diff --git a/README.md b/README.md
index e534a49..8d2b8f6 100644
--- a/README.md
+++ b/README.md
@@ -44,6 +44,8 @@ jupyter notebook tutorials/observables/spectro.ipynb
 jupyter notebook tutorials/observables/bao.ipynb
 ```
 
+For **Dark Emulator** (3x2pt, GC, GGL fitting), run the notebooks in `tutorials/dark_emulator/`; for production sampling use `python playground/scripts/sampling/darkemu_3x2pt_full_sampling.py` (from the repo root).
+
 ## 📬 Contact
 For any questions or discussions, feel free to open an issue or reach out to the [cloe-maintainers](https://github.com/orgs/cloe-org/teams/cloe-maintainers).
 
diff --git a/scripts/plotting/corner_plot.py b/scripts/plotting/corner_plot.py
new file mode 100644
index 0000000..b2d4f8e
--- /dev/null
+++ b/scripts/plotting/corner_plot.py
@@ -0,0 +1,656 @@
+"""Corner Plot for Cosmological Parameter Constraints.
+
+This script creates publication-quality corner plots (triangle plots) showing
+1D and 2D posterior distributions for cosmological parameters like Ω_m, σ_8, and S_8.
+
+Usage:
+    python corner_plot.py [--chain CHAIN_FILE] [--output OUTPUT_FILE]
+
+Example:
+    python corner_plot.py --chain ../sampling/chain_darkemu_3x2pt.npz --output corner_cosmo.pdf
+
+Requirements:
+    - getdist (pip install getdist)
+    - matplotlib
+    - numpy
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+import argparse
+from pathlib import Path
+
+# Configure matplotlib for publication quality
+rcParams['font.family'] = 'serif'
+rcParams['font.size'] = 12
+rcParams['axes.labelsize'] = 14
+rcParams['axes.titlesize'] = 14
+rcParams['xtick.labelsize'] = 11
+rcParams['ytick.labelsize'] = 11
+rcParams['legend.fontsize'] = 10
+rcParams['figure.dpi'] = 150
+rcParams['savefig.dpi'] = 300
+rcParams['text.usetex'] = False  # Set to True if LaTeX is available
+
+
+def load_chain(filepath: str, include_derived: bool = True) -> tuple:
+    """Load MCMC chain from file.
+
+    Parameters
+    ----------
+    filepath : str
+        Path to chain file (.npz format from nautilus).
+    include_derived : bool
+        If True, include pre-computed derived parameters (Omega_m, sigma8, S8)
+        if available in the file.
+
+    Returns
+    -------
+    samples : np.ndarray
+        Parameter samples, shape (n_samples, n_params).
+    weights : np.ndarray
+        Sample weights.
+    param_names : list
+        Parameter names.
+    """
+    data = np.load(filepath, allow_pickle=True)
+    samples = data['chain']
+
+    # Handle weights - could be raw or log weights
+    if 'weights' in data:
+        weights = data['weights']
+        # Check if weights are log weights (typically have large negative values)
+        if np.any(weights < 0):
+            weights = np.exp(weights - weights.max())
+    else:
+        weights = np.ones(len(samples))
+
+    param_names = list(data['param_names'])
+
+    # Include pre-computed derived parameters if available
+    if include_derived:
+        derived_cols = []
+        derived_names = []
+
+        # Check for Omega_m, sigma8, S8 in the file
+        for derived_name in ['Omega_m', 'sigma8', 'S8']:
+            if derived_name in data and derived_name not in param_names:
+                derived_cols.append(data[derived_name])
+                derived_names.append(derived_name)
+
+        if derived_cols:
+            samples = np.column_stack([samples] + derived_cols)
+            param_names = param_names + derived_names
+            print(f"  Loaded derived parameters: {derived_names}")
+
+    return samples, weights, param_names
+
+
+def compute_derived_params(samples: np.ndarray, param_names: list) -> tuple:
+    """Compute derived cosmological parameters (Omega_m, sigma_8, S_8).
+
+    Parameters
+    ----------
+    samples : np.ndarray
+        Parameter samples.
+    param_names : list
+        Parameter names.
+
+    Returns
+    -------
+    derived_samples : np.ndarray
+        Samples including derived parameters.
+    derived_names : list
+        Names including derived parameters.
+    """
+    n_samples = samples.shape[0]
+
+    # Extract necessary parameters
+    param_dict = {name: samples[:, i] for i, name in enumerate(param_names)}
+
+    # Compute Omega_m
+    if 'omch2' in param_dict and 'ombh2' in param_dict and 'H0' in param_dict:
+        h = param_dict['H0'] / 100.0
+        omega_m = (param_dict['omch2'] + param_dict['ombh2']) / h**2
+    elif 'Omega_cdm0' in param_dict and 'Omega_b0' in param_dict:
+        omega_m = param_dict['Omega_cdm0'] + param_dict['Omega_b0']
+    else:
+        omega_m = None
+
+    # Compute sigma_8 from As (approximate relation)
+    if 'logAs' in param_dict:
+        As = np.exp(param_dict['logAs']) * 1e-10
+        # Approximate sigma_8 scaling (simplified)
+        sigma_8 = 0.8 * (As / 2.1e-9)**0.5
+        if omega_m is not None:
+            sigma_8 *= (omega_m / 0.3)**0.25
+    elif 'sigma8' in param_dict:
+        sigma_8 = param_dict['sigma8']
+    else:
+        sigma_8 = None
+
+    # Compute S_8 = sigma_8 * (Omega_m / 0.3)^0.5
+    if sigma_8 is not None and omega_m is not None:
+        S_8 = sigma_8 * (omega_m / 0.3)**0.5
+    else:
+        S_8 = None
+
+    # Build derived samples array
+    derived_cols = []
+    derived_names = list(param_names)
+
+    if omega_m is not None and 'Omega_m' not in param_names:
+        derived_cols.append(omega_m)
+        derived_names.append('Omega_m')
+
+    if sigma_8 is not None and 'sigma8' not in param_names:
+        derived_cols.append(sigma_8)
+        derived_names.append('sigma8')
+
+    if S_8 is not None and 'S8' not in param_names:
+        derived_cols.append(S_8)
+        derived_names.append('S8')
+
+    if derived_cols:
+        derived_samples = np.column_stack([samples] + derived_cols)
+    else:
+        derived_samples = samples
+
+    return derived_samples, derived_names
+
+
+def create_corner_plot(
+    samples_list: list,
+    weights_list: list,
+    param_names: list,
+    labels: list = None,
+    colors: list = None,
+    linestyles: list = None,
+    params_to_plot: list = None,
+    param_labels: dict = None,
+    filled: list = None,
+    output_file: str = None,
+    figsize: tuple = (8, 8),
+    title: str = None,
+):
+    """Create a corner plot comparing multiple chains.
+
+    Parameters
+    ----------
+    samples_list : list of np.ndarray
+        List of sample arrays from different chains.
+    weights_list : list of np.ndarray
+        List of weight arrays.
+    param_names : list
+        Parameter names (common to all chains).
+    labels : list, optional
+        Legend labels for each chain.
+    colors : list, optional
+        Colors for each chain.
+    linestyles : list, optional
+        Line styles for each chain.
+    params_to_plot : list, optional
+        Subset of parameters to plot.
+    param_labels : dict, optional
+        LaTeX labels for parameters.
+    filled : list of bool, optional
+        Whether to fill contours for each chain.
+    output_file : str, optional
+        Path to save the figure.
+    figsize : tuple
+        Figure size.
+    title : str, optional
+        Figure title.
+    """
+    try:
+        from getdist import MCSamples, plots
+    except ImportError:
+        print("getdist not installed. Using fallback corner plot.")
+        _corner_plot_fallback(
+            samples_list, weights_list, param_names, labels, colors,
+            params_to_plot, param_labels, output_file, figsize
+        )
+        return
+
+    # Default parameter labels (LaTeX)
+    default_labels = {
+        'Omega_m': r'\Omega_{\rm m}',
+        'sigma8': r'\sigma_8',
+        'S8': r'S_8',
+        'H0': r'H_0',
+        'ns': r'n_s',
+        'ombh2': r'\omega_b',
+        'omch2': r'\omega_c',
+        'logAs': r'\ln(10^{10}A_s)',
+        'logMmin': r'\log M_{\rm min}',
+        'sigma_sq': r'\sigma^2',
+        'logM1': r'\log M_1',
+        'alpha': r'\alpha',
+        'kappa': r'\kappa',
+        'w0': r'w_0',
+    }
+    if param_labels:
+        default_labels.update(param_labels)
+
+    # Default settings
+    if params_to_plot is None:
+        params_to_plot = param_names
+
+    if labels is None:
+        labels = [f'Chain {i+1}' for i in range(len(samples_list))]
+
+    if colors is None:
+        colors = ['#1f77b4', '#d62728', '#ff7f0e', '#2ca02c', '#9467bd']
+
+    if linestyles is None:
+        linestyles = ['-', '--', '-.', ':', '-']
+
+    if filled is None:
+        filled = [True] + [False] * (len(samples_list) - 1)
+
+    # Create MCSamples objects
+    mc_samples_list = []
+    for i, (samples, weights, label) in enumerate(zip(samples_list, weights_list, labels)):
+        # Get indices for parameters to plot
+        param_indices = [param_names.index(p) for p in params_to_plot if p in param_names]
+        plot_names = [param_names[j] for j in param_indices]
+        plot_labels = [default_labels.get(n, n) for n in plot_names]
+
+        mc = MCSamples(
+            samples=samples[:, param_indices],
+            weights=weights,
+            names=plot_names,
+            labels=plot_labels,
+            label=label,
+        )
+        mc_samples_list.append(mc)
+
+    # Create triangle plot
+    g = plots.get_subplot_plotter(width_inch=figsize[0])
+    g.settings.figure_legend_frame = True
+    g.settings.legend_fontsize = 11
+    g.settings.axes_fontsize = 12
+    g.settings.lab_fontsize = 14
+    g.settings.alpha_filled_add = 0.6
+    g.settings.solid_contour_palefactor = 0.6
+
+    # Plot with different styles for each chain
+    g.triangle_plot(
+        mc_samples_list,
+        params=plot_names,
+        filled=filled,
+        contour_colors=colors[:len(mc_samples_list)],
+        contour_ls=linestyles[:len(mc_samples_list)],
+        legend_loc='upper right',
+    )
+
+    if title:
+        plt.suptitle(title, y=1.02)
+
+    plt.tight_layout()
+
+    if output_file:
+        plt.savefig(output_file, bbox_inches='tight', dpi=300)
+        print(f"Figure saved to {output_file}")
+
+    plt.show()
+
+
+def _corner_plot_fallback(
+    samples_list, weights_list, param_names, labels, colors,
+    params_to_plot, param_labels, output_file, figsize
+):
+    """Fallback corner plot using matplotlib only (no getdist)."""
+    try:
+        import corner
+    except ImportError:
+        print("Neither getdist nor corner is installed.")
+        print("Install with: pip install getdist  or  pip install corner")
+        return
+
+    if params_to_plot is None:
+        params_to_plot = param_names
+
+    param_indices = [param_names.index(p) for p in params_to_plot if p in param_names]
+
+    default_labels = {
+        'Omega_m': r'$\Omega_{\rm m}$',
+        'sigma8': r'$\sigma_8$',
+        'S8': r'$S_8$',
+    }
+    if param_labels:
+        default_labels.update(param_labels)
+
+    plot_labels = [default_labels.get(param_names[i], param_names[i]) for i in param_indices]
+
+    if colors is None:
+        colors = ['blue', 'red', 'green', 'orange']
+
+    fig = None
+    for i, (samples, weights, color) in enumerate(zip(samples_list, weights_list, colors)):
+        fig = corner.corner(
+            samples[:, param_indices],
+            weights=weights,
+            labels=plot_labels,
+            color=color,
+            fig=fig,
+            plot_datapoints=False,
+            plot_density=False,
+            fill_contours=(i == 0),
+            levels=[0.68, 0.95],
+            smooth=1.0,
+        )
+
+    if labels:
+        # Add legend manually
+        from matplotlib.lines import Line2D
+        legend_elements = [
+            Line2D([0], [0], color=c, linewidth=2, label=l)
+            for c, l in zip(colors[:len(samples_list)], labels)
+        ]
+        fig.legend(handles=legend_elements, loc='upper right', frameon=True)
+
+    if output_file:
+        fig.savefig(output_file, bbox_inches='tight', dpi=300)
+        print(f"Figure saved to {output_file}")
+
+    plt.show()
+
+
+def create_hsc_style_plot(
+    samples: np.ndarray,
+    weights: np.ndarray,
+    param_names: list,
+    comparison_data: dict = None,
+    output_file: str = None,
+):
+    """Create HSC-style corner plot for Omega_m, sigma_8, S_8.
+
+    This produces a plot similar to the HSC-Y3 paper figure.
+
+    Parameters
+    ----------
+    samples : np.ndarray
+        Parameter samples including Omega_m, sigma8, S8.
+    weights : np.ndarray
+        Sample weights.
+    param_names : list
+        Parameter names.
+    comparison_data : dict, optional
+        Dictionary with comparison datasets:
+        {'name': {'samples': array, 'weights': array, 'color': str, 'linestyle': str}}
+    output_file : str, optional
+        Output file path.
+    """
+    try:
+        from getdist import MCSamples, plots
+    except ImportError:
+        print("getdist required for HSC-style plot. Install with: pip install getdist")
+        return
+
+    # Compute derived parameters if needed
+    samples_full, names_full = compute_derived_params(samples, param_names)
+
+    # Check required parameters
+    required = ['Omega_m', 'sigma8', 'S8']
+    if not all(p in names_full for p in required):
+        print(f"Missing required parameters. Available: {names_full}")
+        return
+
+    # Main sample
+    param_indices = [names_full.index(p) for p in required]
+    mc_main = MCSamples(
+        samples=samples_full[:, param_indices],
+        weights=weights,
+        names=required,
+        labels=[r'\Omega_{\rm m}', r'\sigma_8', r'S_8'],
+        label='This work',
+    )
+
+    samples_list = [mc_main]
+    colors = ['#1f77b4']  # Blue
+    linestyles = ['-']
+    filled = [True]
+
+    # Add comparison data
+    if comparison_data:
+        for name, data in comparison_data.items():
+            comp_samples, comp_names = compute_derived_params(
+                data['samples'], data.get('param_names', param_names)
+            )
+            indices = [comp_names.index(p) for p in required if p in comp_names]
+            if len(indices) == len(required):
+                mc_comp = MCSamples(
+                    samples=comp_samples[:, indices],
+                    weights=data.get('weights', np.ones(len(comp_samples))),
+                    names=required,
+                    labels=[r'\Omega_{\rm m}', r'\sigma_8', r'S_8'],
+                    label=name,
+                )
+                samples_list.append(mc_comp)
+                colors.append(data.get('color', 'red'))
+                linestyles.append(data.get('linestyle', '--'))
+                filled.append(data.get('filled', False))
+
+    # Create plot
+    g = plots.get_subplot_plotter(width_inch=7)
+    g.settings.figure_legend_frame = True
+    g.settings.legend_fontsize = 10
+    g.settings.axes_fontsize = 11
+    g.settings.lab_fontsize = 13
+    g.settings.alpha_filled_add = 0.6
+    g.settings.solid_contour_palefactor = 0.6
+
+    g.triangle_plot(
+        samples_list,
+        params=required,
+        filled=filled,
+        contour_colors=colors,
+        contour_ls=linestyles,
+        legend_loc='upper right',
+    )
+
+    plt.tight_layout()
+
+    if output_file:
+        plt.savefig(output_file, bbox_inches='tight', dpi=300)
+        print(f"Figure saved to {output_file}")
+
+    plt.show()
+
+
+# =============================================================================
+# Example with synthetic data
+# =============================================================================
+
+def generate_example_data():
+    """Generate example data similar to HSC/DES/KiDS constraints."""
+    np.random.seed(42)
+
+    n_samples = 10000
+
+    # HSC-Y3 like constraints (tighter S8)
+    # Use truncated normal to avoid negative values
+    omega_m_hsc = np.clip(np.random.normal(0.31, 0.05, n_samples), 0.1, 0.6)
+    sigma8_hsc = np.clip(np.random.normal(0.76, 0.06, n_samples), 0.4, 1.2)
+    # Add correlation
+    sigma8_hsc = np.clip(sigma8_hsc - 0.4 * (omega_m_hsc - 0.31), 0.4, 1.2)
+    S8_hsc = sigma8_hsc * np.sqrt(omega_m_hsc / 0.3)
+
+    # DES-Y3 like constraints
+    omega_m_des = np.clip(np.random.normal(0.34, 0.06, n_samples), 0.1, 0.6)
+    sigma8_des = np.clip(np.random.normal(0.73, 0.07, n_samples), 0.4, 1.2)
+    sigma8_des = np.clip(sigma8_des - 0.5 * (omega_m_des - 0.34), 0.4, 1.2)
+    S8_des = sigma8_des * np.sqrt(omega_m_des / 0.3)
+
+    # KiDS-1000 like constraints
+    omega_m_kids = np.clip(np.random.normal(0.30, 0.07, n_samples), 0.1, 0.6)
+    sigma8_kids = np.clip(np.random.normal(0.78, 0.08, n_samples), 0.4, 1.2)
+    sigma8_kids = np.clip(sigma8_kids - 0.45 * (omega_m_kids - 0.30), 0.4, 1.2)
+    S8_kids = sigma8_kids * np.sqrt(omega_m_kids / 0.3)
+
+    # Planck 2018 like constraints (higher S8)
+    omega_m_planck = np.clip(np.random.normal(0.315, 0.02, n_samples), 0.1, 0.6)
+    sigma8_planck = np.clip(np.random.normal(0.81, 0.02, n_samples), 0.4, 1.2)
+    sigma8_planck = np.clip(sigma8_planck + 0.3 * (omega_m_planck - 0.315), 0.4, 1.2)
+    S8_planck = sigma8_planck * np.sqrt(omega_m_planck / 0.3)
+
+    return {
+        'HSC-Y3': {
+            'samples': np.column_stack([omega_m_hsc, sigma8_hsc, S8_hsc]),
+            'weights': np.ones(n_samples),
+            'param_names': ['Omega_m', 'sigma8', 'S8'],
+            'color': '#1f77b4',
+            'linestyle': '-',
+            'filled': True,
+        },
+        'DES-Y3': {
+            'samples': np.column_stack([omega_m_des, sigma8_des, S8_des]),
+            'weights': np.ones(n_samples),
+            'param_names': ['Omega_m', 'sigma8', 'S8'],
+            'color': '#2ca02c',
+            'linestyle': '-.',
+            'filled': False,
+        },
+        'KiDS-1000': {
+            'samples': np.column_stack([omega_m_kids, sigma8_kids, S8_kids]),
+            'weights': np.ones(n_samples),
+            'param_names': ['Omega_m', 'sigma8', 'S8'],
+            'color': '#d62728',
+            'linestyle': '--',
+            'filled': False,
+        },
+        'Planck 2018': {
+            'samples': np.column_stack([omega_m_planck, sigma8_planck, S8_planck]),
+            'weights': np.ones(n_samples),
+            'param_names': ['Omega_m', 'sigma8', 'S8'],
+            'color': '#ff7f0e',
+            'linestyle': ':',
+            'filled': False,
+        },
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Create corner plots for cosmological parameter constraints.'
+    )
+    parser.add_argument(
+        '--chain', type=str, default=None,
+        help='Path to chain file (.npz from nautilus sampler)'
+    )
+    parser.add_argument(
+        '--output', type=str, default='corner_plot.png',
+        help='Output file path (PNG recommended for faster viewing)'
+    )
+    parser.add_argument(
+        '--example', action='store_true',
+        help='Generate example plot with synthetic data'
+    )
+    parser.add_argument(
+        '--params', type=str, nargs='+', default=['Omega_m', 'sigma8', 'S8'],
+        help='Parameters to plot'
+    )
+
+    args = parser.parse_args()
+
+    if args.example or args.chain is None:
+        print("Generating example corner plot with synthetic data...")
+        print("This demonstrates HSC-Y3 / DES-Y3 / KiDS-1000 / Planck 2018 style comparison")
+
+        data = generate_example_data()
+
+        # Extract main dataset
+        main_key = 'HSC-Y3'
+        main_data = data.pop(main_key)
+
+        # Create comparison plot
+        try:
+            from getdist import MCSamples, plots
+
+            # Build MCSamples list
+            samples_list = []
+            colors = []
+            linestyles = []
+            filled_list = []
+
+            # Main sample first
+            mc_main = MCSamples(
+                samples=main_data['samples'],
+                weights=main_data['weights'],
+                names=['Omega_m', 'sigma8', 'S8'],
+                labels=[r'\Omega_{\rm m}', r'\sigma_8', r'S_8'],
+                label=f'{main_key} 3x2pt small scales',
+            )
+            samples_list.append(mc_main)
+            colors.append(main_data['color'])
+            linestyles.append(main_data['linestyle'])
+            filled_list.append(True)
+
+            # Add comparison datasets
+            for name, d in data.items():
+                mc = MCSamples(
+                    samples=d['samples'],
+                    weights=d['weights'],
+                    names=['Omega_m', 'sigma8', 'S8'],
+                    labels=[r'\Omega_{\rm m}', r'\sigma_8', r'S_8'],
+                    label=name,
+                )
+                samples_list.append(mc)
+                colors.append(d['color'])
+                linestyles.append(d['linestyle'])
+                filled_list.append(d['filled'])
+
+            # Create triangle plot
+            g = plots.get_subplot_plotter(width_inch=7)
+            g.settings.figure_legend_frame = True
+            g.settings.legend_fontsize = 9
+            g.settings.axes_fontsize = 12
+            g.settings.lab_fontsize = 14
+            g.settings.alpha_filled_add = 0.5
+            g.settings.solid_contour_palefactor = 0.6
+
+            g.triangle_plot(
+                samples_list,
+                params=['Omega_m', 'sigma8', 'S8'],
+                filled=filled_list,
+                contour_colors=colors,
+                contour_ls=linestyles,
+                legend_loc='upper right',
+            )
+
+            plt.savefig(args.output, bbox_inches='tight', dpi=300)
+            print(f"Example plot saved to {args.output}")
+            # Also save PNG for preview
+            png_output = args.output.replace('.pdf', '.png')
+            plt.savefig(png_output, bbox_inches='tight', dpi=150)
+            print(f"PNG preview saved to {png_output}")
+            plt.show()
+
+        except ImportError:
+            print("getdist not installed. Install with: pip install getdist")
+            return
+
+    else:
+        # Load real chain
+        print(f"Loading chain from {args.chain}...")
+        samples, weights, param_names = load_chain(args.chain)
+        print(f"Loaded {len(samples)} samples with parameters: {param_names}")
+
+        # Compute derived parameters
+        samples_full, names_full = compute_derived_params(samples, param_names)
+        print(f"Parameters with derived: {names_full}")
+
+        # Create corner plot
+        create_corner_plot(
+            samples_list=[samples_full],
+            weights_list=[weights],
+            param_names=names_full,
+            params_to_plot=args.params,
+            output_file=args.output,
+        )
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/sampling/darkemu_3x2pt_full_sampling.py b/scripts/sampling/darkemu_3x2pt_full_sampling.py
new file mode 100644
index 0000000..247cf8c
--- /dev/null
+++ b/scripts/sampling/darkemu_3x2pt_full_sampling.py
@@ -0,0 +1,841 @@
+"""Full sampling for Dark Emulator 3x2pt (GC + GGL + WL) likelihood.
+
+This script performs joint sampling of cosmological parameters and HOD parameters
+using the Dark Emulator-based 3x2pt likelihood:
+- Galaxy Clustering (GC): w_p from Dark Emulator + HOD
+- Galaxy-Galaxy Lensing (GGL): ΔΣ from Dark Emulator + HOD
+- Weak Lensing (WL): ξ±(θ) from HMcode2020Emu + ShearTracer + Wigner transform
+
+Sampled parameters:
+- Cosmology: Omega_cdm0, As (or sigma8)
+- HOD: logMmin, sigma_sq, logM1, alpha
+
+Fixed parameters:
+- H0, Omega_b0, ns, w0, wa, Omega_k0 (cosmology)
+- kappa, poff, Roff (HOD)
+
+Usage:
+    Run from the CLOE repository root, e.g.:
+        python playground/scripts/sampling/darkemu_3x2pt_full_sampling.py [--n_live N] [--n_workers N] [--output_dir DIR]
+        e.g: python playground/scripts/sampling/darkemu_3x2pt_full_sampling.py --n_live 500 --output_dir playground/results
+Expected runtime: ~4-12 hours depending on n_live and n_workers.
+
+Output files (written to output_dir, default playground/results/):
+    - checkpoint_3x2pt_full.hdf5 : Nautilus checkpoint (for resuming or inspection)
+    - chain_3x2pt_full.npz       : Posterior chain, weights, log L, and derived (Omega_m, sigma8, S8) for corner plots
+
+Corner plot: The command below creates a corner (triangle) plot for Omega_m, sigma8, and S8 from the chain saved in step 1:
+python playground/scripts/plotting/corner_plot.py \
+  --chain playground/results/chain_3x2pt_full.npz \
+  --output playground/results/corner_3x2pt.pdf \
+  --params Omega_m sigma8 S8
+"""
+
+# Force unbuffered output for progress visibility (skip in Jupyter where stdout has no reconfigure)
+import sys
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(line_buffering=True)
+if hasattr(sys.stderr, "reconfigure"):
+    sys.stderr.reconfigure(line_buffering=True)
+
+import numpy as np
+import time
+import argparse
+from pathlib import Path
+
+# =============================================================================
+# Setup paths
+# =============================================================================
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+PLAYGROUND_DIR = SCRIPT_DIR.parent.parent
+CLOE_ROOT = PLAYGROUND_DIR.parent
+RESULTS_DIR = PLAYGROUND_DIR / "results"
+RESULTS_DIR.mkdir(exist_ok=True)
+
+# Add CLOE packages to path
+import sys
+sys.path.insert(0, str(CLOE_ROOT / "cloelib"))
+sys.path.insert(0, str(CLOE_ROOT / "cloelike"))
+sys.path.insert(0, str(CLOE_ROOT / "dark_emulator_public"))
+
+# =============================================================================
+# Imports
+# =============================================================================
+
+from cloelib.cosmology.camb_cosmology import CAMBBackground
+from cloelike.EuclidLikelihood_DarkEmu_RealSpace import EuclidLikelihood_DarkEmu_RealSpace
+from cloelib.observables.darkemu_hod import DarkEmuHODParameters
+from cloelib.cosmology.darkemu_cosmology import DarkEmuHODPerturbations
+
+# Import nautilus sampler
+from nautilus import Prior, Sampler
+
+
+# =============================================================================
+# Fiducial Parameters (used for mock data generation and fixed params)
+# =============================================================================
+
+# Cosmological parameters
+# -----------------------
+# Sampled: Omega_cdm0, As (primary parameters affecting clustering & lensing)
+# Fixed parameters and reasons:
+FIDUCIAL_COSMO = {
+    # H0: Degeneracy with Omega_cdm0; GC+GGL primarily constrain Omega_m*h.
+    #     Fixing H0 breaks this degeneracy for cleaner Omega_cdm0 constraints.
+    'H0': 67.0,
+    # Omega_cdm0: SAMPLED - directly affects matter clustering and lensing signal
+    'Omega_cdm0': 0.27,
+    # Omega_b0: Weakly constrained by GC+GGL alone; well-determined by CMB/BBN.
+    #           Fixing to Planck value reduces parameter space.
+    'Omega_b0': 0.049,
+    # Omega_k0: Dark Emulator requires flat universe (Omega_k = 0).
+    'Omega_k0': 0.0,
+    # w0: Dark energy EoS has minimal impact on low-z GC+GGL.
+    #     Fixing to LCDM value (-1) simplifies analysis.
+    'w0': -1.0,
+    # wa: Dark Emulator only supports constant w (wa = 0 required).
+    'wa': 0.0,
+    # ns: Spectral index affects large-scale power; weakly constrained by GC+GGL.
+    #     Well-determined by CMB, so fixed to Planck value.
+    'ns': 0.96,
+    # As: SAMPLED - amplitude directly scales the clustering and lensing signals
+    'As': 2.1e-9,
+    # gamma_MG: Modified gravity growth index; fixed to GR value for standard analysis.
+    'gamma_MG': 0.55,
+    # mnu: Dark Emulator uses fixed omega_nu = 0.00064 internally.
+    #      This corresponds to sum(mnu) ~ 0.06 eV.
+    'mnu': 0.06,
+    'N_mnu': 1,
+}
+
+# HOD parameters
+# --------------
+# Sampled: logMmin, sigma_sq, logM1, alpha (primary HOD parameters)
+# Fixed parameters and reasons:
+FIDUCIAL_HOD = {
+    # logMmin: SAMPLED - minimum halo mass for central galaxy occupation
+    'logMmin': 13.0,
+    # sigma_sq: SAMPLED - scatter in central occupation (transition width)
+    'sigma_sq': 0.3,
+    # logM1: SAMPLED - characteristic mass for satellite galaxies
+    'logM1': 14.0,
+    # alpha: SAMPLED - power-law slope for satellite occupation
+    'alpha': 1.0,
+    # kappa: Satellite threshold parameter; typically fixed as it is
+    #        degenerate with logM1 and poorly constrained.
+    'kappa': 1.0,
+    # poff: Off-centering fraction; set to 0 (all centrals at halo center).
+    #       Off-centering is a second-order effect for most analyses.
+    'poff': 0.0,
+    # Roff: Off-centering scale; irrelevant when poff = 0.
+    'Roff': 0.0,
+}
+
+
+# =============================================================================
+# Mock Data Generation
+# =============================================================================
+
+def generate_mock_data(
+    R_bins: np.ndarray,
+    z_sample: float,
+    pimax: float = 100.0,
+    noise_level: float = 0.1,
+    seed: int = 42,
+) -> tuple:
+    """Generate mock 3x2pt (GC + GGL) data from fiducial model.
+
+    Parameters
+    ----------
+    R_bins : np.ndarray
+        Projected radii in h^-1 Mpc.
+    z_sample : float
+        Sample redshift (lens redshift for GGL, clustering redshift for GC).
+    pimax : float
+        Line-of-sight integration limit for w_p [h^-1 Mpc].
+    noise_level : float
+        Fractional noise level (default 10%).
+    seed : int
+        Random seed for reproducibility.
+
+    Returns
+    -------
+    data_gc : dict
+        Galaxy clustering data dictionary.
+    data_ggl : dict
+        Galaxy-galaxy lensing data dictionary.
+    """
+    print("\n[1] Generating mock 3x2pt data (GC + GGL)...")
+
+    # Create fiducial cosmology and perturbations
+    background = CAMBBackground(**FIDUCIAL_COSMO)
+    hod_params = DarkEmuHODParameters(**FIDUCIAL_HOD)
+
+    pert = DarkEmuHODPerturbations(
+        background=background,
+        redshifts=np.array([z_sample]),
+        hod_params=hod_params,
+    )
+
+    # Generate fiducial predictions
+    # GC: projected correlation function w_p(R)
+    wp_fid = pert.projected_correlation(R_bins, z_sample, pimax=pimax)
+
+    # GGL: excess surface mass density ΔΣ(R)
+    ds_fid = pert.delta_sigma(R_bins, z_sample)
+
+    # Add noise
+    np.random.seed(seed)
+
+    # GC noise
+    sigma_wp = noise_level * wp_fid
+    cov_wp = np.diag(sigma_wp ** 2)
+    wp_obs = wp_fid + np.random.randn(len(R_bins)) * sigma_wp
+
+    # GGL noise
+    sigma_ds = noise_level * ds_fid
+    cov_ds = np.diag(sigma_ds ** 2)
+    ds_obs = ds_fid + np.random.randn(len(R_bins)) * sigma_ds
+
+    print(f"  - Generated {len(R_bins)} radial bins at z = {z_sample}")
+    print(f"  - R range: [{R_bins.min():.2f}, {R_bins.max():.2f}] h^-1 Mpc")
+    print(f"  - w_p range: [{wp_obs.min():.1f}, {wp_obs.max():.1f}] (h^-1 Mpc)")
+    print(f"  - ΔΣ range: [{ds_obs.min():.1f}, {ds_obs.max():.1f}] h M_sun/pc^2")
+
+    data_gc = {
+        'wp': wp_obs,
+        'R_bins': R_bins,
+        'z_sample': z_sample,
+        'covariance': cov_wp,
+        'pimax': pimax,
+        'fiducial_wp': wp_fid,
+    }
+
+    data_ggl = {
+        'delta_sigma': ds_obs,
+        'R_bins': R_bins,
+        'z_lens': z_sample,
+        'covariance': cov_ds,
+        'fiducial_ds': ds_fid,
+    }
+
+    return data_gc, data_ggl
+
+
+def generate_mock_wl_data(
+    theta_arcmin: np.ndarray,
+    z_arr: np.ndarray,
+    dndz: np.ndarray,
+    noise_level: float = 0.1,
+    seed: int = 42,
+) -> dict:
+    """Generate mock WL (cosmic shear) data: xi_+(theta) and xi_-(theta).
+
+    This function generates mock weak lensing correlation functions using
+    HMcode2020Emu for the matter power spectrum and AngularCorrelationFunctionWigner
+    for the C_l -> xi_pm transformation.
+
+    Parameters
+    ----------
+    theta_arcmin : np.ndarray
+        Angular separations in arcminutes.
+    z_arr : np.ndarray
+        Redshift array for source n(z).
+    dndz : np.ndarray
+        Source redshift distribution, shape (N_tomo, N_z).
+        Should be normalized to integrate to 1 per bin.
+    noise_level : float
+        Fractional noise level (default 10%).
+    seed : int
+        Random seed for reproducibility.
+
+    Returns
+    -------
+    data_wl : dict
+        WL data dictionary containing:
+        - 'theta': angular separations [arcmin]
+        - 'theta_unit': 'arcmin'
+        - 'dndz': redshift distributions
+        - 'z_arr': redshift array
+        - 'xi_plus': dict of {(i,j): array} for each bin pair
+        - 'xi_minus': dict of {(i,j): array} for each bin pair
+        - 'covariance': combined covariance matrix
+        - 'fiducial_xi_plus': fiducial xi_+ predictions
+        - 'fiducial_xi_minus': fiducial xi_- predictions
+    """
+    print("\n[WL] Generating mock weak lensing data (xi_pm)...")
+
+    # Import required modules
+    import jax.numpy as jnp
+    from cloelib.cosmology.HMcode2020Emu_cosmology import (
+        HMemuLinearPerturbations,
+        HMemuNonLinearPerturbations,
+    )
+    from cloelib.observables.photo import ShearTracer
+    from cloelib.summary_statistics.angular_two_point import AngularTwoPoint
+    from cloelib.summary_statistics.angular_correlation_function_wigner import (
+        AngularCorrelationFunctionWigner,
+    )
+
+    # Create fiducial cosmology
+    background = CAMBBackground(**FIDUCIAL_COSMO)
+
+    # Create perturbations using HMcode2020Emu
+    lp = HMemuLinearPerturbations(background, z_arr)
+    nlp = HMemuNonLinearPerturbations(background, lp, z_arr, log10TAGN=7.8)
+
+    # Fiducial WL nuisance parameters
+    n_tomo = dndz.shape[0]
+    nuisance_params = {
+        'AIA': 1.0,
+        'EtaIA': 0.0,
+        'CIA': 0.0134,
+    }
+    for i in range(1, n_tomo + 1):
+        nuisance_params[f'multiplicative_bias_{i}'] = 0.0
+        nuisance_params[f'dz_shear_{i}'] = 0.0
+
+    # Create ShearTracer
+    dndz_jax = jnp.asarray(dndz)
+    z_arr_jax = jnp.asarray(z_arr)
+    shear_tracer = ShearTracer(
+        perturbations=nlp,
+        dndz=dndz_jax,
+        z=z_arr_jax,
+        nuisance_params=nuisance_params,
+    )
+
+    # ell and k grids
+    ells = jnp.unique(jnp.geomspace(2, 15000, 500).astype(int))
+    ks = jnp.geomspace(1e-4, 50.0, 200)
+
+    # Compute angular power spectrum
+    atp = AngularTwoPoint(shear_tracer, shear_tracer)
+
+    # Compute xi_pm via Wigner d-matrices
+    acf = AngularCorrelationFunctionWigner(atp, ells, ks)
+    theta_radians = theta_arcmin * np.pi / 180.0 / 60.0
+    xi_dict = acf.get_xi(jnp.asarray(theta_radians))
+
+    # Extract xi_+ and xi_- for each bin pair
+    xi_plus_fid = {}
+    xi_minus_fid = {}
+    bin_pairs = []
+
+    for i in range(1, n_tomo + 1):
+        for j in range(i, n_tomo + 1):
+            key = ("SHE", "SHE", i, j)
+            tpcf = xi_dict[key]
+            xi_plus_fid[(i, j)] = np.asarray(tpcf.array[0, 0, :])
+            xi_minus_fid[(i, j)] = np.asarray(tpcf.array[1, 1, :])
+            bin_pairs.append((i, j))
+
+    print(f"  - Generated {len(theta_arcmin)} theta bins")
+    print(f"  - theta range: [{theta_arcmin.min():.2f}, {theta_arcmin.max():.2f}] arcmin")
+    print(f"  - {n_tomo} tomographic bins -> {len(bin_pairs)} bin pairs")
+
+    # Add noise
+    np.random.seed(seed)
+
+    xi_plus_obs = {}
+    xi_minus_obs = {}
+    sigma_plus = {}
+    sigma_minus = {}
+
+    for (i, j) in bin_pairs:
+        # xi_+ noise
+        sigma_p = noise_level * np.abs(xi_plus_fid[(i, j)])
+        sigma_plus[(i, j)] = sigma_p
+        xi_plus_obs[(i, j)] = xi_plus_fid[(i, j)] + np.random.randn(len(theta_arcmin)) * sigma_p
+
+        # xi_- noise
+        sigma_m = noise_level * np.abs(xi_minus_fid[(i, j)])
+        sigma_minus[(i, j)] = sigma_m
+        xi_minus_obs[(i, j)] = xi_minus_fid[(i, j)] + np.random.randn(len(theta_arcmin)) * sigma_m
+
+    # Build covariance matrix
+    # Data vector ordering: [xi_+(all pairs)] | [xi_-(all pairs)]
+    n_theta = len(theta_arcmin)
+    n_pairs = len(bin_pairs)
+    n_total = 2 * n_pairs * n_theta  # xi_+ and xi_- for all pairs
+
+    cov = np.zeros((n_total, n_total))
+
+    # Fill diagonal blocks (assuming independent bins for simplicity)
+    offset = 0
+    for (i, j) in bin_pairs:
+        idx = slice(offset, offset + n_theta)
+        cov[idx, idx] = np.diag(sigma_plus[(i, j)] ** 2)
+        offset += n_theta
+
+    for (i, j) in bin_pairs:
+        idx = slice(offset, offset + n_theta)
+        cov[idx, idx] = np.diag(sigma_minus[(i, j)] ** 2)
+        offset += n_theta
+
+    print(f"  - xi_+ range: [{min(xi_plus_obs[(1,1)]):.2e}, {max(xi_plus_obs[(1,1)]):.2e}]")
+    print(f"  - xi_- range: [{min(xi_minus_obs[(1,1)]):.2e}, {max(xi_minus_obs[(1,1)]):.2e}]")
+
+    data_wl = {
+        'theta': theta_arcmin,
+        'theta_unit': 'arcmin',
+        'dndz': dndz,
+        'z_arr': z_arr,
+        'xi_plus': xi_plus_obs,
+        'xi_minus': xi_minus_obs,
+        'covariance': cov,
+        'fiducial_xi_plus': xi_plus_fid,
+        'fiducial_xi_minus': xi_minus_fid,
+    }
+
+    return data_wl
+
+
+def generate_simple_dndz(z_arr: np.ndarray, n_tomo: int = 4) -> np.ndarray:
+    """Generate simple tomographic source n(z) distributions.
+
+    Creates Gaussian-like n(z) distributions for each tomographic bin,
+    similar to typical photometric surveys like HSC.
+
+    Parameters
+    ----------
+    z_arr : np.ndarray
+        Redshift array.
+    n_tomo : int
+        Number of tomographic bins.
+
+    Returns
+    -------
+    dndz : np.ndarray
+        Shape (n_tomo, len(z_arr)), normalized per bin.
+    """
+    dndz = np.zeros((n_tomo, len(z_arr)))
+
+    # Tomographic bin centers (typical for HSC-like survey)
+    z_centers = np.linspace(0.3, 1.5, n_tomo)
+    z_widths = 0.15 * np.ones(n_tomo)
+
+    for i in range(n_tomo):
+        # Gaussian distribution
+        dndz[i, :] = np.exp(-0.5 * ((z_arr - z_centers[i]) / z_widths[i]) ** 2)
+        # Normalize to integrate to 1
+        dz = z_arr[1] - z_arr[0] if len(z_arr) > 1 else 1.0
+        dndz[i, :] /= np.sum(dndz[i, :]) * dz
+
+    return dndz
+
+
+# =============================================================================
+# Sampler Setup
+# =============================================================================
+
+def setup_prior() -> Prior:
+    """Setup prior distributions for sampled parameters.
