diff --git a/fitstack/chaintools.py b/fitstack/chaintools.py new file mode 100644 index 0000000..cfee233 --- /dev/null +++ b/fitstack/chaintools.py @@ -0,0 +1,714 @@ +# This contians some functions to work with MCMC chains of CHIME fitstack output + +from typing import List, Dict, Tuple, Any, Union, Optional +import numpy as np +import scipy.linalg as la +from scipy.optimize import minimize_scalar +from getdist import MCSamples +from cora.signal import lssmodels +from cora.util import cosmology +from fitstack.containers import MCMCFit1D + + +def create_unified_config(): + """Create unified configuration dictionary. + + We are putting the redshift and redshift range for different tracer (cross-corr) and + sub-bands (auto-ps) here as a dict. So that this can be loaded in later. + If there is a new set of tracer for a new redshift range or a new subband for auto-ps + analysis, just add that in the config accordingly. + """ + cosmo = cosmology.Cosmology() + + config = { + # Cross-correlation tracers and auto-corr bands + "QSO": { + "z_eff": 1.2034, + "z_range": [1.0038, 1.365], + "type": "cross", + "bias_key": "eboss_qso", + }, + "LRG": { + "z_eff": 0.8372, + "z_range": [0.8065, 0.8728], + "type": "cross", + "bias_key": "eboss_lrg", + }, + "ELG": { + "z_eff": 0.9576, + "z_range": [0.8253, 1.0264], + "type": "cross", + "bias_key": "eboss_elg", + }, + "QSOb0": { + "z_eff": 0.9714, + "z_range": [0.8501, 1.0073], + "type": "cross", + "bias_key": "eboss_qso", + }, + "QSOb1": { + "z_eff": 1.117, + "z_range": [1.0651, 1.1631], + "type": "cross", + "bias_key": "eboss_qso", + }, + "QSOb2": { + "z_eff": 1.3028, + "z_range": [1.2262, 1.3931], + "type": "cross", + "bias_key": "eboss_qso", + }, + "QSOb00": { + "z_eff": 0.8448, + "z_range": [0.8179, 0.8666], + "type": "cross", + "bias_key": "eboss_qso", + }, + "QSOb01": { + "z_eff": 0.9857, + "z_range": [0.9624, 1.0117], + "type": "cross", + "bias_key": "eboss_qso", + }, + "QSObandb": { + "z_eff": 1.159286, + "z_range": [1.0006, 1.335], + "type": "cross", + "bias_key": "eboss_qso", + }, + "bandb": { + "z_eff": 1.159286, + "z_range": [1.0006, 1.335], + "type": "auto", + "bias_key": "None", + }, + } + + # Calculate properties for all entries + for name, cfg in config.items(): + z = cfg["z_eff"] + cfg.update( + { + "b_HI": 1 + lssmodels.bias["HI"](z), + "omega_HI": lssmodels.omega_HI.evaluate(z), + "f": cosmo.growth_rate(z), + } + ) + + if cfg["type"] == "cross": + cfg["b_g"] = 1 + lssmodels.bias[cfg["bias_key"]](z) + + return config + + +def scale_params(chain: MCSamples, identifier: str, config: Dict): + """ + Scale the parameters actually fit to their values at the effective redshift. + + Parameters + ---------- + chain : MCSamples + MCMC samples chain to which derived parameters will be added. + identifier : str + Key used to extract properties from the configuration dictionary. + config : dict + Dictionary containing tracer or band properties. + + Returns + ------- + None + Modifies the input `chain` in place by adding scaled parameters. + """ + props = config[identifier] + + chain.addDerived( + chain["omega"] * props["omega_HI"] * 1e3, + "omega_scaled", + label=r"10^3 \times \Omega_\mathrm{HI}", + ) + chain.addDerived( + chain["b_HI"] * props["b_HI"], "b_HI_scaled", label=r"b_\mathrm{HI}" + ) + + if props["type"] == "cross": + chain.addDerived( + chain["b_g"] * props["b_g"], "b_g_scaled", label=r"b_\mathrm{g}" + ) + + +def _calc_omega_bHI_cov( + c: MCSamples, config: Dict[str, Any], identifier: str, fscale: bool = False +): + """ + Worker routine to calculate the omega_b_HI × omega covariance. + + Parameters + ---------- + c : MCSamples + MCMC samples chain containing derived parameters. + config : dict + Dictionary containing tracer or band properties. + identifier : str + Key to identify the tracer/band in config (e.g., 'QSO', 'bandb'). + fscale : bool, optional + If True, scale by the growth rate `f`. Default is False. + + Returns + ------- + ndarray + 2×2 covariance matrix of [omega_b_HI, omega]. + """ + omega = c["omega_scaled"] + omega_b_HI = omega * c["b_HI_scaled"] + + if fscale: + f = config[identifier]["f"] + omega_b_HI /= f + + return np.cov([omega_b_HI, omega]) + + +def calc_fmu2( + c: MCSamples, config: Dict[str, Any], identifier: str, fscale: bool = False +) -> float: + """ + Calculate the effective f μ² for the chain. + + Parameters + ---------- + c : MCSamples + MCMC samples chain containing derived parameters. + config : dict + Dictionary containing tracer or band properties. + identifier : str + Key to identify the tracer/band in config. + fscale : bool, optional + If True, scale by the growth rate `f`. Default is False. + + Returns + ------- + float + Effective fμ² value. + """ + evals, evecs = la.eigh(_calc_omega_bHI_cov(c, config, identifier, fscale)) + return evecs[1, 0] / evecs[0, 0] + + +def calc_fmu2_single( + chain_list: List[MCSamples], + config: Dict[str, Any], + identifier: List[str], + fscale: bool = False, +) -> float: + """ + Calculate a single best-fit f μ² value across multiple chains. + + Parameters + ---------- + chain_list : list of MCSamples + List of MCMC sample chains. + config : dict + Dictionary containing tracer or band properties. + identifier : list of str + List of identifiers corresponding to each chain. + fscale : bool, optional + If True, scale by the growth rate `f`. Default is False. + + Returns + ------- + float + Best-fit effective fμ² value. + """ + iC = np.sum( + [ + la.inv(_calc_omega_bHI_cov(c, config, identifier, fscale)) + for c in chain_list + ], + axis=0, + ) + + evals, evecs = la.eigh(iC) + return evecs[1, 1] / evecs[0, 1] + + +def add_derived( + c: MCSamples, fmu2: float, cross_corr: bool = True, sublabel: str = None +): + """ + Add a set of useful derived parameters to the MCMC chain. + + Parameters + ---------- + c : MCSamples + MCMC samples chain where derived parameters are added. + fmu2 : float + Effective fμ² value to be included in derived calculations. + cross_corr : bool + If True derived parameters will be based on HI-galaxy cross-corr. Default is True. + sublabel : str, optional + Subscript label for derived parameter names. Default is None. + + Returns + ------- + None + Modifies the input `c` in place by adding derived parameters. + """ + params = c.getParamNames().list() + + subscript = "" if sublabel is None else f"_{{{sublabel}}}" + c.addDerived( + c["omega_scaled"] * c["b_HI_scaled"], + "omega_b_HI", + label=r"10^3 \times \Omega_{\rm HI} b_{\rm HI}", + ) + + if cross_corr: + if "FoGh" in params: + + c.addDerived( + (c["FoGh"] * c["FoGg"]) ** 0.5, "FoG+", label=r"\alpha_\mathrm{FoG,+}" + ) + c.addDerived( + np.log(c["FoGh"] / c["FoGg"]), "FoG-", label=r"\alpha_\mathrm{FoG,-}" + ) + + # A_HI(stack) = (Omega_HI * b_HI + Omega_HI * ) + c.addDerived( + c["omega_scaled"] * c["b_HI_scaled"] + fmu2 * c["omega_scaled"], + "omegabfm2", + label=(r"\mathcal{A}_\mathrm{HI}" f"{subscript}"), + ) + + else: + # A_HI(auto) = (Omega_HI * b_HI + Omega_HI * )**2 + c.addDerived( + (c["omega_scaled"] * c["b_HI_scaled"] + fmu2 * c["omega_scaled"]) ** 2, + "omegabfm2", + label=(r"\mathcal{A}_\mathrm{HI}" f"{subscript}"), + ) + + +def apply_limits( + c: MCSamples, + copy: bool = True, + **kwargs, +) -> MCSamples: + """ + Apply cutoff limits to a set of parameters in the MCMC chain. + + Parameters + ---------- + c : MCSamples + MCMC samples chain. + copy : bool, optional + If True, operate on a copy of the chain. Default is True. + **kwargs : dict + Parameter name → (lower, upper) cutoff values. + + Returns + ------- + MCSamples + Filtered MCMC chain with updated parameter ranges. + """ + if copy: + c = c.copy() + + params = c.getParamNames().list() + + for param in kwargs.keys(): + if param not in params: + raise ValueError( + f"Got keyword argument {param} that does not map to parameter." + ) + + lower, upper = kwargs[param] + cond = (c[param] > lower) & (c[param] < upper) + c.filter(cond) + c.setRanges({param: (lower, upper)}) + + return c + + +class MultiIndex(dict): + """A dictionary indexed by multiple keys that can be queried and manipulated.""" + + keynames: str + _keyname_ind: Dict[str, int] + + def __init__(self, *keynames: str): + self.keynames = keynames + + self._keyname_ind = {k: ii for ii, k in enumerate(keynames)} + + def __getitem__(self, key: Any): + """Get the item indexed by the keys.""" + + if not isinstance(key, tuple): + key = (key,) + + return super().__getitem__(key) + + def __setitem__(self, key: Tuple, val: Any): + + # Validate key format + if not isinstance(key, tuple) or len(key) != len(self.keynames): + raise ValueError("Invalid key") + + super().__setitem__(key, val) + + def filter(self, **kwargs) -> "MultiIndex": + """Filter values matching given values. + + Parameters + ---------- + kwargs + A series of keyname=keyvalue combinations for the filtering. + + Returns + ------- + filtered + A MultiIndex containing only the matching entries. + """ + + key_ind = [] + key_sel = [] + + for keyname, sel in kwargs.items(): + if keyname not in self.keynames: + raise KeyError("Invalid selection") + + key_ind.append(self._keyname_ind[keyname]) + key_sel.append(sel) + + new_keynames = tuple(kn for kn in self.keynames if kn not in kwargs.keys()) + + new_md = self.__class__(*new_keynames) + + for k, v in self.items(): + + for ind, val in zip(key_ind, key_sel): + if k[ind] != val: + break + else: + newkeys = self._remove_key_ind(k, key_ind) + new_md[newkeys] = v + + return new_md + + def groupkey(self, keyname: str) -> List[Tuple[Any, "MultiIndex"]]: + """Return a list of pairs of a keyvalues, and all the matching items. + + Parameters + ---------- + keyname + The name of the key to group by. + + Returns + ------- + results + A list of pairs of each value within the keyname key, and a `MultiIndex` + giving all the matching entries. + """ + + if keyname not in self.keynames: + raise ValueError(f"Keyname={keyname} is not known.") + + ind = self._keyname_ind[keyname] + + # Extract all the possible values for keyname + kvals = set(k[ind] for k in self.keys()) + + return [(kval, self.filter(**{keyname: kval})) for kval in kvals] + + def fold(self, keyname: str) -> "MultiIndex": + """Move keyname from the index into a list of pairs of keyvalues and items. + + Parameters + ---------- + keyname + The key name to move. + + Returns + ------- + res + A MultiIndex for all the remaining keynames. Each item is now a list of + tuples of the keyvalues for the moved keyname, and the original items. + """ + + if keyname not in self.keynames: + raise ValueError("Unknown keyname") + + key_ind = (self._keyname_ind[keyname],) + + new_md = MultiIndex(*self._remove_key_ind(self.keynames, key_ind)) + + for k, v in self.items(): + k_val = k[key_ind[0]] + new_k = self._remove_key_ind(k, key_ind) + + if new_k not in new_md: + new_md[new_k] = [] + + new_md[new_k].append((k_val, v)) + + return new_md + + def _remove_key_ind(self, key: Tuple, ind: Tuple[int]): + return tuple(kc for ii, kc in enumerate(key) if ii not in ind) + + +def hpd_chain( + chain: Union[MCSamples, list[MCSamples]], + param: str, + lim: float = 0.68, +) -> Tuple[float, Tuple[float, float]]: + """Get the highest posterior density estimate for param. + + Parameters + ---------- + chain + A single chain or a list of separate chains. + param + The parameter to estimate. + lim + The fraction within the credible interval. + + Returns + ------- + mode + Get the most likely value, i.e the distribution peak. This is an array if + `chain` is a list. + err + A tuple of the negative and positive errors. This is a [2, nchain] array if + `chain` is a list. + """ + + if isinstance(chain, list): + modes, errs = zip(*[hpd_chain(c, param, lim) for c in chain]) + return np.array(modes), np.array(errs).T + + density = chain.get1DDensity(param) + low, high, *_ = density.getLimits(lim) + + res = minimize_scalar( + lambda x: -density.Prob(x), bounds=(low, high), method="bounded" + ) + mode = res.x + + return mode, [mode - low, high - mode] + + +def cosmo_from_dict(cosmo: dict) -> cosmology.Cosmology: + """Create a Cosmology instance from a dict of omega_m, omega_l, H0 parameters.""" + omega_m = cosmo.get("omega_m", 0.3) + omega_l = cosmo.get("omega_l", 0.7) + H0 = cosmo.get("H0", 70.0) + return cosmology.Cosmology(omega_b=0, omega_c=omega_m, omega_l=omega_l, H0=H0) + + +def dXdz(z: float, cosmo: cosmology.Cosmology, delta_z: float = 1e-3) -> float: + """Finite difference redshift derivative.""" + + X1 = cosmo.comoving_distance(z + delta_z) + X0 = cosmo.comoving_distance(z) + + return (X1 - X0) / delta_z + + +def cosmo_convert_HI( + z: float, new_cosmo: cosmology.Cosmology, old_cosmo: cosmology.Cosmology +) -> float: + """Construct the factor to convert an HI fractional density between cosmologies. + + The factor should multiply the old value. + """ + + # +2 powers of H0 from the critical density + # -1 powers as the comoving distance is in Mpc/h And then a distance derivative + # ratio (which turns out to be the same for both 21cm and DLAs) + + return (old_cosmo.H0 / new_cosmo.H0) * dXdz(z, old_cosmo) / dXdz(z, new_cosmo) + + +def combine_data(dset: dict, cosmo: cosmology.Cosmology = None) -> dict: + """Extract and convert the Omega_HI data. + + Parameters + ---------- + dset + Omega_HI data formatted according to the schema. + cosmo + A common cosmology to convert the results to. + + Returns + ------- + res + A dictionary containing arrays of all the data in the dataset. Keys are: + - `z`: the mean redshift + - `zerr`: only present if a `z_range` was in the input. Shape [2, numz], giving + the lower and upper error bars. + - `omega_HI`: the actual data. + - `omega_HI_err`: shape [2, numz] giving lower and upper error bars. + """ + + data_list = dset["data"] + if not isinstance(data_list, list): + data_list = [data_list] + + def parse_z(d): + + if "z" in d: + z = d["z"] + elif "z_range" in d: + z = 0.5 * (d["z_range"][0] + d["z_range"][1]) + else: + raise ValueError("Couldn't figure out a redshift") + + if "z_range" in d: + zerr = (z - d["z_range"][0], d["z_range"][1] - z) + else: + zerr = None + + return z, zerr + + def parse_value(d): + + mul = 10 ** (-int(d["exp"])) + + value = d["value"][0] * mul + + # If there are multiple error terms combine them in quadrature, and turn into a + # two sided error spec + if len(d["value"]) > 2: + err = np.sum(np.array(d["value"][1:]) ** 2) ** 0.5 * mul + err = (err, err) + + # Deal with two sided errors (note that the errors in the files are reverse + # compared to matplotlib) + elif isinstance(d["value"][1], list): + err = (mul * d["value"][1][1], mul * d["value"][1][0]) + + else: + err = (mul * d["value"][1], mul * d["value"][1]) + + return value, err + + z, zerr = zip(*[parse_z(d) for d in data_list]) + + z = np.array(z) + + if zerr[0] is None: + zerr = None + else: + zerr = np.array(zerr).T + + if cosmo is not None: + old_cosmo = cosmo_from_dict(dset["cosmo"]) + conversion_factor = cosmo_convert_HI(z, cosmo, old_cosmo) + else: + conversion_factor = 1.0 + + if "DLA" in dset["type"]: + conversion_factor /= dset.get("mu", 1.0) + + omega_HI, omega_HI_err = zip(*[parse_value(d) for d in data_list]) + omega_HI = np.array(omega_HI) * conversion_factor + omega_HI_err = np.array(omega_HI_err).T * conversion_factor + + return dict(z=z, zerr=zerr, omega_HI=omega_HI, omega_HI_err=omega_HI_err) + + +def extract_priors(chain: MCMCFit1D) -> Dict[str, Tuple[float, float]]: + """ + Extract uniform priors from a chain file. + + Parameters + ---------- + chain : MCMCFit1D + MCMC chain object containing configuration and parameter information. + + Returns + ------- + dict of str → (float, float) + Dictionary mapping parameter names to `(low, high)` prior ranges. + Returns an empty dictionary if no configuration is found. + """ + if "config" not in chain.history: + return {} + + task_list = chain.history["config"]["pipeline"]["tasks"] + + mcmc_task = [t for t in task_list if "RunMCMC" in t["type"]] + + if len(mcmc_task) != 1: + raise ValueError( + f"Config must have only one RunMCMC task. Found {len(mcmc_task)}." + ) + + params = mcmc_task[0].get("params", {}).get("param_spec", {}) + + priors = {} + for pname, pspec in params.items(): + + if pspec["prior"] != "Uniform" or pspec.get("fixed", False): + continue + + low = pspec["kwargs"]["low"] + high = pspec["kwargs"]["high"] + + priors[pname] = (low, high) + + return priors + + +def gdchain( + chain: MCMCFit1D, + latex_names: Optional[Union[list, dict]] = None, + thin: Optional[int] = None, + **kwargs, +) -> MCSamples: + """ + Convert a CHIME MCMCFit object into a `getdist.MCSamples` object. + + Parameters + ---------- + chain : MCMCFit1D + MCMC chain object containing datasets and parameter mappings. + latex_names : list or dict, optional + Either: + - A list/tuple of LaTeX-style labels for parameters, matching the number of parameters. + - A dictionary mapping parameter names to LaTeX labels. + - If None, parameter names are used as-is. + thin : int, optional + Thinning factor to reduce the number of samples. Default is None (no thinning). + **kwargs : dict + Additional keyword arguments passed to `MCSamples`. + + Returns + ------- + MCSamples + A `getdist.MCSamples` object containing the chain samples and parameter labels. + """ + samples = list(chain.datasets["chain"][:].transpose(1, 0, 2)) + loglikes = list(chain.datasets["chisq"][:].T / 2) + params = chain.index_map["param"][:] + + if latex_names is not None: + + if isinstance(latex_names, (tuple, list)): + if len(latex_names) != len(params): + raise ValueError( + "Latex names must be a dict, or a list with the same length as the" + "number of parameters." + ) + kwargs["labels"] = latex_names + + if isinstance(latex_names, dict): + kwargs["labels"] = [latex_names.get(param, param) for param in params] + + samples = MCSamples(samples=samples, names=params, loglikes=loglikes, **kwargs) + + if thin: + samples.thin(thin) + + return samples diff --git a/fitstack/chisq.py b/fitstack/chisq.py new file mode 100644 index 0000000..7c13b85 --- /dev/null +++ b/fitstack/chisq.py @@ -0,0 +1,1002 @@ +"""Routines for chi^2 tests of auto power spectrum measurements.""" + +import logging +import inspect + +import numpy as np +import scipy.optimize +import scipy.stats + +from caput import config, mpiarray + +from draco.core import task + +from . import containers +from . import utils +from . import models +from . import priors +from . import stats + +# Set up logging +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) + +try: + from tqdm.notebook import trange + + TQDM_IMPORTED = True +except ImportError: + logger.warning("Error importing tqdm") + TQDM_IMPORTED = False + + +BOUNDED_MINIMIZATION = ["L-BFGS-B", "Nelder-Mead", "Powell", "TNC"] + + +def get_param0(fit, param_to_fit): + """Get best-fit parameters from MCMC results. + + The intent is that the parameter values from this routine can + be used as an initial guess for a direct chi^2-minimization + procedure. + + Parameters + ---------- + fit : containers.MCMCFit + Container containing MCMC results. + param_to_fit : list of str + List of parameter names to retrieve. + + Returns + ------- + theta_min : np.ndarray + Array of parameter values matching names in param_to_fit. + """ + + # Get chi^2 and parameter values at each sample from chain, + # along with parameter names + chisq = fit["chisq"][:] + chain = fit["chain"][:] + param = list(fit.index_map["param"][:]) + + # Find index of chain sample with smallest chi^2, then get + # corresponding parameter values + imin = np.argmin(np.abs(chisq.flatten())) + theta_min = chain.reshape(-1, chain.shape[-1])[imin] + + # Identify mapping from array of desired parameters to ordering + # of parameters in chain results + ifit = np.array([param.index(pfit) for pfit in param_to_fit]) + + # Return correctly-ordered parameter values + return theta_min[ifit] + + +def get_bounds(mdl, scale_bound=0.0): + """Get parameter bounds to use in chi^2 minimization. + + Parameters + ---------- + mdl : models.Model + Container with model information. + scale_bound : float, optional + If nonzero, expand parameter bounds by this factor. + Default: 0. + """ + + # Get parameter names + param = mdl.param_name_fit + nparam = len(param) + + # Make empty arrays to hold lower and upper bounds for each parameter + lb = np.zeros(nparam, dtype=np.float64) + ub = np.zeros(nparam, dtype=np.float64) + + # Loop through parameters + for nn, name in enumerate(param): + + # Set parameter bounds based on whether prior is uniform or Gaussian + prior = mdl.priors[name] + if isinstance(prior, (priors.Uniform, priors.PowerLaw)): + lb[nn] = prior.low + ub[nn] = prior.high + else: + lb[nn] = prior.loc - 5.0 * prior.scale + ub[nn] = prior.loc + 5.0 * prior.scale + + # Expand bounds if desired + if scale_bound > 0.0: + db = ub[nn] - lb[nn] + lb[nn], ub[nn] = lb[nn] - scale_bound * db, ub[nn] + scale_bound * db + + return scipy.optimize.Bounds(lb, ub) + + +def get_powerspectrum1d_LOO_invcovariance(mocks, iLOO, fit_cont, hartlap=True): + """Compute leave-one-out inverse covariance from mocks. + + This routine computes the inverse covariance from all mocks + except the one specified by index iLOO. + + Parameters + ---------- + mocks : np.ndarray[nmock, npol, nk] + Array of mocks + iLOO : int + Index of mock to leave out. + fit_cont : containers.MCMCFitPowerSpectrum1D + Container that mocks came from, which other metadata will + be drawn from. + hartlap : bool, optional + Apply the Hartlap factor to the inverse covariance. + Default: True + + Returns + ------- + Cinv : np.ndarray[npol*nk, npol*nk] + Inverse covariance. + hartlap_factor : float + Hartlap factor applied to inverse covariance (1.0 if factor + not applied). + """ + + nmock, npol, nx = mocks.shape + nmock -= 1 + + # Get quantities for constructing inverse covariance + ifit = fit_cont.attrs["ifit"] + flag_before = fit_cont.attrs["flag_before"] + + # Compute covariance. (Relevant pols were already selected in + # mocks, so no need to sub-select pols here) + C = utils.covariance(np.delete(mocks, iLOO, axis=0).reshape(nmock, -1), corr=False) + if flag_before: + C = C[ifit][:, ifit] + + # Invert the covariance matrix to obtain the precision matrix + Cinvfit = np.linalg.pinv(C) + + # Compute and apply the Hartlap factor to the inverse covariance + if hartlap: + hartlap_factor = (nmock - Cinvfit.shape[0] - 2.0) / (nmock - 1.0) + Cinvfit *= hartlap_factor + else: + hartlap_factor = 1.0 + + if not flag_before: + Cinvfit = Cinvfit[ifit][:, ifit] + + Cinv = np.zeros_like(C) + for ii, oi in enumerate(ifit): + Cinv[oi, ifit] = Cinvfit[ii, :] + + return Cinv, hartlap_factor + + +def initialize_powerspectrum1d_min_chisq_ingredients( + mcmcfit_cont, + mcmcfit_cont_for_mocks=None, + model_kwargs=None, + param_spec=None, + param0=None, + extra_starts=0, + extra_start_log_bounds=None, + extra_start_log_samplenegative=True, + bounded=True, + scale_bound=0.0, + force_real=True, + add_mock_to_data=None, + initialize_with_mock=None, + seed=0, +): + """Prepare parameters and data structures for chi^2 minimization. + + See docstring of `powerspectrum1d_min_chisq_fit()` for parameter + definitions, except `initialize_with_mock`, which is either an + integer denoting the index of a mock with which to initialize + the signal model, or `None` if the model is to be initialized + with the data. + + Returns + ------- + signal_model : models.Model + Signal model. + null_model : models.NullModel + No-signal model. + mock_data : np.ndarray[nmock,npol,nk] + Array of noise mocks. + fit_kwargs : dict + kwargs for signal model. + param0_points : np.ndarray[npoint,nparam] + Array of random starting points in parameter space. + param_bounds : scipy.optimize.Bounds + Bounds for each parameter. + fit_cont_for_mocks : containers.MCMCFitPowerSpectrum1D + Container containing mocks. + out : containers.ChisqPowerSpectrum1D + Container to store chi^2 results and associated information. + """ + + def _re(x): + return np.real(x) if force_real else x + + if model_kwargs is None: + model_kwargs = {} + + if param_spec is None: + param_spec = {} + + # Load MCMCFitPowerSpectrum1D container + if isinstance(mcmcfit_cont, str): + fit_cont = containers.MCMCFitPowerSpectrum1D.from_file( + utils.find_file(mcmcfit_cont) + ) + else: + fit_cont = mcmcfit_cont + + # Load separate MCMCFitPowerSpectrum1D container with mocks, if specified + if mcmcfit_cont_for_mocks is None: + fit_cont_for_mocks = fit_cont + else: + if isinstance(mcmcfit_cont_for_mocks, str): + fit_cont_for_mocks = containers.MCMCFitPowerSpectrum1D.from_file( + utils.find_file(mcmcfit_cont_for_mocks) + ) + else: + fit_cont_for_mocks = mcmcfit_cont_for_mocks + + # Get polarizations from input container + pol = fit_cont.index_map["pol"] + pol_fit = fit_cont.attrs["pol_fit"] + if pol_fit == "joint": + ipol = np.arange(len(pol)) + else: + ipol = list(pol).index(fit_cont.attrs["pol_fit"]) + if isinstance(ipol, int): + ipol = [ipol] + + # Get mocks from MCMCFitPowerSpectrum1D container + mock_data = fit_cont_for_mocks["mock"][:, ipol] + nmock, _, nk = mock_data.shape + + # Set quantities needed for model evaluation + model_kwargs["combine"] = False + input_model_kwargs = fit_cont.attrs["model_kwargs"] + for key in input_model_kwargs.keys(): + if key not in model_kwargs.keys(): + model_kwargs[key] = input_model_kwargs[key] + + # Set quantities needed for fit to data + fit_kwargs = {} + fit_kwargs["k1D"] = fit_cont.k1D + fit_kwargs["inv_cov"] = fit_cont["precision"][:] + fit_kwargs["transfer"] = None + fit_kwargs["pol_sel"] = ipol + + # Set data to fit to + if initialize_with_mock is None: + fit_kwargs["data"] = ( + _re(fit_cont.spectrum.data.local_array[ipol]) + if type(fit_cont.spectrum.data) is mpiarray.MPIArray + else _re(fit_cont.spectrum.data[ipol]) + ) + # If desired, add one of the mocks to the data + if add_mock_to_data is not None: + fit_kwargs["data"][:] += mock_data[add_mock_to_data][:] + else: + fit_kwargs["data"] = _re(mock_data[initialize_with_mock]) + + # Set parameter priors + input_param_spec = fit_cont.attrs["param_spec"] + for key in input_param_spec.keys(): + if key not in param_spec.keys(): + param_spec[key] = input_param_spec[key] + + # Initialize signal model, based on name stored in + # MCMCFitPowerSpectrum1D container + signal_model_name = fit_cont.attrs["model"] + signal_model_class = getattr(models, signal_model_name) + signal_model = signal_model_class(**{**model_kwargs, **param_spec}) + signal_model.set_data(**fit_kwargs) + + # Initialize null model + null_model = models.NullModel(**model_kwargs) + null_model.set_data(**fit_kwargs) + + # Determine initial guess for parameter values, and parameter bounds (if needed) + if param0 is None: + param0 = get_param0(fit_cont, signal_model.param_name_fit) + param_bounds = get_bounds(signal_model, scale_bound=scale_bound) + + # Determine additional starting points for optimizer, based on Latin hypercube + # sampling of the parameter space + param0_points = [param0] + if extra_starts > 0: + # Initialize Latin hypercube sampler + sampler = scipy.stats.qmc.LatinHypercube(d=len(param0), seed=seed) + # Draw extra_starts d-dimensional samples from the unit Latin hypercube + other_param0 = sampler.random(extra_starts) + # Scale the samples to cover the desired parameter bounds + if extra_start_log_bounds is None: + other_param0 = scipy.stats.qmc.scale( + other_param0, param_bounds.lb, param_bounds.ub + ) + else: + extra_start_log_bounds = np.asarray(extra_start_log_bounds) + if extra_start_log_bounds.shape != (len(param0), 2): + raise RuntimeError( + "extra_start_log_bounds must have shape (nparam, 2))" + ) + other_param0 = scipy.stats.qmc.scale( + other_param0, + extra_start_log_bounds[:, 0], + extra_start_log_bounds[:, 1], + ) + other_param0 = 10.0**other_param0 + if extra_start_log_samplenegative: + rng = np.random.default_rng(seed=seed) + other_param0 *= rng.choice([1.0, -1.0], size=other_param0.shape) + + # Make a list of param0 plus the other starting points + param0_points = np.concatenate([[param0], other_param0]) + + if not bounded: + param_bounds = None + + # Create container for results + out = containers.ChisqPowerSpectrum1D( + mock=np.arange(nmock, dtype=int), + param=np.array(signal_model.param_name_fit), + pol=np.array(pol)[ipol], + k=nk, + ) + + return ( + signal_model, + null_model, + mock_data, + fit_kwargs, + param0_points, + param_bounds, + fit_cont_for_mocks, + out, + ) + + +def powerspectrum1d_min_chisq_fit( + mcmcfit_cont, + mcmcfit_cont_for_mocks=None, + model_kwargs=None, + param_spec=None, + param0=None, + extra_starts=0, + extra_start_log_bounds=None, + extra_start_log_samplenegative=True, + method="L-BFGS-B", + options=None, + scale_bound=0.0, + force_real=True, + add_mock_to_data=None, + center_mocks_on_data=False, + save_bestfit_models=False, + use_LOO_covariance=False, + use_LOO_hartlap=True, + seed=0, + n_mc_ks=10000, + n_mc_ad=10000, + eps_F=1e-8, + minimize_tol=1e-3, + verbose_notebook=False, +): + """Compute the minimum chi^2 for 1d power spectrum data and mocks. + + Parameters + ---------- + mcmcfit_cont : containers.MCMCFitPowerSpectrum1D or str + Container (or container filename) with information about data, + mocks, covariance, and signal model. + mcmcfit_cont_for_mocks : containers.MCMCFitPowerSpectrum1D or str + Container (or container filename) containing mocks to use for fits. + If not specified, mocks from `mcmc_fit_cont` are used. + Default: None. + model_kwargs : dict, optional + Dictionary that contains any keyword arguments that should be passed + to the model class at initialization. Arguments used to generate + `mcmc_fit_cont` will be inherited, unless explicitly overridden. + Default: None. + param_spec : dict, optional + Dictionary that specifies the prior distribution for each parameter. + See the docstring for the models.Model attribute for the correct format. + Arguments used to generate `mcmc_fit_cont` will be inherited, unless explicitly + overridden. Default: None. + param0 : array_like, optional + Starting guess for parameter values. If not set, determined from input chain. + Default: None + extra_starts : int, optional + Also run the optimizer from this number of randomly-chosen points in parameter + space, and keep the best-fit point out of all the runs. Default: 0. + extra_start_log_bounds : np.ndarray[nparam, 2], optional + If specified, `extra_starts` points in parameter space will be randomly chosen + in log(parameter), with lower and upper log bounds specified by each column of + this array (e.g. [[-2, 6], [-2, 2]] chooses points with log10(param1) between + -2 and 6, and log10(param2) between -2 and 2). Default: None. + extra_start_log_samplenegative : bool, optional + If sampling starting points in log, randomly choose the sign of each parameter + for each starting point. This allows for both positive and negative parameter + values to be sampled, with absolute values with a specified log range. + Default: True. + method : str, optional + Method for `scipy.optimize.minimize`. Default: L-BFGS-B. + options : dict, optional + Dictionary of options for `scipy.optimize.minimize`, Default: None. + scale_bound : float, optional + Scale allowed bounds for parameters by this factor. Default: 0.0. + force_real : bool, optional + Force input datasets to be real. Assumes that input datasets have + been previously examined to verify that imaginary parts are small and/or + unimportant. Default: True. + add_mock_to_data: int, optional + Add mock with this index to data before performing fit. This is intended + as a quick way to add a noise realization to an input signal-only + simulation. Default: None. + center_mocks_on_data: bool, optional + Add best-fit signal power spectrum (fit to data) to each mock. + Primarily used for determining the effective number of degrees of + freedom in the model, centered on the region of parameter space + inhabited by the data. Default: False. + save_bestfit_models : bool, optional + Whether to save best-fit model evaluations for data and each mock, as + datasets in output container. Default: False. + use_LOO_covariance : bool, optional + If True, use leave-one-out covariance for each mock. Default: False. + use_LOO_hartlap : bool, optional + Apply Hartlap factor to LOO inverse-covariance. Default: True. + seed : int, optional + Random seed for generating starting points for optimizer. Default: 0. + n_mc_ks : int, optional + Number of Monte Carlo samples for Monte-Carlo-calibrated KS tests + that compare set of Delta chi^2 values to specific distributions. + Default: 10000. + n_mc_ad : int, optional + Number of Monte Carlo samples for Monte-Carlo-calibrated AD tests + that compare set of Delta chi^2 values to specific distributions. + Default: 10000. + eps_F : float, optional + Step size used for numerical approximation of the Jacobian used + in the L-BFGS-B optimizer when fitting an F distribution. + Default: 1e-8. + minimize_tol : float, optional + `tol` argument for `minimize` routine used for chi^2-minimization. + Default: None. + verbose_notebook : bool, optional + Whether to print status updates when evaluating in a jupyter notebook, + using the `tqdm` package. Ignored if `tqdm` is not installed. + Default: False. + + Returns + ------- + out : containers.ChisqPowerSpectrum1D + Container containing chi^2 results and associated information. + """ + + def _re(x): + return np.real(x) if force_real else x + + if options is None: + options = {} + + ( + signal_model, + null_model, + mock_data, + fit_kwargs, + param0_points, + param_bounds, + fit_cont_for_mocks, + out, + ) = initialize_powerspectrum1d_min_chisq_ingredients( + mcmcfit_cont, + mcmcfit_cont_for_mocks=mcmcfit_cont_for_mocks, + model_kwargs=model_kwargs, + param_spec=param_spec, + param0=param0, + extra_starts=extra_starts, + extra_start_log_bounds=extra_start_log_bounds, + extra_start_log_samplenegative=extra_start_log_samplenegative, + bounded=method in BOUNDED_MINIMIZATION, + scale_bound=scale_bound, + force_real=force_real, + add_mock_to_data=add_mock_to_data, + initialize_with_mock=None, + seed=seed, + ) + + nmock, _, _ = mock_data.shape + + # --------- + # Compute Delta chi^2 for data + # --------- + + # Run minimizer for each starting point, saving run that yields lowest + # chi^2 + for pi, params in enumerate(param0_points): + test_resd = scipy.optimize.minimize( + signal_model.negative_log_likelihood, + params, + method=method, + bounds=param_bounds, + options=options, + tol=minimize_tol, + ) + + if pi == 0: + # Store results from first run. If success==False at this step + # and no other result is better, we'll keep this result. + bestfit_starting_point_idx = 0 + resd = test_resd + bestfit_negloglike = signal_model.negative_log_likelihood(resd.x) + else: + # If this run succeeds and gets a lower chi^2 than the previous best + # run, update the best-run variables with this one + test_negloglike = signal_model.negative_log_likelihood(test_resd.x) + if test_resd["success"] and (test_negloglike < bestfit_negloglike): + bestfit_starting_point_idx = pi + resd = test_resd + bestfit_negloglike = test_negloglike + + data_signal_bestfit_param = resd.x + + # Save results, along with data itself, to output container + out.attrs["data_signal_success"] = resd.success + out.attrs["data_signal_chisq"] = 2.0 * signal_model.negative_log_likelihood(resd.x) + out.attrs["data_signal_bestfit_param"] = data_signal_bestfit_param + out.attrs["data"] = fit_kwargs["data"][:] + out.attrs["data_bestfit_starting_point_idx"] = bestfit_starting_point_idx + + # Save chi^2 for null model and data + out.attrs["data_null_chisq"] = 2.0 * null_model.negative_log_likelihood([]) + + # Save best-fit model prediction for data + if save_bestfit_models: + # Save best-fit model prediction for data + out.add_dataset("data_bestfit_model") + out.datasets["data_bestfit_model"][:] = signal_model.model( + signal_model.get_all_params(data_signal_bestfit_param) + ) + + # --------- + # Compute Delta chi^2 for mocks + # --------- + + # Dereference datasets for mock fitting + success = out["success"][:].view(np.ndarray) + chisq_null = out["chisq_null"][:].view(np.ndarray) + chisq_signal = out["chisq_signal"][:].view(np.ndarray) + bestfit_param = out["bestfit_param"][:].view(np.ndarray) + mock_bestfit_starting_point_idx = out["mock_bestfit_starting_point_idx"][:].view( + np.ndarray + ) + + # Initialize dataset to store best-fit model predictions for mocks + if save_bestfit_models: + out.add_dataset("mock_bestfit_models") + + # Loop over mocks + if verbose_notebook and TQDM_IMPORTED: + mock_iter = trange(nmock) + else: + mock_iter = range(nmock) + + for mm in mock_iter: + + # Print progress update to log + if (mm % 100) == 0: + logger.info(f"Fitting data realization {mm} of {nmock}.") + + # Update models to consider mock data + fit_kwargs["data"] = _re(mock_data[mm]).copy() + if center_mocks_on_data: + fit_kwargs["data"] += signal_model.model( + signal_model.get_all_params(data_signal_bestfit_param) + ) + if use_LOO_covariance: + fit_kwargs["inv_cov"], LOO_hartlap_factor = ( + get_powerspectrum1d_LOO_invcovariance( + mock_data, mm, fit_cont_for_mocks, hartlap=use_LOO_hartlap + ) + ) + signal_model.set_data(**fit_kwargs) + null_model.set_data(**fit_kwargs) + + # Minimize negative log-likelihood for fitting signal model to mock data, + # and save results + for pi, params in enumerate(param0_points): + test_resd = scipy.optimize.minimize( + signal_model.negative_log_likelihood, + params, + method=method, + bounds=param_bounds, + options=options, + tol=minimize_tol, + ) + + if pi == 0: + # Store results from first run. If success==False at this step + # and no other result is better, we'll keep this result. + bestfit_starting_point_idx = 0 + resd = test_resd + bestfit_negloglike = signal_model.negative_log_likelihood(resd.x) + else: + # If this run succeeds and gets a lower chi^2 than the previous best + # run, update the best-run variables with this one + test_negloglike = signal_model.negative_log_likelihood(test_resd.x) + if test_resd["success"] and (test_negloglike < bestfit_negloglike): + bestfit_starting_point_idx = pi + resd = test_resd + bestfit_negloglike = test_negloglike + + success[mm] = resd.success + chisq_signal[mm] = 2.0 * signal_model.negative_log_likelihood(resd.x) + bestfit_param[mm] = resd.x + mock_bestfit_starting_point_idx[mm] = bestfit_starting_point_idx + + if save_bestfit_models: + out.datasets["mock_bestfit_models"][mm] = signal_model.model( + signal_model.get_all_params(resd.x) + ) + + # Save chi^2 for null model and mock. + # If mocks are data-centered, null model is best-fit signal model for data. + # If mocks are not data-centered, null model is no-signal model. + if center_mocks_on_data: + chisq_null[mm] = 2.0 * signal_model.negative_log_likelihood( + data_signal_bestfit_param + ) + else: + chisq_null[mm] = 2.0 * null_model.negative_log_likelihood([]) + + # --------- + # Compute detection significances based on fitted distributions + # --------- + + # Fit a chi^2 distribution with a free number of d.o.f. to the + # Delta chi^2 values for each mock + dchisq_mocks = chisq_null[success] - chisq_signal[success] + ndof_mocks_chisq = stats.fit_chi2_to_array(dchisq_mocks) + out.attrs["chisq_distribution_ndof"] = ndof_mocks_chisq + + # Compute Monte-Carlo-calibrated p-values for KS and AD tests + # comparing the set of Delta chi^2 values to the best-fit + # chi^2 distribution + logger.info("Computing KS p-value for chi^2 distribution") + ks_p_chisq, _, _ = stats.compute_MC_calibrated_distribution_test( + dchisq_mocks, + test="KS", + dist="chi2", + seed=seed, + verbose=False, + n_mc_sims=n_mc_ks, + ) + out.attrs["chisq_distribution_ks_pvalue"] = ks_p_chisq + logger.info("Computing AD p-value for chi^2 distribution") + ad_p_chisq, _, _ = stats.compute_MC_calibrated_distribution_test( + dchisq_mocks, + test="AD", + dist="chi2", + seed=seed, + verbose=False, + n_mc_sims=n_mc_ad, + ) + out.attrs["chisq_distribution_ad_pvalue"] = ad_p_chisq + + # Compute p-value and "number of sigmas" for Delta chi^2 value for data, + # using fitted chi^2 distribution + dchisq_data = out.attrs["data_null_chisq"] - out.attrs["data_signal_chisq"] + data_pvalue_chisq = scipy.stats.chi2.sf(dchisq_data, ndof_mocks_chisq) + data_nsigmas_chisq = scipy.stats.norm.isf(data_pvalue_chisq) + out.attrs["data_pvalue_chisq"] = data_pvalue_chisq + out.attrs["data_nsigmas_chisq"] = data_nsigmas_chisq + + # If Hartlap factor was applied to inverse covariance, undo it + # in Delta chi^2 values + if use_LOO_covariance and use_LOO_hartlap: + scaled_dchisq_mocks = dchisq_mocks / LOO_hartlap_factor + scaled_dchisq_data = dchisq_data / LOO_hartlap_factor + else: + scaled_dchisq_mocks = ( + dchisq_mocks / mcmcfit_cont_for_mocks.attrs["hartlap_factor"] + ) + scaled_dchisq_data = ( + dchisq_data / mcmcfit_cont_for_mocks.attrs["hartlap_factor"] + ) + + # Fit F distribution to Delta chi^2 values + if use_LOO_covariance: + n_for_F = nmock - 2 + else: + n_for_F = nmock - 1 + n_data = len(mcmcfit_cont.attrs["ifit"]) + ndof_mocks_F, _ = stats.fit_F_to_scaled_array( + scaled_dchisq_mocks, n_for_F, n_data, p_eff_0=None, eps=eps_F + ) + out.attrs["F_distribution_ndof"] = ndof_mocks_F + + # Compute Monte-Carlo-calibrated p-values for KS and AD tests + # comparing the set of (appropriately-scaled) Delta chi^2 values + # to the best-fit F distribution + logger.info("Computing KS p-value for F distribution") + ks_p_F, _, _ = stats.compute_MC_calibrated_distribution_test( + scaled_dchisq_mocks, + test="KS", + dist="F", + n_for_F=n_for_F, + p_for_F=n_data, + seed=seed, + verbose=False, + n_mc_sims=n_mc_ks, + ) + out.attrs["F_distribution_ks_pvalue"] = ks_p_F + logger.info("Computing AD p-value for F distribution") + ad_p_F, _, _ = stats.compute_MC_calibrated_distribution_test( + scaled_dchisq_mocks, + test="AD", + dist="F", + n_for_F=n_for_F, + p_for_F=n_data, + seed=seed, + verbose=False, + n_mc_sims=n_mc_ad, + ) + out.attrs["F_distribution_ad_pvalue"] = ad_p_F + + # Compute p-value and "number of sigmas" for Delta chi^2 value for data, + # using fitted F distribution + scaled_dchisq_data *= stats.F_scaling(n_for_F, n_data, ndof_mocks_F) + data_pvalue_F = scipy.stats.f.sf( + scaled_dchisq_data, dfn=ndof_mocks_F, dfd=n_for_F - n_data + 1 + ) + data_nsigmas_F = scipy.stats.norm.isf(data_pvalue_F) + out.attrs["data_pvalue_F"] = data_pvalue_F + out.attrs["data_nsigmas_F"] = data_nsigmas_F + + # --------- + # Compute detection significance based on fitting single amplitude + # --------- + + # Re-initialize signal model with data + ( + signal_model, + _, + _, + _, + _, + _, + _, + _, + ) = initialize_powerspectrum1d_min_chisq_ingredients( + mcmcfit_cont, + mcmcfit_cont_for_mocks=mcmcfit_cont_for_mocks, + model_kwargs=model_kwargs, + param_spec=param_spec, + param0=param0, + extra_starts=extra_starts, + extra_start_log_bounds=extra_start_log_bounds, + extra_start_log_samplenegative=extra_start_log_samplenegative, + bounded=method in BOUNDED_MINIMIZATION, + scale_bound=scale_bound, + force_real=force_real, + add_mock_to_data=add_mock_to_data, + initialize_with_mock=None, + seed=seed, + ) + + # Define chi^2 function based on taking best-fit model and + # scaling with free amplitude (with fiducial value 1.0) + def signal_chi2_at_amplitude(amp): + return 2 * signal_model.negative_log_likelihood( + out.attrs["data_signal_bestfit_param"], amp=amp + ) + + # Find minimum of this chi^2 function + signal_chi2_min = signal_chi2_at_amplitude(1.0) + + # Find amplitude values on either side of fiducial value + # where chi^2 function increases by 1 + bf_amp_lo, bf_amp_hi = stats.find_symmetric_roots( + signal_chi2_at_amplitude, signal_chi2_min + 1.0, 1.0, 0.0, 5.0 + ) + + # Detection significance is Delta(amp)/amp. + # Avearge these values computed with low and high values + # of amplitude + data_nsigmas_ampfit_lo = 1.0 / (bf_amp_hi - 1.0) + data_nsigmas_ampfit_hi = 1.0 / (1.0 - bf_amp_lo) + data_nsigmas_ampfit = 0.5 * (data_nsigmas_ampfit_lo + data_nsigmas_ampfit_hi) + out.attrs["data_nsigmas_ampfit"] = data_nsigmas_ampfit + + # Same other useful information + out.attrs["param0_points"] = param0_points + out.attrs["seed"] = seed + + # Return the output container + return out + + +class PowerSpectrum1DMinChisqFit(task.SingleTask): + """Pipeline task that calls the powerspectrum1d_min_chisq_fit function. + + Enables the user to call the powerspectrum1d_min_chisq_fit method + with caput-pipeline, which provides many useful features including + profiling, job script generation, job templating, and saving the + results to disk. + + See the arguments of the powerspectrum1d_min_chisq_fit method for a + list of attributes and their default values. + """ + + model_kwargs = config.Property(proptype=dict) + param_spec = config.Property(proptype=dict) + param0 = config.Property(proptype=list) + extra_starts = config.Property(proptype=int) + extra_start_log_bounds = config.Property(proptype=list) + extra_start_log_samplenegative = config.Property(proptype=bool) + method = config.Property(proptype=str) + options = config.Property(proptype=dict) + scale_bound = config.Property(proptype=float) + force_real = config.Property(proptype=bool) + add_mock_to_data = config.Property(proptype=int) + center_mocks_on_data = config.Property(proptype=bool) + save_bestfit_models = config.Property(proptype=bool) + use_LOO_covariance = config.Property(proptype=bool) + use_LOO_hartlap = config.Property(proptype=bool) + n_mc_ks = config.Property(proptype=int) + n_mc_ad = config.Property(proptype=int) + eps_F = config.Property(proptype=float) + minimize_tol = config.Property(proptype=float) + seed = config.Property(proptype=int) + + def setup(self): + """Prepare all arguments for the powerspectrum1d_min_chisq_fit method.""" + + # Use the default values from the powerspectrum1d_min_chisq_fit method, + # so we do not have to repeat them in two places. + signature = inspect.signature(powerspectrum1d_min_chisq_fit) + defaults = { + k: v.default if v.default is not inspect.Parameter.empty else None + for k, v in signature.parameters.items() + } + + self.kwargs = {} + for key, default_val in defaults.items(): + if hasattr(self, key): + prop_val = getattr(self, key) + self.kwargs[key] = prop_val if prop_val is not None else default_val + elif key in ["mcmcfit_cont", "mcmcfit_cont_for_mocks", "verbose_notebook"]: + continue + else: + self.log.warning( + "PowerSpectrum1DMinChisqFit does not have a property " + f"corresponding to the {key} keyword argument to " + "powerspectrum1d_min_chisq_fit." + ) + + def process(self, mcmcfit_cont): + """Run the chi^2 minimization. + + Parameters + ---------- + mcmc_fit_cont : containers.MCMCFitPowerSpectrum1D + Container with information about data, mocks, covariance, and + signal model. + + Returns + ------- + out : containers.ChisqPowerSpectrum1D + Container containing chi^2 results and associated information. + """ + out = powerspectrum1d_min_chisq_fit( + mcmcfit_cont, verbose_notebook=False, **self.kwargs + ) + + return out + + +class PowerSpectrum1DMinChisqFit_Split(PowerSpectrum1DMinChisqFit): + """Calls the powerspectrum1d_min_chisq_fit with split-mock approach. + + This task takes two input containers: the first one determines most + aspects of the procedure, while the second one only provides the mocks + that will be used for fitting. This allows for the inverse covariance + to be generated from a different set of mocks than the set used for + fitting. + """ + + model_kwargs = config.Property(proptype=dict) + param_spec = config.Property(proptype=dict) + param0 = config.Property(proptype=list) + extra_starts = config.Property(proptype=int) + extra_start_log_bounds = config.Property(proptype=list) + extra_start_log_samplenegative = config.Property(proptype=bool) + method = config.Property(proptype=str) + options = config.Property(proptype=dict) + scale_bound = config.Property(proptype=float) + force_real = config.Property(proptype=bool) + add_mock_to_data = config.Property(proptype=int) + center_mocks_on_data = config.Property(proptype=bool) + save_bestfit_models = config.Property(proptype=bool) + use_LOO_covariance = config.Property(proptype=bool) + use_LOO_hartlap = config.Property(proptype=bool) + n_mc_ks = config.Property(proptype=int) + n_mc_ad = config.Property(proptype=int) + eps_F = config.Property(proptype=float) + minimize_tol = config.Property(proptype=float) + seed = config.Property(proptype=int) + + def process(self, mcmcfit_cont, mcmcfit_cont_for_mocks): + """Run the chi^2 minimization. + + Parameters + ---------- + mcmcfit_cont : containers.MCMCFitPowerSpectrum1D + Container with information about data, mocks used for + covariance computation, covariance, and signal model. + mcmcfit_cont_for_mocks : containers.MCMCFitPowerSpectrum1D + Container with mocked to be used for fitting. + + Returns + ------- + out : containers.ChisqPowerSpectrum1D + Container containing chi^2 results and associated information. + """ + out = powerspectrum1d_min_chisq_fit( + mcmcfit_cont, + mcmcfit_cont_for_mocks=mcmcfit_cont_for_mocks, + verbose_notebook=False, + **self.kwargs, + ) + + return out + + +def combine_dchi2_results(cont_list): + """Make array of highest :math:`\Delta\chi^2` value for each noise mock. + + Parameters + ---------- + cont_list : list + List of `fitstack.containers.ChisqPowerSpectrum1D` containers with + the results of :math:`\chi^2` minimization. + + Returns + ------- + dchi2_max_arr : np.ndarray[nmocks] + Array of highest :math:`\Delta\chi^2` value for each mock, + over :math:`\Delta\chi^2`values computed from each input + container. + """ + + _DCHI2_FAILURE_VALUE = 0 + + # Make array of dchi2 values for each key + dchi2_2darr = [] + for cont in cont_list: + # Get dchi2 values for this container + dchi2_single = np.abs(cont["chisq_null"][:] - cont["chisq_signal"][:]) + # For any mock where the minimizer reported failure, + # replace its dchi2 value with a small value, so that + # it's ignored when we take the maximum dchi2 over + # all keys + dchi2_single[~cont["success"][:]] = _DCHI2_FAILURE_VALUE + + dchi2_2darr.append(dchi2_single) + + dchi2_2darr = np.array(dchi2_2darr) + + # For each mock, take maximum dchi2 value over all containers + dchi2_max_arr = np.max(dchi2_2darr, axis=0) + + # If there's a mock for which no key had a successful fit, + # remove it from the list by checking its dchi2 value + dchi2_max_arr = dchi2_max_arr[dchi2_max_arr > _DCHI2_FAILURE_VALUE] + + return dchi2_max_arr diff --git a/fitstack/chisq_test.py b/fitstack/chisq_test.py new file mode 100644 index 0000000..e239297 --- /dev/null +++ b/fitstack/chisq_test.py @@ -0,0 +1,339 @@ +"""Routines for chi^2 tests of stacking measurements. + +Used for CHIME-eBOSS stacking analysis in arXiv:2202.01242. +""" + +import logging +import inspect + +import numpy as np +import scipy.optimize + +from caput import config, pipeline + +from draco.core import task + +from . import containers +from . import utils +from . import models +from . import priors + +# Set up logging +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) + + +def _all_subclasses(cls): + return set(cls.__subclasses__()).union( + [s for c in cls.__subclasses__() for s in _all_subclasses(c)] + ) + + +SIMULATION_MODELS = [ + c.__name__ + for c in [models.SimulationTemplate] + + list(_all_subclasses(models.SimulationTemplate)) +] + +BOUNDED_MINIMIZATION = ["L-BFGS-B", "Nelder-Mead", "Powell", "TNC"] + + +def get_param0(fit, param_to_fit): + + chisq = fit["chisq"][:] + chain = fit["chain"][:] + param = list(fit.index_map["param"][:]) + + imin = np.argmin(np.abs(chisq.flatten())) + theta_min = chain.reshape(-1, chain.shape[-1])[imin] + + ifit = np.array([param.index(pfit) for pfit in param_to_fit]) + + return theta_min[ifit] + + +def get_bounds(mdl, scale_bound=0.0): + + param = mdl.param_name_fit + nparam = len(param) + + lb = np.zeros(nparam, dtype=np.float64) + ub = np.zeros(nparam, dtype=np.float64) + + for nn, name in enumerate(param): + + prior = mdl.priors[name] + + if isinstance(prior, priors.Uniform): + lb[nn] = prior.low + ub[nn] = prior.high + else: + lb[nn] = prior.loc - 5.0 * prior.scale + ub[nn] = prior.loc + 5.0 * prior.scale + + if scale_bound > 0.0: + db = ub[nn] - lb[nn] + lb[nn], ub[nn] = lb[nn] - scale_bound * db, ub[nn] + scale_bound * db + + return scipy.optimize.Bounds(lb, ub) + + +def chisq_test( + restricted, + unrestricted, + model_kwargs=None, + param_spec=None, + method="L-BFGS-B", + options=None, + scale_bound=0.0, + required_pol=None, +): + + if required_pol is None: + required_pol = ["XX", "YY"] + + if model_kwargs is None: + model_kwargs = {"restricted": {}, "unrestricted": {}} + + if param_spec is None: + param_spec = {"restricted": {}, "unrestricted": {}} + + if options is None: + options = {} + + fr = containers.MCMCFit1D.from_file(utils.find_file(restricted)) + fu = containers.MCMCFit1D.from_file(utils.find_file(unrestricted)) + + pol_sel = np.array([list(fr.pol).index(pstr) for pstr in required_pol]) + + for key in ["restricted", "unrestricted"]: + model_kwargs[key]["pol"] = ["XX", "YY"] + model_kwargs[key]["combine"] = False + model_kwargs[key]["sort"] = True + + fit_kwargs = {} + fit_kwargs["freq"] = fr.freq + fit_kwargs["data"] = fr.stack[pol_sel] + fit_kwargs["inv_cov"] = fr["precision"][:] + fit_kwargs["transfer"] = None + + eval_kwargs_r = {"freq": fr.freq, "transfer": None} + + # Prepare the model + namer = fr.attrs["model"] + Modelr = getattr(models, namer) + modelr = Modelr(**{**model_kwargs["restricted"], **param_spec["restricted"]}) + + nameu = fu.attrs["model"] + Modelu = getattr(models, nameu) + modelu = Modelu(**{**model_kwargs["unrestricted"], **param_spec["unrestricted"]}) + + # Specialized fit keywords + fit_kwargs_r = {key: val for key, val in fit_kwargs.items()} + if ( + (namer in SIMULATION_MODELS) + and ("DualPol" not in namer) + and ("Split" not in namer) + ): + fit_kwargs_r["pol_sel"] = pol_sel + eval_kwargs_r["pol_sel"] = pol_sel + + fit_kwargs_u = {key: val for key, val in fit_kwargs.items()} + if ( + (nameu in SIMULATION_MODELS) + and ("DualPol" not in nameu) + and ("Split" not in nameu) + ): + fit_kwargs_u["pol_sel"] = pol_sel + + # Construct data realizations + mr = fr["mock"][:, pol_sel] + + nmock, npol, nfreq = mr.shape + + # Create output container + out = containers.ChisqTest( + mock=np.arange(nmock, dtype=np.int), + restricted_param=np.array(modelr.param_name_fit), + unrestricted_param=np.array(modelu.param_name_fit), + ) + + # Fit the restricted model + # -------------------------- + modelr.set_data(**fit_kwargs_r) + + if modelr.nfit > 0: + + # Determine the starting point and parameter bounds + param0r = get_param0(fr, modelr.param_name_fit) + nparamr = param0r.size + if method in BOUNDED_MINIMIZATION: + boundr = get_bounds(modelr, scale_bound=scale_bound) + else: + boundr = None + + resdr = scipy.optimize.minimize( + modelr.negative_log_likelihood, + param0r, + method=method, + bounds=boundr, + options=options, + ) + + out.attrs["restricted_success"] = resdr.success + out.attrs["restricted_chisq"] = 2.0 * modelr.negative_log_likelihood(resdr.x) + out.attrs["restricted_param"] = resdr.x + + thetar = modelr.get_all_params(resdr.x) + + out.add_dataset("restricted_param") + paramr = out["restricted_param"][:].view(np.ndarray) + + else: + + out.attrs["restricted_success"] = True + out.attrs["restricted_chisq"] = 2.0 * modelr.negative_log_likelihood([]) + + thetar = modelr.get_all_params([]) + + yr = modelr.model(thetar, **eval_kwargs_r)[np.newaxis, ...] + mr + + # Fit the unrestricted model + # -------------------------- + modelu.set_data(**fit_kwargs_u) + + # Determine the starting point and parameter bounds + param0u = get_param0(fu, modelu.param_name_fit) + nparamu = param0u.size + if method in BOUNDED_MINIMIZATION: + boundu = get_bounds(modelu, scale_bound=scale_bound) + else: + boundu = None + + resdu = scipy.optimize.minimize( + modelu.negative_log_likelihood, + param0u, + method=method, + bounds=boundu, + options=options, + ) + + out.attrs["unrestricted_success"] = resdu.success + out.attrs["unrestricted_chisq"] = 2.0 * modelu.negative_log_likelihood(resdu.x) + out.attrs["unrestricted_param"] = resdu.x + + # Dereference datasets + successr = out["restricted_success"][:].view(np.ndarray) + chisqr = out["restricted_chisq"][:].view(np.ndarray) + + successu = out["unrestricted_success"][:].view(np.ndarray) + paramu = out["unrestricted_param"][:].view(np.ndarray) + chisqu = out["unrestricted_chisq"][:].view(np.ndarray) + + # Loop over mocks + for mm in range(nmock): + + # Print progress update to log + if (mm % 100) == 0: + logger.info(f"Fitting data realization {mm} of {nmock}.") + + # Fit restricted model + fit_kwargs_r["data"] = yr[mm] + modelr.set_data(**fit_kwargs_r) + + if modelr.nfit > 0: + resr = scipy.optimize.minimize( + modelr.negative_log_likelihood, + param0r, + method=method, + bounds=boundr, + options=options, + ) + + successr[mm] = resr.success + paramr[mm] = resr.x + chisqr[mm] = 2.0 * modelr.negative_log_likelihood(resr.x) + + else: + successr[mm] = True + chisqr[mm] = 2.0 * modelr.negative_log_likelihood(modelr.default_values) + + # Fit unrestricted model + fit_kwargs_u["data"] = yr[mm] + modelu.set_data(**fit_kwargs_u) + + resu = scipy.optimize.minimize( + modelu.negative_log_likelihood, + param0u, + method=method, + bounds=boundu, + options=options, + ) + + successu[mm] = resu.success + paramu[mm] = resu.x + chisqu[mm] = 2.0 * modelu.negative_log_likelihood(resu.x) + + # Return the output container + return out + + +class ChisqTest(task.SingleTask): + """Pipeline task that calls the chisq_test function. + + Enables the user to call the chisq_test method with caput-pipeline, + which provides many useful features including profiling, job script + generation, job templating, and saving the results to disk. + + Attributes + ---------- + max_iter : int + Number of times to call the chisq_test method. + Defaults to 1. + + See the arguments of the chisq_test method for a list of + additional attributes and their default values. + """ + + max_iter = config.Property(proptype=int, default=1) + + restricted = config.Property(proptype=str) + unrestricted = config.Property(proptype=str) + param_spec = config.Property(proptype=dict) + model_kwargs = config.Property(proptype=dict) + options = config.Property(proptype=dict) + method = config.Property(proptype=str) + scale_bound = config.Property(proptype=float) + required_pol = config.Property(proptype=list) + + def setup(self): + """Prepare all arguments to the chisq_test function.""" + + # Use the default values from the chisq_test method, + # so we do not have to repeat them in two places. + signature = inspect.signature(chisq_test) + defaults = { + k: v.default if v.default is not inspect.Parameter.empty else None + for k, v in signature.parameters.items() + } + + self.kwargs = {} + for key, default_val in defaults.items(): + if hasattr(self, key): + prop_val = getattr(self, key) + self.kwargs[key] = prop_val if prop_val is not None else default_val + else: + self.log.warning( + "ChisqTest does not have a property corresponding " + f"to the {key} keyword argument to chisq_test." + ) + + def process(self): + """Fit a model to the source stack using an MCMC.""" + + if self._count == self.max_iter: + raise pipeline.PipelineStopIteration + + result = chisq_test(**self.kwargs) + + return result diff --git a/fitstack/containers.py b/fitstack/containers.py index aeefb4a..b97a752 100644 --- a/fitstack/containers.py +++ b/fitstack/containers.py @@ -1,8 +1,56 @@ """Containers for storing data products and fit results.""" +from typing import ClassVar + import numpy as np -from draco.core.containers import * +from draco.core.containers import ( + ContainerBase, + FrequencyStackByPol, + MockFrequencyStackByPol, + Stack3D, + PowerSpectrum1D, + PowerSpectrum2D, +) + + +class MockContainer(ContainerBase): + """Container where some datasets have a mock axis and others do not.""" + + _non_mock_datasets = () + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.attrs["non_mock_datasets"] = self._non_mock_datasets + + @property + def non_mock_datasets(self): + """Get the list of datasets without a mock axis.""" + return self.attrs["non_mock_datasets"] + + +class MockStack3D(MockFrequencyStackByPol, Stack3D): + """Container for holding a frequency stack split by pol for multiple mock catalogs. + + Adds a `mock` axis as the first dimension of each dataset. + """ + + _axes = ("mock",) + + _dataset_spec = { + "stack": { + "axes": ["mock", "pol", "delta_ra", "delta_dec", "freq"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "weight": { + "axes": ["mock", "pol", "delta_ra", "delta_dec", "freq"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + } class StackSet1D(FrequencyStackByPol): @@ -59,6 +107,125 @@ class StackSet3D(Stack3D): } +class PowerSpectrumSet1D(PowerSpectrum1D): + """Container for data required to perform a model fit with a 1d power spectrum.""" + + _axes = ("mock",) + + _dataset_spec = { + "mock": { + "axes": ["mock", "pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "template": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialise": False, + "distributed": False, + }, + } + + +class MockPowerSpectrum1D(PowerSpectrum1D, MockContainer): + """Container for multiple 1d power spectra. + + This will most commonly be used to store several noise power spectra, + for use in computing a covariance matrix. The spectra will be indexed + by the `mock` axis, to carry over conventions from the stacking analysis. + + The `spectrum` and `samp_var` datasets will vary from mock to mock. + `var` may be the same for every mock, but we allow for it to vary. + The other `PowerSpectrum1D` datasets (`neff`, and `k1D`) will + be the same for every mock, so we only redefine them here to ensure + that they're not distributed by default. + """ + + _axes = ("mock",) + + _non_mock_datasets = ("neff",) + + _dataset_spec: ClassVar = { + "spectrum": { + "axes": ["mock", "pol", "k"], + "dtype": np.complex128, + "initialise": True, + "distributed": False, + }, + "samp_var": { + "axes": ["mock", "pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "var": { + "axes": ["mock", "pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "neff": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "k1D": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + } + + +class MockPowerSpectrum2D(PowerSpectrum2D, MockContainer): + """Container for multiple 2d power spectra. + + This will most commonly be used to store several noise power spectra, + for use in computing a covariance matrix. The spectra will be indexed + by the `mock` axis, to carry over conventions from the stacking analysis. + + The `spectrum` dataset will vary from mock to mock. `weight` and `neff` + may be the same for every mock, but we allow for them to vary. `mask` + will be the same for every mock, so we only redefine it so that it's + not distributed by default. + """ + + _axes = ("mock",) + + _non_mock_datasets = ("mask",) + + _dataset_spec: ClassVar = { + "spectrum": { + "axes": ["mock", "pol", "delay", "uv_dist"], + "dtype": np.complex128, + "initialise": True, + "distributed": False, + }, + "weight": { + "axes": ["mock", "pol", "delay", "uv_dist"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "neff": { + "axes": ["mock", "pol", "delay", "uv_dist"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + "distributed_axis": "delay", + }, + "mask": { + "axes": ["pol", "delay", "uv_dist"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + } + + class MCMCFit(ContainerBase): """Base container for the results of a model fit.""" @@ -79,7 +246,7 @@ class MCMCFit(ContainerBase): }, "fixed": { "axes": ["param"], - "dtype": np.bool, + "dtype": bool, "initialise": True, "distributed": False, }, @@ -198,13 +365,13 @@ class MCMCFit1D(MCMCFit, StackSet1D): }, "freq_flag": { "axes": ["freq"], - "dtype": np.bool, + "dtype": bool, "initialise": True, "distributed": False, }, "flag": { "axes": ["x"], - "dtype": np.bool, + "dtype": bool, "initialise": True, "distributed": False, }, @@ -265,3 +432,155 @@ class MCMCFit3D(MCMCFit, StackSet3D): "distributed": False, }, } + + +class MCMCFitPowerSpectrum1D(MCMCFit, PowerSpectrumSet1D): + """Container for a model fit to 1D power spectrum and all associated data.""" + + _axes = ("x",) + + _dataset_spec = { + "cov": { + "axes": ["pol", "pol", "k", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "error": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "k_flag": { + "axes": ["k"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + "flag": { + "axes": ["x"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + "precision": { + "axes": ["x", "x"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "model_min_chisq": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "model_percentile": { + "axes": ["pol", "k", "percentile"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + } + + @property + def ndof(self): + """Return the number of degrees of freedom.""" + return np.sum(self.datasets["flag"][:]) - self.index_map["param"].size + + +class ChisqTest(ContainerBase): + """Container for best-fit chi-squared results for stacking.""" + + _axes = ("mock", "restricted_param", "unrestricted_param") + + _dataset_spec = { + "restricted_success": { + "axes": ["mock"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + "restricted_chisq": { + "axes": ["mock"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "restricted_param": { + "axes": ["mock", "restricted_param"], + "dtype": np.float64, + "initialise": False, + "distributed": False, + }, + "unrestricted_success": { + "axes": ["mock"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + "unrestricted_chisq": { + "axes": ["mock"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "unrestricted_param": { + "axes": ["mock", "unrestricted_param"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + } + + +class ChisqPowerSpectrum1D(ContainerBase): + """Container for best-fit chi-squared results for 1d power spectrum.""" + + _axes = ("mock", "param", "pol", "k") + + _dataset_spec = { + "success": { + "axes": ["mock"], + "dtype": bool, + "initialise": True, + "distributed": False, + }, + "chisq_null": { + "axes": ["mock"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "chisq_signal": { + "axes": ["mock"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "mock_bestfit_starting_point_idx": { + "axes": ["mock"], + "dtype": int, + "initialise": True, + "distributed": False, + }, + "bestfit_param": { + "axes": ["mock", "param"], + "dtype": np.float64, + "initialise": True, + "distributed": False, + }, + "mock_bestfit_models": { + "axes": ["mock", "pol", "k"], + "dtype": np.float64, + "initialize": False, + "distributed": False, + }, + "data_bestfit_model": { + "axes": ["pol", "k"], + "dtype": np.float64, + "initialize": False, + "distributed": False, + }, + } diff --git a/fitstack/matched_filter.py b/fitstack/matched_filter.py index 9fc777d..ccc904f 100644 --- a/fitstack/matched_filter.py +++ b/fitstack/matched_filter.py @@ -12,6 +12,8 @@ from draco.util import tools from draco.core import containers +from . import utils + def _rolling_window(a, window): shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) @@ -19,9 +21,7 @@ def _rolling_window(a, window): return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) -def _apply_shift(arr, freq, shift): - - slc = (None,) * (arr.ndim - 1) + (slice(None),) +def _apply_shift(arr, freq, offset): df = np.abs(freq[1] - freq[0]) tau = np.fft.rfftfreq(freq.size, d=df * 1e6) * 1e6 @@ -155,7 +155,7 @@ def fit_histogram( # Define the fitting function def gauss(x, peak, mu, sigma): - return peak * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2)) + return peak * np.exp(-((x - mu) ** 2) / (2.0 * sigma**2)) # Perform the fit par, var_par = curve_fit( @@ -217,9 +217,9 @@ def convolve_template(data, template, pp=None, weight=None, max_df=6.0, offset=N ikeep = np.flatnonzero(np.abs(template.freq[:]) <= max_df) if pp is None: - d, w = combine_pol(data) + d, w = utils.combine_pol(data) w = weight if weight is not None else w - t, wt = combine_pol(template) + t, wt = utils.combine_pol(template) else: d = data["stack"][pp] w = weight if weight is not None else data["weight"][pp] @@ -296,7 +296,7 @@ def process_data_matched_filter( mock_stack[0:mpol, mm, :] = mck["stack"][:] mock_weight[0:mpol, mm, :] = mck["weight"][:] - ms, mw = combine_pol(mck) + ms, mw = utils.combine_pol(mck) mock_stack[-1, mm, :] = ms mock_weight[-1, mm, :] = mw @@ -312,12 +312,12 @@ def process_data_matched_filter( (noise_scale_factor[:, np.newaxis] * exp_std_mock) ** 2 ) else: - weight = tools.invert_no_zero(obs_std_mock ** 2) + weight = tools.invert_no_zero(obs_std_mock**2) # Calculate the standard deviation of the mocks by fitting to histogram dcmock = {} for pp, pstr in enumerate(pol): - dcmock[pstr] = cal_utils.fit_histogram( + dcmock[pstr] = fit_histogram( scale * mock_stack[pp].flatten(), bins=bins, rng=brng, @@ -372,7 +372,7 @@ def process_data_matched_filter( # Calculate the standard deviation of the convolved template by fitting to histogram dcmamp = {} for pp, pstr in enumerate(pol): - dcmamp[pstr] = cal_utils.fit_histogram( + dcmamp[pstr] = fit_histogram( scale * mamp[pp].flatten(), bins=bins, rng=brng, @@ -394,8 +394,8 @@ def process_data_matched_filter( results["distribution_mock"] = dcmock results["distribution_mock_convolved"] = dcmamp - dd, dw = combine_pol(data) - td, tw = combine_pol(template) + dd, dw = utils.combine_pol(data) + td, tw = utils.combine_pol(template) results["data"] = np.concatenate((data["stack"][:], dd[np.newaxis, :]), axis=0) results["template"] = np.concatenate( diff --git a/fitstack/mcmc.py b/fitstack/mcmc.py index 02f5c80..686db7a 100644 --- a/fitstack/mcmc.py +++ b/fitstack/mcmc.py @@ -1,9 +1,11 @@ """Perform an MCMC fit of a model to a source stack.""" + import logging import inspect import numpy as np import emcee +import h5py from caput import config, pipeline @@ -28,119 +30,121 @@ def _all_subclasses(cls): SIMULATION_MODELS = [ c.__name__ - for c in [models.SimulationTemplate] + for c in [ + models.SimulationTemplate, + models.AutoSimulationTemplate1D, + models.AutoSimulationTemplate2Dto1D, + ] + list(_all_subclasses(models.SimulationTemplate)) + + list(_all_subclasses(models.AutoSimulationTemplate1D)) + + list(_all_subclasses(models.AutoSimulationTemplate2Dto1D)) +] + +PS1D_SIMULATION_MODELS = [ + c.__name__ + for c in [models.AutoSimulationTemplate1D] + + list(_all_subclasses(models.AutoSimulationTemplate1D)) +] + +PS2D_SIMULATION_MODELS = [ + c.