diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9f5876b..9326b86 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -54,6 +54,7 @@ jobs:
           - test_template_smeared_king_pdf
           - test_utils
           - test_fitting
+          - test_wrapper
 
     steps:
       - name: Checkout code
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 27653fc..ce6e548 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.8
+    rev: v0.16.10
     hooks:
       - id: ruff
       - id: ruff-format
diff --git a/docs/conf.py b/docs/conf.py
index 76dc354..f513987 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,5 +1,5 @@
-import sys
 import os
+import sys
 
 sys.path.insert(0, os.path.abspath(".."))
 
diff --git a/docs/examples.rst b/docs/examples.rst
index f9cc3b4..25b1bd0 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -141,7 +141,7 @@ bin.
    )
    results = fitter.fit_all_bins(verbose=True)
 
-   alpha_fit = results["alpha"]  # shape (n_gamma, n_logE, n_dec)
+   alpha_fit = results["alpha"]  # shape (n_extension, n_gamma, n_logE, n_dec)
    beta_fit = results["beta"]
 
    # Continuous evaluation between bin centers:
@@ -192,8 +192,9 @@ above:
    from kingmaker.wrapper import KingSpatialLikelihood
    import numpy as np
 
-   # Source catalog for the signal-subtraction (marginalized) path.
-   catalog_decs = np.radians(np.linspace(-60, 60, 13))
+   # Stand-in "data" events and a point-source position for one trial.
+   data_events = signal_events[:1000]
+   source_ra, source_dec = 0.5, 0.2
 
    wrapper = KingSpatialLikelihood(
        signal_events=signal_events,
@@ -202,14 +203,10 @@ above:
        cache_parameters=False,
        # Enable the RA-marginalized path for signal-subtraction likelihoods.
        enable_marginalization=True,
-       marginalization_source_decs=catalog_decs,
+       marginalization_source_decs=np.array([source_dec]),
        marginalization_angular_cutoff=np.radians(10.0),
    )
 
-   # Stand-in "data" events and a point-source position for one trial.
-   data_events = signal_events[:1000]
-   source_ra, source_dec = 0.5, 0.2
-
    # Per trial: cache per-event parameters once, then evaluate as needed.
    # set_events precomputes both the standard and marginalized PDF matrices.
    wrapper.set_events(
diff --git a/examples/extended_source_demo.ipynb b/examples/extended_source_demo.ipynb
deleted file mode 100644
index c298ceb..0000000
--- a/examples/extended_source_demo.ipynb
+++ /dev/null
@@ -1,1107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a1b2c3d4",
-   "metadata": {},
-   "source": [
-    "# Extended Source King PDF Demo\n",
-    "\n",
-    "This notebook demonstrates `ExtendedSourceKingPDF`, which evaluates the convolution of the King PSF with a Rayleigh (2D Gaussian, flat-sky) source extension. The convolution is computed via a Gauss-Laguerre quadrature over the inverse-gamma scale mixture representation of the King distribution, and cached in a 4D lookup table over **(α, β, extension, ψ)** at construction time.\n",
-    "\n",
-    "**When to use this class**: whenever a source is spatially extended with a characteristic radius r₀ ≲ 5°. Passing r₀ → 0 recovers the flat-sky King PDF; passing r₀ → α smooths the core noticeably.\n",
-    "\n",
-    "## Contents\n",
-    "1. [Setup and table construction](#1.-Setup-and-table-construction)\n",
-    "2. [Effect of extension on the PSF](#2.-Effect-of-extension-on-the-PSF)\n",
-    "3. [The ring effect: probability redistribution](#3.-The-ring-effect:-probability-redistribution)\n",