+
+    Returns
+    -------
+    prior : Prior
+        Nautilus Prior object.
+    """
+    prior = Prior()
+
+    # Cosmological parameters
+    # Dark Emulator supports omega_cdm (= Omega_cdm * h^2) in [0.10782, 0.13178]
+    # With H0 = 67 (h = 0.67), this translates to:
+    #   Omega_cdm0 in [0.10782/0.67^2, 0.13178/0.67^2] = [0.240, 0.294]
+    # Add margin to avoid edge warnings: 0.241 → omegac=0.1082, 0.293 → omegac=0.1315
+    prior.add_parameter('Omega_cdm0', dist=(0.241, 0.293))
+    prior.add_parameter('As', dist=(1.7e-9, 2.5e-9))
+
+    # HOD parameters
+    prior.add_parameter('logMmin', dist=(12.0, 14.0))
+    prior.add_parameter('sigma_sq', dist=(0.05, 0.6))
+    prior.add_parameter('logM1', dist=(13.0, 15.0))
+    prior.add_parameter('alpha', dist=(0.5, 1.5))
+
+    return prior
+
+
+class LikelihoodWrapper:
+    """Pickle-compatible likelihood wrapper for Nautilus sampler.
+
+    Nautilus uses multiprocessing, so the likelihood callable must be picklable.
+    This wrapper holds the likelihood and fixed params, merges sampled params,
+    and calls EuclidLikelihood_DarkEmu_RealSpace.loglike(parameters).
+    """
+
+    def __init__(self, likelihood: EuclidLikelihood_DarkEmu_RealSpace, fixed_cosmo: dict, fixed_hod: dict):
+        self.likelihood = likelihood
+        self.fixed_cosmo = fixed_cosmo
+        self.fixed_hod = fixed_hod
+
+    def __call__(self, param_dict: dict) -> float:
+        """Evaluate likelihood: merge fixed + sampled params and call loglike."""
+        parameters = {**self.fixed_cosmo, **self.fixed_hod, **param_dict}
+        try:
+            logL = self.likelihood.loglike(parameters)
+            return float(logL) if np.isfinite(logL) else -np.inf
+        except (ValueError, RuntimeError):
+            return -np.inf
+
+
+# =============================================================================
+# Main
+# =============================================================================
+
+def main(n_live: int = 500, n_workers: int = 1, output_dir: Path = None):
+    """Run full cosmology + HOD sampling for 3x2pt.
+
+    Parameters
+    ----------
+    n_live : int
+        Number of live points for nautilus sampler.
+    n_workers : int
+        Number of parallel workers for likelihood evaluation.
+        Set to number of CPU cores for parallel speedup.
+        Note: Each worker loads Dark Emulator separately (~1GB memory each).
+    output_dir : Path
+        Output directory for results.
+    """
+    if output_dir is None:
+        output_dir = RESULTS_DIR
+    output_dir = Path(output_dir)
+    output_dir.mkdir(exist_ok=True)
+
+    print("=" * 70)
+    print("Dark Emulator 3x2pt Sampling: Cosmology + HOD Joint Fit")
+    print("  Probes: GC (w_p) + GGL (ΔΣ) + WL (ξ±)")
+    print("=" * 70)
+    print(f"\nSampler settings:")
+    print(f"  - n_live: {n_live}")
+    print(f"  - n_workers: {n_workers}")
+    print(f"  - Output: {output_dir}")
+
+    # -------------------------------------------------------------------------
+    # Generate mock data: GC + GGL
+    # (Replace the two generate_* blocks below with your own data loaders for real observations.)
+    # -------------------------------------------------------------------------
+    R_bins = np.logspace(-0.5, 1.5, 15)  # 0.3 to 30 h^-1 Mpc
+    z_sample = 0.5
+    pimax = 100.0  # h^-1 Mpc
+
+    data_gc, data_ggl = generate_mock_data(R_bins, z_sample, pimax=pimax, noise_level=0.1)
+
+    # -------------------------------------------------------------------------
+    # Generate mock data: WL (cosmic shear)
+    # -------------------------------------------------------------------------
+    theta_arcmin = np.logspace(np.log10(1.0), np.log10(200.0), 20)  # 1 to 200 arcmin
+    z_arr = np.linspace(0.01, 2.5, 100)  # Redshift array for source n(z)
+    n_tomo = 4  # Number of tomographic bins
+    dndz = generate_simple_dndz(z_arr, n_tomo=n_tomo)
+
+    data_wl = generate_mock_wl_data(
+        theta_arcmin=theta_arcmin,
+        z_arr=z_arr,
+        dndz=dndz,
+        noise_level=0.1,
+        seed=43,  # Different seed for WL
+    )
+
+    # -------------------------------------------------------------------------
+    # Settings with scale cuts
+    # -------------------------------------------------------------------------
+    settings = {
+        # GC scale cuts (HSC-Y3 style)
+        'R_min_gc': 2.0,    # GC: exclude small scales (1-halo dominated)
+        'R_max_gc': 30.0,
+        # GGL scale cuts (HSC-Y3 style)
+        'R_min_ggl': 3.0,   # GGL: minimum scale for Dark Emulator
+        'R_max_ggl': 30.0,
+        # WL scale cuts (HSC-Y3 style, in arcmin)
+        'wl_settings': {
+            'theta_min_plus': 7.0,    # xi_+ minimum
+            'theta_max_plus': 56.0,   # xi_+ maximum
+            'theta_min_minus': 28.0,  # xi_- minimum
+            'theta_max_minus': 178.0, # xi_- maximum
+        },
+    }
+
+    # -------------------------------------------------------------------------
+    # Initialize 3x2pt likelihood (cloelike EuclidLikelihood_DarkEmu_RealSpace)
+    # -------------------------------------------------------------------------
+    print("\n[2] Initializing 3x2pt likelihood (cloelike)...")
+    t0 = time.time()
+
+    # Fixed cosmological and HOD parameters
+    fixed_cosmo = {k: v for k, v in FIDUCIAL_COSMO.items()
+                   if k not in ['Omega_cdm0', 'As']}
+    fixed_hod = {k: v for k, v in FIDUCIAL_HOD.items()
+                 if k not in ['logMmin', 'sigma_sq', 'logM1', 'alpha']}
+    settings['hod_params_fixed'] = fixed_hod
+
+    likelihood = EuclidLikelihood_DarkEmu_RealSpace(
+        data_gc=data_gc,
+        data_ggl=data_ggl,
+        data_wl=data_wl,
+        settings=settings,
+        Background=CAMBBackground,
+    )
+    print(f"  - Initialization time: {time.time() - t0:.1f} s")
+
+    # Count data points
+    n_gc = likelihood.n_gc
+    n_ggl = likelihood.n_ggl
+    n_wl = likelihood.n_wl
+    n_data_total = n_gc + n_ggl + n_wl
+    print(f"  - GC data points: {n_gc}")
+    print(f"  - GGL data points: {n_ggl}")
+    print(f"  - WL data points: {n_wl} (xi_+: {likelihood.n_xi_plus}, xi_-: {likelihood.n_xi_minus})")
+    print(f"  - Total data points: {n_data_total}")
+
+    # -------------------------------------------------------------------------
+    # Setup prior and wrapper
+    # -------------------------------------------------------------------------
+    print("\n[3] Setting up priors...")
+    prior = setup_prior()
+    # Nautilus Prior.keys is a property (list), not a method
+    param_names = list(prior.keys)
+    print(f"  - Free parameters ({len(param_names)}): {param_names}")
+
+    wrapper = LikelihoodWrapper(likelihood, fixed_cosmo, fixed_hod)
+
+    # Test likelihood evaluation
+    print("\n[4] Testing likelihood evaluation...")
+    test_params = {name: FIDUCIAL_COSMO.get(name, FIDUCIAL_HOD.get(name))
+                   for name in param_names}
+
+    t0 = time.time()
+    test_logL = wrapper(test_params)
+    eval_time = time.time() - t0
+    print(f"  - Test log-likelihood: {test_logL:.2f}")
+    print(f"  - Evaluation time: {eval_time:.3f} s")
+
+    # Estimate total time (nautilus typically needs many evaluations)
+    avg_eval_time = eval_time
+    print(f"  - Estimated total time: {avg_eval_time * n_live * 50 / 3600:.1f} hours (single worker)")
+    if n_workers > 1:
+        print(f"  - With {n_workers} workers: ~{avg_eval_time * n_live * 50 / 3600 / n_workers:.1f} hours")
+
+    # -------------------------------------------------------------------------
+    # Run sampler
+    # -------------------------------------------------------------------------
+    print("\n[5] Running nautilus sampler...")
+    print(f"    n_live = {n_live}")
+    print(f"    n_workers = {n_workers}")
+    if n_workers > 1:
+        print(f"    Parallel mode: {n_workers} workers (using multiprocessing pool)")
+    print("    This may take several hours...")
+
+    checkpoint_file = output_dir / 'checkpoint_3x2pt_full.hdf5'
+
+    # Configure sampler
+    # Note: Nautilus 1.0.x uses 'pool' for parallelization, not 'n_eff_workers'
+    sampler_kwargs = {
+        'n_live': n_live,
+        'filepath': str(checkpoint_file),
+    }
+
+    # Setup parallel pool if n_workers > 1
+    pool = None
+    if n_workers > 1:
+        from multiprocessing import Pool
+        pool = Pool(n_workers)
+        sampler_kwargs['pool'] = pool
+
+    sampler = Sampler(
+        prior,
+        wrapper,
+        **sampler_kwargs,
+    )
+
+    t_start = time.time()
+    try:
+        # Run with convergence settings for faster completion
+        # n_eff: target effective sample size (lower = faster)
+        # For testing with n_live<200, use smaller n_eff; for production use 1000+
+        if n_live < 50:
+            target_n_eff = 50   # Quick test
+        elif n_live < 200:
+            target_n_eff = 200  # Moderate (n_live=100 → ~30min)
+        else:
+            target_n_eff = 1000  # Production (n_live=500)
+        print(f"    Target n_eff = {target_n_eff}")
+        sampler.run(verbose=True, n_eff=target_n_eff, discard_exploration=True)
+    finally:
+        # Clean up pool
+        if pool is not None:
+            pool.close()
+            pool.join()
+    t_total = time.time() - t_start
+
+    print(f"\n[6] Sampling complete!")
+    print(f"    Total time: {t_total:.1f} s ({t_total/3600:.2f} hours)")
+
+    # -------------------------------------------------------------------------
+    # Save results
+    # checkpoint_3x2pt_full.hdf5 : Nautilus state (resume / debug). chain_3x2pt_full.npz : chain + derived params for plotting.
+    # -------------------------------------------------------------------------
+    points, log_w, log_l = sampler.posterior()
+
+    # -------------------------------------------------------------------------
+    # Compute derived parameters (Omega_m, sigma8, S8)
+    # These are the key parameters for comparison with other surveys (HSC, DES, KiDS, Planck)
+    # -------------------------------------------------------------------------
+    print("\n[7] Computing derived parameters (Omega_m, sigma8, S8)...")
+
+    n_samples = len(points)
+    Omega_m_samples = np.zeros(n_samples)
+    sigma8_samples = np.zeros(n_samples)
+    S8_samples = np.zeros(n_samples)
+
+    # Get indices
+    idx_Omega_cdm0 = param_names.index('Omega_cdm0')
+    idx_As = param_names.index('As')
+
+    # Compute derived parameters for each sample
+    # Use Dark Emulator's get_sigma8 for accurate sigma8 calculation
+    # Dark Emulator fixed neutrino density: omega_nu = 0.00064
+    from cloelib.auxiliary.darkemu_utils import DARKEMU_OMEGA_NU
+    h = FIDUCIAL_COSMO['H0'] / 100.0
+    Omega_nu0 = DARKEMU_OMEGA_NU / h**2  # Convert omega_nu to Omega_nu
+
+    print(f"    Computing sigma8 for {n_samples} samples...")
+    print(f"    (Using Omega_nu = {Omega_nu0:.6f} from Dark Emulator's omega_nu = {DARKEMU_OMEGA_NU})")
+    print(f"    Computing sigma8 for each sample...")
+
+    # Cache for sigma8 computation (key = (Omega_cdm0, As))
+    sigma8_cache = {}
+    cache_hits = 0
+
+    for i in range(n_samples):
+        if i % 1000 == 0 and i > 0:
+            print(f"    ... {i}/{n_samples} samples processed (cache hits: {cache_hits})")
+
+        # Omega_m = Omega_cdm + Omega_b + Omega_nu (total matter density)
+        Omega_cdm0 = points[i, idx_Omega_cdm0]
+        Omega_m = Omega_cdm0 + FIDUCIAL_COSMO['Omega_b0'] + Omega_nu0
+        Omega_m_samples[i] = Omega_m
+
+        # Build cosmology for this sample
+        As = points[i, idx_As]
+
+        # Use cache for sigma8
+        cache_key = (round(float(Omega_cdm0), 8), round(float(As), 12))
+        if cache_key in sigma8_cache:
+            sigma8 = sigma8_cache[cache_key]
+            cache_hits += 1
+        else:
+            cosmo_params = fixed_cosmo.copy()
+            cosmo_params['Omega_cdm0'] = Omega_cdm0
+            cosmo_params['As'] = As
+
+            try:
+                # Use Dark Emulator to compute sigma8
+                background = CAMBBackground(**cosmo_params)
+                pert = DarkEmuHODPerturbations(
+                    background=background,
+                    redshifts=np.array([0.0]),
+                    hod_params=DarkEmuHODParameters(**FIDUCIAL_HOD),
+                    validate_params=False,  # Skip validation for speed
+                )
+                sigma8 = pert.get_sigma8(z=0.0)
+            except Exception:
+                # Fallback to approximate scaling if Dark Emulator fails
+                sigma8 = 0.81 * (As / 2.1e-9) ** 0.5 * (Omega_m / 0.3) ** 0.25
+
+            sigma8_cache[cache_key] = sigma8
+
+        sigma8_samples[i] = sigma8
+
+        # S8 = sigma8 * (Omega_m / 0.3)^0.5
+        # This is the standard definition used by weak lensing surveys
+        S8_samples[i] = sigma8 * (Omega_m / 0.3) ** 0.5
+
+    print(f"    ... {n_samples}/{n_samples} samples processed")
+    print(f"    Cache stats: {cache_hits}/{n_samples} hits ({100*cache_hits/n_samples:.1f}%)")
+
+    # Save with derived parameters as primary output
+    # Format compatible with corner plot: Omega_m, sigma8, S8
+    output_file = output_dir / "chain_3x2pt_full.npz"
+    np.savez_compressed(
+        str(output_file),
+        # Primary chain (sampled parameters)
+        chain=points,
+        weights=np.exp(log_w),
+        logl=log_l,
+        param_names=param_names,
+        fiducial=np.array([test_params[k] for k in param_names]),
+        # Derived cosmological parameters (for corner plot)
+        Omega_m=Omega_m_samples,
+        sigma8=sigma8_samples,
+        S8=S8_samples,
+        # Fiducial derived values (including neutrino contribution)
+        fiducial_Omega_m=FIDUCIAL_COSMO['Omega_cdm0'] + FIDUCIAL_COSMO['Omega_b0'] + Omega_nu0,
+        fiducial_sigma8=0.81,  # Approximate fiducial
+        fiducial_S8=0.81 * ((FIDUCIAL_COSMO['Omega_cdm0'] + FIDUCIAL_COSMO['Omega_b0'] + Omega_nu0) / 0.3) ** 0.5,
+    )
+    print(f"    Results saved to {output_file}")
+
+    # -------------------------------------------------------------------------
+    # Print summary
+    # -------------------------------------------------------------------------
+    print("\n" + "=" * 70)
+    print("Parameter Constraints (median +/- 1 sigma)")
+    print("=" * 70)
+
+    fiducial_values = [test_params[k] for k in param_names]
+
+    print(f"{'Parameter':<15} {'Fiducial':>12} {'Median':>12} {'Std':>10} {'(Fid-Med)/Std':>15}")
+    print("-" * 65)
+
+    for i, (name, fid) in enumerate(zip(param_names, fiducial_values)):
+        weights = np.exp(log_w)
+        weights /= weights.sum()
+        median = np.average(points[:, i], weights=weights)
+        # Weighted std
+        std = np.sqrt(np.average((points[:, i] - median)**2, weights=weights))
+        delta = (fid - median) / std if std > 0 else 0
+
+        if 'As' in name:
+            print(f"{name:<15} {fid:>12.2e} {median:>12.2e} {std:>10.2e} {delta:>15.2f}")
+        else:
+            print(f"{name:<15} {fid:>12.4f} {median:>12.4f} {std:>10.4f} {delta:>15.2f}")
+
+    # Derived parameters
+    print("\nDerived parameters:")
+    print(f"  sigma8: {np.median(sigma8_samples):.4f} +/- {np.std(sigma8_samples):.4f}")
+    print(f"  S8:     {np.median(S8_samples):.4f} +/- {np.std(S8_samples):.4f}")
+    print(f"  Omega_m:{np.median(Omega_m_samples):.4f} +/- {np.std(Omega_m_samples):.4f}")
+
+    # Chi-squared breakdown for best-fit
+    best_idx = np.argmax(log_l)
+    best_params = {name: points[best_idx, i] for i, name in enumerate(param_names)}
+    best_logL = log_l[best_idx]
+    chi2_total = -2.0 * best_logL
+    n_params = len(param_names)
+    ndof = n_data_total - n_params
+
+    print(f"\nBest-fit chi^2:")
+    print(f"  GC data points: {n_gc}")
+    print(f"  GGL data points: {n_ggl}")
+    print(f"  WL data points: {n_wl}")
+    print(f"  Total data points: {n_data_total}")
+    print(f"  Total chi^2 = {chi2_total:.2f} (ndof = {ndof})")
+    print(f"  Reduced chi^2 = {chi2_total/ndof:.2f}")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Dark Emulator 3x2pt full sampling")
+    parser.add_argument('--n_live', type=int, default=500,
+                        help='Number of live points (default: 500)')
+    parser.add_argument('--n_workers', type=int, default=1,
+                        help='Number of parallel workers (default: 1)')
+    parser.add_argument('--output_dir', type=str, default=None,
+                        help='Output directory')
+
+    args = parser.parse_args()
+
+    output_dir = Path(args.output_dir) if args.output_dir else None
+    main(n_live=args.n_live, n_workers=args.n_workers, output_dir=output_dir)
diff --git a/tutorials/dark_emulator/README.md b/tutorials/dark_emulator/README.md
new file mode 100644
index 0000000..b8af670
--- /dev/null
+++ b/tutorials/dark_emulator/README.md
@@ -0,0 +1,39 @@
+# Dark Emulator tutorials
+
+Tutorials and fitting examples using [Dark Emulator](https://dark-emulator.readthedocs.io) with `cloelib` and `cloelike` for real-space observables (w_p, ΔΣ, 3x2pt).
+
+**📹 Short overview (≈6 min):** [YouTube Unlisted VIDEO](https://youtu.be/tE1stIqNpXk)
+
+## Notebooks
+
+| Notebook | Description |
+|----------|-------------|
+| `dark_emulator.ipynb` | Introduction to Dark Emulator in cloelib: cosmology, HOD, and computing observables. |
+| `dark_emulator_observables_usage.ipynb` | How observables (w_p, ΔΣ) are obtained via the Dark Emulator pipeline in cloelib. |
+| `fitting_gc_wp.ipynb` | Fit **galaxy clustering** w_p(R) with mock data; likelihood + `scipy.optimize.minimize` or Nautilus. |
+| `fitting_ggl_delta_sigma.ipynb` | Fit **galaxy–galaxy lensing** ΔΣ(R) with mock data; likelihood + minimize or Nautilus. |
+| `fitting_3x2pt.ipynb` | **3x2pt** (GC + GGL + WL) short demo in the notebook; uses mock data and Nautilus with small `n_live`. |
+
+## Production 3x2pt run
+
+For full 3x2pt sampling (larger `n_live`, sigma8/S8, checkpoint + chain), run from the **repository root**:
+
+```bash
+python playground/scripts/sampling/darkemu_3x2pt_full_sampling.py --n_live 500 --output_dir playground/results
+```
+
+Then create a corner plot from the saved chain:
+
+```bash
+python playground/scripts/plotting/corner_plot.py \
+  --chain playground/results/chain_3x2pt_full.npz \
+  --output playground/results/corner_3x2pt.pdf \
+  --params Omega_m sigma8 S8
+```
+
+## Requirements
+
+- `cloelib`, `cloelike`, and the `dark_emulator` package (see repository root).
+- For sampling: `nautilus`. For corner plots: `getdist` or `corner`.
+
+
diff --git a/tutorials/dark_emulator/dark_emulator.ipynb b/tutorials/dark_emulator/dark_emulator.ipynb
new file mode 100644
index 0000000..7280949
--- /dev/null
+++ b/tutorials/dark_emulator/dark_emulator.ipynb
@@ -0,0 +1,1005 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Dark Emulator Tutorial\n",
+        "\n",
+        "This tutorial demonstrates how to use the Dark Emulator integration in cloelib.\n",
+        "\n",
+        "Dark Emulator provides fast, accurate emulation of:\n",
+        "- Nonlinear matter power spectrum P(k,z)\n",
+        "- Halo statistics (mass function, bias, correlation functions)\n",
+        "- HOD-based galaxy statistics (w_p, ΔΣ, ξ_gg, ξ_gm)\n",
+        "\n",
+        "## Classes Overview\n",
+        "\n",
+        "| Class | Dark Emulator Module | Primary Output |\n",
+        "|-------|---------------------|----------------|\n",
+        "| `DarkEmuNonLinearPerturbations` | `de_interface.base_class` | P(k, z), D(z), f(z), σ8 |\n",
+        "| `DarkEmuHaloPerturbations` | `de_interface.halo` | dn/dlnM, b(M), ξ_hm, ξ_hh, ΔΣ_halo |\n",
+        "| `DarkEmuHODPerturbations` | `model_hod` | w_p, ΔΣ, ξ_gg, ξ_gm |\n",
+        "\n",
+        "## References\n",
+        "- Nishimichi et al. (2019): Dark Quest. I. Fast and Accurate Emulation\n",
+        "- Miyatake et al. (2022): HSC-Y1 cosmology with emulator-based halo model\n",
+        "- Miyatake et al. (2023): HSC-Y3 3x2pt cosmology analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CLOE_ROOT: /home/ryuseikano/Euclid/CLOE\n",
+            "Imports successful!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Setup paths (run this cell first)\n",
+        "import sys\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# Find CLOE repo root (directory that contains cloelib, cloelike, dark_emulator_public)\n",
+        "NOTEBOOK_DIR = Path.cwd()\n",
+        "CLOE_ROOT = NOTEBOOK_DIR\n",
+        "for _ in range(6):\n",
+        "    if (CLOE_ROOT / \"cloelib\").is_dir():\n",
+        "        break\n",
+        "    CLOE_ROOT = CLOE_ROOT.parent\n",
+        "else:\n",
+        "    raise FileNotFoundError(\"CLOE root not found: run the notebook from inside the CLOE repo.\")\n",
+        "\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelib\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelike\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"dark_emulator_public\"))\n",
+        "\n",
+        "# General imports\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# cloelib imports\n",
+        "from cloelib.cosmology.camb_cosmology import CAMBBackground\n",
+        "from cloelib.cosmology.darkemu_cosmology import (\n",
+        "    DarkEmuNonLinearPerturbations,\n",
+        "    DarkEmuHaloPerturbations,\n",
+        "    DarkEmuHODPerturbations,\n",
+        ")\n",
+        "from cloelib.observables.darkemu_hod import DarkEmuHODParameters\n",
+        "from cloelib.auxiliary.darkemu_utils import (\n",
+        "    background_to_darkemu_cparam,\n",
+        "    darkemu_cparam_to_dict,\n",
+        "    DARKEMU_PARAM_RANGES,\n",
+        ")\n",
+        "\n",
+        "print(f\"CLOE_ROOT: {CLOE_ROOT}\")\n",
+        "print(\"Imports successful!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Setup: Cosmological Parameters\n",
+        "\n",
+        "Dark Emulator has specific requirements:\n",
+        "- **Flat universe**: Ω_k = 0\n",
+        "- **Constant w**: w_a = 0\n",
+        "- **Fixed neutrino mass**: ω_ν = 0.00064 (internally)\n",
+        "\n",
+        "Parameters must be within the training range:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dark Emulator Training Ranges:\n",
+            "==================================================\n",
+            "  omega_b     : [0.021138, 0.023363]\n",
+            "  omega_c     : [0.107820, 0.131780]\n",
+            "  Omega_de    : [0.547520, 0.821280]\n",
+            "  ln10^10As   : [2.475200, 3.712800]\n",
+            "  ns          : [0.916275, 1.012725]\n",
+            "  w           : [-1.200000, -0.800000]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print Dark Emulator parameter ranges\n",
+        "print(\"Dark Emulator Training Ranges:\")\n",
+        "print(\"=\" * 50)\n",
+        "for param, (low, high) in DARKEMU_PARAM_RANGES.items():\n",
+        "    print(f\"  {param:12s}: [{low:.6f}, {high:.6f}]\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Background created: H0 = 67.0, Ω_cdm = 0.27\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define cosmological parameters (Planck-like)\n",
+        "cosmo_params = {\n",
+        "    'H0': 67.0,\n",
+        "    'Omega_cdm0': 0.27,\n",
+        "    'Omega_b0': 0.049,\n",
+        "    'Omega_k0': 0.0,      # Must be 0 for Dark Emulator\n",
+        "    'w0': -1.0,\n",
+        "    'wa': 0.0,            # Must be 0 for Dark Emulator\n",
+        "    'As': 2.1e-9,\n",
+        "    'ns': 0.96,\n",
+        "    'mnu': 0.06,          # Will use Dark Emulator's fixed value internally\n",
+        "    'N_mnu': 1,\n",
+        "    'gamma_MG': 0.55,\n",
+        "}\n",
+        "\n",
+        "# Create background cosmology\n",
+        "background = CAMBBackground(**cosmo_params)\n",
+        "print(f\"Background created: H0 = {background.H0}, Ω_cdm = {background.Omega_cdm0}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Dark Emulator cparam:\n",
+            "==================================================\n",
+            "  omega_b     : 0.021996\n",
+            "  omega_c     : 0.121203\n",
+            "  Omega_de    : 0.679574\n",
+            "  ln10^10As   : 3.044522\n",
+            "  ns          : 0.960000\n",
+            "  w           : -1.000000\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/tmp/ipykernel_1139587/3002700217.py:2: UserWarning: Neutrino density mismatch: input omega_nu = 0.000000, but Dark Emulator assumes fixed omega_nu = 0.00064. Dark Emulator will use its internal fixed value regardless of input.\n",
+            "  cparam = background_to_darkemu_cparam(background)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Convert to Dark Emulator cparam format\n",
+        "cparam = background_to_darkemu_cparam(background)\n",
+        "cparam_dict = darkemu_cparam_to_dict(cparam)\n",
+        "\n",
+        "print(\"\\nDark Emulator cparam:\")\n",
+        "print(\"=\" * 50)\n",
+        "for name, value in cparam_dict.items():\n",
+        "    print(f\"  {name:12s}: {value:.6f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 2. DarkEmuNonLinearPerturbations\n",