__name__ + for c in [models.AutoSimulationTemplate2Dto1D] + + list(_all_subclasses(models.AutoSimulationTemplate2Dto1D)) ] PERCENTILE = [2.5, 16, 50, 84, 97.5] +# Co-pols are stored as XX, YY for stacking and XX-XX, YY-YY +# for power spectra. Internally, we always use XX, YY, but we +# need to map these to the relevant strings depending on the input +# data. +_STACKING_POLNAME = {"XX": "XX", "YY": "YY", "I": "I"} +_PS_POLNAME = {"XX": "XX-XX", "YY": "YY-YY", "I": "I-I", "Q": "Q-Q"} -# main script -def run_mcmc( + +def initialize_mcmc_ingredients( data, mocks, + data_2d=None, transfer=None, template=None, pol_fit="joint", + required_pol=None, + pol_stokes=False, model_name="Exponential", scale=1e6, nwalker=32, nsample=15000, max_freq=None, - flag_before=True, + min_k=None, + max_k=None, + flag_before=False, normalize_template=False, mean_subtract=True, recompute_weight=True, model_kwargs=None, param_spec=None, seed=None, + flag_ind=None, + hartlap=True, + force_real=True, ): - """Fit a model to the source stack using an MCMC. + """Initialize ingredients for MCMC. - Parameters - ---------- - data : FrequencyStackByPol or str - Stack of the data on the sources. This can either be a - FrequencyStackByPol container or the name of a file that - holds such a container and will be loaded from disk. - mocks : MockFrequencyStackByPol, list of FrequencyStackByPol, str, list of str - Stack of the data on a set of random mock catalogs. - This can either be a MockFrequencyStackByPol container - or a list of FrequencyStackByPol containers. It can also - be the name of a file or a list of filenames that hold - such containers and will be loaded from disk. - transfer : FrequencyStackByPol or str - The transfer function of the pipeline. The model for the stacked - signal will be convolved with the transfer function prior - to comparing to the data. This can either be a - FrequencyStackByPol container or the name of a file that - holds such a container and will be loaded from disk. - If None, then a transfer function is not applied. Default is None. - template : FrequencyStackByPol or str - Template for the stacked signal. This can either be a - FrequencyStackByPol container or the name of a file that - holds such a container and will be loaded from disk. - Note that not all models require templates. Default is None. - pol_fit : {"XX"|"YY"|"I"|"joint"} - Polarisation to fit. Here "I" refers to the weighted sum of the - "XX" and "YY" polarisations and "joint" refers to a simultaneous - fit to the "XX" and "YY" polarisations. - model_name : {"DeltaFunction"|"Exponential"|"ScaledShiftedTemplate"| - "SimulationTemplate"|"SimulationTemplateFoG"| - "SimulationTemplateFoGAltParam"} - Name of the model to fit. Specify the class name from the - fitstack.models module. - scale : float - All data will be scaled by this quantity. Default is 1e6, under the - assumption that the stacked signal is in units of Jy / beam and we would - like to convert to micro-Jy / beam. Set to 1.0 if you do not want to - scale the data. - nwalker : int - Number of walkers to use in the MCMC fit. Default is 32. - nsample : int - Number of steps that each walker will take. Default is 15000. - max_freq : float - The maximum frequency offset to include in the fit. If this None, - then all frequency offsets that are present in the data stack - will be included. Default is None. - flag_before : bool - Only relevant if max_freq is not None. The frequency offset flag will - be applied prior to calculating the inverse of the covariance matrix. - Default is True. - normalize_template : bool - Divide the template by it's maximum value prior to fitting. - Default is False. - mean_subtract : bool - Subtract the sample mean of the mocks from the data prior to fitting. - Default is True. - recompute_weight : bool - Set the weight dataset to the inverse variance over the mock catalogs. - This is only used when averaging the "XX" and "YY" polarisations to - determine the "I" polarisation. Otherwise whatever weight dataset - is saved to the file will be used. Default is True. - param_spec : dict - Dictionary that specifies the prior distribution for each parameter. - See the docstring for the models.Model attribute for the correct format. - model_kwargs : dict - Dictionary that contains any keyword arguments that should be passed - to the model class at initialization. - seed : int - Seed to use for random number generation. If the seed is not provided, - then a random seed will be taken from system entropy. + See run_mcmc docstring for parameter info. Returns ------- - results : MockStack1D - Container with the results of the fit, including - parameter chains, chi-squared chains, autocorrelation length, - acceptance franction, parameter percentiles, best-fit model, - and model percentiles. Also includes all data products - and ancillary data products, as well as the covariance and - precision matrix inferred from the mocks. + results : containers.MCMCFit + Container data products and ancillary data products, as well + as the covariance and precision matrix inferred from the mocks. + model : models.Model + Model to be used in chain. + eval_kwargs : dict + Keyword arguments to be used when evaluating model. + npol : int + Number of polarizations to fit. + nx : int + Number of data points to fit. """ - required_pol = ["XX", "YY"] - combine_pol = True + # Function to take real part if force_real is set + def _re(x): + return np.real(x) if force_real else x + + if isinstance(data, str): + with h5py.File(utils.find_file(data), "r") as handler: + container_type = handler.attrs["__memh5_subclass"] + if container_type == "draco.core.containers.FrequencyStackByPol": + polname = _STACKING_POLNAME + elif container_type in [ + "draco.core.containers.PowerSpectrum2D", + "draco.core.containers.PowerSpectrum1D", + ]: + polname = _PS_POLNAME + else: + raise RuntimeError( + f"Container type of data argument ({container_type})" + " not recognized" + ) + else: + if isinstance(data, containers.FrequencyStackByPol): + polname = _STACKING_POLNAME + else: + polname = _PS_POLNAME + + if pol_stokes: + if required_pol is None: + required_pol = [polname["I"], polname["Q"]] + combine_pol = False + else: + if required_pol is None: + required_pol = [polname["XX"], polname["YY"]] + combine_pol = ( + True + if ("DualPol" not in model_name) and ("Split" not in model_name) + else False + ) if param_spec is None: param_spec = {} @@ -152,168 +156,272 @@ def run_mcmc( if isinstance(data, str): data = utils.load_pol(utils.find_file(data), pol=required_pol) - # Load the transfer function + # From the input container, determine the dataset corresponding to the observable + # (stack or power spectrum) + if isinstance(data, containers.FrequencyStackByPol): + dset = "stack" + elif isinstance(data, containers.PowerSpectrum1D): + dset = "spectrum" + else: + raise RuntimeError(f"Input container {type(data)} not supported") + + # If using 1d power spectrum measurements with a 2d power spectrum model, load + # 2d measurements + ps2d_model = model_name in PS2D_SIMULATION_MODELS + if dset == "spectrum" and ps2d_model: + if data_2d is None: + raise RuntimeError( + "Must specify data_2d if fitting 1d power spectrum " + "measurements with model that starts in 2d" + ) + if isinstance(data_2d, str): + data_2d = utils.load_pol(utils.find_file(data_2d), pol=required_pol) + + # Load the transfer function (not implemented for power spectra) if transfer is not None and isinstance(transfer, str): + if dset == "spectrum": + raise NotImplementedError( + "Transfer function convolution not implemented for power spectrum" + ) transfer = utils.load_pol(utils.find_file(transfer), pol=required_pol) # Load the templates if template is not None: template = utils.load_mocks(template, pol=required_pol) - # Load the mock catalogs + # Load the mock catalogs or noise power spectra mocks = utils.load_mocks(mocks, pol=required_pol) nmock = mocks.index_map["mock"].size - # If requested, use the inverse variance over the mock catalogs as the weight - # when averaging over polarisations. + # If requested, use the inverse variance over the mock catalogs + # or noise power spectra as the weight when averaging over polarisations. if recompute_weight: - inv_var = tools.invert_no_zero(np.var(mocks.stack[:], axis=0)) - axes = mocks.stack.attrs["axis"][1:] + axes = mocks[dset].attrs["axis"][1:] for container in [data, mocks, template, transfer]: if container is not None: - expand = tuple( - slice(None) if ax in axes else None - for ax in container.weight.attrs["axis"] + if dset == "stack": + inv_var = tools.invert_no_zero(np.var(_re(mocks.stack[:]), axis=0)) + expand = tuple( + slice(None) if ax in axes else None + for ax in container.weight.attrs["axis"] + ) + container.weight[:] = inv_var[expand] + else: + variance = np.var(_re(mocks.spectrum[:]), axis=0) + expand = tuple( + slice(None) if ax in axes else None + for ax in container.var.attrs["axis"] + ) + container.var[:] = variance[expand] + + # For the simulation template, we need to provide parameters to average the polarisations + if model_name in SIMULATION_MODELS: + if dset == "stack": + if "Split" not in model_name: + model_kwargs["weight"] = ( + inv_var if recompute_weight else _re(data.weight[:]) + ) + else: + if ps2d_model: + # If our power spectrum model starts from 2d, take weights and signal_mask + # from data_2d + _, weight_meas_2d, _, _, signal_mask_2d, _ = utils.initialize_pol( + data_2d, + pol=required_pol, + combine=combine_pol, + return_signal_mask_and_neff=True, ) - container.weight[:] = inv_var[expand] + model_kwargs["weight"] = _re(weight_meas_2d) + model_kwargs["signal_mask"] = signal_mask_2d + + model_kwargs["nbins"] = data.k1D.shape[-1] + else: + model_kwargs["weight"] = tools.invert_no_zero(data.var[:]) - # For the simulation template we need to provide parameters to average the polarisations - if model_name in SIMULATION_MODELS: - model_kwargs["weight"] = inv_var if recompute_weight else data.weight[:] model_kwargs["pol"] = required_pol model_kwargs["combine"] = combine_pol model_kwargs["sort"] = True - # Determine the frequencies to fit - freq = data.freq[:] - nfreq = freq.size + # Initialize data arrays. + # We use x to stand for either frequencies or k values, depending on input data type + data_meas, weight_meas, pol, x_meas = utils.initialize_pol( + data, pol=required_pol, combine=combine_pol + ) - isort = np.argsort(freq) - freq = freq[isort] + # Sort frequencies or k values, then determine which values to use in fit + isort = np.argsort(_re(x_meas), axis=-1) + x = np.take_along_axis(_re(x_meas), isort, axis=-1) + nx = x.shape[-1] - if max_freq is not None: - freq_flag = np.abs(freq) < max_freq + if dset == "stack": + if max_freq is None: + max_freq = 1e10 + x_flag = np.abs(x) < max_freq else: - freq_flag = np.ones(nfreq, dtype=bool) - - freq_index = np.flatnonzero(freq_flag) - freq_slice = slice(freq_index[0], freq_index[-1] + 1) - - # Initialize all arrays - data_stack, weight_stack, pol = utils.initialize_pol( - data, pol=required_pol, combine=combine_pol - ) - data_stack = scale * data_stack[..., isort] - weight_stack = weight_stack[..., isort] / scale ** 2 + if min_k is None: + min_k = 0.0 + if max_k is None: + max_k = 1e10 + x_flag = (np.abs(x) > min_k) & (np.abs(x) < max_k) + + # Use pol-independent flags, for simplicity + x_1d_flag = np.all(x_flag, axis=0) + x_1d_index = np.flatnonzero(x_1d_flag) + x_1d_slice = slice(x_1d_index[0], x_1d_index[-1] + 1) + + # If working with stacks, define 1d frequency axis for later convenience + if dset == "stack": + freq = np.mean(x, axis=0) + + # Sort data and weight arrays + data_meas = scale * np.take_along_axis(_re(data_meas), isort, axis=-1) + weight_meas = np.take_along_axis(_re(weight_meas), isort, axis=-1) / scale**2 npol = len(pol) - mock_stack, _, _ = utils.initialize_pol( + # Initialize array for mocks + mock_meas, _, _, _ = utils.initialize_pol( mocks, pol=required_pol, combine=combine_pol ) - mock_stack = scale * mock_stack[..., isort] + mock_meas = scale * np.take_along_axis( + _re(mock_meas), isort[np.newaxis, ...], axis=-1 + ) + # Initialize array for transfer function if transfer is not None: - transfer_stack, _, _ = utils.initialize_pol( + transfer_meas, _, _, _ = utils.initialize_pol( transfer, pol=required_pol, combine=combine_pol ) - transfer_stack = transfer_stack[..., isort] + transfer_meas = np.take_along_axis(_re(transfer_meas), isort, axis=-1) + # Initialize array for template if template is not None: # Use the mean value of the template over realizations - template = utils.average_stacks( + template = utils.average_data( template, pol=required_pol, combine=combine_pol, sort=True ) - template_stack = scale * template.stack[:] + template_meas = scale * _re(template[dset][:]) if normalize_template: max_template = np.max( - template_stack[..., freq_slice], axis=-1, keepdims=True + template_meas[..., x_1d_slice], axis=-1, keepdims=True ) - template_stack = template_stack / max_template + template_meas = template_meas / max_template - # Subtract mean value of mocks + # Subtract mean value of mocks from the mocks and data if mean_subtract: logger.info("Subtracting the mean value of the mocks.") - mu = np.mean(mock_stack, axis=0) - mock_stack = mock_stack - mu[np.newaxis, ...] - data_stack = data_stack - mu + mu = np.mean(mock_meas, axis=0) + mock_meas = mock_meas - mu[np.newaxis, ...] + data_meas = data_meas - mu # Prepare the model Model = getattr(models, model_name) - model = Model(seed=seed, **{**model_kwargs, **param_spec}) - - param_name = model.param_name - nparam = len(param_name) + model = Model(seed=seed, force_real=force_real, **{**model_kwargs, **param_spec}) # Calculate the covariance over mocks - cov_flat = utils.covariance(mock_stack.reshape(nmock, -1), corr=False) - - cov = utils.unravel_covariance(cov_flat, npol, nfreq) + cov_flat = utils.covariance(mock_meas.reshape(nmock, -1), corr=False) + cov = utils.unravel_covariance(cov_flat, npol, nx) # Determine the polarisation to fit - if pol_fit in ["XX", "YY", "I"]: - ipol = pol.index(pol_fit) + if pol_fit in polname.keys(): + ipol = pol.index(polname[pol_fit]) npol_fit = 1 C = cov[ipol, ipol] - ifit = freq_slice + ifit = x_1d_index elif pol_fit == "joint": - ipol = np.array([pol.index(pstr) for pstr in ["XX", "YY"]]) + if pol_stokes: + ipol = np.array([pol.index(pstr) for pstr in [polname["I"], polname["Q"]]]) + else: + ipol = np.array( + [pol.index(pstr) for pstr in [polname["XX"], polname["YY"]]] + ) npol_fit = len(ipol) C = utils.ravel_covariance(cov[ipol][:, ipol]) - ifit = np.concatenate(tuple([p * nfreq + freq_index for p in range(npol_fit)])) + ifit = np.concatenate(tuple([p * nx + x_1d_index for p in range(npol_fit)])) else: raise ValueError( - f"Do not recognize polarisation {pol_fit}, " - "possible values are 'XX', 'YY', 'I' or 'joint'" + f"Do not recognize polarisation {pol_fit} " + "(possible values are 'XX', 'YY', 'I', 'Q', or 'joint')" ) + # Apply extra flagging if specified + if flag_ind is not None: + logger.debug(f"Flagging out extra data: starting with {len(ifit)} samples.") + ifit = [fi for ii, fi in enumerate(ifit) if ii not in flag_ind] + logger.debug(f"Ending with {len(ifit)} samples.") + else: + logger.debug("No samples flagged out.") + + # Make array of coordinates for MCMC, as list of (pol, coord_value) tuples pol = np.array(pol) - x = np.zeros(nfreq * npol_fit, dtype=[("pol", "U8"), ("freq", np.float64)]) + x_coord_name = "freq" if dset == "stack" else "k" + x_for_mcmc = np.zeros( + nx * npol_fit, dtype=[("pol", "U8"), (x_coord_name, np.float64)] + ) for p, pstr in enumerate(np.atleast_1d(pol[ipol])): - slc = slice(p * nfreq, (p + 1) * nfreq) - x["pol"][slc] = pstr - x["freq"][slc] = freq + slc = slice(p * nx, (p + 1) * nx) + x_for_mcmc["pol"][slc] = pstr + x_for_mcmc[x_coord_name][slc] = x[p] # Create results container - results = containers.MCMCFit1D( - x=x, - freq=freq, - pol=pol, - mock=nmock, - walker=nwalker, - step=nsample, - param=np.array(model.param_name), - percentile=np.array(PERCENTILE), - ) + if dset == "stack": + results = containers.MCMCFit1D( + x=x_for_mcmc, + freq=freq, + pol=pol, + mock=nmock, + walker=nwalker, + step=nsample, + param=np.array(model.param_name), + percentile=np.array(PERCENTILE), + ) + results["weight"][:] = weight_meas + results["freq_flag"][:] = x_1d_flag + + else: + results = containers.MCMCFitPowerSpectrum1D( + x=x_for_mcmc, + k=nx, + pol=pol, + mock=nmock, + walker=nwalker, + step=nsample, + param=np.array(model.param_name), + percentile=np.array(PERCENTILE), + cosmology=data.cosmology, + ) + results["var"][:] = tools.invert_no_zero(weight_meas) + results["k_flag"][:] = x_1d_flag + results["k1D"][:] = x results.attrs["seed"] = str(model.seed) results.attrs["model"] = model_name - results.attrs["pol_fit"] = pol_fit + results.attrs["pol_fit"] = ( + polname[pol_fit] if pol_fit in polname.keys() else pol_fit + ) - results["mock"][:] = mock_stack - results["stack"][:] = data_stack - results["weight"][:] = weight_stack - results["freq_flag"][:] = freq_flag + results["mock"][:] = mock_meas + results[dset][:] = data_meas results["fixed"][:] = True results["fixed"][:][model.fit_index] = False if transfer is not None: results.add_dataset("transfer_function") - results["transfer_function"][:] = transfer_stack + results["transfer_function"][:] = transfer_meas if template is not None: results.add_dataset("template") - results["template"][:] = template_stack + results["template"][:] = template_meas results["cov"][:] = cov - results["error"][:] = np.sqrt(np.diag(cov_flat).reshape(npol, nfreq)) + results["error"][:] = np.sqrt(np.diag(cov_flat).reshape(npol, nx)) # Invert the covariance matrix to obtain the precision matrix Cinv = np.zeros_like(C) @@ -323,6 +431,13 @@ def run_mcmc( Cinvfit = np.linalg.pinv(C) + # Compute and apply the Hartlap factor to the inverse covariance + if hartlap: + hartlap_factor = (nmock - Cinvfit.shape[0] - 2.0) / (nmock - 1.0) + Cinvfit *= hartlap_factor + else: + hartlap_factor = 1.0 + if not flag_before: Cinvfit = Cinvfit[ifit][:, ifit] @@ -333,39 +448,243 @@ def run_mcmc( results["flag"][:] = np.diag(Cinv) > 0.0 # Set the data for this polarisation - y = data_stack[ipol] + y = data_meas[ipol] - fit_kwargs = {"freq": freq, "data": y, "inv_cov": Cinv} - eval_kwargs = {"freq": freq} + if dset == "stack": + # freq is 1d array of frequencies + fit_kwargs = {"freq": freq, "data": y, "inv_cov": Cinv} + eval_kwargs = {"freq": freq} + else: + # Reminder: x is 2d array of [pol,k] + fit_kwargs = {"k1D": x[ipol], "data": y, "inv_cov": Cinv} + eval_kwargs = {"k1D": x} if transfer is not None: - fit_kwargs["transfer"] = transfer_stack[ipol] - eval_kwargs["transfer"] = transfer_stack[:] + fit_kwargs["transfer"] = transfer_meas[ipol] + eval_kwargs["transfer"] = transfer_meas[:] else: fit_kwargs["transfer"] = None eval_kwargs["transfer"] = None if template is not None: - fit_kwargs["template"] = template_stack[ipol] - eval_kwargs["template"] = template_stack[:] - - if model_name in SIMULATION_MODELS: + fit_kwargs["template"] = template_meas[ipol] + eval_kwargs["template"] = template_meas[:] + + if ( + model_name in SIMULATION_MODELS + and ("DualPol" not in model_name) + and ("Split" not in model_name) + ): fit_kwargs["pol_sel"] = ipol eval_kwargs["pol_sel"] = slice(None) + # Save model_kwargs, param_spec, and a few other quantities + # to results container for later reference + results.attrs["model_kwargs"] = model_kwargs + results.attrs["param_spec"] = param_spec + results.attrs["pol_sel"] = ipol + results.attrs["ifit"] = ifit + results.attrs["flag_before"] = flag_before + results.attrs["hartlap_factor"] = hartlap_factor + model.set_data(**fit_kwargs) + return results, model, eval_kwargs, npol, nx + + +def run_mcmc( + data, + mocks, + data_2d=None, + transfer=None, + template=None, + pol_fit="joint", + required_pol=None, + pol_stokes=False, + model_name="Exponential", + scale=1e6, + nwalker=32, + nsample=15000, + max_freq=None, + min_k=None, + max_k=None, + flag_before=False, + normalize_template=False, + mean_subtract=True, + recompute_weight=True, + model_kwargs=None, + param_spec=None, + seed=None, + flag_ind=None, + hartlap=True, + force_real=True, + progress=True, +): + """Fit a model to the source stack or power spectrum using an MCMC. + + Parameters + ---------- + data : FrequencyStackByPol, PowerSpectrum1D, or str + Measurements of stacking or power spectrum. + This can either be a FrequencyStackByPol or PowerSpectrum1D + container, or the name of a file that holds such a container + and will be loaded from disk. + mocks : container, list of containers, str, or list of str + Mocks for estimating a noise covariance. + This can either be a MockFrequencyStackByPol or + MockPowerSpectrum1D container, a list of FrequencyStackByPol or + PowerSpectrum1D containers, or the name of a file or a list of + filenames that hold such containers and will be loaded from disk. + data_2d : PowerSpectrum2D or str + Measurements of 2d power spectrum, either as a PowerSpectrum2D + container or filename. When fitting to 1d power spectrum + measurements with a model that starts in 2d, weights and + (kpara,kperp) masking will be taken from here. Ignored if not + needed. + transfer : FrequencyStackByPol or str + The transfer function of the pipeline (only implemented for stacking). + The model for the stacked + signal will be convolved with the transfer function prior + to comparing to the data. This can either be a + FrequencyStackByPol container or the name of a file that + holds such a container and will be loaded from disk. + If None, then a transfer function is not applied. Default is None. + template : FrequencyStackByPol, PowerSpectrum1D, or str + Template for the stacked signal. This can either be a + FrequencyStackByPol or PowerSpectrum1D container, or the name of + a file that holds such a container and will be loaded from disk. + Note that not all models require templates. Default is None. + pol_fit : {"XX"|"YY"|"I"|"Q"|"joint"} + Polarisation to fit. Here "I" refers to the weighted sum of the + "XX" and "YY" polarisations for stacking measurements and the + unweighted sum of "XX" and "YY" for power spectrum measurements. + "joint" refers to a simultaneous fit to the "XX" and "YY" or "I" + and "Q" polarisations. + required_pol : list + Polarizations to load from file. If None, defaults to ["XX", "YY"] + for stacking measurements and ["I-I", "Q-Q] for power spectrum + measuerements. + pol_stokes : bool + If True, assume that all input files contain Stokes parameters + instead of instrumental polarisations. Default: False. + model_name : {"DeltaFunction"|"Exponential"|"ScaledShiftedTemplate"| + "SimulationTemplate"|"SimulationTemplateFoG"| + "SimulationTemplateFoGAltParam"|"AutoConstant"| + "AutoSimulationTemplate2Dto1D"} + Name of the model to fit. Specify the class name from the + fitstack.models module. + scale : float + All data will be scaled by this quantity. Default is 1e6, under the + assumption that the signal in a stacking analysis is in units of Jy / beam + and we would like to convert to micro-Jy / beam. + Set to 1.0 if you do not want to scale the data. + nwalker : int + Number of walkers to use in the MCMC fit. Default is 32. + nsample : int + Number of steps that each walker will take. Default is 15000. + max_freq : float + The maximum frequency offset to include in a stacking fit. If this None, + then all frequency offsets that are present in the data stack + will be included. Default is None. + min_k, max_k : float + The minimum or maximum k values to include in a power spectrum fit. + Default is None. + flag_before : bool + Only relevant if max_freq, min_k, or max_k is not None. + The frequency offset flag will be applied prior to calculating the + inverse of the covariance matrix. Default is False. + normalize_template : bool + Divide the template by its maximum value prior to fitting. + Default is False. + mean_subtract : bool + Subtract the sample mean of the mocks from the data prior to fitting. + Default is True. + recompute_weight : bool + Set the weight dataset to the inverse variance over the mock catalogs + in a stacking analysis, or set the 1D `var` dataset to the variance over + the noise power spectra in a power spectrum analysis. + This is only used when averaging the "XX" and "YY" polarisations to + determine the "I" polarisation. Otherwise whatever weight/var dataset + is saved to the file will be used. Default is True. + param_spec : dict + Dictionary that specifies the prior distribution for each parameter. + See the docstring for the models.Model attribute for the correct format. + model_kwargs : dict + Dictionary that contains any keyword arguments that should be passed + to the model class at initialization. + seed : int + Seed to use for random number generation. If the seed is not provided, + then a random seed will be taken from system entropy. + flag_ind : list + List of extra indices to flag. These are indices into the flattened data *after* + all other selections have been applied. + hartlap : bool + Apply the Hartlap factor to the inverse covariance computed from mocks. + Default: True. + force_real : bool + Force input datasets to be real. Assumes that input datasets have + been previously examined to verify that imaginary parts are small and/or + unimportant. Default: True. + progress : bool + Whether to display emcee progress bar. Default: True. + + Returns + ------- + results : MCMCFit + Container with the results of the fit, including + parameter chains, chi-squared chains, autocorrelation length, + acceptance franction, parameter percentiles, best-fit model, + and model percentiles. Also includes all data products + and ancillary data products, as well as the covariance and + precision matrix inferred from the mocks. + """ + + # Initialize MCMCFit container for results, theory model, kwargs for + # model, and lengths of data to fit + results, model, eval_kwargs, npol, nx = initialize_mcmc_ingredients( + data, + mocks, + data_2d=data_2d, + transfer=transfer, + template=template, + pol_fit=pol_fit, + pol_stokes=pol_stokes, + model_name=model_name, + scale=scale, + nwalker=nwalker, + nsample=nsample, + max_freq=max_freq, + min_k=min_k, + max_k=max_k, + flag_before=flag_before, + normalize_template=normalize_template, + mean_subtract=mean_subtract, + recompute_weight=recompute_weight, + model_kwargs=model_kwargs, + param_spec=param_spec, + seed=seed, + flag_ind=flag_ind, + hartlap=hartlap, + force_real=force_real, + ) + + # Set seed for emcee + if seed is not None: + np.random.seed(seed) + # Determine starting point for chains in parameter space pos = np.array([model.draw_random_parameters() for ww in range(nwalker)]) nwalker, ndim = pos.shape + nparam = len(model.param_name) + # Create the sampler and run the MCMC - sampler = emcee.EnsembleSampler(nwalker, ndim, model.log_probability) + sampler = emcee.EnsembleSampler(nwalker, ndim, model.log_probability_sampler) - sampler.run_mcmc(pos, nsample, progress=False) + sampler.run_mcmc(model.forward_transform_sampler(pos), nsample, progress=progress) - chain = sampler.get_chain() + chain = model.backward_transform_sampler(sampler.get_chain()) # Compute the chisq chisq = results["chisq"][:].view(np.ndarray) @@ -395,6 +714,11 @@ def run_mcmc( # Discard burn in and thin the chains flat_samples = results.samples(flat=True) + if len(flat_samples) == 0: + raise RuntimeError( + "After thinning and burn-in removal, chain has no samples remaining!\n" + "Re-run with larger number of samples." + ) # Compute percentiles of the posterior distribution q = np.percentile(flat_samples, PERCENTILE, axis=0).T @@ -406,7 +730,7 @@ def run_mcmc( results["span_upper"][:] = dq[:, 2] # Compute percentiles of the model - mdl = np.zeros((flat_samples.shape[0], npol, nfreq), dtype=np.float32) + mdl = np.zeros((flat_samples.shape[0], npol, nx), dtype=np.float32) for ss, theta in enumerate(flat_samples): mdl[ss] = model.model(theta, **eval_kwargs) @@ -436,6 +760,7 @@ class RunMCMC(task.SingleTask): """ max_iter = config.Property(proptype=int, default=1) + data_2d = config.Property(proptype=str, default=None) data = config.Property(proptype=str) mocks = config.Property(proptype=_list_or_glob) @@ -443,6 +768,8 @@ class RunMCMC(task.SingleTask): template = config.Property(proptype=_list_or_glob) pol_fit = config.Property(proptype=str) + required_pol = config.Property(proptype=list) + pol_stokes = config.Property(proptype=bool) model_name = config.Property(proptype=str) scale = config.Property(proptype=float) @@ -450,6 +777,8 @@ class RunMCMC(task.SingleTask): nsample = config.Property(proptype=int) max_freq = config.Property(proptype=float) + min_k = config.Property(proptype=float) + max_k = config.Property(proptype=float) flag_before = config.Property(proptype=bool) normalize_template = config.Property(proptype=bool) mean_subtract = config.Property(proptype=bool) @@ -458,6 +787,11 @@ class RunMCMC(task.SingleTask): param_spec = config.Property(proptype=dict) model_kwargs = config.Property(proptype=dict) seed = config.Property(proptype=int) + flag_ind = config.list_type(type_=int) + hartlap = config.Property(proptype=bool) + + force_real = config.Property(proptype=bool) + progress = config.Property(proptype=bool) def setup(self): """Prepare all arguments to the run_mcmc function.""" @@ -469,7 +803,6 @@ def setup(self): k: v.default if v.default is not inspect.Parameter.empty else None for k, v in signature.parameters.items() } - self.kwargs = {} for key, default_val in defaults.items(): if hasattr(self, key): diff --git a/fitstack/models.py b/fitstack/models.py index 7c7dd43..2a8278c 100644 --- a/fitstack/models.py +++ b/fitstack/models.py @@ -1,5 +1,7 @@ """Define models that can be fit to the source stack.""" + import inspect +import logging import numpy as np @@ -10,6 +12,9 @@ from . import utils +logger = logging.getLogger(__name__) + + class Model(object): """Baseclass for emcee models. @@ -49,7 +54,7 @@ class Model(object): param_name = [] _param_spec = {} - def __init__(self, seed=None, **param_spec): + def __init__(self, seed=None, force_real=True, **param_spec): """Initialize the model. Parameters @@ -58,6 +63,11 @@ def __init__(self, seed=None, **param_spec): Seed to use for random number generation. If the seed is not provided, then a random seed will be taken from system entropy. + force_real : bool + Force input datasets to be real. Assumes that input + datasets have been previously examined to verify + that imaginary parts are small and/or unimportant. + Default: True. param_spec : dict Specifies the prior distribution for each parameter. See the description of the class attribute of the @@ -72,10 +82,15 @@ def __init__(self, seed=None, **param_spec): self.seed = seed self.rng = np.random.Generator(np.random.SFC64(seed)) + self.force_real = force_real + defaults = self.default_param_spec() self.param_spec = {} for name in self.param_name: - self.param_spec[name] = param_spec.get(name, defaults[name]) + if name in param_spec: + self.param_spec[name] = param_spec[name] + else: + self.param_spec[name] = defaults[name] self.priors = {} for name, spec in self.param_spec.items(): @@ -100,8 +115,11 @@ def __init__(self, seed=None, **param_spec): name for name in self.param_name if self.param_spec[name]["fixed"] ] + def _re(self, x): + return np.real(x) if self.force_real else x + def set_data(self, **kwargs): - """Save any ancillary data needed to evaluate the probabily distribution. + """Save any ancillary data needed to evaluate the probability distribution. Parameters ---------- @@ -153,13 +171,15 @@ class attribute and -Inf if they are outside the ranges. return log_prior - def log_likelihood(self, theta): + def log_likelihood(self, theta, amp=None): """Evaluate the log of the likelihood. Parameters ---------- theta : list Values for the fit parameters. + amp : float, optional + Scale model by extra amplitude. Default: None. Returns ------- @@ -170,11 +190,30 @@ def log_likelihood(self, theta): theta_all = self.get_all_params(theta) mdl = self.model(theta_all) + if amp is not None: + mdl *= amp residual = np.ravel(self.data - mdl) return -0.5 * np.matmul(residual.T, np.matmul(self.inv_cov, residual)) + def negative_log_likelihood(self, theta, amp=None): + """Evaluate the negative log of the likelihood. + + Parameters + ---------- + theta : list + Values for the fit parameters. + amp : float, optional + Scale model by extra amplitude. Default: None. + + Returns + ------- + nlogL : float + Negative logarithm of the likelihood function. + """ + return -self.log_likelihood(theta, amp=amp) + def log_probability(self, theta): """Evaluate log of the probability of observing the data given the parameters. @@ -214,7 +253,8 @@ def get_all_params(self, theta): """ theta_all = np.copy(self.default_values) - theta_all[self.fit_index] = theta + if self.nfit > 0: + theta_all[self.fit_index] = theta return theta_all @@ -258,9 +298,91 @@ def default_param_spec(cls): return param_spec + def forward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Take a sample (or set of) and transform into the basis used by the sampler. + + Use this to transform into a basis that is more easily traversed by the sampler. + Must be an inverse of `backward_transform_sampler`. + + Parameters + ---------- + sample + A 1D array containing a single sample, or a 2D array containing rows of + samples. + + Returns + ------- + transformed_samples + The sample transformed into the samplers basis. + """ + return sample + + def backward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Take a sample (or set of) and transform from the basis used by the sampler. + + Use this to transform from a basis that is more easily traversed by the sampler. + Must be an inverse of `forward_transform_sampler`. + + Parameters + ---------- + sample + A 1D array containing a single sample, or a 2D array containing rows of + samples in the basis used by the sampler. + + Returns + ------- + original_samples + The sample(s) transformed into the original basis. + """ + return sample + + def log_probability_sampler(self, theta: np.ndarray) -> float: + """A log probability function in the sampler's basis. + + Parameters + ---------- + theta + Coordinate vector in the samplers basis. + + Returns + ------- + lp + The log probability of the sample. + """ + return self.log_probability( + self.backward_transform_sampler(theta) + ) + self.log_transform_measure(theta) + + def log_transform_measure(self, theta: np.ndarray) -> float: + return 0.0 + + +class NullModel(Model): + """Model that just returns zero. + + Intended for assessing a zero-signal null hypothesis. + """ + + param_name = [] + + def model(self, theta): + """Evaluate model. + + Parameters + ---------- + theta : array_like + Array of input parameters (unused). + + Returns + ------- + model : float + Null model value (zero). + """ + return 0.0 + class ScaledShiftedTemplate(Model): - """Scaled and shifted template model.""" + """Scaled and shifted stacking template model.""" param_name = ["amp", "offset"] @@ -270,7 +392,7 @@ class ScaledShiftedTemplate(Model): "value": 1.0, "prior": "Uniform", "kwargs": { - "low": 0.0, + "low": -10.0, "high": 10.0, }, }, @@ -279,14 +401,14 @@ class ScaledShiftedTemplate(Model): "value": 0.0, "prior": "Uniform", "kwargs": { - "low": -1.0, - "high": 1.0, + "low": -0.8, + "high": 0.8, }, }, } def model(self, theta, freq=None, transfer=None, template=None): - """Evaluate the model consisting of a scaled and shifted template. + r"""Evaluate the model consisting of a scaled and shifted stacking template. .. math:: @@ -387,8 +509,8 @@ class Exponential(Model): "value": 0.0, "prior": "Uniform", "kwargs": { - "low": -1.0, - "high": 1.0, + "low": -0.8, + "high": 0.8, }, }, "scale": { @@ -403,7 +525,7 @@ class Exponential(Model): } def model(self, theta, freq=None, transfer=None): - """Evaluate the exponential model. + r"""Evaluate the exponential model. .. math:: @@ -452,7 +574,17 @@ def model(self, theta, freq=None, transfer=None): class SimulationTemplate(Model): """Model consisting of a linear combination of templates from simulations.""" - param_name = ["offset", "omega", "b_HI", "b_g", "NL", "FoGh", "FoGg", "M_10"] + param_name = [ + "offset", + "beam_error", + "omega", + "b_HI", + "b_g", + "NL", + "FoGh", + "FoGg", + "M_10", + ] _param_spec = { "offset": { @@ -464,13 +596,22 @@ class SimulationTemplate(Model): "high": 0.8, }, }, + "beam_error": { + "fixed": True, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.5, + "high": 1.5, + }, + }, "omega": { "fixed": False, "value": 1.0, "prior": "Uniform", "kwargs": { - "low": 0.0, - "high": 5.0, + "low": -10.0, + "high": 10.0, }, }, "b_HI": { @@ -479,7 +620,7 @@ class SimulationTemplate(Model): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 8.0, + "high": 10.0, }, }, # This is the only parameter which is reasonably constrained. @@ -498,8 +639,8 @@ class SimulationTemplate(Model): "value": 1.0, "prior": "Uniform", "kwargs": { - "low": -1.0, - "high": 7.0, + "low": 0.0, + "high": 5.0, }, }, "FoGh": { @@ -508,7 +649,7 @@ class SimulationTemplate(Model): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 4.0, + "high": 5.0, }, }, "FoGg": { @@ -517,7 +658,7 @@ class SimulationTemplate(Model): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 4.0, + "high": 5.0, }, }, "M_10": { @@ -526,11 +667,14 @@ class SimulationTemplate(Model): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 25.0, + "high": 20.0, }, }, } + _template_class = signal.SignalTemplate + _template_kwargs = ("symmetrize", "reverse") + def __init__( self, pattern, @@ -538,25 +682,31 @@ def __init__( weight=None, combine=True, sort=True, + symmetrize=False, derivs=None, - factor=1e3, + factor=1e6, aliases=None, *args, - **kwargs + **kwargs, ): + if derivs is None: + derivs = {"lin": (-1.0, 1.0)} + if aliases is None: - aliases = dict(shotnoise="M_10") + aliases = {"shotnoise": "M_10", "lin": "NL"} - self._signal_template = signal.SignalTemplate.load_from_stackfiles( + self._signal_template = self._template_class.load_from_stackfiles( pattern, pol=pol, weight=weight, combine=combine, sort=sort, + symmetrize=symmetrize, derivs=derivs, factor=factor, aliases=aliases, + **{k: v for k, v in kwargs.items() if k in self._template_kwargs}, ) super().__init__(*args, **kwargs) @@ -575,9 +725,13 @@ def model(self, theta, freq=None, transfer=None, pol_sel=None): param_dict = {k: v for k, v in zip(self.param_name, theta)} offset = param_dict.pop("offset") + beam_error = param_dict.pop("beam_error") model_init = self._signal_template.signal(**param_dict)[pol_sel] + if (model_init.ndim > 1) and (model_init.shape[0] > 1): + model_init[1] *= beam_error + model = utils.shift_and_convolve( freq, model_init, offset=offset, kernel=transfer ) @@ -588,7 +742,17 @@ def model(self, theta, freq=None, transfer=None, pol_sel=None): class SimulationTemplateFoG(SimulationTemplate): """Model based on templates from simulations convolved with a FoG damping kernel.""" - param_name = ["offset", "omega", "b_HI", "b_g", "NL", "FoGh", "FoGg", "M_10"] + param_name = [ + "offset", + "beam_error", + "omega", + "b_HI", + "b_g", + "NL", + "FoGh", + "FoGg", + "M_10", + ] _param_spec = { "omega": { @@ -596,8 +760,8 @@ class SimulationTemplateFoG(SimulationTemplate): "value": 1.0, "prior": "Uniform", "kwargs": { - "low": -5.0, - "high": 5.0, + "low": -10.0, + "high": 10.0, }, }, "b_HI": { @@ -605,8 +769,8 @@ class SimulationTemplateFoG(SimulationTemplate): "value": 1.0, "prior": "Uniform", "kwargs": { - "low": -5.0, - "high": 5.0, + "low": 0.0, + "high": 10.0, }, }, "FoGh": { @@ -615,7 +779,7 @@ class SimulationTemplateFoG(SimulationTemplate): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 8.0, + "high": 5.0, }, }, "FoGg": { @@ -624,44 +788,16 @@ class SimulationTemplateFoG(SimulationTemplate): "prior": "Uniform", "kwargs": { "low": 0.0, - "high": 8.0, + "high": 5.0, }, }, } - def __init__( - self, - pattern, - pol=None, - weight=None, - combine=True, - sort=True, - derivs=None, - convolutions=None, - delay_range=None, - factor=1e3, - aliases=None, - *args, - **kwargs - ): - - if aliases is None: - aliases = dict(shotnoise="M_10") - - self._signal_template = signal.SignalTemplateFoG.load_from_stackfiles( - pattern, - pol=pol, - weight=weight, - combine=combine, - sort=sort, - derivs=derivs, - convolutions=convolutions, - delay_range=delay_range, - factor=factor, - aliases=aliases, - ) - - super(SimulationTemplate, self).__init__(*args, **kwargs) + _template_class = signal.SignalTemplateFoG + _template_kwargs = SimulationTemplate._template_kwargs + ( + "convolutions", + "delay_range", + ) class SimulationTemplateFoGAltParam(SimulationTemplateFoG): @@ -672,7 +808,17 @@ class SimulationTemplateFoGAltParam(SimulationTemplateFoG): SimulationTemplateFoG models. """ - param_name = ["offset", "omega", "omega b_HI", "b_g", "NL", "FoGh", "FoGg", "M_10"] + param_name = [ + "offset", + "beam_error", + "omega", + "omega_b_HI", + "b_g", + "NL", + "FoGh", + "FoGg", + "M_10", + ] _param_spec = { "omega": { @@ -684,7 +830,7 @@ class SimulationTemplateFoGAltParam(SimulationTemplateFoG): "high": 5.0, }, }, - "omega b_HI": { + "omega_b_HI": { "fixed": False, "value": 1.0, "prior": "Uniform", @@ -710,7 +856,7 @@ def model(self, theta, freq=None, transfer=None, pol_sel=None): offset = param_dict.pop("offset") - omega_bHI = param_dict.pop("omega b_HI") + omega_bHI = param_dict.pop("omega_b_HI") param_dict["b_HI"] = omega_bHI * tools.invert_no_zero(param_dict["omega"]) model_init = self._signal_template.signal(**param_dict)[pol_sel] @@ -720,3 +866,1017 @@ def model(self, theta, freq=None, transfer=None, pol_sel=None): ) return model + + +class SimulationTemplateFoGTransformDualPol(SimulationTemplateFoG): + + param_name = ["offset", "omega", "b_HI", "b_g", "NL", "FoGh", "FoGg", "M_10"] + + def __init__(self, pol, *args, **kwargs): + + self.param_base = [par for par in self.param_name] + defaults = self.default_param_spec() + + param_name = [] + for pstr in pol: + for name in self.param_name: + key = f"{name}_{pstr}" + param_name.append(key) + kwargs[key] = kwargs.get(name, defaults[name]) + + self.param_name = param_name + self.pol = pol + + super().__init__(pol=pol, *args, **kwargs) + + def model(self, theta, freq=None, transfer=None): + + if freq is None: + freq = self.freq + + if transfer is None: + transfer = self.transfer + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + model = [] + for pp, pol in enumerate(self.pol): + + param_pol = {k: param_dict[f"{k}_{pol}"] for k in self.param_base} + + offset = param_pol.pop("offset") + + model_init = self._signal_template.signal(**param_pol)[pp] + + model.append( + utils.shift_and_convolve( + freq, model_init, offset=offset, kernel=transfer + ) + ) + + return np.array(model) + + def forward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI basis.""" + + newsample = sample.copy() + for pol in self.pol: + ind_omega = self.param_name_fit.index(f"omega_{pol}") + ind_b_HI = self.param_name_fit.index(f"b_HI_{pol}") + newsample[..., ind_b_HI] = sample[..., ind_omega] * sample[..., ind_b_HI] + + return newsample + + def backward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI basis.""" + + newsample = sample.copy() + for pol in self.pol: + ind_omega = self.param_name_fit.index(f"omega_{pol}") + ind_b_HI = self.param_name_fit.index(f"b_HI_{pol}") + newsample[..., ind_b_HI] = sample[..., ind_b_HI] / sample[..., ind_omega] + + return newsample + + def log_transform_measure(self, theta: np.ndarray) -> float: + meas = 0.0 + for pol in self.pol: + ind_omega = self.param_name_fit.index(f"omega_{pol}") + meas -= np.log(np.abs(theta[..., ind_omega])) + + return meas + + +class SimulationTemplateFoGTransformSplit(SimulationTemplateFoG): + + param_name = ["offset", "omega", "b_HI", "b_g", "NL", "FoGh", "FoGg", "M_10"] + + def __init__(self, splits, restricted=True, *args, **kwargs): + + self.param_base = [par for par in self.param_name] + defaults = self.default_param_spec() + + usplits = np.unique(splits) + + if not restricted: + param_name = [] + for split in usplits: + for name in self.param_name: + key = f"{name}_{split}" + param_name.append(key) + kwargs[key] = kwargs.get(name, defaults[name]) + + self.param_name = param_name + + self.restricted = restricted + self.splits = np.array(splits) + self.index_splits = { + name: np.flatnonzero(self.splits == name) for name in usplits + } + + print(self.param_name) + print(self.index_splits) + + super().__init__(*args, **kwargs) + + def model(self, theta, freq=None, transfer=None): + + if freq is None: + freq = self.freq + + if transfer is None: + transfer = self.transfer + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + model = np.zeros((self.splits.size, freq.size), dtype=np.float64) + + for name, index in self.index_splits.items(): + + param_split = {} + for k in self.param_base: + lookup = f"{k}_{name}" if not self.restricted else k + param_split[k] = param_dict[lookup] + + offset = param_split.pop("offset") + + model_init = self._signal_template.signal(**param_split) + + model[index] = utils.shift_and_convolve( + freq, model_init, offset=offset, kernel=transfer + ) + + return model + + def forward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI basis.""" + + newsample = sample.copy() + + if self.restricted: + index = [ + ( + self.param_name_fit.index(f"omega"), + self.param_name_fit.index(f"b_HI"), + ) + ] + else: + index = [ + ( + self.param_name_fit.index(f"omega_{sp}"), + self.param_name_fit.index(f"b_HI_{sp}"), + ) + for sp in self.index_splits.keys() + ] + + for aa, bb in index: + newsample[..., bb] = sample[..., aa] * sample[..., bb] + + return newsample + + def backward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI basis.""" + + newsample = sample.copy() + + if self.restricted: + index = [ + ( + self.param_name_fit.index(f"omega"), + self.param_name_fit.index(f"b_HI"), + ) + ] + else: + index = [ + ( + self.param_name_fit.index(f"omega_{sp}"), + self.param_name_fit.index(f"b_HI_{sp}"), + ) + for sp in self.index_splits.keys() + ] + + for aa, bb in index: + newsample[..., bb] = sample[..., bb] / sample[..., aa] + + return newsample + + def log_transform_measure(self, theta: np.ndarray) -> float: + + if self.restricted: + index = [self.param_name_fit.index(f"omega")] + else: + index = [ + self.param_name_fit.index(f"omega_{sp}") + for sp in self.index_splits.keys() + ] + + meas = 0.0 + for aa in index: + meas -= np.log(np.abs(theta[..., aa])) + + return meas + + +class SimulationTemplateFoGTransform(SimulationTemplateFoG): + """An FoG damped template that samples in a decorrelated basis. + + This uses an alternative basis replacing various parameters to decorrelate the + chains: + + - `b_HI -> omega_b_HI = omega * b_HI` + - `FoGh -> FoG+ = log(FoGh * FoGg) / 2` + - `FoGg -> FoG- = log(FoGh / FoGg) / 2 + + However, the chains are returned (and priors applied) in the original basis. + + Parameters + ---------- + pattern + Glob pattern to find the signal template modes. + data_reverse + Reverse the frequency offset axis in the data before evaluating the likelihood. + This is useful for testing issues in the signal generation. + """ + + def __init__(self, pattern: str, data_reverse: bool = False, *args, **kwargs): + self._data_reverse = data_reverse + logger.debug(f"Reversing the data before sampling: {self._data_reverse}") + super().__init__(pattern, *args, **kwargs) + + def forward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI, FoG+, FoG- basis.""" + + newsample = sample.copy() + ind_omega = self.param_name_fit.index(f"omega") + ind_b_HI = self.param_name_fit.index(f"b_HI") + + newsample[..., ind_b_HI] = sample[..., ind_omega] * sample[..., ind_b_HI] + + # Transform to FoG+ and FoG- parameters for sampling + newsample[..., 5] = 0.5 * np.log(sample[..., 5] * sample[..., 6]) + newsample[..., 6] = 0.5 * np.log(sample[..., 5] / sample[..., 6]) + + return newsample + + def backward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega_b_HI, FoG+, FoG- basis.""" + + newsample = sample.copy() + + ind_omega = self.param_name_fit.index(f"omega") + ind_b_HI = self.param_name_fit.index(f"b_HI") + + newsample[..., ind_b_HI] = sample[..., ind_b_HI] / sample[..., ind_omega] + + # Transform back to FoGh and FoGg + newsample[..., 5] = np.exp(sample[..., 5] + sample[..., 6]) + newsample[..., 6] = np.exp(sample[..., 5] - sample[..., 6]) + + return newsample + + def log_transform_measure(self, theta: np.ndarray) -> float: + """The measure for the coordinate transform.""" + + # The measure for the transform for Omega_b_HI + ind_omega = self.param_name_fit.index(f"omega") + measure = -np.log(np.abs(theta[..., ind_omega])) + + # The log-measure for the transform for FoG+/- transform: 2 * FoGh * FoGg + measure = 2 * theta[..., 5] + np.log(2.0) + + return measure + + def log_likelihood(self, theta): + """Evaluate the log of the likelihood. + + Parameters + ---------- + theta : list + Values for the fit parameters. + + Returns + ------- + logL : float + Logarithm of the likelihood function. + """ + + theta_all = self.get_all_params(theta) + + mdl = self.model(theta_all) + + if self._data_reverse: + # Reverse the frequency axis + data = self.data[..., ::-1] + npol, nfreq = data.shape + inv_cov = self.inv_cov.reshape(npol, nfreq, npol, nfreq) + inv_cov = inv_cov[:, ::-1, :, ::-1].reshape(npol * nfreq, npol * nfreq) + else: + data = self.data + inv_cov = self.inv_cov + + residual = np.ravel(data - mdl) + + return -0.5 * np.matmul(residual.T, np.matmul(inv_cov, residual)) + + +class AutoConstant(Model): + """Power spectrum model that's a constant in k with a free amplitude.""" + + param_name = ["amp"] + + _param_spec = { + "amp": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 10.0, + }, + }, + } + + def model(self, theta, k1D=None, transfer=None, template=None, pol_sel=None): + """Evaluate constant-power-spectrum model. + + Parameters + ---------- + theta : [amp] + One-element list containing the model amplitude. + k1D : np.ndarray[npol, nk] + K values for each pol. If not provided, method will use + the `k1D` attribute. + transfer, template, pol_sel + Unused arguments. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if k1D is None: + k1D = self.k1D + + amp = theta[0] + + model = np.full(k1D.shape, amp) + + return model + + +class AutoScaledTemplate(Model): + """Scaled power spectrum template model.""" + + param_name = ["amp"] + + _param_spec = { + "amp": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 10.0, + }, + }, + } + + def model(self, theta, k1D=None, template=None, transfer=None, pol_sel=None): + """Evaluate the model consisting of a scaled power spectrum template. + + The power spectrum template is simply multiplied by a free amplitude. + + Parameters + ---------- + theta : [amp] + One-element list containing the template amplitude. + k1D : np.ndarray[npol,nk] + K values for each pol. (Not actually used in model evaluation.) + template : np.ndarray[..., nk] + Signal template. + transfer, pol_sel + Unused arguments. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if k1D is None: + k1D = self.k1D + + if template is None: + template = self.template + + amp = theta[0] + + model = amp * self._re(template) + + return model + + +class AutoSimulationTemplate1D(Model): + """Linear combination of 1D power spectrum templates from simulations. + + Note that Finger-of-God damping is *not* varied in this class: + the `FoGh` parameter have no effect, and the `FoGs` parameter just + switches between the alphaFoG=1 shot noise template (if `FoGs != 0`) + or the alphaFoG=0 template (if `FoGs == 0`). To force the no-FoG + form of shot noise, keep the `FoGs` parameter fixed to zero. + + The derived class `AutoSimulationTemplate1DFoG` should be used to + vary Finger-of-God damping. + """ + + param_name = ["omega", "b_HI", "NL", "FoGh", "SN", "FoGs"] + + _param_spec = { + "omega": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 5.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + "NL": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -1.0, + "high": 7.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 4.0, + }, + }, + "SN": { + "fixed": True, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 4.0, + }, + }, + "FoGs": { + "fixed": True, + "value": 0.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 4.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate1D + _template_kwargs = () + + def __init__( + self, + pattern, + clustering_filename_pattern="*.h5", + shotnoise_filename_pattern="*.h5", + pol=None, + combine=True, + sort=False, + factor=1, + nbins=7, + logbins=True, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) + + self._signal_template = self._template_class.load_from_ps1Dfiles( + pattern, + clustering_filename_pattern=clustering_filename_pattern, + shotnoise_filename_pattern=shotnoise_filename_pattern, + pol=pol, + combine=combine, + factor=factor, + nbins=nbins, + logbins=logbins, + force_real=self.force_real, + **{k: v for k, v in kwargs.items() if k in self._template_kwargs}, + ) + + def model(self, theta, k1D=None, template=None, transfer=None, pol_sel=None): + """Evaluate the model. + + Parameters + ---------- + theta : np.ndarray[6] + Parameter values, ordered as + ["omega", "b_HI", "NL", "FoGh", "SN", "FoGs"]. + k1D, template, transfer + Unused arguments. + pol_sel : np.ndarray + Indices of pols to evaluate for. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if pol_sel is None: + pol_sel = self.pol_sel + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + model = self._signal_template.signal_1D(**param_dict)[pol_sel] + + return model + + +class AutoSimulationTemplate1DFoG(AutoSimulationTemplate1D): + """Power spectrum model with varying multiplicative FoG damping. + + To vary FoG damping in the clustering signal but use the no-FoG + form of the shot noise template, keep the `FoGs` paramter fixed + to 0 but vary the `SN` parameter. + """ + + param_name = ["omega", "b_HI", "NL", "FoGh", "SN", "FoGs"] + + _param_spec = { + "omega": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate1DFoG + + +class AutoSimulationTemplate1DFoGTransform(AutoSimulationTemplate1DFoG): + """Power spectrum model with FoG and more efficient sampling. + + This uses an alternative basis that makes the following replacement: + + - `b_HI -> omega_b_HI = omega * b_HI` + + However, the chains are returned (and priors applied) in the original basis. + """ + + def forward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, Omega*b basis.""" + + newsample = sample.copy() + newsample[..., 1] = sample[..., 0] * sample[..., 1] + + return newsample + + def backward_transform_sampler(self, sample: np.ndarray) -> np.ndarray: + """Transform to an Omega, b basis.""" + + newsample = sample.copy() + newsample[..., 1] = sample[..., 1] / sample[..., 0] + + return newsample + + def log_transform_measure(self, theta: np.ndarray) -> float: + """The measure for the coordinate transform.""" + + # The measure for the transform for Omega*b + measure = -np.log(np.abs(theta[..., 0])) + + return measure + + +class AutoSimulationTemplate1D_Omega2(AutoSimulationTemplate1D): + """Version of AutoSimulationTemplate1D that samples in Omega_HI^2. + + This uses a uniform prior on omega^2. However, this class is mostly + intended for chi^2 minimization, for which the prior doesn't matter. + """ + + param_name = ["omega^2", "b_HI", "NL", "FoGh", "SN", "FoGs"] + + _param_spec = { + "omega^2": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -400.0, + "high": 400.0, + }, + } + } + + _template_class = signal.AutoSignalTemplate1D + _template_kwargs = () + + def model(self, theta, k1D=None, template=None, transfer=None, pol_sel=None): + """Evaluate the model. + + Parameters + ---------- + theta : np.ndarray[6] + Parameter values, ordered as + ["omega^2", "b_HI", "NL", "FoGh", "SN", "FoGs"]. + k1D, template, transfer + Unused arguments. + pol_sel : np.ndarray + Indices of pols to evaluate for. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if pol_sel is None: + pol_sel = self.pol_sel + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + omega2 = param_dict.pop("omega^2") + # Need to allow omega to be complex so that omega^2 can be negative + # when model is evaluated + param_dict["omega"] = (omega2 + 0.0j) ** 0.5 + + model = self._signal_template.signal_1D(**param_dict)[pol_sel] + + return model + + +class AutoSimulationTemplate1DFoG_Omega2(AutoSimulationTemplate1D_Omega2): + """Version of AutoSimulationTemplate1DFoG that samples in Omega_HI^2. + + This uses a uniform prior on omega^2. However, this class is mostly + intended for chi^2 minimization, for which the prior doesn't matter. + """ + + param_name = ["omega^2", "b_HI", "NL", "FoGh", "SN", "FoGs"] + + _param_spec = { + "omega^2": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -400.0, + "high": 400.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate1DFoG + + +class AutoSimulationTemplate2Dto1D(Model): + """Linear combination of 2D power spectrum templates from simulations.""" + + param_name = ["omega", "b_HI", "NL", "FoGh", "M_10"] + + _param_spec = { + "offset": { + "fixed": False, + "value": 0.0, + "prior": "Uniform", + "kwargs": { + "low": -0.8, + "high": 0.8, + }, + }, + "omega": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 5.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + "NL": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -1.0, + "high": 7.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 4.0, + }, + }, + "M_10": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 25.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate2D + _template_kwargs = () + + def __init__( + self, + pattern, + filename_pattern=None, + pol=None, + weight=None, + signal_mask=None, + combine=True, + sort=False, + derivs=None, + factor=1, + aliases=None, + nbins=10, + logbins=True, + slow_1d_binning=False, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) + + if derivs is None: + derivs = {"lin": (-1.0, 1.0)} + + if aliases is None: + aliases = {"shotnoise": "M_10", "lin": "NL"} + + self.slow_1d_binning = slow_1d_binning + + self._signal_template = self._template_class.load_from_ps2Dfiles( + pattern, + filename_pattern=filename_pattern, + pol=pol, + weight=weight, + signal_mask=signal_mask, + combine=combine, + derivs=derivs, + factor=factor, + aliases=aliases, + nbins=nbins, + logbins=logbins, + force_real=self.force_real, + **{k: v for k, v in kwargs.items() if k in self._template_kwargs}, + ) + + def model(self, theta, k1D=None, template=None, transfer=None, pol_sel=None): + """Evaluate the model. + + Parameters + ---------- + theta : np.ndarray[5] + Parameter values, ordered as ["omega", "b_HI", "NL", "FoGh", "M_10"]. + k1D : np.ndarray[npol,nk] + K values for each pol. (Not actually used in model evaluation.) + template, transfer + Unused arguments. + pol_sel : np.ndarray + Indices of pols to evaluate for. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if pol_sel is None: + pol_sel = self.pol_sel + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + if self.slow_1d_binning: + model = self._signal_template.signal_1D_slow(**param_dict)[pol_sel] + else: + model = self._signal_template.signal_1D(**param_dict)[pol_sel] + + return model + + +class AutoSimulationTemplate2Dto1DFoG(AutoSimulationTemplate2Dto1D): + """Power spectrum model with varying FoG damping via multiplicative kernel.""" + + param_name = ["omega", "b_HI", "NL", "FoGh", "M_10"] + + _param_spec = { + "omega": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate2DFoG + _template_kwargs = AutoSimulationTemplate2Dto1D._template_kwargs + ( + "convolutions", + "kpara_range", + ) + + +class AutoSimulationTemplate2Dto1D_Omega2(AutoSimulationTemplate2Dto1D): + """Version of AutoSimulationTemplate2Dto1D that samples in Omega_HI^2. + + This uses a uniform prior on omega^2. However, this class is mostly + intended for chi^2 minimization, for which the prior doesn't matter. + """ + + param_name = ["omega^2", "b_HI", "NL", "FoGh", "M_10"] + + _param_spec = { + "omega^2": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -25.0, + "high": 25.0, + }, + } + } + + _template_class = signal.AutoSignalTemplate2D + _template_kwargs = () + + def model(self, theta, k1D=None, template=None, transfer=None, pol_sel=None): + """Evaluate the model. + + Parameters + ---------- + theta : np.ndarray[5] + Parameter values, ordered as ["omega^2", "b_HI", "NL", "FoGh", "M_10"]. + k1D : np.ndarray[npol,nk] + K values for each pol. (Not actually used in model evaluation.) + template, transfer + Unused arguments. + pol_sel : np.ndarray + Indices of pols to evaluate for. + + Returns + ------- + model : np.ndarray[..., nk] + Model for the signal. + """ + + if pol_sel is None: + pol_sel = self.pol_sel + + param_dict = {k: v for k, v in zip(self.param_name, theta)} + + omega2 = param_dict.pop("omega^2") + # Need to allow omega to be complex so that omega^2 can be negative + # when model is evaluated + param_dict["omega"] = (omega2 + 0.0j) ** 0.5 + + if self.slow_1d_binning: + model = self._signal_template.signal_1D_slow(**param_dict)[pol_sel] + else: + model = self._signal_template.signal_1D(**param_dict)[pol_sel] + + return model + + +class AutoSimulationTemplate2Dto1DFoG_Omega2(AutoSimulationTemplate2Dto1D_Omega2): + """Version of AutoSimulationTemplate2Dto1DFoG that samples in Omega_HI^2. + + This uses a uniform prior on omega^2. However, this class is mostly + intended for chi^2 minimization, for which the prior doesn't matter. + """ + + param_name = ["omega^2", "b_HI", "NL", "FoGh", "M_10"] + + _param_spec = { + "omega^2": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -25.0, + "high": 25.0, + }, + }, + "b_HI": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": -5.0, + "high": 5.0, + }, + }, + "FoGh": { + "fixed": False, + "value": 1.0, + "prior": "Uniform", + "kwargs": { + "low": 0.0, + "high": 8.0, + }, + }, + } + + _template_class = signal.AutoSignalTemplate2DFoG + _template_kwargs = AutoSimulationTemplate2Dto1D._template_kwargs + ( + "convolutions", + "kpara_range", + ) diff --git a/fitstack/priors.py b/fitstack/priors.py index 49f31ca..1338c70 100644 --- a/fitstack/priors.py +++ b/fitstack/priors.py @@ -1,6 +1,7 @@ """Define statistical distributions that can be used to place priors on parameters.""" import numpy as np +from scipy.integrate import quad class Prior(object): @@ -96,7 +97,7 @@ def __init__(self, loc=0.0, scale=1.0, **kwargs): self.loc = loc self.scale = scale - self.norm = 1.0 / np.sqrt(2.0 * np.pi * self.scale ** 2) + self.norm = 1.0 / np.sqrt(2.0 * np.pi * self.scale**2) def evaluate(self, theta): """Evaluate the probability of observing this value of the parameter. @@ -112,7 +113,7 @@ def evaluate(self, theta): The probability of observing the input parameter value. """ - return self.norm * np.exp(-((theta - self.loc) ** 2) / (2.0 * self.scale ** 2)) + return self.norm * np.exp(-((theta - self.loc) ** 2) / (2.0 * self.scale**2)) def draw_random(self): """Draw a random value of the parameter from the prior distribution. @@ -124,3 +125,67 @@ def draw_random(self): """ return self.rng.normal(loc=self.loc, scale=self.scale) + + +class PowerLaw(Prior): + """Power-law prior distribution.""" + + def __init__(self, low=0.0, high=1.0, power=0.0, **kwargs): + """Set the lower and upper boundaries of the distribution, and the power. + + Parameters + ---------- + low : float + The lower boundary. + high : float + The upper boundary. + power : float + Power for power-law prior + """ + + super().__init__(**kwargs) + + self.low = low + self.high = high + self.power = power + + def _power(x): + return x**power + + self.norm = 1.0 / quad(_power, low, high)[0] + + def evaluate(self, theta): + """Evaluate the probability of observing this value of the parameter. + + Parameters + ---------- + theta : float + The parameter value. + + Returns + ------- + prob : float + The probability of observing the input parameter value. + """ + + return ( + self.norm + * ((theta >= self.low) and (theta <= self.high)) + * theta**self.power + ) + + def draw_random(self): + """Draw a random value of the parameter from the prior distribution. + + Returns + ------- + theta : float + Random value of the parameter. + """ + u = self.rng.uniform(low=0, high=1) + + if self.power == -1.0: + return self.low * (self.high / self.low) ** u + else: + c = 1.0 + self.power + return (u * (self.high**c - self.low**c) + self.low**c) ** (1.0 / c) diff --git a/fitstack/signal.py b/fitstack/signal.py index f8dad65..5dd4a4c 100644 --- a/fitstack/signal.py +++ b/fitstack/signal.py @@ -1,17 +1,21 @@ import logging import re import glob -import os -from typing import Dict, Optional, Tuple, List +from typing import Dict, Optional, Tuple, List, Callable from pathlib import Path import numpy as np from scipy.fftpack import next_fast_len +from scipy.optimize import curve_fit, OptimizeWarning +from scipy.interpolate import CubicSpline from draco.util import tools -from draco.core.containers import FrequencyStackByPol, MockFrequencyStackByPol +from draco.core.containers import FrequencyStackByPol, PowerSpectrum1D, PowerSpectrum2D +from draco.analysis.powerspec import get_1d_ps from . import utils +from cora.util import cosmology +from cora.util import units as u logger = logging.getLogger(__name__) @@ -22,7 +26,7 @@ class SignalTemplate: Parameters ---------- derivs - A dictionary of derivates expected, giving their name (key), and a tuple of the + A dictionary of derivatives expected, giving their name (key), and a tuple of the parameter difference used in the simulations (between the perturbed sim and the base values) and the fiducial value of the parameter. factor @@ -36,7 +40,7 @@ class SignalTemplate: def __init__( self, derivs: Optional[Dict[str, Tuple[float, float]]] = None, - factor: float = 1e-3, + factor: float = 1.0, aliases: Optional[Dict[str, str]] = None, ): @@ -49,6 +53,9 @@ def __init__( self._derivs = derivs self._factor = factor self._aliases = aliases if aliases is not None else {} + logger.debug(f"Using deriv modes: {self._derivs}") + logger.debug(f"Using aliases: {self._aliases}") + logger.debug(f"Using factor: {self._factor}") @classmethod def load_from_stackfiles( @@ -58,6 +65,8 @@ def load_from_stackfiles( weight: np.ndarray = None, combine: bool = True, sort: bool = True, + symmetrize: bool = False, + reverse: bool = False, **kwargs, ): """Load the signal template from a set of stack files. @@ -79,6 +88,10 @@ def load_from_stackfiles( is the weighted sum of the XX and YY polarisation. sort Sort the frequency offset axis in ascending order. + symmetrize + Explicitly symmetrize the templates. + reverse + Reverse the templates. Useful for testing symmetry effects. **kwargs Arguments passed on to the constructor. """ @@ -121,10 +134,25 @@ def load_from_stackfiles( mocks = utils.load_mocks(stack_files, pol=pol) mocks.weight[:] = weight[np.newaxis, :] if weight is not None else 1.0 - stacks[key] = utils.average_stacks( + stacks[key] = utils.average_data( mocks, pol=mocks.pol, combine=combine, sort=sort ) + if reverse: + logger.debug(f"Reversing stack {key}") + stacks[key].stack[:] = stacks[key].stack[..., ::-1] + stacks[key].weight[:] = stacks[key].weight[..., ::-1] + + # TODO: this presumes that 0 is the central element + if symmetrize: + logger.debug(f"Symmetrizing stack {key}") + stacks[key].stack[:] = 0.5 * ( + stacks[key].stack[:] + stacks[key].stack[..., ::-1] + ) + stacks[key].weight[:] = 0.5 * ( + stacks[key].weight[:] + stacks[key].weight[..., ::-1] + ) + # Create the object and try and construct all the required templates from the # stacks self = cls(**kwargs) @@ -159,13 +187,14 @@ def _check_load_stack(key): return ( self._factor * stack.stack[:], - self._factor ** 2 + self._factor**2 * tools.invert_no_zero(stack.attrs["num"] * stack.weight[:]), ) # For all linear component terms load them and construct the various HI,g,v # combination terms for term in compterms: + logger.debug(f"Combining mode {term}") s00, v00 = _check_load_stack(f"00-{term}") s01, v01 = _check_load_stack(f"01-{term}") @@ -196,6 +225,7 @@ def _check_load_stack(key): # For the expected derivative modes combine the perturbed entry and the base # templates to get the derivative templates for name, (delta, _) in self._derivs.items(): + logger.debug(f"Interpreting derivative mode {name}") if name not in stack_modes: raise RuntimeError(f"Expected derivative {name} but could not load it.") @@ -205,7 +235,7 @@ def _check_load_stack(key): # Calculate the finite difference derivative fd_mode = (s - sb) / delta - fd_var = (v + vb) / delta ** 2 + fd_var = (v + vb) / delta**2 self._stack_comp[name] = (fd_mode, fd_var) @@ -213,6 +243,7 @@ def _check_load_stack(key): # bias and Kaiser factors (such as shot noise) noncompterms = [k for k in stacks.keys() if "-" not in k] for term in noncompterms: + logger.debug(f"Interpreting non-component mode {term}") self._stack_noncomp[term] = _check_load_stack(term) def signal( @@ -262,7 +293,10 @@ def _combine(vec): # before adding in the non-component contributions signal = self.convolve_pre_noncomp(signal, **kwargs) - # Add in any non-component contributins + # Scale by the overall prefactor + signal *= omega + + # Add in any non-component contributions for name, stack in self._stack_noncomp.items(): name = self._aliases.get(name, name) @@ -277,9 +311,6 @@ def _combine(vec): # after adding in the non-component contributions signal = self.convolve_post_noncomp(signal, **kwargs) - # Scale by the overall prefactor - signal *= omega - return signal def convolve_pre_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: @@ -353,11 +384,27 @@ def __init__( self._delay_range = delay_range super().__init__(derivs=derivs, *args, **kwargs) + logger.debug(f"Using convolutions: {self._convolutions}") + logger.debug(f"Fitting delay range: {self._delay_range}") def _solve_scale( self, base: FrequencyStackByPol, deriv: FrequencyStackByPol, alpha: float ) -> np.ndarray: - """Solve for the effective scale of the FoG damping. + r"""Solve for the effective scale of the FoG damping. + + Note that the scale parameter returned by this function is different from + the scale parameter defined in the eBOSS stacking paper: if :math:`s` is the + code parameter and :math:`\sigma_{\rm eff}` is the paper's parameter, then + + .. math:: + + s = \sigma_{\rm eff} / \sqrt{2} + + Therefore, the FoG kernel is defined as + + .. math:: + + H(\tau, s) = 1 / (1 + (s \tau)^2) Parameters ---------- @@ -371,47 +418,77 @@ def _solve_scale( Returns ------- scale : np.ndarray[npol,] - The effective scale of the transfer function: - H(\tau) = 1 / (1 + (scale * \tau)^2) + The effective scale of the transfer function. """ nfreq = self.freq.size df = np.abs(self.freq[1] - self.freq[0]) tau = np.fft.rfftfreq(nfreq, d=df)[np.newaxis, :] - tau2 = tau ** 2 + tau2 = tau**2 + + if base.stack[:].ndim > 2: + # Dealing with the Stack3D container. + # Use the central pixel to determine the effective scale. + sel0 = ( + slice(None), + np.argmin(np.abs(base.index_map["delta_ra"][:])), + np.argmin(np.abs(base.index_map["delta_dec"][:])), + ) + else: + sel0 = slice(None) - mu_fft_base = np.abs(np.fft.rfft(base.stack[:], nfreq, axis=-1)) - mu_fft_deriv = np.abs(np.fft.rfft(deriv.stack[:], nfreq, axis=-1)) + # FoG kernel acts in delay space, so we FFT the stacks from freq to delay + mu_fft_base = np.abs(np.fft.rfft(base.stack[sel0], nfreq, axis=-1)) + mu_fft_deriv = np.abs(np.fft.rfft(deriv.stack[sel0], nfreq, axis=-1)) + # Get variance of base and deriv delay-space stacks, for usage in + # error propagation var_fft_base = np.sum( - tools.invert_no_zero(base.attrs["num"] * base.weight[:]), + tools.invert_no_zero(base.attrs["num"] * base.weight[sel0]), axis=-1, keepdims=True, ) var_fft_deriv = np.sum( - tools.invert_no_zero(deriv.attrs["num"] * deriv.weight[:]), + tools.invert_no_zero(deriv.attrs["num"] * deriv.weight[sel0]), axis=-1, keepdims=True, ) + # Compute ratio of base and deriv stacks, and compute variance of + # ratio using error propagation ratio = mu_fft_base * tools.invert_no_zero(mu_fft_deriv) - var_ratio = ratio ** 2 * ( - var_fft_base * tools.invert_no_zero(mu_fft_base ** 2) - + var_fft_deriv * tools.invert_no_zero(mu_fft_deriv ** 2) + var_ratio = ratio**2 * ( + var_fft_base * tools.invert_no_zero(mu_fft_base**2) + + var_fft_deriv * tools.invert_no_zero(mu_fft_deriv**2) ) - y = (ratio - 1.0) * tools.invert_no_zero(alpha ** 2 - ratio) - - w = (alpha ** 2 - ratio) ** 4 * tools.invert_no_zero( - (alpha * 2 - 1.0) ** 2 * var_ratio + # If each delay-space signal was exactly proportional to H(tau) as defined + # in the docstring, the ratio would be equal to + # H(tau,alpha*s)^2 / H(tau,s)^2 . + # This might not exactly be true because of how the data were processed, + # but we'll assume it's true and fit for an effective value of s. + # To do so, we write + # ratio = H(tau,alpha*s)^2 / H(tau,s)^2 + # and then solve for y, defined to be kpar^2 s^2. + y = (ratio - 1.0) * tools.invert_no_zero(alpha**2 - ratio) + + # We then compute weights w that are equal to the inverse variance of y, + # computed via error propagation. We also zero out tau values that are + # beyond the desired fitting range + w = (alpha**2 - ratio) ** 4 * tools.invert_no_zero( + (alpha**2 - 1.0) ** 2 * var_ratio ) w *= ((tau >= self._delay_range[0]) & (tau <= self._delay_range[1])).astype( np.float32 ) + # From the definition of y, we know that s^2 = y/tau^2. We optimally + # estimate s^2 by taking an inverse-variance weighted average of y/tau^2 + # over all tau values. (We'll only use s^2 in calculations, so it + # makes sense to estimate s^2 instead of s.) scale2 = np.sum(w * tau2 * y, axis=-1) * tools.invert_no_zero( - np.sum(w * tau2 ** 2, axis=-1) + np.sum(w * tau2**2, axis=-1) ) return np.sqrt(scale2) @@ -459,8 +536,11 @@ def convolve_pre_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: fslice = slice(0, nfreq) # Determine the delay axis + tbcast = (np.newaxis,) * (signal.ndim - 1) + (slice(None),) + sbcast = (slice(None),) + (np.newaxis,) * (signal.ndim - 1) + df = np.abs(self.freq[1] - self.freq[0]) - tau = np.fft.rfftfreq(fsize, d=df)[np.newaxis, :] + tau = np.fft.rfftfreq(fsize, d=df)[tbcast] # Calculate the fft of the signal fft_signal = np.fft.rfft(signal, fsize, axis=-1) @@ -469,21 +549,1371 @@ def convolve_pre_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: # Assumes a Lorentzian in delay space. fft_transfer = np.ones_like(fft_signal) + # Loop over parameters corresponding to distinct kernels we'll need to + # convolve the signal by for name, (_, x0) in self._convolutions.items(): - scale0 = self._convolution_scale[name][:, np.newaxis] - + # Get aliased name of parameter and parameter value name = self._aliases.get(name, name) if name not in kwargs: raise ValueError(f"Need a value for convolution parameter {name}") - x = kwargs[name] + # Re-scale effective convolution scale alpha = x / x0 scale = alpha * scale0 + # Accumulate kernel into delay-space transfer function fft_transfer *= (1.0 + (scale0 * tau) ** 2) / (1.0 + (scale * tau) ** 2) + # Multiply signal by transfer function and ifft back to frequency-space signalc = np.fft.irfft(fft_signal * fft_transfer, fsize, axis=-1)[..., fslice] return signalc + + +class AutoSignalTemplate1D: + """Power spectrum templates from pre-simulated modes and input parameters. + + Parameters + ---------- + factor : float + A scaling factor to apply to the sims. + nbins : int + Number of 1d k bins. Default: 10. + logbins : bool + Whether bins should be log-spaced. Default: True. + force_real : bool + Force input datasets to be real. Assumes that input datasets have + been previously examined to verify that imaginary parts are small + and/or unimportant. Default: True. + """ + + def __init__( + self, + factor: float = 1, + nbins: int = 7, + logbins: bool = True, + force_real: bool = True, + ): + self._factor = factor + self._nbins = nbins + self._logbins = logbins + self.force_real = force_real + logger.debug(f"Using factor: {self._factor}") + logger.debug( + f"Using {self._nbins} " + f"{'log-spaced' if self._logbins else 'linearly-spaced'} bins" + ) + + def _re(self, x): + return np.real(x) if self.force_real else x + + @classmethod + def load_from_ps1Dfiles( + cls, + pattern: str, + clustering_filename_pattern: str = "*.h5", + shotnoise_filename_pattern: str = "*.h5", + pol: List[str] = None, + combine: bool = True, + force_real: bool = True, + **kwargs, + ): + """Load the signal templates from a set of 1d power spectrum files. + + This will load the ps1D files from each location and try to + compile them into a set which can be used to generate signal + templates. + + The clustering-signal templates should be stored in directories + with names of the form `*_bias_b_Pk_p_FoGh_f`, where: + - `b` is one of `0`, `0.5`, or `1`, corresponding to the ratio of + b_HI and the fiducial b_HI value + - `p` is one of `lin` or `nonlin`, and indicates the nonlinearity + of the matter power spectrum + - `f` is a float indicating the value of the Finger of God damping + parameter, relative to the fiducial value (`f=1`) + + If present, the shot noise templates should be stored in + directories with names of the form `*_shot_FoGs_f` where `f` is + defined as above. + + Parameters + ---------- + pattern + A glob pattern that isolates the directories containing the + signal templates. + clustering_filename_pattern + A glob pattern that specifies the filenames containing the + clustering-signal templates. + shotnoise_filename_pattern + A glob pattern that specifies the filenames containing the + shot noise templates. + pol + The desired polarisations. + combine + Add an element to the polarisation axis called I that + is the weighted sum of the XX and YY polarisation. + force_real + Force input datasets to be real. Assumes that input datasets + havebeen previously examined to verify that imaginary parts + are small and/or unimportant. + **kwargs + Arguments passed on to the constructor. + """ + + dirs = glob.glob(pattern) + + matching_clus, matching_shot = {}, {} + + # Examine the format of each directory + for d in sorted(dirs): + logger.debug(f"Processing directory: {d}") + + # Parse directory name for clustering or shot noise parameters + clus_re = re.search(r"bias_([0-9\.]