-    "4. [Extension vs PSF width: when does it matter?](#4.-Extension-vs-PSF-width:-when-does-it-matter?)\n",
-    "5. [Normalization check](#5.-Normalization-check)\n",
-    "6. [Multi-source evaluation with `evaluate()`](#6.-Multi-source-evaluation-with-evaluate())\n",
-    "7. [Performance benchmarks](#7.-Performance-benchmarks)\n",
-    "8. [Summary](#8.-Summary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b2c3d4e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from timeit import timeit\n",
-    "\n",
-    "from kingmaker.pdf import ExtendedSourceKingPDF, KingPDF\n",
-    "from kingmaker.utils import angular_distance\n",
-    "\n",
-    "plt.style.use(\"seaborn-v0_8-darkgrid\")\n",
-    "rng = np.random.default_rng(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3d4e5f6",
-   "metadata": {},
-   "source": [
-    "## 1. Setup and table construction\n",
-    "\n",
-    "`ExtendedSourceKingPDF.__init__` accepts optional grid arrays for each axis:\n",
-    "\n",
-    "| Parameter | Default range | Axis in table |\n",
-    "|---|---|---|\n",
-    "| `points_alpha` | 0.05° – 180° (30 pts, log) | log₁₀ α |\n",
-    "| `points_beta` | 1.01 – 10 (20 pts, log) | log₁₀ β |\n",
-    "| `points_extension` | 0.05° – 5° (20 pts, log) | log₁₀ extension |\n",
-    "| `points_psi` | 0 ∪ logspace(1e-4 rad, π) (501 pts) | ψ |\n",
-    "\n",
-    "The table is built once at construction using 32-point Gauss-Laguerre quadrature for each (α, β, extension) triple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d4e5f6a7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Table shape : (30, 20, 20, 501)\n",
-      "             (n_alpha=30, n_beta=20, n_ext=20, n_psi=501)\n",
-      "Memory      : 48.1 MB\n",
-      "Build time  : 1.4 s\n",
-      "\n",
-      "Alpha range : [5.0e-02 1.8e+02] deg\n",
-      "Beta range  : [ 1.02329299 10.        ]\n",
-      "Ext. range  : [0.05 5.  ] deg\n"
-     ]
-    }
-   ],
-   "source": [
-    "t0 = time.perf_counter()\n",
-    "ext_pdf = ExtendedSourceKingPDF(angular_cutoff=np.radians(5))  # default grid\n",
-    "t1 = time.perf_counter()\n",
-    "\n",
-    "print(f\"Table shape : {ext_pdf._table.shape}\")\n",
-    "print(\n",
-    "    f\"             (n_alpha={ext_pdf._table.shape[0]}, n_beta={ext_pdf._table.shape[1]},\"\n",
-    "    f\" n_ext={ext_pdf._table.shape[2]}, n_psi={ext_pdf._table.shape[3]})\"\n",
-    ")\n",
-    "print(f\"Memory      : {ext_pdf._table.nbytes / 1e6:.1f} MB\")\n",
-    "print(f\"Build time  : {t1 - t0:.1f} s\")\n",
-    "print()\n",
-    "print(f\"Alpha range : {np.degrees(ext_pdf._points_alpha[[0, -1]])} deg\")\n",
-    "print(f\"Beta range  : {ext_pdf._points_beta[[0, -1]]}\")\n",
-    "print(f\"Ext. range  : {np.degrees(ext_pdf._points_extension[[0, -1]])} deg\")\n",
-    "\n",
-    "king = KingPDF()  # point-source reference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5f6a7b8",
-   "metadata": {},
-   "source": [
-    "## 2. Effect of extension on the PSF\n",
-    "\n",
-    "For a fixed PSF (α, β), increasing the source extension r₀ broadens the observed angular profile. The convolution is computed via the scale mixture trick:\n",
-    "\n",
-    "$$p_{\\rm extended}(\\psi;\\,\\alpha,\\beta,r_0) = \\frac{\\psi}{\\Gamma(\\beta-1)} \\int_0^\\infty \\frac{t^{\\beta-1}}{\\beta\\alpha^2 + r_0^2 t}\\, e^{-t}\\, e^{-\\psi^2 t / [2(\\beta\\alpha^2 + r_0^2 t)]}\\, dt$$\n",
-    "\n",
-    "Setting r₀ = 0 recovers the flat-sky King PDF exactly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f6a7b8c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/725358620.py:41: UserWarning: Glyph 8315 (\\N{SUPERSCRIPT MINUS}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n",