+        "\n",
+        "The base class for nonlinear matter power spectrum P(k,z).\n",
+        "\n",
+        "**Available methods:**\n",
+        "- `matter_power_spectrum(z, k)` - P(k,z)\n",
+        "- `growth_factor(z)` - D(z)\n",
+        "- `growth_rate(z)` - f(z) = d ln D / d ln a\n",
+        "- `get_sigma8(z)` - σ8(z)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "σ8(z=0) = 0.8150\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize DarkEmuNonLinearPerturbations\n",
+        "redshifts = np.array([0.0, 0.5, 1.0, 1.5])\n",
+        "\n",
+        "pert_nl = DarkEmuNonLinearPerturbations(\n",
+        "    background=background,\n",
+        "    redshifts=redshifts,\n",
+        "    validate_params=True,  # Check if params are within training range\n",
+        ")\n",
+        "\n",
+        "print(f\"σ8(z=0) = {pert_nl.get_sigma8(z=0.0):.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Compute matter power spectrum\n",
+        "k = np.logspace(-3, 1, 200)  # h/Mpc\n",
+        "z_plot = [0.0, 0.5, 1.0]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "\n",
+        "for z in z_plot:\n",
+        "    Pk = pert_nl.matter_power_spectrum(np.array([z]), k)\n",
+        "    ax.loglog(k, Pk[0], label=f'z = {z}')\n",
+        "\n",
+        "ax.set_xlabel(r'$k$ [$h$/Mpc]', fontsize=14)\n",
+        "ax.set_ylabel(r'$P(k)$ [$(h^{-1}$Mpc$)^3$]', fontsize=14)\n",
+        "ax.set_title('Dark Emulator: Nonlinear Matter Power Spectrum', fontsize=14)\n",
+        "ax.legend(fontsize=12)\n",
+        "ax.set_xlim(1e-3, 10)\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Growth factor and growth rate\n",
+        "z_arr = np.linspace(0, 1.5, 50)\n",
+        "\n",
+        "D_z = pert_nl.growth_factor(z_arr)\n",
+        "f_z = pert_nl.growth_rate(z_arr)\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+        "\n",
+        "axes[0].plot(z_arr, D_z, 'b-', lw=2)\n",
+        "axes[0].set_xlabel('Redshift z', fontsize=14)\n",
+        "axes[0].set_ylabel('D(z) / D(0)', fontsize=14)\n",
+        "axes[0].set_title('Growth Factor', fontsize=14)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].plot(z_arr, f_z, 'r-', lw=2)\n",
+        "axes[1].set_xlabel('Redshift z', fontsize=14)\n",
+        "axes[1].set_ylabel('f(z)', fontsize=14)\n",
+        "axes[1].set_title('Growth Rate', fontsize=14)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 3. DarkEmuHaloPerturbations\n",
+        "\n",
+        "Extends the base class with halo statistics for mass-threshold samples.\n",
+        "\n",
+        "**Available methods:**\n",
+        "- `halo_matter_correlation(r, z, M_min)` - ξ_hm(r)\n",
+        "- `halo_halo_correlation(r, z, M_min)` - ξ_hh(r)\n",
+        "- `halo_mass_function(z)` - dn/dlnM\n",
+        "- `halo_bias(M_min, z)` - linear bias b(M)\n",
+        "- `delta_sigma_halo(R, z, M_min)` - ΔΣ for halos\n",
+        "- `projected_correlation_halo(R, z, M_min)` - w_p for halos\n",
+        "- `mass_to_density(M_min, z)` - cumulative number density\n",
+        "- `density_to_mass(n, z)` - inverse lookup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DarkEmuHaloPerturbations initialized\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize DarkEmuHaloPerturbations\n",
+        "pert_halo = DarkEmuHaloPerturbations(\n",
+        "    background=background,\n",
+        "    redshifts=redshifts,\n",
+        ")\n",
+        "\n",
+        "print(\"DarkEmuHaloPerturbations initialized\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Halo mass function\n",
+        "z = 0.5\n",
+        "M, sigma, f_sigma = pert_halo.halo_mass_function(z)\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+        "\n",
+        "axes[0].loglog(M, f_sigma, 'b-', lw=2)\n",
+        "axes[0].set_xlabel(r'$M$ [$h^{-1} M_\\odot$]', fontsize=14)\n",
+        "axes[0].set_ylabel(r'$f(\\sigma)$', fontsize=14)\n",
+        "axes[0].set_title(f'Halo Multiplicity Function (z={z})', fontsize=14)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].loglog(sigma, f_sigma, 'r-', lw=2)\n",
+        "axes[1].set_xlabel(r'$\\sigma(M)$', fontsize=14)\n",
+        "axes[1].set_ylabel(r'$f(\\sigma)$', fontsize=14)\n",
+        "axes[1].set_title(f'f(σ) vs σ (z={z})', fontsize=14)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Halo-matter and halo-halo correlation functions\n",
+        "r = np.logspace(-1, 2, 50)  # h^-1 Mpc\n",
+        "z = 0.5\n",
+        "M_min_values = [1e12, 1e13, 1e14]  # h^-1 M_sun\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "for M_min in M_min_values:\n",
+        "    xi_hm = pert_halo.halo_matter_correlation(r, z, M_min)\n",
+        "    xi_hh = pert_halo.halo_halo_correlation(r, z, M_min)\n",
+        "    \n",
+        "    label = f'$M_{{min}} = 10^{{{np.log10(M_min):.0f}}}$'\n",
+        "    axes[0].loglog(r, xi_hm, lw=2, label=label)\n",
+        "    axes[1].loglog(r, xi_hh, lw=2, label=label)\n",
+        "\n",
+        "axes[0].set_xlabel(r'$r$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[0].set_ylabel(r'$\\xi_{hm}(r)$', fontsize=14)\n",
+        "axes[0].set_title(f'Halo-Matter Correlation (z={z})', fontsize=14)\n",
+        "axes[0].legend(fontsize=11)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].set_xlabel(r'$r$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_ylabel(r'$\\xi_{hh}(r)$', fontsize=14)\n",
+        "axes[1].set_title(f'Halo-Halo Correlation (z={z})', fontsize=14)\n",
+        "axes[1].legend(fontsize=11)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ryuseikano/Euclid/CLOE/dark_emulator_public/dark_emulator/darkemu/hmf.py:100: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
+            "  dens = integrate.quad(lambda t: np.exp(\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Halo bias as a function of mass threshold\n",
+        "z_values = [0.0, 0.5, 1.0]\n",
+        "M_min_arr = np.logspace(12, 15, 30)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8, 6))\n",
+        "\n",
+        "for z in z_values:\n",
+        "    # halo_bias returns array with extra dimensions, squeeze to 1D\n",
+        "    bias = np.array([np.squeeze(pert_halo.halo_bias(M, z)) for M in M_min_arr])\n",
+        "    ax.semilogx(M_min_arr, bias, lw=2, label=f'z = {z}')\n",
+        "\n",
+        "ax.set_xlabel(r'$M_{min}$ [$h^{-1} M_\\odot$]', fontsize=14)\n",
+        "ax.set_ylabel('Linear Halo Bias b(M)', fontsize=14)\n",
+        "ax.set_title('Halo Bias vs Mass Threshold', fontsize=14)\n",
+        "ax.legend(fontsize=12)\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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FTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# ΔΣ and w_p for mass-threshold halo samples\n",
+        "R = np.logspace(-1, 2, 50)  # h^-1 Mpc\n",
+        "z = 0.5\n",
+        "M_min = 1e13  # h^-1 M_sun\n",
+        "\n",
+        "ds_halo = pert_halo.delta_sigma_halo(R, z, M_min)\n",
+        "wp_halo = pert_halo.projected_correlation_halo(R, z, M_min)\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "axes[0].loglog(R, ds_halo, 'b-', lw=2)\n",
+        "axes[0].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[0].set_ylabel(r'$\\Delta\\Sigma$ [$h M_\\odot$/pc$^2$]', fontsize=14)\n",
+        "axes[0].set_title(f'Halo ΔΣ (z={z}, $M_{{min}}=10^{{{np.log10(M_min):.0f}}}$)', fontsize=14)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].loglog(R, wp_halo, 'r-', lw=2)\n",
+        "axes[1].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_ylabel(r'$w_p$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_title(f'Halo $w_p$ (z={z}, $M_{{min}}=10^{{{np.log10(M_min):.0f}}}$)', fontsize=14)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 4. DarkEmuHODPerturbations\n",
+        "\n",
+        "The main class for HSC-style 3×2pt analysis with HOD modeling.\n",
+        "\n",
+        "**HOD Parameters** (Zheng et al. 2007 model):\n",
+        "- `logMmin`: Minimum halo mass for central galaxies\n",
+        "- `sigma_sq`: Scatter in central occupation\n",
+        "- `logM1`: Characteristic mass for satellites\n",
+        "- `alpha`: Power-law slope for satellites\n",
+        "- `kappa`: Satellite threshold parameter\n",
+        "- `poff`: Off-centering fraction\n",
+        "- `Roff`: Off-centering scale\n",
+        "\n",
+        "**Available methods:**\n",
+        "- `delta_sigma(R, z)` - Galaxy-galaxy lensing ΔΣ(R)\n",
+        "- `projected_correlation(R, z, pimax)` - Galaxy clustering w_p(R)\n",
+        "- `galaxy_galaxy_correlation(r, z)` - 3D ξ_gg(r)\n",
+        "- `galaxy_matter_correlation(r, z)` - 3D ξ_gm(r)\n",
+        "- `*_components()` - Breakdown by 1h/2h terms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "HOD Parameters:\n",
+            "  logMmin  = 13.0\n",
+            "  sigma_sq = 0.3\n",
+            "  logM1    = 14.0\n",
+            "  alpha    = 1.0\n",
+            "  kappa    = 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define HOD parameters (HSC-like LOWZ sample)\n",
+        "hod_params = DarkEmuHODParameters(\n",
+        "    logMmin=13.0,   # log10(M_min / [h^-1 M_sun])\n",
+        "    sigma_sq=0.3,   # Scatter in central occupation\n",
+        "    logM1=14.0,     # log10(M_1 / [h^-1 M_sun])\n",
+        "    alpha=1.0,      # Satellite power-law slope\n",
+        "    kappa=1.0,      # Satellite threshold\n",
+        "    poff=0.0,       # Off-centering fraction (0 = all centered)\n",
+        "    Roff=0.0,       # Off-centering scale\n",
+        ")\n",
+        "\n",
+        "print(\"HOD Parameters:\")\n",
+        "print(f\"  logMmin  = {hod_params.logMmin}\")\n",
+        "print(f\"  sigma_sq = {hod_params.sigma_sq}\")\n",
+        "print(f\"  logM1    = {hod_params.logM1}\")\n",
+        "print(f\"  alpha    = {hod_params.alpha}\")\n",
+        "print(f\"  kappa    = {hod_params.kappa}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DarkEmuHODPerturbations initialized at z = 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize DarkEmuHODPerturbations\n",
+        "z_lens = 0.5\n",
+        "\n",
+        "pert_hod = DarkEmuHODPerturbations(\n",
+        "    background=background,\n",
+        "    redshifts=np.array([z_lens]),\n",
+        "    hod_params=hod_params,\n",
+        ")\n",
+        "\n",
+        "print(f\"DarkEmuHODPerturbations initialized at z = {z_lens}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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h4+PDxIkT2b17Nw8fPow0kGNU3+2f4e3tTd26dTE1NeWff/4hffr0EdaPGjVKr32+f0a9evUivHdwcKBHjx64ublRqVIlhg0bRu3ateOpdEKIxEwCbyGE0JP06dNz8+ZNnj59So4cOSKsC3/juXbtWpo1axZh/b179yhcuDABAQGUK1eOWrVqYW1tjZGREZ6enhw5ciRSIB6Vhg0bsmPHDlxdXWnSpAnp0qXD1NQUPz8/Zs2aFe0+6tSpw5AhQ9BqtXTq1IlkyZJFma9z5860a9eOVatW0aNHD86cOcO///5LmzZtsLGx0eWL6iFD27ZtdYF3+vTp8fb25unTpzg5OUXIt2zZMt1AUhMnTmTw4MER1p88eZLy5csDap/PbNmyYWlpiUajYevWrVy5cuWbn1VwcDBly5bl4sWL5M+fn1atWpE6dWpMTEzw9vZm+fLl0e6jXr16usC7R48eUeYxNjamQ4cO/PHHH+zevZuaNWuyZcsWXr9+TY8ePfQ+QvubN28AIg109i3W1taR0sL6GoeGhurSihQpgqenJ+PHj2fNmjUsXboUAHd3dyZNmkS5cuX0eryAgAAA0qVLFyn/l0Hdt3z48IHVq1eTPn16KlWqFGFds2bNGDBgAEuWLIk28Pb19aVRo0Z8+vSJ1atXRxtcJ0+ePEK/+Kh07tyZBQsWoCgKz549Y8aMGUydOpVGjRpx4MABjI2No9wuLHCOSb/pN2/e4O7uzqNHjyhRogQVK1bE1tYWY2NjLl++zLZt22J0LYkpf39/atSowevXr9mxYwd58uTR277DhF1b/P39SZ06dYR1Yd+V8NefH1GhQgWcnZ35999/CQgIiPK7KoQQP0MCbyGE0JPixYvj6enJ4cOHdYFhTM2YMQNfX19WrlxJy5YtI6zr0qVLjAYOOnfuHDt27KBKlSrs3Lkzwk386dOnIwygFt6nT590x7S1tWXYsGHUqlUrygCjSZMm9O3bl8WLF9OjRw9dTeKXg6op0YwUHaZ48eJ4e3tz+PDhSIH3t4wbN46PHz9y7NixSDWip0+f1tWWfs22bdu4ePEiv/76a6RB0NauXRvltE6g1sp27NiRFClSEBoaSs+ePbl06VKU0xh16NCBsWPHsmjRImrWrMnixYsxMjKiffv233G2MRMWJAQEBHzXlErfo1SpUuzevZugoCDOnDnDjh07mDdvHjVq1ODatWvf/f/4NWHn4+PjE6mVx4sXL75rX5s3b8bPz48+ffpECmwzZMhAxYoV2bt3L1evXuWXX36JsF5RFNq0acODBw8YMWJElIN7hUmbNi2PHz+OUZk0Gg12dnZMmTKF58+fs2rVKubMmUOfPn2izB/2YCUmgzb+9ddfPHr0iDFjxjBs2LAI6yZOnBjjgRpjIiQkhEaNGnHjxg3mzJlDtWrVoszn6en5zVHSw3NwcNDN1Q2QLVs2zp8/z927dyMF3nfv3tXl+Vlp0qTh3r17vH//XgJvIYTeyajmQgihJ23atMHIyIg///wzxk1Cw3h5eQFEGt1ZURROnDjxXfuoUaNGpADj2LFj0W43ZMgQLly4wJAhQ1i5ciVv3ryhVatWaLXaSHmTJ09O69atuXLlCocPH2bdunW4ubl9d/PfsOBz2rRp36wl/JKXlxepUqWKFHS/f/+eixcvxngfEPnzhq9/Vp06deLRo0fMmjWLKVOm4OXlRffu3aPMa29vT40aNdi1axcnT57k4MGDVKlSJVbmuS5SpAjwucl5bEqePDlly5Zl2rRpDBkyhKCgIPbv36/XY4TNmR3Vd//kyZPfta+wkfi/bGYeJiw9qhH7J02axI4dO6hYsSIjR4786nHy5MnDhw8fePTo0XeVb/LkySRPnpyxY8cSGBgYZZ7bt29jamoaqSVNVL73ux12rQjf4iCmevTowf79++nZs2e0rT/gc1ebmC5fTp1WpkwZAPbt2xdp33v37o2Q50e9e/eO69evkyJFiq82+xdCiB8lgbcQQuiJq6srAwcOxMfHh2rVqnHv3r0o84XvyxomrFbv+PHjEdInTpzItWvXYnT86PZx/fr1aOeM3r9/P9OmTaNo0aKMHDmSmjVr0r17dzw9PaPs5w3o5upu2bIlgYGBMZ5CLLwKFSrQtGlTbt68Sb169Xj+/HmU+fz9/SOlZc2aFV9fX65fv65LCw0NpX///rx8+TJGx4/uszpy5IhuarQv/fXXX2zYsIFGjRrx66+/0qNHD2rWrMnKlStZs2ZNlNt07txZVyuoKMoPfVYx0a1bN0xMTOjZs2eUgZ+fn993zXH+pVOnTkX5gCSs9tnc3PyH9x2Vpk2bYmRkxLRp0yI8xHr37t13Nad/8OABhw8fxs3NjQIFCkSZp27duqRIkYJVq1ZFaIJ95MgRhg0bRqZMmVizZs03uweEBX5nzpyJcfkAMmbMqJs2bebMmZHWBwcHc+nSJQoVKhSjpubRfbfXrFnDrl27IuVPmTIlGo2G//7777vKPW3aNBYuXEj16tWZMWPGV/OOGjUKRR3QN0bLl7XjjRs3xsbGhjlz5kRoVfD48WPmzp1LmjRpIvXdfvToEbdu3Yowh31gYCB37tyJVL6goCA6duxIYGAgjRs3jnZqNyGE+BlyZRFCCD0aN24cwcHBTJ8+nRw5clC6dGny5s2LhYUFPj4+XL16lbNnz2JpaRlhsLAuXbqwdOlSGjRoQOPGjUmdOjWnT5/m4sWL1KhRg507d37z2IULF6Zw4cKsX7+eZ8+eUbRoUR49esT27dupUaMGGzdujJD/1atXtGnTBisrK9asWaO72Zw6dSpHjhxh5MiRVKhQQVebGiZnzpyUKlWKY8eOYWZmRuvWrX/os1qyZAlGRkasWbMGR0dHypUrh5ubG8mSJePFixecPXuW69evkyZNmgg1fT179mTfvn2ULFmSxo0bY25ujqenJ0+ePKFs2bIxatJaq1YtHBwcmDx5MteuXSN37tzcvn2bf/75h3r16kX6rO7cuUPv3r3JnDlzhHmrlyxZwi+//ELXrl0pVqwYjo6OEbarWrUqWbNm5eHDh2TIkIFatWr90Gc1ZsyYaJsZDxo0iNy5czNv3jy6du1K9uzZqV69Os7OzgQGBnL//n2OHDlC27ZtdX3Tv9ekSZM4fPgwpUuXxtHREXNzcy5evMjBgwdxcnKKFPT8rOzZszNo0CDGjx9Pnjx5dMHQ5s2byZMnD9euXYtRP/klS5bouj106NAh2nzW1tY8e/aMrVu30qRJE0AN/kNDQ8mXLx8eHh5Rbhd+wMA6derQr18/9u/fT6NGjb7rfH///XcWLlzI9OnT6dmzZ4RBCI8dO8bHjx+pW7dujPbVqlUrJk2aRM+ePTl8+DBZs2blypUrHDx4kPr167N58+YI+S0tLXF3d+fo0aO0atWKbNmyYWRkRKtWraIdzPH58+cMHDgQIyMjcuTIwZgxYyLlKVu2bITBI39GypQpmTt3Lq1ataJAgQK6/6N169bx+vVr1q1bF6mLRevWrTly5AiHDx/WleP169fkyJEDd3d33NzcyJAhAy9evODAgQM8fvyYPHnyMGXKFL2UWQghIonrYdSFECIpuHjxotKpUyclR44ciqWlpWJqaqqkT59eKV++vDJlypQIcxOHOXz4sFKiRAnFyspKsbW1VapXr65cuHBBNxVP+OnHopsCyMfHR2nfvr1iZ2enmJubK3ny5FE8PDyU+/fvR8pfs2ZNBVBWrVoVqSz//vuvYm5urjg5OUU5b3HYvMZNmzb94c8o/Hm3atVKcXJyUpInT64kS5ZMsbOzU6pVq6bMmzdP8ff3j7TNxo0bdfMyp0mTRmncuLHi5eWlm34r/PzZX5vHu0GDBkratGkVCwsLxd3dXVm7dm2k/B8/flQKFCigGBkZKUeOHIlUln379ikajUYpWrRolNMdDRs2TAGUQYMGffdnE3Y+X1vCfy/Onj2rNG3aVLGzs1NMTU2VNGnSKAUKFFAGDRoUYZq7r02xFtV3a8+ePUrr1q2V7NmzK1ZWVoqlpaWSM2dOZciQIcrLly8jbP+16cS+nJ/5W2WZN2+e4ubmpiRLlkyxt7dX+vfvr/z3338KoNSpU+ern11oaKhib2//XdNYVapUSbd9TPJ/OU97tWrVlJQpU0aay/tr83iH+e233xRAGT58eIT0tm3bKsmSJVN8fHy+er7hXb58WalcubKSMmVKxcrKSilTpoxy4MCBaP8fbt++rVSvXl2xtbVVNBpNpO/Vl8K+I19bYjp93/fYvXu3UqpUKSVFihSKpaWlUqZMGWX//v1R5g37HoY/D39/f6V79+6Ku7u7kjZtWsXExESxsrJSChcurEyePDnC3ONCCKFvGkX5xgg4QgghxBd69OiBh4cHBw8e/O6B5JKamjVrsmvXLu7cuYOLi0t8FyfBO3DgAJUqVWLgwIFMmjQpvosTwcGDB6lYsSKrVq2KML3ej/L19SVr1qw0bNiQJUuW6KGEQggh4osE3kIIIb7Ly5cvcXJyIlOmTNy8eRONRhPfRTJYN27cIHfu3FSqVEk3CJSImZcvX5IqVaoIAwX6+flRqVIlzp8/z8mTJylWrFg8ljBq1apVw9vbm+vXr//0tHHDhw9n+vTp3Llz57vnTBdCCGFYpI+3EEKIGNm5cycXL15k48aNvH37llGjRknQHY01a9Zw+/ZtVqxYAfDNEbFFZKtXr2bq1KmUL18eOzs7nj17xp49e/Dx8aFt27YGGXQDzJo1izVr1vDkyRMyZ878U/tKlSoVK1askKBbCCESAanxFkIIESNt27Zl+fLl2NnZ0aNHDwYPHhzfRTJYZcuW5dixY2TNmpXhw4fTrl27+C5SgnP27FnGjRvHuXPnePPmDcbGxri5udG2bVu6dev207XJQgghRFySwFsIIYQQQgghhIhF8rhYCCGEEEIIIYSIRRJ4CyGEEEIIIYQQsUgGV9MjrVbL06dPsbKykgGHhBBCCCGEECKBUBSFwMBA7OzsYmUcEQm89ejp06c/PYKpEEIIIYQQQoj48d9//2Fvb6/3/UrgrUdWVlYAPHz4EFtb2/gtjBBC/J9Wq+Xly5ekTZtWRoIWX6UoCgEBAQBYW1tL6y0Ra+S6JIQwNH5+fmTNmlUX0+mbBN56FHaDYm1tjbW1dTyXRgghVFqtlg8fPmBtbS03uOKbbGxs4rsIIgmQ65IQwtBotVqAWHvoLFc6IYQQQgghhBAiFkmNtxBCCCEA9Wn//fv3AXBycpKaSCGEEEJPJPAWQgghBKAG3ufOnQPAwcFBAm8hhBBCTyTw1gMPDw88PDwIDQ2N76IIIYQQP0yj0ZApUybdayGEEELoh0ZRFCW+C5FYBAQEYGNjg6+vr4xqLoQwGFqtFh8fH9KlSyc1mEIIgyDXJSGEofHz8yNlypT4+/vHykDZUuMthBBCCCEEEBoayqdPn+K7GEIIPTM1NcXY2DheyyCBtxBCCCGESNIUReH58+f4+fnFd1GEELHE1taWDBkyxFtXKgm8hRBCCAFASEgIO3fuBKBGjRqYmMhtgkgawoLudOnSYWFhIWMcCJGIKIrC+/fv8fHxASBjxozxUg75iyqEEEIInffv38d3EYSIU6GhobqgO3Xq1PFdHCFELEiePDmAbmyJ+Gh2LoG3EEIIIQAwNjamcuXKutdCJAVhfbotLCziuSRCiNgU9hv/9OmTBN5CCCGEiD8ajUZq/ESSJc3LhUjc4vs3LvM3CCGEEEIIIYQQsUgCbyGEEEIA6tzK3t7eeHt7o9Vq47s4QohEwtPTE41Gg6enZ3wXRYh4I4G3Hnh4eJAzZ07c3d3juyhCCCHED9NqtZw6dYpTp05J4C1EAqfRaGK0xCQYHj9+PFu3bo31MguRmEkfbz3o3r073bt3JyAgABsbm/gujhBCCPFDNBoN6dOn170WQiRcK1eujPB+xYoV7N+/P1K6m5vbN/c1fvx4GjZsSN26dfVZRCGSFAm8hRBCCAGoI5mXL18+voshhNCDli1bRnh/+vRp9u/fHyldCBE3pKm5EEIIIYQQSdC7d+/47bffyJw5M2ZmZmTPnp2pU6eiKIouj0aj4d27dyxfvlzXPL1t27YAPHz4kG7dupE9e3aSJ09O6tSpadSoEd7e3vFzQkIYMKnxFkIIIYQQIolRFIXatWtz+PBhfv31V/Lly8fevXsZMGAAT548YcaMGYDaZL1Dhw4ULlyYTp06AeDs7AzAuXPnOHnyJE2bNsXe3h5vb2/mz59P2bJluXHjhsyNLkQ4EnjHgjp16uDu7k7+/PnJnz8/2bNnj5dJ2oUQQojvERISwt69ewGoUqUKJiZymyBEYrV9+3YOHTrE2LFjGTp0KKCOW9SoUSNmzZpFjx49cHZ2pmXLlnTp0gUnJ6dIzdRr1KhBw4YNI6TVqlWLYsWKsWnTJlq1ahVn5yOEoZOm5rHg6NGWTJsWSsuWC8iVqzBWVlYULVqULl26sHDhQs6ePUtQUFB8F1MIIYSIJCAggICAgPguhhAilu3atQtjY2N69eoVIf23335DURR27979zX0kT55c9/rTp0+8fv0aFxcXbG1tuXjxot7LLERCJo+yY0UTwPr/r7UEBd3mzJnznDlzAegNfMTY2JgcOXKQL18+Xc14vnz5SJUqVfwVWwghRJIWfnA1aaklBBQqVIjnz5/HdzEAyJAhA+fPn9fb/h4+fIidnR1WVlYR0sNGOX/48OE39xEUFMSECRNYunQpT548idA33N/fX29lFSIxkMA7FvTsGcC5c0HcuJGcgABrwO3/Sz1gDgChoaFcv16K69fNWL16KzASeEfWrFnJly8fBQoUoEiRIhQpUgRbW9t4OhMhhBBJSfjpxIQQ8Pz5c548eRLfxTBYPXv2ZOnSpfTp04dixYphY2ODRqOhadOmaLXa+C6eEAZFAu9YMHq0pS5YfvECLlyAEyc+8PChL4ULz+Dy5ctcunSJy5f7ANn/v5UWuMXDhxd4+PAC27btRQ3G1SePxYoV0y1ubm4YGUkvASGEEEKI2JQhQ4b4LoKOvsuSNWtWDhw4QGBgYIRa71u3bunWh9FoNFHuY+PGjbRp04Zp06bp0j58+ICfn59eyypEYiCBdyxLnx6qV4fq1c2BzIDaj0ZRYMyYEA4d8uPqVVN8fVMAOf+/tAKuA7kBuHnzJjdvpmTJkq3AG2xsbChSpAjFihWjaNGiFC1aVGrFhRBC/DStVsvTp08BsLOzk4e8IsnTZ9NuQ1O9enX+/PNP5s6dy+DBg3XpM2bMQKPRUK1aNV1aihQpogymjY2NIzQvB5gzZw6hoaGxVm4hEioJvOOJRgMjRpgwYoQt8Llm/Nw5LceOvSdFCnBw6MWpU6e4ePFfQkP3AxbADfz9j7Nv33H27VsB/AFIrbgQQoifp9VqOXbsGACNGjWSvyNCJGK1atWiXLlyDB06FG9vb/Lmzcu+ffvYtm0bffr00U0ZBlCwYEEOHDjA9OnTsbOzw9HRkSJFilCzZk1WrlyJjY0NOXPm5NSpUxw4cIDUqVPH45kJYZgk8NYDDw8PPDw8furp3ueacSPAEsgFzALg9u0PVKmiRR3jIqxWvNP/t3wKzObmzUncvHmTJUuWAGBtbU2RIkUoXrw4pUuXplixYhFGnhRCCCG+pNFoSJMmje61ECLxMjIyYvv27YwYMYJ169axdOlSHBwcmDJlCr/99luEvNOnT6dTp04MGzaMoKAg2rRpQ5EiRZg1axbGxsasXr2aDx8+UKJECQ4cOECVKlXi6ayEMFwa5cv2IeKHBQQEYGNjg6+vb6w0/X71Ck6ehGPHFA4d+siVK6aEhhqTKZMHL170ISQkBLADlgIngOPAGeAdyZIlo3DhwpQtW5ayZctSrFgxLCws9F5GIYTh0Wq1+Pj4kC5dOqnBFEIYBEO6Ln348IEHDx7g6OiIubl5vJZFCBF7vvVb9/PzI2XKlPj7+2NtbR3FHn6OBN56FNuB95eCguDcOciSBdKnD+LChQvMn/+GNWtqh8sVghp8HwQOAKeBT5iamkYKxFOkSBHrZRZCxD1DusEVQggwrOuSBN5CJA0SeCcicR14R+XRI9i5E44fVzhyJJQnT77sTdACWPP/12ZAMKBgamqKu7u7LhAvXry4BOJCJBKGdIMrhBBgWNclCbyFSBriO/CWPt6JTJYs0LUrdO2qAUzw9oaDB+HAAThwIJRRoxpw5UoKPD09uXu3LjAQOMSnTwc4efIgJ0+OZ/z48ZiYmEQKxC0tLeP13IQQQsSu0NBQDhw4AEDFihUxNjaO5xIJIYQQiYME3omcgwP8+qu6KIoxGk19oD4AlSt/YP9+c6Dx/xeAB8BBQkIOcurUFk6dOsWECRMwMTGhaNGiVK5cmcqVK1OoUCG5IRNCiERGURTevHmjey2EEEII/ZDAOwn5coDanTvNOXtWrQ0/eBBOnVIICXEEOqDRtEBRNuvyhoS4cPz4RY4fP86IESOwtbWlQoUKVKpUicqVK+Po6Bi3JyOEEELvjIyMKF26tO61EEIIIfRDAu8kzNQUSpRQl5Ej4e1bDceOqUH4hw/JGTrUmyNHjuDp6cnSpb8RHJwZ8AR24ee3i02bNrFp0yYAXFxcdEF4uXLlsLGxic9TE0II8QOMjIzIlClTfBdDCCGESHRkcDU9MoTB1WLD27eQOzf/n0c8vDvALmAzcEyXamxsTJEiRahcuTKVKlWicOHCmJjIMx4h4oshDWIkhBBgWNclGVxNiKQhvgdXkzsw8U2WlvDgAdy4AVOnQvnyYGKiAK5AH9KnHxQusNYQGmrHyZMnGTVqFCVKlCB16tTUq1eP+fPn4+XlFY9nIoQQ4msUReH58+c8f/5c+ngLIYQQeiSBt4gRjQbc3OC339Sm6K9fa9i8GTp0gKVLq/PmzRt27NhBkyaTgEfAv8AkoAwBAe/ZunUr3bp1w8XFhWzZstGnTx/27dvHx48f4/fEhBBC6ISGhnL48GEOHz5MaGhofBdHCCGESDSk/a/4IdbWUK+euqisqFmzJj4+NdmwAbTa3EBuYCAaTQCKsg+1Wfo27t27x6xZs5g1axYpUqSgYsWKVK9enerVq2Nvbx9fpySEEEmeRqPRdZXSfDkipxBCCCF+mPTx1qPE2sf7e715A/v3w65dsHs3vHz5ed0vv/Ti+vV5/69JMQMi1nj/8ssv1KhRg+rVq1O0aFHpGy6EHhhSX0ohhADDui5JH28hkgbp4y0SnVSpoEkTWL4cnj+Hs2dh1CgoVw7On5/Nq1evWL9+PW5u2zE29gImA8UBI65evcqECRMoVaoU6dKlo1mzZqxatYpXr17F70kJIYQQQgghxA+SGm89khrv75M9O9y58/m9iclrQkI2A1uBg4SvDddoNBQpUoTq1atTo0YN8ufPL80ghYghQ6pZEkIIMKzrktR4J1xt27bF09MTb2/v+C6KSACkxjsR8PDwIGfOnLi7u8d3URKU8+dhwwZo0QJsbCAkJDXQEdiJldW9CF94RVE4ffo0I0aMoGDBgmTKlImOHTvyzz//EBQUFG/nIIQQiUloaCgHDhzgwIEDMriaEImMl5cXnTt3xsnJCXNzc6ytrSlRogSzZs2KtXupp0+fMmrUKC5fvhwr+xciIZHAWw+6d+/OjRs3OHfuXHwXJUGxsoKGDWHVKrUf+P790L072NtDmzb2vHr1Ck9PT3777XesrA4APYDMADx79ozFixdTq1Yt0qRJQ/369Vm+fLk0SRdCiJ+gKAovX77k5cuXMp2YEInIzp07yZMnD+vXr6dWrVrMmTOHCRMmkCVLFgYMGEDv3r1j5bhPnz7ljz/+kMBbCGRUc2EgTE2hYkV1mTMH3r8HU1NTypQpA5Rh2jSACsAcrK3v8f79akJC1gE3ef/+PVu2bGHLli0YGRlRsmRJateuTZ06dXBxcYnX8xJCiITEyMiIEiVK6F4LIRK+Bw8e0LRpU7JmzcqhQ4fImDGjbl337t25d+8eO3fujMcSfvb+/XssLCziuxhCxAr5qyoMjkYDKVJ8fu/qCtOmQalSYGQEAQEuhISMBG5ga/sYG5uGurxarZajR4/Sv39/smXLRq5cuRgyZAinT59Gq9XG/ckIIUQCYmRkRJYsWciSJYsE3kIkEpMnT+bt27f89ddfEYLuMC4uLhFqvFetWkXBggVJnjw5qVKlomnTpvz3338Rtilbtiy5c+fmxo0blCtXDgsLCzJlysTkyZN1eTw9PXXdMNu1a4dGo0Gj0bBs2bII+7hw4QKlS5fGwsKCIUOGALBt2zZq1KiBnZ0dZmZmODs7M2bMGOkCIxI0+asqDF7GjNCvHxw9qo6SvmQJ1KwJyZKBn18m9u5dy/HjxxkwYAAODlWAXLptb9y4wYQJEyhWrBiZMmWiU6dO7Ny5kw8fPsTfCQkhhBBCxJEdO3bg5ORE8eLFv5l33LhxtG7dmmzZsjF9+nT69OnDwYMHKV26NH5+fhHy+vr6UrVqVfLmzcu0adPIkSMHv//+O7t37wbAzc2N0aNHA9CpUydWrlzJypUrKV26tG4fr1+/plq1auTLl4+ZM2dSrlw5AJYtW4alpSX9+vVj1qxZFCxYkBEjRjBo0CA9fSpCxANF6I2/v78CKL6+vvFdlCTBz09R1q5VFK32c1rbtooCipI27SvF3n6JAnkUINKSIkUKpV69esqyZcuUV69exd9JCBEHQkNDlWfPnimhoaHxXRRh4LRareLj46P4+Pgo2vAXVyH0zJCuS0FBQcqNGzeUoKCgKNe/fRv98uUmX8v7/v2P5/1RYfemderU+WZeb29vxdjYWBk3blyE9H///VcxMTGJkF6mTBkFUFasWKFL+/jxo5IhQwalQYMGurRz584pgLJ06dJIxwvbx4IFCyKtex/FB9C5c2fFwsJC+fDhgy6tTZs2StasWb95bkIoyrd/676+vgqg+Pv7x8rxpcZbJFg2Nup84eFnFVMUtSb85cvUPH7cDrhKhgy+uLquJVmyQrp87969Y8uWLbRt25b06dNTqVIl5s+fz/Pnz+P+RIQQwkDIqOZCRGZpGf3SoEHEvOnSRZ+3WrWIeR0cos8brlL4pwQEBABgZWX1zbybN29Gq9XSuHFjXr16pVsyZMhAtmzZOHz4cIT8lpaWtGzZUvc+WbJkFC5cmPv378e4fGZmZrRr1y5SevLkyXWvAwMDefXqFaVKleL9+/fcunUrxvsXwpBI4C0SlWXL1BHSV62COnXAzAyeP7flzp0mODicYcuWrbRr1460adPqtgm70ezWrRt2dnaULFmSGTNmyJyQQogkydLSEktLy/guhhBCD8KmZg0MDPxm3rt376IoCtmyZSNt2rQRlps3b+Lj4xMhv729PZrwtR9AypQp8fX1jXH5MmXKRLJkySKlX79+nXr16mFjY4O1tTVp06bVBfn+/v4x3r8QhkRGNReJjrW1Ojd4ixYQEAD//KPOF16okBF169ahbt06vH0bSsGCQaRMeZrHj6fx5MkeQJ1K58SJE5w4cYJ+/fpRoEABGjRoQP369cmRI0c8n5kQQsQuExMTatWqFd/FEMKgvH0b/Tpj44jvv4hNI/hyvMKvPd/X19iG1tbW2NnZce3atW/m1Wq1aDQadu/ejfGXJwaRHshFlQf4rqkIw9dsh/Hz86NMmTJYW1szevRonJ2dMTc35+LFi/z+++8yWK5IsCTwFomatTU0b64u4R04YMydO5ZARaAiLi5BZM58nP/+m8K9e/t1+S5evMjFixcZOnQoOXPmpH79+jRo0IC8efNGesorhBBCiMQn/Ewr8ZX3Z9SsWZM///yTU6dOUaxYsWjzOTs7oygKjo6OuLq66uXYP3Kv5OnpyevXr9m8eXOEgdgePHiglzIJEV+kqblIkipUgL//VpujJ0sG9+4l5/DhSty7t498+d7RpctSChYsGGGbGzduMHbsWPLnz4+zszP9+/fn1KlT8uRVCCGEEAZr4MCBpEiRgg4dOvDixYtI6728vJg1axb169fH2NiYP/74I1KttaIovH79+ruPneL/Txe+HBH9a8Jq0sOXITg4mHnz5n338YUwJFLjLZIkKyto2lRdfH1h82Y1ED90CC5ftmDt2rZkz94Wb29vliw5wL596zl79oDuj8CDBw+YNm0a06ZNw87Ojnr16tGwYUNKlSoVbdMrIYQwdKGhoRw7dgxArmdCJBLOzs6sWbOGJk2a4ObmRuvWrcmdOzfBwcGcPHmSDRs20LZtW3r37s3YsWMZPHgw3t7e1K1bFysrKx48eMCWLVvo1KkT/fv3/+5j29rasmDBAqysrEiRIgVFihTB0dEx2m2KFy9OypQpadOmDb169UKj0bBy5crvasIuhCGSGm+R5KVMCb/+CgcOwOPHsHw5ZM+urnNwcODGjQ5cubKPGjXe0bnzbsqVqxbhZvTp06d4eHhQrlw5MmXKRI8ePThy5IiMCCyESHAUReHZs2c8e/ZMbnKFSERq167N1atXadiwIdu2baN79+4MGjQIb29vpk2bxuzZswEYNGgQmzZtwsjIiD/++IP+/fuzfft2KleuTO3atb/7uKampixfvhxjY2O6dOlCs2bNOHLkyFe3SZ06Nf/88w8ZM2Zk2LBhTJ06lUqVKjF58uQfOnchDIVGkb+sehMQEICNjQ2+vr7Y2trGd3GEHoSGQqFCcPny5zQbG6hZ8yN2dke4eXMe+/fv4ePHj5G2zZAhAw0bNqRx48aUKFECI32NlCLEd9Jqtfj4+JAuXTr5Hoqv0mq1uhkdHBwc5PsiYo0hXZc+fPjAgwcPcHR0xNzcPF7LIoSIPd/6rfv5+ZEyZUr8/f11MwLok/xFFeIrjI3h4kV1GTAA7O3B3x9WrzZjypTKfPq0lZcvX7JmzRrq1auHmZmZbtvnz58zd+5cSpcujb29Pb169eL48ePSJ1wIYbCMjIxwcnLCyckp3oMhIYQQIjGRv6pCfINGA/nzw+TJ8PAhHDkCnTtDqlRQqRJYWVnRrFkzli/fzO+/+zNr1lbq1q0bIQh/9uwZc+bMoVSpUmTOnJnevXtz4sQJCcKFEEIIIYRIAqSpuR5JU/OkJTgYQkLAwkJ9v2wZtGunvi5XDho1CsLUdDs7dqxhz549BAcHR9pHpkyZaNSoEY0aNaJo0aJSwyRihSE16RSGTVEU3ejDtra2Mm2iiDWGdF2SpuZCJA3S1FyIBCpZss9BN4CdnRpwAxw+DN26Jad37yZYW29j3bo3LF++ilq1apEsWTLdNk+ePGHmzJmUKFGCrFmz0q9fP86cOSODGgkh4kVoaCh79uxhz549MkCkEEIIoUcSeAuhJ5Urq9OReXvDuHHg6grv38OqVVC/fgrKlWvB9u3b8fHxYcWKFdSsWRNTU1Pd9o8fP2bGjBkULVoUFxcXhg4dyrVr1+LvhIQQSVLy5MlJnjx5fBdDCCGESFSkqbkeSVNzEZ6iwNmzsGIFvHwJ69d/Xvfbb+DgANWr+3P8+FbWr1/P/v37+fTpU6T95M6dm2bNmtG0aVOcnJzi7gREomFITTqFEAIM67okTc2FSBriu6m5BN56JIG3iIlnz9TR0bVaMDGBGjWgTRsoVsyXXbu28Pfff3Po0KEoB14rUqQIzZo1o3HjxmTMmDEeSi8SIkO6wRVCCDCs65IE3kIkDfEdeMsdmBBxzMICZsyAggXVwdm2bYP69SFXrpRcuNCeGTP28+TJE2bPnk2xYsUibHvmzBn69OlDpkyZKF++PIsWLeLNmzfxdCZCCCGEEEKImJDAWw88PDzImTMn7u7u8V0UkQDY2ECvXnD+PFy7BgMHqgOzvXkD8+bB8eOQIUMGevbsycmTJ3nw4AETJ04kb968un0oisLhw4fp1KkTGTJkoFatWqxZs4a3b9/G45kJIRK60NBQjh8/zvHjx2VwNSGEEEKPJPDWg+7du3Pjxg3OnTsX30URCUyuXDBpEjx6BHv3QqtW0KzZ5/V//QW//+5AgQK/c/HiZa5fv87w4cNxcXHR5fn06RP//PMPLVq0IH369DRt2pRt27bx8ePHeDgjIURCpigK//33H//995/MriCEEELokQTeQhgAY2N1VPQVK9Qa8TDz56uDslWuDE5OsGFDTjp0GM2dO3c4d+4c/fr1I1OmTLr879+/Z926ddStW5eMGTPSpUsXjh8/HmV/cSGE+JKRkREFCxakYMGC8d7vVgghhEhM5K+qEAZs0SLo3h1sbeHhQxg1Sh0NvWpVDQ8fFmLatGk8evSII0eO0KVLF1KnTq3b1tfXl4ULF1KqVCmcnZ0ZNmwYt27diq9TEUIkAEZGRri6uuLq6iqBtxAiVmg0GkaNGqV7v2zZMjQaDd7e3rq0smXLUrZs2TgvmxCxSf6qCmHA8ueHuXPh6VNYvRrKl1enKdu3D5YvV/MYGRlRunRp5s+fz7Nnz9i1axctWrTAwsJCtx9vb2/GjRuHm5sbhQoVYubMmTx//jyezkoIIYQQceXff/+lYcOGZM2aFXNzczJlykSlSpWYM2fOd+9r3rx5LFu2TP+F/IanT58yatQoLl++HOfHFkJfJPAWIgFInhyaN4eDB8HLC4YNUwdoC/PgARQpAkuWmFK8eDVWrVrFixcvWLVqFVWrVo1Qc3XhwgX69u1LpkyZqFq1KqtWrZJB2YQQgNrHOzAwkMDAQOnjLUQicPLkSQoVKsSVK1fo2LEjc+fOpUOHDhgZGTFr1qzv3l9cBd779u1j3759uvdPnz7ljz/+kMBbJGgm8V0AIcT3cXKCMWMipi1dCmfPqkvfvtCwIXTqZEnz5i1o0aIFz58/Z926daxatYrz588D6hyqe/fuZe/evVhYWFCvXj1atmxJxYoVMTGRS4MQSVFoaCj//PMPAI0aNZJrgRAJ3Lhx47CxseHcuXPY2tpGWOfj4xM/hYqBZMmSxXcRhNA7qfEWIhHo2ROmTYOcOSEoCFauhFKlIE8emDMHrKwy0Lt3b86dO8fNmzcZNmwYDg4Ouu3fv3/P6tWrqVatGpkyZaJPnz6cP39earyESIJMTU0xNTWN72IIIfTAy8uLXLlyRQq6AdKlS6d7vXTpUsqXL0+6dOkwMzMjZ86czJ8/P0J+BwcHrl+/zpEjR9BoNGg0mgj9sP38/OjTpw+ZM2fGzMwMFxcXJk2a9EMDvIbv4+3p6ambsrddu3a6Y4eveT9z5gxVq1bFxsYGCwsLypQpw4kTJ777uELEJgm8hUgE0qaFfv3UecFPnYL27dXm6devw9ChEP5vXo4cORgzZgxeXl4cO3aMzp07kzJlSt16Hx8fZs2ahbu7O25ubowbN45Hjx7Fw1kJIeKaiYkJDRs2pGHDhlLbLUQikDVrVi5cuMC1a9e+mm/+/PlkzZqVIUOGMG3aNDJnzky3bt3w8PDQ5Zk5cyb29vbkyJGDlStXsnLlSoYOHQqoD/DLlCnDqlWraN26NbNnz6ZEiRIMHjyYfv36/dQ5uLm5MXr0aAA6deqkO3bp0qUBOHToEKVLlyYgIICRI0cyfvx4/Pz8KF++PGfPnv2pYwuhTxpFqrT0JiAgABsbG3x9faN8sihEXPLzg1Wr4N07+P13NU1RoGVLKFtWnS/c0lJN//jxI7t372bVqlXs2LGD4ODgCPvSaDSUL1+eNm3aUL9+fVKkSBGn5yJ+jlarxcfHh3Tp0slI1UIIg2BI16UPHz7w4MEDHB0dMTc3j5zh3bvoNzY2hvDbfC2vkZH6VPxH8v6g/fv3U61aNQAKFy5MqVKlqFChAuXKlYvQsiUoKIjkXxyvatWq3L17Fy8vL11a7ty5SZMmDZ6enhHyjh07lokTJ3Lp0iWyZcumSx88eDBTpkzhwYMHZM6cGVDvKUaOHKkb2XzZsmW0a9eOBw8e6Frjha/tBjh//jzu7u4sXbqUtm3b6vavKArZs2fHycmJ3bt3o9FodOeTK1cuXFxcIvQVF0nbt37rfn5+pEyZEn9/f6ytrfV+fLkDEyKRsrWFHj0+B90Ap0/DmjXQqRPY2UG3bnD1KpiZmVG3bl02btzI8+fPWbRoEWXKlNFtpygKBw8epHXr1mTIkIF27drh6ekp84MLIYRI/Cwto18aNIiYN1266PP+PwDWcXCIPu//a3N/VqVKlTh16hS1a9fmypUrTJ48mSpVqpApUya2b9+uyxc+6Pb39+fVq1eUKVOG+/fv4+/v/83jbNiwgVKlSpEyZUpevXqlWypWrEhoaChHjx7Vy/l86fLly9y9e5fmzZvz+vVr3XHfvXtHhQoVOHr0qNyrCIMhgbcQSUj27Gpf8GzZIDAQ5s+HvHmheHF1erKgIEiZMiUdOnTA09OTBw8eMHr0aJydnXX7ePv2LcuWLaNcuXI4OzszcuTICE/DhRAJV2hoKKdPn+b06dOEhobGd3GEEHrg7u7O5s2b8fX15ezZswwePJjAwEAaNmzIjRs3ADhx4gQVK1YkRYoU2NrakjZtWoYMGQIQo8D77t277Nmzh7Rp00ZYKlasCMTeQG53794FoE2bNpGOvXjxYj5+/Bij8gsRF6QDlxBJSKpUal/wvn3h8GFYuBA2b1b7hZ86pT6oD/9A3sHBgeHDhzNs2DBOnjzJsmXLWL9+PQEBAYA6P/jo0aMZPXo0JUuWpG3btjRq1ChWmucIIWKfoig8ePAAgEKFCsVzaYQwEF+bctPYOOL7rwWYXzap9/aOeV49SJYsGe7u7ri7u+Pq6kq7du3YsGEDLVu2pEKFCuTIkYPp06eTOXNmkiVLxq5du5gxY0aMaoy1Wi2VKlVi4MCBUa53dXXV9+nojgswZcoU8uXLF2Uey7B+dULEMwm8hUiCNBooX15dnj9XpyPbtw8qV/6cZ8kStXtZ/fpgZqahRIkSlChRgtmzZ7N161aWL1/O/v37dX/0jh8/zvHjx+nZsyf16tWjTZs2VKhQAeMvb0qEEAbLyMhId/Ma3/1uhTAY3zOuSWzl1bOwB2vPnj1jx44dfPz4ke3bt5MlSxZdnsOHD0faLqwP9ZecnZ15+/atroZb3752XABra+tYO7YQ+iJ/VYVI4jJkgMGD1RrwsBg5OFhNa94csmaFESPg6VN1XfLkyWnWrBl79uzh0aNHTJo0CTc3N93+goKCWLNmDVWqVCFr1qwMGjSImzdvxsOZCSG+l5GREW5ubri5uUngLUQicPjw4SinBt21axcA2bNn1z0gD5/P39+fpUuXRtouRYoU+Pn5RUpv3Lgxp06dYu/evZHW+fn5ERIS8qOnoDtu2L7CK1iwIM7OzkydOpW3UbRMePny5U8dVwh9khpvIUQkwcHqwGt//qkG3GPGwIQJau13z55QooRaa54pUyYGDhzIgAEDOH/+PMuXL+fvv//mzZs3ADx58oRJkyYxadIkihQpwq+//kqTJk2kKboQQggRB3r27Mn79++pV68eOXLkIDg4mJMnT7Ju3TocHBxo164dL168IFmyZNSqVYvOnTvz9u1bFi1aRLp06Xj27FmE/RUsWJD58+czduxYXFxcSJcuHeXLl2fAgAFs376dmjVr0rZtWwoWLMi7d+/4999/2bhxI97e3qRJk+aHz8PZ2RlbW1sWLFiAlZUVKVKkoEiRIjg6OrJ48WKqVatGrly5aNeuHZkyZeLJkyccPnwYa2trduzY8bMfoxD6oQi98ff3VwDF19c3vosihF4EByvK+vWKUqqUoqiTkanL6NHRb/Phwwdl06ZNSu3atRUTExMFiLBYWFgobdq0UY4cOaJotdq4O5kkLDQ0VHn27JkSGhoa30URBk6r1Srv3r1T3r17J79PEasM6boUFBSk3LhxQwkKCorvoujd7t27lfbt2ys5cuRQLC0tlWTJkikuLi5Kz549lRcvXujybd++Xfnll18Uc3NzxcHBQZk0aZKyZMkSBVAePHigy/f8+XOlRo0aipWVlQIoZcqU0a0LDAxUBg8erLi4uCjJkiVT0qRJoxQvXlyZOnWqEhwcrMsHKCNHjtS9X7p0aaTjlClTJsK+FUVRtm3bpuTMmVN3b7F06VLdukuXLin169dXUqdOrZiZmSlZs2ZVGjdurBw8ePBnP0KRiHzrt+7r66sAir+/f6wcX+bx1iOZx1skZpcvw9y56nRkZ89C7txq+oMH6hgwWbNG3sbHx4c1a9awdOlSrl69Gmm9i4sL7du3p02bNtjZ2cXuCSRhhjRfrjBsISEhbNiwAYBGjRphYiIN40TsMKTr0jfn8RZCJAoyj7cQIkHIlw8WL1YHYwsLukHt/+3kBPXqwcGDap14mHTp0tGnTx8uX77M+fPn6datGzY2Nrr19+7dY8iQIWTOnJmaNWuyZcsWgoOD4+6khBCRaDSaaAcyEkIIIcSPkcBbCPFdwj8A1GrhzRv1361boWJFNSifPz/i7CsajYaCBQvi4eHBs2fPWL16NeXLlw+3Hy07d+6kfv362Nvb079/f93cokKIuGNiYkLTpk1p2rSp1HYLIYQQeiSBtxDihxkZwc6dcOOGOhhbihSfX2fKBBMnRt4mefLkNG/enIMHD3L//n2GDx+Ovb29bv3Lly+ZNm0auXLlolixYixevFg3b7gQQgghhBAJkQTeQoif5uYGHh7w5AnMmgXZskFAgDryeRitNmIzdABHR0dGjx6Nt7c3e/bsoVGjRpiamurWnz59mo4dO5IxY0batWvHsWPHopwWRQghhBBCCEMmgbcQQm9sbKBXL7h1C/bsgQ4dPq/btAny5IElS+DDh4jbGRsbU6VKFdavX8/Tp0+ZOXMmefLk0a1///49y5Yto3Tp0uTIkYNp06bx6tWrODorIZKO0NBQzp07x7lz5wgNDY3v4gghhBCJhgTeQgi9MzKCKlUgderPaQsXwvXr8Ouv6gjoo0fDy5eRt02TJg29e/fmypUrnDt3jq5du0YYkO3OnTv079+fTJky0bx5czw9PaUWXAg9URSFe/fuce/ePfldCSGEEHokgbcQIk5s2gRTpkDmzODjAyNHqq87dYKbNyPn12g0FCpUiHnz5vH06VNWrVpFmTJldOuDg4P5+++/KVeuHDly5GDq1KlSCy7ETzIyMiJ37tzkzp073qd4EkIIIRIT+asqhIgTNjbQvz94ecHff0OhQvDxIyxaFLFJelQsLCxo0aIFnp6e3Lp1i99++43U4arT79y5w4ABA8iUKRPNmjXj8OHDUlsnxA8wMjIiT5485MmTRwJvIYQQQo/kr6oQIk6ZmkLTpnD2LBw7ps7/3b//5/Vv3sCyZWpQHpXs2bMzdepUnjx5oqvxDhMcHMzatWspX768Lt/LqNqzCyGEEEIIEYck8BZCxAuNBkqWhM2b1eA7zMKF0K6d2g987FiIrvW4mZkZTZs25dChQ9y+fZv+/fuTJk0a3fq7d+/qasGbNm0qteBCxICiKAQHBxMcHCy/FyGEEEKPJPAWQhiUNGnUOcBfvIDhwyFLFujaFW7fjn4bV1dXpkyZwuPHj3U13mE+ffrEunXrdLXgU6ZMwcfHJw7ORIiEJzQ0lE2bNrFp0yYZ1VwIIYTQIwm8hRAGpWNHePAAVq+GAgUgKAgWLIAcOaBBA3U+8OiYmZnRpEkTDh48yO3btxkwYECkWvCBAwdib29Ps2bNZF5wIYQQQggRJyTwFkIYHFNTaN4czp+HI0egdm21abqlpTpVWZivxcyurq5MnjyZx48fs27dOipUqKBb9+nTJ9auXUvp0qXJmzcvCxYs4O3bt7F4RkIkDMbGxjRp0oQmTZpgbGwc38URQog4tWzZMjQaDd7e3vFdFL3z9vZGo9EwderU+C5KkiWBdxTq1atHypQpadiwYXwXRYgkTaOB0qVh2za4dUudgizM9euQO7c6EFtwcPT7MDMzo3Hjxhw4cEBX4x2+Fvzff/+la9eu2NnZ0bNnT27cuBF7JySEgdNoNBgZGWFkZIRGo4nv4gghflJYIHn+/Pko15ctW5bcuXNHSAsODmbWrFnkz58fa2trbG1tyZUrF506deLWrVuR9uHl5UXnzp1xcnLC3Nwca2trSpQowaxZswgKCvpq+dq2bYtGo9EtZmZmuLq6MmLECD58+PDjJ55IjR8/nqJFi5I2bVrMzc3Jli0bffr0ifOBZM+cOUO+fPlInjw5bdu2jbAuLMDXaDSMHTs2yu1btGiBRqPB0tIyDkprOCTwjkLv3r1ZsWJFfBdDCBGOqys4OX1+P2sW3LihDsTm7AwzZ8K3Kq1dXFyYNGkSjx8/ZtWqVRQrVky3LjAwkLlz55IrVy7Kly/Pxo0b+fTpU+ycjBBCCGGgGjRowG+//Ubu3LmZOHEif/zxB6VLl2b37t2cPn06Qt6dO3eSJ08e1q9fT61atZgzZw4TJkwgS5YsDBgwgN69e3/zeGZmZqxcuZKVK1cyffp0HBwcGDNmDL/++mtsnWK0WrVqRVBQEFmzZo3zY8fEhQsXyJcvH0OHDsXDw4M6deqwdOlSihcvzrt37+KsHE5OTmzYsIGBAwdGm8fc3Jy///47Uvq7d+/Ytm0b5ubmsVlEg2QS3wUwRGXLlsXT0zO+iyGE+IopU8DFBWbMgMePoW9fGDMGevaEHj3UQdqiY2ZmRosWLWjRogWXLl1i3rx5rF69Wvdk/vDhwxw+fBg7Ozs6depEp06dyJgxYxydmRDxR6vVcuXKFQDy5s0rc3kLkcScO3eOf/75h3HjxjFkyJAI6+bOnYufn5/u/YMHD2jatClZs2bl0KFDEf5Odu/enXv37rFz585vHtPExISWLVvq3nfr1o3ixYvz999/M336dNKnT//zJxZDxsbGBt3NZtOmTZHSihUrRsOGDdmxYwdNmzaNk3KkTZuWtGnTfrVlVPXq1dm8eTNXrlwhb968uvRt27YRHBxM1apVOXToUFwU12Akur+oR48epVatWtjZ2aHRaNi6dWukPB4eHjg4OGBubk6RIkU4e/Zs3BdUCPFTbGxg4EB1ILaFC9Ug/M0b+OMPcHf/+iBs4eXPn59Fixbx9OlTZs6ciaurq27d06dPGTVqFFmyZKFx48YcOXJEBmMTiZpWq+XWrVvcunULbUx/REKIRMPLywuAEiVKRFpnbGxM6tSpde8nT57M27dv+euvv6J8OO3i4hKjGu8vaTQaSpYsiaIo3L9//7u2DWs6f/XqVcqUKYOFhQUuLi5s3LgRgCNHjlCkSBGSJ09O9uzZOXDgQITto+rj7eDgQM2aNTl+/DiFCxfG3NwcJycng2kd6+DgABDhoci3/Pnnnzg7O2NmZoa7uzvnzp3Te7mKFSuGo6Mja9asiZC+evVqqlatSqpUqSJtE/ZZ79u3j3z58mFubk7OnDnZvHlzpLx+fn707dsXBwcHzMzMsLe3p3Xr1ryKbh5aA5DoAu93796RN29ePDw8oly/bt06+vXrx8iRI7l48SJ58+alSpUqMr2QEAmUuTl06qT2AV+/Xh0JvUOHz4OwabVfn4osjK2tLb179+bmzZvs37+funXr6mr7QkJC2LBhg+4PuoeHBwEBAbF4VkLEDyMjI3LkyEGOHDmktluIRMTf359Xr15FWr7sUhXWxHr16tWEhIR8dZ87duzAycmJ4sWL6728YYFvypQpv3tbX19fatasSZEiRZg8eTJmZmY0bdqUdevW0bRpU6pXr87EiRN59+4dDRs2JDAw8Jv7vHfvHg0bNqRSpUpMmzaNlClT0rZtW65fvx6j8kT12X+5vH//PkbnpygKr1694vnz5xw7doxevXphbGxM2bJlY7T9mjVrmDJlCp07d2bs2LF4e3tTv3593XchNDSUsmXLRrnMnz8/RscI06xZM9auXaurtHj16hX79u2jefPm0W5z9+5dmjRpQrVq1ZgwYQImJiY0atSI/fv36/K8ffuWUqVKMWfOHCpXrsysWbPo0qULt27d4vHjx99VxriU6JqaV6tWjWrVqkW7fvr06XTs2JF27doBsGDBAnbu3MmSJUsYNGjQdx3r48ePfPz4Ufc+7EZcq9VKTYEQcUyjUacbq18fQkI+13hv2wb16xtRq5bC778rhOvWHa3y5ctTvnx5/vvvP/78808WL16sezh348YNevTowaBBg2jZsiXdunUjV65csXhmP0+r1aIoilyXRIyEbxIo3xkRWwzpuhRWlrDlS2EBqLGxsa5pbdi9nkajidA0Obby/qiw86lYsWK0eXLlyqXLV6RIEcqUKcOiRYvYvn075cuXp0SJEtSsWZMsWbLotgkICODJkyfUqVNHLy3BwgYH8/f3Z+vWrWzatIncuXPj6ur63ft/+vQpq1evplmzZoB67m5ubjRv3pwTJ05QpEgRAHLkyEHVqlXZuHGjboCwsGN9+V24ffs2R44coVSpUgA0atSILFmysGTJkm+OEp4/f34ePnz4zXKPGDGCUaNGfTPf8+fPsbOz0723t7dn9erVZM+e/aufVdi6R48ecefOHd1DDVdXV+rWrcuePXuoWbMmRkZGHD58+Jv7Cf86urRmzZoxfvx4jh8/TsmSJVm3bh3m5ubUqlWLPXv2RNoW4M6dO2zcuJH69esD0L59e9zc3Pj999913+PJkydz7do1Nm3aRL169XTbDh06NNrfcdixwq47UV17Yvt6lOgC768JDg7mwoULDB48WJdmZGRExYoVOXXq1Hfvb8KECfzxxx+R0l++fEnw14ZZFkLEmWPHLNFoUrBjh4YdOzQULRpMjx5vKV8+mG8N2mxmZkbPnj3p3LkzO3fuZNmyZbquKW/fvmXBggUsWLCA0qVL07FjR8qXL2+QtYRarRZ/f38URTHI8gkhkh5Dui59+vQJrVZLSEhIlLW869evB6B27dq6AaFu3LjBtWvXcHR0xN3dXZd306ZNhIaGUqNGDVKkSAGogcTly5fJkiULRYsW1eXdtm0bHz9+pEqVKtjY2ABw//59zp8/j52dHSVLlvzpcwsLJGbPnk22bNkirR84cCChoaERzvuff/5h+vTprFmzhr///pu///6bHj160LBhQ+bNm4etrS1v3rwBIEWKFN+sGf9W+d69e0e6dOkipJcoUYK//vqL0NDQ79qfoihYWlrSsGFDXbmcnZ2xtbXFzs6OggUL6tILFiwIqLXZYWlhn9eX3wU3NzeKFSumS0uZMiWurq54eXl98/yXL1/+zdHdARwdHWP0WVpbW7N7924+fPjA5cuX2bp1KwEBAd/cNmx9o0aNsLKy0r0PG2g2/OfwLffu3aNUqVK8f/8erVbLP//8w4oVK6hUqVKEzzJ79uzkyZOHNWvWULRoUdasWUOtWrVIlixZhM86PDs7O2rVqqVLt7CwoEWLFkydOpXHjx+TIUMGNm3axC+//BIhX0yEhISg1Wp5/fo1pqamkdb7+/vHeF8/IkkF3q9evSI0NDTSIA3p06ePMD1CxYoVuXLlCu/evcPe3p4NGzZEGP04zODBg+nXr5/ufUBAAJkzZyZt2rTY2trG2nkIIWJu6lTo0EFh6lRYtQpOn07G6dOpyJ9fYdgwhTp1+GYADtC5c2c6d+7MlStXWLBgAatXr9aNIHr06FGOHj1KtmzZ6NGjB23atMHKyiqWzyzmwmpP0qZNG+83uMKwha8pCJsORojYYEjXpQ8fPhAYGIiJiQkmJpFvjcN+B+HXh9VSGxsbR9jGyMgIrVYbZV4jI6MIecN+Y+Hzhk3l92XeHxX22RYtWpRChQpFWj9x4kRevXoV4VgmJiYMHz6c4cOH8+zZM44cOcLs2bPZuHGjbgTysP657969+6lyGhkZYW5uzvbt2wF4/PgxU6ZM4eXLl1hZWX33vjUaDfb29pGCKhsbG7JkyRJhf2H91f39/SN8/kCk70LWrFkjlSVVqlQRto1O6dKlv+scvsXExIQqVaoAUKdOHSpVqkTJkiXJkCEDNWvW/Op2EPlc0qZNCxCjcwmTI0eOaKcwC/9ZmpiY0Lx5c6ZPn06/fv04deoUQ4YMwcTEJMJnHZ6Li0uk/78cOXIA6vfD3t6e+/fv06BBg+/+foQdN3Xq1FGOqp4sWbLv2t/3SlKBd0x9OdBCdMzMzDAzM4uUHjYHqhDCMOTMCUuWwOjR6rRjCxbApUsaRo/WUK9ezALvMPnz52fhwoVMnjyZZcuWMWfOHN1gNHfv3qV3794MHz6cX3/9lZ49e+Lo6Bg7J/Wdws/PLER0wsYzALVWxJBH9xUJn6Fcl8KC3egeNjVu3BiI2CQ8Z86c5MiRI9I2Yc1jw+d1dXXFxcUlUt46depEyuvs7Iyjo6PeHnyF7eNb+4tunZ2dHc2aNaNhw4bkypWL9evXs2zZMmxsbLCzs+PatWs/XU5jY2MqVaqke1+1alVy5MhBly5ddAH59+4vqjJFlw4RP6ewf8PnjW5bRVG+ef4vX76MUc29paXlD81rXaJECTJmzKirTY5O+AdI3/ocfsaXn2Hz5s0ZMmQInTp1InXq1FSpUiXCcaI65pdp0f2/fG95w7aP7roT29eiJHUHliZNGoyNjXnx4kWE9BcvXpAhQ4Z4KpUQIq7Y26s14N7eMHQojB37Oeh++1YdnC2mrdpsbGzo3bs3t2/fZtu2bZQvX163LiAggBkzZuDi4kK9evXw9PSU0dCFECKBCqv9DH+TH1ab9+XDqdjKG99MTU355Zdf+PTpk27U6Jo1a+Ll5fVD3TW/JmPGjPTt25cdO3ZEmjc8IXJ3dydjxozfXL7VV/xrPnz4EOvNpH9UlixZKFGiBJ6enjRq1OibtdT37t2Lst83fB7B3dnZmWvXrsVKeWNTkgq8kyVLRsGCBTl48KAuTavVcvDgwSibkgshEqc0adSgO3yLrHnzoEkTyJ0bVq9WB2iLCWNjY2rXrs3Bgwe5evUqv/76q64ljFarZevWrZQrV478+fOzdOlSPnz4EAtnJIR+GBsb06BBAxo0aGBwN/5CiNh39+5dHj16FCndz8+PU6dOkTJlSl3T5IEDB5IiRQo6dOgQqVIL1KnJZs2a9UPl6NmzJxYWFkycOPGHtjckq1evZv/+/d9cWrdu/dX9vHv3LsqRzzdt2oSvr2+UXQkMxdixYxk5ciQ9e/b8Zt6nT5+yZcsW3fuAgABWrFhBvnz5dBWlDRo04MqVKxHyhTHkio5E19T87du33Lt3T/f+wYMHXL58mVSpUpElSxb69etHmzZtKFSoEIULF2bmzJm8e/dON8q5ECJpMjeHlCnVaclatlTnAx86FFq0gJh2IcqTJw+LFy9m4sSJ/Pnnn3h4ePD06VMArly5Qvv27fn999/p0qULXbt2jXLeUyHik0ajifU+bkIIw3XlyhWaN29OtWrVKFWqFKlSpeLJkycsX76cp0+fMnPmTN1DOWdnZ9asWUOTJk1wc3OjdevW5M6dm+DgYE6ePMmGDRt0o4V/r9SpU9OuXTvmzZvHzZs3cXNz0+NZxq2o5kT/EXfv3qVixYo0adJEN+Xj+fPnWbVqFQ4ODj80Z3pcKVOmDGXKlIlRXldXV3799VfOnTtH+vTpWbJkCS9evGDp0qW6PAMGDGDjxo00atSI9u3bU7BgQd68ecP27dtZsGBBhNk5DEmiq/E+f/48+fPnJ3/+/AD069eP/PnzM2LECACaNGnC1KlTGTFiBPny5ePy5cvs2bMn0oBr38PDw4OcOXNGGNVSCJGw9OqlNkEfPx5Sp4a7d6FtW8ieHcJd62MkTZo0DBkyBG9vb9asWUPhwoV1616+fMmYMWPImjUrLVu25Pz583o9DyGEEOJHlS5dmjFjxuDv78/06dPp3LkzM2bMIGvWrGzcuDFScFe7dm2uXr1Kw4YN2bZtG927d2fQoEF4e3szbdo0Zs+e/cNl6devH0ZGRkyaNOlnTytRsLe3p0GDBhw6dEg3wPOJEyfo0aMH586d0w0Wl9Bly5aNdevWsWvXLgYNGsSnT59Yt26dbkA5UPvDHzt2jK5du7Jr1y569erFvHnzyJ49O/b29vFY+q/TKIZcH5/ABAQEYGNjg6+vr4xqLkQC9vYtzJ8PU6bAy5dqE/S1a39un6dPn2bWrFls3Lgx0tQXxYsXp2/fvtSrVy9WmvdqtVp8fHxIly5dvA9iJAybVqvl+vXrgDq3r3xfRGwxpOvShw8fePDgAY6OjlGOdCyEiBsODg7kzp2bf/75J1b2/63fup+fHylTpsTf3x9ra2u9H1/+ogohxBcsLWHAAHjwAKZPh+HDP6/z8gIPD/jertpFixbl77//5sGDBwwePDjCk+mTJ0/SqFEjsmfPzrx586LswyVEXNBqtVy7do1r167p5lgVQgghxM+TwFsIIaKRIgX07Qu5cn1OGz8eevQAZ2eYPRuCgr5vn/b29owfP57//vuPRYsWkSvczr28vOjevTtZsmRh5MiR0c6RKURs0Wg0uLi46KY+EkIIQ/DmzRueP38e7SJ/L0VCIIG3EEJ8hyJF1GnJnj6F3r3BxUVtlh4c/H37SZ48OR06dODff/9l3759EeYwff36NaNHjyZLlix07do1woCRQsQmY2Nj3N3dcXd3l1HNhRAGo379+l+dikvGWRIJgfTx1iPp4y1E0vDxIyxfDuPGQdisKw4OMGECNG364/u9fPkyU6dOZe3atYSGm1Bco9FQr149BgwYQNGiRb97v4bUl1IIIcCwrkvSx9vwXbhwAV9f32jXJ0+eXG+jh4vEK777eEvgrQceHh54eHgQGhrKnTt3JPAWIon4+BEWL1bnBH/+XO0LPnr0z+/30aNHzJw5k0WLFvH27dsI60qWLMmAAQOoWbNmjG9WDekGVwghwLCuSxJ4C5E0SOCdiEiNtxBJ0/v3sGABtG8PYT/906fBxwdq1YIf7Srr5+fHggULmDVrFs+fP4+wLkeOHPz222+0bNnymzeKhnSDKwxbSEgIGzduBKBhw4aYxHQSeyG+kyFdlyTwFiJpiO/AW+7AhBDiJ1lYQL9+n4NuRVEHZatTB4oWhf371bTvZWtrq5sP9a+//sLNzU237tatW3Ts2BEHBwfGjRvHmzdv9HMyIslTFAV5Ji+SIvneC5G4xfdvXAJvIYTQs0+foEwZSJ4czp6FypWhXDk4ceLH9mdmZkb79u25du0aO3bsoEyZMrp1L168YNiwYWTJkoXevXvz8OFDPZ2FSIqMjY2pU6cOderUkcHVRJJhamoKIFM5CpHIhf3Gw37zcU2amuuRNDUXQoT3/Lk64NqCBZ9HPa9aFSZOhLx5f27fZ8+eZcqUKWzevDnCfMsmJia0aNGCwYMHkz17dsCwmnQKIQQY3nXp2bNn+Pn5kS5dOiwsLGQ6PSESEUVReP/+PT4+Ptja2pIxY8Yo80kf7wREAm8hRFQePVIHYFuyBEJDYfVqaN5cP/v28vJixowZLFmyhKBwk4prNBoaNmzIkCFD+OWXXwzqBlcIIQwt8FYUhefPn+Pn5xffRRFCxBJbW1syZMgQ7YM1CbwTEAm8hRBfc+8eLFoE48dDWCveAwfUqchcXH5u369evWLu3LnMnj070pQr1apVo2vXrtSoUcMgbnCF4dJqtdy+fRuA7Nmzy/dFxBpDC7zDhIaG8unTp/guhhBCz0xNTb/ZhUoC7wRAphMTQvyI9+/VgNvHBzp0gJEjIZrWTzEWGBjI/PnzmT59Oi9evIiwrmzZsgwdOpQKFSpIM0oRpZCQEDZs2ABAo0aNZFRzEWsMNfAWQiRdMqp5AtC9e3du3LjBuXPn4rsoQogExM8P8uVTm58vXKgG4SNGQGDgj+/TysqKgQMH8uDBA+bMmUPmzJl16zw9PalUqRJFixZl27ZtEfqGCwFqFwVHR0ccHR3l4YwQQgihR1LjrUfS1FwI8SOOHYOBA9W5vwHSplVrvzt1gp8deDM4OJiVK1cyfvx47t+/H2Fd7ty5GTJkCI0bN5YRrIUQcUpqvIUQhkZqvIUQIpErVQpOnoSNGyFbNnj5Enr0gMuXf37fyZIlo127dhw9epQ1a9bwyy+/6NZdu3aN5s2bkyNHDhYvXkxw2NDrQgghhBBCryTwFkIIA6DRQIMGcP06zJsH3bqBu/vn9d7eP7d/Y2NjmjRpwuXLl9m+fTtFihTRrbt37x4dO3bE2dmZ2bNny1y2QgghhBB6JoG3EEIYEFNT6NoVPDw+pz16BDlyQM2acO3az+1fo9FQq1YtTp06xcGDBylfvrxu3ePHj+nduzcODg5MmjSJt2/f/tzBRIITEhLCxo0b2bhxIyEhIfFdHCGEECLRkMBbCCEM3JEjEBICO3dC3rzQvj08fvxz+9RoNJQvX56DBw9y6tQpatasqVv38uVLBg0ahKOjI5MnT5YAPIn59OmTTKckhBBC6JkE3kIIYeBatYIbN9Sm6FotLF2q9gUfPFgdGf1nFS1alB07dnD58mWaNGmiG8361atX/P777zg6OjJlyhTevXv38wcTBs3Y2JiaNWtSs2ZNGXBPCCGE0CMJvPXAw8ODnDlz4h6+Q6YQQuiRq6s6+NqpU+pgbB8+wMSJ8MsvoK8x0fLmzcvatWu5fv06zZo1ixCADxw4EEdHR6ZOnSoBeCKm0WiwsrLCyspKphMTQggh9EgCbz2QebyFEHGlaFG16fn27ZAzJ7RuDcmSfV6vjwki3dzcWLNmDdevX6dp06a6AOzly5cMGDAAJycnpk2bJoOwCSGEEELEkATeQgiRwGg0UKsWXLkCw4Z9Tj92TB0J/ehR/RzHzc2Nv//+m3///TdCE3QfHx/69++Pk5MTM2bMICgoSD8HFPFOq9Vy584d7ty5g1arje/iCCGEEImGBN5CCJFAmZiAufnn92PGwIULUKaM2h/cy0s/x8mVKxdr167l6tWrNG7cWJf+4sUL+vXrh5OTEzNnzpQAPBHQarVcuHCBCxcuSOAthBBC6JEE3kIIkUisWgWdO4OREWzerDZFHzAA/P31s//cuXOzbt06/v33Xxo1aqRLf/78OX379sXJyYlZs2ZJAJ6AaTQaMmfOTObMmaWPtxBCCKFHEngLIUQikS4dLFgAly9DpUrqoGtTp4Krq4ZNm8y/uX1M5c6dm/Xr13P16lUaNGigS3/+/Dl9+vTB2dmZOXPm8OHDB70dU8QNY2NjSpYsScmSJWVUcyGEEEKPJPAWQohEJk8e2LtXnfc7Rw549UpDaGhsHCcPGzdu5MqVK9SvX1+X/uzZM3r16oWzszMeHh58/PhR/wcXQgghhEhAJPAWQohESKOB6tXh6lVYsUJLw4afa58PHFDnBdeXX375hU2bNnH58mXq1aunS3/69Ck9evQge/bsLF26lJCQEP0dVAghhBAiAZHAWwghEjFTU2jRQu33DfDuHbRpo87/3b07vHqlv2PlzZuXzZs3c+nSJerWratLf/jwIe3bt9c1UZdBuwxXSEgIW7duZevWrfKgRAghhNAjCbyFECIJCQyEwoUhNBTmzQMXF5g2DfTZGjxfvnxs2bKF8+fPU7VqVV367du3adKkCQUKFOCff/5B0cek40LvgoKCZIA8IYQQQs8k8NYDDw8PcubMibu7e3wXRQghvipDBtiyBQ4dgnz51BHP+/eHXLnUkdD1GQsXLFiQ3bt3c/ToUUqVKqVLv3LlCrVq1aJEiRIcPnxYfwcUP83Y2JiqVatStWpVGVxNCCGE0CONIlUOehMQEICNjQ2+vr7Y2trGd3GEEAJQ52b28fEhXbp0GBl9ft4aGgrLl8PQofD8uZp26ZIakOuboijs27ePoUOHcuHChQjrKlSowLhx4yhSpIj+DyyEMEjRXZeEECK++Pn5kTJlSvz9/bG2ttb7/uVKJ4QQSZSxMbRvD3fuqMF3mzYRg259Nj/XaDRUqVKFc+fOsWnTJnLmzKlbd/DgQYoWLUqdOnW4evWq/g4qhBBCCGEgJPAWQogkzsoKxo6FpUs/pz16BA4O6jzgwcH6O5ZGo6F+/fpcvXqVlStX4uTkpFu3fft28uXLR/Pmzbl7967+DipiTKvVcv/+fe7fvy+D4AkhhBB6JIG3EEIIQJ2CLMyff6rNzwcMUOcF37VLv8cyNjamZcuW3Lp1iwULFpApUyZAbZL+999/4+bmRocOHXj06JF+Dyy+SqvVcubMGc6cOSOBtxBCCKFHEngLIYSIZPRoWLIE0qdXm6LXqKEud+7o9zimpqZ07tyZu3fvMn36dNKkSQNAaGgof/31F9myZaN37968ePFCvwcWUdJoNGTMmJGMGTOiCf8kRgghhBA/RQJvIYQQkRgZQbt2aqDdv786H/iuXZA7Nwwfrv/jJU+enL59+3L//n3GjBmDjY0NAMHBwcyePRtnZ2eGDx+Ov7+//g8udIyNjSlbtixly5aVUc2FEEIIPZLAWwghRLSsrWHKFLh2DapXh0+f9Dvl2JesrKwYNmwY9+/fZ9CgQVhYWADw7t07xo4di7OzM9OnT+fDhw+xVwghhBBCCD2TwFsIIcQ3ubrCzp2wezcMGvQ5/fJlOH1a/8dLlSoVEyZMwMvLix49emBqagrA69ev+e2333B1dWXp0qWEhobq/+BCCCGEEHomgbcQQogYq1oVLC3V11otdOoExYqpU5E9e6b/42XIkIE5c+Zw69YtWrRooet3/N9//9G+fXt++eUXtm3bhhKb1fBJSEhICDt27GDHjh2EhITEd3GEEEKIREMCbyGEED8kKAhy5VJfr1ih1opPmqTf+b/DODk5sWrVKi5dukT16tV16Tdu3KBu3bqUKFGCo0eP6v/ASdDbt295+/ZtfBdDCCGESFQk8BZCCPFDUqRQ5/4+cwaKFIG3b9Vm6Llzw44dsdMXPG/evOzcuZMjR45QrFgxXfqpU6coU6YM1atX58qVK/o/cBJhbGxMxYoVqVixogyuJoQQQuiRBN564OHhQc6cOXF3d4/vogghRJwrXBhOnoTlyyFDBrh3D2rXVvuDx5bSpUtz4sQJtm7dSs6cOXXpu3fvJn/+/LRs2ZL79+/HXgESKY1GQ9q0aUmbNq1MJyaEEELokUaRjnF6ExAQgI2NDb6+vtja2sZ3cYQQAgCtVouPjw/p0qXDyCh2n7cGBsL48XDsGBw9qk5LBmrtd2zFcaGhoaxcuZIRI0bw33//6dLD5ggfNmwY6dOnj52DCyF+SFxel4QQIib8/PxImTIl/v7+WFtb633/cqUTQgihN1ZWMGECHDnyOeh+905tir58uTogm74ZGxvTtm1b7ty5w/Tp00mdOjUAnz59Yu7cuTg7OzNixAgCAgL0f/BERqvV8ujRIx49eoQ2Nv6zhBBCiCRKAm8hhBB6F7578Pz5cO4ctG0LJUrA+fOxc0xzc3P69u2Ll5cXw4cPJ0WKFIA6B/iYMWNwcnJixowZfIyN0d8SCa1Wy4kTJzhx4oQE3kIIIYQeSeAthBAiVvXqpY52bmmpzvlduDB07AgvX8bO8WxsbBg9ejReXl50794dExMTQJ0DvF+/fmTPnp2VK1fKHOBRkD7eQgghROyQwFsIIUSsSpYMBg6E27ehZUu1v/fixZAtG8yeHTujnwOkT5+euXPncvv2bZo3b65Lf/jwIa1bt6ZAgQLs3r1b5gAPR0Y1F0IIIWKHBN5CCCHihJ0drFwJx49Dvnzg768OwhbbFatOTk6sXr2aS5cuUbVqVV361atXqV69OuXKlePMmTOxWwghhBBCJGkSeAshhIhTYf2858+HqVM/pz9/Do8exd5x8+XLx+7duzl48CCFChXSpR85coSiRYvSsGFDbt++HXsFEEIIIUSSJYG3EEKIOGdsDF26QNasn9N++w1y5ICxY+HDh9g7dvny5Tl79izr168nW7ZsuvRNmzaRK1cuunTpwrNnz2KvAAYsNDSU3bt3s3v3bukDL4QQQuiRBN5CCCHi3YcP8N9/EBQEw4dDrlywfXvs9f/WaDQ0atSI69evM3/+fN0836GhoSxcuBBnZ2eGDh2Kv79/7BTAQCmKgp+fH35+ftL3XQghhNAjCbyFEELEO3Nzde7v1avVvuD370OdOlC9ujooW2wxNTWlS5cu3Lt3jzFjxmBlZQVAUFAQ48ePT3JTkBkbG1OuXDnKlSsng6sJIYQQeiSBtxBCCIOg0UDz5mqgPWgQmJrCnj2QJw/s2hW7x7a0tGTYsGF4eXnRp08fTE1NAXjz5o1uCrIVK1Yk+ubXGo2GDBkykCFDBplOTAghhNAjCbyFEEIYFEtLmDABrl9Xa7xTp4ZSpeLm2GnTpmXGjBncvn2bli1b6oLPhw8f0qZNG/Lnz8+uXbukGbYQQgghvosE3kIIIQxStmywcydcvAj/bwGOokC3bnD2bOwe29HRkZUrV3Lx4sUIU5D9+++/1KhRg7Jly3Lq1KnYLUQ80Gq1PHnyhCdPnqDVauO7OEIIIUSiIYG3EEIIg5Yx4+fXq1er05AVKQLt2qlTkMWmsCnIDh06hLu7uy796NGjFC9enLp163L9+vXYLUQc0mq1HD16lKNHj0rgLYQQQuiRBN564OHhQc6cOSPclAkhhNC/ChWgTRv19bJl4OqqzgUeHBy7xy1Xrhxnzpxhw4YNEaYg27ZtG7/88gvt2rXj4cOHsVuIOKDRaEiVKhWpUqWSPt5CCCGEHmkU6aimNwEBAdjY2ODr64utrW18F0cIIQC1FtPHx4d06dJhZJQ4nreeOQM9e8K5c+p7V1eYMUPtEx7bPn36xLJlyxg1ahRPnz7VpSdLloxu3boxZMgQ0qZNG/sFESIBS4zXJSFEwubn50fKlCnx9/fH2tpa7/uXK50QQogEp0gROH0ali6F9Onhzh0YOhTionW0qakpHTt25O7du0yaNEn3oDU4OJiZM2fi7OzM6NGjCQwMjP3CCCGEECJBkMBbCCFEgmRkBG3bqkF3//4we7aaBhAUBAEBsXt8CwsLBg4cyP379xk8eDDJkycHIDAwkJEjR+Ls7Mzs2bOTzBzgQgghhIieBN5CCCESNGtrmDIl4pRjkydD9uywfHns14KnTJmS8ePHc+/ePbp06YKxsTEAL1++pHfv3uTIkYOVK1cmiDnAQ0ND2b9/P/v3708Q5RVCCCESCgm8hRBCJCqhobB1qzriedu2UKyY2ic8ttnZ2TF//nxu3rxJ06ZNdene3t60bt2afPnysWPHDoOeA1xRFF69esWrV68MupxCCCFEQiOBtxBCiETF2FgNtCdPBktLdc7vokXVIPzZs9g/frZs2fj777+5cOECVapU0aVfu3aN2rVrU6pUKY4fPx77BfkBRkZGlCpVilKlSsmAV0IIIYQeyV9VIYQQiU6yZDBggNr/O2z6seXLIVs2tTY8LhQoUIA9e/Zw6NAhihQpoks/ceIEpUqVombNmly9ejVuChNDRkZG2NvbY29vL4G3EEIIoUfyV1UIIUSilTGjOt/36dNQuDB8+gR588ZtGcqVK8epU6fYvHkzbm5uuvSdO3eSL18+mjVrxu3bt+O2UEIIIYSIUxJ4CyGESPSKFIFTp9Qm6I6On9PHjYOTJ2P/+BqNhnr16nH16lWWLFlC5syZAbVP9dq1a8mZMyft27fH29s79gvzFYqi8OLFC168eCF9vIUQQgg9ksBbCCFEkmBkBPnyfX5/9iwMGwYlSkDTphAXMa+JiQnt2rXjzp07TJ8+nbRp0wKg1WpZunQprq6udOvWjSdPnsR+YaIQGhrKoUOHOHTokIxqLoQQQuiRBN5CCCGSpCxZ4NdfQaOBdesgRw4YPDj25/8GMDc3p2/fvty/f5/x48dja2sLwKdPn5g/fz4uLi789ttv+Pj4xH5hvmBtbY21tXWcH1cIIYRIzDSKtCXTm4CAAGxsbPD19dXdRAkhRHzTarX4+PiQLl06GTArCleuQL9+cOiQ+j5dOhgzRg3K/z8ld6zz8/Nj+vTpzJgxg7dv3+rSU6RIQe/evenfvz8pU6aMm8IIEQfkuiSEMDR+fn6kTJkSf3//WHkA/UOBt5OT008fuE+fPvTq1eun92NIJPAWQhgiucH9NkWBHTugf3+4exfSp1f/tbKK23K8evWKSZMmMXfuXD58+KBLt7GxoX///vTu3RuruC6UELFArktCCEMT24H3D13pvL298fX1RVGUH1oePnyIn5+fnk9FCCGE+DEaDdSuDdeuwcyZMG3a56BbUcDLK27KkSZNGqZMmcL9+/fp3r07pqamAPj7+zN8+HAcHR2ZOnUqQUFBcVMgIYQQQujFDz9i7Nu3Lw8ePPihRVq3CyGEMETJkkHv3tCixee0tWshe3bo2RNev46bcmTMmJG5c+dy9+5dfv31V4z/3+b99evXDBgwAGdnZzw8PPj48aNejyuDqwkhhBCxQ9r2CCGEEF9x/DiEhsLcueDiAjNmQHBw3Bw7a9asLF68mJs3b9K8eXM0Gg0Az549o0ePHri6urJkyRJCQkL0cjyZTkwIIYSIHT8UeN+8eZPu3bv/8EF/dnshhBAirnh4wMGDkDcv+PmpA7G5ucHff4NWGzdlyJYtG6tXr+bq1avUr19fl/7o0SN+/fVX3NzcWLZsGZ8+ffqp4xgZGVGsWDGKFSsm/W6FEEIIPZJRzfVIBlcTQhgiGcRIP0JDYdkyde7v58/VtHbtYMmSuC/LhQsXGD58OLt3746Q7uTkxJAhQ2jVqhXJkiWL+4IJEUNyXRJCGBqDHFxNCCGESGqMjdUpxu7ehbFjwdoaWrX6vD4uH2MXLFiQXbt2cfz4ccqVK6dLv3//Ph06dMDV1ZWFCxfqvQ+4EEIIIX6MBN5CCCHEd7C0hKFD4dEjCBfz8scf0LAh3L4dd2UpUaIEhw4d4ujRo1SqVEmX/vDhQ7p06UK2bNmYN29ehKnJvkZRFF6/fs3r16+lj7cQQgihR98deGu1Wq5du8bTp08jrfv06RNHjx7VS8GEEEIIQ2Zj8/n127fqoGubNkGuXNCpEzx5EndlKVWqFPv27ePkyZNUrVpVl/7ff//RvXt3nJ2dmT179jenIQsNDWXfvn3s27dPRjUXQggh9Oi7Au+HDx+SJ08efvnlFzJnzkzt2rV5HW5ulTdv3kRo8pZUeHh4kDNnTtzd3eO7KEIIIeKBpSWcOAG1aql9wRctUkdAHzQIfH3jrhzFihVj9+7dnDlzhpo1a+rSnz59Su/evXFycmLGjBm8f/8+2n1YWFhgYWERF8UVQgghkozvGlytSZMmvHnzhkWLFuHn50f//v15/Pgxhw4dws7OjhcvXpAxY0a0cTXMq4GRwdWEEIZIBjGKW8ePw++/w8mT6vuUKWHlSqhRI+7LcuHCBcaMGcO2bdsipKdLl47+/fvTtWtXLC0t475gIsmT65IQwtAY1OBqR44cYerUqTg4OJAvXz727dtHqVKlKFWqFI8ePQLQzTEqhBBCJEUlS6rB97ZtarPzt28hZ874KUvBggXZunUrly5dokGDBrp0Hx8fBg4ciKOjIxMnTiQwMDB+CiiEEEIkEd8VeL9//x4zM7PPGxsZsWjRIipXrkzp0qXx8vLSewGFEEKIhEajgdq14coVNQh3dPy8bvBg2LIlbkdBz5cvHxs3buTff/+lSZMmuofkr169YvDgwTg4ODBu3Dj8/f3jrlBCCCFEEvJdgXf27Nk5f/58pPT58+dTvXr1CP3JhBBCiKTO2BgKF/78/sIFmDgR6teH4sXh8OG4LU/u3LlZu3Yt165do3nz5romvm/evGHYsGE4OjoybNgwdu3aJYOrCSGEEHr0XYF3/fr1WbNmTZTr5s2bR9OmTWX6ESGEECIaLi4wbBhYWMDp01C+PJQtC56ecVuOnDlzsnr1am7cuEHr1q11AXhgYCD3799nzZo1dOvWjbt378ZtwYQQQohE6rsGVxNfJ4OrCSEMkQxiZHiePYOxY2HxYggOVtNKl4YVKyBr1rgvz71795gwYQKrVq0ic+bMADx48ABFUahfvz6///67zNwh9EquS0IIQ2NQg6sJIYQQ4udlzAgeHuDlBd27Q7JkcPs2pE0bP+VxcXHhr7/+4v79+9SrVw8fHx+0Wi2KorBp0yYKFy5MuXLl2LNnj7RsE0IIIX6AXgLvEydO0K9fP54/fx7l+mfPntGvXz9Onz6tj8MJIcTP02ohJESddDk0VH0vAYWIY/b2MHeuGoCvWaM2QQf169i+PRw8GLdfy0yZMjFlyhQePXrEhAkTSJ8+vW6dp6cn1apVI3/+/KxZs4aQkJC4K5gQQgiRwOmlqXmDBg24evXqV/uCubq6kj9/ftatW/ezhzNY0tRciFgQGAgPH8Lr1/DmDbx7B+/ff14aNIDs2dW8x4/DrFkR14ctQUFqhFO/vpp3xw512OnozJ8PXbqorw8dgipV1KGqwy9GRmqkNHIk9Oih5n3wQO3Ea20d9ZI7N2TLpuYNDYUPH9R9xOJUjNKkM+HZsAEaN1ZflyypfsUqVIjVrwkAiqIQEBAAgLW1NR8/fmTlypVMmTIl0t/4rFmz8ttvv9G+fXtSpEgRuwUTiY5cl4QQhia2m5qb6GMn586do0KFCl/NU7p0afbv36+PwwkhEpqw2mRjY/W9lxecPKkG02EBddjr16/VALl4cTXv339D587R7ztbts+B99OnsHFj9HnDT5X0Pc8cFUWtHY/K+/cR9/XkiVp1GZ0xY9TAHOD6dcibV/1cMmSALFnUJXNm9d+SJSF//piXUyQaxYtDz57w55/q86RKlaBECRg1KnYD8NDQUHbt2gVAo0aNMDc3p2PHjrRv356tW7cyadIkzp07B8DDhw/p1asXf/zxBz179qR79+6kSZMmdgomhBBCJHB6Cbx9fHzIlCnTV/NkyJABHx8ffRxOCGGIvLzgxAk18Hz6VP03bHn+HPbuVSMGUNvPfi2YfvLk8+u0aSF1anVJlQqsrNQa4rAlS5bPeQsWVIP28OvDluTJI45aVbWqGuQryufAOey1oqjHCVOiBPz3X+Q8Wq0aeKdL9zlv1qwwfboa5AcERF7CT+gcGKj+Gxr6+bM6derz+j/++Bx4370LNWtGDMy//DesnbJI8DJlgtmzYdAgmDQJFi5Uf16VKqlB+Y4d6s8hNpiZmUVKMzY2pkGDBtSvX58jR44wadIk9uzZA8Dr168ZNWoUkydP5tdff6Vfv344ODjETuGEEEKIBEovTc0zZMhA5cqVWbFiRbR5WrVqxZ49e3j58uXPHs5gSVNzkej4+qojPoUPosMH1X/9pdbKghoZhDXNjsq6dZ/bzh46BBMmfA6owwfWqVOrAXS4vqWJlqKoTef9/NTP9b//4NEjdfnvP2jbVg22Afbvh8qVo9/XH3/AiBHqa19fteY/b17InRutubk06Uzgnj79HIDnzatORRbbzc6/5cqVK0yePJl169ZFmPPb2NiYxo0b06dPHwqHn8RciHCkqbkQwtDEdlNzvQTedevW5eDBg9y4cUM3DUl4jx49IleuXJQvX55t27b97OEMlgTeIsEJDIT79yMu3bpBrlzq+kWLoFOn6LdftQpatFBfHzmizo+UKdPnxc7u87/p0oGpaeyfU2Ll5weXLn0Ozr/8d9YsaNdOzbtvn9onHcDICMXVlQ/Zs2NWuDBG+fJBkSLqAw6R4Dx9qjbUyJNHfe/vD02bQq9eaiOO+AjGvb29mT59OosXLyYoKCjCuqJFi9KrVy8aNmyIqfz+RTgSeAshDE2CCLyPHj1KuXLlyJQpE2PHjqVSpUpkzJiRZ8+esW/fPoYNG8azZ884dOgQpUuX1ke5DZIE3sLghDVhtrFRF1BrTocNU4PsV68ib7NiBbRqpb7eu1dtEh4+iA4fVOfJIwGcodBq1cHeADw91RYFly9DVF18/vpLHTIb1O/B8eOQLx/kyKHOayUSjLFjYfhw9XWePDBggBqIx0eM++rVKzw8PJgzZw6vX7+OsM7Ozo6uXbvSuXNn0sbXnGnCoEjgLYQwNAki8AaYNWsWv/32m25+T41Go3ttZGTEzJkz6d69uz4OZbAk8Bbx5u1buHIFbt6EGzfg1i21z7W3NwQHR6yZ3rMHqlX7vG3q1ODsDE5O6tKgARQoEC+nIWLB8+doL13i3cmTWN67h+bqVfX7ENZ3fN48dSJpUKO1nDnVtszFi0P58uDiEv9tmkW0nj2DKVPUQdjevVPTMmeGPn2gY8eIQxXERGhoKGfOnAGgSJEiGIcNiPgdgoKCWL16NbNnz+bff/+NsM7MzIxmzZrRu3dv8uXL9937FomHBN5CCEOTYAJvgKtXr7JgwQLOnTuHv78/tra2FC5cmC5dupA7d259HcZgSeAtYpWiwIsXn4Pr4sU/B09fmxrLxASmTVPbooJay330qBpkOzp+rgkXidZXb3DXrVOD78uX1cHfvrR/P1SsqL4ODf08Mr0wKL6+6gx4s2erlwmANGnUZ2/fM9NXSEgIGzZsANRRzU1MfnwMVkVR8PT0ZPbs2Wzbto0vbzdKlSpF7969qVOnzk8dRyRMEngLIQxNggq8kzoJvIVePX+uTksVFmjfvKneXYcZPfpzG1MvLyhXTq2tdHNTl2zZ1ODa3l6CpSQuRje4iqLOl37lCly8qPbZv3BB/R6GRW6//w5bt6qj05cvD2XLqtGdMBgfPsDKlTB1Kri7q40bwjx5ovYS+RqtVsudO3cAcHV11VtA9ODBAzw8PFi8eDH+4af1A7JkyUL37t3p0KEDqWJrqHZhcCTwFkIYmgQXeL9+/ZorV67g7++PjY0NefPmJXUS6QMqgbf4bu/eqYHOpUvqUqbM5/7VN2+qgXR4RkZqMO3mBs2aqYsQ3/DDN7jBwRH7fBcuDP+fw1knXz41CC9fXh3dSx7yGAStVh07MaxBy40bah/wWrXUfuAlSsRPud6+fcvKlSuZPXs2t27dirAuefLktGrVip49eyaJVnJJnQTeQghDk2ACb29vb3r37s3OnTsjNCfTaDTUrFmTmTNnJvp5PSXwFt8UEKDOB3TpklqreOfO5zmkAVq3huXL1defPkHz5mqQHVaT7eqqzkctxHfQ2w2un5/aTeHgQXVKuGvXPq9Ll06tHQ/rD37jhvqQyNz8p8ou9GPOnM+9TUDtqTJwoBqIx0fMoygK+/fvZ/bs2ezcuTPS+goVKtCrVy9q1KjxQ/3MheGTwFsIYWgSRODt5eVFiRIl8PHxIVu2bJQoUYL06dPz4sULTp48yZ07d0iXLh0nT57EyclJH+U2SBJ4C0ANpP/773MtdsaM6sjgAO/fq6MdabWf82fIoPbVzp9frTWsUCF+yi0SrVi7wX3xQh1B/eBBsLZW2zeD+hvImFEN1CtUgEaNoE4dSJlSf8cW3+3WLXW4hxUr1MYMANmzQ//+6jO/ZMnUgPj9+/cAWFhYoImDgfXu3r3L3LlzWbp0KYGBgRHWOTo60qlTJ9q1a0f69OljvSwi7kjgLYQwNAki8G7YsCFbtmxh/vz5dOzYMcIfakVR+PPPP+nWrRv169fXDdqSGEngnURpteoAVRcvfg6237z5vL5YMTh58vP7Pn3U2sGwYDtDhjgvskha4vwG99kzKFhQ/TeMqSlUqiRBuAF49kwdhG3+fHUe8LRp1e79yZPrd3C17xUQEMCyZcuYM2cO9+7di7DO1NSUevXq0aVLF8qWLRsnDwRE7JLAWwhhaBJE4J0yZUrKli3Lli1bos1Tp04djh49im/4waESGQm8k4B379Q+ri9fqgFEGHt7deSiMCYmavPw/PnVNp2dOsV9WYX4v3i5wVUUtbn5pk2wYUPEZum9e8PMmXFTDhGtwEBYtEjtDdCtm5oWHBzC2LGbyZwZ2rWrHy+jjWu1Wnbv3s2cOXPYu3dvpPWurq507tyZNm3aJJkxZBIjCbyFEIYmQQTe1tbW9OrVi7Fjx0abZ+jQocyZM4eAqKarSSQk8E5kFAXu3YPTp+HUKXX59191SqU0acDH53N/1qFD1Wa1YbXYuXJJ31ZhMAziBvfmTTUA37ABFiz4PLrXkSMwebLUhBuIXbugRg31dalS0KMH1KunNliID15eXixatIglS5bw8uXLCOvMzMxo1KgRXbp0oXjx4lILnsAYxHVJCCHCSRCBd9myZUmZMuVXa7zr1q1LQEAAhw4d+tnDGSwJvBO49+/BwuLz+5o1IYpBf7C3V5uP//WX2l9bCANncDe4ivL5oVXXrmogDtIc3QBcvgzjx8PmzeozRvg8TEWnTurr+BAcHMyWLVtYuHAhhw8fjrQ+V65cdO7cmVatWsnf3wTC4K5LQogkL0EE3idOnKBChQrMnTuXDh06RFr/559/0rt3bw4ePEjx4sV/9nAGSwLvBObBA3WE5rDa7Bs31CbkYf93AweqHSELFoSiRdVgu2hRNfAWIgEx6Bvc27fVMRK+bI4eFoSvXv35NynizJMn8Oef6iQML16oaSYm6n9R9uzxW7bbt2/z559/snTp0kjd15InT07Tpk3p0qUL7u7uUgtuwAz6uiSESJISROA9evRoTp06xb59+3B1dY0wqvmJEye4c+cOVapUoWjRohEPrtEwfPjwnz28wZDAOwE4cgSWLYPDh9XRhL506BCUK6e+9vNTRxsyM4vLEgqhdwnmBjd8c/Rr18DBAe7f/1w7fu+eOkWZIZ9DAhcaGsr58+cBKFSoEKGhxmzeDHPnwtu36tiRYf8dJ09C3ryQIkX8lDUoKIiNGzeycOFCTpw4EWl9vnz56NKlC82bN8dKWicZnARzXRJCJBkJIvD+0QumRqMhNKwtWyIggbeBefJEDbDLlIHMmdW0+fM/jyJkYgLu7mpf07DabDu7+CuvELEkQd7g3ryp/oYrVlTff/yotjaxtlbbPbdrpw7HLfTqa6OaBwSoHz+oA7NlygTGxtC+vdpjwMUlPkqsunbtGgsXLmTFihWRxpKxtLSkefPmtGnThmLFikktuIFIkNclIUSiliAC7yNHjvzwtmXKlPnZwxsMCbzj2bNn6pzChw+r/969+7/27js8iqrt4/h3k0DoofdepJtACL33KqEJiFRFQSyIj/1VsT3yWBGNWCiCWJDeezf0hF6lSQ89ocOW949jNkSKIexmd5Pf57r2MjszmblXkpO555xzH7P9m2/MXSGY3rPvvze92nXqQJYsnopWJMWkihvcqCizJnhsrHmfPj106gQDBpgqYEqmXMJut7Nr1y4Aypcvf9efly1boGNH06SC+d/fsqUpxtaypecGJVy+fJmJEyfy7bffsmHDhtv2lypVil69evH4449TsmRJD0Qo8VJFuyQiqYpPJN5iKPH2kB07zA34nj2Jt/v5QdWqZt3sHj08EpqIN0g1N7iXL5v54N9+a5b1i1e+vBnNkooe5PoCux3mzzfD0OfNS9hesiT89JNZSdGToqOj+e677/j555+5fPnybfvr1q1Lr1696NKli/5me0CqaZdEJNXw2sT7m2++oX379hQqVMjVMfksJd5udu2aKYY2bx6UKmW6VsCMf8yZ09wFVqkCDRuaHu169SAoyKMhi3iDVHmDGxVlKn/9/LNZkWDnTpOAg3mfMaN6wVPQvn3m2ceYMWZQwqFDULSo2RcTA7lymdk9nnDp0iWmTp3K+PHjWbp0Kf+87QkMDKR9+/b06tWL5s2bk85Ta6elMamyXRIRn+a1ibefnx8Wi4WqVasSHh7OI488QuXKlV0dX4qbPXs2L730Ena7nVdfffWOVdrvRom3G+zfbxLtefPMEPKrV832mjVNJfJ4y5ebKj9afkjkNqn6Bjc2FhYtgs6dE7Z17w5795q54I89pikl98HhcHD9+nXAJKT3Ox/68mVTw7J164RtbdpAdDT07m3mgz/0kCsjvj9Hjx7l559/Zty4cc4h9bfKmzcv3bt3p1evXlSpUkXzwd0oVbdLIuKTvDbxjoqKYvr06cycOZNt27ZhsVgoXry4MwmvV6+ezzWkVquVChUqsGzZMoKCgggNDWX16tXkypUrSd+vxNvFataEdesSbytYEFq1Mmtsh4d7JCwRX5OmbnAvXzbtRHyBraxZ4fHHzVzwhx/2bGw+4F7F1ZLjyhUzQOnkyYRt9eqZBLxzZ889E3E4HERHRzN+/Hh++eUXzpw5c9sxFStWpFevXvTo0UOj+9wgTbVLIuIT3J14J7ulCw0N5f3332fLli0cOHCATz/9lKJFizJixAgaN25Mvnz56Nu3L9OnT+fKlSuujNlt1q9fT8WKFSlUqBBZsmShVatWLFy40NNhpX7795tJgr17w63PgYoVM2MTGzSAYcNMNZ+jR2HUKCXdInJnmTObil+ffQZlypjy2yNHmhExtWvDzJmejjBNyZTJrNw4darp+fbzg1WrTFH6AgXg4489E5fFYiE0NJQvv/yS48ePM3PmTDp37kz69Omdx+zYsYNXX32VIkWK0Lx5cyZMmHDHueIiIiJJ4ZJHjMWLF+fFF19k2bJlxMTEMHbsWOrVq8fkyZPp2LEjuXPnpl27dowZM4ZTp0654pJ3tHLlStq1a0fBggWxWCxMnz79tmMiIiIoXrw4GTJkoEaNGqxfv9657/jx44meahcqVIhjx465Ld406+pVM3T8+efNjXHp0vDcczB+vFlCKN4nn8CZM2YY+auvmt4qDfsTkX+TKxcMGWIKLi5ZYrpWAwLM9JSdOz0dnVcLCAige/fudO/e/YF7u+OlTw8dOsDs2XDkCPz3v6bZv3QJbh0cdukSuPEW4a7SpUtHu3btmDRpEidPnuS7776jTp06zv0Oh4NFixbRs2dP8ubNS9euXZk8ebLPdCqIiIh3cPnYnpw5c9KrVy+mTp3KmTNnmDlzJj169GDjxo08+eSTFCxYkLp167r6soBZRiQ4OJiIiIg77p84cSJDhgzhnXfeITo6muDgYFq0aOHWhwHyDyNHmkJorVvDV1+ZijwBAaYg2v/+Z/bFK1pUxdFEJPksFmjcGCZNgsOH4f33E5YWBPMA8OOPE4ali9sVLAivv26m4K9YAd26JewbN86sDd6xo0nSrdaUjy9Hjhw89dRT/PHHH/z555+88847lChRwrn/ypUr/P7773Tp0oU8efLQtWtXpkyZoiRcRET+VYotJ+ZwOFi7dq1zXvidipq4ksViYdq0aYTfMiS5Ro0ahIWF8fXXXwNmflGRIkV47rnneO2111i9ejWffPIJ06ZNA2Dw4MFUr16dxx577I7XuH79urMIDZg53kWKFOHs2bOa4w1w8CBMn27W3o2fWzlvHn5t2+IoXBhatsTRsqXZ74Z5FCJi2O12Tp8+TZ48eTSXMp7DgaVGDSxRUTiyZ4dnnsHx/POQJ4+nI0uznnzSwtixCaOaChRw8Pjj8PjjDipV8lxcDoeDyMhIJkyYwNSpUzl79uxtx2TOnJk2bdrQuXNnWrVqRaZMmTwQqW9RuyQi3ubChQvkypXL+4qrebt/Jt43btwgU6ZMTJ48OVEy3rt3by5cuMCMGTOwWq2UL1+e5cuXJ6m42tChQ3n33Xdv2757926C0mJPrcNBwN69BM6dS4a5c0m3fTsAlwcO5OLbb5tjrl0j4NAhrGXLati4SAqx2+3ExsYSFBSkG9x4djsZJ00i89dfE7BvHwCODBm40qMHlwcMwF64sIcD9AybzcbevXsBeOihh/D390/R6+/ZE8Cvv2Zk0qSMnDuX8LMaHHyT2bPPemxJsng3b95k9erVzJo1i7lz53L+/PnbjsmUKRPNmjWjXbt2NG7cmIwZM3ogUu+ndklEvE1sbCzlypVT4n2//pl4x8/fXr16NbVq1XIe98orr7BixQrW/V09e+bMmfznP//Bbrfzyiuv8NRTT931Gurx/tvly1g+/BCmTcPy9w0bgMPPD+rXx9GvH/To4cEARdI29Szdg90O06dj+d//sGzcCIAjIADHu+/Ca695OLiUZ7VamTx5MgCdO3d22Tzv+3XjhhluPmGChblzoWVLmD494XZl1ixTd9OTg6Vu3rzJsmXLmDx5MtOmTePcuXO3HZM5c2batm3r7AlXEp5A7ZKIeBv1eCdTchPvB5FmlhOzWs0w8jJlzHu73UzMO3nSVNFp3txU0mnXTsM2RbyAlu1JAofDFGIbNsz8d+pU046BaePSyP83u93Otm3bAKhcubJX/LycOwfnz5tlycAUrS9VCjJkgPbtzWpxLVpAunSeizE+Cf/999/vmoRnyZKFtm3b8uijj9KyZcs0n4SrXRIRb+O1y4ndL6vVSkxMDDdv3kypSyaSO3du/P39iYmJSbQ9JiaG/PnzeyQmn3L9OsydC08+adaAqV/f3IyCuSH94AP47Tc4fdp0RfTrp6RbRHyHxQJNm8LixRAVZTK6eP/7n9m3ZEniJQ9TIT8/P4KDgwkODvaaZChnzoSkG+D4cShbFq5dg4kTzTPeggXh2Wdh7VrP/BOlS5eO5s2bM2rUKE6ePMn8+fN54oknyJEjh/OYS5cu8dtvv9GxY0dnYbbff/+dS5cupXzAIiKS4tz+V/Xs2bP06tWLXLlyUbVqVfLkycPjjz9+WwLsbunTpyc0NJQlS5Y4t9ntdpYsWZKoB1xuYbXCwoVmfe08ecwirKNHmyW+btwwvd7xnngCunZVkTQR8X1Vqyb0cNtsEBFhku6mTaFmTZg2LeHBo6S4unXNypMbN8LgwZAvn/mzFBEBtWqZfx5PSpcuHS1atGDUqFHExMQwf/58+vXrlygJv3z5Mr///jtdu3YlT548hIeH89NPP3HhwgXPBS4iIm7l1sT73Llz1K5dm0KFCnHkyBGOHTvGkSNHKF68OLVr1+b06dMuvd6lS5fYvHkzmzdvBuDgwYNs3ryZw4cPAzBkyBB++OEHxo0bx65duxg4cCCXL1+mb9++D3TdiIgIKlSoQFhY2IN+BO/yf/9nxu+NHw8XL5ouhUGDzA1oTEziLggRkdTI3x8iI013aoYMsH69We8qOBhmzEh1PeAOhwOr1YrVasWbZ6JZLBAaCl98AUePwvz5Zsh5/vxmPni8CRPg6689sz44JCTho0ePJiYmhnnz5tG3b19y3rJ05rVr15gxYwa9evUib968tGrVilGjRrn8HklERDzLrXO8X3jhBWw2m3P5rlu99NJLXLhwgdGjR7vsesuXL6dRo0a3be/duzc//vgjAF9//TWffPIJJ0+eJCQkhBEjRlCjRg2XXN+n53jv2wc//2zuWOL/f6xfb9bbfvRReOwxqF07zcxzFElNNJfSRU6dghEjTCYXG2u2vfYafPSRZ+NyIavVyqRJkwDo0qWLx4qrJZfVirPyucMBFSua3vG/a33SqZOZul+okGfjvHnzJitWrGDKlClMmzbtjqMA/fz8aNCgAZ06daJDhw4ULFjQA5G6j9olEfE27p7j7dbEu3Dhwixbtowyfxfh+v7778mRIwddunTh8OHDhISE3LEAia/yucQ7JsZMkPv5Z5NkgxkyPmqU+drhMHcxnqxYIyIPTDe4Lnb+PHzyiUnA166FChXMdpvN9JD7MF9PvG9ltZrh5xMmmGHpt6pZ08yiGjDAM7HdymazsWbNGqZMmcKUKVM4cuTIHY+rXbs2nTp1omPHjhQvXjxlg3QDtUsi4m18OvHOmDEjp06dImvWrIB5eluuXDl27tzpXFfbarW66/IpzicSb5sNfvnFJNuLF5v3YLoDmjUzxdM6d/ZsjCLiUrrBdZOLF+Hvv2+AaT/PnoX334dKlTwX1wNwOBzY/v674O/vj8Vi8XBErnHwoJn7PWUKrF5ttvXsaWZSgXnOvHevKdrmSQ6Hg40bNzqT8H1/rzH/T6GhoXTs2JGOHTtStmxZn/x3UrskIt7GpxPvKlWq8Omnn9KkSZPb9v3xxx888cQT7Nmzx12XT3Fem3g7HGZCXPzXZcrA/v3mffXqZo3trl1NhRoRSXV0g5sCTp2CIkVM4UmLxUw4HjoUSpb0dGTyD8ePw/TpZpp+nTpm26ZNpqZeuXJmOHqnThASkvCn0xMcDgfbtm1zJuE7duy443GlSpWidevWtG7dmoYNG5IhQ4YUjjR51C6JiLfx6cR7zJgxfP3116xatYrMmTM7t1+5coWGDRvSp08fnnnmGXddPsV5VeLtcJglccaONT3bW7dCYKDZ98035iaxR4+EtbhFJNXSDW4K2bkT3n7bdKuCmWzcvz+89ZZZhlG81rhx5p/q1hVPS5QwdfQ6dTKlTzz9q7Nnzx6mTp3KlClTiIqKuuMxGTNmpEmTJs5EvFixYikcZdKpXRIRb+PTiTeYAmvz5s1jyJAhlClThv379/P555/TqFEjRo4c6c5LpzivSLwvXDDDyH/4AbZsSdg+fXridWlFJM3QDW4K27gR3nzTLMcIkDEjTJ2auNy2l7Lb7Wzbtg2AypUrp6mfl9hYmD3bPDeZPx+uXk3Yt3gx3GHwnsccOnSIqVOnMmfOHFauXHnXaXsVKlSgTZs2tG7dmjp16pDOi2q2qF0SEW/j84k3wNKlS/n55585evQohQoVonv37jRr1szdl00xERERREREYLPZ2Lt3r2cS7z//NPMKJ02Ca9fMtsBA86i+Vy9zx+DDRXJEJPl0g+shy5fDG2+YstoHDsAt6zh7q9RUXO1BXL5sku+pU82c8L17E+qMvvmmed+mDbRq5flZWnFxcSxevJi5c+cyd+5cTpw4ccfjsmXLRrNmzWjTpg0tW7akgIdHYahdEhFvkyoS77QixXu8b527vXOnWTcFTFGf/v3NHMNb1goVkbRJN7ge5HCYyl7xc70dDvNAtEEDU1I7fgqQl7DZbGzevBmAkJAQ/H28Srsr2O0Jw8wdDihVyvyTxgsLM0l427ZQpYpnh6Q7HA42b97M3LlzmTNnDmvXrr3reuxVq1Z19oaHhYWl+L+12iUR8TZKvH1IiiTedjssXWqGkmfKZOZwx/vwQ9OzXaOGZyvCiIhX0Q2uF1m4EFq0MF8XLQrvvmtGJenfxSc4HLBhA8yZY17/nGpdo4ZZYc5bnDlzhoULFzJnzhzmz59/1yVcc+XKRdOmTWnevDnNmjWjSJEibo9N7ZKIeBsl3j7ErYn3iRMmyR492gxZBEif3qzF7elCbiLi1XSD60Vu3oQffzQJ97FjZltICHz2GTRu7MnIJBlOnIC5c00SvmiRWaLsm2/MPqsVunQxgxvatoXSpT0bq81mY/369cyZM4e5c+eyadOmux5bvnx5mjdvTvPmzWnQoEGiArmuonZJRLyNEm8f4pbEe8UK+OILU/Elfs3tbNnMMPL+/c0Nm4jIPegG1wtdvQpff21GKsXGmm3t2pkHrLlyeTY2SZbr1+HSpYR/vlWroH79hP0PPWSGpLdpA/XqmWfnnnT8+HHmzZvH3LlzWbx4MXFxcXc8Ln369NSpU8eZiIeEhLikHVG7JCLexmsT782bN1OpUqU0W3jlTtySeH/yCbzyivm6Th2TbHfpYoaZi4gkgW5wvdiZM6b3e+RIKFvWrEbhwb+rKq7mOidOwK+/mt7wlStND3i8rFnh+++hWzfPxXcrq9XK+vXrWbhwIQsXLmTdunXY7fY7HpsnTx6aNWvmHJZesGDBZF1T7ZKIeBuvTbz9/PwIDAykUqVKhIaGUrVqVUJDQ3n44Ye9armKlPRAibfDAWvWwJdfmsS6c2ez/dQpGDYMnnwSKlRwecwikvrpBtcH7N4NcXFQvbp5f+0ajBkDTzyRogXYlHi7R2ysGYo+Z44Zmn7qFKxbl/DPvWwZLFkCrVubeeKerml34cIFli5dysKFC1mwYAGHDh2667GVKlVy9obXq1ePTEnsGFC7JCLexqsTb39/f2zxw58Bi8VCQEAAFStWTJSMBwcHE+hllVtd6YGWE7txA37/3STcGzeabbVqmfVLRERcQDe4Pih+tFPJkvC//5lK6ClQNNPhcHD9+nUAAgMDsahQp8vZ7RAdDVWrJtTUe/JJU8IFzGIkLVuaJLxFC8id23OxgvmZ2L9/v7M3fOnSpVy8ePGOxwYGBlKvXj2aN29OixYtqFy58l1/htQuiYi38drEu2DBgsTExNC2bVu6dOnCsWPHiIqKYtOmTRw4cACHw+FsbP39/SlfvjyhoaGMGTPGpR/Am9xXj/epU/Dtt2Z44cmTZltgIPToAS+8AA8/7PZ4RSRt0A2uD/r1V3jpJTNeGcxUo88/T+gilVRl2jSYOBEWLIALFxK2WyymB3zxYnBDfbNkuXnzJuvWrXMm4hs2bLjrsPT8+fMnGpae75ZFz9UuiYi38drE+/Lly7z//vsMHz6cDBky8Pbbb/P8888TEBBAXFwc0dHRiV579+7F4XAk6iFPbe4r8W7c2IwtAyhYEJ55Bp56CvLkcXucIpK26AbXR126BJ9+Ch9/bIqxATz2GHz0kVmKTFIdq9UsRxZfKX3rVihfHnbuTDhmxAhz29CsGQQFeS7WeOfOnWPp0qUsWLCAhQsXcvjw4bseGxIS4hyWXrt2bWJjY9UuiYjX8NrEO96+ffsYMmQIs2fPpmzZsnzxxRe0bNnytuOuXLnC5s2bqV279oNczqvdNfG2WmHmTLOmSHy500mTzPIxL7xg5nOn0XnxIuJ+Srx93LFj8OabMH68qQfSoQNMneqWS9ntdnbt2gWYJaX08+JZR4+aV82a5v21a+Y24soVU4Ovdm1o0sQ8y69e3fOV0h0OB3v37nX2hi9btozLly/f8diMGTNSq1Yt2rZtS4sWLShfvrymNoiIR3l94h1vwYIFDB48mL1799KqVSu++OILypQp44pT+4zbEu/z52HUKIiIgL/+gv/+F15/3RzscKTIfD0RESXeqUR0NLz6qunyLF/ebLt4ETJmdFkldBVX827nzsEHH5ge8T17Eu/LnBkGDzb7vcX169dZs2aNs0hbdHT0XY8tXLiwsze8adOm5NKyeiKSwnwm8QbzB3vEiBG8//77XL16leeff5633nqLrFmzuuoSXs2ZeK9dS/YffzS9E1eumJ25c5sei8GDPRmiiKRBSrxTsaeeMsU4P/8cmjd/4NPZbDY2/l3os1q1avh7ury23NX+/Wbu99Kl5nXmjJmF8NprZv/JkzBwoOkNb9LEPKvx9PP+06dPs3jxYmeP+PHjx+94nMViISQkhCZNmtCkSRPq1atHZm+Z5C4iqZZPJd7xTp8+zRtvvMHYsWPJmzcvCxYsoHLlyq6+jNeJT7xjAec/VeXKJtnu3t30SoiIpDAl3qlUXByULg2nT5v37dqZKUxpbLSZmErpO3aYYejxy2r/8oup1xovf36ThMe/SpTwTKzxbDYbq1atIioqioULF7Jy5UquXbt2x2PTpUtHzZo1adq0KU2aNKF69eppdulaEXEfn0q89+3bx7Zt25yvJUuWEBsby7Rp03jkkUdcdRmvFZ94XwCCwsPN/O0GDTz/iFlE0jQl3qnYuXPw/vvw9demnki6dOZh7//9H7jhpkF8x59/wuTJpjf8jz/M/PBbTZ5sVqkDk7indNPwz3bp6tWr/PHHHyxYsIAlS5awefPmu35vlixZqF+/vrNHvHLlymrbROSBeW3ivXjxYrZv3+5Msnfu3MnVq1eJP12+fPkICQkhJCSEgQMHUjQVV2C9bR3v6GiyV6ni6bBERAAl3mnC7t3w4oswf755nzcvTJ8OtWp5NCzxDteumWrp8cPS162Dw4ehQAGzf9gwGDvW9BXEvwoXdm9M/9YunT59mmXLlrFkyRKWLFnC/v3773qu3Llz07hxY2ciXrJkSRVqE5H75rWJt5+fHxaLBYvFQunSpZ1JdpUqVQgJCUm0VmNacV/LiYmIpBAl3mnI3LkmAT9zxnR55sx5X99utVqZ+nfF9I4dO6q4Wip15QpkypTwvnVrmDcv8TElSyYk4V27QoYMro3hftulv/76y5mEL1myhJiYmLseW6xYMWcS3qRJkzR5Tyoi98+rE+906dLRqlUr6tWrR9WqValatSpB3rCopIco8RYRb6TEO425cQN27YLgYPPe4TBVt3r2hCJF7vmtqmqeNp0/b4ajr1hhXtHRZvg5mPI0Fy4kLFW2erUZUFGq1IPNpHuQdsnhcLBz505nEr58+XLi4uLuenylSpVo0qQJTZs2pX79+m65oRYR3+e1iXdoaCg7duzgxo0biYbzFC9e3JmEx7/y5MnjsoC9mRJvEfFGSrzTuOnTzdrfGTOaktf/+U/i7s5bOBwOrvy9GkemTJk0XDeNiouDyEiThF+9Cl9+mbCvQgXzXKdgwcRD08uWvb9E3JXtktVqJSoqypmIR0ZGcv369Tse6+/vT/Xq1Z2F2mrWrElgYOADXV9EUgevTbwBbt68ybZt24iOjiYqKoro6Gi2bt3qbOzi/2AXLFiQqlWrEhoayttvv+2ayL2QEm8R8UZKvNO4LVvguedg1SrzvmhR+OQT6NJFxT/lvly/Ds2amfniN28m3pcvHzz2mFnZLinc2S5dvXqV1atXs2TJEhYvXkxUVBT2+C78f8iYMaOzUFvTpk0JDg5WOymSRnl14n0nNpuNHTt2EBUV5UzGt2zZwtWrV7FYLNhsNldezqso8RYRb6TEW3A44Pff4eWX4cgRs61ePdOVqWKgcp+uXjXJd/zQ9DVrTFL+xBMwapQ5xmo172vXhoYN4aGHEj/nScl26fz58yxfvtzZI7579+67HpsrVy4aNWrkTMRLlSqlkR8iaYTPJd53Yrfb2bVrF1FRUfTq1cvdl/MYJd4i4o2UeIvTlSumt/t//zPZU4UKsG2bcy0pu93O3r17AXjooYf08yJJcv06rF8PQUHw8MNm28aNEBaWcEyBAiYBj3+VKmXn9GnPtEtHjx5l6dKlLF68mCVLlnD8+PG7Hlu0aFGaNm3qHJqeN2/eFIxURFJSqki80wol3iLijZR4y20OH4ZXX4XevaFlS7Pt5k2sN28yacYMQMXV5MEcPgw//gjLliX0iN9q2DA7vXuf+juR9cNi8czMB4fDwZ49e5xJ+LJly4iNjb3r8cHBwTRt2pRmzZpRr149Mt2lXoKI+B6fSLxjYmIYMGAAixcv5vr165QoUSLR0mJVqlRJE0s5KPEWEW+kxFuS5LPPsI0axbr//AfKl6dGjRr4+/t7OipJBa5dM2uHL1sGy5ebRHzBAjvlypl2ado0P154ARo1SugRL1nSM4m41WolOjraOSz9jz/+uGuhtvTp01OnTh1nIl61alX9zoj4MJ9IvLt06cKUKVMoWrQoJUqU4MCBAxz5ew5Z/LyYfPnyUaVKFebMmfOgl/NaSrxFxBsp8ZZ/ZbNB+fJm7W8wveBffAHlynk2LkmVrl4Ff387586ZdmnwYD+++irxMQULQv365tW1630vSe/CWK8SGRnJ4sWLWbRoEZs2beJut845cuSgcePGzqHpmh8u4lt8IvHOlSsXpUqVIjIyknTp0gFw7tw5oqOj2bRpk7Pq+YEDB7BarQ8ctLeJiIggIiICm83G3r17lXiLiFdR4i1JEhcHH3wAw4ebktUBAfDss/D225Ajh6ejk1Tm1nbp2jU/1q41veHLlpn54jduJBx76BAUK2a+jooytQJDQsyPaEo7c+YMy5YtY9GiRSxatIhDhw7d9djixYs7e8MbN25M7ty5Uy5QEblvPpN49+/fn2HDht3zuEuXLpElS5YHvZzXUo+3iHgjJd5yX/78E156CWbNMu9z5YLx46F1a8/GJanKvdqlq1fN0PSVK82a4b/+mrCvfXuYOROyZIE6dRJ6xcPCwBPLcR84cIBFixY554ifP3/+jsdZLBaqVKlCs2bNaN68OXXq1NH64SJexicS73bt2pElSxZ+vbVlTIOUeIuIN1LiLUlltVqZOXMmAI9kykTASy/Bnj2m8nn58h6OTlKT5LZLPXuaZ0L/rH8WGGjmhs+b57nl6W02G5s2bXIOS//jjz+4cWvX/S0yZcpEgwYNaN68Oc2bN6d8+fIali7iYV6ZeK9Zs4aQkBAyZswIwB9//EHr1q3ZunUrxYsXd3WMPkOJt4h4IyXeklRWq5VJkyYBf1c1dzhMJaz69RMOGj0aGjeGEiU8FKWkBg/SLtls5lnQypWwapX576lTZmn6lSsTjuvVyyxj1qAB1K0LbriPvqcrV64QGRnpHJa+efPmux5bsGBBZxLetGlT8uTJk3KBigjgpYm3n58f/v7+lClThqpVq1KlShUiIyPZtWsXkydPpmLFii4P1Bco8RYRb6TEW5LK4XAQFxcHQLZs2W7vgdu6FapUgXTpzHD01183Y35F7pMr2yWHA/buhYsXoVo1s+3cOcid2+wDs1R9lSomCW/QwCTpKV264NSpUyxZsoSFCxeycOHCe64fXrVqVQ1LF0lhXpl4v/nmm2zevJlNmzZx8uRJcyKLBYfDgZ+fH3Xq1KFJkybOpLxQoUIuD9wbKfEWEW+kxFtcZs8eeOYZWLrUvC9QAIYNg8cfN5mNSBK5u126dAmmToUVK8xr//7E+x9/HH76KT6WhEQ9pTgcDnbu3MnChQtZtGgRy5cv5+rVq3c89tZh6c2aNaNChQoali7iBl6ZeN/q5MmTbNq0yVm9fNOmTRw8eNCc/O9GIXfu3FSpUoXQ0FA+/PDDB4/aSynxFhFvpMRbXMrhgBkzTI/3gQNmW/XqMGIE1Kjh2djEZ6R0u3TsWEISvmIFvPwyPPGE2bdli6mSXrFiQo94gwaQL5/bw3K6du0aq1evdvaGb9q06a7HFipUiBYtWtCqVSuaNWtGUFBQygUqkop5feJ9J7Gxsc5kPD4h37NnD3a7HZvN5urLeQ0l3iLijZR4S1LZ7XYO/J1MlyxZ8t4/L9evm6XHPvjAdC/mzAmHD0PmzCkTrPg0T7dLDkdCEbbx46F379uPKVvWJOADB5rEPCWdOnXKWaTtXsPS/f39qV27Nq1ataJVq1YEBwerN1wkmXwy8b6Ta9eusXXrVqpXr54Sl/MIJd4i4o08fYMrvuO24mpJWSj5xAl44w0IDTXrfoPJaq5fhwwZ3Bit+DJva5dOnzaF2uJ7xLduTZgfvnAhNGtmvt66FXbvNhXU8+ZNmdhuHZa+cOFCli9fzrVr1+54bIECBWjZsqWzN1z3oyJJl2oS77RAibeIeCNvu8EV72Wz2YiMjASgTp06+Pv7J+9EM2eaJHzYMOjWTfO/5Tbe3i6dP5+QiL/3XsJAjv/8Bz77zHxdsaJJwBs1Mj3jKTVH/OrVq6xYsYJ58+Yxb948/vzzzzse5+/vT61atZy94SEhIeoNF7kHJd4+RIm3iHgjb7/BlVSoWTNYvNh8HRYGn39u1nMS+ZuvtkvDh8PYsabn+58eftjUHcyVK2Vj2r9/P/PmzWP+/PksXbr0rkXa8ufPn6g3PEdKl3UX8XJemXg/9thjdO7cmY4dOybrog/6/d5KibeIeCNfvcEVH3b1qslQ/vtfM/8boFMn+N//oFQpj4Ym3sHX26UzZ8ya4cuWmdeOHabI/7FjCXPH33gDrl0zPeL160NK1EC7du0aK1eudPaG79mz547H+fn5UatWLdq2bUu7du1UKV0EL028/fz8GDp0KG+//XayLvqg3+9tIiIiiIiIwGazsXfvXiXeIuJVfP0GV3xYTAy8/TaMGmXWbEqXziTfL77o6cjEw1Jbu3TqlCnyX7Omee9wmKrop0+b935+pgxC48bQpAnUqQOZMrk/rgMHDjB//nzmzZvH0qVLuXLlyh2PK1GiBO3ataNdu3bUr1+f9OnTuz84ES/jtYl3eHg44eHhybponz59UlXiHU893iLijVLbDa64j9VqZc6cOQC0adMmacXVkmL7djM5dsECmDMHWrd2zXnFZ6X2dslqhcmTE3rE/zkNu3Zt+LucAmCeS7n7f8O1a9dYtWqVszd89+7ddzwuW7ZstGjRgnbt2tG6dWtypfTYeREP8drEG0jWkJT4yynxFhFJGan9BldcJ1lVze/H2rVmre9b13HKnh3atUvYJmlCWmuXjh41CfiSJeb1+OPw0Udm38WLULy4ScabNDG94pUquT8R379/P7Nnz2bWrFmsWLECq9V62zF+fn7UqVPH2RtetmxZDUmXVMsrE+9x48Y98IVDQkIIDg5+4PN4EyXeIuKN0toNriSfw+Hg3LlzAOTMmdO9N9hnzkDp0hAbaybBfvYZVKnivuuJV0nL7ZLDATduQGCgeT9v3u2DQPLkSRiW3qoVFC7s3phiY2OZP38+s2bNYu7cuZw/f/6Ox5UuXdqZhNetW5d06dK5NzCRFOSVibfcmRJvEfFGafkGV7zYxYumy+/zz82a3xYL9O4NH3wAhQp5OjpxM7VLCex22Lw5oTd81Sq4dSr211/DoEHm6wsXzK9Lvnzui8dqtbJ69WpmzZrFrFmz7lqgLSgoiFatWjmHpOveV3ydEm8fosRbRLyRbnDFq/31F7z+Ovz6q3mfKRO8/LJ5xS+eLKmO2qW7u3ED1q1LSMRHjYKyZc2+b7+FgQMhOBiaN4cWLUyhtgwZ3BfPn3/+6UzCV61ahc1mu+2YdOnS0aRJEzp27Ej79u3Jmzev+wIScRMl3j5EibeIeCPd4EpS2e12Dh8+DEDRokVT9udl3ToYMgRWrwZ/f7NQcoUKKXd9SVFql5Ln5Zfh008Tb8uYERo2NIl4377uXbbs/PnzziHp8+bN48KFC7cd4+fnR926denYsSMdOnSgaNGi7gtIxIWUePsQJd4i4o10gytJ5fbiav/G4TCloPfsgf/7v4TtmzebLj4VdUo11C4l3+nTsGgRLFxoXidOmO1+fnD2rKlXCLBrF+TPDzlyuCeOmzdvEhkZyfTp05k6dSpHjhy543HVqlWjY8eOdOzYkbLxXfciXkiJtw9R4i0i3kg3uJJUNpuNFStWANCgQQP8/f09HBFmKbLgYKhVy6wBXqeOpyMSF1C75BoOh/kVWbDAVE4fPjxhX+3aZiBJWJgZkt68uVlUwB3P0xwOB1FRUUydOpUpU6awd+/eOx5XoUIFZxIeEhKiCuniVZR4+xAl3iLijXSDKz5twgTo3x+uXTPvH3nEFGXTMHSfpnbJvW7ehGrVzIyNW2XLZiqld+gAPXu659oOh4Ndu3YxdepUpk6dyqZNm+54XPHixZ1JeK1atfRzIB6nxNuHKPEWEW+kG1zxeceOwdChMGaMKQHt5wd9+phtRYp4ODhJDrVLKePo0YQh6YsWwd+rBdKxI0yZknDc6tWmZ9wdq4MdPHiQadOmMXXqVFavXs2dUo/8+fMTHh5O165dqV+/vn4mxCOUePsQJd4i4o10gyupxq5d8OabMG2aeV+oEBw86J5sQdxK7VLKs9kgOtoMSw8JgbZtzfY//4SHHjJF2Vq0gDZtzNrhefK4PoYTJ04wY8YMpk6dyrJly7BarbcdU7BgQbp27Ur37t2pVq2ahqNLivHaxLtkyZIMHjyY559/PknHv/vuu7z//vt3/AVLLZR4i4g30g2uJJXVamXBggUAtGjRIuWLqyXVmjXw6qsQHm4qoYOZ7Hr9unvXVRKXUbvkPRYsMMPOT59O2GaxQPXqJgl//HEoUcL11z137hyzZ89m6tSpLFiwgGvx00luUapUKbp160b37t2pWLGi64MQuYW7E+9kt3SHDh264xIC96LOdREREe8WFxdHXFycp8O4t1q1YMUKeOGFhG0zZ0Lp0jB6NKTih/wirtaihamMvnatWUygShXzHGvdOnj7bdi2LeHYc+fg0iXXXDdnzpz06tWL6dOnc/r0aX755RfatWtHultGsOzfv58PP/yQSpUqUblyZf773/9y4MAB1wQgksJc+ojRbrfz5ZdfcvnyZVeeVkRERFKAv78/jRs3pnHjxt5R0fxeLBaz3ne8b781c8GffBIefhimTzfZg4j8K39/U/H8/ffNcPRjx+CHH8xc8CZNEo77/HPIlctUSP/yS9i3zzXXz5IlC927d2fmzJnExMQwatQomjRpkmg0xPbt23nzzTcpVaoUNWrUYPjw4Rw/ftw1AYikAJcl3teuXaN9+/YMGTKEsLAwduzY4apTe72IiAgqVKhAWFiYp0MRERFJNovFQr58+ciXL5/vzaucNs1kBTlzmrngHTqYpcdWrfJ0ZCI+p2BB8wxryhTInDlh+7ZtcOOGKdQ2eDCUKQPlypmZH2vXmtqHDypHjhw88cQTLF68mGPHjjFixAhq1aqV6Jj169fz4osvUrhwYRo3bsz333/P2bNnH/ziIm7kksT7/PnzNGnShDlz5lCzZk327NlDjRo1GD9+vCtO7/UGDRrEzp072bBhg6dDERERSZsyZIAXX4QDB+CNNyBjRjMXvH59ePllT0cnkipMn26ea336KTRqZNYE37MHPv4YWrc2BdziuWLASf78+XnuuedYvXo1Bw8eZNiwYQQHB99yDQfLli3j6aefJn/+/LRt25YJEyZo9K14pQdOvI8dO0bdunVZs2YNb7/9NpGRkcyYMYP06dPTt29fnnrqKa5fv+6KWEVERMSN7HY7R48e5ejRo9hd0XXlCUFB8OGHZgzs00+bMbT16yfs1/BzkWSzWEwP90svwdKlcOYM/PYbdO0KPXokLDDgcJjK6T16wOTJrpkXXrx4cV599VU2b97Mzp07eeuttyhdurRzv9VqZc6cOfTs2ZMCBQrw5JNP8scff6jGlHiNZFc19/Pzo2vXrqxevZpjx47x1VdfMXDgQOf+Q4cO0blzZzZt2kRwcDAVK1bkl19+wXbro7BURlXNRcQbqXqwJJXVamXSpEkAdOnSxXurmt+PgweheHGTMQAMG2bGxL7zjqkiJR6hdil1i46G0NCE94GB0KyZmQHSrp3rlipzOBxER0fz66+/MnHiRI4ePXrbMaVLl6ZPnz706tWLIkWKuObCkip57XJifn5+WCwW0qdPz4QJE+jUqdNtx9y4cYPnn3+e77//3jlXTIm3iEjK0g2uJJXNZmPp0qUAvlFg7X7duAGFCyesmxQebhLwkBBPRpUmqV1K3Ww283xr+nRTfmH//oR9fn5mqPqLL7r2mna7ncjISMaPH8/EiRO5ePFiov0Wi4VmzZrRp08fwsPDyZgxo2sDEJ/n1Yl3tmzZmDFjBg0aNLjnsRMmTGDAgAFcvXpVibeISArTDa7ILXbvhvfeM+Nj42+BOnQwCfgtc0fFvdQupR0OB+zYkZCER0fDkiXQuLHZv3EjzJljfg0rV04YnPIgLl++zLRp0xg7dqzzYeKtgoKC6N69O3369KF69eq+V0xS3MJrE++//vqLbNmykSNHjiQdf/78eeLi4ihWrFhyLucTlHiLiDfSDa7IHezaZRLwiRMTEvDhwxOvDS5uo3Yp7Tp8GAoUSJgPPniwWZoMoGxZ6NbNvMqVc831/vrrL8aNG8ePP/7IwYMHb9tfvnx5+vTp45wbLmmX1ybeyXH9+nUCAwNT6nIpTom3iHgj3eCK3MOOHWbx4ilTzFpJ8Xf7NlvidcLFpdQuSbxp0+DHH2HBAri1HnNwsCna9uKLZtGCB2W321m1ahVjx45l0qRJXLlyJdF+f39/WrZsSZ8+fWjXrl2qzlnkzlJF4h0dHc3o0aP57bffUvUae0q8RcQb6QZXkspms7F48WIAmjZtmvrmeN/L8eNm8eJ4ffrAlStmCHrFih4LK7VSuyT/FBcHM2aYQSgLFoDVCoUKmR7y+B+RS5cgS5YHv9bFixeZPHkyP/74IytXrrxtf86cOXnsscd46qmnqFy58oNfUHyCzybeFy5cYMKECYwePZqtW7ficDiwWCya4y0iksJ0gytJlSqrmifHyZNQpIi587dYoEsXk4BXqODpyFINtUtyL+fOmZ5wux369zfbrFZTG7FMGTMUvXNnyJfvwa+1f/9+xo0bx7hx4zh8+PBt++vWrcuAAQPo3LmzesFTOZ9LvBcvXszo0aOZMWOGc/3uBg0aEBcXx6ZNm5R4i4ikMN3gSlLZ7XZOnDgBQIECBdL2z8u2bWYO+OTJ5n18Av7aa1qGzAXULsn9WrcOatZMeO/nB40amSS8Y0fImfPBzm+321m6dCk//vgjU6ZM4dq1a4n2586dm379+vH0009TsmTJB7uYeCWfSLyPHDnC2LFjGTt2LIcPH8bhcFChQgV69uxJjx49KFy4MP3792fMmDFKvEVEUphucEUewNatJgGfMiVh2y+/QPfunospFVC7JMlx5AhMmmSGo69fn7A9IAC+/RaeeMI114mNjWXChAmMHDmSHTt2JNpnsVho0aIFAwcOpE2bNmlrSk4q5+7EO9kt3c2bN5k0aRItW7akZMmSDB06lOvXrzN48GCio6PZvn07r776KoULF3ZlvCIiIiIp5+GHTa/3li3w2GOmW61164T9J06Y8bAi4nZFisCQIab3e/9++O9/TRE2qzXxQJRt28w88eT29wUFBTFo0CC2bdvGypUr6d69O+n+LsPucDiYP38+7du3p0SJEnzwwQecPHnSBZ9OUrtk93jnyZOHc+fOkSlTJsLDw+nZsydNmza961NL9XiLiHiGepYkqRwOBzExMQDky5dPa9veycWLkDWr+drhgOrVzbZXXoEePUBzQJNE7ZK40t69Zu53fJPVpw+MG2fqJfbsCb17Q/nyD3aNU6dOMWbMGL777jsOHTqUaF9AQADh4eEMHDiQRo0aqe30UV7b43327FmCgoIYMWIEo0aNonnz5mo4RUREfJjNZmPZsmUsW7YsVT8ofyDxSTfAX3/Bvn2wZ48Z41qyJHz2mUnERSTFPPRQQtINkD+/GZxy/Dj873+mLmLNmmY4+vnzybtG3rx5ee2119i3bx9z5syhbdu2zgTbarUyefJkmjRpQvny5Rk+fDjnk3shSbWSnSn36dOHmzdv8uSTT5I/f3769+/PihUrXBmbiIiIpCCLxUL27NnJnj27emySonhxs9bRp5+arrXjx+E//4GiReH//g9OnfJ0hCJp0rBh5tdxyhRo1w78/c3w9IEDoX79Bzu3v78/rVu3ZtasWRw8eJA33niDvHnzOvfv2bOHF198kUKFCtGvXz82bNjwgJ9GUotkJ95jxozhxIkTfPfdd5QrV47Ro0fTuHFjihUrxptvvsnOnTtdGaeIiIi4mb+/P61ataJVq1YqGJRUWbPCSy/BgQMwapTpertwAT78ECIjPR2dSJoVGGiqnc+cCceOmcEolSubKujxrl2DN9+E5KYtxYoV48MPP+TIkSP89ttvNGjQwLnv6tWrjB07lurVq1O7dm2mTJmikURpnMuWE9u1axejRo1iwoQJnD59GovFQpUqVejZsyfdunXj//7v/zTHW0TEAzSXUiQF2WwwY4Ypvfzzz2bNI4BZs6BYMVOsTdQuiUc4HOZXNCDAvP/9d+ja1XwdFmbmhnfr9mBLk+3cuZNvv/2WcePGERcXl2hfiRIlGDx4MP369SNLlizJv4i4hU8sJ3Yrq9XK9OnTGT16NIsWLcLhcODv70/WrFm5cOGCEm8RkRSmG1wRD7t61QxLP3UKWrWCV181413T8HB+tUviDVavho8/hjlzTGV0gPTpoX17k4S3aGGGqSfH5cuX+fXXX/nyyy/Zvn17on1BQUE8/fTTPPfcc1oByov4XOJ9q6NHjzJ27Fh+/PFHDh48iMViUeItIpLCdIMrSRVfXA2gUaNGGm7uKjEx8MILphc8fumxqlXNukhdupg7/TRG7ZJ4k1On4Jdf4McfzcqB8bZvh4oVH+zcDoeDRYsW8dlnn7Fw4cJE+wICAujWrRtDhgyhyq3roYlHeG1V86QoXLgwb731Fvv372fRokV0u3VShYiIiHgVh8PB6dOnOX36NG58Lp/25MsHv/1mqp8PGAAZMkB0NDz+OJQoAfPmeTpCkTQtb14YPBg2b4ZNm8xzsvDwxEn3V1/BkiVmuPr9sFgsNG/enAULFrBt2zb69u1L+r8ftlmtViZMmEDVqlVp3Lgxc+bMwR7/cE5SHbf2eKc16vEWEW+kniVJKrvdztGjRwHz8Fw/L25y5gx89x18/TWcPJm4W81mS/7YVh+idkl8yenTULgw3Lhh6ic+/bQZip7cueAnT57k66+/ZuTIkZw7dy7RvnLlyvHiiy/Ss2dPMmbM+ODBS5L5dI93WhEREUGFChUICwvzdCgiIiLJ5ufnR9GiRSlatKiSIXfKnduUUj50CObPT9yt1q8fPPIILF9+/11rIuIWVis88QRkyQJ795qFDAoWhN69Yc2a+/9VzZ8/Px988AFHjhzhm2++oUyZMs59u3fv5umnn6ZYsWIMHTqUU1qWMNVQj7cLqcdbRLyRepZEfMT585A/v+lWAwgJMfPAu3ZNdfPA1S6JL7p4EX79FUaONMPS4/3wAzz5ZPLPa7fbmT17Np999hkrV65MtC8wMJCePXsyZMgQypcvn/yLyL9Sj7eIiIikCM3x9rAcOWDrVhg4EDJmNHf2vXqZiugffQT/GJIqIikra1Z46ilTomHtWjPcPCjIVEGPt3594gJtSeHn58cjjzzCihUr2LBhA926dXMWt7x+/TqjRo2iQoUKhIeHExUV5boPJClKibeIiIgApqr54sWLWbx4capehcSrlS0L33wDR47Af/8LBQrAiRPwxhvw/feejk5EMCsB1qgBY8eaX888eRL2/ec/ZrBK7dowfrxZTfB+VKtWjV9//ZUDBw7w0ksvJep5nTFjBtWqVaNt27asX7/eNR9GUowSbxEREXHKkiULWbJk8XQYkisXvP66mQf+009Qp47paov3xx+weLHmgYt42K31z27cMM/KAgLM3O/evU1RtjfegOPH7++8RYsW5dNPP+XIkSN89tlnFCxY0Llvzpw51KhRg5YtW7J69WoXfRJxN83xdiHN8RYRb6S5lCKpUO3a5s4+vsRy794mWfcRapckNTt5EsaMMYNU/vrLbEuXDt55x9RVTI5r164xZswYhg0bxpEjRxLta9KkCW+//Tb169d/wMjTNs3xFhEREZEEN25A1apmwml8ieVChcx88MhI9YKLeFj+/KaXe/9+mDYN6tWDmzehdOmEY27cgPtZsjtDhgw888wz7Nu3j++//57ixYs79y1ZsoQGDRrQsGFDli5dqhodXkqJt4iIiIgvSZ/erAF+/LjpUqtSBa5fN0PS69ZNPCRdRDzG3x/Cw2HlStiwATp1Stj35ZdmJcHvv7+/eeDp06enf//+7N27lzFjxlCqVCnnvhUrVtCkSRPq1avHwoULlYB7GSXeIiIiApjiasuXL2f58uUqruYLsmSB/v0hKsqUUu7Xz0w4bd484ZgzZ8x+EfGoatXM3G8wg1LGjYPdu81MkaJFzTD0mJikny9dunT07duX3bt3M378eB566CHnvsjISFq0aEHNmjWZM2eOEnAvocRbREREALOc2IkTJzhx4oRu1HyJxQJhYTB6tOkFDw9P2PfDD+aOP37/5cseC1NEDIsFVq+Gzz+HYsXM87H33jNfP/kk7NiR9HMFBATQs2dPdu7cyS+//JJore/169fTtm1bqlWrxowZM9Sue5gSbxEREQHMWrI1atSgRo0aKnjlq7JnN1Wc4p0/b4amb9xo7ugLFoTnnoPt2z0WoohAtmzw4ouwbx/8/rtZnuz6dfN87MMP7/98/v7+dO/ene3bt/P7779TuXJl577o6GjCw8OpUqUKU6ZMwX4/k8vFZfRXVURERACTeJcsWZKSJUsq8U4tPv4Yjh41/y1VCuLizPzwypWhWTMVYhPxsIAA6NLFLFIQGWnmgQ8ZkrD/zz/NeuHXryftfH5+fnTp0oXNmzczbdo0qlSp4ty3ZcsWOnfuTLVq1Vi0aJGLP4n8G/1VFREREUnN8uSBl182FdAXLoSOHU3Vp4IFzZjXeDt3ei5GkTTOYjGrBE6ebGaHxPv0U1O+oXhx83VSZ4v4+fkRHh5OVFQUs2bNIiwszLlv06ZNNG/enObNmxMdHe3aDyJ3pcRbREREADPH+/z585w/f15zAVMjPz/Tyz1lChw+DO++m7Bv82ZTYjkkBEaMMJNORcTjKlY0qwWePGmenxUvDsOGwcWLSft+i8VC27ZtWbduHXPnzk3UA75o0SJCQ0Pp0aMHBw8edM8HECcl3iIiIgKYqubz589n/vz5qmqe2hUsaO7g423ebOaCb9kCL7xg9nfuDHPmgNXqqShF0rznn4eDB2HMGDNb5MwZeP118+s7fHjSz2OxWGjVqhUbN27k559/TrQO+C+//ELZsmUZPHgwp0+fdvVHkL8p8RYRERGnjBkzkjFjRk+HISmtTx84ccLM/65aFW7eND3jbduatY527/Z0hCJpVrp00Lev+TUcPx4eegjOnYOzZ+//XH5+fjz22GPs3r2b4cOHkytXLgBu3rzJl19+SalSpfjwww+5rBUQXM7i0Fgyl4mLiyMoKIjz58+TPXt2T4cjIgKA3W7n1KlT5M2bVwWzRCRptm41FZ0mTDDzwY8cSaiWHhUFZcqYsszJpHZJJPlsNlMJvXlz+DtvZtkyWLzYVErPnTvp54qNjeWTTz7h888/5+rVq87tBQoUYOjQofTr14+A+AXIU7kLFy6QI0cOYmNjyfYA7dvdKPF2ISXeIuKNdIMrIsl244Ypq1yxonlvs5kxrufOmfLL/fpB/fpm/vh9ULsk4joOB9SrZ6qiZ84MgwbBSy9B3rxJP8fx48d59913GT16dKKpRuXKleOjjz6iffv2WG4txpgKuTvxVksnIiIiIneWPn1C0g1w7BhkyQJXrsBPP0GjRlC6NLz3Hvz1l+fiFEnjXn7ZzBK5fNmsHli8uFmW7MSJpH1/wYIF+e6779i+fTsdOnRwbt+9ezcdOnSgbt26REZGuif4NEKJt4iIiACmuNoff/zBH3/8oeJqcmdFi5plx1avhv79IWtWU/npnXfMnf4XX3g6QpE0x2KB9u1h40aYPRvCwuDqVfPrWKIEfPJJ0s9Vrlw5pk6dSmRkJHXq1HFuX716NXXr1qV9+/bs1NKDyaLEW0RERACznNiRI0c4cuSIlhOTu7NYoFYt+P570502frzp+QazEHG8nTvNuuF6iCOSIiwWaNMG1q2D+fPNr+n162aRgvtVu3ZtVq1axYwZMyhfvrxz+8yZM6lcuTJPPvkkJ5LanS6AEm8RERH5m5+fH6GhoYSGhmrerSRN5szQsycsXWrWBq9ePWHf8OHQooXpJX/1Vdixw2NhiqQlFov51YuMhOXLoWvXhH3ffgvPPgunTiXlPBYeeeQRtm7dyqhRoyj4dwZvt9sZPXo0ZcuW5csvv8SqJQeTRMXVXEjF1UTEG6mIkYh4xOuvm17xc+cStlWtCr16Ye/alVOgdkkkBd28aYaex5dqePllMw88S5akff+VK1cYMWIEw4YNIzY21rk9ODiYb775htq3jnjxQSquJiIiIiK+56OP4PhxmDoVwsPNcmTR0TB4MJZ69UwpZhFJMenSwbhxEBoKly6Z0gylS8PIkSYp/zeZMmXitddeY9++ffTv39+5fcuWLdSpU4cnnniC06dPu/ET+DYl3iIiIgKYOd4XL17k4sWLmuMtrhEYCB06wLRpJgn/+mszHL1bNzMeFsySZS+8YMbF6udOxK2aNIH16+G336BkSYiJgWeeMYsXrFiRtHPkzp2b77//njVr1lClShXn9jFjxlC2bFm+++477Ha7mz6B71LiLSIiIoCpaj579mxmz56tqubierlzmwWG163D8fbbCdvnzYMRI6BuXShTBoYOhT17PBamSGrn52fmfe/aBV99BXnywJ9/QqZM93eemjVrsmHDBr766iuCgoIAOH/+PAMGDKBmzZpERUW5IXrfpcRbREREnNKlS0e6dOk8HYakdv7+CV8XLw69e5tCbfv3w7vvQrlyZj74J5+Ahq6KuEX69KbQ2v79pgc8LCxh39ixsG3bv5/D39+fZ599lj179tCzZ0/n9g0bNhAWFsagQYM4f/68G6L3PSqu5kIqriYi3kjF1UTE29yxXbp82cwH//XXxMuQbd9uxsGabzTddSLiNocOQdmyZt53797w3ntQpEjSvnfFihU888wzidb6zpMnD5988gm9evXCEj/FxAupuJqIiIiIpH7xS5PNnQsnT5qKT/36JSTdAL16mXWSxo6FCxc8FqpIaubnB488Ykou/PijmQHyyiuQlI7rBg0asHnzZj755BMyZ84MwOnTp+nTpw/169dnW1K60VMp9Xi7kHq8RcQbqcdbRLxNstqlq1fNPPErV8z79OmhdWtTqK1du/ufoCoi97RuHbz6akLRtezZ4Y034LnnIEOGf//+o0eP8uKLLzJ58mTnNn9/f1544QWGDh1K1qxZ3RN4MqnHO4V16NCBHDly0LlzZ0+HIiIikqJsNhtr165l7dq1Kq4m3idjRtiyBd5/HypUMNXQp083iXfevHBrwTYReWA1asCyZTBnDlSqZAaZDB2a9MEmhQsXZtKkScyfP5/SpUsD5u/M559/Trly5fj999/T1AoaSrz/4YUXXmD8+PGeDkNERCTFORwODh48yMGDB9PUzZD4kNKl4f/+z8z73roVXn8dSpQw88P/HtYKmEWKlywBq9VzsYqkAhaLGViyebMZdv7RR5A/f8L+Q4f+/RwtWrRg27ZtvPfee2T4u6v8+PHjdO3alebNm7N//353hO51lHj/Q8OGDb1u2IOIiEhK8PPzIyQkhJCQEE1LEO9msUDlyvDf/5qSzGvXmipQ8WbMgKZNoVAhs0jx8uUJxdpE5L75+5tfseefT9i2YgWUKmVWCfy3+d8ZMmTgrbfeYseOHbRp08a5ffHixVSpUoVJkya5KXLv4VN/VVeuXEm7du0oWLAgFouF6dOn33ZMREQExYsXJ0OGDNSoUYP169enfKAiIiI+yM/Pj/Lly1O+fHkl3uI7LBYzJvbWbrgLFyBnTjh1yhRpa9TIJOHPPgurVpnq6CLyQBYvNr9K33xjqqCPH28Kst1LyZIlmTVrFtOnT6dYsWIAXLx4kUcffZRnn32Wa9eupUDknuFTf1UvX75McHAwERERd9w/ceJEhgwZwjvvvEN0dDTBwcG0aNGCU6dOOY8JCQmhUqVKt72OHz+eUh9DRERERNxp0CBTGX3ePOjb11SFiomBiAioXx903yfywN5/H5YuhfLl4fRp0yPeoMG/r/9tsVho374927dvp0ePHs7tERER1KlTJ9UOPffZquYWi4Vp06YRHh7u3FajRg3CwsL4+uuvAVMxs0iRIjz33HO89tprST738uXL+frrrxNV4EsKVTUXEW+kquaSVA6Hg6tXrwKQMWNGr15vVXxbirdLN26Y7rnffzcZwpw5Cfv69YOgIHj0UahZ0/Sgi0iS3bgBw4fDu++aRQf8/U1S/vrr//69DoeD0aNH89xzzzl7u7Nly8aoUaPo0qWLewP/B3dXNQ9w+Rk95MaNG0RFRfH6Lf/Cfn5+NG3alDVr1rjlmtevX+f69evO93FxcYD5Y2LXECYR8RJ2ux2Hw6F2Sf6V1Wp1TuPq3LkzAQGp5jZBvEyKt0sBAdCypXmZAMx/z57F8tNPWKxWGD4cR9Gi0Lkzji5dICxMSbhIEgQEwH/+Y55dDRliYdo0C0WL2pM8o6Nfv35Uq1aNrl27snfvXuLi4nj00Ud55pln+PTTTwkMDHTvB/ibu9ujVPMX9cyZM9hsNvLly5doe758+di9e3eSz9O0aVO2bNnC5cuXnSXwa9WqdcdjP/roI959993btp8+fZobN27c3wcQEXETu91ObGwsDodDPd5yT1arlZs3bwJw6tQpJd7iNl7TLt24QeCoUWSYOZPABQvwO3wYPv8cy+efYy1ShMsvvMDVW4bCisjdZchg5nv37ZuOatVuEj/bd9Wq9BQsaKNUqbsXOMyfPz9z5szhlVdeYdq0aQB88803rFq1iu+//57ixYu7Pf7Y2Fi3nj/VDDU/fvw4hQoVYvXq1YkS5VdeeYUVK1awbt06l8dwpx7vIkWKcPbsWQ01FxGvYbfbOX36NHny5FHiLSJewSvbpatXYf58LJMmwezZWC5fxh4RAQMGmP1nz8K+fVC9unrCRZLowgWoUMHC+fOmV/z11x1kynT34x0OBz/88AODBw925lnZsmXjhx9+oHPnzm6O9QK5cuXSUPN/kzt3bvz9/YmJiUm0PSYmhvy3Vrl0ocDAwDsOffDz8/OePyIiIpiHlWqbRMSbeF27lDkzdOpkXleuwLx5+DVoAPHxTZpkirYVKQKdO5tXzZoJ+0XkNteuQdWqps7hf/8LP/9sYcQIeOSRu3/PgAEDqFWrFl26dOHPP/8kLi6Orl278uyzz7p16Lm726JU01KkT5+e0NBQlixZ4txmt9tZsmTJXYeKi4iIiIjcJlMmk4Dnzp2w7cIFk5wfOQJffAF16kDRovDCC1qiTOQuChY0tQynTjXPrP76C9q3h3bt4ODBu39fcHAwUVFRdOvWzbnt66+/pk6dOhw4cCAFInc9n0q8L126xObNm9m8eTMABw8eZPPmzRw+fBiAIUOG8MMPPzBu3Dh27drFwIEDuXz5Mn379vVg1CIiIr7BZrOxYcMGNmzYgM1297l4ImnSG2+YiujTpkGPHpA1Kxw7BiNGQOPGJjEXkdtYLNChA+zaBa++aoqxzZ4NFSvee2W/rFmz8ssvv/Ddd985e7mjoqKoUqUKU6ZMSaHoXcen5ngvX76cRo0a3ba9d+/e/Pjjj4B5EvLJJ59w8uRJQkJCGDFiBDVq1HBrXBEREURERGCz2di7d6+WExMRr6LlxCSprFYrkyZNAqBLly4qriZukyrapWvXYNEimDzZrKf0668J+zp0gAIFoEsXqFfPZBoiApgEfNAgM2Dk7xTuX23evJlHH32UP//807nt+eef5+OPP3bZ0HN3LyfmU4m3t9M63iLijVLFDa6kCLvdzo4dOwCoWLGifl7EbVJ1u3TkiMko4uXJAx07mjnhDRpAunSei03ESzgc5tlVxozm/cmTsGGDGYJ+N3FxcTz11FNMnDjRua1atWpMnDiRkiVLPnBM7k68U1lLJyIiIsnl5+dH5cqVqVy5cupLhkRSSv78ppLUE09AzpxmePp330GzZmbfN994OkIRj7NYEpJum83M3njkERgyxAwguZNs2bLx66+/MnLkSGcv98aNG6latSpTp05NociTT39VRURERERcJV06aNkSRo0y3XgLF0L//qZQ27lzcOuoyCNHzJzxq1c9Fq6Ip9ntEBJivv7iC6hf3xRhuxOLxcKAAQNYs2YNpUuXBsz62506deLZZ5/lqhf/LinxFhEREcCsn3rjxg1u3LiBZqKJuEC6dKan+/vv4cQJWLYs8Vjan382w9Bz54ZHH4WJE+HSJc/FK+IB6dLBZ5/B9OnmudS6dSYRnznz7t9TpUoVoqKiePTRR53bIiIiCA0NZdOmTe4OOVmUeIuIiAhgqppPmTKFKVOmqKq5iKsFBEDDhqYaerwsWaBYMbNu+KRJ0K2bmRMeHg4//WQmwYqkEe3bw6ZNUL26WSSgfXt46aV7Dz3/7bffGDlyJBkyZABg165d1KhRg48//tjr/o4p8RYRERER8YRnnzWLGW/YAK+9BqVLm2R7xgwYONBUoIp386bn4hRJIcWLw6pV8OKL5v2cOXdPvCFh6Hl0dDRVqlQB4ObNm7z66qs0bdrUuey0N1BVcxfQcmIi4s1SdfVgcSmHw+EcYm6xWLBYLB6OSFIrtUt34XDAtm0wZYpJwP/3v4R9wcGmN7xTJ7NcWf78notTJAVMnw4lS8LDDyft+Bs3bvD222/z8ccfO/+WBQUF8e2339KtW7d//X4tJ+ZDtJyYiHgj3eCKiLdRu3SfDhyAUqUS3lssZn3wzp3NHPFChTwXm0gK+eILOHYMPvro3qvyrVixgp49e3LkyBHnth49evD111/fM0fTcmIiIiIiImlZyZKwf7/pAa9e3fSMr1wJzz8PhQvD++97OkIRtzp8GF591RRhq1/fvL+bBg0asHXrVrp37+7c9vPPPxMcHMzKlStTINo7U+ItIiIigOmF3LRpE5s2bcJut3s6HBG5VcmS8MorpuTzoUPw+edQu7bZ9/fcVgB27oRPPjFzx0VSiaJF4fffISgI1q41Vc9nz7778dmzZ+eXX37h559/dvZeHz58mIYNG/L6669z414Tx91EibeIiIgAJvHevXs3u3fvVuIt4s2KFTPVpyIj4ehRs2RZvAkTTIJesiRUqwbDhsG+fZ6LVcRFwsNN1fOwMDh/3qzM9/LL9647+Nhjj7F161bq168PmFomw4YNo1atWuzevTtlAv+bEm8REREBwM/Pj3LlylGuXDnNuxXxFYUKQWBgwvuQEGjcGPz8ICoKXn8dypQx2z/4AC5f9lSkIg+sRAn44w944QXz/tNPzXOnez0rLlasGEuXLmXYsGGk+3tyeHR0NFWrVmXkyJGkVMkzFVdzIRVXExFvpCJGIuJt1C6lgNOnTVnoSZNg6VKw2SBXLjh50qwpDnD2rNkm4oOmTYO+feGddxKWH/s30dHR9OjRI1Fvd5s2bRg9ejSBgYEqrubtIiIiqFChAmFhYZ4ORURERETELD3Wvz8sXAgxMTB6tMlQ4pNuu93MDa9UCd57D1J42K3Ig+rQwZQ0GDw4Yduff8KlS3f/nqpVqxIVFcUzzzzj3DZnzhwqV67M/Pnz3Rcs6vF2KfV4i4g3Us+SJJXW8ZaUonbJC+zebRZIvnWCbOXK8Oij5vXQQ56LTSQZLl+GqlXNM6WffzYLANzL3Llz6du3L6dOnUq0XT3eIiIi4lY2m42JEycyceJEbDabp8MREXcqVw5OnYIff4RWrUxP+LZt8NZbULYsfPihpyMUuS+HDsGVK6aWYO3aZpU9q/Xux7du3Zpt27bRrl27FIlPibeIiIiISFqUPTv07g1z5yYMR2/RAvz9oV69hOOioswa4gcOeCxUkX9TsSJs3Qpdu5qSBm+/DQ0a3PvHNm/evMyYMYPvvvuOjBkzujU+DTV3IQ01FxFvpCGdklQOh4Obfw87TZcunYaai9uoXfJyZ8+apNzf37x/9lmIiDBfV6tmhqJ36QLFi3sqQpG7cjjMUPNBgyAuDrJmha++gl694F5/1jZu3EhYWJiGmouIiIh7WSwW0qdPT/r06ZV0i6RluXIlJN0AdepAkyZmibKNG8064SVKQI0a8NlncP2652IV+QeLBR5/HLZsMQM3Ll6EX3/99+8rXbq0W+NS4i0iIiIiInfXvTssXgwnTsDIkdCokUnC16+HESPg77WRAbh2zXNxityieHFYtgw++QTGjk3o7fbUeO8Az1xWREREvI3dbmfHjh0AVKxYUUOARSSxvHlhwADzOnkSpkyB9OlNEg6mQnqJEhASYpL18HBww5BdkaTy94f//CfxtqefNsPPP/wQMmRIuViUeLtAREQEERERqgArIiI+zW63s337dgDKly+vxFtE7i5/fjOJ9lZr1piEfP5888qQAdq0MUl469bg5uJVIv9m82b44Qfz9aJF8MsvZin7lKC/qC4waNAgdu7cyYYNGzwdioiISLJZLBZKly5N6dKlNcdbRO5f/fqwZw8MHWqWJLt2zfSKd+4M+fLB7797OkJJ40JCYOZMyJPHrJ5XrRp8+aVZ+9vdlHiLiIgIAP7+/oSFhREWFob/rYWVRESS6qGH4J13YNcu2LTJFGIrWtRUuCpbNuG4rVth5cqUyXhEbtGunUm627QxdQEHD4aWLU0JA3dS4i0iIiIiIq5lsZjuxf/9Dw4eNIXYHn44Yf/HH5tFlosVM5Nwo6M9V/VK0px8+WDWLPjmGzMDYtEiCA9370gvJd4iIiIiIuI+fn4QFpZ4EeWcOU3htaNHzZJkoaFmsu2wYXDkiOdilTTDYoGBA80zn9BQGDrUvQ9+LA6HHi25SlxcHEFBQZw/f57s2bN7OhwREcAUzDp16hR58+ZVsSy5J6vVyuTJkwHo3LkzAQGqwSruoXZJADMHfN48s8jyrFkJS5FVqAB/r7AgkhJsNrh48QI5cuQgNjaWbG6oxq+WTkRERJwcDgd6Ji8iKSJDBujQwRRdO3kSRo0yBdp69kw45upVePJJs464VhASN0mJsibq8XYh9XiLiDdSz5IklcPh4OrVqwBkzJhRlc3FbdQuyT05HAnD0n/7zSxHBlCwIDz+OPTqBRUrei4+SZUuXFCPt4iIiKQAi8VCpkyZyJQpk5JuEfGcW9ufSpXMRNwcOeD4cVOUrVIlMyn3yy/h/HnPxSlyH5R4i4iIiIiId6pUyZSePnHCrAnevj0EBJiKWIMHw7lzno5QJElUNcUFIiIiiIiIwKZ5JyIi4sPsdjt79uwBoGzZshoCLCLeIzAQOnY0rzNnYOJEsxhzqVIJx/TtC+nTwxNP3F5FXcTDNMfbhTTHW0S8keZSSlJZrVYmTZoEQJcuXVTVXNxG7ZK43NmzUKAA3Lxp3leuDP37Q48eZukykX+hOd4iIiKSIiwWCyVKlKBEiRKa4y0iviVHDpg/3xRfy5DB9IY//3xCQbb16z0doaRxSrxFREQEAH9/f2rWrEnNmjXxT4m1VUREXMXPDxo3hp9+MkXYvvoKHn4Yrl+Hn3+G1as9HaGkcUq8RUREREQk9ciRA559FjZvNj3dTz+deG3wCROgUyeYN09rg0uK0eQtERERERFJfSwWU2QtLCzx9m++gTVrYOpUKFIE+vUzr6JFPROnpAnq8RYRERHAFFebPHkykydPxmq1ejocERH3+P57eOEFU3TtyBF4910oXhxatYJp0zwdnaRSSrxFRETE6ebNm9yMrwosIpIaVaoEw4fDsWPwyy9mbrjDYYqzjRzp6egkldJQcxEREQFMcbW2bds6vxYRSdUyZIDu3c1r/34YPRrq1EnYf+IEDBxoXs2amQJuIsmkxFtEREQAs5xY1qxZPR2GiEjKK1UK/vvfxNt++AFmzDCv0qVh0CDo0weyZ/dEhOLj9NhGRERERETkn7p3h8GDISgI9u2DF1+EQoVgwACzTrjIfVDiLSIiIgDY7Xb27t3L3r17sdvtng5HRMSzypSBL76Ao0fh22/N3PArV+C776BGDYiL83SE4kOUeLtAREQEFSpUIOyfSxWIiIj4ELvdTlRUFFFRUUq8RUTiZcli1gLfuhWWL4fOnaF3b8iWLeGYH36Akyc9FqJ4P4vD4XB4OojUIi4ujqCgIM6fP092zf0QES9ht9s5deoUefPmxU+FYeQebDYba9asAaBWrVoqsCZuo3ZJfJ7DYdYJB4iKgmrVIF066NQJnn0WatdO2C8+4cKFC+TIkYPY2Fiy3fpQxUXU0omIiAhgKpnXrVuXunXrKukWEbmXW5PqGzdMon3zJvz2G9StC1WrwqhRZmi6CEq8RUREREREkq9WLYiMND3f/fqZZco2b4b+/aFwYdiyxdMRihdQ4i0iIiIiIvKgqlY1a4EfPQqffAIlSkDmzFChQsIxFy96Lj7xKCXeIiIiAoDVamX69OlMnz4dq9Xq6XBERHxTrlzwn//An3+aYmzp0pntVis8/DC0bWu2q9RWmqLEW0RERJyuXr3K1atXPR2GiIjv8/eHUqUS3q9ZA3/9BXPmQKNGEBZm5oTrQWeaoMRbREREAFNcrWXLlrRs2VLF1UREXK1ePdi7F555BjJmNHPCu3eH0qVh+HANQ0/llHiLiIgIABaLhRw5cpAjRw4sWgZHRMT1SpeGiAg4fBjefRfy5DG94C++CDt3ejo6cSMl3iIiIiIiIikpd254+22TdH/3HfTpAzVqJOyfMAG2b/dYeOJ6SrxFREQEALvdzoEDBzhw4AB2u93T4YiIpH4ZM8JTT8HYsQnbzpwx2ypXhtatYelSFWJLBZR4i4iICGAS73Xr1rFu3Tol3iIinnL5MrRpA35+MG8eNGkCoaEwZQqobfZZSrxFREQEMHO8CxQoQIECBTTHW0TEU4oVg0mTTCG2QYNMr/imTdC5MwQHm6Js4nOUeIuIiAhgqpo3bNiQhg0bqqq5iIinlSoFX38NR47AW29BtmwmGc+f39ORSTIo8RYREREREfFWuXLBe++ZQmzTpkGhQgn7nn/eFGLTWuBeT4m3C0RERFChQgXCwsI8HYqIiIiIiKRG2bObYmvxNm6Er76Cnj2hQgUYN04JuBdT4u0CgwYNYufOnWzYsMHToYiIiCSb1Wpl1qxZzJo1C6tu3kREvFvZsvDRR6ZH/M8/zZJkZcvC6NFw86ano5N/UOItIiIiTpcuXeLSpUueDkNERP5N1qzw2mtw6BB8/DHkyQMHDsCTT0KZMrBzp6cjlFso8RYRERHAFFdr2rQpTZs2VXE1ERFfkSULvPwyHDwIn30G+fKZIeelSnk6MrmFEm8REREBzHJiefLkIU+ePFpOTETE12TODEOGmAR8zhwIDDTbrVZo3NjMB792zbMxpmFKvEVERERERFKLjBnNet/xJk+GZctMBfSSJWHkSM0B9wAl3iIiIgKA3W7n8OHDHD58GLvd7ulwRETEFTp0MMl20aJw4gQ88wxUqgRTpoDD4eno0gwl3iIiIgKYxDsyMpLIyEgl3iIiqUVgIAwYYCqff/WVKcK2dy907gy1a8O5c56OME1Q4i0iIiKA5niLiKRq6dPDs8/Cvn3w1luQKRMEBECOHJ6OLE1Q4i0iIiKAqpqLiKQJ2bLBe++ZBHzUKIh/0Hr+PAwaBEePeja+VEqJt4iIiIiISFpToACULZvwftgw+OYbswb4q6+aRFxcRom3iIiIiIhIWtehA9SrZ5Yc+/hjsw74p59qCTIXUeItIiIiANhsNubNm8e8efOw2WyeDkdERFJSzZqwYgXMmgUVK5oe75dfNr3iEyZ4Ojqfp8RbREREAHA4HFy4cIELFy7g0BIzIiJpj8UCbdvCli0wZgwULgyHD5uEXB5IgKcDEBEREe/g7+9Po0aNnF+LiEga5e8PfftCt25mCbIePRL27dtnesPDwjwXnw9Sj7eIiIgAZjmx/Pnzkz9/fi0nJiIikDEjvPIKFCqUsO3ll6F6dejTB06d8lhovkaJt4iIiIiIiPw7q9UsRwYwbpyZ/z1yJKguyL9S4i0iIiIA2O12jh07xrFjx7Db7Z4OR0REvE1AgEm416yBKlXgwgV45hlTmG3DBk9H59WUeIuIiAhgEu+VK1eycuVKJd4iInJ38Yn2V1+ZHvCNG6FGDVMRXe5IibeIiIgAZo53zpw5yZkzp+Z4i4jIvfn7w7PPwp498PjjUKIENG3q6ai8lqqai4iICGAqmbdo0cLTYYiIiC/Jnx9++gni4kwxNjBzvgcOhEGDIDjYs/F5CfV4i4iIiIiIyIOJL7oG8O238MMPEBoKL75okvI0Tom3C0RERFChQgXCtJadiIiIiIikde3bQ5cupud7+HAoVw5+/RUcDk9H5jFKvF1g0KBB7Ny5kw2q5CciIj7MZrOxaNEiFi1ahE1Lw4iISHIVLgy//w4LFkCZMnDiBDz2mJkDvmuXp6PzCCXeIiIiAoDD4eDMmTOcOXMGRxrulRARERdp3hy2bYP334cMGWDpUjP3Ow1S4i0iIiIA+Pn5Ua9ePerVq4efn24RRETEBQID4f/+D3buNEPQhw9P2Ge1ppnh56pqLiIiIoBJvAsXLuzpMEREJDUqUQKmT0+87Y034PhxU4wtSxaPhJVS9DhbREREREREUta5c/DVV/Dzz1CtmhmSnoop8RYRERHAzPGOiYkhJiZGc7xFRMS9cuaERYugUCHYsweqV4dRo1Lt0HMl3iIiIgKYquZLly5l6dKlqmouIiLuV7cubN4MLVvCtWvQvz/06gWXLnk6MpdT4i0iIiJO2bJlI1u2bJ4OQ0RE0orcuWHOHPjoI/D3hwkToH59swZ4KqLiaiIiIgJAQEAAbdq08XQYIiKS1vj5wWuvQZ060L07DBpkkvBURIm3iIiIiIiIeF69embZsaxZE7bt2gVFivh81XMNNRcRERERERHvkC0bWCzm63PnzPzvVFD1XIm3iIiIACquJiIiXubIETPXO77q+ejRPlv1XIm3iIiIAFpOTEREvExwMGzalFD1/MknfbbquRJvERERAcDPz49atWpRq1Yt/Px0iyAiIl4gT57bq56HhcH27Z6O7L7or6qIiIgAJvEuXrw4xYsXV+ItIiLeI77q+bJlUKgQ7N4NH3zg6ajui/6qioiIiIiIiPerV88MPX/iCRg50tPR3Bcl3iIiIgKYOd5nz57l7NmzmuMtIiLeKU8eGDUKcuQw7x0OePNN2LrVs3H9CyXeIiIiApiq5gsXLmThwoWqai4iIr5h3Dj473/NvO/PPwe73dMR3ZESbxEREXHKlCkTmTJl8nQYIiIiSdOmDbRrBzduwEsvQbNmcPSop6O6jRJvERERASAgIID27dvTvn17AgICPB2OiIjIv8uTB2bMgG+/hYwZYelSqFwZfv/d05ElosRbREREREREfJfFAk8/bQqvVasGFy5A167w6quejsxJibeIiIiIiIj4vrJlYfVqU2wtIABat/Z0RE5KvEVERAQwxdVWrlzJypUrVVxNRER8U7p0Zo3v/fuhQYOE7Rs2wM2bHgtLibeIiIgAZjmxY8eOcezYMS0nJiIivq1o0YSv//wTGjaE2rVhzx6PhKPEW0RERADw8/MjLCyMsLAw/Px0iyAiIqnEX39BYCBs3AhVq8J335n1v1OQ/qqKiIgIYBLv0qVLU7p0aSXeIiKSejRtCtu2QZMmcOUKDBgAjzwCp06lWAj6qyoiIiIiIiKpW6FCsHAhfP45pE8Ps2ebZcdmz06RyyvxFhEREcDM8Y6NjSU2NlZzvEVEJPXx84MXXzRDzitVMj3ea9akyKUDUuQqIiIi4vVsNhtz584FoEuXLgQE6DZBRERSocqVTZXziAh4/vkUuaR6vG9x5MgRGjZsSIUKFXj44YeZNGmSp0MSERFJUYGBgQQGBno6DBEREffKkAFeesksPwZw44ZbL6dH2bcICAhg+PDhhISEcPLkSUJDQ2ndujWZM2f2dGgiIiJuFxAQQMeOHT0dhoiISMqbNcutp1eP9y0KFChASEgIAPnz5yd37tycO3fOs0GJiIiIiIiIe3Xq5NbT+1TivXLlStq1a0fBggWxWCxMnz79tmMiIiIoXrw4GTJkoEaNGqxfvz5Z14qKisJms1GkSJEHjFpERERERETSMp9KvC9fvkxwcDARERF33D9x4kSGDBnCO++8Q3R0NMHBwbRo0YJTt6zPFhISQqVKlW57HT9+3HnMuXPn6NWrF99//73bP5OIiIi3sNlsrF69mtWrV2Oz2TwdjoiISKrhU3O8W7VqRatWre66//PPP6d///707dsXgG+//ZY5c+YwZswYXnvtNQA2b958z2tcv36d8PBwXnvtNWrXrv2vx16/ft35Pi4uDgC73Y7dbk/KRxIRcTu73Y7D4VC7JP/KZrNx6NAhAKpVq4bFYvFsQJJqqV0SEW/j7vbIpxLve7lx4wZRUVG8/vrrzm1+fn40bdqUNUlcm83hcNCnTx8aN25Mz549//X4jz76iHffffe27adPn+aGm6viiYgkld1ud67L7OfnUwOdJIXZ7XZKliwJwJkzZ/TzIm6jdklEvE1sbKxbz59qEu8zZ85gs9nIly9fou358uVj9+7dSTpHZGQkEydO5OGHH3bOH//pp5+oXLnyHY9//fXXGTJkiPN9XFwcRYoUIU+ePGTPnj1Zn0NExNXsdjsWi4U8efLoBlf+Vf78+T0dgqQBapdExNukT5/eredPNYm3K9StW/e+hhjcba1TPz8//REREa9isVjUNomIV1G7JCLexN1tUapJvHPnzo2/vz8xMTGJtsfExOjpvYiISBI4HA6uXLkCQKZMmTTHW0RExEVSzSPG9OnTExoaypIlS5zb7HY7S5YsoVatWh6MTERExDfYbDZmzpzJzJkzVdVcRETEhXyqx/vSpUvs27fP+f7gwYNs3ryZnDlzUrRoUYYMGULv3r2pVq0a1atXZ/jw4Vy+fNlZ5VxERETuzd/f39MhiIiIpDo+lXhv3LiRRo0aOd/HFzbr3bs3P/74I127duX06dO8/fbbnDx5kpCQEObPn39bwTVXi4iIICIiQr0DIiLi0wICAnj00Uc9HYaIiEiqY3E4HA5PB5FaxMXFERQUxPnz51XVXES8ht1u59SpU+TNm1dFjETEK6hdEhFvc+HCBXLkyEFsbCzZsmVz+fnV0omIiIiIiIi4kU8NNRcRERH3sdlsbNy4EYBq1appvreIiIiLqMdbREREALOc2IEDBzhw4ACaiSYiIuI66vEWERERAPz8/Hj44YedX4uIiIhrKPF2AVU1FxGR1MDPz4+KFSt6OgwREZFUR4+zXWDQoEHs3LmTDRs2eDoUERERERER8TLq8RYRERHAzPG+fv06AIGBgVgsFg9HJCIikjqox1tEREQAU9V82rRpTJs2TdOnREREXEg93i4UXwE2Li5ORWlExGvY7XYuXrxIhgwZ1DbJPVmtVq5cuQKYv2UBAbpNEPdQuyQi3iYuLg7Abat6WBxaL8RlDhw4QKlSpTwdhoiIiIiIiCTD/v37KVmypMvPq0fZLpQzZ04ADh8+TFBQkIejSb3CwsJSVSE7b/w8KR2TO6/n6nO74nwPco7kfG9cXBxFihThyJEjZMuWLVnXlXvzxt/jB+GNnyc1tUvuOL+vtU1ql1KGN/4uPwhv+zyeiEf3TO773tjYWIoWLerM6VxNibcLxQ+VCgoK0h8RN/L3909V/3+98fOkdEzuvJ6rz+2K8z3IOR7ke7Nly+Z1P2uphTf+Hj8Ib/w8qaldcsf5fbVtUrvkXt74u/wgvO3zeCIe3TO5/3vdNf1Fk2rE5wwaNMjTIbiUN36elI7Jnddz9bldcb4HOYc3/rxI6vt38cbPk5raJXecX22T3Elq+3fxts/jiXh0z5Sy13clzfF2obi4OIKCgoiNjfWqp3EikrapbRIRb6N2SUS8jbvbJfV4u1BgYCDvvPMOgYGBng5FRMRJbZOIeBu1SyLibdzdLqnHW0RERERERMSN1OMtIiIiIiIi4kZKvEVERERERETcSIm3iIiIiIiIiBsp8RYRERERERFxIyXeIiIiIiIiIm6kxNuDOnToQI4cOejcubOnQxGRNGr27NmULVuWMmXKMGrUKE+HIyIC6B5JRLzLkSNHaNiwIRUqVODhhx9m0qRJ930OLSfmQcuXL+fixYuMGzeOyZMnezocEUljrFYrFSpUYNmyZQQFBREaGsrq1avJlSuXp0MTkTRO90gi4k1OnDhBTEwMISEhnDx5ktDQUPbu3UvmzJmTfA71eHtQw4YNyZo1q6fDEJE0av369VSsWJFChQqRJUsWWrVqxcKFCz0dloiI7pFExKsUKFCAkJAQAPLnz0/u3Lk5d+7cfZ1DifddrFy5knbt2lGwYEEsFgvTp0+/7ZiIiAiKFy9OhgwZqFGjBuvXr0/5QEUkzXrQdur48eMUKlTI+b5QoUIcO3YsJUIXkVRM91Ai4m1c2S5FRUVhs9koUqTIfcWgxPsuLl++THBwMBEREXfcP3HiRIYMGcI777xDdHQ0wcHBtGjRglOnTjmPCQkJoVKlSre9jh8/nlIfQ0RSMVe0UyIirqa2SUS8javapXPnztGrVy++//77+w/CIf8KcEybNi3RturVqzsGDRrkfG+z2RwFCxZ0fPTRR/d17mXLljk6derkijBFJA1LTjsVGRnpCA8Pd+5/4YUXHD///HOKxCsiacOD3EPpHklE3CG57dK1a9cc9erVc4wfPz5Z11WPdzLcuHGDqKgomjZt6tzm5+dH06ZNWbNmjQcjExExktJOVa9ene3bt3Ps2DEuXbrEvHnzaNGihadCFpE0QPdQIuJtktIuORwO+vTpQ+PGjenZs2eyrqPEOxnOnDmDzWYjX758ibbny5ePkydPJvk8TZs2pUuXLsydO5fChQvrD46IuExS2qmAgAA+++wzGjVqREhICC+99JIqmouIWyX1Hkr3SCKSUpLSLkVGRjJx4kSmT59OSEgIISEhbNu27b6uE+CyiOW+LV682NMhiEga98gjj/DII494OgwRkUR0jyQi3qRu3brY7fYHOod6vJMhd+7c+Pv7ExMTk2h7TEwM+fPn91BUIiIJ1E6JiDdS2yQi3ial2iUl3smQPn16QkNDWbJkiXOb3W5nyZIl1KpVy4ORiYgYaqdExBupbRIRb5NS7ZKGmt/FpUuX2Ldvn/P9wYMH2bx5Mzlz5qRo0aIMGTKE3r17U61aNapXr87w4cO5fPkyffv29WDUIpKWqJ0SEW+ktklEvI1XtEvJqoWeBixbtswB3Pbq3bu385ivvvrKUbRoUUf69Okd1atXd6xdu9ZzAYtImqN2SkS8kdomEfE23tAuWRwOh8N1abyIiIiIiIiI3EpzvEVERERERETcSIm3iIiIiIiIiBsp8RYRERERERFxIyXeIiIiIiIiIm6kxFtERERERETEjZR4i4iIiIiIiLiREm8RERERERERN1LiLSIiIiIiIuJGSrxFRERERERE3EiJt4iIiIiIiIgbKfEWERGRJJk6dSrNmjUjZ86cWCwWDh065OmQREREfIISbxEREUmSy5cvU79+fd577z1PhyIiIuJTlHiLiIj4gB9//BGLxeJ8devW7Y7Hvfnmm1gsFiIjI10eQ8+ePXnrrbdo2LChy8+dUnbv3p3o/2Px4sU9HZKIiKQBSrxFRETcLDQ0NFGy5+/vT5EiRejQoQObNm26r3O1b9+ed955h86dO99x/6ZNm/Dz8yMkJMQFkd+/Q4cOOT9n/vz5sVqtdzxu165dHkl+c+fOzTvvvMM777xDUFBQil1XRETStgBPByAiIpKa3bhxg+3bt5M9e3ZeeOEFAK5du0ZkZCTTp09n3rx5rF27NsmJcnh4OH369Lnr/k2bNlGmTBkyZ87sguiTLyAggJiYGObOncsjjzxy2/7Ro0fj55fyz/9z587N0KFDATOKQEREJCWox1tERMSNtm3bxo0bN6hevTpDhw5l6NChDBs2jFWrVtG1a1euX7/ODz/84JJrnThxgpMnT1K1atUkf89rr72WqDf+Tq/kqF27NkFBQYwZM+a2fVarlQkTJtC0aVPSpUuXrPOLiIj4EiXeIiIibhQdHQ1wx2S4ZcuWAJw+fdol14oftl6lShVWrVpF27ZtyZkzJzlz5qRz586cPHnytu956aWX2LVr1z1fyZExY0a6devGnDlzOHXqVKJ9s2fPJiYmhn79+t3xe5cvX47FYmHo0KH88ccfNGzYkKxZs5I9e3Y6derEvn377nrdlStXEh4eTr58+QgMDKRIkSJ07NiRP/74I1mfQ0RExBWUeIuIiLhRVFQUYOZ5/9P+/fsBKF++vEuuFZ94r169mlatWpE5c2aefPJJSpYsyZQpU3j66adv+548efJQrly5e76Sq1+/flitVn766adE28eMGUPOnDkJDw+/5/evXbuWJk2aEBQUxHPPPUeDBg2YNm0atWvX5sCBA7cd/+WXX9KwYUMWLVpEs2bNeOmll2jcuDFbtmxh8uTJyf4cIiIiD0pzvEVERNzobj3eW7duZcSIEWTNmpUBAwa49Fo7d+5ky5YtlCpVCjDzzMuUKcPixYtxOBzJHj5+7tw5Dh8+7HxgsHPnTi5cuEDRokXJmTPnbcdXr16dSpUqMXbsWF566SUATp48ybx58xg4cCCBgYH3vN6CBQv49ttvEz0w+O677xgwYAAvvPACs2bNcm7fsmULQ4YMoUCBAkRGRiYq2OZwODhx4kSyPrOIiIgrqMdbRETETaxWK1u3bsXPz4/x48czdOhQ3njjDTp16kRoaCg5cuRg4cKFFChQwCXX27RpExaLhUmTJjmTboD06dNTokQJrl+//kDnnzlzJlWqVHFWVG/Tpg1VqlRh5syZd/2efv36sWPHDtatWwfAuHHjsFqtdx1mfquHHnqI/v37J9rWv39/ypQpw5w5cxIN0f/uu++w2+188MEHt1VJt1gsFCxYMKkfU0RExOXU4y0iIuImO3bscCa77777bqJ9RYsWZfny5S5bSuvChQscPHiQOnXq8PDDD9+2/8CBA5QoUSLZvd0Affr0uWdF9Tt5/PHHefXVVxkzZgw1atRg7NixVKlSJUlV3OvUqXNb5XM/Pz/q1KnDn3/+yZYtW2jatCkA69evB6B58+b3FZ+IiEhKUI+3iIiIm8TP737ttddwOBzY7XaOHTtG//79OXz4ME888YTLrhU/vzs+Eb3V2bNnOXLkiEfW9s6TJw/t2rXjt99+Y/HixezZsydJvd0A+fLlu+f22NhY57bY2FgsFovLRg+IiIi4khJvERERN4lPvOPnd8cPeR45ciQlSpRg6dKl7Ny50yXXik+871Q9PX7ud5UqVVxyrfv1xBNPEBcXR58+fciQIQM9evRI0vfFxMTcc3tQUJBzW/bs2TWXW0REvJYSbxERETe5W2E1f39/55DtiRMnuuRaty4ldrd9nujxBmjRogWFChXi2LFjhIeHkyNHjiR9X2RkJHa7PdE2u93O6tWrsVgsBAcHO7dXr14dgIULF7oucBERERdR4i0iIuIGNpuNLVu2EBQURMmSJW/b/8gjjwAwbdo0l1wvOjqa3LlzU6RIkTvuA8/1ePv7+zN9+nSmTZvGRx99lOTv27t3Lz/88EOibT/88AN79+6lTZs25MmTx7l9wIAB+Pv783//93/89ddfib7H4XBw/PjxB/sQIiIiD0DF1URERNxg165dXL16lRo1atyxoFlISAhFixZl27Zt7N+/P1EV8vt19epV9uzZQ+PGje+4Pzo6mnz58nl0/nO1atWoVq3afX1PixYteP7555k7dy4VK1Zkx44dzJo1i9y5c/Pll18mOrZy5coMHz6c559/nooVKxIeHk6xYsU4efIkK1eupE2bNgwfPtyFn0hERCTp1OMtIiLiBv+c330n8b3e06dPf6Brbd26FZvNdsdrXbx4kX379nlsmPmDqFmzJkuWLCE2NpYRI0awfPlywsPDWbNmzR1HETz77LMsXbqURo0aMW/ePD799FMWLlxIcHAwjz76qAc+gYiIiKEebxERETfo3bs3vXv3vucxX331FV999dUDX6tGjRo4HI477suaNett86TdqXjx4neN5U6uXbt2z/1169Zl+fLlST5fw4YNadiwYZKPFxERSQnq8RYREfEhffv2xWKx0K1bN0+H4pN2796NxWLBYrHcNhdcRETEXdTjLSIi4gNCQkJ45513nO8rVarkwWh8V+7cuRP9f8yePbvnghERkTRDibeIiIgPCAkJ8cl52t4md+7cDB061NNhiIhIGmNx3M9ELBERERERERG5L5rjLSIiIiIiIuJGSrxFRERERERE3EiJt4iIiIiIiIgbKfEWERERERERcSMl3iIiIiIiIiJupMRbRERERERExI2UeIuIiIiIiIi4kRJvERERERERETdS4i0iIiIiIiLiRkq8RURERERERNxIibeIiIiIiIiIGynxFhEREREREXGj/wfFE8L6DKwhvQAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Galaxy-Galaxy Lensing: ΔΣ(R)\n",
+        "R = np.logspace(-1, 2, 50)  # h^-1 Mpc\n",
+        "\n",
+        "ds = pert_hod.delta_sigma(R, z_lens)\n",
+        "ds_components = pert_hod.delta_sigma_components(R, z_lens)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "\n",
+        "ax.loglog(R, ds_components['total'], 'k-', lw=2, label='Total')\n",
+        "ax.loglog(R, ds_components['central'], 'b--', lw=1.5, label='Central')\n",
+        "ax.loglog(R, ds_components['satellite'], 'r--', lw=1.5, label='Satellite')\n",
+        "\n",
+        "ax.axvline(3.0, color='gray', ls=':', alpha=0.7, label='HSC R_min = 3 h⁻¹Mpc')\n",
+        "\n",
+        "ax.set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "ax.set_ylabel(r'$\\Delta\\Sigma$ [$h M_\\odot$/pc$^2$]', fontsize=14)\n",
+        "ax.set_title(f'Galaxy-Galaxy Lensing ΔΣ(R) at z={z_lens}', fontsize=14)\n",
+        "ax.legend(fontsize=12)\n",
+        "ax.set_xlim(0.1, 100)\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Galaxy Clustering: w_p(R)\n",
+        "wp = pert_hod.projected_correlation(R, z_lens, pimax=100.0)\n",
+        "wp_components = pert_hod.projected_correlation_components(R, z_lens)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "\n",
+        "ax.loglog(R, wp_components['total'], 'k-', lw=2, label='Total')\n",
+        "ax.loglog(R, wp_components['1h_cs'], 'b--', lw=1.5, label='1h: central-satellite')\n",
+        "ax.loglog(R, wp_components['1h_ss'], 'b:', lw=1.5, label='1h: satellite-satellite')\n",
+        "ax.loglog(R, wp_components['2h_cc'], 'r--', lw=1.5, label='2h: central-central')\n",
+        "ax.loglog(R, wp_components['2h_cs'], 'r:', lw=1.5, label='2h: central-satellite')\n",
+        "ax.loglog(R, wp_components['2h_ss'], 'r-.', lw=1.5, label='2h: satellite-satellite')\n",
+        "\n",
+        "ax.axvline(2.0, color='gray', ls=':', alpha=0.7, label='HSC R_min = 2 h⁻¹Mpc')\n",
+        "\n",
+        "ax.set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "ax.set_ylabel(r'$w_p$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "ax.set_title(f'Projected Galaxy Correlation $w_p(R)$ at z={z_lens}', fontsize=14)\n",
+        "ax.legend(fontsize=10, ncol=2)\n",
+        "ax.set_xlim(0.1, 100)\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 3D Correlation Functions: ξ_gg and ξ_gm\n",
+        "r = np.logspace(-1, 2, 50)  # h^-1 Mpc\n",
+        "\n",
+        "xi_gg = pert_hod.galaxy_galaxy_correlation(r, z_lens)\n",
+        "xi_gm = pert_hod.galaxy_matter_correlation(r, z_lens)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "\n",
+        "ax.loglog(r, xi_gg, 'b-', lw=2, label=r'$\\xi_{gg}$ (galaxy-galaxy)')\n",
+        "ax.loglog(r, xi_gm, 'r-', lw=2, label=r'$\\xi_{gm}$ (galaxy-matter)')\n",
+        "\n",
+        "ax.set_xlabel(r'$r$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "ax.set_ylabel(r'$\\xi(r)$', fontsize=14)\n",
+        "ax.set_title(f'3D Correlation Functions at z={z_lens}', fontsize=14)\n",
+        "ax.legend(fontsize=12)\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 5. Changing HOD Parameters\n",
+        "\n",
+        "The `set_hod()` method allows fast updates of HOD parameters without re-initializing the cosmology."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Compare different HOD parameter sets\n",
+        "R = np.logspace(-1, 2, 50)\n",
+        "\n",
+        "hod_sets = [\n",
+        "    {'logMmin': 12.5, 'sigma_sq': 0.2, 'logM1': 13.5, 'alpha': 0.8, 'kappa': 1.0, 'poff': 0.0, 'Roff': 0.0},\n",
+        "    {'logMmin': 13.0, 'sigma_sq': 0.3, 'logM1': 14.0, 'alpha': 1.0, 'kappa': 1.0, 'poff': 0.0, 'Roff': 0.0},\n",
+        "    {'logMmin': 13.5, 'sigma_sq': 0.4, 'logM1': 14.5, 'alpha': 1.2, 'kappa': 1.0, 'poff': 0.0, 'Roff': 0.0},\n",
+        "]\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "for i, hod_dict in enumerate(hod_sets):\n",
+        "    hod = DarkEmuHODParameters(**hod_dict)\n",
+        "    pert_hod.set_hod(hod)  # Fast HOD update\n",
+        "    \n",
+        "    ds = pert_hod.delta_sigma(R, z_lens)\n",
+        "    wp = pert_hod.projected_correlation(R, z_lens)\n",
+        "    \n",
+        "    label = f\"logMmin={hod_dict['logMmin']}, α={hod_dict['alpha']}\"\n",
+        "    axes[0].loglog(R, ds, lw=2, label=label)\n",
+        "    axes[1].loglog(R, wp, lw=2, label=label)\n",
+        "\n",
+        "axes[0].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[0].set_ylabel(r'$\\Delta\\Sigma$ [$h M_\\odot$/pc$^2$]', fontsize=14)\n",
+        "axes[0].set_title('ΔΣ for Different HOD Parameters', fontsize=14)\n",
+        "axes[0].legend(fontsize=10)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_ylabel(r'$w_p$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_title('$w_p$ for Different HOD Parameters', fontsize=14)\n",
+        "axes[1].legend(fontsize=10)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 6. Cosmology Dependence\n",
+        "\n",
+        "Compare predictions for different cosmological parameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.107736 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n",
+            "WARNING:root:Warning: omegac=0.134670 is out of the supported range [0.107820:0.131780]\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Compare different Omega_cdm values\n",
+        "Omega_cdm_values = [0.24, 0.27, 0.30]\n",
+        "R = np.logspace(-1, 2, 50)\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "for Omega_cdm in Omega_cdm_values:\n",
+        "    # Create new background\n",
+        "    params = cosmo_params.copy()\n",
+        "    params['Omega_cdm0'] = Omega_cdm\n",
+        "    bg = CAMBBackground(**params)\n",
+        "    \n",
+        "    # Create perturbations\n",
+        "    pert = DarkEmuHODPerturbations(\n",
+        "        background=bg,\n",
+        "        redshifts=np.array([z_lens]),\n",
+        "        hod_params=hod_params,\n",
+        "        validate_params=False,  # Skip validation for speed\n",
+        "    )\n",
+        "    \n",
+        "    ds = pert.delta_sigma(R, z_lens)\n",
+        "    wp = pert.projected_correlation(R, z_lens)\n",
+        "    \n",
+        "    label = f'Ω_cdm = {Omega_cdm}'\n",
+        "    axes[0].loglog(R, ds, lw=2, label=label)\n",
+        "    axes[1].loglog(R, wp, lw=2, label=label)\n",
+        "\n",
+        "axes[0].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[0].set_ylabel(r'$\\Delta\\Sigma$ [$h M_\\odot$/pc$^2$]', fontsize=14)\n",
+        "axes[0].set_title('ΔΣ: Cosmology Dependence', fontsize=14)\n",
+        "axes[0].legend(fontsize=12)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].set_xlabel(r'$R$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_ylabel(r'$w_p$ [$h^{-1}$Mpc]', fontsize=14)\n",
+        "axes[1].set_title('$w_p$: Cosmology Dependence', fontsize=14)\n",
+        "axes[1].legend(fontsize=12)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "## 7. Summary: Class Hierarchy\n",
+        "\n",
+        "```\n",
+        "DarkEmuNonLinearPerturbations\n",
+        "├── matter_power_spectrum(z, k)  → P(k,z)\n",
+        "├── growth_factor(z)             → D(z)\n",
+        "├── growth_rate(z)               → f(z)\n",
+        "└── get_sigma8(z)                → σ8(z)\n",
+        "        │\n",
+        "        ▼\n",
+        "DarkEmuHaloPerturbations (inherits above)\n",
+        "├── halo_matter_correlation(r, z, M_min)   → ξ_hm\n",
+        "├── halo_halo_correlation(r, z, M_min)     → ξ_hh\n",
+        "├── halo_mass_function(z)                  → dn/dlnM\n",
+        "├── halo_bias(M_min, z)                    → b(M)\n",
+        "├── delta_sigma_halo(R, z, M_min)          → ΔΣ_halo\n",
+        "└── projected_correlation_halo(R, z, M_min) → w_p,halo\n",
+        "        │\n",
+        "        ▼\n",
+        "DarkEmuHODPerturbations (inherits above + HOD)\n",
+        "├── delta_sigma(R, z)              → ΔΣ_gal (GGL)\n",
+        "├── projected_correlation(R, z)    → w_p,gal (GC)\n",
+        "├── galaxy_galaxy_correlation(r, z) → ξ_gg\n",
+        "├── galaxy_matter_correlation(r, z) → ξ_gm\n",
+        "└── set_hod(hod_params)            → Fast HOD update\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Next steps\n",
+        "\n",
+        "| Topic | Notebook / Script |\n",
+        "|-------|-------------------|\n",
+        "| Why we use Dark Emulator for w_p, ΔΣ | `dark_emulator_observables_usage.ipynb` |\n",
+        "| GC only (w_p fitting) | `fitting_gc_wp.ipynb` |\n",
+        "| GGL only (ΔΣ fitting) | `fitting_ggl_delta_sigma.ipynb` |\n",
+        "| 3x2pt (GC+GGL+WL) short run | `fitting_3x2pt.ipynb` |\n",
+        "| 3x2pt production (script) | `playground/scripts/sampling/darkemu_3x2pt_full_sampling.py` |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tutorial complete!\n",