+)_Pk_([A-Za-z]+)_FoGh_([0-9\.]+)/", d) + shot_re = re.search(r"shot_FoGs_([0-9\.]+)/", d) + + if clus_re: + # bias will be "0", "0.5", or "1" + bias = clus_re.group(1) + # pk_type will be "nonlin", "lin" + pk_type = clus_re.group(2) + # alphaFoG_clus with be a float + alphaFoG_clus = clus_re.group(3) + # Create a composite key that identifies the templates + key = f"clus-{bias}-{pk_type}-{alphaFoG_clus}" + elif shot_re: + # alphaFoG_shot will be a float + alphaFoG_shot = shot_re.group(1) + # Create a composite key that identifies the templates + key = f"shot-{alphaFoG_shot}" + else: + logger.info(f"Directory {d} does not match expected format, rejecting") + continue + + # If key has been encountered before, raise error + if key in matching_clus.keys() or key in matching_shot.keys(): + raise ValueError( + "Did not find a unique set of modes at this location. " + "You might need to refine the pattern." + ) + + # Check that we're working with a directory + d = Path(d) + if not d.is_dir(): + raise ValueError("Glob pattern for templates must point to directories") + + if key.startswith("clus"): + matching_clus[key] = d + else: + matching_shot[key] = d + + # For each template type (clustering vs. shot noise) and each directory, + # load templates and average them together + ps1Ds_clus, ps1Ds_shot = {}, {} + for matching, ps1Ds, filename_pattern in zip( + [matching_clus, matching_shot], + [ps1Ds_clus, ps1Ds_shot], + [clustering_filename_pattern, shotnoise_filename_pattern], + ): + for key, d in matching.items(): + ps1D_files = sorted(list(d.glob(filename_pattern))) + + if len(ps1D_files) == 0: + logger.info("No files found at matching path.") + continue + + mocks = utils.load_mocks(ps1D_files, pol=pol) + ps1Ds[key] = utils.average_data( + mocks, pol=mocks.index_map["pol"], combine=combine, sort=False + ) + + # Create the object + self = cls(**kwargs) + self.force_real = force_real + + # Set flag if shot noise templates are present + self.has_shot = len(ps1Ds_shot.keys()) > 0 + + # Construct all the required templates from the averaged inputs + self._interpret_ps1Ds(ps1Ds_clus, ps1Ds_shot) + + return self + + def _interpret_ps1Ds( + self, + ps1Ds_clus: Dict[str, PowerSpectrum1D], + ps1Ds_shot: Dict[str, PowerSpectrum1D], + ): + """Generate the required templates for the 1d power spectra.""" + + # Sort tuples of Pk strings and FoGh values based on which bias values + # they exist for + clus_keys_temp = {"0": [], "0.5": [], "1": []} + for key in ps1Ds_clus.keys(): + key_split = key.split("-") + for b_str in ["0", "0.5", "1"]: + if key_split[1] == b_str: + clus_keys_temp[b_str].append((key_split[2], key_split[3])) + + # Collect tuples that exist for all 3 bias values + clus_keys = [] + for key in clus_keys_temp["0"]: + if (key in clus_keys_temp["0.5"]) and (key in clus_keys_temp["1"]): + clus_keys.append(key) + logger.info( + "Found the following clustering templates " + f"for all 3 bias values: {clus_keys}" + ) + + # Find FoGs values from shot noise templates + shot_keys = [k.split("-")[1] for k in ps1Ds_shot.keys()] + logger.info(f"Found the following shot noise templates {shot_keys}") + + self._ps1D_modes = {} + + # Get the first k1D axis as reference + self._k1D = next(iter(ps1Ds_clus.values())).k1D[:].copy() + self._k1D.flags.writeable = False + + def _check_load_ps1D(ps1Ds, key): + """Validate the 1D power spectrum and extract the template/variance.""" + + if key not in ps1Ds: + raise RuntimeError(f"Power spectrum {key} was not loaded.") + + ps1D = ps1Ds[key] + + if not np.array_equal(ps1D.k1D[:], self._k1D): + raise RuntimeError( + f"k1D values in power spectrum {key} do not match reference." + ) + + return ( + self._factor * self._re(ps1D.spectrum[:]), + self._factor**2 + * self._re(ps1D.var[:]) + * tools.invert_no_zero(ps1D.attrs["num"]), + ) + + # Load clustering templates and construct the various HI,v + # combination terms + for term in clus_keys: + logger.debug(f"Combining clustering mode {term[0]}-{term[1]}") + + s0, v0 = _check_load_ps1D(ps1Ds_clus, f"clus-0-{term[0]}-{term[1]}") + sh, vh = _check_load_ps1D(ps1Ds_clus, f"clus-0.5-{term[0]}-{term[1]}") + s1, v1 = _check_load_ps1D(ps1Ds_clus, f"clus-1-{term[0]}-{term[1]}") + + # Initialize arrays for b_HI = 0, 1/2, 1 + template_mean = np.zeros((3,) + s0.shape) + template_var = np.zeros((3,) + s0.shape) + + # Calculate the template for each component + ## s_hh = 2 [s(1,1,0) - 2s(1,1/2,0) + s(1,0,0)] + template_mean[0] = 2 * (s1 - 2 * sh + s0) + ## s_hv = s(1,1/2,0) - s(1,0,0) - 1/4 shh + template_mean[1] = sh - s0 - 0.25 * template_mean[0] + ## s_vv = s(1,0,0) + template_mean[2] = s0 + + # Calculate the variance of each component, using error propagation + template_var[0] = 4 * (v1 + 4 * vh + v0) + template_var[1] = vh + v0 + 0.0625 * template_var[0] + template_var[2] = v0 + + self._ps1D_modes[f"clus-{term[0]}-{term[1]}"] = ( + template_mean, + template_var, + ) + + # Load shot noise templates + for term in shot_keys: + logger.debug(f"Loading shot noise mode {term}") + self._ps1D_modes[f"shot-{term}"] = _check_load_ps1D( + ps1Ds_shot, f"shot-{term}" + ) + + def signal_1D(self, *, omega: float, b_HI: float, **kwargs: float) -> np.ndarray: + """Return the 1D power spectrum template for the given parameters. + + Parameters + ---------- + omega + Overall scaling. + b_HI + Scaling for the HI bias term. + **kwargs + Values for all other parameters (e.g. NL, FoGh, SN, FoGs). + + Returns + ------- + signal + Signal template for the given parameters. An array with shape + [pol, k1D]. + """ + + def _combine_kaiser(vec): + # Combine templates needed for Kaiser factor + return b_HI**2 * vec[0] + 2 * b_HI * vec[1] + vec[2] + + # Check that NL is present in kwargs + if "NL" not in kwargs: + raise ValueError("Need a value for parameter NL") + + # Rescale each template before combining. + # Template filename may have alphaFoG=1 or alphaFoG=1.0, + # so we try both + try: + nonlin_signal = self.rescale_templates( + self._ps1D_modes["clus-nonlin-1"][0], + template="clus-nonlin", + alpha_par="FoGh", + **kwargs, + ) + except KeyError: + nonlin_signal = self.rescale_templates( + self._ps1D_modes["clus-nonlin-1.0"][0], + template="clus-nonlin", + alpha_par="FoGh", + **kwargs, + ) + + try: + lin_signal = self.rescale_templates( + self._ps1D_modes["clus-lin-1"][0], + template="clus-lin", + alpha_par="FoGh", + **kwargs, + ) + except KeyError: + lin_signal = self.rescale_templates( + self._ps1D_modes["clus-lin-1.0"][0], + template="clus-lin", + alpha_par="FoGh", + **kwargs, + ) + + # Combine clustering templates according to Kaiser factor and matter + # nonlinearity prescription + nonlin_signal = _combine_kaiser(nonlin_signal) + lin_signal = _combine_kaiser(lin_signal) + signal = kwargs["NL"] * nonlin_signal + (1 - kwargs["NL"]) * lin_signal + + # Scale by the overall prefactor (omega**2 for auto-correlation). + # If we sampled directly in omega^2, this omega may be complex, + # so we need to take the real part here to avoid having omega**2 + # evaluate as a compex number with zero imaginary part. + signal *= np.real(omega**2) + + if self.has_shot and kwargs["SN"] != 0: + if "shot-1" in self._ps1D_modes.keys() and kwargs["FoGs"] != 0: + # If shot-1 is present and FoGs parameter is nonzero, + # recale FoGs=1 template + shot_signal = self.rescale_templates( + self._ps1D_modes["shot-1"][0], + template="shot", + alpha_par="FoGs", + **kwargs, + ) + elif "shot-0" in self._ps1D_modes.keys(): + # If shot-0 is present but shot-1 is not, + # or FoGs parameter is zero, use shot-0 + # as shot noise template + shot_signal = self._ps1D_modes["shot-0"] + + signal += kwargs["SN"] * shot_signal + + # If desired, rescale entire signal + signal = self.multiply_signal(signal, **kwargs) + + return signal + + def rescale_templates(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Override in subclass to rescale templates.""" + return signal + + def multiply_signal(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Override in subclass to multiply entire signal by a function.""" + return signal + + @property + def k1D(self): + """Get k1D values the template is defined at.""" + return self._k1D + + @property + def params(self): + """The names of all the parameters needed to generate the template.""" + return ["omega", "b_HI", "NL", "FoGh", "SN", "FoGs"] + + +class AutoSignalTemplate1DFoG(AutoSignalTemplate1D): + """Power spectrum templates from pre-simulated modes and input parameters.""" + + def _interpret_ps1Ds( + self, + ps1Ds_clus: Dict[str, PowerSpectrum1D], + ps1Ds_shot: Dict[str, PowerSpectrum1D], + ): + """Generate the required templates for the 1d power spectra.""" + + super()._interpret_ps1Ds(ps1Ds_clus, ps1Ds_shot) + + self._sigma2_for_amplitude = {} + self._FoG_shape_splines = {} + + for template in ["clus-nonlin", "clus-lin"]: + self._sigma2_for_amplitude[template] = self._solve_sigma2_for_amplitude( + template + ) + self._FoG_shape_splines[template] = self._compute_FoG_shape_splines( + template + ) + + if self.has_shot and "shot-1" in self._ps1D_modes.keys(): + # If shot-1 is present, set up splines + self._sigma2_for_amplitude["shot"] = self._solve_sigma2_for_amplitude( + "shot" + ) + self._FoG_shape_splines["shot"] = self._compute_FoG_shape_splines("shot") + + def ratio_amplitude_func(self, alpha: np.ndarray, sig2: np.ndarray) -> np.ndarray: + r"""Function to rescale template ratios to a common amplitude. + + The following function is a good way to rescale template amplitude + ratios at different :math:`\alpha_{\rm FoG}` ratios: + + .. math:: + + r(\alpha_{\rm FoG}) = \frac{(1+\sigma^2)^2}{(1+\alpha_{\rm FoG}^2\sigma^2)^2} + + Parameters + ---------- + alpha + Float or array of :math:`\alpha_{\rm FoG}` values. + sig2 + Float or array of :math:`\sigma^2` values. + + Returns + ------- + func + Float or array of function values. + """ + return (1 + sig2) ** 2 / (1 + sig2 * alpha**2) ** 2 + + def _solve_sigma2_for_amplitude(self, template: str) -> np.ndarray: + r"""Solve for effective scale for rescaling template amplitudes. + + If :math:`r(\alpha_{\rm FoG})` is defined as the ratio of a template + to its value for :math:`\alpha_{\rm FoG}=1` at the lowest k bin, + we assume that :math:`r(\alpha_{\rm FoG})` is well-described by + + .. math:: + + r(\alpha_{\rm FoG}) = \frac{(1+\sigma^2)^2}{(1+\alpha_{\rm FoG}^2\sigma^2)^2} + + for some constant :math:`\sigma^2`. This routine solves for + :math:`\sigma^2`. + + Parameters + ---------- + template + Name of templates to rescale. + + Returns + ------- + sigma2 + Effective scales used in rescaling function. Array with shape + `[term, pol]` where `term` denotes hh, hv, or vv. + """ + # Get alphas and template ratios for desired template + alphas, template_ratios = self._get_template_ratios(template) + + # For each of the hh, hv, and vv templates, solve for the effective + # scale sigma^2. Set assumed uncertainties equal to data, which ensures + # that each point receives the same relative weight in the fit + # (otherwise, much smaller values will be deprioritized in the fit) + sig2_list = np.zeros(template_ratios.shape[1:3], dtype=float) + for termi in range(sig2_list.shape[0]): + for poli in range(sig2_list.shape[1]): + # If fit fails, it's likely because the effective scale + # should be very close to zero so the desired relative + # tolerance in curve_fit is not achieved. In this case, + # we set the scale to zero + try: + popt, _ = curve_fit( + self.ratio_amplitude_func, + alphas, + template_ratios[:, termi, poli, 0], + sigma=template_ratios[:, termi, poli, 0], + p0=1.0, + ) + sig2_list[termi, poli] = popt[0] + except OptimizeWarning: + logger.info( + "Fit for amplitude-scaling effective scale " + "did not converge. Setting scale to 0." + ) + sig2_list[termi, poli] = 0.0 + + return sig2_list + + def _compute_FoG_shape_splines( + self, template: str + ) -> Callable[[np.ndarray], np.ndarray]: + """Compute cubic splines in alphaFoG for chosen template. + + Ratios of templates to alphaFoG=1 versions are computed and + rescaled to a common amplitude, and then separate cubic splines + are fit to the rescaled ratio at each k. + + Parameters + ---------- + template + Name of template to compute splines for. + + Returns + ------- + splines + Function that evaluates spline, with return value with + shape `[term, pol, k]` where `term` denotes hh, hv, or + vv. + """ + # Get alphas and template ratios for desired template + alphas, template_ratios = self._get_template_ratios(template) + + # Rescale template ratios to common amplitude + template_ratios /= self.ratio_amplitude_func( + alphas[:, np.newaxis, np.newaxis, np.newaxis], + self._sigma2_for_amplitude[template][np.newaxis, :, :, np.newaxis], + ) + + # Compute cubic splines in alphaFoG for each term, pol, and k + splines = CubicSpline( + alphas, template_ratios, axis=0, bc_type="not-a-knot", extrapolate=True + ) + + # Store the values of the ratios at the min and max input alphas + ratios_alpha_min = template_ratios[0] + ratios_alpha_max = template_ratios[-1] + + # Define a function that extrapolates the cubic spline results + # with constant values if alphas are provided that are outside the + # range of simulations we've loaded + def evaluate(alpha): + alpha_ = np.asarray(alpha) + spline_output = splines(alpha) + + below = alpha_ < alphas[0] + above = alpha_ > alphas[-1] + + spline_output = np.copy(spline_output) + + if np.any(below): + spline_output[below] = ratios_alpha_min + if np.any(above): + spline_output[above] = ratios_alpha_max + + return spline_output + + return evaluate + + def _get_template_ratios(self, template: str) -> Tuple[np.ndarray, np.ndarray]: + """Get ratios of templates to alphaFoG=1 templates. + + Parameters + ---------- + template + Name of template to fetch ratios for. + + Returns + ------- + alphas + Sorted array of alphaFoG values. + template_ratios + Ratios of templates. Array with shape `[term, pol, k]` + where `term` denotes hh, hv, or vv. + """ + # Gather template ratios into a single array with axes + # [alphaFoG, term, pol, k] where term denotes hh, hv, or vv + alphas = [] + template_ratios = [] + for key in self._ps1D_modes.keys(): + if not key.startswith(template): + continue + + a = float(key.split("-")[2]) + alphas.append(a) + # Template filename may have alphaFoG=1 or alphaFoG=1.0, + # so we try both + try: + template_ratios.append( + self._ps1D_modes[key][0] / self._ps1D_modes[f"{template}-1"][0] + ) + except KeyError: + template_ratios.append( + self._ps1D_modes[key][0] / self._ps1D_modes[f"{template}-1.0"][0] + ) + + # Sort array based on alpha_FoG values + sort_idx = np.argsort(alphas) + alphas = np.array(alphas)[sort_idx] + template_ratios = np.array(template_ratios)[sort_idx] + + return alphas, template_ratios + + def rescale_templates(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Rescale alphaFoG=1 templates to different values.""" + + # FoG kernel only depends on alpha^2, so we force alpha<0 values + # to evaluate the splines at |alpha| + alpha = np.abs(kwargs[kwargs["alpha_par"]]) + template = kwargs["template"] + + # Apply amplitude rescaling + signal_out = signal * self.ratio_amplitude_func( + alpha, self._sigma2_for_amplitude[template][..., np.newaxis] + ) + + # Apply shape rescaling + signal_out *= self._FoG_shape_splines[template](alpha) + + return signal_out + + +class AutoSignalTemplate2D: + """Power spectrum signal templates from pre-simulated modes and input parameters. + + Parameters + ---------- + derivs : dict + A dictionary of derivatives expected, giving their name (key), and a tuple of the + parameter difference used in the simulations (between the perturbed sim and the + base values) and the fiducial value of the parameter. + factor : float + A scaling factor to apply to the sims. + aliases : float + Allow the parameters to be given by more meaningful names. + nbins : int + Number of 1d k bins. Default: 10. + logbins : bool + Whether bins should be log-spaced. Default: True. + force_real : bool + Force input datasets to be real. Assumes that input datasets have been previously + examined to verify that imaginary parts are small and/or unimportant. + Default: True. + """ + + def __init__( + self, + derivs: Optional[Dict[str, Tuple[float, float]]] = None, + factor: float = 1.0, + aliases: Optional[Dict[str, str]] = None, + nbins: int = 7, + logbins: bool = True, + force_real: bool = True, + ): + + if derivs is None: + derivs = { + "NL": (0.3, 1.0), + "FoGh": (0.2, 1.0), + } + self._derivs = derivs + self._factor = factor + self._aliases = aliases if aliases is not None else {} + self._nbins = nbins + self._logbins = logbins + self.force_real = force_real + self._mcmc_binning_cache = None + logger.debug(f"Using deriv modes: {self._derivs}") + logger.debug(f"Using aliases: {self._aliases}") + logger.debug(f"Using factor: {self._factor}") + logger.debug( + f"Using {self._nbins} " + f"{'log-spaced' if self._logbins else 'linearly-spaced'} bins" + ) + + def _re(self, x): + return np.real(x) if self.force_real else x + + def _cache_mcmc_binning(self): + """Cache quantities needed for binning 2d power spectrum to 1d.""" + cache = {} + + for ipol in range(self._signal_mask.shape[0]): + kpp, kll = np.meshgrid(self._kperp, self._kpara) + k = np.sqrt(kpp**2 + kll**2) + + # Apply signal window if present + if self._signal_mask is not None: + k = k[self._signal_mask[ipol]] + weight = self._ps2D_weight[ipol][self._signal_mask[ipol]] + + # Flatten arrays + k1D = k.flatten() + w1D = weight.flatten() + + # Calculate bin edges + kmin = k1D[k1D > 0].min() + kmax = k1D.max() + + if self._logbins: + kbins = np.logspace(np.log10(kmin), np.log10(kmax), self._nbins + 1) + else: + kbins = np.linspace(kmin, kmax, self._nbins + 1) + + indices = np.digitize(k1D, kbins) + + # Pre-compute weight sums for each bin + w_sums = np.zeros(self._nbins) + for i in np.arange(len(kbins) - 1) + 1: + w_b = w1D[indices == i] + w_sums[i - 1] = np.sum(w_b) + + cache[ipol] = { + "indices": indices, + "kbins": kbins, + "w1D": w1D, + "w_sums": w_sums, + } + + self._mcmc_binning_cache = cache + logger.debug("MCMC binning calculations cached") + + @classmethod + def load_from_ps2Dfiles( + cls, + pattern: str, + filename_pattern: str = None, + pol: List[str] = None, + weight: np.ndarray = None, + signal_mask: np.ndarray = None, + combine: bool = True, + force_real: bool = True, + **kwargs, + ): + """Load the signal template from a set of 2d power spectrum files. + + This will load the ps2D files from each location and try and compile them into + a set which can be used to generate signal templates. + + The signal templates should be stored in directories with names of the form + `*_compderiv-d-par`, where: + - `d` is one of `0`, `h`, or `1`, corresponding to the value of b_HI + - `par` denotes a specific combination of other parameters + + Parameters + ---------- + pattern + A glob pattern that isolates the directories containing the base + signal templates. + filename_pattern + A glob pattern that specifies the filenames containing the base + signal templates. + pol + The desired polarisations. + weight + The weight to use when averaging over polarisations and binning + from 2d to 1d. Must have shape [npol, kpara, kperp]. + signal_mask + Boolean mask to use when binning from 2d down to 1d. + Must have shape [npol, kpara, kperp]. + combine + Add an element to the polarisation axis called I that + is the weighted sum of the XX and YY polarisation. + force_real + Force input datasets to be real. Assumes that input datasets have + been previously examined to verify that imaginary parts are small + and/or unimportant. Default: True. + **kwargs + Arguments passed on to the constructor. + """ + + dirs = glob.glob(pattern) + + if filename_pattern is None: + filename_pattern = "*.h5" + + matching = {} + + # Find directories which match the right format + for d in sorted(dirs): + ###mo = re.search(r"_compderiv-([^\/]+)", d) + mo_comp = re.search(r"bias_([0-9\.]+)_Pk_([^_]+)/", d) + # Check for shotnoise directory + mo_shotnoise = re.search(r"template_shotnoise", d) + + if mo_comp: + bias = mo_comp.group(1) # This will be "0", "0.5", or "1" + pk_type = mo_comp.group(2) # This will be "base", "lin", "FoGh" + # Create a composite key that identifies the templates + key = f"{bias}-{pk_type}" + + elif mo_shotnoise: + key = "shotnoise" + else: + print(f"Directory {d} does not match expected format, rejecting") + continue + + logger.debug(f"Processing directory: {d}") + + if key in matching: + raise ValueError( + "Did not find a unique set of modes at this location. " + "You might need to refine the pattern." + ) + + d = Path(d) + + if not d.is_dir(): + raise ValueError("Glob pattern for templates must point to directories") + + matching[key] = Path(d) + + # For each directory load all the ps2D files and combine them + ps2Ds = {} + for key, d in matching.items(): + ps2D_files = sorted(list(d.glob(filename_pattern))) + + if len(ps2D_files) == 0: + print("No files found at matching path.") + continue + + mocks = utils.load_mocks(ps2D_files, pol=pol) + ps2Ds[key] = utils.average_data( + mocks, pol=mocks.index_map["pol"], combine=combine, sort=False + ) + + # Create the object + self = cls(**kwargs) + self.force_real = force_real + + # Save signal mask and ps2D weights, for later use in binning + # 2d power spectrum to 1d + self._signal_mask = ( + signal_mask + if signal_mask is not None + else next(iter(ps2Ds.values())).mask[:].copy() + ) + self._ps2D_weight = ( + self._re(weight) + if weight is not None + else self._re(next(iter(ps2Ds.values())).weight[:].copy()) + ) + + # Try and construct all the required templates from the stacks + self._interpret_ps2Ds(ps2Ds) + + return self + + def _interpret_ps2Ds( + self, + ps2Ds: Dict[str, PowerSpectrum1D], + ): + # Generate the required templates from the 2d power spectra + + # Find all entries that have the linear component structure + compterms = [k.split("-")[1] for k in ps2Ds.keys() if k.startswith("0-")] + + ps2D_modes = {} + + # Get the first kpara, kperp axes as references + self._kpara = next(iter(ps2Ds.values())).kpara[:].copy() + self._kperp = next(iter(ps2Ds.values())).kperp[:].copy() + self._kpara.flags.writeable = False + self._kperp.flags.writeable = False + + def _check_load_ps2D(key): + # Validate the 2D power spectrum and extract the template and its variance + + if key not in ps2Ds: + raise RuntimeError(f"Power spectrum {key} was not loaded.") + + ps2D = ps2Ds[key] + + if not np.array_equal(ps2D.kpara[:], self._kpara): + raise RuntimeError( + f"k_par values in power spectrum {key} do not match reference." + ) + + if not np.array_equal(ps2D.kperp[:], self._kperp): + raise RuntimeError( + f"k_perp values in power spectrum {key} do not match reference." + ) + + return ( + self._factor * self._re(ps2D.spectrum[:]), + self._factor**2 + * tools.invert_no_zero(ps2D.attrs["num"] * self._re(ps2D.weight[:])), + ) + + # For all linear component terms, load them and construct the various HI,v + # combination terms + for term in compterms: + logger.debug(f"Combining mode {term}") + + s0, v0 = _check_load_ps2D(f"0-{term}") + sh, vh = _check_load_ps2D(f"0.5-{term}") + s1, v1 = _check_load_ps2D(f"1-{term}") + + # Initialize arrays for b_HI = 0, 1/2, 1 + template_mean = np.zeros((3,) + s0.shape) + template_var = np.zeros((3,) + s0.shape) + + # Calculate the template for each component + ## s_hh = 2 [s(1,1,0) - 2s(1,1/2,0) + s(1,0,0)] + template_mean[0] = 2 * (s1 - 2 * sh + s0) + ## s_hv = s(1,1/2,0) - s(1,0,0) - 1/4 shh + template_mean[1] = sh - s0 - 0.25 * template_mean[0] + ## s_vv = s(1,0,0) + template_mean[2] = s0 + + # Calculate the variance of each component, using error propagation + template_var[0] = 4 * (v1 + 4 * vh + v0) + template_var[1] = vh + v0 + 0.0625 * template_var[0] + template_var[2] = v0 + + ps2D_modes[term] = (template_mean, template_var) + + self._ps2D_comp = {} + self._ps2D_noncomp = {} + self._ps2D_comp["base"] = ps2D_modes["base"] + + # For the expected derivative modes, combine the perturbed entry and the base + # templates to get the derivative templates + for name, (delta, _) in self._derivs.items(): + logger.debug(f"Interpreting derivative mode {name}") + + if name not in ps2D_modes: + raise RuntimeError(f"Expected derivative {name} but could not load it.") + + s, v = ps2D_modes[name] + sb, vb = ps2D_modes["base"] + + # Calculate the finite difference derivative + fd_mode = (s - sb) / delta + fd_var = (v + vb) / delta**2 + + self._ps2D_comp[name] = (fd_mode, fd_var) + + # Load any non-component type terms. These are terms which sit outside the usual + # bias and Kaiser factors (such as shot noise) + noncompterms = [key for key in ps2Ds.keys() if "-" not in key] + for term in noncompterms: + logger.debug(f"Interpreting non-component mode {term}") + self._ps2D_noncomp[term] = _check_load_ps2D(term) + + def signal_2D(self, *, omega: float, b_HI: float, **kwargs: float) -> np.ndarray: + """Return the 2D power spectrum signal template for the given parameters. + + Parameters + ---------- + omega + Overall scaling. + b_HI + Scaling for the HI bias term. + **kwargs + Values for all other derivative terms (e.g. NL) and non-component terms + (e.g. shotnoise). + + Returns + ------- + signal + Signal template for the given parameters. An array of [pol, kpara, kperp]. + """ + + def _combine(vec): + # Combine the bias terms and templates to get a new template + return b_HI**2 * vec[0] + 2 * b_HI * vec[1] + vec[2] + + # Generate the signal for the base model + signal = _combine(self._ps2D_comp["base"][0]) + + # Add in any derivative contributions + for name, (_, x0) in self._derivs.items(): + + ps2D = _combine(self._ps2D_comp[name][0]) + + name = self._aliases.get(name, name) + if name not in kwargs: + raise ValueError(f"Need a value for deriv parameter {name}") + + x = kwargs[name] + + signal += ps2D * (x - x0) + + # Multiply signal by a Fourier-space function + # before adding in the non-component contributions + signal = self.multiply_pre_noncomp(signal, **kwargs) + + # Scale by the overall prefactor (omega**2 for auto-correlation). + # If we sampled directly in omega^2, this omega may be complex, + # so we need to take the real part here to avoid having omega**2 + # evaluate as a compex number with zero imaginary part. + signal *= np.real(omega**2) + + # Add in any non-component contributions + for name, ps2D in self._ps2D_noncomp.items(): + + name = self._aliases.get(name, name) + if name not in kwargs: + raise ValueError(f"Need a value for non-comp parameter {name}") + + x = kwargs[name] + + signal += ps2D[0] * x + + # Multiply signal by a Fourier-space function + # after adding in the non-component contributions + signal = self.multiply_post_noncomp(signal, **kwargs) + + return signal + + def signal_1D_slow( + self, *, omega: float, b_HI: float, **kwargs: float + ) -> np.ndarray: + """Return the 1D power spectrum template, binned from 2D template. + + Uses `get_1d_ps` from `draco.analysis.powerspec`, which re-calculates + several quantities that don't change if model parameters are changed, and + is therefore slower than the cached implementation in `signal_1d`. + + Parameters + ---------- + omega + Overall scaling. + b_HI + Scaling for the HI bias term. + **kwargs + Values for all other derivative terms (e.g. NL) and non-component terms + (e.g. shotnoise). + + Returns + ------- + signal + Signal template for the given parameters. An array of [pol, k]. + """ + + _signal_2D = self.signal_2D(omega=omega, b_HI=b_HI, **kwargs) + + signal_1D = np.zeros((_signal_2D.shape[0], self._nbins)) + + for ipol in range(_signal_2D.shape[0]): + + _, signal_1D[ipol], _, _, _ = get_1d_ps( + _signal_2D[ipol], + self._kperp, + self._kpara, + self._ps2D_weight[ipol], + self._signal_mask[ipol], + self._nbins + 1, + self._logbins, + ) + + return signal_1D + + def signal_1D(self, *, omega: float, b_HI: float, **kwargs: float) -> np.ndarray: + """Return the 1D power spectrum template with cached binning schemes. + + Parameters + ---------- + omega + Overall scaling. + b_HI + Scaling for the HI bias term. + **kwargs + Values for all other derivative terms (e.g. NL) and non-component terms + (e.g. shotnoise). + + Returns + ------- + signal + Signal template for the given parameters. An array of [pol, k]. + """ + + _signal_2D = self.signal_2D(omega=omega, b_HI=b_HI, **kwargs) + signal_1D = np.zeros((_signal_2D.shape[0], self._nbins)) + + if self._mcmc_binning_cache is None: + self._cache_mcmc_binning() + + for ipol in range(_signal_2D.shape[0]): + cache = self._mcmc_binning_cache[ipol] + indices = cache["indices"] + w1D = cache["w1D"] + w_sums = cache["w_sums"] + + if self._signal_mask is not None: + p1D = _signal_2D[ipol][self._signal_mask[ipol]].flatten() + else: + p1D = _signal_2D[ipol].flatten() + + # Compute binned power spectrum using cached values + with np.errstate(divide="ignore", invalid="ignore"): + for i in np.arange(len(cache["kbins"]) - 1) + 1: + bin_mask = indices == i + p = np.sum(w1D[bin_mask] * p1D[bin_mask]) / w_sums[i - 1] + signal_1D[ipol, i - 1] = p + + return signal_1D + + def multiply_pre_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Override in subclass to multiply signal by function pre-non-components.""" + return signal + + def multiply_post_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Override in subclass to multiply signal by function post-non-components.""" + return signal + + @property + def kpara(self): + """Get k_para values the template is defined at.""" + return self._kpara + + @property + def kperp(self): + """Get k_perp values the template is defined at.""" + return self._kperp + + @property + def params(self): + """The names of all the parameters needed to generate the template.""" + return ( + ["omega", "b_HI"] + + [self._aliases.get(name, name) for name in self._ps2D_comp.keys()] + + [self._aliases.get(name, name) for name in self._ps2D_noncomp.keys()] + ) + + +class AutoSignalTemplate2DFoG(AutoSignalTemplate2D): + """Create signal templates from pre-simulated modes and input parameters. + + Multiplies the 2d power spectrum with a kernel to simulate FoG damping, + in contrast to the AutoSignalTemplate2D class, which uses a linear model for + the FoG damping. + + Parameters + ---------- + derivs + A dictionary of derivatives expected, giving their name (key), and a tuple of the + parameter difference used in the simulations (between the perturbed sim and the + base values) and the fiducial value of the parameter. + convolutions + A dictionary of the expected convolution parameters, giving their name (key), + and a tuple of the parameter difference used in the simulations (between the + perturbed sim and the base values) and the fiducial value of the parameter. + kpara_range + The lower and upper boundary of k_parallel that will be used to fit for + the effective scale of the base convolution kernel. + Defaults to (0, 5) Mpc^-1. + """ + + def __init__( + self, + derivs: Optional[Dict[str, Tuple[float, float]]] = None, + convolutions: Optional[Dict[str, Tuple[float, float]]] = None, + kpara_range: Optional[Tuple[float, float]] = None, + z_eff: Optional[float] = None, # effective redshift + *args, + **kwargs, + ): + + # Set default z_eff if None + self.z_eff = z_eff if z_eff is not None else 1.0 + cosmo_cora = cosmology.Cosmology() + self.H_z = cosmo_cora.H(self.z_eff) * u.mega_parsec / 1000.0 # In km/s/Mpc + + if derivs is None: + derivs = { + "NL": (0.3, 1.0), + } + if convolutions is None: + convolutions = { + "FoGh": (0.2, 1.0), + } + if kpara_range is None: + kpara_range = (0.0, 5.0) + + self._convolutions = convolutions + self._kpara_range = kpara_range + + super().__init__(derivs=derivs, *args, **kwargs) + logger.debug(f"Using convolution parameters: {self._convolutions}") + logger.debug( + f"Fitting effective FoG scale over k_para range: {self._kpara_range}" + ) + + def _solve_scale( + self, base: PowerSpectrum2D, deriv: PowerSpectrum2D, alpha: float + ) -> np.ndarray: + r"""Solve for the effective scale of the FoG damping. + + Note that the scale parameter returned by this function is different from + the scale parameter defined in the eBOSS stacking paper: if :math:`s` is the + code parameter and :math:`\sigma_{\rm eff}` is the paper's parameter, then + + .. math:: + + s = \sigma_{\rm eff} / \sqrt{2} + + Therefore, the FoG kernel is defined as + + .. math:: + + H(k_\parallel, s) = 1 / (1 + (s k_\parallel)^2) + + Parameters + ---------- + base + 2d power spectrum from simulations with the base parameters. + deriv + 2d power spectrum from simulations with the FoG parameter perturbed. + alpha + The ratio of the FoG parameter for deriv relative to base. + + Returns + ------- + scale : np.ndarray[npol,] + The effective scale of the transfer function. + """ + + kpara2 = self.kpara[np.newaxis, :, np.newaxis] ** 2 + + # Take real parts of input spectra, so that output scale is also real + ps2D_base = self._re(base.spectrum[:]) + ps2D_deriv = self._re(deriv.spectrum[:]) + + # Get variance of base and deriv ps2D, for usage in error propagation + var_ps2D_base = tools.invert_no_zero(self._re(base.weight[:])) + var_ps2D_deriv = tools.invert_no_zero(self._re(deriv.weight[:])) + + # Compute ratio of base and deriv ps2D, and compute variance in ratio using + # error propagation + ratio = ps2D_base / ps2D_deriv + var_ratio = ratio**2 * ( + var_ps2D_base * tools.invert_no_zero(ps2D_base**2) + + var_ps2D_deriv * tools.invert_no_zero(ps2D_deriv**2) + ) + + # If each power spectrum was exactly proportional to H(kpar) as defined in + # the docstring, the ratio would be equal to + # H(kpar,alpha*s)^2 / H(kpar,s)^2 . + # This might not exactly be true because of how the data were processed, + # but we'll assume it's true and fit for an effective value of s. + # To do so, we write + # ratio = H(kpar,alpha*s)^2 / H(kpar,s)^2 + # and then solve for y, defined to be kpar^2 s^2. + r_sqrt = ratio**0.5 + y = (r_sqrt - 1.0) * tools.invert_no_zero(alpha**2 - r_sqrt) + + # We then compute weights w that are equal to the inverse variance of y, + # computed via error propagation. We also zero out kpar values that are + # beyond the desired fitting range + w = ( + 4 + * ratio + * (alpha**2 - r_sqrt) ** 4 + * tools.invert_no_zero((alpha * 2 - 1.0) ** 2 * var_ratio) + ) + w_mask = (self.kpara >= self._kpara_range[0]) & ( + self.kpara <= self._kpara_range[1] + ) + w *= w_mask[np.newaxis, :, np.newaxis] + + # From the definition of y, we know that s^2 = y/kpar^2. We optimally + # estimate s^2 by taking an inverse-variance weighted average of y/kpar^2 + # over all (kpar,kperp) values. (We'll only use s^2 in calculations, so it + # makes sense to estimate s^2 instead of s.) + scale2 = np.sum(w * kpara2 * y, axis=(-1, -2)) * tools.invert_no_zero( + np.sum(w * kpara2**2, axis=(-1, -2)) + ) + + return np.sqrt(scale2) + + def _interpret_ps2Ds(self, ps2Ds: Dict[str, PowerSpectrum1D]): + + super()._interpret_ps2Ds(ps2Ds) + + base = ps2Ds["1-base"] + + self._convolution_scale = {} + + for name, (delta, x0) in self._convolutions.items(): + + key = f"1-{name}" + + alpha = (x0 + delta) / x0 + + if key not in ps2Ds: + raise RuntimeError(f"Expected derivative {name} but could not load it.") + + # Determine the effective scale + scale = self._solve_scale(base, ps2Ds[key], alpha) + self._convolution_scale[name] = scale + + def _get_factor(self) -> float: + """Calculate the conversion factor connecting tau and k_parallel. + + C = -1/(2 pi nu21) * c/H(z) * (1+z)² + + Returns + ------- + C : float + Conversion factor + """ + + C = ( + (-1.0 / (2 * np.pi * u.nu21)) + * ((u.c / u.kilo) / self.H_z) + * (1 + self.z_eff) ** 2 + ) + + return C + + def multiply_pre_noncomp(self, signal: np.ndarray, **kwargs) -> np.ndarray: + """Multiply the 2d power spectrum with the relative FoG kernel. + + Parameters + ---------- + signal : np.ndarray[npol, nkpara, nkperp] + The 2d power spectrum before adding the non-component contributions. + kwargs : dict + All parameter values. + + Returns + ------- + signal : np.ndarray[npol, nkpara, nkperp] + The 2d power spectrum after multiplication with the relative FoG kernel. + """ + # Calculate the conversion factor + C = self._get_factor() + + # Loop over parameters corresponding to distinct kernels we'll need to + # multiply into the signal + for name, (_, x0) in self._convolutions.items(): + + # Get scale corresponding to base template + scale0 = self._convolution_scale[name][:, np.newaxis, np.newaxis] + + # Get aliased name of parameter and parameter value + name = self._aliases.get(name, name) + if name not in kwargs: + raise ValueError(f"Need a value for convolution parameter {name}") + x = kwargs[name] + + # Re-scale effective convolution scale + alpha = x / x0 + scale = alpha * scale0 + + # Multiply kernel into signal + signal *= ( + 1.0 + (scale0 * C * self.kpara[np.newaxis, :, np.newaxis]) ** 2 + ) ** 2 / ( + 1.0 + (scale * C * self.kpara[np.newaxis, :, np.newaxis]) ** 2 + ) ** 2 + + return signal diff --git a/fitstack/stats.py b/fitstack/stats.py new file mode 100644 index 0000000..1dd81c5 --- /dev/null +++ b/fitstack/stats.py @@ -0,0 +1,556 @@ +import logging + +import numpy as np +import scipy.optimize +import scipy.stats + +from statsmodels.stats.diagnostic import anderson_statistic + + +# Set up logging +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) + +try: + from tqdm.notebook import trange + + TQDM_IMPORTED = True +except ImportError: + logger.warning("Error importing tqdm") + TQDM_IMPORTED = False + + +BOUNDED_MINIMIZATION = ["L-BFGS-B", "Nelder-Mead", "Powell", "TNC"] + + +def F_scaling(n, p, p_eff): + """Compute chi^2-to-F scaling factor: + + From Sellentin+Heavens 2015 (arXiv:1511.05969), a chi^2 + value computed with an estimated covariance will follow + an F distribution if the value is multiplied by + + .. math:: + + (n - p + 1) / (n p) . + + If this is used in the context of fitting a model with + degenerate parameters, the F distribution may not be + exact, so we allow for an effective number of degrees + of freedom by allowing the `p` in the denominator to + differ from the one in the numerator. + + Parameters + ---------- + n, p, p_eff : float + Parameters in the rescaling factor. + + Returns + ------- + factor : float + Rescaling factor. + """ + return (n - p + 1) / (n * p_eff) + + +def fit_chi2_to_array(vals): + """Fit a chi^2 distribution to a set of values. + + Parameters + ---------- + vals : np.ndarray + Values to fit to. + + Returns + ------- + ndof : float + Best-fit number of degrees of freedom. + """ + return scipy.stats.chi2.fit(vals, floc=0, fscale=1)[0] + + +def fit_F_to_scaled_array(vals, n, p, p_eff_0=None, eps=1e-8): + """Fit an F distribution to a rescaled set of values. + + We take the input values, rescale by + + .. math:: + + (n - p + 1) / (n p_{eff}) , + + and then fit for :math:`p_eff` by maximizing the likelihood that + the rescaled values are drawn from an :math:`F_{p_{eff}, n - p + 1}` + distribution. + + Parameters + ---------- + vals : np.ndarray + Values to fit to. + n, p : float + Parameters of the target `F` distribution. If the input values + are :math:`\chi^2` or :math:`\Delta\chi^2` values computed using a + covariance matrix that was estimated from N simulations, one should + specify :math:`n=N-1`. `p` should be the length of the data vector + p_eff_0 : float, optional + Initial guess for :math:`p_{eff}`. If not given, a :math:`\chi^2` + distribution is fit to the (unscaled) values and the best-fit + number of degrees of freedom is used. Default: None. + eps : float, optional + Step size used for numerical approximation of the Jacobian used + in the L-BFGS-B optimizer. Default: 1e-8. + + Returns + ------- + p_eff : float + Best-fit :math:`p_{\rm eff}` value. + resd : scipy.optimize.OptimizeResult + Full fit results. + """ + + _MIN_P_EFF = 1e-6 + + def _neg_log_likelihood(x): + + p_eff = x[0] + + # p_eff <=0 is impossible, so return infinite negative log likelihood + # in that case + if p_eff <= 0: + return np.inf + + # Set scaling factor to rescale input values into F-distributed form, + # and rescale values + s = F_scaling(n, p, p_eff) + vals_scaled = vals * s + + # Evaluate pdf of proposed F distribution at scaled values + pdf_vals = scipy.stats.f.pdf(vals_scaled, p_eff, n - p + 1) + + # If zeros or infinities are detected, return infinite negative log + # likelihood + if np.any(pdf_vals <= 0) or not np.isfinite(pdf_vals).all(): + return np.inf + + # Return negative log likelihood, corresponding to probability + # that given values are distributed like F_{p_eff, n - p + 1} + return -np.sum(np.log(pdf_vals) + np.log(s)) + + # If no initial guess for p_eff provided, set to best-fit number of d.o.f. + # for a chi^2 distribution + if p_eff_0 is None: + p_eff_0 = fit_chi2_to_array(vals) + + # Fit for p_eff using L-BFGS-B + resd = scipy.optimize.minimize( + _neg_log_likelihood, + x0=np.array([p_eff_0]), + bounds=[(_MIN_P_EFF, None)], + options={"eps": eps}, + method="L-BFGS-B", + ) + + # Return p_eff and full fit results + p_eff = resd.x[0] + return p_eff, resd + + +def compute_MC_calibrated_distribution_test( + vals, + test="KS", + dist="chi2", + seed=0, + n_for_F=998, + p_for_F=16, + verbose=False, + n_mc_sims=1000, + bootstrap=False, + return_ndof=False, +): + """Compute Monte-Carlo-calibrated distribution test. + + For a set of values, we compute the specified test statistic + (KS or AD) comparing the values to the specified distribution + (chi^2 or F) with `n_dof` parameter fit to the values themselves. + We then generate `n_mc_sims` draws of the same number of values + from the best-fit distribution, re-fit the distribution to the draw, + and compute the test statistic for each draw and the corresponding + best-fit distribution. The number of draws with test statistic + greater than the data test statistic is the p-value for the test, + but calibrated via Monte Carlo. We also return the data test + statistic and test statistics for each draw. + + Alternatively, this function allows one to draw bootstrap resamples + of the input set of values instead of generating Monte Carlo + draws from the fitted distribution. + + Parameters + ---------- + vals : np.ndarray + Array of values to compare to a distribution. + test : str, optional + Test to apply. Must be one of "KS" (Kolmogorov-Smirnov) + or "AD" (Anderson-Darling). Default: "KS". + dist : str, optional + Distribution to test. Must be one of "chi2" or "F". + Default: "chi2". + seed : int, optional + Seed for random number generator. Default: 0. + n_for_F, p_for_F: float, optional + n and p parameters for "df2" of the F distribution. + The p parameter for "df1" will be fit separately from + `p_for_F`. Defaults: 998, 16. + verbose : bool, optional + Use `tqdm` package to display progress bar. Default: False. + n_mc_sims : int, optional + Number of Monte Carlo realizations of distribution. + Default: 1000. + bootstrap : bool, optional + Draw bootstrap resamples of original set of values, instead + of drawing from best-fit distribution. Default: False. + return_ndof : bool, optional + Return array of fitted n_dof values for each Monte Carlo + realization. Default: False. + + Returns + ------- + p_cal : float + Calibrated p-value corresponding to chosen test. + statistic_data : float + Test statistic evaluated on input values. + statistic_sim : np.ndarray + Array of test statistics computed via Monte Carlo. + ndof_sim : np.ndarray + Array of fitted n_dof values for each Monte Carlo realization. + Only returned if `return_dof` is True. + """ + + _TESTS = ["KS", "AD"] + _DISTS = ["chi2", "F"] + + # Ensure that valid test statistic and distribution are being requested + if test not in _TESTS: + raise NotImplementedError( + f"Test {test} not implemented (must be one of {_TESTS})" + ) + + if dist not in _DISTS: + raise NotImplementedError( + f"Distribution {dist} not implemented (must be one of {_DISTS})" + ) + + # Fit specified distribution to input values, and compute desired + # test statistic + if dist == "chi2": + ndof_data = fit_chi2_to_array(vals) + + if test == "KS": + statistic_data, _ = scipy.stats.kstest(vals, "chi2", args=(ndof_data,)) + elif test == "AD": + statistic_data = anderson_statistic( + vals, scipy.stats.chi2, params=(ndof_data,) + ) + + elif dist == "F": + ndof_data, _ = fit_F_to_scaled_array(vals, n_for_F, p_for_F) + + # Set second parameter for F distribution + df_den = n_for_F - p_for_F + 1 + + # Note that input values need to be scaled before comparing to + # F distribution + if test == "KS": + statistic_data, _ = scipy.stats.kstest( + vals * F_scaling(n_for_F, p_for_F, ndof_data), + "f", + args=(ndof_data, df_den), + ) + elif test == "AD": + statistic_data = anderson_statistic( + vals * F_scaling(n_for_F, p_for_F, ndof_data), + scipy.stats.f, + params=(ndof_data, df_den), + ) + + # Define lists to store fit parameters and test statistics for + # each Monte Carlo simulation + ndof_sim, statistic_sim = [], [] + + # Initialize random number generator + rng = np.random.default_rng(seed=seed) + + # Get size of dataset + n_mocks = len(vals) + + # Use tqdm to print status during loop over Monte Carlo simulations, + # if desired + if verbose: + s_range = trange(n_mc_sims) + else: + s_range = range(n_mc_sims) + + # Loop over MC sims + for s in s_range: + if dist == "chi2": + # Generate n_mocks random draws from chi^2 distribution + # or input values, and fit n_dof + if bootstrap: + sim_vals = rng.choice(vals, size=len(vals), replace=True) + else: + sim_vals = rng.chisquare(df=ndof_data, size=n_mocks) + + ndof = fit_chi2_to_array(sim_vals) + + if test == "KS": + statistic, _ = scipy.stats.kstest(sim_vals, "chi2", args=(ndof,)) + elif test == "AD": + statistic = anderson_statistic( + sim_vals, scipy.stats.chi2, params=(ndof,) + ) + + elif dist == "F": + # Generate n_mocks random draws from F distribution + # or input values, and fit n_dof + if bootstrap: + sim_vals = rng.choice( + vals, + size=len(vals), + replace=True, + ) + ndof, _ = fit_F_to_scaled_array(sim_vals, n_for_F, p_for_F) + sim_vals *= F_scaling(n_for_F, p_for_F, ndof) + else: + sim_vals = rng.f(dfnum=ndof_data, dfden=df_den, size=n_mocks) + ndof = scipy.stats.f.fit(sim_vals, fdfd=df_den, floc=0, fscale=1)[0] + + if test == "KS": + statistic, _ = scipy.stats.kstest(sim_vals, "f", args=(ndof, df_den)) + elif test == "AD": + statistic = anderson_statistic( + sim_vals, scipy.stats.f, params=(ndof, df_den) + ) + + ndof_sim.append(ndof) + statistic_sim.append(statistic) + + ndof_sim = np.array(ndof_sim) + statistic_sim = np.array(statistic_sim) + + # Compute p-value for test statistic evaluated on input values, + # as fraction of simulations with test statistic greater than + # data value + p_cal = np.mean(statistic_sim > statistic_data) + + if return_ndof: + return p_cal, statistic_data, statistic_sim, ndof_sim + else: + return p_cal, statistic_data, statistic_sim + + +def compute_MC_calibrated_LOBO_distribution_test( + vals_block, + vals_other, + test="KS", + dist="chi2", + seed=0, + n_for_F=998, + p_for_F=16, + verbose=False, + n_mc_sims=1000, +): + """Compute Monte-Carlo-calibrated leave-one-block-out distribution test. + + This routine takes separate "block" and "other" values to test. The + desired distribution is fit to the "other" values, and then the test + statistic is used to compare the "block" values to this distribution. + The test statistic is calibrated by generating many Monte Carlo + realizations of the "block" and "other" values, re-fitting the + distribution to the "other" values, and re-computing the test + statistic for the "block" values. + + Parameters + ---------- + vals_block, vals_other : np.ndarray + Arrays of values (see docstring for explanation)). + test : str, optional + Test to apply. Must be one of "KS" (Kolmogoriv-Smirnov) + or "AD" (Anderson-Darling). Default: "KS". + dist : str, optional + Distribution to test. Must be one of "chi2" or "F". + Default: "chi2". + seed : int, optional + Seed for random number generator. Default: 0. + n_for_F, p_for_F: float, optional + n and p parameters for "df2" of the F distribution. + The p parameter for "df1" will be fit separately from + `p_for_F`. Defaults: 998, 16. + verbose : bool, optional + Use `tqdm` package to display progress bar. Default: False. + n_mc_sims : int, optional + Number of Monte Carlo realizations of distribution. + Default: 1000. + + Returns + ------- + p_cal : float + Calibrated p-value corresponding to chosen test. + statistic_data : float + Test statistic evaluated on input values. + statistic_sim : np.ndarray + Array of test statistics computed via Monte Carlo. + """ + + _TESTS = ["KS", "AD"] + _DISTS = ["chi2", "F"] + + # Ensure that valid test statistic and distribution are being requested + if test not in _TESTS: + raise NotImplementedError( + f"Test {test} not implemented (must be one of {_TESTS})" + ) + + if dist not in _DISTS: + raise NotImplementedError( + f"Distribution {dist} not implemented (must be one of {_DISTS})" + ) + + # Fit specified distribution to "other" input values, and compute desired + # test statistic using "block" input values + if dist == "chi2": + ndof_data = fit_chi2_to_array(vals_other) + + if test == "KS": + statistic_data, _ = scipy.stats.kstest( + vals_block, "chi2", args=(ndof_data,) + ) + elif test == "AD": + statistic_data = anderson_statistic( + vals_block, scipy.stats.chi2, params=(ndof_data,) + ) + + elif dist == "F": + ndof_data, _ = fit_F_to_scaled_array(vals_other, n_for_F, p_for_F) + + # Set second parameter for F distribution + df_den = n_for_F - p_for_F + 1 + + # Note that input values need to be scaled before comparing to + # F distribution + if test == "KS": + statistic_data, _ = scipy.stats.kstest( + vals_block * F_scaling(n_for_F, p_for_F, ndof_data), + "f", + args=(ndof_data, df_den), + ) + elif test == "AD": + statistic_data = anderson_statistic( + vals_block * F_scaling(n_for_F, p_for_F, ndof_data), + scipy.stats.f, + params=(ndof_data, df_den), + ) + + # Define lists to store fit parameters and test statistics for + # each Monte Carlo simulation + ndof_sim, statistic_sim = [], [] + + # Initialize random number generator + rng = np.random.default_rng(seed=seed) + + # Get sizes of datasets + n_mocks_block = len(vals_block) + n_mocks_other = len(vals_other) + n_mocks_total = n_mocks_block + n_mocks_other + + # Use tqdm to print status during loop over Monte Carlo simulations, + # if desired + if verbose: + s_range = trange(n_mc_sims) + else: + s_range = range(n_mc_sims) + + # Loop over MC sims + for _ in s_range: + if dist == "chi2": + # Generate n_mocks random draws from chi^2 distribution, + # and fit n_dof + sim_vals_total = rng.chisquare(df=ndof_data, size=n_mocks_total) + sim_vals_block = sim_vals_total[:n_mocks_block] + sim_vals_other = sim_vals_total[n_mocks_block:] + + ndof = fit_chi2_to_array(sim_vals_other) + + if test == "KS": + statistic, _ = scipy.stats.kstest(sim_vals_block, "chi2", args=(ndof,)) + elif test == "AD": + statistic = anderson_statistic( + sim_vals_block, scipy.stats.chi2, params=(ndof,) + ) + + elif dist == "F": + # Generate n_mocks random draws from F distribution, + # and fit n_dof + sim_vals_total = rng.f(dfnum=ndof_data, dfden=df_den, size=n_mocks_total) + sim_vals_block = sim_vals_total[:n_mocks_block] + sim_vals_other = sim_vals_total[n_mocks_block:] + + ndof = scipy.stats.f.fit(sim_vals_other, fdfd=df_den, floc=0, fscale=1)[0] + + if test == "KS": + statistic, _ = scipy.stats.kstest( + sim_vals_block, "f", args=(ndof, df_den) + ) + elif test == "AD": + statistic = anderson_statistic( + sim_vals_block, scipy.stats.f, params=(ndof, df_den) + ) + + ndof_sim.append(ndof) + statistic_sim.append(statistic) + + ndof_sim = np.array(ndof_sim) + statistic_sim = np.array(statistic_sim) + + # Compute p-value for test statistic evaluated on input values, + # as fraction of simulations with test statistic greater than + # data value + p_cal = np.mean(statistic_sim > statistic_data) + + return p_cal, statistic_data, statistic_sim + + +def find_symmetric_roots( + func, + root, + x0, + x_lo_bound, + x_hi_bound, +): + """Find roots of a function on either side of a certain point. + + Parameters + ---------- + func : callable + Scalar function to use for root-finding. + root : float + Value of desired root. + x0 : float + Point dividing two search regions for roots. + x_lo_bound, x_hi_bound : float + Lower and upper bounds of search regions for roots. + + Returns + ------- + root_lo, root_high : float + Roots on either side of `x0`. + """ + + resd_lo = scipy.optimize.minimize_scalar( + lambda x: np.abs(func(x) - root), bounds=[x_lo_bound, x0] + ) + resd_hi = scipy.optimize.minimize_scalar( + lambda x: np.abs(func(x) - root), bounds=[x0, x_hi_bound] + ) + + if not resd_lo.success or not resd_hi.success: + raise RuntimeError("One of the root-finders failed") + + return resd_lo.x, resd_hi.x diff --git a/fitstack/utils.py b/fitstack/utils.py index 5a7918e..e99d45e 100644 --- a/fitstack/utils.py +++ b/fitstack/utils.py @@ -1,4 +1,6 @@ """Utililites to prepare the data for the fit.""" + +import logging import os import glob from pathlib import Path @@ -12,8 +14,42 @@ from . import containers +logger = logging.getLogger(__name__) + def covariance(a, corr=False): + """Calculate the sample covariance over mock catalogs or power spectra. + + Parameters + ---------- + a : np.ndarray[nmock, nx, ...] + Array of mock data. + corr : bool + Return the correlation matrix instead of the covariance matrix. + Default is False. + + Returns + ------- + cov : np.ndarray[nx, nx, ...] + The sample covariance matrix (or correlation matrix). + """ + + am = a - np.mean(a, axis=0) + + cov = np.sum(am[:, np.newaxis, :] * am[:, :, np.newaxis], axis=0) / float( + am.shape[0] - 1 + ) + + if corr: + diag = np.diag(cov) + cov = cov * tools.invert_no_zero( + np.sqrt(diag[np.newaxis, :] * diag[:, np.newaxis]) + ) + + return cov + + +def covariance_low_mem(a, corr=False): """Calculate the sample covariance over mock catalogs. Parameters @@ -32,9 +68,15 @@ def covariance(a, corr=False): am = a - np.mean(a, axis=0) - cov = np.sum(am[:, np.newaxis, :] * am[:, :, np.newaxis], axis=0) / float( - am.shape[0] - 1 - ) + nmock, nsample = am.shape + + cov = np.zeros((nsample, nsample), dtype=np.float64) + + for aa in range(nsample): + + for bb in range(nsample): + + cov[aa, bb] = np.sum(am[:, aa] * am[:, bb]) / float(nmock - 1) if corr: diag = np.diag(cov) @@ -45,35 +87,35 @@ def covariance(a, corr=False): return cov -def unravel_covariance(cov, npol, nfreq): +def unravel_covariance(cov, npol, nx): """Separate the covariance matrix into sub-arrays based on polarisation. Parameters ---------- - cov : np.ndarray[npol * nfreq, npol * nfreq] + cov : np.ndarray[npol * nx, npol * nx] Covariance matrix. npol : int Number of polarisations. - nfreq : int - Number of frequencies. + nx : int + Number of frequencies or k bins. Returns ------- - cov_by_pol : np.ndarray[npol, npol, nfreq, nfreq] + cov_by_pol : np.ndarray[npol, npol, nx, nx] Covariance matrix reformatted such that cov_by_pol[i,j] gives the covariance between polarisation i and j as - a function of frequency offset. + a function of frequency offset or k. """ - cov_by_pol = np.zeros((npol, npol, nfreq, nfreq), dtype=cov.dtype) + cov_by_pol = np.zeros((npol, npol, nx, nx), dtype=cov.dtype) for aa in range(npol): - slc_aa = slice(aa * nfreq, (aa + 1) * nfreq) + slc_aa = slice(aa * nx, (aa + 1) * nx) for bb in range(npol): - slc_bb = slice(bb * nfreq, (bb + 1) * nfreq) + slc_bb = slice(bb * nx, (bb + 1) * nx) cov_by_pol[aa, bb] = cov[slc_aa, slc_bb] @@ -85,28 +127,28 @@ def ravel_covariance(cov_by_pol): Parameters ---------- - cov_by_pol : np.ndarray[npol, npol, nfreq, nfreq] + cov_by_pol : np.ndarray[npol, npol, nx, nx] Covariance matrix as formatted by the unravel_covariance method. Returns ------- - cov : np.ndarray[npol * nfreq, npol * nfreq] + cov : np.ndarray[npol * nx, npol * nx] The covariance matrix flattened into the format required for inversion and subsequent likelihood computation. """ - npol, _, nfreq, _ = cov_by_pol.shape - ntot = npol * nfreq + npol, _, nx, _ = cov_by_pol.shape + ntot = npol * nx cov = np.zeros((ntot, ntot), dtype=cov_by_pol.dtype) for aa in range(npol): - slc_aa = slice(aa * nfreq, (aa + 1) * nfreq) + slc_aa = slice(aa * nx, (aa + 1) * nx) for bb in range(npol): - slc_bb = slice(bb * nfreq, (bb + 1) * nfreq) + slc_bb = slice(bb * nx, (bb + 1) * nx) cov[slc_aa, slc_bb] = cov_by_pol[aa, bb] @@ -124,7 +166,7 @@ def _centered(arr, newsize): def shift_and_convolve(freq, template, offset=0.0, kernel=None): - """Shift a template and (optionally) convolve with a kernel. + """Shift a stacking template and (optionally) convolve with a kernel. Parameters ---------- @@ -171,148 +213,390 @@ def shift_and_convolve(freq, template, offset=0.0, kernel=None): return model -def combine_pol(stack): +def combine_pol(cnt): """Perform a weighted sum of the XX and YY polarisations. Parameters ---------- - stack : FrequencyStackByPol, MockFrequencyStackByPol - The source stack. + cnt : container + Input container. Can be one of FrequencyStackByPol, + MockFrequencyStackByPol, PowerSpectrum1D, MockPowerSpectrum1D, + PowerSpectrum2D, MockPowerSpectrum2D. Returns ------- - z : np.ndarray - The weighted sum of the stack dataset for the - XX and YY polarisations. - wz : np.ndarray - The sum of the weight dataset for the - XX and YY polarisations. + output : dict + Dictionary with the following keys: + - "z": Weighted sum of the relevant dataset for the XX and YY + polarisations. + - "wz": The sum of the weights for the XX and YY polarisations. + - "x": The weighted average of the independent coordinate (frequency + lag, k, or dict with kpara and kperp keys) for the XX and YY + polarisations. + In addition, if the input is a PowerSpectrum2D container, the dict + also contains: + - "mask": Boolean AND of mask for XX and YY. + - "neff": Effective number of modes for XX+YY weighted sum. """ - y = stack["stack"][:] - w = stack["weight"][:] + _dset_name = {"stack": "stack", "ps2D": "spectrum", "ps1D": "spectrum"} - ax = list(stack["stack"].attrs["axis"]).index("pol") - pol = list(stack.pol) + # Determine data type, names of co-pol combinations, and whether input + # container contains mocks + if isinstance(cnt, containers.FrequencyStackByPol): + data_type = "stack" + copol_names = ["XX", "YY"] + is_mock_cont = isinstance(cnt, containers.MockFrequencyStackByPol) + else: + copol_names = ["XX-XX", "YY-YY"] + if isinstance(cnt, containers.PowerSpectrum2D): + data_type = "ps2D" + is_mock_cont = isinstance(cnt, containers.MockPowerSpectrum2D) + else: + data_type = "ps1D" + is_mock_cont = isinstance(cnt, containers.MockPowerSpectrum1D) + + # Get list of pol names in input container + pol = list(cnt.index_map["pol"]) + + # If operating on power spectra, check that ordering of k-bin centers is + # identical for the two polarizations. (Otherwise, we shouldn't combine them.) + if data_type == "ps1D": + isort = np.argsort(cnt.k1D) + ax = list(cnt.k1D.attrs["axis"]).index("pol") + slc_XX = (slice(None),) * ax + (pol.index("XX-XX"),) + slc_YY = (slice(None),) * ax + (pol.index("YY-YY"),) + if not np.allclose(isort[slc_XX], isort[slc_YY]): + raise RuntimeError( + "Power spectrum k bins have different ordering " + "for different polarizations, so can't combine" + ) + + # Get input data dataset + y = cnt[_dset_name[data_type]] + + # Get input weights + if (data_type == "stack") | (data_type == "ps2D"): + w = cnt["weight"][:] + else: + w = tools.invert_no_zero(cnt["var"][:]) + # Get index of pol axis in data dataset + ax = list(cnt[_dset_name[data_type]].attrs["axis"]).index("pol") + + # Set input weights to zero, except for desired co-pol elements flag = np.zeros_like(w) - for pstr in ["XX", "YY"]: + for pstr in copol_names: pp = pol.index(pstr) slc = (slice(None),) * ax + (pp,) flag[slc] = 1.0 w = flag * w - wz = np.sum(w, axis=ax) + output = {} + # Get sum of weights across pols, and weighted sum of data across pols + wz = np.sum(w, axis=ax) z = np.sum(w * y, axis=ax) * tools.invert_no_zero(wz) - return z, wz + # Compute coordinates (freq/k) and other ancillary datasets for + # averaged data + if data_type == "stack": + # Frequencies are identical for XX and YY, so no average needed + x = cnt.freq + + elif data_type == "ps2D": + # k_par and k_perp are identical for XX and YY, so no average needed + # here either + x = {"kpara": cnt.kpara, "kperp": cnt.kperp} + + # Compute effective number of modes + neff = cnt["neff"][:] + output["neff"] = wz**2 * tools.invert_no_zero( + np.sum(w**2 * tools.invert_no_zero(neff), axis=ax) + ) + # Compute boolean AND of mask across co-pol elements. + # Mask has different shape than data/weights, so we need to + # determine the pol axis again + mask = cnt["mask"][:] + ax = list(cnt["mask"].attrs["axis"]).index("pol") + ipol = np.array([pol.index(pstr) for pstr in copol_names]) + slc = (slice(None),) * ax + (ipol,) + mask = mask[slc] + output["mask"] = np.all(mask, axis=ax) + else: + x = cnt.k1D[:] + if is_mock_cont: + # Check that weights are identical for each mock + if not np.allclose(w, w[0]): + raise RuntimeError( + "Weights in MockPowerSpectrum1D are different for each mock." + "The current code implementation cannot handle this." + ) + # Just use weights for first mock, modifying ax to account for + # fact that we've selected a single element of the mock axis + x = np.sum(w[0] * x, axis=ax - 1) * tools.invert_no_zero(wz[0]) + else: + x = np.sum(w * x, axis=ax) * tools.invert_no_zero(wz) -def initialize_pol(cnt, pol=None, combine=False): - """Select the stack data for the desired polarisations. + output["z"], output["wz"], output["x"] = z, wz, x + + return output + + +def initialize_pol(cnt, pol=None, combine=False, return_signal_mask_and_neff=False): + """Select the data for the desired polarisations. Parameters ---------- - cnt : FrequencyStackByPol - The source stack. + cnt : FrequencyStackByPol, PowerSpectrum1D, or PowerSpectrum2D + Container with stack or power spectrum. pol : list of str The polarisations to select. If not provided, - then ["XX", "YY"] is assumed. + then ["XX", "YY"] is assumed for stack or ["XX-XX", "YY-YY"] + is assumed for power spectrum. combine : bool Add an element to the polarisation axis that is the weighted sum of XX and YY. + return_signal_mask_and_neff : bool + Also return mask and neff for 2d power spectrum. + Ignored if input container is not PowerSpectrum2D. + Default: False. Returns ------- - stack : np.ndarray[..., npol, nfreq] - The stack dataset for the selected polarisations. + data : np.ndarray[..., npol, nx] or np.ndarray[..., npol, nkpara, nkperp] + The stack or power spectrum dataset for the selected + polarisations. If combine is True, there will be an additional - element that is the weighted sum of the stack for + element that is the weighted sum of the + stack/power spectrum for the "XX" and "YY" polarisations. - weight : np.ndarray[..., npol, nfreq] + weight : np.ndarray[..., npol, nx] or np.ndarray[..., npol, nkpara, nkperp] The weight dataset for the selected polarisations. If combine is True, there will be an additional element that is the sum of the weights for the "XX" and "YY" polarisations. + cpol : list of str + List of polarizations in output arrays. + x : np.ndarray[..., npol, nx] or dict + Frequencies or k values for the selected polarizations. + If dealing with 2d power spectrum, dict has kpara and kperp keys, + each as np.ndarray[..., npol, nk]. """ - if pol is None: - pol = ["XX", "YY"] + _dset_name = {"stack": "stack", "ps2D": "spectrum", "ps1D": "spectrum"} - cpol = list(cnt.pol) - ipol = np.array([cpol.index(pstr) for pstr in pol]) + if combine and ("I" in pol or "Q" in pol): + raise RuntimeError("Cannot combine polarizations if Stokes parameters provided") + + if isinstance(cnt, containers.FrequencyStackByPol): + data_type = "stack" + if pol is None: + pol = ["XX", "YY"] + else: + if pol is None: + pol = ["XX-XX", "YY-YY"] - num_freq = cnt.freq.size + if isinstance(cnt, containers.PowerSpectrum2D): + data_type = "ps2D" + else: + data_type = "ps1D" + + # Get indices of requested polarizations in pol axis of input container + cpol = list(cnt.index_map["pol"]) + ipol = np.array([cpol.index(pstr) for pstr in pol]) num_cpol = ipol.size + # Set number of output pols (input+1 if adding combined pol) num_pol = num_cpol + int(combine) - ax = list(cnt.stack.attrs["axis"]).index("pol") - shp = list(cnt.stack.shape) - shp[ax] = num_pol + # Determine name of dataset with data + if isinstance(cnt, containers.FrequencyStackByPol): + dset = "stack" + else: + dset = "spectrum" - stack = np.zeros(shp, dtype=cnt.stack.dtype) - weight = np.zeros(shp, dtype=cnt.stack.dtype) + # Get index of pol axis in input data dataset, then make shape of + # output data dataset, adjusting pol axis length if adding combined + # pol + ax = list(cnt[dset].attrs["axis"]).index("pol") + shp = list(cnt[dset].shape) + shp[ax] = num_pol + # If input is MockContainer, some datasets (e.g. mask for PowerSpectrum2D) + # will not have a mock axis, so the shapes of these datasets in the + # output container will not be the same as the data dataset. So, + # we need to separately track the pol axis and output dataset shape + # for these containers. + if isinstance(cnt, containers.MockContainer): + nomock_dset = cnt.non_mock_datasets[0] + ax_nomock = list(cnt[nomock_dset].attrs["axis"]).index("pol") + shp_nomock = list(cnt[nomock_dset].shape) + shp_nomock[ax_nomock] = num_pol + else: + ax_nomock = ax + shp_nomock = shp + + # Make arrays for output data, weight, and coordinate datasets + data = np.zeros(shp, dtype=cnt[dset].dtype) + weight = np.zeros(shp, dtype=cnt[dset].dtype) + if data_type != "ps2D": + x = np.zeros(shp, dtype=cnt[dset].dtype) + else: + x = { + "kpara": np.zeros( + tuple(shp[:-2]) + (len(cnt.kpara),), dtype=cnt.kpara.dtype + ), + "kperp": np.zeros( + tuple(shp[:-2]) + (len(cnt.kperp),), dtype=cnt.kperp.dtype + ), + } + + # Make slices for transferring desired pols in datasets from + # input to output container slc_in = (slice(None),) * ax + (ipol,) slc_out = (slice(None),) * ax + (slice(0, num_cpol),) + slc_in_nomock = (slice(None),) * ax_nomock + (ipol,) + slc_out_nomock = (slice(None),) * ax_nomock + (slice(0, num_cpol),) + + # Transfer data and weights + data[slc_out] = cnt[dset][slc_in] + if (data_type == "stack") | (data_type == "ps2D"): + weight[slc_out] = cnt["weight"][slc_in] + else: + weight[slc_out] = tools.invert_no_zero(cnt.datasets["var"][slc_in]) + + # Transfer coordinates. + # These are handled separately from "non-mock" datasets + if data_type == "stack": + x[slc_out] = cnt.freq[..., :] + elif data_type == "ps2D": + x["kpara"][slc_out] = cnt.kpara[..., :] + x["kperp"][slc_out] = cnt.kperp[..., :] + else: + x[slc_out] = cnt.k1D[:] + + # If we want the extra datasets in PowerSpectrum2D, + # take appropriate slices of input mask and neff + if data_type == "ps2D" and return_signal_mask_and_neff: + neff = np.zeros(shp, dtype=cnt.neff.dtype) + neff[slc_out] = cnt.neff[slc_in] - stack[slc_out] = cnt["stack"][slc_in] - weight[slc_out] = cnt["weight"][slc_in] + signal_mask = np.zeros(shp_nomock, dtype=cnt.mask.dtype) + signal_mask[slc_out_nomock] = cnt.mask[slc_in_nomock] if combine: + # Make slices that select combined pol in outputs slc_out = (slice(None),) * ax + (-1,) - temp, wtemp = combine_pol(cnt) - stack[slc_out] = temp + slc_out_nomock = (slice(None),) * ax_nomock + (-1,) + # Compute data, weights, coords for combined pol + cp_calc = combine_pol(cnt) + temp, wtemp, xtemp = cp_calc["z"], cp_calc["wz"], cp_calc["x"] + data[slc_out] = temp weight[slc_out] = wtemp + # Transfer coords (and mask+neff, for PowerSpectrum2D) + if data_type == "ps2D": + x["kpara"][slc_out] = xtemp["kpara"] + x["kperp"][slc_out] = xtemp["kperp"] + signal_mask[slc_out_nomock] = cp_calc["mask"] + neff[slc_out] = cp_calc["neff"] + else: + x[slc_out] = xtemp cpol.append("I") - return stack, weight, cpol + if data_type == "ps2D" and return_signal_mask_and_neff: + return data, weight, cpol, x, signal_mask, neff + else: + return data, weight, cpol, x -def average_stacks(stacks, pol=None, combine=True, sort=True): - """Calculate the mean and variance of a set of stacks. +def average_data(cnt, pol=None, combine=True, sort=True): + """Calculate the mean and variance of a set of stacks or power spectra. Parameters ---------- - stack : MockFrequencyStackByPol - Set of stacks to average. + cnt : MockFrequencyStackByPol, MockPowerSpectrum1D, or MockPowerSpectrum2D + Container with stacks or power spectra to average. pol : list of str The polarisations to select. If not provided, - then ["XX", "YY"] is assumed. + then ["XX", "YY"] is assumed for stacks or ["XX-XX", "YY-YY"] + is assumed for power spectra. combine : bool Add an element to the polarisation axis that is the weighted sum of XX and YY. Default is True. sort : bool - Sort the frequency offset axis in ascending order. + Sort the frequency offset or k axis in ascending order. + Ignored if working with 2d power spectra. Default is True. Returns ------- - avg : FrequencyStackByPol + avg : FrequencyStackByPol, PowerSpectrum1D, or PowerSpectrum2D Container that has collapsed over the mock axis. - The stack dataset contains the mean and the weight - dataset contains the inverse variance. + The stack or spectrum dataset contains the mean. For stacks, + the weight dataset contains the inverse variance, + while for power spectra, the var dataset contains + the variance (1d) or the weight dataset contains the + inverse variance (2d). """ - sarr, _, spol = initialize_pol(stacks, pol=pol, combine=combine) - nstack = sarr.shape[0] + if isinstance(cnt, containers.MockPowerSpectrum2D): + darr, _, dpol, dx, dmask, dneff = initialize_pol( + cnt, pol=pol, combine=combine, return_signal_mask_and_neff=True + ) + else: + darr, _, dpol, dx = initialize_pol( + cnt, pol=pol, combine=combine, return_signal_mask_and_neff=False + ) + ndata = darr.shape[0] - freq = stacks.freq - if sort: - isort = np.argsort(freq) - freq = freq[isort] - sarr = sarr[..., isort] + # freq/k should always be real + dx = np.real(dx) - avg = containers.FrequencyStackByPol( - pol=np.array(spol), freq=freq, attrs_from=stacks - ) + # If requested, sort by freq/k + if sort and not isinstance(cnt, containers.MockPowerSpectrum2D): + isort = np.argsort(dx, axis=-1) + dx = np.take_along_axis(dx, isort, axis=-1) + darr = np.take_along_axis(darr, isort, axis=-1) + + # Make new container with mean and variance over mock axis + if isinstance(cnt, containers.MockFrequencyStackByPol): + avg = containers.FrequencyStackByPol( + pol=np.array(dpol), freq=cnt.freq, attrs_from=cnt + ) + avg.stack[:] = np.mean(darr, axis=0) + avg.weight[:] = tools.invert_no_zero(np.var(darr, axis=0)) + elif isinstance(cnt, containers.MockPowerSpectrum2D): + avg = containers.PowerSpectrum2D( + pol=np.array(dpol), + delay=cnt.index_map["delay"], + uv_dist=cnt.index_map["uv_dist"], + attrs_from=cnt, + distributed=False, + ) + avg.spectrum[:] = np.mean(darr, axis=0) + avg.kpara[:] = cnt.kpara[:] + avg.kperp[:] = cnt.kperp[:] + avg.mask[:] = dmask + avg.neff[:] = dneff + + if darr.shape[0] == 1: + # Set weights to unity for single mock + avg.weight[:] = np.ones_like(avg.spectrum[:], dtype=int) + else: + # Variance calculation for multiple mocks + avg.weight[:] = tools.invert_no_zero(np.var(darr, axis=0)) + else: + avg = containers.PowerSpectrum1D( + pol=np.array(dpol), k=cnt.index_map["k"], attrs_from=cnt, distributed=False + ) + avg.k1D[:] = np.mean(dx, axis=0) + avg.spectrum[:] = np.mean(darr, axis=0) + avg.var[:] = np.var(darr, axis=0) - avg.attrs["num"] = nstack - avg.stack[:] = np.mean(sarr, axis=0) - avg.weight[:] = tools.invert_no_zero(np.var(sarr, axis=0)) + avg.attrs["num"] = ndata return avg @@ -330,7 +614,8 @@ def load_pol(filename, pol=None): filename : str Name of the file. pol : list of str - Desired polarisations. Defaults to ["XX", "YY"]. + Desired polarisations. Defaults to ["XX", "YY"] for stack + or ["XX-XX", "YY-YY"] for power spectrum. Returns ------- @@ -339,52 +624,78 @@ def load_pol(filename, pol=None): the requested polarisations. """ - if pol is None: - pol = ["XX", "YY"] - - pol = np.atleast_1d(pol) - with h5py.File(filename, "r") as handler: container_path = handler.attrs["__memh5_subclass"] fpol = list(handler["index_map"]["pol"][:].astype(str)) + if container_path in [ + "draco.core.containers.FrequencyStackByPol", + "draco.core.containers.MockFrequencyStackByPol", + ]: + if pol is None: + pol = ["XX", "YY"] + elif container_path in [ + "draco.core.containers.PowerSpectrum2D", + "draco.core.containers.MockPowerSpectrum2D", + "draco.core.containers.PowerSpectrum1D", + "fitstack.containers.MockPowerSpectrum1D", + ]: + if pol is None: + pol = ["XX-XX", "YY-YY"] + else: + raise RuntimeError(f"Container type of file ({container_path}) not recognized") + + pol = np.atleast_1d(pol) + ipol = np.array([fpol.index(pstr) for pstr in pol]) Container = misc.import_class(container_path) - return Container.from_file(filename, pol_sel=ipol) + return Container.from_file(filename, pol_sel=ipol, distributed=False) def load_mocks(mocks, pol=None): - """Load the mock catalog stacks. + """Load the mock catalog stacks/noise power spectra. Parameters ---------- - mocks : list of str, FrequencyStackByPol, or MockFrequencyStackByPol; or glob - Set of stacks on mock catalogs. This can either be a - MockFrequencyStackByPol container or a list of - FrequencyStackByPol or MockFrequencyStackByPol containers. - It can also be a filename or list of filenames that + mocks : list of str; container; list of containers; or glob + Set of stacks on mock catalogs or noise power spectra. + This can either be a MockFrequencyStackByPol, MockStack3D, + MockPowerSpectrum1D, or MockPowerSpectrum2D container; a list of + FrequencyStackByPol, Stack3D, PowerSpectrum1D, or PowerSpectrum2D + containers; or a filename or list of filenames that hold these types of containers and will be loaded from disk. pol : list of str - Desired polarisations. Defaults to ["XX", "YY"]. + Desired polarisations. Defaults to ["XX", "YY"] for stacks or + ["XX-XX", "YY-YY"] for power spectra. Returns ------- - out : MockFrequencyStackByPol - All mock catalogs in a single container. + out : MockFrequencyStackByPol, MockPowerSpectrum1D, or MockPowerSpectrum2D + All mock catalogs or power spectra in a single container. """ + if isinstance( + mocks, + ( + containers.MockFrequencyStackByPol, + containers.MockStack3D, + containers.MockPowerSpectrum1D, + containers.MockPowerSpectrum2D, + ), + ): + if pol is None: + if isinstance(mocks, containers.MockFrequencyStackByPol): + pol = ["XX", "YY"] + else: + pol = ["XX-XX", "YY-YY"] - if pol is None: - pol = ["XX", "YY"] - - pol = np.atleast_1d(pol) - - if isinstance(mocks, containers.MockFrequencyStackByPol): + pol = np.atleast_1d(pol) if not np.array_equal(mocks.pol, pol): raise RuntimeError( - "The mock catalogs that were provided have the incorrect polarisations." + "The mock catalogs/power spectra that were provided have " + "incorrect polarisations." ) out = mocks @@ -394,6 +705,28 @@ def load_mocks(mocks, pol=None): if isinstance(mocks, str): mocks = sorted(glob.glob(mocks)) + if pol is None: + with h5py.File(mocks[0], "r") as handler: + container_type = handler.attrs["__memh5_subclass"] + if container_type in [ + "draco.core.containers.FrequencyStackByPol", + "draco.core.containers.Stack3D", + ]: + pol = ["XX", "YY"] + else: + pol = ["XX-XX", "YY-YY"] + + else: + if pol is None: + if isinstance( + mocks[0], (containers.MockFrequencyStackByPol, containers.Stack3D) + ): + pol = ["XX", "YY"] + else: + pol = ["XX-XX", "YY-YY"] + + pol = np.atleast_1d(pol) + temp = [] for mfile in mocks: if isinstance(mfile, (str, Path)): @@ -401,7 +734,8 @@ def load_mocks(mocks, pol=None): else: if not np.array_equal(mfile.pol, pol): raise RuntimeError( - "The mock catalogs that were provided have the incorrect polarisations." + "The mock catalogs/power spectra that were provided have " + "incorrect polarisations." ) temp.append(mfile) @@ -412,11 +746,30 @@ def load_mocks(mocks, pol=None): boundaries = np.concatenate(([0], np.cumsum(nmocks))) - out = containers.MockFrequencyStackByPol( - mock=np.arange(boundaries[-1], dtype=int), - axes_from=temp[0], - attrs_from=temp[0], - ) + if isinstance(temp[0], containers.FrequencyStackByPol): + out = containers.MockFrequencyStackByPol( + mock=np.arange(boundaries[-1], dtype=int), + axes_from=temp[0], + attrs_from=temp[0], + ) + elif isinstance(temp[0], containers.Stack3D): + out = containers.MockStack3D( + mock=np.arange(boundaries[-1], dtype=int), + axes_from=temp[0], + attrs_from=temp[0], + ) + elif isinstance(temp[0], containers.PowerSpectrum2D): + out = containers.MockPowerSpectrum2D( + mock=np.arange(boundaries[-1], dtype=int), + axes_from=temp[0], + attrs_from=temp[0], + ) + else: + out = containers.MockPowerSpectrum1D( + mock=np.arange(boundaries[-1], dtype=int), + axes_from=temp[0], + attrs_from=temp[0], + ) for mm, (mock, nm) in enumerate(zip(temp, nmocks)): @@ -425,8 +778,24 @@ def load_mocks(mocks, pol=None): else: slc_out = boundaries[mm] - out.stack[slc_out] = mock.stack[:] - out.weight[slc_out] = mock.weight[:] + if isinstance(temp[0], containers.FrequencyStackByPol): + out.stack[slc_out] = mock.stack[:] + out.weight[slc_out] = mock.weight[:] + elif isinstance(temp[0], containers.PowerSpectrum2D): + out.spectrum[slc_out] = mock.spectrum[:] + out.weight[slc_out] = mock.weight[:] + if mm == 0: + out.mask[:] = mock.mask[:] + out.kpara[:] = mock.kpara[:] + out.kperp[:] = mock.kperp[:] + else: + out.spectrum[slc_out] = mock.spectrum[:] + out.samp_var[slc_out] = mock.samp_var[:] + if mm == 0: + out.var[:] = mock.var[:] + + if isinstance(temp[0], containers.PowerSpectrum1D): + out.k1D[:] = mock.k1D[:] return out diff --git a/notebooks/README b/notebooks/README new file mode 100644 index 0000000..4cf744f --- /dev/null +++ b/notebooks/README @@ -0,0 +1 @@ +# This contains several example notebooks for analyzing MCMC chain output from fitstack. There are two different notebooks - stacking and auto-ps analysis. diff --git a/notebooks/chain_analysis_auto_ps_fitstack.ipynb b/notebooks/chain_analysis_auto_ps_fitstack.ipynb new file mode 100644 index 0000000..0c9ed90 --- /dev/null +++ b/notebooks/chain_analysis_auto_ps_fitstack.ipynb @@ -0,0 +1,1129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import glob\n", + "\n", + "import numpy as np\n", + "from cora.util import units\n", + "\n", + "from draco.util import tools\n", + "from fitstack import containers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary cache directory at /tmp/matplotlib-hlcji1cs because the default path (/home/h/halpern/arnab92/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import getdist\n", + "from getdist import plots, MCSamples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from draco.core.containers import FrequencyStack, FrequencyStackByPol, MockFrequencyStack, FormedBeam" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import getdist\n", + "from getdist import plots, MCSamples, chains, mcsamples\n", + "from cora.signal.lssmodels import bias\n", + "from cora.signal.lssmodels import omega_HI\n", + "from cora.signal import corr21cm\n", + "from cora.signal import lssmodels\n", + "from cora.util import cosmology" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams['font.family'] = 'serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm' # or 'stix'\n", + "plt.rcParams.update({\n", + " 'font.size': 24,\n", + " 'axes.labelsize': 14,\n", + " 'axes.titlesize': 14,\n", + " 'xtick.labelsize': 14,\n", + " 'ytick.labelsize': 14\n", + "})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Richard's chaintools" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from fitstack.chaintools import ( \n", + " calc_fmu2_single,\n", + " calc_fmu2,\n", + " add_derived,\n", + " apply_limits,\n", + " hpd_chain,\n", + " create_unified_config,\n", + " scale_params,\n", + " hpd_chain,\n", + " combine_data\n", + ")\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First load the chains. Note I have already ran cahintool.gdchain and save it in a pickly file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "chain_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/chains/\"\n", + "# chain_path_fixed = \"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/NL_fixed/chains/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/chains/'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_path" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "alphaFoG_power=-1.0\n", + "alphaFoG_min=0.1\n", + "alphaFoG_max=10.0\n", + "nsample=500000\n", + "nwalker=32" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/chains/chain_autops_pow-1.0_FoGmin0.1_FoGmax10.0.gdchain'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_path + f\"chain_autops_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.gdchain\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<_io.BufferedReader name='/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/chains/chain_autops_pow-1.0_FoGmin0.1_FoGmax10.0.gdchain'>\n" + ] + } + ], + "source": [ + "with open(chain_path + f\"chain_autops_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.gdchain\", \"rb\") as fh:\n", + " print(fh)\n", + " chain_all = pickle.load(fh)\n", + " chain_all.label = \"All parameters\"\n", + " chain_all.tracer = \"bandb\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scale the parameters actually fit to their values at the effective redshift." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# First load the dict containing all the relevant param values \n", + "config = create_unified_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'QSO': {'z_eff': 1.2034,\n", + " 'z_range': [1.0038, 1.365],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5782310960156978,\n", + " 'omega_HI': 0.000642563420124033,\n", + " 'f': 0.9015817540176252,\n", + " 'b_g': 1.92122457368},\n", + " 'LRG': {'z_eff': 0.8372,\n", + " 'z_range': [0.8065, 0.8728],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_lrg',\n", + " 'b_HI': 1.410682907405958,\n", + " 'omega_HI': 0.000576174260879225,\n", + " 'f': 0.8450640065556718,\n", + " 'b_g': 2.4319062430400002},\n", + " 'ELG': {'z_eff': 0.9576,\n", + " 'z_range': [0.8253, 1.0264],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_elg',\n", + " 'b_HI': 1.469278654771197,\n", + " 'omega_HI': 0.0005985416343037188,\n", + " 'f': 0.8671616829717421,\n", + " 'b_g': 1.57532},\n", + " 'QSOb0': {'z_eff': 0.9714,\n", + " 'z_range': [0.8501, 1.0073],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.475745886048952,\n", + " 'omega_HI': 0.0006010697096585794,\n", + " 'f': 0.8694469155412741,\n", + " 'b_g': 1.65145627288},\n", + " 'QSOb1': {'z_eff': 1.117,\n", + " 'z_range': [1.0651, 1.1631],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5413109085178034,\n", + " 'omega_HI': 0.0006273248712423942,\n", + " 'f': 0.8908845170491495,\n", + " 'b_g': 1.8172619419999998},\n", + " 'QSOb2': {'z_eff': 1.3028,\n", + " 'z_range': [1.2262, 1.3931],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.6192458573000803,\n", + " 'omega_HI': 0.0006598021460724086,\n", + " 'f': 0.9123331729462839,\n", + " 'b_g': 2.0459639795199998},\n", + " 'QSOb00': {'z_eff': 0.8448,\n", + " 'z_range': [0.8179, 0.8666],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.4145047645417193,\n", + " 'omega_HI': 0.000577603166324638,\n", + " 'f': 0.846581327620715,\n", + " 'b_g': 1.5168673571199998},\n", + " 'QSOb01': {'z_eff': 0.9857,\n", + " 'z_range': [0.9624, 1.0117],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.4823976713829805,\n", + " 'omega_HI': 0.0006036819251256054,\n", + " 'f': 0.8717653564445413,\n", + " 'b_g': 1.66721878822},\n", + " 'QSObandb': {'z_eff': 1.159286,\n", + " 'z_range': [1.0006, 1.335],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5595435204378436,\n", + " 'omega_HI': 0.0006348134105155576,\n", + " 'f': 0.8962895921543366,\n", + " 'b_g': 1.8676248854832878},\n", + " 'bandb': {'z_eff': 1.159286,\n", + " 'z_range': [1.0006, 1.335],\n", + " 'type': 'auto',\n", + " 'bias_key': 'None',\n", + " 'b_HI': 1.5595435204378436,\n", + " 'omega_HI': 0.0006348134105155576,\n", + " 'f': 0.8962895921543366}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the chains\n", + "scale_params(chain_all, config=config, identifier= \"bandb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['omega', 'b_HI', 'NL', 'FoGh', 'omega_scaled', 'b_HI_scaled']\n" + ] + } + ], + "source": [ + "params = chain_all.getParamNames().list()\n", + "print(params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate the fmu2 from the chain (Omega_HI * b_HI) - Omega_HI plane" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fm2=0.782\n" + ] + } + ], + "source": [ + "fm2 = calc_fmu2(chain_all,config,identifier=\"bandb\", fscale=False)\n", + "print(f\"{fm2=:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fmu2=0.782\n" + ] + } + ], + "source": [ + "fmu2 = calc_fmu2_single(\n", + " [chain_all],config,identifier=\"bandb\", fscale=False)\n", + "\n", + "\n", + "print(f\"{fmu2=:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a set of useful derived parameters\n", + "# Note: The fm2 param is 0.64 as estimated from stacking with fixed NL parameter for this data\n", + "# So, I am using that instead of fmu2 estimated from this chain\n", + "\n", + "add_derived(chain_all, fmu2 = 0.64, cross_corr=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply cutoff to a set of parameters.\n", + "apply_limits(\n", + " chain_all,\n", + " omega_scaled=(0, 5),\n", + " b_HI_scaled=(0, 10),\n", + " FoGh=(0, 5),\n", + " copy=False,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "orig_params = [\n", + " \"omega_scaled\",\n", + " \"b_HI_scaled\",\n", + " \"omega_b_HI\",\n", + " \"omegabfm2\",\n", + " \"NL\",\n", + " \"FoGh\", \n", + "]\n", + "\n", + "original_param_limits = {\n", + " \"omegabfm2\": [0, 10],\n", + " \"omega_scaled\": [0, 5],\n", + " \"b_HI_scaled\": [0, 5],\n", + " \"omega_b_HI\": [0, 4],\n", + " \"NL\" : [0,5],\n", + " \"FoGh\": [0,5],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "save_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/plots/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "save = False" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.159286" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z_eff = config[\"bandb\"][\"z_eff\"]\n", + "z_eff" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:fine_bins_2D not large enough for optimal density: b_HI_scaled, omega_b_HI\n", + "WARNING:root:fine_bins_2D not large enough for optimal density: omega_b_HI, omegabfm2\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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kXNWrV7e4HRwcXORjci9LlJ2dbbUyLhFReeb6FTrs7MBVHc4k6DF2XQaGtOIfbiJ7uXTpEvbt2wcACAoKKlZPUFpamkWP3tKlS/HOO+/YvY2AsVppWahUqRJu3rxpvm3qCbZFRkaGxW1PT898w5Y7duxo14CWe83BouRtX2BgYJGPadWqFVq1aoU5c+bgyJEjWLt2LX799VeLYcRarRaffPIJMjIy8MUXX9jeeAfTaDTo378/fv/9d/zwww8YPny4s5tE5Zyvry9q1aqF//77D4BtIwoUCoXF7YJGEhARlVfs8cwjKcP4Ri3T9vdYRGQDU2+nr68vrl+/joSEBJu/Fi9ebHGsc+fO5Vs2w5q8PW22hCeVSlWMZ1Vyuau6Asb5jbbKu2/NmjXt0aRC5V3+oTC3b9+2uJ23d6cwgiCgVatW+OKLL3D16lVs27YNLVq0sNhn7ty5uHr1qs3HdCS1Wo0XX3wR27Ztw/r160sUOpOTkxEdHY2ZM2di4cKFuHTpkgNaSuVNq1atzP+3Zah83rnO/v7+dm8TEZEjMXjmkaUtf0O4iFydXq83D4/t379/vjmARenZs2e+NSTzhlFrcg9NA2wLTxcvXixW20qqQ4cOFrdzLwFSlLNnz1rctrVAUGnYOr9SFMV830Nbl2LJSxAEdO3aFYcOHcITTzxh3q7X67Fjx44SHdOe1Go1XnjhBfz999/4888/0aNHjwL3nTlzZr4ldADgq6++Qt26dXH48GFUr14d//33Hxo2bIhRo0ZBr9cDMAb5Jk2amJeFuX79uvl2kyZNcPv2bZv2KY63334bnp6e8PDwcOjXSy+9VKx2VSTPPPOM+f+JiYlF7p87nCqVStSrV88h7SIichQOtc2DwZPI/rZt22Z+41uSHiGFQoFXX30VX375pXlbdHQ0vvrqq0KL8gQFBVncvn79OipVqlTg/vHx8cUuYFPSuZUvvPACpk2bZr79559/2jx8dPv27fmO5WiHDh2yab+jR49a9BrXq1cPtWrVyrffkCFDcPToUXz88cdFVp6VSqX44IMPEBMTY96WtzhTWTMYDBgwYAAOHDiA7du3Fxquk5KSMG3aNGzatMli+5QpU/Dpp5/i559/xssvvwzA+MFM8+bN0bdvX/j6+mLWrFkIDw/HiRMnADy43ky3c7NlH1t9/vnnGDVqlMPn0+atgOxOXnjhBXh6eiIrKwsXLlwocv+EhATz/59++ul8Q2+JiMo7Bs88snJN79LoRChk5adgB5GrMg2zbdKkSb5hk7YaPny4RfBMS0vDr7/+Wmh13KioKIvbhw8fRrNmzQrc/8cff7RauKcwuXtVrQ3lzc7ORrt27QAAvXr1wnvvvQcAaN68OTp37mzuuTt58iSOHz+O5s2bF3q+K1euYO/evebbzZo1w9NPP12sNpfEH3/8gfv37+cL83n99NNPFrdHjx5tdb+rV6/izJkzOHLkiE1LnuStAlrY0hNnzpzBn3/+idDQULz00kt2rywLAOPGjcPOnTuxY8cOtGzZssD9MjMz0b9/f4SEhODxxx83bz906BCmTZuGRo0amUOnSZ8+ffDZZ59h7ty5eOutt5wSzpRKJWrXrl3m53Unvr6+GDRoEL7//nvExsZCpVIVumzR8ePHzf8fP358GbSQiMi+ONQ2j2zdg093U7LY+0lUWvfu3cPmzZsBAK+//nqJj1O/fn20b9/eYltRw207dOhgUVl00aJF5uGLef3777+YOXNmkcEqr9xVWBMTE/MF1xs3buD48eM4fvx4vuUP5s2bZ9FjO378+EKLG4miiPHjx5ufg0KhKLP1/LKzs82huSDnz5/HokWLzLfr1q1bZA/3qlWrbCqslLu3UyKRoHPnzlb327hxI5o0aYK33noLL7/8Mp588slirQNqi6VLl2L+/PkwGAzo1asX2rZti8mTJ2P79u3m55KdnY1169bh0UcfxY4dOzB9+nSLa/F///sfDAZDvuWCTLp06QKdToctW7bYte2UX1paGnr27Alvb2+0a9cON27cKLNzf/rpp/D390d2djZ+/vnnAveLjY01z/197bXX0KlTp7JqIhGR/YhuKjU1VQQgpqamWmyfuCFDxJtJIt5MEi/c0TmpdVReFXTdUMHmzJkjAhC9vLxK/X1bsWKFCMDi6+LFi4U+5vnnn7fY/7nnnhNPnTplvj85OVmcP3++GBgYKHbp0kUcNGiQxf5FWblypcX+f//9t8X9kyZNMt/3559/5nt8dHS0KJVKzfv07NlTTE5Ozrdfenq6OGTIEPN+giCICxcuLLJ9JbV06VKL59W3b18RgPj++++LGo0m3/6nTp0Sa9eubd7fw8NDPHDgQIHH79Chg3nfJ554Qrx9+3aB++7cuVMMCAgw7//mm28WuG/r1q3zXSPbt28v1nMvzJkzZ0QPDw+xY8eO4q5du8TLly+Lb7zxhsX5AgICRIlEYr792muvWRwjPT1dVCgUIgBx8+bNVs8zf/58EYA4YcIEi+22XJe2XrsVyfbt283POzw8vFiP/fjjjy1+fq+88kqZnVsURXHdunWiVCoVAwICxBs3buS7PzMzU2zbtq0IQGzdurWYnp5e7HMQEZUHHGqbR+45nsmZ7PEkKokDBw6gZ8+eAIDU1FQAxmGouYth5J6vVJg1a9bgzTffBACrvZWPPvooPDw8AADr169HmzZtLO7/6quvcODAAdy7dw8AsGnTJmzatMlcOCU5ORkA0LhxYyxbtgyTJ0+2eHzuYY7Lli1D165dLe7v2bMnPvzwQ/OyH88//zxef/11VK5cGYcOHcLatWsBGCtYPvXUU/na37t3b/j5+aFfv35ISUnB+vXrERMTgxdffBH169eHRCLBpUuX8Ntvv+Hu3bsAjMN7ly1bht69e9v0PbSHkSNHIigoCP/73//wyy+/4Pnnn0fNmjWRlZWFo0ePYvPmzebeWi8vL2zYsKHQeY+5C0zFxMQgMjISTz/9NJo1a4bKlStDr9fj1q1b+Pvvvy3mlw4YMACzZs0qVttFO81TFEURI0aMQNeuXfHrr79CJjP+Cf36669RqVIlTJ06FQCQkpJifsxrr72G7777zuI4V69eNQ/LnjBhAj744IN858rKykJISAjX47RCrVYjISEBmZmZSEhIwD///GPxPb59+zZ69uyJl156CXXq1EFQUBC8vb0LHJ4tlUotbpt+rmVxbsD4GrJixQoMHToUbdu2xdSpU9GuXTtotVqcPHkSs2bNwokTJzB06FB89913vCaIyHU5O/k6S0E9V0N+TjP3eG47q3ZS66i8Yo+nbWJiYvL1OuX9slXenrfCvmJiYqwe4/Lly2Lnzp2tPkYikYivvvqqmJaWJoqimK/HM/fXb7/9ZvX4Z86cERs3blzg45555hkxISGh0OeZmJgoTpw4UQwKCirwOP7+/uKoUaPEW7du2fz9K6m833fT93bFihViZGSk1fZJpVLx+eefF//7778ij6/T6cRNmzaJAwYMEKtUqVLoz1UQBLFNmzYFfv9zW79+vUUPcuvWra320JbEzp07xerVq1vtkRZFYy9lSEiIGBAQIHbs2FHcuHGj1f0OHz5sbt++ffuK1QZbfn+K+zvmamx5fcn7VaNGjQKPl56eLvbq1Uv09vYWO3bsWOjvl73PndvFixfF0aNHi3Xr1hWVSqWoVCrFunXrisOGDRMPHTpUzO8SEVH5I4iig0vWlVMqlQr+/v5ITU21mMz/8op0rIo1fhK96lVv9GvGTxbpgYKuG3INV69exf79+xEfHw+dToeIiAh06tQpX+GakoqNjcXx48eRmJgIQRAQGhqKNm3aFGvZA4PBgKNHj+L8+fO4e/cuRFFEcHAw6tWrh1atWhXaG2NPy5Ytw5AhQ8y3Y2JiLJZtOXbsGM6ePYs7d+5AKpWiWrVqeOKJJxAcHFzsc4miiMuXL+PMmTO4deuWuSqur68vatWqhWbNmhWrwM6pU6ewY8cOhISEoHfv3uWu+md8fLz5mouOji5Wz7WpYm1hf7pt2YeIiKiscahtHhxqS1RxRUZGIjIy0mHHb9asWaFVc20hkUjQqlUri8Xly6MWLVqUuEJxXoIgoG7duqhbt65djtewYUM0bNjQLsdyhLCwMERFReHs2bO4cuVKgfuJoojU1FQEBASUXeOIiIgchFVt88i9nAqr2hIRkSOMGjUKAMxzgK357bff8PXXX5dVk4iIiByKwTOP7Nw9ngyeRETkAKNHj0bbtm1x/PhxHD58ON/9Go0Gs2fPxtChQ53QOiIiIvtj8MwjWyciyMs4P4ZDbYmIyBEkEgl+//13tGvXDn369MGBAwfM9928eRM9e/bEpEmTUK1aNfP2y5cvm/8fGxtr9bi27ENEROQMnOOZh1YPBHkJSMkSkZJlKPoBREREJVCpUiXs2rULK1euxNtvvw2VSgWlUonw8HB8+OGHaN26NQDjEh1PPPEErl69an5sixYtEBQUhG7dumHFihU27UNERORMrGqbpzpp41mp0OhE3E0X0bSaFH+NZuVSeoBVbakie/TRRxEXFwfAuI6kqbosAAQGBpqrw/bt2xdfffWVU9pIRERErok9nnnoDIBMKiDQi0Ntici93Lt3D3fu3LF6X3Jysvn/qampZdUkIiIiqiAYPPPQG0Qo5AICPAUGTyJyK9euXXN2E4iIiKiCYnGhPHQGQCYBAj0FVrUlIiIiIiKyAwbPPHT6nODpJUFqlgiDgeGTiIiIiIioNBg889AZREglxqG2BhFIUzN4EhERERERlQaDZx65h9oCLDBERERERERUWgyeeegNgEwKBOQEzxTO8yQiIiIiIioVBs88jD2eAgK9cno8GTyJiIiIiIhKhcEzD+MczwdDbdnjSUREREREVDoMnnmY5ngGcI4nERERERGRXTB45qE3PFhOBQCSswxObhEREREREZFrY/DMwzTHk8WFiIiIiIiI7IPBMxeDQYQoGqvamosLcagtERERERFRqTB45qLLGVUrFR7M8bzP4ElERERERFQqDJ65mIKnTALIpQIqeQu4k8Y5nkRERERERKXB4JmL3hQ8pcbezjA/CW6nMngSERERERGVBoNnLjqDcVitLOe7Eu4nIF7FobZERERERESlweCZS+6htoCxxzM1W0SmhuGTiIiIiIiopBg8c9Hpjf9KcwVPAIhXcbgtERERERFRSTF45qLP6diUSYxzPMP9GTyJiIiIiIhKi8EzF53eco6nqceTBYaIiIiIiIhKjsEzF/McT6nx33B/Y88nCwwRERERERGVHINnLqbgmbOaCns8iYiIiIiI7IDBMxfzciq51vEEOMeTiIiIiIioNBg8c9HnWU7FQy4g0EvAbQZPIiIiIiKiEmPwzCXvOp4AUM1fgpspDJ5EREREREQlxeCZi2kdT9NyKgBQLcAYPEWRBYaIiIiIiIhKgsEzF9McT2mu70pEgAQZGiAli8GTiIiIiIioJBg8c8lZxtNiqG1EoPFGHIfbEhERERERlQiDZy7mobbSB9siAnKCZzKDJxERERERUUkweObyoLjQgzme5uDJHk8iIiIiIqISYfDMxTzH80HuRDUGTyIiIiIiolJh8MzF3OOZa6htNQ61JSIiIiIiKhUGz1z0Vtbx9FIIqOQtsMeTiIiIiIiohBg8c7E2xxMwzvNk8CQiIiIiIioZBs9cdDnrqeQeagsYg+fNFANEkWt5EhERERERFReDZy6mHk+pZYcnqgVIoNYBiRkMnkRERERERMXF4JmLtTmeANfyJCIiIiIiKg0Gz1xMy6nI8nR5RgRySRUiIiIiIqKSYvDMRVdAj2f1nOB5/T6DJxERERERUXExeOZinuOZ57tSt7Kx2tCFe/oybhEREREREZHrY/DMRZeTK/MupxLqJ8DfQ8C5OwyeRERERERExcXgmYs+Z7mUvENtBUFAVKiUwZOIiIiIiKgEGDxzedDjmf++h0MkSFCJSM7kPE8iIiIiIqLiYPDMxVxcSJr/vodDjBvZ60lERERERFQ8DJ65mIsLCUK++xqFywAA/95i8CQiIiIiIioOBs9c9OZ1PPPf17SacWPsTQZPIiIiIiKi4mDwzKWgdTwBINhHgqr+Av65pSvbRhEREREREbk4Bs9cCgueANC0mgynbuuh1Ytl1ygiIiIiIiIXx+CZiy4nUEol+ed4AkDTqlJo9CwwREREREREVBwMnrkU1ePZrJqxwNA/nOdJRERERERkMwbPXEwjaAseamsqMMR5nkRERERERLZi8MxFl9ORKZNaH2pbPVCCIC+BlW2JiIiIiIiKgcEzF13OcioF5E4IgoCW1WU4FqeDRscCQ0RERERERLZg8MzFPMfTyjqeJm1ryZCt5TxPIiIiIiIiWzF45qIvorgQALSNNBYY2ndVWwYtIiIiIiIicn0MnrmYejwLWE0FANCyugxSCbD/KgsMERERERER2YLBMxedQYRMYpzLWRBvpYBm1aT4+4oOBgPneRIRERERERWFwTMXnb7w+Z0mT9WTIzFDxD+3OM+TiIiIiIioKAyeuegMBVe0ze2Zh+UAgG3nOM+TiIiIiIioKAyeuegMgKywCZ45WteUwc9DwB8MnkREREREREVi8MxFZxBtGmorlwro/JAMB6/pkJxpcHzDiIiIiIiIXBiDZy56Q+FLqeTWtb4cBhH46yKr2xIRERERERWGwTMXXXGC58MKAMAf5zQObBEREREREZHrY/DMRacXbZrjCQDVAiRoGCbFH+e1EEUuq0JERERERFQQBs9cdAZAWozvSNeH5bidKuJUPJdVISIiIiIiKgiDZy7FGWoLAN1yllXZeJrVbYmIiIiIiArC4JmLzgDIbFnIM0e7WjIE+whY8w/neRIRERERERWEwTMX4xxP2/eXSQW81FiB0/F6nLrN6rZERERERETWMHjmoheLN9QWAAa3VAIAvvlb7YAWERERERERuT4Gz1yKO8cTAFrWkKFtpAwrjqpxN83gmIYRERERERG5MAbPXHR6QGrjciq5vf2EB9Q64Nu/sx3QKiIiIiIiItfG4JmLzlC8OZ4mPR6Ro26wBPP3qZGl4ZqeREREREREuTF45mKsalv8x0kkAsY97oH7mSJ+O8UKt0RERERERLkxeOZSkjmeJgOaK6CUAYsPscgQERERERFRbgyeuegNgKwEczwBINBLgp6NFNh1SYf/EvV2bhkREREREZHrYvDMRWcQIS3Fd2RYa+PSKksOs9eTiIiIiIjIhMEzF52+5ENtAeCJOjJEVpJg6RE1MllkiIiIiIiICACDp4XSzPEEjEWGJnb0wO1UER9sybRfw4iIiIiIiFwYg2cOg0GEQSz5HE+TkW2VaF9Lhnl71Nh5UWun1hEREREREbkuBs8c+pyRsSVZTiU3qUTAigHe8FEC43/LhMHAIbdEREREROTeGDxz6HIK0ZZmqK1JzUpSjG3ngdPxemw6w15PIiIiIiJybwyeOUw9ntJSDrU1mdDRA55yYNqfWRBF9noSEREREZH7YvDMoctJnvbo8QSAKr4SvP6YEsfi9Pj2by6vQkRERERE7ovBM4fOYPzXXsETAD55xhP1q0gwYUMmtpzR2O/ARERERERELoTBM4cjgqe/pwQbh/siyEtAr6Xp2Pcf53sSEREREZH7YfDMYQ6eUvvM8TSpGyzFX6N9IZcC/ZanIzHdYNfjExERERERlXcMnjlMczztnDsBAI3CZfi+tzdupYoY/EsGiw0REREREZFbYfDM8aDH0zHHH9BCiaGtFNhyVou+yzNw/o7eMSciIiIiIiIqZxg8c+gdMMczr697euO5BnL8ekKDVnNVOHVb57iTERERERERlRMMnjl0BtNyKg4Ya5vDWylg43Bf/DnKF9laET1+5JxPIiIiIiKq+Bg8c2hzRr46ssfTpPNDcizs441r9w3oszwdegPnfBIRERERUcXF4JlDkxM8lbKyOd/gVkqMe1yJmEs6zNqZXTYnJSIiIiIicgIGzxyanKq2CpnjhtrmNauHFxqGSfH+lix0+EaFX09oyuzcREREREREZYXBM4cmp86PwkFVba1RygSsGeSDx2vLcPSGDn2WpePVn9KhyubQWyIiIiIiqjgYPHOYezwdsZBnIR4OlWLPG364PiUAPR6RY+UxDdp9pcI9Fh0iIiIiIqIKgsEzh9rU41lGczzzCvaRYMMwH8x8zhOn4vVo95UKR67rIIrs/SQiIiIiItfG4JnjQXGhsu3xzE0QBEx60hPf9/bCtfsGtJqrQuUPUvC/HVnQ6hlAiYiIiIjINTmpf6/80ehMQ22d3BAAI9p6oE2kDAv2q/HXRS0+2JKFX09osKC3N1rVkEIQnBeOiaj802q10Ov1zm4GERER5SKRSCCXy932vTyDZw5Tj2d5CJ4A0DBchu96y6DRiZj+Vzam/ZmFx+apUL+KBK+38cDINkp4KtzzoiUi61QqFRITE6FWq53dFCIiIrJCKpXCy8sLVapUgUKhcHZzyhSDZw5zj6cTh9pao5AJmNLVE32aKLDwQDbWnNDgrQ2ZmLUrC9/09MZLTdzrgiUi61QqFW7dugUfHx9UrlzZrT9RJSIiKm9EUYRer0dWVhZSU1Nx7do1VKtWDV5eXs5uWplh8MxR3no883o4VIp5Pb0xs4cXFh9S49PtWei9LB0fPe2BqV09IZHwDSaRO0tMTISPjw+qVavGwElERFRO+fj4ICgoCNevX0diYiKqV6/u7CaVGRYXymFeTqWc9XjmpZQJGN3OA7Fv+6NldSk++zMbLeeq8NWebNxI5pwuInek1WqhVqvh7+/P0ElERFTOSaVSBAUFISMjAzqdztnNKTMMnjk0OT9zpYv0AYf7S7DnDT+81dEDF+7qMf63TNT4JBX9l6cjQ80KuETuxFRISC6XO7klREREZAulUgkADJ7u6MFQW9fpLfCQC5jzghfufhaI31/zQfcGcqz+R4N2X6vw/f5s3Es3OLuJRFSG2NtJRETkGtzxbzaDZw51OVpOpbg8FQJ6PKLAxtd8MKWLB07e1mPUr5mImJqCL2OyYDCwB5SIiIiIiJyHwTOHuceznM/xLIwgCJj6jBcSPw/AmkHeiAySYOLvWejyfRo2ndYgnUNwiYiIiIjICRg8c2hcuMczr0AvCfo0VeLYRH+8/pgSf13UoceP6Qj5KBnLDqshigygRERERERUdhg8c6hz5vVWhOBp4q0UsLCvN6586I9venmhsrcEQ1ZloMkXKiw6mM0huEREREREVCYYPHNkaIwhzFvpukNtC1KrshRj23vg+EQ/jO+gRFyKAa+vyUTbr1Q4ddt9KmkREREREZFzMHjmyNCIkEsBuQtVtS2uyj4SzH3RG3FTAvD2Ex44GqdHs9kqTPgtAydu6jgEl4iIiIiIHMJFVq10vAyNCG9FxQ2duXkrBXzxvBcGNFfg9egMzNujxrw9arSsLsX8l7zRojovC6KK6EayHonprvUBU2UfAdUDK9AcCCKqcAwGAyQS1+7LqQjPgco/JowcGRrAW+HsVpStJtVkODTeDwev6fBLrAY/HFCj5VwVhrdWYtqzngj24QsQUUVxI1mPhz5PRbaLja73kAEXPvBn+CSicufKlSsYM2YMZs6cicaNGzu7OaWyYMECnD17FjNnzoSPj4+zm2PhwoULGDJkCA4ePIilS5di8ODBzm4SlRCTRY4Mtfv0eOYmkQhoW0uO+S95499J/uhUV4YfDqpR7/NUfLglE7dTDc5uIhHZQWK66HKhEwCydXC5XlqisrB8+XL4+/tj5cqVzm6KW9q7dy9atGiBkJAQPPLII85uTqn16dMH+/fvR6tWrRAfH+/s5gAA9Ho9Zs2ahSZNmuDgwYPFfvzOnTvxzDPPoFKlSvDy8kLDhg0xY8YMaDSaErfJEcd0JwyeOTI0YoUsLFQcUaFS7Bjli3VDfFDFR8DnO7JR/ZMUvLk+A5kavvEjIqoI9Ho9Tpw4gTfeeANdu3Yt0TEMBgNmzpyJRo0aoUOHDmjevDnGjx+P1NRUO7eWChIdHQ2VSoU1a9Y4uylu5/Lly+jRowcef/xxLFu2DFKp64/ICA4ORkxMDFQqFbp16+b0IJWUlIQ2bdpg1qxZWLJkCWrUqFGsx3/55Zfo3LkzNBoNtmzZgpMnT6JPnz744IMP0LFjR2RmZha7TY44prth8MyRkiXC38O9gycACIKAno0VOPeePzYP98FjNWX4eq8aTb5Ixa6LWhYgIiJyYd26dUP37t2xceNGfPvtt8jOzi7Rcd566y3MmTMHGzZswJ49e7Bv3z7ExsbiySefhFqttnOryZqxY8fi0Ucfxbhx45zdFLczcuRIpKen44svvoAgVJz3joGBgfjoo49w4sQJzJo1y6ltiYuLQ0REBM6ePYv+/fsX67EHDx7E22+/jcjISGzevBmtW7dGnTp18NFHH2HSpEk4ePAgJk6c6PRjuiMGTwB6g4j7mWKFm9OoVqsxderUEr0JkEgEPNtAgT1jfTH3BS/cTDHgye/S0GquCqtj1dDqy0cALc1zJCJyN1u3bsW2bdvw8ccfl/gYhw8fxtdff40JEyagVq1aAABPT0/MmDEDx48fx9y5c+3VXCrEM888gyNHjuDpp592dlPcysGDB7Fz5048//zzqFevnrObY3dDhw5FpUqV8OWXXzq1B69hw4ZYu3YtqlSpUuzHTpkyBaIoYsyYMfD09LS4b/z48ZBKpVi0aBHi4uKcekx3VLGSVgmlZIkwiECwT8X51AowhrJPPvmkVKFMIhEwvqMHzk72x5h2SpxJ0KP/igzU+iwVs3Zm4W6ac+eA2uM5EhGR7ZYuXQpRFPHiiy9abG/Tpg2qVKmCH3/80Uktc75ly5ZBEATz1+DBg3HmzBn07t0bISEhUCqVqFOnDj7++GOLN/X79u2zeFzHjh2hUqkwfvx41KxZE3K53Lz92rVrFvvWrFnTalvi4+MxYcIE1KtXDx4eHggICED79u3x448/Qq/XW+xbnPOXlLVzpKamYuTIkQgJCYGvry8ef/xx7NmzB4BxSPhnn32GyMhIeHh4oEGDBvj5558LPP7evXvxxhtvoEmTJggMDISnpycefvhhvPvuu0hOTrbYt2PHjhZtEQQBU6dOBQBMnTo13325rVixAgDQuXPnEn8vyjOZTIaOHTsiOTkZmzZtclo7Sjp8OTExEbt27QIAPPXUU/nuDwkJQcOGDaHX67F27VqnHdNdMXgCiFcZw1Owd8UKnvZUs5IU375kXAN0endPiKKIdzdlIXxKCp75Pg0/HMhGPAsRERFVeLt374ZUKrXa2xMVFYUrV67g9u3bxTpmfHw8Pv74Y7z99tsYMWIEfvzxRxgMrvc3pW/fvoiPj8e8efMAAGfOnMEzzzyDZ555BocPH8ahQ4fw2GOP4bPPPsOTTz5pDp+mgi7r1q0DAGRnZ+PZZ59FvXr1EBMTgx07dph7fiIiIhAfH48jR44U2I4jR46gYcOGWLFiBSZPnoxz585hz549iIqKwvDhw9G9e3eLD2yLc/6SsnaOPn364Mknn8ShQ4ewcuVKXLhwAV26dMHx48fx1ltvITQ0FLt27cLmzZuhVqvxyiuvYMuWLVaP//TTT2PTpk14//33cezYMcTGxuKdd97Bzz//jJYtWyIxMdG87/r163Hx4kWEhIQAAN599128/fbbAIC3334ba9euhVKpxN69e/MV2vnzzz8BGHvkiuKq13WjRo0AADt27HByS4rv2LFj0Ov1EAQB9evXt7qPaXthv0OOPqa7YvAEcCbe+MnfwyGuPznc0YK8JZj8lCeufhyAXwf74NkoOXZd0mJEdCbCp6SgwYxUvLQ0DR9tzcQvx9U4eFWL0/E6XL+vR1KGARlqERqdyLmiROQU//zzD1555RW0a9cOy5YtQ1ZWFj7++GOMHTsWL774IsaOHcsRFIXQ6XT477//4O3tbXXNP19fXwDAtWvXbD7m4sWL0aZNGzz77LOYPXs2vvvuO2zZsgXDhg0z75Oamlpob1d54enpidDQUPj7+wMwvmH9+uuvMXToUNSsWRNNmzbFihUrzGHL1Msml8sRGhqKoKAgAMbhzC+//DJGjx6NyMhIdOzYEa+//jqCgoIglUoRGhqK4OBgq21QqVR48cUXkZSUhOjoaAwdOhSRkZFo3LgxFi5ciH79+uGPP/7Au+++a35Mcc5fUtbOMXToUPTu3RuRkZF44YUXMG3aNKjVagwZMgRVq1bF8OHDERkZiaeeegrffvstAODrr7+2evwaNWpgzZo16NOnD2rXro2HH34YQ4cOxerVq3H58mV89tln5n2DgoJQt25dc+/83LlzcfHiRQCAVqvFW2+9hU8//RTt27dHaGio+XEZGRm4evUqAKBatWqFPl9Xvq6rV68OADh16lSxHjd48OB8vcW2fJl+D+zhypUrAIzzVRUK6+skhoWFWezrjGO6K7ddx9M0zKThp3G4ne0NGICoIAEqVcV5w6FSqSz+tbenaxm/VNlS/HVBg23ntYiN0+G3Ywass+EDPakEkEsBhVSATGq8LZUAUkGAVAAkOR3QggAIAGD6NxdRNAB9d6LprPuQSlIs7ss7398eBQC0WcaKjffv3y/1sci9iKKItLQ0hIeHc5FuJ/rss8+wevVqLFy4ECNHjsTWrVvx2Wef4aGHHoJOp0NUVBTefvttfPPNN85uarmUmpoKrVZbYO+XKXja+hr5/fffY/To0YiJiUGrVq0AGIfYffzxx2jWrBkGDhyITp06Yfv27fD29rbPkyhD1atXx/PPP2+xTRAEjB07Fjt37sT333+PTz/9FB4eHhb7eHl5YciQIRbbcgenwixevBi3b99Gw4YN8eSTT+a7f+LEiVi9ejUWLFiA999/3+rPsjTnt5Wvry969uxpse3RRx8FYAw8gwYNsrjPdH3ExsZaPd6FCxesbn/ssccAAJs2bcJXX31lcV/37t0xfPhwLFq0CP369UNsbCyGDRuGWrVqmXtAc7t+/br5g/PKlSsX+Nxc/bo2PTdTyLZVWFgYHnrooRKfzx5M73nzzsPMzcvLCwBsrsLtiGO6K7cNnpcvXwYA3JjzYO2lRguc1RrHioiIcHYTrNLnfJWspqKl/+xwjOKIjIws4zNSRREXF1fkJ+XkGBcuXED9+vWhUCgQFxcHtVqNDz74wPxGSSaToXnz5li6dCm+/PJLyOXyfMf48ssvsXHjxmKfWxAEfP311zYNzyvPsrKyACBfUDIx9QbY8ubr/PnzmDBhAvr3748OHTpY3Ne4cWMolUpER0ejU6dO+O2337B06VIAwLZt27BixQpER0cjPDwcffr0gSAIUKvVuHjxIv766y8sXboUr776qs3P6/Tp05g1axY0Gg2qVq0KLy8vtG/fHsHBwdixYwcmTZpk87Fyq1+/vtUPPU0BKy0tDcePH0fbtm0t7q9Ro0aB3+OibN++HcCDoJZX06ZNoVAooNFoEBMTg759++bbpzTnt1X16tXz/Y6Zeop9fHzMw2BNAgICABT8oYZKpcLXX3+NzZs34+rVq8jMzLQYXXXz5k2rj/vyyy+xc+dOXLp0CS1btkR8fDz+/fdfqx8Q5v4gX6lUWj1eSa5rR1zTQMmva9NzS0tLK9b5pk+fjunTpxfrMeRe3DZ41qlTB4Bx/oWfn5/FfXsu67D+pBrr/7Vcw+jJenJMfcYTNYNcY0huWloaoqKicPbsWfOn0BVNWT/HW7duoXXr1oiLi8t33RAVRqVSISIiosL+LrqCtLQ0DBgwAACwZ88ePPTQQ2jcuLHFPklJScjIyEBSUpLFEDuTt956C2+99VaZtLc8CgwMLPT+9PR0AAUH09zmzJmD7OxsjBo1Kt99EokElSpVwtWrV3Hw4EFzgRwA5jmTBw8eRMeOHTFnzhyLxy5btqzAkGHNTz/9hLfffhvR0dF4/PHHzdu3bt1q7hErqbzhySR3L6O1thY0jNYW169fz3eO3KRSKSpVqoT4+HjzvvY8v60qVaqUb5sppFsb0mu6z9ocybt376Jt27a4fPkyBgwYgM8//xwRERHm8Fi3bl1otVqr7fDx8cHy5cvRoUMHnDt3DrNnzzYPNc1Lp9OZ/19Q8ZuSXNf2vqaB0l3XpueW+/m6CtN7M9OHZNaY5labPuhwxjHdldsGT9MvVbVq1fIFiKpZWihvqAFfy+DpHSSHf2VPVAt3jW+b6ZO5qlWrVtiQ5Kzn6OfnV2G/p+RYFWnNN1fTokULAMbXjePHj+d74yWKImJjYyGTycrkjXdB1Go1XnzxxWIvZdC9e3erwwPtydvbG97e3gUWSMnIyABQdEAFgLVr18Lf3x9t2rSxer9cLkdWVhY++eQTrFq1yuY2Dhw4EB988IFN+x47dgxDhw7FmjVrLN6cA8Y1T/v06WPzeYujqDoHpXmdsMdrTFm8ThV2juKe/7PPPsPly5fRuXNn/PTTT8VuS/PmzVG1alXExcVh8eLFGD16tNVhlbmHxWo0Gqu9no64rotzTQOlv65N89yLOwz4vffew2+//VasxwDGNWnHjh1b7MdZU7t2bQBAcnIyNBqN1TmZpoJRpn2dcUx35RoJqowpZIDOyt9UhUxAhib/diIich179uyBXq/HE088YbH933//RVJSEjp16lTiUv72oFQqsXXrVqedvyiRkZEF9r4kJSUBQJHzvJKSkpCSkoJOnToVOuf50KFDWLNmjU1BNikpCb6+vlAoFDZ/MDhlyhRUqVIFL7zwgtX7X3vtNfz66682HcuaO3fuWN1+9+5d8//tPfS+Ro0aOHfuXIHn1uv15p9TjRo17HpuZ9m9ezcAoGvXriV6/OTJk1GtWjUEBQXh33//xTvvvGMuZpRb7lEQKpUq3wdU9r6uS3JNA6W/rk0f6hfUY1+Q+Pj4AufaFiZ3xeHSatGiBaRSKfR6Pc6fP2+u0Jvb+fPnAQAtW7Z02jHdFYOnFUqZAJ0+/6eRCimQoWE1ViIiV2Zajy3vmoQbNmwAAIwePbrAx86ZM6dEa9tVlDmegLHHZNasWbh586ZFaNLpdDhz5gwiIyNRtWrVQo9hmttX2BIdEokETZs2zbdeaEG+++47DBw4EDVr1sTEiRMBGIf+Tps2DYGBgZDL5YiPj8cHH3yAgIAAqNVq7NixA48//niBPWwPP/wwunTpYtP5rTl//jxEUcx3/KNHjwIwFthp3rx5iY9vTZcuXfDHH3/g0KFDVu+PjY0199rk/fDFVZl64K31JN+6davQx+7cuROLFy/GP//8A61Wi+bNm2P+/Pno3r17viAbFhYGPz8/qFQqJCQk5Aue9r6urV3TgOOv64SEBABFf4CU17Jly7Bs2bJiPcbeKleujE6dOmHHjh3466+/8oXEO3fu4NSpU5BKpXjppZecdkx3xdKKVigL7PEEMl0oeCqVSkyZMqXACfAVgbOeo9bKBxNE5Bp27dqF8PBwizeHqamp+P777/HMM8+gV69eBT524sSJ2L17d7G/YmJiXC507t692+pQQFMl0rwLpW/evBkZGRn5qqFa4+fnh+bNm+PevXtW7//9999x//59c6AwGAzmavS5HT9+HJMnT0b//v3xySefmLcrlUqIoohnn30WTz31FN5991289dZb5jCh1+tx//59aLXaQnt1JBKJuSpqSdy4cQO///67xTZRFM29aSNHjrR7EZ9hw4ahatWqOHPmjLnQUG5ffvklAGDUqFGlWpvz8uXLEAShXMxpM/UyWVvjc/Xq1QU+LiUlBYMHD8aMGTNQt25dREVF4YsvvgAADB061NwznFu7du0AWK+ia4/rurBrGkCZXNem3ru8w3RdxSeffAJBEDB//nxkZ1uWsJw3bx70ej1ee+21fMU3ly1bhsjISIwbN85uxyRLDJ5WKGUCrE2/UEgFbDxtfXJ6eaRUKjF16tQKHzyd8Rx/O8kx10SuKDExEadOnUJWVhYOHjwIwDhXa9CgQYiMjCz0TWpFYhqGmZycbLWASGpqKp5++mm8/PLL+OOPPyzua9WqFXr16oX//e9/uHTpknn/KVOmoGHDhjZXgJ07dy4OHTpk0TN3+vRpjBkzBjdu3MDHH3+My5cvQ6fT4aOPPjL/vHJr3rw5ZsyYgVWrVlmsjwgY3+THxsbiqaeeMm/r0KEDLl68iM2bN6NKlSqQy+Xmgki5ffjhh+jZsyfatWuHrl27Ijo62qbnlFeXLl3w4YcfYunSpbh27RpOnDiBQYMGYdeuXWjdurV5/UK9Xo+EhARzxVaNRoOEhAQkJCTkCyamfU3hJu9jfX19sWHDBlSuXBn9+/fH4sWLcfXqVfz7778YOXIkVq9eja5du2LmzJn5jmnL+U1Mw4Vr1qxp0/eioHOYvv/WnpOpOvL9+/fNvXB59wVg7u2LiYnBkCFDcOzYMZw/fx5ffPGFxTIwpselp6cjISEBY8aMQVhYmMWHTS+99BLatWuH+Ph4DB8+HAkJCRZFZUzDVwvqUS7tdV3YNQ2UzXV94MABCIKAHj16WL2/rJh+7rmvw9TUVPM2jcb6e7HHHnsMs2fPxn///Yfu3bvj8OHDuHz5MqZNm4ZZs2ahdevW+Qo4AcDs2bNx7do1fPPNN/k+dCjpMckSg6cVygIGIHvKgWwte7oI+Pbv7CKLQxCVJ5V9BHi44OQKD5mx7faya9cuiKKIH374AStXrsTw4cPRu3dvtG7dGrt3767wRcNeffVVNGrUyDy37+TJkwgODsajjz6KefPmmffz8fFBu3btEBERgYcffjjfcVatWoVRo0ahZ8+eaNu2LVq1aoXmzZvjr7/+svmDwPbt22Pfvn1YtGgRXnvtNYwcORJ//fUXPv30U7zxxhsYN24cRo4ciWHDhqFu3brmnqaCmIpHmZw7d868tl5uXl5eOH36NKRSKZ555hkcP3483+v5tGnTsHr1auzfvx+PPvpoiYsMhYaGYtu2beag2apVK+zfvx8ffvghdu7caW5fXFycRQA6ePAgwsLCEBYWhri4OItjmvY19fLdvHkTYWFhFmtitmjRAidPnsSgQYMwY8YMPPzww2jfvj1Onz6NRYsWYfPmzRY/p+Kc3yQmJgYAbB5aWNA5Zs+eDQBWn9Obb74JwNjLHhYWZj5WWFiYeUkawLhSweHDh9G3b19s3rwZjz32GDp27IgjR45gz549+R43e/ZshIWF4ZdffsHRo0cRHh5usc++ffsAAL/99hvCwsKwZs0a8/0DBgxApUqVsGbNGnMRntzseV3nvaYBx1/X//77L06ePImuXbuiXr16BbatLJh+7mFhYeZ55ePHjzdvO3DgQIGPfeutt7Bjxw7IZDJ07doVDRs2xOrVq/H5559jz549Vgsnvfbaa/Dx8UG/fv2sVl0uyTHJkiC66btnlUoFf39/pKam5nujEZ9qwOtrMrD5rGXv5qROHriaZED0EJ+ybCqVIzdv3jQOoxh5FX+/UxXtauVf54/ImsJec0orOzsbV69eRWRkZKHD9m4k65GY7lov+ZV9BFQPtF+hn5EjR+LHH39EUlJSuRgiSCVXs2ZNdOzYscA5ZdHR0Xj55ZehVqvNxaL0ej18fHywdOlS9OvXD7GxsXjsscfwyy+/5BtirdfrIZPJMGXKFHPPpK2WLVuGIUOGYNCgQU6f8+YI9+7dQ4MGDeDh4YF//vnH6pv0imzp0qUYOnQovvzyS0yYMMFuxy3qmgYcf113794df/31F06cOIH69evb7blRfrb+7a5IXPDzb8eT57zH+aKHF97Z+KCcvUIKaDi3j3KsPKph8CSXUj1QiupFFwet0Hbt2oXmzZszdLqB559/HlFRUdixY4e5SMxff/2FevXqmXsHmzVrhmXLlmHMmDEIDAxEp06dzI/fvHkzKleu7JS2l2c6nQ6dO3eGVCrF77//7nahEwCGDBmCI0eO4KOPPkKjRo3w5JNPltm5HXldT58+HVu3bsXPP//M0EkOweBphSxnALIhT2ewTCpAbwAMBhESCdfic2cNw6VY+68G3/TygkLGa4HIFVy+fBmXLl2yeQ4ilU/btm3DypUrERcXhw0bNmDIkCHo2LEjBg0aZLGfUqnErl27MH36dJw4cQJarRYpKSnYtWuXxTp8/fv3xyOPPILZs2dj0aJFCA8Ph1wuR0REBPbs2YMdO3aU9VMs12QyGWbOnIkmTZoUe7mNiuS7775D1apV8cILL+D06dOlWprG1msacNx1vXr1asyePRu///47nnvuuRI/F6LCcKitlWFv6WoR/Zeno02kDO9vybK4r3uUHNGDfeCpYNhwR6ahtp/8noApu+TY8roPukXlX0iYKK/yMNTWnY0aNQq//vqreYjtQw89hG3btiEoKMjZTaMKJCsrC6mpqVizZg3Gjx+PPn364KuvvoKPjw98fDhNpyK6ePEiAgMD8y2t4mrOnj2L8PBwBAQEOLspbsMd/3azuJAVipyhtnorS6oAQLbOLbM65dKzkTFsropldVsiV7BgwQIkJiZCFEWkpKTg8OHDDJ1kd2vWrEFYWBjGjx8PwDgfL3cBHap46tWr5/KhEwCioqIYOsnhONTWCrkUgGB9LU8AyHadFVXIQaoHSdGqhojNZ7TQ6kXIpewBJyJyd4MHD8bgwYOd3QwionKJPZ5WCIIAmQTQGaz3bKZr2ONJwAsNFUjJErH3Sv418IiIiIiI6AEGzwLIJILVHk+FDEjOZPAkY/AEgA2nONyWiIiIiKgwDJ4FkEkArT7/dk+5AFU2gycB9UOkeKiKBBtOafMt0kxERERERA8weBZALrU+1FYpE5CuZsggo+cfUeBmigGxN618SkFERERERAAYPAsklwpWezwVUmDzWVYXIqMXGsoBcLgtlQ/seSciInIN7vg3m8GzAM9Gya0GT7kU0HI5FcrRqoYMIb4CNpzihxHkPBKJ8aVcr2fPOxERkSsw/c02/Q13B+7zTItJISt4qK2aRUwph0Qi4LkGCpyO1+P6fb7pJ+eQy+WQSqXIyspydlOIiIjIBmlpaZDL5ZDL5c5uSpmpUMFz/vz5EAQBgiDg2rVrpTqWXALorVS19ZQLyNKKbtk9TtZ1izK+YGw7x15Pcg5BEODl5YXU1FT2ehIREZVzWVlZUKlU8PX1hSC4z1rwMmc3wF5u3LiByZMn2+14MqlgNXh6yAGDCGRqAG+l3U5HLuzJenLIpcbgObKth7ObQ26qSpUquHbtGq5fv46goCAolUq3+mNGRERUnomiCL1ej7S0NKhUKiiVSlSuXNnZzSpTFSZ4jhgxAkFBQUhPT7fL8eRSY8DM64MtWegeJUeaWoS3km/qCPDzENC+lgx/XdRCrROhlPG6oLKnUChQrVo1JCYmIj4+3tnNISIiIivkcjkCAgJQuXJlSKVSZzenTFWI4LlixQrs3LkT69atQ48ePexyzIKG2ppkajjUlh7oFiXHrks67L2iQ+eH3GesPpUvXl5eqF69OnQ6HXQ6TkYnIiIqTyQSCeRyuduOSHL54Hn37l1MmDAB7777Lho2bGi34ypkAnQGEQv7eGNEdIbFfYIAZDB4Ui7PPKzA279nYetZDYMnOZ1MJoNM5vIv70RERFSBuHxxobFjx6JKlSr48MMP7XpcpQzQGayvseMhF5CazeBJDzwcIkGNQAm2co1XIiIiIqJ8XDp4/v7771i7di1+/PFHKJX2rfTjKRcgisbwmZeXHEjJYvCkBwRBQLcoOS7eM+DyPVYVJSIiIiLKzWWDZ2pqKkaPHo3Ro0ejbdu2JT6OSqWy+FKr1QAAb4Vx7LXGyjQpT7mA306yZ8sdqNVqi+sjLS2twH25rAoRERERkXUuGzwnTpwIqVSK6dOnl+o4ERER8Pf3N3+ZjmcKnmq99aG22Vr2eLqD6dOnW1wfUVFRBe77RB05lDJwuC0RERERUR4uWX1i165dWLJkCTZv3gxfX99SHSsuLg5+fn7m26Yhux459WG0VkZNesiAbB2Dpzt477338NZbb5lv37p1q8Dw6a0U0LGOHDGXtcjUiPBSuGfFMiIiIiKivFyuxzMzMxPDhw9H//790a1bt1Ifz8/Pz+LLFDxlEmP1WmtzPD3lAjQ6QGulN5QqFqVSaXF9FPVBxzMPy6HWATGX2OtJRERERGTicsHz6NGj+O+//7B+/Xr4+PhYfDVo0MC8X4MGDczbf/7552KfRxAEyCSAzkq4NPVkpbLAEOXBeZ5ERERERPm53FDbli1b4tKlS1bvu3XrFjp27AgA2Lp1K6pWrQoACAkJKdG5ZBLBao/nOxsz0T1KDlW2iMo+JTo0VVB1g6WoGyzBupMafPmCFxQyDrclIiIiInK54Onp6Yk6depYvS/3guk1atRAzZo1S3UuqQTQWwmeACARgHQNezwpv5FtlJj4exZWxWowqKV9l/khIiIiInJFLjfUtizJJIDOYD1cKmUC0tUMnpTfa495wM9DwJyYbIgirxEiIiIiogoRPO/fv4+EhATcu3fPvO3evXtISEjA/fv3S3xcudR6VVsA8FQAyZkMFZSfn4eA1x9T4lS8HjsuWFkIloiIiIjIzVSI4NmzZ0+EhYWhZcuW5m0tW7ZEWFgYevbsWeLjPttAAU0BwdNHIWDtv5oSH5sqtnGPKyGTAHNispzdFCIiIiIip6sQwXP37t0QRdHq1+7du0t8XC85oNGJWNjHO/99CgGZzJ1UgIhAKXo3UeDPCzpcvlfApxdERERERG6iQgRPR/GUC9AWUFzISy4gSyvCUMAcUKIhrYyFhX45zk8oiIiIiMi9MXgWwkMuFDjH00sBiCKgymbwJOs61ZUh1E/Az8fVLDJERERERG6NwbMQnnJAq7ceGCZvNs7dS8xgoCDrpBIB/ZoqcPGeAcfjONyWiIiIiNwXg2chvBQC9AZAX8BwWoUMSM5i8KSCDWhuHG678pjayS0hIiIiInIeBs9CeMkFAIC6gBUxPOUCUhk8qRDNI6RoFC7FksNqJKYXMGGYiIiIiKiCY/AshLfC+K9aZz1cesoF/HqChWOoYIIg4OMunkhXA1/synZ2c4iIiIiInILBsxA+ysJ7PL0VQIaGPZ5UuBcbytG0mhTz9mTjTHwBFxMRERERUQXG4FkImVSATApoCigw5JmzpApRYSQSAT/08YZeBIauyoCugOuJiIiIiKiiYvAsglxa8JIqHnIBGl3BxYeITFpUl+GdJzxw5IYe3x9goSEiIiIici8MnkVQSAGNHljYxzvffR4y47/pagZPKtqUrp4I8RXw5e5sGPhhBRERERG5EQbPIiikgKaA4kKmtTzvZzJEUNE85AKGtVbiapIBh69zricRERERuQ8GzyJ0b6CAuoChtgAglwKJGQyeZJtejYylktef1Dq5JUREREREZYfBswg+SgHqQgoI+SgFrs9INmtaTYoagRKsP6mBKPIDCyIiIiJyDwyeRfBVAtmFjIr0Vgj49V/2XpFtBEHAi43k+C/JgNPxhXSlExERERFVIAyeRfBWCtDoxAJ7p/w9BaRmGdh7RTZ7oaFxuO2GU/zAgoiIiIjcA4NnEbzkAgwioCtgNK2f0rikSoambNtFrqttpAyVvAVsOMWLhoiIiIjcA4NnEbwUAgDjkirW+CiN9ydlcJ4n2UYmFfBcAzlib+px/T6H2xIRERFRxcfgWQRv46jIApdUefO3TMilQIKKQ23Jdr2bGC+shQfUTm4JEREREZHjMXgWwTunx1NdSIEhPw8B99jjScXQtb4cUaFSfLdPjdQsXjtEREREVLExeBZBJhUglwJqnYiFfbyt7uOtFLDuX87XI9tJJALee8oDqdki5u7OdnZziIiIiIgcisHTBkqZUGiPp49CQBqzAxVT/2YKPBwiwZe7szlHmIiIiIgqNIcFz5MnT9p1P2dSyAC1vuA5nP6eAtQ6EWnZnOdJtpNKBHz6jBfS1MCcGH5yQUREREQVl8OC57fffmvTfvPnz3dUE+zGQyYgu5AlF/09jPNAE9LYa0XF07ORHA1CpZjPuZ5EREREVIHJHHXgdevWQacrZHxqjo0bN2LhwoWOaoZd9GqswMqjBVcfHbsuE32bKHArxYC6wdIybBm5OolEwOSnPDDwpwx8t0+N9zp7OrtJRERERER257DgmZqaipiYGJv2K+8CvQRkaAofRhvgJWDFMQ061pWXUauooujXVIGPtmbhi5hsDGqpRLg/p14TERERUcXisODZu3dvrFq1qsj9+vfv76gm2E2QlwCtvuC1PAHjcNsriRwqScUnkwr4uqcXevyYjsG/pOOPEb6QSARnN4uIiIiIyG4c1rXSq1cvm/Z76aWXHNUEuwnwNIaALG3BwTMgp8AQ5+lRSTz3iAIj2iix44IO3+0veFg3EREREZErcljwjIyMtGm/mjVrOqoJdhPgKQACkKFBgWt5+nsY94lXsbItlczs571Qp7IE72zMxPk7emc3h4iIiIjIbhwWPL/77ju77udMUokAT7mAzELmecqkArwVAuJV7PGkkvFRClj5ig80OmDQz+nQG/ghBhERERFVDA6b47l69WpcvXq1yP2OHDmCxYsXO6oZduMlL3yo7YjoDIxqq8SKo2o8wQJDVEKta8owqZMHZuzMxtzd2Xi7E6vcEhEREZHrc1iPpyiK+b6uXLmSb5urUMoEZBexOkygp4DkTBEG9lRRKUzp6on6VST4aFsWLt7lkFsiIiIicn0O6/Hs27cvli5darHtvffew/Tp0y22DRs2zFFNsKsXG8nxy3FNofsEekmgN+hxJ01EmD+rklLJeMgFLOnvjbZfp2HY6gzsGcsqt0RERETk2hzW4/nGG2/k2yYI+d88jxkzxlFNsCtvhQBNET2evkpALgVupnKeJ5XOY5FyjH9ciX3/6TB/H6vcEhEREZFrc1jwrFSpUr5t1obWWtuvPPJVCsjWGYcHF1TZVhAE+HsKWHqYQYFKb9qzXqhdWYLJmzNxNYlDbomIiIjIdTkseOYdUgtY7/GcMWOGo5pgV/6eAvQGQFPI+/8R0RkI8JQgOZNzPKn0vBQCfuzrjUyN8dpypTnRRERERES5OWyOZ3R0NDQayzmRx48fR0JCgsW2zZs3Y8GCBY5qht0EeBpDc5ZWhFJW8Hy7QE8BlxNFaHQiFIXsR2SLjnXlGP6YEosOqrH1rBbPNlA4u0lERERERMXmsOCpUqkQExOTb3vebcnJyY5qgl35eQgQBCBDAwQUssKFn4cAiEC8yoAaQdKyayBVWJ8+44mfjqkx5Y8sdIuSWx05QERERERUnjksePbp0we//PJLkfsNGDDAUU2wK6lEgJdCQIa68OGOnnJjVdKbKQyeZB+hfhKMbueBOTHZ2HRGix6PsNeTiIiIiFyLQ+Z4pqen47nnnsPMmTOxfPlyxMXFFbjvwIEDHdEEh/BVCkgrIniO/DUTAZ4Clh0pfOkVouKY1MkDXgrgzfUsNERERERErsfuPZ4nTpxAly5dkJiYCFEUIQgCBEFA9+7dMXfuXERGRlrs37VrV3s3wWH6NFVg+ZGiK9YG+wg4m6CH3iBCyvUXyQ6q+Eqw4CVvDF6VgRZzVFj1qg+eri93drOIiIiIiGxi9x7Pt956C2+++Sa2bduGVatW4Z133kGTJk2wadMmNGrUCDt27LD3KctMoKeATI2xx7OgJVUAINhHAr0BiEvmep5kP6+2VGLb6z4QAXRdmIbpO7JY6ZaIiIiIXILdezzr1KmD999/33y7b9++AIAbN25gzpw56N27N44dO4Y6deqU+lxxcXFYuXIltm/fjtOnT0OlUsHf3x+NGjXCyy+/jCFDhkAqtd88y0AvAVo9iqxY660Q4KMUcO6OHjUrcZ4n2U+XhxU49pYUPZek4/0tWTgap8NPr/jAS8GedSIiIiIqv0rc4/ntt9/i8OHD0Ost55sVVHGzevXq+Oqrr7Bo0SJMmTKlpKc127NnD2rXro2pU6eiVatW2LZtGy5cuIBly5YhJSUFw4cPR6dOnZCenl7qc5kE5iypkqktvJdpRHQGQv0kWHpEwx4psrtalaU4MN4PA5or8NtJLQb+lM7rjIiIiIjKtRIHz48++ght2rSBv78/nnrqKXz22WfYvXs3unbtihkzZhT4uN69e9ulFzI5ORlarRbTpk3DrFmz0LJlS9SqVQvdu3fH3r17ERERgb1792Lq1KmlPpeJv6cACECmDXWDwvwEZGlEJKgYCMj+vBQCVr7ijf7NFFh/UoufjrGYFRERERGVXyUeahscHIx+/fohNTUVu3fvxq5duyAIAuRyY8GTq1ev4uWXX8Zjjz0GhcJy+YeUlJRSNdpEIpHgtddey7fdx8cHAwcOxP/+9z+sXr0as2fPtsv5pBIBnjKhyB5PAPD3EOApF3D+rh5h/g4pHkxuThAEzH/JC3uuaDFufSaerCdHOK81IiIiIiqHShw8q1SpgqlTpyIkJAQAcP78eezatQu7d+/Gnj17sGjRIvz4449QKpV49NFH0aRJE3h7e2Pv3r1o3LhxqRv+3HPPQaVSwdvbepGfatWqATD2jNqTjxJIVz8oMDQiOsPqfoIgINRPwI8H1XiiLquPkmMEekmwqK83nv0hHa+vycCm4T4FDncnIiIiInKWEgfPLVu2wN/f33y7fv36qF+/PkaPHg0AOHPmDHbt2oU9e/bgxIkTOHLkCGrUqIFhw4ZhwoQJpW64VCotMHQCQHx8PACgUaNGhR5HpVJZ3FYqlVAqlQXu37+5EssOF72kyojoDHz6jCeuJumgyhbh58Ew4IrUajXU6gc/77S0NCe2xrpuUQoMelSB5Uc1GP9bJr7o4VVo8SsiIiIiorJW4uCZO3Ra06BBAzRo0ABvvPFGSU9RKlu2bAEAjBs3rtD9IiIiLG5PmTKl0Hmhob4C0tWiTWt0VvYWIJUAF+/q0aK63QsIUxmYPn06PvnkE2c3o0hf9/JGXIoBX+9VY89lHVa84o1G4bzmiIiIiKh8qJATwrZv347Y2Fh069YN/fr1K3TfuLg4pKammr/ee++9Qvev6i+BQQRSs4ue5ymVCAj2kWDB/qJ7SKl8eu+99yyuj7Nnzzq7SVb5eQj4Y6QvPn3GE2cS9GgzT4Xt51hwiIiIiIjKhwoXPO/evYthw4ahXr16WL58eZHz3fz8/Cy+ChtmCxjX8lTKBCRnPpjnWZAR0RkI9RWQmGGAWsfqtq5IqVRaXB++vr7OblKB5FIBH3XxxN/j/OAhF9B9UTrWxPJDDyIiIiJyvgoVPFNSUvD0009DLpdj586dqFy5st3PIQgCAr0E3M+0LUiG+EoAETiboC96ZyI7aF1Thv3j/BDmJ8GrP2fg8DWds5tERERERG6uwgTPpKQkdO7cGVqtFvv37zdXtXWEIa0USM4UIYpFh0+FTECInwRf7cl2WHuI8nooRIotr/tAJgW6/ZCG539Mw7M/pOEQQygREREROUGFCJ7x8fHo0KEDRFHEnj17EB4e7tDzhftJoNaJyLbxPXxEgARJGSKSMw0ObRdRbg3DZVj1qg8kArDtnBZbz2rR7Yc03EnjdUhEREREZcvlg+e1a9fQvn17BAYGYteuXRbDa+/cuYOuXbvizp07dj1nqJ/x26ayocDQiOgMBPsY54WeuMXhtlS2ejyiwJ3PAqCeHYgNw3yQnCli3LpMZzeLiIiIiNyMSwfP8+fPo3379qhTpw62b98OPz8/i/uzsrKwfft2ZGVl2fW8HnIB3goBqVlFFxgCAIkgoFqABAv2qWEwsMgQlS2JRIAgCHi+oQIvNZYj+oQGiw+x6BARERERlR2XXejvzJkzeOKJJ3Dv3j0EBgbi8ccfz7ePRuO45SR8PQSkqW0LkSOiM/Dl8164kqjHpXsGPBQidVi7iAqzoLc3jtxQ4bXVGThyXYfZz3vB16Pwys9ERERERKXlssFzx44duHfvHgDg1KlTZX7+l5sr8MMB23uNvJUCqvhKMGNnFpa+7OPAlhEVrLKPBAfe9MPQVen44aAaJ2/rsH2UH/wYPomIiIjIgVx2qO348eMhiqJNXzVr1rT7+cP9JEjXiNAUY33O2pUkSEwXcSuFxV3IeaoGSPDHSF98+LQHDl3Xo97nKej4jQrbzzluhAARERERuTeXDZ7OVj3Q+K0zredZ1DzPEdEZqOQtIMBTwKfb7TvnlKi4BEHAp8944osenqjsLcGh6zo8uygda2KNvfi2LBVERERERGQrBs8S8lQICPAQcCfd9t5LQRBQq7IUt1UGpHBpFXIyQRDwdidPnJ7sjzOT/RHuJ8HLKzPQ8stU+L6bjEdmpOLCHVZiJiIiIqLSY/AshWGtlbiXLhardyjMV4CHTMDROL6hp/KjdmUpdo/1RfMIKf5LMqBxVRnO39Wjy/dpiEvmtUpEREREpcPgWQp1gqXI0ojIyJkaZ8twW4lEQM0gCRbsyy7W/FAiR6tVWYojb/kj8fNA7H/TD8tf9sb1ZAM6zU/DkkNqbD+nQaaG1ywRERERFR+DZymE+wmQSYF7xRhuCwA1AiUwiMDxOJ2DWkZUegNaKLGwjxeu3jdg2OoMdF2Yjnqfp/C6JSIiIqJiY/AsBYlEQLCPBHfSbe8FGhGdAYVMQM0gKWbHsNeTyrfX23jg6kf+2DzcB7N6eOJ+poiO36qw57LW2U0jIiIiIhfC4FlKo9oqkZRhgN5gW3VbkzqVJdAbgNibnD9H5VtEoBTPNlDgnU6e2DnaF1JBQNeFadh2lsuvEBEREZFtGDxLqW6wFAYDcK8EvZ4RARLM25PNpSvIZTwWKUfMWF/4KAQ8vzgdn/yRhc//zOIaoERERERUKAbPUvJRCvD3FHAv48E8T1t7PWsGSZGhFnH+DpdWIdfRtJoMe9/wQ7CPgKl/ZOHDrVnoujAdr/6UDlU2P0QhIiIiovwYPO1gWGsl7qiKt6zKiOgM+HoICPeXYOofWZzrSS7l4VApTk7yx5bXfbBvnC+ef0SOlcc0aDwrFe9uzETfZen4eGsm0tW8romIiIgIkDm7ARXBQ1WkyNKKUGWL8PcUivXYBqFS7Lmixdu/Z+LrXrb1lBKVB5W8JegWpQAAtImUYdFBNd7ZmIVZu7LN+6yK1eCbXl7I1gLn7ujx1ENyPFqdLztERERE7oY9nnYQ5ifAUyEgXvWgd8eW4bYjojPgIRcQFSLF1fsG3EzhkFtyTYIg4PU2Hkj4NAAXP/BH5qxAfNvLC3EpBjyzMB0vLknH+1uy0PJLFd75PRM6PXtCiYiIiNwJg6cdCIKAYa0UuK0ylKhQULUACYK9Bb4hJ5fnqRBQN1gKT4WAMe09cHKSP2b18MTift7YNcYX7WrJMDsmG49/k4ZVx9XI0vB6JyIiInIHDJ528kiYDBlqsdjFVUZEZ0AQBDQOlyFbJ+Lt37Mc1EKislevihTvdPLE0NZKPFFXjl1jfDHxCQ8cuaHDyyszED4lBSPWZGDLGQ1Ss9jjT0RERFRRMXjaSbi/AG+FgJupxa9uOyI6A54KAQ1CpbiSpMe1JK7tSRWTXCpg9vNeSPg0AN/08kJkJQl+OKhG90XpqPKhMYReS9JDpxcRn/pgfVwiIiIicm2s8mEngiBgZFslvv1bjYeriJBIildkCAAiAiS4m27s9Vw2wBs+yuIfg8gVVPaRYGx7D4xt74GLd/X466IWq2I1+OGgGj8cVEMqAfQGoKq/gPc7e+K11kooZPx9ICIiInJV7PG0o6bVpNDoRdwuZpEhIPeQWylkEmDCb5mOaiZRuVKvihSj23ng73F+2D3WF6+0UODFhnKMaKOEQQTGrM1Evc9TseSQ5ZzQpAwDlyEiIiIichHs8bQjf08JqgVIcPGuHuF+QrF7PUdEZ2BhH280Cpfi4DUdzsTr0CCMPyJyHx3qyNGhjtx8+8sXvPDdvmzM2JmNYaszMPH3THSqK8O5O3qcu2OAv4eAfs0UeLS6DNUDJehYRwa5lD2jREREROWNIJakDGsFoFKp4O/vj9TUVPj5+dntuCmZBgz6OQMNw6WoHig1bx8RnWHzMRb28cap2zrcTDVgQW9vhPqxY7q8uHnzJiIiIux+3VDhVNkiVhxVY/EhNU7c0qNagASd6spw4pYeJ28/mBMd5idgWGslHqspg4dMQN1gCSJy/R46k6Nec4iIiIhcAbvT7CzAS4KqARL8l2RARIAEgmDsfVnYx7tY4TMqVIp0jYhx6zKx5GXO9yT35uchmOeEanSieb6nKIq4dt+Ai3f1iL2px6JDakz7M9visU8/JMOcF7zwCEcPEBERETkNezwd0PsQn2rA62sy8FikDJW9H/RWFrfXU6MTse+qDjIJ8GM/b3jIGT6djT2e5ZvBICLmsg63UgzI1IrYc1mH6BMaAMBDVSRQSAU894gcEzt6IMCrbEcSsMeTiIiI3BmDp4PeBA75JR0GEWhTU2bu9QSKHz4z1CIOXNNBKQMW9fWGp4Lh05kYPF3PPzd1mPpHFq4kGpCcZcDtVBGVvAU8VlOGu2kG3Ew1QCoICPUT0LuJAmPaecDLAb9nDJ5ERETkzjh50EGmdfPC/QwRCWklz/UjojPgrRTQJlIGjQ4YviYDalbxJCqWptVk+P01X5ye7I+4KQH46RVvBHsL+PO8FvEqETWDpKgWIMHVJAMmbcxCvc9TsOSQ2ryGqN4gYuNpDZYeViMtm79/RERERCXBHk8H9j6MjM5AapaIDnVkkJSw1xMw9nxmakTs+08HLwWwqJ83lFzT0CnY41lxafUiFh9SY8q2LNxNF9EwTIr2tWXYcUGLS/cMAIBgHwFj23nARwkYRMDfU0AlLwlqVZKgTrC00LnY7PEkIiIid8bg6cA3gck5FW4fCZOiZpBlZc2ShM90tYiD14xzPhf09irzOWrE4OkO0rJFzNyZhW/+VkOVLaKyt4BRbZUI8ZVgxs5s3EwxFPjYUD8BdStLUTdYgrrBUjwcIsUjYVLoDcBvx5Iw+dkQXjtERETklhg8Hfwm8K8LWszfl42OdeTwlJe81xMwhs8srYgj13XQ6IGFfbwQ5M3wWZYYPN2H3iDibpqIYB8Bspy1QbO1ImJv6iAVBEgkQGqWiHvpBlxONOByoh6X7hlw6Z4eiRlWXlbVKuD7SF47RERE5JYYPB38JlCrFzHwpwz4KAU8GiEtcaEhE1O128M54fO73l6oxPBZZhg8yRYpmQZcSjTgbIIep+KN64w+GpKJfo8F89ohIiIit8TgWQZvAq8l6fHGukw0j5Ah3N8yJJY0fKp1Io7e0CFDI2J6dy/UqyIt+oFUagyeVFKc40lERETujF1lZaBmJSlqBElwKl6HbK1lzl/Yx7vYxxsRnQGlzLgcRIivBBN/z8TfV7TmKpxERERERETlCYNnGfnyBS9IJQLOJOjz3VfS8CmVCGhSVYYGoVLMjsnGqz9l4EZy/uMTERERERE5E4NnGVHIBPzvWU/cTjUgQVVwVcziMA3TrVVJio615VDKBYxZm4kj13Vw0xHURERERERUDjF4lqE6wVJEBErw720d0tWlH3ILPAif3koBrapLUb+KFJ9tz8LI6EyouNg9ERERERGVAwyeZWzei17wVgjGJVF09gufI6IzIAgC6gZL0bqmDMlZIgauTMfuS5z7SUREREREzsXgWcYUMgHf9faGXgRib+nzhcKShk/gQe9nsI8ET9SR4eFQKebtycYrKzNwq5BF74mIiIiIiByJy6k4aWmD26kGjFmbAS+5gKbVZPDzEPLtU5KlVkxMATZTI+LkbR3uZYgI8ZHg8+6eCPHl5w0lxeVUqKSc/ZpDRERE5ExMIE4S7i/Bkv7ekEmAA1e1yFDnz/+l7f0cEZ0BL4WAVjVkaBEhg0Yv4vXVGRi3LiPfHFMiIiIiIiJHYfB0okAvCX7s7w0vhYAjN3RQ6+wbPgGY536G+UnQJlKGBmFSxKtEDPwpHbsualmAiIiIiIiIHI5DbcvBsLe0bBHDV2dALwINQqUI8xMgCJZDb0sz7NbEFGL1BhFXEg24dt8ArV5EuL8EEzp6oG6wJN95yRKH2lJJlafXHCIiIqKyxuBZTt4EZmlETPw9E3HJBlQNkKBJuBQSiX3nfZqYAqjBIOJmqgFxyQbczxTh7yng3Sc9UD9ECrmUAdQaBk8qqfL2mkNERERUlhg8y9mbwCuJerz9eyb8PQQ0rSqDp8Ix4ROwHMabli3i/F097qQZoJAKmPyUB6JCpfCycn53xuBJJVVeX3OIiIiIygKDZzl8E5igMmDCb5lQ60Q0DJehqr/1qbiOCKBqnYiL9/S4kWyAQQSCvAR80NmTw3BzMHhSSZXn1xwiIiIiR2PwLKdvArV6ERM3ZOJqkgGRlSR4OEQKqZWht4BjAqhWLyIpQ8SNFAPuqAzwVggY3kaJqBApQq3MQXUXDJ5UUuX9NYeIiIjIkVjVtpySSwV83csb05/zRFyKAXuu6JCYbrC678I+3tar3+rUwKGZxn9tYFqCZUR0BuRSAaF+ErSsLkOHOnJU8RXwwwE1Xl+TgVdWZiA2znoV3rKmVqsxdepUqNW2PUcqmjt8T93hORIRERGVJ+zxdIHeB1W2sfczQWVA7cpSPFRFUmDvJ5CrB1StAr6PBEZeBZQle465A60oikjJEnE92YBbqQZABIJ9JHi7kwfqBUusFkNytLL+ObpDj6cr/W6UlDOeozt8X4mIiIgKInN2A6hofh4CFvXzxr+3dJiyLQs3kvWoHihFZJDEavEhU1gcsVJV6nPnHsa7sI83Ar0EBHpJEBUi4k6aAbdUIt7ZmAkPmYCRbZVoXk2KAC92pBMRERER0QMMni6kcVUZfhrog39u6vD1XjWuJOkR5idBozApFLL8AXReT2+M/z7n3y2lP3/eEBoRKEVEIJChERGXbMD3+9XI1okI9hYw6UlPPFSFBYmIiIiIiMiNg6derwdgHP7mahoHAz+8KOLyPQP+tyMLe1OANpGyfCEvOyPN/O+8Z30BAOPX26cQUe7e1Hk9vVHDB4jwEnEvXcTNFAPGr05FmJ8EX/WyMvfUjkw/v7L6OaakpJTp+ZyhrL+nzuCM52i6du7fv19m5yTXJ4oi0tLSEB4eDomEo0mIiMh1ue0cz6NHj6Jly5bObgYREVGR4uLiUK1aNWc3g4iIqMTctsezTp06AIAzZ85U2EIfaWlpiIqKwtmzZ+Hr6+vs5jhEWT/HuLg4tGnThteNi3PGc7x16xZat26NuLi4CnvtkP2pVCpERERU2N9FIiJyH24bPKVSKQCgWrVqFfZNoGkYYdWqVfkc7YzXjWtz5nP08/OrsN9XchzOlyciIlfHCSNERERERETkUAyeRERERERE5FAMnhWYUqnElClToFQqnd0Uh3GH51jW3OF76g7PkYiIiKg8cduqtiqVCv7+/khNTeV8K7LZzZs3ERERweuGiq2iXzsL92fjk+1ZUEgFzHjOE/2aMdTbA/9WERFRRcEeTyIiKpWF+7Mx8tdMSCUCMrUi+q/IwI4LWmc3i4iIiMoRBk8iIiqx6/f1mLAhE3UqS/DvO344MsEPfh4CXl+TAa3eLQfUEBERkRUMnkREVGLvbMxClhZY1NcbQd4S1KwkxftPeeDafQN+Oa5xdvOIiIionGDwJCKiEjl/R4+1/2rQ4xE5OtaVm7ePaucBbwXww0G1E1tHRERE5QmDJxERlcjMnVkQReD9pzwttvt5COjVWIEDV3W4kqh3UuuIiIioPGHwJCKiYruRrMdPxzToVFeGVjVl+e5/9VFjVdtVsRxuS0RERA4MnidPnrTrfkREVH58t08NnQGYnKe306RDbRn8PQRsO8fqtkREROTA4Pntt9/atN/8+fMd1QQiInIAtU7E4kNqPFRFgqfq5e/tBACZVMBTD8lw+LoOKZmGMm4hERERlTfW3zHYwbp166DT6Yrcb+PGjVi4cKGjmkFERHa29oQGiRkiPnzaE4IgFLhfl/pyrPtXi52XdOjVWFGGLSQiIqLyxmHBMzU1FTExMTbtR0REruO7/Wp4yoFBjxYeJjvlVLrd95+WwZOIiMjNOSx49u7dG6tWrSpyv/79+zuqCUREZGcX7+px4KoOg1sqEOBV+GyNWpUkqOwt4PB1VrYlIiJydw6b49mrVy+b9nvppZcc1QQiIrKzX44b1+Yc2EJZ5L6CIKBlDRlib+qg1YuObhoRERGVYw4LnpGRkTbtV7NmTUc1gYiI7EgURfx8XINwfwEd6tg2YKZldRnUOuDkbfZ6EhERuTOHBc/vvvvOrvsREZFzHYvT43KiAf2aKiGVFFxUKLeW1aUAgONxRRebIyIioorLYXM8V69ejatXrxa535EjR7B48WJHNYOIiOxkVc4w2wHNbS8U9EiYMXieSWCPJxERkTtzWI+nKIr5vq5cuZJvGxERlX+iKGLDaS0iK0nQtJrU5sdVC5DAVwmcZfAkIiJyaw7r8ezbty+WLl1qse29997D9OnTLbYNGzbMUU0gIiI7OXfHgKtJBox7XFno2p15CYKAqFApzt5h8CQiInJnDuvxfOONN/Jts/ZmZcyYMY5qAhER2cmm0xoAwHMNir8eZ1SIFLdTRaRkGuzdLCIiInIRDguelSpVyrfN2tBaa/sREVH5sumMFn4eAh6vXfyBMlGhnOdJRETk7hwWPPMOqQWs93jOmDHDUU0gIiI7SEw34OA1HbrUl0Mhs32YrUn9EGPwvHSPPZ5ERETuymFzPKOjo6HRaCy2HT9+HAkJCRbbNm/ejAULFjiqGUREVEpbz2phEIHuUfISPb5WJWPwvHqfPZ5ERETuymHBU6VSISYmJt/2vNuSk5Md1QQiIrKDzWe1kAhAtxIGz5pBxsE1/yWxx5OIiMhdOSx49unTB7/88kuR+w0YMKDE57hz5w62bduGrVu34tixY7h9+zZEUURYWBhatWqF0aNHo0OHDiU+PhGRu9PoRPxxToPHaspQ2adkszO8FAJC/QQGTyIiIjdm9zme6enpWLVqFRo3bozly5cjLi6u0P0HDhxY4nM9//zzGDJkCABgyZIlOH/+PI4cOYI33ngDW7duRceOHfH111+X+PhERO5u7xUd0tTAcw1K1ttpUquSFFeTONSWiIjIXdm1x/PEiRPo0qUL7t27Zy4kJAgCunfvjrlz5yIyMjLfY7p27Vqqcz711FOIjo622Na4cWMEBATgtddew/vvv48RI0ZAqVSW6jxERO5o+3ktAODZUgdPCQ5c1SFTI8JLUfwCRUREROTa7Bo833rrLbz55pto0aIFkpOT8c8//+Cvv/7Cpk2bsHPnTqxfvx6dO3e22/mmT5+OsLAwq/e1bNkSAJCRkYGMjAwGTyKiEth1SYsqPgIa5CyJUlK1KhkH2Fy7bzAvr0JERETuw67Bs06dOnj//ffNt/v27QsAuHHjBubMmYPevXvj2LFjqFOnjl3O98QTTxR434EDBwAAzZs3R1BQkF3OR0TkTu5nGPDPLT36NlFYXQ6rOGoGGcPmtft6Bk8iIiI3VKI5nt9++y0OHz4Mvd5yvk5Bb0yqV6+Or776CosWLcKUKVNKckqb6PV6xMXFYe7cuZgwYQKaNWuG1atXO+x8REQV2Z4rOogi8GS90n9GWS3A+OfmVioLDBEREbmjEr2b+Oijj6BSqeDp6YnWrVujQ4cOaN++Pbp27YoZM2Zg8uTJVh/Xu3dvbNq0qVQNLsjYsWOxYMECGAwGBAYGYvr06Rg7diyk0sI/WVepVBa3lUolh+WSmVqthlqtNt9OS0sDwOuGilbQteNKdl0yzu/sVLd08zsBoJq/MXjeTGHwJCIickcl6vEMDg7G66+/jh49euDs2bOYMmUKnnzySfTv3x9Tp07FiBEjsGfPHmg0mnyPTUlJKW2brZoyZQrOnj2LXbt24ZVXXsHEiRPRvn17XL9+vdDHRUREwN/f3/w1ffp0h7SPXNP06dMtro+oqCgAvG6oaAVdO65k1yUdagRKEFmp9AXQTT2eDJ5ERETuSRBFUSzug9q1a4d169YhJCQEAHD+/Hns2rULu3fvxp49e8xVbZVKJR599FE0adIE3t7e2Lt3Lxo3boz58+fb/Ynk9c0332DcuHEIDw/HqVOn8s3zVKlU8Pf3R1xcHPz8/Mzb2XNFueXttbp16xaioqJ43VCRCrp2UlNTLa6d8ipBZUDYxykY0lKBJS/72OWY/pOT0bqGFNtHlf/nX16Y/la5ynVDRERUkBIFz9TUVPj7+xd4/5kzZ7Br1y7s2bMHJ06cwK1bt1CjRg0MGzYMEyZMgExm15pGVun1etSsWRM3b97E9OnT8w3/5R9zKombN28iIiKC1w0Vm6tdO6uOq/HyygysfMUbr7Swz4cqDWakAgDOTC747wdZ4t8qIiKqKEqUAAsLnQDQoEEDNGjQAG+88UaJGmUPUqkUUVFRuHnzJg4ePOi0dhARuaJdl3QAgCfqlH5+p0lVfwGHr+uL3pGIiIgqnNJP3HGS27dv4/vvv89XWTc3b29vALA615SIiAq265IWD1WRoGqA/f5MVPOXQJUtQpVd7IE2RERE5OJcNnhevHgRo0aNwpUrVwrc5/Tp0wCAunXrllWziIhc3rUkPf5LMtilmm1u5iVVWGCIiIjI7TgseJ48edKu+xXku+++s7r9p59+wqVLlyAIAl599dVSnYOIyJ3EXDYOs3VY8ORankRERG7HYcHz22+/tWm/kla4NRUo+vrrrzFw4EDExMTg6tWrOHjwIN577z0MGTIEEokEc+bMQYsWLUp0DiIid2Rav7NjHfsWgjMFzzj2eBIREbkdh5WXXbduHXQ6XZH7bdy4EQsXLiz28du1a4fY2FisXr0ae/fuxUsvvYTU1FQoFApERERg0KBBGD16NJo1a1aS5hMRuSVRFLHzohaNw6Wo7GPfzybD/YzHi1cxeBIREbkbhwXP1NRUxMTE2LRfSTVt2hRNmzYt8eOJiMjShbsGxKtE9Gtq/z8PoTnBM4HBk4iIyO04LHj27t0bq1atKnK//v37O6oJRERUTKZhtp3q2Xd+JwAE+wiQCEBCGqvaEhERuRuHzfG0dV4l518SEZUfuy5pIZUAj9e2f/CUSgRU8RU41JaIiMgNOSx4nj171q77ERGRYxkMImIu6fBohBR+HoJDzhHqK+FQWyIiIjfksKG2q1evxtWrV4vc78iRI1i8eLGjmkFERDY6eVuP+5miQ4bZmoT6SrAvUeuw4xMREVH55LAeT1EU831duXIl3zYiIiofzPM77bx+Z26hfgLS1UC6mq//RERE7sRhPZ59+/bF0qVLLba99957mD59usW2YcOGOaoJRERUDLsu6aCQAm1qOuxPA8JyKtveSTPARyl12HmIiIiofHFYj+cbb7yRb5sg5J8zNGbMGEc1gYiIbKTVi9hzRYs2kTJ4KhwzvxMwDrUFuJYnERGRu3FY8KxUqVK+bdaG1lrbj4iIytbxOD3S1Y4dZgsYh9oCQIKKQ22JiIjcicOCZ94htYD1Hs8ZM2Y4qgmlcjpehzFrM5Ct5ZsjIqr4HszvdNwwWwAIzRlqm5DGHk8iIiJ34rB3GNHR0dBoNBbbjh8/joSEBIttmzdvxoIFCxzVjBIRRRHTtmcjQyPi0DUdOjq4B4CIyNl2XNDCRwk8Wt2xwdM0x5NLqhAREbkXh73DUKlUiImJybc977bk5GRHNaHEElQiMjQiqvhK8M3f2WgTKYNC5rg5T0REzpSuFrH/qg5d6ssd/lpnmuOZkMbRJERERO7EYcGzT58++OWXX4rcb8CAAY5qQondSDZALgWaVJVi50UDjtzQoV0t9noSUcW094oWWj3w9EOOf53zUQJeChYXIiIicjcOm+P56quv2rTfwIEDHdWEElt6RI0ALwmUMgF1Kksxe1c2h4URUYW144JxfmfnMgiegiAgzE/C11QiIiI347Dg2bVrV7vuV5aSM0UEehqHm9WpLIG/p4BJGzOtVuUlInJ1Oy7oUC1AgoeqOOxPgoVQXwmLCxEREbmZsnmX4ULUOhGZGhH+HsbgKZEIeCRUiuRMERfu8o0SEVUst1IMOJOgR+d6MquVxx0h1E/AnTQRBgM/zCMiInIXZRI8L126BK1WWxanKrX7GcY3Qj7KB2/A/D0lqBogwcdbs6DR8Y0SEVUcf100vjY/Xb/s5rGH+kqgNwBJmXw9JSIichelDp4JCQkYMmQIqlSpAh8fH7Rp0wZLliyBwfCgd3Dfvn3o3bs3mjZtimHDhpX2lA6VlGmAIABeed6DNQiVQmsQcei6zjkNIyJygO3njcHzyXplFzxNS6qwwBAREZH7KFXwvHXrFlq3bo0VK1YgMTERmZmZOHToEIYPH44WLVrg33//BQAMGTIEv/32G+Lj47Fs2TJ7tNth4lUiNp3RYtTaTIyIzjBvV8oE1K0sxZyYbNxK4ZslInJ9Wr2Ibee0aFVDimCfspt5EWoKnql8LSUiInIXpVpOpV+/frhx4wYee+wxdOvWDSEhIUhKSsLRo0exY8cOtGzZEu+//z4+/PBDSKVSBAYG4t69e/Zqu0Os+UdT4H21KktwP1PEuPUZWPqyD/w8uLYnEbmuff/pkJIl4vlHFGV63lBf42sn1/IkIiJyHyUOntu3b8fRo0cRHR2Nl156Kd/9Go0GmzZtws8//4ymTZti0qRJdp/nGRcXh5UrV2L79u04ffo0VCoV/P390ahRI7z88ssYMmQIpFJpsY6ZrbV8IzQiOgML+3gDACSCgKbVpPj7PxHjf8vE4n5eZVaMg4jI3n4/ZfygrccjZbtOMYfaEhERuZ8Sj61as2YNZs2aZTV0AoBCoUCvXr2wfv167Nu3DwqFAj169MCAAQNK3Njc9uzZg9q1a2Pq1Klo1aoVtm3bhgsXLmDZsmVISUnB8OHD0alTJ6Snp9t8TFEUkaUt/BN4uVRA06pSJKUbsPMi53sSkfG1Q6d3rd47URSx8YwWtSpJEBVavA/oSssUPLmWJxERkfsocY/nyZMn8f3339u0r5+fH/r06YM+ffqU9HT5JCcnQ6vVYubMmZg0aZJ5e61atdCxY0dERUVh7969mDp1KmbPnm3TMdPUgMZKlszd6wkAgV4SNK4qw1d7slHFV0Cj8FKNWCYiF5apETF2bQZU2cCP/bwQ4OUaq1SdSdDjapIBEzooy3zkRhVfARLBOKeeiIiI3EOJ3yF5eXlBoSjbeUF5SSQSvPbaa/m2+/j4YODAgQCA1atX23y85EwDNp+1Phw4d6EhAKgWIEHNIAk+257tcj0dRO5MozOuH5mUYcDfV7TI1Ii4n2HAoJ/Tceme3ryfTi/iWpIeOy9qMTI6A5fu6SGKD37X9QbjcUZFZyA1W4TWIGLwLxkY/HM69lzWmpdeytKIOHxNh3HrMvK1xZk2nDK+1vUo4/mdACCVCAj2ETjUloiIyI2UuKvO2aHzueeeg0qlgre3t9X7q1WrBsDYM2qrdHXxAmSdYClupGgRe1OPljXY60lUnphC4pVEA6QS47+/HFcjKVOEp0yAIBh/5yUSwLT601u/ZcJTIUACIEsnwmAAvBQCPOTG+/w8BAx8VIFVsRqkZIowiIAgAI/XlkMuAa7e10OjB77ak40F+wX4eQi4m2Y8uJ+yfM0HX/OPBlV8BLSr5ZzXrjA/CYMnERGRGynxOw6VSoW0tDT4+vrasz02k0qlBYZOAIiPjwcANGrUyOZjZhVR+yjvkFtPuYBalaT4344szHvRCzUrle08KSJ3k6URkakV4SkXsO8/HaQSYMVRNbwVAjLUItQ64zBOrR64k2aAv6eAxHRjAPWUC6jsI+CRUAmu3TcgXS3i0eoyJGYYcDXJgLa1ZMjQGI8BEVDKgMreEngqjIExNcuA68kG/HRMA1+lgOrhEmRpAT8PwVzhOirU+JKaFSziwj09tHrg4RApqgVIkJ5Ufj6cOh2vw+l4Pca0U0ImdU4gDvOTYO8V+xacIyIiovKrxO+E2rdvj08++aTI+ZOiKOKvv/7Cxo0bcezYMcTHx+PatWslPa3NtmzZAgAYN25cofupVCrz/28nGQC9FpAWXOExb/h8KFiCTI2IN9dn4ovnvVCvCsNnRaJWq6FWq82309LSAFheNwCgVCqhVCrLtG0ViVYv4n6miBBf4+j/u2kGTN6chUmdPOCfE+o+35GF68kGQASkOZMEPOQCAjwF3EsX4aUAwv0luJFsgFYvolqABIkZIlrXlEEhBXyUAqQS47GqBUigNwAKmYBQPwnqBYtQyAQEeRXcRn9PCRp52jY7wVMhoEGwHjqt8drRZQPZmWkl/O7Y36pYYzXb/s2cN3IlzE9AhsbY6+xTznqDiYiIyP5KHDwHDx6Mpk2bAgA++eSTfL2Px48fxy+//II1a9bgmWeewfvvv49u3bohLi6udC22wfbt2xEbG4tu3bqhX79+he4bERHx4Ea9nkDtbkC9Fwt9jMUSKxIBzapJcfI2MGljJr7r7Y1wf9coLkJFmz59Oj755JN82y2uGwBTpkzB1KlTy6hV5V9csh43UwwI95egRpAUmRoRl+7p8d0+Nbo+LEetShI8EiaFVCIgSyNi7LpM3E0zIMRPgp6N5PjhgBp6A/DO75kWx21SVQpPuYDUbBERARIoZPkDS91gCTR6Yw9nQaQSwRxeAVg9Tmn9sXIWNi/53O7HLS1RFLE6VoOIAAkeq+m8XtjQXEuq1A3mB3ZEREQVnSDmrpZRTMOGDcPSpUvh6+uLtm3bonLlykhOTkZsbCwSEhJQu3ZtLF68GO3btwcA1K9fH5cuXYJery/iyCV39+5dNGvWDN7e3ti/fz8qV65sdT/Tmp9xcXHw8/MDALwWrcGv/+oAadG9ALl7PQHAIIo4cl2HdDWwbIA3vBT8BL8iyNvjeevWLURFRVlcNwB7PHO7nWrAqF8zgJz5j8MfU+L7A2oIAIJ9JBABJKYb5/ZJBEBvMPZg1g+R4m66CLVWRLi/BNUDJfj7ig5+nsZeTakA1K7sOgFFq1GbezwBIOXebUwd0ASpqakW105Z2/+fFu2+TsM7nTwwq0chXbwO9u3f2XhjXSb2vOGLx2uX7TqirsT0t8rZ1w0REVFplerj7gULFiAuLg5//fUX/vjjDwiCAFEUIQgCxowZgxkzZsDLy/jGJj093Tzv0lFSUlLw9NNPQy6XY+fOnQWGztz8/Pwe/DGXpQNSjU3nyjvkViIIaFZNhr1XdHhzfSZ+6OtV5ksUkP3lDZSmIbYW142buHBHb+6dqhpg7K3K1Ii4eFePYB8J7mca8O3faiSoDKjkLaBFhAx/XdTi+/1qRIVKUT1QAnnOfMIMtYiUbGNVWIVMQKCnAIVMQK1KlufsVFcGQYBL/i7JFUrIFQ+uneyM8jHU9vsDxjA8rJVzPygxreUZn8oCQ0RERO6gVMFToVDgjz/+wNKlS7Ft2zZkZmaiTp06GDJkiHkYLgBMmjQJFy9exKuvvormzZuXutHWJCUloWvXrtBqtdi/fz/Cw8OLfQy9oXidv3nDp0ImoGk1KQ5c1eHSPQPne5LLEkUR8SoRogiE5BTrmbQxEwYAUgEY1VaJ305pcS/dAI0e5t7NIC8BDcOlqOovgVQioF0tOe5nGlA90PJ3wVspwNuGeX0SiesFzvIsMd2AX09o8ERdGR4Kce7rU5if8WfLtTyJiIjcQ6kn+EgkEgwbNgzDhg0rcJ9Zs2aV9jSFio+PR+fOneHh4YE9e/bY1NNpjb4EH7znDZ+VvCUI85fg421ZWNrf21wRk8hVGAwiRkRnIiFnqQupxPihikImoENtGWJv6vDdfjUqeUsQGSRFZCUJ1DrAUw5z8R4TH6UAH6VrfwCTew3fvEPsTfct7OOdb63ffNKcv47n8qNqqHXAyDbOHxZu6vFMSGOPJxERkTsoP/X9S+jatWt46qmnEBYWhi1btlgMf7xz5w4GDRqE5cuXIyQkpNDjGAwifj9tn9L+DUKl+PuKDq/+nIGvenqx2BCVe6Io4nS8HgYRSFOLuJNmQOuaMnjKBSRlGJCaLaJGoLGYT8vqMoiwDJlyJ2fLIkNfHqYAWdzHFbR/cY/jDBqdiG/2qlHFR8ALDZ27DjMAhPo+KC5EREREFZ9LB8/z58+jc+fOaNCgAdavX2+eT2qSlZWF7du3Iysrq8hjpWSVfLiXtfU9O9Yx9gyNXZuBJf29EeDF8EnOJYoidAZALhWQqRHxd84aiutOapGSKSJbJ+bsB0SFShHsY7xm8/ZYOnL4a1kFOFcIiva2+JAa15MN+KKHp0Oq+BaXp0KAv4fA4ElEROQmXDZ4njlzBk888QTu3buHwMBAPP744/n20WhsKxQEAKnZpZtnZG2+56PVZdj3nw5j12VicX9vKMvBmz1yT9laESOjM5CUIcJDLiBba/zXQ2ZcC7N6kARhvhIIApCtE1HZ274flBRrSCrZXZZGxLQdWQj1EzC6nYezm2MW5idwjicREZGbcNnguWPHDty7dw8AcOrUqVIfL0NT+jc/ecOnVCKgRXUZDlzVYczaTCxipVsqY6lZBsgkAt5cn4kMDdC0mgzpahFpahENw6TwsLLWpY8NRX8KU1iwZOh0ju8PqHE7VcQ3vbzK1VJPYf4SnLztuOW1iIiIqPxw2eA5fvx4jB8/3m7HU+vsc5y84dNbIaBZNSkOXNNh7xUdHq8tY/gkh9PoRIyIzkBiuvEDFakEaF1ThiA7DvlmiHQN6WoR0//KQkSABMMfc35RodzC/CSIuaSDRieWi+G/RERE5DguGzztTaNz3HCvSt4SNAyTYk5MNn49IcE3vdjzSfZ3L92AiRsyYRABjQ7QiyJa1ZBBZwC8FYC/Z8lCJwOma/tmbzbupYtY1Nez3A33NxUYupNmQESga1c/JiIiosLZJXjev38fABAUFFTkvufPn0f9+vXtcVq70tqxvkXeXk8AqBkkhYdMwPE4Hcatz8TXPRk+yb7e25wFnQGo4iNBplZEncpSBBQzbDJkViypWQZ8EZON2pUlGNSyfPV2ApZreUYEOrkxRERE5FB2GXd39OhRtG3bFnFxcYXu9/HHH2PMmDH2OKXd6ew8zcjaG/hQPwmaRchw7b4Bf/9np7G95PbS1SJO3dbhjsqAusFSRIVK0SJCZnPoHBGdYf6iimXu7mwkZ4qY2tUTcmn5+6DLtJYnK9sSERFVfHbp8ezSpQumTZuGTp06Ydu2bahTp06+fSZOnIhTp05h06ZN9jil3ekdMNLWWs9nmJ9x2O0XO7NRyUtAgzCOdqbiMxhEjF6biQSVAfqc9+z+ngJCfYsOFwyY7iEpw4Avd2fj4RAJ+jdz/rqd1piC5+1UBk8iIqKKzm6pp1evXvDx8UGXLl2wceNGNGjQwHzfqFGjkJCQgM2bN0OhKJ9vgDLtUNXWmoKG3aarRXywJQtLX/ZGINf4JBto9SLkUgF30wx4d1Mm7meKaBAqhbdCQICnUGCPFoOme/piVzbS1MCnz3hB6sC1V0ujWoDxte8mgycREVGFZ9futi5dumD58uXo0aMHoqOj0axZMwwaNAh6vR5r166FVFp+i0dsOGX7mp/FZS18RoVIcTf9/+zdeXhU1fkH8O+5984+2UNCAgm7CAiiVnDBfbdYd9Raa11arXXBpS79tQVbK1Vra+vS0lr3fcPduoFYt4oiiiggewiEQPbMPvee3x83E7YEsszMvTPz/TxPHshkMnNm5uTe8973nPdI/OrVEP6+w8+IdlTXauBnTwfg1gRCMYkCj8BBQ7VdXrRgwGm9bf/um+p9uPGB9DzvxhYDd/83jImDVJw2wZGeJ+2DROBZ08TAk4iIKNslfZ7nlClT8Oyzz2LatGkYPnw4hg0bhn/84x+2L6QTTfOSS0URGD1AxcLaONY26hhSbN+gnKw3440QvE6BQQUKijwCA/yiy78pBpvWmz3N1+XFpnT6zRshBKPAH77vgWLTbCcAeJ0CJT6BmmYGnkRERNkuJQsMx40bhyFDhuCDDz7ANddcY/ugEwBiKd7DvKuBaGWBwIZWBVe9EMSsqV6MGcjgk7bXEjJw7UshbGo1MHmIhrK8rjOcDDiTa8e/1W3f354ElFYGnV/WxvHA/yI4ZrSGE8bYN9uZUFWoMPAkIiLKAUlfXBgIBHD88cdjn332wWeffYbLLrsML7/8crKfJuniRur28UzYMTgQQmC/KhVVRQqufyWIL9az0i2ZdEOiIWDgF88F0R6W2L9656CT1WiTY8cgsaugcfY0X+eXnUkpce1LQQgAd56cGVs2VRUqWN9sQMrUH4OJiIjIOknNeLa0tOC4447Dcccdh5tvvhkA8Pbbb+P4449HMBjE2WefncynSyo9TRfcd8x8KkJgr4EqnKrAb98I4a5TvRhRysxnLtvYYuDqF4MIRCQcKjBluAN+1/YBBIPN5Ej8Ldo9oOypRxZE8e7yOH52oAvjKzOjYnZVkYJIHNjcLlHWg6rMRERElJmSNjLZsmULjjnmGJx77rm47rrrOm8fMWIE5s6di2OPPRbBYBAXXnhhsp4yaaSUSEPCs9OOwacQAnsMUBCMSlz7YhB3nsLgM1e1hiUufz6AfLfAxEoNPpeAS9s6GGfA2XOJtZbbfp/Napp0XPlCEIMKBG47yWN1c3qsKlFgqNnodio5ERERZb6kBJ4bNmzA0UcfjSuvvBKXXnrpTj+vqqrCvHnzOoPPyy+/PBlPmzRxC5YXdRV87j1IhQRw7YtB/OtsHwb4OQjLJaGoxOXPBeBUBfav0qCpDDj7YtuAM9uDzYS4LvHjxwNoDUs8+5M8FGbQFk1V21S23a/K4sYQERFRyiQl8Fy2bBlmzpyJadOmdXufgQMHYt68ebj99tuT8ZRJlc5s57a6mnY7sVLFhxEz8/nQD322rkhJyROISPzs6QBiOjBpCIPO3dldQJkrAWfCr14L4b0VcVx1qAvH7mn/gkLbqiramvEkIiKi7JWUwPOII47o0f1KSkpw2223JeMpkypd6zt7QlEE9h6k4b+rYrjkmSD+epoXXieDz2wW082gUzeAg4dp8HR83gw4t8q1QLI3nl0UxR1zwzh4mIY7TvZa3Zxe23aqLREREWWvzJmPlUJWZTyBroOLfLfA5CEamkMSlz4TgG5lAynlrpkTRCgmccBQBp0Adqoey6Cze9/U6bjgiXZU5As8+xM/HGrmXaQaVKBACHOqLREREWWvzCh7mGJWV/Hvao/PUp+CA4YI/HdVDO8uj2fc9DnaPd2QuO6lENY0Gti3SuvMbOdC0Gn3vTB7y5AScR1wdhSCCsckXBoQiQPtUQmvI/kBYUvIwKn/bkNUB567wI+Kgsy8jujUBMrzBDOeREREWY6Bp010FXzmuQUmVGq4+/0wXlocxV2neTMyo0Fdu3pOEOuaDEyoVFGZL7I+4MykQDJBSgndAIQAAlHzNlUAXqcZVDpVc3r85zU66loNHL2HAzUtBpZt0uF3CYRisnMqvzeavH16DUPi/McDWL7ZwL1neHHQsMy+MDWkSMGaRt3qZhAREVEKMfCEOai0g66Cz8GFCjRFw6LaOK56IYh7z8iMTeFp176t07G6wcA+gzUMLlQYdNqEISVWbDYQjEmUeM0sXENAQlEAY5uEnEMFYjpQ4BHYZ5CGulbzh+8sjwEAztrXiacXRjGoUMGdJ3tR22Lg+ieSN7Xi1nfCeOnrGH68vxM/P9iVtMe1yohSFf9bqyMck3CnIDtMRERE1suqwHPZsmW44IIL8PHHH+PBBx/ET37ykx79ntYxQ+2WEz349euh1DWwB7oKPgfmK9hHaPh0XRzvLo/jqD00Bp8Z7MvaOH79eghDixUMKsieTGcmBJctIQMtYYm4Yf7dl3gVtEcllmzUYUhAwiz2VOwVWLTBgEMBbj7BA0UAgzqK4ETjZiEcTQFmvRPCeyti8DgF/nKKF19uiGOvChXVRSpOHe+E19mxT2+Zir+d5sVLV/f/Nbz+TRS/fSOEfQer+MeZvqw4FgwvMd/bNY0G9iznHsZERETZKCsCT13Xceedd2LGjBkIh8O9/n2nCkAAUX3nwbMVQUFXwWd5noKJlSrufj+MF75SmPnMUG1hiZn/CWF4iYKx5SoufTZodZP6zMpAc3O7gcUbdYwpV1GRbwYtUkqs2GKgOSQxrFiBxylQ32ZgY6sB3QB0ab7/qgI4VIGYLqEb5vTOigIF5+/vRHNIYu9BZhZ6S7uZxSztYj/dRBD677N92NQmMahQgd8lMKjQ2Xkfn2v7v89kbI30xfo4fvhIAMVegRcu9HcWo8p0I0rMYHPlFp2BJxERUZbK+MCzoaEBJ554IlauXIkHHngAN910E9auXdurx1AUAY9DIBDdeSpcYnBth6xUVZEKlyawYF0c81fEcfiozF7XlYuueykIhyqwZ1nmBp3pDjijcYm2iEQkDrg1M4D8dF0cAsBnNXEMzFPQFpGIxiViBpDvEvh4jbmeUlWAinwFp+3thJTAyFIFw0oUCCGgGxI1TQa2BCT2HazuFBh2FXDuqNCroDBNO5gs3hDHsX9vQ0SXePmneRhSnD0B2vBS871e1cACQ0RERNkq4wPPmpoaVFVV4ZVXXkFZWRluuummPj1OsVdgY4uBYcVKlwV8th1spyMITTzHjoP8sjwF4ypU/Pm9MEYNUDszL2R/39Tp2NBiYPIQDZc9l1lBZzqCzXBMYnO7gQKPgsaggcagRFtYojXSMQd2G4MLzaz/13U67novjAE+gdP3dmFQoYKRpQqaQxKtYYlBBQq0bgpyqYrA0BIVQ0tS/tL67Z1lMZz+YDvCMYmXLvbj0BHZddEpkfFc1cACQ0RERNkqKYFnTU0NmpubMX78+GQ8XK+MHz8ezz33XL8f55YTPfjZ0wHM/S6GvSo0VOSJbqfGzZ7mszQDOqRIQV2bxDUvBvHgD32d23CQfUXiEjPeCKGqSMFv3rB2HXFPJCPQDMckGgISQgAuDdgSkFjfbGCvChUD/AJfbTDXVbo1oCUs0Rwy115C6lAV82LQTya7UFWoYFCBAr8LWN9sYHO7xMRBZoZyQqWGB37o3+m5i7wCRWnKRKaSlBJ/nR/BdS8Hke8WePuyvKwLOgGgIl/ApQErtzDjSURElK16FXjeeuut+OCDD7DffvvhV7/6FTweD/7whz9gxowZkFLioIMOwquvvoqCgoJUtXcnqpqc6WalfgWPn+fHdS8FsbAmDqcGjClXMbhA6TIATVcGtKv1nkII7DNIxQer4vjpUwHcc4YXRV5mPu1s/oo4DCkxttyekwySldEMRCUaAgaCUWBNo45YIo6QgFMD8t0Cn66Lw6kCugH4XQLNIeDc/Zwo8grsN1jD5oCBivyuZx5UFamoKkpKU20vEJH46dMBPLkwijHlCuZcmIfRWbr+UVEEhhUrnGpLRESUxXo8Cp4xYwbuv/9+VFdX4/7778fLL7+Mhx9+GP/+979x+eWXw+l04r333sONN96Iv//976lsc8p4nAL3nulDS8jAb14P4asNOlZsNrBflYoCT/eBXarXgXYVfLo0gQOGavhsXRwXPxXAvWf4MDCfwacd1bUauPeDMMaUq7jiBXtNse1PwKkbEo1BCUOaaynr2wysbDA69rkUGFyo4M5TvBAAgjEgzwVICSytN7AlYGBkqYrKgp37bLUzO4Or3qhp0jH1X+34aoOOM/Z24IFz/MhzZ/fMhhGlKuZ+F4NhyKQUYiIiIiJ76XHguXnzZtTU1EBRzIHinDlzMH36dHz55ZfIy8sDYE4L6+kWJnbR2tq63fculwsFHhf+droPzUEDV78YxEdr4hhWrGLUAAXqLgZEqQxAuwo+fU6Bg4dpWFATx2XPBnDnKV4MK+GgPZkikQgikUjn921tbQC67jcu1877KcZ0c0p0kUfgtnd7X3E52foTaMZ0s2qsKszM5dJ6HW3hrYsvXZrAb4/zYL8uCvXkd3RLIYCxA1UA2d9Pu+s7u/N5TRwn/asNdW0Sf5zqwfVHuXOigvXoMhWvfRPDuiYDQ3kcIyIiyjo9Djyrqqo6g04AOPXUU7Fq1arOoBMwp4BWV1cnt4UpVlVVtd33M2bMwMyZMwGYFSv/dZYPH6yK46/vhxGImtUvdzcITNUa0K6CT00VmFSt4csNOq56IYg/fN+D8ZX2nM6ZiWbNmoWbb755p9t31W+2de2LQYRjEk8ujKWqibvV22BTN8wKsi7NXFPZGjHXarZHJBwqYEggrgMD/AJ3n+tDnksgqgO+jj0rydRd39mVV76O4uxH2iEl8PwFfpw6wbn7X8oSY8rN88u3m3QGnkRERFmoxxFKJBLBM888gwULFuCOO+4AYAafCa+88gqKi4vh8XiS38oUqqmpQX5+fuf3O2atNFXg8FEODClWcNULQXy6zlz7mb+baW+pWgPaVfCpKuaaT7cG/N9rIfztNC8Hbkly00034Zprrun8vra2FmPHjt1tvwGApqCBNY0GJlSqmLPYmsBzV0FnTDcrv8Z0M5gMx82Ac2OrgUBEwqmZ6zDz3QIXTHZhYJ7AyFIVhgSiutxuXbGT1zp20l3f6YqUEvd+EMFVLwRR6hN45ad5mDQkt97UMR3rV7/dpOOErt8mIiIiymA9Htn8/Oc/xyWXXAKHY2tFxeHDh3f+/6KLLoKiKHjooYeS2sBUy8/P3y6A6M6wEhX3nOHFTa+EMH9FDMNKFIwbuPvsJ5D8ILS7gkNjylW0R4EbXw3h0R/5uizOQr2z4xTaxBTbnvSbX70agtcp8Ie30zvFdlfBpiElWkISG1slVjfqMLap5eLUAI9DoNAjcMNRbjQEJCYP0bpcW+gD+9budNd3thWMSnxeE8ef3wvjxcUx7DFAwRuX5GF4ae5dONqzM/BkgSEiIqJs1OPAs7y8HC+++GK3P3/xxRfh8/mw9957J6NdtlRdpOKx83xYvFHHr18LwaEK7DFA6dX0wmRNw+0u+BxfoWL+ilhHtVsf/C4GCFZoDhqobTHwypL0ZDq7CzYNaa7BbI+Y02ZrWwyEY2Y286ajPRg7UIXfCRZzscD9H4dxzYtBtHUsAz1nXyf+fqZ3l4XMslmJT8EAv8C3m7iXJxERUTZK2lyugw46KFkPZWtCmHsH/uY4D373nxDWNhoYN1DFoMKeDxZTWYTI4xA4aJgDn62L44InArjvTC8G+HNzIGsVKSWufSkEXxr2V+0u4JRSYmWDgZVbdETj5m0ep8BlU1wYX6GixCe4HtNCry2J4qdPBzGsRMGvjnHhmNEO7FeVW1NruzKmXMXXG3VIKdk/iYiIskzSRjp1dXVYs2YNGhsb0dLSArfbjaKiIowePRoVFRXJehrb2L9awwM/9OF3/wlh4fo4mkI9n3qb0N/sZ1dZT8Bck3fICA2fro3jsmeDeOAcX9ZvxWAnm9ok6tsMvPpN6rKdXX3um9sNrG4wIAHEdYnmkMSVh7lRma8gzy0wqIDBph1IKfGbN0Lwu4APrszvckuZXDWmXMX7K+PY3C5Rlse+SkRElE36FXjGYjH85S9/wf3334+VK1d2e79hw4bhwgsvxC9/+cvt1ohmugF+BXef4cOSjXH86tUQdAMYX6lC6WXwCfQ9+9ld8OlQBSYN0fDflXFc+UIA/z7bx+mUaTLjjVDKpjjv+FnrhsR3mw3UNJtTaEv9Ai5NIBoH7jjZiz3Kcm+toN2tajDwxXqJnx/sYtC5g/EVZn/9aoOOo0fzvSEiIsomfQ48W1tbcdRRR+Hzzz9HWVkZDjvsMOTl5SEvLw9OpxPt7e0IBAJoaGjAkiVL8Otf/xrPPvss3n77bZSWlibzNaCxsRHRaBQAoOvm+qCWlhbU1dUBAIqLi+F0pm5bgnEVGv50ihe/fCmIYExifIXW68AjFdNvHarAflUqPlwdx8+fDeKvp3nhdjD4TKW2sMSGVgOvJnlt57YBZ1PQQHNIYl2TgdawhKoAVx/uxohSFYN7MeWbrPHO8hgADd8fmz0X4ZJln8Fm4LlwfRxHj+b7Q0RElE36HHjOmDEDXq8XH374IQ488MBd3lfXdbzyyiuYOXMmbrrpJvzrX//q69N26bTTTsP8+fO3u2369OmYPn06AGDevHk4/PDDk/qcOxo1QMV9Z/pw3UtBzFsRw76DtF6t+0zoy/Tb7rKeAFDgUbB/tYaF6+M477EA/nWWF4VeBiep8k2djmQWE058roaUqG+TWNWgoyFg7qdZlqfg18d6MLhQ4VTqDDJ/RQyaouGwkQysdrR3pQYhgIXrWWCIiIgo2/Q58Pzggw/wwQcfdLl/4Y5UVcUpp5yCI444YrdBal+89957SX/MvqgsUPDYj3y4+sUgvqg1132OLFV7nWVMdvZzgF/BESMd+GhNHNPnBPHgD31c65cCwajEbe+G8NLXycl2JvrBxlYDSzbqCMUkyvIU3HOGB9VFvaumTPaxrF7HHmUqK053wecS2LNMwRfr41Y3hYiIiJKsz4Gn3+/vUdC5rYKCApSUlPT1KTOCogjcdaoXH66O42/zI9jUFsdBwzR4+jDFtTf7f+4q6wkATk1g4iAV/10Vx7zv4jh8pMY1n0n27SYdhuz/48ye5kN7RGJRbRz1bRKRuERVkYLfn+hFiY/Z6ky3ptHA96v5OXZnn8Eanvg8itawRD4z+URERFmjz6OfxsZGrF27tle/s2LFii43Uc82QghMGe7AQ+f6oAjgvyvj2NLev03RZ0/z7TKw7IlCj4LxFSr+Oj+Mi58OIhpPQpREAADDkLhzXrhf2c7Z03y453QvvtwQx7wVMTQFJa4+3IV7zvDivjN9DDqzRFw3p+ZT1/btWOe5qJZZTyIiomzS55HsD3/4Q0yePBm33347li9fvsv7btiwAX/7298wZcoUnHbaaX19yozjdQo8dK4PJT6BT9bGsbrB3J+uP/obfA4tVnHwcA0tIQOXPReEkYwUHWHFFgNt4b6/l7On+VDXamDed3FsapW49fsePPFjHw4e7sCQYgYp2WbUAF5E6M7+1eZEnE/WMPAkIiLKJn2eanv99ddj48aNuOmmm3DTTTdBVVX4fD7k5eXB7XYDAAzDQFNTE5qbmyGlxOmnn45f//rXSWt8JnBpAv+Y5sUHq+K4fW4YwZjEuIH92z512zWgfQlECzsKDn3csebzr6d5uV6wn1Y16H3et/Pu07z4akMcaxsNVBUpuPNkLzxOfh7ZjBnP7k2q1uBUgfdXxnH9UVa3hoiIiJKlzxGQEAJ33XUXzjnnHDz44IOYP38+Vq1ahZaWls77+Hw+DBs2DD/96U9x2mmnYdKkSUlpdKYRQuCQEQ4UewVufCUEl6ZjREn/i8P0J/tZ7FWwzyANn6+P44NVcRwyghU2+yoSl7j/42iffnf6YW68szwGVQFu+4EXYwcyIMkFDDy753aYexB/uDoOw5Bci05ERJQl+pd6AzB58mRMnjwZgLltSktLC0KhEIqKiuD1evvdwGwyrkLD/x3rwR/eDqElJLHPINXSQVVlgYKoruL2d8N4ZlEUd53qhcpBXq8tWBfH81/1PvA8c28n1jbquP4oN75XpcGp8b3PBW4HUJnPz3pXDhmu4YNVcXxdp2NCZb9PU0RERGQDSV1opKoqiouLMWjQIAad3ThgqIa/nebFpjYDX23U0RIyYPRz3Wd/DC1WsV+VhpomA1c+H+z3GtRc9OD/ehd0XnO4Gyfv5YDfLfDoj/w4aJiDQWcOGV5i7QWnTHDoCDPYnL+C6zyJiIiyRdorXPztb39L91PazrASFbed5MWGFgPvr4zjo9Vx6BYW+aksULBflYZ1zQbmfhdn8NkLrWGJhkDPKxafsbcTaxp1XH24G/ef5YWPeznmnBGlLCy0O1OGO+BUgTe+Tc6euERERGS9tI+AHn/88XQ/pS2NLlfxzE/8uOcML1rDEkvq+l/xtj/K8xTsXWlutfLfVcwy9NRNrwZ7vIXKGXs74dKAx8/z47CRDma9ctSIUq7v3B2/S+DIUQ68uzyG9ggvhBEREWWDtAaeuq5jxYoV6XxKW3NqAkOKVcya6sW6JgOLanU0Bg3LAtDqIhVjylXcOTeM+rb+7TuaK5qCPfusTt7LgTy3wP1n++B2MODMZQw8e+YHezkQ1YG3ljLrSURElA36XLXhhBNOQCQS6dXvJLZWoe2NGajiz6d4ccMrQaxvNlBZoGDfwaolW5wMK1FQ12qu9/zn2T7kuxkkdac5aODJhbtf3zl1nANVRQr+eqqXWU7CiBJOte2Jk/Zy4rLngnjhqyhO29tpdXOIiIion/oceI4cORL33ntvr3+P+0V2beQAFc/+xI9VDQaueTGI5ZsFRpelPzOiCIHvdezx+ePH2nHVYW4cPlLj59aFG14J7fY+U8c5cPVhbhwxiu8hmUaUMOPZE4MLFUwZruGFr6JoDUteBCMiIspwfb70fvHFF+Pmm2+GYRg9/tq4cSNcLlcy259VFEVg5AAVvz3Og+X1Oj5eE0M4lv5pty5NYMowDXuUqfjLe2F8tJprPruyuX3X05GnjmXQSTsr51YqPXbhZBdCMeCphb2bXUNERET20+fAc/z48fjkk0969Tvl5eUYNWpUX58yZ3yvWsO9Z3oRjgEfr4kjEk9/8KmpAiNLVYwuU3HH3DAae1G5NRc0BgzMWdz92rOp4xwYOUDFkXs4GHTSdtgfeu7MiU74XcDsjyKstk1ERJTh+hx4KkrffnXixIl9fcqcUl2k4p9n+SClGXxakfkEzPVofpfAL54L9mrbkGw3441dT7MdXKDgz6d40tQaouzkdwlcNNmFhet1FhkiIiLKcP2qcvHaa6/1+ncefvjh/jxlTslzC/zzbDP4fG9FbLdTO1NBUQQmVWtQFeDCJwL4eDUHfwDwyILup/5dMMmJe87wMrNFlAS/PNIDpwr87q0ws55EREQZjOUVbS7fLfDwuT6U5Sn4dG0cNc162tvgdghMGa5hdJmKW98O4/2VDD67c8beTtx9ug8qq9cSJcWgQgU/O8iFj1bHe1RFmoiIiOyJgWcGcGoC953hxWVTXPhqg47WcPqv+ivCLHw0oVLFHe+G8ckaFhza0dRxDvz+RA98LgadRMn0uxM8KPMLXD0naMnMDyIiIuo/Bp4ZQlEEjhntQJ5LYP6KGD5cFUNjwIBhpDcIHVKsYuxAFX94K4T/MvO5nSsOcWPPcm6VQZRsRV4Fd5/uRX27xDmPtCOuc8otERFRpmHgmUEUReDBH/pwx8lexA3gw9VxzF2R/qq3I0pVjK9UcTszn51OHe/A96oYdBKlyrR9XLj8EBfeXR7HRU8FoKf5ohsRERH1DwPPDONQBfYsV/HIj3yYfZYPAsDC9XHE0pwBGFqsYsxAFX94O4S1jelfd2o3NxzlQbGPf05EqfTnU7w4ZbwDjyyI4txHAwhEGHwSERFlCo6UM5SqCFQWKLjrVC9awxLvLI/h641xbGw1OjMBsWgEr/z794hFU7P5+ogSBWV+BTe9Gkr7lN+ESCSCmTNnIhKxboP5M/Z2YszA7Ml22uE9TbVceI27YrfX39P2OFSBp8/348yJTjz9RRQH3tWKReuTO+vCbu8NERFRthAyR+vTt7a2oqCgAC0tLcjPz7e6Of0SjEosqo3j7vcjaI9KeBwCQ4sVDHQFcO3xZbjrrXp4fKl5je0RiXnfxfDnU70YNSD9wVe6P8f169ejqqoKuHQ14DKfb+F1+dhnsJby506XbPrb6I4VrzHRd+zwvtrtM+5te6SUuGNuGL96LQQpgQsnu3DN4e6kXADK9PeGiIjIrrJntJzDvE6Bg4Y5cNAwB1pCBj5ZE8ffP4xghSGA0WfgqzpgWJkBhwrkuQQ0NTlVV6WUnVN8073O1E4q8jlxgCidhBC4/igPThjjwPQ5Qdz/SQT3fxLBhEoVJ45x4ODhGvauVDG4UOF+ukRERDbBwDPLFHgUHDfGiQOHOfDOEh1zYkEEowL/W2tOR1MVoNAj4HUKqMLco1NTgDK/Aq/TfAwpzUJGXTGkRFwH1jYZWNNoIByTKPEJS7KddnDxAS6U53FgS2SF8ZUa3rksD/9bq+NfH4fx+rcx/PHdMPCu+fMir8CQIgWDCxUMKlBQ6hMo9CS+lM7/F3gEXBqgCoFQUALuIjSHJBSXhKYAmmIeOxnEEhER9V3OBp66bhbEaW1ttbglqTOpvB1Y9TruOC4M4XQgpkusazKwodXAa0tiiBsS0TgQ1SViOqB0JO4MY2tAqiqAIoCoDoSiEomlnKoCXHqwGyNKFAwqVBAJtsGKFVGJzy9dn2Nzc7P5n2gbAOCGQwrR1taWludOl3S/p1aw4jUm+o4d3le7fcb9bc/YYuAv3wfuPEHBkjodi2p1LN4Qx5I6HRtaDLxdYyDSm92ffrIQQ2Y2AWja7mYhtgah5r8CndfoBCA67tPx7db/d/xs2/8ngliBbX6W+Pk2j6WHWgAAjY2NvXgBlOuklGhra0NlZSUUhbNyiMgecnaN54IFCzBp0iSrm0FERESUEjU1NRg8eLDVzSAiApDDGc+RI0cCAJYsWZK1BRva2towduxYfPPNN8jLy7O6OSmR7tdYU1ODgw46iP0mw1nxGu3Ud+z2GdupPXZqCwDU1tbigAMOQE1NjeX9hjJHa2srqqqqbNGHiYgScjbwVFVzTeLgwYOz9mSemLY2aNAgvsYkY7/JbFa+Rjv0Hbt9xnZqj53asq38/HxbtYcyA9clE5GdcOI/ERERERERpRQDTyIiIiIiIkopBp5ZzOVyYcaMGXC5XFY3JWVy4TWmWy68p7nwGnfFbq/fTu2xU1uIiIiySc5WtW1tbUVBQQFaWlq4boZ6bP369aiqqmK/oV5j36G+yIZ+82VtHBc+GcDKLQauPNSFm0/wcO1hinGMQ0R2xIwnERERpcSGFgNH39eGJXU6Sv0Cv38rjLvft2LXZyIishoDTyIiIkqJ618OYktA4unz/fjylwUYWargN2+E0BgwrG4aERGlGQNPIiIiSrqlm3Q8uTCKE8Y4cPJ4J3wugd+d4EFrWOL+T5j1JCLKNQw8iYiIKOluezcEQwIzj/d03nbGRCfK8wQeWRC1sGVERGQFBp5ERESUVK1hiae/iGLKcA2ThmidtztUgZP3cmJJnY6VW3QLW0hEROnGwJOIiIiS6pkvIgjFgAsm7bwtzSnjHQCAlxYz60lElEsYeBIREVFSPbIgCq8TOHOic6efHbmHA34X8NLXMQtaRkREVmHgSUREREmzqc3AB6vj+ME4J/LcO+/X6dIEjhzlwMdr4ghFc3IrcSKinMTAk4iIiJLmpcVRSAmcOsHR7X0OHqYhpgOf1cTT2DIiIrISA08iIiJKmjlfxeDSgBPG7DzNNmHKcLPg0IerGXgSEeUKBp5ERESUFIGIxNzvYjhqD0eX02wT9qvS4NIYeBIR5RIGnkRERJQU76+MIaoDx+/Z/TRbwFzn+b0qDR+ujkNKrvMkIsoFDDyJiIgoKd5aZlaqPWb0rgNPAJg8RENTUGJ1g5HqZhERkQ0w8CQiIqKkeGtZHFWFCkaX7X54MXGQCgBYVKunullERGQDDDyJiIio32qbDXxTp+PYPR0Qovv1nQlbA0+u8yQiygUZHXiuXLkSl19+OcaMGQOv1wun04mqqiqcffbZ+OSTT6xuHhERUc54OzHNdg+tR/ffs1yFS2PGk4goV2Rs4Dl//nzsvffemD17Ns466yx89NFH+Oabb3DnnXfis88+w0EHHYTZs2db3UwiIqKc8PbyGIQAjtpj9+s7AcChCuxVoeILBp5ERDkhYwPP6dOnIxAI4LrrrsPMmTMxceJEjBw5EtOmTcOrr74KKSWuuuoq1NfXW91UIiKirCalxPwVMYyvUFHq7/nQYuIgDeubDWxpZ4EhIqJsl7GB57JlywAAU6ZM2elne+65J0pKShCJRPDBBx+ku2lEREQ5ZU2jgdoWiUNH9GyabcL4CnOd5zebmPUkIsp2GRt4Tpw4EQDw9ddf7/SzjRs3orGxEQDg8XjS2SwiIqKc89+VZoGgQ4b3LvAcU24Gnt/WMfAkIsp2GRt4/uMf/0BVVRV+//vf47HHHkMgEICu6/jiiy9w2mmnQUqJkpISHHDAAVY3lYiIKKu9vyoRePZsfWfCnuXmMGRpPQNPIqJs17tLkzYyYcIELF26FPfccw8uv/xynHfeeVBVFbpunrzKysrwxBNPoKioaJeP09raut33LpcLLpcrZe2mzBKJRBCJRDq/b2trA8B+Q7vHvkN90V2/sbv/roxhZKmCioLeXc8eXKDA5wSWbuIaTyKibJexGc/m5macd955uPHGG3Heeefh448/xqJFi/DPf/4Tl1xyCT766CMcddRRu32cqqoqFBQUdH7NmjUrDa2nTDFr1qzt+sfYsWMBsN/Q7rHvUF9012/srK7VwPLNRq/XdwKAogiMLlPxLdd4EhFlPSGllFY3oreklDjooIPwySef4Morr8Rf//rX7X7+6aef4ogjjsCsWbNw5ZVXdvkYra2tKCgoQE1NDfLz8ztvZ/aBtrVj9qG2thZjx45lv6HdYt+hvuiu37S0tGzXb+zkuUVRnPlQOx48x4efTO59Xz730XY88XkUgduL4HWKFLQw9yTGOHbuN0SUezJyqu3cuXPxySefAABuuOGGnX4+adIkHHjggbjqqqtQVlaGs88+u9vHys/P50GZurVjUJCYJsl+Q7vDvkN90V2/sbP3V8YAoE8ZTwDYs8wsMLS8XsfEwRk5LCEioh7IyKm2n3/+OQBzAFdZWdnlfRLTk/75z3+mrV1ERES55v2VcVQWCAwr6duQorOyLafbEhFltYwMPBOE6H5KjqKYL622tjZdzSEiIsoprWGJrzbqmDLMsctz8q6wsi0RUW7IyMBz9OjRAICWlhZs2rSpy/ssXboUADBw4MC0tYuIiCiXfLo2DimBg4b1fYrsqAEqFMHKtkRE2S4jA89jjjmmM6D8y1/+stPPFy9ejLfffhsAcMYZZ6S1bURERLnik7Xm/p0HDu174OnSBIaXKJxqS0SU5TIy8PR6vXjiiSfg9/txxx134P/+7/+wZMkSrF27Fk899RS+//3vwzAMnHTSSfj5z39udXOJiIiy0sdr4nBpwMRBar8eZ9QAFSu26MjAQvtERNRDGRl4AsARRxyBxYsX4/LLL8ecOXOw//77Y+TIkZg+fTrGjRuHxx57DC+99BI0jRXyiIiIkk1KiU/WxLFflQan1r9tUEaWKgjFgI2tDDyJiLJVRkdlQ4cO3WkPTyIiIkq97zYbaAzKfk2zTRhRamZMV27RUVmQsdfEiYhoF3h0JyIiol77eI25vvOAIckIPM3hyMotLDBERJStGHgSERFRryUCz6RkPEvMjOeKLSwwRESUrRh4EhERUa99siaOqkIFgwr7P5QYVqJACGY8iYiyGQNPIiIi6pW2sMTijToOSEK2EwDcDoHBBQpWNjDjSUSUrRh4EhERUa8sWBeHIZMzzTZhRKnCjCcRURZj4ElERES98sna5K3vTBhRqqIxKNEUZPBJRJSNGHh2Y0OLge82c8oPERHRjj5eE4dTBfYZrCbtMUeUsLItEVE2Y+DZjetfDuKaOUGs4XoTIiKiTlJKfLImjn0Hq3BpImmPu3VLFZ53iYiyEQPPbkTNWUSY9U7Y2oYQERHZyMotBrYEZFKn2QLAyFIze7qygRlPIqJsxMCzC8GoRCgmMahQwcZWAw0BngSJiIiAbfbvHOZI6uMy40lElN0YeHZhc7sZaI4pV+FzCtzwcghxXVrcKiIiklLiy9o4PlgVg2HwuGyFROB5wJDkre8EgAKPghKfwAqu8SQiykoMPLvQFpEQAnBrwLiBKjYHDFz5QtDqZhER5TQpJS5/PohfvxbCbe+E0Rxi4GmFT9bGMahAoKoouYEnYBYYYsaTiCg7MfDsQltYwqUJCCFQlqdgn0EaapoM1LfxKiwRkRWklLji+SDWNxko8Qk4NaDIm7zCNtQzwajEVxt0HJDk9Z0JI0pV1LZIhKK8qEBElG0YeHahLWJmOxMq8gXy3AK/fDkInVO7iIjSSkqJd5bHsbbJwH5VGhRFoNSnQAgGnun2xfo4dAOYVJ2awHNkxzrPVawoT0SUdRh4duGFr6JwO7YOaIQQ2HewhqagxJI6ngyJiNJFNyR+9nQQ97wfxsRBKkp8Ag3tBi6b4rK6aTnp03XmOTBVgeeIjsq2q1jZlogo6zDw7EIoJrfLeAJAvlugskDBrW+F0RzkCZGIKNWicYlLnwliS8DAgcM0VBWqaApKGHLr1huUXgvWxSEEsF9VagLP4SWJyrY8zxIRZZuUBp5fffVVUu+XLpE44HLsPIVrTJkKXQI/fjyAj1fHICWn3RIRpUIgInHBEwE0BAxMrtZQ7DVPVxvbDOS7BXwuTrO1wqfr4hhTriLPnZr3f0TnXp6cXURElG1SGnjec889Pbrfvffem8pm9FokvnPGEwA8ToGj9tCwZ7mKW98J4/LngiznT0SUZM1BAxc/FYBuAIeOcKDYZ56qdEOittnAlYe6LW5hbmoIGFi5xcCk6tRlmyvyBTwOZjyJiLJRaubKdHj++ecRj8d3e7+XX34Zs2fPTmVTeiymS8R1wKV1fTVXEQIjS1X4nQIL18dxzYsh3HWaN82tJCLKPnFd4pcvh7CqQYfPKXDgUG279fab2iR0CYyr4DRbK3y2zjyf75+i9Z2AWVNheInKLVWIiLJQSgPPlpYWzJs3r0f3s4tQzPzXsZtxzcB8BaPLVCyr1xGMSnidnPZFRNRX4ZjEJc8E0BKSGFuuorpIgapsf1xd16Sj3K/Az2m2lkh1YaGEEaUK3vg2Bt2QO/UBIiLKXCk9e5x55pl48sknd3u/c845J5XN6JVwzJw661R3f7IbUqRg5RYDV70QxB++78EAv2B5fyKiXmoKGrji+SAicWDKcAfyu1g/GI5JbA5I3H6Sx4IWEmCu73SqwITK1GacR5QoiOnA+mYDQ4qZ3SYiyhYpDTxPP/30Ht3vjDPOSGUzeiUcNwNPrQerXzVVYN/BKj5fH8dFTwbgdgjMPN6NcRWpvRpMtCMpJcIxoCUs0RKSaI1ItIUlwnGJmA4YEhAAXBrgdQr4XQJ5HV/5bgGvE7xoQpb4pk7Hr14Ndk6t7S6bua7JgFMFRg1gMXYrSCmxYF0cEwepcHazFCVZOgsMbWHgSUSUTVIaIQ0bNqxH9xs6dGgqm9Er4R5OtU0o9Ss4ZrQDbWGJtU0GbnwlhD9M9WBCJYNP2j0pJVrDEs0hibaIRCBiXvyI6uZ6N12aQaPs+Fc3gDeXxmBIIG6Y65FjOhDRJYxtanE4NcCtCThUQFUEhDAfI25IRONmAa3YNkuoFAE4VPP+5u8AmiJw4lgH3Jq55tmlAR6H+a9TE1AFOh9X72ibbphbXUiYga6qCDhV8/d8TsDvEtB6MJuAsl8wKvHR6jj+Oj+MoSUKxpar3U6rlFJiXZOBSw92s/9YpKbZwKY2iTMnpv7cNqK0Y0uVBh1HwpHy5yMiovRI6Rnkvvvuw7///e+k3S8dAlEJVQEuey6I2dN8PfodRQgUeAQmeBRoio7fvBbC7LN8GJjPK/O0lWFIbGqTqGk28NCnEbSGJdojEnpHwCiEOcXboZoZd0UAimIGcEKYwaNAIkgEvIqA1hHYJQJNj8OsCNmTdVFxXSIcN6cwRuJAVDeD0bghETPMQPK1JTHEEgGuYf5OzIAZWXZDETAbCmwXDCc4OwJYj0PgtAnmtMo8t4DfKToDW00VUEQi2DYD8UhcIhQ199lNtDdRVFpTzODY4zAzul7n1vdCU5jNtZOYLvH+yjjufj8MRZjTNneX1apvlwjFJPYZxOyXVRakaX0nsDXjuYqVbYmIskpKzyBPPfUUVq9evdv7ffrpp7YJPFvDEp6OKoqXPBPocfCZMKZcQWPQwJUvBHHVoS6MKVc7twKg3BOJS6zYrONv70ewud1ATDeDxgKPQKlPYGixAn9HoOTS0hsgaaqAX0WvC7VIaQZ8iUysEGawmQiMt30NUkrEjY6sbNwMGEMxiVDM/PfJhVGE40AktjWI3B1VMbOziYwrYGZcuwuKFQFoHZlfh5LI5pqvf+o4B9wd2Vy3JuBxovP7RCDrcZgXAxi89l9jwMAvngsiEpcYXaZiSJHSowzmmkYDA/wCpX4eS63y6drUV7RNGFKkQBHAygYGnkRE2SSlZxApJaTcfhS4atUqDB8+PJVP2y9tEYmnvoj2+feFENhnkIbPauL483thxA3gpqM9GF+hcsPzHGEYEss3G/jT3DA2txswAJT6BPYoU1HqU5DnyuwgRggz6OtJ7kmIrdN3d1X5ORGgJqYYJ4JadAS1miKgKeicOryrx4np5uMkMriJLG68I4ub+H9cB15cHDMD1o77xQzZZZZWUbbPRmsdge9xezo6vjd/7urM5pqvNxHEagrQHsndPX/r2wz8/s0Q1jUZyHMLHDbCAU8PK4EHoxL17QZuP4nbVlnp03Vx5LsF9kjDGlunJlBdpHBLFSKiLJPSwPOss87Cgw8+uN1tN910E2bNmrXdbRdddFGfHv+hhx7CBRdcsMv7PPvss70qXvTK17Htvu9L1tPnEjhspAOGlFhWb+APb4XgdQr89jg3RpSq2+1LR9lleb2OGW+E0B6VGOBXMGGQinK/kvJiHJlu2wC1c55uHx/HqZnTefv6OLqRmHJsTguNdgSlWwNZM3A1JPDWshgMA9ClOWV62/vtcM0NoaZAn19XJorrEkvqdNw5L4ymoITPKTCuQkV1oQJlN1PBL3lm63t11aEuuDWB0WXMdlpFNyQ+r4lj/2ptt59dsowoVfDZOh1Syoy+UEdERFulNPC84oordrqtqxPIL37xiz4/h8fjQXV1dbc/z8/P79XjRfWdsxJ9CT4Bc+3nmHIVw4oVfF4Tx42vhKAqwPTD3Dh4uAYHi2RkjY0tBv7vtSA2t0tUFijYv7r76pxkb6oioHbGOH37DLedYhzvKLrUvFnDu0lrpX3phsT/1sZx57wwYjpQka9gz