqammD17tso+VFarVi15u88++6xIrwPwpWLGjx+PmjVrwtLSEqamprh79y6ePn1a6H7Jzc3FrFmzUKdOHVhbW8PU1BRHjx595+MIIUTXFPC1S0iZN306v5SkR48eISsrS+V4QzgeA4Bbt24hNzcX1atXVzmmOX36tMoxjbGxMTw9PeW3HRwc5MdW1tbW6N+/P/z8/PDFF19g6dKlKkNFi8LQ0BDffvst1q9fDwC4du0abt++jf79+7/vW5fTucIwTZo0wYULFzB69Gi0aNEC7O0yh4aGhti1a5eWo9McY2MgJUV7r10choaGaN++Pdq3b4+pU6di8ODBCAwMLPIfaEpKCho2bIgtW7bku69y5coqiUtxmZiYFPsx06dPR+/evXHw4EEcPnwYgYGB2L59O7p27aqVgi3v85oGBgYqt0UikdptMpnsg2JTlvL2D3bt2rX5kjg9PT21jzl06BCys7MBFO99jh8/HkFBQfjll19QtWpVGBkZoXv37sjKyir0cQsXLsTSpUuxZMkS1KlTByYmJhgzZsw7H0cIIbpGg1/vhJSqkk4AiyIlJQV6enq4evVqvmMY5WIu6o6thNwEADZs2IBRo0bhyJEj2LFjBwICAhAUFIQmTZoUOZbBgwejfv36eP78OTZs2IA2bdrAzc3tPd+Zgs71BAJAvXr1EBwcjKioKBw4cAD//PMPIiMj0bFjR22HpjEiEWBiop3L2xoa783b2xupqakq2y5cuJDvds2aNQEADRo0wIMHD2Bra4uqVauqXCwsLGBmZgZ3d/dCJ8AaGBggNzf3nbHVrFkTz549w7Nnz+TbwsLCkJCQAG9vb/m26tWrY+zYsTh27Bi6deuGDRs2AADq1q1bYByenp6QSCQqy2NkZ2fj8uXLKs+dd3+8efMG9+/fl++PvAp7TU0pyn6pWbMmLl68qPI45fdhZ2cHR0dHPH78ON/vsaBeejc3N3kbJyenIr0OwIvz9O/fH127dkWdOnVgb2+fr5CQRCLJ9zdx7tw5dO7cGX379kW9evVQpUoVWo6DEFJuZGYqrlMSSEjBPD09YWBgoHK8IRyPAcBHH32E3NxcxMXF5Tumsbe3L9ZrffTRR5g8eTLOnz+P2rVrY+vWrWrbqTtuAYA6deqgUaNGWLt2LbZu3YqBAwcW6/ULopNJoMDW1hafffYZOnXqBBsbG22HU+HEx8ejTZs2+Ouvv3Dz5k1ERERg165dWLBgATp37qzSdteuXVi/fj3u37+PwMBAXLp0SV7tqE+fPrCxsUHnzp3x33//ISIiAsHBwRg1ahSeP38OgPfO/frrr1i2bBkePHiAa9euYfny5fLnF5LEmJgYeaVOddq1a4c6deqgT58+uHbtGi5duoR+/fqhVatWaNSoEdLT0zFixAgEBwcjMjIS586dw+XLl+UJ2uTJk3H58mX88MMPuHnzJu7du4fVq1fj1atXMDExwbBhwzBhwgQcOXIEYWFhGDJkCNLS0jBo0CCVOGbOnIkTJ07Iu/RtbGwKXBsmMDAQ27ZtQ2BgIO7evYtbt27h559/LvbvqzDv2i8AMHr0aKxfvx4bNmyQ/x7v3Lmj8jwzZszAvHnzsGzZMty/fx+3bt3Chg0bsGjRoiLHIpwx++WXX/DgwQOsWLECR44cUWlTrVo17N27F6Ghobhx4wZ69+6dr1fT3d0dZ86cwYsXL/Dq1Sv544KCgnD+/HncvXsX33//fb4KqIQQoquU//2pKd5MCHnL1NQUgwYNwoQJE3Dy5En58Zgw+qx69ero06cP+vXrh7179yIiIgKXLl3CvHnzcPDgwSK9RkREBCZPnoyQkBBERkbi2LFjePDgQYEn/d3d3XHz5k2Eh4fj1atX8pFSAO8NnD9/PhhjhVaIL5YPnlVYCoQqOkX1/PnzEorkw5TVybHvKyMjg02aNIk1aNCAWVhYMGNjY+bl5cUCAgJYWlqavB0AtnLlSta+fXsmlUqZu7u7ygRXxhiLjo5m/fr1YzY2NkwqlbIqVaqwIUOGqOyrNWvWMC8vL2ZgYMAcHBzYyJEj5ff9888/rGrVqkxfX5+5ubkxxvJPuBVERkayL7/8kpmYmDAzMzP29ddfs5iYGMYYL0jzzTffMBcXFyaRSJijoyMbMWIES09Plz8+ODiYNW3alEmlUmZpacn8/PzYmzdvGGN8Au/IkSPl76NZs2bywiqMKQrD/Pvvv6xWrVpMIpGwxo0bqxTTyVsYhjHG9uzZw+rXr88kEgmzsbFh3bp1K/D3ou59q5ts3KpVK3kFrHftF8GcOXOYjY0NMzU1Zf7+/mzixIn5XmvLli3yWK2srFjLli3Z3r17GWNFKwzDGGN//PEHc3Z2ZkZGRuyLL75gv/zyi8o+iYiIYK1bt2ZGRkbMxcWFrVixIt/7CQkJYXXr1mVSqZQJX3Xx8fGsc+fOzNTUlNna2rKAgADWr1+/fPuGEEJ00Z07isIwxagnRkiJKcvHvsnJyaxv377M2NiY2dnZsQULFqgcS2RlZbFp06Yxd3d3+bFn165d2c2bNxlj6o/X9u3bJz/miImJYV26dGEODg5MIpEwNzc3Nm3aNHmRvrzHa3Fxcax9+/bM1NSUAWCnTp1SidXY2Dhfgb4PIWJMaeBqGWVnZ4cuXbpg8ODB+Pjjj9W2SUxMxM6dO7F06VJ89913GDVqVClH+W5JSUmwsLBAYmIizM3NtR1OqRGJRNi3b1+BPV0VSXBwMFq3bo03b97kWwuQEEII+RDnzwPC4U/lysDhw9qNh5CKeuyraU+ePIGnpycuX76MBg0aaOQ5daIwTFhYGObMmYP27dvD0NAQDRs2hKOjIwwNDfHmzRuEhYXhzp07aNCgARYsWFCu5gYSQgghhBTF69eK6/HxfF7gB9RWI4RoWXZ2NuLj4xEQEIAmTZpoLAEEdGROYKVKlbBo0SJER0djxYoVqFatGl69eoUHDx4A4HPKrl69ipCQEEoACSGEEFIhKc8JlMmAxETtxUII+XDnzp2Dg4MDLl++jDVr1mj0uXViOGh5QV3ihBBCCCkpy5YBf/6puL19O1C1qvbiIYSOfcsunegJJIQQQgghhctbHFt5eCghhCijJJAQQgghpBzIO/wzz5K9hBAiR0kgIYQQQkg5kHdtwJQU7cRBCCn7KAkkhBBCCCkHcnL4T5GI/6SeQEJIQXQuCUxPT0daWpr8dmRkJJYsWYJjx45pMSpCCCGEEO3KzuY/raz4T0oCCSEF0bkksHPnzvjzbemrhIQE+Pj44Ndff0Xnzp2xevVqLUdHCCGEEKIdQhJoacl/UhJICCmIziWB165dQ4sWLQAAu3fvhp2dHSIjI/Hnn39i2bJlWo6OEEIIIUQ78iaBNCeQEFIQfW0HUFxpaWkwMzMDABw7dgzdunWDWCxGkyZNEBkZqeXoCCGEEEK0g3oCSXkwfbpm2pDC6VxPYNWqVbF//348e/YMR48eRYcOHQAAcXFxtAglIYQQQios6gksH3JzgVOn8q/7SIgm6VwSOG3aNIwfPx7u7u7w8fGBr68vAN4r+NFHH2k5OkIIIYQQ7aCewPIhOBiYMAFYulTbkZRd06dTb+CH0rnhoN27d0fz5s0RHR2NevXqybe3bdsWXbt21WJkhBBCCCHaQ0lg+RAVxX8+f67dOMoiSvw0R+eSQACwt7eHvb29yrbGjRtrKRpCCCGEEO2jJSLKh+Rk/jMhQathlEnvSgIpSSw6nUgCx40bV+S2ixYtKsFICCGEEELKJpoTWD4ISWBFnRO4ceOHP9bdXf39wcHv/9zljU4kgdevXy9SO5FIVMKREEIIIaSi+/df4NIl4NYtoEMH4Icfivd4xoBdu4CaNYE6dTQTE2P5ewJTUvh2OjzSLUISmJQEyGSAuJgVPMLCgIAAYMQIoE0bzcenCz75hP+knsGC6UQSeOrUKW2HQAghhBCC58+BGTMUtzdvVk0C37zhvQ316wMeHuqf4+JFYMECfr12bWD0aOBDa9vl5iquCz2BMhmQmQkYGqq2TUoCliwBunQB6tb9sNfVNMaAOXN47FOnVswEVkgCGeO/K+H3+S6MATdv8v0WFQVMnAhcuVJiYZZpQo9gcXoVC+o9LAlloUdSJ5JAQgghhJCyIDxc9XZ2tmpvze+/814+APjtN6Bhw/zPce2a4vrt28DatcCqVepfb+dOnniOHVt4QiT0AgKAuTlvyxifF5g3CZwzBzhxAjh0CLhwoeDnLC1btwKnTwNNmwIvXgD79/PtffoAnp5aDU0rhCQQ4CcVipIEMgbMnw/s2VNiYekEIZErC0lWWaezSWBYWBiePn2KrKwsle1ffvmlliIihBBCSGkRKig6Opbu6z54wH9+/jlPomQyfqBeqRI/EH/4UNH25k31SeCNG7wtwJO1mBh+XSbjj9fTA5ydeWIp9Bh++ilQtSrv5alVC+jXT/U5lZNAiQSQSoGMDMDPD2jXDujcGfjrL/