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/725358620.py:41: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n",
-      "/Users/mjlarson/icecube/icetray/scripts/king/venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 8315 (\\N{SUPERSCRIPT MINUS}) missing from font(s) Arial.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n",
-      "/Users/mjlarson/icecube/icetray/scripts/king/venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Observation: extension broadens the core and suppresses the peak.\n",
-      "             The PSF tail is unaffected for r₀ << α.\n"
-     ]
-    }
-   ],
-   "source": [
-    "alpha_fixed = np.radians(1.0)  # 1 degree PSF\n",
-    "beta_fixed = 2.5\n",
-    "extensions_deg = [0.0, 0.3, 0.5, 1.0, 2.0, 3.0]  # degrees\n",
-    "\n",
-    "psi_deg = np.linspace(0.01, 8.0, 800)\n",
-    "psi = np.radians(psi_deg)\n",
-    "alpha_arr = np.full_like(psi, alpha_fixed)\n",
-    "beta_arr = np.full_like(psi, beta_fixed)\n",
-    "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "colors = plt.cm.viridis(np.linspace(0.1, 0.9, len(extensions_deg)))\n",
-    "\n",
-    "for ext_deg, color in zip(extensions_deg, colors):\n",
-    "    if ext_deg == 0.0:\n",
-    "        # Point source: use KingPDF directly (exact spherical form)\n",
-    "        pdf_vals = king.pdf(psi, alpha_arr, beta_arr)\n",
-    "        label = \"r₀ = 0 (point source)\"\n",
-    "        lw, ls = 2.5, \"--\"\n",
-    "    else:\n",
-    "        ext = np.radians(ext_deg)\n",
-    "        pdf_vals = ext_pdf.pdf(psi, alpha_arr, beta_arr, np.full_like(psi, ext))\n",
-    "        label = f\"r₀ = {ext_deg:.1f}°\"\n",
-    "        lw, ls = 2.0, \"-\"\n",
-    "    ax1.plot(psi_deg, pdf_vals, color=color, lw=lw, ls=ls, label=label)\n",
-    "    ax2.semilogy(psi_deg, pdf_vals, color=color, lw=lw, ls=ls, label=label)\n",
-    "\n",
-    "for ax in (ax1, ax2):\n",
-    "    ax.axvline(\n",
-    "        np.degrees(alpha_fixed),\n",
-    "        color=\"grey\",\n",
-    "        lw=1.2,\n",
-    "        ls=\":\",\n",
-    "        label=f\"α = {np.degrees(alpha_fixed):.0f}°\",\n",
-    "    )\n",
-    "    ax.set_xlabel(\"Angular separation ψ (degrees)\", fontsize=13)\n",
-    "    ax.set_ylabel(\"Probability density (sr⁻¹)\", fontsize=13)\n",
-    "    ax.legend(fontsize=11)\n",
-    "\n",
-    "ax1.set_xlim(0, 8)\n",
-    "ax1.set_ylim(bottom=0)\n",
-    "ax1.set_title(\n",
-    "    f\"Linear scale   (α = {np.degrees(alpha_fixed):.0f}°, β = {beta_fixed})\",\n",
-    "    fontsize=13,\n",
-    "    fontweight=\"bold\",\n",
-    ")\n",
-    "ax2.set_xlim(0, 8)\n",
-    "ax2.set_ylim(1e-1, None)\n",
-    "ax2.set_title(\"Log scale — tail behavior\", fontsize=13, fontweight=\"bold\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"Observation: extension broadens the core and suppresses the peak.\")\n",
-    "print(\"             The PSF tail is unaffected for r₀ << α.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7b8c9d0",
-   "metadata": {},
-   "source": [
-    "## 3. The ring effect: probability redistribution\n",
-    "\n",
-    "Because the PDF must integrate to 1, probability removed from the core at ψ < r₀ must appear elsewhere. The ratio p_extended / p_point shows:\n",
-    "- **Deficit** at ψ < r₀ (fewer events land near the nominal source direction)\n",
-    "- **Enhancement** near ψ ≈ r₀ (events from the source edge with near-zero reconstruction error pile up here)\n",