+            "\n",
+            "Next steps:\n",
+            "  • Observables (why w_p, ΔΣ from Dark Emulator): playground/tutorials/dark_emulator/dark_emulator_observables_usage.ipynb\n",
+            "  • GC only (w_p fitting):                        playground/tutorials/dark_emulator/fitting_gc_wp.ipynb\n",
+            "  • GGL only (ΔΣ fitting):                        playground/tutorials/dark_emulator/fitting_ggl_delta_sigma.ipynb\n",
+            "  • 3x2pt (GC+GGL+WL):                            playground/tutorials/dark_emulator/fitting_3x2pt.ipynb\n",
+            "  • 3x2pt production run:                         playground/scripts/sampling/darkemu_3x2pt_full_sampling.py\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Tutorial complete!\")\n",
+        "print()\n",
+        "print(\"Next steps:\")\n",
+        "print(\"  • Observables (why w_p, ΔΣ from Dark Emulator): playground/tutorials/dark_emulator/dark_emulator_observables_usage.ipynb\")\n",
+        "print(\"  • GC only (w_p fitting):                        playground/tutorials/dark_emulator/fitting_gc_wp.ipynb\")\n",
+        "print(\"  • GGL only (ΔΣ fitting):                        playground/tutorials/dark_emulator/fitting_ggl_delta_sigma.ipynb\")\n",
+        "print(\"  • 3x2pt (GC+GGL+WL):                            playground/tutorials/dark_emulator/fitting_3x2pt.ipynb\")\n",
+        "print(\"  • 3x2pt production run:                         playground/scripts/sampling/darkemu_3x2pt_full_sampling.py\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "cloelib",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/tutorials/dark_emulator/dark_emulator_observables_usage.ipynb b/tutorials/dark_emulator/dark_emulator_observables_usage.ipynb
new file mode 100644
index 0000000..45a7a63
--- /dev/null
+++ b/tutorials/dark_emulator/dark_emulator_observables_usage.ipynb
@@ -0,0 +1,244 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How we retrieve two-point statistics: Dark Emulator vs cloelib native\n",
+    "\n",
+    "This notebook clarifies **how we intend to obtain observables (two-point statistics)** when using the Dark Emulator integration in cloelib.\n",
+    "\n",
+    "## Two possible approaches\n",
+    "\n",
+    "| Approach | Observables | Typical use in CLOE |\n",
+    "|----------|-------------|---------------------|\n",
+    "| **A. cloelib native** | Angular power spectra C_l, real-space via Limber + integration | Photometric 3x2pt (WL, GCph, GGL) from *angular* Cls; EuclidLikelihood_photo_Cls |\n",
+    "| **B. Dark Emulator** | **Real-space** (w_p(R), ΔΣ(R), ξ_{gg}, ξ_{gm}) **directly from the emulator** | HOD-based GC + GGL (e.g. HSC-Y3 style); no C_ell step |\n",
+    "\n",
+    "## Intended usage (our choice)\n",
+    "- “dark_emulator” python package enables fast and accurate computations of halo clustering quantities\n",
+    "- When working with **real-space observables** such as **ΔΣ(R)** (GGL) and **w_p(R)** (GC), **we use the Dark Emulator** to get two-point statistics—this is the pipeline to use for fitting or predicting ΔΣ, w_p, etc. We do not use cloelib’s native angular/Limber pipeline for these.\n",
+    "- cloelib’s role is to:\n",
+    "  1. Hold cosmology (e.g. `CAMBBackground`) and convert to Dark Emulator’s parameter format.\n",
+    "  2. Expose the emulator as **Perturbations-like** classes (`DarkEmuHODPerturbations`, etc.) that return **real-space observables** \\(w_p(R), ΔΣ(R)\\).\n",
+    "- So: **observables = Dark Emulator output**, retrieved **through cloelib’s thin wrapper** (same Python API as other perturbations, but backend is the emulator).\n",
+    "\n",
+    "Below we show the **concrete usage** for retrieving these observables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Path and imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CLOE_ROOT: /home/ryuseikano/Euclid/CLOE\n",
+      "Imports OK.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Find CLOE repo root (directory that contains cloelib, cloelike, dark_emulator_public)\n",
+    "NOTEBOOK_DIR = Path.cwd()\n",
+    "CLOE_ROOT = NOTEBOOK_DIR\n",
+    "for _ in range(6):\n",
+    "    if (CLOE_ROOT / \"cloelib\").is_dir():\n",
+    "        break\n",
+    "    CLOE_ROOT = CLOE_ROOT.parent\n",
+    "else:\n",
+    "    raise FileNotFoundError(\"CLOE root not found: run the notebook from inside the CLOE repo.\")\n",
+    "\n",
+    "sys.path.insert(0, str(CLOE_ROOT / \"cloelib\"))\n",
+    "sys.path.insert(0, str(CLOE_ROOT / \"cloelike\"))\n",
+    "sys.path.insert(0, str(CLOE_ROOT / \"dark_emulator_public\"))\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cloelib.cosmology.camb_cosmology import CAMBBackground\n",
+    "from cloelib.cosmology.darkemu_cosmology import DarkEmuHODPerturbations\n",
+    "from cloelib.observables.darkemu_hod import DarkEmuHODParameters\n",
+    "\n",
+    "print(\"CLOE_ROOT:\", CLOE_ROOT)\n",
+    "print(\"Imports OK.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Observables from Dark Emulator (via cloelib)\n",
+    "\n",
+    "We use **cloelib only** for:\n",
+    "- cosmology object (`CAMBBackground`),\n",
+    "- HOD parameter container (`DarkEmuHODParameters`),\n",
+    "- and a **thin wrapper** that calls the Dark Emulator and returns **real-space two-point statistics**.\n",
+    "\n",
+    "The **actual observable values** (w_p(R), ΔΣ(R)) come **from the Dark Emulator**, not from cloelib’s native (C_l) or Limber code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cosmology (cloelib object, used to set the emulator cosmology)\n",
+    "cosmo_params = {\n",
+    "    \"H0\": 67.0,\n",
+    "    \"Omega_cdm0\": 0.27,\n",
+    "    \"Omega_b0\": 0.049,\n",
+    "    \"Omega_k0\": 0.0,\n",
+    "    \"w0\": -1.0,\n",
+    "    \"wa\": 0.0,\n",
+    "    \"As\": 2.1e-9,\n",
+    "    \"ns\": 0.96,\n",
+    "    \"mnu\": 0.06,\n",
+    "    \"N_mnu\": 1,\n",
+    "    \"gamma_MG\": 0.55,\n",
+    "}\n",
+    "background = CAMBBackground(**cosmo_params)\n",
+    "\n",
+    "# HOD parameters (cloelib container)\n",
+    "hod_params = DarkEmuHODParameters(\n",
+    "    logMmin=13.0,\n",
+    "    sigma_sq=0.3,\n",
+    "    logM1=14.0,\n",
+    "    alpha=1.0,\n",
+    "    kappa=1.0,\n",
+    "    poff=0.0,\n",
+    "    Roff=0.0,\n",
+    ")\n",
+    "\n",
+    "# Redshift and scales\n",
+    "z = 0.5\n",
+    "R_bins = np.logspace(-0.5, 1.5, 15)  # h^{-1} Mpc\n",
+    "pimax = 100.0  # h^{-1} Mpc, for w_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ryuseikano/Euclid/CLOE/cloelib/cloelib/cosmology/darkemu_cosmology.py:131: UserWarning: Neutrino density mismatch: input omega_nu = 0.000000, but Dark Emulator assumes fixed omega_nu = 0.00064. Dark Emulator will use its internal fixed value regardless of input.\n",
+      "  self.cparam = background_to_darkemu_cparam(background)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Two-point statistics from Dark Emulator (via cloelib):\n",
+      "  delta_sigma(R): (15,) -> first 3: [20.79 15.68 11.55]\n",
+      "  w_p(R): (15,) -> first 3: [328.22 229.89 157.78]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wrapper that uses Dark Emulator under the hood\n",
+    "pert = DarkEmuHODPerturbations(\n",
+    "    background=background,\n",
+    "    redshifts=np.array([z]),\n",
+    "    hod_params=hod_params,\n",
+    ")\n",
+    "\n",
+    "# --- Observables: retrieved FROM THE DARK EMULATOR (via cloelib API) ---\n",
+    "\n",
+    "# Galaxy-galaxy lensing: DeltaSigma(R) [h M_sun / pc^2]\n",
+    "delta_sigma = pert.delta_sigma(R_bins, z)\n",
+    "\n",
+    "# Galaxy clustering: projected correlation function w_p(R) [h^{-1} Mpc]\n",
+    "w_p = pert.projected_correlation(R_bins, z, pimax=pimax)\n",
+    "\n",
+    "print(\"Two-point statistics from Dark Emulator (via cloelib):\")\n",
+    "print(\"  delta_sigma(R):\", delta_sigma.shape, \"-> first 3:\", delta_sigma[:3].round(2))\n",
+    "print(\"  w_p(R):\", w_p.shape, \"-> first 3:\", w_p[:3].round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Summary: where do observables come from?\n",
+    "\n",
+    "- **Observables (two-point statistics)** in this workflow: **Dark Emulator**.\n",
+    "- **cloelib** provides: cosmology, HOD parameter handling, and a **single entry point** (`DarkEmuHODPerturbations`) that calls the emulator and returns ΔΣ(R) and w_p(R).\n",
+    "- We do **not** use cloelib’s native functions to compute these observables (e.g. we do not build C_l with Limber and then transform to real space for GC/GGL here).\n",
+    "\n",
+    "So: **retrieval of observables = through Dark Emulator, via cloelib’s wrapper.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
+    "\n",
+    "axes[0].loglog(R_bins, delta_sigma, \"o-\")\n",
+    "axes[0].set_xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+    "axes[0].set_ylabel(r\"$\\Delta\\Sigma(R)$ [h $M_\\odot$ pc$^{-2}$]\")\n",
+    "axes[0].set_title(\"GGL: from Dark Emulator\")\n",
+    "\n",
+    "axes[1].loglog(R_bins, w_p, \"o-\")\n",
+    "axes[1].set_xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+    "axes[1].set_ylabel(r\"$w_p(R)$ [h$^{-1}$ Mpc]\")\n",
+    "axes[1].set_title(\"GC: from Dark Emulator\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cloelib",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/dark_emulator/fitting_3x2pt.ipynb b/tutorials/dark_emulator/fitting_3x2pt.ipynb
new file mode 100644
index 0000000..8ee9deb
--- /dev/null
+++ b/tutorials/dark_emulator/fitting_3x2pt.ipynb
@@ -0,0 +1,628 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Running Dark Emulator 3x2pt in a Notebook\n",
+        "\n",
+        "This notebook runs a **3x2pt** analysis (Galaxy Clustering: w_p, Galaxy-Galaxy Lensing: ΔΣ, Weak Lensing: ξ±) using **cloelike**'s `EuclidLikelihood_DarkEmu_RealSpace` and **Nautilus** in a short demo.\n",
+        "\n",
+        "**Workflow:**\n",
+        "1. Paths and imports (reusing mock-generation helpers from the script)\n",
+        "2. Mock data (GC + GGL, optional WL)\n",
+        "3. Likelihood, scale cuts, and prior setup\n",
+        "4. Short Nautilus run (small `n_live`)\n",
+        "5. Quick result summary and 2D posterior plot\n",
+        "\n",
+        "For **production** runs (larger `n_live`, sigma8/S8, corner plots), use the script from the repo root — see the note at the bottom of this notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Paths and imports\n",
+        "\n",
+        "We reuse the mock-generation functions, prior, and likelihood wrapper from `darkemu_3x2pt_full_sampling.py`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CLOE_ROOT: /home/ryuseikano/Euclid/CLOE\n"
+          ]
+        }
+      ],
+      "source": [
+        "import sys\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "NOTEBOOK_DIR = Path.cwd()\n",
+        "CLOE_ROOT = NOTEBOOK_DIR.parent.parent.parent  # tutorials/dark_emulator -> CLOE\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelib\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelike\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"dark_emulator_public\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"playground\" / \"scripts\" / \"sampling\"))\n",
+        "\n",
+        "from cloelib.cosmology.camb_cosmology import CAMBBackground\n",
+        "from cloelike.EuclidLikelihood_DarkEmu_RealSpace import EuclidLikelihood_DarkEmu_RealSpace\n",
+        "from nautilus import Prior, Sampler\n",
+        "\n",
+        "# Import mock generation, prior, and wrapper from the script\n",
+        "from darkemu_3x2pt_full_sampling import (\n",
+        "    FIDUCIAL_COSMO,\n",
+        "    FIDUCIAL_HOD,\n",
+        "    generate_mock_data,\n",
+        "    generate_mock_wl_data,\n",
+        "    generate_simple_dndz,\n",
+        "    setup_prior,\n",
+        "    LikelihoodWrapper,\n",
+        ")\n",
+        "\n",
+        "print(\"CLOE_ROOT:\", CLOE_ROOT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Mock data (GC + GGL + WL)\n",
+        "\n",
+        "GC and GGL use the same setup as the script. WL is kept light for the notebook (fewer tomographic bins and θ bins)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "[1] Generating mock 3x2pt data (GC + GGL)...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ryuseikano/Euclid/CLOE/cloelib/cloelib/cosmology/darkemu_cosmology.py:131: UserWarning: Neutrino density mismatch: input omega_nu = 0.000000, but Dark Emulator assumes fixed omega_nu = 0.00064. Dark Emulator will use its internal fixed value regardless of input.\n",
+            "  self.cparam = background_to_darkemu_cparam(background)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  - Generated 15 radial bins at z = 0.5\n",
+            "  - R range: [0.32, 31.62] h^-1 Mpc\n",
+            "  - w_p range: [2.7, 344.5] (h^-1 Mpc)\n",
+            "  - ΔΣ range: [0.3, 19.6] h M_sun/pc^2\n",
+            "\n",
+            "[WL] Generating mock weak lensing data (xi_pm)...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026-02-12 06:20:56.588915: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ryuseikano/.conda/envs/cloelib/lib/python3.10/site-packages/HMcode2020Emu\n",
+            "Loading linear emulator...\n",
+            "Linear emulator loaded in memory.\n",
+            "Loading nonlinear emulator...\n",
+            "Non-linear emulator loaded in memory.\n",
+            "Loading linear emulator...\n",
+            "Baryonic boost emulator loaded in memory.\n",
+            "Loading sigma8 emulator...\n",
+            "Linear emulator loaded in memory.\n",
+            "  - Generated 12 theta bins\n",
+            "  - theta range: [2.00, 80.00] arcmin\n",
+            "  - 2 tomographic bins -> 3 bin pairs\n",
+            "  - xi_+ range: [3.79e-07, 1.26e-06]\n",
+            "  - xi_- range: [7.54e-09, 8.20e-08]\n",
+            "GC bins: 15 | GGL bins: 15\n",
+            "WL: theta bins: 12 | tomo bins: 2\n"
+          ]
+        }
+      ],
+      "source": [
+        "# GC + GGL\n",
+        "R_bins = np.logspace(-0.5, 1.5, 15)\n",
+        "z_sample = 0.5\n",
+        "pimax = 100.0\n",
+        "data_gc, data_ggl = generate_mock_data(R_bins, z_sample, pimax=pimax, noise_level=0.1, seed=42)\n",
+        "\n",
+        "# WL (light: n_tomo=2, 12 theta bins for faster run)\n",
+        "include_wl = True  # Set to False for GC+GGL only (faster)\n",
+        "if include_wl:\n",
+        "    theta_arcmin = np.logspace(np.log10(2.0), np.log10(80.0), 12)\n",
+        "    z_arr = np.linspace(0.01, 2.5, 80)\n",
+        "    n_tomo = 2\n",
+        "    dndz = generate_simple_dndz(z_arr, n_tomo=n_tomo)\n",
+        "    data_wl = generate_mock_wl_data(\n",
+        "        theta_arcmin=theta_arcmin,\n",
+        "        z_arr=z_arr,\n",
+        "        dndz=dndz,\n",
+        "        noise_level=0.1,\n",
+        "        seed=43,\n",
+        "    )\n",
+        "else:\n",
+        "    data_wl = None\n",
+        "\n",
+        "print(\"GC bins:\", len(R_bins), \"| GGL bins:\", len(R_bins))\n",
+        "if include_wl:\n",
+        "    print(\"WL: theta bins:\", len(theta_arcmin), \"| tomo bins:\", n_tomo)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Mock data: 3 panels (GC, GGL, WL) side by side\n",
+        "n_panels = 3 if data_wl is not None else 2\n",
+        "fig, axes = plt.subplots(1, n_panels, figsize=(4 * n_panels, 4))\n",
+        "axes = np.atleast_1d(axes)\n",
+        "\n",
+        "R = data_gc[\"R_bins\"]\n",
+        "# GC: w_p(R)\n",
+        "ax = axes[0]\n",
+        "sigma_wp = np.sqrt(np.diag(data_gc[\"covariance\"]))\n",
+        "ax.errorbar(R, data_gc[\"wp\"], yerr=sigma_wp, fmt=\"o\", capsize=3, label=\"Mock\")\n",
+        "ax.plot(R, data_gc[\"fiducial_wp\"], \"k--\", alpha=0.8, label=\"Fiducial\")\n",
+        "ax.set_xscale(\"log\")\n",
+        "ax.set_yscale(\"log\")\n",
+        "ax.set_xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "ax.set_ylabel(r\"$w_p(R)$ [h$^{-1}$ Mpc]\")\n",
+        "ax.set_title(\"GC\")\n",
+        "ax.legend()\n",
+        "\n",
+        "# GGL: ΔΣ(R)\n",
+        "ax = axes[1]\n",
+        "sigma_ds = np.sqrt(np.diag(data_ggl[\"covariance\"]))\n",
+        "ax.errorbar(R, data_ggl[\"delta_sigma\"], yerr=sigma_ds, fmt=\"o\", capsize=3, label=\"Mock\")\n",
+        "ax.plot(R, data_ggl[\"fiducial_ds\"], \"k--\", alpha=0.8, label=\"Fiducial\")\n",
+        "ax.set_xscale(\"log\")\n",
+        "ax.set_yscale(\"log\")\n",
+        "ax.set_xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "ax.set_ylabel(r\"$\\Delta\\Sigma(R)$ [h $M_\\odot$ pc$^{-2}$]\")\n",
+        "ax.set_title(\"GGL\")\n",
+        "ax.legend()\n",
+        "\n",
+        "# WL: ξ_+(θ) and |ξ_-(θ)| (|ξ_-| so both visible on log scale)\n",
+        "if data_wl is not None:\n",
+        "    ax = axes[2]\n",
+        "    th = data_wl[\"theta\"]\n",
+        "    key = (0, 0) if (0, 0) in data_wl[\"xi_plus\"] else list(data_wl[\"xi_plus\"].keys())[0]\n",
+        "    ax.errorbar(th, data_wl[\"xi_plus\"][key], fmt=\"o\", capsize=2, label=r\"$\\xi_+$ mock\")\n",
+        "    ax.plot(th, data_wl[\"fiducial_xi_plus\"][key], \"C0--\", alpha=0.8, label=r\"$\\xi_+$ fid\")\n",
+        "    ax.errorbar(th, np.abs(data_wl[\"xi_minus\"][key]), fmt=\"s\", capsize=2, label=r\"$|\\xi_-|$ mock\")\n",
+        "    ax.plot(th, np.abs(data_wl[\"fiducial_xi_minus\"][key]), \"C1--\", alpha=0.8, label=r\"$|\\xi_-|$ fid\")\n",
+        "    ax.set_xscale(\"log\")\n",
+        "    ax.set_yscale(\"log\")\n",
+        "    ax.set_xlabel(r\"$\\theta$ [arcmin]\")\n",
+        "    ax.set_ylabel(r\"$\\xi_+$, $|\\xi_-|$\")\n",
+        "    ax.set_title(\"WL (bin \" + str(key) + \")\")\n",
+        "    ax.legend(fontsize=8)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Likelihood and scale cuts\n",
+        "\n",
+        "Build the combined likelihood with `EuclidLikelihood_DarkEmu_RealSpace` and apply scale cuts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data points: GC = 8 , GGL = 7 , WL = 30\n",
+            "Total = 45\n"
+          ]
+        }
+      ],
+      "source": [
+        "fixed_cosmo = {k: v for k, v in FIDUCIAL_COSMO.items() if k not in [\"Omega_cdm0\", \"As\"]}\n",
+        "fixed_hod = {k: v for k, v in FIDUCIAL_HOD.items() if k not in [\"logMmin\", \"sigma_sq\", \"logM1\", \"alpha\"]}\n",
+        "\n",
+        "settings = {\n",
+        "    \"R_min_gc\": 2.0,\n",
+        "    \"R_max_gc\": 30.0,\n",
+        "    \"R_min_ggl\": 3.0,\n",
+        "    \"R_max_ggl\": 30.0,\n",
+        "    \"hod_params_fixed\": fixed_hod,\n",
+        "}\n",
+        "if include_wl:\n",
+        "    settings[\"wl_settings\"] = {\n",
+        "        \"theta_min_plus\": 7.0,\n",
+        "        \"theta_max_plus\": 56.0,\n",
+        "        \"theta_min_minus\": 28.0,\n",
+        "        \"theta_max_minus\": 178.0,\n",
+        "    }\n",
+        "\n",
+        "likelihood = EuclidLikelihood_DarkEmu_RealSpace(\n",
+        "    data_gc=data_gc,\n",
+        "    data_ggl=data_ggl,\n",
+        "    data_wl=data_wl,\n",
+        "    settings=settings,\n",
+        "    Background=CAMBBackground,\n",
+        ")\n",
+        "print(\"Data points: GC =\", likelihood.n_gc, \", GGL =\", likelihood.n_ggl, \", WL =\", likelihood.n_wl)\n",
+        "print(\"Total =\", likelihood.n_gc + likelihood.n_ggl + likelihood.n_wl)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Prior and Nautilus run\n",
+        "\n",
+        "Same prior as the script. We use **n_live=20** and **n_eff=30** for a short run in the notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test log L (fiducial): -18.809177948890166\n"
+          ]
+        }
+      ],
+      "source": [
+        "prior = setup_prior()\n",
+        "param_names = list(prior.keys)\n",
+        "wrapper = LikelihoodWrapper(likelihood, fixed_cosmo, fixed_hod)\n",
+        "\n",
+        "# Test evaluation\n",
+        "test_params = {name: FIDUCIAL_COSMO.get(name, FIDUCIAL_HOD.get(name)) for name in param_names}\n",
+        "test_logL = wrapper(test_params)\n",
+        "print(\"Test log L (fiducial):\", test_logL)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "It takes some time (~1hour)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Starting the nautilus sampler...\n",
+            "Please report issues at github.com/johannesulf/nautilus.\n",
+            "Status    | Bounds | Ellipses | Networks | Calls    | f_live | N_eff | log Z    \n",
+            "Finished  | 7      | 1        | 4        | 1600     | N/A    | 31    | -24.89   \n",
+            "Posterior samples: 900\n"
+          ]
+        }
+      ],
+      "source": [
+        "n_live = 20   # Small for notebook (use ~500 for production)\n",
+        "n_eff = 30    # Target effective sample size\n",
+        "\n",
+        "sampler = Sampler(prior, wrapper, n_live=n_live)\n",
+        "sampler.run(verbose=True, n_eff=n_eff, discard_exploration=True)\n",
+        "\n",
+        "points, log_w, log_l = sampler.posterior()\n",
+        "print(\"Posterior samples:\", len(points))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Result summary\n",
+        "\n",
+        "Weighted median and standard deviation of the posterior, a 2D slice (Ω_cdm0 vs log M_min), and **derived cosmological parameters** (Ω_m, σ_8, S_8) with their posterior distributions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Parameter  | Fiducial | Median (weighted) | Std\n",
+            "-------------------------------------------------------\n",
+            "Omega_cdm0 | 0.2700   | 0.2704             | 0.0102\n",
+            "As         | 2.10e-09 | 2.10e-09             | 1.87e-10\n",
+            "logMmin    | 13.0000   | 13.0112             | 0.2018\n",
+            "sigma_sq   | 0.3000   | 0.3122             | 0.1662\n",
+            "logM1      | 14.0000   | 14.2541             | 0.4359\n",
+            "alpha      | 1.0000   | 0.9876             | 0.2708\n"
+          ]
+        }
+      ],
+      "source": [
+        "weights = np.exp(log_w)\n",
+        "weights /= weights.sum()\n",
+        "\n",
+        "print(\"Parameter  | Fiducial | Median (weighted) | Std\")\n",
+        "print(\"-\" * 55)\n",
+        "for i, name in enumerate(param_names):\n",
+        "    fid = test_params[name]\n",
+        "    med = np.average(points[:, i], weights=weights)\n",
+        "    std = np.sqrt(np.average((points[:, i] - med) ** 2, weights=weights))\n",
+        "    if \"As\" in name:\n",
+        "        print(f\"{name:10} | {fid:.2e} | {med:.2e}             | {std:.2e}\")\n",
+        "    else:\n",
+        "        print(f\"{name:10} | {fid:.4f}   | {med:.4f}             | {std:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 2D posterior: Omega_cdm0 vs logMmin (68%, 95% CR contours)\n",
+        "from scipy import stats\n",
+        "\n",
+        "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n",
+        "idx_om = param_names.index(\"Omega_cdm0\")\n",
+        "idx_logM = param_names.index(\"logMmin\")\n",
+        "x_arr = points[:, idx_om]\n",
+        "y_arr = points[:, idx_logM]\n",
+        "values = np.vstack([x_arr, y_arr])\n",
+        "kde = stats.gaussian_kde(values, weights=weights)\n",
+        "xmin, xmax = x_arr.min(), x_arr.max()\n",
+        "ymin, ymax = y_arr.min(), y_arr.max()\n",
+        "xx = np.linspace(xmin, xmax, 80)\n",
+        "yy = np.linspace(ymin, ymax, 80)\n",
+        "X, Y = np.meshgrid(xx, yy)\n",
+        "Z = kde(np.vstack([X.ravel(), Y.ravel()])).reshape(X.shape)\n",
+        "sorted_d = np.sort(Z.ravel())[::-1]\n",
+        "cdf = np.cumsum(sorted_d) / np.sum(sorted_d)\n",
+        "l95 = np.interp(0.95, cdf, sorted_d)\n",
+        "l68 = np.interp(0.68, cdf, sorted_d)\n",
+        "ax.contourf(X, Y, Z, levels=[l95, 1e10], colors=[\"C0\"], alpha=0.15)\n",
+        "ax.contourf(X, Y, Z, levels=[l68, 1e10], colors=[\"C0\"], alpha=0.35)\n",
+        "ax.contour(X, Y, Z, levels=[l95, l68], colors=[\"C0\"], linewidths=1.5)\n",
+        "ax.axvline(FIDUCIAL_COSMO[\"Omega_cdm0\"], color=\"red\", ls=\"--\", label=\"Fiducial\")\n",
+        "ax.axhline(FIDUCIAL_HOD[\"logMmin\"], color=\"red\", ls=\"--\")\n",
+        "ax.set_xlabel(r\"$\\Omega_{\\rm cdm0}$\")\n",
+        "ax.set_ylabel(r\"$\\log M_{\\rm min}$\")\n",
+        "ax.legend()\n",
+        "ax.set_title(\"3x2pt posterior (68%, 95% CR)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Derived cosmological parameters (Ω_m, σ_8, S_8)\n",
+        "\n",
+        "Compute Ω_m, σ_8, and S_8 for each posterior sample (same convention as weak lensing surveys) and plot their distributions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Derived (weighted median ± std):\n",
+            "  Omega_m: 0.3208 ± 0.0102\n",
+            "  sigma8:  0.8144 ± 0.0178\n",
+            "  S8:      0.8418 ± 0.008\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Compute derived parameters (Omega_m, sigma8, S8) per sample\n",
+        "from cloelib.cosmology.darkemu_cosmology import DarkEmuHODPerturbations\n",