68J07wdjk5vAdRT+/D98v7evwYEMo5XL2qet++fcCQIYCFRenHpU3KSWBRi8PcuqU+AUxIKHpPYnnw5An/WVCvXv/+NERUoHNJ4OPHj9G1a1fcunULIpEI7O23qDAfMFd5PAQhhBBCyp2cHODbb3kideQIT3qKQ0g28iZoMhk/iIyIANLT+VDKzEyejJmb82QkJARIS1O0f/0a6NmTJ4eGhkBsLE+sxGLgl1+A48cBExPg2TO+PTISiI7m152d+fXISGDoUN5DmJvLE0ORCDAz4/HIZMDgwbxX78kTnrSdPg0YG/P2OTk83mfP+H1Ll/KkMC6O76OtW3nvpETCLwYGivf86BFQvfp7/iI04PffC75v+3bg+nVgy5bSi6csUJcEvn4NhIbyuW7q5gjm7aEW3L8PlPcC+v37azsC3aRzSeDo0aPh4eGBEydOwMPDA5cuXUJ8fDx+/PFH/PLLL9oOjxBCyDukp/MD3/h44NUr3ovi4gLUq8cPtAl5l+RkIDGRX3/5EnByKvpj09KAQYOArCw+fC4piSdL9+/zghpCgieIj+dtXFz49dev+fYdO/jjMjJ44peXTMb/vkNDeYx5l2vQ0+PJYW4uf80DB/hnwdkZcHAAnj7l8QiE5DInh18uXlQd5pmdzQvBiETAtm28jakpvy8mhiezeV/bxAQYMIAnhaNHA97e/HWeP+fDMl+94u/9yRO+vywsAH19fjEx4T1MlpY8WZVIFPeJxTz5fPOGxyN8roXk1sGBP65SJeDMGX7f4MHAunX592NByU1ZxRhw+DBQowZQpcr7PUfe4aAAMGwYT9gnTgS6dePfo2Zm/Pe8fj2we7f653rwoPwngUWd9+fuTsNElelcEhgSEoKTJ0/CxsYGYrEYYrEYzZs3x7x58zBq1KgiVxIlhBCiWbm5/MA8IUHxU7geH6/oYRGGvqnj6ckPnBIT+cH269f8gNXMjB+AVqrEq921bq3am0EqFuVELTa28CRQ6K2LjOTz306cUAzpnDaNJy3KDA35sEszMz6kcssWnuBERfG/cQMDnsjUqwfcu8dfXxh+KZXyv1eZjF+cnHjyl53NEyHG+OsZGADLlvGeyM8/50liTg4/SDUy4gmWhQW/npPDk6ru3fnn6MwZHkePHkCDBvz59PT4c0ybxq/7+yseJxLx4acHDvA4qlThCd2bN/xkTHQ0f99Dh/JEN6/UVJ6QAoCtLf8MFkV2Nh/qKZMB1tZA5co8aX7zhr83R0dFAm5nx/dV37680E7euY8lNWRVqKhanJ7k5GQ+j7NJE/VzNC9c4L8HoHhFWYKD+X6qUUM1YRd6Ah894j8PHOBJ5q1bwLFjfCjtkSMFP68wPJJwQtXQvCpicqhzSWBubi7MzMwAADY2NoiKioKXlxfc3NwQXgqni1auXImFCxciJiYG9erVw/Lly2mhekJIhcIYPyC5epUf5D17xg+whaFnRWFqyg8KbWz4wfbDh/xA89EjxcGOspcvFdePH+cl8L/4gp8Rd3bWzPsiZZ+QWJw4oejdGjeOJ1gpKTxZMDTkF6mUb3v1iicigowM/lMi4ZU53dx4YuPpyXvCqlRRJBxZWfkPDg0MgAkT+N/e/PmKHpgBA4ANG1Tbxsfzn3l7uCdMUJTur1KF9+ooi43lMRgZKbaJRPxzIPT+/fMPH4bq5cVvP37Me9csLICRI/Pvt9mz+WdlwADew3j0KPDHH4q5lVlZ/HMlJGTOzvzzuWoV/1wLJ2OWL+fPl5amONGTlKQYkpqSwj/fZ8/yfQ/w/S/0lpqZ8cebmvLXFp5rxgxFwmlgwKul3rzJX7tHD57sCstdeHkBLVoAvr6K3s68srJ4TEZGBRfUmTuXJ1A7dhR9bun48fy7b84cPt8yr3v3FNcfPOBVWAcMABo1Um0XFsbff7t2PEkfP55vP3pUtV3eOYFZWYp5p/v3F54AAnxepYkJT37q1y+8bXmXNyFWnjdYUHJYHLqWSOpcEli7dm3cuHEDHh4e8PHxwYIFCyCRSPD777+jyvv2uxfRjh07MG7cOKxZswY+Pj5YsmQJ/Pz8EB4eDltb2xJ9bUIIKW2vXvGETOjNS07mt69cUQyJU8fcXDFMTDgotbLivQweHvyirtCDMOclOpq3t7bmF5mMH2QmJfGDqn/+4QfDf/7Jew1atAB69+a9KhWxnHx5JRRJuXmTH1jfu8dv5+TwpEEYJhcZqdo7lXfYJcB7xGxs+N9IpUp8+Ji+Pu+xKWg+3IkTwKxZqtsaNeJJkTAnS1iPD+AnJYQkUCLhB+sCR0e+bpuwlES9eor77OyAO3feuTsQGckvyqZO5QmMSMT3C6C+h9zAQHVZi7p1+WXCBP64zz7j+9PJiZ/MmT+fJ4F37/LETki0cnJ4URpjY/Uxjh7N109cv54nMw4OfO7m9u2qhWsAwNWV/64sLICvvuJJYng4TwKF+YyZmTzxe/BAtbfu0SNelEdfnyfuLi48dmtr/jdy+zYf3iuT8cdZWgL29nxJjM8+U+yjffv4z82bgZ9+yv9+bt/m+0EorJKcrJhPunOn+iRQ+X326sV/3rqlGPYqEAr7/P47P7EliIhQbZeQoHoSIylJcf358/yvP2IEsGIFT/yEz8Jff/FeXF1PAosz94+Kv7ybziWBAQEBSH37Vz1z5kx8/vnnaNGiBSpVqoTt27eX6GsvWrQIQ4YMwYABAwAAa9aswcGDB7F+/XpMmjSpyM+TkpICsZpZvfr6+jBUGuCfkpJS4HOIxWIYK30LF6dtWloaZMrfKMVse/cuP/M0ZIgY9vZFe14AMFU6XVecthkZGcgR/rt9YFtjY2P5vi+ptllZWfmq1r5vW0NDQ+i/HatUFtrm5OQgQziNroZEIoHk7X/qstBWJpMhLe8En/dsq/z5LKm2QOGf5bxtk5JSkJDAk6eEBN4zEh3Nzy7HxoqRk2Msn0PEWArMzflBkoUFP0AV5uSlpophbW0sT7zi4tLw4IFM3ouhjA/DN4ahIT9T7+mZBkdHGVxd+YGuhYWiF6Ww7xN1b9Pa2lTeO1LQd0STJjzhCw01xZ49wPnzQHBwBk6ezIGnJz/Id3HhlypV+Puh74jSa/shn2WZjCc5t2/zA+3r1yVITBQ+nzmQyXhbMzPA3l6EpCQ9SKVA27YyfP+9PqysJNDXB1JScpCUlIGMDH4gbGPD/w6Ev8v//pNAX58/b2qqDCkp+T+fjAHjx+tDLNaHSMTf29dfyzBgQJrKUNQ3b8TIzeW/q8qVAYB/PvX0ZDAxyZIfsLu6MlSpkovKlfVhYCAMu+RtbWxkyM3l+7dDBwY/PxmsrBhCQ8V49gzYv98AYrGBPFE0MMhAvXoMFy+K8OABcONGDqpWBRITRZDJJDBQygKLcmygr89PpOzbl4EbN/h969fLMG6cDCEh/P35+gJ37xoiOZl/z9jb5/98ymTAmTP6EIlEmDVLiqws3ms5eHAaoqNFOHqUn6GZNy8Xkyfr4dYtXtSvWTMpZs/G299HGgYMECEqSoSAgBxMmaKPlBRg4sQcODsDJiamyM4GLl8GTp5MQ2SkDNeuqS65IdDTM4VIxL/rYmIyEBWVg2vXeE9mo0YiDBrEkJvLf7dJSYZ48kT4u8tAamoOIiNFOHtWD25uDO3by+Dry7B0qT4Yk0AkEiMtTfG5X7hQjORkEaZPz8XTp4q/CbE4f1uAf18Lrx0cLMPRo2L58965AzCWA5mMt42LY3j+PFfePjpa8bxPn6q2BYBu3XJQo4YIBgYMgwfrQyw2gEikB3d37XxHFPY3+D4oudMcEWNFHbxTdr1+/RpWVlbyCqElISsrC8bGxti9eze6dOki3+7v74+EhAT8/fff+R6TmZmJTKWB3UlJSXBxcUGzZs3kHxxl3t7eWKW0UFD79u2RnffU2VtVq1bFOqUZ1B07dizwQNPZ2Rl//fWX/HbXrl3xRjiFmoetrS127twpv92jRw/ExcWptLl7l5+FdXe3wq1b++Tb+/bti+fqTkuBH9QcOnRIfnvw4MF4qFxHW4mBgQGCgoLkt3/44QeEKc+OzyNYqf997Nixhc4LPXDggPyAcPLkyQgJCSmw7c6dO+U9vNOnT1d5nbw2bdoENzc3AMC8efNwNO94DiUrV65ErVq1AABLlizBfmFBJDV++eUXNHo7hmT16tXYsWNHgW1nz56N5s2bAwA2bNiATZs2Fdh28uTJ8Ht7CnPHjh1YvXp1gW3Hjh2Lzp07AwD+/vtvLF68uMC2w4YNQ8+ePQEAR48exbx58wps6+/vLz+hcvbsWQQEBBTYtmfPnhg2bBgA4MqVKxgvjJtRo0uXLhgzZgwA4M6dOxg+fHiBbf38/DB58mQAQGRkJPz9/Qts+8knn2D62/8+cXFx6NGjR4FtfX195e89JSUFn3/+ORjjZ9RTUxVDv8RiwMXlI3zzzWLk5vKhbEuXfgI9PUUVP5GIPyY1FRCJvOHsvAqM8bZ377YHY+q/I6TSqnB3V3xHPHjQETKZ+u8IicQZHh6K74iHD7siN/fN2+fhcejp8bPulSrZ4vffd6JOHX42Xd13hMDKygr79pXsd0RkJNCz5w+4ezcM6s4rWVsDQUHB8t4e+o5493cEY8Do0ZPh4+MHAwPg1KmifUdkZwObNv2NVasWIzOTJwXCEYbws2XLYWjYsCdkMuD27aM4dmweGONDNFNTVRc8r1TJH87OA1CvHiCRnMXJkwEwNuZ/j2/eWCEigo/+sbR8g//972O13xEJCRaIjbWDu/sTSKX8INXDwx+3bg14G3s41q79Xv6aubl6ePnSBoaGGXj0qCrMzZvDwaEr2rcHhg7N/x2RkWGIsLCasLZ+jf79PdC06XRMmQIEBLzCkCFTkZ7OT4LY20fDySlKnhw0b+4j/47Yti0V3303DSIRQ+3atyCRqH6mq1dvjGvXFshvP3/+PWrUCMejR1WQkGAlf+7kZFM8e/Y5mjcfir17eduiHkc8fQrUqTNV/h0hkWShdu1bePyYv0adOoCHx68IDwcWLwYWLVIcR2RlGSAx0QLm5km4fbsO9PUrw9Nz0tt9DYhEPfDixWs8flwFlpYJsLF5hdDQ+gBE0NOzxNatU9GxI4+pb9++ePbsOQARRCKG27drITPTENWrh8PQ0ASHD++QD60dPHgwbt9+iLQ03mOYlcV74aRSwMTEGA4Oh1CvHjBmDPDjjz/g+vUwxMYCKSlVIZNZQF8/Djk5thCJAEfHX+W9yS9ejEV6+nWkp9cGY9J8+83JaQ5sbXkCn5LyBywsdiMszBsAUK3afcTE2CM5mQdZpUogDAz49U8/nY6zZ4MBAMnJprh/n4/jFYlkYEwMd/fJkEpt0KYNsGXLPiQlnQUAGBunws0tEnfvestjcHUdByMjJ0gkwLNnh8DYHuTkGMDUNBnOzi8AADKZGNevfwQXlxEwNvbAgQPAvn2lfxyRk5ODc+fOITExEeZFrPyliUXbi9JrWNETSp3rCZw5c2ah908TZuNq2KtXr5Cbmws7OzuV7XZ2drinPABcybx58zBDefxFOZCVpZiQHxXFhyVQNT9C8nvzhg8BunKFD0tKTYXaJCUpiS8ULVBXZVBgaKhaMEAgVOQzMFAUp6hShQ8VE5K3kSP5UKacHH6gJBbz9gYGfBjV//7HexTfvOFnymUyPpcmbzEGW1tejKKscHNTFMdISODJhHDJzOTvqXdvPneod2/1v4PyQjg5kPcik/GfR4/yYYcpKcB///EhderaM8aHxglDdsVi3mtsYcFPSqSl4W3PBm8/Ywaf95SaytsVcE4AAB9iKeTgiYn5/95FIt57Z2bGf19TpvC/0bNnFY8DAJlM8YeZlVVwVY9Hj6oCAJ49c0XVqvykQmam4rHp6aonj6Oj7REba5/ved6WIsjH0DAD9erdgFgsA+CBDh34HK9Xr2SwskqQJ4FGRvzMj55e/j9APz8GD48ImJklwcAgf2+ykVE29PUVwz2lUv4lYGX1BgkJVnjzxhqOjlFgjL8XNeeY38nVFXBze4GnT82Qm6uPrCwJUlJMkZJi+va1UuDoyIdrRkUBWVn6ePrUBZUrv0RkpBtSU03l71GZgwPvOTQwyIGX132lexT7vVUr1cfw8/n8rIGwv+LibJGU5ISJE4E1axRthfmfeUVGOkMiAU6e5EN6raz4cFBbW+DyZQtkZwM5OfwEDmP8ezg1lT+Xt7cYMTF6iIzkCaBQ5Ef47nB0ZPLfRWKiFLGx1eSvm5JiWuDfY2KiIqFMT1dM9mSMnxhwdZUhNpZ/Nvl7z0Vurh7S0kxUEkBlQuebkVE6nJxUJ1Pzv0mFPIevZdr7LPlQ0RO696FzPYEfffSRyu3s7GxERERAX18fnp6euKZuTIAGREVFwcnJCefPn4evr698+8SJE3H69GlcvHgx32MK6gl88eKF2rMhujAcdO9exaKuYrEYI0YYY+DAdz8vQMNB36dteRrqpY22pTUcNCeHz0MJC+MH2Xfu6CMmRtFWOLtubMznr1SvrpgzlJ2tDz09Q3mFv8zMFPnBcVwc7zWsUYMXsKhXTx/u7oYQi/mBeUZGisrwS2XaHDL+Pm0BzX9HhIfzUvlnzpjKD+Ds7DLQuXMOWrfmSUx8PJ9fGBPDD25jYowRHy+GgwPg4pIBD48cVKrEf1eZmfx37enJfyemppr7jsjJ4ckZP1lgjLQ0MVJTgdevs5CUlIWUFMX9aWmK6xkZhkhP50PmkpOzkJNT8GdZLDaEWMw/yzJZFmSywtsaG+u/La7x7rbC8xoY5MDBIQP29jxxEokg/3vlJx4kkEolEIsVQzzFYp4s1KrFT14ISUxhn/udO8VYvpzvz0qVgEOHxGrbtmjBn8zFBdi6lf9+fv9diq1b+ZDJgAAZ2rVTfJYHD9ZDeLgiQRGGg44YAfTrV7zviNTUNGzYIMbNmyLMmZMLE5OC277reYcMMcTdu/x2v35pGDhQhvR04Msv9ZGRAaxdm4OEBBEmTpTA29sAwuCf9zmOWLhQjH/+EcPFhRd9kkqBI0dk2LDBGFu2AH36AJGRGTh9ms+Vy1u4RCQSQSzmCc9XXwGjR+f/LPfrp4eICBEkEhEuXlQkR3k/9yNH6iE0VPH70NMzlFfcLOg7IjkZ+OILfQjDbf/8E6hShX8+09IAPz/+N8EY/9zxNR0NYWjI/15//jkDIlEufvxR9YuVMf4dsHKlBA8eiLF2LSCTZYMxRfe1tzfDw4ciZGUBs2fnon59IwQEiHHrFuDjk4VZs7JgYABMmaKH//5TvK9evWRwczPEwoXit6+Vgy+/zML+/WoWBIRiOKjQdvz4THzxRf7D+RYtFMNBr1zRznFEUlISnJycNNITSL17mqVzPYHqhvEkJSWhf//+6Nq1a4m9ro2NDfT09BCb57RlbGws7O3znzUEAKlUCqk0/1ACU1NTlQOYghSlzfu0Vf7SL27bCxcUE7HDwvhk7759+QHthzxvYQzVneYrw22VD1zKW1t9ff0i/62VhbZisbhE2sbHi3Hrlilu3+ZFK8LCVItACDw8gDp1xKhTxxR16vADXHWL/Koq+me5OG1L6ztCG20L+nw2bMgvL17wxbIPHABiYw3x+++FL1AN8ITw6tWCP/eWlrx3sVEjft3ExFC+cLeQoCknbqrXJW8vim3qeng5ydtLUfC5bmIxL+ShfDExyXtdAhMTidr7hdt6ejy+S5ckOHtWggsX+DYvL34io3p1xTIGRkY86bOy0odIVNS/NX0U9W847+c+J0dx8iMxUfVzJbRNTla00dfn7+vePdXqixkZYojFppg3T7HsiPAYb2/gxx950Y7evYv/fWJmZopRo4rW9l3PW7Mm5Elg1arG8t9T69Z8mYBz53jRD6GHX/A+xxH+/sDBg/wzoKcH+Pjwv3FhGY6oKOD6dUPo6UFlH6vj4KD+szxvHvDrr8DYsarb87ZVnsuZV0HfEcKSF4Ljx4GLFw3Rowc/GaD8fOp6TR88MISTk/rX1dcHPv6YV3b97jtgwAAD3L6t2OFCkXp9faBDB/678PDgo0IuXpRg504JEhP5XGY9PX5yz9ub7wflw1uRSB89e+rj33/Vv3dlIpE+3N311VZJzfsetHEcUdjJvIJ8yOLvQhJIyeC76VwSqI65uTlmzJiBL774At9++22JvIZEIkHDhg1x4sQJ+ZxAmUyGEydOYMSIESXymmVNUpKiKtaMGbwCVWwsP7Dq1k27sRFSUrKy+IHj7dv8H/nNm+qHbJqZAXXqKC61ahU8hIyULicnXgVxxAggKIiX9A8L478fYZkKe3teBVAoix8VpViuIiFBseQAwP8GEhL4Wl2HD2s2VmGNuLzJWd4kTX1ixy9SqeaqpJqa8gNeoWBPWaHcccYYL3CU93yscpXFV694gt63b/7n2b9f8bsU8gofH1451M5OtZKntnh7Qz7PT3ktv+bNeRJ4+zb/zgE+fP3MKlWAli0V1SyFwU9CEhgeXnjiV7cu/4wABS+74OX17hMxAFR6T4sqb8mDP//kP2fN4r2Y73LjhuKzro7wvS4S8SRVHScnxe+hSRNe0RjgQz2Vk9RFixTPUb8+/99x6xZPDr28+H5WnidbkAL6IuDvD2zalD/ZJgQoJ0kgACQmJiIxMbFEX2PcuHHw9/dHo0aN0LhxYyxZsgSpqany4hbl3blz/MvI05Of2erTh3+BrV7N11cp6MuQEF3BGB8WeOuW4hIenr+0uVjMF5OuXZsf8NSpww/M3t3LR7TJyAj48kt+edfi04XNe8zJ4Qe5Z88q5nsKvX1Cj1NhSVxh10tiQezyKO/oybi4wpPAtDT++8orbzGatDR+cL94cfEWEC9pystYKK+LWe3tdLQHDxSjEd5nTmBeY8cqksCmTfnPjz7iSbKwrmBBevRQJIEfunpW3t4tsVixVmBSkuLk3NGjwLBh/L7CJjlt2fLu17x1