-    "- **Recovery** in the tail (shape controlled by α, β)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b8c9d0e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/3826809362.py:34: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dotted vertical lines mark each extension radius r₀.\n",
-      "The ring enhancement peaks near ψ ≈ r₀; the deficit is at smaller ψ.\n"
-     ]
-    }
-   ],
-   "source": [
-    "alpha_fixed = np.radians(1.5)  # 1.5 deg\n",
-    "beta_fixed = 2.5\n",
-    "extensions_deg = [0.2, 0.5, 1.0, 1.5, 2.5]\n",
-    "\n",
-    "psi_deg = np.linspace(0.05, 7.0, 600)\n",
-    "psi = np.radians(psi_deg)\n",
-    "alpha_arr = np.full_like(psi, alpha_fixed)\n",
-    "beta_arr = np.full_like(psi, beta_fixed)\n",
-    "\n",
-    "# Reference: flat-sky King (the quantity our table matches at r0→0)\n",
-    "p_point = king.pdf(psi, alpha_arr, beta_arr)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "colors = plt.cm.plasma(np.linspace(0.15, 0.85, len(extensions_deg)))\n",
-    "\n",
-    "for ext_deg, color in zip(extensions_deg, colors):\n",
-    "    ext = np.radians(ext_deg)\n",
-    "    p_extended = ext_pdf.pdf(psi, alpha_arr, beta_arr, np.full_like(psi, ext))\n",
-    "    ratio = p_extended / p_point\n",
-    "    ax.plot(psi_deg, ratio, color=color, lw=2.0, label=f\"r₀ = {ext_deg:.1f}°\")\n",
-    "    # Mark the extension radius\n",
-    "    ax.axvline(ext_deg, color=color, lw=0.8, ls=\":\", alpha=0.7)\n",
-    "\n",
-    "ax.axhline(1.0, color=\"black\", lw=1.5, ls=\"--\", label=\"point source (ratio = 1)\")\n",
-    "ax.axvline(\n",
-    "    np.degrees(alpha_fixed),\n",
-    "    color=\"grey\",\n",
-    "    lw=1.2,\n",
-    "    ls=\"-.\",\n",
-    "    label=f\"α = {np.degrees(alpha_fixed):.1f}°\",\n",
-    ")\n",
-    "ax.set_xlabel(\"Angular separation ψ (degrees)\", fontsize=13)\n",
-    "ax.set_ylabel(r\"$p_{\\rm extended}(\\psi)\\;/\\;p_{\\rm point}(\\psi)$\", fontsize=13)\n",
-    "ax.set_title(\n",
-    "    f\"Probability redistribution due to source extension   (α = {np.degrees(alpha_fixed):.1f}°, β = {beta_fixed})\",\n",
-    "    fontsize=13,\n",
-    "    fontweight=\"bold\",\n",
-    ")\n",
-    "ax.set_xlim(0, 7)\n",
-    "ax.set_ylim(0.5, 1.5)\n",
-    "ax.legend(fontsize=11, ncol=2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"Dotted vertical lines mark each extension radius r₀.\")\n",
-    "print(\"The ring enhancement peaks near ψ ≈ r₀; the deficit is at smaller ψ.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9d0e1f2",
-   "metadata": {},
-   "source": [
-    "## 4. Extension vs PSF width: when does it matter?\n",
-    "\n",
-    "The observable effect of a finite source extension depends on the ratio r₀/α:\n",
-    "- **r₀ ≪ α**: the source looks point-like; the convolved PDF is indistinguishable from the King PSF\n",
-    "- **r₀ ~ α**: the core is measurably broadened\n",
-    "- **r₀ ≫ α**: the PDF is dominated by the source extension; the PSF is a small perturbation\n",
-    "\n",
-    "The cell below sweeps a grid of (α, r₀) values at fixed β and plots the peak suppression — defined as the ratio of peak values p_extended(ψ_min) / p_point(ψ_min) — as a 2D map."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d0e1f2a3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/4292982122.py:42: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Blue region (below diagonal): r₀ < α — extension barely changes the PSF.\n",
-      "Red region  (above diagonal): r₀ > α — significant suppression near the core.\n"