2EqSrw9225q26ATMKvZXn24O20BD+1sWb2Btkh61ncmjChR8e7yOBqDEiU+fvZERNkgpWeRkpKSnW7bceptd/frqUmTJuG9997r8+/vSE/BkhK3Q+Dg4Q7EdIm1jQbumh/G394HqotU/PkUDwdUGSyuS1zzYhCrGw0UeQQOHKahlGt6c972GVwAEIAnu/uFlBLL6w3M/E8IwZjE8BLzoltvZnjsGHT+3zFuLN6gY9/BrBJupQXr0re+M2F4SUdl2y0GSnhMJSLKCik9mu84pRboOuP5xz/+MZXN6JV4N4HnjgOivnCoAiMHqDhqDwdGlqpY1aDj6zquYclUG1sMnP94ADXNBiZWqjiYQSflqNpmA+c9FsB1LwXhdwkcPtKBMeW9W1bQ1TF2TaOBygKF6+Mt9sV6M/Dcryp9VYW37uXJcyQRUbZI6eXLZ555BtHo9oV6Pv/8c9TV1W1326uvvoq///3vqWxKj3UXeAJ9n3K7I5dmBqDtUYmb/xPC1Ye7MbZcRVEPp6KR9a5/OYDakECxR+DQEQ74elgohSjbfLE+jhlvhFDiM9e257t797ewq4t6LSGJP7KokOW+qNVR5DUL/qTL1r08WdmWiChbpDTwbG1txbx583a6fcfbmpqa+vwcbW1tuOWWW/DSSy9h3bp1UFUVI0eOxMknn4xLLrkEfr+/x48lpUQkvuvKk8kKPgFg3EAVMR34y3thRONAvlvgdyd6MKigd9PTKP1qmw1MHKZicKHCiwWUc0JRiY2tBu6cF8a6JgN7lKnYY0Dv/xZ2FXT+7EAXglGJwYWcRWAlKSUW1erYr0pN67GOGU8iouyT0sBz2rRpeOKJJ3Z7v3PPPbfPz7Fw4UKUlJTgt7/9LUaMGIH6+no89NBDuO666zB79my8+eabGDZsWLe/39ra2vn/YFQiFtv91dXEYKm/AahDFdi/WoNhSDQGJb7dpGP6C0E4NfOxuWed9SKRCCKRSOf3bW1tAIB9B0ZQ7AwgHDRv1xwuOJwuK5pINhWLRhCPbe074aDZd7Y95gCAy+WCy5UZfactLHHDK0GsbzYgpbl1zH5VGioLen+s2t3yhbpWA7870dPXpmas7o45VlndYKA1LDGxMn3TbAHzQmypT2DlFmY8iYiyRUoDzx//+Mc9ut95553Xp8cfM2YM/vjHP+L666/vvBI7duxYHH744XC73Zg9ezZOOeUUfPHFF1CUrgdGVVVVW7/xlGDU+Q8DrgN79PzJyn4qikCpX2CKTyAYM6euXfxUAKU+BZcf4sJEFtawzKxZs3DzzTfvdPvvzhyx3fdTL/w/nHTRb9LVLMoA/3n0drz6wB92un27Yw6AGTNmYObMmWlqVd9E4mal2j/PC8OlCexdqaIsT4GrD9sE9XS9vKaas0JyTXfHHKssqjUzjvtYcB4aUapgVQMznkRE2ULIrsrMpsG3336LMWPGpOzxN27ciKqqKui6jldeeQVTp07d7uetra0oKChATU1N55Yr65olxv8pAii9O8Ema+ptQjgm8fGaOBQBtIbN7TlOHOvA5CEaBuYzC5pOO2YfamtrMXbsWPxxzkq4fXmdtzPjSTvaMePZvHkDZp47cbtjDmDvjGdT0MBv3wihttmAhDn9cUSJArUPlbh7U6DtlPEOXHO4G4eMcPT6eTJdd8eclpaWXm8Plgy/eT2IW94K46vr8zG+Mr3B57mPtuOJz6MI3l4ED9fR90pijGNVvyEi6oplqbTLL78cs2fPxsiRI1Py+BUVFRg0aBDWrVuHDz74YKfAMyE/P7/zoOwM64DS+6uryZp6m+B2CBwxyhxwrW82sLpBx5MLo3jgfxEUewUumOzC5CFan7IN1Ds7BgWJaZJuXx48vv6dzJNRKTlZkn3xhACHc/uLEeGAOWVy22OOHemGRE2TgdvnhrG+2YDHYRZDqy5U4OzjMacvfX3SkNyc6dHdMccqi2p1uDRgz/L0Z59HdGypsrrRwNgczH4TEWUbS87sa9euRTAYxA033IDnn38+Zc9TUVGBdevWobGxsUf3j+r9S/4ms/BQwuBCBYMLFRiGxOpGA20RibveC8PjEPjnWT5uM5AB7BRgdicVbWQwm1lawxKfrYvj3g+2FjubUGkW0FL6WFSmL/3qlPEOXHGImxfWbOKL9XHsVaHCoab/89i2wBADTyKizNfnwPOTTz7ZaauUBMMwEIlEEA6H0dzcjMbGRmzcuBErVqzAokWLsHbtWgCA19v3MvmhUAjz58/HoYce2u3jJLZtKSws7NFjxpKwlCQVwSdgrgNNnISDA8ypuD95IoDzJzmhCKCqUEG+W2BwYd+mwVHyZEKgmQ79eR8YtKaHYUis2GJmN+vbDDhVgaHFZrDZ3y2C+vr5KwKYMjw3s512s7ndQG2LxAljrPk8OrdUYYEhIqKs0OezybRp01BbW9ur39l2OemAAQNwxRVX9PXpsWnTJpxwwgmYN28eDj/88J1+Xl9fj/Xr1wMADjywZ8WC9CSd25I99XZHXqfAIcM1LK3X8dRCM/hvCUtAmj+770wvSnxcC5pODDaTi0FragSjEt9t1tESlmgJSTy6IIpQTKLUL7DPYA0VeQJKPy9c9fdv4boj3H2e0kvJ9WVHYaGJg6zJNo4oMZ93BbdUISLKCn0OPP/1r3/hRz/6ERoaGjBo0CB4PB44HA5omgan0wmfz9e5lsnv9+Nf//oXfvOb3+CAAw7A2LFjMWTIkKS8gAcffLDLwPOWW26BrusYNWoUTjrppB49VjzJF1VTlf0EAKcmMGGbQg9SSgRjwOc1cfz82SDO39+JuAGMKVexRxmnKKUKA0772d1nkouBaVtY4qZXg6hpMgBhboPi0syp/FWFSlKm7Cfjb+Gsic6cXdtpR1/UxgEA+wy25hwyMF/A7wKWb2bgSUSUDfp8hj/uuOOwcOFCzJo1C/Pnz8d1112H888/v9ttS/7973/jrLPOwtixY/vc2G2pqrmZ9SOPPIJ4PI6f/exnGDp0KDZv3owHH3wQ9913HyorKzFnzhxoWs9eZnsk+QV+U539TBBCwOcEJlVrWLxRxzNfRCEEcH9AYvrhbuwzSEU4jj7tt0c7m/5CAHAxoM9E/Q2QMilw1Q2Jhet13Pp2CA5FYK8KFYMKFGhJXK+XzIsv1x3p5lIBG1lUq0MIbHeRM52EENizTMWyek61JSLKBv06m1RVVeG+++5DXV0d/vSnP2HvvffGddddh/POO6/bADRZqqqqsGzZMjzxxBN49913ccYZZ6CpqQk+nw+jRo3CzJkzccUVV6C4uLjHj9kSTt3OMukKQN0Ogf2rt36s65p0/G1+GEbHS7v6cDcOHq7BMMDy9ER90OdAqy312fFERdq6NomHPo1gc7uBuA4MK1Exuiy567+Tne0/bYIDe1s0pZO69sX6OEaVKvBbWMRudJmKz2qiCEQki+kREWW4pFzGHDhwIP70pz/hyiuvxG9/+1vceuutuPXWW3H66acn4+G7NWrUKMyYMQMzZsxIyuO9tqTrYknJlMrpt12pLlIxwKcgEJNoj0j85b0w/vKe+bOB+Qr+fIoXeW6ezIkymWFILKrVMeudMMIxCacGFHgUjChVMShfSepFplRNL7/6cLcllVOpa8GoxLJ6A2fs7bS0HYltXJZv1rHPYE7DJiLKZEk9ildXV+Ohhx7CV199hauuugr33nsv7r77bowbNw6ij+X40ykZVW17Il3ZzwSPU8DjFCj1AYUegVAMkBJYuknH+Y+348IDXJDSXA86pFjh4I8oAwQiEiu26Jj9UQSb2yUicYkhxQqGl2j9rki7o1SvZT55Lwf2ZVBhK4s36DCkdes7E/bsqFGwdBMDTyKiTJeSo/iECRMwb948PPnkkzj++ONx8skno6qqKhVPlVTJLi60O+kOQAGg0KOg0GP+v8wvsLLBwLNfRCEBNAXN+bgjSlXcdVrft7ohotSIxCW+2qDj7vfDaApJKACKfQLVRUpStkDZUbqKZ113pBteTv23lUUdhYWsqmibMLrMXLazrJ4FhoiIMl1KLx+ec845OPHEE3Httddi9erVWL16ddKKC6WCnrolnrtkRQAKAJoqMLpMxeiOK8pNQQNbAhJLN+l4Z1kMhgQeWRDBzSd4OvcQJaL0agkZqG+XWNmR3ZQSqMhXMLJURalPJLVQUEI6qzWfOt6B/ZjJsp0vOrZSsTrLOGqACiGApSwwRESU8VJ+RikoKMD999+PadOm4dJLL8WHH36I3/3udz2uNJtOumFR5NnBqgA0ociroMgLaApw3wdhiI5tF659MYirD3fjsc/MzOgvprgwvkJNyYCXiEwbWwzc9GoQDQHzuOR3CYwqVVFdpKRsn0srtge6daqXhc5saFFtHAPzBcrzrK2E7nYIDCtWsHQTM55ERJkubdHfsccei88//xwXXXQRJk+ejDlz5qC6ujpdT98j0tq4s1O6CxDtaFiJiqHF5mBDSmBJnY6/vR9GoUeBlBIz/xOCSxX4xSEuHDRM45pQon4yDIn1LQZqmgw0hySeWRRFY0CiyCtwwFANhR6R0r8zq/ajPX9/Z+dUSrIP3TCndB82wmF1UwCYBYbmfReDYUgo3G6HiChjpTXtWFRUhBdeeAFXXHEFDjjgAMydOxd77rlnOpuwSxYnPLdjdfYzUQxKCGB8pYbxlVt/FopKrG7Ucdf8MO7/WOChc33ce4+oD9ojEh+vieMfH4YRjZvZHZ8T8DsFRpSoKM8TKSvMZlWwmTB1rAO/P9GTEYXncs3yegOhmPWFhRJGD1Dw+jdATbOBIcX2aBMREfWeJfNdZ82ahXvvvRcnn3wyli5dapuBx0tfx6xuwk6sDkC74nEKjB2oobpI4r0VMSzdpGNchf2mThPZUTgm8cWKGB76NILGoIRTFRhWoqIyX4HPibQcD60OOgGgPF9BVRGDCDv6wiaFhRISW6p8u0ln4ElElMEsiRZeffVVAMCKFSvQ0NCA0tJSK5qxHWmXebbdsGMA6ncJFHsF7vlvBH+fxsCTqCcueSYAxaVggF/BhAoFlQVKWmYM2CHYTJg61oHfHOu2uhnUjUU2KSyUMG6gGWx+vVHH8WMsbgwREfVZ2s8qjz76KC688EIAwPnnn2+LoBMAInGrW9AzdgtAXZpI+zY0RJlMNySOGOGA25GemR52CjgTLj/ExcyVjX2xPg6/CxhRYo/1t+MrzaHKlxtYYIiIKJOlPfA877zzsO+++6KlpQUHHXRQup++W20Re2c8d2R1AaKE9ojEBZNdVjeDKGOMGqCmJei0Y8AJAKdNcGDKcHsUraGdSSmxqFbH3pWabQr55LsFhpUo+IqBJxFRRrPkcua4ceNsFXQCZgCVaaweWAaiEm0RicoCe1wVJ8oE5f7U/71YfWzoztSxDtx2khc+lz0CGtpZbYvEloC0zfrOhAkVKr7dpCMaz7xzNRERmRgxdMiUqbY7snKAuWKzDq9DYE9uh0DUY84UzzOxa9AJACU+gZED7BXQ0PYWdRQWssv6zoS9B6mI6cCyemY9iYgyFSOGDhFeRe2VQESiptnAjUe7bTMdiygTpLJqrZ2Dzh/s5cDNJ3isbgbtxhfrzcDOdhlPrvMkIsp4DDw7xHgu65UVDWa2c0KlvQYnRLnKzkEnANx4lJsFhTLAoto4NGVrJVm7SJxruM6TiChzMfDskMmVWdM94IzGJWqbDVx7JLOdRHZg96Dz3P2cmDTEXlM3qWtf1OoYU56eAli9MaJEgdfJwJOIKJMx8OwQNzjVtqdWNRhQFTDbSWQDdg86p45z4C+netOyVyn1T3PQwOoGA/sMtt+xXVEExleo+GpDhhZkICIiBp4JegZnPNO5rUp7RGJlg45rj3DDpXEgSUS7VpmvYEAaKvlS/yXWT04cZM/s9MRBGja2SmxsyeATNhFRDuNooEMmB57pEtclFqyLo8AtcACnzRFZzu7Zzp9McuKvp3mtbgb10KJaM/Dcx2aFhRImVZvnnf+tZdaTiCgTMfDswJm2uyalxFcbdUR1ibtP93JtJ5ENpHO2Q29NHefAnSd7bbdWkLr3xXozoNvbpoHnAUMZeBIRZTKmrTrojDx3aX2zgdoWA3891YsCD69XEO3OjkFhU70PNz5gUWMsMKRIQbGPx4pMsqhWx9BiBUVee35ue5YpyHcLfMLAk4goIzHw7BCKWd2CvklHxiMYlfi6TsfwEgXDS+15JZwoneycabSDsyY68TdOsc0okbjEkjodU8c5rG5KtxRFYFK1io/XxKEbkgWriIgyDAPPDi9/HbW6CbakGxKf1cThdQj86WQOJCk3ZFJgOXuaz1ZrPX8wzoE7TvZwOn6G+aZOR9wA9rFpYaGEyUM0vLM8jiV1OiZU2rutRES0PR61O0QzYGuwdA+GzaBTRzAqcf/ZPjhUDiQp+2RSkNmdxGuwOgD9wTgH/u9YD6qKODMi03yxvqOirQ23UtnWtus8GXgSEWUWHrU7xG0eeFoRdC5YF0dzSOKeM3xcq0UZKRuCykzi0gT2tXngQl1bVGuum7RrRduEyR0V1T9ZE8dPD7S4MURE1CsMPDvEbFxcKN2DZynNtT5NIYn7zvChooBBJ9kfg0xrnbWPE/840wuNMyMy0he1Ooq9AoML7X28H+BXMLJUwfsrWWCIiCjTMPDsYLe406pBtG5ILKrVsaHVwB0/8DLoJFtikNk1q6bcTh3rwMzjPSi0aTVU2jXDkPiyNo79qzUIYf8LB0eOcuCfH0ewrklHNad1ExFlDAaeHXTDuue2yyBaSomF63U0Bs1tU1jBluzALn8fmSSdAejUsQ5UFSkYXcagM1OtajDQFgH2GZwZQ4KjR5uB57vL47hgMs9TRESZIjPOMt2oqanBo48+ijfffBNff/01WltbUVBQgAkTJuCHP/whLrjgAqjq7k9KUkpICzKedhpQS2lumbKpzcDdp3sxpJgnc0ofO/0tZJN0VLytLFBw35n8/DLZotqOwkI2X9+ZcMRIc+jyzvIYLpjssrg1RETUUxkbeM6fPx/HHHMMAGD69Om44447UFpaim+++Qa//e1v8dOf/hSPPvooXnvtNfj9/l0+VjqznXYcYMd1iS836NjYauC2kxh0UvLZsd/nilQGnz/Yy4Fbvu9JyWNT+ixcnxmFhRJK/Qr2Gazi3eUxSCkzYnowERFlcODZ1NSEWCyG2267Dddff33n7cOHD8fhhx+OsWPH4v3338fMmTPxpz/9aZePla71nXYcfDcEDHxWE4eUwF9P82JYSWYMPMie7NjHKTXB56njHZh9lg8D/Jxim+k+r4nD6wT2LM+c4//Rezhwx9wwltTp2KsiY4cyREQ5JaOP1oqi4OKLL97pdr/fj/POOw+33nornnrqKcsCT7sPwje1Gfi8Jo4BfgV3neqFz8WrxtQ7du/jtFUy132evJcD1xzhZtCZBRJr+ycO0qAqmXMOOGqUhjvmAm8tjTHwJCLKEBl7tD7ppJPQ2toKn6/rge/gwYMBmJnR3Ulm4JkpA/FVDTqWbNRRVaTg7tO9GTXgoPTLlH5Nu9ffAHTqWAcuOciFA4Zk7OmDtlHTbGBLQOKcDNt/9bCRDvicwIuLY7jmCE73JiLKBBk7clBVtdugEwA2btwIAJgwYcIuH6e1tRVKRCIWiwGRAKA6Aa3nxQoybUBuGBIrGgws26TjpmPcOGiYw+om2VokEkEkEun8vq2tzfxPtG37O6quXvUbO8q0vmx3sWgE8djWvhMOmn0mHNi+72gOFxzO9Pedvky/PXc/JxwqcPwYB9fVpUi3x5wU+bzGLCy0b4ZUtE1wOwS+P9aJZ7+Moq7VwMB8Zt+JiOwus840vfDaa68BAK688spd3q+qqgpw+IF9LwOWPAaM+xFwwA27ffxMHKQ3Bg18vVFHW1ji18d6MHlo1n78STNr1izcfPPNO//ggR0uaEy+vkf9xg4yse9mov88ejtefeAPO91+46kjtvt+6oX/h5Mu+k26mtVnp453IG4AfznVy6Azhbo95qRIorDQflWZlfEEgNP3duCZRVG8tDiKSw52W90cIiLaDSGlFRuJpNabb76J448/HieeeCJeffXVLgdJia1XampqAGceLn42hje/3nXGM1MH7FJKbGiR+KI2jmKvwJ9O9qKUa7N6ZMfsQ21tLcaOHQtc+BXgzNt6RxtmPDO1v9qFlBJxA4jrQNwAdCmhG2YVbEOa/+od90ncrksJwwB0CUSjMeh6DIYUkBJob9yAOdfujT/OWQm3b2vfsSrjua2eZD5Pn+DEEz/2wakx6Eyl7o45LS0tyM/PT/rznTi7DfNWxND2xyJoamZ9tm1hiQG/bsKhIzS89fPkvzeZLDHGSVW/ISLqi6xLedXX1+Oiiy7CHnvsgYcffni3V+bz8/MBZx4cjnbAtXMwlumD9+aQgS/W62iPSIwoVZit6CWXywWXa2tQ0Nraav7HmQe47HMyz/R+miq6IRHVgWhcItYRQMYMibhuBooxw9xOKGZgu/vFOm7DLi7LKQJQFUBVhPlv5/eAIgQU1YETx3mhCEAAaKxvwxwAbl8ePD779B1g19Nuz93PibawxIzj3Qw606DbY04KSCnxeU0ce1eqGRd0AkCeW+C4PR14/ZsYGgMGin28oEpEZGdZFXg2Nzfj2GOPhcPhwLvvvovS0tI+P1amD+SllGgMSvxvbSLL6cOgQp6UM12m98vd6cwydmQaE0GiedvWjOOOGUhdJn7H/P2YDkR1M/u4o0Rw6FAENBXQFMChCpy8lwNep4DHAXgcAh6HgEsD3JqAUwMcHffTVPP/Si8Lcq1fb+/11F0Fn9MPc2N1g477z+G2KdmotkWivl3ijImZOxSYNtGJl7+O4cmFUfziEE63JSKys8w92+ygoaEBxx9/PGKxGD788ENUVlb2+Hd3HD9m8uA+HJP4brOO2hYDMR0YmK/gH9NYtTYTZVo/lNLMLoZjEuG4mT2MdgSN5tRUCV1uP1U1ESiaweIusozCzChqioCmbA0ezUyjwPFjHHBqgFM1A0W3Y2sAuW0w6dJg+d+CnVc3JPpcKCrx1UYdK7bomHGch0Fnlupc35lhhYW2ddoEJwrcQfz7fxEGnkRENpe5Z5ttbNy4Eccccwzcbjfmz5/f60yn1jGmuuXEzB1g6YbEuiYD327S4VAFfnmkGwPzFQwtVji1NkPYPdDUDTOgDMckgjGJUBTmvx3/D8W3zzCqCuBUxQ5BopkpPH5PBxyqGSi6NDMgdGkCbsfWwLHzNg1wqMiafhyKAl6rG7ELuiGxoCaOuA7cc7oXQ4ozr+gM9cznNZlbWCjB4xT44X5O/P3DCBbWxLFvVVYMa4iIslLGH6HXrFmDo48+GhUVFXjttde2W0S/adMmnH/++Xj44YdRXl7e7WM4O8658S6m5e1OJC4RjMqOwbI5xa8tIlHgFtgSkFAE4HWaA2aXZga52w6gm4IGdAmUeAWEEGgJGTAkUOAWiBvdD7gNQ2JDq0RzyIAiBGqadcR04OrD3ThkuJaR63VyTboDTUNKhGNmn+1cz7hD8ZxEYRxzCquZjdx27WOConRkEx0dGQePQIHb/Mp3C/hdgusBu9EYMlCS5MeM6eaUZFUBhEisP+3d+28YEutbDKzYYiAal/j3OT4UeTPzQhz1zGc1OlwaMHZg5gaeAHDxAS78/cMI/v2/CANPIiIby+gj9NKlS3HMMcdg3LhxeOGFF+D1bp9HCIVCePPNNxEKhXb5OErHWq9ofPspcDHdXCcZ04H6dgN+l4BuAC0hAxBmJqam2eicGigEsLtZdGbhEUBTzUF6XasZ7RZ4zMxQYyDxYACkuVdZsVdAwgxa81wCzSGJujYDUgL5bgHdkLhsihsHDNHgc3Gwb2epDDalNPtqOA4Eo2YmMhCVCEbR8a+EsU3/VBNrFrebuiqgCnOfRkfntFXA5xTwOwXyOgJLnzN7MpDptmKzgbJiA05VIBw316kmgkUhzIsAoZhEIGJ+buGO45JTNbPASsdxJjGtuT0qEY3v/DyJY40hzf97HObFAEWYa1QLPAKlPgV+FxCJAV9uiGNLQGJwoYJbTvQy6MxyhiHx8Zo4vlelwZHhFyr3rdIwcZCKxz6L4tbve1DgYd8lIrKjjA08lyxZgiOOOAKbN29GUVERDj300J3uE41Ge/x4fqdAW0QiGpfY3G5e+d/SbnQO1Iu8Ag0BCYdqZiMjcYnGAHDz8R4ML1HQHJJoi0h4HObAfMUWHXsMUKEqQCRuDijbwhLtEXOdW1NQ4vHPI/jDVA8KPQKLN+h46esY7jrN/H5DiwGPQ6CuzcBD/4tCwlwPV9dqwKUJzOx4Xp5g7a+/wWZcl4jEgXDc/DeS+Fc3A47E9+Edprq6NAGv0wwaLznIhWKfQLFXQZ7LzMJn+mAzU6kK8P7KLiLFbSiK+bn5nAJn7eOEAPDi4hgCUTNQVYR50eDsfZ0o9ioo8ZnTk2P61oJLwZj5f00xg9SW8NaLD68uiWF5vY4l+tY0tksTmD3Nh4oCHlNywbJ6A01BiYOGZewwYDtXH+bG+U8E8I8PI7jhaI/VzSEioi5k7Bnn7bffxubNmwEAixcv7vfjnbWvE7M/jGB1gwEhgDK/gt+d6MGoASocKnY7SC/cYdFWWd7Og7fyvO2/P3r01iqX1UUqvj/O2fl9SUdZ+JEDVEwZvvV+Ukpmmmyur4FmXJdoDUs0h6V5ISNsZi23neIKAbg6Ml9ODZg6zgm/E/C5BPI6vvLdAgUeBpZ29e9zfIgoPkR1wO8EnJq5z6cuzSyUQzUvGOz4d37CWGc3j9h7p05wwjAkNrWZfc3tAKoKFU7RzyEfro4BAA7OksDznP2c+PXrIdz1fhhXHeaG28G+TERkNxl7xpk+fTqmT5+etMc7fKQDTlWgqlDBsBLFtictBp321NtgMxI3g8zWsERLWKIlZE6ZhDQDygKPggsmu1DoMQPJPJc5zdXr6P02HmQvQoguL0x1/DRt7VAUgYoCgYqCtD0l2chHq82s+4FDM3YYsB2HKnDdEW5cNSeIhz+N4JKDWeGWiMhusuOMkwR+l8Cxe9p7nz2yn54EnNG4uVdec8hAW0SiLWwGnoC5hjffLfDTA12oyFdQWWB+zwsMRJRKH66OY9QAZRcXQTLPRQe4cMvbIfzurRB+9D0Xax4QEdkMA0+iPrjrNB88vq6Dzrgu0RSSaAhIbG430ByWUGAWczljohNlfgVlfoHyPIUDIyJKuy3tBpZvNnD+/smbvm0HPpfA70/w4NJng7h9bgg3n2DnjYuIiHIPA0+ifpBSoi1iVjpuDks0Bc3ps1KaxWF+tL8TI0pUDC9V4OL2IkRkAx+tMafZHjw8+4YAFx3gwj0fRHDH3DAuOsCF6qLM3iqGiCibZN9ZhygNWkMG1rbr2NBiIBCVUBSz2vG5+zlRVaSgukhFvpuBJhHZz7zvzMJChwzPvuUlmipw16leHH1fGy5+KoD/XJLHdfFERDbBwJOoDz5cHUd+voGLD3RhrwoVA/MEBzdElBHeWhZHVaGC0WXZs75zW0ft4cClB7nwj48iuPeDCK44lIWGiIjsgIEnUR/cfboPIwf5WASIiDLK+mYD39TpuHCyM6uPX3862Yt3lsdw/StBTB6iYdIQDneIiKyWnZc7iVKsPF/J6kEbEWWnt5eZ02yPHZ1902y35XMJPPMTPxQB/OD+Nqxt1Hf/S0RElFIMPImIiHLEK0uiUIQ5HTXb7TNYwxPn+VHfLnHM39tQ08Tgk4jISgw8iYiIckB7ROKNb2M4YpSGUn9unP5PHu/EP6d5sWKLgSl/a8OyTQw+iYiskhtnHiIiohz36pIowjHgzL2za//O3bn4QDcePdeH2hYD+/+5BS98GbW6SUREOYmBJxERUQ54cqE5zfbUCbkVeALAud9zYe4v8uB1Cpz+YDvOf7wdW9oNq5tFRJRTGHgSERFludUNOl5ZEsNJ4xwoy8vNU/+hIxxYeF0Bpo5z4JEFUYyZ1YL7PggjEpdWN42IKCfk5tmHiIgoh9z7QQRSAlcdltt7WlYWKHj5Yj+e+YkfDhX4xXNBjLqlBX+bH0ZTkBlQIqJUYuCZxSKRCGbOnIlIJGJ1U1ImF15juuXCe5oLr3FX7Pb67dQeO7UlWdY16fj7h2FMqFRx+Mhd72eZSa+/r20VQuDMiU6s/HUh/nyKF5G4xFVzgqic0YwfPtKOpxZGkhqEZtJ7SkSUSkJKmZNzTFpbW1FQUICWlhbk5+db3ZyU4GtMvvXr16OqqorvaYaz4jXaqe/Y7TO2U3vs1Bag//1GSonTHmjHi4tjePeyPBy5m21U7Pb6dyVZbQ1FJV74Kop/fRzB/JVxAICqAPtXqZg0RMOkag3fq9IwolSBpvZ+/2Yr3tNM+hyJKHfs+tInERERZSQpJX79eggvLo7hh/s5dxt05iqPU+Dc77lw7vdc2Nhi4I1vY3jtmyjeXxnHJ2sjAMxMpVMFRpQqGF2mYtQAFdVFCgYVKBhcaP5bniegKr0PTImIcgUDTyIioizSGpb4dG0cf5oXxptLYzhwqIZ/TvNZ3ayMUFGg4MIDXLjwABeklFjTaGDBujg+r9GxrF7H8s06Xvsmhpge2+l3VQUo8wsUeRUUeQSKvAKFHgGvqgMHz8Af3tVR4A/BrQEeh4DbIeDWALdDwKkCDlVAUwBNhfmvIuDY5v+J2zvvl/jq+F5VAAFACMAwcnIyGxHZXM4GnrpubiLd2tpqcUtSJ/Ha+BqTp7m5Oa3PZwX2m9SwU9+x22dsp/bYqS3A1n6z9+9rAGceDAlICRgADCnN7wEYBmBIQDeAltDWoOO8/V24daoKPdKG1h4sMbTb69+VdLS1xAEcP8L8MimI6wI1zQY2tJhfG1sN1Hb8W9cm0RKSWNVsoDkkEUpsGTr+J7j9rRYALSlr63bC5vM0Njam5/koK0gp0dbWhsrKSigKy8BQ8uXsGs8FCxZg0qRJVjeDiIiIiMg2ampqMHjwYKubQVkoZzOeI0eOBAAsWbIEjXEfZr4RwrvLd546AwB/PCk1U5QMKbGkTofXITCiVEVzyJzSc/cZPuS5+r9OpK2tDWPHjsU333yDvLy8JLTYftL9GmtqanDQQQcB534IuJLzfKnqX30VDrZh5rkTMfPxRXB7s7PfLNvQhgdn341PH74GFaXpKbyR6DtLlizpU7GPuC5x+fNB5LkE9q5UIUTfjxGp/IxjusR/V8Vw+RQ39qnq2SnGTscqO7UF6H+/AYCbXgkiqgP7Dt7955GKvlHbouObOh03n+DBkGI1KY8J2O+z6o4V7aytrcUBBxyAmpoaFheiHmttbUVVVZWt/54os+Vs4Kmq5slv8ODBcEZ88BQFgbyuA8+istQFBoeWb/1/qMWApz2O0cP8UJJQoCAx/WjQoEFZe+Kx7DXmVQKu5DxfKvtXX4QC5ntaOKASHl929pu9PK0ABO74rAwP/bgEXmf6CoIMHjy4T331mzodSl4QB+7hgNvRv/am+jOujsXx8lrgpAN71rftdKyyU1u21dd+AwAXHh3Dvz6OoKhs98WFUtE3isqAkCuOf3wp8eh5/qQ8JmDfz2pHVrYzPz/f1u8N2VN/LmwS7QoncMNciG8HUV3CqSIpQScRdU9VACx7FjED+MnjAXxZG7d9MY5MWhXhcwLhWOa0N9u5NCCevG0p+2RosYLmkERLyOKGEBGRZRh4ArBLnBeNA84+7BFGme2SZwJWNyE3RVpwYJVEqV/gN6+HcO6jZgCq2zQA3aNMhdchsHB9HIbNg1BFCNj0bcxJDlVY3q8LPea5rb6dHYOIKFcx8ERH9sMGAlEJTxK3WXO5XJgxYwZcLlfyHtRmcuE1ppvmcGHqhf8HzZG972niNfo8LkwcpOGIkQ4Uec0A9OyHA5i/IobGgL0yMw5V4K+ne9Eckljd0L+2pfozFsKstNpTdvo7tlNbksWp9jzjmaq+4VAFnBrQFExe4Jkpn5WV7dzUauA3rwfx9rKulxIREaVTzla1bW1tRUFBAVpaWtBm+HHpMwG8+k3XB+bZadj/LK5LvLM8hmuPcOPg4dzk267Wr1+Pqqoq4NLVSVvjCaSnj9HuhaISqxt1rG+WiMQlCjwCF0x2YWy5ioH5ol/rXhJ9p6WlpV9rri58oh0OVWC/HhbuscLKLTpWNxh45oLkrefLVcnoN8vrdVz7YhAnjnVAtXCKz7zvYrj0YBcOG8lzXKol+s33bl2Hzzb5oCrAgmvysU8PCkxR7tp2bMy1wZQKNsn1WcsOGc+6NgndAE8KRBbyOAXGDtRw9GgNBwzVUOQVmP1RBD97OoBpDwXw1tIYtrRblwld16Rjc0CiLM/eU/LdDoFwXFo+vZNM7o44L6Zb2w7A3IOU0uezdToOHqZBN4A754Wtbg4R5ThGObDHGs/2iITfJdJaXZOIuqYIgQF+gQF+BXsNlGgNS9S1Sfz7kwjujkoMzFfwx5M8KPGl76pVNC5x3UshlOcpGFxgg6tlu+B3mgFGU9BcQ0vWcnSc5HSLZ49L2ONCb655/DwffvhoAC9/HUU4JvtdFZuIqK94CoA9As+4IaHx08hZLDBkX0IIFHgUjC5TceQoDftXawhGJS58IoBP1sTTVm32yw06onGJCRX928MzHfwd+xA3BJjesgOtY+tM3eKPQ6B3a3+p/w4YpmFIsYrTJjjQFgH+uypudZOIKIcx1MHur8AyKCAiwAxCB+YrOGSEhtFlKv7wdghXvRBMS/B5/8cRDPArGZGtUBWzkExLmGGGHSQualpdDVkR9pjum0uOHmXOsz6y49/3V7LIEBFZh4En7JHxdKgCsY5pUFJKbGwxEI1z0EZkR4oQGDlAxX6DNaxpNPDeitRnEUIxCa8z5U+TNB6HYOBpEy7NPMnFLQ76XA6B5xZFrW1Ejjl0hLmiakKligK3wPw0HKuIiLrDwBPmINJqeS6B9ohEXauBi54M4GdPB3DZc0Grm0VEu1BZoGBCpYo/zwtj2abUjupjunmBKlMUuAUeXRCxuhkEwOMwt7iJWBxzlPoENrUZiPCiatrs21H9WlUEDhyqYUFNHHGr51wTUc5i4GkTA/xmYaGfPhVAWwQo9Qu0hHhyILK76iIVVUUKfvVaEG0pzPCpCjJqwFhZoKApKC2tAkwmIQQ8DoFgzNr+U11oDjlueJkXVdNl24tV+1apCMeA5Zv5N0lE1mDgaRMOVWDKMA2Th2g4YIiG9ghQbvMtE4jItNdAFQ5F4KoXAjBStIVIvjv9U1cveSbQ5zXupT4z2Fm8kYv67MDnBAJRawNPpyYwukzF6kYDgUjmXETJFvsMMrOfX6zndFsisgYDT9inyp5TE8hzCXyyNg6HCtx2ktfqJhFRD2iqwL5VKhqCElekqNjQRQe40BSUaaui29+iakIIlPoFHuF0W1s4ZYITYYszngBQXaRAAFhWzwsS6bbPYLO88Re1fO+JyBoMPG3m6zodmgI8+EMffC5mPHMJqydntkKPgv0Ga6hpMnD5c0HoSc58lvoEdMOadXp97ZtOVVhe0IZM+S6BkA0KmqqKmQlvZeGptBtWrCDfLZjxJCLLMPC0kZgusanNwK+OcXdWISSizDEwX8H+1RpqWwz8+LEAapqSF3UVe83DdbvF0yV7QzfkbrerovTwu4RtivroBuDUrG5F7lEUgb0rVXy5gVeDiMgaHBLYSGtYQkpzKhIRZabyPAWHjXDApQGXPRvEz58JJCW743cBbodAU9AewUNPxA1zGjJZz+MAYgbSNlV7V6K6hCcD9qPNRuMGqmgISNS3scAQEaUfrzkCsMvpLxQz9xTNd9ulRUTUFz6XwOQhGuraJJZs1HHeo+0o0PtXyVMIgSKvQHMGVbvWJXDcng6rm0EANEUAEjAkYPW1AEWY7aD0G1dhrvNcUqejLI8XuYkovXjUgXkStIO4IaGp5gCTchPXeWYPIQQq8hUcMUrD+Eo1KQHjSeMciKdhxJ6sfmgYgINnGVtwmPEGdBskupyasLzCbq4aW252hG/qON2WiNIvq4YE9957L4QQEEJgzZo1Pf49xnlElCqqIlBdpOLAof2bYCKlxJMLo/Cz6Bj1QaJYXdQG8YbHgYzK3GeTbTOeRETpljWB57p163DjjTf26Xc5jCM7YdYzOyn9vMK1dJOBtojEkCI1SS3q3uxpvqQ8jkMFXl1ig1KqhCKP2f/skGn0OAReWsx+YYUyv0CxVzDwJCJLZE3geckll6C4uNjqZiTVlnYj6VsyEFFm+uM7IVTkK2lbA56M4NPnFGiP8BhmB05NwKkBIRvs5akq4LnNIkIIjBuocqotEVkiKwLPRx55BO+++y7uueceq5vSL6ow199IKbFgXRwXPBHAT58K2KIKIRFZJ6ZLNIYkKvPTd8jeMfPel0x8gcdcyxdg8GkLLk1Ysg/sTtgdLDV2oIotrGxLRBbI+MCzvr4eV199NW644QaMHz++T4/Rk3NgOqY/+lzmBvGb2iT+PC8Mn1Ngc7tEXSvP0rmG021pW4YEIGH5npi97ZfFXgEIYG0TB7h2oCnmRYyeuOSZQMqOQx6nQHsEMJj1tMS4gVznSUTWyPjA8/LLL0dZWRl+/etfW92Ufiv0CBR4BH72dACBqMR+VebJIRl7ABJR5nJpAh6HSNuxYFcBR2+CEYcqUOQRuPeDcDKaRf2kKkBvJ9CkIvisyFMQ0SUWb2TgY4WxA1nZloiskdGB50svvYTnnnsO999/P1wul9XN6TdFCEyq1rDHABUHDNE6N9h2pL6WCNkQs56UoBsSigJEe5itspMBfgWb2zKv3dlIoGf7ZyZjmvWu+FwCQ4sV3PyfEC+sWoAZTyKySv/q+1uopaUFl112GS677DIcfPDBfX6c1tZWqFGJWCwGRAKA6gQ064JYt0NgjzLzpBDuKALBwNM6kUgEkUik8/u2tjbzP9G27e+ouiztN2Q/sWgE8djWvhMOmn2mtbV1u/u5XK5dXjhrChq4ek4Q4ZhEVWHmHbJLvALL6yXawhJ5aSqMlMm6O+b0tt90RQhhm+WVY8tVbGmXuOqFAB44x8f9q/up23NVF8rzBAo9At9uYuBJROmVsRnPa6+9FqqqYtasWf16nKqqKlRWVOLNR+8AHp8CfHZXt/dNdwYqMSWqv9swUN/NmjULBQUFnV9jx441f/DABOAfw7Z+7aLf9AeznpnrP4/ejunHlnV+zTx3IgDzmLNtn9rVMSwal/jZ00FEdeDgYVraKtruTm/6pc9ptrmFma0e6e6Y05t+0x0hej/VNlVURWCfwWaRm2X1XAPcX92eq7oghMDYgSoDTyJKu8y7fA5g7ty5eOCBB/Dqq68iLy+vX49VU1MD1Z2HC56O4c2vLzMznjZjk3FCTrrppptwzTXXdH5fW1trntAv/ApwbtP3VGY7aXvHn3c9jj77qs7vmzdvwMxzJ6Kmpgb5+fmdt+8qa9UalgjHJPau1FDgyczrhInrZtw+o2e6O+b0pt90RxE9m2qbLoUeBQN8An95L4zZZyVn79hc1e25qhtjylV8tDqOxoCBYl9mHluIKPNkXOAZDAbx05/+FOeccw5OPPHEfj9efn4+VFceHI52wGWvg29ifKDYI8mRk3acztY53c2ZB7jyu/mt5LrkmUBS9lSk9HI4XXA4t/adcMCc+pafn79dALErJT6BIq/Ad5t1DPCLjJyOmAh0HGrmtd0K3R1zetNvunPyXg48uyjar8dItny3gi0BZjz7q9tzVTfGlpvjnW836Th4uL3GPkSUvTLuaLNgwQKsWrUKL7zwAvx+/3Zf48aN67zfuHHjOm9//PHHd/mYdh3LJQZsVm+hQETWEELgtpO8aAxK1LfbKFXVC1uXDFjbDjIrpwf7GHematq/ISX7hgXGlHdUtuV0WyJKo4zLeE6aNAnfffddlz+rra3F4YcfDgB4/fXXMWjQIABAeXn5Lh/Truc8DtiIqKJAQaFHYHO7gfK8zLsKlSiOFoxmZuCcTYq8CkJxCcOQUHZxYpk9zZe29eWRuLldEKVXYkuVb1nZlojSKOMCT4/Hg5EjR3b5M03b+nKGDBmCoUOH9ugx7ZrxDMfNgVqiOAcR5Sa7HqN6wqGa1brXtxgYXsoS3VbyuwBIIKoDbptcw9ANYOo4h9XNyDlVhQq8TuCbTZzmTETpY5NTj7XsOqjbEpDwOgW8DDyJclpMB5xpWiPZ00xXT+8nhECZX+D+jyO7vzOlVKIP6TaKNRwq8OqSmNXNyDmKIjCmnJVtiSi9siLwbGxsRF1dHTZv3tx52+bNm1FXV4fGxsbd/r4dwzpDStQ0GbjkIFZLJW6rkuvcDiAUy9ypqqU+Bc0hycq2FkvMru3rx5CK45DXKRDgNGxLjClXsa7JQHuE7z8RpUdWBJ6nnXYaKioqMGnSpM7bJk2ahIqKCpx22mm7/X07ZjwjcSASlxg5ICs+IiLqh3P3c6EplLmDQ4/TXLMesFdB1ZyTCDzt1JPyXGbgGdPt1KrcMLajwNBSZj2JKE0ybo1nV957771+/b4N4064VEBTgZomA9VFXBdFlMvyXALRuNWt6D87HmtzidZxHdOQEnb5NPJcAlICDQGJgfn2aFOu6KxsW6fje9VZMRwkIptjOg32zHgqisDwEhW3zw3zaiRRjnvw0wh8Npx139Opl4kK3XY81uaSRPXYmI1OKT6XuWXY+mYbLTzNEZ2VbTnGIKI0YeBpY6MGKKgqVPDLl4K44vkA4pyKlNO4zjM3bWozsKHFQFVheg7Xs6f5kv6YiaI2XMtnLbcDUBQgbKP1wooQGOBXcPf7YUhpn3blguElCpwq9/IkovRh4Am7TDjamSIEJlRq2LdKw/pmA5c9F+SJmSiHBCISV70QRJFHYHCaAs9U8DmZ1bIDIQT8ToF2m621HVmqoDEosZRbe6SVpgrsUcbKtkSUPpk7kkkiu0//GlSgYP9qDRtbDKxu4Ik5lzHrmTvCMYmfPR2AlMB+VRoUux+odkFRBEp9Cv7xIbdUsZrfJfpVxTQVx6Air4LBhQpu/k8IBisfp9WYchUrtxiIxPm+E1HqMfDMEKU+BX6XwLomBp5EuWD6nCCiOnDQUA1uR+YGnQkD/AINAYOzNix2xkSnLbfmGTVARSAqsZbnuLQaW67AkMDyemY9iSj1GHjCnH6UCeKGudk2EWW3uC5R22JgzzIFHmdmHJ92p8SnIKYDm9rsF/TkkjyXQKSfFZJTkfX0uwQ8TsHp2GmWqGzL6bZElA4MPDNEICIRjklUFfEjy3Wcbpv9VAVwa9asxett/+rp/fNcZlXVZcysWMrjgG33zCxwCzz2mc0WoGa5RGXbb+r4d0lEqccoJqGHSQWrBv0NQQOqYq73JKLsJoTAlYe6sLZR79hzMfMJIVBZIPD3DyKcbmshhyqgG+j3Z5CKc2GBW6ApJNk/0miPMhWKAL5lYSciSgNGMQk2P8+FYoDXKaAq2THtjoh2bXSZipgOtGdRPZ7BhQoCUcnpthZKLNewYw2fAX6BUFSivt2GjctSLk1gRKnCLVWIKC0YeGaIuCGh8dOiDpxum/0KPOZFJjvtudidnvbHAreAUwPWNjK7YhVPR6Gq3a3zTMV+rrtT6BFwOwQL3aTZmHIVy+t17hVORCnHUKYPrBj0G4a57ouIcoNTBZwa0NaPrS/6IpUBhxACHodAa5pfE21V7DUDz0DUfp+BEAIlPoGHPuU6z3QaW64iqgOruF0bEaUYQ5kMEdUBjdNsiXKGEAID/Arq27NrMCglwEOZdbxOAZcm0Bruf+CZiouw+W6BtiS0jXqOlW2JKF0YeGaI1rDE2fs6rW4G2Qin22a/Cye7khIgpENP+2MkDviyZIuYTOVzAUEbZjwBwO8UCMWkbSvvZiNWtiWidGHgmQFCUYn2iMTQYn5ctD0Gn9kjGJW44rkAfvJ4O95aGsNXG+K4+/1w55q8dEl1n4rpEj5eQ7OUWxOIJinGSHZ/cTvMf9s5HTtt9mTGk4jShJFMB2HjC/Cb2g1oKjoDTyklVjfoqOVG20RZ45cvB7Gh1YDXKfDQpxH8+rUQDAnsM0izumlJJQQQ4/jWUlPHORDdTUbRqotaLjWxBtWSp89JfpdAdZGCJcx4ElGKZdeIph96G3he8kwgbVX/InGz5LmqCIRjEpc9G8DmjnLzNxztxpThjrS0g+wpnX2RUicSA4q9CiZmWaC5ozyXwMZWXjSzktcpEN1NVVurKB2Xw1lhNb0mVKp4a2kM0biEU7PxlXgiymjMeHaw82G2xCcQiEhc+GQA5z0WQFsEOGSEhrEDVdz+bhgNAQ7ich2n3Ga+Kw51YVObgeZQdv89D/AreHhBFFIysLCK14GkTbVNto6EJ2LZ/WdgO/tXaYjqwOKNNu0YRJQV0h54tre345tvvkFzc3O6n3qX7FxlsdSnYOIgFU4VqC5ScOgIDYUeBcNKFHidAv9ba9NL15RWDD4z24RKFUVegaX12T3iLvMLhKISzSEGnlbJdwuE4xKGDYN/TTW33Klpyu6/A7vZv9pc5/nZOo4niCh10h54CiHw3Xff4bbbbsOQIUOwxx57pLsJXbLzGk8AqCpSse9gDaPLVLg6psEoQmBQgYJHuOcZUcYTQuBXR3uwuc1AIIsLqxR4zOPXprbsfY12V+BRAGlO706GZF/0qi5W8Lf3wxlT0TkbfK/anOK/oIYZTyJKnbQHnj6fDyeffDJmzZqF//73v6ivr093E7okbD3Ztnt+l0AwxpMzmZj1zGyjBihwakBdW/Zme1TF3EeyhRlPy+S5zH/D8e4/AyvXjY8oUeDSBG54OWhZG3LNAL+CIUUKFjDjSUQpZOkaz+rqakydOtXKJnSye8azK9G4REPAgFPNwMYT0U4URaDIq6AlyzM9bgfQmsVZXbtzd2zRE0/i9Y1kXvRSFYE9BiiobTEQsul+o9lo/2oVS+p02+7xSkSZL2WB52OPPdaj+w0cODBVTchqzSED734XQ02TgRuOdlvdHLIRZj0z24ljHEkNCNKpp33P5xR45gsuEbCKo+PMr9u4n5XlKZASrICcRt+r0qAbwKJaZj2JKDVSFnjOnTu3R/djZcO+WdtkwOsQeOp8P/avzu7tF4hyiYS9q2wng88p0M6Mp2UURUBTsdu9PK3kVAGnBmzK4mnndpMYSyxYx3WeRJQaKYtY5syZ06P7zZs3D3feeWeqmtFjmTbQC8eAafs44XFmWsspHbi3Z+bSFEA37BsQJEOeW2DFFgndkFDtXFI8i3kdAgEbJ52FECj1KfjHhxEcOIx7VafDflVmZVuu8ySiVElZ4NnW1oZ58+bt9n7r169PVROymiElVO7CSpRV6loNPPxpBEOKVaubklJ5LgEpgS0BifI8Bp5W8LuSn3VO9gWvYcUKPlwdx8otOkaUZvffhB0UeBSMLlO4RRsRpUzKQpdp06Zh9erVu/2aNm1aqprQK5mWXxBCIMuTIkQ5ZUOLgcueDaDAIzCqNHOvKvVknaffae6dzGmU1jl7XycCNi8iU+xTUJ6v4Ob/hKxuSs44eJiGFVsM/m0SUUqkbHTz4x//uEf3O++881LVhKzmUIBXvjY3YdvcbqApyJMEbY9FhjJHY8DAFc8HUOhVMKlag2Zhpep0TNFWFAG/S6Cee3laJt8tEM6ArbiGFStoCkq0hHiOS4cpw81pzR+uYtaTiJIvZYFnaWlpj+53/PHHp6oJWc3vMvfBq202cOETAfz4sUBWbzpPlM2ufyUEtyawf5WaM2se89wCTy208SLDLJfnEojpyV9PnOwLXv6OOgZtkaQ+LHVjynBzBdYHq2IWt4SIslHKAs9bb701VQ9NAAYXKAjHJS59JoBCj3lirmnmFWHaHrOe9hfTJerbDIwstUfQmYw+07PptgLtNp/qmc0KOs4bwV3E/nYoUJaovOthfaG0GFmqYIBf4MPVzHgSUfKlrLjQiy++iOnTp6O4uLjztsTWKUIILF68GA0NDaioqMDjjz+eqmZkLZ9L4OBhGtojEpUFZgGGVQ069ixnAQbaHivc2lsoBkgJeJxWtyS9fE6BUFTCMCQUGwTcuabYKwABtEUk8tz2ff8Te9q6NPu2MZsIITBluIZXvo4hEJHwufi+E1HypCzw/MEPfoC77rprp9vb29tx3XXX4fnnn8e0adPw17/+NVVNSDmrB/RFXgVFXvP/5XkKHvxfFMfv6eAgjiiDJKY65tqfrdcJGBJoDUsUenPsxduAQxXwOwXaUrBEI5nnRkfHeudAVMLPICgtpgzTMOerGD5dF8cRo5hqJqLkSdlU25tuummn2+bNm4cJEyZgzpw5ePrpp/HUU0/1eC1oqokMP58lpt4ured0W9oZp9za14YWAxBmBjCX+F1mxm1jK6fbWsXnFAjafLqz3wU4NWB5vW51U3LGwR37pnK6LRElW8oCz8mTJ3f+PxgM4he/+AWOPvpo7Lfffvj6669x5plnpuqp+yTTh3w+l8DAPAW3v8uy89Q1Bp/29Of3IhjgVzozO7nCoQoUuAXWNDKgsMrUcQ5Ebf72K0KgqlDB396PIKbbO0jOFvsMVuFxAO+vZIEhIkqulG8W995772H8+PF45pln8MQTT+DZZ5/FgAEDUv20vZbpGU8AqC5S0BCQaA3z5EyUCdrCEvXtBoYUZe6+nd3pyYWOAX4FD38a7Vz/T+nldiBlwVwyL3SNKFUR0yUWrGMGLh2cmsAhwzX8d1UcIZtnxIkos6RstBMMBnHFFVfgqKOOwsSJE7FkyRKcddZZO91v5cqVqWpCzin1mVPX1rO6LXWDWU972dBiABIY4MuCK199MDBPIBCV2MT9PC1h7uVpdSt2z6UJDCtRcee8MKJx9pV0OH6ME+EYs55ElFwpCzzHjx+Pp556Co8//jief/55lJWVdXm/GTNmpKoJvZINwz5VEfA5BLYEGHgSZYL2qIRDBbQcm2abUOgRcDsElm6y+XzPLJXnEojEZUZknEeUKNANYFEt+0o6HLenuc7zzaUMPIkoeVJW1ba+vh4/+tGPsGzZMvzud7/r8j5NTU149tln8dhjj6WqGTlHVYAYz8u0C1ZXY6atonFpi707rSKEQJlf4OEFERzO6plpl+8WMCQQiZvTbu3MqQkMzFfw9w/DmDTEb3Vzst6YcgWDCxW8uYzTm4koeVIWeJ5//vm45557dnu/xYsXp6oJOSkSNzdmJyL7a4sArpQdhTOD3yWwud3+GbdslNexPUkkLuF2JP+8keyLXHkugeYg+0o6CCFwwhgH/vVxBKu26Bheyj3Ciaj/UjbVdurUqT2637XXXpuqJuSkmC7hzbGN6Kn3uNbTHp75IprVexP2pJ+pCqBnwFTPbJToe2EmtagLp4w30+BzFkctbgkRZYukB55PPPEEXn75ZRx55JE7/cwwDHz00Ufb3XbCCSckuwk5zZC5txE9UaZqj0rOUCDLOFRAUYBoCrcpSeZFLodqtjUT1qRmg6P2cCDPBbzwFdd5ElFyJD3wnDp1KiKRCH7yk5/g5ptvRktLS+fPhBB4/fXX8cgjjyT7afutr9up2C1z5FCBIM8R1AN267u5KKZLOHN8qq0qAJ310CwhhIBTFYhnSF2AEq+CmA6sa2KHSQeXJvD9sU58vCaOjS18z4mo/5IeeObn5+PMM8/EE088geOOOw7nnHMOXn75ZQDmSe7mm2/GvHnz5sFl3wAAe4hJREFUkvJcNTU1uPXWW3HYYYehpKQEDocDpaWlOPLII3H//fdD1zPkbJpExT4Ff54XxltLY1iwLg7d4JVhIrIvpyagG+Y6Q0o/TQGiGXKqzHML5LsF7pgbtropOePUCQ5ICbz0NafbElH/pWSN53vvvYfLLrsMN998MwKBAKZNm4YDDzwQkydPRllZGSorK/v9HPPnz8eIESMwc+ZMTJ48GW+88QaWLVuGhx56CM3NzfjpT3+KI488Eu3t7T16vGyZ7Day1PxIH1kQwS1vhXDpM0FOS6JuMetpLSmz59jTnd31scRU4y0sMGQJTUHKL1Am8zhTWaBgY6sBgxdV0+KEMU64NGDOVww8iaj/khp4Silx4YUX4sgjj8SDDz6IxYsXY926daioqEB9fT2WL1+O3/zmN91ur9IbTU1NiMViuOWWW3D77bdj0qRJGD58OKZOnYr3338fVVVVeP/99zFz5sz+v7AMUuxVcMQoBw4d4cCBQzTUtRpY1cApMtQ9Bp/WCEUlYjrgSUE10UzidZpLBGqaeZyygqYCsV289XbbeqkyX0E0DqzldNu0yHMLHDPagbnfxdHIPcKJqJ+SGng+8MADaG5uxsKFCxEMBrF+/XqsXr0aq1evxsqVK/Hwww/D4XBAVZNTlltRFFx88cU73e73+3HeeecBAJ566qmkPFcmKvYpyHcLrNzCkwXtGoPP9NvYav5d5rtzO/AUQqDEp+D+TyJWNyUnOVWBaAZVtfW5zOm239RlyPzgLDBtohNxA3ieWU8i6qekBp6fffYZnnvuOUycOBGii2o9P/jBD/Dtt9+iqamp38910kknobW1FcXFxV3+fPDgwQCQlOfaHTsP2gs9Ak8u5ICOds/O/Tgb1TQb8DhFSvZPzDQD/AINAU6ftMKpExyIpmF9bTKPLxX5Ch78X5T9JU1OGe+ExwE8/hkDTyLqn6QGnn6/H4qy64f8+c9/jkcffbTfz6WqKny+7qcAbdy4EQAwYcKEXT5Oa2srWltbEYuGgWgAiGdXkOZ1CoR4ruizSCTS2UdaW1vR1tZm/iDaBkRat35lSb+55JkAA9AkiUUjCAVaO7/CQbPvJPrSvz9ogU8JIRbNjr6zK7vrUyVeBXEd2NTGQKK7Y862t7W2tiISSU6/8TpF2ooLJevYUlmgIBKXWNPI2TwJ3Z6rkiDPLXDyXk7MXxlHTRMzzUTUd0kNPL1eLxYsWLDL+4wbNw5Lly5N5tN26bXXXgMAXHnllbu8X1VVFQoKCvDsX68FXvsx8NldKW9bOrk0IMJ9z/ps1qxZKCgo6PwaO3as+YMHJgD/GLb1K8v6DYPP/vvPo7dj+rFlnV8zz50IYOsx59G/zcBDN07Ffx693dqG2oDfBajK1unHuay7Y06i3yS+Zs2alZTnc2sipft4poLfJVDiE/jdmyGe2zp0e65KknO/5wQAPLmQV7KJqO+ETOJRu66uDgcffDAefvhhTJkypdv7/exnP8M///nPZD3tTt58800cf/zxOPHEE/Hqq692Oe23tbUVBQUFqKmpQX5+Pi55NoqnPg8BigZorj49r92KMADAloCBj1fH8fiP/Tm/lqwvIpHIdpmF2tpa84R+4VeAM2/rHVVXn/uNndmxT2eKWDSCeGxr32nevAEzz52ImpoaOD15+PFTMXxvkERFoQsOp336TqouOuyuL324KoYz93Fi6jhnSp4/U3R3zEmcqxJcLhdcrv73m2/qdNzwShDfH+uA0s2G1snsE8k6pjSHDPx3VRwzjvPge9U5vhkuuu83LS0t2/WbvorpEpW/bUaJT+CbGwugKBxPZKPE2DhZ/YZoR0nNeA4cOBC/+tWvcNhhh+HHP/4xPv74453u8+mnn8IwUndVu76+HhdddBH22GMPPPzww10GndvKz89Hfn4+XG434PRlXfBQ5BFQFWBZPafH9IXL5ersI/n5+cjL6wg2nXmAK3/rV5b1mwROve07h9MFjy+/88vtNftOfn4+4MyDw+FCUX6erYLOVNpdPyryCjzxObMp3R1ztr0tPz8/KUEn0FHcSgKRWFIebreSdTwp9CgYWarilrdC2NzOTHm356okcagCFx/gwrJ6A28vS1NnIaKsk/R9PC+66CLce++9eO655zBlyhQUFxfjmGOOwbnnnotjjz0WRx999G6nv/ZVc3Mzjj32WDgcDrz77rsoLS3t8e/uJj7NWKoiUFmg4G/zueE29R2Dz+RKrKnTUrKTct9Z+TkP8CtoC0u0hjl1Mp0SM2FCaSgwlGyjB5iV26+Zw/2q0+GyKS6oCvCX+dm/Lp2IUiMlw55LL70UX3zxBS688EIAwLvvvosnn3wSTU1NeP3113db8KcvGhoacMwxxyAWi+HDDz/srGpL5r5nzSEO6Kh/GHwmj6NjR6kMW1qXUsVeAUUAaxo5OyOdfB0zmyNp3FIlWccSRRGYUKmhOSSxgtuGpVxVkYpz9nXizaUxfLCKWU8i6r2UXW8fPXo0/vWvf6GxsRFbtmxBS0sLFixYsMu1n321ceNGHHbYYZBSYv78+aisrOz1Y2RpwhMAUOITEAKsRkf9xqm3yeFzmkecSAZmmfpjV31HVQT8LoF6VrZNK1Uxt/QJRjPzfc93CxR5Bf7yHmf1pMPNx3ugKcCNr7CwExH1XlomehUXFyd9vUHCmjVrcMghh6CoqAhz587dbnrtpk2bcPzxx2PTpk27fZxkHD7tOiBXFXPD7fs+5PQYSg679vVM4XGY/8Z4LWg7HodAW4SD2XTzu4D2NL/vyTyGlPgUtITYb9JheKmKSw924cPVcTy7iGuyiah3bLbCqHeWLl2KQw45BCNHjsSbb765UwWuUCiEN998E6FQyKIW2kd1kYL1zQbWchobJQmDz75LTGtUM/oInHwOFXhtCafwpdvZ+zh3uRTD7tWt81wC7REJ3WDwmQ43H+9BiU/g6heDaOMSHiLqhYytQb5kyRIcccQR2Lx5M4qKinDooYfudJ9oNP1X4y55JmDLk3R1oYItAYmrXgji72f6UFHAES/1XyL4tGOft7OGgLkeLTHl1g7scCHBpQm0RbhWL91K/QraM3SqLQAUegQMCWxoMVBVpFrdnKxX7FNw20leXPxUAL9/K4Tbf+C1uklElCEyNvp4++23sXnzZgDA4sWL8fnnn+/0tXjxYkvaZocB3I4URWDfwSqKvALXvhSEwSvDlER27PN2Vtsi4dS2Trm1ml0+v8SUTx6f0svvFIjryNiMoc8J+FwCX2/kjJ50uWCSE5OHqPjLe2F8U8f3nYh6JmMDz+nTp0NK2aOvoUOHpr19dhnIbUsRAhMqNAQiEgvX80RBycXCQz334P8iKPIou91nOBvtqo8UehToBlDfnpkBUKZLd29M1vFCCIGhxQpmfxRBS4gZ83RQFIH7zvBBl8DlzwdYaIiIeiRjA89ky5Xhn88lUF2k4I65YWYVKCUYfO6alBKNQYkirz2OOnb6vPJcgNshsKyeF8bSKapLKMIMJjLVkCIFHofA9Dmc0ZMu+1ZpuPQgF+Z9F8fTX7DQEBHtHgPPDrmUeRhWoiIYlVjdyCvDlBrMfnavNSwRjkkU2yTwtBMhBCoLBO79IMLgIY2iunWFrpJ1nFA7lpM0BiXmrUjjpqQ57pYTPSj1CVzDQkNE1AMMPDskewho52IrfpdAgUdgObMKlGIMQHdW22JACLMgSq7aVZ8YWqwiFJVYsYUXxtJFSiAbrr0WeBSMr1Bx13thrOO+1WmRKDS0sVVi+hxOuSWiXWPgmaNKfQoe/YxTYyg9GHxuVdcmkecSUDN4WmMq+ZwCRV6B5ZsZOKSLpgC6hXF+Mo8PVYUKin0Cf3grnLTHpF37ySQnThzrwAP/i+Ke/3K/cCLqHgPPhBwbA5b6BNrCEs1BZhUoPZj9NL34VRR+V44dcHop3y3w/Je8MJYuboeAbmRuVdttCSFQ6lPQFsn815IpFEXgifN82LNMwfQ5Qcz5in+7RNQ1Bp4dkj0MtPsAu9Qn4NSALzcwq0DpZfe/jVSL6RIatxrcJacqEOUyvbQpcJtnwHDMujYk87ggZc5dS7ZcgUfB65fkoTxP4JxH2vHedxZ2JiKyLQaeOUpRBKqLVNzzXxbxoPTL5eynIQHOst01lwaE4zwupUteIvDMkvdcUYCYAa43TLNhJSrevDQPHofAD+5vw8IaXj0iou0x8Oxw7J422ck9jaoLFYRjEisbON2WrJGLwachAc0mkaeV7/+unjvPZWY8WSUzPbwOAAKIWBwnJKs/DvAJhKISG1rYf9JtfKWGV3/qR9wAjp/dxiKGRLQdBp4dUjEOtPug2ucSKPULzHo7ZHVTKIflWvYzEjczetS9Qo+AEEBNMy+KpYOiCDhVIJIlGc9Cj1m5fcZ/eG6zwsHDHXjuAj+aghLH/L0NNawwTEQdGHh2SFUCwu4D6lEDVGxul/imjicGslauBKDhmITXYX3G087vtaYK5LkENrQw8EwXtyYQzpKZkUII7FmmYlOrwW1VLHLiWCcePteHmmYDh9/ThqWb+DkQEQPPTqkcBtp5gFfqUzC0RMGvXg1iIwd5ZAO5EID6nNYHnnbncwk8u4jVMdPF7RC2yHgm62+/LE9BkVfgj+9wWxWr/HA/Fx4424d1TQYOvKsVc5ez4BBRrmPgSRg3UEWRV+CaF4MsNES2kQhAszEI9TqtboE97Oqz9TiAYJTHo3SZOs6BaJYlpQb4FTQG2Yes9JPJLrx5aR4A4Ji/t+GP74Q4ziDKYQw808TOA2hFCIyv0NAeYaEhsic7//30llMDVIuLC2XCe5nnEmiLSA5S08StATEbZDyB5PVPn9MsMsTqttY6cg8HPr0mH3tVqLjp1RBOur8d9W0caxDlIgaeHdJ1WrLrgM/vEij0CFagI1vLhiyoU2XQua3u2lPkUaAbQF0bg4Z08DqFrTKeyeinLs2sIm3l/qRkGjVAxSfT83HxAS68/k0MI29pxsw3ggxAiXIMA88O6byobreBX0KxT+CJz7mmijLDtkGoXf+muqKp1j13Jr1Pfpe57nDlFhtFQ1ms0CMQjEkYWZQdTKylrm9ncGMHHqfAv8724aWL/agsUHDzm2FUzWzGBU+0YxmLDxHlBAaeHdI9m8uOA8ASr4LWiESI66ooA2VKEOqyKONp9/dlR0IIDPAL3P9JxOqm5ISKfAWGYa+9U/vbZz0OczbP7XNZYMhOfrCXE0tuLMALF/px4FAND30axdg/tuCXLwW5rpsoy3E3uQ5vL+NcnBKfgCqAxRt1TBrCrkGZa9sB6+xpPgtbsjOPI73Pl2kB57ZKfQpqm+OI6xKaxVOUs90Av4DbIbCpXaLAY3VrkkMIgWElCpbU6WgNS+S72YfsQlUETp3gxKkTnPjfmjiufjGIP80L48XFUTx8rg8HDUvzgZKI0oIZTwvZbUDoUAWqChX8aW4YMZ1XHSk72C0T6knjVip2ec19VeQRMCSwies8U04IgfI8gbrWnaelZnI/qipU4NIE/u+1oNVNoW5MHqrhv1fm4a5TvdjQauDQu9twy1shRG1S7IqIkoeBp8XsdkIfNUBFzJD45Us8SVP2sUMQ6tFSH3ha/Rp7q7u2epyAIoDGINfopcM1h7vREpII2Gi6Y3/7saoIVBcpqGu1z2uinamKwFWHufH5tQXYa6CK37wewohbWnDjK0EsWh9nZWKiLMHA0wbsMBhOcDsE9hqoYmWDgbWNXOxP2cuqvztvijOedjiOJIsiBDwOwb0Y02RosQKnhi6znlbqb58u8giEYxKBCPuR3e1ZruJ/1+Tjjh94oCnAbe+Gsc+fWjF2Vgtu/k+IRYiIMhwDzw52Wflhh0Hj4EIFA/MUXP9yiFNuKSekMwD1OlPzuHa5eJVsBR6Bhxew2nY6KIpAgdvcPzWbFHoEFAEs43ZhGcGlCVx3pAerflOAj67Kw5WHutAclpj5nxD2nNWCo+9rxStfR7nHL1EGYuDZwU6HL6sHj0IIjK9QEdUlPlgVt7QtROmUji1aVCW5l7myJeDs7jWU5Qk0tBsIx+x0lM5eZ050ot2GgWd/+rhDFRhUqOC2d8OI82JqxhBC4MBhDvz1NB/WzyzE3F/k4Uffc+L9lXH84P52jL61BXfOC2Ezt8shyhgsXWpTlzwTsLQap9shMKpUxV/nhzF5iJby6YFEdrTjYNcuFXKzIdDsqYF5Cr6EjpVbdIyr4Ckr1crzFLSGzf08FZE9x/3RZSrmfRfDR2viOHQEK6ZmGlUROGKUA0eMcuD2kwz8/cMw/vFRBNe9FMJNr4YweYiGA4dqOGyEhinDNRR4mFchsiOexTvYcd36jsFnuoPRocUKVjbo+N/aOI4YxRM1UVcBXzr/JnMp4ExwqOb0z3VNBsZVWN2a7FddpEA3gOaQRLHXXoFnf86BHofA6DIVd84NY9xAFSU+BiaZqqJAwe9O9OLXx3rw8tcxPLIggg9Wx/HBqjjumGsWJNtnsIrDRzpw+EgNhzAQJbINBp4dbBh3Ath5oJnO/Qk1VWBYiYq73w/j4GEanGmoxkmUadIRjOZKwNldYJHvFnhmURQnjE3RAlnqVOIT8LkENrQYKPZm12B9WImCNY0GFq7Xcczo7HptucipCZwx0YkzJjohpcS3mwzMXxHDeyvieG9FDHfOC+POeWYguu9gFYcM13DoCAcOHaGhmBceiCzBwLODHTOeu5OODOjQIgUrNuv4coOO/avZXYh6ostAsa3nwWOuBJo95XMJ1Ldn4EE6AwkhcPkhLvx5XhijyyQcqsDsaT7b9Mn+nPcUIVDmF3h0QQTHjOYsnmwihMDYgSrGDlTx8ymAlBLL6g3M+y6GeSvieH9lDH+ZH8Ff5kegCGDyEA1H76Fhn8EaJlSqGFasQEny+nsi2hkjiQ5GJkaeaeDUBCryFdz9fhiP/MhvdXOIspJdBvV2VeA2t8Noj0j4XRwcptr+1RpERxXYvbJsXW2JT8Hapjj7UpYTQmDPchV7lm8NRFdsMTOiby2L462lMXy8ZmvxRL8L2GugitFlKvYo+//27js+ijL/A/jnmdmSzaaHJEAIHUVQRFAUUKwoIqKHiqBnBUQ59edhvVPPemI972zoWcDesMAJwintVEQRLIiCKCABAgnpZbNl5vn9MdmQkJ7M7uxuPu/XK68ks5OZZzPPPjPfp6oYmKnisCwV/TMU2FXmEyKzxNYdpQOiNe4MR6tn92QFX/8eQKlH5zgJIpMx6GxZiksAAthZrGNQV9Xq5MQ8p01g9klx+MeqagzuKiEibJKhjtz3shIF7Crw1e8BnHoIWz07CyEEBmSoGJChYvpIIKBJ/Jin4Yc9Gn7I0/D97gB+3Kth7e/1l9yxKcBhWSqOzFYxKEtFz1QFOakKeiQr6JGicAgSURsx8KwRrYEnEPqZNzPcAg4b8P0eDWP6MfAkotBpLKiwqwIpcQI7ijQGnmHSr4sKXwAoq5ZIdkXew3V7g09VEeiXruKpz6oxsjdnbO+sbKrA0B42DO1R/zG4rFril3wNm/M1bN6nYdNeIzh97ZuGawkrAshOVtA7TUGfdAU5KQqykxWkuwXS4hUMzFKRnSwiruKGyEoMPGtEcdzZQDAQNSsAVRSBnqkHllZxsoaPyBRs7Wy9NLfAG+t9GM8JhsIiK1HAZRfYUyaR7EJEjfPsqN5pCn7dr2HDrgCO78tWTzogKU7g6J42HN2zYUC6tUDDrhIduSU6cot1/F6sY3uhEaR+1sSa513cAkf1UDGshw1HZasYmm1DZqJAvEPAoYJBKXU6DDxr6LEUedYwcwbcfukKdhZruOH9KjxzQTwLSyIKq+Q4BTsKA9B0CZWTgIScEAJ/OsGJf62uRv8ukTnOrb2tnjZVIDtFwdOfeRl4UqskxQkMz7FheE7jr1d6JXaV6NhTpqOoSqKgQsemvRo27NKwZnsAn2xpGJiqCtA1UeDwbjYc3k1F7zQFqgCEMFpTnTaBnqkK+nUxWlP53EWxgIFnjWjuatsaHW0FddgEhmbb8PXOANbt1DCiF7MOUUfESutRKDQWUCQ6jQrCoiqJjAQ+gIXDsb2MSYY25mk4Kju2ujh3TzIqMvLLdWQmcggJdYzbKXBolopDsxp+TjRdYmuBjm93BfDDHg0lHokqv0SlTyK3WMfn2/xYttnf7PGzEgVG97FhdB8bhufYkOAU0HTAr0nEOwSyEhVkJIiIrCAiqovRQ41YbPFsTEcC0KxEBQMzVfz9vx48d6EbXZN4syai8HDXjMUrrpLI4ATbYRFnF3jgrHjctLAK/buoEdndtr2tnmnxAm6HwIZdAYw7jN23KXRU5cAMu1OHN3xd1yV+L9axq0SHLo2GEF0CHr+xffM+DWt2BLDwRz/e/6H5ADXdbQShWYkCXWu+G78bP3dPUtA/Q+WMzmQZBp41OkncWau9AWj/Lgryy3XcuqgK8y92s+sHEYWFTTVmIy2r7myltbUOyVTgcgjklmgY3DUyHxnaE3wKYXS3fekrHwNPspSiCPRJV9EnvfleBZVeiXU7A9iYp6E6IGFTBGwKUOmT2FeuY2+ZxL4KHfvKJX7Yo2FlZePjTgHA7QBcdmMct63mtElOgWxXhZlvjaiByLyLWCDWu9o2pa3jQIUQGNxNxWe/BfBLvt5otxIial6ktRpFC5sq4A100sLaIkIYS6vM+dSDnikyIls92ystXuCXfAmPT8LF2W0pwrmdAicNsOOkAa0bl+zXJPLLjaDU+JLYVarjl3wNRVUSHr+Ex290BZYASjwSn+xsvkWVqKMYeNborIFnXa1tBU1xKeiapODeZR68dglbPYnIfI0uq6IAPq2JP6CQOaaniuQ4gbW/BzCqT2Q+NrSn1TOxprthXpmOvl1YiUqxxa4KZKcYLfutVVIikPp0CBNFnR4H6dVg3HnAzHcqa7+aMjBTRblX4ttdfAokovBw2gVKPSytw01VBJ6b7IYqgK0FmulrRVslzi6Q7BJ4eEW11UkhiggKZwynEGPgWYMtnm2TGCfQJ13B/f/1oMLLfx4RhV6SE3j3u4YLuVPouRwCfxkbh9wSHUVVekQGn+3pAtwvXcWeUh37K/QQpIiIiOpi4FlDss2zUc3dyA/NUGFTBWZ/WBXGFBFRZ3Fw+ZPiUlDulfBxnKclhnRX0TVRwde/B1BYGRuBWrdkgQSnwB1LPFYnhYgo5jHwpHazqQJHdleRV6rjx7ymZ08jIjJDiktASmBfeWwEPdFGCIG5F8QjNV7B97s1zD0/3uokNdDWVk9FCAzoYrR6lnPGZCKikGLgSR2SkaCgd5qCuz72oJJdbokohFx2wK4CeWUsa6xiUwUePccFT0Bic76OZy+IvOCzrbomCagK8PM+zllARBRKDDxrCHBAdXsNzFKhKuxyS0ShJYTRLbKoii2eVkqNV3DvmS78Vqjhhzwt4oLPtrZ6qopAaryC3aXMV0REocTAs0ZMrggS8AJrHzK+h5BdFTiim9FVaXtheGuMvV4v7r77bni9oX2PnUqY8o2lLH6Plk/MEmnXuJn0HBxEOG0CVSGcX6izlymtff9HZtvw8Nnx2FmkI69MWpOnTczHdgX4+OfQrGHY2fNUqHSG/2tneI/UuTDwrBGTgafmBb562PgeYlmJCjITjS63MoxTBHu9Xtxzzz0slM0Uxnxjmc7wHpsTae+/DelRROgCBIBlSlve/2FdVfRMU/Dd7oA1426byTdtbfXUJTBuoN2slNXT2fNUqHSG/2tneI/UuTDwrKHGYuDZAcHa67bUYh+SoaC4SmJnMbsrEVFoxGQlYRR7YlI8uicbM93+7XSX9a357aRLQOUTERFRSLGYrcEbzgHtCToBY9xParzAnE+5GDdRS6L1Ad1qAuDiVxFEVQSeucCNPulGy+eOosgZ89mWVk+bAiz5KXQt6UREBNisToBVNM0Yi1hWVgYAkN4KwBtjC5P7yut/b4V/TnLDU1nW7lNmOTVsyddQVByALQzNyMHrF/weaiUlJcYPbfifRp125JuoY8V79JQCAKorD5zzn2cBN7zf9kXvOyzSrnEL6Zn5ahn+OckI1P2eAPwaUFYWmvHk4S5TWhIsc8KVnva+//tOk/jfbwE883k1KjNV/PMsNfR5uxX5uG7eaZY3gEp/aPKVFXkq3PnGCpH2WQ0Fq55xioqKwnI+ig1SSpSXl6N79+5QlOZb8oQM54C8CLJu3TqMGDHC6mQQERERERFFtdzcXPTo0aPZfTpti2f//v0BAJs2bUJSUpLFqQmN8vJyDBo0CD/99BMSExOtTk5IhPs95ubmYtSoUcw3Uc6K9xhJeSfSrnEkpSeS0gKEP99E2vtvTrSktbOXN6ESLde/I8L9Hnfv3o3jjjsOubm5MZtvyHxlZWXIyclpVR7ttIGnqqoAgB49esTshyvYNSM7O5vv0WTMN9HNyvcYCXkn0q5xJKUnktJSV7jyTaS+/8ZES1o7e3kTKtFy/TvCqveYlJQUs/9TCh3Ritn/OKUOERERERERhRQDTyIiIiIiIgopBp4xzOl04q677oLT6bQ6KSHTGd5juHWG/2lneI/NibT3H0npiaS0WCGa3n+0pDVa0hltOsP/tTO8R+pcOu2stmVlZUhOTkZpaSn7sVOr7dq1Czk5Ocw31GbMO9QezDfUHsw31B51882mQheOf6IcG29NxqCuqtVJowjWlpiKLZ5ERERERFTrr4s90CXw7BfVVieFYggDTyIiIiIiqlXlMzpEuh0tz1RK1FoMPImIiIiIqJZdNQLOgG5xQiimMPAkIiIiIqJa9pphnT6tU04FQyHCwJOIiIiIiGo5agJPv2ZtOii2MPAkIiIiIqJaTpvR1dbHwJNMxMCTiIiIiIhqOWzGd2+AXW3JPAw8iYiIiIioll2pafEMWJwQiikMPImIiIiIqFawxZOTC5GZGHgSEREREVGt4BhPTi5EZmLgSUREREREtYKz2nKMJ5mJgScREREREdVy1K7jaW06KLYw8CQiIiIiolo2NTi5EFs8yTwMPImIiIiIqJasiTf9urXpoNgSU4Hn008/DSEEhBDYsWOH1ckhIiIiIoo6wXZOLqdCZoqZwHPnzp247bbbrE4GEREREVFUC7Z4cnIhMlPMBJ4zZ85EWlqa1ckgIiIiIopqsiby5ORCZKaYCDxfeeUVLF++HE899ZTVSSEiIiIiimp6cIynxhZPMk/UB575+fn485//jFtvvRVHHHGE1ckhIiIiIopqtWM82eJJJor6wPPaa69FZmYm7rjjDquTQkREREQU9Q6M8bQ2HRRbbFYnoCMWLlyIBQsW4LPPPoPT6bQ6OUREREREUe9Aiye72pJ5ojbwLC0txaxZszBr1iyMHj263ccpKyur97vT6WQQS7W8Xi+8Xm/t7+Xl5QCYb6hlzDvUHsw31B7MN9QeTeUb4MAYTy6nQmaK2q62N954I1RVxZw5czp0nJycHCQnJ9d+dfR4FFvmzJlTL38MGjQIAPMNtYx5h9qD+Ybag/mG2qOpfAMc6Gqrs8GTTCRkcL7kKLJixQqcdtpp+OijjzB+/Pja7Tt27ECfPn0AANu3b0fv3r2bPEZZWRmSk5ORm5uLpKSk2u2sDaS6Dq4N3L17NwYNGsR8Qy1i3qH2YL6h9mC+ofZoKt+Ulpbi1qUqnl1jvCb/yeUKqWnBmKq0tLReedOYqOtqW1VVhRkzZmDq1Kn1gs72SkpKavGfRJ3XwTfpYLcl5htqCfMOtQfzDbUH8w21R1P5BjgwxhMw1vQUQoQxZRSroi7wXLduHbZt24Y9e/YgISGh3mt1G28HDx5c+yF57rnncPHFF4c1nURERERE0ahuf0ifBjijLmKgSBR12WjEiBHYunVro6/t3r0bJ510EgBgyZIlyM7OBgBkZWWFK3lERERERFGt7thOb4CBJ5kj6rKRy+VC//79G33NZjvwdnr16tXsGE8iIiIiImqobldbb0ACYFdb6riondWWiIiIiIjMV3f4WrU/6uYhpQgVdS2ejSkqKoLP50NBQUHttoKCAsTFxcHhcCAtjbNxERERERG1hjyoqy2RGWKixXPSpEno1q0bRowYUbttxIgR6NatGyZNmmRhyoiIiIiIokv9MZ5s8SRzxESL56pVq6xOAhERERFRTKg/xtOyZFCMMbXF84cffjB1PyIiIiIiCq+6XW2r2eJJJjE18Hzqqadatd/TTz9t5mmJiIiIiMgkbPGkUDC1q+17772HQKDl3Llo0SI899xzZp6aiIiIiIhMwDGeFAqmBp6lpaVYuXJlq/YjIiIiIqLIw1ltKRRMDTwvuOACvPnmmy3uN3XqVDNPS0REREREJqkbeHq4jieZxNQxnuedd16r9jv//PPNPC0REREREZmkbqhZ6bMsGRRjTA08+/Tp06r9evfubeZpiYiIiIjIJHVbPCu8bPEkc5gaeD7zzDOm7kdEREREROGl14k8yxl4kklMHeP51ltvYfv27S3u9/XXX+PFF18089RERERERGSCuqEmWzzJLKa2eEopG3z99ttvDbYREREREVFkYldbCgVTWzwvvPBCzJs3r962v/zlL5gzZ069bdOmTTPztEREREREZBK2eFIomNried111zXYJoRosO1Pf/qTmaclIiIiIiKT6DrgtAGqwjGeZB5TA8/09PQG2xrrWtvYfkREREREZD0JQAgg0SnY4kmmMTXwPLhLLdB4i+eDDz5o5mmJiIiIiMgkUgICQIJToILreJJJTB3j+c4778Dnq587169fj71799bb9tFHH2Hu3LlmnpqIiIiIiEwQbPFMcHCMJ5nH1MCzrKwMK1eubLD94G3FxcVmnpaIiIiIiEyiS0ARRotnYRUDTzKHqYHn5MmT8cYbb7S438UXX2zmaYmIiIiIyCTBrraJcQK/F+tWJ4dihKljPC+99NJW7XfJJZeYeVoiIiIiIjKJlMY8LQkOTi5E5jE18Bw3bpyp+xERERERUXhJHJhcyOMHAhqDT+o4UwNPIiIiIiKKblJKKIoReAJApY+BJ3UcA08iIiIiIqqlB8d41gSe5V5r00OxIeyBp5QSmzdvxg8//AApWXtCRERERBRJgsuppMYbgWdRFScYoo4Le+A5Y8YMjBo1ClJKPPfcc/jwww/DnQQiIiIiImpCcFbb9GDgWdn2xiJvQKKgggErHWDqciqtcc4552DAgAE48sgjceSRR2Lz5s3hTgIRERERETVBwljHM91tBJ7tWcvzqrcr8fa3Ptw+1oXqgMQ1o+PQI4Wj/DqzsAeeZ599Ns4+++za3wcOHBjuJBARERERURN03ehqm+42AsXCyta3XJ73Ujl+zNPwS4HxN3/72AMA2JSn4cPpieYnlqIGqx2IiIiIiKhWcIxnsKttYSu72m7KC+D9H/y1QecTk+Lx9mVuxDuARZv8+GKbP1RJpigQlsBzwYIF4TgNERERERF1UO0YzzZ0tV3xix+HP1QGALjx5Dg8PNGFa0Y7MfkoJ/57dSKkBCa/XIHyak4u2lmZ2tX2kUcegcfjqf39jDPOwLHHHosXX3wRGzduhKqqAIDU1FRcd911Zp6aiIiIiIhMUNviWdPVdm9Zy11t535RXfvzrafGISPhQPvW6L52zDreiWc+92LIw6XYdmcyhBCmp5sim6mB59ixYzFhwgRceOGFmDZtGg477DAAQEVFBe677z4AQEZGBlavXm3maYmIiIiIyCS6BBQhEO8QGJChYN3OQLP755frWPSjHyf0teH1S9z1gs6gmaOMwHNHkY4t+ToGZqmhSj5FKFO72u7fvx/PP/88HnvsMQwaNKi2JiMzMxNerxeBQADPP/88Vq5caeZpiYiIiIjIJMGutgAwuo8NvxToTU4wtHZHAFl3lsCnATedEoec1MYDyiHdbVh8VQIA4NNfONazMzI18Pzss89w5plnNtg+aNAg2O12KIqCiRMnIjc318zTEhERERGRSaSUCPaEPaan0UFyfa7WYL+9ZTrGPFlW+/uEQfZmjzumnx02Bfh0CwPPzsjUwLOqqqrR7cFutkF+PzMbEREREVEkkjjQ4nl0jhF4Ntbd9uZFVfDXxKPrZidBUZoft5ngFDiutw0rfw1A0znJUGdjauC5e/fuVu23Z88eM09LREREREQmkRJQaqKEI7NVOFTg8231A8+r36nEa9/4kJkgUPZgKo7u2bqpY07oa0NZtcTP+xq2oFJsMzXwtNvt+OKLL5rdZ9WqVbDbm2+GJyIiIiIia+h1xng6bQIn9rdh1W9+BDSjlXJ/hY7n1ngBAHeNcyExrvUz1I7sbQSoX2xrfsIiij2mBp4333wzzj//fCxatKjR1z/44ANcdNFFuPnmm808LRERERERmSS4nErQcb1sqPYDpz9bjif/V403N/gAAA+cZazV2RZj+tngsgOvfOMzMcUUDUxdTuXwww/HI488ggsuuABZWVkYOHAgsrKyUFFRgfXr16OgoADz5s3D4MGDzTwtERERERGZpO6stgAwvGac58qtAazcarRUprsFrh7tbPN6nMkuBecd6cBr3/iQX64jM9HUdjCKYKZf6T/+8Y/44osvcMQRR2D16tV4/fXXsWTJEgwbNgxr1qzBlClTzD4lERERERGZRAKoO0/QGQPtmDnKianDHLXbJg1xIDW+faHEif2MQHbNdna37UxMbfEMOvroo7F48WL4fD4UFRUhMzMTisLaDCIiIiKiSKfr9bvaxtkFnp3shpQSH//sR4lHYliPxtfrbI3RfYz5Xv73mx/nDnG0sDfFipBGgw6HA1lZWVi8eDHmzJmDd999F4EAazaIiIiIiCJV3eVU6hJCYOH0BEwYbMcFQ9sfMA7MUtCvi4KX1/lQ4eWyKp1FSFo8g0pLSzFu3Dj88ssviI+PR2FhIR544AGsWLECqampoTw1ERERERG1g5T1WzzrGtPPjjH9OrZChRACt50ahxlvV+GtDV5MHxnXoeNRdAhpi+cDDzyAhx56CIWFhcjNzUVFRQUefPBBPPDAA6E8LRERERERtZMxq23bJg1qq4mHGy2mX+/kep6dRUgDz65du2LMmDEHTqYoOOOMM5CcnGzK8XNzc/HAAw/gxBNPRHp6Oux2O7p06YJTTjkFL7zwAjSNGZmIiIiIqC10WX9yoVDITFSQnSzw7S4Ow+ssQhp4NjWe0+Ho+CDi1atXo1+/frj77rtx7LHH4uOPP8aWLVswf/58lJSUYMaMGTjllFNQUVHR4XMREREREXUWUspGx3ia7ageNvywR4Nf4zjPziCkgWdRURFWrFhR+7uUEv/973+Rn5/f4WMXFxfD7/fj/vvvx8MPP4wRI0agb9++mDBhAv73v/8hJycH//vf/3D33Xd3+FxERERERJ1Fc2M8zXRUtgqfBvy8j70UO4OQBp533HEH7r33XiQnJ6Nbt25wOp245ZZb8Ne//tWU4yuKgunTpzfYnpCQgEsuuQQA8NZbb5lyLiIiIiKizqCpWW3NNqS7Mc/pj3kMPDuDkM5q63a7sWrVKnz44YfYvHkz+vXrh3POOceUrrZnn302ysrK4Ha7G329R48eAIyWUSIiIiIiah0pASWkzVOGwV2NtUA37WXg2RmYGni+9tpr2LVrF0pLS5GcnIwbb7wRdrsd5557rpmnAQCoqtpk0AkAeXl5AIAhQ4Y0e5yysrJ6vzudTjidzo4nkGKC1+uF1+ut/b28vBwA8w21jHmH2oP5htqD+Ybao6l8AxiTC4WjxbN/hgKHyhbPzsLUuozLL78c3bt3x9///nfcdtttsNs7tsZPRyxevBgAcP311ze7X05ODpKTk2u/5syZE47kUZSYM2dOvfwxaNAgAMw31DLmHWoP5htqD+Ybao+m8g0QXE4l9GmwqwIDs1QGnp2EkFKaNo3UuHHjsHTpUgDGjLZ79uyp93rPnj3NOlWzli1bhnHjxmH8+PH46KOPGl2HqKysDMnJycjNzUVSUlLtdtYGUl0H1wbu3r0bgwYNYr6hFjHvUHsw31B7MN9QezSVb0pLS3HUv3SkxQusu9GcJRCbc9ErFXjrWx/KH0yF2xmOdlYyUzCmKi0trVfeNMbUrraHHHJIvUS88soruPfeezFt2jQcffTRmDZtmpmna1R+fj6mTZuGQw45BC+//HKLi98mJSW1+E+izuvgm3Sw2xLzDbWEeYfag/mG2oP5htqjqXwDGC2eoV7HM+jwbirkBmD9rgDG9LOutySFnqmBZ93Mm5aWhjvuuAM//fQT5s6dW2+/V199tXbWWTOVlJTg9NNPh91ux/Lly9GlSxfTz0FEREREFMt0PTxdbQHg2F5GOPLQ8moGnjEu5PNV9e7du8G2VatWmX6ewsJCjB07Fn6/H1988UXtrLbtFdAkNu/TUOrRTUohEREREVHkC9dyKgBw6iF2nNDXhuW/+OELmDYCkCKQqS2eb7/9NgoLC+ttW79+Pfbu3Vtv24cffogXX3zRtPPm5eVh7NixiIuLw+rVqzvc0qnpEtPeqkRRpYSqALeeGodjetpgU9nvnIiIiIhim7GcSviee08eYMNn2wLYkq/hiO4hXe2RLGTqld2zZw9WrlzZYPvB2+pO19xRO3bswGmnnYZu3bph8eLF9cYy7Nu3D5dddhlefvllZGVltfqY63M1lHgkTuhnw75yiYeWV8OuCtw/3oVDs1TT0k5EREREFGl0KcPW4gkAQ2qCze/3MPCMZaZe2cmTJ+ONN95ocb+LL77YlPNt3rwZY8eOxeDBg/H+++8jPj6+3usejwfLli2Dx+Np9TGllPjHqmr0TFWQ4lKQ4gJ6pSrYnK/hpoVVuO20OIzqY2tx0iIiIiIiomgUzsmFAODI7kbDzg97uKxKLDM18Lz00ktbtZ8ZEwtt2rQJJ598MgoKCpCamooxY8Y02Mfn87X5uAUVEpVeiaOyD7RsxtkFhmbbkByn4cHl1ejiFnjo7HhkJoZ8iCwRUdTzBSQKKiQKq3SUeiSqA4AqgIwEgUMyVThtrMjr7Kr9EsVVEiUeCW/NGC+7CqTGK+jiFnAwjxCFlS7DN7kQAPRNVxDvADYy8IxppgSeb7zxBhISEjBu3LgGr+m6jrVr12LUqFG12xrbr60++eQTFBQUAAA2btzY4eMFbcnX4LQJpLgaftr6pKtIcQlszNMw/a1K9ElX8OCEeLgcvCESEQVJKbGjSMfP+zS89o0P5V4JSKP23GUXsKnGQ02lV0JRgL+c5sKIXuxa1ZmUV0tsK9Tw3BoviqskKrw1E4oIo1ICMPKIlICqAHee4cLwHOYRonCRMnyTCwHGeNLDslRs2svAM5aZUopPmDABy5Ytw+WXX45DDz0UN9xwA5KTjQVnhRBYsmQJfv3111a3iLbGDTfcgBtuuMG04wW9uNaLzETRZFfa1HgFJ/QV2FmsY3O+hotfrcAdp7twVA+V3W+JqFPLL9fxY56GZ7/wwuOXSHYJZCYoODRTIMkp4HIASp1y0heQ2Fao475lHsw524XDuzGwiFUen8T2Ih3bCzW8+o3PqHQQQGq8QLckBanxAgkOAZf9wIQmui7h8QO5JTru/tiDfl1UPHauC2o4+/8RxTBdl8grk/D4JXqk1O/FZ0wuFN70DO6qYn2uD2XVEklx/JzHIlPu8klJSbjgggtwwQUXYO3atZg6dSquvvpqTJw4EUII3HPPPZg+fbqpgWcoBDSJYo9ETmrznzQhBHqlqeierGBrgYa7lnrgsgmkuQVuOjkO/booDEJj3Pe7A0ipCEAIQNONr4AOBHQJTTdq6oMUYdTYCxh5R1WMbcHtigDUmu02BbCpgE0RcKhG61DdBzGiSFNcpWNjnoa5n3tR4ZWIswv0SFHQI1lBYgsPDg6bwMAsFWXVEv9Y5cVLUxl4xhIpJTbt1fDYymoUVhqFYnKcEWh2cQukxYtmg0hFEXA7gYFZKuIdwI95Gq56uwovTInnPZY6Nb8mUV4tkRovUFQlMfuDKng149kj1SXw2LnxSHAanxEpJfaVS2wv1FBYJVFYaXRp79dFwbyvvPAFjGOqCnD7yANLCOphbvEEjMATAH7aq+G43rwfxCLTruqqVavwzjvvYPv27aiqqsLkyZNx1FFHQdd1/Prrr7j66qvNOlXI7C7VoetAenzrqnjsqsCgrjb0SJHYW6ajoFLHnz+oQmaigjkTXBwDGsMeWu6B4jQWOQ4GkYoQtT/XfSaSEtABwOhtCF1KY5us/1qTBOBUBeLsRiB67hF2JDgF4h0CcTYjHwbPqQjjRqEIo6YymKZgUGtXBewK4LCBrQbULn5NYleJju2FOl76yotSj4TDBnRPVnBktopUV9M9RprSJ13B2h0BrM8NsDtlDAhoEr8U6HjgEw9KqyW6JSkY1kNFRoKAvZ3LkvVMVWFXBb7JDeDyNyrx0Nnx6JrEeyxFBymN1vu9ZTp2leoorDQqqeMdRqBYHQBe/cYHBcAfj3YgM1FBissY9hVfM5zL45P4db+GZz73Iq9Mh6YDaW6BPxzhQFGV8RCRkSBQ7JGY+XYlbjk1Di67wAOfeFBYaZTTwYpyAPh0C3BoloruSQocKrBssx/3LjswGWe4JxcCDgSemxh4xqwOX1UpJaZNm4b58+fD6XQiPT0ddrsd3bp1Q35+PoqKinDXXXfhuuuuMyO9IZVboiOupoWpLZLiBJLiVBwCFUWVRu3/tDcr0SVB4LZTXeifofAhP8aMPdQBl9th2vHqBqOaDmg1332ahC8AeAPGTas6IPHudz54A8Zrfs0IXttDVQCHKuCwGRNoXXiUA92SBHJSFCS7+EBHB1T7JTbmaXjm82oUVUroEnA7BDISjTE56W5RrwttW2UkKOjXRcUDn3jw1mUJ7Q5OyFq7SnTM+dSDvFIdfh3omqjgyO6qaeVJtyQFJ/S11bR8VuIvp8VhZJ823rCJDlJWLbFxTwAuu8CR2Wq957Uf8wJ48n9e6BI45wg74u0CThuQGCcQZxOo8EpU+SUcqkBmgkBmogKXHfj6dw2Pr64GYFQGewPG/RoAXA5j6IGiAF6/RJXfuB8H5xZ59gsvKv2ytkLaYTN6QVX5JRQAKfFGuZvgFPh5n4YX13pr06soAsf1UvHTXg1/W+KBLo3jju5jQ2q8gF8DPt8egK4DI/vY4K4zR8mR2SrW/3zggSLckwsBwOHdjMDz+90BAM7wnpzCosOB50svvYSSkhJs2LABRx55ZIOa7kWLFiE3NxeqGvnrX87/yoe0+LbX1teV5lYwpp9AQYXEtiIdNy2sgl01tl8+woGeqUYXNHadpLoUYTRVqjBmcjyg+XwipYTEgeAz2Ioa3Bb8bgS0RsAQqKnx9GsSPg3waYDHLzH/Ky/KvUYA7HYa3eAuO8aJdLeA22EEqIowjuULAJU+idJqiaIqiQXf+VAdkAhoxo0qGNROGmJHultBRoIx1s/tZL6PFpoukVus45GV1dhdYlSRZyUarZrp8cL0SdX6dVGws1jD9e9V4ZkL2JUyWui6xE/7NDy8vBrFVca4rAEZxlAUl938a5jiUjC6j8Bv+3U88Ek1/joWGJZj48zI1Crl1RJFVTqEMCrPnDaB5b/48VJN8JbsErhouMMI9HyyZt4PBTYFeO97H3wBwK8bAaMujaExDlUgoMvaLquKYtwn+6QpcNoEpDSCR7dDINEpENeKz4WmG7N/V/mMibc0HUhwCqS76/cayEhQ4PFLVPslVEUg0WkM6RnRywZdl9Ak6u3vsAGnDGi8sqZnqgp3XzuW1vwe7smFjDQoyE4WWP1bIMxnpnDpcOD5zTffYMGCBVCaGIE8ceJEXHvttSguLkZqampHTxdS5V6JnqkKZr5TWW/7c5PdbTqOEAKZiUbNV6VPYl+5jqIqiX+t9qLaL+GyC1w3xokjs20cPE0dIoQwbgw12aj56p3W3eyKqyT2V0oUe3Q8vMJTezNtjE01bqYuu0BSnIBdEZBA7U34zQ0+VPoO3JCdNoEEJzDpSAcSaoJZKQGP3wh+P9rkh1ZzsztrkB3JLuO4wUlH1Jobul8zWn99AePnQE1QrQhj3KDLbowlS4tnsNtaWs0kEzuKNMz7yofCSqMrV1KcMQ4zJ0UJaUuk0yYwPMeGr34P4NtdGoaxy21Ek1Jie6GOv33sQVm1RNckBaP7qEjtYOVtawgh0D9DhSaBBz6tBmpadV75o5sVFp2QNyDxe5FeW3EqhBHAZCQceC6VUuKnvRr+8pGn2V5CTpvAO9/6au8nh3dT0Se94Z1VSuOeE2wdlVLCGzCeIz1+ieQ40aGWflURcDtqepYkNL+vMRdEw3yvKAJtTUHdRhEp0aGeLO0hhMDJA+x47RsfCir0eteQYkOH7+wJCQlNBp1B11xzDV599VVcf/31HT1dyPg1o7tEQiMPqTPfqWxz8Bnkdgj0TVfRN934vcIrsbNYw+OrquHXDtSuZSQo6J6koGtS85MtEIWSqgh0SRDokgAEw1h/TXdfTRo31+AESQ4VrQ5Eqv0S5d4DXx/+YHQXDujGQ4JdAew2Y0IlhzBaapf87IfHJ2sDzAZEzWRMyoExrlIaQWjdLshOm0CSS+CPwx3ISBBIcBo1xro0jltd043Zr8naCaBsCuCqGVMb7zC6VsXZREyNjfUFJHYW6/i9WMcr67wo8UjouvH/SncLDO6qokuCUq8rVqhlJCjolqTgqc+q8dJFLTxtkWWKKnXcuLAK+yskMhIVjOlnTSXqoZnGpEPf7dLgDEHrKkUOX8AI7Cq8EnvKdOwu1fHRJj+q/RKlHiMItKuo7c4KGDMml3uNci3Y+yc7RcGgLOPe5g1IBHTjPuKouf+0tnwXQqDu7U8IYx6G1rRmRgtdyrB3tQWAU2sCz7uXevD0+e179qbI1eHAMz4+HuvWrcMxxxzT5D6DBw/G008/3dFThdS+cqNf4t1LPY2+3pHgs64EpzEh0aGZRqtSfoWOtzb4UOE1HpZVBeiapODBCS6ktHKSI6JQsquiTvff9t2F4uxG96KWam6bouvGA0JwtmCbUjNTcBN3RV0eeEgpqza+nv/SiyqfrDfjMFAT+KrGzMLAgQmgmho/G5ysSVWMB4+6EzdNGGw3AlaH0T3ZrgJ2xfheUGz92mTegMSmPA1PfWbMMqpLo0xKixfomaIgNV6B29H0/zUcjEqE2Hl4iyWaLrFmewCPrqxGgkNgdF8b0iy+T6XHK3DYNPgCEj/maeiTrjZagUyRR9aUs8FyOtiVNPj5r/BKbNgVwDOfe1HpPVAYq4oRVDpUgWSXQO80FZkJAo6aMZdf/R6AgNHrpWeqAqWmZ1Cc3fi7YCteLAWJoWDF5EIAcMkxDtz7Xw8WfOfDU+dx2EWs6XDgec0112D06NF4+eWXcfzxxze5XyAQ2f21Cyv1FtcrMiv4BIyH1qxEgayamW+llKj0Afsrjdkir3izEnee7sIR3VVOtBGBbni/EnC2btyyWXmmM1MUAUcbnm8VIWpbLOsGu7o0xqFqNS2tzQWwwYcivwb4dePvAnWWzdEloNdMBOWvmejpo01+Y0Iozaihr7u0jqe48UqtUJNSIrdEx4OfVmNPqQ4JY6zmkGwFGW4l4h6+UlzGhBnsZhVZfi3Q8LePPajySRyaqaJvWmTMVRDvEBjZ246NeQHcsdgDCSAnVcHDZ8ezm32E2leu447FHuyv1BE4qD7OZTeCyUqf0TvGoQLZyQoyuqqwq8YM7wevB1xXglPg1EM44ZQZrJhcCDCej88aZMdTn3mxq0RHTmrkzxFDrdfhwLNr167461//ihNPPBEXX3wxrrnmGowcObLePl9//TV0XW/iCJGhtFq2aiKE4PhPs4MJIYyxbwlOYyzVz/s03L3UAwFjBrNrT4jDEd1UOG3GZDA2JXa6/MW6g8cMdwSD2I5RhNFdtjVEzb7G/u3/rOnSCECL8u1Y3u6jtJ4vIJFfYSzx9NJXXuSXS3gDxqQvh2Wp6GHSWM2W8nV782qvVAV7SnVc/U4lnpjkRnYKg08rFVbq+MtHxky1XZMUHJNji7iALilOYHQfOzTd6Ia5eZ+OKS9X4NnJzD9WK6+W2JKv4bk1xuR1vprZXVNcRnnkshvLgsXZBTx+Y5x5pU8iK1HBwEyBjAQOP7KKlNa0eALA0Tk2AF58k6sx8IwxpszeMG3aNPj9fsyePRuvv/46kpOTMXz4cGRmZqKgoABr167F559/bsapQqbEI9u0jIqZrZ8HUxWBw7vZMCBDYn+F0R337//11A5213Sja+BDE+MxIIMfyM6kLUEsg9TIoAgBpWbmQ7NJaXTZ/22/jnk1a2pW+owmVptqdDXrk64gK1ExbQxea/Nge8tIRRE4rrcN63YGcNPCKrx5Gcd6WuWbnQHcu8wDt8O4JpHeAq0qAjkpKqQEvt+todijM/C0iDcg8f1uDff/1wNFAF0SFPRIUeCsmZCubpfaIKdNIMVlUYKpAd2CWW2Djs4xnm2/+j2APwwxb+k6sp5p0wZeffXVOPnkk/Hoo4/ivffew/LlRt3+8OHDsWTJEgwZMsSsU4XEe9/5kORq20csVK2fQU6bQHaKQHaKgsM1iYIKiYAuEWcT2FWq4+aFVZg5yokPNvpR6ZVwOQT+dLyzZsIF1hB2dma0tDJ4jSxSGmOhdhbreHaNFwUVEh6frF2yqUeKEWAmxgnE280dK9me/NTe4FNVBHJSVWzYFUBAk7BxuEFYVfkkbl5YhZ3FOg7NVNE/Q2mya2Nj+cLqckOr6WD1yIpq3HhyHIZ05wzJZtJ1iRLPgbWly6uNpT/KqiXe3OBFpdfYDgB90hUMzFTZahmFpEVdbQHgsCwVyXECa7ZH9jA9ajtTS+NDDz0Uzz//PJ5//nkUFRXBbrcjMTHRzFOETKUPeOUbb8s7NiKUrZ9BdlWge/KBEqBLgsBWh8Dr631w2Y2lWyq8EncvNaYK75Ou4PFz4yNiDA5Fr0h8qOxsSj06thboeP5LL4qqjPXahDC6quWkKMhMMMZEhWLaezMqL9pbPqbHG0sB/JinYWgPBg7hsqdUx/+9XwUAGJ5jQ/fkhi2GLeWLUFfKtiQ4wdDOYh23f+TBY+fG45BM9g7qqN0lOu5Z5kFBRf2xmWrN5GpOm9HtOSNBIN4u0MVtTPhD0cmqyYUAo+fLyN42rPrND19AMh/FkJDdzdPS0kJ1aNPpuoRXa2Zhp1YI941WEQKHZqo49KCbqV+T2FOq48e9Gqa9VYmrR8UhM1Gge7LCBbbJFO0NRhiwNk9KoxVhX7mx9u8b630orZao9EqoitGi2SdNQWq8QIortOOezByXHDxeW69/XE2F2tOfe/H8FAae4bBxTwB3LvEgza1geI+GE9uZnS9CKSPBWMexsNKPR1dW498XsvzpiOIqHX9aUFk7VjzFJeBUBWyqMecEZx6NPVZNLhQ0uq8NSzf78e0uDcf25j0gVvBKwli8Hh2LO2uFo/WzOXZVoFeaUdv7234dD6/wwBcwaq2ykhTceXocB2qTJUI1GU2021eu495lHuwrMyYBAowxUIlxAtnJCtLijeVOwtFVLdICi0SnQF5ZZE9MFwuklPhiewAPfVqNvl0UHJapNugtE86u1mbRpYSEhJQMitojoEkUVkn8XqTjX6ur4bAJjOptY0+qTsKYXMi6az2qJthcsyPAwDOG8ErCGJdgJqu7GQFAultButvoIlXtlyioMBaKn/VuFTISBB47Nx6pXCeUIkhHg55oC1yllJj9oQe/7teQ6DQmAeriFkh0irCPaQxHwNmeICTFJbB1v9HqG2kzqcaKsmqJG96vREGFxBHdVfROq18x2dG8YeX9UABIcAgUVOjYsk/DoVmsdG2N3SU67vzYg8IKHbo0Jirr4lbQLz38S+jUzX/RVsZHOysnFwKAEb1sUBXgi+1+/PmkOAtTQmZi4AljKZVQiIQAFDC6rOWkGssoFFZKbNqr4aq3qzDvIjcX2qaYEW0z/n6xPYBt+zUc2d1YQsmKrmrhbuFsa/DZxS2gCmOcJ2u8zecLSFz1diUEjG5taXUqI0PR3RoI72dPCGM23h/zNNy8qAp/P8uFIzjRUAO6fqCLf16ZjrlfeJEaL3Bktg1JccZSb1a0fB2cB+uWH6Equ1pz/Ei4f4SalMZzsZWN2wlOgeE9VKzcGoCmS05QFSNYAgOo8BqzQoZKpASgQgh0SRAY1Ufgs98C+L/3q/DClHiOzaBOp6mHinB9RvdX6Hh0RTUGZKroaUHXdyu71LYl+LSpAj1SFDy+qhpvXuZmWWWiSq/EnxZUwheQOKm/HXF11rGOtC7XHaEqAkO6q1AV4I7FHjxxXjx6pbHlEzC6+W/IDeDFtT54AxKKYrQQH5alom96aCvD2pvHQp03W3P8SHmmC6WauNPSMZ4AcOZhdtyzrBpf/R7AqD5tWPOQIhYDTwBV/vDMmGX1eJcguypwZLaKNdsDyC3RLXnwJYpE4ZjFt9Ircf1HVUiKE+ifHv7u7pEQVLSlLOyZquD3ogD2V0pkJDDwNIPHJzHtrUpICYzsbQtr0GlVy2f/Liq2F+rYVqh36sBT0yVyi3U8vKIauSU63A6B3ukKuiYqSHR2fJKgSChfwiWWW0WD/QCtDjzPGuzAPcuqseQnPwPPGMHAE4A3ANjD9PwXKTVl6W4FCU6BLfkMPImaY9qDVLlxnDuXVEEgESN6hneSjmh9IPTWLOPm5trEptB0iRs+qIKmAyf0DW/QWVe474UlHuNROi2+8+ajggod1y2oQqVPIsEpcFS2DdnJos3BZrSWJaFm9XOdmYItnlb3bh3eQ0VmgsDin/y4/yxr00Lm4OwyMJYgUcL8n5j5TqXlhXdWosALX3qh6aEZ40pEDe0q0TGoqxq2dckioaxpTGvTFNCNWncnq0k7TNclrn6nCvvKdQzNViOie224zpuRIJCRqOCOxR78WqC1/AcxpqRKx58WVMFhA04ZYMfJA+zo0Yax5cFyJBLLEjJf8LHQ6moaRRE48zA7vtut4ee9ne9zG4sYeALQdOtqdawsxHulqfAGjAeRKh+DT6JwcDsEuiWFp2t/pD8ktiZ9SU4BKYF95SyjOmrNjgDyK3Qc19uGjITQTSTUVuHIq6oiah94uiZ1nkefCq/E8l/8uPLNStgV4JietlbNEF030LQ6f0S65ya7Y6q1EzjQ1dbqFk8AuPQYJwDglXVei1NCZmAdcg0rP1tWjf10OwSO7WXD+lwNU1+pQFq8wF9Oc+GQTHa9JQqVNHfbu7a1VSw9KCY4AbtqtBR3T+48AYPZqv0S/1hZjQFd1Hqz10aSUN8Le6Up2Feu488fVOH5KbEVKDTlqrcrEBevom8XFf3SlWZnBo2lciMcYi3YrEuvWT7Z6jGeAHBSfxuS4wTW/h6wOilkAgaeMD5YVtelW7VWVbpbwckDjEXa95ZJ3LSwCq9fkoDEuAgobYhiUFIIP1vR+ODYUrBR5Qf8Wucem2eG/3u/Cqoi0DstMoPOoFAGn66arsWR8DAdLunxCo47xNZkwBmNZUY41c2LnWlN0drJhSxNhUFRBIblqFifq0HXZdjXkiVzMfCE8cGSVkeedYS7BdSuCvRMVdEtSWLpz8Y6XolxbPUkCgWnGpqbZqw+QJZ6jFXM+1gwA3CsyC/XsadUx4hetrCNLe6IUE08lFemw6YCz5wfb+pxI5mxlEzDax6r5YVZGst7sR5s1hUc4xkpQd7ROTas3BrAr/t19sqLcgw8AdiUAx+yzsyuCiS7BB5ZUd1puiERhVuKKzJu5JGkucq26oCEQwUXD++A7/doiHcIZEbZcjRmVsLqUqLYI2FTBGwhqvyJRAe/VwacjYv1oFJKCV0aYzaDQz1KPDq2FugorpJQhNEVPa2mFUZGyORCQcN7GMHmkp/8DDyjHANPAA6bgKZbnYrIcFiWirU7Avhtv4Z+XfjhJjKb0x6aW/lzk90x+VCZXyGR7mZrZ0fM/8qLrMTQjy0OBbOCz71lEgUVOp6c1HlaO+uKxbKho6Ip2NR0ifwKiRKPhKYba88nxwmkxQuoClDpBcq9ElV+iQqvhE8DXHYj0CyqlCirNgJPVQHiHQICQFm1RGq8wE2nxKHSJ/Gv1dXwl/oBRNbkQgBw5iAHspOrcOt/qjBlmKNTTRAWaxh4AnCogJ+BJwAgI0FBVpKCuz724NU/uqPyQYWIYoOUEiVVOm47zWV1UqJahU+ib5emH9QivdLCjOBzZ4mOLm6B3umdr0I1kq9tOEVioFntlwjoRmBZ4jGCy4AuEe8QcNsFNGkElPsrdPh1Y5ZvmwJ4AxJbalYjEKKmhVIALptAQpzAhEF2LNzogy6BC4c50DNVgVMVqPIb59El0DtNQd/0A0vqDM1WseaHOCyFsfRS8NiRIClO4IlJbpw3rwKvfePFTafwnhCtGHjCmHDAr7GvbdDATBX/+82P69+rwj8nxbOLGxFZosQj4dcQ8RPiRDqHKuBvYQm8WA4+d5XoKCjX8a9O2Np5w/uVgDPJ6mSEVbgDTCmNFkYBwKYCSiPRmi9gdPUO9q6r8knsKdONMew1VMWo/D/vSAcWfOfD/gqjC6zbKXDLqS4c3k1FQp2lcCq9ErtLdfg1ibR4BRkJ9buRTzrS0ab3ke5WcFSOERZEWosnAJw12I6kOIH3f/Az8IxiDDwBJDgFAhG2Lq1VS6wARs3SsB42fLc7gD++Wol/nBuPblzGgIjCrNgj4bABGVE2NjHSSNm6CfSamsEzUrTnvljpk/h+TwD9uhhLilDsCPczkqZL+ALGuHOPHyitlijx6CjxyAPPkKJ+sGZTjEC0OiCNaK7mtTibQEaCwD1nupDoNLrLJscdCBwnDG45aHQ7RcjGO9aO8YygotdpE5gw2I431vuwp5TLa0UrBp7gZB+N6Z6sINllx/e7A/jTgkq8fkkCXA7+n4gofAoqJFLjFXb576A0t8DuUr1et7qWBB/qIzEAbS1fQOKbnQG4HQKPncsWkmgXikBT143WyoBu5BePH/BqEt4A4NMkdN1YzqnCK1Hpl7VNgUIYjRYXHuVAdrKCLm4BCaMlU6up6BEC8AWMY6e4jKWMnDZERXkWaZMLBf3hCAfeWO/Dhxt9mHV8nNXJoXZg4Akg0RlpH63IGIvgdggcnWPDss1+7CrVMSCDtcVE7fHcZDeK89247aXQnyeaA4W6yqsl8it0PHx25+seabZ7z3RhxtuV2Lpfx4AubQvkozVP+QISX+8MwKcBz1/IISPRqKPPQVpNUKnpgF+TNcElUOk3Jtsp89QPJgEjWHTaBJw2Y/4PRRE4a7AdafECmQkKEuME3A6jZ1gs56lI7GoLAOMOs8NlBx5aXo0LhjqQkcBWz2jDwBNAnB2RV60TIRw2AbdD4F+rq/HU+dYHw0SRLBIqjKI1UKhLSokf9waQHCcwMIsPFh3VNUnB385w4d5lHpR6JA7vqrapB0u0tX5W+YygM6ABz06OR0o881C0aE8ZWuGVyCvTUeWX8PqNrrBVPtnouGZFGPN6JMYJXHO8ExkJChKcxjhot0MgwRkdLZKhpkdgV1vAaGV+ZGI8rn2vCs9/6cVfx7InQ7Rh4AmjkLHzvtSkQzJVfLsrgN+LNPRKY6snUV2REGzGmmKPxP4KiacviOdDoEmO6WnDE5Pi8ZePPFj5qx9DutvQI6VtN75oqNQo9ej4ckcALrvA81PikezizT2Staf8DE7mU1ZtTK6zq0SH02YEjRMPdyDRKZDiEkiKE3DZjTXK42yAyyHgUBlYtkawq22ktXgCwMxRTtyxxIOPNvkZeEYhBp41HJ1oQem2yk4WyC0RuG9ZNV6YyodsoqBIDTqjIUBozs5iHckugZ6prOgyU590Fa9f4sbyXwJ48n/VKPEoOCxLjZkug0ZLp4Zkl8C/L3TDzvt6xGlPmalLo2vs/kqJokqJoiq9tjUzKU7gvvEuHNFNhRIj+TgS6BE6xhMAbKrAmYfZ8da3PnyzM4CjezKUiSa8WjXsEfJ8E4kPskIYYxu2F3KxU6KgSPys1hVt3SODqv0Se0p13H46a7JDQVUETh9oR/dkgTuXeFBaLTEs29bqrreRWqmxv0LHhl0anDZg7gUMOiNJa8tKXZcorZYo9xpLj5R5JKoDxpqVUhpjL9PdAred5kJGgqjpJsvrHAq1LZ4RGszfeboL737nw6wFlfjqz0lsxY4iDDxr2CIk8IxUcXZjOnBfQMJh4wecYlukB5VtEamBQl11/9+b8zU4bQJDs1koh9Lh3Wx4drIbN7xfhc+2BXBMTxWprRwLaVWeaupzubdMx/rcALKSFDx5XjycvEdZrq1l6P5KHRtyNXgD0lhaxCVw8dEOpLgUuB1AVqKxTiUDjPDQayLPSP1vH9ZVxfVj4vCPVdX4cKMffxjStjVLyToMPGtEQlejSH7YzXALCAF8v0fDMezWQDEgkj9vnZXHJ7GrRMd9411ssQqDrEQF8y9245p3q7BmRwDH97FF1ZhIKSW2FujYkq+hV5oRdDIwsVZry9VqvzEhUGm1REVNC2fXRAUPnh2PtHgGmFYLzmobyZfh5lPi8OJaL659rxIn9be1uuKMrMUIokYExJ0RzWETyElR8OiKarx+ibt2kWOiaMSgM3LUvRa5JTocqsDh3djaGS5Om8ALF8Zj8vxK7CmTSI7QHs6NfWb3lElsyddw95kuDM/h44yVmitTfQGjC21+hY6SKokKn4QvYPQ0S41XMGWYA33SVByaqURs187OJpInFwrqmqTgiUnxuOyNSlz5ZiU+mJZodZKoFVhSU6sNyFCxq8SPdTsDGNnHbnVyiNqFQWdk8msSO4p0zBztjIgeKJ2JogjccJITDy+vRp80BXH2lv//4exu29hndm+Zju93B9ArTWHQaaGWytNfCzRsztcgJZAYJ3DxcAfS3QoyEwRyUhX2bIhQwRk9Iv3qXHKMA8u2+PHGeh8eW+nBjSdHaM0Z1WJpXUPKlvcJteBNvKlJQax+YHbZjVbPJz/z4rjeNnaFoag0851Kyz9L4RLpYzvr2laoQ0JidB/elqyQlahAytZ3rbMyb+0t07EuN4CeqQr+NSnesnR0di2Vo+XVEpvzNfz5pDgc09PGiYCiiFYTeaoR3ntVCIGHz47Hiq1+3LTQg5wUBZOPclqdLGoG7/A1AnoERJ41mrqhN7Y93A/QPVMV7CgK4NtdGoaxlpmiVCR8lswQTYFlY+r+zwsqdFx5rBPxrZxdlcyVUPN/L6yU6J4cOdfg4M+llBI/79OQnazgKY7ptExL5WVRpY4vtgeQ7BIY08/GXgxRJlCzXE00tEhnpyhYf2MyRvyjFJe9UYnMBAUnDWCvvEjFyKGGL2B1CtqntQ+eZj1UJ7sU9M9Qcc8yD+ZNdSPNHeHVYUStFPIgrrzp40d7ANledVuffRrYImIhf03la2t6/1iZXyt9QIVX4uGJLgadEayk2shIVx7L1qdoFCwPHFEy3L57soJF0xNx8tPlGP/vctxyShxmHR+HzEQ+o0YaXpEaPs3qFIRAwAusfQgIeDHznUpTHhZ0KZGZIKDrwNYC69f19Hq9uPvuu+H1eq1OSuyok29iVgS8x+Bn0pKH+Ah4/3X5fV5sXrMIr31tfQDeGcuUXSU6/vRuFbomKchw+fCfF++D39fw/VuRXw8+X1xNdfn+Shk11ypa0tkWLeWDPmkKBndT8fiqalwwrwLVfvN7lcXi//VgVr3HYGNMpKxx3xrDcmxY8adEuB0C9yyrxtGPlWHLvoYP98w31mLgWSMuFtt+NS+w7h94ZIINT06Kx7/+EI9qv0S1X8IbML6q/RKVXomyaomiKh0FFTr2lunYXaJjZ7GG3/Zr+Gmvhg27Avh8mx9Lf/ZjzfYAUlwCg7paXyJ5vV7cc889EfnhilqaF/jqYeN7rNJ8wA8vGt/DbNt+DbnFGu4eZ+EkCBF0jbcVavj6dx++//57nH+E9S1YnbFM+bnm4azEI/HV7358tGoDNu3xYmuBhu2FGnYWa5a2cu4s1rC1QMOPeQF8s8tIa6lHRs21ipZ0ttWOIuMZYfM+Dd/tDuDLHX6s/tWPlVv9WPlrAFsLjGvl14wu3GaL1f9rXVa9x4BmXK9o6Gpb1/AcG3belYLnJscjr0zHUY+W4up3KvHpFn/te2K+sVYshlvtcvvpLpR4JD76yW/aMS8e7qjttnRwjyApjXWSpDS+gm2HwZoAedA+9f4WDbtEBX+s+zc+XwKWHTMbn/4mYLe37X0pwhhUblMEHDYgzi4wdZgDWYkKeqUpXGcrlil24IjLje8m+cMRB45VN6/W2xbMu3V3ChG/341lQ6bhjMPdsNvDMxbEU2zHcgA7S3TISg2aDkwYFOZxKDUf2YA/AUtH3IQzj0jAx7+ENwkAMGGwvfYaby3Q4FYAbF2I43r+NfyJIZx2iA2Dstz4pUBDXlEAr9ndKPIIFPl0aNKYA2HiYDsOngrBzPvlwWo/GwL4MU9DnE3AaQPcToF7znThqB4qystDdnpqRN1rogpgS74GVRGwK4DTBkwa4oDbIaAqxjNPnE0gLV6gR0rrZkqmyBHsBRgtXW3rcjkErhoVhx4pCu5Z6sFza7x4bo0X6W6BUb1tGJmjA10OR1GVRFKS1antfBh41hiYpeKJ8+KRkeBBiUdCk8YzmqoAqiKgCiMYEzUFrqIAihAQwthPCOD0Q+01+wN2xagpCo6nPziAFOJAcKeIA1NW1y7aC2O7UhPc1Y3x6p4zuO/BP6sKUF2lY9kdC/DgWX9FSmI8lJqoNhjsAvWDS4dqfLcr4FpaHdBUMBG8bqhz/US912ryU/B11LmuB+0L1Mk34sDPok6ePH2gvXZ7MO8qB38pokFaKqsUTFz0GxbNcCPBfWBscO1+DY4pGrzWWJ1E3U1182vt+zv4fTTxvpv6e0U0lsb6n9Hgz+XlOvrf/CLmTvorEhPdDT4/jaUVNelqbL+m3k/d33fvcqPPbcDLFycgKSkRui5RHTDWuNOkMYtg3cqogyuTUPu7bLBf8Lte57N98Gt1/7aiQsfSvy3GnWPvwIPnupv8m4Mrvw78Luudr256gPrlGBAsL42y02ETiLMZSyt0cSuIdwiUlUm8esUWVmZZRAiB7BSB7BQFZWUKsGUB/n3Bi0hKSqi3n5QSPg2o8klU+wGfZvyu6UZwqtfk42C+0OvkG8DIF3WvcPCeFLy/2lTAXnM/cjsE4h0CbgeYLzrgouEOON1OKAI4Y6AdThvgVFE72c/B9wVVAVQhYFMBW+3zzIGgP9Fp/MxrEts8fuPT6oriCoPxgxwYP8iBtTsC+PQXPz7a5MPSzX78ZxOAi1dj8GMaBmaVoluSguE5KronKUiME0hxCdhVgeSan5NdAm6HqP08qILPyB3RaQNPWXMnLCsrq92Wbgf+OaHRvVt51MgaKFqmlQMl29DFVo6klgoPDZAa4PUBkdcw37Tg9at7HcNxvp9uUpGcpNberOsFN9AbVg606iZtdjNf+/JjWVkFsOszHNWlAklJMdYbv+Zf7NPLAU8h7Fo5HHrbbyC1LbNtUFVpNM8cnFeVmq961RWiiZ9NUqaVA4U/I8dVjqQE626ggWqgrDr8n+PmRFJaAOvKuObOpwJwC8BtQ+ieIiSge4HyZm5IkXatmmJFOoPnemicjqSk4L2gg88oOuDzAOEfoNC4aLn+HWHV5//85/IARyIShY6ysmh6KmxoUBow6Djg+uMEPD4VL68txa33PYUBE2ahsFjBj7/rWPxt24+rKEbljK2moeng8bA21ajEUdD8MlXNvtZCGg5u0AiSug5MWY6jHiqCUEpaOErL7j0zHhMOdzT5ejDfSNnyk5GQrdkrBu3atQs5OTlWJ4OIiIiIiCiq5ebmokePHs3u02kDT13XsWfPHiQmJrLLCLWapmn49ddf0b9/f6hqFA5+IMsw71B7MN9QezDfUHsw31B7SClRXl6O7t27Q1Ga7ynXaQNPIiIiIiIiCo8YG8BFREREREREkYaBJxEREREREYUUA08iIiIiIiIKKQaeFPWefvrpmvUrBXbs2GF1cihKdOZ8s2XLFowaNQpCCMyfP9/q5BARURM6872K2i9S8w0DzxiTm5uLBx54ACeeeCLS09Nht9vRpUsXnHLKKXjhhRegaZG11mhH7dy5E7fddpvVyYh6zDedg6ZpePjhhzF06FB8+eWXlqYlkvLcvn37MH/+fEyePBl9+/ZFXFwcnE4nevfujQsvvBCrV68OW1qsEEnXoiXz58+vfZhq6mvBggVWJxMA8Ntvv+Haa6/FYYcdhvj4eDgcDuTk5GDKlClYu3at1cmLStGUV83QWe9VZmO+iSCSYsaqVauk3W6Xdrtd3nzzzfKrr76Sv/32m/zPf/4jjzrqKAlAjhkzRpaXl1udVNOMGzdO9uzZUwKQAOT27dutTlLUYb7pHPlm//79csSIETI9PV2+8cYbslevXhKAnDdvXtjTEml57thjj5UA5AUXXCBXrlwpt2/fLr/77jv56KOPyoSEBAlA/utf/wpLWsIt0q5FS+bNmyddLpc89NBDm/xatmyZ1cmUq1atkm63W9psNnnXXXfJb7/9Vm7dulW+/fbbsl+/flIIIZ999lmrkxlVoi2vmqEz3qvMxnwTWfmGgWcM+eCDDyQA+dBDDzV4rby8XObk5EgA8sYbb7QgdeZ7+eWXpd1ul4sWLYrID1e0YL7pHPnm22+/leedd57ct2+flFJaGnhGWp479thj5Wmnndboay+88IIEIN1ut6yurg5LesIp0q5FS+bNmydPPPFEq5PRoqFDh0oA8rbbbmvw2s8//ywBSKfTWft5pJZFW17tqM56rzIb801k5Rt2tY0xiqJg+vTpDbYnJCTgkksuAQC89dZb4U6W6fLz8/HnP/8Zt956K4444girkxP1mG9i3xFHHIEFCxYgMzPT6qQAiKw8N2fOHDz55JONvjZixAgAQGVlJSorK8OSnnCLpGsRK7Zs2QIAOP744xu8NnDgQKSnp8Pr9eLzzz8Pd9KiWmfJq535XhUKzDeRg4FnDDn77LNRVlaGtLS0Rl/v0aMHAKC4uDicyQqJa6+9FpmZmbjjjjusTkrUY77pHFRVtToJtSItz5188skYOHBgo6+tWbMGADB8+PAm0xvNIu1axIqhQ4cCAH788ccGr+Xl5aGoqAgA4HK5wpmsqNaZ8mpnvleZjfkmstisTgCZR1VVuN3uJl/Py8sDAAwZMiRcSQqJhQsXYsGCBfjss8/gdDqtTk7UY76hcIv0PKdpGvbs2YMFCxbg9ttvx7Bhw2KiNrwxkX4tGlNeXo77778fCxcuxM6dO6GqKvr3749zzjkHM2fOREJCgtVJxLPPPosJEybgvvvuQ3Z2Nv7whz8gLi4OP/zwA2bNmgUpJdLT03HcccdZndSoEY15tT14rzIX801kYeDZiSxevBgAcP3111uckvYrLS3FrFmzMGvWLIwePdrq5HQKzDcUblbmuWuvvRZz586FrutITU3FnDlzcO2110ZUi3E4ReLnf8OGDUhPT8ff/vY39OvXD/n5+Zg/fz5uuukmPPfcc1i2bBn69OljaRqHDBmCzZs346mnnsK1116LSy65BKqq1s6emZmZiTfeeAOpqamWpjOWRGJebSveq8KP+SbMrB5kSuGxdOlSCUCOHz9e6rpudXLabdq0aTInJ0eWlZXVbtu+fXtEDqCOBcw3scvKyYWaY3Wey8/Pl5s3b5YrVqyQ1113nVRVVY4cOVLu2LEj7GmxmtXXojFr166VDz74YKPpmTlzpgQghwwZIjVNsyB1BxQXF8tJkyZJIYS89tpr5Zdffik3btwo//3vf8uZM2fKX3/91dL0xZpIzKvtwXtVeDHfhB8Dz05g3759Mjs7Wx5yyCGyoKDA6uS02/Lly6UQQi5evLje9kj9cEU75pvYFomBZyTmuSeeeEICkN27d5eFhYVWJydsIvFatGTPnj1SVVUJQP7nP/+xLB26rsvjjjtOApDXX399g9e/+uorGR8fH7NL9IRbNObVxvBeFV7MN9bg5EIxrqSkBKeffjrsdjuWL1+OLl26WJ2kdqmqqsKMGTMwdepUjB8/3urkxDzmGwq3SM1zs2bNQo8ePbBnzx78+9//tjo5YRGp16Il3bp1Q3Z2NgBYOlvsihUrsHbtWgDArbfe2uD1ESNGYOTIkfi///u/mB07HC7RmlcPxntVeDHfWIeBZwwrLCzE2LFj4ff78cUXX9TO3BWN1q1bh23btuH9999HQkJCva/BgwfX7jd48ODa7a+//rqFKY5ezDfMN+EWyXlOVVUMGjQIAPDll19anJrQi+Rr0RrdunUDgNpZY62wfv16AEBSUhK6d+/e6D7BPNVZKjNCIdrzal28V4UP8421+YaTC8WovLw8jB07FnFxcVi9enXU1uYEjRgxAlu3bm30td27d+Okk04CACxZsqS2xjsrKytcyYsZzDfMN+FmdZ7bs2cPFi1ahBkzZjQ5gVBwRkSfzxfOpIWd1deiJR6PB6tXr8aYMWMQHx/f6D579+4FAKSkpIQxZY0TQjT5mqIY9f67d+8OV3JiSqTn1bbivSo8mG8iIN9Y3deXzLd9+3bZr18/efzxx8vS0tJ6r+3du1eeccYZcu/evRalznyR2o892jDfdC6RMMYzEvLcypUrJQC5ZcuWJvcZMGCABCCvu+66kKbFSpFwLVoS/MyuXLmy0df37dtXO8bz/fffD2/i6vjwww9ry5am/mdnnHGGBCDHjBkT5tRFv2jIq2bq7PcqszDfRAZ2tY0xmzdvxgknnID+/ftj2bJlSEpKqve6x+PBsmXL4PF4LEohRSLmGwq3SMtzzzzzTKPbX3vtNWzduhVCCFx66aVhSUu4Rdq1aMm8efMa3X7//fdD0zQMGDAAZ599dphTdcDYsWPRtWtXAMDjjz/e4PWNGzfik08+AQCcf/75YU1btIu2vEqRgfkmcrCrbQzZtGkTTj75ZBQUFCA1NRVjxoxpsE8sdRUrKiqCz+dDQUFB7baCggLExcXB4XAgLS3NwtRFD+abzpNvgu8dQO16gqWlpbXdE9PS0uBwOEKejkjKczabcRt84oknUFhYiCuvvBK9e/fG3r17sWjRIjz66KNQFAWPPvoojj766LCkKZwi6Vq0RFVVCCHwyiuvIBAI4KqrrkLv3r1RUFCAefPm4ZlnnkH37t3xwQcf1F5XK8THx+ONN97AxIkT8cgjj0BVVVx00UVISEjAl19+iVtuuQW6ruPss8/GNddcY1k6o0005VUzdOZ7lZmYbyIs31jd5Ermefzxx2ub1Vv6iqRm9/Y68cQTm3x/J554otXJixrMN50n3zT33tFMF0azRVqe27Bhg7zlllvkcccdJ9PS0qSqqtLlcslDDjlETps2Ta5fvz7kabBKpF2Llvzyyy/y7rvvlieccILs0qWLVFVVJiUlyeHDh8u77747opa82b59u7z++uvlYYcdJl0ul7TZbDIrK0uOGzdOvvbaa1G9bqAVoi2vdlRnvleZifkmsvKNkFLKVsaoRERERERERG3GMZ5EREREREQUUgw8iYiIiIiIKKQYeBIREREREVFIMfAkIiIiIiKikGLgSURERERERCHFwJOIiIiIiIhCioEnERERERERhRQDTyIiIiIiIgopBp5EREREREQUUgw8iYiIiIiIKKQYeBIREREREVFI2axOAHXM3r17MXfuXDgcDuzatQsZGRm4++67oSisUyCixrHcICIionBj4BnlZs6cialTp2LKlCnQNA05OTlwOBy44447rE4aEUUolhtEREQUbqzejnI2mw3r1q0DAKiqip49e+Lzzz+3OFVEFMlYbhAREVG4scUzyr333nu1P0spsWPHDlx55ZUWpoiIIh3LjeaVlJTgrLPOQn5+PgoKCnDvvffi+uuvtzpZREREUY0tnmGkaRq+++47XHfddRg3blyT++m6joceeghDhgzBiSeeiOHDh+OGG25AaWlps8f/8MMPkZGRgdtvv93spLfZDz/8gNmzZ+OYY47BKaecghNOOAHDhg3DFVdcgaVLl1qdPKKowXIj/OVGSkoKFixYgFNPPRWlpaU45phjwnZuatqyZcuQk5ODNWvWWJ0UIiJqBwaeYTJ+/HhMmDABixYtwlNPPYXq6uom9509ezYee+wxfPjhh1i9ejU+//xzbNiwAaeeeiq8Xm+D/X/77Tc8+uijePzxx/Hyyy/D7XaH8q00y+v1Ytq0aTjqqKMgpcSnn36KFStW4LPPPsOaNWswdOhQnHPOOTj++OOxa9cuy9JJrVNSUoLRo0djwIABSElJwRNPPGF1kjoVlhvWlRvdunVDaWkpXC4Xjj766LCck5q2c+dO/PGPf8SuXbvg8/msTg4REbWHpLADIE888cRGX1u7dq0UQsgHHnig3vYvvvhCApBz5sxp8rilpaWyd+/e8t577zUzua0WCATk2LFjJQB5ww03NLnf22+/LQHInJwcuXv37jCmkNpjz549cubMmRKAXLNmjdXJ6bRYboS/3Ojatas8+eSTw3Iuat4111wjAUgAcuXKlVYnh4iI2oEtnhFm3rx5kFLiD3/4Q73to0aNQmZmJl544YUm/zYpKQkTJ07EXXfdhd9//71V51uxYgV2797d4n5Lly7F/v37m91n/vz5+OSTT5CQkIB77723yf0mT56MkSNHIjc3F3/5y19alU6yDlt+Ih/LDfP9/PPP2Lt3L8aMGRPyc1Hz8vLysGrVKmRnZ1udFCIi6gAGnhFm1apVUFUVhxxySIPXBg0ahN9++w179uwBAHzxxRfIyMjAwoULa/dxOByQUqKkpKRV5ysoKMBpp52G/Pz8Jvd577338H//938tHmvJkiUAgNGjRyMxMbHZfcePHw8AWLx4cavSSdZatWoVjjvuONjtdquTQo1guWG+lStXAgADzwjw6KOP4sYbb4TL5bI6KURE1AEMPCNIIBDAtm3b4Ha7G13IPfhQtmPHDgCA2+2GEAJpaWkAjNkpV61ahZEjR2LIkCGtOueFF16I66+/HqeffjqKi4sbvL5s2TLccsstWLZsGbp06dLssaqqqgCgxf0AICsrq97fUORiy09kY7kRGitXroTdbkdxcTGuuuoqzJgxAyeffDK+/fbbkJ+bDti5cyeWLl2Kyy67jBVfRERRjsupRJDS0lL4/X5kZmY2+nrwAbKoqAgAMHToUMyfPx+rV6/GsmXLsHPnTgwbNgwPPvgghBCtPu8111yDiooKjBs3Dp9++mnteT7//HNcffXVWLp0KXr37t3icfr37w8AjT6IHqywsBAAWv2gS9Zhy09kY7lhPiklVq9eDQDYsmUL/v3vfwMA/v73v+OMM87Ar7/+iqSkpJCmgQx33nknbrzxRthsNthsfGSh+vLy8jB37lxUVVWhvLwcxxxzDK688spGK+EoNvCaRzkLx5d2WmhikpDc3FwJQPbr16/Rv7v88sslAPnaa6+FJF133nmnPPHEE2VVVZVcv3697N27t/z+++9b/ffr16+XQgjZtWtXqet6s/tOmjRJApD//ve/O5psCrHzzz9f2u12uWDBAjljxgw5ffp0edJJJ8kNGzZYnbROheVG+MqNjRs3SgBy2rRp9bYvXbo0pP9Lqu/777+X/fv3l36/X0op5fDhwyUAuXz5cotTRpHghRdekL1795Zr166VUhoTlZ177rny8ssvr92npKSEn9cYwmse/Vg9EEFSU1Obfb2iogIAEBcXF5Lz33vvvRg2bBjOOussnH/++Xj77bfb1LIwbNgw/OlPf8LevXvxzTffNLmf1+vFJ598giFDhuCKK66o99rHH3+MqVOnQlVVpKam4qqrrsL27duxfv16XH755XC5XIiPj8cVV1yBRx55BFOmTIGqqkhLS8P06dPx3XfftfftUyNkIy0/zz//PE477TScccYZKCsrsziFxHLD/HIj2Mp/8cUX19sebJXduXNnq8+3bt06vPvuuyyr2uH666/H3//+99qWzuCSP40tD0Sdy7PPPosZM2Zg/vz5OPbYYwEAqqrib3/7G+bPn48VK1YAMLr9W7lUFJmH1zxGWB35dkZoZlkEt9st+/Tp0+hrZ555Zshre3/88UcZFxcnTz31VBkIBNr89xUVFbJfv37NLouwYMECabPZ5Pr165vcp1evXvKyyy5rsP3EE09s8L9ral/quNa0/EyZMkUqiiJTUlLkjBkz5LZt2+Q333wjL7vsMhkXFyddLpe8/PLL5ddffy3feecdeeGFF0pFUWRqaqqcNm2a/Pbbb615c1GG5Ub4yo1JkyZJp9MpPR5Pve233HKLBCBffPHFNp+vpXNSfW+88UaD/+GECRMkAPn+++/X275kyRKWQ53Izz//LOPi4uRFF13U4DVN06TT6ZQzZ86UUko5ZcoU6fF45JIlS+SsWbNqy8ry8vLav/nyyy/lxIkTJQB5zjnnyK+++qpenkpOTpaXXXaZfPnll8P2Hqm+9l7z4DXs0aOHnD17trzxxhvltddeK08//XSpKEqbr+nGjRvlJZdcIi+88EI5e/Zseccdd8hly5bJDRs2yIceeqhVx+js5RUDTws09wB5+OGHy5SUlEZfGzFihAQgd+3aFZJ0/frrr7J3795y8eLFcvr06fKPf/yj1DStzcdZvXq17NatW5MPoOeee6688847mz0GA8/I8MQTT0gAcsWKFfW2v/nmmxJA7bqRfAAPPZYb4Ss3+vbtK48//vgG24cPHy6FEHL79u1tPl9L56QDiouLZXZ2doMu2xdddJEEIF966aVG/47Xo3OYPn26BCA/++yzRl/v3r27PP300+WaNWvk3/72t3qvzZw5U6alpclZs2Y1+LvrrruuwTbmkcjQkWve1DWcN2+e/Pvf/97qNLz66qsyKytLrl69ut72xYsXy+7du8u77rqr1cdqLl2xXl6xq22EGT9+PEpKSrBr16562wOBADZt2oQ+ffqEZC2z3NxcnHHGGfjHP/6B8ePH47nnnkMgEMCsWbNafYybb74ZgDEJzZlnnolPP/20wT6lpaXYunUr7rjjDgDASy+9hC1btpjzJsh0q1atgtPpxMiRI+ttD87sGZxllKzFcsNcu3fvxtChQ+tt+/3337FhwwacddZZrZo0idrvpptuwsSJExt02T54oizqnBYsWIDk5GSMGjWq0dftdjs8Hg/uuece3HDDDfVe69q1K5588knMnTu3tmtmUHCmb4o8HbnmTbnkkktaPVzom2++wZVXXom5c+c2mGhx/PjxmDx5cquOQ1xOJeJMmjQJgPEhq+ujjz5CZWVlg7FNZti7dy/Gjh2Le+65p3YBekVR8OqrryIvLw+zZ89u1XG2bNmC8vJyAMDtt9+O//znPw32Wbp0KW655ZbadQMXLlzIh7gI9t133+GYY45pMD5w+fLlEELglFNOsShlVBfLDXP16tWrwRihf/zjH3C5XHj88cdDdl4C/vvf/+LFF1/Exx9/DLvdXjubrc1mq51dOC8vz+JUklUKCwtRUlKC4cOHNzuL6dq1azFz5sxGx8BfdNFFOPfcczFt2rTaMfAUucy45gcfz+fzQVXVVs9OftdddyEzMxPnnntuo69Pnz69VcchLqcSdvv27QNgTFIRCAQaTA9/7LHH4rzzzsMDDzyAs846CwMGDEBpaSnuuusuHHHEEbjllltMTU9hYSHGjh2LG2+8scFEGjabDe+88w7OOuss3HnnnbjvvvuaPVbPnj2RnZ2Nbt26QQiB5OTkBvusXbsWixYtwgMPPIDi4mJ07doVTqez0eOtX78et912W71t27ZtQ9++fdv4Lqm9du/ejfHjx9fbxpaf8GO5Ed5yY9q0afjwww8hpYQQAu+++y7mzZuH9957r3b5FzPPR4bi4mLMnDkTCxcubPT/98477+C+++7Dr7/+akHqKBIE13JtavkowKgAO+qoo2orxBozd+5cDB48GDfffDPmzp1rejrJPGZd86BnnnkGl1xyCXr37o0bb7wRgDEJ3/3334/U1FTY7Xbk5eXh9ttvR0pKSu3EdmPGjGlyybHDDjsMZ5xxRjveXefDwDNMLr30Unz33Xf45ZdfAAA//PADMjIy0L9/f1x88cX1uga8+eabuP/++zFp0iQkJSWhsLAQo0aNwoMPPtjkw1Z7PfTQQ5g+fTpmzJjR6OtOpxMLFy7EhAkT8NNPP2HQoEFNHmv48OF4+umna1svevTo0WCf/fv3Y9u2bbW/n3nmmc0e78EHH6y3be3atc2+HzJXW1p++ABuPpYbhnCXG3/+85+xfft2nH322UhNTUUgEMC6detw6KGHhuR8ndUrr7yCSy+9FIAxg/aVV16JyZMnY+LEiY3uv3PnTgBodkZglkOxLSkpCcOHD0dBQUGjry9cuBBFRUVIT08HAOi6DiklVFWtt19WVhaeeuopXHTRRTj//PNx6qmnhjzt1D5mXPNgufD777/j3XffxSWXXALAuFdJKWsrSk877TQAwOrVqzFu3Dh88cUXKCoqgt/vb3ZokaIoDYYktUanLK8sHF9K1CROLhQZHnroITly5Mja9RXfeecdmZiYKD/++ON6+3XWQfIUWcJdbjDft9/GjRulqqry9ddfl2VlZfLaa6+VQ4cOlV6vt8m/2b59uwQgAcht27Y1eJ3Xo3P43//+J91ut/zyyy9rt23cuFHOmjVLPvHEE/Kxxx6TKSkp0u/3y7/+9a/1JqQ5eAKY8847T/bq1UuWlZU1OjlMU3mkvLxclpWVmfWWqAUdueYHX8OrrrqqdoI4KaX84IMPZEJCQoNzpqamyg8//FAGAgFpt9vlxIkTG+xz++23yz/84Q9y9OjR8owzzpBvv/12q99TZy2v2OJJRE1qS8sPEVFrlZaWQtO02q7aWVlZ+PLLL+FwOJr8m969eyMjIwMFBQWYOnUqXC4XnnrqKQwePDhcyaYIcMIJJ+Dzzz/Hk08+iRdeeAE2mw0DBw7Evffei/T0dAQCARQUFGDatGk4+eSTcfzxxzd5rGeeeQaDBw/GTTfdhG7durU6DZ9++in69++Pww8/3Iy3RC0w85offfTR9X7/+eefER8f32C/+Ph4/PjjjzjnnHNw5plnYv369bXDL4Luv/9++Hw+OJ1O3HHHHZxkqBUYeBJRk+x2O8e/EJHpRo0ahcsuuwwvv/wy+vXrh4ULF6JPnz4t/t11112HBx98EF27dsXDDz+MQw45JAyppUgzdOhQvPjii42+ZrPZMGfOnFYdJzMzE08//TQuvPDC2knaWmPt2rUNZjel0DLrmh88RKRfv34oLCyEpmm13XM1TUNhYSH69esHwJhcaOTIkXj//fdx3nnn1fv74N8c3J2bGsdZbSmifPzxx7jooouQm5uLjz76CDNnzsT27duxfv16XHnllfj666/x9ddf44orrsAjjzyCKVOmIDc3F4sWLcKMGTOaHftDRLEp3OVGW863bt06LFiwgGXVQYQQmD9/Pvbt24ctW7a0utXyzjvvRGVlJT788EMGndRmJSUlDbZNnjwZF1xwAQoLC1t1jJ07d+KNN97g8isx4pxzzsGgQYPwySef1G779NNPccghh9RWRgwbNgzz58/Hn/70pwbL8Hz00Ufo0qVLg+MuWrQI06ZNg6ZpoX0D0cbqvr5EFL2WLFkip06dKhVFkenp6fKqq66S27Ztk99884284oorpMvlki6XS15++eXy66+/lu+++6688MILpaIoMjU1VU6fPl1+++23Vr8NIopiLIeoJYsXL5annHKKtNlscurUqfKXX36p93p+fr6cMmVK7e9181TPnj3lDTfcIGfPni0vuugimZSUJPv27Rvut0BtVPcaJicny8svv1zOnz+/0X0LCgrk7Nmz5Zw5c+S9994rZ8+eLffv399gvx9++EFeeumlcsqUKXL27Nny1ltvlU899ZTctGmT/Oc//1lv3zlz5sjk5GS5adOmJtPVGcsrIaWUVge/REREREREseLbb7+FEAJDhw61OikRg11tiYiIiIiITLR27VocccQRVicjojDwJCIiIiIiMsnPP/+MrKwsTjp0EHa1JSIiIiIiMomu61AUtu8djIEnERERERERhRRDcSIiIiIiIgopBp5EREREREQUUgw8iYiIiIiIKKQYeBIREREREVFIMfAkIiIiIiKikGLgSURERERERCHFwJOIiIiIiIhCioEnERERERERhRQDTyIiIiIiIgopBp5EREREREQUUv8PE3xxvTTfNLkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 20 # Axis label font size\n", + "g.settings.lab_fontsize = 20 # Parameter label font size \n", + "g.settings.legend_fontsize = 20 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 20\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "label = fr\"$p={alphaFoG_power}$\" + r\" $\\alpha_{\\rm FoG}$ prior, max($\\alpha_{\\rm FoG}$) =\" + fr\"{alphaFoG_max}\"\n", + "g.triangle_plot(\n", + " [chain_all],\n", + " orig_params,\n", + " filled=True,\n", + " #legend_loc=\"upper right\",\n", + " legend_labels=[label], \n", + " legend_loc=(0.4, 0.8),\n", + " param_limits=original_param_limits,\n", + " #line_args={'lw': 2.5}, # Line width for contours\n", + " filled_compare=True, # Better filled contours\n", + " diag1d_kwargs={'normalized': True, 'lw': 2.5} # 1D marginalized plots\n", + " )\n", + "plt.suptitle(fr\"Auto-ps: $z_\\mathrm{{eff}} = {z_eff:.2f}$\", va=\"top\")\n", + "if save:\n", + " g.export(save_path+f\"origparams_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.pdf\")\n", + " g.export(save_path+f\"origparams_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# reduced params plot \n", + "new_params = [\n", + " \"omega_scaled\",\n", + " \"b_HI_scaled\",\n", + " \"omega_b_HI\",\n", + " \"omegabfm2\",\n", + "]\n", + "\n", + "param_limits = {\n", + " \"omegabfm2\": [0, 10],\n", + " \"omega_scaled\": [0, 5],\n", + " \"b_HI_scaled\": [0, 5],\n", + " \"omega_b_HI\": [0, 4],\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "chain_all.label = (\n", + " fr\"All parameters - $\\chi^2_\\mathrm{{min}} = {2 * chain_all.loglikes.min():.1f}$\"\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'All parameters - $\\\\chi^2_\\\\mathrm{min} = 10.9$'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_all.label" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:fine_bins_2D not large enough for optimal density: b_HI_scaled, omega_b_HI\n", + "WARNING:root:fine_bins_2D not large enough for optimal density: omega_b_HI, omegabfm2\n", + "/tmp/ipykernel_255440/2662632556.py:49: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 20 # Axis label font size\n", + "g.settings.lab_fontsize = 20 # Parameter label font size \n", + "g.settings.legend_fontsize = 20 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 20\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "label = fr\"$p={alphaFoG_power}$\" + r\" $\\alpha_{\\rm FoG}$ prior, max($\\alpha_{\\rm FoG}$) =\" + fr\"{alphaFoG_max}\"\n", + "g.triangle_plot(\n", + " [chain_all],\n", + " new_params,\n", + " filled=True,\n", + " legend_labels=[label], \n", + " legend_loc=(0.35, 0.8),\n", + " param_limits=param_limits,\n", + " filled_compare=True, # Better filled contours\n", + " diag1d_kwargs={'normalized': True, 'lw': 2.5} # 1D marginalized plots\n", + " )\n", + "plt.suptitle(fr\"Auto-ps: $z_\\mathrm{{eff}} = {z_eff:.2f}$\", va=\"top\")\n", + "\n", + "# Optional: Further customize the plot after creation\n", + "axes = g.subplots\n", + "for i in range(len(new_params)):\n", + " for j in range(i+1):\n", + " ax = axes[i, j]\n", + " \n", + " # Make tick labels larger and clearer\n", + " ax.tick_params(labelsize=14, width=1.5, length=6)\n", + " \n", + " # Add grid for better readability (optional)\n", + " ax.grid(True,color='gray', alpha=0.3, linewidth=0.7)\n", + " \n", + " # Ensure labels are not cut off\n", + " ax.margins(0.02)\n", + "\n", + "# Adjust layout to prevent label cutoff\n", + "plt.tight_layout()\n", + "\n", + "if save:\n", + " g.export(save_path+f\"reducedparams_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.pdf\")\n", + " g.export(save_path+f\"reducedparams_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.png\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.552011640343898 [1.0994722525103802, 0.7969145282179078]\n" + ] + } + ], + "source": [ + "# Estimate the A_HI(auto) from the chain\n", + "mode_all, err_all = hpd_chain(chain_all,\"omegabfm2\")\n", + "print(mode_all, err_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.8846781264565835" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sqrt(mode_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Omega_HI constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add a prior on the value of b_HI of 20% fractional" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "llprior = 0.5 * ((chain_all[\"b_HI\"] - 1.0) / 0.2) ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "re_weight = True\n", + "if re_weight:\n", + " chain_all.reweightAddingLogLikes(llprior)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.0006, 1.335]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z_range = config[\"bandb\"][\"z_range\"]\n", + "z_range" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "za_eff = np.array(z_eff)\n", + "zerr_eff = np.abs(np.array([z_range]).T - za_eff)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array(1.159286),\n", + " array([[0.158686],\n", + " [0.175714]]))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "za_eff, zerr_eff" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# param values of Omega_HI from the chain\n", + "ms_all, errsa_all = hpd_chain(chain_all, \"omega_scaled\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8341956818686768, [0.1541242185527839, 0.17813662881534975])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms_all, errsa_all" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Load other data\n", + "import yaml\n", + "with open(\"otherdata.yaml\", \"r\") as fh:\n", + " data = yaml.safe_load(fh)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Process GBT numbers\n", + "# WiggleZ, ELG, LRG order\n", + "om_b_r = np.array([0.70, 0.55, 0.45])\n", + "errs = np.array([0.12, 0.11, 0.10])\n", + "\n", + "r = np.array([0.9, 0.7, 0.6])\n", + "b_HI = 1 + lssmodels.bias[\"HI\"](0.78)\n", + "\n", + "om_GBT = om_b_r / b_HI / r\n", + "\n", + "# Calculate errors for GBT, and add in a 20% uncertainty on the bias\n", + "err_GBT = om_GBT * ((errs / om_b_r)**2 + 0.2**2)**0.5\n", + "z_GBT = np.array([0.74, 0.78, 0.82])\n", + "\n", + "\n", + "types = [\n", + " (\"HI_direct\", \"HI direct\", \"o\"),\n", + " (\"HI_stack\", \"HI stacking\", \"v\"),\n", + " (\"HI_im\", \"HI intensity mapping\", \">\"),\n", + " (\"DLA\", \"Damped Lyman alpha\", \"s\"),\n", + "]\n", + "\n", + "c = cosmology.Cosmology()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "markers = [\"o\", \"v\", \"^\", \"<\", \">\", \"8\", \"s\", \"p\", \"P\", \"*\", \"h\", \"X\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams['font.family'] = 'serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm' # or 'stix'\n", + "plt.rcParams.update({\n", + " 'font.size': 24,\n", + " 'axes.labelsize': 24,\n", + " 'axes.titlesize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "save" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(num=1, figsize=(20, 10), dpi=250, linewidth=1.5)\n", + "\n", + "# Create axes (assuming you want a single subplot, modify if you need multiple)\n", + "ax = plt.gca()\n", + "\n", + "# plot the CHIME data\n", + "yerr = np.array([[errsa_all[0]], [errsa_all[1]]])\n", + "plt.errorbar(\n", + " za_eff,\n", + " ms_all,\n", + " xerr=zerr_eff,\n", + " yerr=yerr,\n", + " marker='o',\n", + " color='green',\n", + " ls=\"\",\n", + " label=f\"CHIME-auto-ps\",\n", + ")\n", + "\n", + "# Now plot all other surveys \n", + "for type_, label, marker in types:\n", + " z = []\n", + " om = []\n", + " z_err = []\n", + " om_err = []\n", + " labelled = False\n", + " if type_ == \"HI_im\":\n", + " # Add in the GBT points by hand rather than trying to insert into the OmegaHI\n", + " # data file\n", + " plt.errorbar(\n", + " z_GBT,\n", + " om_GBT,\n", + " xerr=None,\n", + " yerr=err_GBT,\n", + " label=label,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.4,\n", + " fillstyle=\"none\",\n", + " )\n", + " continue\n", + " for name, dset in data.items():\n", + " if dset[\"type\"] != type_:\n", + " continue\n", + " if type_ == \"HI_stack\" and name in [\"delhaize2013\", \"chen2021\"]:\n", + " continue\n", + " ds = combine_data(dset, cosmo=c)\n", + " z = ds[\"z\"]\n", + " z_err = ds[\"zerr\"]\n", + " om = 1e3 * ds[\"omega_HI\"]\n", + " om_err = 1e3 * ds[\"omega_HI_err\"]\n", + " l = None if labelled else label\n", + " labelled = True\n", + " \n", + " plt.errorbar(\n", + " z,\n", + " om,\n", + " xerr=z_err,\n", + " yerr=om_err,\n", + " label=l,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.7,\n", + " fillstyle=\"none\",\n", + " )\n", + "\n", + "za = np.linspace(0, 6)\n", + "omHI = lssmodels.omega_HI.evaluate(za)\n", + "plt.plot(za, 1e3 * omHI, \"k-\", label=\"Fiducial\")\n", + "\n", + "plt.xlim(0, 2)\n", + "plt.ylim(0, 1.75)\n", + "plt.xlabel(\"Redshift $z$\")\n", + "plt.ylabel(r\"$10^3 \\times \\Omega_\\mathrm{HI}$\")\n", + "plt.grid(color='gray', alpha=0.3)\n", + "\n", + "## Fix up the legend\n", + "# get handles\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "# remove the errorbars\n", + "handles = [h[0] if ii > 3 else h for ii, h in enumerate(handles)]\n", + "# use them in the legend\n", + "ax.legend(handles, labels, loc=\"upper right\", ncol=1, numpoints=1)\n", + "\n", + "# Add text labels (modify as needed for your specific case)\n", + "ax.text(\n", + " 0.02,\n", + " 1.0 - 0.03,\n", + " \"Full parameters\", # Change this label as needed\n", + " transform=ax.transAxes,\n", + " fontsize=\"large\",\n", + " bbox=dict(fc=\"w\", ec=\"w\"),\n", + " verticalalignment=\"top\",\n", + ")\n", + "\n", + "if save: \n", + " plt.savefig(save_path+f\"omegaHI_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.pdf\")\n", + " plt.savefig(save_path+f\"omegaHI_pow{alphaFoG_power}_FoGmin{alphaFoG_min}_FoGmax{alphaFoG_max}.