S7GPmzYFLl4sOBGzsVFcb9MGGDiQVypVnrnUoQMfPt6tm6KnUCoFTp9W/V1JJHzdxmvX+HBIkYj3tCsvx1O7Nn/PyvT1eY+rOj/8wJexqFr13e+7LNm4Uf32wnoIqeev+HQuCVy2bJnKbcYYoqOjsXnzZnz22Wcl+to9e/bEy5cvMW3aNMTExKB+/fo4cuRIvmIx5dWpU/ynsKBm9+7Av//yfz6zZ/OhHbRGF9El6el8iJVy0qduWQQrK0UPX926fGhWMUYrkjLoQ5ItfX2eJJalAjkVTd61AGNi+GdT2ePHqrfVFUVMT8/fc2ZvX7YSQIAnEc2a8e8dYc06gCcLfFkMPn8P+PCeQICf1Fq3jj+vqyvfZmzMEwqlgr9ylSrxobPVq/P2QrFn4bHvK29PoEzG5/IaGQGTJvE1CQVvCz3LNWrE/wbyrmk6ZAiwa5diLmOHDrw3VZCRAfkwzObNedGjceP4SYS8a0oqn/y2tOS/pxo18r8PV1fewyr8Tdapoz5ZF4tVF5VXTmhnz+bPI6wvKPD1VV8cB+DfcwWtg1mWfUhhGEoGi07nksC85enFYjEqV64Mf39/ean3kjRixIgKM/xTWVoaIFRKF5JAiQSYOZN/IZ05w780v/xSayESUijG+EGSMI/v1i1+AiPvdAVhzpNy0ufgQCc4CClL8iaB6iqSvnihels5YRDs3Jk/afrQxKUkiMXA0qX5twtzzh48UCw4r6kEVt3C4t27q08Ca9fmyZRgzx7+O/rQEULqhoMmJ/MkUN3vU5mbG7BgAV9U/vFjPpy2ShWeBMbE8GOWpk15752QBDo68p5OocaTjQ3f99Om8RoISiuEAYB8WQlAUU23IH5+fOQUUPRF25XnCn/6qfq55y1bFu25yjtK/opP55LACOXxHaTUHD/Oz5i6uqqe5apWjQ/BWLaM9wS2a0c9JKRsSE3lB0XCPL7bt3kBibxsbVXn8tWoUfh8EEKI9gnDQYWDdnVJoDBscfhwYOXKgp8r73Dvtm01E2NpqVaNJ4HCcrqa6AksiJcXPymWt/hK3mTv7ZKYH0xdsZOkJD46412srPgSVt98w5O6+vV5b6pYzIe7tmvH5+spn+Dr3x+YO1dxWxjuaW3Nh1bmVZwkcMAAfhx14gRQ1IFreQthKyf4VarwuYedOhXtuXQJJXSlQ+eSQFJ6hHH3gOLMX5cu+XtE+vblE+ufPgWOHCl+kRjG+GTpxETg88+px4UUn0wGPHmiOqzz8eP8c0MkEp7kCfP46tT58DkrhJDSJ/QEurvzZE9dsSYhUWnVio8CCAri62Hevcurxarzww9ACRYaLxHCfC9heKMm5gQWZu1aXg+ge3cgNJQXHmndumReS11PYFKSYuiroEoVnjisXq0YtaScKOrrQ74gPcCTw2bNFLcXL+Z/L127Ar/9ppgWoDznT53iJIFiMS9OVZzBZOoKa27bxucWdupUfo+XhCUi8i4VERxcyoGUczqRBI4bN67IbRcJi9iRD7JzJ18wevJkfubv1i0+TO7zz/O3FYv5P4NFi/gQkK5dFV9MOTm8MpelJdC5c/55OElJwPz5iqEYhoZA+/Yl+tZIOZCUpJrw3b6df3gYwHsJhCGdderwM+YleZacEFI6hJ5Ad3debj9vT2ByMr8AvOdq6lT+/8zAgPfCNGgAjB+v+piNG/mwRl2Tt9etpL/j7O35MEsAaNwYGDSodAoaubryk83Xr6tWSAV4j5i3t2rRnKL0FgpatFBcr1ePLzIPvDsJzDsnUNO+/54npb16KbZVq6YoCFReFTQnUF0PIfUavj+dSALzrg147do15OTkwMvLCwBw//596OnpoWHDhtoIT+fk5PAhBgUtH5SUxIfOpKfzpSCE8titWhU8vv/zz4EVK/jZqbAwXqo6O5ufdRUKyuzbxyeLv/21IS6O//NQHlaybBl/nbI2KZ9oT24uL9MvzOO7dYsfCORlaMj/7pSHdlLFWkLKJ+GkT5Uq/GdsLK9gXbUqX9ZB+L9iacnnjwGqyZG6aQvKvTq6pLSTwLxKMgFUHprv7My/+4V5dcqE4xTlJPB9k7JGjRRJ4LuORZT/ZkpiGsGgQbzHUheLu5Q0Sv4+nE4kgaeELAK8p8/MzAybNm2C1dvTPG/evMGAAQPQQvlUDinQ6NF8HZxVq/JXUwN4CeXUVN7Dl5vLyxUD+SdEKzM35z14Bw/y3kB7e2DOHF4wRiLhl7t3eRGZhQt5xa3p0/k/aicnfpZ22jR+e8sWPnaeVEyvXyuSPWEh9rzzIgB+4KOc8Hl6Unl9QioKIQkUeoTi4vj/NiMj1bXYClqnTt0wQ11NAp2c+P9rYehgeRrt0KoVX3KiQQNFYqasXz++bp+wRLSwliHw/klgly58Prm397vbKp9MKE7PY1GJxUWLo7wpyrBPoUhhUduT/HQiCVT266+/4tixY/IEEACsrKwwe/ZsdOjQAT/++KMWoyv7bt/ma94AfGjM1q18WM3q1fwMWqdOfLw5wMsR790LXLnC5001aVL4c3/1FU8CDxzgVbcY48nfokV86IKQFE6axKtkXbrEe2+WLeMH9CNG8ERwwwZeZTTvP+QrV/giz5Mnv3vsfVmRlQVcuKCYs8IYf+81a2o7srIhO5uvs6Y8tFPdGlSmpnyYlvJC7LryN0AI0az0dEXVxLyVPNPT+U/he6Sg9dPUldTX1REoBgaKXjLhdnmhpwdMnMivX7mi2C6V8mUbunVTnRennPS/b1ImkfBRUEX166/8pEN5H6JZmpQTvIJQT+CH07kkMCkpCS9fvsy3/eXLl0gWJgCQAimvkxQbC4wcyf9xpKTwbevW8bOJXl68Z69pU76tefN3L4Rdpw4fsnD/Pr9dqxY/MyuspbVgAf8yF5aTAPiXuDCU5dNPeQnmsDA+j3DsWNXnX7SIP3edOnyh+rLu+XP+foX9IfjrL54E+vjwJDEzkxcr6dRJdw9Ciio2VjXhu3cvf8lrkYgP8RLm8dWuzef90ELshBBAse6bVMrnbOnrK0r6A7zImFA4RLlnSJm5ecnGWNrc3ctnEqhM+RDv5En1wy81MRy0uFq1Kp3XqUiExeLzFoZRRj2BH07nksCuXbtiwIAB+PXXX9G4cWMAwMWLFzFhwgR0K25Zygrm1SteHQ3gPW7z5ytKSnt78x7BJ0/47aFD+cG4qSkwZkzRnl8kAn75Bbh6Ffj4Yz4kVJm+PjBvHk88r13jX5zKVdjEYj4JevRoXm30u+8UQ3ZiYhTJVGTku2PJzeVJZKVKfF5iSf1TTEvjvavh4bxEt1TKE2F9fb7AbGoq77Fq1IjPU3n1is+RvHuXX5T99hvQsyeP/c4d4M0b4OuveUUzXawAlpnJkzxhLt/t2+rLuFtYKIq31K7NTx6oG6pFCCGAonJjpUr8u9HcXHVB8KdP+fcxwIeJq1O5Mv9/ZWEBvHypqLCpq7y8+AlWoPwmgcpLeRQ0/87YmJ/sFonK734g+anrOaTE8N10Lglcs2YNxo8fj969eyP77TeCvr4+Bg0ahIULF2o5urJt715+trRePT7c0sCAVwD98ktg8GD+pXnsGP+ibd78/V7D0bHgORgA/+JevpwP6/j44/zJja8v7xmMjAT+/hvo3ZtvF/65AeqLguT18CGvGAfwRGzevA+fL5aUxPeZoSFfhHjHDh6jUKVOnXr1+GsrL0Pw5g1fvPbFCz5/RSQCDh/mvWR517K6fZu3nTJFdb/KZDzBcnYuO2e0ExN5aW5hTb7wcJ7QKhOLeZKsPJfP2Vk3k1xCiHYoJ4EA7/EpKAksbIheUYac6Yovv+RLNwD5l08oL777jv9vHzSo8HYFJf5EdxTUA0iJnWbpXBJobGyMVatWYeHChXj06BEAwNPTEybUdVCorCw+nw7gC6cCvEx23gVLi7qA6YeQSlXX51EmFvOhnnPn8qGh33zDtxU3CVTucTp5Epg5EwgMLP6QwogI4Phxvrjrw4eKGJXX7rGz4z1YXl68gEl4OE/wWrfmPZt512yysso/nPX773kieOQIP7Dx9uZDdDds4HM4u3ThCZOPD98eFMTPXtvY8LPZ2i5rHhnJTyS8eaO6vVIl1TX5atZUPxeHEELe5dAh/n0sVIIUqv/mnR986RL/ntTXBzw8SjdGbXFwUCwa37SptqMpGVWr8pPZpPwTTtDQvL+SpXNJoMDExAR11ZW2JGqFhPAzpba2Jbeoq6Z07MiXm4iK4md9fHxUJ4THxfHeN3UlvpXbAHxIalwcL1hjbMzn6BWl1+nmTT4XUuhNVCYkgE2b8p5KH58P78kyMOBncr/8UnW7nx/vSbx8mVd0vXFDcZ9IxIeXfvcdL7bj7MwX7o2L40V8mjYtnTmGcXHA8OE8AXR2Blq25Elp3bo8QaZePkKIJkybxn8KJwWFnsC8oyGOH+c/q1SpWEMCN27ka+h9/LG2IyHkwwhzAkui54969Q8eTAAAH