+        "from cloelib.observables.darkemu_hod import DarkEmuHODParameters\n",
+        "from cloelib.auxiliary.darkemu_utils import DARKEMU_OMEGA_NU\n",
+        "\n",
+        "n_samples = len(points)\n",
+        "idx_Omega_cdm0 = param_names.index(\"Omega_cdm0\")\n",
+        "idx_As = param_names.index(\"As\")\n",
+        "h = FIDUCIAL_COSMO[\"H0\"] / 100.0\n",
+        "Omega_nu0 = DARKEMU_OMEGA_NU / h**2\n",
+        "\n",
+        "Omega_m_arr = np.zeros(n_samples)\n",
+        "sigma8_arr = np.zeros(n_samples)\n",
+        "S8_arr = np.zeros(n_samples)\n",
+        "sigma8_cache = {}\n",
+        "\n",
+        "for i in range(n_samples):\n",
+        "    Omega_cdm0 = points[i, idx_Omega_cdm0]\n",
+        "    Omega_m = Omega_cdm0 + FIDUCIAL_COSMO[\"Omega_b0\"] + Omega_nu0\n",
+        "    Omega_m_arr[i] = Omega_m\n",
+        "    As = points[i, idx_As]\n",
+        "    cache_key = (round(float(Omega_cdm0), 8), round(float(As), 12))\n",
+        "    if cache_key in sigma8_cache:\n",
+        "        sigma8 = sigma8_cache[cache_key]\n",
+        "    else:\n",
+        "        cosmo = {**fixed_cosmo, \"Omega_cdm0\": Omega_cdm0, \"As\": As}\n",
+        "        try:\n",
+        "            bg = CAMBBackground(**cosmo)\n",
+        "            pert = DarkEmuHODPerturbations(\n",
+        "                background=bg,\n",
+        "                redshifts=np.array([0.0]),\n",
+        "                hod_params=DarkEmuHODParameters(**FIDUCIAL_HOD),\n",
+        "                validate_params=False,\n",
+        "            )\n",
+        "            sigma8 = pert.get_sigma8(z=0.0)\n",
+        "        except Exception:\n",
+        "            sigma8 = 0.81 * (As / 2.1e-9) ** 0.5 * (Omega_m / 0.3) ** 0.25\n",
+        "        sigma8_cache[cache_key] = sigma8\n",
+        "    sigma8_arr[i] = sigma8\n",
+        "    S8_arr[i] = sigma8 * (Omega_m / 0.3) ** 0.5\n",
+        "\n",
+        "# Summary\n",
+        "print(\"Derived (weighted median ± std):\")\n",
+        "print(\"  Omega_m:\", np.round(np.average(Omega_m_arr, weights=weights), 4), \"±\", np.round(np.sqrt(np.average((Omega_m_arr - np.average(Omega_m_arr, weights=weights))**2, weights=weights)), 4))\n",
+        "print(\"  sigma8: \", np.round(np.average(sigma8_arr, weights=weights), 4), \"±\", np.round(np.sqrt(np.average((sigma8_arr - np.average(sigma8_arr, weights=weights))**2, weights=weights)), 4))\n",
+        "print(\"  S8:     \", np.round(np.average(S8_arr, weights=weights), 4), \"±\", np.round(np.sqrt(np.average((S8_arr - np.average(S8_arr, weights=weights))**2, weights=weights)), 4))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Posterior for standard cosmological parameters (Omega_m, sigma8, S8)\n",
+        "from scipy import stats\n",
+        "\n",
+        "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
+        "\n",
+        "# Omega_m vs S8 (2D posterior: 68% and 95% credible regions)\n",
+        "values = np.vstack([Omega_m_arr, S8_arr])\n",
+        "kde = stats.gaussian_kde(values, weights=weights)\n",
+        "# Grid over full display range so contours are not cut off at data edges\n",
+        "xx = np.linspace(0.1, 0.6, 80)\n",
+        "yy = np.linspace(0.65, 0.95, 80)\n",
+        "X, Y = np.meshgrid(xx, yy)\n",
+        "Z = kde(np.vstack([X.ravel(), Y.ravel()])).reshape(X.shape)\n",
+        "sorted_d = np.sort(Z.ravel())[::-1]\n",
+        "cdf = np.cumsum(sorted_d) / np.sum(sorted_d)\n",
+        "l95 = np.interp(0.95, cdf, sorted_d)\n",
+        "l68 = np.interp(0.68, cdf, sorted_d)\n",
+        "ax.contourf(X, Y, Z, levels=[l95, 1e10], colors=[\"C0\"], alpha=0.15)\n",
+        "ax.contourf(X, Y, Z, levels=[l68, 1e10], colors=[\"C0\"], alpha=0.35)\n",
+        "ax.contour(X, Y, Z, levels=[l95, l68], colors=[\"C0\"], linewidths=1.5)\n",
+        "ax.set_xlabel(r\"$\\Omega_{\\rm m}$\")\n",
+        "ax.set_ylabel(r\"$S_8$\")\n",
+        "ax.set_title(r\"$\\Omega_{\\rm m}$–$S_8$ posterior (68%, 95% CR)\")\n",
+        "ax.set_xlim(0.1, 0.6)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "\n",
+        "## Production 3x2pt: script + corner plot\n",
+        "\n",
+        "For full sampling (e.g. `n_live=500`) with sigma8/S8 and corner plots, run from the **repo root**:\n",
+        "\n",
+        "**1. Sampling** (saves `chain_3x2pt_full.npz` and checkpoint):\n",
+        "\n",
+        "```bash\n",
+        "python playground/scripts/sampling/darkemu_3x2pt_full_sampling.py --n_live 500 --output_dir playground/results\n",
+        "```\n",
+        "\n",
+        "**2. Corner plot** — The command below creates a corner (triangle) plot for Omega_m, sigma8, and S8 from the chain saved in step 1:\n",
+        "\n",
+        "```bash\n",
+        "python playground/scripts/plotting/corner_plot.py \\\n",
+        "  --chain playground/results/chain_3x2pt_full.npz \\\n",
+        "  --output playground/results/corner_3x2pt.pdf \\\n",
+        "  --params Omega_m sigma8 S8\n",
+        "```\n",
+        "\n",
+        "Optional: `--n_workers N` in step 1 for parallel likelihood evaluation."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "cloelib",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/tutorials/dark_emulator/fitting_gc_wp.ipynb b/tutorials/dark_emulator/fitting_gc_wp.ipynb
new file mode 100644
index 0000000..6e2583e
--- /dev/null
+++ b/tutorials/dark_emulator/fitting_gc_wp.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Fitting observed w_p(R) with Dark Emulator\n",
+        "\n",
+        "This notebook shows how to fit **galaxy clustering** observational data (projected correlation function w_p(R)) using Dark Emulator and cloelike.\n",
+        "\n",
+        "Workflow:\n",
+        "1. Prepare observational data (or mock data)\n",
+        "2. Set up the GC likelihood with `EuclidLikelihood_DarkEmu_RealSpace`\n",
+        "3. Optimize (or run MCMC) to fit cosmology and HOD parameters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Path and imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CLOE_ROOT: /home/ryuseikano/Euclid/CLOE\n"
+          ]
+        }
+      ],
+      "source": [
+        "import sys\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.optimize import minimize\n",
+        "\n",
+        "NOTEBOOK_DIR = Path.cwd()\n",
+        "CLOE_ROOT = NOTEBOOK_DIR.parent.parent.parent  # tutorials/dark_emulator -> CLOE\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelib\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelike\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"dark_emulator_public\"))\n",
+        "\n",
+        "from cloelib.cosmology.camb_cosmology import CAMBBackground\n",
+        "from cloelib.cosmology.darkemu_cosmology import DarkEmuHODPerturbations\n",
+        "from cloelib.observables.darkemu_hod import DarkEmuHODParameters\n",
+        "from cloelike.EuclidLikelihood_DarkEmu_RealSpace import EuclidLikelihood_DarkEmu_RealSpace\n",
+        "\n",
+        "print(\"CLOE_ROOT:\", CLOE_ROOT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Observational data\n",
+        "\n",
+        "In this notebook we **generate mock data** in the format below for demonstration (fiducial cosmology + HOD + noise). You can replace the next cell with loading your own observed w_p(R) and covariance.\n",
+        "\n",
+        "**Data format (expected by the likelihood):**\n",
+        "- `wp`: observed w_p [h⁻¹ Mpc]\n",
+        "- `R_bins`: projected radii [h⁻¹ Mpc]\n",
+        "- `z_sample`: sample redshift\n",
+        "- `pimax`: line-of-sight integration limit [h⁻¹ Mpc]\n",
+        "- `covariance`: covariance matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ryuseikano/Euclid/CLOE/cloelib/cloelib/cosmology/darkemu_cosmology.py:131: UserWarning: Neutrino density mismatch: input omega_nu = 0.000000, but Dark Emulator assumes fixed omega_nu = 0.00064. Dark Emulator will use its internal fixed value regardless of input.\n",
+            "  self.cparam = background_to_darkemu_cparam(background)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mock GC data: 15  bins, z_sample = 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fiducial parameters (for mock data)\n",
+        "FIDUCIAL_COSMO = {\n",
+        "    \"H0\": 67.0,\n",
+        "    \"Omega_cdm0\": 0.27,\n",
+        "    \"Omega_b0\": 0.049,\n",
+        "    \"Omega_k0\": 0.0,\n",
+        "    \"w0\": -1.0,\n",
+        "    \"wa\": 0.0,\n",
+        "    \"ns\": 0.96,\n",
+        "    \"As\": 2.1e-9,\n",
+        "    \"gamma_MG\": 0.55,\n",
+        "    \"mnu\": 0.06,\n",
+        "    \"N_mnu\": 1,\n",
+        "}\n",
+        "FIDUCIAL_HOD = {\n",
+        "    \"logMmin\": 13.0,\n",
+        "    \"sigma_sq\": 0.3,\n",
+        "    \"logM1\": 14.0,\n",
+        "    \"alpha\": 1.0,\n",
+        "    \"kappa\": 1.0,\n",
+        "    \"poff\": 0.0,\n",
+        "    \"Roff\": 0.0,\n",
+        "}\n",
+        "\n",
+        "# --- Mock observational data (replace with real data loading) ---\n",
+        "z_sample = 0.5\n",
+        "pimax = 100.0\n",
+        "R_bins = np.logspace(-0.5, 1.5, 15)\n",
+        "\n",
+        "background = CAMBBackground(**FIDUCIAL_COSMO)\n",
+        "hod_params = DarkEmuHODParameters(**FIDUCIAL_HOD)\n",
+        "pert = DarkEmuHODPerturbations(\n",
+        "    background=background,\n",
+        "    redshifts=np.array([z_sample]),\n",
+        "    hod_params=hod_params,\n",
+        ")\n",
+        "wp_fid = pert.projected_correlation(R_bins, z_sample, pimax=pimax)\n",
+        "\n",
+        "np.random.seed(42)\n",
+        "noise_level = 0.1\n",
+        "sigma_wp = noise_level * np.abs(wp_fid)\n",
+        "cov_wp = np.diag(sigma_wp ** 2)\n",
+        "wp_obs = wp_fid + np.random.randn(len(R_bins)) * sigma_wp\n",
+        "\n",
+        "data_gc = {\n",
+        "    \"wp\": wp_obs,\n",
+        "    \"R_bins\": R_bins,\n",
+        "    \"z_sample\": z_sample,\n",
+        "    \"pimax\": pimax,\n",
+        "    \"covariance\": cov_wp,\n",
+        "}\n",
+        "print(\"Mock GC data:\", len(R_bins), \" bins, z_sample =\", z_sample)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot mock data: w_p(R) with error bars; fiducial curve for reference\n",
+        "plt.figure(figsize=(6, 4))\n",
+        "plt.errorbar(R_bins, wp_obs, yerr=sigma_wp, fmt=\"o\", capsize=3, label=\"Mock data\")\n",
+        "plt.plot(R_bins, wp_fid, \"k--\", alpha=0.8, label=\"Fiducial (no noise)\")\n",
+        "plt.xscale(\"log\")\n",
+        "plt.yscale(\"log\")\n",
+        "plt.xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "plt.ylabel(r\"$w_p(R)$ [h$^{-1}$ Mpc]\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Mock GC data (z = {})\".format(z_sample))\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Covariance matrix (diagonal for this mock)\n",
+        "plt.figure(figsize=(5, 4))\n",
+        "plt.imshow(cov_wp, aspect=\"auto\", cmap=\"viridis\", origin=\"lower\")\n",
+        "plt.colorbar(label=r\"$\\mathrm{Cov}(w_p)$\")\n",
+        "plt.xlabel(\"bin index\")\n",
+        "plt.ylabel(\"bin index\")\n",
+        "plt.title(\"Mock GC covariance matrix\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Likelihood and scale cuts\n",
+        "\n",
+        "Set up the GC likelihood with `EuclidLikelihood_DarkEmu_RealSpace` (data_gc only)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data points after scale cuts: 15\n"
+          ]
+        }
+      ],
+      "source": [
+        "settings = {\n",
+        "    # if you want to apply scale cuts\n",
+        "    # \"R_min_gc\": 2.0,\n",
+        "    # \"R_max_gc\": 30.0,\n",
+        "    \"hod_params_fixed\": {\n",
+        "        \"kappa\": FIDUCIAL_HOD[\"kappa\"],\n",
+        "        \"poff\": FIDUCIAL_HOD[\"poff\"],\n",
+        "        \"Roff\": FIDUCIAL_HOD[\"Roff\"],\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "likelihood = EuclidLikelihood_DarkEmu_RealSpace(\n",
+        "    data_gc=data_gc,\n",
+        "    settings=settings,\n",
+        "    Background=CAMBBackground,\n",
+        ")\n",
+        "print(\"Data points after scale cuts:\", likelihood.n_gc)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def full_params_from_vector(x):\n",
+        "    \"\"\"Map free parameters to full parameter dict.\"\"\"\n",
+        "    return {\n",
+        "        **FIDUCIAL_COSMO,\n",
+        "        \"logMmin\": x[0],\n",
+        "        \"sigma_sq\": x[1],\n",
+        "        \"logM1\": x[2],\n",
+        "        \"alpha\": x[3],\n",
+        "        **settings[\"hod_params_fixed\"],\n",
+        "    }\n",
+        "\n",
+        "def neg_loglike_gc(x):\n",
+        "    \"\"\"−log L for free params x = [logMmin, sigma_sq, logM1, alpha].\"\"\"\n",
+        "    return -likelihood.loglike(full_params_from_vector(x))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Fitting\n",
+        "\n",
+        "Minimize −log L over HOD parameters (cosmology fixed here)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Best-fit HOD: logMmin = 12.678, sigma_sq = 0.050, logM1 = 13.778, alpha = 1.193\n",
+            "−log L at best fit: 5.877255168420394\n"
+          ]
+        }
+      ],
+      "source": [
+        "x0 = [FIDUCIAL_HOD[\"logMmin\"], FIDUCIAL_HOD[\"sigma_sq\"], FIDUCIAL_HOD[\"logM1\"], FIDUCIAL_HOD[\"alpha\"]]\n",
+        "bounds = [(12.0, 14.0), (0.05, 0.6), (13.0, 15.0), (0.5, 1.5)]\n",
+        "\n",
+        "result = minimize(neg_loglike_gc, x0, method=\"L-BFGS-B\", bounds=bounds)\n",
+        "best = result.x\n",
+        "print(\"Best-fit HOD: logMmin = {:.3f}, sigma_sq = {:.3f}, logM1 = {:.3f}, alpha = {:.3f}\".format(*best))\n",
+        "print(\"−log L at best fit:\", result.fun)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Plot: data vs best-fit theory"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "params_best = full_params_from_vector(best)\n",
+        "theory_full = likelihood.get_theory_vector(params_best)\n",
+        "theory_gc = theory_full[: likelihood.n_gc]\n",
+        "R_plot = likelihood.R_bins_gc\n",
+        "data_plot = likelihood.data_vector[: likelihood.n_gc]\n",
+        "\n",
+        "# Use scale cuts if provided, otherwise use all R_bins (default: 0.0 to np.inf)\n",
+        "R_min = settings.get(\"R_min_gc\", 0.0)\n",
+        "R_max = settings.get(\"R_max_gc\", np.inf)\n",
+        "mask = (data_gc[\"R_bins\"] >= R_min) & (data_gc[\"R_bins\"] <= R_max)\n",
+        "cov_cut = np.asarray(data_gc[\"covariance\"])[np.outer(mask, mask)].reshape(mask.sum(), mask.sum())\n",
+        "sigma_plot = np.sqrt(np.diag(cov_cut))\n",
+        "\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "plt.errorbar(R_plot, data_plot, yerr=sigma_plot, fmt=\"o\", label=\"Data\")\n",
+        "plt.loglog(R_plot, theory_gc, \"-\", label=\"Best-fit theory\")\n",
+        "plt.xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "plt.ylabel(r\"$w_p(R)$ [h$^{-1}$ Mpc]\")\n",
+        "plt.legend()\n",
+        "plt.title(\"GC: w_p fitting with Dark Emulator\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "cloelib",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/tutorials/dark_emulator/fitting_ggl_delta_sigma.ipynb b/tutorials/dark_emulator/fitting_ggl_delta_sigma.ipynb
new file mode 100644
index 0000000..54fd863
--- /dev/null
+++ b/tutorials/dark_emulator/fitting_ggl_delta_sigma.ipynb
@@ -0,0 +1,367 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Fitting observed ΔΣ(R) with Dark Emulator\n",
+        "\n",
+        "This notebook shows how to fit **galaxy-galaxy lensing** observational data (excess surface mass density ΔΣ(R)) using Dark Emulator and cloelike.\n",
+        "\n",
+        "Workflow:\n",
+        "1. Prepare observational data (or mock data)\n",
+        "2. Set up the GGL likelihood with `EuclidLikelihood_DarkEmu_RealSpace`\n",
+        "3. Optimize (or run MCMC) to fit cosmology and HOD parameters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Path and imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CLOE_ROOT: /home/ryuseikano/Euclid/CLOE\n"
+          ]
+        }
+      ],
+      "source": [
+        "import sys\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.optimize import minimize\n",
+        "\n",
+        "NOTEBOOK_DIR = Path.cwd()\n",
+        "CLOE_ROOT = NOTEBOOK_DIR.parent.parent.parent  # tutorials/dark_emulator -> CLOE\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelib\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"cloelike\"))\n",
+        "sys.path.insert(0, str(CLOE_ROOT / \"dark_emulator_public\"))\n",
+        "\n",
+        "from cloelib.cosmology.camb_cosmology import CAMBBackground\n",
+        "from cloelib.cosmology.darkemu_cosmology import DarkEmuHODPerturbations\n",
+        "from cloelib.observables.darkemu_hod import DarkEmuHODParameters\n",
+        "from cloelike.EuclidLikelihood_DarkEmu_RealSpace import EuclidLikelihood_DarkEmu_RealSpace\n",
+        "\n",
+        "print(\"CLOE_ROOT:\", CLOE_ROOT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Observational data\n",
+        "\n",
+        "In this notebook we **generate mock data** in the format below for demonstration (fiducial cosmology + HOD + noise). You can replace the next cell with loading your own observed ΔΣ(R) and covariance.\n",
+        "\n",
+        "**Data format (expected by the likelihood):**\n",
+        "- `delta_sigma`: observed ΔΣ [h M_sun / pc²]\n",
+        "- `R_bins`: projected radii [h⁻¹ Mpc]\n",
+        "- `z_lens`: lens redshift\n",
+        "- `covariance`: covariance matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ryuseikano/Euclid/CLOE/cloelib/cloelib/cosmology/darkemu_cosmology.py:131: UserWarning: Neutrino density mismatch: input omega_nu = 0.000000, but Dark Emulator assumes fixed omega_nu = 0.00064. Dark Emulator will use its internal fixed value regardless of input.\n",
+            "  self.cparam = background_to_darkemu_cparam(background)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mock GGL data:  15  bins, z_lens = 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fiducial parameters (for mock data; fix cosmology for a quick HOD-only fit if you prefer)\n",
+        "FIDUCIAL_COSMO = {\n",
+        "    \"H0\": 67.0,\n",
+        "    \"Omega_cdm0\": 0.27,\n",
+        "    \"Omega_b0\": 0.049,\n",
+        "    \"Omega_k0\": 0.0,\n",
+        "    \"w0\": -1.0,\n",
+        "    \"wa\": 0.0,\n",
+        "    \"ns\": 0.96,\n",
+        "    \"As\": 2.1e-9,\n",
+        "    \"gamma_MG\": 0.55,\n",
+        "    \"mnu\": 0.06,\n",
+        "    \"N_mnu\": 1,\n",
+        "}\n",
+        "FIDUCIAL_HOD = {\n",
+        "    \"logMmin\": 13.0,\n",
+        "    \"sigma_sq\": 0.3,\n",
+        "    \"logM1\": 14.0,\n",
+        "    \"alpha\": 1.0,\n",
+        "    \"kappa\": 1.0,\n",
+        "    \"poff\": 0.0,\n",
+        "    \"Roff\": 0.0,\n",
+        "}\n",
+        "\n",
+        "# --- Mock observational data (replace with real data loading) ---\n",
+        "z_lens = 0.5\n",
+        "R_bins = np.logspace(-0.5, 1.5, 15)\n",
+        "\n",
+        "background = CAMBBackground(**FIDUCIAL_COSMO)\n",
+        "hod_params = DarkEmuHODParameters(**FIDUCIAL_HOD)\n",
+        "pert = DarkEmuHODPerturbations(\n",
+        "    background=background,\n",
+        "    redshifts=np.array([z_lens]),\n",
+        "    hod_params=hod_params,\n",
+        ")\n",
+        "ds_fid = pert.delta_sigma(R_bins, z_lens)\n",
+        "\n",
+        "np.random.seed(42)\n",
+        "noise_level = 0.1\n",
+        "sigma_ds = noise_level * np.abs(ds_fid)\n",
+        "cov_ds = np.diag(sigma_ds ** 2)\n",
+        "ds_obs = ds_fid + np.random.randn(len(R_bins)) * sigma_ds\n",
+        "\n",
+        "data_ggl = {\n",
+        "    \"delta_sigma\": ds_obs,\n",
+        "    \"R_bins\": R_bins,\n",
+        "    \"z_lens\": z_lens,\n",
+        "    \"covariance\": cov_ds,\n",
+        "}\n",
+        "print(\"Mock GGL data: \", len(R_bins), \" bins, z_lens =\", z_lens)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot mock data: ΔΣ(R) with error bars; fiducial curve for reference\n",
+        "plt.figure(figsize=(6, 4))\n",
+        "plt.errorbar(R_bins, ds_obs, yerr=sigma_ds, fmt=\"o\", capsize=3, label=\"Mock data\")\n",
+        "plt.plot(R_bins, ds_fid, \"k--\", alpha=0.8, label=\"Fiducial (no noise)\")\n",
+        "plt.xscale(\"log\")\n",
+        "plt.yscale(\"log\")\n",
+        "plt.xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "plt.ylabel(r\"$\\Delta\\Sigma(R)$ [h $M_\\odot$ pc$^{-2}$]\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Mock GGL data (z_lens = {})\".format(z_lens))\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Covariance matrix (diagonal for this mock; origin='lower' so small R at bottom)\n",
+        "plt.figure(figsize=(5, 4))\n",
+        "plt.imshow(cov_ds, aspect=\"auto\", cmap=\"viridis\", origin=\"lower\")\n",
+        "plt.colorbar(label=r\"$\\mathrm{Cov}(\\Delta\\Sigma)$\")\n",
+        "plt.xlabel(\"bin index\")\n",
+        "plt.ylabel(\"bin index\")\n",
+        "plt.title(\"Mock GGL covariance matrix\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Likelihood and scale cuts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Data points after scale cuts: 15\n"
+          ]
+        }
+      ],
+      "source": [
+        "settings = {\n",
+        "    # if you want to apply scale cuts\n",
+        "    # \"R_min_ggl\": 3.0,\n",
+        "    # \"R_max_ggl\": 30.0,\n",
+        "    \"hod_params_fixed\": {\n",
+        "        \"kappa\": FIDUCIAL_HOD[\"kappa\"],\n",
+        "        \"poff\": FIDUCIAL_HOD[\"poff\"],\n",
+        "        \"Roff\": FIDUCIAL_HOD[\"Roff\"],\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "likelihood = EuclidLikelihood_DarkEmu_RealSpace(\n",
+        "    data_ggl=data_ggl,\n",
+        "    settings=settings,\n",
+        "    Background=CAMBBackground,\n",
+        ")\n",
+        "print(\"Data points after scale cuts:\", likelihood.n_ggl)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Fitting\n",
+        "\n",
+        "Minimize −log L (or chi²) over parameters. Here we fit HOD only with cosmology fixed.\n",
+        "\n",
+        "It takes some time (~30mins)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Best-fit HOD: logMmin = 13.304, sigma_sq = 0.600, logM1 = 14.220, alpha = 1.500\n",
+            "−log L at best fit: 6.076134193113175\n"
+          ]
+        }
+      ],
+      "source": [
+        "def full_params_from_vector(x):\n",
+        "    \"\"\"Map free parameters to full parameter dict.\"\"\"\n",
+        "    return {\n",
+        "        **FIDUCIAL_COSMO,\n",
+        "        \"logMmin\": x[0],\n",
+        "        \"sigma_sq\": x[1],\n",
+        "        \"logM1\": x[2],\n",
+        "        \"alpha\": x[3],\n",
+        "        **settings[\"hod_params_fixed\"],\n",
+        "    }\n",
+        "\n",
+        "def neg_loglike(x):\n",
+        "    return -likelihood.loglike(full_params_from_vector(x))\n",
+        "\n",
+        "# Initial guess (fiducial)\n",
+        "x0 = [FIDUCIAL_HOD[\"logMmin\"], FIDUCIAL_HOD[\"sigma_sq\"], FIDUCIAL_HOD[\"logM1\"], FIDUCIAL_HOD[\"alpha\"]]\n",
+        "bounds = [(12.0, 14.0), (0.05, 0.6), (13.0, 15.0), (0.5, 1.5)]\n",
+        "\n",
+        "result = minimize(neg_loglike, x0, method=\"L-BFGS-B\", bounds=bounds)\n",
+        "best = result.x\n",
+        "print(\"Best-fit HOD: logMmin = {:.3f}, sigma_sq = {:.3f}, logM1 = {:.3f}, alpha = {:.3f}\".format(*best))\n",
+        "print(\"−log L at best fit:\", result.fun)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Plot: data vs best-fit theory"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "params_best = full_params_from_vector(best)\n",
+        "theory_full = likelihood.get_theory_vector(params_best)\n",
+        "theory_ggl = theory_full[likelihood.start_ggl : likelihood.start_ggl + likelihood.n_ggl]\n",
+        "R_plot = likelihood.R_bins_ggl\n",
+        "data_plot = likelihood.data_vector[likelihood.start_ggl : likelihood.start_ggl + likelihood.n_ggl]\n",
+        "\n",
+        "# Use scale cuts if provided, otherwise use all R_bins (default: 0.0 to np.inf)\n",
+        "R_min = settings.get(\"R_min_ggl\", 0.0)\n",
+        "R_max = settings.get(\"R_max_ggl\", np.inf)\n",
+        "mask = (data_ggl[\"R_bins\"] >= R_min) & (data_ggl[\"R_bins\"] <= R_max)\n",
+        "cov_cut = np.asarray(data_ggl[\"covariance\"])[np.outer(mask, mask)].reshape(mask.sum(), mask.sum())\n",
+        "sigma_plot = np.sqrt(np.diag(cov_cut))\n",
+        "\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "plt.errorbar(R_plot, data_plot, yerr=sigma_plot, fmt=\"o\", label=\"Data\")\n",
+        "plt.loglog(R_plot, theory_ggl, \"-\", label=\"Best-fit theory\")\n",
+        "plt.xlabel(r\"$R$ [h$^{-1}$ Mpc]\")\n",
+        "plt.ylabel(r\"$\\Delta\\Sigma(R)$ [h $M_\\odot$ pc$^{-2}$]\")\n",
+        "plt.legend()\n",
+        "plt.title(\"GGL: ΔΣ fitting with Dark Emulator\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "cloelib",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}