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pipe_ps", + "language": "python", + "name": "pipe_ps" + }, + "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": 4 +} diff --git a/notebooks/chain_analysis_qso_stack_fitstack.ipynb b/notebooks/chain_analysis_qso_stack_fitstack.ipynb new file mode 100644 index 0000000..6e23936 --- /dev/null +++ b/notebooks/chain_analysis_qso_stack_fitstack.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import glob\n", + "\n", + "import numpy as np\n", + "from cora.util import units\n", + "\n", + "from draco.util import tools\n", + "from fitstack import containers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary cache directory at /tmp/matplotlib-llnjbumc because the default path (/home/h/halpern/arnab92/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import getdist\n", + "from getdist import plots, MCSamples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from draco.core.containers import FrequencyStack, FrequencyStackByPol, MockFrequencyStack, FormedBeam" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import getdist\n", + "from getdist import plots, MCSamples, chains, mcsamples\n", + "from cora.signal.lssmodels import bias\n", + "from cora.signal.lssmodels import omega_HI\n", + "from cora.signal import corr21cm\n", + "from cora.signal import lssmodels\n", + "from cora.util import cosmology" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams['font.family'] = 'serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm' # or 'stix'\n", + "plt.rcParams.update({\n", + " 'font.size': 24,\n", + " 'axes.labelsize': 14,\n", + " 'axes.titlesize': 14,\n", + " 'xtick.labelsize': 14,\n", + " 'ytick.labelsize': 14\n", + "})\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Richard's chaintools" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from fitstack.chaintools import (\n", + " calc_fmu2_single,\n", + " calc_fmu2,\n", + " add_derived,\n", + " apply_limits,\n", + " hpd_chain,\n", + " create_unified_config,\n", + " scale_params,\n", + " hpd_chain,\n", + " combine_data\n", + ")\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First load the chains. Note I have already ran cahintool.gdchain and save it in a pickly file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "chain_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_path" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "weight = 'uniform'\n", + "param = [\"transform_shot_noise_nonlinear_free\",\"transform_nonlinear_fixed\"]\n", + "pol = \"joint\"\n", + "tracer = \"QSObandb\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/chain_joint_QSObandb_uniform_transform_shot_noise_nonlinear_free.gdchain'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " chain_path + f\"chain_{pol}_{tracer}_{weight}_{param[0]}.gdchain\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<_io.BufferedReader name='/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/chain_joint_QSO_uniform_transform_shot_noise_nonlinear_free.gdchain'>\n", + "<_io.BufferedReader name='/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/chain_joint_QSO_uniform_transform_nonlinear_fixed.gdchain'>\n" + ] + } + ], + "source": [ + "with open(chain_path + f\"chain_{pol}_QSO_{weight}_{param[0]}.gdchain\", \"rb\") as fh:\n", + " print(fh)\n", + " chain_all = pickle.load(fh)\n", + " chain_all.label = \"All parameters\"\n", + " chain_all.tracer = tracer\n", + "with open(chain_path + f\"chain_{pol}_QSO_{weight}_{param[1]}.gdchain\", \"rb\") as fh:\n", + " print(fh)\n", + " chain_nlfix = pickle.load(fh)\n", + " chain_nlfix.label = \"Fixed non-linear\"\n", + " chain_nlfix.tracer = tracer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scale the parameters actually fit to their values at the effective redshift." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# First load the dict containing all the relevant param values \n", + "config = create_unified_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'QSO': {'z_eff': 1.2034,\n", + " 'z_range': [1.0038, 1.365],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5782310960156978,\n", + " 'omega_HI': 0.000642563420124033,\n", + " 'f': 0.9015817540176252,\n", + " 'b_g': 1.92122457368},\n", + " 'LRG': {'z_eff': 0.8372,\n", + " 'z_range': [0.8065, 0.8728],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_lrg',\n", + " 'b_HI': 1.410682907405958,\n", + " 'omega_HI': 0.000576174260879225,\n", + " 'f': 0.8450640065556718,\n", + " 'b_g': 2.4319062430400002},\n", + " 'ELG': {'z_eff': 0.9576,\n", + " 'z_range': [0.8253, 1.0264],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_elg',\n", + " 'b_HI': 1.469278654771197,\n", + " 'omega_HI': 0.0005985416343037188,\n", + " 'f': 0.8671616829717421,\n", + " 'b_g': 1.57532},\n", + " 'QSOb0': {'z_eff': 0.9714,\n", + " 'z_range': [0.8501, 1.0073],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.475745886048952,\n", + " 'omega_HI': 0.0006010697096585794,\n", + " 'f': 0.8694469155412741,\n", + " 'b_g': 1.65145627288},\n", + " 'QSOb1': {'z_eff': 1.117,\n", + " 'z_range': [1.0651, 1.1631],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5413109085178034,\n", + " 'omega_HI': 0.0006273248712423942,\n", + " 'f': 0.8908845170491495,\n", + " 'b_g': 1.8172619419999998},\n", + " 'QSOb2': {'z_eff': 1.3028,\n", + " 'z_range': [1.2262, 1.3931],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.6192458573000803,\n", + " 'omega_HI': 0.0006598021460724086,\n", + " 'f': 0.9123331729462839,\n", + " 'b_g': 2.0459639795199998},\n", + " 'QSOb00': {'z_eff': 0.8448,\n", + " 'z_range': [0.8179, 0.8666],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.4145047645417193,\n", + " 'omega_HI': 0.000577603166324638,\n", + " 'f': 0.846581327620715,\n", + " 'b_g': 1.5168673571199998},\n", + " 'QSOb01': {'z_eff': 0.9857,\n", + " 'z_range': [0.9624, 1.0117],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.4823976713829805,\n", + " 'omega_HI': 0.0006036819251256054,\n", + " 'f': 0.8717653564445413,\n", + " 'b_g': 1.66721878822},\n", + " 'QSObandb': {'z_eff': 1.159286,\n", + " 'z_range': [1.0006, 1.335],\n", + " 'type': 'cross',\n", + " 'bias_key': 'eboss_qso',\n", + " 'b_HI': 1.5595435204378436,\n", + " 'omega_HI': 0.0006348134105155576,\n", + " 'f': 0.8962895921543366,\n", + " 'b_g': 1.8676248854832878},\n", + " 'bandb': {'z_eff': 1.159286,\n", + " 'z_range': [1.0006, 1.335],\n", + " 'type': 'auto',\n", + " 'bias_key': 'None',\n", + " 'b_HI': 1.5595435204378436,\n", + " 'omega_HI': 0.0006348134105155576,\n", + " 'f': 0.8962895921543366}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('QSObandb', 'QSObandb')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_all.tracer, chain_nlfix.tracer" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the chains \n", + "scale_params(chain_all,config=config, identifier= chain_all.tracer)\n", + "scale_params(chain_nlfix, config=config, identifier= chain_nlfix.tracer)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['offset', 'omega', 'b_HI', 'b_g', 'NL', 'FoGh', 'FoGg', 'M_10', 'omega_scaled', 'b_HI_scaled', 'b_g_scaled']\n" + ] + } + ], + "source": [ + "params = chain_all.getParamNames().list()\n", + "print(params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate the fmu2 from the chain (Omega_HI * b_HI) - Omega_HI plane\n", + "# The fmu2 value is estimated from the NL-fixed chain in the stacking paper and we do the same here" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fm2=0.644\n" + ] + } + ], + "source": [ + "fm2 = calc_fmu2(chain_nlfix,config,identifier=chain_nlfix.tracer, fscale=False)\n", + "print(f\"{fm2=:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fmu2=0.644\n" + ] + } + ], + "source": [ + "fmu2 = calc_fmu2_single(\n", + " [chain_nlfix],config,identifier=chain_nlfix.tracer, fscale=False)\n", + "\n", + "print(f\"{fmu2=:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0madd_derived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgetdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmcsamples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMCSamples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfmu2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcross_corr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msublabel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSource:\u001b[0m \n", + "\u001b[0;32mdef\u001b[0m \u001b[0madd_derived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m Effective fμ² value to be included in derived calculations.\u001b[0m\n", + "\u001b[0;34m cross_corr : bool\u001b[0m\n", + "\u001b[0;34m If True derived parameters will be based on HI-galaxy cross-corr. Default is True.\u001b[0m\n", + "\u001b[0;34m sublabel : str, optional\u001b[0m\n", + "\u001b[0;34m Subscript label for derived parameter names. Default is None.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m None\u001b[0m\n", + "\u001b[0;34m Modifies the input `c` in place by adding derived parameters.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetParamNames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubscript\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msublabel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mf\"_{{{sublabel}}}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDerived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"omega_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b_HI_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"omega_b_HI\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"10^3 \\times \\Omega_{\\rm HI} b_{\\rm HI}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcross_corr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"FoGh\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDerived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FoGh\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FoGg\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"FoG+\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"\\alpha_\\mathrm{FoG,+}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDerived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FoGh\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FoGg\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"FoG-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"\\alpha_\\mathrm{FoG,-}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# A_HI(stack) = (Omega_HI * b_HI + Omega_HI * )\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDerived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"omega_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b_HI_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfmu2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"omega_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"omegabfm2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"\\mathcal{A}_\\mathrm{HI}\"\u001b[0m \u001b[0;34mf\"{subscript}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# A_HI(auto) = (Omega_HI * b_HI + Omega_HI * )**2\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDerived\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"omega_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b_HI_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfmu2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"omega_scaled\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"omegabfm2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"\\mathcal{A}_\\mathrm{HI}\"\u001b[0m \u001b[0;34mf\"{subscript}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFile:\u001b[0m /gpfs/fs0/scratch/h/halpern/arnab92/venv_ps/venv/lib/python3.11/site-packages/fitstack/chaintools.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "add_derived??" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a set of useful derived parameters\n", + "add_derived(chain_all, fm2, cross_corr=True)\n", + "add_derived(chain_nlfix, fm2, cross_corr =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply cutoff to a set of parameters.\n", + "apply_limits(\n", + " chain_all,\n", + " FoGh=(0, 5),\n", + " FoGg=(0, 5),\n", + " omega_scaled=(-10, 10),\n", + " b_HI_scaled=(0, 10),\n", + " copy=False,\n", + ")\n", + "\n", + "apply_limits(chain_nlfix, omega_scaled=(-10, 10), b_HI_scaled=(0, 10), copy=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "orig_params = [\n", + " \"offset\",\n", + " \"omega_scaled\",\n", + " \"b_HI_scaled\",\n", + " \"b_g_scaled\",\n", + " \"M_10\",\n", + " \"NL\",\n", + " \"FoGh\",\n", + " \"FoGg\",\n", + "]\n", + "corr_params = [\n", + " \"omega_scaled\",\n", + " \"b_HI_scaled\",\n", + " \"omegabfm2\",\n", + " \"FoGh\",\n", + " \"FoGg\",\n", + " \"FoG+\",\n", + " \"FoG-\",\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "save_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/plots/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "save = False" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.159286" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z_eff = config[chain_nlfix.tracer]['z_eff']\n", + "z_eff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plot all parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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IiLXh07wUdJ2Tj6wN/KWJKJAmT56M9u3bu55v3bq1wdNwv/rqK+Tl5bmejxs3rtpIcBWn0wmLxQKLxYJTp0751N+ZU1eTk5NrPffyyy93K1oTiPu94IILar3fULDZbLj44ovx888/4/vvv8eECRNqPXf+/Pn4/PPP3Y698MILaN++PTZu3IiWLVvi4MGD6NatG6ZNmwZZll3nHT9+HJmZma4tiP766y/X88zMTI9e98aMGTNgNBphMBgC+rj00ku9iquxOe+881x/Pv1zXpvTk169Xo8OHToEJC4iosYmvOZ1kVf2/WXFxI9FXCtuwSvPjoVkNODpCjsufWgzLliWiC3N8pHaIjHUYRI1SpIkYcGCBbjwwgtde07OmDHD5wI9DofDVbkWAIxGIxYuXOjRtb/++mu1NXueOHONXm17rgKARqPB4sWLMWzYMFcy5s/7jY6OxgsvvOBTW4GgKAquvvpqrF+/HitXrqw25fp0+fn5eOKJJ/Dll1+6js2cOROzZ8/Ge++9h6uuugoAcOWVV6J37964/PLLER0djaeeegpA5ZTurKwsAHCtVa56XqW+173x5JNPYtq0aQHfhzY1NTWg7Ye7iy++GEajEeXl5fjjjz/qPf/0Kf9jxoypNgWZiIhqEdJNg8JMJO1p6pQVtc/du9Rh/16rOktL3F4rLS5Xu9y6TZ1wx48hii54Iuk9o8bpkUcecdtv8rXXXvOpnXvvvdetnbfeeqvO80/fG3PkyJFe9+d0OtWOHTu62mjbtq1H182dO9fv9ysIgsd7yv7222/qc889p77//vt+3f/1TLfeeqsaFxenbty4sc7zSktL1dGjR6tt27ZVZVlWVVVVf/31V1UURbV79+41XtO1a1dVo9GoJ06cqPZa1c+kNvW93hhF4j60VaZOnaoCUA0GQ73/TrVv397Vz5o1axoQMRHR2eXs+lexHpGUHL22fJ9qvu2Iuv+XbTW+vv7nfarwn1z168+2Bjmy4Iqk94waJ1mW1cmTJ7t+ERVFUV22bJlXbTzxxBNuSeKTTz5Z7zWn/5INQH311Ve96vPMRPyDDz7w+Nrp06f77X4FQVBffPFFj6774osvVI1G47p2yJAhqsPh8KpvT7z55psqADUmJkZt3ry5OnDgQPW+++5Tv/vuO7W4uFhVVVUtLy9XP/nkEzUjI0MFoH788ceu6y+88EIVgPrMM8/U2P7dd9+tAlBff/31aq8xoa2uIQml1WpVJ06cqJpMJnXQoEHqX3/9FdT+c3Jy1NjYWBWAunDhwlrP27p1q6uPG2+80esYiYjOZmfXv4r1iJTkqNyuqM3+s1+9567/1nne1ff/pPa8ZZNr1KAxipT3jBo3WZbVu+66yy1BvOSSS9S9e/fWed2WLVvUoUOHuq4xGo3qG2+84VGfZya0oiiqd9xxh5qTk1PndUePHlWnTJnidu11113n8b1WefHFF92SS1/uNyoqSv3ss8887nPAgAFucQNQV65c6XXsddm9e7dqMBjU4cOHq2vXrlX379+v3n777W59xsXFqaIo1piAlJSUqDqdTgWgfvXVVzX28corr6gA1DvvvLPaa0xoq1u5cqXrvtPS0ry69tFHH3V776655pqg9q+qqrp8+XJVkiQ1Li5OPXLkSLXXy8rK1EGDBqkA1AEDBqglJSU1tEJERLURVDXAi2giiNVqRWxsLCwWC2JiYkIdTq1e+ng/Zv0kYN8tTiR26VjreX/8fgIZizX4aughnDepXxAjDJ5Iec/o7LBq1SpMnz4dv//+u+tY165dMXToUKSmpiIuLg4FBQU4fvw41qxZgwMHDrjOGz16NJ599ll069bNo7527NiBkSNHIj/fvaq5VqvFkCFD0KtXLyQnJ8NkMqGsrAxHjx7Ftm3bsH79eiiK4jp3xowZeOKJJ3zaGmjdunWYMWMGNmzY4PX9jh07FgsWLEDnzp097u+cc85x6wsAvvvuO4wdO9br2GuiqiqGDh2KpKQk/Pe//3XbPuixxx6rcbueG2+8EQsXLnRty/Lbb7+53sP27du77S1apby8HBaLBddddx3mzp3r9lrVGtna/mmu7/VIZ7PZcPLkSZSVleHkyZPYvn07Fi5c6PbZmThxIi699FK0a9cOCQkJMJvNaNq0aY3tzZ49GzNnznQ9nzJlCt56662g9V/l/fffx/XXX4/k5GTMmjULgwcPhsPhwM6dO/HUU08hKysL119/PRYuXMh9homIvBXSdDrMRMJon6Ioaoc7/1Dvmu7ZerOJ96xTh932c4CjCp1IeM/o7CLLsvr555+7pjnijBHF0x9NmjRR/+///k9dt26dT305nU71xx9/VO+991514MCBqsFgqLO/qkdycrJ62223qXv27PHLPX/11VfqxIkTXVMra3skJiaqV1xxhfrLL7/41M+nn36qSpLkam/AgAF+XUe7Zs0atWXLlmphYWGNr7/yyitqSkqKGhcXpw4fPlxdsWJFtXM2btzois+X+6y61tfXI92ZMw88edQ1DbikpESdNGmSajab1eHDh6vHjh0Lav+n27dvn3rLLbeo7du3V/V6varX69X27durN9xwg7phwwYvfkpERHQ6jtCeJhJG+77fVoDxS53YO34v2o0dWu/563/+E4OWJ2LPvwrRuVfbIEQYXJHwntHZy+l0YteuXfjjjz9w4sQJlJSUYP78+SgtLUXz5s2RlZWFxET/VSJ3OBw4cOAADh48iGPHjqG4uBhlZWXQ6/WIjo5GamoqunfvjvT0dNdInz85nU5s2bIFR44cQU5ODqxWK+Li4pCcnIw2bdogMzPTp5Hg0+3atQurVq1CSkoKLrvssrCrBHvixAmkpaUBqNyr97LLLvPq+rN9hJaIiMhbTGhPEwnJ0QWzdkPIPogVr42HIEn1nq+qKrr9ZwdGxeXi+cdHByHC4IqE94zodAsWLMBdd90FAOjXrx/ef/99tG3b+L5sOpt16dIFe/bswdy5c3H//ffXeI6qqrBYLIiLi3M7zoSWiIjIOw37qpyC6niRjG8LUzC1W5lHySxQ+cvPjV0q8H5BOuxlFQGOkIjqc8cdd+DSSy8FAGzatAldu3bFLbfcgpUrVyI7Oxs2my3EEVJDTZs2DQDwySef1HrOZ599hhdffDFYIRERETVaTGgjyHvfZ6Op7SRGTx7s1XX/mtwNFikK36zICkxgROQxQRDw3nvvuaaiVlRUYNGiRRg3bhxatmwJg8GAl19+OcRRUkPccsstGDRoELZu3YqNGzdWe91ut+OZZ57B9ddfH4LoiIiIGhcmtBHk/a02XCLsgC6tmVfXJSSYMUJ7CB9tLg1QZETkDZ1Oh48//hgLFy5EUlJSqMMhPxNFEV988QUGDx6MyZMnY/369a7Xjh49iksuuQT33nsvmjdv7nbd/v37XX/etm1btXbre52IiOhsxIQ2Quw95USWrQmu6u1bOf/JPXX4xtYO5ZZiP0dGRL6aNm0aDh06hLfeesu1DYjZbG5w4SQKvcTERKxduxYzZ87EjBkz0LVrV/Tu3RvTpk3Dww8/jIsvvth17vHjx9GxY0dkZGS4jvXp0wdJSUn417/+Ve/rREREZzMWhTpNOBcYeuidQ/j05xzserIVNMmpXl9vtVYg+eFCvNN7Pyb/35AARBga4fyeERERERFRYHEYIEJ89puCizR7fEpmASAmxoBR+sP4b5bDz5ERERERERGFBhPaCHAwT8bv9nhc1N3QoHYu6a7DKkcbOFjtmIiIiIiIGgEmtBHg0/W5aFl+FL1G9WpQOxPGd0KJZMIP3//mp8iIiIiIiIhChwltBFixpRhjbNuga9ehQe0kJZrRTziCLzYV+ikyIiIiIiKi0GFCG+YKShWst8ThwjYOCILQ4PbOT7fju6JUsBYYERERERFFOia0Ye7LrFJEyyUYMaJho7NVJo5ug4O6Zti96Q+/tEdERERERBQqTGjD3FfrT2GEZSOi+g3wS3sZGalIl0/hqx+y/dIeERERERFRqDChDWOKouLHEwacm1gEQa/3W7sj4/Ox+pj/2iMiIiIiIgoFJrRh7LeTMvIUE0b1ivdru2MyY7AebVFuKfZru0RERERERMHEhDaMfb85D23LDqHN4IZt13OmMSM7wCFo8cPq3/3aLhERERERUTAxoQ1jq3dYMNi+B9r0dn5tNzbWgD7CX1iZZfFru0RERERERMHEhDZMOWQVvxTEYESazS/b9ZxpVLMKrM1P8Hu7REREREREwcKENkxtPOxAuarBuf1TA9L++EFN8ZsuHccOnghI+0RERERERIHGhDZMrdpwAl1L/kDTgX0D0n7fAW0Q77Tgu9X7A9I+ERERERFRoDGhDVOrf6/AEOyHpklKQNrXSCKGGY5i5T45IO0TEREREREFGhPaMFRqU7G5OB4j0tWA9jOqvYT/ladBUZSA9kNERERERBQITGjD0M/7yiCoCoYNbBXQfsYNT8cpbRJ2bTkU0H6IiIiIiIgCgQltGPp+w0n0Lt6JhH6BWT9bpW3Hpkh3HMfKdUcD2g8REREREVEgMKENQ2sOqBiqy4YYFR3wvobH5GLNX1LA+yEiIiIiIvI3JrRhJr9Uwa7yOJzbLjhvzagMI9bLrWC3OYLSHxERERERkb8woQ0za/aUIUouwznntA1Kf2NGdUCZaMC6n/8MSn9ERERERET+woQ2zKzalIP+1iyYe/UOSn9JTRPQw3kY32/OC0p/RERERERE/sKENsz8cFjEUNNxiCZz0PockWTFDyeMQeuPiIiIiIjIH5jQhpHsQhkHHDE4t6MuqP2OzozFVrSCxVIe1H6JiIiIiIgaggltGFm1uwxJ9nz0PKdDUPsdNrITNKoTq9fuC2q/REREREREDcGENoys2pyLQdatMGQGZ/1sFWNMFAaoB7BqR3FQ+yUiIiIiImoIJrRhQlVV/HBUh2HRORANwV/Pem5aBX7Mjw16v0RERERERL5iQhsmfj+l4JRswsiM0BRnGtOvCf6QmuHocWtI+iciIiIiIvIWE9owsfq3ErSqOIoO53QLSf99hnRGoqMQ363ZH5L+iYiIiIiIvMWENkys2laIgdat0HfrGZL+Jb0eQ6TDWP17RUj6JyIiIiIi8hYT2jDglFX876Qew+MKIOj1IYvj3NYyfipOhqqqIYuBiIiIiIjIU0xow8DWbBlWRY9zu0WHNI6xg1vgpJSA3/7ID2kcREREREREnmBCGwZW/1aCjNJ9aD4gNNONq7Tv3QGtbcfw3U+HQxoHERERERGRJ5jQhoFVOywYVLwN+i7dQxqHIEkYbjyKNQeUkMZBRERERETkCSa0IVZuV7Eh14gRiRYIutCtn60yqoMW6yrS4JC5jpaIiIiIiMIbE9oQ++WQE4oKDOuZEOpQAACjh7dBqWDA+i3HQh0KERERERFRnZjQhtj3O4vRy7oTCX37hjoUAECTjunobtuPletPhDoUIiIiIiKiOjGhDbHVv5ViSNl26Lt0C3UoAABBEDAiNhdrs7WhDoWIiIiIiKhOTGhDKK9EwQ6LCec2s0HQhE8CObqLGVvlNFjLWRyKiIiIiIjCFxPaEFr7pwPRShn692sV6lDcDD23MzSKE2t++SvUoRAREREREdWKCW0Ifb+9CAMKNyO6/zmhDsVNVPOm6G//A6u25IU6FCIiIiIioloxoQ2hNfscGCr/Dk3rtqEOpZoRyRasPWUOdRhERERERES1YkIbIgfzZByuMGFUWxGCIIQ6nGrG9IzHH0jF8QJ7qEMhIiIiIiKqERPaEFn1hx1p9hxknJMR6lBq1Gd4NyQ4CvHdj4dDHQoREREREVGNmNCGyMotBRhUuBGmfgNDHUqNtAkJGKzsw6pdxaEOhYiIiIiIqEZMaEPA7lSx+i8NRukPQ0pqEupwajWyWQV+LIiHqqqhDoWIiIiIiKgaJrQh8PNBJ8plEWO7R4U6lDqN7ZeCk0Ic9mSXhzoUIiIiIiKiapjQhsCX20vQz7odKcMGhzqUOrUf3AOty7O5jpaIiIiIiMISE9oQ+HpHGc4t2Qx9j16hDqVOosmM4ZpD+HYPKx0TEREREVH4YUIbZH/mythfZsT4NjIEjTbU4dTrwi4a/FzeFJZyJdShEBERERERuWFCG2Rf/WZDK9txdB3SLdSheGTM+O7QyXZ8/eORUIdCRERERETkhgltkH25qRAj8n+GadDQUIfikajWLXGucxc+21AQ6lCIiIiIiIjcMKENoqIyBb+c0GFs7AlI8YmhDsdjF7Zx4PvCZDhkbt9DREREREThgwltEH252wGzUo5R56SFOhSvTBjXHmXQYe3mU6EOhYiIiIiIyIUJbRB9vL4Qo3N/QOzIsaEOxStNunXGORW78emPx0IdChERERERkQsT2iAprlCx6rCEC/X7oG2VHupwvCIIAiY0K8IXJxIgK5x2TERERERE4YEJbZB8vccOvVyBsYNbhDoUn1wxvi3yYMYPnHZMRERERERhggltkHy8rgCj8n5C/OjImm5cpVmfbhhasRPLvj8a6lCIiIiIiIgAMKENCmuFiu8OSrhQF3nTjasIgoDL25bji9wU2JycdkxERERERKHHhDYIPsmyweAsw/lDmoU6lAaZfEk32FQJK9YcCnUoRERERERETGiD4e0f8zEhZyXiL5gQ6lAaJL5da4yVd2LZ/wpCHQoRERERERET2kA7lC/j55NGXJF8DJqUpqEOp8GmZAr4tqQlThZUhDoUIiIiIiI6yzGhDbBlv5aiXflhDL6gX6hD8YsLrjgHafZTePWjvaEOhYiIiIiIznJMaANIVlS8+UsxLi1aDfPw0aEOxy80MTGYknAAb+w1c09aIiIiIiIKKSa0AfT1bjtOlmtwXV8NBL0+1OH4zb8v64iTagy+/ulIqEMhIiIiIqKzGBPaAHrp2zxckPs90q+YFOpQ/KpZZmdc6NyK578rDHUoRERERER0FmNCGyD7cmSsPm7Ev1MOQdu8VajD8bsZ55rwg60ltuzJD3UoRERERER0lmJCGyBPfZWHXsW7MOSKkaEOJSAGTByC4eXbMeeDw6EOhYiIiIiIzlJMaAMgu1DGOztF3CH8AEO/gaEOJyAEScI9AxR8YW2J3w8XhzocIiIiIiI6CzGhDYD5X+WjfekBXPJ/wyAIQqjDCZhxVw3BgPLdeODN/aEOhYiIiIiIzkJMaP3sSKGMN7YCdyqrYBo8PNThBJSoN2DOMDu+sLbC+h25oQ6HiIiIiIjOMkxo/ey+D06hc/HvuOrG4Y16dLbK0CtG4oKKTbjn3eNQVe5LS0REREREwcOE1o82HHbgoz90eDz6J5jOGRLqcIJCkCTMmxSHLbZkvL3iYKjDISIiIiKiswgTWj9xyCqmvnUSF+avxpjpV4U6nKDqMnoA7tavw4w1EvIs9lCHQ0REREREZwkmtH4y56tCZOc78cyAImjT24U6nKB75N4RSLIX4ObndnPqMRERERERBQUTWj9Yf8iBJ36Q8WTJMrS7+fpQhxMSxiaJWDraiq8KU7Hw432hDoeIiIiIiM4CTGgb6KRVwaULc3F5zle4/oFLIWh1oQ4pZAZMGo4n49bh7nWxWL8jJ9ThEBERERFRI8eEtgFKbComvHACyYUH8MKV8dB1zAh1SCF314MXYJJzEya8UYZ9f1lDHQ4RERERETViTGh9VPp3Mpt3LA8fdclC4oUXhzqksCAaDHjz8cHoYT+AUc+exJ9MaomIiIiIKECY0PrgVLGCofOO4cChfHzafCU63HFbqEMKK/rEBHz2cAba249i8DN52LLrVKhDIiIiIiKiRogJrZd+2mdDn9nHIGcfwuou69DjoXsgCEKowwo7Mc2b4qsne2KI8gcGL1Gw6L1drH5MRERERER+xYTWQ3klCm55PRvnvlKMESdWY9WkErS/bWqDk1mbzYZZs2bBZrP5KdLwYUyMx8cLzsXjyVtxx8Ym6DP1J+zYdTTUYRERERERUSMhqBw2c7FarYiNjYXFYkFMTAwA4ECuE6+sOIrXd+mQUnYCT0T9iEn3XglNalrA+myMNq/cgH8vy8fOxH6YaNyHey5uin790xv8hcDZ8vMjIiIiIqLqNKEOIBxtPFCOX3fn4cvfHNhS0QRdSo5jtmkHbvx3b0T1uTvU4UWkjudkYMe4WHz6zlos2KTDgA/j0P7d33BRSj5GZMZjyNB2iI41hzpMIiIiIiKKIByhPU3VaB9uPohOcg7GivtwUVcJgy4aBF3LVgHts7GPMJ55n3u3H8S73/2F746ZsUNsDVUAWjpz0UFbiHaxTqTFiEiN1yG1iRGJ8UaYzHpERelgjjYiOtoArUEHjSSeNT8/IiIiIiKqjiO0p5FlGQCwbfQOtO6bCSmuMwCgAkCFNTDbz1j/btcaoPbDxZn3mdY2CffemoR7ARRby7B5w0H8ftCCP3IV7Dqlw9rjeuRBj3wpCoogAVAB2P5+/E1VIVYUAAAKCgqCej/kG1VVUVxcjLS0NIgil/ATERERUcNwhPY0mzdvRr9+/UIdBlGjl52djebNm4c6DCIiIiKKcByhPU27du0AALt37w7a9NXi4mJkZGRgz549iI6ODkqfoRCo+zx27BgGDBiA7OxsTjmOAFarFS1atGjUn3UiCj+yLMPhcIQ6DKKA02q1kCQp1GEQBRUT2tNU/Q+gefPmQUuOqqbgNmvWrFEnZIG+z5iYmEb982tsuHczEQWDqqo4efIkioqKQh0KUdDExcUhNTWV/9bSWYMJLRERETVKVclscnIyTCYTf8GnRk1VVZSVlSEnJwcA0LRp0xBHRBQcTGiJiIio0ZFl2ZXMJiYmhjocoqAwGo0AgJycHCQnJ3P6MZ0VWGY0xPR6PWbOnAm9Xh/qUALqbLlPIiIKD1VrZk0mU4gjIQquqs88143T2YIjtCGm1+sxa9asUIcRcGfLfRIRUXjhNGM62/AzT2cbjtASERERERFRRGJCS2eV07dd5hbMRERERESRjVOOI4zqdEKtKIdaUQHVboPqcECVnYAsA4p82pkCIEkQNBpAo4Wg10PQGyCaTBA02pDFH0rTPi7FrhMyfpkeg/J1PyL34buQ+OATMI8eH+rQiIgoiJwnj0MuKgxZ/1JcPDSpaQHtIzMzE0eOHEFMTAwOHz4MALjxxhvx/fffIzs7G4cOHULr1q0DGgMRUTAwoQ1TqtMB5/FjKPn0AyilJZVJrK0CqqxA0Gork1KNBoJGAgQREEUI4mkD7qoKVVEARQEUuTIRdjigOuwQBBGC0QQxKgpRF10GTct0iMbGXzRj8Xqb68/WD5dCLSlG4UtPwTRiTGXiT0REjZ7z5HEcmzQGsNvqPzlQdHo0W/59g5PaY8eOoWXLlvj3v/+NRYsWub2WlZWFKVOm4Mcff3Qde/311/H222/juuuua1C/jVlWVhY+//xz3HHHHYiLiwt1OETkAf4WH2ZUuw2FL8yHnHMSQlQ0pJhYSEnJlSOrekNlEtuAxf6qqgJOJ5TyMqglxShe/gGUwgJIySmIv/OhsyKxU2UZFds2IXHWU8h//AHYdmyFoXf/UIdFRERBIBcVhjaZBQC7DXJRYYMT2nfffReiKOKjjz7C888/z50E/CArKwuPPfYYpkyZwoSWKEJwDW2YKXh6NlSnA4a+A2Do3hPa1m2gSU6BGBVdOTLbwMp1giBA0GohxcRCk9Yc+s5dYeh3DlSbDYUvzvPTXYQ354mjgN0OQ69+MPTqj7Ifvg91SERERF778MMPMXfuXBQWFmLFihWhDoeIKCSY0IYRpaICSlEBdB06QdDqgtavoNFC26Yd5JxTQeszlByHD0IwGCGlNIWh/0BUbNsU6pCIiIi8snnzZnTq1Am33nor4uLisHTpUr+1vWLFCmRmZkIQBNx3332YNm0aevXqhYSEBEyaNAk5OTmuc/Py8nD77bcjMzMTPXv2RPfu3fHggw+ioqLCdc7ixYuRkZEBQRCwcOFC3HzzzejduzckScIdd9wBAJgzZw769euH3r17o1u3brjyyitx7NgxVxtbtmxBZmYmdDodpkyZgqeeegr9+/dHSkoKHnroIQDAkiVLMHDgQDRr1gwPP/xwtfs6cuQIJk+ejFatWqFt27YYMWIENm3653eAhx56CI8++igAYPz48cjMzMSll17q8fWe3Ofbb7+Nnj17omfPnujWrRuuueYabN++vQHvFhExoQ0jzmPZEGPiIIhS0PsWDEZAVaEUW4Ped7A5jxyGpmVrCKIIQ+/+cPy5N6TFQYiIiLy1dOlS3HzzzTAajfjXv/6FlStX4tQp/3wxPWHCBGRlZQEAFi5ciIsvvhjbtm3DwYMHceDAAVxyySWuc/fv34+1a9fihx9+wPbt2/Hzzz/j559/xr333us6Z+rUqfjmm28AAC+++CJuv/12bN26FU8//bTrnKeeegqvv/46tm7diqysLKSmpuLCCy+ELFcWvOzTpw+ysrKQlpaGlStXokePHti4cSOWLVuGOXPm4Pbbb0fLli2xfv16vP7663jyySexevVqV/v5+fkYPHgw7HY79u/fjwMHDuD888/HiBEjsHfvXgDAk08+idmzZwMAvvnmG2RlZeGTTz7x+Pr67vPnn3/G1KlTsXz5cmzfvh0bNmzA0aNH8cUXX/jlfSM6WzGhDSPFH7wFKalJSPoWBAFSfAIsS18NSf/BJOeegiY5BQCg69QVgskM2/bNIY6KiIjIM3a7HVlZWRg+fDgA4Oabb4bT6cR7773n974GDhyIsWPHAgDi4uLw0EMPYd26da5ksVu3bvjuu+8QHx8PAIiNjcXVV1+NN954o8bt8UaOHImuXbu64r7//vsBABs2bED37t0BAJIk4eabb8b27duxZcuWam00bdrUFdOYMWMQHR2NdevWuY6dd955iIqKwg8//OC6ZsGCBcjOzsYzzzwDrbZyt4c777wT8fHxmDev/iVX3l5f031u3LgRer0eaWmVa6fNZjMef/xxDBgwoN7+iah2TGjDhFJRDsVSBKlJcshikFLT4Dx+rNHvz+rMz4eYkAQAEDQa6Lv3QkXW1hBHRURE5JmvvvoKkyZNcj3PyMjAkCFD/DrtuEqXLl3cnvfu3RsA8OuvvwKoTMp++uknDB8+HF27dkVmZibmzJmDsrIynDx5slp7nTt3dv3ZbDYjNTUVAFBYWIiLL77Y1UbVKPDBgwertdGuXTu35/Hx8dWOJSQkuPW/evVqJCcnu50nSRI6derkVgm6Nt5eX9N9Dh48GCUlJejXrx9ee+015ObmYsiQIRg3bly9/RNR7cK6pO3hw4fRtWvXav+TAoAff/yxzupzDbk2FByHDkBMSIQghe4tEWNiIWg1cB4+AG169Z9bpBMFQFGBssIixHTq5Dquz+iGiu1cR0tERJHh3XffxR9//OGWwBYWFuLIkSPIyspCZmam3/qKiYlxe141Env8+HEAletGb7nlFnzyySeuJLRqayCbrXo16aioqGrHduzYgREjRuDWW2/FJ598Ao1Gg8OHDyM9Pb3GNkwm960GBUGo8VjVdGWgcq2v1Wqt9rMpKiry6It8b6+v6T4HDBiAn376CfPnz8ett96KW265BRdddBFefPFFNGvWrN4YiKhmYZ3QApVrJjz55szf1wZbyX+XQWoa2v+ZCYIATVoLWN99HYmPNL6KxwYtUGYHSgqKEZ+Q6Dquy+gO6wdvQ5VlCFLw1y8TERF5Kjc3F4qiYPfu3W7Hi4uLkZiYiKVLl/o1obVa3WtrFBQUAIBr2uw777yDrl27uq2r9dZHH30Em82Ghx56CJoAbR+YlJQERVFca4ODfX2VwYMHY/DgwTh16hTeeOMNPPHEE7j88svxyy+/NKhdorMZpxyHAaW0BEpJMaT4xPpPDjApIRGyxQJVUUIdit8ZNJVbHpVZSiGdntB26Qa1vAyOQwdCFRoREZFH3nvvPdda0dNFR0dj6NCheP/99+F0Ov3W35mJ89atlUt0zjnnHACAzWartqVgTVON61I1Cnt6O962UZ/Ro0fj6NGjroS8ysqVK/HII4+4nletj60adV25ciUKCgo8vr4u77//Pr788ksAQEpKCh588EHceOON2Llzp8/3RURMaGtktVrdHjVNd/Eny+svQ0pKCYvRQUGjgaDTQbEWhTqUGtlsNrf3pri42ONr9X9/6VtaUg7xtC8PNEnJkJJTYN+zw9/hEhER+dU777yDCy64oMbXLrjgAuTk5Lgq7frDjh07sHLlSgCV02vnzJmDQYMGYdSoUQCA888/H7t27cKqVasAVCaiS5Ys8aqP8ePHAwCee+45AJVFr+bPn++vWwBQWcApLS0Nd9xxB+x2OwAgOzsb06dPR48ePVznpaenAwCOHj2K4uJiTJw4EcXFxR5fX5d9+/Zh/vz5rlHv8vJybNu2Deeee64/b5XorBP2Ce2pU6dwzTXXoF+/fujQoQOuuuoq7Nq1K6DXtmjRArGxsa7H3LlzG3obtVIVBc4Tx6AJ8XTj04lR0ZBP+febUX+ZO3eu23uTkZHh8bX6v0doy1UdxGj3NUG6jO6w7fHsc0VERJFLiosHdPrQBqHTV8bhhfz8fPTo0QM7d+7EhRdeiD/++MPt9RdffBEvv/wyAOCmm25Cly5dsGLFChw/fhyZmZnIysrCjTfe6LbP6uuvv15vvzfeeCNWrVqFPn36oG3btmjbti0+/fRT1+sPPvgg/vOf/2DKlCno3bs3brrpJlx44YWuPj755BN89NFHrqT10UcfRWZmptv61pEjR+LVV1/FBx98gI4dO+K8887DoEGDXOfff//9OHDgADIzM3H8+HGsWLECEydORH5+fr3HRowYAaCySNQvv/wCp9OJtm3bomfPnpg8eTJmz57tttfswIEDccMNN+Daa69F//79cd9996FVq1YeXV/ffU6cOBGtW7fGgAEDkJmZid69e6NLly548803PfkIEFEtBDWMS9pmZ2dj/PjxePvtt9G7d29YrVbcfvvt+O9//4uffvoJffv29eu1VqsVsbGxyM7OdiuCoNfrodcH5h8/x1+HYHlzIQyZvQPSvi+ceTlwHj2CxFlPV5tGFGo2m81txPzYsWPIyMiAxWKpVrjiTJ3nFGFvjoJPd9yAC955HtoWrVyvWd5ajLIfVqLpO58FLHb65++YJ+8XEVFDVFRU4NChQ0hPT4fBYHB7zXnyeEj3H5fi4qFJTQtZ/54SBAEzZ87ErFmzQh0KeaGuzz5RYxTWRaFatGjhNqIaExODxYsX45tvvsGDDz7omt7i72tjYmKC9su29b03oEltGpS+PCUlNoHzr0NwHjsCbfNW9V8QRGd+uXBmsYq6SGJlcl4h6SGa3asP6rp0R9GrL0C12SAE6MsLIiIKD5rUtIhIKImIqH5+m3JcV4JY12veMhqN6NatGzZs2BDUawNBlWUohfmQEpNCHYobQRCgad4S1rdfDXUoflU1FaFcNFRLaPUZ3QDZCfu+PcEPjIiIiIiIfOK3Edo5c+Zg8+bNNb62atUqjB492us2LRYLjEYjdDqd23FJktzWJPj72mBRCgsg6A0QtLr6Tw4yKTEJ9v37oCoKBDHsl1p7RFYqU9oKrbnaKKwYFQ1N6zaw7d4JfbeeoQiPiIgoLKxYscK11nbx4sXYsWMHPvuMS3KIKDz5LVORZRkOh6PGh6/l46dPn47ly5e7HbPb7di1axd69erldvzEiRM+XxsqSkUZhFAXpqiFoNFC0OnDttqxL/7OZ1FhjK3xdX2XHrDvZul8IiI6u02YMAFZWVlQVRUnT55kMktEYc1vI7QPPvggxo0bV+Nr/fv397ndp59+GsOHD0fTpk0hyzLuuece5ObmYtmyZa5zPvjgA1x11VWYN28e7rvvPq+uDSlFBcKs6NLpRJMJSmEBpLiEUIfiF1UJrc1Y8/pofUZ3WD9aGsSIiIiIiIioIfw2Qvvnn3/69Fpd7r77bgwcOBDjxo1Djx490LJlS+zevRurV692m8IcHx+P6OhoJCcne31tKKlOBxDG03lFcxRKPv841GH4jSuh1UfX+LquS3c4jxyGbCkKXlBEREREROQzv43QPvzww3jppZeqHVdVFbm5ubj99tu9brNbt26uPdXqMm7cuGrVbj29NpRKv/oUgsEY6jBqJcbGwZF9ONRh+I2sVP63vLaEtn1HQKuF/fddMA4YEsTIiIiIiIjIF35LaG+44QZMnz7d9fyTTz7BpZdeClVV8eqrjatarr/IhQXQNmse6jBqJcbEQi0phuqwh2XhKm+5Rmi15hpfF3R66DpmwLZ7JxNaIiIiIqII4LeEdvr06WjV6p89S5s0aeJ6Pm3aNH9102goJcVQS0sgxofv+lRBkiBERcN58gS0LcJrP1pfKGplRltWS0ILAPou3VkYioiIiIgoQoT1GtrGrOjVFyClNIUgSqEOpU5SbDyK/xsmRbQaSFEBIxwo1dSe0OoyesC2eydUVa31HCIiIiIiCg9+G6F97LHHsGnTJtfz7du34+jRowCAtWvXYuTIkf7qKuIpFeWQTx2Hvle/UIdSLzEhEfY/9kBVVQhhXJHZE4oCRKMcJULt65b1XXtAKciD89gRaJtH/qg0EREREVFj5reEVhAEOBwO1/OuXbu6ntvtdn910yhYXn0BUlIyRL0h1KHUS4yKhgABzuNHoW3WItThNIisAglqOUqE2n/umhatICU2gW37Fia0RESN1HGLgsIyJWT9x5tEpMWG7y4Hvrj11lvx5ZdfIjs7G4cOHULr1q1DHZJHFi9ejBdffBG///473nrrLUyZMiXUIRGRl/yW0M6cObPWUdjBgwf7q5tGQc7LhbZdh1CH4RFBEKBp0RKW119G0sz5oQ6nQRQViFbKUKLWXOUYqLxffc++qNi+GVEXTgpidEREFAzHLQqGv2yFpTx0S0tijQJ+vC3Gq6Q2JycHY8aMwZEjR1BYWIgePXpUO+fkyZOYOnUqZs2ahY0bN+K8887Dd999h379Aj8j7JVXXkHfvn1x3XXXBbwvf5o6dSrGjRuH9PR0t+NHjhxBr169sGTJEkycODFE0RGRJ/yW0NY1pZjTjd2pFWUQTaZQh+ExqUkKHH8dgpyfBykxKdTh+ExRgGi5BLlqYp3nGXr2hfX9N4MUFRERBVNhmQJLuQqNCGhDUMbCIQOWchWFZYpXCW1ycjKysrIwZcoULF26FFlZWdXOmTVrluvPZrMZrVq1gtlce90Iqp1er0erVq0QExMT6lCIqB4NSmjXrl2Lb775BgAwYcIEDB06FACwatUqbNiwAS1atODUjRqoigKEeTGo0wmiCE1yKixLFyPhrodDHY7PFFVFjLMEhxRtnefpe/WD8+nH4Dx1ApqUpkGKjoiIgkkrAXpNKGpDqHAGaLbzFVdc4fpz165dsX379sB0dBZISUnB1q1bQx0GEXnA5wUcDzzwAEaNGoXnnnsOzz33HEaMGIHx48fj0KFDGD16NK666irccMMN/oy1UVCdDgACEGEFlqSkJpDzciO6+q+sAtEOK4rlur/H0bZpBzE2DrasLUGKjIiIyHeHDx9G69at0alTJ3Tq1AkrV65EZmYmBEFwjdouWLAAaWlpEAQB7du3x+HDh/HWW2+hRYsWaNKkCaZOnepqb9euXTjvvPOQnp6O9PR0XHjhhdi3b59bnyUlJbjhhhuQmJiInj174qabbkJpaWm9sW7ZsgWZmZnQ6XSYMmUKnn76aZxzzjlIS0vDzTffXK3uSkFBAW6++Wa0bt0aHTp0QGZmJj766COf2/PErl273NqskpmZiYSEBLRu3Rrffvstzj33XDRv3hyjR492FUKtoigK5syZg/bt27vel2effdbtnIMHD+L6669HZmYmevbsiczMTDz77LOQZdl1zqOPPop27dpBEAT897//xZVXXokePXpAEAQ8//zzXt8bUWPkU0K7du1afPjhh3jttdfw22+/YdOmTVi6dCni4+Nxzjnn4KmnnoJOp4vo5CdQ5IJ8iCZTxFUMFsxRgKpCsRSGOhSfKSoQbbegWJbq/GwKogh9Zh9UbN8cxOiIiIj8Y+zYsdWmJN95551Yt24dzGYz+vfvj9atW+Oaa65Bs2bN8P3332Px4sUAgP3792Pw4MFo06YNDh48iIMHD6J169YYMmQI8vLyXO3deOON+P7777F9+3Zs374dU6dOrZaw1aRPnz7IyspCWloaVq5cic6dO+PXX3/F6tWr8cYbb+Dtt992nWuz2TBq1Cjs3bsXv/32G/bt24ennnoK1157Ld58802v2/NUt27dXG2eLisrCxMmTEBhYSF+/fVXrF27Fnv37sWRI0dw7733up1722234dlnn8WKFSuwd+9efP7555g3bx5mz57tOmfTpk04fPgwNmzYgO3bt+O7777Dq6++ihdeeMF1zuzZs/H6668DAJ599lk8/fTT2LFjB26//Xav74uosfIpoV2+fDk2btyIG2+8ERkZGejTpw+uueYavPfeezh8+DA6duyIJUuW4LLLLvN3vBGv+IO3IcbFhzoMrwmCACkuHs5jR+s/OUwpSmVC61RF2Jx1n2vo2Re2bUxoiYgoPGVmZroe48eP9+ia9PR0zJkzB++99x6WL1+O2bNn49xzz0XPnj1d58yaNQtOpxNz586FIAgQBAFPPPEELBYLXn75ZQDA3r178fHHH+O2225Dy5YtAQC9e/f2umZKSkoKLrjgAgBARkYGOnXqhB9//NH1+rJly7B9+3bMmTMHUVFRAIAxY8bgggsuwH333Vdt9LW+9vyluLgYd9xxBwAgKioKo0ePduvnzz//xOLFizFt2jR07twZANCpUyfccMMNeOqpp1wj2WPHjsWHH34Ig6Fy94XU1FRccsklWLJkSY39Tpo0Cc2bNwdQuV3mtdde6/d7I4pEPq2hbdeuHZKTk2t8zWAw4KKLLsJFF13UoMAaK7mwANoWkbkdjBgdg9KvlkPfpXuoQ/GJogLRFUUAgGKbCoO29lFyfc++KHx+LuTCfEjxdReRIiIiCrbTR2APHz6M4cOHe3Tdbbfdho8++gg33XQT0tLSqq0TXb16Nbp27epWDCk2NhbNmzd3JW0bN26Eqqro1auX27Vdu3b16h46dHDf8SEhIQGnTp1yiwWoHIU9Xb9+/fDZZ59h+/bt6N+/v8ft+UtSUhISEhJq7WfNmjVQVRWDBg1yu65r164oLS3F5s2bMXz4cERHR2PRokX48MMPYbFYoNFocPLkSRQW1jwbrio5BoD4+MgbHCEKFJ8S2qpvycg7qtMJpaQYYmxsqEPxiWAyQzl1MtRh+ExRgRi5BABQXKGiSR0fY12HzhDMZti2b4Hp3LFBipCIiMh7rVu3xuHDhz06VxRFLFy4EJmZmbj44ouh1+vdXs/Ly0N5eTkyMzPdjttsNjidldObTp6s/F0gLi7O7ZxYL3+/MZ2x44Moim7rR/Py8mAymarFWJVMnj4F2pP2brzxRmzZ8k99jNdff71asuxr3IryT6WvqrjuuOMOPPDAA67jNpsNKSkpKCoqAgA8/PDDeOGFF7BmzRoMHDgQQOUI+WOPPVZjv/z9m6hmPiW0x44dq/ecsrIyZGdno2PHjr500SjJebkQjSYIkt92Swoq0WiCWl4KVVUjbg2wqqqVCa2zGABgqah7fbeg0UDfvTcqtm9mQktERI3K+++/j/79++Ptt9/G//3f/2HYsGGu15KSkpCRkYG1a9fWen3TppU7AJw5kliVqPlLUlISysrKYLPZ3JLagoIC1+veqFqLGmhVcS1ZssS1A0hN3nnnHYwePdqVzBKRb3xaQysIAp555hm3b6OAympt8+fPxwUXXIDZs2cjIyPDL0E2FsUfLYUYGxfqMHwm6HSApIFiKQp1KF6rqgEV4yyBJKgoKKu/YJmhZ1/YWBiKiIgakS1btmDdunX43//+hz59+uDGG29EeXm56/XRo0djz549cDgcbtctW7YML730EgCgf//+EAQB27Ztcztn9+7dfo111KhRAIDNm93/Ld68eTOSkpLc1v6Gk1GjRkEQBOzYscPteEVFBS699FLk5uYCqByxPXOAoGr0m4g851NCe+edd+LFF19Eeno6LrnkElx00UXo1KkTOnbsiKNHj+K9997DjBkzWOX4DHJRAaT4hPpPDGNSfAKsy2ouVhDOlL8/iiJkJJmAvJL6NwHU9+oL+77foRRbAxwdEREFm0MGbE416A+HXH9sAbtnhwPTpk3Da6+9Bp1OhzfeeAN//fUXHnnkEdc5s2bNgs1mw8yZM12/x/3222944IEH0LdvXwBAx44dceWVV2LhwoU4cuQIAGDr1q1YsWKFX+O99tpr0bNnTzz44IMoKalcMrR69Wp89dVXmDdvHnQ6nV/785d27drh1ltvxdNPP+3a7sjhcLgKWTVp0gQAcP7552PVqlXYtWsXAGDfvn1uWxIRkWd8SmhjYmKwevVqNG/eHJ9//jm+/PJLJCQk4Oeff8ZLL72EoqIivPzyy2jRooW/441Yqs0GtaQ4okdoAUBKSoacmxPqMLzmSmhVFUlmEXml9X/Zos/oBkFvQMW2TQGOjoiIgiXeJCLWKMCpAOWO4D+cChBrFBBv8u5XsJycHGRmZrqSxszMTFx++eU1nlu1Dy0ALF68GBMmTMBnn32GjIwM7N27F08//TQA4IUXXoBOp8OCBQuQmZmJoqIitG3bFuvWrcOuXbvQsmVL9O7dG7fddhuWLl2KAQMGuPp47bXXMGbMGPTs2RM9evTA008/7dq6Zvz48a5tgM504MABZGZm4vjx41ixYgUmTpwIABg4cCC2bNni2lfWbrdDr9dj9erV6NSpE7p06YIOHTpgxowZWLp0KW644Qav26vJ4sWLXVWiH330UUydOtW1D21Vm1XrbEeMGIEVK1bg+PHjyMzMRH5+Pv7zn/+47jUzMxPr1693/Wxvu+02XHDBBcjIyEDfvn0hSRI++OADV98vvPACLrvsMowePRrnnHMOHnnkEUyYMMGtrQULFuDGG28EULkGeOxYLoMiOpOgNnAY1Wq1QlEUt8IAf/75J2JjY2uthByurFYrYmNjYbFY3Kr7+YN9/x8o/nBpxFYIrqKqCio2rkfCg09ACoPk/OjRo2jRokW971mFQ4XxnkJ8sWMKnrtkGUa012HmOGO97Z/6zw3QtmyNhBmP1Hsu1S+Qf8eIiE5XUVGBQ4cOIT093bUtSpXjFgWFZfXP1AmUeJOItFifxhSI6lXXZ5+oMWpwdaKafilt3759Q5ttdIo/XAopqUmow2gwQRAhJSXD+vZixE+/P9TheEz++/cWUSMhKUpEfqlnv8gY+g5A6VefBTAyIiIKtrRYJpRERI0F/28eBEpZKRRLEaTEyE9oAUBKToGc6/993QKpasqxRqf1eMoxABj6DoTj4J+Q8/PqP5mIiIiIiIKKCW0QWJa8BCmpCQRNZG7XcyYxOgaq0wm5qOaNv8OR8vfMekmnQ5JZQJ6HI7S69p0gxsSiYsuvgQyPiIiIiIh8wIQ2wFRVhfPkCWiaNgt1KH4jCALE2DjIp46HOhSPuYpC6XVejdAKkgR9r36o2LwhgNEREREREZEvmNAGmPP4UQgaCWJ04yqAI5rMKPn681CH4bGqNbSSVoukKAF5JZ7XQjP2HYiKzesDFBkREREREfmqwQntpEmTsH///mrH9+7dW2s5eU8dOHAAM2bMQO/evdG7d2906NABQ4YMwddff+3R9Tk5Obj66qvRsWNHdOzYEZdeeimOHj3aoJi8ZX3nNUgpTQPeT/6cR6o9AkkwGKFWlNd/YpioGqGVdBqvphwDlYWhnMePwnEsO0DRERERERGRLxqc0L7xxhuYMWMG9uzZ4zr222+/4eGHH8brr7/eoLa//fZbfPjhh/joo4+wdetW7N27F+eccw4mTJiAn376qc5r7XY7Ro8eDbvdjt27d2PPnj0wm80YMWKEa3PuQFMqKqAU5EOTnBqQ9utLXgOZ2IpR0VCKrWjgrk9B809CWznluNwBlNk9i13Tui2kpGRUbOG0YyKiSBMp/04R+Qs/83S2aXBCGxcXh2XLluGRRx5BVlYWtm/fjsceewxLly5FdHR0g9pu1qwZZs2ahXbt2lUGK4p48MEHoSgKvvjiizqvXbp0KXbu3In58+dDo9FAkiTMnz8fBw8exKJFixoUl6csb7wMMSERglbr97a9SVQDkdQKRiMEUYJ86oTf2w6EfxJaLZLMAgB4PEorCAIMfQagYjMLQxERRQrt3//2lpWVhTgSouCq+sxrA/D7J1E48kvZ3ejoaLz33nuYNGkSRFHEJ598Ar1e3+B2J06cWO2Y1WoFADRpUvcWOMuXL0fLli3Rpk0b17HU1FRkZGRg+fLluOeee2q9tqqPKnq93qf7kfNyoW3Wwuvr6uNLgpo/5xEkPvi432IQBAFSalNYl76KhPse81u79bHZbLDZbK7nxcXFHl3nltBGVX6Pk1uiomW8Z/0a+p6DwleehaooEEQuPSciCneSJCEuLg45OTkAAJPJBEEQQhwVUeCoqoqysjLk5OQgLi4OkiSFOiSioPDbPjIbN25EVFQUBEHAxo0bMXToUH817XLs2DHceuut6NWrF2699dY6z925cyc6dOhQ7Xh6ejrWrFlT57UtWrgnoTNnzsSsWbO8ilVVFCjFVoixcV5dV5+GjLb6O6mVkpJhy9oCVVWD9kvC3Llz8dhj3ifQsvLPtj1mHRClB05YvVhHO3AolCcehG1XFgw9enndPxERBV9qauWSn6qkluhsEBcX5/rsE50N/JLQrly5Eu+++y7ee+89qKqK6667Dg6HAyNHjvRH8zhw4ADGjh2LAwcO4LzzzsPnn3+OmJi6qwbn5eWhd+/e1Y7HxMSgrKwM5eXlMBqNNV6bnZ3t1r4vo7OKtQiCTufXvWf9MXXYn0mtaDAAqgq1ohyC0eSXNuvzwAMP4K677nI9P3bsGDIyMuq9rmqEVqPTQhAEtIyX8FeB5wmtJikZ+p59Ubb6Gya0REQRQhAENG3aFMnJyXA4HKEOhyjgtFotR2bprNPgbOuLL77AF198gbffftv1F2jp0qW48cYbYbfbcd555zU4yLZt22L//v2wWq149NFH0aNHD6xYsQKDBw9ucNs1iYmJqTdhro9SWADRZPZTRP8kswoqR0JFuC/4lyHCIWigCCJkQXI7T1QV6FQ7tKrsastfSa2g00MtLQWClNCeOf37zOnhtflnH9rKa1vGiThS6HlCCwDm0eNheXMR4u98kNOOiYgiiCRJ/CWfiKiRanBCu3//frzxxhtuU04lScIbb7yBBQsW+CWhrRITE4MFCxZg5cqVuOWWW7Bz585az01KSqpxfaXVaoXJZKp1dNZfSj7/GKI5qkFt5Nh1+MAwFEUVgPX6AbDJ//yMz6xfJwiARlAhCpWVvgSh8gxVFaAAcCqAJABpJieuWDQR8FdSq9VCqShHuP+acPoaWgBoGe99QmsaMRYFT8+GLWsLDL36+TtEIiIiIiLyUoMT2rvvvrvG46Io1vqap8rLy2EwGNySZUEQ0K1bN3zyySew2Wy1Tgfu3r079u7dW+34oUOH0K1btwbF5QmlxApN02ZeX2d1SnhHPwLZ1sokNVEGUuVCdEhSoJcqE9YqVVXZPV2+Wu4UcLhEg2f/70voJRWpQjSusf2EJK3D4zbOJBoMUIo9GyUNJfnv3FX6+/PSKkHE13u8m34mJSbBeM4QlKz4hAktEREREVEYCOt5k+eddx42bKi+9+fhw4cRExMDnU7nOnbihPv2MZdccgn++usvHD582HXs1KlT+P333zFp0qSAxQxUVplTiq0Qoz2ftqyqwEPlozCrfASK7UCXJsCgFkB7IQ8pRhlGjXsyC1Qmst4kokaNis5xDgxLrUDnWAccRRY84xiGu4tH4UnnKJTJ3n8cxKholK74r9fXBZtrDa2+8jPjywgtAERNvAJlq7+BbLX4MzwiIiIiIvJBWCe0QGWF4fz8fACVieKLL76IzZs34z//+Y9r5PaDDz5AWloa5s+f77puypQp6NatG+677z44nU4oioL7778f6enpmDZtWkBjlvNyIGj1EHT1F5NSVeCYTY9nMAoWGzCgGdA1GYg3AnJ+XkDiEwQgTq+gQ6wD55jzMLAFUGIHdpV4v2+wGJ8IuSA/7DfxVv6OTzwtoT1uUeCQvYvbOGg4xOhYlH63wu8xEhERERGRd8I6oX3yySfRrFkzDBs2DJmZmejcuTM++ugjvPvuu25bt8THxyM6OhrJycmuYzqdDqtWrYIkScjIyEDnzp1htVqxdu1aREU1bG1rfYrffQNiYqJH5z6DUXhZHgKtCAxsDhhDsAe2XgN0TgI+1/bFPSWjsEAYhQrFs4+GaDRC0Ggh554KcJQN41pDW5XQxolQVOC4xbtRWkGjgXnCJJR8+mHYJ/FERERERI2d//aUCYBBgwZh0KBB9Z43bty4GqvdpqSk4P333w9EaLVSVRXO3BzoO9W9lYxDEfBXhREnZWBgC0B3RlWlQI3O1kTOz0N8YhIGtgCsNuCoFXi47Fw8oP8Jidr615mKCQko/uBtxE+/PwjR+kb5O2+tqnLcLE6EKACHCxS0SvCupFX0xZfDuvQ1VGz8BcYBQ/wdKhEREREReSisR2gjkVJYAKgKhKiap+/mO7R4qHwU7ikeiaXiQHRLDm0ye6YYPZDRBGgRA7yCYR5dI8UnQi4IXcyekF1raCsTWq0koGW8iAN53q+j1TRtBvN5F8Hy5kJ/hkhERERERF5qUEKbl5cHRfE+IWjMrO++Dikhya0y8+ly7TqIAjAiHejXDEgMzvat9ToziW5iAooqgMXaUfVeK8bGQa2ogFxUGKjwGsxVFMrwTyGx9k1E/Jkr+9Re7JSpsGVtRcW2Tf4Ij4iIiIiIfOBxQvvNN9+gb9++eOyxx2C323HxxRcjOTkZLVu2rHM/2LONXJgPKaHm9bMORcBbOAcJRlSrWOy6PoSjs6f3Ha0H+jQFjliBeUrdSa0gitCkpsGy5MVAh+gz5YxtewCgfRMJ+/N8S2i1rdJhGj0eRUte8kd4RERERETkA48T2lWrVuHxxx+HRqPBLbfcgquvvhp//PEH3n//fbzwwguBjDFiKOVlUEtKIMbF1fh6sSzBqQDtEmq+PpTJbE2i9UDftMp1tS+IdSe1mrTmkHNPQSkvC1J03pGdTgCAZPgnoW2XJOHPXN9nGMTddDts2zahfNP6BsdHRERERETe8zihHTZsGMaNG4eHHnoIY8aMwWWXXYb27dtj6NChGDNmTCBjjBiWNxdCTEiEINZcZEgAYJMBu2+DgkFxZlKtEYEeKcCfBaiz8rGg00FKbALL6y8HOkSfyHY7AEAyGFzH2jcRsT9P9rlasbZ1W5jHX4yiRc+x4jERERERUQh4nNCWlpaia9euKCwsxMSJE13HL7vsMpSXlwckuEgj55yEJjm15tdU4CnHcKSaK5PEaq+H0ejsmbHopcqYbfVs5SM1SYGcnxvI0HzmtFdWaz59ynFGioRSO3Awv2GjtPa9e1D+89oGx0hERERERN7xOKG9+uqr8eWXXyI+Ph5a7T+bpY4ZMwbnnXdeQIKLJHJRIVSbDWJ8fLXXTtp0eMw+CkYt0CO1+vrZcEpmayKJQNMo4HllOGxKLYt/AYixsVBKSqA67EGMzjOKzQFBVSCeNkKbniiiSZSADYedPrerSWuOqIsno2jx81BZII2IiIiIKKi8qnKcnp5e7dhNN92ElJQUvwUUqaxvL4aUlAxBcP+R5jm0eKJ8KFLMlVN3I8WZSXanpMqR2meUkbVeI4gSxOhoOE+eCHR4XpPtdkiqAkH/T0IrCAIGtNLg1wYktAAQe/00OI8cQtnqbxsaJhEREREReaHB+9Du2rUL33zzDT744AN89tln2Lp1K+z28BuhCzQ5LwdSk+Rqx02iDFGoXD9bU2XjcB+drSIKQPtEoKAcyK4w1HqeFJeA4o/eCWJknpHtdohQIJw25RgABrRueEKraZKC6MuuQdGi56CUlTaoLSIiIiIi8pxPCa0sy7j//vuRkpKCHj164IILLsDVV1+NSZMmoW/fvkhOTsZ999131iS2ckE+VFmGGB3jdjzHrsNDpeciLRpoGh2i4BrgzGRbJwFt4oGl0uBar5ESkiDn54VdkST57ynHZya0g9I12HFcRomtYfHG3nArAKBg3sywu3ciIiIiosbKp4R2xowZsNls+Oyzz3D06FFYLBZUVFQgLy8Phw4dwvvvv48TJ07g7rvv9ne8Ycny1iJomqRAENyHYCsUEQ4FSI6AQlCeSjEDFhvwiqbmbXwEsxkQEHbFoWSHAyJUQKN1O963pQYCgE1/NWyUVoyKRtLcF1G6+huUfvlJg9oiIiIiIiLP+JTQJiUlYcGCBRg4cCDS0tIQHR0NnU6HhIQEtGrVCuPHj8c777yDpKQkf8cbdlSnE3LOSUipTau99m3MYOgkwFDzLj4RoVrFY03l3rSHi4DD5cZq5wuCAE2TFFiXvhakCD1TldCe+aWDSSegZ3MJ6w41LKEFAH2nLki440EUPDUb9v37GtweERERERHVzaeE9vQqx3XRaDS+NB9RLEtehBgVDdFoqvaaXQZi9EC0vvp1kTg6W8WoBVKjgM9Mg2p8XUpOhZxzEqocPhvuynYHJNRchXhQusYvCS0ARF12NYyDhyP3gduhlJf5pU0iIiIiIqqZTwltTk4O7rzzTmzYsAG5ubkoKyuDoihQFAWFhYXYtm0b7r33Xvz111/+jjesqKoK57Gj0DRrUe217AoDDhdVTtE9k7+T2Xs2JVR7+FNN8RbbgKGWTTWeL5pMEExmOP466Nc4GkK2OyCg5rWtg9K1WH/IAYfc8LWvgiAg8eE5gN2Ogqcea3B7RERERERUO58S2rlz50Kv12Ps2LFITU1FdHQ0tFottFotkpKSMG7cOFRUVGDBggX+jjesyKdOAKoKMc5979lXNKOwwD4YXZOB5jG1XOwHpyevpoJjbg9/J7hnJrVt4oEPxX54Sap5La0mtSmK33+rwf36i2x3Vq6hrcGoDhqUOYB1B/0zSitGRSNpzvMo/XYFSr75wi9tEhERERFRdT7NCdZqtZg3bx5mz56NHTt2IDs7G+Xl5YiLi0OzZs3QvXt3iGKDdwQKe9Z3XoOUkuq2LlNVgS6523BI2wtJ1WchN2h0tqbE1FRwrMZzzzx+z6ZmAICn+xX43P/p4o1Av2bAr9mAxaRBrMY9GZQSkuDY/wdUhx2CVueXPhtCdjhq3DYJAOJMIgala/D1HgeGt/dsOn199F16IP62GSiYPxP6Lt2hbVV9D2ciIiIiImqYBi1y1el06Nu3L/r27Vvj64sWLcK0adMa0kXYUhUFcn4e9D16ux2fr4xCiRHoGl/9Gl+T2dMT2doS2PqYCo6hLKGZqy1fEls5Pw9S4j+FvgwaID0eeLZsOB6V1kAj/DMCKmg0EKNj4TyWDW3rtj7F7E+Ks/YRWgA4P0OLpZvtePoi//UZfdV1qNi6ATl33IiUV9+DJjnVf40TEREREZFvU449tW7dukA2H1LyqRMQdDqIxn8q/b6iGYXCCqBXKpBYw+ist2qaUtwQNU1JbqiWsYBRAzxYNhIOxX0IVExMQvGHSxvchz/IDmetI7QAMD5Diz0nZRzO918hK0EUkTTnBUhNUnBq2rVwHj/qt7aJiIiIiMjHEdrLLrsM+fn59Z63YcMGX5qPCJalr0I6Y8TNJlcmstoatunxdnT29EQ2EE4fsfVmtPbMUVpRALomA1uPA4u1I3G7vNr1mpSYBNtfh6DKMgQptHsXyTY7JKH2EdouqRJaxov4eo8Dtw7xX6yi0YTk55cg955bcPKGy5H8wuvQdejst/aJiIiIiM5mPiW01113HZ5//nkMGlTzti1AZQXgffsa516cSmkJlII86Nq0dx0rdGhwtALolNiwtgOdyJ7OH9OQqySZgLwyAKdtUSTqDRBMJjiPHgn5GlLZXvsaWqCyOvGErlq8v82OWwbrq+1X2xCiyYzk55cg77H7cfLfV6HJ0wth7HuO39onIiIiIjpb+ZTQjh8/HmvWrMHMmTPrPO/48eM+BRXuLK+9CKlJKoTT9uO1KSJ0EtA0uvr5nozO+mOdrC+q+vJmtPbMUVoASIkC/rIAR1U9mhtsruOalKawLnsNiQ/P9W/gXqovoQWA/ww1oNMcC37c78QIPxWHqiJodUia/QyKXnoKOf+5AUmPPQXzmAv82gcRERER0dnG5zW0Q4cOrfecSy+91Nfmw5Yqy3CePA5NWnO34+/qh8LUoBJb/lkn25C+gZorKXvCoAEymgDPOYYg1/5PVWMpOQVKcTHkgvqnqAeSYrdDrGfUtX0TCVf11mH2yvKAxCCIIuKn34/42+9B3iN3w/L2q1Bl/63ZJSIiIiI62/ic0F50Uf3lYMeMGeNr82HLefQIBKMJoumfqk+LtaOQXwZ0Sa5+vrejs6HkTVJb030lmYCOicBT9qE4YauceyyIEjTNWqDo1ef9Gqu3ZIcDYn1DtAAeGGXEj/ud2HHMP3vS1iTmquuQ9MQCWN5ejJM3TIZ93+8B64uIiIiIqDEL+81ic3JycPXVV6Njx47o2LEjLr30Uhw9Wn+12MOHDyMqKgqZmZnVHkVFRT7HY/3gLWia/JO5qmrlVNuOiYDujFpC9SWzVZWGQzkyeyZvqiDXdH9p0UCHRGBuxRDXMU3TZlAK86GUFPs9Xk857U5IHnzaM1IlDErXYOEvtvpPbgDz6PFI+/hbSE1ScOKai5B7/+2w/f5bQPskIiIiImpswjqhtdvtGD16NOx2O3bv3o09e/bAbDZjxIgRKCkpqff6Pn36ICsrq9ojLi7Op3hUpwNKfh6kpCYAgDyHFo/aRkEjAvHGei4+QzCLP/miIVOQm5gArQi8qhsFoHJPWikpBZY3F/o1Rm9UrqH1rNDT3SMMWLLBhkW/VAQ0Jk1yKpKfXoiUJR9AKS/HyX9NRM49t0AuDO30bCIiIiKiSBHWCe3SpUuxc+dOzJ8/HxqNBpIkYf78+Th48CAWLVoU9Hgchw9CjImFoNPD6pQwp3wY0qKBvmmoVnCottHZM/eWDWeejNbWdJ+SWLmVz8FCQP57pxwpqUlI19EqTidEybOEdmJ3HRZcbMJD35Qjr0QJcGSAoUdvpLzwOpq++wXkE8dw4pqLYfstK+D9EhERERFFurBOaJcvX46WLVuiTZs2rmOpqanIyMjA8uXLgx5P8YdvQ0pOQYUi4hl5BJrFAM1jAE93eDk9kQ33ZPZ0vozWJhgBsw54oGwUDpUbIUZFQy2tf1Q9UBS706M1tFVuGaxH6wQRE14vgazUvn+tP+k6ZiD1jY9hPGcITt50FfJm34+KHVuhqsHpn4iIiIgo0oR1Qrtz506kp1ffvzQ9PR27du2q9/pTp07hmmuuQb9+/dChQwdcddVVHl1ntVrdHjabDaqqQi4qgpSQiHmOcxGtA9p7MRs3UkZla+NtUisKQM9UICMJWKQMwp+2GKhKwyv62mw2t/emuNizdblOpxOi6PnHXSsJ+OqmaOw87sR7W+y+hus1Qa9H4sNz0OTpVyDnnsKpm67E8UmjUfzxu1AqAjsFmoiIiIgo0oR1QpuXl4fo6Oobu8bExKCsrAzl5bVvryJJEjQaDe68805s2rQJW7ZsgVarRf/+/bF58+Y6+23RogViY2Ndj7lz51ZWf1IVLNSOQbmzsvCRJyOz4Vj4yVfeFIyqkmgCOicBb5Z0xV/65vVfUI+5c+e6vTcZGRn1XqOqKmSHExoPpxxXSYsVMWOEAbcuL8W3e4KX1AKAafAIpLz0Fpp9+RPM512EoiUv4thFw2F5+9WQFtciIiIiIgonYZ3QNkSLFi2wa9cu9O7dG0BlErx48WKYzWY8+OCDdV6bnZ0Ni8XiejzwwAPILQUWJUxC3qFj6NW0+prZmkT6qGxtTh+tlRKT6j0/FRaMyPsJ76VNwuzvyuGQfZ9C+8ADD7i9N3v27Kn3GtVWAQWiVyO0VR4eY8Qtgwy49O0SfLw9sJWPa6JJaYq4m25HsxU/InbKVBT/910cvXAYChc+F/K9fYmIiIiIQk0T6gDqkpSUVOOUUqvVCpPJBKPRu9LCRqMR3bp1w4YNG+o8LyYmBjExMQAAWVExd1UFjhaVo2NmLwzZ9hHsfx6BpU1PKAZzjdff/b0AoAlMBUe8ii+SmAqOQUxIwt3f/7Mf77Nj3BNVqcyKqOzfYT72Jyzt+6B3+7bYmyPjjk/L8PAYI5rGep9g6vV66PV613Or1VrvNYrVAqcgQauV6j33TBpJwLwLjYjSC7hqWSm0koCJ3XVet9NQotGEmCunIPrSq1D67RewLF2C4vffQtTFlyH60mugaZUOwdPF3EREREREjURYJ7Tdu3fH3r17qx0/dOgQunXrVue1FosFRqMROp178iFJEmTZ87Wc/1lehjKHin4tNYjRRyNvzL8QveMnNF3/KSriU1Ge3Aq2xGaQDea/E1nALJdVXpxQOXqpFNS9H22kERP+GZWtutdSyYSHvqnAs31zoC88CUP+MUgVpShNa4/c0f+CbIyCDkCXVAlHChU8+HUZHhljRJsk75NMbynFxXAKGmjP3CjYQ4Ig4JGxRug1wKVvleCecw148nwjJC+KTPmLoNUhasJlMJ9/Ccp++B7Wtxej+KNlEBMSoW3THtpWbaDv2gOm4aMhRlWfrk9ERERE1JiEdUJ7ySWX4Oabb8bhw4fRunVrAJWFnn7//ffKda2nOXHiBJo2bep6Pn36dIwdOxZXXnml65jdbseuXbvQq1cvj2NITxSRU6Jiz0kZFU5AUUXo4kZAEzsEJmsODIezof9tMyRRQEZODIq1USjTRKFMY0KpZIJT0gEJLV3tCYBbcSRPUqIaz/E2l6phlq9HE39F9yRQVGSYHKUwOstgksthdpYiylEMs1wKUVGwd28cbOZEVDTrDrspDgoEyCcAp+KAU6lcimzUAqnRIhLNwUkIlWILnKLGpxHa09070oiOyRKueKcEe07KeO5iE9o1CXxCXhNBkmAedR5MI8fBefQIbLuy4PjrAJyHD6Lw+XnIn/MwjIOGwzzqPBh6D3DtnUxERERE1JiEdUI7ZcoUvPzyy7jvvvvw3nvvQRRF3H///UhPT8e0adNc533wwQe46qqrMG/ePNx3332u408//TSGDx+Opk2bQpZl3HPPPcjNzcWyZcs8jmHGue7TmmVFRYUDsMsqHHI0HHJbOBUVNmsxzs/PR/Hq7yHbKuCssEO226HIAqDTYW9MZ8haPZxaA2StAU6NDoqkgyxpoIhaKJIGsqiBKmkgW4tdyaaKyiQQpz0//b+eEM74bxUpOhqCqkCUHRBlJ0TFCVF2QJKdkGQbRIcNGnsFNM4KaJw2aOzlEGUHFJMR7eUTEHV6mAcOhRTbBvq4WGjMJmglARqxskqwJFTuSasRAZ1GgE6q/G+wKcVWOHVGaDUNXzJ+UTcdPr0uCrNXlqPH0xY8Md6E24fovS445S+CIEDbohW0LVq5jqlOByo2rkfpyi+RP/dRqKUl0LRoBX3PvjANOReGAUMgGgwhiZeIiIiIyJ/COqHV6XRYtWoV7rzzTmRkZEAQBHTt2hVr165FVFSU67z4+HhER0cjOTnZdezuu+/Gq6++inHjxgGorJjcuXNnrF69GiNGjPA5JkkUYNYD5jPTw4Q4oHUc0PufRFtVVag2G9TSEgwvL4VaVgalrBRla76FarcDshOq0wk4nVBlGXA6oMoKBEkCJAkQJQiSCIgiIIgQRKGytLIgwC09FYQzsl4VgAqoKlSl8r9QFEBVoMqV/4XsrPyzKELQaCBIGkAj/f1fDQStDoJWC/OECRCMZggmE0STGYLRFHFrNZViK5x6Exo4QOtyXoYO4zpr8dp6G+7/qhxvbrRhSj8dpg40wKwP/c9G0GhhHDQMxkHDoMoyHPv/QMWOrajY/CvyHrkbEAQY+g+CrkNnaFu2hpTaDJqmaZCSkiH4UDiLiIiIiChUBFVVfS8528hYrVbExsbCYrG4ikIFm6qqgN0O1WE/Ldl1AopSmfS6ktO/k9ZqhL8T4MrEV5AkQBArk1WxMlEWNBoIWi2g0UZ8AnP06FG0aNGizvfM+v5buOtHIwoGnI9Pr/fvutKcYgXP/ViBd7fYUGIDxnbSYv6FRrRODM1U5PooFeWoWP8/lP/6MxyHD8CZ/Rfk/NzKFzVaaFJSoW3fCcYBg2EYMATaZi382n84/B0jIiIiosYjrEdoz0aCIAB6PYTTKvlSw8j5uZCNGdAGoIhTcrSIeReacP9IA77e48CCnyqQ/rgF/VpKuG2IAZN76qAPwTTr2ogGI0znjoXp3LGuY6rNBufJ43CePAbnieOw794Jy1uLUTBvJqTkFEgpTaFJSoaUmgZtejto09tCm94OUmxc6G6EiIiIiAhMaOksIOecgmzo67cpxzWJM4m4uo8eV/bSYedxGW9vsuG25WWY/lkZxnbUYuogPYa10wYugAYQ9HpoW6VD2yq98sDFk6GqKhyH9sO+ZxfkvFzIeafgzD6M8v+tgfP4UUBVKysrt2oDwWiCaei5iLrkyoibjk5EREREkS2y55s2AjabDbNmzYLNZgt1KAEVyvt05uVANpirJbSBiEkUBWQ21+D5S8w4OisOL11igqwCw18uRtd5Ftz9eRnySpQarw2nz4IgCFCbtcQzW3bCcOUUJMx4FMkLlqDZ52vR4n870fTdL5Bw10Mw9BkAbcvWKFwwByVf/DfUYRMRERHRWYZraE8TivV9Z8uawkDdpydraI9OGI67zlmIuDat8Nrl5oDHVJM/TslYsduONzbYcCBPwc0D9bhjmMFt259w+yx4E49l2RJY330DzVf8VOd0+XC7RyIiIiKKbByhpUZNtlognziGckMsonShi6NjioR7zjVi572xeO9aM9YfdqLTXAsufr0Ym484QxeYn0RPugpwOlHy3YpQh0JEREREZxEmtNSo2XZuA3Q6lEpmRBtCv75TpxEwuaceW++OwZpboiGrQL/nrBjxshVLNiqAMTHUIfpENJkRdcmVsL7zGlSHPdThEBEREdFZgkWhTiPLMoDKaZHBUtVXMPsMhUDdZ1FREQDgh2+3wKA3QJYVKH8/HGXlKPn+K0j9/w/b/7JgXHsjrNZ/RkND/bPvmQy8dzmw6ziwbHMpnlltA/61CV3nFqBNkhWt4iW0ThDRKkFEvEmEXgL0GgFVOy0J+HtLYvyzK7EgAD2a+eevtdc/n4smw/LJ+3A89iBM510IQW/AmTWiLNZiAEBBQYFfYqTAU1UVxcXFSEtLgxjh23wRERFR48M1tKfZvHkz+vXrF+owiIjCTnZ2Npo3bx7qMIiIiIjccIT2NO3atQMA7N69O2AFaxZ9fgT6Y38goU9vAEB5WQnu/dcgPPXOOhhNUQHps0FUFfk//oRB/xqPTmkGn5spLi5GRkYG9uzZg+joaL+Fl52djYEDB9b6njkO7UfJ15/htSaXYemBWFzZ2orr3rsGAFAqKxi1+whWd2mJ5tPvdV2j2Gyw79mFxAcf91ucngjUzyic4jl27BgGDBiA7OxsFoWKEFarFS1atAiLzyQRERHRmZjQnkaSKivONm/ePGC/bBuMVqSmNEVUkzQAQFlp5RTMhKSmMJnD8xdGObkZ4kwxaN482ec2qqatNmvWLCA/29reMwdkWE0GJKR3gpQrwhxvRlNd5ce+WK7cPidFp0GzpH/Wrqqqioq//kRSkyZ1Vuz1t0D/jLwVyHhiYmLC4h7Jc9xjmIiIiMIRF0QFWYWlGDqT7yOdoaAYoyFbLKEOwyeC3gDV4XSt5fRkfr0gCBCMZshFXOdJRERERBTOmNAGmWx3QNJHVkIrREXh583HQx2GTwStDqosu4omebpgXDAaoViKAhQVERERERH5AxPaIJOcNjh1RtdzrVaHS6bcA602hJuk1sMQF4vy4rIGtaHX6zFz5kzogziFFwAEjQZQZNdzWfln2qROEDC9aTx0NUylFI0mlH79WVBirBKqn1Ftwi0eIiIiIqIzcQ1tkIlOO2TdPwmCVqfHpdfdW8cVoadPiENp2X6oigLBx2079Ho9Zs2a5d/APCFJgKpC/DtnVU6PSRRwZ7Oa930VjEbIBfmBj+/0eEL1M6pFuMVDRERERHSmRj1C+7///Q8jRoxAu3bt0KpVK4wfPx579uwJaUwOpwKNIbJGvHQGPcolIxRrUahD8Z4gAKqKIZZNANxHaOu8TG+AWlERyMiIiIiIiKiBGm1C+9NPP2HkyJE477zzsH//fhw+fBitW7fG0KFDkZ2dHZKYyu0qZKcMnSGy1tCKAuDUmyHn5YU6FK8JgugaoRUUJz44aPbsOoMBqo0JLRERERFROGu0Ce0jjzyClJQUzJgxA0Bl5dp58+ahtLQUTz75ZEhiOpJvRxOhFNBE3kxvRW9C6ZefhDoM74kCVFWFUE85qPw5j7g9F3R6qA4HVKczkNERAOfJ47Dt2g5V9bRkFxERERFRpUab0G7ZsgWdOnWCeNqaz5iYGLRu3RorVqyo81qr1er2sNlsfonp/Q2lSNI5gQjcz1HQ61FR7p+fQ0PYbDa396a4uHIf31rfs6oRWi/7EQQBgk4PpbTEvzdwlqnt/covqVzNXLFjG45dMgonr5+MwufnhjJUIiIiIopAjTahNZvNUBSl2nFRFHHixAlYrdZar23RogViY2Ndj7lz/fOLdlmZDVG6yByFMhg0sNhCn4jPnTvX7b3JyMgAUMd7JkmAqkAUvP+5CwYDlOLaPydUv9rer21HnVBVFYVPPwbzmAuQsvhdFH+0DKVrvg1xxEREREQUSSJv7quHevbsiR07dsDhcECr1QIALBYLDh06BKByRC8mJqbGa7Ozs91e89e2JYLTCYTx9jx1MeolWJyh/7g88MADuOuuu1zPjx07hoyMjFrfM+Hv0XARKqB6l5CLej2UkmI/RH32qu392pIt43xpE+z7/0CTZxZBk5qGmGtvRNFLT8M0bBQEjTaEURMRERFRpGi0I7RPPPEE8vPz8eCDD8Jut6O8vBx33nknJEkCABiNxlqvjYmJcXv4K6E1wo4S0eSXtoLNpBWwXd8h1GFAr9e7vTfR0dEA6nnPBAGiqgAC6llJ604wmlD6zed+jf9sU9v7tfWoE2U/rIShV39oUtMAADHX3gi5qAClq74JZchEREREFEEabULbr18/rF69Gjt37kSXLl0wdOhQdO3aFddccw3MZjPi4+ODHlOsaIcFtSfS4cyoUVGmhn6E1ieCCOnvHWgFAA5B8uwykwlqWWkAAzt7bTvqROmPq2EaPtp1TIqJRdT5l6Dkv++FMDIiIiIiiiQRmqF4Zvjw4Rg+fLjbsfHjx2PAgAFuxaKCZXQbFV/mROaUY7Ok4KQambELogANZNdzp6CBVpXruKKSaDTDUVYWyNDOWvklKv4qApoOGeF2PPqyq3H8snGw/7Ebuo5dQhMcEREREUWMRjtC+9dff2HHjh1uxywWC37++WfcdNNNIYlJkeXKqrsRKForw6IaInNrlb9HaFVUjtDKno7QGo1Q7RXcuicAYjVO7Go2EJqmzdyOa1u3haHvQBRzlJaIiIiIPBCZ2ZUHfvjhB1xxxRUoKCgAAJSWluKmm27CqFGjcPnll4ckplV/OGDUhb5SsC90EmAQnCgoi8CEVhQB5Z+4ZXiY0IoiRIMRSlFhoCI7a3UXjuG3FoNqfC3qkitQuuobKOUcHSciIiKiujXahLZr165o1qwZevbsiczMTAwdOhQ9e/bExx9/HLKYiqw2xOqrbyUUEQQRMbChMBITWggQ8c/P3enhCC0ACOYoyIX5gQjqrJZRuAtZxo41vmYaci4ESULZD6uCHBURERERRZpGu4a2T58+WL16dajDcFNsUxFj1MAR6kB8oGi00Cp2OOpfehp+xL+rHAMQBEDxYtq3aDKjZMUnSLj74UBFd1bqfmw9liZfAbtThU7jPmtB0OthGj0epV9/iqjxF4UoQiIiIiKKBI12hDbclNpUaJ02SKbIrHKsaA1QnU5oPB/cDBuCIEJS/xmh9SqhjYqGWsq9aP0ts3gPbIqIbUdr/oYk6vxLULH5VzhPnghyZEREREQUSZjQBom1QkWUswROQ1SoQ/GJqtHCAQla2R7qULwnCpAgo6qelUOoeWJC/pxHql8aFQWlpCQyi2GFsYSUWHRNlfDzwZrnK+i6ZULTohVKv1sR5MiIiIiIKJIwoQ0Sa7mMaEcxnMboUIfiM6fOCLW0JNRheE8Q/5lyDMAuaD2/VqsDBCEy7zuMadu0x5C2GvxysOYK0oIgwDz+YpR8/Sm/TCAiIiKiWjGhDZJiSxl0WhGQInfZsqQ3oLTIGuowvCcKwGlTjuVaRmhrIggCRLMZcmFBICI7a2nT22FwemVCqyg1J6xR4y+G8/BB2PfsDHJ0RERERBQpmNAGyartRdAa9KEOo0GMZh1W/noq1GF4TRBEaCH/vQ+t6lWVYwAQTFGQC1jp2J8qR2i1KChTsTen5srfmqbNoO/dH6XffBHk6IiIiIgoUjChDRJ7WTk0JkOow2iQ6BgjLKWRuYZWq/xTfEj2oigUAIgmE8q+Y1LlT7r0NmgeJ6JVvIifD9Re9ztq/EUoXfkVVEcEfu6IiIiIKOCY0AZJaVEJTCZdwNqX8/PcHoEQFWtCcZlS6xTR8CVAqtqH1sttewBAMJmhlJcFIK6zl6Z5KwCoXEd7qOZ1tABgOncc1IoylK//OVihEREREVEEYUIbBDanigq7DLMxMOtna0pgA5HUClGxMDmKYamIsIRWECBAAQQRAoCtUxZ4d7nBALW8PDCxnaUETeXfhSFtNPhpv7PWwk9iVDSMw0ah9JvPghkeEREREUUIJrRBcNyiIBGlgM7/U47rSlz9ndQqOgNMqg0lJTa/thsMwt//VVVAVoQ6z612rU4PVXZy2msAjOygRXaRgv15Na+jBYCo8RNR9vNayFZLECMjIiIiokjAhDYI3t9qR4JaClnr36JQniSsfk1qBQGC3gB7can/2gwGQQCgAkJlYuusY4C5pr1oBUGAaDBBsTCh8rc2iSJaJ4hY/Uft62gN/QdBjI5F2epvghgZEREREUWCyN1DJoKUO1Q0lcug+DGhrUpU79mUUOPrT/cL0DYzOgNs1mIASYFpPxAEoXJo9m+y6t0ILQAIZjPkgjxISU38GdlZTxAEjOqgxep9DkwbXPMMBkGjgXnchSj95gtEX3JlkCMkIiIionDGEdogcCqA3lkGJQBTjgHAVHDM7QG4J7r+HKXVGPUoLYrAEVpVdU07ln1YAiyao1Dy5Sd+DYsqjeqgwdo/nZDrKDYWNf5i2HZshePoX0GMjIiIiIjCHRPaYFBVaBw2yDqjX5o7fXS2KoE9XU1Jrb+YzHqs3h5Ze7IKfye0l3dRAUnjW0IbHQOl2Or/4AjndtCiqFzFtqNyredoO3SGtm0H7klLRERERG6Y0AaB5LDBKWkByX8zvOtLVs9Mav01ShsTa0RRSe2JR1gSRUBRIImACkD1YcqxGB0NpaQEqhxh9x4BmkSJyGwm1bmOVhAEmM+/GKXffF5rRWQiIiIiOvswoQ0CyVYGpyHKb+1VJak1jc6err7XfWGIjYZgL0dRWe1VacOOKEJVFEhC5exjhw+hC5IGotEEOTfH//ERRnXQYtW+2hNaADCPmwDniWOw7dgapKiIiIiIKNwxoQ0CsbwUitE/CW3VSKs3yao/px7LhiikKEXIzo+crXsE4Z8RWqiA04cRWgAQ4+JQ/PE7/g2OAABjOmnxy0EniuvY41jTJAWGAUNQ8vnHQYyMiIiIiMIZE9ogkIsKALN/Elpvk9PTE1+/TDsWRUQZNfh8Q1HD2woS47BRgKJABCAoTnx00OxTO1JsHJSiQv8GRwCAoW010GuAVXVMOwaA6ImXo2z1N9yTloiIiIgAMKENCmdFBaSo6Aa348vobNX5/hyl1ZgMkEsjqNKxRgNVVSD+vQ+t+vejNjXtRQsAYmw8lGILVKczEFGe1fQaAWM6avHVbnud5xkHj4AYE4fSbz4PTmBEREREFNaY0AaBo8IOTVTDR2gDUbXYF6ohCqgoC3UYHhMkqXKE9u+iUAIAGZL37Wg0EMxRcJ70/9pkAi7sosPXexxQ6ti+R9BoEDXhUpR8+iGLQxERERERE9pAczgVqA47RD+M0AINK/R0z6YEv0w7dupNEEsjaMqnpKlcQ3va0lm7qPWtqfhEFH/EdbSBcF6GFrmlKrZk111JOuriyXD8dZDFoYiIiIiICW2gFRdXwCAqUDW+JVBV7vq2YVWF/Vnx2KHRQ+eMoKJQkuSqcgxUjtDaRZ1PbUnxCVAKCvwXHLmkRIvo11LCl/VMO9akpsE4cChKPv0wSJERERERUbhiQhtgJUVl0Gv982MOxDY8vnCIOmiVuov3hJWqEdqqKccCYBd8+4JBiIqG6rBDKSn2b4wEALigiw5f7a7/sxU18QqUrvkWMot0EREREZ3VmNAGWHFxOXQ679drBoK/ikOpogTIdU8LDSdVa2g1p33aHYLGt7YEAWJsHJwnwuPLhcZmQlctso7JOJhX9+fLOHAYpLh4FociIiIiOssxoQ2w73aVwRAmCW2Vhq6j1UoC7JFUj0dTmdAOs25yFYXydcoxAIixcSj57CO/hUf/6NZUQsdkER9n1T3tWNBoEHXRZBSzOBQRERHRWY0JbYCVl9lhMjQsob3rW8Wv040bOkobZRBgcTZsTXAwCZIGqqJAKyiACghQYasnoa1t6x4AEGNioRRHUFGsCCIIAi7vqcNH2+tOaAEg6qLL4Dz6Fyo2rQ9CZEREREQUjpjQBlhpUQmMRt9HA/3NH4mxWS9BUVQUV0TIyJhGAygyJAFQUbkZra9VjgFANEdBKS+HaoucwliR5PKeemQdk7Evp+5px5qUpjCNGg/rsiVBioyIiIiIwg0T2gCz2xzQmk2hDsOvFK0OSWoxjlsaVnk5WASNBpAViKhMwAUAm/9vge/tiSKkmBiuow2QjFQJXZtKHo3Sxv7rJlRsXAf7H7uDEBkRERERhRsmtAEmV9ggRseEOgw3poJjDdoGSNEakKBY8dG2Cj9GFUB/Tzk2iIprDa1DEeq7qk5ifCKK/7vML+FRdVf01OHD7fZ618fqOnSGYcAQWJa9HqTIiIiIiCichE1Cu2rVKp9eC2flFU7onOWQzb4ntP5eP+sXogi9VoKjPDKm3Ap/TznWif8k8Q1NaKWERMj5eSxIFCBX9NJhz0kZW7Prr6Yd+3//Rtmqb+D461AQIiMiIiKicOLb3iUBMGfOHGzevLnG11atWoXRo0cHOaKGy8+xwKgTKvdBbWQkgx5yWRmAhm8DFGiCRgNVlqER/p5yLADOBuahoskMQaOFfOoENKlpfoiSTtc2ScK57TVY8qsNfVrW/fdH37s/9L36oWjRc2gy76UgRUhERERE4SBsRmhlWYbD4ajxIUfQnqenO3ncgihz+BSEOlNDtu8RTaa/E9oIoNECigIRClTV8ynHdVU6BgCpSTILEgXQTefo8f42G0psdX/7IAgC4m+7B2VrvoPtt6zgBEdEREREYSFshg4ffPBBjBs3rsbX+vfv73V7hw8fRteuXdGuXbtqr/3444+Ii4vzuk1vfbfxFJKiDT5f35B1rp64Z1MCnjvPt2slcxTshZGxdY0gioAgQAdn5XMAdrlhU44BQEpOhW37ZqhOZ+W0ZvKrid11uG15GT7abscNA/R1nqvv0h2mUeeh4LknkbrkQwhSeO39TERERESBETYjtLUls/W9Vpc+ffogKyur2iMYySwAVJRVwBBtblAbgVo/29B2pegYOCtsEbOGVJBEaFUZVdF+fKhh7wsAiAYDxKgort0MEL1GwP/11WPhLxUefc7ipz8Ax8E/UfzJe0GIjoiIiIjCQdgktC+9VPvat7peC2dKRQVgjgp1GAGhmGNgdJSirP6dVcKDpIFOcUAFYC6sTObtQsNHVaWUNBS/xwq7gXL7ED12HJex9k9nvedqUpsi/vZ7UfTKs3Bk/xWE6IiIiIgo1MImoX344YfRoUOHao/27dvjkUfqXsvob1ar1e1hs3lfzVdVVUj2CjiN0T7F0JD1rcHox2mKgdFRgjJ7cPeitdlsbu9NcXExgPrfM0GUICgyBACl8c0gACgXjfX2V+862qQmUEpLIRcV+HpLjVpt75enWidKuLynDk+tKffo/KiJV0DfoxfyHrkLqtPhS8hEREREFEHCJqG94YYbsGrVKtfj5ptvdv152rRpPrV56tQpXHPNNejXrx86dOiAq666Crt27ar3uhYtWiA2Ntb1mDt3rtd9qzYbVAhQtXWv/YtUqkYLvSSgpKg0qP3OnTvX7b3JyMgA4MF7JklQFRmT0yvjFQSgXPJ9fXMVQRShSU2DZcnLDW6rMart/fLGveca8P0fTmQdrX+UVhBFJM6cD+exbL4nRERERGeBsElop0+fjlatWrkeTZo0QatWrdC6dWufElpJkqDRaHDnnXdi06ZN2LJlC7RaLfr371/r9kBVsrOzYbFYXI8HHnjA6/6V4mKUas0whHGtIFPBMdyzyfdtd7QGHawFVj9GVL8HHnjA7b3Zs2cPAA/eM0kCZBm6mCioqCwMZRP9U4Fa07QZ5NyTUO2RsS9vMNX2fnmjRzMNxnbSYt6aCo/O1yQlI/HRebC8vRgVWzd63R8RERERRY6wSWj//PNPn16rTYsWLbBr1y707t0bABATE4PFixfDbDbjwQcfrPPamJgYt4de7/0oa15eCfQ6DTQ+/oTv2ZQQsIJQZ/J12rHOZMDq7UX+DaYeer3e7b2Jjq6c0l3feyZIEiA7oRHxd0KrosyDKceeEPR6iHEJKHrtRb+015jU9n5565ExBnycZfdolBYATEPORfRl1yDvoTuDNn2fiIiIiIIvbMYPZ82ahU2bNrmeb9++HUePHgUArF27FiNHjmxwH0ajEd26dcOGDRsa3FZ9jueVI87o29Ywlb+A+z5yGiyS2YzygghZOypJUGUZWgmAWjnleMP1L6Hzy/XvW5Q/5xEkPvh4nedo0prD/udeqKoKQWj4lkDkblAbLS7I0OKBr8rx7VTPkuL46ffB9lsW8h6+C8kvv8WtfIiIiIgaobAZoRVFEU6nEw6HAw6HA127dnX92W73vpSuxWKp8TpJkiDLsj9CrtOqbfkwm7Q+XduQacDeasi0YzEqCnJZcNfQ+kqQNIAsnzZCC1T4YS/aKmJMLARRgvPIYb+1Se7mXGDEyj8c+OFPz4o9CVodmsx5AfY/dsPyOtfTEhERETVGYTFCW1BQgPLycmzevBnt2rXD1KlT0bFjR9frgwcP9rrN6dOnY+zYsbjyyitdx+x2O3bt2oVevXr5Je66OMvt0DZgv9tgTTeuIufnQUpM8uoaKTYOjrL9kTEqWTVC+3dCKwpAudN/3+cIggBNi5awvLkQibOeCv+fRwTq2lSDf/XR4Z4VZdh4Zwwksf6fsSatORJnPoXce2+BvmsPGAcND3ygRERERBQ0YTFCO23aNAwbNgytW7fGunXrkJmZ6bbO1dfpxk8//TROnDgBAJBlGffccw9yc3Mxc+ZMv8RdF8VWDpgiZw9aX0ZpDSYDSqCDUloSgIj8SxDFyqJQ0j8jtDbFv0mnlJQMKDKchw/4tV36x5wLTNiXI+O19Z4X4DING4nYG25F7n23s0gUERERUSMTFgnt0KFD8cwzz+Cll17Cpk2b8OeffyI7OxvPPPOMz23efffdGDhwIMaNG4cePXqgZcuW2L17N1avXo3Ro0f7MfrqVFWFs8IGMcr7Ajh3fRvcfV0B30eDdRoBksGAvBPhv47WeO5YqIqMUcUbKtfQArB5MfO8vv1ogcpRWm2b9ih67UWoDu+nyVP90mJFPD7ehAe+KsepYs//rsTedDuiJl2JnLtuhm3n9gBGSERERETBFJKEduDAgbjjjjuwYsUKWK1W19rZKs2bN8eyZcuQl+d7ddJu3brh5Zdfxo4dO7Bjxw4cO3YMq1evxogRI/xxC3VSLEUolczQG3zbFibY040bIi5Kg2PHi0MdRr0ErQ5wytAKKlQIEATg08NmVAj+2bqnihQXDzE2DoUvPuXXdukftw7Wo3WCiNuXl0FVVY+uEQQB8Xc8APN5F+HUf65jUktERETUSIQkoa2oqIDdbsf999+PpKQkvPTSSxg/fjzWrFmD8vJy13kJCeFf6bcmluO5UI0m6CKsqKov25vEROvx3Zb8AETjX4JOD9XpgEFUoKLySwMBQInG7Pe+dOntIOechDP3lN/bJkAjCVh6tRlf7LJj6SbPR8IFQUDCvTNhHleZ1Fbs2BbAKImIiIgoGEKS0M6YMQMLFy7Enj17kJ2djSeffBJt27bFLbfcgri4OPTs2RMDBgxAbGxsKMJrsMNffgdzlPd7nN71rRKy0Vlfqx1HxxhRXObZ3qChJOj1gOyESfpnnrEoAKWSyeM2PJl2DACCTgdtm3YoemE+1CBU1D4b9WimwfwLTbhteSn253r+MxZEEQn3zYL5vIuR85/rUbFjawCjJCIiIqJAC0mV46uuusr155SUFFx++eW4/PLLAQC5ubk4dOgQ2rRpg6Qk76ruhotfnK0QFRc5BaEawhAdBZvtJOxOFTpN+Fb2FXQ6qE4n9KKCqlmqAlQUS/4foQUAqUkK5NxTKHrpKcTf8UBA+jjb/WeoHt/tdeDypSX4+T8xMOk8+/xVjdQKooic/9yA5BdehyGzT4CjJSIiIqJACIuiUKdr0qQJ+vXrF7HJrKqqsJY6EJUYE+pQfOLttGPZFIUEpwWnrOE9ElmV0BpFGSqAsoRmEARg/f+9GJj+BAHadh3hPHEMckH4T8mORKIoYNk1ZhSWq7j23RIoimfraYG/19TOeARRF16CnP/cgIrtmwMYKREREREFStgltJFOLipEgRCF6Ci9d9f5sH41LIgSYvQqPlxXGOpI6iToDYDTCZ2o4pLWpVDVyg9/mdO7UWVPpx0DgKg3QNu6DQoX/D979x3eVnn2cfx7hvbyjmf2ICGBhJ0AYYQRKBRKKVCgrDI6KQVS9iqEEDYvbYHSFiiztMxSVsIKhL0DYWbajjM8JWtL57x/CJs4iRMPyZLs+3NdusCydM4tPZKjn551rQw9zpBSt8r/zvDw0tcJLnomvO07bERRFArPuwz3D49m/e9OJ/LhuxmqUgghhBBCZIoE2jRrqm9EdzrQe/nMznm3KK9WN96Yy2kl7M/tvWg7FoUyTROr+t1etAqEEpl9C2jDKlBsNlpvuy6j5xnKJpZrPHaam5tfjfDXNyO9um8q1F6K+4ifpEKt7FMrhBBCCJFXJNCm2er/vYjbbc92GX3S14WhNJeLRHswAxWlj6LroKhgJHEWuFOBFogbEFcytxy1oihYx21HYv1a4nWrMnaeoW7WeAt3HePiV/8J8e+Pe7cHsKIoFJ57Ce6jjmP9OWcQev3lDFUphBBCCCHSTQJtmi02RuLwZWahoYHS2+HPpsuL2dqcoWrSR7FYMOMJbDqYJrha6nl8pYs2vXfznXsz7BhS83et221P6+03YAT8vbqv6LnT9rAx/3AHJ9zfznNL+xBqz7kI70lnsGHOr/D/65893uNWCCGEEEJkjwTaNAsFYzh8nl7d59znjAxVMzAMbwGEcnvIMYCiWyARx6alhhxDauueNr137dUXmq8AvWYEzddfhZmIZ/x8Q9V5+zm4cJadH/2jnSc+7X2oLTjjtxRfPp+W266jee6lmLFohioVQgghhBDpIIE2jYxIhPakisPT871NO+Tr/FkA1VtAPBrHTOT2frSKRceMx5kdfLtz6x5VMXn1tLt6faze9tIC6JXVqC5XKtRK71/GXHWIgytnO/jJve387a3ezakFcB96BOV/fYjwm6+x9hcnEq+VoeJCCCGEELlKAm0aGa3NhK0eLFru7se6LX2ZR2uz6kStbkJrN2SoqjTRdMxEAqeWxCTVRgrQHu/b26DXQ48VBcu4CZixGC23zO3TOcW2KYrChQc4uOsYF7/4d4i5L4Z7/QWCbfJUyv/5OKrTRcNPD8P/yH2YRn6PpBBCCCGEGIwk0KZRsrWFuM2F1otnNVe36+lNXYoCbo+NhtrcDrSKJTXk2KslMczUXrSaAv4+Bto+1aBq2CZNIdnUSMufbxyw8w5FP9/DxmOnurlmQZjfPR7q1T61AHpJGWW330PhuZfQesctrP35MUQ++TBD1QohhBBCiL6QQJtG/v89iWF1oOZvB22n3vbSenxO/re4IUPVpIeiWzDjcTx6gh+PSq3KrCigKRDQ+raQV1+GHisWC7bJO5KoXUXrX2/r03lFzxwxxcqLv/Dwz/dinHB/kFiid6FWURQ8Rx1H5b+ew1I9gnVnHMeGi86WFauFEEIIIXKEBNo0ag0Z2ByWXt0nF/ef7Us9nmIffn9uL6DjPPBQzHgMu5oaOmqaqcf67xUumiyFA1qLandg235H4su+kTmaGbb3GAuvn+3htWVxDrs7QFu490OH9fIKSq6+ifJ7/kOyaQNrfnIIzbfMI+lvy0DFQgghhBCipyTQptErygR8bmu2y0ib3gw7dhd6aIspJMO9X4RnoCgOF2YstfKtUzPpiDWqYrLw53/r83H70ksLoLrdWCdNofVPNxCvr+3z+cW2TanUefN3XurbTPa4xc83G5J9Oo5t+x0YdtdDlF57K+HXX2LNUQfgf/hezHjvVlQWQgghhBDpIYE2TUzTZF1Io7TI0eP75Pp2Pb0ZdqzrKlaHjcaGpgxW1D+q2925DYuryLPRSsfQHM3OW0Hz+rBOnEzr/80nsaYuKzUMFSOLNd46x8uYEo3db/HzZC+39emgKArO/Q6i8l/P4vv5r2n7+59Zc+yhhF5+QVavFkIIIYQYYBJo0yTcHqHNtFHgtfXqfrk23LhDX+pyuuxsaGjJQDXpoTicnT20Lgvf99ACkaRCRO1d222sr720kNqj1rrd9rTcdp301GaY167w1OlufrOXjaPvbecn9wRY6+/bF0uKxYr3p6dQ+fhCnDMPYMOlv6fhxCMIvvQcZrJvPcBCCCGEEKJ3JNCmyZqGNgrsyqBYEKqvbB4nL729NttldEt1OjFjUUzT5AfBNzHN77buUeDxlS4arGX9On6/Qm1BYWdPbXzVin7VIbZOUxX+eKiT98/1sqLZYOK8Nu5cHOn1glGdx/P6KDznQqoefwn7tF1pumIO9YfvQ8vt1xNfuSzN1QshhBBCiI1JoE2TlsZ2HM6ez5/N1e16NtWbYdFWj5toKJzBavpHsVhRFBUSCYotcY4d3U6oqApncz2aYrLgtLuzWp/mK8C6/Q60/uUmWv9yU1ZrGQqmVuu8fY6XSw+yc9EzYcbPbeOuxRHCsb4FW728gqLzL6PqmUX4TvkFkfffYc1PZrPuN6cQev1l2cdWCCGEECIDJNCmyWvvNaA7ez5/NhdXN95Ub+uz+DxEQ5Gcnkeo2OyY0QiFepxoUumcR6sp0BTR+n38/vTSAmgeL7YddyKxpo6meZdhRnN75eh8p2sK5+3nYMVlPk7b3cZF/wsz/KpWrnguxLpA3wKoVlCE55gTqbjvMcrvfxKtpIwNF/wmtYDUg/8gXl+b0+8RIYQQQoh8IoE2TeKhMLrbne0yssrhsNGuOjEC/myX0i3FbseMRFAVcFs2XukYUKBJL+j3OfobalWHE9vUXVB0ncYr58gQ5AFQ4FS5fLaD2isK+OMhDh75MMbwK1s57aF2lqxJ9Pm4tu22p+TK66l+5nXcPzwa/0P3sObI/ambPZ11vz2Nlj/fRHDhcyTWrknjoxFCCCGEGDok0KZJOBTF5vVku4yM6OmwY5sGYZuXeGPuDqdWbHaMaGproYISD8ZGHWX/XuHkybMeTst5+htqFU3DOnYC1vETab3rNprmXoLRHkhLbaJ7LpvCL/ey88VFPh47zc3KFoMdrvdz8B1+Xvgi1ueeVa2oGN9pv6LqmUVUPraAwnMuwjpmPLHPPqH52kupP3wf1hx/OK133ELsq6XSgyuEEEII0UMSaNMgFo4QioOjoGeBNte369lYb4YdKwpYnHbWPvFEBivqH9fBh2NGUoH2yPCbGKbSOY9WV2BdWCeXooRWUIh9511RXW6a/nghLf83HzPR9x5D0TOqqnDY9lZe/rWXD8/3UuZROezudqbM9/OPt6NE4n17lSiKgmX4SNyHHEHhORcy7I5/Uv3S+1Q89F+cs2YTfmsRDScewZqjD6blLzcT+fh9zEQ8zY9OCCGEEGLwkECbBvWrGnE5Lahaz5/OXJ8/21c2j4vmQO4GLtVXgBkOAjDMGuPoUcHvf6eARTX7vdpxh/720nZQVA3LiFHYpu6CEfDTeNm5tPz5Rgk6A2Ratc79J7pZeXkBh21v4bynQoy4qpWrXwizob3/X04pioJ13HYU/PzXVPzzCSqfeAn34T8m8v5brDvrBGoP2I31c35F4PGHia9aIb23QgghhBAbkUCbBquffg6X15ntMjKqp6syOwt9vB2vytkVXdXCIoxQCACfnkCBLsOOH1nu4rFT70/b+dIVagFUhwPbpClYJ07GaG2h8eJzaL55rgxFHiBVBSrXHe6k9soCLjvYwb3vRqm8vJXZdwb4+9tR1vdxEalNWaqH4zvlLCr+8W+qX3yH4kuvRfMV0Pb3v7Dm6IOoO2h31p/3C9ruu4voZx/LFxtCCCGEGNL0bBcwGLyfqMBSXtij2+bTcOONzXm3iJsP2fbtinw21mmFJJsb0UvS09OZTqrbgxmPYSYTKJpOWZmb5Eqjc9hxsLCK1phKQHPhSQa3fcAeaLr2Moovvjotx4LUSsjapCkY4XBqNeQ/XoBWMoyCX52L6h6c87hzidum8Ju97fxyTxuvfpvgPx/HuPiZEKc/YjKlQmP/cTr7j7ewzxgdn6N/3xlqvgJcBxyC64BDME2TZEM90U8/JPrpRwRfeIbWP92I4nBi23Fn7Dvthn3n3bFOmoyiW9L0aIUQQgghcpsE2n4yE3Fi7SHcRcU9vk++DTd2NtcTKqrq0W19dvjKWUrDA/dTc855Ga6s9xRVRXW6MIJBNK+PE2Kv0zZmKv9e4Ur9XoEnVjkZdtZjnPmX2Wk7b0dPbTqDrepwYB0zDnP4CBJ1tTT98UK04hJ8Z5yN1ovXo+gbTVWYNd7CrPEW/nS0k4/rk7z8TZyXvk5w99vtROKwU7XGnqN09hptYc9ROhW+vgdcRVHQK6vRK6txzf4hAMnWFqIfv0/kw3cJLnyW1jtuRrHZse24E/add8e2027YJk5GsdrS9bCFEEIIIXKKBNp+itfV0WgrodxpzXYpGZdsakQrLtnqbVQFikvc/K9xDL8YoLp6S3W5MdsD4PVRaYsSalE6hx07m+sxC6tYF9YIqXacRiSt5053by2AYrFiGTUGvXo4iYZ6mq+/EsVux/vTU7CMGithZgBoqsLONTo71+jM2R9iCZN3ViVYtCzB4hUJ7nk3iD9iMrpYZa/ROnuN1tlzlIXtylRUVen7eQsKce57IM59DwQg6W8j+lEq4IZefoHWO25J3a64FG1YOXpZOdqwCrSycvRhFWjDyrGMGovm9aXleRBCCCGEGGgSaPtp2UOPohQcjL0Hz2RqHmpRxmvKlJ4OOy4sL6RxeQIzEc/JoY+uw35M8JnHgFQAL7UnSZp0DjtWFHhipZNhv3gyrb20HTIRagEUiwXL8JHo1cMxWpoI/OchjNYW1IJCvCf8HH34SBRVps0PBKuusPcYC3uPSb3+k4bJ52uTvLE8FXCveTHC6pYQRU6FGaN09hyVCsNTqzRK3X1vI83rw7nPLJz7zALACPiJffsVyXUNJNavJbluLYmGeqKffEBiXQNGcxMAemU11omTU5ftJmOdMAmtoGfTKIQQQgghskkCbT+YpsmC0HCKxvTsg9+cd4vybrhxh94MOy7yWFluLSZUtwbXyBEZrqz3tGHlGAF/588nOZbQPnpH/rMyNey4o5d2Q0RjvaWYsnhT2mvIxBDkDoqqpnrkiksx43GSjetpu+cOzGgEvbwS35lnozpdaT+v6J6mKuxQqbNDpc6v9kpdV9uSZPGKBG8sT/DoxzEufy5MPAmVPoWpValw2/HfMcV968lVPV7s03bt9vdmNEps2dfEvvyM2BefE1r4HK133gaJOFpFFdYJk4iMGN3Xhy2EEEIIkXFDItA2NDRw6qmn8sILL6R1y4tk43rqlELGFw+dcNDTYcdOn4vljz7BlD+cMzCF9YJWVIIZjWDGYygWK9W2CHFT2WIvrfqzR5jzj9noJDNSSyaDLaR6bfWKKvSKKoxgO4k19TRdOQfV48N91HGp4acFRSj6kPhTkFNqCjWOK9Q4bqfUkPBYwuTL9Uk+rkvycX2Ct1YmuGNxlJaQicsKO3aG3FTQnVyu4bD2fbgygGKzYZs0BdukKZ3XmfEYsWXfEPvyc2Jffk7kzUX9OocQQgghRCYN+k+xjz/+OOeeey4WS/qHvq68/yFMz3RcPZg+m6+rG2+qp8OO7QVempbn5nYiiqqi+gpJtrSglw1DUWB0lZsTNT8PL3cD3/dI+6wG9/36f/z8z+kferyxTAdbSM0dto6bgDl6DMnmJoLPPI4RCGBEQqhON1pBIZ7jT0MbVo6i9C8oid6z6t/34p5EKuSapkltq8HH9d8F3TUJnvsizoomA1WB7YalAu6OlamQu90wlUqviq71vf0UixXbdttj2257ACx+Pzwsc2yFEEIIkZsGfaCdP38+CxYsYO7cuXz77bdpPfYL0TEUDOv5Nin5Oty4Q2+GHdsKfXySrGAfw8jJeZvuHx9P+5P/Qi8bBsBZ8YVckNijSy+ts7me+76p4siRIR753bMcd9uhGa9rIIKtounopcOgNPXYzWQSoz2A0dJM65+ux0wk0EpK8Rx3Mnp5lfTeZpGiKAwv1BheqPHDyd9f3xoy+LTh+97cRz6KcdmzYWLJ1AiJSp/K8EKVmgKVSq9CuVdlmEel3JP6/wqvSolL6deCVEIIIYQQuWDQf1JdvHgxegY+kJumyQZ/grLJBdu8bb4vBrWpngw7djssrNMLMFpbcnILGcvI0RjNTZjJJIqmoSkwdrib47QAjy7/fgh5x9DjHw4PUWuroCbaMCD1DUSw7aBoGpqvAM1XkHpeQkGSTY203f0nzFA7ituL5vXhPuqnaKVlMv82BxQ4VWaOUZk55vuRJ/GkSW2LQW2rweqW7y/fNhosXpFgbcBkXcAgmkjdXleh3JsKvMML1e+Cs8qIQvW7n1WKXRJ4hRBCCJHbBn2g7UuY9fv9XX622WzYbF23Pmla20JYd1Dg3faWKHPeHTxhFno27Nhnh6+tPsKNzb3ao3dbotEo0Wi08+dAIAD0rM02pjqcqF4fycb16MMqADgrupBL2Z3fTW7j/z7/vpc2VFTFU6udmMf/k7MeOC4ji0R1pyPYwsCEWwDV6UJ1urDUjMBMJDD8bRj+Nvz/vCu1mJaqoTqdKG4v3p+dnurt7UZ37SXSz6IpjC7RGF2idXsb0zRpDZus9Zus8RusaTOob0uF4K83JFnwVZzVLQZtkdRaAw4LVDnaB+ohCCGEEEL02qAPtH1RU1PT5ecrrriCK6+8sst197y0gXKPQk+nGub7cOMOPR12rCrgdVn45qkXmDZnXNrOP2/ePK666qrNru9Jm23K+7MzaPvHX9DKUnNGFQXOL/iAqxp3wdhk6HGoqIqnVzvhxEc4+aGTGR5dk7bH1FMD2WvbQdF1tKLizl520zQxo1HMUBAj4Kfl5rloxSUUnnfZFufddtdeIjsURaHQqVDohInl3QfftvD3Pb1f1iY4bwBrFEIIIYToDcVM57K/OeyUU07hvvvu2+oqx36/H5/PR21tLV6vt/P6LfX2nX/De+xU0I4yYcqmh+nivBcVzGRy0ARaSAW9G3Zr3uaw43XfrCbW1MQf/nBg2s69aY9ffX09kyZN6lGbbco0TZouPRfLmHFohd/3ov9ZP4BV9QEeX+lCUb7/MiJUVIVhwhEjQlS7Epx0+yFkc0DmQAbb7piJBNHPPkmF2rMv2Oz33bVXW1tbl/YSuavj76K0mRBCCCFyUe6t1pMDvF5vl8umwaix3UCLBnEWbHtBqMEWZiEV8HoyjLqg0EFLML2rO9tsti5t4/Gk2mBbbbYliqLgO/3XxFd8i2l+X+evEwspKvFw4th2zO96aiH1uFUFnlrlpCmicftZLxBUHWl9fL3RdO1lnZdsUXQd63aTSNStxmjffDhxd+0lhBBCCCFEOkig7YM7F0epSa4n7tr6VhbnvTi0F1SxezwYsRiBSO4OAtBHjkGxWEnU1Xa5fo66EKvPy09GBbcYav+13IXXYnDzyU9z/2+fI5nlt9LG4XagQ65qd6ANq6D1rlsH7JxCCCGEEEKABNo+Wes3qImvJ2l3b/O2g613dmPbCuym1Ua54ueORbm7qIyiKBSeewmJ+lqSba2d12sKXKwvpGyYh59MUTvn1HbMq3W11HPnl17+u9pJW0zl+tNeYIW9BiOrg5C7Gshwq1dWk1zXgJnIzb2HhRBCCCHE4CSBtpfWBQyUZIJCLYqpW7Z9h0GqR0FdUaiwRlnXEst8Qf2guj0UnH0BsS8+22zY7O/NhVR74YhJKuftpW6xt/aBZW4eX+XkiZ/9net//gJfO0YRV7pfcCcbMt17q9rtqB4v8ZUr0n5sIYQQQgghuiOBtpf+8nqUKlcCQ7du9XbnvajgSoa2eTwzGaP5/Tsxk7kd+vqq1BIlHE2yvjXClVde2WWBoGyLRqOdNVmqaij45e+JfvYJydaWLrf7dWIhF9kWUe+HY3ZQuWI/tUtvrbO5Hk2BR1e4eGylixdOupNrT36eO3/5POstxfR0wHXUMLmlvomokfkh2lsaorxp4I3G41z7yH+IxnvW66qVDiPwr/vSVuPG7ZMP8qnefKpVCCGEEGJrBv0qx3PmzGHBggWsXr2alpYWdtxxRwDeffddrNauoXRbq3kahskv/x1ir9IQNW8+SsPMY7s9b0egNZobt1qfEWtn5b0zGXnKIlTrtocw5xK1qISg5uSmg7p/CVUs+hevTToGU1O46sjStK+UWldXR01NTZ+Ou6X2TjTU03LrPPSqGvTqGhSl63c+d1kP4OsmcFlgTBFc8+r3i0ltvKWRacJvJvlpCGvEDYVSe5Ij7z6B4ngLGlteKCuQNJjy0XKWTBuNR8v+d00b1zPysrnbvL0ZjxN5701K5v0fSjejF3rTXvm2um4+1dubWvPpcQkhhBBi6Bn0+9DecMMNaTtWfZuB06pgt+soyUS3txvqi0F1Mk3UWJjSAjufr8uPniC9ooriy+bRfNPVJJsbsY6ZgOr+/ouGs2ILMdxwt+0Alm6AH09WqXDDn95VCJcOh2QSAEWBP3+R+vBvmPDriX7+efLDBBMKRTaDg+7/JeXRDfiSm68MnIu2Nky5Y/sgxWJBcXtJ1NViGTl6oEoTQgghhBBD2KAPtOl0zzsxSl0KhtWOmoiCYYC65Z60ngw3Huy0SBBTt1LgtdK+Ogo5Nq+0O6rbQ/Hl82m742ain32MVliEXjMS1elM/V5JBVtcsDZq5d74TA4fb+K1wTC3yk1vKajffadhJpMous4dX6bCrWnCeVPaeOXkv9AS1UiYUGQzmPXPX2GPNIKS/Z7Z3uoIu8UXX41WVEzg0fsp+sMVWa5KCCGEEEIMBRJoN5L8rnfN7/dv8ffrmkKMLlYJhVT8pkK0qYGks+sQvEteTiUZ47tAa8S2vsKvEQt2+W++UAuLMaIBHAT4/X+dzN1/82HHzoblNNvdREMBrMkAOEu7fW77qrW1Fei+zbam4z7d3Vc98Qys0Sitf/8TyffeRnW50IaPRHN/v5eqkxC/Sj5NTFWob7fzeHQPfjAi9bsSJ1S44brFSuc8WgO48YOO0JrEMFM9uIljrqMpGIfAzdx83LkUuS2c8PefYDHTu49vb7QnjS7/3RZLKIRhdxJbsRytrQ1F2XykQm/aa1vtk2vyqd7e1NrRZs3NzZksSaSRaZoEAgEqKytRu/nSVQghhBgsBv0c2t5477332G233bJdhhBCCNFvtbW1VFdXZ7sMIYQQIqOkh3YjY8eOBeDzzz8fsMVPAoEAkyZNYunSpXg8nm3fIU9l6nHW1tYyY8aMPrVZrj33Q6Ge3rRXrj0f25JP9fam1vr6evbYYw9qa2tlUag84ff7qampyfnXoRBCCJEOEmg3ommpOZ7V1dUD9sGtY8hfVVXVoP6wmOnH2Zc2y7XnfijV05P2yrXnY1vyqd6+1Or1enP+cYmutjTsXwghhBhsZHKNEEIIIYQQQoi8JIFWCCGEEEIIIURekkCbZTabjSuuuAKbzZbtUjIqFx9nrtUk9eTW+Xsrn+rNp1qFEEIIIbZGVjneiN/vx+fz0dbWJnPF8kRdXR01NTXSZnlC2iv/SJvlH/m3TAghxFAiPbRCCCGEEEIIIfKSBFohhBBCCCGEEHlJtu0RQggxpJimyVNPf8aLHzQxwp3kzBN3pLCyJNtlCSGEEKIPpIdWCCHEkBFLGPzkknc55qUyvmqzcOvq4ex0dQNfvftFtksTQgghRB9IoBVCCDFknH3t27zaVsLiIxp46bY9+Xr+CIY7Y/zwngjtDRuyXZ4QQgghekkCrRBCiCHhmRe/5u6mcTywzzp2nTUVAI/LypOXb09A93DJLW9nt0AhhBBC9JoEWiGEEINeImkw55kop1g+YPYxM7r8rtBn5+aDktwZ2ZVl7yzJUoVCCCGE6AsJtEIIIQa9+x/9jFVmEVeePmmLvz/2sPGMVxuZ96/VA1yZEEIIIfpDAq0YMv74fJgLng5luwwhxAAzTZOb3zI51fUZNdsN3+JtFEXhov0tPBifxoavVw5sgUIIIYToMwm0YshYFzBY325Q12pkuxQhxAB6/c2VfK5U8dujR231dj/5wXg8SpT7/v35AFUmhBBCiP7K6UD78ccfc8YZZzBx4kSmTJnCpEmTOPvss9mwoWcrUa5fv54TTjiBCRMmMGHCBI4++mjq6uoyXLXIVZEE7FStM/f5dtbefhNN8y6j+ea5JBrqMQ0JuUIMVn/+31r2T3zOdruM3+rtLJrCCTUbuLd2GEY8PkDVCSGEEKI/cjrQHnfccTQ3N/P++++zZMkSFixYwIsvvsiee+5JOBze6n1jsRgHHnggsViMzz//nKVLl+Jyudhvv/1ob28foEcgckUkbqKrUOY0GfvtIv7Zvj1qQRGKqtL6l5tpvPh3NN94NYkN6zBNM9vlDnrRhDzHYmC0ByL8t30Ep+7Us9uf+ePxfG4fzYcLPshsYUIIIYRIi5wOtADz58/H5XIBUFVVxZw5c/jmm2949tlnt3q/++67j08//ZT58+ej6zqapjF//nyWL1/OHXfcMRClZ1QwarLWb7C8McGX9RE+Xx1i6eog364JU9sUpyVkkDQkNHSIxE0sGrg+e5OJTj8NI3bm+pKfkageg32nXbFNmYqi67Tcci1Nl59H659vxIhEsl32oHX+kyHOfSLEtS+GWRcw5EsEkTGP//dzNDPJj47euUe3nzi6gMms4dHXGzNcmRBCCCHSQc92AVvz6aefYrVau1xXWVkJQEtLy1bv+9hjjzF8+HBGjx7deV15eTmTJk3iscceY86cOd3e1+/3d/nZZrNhs9l6W37aRCNxVq9q5Ik3m/BvaCUYSWCJR3AnQ9iNCJoKqqJgoJI0IW5ABAvtmhOrRcNlV3G7bfxw7wqqh5dgK/CiKErWHk9/RKNRotFo58+BQADYdptFk2AhiWfVEtbvdjg7OxS+aoKL2/dn+1I4nYWoI0ahDx+J0dpCYl0DTZedi+r14jnmJCyjxqBYs/cayFfdtdfe+jckmzWavg1z/fsWYoqN8iIHR02zM6LSjb2kCNXuyFbZQ1p3bZav/vVRnEMtX+P0zerxfY4aE+GRzyuZn0ig6Dn9z6QQQggx5OX0v9SbhlmAr7/+GkVRmDlz5lbv++mnnzJ+/ObzpUaNGsVLL7201fvW1NR0+fmKK67gyiuv3HbBadTe7Oee/yyleYOfQCCKz6Hg89ooK3Hi8BagOl0YNgeGboMthFMlmUCJRoiFwoQDIQL+MA/8byXt7V/hUWMUFTo4+qBRlE8YkVdBbd68eVx11VWbXb+tNoslTJz+9cS8JSRcPjRgUilUeuDjtbDWaqXcFkNRFLTCIrTCIsxkgmRzE4F/P4DR1opWUor35F+gDSvP2y8EBlp37fXkOYcSTJoEDYOEabLnYadSuNex/OsVaAsnKY61UqJHmKGtpsynUXLsCWilwyRcDIDu2iwftfkjLIyO5OFdv+3V/Y49dAx/XGbyyWufMnVWD8cqCyGEECIrFDOPxvolk0mmTZvG7rvvzt13373V21qtVg4++GD++9//drn+xBNP5MEHHyQUCuFwdO0B8vv9+Hw+amtr8Xq9ndcPVA9tMp7gmyWr+Pez3+APJigqdFJUUYSrvBQtTedPJk38re20rWuhpdGPLdhCaYGdnx65HSXjRqCouT0KfdPeo/r6eiZNmrTNNlvWmOThv73KDsPtBKsndDnm2nb4ohEutC2izBrb4nnNWIzE+rUkGtagqCp6ZRW+03+TV18GZEN37XX7fz7B4fR0Xm+xWLF891wmDGgNm7S1hQm0tBPyB3GFWyiJNbGLVk9lgcbI436CZVi5BNwM6K7N2traurzH8sHD//qQM94oo/EaH3afZ9t32Mik333Gj3yrmPvHH2Souszp+LcsH9tMCCGE6K28+jR49dVXY7FYuPXWWzN6Hq/XO6AfAqKhCHfc9yFr61qw2S0MqyljVE0lmiX9zaNpCoXFHgqLPYwE/MEYLavXcv3Dy3AnP2V20Xp2/vUp6FZL2s+dDpsG1Y6hxttqs2jCxBnYQKRk+ma/K3enOrlvbJrJ+Syi1BLbrNNbsVqxVA9Hr6rBaGslsaaOxkt+j1pYhOeYk9Crh6Pa7el5kINId+3lcHpwurYcMHQVSlwKJS4nVDoxTQjGR+EPJni9qZlAc4D4X7+mIPY2bpeVXbU6ag45iPLqIuxFBTn/pUyu667N8tH/Pg6yj7oMu2+fXt/3kKogLy4vZG4G6hJCCCFE+uRNoL3nnnt49NFHefXVVzsXidqakpKSLc798vv9OJ3OzXpnsyEWS/Cnv79PQ20jRQUOxu8yHkdx4YDW4HVZ8U4cTs12w2lb28QLy5w8dtWLDBsxjN+cMg2bVRvQejIl2upH01WS9i2/doa5IBiDmwIzsRhQ7YUzowtRNw22ioJWUIhWUIgZi5Fs2kDgkXsw2lpRHC60wiK8J52JVlwyAI9qaFAUcFvBbdWhsAwoI2FAeyhOtLmVxa3DCD27mmBoGa5kkAK7wu6soNBhUnXALJxeB6rTheJ0pv4rvepDgmGYLAhUctn4VX26/w9mlHNrvZP1366ibOyINFcnhBBCiHTJi0B7//33c9NNN/Hyyy9TVlbWo/vssMMOfPnll5tdv2LFCqZMmZLuEnvFNE3+9PhKaj/+iiInTN59O6yFBVmtSVWgsKKYwopioo2NrPtiGZdds5DKHbbj7KOGo26a7PJMrLmFZxt8LHlR4aaDtjzKfnQhjCpIDUGuC8DFyQP4rfY6DjVJkSWx2e0VqxW9ogq9ogrTMDD8bSRbmmm+8Y8oFgveE36OZcw4FD03e7vzma5CgdsC7lIYXgqAYUIwnKC9tZ1XAmMJheKEFsRQE+0UGCspTraxR+AjypUAdocVxZEKuK7Dj0YrKkL1FUrv7iDy3od1rNcLOXz/vrXpXnvU4PpXPc8tXMbJEmiFEEKInJXzgfaBBx5g/vz5LFy4kPLycgCeeeYZ1qxZw5lnntl5u4aGBioqKjp/PuqoozjrrLNYuXIlI0eOBGDdunV88cUXzJs3b0Afw8Za/FFuvvNd9EAzEyeNxF5VlbVaumMrKWH4XsVU1tey6pMPuWRZLeedsQslRfk7pDbw2kv4rdv+IkNRoMKTuqxshdsDexOLwpXKK3i05JbW30rdT1W/77kdORqjpZnAo/djBNpQC4rwHH0C+vCRsnJvBqkKeJw6HmcBUNB5fTQBgRi0RuDhyP74Q3GcRoRiI8C+LW/hefBxXOEWXLEANpcD1e3G9cOfoJeUoRYWoWiDY5TCUPP0a/VMjuoM375n2/Vsyqqr7Ouo5/mvkpyc5tqEEEIIkT45HWgffPBBzjjjDK6++moWLlzYef3rr7/eJbw+/PDDHH/88Vx33XVccMEFAJxyyin86U9/4oILLuDBBx9EVVUuvPBCRo0axS9/+csBfywANz+1ljXvf8KYIijYd1ewbL6Kc85QFPTq4YwtLyew5DPm3/wyVdMmc86Ph2e7sj5ZqlbTrrtxJUOc96KT63fe0Pm77oYHjyxIXZa3wMXN++G0wFX2l3FoxlbPpSgKWlExWlExZiz6/UrJrS0oThdaQSHuI49BLShE9fiG5MJGl7ysoNq67y1PJ5ueupQ4Uz8bpoX2mAV/1MNLJT8mGIdwHCKxJNZoEE/cj/up9bjCX+GItTNBWYfDbsHl0Ck/6AAKi91objeq2yNhN4e9UGvjoKK1/VqRfPZEnaveH4ERj6Hm8t9rIYQQYgjL6U/Sv/3tb4lEIlvcM/aKK67o/P/CwkI8Hk+X4chWq5UFCxbw+9//nkmTJqEoCpMnT+bll1/G7XYPSP0dYgmTax5cifr1x+w0sRJl+JgBPX9/mLoV97SdmLq2jq8//Iir1zRw0a92Rdfya2imGgkS1J1ogJlMdvldsqmx8/+3FG47hiJ/0wzzovtTqMPZxkK0HnxOVqw29PJK9PLK1LDktlaMtlb8D/4DIxzCjERQ7HZUtwf3j47DUjMC1TWwr89scCVDhPFw3oupJ3Eggm0HVQGvLXXpSiOW9BJJeIkkqokloTWW4O1gADPYTjwUIfRMI/HYWgqTfooSbUxLrqDAbuC2aTjsOr6Df4DmdqG4PahuL4rNJls8ZUF7e4RPzCqunhrp13F+MGsMv/7E5MM3vmSX/XZIU3VCCCGESKe82rYn0zKx1YE/YnLVX5dQs+ELKnbdAaOgNC3HzYpQkFXvLSGoObn493vhcGS/x6Kuro6ampqttplpmvzlkvu52jgYjxLDaG4kVFTFDbs1d3vcLQVbw0z11jaHvzsuqbmcV1gWdjsUeVtMw8AMhzACfpKtLRgtTShON1pJKZ7jTkYrKBxUc3A72mv07z9HtXk626KDomkDGm77ImFAIAptYYNgMEo0GCEejZGMxjDjMayJKM5kEG/Mj1uNs4NZh9Oh4bBZ8B14EC63A6fLjmq1gMWCYrGgWG0529vbk/dYrnnumU854sVhNF/pwF3Uv5rH/PYLzqyq5YILD0pTdZkn2/YIIYQYSnK6hzbfNQUNrr7rEyYHluLdczpJZ55/sHC6GL7nLqz74FOuvv5VLjxnb7y+3J8TakbCGKpO0tQxmtd0Xj/n3aJuQ22yqXGzUKsqMLYoFWjWBMBlga+b4au4i+fc0zkt/io+ffPFo7ZGUVUUlxvV5f6uFzeJ0dJCsrmJ1luvw4iEUGx2NI8X94+OS20PNAh6cI2WJrCm9jp1Ntd3Xh8qquLc576/3Q27NefcitG6CoUOKHSoUOQAvn8PmCbEDYgkIBSH1lCMhe0hEuEQRiRC8vlmEvEkiUQSpxnDaUZxGhFcZojtYquxWxQsVguq1Ypis+OedTD2Qi++Eh9ul1V6e3votU9b2MGI4i7atd/H2ruwmdfqrVyQhrqEEEIIkX4SaDNkfcBg7t2fsGv7Z1imz+x2u5h8o+g65btNQ//kM669+TUu/P0+FBTkdqg1gu3ELQ7YaKSxs7meUFHVNkMtbN5bq6sw3Jf6/7HAnxqmUxSGG9mXq/WF9IeiamjFJZ3nNE0DMxjCCLQReOwhjNZmFLsDraQM70lnoBUU9et8uWbTcDvn3SIgNWc5F8PtphQFrFrq4rWR2m+ozMrGi1RBqrc/kvhu7m4SGuMmL0cTGNEoxMKo0ShqLITywmqMSJhoJEFcs+K2aXidKjONr6jaby+8bis2hxUsVhSbDdXuQLE7huS87I0tXmdnRmF69s/df6KT3y4uIhGNottkyychhBAi1wztTz0Z0hQ0uObvn7FH+6doe+yLMUjCbCdFoWTHyWhLPuO6W17n4vNm4vXm7grIZihEwmKHaNfrO0LttnQXbAFKnXDAqFTv7Vt1MC95AIFYat7t6ZH+hVsARVFR3G5Utzu1PZBpYLS1kWzcQPN1V4Ciono8qA4nrsN/jF5ZjeocHK+3fA+3W6Mq4LSkLikKYPnusoUeeNPECAYJ+dtpD0R5LrQLwVdCRBJBjEQS3UigGXH0ZAxLMoZFBc1qwWLVcdpUZiS/omL/ffB6ndh8blSPF8XhHJQ9vtFInPfNGn43cXVajnfgvqPxv5Pk3Te+YcasyWk5phBCCCHSRwJtmgWjJlfd+zW7tX6IPn2fjPbMnvvc1lfbBbj5kAwt3qQoFE6ZjPLRx8y97U0uP38mLkduvpzMSJikbqW7j+5b66XdWHfBtmN9rJ0qoM4PhXb4YgOst1nRFWOLe9j2laJstD3QmHGp+bftAcxwmMC//onR1oLqLcB7ylnoFdU5Oy+ztwZzuO0RRUF1u3G73biB8o1+ZZjfX5IGJA0TIxolGYkQD0cIhmI8FSki/EqYSDSAGl+NPR7CYsRRLTqarqUumopm0VGtFvbasRSnz4W7uAB/e7K7qnLSO28vJ6oUs+8+49JyvIoKHxMSn/LyB03MmJWWQwohhBAijXIzgeSppGFy5X/Wsv3a97DvPp24w5PW428aYDf+kN/9fbr2QKY14CoKBVN3pPrtd7nmT+9y9bnT0Xuy9O8AM0JBEhb7ZqsbQ8+GHm+qu2Br11NzbCG17+n89pmYwBgH/DrR/97aTSmKguJ0demRNZMJkuvX0Xb37Rjt7akFh3QdxenCc+zJWEaNQVHza4XqTQ35cLsJVUldANAAFLDZoZtRE0kjNc83mUhghsOYsSiJWAIjHseIJ0jGYrz+Xj1GJEIiFGa9v22gHkpavPbBeiYkwpRUTU3bMfcuaOaVeiuXpu2IQgghhEgXCbRpNPd/foq/eothU7Yj7C1OyzHPe1HpEsR6EmI3tvmCO6kP/mlbTVZVKd11GpFFb3PNg8u58qTc25IotPA5krZDuv19X0ItbHnhqA7blaQuoTh82AArVAejHOFe195biqajV1SlhicbScx4HOIJjGAA//13Y8ai+E79JZYx4/M+2MLm74c571bREW5haAbcbdHU70YV6DrYPcBWvngzTQrrlsGfBqq6/ltcpzHD0/P3cU/sP9HJw29XEovGsdoGz6rjQgghxGAggTZNVjcn8X/6CTMrbQTLR/X7eBsH2d6G2O5saTXZdPTYmhYbI3aayGfvfsbNzxVy7iG5tVCRGY+RdG19MZeezqfd1Nbm10JqjuS4YvhH655cTfp7abdGUTUUmwY2UnNwh1WQbG3B/9A/MCMRtJJSXIccmdoyxuFAKypGdTgHtMZ021bvbQcJuT2kKCTt+fOaSCYN3k5UcevkurQed9a+Ywi+p/DOW8vZe98JaT22EEIIIfpHAm0axJMmNz34FdNZRXDCgf0+XkcvarqC7JZ0HLtjSHJ/g22ysIztR6zh7Q8+x7/fXnjtOTT0OJEgqVm6nUO7sd720nbY1sJRS9fDJ3EPY50hXFr25iR2zL812ttJtjQRfOYxzEQCMx7DbG9PbRFUVIznp6eglZbl9R64W+697WB0trOE28Hjk49W06Z52W+vkWk9bllVMRPin7DogzYJtEIIIUSOkUCbBtc952f4uiVYdt+ZmNa/p3QgwuzGOnomz33O6HeoNcZNZrs1r3LDo6u4+qSR6SkwDcxEAqMHiyP1dejxxrYUbFUFxhTBI/7d0eJwjTawPbVbon63cvLGTNPACAQwmptoveMWjFA7itWG6nSi+grx/fzXeb2C8pZ7b2HTHlwJuPnr1XfWMDweYvj4iWk/9h7eVt6oGxyLrAkhhBCDiQTafvJHTBqXfs1+FXbCvrI+H6djiPFABdmNpS3UajrFkyew8pMvWNNaTWVBjry8kkkM1UJPumj7OvR4s1NuEmyH+6DGC4troT5qo8oW3drds0JRVDSvD83rwzJyNKZhYEYiGKF2jJZmmq6cg1ZWQcEvf4/q2sLWMnlk0/eZBNzB4Y2VBtMd61CUSWk/9sxxNs7/cBjJRBJNl2ArhBBC5Ir8XxUmy258roVJ/i+Jjp/W52NkM8x2cDbX42yu79FWQFsTL61mrCvEnx/8PE2V9Z+ZTGCoPf8A6myuZ867RRsFnL5LNjV2hltFgVEFcKe5N1Ejh4Zkd0NRVVSnE72kDOu47bDvvDsATVf+gcarLqD5+iuJfvHZFlePzjcdr/+NLx2vgXOfMzj3OaOzHUVuMk2Tt8LD2HtkZv5Z23evEbToPj77aFVGji+EEEKIvsmLQNvQ0MDs2bNRlNwKAcGoSds3y6ioLsKw9W3hlFwIsxvrd6hVFDzbTSDRUE9TIH37r/ZLMoHZy6Hg6W6PjjBU4UkF2zmBWSTM3Ho9b4titWEdMw777jOwVA9HdXto//f9NF9/ZbZLy4iNwy3QGW47vqSQgJtbvv16LWv1YvbZvf8jLLZk9PgKqhIbeO2d9C44JYQQQoj+yflA+/jjjzN9+nSWLVvW6/suWrSI/fbbj7FjxzJixAgOPfRQli5dmrbabnu5nTH+b4iM6vvwtlwKsx36