1RJREFUExV0IgkcN24cZs2aBRMTk3cuHE+LxavXsiWwfj1PBPP2TJU1hoZ88fn16/m8uq++4sNYXV35kMyEBD7cRVhvUB0hCWzeHKhfny9BsWsXTwRHjCg4MXn2jM+VFCqoisU8mWrXju9DAwPF8hmlsQadqyuv3BoVxWO6fJn//tq140nWrFnA6dO8p1PZ7t18PmebNjyRbNSoZJZPSE7mczxjYnisf/xRMmWyCSEkLyEJzNsTKFQO1fV5fsUllb67ijchuoCGg5aOMp4OcNevX5fP/8u7cLwyEXU5FEgkUr8mYFnVqxcfGhkVxecQAjwJu3mTJ4GRkYUngUIBWVtbXnU0LY0PMd20iRcdGThQtb1MxpfLWL2aH0Do6wOffw74+yvWoRIICw+XJkdHXkRHuZAOwNdc/P13Xk3VwoIPk7K25l+UcXHAP//wi7U1L7iir88TWV9fXo007/BYxvg+vnCBz+u7d49Xd50/X33iPGcOX8jdxob33lICSAgpCXmrCAMFJ4GCypVLLh5CSMkRihQqK6xSKHk/OpEEKi8Wr3ydlF9WVnw+3MiRiqqgLVvynsCbN989LzA2lv8UCrJ068bXj1q8GFi1ivf4DRjAhy8GB/Ohn8LrNG7MC7EUVFq8LBGLeSXXIUP4dSFRGz+eDw09epQPjXr9mi+gLDh2jM+5HDuWzyVJSuJJ3+7dPKlTduIEf2zLlqrbX75ULN67aFHhBYEIIeRDqFkZqsA5gXnvJ4ToloISvoKKOVEP4fvRiSRQWXp6OhhjMH47ISwyMhL79u2Dt7c3OnTooOXoiCZVqsSrnc2erahqevMmv+9dSaByT6CgTx+eCK5Zw9cpPHCAzxmMjub3m5jwpKhzZ92bx5Z3qKdYzHvwGjQAJkzgw0hjY3mP58uXwLZtvIDN0KH5n0sq5V+09erxYjh79/JevrxrRR46xJ+vfn1eyIYQQkqKuiTQxob/LGiKA41MIKRioCUi3o/OJYGdO3dGt27dMHToUCQkJKBx48aQSCR49eoVFi1ahGHDhmk7RKJBJia8MIrA1ZX/fNdagcKcwLzDgQYP5j19Gzfy4gLR0Xye4Dff8CSxoDPKukxfnw//VNajB0+G//mHJ5Dm5vyA6rPP+DBRodBCcjLvSXz8mFcv7dSJb2eML48B5C9mQwghmiZ8p1ta8h6+hARe+AVQrdasrLQWCyeEEF2kc0ngtWvXsHjxYgDA7t27YW9vj+vXr2PPnj2YNm0aJYHlnJsb//n0KU9E1PXYpaXx8uAAr06ZV926fPji48e8p6tJk7Kz1l5psbICJk/mVUUL6/U0MwP69weWLeNJY/v2vOLojRv8d2BkxIvUEELIhzp2jA8Dq149/32vXvGfjRvzucjZ2Yrqx8rJnrk5H94OUE8gIbqKlogoHTqXBKalpcHMzAwAcOzYMXTr1g1isRhNmjRB5Lu6h4jOExYWT07mSxKom/MhDBsyNi58GYkqVRRnkiuqogx77dmTDx+NjubVP4cO5T2IAE8KC9vHhBBSFBcuAP/7H7+uvCSQQOgJtLHh31vKy998+ilw9ixfruevvygJJETXCUM58w7zpCGemqVzSWDVqlWxf/9+dO3aFUePHsXYsWMBAHFxcTCvaN05FZBUyguZREXxnih1SaBwsKA8H5C8P6mUJ36zZvEk8PZtxdzMzp21GxshRHelpgIBAUBiIlCjRuFt1c3zFkgkwIIF/PqOHYrtlAQSopuoJ7B0iN/dpGyZNm0axo8fD3d3d/j4+MD37WSnY8eO4aOPPtJydKQ0CPMCCyoOQ0mg5n35JTBuHD/YuniRF9hxddWtZUcIIWWLRMIrD9+8qVqVOO9yEBkZvLgVwEeDFIYxxXWpVDNxEkJIeaRzPYHdu3dH8+bNER0djXr16sm3t23bFl3zLqJGyiVXVz50qKDRv5QEap5IBPTuDTRrBsyYwQ/a+vbVvSqqhJCyw8CAF/9KTQXCwhTb37xRnc+9dy9f5sbRkVcpLoxyEkgI0U3UA1g6dC4JBAB7e3vY29urbGvcuLGWoiGlTSgOo3zQcPEicPo0MGpU4cOGyIdxc+NrKr56RfuXEPLhLC15EpiertiWNwncv5//7N+/4OUgCCGEFI9Ofp3+999/+O233/Do0SPs3r0bTk5O2Lx5Mzw8PND8XacJic5r1gz49Vc+POj+fb7W3+TJvBiAm1v+heKJZonFtG8JIZphaQm8eKG67c0bxfXHj/nFwIAXoirK8xFCdJu6nkDqHdQ8nZsTuGfPHvj5+cHIyAjXr19HZmYmACAxMRFz587VcnSkNDg7Kw4G1q8HNmxQVIM7fZp6AgkhRFeoS9qUk8Djx/lPX1++ZM27/O9/gIcHX0aCEKKbgoPzX9QtCE8+jM71BM6ePRtr1qxBv379sH37dvn2Zs2aYfbs2VqMjJSmAQOAo0eBEydUhwdduaJYsiDvQvGEEELKFnVJYEKC4npEBP/ZqFHRnq9KFWDXrg+NihCiTVQdtHToXBIYHh6Oli1b5ttuYWGBBOX/HKRcq1oVaNkSOHOGLxrcqBEvHPD4ceELxRNCCCk73tUTSCM7CKl4aJ3A0qFzSaC9vT0ePnwId3d3le1nz55FlYq+8ncFM3AgTwIBXhDm9GmeBAK8d5DmhhBCSNmm/D0tEvHqnm/eAM+e8fneQhJIIzsIqTioJ7B06FwSOGTIEIwePRrr16+HSCRCVFQUQkJCMH78eEydOlXb4ZFSVLs2/4IwMAC8vfkBxB9/8PsqV+YFTAghhJRdykmglxdw7x5w6BCvCNqzJyWBhBBSUnQuCZw0aRJkMhnatm2LtLQ0tGzZElKpFOPHj8fIkSO1HR4pZZ9/rrheowYfAhobSwcMhBCiC5STwOrVeRIoLBa/Y4fiPhubUg2LEELKPZ1LAkUiEaZMmYIJEybg4cOHSElJgbe3N0xNTZGeng4jIyNth0i0RCTiQwh27ODDiAghhJRtyklgw4bAP//kb2NhAUgkpRYSIUTLaBho6dDZAXMSiQTe3t5o3LgxDAwMsGjRInh4eGg7LKJlAwYAn30G9O2r7UgIIYS8i4mJ4rqvL2Bqmr8NjewghBDN05mewMzMTEyfPh1BQUGQSCSYOHEiunTpgg0bNmDKlCnQ09PD2LFjtR0m0TIbG2DWLG1HQQghpCgcHBTXraz47QcPVNvQUFBCKpaC1gSk6qCapTNJ4LRp0/Dbb7+hXbt2OH/+PL7++msMGDAAFy5cwKJFi/D1119DT09P22ESQgghpIjMzPi6flIpH9Lv4ZE/CUxP105shBDtoGSvdOhMErhr1y78+eef+PLLL3H79m3UrVsXOTk5uHHjBkQikbbDI4QQQsh7UJ7JMXYsEBUFdO0KJCYCy5YBX3yhvdgIIaS8EjHGmLaDKAqJRIKIiAg4OTkBAIyMjHDp0iXUqVNHy5EVXVJSEiwsLJCYmAhzc3Nth0MIIYSUaa9f82GidK6XEN30Pse+QmEYKhBTsnSmJzA3NxcSpfJg+vr6MFU3g5wQQggh5YK1tbYjIISUNmE4aN65gTRMVLN0JglkjKF///6QSqUAgIyMDAwdOhQmyqXFAOzdu1cb4RFCCCGEEEI+ECV7pUNnkkB/f3+V231pDQBCCCGEEELKFXXDQGloqObpzJzA8oDmBBJCCCGEkIqCjn3LLp3pCSSEEEIIIYSUb9QTWDooCSSEEEIIIYSUCZTwlQ6xtgPQFe7u7hCJRCqX+fPnazssQgghhBBCCCkW6gkshpkzZ2LIkCHy22ZmZlqMhhBCCCGEEEKKj5LAYjAzM4O9vb22wyCEEEIIIYSQ90bVQYvI3d0dGRkZyM7OhqurK3r37o2xY8dCX7/gPDozMxOZmZny24mJiXB1dcWzZ8+oQhIhhBBCCCnXkpKS4OLigoSEBFhYWGg7HKKEegKLaNSoUWjQoAGsra1x/vx5TJ48GdHR0Vi0aFGBj5k3bx5mzJiRb7uLi0tJhkoIIYQQQkiZkZycTElgGVOhewInTZqEn3/+udA2d+/eRY0aNfJtX79+Pb7//nukpKRAKpWqfWzenkCZTIbXr1+jUqVKEIlEHxZ8CRHO2FBvpebQPtU82qeaR/u0ZNB+1Tzap5pH+1TzaJ9yjDEkJyfD0dERYjHVoyxLKnRP4I8//oj+/fsX2qZKlSpqt/v4+CAnJwdPnjyBl5eX2jZSqTRfgmhpafk+oZY6c3PzCv2lVRJon2oe7VPNo31aMmi/ah7tU82jfap5tE9BPYBlVIVOAitXrozKlSu/12NDQ0MhFotha2ur4agIIYQQQgghpORU6CSwqEJCQnDx4kW0bt0aZmZmCAkJwdixY9G3b19YWVlpOzxCCCGEEEIIKTJKAotAKpVi+/btmD59OjIzM+Hh4YGxY8di3Lhx2g5N46RSKQIDAwuc50iKj/ap5tE+1TzapyWD9qvm0T7VPNqnmkf7lJR1FbowDCGEEEIIIYRUNFSmhxBCCCGEEEIqEEoCCSGEEEIIIaQCoSSQEEIIIYQQQioQSgIJIYQQQgghpAKhJLACOXPmDL744gs4OjpCJBJh//7973xMcHAwGjRoAKlUiqpVq2Ljxo0lHqcuKe4+3bt3L9q3b4/KlSvD3Nwcvr6+OHr0aOkEq0Pe529VcO7cOejr66N+/folFp8uep99mpmZiSlTpsDNzQ1SqRTu7u5Yv359yQerI95nn27ZsgX16tWDsbExHBwcMHDgQMTHx5d8sDpi3rx5+Pjjj2FmZgZbW1t06dIF4eHh73zcrl27UKNGDRgaGqJOnTo4dOhQKUSrG95nn65duxYtWrSAlZUVrKys0K5dO1y6dKmUIi773vfvVLB9+3aIRCJ06dKl5IIk5B0oCaxAUlNTUa9ePaxcubJI7SMiItCpUye0bt0aoaGhGDNmDAYPHkxJi5Li7tMzZ86gffv2OHToEK5evYrWrVvjiy++wPXr10s4Ut1S3P0qSEhIQL9+/dC2bdsSikx3vc8+7dGjB06cOIE//vgD4eHh2LZtG7y8vEowSt1S3H167tw59OvXD4MGDcKdO3ewa9cuXLp0CUOGDCnhSHXH6dOnMXz4cFy4cAFBQUHIzs5Ghw4dkJqaWuBjzp8/j169emHQoEG4fv06unTpgi5duuD27dulGHnZ9T77NDg4GL169cKpU6cQEhICFxcXdOjQAS9evCjFyMuu99mngidPnmD8+PFo0aJFKURKSCEYqZAAsH379hXaZuLEiaxWrVoq23r27Mn8/PxKMDLdVZR9qo63tzebMWOG5gMqJ4qzX3v27MkCAgJYYGAgq1evXonGpcuKsk8PHz7MLCwsWHx8fOkEpeOKsk8XLlzIqlSporJt2bJlzMnJqQQj021xcXEMADt9+nSBbXr06ME6deqkss3Hx4d9//33JR2eTirKPs0rJyeHmZmZsU2bNpVgZLqrqPs0JyeHNW3alK1bt475+/uzzp07l06AhKhBPYGkQCEhIWjXrp3KNj8/P4SEhGgpovJHJpMhOTkZ1tbW2g5F523YsAGPHz9GYGCgtkMpF/755x80atQICxYsgJOTE6pXr47x48cjPT1d26HpLF9fXzx79gyHDh0CYwyxsbHYvXs3OnbsqO3QyqzExEQAKPQ7kv5XFU9R9mleaWlpyM7Opv9VBSjqPp05cyZsbW0xaNCg0giLkELpazsAUnbFxMTAzs5OZZudnR2SkpKQnp4OIyMjLUVWfvzyyy9ISUlBjx49tB2KTnvw4AEmTZqE//77D/r69LWmCY8fP8bZs2dhaGiIffv24dWrV/jhhx8QHx+PDRs2aDs8ndSsWTNs2bIFPXv2REZGBnJycvDFF18Ue9hzRSGTyTBmzBg0a9YMtWvXLrBdQf+rYmJiSjpEnVPUfZrXTz/9BEdHx3zJNin6Pj179iz++OMPhIaGll5whBSCegIJ0ZKtW7dixowZ2LlzJ2xtbbUdjs7Kzc1F7969MWPGDFSvXl3b4ZQbMpkMIpEIW7ZsQePGjdGxY0csWrQImzZtot7A9xQWFobRo0dj2rRpuHr1Ko4cOYInT55g6NCh2g6tTBo+fDhu376N7du3azuUcuN99un8+fOxfft27Nu3D4aGhiUYnW4qyj5NTk7Gt99+i7Vr18LGxqYUoyOkYHTKnBTI3t4esbGxKttiY2Nhbm5OvYAfaPv27Rg8eDB27dpFZ1Y/UHJyMq5cuYLr169jxIgRAHgCwxiDvr4+jh07hjZt2mg5St3j4OAAJycnWFhYyLfVrFkTjDE8f/4c1apV02J0umnevHlo1qwZJkyYAACoW7cuTExM0KJFC8yePRsODg5ajrDsGDFiBA4cOIAzZ87A2dm50LYF/a+yt7cvyRB1TnH2qeCXX37B/Pnzcfz4cdStW7eEI9Q9Rd2njx49wpMnT/DFF1/It8lkMgCAvr4+wsPD4enpWeLxEqKMkkBSIF9f33xltoOCguDr66uliMqHbdu2YeDAgdi+fTs6deqk7XB0nrm5OW7duqWybdWqVTh58iR2794NDw8PLUWm25o1a4Zdu3YhJSUFpqamAID79+9DLBYX+QCSqEpLS8s3XFlPTw8AwBjTRkhlDmMMI0eOxL59+xAcHFykz6+vry9OnDiBMWPGyLfR/yqF99mnALBgwQLMmTMHR48eRaNGjUo4St1S3H1ao0aNfP+nAgICkJycjKVLl8LFxaUkwyVELUoCK5CUlBQ8fPhQfjsiIgKhoaGwtraGq6srJk+ejBcvXuDPP/8EAAwdOhQrVqzAxIkTMXDgQJw8eRI7d+7EwYMHtfUWypzi7tOtW7fC398fS5cuhY+Pj3zOipGRkUqPS0VXnP0qFovzzcOwtbWFoaFhsea8lHfF/Vvt3bs3Zs2ahQEDBmDGjBl49eoVJkyYgIEDB9JIgLeKu0+/+OILDBkyBKtXr4afnx+io6MxZswYNG7cGI6Ojtp6G2XK8OHDsXXrVvz9998wMzOTf0daWFjI/+769esHJycnzJs3DwAwevRotGrVCr/++is6deqE7du348qVK/j999+19j7KkvfZpz///DOmTZuGrVu3wt3dXf4YU1NT+Umhiqy4+1Td/yNLS0sAoP9TRHu0WJmUlLJTp04xAPku/v7+jDHG/P39WatWrfI9pn79+kwikbAqVaqwDRs2lHrcZVlx92mrVq0KbU+49/lbVUZLROT3Pvv07t27rF27dszIyIg5OzuzcePGsbS0tNIPvox6n326bNky5u3tzYyMjJiDgwPr06cPe/78eekHX0ap258AVP73tGrVKt935s6dO1n16tWZRCJhtWrVYgcPHizdwMuw99mnbm5uah8TGBhY6vGXRe/7d6qMlogg2iZijMagEEIIIYQQQkhFQdVBCSGEEEIIIaQCoSSQEEIIIYQQQioQSgIJIYQQQgghpAKhJJAQQgghhBBCKhBKAgkhhBBCCCGkAqEkkBBCCCGEEEIqEEoCCSGEEEIIIaQCoSSQEEJImTN9+nTUr19f22HIiUQi7N+/v9iPCw8Ph729PZKTkzUflJJXr17B1tYWz58/L9HXIYQQUj5QEkgIIRXUmjVrYGZmhpycHPm2lJQUGBgY4JNPPlFpGxwcDJFIhEePHpVylKVL08nn5MmTMXLkSJiZmWnsOdWxsbFBv379EBgYWKKvQwghpHygJJAQQiqo1q1bIyUlBVeuXJFv+++//2Bvb4+LFy8iIyNDvv3UqVNwdXWFp6enNkLVSU+fPsWBAwfQv3//Unm9AQMGYMuWLXj9+nWpvB4hhBDdRUkgIYRUUF5eXnBwcEBwcLB8W3BwMDp37gwPDw9cuHBBZXvr1q0BAJs3b0ajRo1gZmYGe3t79O7dG3FxcQAAmUwGZ2dnrF69WuW1rl+/DrFYjMjISABAQkICBg8ejMqVK8Pc3Bxt2rTBjRs3Co133bp1qFmzJgwNDVGjRg2sWrVKft+TJ08gEomwd+9etG7dGsbGxqhXrx5CQkJUnmPt2rVwcXGBsbExunbtikWLFsHS0hIAsHHjRsyYMQM3btyASCSCSCTCxo0b5Y999eoVunbtCmNjY1SrVg3//PNPofHu3LkT9erVg5OTk3ybup7GJUuWwN3dXX67f//+6NKlC+bOnQs7OztYWlpi5syZyMnJwYQJE2BtbQ1nZ2ds2LBB5Xlq1aoFR0dH7Nu3r9C4CCGEEEoCCSGkAmvdujVOnTolv33q1Cl88sknaNWqlXx7eno6Ll68KE8Cs7OzMWvWLNy4cQP79+/HkydP5L1dYrEYvXr1wtatW1VeZ8uWLWjWrBnc3NwAAF9//TXi4uJw+PBhXL16FQ0aNEDbtm0L7MXasmULpk2bhjlz5uDu3buYO3cupk6dik2bNqm0mzJlCsaPH4/Q0FBUr14dvXr1kg93PXfuHIYOHYrRo0cjNDQU7du3x5w5c+SP7dmzJ3788UfUqlUL0dHRiI6ORs+ePeX3z5gxAz169MDNmzfRsWNH9OnTp9Bet//++w+NGjUqdP8X5OTJk4iKisKZM2ewaNEiBAYG4vPPP4eVlRUuXryIoUOH4vvvv883B7Bx48b477//3us1CSGEVCCMEEJIhbV27VpmYmLCsrOzWVJSEtPX12dxcXFs69atrGXLlowxxk6cOMGA/7d3dyFNtmEcwP97tZEs7WCNkeUsE6XMVh1UFjoX1Ywo+qAvdxBhUR0YCAVBRqBBHsiCInIw0IjmQQXlgasIXBgGjdGkr2W0ZUcW1oJNLWlc74H4vD5qJaZv0PP/wWD3B9f1PJ7Ixb37viHd3d3jxggEAgJA4vG4iIg8ffpUdDqdMj+ZTMq8efPkypUrIiLS3t4uGRkZ8vXrV1WcRYsWidvtFhGRs2fPitVqVY15vV7V/NraWikqKhIRkWg0KgDE4/Eo4y9evBAA8urVKxER2bt3r2zZskUVw+l0yuzZs5X