G2qTReXUOGL89amV6Suqj0zTxEwkSQRDvb5vRy9duiSbGjGbG9m1EgrtUBfZ8j6huU7RdLSiYvSKKqyTdsBoayXZmt5tUnLNlnpuJeDmllcXr6Ik3sKknfq/ynx3pjvW8frKNGx3JoQQQoi0yflAO3/+fBYsWMCee+7Zq/u99tprzJo1i0MOOYRvv/2WlStXMnLkSGbOnEltbW1aattQu56KAgtJx1b2cdyK/g7vzbT+1Fc4qprG5bUYRpY//CUTqVWZlL691NMdaiEVbEuc8Kitd6/pXKRoGlpJGf77/prtUgbMlnpuNx2enOvv7cHotW9i7GGpQ+3BAnB9tedIjbfDwzJ2fCGEEEL0Xs4H2sWLFzNu3Lhe3++yyy5j2LBhnH/++QAoisJ1111HMBhk7ty5/a6rsd3Asr4Wa3VNn+5/3oup4aa51jvboaOujjp7S62oxhNppW6NP51l9ZoZj6P0c+VpIO2h1htuoqkxkNZjZovmK8Dwt2W7jKzY0tzb77fEknA7kN4KFLNndWaf6333qKLOUsaKr9Zk9DxCCCGE6LmcD7S63rcw8v7777Pddtuhqt8/RK/Xy8iRI3n66ae3el+/39/lEo1uviLt3xaHqAnXESmt7lN9uTjUeFPO5vq+L/ij6ZSUunnwyS/TWlM0Gu3SNoFAKhR222aJBKj9e5lnop08FpNwQqH+umvSfuyBprjdGMH2Lf6uu/YyYkGMWHvnxUzGBrLkjNtauM31ocnxWJRQMNB5CYe23La5ZE19C8v1CvbbuTSj55kydTgFCT+vvLEio+cRQgghRM/lfKDtK5fLhWFs/m29qqo0NDTg93ffc1hTU4PP5+u8zJs3b7Pb+Nc3U+jWMSy9nwfZ117PbOlrvd7KMlqb0tsLOW/evC5tM2lSav5yd21mJuIovVjhuDvpHnqsKlBiN6izVdB07WU0XXtZ2o490BSbHTORwIxt/sVPd+21+qFDWHnvzM5Ly0f/GOiyB0x3i0vl6vzbpx68jdMPHd15+cNJuT80/tVFy/Ak2tl5Ru9H8/SGpqnsYannjW9yb9stIYQQYqjKkY1C02/atGl88sknxONxLBYLAG1tbaxYkfpm3e/34/V6t3jf2traLr+z2Wxdfm+aJoF1jbiLfYT7UFs+9M52+H5f1t5/92EtG4b60Qqa26IU+WzbvkMPXHTRRZx77rmdP9fX1zNp0qTu2yxpgKqQi8u4FNuSvHDyXYy982AAmq69jOKLr85yVb2nKAqqzY7R3o5W1LWdu2uv4cc/h2p1fX8MzTpg9WbTxu/7rnvfwsb732Zz79sjTvgdhx7zy86fmxsbcj7ULvoiyC7qanTb8Iyfa8/qJA8sT+8UBCGEEEL03aDtob3mmmtoamri4osvJhaLEQ6H+f3vf4/23YIhDoej2/t6vd4ul00DbWvYxBFpg8LMDm/Ld6amU+TSWLViQ9qOabPZurSNx5NakKu7NjONZJ8XhNpUuntpfVaDQLxr73e+9tQqNtsWhx13116q1YVqdXdehkqg3Vh3c2+zvfetxWrD6fJ0XhxO94DX0FuLW3zsOWxgek332XkYX1mGs2FN04CcTwghhBBbN2gD7W677cbChQv59NNP2X777Zk5cyaTJ0/mxBNPxOVyUVhY2Odjb2g3KYk1E/f0/hj5Nty4Q5+HHRc4eObdljRX0wuGmdr4NQc5dJNIcvPa8jLUWq2Y4d5vjSS62lK4zbUhybmmtTXEUrWKfXbo+9/03thtj9HYjQiLXvt2QM4nhBBCiK0btIEWYN999+WFF17gm2++4b333uPcc89l1apV7LHHHl0Wi+qtRz8IU5Dwk7D3fruefBpu3KE/i0N5vE78zVlc6dg00h5o09VLqympubRRxZKW42WTYrFihPsyAF90Z+NQC0iw7caiRd+im0lmzBw/IOez2S3srNTx2he5v1iWEEIIMRSkPdAuWrRos+s+//xzYrHMrmLa0NDQ5edVq1bxySefdLmura2N119/nTPOOKNf54oEwjhsWr9Xzx0KbB4X8UiceDI7s1hNI72BNt1fRlhVk7C2+fD3fOulVXSd8EvPZbuMQWfTUAsSbDf12qetTEuuxOkbuKHRM4ZFeKsp94diCyGEEENB2hPZp59+yueff9758+OPP85dd93FnXfeme5TdXr44YeprKxk/vz5nde98sorHHfccTQ3NwMQDAY544wzOOCAAzj22GP7db5IJIrDNmjX00orw+7EbQTxR3JxWabsc2gmAc217RvmOk3r+xZPYqu2FGoBCbXfeXO9gxnFA9tbus8OhXyijSTYOjj2khZCCCHyWdoD7Weffcbll1/OU089BcDTTz/Ntddey/DhfVt9cs6cOUydOrVz79ipU6cyderULj2+hYWFeDweysrKOq+bPHkyVVVVTJs2jalTpzJz5kymTZvGo48+2o9HlxKPJbFYpXe2JwyLDWcyQjCWvUCbo1NoAXBZTNr0La+2nU+9tIqmgSGBNlM65tWmc1GywSAcjvMhNczcvvfTP/pj75njSCoqby76ekDPK4QQQojNpb2b8fDDD+cHP/gBzz2XGn7Y0tKC2+3u3Dqnt2644YZt3mb27Nmb7Su7yy67sHDhwj6dc1sSCQNNy+GUlENMVcNqxokmsl1JbnLqBm+cdBuT75id7VL6R1VTw7vFgEo2NWZ1i59se3PxMhJKMfvsk9n9Zzfl9dqZbHzBa5+2cOAPB/TUQgghhNhE2gPt888/zyuvvILFYsFut1NfX09DQwPr169P96myxgC0nNzZNDcpmGQz65gm5OrXDw7NZP0WVjrOO4qSeqKFGECvfdTIpHiQwvKdB/zc04vbeXOdfcDPK4QQQoiu+h1oW1pamDdvHl988QVjx47luOOOw+/3s/fee7N8+XJef/11rrjiCg444IB01JsTFFUjqwktjyhGkqSqZ239LEVRIIe/fLBpJtHBEGhFRoWKqrZ4/VDunQVYvEZnRkFrVs69z0Q3D71eTCISQbdLsBVCCCGypd8x4xe/+AWGYTBy5EgWL17MAQccwKJFi3C73eywww44HA6uv/56DjrooHTUmxNUXSMZkzG0PaHGY8RUK1lbQ0tRcjnPYlVN4sYgCLRm7u73m+86wuwNuzVzw27NWa4mdyQSSd5LVDFzfHbC5L77jCGgu/ngzW+ycn4hhBBCpPQ70M6cOZMbb7yR22+/nXfffZdvvvmGuro6brzxxnTUl5N0u5VkNJrtMvKCFgsT0R04LVkKO4qa00NhLSrEzUEQBE3zu95wkU4bh9mNacUlQ7539sP3VxHQXOy79+isnL+i3MuoxFoWfbAuK+cXQgghREqvA+2MGTM455xzePrpp/H7/SQSCeLxeOfvq6uruf/++2lsHLxbSthdDsLRZJ9WdVU0LQMV5S4tHCCgufHYsxR21PTO7exu6Gdfba284ouvTuu5MiqZhCH22s60LYVZCbLfe+WdNYyO1VM1piJrNUz3NvNGvbzuhRBCiGzqdaCNRCLEYjEuvPBCSkpKuP322zn00EN56aWXCIfDnbcrKhq820u47RrNlkL0kH/bNx4EQkVV3HxI3zrz434/it2OTc9OoFVUDcz0znfOxLDP3O1D7hkzmQRN9mZOh1BR1WZhVoLs5l5frTDDvSGrNew9zsY78SqMhExBEUIIIbKl1ynl/PPP5y9/+QtLly6ltraWuXPnMmbMGH71q19RUFDAtGnT2GOPPfD5fJmoNyecsIuVDfZSrP7e90LfdJCZ9l6+XBZsC+EpLcxeAbqGaRgoSv8jYybarWMF5k3jfl71zgJmPI5zVp5vPZRlmwbZG3ZrliDbDcMweCtaycwxfdsOLl323XMk6ywlfP3R8qzWIYQQQgxlve5SOf744zv/f9iwYRx77LEce+yxAGzYsIEVK1YwevRoSkoG74ewCq/KBncVlqalUNn7/Q+HzLBjw8DfFsI9OXuBVtH0tK5Ine7e2ZgButo1bOdbmAUw4zFUuyPbZeSdTb8k6QixMHj/fqbDkk/radZ97L9nlpZP/86EcSWUJlfw2ju1bLfr+KzWIoQQQgxVaR0jWFpaSmlpaToPmZPsFgVbURHBr5v6vLprqKgKZ3N9BqpLr/4MN7a2bWCNXsK5M71prqoX9FSgVaBf43oz1aseSqg49e8Ly8cwC2BGoyguV7bLyBsbv56+D7EgQbZnXnmrjpqYj9HbT8xqHYqisLtjHW+sMDgrq5UIIYQQQ5dMeuuj4hI3a772Ue3fQMxX1qv73nSQybnPZaiwHBKvryXqLaXUnb1eFEXXMZPJzYb09kUm5s6uD2uUlnnyNsh2MKMRVHcWv7jIYVv6MkTRNG46qOOLDAmxvfXaCoMZjrUoyqRsl8LeI1Tu/qwMU1b6FkIIIbJCAm0f/XIvG39cMppx9ct6HWgh9YE213tp+9M7i5Fkw5oWSnfZKb1F9ZZu+a6H1uxzB22memeTJqxVfVyTeAXyeBS6mUxgxmKoHgm0W3qtdO2B7ZDvy4Blj2mavBUaxmUTarNdCgD77F7JBd+4aPi6lsoJw7NdjhBCCDHkZHcCUg81NDQwe/bsnPr2u9CpolSNpH3NWpRk71e4vOkgszPU5qL+1mVZu4oVlnJ+PTu7Q9AVRUHRNHSXs0/3724f0P7SiksIukpwWsCl9X77p1xiBAKobvfQmRu+iY7FnDZd0OnmQ1RuPkSVRZ3S7Muv1rFOL2L/6dXZLgWAnaZW4UkGeeX1FdkuRQghhBiScj7QPv7440yfPp1ly5b16n7vv/8+hxxyCBMnTmTKlCnstttu/Pvf/05rbZWlTr5xjMJV/3Wf7v/9kMPc0vHBvM+9s6ZJy7ercI8ciTdb+89uTNfRjL5vq5GJMAsQjIPbmtZDZ0WycT1aweDdpmtLthRiNw6wEmIz56XXVlAeb2TCtNHZLgUAi64yw1rHS9/I1j1CCCFENuR8oJ0/fz4LFixgzz337PF9Vq5cyaxZsygpKWHJkiUsWbKE0047jWOOOYb//ve/aavtd/vaqR+2PcryL8DoWy/bzYeoOdlL2+cwC+jra/km5uW3R+XG8DtF01D70Iue7nbZNOi0x+DQ0DtpPcdAMo0kifXrSG5Yj+/Ms7NdTkZtHGA3/sJHemEH3uvfxplhb0DNoREB+9YkWdTe+6knQgghhOi/nA+0ixcvZty43m2N8+yzz+L3+zn33HPR9dQ04V/84hd4vV4eeuihtNVm0RSqqgv43DICd+2XfT5OLoXafs2bBTAMNnyxDOfYcZR6cuQDp6aj9rKHNp1DjbfUY7e8BdYEYIQj3O/jDyTTNEn624h+tZTI22+QaKin8NxLUZ2Da4XjbQXYfr1HRJ+ZpsniUBkzx+TW8g8H7j6MZZYqVi1fn+1ShBBCiCEntz4VbEFHIO3LfRKJ70OMaZoYhkEymd75inP2t3NOwzS2+/Zx1MoxGBZ7n45z8yEq5z6X+uCcjYWi+j3MuMPqZSwzi7nymJH9LypNFF1HN+I9vn26w+ym1rZDnR/mul7GqaVvj9x0M6IREmvqMdpaMGMxAMx4HNVuRxtWQeE5Fw2KvWe7W8gJttx+Inu+/nQl9ZYy9t/Lne1Supi662iKH1zJgteaOH209NQKIYQQAynnA21fHHfccdxyyy1cc801PPjggzidTubNm0c0GuUXv/jFNu/v9/u7/Gyz2bDZbFu8rVVXGFPj4931O7LXl+/ROmXvPtedCrXGgK9+nK4wq0QjfPtVA5W77oHHnpkerGg0SjQa7fw5EAgA22gziwUtGevVurL9DbPdBaFlzbC6DS52vJazYdY0TZJr1xBfuRxtWAUFvzwX1eUGRUGxWlGsW34vbEl37WXEgl1up2hWFG3gJhRvGmI3bu9U2w3dIBuPRYnHY50/h0PtWaymq+dfW01FooLJU3o3aifTNF1jT301L39j5fRsFyOEEEIMMYMy0Hq9Xl566SVOPfVUSkpKcLvd+Hw+FixYwD777LPN+9fU1HT5+YorruDKK6/s9vbn7mfj/ObJ1C9ZRlFTPdHivg8fvvkQlfNeVAYk1G78oT4dQyhblnxGuLCcS46o6PexujNv3jyuuuqqza7fWpspFit6PLrZfbYkHUO/txRmDTPVK1sfgGtyuGfWCAWJr1iGGY1SdPHV/V7sqbv2Wv3QIV1+LtjpTIp22faXTf3RXYgd6gF2U089eBuP33tDtsvYogUrVPZ3NaAo47Ndymb2G57khhWlsh+tEEIIMcAU0zRzc6ndTZxyyincd9999KTcr776ilmzZnHooYdy6623YrfbefTRR/n1r3/NAw88wCGHHLLF+/n9fnw+H7W1tXi93++pubUe2s77RkwufqCOA+ufIzLjEAxb/4dinvtcKvSkO9imO8gCxOtrWfL5Wi75w/4UeHveg9dbm/b41dfXM2nSpK22WfTTj/jTy23Ma5iEr7X757K/Q423Njz13frUzqPn669SoGd3NVTTSGK0tWEE/JiRMGY8ntpLNhIF00CvrKbg1+enZRue7tpr+PHPoVq/n3ebiR7abffCii3ZtIe2ubGBP5y0J21tbV3eYwNfV4KS89bwfzuv4eRT9shaHd1Z+t43bP9gMV+cFWW7iZn7Uq8nOv4ty3abCSGEEANhUPbQXnbZZbS2tnLbbbfhcKSC5XHHHce//vUvTj75ZNasWbPVubler7fXHwK8doWxY0t5vWUasz59jdadDwK1f2Fx495a6H+w3fgD/g27NaftQ70ZbOerJbWM3G1qRsMsbP7lQsdQ4621meJyo8fXgdp9QOtPmN3W89gWgVgS5rsW9vrY6WImEySbm0g2bsBoaUZxuVC9PtxHHoNic6SGEjtdqL6CtPYudddeqtWFak3/PMhNX+MdpBe25yxWG6Zmoy1sEGhpp3Zlbixc9uYb3xJQS5h9QG4NN+6w3c5jqLr3a557qTHrgVYIIYQYSgZloF2yZAnV1dWdYbbD+PHjefLJJ1mxYkWvV07uiXP2tXNlcBIfftzE1K/fx7/dbv0+Zmqv2lQwPu/F4ZgbLWq1rYC7peGz3/fIpunDfSLBivc+x1M1jF8fmRvb9GxKdTrRYpFuf9/XMNtdkE0aoKnQGoFvmiEch0oP9GoSbxoYkQjJ5kaM5kYMf1sqwP7kZ1hGjh4UizmBDCPui4SR+oIlnoRYwiARjRONRImHokRDEcLBMGY4TEmsiSK7yfT2j7gr20UDz7+1lh2SQYZV75ztUrZIVVVmeRp4fpmV32e7GCGEEGIIGTSBtqGhgYqK1LfiZWVlfPzxxyQSiS49satWrUJRFAoLCzNWx+WHOPlDeA+Wf/YiI51LaR8+KW3H3jjcAp2rIncnnb2wW2QYrHnvY2JWF5f/YtfMnaefFKeLCf4vQd18wa6+hNmtPaehOLy+Gspd0ByBX2hvklQUxiRDGdsky4zHMMJhzGgEMxLBDAUxAgHMeAytqBjviaejV9X0ajGnXCWLOaUkjNTF+O5LEiURR42GIRYhEY2RiMaIRRNEI3GisSSxeJJY3CCWAMwkdjOOzYxjI45NA69VY2pyFV6HRsXsAymoGIlWXIJisVJXtyfcOD+rjxfgpQYHs4YFsl3GVv1gipNT3q4mEopgd/ZtxXshhBBC9M6gCLQPP/wwxx9/PNdddx0XXHABv/3tb/nJT37C5Zdfzty5c1EUhVdeeYXHH3+cY489lpKSzH3oVVWFa47wcUFsfxxfPU+pbiVUOTYj59r2/NcMfrg3TZo+/JimmIXL5+yJpuXuvpyqy40Sj6FYt7wQU0/DbHdBNmFAewy8Nvi2GYb7UsOMxxTC+ESoz3V3xzRNDH8byfXrSLY0QSKB4nCi2GwodgeuI45BKy1DKypB6eew91ww1HphY0mIJFI9qAkDokkIx03CoTiRSIxYOIoaDWOPBbHEQmixMBhJDIsNVdfQdQ2rRcNi1dhnx2I8Hhserw2X247TacVqs6BoOmgaaFpeLGC0oa6RD7VRXLtHU7ZL2arZsyeSeCfMwgVfctgRU7NdjhBCCDEk5HygnTNnDgsWLGD16tUATJ06FYB3330XqzW1iExhYSEej4eystT+f0cffTTPP/881113HZMmTULTNFRVZe7cuZx99tkZr9luUZj742IuevQgdlr6PEWYhCpzc95XnxgGrR9+SG27hcvO2xu73ZLtirZK0XV0i4Y9GUYtKsFobgRSQaknYXZbvdxfbICGdtDVVKido76E1WFCmtZ+Mk0TMxbFbA+QbGsl2diIoqloZeUUXXAlqq8wL0JJb21pPuxgCbHx70JrLAmRuEEkHCPYHiUQjKHEIrgT7djjIWzxMPZEmJ1DX+PT4/isJh67gs1px3XUj9AKRqXmPdsdg/I10OGp577Ck6xin31zb3XjjXmLvUw3P+N/H8Q57IhsVyOEEEIMDTkfaG+4YdvbR8yePXuzfUgPPvhgDj744EyVtU1um8K1Pynlkv/MZtIXC6iOhgiM3AHy/EOnkojR9O4HrE64ufS8PXG7Bm7v0P6w2Cw4AmFM3Qn0fHue7sJsLJnahmd9MDVHdr7nZRKmgldPbvH2vWFEwhhtrRiBAEawHTPUDoqK6vagen0UnX8ZalHxoAww3S9clh8h1jBTvarxeBIjFCQRDJIMRYhHIkQiCcJRg/Y4qEYShxnFYURxGBFKLXCIuZxSh4HbYcH1wyNQPSNQXa5U77vVNih62/vq6S8MDnKsxmIZme1StungETH+vqJEtu8RQgghBkjOB9p85rErXHdMKZc9fgj+r15nUvtiApP2AC0/n3Yl6GfNu5+w3lHG5efvgcve/21dBorVZsHREiKEs/O67npnt9YjG4ylwuyKVlCA01lMqSvWr31ljWgEo7mJZFsrhr8NTDO1gNNhP0YtLkErKkZ1OLd9oDyiFhaj2jwABLXvH5tCx1xxyMUQG0tCIAqBGAQjSSLtYWLhKEY0ihmNoMfCWOMh7IkwulVHt1mx2KxYHFYO27uCQq+dQp8Nh9OKYkldsFol+GxFJBLj5fgo7ppal+1SeuSHB43mkn+4+eKDZUzaJTPTTYQQQgjxvfxMVnnEaVWY/5NC/vi//Xjjs4/Y/e0XiU/bi6Qzv/YGVNes4pslK4lVjOaqs3bEZsmv3iLvvrNwPuAnRAnh0uGQ3Lwntbsga5ip1YqbwxBNgFUDhw6XWBbSl61TTdPADIZItrWQbFyPGQqhFhbh/tFx6OUVg3YI8caCmhN1oyD7fYjNDbGESTgSIxKMEgrGCIZjBENxiEYpTLRRGGtlz/AXFFlN3HYVm9NCwWGHofvKUH0FqB7vkO5RTacXX/iCuDKMww6dmO1SemT7KTWMTizlPwsauFwCrRBCCJFxEmgHgK4p/PGHXm7x7MbLHxey/eLFlE0aS7RybM4PQVbiUaKffcznjSpF0/bgkh9X5mXYshYV4ko00Pjdz6kFtXrWA1jnTy3y9Cvldarc0V43mWmamOFwagudlmaMQBtYbWi+Anyn/gq9ejiKlj+93emS7RAbTaR6/xItrcTa2oi2hwlFEoRiSfR4DIeaxGkFn11ldvIbShxQ/uNDUb0jUL0+FOdxefleyDdPvt3MnkoQX8mMbJfSI4qicGR5E0/UFXB5tosRQgghhgAJtAPo9/s5CM6YwrVPlLH6i4/Yvu41rFOmkXD6sl3a5kwTtX45dV+sosE9nHN+N5XqkvzdhsJaWIgr0U5QdfQoSBlmKsRGEqnhxRdYF1FqjfX4fEYknBpG7G/rHEasFRXjPekMtPIqVHv+PpfpMHf/gQmzHSsGRxIQihpEAkFC/iCRQAh7uBV3zI/V6cDucfKDvSspLnFTWOzG4XWh6Lm92NlQEI8l+G9gOJdvvzbbpfTKsbOquPlfhXz7+WrGbp+b+3MLIYQQg4UE2gHmsinMPa6cFRsO4PYHPqP09Y8YXeWGcdtj2BzZLg8Ac8NaGr/4llVxF6U7zGDeUVVYtPzuibK5nexYZeGrmB/ofrh3NJHagmd1G4QT4LLACB+UJrcdZs1EnOT6dSTWrcWMRtCKivEc9VO08spUj5705mWMYabmNwdi4I+CP5wk7A+ih9txx9rwRFvxRlvZ01hNiUuh2Gel+NQT0ErKhmTveL5Y8MJSWtVyjjsic3uHZ8Iuu49ixANf8shz67lUAq0QQgiRURJos2RUqYUbfjeVW54fwdtLvqby1TepqfKijp6Qlfm1hmESrG+gaUUdjRGV0vETufKYcXjsg2MeoEWDiLOA+ZPXEeom0LZG4KMGcFnBY/tujqwCbGXhYtM0MNraSK5rINnciFpQhO/M36JX1sgcygxJGqm2ao2APwbBiEEsFMaTCFAUbaEisp6jmt+l2AlOrxPXkUejFY9HKyhMLcIk8sZDb7ayL22UVu6d7VJ6RVVVjixbz5MrnFya7WKEEEKIQU4CbRZpqsL5hxYRP3h3bnl+PO8sXUbxoo+oLlDwDq8iVjYcM4PDHpMGtLYE8detpWVdCw7doHT8KH5/9Hjs1sHVa2XVFCK+cuxNn25xT2DDhKUb4DeWxYy2h7s9jhmNYgT8GMF2jPYAhr8Vxe5AKx1G8e8uHHSrEeeCSCI1/LstYuJvCxP2BylMtFIaa+Kg1vcoSLRRYFfR3C5UtwfPCSeilfxQhgznuWg0wX/bR3Lj9iuyXUqf/PTgEdz2iI+vP1rG+Gljsl2OEEIIMWhJoM0BFk3hDz8owjikkG/X7cB9z9US+rqe6k9fpbjAhrusCIqHEfcUgdr3oGma4I+a+Bv9+De0EGxpozjWSmGph1+cuSOFNeWDdlisVYewuwT7ijowkl2ex7YIfLgWihwwWt88zJqGQXLDOhJr6jAjEVSvF9XpxvPTU9ErKlGdroF8KINCwVfvoNZMIOYrxbB2nU8cS5g0t0Vp80do9UcxwyHK4s1UhNdxZOJryj0KVo8b90+ORS3YO7Wi8CB93Q5l/336U6JKBUcfOSXbpfTJbruPZOyDS7nn6TXMk0ArhBBCZIwE2hyiqgrjK2zMPW0sodgYlq2N8OjLa/CvbcS5YhUl8Y9wOa043HbsLgcWtwvsTgyrnaTFhqnpqaBmmpjJJLFIlFB7lPb2CIFAlEB7DHekhWJrgh/baxl/6lE4q6uHxBxCTVUwrDYirkKc61YSqhiDYcL6IHzZCGfrixm5SZg1jSTJDeuJr16JarfjO/NsGUqcJkmbE9/KT7H6m0iYCo1aAetMFxviNiIJKNZj7Mdyqp1xitw67uOOQyvdW3rAh5B73gpziP41heX7ZLuUPlEUhZ+NbuXv31RxTTyBZpF/boUQQohMkH9hc5TTqjBluIMpp4zBNEfTHDJZ3xrjicWNhAJBwi1+YuvjWOJN2JIRLEYM1UhiADFTI6ZYsOoKbiu4HRpH8Q2jjz8Ed+UuQ7ZH0apBy6Q9GfbxiwSKh/PBhtSQ1HMsb1BjjwCp3ljD30aycT3JDetRPR4KfzMHvbI6m6UPOk0TprPSdLMhYNDWHserxylxmJy1m4PyYjuaVYYLD2X1tc28mBjHk/uuznYp/XLa0RP5440JFjz7CbOP2Dnb5QghhBCDkgTaPKAoCsUuhWKXnYnHdA1WkbhJJG6SMFLzQDUVbLqC05Lq8f3eQQNbdA6yahAorMFTXM3qxe9TWD6cs9XXMGMxYuEwRrAdsz2A4nCilZRSfNk8VM/AL9A1FLy7KkFVmckv9rJTU+DEqsuQYfG9v/37CyoThcw+fGq2S+mX6poi9lff5B+Losw+ItvVCCGEEINTXgTahoYGTj31VF544QVMc2D2r8wXdouC3SJhoCfsFoVI3OSTUQcQtK7hnIZ7SWKiWK24Dv4hamERWmkZqj03tk8azG49yonPJ8OHxeaSSYP7lhfws4o1aJZJ2S6n307fy8VJr41i7YoGykdVZLscIYQQYtDJ+UD7+OOPc+6552Kx9G4I4imnnMIbb7yB2+3ucn1LSwtr166ltbUVh0OCy1DisCi0hE3q20xuPH4MHvs12S5pyJJFnER3nn7yE+rUSs46oTjbpaTFUUdO4Q+vfMXtD9Yz91IJtEIIIUS65fzqNvPnz2fBggXsueeevb7v3/72Nz7++OMul9mzZ3PkkUdKmB2CfrarlRXNSWoKVDx2CVRC5KKbF8U4yrqUmtHl2S4lLXRN5Zfjm7m7YSSRYCTb5QghhBCDTs4H2sWLFzNu3Ob7hm7LWWedxcSJE7tcFwwGeeSRRzjzzDPTVZ7II6OLVa473MklB9m3fWMhxIB77+3lvKGMY86Rg6sn8xcnTSWi2rj33rezXYoQQggx6OR8oNX1vo2Knj59OsOGDety3aOPPkpZWRn7779/OkoTeUZRFIZ5VBnuKkSOuurRNexnfMHOe22X7VLSylfg4syKVcz7vJRYSHpphRBCiHTK+Tm06XT33XdzxhlnbDPQ+P3+Lj/bbDZsNlsmSxM9FI1GiUajnT8HAgFA2ixXSXvln+7aLNPefP0b/mdMYvGRDQNyvoF20Zk7cvdVLdz1t7f47dn7ZbscIYQQYtDI+R7adFm6dCkffPABp5xyyjZvW1NTg8/n67zMmzcv8wWKHpk3b16Xtpk0KbUKqrRZbpL2yj/dtVkmmabJxY818QOWMGP/7TN+vmwoLvHwm+pVXPdVBcEW/7bvIIQQQogeUcw82QfnlFNO4b777uvztj3nnnsudXV1PProo93exu/34/P5qK2txev9fv9R6T3KHZv2HtXX1zNp0iRpsxwl7ZV/umuztra2Lm2WTg8/9D4nvT2c90/0s+NuYzNyjlzQ1hZmwiX1nFi0nBv/mLm9wTv+LctkmwkhhBC5YkgMOY7FYtx///08/PDDPbq91+uVDwE5atPg0zF0VdosN0l75Z/u2ixT/K1BznvTy29KPmPH3Qb3+gY+n4Ob9gpw6ps7cvLbXzJlj8E1V1gIIYTIhiEx5PjJJ5/E5/Mxa9asbJcihBBiI7+e/wG6meTq3++e7VIGxPHHTmUffSU/+2eASHso2+UIIYQQeW/QBNqGhu4XEvnb3/7Wo8WghBBCDJx7//kOD4e2458/jOP2ubJdzoBQFIV/ztmONWoR5859I9vlCCGEEHlvUATahx9+mMrKSubPn7/Z71atWsWiRYs49dRTs1CZEEKILVm86Gt+9V41l1V+zr4H75DtcgZURYWP+34Q5a+hqfzl1oXZLkcIIYTIazkfaOfMmcPUqVN5+umnAZg6dSpTp04lFot13qawsBCPx0NZWdlm9//73//O4YcfvsXfCSGEGHgff7CKHz5q4ceOr7js/JnZLicrDjlkErfssJqzV+zAg397NdvlCCGEEHkrb1Y5HgiyMmT+qauro6amRtosT0h75Z90t9nLC5byo6dd7Kuv5D/XTcdis6ahyvx1zf+9w5XLRnFjzSf87rwD0jI1Rv4tE0IIMZTkfA/tYBeNRrnyyiu7bJMxGOXi48y1mqSe3Dp/b+VTvdmoNRFPcMX1izj4vyUc6/6Wx+bP6FeYzafne2vmnDWVk+KPMWf1jhx37ku0rmvOdklCCCFEXpEe2o1k41vtofJNeqYeZ396j3LtuR8K9fSmvXLt+diWfKq3N7X2t4fWMAyefPxjLn1Vo54C/m+nOk46dUa/eyLz6fnemo7HsfClpZz+OIRMncvGr+asM/fE4rD365j5/twIIYQQPTEk9qEVQggxcAzD4LOPV/HUS6u5b2URq7RqTvJ8xlVnllM9as9sl5eTdt2lis92t3HdnR9w4bcTuOb81ZxUVstxB1YzdY9xqKoMqBJCCCG2RAKtEEKIXjMMg0BLO3V1rTSsaaN+XZAv68J81qzzYbSMNZZSRsZLOL5yPWceU8KIcftnu+Sc53LZuPq8GZzTGOBvD6/ivq9KuOHRUsof/JadrOuYWpJgbIWNmnIXw4cXUjrMh8fnRLdasl26EEIIkTUSaDeSTCaB1HCtgdJxroE8ZzZk6nG2trb2+bi59twPhXp601659nxsSz7V25taO9ps4m8/IGYrIKpYiCpWYspG819NN4WGwSglzHYuP78bFWTW3gbjJtYANT0+V6YeQy7b0uOwWOGXJ0/hl8Dyb9ey4LWVfFIX59nVLupWOWhUvaAYQAvQgs2I4jLDWEliMZMokVYAmptlPm6+ME2TQCBAZWWl9MYLIUQvyRzajbz33nvstttu2S5DCCGEEENQbW0t1dXV2S5DCCHyivTQbmTs2LEAfP755wO2kEYgEGDSpEksXboUj8czIOfMhkw9ztraWmbMmNGnNsu1534o1NOb9sq152Nb8qne3tTan/dYJuXT8701mXgc9fX17LHHHtTW1uZUm4nu+f1+ampq8vq1LIQQ2SKBdiOapgFQXV09oKscA1RVVQ3qDx6Zfpx9abNce+6HUj09aa9cez62JZ/q7UutA/l3sSfy6fnemkw+Dq/Xm9fPzVCUjn2IhRBiqJGJGkIIIYQQQggh8pIEWiGEEEIIIYQQeUkCbZbZbDauuOIKbDZbtkvJqFx8nLlWk9STW+fvrXyqN59q7c5geAwweB6HEEIIkS2yyvFG/H4/Pp+PtrY2mXeUJ+rq6qipqZE2yxPSXvlH2iz/SJvlH/n8IYQQfSc9tEIIIYQQQggh8pIEWiGEEEIIIYQQeUm27RFCCCFExkWXfETg0QeILv0U1evDuc+BeI45EdXpynZpQggh8pj00AohhBAiY0zDoOXW61j782MxwiG8x5+KY8Y+BP7zIA0/PYzYt19nu0QhhBB5THpohRBCCJERpmHQdNUFhBa9RNnt9+DYfc/O33lPOI2mqy9i3VnHU/63R7CMGpvFSoUQQuQr6aEVQgghREa03nUrodcWUH7nA13CLIDqdFFyzS3YdtqN9ef9AiPgz1KVQggh8pkEWiGEEEKkXfit1/Hfexelc2/DOmHSFm+jaBolV90AikrL7dcPcIVCCCEGAwm0QgghhEirpL+NpqsvwvvTU3Hsuc9Wb6s6XRRfcg3tTz5K5MN3B6hCIYQQg4UEWiGEEEKkVesdt6A4XRT88vc9ur19p91wH3EMzTf8EdMwMlydEEKIwUQCrRBCCCHSJr7iW9qfeISicy9Bsdl6fD/fWWeTqF1FaOFzGaxOCCHEYCOBVgghhBBp0/J/87HvOgPHjJm9up9eUobnmBNpves2zEQiQ9UJIYQYbCTQCiGEECItoks+IvzGqxSe/Yc+3d970hkk1zUQXvRSmisTQggxWEmgFUIIIURatN17F459D8Q6brs+3V8rKMJ1yBH4H7kvzZUJIYQYrCTQCiGEEKLfYt9+TXjRS/hOOatfx/EcdzLRj94j9tXnaapMCCHEYCaBVgghhBD95v/nX7HvOgPb9jv26zjWMeOw7zaDwL8fTFNlQgghBrO8DbQNDQ3Mnj0bRVGyXYoQQggxpCUbNxB88X94f3Z6Wo7nPuIYggufxQiH0nI8IYQQg1deBtrHH3+c6dOns2zZsm5vs3LlStxuN1OnTt3s0traOnDFCiGEEINc4Ml/oVdWYd99z7Qcz7nPAaCohF5ZkJbjCSGEGLz0bBfQF/Pnz2fBggXMnTuXb7/9ttvb7bLLLrz66qsDV5jIC6ZhkGxuRLFYUb0+6eUXQoh+MBMJ2p94BO8JP0dR0/M9uWKz4Tr4MILPPI770CPSckwhhBCDU14G2sWLF6PreVm6yDIj4Kdp3uUoqoqZiKNoOnpVNb6zzkHRtGyXJ4QQeSe86CWMtjZchx2V1uO6DzuKtacdQ6KhHr2iKq3HFkIIMXjkZSrMdJj1+/1dfrbZbNhstoyeU/RMNBolGo12/hwIBICetZlpmjTfeDV6RSWW6uGYponR1kqibhWNl56L7/TfYBk9Vnps06g/7SWyQ9os/3TXZgMl8NhDOA8+DM3rS+txrdvviD58FMEXnun3yslCCCEGr7ycQ9tT69at48QTT2S33XZj/PjxHH/88SxZsmSb96upqcHn83Ve5s2bNwDVip6YN29el7aZNGkS0LM2S9SvhmQSvaoGAEVR0AoKsU2einX8dvjvvYPmqy8k2doyoI9pMOtPe4nskDbLP9212UBIrF1D5L238BxxTNqPrSgKrlmzCb30XNqPLYQQYvBQTNM0s11EX51yyincd999bOkh1NbWcuihh3Lvvfey88474/f7+e1vf8u///1vXnvtNXbdddfN7uP3+/H5fNTW1uL1ejuvz6WeCNM0MQJ+kuvXEfzvfzAiYYjHMY0kAIqmoVisKHYHrsOPRispHVTzRDftiaivr2fSpEk9arOm6y5H9XixVFZv8dimaZBYU09i9Ur0qhoKfjNHhiH3U3fttfSKCxh22I9QfAWoBYXYvb6ceY8Ndf15j+UDM5nEjEYw43EwTRRdR7HZUCzWbJfWZ921WVtbW5c2y4S2f/yF9v89SeV/XsjIvzOxb76k4fjDqXziJSzVw9N+/FzR8fljINpMCCEGm7wcctwTNTU1XXpjvV4vd955J88++ywXX3wxCxZ0v3Ki1+vNqX9QTNMk2VCP/8F/kGxuBNNE9XhRXW60ouLUB7GOhTiSScx4DDMcJvDIvRjtgdTtCwrxHPMzLMNHouiW7D6gftj0Q3THMMhttZlpmhjNTVhGju72NoqiYqmqQSsuJb7sK5ouPx/fWb9DrxkxaL4QGGjdtZfTboOF/yMZDpMIBYnpOqrHi/uHR6OXV6EWl8hzniV9fY/lIjORILF2De3/fgCj3Y8RCmHGoqm/gZoGKGAamPEYiqKiOByoLjeuI45Br6xO+xDaTOmuzTLNNE3a//cE7sN+nLH3q2XsBPThowi9/Dy+k87MyDmEEELkt0EbaLfE4XAwZcoU3n777WyX0iNGJELbX28j0bAGRdfQSodh235HFKezVx8ejHAYo7WZwIN/x/D70UpK8Z7yC/RhFRmsPrcYrc2g66g2+zZvq9rtWCftgNHcRNtfbwNNw/uzM7GMHI0ii5GlhaVmBFanE0h9KDYjYYyAn+CzT5Fsa4F4PPUlzI9/il49AtXlznLFIl+YySSJ2pX4H7oHo6UZxeVC8xWiV1SjuFyp3lil62wb0zQhkcAIhzCD7bQ/8QhGWyuKpqf+Xp72KzRfQXYeUA6LfvoRidpVuDK4CrGiKDhnzSb00gsSaIUQQmzRoP103tbWhsPhwGrtOoxM0zSSyWSWquoZIxKm9S83k1xbj1ZUgm3i9qievveMqA4HqqMKvaIKMxYjsX4tLbddh2Kx4jvtV+jDRw763rDk+nW9eg4VRUErLkEtKsZoaiTw0N8x2tvRSkrxHH8aemV12ranGOoURUFxOFEdTigrB8CIRjBaWwg89jBGawuKzYZWVILn+FPRysoH/etV9J4RCdN2560k1q5BsTvQh5VjHTu+R0OJFUUBiwXN4gOvL/W30jQx2gMkN6yj+dpLUV0uvKf8UkZsbCT4zOPYd98z41+OumbNxn/PHSTW1KF3M2VECCHE0DVoA+3vfvc7Dj74YH760592XheLxViyZAk77bRTFivrnplI0PqXm0jUrUYrLcM2bTdU+7Z7FHtDsVqxVA9Hr6om2dhI2923g65R8Ovz0UuHpfVcuaT9mcf61MunKApaSSlaSSlGJEKycT1td/8fZiSCVjoM3+m/RisoykDFQ5tqs6MOq0AfVoFpGhiBAEZTI62334AZj6EVFeM55mfoVTUo1vybxynSx4xFaf3zTSQa6tFKSrFNnorqcvX7uIqioHm8aB4v5sjRJBs30Hb3/wHg+/lv0EeMGtLB1oxGCS74H8UXXpXxc1nGT0SrqCL0xit4j/lZxs8nhBAivwzaQAtwww03sO+++1JRUUEymWTOnDls2LCB+++/P9ulbSaxdg0tt1+P6nBim7pzqrcqgxRFRS8tQyspIbluLS03Xo1WWkbh7y7M68VRumOGQ/0eMqja7ajVw7FUD8cIh0mua6B5XmqhKd/pv0UbJj2HmaAoKprXh+b1YRk1BiMSJtnchP+Bv2H421A9PtSiYrwn/hzVVyhtMESYpknbX28jvmIZWmERtmm7pv0LwA6KqqGXlaOVDsNobqTtb38CXafwt39AKy7JyDlzXfjt1yGRwDFzVsbPpSgKzr33J/z6yxJohRBCbCYvA+2cOXNYsGABq1evBmDq1KkAvPvuu51DjM877zzuuusuZs+eDUBjYyMTJ05k4cKF7Lffflmpe0vMZJKW264juX4t1jHj0UpKB/T8iqKil1emFkJa8S2NV8yh8HcXDrr5tUY4jGJP35cEqsOBOnI0es0IkuvX0vJ/16HoOp7jT8M6eqz0GmaQanegVlZDZTVmMoHR0kKypYnm+VcCoPkKcP/oOLTyykG1wrf4nhEJ03Lj1ZiRCNZJU9D6MSWjN1JTEUpRi4pJrm2g+for0YZVUHj2BUNufn1wwf9w7L0fqrP/veE94dhrPwLnPYIRbJc59UIIIbrIy3+Bb7jhhm3eZsqUKfzpT38agGr6zmgP0HzDVSgWK/adds1qz6hisWAdP5Fk4wZabp6LXjOCgl+fPyjCQGrRoQhKBnpvFE1Dr6hCK6/EaGmm/bGHMFpbUj2GJ52Zmms7CJ7DXNWxaI9WUppq53AYo62FwGMPYfjbvlsR3IPq9uI+8li0klIUl1vaJI8lNqyj5ea5aCVlWCdOzspcdkVRU+/74lJi335F05VzKDz3UrSi4gGvJRuMSJjwopcpvnL+gJ3TvvNuKLqFyDuLce5/8ICdVwghRO7Ly0A7GCQ2rKPlpmvQK6vRq4fnzAdsraQUm9tNbOkSWuZfQeGcK/J+L1YzEk7tNbnR42i69rIutym++Op+nUNRFLSiYrSiYsx4jMSG9bTdeQtmMoleXonv9N8MWE/GUKUoCorTiep0oldUAd8tLhXwYwYC+O//K0Z7e2rvUZcb1e3BfcRPUns1y1DlvBBfuZzWO27GMnY8eklZtstBsVqxTpxMcv1amudfgWXMOArO/F22y8q48OJXAXDM2HfAzqlYbdh335PQG69IoBVCCNGFBNosiNfX0vp/87GOm4BWPLBDjHtCtTuw7bgTsa++oHnuxRRd+Me8HkJrBoMoG612vWmY3fi6/gZbAMVixVJZjV5RlVolde0amq6cg+otwHP8qXm/F3A+UW321FZNG4UfMxbFaG/HCLYTePR+jEAAEvFUT67Hh/vHx6OVlWdsPqbom9a7biW+/Fts2+/Qr1Xf001RFPRhFaguN9GlS2j5v/kU/PYPg/oLktDC53DM3H/A3yPOvfen5c83YhqGrDIvhBCikwTaAdYZZidOzul9DRVNxzpxe+LLvqHpmksouviavP2Ab4RDPR7O3XTtZWkJtbDJKqmjx5Js3EDggb9jBNpQvQWohUVDdiGj5pvmEtc2/0Carud+axSrDa3I1mV4qBmPpVZS9rfhv/cOjIAfxWpH9XpxH3lMKuAWFA25dsoVrXf/H/EVy7BNmZaWFYwzQXV7sO+4M9HPPqHllrkU/v6SQfl6MUJBwq+/QsncWwb83I4996XpmouJLf0U2+SpA35+IYQQuUkC7QBKbFiXF2G2g6KoWMaMJ75iGc1zL6Hokrl5GWrNSBgsqR7RLfXObiqdobaDounoHdvQJOIkW1owWptTCxl9t+K097Rf5cXrIpPSPRS8pxSLtXPIOIBpGpjBIIa/jcDjj2AE/BCPobg8qG43rh8chVZYhFpQiGKzD8rgkisSDfXEv/0a2+QdczbMdlBsNmw7TCO65GNab51H4e8vznZJaRd+4xXQdRx77D3g59aKS7BuvwPh11+RQCuEEKKTBNoBYrQHaLnpmtQw4zwKLYqiYBk1JhVq511G8aVz825bHzMS7nXN6RyCvClFt6CXlkFpWWoho/YAiQ3raL72UlSXC8/xP8cycpQMSyaz7bA1iqKiuD2obg96ZTUAZjyOEWzHaA8QfPrfGKEgZjiUur3d8d3Fjuvgw1FdqfsqbrcE3n4wAn5abp2HdcIkVLcn2+X0iGKxYJuyI5GPP6T1LzdR8Kvzsl1SWgUXPItznwNQbNmZhuLYaz9CLz1PwS9/n5XzCyGEyD0SaAeAmUjQfP2V6FU1OTlndls6Q+3XX9A8/0qKLr4mr+YvhRY+h2LpWzjMRG/txhRFQfF4sXq8mCPHkGzaQODhf6T2V/X6vlud95jUFjQZ3ps4l23cczvQ4baDYrGgFRSiFRR2ud6MxzAiEcxwGDMaIfjsk5jRCGYkihmLpOb7We0oNiuK1YZis+E66DBUlxvF40Xz+LIWDnKZmUjQfMNV6DUj0AqLsl1OrygWK7bJOxD95EMS6xoGzTZoRnuA8JuvUTo/ezsIOGbsQ9udt5JoXJ8TC4MJIYTIPgm0A6Dllrkodgd6VU3aj721IbTp/OCvKAqWcdsR+/xTWm6eS9H52x66myvMRLxfKwwPVC+hoqropcPQS4dhJuIYba0YgQD+B/+O4W9DcbrxHn8qljHj837l6f7IVq9tdxSLFc1ihW4WKjKTCcxoFDMWSwXdWIzg809jRiLfBd8wqNp3KzS7cf3gR2hFJWiFRUM66LbcOg/FZu/sIc83qsOJddwEWm69jpKrrs/rhfU6hBa9jGqz49h9z6zVYJ0wCbWwiMjbb+A+7Kis1SGEECJ3DGigbW1tRdM0PJ78GDqWDvGVy0i2NGOftmvahh32ZB7oprdLy+q9qop14vZEPvqA1rtupeCsc/p9zIFgJhKg9/+lnune2o0pugWtuLSzR980DIyWJgKP3IcRCqJXVlNw1jlDOvDkQq9tTyiajuLUoZsvVUzThFgMIxTECAUJ/vc/qeHMoWAq6H43lFmx2XAd8AMUpwvV5UptPeRyD8ovN+L1tSQb12Pfabe0D9ceqC8BgdR7uLmJllvnUfSHK9N67GwILfgfjn0PzO6e6aqKY/e9CL/9ugRaIYQQwAAE2rq6Os4++2zefvttdF0nHo9jGAa77LILt956K+PGjct0CVljRMK03nkr1omTUfoZqHoaYrd1/37vt6pbsE2aTHTJxyRbm9EK8mAoYDKBoun9fg4hi3M6VbUz4Brt7cTrVtF42e/RSofhPfkstNJhQ3qeZq712vaGoihgs6HZbF2G1nYG3UhqKLMZjRJ88RnMWNfeXsVqRXU4UZxOXIf+6PvFquyOvHxNmIk4rX++Eeu47fo8VWBjvXnfZ2JRMsuosUQ+eIfE2gb08vwdemwE/ITffoOym+/KdinYp8+k5ea5mMnkoPxCRwghRO9kPNDedddd3H777VRVVXW5/ptvvuFvf/sb8+fPz3QJWdNyy7VopcPQvL5+HScdQWzTY/Xng5rqcmMZMYqWm+dSfMX1Of+BwkwkIc01DmRv7aZUtxvbdttjRCMk162l9bb5qWHVHi+qx4vn2JPQysrzap5zuuRLr21PbBx0u2OaRmqubjiU6t195rHvendDYJqpUGuzoVitKLoF5wGHfj+X1+5AcThQHS6wWnMm/LbefgOq29NlW6W+yJUvsBRdxzJqDK1/uYniq27Imee5t0KvLkB1ubHvuke2S8Gxx140tbUQ+/JzbNvvkO1yhBBCZFnGA+20adM2C7MA48aNY+rUqZk+fdbE61Zj+P3Yd96tz8dIZ5Dt7th9/aCmlVeSbNpA659vpPDsC9JZWvpl6Fv8bPcKqjY76vCRWIaPxIhEMAJtGIEArX++CTMaQS0qxvOTn6FXVg3JBaWytQXQQFIUFcXhAIdjswBoxuOYkXBq/m48hhmPE1zwDCQSmIlE6vfxGMSimCaodvt3IdeJ69Aj0QoKUX0FqZ7iAWK0B0g01GPbadd+HSfdfzv7/feydBiJ+jriy7/FOiY/RyUFFzyLc/+DcmL1da2oGOt22xN+a5EEWiGEEJkPtN988w0///nP2XXXXXG5XGiaRjAY5OOPP6agoCDTp88KM5mk9Y6bsY4Z1+cglckwu+l5+vIhTVEULGMnEP3ofYz2QE5vqWEaSVAz14uczd7aDqrdntojuHQYAEY0gtHUmFoxua0NLFa0ggLcR5+ApXr4oFigprcGU+9tTygWS2rIbg/emmYigRkJp4Y3h8OpbYnCIcxQiGZ/IPPFfqf1TzegV1Sh2vq233Wm/2726+/liFH47/kLxVffnHe9tMnWFiLvvknZ//0926V0su+xN5G334DTf5PtUoQQQmRZxgPtBRdcwFtvvcUrr7zC559/jmEYVFVVcfLJJ7Pbbn3vvcxlrXfcjGq3oxWX9On+AxVmNz5fXz6kqXYHemU1LbdfT/FFORwQDAO0zA6/zXZv7aZUmx21shq9sjq1120wSLKthcB3KyarvkI8x56EZfioIbmw1FALt9ui6Hrnvrubsq1dB9yf8RqSba2pBfR26duQ1lz/ElAtLIJVKolVy7GMHJOByjIn9OoCVJ8P+06582+2Y/re+O+/GyPgR+1mhXEhhBBDw4Cscjx9+nSmT5+e1mM2NDRw6qmn8sILLwzokLhtMeMxErWrsE2e2qf7D3SY3fi8ffmQplfXEHn/HZKNG9BKcnSP3WQSJYM9tBvLtWAL3+1163ajut1QVYOZiJNsbiLwr39itLWguL1oRcV4TzoTzVeQ7XIHnITbrevvgnY91XbXbegVVX06Xz58CagoCnr1cNruvYuSK6/PUGWZEVrwP5z7zx6w10JP2HaYhmK3E37vTVz7z852OUIIIbIoq6vGvPrqq3263+OPP8706dNZtmzZVm+3fv16TjjhBCZMmMCECRM4+uijqaur69M5e6r1LzejFhSmwkMvZSvM9uf8iqajVw+n9a5b019QmphG+heF2pZst+XWKLoFvawc2/Y7YN99TyxVNZjhMM3XXkrjVX8gXrcqp74kGkhN117WeREDx4xGSTauQ6/YfL2FXNWX14hWUooZCZNsasxARZmRbG4i8v7buA44JNuldKHoFuy7ziDy1hvZLkUIIUSWZTzQvv766yxatGiLl3vvvbdPx5w/fz4LFixgzz2739w9Fotx4IEHEovF+Pzzz1m6dCkul4v99tuP9vb2Pj6arTMTcRJr6rAMH9nr++bKB+i+1KGXV2L420i2Nmegov4xTTM15DgLK/7mSptujaLpaMUlWMdNwL7bDPTyStruuJWmK/9A7NuvMWPRbJeYNRJuB07r325HLShCsfZ+f9Nstk9vz60oauo9ds9fMlRR+oVefh6tqATb1F2yXcpmHHvsRfitRUP2CzghhBApGR8/9MUXX3D33XczefLkzX73/vvv9+mYixcvRt/G0Kf77ruPTz/9lCeeeKLztvPnz6eqqoo77riDOXPm9OncW9N2122oXh+q05X2Y+cyRdPQK6po++vtFP3himyX01UyCYqatUVYcnEIcncUVUUvK0crLSPZmFpQKtnWhupwoHp9eI49Gb2qJue3acoEGZacWcn1a7GMGN3r++Xjlw3asHKiH3+QN3uoBhc8i3PW7Jys1TF9Js3XXUF8xbdYR+fn6tFCCCH6L62B9sEHH+SEE07oct2ZZ55JLBbjN7/ZfCXCO+64o0/n2VaYBXjssccYPnw4o0d//yGpvLycSZMm8dhjj6U90JqmSWJNPZZxE3p931z7UNaX+WF6RSWRD97BjEVzawXdZBIlwwtC9UQurITcU4qiopeWoZeWYRoGRrAdo62Vtn/8GTMcRh9Wgfe0X6EVFGa71KyQcJteRnsAMxxG7eWq97nyd7O37231u/1/E/W1fRrNM5ASG9YR/eg9Cn99frZL2SK9shp95Ggib70ugVYIIYawtH7SE5dp2AAA7tZJREFUX7hw4RavP/3007d4/S9/+ct0nr6LTz/9lFGjRm12/ahRo1iyZMlW7+v3+7tcotFtD7tMrl0DCqheX59rziW9HkpntaH6Cmn7258yVFFKNBrt0jaBQGpLke7azEwmQMl+oIXc+QDeG4qqonm8WKqHY99xZ2w7TMM0TZrnXUbT1RdiBPxbvX937dWeNAhsdIka+TlkcDAOSY7G4/hDoc5LezgM9O3vYk/EV61AKypGyZH36UDQSsoIPHxP2o7X3fusv0IvPY82rALrlKlpOV4mOKbPJPzma9kuQwghRBal9RPEAw88wKxZs7j66qtZtGgRsVgMALu9656CHddnUmNjIx7P5ltQeL1eQqEQ4e8+pG1JTU0NPp+v8zJv3rxtns//z7+iDavo9dDWwfRhWC+vJLFubUbPMW/evC5tM2nSJGArbZal+bPdyff2Vp0urKPHYt9tBqqvkKZrL8XcSrDprr2mf7qSKR8t77z8pSH35l/3xmCab3vTY09RdeLPOy+7/C41mqUvfxd7ov2JR1ALi3t1n1x7nntbj1ZUQrK5KW1zP7t7n/VX6MX/4TrgkJzeN9cxfSaRj97DCIeyXYoQQogsSesn/RNPPJHCwkKeeeYZZs2aRUFBAfvttx9XXnklr776auc3+n/4wx/Sedq0q62tpa2trfNy0UUXbfX2ZiJBsmkDeumwAapwYPT2Q5paWJhawdPflqGK4KKLLurSNkuXLgW6bzMzmYQc+zCWax/G+0JRVSw1I9AKCmm94+Zub9dde721w0iWTBvdeflVRdFAlZ5x+R5sz/vxEdQ/8PfOy/u33QD0/u9iT5iGgdHWilY4tIavqw4HaBpGc1Najtfd+6w/EmvXEF3yEc4Df5CGCjPHvtNuKKpG5IN3sl2KEEKILEnrHNp77rmHl156ifXr1zN79mzeffddXnvtNRYsWMC8efNQVZVdd92VhoYGbr311nSeejMlJSVbHHbl9/txOp04HI5u7+v1evF6e75Re2JNHYrTjWLr3dzRfP7QuyWKoqKVlOK/904Kz74gI+ew2WzYNnqe/f7UkNdu28xIBdpce67zaU7t1uiV1USXfoZpmlvsxemuvdyaiicH5jZnUr7OtbVZLNgsls6fA6HUaJbe/l3sCaO5CcVqQ7H0fHXjXHsvd+jte1orKCTRUIdWXNLvc3f3PuuP4IJn0atqsE7cfEHHXKLYbNh23p3Im4tw7rVftssRQgiRBWn/RDlr1ix++MMf8tRTT1FaWsq1117L4sWLaW1t5ZlnnmGfffZJyz+227LDDjuwcuXKza5fsWIFU6ZMSeu5Ao/cl5YPJbmo10PpiktJNm3IUDV9YJg5OzcvVz+Y94bicoNpbHMu7VA3mIYkp1NiwzrUNIfkfKF6fbT/97Fsl9Gt0IJncR74g5webtzBMX1vwm8tynYZQgghsiQjn/RdLhennHIKiqJwzz330N7ejsPh6Jxf+6Mf/SgTp+3iqKOOYtWqVV1C7bp16/jiiy/48Y9/nNZzJVua0fJ8Dli6qL4CzHAIIxTMdikAmKaRc0OON5bvrwNFUVA9XpIb1me7lLyxcbjN9/bvr+B/H0Nxb77WwVCguj0YaVq8Kd3iK5cT+2IJroMPy3YpPeKYsQ+JutXEV6/MdilCCCGyIKNdV9OmTePkk0/mv//9Ly+++GLn9SeffHImTwvAKaecwpQpU7jgggtIJBIYhsGFF17IqFGj0rq6ctLfBqaB4nSm7Zj5TFFVVF8hibrV2S4lxTQhd/MskP9zLlWni+DT/852GXkr39u/P4xQ+6Dat7s37ag4nZixCGY884sk9lb7c09iGbcd1rG934YuG/SaEehVNdJLK4QQQ1TGx2KqqspPf/pTJkyYwF//+lfq6+uZPn16v445Z84cpk6dytNPPw3A1KlTmTp1apfVk61WKwsWLEDTNCZNmsTEiRPx+/28/PLLuN3ufp1/Y8m1a1C9vrwYltVXvR52XFhE4PGHM1RNLxm53UO7sXwNNYrdjhmNZLuMvDcUg60ZCqH24svAwfT8KIqKaneSbGvNdildmIZB8Pn/4j70yGyX0mOKomCfPpOIBFohhBiS0roo1KaCwSDLli1j+fLlLFu2jG+//Za5c+dywQUX8Ktf/arPx73hhht6dLthw4bx0EMP9fk8PdH+1KODZu/ZdFF9hcRrV2W7jBQTcr6LdiP5uFiUYrVhDsBWXENFR2jLt9dBb5nRKJgG9GJBqMFGcTgw/G1QUpbtUjpFP/mA5No1OGcfnu1SesUxYyaNF/0OMxrt9QKNQggh8ltaA+3VV1/N119/zbJly1i2bBmNjY0AVFRUMHr0aMaMGcPpp59OaWlpOk+bVUZ7O5ZebtczmHoZtkRxOMAwMNoDqFmfH5f7Q47znWKxYCbi2S5j0MnHLzd6w2gPoNjsg3p0y7Yodjtme3u2y+gi+OxT2HebgZ5DIbsn7LvsgWkkiXz0Lo499s52OUIIIQZQ2gPtYYcdxk9/+lNGjx7debEN0m9LTdPEDAUH1RywdEgtFOQh2bg++4HWNMm3RJt3QUbXMROJbFcxKOXda6EXjHAQxTo4/23oKcVqI7jgf9h2mJbtUoBUr3lw4bMUzbki26X0mupwYp+2K+G3XpdAK4QQQ0xa59Duv//+HHrooaxbt4533nmHd955h4aGhs1u99prr6XztFljhkOgqigb7dkoUlSXh/Yn/pXtMsQAUDQNkslslzFoDdYRHWY4DEP8b6diseTUolCh11+GZBLnfgdmu5Q+cUyfSfjNwfH5QgghRM+ltYf2Jz/5CT//+c87f16xYgWvvvoqy5YtQ1EURo4cyYwZM/jjH//ISy+9lM5TZ4UR8KPY7NkuY0D0tqdIcbpINubIVi751UGbf1Q1tfiWyJjB2FNrxqIoekaXcciK3rSVouuQQ6Mb2p/8F66DfoDqyM9V++3TZ9Jy23XE62uxVNVkuxwhhBADJK2fJjYOswCjRo1i1KhRnT+vXr2a559/nnfeeSedp80aMxxCsQ7dBU22RnU6SURk5du+yqsAoyiYppntKga9vHpN9IAZj4M2+AJtr2g6GLkxuiFet5rIO4spv/c/2S6lzyyjx6JVVhN+/WUsx2V+e0AhhBC5IePb9mxs+PDhnHnmmfzoRz8ayNNmjBGJyHDjbig2W+5s5SJZS4jck0yiqAP6T1DuURTMHBnd0P7Uv1N7z07aIdul9JmiKDj3OYDwa/k/AkwIIUTPZeXTxKWXXpqN06adGY2CLoF2i3QLZjKBKXMrBz8zb7b6FSK3KEpOfOFmJuIE//sYnh8dl/erTjv3OYDIR+/m3P6+QgghMicrgXbChAnZOG36JeLSw9ANRVFQdAtmJJztQsiJT4yDmWmAIu8D0QdDfai6mRvbioVffwWjPYDrkB9mu5R+s+24M6rbQ3ixLA4lhBBDhXwK7YfwopdSC+KILdP01Dy5rMqNHpDBzDQMeR+I3tN1TDM3httmjWHkxJeigSf+hfPAQ7O/zVoaKLqOY6/9CL+2INulCCGEGCDZ/5c0j5mmjLXcGkVVsr/6rQL5mGjzavGfZBI0LdtViDyjWKyQGOJTEowkqNl978TrVhN5+3U8Pzouq3Wkk3PmLMJvvZ6aFiSEEGLQk0ArMkjJ/pDCHJmjNqglkyhZ/lA+FOTVlxw9oFhtmIlsj+DILjORgCxvXRT49wNYt9se65SpWa0jnezT9wYjSfjdxdkuRQghxACQQNsPipIDgS2XmSaoWe7BVlVpowwzk8msfygf7AZbmAVQ7PYcmJKQfr1pKzMeR8niwoJGe4D2px7Fc9wpeb8Y1MZUhxP7HnsTeun5bJcihBBiAEig7QfHPgdkf0jtAOnLB2rTSGb1wxqAouRfoM238GIm4igSaDMm314PPaW6XBCLZbuMrDLjMZwHHpq187c/8ziq3YnrwEOyVkOmuA48lNBrCzFjMuxYCCEGOwm0/aBYrZjJRLbLyF2JBFj+n737jpejKv8H/jkz29vtN50ECJZg6MWCDVAwIoiAooAiTXogJKRAOiQEpAlIF5QmSAQFwldBv36Fnw2VmlATQm56bt2+OzPn/P7Y3JDALVtm9pzZfd6v133pvbl352HPzpl55pzzHJ/cGDQaRXecaVJC65BaTWYBQAtHIPLZQi2COiVyOWjhiJxjWxYSj/4KkZNOKaxnrjHBLx4OGAYyf39RdiiEEEIcRnehFSisAaOEdiCC88IIrd8vNxCmueqG2Y0JjMjn5T+4qDFu/ByUivn8ANj2B1/1uZ+3yGalVRbOvPgXmFs3I/qd2ikGtTMtFEbwsK8g9dwzCH35CNnhEEIIcVDNjtCuXbsWkUgE++2338e+ent7bTkGCwQBo/Qpc/VwsyryeTCfX/66LBetoXXr50Lkcggf9S3ZYdSEljmLXfs5KAcLBsFL2Ku61t4bkc1AizVIOXb8oXsQPvpY6E0tUo5fDaGvTUHmhT+DZ7OyQyGEEOKgmh6hPeigg/CXv/zFsdfXwhHaFmAQIpeVPzoLFPZ4rPe9Lh0m86a8VtRaolYsFghCZNJANCY7lKoTRr6wD20wVPVjZ//7L+Re+Q9arlhS9WNXU/ALXwEAZP72F4QPP1puMIQQQhxT0wmt07RYDDybgRBC/kikg8oqCJVJQwsEHYimRLruihFaNyc0PJ2C3tgkOwzXcXOb20ULhSHSadlh2KaUNuWpFFg4LOXa0Xff7QgdOQXe8btX/djVpAWCCH35SKSe/T0ltIQQUsMooR1APB7f5Xu/3w//AKONzOsD83gLo5EqJG8K4ekUWChs++vmcjnkdhoVTyQSAIZoM1YYoW2ZsxhdS+baHo8d3JzY8GwWEAJskHWAg7VX0tp11NzHGPyyt3iqEtXbO2cYyO20nU4yU5gSXGy/WIrIt7+L+IP3VPQabsWTCWhhe9bPDnaeDfi7K19D9h8vYtTDT9lybNWFj/kOtl58JqzuLujNtTu9mhBC6lnNrqEFgC1btuDUU0/FIYccgk984hP4wQ9+gNdff33Yvxs3bhwaGhp2fC1dunTQ39UiEYhksuTYVL+prZRIJRE53v5iI0uXLt2lbSZNmgRg8DZjHh1C4a2V3P454N2d0BqbBh1lGqy9PvfaWkx+ec2Or59v6q5m2FK4ZX3s9ct/hzGnnrnj66CpMwCU1i8WS29pAy+x/3TDe1gMnkwgcuyJtrzWYOfZQPruux3BLx0B316fsuXYqgsc9Fno7SOQevZ3skMhhBDikJododV1HR6PB5deeikOPPBAxONxXHTRRTj00EPxf//3fzj44IMH/duOjg7EYh+u6RpqFEKLxMATceitbbbGr4qyphsLAZ5MOPKezJ49G9OmTdvx/YYNGzBp0qTB20zTAa7mlGO335gLy4SxvgNNF18+6O8M1l5/32cCIvqHz9N8NGVfGZedcBwuPPbDvVE3dXXjoKkzSuoXi8UiUUAI8FwWmj9Q8evJVGo783gf9BGjbDn2YOfZR+XffQuZ/3seI+9/3JbjugHTNESO+Q6Sv38c0R/8uKaXBxFCSL2q2YR23Lhxu4zGxmIx3HHHHVixYgXmzJmD5557btC/jcViu9y4DSVy4inou+dWlLPphMrTYCsh0mnA44XmQLGTj05z7J8GOVibMa2QNAnFCkO5LcnZGc9lwft6Ya7vgN7UDM+oMYP+7mDtFdE1RPXanSDi5vb1e73w77SNTiJdmHJcSr9YLMYYtFgDeF8vtPaRtr62ykQuB1gWtIZGW15vsPPso3pvvxGBz38Z/r33teW4bhH+5vHou/sW5Fe9Dv/e+8gOhxBCiM1qNqEdSDAYxOTJk/GPf/zDttf0jBgJnkxAWCaYXltvZ7k35TzeC12lqrcaU2qU1m3JjhAcvK8PVncnrO4uwDCgNTQi9uNz4d19ouzwlOK2tlVB5NvfRerp3wIlJLSqPQwstd2tvh5oDY1VHS3MvvofZF78X4x6sP6m3nrHjEPgoM8h+cSjlNASQkgNqq0MbCd9fX0IBoPw+Xy7/FzXdViWZdtxmNcHLRoD7+2F3tJa8t+rdmNmB6unG9GTTpUdxg5M0wAF1tG6LdkRnMPctAHmhg4wjxd6axuaLp0DvaWNpu3txG3tqhrPmN1g9XTXfLX4nfGebkS+/b2qHU8Igd5bf4rQ14+B7xOfrtpxVRI96RR0zrsMjRdOp6rshBBSY2p2zt/UqVOxfPnyXX6Wz+fx+uuv44ADDrD1WHpzS2HkqoaUe5MuOAfv64Fn3HibI6qApkNw+x5ilMNtSQ9PJZF7+d/gPd1ouuxKtC6+Hk1TZ8HT2l43Scdw3FLkSXV6YxOgaRApdxaHKjUOIQSsnu6q9pGZ//cX5F5/BY3nTq3aMVUT/PKR0JpbkXzyMdmhEEIIsVnNJrQAcN1112HTpk0AAMuyMGPGDGzbtg3z58+39TixH54Dq7sTosz9TlW5MbMD7+0BC4YdWT9bNqZJnXLspvYVQsDcuB6511+BZ+w4NF+5BJ7WdtlhKaM/iXVTm7qB3tIGq6tTdhhVwZOJwoyHKi3LEEYevT9bhuh3ToZ3rEIPGquM6TqiJ52KxG8ehDCN4f+AEEKIa9RsQnvZZZfh85//PI4++mjsu+++2G233bBy5Uo8//zz+NrXvmbrsfSGRjCvF7yv19bXlaWSm3Vr2xbobYolQBoDJBWFUj3xEUJAGHnwZBLmpg3IvfxvmFu3oHn2YjSeeymNxm5HSayzYqeeCWvb1pL/TnablHN8q3NrVaviJx59AFZ3Nxp+Ur+js/0ix50EHu9D+s9/lB0KIYQQG9XsGtrJkyfj1ltvrdrx9LYRhWSuzLU5qqylreQGUVgmrO5OtFw808aIKsckjdDKvtkejDBNmJs2wOrcBp5KgukamM8PFo6g4awL4dltAiWy26nahrVGbx8JwS3wZBJaJFLS38rqO8vd0szathXN0+c5ENHHmV3bkLz7FjRNnQXdporKbqbHGhA57iT03Xc7Qkd+Y0cVfEIIIe5WswlttTWccT66Fs2E2GMvMF0v6zVkJ7WV3rxbW7dAa2iCFgrbFJFNNK2q2/aomgQJIWBt3Qzj/dXQmprReO4l0NtHgHnK2XSqdqnafrWMMQZP2wiY2zbDF6ndytk83gem69CaW6pyvN6f34Dg+N0ROe6kqhzPDWI/OgeJJ36N9F/+iPDhR8sOhxBCiA3o8aRNtEgUWrQBVte2il5H1s10pccVQsDcsB4NPz7PpohsVMUqx6omQzybRX7VazA3rEfTjHlombUIntFjKZndCU0rlit2xnmwtm4p6+FTtdut3ONZmzdCHzGqajMg0v/7RzTPXFj2Q9Za5GkbgejxJ6Pv7lshFKh+TwghpHKU0NoodtpZMDduqPh13HJztjPe3QnoGvRRY2yIyF7V2rZHxWSIZzPIr3kXuZdfghZtQMuCa6nI006oyNPghGXB6u5Cft37VTme3tgMFgiAd5VXMb5abVh2BXjDgNXViYazLrQ5osFFv/9j2nd1ALEfnQNj3ftIP/+s7FAIIYTYgBJaG3l22x0inwNPxCt+LdVvznYmhIDxwVo0nHG+mmsvNQ2wce/hgaiUEAkhYHV3IbfyVeRe/jcAhpb5y9A0dRaN1GxHSezHiXweVncXjLVrkH31v8j840UYHR9UdRQ/dsqZMDeX/1DQ6Tat5PXNzRuhtbRWtQJ844/Ortqx3MTTNgKxk09Hzy3XgmezssMhhBBSIVpDayOmafCMHgtj/Tr4P/2Zil/P6TW1dt38Wdu2AIzBM34PW17Pdprm2D60qiRFQgiIbAZWVyeszZsAjSF26lnw7rkXTSveTpW2kkFwDpHLQRh5iHwOyOch8nmIXBY8l4VIpwHBdyydaDjrAnhGjgbzeJFdvx5YcE1V4vTuPhE8mQRPp8pei+9Uv1lRwTxuwdy4vmrFoPoxn7+qx3OThjPOQ2rFk4g/cDcaz75IdjiEEEIqQAmtzRrPvQSdV04DT6ehhSp/Et9/E2XnDZqdN/bCsmCsXYOmi2eqOTqLwv6DTkw5lp0g8XSqUKm4rxc8mQB0HXpjExovnA595Ghl26PaZLdTNQluQaTT4MkEeCoFkU6CZzIQ+TyYz1eoZu3zgXl9YD4fIsd/DywSK2w9Fo5I/8wwjweeUWNgrl8H3yc+Xfbr2J3UVlwwb8tmaOFIVbfrIUPTwhE0XjQD3UvmIvyNY+t6j15CCHE7Smhtxry+wijtuvfh/9Tetr2uHYmtEzf2ZscH0KIxeBRcO7uDrkPYPOVYVpIkhADv7YHRsRYik4He2o7Yj34Cva1dverSktVyIiu4BZHJgKfTEJk0eDoFkUqCZzPQAiGwSAThb3wbenML9MamQrLqki1KGs69BF3zLgPP7g4tECj7dexKaisumMctGB0foGnq7IpjIfYKf+M4pJ55Al0LZ2HEHQ/SkgxCCHEpSmgd0Hjepeicexl4MgEtErX1tctJbJ26seepFMzNG9EyrzrTEcvFNN3WNbSyEiUrEYex5j3AMBD78bnw7j6RbsAGUEuJrOC8MOKaSmxPWlOFJDafgxYIggWDYKEwoif8AHpzK7TGJjCPu7t1LRCEPnIMzPUfwDfxkxW9ViUPAu36HJmbNkILh+EZOcqW1yP2YYyhZe412HjyFMQfvg8Np50lOyRCCCFlcPedj6KYzw/v+N2RX/Me/JP3c2Qan+ybdiE48u+8Ce/43aGFI1JjGU7w8KOQ/vMfKh6xkfWe80waxgfvg/f1wjt+dzT85BLXjLZVk+xzohLCsiBy2cKoayYNkU6BpwprSZk/AC0cgRaOIHra2dCamqFFYzX9GWg8fxq65k0HHzPOliJKO382huoD7P4MCcOA2fEBmmcutPV1iX08I0eh+fIF6Fo0C/7J+yGw30GyQyKEEFIiSmgd0nDupeiaPx3Wti3wtI+UHY7tzA/Wgnm9aDhvmuxQhsV8fsA0K3qNaidLQgjweB/MjevBe3vgGTMOzdPngnl9VY3DDdyUyArTAE8ktq9xTUJkMxDZLIRpFhLXYBAsGEL42JMK04VbWuuyqJcWCMIzdjcY76+Gf9JkW1+7mp8XY9370FvaoDe3VO2YpHSRKcch9/rL2DbzIoz61W/hGUGj6YQQ4iaU0DqEaRoaL7ocPddfVdhf0Vc7iYjV2wNzyya0zLtGehGZYjCfD8IqP6Gt5g2wEAK8uwvGuvchLAve0WPRPGMeJbIDcEMiK7gF3tcHq7cbvKcHIpsGi8SgRaKIfuf70BqaoMViYMGQK86lamo8/zJ0zr0MVk839KZm2eGUjCcTsLZtRcvC62SHQorQPG0OjDXvYsuFp2PkXQ9Db6KHEIQQ4haU0DrI0z4SnhGjkH/3LfgmTa6JG1aezSL/9io0XnS5a4oQMa8PKDOhrVbSJIQA7+uF8cEawLQK26aM36MmPjN2UjmJFUIUpg2nkoWtZ+K94Ik4WCgCvakZjRdMh97WTuuei8Q8HjSeewl677wZgQMOdtX7JjhH/p234N19T2iBoOxwSBGY14f26+/AlvN/hC0XnI4Rt9wHvaVVdliEEEKKQAmtwxqnzkLXwpkwN66Hd8w42eFURFgm8qteh2fMOFf9tzCfD8IsvShUNZInwTmszm0wN3RAWCYaTj8X3omfpET2I1RKZAW3ClvibF/jKjJpiEwWPJcB8/qghULQwlHEfnRuYS9XP+0FWi7v+N2hRSIwPngfvj0myg6naGbHB2A+Hxp+consUEgJtEgU7bf8AlunnolNZ5yE9hvvgm+PvWSHRQghZBiU0DqM6TqaZ8xD11VzCvsQNjbJDqksQnDk31oFLRJB44UzZIdTEubzlzxC63QCJQwD5uaNMDeuBwuG0HDGeTQiOwAVElnBOXi8D7y3B1ZfD3gyCS0YhBaOIPSNb0NvaIAWayxMHa7D9a5Oa5p2BboWXA5r+xZEqrMScZibNqBl7lI6n11Ib2jEiDseRNe86dj845PQfNkVCH/rRGpLQghRWO2WyVSIFo2haeos5N9aCZ5O7fJvOcPAkl8/jpxhSIpueEIIGO+8BQiBpunzyrqw53I5LFiwALlczoEIh8b8fogBikLluMCNG7qQ42KXnzuVRAnLhLltK3JvvoHsS3+HSKfQPH0eWucvg3fCnsjn89Leo4HIarOWOYvRMmcxIjPmSTk3hBDgqRSMDR3IrXwV2X+8AOP91YAQaDjrIrQtvRmti29A86xFCOx7ALwT9oTe3IK8xZVqv6HIPB9LpQWCaLxwBvJvr4LIfxivin2nMA3k31yJxnMvKXrLNje1xUCqGX+1jqUFgmi95hZEzroQW66+EpvP/yHyb69y9JiEEELKx4QQYvhfqw/xeBwNDQ3o6+tDLBaz/fV777wJxto18O+z/451VfF0GmNOPRMbHrwXsVDl21PYTQgB4723wdNptFx5ddkjUE69t+vXr8e4ceOGfF1h5LFt9lSEvvBlAB9u25GwOCa/vAav778HovqHz3bsTGj7izyZWzaB93ZDizUgcuKp8O4+EVogsMvvOv35K5UT8fS3VzGf92qeGzyTAe/rgdXbA97bA2g69KYmRE44BZ4x4z7WVgPGq1j7DaWUWIs5x6qh56alsHq74Z+8P5imKdd3CiGQX/U6WCCI5unFbw/m5HlWjTar5ue+2udYPB7H/u2teOEnP4Tx978i+IUvI3rSaQh89jDbt81yU/9BCCGqoSnHVdT4k0vQe9tPkXvtZfgn72fL/opOKhQ2eRMin0PLFVe5dzqlxwsIAcEtMG3owjJ2JbNCcFhbt8BY9wGYx4PYKT+Gd8JEWk+pCJHPb09eu2H19gKCQ2toRPQ7J8Mzehy0KN1QqqZx6ix0L7kSxntvw7vXp2SH8zHmB+9DmCaaL50jOxRiozU5A5HFN8D/wWrEH/0Vtk77CbSGBoS+eDgCh3wevkmT4RmzG01JJoQQiSihrbLGC6aj9/YbkHvtZfg+/ZlCsqWgwtS5NwBNR8sV5Y/MqoAxVigMlTfAAoMntHYks8KyYG3dDGP9OjC/H00XToc+agzd7EgmLHPX7XPyWWixRkSO/x48o8dCa2ymNlIcYwzNl89H1+I5MD94H1Bof29z0waY27YUtjJzUTVmUjz/5P3RNnl/WNOuRObFPyP91z+j+9qF4H290GIN8IwdD8/osfCMHgOtsQlatAF6rAEsGgPz+cG8XjCfD8zrK2zj5/UWHrBqDGAaeDIp+z+REEJcixLanVhWoRJuPB539DjaKWdB6/gAPY8/hNzoQrXgRDrj6DFLZXV1wtI9aLpgxvbYKouv/z21+73t7e0t6nXTTS3IZ7JgnMN7yWx0X381khYHACQtjubLrkA8na44HqPjA4hkErHTz4Nn5GikASCRKOpvnXqPyuVEPP3tVcznvf937Dg3cm+8CoBBa2xE9MfnQW9pA9M05ADkgKLbaCiqtd9QSom12HOsWjwXTEfvbdcjGy6sUZXddwohYGzZgobzLkMybwD50tb0OnmeVaPNqvm5r/Y5NuDxPF7gK0ch8JWj4BcC5sYO5N95G+amDuQ2bYS58nXwvj7wZBw8kQBP9gF8+JVdcaNQ56G7u9uR/xZiLyEEEokERo8eDc3m6eeEkNLRGtqdvPTSSzjkkENkh0EIIYQQQhTX0dGBsWPHyg6DkLpHI7Q7mTixsM/hypUrHSvKIIRA99VXwL/PAWBeL5KZDA6aOgP/vvk6RIJBR45ZCmPzRohMBk3nT7P1dROJBCZNmoRVq1YhGi2u+mcxOjo68PnPf37QNsu99jIy/3wR3rG7AQB6bv3pjn9LWRxHrlyH5/feDeHtRaGaLpxeUTz5d95E9PjvwbPb7iX/rVPvUbmciKe/vZ6bNA4Rjz7k++3UuZH/4H0gk0LjxbNsnR6qWvsNpZRYhzvHqq3npiXQ20chEwgq03fm33oD0e/9CJ7Rpd/YOnmeldNmPTdfA62lFZ6WtqJ+v5rXsMGO5bbr1kA2bNiAz372s+jo6FDiPCNDi8fjGDdunPJ9PSH1ghLanejbb27Hjh3r2AXF6utFoKkRwVGF9V/901xHtTQrUamTB/zIvfkG2mx+4tg/ZWvMmDGOvLeDtVl2ywZkmhrhbW0BAAR8H37kE9unHI/weXZUOW7Z/nvlysdHgv37b2j6/BdL/lun36NSORnPSL8XUV0b8v126twQLU2FarSP3oemGfNtWzurWvsNpZxYnewXS+H3euAfOxbJ7ZOLVOg78yNHIRwKwF9Gv+nk56bUNhOmCT8sBPbYE8xbXN2Eal7DBjsWD/iQf3sVWl123RpILBZT4jwjxaHaC4SogSb+V5nVtQ1aOCI7jEGxcBiwTPBk5WsKVZD565+AEi44/Vv6lEsLh8FTVNyjWJW+3+VgTIPvU3uDJxPovfmaqh+flE/kcxCGAVbENkrVxEIhpJ79newwKmZu2gAWjhadzKqChcPg2SyES/fyJYQQUhlKaKss9eRj0CLqTlFhjEGLNcDctEF2KLYQQpSU0FZKi8TAE2oUzyGDY7oH/r33hbl1C3pu++nwf0CUYPX2QAuFlBsV0YIh8HRKdhgVSzz6K+hNzbLDKBljGrRoFOa2zbJDIYQQIgEltFXGU0lokQ9HaP1eL2Z/9wT4FXoirjU2IfnbR2x9Tb/fj/nz58Nf9X1YB6955mMMU0c1wWfjzTELhQDTLGuUVt57NLBqxTPYKK3T5wbz+eCfvC/M9evQe8eNFb+eau03FDfFujPe0w22ff9ulfpOLRyGSJWX0KrUFrynG3pzacsuqtkOQx1LjzYg+fjD9h5PobYhhBAyOKpyvJN4PI6Ghgb09fU5toZl28wL4d/vIGiKTZnbGU8lkVv1BtqW3CQ7lGGtX78e48aNG7TNuq9fDObz7ygKVcwU10r3o829+QaiJ/wAvk9Oquh1alF/e72+/x471i33s2Mf4HLwdAq511+Bd8+90Hj2xVJiUNlw51g1dd9wFZjugXe3CVLj+CghBLL/eAEtC38KLSi/FkI5bcaTCXQtmoXAZw9TbgS8GFZXJ8yN69Eyz53LCFQ6z8jwqnG/SAgpHo3QVhFPpwDBwRR/2stCYcDM18QUOghUdcoxAOjNLUg8/lBVj0nKp4XC8O+9D4z33oHxwfuywyFDEOl0YRaEYhhjYKEIrO4u2aGUzdzQAa2xyZXJLABoscJyD3pGTwgh9YcS2iqyujoLBTcUv2HYsY5280bZodig+jc3enMLeG8PhGlU/dhuJqNAVD8tEoVv733Q+/PrYXR8IC0OMjSeTkELhWWHMSAtHAHv7pQdRtkSv33Eletn+zGvD/D6wHu6ZYciDU8lYaxbC3PbFkrsCSF1hRLaKko+8WulC0LtTIs1IvnEr2WHUTnxYVn9aiVMzOsrFCihxMhV9GgMvkmT0XvrdZTUKkiYJkQuB6bAft0D0cJhpFY8KTuMsgghwHt7oDU2yQ6lIlosBnNrfRWGEqaBxOMPYdOpx6HjK/tj4wlfw4Yph2H91w9F5+LZyK16XXaIhBDiOEpoq4gnE7sUhFKZFo2Bx2uhWm/pT6ntSHz1thGIP3xfxa9Tb2SO0gKAHmv4MKldu0ZqLGRXvK8XWjAIxtS8bLFwBDzpzi27eFcnoHugBdR8WFAsLRpD6ve/kR1G1RhrV2PTD7+D3ttvROALX8bIXzyGMStexOjf/A+aLp0Da8smbD79BGy7/AKY27bIDpcQQhyj5p1BjeLJJLSwS0Zoo1HwVBLCNGWHUhkJa2gBQG9tK0w7pn0RXUePNcC/9z7ovf0G9N55s+xwyHZWT3dhfb+itHAEPJOCsCzZoZQs/sh9rp5u3E+L1s+2aZl/vohNp58Az6gxGP34H9B03jT4J+8PT9sIeCfsiciUb2PErfdj5P3LYW7bgk2nHIvMP16QHTYhhDiCEtoqEfkcRD4HFnLHE3Cme6AFQ7C6tskOpTKCS0lomccLrbEJ+TXvVv3Ybid7lBYo3Bj7J+8H4/33aJ9aRaSeelzZ9bMAwHQdmj8I3tsjO5SS8Z5u6C6fbgz0P1RI13z9gszf/oqtl56D6AmnoO26n0NvGnyrJf+kyRh59yOIfOsEbJ16FvoeuLuKkRJCSHVQQlslVuc2aOGIstPlBqJFY7BomlLZPCNGIfHrX8oOw5WUSGrDEfj3PQDmxvXoueFqKrIiGU+nlKxwvDMWicDq3Co7jJII0wBP9Ll+/SwAME2DForA6nRvca7h5N97B9tmX4zYaWeh8cLpYNrw9xTM40XTRZejdclN6P35jei94ybqzwghNcU92ZXLJZY/DC3qjunG/bRoFMmnlssOw7W05maIXBZWj3u38qh3WiCIwL4HwIr3onvp3Jof+VEZT6WghdSuQaBFokg+9bjsMEpibtwAFomCeTyyQ7GFFo3C2labhaF4MoFt089F8PNfQuO5l5a8Y0L4iG+g/fo7EH/wHvT+/AaHoiSEkOqjhLZKeCLumgrH/QrrkRKyw6iMEGVNObZjhJAxDZ72kej7xc8rfq16pMIoLVCoWu2fvB/AGLoWzQJPuvyccCFhmhD5rLIVjvtpkajrPh+JR3855JRVt9GiMSSf/q3sMBzRfeMSQNfRMu+asrf/C37+S2i7/k7EH7wX8UfutzdAQgiRpG4S2i9+8YtgjGHt2rVSjl+ocByTcuxysVAIMHKuLmwke1KVPnI0rK1baGTP5Zimw/fJSdBb29F11RyYmzfJDqmuWL3d0AKhoqZXyqSFIxCppGumcwohYHV3QW9plR2Kbdz4UKEY6Rf/F6mnf4vW+ddCC1Y29T546BfQuvBa9Ny0FKnnVtgUISGEyKP23YFNli9fjhdffFHa8Xk2Cxh55dd/fRRjGlg4SuX+K6AFg9CiURir35EdiiupMkoLFPYz9o4bD99en0LPjVej97afuiZxcTve3QUWVrcgVD/m9QIeL3i8T3YoRbG6tgFMU7rYVqlYKASRKxRhrBU8m0X3tQsR+8GP4d9nf1teM/z1Y9B00eXoWjgT+bdX2vKahBAiS80ntPl8HrNmzcKUKVOkxWB1bimsUZJQbbdSWiyG5PKHZYdRvjKnHNvJM3oc4g/cQ8lPjdCbWwrFojZtRM+y+TT6XgXJp5e7JunSwhHXVIdPPHAP9NY22WHYijFtexvUTmGoxMO/APJ5NJx1oa2vGz3lDIQOPwpbp59PtR4IIa5W8wntbbfdhoMPPhgHH3ywtBiSyx+BFnXXdON+WiRaN/v6OUVrbAJ0HcZq2sKnHCqN0vbTgiH49zsQQghaV1sFIpWC5oIRWqDQZ6Z+9xvZYQxLCAGzc2vNJbRAf2Eod1WbHoy5bQv67r8TjedPgxa2tygaYwzNc66C3tSMzrmXQXBu6+sTQki11HRC293djeuuuw5Lly6VGocbC0L106IN4Im4y0cXyxuhtSuRYozBu/tE9N17K43mlUnFpJbp29fVto1A1+LZMDZ0yA6pZhW27FG7wnE/LRwBT6n/gIN3dwGcu/baNBQtEkXymdooDBW//054xu6G8DHfceT1tUAArUtuRu6NVxH/5V2OHIMQQpxWG3X6B7Fo0SKceuqpGD9+fEl/F4/vOiLp9/vh9/vLjoMnE/DuuVfZfy8T2/7fLVJJMAVufHK5HHI7FalKbK/CPGibKZKI6w2N0GIN6PnZMjRPu1J2OFUzWHslrV1HAnyMwa+5b0o+YwzesbtBi0TRe8u18IwZh8YLZ7hyeUG/ks8xh4l8DsIwwAIBx49lBxaOgCeTVT1mOW0W/+Wd0NvaXf1ZHYwWjsLsWCc7jCEN1mY7M7dtQeLJR9F21Y2OFkTzjt0NLVcuQeeVl8J/wMEI7HugY8cihBAn1OwI7bvvvovHHnsMV1xxRcl/O27cODQ0NOz4qmSEl6dTAOdgfnfcjH0UYwxaNAZzixr7+i1dunSXtpk0aRKAYdpMkfs13557wdq6Bcb6D2SHUjWDtdfnXluLyS+v2fH1803dw76WiqO0/fTGJvj3PRDWtq3oXnKlqwvSlHWOOcjq6YYWCrkm8WKBAGBZ4NlM1Y5Zapv1Tzf2tI2oWozVxMIh8FwWwsjLDmVQg7XZzuK/uhve8Xsg+OUjHY8nfOQ3EDn2RHTNm164byGEEBep2YR25syZmDVrFhoaGkr+246ODvT19e34mj17dtlxWJ3boLm0IFQ/LdaA5JO/lh0GAGD27Nm7tM2qVasADNFmQkCVjJZ5ffB9am/03nJd3RTgGKy9/r7PBLy+/x47vs4f1Sw50sppgQD8++4P5vOjc8HlMDvduYav5HPMYVZ3l+1rB53EGAMLh6talKjUNrO2bQEYA3PR+1qKQmGo6rZBqQZrs35W5zYkn/g1Gs48v2rbVTVdUvh89PxsWVWORwghdqnJhPaFF17AG2+8gfPOO6+sv4/FYrt8VTKtLvnEr8Ei7r5p0KIxZQpD+f3+XdomGi1Mg7azzXZm96ig3tAI7x4T0X3tQlh9vba+tooGa6+IriG601ex041VHqUFtu9XO/ET8E7YAz3XLULPLde6bv15tc+x4aSe+S2YSyoc99MiUfAqJlOltln8V3fB0zbC1Q9ah6OFo7AUfqg0WJv1i//mAXhGj0Xoq0dVLSYtFEbL/GVIPvEoMn9/oWrHJYSQStVkQvvcc8/BsiwcfPDB2G+//bDffvvhjjvuAABMmTIF++23H1asqM5m4jyVdNXowkAKG9Un3VsBUbF7Nk/7SHh3m4DupXPrIqm1m+pJLQB42kbAv9+BsLo60bVwJqzeHtkhuRZPpVzXh2rhCFIrnpAdxoAE57C2bYXeXpvTjftpkQhSz6jZBsMRuRySv/01oif/qGqjs/0CBxyC6Mmno+uq2co8yCaEkOHUZEK7aNEirF69Gq+88sqOr3PPPRcAsGLFCrzyyitV25eWJ5PQwvKLKVWCeTzQAgFY3epO3xqUEGCqZbQAPCNHwzt+d3RfM4+2fKlRWiAI/z77Q29tQ/eSK9Fz01Kqcl0iIQREOuWaPWj7FSodV7cwVLHMjR1ggQC0YEh2KI4qPIh1Z9+a+sNTgGUh/I3jpBy/afsWQd3XXyXl+IQQUqqaTGhVIUwDIp8FCwVlh1IxLRqDpUhhqFrhGTkantFj0X3NfFcXEZLBDaO0wPYqyGPGwX/AweDpFDrnXob8e++4bhqyLCKTLqyD9/lkh1ISFg6DZ9IQliU7lI9JPHgv9BotBrWzHW3gsodIQgjEH/0VIsedJO2hA/P70TJ3KVLP/g6Zv/1VSgyEEFKKmk9oV6xYMeCU42qwenugBUJgzLm3uWvJ3B1fTtKiMaSeetzRYzimwgFaJ99bz5hx0GIN6L5uESU5NUzzB+D/9Gfg++QkxH91J7oWzIC5aYPssJRndXeBhcOuW+vJNB1aIAjeM3z17moSpgmruxOetnbZoTiOaTq0oNqFoQaSe+2/MN57G9GTTpUah3/y/oie/CN0LZ2r7GwDQgjpV9P70AKFBLZa04s/ivd0g4WdmSo3UJLV/7OWOYttP54WbYCxocP213WeOlWOB8IYg3fPvZB79T/ou+NGNJ43TXZIrtG1ZK4jn3Un6Q2N0PY7CNa2Lej52TJokSgaL5wBvaFRdmhK4j1d0EL2rp8d7AGV3Z8lFo7A6toKvbXN1tethLn+A7BwFMxXnYJesmnRQmEoz4hRskMpWvK3v0bkS0fAM3qs7FDQeN6lyPz1T+i59adomblAdjiEEDKomh+hlSn51HJH1n4NN2LoxIgtC4cgcjmIHE2NtRvTNPg+8WkYa9eAZ9Kyw3EVt0w93hljDJ72kQgcdCi0aAzdV1+B7uuvomnnA0iteBKajQ8Fh/q82P1Z0iJRJH+/3NbXrFT8kfvrYnS2nxaJIvWUWm0wnPQL/4voiafIDgNAoQ5Ay5VLkFz+MLL/+afscAghZFCU0DpIpJO23owBpd102XmDxpgGLRJ1396aAoAN0xUdn9IdjkBvH4neW6519Di1yI1JLVCYEukdNx6BAw8BTAOd86aj944baer5Tng6ZduWPcV8Tux8GKhFY0oVJRKWBd7TBb2lVXYoVVPYck6dNiiG3j4CgYM/JzuMHQIHHorId05G19VXgGczssMhhJAB1fyUY5l4yr6bMUD+jbsWjSH5+ENomjpLahylcU9y4N1tPLL//ies3m7ojc2ywyFVwnx++D7xaViJOIz33kHXwplomjoTelOL7NCk2lHh2IaHgjL6zv5Kx4Lzqm+9MhBz03qwYFjKdOPh3n+nlg6wUBg8l4XI51wzzTo85TglPi87a7pwBjae/E303Xmzy67/hJB6oVavWUNEPgdhGGABeyocl3tDZueNnBaN0r50DmIeLzxjxqH39htlh+I6sh/22EGPxuDf70B42trRfc38wjY/ClbJrRaRSgK6B8zjrfqx7fg8MY8HzOcHV2QP4sQjv6z66GyxI95OFTZkmgYtHIHVuc3213ZK5OhjZYfwMVokipbZixF/+D7kVr4qOxxCCPkYSmgdYvV0QwuFbKnOWemFvlan0BXFpinHQHWSJs/oMeC9PbC6uxw/Vq2phaSWMQbP6LHw738QeDKBrvnTYW7ZJDssKazuLltqEMh8GKhFIrAUWKYhhIDV3QW9uXqj/uW8f06cw1o0isTyh21/Xad42kfKDmFAwS98GeFvHIeuRbNpvT8hRDmU0DqkkNA6U+G4HLaMOPgDgBDuS2pdhOkeeMbuht7bb5AdCpFI8wfgmzQZnt0moOeGq9F9/WIIIy87rKqyerorXrIh+2GgFo0h+Xv5253xvl4AwtYlMIOpdLTV9uJc0RjNLLJJ06VzYPX2oO++O2SHQgghu6CE1iGpZ34LFq58uwmVRp0YY9CiMZhbNssOpaZ5Ro0BT8TrdmSuEiqdL5VijMHTNgKBAw+BMAx0zpsBY/062WFVTfp/fgctFJIdRkW0cESJB4DmhnXQG5sc38/XrvPP1qUykagSbVAL9IZGtMxcgL777kD+nTdlh0MIITtQQusQkUopNUIL2DSFLtaA5JO/tiGaarG3KFQ1Eiam6/BO2AO9t10Pwbnjx6s1tZTUAgDz+uD/5CT4Jn4Cvbf9FF3XzKuLaqOVVjhWIbnSIhGIVFJ65erkE49Cc7jQnKrnHQuGACNPW87ZJHT4UQh95Uh0XTUHwjRlh0MIIQAooXWMHdtNqHiDoEVj4HGavuU0vX0koGvo/fn1skNxJRXPnUrpzS0IHHgImNeLrvkzanqLn0KF47TrR2iZ1wfoeqHAlSSCc/B4L/TGRseO4cT5ZtdrMsbAwhFYXe4pDKW65hnzYW5cj/hD98oOhRBCAFBC6wiRz0GYJphfvW0CKl8TFi1sReGmJ7MOT7NzAmMMvr0+DbPjA/ft/auIWkxqme6Bb4+94P/MvjA3bkDXwpmwujplh2U7kc0AbHtCqICKRmlDYalF3qzuTjCv37Fta5w8z2wraBiOUkJrI72lFU3TrkTvXT+DsXa17HAIIYQSWidYvT3QgvZUOFYN0z3QgiHX3Bw4MYBVrURJCwbhnfgJ9Fx/Fa0BK1MtJrVAYV2gf78D4Gkfge5rF6D7p4tqakol7+stTBUtk0rtzkJhWD3d0o6f+PUvoTk4OusGWjiM1IonZYdRU8LfOBbBQ76Aznkz6q5gHSFEPZTQOqBwM1bZ/rMqP/XWYg1IPPaATdGQoXha2+EZPRbdS+eBZ9KywyEKYUyDZ9QYBA48BOACnfMuQ+9tP62JvWt5X+GhYC3QQiGk/+d30o7P+3qhxRocee1qPDiwpUJ/OAIucdp3LWKMoWXuUphbNqH3jptlh0MIqXOU0Dog9cwTNXMzNhAtFgOP98kOo254x+4Gva0d3UuurItiQHZTabTOCczrg2+vT8L/mX1hdXWic+5lyK9+19Xra1PPPFn2CK1T7V3u67JgCDwj57wVQoDH+6DFGm1/bTedV1o4DJ5OUZE9m+nNLWidvwzxB+9B5l9/kx0OIaSOUULrAJ5JVzRCq/pTby3aUPf7+lX7Zs6z2wToTS3ovvoK8Gy2qseuBW66+S6XFonCP3k/+CZ+AvFf3YmuuZchv8adiS3PZsAClc1yUQULBiEkza7g8T5A06AFAlKOb5dKz1+me8C8fnoQ64Dg57+E6Mk/QteCy2H1yptaTwipb8omtKZp4je/+Q3++Mc/4rXXXpMdTklENlPR+i/VsUAAEMIl6zrddzM/EMYYPBP2gNbUjO4lV0Dka2e9ZLXUQ1ILAHpTM/z7Hgjv7nsi/ss70Xnlpej9+fWuWucmMhloFS7bUAXz+SEsU8r7b23d7Mh0YzeeS1o4DE4JlyOaLrgMWmMTuhbPceUDNEKI+ymb0Ho8Hpx00kkIhUI45phjZIdTEpHNuuKJeNlT6BiDFo3B3LzJ5ojcpdo3dYwxeCfsCS0aQ9fSee6qNK0IN96Il4MxBr25Bf59D4Rvr0/B6ulG5xWXoHvZfBgbOpSeeimEgMiWN8vF6fYt5/UZY9ACQfC+6o8OJp95wvaEVtY5VHHth1AYyd/9xqZoyM6Yz4+2q29E9p//D8nlD8sOhxBSh5RNaPsddthhOP/882WHUTRh5AtFWcrcbsItN9xarAHJJx+VHUbdYYzBu+cnwLxedF+7gJ6Gl8Et55gdGGPQGxrhnzQZgQMPBQuF0XfnTeicfXEhuX3/PeVG+0UmDWg6mMcrOxTbsEAQVry36scV8T7oDhWEchsWDlNhKAd5d5+IpmlXoOempci/97bscAghdUaZhPaBBwavmtve3l7FSCrD43FogYBrtuwp9+ZeizWAJ2g9kgyMMfg++WmIfB49N1xFSW0Z6imp7cd8fnjH7obA/gfDv9+BYOEI4g/eg84rLkHnwpnoueVaWAqsMeTxvsKyhhrCgiEpdQdEPgcWDtv2erLPm8r2A45ApFI2RkM+KnL89xD84uHYNvNClyxJIoTUCo/sAPrdfffd6OjoGPDfXnzxRZxxxhlVjqg8PBGvuZuxgWiRKHgqBWGaYB5lPkZV17VkLlrmLK76cZmmwz9pMnKvvYzeny1D09RZVY/B7WS1nQq0QBDamHHAmHEQlgXe2wOruxPdV18B5vdDbx+JhrMukrJ0gvf1llUQqlrJVjmfGy0YRPp/fo/Avgc6FNUgx43EwJgyz62lYqEQeC5T99csJzHG0HLlEmz60QnoWjQLrctudc3DfUKIuyl1pTMMY8CvlIueqvJkHMxfG8VMhsJ0HVowBKtrm+xQhlejF1Tm9cL/mX1hbtmMnluulR2OK8kecVIB03XoLa3w7fUpBA79PLwT9gRPxNE1bxq6r1sIq8oji6kVT9bctmeytu5hDfZNN1blXCm79oOmFdYy9/bYHBHZmRaOoO3aW5H5x4uIP3iv7HAIIXVCmceU8+bNw5FHHjngvx122GFVjqZ8qT88VfYIrcxiG+WMVGmxGKxtW+AZMcqBqNxD5kgf8/vhn7wfcq+/jJ6fLUPTxTOlxOFm9TxS+1GMadCbmqE3NUPkczA3bkDvzcuqGgNPp6C3umeZSTFYMAghYQ9pLRqr+jFVxsIRWN2d0FvbZIdS03x77IWWK69G57zpO9bvE0KIk5QZoX3ppZcG/bcjjjiiipFURmSzYP7an3IMFG6Wkk8tlx1G3dMCAQT2OQDW1s3ovp7W1JZDldEnlTCfH94Je8C/7wFVPa7IZKCFShuhrXb7lXo85vNDmNXfukeLRG15nVo5P7RwBMmnHpcdRl0If/0YRE88BdtmXwxz80bZ4RBCapwyCe0TTzyBhx9+GA899BAeeughPPzww7j66qux11574ZxzzpEdXtHcsmWPHbRoTEqhk5JVIcGTfcPH/H749zkAPN6HnmsXKL0ti6pkt6GqqrneUHAOnk3X3D7ejDFowSCsKk93Zbpe1eNVS9nFDMMRCKp0XDVNl8yCd4+9sPWyc8EzadnhEEJqmDIJ7ZIlS/CDH/wAp5xyCk455RRs3boVt99+O2bOnIm77rpLdnhFE7nyRmhl30yXtb9iMAQYBl2oFMG8Xvgn7wuRy6J72XxKassg+zysdzwRB/P6ajIRY6GwK9dv1tI5oUUi4MkkzWKpEubxou2an0EkE+haNIved0KIY5RJaPvXz65cuRKf/exn8de//hUvvfQSzjrrLMmRFU+YxvY9aGtn/8ShMMagRWOwtm6RHYoSVLjxY7oHvr33AQyjsE8tJbUlU6Ed6xXv6YZW4jYzMmsPlEILhpCiJRq2KeshrM8PMEajtFWkNzaj7fo7kfnbX9F3z62ywyGE1ChlElrTNDF//nxMmTIF06dPx29/+1uMGuWuYkM8lQLz+euqTL0WjSH520dkhzGE+mmLfkzT4Zs0Gcjn0HPdQnoqXgZKauVI/u4xsJB9+6aqhIXC4Gn3VOwHavM80MIRWN1dssOoK76Jn0Dr4uvRd+9tSD7zhOxwCCE1SJmEdv/990dHRwdeeeUVnHjiibv827Jl1a2yWS6eTIAF/LLDKFs5Ny+uWUdbJarcADK9kNTyTIaS2jKp0pb1hKdT0Go0odXC7ktoaxGLRJB88lHZYdSd0JeOQPP0uehaPAeZf7wgOxxCSI1RJqGdOHEijjjiCDz77LM7ikP1/+/y5e6YpiVSSVeun61Ef0KrasLEGKpSFGpnqrQn0z3wf2Yf8Ewa3dfMK0yHJyVRpS3rhUgloYUjssMoWimfDxYMQuRzEKbhYET2ccNnv6yHsKEweDLhQDRkONETT0HstLOwbeZFyL+9UnY4hJAaosw+tBdffDG++tWvDvhvbW3u2DMu9ezvoPncO0JbDubzAR4veF8P9MZm2eEMoP6mHO+skNTui/xbq9B11Ww0X74AWo1VkHUa7VNbHcKywLMZsBK27HFD0tWPMQ1aIAje20v7oEqkhaMwO9bJDqNuNZ4/DdbWzdg69WyMuPdReMeMkx0SIaQGKDFCe++996K9vX2Xn/33v/9FLpcDAHz961+XEVbJRC4H5nd3QlvWE++YwoWhJIzQAmrdaDNNh+/Tn4Eea0TXwpkw1q6RHZLrqNSetYr39kDzB8G02qtw3I+FwrB6aP2mTCwUAs9lIExTdih1iTGGliuvhnevT2LrBT+CuXWz7JAIITVAiYT2tNNOw3vvvYd58+bhjTfeAABMmDAB99xzj+TISiPyuUIVxTqjRaJI/u43ssMYRH2P0PZjjME7YQ/4PrU3eu/6GbqumgOrr1d2WK5CSa2zrO5OsBIrHLuNFo4g6YJKx276rJcaK9P6R8rdt4VSrWBeH9qu+zn09pHYcv6PYHV1yg6JEOJySiS0XV1dWLVqFTZs2IApU6bglFNOwY9+9CNs2rSpotd95ZVXcPbZZ+PTn/40Jk+ejEmTJuHiiy/Gtm3bbIp8V+WM0LrpxmEwWqxB3cJQkkZoATXbVm9oRODAg6HFGtB99RXovnYBrScrQdeSuUq2ay1IPrXcVetn+5W0jjYUgqDCUNKxcARWNyVRMmmBINpvvAtaNIotF54Oix4wEEIqID2hffPNN3HooYfixRdfBOccRx11FMLhMGKxGObPn1/Ra5988sno7u7Gv//9b7z++ut47rnn8Mc//hFf+MIXkMlkbPov+JDIu3/KMVDG/orhCHg6qegULiYtoVUV03R4x41H4KDPgnm86Fo0Cz03LaWiUSWgpNZ+Ip0sqcKxG9tAC4XB02nZYQzJje9rqbRQGMmn1R8pr3VaOIIRP/sFmK5j60U/hhXvkx0SIcSlpCe0jz32GP773//imWeewX333Ye7774bd911F7761a8iHq981G/ZsmUIb5/GNmbMGMyYMQPvvvsuVqxYUfFr70xYVqF6pddr6+u6AdN1aMGQmk+8GYOAvIRW5ZtD5vXCu/ue8B9wMHgyga4FM2D1dssOyzVUbls34ql0zU85ZoEgRC6r6MM/9yrnIaxIJR2KhpRCi8bQfut9EJaFLeeeStOPCSFlkZ7QtrS0oLW19WM/P/300/Hkk09W9NqvvfYaJk6cuMvPRo8eDQDo6Rl8eks8Ht/lq7841VBENgPm9YEx6W+pFFo0Bmub84WhcrncLm2TSBSmyw7ZZpIHaFVPfDR/AL5Jk+EZNQbdS+fB6PjAttcerL2SFkdip68cd+couuptW46cYSCeTu/4Sm6fzVJOv1gsYeQhjBxYoPRtz9xkx/pNm9evD3aeJdKZXdoyZ7hjyyCnsXAYPCV36vdgbVaP9MZmjLj9ATC/H5vP+QHMzRtlh0QIcRnp2Vd7ezseffTjm5x7PB5YFU6B9Pl8H/vZO++8A8YYvvSlLw36d+PGjUNDQ8OOr6VLlw57LJ5Jg3k/fjy3KvmJdzRWlWInS5cu3aVtJk2aBGCINitsROt4XG7HGINn1Bj4Jk1G720/Rc9tP7Vlb+HB2utzr63F5JfX7Pj6+Sb3jgzXWlJ7/fLfYcypZ+74OmjqDADl9YvFsnp7oAVDrn0gWOo6WqvP3vWCg51nnzrnwl3a8vrlvxvydWrtszwY5g8A27eJkmWwNqtXekMjRtx6P/T2Edh89vdhrFsrOyRCiIswYcddawWEEDjmmGPQ2tqKCy64AAcffDAYY9i0aRPuvfdeXHnllbYdy7Is7L///jj00ENx9913f+zf4/E4Ghoa0NHRgVgstuPnfr8f/mHWxhofvI/4r+6C/zP7Fh2P6jcPpey9yZNJ5N9aidarb3QwosJT7Z1HhjZs2IBJkyYN2mZdS+dCb26Bp32k9PfbLXuZ8kwG+VWvg0UiaJ52RUWVuwdrr7/vMwER/cPkxccY/Jr7K1K7pY2HkjOMXUbyNnV146CpM8rqF4uVf+9tJB57AP5Pf6ao35d9Lg+k2LbPv78aTNfRNHWWbcce7Dx7665bEQ0Fd/zc7/XCP8SyGBXf12KVeu5lX/0PGs+ZCs/osQ5FNLTB2qyvr2+X86zeiFwO22ZfjNzrL6Ptp7cjsO+BskMaUP/9Yr23FyGqkP44nDGGxx9/HMFgEIcddhii0SgmTJiAww47DKeddpqtx1q8eDG8Xi9uuummIX8vFovt8lXMTRvPpMEGGBGuFywcAs9lIfL2TUMciN/v36VtotEogPLarNrccrOoBYPw73cgmKajc/4M5N95s+yCUYO1V0TXEN3pqxaSWcA9bTwUv9eLWCi04ysSLCRETp5jqad/Cy0Ysu31VKYFg+AZewtDDXaeRUPBXdpyqGTW7cpZRyuz7sNgbVbvmN+PtmtvQ+jIKdhy/g+R+uPTskMihLiA9IQWAILBIO644w588MEHeOihh3DLLbfgtddew/jx4207xn333YfHHnsMzz777I4iUXYSmTRQQ1OOgRKn0TGtcIPQ6cyWSGVjVOW4HEzX4dvrk/B9chLij9yPzisvRc/PlkEYedmhKa8Wktpq45k0WDA4/C/WABYMQThQZb9S9fa51cIRpJ55QnYYZADM40Hz5fPRdMF0dM69DH2/+LktS2AIIbVLiYS236hRo3DcccfhW9/6lq1J5wMPPIDrr78ef/7zn9He3m7b6+4s/adn63qEFiiso00sf1h2GLtQbdzPbTeNekMjAvvsD/+kyeCpJDrnXgZz0wbZYSnPbe0sm8hmwQLuTmiLbXMtGCw8ACVSsXAEnCodK4sxhtgPfoy2a29D3313oGvRLHqgSggZlFIJrRMefPBBLFu2DM8//zxGjhwJAHj66adx11132Xockc/XVFGocmiRCHii8q2WbKXgCK0bkx0tEoX/U3vD94lPo+empTC3bpYdkvK6lsx1ZVvLIHLZQqGeIrj+PfX6AMEhbKwSTQpK+Wxo4TB4OgXBuYMRkUqFvnwkRtz5ELJ/fwFbLj6T9qolhAyophPahx56CGeffTZOP/10PP/883jwwQfx4IMP4qmnnsLGjfaWhReGAVbD65OKoUVi4EnFnngrmNC6md7UDO+en0DPzdfQjWCRXJ+AOUxYFoSRB/PXxwNBxhhYIAgrQTfmMjHdA+YLgNtccZrYzz9pMkbe/zh4bw82/+g7yL/7luyQCCGKqemE9qKLLkI2m8WMGTNw2mmn7fiye3QWAGAYJY3QuuUmt7TtKIKAkVNs5EG1SccFbmn/geitbWA+P4z33pYdimu4ub2dJrZveebWLXvKwQJBcIVGmur186mFw7C6umSHQYrgGTkaI+99FL5Jk7H5xychuWLoLagIIfWlpu8guru7IYQY8GvBggW2HksY+bofoWVMAwvJrRz5MQw0Qmszxhi8Y8Yh/tC9skNxFZqCPDCeSoIpWJXcSSwQBE8kZIdR97RIFKnf/0Z2GKRIWiiM1qtuROMFl6Fr0Sx0X7uQ1tUSQgDUeEJbLUIICNMA6jyhBQrraK0uhSodM6ZsdUQ3JzdaczNEJgOepJvyUrm53Z0g0qmaqT9QbNsyvx9p2o7EESWvo6XCUK7CGEPs+6djxO2/QvrPf8DmM74LY+1q2WERQiSjhNYORh5gGphGb6d6WyGoOeW4n1uTG8Y0aM0tMDo+kB2KK9Fo7Yd4JlN3FeK1QAAip97WPfWGKh27V2D/gzHq4aegt7Zj06nfRmL5w8o+vCaEOI8yMBsUKhzX7uhsaetow+BphbakoCnHjtEbm5B84teyw3A1SmqB9B+fLnqEtlbeL+YPKFZroD4xfwCwTGoLl9KbW9B2w51ounQ2em5cim3TfgKrm9ZEE1KPKKG1gchmwXSP7DCUoIVCEOmUQk9K1R6hBdx7k67FGtTbpsmF3Nr+dhGmWXfLNZjfT0mUAhhj6tV9ICVhjCF6wg8w6sEnYXVuxcaTpyD57O8UugchhFQDJbQ2EEYe8Oiyw1AC8/oK61azikync8kIrRuTGhYIAqYJrkpbu5gb298uwsiDeersgaDHC2GZhWReslr87JW0jjYUolG9GuCdsCdG/uIxRL/3Q3RdNQdbLzwdxrq1ssMihFQJJbQ2EIZBI7Q7YcEgeJ8aW1Iw0D60TmGMgYUjsLpodMMOdbuu1jRrpihUsRhjYF6fOg/+6hgLR5Ba8aTsMIgNmNeHxjMvwOhfrwAAbPz+N9F7z60QeZoNQUito4TWBsI0ACoItUNhSwo1Elow9acc93NjMqOFI+A9NLphJzd+DiohTAOotxFaAMzrpdkNCtBCYfA0FYaqJd5x49F+6/1onbsUid88iI0nfxOpPz1L05AJqWGUhdnBNAG9+CnHbrxhLWkKlz+gzBPv4JeOcNUIrds+GywYROrZ38sOo+a47XNQCWGaNTXluOite7xeiGzW4WjIcLRQGEKlQobEFowxhI8+FmMe/yNCX/kaOudNx+Yzv4vsK/+WHRohxAGU0NpAcA7mopFAp7FAQK2pdC5KaN1GCwQhsnQzSCpgWfW5ZMPjpcJQKvD5AM7B6eFCTdKiMTRdPBNjHv8jvOMmYMs5P8CW836IzL/+RiO2hNQQSmjtIISrprY6jfkD6qxZ0TTXJbRuGp1jgSDdCDrETZ+DSgjTrMuieszjUaefrEFFj5QzBhYKgfd2OxwRkckzagxaF16HUY88A72tHVsvPgObf3wi0v/3PATnssMjhFSIElpiO+ZTaEsKTYOAuxJaN2GBAEQuS0+6HVLrSa0QAuAWoNVfQgtdBwxDdhQEgBYMwertkR0GqQLfnnuhddFPMfrxP8L3qb2xbfbF2PT9Ywpb/ShQdZwQUh5KaO3AqJLuzpjfr9DIA4Mb81m3JDJM18GYps4DDOIulgUwraglG245J4rFmAZh0Q20ClgojNSKJ2SHQarIO3Y3tMxahDG//wuCn/8SupfOw8YTv47Ebx+h6xkhLkQJrQ0YJbS78nggLK7E006mUds4zuejdbQOqrVEbhfcAqvXCvG6jsxfnpMdBUGhuB0VhqpPntZ2NE2dhTG//wvC3zwevbddj/XHfBE9N19D+9gS4iJ1eidhM91DazB2whgD8/kgMgrcILhwDW0/tyQyVK2VlEtwXr/1BxijqfqK0AIhtQoZkqrTG5vQePZFGPPU/6Hx/MuQ/fc/sPGEr2HLBT9C6rkVtMUWIYqjhNYGzOMprAOrcaUkWMzrA0+nHIymWAyunHPsIszjpZtBUp66L6gnv29qmbNYdgiOKbowVDAIns3QAwYCLRRG9PjvYdQDT2LkL38LfeRodF01B+u//llsu+ISpP/yHE1JJkRBdbhXgv2Y3w9h1n5CWwrm80JkFEhyXDxC6xoeD0Q+LzsKQggpC9P1woO5VBIsEpUdDlGEf9Jk+CctBZ8xH9m//R9Sz61A55XTAMZgTN5fdniEkJ1QQmsD5vMDCqwXVQnzeCFy8hNaWkPrPKZ7IAxKaEkZqP4AUQQLBMATcWiU0JKP0AIBhA4/CqHDjwLPpJF96e/Y+mda/06ISmjKsQ1YIFDSDX0tT/HawesDz6owLYdumB2na0oUACPuw1gdz6AQovDfT5TAAkHweJ/sMIjitGAIoS8dgeZpc2SHQgjZCV1NbcCCIQjToPU3O2EeDzJ/elZ2GIV9aKldHMWYRjMUSHl0DULUaUE9zhH80hGyoyDbaf4AUs/+TnYYhBBCykAJrQ2YpoF5vACtI9yBeTxqjNrV8whQtWgaMn/9k+woiBvpHoDzunzoJDgHPF7ZYQCok1lDw2CBABW3I4QQl6KE1ibM7wfPqzDFVhEeD4SlQkJLU44dR9uPkDIxxrY/dKrDUVrLAvOqkdCS7VOOc7T9GCGEuBEltDZhfnq6uzOm64ACCS3T6nlLEFILan30jOk6UIdV4oVlUkJbBUVv3eP3QyhR94EQQkipKKG1CQsGIbL0dHcH3QNhKTDqoun1OfpDiFvoxc3mqLnE3jQLFfKJEpjfD5HP0WwTQghxIUpobRKecjxEJi07DHVoikwjpH1onSdEYeoosV3NJXEDYB5PXRYVE6YBFgjIDmOHevisDYVpeqH2A13HCSHEdSihtYne2ASeThX9+zV/88AYwBVIJDUNQoU4yuCazwhVa3WEa9q/QszrVaOAXJUJwwDzq5PQku21MJIJ2WEQQggpESW0NtGbWiDSKZqutF1hvE7+e8F0mnLsNJWqtRIX8npL2se7Zhh5aKGQ7Ch2US8PUQbDfH6IVFJ2GIQQQkpECa1NmN8PeLxUGGq7QiqrwDRUXVdjpLiWUbVW29VTYsF8Poga2vKsmLYTllV4+On1VSEiUizm95c004oQQogaKKG1kRaJ0nSlfpwDClQYZppeiMVl3JTQCMsE89GNuV3c1PZ2CB35TYg62/JM5HJgPr/ja8+FVXr16Hr7/O2M+fxI/eFp2WEQQggpESW0NtIiUfBE8QltTd84cF5IJmXT9cKUWBdx3efCpLWAdmiZs9h9bW8DLRKFyNVbQputyjljbtns+DFqSX+lY0IIIe5CCa2NIieeAh7vkx2GEoRpFqb7SsY8HoDX3x6X1STyalVrdaN6TGT7adFo0Us1auV9EtkMtCqcM9bG9UVtifRRtfI+l4r5/AAltIQQ4jqU0NrI0z4CPJWEKCGBynGBGzd0IeeCdZ4l3eSYxi7rKnO5HBYsWIBctUdiPN5BE1rV3vscF7hzj32RMwzZoQAAcoaBJb9+fNh4hJGHFrS/uI1q7TOccuOVkTwU27bVoMUaITLpkgvque3zsTOezYAFggCc7Ru1aAzmhvVl/W2xn8tqtoPTx2I+P0Tuw/Xc0q5binPb++K2eAkhpaOE1kbM64MWDoPH40X/TV4I3LypB/kaq44s8nmwnQqe5HI5LFy4sOoXFObxQFgDTzlW7b2PTJuDpY8tVyLJAApJz3DxCM4hjDxYKGz78VVrn+GUGq/MKcbFtG21aIEAoOlAiYWhVPx8FNueIp1G+FsnAHC2b2w452KYGzocndJdzXZw+liFAmUfvleyrluqc9v74rZ4CSGlo4TWZnpjM3hvT9G/33zZFQ5GI4/IZRE6+ljZYRSmPQsBofjWPW6d4idyWTBfAEyjrqQUbm1vp7BQuK6qy/J0GnpTs+PH0WMN8Iwcjfz775X193X3OfV4Cg/pTPkPegghhBSP7kJtFv3eD2H1dMsOQzqezUKLxmSHAcZYYS9ay1L25kzVuIoh0mlooaDsMFyjXgs/DUeLRIquEO/2909YFkQ+Cy3WWJXjNV40AzweL/u65Pb3uxSMMTCfDzydlh0KIYSQEnhkB6ASa/sWB/ESpgx/lAiGEe/tRa6vr6i9ORPpQjGU5CDTYlXiLeEin+3thUfToW1/L+Mf+V+79Pb2Dvu6CYsjF09CC/iR2Ol97n/PZb73zZddgfj297X/s9D/v7IVE4/R3QWh6fAW2a797VXMe65C+5RiuHh3bmvZSvms9SWTAOw/d3fGD5+C5G8fga+lrajfT1hcuc9Hse3LkwkYmo5AqjAi7UTf+NF+UfvhOei6+xb49z2gUCivRN5LZqP7+qsH/LdqtkO5xyrl2pUTAvqWLfCAOXbdGkgx1zJVVPN9sYOT51h3Nw1guIUQAolEAqNHj4ZGs8pqDhOlVuKoYS+99BIOOeQQ2WEQQgghhBBCbNbR0YGxY8fKDoPYjEZodzJx4kQAwMqVKxGLDT9dNv/eO0j94Sn49phY1Ov33PrTj/0sZXEcuXIdnt97N4R19Z8YNV04vajfy3+wBuGvfh2+T+4NAEgkEpg0aRJWrVqFaDRqWzwdHR34/Oc/X1SbGe+/h+QzTyD17O8BqPfelxNPse1RjmQmg4OmzsC/b74OkeDw04rz772NyLdOgnfC7oP+Tn97PTdpHCKeobd1Uq19huOGePs/L6W07fpt23DkFQuL7heLJUwTXUuuRPDgzw37u27sO4s5N/PvvQ125BRM/vo3bO0bP9ov8lQSPTcvQ2D/g4r6+4He78FUsx3sONZQ7SIER/bf/0TL3KVgjDl23RpIKX2jbKqfe/3K6e+KtbGrC4fPno+Ojg5b+0XinHg8jnHjxjl+LhM5KKHdib5939SxY8cW1UEZRhbxSAj+1paiXj/g+/jb3T8FdoTPg6jCF4Z+LUX+t+a6tiA6dhx825+C9U/1GTNmjCOdfzFtluvehlRrKxLb20G1976ceIptj3L0T58c1dKMWGj4bXlym0KITdgN3iKefI70e4f9b1StfYajcrwfXQdZatsCxfeLxRJCwB8JI9jSBMaGfr/c2HcWc27mNoegjR0HwJm+sb/NeCKOYEMUwQquVYOpZjvYcayh2oVnMsi1NKNtXKFNnL5uDaSYvlE21c+9fv1tXU5/V6xYLEYJrcswxmSHQBygbk/kAswfoGqIgzFNML9fdhS7SD2/oqh1zW6hWrEWYRpgPrXanKj3OelXKNimAVbx+3bXGmFaVemTCiuL6CZuOCKXBfMHZIdBCCGkRJTQVoD5A4Bhyg5DScKygJ32oVWCkQfzKRZTDRGGARagiscqUTWZ3UH3FPqKemWaQDUeAglB+SyGPx8KCS09lKsFyvd9hBBbUUJbAeb3VzxC62MMU0c1wVdrUyAsc5fk0e/3Y/78+fBLvFkQhgHm+XA0RLX3vpR4qnGx9nu9mP3dE+AvcgRJGAa0gH2jG6q1z3BUi3eoz0ipbesUpnsKSV0ZVHu/yyEsE/5o1Pm+0cER2mq2g9PHErkctJ1GaFW4bqnIbeeeKv0dIcQ5lNBWgAWCEIaBSgpF+zWGS8e0wK+548JQLGGau0w/9fv9WLBggfSEFjtd0FR774uNp1pPnv1eL+acfGJRNwHCsgo3zTaOyqvWPsNRKd7hPiOltK2jPMWN0A7036PS+10uYRoIRCJV6BudG6GtZjs4fSyRyyF09LEfHk+B65aKVD/3PtpfKNPfEUIcQwltBZjHU7hJ4MVNmXP7FJiS4jdN5ab3ihqYcqzqZ0iYBpjXS8UWFKDqZ2QgzKOXPULrdoLzwkMgTxVusoUAoznHwxK5LLQIVUAlhBC3oSrHFWJeX2Eqq05vZT8hBIRlqlcgyMiDqbautwRKJyqGuct0blJ9Sn8+BqN7IGowoS2qLczCOVOVh0BUE6ooIksJLSGEuBGN0FaIebx1O8IwKM4BxsB0dfbSE5xDWBxQKKZSqJ6sCMsEFN87sZap/vkYDPN4Aas++09hGoCnSg9CqcrxsIQQ4LksNNqj0tXc2hcS55mbN8oOgTiIEtoKMa+3sDaTfMhUb7RO5PNgHt2VU2JdcYG2LKUeYNQTV3w+BuOpzRHaYgjTLCxbqcrBqMrxsEwDTNNcPYuHEDKw/Op3seXC02WHQRxECW2lPB4IIy87CqUI06jejVqxTANw4bRwtyQrgluARglttbnl8zGY0BFH1+9e3lV88CdozvGw5wrPZsFsrNJOqs/t/SFxRvbV/2DL2SfDv/e+skMhDqKEtkLM66vbEYbBCNOs3lS6IgnTADT6uDuGc3p/q6wWbt6Yzw/U6QyXqk85ru98dlgimynsLU8IqRnp/3seWy/4EcJHH4uWedfIDoc4iO5AK8S83rq9IRtUNafSFUmYJhiNIDrHsqgwWhXVQjIL9O/lXZ8PBIVRqAxO1CCyWbBgUHYYpEy10icS+ySeeBTbZl6IhjMvQNOMebQsqsbRHWiFQl+bgtSKJ2WHoRSh4vRe03LdCKKbLtCFUXm6WDjNTZ+JYjB/oG4TWphm9R4CUVGoYYlsFuFvHi87DEJIhYQQ6LvnVvTdexta5lyFyLEnyg6JVIFiWYf7sECIikJ9hDBN5UYehGUCmoauJXNlh1KThGkgdMQ3ZIdR02otmQUAzR8orG+vQ8I0ED762CodjKYcD0dkM9BiDbLDIGWoxb6RlEdYFrqvXYDUM0+i7bqfI/TFw2WHRKrEXUNWCmKBQEkJbV10vIahXJVjWBaYy0Zo3UTk89ACNF3PKbXab9T9lONqrdkUAGW0Q+OU0BLiajybxbZZFyH9p//BiJ//ipLZOlM3d/hf/OIXwRjD2rVrbX1dLRCsizW0pdxQC9NE6GtTHIymdG4rCuW2BEbkcmDhiOwwapLbPgulYD5/3Y7QwqxiQov6HqEd7hwSnEPkctAitAet29Ry/0iKZ8X7sPXiHyP/1kqMvPsR+PfZX3ZIpMrcc4dfgeXLl+PFF1905LVZIFC/204MQhh59apFGnnlClXVEpHNQovGZIdRc2r+Zs3ng7A4hODD/qpb3oti4xSGQdvEKEJks2A+PxWNIcSFzC2bsOWcH4DH4xh576Pw7j5RdkhEgppPaPP5PGbNmoUpU5wZMWSBIISRhxDCkdd3JdNU7kZNGAbgkpsVt9y49xOcQ+RpdMNubvsclIMxBubRgTqcdiwMs8oP/up4iHYYIpuBFqIlE25TD30kGZrx/nvYfOb3oMUaMPLuR+BpHyk7JCJJzSe0t912Gw4++GAcfPDBjrw+0/XCU906vCEbTGHkQa2bA5HPu2LbHjdeoEU2C+an0Q07ufFzUDaPtz7X0ZpG9dad0wPXIfFMGiwQkh0GKUFd9ZFkQNlX/4vNZ50M/6TJGHHLfTRLrM7V9BzM7u5uXHfddfj73/+O++67r+i/i8fju3zv9/vh9/sH/wOvrzDNVrHKvrIIB2/Ucrkccrncju8TiQSA4dss/ZfnlJ9y7NYLtMhmoA2yf+Ng7ZW0dp1i6mMMfo1GkAD5n4OcYSC3U12AZCYDoIx+sUisDhNaITiEZQE+ny2vN1y/aCUSyObzYIYBP12nPkZk0gh/47iqHpP6RncZrF8kcqT/70/ovGIqwsecgGbaY5agxhPaRYsW4dRTT8X48eNL+rtx48bt8v38+fOxYMGCQX+feX20dc92QojCaKhDU46XLl2KhQsXfuznw7aZZQE23HyTj+PZzKAj8oO11+deW7vL91NHNeHSMS1OhOcqspNZALh++e+w9LHlH/t5qf1isZjXUxeF9XZhmGAeDxizJ1EZrl8c6/Pgs9Eg9jpiPeacXF97MhZzTvF0GlpjUxWi+RD1jeWT0U8O1i+S6kv+/jfoWjIXDWdfhIYzzretHyXuVrMJ7bvvvovHHnsMb775Zsl/29HRgVjsw6kLw41CMK8XIp8v+vVb5iyu3f1QLQtgzLFte2bPno1p06bt+H7Dhg2YNGnS8G1mmUpv26NCIlMukU4Pup/mYO31930mIKJ/2B6+Or8gqdT+l51wHC489sOaA5u6unHQ1Bkl94tF83jrrrCeMA3Axj5yuH7R2rQBqV/dieC+B9p2zFoiMmnojc1VPSb1je4yWL9IqkcIgfj9d6D3zpvRPHMhosd/T3ZIRCE1m9DOnDkTs2bNQkND6fvKxWKxXW7chsN8hSnHZPtaVZum0Q3ko9Mc+6fUDddmgnNli0KplMyUQ2RS0JoGvhkcrL0iuoaoru4DhmpSrf39Xu8u01IT6cLUulL7xWLV5ZRj07R1icpw/aKR6IPl8xc93bimH7p+hDDNQntUedsx6hvLI6u/HKxfJNUhOEfPDVch+eRjaFt2K0JfPlJ2SEQxNZnQvvDCC3jjjTfw6KOPVuV4zOsDShihdZuS9qA1nE1oy8YtJYtCqZbMlIOnqz+6UStqof0rxTye+iuqZxiFqdbVIgRooG9gPJ2CFgrRtEUXoP6yPol8Dp0LZiL7jxfQfst9COzvTJFX4m41mdA+99xzsCxrl8rGmzdvBgBMmTIFPp8PS5YssW0rn9BR30LqqcdteS23E/l8IcFXjYIjtLVwcRaGAVhW1Uc33K4W2t4uoSO+gdQfny7qd2tl5NDuKcekfCKdghYKyw6DEDIAnkpi24wLYKxdjRF3PQzfxE/KDokoqiYT2kWLFmHRokW7/GzBggVYuHAhVqxYgQkTJth6PC0Ygsjnhv/FOlAYoVWv+JKwOKDwGlq34skEWCRCoxsloGR2VywQqJk1tMW2rTCMKlfFF6jHfWiLKwiVQqjKFY5J6ajfrD9WVye2XnIWeCaDkfc+Cs+oMbJDIgqjO3wbsHC4pKJQNS2XU3PKseBKJV21cnHmiTi0CO39VqxaaXc7MX+g/qocmyZCXz1KdhQEgEgloTdRFWGVUb9Zf4z167D5rO8Buo6R9zxCySwZVk2O0O5sxYoVmDNnzsemHL/yyiu2HaMwQksJLVBY6xA5/mTZYXwcV2eEtpYuzlbXNjScM1V2GMqrpTa3GwsG627bM2EYhUS+agdEPQ7QFoWnUtBbWmWHQQjZLv/2Smy5+Cz4PjkJbctugRYMyQ6JuEDNJ7RTpkyxba3sYFgoDGHkIQQHY2okTbLwXE7J9ZSFKcdqraF1O3PrZsDi8IweKzsUpVEyOzQWqMOE1jQG3buZVI/I5woFs2gNrbKo/6wv2f/+C1unnYPQl45Ey7yljm0BSWpPfWdfNmGatr3ScfE3ZbXaSYtcDlpEvYQW3AJTYCuEWmh3YZrIr3kPxvur0XTpHKX395WtFtrbaVowCGHUVw0CYRhggWqO0NbfEG1R62eTSWhUA0BZ1H/Wl/SL/4utF5+ByLdORMuCaymZJSWp+RHaamE+P3g+B92vXkGkSpS0ZY8QEPksNCVHaE3lqhy7jRAC1tbNMNaugd7UjJb5y2gq0BDoZqw4/VXRhWWC6XVySTLy0Ko5QitEveWzRSkUtYvKDoMMgPrP+pL649PonDcDDWeej4azLqSHTKRk0odWnnvuubL+TTXM74fIZmWHIZdpfDharRDBOcCF9CnHbr5A82QCuddehrlxPZqmXYHmmQspmR2Cm9taBub1QxQ5w6UW3lthGGChKp4/dThCWwyeTCBy7EmywyCkriV++wg6516Gpqkz0Xj2RZTMkrJIfxy+ZMkSvPTSSwP+23PPPYevfe1rVY6oPMzvr/ute0QuV91CJ8UyDDBdR/fSebIjcR2ezcJYuxq8rxfe8buj4SeX0BTjYdRCwlVtzOeFMPJAsPbXlQrBC2toq9pXiioeyz14Mgm9tV12GOQjqA+tH/GH70PPz5ah5YqrETn2RNnhEBeTntBalgVjkIIglmVVOZryhb52DNLPPyM7DKlENqtkQiuMPOCR+1F32wVaCAFr80YYa9fAM2oMmqfPVW7kXUVua2dVMJ8fIufuB4JFt71hgHm89GDIQcW0hTDygGlAa2h0PiBSNOpD60ff/Xei946b0Lr4BoS/5mzxVlL7pCe0c+bMwdFHHz3gvx166KFVjqZ8WjRa91OOeU7RhDafB5O4ftZtF2iRyyH/3lsQ+Tya5yyG3tgsOyRXcFs7q4T56meGi8jnq75Xt+AcoGl8u+CJwvpZmt6oDupD60fv3beg7xe3o23pzQh99euywyE1QPoj4sGS2eH+TTVaNFZyQltrnbfIZRH6+jGyw/gYYeSpIFQRhGXC6PgA2ZdfghaOomX+tZTMFqnWzuVqC31tSt3s5S1yOTBfbRUPdCOeiEOPxmSHQbajPrQ+CCHQc/sN6LvvdrRdeysls8Q20hPaW265pax/U40WjUHkMrLDsFWpFxiRzUJTsGKkME1pBaHccJEWlglj3VpkX/oHeDKB5tmLCtvx0EOAorihjVWnhcIQufqY4SLyuaqP0EKg5BFat36ui42bJ/oQ+c73HY6GFMOtnzVSGiEEem+5FomHfoH26+9E6IuHyw6J1BDpU46vvPLKARNXIQS2bduGiy66SEJUpWP+AAQXEKYJJnm9piwil4UWVS+hhWkqsQetanbehkdraETz7MXQG5tkh+UqdCNmDy0SLWnKccucxehaMtfBiJwjpXie4NU9nuKEEODxOPQRI2WHQkhdEEKg54arkXzyMbTdeDeCB39OdkikxkjPvM4880xMnTp1x/ePP/44TjzxRAghcOedd0qMrDSMMWiBIEQ2U7f72olsFpqKU7i4BTBKaHdmdXfBWLsa0HQ0TbsCnna6sSsVJbP2YeEIRNa9a2hL+SzwXBbR4092MJoBCEFraHci0inA66OtxxRA/WjtE5yj+9qFSD37O7TffA8CBxwiOyRSg6QntFOnTsX48eN3fN/W1rbj+/POO09WWGVhwSB4JqPktFunCcuE4BZYQL1tN4QlpyCKihdqITiM1e/C6ulB4zkXwTN+DyqKUgYV29bNtHAEIp+DEKLmP48il63+Q886SWiLnm4c74Pe0OBwNGQ41I/WPsE5upfMRer5FWj/2S8Q2PcA2SGRGiV92Ordd98t699UxLaP0JaiVjp0kS1Mo1PyZtQyaYuM7Yz3V4OnU2hdsAzeCXuq2V6Kq5VzViXM4wHzeuui0rHI5qr/0FNwOtd3YvX1IvLt78oOo65RP1r7diSzf3oWI267n5JZ4ijpI7QLFy7Ev/71rx3fv/zyy1i/fj0A4M9//jOOOOIIWaGVLPyN45B6ernsMGxRckGoXBZaQL0tewBAWFbVi0KpeLE2O7fC6uxE64JrqcpqmVRs11rB/IFCpXgFt/6yixAcIl/9hFbw+hihLYYQAryvF/qosbJDqVvUj9Y+wTm6l81H6vkVGHHbL+Hfex/ZIZEaJz2hZYzBMIwd33/mM5/Z8X3eZds4aLEYeKa2Kh0XS2QzSk43BgBYJlDnI7Q8nYbx3jtomj4XzE/JbDnoJsxZLBCo+UrHIpcH83qrX0G8DvahLfb8FNkMwJia9R7qQK30o1YiLjsEZQkh0H3dIqT+8BRG3HIfJbOkKqQntPPnzx90FPawww6rcjSV0RuaSp5yXCt4LovQ174pO4wBCcuq6pRj1S7YwjSRX/U6vBP2gKdthOxwXEm1Nq1FWv8IbZFUqXRcymdD5LJgMmayiNpPaIvFe3ugNTTSFGwJaqUfNTdtgPHu27LDUJIQAj3XL0ZqxZNo/9kv4J+8v+yQSJ2QPmw11JRiN003BrZX6jTyhSmudaawB62aT7wz//vHuh2hFUIg//ZKaI2NaDxvmuxwXKlWbsJUF5ry7Zp/ICiymepv2QMANOV4B6u3B1Haf7bqaqEfFZwj/947MDesR+NFM2SHoxwhBHpuXILk75cXqhnTmllSRfV5l+8QpmmFrXtKnHZcEx19VtE9aFG4CKFKU/xUa0tzQweEaaFp2pWyQ3El1dqzlmnR2l+yIbJZaBKWZogyR2jd8vkverqx4OC9PfCMGedwRGRnbvkcDUUYBvIrX4XIZtAy7xroMaqSvTMhBHp/tgzJJx5F+413IbDfQbJDInVG6YR21apVeOedd2SHURIWDIFnUrLDqEg5Fx+RU3QPWgDg1Z1yrAphmjA7PkDTpXOqv2avBtTCTZib6A2NNb+GlmczCE/5dvUPbFlgtBc3eCIB5vNDC0dkh1I3aqEf5ekUsq/8BywcRfMVV1Mdio8QQqD31p8i8ZuHCsnsgYfKDonUIWWvcO+99x6++c1vIhQKYfny5ci45Mk9C4ZKHqF1O2FZEJYJpuom9ZZVlRFa1S7cVncntFgD9IZG2aG4jmptWQ9YKAyRz0Pw4pdsuK2dRDYDTcbITg3vQ1vKZ4D3dENranYwGrIzt52fA7F6upF77WV4dxuP5suurMuH40MRQqD39huRePSXaLvhTgQO+qzskEidkl4U6qPS6TRCoRAmTpyINWvWgDGGsWPdU14/8s1vI/nEo7LDqCqRzaq7By22Tzmuw9EJ3tuDCK0VK1kt3IS5EdO0wtY9mSxYOCw7nKKUvL1ZVs5Mlsxf/1SzCW0prJ4uNJwzVXYYdaEW+lFz0wYYH7yPpktmwzNqjOxwlNR3188Qf+hetF9/B4KHfF52OKSOSU9of/Ob3+zYpueb3/wmFi9ejAMOKCwkD4VC+Pa3vy0xutJpDU3gmXTJf6dKxc5yqLwHLQCAc8efqsq+ePNEHFZ3F+DxwDNyNKBp4Ik49LZ2qXG5jex2rHdaKAiRTQMuSWhLISwLwjTAJEx3FULU5LKDkipM53MQ6XShfySOcns/KgSHsWY1rJ4uNM+5imY5DaL37lvQ98s70X7d7Qh+9ouywyF1Tvqwlcfjwd///nd89rOfRUNDA7Zs2YLnnnsODz/8sOv2oQUArbEJIp2GEEJ2KGUp50LEVd6DFihsWVGj04R4OoXc668g9+ZKgBcKnuRWvgZz43pAAHorJbTFcvtNWC1ggVDNFobqr3AsZSZLDU85LpbV3QWtqbkmE3uVuL0fFYaB/BuvQWRSaJ23jJLZQfTeexv6fnE72pbdiuAXviw7HELkJ7R9fX245ZZbsMceewAo7D1733334emnn0YikZAcXem0QLBw47B91LkeiGwWoa8fIzuMQQkuHE1oZV3Azc0bkXv1v9BaWtF69Y1omnYFmudcBS0cgdW1DU2XXansNHDVuP0mrFaEv3FszW7dI7IZaEFJD/4q2IdW1XOj1Lisrk5Ev3uaQ9EQQN3PSrF4qr/4UwTNVyyh4k+D6L37FvTdexvalt2C0BcPlx0OIQAUmHJsmuYu3//kJz/Z8f/dOsrJQiHwTBq6z1fS37l12rHIZtStcAxUdDOnKnPTBhjr1xWmQzU27fg50zQ0T3ffZ0gmt9+E1RIt1ghR4pINWf1mqZ8bnsmABSQVzhOirh9uCdME7+uBd9wE2aHULLf3o2bnVhjvvQPv7nui8dxLZYejJCEE+u68GX0P3I32636O4Be+IjskQnaQntCuXLly0H977bXXqhiJfbRgCCKdAupkqorIZKDHGmWHMTgHR2hlXMR5NgNj7Ro0X3E1TYeqkNtvwmqN1tBY01OOpWzZAwC8tpZdlDw6290JraGRRtwc4uZ+VHAOY+0aWJ1b0TR9LjxtI2SHpCQhBHp/fgPiD/8C7T+9A8HP0ZpZohbpV7jJkyfjJz/5CbZs2bLjZxs3bsQZZ5yxoziU24SOPg48XXphKNnK2n9WCPBsGlqDwpuMC15ToxPmhvXQR46mZLZCbr4JqxaeTCL//uqqHU+LRCGMPIRV/NY9biEyGWiSHvyJOl9Da3VuQ/Qkmm7sBDf3ozybQe61lyEyKbTMv5aS2UEU9pm9DolH7kP7DXdSMkuUJD2hPeOMM/DpT38ae+65J1pbW9HS0oKJEydi0qRJOP3002WHVxa9qRkikyrrb912cRD5HJjuAfOWNr26qrgzN3My2kpYFqytm9F4zsVVP3Ytcdt5Vk1CCFi9PYViYytfBStx6UQlmKZB8wdL3svbDe3JM2losh5C1VBCW/JWSaYB3tsD74Q9HYqofrnhvBuMuXULcq/8B3prG5qvWKL2Tg0SCSHQe/MyJB59AG033IXgoYfJDomQAUmfcgwAl1xyCU499VS88MIL0DQNhx12GFpaWmSHVTatscmVI7TlEJkMWFDSurAiFUYnpD+7sYW1ZRO0WAO0SFR2KK7l5pswJwkhwLu7YKxbC1gWYj88G96Jn0Ru0yZg4bKqxdFfg0CLVH97m2KVnFRxCyKfl3feVrgXt1vrOwCF0VmtsYmmG9vMrf2oMA3kV78LnoijacY8eGgngEEJIdBz4xIkn3gU7TfdjcBBn5UdEiGDUiKhBYDW1lYcf/zxyGazePPNN+H3+xFR+IZmKFo0BmEYEKYJ5lHmLR5SuRcnkUnLq9xZLMEBzf2jEzyVgrFuLZovXyA7FNdy602Y06y+XhhrVwOmhYYzz4dnwp7SpulrwVDJhaFUJzIZsEDA8f2wBw+gNopClXP+mls3o+FHPxn+F0nR3NqPWr09yL/zJvTmVrQuvA7M45UdkrKEEOi5fjGSv1+O9pvvQeCAQ2SHRMiQlMq2XnjhBUyfPh1erxe9vb245ZZb8NWvflV2WCVjjEELhcHTKeix0teWuulpOE+nETr6ONlhDM2B6XbVvqDzdBq5la/Cu8dE6C2tVT12rXDrTZiTeCoJY+0a8FQSDWecD++en5CXdG0X/ubxSPz2Eakx2I1n0tCkzmSpnSnHpeDZDEQqBQ9VN7aNG/tRYVkw1q6G1dWJxvMvg3fceNkhKU1wju7rFiG14km0/+xeBPY7SHZIhAxLqXmYq1atwj//+U+8+OKLeOONN1xb5RgAtHAYIpWUHYbjRCa1y7YxSuIczMVTjnkmjdzrL8M7fnc0/uQS2eGQGsBTSeTeWonc669Aa2hE6+Ib4NvrU9KTWaCwZKPUNbRA9W60yyqel06DhSQmtA7vxV0N5bzv1pbN0NtHgOm6AxHVHzcms1a8D9n/vgRhmmiZfy0ls8MQloXuJVci9eyTaP/ZLyiZJa6h1Ajt+PG7djSjR4+u6PVeeeUV3HbbbXjxxRfh8XhgWRaOPPJIzJ07F21tbRW99nDCU76N1P885egx7FLJRYqn09Camm2MxgHCvVtWCMtCfuXr8O42gfbGq4Abb8TsJoQA7+uFuX4deCoJz+ixaJ4+V7mCbnpjoaieqJFpskDhoVT0+JOlHV/YsBe3zJlD5VXg5zC3bELz9HkORFR/3NaHCsuC8cH7sLZtQeO5l1BRsCIII4/OedORfenvGHHbr+Dfex/ZIRFSNKUS2jfeeAPJZBJtbW1Ys2YNNm/eXNHrnXzyydh7773x73//G+FwGBs2bMARRxyB//mf/8Grr76KoINrP7XmVvAKRmjdMO1YmCaEYUCLxmSHMiTBha1raKt5YTc+WAMWiaDx/Muqdsxa47YbMbsJwWF1dcLsWAdhmfCO3Q3Nsxcru76f+f2AphcqqPtLqzyqar8p0mloMmey1MAIbal4dzeYz0dLNCrkxv6TJxPIv7UKLBxGy4JroQUUr/OhAJ5JY9vlF8J4722MuPNh+PbcS3ZIhJREqSvcxRdfjNWrV+O6667DmjVrcPnll1f8msuWLUM4HAYAjBkzBjNmzMC7776LFStWVPzaQ9Fb2iBSyUKF3RrF0ylooZDSoyhCiMIIrQunHFuJOKxtW9F06RzZobiWG2/G7CIsE+bG9cj++18w169Dw4/PRetVN6LxvGnKJrP9WCgEoWCl+HL36i4szZA4k8XFe3GXew6bmzYg9oMzbI6mvrit/xSCw1i3Frk3XoVntwlomXMVJbNF4Ik4tl50BsyOtRhx9yOUzBJXkn5XM2fOHDQ3N2P8+PE48sgjMXPmTMycOdOW137ttdfg+8geiv3TmHt6egb9u3g8vsv3fr8f/hJL/muBAODxQGSzYApXAa7kgiVSSWjh6laizuVyyOVyO75PJBIAhmgzzgEw227mqnWBF9yC8c6b8O4+0dUX5MHaK2nxXX7Pxxj8NleidtvNmF14MglzyyZYWzdDa2hE08WXwzOy+OUbJZ9jDtBCYfBMGrrqyxmKIHI5QPc4um3McG2WyeaQy+YQCBjwe8uv7FrtEfByz2GeyYAnE/AqfGMus28shtv6T57NIP/2m2AaQ8vcpbZvkTVYe7md1d2FLRf9GDBNjLjn17SNEXEt6cNWr7/+OqZPn46TTjoJTU32Tsn6aDILAO+88w4YY/jSl7406N+NGzcODQ0NO76WLl1a1vG1cAQ8WX6np/oFhaeSCH/z+Koec+nSpbu0zaRJkwAM0WbcAjRNyWmIQzHeXw0WCqPhnItlh1KRwdrrc6+txeSX1+z4+vmmbluPq/q5YzeeycBYtxbZ//wTuVWvgek6WuYuRcsVV5eUzAJlnGMOCE/5NkQqVdbfqtb2Ip2CFgo7eozh2uzK+x/E5AsuwfXLf+doHHaqpB3Njeuhjxil9LYssvrGYqh2Dg3H3LYFuVf+A725Bc1zr3Fkv+fB2svNjPXrsPnsk8G8Xoy48yFKZomrMSF5Tuztt9+O8847DwDwi1/8AjfccAMuu+wy/PjHP7b9WJZlYf/998ehhx6Ku++++2P/Ho/H0dDQgI6ODsRiH64LLXckouempQDn8O5efjECJxOxSi9a2Vf+g8ZzL4Fn1BibIhreR5+SbtiwAZMmTRq0zXg2g65505H+3z/acvxqXOitrk7kV7+D1gXXOTqqUw2Dtdff95mAiP7h8zQ7RyHcdjNWLsEtWNu2wty8ESKTgd7ajthpZ0MfMbKiGQmlnmNOMDeuR+9dNyOw74Fl/b0T/Wa5nytj/TqIXA7N053ry4drs76Fl0ObtA8C0WhFI7T9nH5AWNHMIdNE9qW/oWXeMkcSG7vI6BuL4ab+U1gWjDXvwurtRdO0OY4mZIO1V19f3y79olvk3nwDWy85C75P7o22a37m+EM3FfTf47u1zcjQpE853vmG6IwzzoBhGDuS2fvvvx+nn366bcdavHgxvF4vbrrppiF/LxaL2fJhjxx/MuK/urOi11C2yIng4Klk1QtufPQmun9K3aBtZlm2FUOpxoWep9PIv/sWmi69wvXJLDB4e0V0DVFd+gQRVxLcgrlxA8z166BFomg4/Tx4xo23bWuSks8xB2hNzRDp8isdq9Rv8lQS0eO/5+gxhmuznNeLQDgMZkMyqzpz80ZoTS1KJ7OAmn2jm5JZnk4h/+ZKsHAYrQuWgfmcvV4O1l5ulPn7C9g280KEjjgaLVdcpfRMBkKKJT2hffzxx3epZvzyyy+jq6sLAPDcc8/ZltDed999eOyxx/CXv/xlR5Eop+lt7eCJuJLbT1R64RLpNJjP7/hFpGK88u0qqoWnUsitfBXe3feEZ+Qo2eG4kptuyMphxfuQf+dNaMEQmmcuhN7cIjskR2jBUKGQWz4PKPBgp7JaAyloTZLbybIAG5MkJx8YVPRecw5zQwdt1VMit/Wb5tYtMNa8C+/43dFw3jTl7q9UlnzmCXQtnoPYj85G47mX0ntHaob0hDaZTMIwjB3ff+Yzn9nxPed8sD8ryQMPPIDrr78ef/7zn9HeXr01AlooDOgeiGwGLBgq+3VUGm3ox5MJaFG1n4ADhSlJdlQ4dvKCL4SAtWUTjLVr4N1jIhp/coljx6plbrspK4UQAmbHBzA3ri/sqbj7RNkhOU4LhwuzQMpMaFXoN4Xg4JmU9AcPgluAZs8IvpMqPYetrZuhhcPQW53dZ76WuKnfFJzDeP89WN1daJ4xn9q5BEIIxO+/A7133ozm6XMRPfEU2SERYivpCe38+fNxxBFHDPhvhx12WMWv/+CDD2LZsmV4/vnnMXLkSADA008/jY0bN+Kcc86p+PWHo0Wi4Il4YcShAnbenNlxAeOJOCLHfMeGaJwmAIUfQAohYKx5F7y3p6ZH3JzmppuyUgnLRP7tNyGMPFrmXVP1yuKysHCkkNBKPicqGjFMZ8D8AalT+gTnhW7Q5n1oVbsmCcFhdKxD00UzbIioPrip3+TZLPJvvQHm86N1/rU1sSSnWoSRR9eSuUg/twJt19yC0Fe+JjskQmwndRHbvffeuyPJ7Pff//53x8L7wRLdYj300EM4++yzcfrpp+P555/Hgw8+iAcffBBPPfUUNm7cWNFrF0uLNYAn1FlrYdcFjCfi0NtGDv+LsgkBpnBGa7y/GjyZQMu8a6TfuBP18GwWuVf/C+bxoGVu/SSzABA55gTwVLKi15B9w85TCfltZhhgDq3JtOP9tauNrG1bwXy+qhYpdDPZ50YprJ5u5F75N/TWdjTPuYqS2RJYvd3YcsHpyP7jRYy4+xFKZknNkprQnnbaaXjvvfcwb948vPHGGwCACRMm4J577rHl9S+66CJks1nMmDEDp5122o6vu+66y5bXL0b0xFNsS2hVuQAJywJPp90z3afCfNap993YuB68uwvNsxarvxZZYaqcF3azurYVbuJGjELT5QtsK/rkFnpLG0SFCS1Q2eej0s8WTyalFycSRh7QnZuMJfP97ScEh7FuLRrOutCW16tlLXMWu6bPFELAWLcW+XfeRNMls9F00eW05rMExtrV2PzjkyDSKYy8/3H4P/0Z2SER4hipU467urqwatUqbNiwAVOmTMEXv/hFxONx7Lvvvra8fnd39fdv+yi9tQ08lYLgFpgNa5gqmeZl5+isFo6AeaTPWHctq6cb5rq1aLlyCbRAQHY4ruWWG7NS8GQCxrq14Kkkmi6dU/I+srVCa2oGz2YgLEtKMm/L0oxkAg1nnm9DNOUTRh7MwYQWKP26ZPd5a23bCub1wjNmnK2vW2vc1F8Kw0D+nVUQpoWWuUvlz3Rwmcw/X0Tn7KnwH3goWhf9tOJlb4SoTlpG8uabb+Koo47C5MmT0d7ejqOOOmrHk7f58+fLCst2zOuDFgyBJ5PQYw22vGY5Sa2dFzIe74Nm03+L46Tusjwwnssi//YqNE2dBS1Ke6HVO2EY4PE+WPFe8O5uCMuEZ/RYNM9cUNfbKTBdhxYMg6dT0Cs8T2QkXEIIiGQCuoN7YxYVRy4PVOHhY/97Ntj77FQy1T8620jVbofkpmSWJxPIrXoDemsrmi+ZU3ezUypRKP50J3rvuBGx085G4/nTbF8/T4iKpCW0jz32GP773/+itXXXfUzvuecexONxtLTUznpCLRYrrDm1MQks5QbN7gsZj/cidprzBbVUYPd7JwRH/q1V8IweC8/osba+dr1x0w3aRwnDgLl1M6xtWyHSSWjRBmixBjReOAP6iJF0Y76dFolAJBOADQ9+hku4Pvp7lRLZDODxQAsEbXm9suPI56o6m6ba56W1ZTOYzw/v2N2qely3cFs/aW7ZBOP91fDuuRcaz5kqOxxX4ckEOhfORPZff0Pr0psRPvxo2SERUjXSEtqWlpaPJbMAcPrpp+OXv/wlzjzzTAlROSNy3HeRePwhwObpUMMltU5cyITg4PE4dFftk6pOcmCufR9M09B48UzZoRAJhGXC7FgHc9N6aE0taDj7QnhGjaURiEGEjzkBqaeXAzYW+hms37T9wV8iLn39LACIXLYqI7QyCG7BWLcWTVNnyw5FSW5KZgXnMNa8C6u3B82zFkKXvXezy+TXvIttMy4AGMOo+x+vi63dCNmZtKtce3s7Hn30UXzve9/b5ecejweWZUmKyhn6iFGOVTqu9gVLJJNgfr/0UQc3MrdsgrltC1rmL6MRuAq56Uatn9XXi/zbb0JraEDLvGVKJDuq01vbwJOVF4b6qGp8fngiocSSApHNgHlrc+q6uWkjtHAYHlc9YHWe2/pHnssi/2b/ljzLqEhiiVL/83t0LZmLwKFfQOt8uraQ+iQtoT3ppJNwzDHHYMWKFbjgggtw8MEHgzGGTZs2YevWrbLCcoQWawA4B89lofndXQDI6uuF1tAoO4yqsPOmwNy0Aca6tWietZAeBlTIbTdrQgiY69bC3LwRjedfBu9uE2SH5Bp6Syt4xr6ietXEE3E0nnuJ7DCQem4FmNcnOwzbCdOE2fEBmi9fIDsUpbitf7T6epF/ayU8o8eikaoYl4QnE+i+bhFSf3gajRdMQ+zUs+j9I3VLWkLLGMPjjz+OSy+9FIcddhh8Ph9aW1uh6zr+/Oc/ywrLEYwxaNEYeDwOrc3dCS3v60X05NNlh+Eqxvp1MDdtQPOcq2xdR03UJ0wT+bdXQZgmWuZdAy0Ulh2SqzCPt1BUL1V5YahqEpyDp5JqbG2Wz4HV4ENIs+MD6E3N0Fs+vnSpHrktkRVCwNy0Aea6tWi8aAa8Y8fLDslVcm+8gs4rpwG6jpH3/Ya25CF1T+rCmmAwiDvuuAPz58/Hv/71L2iahsMPPxzhcO3d9GnRQmEotMmteFkJIQR4vA+e0W7buF5eqWOrtwfmho7C9jw0Dahibrpp4+k08qteh9bQiObZi2mdbJm0SBQiEbelMFS18GQCWjCkxMioyOdrbgonz2Zhbt6IlnnXyA5FOjf1if0Et2C89w54IoHmK66mB70lEPkc+u6/E32/uB2RY45H07Qr6EEpIZCc0PYbNWoUjjvuONlhOCpy4inou/tnssOoiEilAI+3LjpPW7btsCzk33kTjRdOp2TWBm66cbO6tiH/7tvwjt8djedfJjscVwsfexJSv/+N7DBKwhNxaDE1EnCRy4H5ayuhNd5/D57RY2uyX22+7ArEQqGqF3ysFpHLIbfqdbBAAC3zr1HioY9bZF/9L7qvngOrrw+tS26kKsaE7ESJhLYeeNrawZNJCM5duyeYFe+F3uCyJ6kM0gZozY4PoMUaaCpVHRGWCeP9NbC6O9E07Qp4RlCxmkrpbe2OFdVzCo/3IXrSqbLDKMyqyWXBXF67YWdWXy94Io7my2tnv/qBuDlpHYwV70P+zTdovWyJeDKBntuuR/K3jyDyrRPQePFMGtUm5CMooa0S5itUBhapJJiLps7tjPf2IPrd02SH4Qoil4O5aQNNibOJG27urK5O5Fe/A62hEa0Lrqu5UTFZ9KYW8GwWwjLBdPUvWf1LM3QFHmaIbAZM06q6D62ThOAwVr+DhrMuAvPUZuXmWmVu3ghj7Ro0XjCdCuMVSVgWUs/8Fr0/vxEsGET7rfcjePDnZIdFiJJq4yrnElosBisRV2Irh1IJIcD7euEZPVZ2KKXZ/gR4uD177WasWwt9xKianBJHdsUzGRhr3gHPZAo3a2N3kx1STWG6Di0cBk8mobuguJHI5QAhlOjneTwOFqydqurmpo2AxwvvHrTHplsU9pd9D1ZvN5pnL4be2CQ7JFfI/uef6L7hapjr16Hh9J8g+v0fQwvUzkwLQuxGCW0VhY89CcknH5UdRll2rJ8NR2SHUjJR4pzjSkcDeSYDq3MrWhZdX9HrkAJVR2cF5zDXr4O5oQOesbuhefZVVPjJIVq0ATwRd0VCy+O90GINSkyn5PEesGBIdhi2EPkczO1bn6nw3pLhCSOP3JtvgOk67S9bpNzKV9F39y3I/O2viBx7IkbcfK8a1dIJURwltFXkaRvhurVg/ay+HuiNjbLDcAVz/QfwjBpDT1NrGE8lkX97FZjPX9iOh0biHRX59neReOwB2WEUhcfjhb3HFZD8n6fQEK2Nz2b+/dXQR4yC3tQiOxRSBJ5MIrfqdXja2tF46Rx6CDEEIQRyr/4Hfff+HNl/vIDgV76GUQ/+Dr5PfFp2aIS4BiW0VaQ1NUPkchCm6bo1Tbyvl9bPFkGYBqxtW9Gy6KeyQ6kJqo3OCiFgblwPs+MDeHffEw0/uYRu1KpAd9HDQB7vQ+MPz5YdRkE6BW3ESNlRVMzq7QHv60Ur9auusKPK+x57ofEnU2WHoyyRyyH13DNIPPor5N9ehdBXv45RDz8F316fkh0aIa7jrqzK5ZimQQtHwFPuWAvWb8f62VFu23+2dJUmUFbnNmiNTdBqZJqfTMols/k88u++CWEYaJ5zlavOYbfTYg2AZULkc0pPWxSWCZ5JQ29VY79xnkqBRdy3TGRnglvIv/s2Gs+9lLZ4UZwQorAMY+N6NF12JTzt7n+YYjchBPJvvoHUiieQ+sPTEKaJyLEnoXXpz6j+AiEVoIS2yrRIBDyZcNXNsEinAd1D0yqLYHVuQ/SUM2SHQWxm9fYg//Yq6O0j0Tx1Fq2VrTLGGLRorLCOtkXd9WQ8kYAWiajz+RBC6QcAxTA71kELh+Edv7vsUMgQBOcw3nsbPJlEy5VL6H5hJ0IIGO+9jfRf/4T0H56C8f5q+Pc/GI0XTEf469+EFgrLDpEQ16OEtsrCx5yA1O9/IzuMkvB4r4vXz7Kq7UMruAUe76WnrDZQZXRWCAFzQwfM9evQeNEM2lNYIi0SA08k1E5o433KrJ8FCg9Q3TwlnqfTMDeup+3PFCdMA/k33wA0HS3zltJIOgCeSSP3yn+QefF/kX7hz7A2bYB34icROupbiHzjOPftGEGI4iihrTK9uQU8lZQdRkmsvl5Ev3Oy7DCUx+NxsFDE9SMipEAIAWP1u+DxXrTMXUojDpJFvvN9xH91p+wwhsQTccQUmqHBXPyZFUIg/97b8Ow2gc49hfFcFvk3XoPW2IimaVeCaZrskKTgyQRyr/4X2ZdfQu6//0Ju1euAxhA48FDETj0TocO+SkksIQ6ihLbK9KZm8HQaQghXPDnvXz+rj3Tp+lnGUK0hWt7XC81FU8lVpcLobP8UMZ5Jo2XuUnpIoQC9rR08EVe27xRCFKZEt42QHcoObk4Era2bActE4/mXyQ6FDIKnU8i98So8o8ag6eKZssORomvpXCRXvw1j7RrA64X/M/shcOgX0HDepfBP3g9aoHb2gSZEZZTQVhnz+cG8Xohs1hUb3otcFhBCqWl0qrL6etHw4/Nkh0FsYH7wPniqsBaMps+pQQuFAd0Dkc0oubeqyOUAAEyhvbq1aEx2CGURhgHj/dVomj63bkf8VMcTceRWvgbv7nui8dxLZYcjj2Uh8p3vwz9pH/g+OQnMTw8/CZGBEloJtFAIIpMGXJDQ9q8JU3FERCWCWxDJODwjR8kOhVTI3LoF5tYttBZMQVokWii8pGJCm0xAi0aV6iuZ1ys7hLIYH6yB3toOj0Kj3eRDVl8v8m++gcbzpsE7YQ/Z4UjVcuUSxGLufHBESC2hR58SsGAIPJOWHUZRVCtyoire2wsWjtLU1ArJnm7M0ykYa95F82VXKpk01TstGgNPJmSHMSCeSkILu3eKryp4MgGrc1vdTmFVndXbU0hmL7q87pNZQog6KKGVIHz0sRCZjOwwisLjfYh+74eywyifEACKGzGpJJkyt22B3qpu9VUyPME58m+tgnf87tSWiop85/vgibjsMAbEU0mEjz1JdhiuJoRAfvU78E7Yg6ZuKsjq7UH+rZVomjqLqvkTQpRCCa0EWrQBIqt+QitMEzyTUXqbjOGJYvPZ8o8gOHh3FxpOP9fZA9U42aOz5rq1YH4/Gs6bJjUOMjhP2wiIVAJCVGkvrhLwdAp6U7PsMFzN6twKWBwN9bwmU1FWvG9HMkvVegkhqqGEVgItGoPIZmWHMSyeTBT2MdR12aEojcfjYH6/qyuK1jueTMDcvBFN065Qag0k2RXz+wGvDyKt1pINwTlENktVzisguAXj/TVoPO9SOgcVw5MJ5Fe9jsaLZlAySwhREiW0EmiRCHjOBQltvA9alNbPDof3dENvapEdhqvJHJ0VnCP/zpvw7jGxUEmXKE2LRMGTak07FtkMmN9PD/8qYG7aCC0cpoRJMTydRm7la2g871J4x46XHQ4hhAyIEloJmM8PxgpTelXGE32Ifudk2WFUiDm+Da3V043o90939iDEMeb6dYWpxudMlR0KKUIhoU3KDmMXIpOhImIVEKYJs+MD2nNWMSKfQ+6NV+Edvzu8E/aUHQ4hhAyKElpZfH6IfE52FIMSQoAnEtDbR8oOpTKMwcmMVuRyENm0+98niWSOzvJ0CuaGDjRdMoemObpE5IQfKFfpmGczYAH1t2FTlblxPbTGJujNNNNFFcIykVv5GjwjRqKR6goQQhRHCa0kzOuDyOdlhzEokc0CjEELR2SHUhnm7Ait2bkVeksbTTV0ISEE8u++Be/43Wn9s4t4WtvBkwkIwWWHsoPI5RD6+jGyw3AlYZkwN65H43lUCEoVQgjk334TWjiCxqmzZIdDCCHDooRWEub1QhiG7DAGxRN90KK0WfhwrM6tiP7gDNlhkDJYmzYAABpomqOrML8fzOeDSKtTKV7ksu5/+CeJuWkjtIZG6I1UIVoV5gfvQ5gGmqbPo5krhBBXoIRWEub1AaaBnGFgya8fR06x5JYnErYmtLlcDgsWLEAuV+Vp1mLwbXtyXODGDV3I8fKGcIWRh0ilbCtiIu09GkQ14hlqurGT5wbPZmB88D6aLplt2w2bau03FDfFOhAtEkW2t0eZvlPkc9Ai5SW0bm+LSs5TITjMjevRcM7FxR2ryu+V29umHOa2LTC3bkHzzIWDzjxy2/vitnid4Kb3wE2xEnVQQisJ83ggjEJCu/Sx5UrclO2MJ+KInniKba+Xy+WwcOFCCQkt376O9uPyQuDmTT3Il7mnpdXXC62hwbbpxtLeo0HIjsepc0MIgfw7b8EzdjdbR4Vkv1+lcFOsA9EiUeT7epTpO0UuD1ZmhWy3t0Ul56nVuQ0sEICntb24Y1X5vXJ725SKp1MwVr+L5suuhDbEmnC3vS9ui9cJbnoP3BQrUQcltJIEDz8KwrJkhzEgwTl4Kgm9tU12KJUzLTBt+I95OYWJRDIJLUxrL8slqxiUuaEDEByNF86QcnxSucgJPwBSKdlh7CCMHLQgFYUqlblxAxp+eI7sMAgAYVnIv7kS3vG718a1nxBSVyihlYR5fYCl5rY9Ip2CFgiA+fyyQ6mYMA1Ac6ZgkzDyCB19rCOvTZzBkwmY69eh+bK5RT3oIGrytLZDpJKDrSaoKmGZAFihTydF4+kURDYDz/g9ZIdCABhr3gMLh9FAFY0JIS7kkR2ASqztI6bxeNzxY+VzOSRTaeS2FzZJKFTgxOzsBPf44LPxfeh/T+1+b3t7e4d8XaOnG0nTRD6dRsLatSpqcvv3SYsjnk6XfOx83oAVj8Nv03+TU+9RuZyIp7+9innPEzafG0II5F97BfrY3ZAUAGx+n1Vrv6GUEutw55gsCQFENSa97+TZLPJcIFDm++PkeVaN96bc89RY3wERa4S/hD2Fq32OVfN41Wyzj+LJBPKbN6FlzmIkEsNvieWmvg5w9hyr5/fAKU7fL3Z3d9v6usQ5QggkEgmMHj0a2jCDEEyIMhcQ1qCXXnoJhxxyiOwwCCGEEEIIIaTudXR0YOzYoQuw0gjtTiZOnAgAWLlyJWKx4iv89t5zKwDAO2Zc0X/Tc+tPAQApi+PIlevw/N67IayrOQWy6cLpRf+usaEDwUO+AP8+++/y80QigUmTJmHVqlWIRu1bd9rR0YHPf/7zg7ZZzy3XQmtqgaeldcd73k+1936oeEppA7skMxkcNHUG/n3zdYgMsT4w37EWoS98Bf699x32Nfvb67lJ4xDxDD0VXLX2GU414rXrc1Bs2wLA+m3bcOQVC0vuF4vVc+dNYF4fvKPGFPf7ivedxbTRjuI7l893pG8cql8URh7dy+YjcOBni369j/adO6tmO1RyrGLaJfuff6Fp+ocFkZy6bg2klL5RNpnnXjl9YCn9XbH6+8WXbroW0VDIltcczlDn4XBU7S8H4lSsm7N5HP/ORnR0dDhyLSP2i8fjGDduXFH9LyW0O9G3V6sdO3ZsSR/21uNPQvpPz8LX2lL03wR8hbe+fxrsCJ8HUUU7mZYS/rvyiV6ERo9C4CNPUvqnjowZM8aRjmSwNgvGotCbm+BpbdnxnvdT7b0fKp5S2sAu/VOCR7U0IzbEBTvXtQXRCbvDN8zTs52N9HuHfc9Va5/hOB2vnUW0im3bnZXaLxYr3NICCAFvkZ9x1fvOYs5Vq0+H0dyI1rFjHe0bB2oznkoi0NiIYBnXq4FUsx0qOdZw7SJME5lICG17TtyxlZfT162BFNM3yibr3Cu3DyynvyvW6NYW219zMGMWLEXXkrll/a2q/eVAnI41FotRQusyxWyvqPan2iWYxwN8ZH1m3eLctm1sbCEEmKZC6Zgali9/yxJCQkccrWzFd8dYFpgu53myME2ACqJ9jMhmoAWCtu1LTYgTZO0OQIjq6KpmA+bxKluxuOq4BXi8sqP4kBAAc/fHXPULmMjnoQWr84S6Xqn+GagE86lb8d0pwjQBWQ/+TBPMocrvbsYzGTDaeom4QC1fDwgpl7vv9FXh9ZY9wuBjDFNHNcFXI0+FxSAjtH6/H/Pnz4ffX+WtgAQHBnlvVXvvVYvH7/Vi9ndPgN879AMKYeTBwvaP0Kr2fgzHqXiduHkptm2rwusrJHglUvHzUXRbWWZhZg+q3zcK0wBsnMZXzXZw8lgimwEL7JrQSrtuKU7GuVdJP6hUf2eTUt8PFfvLwbgpVqIOWkNrA+b1FUYmy+DXGC4dU/31kY6xLMDz8Y+V3+/HggULqh6OEGLQhFa1936geGQ+ifV7vZhz8olD/o7gHMI0PnYjaMvxFWuf4TgRr1PtX0zbVku5I7Ru+3zsTFjWjhHaaveNhSnH9o3QVrMdnDyWyGYR/ubxux5P0nVLdW4791Tq72RxU5u5KVaiDhqhtQGrYIS25nBemIKtCi5ovZiDRD4P5vXRujNSNub1QZh11n9alrxpv6YJpnhRGLsV82BIZNPQGhqqEA0h9qCpx4R8qL6uag4prKGtsxuyQQhu7ZhKp4QhRmhV54aLlcjnwGg6niPc0P52YD4fRL2toeUWgocfJefYNo/Q1gqeyUCLNcoOgwygXvrCctB7Q0gBJbR28Hrrb4RhMBYfcMqxNILT6KGDRC4H5qOE1m71dJPCfP76eyBoWfJmspgGGM1a2YXgvFDcLuLsXrOEOKGerhekfoh8Dj133FT079NVzQbMV/oa2prtgLjEG7WBuHSE1i2fD5GnhNZubml7uzBv/VWJF5ZV+O+WcWwaof0Ykc2C+f2U6BPXqrfrBqlt+ffexqbTT0TqD08V/TfUe9ugsIa2vm7IBiMUG6EV3P3b9qhM5HMIfW2K7DBqRl3elHh9EBYvFHCrFxITWliWrVWOa4HIZqDRlj1Kqss+kZA6JThH/MF7semHx8M7djeMuu83Rf8tXdXsoHsALiA4lx2JfMqtoeWA5q4RWjddwEUuBy0ckR0GcTHGWKFwW5mV4l3JsgBpI7RGXe1DW0x/yjNpsADtpU3czU33DoR8lLlpA7acdxp677kFLVdcjdZlt0JvbC767ymhtQFjDMyj1+Q6sJI7SOUSWndOOXYLkcvSujOb1PPNCNN1oI7qEAjLAvP4pBw783/PU+X3jxCZDMJHf0t2GOQj6rlPLBe9Z8RthBBIPvMENn7/GEAIjH7kGUS+eXzJ9W/oqmYX3VNYm1THhBDbpxyrtYaWuWjKsdsuRiKXo4TWBm5rd9vpnqK3PquJ90rmGlpuUUL7EYUte5pkh0GILWqijyR1wertRuesi9B19RVoOPMCjLj9AXhGjSnrtRQaSnM35vHUXWGTjxEcYEytwhrcfVOO3UIIXigKFQrLDsXV6OZj+whtHU05FpYlr9YA52r10QoobNlDe9ASQki1ZP7f/6Fr8WxozS0Y9asn4Jv4yYper26ual/84hfBGMPatWudOUAJIww1y7IKN6YKEYJTUSiHiFwezOtTrs3dhJLZ7XS9vvpPLrsoFJ2z/QTnhZkm0ZjsUMhOqG+sDL1/RFU8k0bXNfOwddo5CH/z2xh1/+MVJ7NAnSS0y5cvx4svvujoMZjHA5iGo8dQnVDxRokL14zQuu0CJHJZsABVBi2X29rbSYU1tPUzw0Xqtj2cHvLtbMeWPapduwipEF1jiGpyb7yCTaceh8zf/ooRtz+Aposut23rx5q/quXzecyaNQtTpji8tUi9jTAMRLER2sI2IO5aQ+smIpsBCwRkh0FqQQ30n8XePAohCkshZNUa4BysTrbtKaZNRCYNLUgVjlVCiZh96L0kKhCmgd47b8bms06Gf58DMPrhpxA44BBbj1Hza2hvu+02HHzwwfjEJz6BFStWOHYc5vFSUSjVRmgt0zUjEW686IhMBhqN0JbFje3tpLpaQ7u9KFOpFRztOz4H6mjbnuHwTBqMElpCCHGEsXYNOudNh7lpPdqW/gyhr37dkeO4426/TN3d3bjuuuuwdOlSx49VmHJc3wltYYRWoWckFhU/cRLPpBE+5juyw3AdSmYHUE81CCTPZBGcU5XjnYhMBuFvHCc7DEIcQ9ccIoMQAvHHHsCmU4+D3tKC0Y8841gyC9T4CO2iRYtw6qmnYvz48SX9XTwe3+V7v98Pv3/oOd7Bw49G+vnSRoBb5ixG15K5Jf2NyoRlOj5Cm8vlkMvldnyfSCQADNxmXtqewlEik4bWOPRWF4O1V9Liu/yejzH4XbLWudblDAM548N6AMlMBkB5/WKx6mkNrRMzWUrpF0EJ7S54Jg2tobHqx6W+cWCqJl+D9YuJdGaX3/N7vfDLKvg2hFq73yxGjgvkhdjxfeoj5xZxjrltC7oWzULulf+gadoViBz/PcdnJdVsQvvuu+/isccew5tvvlny344bN26X7+fPn48FCxYM+TfM76/7olDVGHlYunQpFi5c+LGfD9Rm86ZfBsia1lcCVS/gQxFCgGfS0Ie5ERysvT732tpdvp86qgmXjmmxMUI1uaGtr1/+Oyx9bPnHfl5Ov1is4OFHIf2nZ215LeU50E+W0i9eyC2aubITkUlDH+bBnBOob3SXwfrFT51z4S7fz/7uCZhz8onVCosM4eebunHzph7ZYdSd1HMr0H3NPHjG745RDz8F77jSBhXLVbMJ7cyZMzFr1iw0NJS+t1xHRwdisQ9L+BczCsF8PlpDa1mAw1OOZ8+ejWnTpu34fsOGDZg0adLAbZbPuSKhdSORzRS27PH6hvy9wdrr7/tMQGSnwjS+OmgnNySzAHDZCcfhwmM/LKK3qasbB02dUVa/WCzm9QFmfUw5dqKfLKVfjC+8vC5GaIsqCGWaEKYJFo5UIaJdUd/4cSr3kYP1i2/ddSuioQ9rSag4Otuv3kZpzx/VjLNGfviwakvexJEr10mMqLbxRBzd1y5E6rkVaDj7QjT86CeF5ZhVUpMJ7QsvvIA33ngDjz76aFl/H4vFdrkJKAbzB2ouoS354mKajo/QfnSaY/+UuoHazMqkXVMUym14MgmtiJvAwdoromuI1kmlVUDtG7WP+uiUuf4pdeX0i8ViXm9hyUI9cKCfLKVfpCnHH+qvcCyjQBf1je4yWL8YDQURC7mnqFg9JbV+jcGPD8/tJJ1Xjsn++x/oXHA5WCCIkb94DP5Jk6seQ00mtM899xwsy8LBBx+842ebN28GAEyZMgU+nw9LliyxdSufwpTjOrkhG4SwLIQOP0p2GB/iHEzxtUduSnR2JlJJaJGo7DBIjWBeL1AnRaGEZQJVfGr9MdwCoyrHALZXOHZRMlLL3HotdKN6SmqJs0Quh56f34DEr+9H9KRT0HjhDGm7X9Tk44pFixZh9erVeOWVV3Z8nXvuuQCAFStW4JVXXrF9X1rm80PU+xpa0xx2Cmo1CW7RCK1DeKIPke98X3YYrkA3akXw+v5/e/cdHlWV/gH8e+/cSe8JARJqqIKAZQEbioJSlP1ZUFBxqQpIR5CmdFFh7Q2VtosNKcuKCggKCOuq64rSWxJaEpKQkB5mbjm/P8bMElKnnnvvvJ/n4dFMMnPfuee2097jUpZjQ+9TztngKcvx/7AyWoOWEELcYT9xFFlDH0TZN18i8fUViJs2l+tSjnRX8xIxOIR6aFXF0VOtF5pGc2h9gDENWnERLImNeIeie4auePmREBTkWDc6ADBFASQ+PaSMMRpyfAWtrBThAyiBD3GdZrfV/Uc6Rvcm4i6mqihc+yGyhj0Ea8tWaPzplwi9uQfvsMxfof36669x3XXXYfny5QAcQ46vu+46728oyNHDwFgApwVXFAhBOqvQ6njIsVFvKFpxMYSQUIghIbxDISYhWIPADJwUyqVzWVEQdienqRmqY9QKjzmj/lTf8mBlZbDExvk4GlIXI94L1ewLvEPwmBH3O+FLycpA9tgnULjyXcQ/twQJS97gkiW+OqacQ3ul/v37e314cXUEQYAg/bGWoo6G3foTUxRHT4teUA+tT2h5F2GJpWUk6kIPC/UXUD20Kr+GP6YotGTPH5imQbtcDjHK9ZUQSGBjqgo1N4d3GIT4DWMMpV9vRv6yhQhq1wFJn34JqXEy77AqoTubN0lWMDkwHsqqpbMeWqZppu+J8DfGGJS8XEQOGck7FGIigbTsGVM4Ts1QFcDHmeiNgpWXQwgJ8euyEqQqIzb8KdlZECMdSRHVvIuco/GMEfc/8S+14BIuzpqIvBfmIHrkODR89++6q8wCVKH1KkFyfekJM11M9DmHVp+HuFHLnZUUA4zB0qAh71B0zajly41FAsAcCYvMTpEhBPMZrs9USpRXQSsrgRgWzjsMYjCMaVDOn0Pko8MBAPLpNNjTThr62kX3K1KT8h++R9aj90E+k47Gf9uE6CdG+Xx5TnfRnc2LBKsEyIGb6Zgpsg4rtNRD601K5nlIjZKo57sW9HDgOkEQHJl/A6CXlskcp2YoCgRaixHAHxmOqULLlRGvlWpuDoSQYEgNEgEAcTMXgpWXwXbwNzCDJ4oipIJ2uRz5SxcgZ8qTCO87AI3XbERQm/a8w6oV3dm8SbKaZukeV280TFMBxiBI1rr/2F8YVWi9SSsvg5qfh+inJvEOhZiRJLk8wsWIHEOOOfXQKjJg8jVo63vv0kpLKMMxcQljDMrZM4geOc75mhgSgrg5S2CJicXl3/4LraSYY4TuM2LjAvEN25GDyBpyP8q+/xYN3/07YifN1FdnVQ2oQutFgmQNmHlgVSiKviqzAJjGdFmhNeKNgzEGOfUEpKbNKbtxLYxYtnohSIHRQwvFDoHXOaQoAPXQAnAs2WOJo+R2vBjxWqlezAUsFkjJzSq9LggCYifPgjWlNWyHfnf8HSEGwxQFBSvfwYURjyC4Y2ckffolQm7szjuseqM7mxcJUuAOOWay/iq0YIx3BKahZGWAyTJinn6Gdyi6ZcQHND0RJMn0DYKMaWCKyrGHVoFg8h7a+mCaCma7DDE6hncoxCAcvbOnET1yXI1TbmKenIjY6XNhTz0B+fxZP0foObqHBS753BlkP/UYij9ZjYTFryJhwTKIkVG8w3KJriq033//fZXXDh8+DLvdziEa14X17mf6B7KaMEUGrDrLFqnDIcdGvGGoBZegnD2N2GlzdZsMgJiAxbUKrRHPJcgKBEniNgedyTJlOcYf82dDwuh6xokRz131Yi4gipCataj176SERMQ/twRqdpYjWZTBGtaNWDbEfYwxFG/8BFmPDYAQHo7Gn36J8N79eIflFl1VaA8cOIDDhw87f960aRPef/99LF++nGNU9ScEh5hmDq3LZFl/PbS0bI/HtOIi2I8dRuzkWbDQeo01oocAzwmSZMi1aF0peybLEHiuU67YTb0OrSvzZ4VwSghF6ocxDcrZdEQ/OaFezxRiRCTin38JWnEx7MePGDoDMjEvJTcbOZNG4dJrLyJ24gwkvrkKUmIj3mG5TVd3tkOHDmHu3Ln45z//CQD44osvsGTJEjRr1qyOd+qDEBwSuEOOFRmCVWcVWvxvDm3ekuc5x2I8WlkpbEcOImbsVF2uOUZMJgByEDhGsvC7TjJZBmjdVWilJRDDI3iHEZCM2Pin5uYAFglSk/o/iwrBwYh/7gVAVWA/esiRONMgjFhGxDWl33yFrMH3QispQuNPtiDy4ccN3wGkqwrtgAEDsHHjRgT9saTBpUuXEBERAavuKkrVE0Lc66E1w8WDKQqgsx5avSWFMlI5M7sNtkO/w9oiBdYWKbzD0TUjlauehd15j+mTQjG7nWvDH5NlmkMLQCspQcQDg3mHQQyAaRrkM+mIGT3J5Qd+QbIibtZiQBRhP3LQsQ60QdB9zZzU/DzkzpqIi/OmI2rISDT64FNY6xhGbxS6aqrdtm0bdu3aBavVipCQEGRkZCArKws5OTm8Q6sXITgETDb3A1lNmCwjrFdf3mFUxhgA/VRojYKpCmyHDkBqlISYMVN4h6NrdNP3HiE42PxTNhS+Q47Ldn0T8PNGGWNgpcWwxCfwDiXgGPF6qWZnQQwJgZTUxK33CxYL4mYuRP7L82A/cgBBHToH/DlI/I8xhrIdXyN/2QJYEhuh8ZoNCGrXgXdYXqWrHtr58+ejV69emDNnDuLj47F371689tprSE42xnBHISQEUIyRwKo2bt10ZDuEkFDvB+MJHSaF0jumabAfPQQxMhIxE2fwDocEEEcOAnM3CPLuoYXqSEplRvW9bzG7DRBEiGE0h5bUjqkK5LOnEe1hdn9BFBE3YwEQFOzoqTXI8GMjNkCQqtS8i7g4YzwuzpuOyMFD0fhvG01XmQV0VqGNj49Hv379EBERgc6dOyM0NBRLly7FPffcwzu0ehGCgsEU1XBZ7byBybL+1idlTDcdtEa4MTDGIJ86DgiiI6MxNQbUyghlaiRCcAhg8h5aJssIu7s/v+0rSsDPoWUlxRAjInmHEXCMeL1Uzp+DGB0DKSHR488SRBFxz84HrNY/5tRSoijiW4wxlGz9JzIH9YOSlYHGa/+BmJHj9JfA1Ut0VaE1OkEUIUgW088Dqw6TZR320NKQY1coZ9KhlZYibsZ8U2dCJfokBAebfsoGk+0QQsL4BaAqECyBXaHVSighFKkbs9ugZJ5HzLhpXvtMZ0+tIDqyHzP9V2qN2BBBACXnAnKnjUXeolmIenwEGq1ej6DW7XiH5VO6fmo9cuQITpw4wTsM10hW888Dq4YeK7R6SQplhBuCfP4s1Iu5iJu9iO+yIgZhhDI1GscIF9cqtEYrBybLEEL5XSfN2kPrynGglRQj/P5HfBgNuZrRzlMAkE+nwdKosdeXq3NWahUZ8iljrFNrxPILVEzTULz+Y2Q+0hdqXi4ar/0nooePNW2v7JV0W6E9deoU7r33XoSFhWHjxo0oLy/nHVK9CAGw9ES1FB3OoYV+hhzrmZKVASXzPOJmLYSouzLUH7q5+4YjB4GxGgNdPhZkme85JisB8WBTG62kGBYvDCEl5qWVFEO9lI/Y8dN98vmCJCFu1iJoJUVQzp72yTZI4LGnnkT2qMG49PYyxIydikYrP0dQqza8w/Ib3VVoy8rKAACtW7dGWloamjRpgoceegihHFu1XSFYpYBbi5ZpGpiqQggO5h1KZTTkuE5KTjbks6cRN3MhzSsjXFX00Bqhx8JdjiHH/HINMEU2bVKo+mB2G6CpECOjeIcSMIzWAMgYgz31BKzNW0II8t0zjWANQtysRVByLkC5kOmz7XiL0coxkDCbDQXLX0fWkP+DGBOLpHVbETXoLwGXTZv7nW39+vWQ/6gA3nvvvVi0aBFuuOEGAEBYWBjuv/9+jtG5wc0hx/GzFyFvyfM+CMgPZBmCZNVfEiFN4x6Tnm8CamEB5LSTiJuxAJboGN7hGIKey9NX1JJiv2xHEEXHDVjV35rW3sAYc1QoOfXQVmwfBlnX3Re04mKIEVHc7wtEv9TcbEDTEO2HJevE0DDEPTsf+S8+DyE4BJbYOJ9vk5jL5V9+RN5L88BKipGw+BWE3dU3YK9v3HtoJUnCv//9b9x0002Ijo5GdnY2duzYgU8++QR2u/GWwBGsVrBA66GV7fqcd2ninh5PaZcvw37sMGImTIclLp53OESntOIiyCeO+W+DktW8iaFUBYJo4ddqLtsdjQYmS/jm6vxZGoniP0ZrAGSKAjk9FTHjpvmtUmCJiUXs5FmwHz8CrazUL9t0l9HK08yUC1nInTUJ2U//BSE3dEXS+m0I79UvYCuzgA4qtIWFhXjrrbeQkpICALjtttuwevVqfPnllygu9k/PgDcJktXQWY7duWAxWYYQpL9W//Lvv9VFUii9YUyD/fhhSElNYG3SnHc4hhFoN3OtpAS2wwcQ/Zcn/bZNwSqZNqkes/PtHdXKywFJhw2PfqQVFyHiwcG8wyA6JZ9NhyU2DlLDxn7drtQ4GdaWrWA7fDDgOkSIa5jNhsJV7yLz4T5QLmSg0ZqNiJ+9mKZRQAcVWuWqyt/o0aOd/2/EuVRhvfuZ9oGsJky2A3rtoeVYodVrBUjJOA9BFBEz4VneoRiGXsvSV7TSUtgO/QZrq7aQmvqv0UOwWk2bg4ApsuP78dp+ebkuGx79hTEGrbgIlsRGvEMJCEa7ZmolxVBzshEzaSaX7ceMmQJLfALsxw/rejkfo5WrmZTt/Q6Zg/ujaN3fEffsPDRa+TmCO3TiHZZucK/QHj58uMbfHThwwI+ReIcQHBJwLWzMrs8hxwygHtqraJfLoZw7g5jJswN6aAqpmVZW5qjMprRGzFMT/btxMy97Jtv5Vmgvl+nyOu0JVx6u2eVywGKBGBbuw4gIYLxKD2Ma7KeOw9oihWsW8tgpswHGoJxO5xZDfRitfI1OPnsaOZNHIXf60wi9vReSN+5AxICHTDd9xFPc90anTp0wevRoZGdnO1/LzMzEiBEjnMmhjEQINt7SE55isoywXn15h1EN4/Xw+5qcehJSclOvr61nZoF089bKSmE7uB/W5i0RM3qy37fvzrJnRikfpvBdMkcrK/Np1la904qLaFgeqZaSlQkIol8SQdVGEEXEPjsfSm42lIs5XGMh/Gllpbj0zivIHNwfzC6j8SdbEDdlNuUBqAH3Cu2IESNwzTXXoFWrVkhISEB8fDxat26NDh06YNiwYbzDc5kQHGzepCY1ke0QQsN4R1EV5yHHeqMWFkArLUXMuGm8QyE6pJUUw3bwN1hbtkLM2KlcYhAk4yx75mpFmsl859CWbd8CIchcPbSu0IoKETHgId5hmJ5RGpgqaLbLUM6kI3byTF2MWhJDQhE7dQ7kUyeg/bGMpR4ZrZyNhGkaSrb+E5kP90Hpti+QsOgVJL6zBkEpgbOmrDu4L9sDAJMnT8aQIUOwd+9eiKKI2267DfHxxsy8KoSEuj1kzqhL9zC7HaIuK7TgVqHV28WeMQY5PRXRI8YG9DqUrtJbOfqKWnDJkfV67BRYW7TiFkdY734o3f4lt+37lCwj7M4+3DbP7DZT9VC6em5qRTR/llTGGIN86gSkpCawxOhnyRypQUNYm7eE/dhhBF93AwQxsNYTDVSMMVz+YQ8uvfMKlLOnETVkJKKGjeY6DN5IuPfQVkhISMADDzyAPn364OzZsygpKeEdklvEkFDD9DBczd2HdybbIYTq8YSjIccVtEv5gKrA2qot71CIzii52bAfO4zYybO4VmaBPxoEZeMt11YfTFEcU1J4bd9uD9ghx0xVoJWXwZKQyDsUUzNaA6B6MQesvFyXCRKjx06FEBoKOS2Vdyg1Mlp569nl339F9ujHkPPMWAR3uRFJm79FzJjJVJl1gW4qtACwd+9e3HHHHZgwYQJuuukm7Nq1i3dIrgsKAlNVXWep8zYmy/pMtMGxh1ZPGGOQz6QjasTTlETABWa/WTPGIJ87Azk9FXHPzofUOJl3SH/kIDDnlA2myJwrtDYIwYFZodWKiiBGRPJbA5joDrPbIaedQszEZ3V5XAiCgLhnnoeafxFqfh7vcIiP2E8eQ84zY5D95GBYEhshacM2xM+YD4ka31ymq6fbI0eO4KeffsK+fftw6NAhY2Y5FgTHkE6TPpRdjTHmaPnXZSsSA4/qrN4qQlr+RYBpsLZszTsUohNMVWE/cRTqxRzEz3kBlvgE3iEBqMhBYMwRLnVSFAgh/CqUzGYzTQ+t68ONCykRno/p7b5XG8YY7KeOw5LYyO9rzrpCCA5G7MQZsJ88BmbX58gVI5W7ntgO/46cZ8Yg67EBYIqCxms3o8Hi12Bt4r9l8sxGV5PpmjevXJBJSUmcIvGQZAWTZdMtkVAtVYEgivqcl0k9tM7e2eiR43WR8MIozHyT1i5fhv3oQQihYYif+xLXzLtX8yQHgd7x7KFligKmqlyTUvGkFhYgevhY3mEQnVBzs8HKyxA3YwHvUOokNU6GJbER7KeOI+iaa+k+bmCMMdh+/RmFq9/D5Z/+hdA7eqPRmo0I7tiZd2imoKtayKFDh1BSUoIGDRogLS0NFy5c4B2SWwSr60tPGBWz2wHdVtxpDq16MQcQREjNW/IOheiAWlgA+7HDkJKbImb8dN09HBk5B0FdmMxvDq1WUgwhOFh35e0PTNPASop03RNndEZqANQuX4acdgqx0+fqsyG+GrETZyBv/rNQc7Mh6TCxmVETmvoLUxSU7dmB4k/WwHZwP8Lu7o/Gn36JoNbteIdmKroacjxx4kSkpqZi2bJlSEtLw7PP6m+ifn0IVitg0sQmV2OyDCEoMFv9q6OnGztjDMrZM4geRb2zrtBTGXqTkpUB+9FDiBk/DbETntXnMeFmDgJ/l5lb21NkbkN+HRVafvN3vcnl4cYlxRBCQgN2/rCvGel6yRiD/cRRSMlNDTVHUZAkxEyeCTntFJjNxjscUk9aSTGKPlqJjAd7I2/es7C2aYek9dvR4IXXqTLrA9ybp2bPno24uDg0b94cvXv3xowZMzBjxgyvfPZvv/2Gd955B/v27YMkSVBVFb1798bzzz+PBg0aeGUb1REkye15YEZr6WJ2u36HVgf4OrRqbg5gESE1pTkZgYwxDXJaKrRL+YibvRiW6BjeIdXImYNAVgATrZnKGANTVW6VKq2kGGKIOSq0rtIKCyDq+Jgn/qNknAMYQ8z46bxDcZmUkAipURLsqScQ3KET73CqMNqzqy/Zjh1GyT/Xo/TrzRAjIhD5yBOIuH+Qru+9ZsC9Qnvw4EFs2bLFJ589ePBgdOzYEb/88gvCw8ORkZGBXr16Ydu2bfj9998R6qulZqxBhhty7NGSPSZ68DQLxjTIZ9IRM+4ZffbE6ZSRehvqg6kK7McOA4w55ssaoZfKanXMNzXTdUVVIFgs3M7Fsq3/NEUPrTvnp1ZUgMjHRvggGmKk66VWUgzl/FnEP7fEsNn+YyY8i7x506FczDFUD3MgUIsKUbrtC5T8cz3kE0cRfEM3xM9ehLBefXWVp8LMuJ/V/fv3d/7/qlWrcO2112L16tVe+/yXX34Z4eGOJWWSk5Mxffp0nDx5El9//bXXtnG1sF59zZup82p67qENYGpONoTgYFiTm/IOhXDCZDtsB36DYA1C3JwlxqjMAhD+SKpnJkxRAI7z9bTL5RACsIeWMQ1aUaEulqQi/DBVge3oYVhT2kCMjOIdjtsESULMhOmQU0/qstPESA0c3qBdLkfpt1uRO2sSzve7BUWrlyP0ljuQtGknGr3/McL7DKDKrB9x76ENvuIha8SIEZBlGcOHDwcArFmzBsOGDXP7sw8cOICgq1r5KzInX7p0ye3PrYsQEhpAc2jtCL/vId5h6IJeLuZMc/TOxk6ayTsUQ9FL+XkDs9lgO/gbLPEJiJky21C99ILVCpgt07GicE1Awy5f1unSar7FSksBaxDE0DDeoZiOka6XcupJWKKjETN6Eu9QPCY1SoIlNg7ymXQEtWrDO5yAo5UUo/ynfSj7djvK934HiCJCe9yFBi+9hdCbbzdMojEz4r7nN2zYUCmb8f79+5GX51hEeseOHR5VaK+uzALAiRMnIAgCbr/99hrfV1RUVOnn4ODgShXvuoghoabrYagJs9shhvnvYcFms8F2RVKE4uJiANWXWaBSs7MghoVBasR/2auayqtErZz0J0gQECwap9KlZ5rtMmwHfoPUqDFiJ7qej8CVc8wX55lgpR5aV9VVZuXFRbBpDCGyjGCDLt3jTgVKLSyAJTrWB9F4jq6N/qHkZEMtLETCgqUefQ7v6+KVYibNRN686dAaNoYYEeHTbblKT3NpbRqDnf1vtYtS1bVkg8AfSx+eOILyH/ai/N97YDuwH0JwCEJv64mEhX9FyM23B2x+Ar3hPuS4pKQEsiw7/1177bXO/9c01w++2qiqipUrV2LkyJFo27ZtjX/XtGlTREdHO/+9+OKLLm1HCKQKrWyHEOK/Cu2LL75YqWw6dOgAwPMyMwumaZDPnUH0U/poia6pvG4+cBqd9qc5/72blc81TiP1NtSG2e2wHfwNUuMktyqzgA7OMcm9Zc90XYaKAsHiuwptbWUWHxONlz9bjyYjn8YrG//psxj0SCssQMSDj/IOo1pGuTZWR9fn2hW08nLIaScRO3W2x1OjuF8XryCGhMLavCXsaSfAGC1PWJN3s/IrnUu9D5+t8z1MtsN2cD+KPlqJnOlP43zfm5E15H6UfvMlgjtdj4Zvr0HTnT+jwQuvI+zOe6gyqyMC43w2fPvtt+jVq5fLv3PH/PnzsWXLFnz//ffOebVXKioqQnR0NM6dO4eoqP/Ns3C1xU0tuIT8pfMR2vVmt2P1ZwuXJzen8p//jbhZC2GJivZiRDW7upU0IyMDHTp0qLbMSt94EWJEJKTGyX7Zn3q4ySsXMqHm5iB+7ku8QwFQc3n9u3MLRFj+157GuxdCD2XnKaYosB3YD0t8AmInz3L7c1w5x3zRE3F5/39QtusbBLVs5fJ79XqeK9lZ0AouIW7mQh9EVHuZRagKit9ZBkuXGxFstbrVQ6uHHhdX9ztjDJd/3If4eS9DDNdXLxZgnGtjdYxwvWSaBtvvv8KS2BCxXshqzPu6eDWmacibNw1S0xaQGugvQZQerhlX99Bm2xX0PnwWhYWFiIqKglZWCvnUCdhPHoX95DHIJ47BfuIImM0Ga0obBHe5AcFdbkRI15t1uf5vIKiol1WUWW24DjleuXIlbrrppkqv/frrr+jYsSOCg4O9WpldvXo1Pv/8c+zevbvayuyVoqKi6txxtRFDw4AA6qEVfZUtuhpX3ywqhvtUV2alfotKHzd4xhjkc2cRO0E/SxLUVF4RFhGRFu4DRADoo+w8xTQN9qOHIEZGIcbDudOunGO+IISEmm4OLZNlwIdDfWsrs9C8HKhR0Qj249QQb3PnHGWlpYBk1WVlFjDGtbE6RrleyqfTIAQFIWbcNK98Hu/r4tUEUUT0mCkoePuvsMTHQxAtfo9B74JFAcH4X2NQyR/nVc4zo1F0IRPqhUwAgNSkGYLatEfIzT0QPfJpBHW63m+dNMR7uFZon3jiCWzduhXr1q3DI488gmuvvRYtWrTAihUrMG7cOK9tZ+3atXjllVfw3XffITHRDy1ZQUFgmgqmaYZND18fTFUAMMpyrBNqXi4EqwSLDubOEv9hjEE+dRwQRcROe95QCaCqIwQHm2/Kho+HHNdGLSqEEIBJkdTCS7DExPAOg3Cg5udBvZiD+HlLDX89rI01uSnE8HAomRmwNmnGO5xK9DSX9mrWdtci5s8DYW2eAmurNrpt9CKu4VqhzcvLw5EjR5CRkYH+/fujR48eKCoqQpcuXby2jY8++ggvv/wydu7ciUaNHEMGvvzyS2RmZuKpp57y2nauJAgCBGuQY36pCdb+qwmzyzqvzJr3RlYd5fw5RA8bY+obuLcZpbehNsr5s9BKSxD//IumaEATgkN0uSSFJ5iqILzvn7lsu2z7FogRkVy27Q3unqNaYQEiH3nCy9EENiNcL5nNBvuJo4idMjsg5jfGjHsG+S/Ng9SoMS0RU0+xT01ABIdedeJb3Cq0R48eRZ8+fdCpUyckJiaiT58+zgfxefPmeWUbH3/8MZ588kksWrQIO3fudL6+d+9eNG7c2CvbqIlgtTqGHZu5QivrfA1aAUCAJExQCwvAZBlS8xTeoRA/UvMuQsk876jM6vlcdIEYHGLKIcdCEJ/M61p5GSwNGnLZNi+MMVp/NgAxpsF2/AikpCa6yPLvD5bYeFji4qGcPwdrC33d//XcS0vMh1uF9vPPP8evv/6KhISESq+vWLECRUVFiI+P93gbEyZMwOXLlzF9etU5hd6qNNfE0UNrroeyKmQ7hGqWRgo0emi1Vs6fhbVJU1P00PmLHsrNE1pZGewnjyF26hxTDZkSQtzvodXtA5SqcButw8rKIPgxz4EesLIyQLQYumdab4xwvVTOngEAxEx4lnMk/hUzbhryFs6ElNSEnslIwOL29BsfH1+lMgsAw4YNw+bNm72yjfz8fDDGqv03f/58r2yjRlYrmGx3++3+unl4sh1mtzt6onVLQCD0z2rlZdCKixD91ETeoRA/YaoK+9FDsDZvCamhb0eb+JtjDq37105fcvd6yRQFAoe1sZndBqaq3HqHPeX2cOOiApo/60VGqMyqhQVQsjIQN+35gGvYFSMiYUlsCPl83cvSEGJW3M76xMRErFu3rsrrkiRBVVUOEXmXYA0Cs+vzocxbeA6jq5cAGXKsZJyH1CiJ5s+4wAgPaDVhjMF+6jjEiAhEj53KOxzvk6wAY2Ca8e8DTorC5VqpFhZADA0LuHn1asElRNw/iHcYxE+YLMN+/Ahinn7GVKNVXBEzdirU7EzdPXca+V5LjIVbhfbhhx/G3//+dwwdOhQ///yzc3HorKws5OTk8ArLa8J69TX9kGMm2xF2d3/eYdTC/A9xTJah5l5A9OhJvEMhfqJmZ4GVlJgio3F1KpLqQTZPYiimKFyGAmqFBRAMulyP273hFfNnk5p4OaLApPcKSUUDnyU+AdbmLXmHw40YHgFLg0aQM87xDoUQLrhVaAVBwIYNGxAaGorbbrsNkZGRaNGiBW677TY88YTxMxMKoWGATofNeQuz2yGE6PhhKQB6aJULmbDEJUAMq31tZWIOWmkJ5NNpiH1mjql75AVJAjNRYiimyBA4ZFwt3fZFwF0bWHk5IAgQIymLaSBQcy6AlZUhdvJs3qFwFzN2CtQLmbrrTNF7owgxB64TDUJDQ7F8+XKcOXMGH3/8Md566y0cOHAAzZs35xmWV4ghIbq7qHgbk+06TzZivt6rKzGmQcnKQNRI763ZHAiMenNlqgr7scOwtmwFS6znSfN0TbJ6lBhKT5imAoxxaYBgZWWG7KH1pAy1ogKIUdFejCZw6e1cuppWXg45PRWxU2ZDkLiuQqkLYkSkI+NxVgbvUAjxO13MnG/cuDH+7//+DwMGDEB4uDlak4WQMI8rtL6+mXj8+XYZop4flvzQQ8vzhq/l5UEICoYUYEtyBCo57RSE8AjEjJnCOxSfEyTJseyZjrh9risKt950rbw04Hpo1YJLiHhgMO8wiI8xpsF+4gikJs1gia+aYDRQRY+eBCXzvO5yEOi9cYQYny4qtGYkBEwPrY4rtCbvoZUzzyHqiSd5h2EoRr2pKhdzoBbkI+6Z53iH4hdmGnLMZH4VWmbX+yiaqjw9R7WiwoBZg9SX9H6tVM6fAwQRMeOrLssYyCyx8RAjIqHmZPMOhRC/ogqtj4gmn0PLNA2M49qK9SEApp1Dq5WVgpWVwdqyFe9QiI9ply9DPnUCsZNn6TuruDdZg9wecqw3TFUATsMhxZBQCILnt3m9V24qaJcvA5oGMTqGdyjEh7TSEigZ5xA3dY4pE+N5Kmr4WCgZ553JVgkJBFSh9ZWgIDBNBdM03pH4BJPtEKxB+r6Z6Dk2DynZF2Bp2AiCxcI7FOJDjGmwHz8CKbkppMRGvMPxm7A779HdkGO3yTK3+X2CwYYbe947WwAxOkbf9yXiEcdQ46OwtkihxF81kJKbAoxBKyzgHUolRmkYI8ZEFVofEQTBMczMLA9lV5NlCFYDZFn9o4XSTBdSxjSoORcQPYKSQbnCiMeAcvYMBFFEzIRneYfiV0JwCPXQeoFgkpwU9aUVFiDizwN5h2F4er5WKufPQbBaER0AuQTcJQgCpKRkSg5FAgpVaH1IsFp1Ow/M0xsWszt6aPUs9PZevEPwCe3SJQjBwbDExPIOhfiQWlgAJSsDsc88F3A9To4KrfvXTj09kDNZ5jaH1kgJobxRZlphIaRGyV6IhuiRVlYK5fxZxE6hocZ1iX5yArSCfDC7jXcolejp2kzMhSq0vmS16j7TsbuYIgMG6qH1BV5lo17MgYUyG5sak2XYjx9BzLhpEMMjeIfjd0JIMKCjHlqPznVFoSHHfsBkO5jdBjHO5Eta+ZhunzkYg/3kcUjNW0KMiOQdju4JQcGwJCRCuZDFOxRC/IIqtD4kSJ5XaPWK2e0QgvTdQ2vGObRM06DmXUT0sDG8QzEUvT6kVYcxBvup47DEN4C1WQve4XDhaQ+tnjBVQVivfly2rftr9B+80jtbVAgxKgqCSI81ZqRmZwFMQ8zTz/AOxTCiho+Fkp1FyaFIQKArvw8JkgSY5KHsakyWuT2k1ZsgmC7LsVZYACE0lFqoTUzNzgIrK0Ps5Fm8Q+FGCAoGk/XTQ+sRWYYQzKdiGUjDMtWiQoiR0bzDMDS9Nvwx2Q75dBpiJ84MqGPaU5aERAiihZJDkYBAFVpfMtHSE1XIdgghOl/fUBBgruosoObnwRJHi8iblVZWCjk9FbHPzOE2TFUPhOAQQDVHYyBTlMBZbskN3nq41YoKEfHwEK98FtEXOT0VlsSGsMTTvc8VgiDA0rAR1JwLvEMhxOeoQutDYXf1Mc2wuasxWYYYot81aM1KK8hH5GPDeYdhKEZpDWaaCvuxI7C2SIElNrDnAQpBQWCK6tFQOb2UO1MVgCq0PsU0DVpJCaREyi3gLr2cL1dTi4ugXspHbIBleveW6JHjoOblgqkq71AI8Smq0PqQEBzilcQm3r7ReOPzmCwboocWJuqjZXYbmN0GS3wD3qEQH5BPp0EICUH02Km8Q+FOEEXHGssq/xEuHl8vFQVCMFVoq+O13tmSYoihYbrPvE9cwxiDnHoC1hYpNMrBTWJYOMTIKKh5F3mHUoleG1CIcVGF1oeEoGDTDjlmigxB9z20gpnqs1ALCyFGxVDSExNSL+VDvZiL2GeepzliFSTJFNdPpqj0MO5jWnERxKgo3mEQL1NzswEGRI+ezDsUQ4scNNSxLwkxMXoy9iEhOEhXS094lRF6aE1GKy6EGEVJT1xhhFZgZrfDfuIoYic8S8P4ryBYJHNcP1XFMNmG/cmb56ZWVIiI/3vEa58XaPR4nWSaCvl0GmLGTqFGPg9ZW6RAK7ykuylwejzuiHFRhdaHzNpDyxgzRKITwYdDjnlciLWiIkQOfNzv2yW+41hb8RgsiY0gJTXhHY6+SJIp5n0xVaGhsD6mFRfDQvNnTUXJzIAYHgGpcTLvUAxPCAqGGB0LNT+PdyiE+AxVaH1ICApyJAQxG1WBYLHof+iriabQMk2DVloCS4NE3qEQL1Kzs8BsNsROmsk7FN0xw7JnTHNUyAWLhXMk+uLNBkEm2wFFhhgd67XPJHwxRYFy/iytOetFkY88AfViLu8wCPEZnddIjE2wem/IsZ6GZjBZAQJ4SREeWFkpxOAQ6ukxEa283LFEz9TZVOGpjsXicQ8t9+umqjqGThOf0YqLIUZG0rBUN3E/R6qhZJ6HGBMLS1xgZ3v3JqlZS2gF+bob9aLH448YE1VofcjRQ2vCi4eqQJCsnn+Or5koy7FWUgwhIoJ3GIai5xulY6jxUUhNmwf8Ej01cfTQGnuEC1NVgBorKvH2eem4NlJCKLNgigIl8zxiKNu7V4khIRAioqAV5PMOhRCfoAqtL0lWQNPAmMY7Eq9iigJI9JDmT1ppCSLufZB3GMRL1KwMQGOIGTeNdyj65YUeWk95XPlSVep99zGtuIgSQpmIkpXh6J2NoSHk3maJT6B5tMS0qELrQ4IgQLCIgGquCi0UxRjD6ATzLNujlZRApOFXpqDZLkM+k47YyTP1Pw+do7Ce9wA6G+HiKuqhrcwXoya00hJYEmhtbnfobRQL01RH7ywt0+MTUUNGQc3PA2P6ejDS23FIjImepnzNIpkuMRRTaQ6tPzHGwOihzTTk1JOQkprQUOM6CFYr9x5aj2kaIFKF1leYLDsSQtFyZqagZl+AGBFBc2d9xBITC4giWGkp71AI8Tqq0PqaaPFaD61eWrGYYYbRmWMOLbNdBiwWiLTub73p5Vy5mnopH1ppCWLGT+cdiv5JVsDojYGq6hilQ3zWOyuER1BCKBNgjEHJOI/oEeN4h2Jqlth4qDSPlpgQ3Wl9TLBYAM3gvQxXU1XACEOOTYKVlECkhFCGx5gGOfUkYp6a5Eh4RGolWK1gmueNgTwbN5imUg+tD2mlJRDDI3mHYUh6a/TTCi4BggALrTvrU5EPP06JoYgpUYXW1yyibobNee0GpqoI63m3dz6L1EkrKaaHNhNQL2QBQUGQmrfkHYohCJLV8HNooWk0Txq+qzyx0hKE3/uATz6b+JeSlQEpKZl6231MatwEWlGhVxoLvUlvDSzEeOhO62OCaHHMozIRpqnGWbbH+COOoZUUIeKBwbzDIB5gqgr57GnEPD2VHtjqS5KMP7qFkkL5lFZWSvMtTYDZbNAKLyH6yQm8QzE9ITgYQmgYtOIi3qEQ4lVUofU1UTR+L8PVVI2SQvkJYwxacTEsDRJ5h2IYemzpVbIyIEZGQWrQkHcohiFIEhjHDPHeOI6YpjnuASbg7v7wWe8sY2BlpRBj4nzy+Wamt2ukknMBloRECNYg3qEEBDE6FlphAe8wCPEqc9xp9UwUdTe0w1OOHtrArdD682GAlZc7EkKF0xxao2KaCiXjHGLGTOEdirFIkvFHt2gaBIFusz5htwOiBWJICO9IiAcYY1CzLyDqL6N5hxIwIh96DFpRAe8wqtBbQwsxFrrT+pggigCr+aHMJstY8tkG2GS5Xp+nixNe01xOCmWz2TB//nzYbDYfBVUNofYsxzaN4bWMPNg0fYxLru5Y0IoKuC1JwaXMrty+zsqnLjWdy2pOtmMpivgETpFVxbts68ORUM97104uNA2hdeQbMEJZ1Ka289Tb96sry1wrL4MQFubVz6+yPYOXja9489xjpSVgmgZLw0ZeiKx6VI6V94GlUWNoRUW6W4+2gtHu/b5ipONWD7FShdbX6phDa5NlvPj5Rn0/lF1N01xetsdms2HBggW6OjHtjOGNrEuw6+SiXt2xoBYWcJs/y7vM9FY+damu/BxLUZxD1PCnOUZWFe+yrReLVOvoFiNcO1k9Gv8MURa1qOk89UXj65VlzsrLIIb6vkJr5LLxFW+ee0puDqQGiT7NLUDlWHkfiCGhgDUIrEyf69Ea7d7vK0Y6bvUQK1Vofc2EQ47BNEp04geMMWgFlyAlNeEdCnGTVuxoBacydJ1gsdQ6usUQAnx6hi9p5eUI6zOAdxiGo4tRXn9gjEG9mIOovzzFO5SAI0ZGUmIoYipUofU1UTD+PLCrMI2ZJtGJnrHSEkC0wMJpyLER6elhDQDUC5mQGjamzMbusJgjy7EgBV7jnz/OQ1ZeRtdGg2NlpQBjEHU0HSNQRAx4CFpxMe8wqtDbPZwYBzUdX6FiPkFRkfdarUoEC6ABUllZtb8vLiuv9N/6KHYz86e1hhhcZbdaYZFVWFzYTxX71Jv7tq7PlZlj/1d876v3W8kfP5e4uD+9tR+vdvWxoBYWQouJ9fo+qy9flFnFZ9Vnn7tTPr4qm/q4uvwYY5BLyxAzdAy3MqyJK2Xrq3O3LkyRURocBtkL1053rpneOJZk0QJAhFTLvvPleebKfaU+qtuP1Z2n/rhG2gUBokVy6T7kKn8e+65cGz3hjbJx57mlOmphIbTYeBT7uGLly3NMb9f2mlwdrxIWgWJF4XrPrIm7z2Z1fq7iaCA1apnpma+f8esz31tgep0VzsH58+fRtGlT3mEQQgghhBBCSMA7d+4cmjSpfeoWVWivoGkaMjMzERkZSUMEDUJVVZw6dQqtW7eGheb16h6Vl/FQmRkPlZnxUJkZC5WX8VCZGQ9jDMXFxUhKSoJYx1RHqtASQgghhBBCCDEkyuxDCCGEEEIIIcSQqEJLCCGEEEIIIcSQqEJLCCGEEEIIIcSQqEJLdCsrKwt9+/alBF0Biso/sFH5BzZ/lj8da+bXo0cPCIKA06dP8w6F1BOVmfHwLDOq0HKQk5ODxx9/HO3atUO7du0wcOBAnD9/vs73paamYtq0abjxxhtx4403om3btujRowe++uorP0TtOne/JwBs2rQJN998M1JTU/267dOnTyMiIgLXXXddlX8FBQVuxeJpuXmyH6vjyXf0xf6pjqfl7y/+2h/e9P333+POO+9E69at0bx5c/Tv3x9HjhzhHVYl7pb/L7/8gn79+uGaa65Bp06d0K1bN6xfv95HUdbut99+w5NPPumMpUOHDpg4cSJyc3Pr9X5vn/fe5stjv77l7419VJ9tGfE89wVPj2leNm7ciH379vEOgwsqM2MxankBOigzRvzKZrOxzp07s4EDBzJZlpmiKOwvf/kLa926NSsuLq71vW+99RZLTk5mJ0+eZIwxpqoqmz59OhNFke3evdsf4debJ9+TMca6devGTpw4wYYOHcpcPUw92XZ6ejq74447XNpeXTwpN0/3Y3U8+Y6+2D/V8aT8/clf+8Nbdu/ezSRJYi+//DJjjDFN09jYsWNZfHw8O3v2LOfo/sed8k9PT2dRUVFsyJAhTJZlxhhj7733HgPAvvjiC1+GW6127dqxBx98kJWUlDDGGDt//jxr164da9OmDSsrK6v1vb44773Nl8d+fcrfW/uoPtsy2nnuK54c07zYbDbWunVr1r9/fwaApaen8w7Jr6jMjMWI5cWYPspMv0+KJvXBBx8wACw1NdX5WlZWFhNFkS1durTW927atIl9+OGHlV67dOkSA8CmTJnik3jd5cn3ZIw5H0jdqdB4sm1fPLh4Um6e7sfqGKFC60n5+5PRHnR79OjBkpOTmaqqztcKCwtZSEgIGz16NMfIKnOn/N955x0GgP3666+VXo+KimKDBw/2eox1adeunbMRq8KKFSsYALZhw4Za3+uL897bfHns16f8vbWP6rMto53nvuLJMc3Lq6++yh599FE2b968gKscMUZlZjRGLC/G9FFmNOTYzzZu3IhmzZohJSXF+VqjRo3QoUMHbNy4sdb3PvDAAxg1alSl14qKigAADRo08H6wHvDkewKAJEnctu1tnpSb3r6Lv3hS/qRmv/zyC9q3b19pgfKoqCi0aNECX3zxBcfIKnOn/CveoyiK8zXGGDRNg6qqXoutvg4cOIDWrVtXei0pKQkAcOnSpVrfG6jnfYX6lL+39hFda+rPk2Oah/z8fCxbtgwvvvgi71C4oTIzFqOVF6CfMqMKrZ8dOHAALVu2rPJ6y5YtcfDgQZc+KyMjA+PGjcMNN9yAcePGeStEr/Dm9/T3trOzszFkyBB069YNbdu2xWOPPebVmF0pN1/tR0++o6/3j9EYaX+Eh4dD07Qqr4uiiKysLGdDixENHjwY7du3x+LFi1FSUgJN07BkyRLYbDaMGTPG7/EEBQVVee3EiRMQBAG33357re/lef10Bc9j39/7yEjnua94ckzzsHDhQgwZMgTNmzfnHQo3VGbGYrTyAvRTZlSh9bOLFy8iMjKyyutRUVEoKytDeXl5nZ+RmpqK1q1bo0mTJlBVFZs3b0ZUVJQvwnWbN74nj21bLBZIkoQpU6bg559/xi+//AKr1Yru3bvjP//5j0dxuVNuvtiPnnxHX+4fIzLa/rj++utx+PBhyLLsfK2wsBDp6ekAYOgKbVRUFL799ltcvnwZCQkJSExMxKpVq7Bjxw7cddddvMODqqpYuXIlRo4cibZt29b6tzyvn/XF+9j35z7i/V31ypVj2t9OnjyJzz//HHPmzOEdiq5QmRmLnssL0FeZUYXWAzt37oQgCHX+69mzp1e326pVK5w6dQqFhYVo27YtunTp4tPMYry+Jw9NmzbFwYMHceONNwJwPBwtX74c4eHhmD17tkef7e9yq4kn39GX+8eIjLY/Fi9ejLy8PMyePRt2ux3l5eWYMmUKLBYLACA0NJRzhO47fvw4unXrhubNmyM/Px85OTl44YUX8OCDD2Lr1q28w8OiRYtgtVrx+uuv8w7FK4x27HsikL6rK/R8TM+YMQMzZ85EdHQ071B0hcrMWPRcXoC+yowmj3jglltuwdGjR+v8u7CwMOf/JyQkoLi4uMrfFBUVISwszKUHyqioKLz22mvYvn07nn76aRw4cKDe73UF7+/pKm9vOzQ0FJ06dcKPP/7olfhcKTd/7UdPvqO394/R6Xl/dOvWDTt37sSLL76Ijh07IiYmBo8++iiCg4Oxdu1axMbG8g7Rbc8//zwKCgrwxhtvOM+LwYMHY926dRg6dCgyMzO5zZdcvXo1Pv/8c+zevRvh4eF1/j3P66cn/Hns895Hej7P/cHVY9qf9u7di0OHDmHdunW8Q9EVKjNj0XN5AforM6rQeiAsLAzt27d36T2dO3fGsWPHqryenp6OTp061fre8vJyhISEVFr8XRAEdOrUCRs2bIDNZkNwcLBL8dSHv7+npzzZdmFhIUJDQ6vMY7BYLG4nlvGk3HyxHz35jr7YP0ZmxP3Rs2fPKqMp+vfvj5tuuqlSsiijOXjwIJo0aVKlItO2bVts3rwZ6enpaNOmjd/jWrt2LV555RV89913SExMrNd7eF4/64v3se/PfcT7u+qNO8e0P+3YsQOqqqJr167O1y5cuADAca0LCgrCkiVL0L9/f14h+h2VmbHovbwAHZaZ3/MqB7j333+/SkrrCxcuMIvFUmWpgczMzEo/33HHHeyHH36o8pldu3Zl0dHRTNM0n8TsDk++55XcWbbFk20PHTqUffLJJ5Ves9lsrGHDhuzWW291KY4KrpTb1fG48l3qy5Xv6I/9U1eser5M+Xt/eOr06dPst99+q/RaQUEBi4iIYJ999hmnqGpWV/lfeXzefvvtLCoqyrkMS4VBgwYxQRBYbm6uz+Ksydq1a1nHjh1ZVlaW87UtW7aw999/v9Lf+eO89zZ/HPu1lb+391Ft2zLaee5L9T2m9SYQl4CpQGVmLEYtL8b4lplxm+MNatiwYejUqRNmzJgBRVGgaRpmzpyJli1bYuzYsc6/+/TTT5GUlISXX3650vvnzZuHvLw8AI4lKd5880385z//wcSJEyv1APLm6ffkue1ly5YhKysLgGNC/vTp05Gbm4t58+a5HVN9yq26eOr7XVxVn+/oz/1jZEbaH7t27cLgwYORn58PACgtLcWTTz6J3r17Y9CgQZyjc83Vx+eECRNQVFSEuXPngjEGwPF9N23ahEGDBiEhIcGv8X388cd48sknMWzYMOzcuRMfffQRPvroI2zZsgWZmZk1fg/Ad+e9t/E89v29j4x0nvtKfY9poh9UZsZC5eUBv1ehCbtw4QJ79NFHWZs2bVjbtm3Zgw8+yM6ePVvpb7Zu3coiIyPZqlWrnK/t27ePDRs2jHXs2JF16dKFtWvXjt1yyy3so48+0lXvbAV3vydjjE2bNo116dKFxcbGMgCsS5curEuXLsxms/l02wcOHGDjxo1jnTt3Zp07d2ZJSUmsV69e7LvvvnNzL9S/3GraF/X5Lq6o73f01/6pjqfl7y/+2h/e8p///If16tWLNWvWjHXp0oXdcMMNbMmSJcxut/MOrZL6lH91x+e2bdtYz549Wfv27VnHjh1Zp06d2NKlS9nly5f9/h0qYq/u37x582r9Hox5/7z3Nl8e+/U9/72xj+qzLaOd575S32NaT7766ivWpUsX1rBhQwaAXXPNNaxLly68w/IbKjNjMWJ5MaaPMhMY+6MpmxBCCCGEEEIIMRAackwIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJCoQksIIYQQQgghxJAk3gEQYlYfffQRwsPDsWfPHtx999249957eYdECCGEEEKIqQiMMcY7CELMJi0tDffddx+OHDmCrVu34rnnnsN///tf3mERQgghhBBiKjTkmBAfSElJwd69ewEA//3vf6l3lhBCCCGEEB+gIceE1OHrr7/GqlWrsGHDBudrdrsds2bNQoMGDaAoCi5evIhly5bBarU6/yYyMhLr1q1DWloa3n33XZ/FV1hYiNdeew2apiE5ORmlpaU4efIkBg4ciF69evlsu4QQQgghhPBGPbQmY7PZ0LZtW/z000+8QzG8zZs3Y+rUqXjttddw8eLFSr+bO3cuFEXBzJkz8dxzzwGA878VgoKCMGjQIPTt2xeDBg3ySYwnTpzAtddei9DQUCxcuBCjR4/G1KlTMX/+fIwcORIzZszwyXYD0ccff4zrr78eN910E+9QiBuo/PRtxYoV6NOnD6xWKxYsWFDj3w0fPhySJGHo0KFYu3atHyMkhBCiVzSH1mTefPNNLFiwADfccAN27NjBOxxTmD9/Pnbv3o3du3cDcDQaNGjQANu2bcMtt9wCAPjXv/6FBx54ANnZ2RAEAQDAGIMgCDhx4gTatWuH7OxsJCYmejW2W2+9FWfPnkV6ejokqfKAiw8//BBPPfUUduzYgd69e3t1u4Fq7NixsFqtePPNN3mHQtxA5advH374Ib766iuEh4fj448/rvL7nTt34r333kN+fj527drFIUJCCCF6RD20JlJWVoZTp05h4cKF2LlzJ77//nveIZnS77//juLiYrRq1cr5WosWLZCbm4sDBw4AAD744AMMHDgQAHDhwgU0btwYcXFxNX7mX//6V2zevLnK6wUFBXjwwQehaVqV3xUWFuKHH37AjTfeWKUyC8BZ2f76669d+n6kZrt378Zdd93FOwziJio/fTt9+jTuuusupKamVvldSUkJcnJycO7cOfTs2dP/wRFCCNEtmkNrIu+88w7Gjh2LVq1aYdmyZXjuueeoUusD586dAwCEh4c7X4uMjHT+rkuXLhgwYAAkScJnn32G7du34+uvv6620llh6tSpGDFiBEJCQtC3b18AQHFxMYYOHYqlS5dCFKu2PVVUclVVrfYzy8rK3PuCpFoXLlzAyZMn6WHaoKj89E8QBKSkpFRbof38889x//334y9/+QuWLl3KITpyNU3TsH79euTk5ODixYsYNGgQOnTowDusgEH739io/LyLemhNoqioCFlZWbjmmmsQFBSEuXPnYu/evdi+fXuVv33//feRnJyMv/71r5Ve79mzJ/7973/7K2TDKi8vBwCEhIQ4XwsODgbg6DUFgMaNG2PEiBEYPHgwVq9ejeuuu67WzxRFEStWrMDatWuxa9culJeXY+jQoVi8eDHatWtX7XtiY2Nx00034fjx49X+/tixYwCAAQMGAHAM1+vRowduvfVWfPPNNzh9+jSmTJmCqKgovPHGG9i6dSt69OiBO+64o9rjJtDt3r0bzZo1w+rVq/H222/jkUceQV5eHu+wSD1R+elbWloaUlJSkJKSgosXL6K4uNj5u19//RWdO3fGzz//DEmSaB60DpSVleG+++6DpmmYMGECxo8fjz59+iAjIwM2mw0rVqzgHaKp0f43Nio/76MKrUm89dZbGD9+vPPnoUOHok2bNnj++eer/O2oUaNQVFSEW2+91fnakSNH8Pvvv6Nz585+idfIYmJiqrxWUlICoHIl11WSJGH16tV477338NBDD+G5555Dp06dan3Pq6++itTUVJw6darK77Zu3Yp77rkHd955JwCgd+/e6NWrF/r164d77rkHLVq0wKRJk5CYmIhJkyahX79+6NWrF+6++2706dPH7e9hVrt27ULLli3x9NNPY/z48WjatCn69OlTayMBAGoo0Inqyu/FF1/kHRb5w549e9CzZ0+kpKRAEARnL62qqti/fz/+9Kc/Yc+ePejevXul6+z777+PPXv2VPqsl156CRs3bsScOXOcI2qId02dOhXx8fF49NFHAQANGjRAp06dsGbNGmzduhXdu3fHd999h/vvvx8dO3aE3W4HAOzYsQNdu3bFCy+8gB07dqBHjx7o0aMH/va3v/H8OoZTn/1f0Yjdt29frFixAu+++y7uu+8+zJw5s87PX7lyJSZNmoQPPvgAq1atwqZNm/D5559X+7d1NZYDdB+8mp7KzyxoyLEJ5Ofno7CwECkpKc7XLBYL5s+fj8cffxybN2/G/fff7/zdr7/+CsYYunbt6nxt6dKlmDZtWqVhtKR6ycnJABy9sfHx8QDg7E1o1qyZx58vCIIzsVRdbr75ZowZMwbr16/HrFmznK+Xl5dj586d+OGHHzyOhzjs2rULb7/9trM33mazoWHDhujatSskScI999wDAJg0aRK2bNmCSZMmAQD69euHn376CZIkUUMBR9WVX3p6OueoSIXU1FQMHz4cgGOES1paGq677jqsW7cODz/8MABHpbciwV15eTlWrFiBNWvW4JVXXnF+zg8//IDjx49j5syZ6N69OyZMmIB//OMf/v9CJpaZmYmVK1dWmdKUmJiI1NRUlJeXO585MjMzsXPnTixevBgLFy7E3XffjezsbAwZMgSAI6FiRdZqUj/13f+dOnXCvn37IEkSRo0aBcCRGG/ZsmW1fv7rr7+Ow4cP44MPPnA+i4wfP77GkRG9e/d2bofug3XTW/mZBfXQmsAbb7yBiRMnVnl98ODBuPbaazF37lxcmcx6z549uPXWW51zOnft2oXjx49j+vTpfovZyDp37oz4+PhKD8NHjhxBZGRknT2qtVEUBcOHD8fYsWOxYcMGLFmyBAcPHqzx70+cOIHTp0/jhRdewN69eyv9bvv27ZgyZQpat26Nb775xu2YiENOTg7S09MrjWr46aef0Lp1a45Rkfqi8jOWinm0p0+fRlxcHKKiolBWVoZffvnFOQc6NDQUEyZMqHLN3bVrl7OxtkmTJvjxxx+rTapH3Pfzzz/DarWiW7dulV6XJAnffPMNHnnkkUqvv/rqq1i9enWt9zNSf67u/ysJgoDmzZvX+Pvc3FzMmTMHCxYsqNSwPnnyZI/jJg5Ufr5BFVqDy87OhizLaNKkSZXfiaKIBQsW4ODBg1i3bp3z9e+//975UHDo0CEsWbIEmzdvRlBQkL/CNjSLxYLBgwfjs88+c7726aef4qmnnnJ7yLGqqhg1ahSGDRuGnj17IjQ0FGvWrMG8efNw9OjRat8THR2N1atXQ5ZldOrUyTnsGQD++9//4vHHH0dqaip+/PHHSu/bv38/1qxZgzVr1mDDhg1uxRto0tPT0aFDB+cIhvPnz+PAgQMYM2YM58hIfdRUfgMHDsT48eOxZcsWjB49Gm+++aZzeS7iP2fOnEGLFi2cP7dq1QqpqanYtm2bM0neDz/8AEEQcPPNN9f6WVlZWZVGGlksFhQUFPgi7IAliiISEhJgsVgqvS4IAnr06FFl6lJcXBxeffVVjBo1ihoXvMDV/V9h5cqVABw9qjabDcuWLcPXX3+N5cuXO3sL9+7di+DgYCQlJVV6b+vWrXHffff54NsEHr2V38WLF01xH6Qhxwb30ksv4aabbqrx4IuLi0NSUhLmz5+Phx9+GIIgYN++fZg1axY+/fRT/Otf/8I//vEPRERE+DdwA9i+fTs2bNiAr776Cvn5+Xjsscdw6623Yty4cXjppZcwefJkLFq0CKqqIioqCosXL3Z7W6+99hoefvhh3H333c7XIiIisGbNGowcORLr1q2rkum4YcOGWL16NRYuXAgAGDZsGK655hoAwPHjx52teBUXwQrXX389hg0bBsCxTMby5cvdjjtQpKSkOIeqAsDMmTMxefJk5/6uaCQAHDcHoi81lV9xcTGCgoIwYMAArF69GnfeeadHoyyIe7Zt24bbb7/d+XNKSgo+/PBDzJkzx/nanj170LVrV4SGhtb6WZqmVXpQVBSlyoMj8cxdd90Fi8WCjIwMJCcnQ5ZlrFmzBiUlJSgqKkJeXh7S09Pxpz/9yfmehx9+GB9//DHeeOMNNGjQoNbPl2UZVqvV11/DsFzd//v378fy5cvx5ptvYuTIkYiPj8dLL72ElJQU9O/fHwDQrVs37N69G6IoVhrRBwDLly/Hvn37kJiYiIULF9b4vEj3wfrRW/n9/PPPprgPUoXWwLKysvDee+/h9ddfr9fff/rpp86hWN9++y169+7tnJBOqurTp0+Ncz0iIiK8moVu2rRp1b4eFRWF9evX1/i+s2fPVvu62Sf/+1uDBg0wfPhwvPzyy8jPz0fnzp0rDdGnRgJ9q6n8SkpKsGfPHnz11Vd44oknDHkTN7Jjx45h8eLF2LRpE7p37465c+fizjvvRPv27TFjxgw0bdoUP/zwA/72t79h06ZNiIqKwsSJE7F06dIaR8MkJyejtLTU+bOqqoiOjvbXVwoIERER+O6777Bq1So0aNAAFovFuaTSggUL8Mknnzjn/F3p3XffRdeuXWsd2cIYwz/+8Y9ah10GOlf3//XXX48xY8ZUaiTYu3cvunfv7vzZarXi6NGjuO2221BeXo7z5887R/6NGTMGP/74I2644YZaOz/oPlg/eiu/Hj16mOI+SBVaA2vcuDEuX77s8vvy8/N9EA3Ru507d+Lbb7+F1WpF9+7d0bZtWyxfvhw5OTl444030LZtW+zcuRMWiwVdu3YN+MQNVxs7dizvEIgHqiu/tLQ09OnTB3fddReHiEj79u3x0UcfVXn9ysrMLbfcgltuuQXvv/9+vT6zd+/ezl6ikydPVuolJN7TsmXLaldRWLJkSY3vqRgt9tJLL1X7XgDYt28foqKivBanWbmz/0eOHOn8/+uuuw5paWnOVRAuXbqENm3aICoqCgsXLsRzzz2H1atXO+dh5ubmOt978uRJtGnTxltfJSDpqfzMch8U2NV904QQQupl586dWLBgAaxWK2bNmoW2bdvivffew7vvvotFixZh0qRJ2Lp1K1544QVYLBbMnj2bGgp0pKysDD169EBSUhJiY2Px5z//GQMHDuQdFqkHxhjee+89vP322/jTn/6EYcOGOR/IZs2ahU6dOmH//v0YNWpUjWt5E9/avn075syZg4ceeggzZ850PlyPGjUKK1ascF4/w8PDMXDgQOTm5uKdd97Bl19+Wefa7aRuFftXFEUMHToUI0aMcP5OURS8/PLLaN26NTIzM9G5c2f06tXL+ftVq1Y5138WBAGRkZEICgrC/fffj5SUFHz55Ze49tprK22H7oPe5a/yM8t9kCq0hBBCAtL06dMxefJkJCcno7i4GLNnz8ZTTz1l2CFXhBDia2fPnkVZWRnat2/POxTihqvLzyz3QRpyTAghJCD16tUL27ZtQ2JiIhRFQatWrWgoHSGE1OLnn3/GQw89xDsM4qary88s90HqoSWEEEIIIYTUqbS0tNLSWMRYzFp+VKElhBBCCCGEEGJIYt1/QgghhBBCCCGE6A9VaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGBJVaAkhhBBCCCGEGNL/AzFSes3mEZXFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 16 # Axis label font size\n", + "g.settings.lab_fontsize = 16 # Parameter label font size \n", + "g.settings.legend_fontsize = 16 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 16\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "#label = fr\"All parameters - $\\chi^2_\\mathrm{{min}} = {2 * chain.loglikes.min():.1f}$\"\n", + "g.triangle_plot(\n", + " [chain_all, chain_nlfix],\n", + " orig_params,\n", + " filled=True,\n", + " #legend_loc=\"upper right\",\n", + " #legend_labels=[label], \n", + " legend_loc=(0.6, 0.8),\n", + " #param_limits={\"omegabfm2\": omegabfm2_limits[tracer]},\n", + " )\n", + "plt.suptitle(fr\"{tracer.split('b')[0]}: $z_\\mathrm{{eff}} = {z_eff:.2f}$\", va=\"top\")\n", + "\n", + "if save:\n", + " g.export(save_path+f\"origparams_{tracer}_{weight}.pdf\")\n", + " g.export(save_path+f\"origparams_{tracer}_{weight}.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plot the reduced set of parameters and the correlated set of parameters as in the stacking paper " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 16 # Axis label font size\n", + "g.settings.lab_fontsize = 16 # Parameter label font size \n", + "g.settings.legend_fontsize = 16 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 16\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "#label = fr\"All parameters - $\\chi^2_\\mathrm{{min}} = {2 * chain.loglikes.min():.1f}$\"\n", + "g.triangle_plot(\n", + " [chain_all, chain_nlfix],\n", + " corr_params,\n", + " filled=True,\n", + " legend_loc=(0.6, 0.8),\n", + " )\n", + "plt.suptitle(fr\"{tracer.split('b')[0]}: $z_\\mathrm{{eff}} = {z_eff:.2f}$\", va=\"top\")\n", + "\n", + "if save:\n", + " g.export(save_path+f\"corrparams_{tracer}_{weight}.pdf\")\n", + " g.export(save_path+f\"corrparams_{tracer}_{weight}.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plot only the reduced set of parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# reduced params plot \n", + "new_params = [\n", + " \"offset\",\n", + " \"omegabfm2\",\n", + " \"M_10\",\n", + " \"NL\",\n", + " \"FoG+\",\n", + "]\n", + "\n", + "param_limits = {\n", + " \"omegabfm2\": [0, 20],\n", + " \"offset\": [-0.5, 0.5],\n", + " \"M_10\": [0, 15],\n", + " \"NL\": [0, 5],\n", + " \"FoG+\": [0, 5],\n", + "}\n", + "\n", + "omegabfm2_limits = {\n", + " \"QSObandb\": [-0.5, 6.5],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "chain_all.label = (\n", + " fr\"All parameters - $\\chi^2_\\mathrm{{min}} = {2 * chain_all.loglikes.min():.1f}$\"\n", + " )\n", + "chain_nlfix.label = (\n", + " fr\"Fixed non-linear - $\\chi^2_\\mathrm{{min}} = {2 * chain_nlfix.loglikes.min():.1f}$\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 16 # Axis label font size\n", + "g.settings.lab_fontsize = 16 # Parameter label font size \n", + "g.settings.legend_fontsize = 16 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 16\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "#label = fr\"All parameters - $\\chi^2_\\mathrm{{min}} = {2 * chain.loglikes.min():.1f}$\"\n", + "g.triangle_plot(\n", + " [chain_all, chain_nlfix],\n", + " new_params,\n", + " filled=True,\n", + " legend_loc=(0.6, 0.8),\n", + " param_limits={\"omegabfm2\": omegabfm2_limits[chain_all.tracer]},\n", + " )\n", + "plt.suptitle(fr\"{tracer.split('b')[0]}: $z_\\mathrm{{eff}} = {z_eff:.2f}$\", va=\"top\")\n", + "\n", + "if save:\n", + " g.export(save_path+f\"reducedparams_{tracer}_{weight}.pdf\")\n", + " g.export(save_path+f\"reducedparams_{tracer}_{weight}.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Estimate the A_HI(stack) from the chain" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.8185925047113227 [1.4148266581213509, 2.6966553913460714]\n" + ] + } + ], + "source": [ + "mode_all, err_all = hpd_chain(chain_all,\"omegabfm2\")\n", + "print(mode_all, err_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.7773715369270857 [0.1969952588079238, 0.19916590309001703]\n" + ] + } + ], + "source": [ + "mode_nlfix, err_nlfix = hpd_chain(chain_nlfix,\"omegabfm2\")\n", + "print(mode_nlfix, err_nlfix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Omega_HI constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add a prior on the value of b_HI of 20% fractional" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "llprior = 0.5 * ((chain_all[\"b_HI\"] - 1.0) / 0.2) ** 2\n", + "llprior_fix = 0.5 * ((chain_nlfix[\"b_HI\"] - 1.0) / 0.2) ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "re_weight = True\n", + "if re_weight:\n", + " chain_all.reweightAddingLogLikes(llprior)\n", + " chain_nlfix.reweightAddingLogLikes(llprior_fix)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.0006, 1.335]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z_range = config[\"QSObandb\"][\"z_range\"]\n", + "z_range" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "za_eff = np.array(z_eff)\n", + "zerr_eff = np.abs(np.array([z_range]).T - za_eff)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array(1.159286),\n", + " array([[0.158686],\n", + " [0.175714]]))" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "za_eff, zerr_eff" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# param values of Omega_HI from the chain\n", + "ms_all, errsa_all = hpd_chain(chain_all, \"omega_scaled\")\n", + "ms_nlfix, errsa_nlfix = hpd_chain(chain_nlfix, \"omega_scaled\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.2395634461912997, [0.6098597743709715, 1.5086329788427901])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms_all, errsa_all" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Load other data\n", + "import yaml\n", + "with open(\"otherdata.yaml\", \"r\") as fh:\n", + " data = yaml.safe_load(fh)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Process GBT numbers\n", + "# WiggleZ, ELG, LRG order\n", + "om_b_r = np.array([0.70, 0.55, 0.45])\n", + "errs = np.array([0.12, 0.11, 0.10])\n", + "\n", + "r = np.array([0.9, 0.7, 0.6])\n", + "b_HI = 1 + lssmodels.bias[\"HI\"](0.78)\n", + "\n", + "om_GBT = om_b_r / b_HI / r\n", + "\n", + "# Calculate errors for GBT, and add in a 20% uncertainty on the bias\n", + "err_GBT = om_GBT * ((errs / om_b_r)**2 + 0.2**2)**0.5\n", + "z_GBT = np.array([0.74, 0.78, 0.82])\n", + "\n", + "\n", + "types = [\n", + " (\"HI_direct\", \"HI direct\", \"o\"),\n", + " (\"HI_stack\", \"HI stacking\", \"v\"),\n", + " (\"HI_im\", \"HI intensity mapping\", \">\"),\n", + " (\"DLA\", \"Damped Lyman alpha\", \"s\"),\n", + "]\n", + "\n", + "c = cosmology.Cosmology()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "markers = [\"o\", \"v\", \"^\", \"<\", \">\", \"8\", \"s\", \"p\", \"P\", \"*\", \"h\", \"X\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams['font.family'] = 'serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm' # or 'stix'\n", + "plt.rcParams.update({\n", + " 'font.size': 24,\n", + " 'axes.labelsize': 24,\n", + " 'axes.titlesize': 24,\n", + " 'xtick.labelsize': 24,\n", + " 'ytick.labelsize': 24\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(num=1, figsize=(20, 10), dpi=250, linewidth=1.5)\n", + "\n", + "# Create axes (assuming you want a single subplot, modify if you need multiple)\n", + "ax = plt.gca()\n", + "\n", + "# plot the CHIME data\n", + "yerr = np.array([[errsa_all[0]], [errsa_all[1]]])\n", + "plt.errorbar(\n", + " za_eff,\n", + " ms_all,\n", + " xerr=zerr_eff,\n", + " yerr=yerr,\n", + " marker='o',\n", + " color='green',\n", + " ls=\"\",\n", + " label=f\"CHIME+eBOSS {tracer}\",\n", + ")\n", + "\n", + "# Now plot all other surveys \n", + "for type_, label, marker in types:\n", + " z = []\n", + " om = []\n", + " z_err = []\n", + " om_err = []\n", + " labelled = False\n", + " if type_ == \"HI_im\":\n", + " # Add in the GBT points by hand rather than trying to insert into the OmegaHI\n", + " # data file\n", + " plt.errorbar(\n", + " z_GBT,\n", + " om_GBT,\n", + " xerr=None,\n", + " yerr=err_GBT,\n", + " label=label,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.4,\n", + " fillstyle=\"none\",\n", + " )\n", + " continue\n", + " for name, dset in data.items():\n", + " if dset[\"type\"] != type_:\n", + " continue\n", + " if type_ == \"HI_stack\" and name in [\"delhaize2013\", \"chen2021\"]:\n", + " continue\n", + " ds = combine_data(dset, cosmo=c)\n", + " z = ds[\"z\"]\n", + " z_err = ds[\"zerr\"]\n", + " om = 1e3 * ds[\"omega_HI\"]\n", + " om_err = 1e3 * ds[\"omega_HI_err\"]\n", + " l = None if labelled else label\n", + " labelled = True\n", + " \n", + " plt.errorbar(\n", + " z,\n", + " om,\n", + " xerr=z_err,\n", + " yerr=om_err,\n", + " label=l,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.7,\n", + " fillstyle=\"none\",\n", + " )\n", + "\n", + "za = np.linspace(0, 3)\n", + "omHI = lssmodels.omega_HI.evaluate(za)\n", + "plt.plot(za, 1e3 * omHI, \"k-\", label=\"Fiducial\")\n", + "\n", + "plt.xlim(0, 2)\n", + "plt.ylim(0, 3.5)\n", + "plt.xlabel(\"Redshift $z$\")\n", + "plt.ylabel(r\"$10^3 \\times \\Omega_\\mathrm{HI}$\")\n", + "plt.grid(color='gray', alpha=0.3)\n", + "\n", + "## Fix up the legend\n", + "# get handles\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "# remove the errorbars\n", + "handles = [h[0] if ii > 3 else h for ii, h in enumerate(handles)]\n", + "# use them in the legend\n", + "ax.legend(handles, labels, loc=\"upper right\", ncol=1, numpoints=1)\n", + "\n", + "# Add text labels (modify as needed for your specific case)\n", + "ax.text(\n", + " 0.02,\n", + " 1.0 - 0.03,\n", + " \"Full parameters\", # Change this label as needed\n", + " transform=ax.transAxes,\n", + " fontsize=\"large\",\n", + " bbox=dict(fc=\"w\", ec=\"w\"),\n", + " verticalalignment=\"top\",\n", + ")\n", + "\n", + "if save: \n", + " plt.savefig(save_path+f\"omegaHI_comparison_full_{tracer}_{weight}.pdf\")\n", + " plt.savefig(save_path+f\"omegaHI_comparison_full_{tracer}_{weight}.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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oydnlamxRU1OTuvTSS3N+sCnXGp06dcq6TufOnVOvvfZaHnf2Dz64A1B86nHLVHLtckYGKB71OLdSbmxRVVWV+r//+7827a+SX2MAlJvx48enNtpoo1b/Tq3kB4TT+frrr1Mbbrhhxvux1VZbpWbPnt2iOW+99daMX3IREakrrrgiod38XSXuCaAcqcnpNaexxYgRI1L19fWtXmPixIk5H7KuqalJ/fnPf87jzv7Bh3YASod63DyVXLuckQFKg5qcWak3tnj66afbtL9Kfp0BUGpyNbbYbbfdUjNnzmz1/F988UVq6623zvm795xzzsnjroqnEp9JrsQ9tVZ1wPfUTjvtFI888kh89dVXUV9fH++++25ccskl0a1bt4z/5sorr4wHHnggbaxDhw7x6KOPxpZbbtmiPAYMGBCPPPJI1NbWpo2PHTs2Ro8e3aI5S82yZcti2LBh8dVXX6WNb7vttvGnP/0p6urqWjTv+eefH8OHD08bS6VScfjhh8eMGTNanG9zVOKeAMqJmtx2N9xwQ4wcOTKqq1t/JBgyZEhMmDAha71bsGBB7L///rFo0aJWrwNAaVKP+ZYzMkDxqMflb4899oiNNtqo2GkA0EbffPNNHH300bHrrrvGu+++2yReXV0dw4cPjxEjRhQhu9L1n//5nxnPdT/84Q/jiSeeiNVXX71Fcx5xxBFZX6uMGjUq/vrXv7ZozpaoxD0BlBM1uW3OOeecuPzyy6N9+/atnmPrrbeOv/3tb7HmmmtmHNPQ0BAHHXRQzJw5s9XrAFC61GO+5YwMUFxqcnnbbLPN4qc//Wmx0wAgD37yk5/EY489lvVvprn06NEjxo8fH7vsskvWcRdeeGE8+eSTrV6nFFTiM8mVuKe20NgCmmnatGkxatSojPELL7wwtt5661bNvd1228X555+fMX7WWWfFG2+80aq5S8H5558fkyZNShvr1KlT3HPPPdGhQ4dWzX355ZfH5ptvnjY2f/78OOSQQyKVSrVq7mwqcU8A5UJNbrvTTz89jjzyyLzMteWWW8b111+fdcyMGTPi3HPPzct62ey8886RSqUK9uODXMD3mXqcH5VSu5yRAYpDPW6b22+/vaB1ONMby5neXGyLSnmNAVAuHnzwwejdu3fccsstac8nffv2jb/85S9xzTXXROfOnYuQYWm66aabYuzYsWlj7dq1i7vuuitWW221Vs09YsSI2HPPPdPGli9fHgcffHAsWbKkVXNnU4l7AignanLbHHDAAXHBBRfkZa5evXrFPffck/VLFubOnZvImThdLoU8I2dqQArwfaEet12l1C5nZIDiUpNb77zzzitoLT788MPT5nHiiSfmfW+V8joDoJysv/768dBDD7WpmfC3OnbsGPfee2/OBhlHHXVUWZ+JKvGZ5ErcU1tobAHNdMIJJ8Ty5cvTxvr27dvmLoUjR46MTTfdNG2soaEhTjzxxJL7BdIc77zzTtaHWs8555xYb731Wj1/+/btY8yYMRnjEydOjFtvvbXV86dTiXsCKCdqctv06NEj64eTWuOwww6LIUOGZB1z3XXXxSeffJLXdQEoHvWYbzkjAxSPelw+pk2bFuPHj29yfcMNN4zBgwcXISMA8uWYY46JAw44IO23jdfW1sYFF1wQU6dOjW222aYI2ZWuOXPmxK9+9auM8WOOOabVDbq+df3118dKK62UNvbhhx/GRRdd1Kb5/1Ul7gmgnKjJbdOhQ4e4/PLL8zrnLrvsEieccELWMQ899FC88soreV0XgOJRj/mWMzJAcanJ5eOrr76Ke++9t8n1zp07xy9+8YsiZARAvv33f/93dOnSJW/z9ejRI6699tqsYz777LO45ppr8rZmIVXiM8mVuKe20tgCmuH++++PF154IWP8ggsuyNphvjnatWsXv/71rzPGX3rppbj//vvbtEYxjBgxIuPD1T169IiTTjqpzWtsv/32WR/+/dWvfhULFy5s8zrfqsQ9AZQLNbntRo0aFZ06dcr7vL/85S+zxuvr6+Omm27K+7oAFJ56zHc5IwMUh3pcXjK9oXzCCSe0+X8nAIor0wcxd9xxx3jttdfinHPOyfghi++zc889N+bOnZs21qFDh/iv//qvNq+x/vrrx1FHHZUxftlll8XHH3/c5nW+VYl7AignanLbHHPMMbHuuuvmfd4zzjgjampqso4p1wesAWhKPeZbzsgAxaUml49bbrklli5d2uT6f/7nf0bnzp2LkBEA+bTFFlvEsGHD8j7vv//7v8fGG2+cdcx1110XjY2NeV87aZX4THIl7qmtPC0HOaRSqfjNb36TMd6rV6/Yd99987LW/vvvH2uvvXbG+EUXXVRW34A3ZcqUGDduXMb4McccEx07dszLWqecckrG2Jw5c+KGG27IyzqVuCeAcqEmt111dXUceOCBicy9/fbbR79+/bKOufvuuxNZG4DCUY/5LmdkgOJQj8vLvHnz4s4772xyva6uLo444ogiZARAkrp27RpjxoyJCRMmxKabblrsdErS559/HrfddlvG+LBhw6Jnz555Wevkk0/OGFu2bFlceumleVmnEvcEUO7U5JY5+OCDE5m3V69esffee2cd8+CDD6b9EA8A5U89/n5yRgYoPWpyaVqxYkXab1evqqqKE088sQgZAZBvBx10UCLzVldX56wVH3/8cbz00kuJrJ+USnwmuRL3lA8aW0AOjz/+eLz55psZ40cccUTevlGtXbt2WR9kffPNN+Oxxx7Ly1qFcMkll2SMVVVVxZFHHpm3tXbfffes3xxwxRVXxLJly9q8TiXuCaBcqMltt+2228Zaa62V2Py77rpr1vgHH3wQ77//fmLrA5A89ZjvckYGKA71uLzcfvvtsWjRoibXDznkkFhllVUKnxAAifn3f//3mD59ehx33HFRVVVV7HRK1hVXXBH19fUZ49m+bbWlNt1009hxxx0zxm+99daYNWtWm9epxD0BlDM1uWXWWWed2HrrrRObP9d7yEuWLImXX345sfUBKA71+PvLGRmgtKjJpevRRx+Njz76qMn13XbbLTbZZJMiZARAvu2///6JzZ3r764REc8880xi6yehEp9JrsQ95YPGFpDDjTfemDWe7wKTa76bb745r+sl5csvv4yxY8dmjPfv3z969eqVt/Wqqqpiv/32yxifOXNmmx+wrsQ9AZQTNbntBgwYkOj82d4o/NakSZMSzQGAZKnHfMsZGaB41OPykUql4rrrrksbGz58eIGzASApPXv2jAcffDAeeuihvH3jaKVatmxZ3HHHHRnjPXr0iB122CGva2Z7LbN06dK466672jR/Je4JoFypya3Tv3//ROf3HjLA94t6/P3mjAxQOtTk0nfNNdekve59ZIDK0LVr1/jhD3+Y2Px9+vSJbt26ZR1TTn93rcRnkitxT/misQVkMWvWrHjyySczxjfYYIPo06dPXtfcYostsv5CGjduXFl0j7377rujoaEhY3yfffbJ+5q55vz973/fpvkrcU8A5UJNzo9836N/tdFGG+Uc8+677yaaAwDJUY/5LmdkgOJQj8vLk08+Ge+9916T6zvttFNsvvnmRcgIgHw7/PDDY/r06VkfEOEfHnvssfjqq68yxvfaa6+ors7vYyxJnyUrcU8A5UhNbj3vIQOQL+oxzsgApUFNLn3Tp0+P5557rsn19ddfP/baa68iZARAviX9d9eqqqqcjTPK6e+ulfhMciXuKV9qip0AlLIHHngg6y+PQYMGJbLuzjvvHH/4wx/SxhoaGuKBBx6IE044IZG18+W+++7LGk/i3m277bbRvn37WLZsWdr4uHHjYt68edG1a9dWzV+JewIoF2pyy2211Vbx6KOP/tO1rbfeOtE1V1111ZxjZs+enWgOACRHPea7nJEBikM9bp1tt902xowZ0+Ra0jJ9y85JJ52U+NoAFMYpp5xS7BTKSjHOkuuvv36su+668fHHH6eNv/HGGzF9+vTo3bt3q+avxD0BlCM1uXl69erV5D3kpBsv1tXVRYcOHWLp0qUZx3gPGaAyqMc4IwOUBjW5+TbeeOMm7yMPGDAg8XWvvfbatNdPOOGEvDeBAqAw7rnnnn96trVnz56Jr5nr8zvl9HfXSnwmuRL3lC9e7UAWTzzxRNb4DjvskMi6O+64Y9b4uHHjElk3X2bPnh2TJ0/OGG/fvn1stdVWeV+3Q4cOMXDgwIzx5cuXxzPPPNOquStxTwDlRE1uuTXWWCOGDBnyTz/du3dPdM3mNLZYvHhxojkAkBz1mG85IwMUj3rcOptsskkcd9xx//SzySabJLrme++9F08++WST6+uss04MHTo00bUBoBStWLEinn766axjyu21TCXuCYDK1rlz5ybvIffq1Svxdbt165Y17j1kACh/zsgAlKOePXs2eR856cYW8+fPjzvvvLPJ9Y4dO8aRRx6Z6NoAJGf33Xf/p7+7brnllomvmevzO+Xyd9dKfCa5EveUTxpbQAb19fXx/PPPZx2TVIHp169f1vj48eOjvr4+kbXz4amnnopUKpUx3rt376itrU1k7Vz3rrV/nKzEPQGUCzW5fCxfvjznmM6dOxcgEwDyTT3mu5yRAYpDPS4v1113Xdp6eeyxx0ZNTU0RMgKA4po4cWJ88803GeOrrLJKrL/++omsndRZshL3BABJyPU+sveQAaD8OSMDQPPcfvvtsXDhwibXDz744JyNIQHguyrl766V+ExyJe4pnzS2gAymTJkSixYtyhivqamJzTbbLJG1+/TpE+3atcsYX7RoUUyZMiWRtfNhwoQJWeObb755Ymv/6Ec/yhp/4YUXWjVvJe4JoFyoyeVj3rx5Ocf06NGjAJkAkG/qMd/ljAxQHOpx+Vi0aFHccccdTa63b98+jjnmmMInBAAloJTPki+//HKsWLGixfNW4p4AIAm53kf2HjIAlD9nZADILZVKxfXXX582dtJJJxU4GwDKXaX83bWUz5M+i5wMjS0gg8mTJ2eNb7DBBtG+fftE1u7QoUPOrrS58iumXLkl9XB1xN+7FWXz3nvvNetDt/+qEvcEUC7U5PLx1Vdf5RzTt2/fAmQCQL6px3yXMzJAcajH5ePOO+9M+810P/vZz2KNNdYofEIAUAJK+Sy5ZMmSmDZtWovnrcQ9AUC+LViwIOc3B3oPGQDKnzMyAOT25z//Of7v//6vyfUddtghtthiiyJkBEA5y/X5nXL5u2spnyd9FjkZGltABrl+eeR6iLet1ltvvazxUv32u6VLl8Zbb72VdUyS9y7XfUulUjF16tQWzVmJewIoJ2py+cj1BmC7du1ixx13LFA2AOSTesy3nJEBikc9Lh/XXXdd2uvDhw8vcCYAUDqK+VqmZ8+eORuAtea1TCXuCQDyrTkfIh00aFDyiQAAiXJGBoDcrr322rTXvY8MQEs1NjbGO++8k3VMOfzdtRKfSa7EPeWbxhaQQa431ZJ+SDjX/NOnT090/dZ69913c3bZT/qPk7W1tVnHtPTeVeKeAMqJmlw+Xn/99azx7bffPrp06VKgbADIJ/WYbzkjAxSPelwexo8fn/bNyYEDB8bWW29dhIwAoPgWLFgQn3zySdYxSb6Wqaqqil69emUd09LXMpW4JwBIQq73kHv16hV9+vQpUDYAQBKckQEgtxkzZsS4ceOaXO/Zs2fst99+RcgIgHL27rvvxuLFizPGq6qqYs899yxgRq1Tic8kV+Ke8k1jC8hgxowZWeNrrbVWouvnmj9XfsXSnLySvHdVVVXRo0ePrGNaeu8qcU8A5URNLh8vv/xy1vhRRx1VoEwAyDf1mG85IwMUj3pcHnzLDgA0VeyzZHPmL7f3kJszv9dnAJSCXO8hH3nkkQXKBABIijMyAOR2/fXXR2NjY5Prxx57bKy00kpFyAiAcpbr76677LJLbLjhhgXKpvWKfZ70WeTiqCnq6lCi5syZE/Pmzcs6pnv37onmkGv+r7/+OubOnRurrrpqonm01HvvvZdzTCHu3ccff5wx3pwcWzq+3PYEUC7U5PIxZ86c+Mtf/pIxvuqqq8awYcMKmNHfTZo0KSZOnBhvvPFGvPXWWzFr1qxYsGBBzJ8/P1asWBF1dXXRqVOn6NmzZ6y77rqx+eabR//+/WOnnXaKrl27FjxfgFKkHhdWqdcuZ2SA4lCPy8Mnn3wSY8eObXK9e/fu8R//8R9FyOgfSv01BgCVrVTOktmU63vI2TgfA1BsDQ0N8dhjj2WM19TUxBFHHFHAjP5u2rRp8dJLL8Ubb7wRb775ZsycOTPmz58f8+fPj+XLl0fHjh2jU6dOsdZaa8UPfvCD6NOnz/8/Iydd3wEgnVKvXc7IAJDd4sWL47bbbmtyvX379nHssccWIaN/KPXXGQCk9/DDD2eNF7u+NFepnCd9FrmwNLaAND799NOcY5L+5bHGGmvkHPPpp5+W3EPCue7dyiuvHB07dkw0h1z3rjn/+7ZkfDnuCaBcqMnl45FHHokVK1ZkjJ955pnRoUOHguTyySefxHXXXRd//OMf48MPP8w69ts/vn7++ecxefLkeOihhyLi7w9R7bDDDnHooYfGgQceGHV1dQXIHKA0qcfJK6fa5YwMUBzqcXkYM2ZM2rPx0UcfHbW1tQXPp5xeYwBQ2crhtUy+30OOKL89AUC+vfDCCzF37tyM8aOOOirWXnvtguQyd+7cuO666+K+++6L6dOnZx27cOHCWLhwYXzxxRfx6quvxiOPPBIREdXV1TFgwIA4+OCD4xe/+EV069atEKkD8D1VTrXLGRkAsrvrrrvim2++aXL9gAMOyPmN6kkop9cZADS1aNGieOaZZzLG+/TpEwcccEABM2q9SnwmuRL3lG/VRV0dStScOXNyjunSpUuiOTRn/ubkWWi5ckr6vjVnjZbet0rcE0C5UJPLx5gxYzLGevbsGSeffHLiOSxatCjOOuus2GSTTeK///u/c35oJ5uGhoZ4/vnn48gjj4x11lknLr744li0aFH+kgUoI+pxcsqxdjkjAxSHelz66uvr4+abb25yvV27dnHccccVNJdyfI0BQGUrh9cy33zzTdbmzf+qEvcEAPmW7T3kurq6OPfccxPPoaGhIUaPHh0bbrhhnHvuuTk/sJNNY2NjvPLKK3HqqafG2muvHSNHjoyvvvoqj9kCQHnWLmdkAMju2muvTXt9+PDhBc2jHF9nANDUHXfcEUuWLMkYv+iii6K6ujxaB1TiM8mVuKd8K4//OqHAmvN/zE6dOiWaQ3PmL/YvkHRy5ZT0fWvOGvkuJuW4J4ByoSaXhwkTJsTkyZMzxi+55JLEOwpGREyePDkuueSSrIf0b1VVVTV73q+//jrOPvvs2GyzzeLpp59uS4oAZUk9Tk451i5nZIDiUI9L33333Zd2/0OHDo0f/OAHBc2lHF9jAFDZcr1GWGmllaJ9+/aJ5pDrtUwqlWrRQ7eVuCcAyKf3338/Hn744YzxUaNGxVprrZV4Hp999lmMHDky7Tfj/quWnJGXLFkSo0ePjo033jjuueeeNmQIAP+sHGuXMzIAZDZhwoR48803m1zv379/bLvttgXNpRxfZwDwzxobG+PKK6/MGN9ll11i3333LWBGbVOJzyRX4p7yTWMLSOPrr7/OOaYUHhJuTp6FliunUvjFu2TJkqivr2/2fJW4J4ByoSaXh4suuihjbPDgwfGLX/yigNn8s44dO8awYcPixhtvjIkTJ8asWbNi2bJl0djYGIsXL44ZM2bEE088EWeddVZsuummWef65JNPYvfdd4+zzz47UqlUgXYAUHzqcWGVeu1yRgYoDvW49JXKt+xkUuqvMQCobOVwloxo2WuZStwTAOTTb3/724zfiv6jH/0ofvWrXxU4o39YaaWVYsiQIXHVVVfFSy+9FDNnzoylS5dGY2NjLFmyJD766KN45pln4oILLoj+/ftnnWvu3Lnx85//PA4//PBYtmxZgXYAwPdNqdcuZ2QAyOyaa65Je71U3kcu9dcZAPyz++67L2bMmJE2tvLKK8ctt9xS4IzaphzOkz6LnH81RVsZStjSpUtzjllppZUSzaE5nWmbk2eh5cop6fsW0fx7V1tb26z5KnFPAOVCTS59Dz/8cMZvae3SpUvcdNNNBc7o77p16xa/+tWv4phjjomuXbumHdOxY8fYYIMNYoMNNojBgwfHRRddFM8991z88pe/jEmTJmWc++KLL44PPvgg7rzzzmjXrl1SWwAoGepxYZRL7XJGBigO9bi0TZw4MSZPntzket++fWPQoEGFT+g7yuU1BgCVrZzOks1ViXsCgHyZMmVK3HrrrWljNTU1cfvttxekVv6rurq6OOWUU+LUU0+NNdZYI+2YDh06xLrrrhvrrrtu/OQnP4lzzjknpkyZEmeddVY89dRTGee+44474sMPP4zHH3886urqktoCAN8z5VK7nJEBIL1PP/00xo4d2+T66quvHgceeGARMvqHcnmdAcA/LFy4MM4888yM8Ysvvjg22GCDAmbUduV0nvRZ5PypLsqqUOKa0xmupibZvjDNeQi0FL99NNe9S/q+ReT/3lXingDKhZpc2pYsWRIjRozIGL/llltinXXWKWBGf7f33nvH//7v/8bIkSMzfmgnk1133TUmTpwYv/71r6OqqirjuHvvvTeOOuqotqYKUBbU4+SVU+1yRgYoDvW4tJXqt+yU02sMACpbJZ4lK3FPAJAPqVQqTjrppGhsbEwbv/jii2PLLbcscFYR2267bbz55ptx8cUXZ/zATib9+/ePP//5z3HjjTdmfej4+eefj6FDh8by5cvbmi4AlFXtckYGgPTGjBkTDQ0NTa4fddRR0aFDhyJk9Hfl9DoDgH/4zW9+E5999lna2F577RUnnXRSgTNqu0o8T1binvJNYwtIoxQeEm7O/M3Js9BK4Rdvvu9dJe4JoFyoyaXt9NNPjw8//DBt7NRTT41hw4YVNqH4e05jx46N7t27t3qO6urqOPfcc+Omm27KOu6OO+6IK6+8stXrAJQL9ThZ5Va7nJEBikM9Ll1ffvllPPDAA02ur7LKKnHIIYcUIaO/K7fXGABUtko8S1bingAgHy677LL461//mjb27//+7zFy5MgCZxRx4IEHxvPPP9/mbys85phjYuzYsVk/uPP000/HGWec0aZ1AKDcapczMgA0VV9fHzfffHOT6+3atYvjjz++CBn9Xbm9zgDg7/7617/G5Zdfnja2/vrrx5133pn1i2tKVSWeJytxT/mmsQWUqOpq//dsrUq8d5W4J4By4Xdwev/zP/8TY8aMSRvbYYcd4rLLLitwRhGnnHJKjB49Om8H8qOOOipOPfXUrGNGjRoVr7/+el7WAyCzSq3HalfrVOp/DwClzu/f9G688ca0b/QdfvjhsfLKKxchI68xACCdSnwtU4l7AqC8TZo0Kf7rv/4rbWzjjTeOO+64o7AJRcR+++0Xd999d7Rv3z4v8w0ePDjne+FXX311PPHEE3lZD4DvH7WrdZyRASg1f/zjH2P27NlNru+zzz6x7rrrFiEjrzMAytW8efPi4IMPjoaGhiaxjh07xoMPPhjdunUrQmaVoRLPk6W+p9LODoqkOS/S0xWCfFq+fHnOMfk6TORTrpySvm8R+b93lbgngHKhJpemjz76KI488si0sY022igefvjhgnQRPP/882PSpEkxadKkmDx5ciLfDnvRRRfFhhtumDG+fPnyGD58eN7XBSgl6nH+VELtckYGKA71uDQ1NDTEjTfe2OR6VVVVnHjiiQXLoxJeYwBQ2SrxLFmJewKAtvjmm2/ioIMOSlufunfvHk888UR06dIl8TyOOeaY/39GnjRpUtxzzz15f4j3pJNOiu222y7rmJNPPjnq6+vzui4AlakSapczMgA0de2116a9Xsj3VCvhdQbA910qlYojjzwyPvzwwyax6urquOeee6Jfv36FTyxPKvE8WYl7yjeNLSCN5vyfcsWKFYnm0JxfULW1tYnm0Bq57l3S9y0i//euEvcEUC7U5NIzf/782GuvvWLu3LlNYmussUY8+eSTsfrqqxckl/XWWy8GDBgQAwYMiP79++ftm2i/q66uLq6++uqsY1566aV48skn8742QKlQj/OnEmqXMzJAcajHpenBBx+Mzz//vMn1PffcM2sTiHyrhNcYAFS2SjxLVuKeAKC1li9fHvvvv3/MmDGjSayuri4ee+yxgp2Te/bs+f/PyAMGDEikFlZXV8dNN92UdcyMGTPi9ttvz/vaAFSeSqhdzsgA8M/+9re/xaRJk5pc7927d+y6664Fy6MSXmcAfN+de+658eCDD6aNXXXVVTF06NDCJpRnlXierMQ95ZvGFpBGhw4dco5ZtmxZojk0pytOc/IstFw5JX3fIvJ/7ypxTwDlQk0uLQ0NDTFs2LCYNm1ak1inTp3iscceiw022KAImSVr8ODBsd5662Udc/nllxcmGYAiUI/LT5K1yxkZoDjU49KU6Vt2TjrppAJnUhjOxwC0ViWeJStxTwDQWscdd1w899xzTa7X1NTEfffdF1tttVURskpWnz59Yqeddso65oorrohUKlWgjAAguyRrlzMyAPyzTO8jDx8+vMCZFIYzMkAy/vCHP8RvfvObtLFRo0ZVRF2pxPNkJe4p3zS2gDS6deuWc8zChQsTzWHBggU5xzQnz0LLlVPS9y0i973r2LFjizoKVeKeAMqFmlw6UqlUHHfccfHUU081ia288srxxBNPxMCBA4uQWfKqqqriyCOPzDrmueeei5kzZxYoI4DCUo/LT5K1yxkZoDjU49Lz2muvxUsvvdTk+g9/+MPYc889i5BR8pyPAWitcjhLRrTstUwl7gkAWuPCCy+M2267rcn1du3axT333BN77713EbIqjKOPPjpr/N13341XXnmlQNkAQG5J1S5nZAD4h1mzZsX999/f5HrXrl3j0EMPLUJGheGMDJBfzzzzTMbfrSeffHL89re/LXBGySiH86TPIuefxhaQxmqrrZZzTNK/QJozf3PyLLRcORXiF2+uNVp63ypxTwDlQk0uHSeffHLceuutTa537NgxHnvssdhxxx2LkFXh7L///lnjjY2N8T//8z+FSQagwNTj8pRU7XJGBigO9bj0ZPqWnRNOOCGqqqoKnE3hOB8D0Bq5XiMsX7488W+myfVapqqqKlZdddVmz1eJewKAlrr88svj3HPPbXK9uro67rzzzhg2bFgRsiqcoUOHRnV19kdwH3rooQJlAwC5JVW7nJEB4B9uuummtHXvsMMOi06dOhUho8JwRgbInxdeeCH23XfftPXkhBNOiKuuuqoIWSWjEp9JrsQ95ZvGFpBG9+7dc46ZP39+ojk0p3Ps6quvnmgOrZHr3iV93yJy37uW3rdK3BNAuVCTS8PIkSPTfmCnY8eO8cgjj8SgQYMKn1SBbbLJJjn/oPziiy8WKBuAwlKPy1NStcsZGaA41OPSMnfu3LjnnnuaXF955ZXj8MMPL0JGheN8DEBrlMNrma5du0ZNTU2z56vEPQFAS1x33XVxxhlnNLnerl27uOOOO+Kggw4qQlaF1alTp9hkk02yjnFGBqCUJFW7nJEB4O8aGhrihhtuaHK9qqoqTjzxxCJkVDjOyAD5MXHixNhrr71i8eLFTWLHHntsxi/iKVeV+ExyJe4p3zS2gDTWWWednGPmzJmTaA6zZs3KOeYHP/hBojm0Rq57t2jRoli6dGmiOeS6dy29b5W4J4ByoSYX39lnnx2jR49ucr1z584xbty4+OlPf1qErAqvuro6+vXrl3XM3/72twJlA1BY6nF5Sqp2OSMDFId6XFpuvfXWWLJkSZPrhxxySKyyyiqFT6iAnI8BaI1yeC2T7/eQI8pvTwDQXDfeeGOcdNJJTa6vtNJKcffdd8ehhx5ahKyKY8CAAVnjU6dOjYaGhgJlAwC5JVG7nJEB4O8efvjh+Oyzz5pc33333WPjjTcuQkaF5YwM0DaTJk2KwYMHx8KFC5vETj755BgzZkxUVVUVIbPkVOIzyZW4p3zT2ALSWH311aNLly5Zx8yePTvRHHLNv8oqq8Sqq66aaA6tseGGG+YcU+x715wcWzq+3PYEUC7U5OI688wz4+KLL25yvWvXrvHUU0/FzjvvXISsiqdv375Z4x999JE/uAIVST0uX0nULmdkgOJQj0tHY2NjXH/99Wljw4cPL3A2xeF8DEBLVeJZshL3BADNcd1118Xxxx8fqVTqn663b98+/vSnP8V//Md/FCmz4sh1Rq6vr49PPvmkQNkAQG5J1C5nZAD4u2uuuSbt9XTNISuRMzJA6/31r3+Nn/70p/HNN980iY0aNSquuuqqimtqEVGZ58lK3FO+aWwBGeT6P+fMmTMTXf+LL77IGi/2L49MmpNXkvculUrFl19+mXVMEsWk3PYEUE7U5OI47bTT4rLLLmtyvVu3bvHMM8/ENttsU4Ssiqtbt25Z4ytWrPAHV6BiqcflKYna5YwMUDzqcWl47LHH4sMPP2xyfdCgQTkf1KkUzscAtFSxz5IR+X8tU4l7AoBcfve738Xw4cObNLWora2Nhx56KPbdd98iZVY8uc7IEZH27wgAUCxJ1C5nZACIeOONN+LFF19scn3DDTeMwYMHFyGjwnNGBmidF198MXbfffeYP39+k9h//dd/xW9/+9siZFUYxT5P+ixycWhsARn06dMna/yDDz5IdP1c8/fu3TvR9Vtr4403jpqamqxjkrx3X3zxRSxdujTrmJbeu0rcE0A5UZMLK5VKxfDhw+N3v/tdk9hqq60Wzz77bAwYMKDwiZWArl275hyTrkMmQCVQj8tTErXLGRmgeNTj0pDpW3aGDx9e4EyKx/kYgJbq0qVLrLPOOlnHJPlaJpVKxUcffZR1TEtfy1TingAgm0svvTROO+20Jtc7duwYjzzySOy1115FyKr4nJEBKDdJ1C5nZADI/D7y8ccfH9XV34+PbzojA7Tc+PHjY/DgwbFw4cImsQsuuCAuvPDCImRVOJX4THIl7infvh+vjKAVcn1gs9gPCZfqB0o7dOiQ81vpkrx3ueauqqqK/v37t2jOStwTQDlRkwsnlUrFscceG9ddd12T2BprrBHjx4+Pfv36FSGz0tCcP7guXry4AJkAFJ56XJ6SqF3OyADFox4X3zvvvBPPPvtsk+s/+MEPYujQoYVPqEicjwFojWK+lpk5c2bOh3da81qmEvcEAOn85je/iVGjRjW5XldXF4899ljsvvvuRciqNDgjA1BukqpdzsgAfJ99/fXXcc899zS5XldXF0cccUQRMioOZ2SAlnn66adjr732ikWLFjWJ/fa3v41zzjmnCFkVViU+k1yJe8o3jS0gg4EDB2aNv//++7Fs2bJE1q6vry/rh4Rz3bu33347sbVzzf3DH/6wWYelf1WJewIoF2pyYTQ2NsaRRx4ZN998c5PYmmuuGc8//3xsvvnmRcisdKRSqZxjqqqqCpAJQOGpx+UpqdrljAxQHOpx8V177bVp6+txxx0X7dq1K0JGxeF8DEBrlPJZsmPHjtGnT58Wz1uJewKAf3XeeeelfYC6U6dO8eSTT8auu+5ahKxKhzMyAOXm+/gesjMyAEm79dZb0zZs+PnPfx7dunUrQkbF4YwM0Hzjxo2LffbZJ5YsWdIkdsUVV6RtNFypSvk86bPIydDYAjLo379/1NXVZYwvX7483nnnnUTWnjZtWjQ0NGSM19XVFb0rTjY77bRT1vibb76Z2Nqvv/561viOO+7YqnkrcU8A5UJNTt6KFSvisMMOi9tvv71JbO21144JEybEZpttVoTMSss333yTc8zKK6+cfCIARaAel6ekapczMkBxqMfFtWDBgvjDH/7Q5HptbW0cffTRRcioeJyPAWiNUj5Lbrvttq1qUlWJewKA7zrrrLPiggsuaHK9S5cu8ec//9nfY8MZGYDy8318D9kZGYAkNTY2xpgxY9LGhg8fXuBsissZGaB5Hn300Rg6dGgsXbr0n65XVVXFNddcE6eddlqRMiuOUj5P+ixyMjS2gAxqa2tj0KBBWce8+uqriayda95ddtklamtrE1k7H3bfffesXfSmT58e9fX1iayd694NHjy4VfNW4p4AyoWanKyGhob4+c9/HnfddVeT2LrrrhsTJkyIjTfeuAiZlZ558+blHLPKKqsknwhAEajH5Smp2uWMDFAc6nFx/f73v48FCxY0uf4f//Ef0b179yJkVDzOxwC0xjbbbJP1m1++/vrr+OijjxJZO6mzZCXuCQC+NXLkyLjkkkuaXF/l/7V333FSldfj+M+ySy9SRFBUUMGKIiBIU0CxC9i7FE2I3cQWy0exRaLBFnujRCV2FKKIKE0iKqhogNgpEQUFpPfd+f2Rn3417M62mV2Yfb9fr/sH93nuOecZZZ972ZkzdevGuHHjolOnTuVQ1ZbHMzIAW5t07V2ekQGoqF577bX4+uuvNzt/8MEHx3777VcOFZUfz8gAhXvppZfixBNPjA0bNvzqfFZWVjz88MMVrilSRGa+JzkT15RKGltAEoX9JX377bfTkrewuFvCD49ktttuu2jTpk2B4+vXr49p06alPG9hcXNycqJHjx4lip2JawLYmtiT02Pjxo1x2mmnxbPPPrvZWLNmzWLSpEmx2267lSrHDz/8EC+88MKvjtmzZ5cqZnn58ccfk45nZ2dHkyZNyqgagLJnP976pGvv8owMUH7sx+UjkUjEAw88kO9YRfyFsudjAEqiKM9sW9u9TCauCQAiIn7/+9/H4MGDNztfv379eOutt6J9+/alir9mzZrNfoc8ffr0UsUsL4U9I0dENG3atAwqAYCiSdfe5RkZgIrqvvvuy/e83yPnzzMyUJE999xzceqpp8bGjRt/db5SpUrxxBNPxIABA0qdY8yYMb/6d9cxY8aUOma6ZeJ7kjNxTamUU94FwJbspJNOij/84Q+xadOmfMcnTpyYlryTJk0qcCwnJydOOumktORNpdNOOy0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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(num=1, figsize=(20, 10), dpi=250, linewidth=1.5)\n", + "\n", + "# Create axes (assuming you want a single subplot, modify if you need multiple)\n", + "ax = plt.gca()\n", + "\n", + "# plot the CHIME data\n", + "yerr = np.array([[errsa_nlfix[0]], [errsa_nlfix[1]]])\n", + "plt.errorbar(\n", + " za_eff,\n", + " ms_nlfix,\n", + " xerr=zerr_eff,\n", + " yerr=yerr,\n", + " marker='o',\n", + " color='green',\n", + " ls=\"\",\n", + " label=f\"CHIME+eBOSS {tracer}\",\n", + ")\n", + "\n", + "# Now plot all other surveys \n", + "for type_, label, marker in types:\n", + " z = []\n", + " om = []\n", + " z_err = []\n", + " om_err = []\n", + " labelled = False\n", + " if type_ == \"HI_im\":\n", + " # Add in the GBT points by hand rather than trying to insert into the OmegaHI\n", + " # data file\n", + " plt.errorbar(\n", + " z_GBT,\n", + " om_GBT,\n", + " xerr=None,\n", + " yerr=err_GBT,\n", + " label=label,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.4,\n", + " fillstyle=\"none\",\n", + " )\n", + " continue\n", + " for name, dset in data.items():\n", + " if dset[\"type\"] != type_:\n", + " continue\n", + " if type_ == \"HI_stack\" and name in [\"delhaize2013\", \"chen2021\"]:\n", + " continue\n", + " ds = combine_data(dset, cosmo=c)\n", + " z = ds[\"z\"]\n", + " z_err = ds[\"zerr\"]\n", + " om = 1e3 * ds[\"omega_HI\"]\n", + " om_err = 1e3 * ds[\"omega_HI_err\"]\n", + " l = None if labelled else label\n", + " labelled = True\n", + " \n", + " plt.errorbar(\n", + " z,\n", + " om,\n", + " xerr=z_err,\n", + " yerr=om_err,\n", + " label=l,\n", + " marker=marker,\n", + " markersize=6,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.7,\n", + " fillstyle=\"none\",\n", + " )\n", + "\n", + "za = np.linspace(0, 3)\n", + "omHI = lssmodels.omega_HI.evaluate(za)\n", + "plt.plot(za, 1e3 * omHI, \"k-\", label=\"Fiducial\")\n", + "\n", + "plt.xlim(0, 2)\n", + "plt.ylim(0, 1.7)\n", + "plt.xlabel(\"Redshift $z$\")\n", + "plt.ylabel(r\"$10^3 \\times \\Omega_\\mathrm{HI}$\")\n", + "plt.grid(color='gray', alpha=0.3)\n", + "\n", + "## Fix up the legend\n", + "# get handles\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "# remove the errorbars\n", + "handles = [h[0] if ii > 3 else h for ii, h in enumerate(handles)]\n", + "# use them in the legend\n", + "ax.legend(handles, labels, loc=\"upper right\", ncol=1, numpoints=1)\n", + "\n", + "# Add text labels (modify as needed for your specific case)\n", + "ax.text(\n", + " 0.02,\n", + " 1.0 - 0.03,\n", + " \"Fixed non-linear parameters\", # Change this label as needed\n", + " transform=ax.transAxes,\n", + " fontsize=\"large\",\n", + " bbox=dict(fc=\"w\", ec=\"w\"),\n", + " verticalalignment=\"top\",\n", + ")\n", + "\n", + "if save: \n", + " plt.savefig(save_path+f\"omegaHI_comparison_fixed_{tracer}_{weight}.pdf\")\n", + " plt.savefig(save_path+f\"omegaHI_comparison_fixed_{tracer}_{weight}.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create figure with two subplots\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(20, 19), dpi=250, \n", + " sharex=True, \n", + " gridspec_kw=dict(wspace=0.0, height_ratios=(1.6, 1), hspace=0.0)) \n", + "\n", + "# Define data for each panel\n", + "panel_data = [\n", + " {\n", + " 'ax': ax1,\n", + " 'ms_data': ms_all,\n", + " 'err_data': errsa_all,\n", + " 'ylim': (0, 11),\n", + " 'label': 'Full parameters'\n", + " },\n", + " {\n", + " 'ax': ax2,\n", + " 'ms_data': ms_nlfix,\n", + " 'err_data': errsa_nlfix,\n", + " 'ylim': (0, 1.7),\n", + " 'label': 'Fixed non-linear parameters'\n", + " }\n", + "]\n", + "\n", + "# Loop through each panel\n", + "for i, panel in enumerate(panel_data):\n", + " ax = panel['ax']\n", + " \n", + " # Plot the CHIME data\n", + " yerr = np.array([[panel['err_data'][0]], [panel['err_data'][1]]])\n", + " ax.errorbar(\n", + " za_eff,\n", + " panel['ms_data'],\n", + " xerr=zerr_eff,\n", + " yerr=yerr,\n", + " marker='o',\n", + " color='green',\n", + " ls=\"\",\n", + " label=f\"CHIME+eBOSS {tracer}\",\n", + " )\n", + " \n", + " # Now plot all other surveys \n", + " for type_, label, marker in types:\n", + " z = []\n", + " om = []\n", + " z_err = []\n", + " om_err = []\n", + " labelled = False\n", + " \n", + " if type_ == \"HI_im\":\n", + " # Add in the GBT points by hand rather than trying to insert into the OmegaHI\n", + " # data file\n", + " ax.errorbar(\n", + " z_GBT,\n", + " om_GBT,\n", + " xerr=None,\n", + " yerr=err_GBT,\n", + " label=label,\n", + " marker=marker,\n", + " markersize=10,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.4,\n", + " fillstyle=\"none\",\n", + " )\n", + " continue\n", + " \n", + " for name, dset in data.items():\n", + " if dset[\"type\"] != type_:\n", + " continue\n", + " if type_ == \"HI_stack\" and name in [\"delhaize2013\", \"chen2021\"]:\n", + " continue\n", + " ds = combine_data(dset, cosmo=c)\n", + " z = ds[\"z\"]\n", + " z_err = ds[\"zerr\"]\n", + " om = 1e3 * ds[\"omega_HI\"]\n", + " om_err = 1e3 * ds[\"omega_HI_err\"]\n", + " l = None if labelled else label\n", + " labelled = True\n", + " \n", + " ax.errorbar(\n", + " z,\n", + " om,\n", + " xerr=z_err,\n", + " yerr=om_err,\n", + " label=l,\n", + " marker=marker,\n", + " markersize=10,\n", + " ls=\"\",\n", + " color=\"gray\",\n", + " alpha=0.7,\n", + " fillstyle=\"none\",\n", + " )\n", + " \n", + " # Plot fiducial model\n", + " za = np.linspace(0, 3)\n", + " omHI = lssmodels.omega_HI.evaluate(za)\n", + " ax.plot(za, 1e3 * omHI, \"k-\", label=\"Fiducial\")\n", + " \n", + " # Set axis properties\n", + " ax.set_xlim(0, 2)\n", + " ax.set_ylim(panel['ylim'])\n", + " ax.set_ylabel(r\"$10^3 \\times \\Omega_\\mathrm{HI}$\")\n", + " ax.grid(color='gray', alpha=0.3)\n", + " \n", + " # Fix up the legend (only for top panel)\n", + " if i == 0: # Only add legend to the first (top) panel\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " # remove the errorbars\n", + " handles = [h[0] if ii > 3 else h for ii, h in enumerate(handles)]\n", + " # use them in the legend\n", + " ax.legend(handles, labels, loc=\"upper right\", ncol=1, numpoints=1)\n", + " \n", + " # Add text labels\n", + " ax.text(\n", + " 0.02,\n", + " 1.0 - 0.03,\n", + " panel['label'],\n", + " transform=ax.transAxes,\n", + " fontsize=\"large\",\n", + " bbox=dict(fc=\"w\", ec=\"w\"),\n", + " verticalalignment=\"top\",\n", + " )\n", + "\n", + "# Add x-label only to bottom panel\n", + "ax2.set_xlabel(\"Redshift $z$\")\n", + "\n", + "# # Adjust layout and display\n", + "plt.tight_layout()\n", + "# plt.show()\n", + "\n", + "if save: \n", + " plt.savefig(save_path+f\"omegaHI_comparison_{tracer}_{weight}.pdf\",dpi=250,bbox_inches=\"tight\")\n", + " plt.savefig(save_path+f\"omegaHI_comparison_{tracer}_{weight}.png\",dpi=250,bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pipe_ps", + "language": "python", + "name": "pipe_ps" + }, + "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": 4 +} diff --git a/notebooks/chain_save_Stacking.ipynb b/notebooks/chain_save_Stacking.ipynb new file mode 100644 index 0000000..0fc4891 --- /dev/null +++ b/notebooks/chain_save_Stacking.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import glob\n", + "\n", + "import numpy as np\n", + "from cora.util import units\n", + "\n", + "from draco.util import tools\n", + "from fitstack import containers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary cache directory at /tmp/matplotlib-azf49izt because the default path (/home/h/halpern/arnab92/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import getdist\n", + "from getdist import plots, MCSamples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from fitstack.chaintools import extract_priors, gdchain" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from draco.core.containers import FrequencyStack, FrequencyStackByPol, MockFrequencyStack, FormedBeam\n", + "from fitstack.containers import MCMCFit1D" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "weight = ['inverse_var', 'uniform']\n", + "pol = 'xy'\n", + "param = [\"transform_shot_noise_nonlinear_fixed\",\"transform_shot_noise_nonlinear_free\",\"transform_nonlinear_fixed\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "latex_names = {\n", + " \"offset\": r\"\\Delta{\\nu}\",\n", + " \"omega\": r\"\\Omega_{\\rm HI}\",\n", + " \"b_HI\": r\"b_{\\rm HI}\",\n", + " \"b_g\": r\"b_{\\rm g}\",\n", + " \"M_10\": r\"M_{10}\",\n", + " \"NL\": r\"\\alpha_{\\rm NL}\",\n", + " \"FoGh\": r\"\\alpha_{\\rm FoG,HI}\",\n", + " \"FoGg\": r\"\\alpha_{\\rm FoG,g}\",\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_shot_noise_nonlinear_fixed/inverse_var/all/polxy\n", + "{'offset': (-0.8, 0.8), 'omega': (-20.0, 20.0), 'omega_b_HI': (-20.0, 20.0), 'b_HI': (0.0, 10.0), 'M_10': (-500.0, 500.0)}\n", + "Removed 0.1 as burn in\n", + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_shot_noise_nonlinear_free/inverse_var/all/polxy\n", + "{'offset': (-0.4, 0.4), 'omega': (-10.0, 10.0), 'b_HI': (0.0, 10.0), 'NL': (0.0, 5.0), 'FoGh': (0.0, 5.0), 'FoGg': (0.0, 5.0), 'M_10': (0.0, 20.0)}\n", + "Removed 0.1 as burn in\n", + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_nonlinear_fixed/inverse_var/all/polxy\n", + "{'offset': (-0.8, 0.8), 'omega': (-20.0, 20.0), 'omega_b_HI': (-20.0, 20.0), 'b_HI': (0.0, 10.0)}\n", + "Removed 0.1 as burn in\n", + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_shot_noise_nonlinear_fixed/uniform/all/polxy\n", + "{'offset': (-0.8, 0.8), 'omega': (-20.0, 20.0), 'omega_b_HI': (-20.0, 20.0), 'b_HI': (0.0, 10.0), 'M_10': (-500.0, 500.0)}\n", + "Removed 0.1 as burn in\n", + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_shot_noise_nonlinear_free/uniform/all/polxy\n", + "{'offset': (-0.4, 0.4), 'omega': (-10.0, 10.0), 'b_HI': (0.0, 10.0), 'NL': (0.0, 5.0), 'FoGh': (0.0, 5.0), 'FoGg': (0.0, 5.0), 'M_10': (0.0, 20.0)}\n", + "Removed 0.1 as burn in\n", + "/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/transform_nonlinear_fixed/uniform/all/polxy\n", + "{'offset': (-0.8, 0.8), 'omega': (-20.0, 20.0), 'omega_b_HI': (-20.0, 20.0), 'b_HI': (0.0, 10.0)}\n", + "Removed 0.1 as burn in\n" + ] + } + ], + "source": [ + "fit = {}\n", + "for ww in weight:\n", + " fit[ww] = {}\n", + " for pp in param:\n", + " path = f\"/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/{pp}/{ww}/all/pol{pol}\"\n", + " print(path)\n", + " fit_1D = MCMCFit1D.from_file(os.path.join(path,f\"mcmc_fit_{pp}_joint_QSO_NGC.h5\"))\n", + " fit[ww][pp] = fit_1D\n", + " \n", + " # Extract the prior ranges from a MCMC fit chain \n", + " ranges = extract_priors(fit_1D)\n", + " print(ranges)\n", + "\n", + " # Convert a CHIME MCMCFit object into something getdist can understand\n", + " chain = gdchain(fit_1D, latex_names=latex_names, thin=20, ignore_rows=0.1, ranges=ranges)\n", + " save_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/stacking/stacking/mcmc_fits/chains/\"\n", + " outname = save_path + f\"chain_joint_QSO_{ww}_{pp}.gdchain\"\n", + " # chain.savePickle(str(outname))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:fine_bins_2D not large enough for optimal density: omega, b_HI\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 16 # Axis label font size\n", + "g.settings.lab_fontsize = 16 # Parameter label font size \n", + "g.settings.legend_fontsize = 16 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 16\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "g.triangle_plot(\n", + " [chain],\n", + " \n", + " filled=True,\n", + " legend_loc=(0.6, 0.8),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pipe_ps", + "language": "python", + "name": "pipe_ps" + }, + "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": 4 +} diff --git a/notebooks/chain_save_auto_ps.ipynb b/notebooks/chain_save_auto_ps.ipynb new file mode 100644 index 0000000..0f6fad6 --- /dev/null +++ b/notebooks/chain_save_auto_ps.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import glob\n", + "\n", + "import numpy as np\n", + "from cora.util import units\n", + "\n", + "from draco.util import tools\n", + "from fitstack import containers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary cache directory at /tmp/matplotlib-h_4mbgf0 because the default path (/home/h/halpern/arnab92/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import getdist\n", + "from getdist import plots, MCSamples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from fitstack.chaintools import extract_priors, gdchain" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from draco.core.containers import FrequencyStack, FrequencyStackByPol, MockFrequencyStack, FormedBeam\n", + "from fitstack.containers import MCMCFit1D, MCMCFitPowerSpectrum1D" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "cyl = 'allcyl'\n", + "pol_sel_iq = 'iq'\n", + "window = 'uniform'\n", + "reg = 'flat'\n", + "taper = 'v2'\n", + "cutabs = 7\n", + "cut = 10\n", + "band='full'\n", + "with_shot = False\n", + "with_scaled_noise = True" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "path_fit = f\"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/cyl_{cyl}/all_time_mask_only/pol{pol_sel_iq}/band{band}_{window}_absorb_nearby_nsig_{cutabs}_{reg}/{cut}Jy_base_{taper}/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/cyl_allcyl/all_time_mask_only/poliq/bandfull_uniform_absorb_nearby_nsig_7_flat/10Jy_base_v2/'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/cyl_allcyl/all_time_mask_only/poliq/bandfull_uniform_absorb_nearby_nsig_7_flat/10Jy_base_v2\n" + ] + } + ], + "source": [ + "path_data = f\"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/cyl_{cyl}/all_time_mask_only/pol{pol_sel_iq}/band{band}_{window}_absorb_nearby_nsig_{cutabs}_{reg}/{cut}Jy_base_{taper}\"\n", + "print(path_data)\n", + "ps1d_data = containers.PowerSpectrum1D.from_file(os.path.join(path_data,\"pspec_1d_all_bandb.h5\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "redshift = ps1d_data.attrs['redshift']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "alphaFoG_power=[0.0,-1.0]\n", + "alphaFoG_min=0.1\n", + "alphaFoG_max=[5.0,10.0,20.0]\n", + "nsample=500000\n", + "nwalker=32" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "latex_names = {\n", + " \"omega\": r\"\\Omega_{\\rm HI}\",\n", + " \"b_HI\": r\"b_{\\rm HI}\",\n", + " \"NL\": r\"\\alpha_{\\rm NL}\",\n", + " \"FoGh\": r\"\\alpha_{\\rm FoG,HI}\",\n", + " \"SN\": r\"SN\",\n", + " \"FoGs\": r\"\\alpha_{\\rm FoG,s}\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n", + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n", + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n", + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n", + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n", + "{'omega': (0.0, 20.0), 'b_HI': (0.0, 10), 'NL': (0.0, 5)}\n", + "Removed 0.1 as burn in\n" + ] + } + ], + "source": [ + "for ii, alpha_p in enumerate(alphaFoG_power):\n", + " for jj, alpha_max in enumerate(alphaFoG_max):\n", + " choise_param = f\"4para_Ombsampling_pow{alpha_p}_FoGmin{alphaFoG_min}_FoGmax{alpha_max}_nsamp{nwalker}x{nsample}\"\n", + " data = os.path.join(path_fit,choise_param,\"mcmc_fit_1D_ps.h5\")\n", + " fit_1D = MCMCFitPowerSpectrum1D.from_file(data)\n", + " \n", + " # Extract the prior ranges from a MCMC fit chain \n", + " ranges = extract_priors(fit_1D)\n", + " print(ranges)\n", + " \n", + " # Convert a CHIME MCMCFit object into something getdist can understand\n", + " chain = gdchain(fit_1D, latex_names=latex_names, thin=20, ignore_rows=0.1, ranges=ranges)\n", + " save_path = \"/scratch/h/halpern/arnab92/data/rev12/bandb/powerspectrums/data/fit_mcmc/chains/\"\n", + " outname = save_path + f\"chain_autops_pow{alpha_p}_FoGmin{alphaFoG_min}_FoGmax{alpha_max}.gdchain\"\n", + " # chain.savePickle(str(outname))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:fine_bins not large enough to well sample smoothing scale - omega\n", + "WARNING:root:auto bandwidth for FoGh very small or failed (h=0.00032261524670257737,N_eff=208108.9267584525). Using fallback (h=0.002483189230471282)\n", + "WARNING:root:fine_bins_2D not large enough for optimal density: omega, b_HI\n", + "WARNING:root:fine_bins_2D not large enough for optimal density: omega, FoGh\n", + "WARNING:root:fine_bins_2D not large enough for optimal density: b_HI, FoGh\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plotter with larger size and better settings\n", + "g = plots.get_single_plotter(\n", + " width_inch=10, # Larger width for better readability\n", + " scaling=True,\n", + " ratio=1 # Square aspect ratio\n", + ")\n", + "\n", + "# Set additional GetDist-specific settings for publication quality\n", + "g.settings.axes_fontsize = 16 # Axis label font size\n", + "g.settings.lab_fontsize = 16 # Parameter label font size \n", + "g.settings.legend_fontsize = 16 # Legend font size\n", + "#g.settings.colorbar_label_fontsize = 14\n", + "g.settings.title_limit_fontsize = 16\n", + "\n", + "# Optional: Customize line widths and contour properties\n", + "g.settings.linewidth = 2.5 # Contour line width\n", + "g.settings.linewidth_contour = 2.0 # Filled contour edge width\n", + "g.settings.alpha_filled_add = 0.85 # Filled contour transparency\n", + "\n", + "g.triangle_plot(\n", + " [chain],\n", + " \n", + " filled=True,\n", + " legend_loc=(0.6, 0.8),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pipe_ps", + "language": "python", + "name": "pipe_ps" + }, + "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": 4 +}