26LzDAEh1dbXSTiQSAkB8Pt+4fxMREavVKjU1Naq+8eJfuHBBsrOzlfaBAwckOztbksmk0pefny/FxcVK+/v372IwGKS5uVkVq6qqSkpLS3/4TERERCIiXAkkItKw0tJS9PX1IRAIoL29HXl5eTCZTLDZbMq+QL/fj5ycHFgsFgBAMBjE1q1bYbFYkJ6eDpvNBmBoDxwALF++HIsXL1ZWAx8+fIiPHz9i9+7dAIDOzk4kEgkYjUbMmjVL+USj0XEPnunr68Pbt29RUVGhmn/u3Lkx85ctW6Z8nzt3LgAoP1V9/fo1Vq1apZo/uv0zI2MbDAZkZGQoscczMDCAmTNnTjj+SAUFBfjnn//+RZvNZhQWFirtlJQUGI3GMfnT0tLQ398/qZxERKQdqX/6AYiI6M/Jzc3F/Pnz0dbWhlgsphR0mZmZyMrKQkdHB9ra2rB+/XoAQwWZw+GAw+HA9evXYTKZ8P79ezgcDgwODipxnU4nvF4vTp06Ba/Xi7KyMhiNRgBDJ5CO3os4bHh/3kiJRALA0H6+1atXq8ZSUlJU7RkzZijfdTodgKF9ilNhZOzh+D+LPWfOHMRisV/GTSaTE8o1kfyfP3+GyWT6ZU4iItI2FoFERBpnt9vh9/sRi8Vw8uRJpb+kpAQ+nw9PnjzBsWPHAADhcBifPn1CXV0dsrKyAEB1uuiw8vJyVFdXIxgM4ubNm2hoaFDGVq5ciZ6eHqSmpqoORPkRs9mMzMxMRCIROJ3OSb9nfn4+AoGAqm90W6/Xj1uUTcaKFSvw8uXLMf0fPnxQtSORyJTkA4Dnz5+Pud6DiIhoNP4clIhI4+x2Ox49eoRQKKSsBAKAzWaD2+3G4OCgciiMxWKBXq/HpUuXEIlE0NLSgtra2jExFyxYgLVr16KiogLJZBLbtm1TxjZs2ICioiJs374d9+/fx7t379DR0YHTp0+PW1ACQ4eynD9/HhcvXkRXVxeePXuGxsZGuFyuCb9nZWUlWltb4XK58ObNG7jdbvh8PmXFcPi5o9EoQqEQent78e3btwnHH83hcODx48djisqenh7U1NQgEong1q1buHbtGmKxGMLh8KRzAUB/fz+CwSA2bdr0W3GIiOjvxyKQiEjj7HY7BgYGkJubC7PZrPTbbDbE43HlKgkAMJlMaGpqwo0bN7BkyRLU1dWhvr5+3LhOpxOdnZ3YsWMH0tLSlH6dTofW1laUlJTg4MGDyMvLw759+9Dd3a3KP9KhQ4fg8XjQ2NiIwsJC2Gw2NDU1YeHChRN+z3Xr1qGhoQEulwtWqxV3795FVVWVat/erl27UFZWBrvdDpPJhObm5gnHH23z5s1ITU3FgwcPVP1Lly5FV1cXCgoKcObMGXg8Huj1epw4cWLSuQDgzp07sFgsKC4u/q04RET099OJiPzphyAiIvoTDh8+jHA4PG3XKly+fBktLS24d+8egKF7Am/fvo1QKDTludasWYPjx4+jvLx8ymMTEdHfhXsCiYhIM+rr67Fx40YYDAb4fD5cvXpVden8VDty5Ai+fPmCeDyO9PT0acvT29uLnTt3Yv/+/dOWg4iI/h5cCSQiIs3Ys2cP/H4/4vE4cnJyUFlZiaNHj/5v+adzJZCIiGiiWAQSERERERFpCA+GISIiIiIi0hAWgURERERERBrCIpCIiIiIiEhDWAQSERERERFpCItAIiIiIiIiDWERSEREREREpCEsAomIiIiIiDSERSAREREREZGGsAgkIiIiIiLSkH8BEPYPwvZbLkgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, axr, axr2 = plotForMoSA.plot_fit(figsize=(10, 5), uncert='no', trans='no', logy='no', norm='yes')\n", + "# You can use norm='yes' to check how ForMoSA rescaled the data\n", + "\n", + "# You can modify the different axes and includ further plotting features\n", + "axr.set_ylim(-5,5)\n", + "\n", + "#plt.savefig('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PLOT Chains of posteriors" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ForMoSA - Posteriors chains for each parameter\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plotForMoSA.plot_chains(figsize=(10,6))\n", + "#axs[i, j] #i=cols, j=0,1\n", + "#plt.savefig('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Access information\n", + "\n", + "You can access different parametes since we are working with a class\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "posteriors_chains = plotForMoSA.posterior_to_plot\n", + "posteriors_names = plotForMoSA.posteriors_names" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "exo_formosa_multi3", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_build/doctrees/nbsphinx/tutorials/format_obs.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/format_obs.ipynb new file mode 100644 index 0000000..676dbf8 --- /dev/null +++ b/docs/_build/doctrees/nbsphinx/tutorials/format_obs.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Observation format\n", + "\n", + "This section will help you convert your observationnal data into the ForMoSA format\n", + "\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "from astropy.table import Table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data ``.fits``\n", + "\n", + "Your observed data (spectroscopy and/or photometry) should be formated in a ``.fits`` file with the following extensions:\n", + "\n", + "- **'WAV'** : (array) wavelength grid\n", + "- **'FLX'** : (array) flux\n", + "- **'ERR'** or **'COV'** : (array or 2D-array) errors or covariance matrix. The covariance matrix should have ``diag(COV)=ERR²``\n", + "- **'RES'** : (array) resolution\n", + "- **'INS'** : (array) instrument name\n", + "\n", + "exemple :" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ColDefs(\n", + " name = 'WAV'; format = 'D'\n", + " name = 'FLX'; format = 'D'\n", + " name = 'ERR'; format = 'D'\n", + " name = 'RES'; format = 'D'\n", + " name = 'INS'; format = '3A'\n", + ")\n" + ] + } + ], + "source": [ + "# CHECKUP FORMAT\n", + "hdul = fits.open('~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits')\n", + "print(hdul[1].columns)\n", + "wav = hdul[1].data['WAV']\n", + "flx = hdul[1].data['FLX']\n", + "err = hdul[1].data['ERR']\n", + "res = hdul[1].data['RES']\n", + "ins = hdul[1].data['INS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "optional extensions can also be used when dealing with stellar-contaminated high-resolution spectroscopy:\n", + "- **'TRANSM'** : (array) transmission (atmospheric + instrumental)\n", + "- **'STAR_FLX'** or **'STAR_FLXi'** : (array or i arrays) star flux or shifted star flux (to account for LSF changes)\n", + "- **'SYSTEM'** or **'SYSTEMj'** : (array or j arrays) systematic model(s) (usually computed from PCA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Format your data\n", + "\n", + "To format your data, you can use the simple Python routine below :" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correction successful\n" + ] + } + ], + "source": [ + "# FITS converter :\n", + "table = Table([wav, flx, res, res, ins], names=('WAV', 'FLX', 'ERR', 'RES', 'INS'))\n", + "hdul = fits.HDUList()\n", + "hdu = fits.BinTableHDU(table)\n", + "hdul.append(hdu)\n", + "hdul.writeto('~/YOUR/PATH/formosa_desk/inversion_targetname/inputs/data.fits')\n", + "print('correction successful')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have multiple observations, we recommand that you create separated ``.fits`` files (e.g ``data_1.fits``, ``data_2.fits``, ...)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pRT3_env", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_build/doctrees/nbsphinx/tutorials_demobetapic_11_1.png b/docs/_build/doctrees/nbsphinx/tutorials_demobetapic_11_1.png new file mode 100644 index 0000000..a2dd2aa Binary files /dev/null and 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When it is not found, a full rebuild will be done. -config: 3d69bd81622fe4392ef94600d12c6fdb +config: d4763c4acadf04200e3866f19bc6a8cf tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_images/ForMoSA.png b/docs/_build/html/_images/ForMoSA.png index d48bd9c..ab8d329 100644 Binary files a/docs/_build/html/_images/ForMoSA.png and b/docs/_build/html/_images/ForMoSA.png differ diff --git a/docs/_build/html/_images/tutorials_demobetapic_11_1.png b/docs/_build/html/_images/tutorials_demobetapic_11_1.png new file mode 100644 index 0000000..a2dd2aa Binary files /dev/null and b/docs/_build/html/_images/tutorials_demobetapic_11_1.png differ diff --git a/docs/_build/html/_images/tutorials_demobetapic_13_2.png b/docs/_build/html/_images/tutorials_demobetapic_13_2.png new file mode 100644 index 0000000..f88ca60 Binary files /dev/null and b/docs/_build/html/_images/tutorials_demobetapic_13_2.png differ diff --git a/docs/_build/html/_images/tutorials_demobetapic_15_1.png b/docs/_build/html/_images/tutorials_demobetapic_15_1.png new file mode 100644 index 0000000..d2bff5e Binary files /dev/null and b/docs/_build/html/_images/tutorials_demobetapic_15_1.png differ diff --git a/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html b/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html index 56a8fd6..61cd1df 100644 --- a/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html +++ b/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html @@ -167,11 +167,12 @@

Source code for ForMoSA.adapt.adapt_obs_mod

                     if len(transm_obs_extract) != 0:
                         transm_obs_extract = np.concatenate((transm_obs_extract, obs_opt[c][1]))
                     if len(star_flx_obs_extract) != 0:
-                        star_flx_obs_extract = np.concatenate((star_flx_obs_extract, obs_opt[c][2]))
+                        star_flx_obs_extract = np.concatenate((star_flx_obs_extract, obs_opt[c][2]), axis=0)
                     if len(system_obs_extract) != 0:
                         system_obs_extract = np.concatenate((system_obs_extract, obs_opt[c][3]), axis=0)
                     # Save the interpolated resolution of the grid
                     res_mod_obs_merge.append(res_mod_cut)
+                    
     
     
                 # Compute the inverse of the merged covariance matrix (note: inv(C1, C2) = (in(C1), in(C2)) if C1 and C2 are block matrix on the diagonal)
diff --git a/docs/_build/html/_modules/ForMoSA/adapt/extraction_functions.html b/docs/_build/html/_modules/ForMoSA/adapt/extraction_functions.html
index b4c4dfe..b7a6f73 100644
--- a/docs/_build/html/_modules/ForMoSA/adapt/extraction_functions.html
+++ b/docs/_build/html/_modules/ForMoSA/adapt/extraction_functions.html
@@ -18,6 +18,7 @@
         
         
         
+        
     
     
      
@@ -42,10 +43,10 @@