-     ]
-    }
-   ],
-   "source": [
-    "beta_fixed = 2.5\n",
-    "psi_eval = np.radians(0.3)  # evaluate near the core\n",
-    "\n",
-    "alphas_deg = np.logspace(-1, 1, 40)  # 0.1 to 10 deg\n",
-    "exts_deg = np.logspace(-2, np.log10(4.9), 40)  # 0.01 to ~5 deg\n",
-    "\n",
-    "alpha_g, ext_g = np.meshgrid(np.radians(alphas_deg), np.radians(exts_deg), indexing=\"ij\")\n",
-    "beta_g = np.full_like(alpha_g, beta_fixed)\n",
-    "psi_g = np.full_like(alpha_g, psi_eval)\n",
-    "\n",
-    "# Clamp to table bounds before querying\n",
-    "alpha_lo, alpha_hi = ext_pdf._points_alpha[[0, -1]]\n",
-    "beta_lo, beta_hi = ext_pdf._points_beta[[0, -1]]\n",
-    "ext_lo, ext_hi = ext_pdf._points_extension[[0, -1]]\n",
-    "\n",
-    "alpha_q = np.clip(alpha_g, alpha_lo, alpha_hi)\n",
-    "ext_q = np.clip(ext_g, ext_lo, ext_hi)\n",
-    "\n",
-    "p_extended = ext_pdf.pdf(psi_g, alpha_q, beta_g, ext_q)\n",
-    "p_point = king.pdf(psi_g, alpha_g, beta_g)\n",
-    "\n",
-    "ratio = p_extended / p_point\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(9, 7))\n",
-    "im = ax.pcolormesh(alphas_deg, exts_deg, ratio.T, cmap=\"RdBu_r\", vmin=0.7, vmax=1.3, shading=\"auto\")\n",
-    "# Diagonal: r0 = alpha\n",
-    "diag = np.logspace(-1, np.log10(4.9), 100)\n",
-    "ax.plot(diag, diag, \"k--\", lw=1.5, label=\"r₀ = α\")\n",
-    "\n",
-    "cbar = plt.colorbar(im, ax=ax)\n",
-    "cbar.set_label(r\"$p_{\\rm extended}(\\psi)/p_{\\rm point}(\\psi)$ at ψ = 0.3°\", fontsize=12)\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.set_yscale(\"log\")\n",
-    "ax.set_xlabel(\"PSF width α (degrees)\", fontsize=13)\n",
-    "ax.set_ylabel(\"Source extension r₀ (degrees)\", fontsize=13)\n",
-    "ax.set_title(\n",
-    "    f\"Core modification ratio at ψ = 0.3°  (β = {beta_fixed})\", fontsize=13, fontweight=\"bold\"\n",
-    ")\n",
-    "ax.legend(fontsize=12)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(\"Blue region (below diagonal): r₀ < α — extension barely changes the PSF.\")\n",
-    "print(\"Red region  (above diagonal): r₀ > α — significant suppression near the core.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1f2a3b4",
-   "metadata": {},
-   "source": [
-    "## 5. Normalization check\n",
-    "\n",
-    "The convolved PDF is normalized in the flat-sky approximation: $\\int_0^\\pi p_{\\rm extended}(\\psi)\\, 2\\pi\\psi\\, d\\psi \\approx 1$.\n",
-    "\n",
-    "Small deviations from 1 arise from:\n",
-    "- **Table interpolation error** (~0.9% with the default grid)\n",
-    "- **Truncation** at `angular_cutoff = 5 deg` (negligible for typical King parameters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f2a3b4c5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   α (deg)       β   r₀ (deg)      ∫p dΩ     error\n",
-      "  --------------------------------------------------\n",
-      "       0.5     2.0        0.3    0.99866   -0.134%\n",
-      "       1.0     2.5        0.5    0.99973   -0.027%\n",
-      "       1.0     2.5        1.5    0.99963   -0.037%\n",
-      "       2.0     3.0        2.0    0.99612   -0.388%\n",
-      "       3.0     4.0        1.0    0.99528   -0.472%\n",
-      "       0.3     1.5        0.1    0.90860   -9.140%\n"
-     ]
-    }
-   ],
-   "source": [
-    "psi_fine = np.linspace(1e-4, np.pi, 80_000)\n",
-    "dpsi = psi_fine[1] - psi_fine[0]\n",
-    "\n",
-    "cases = [\n",
-    "    (0.5, 2.0, 0.3),\n",
-    "    (1.0, 2.5, 0.5),\n",
-    "    (1.0, 2.5, 1.5),\n",
-    "    (2.0, 3.0, 2.0),\n",
-    "    (3.0, 4.0, 1.0),\n",
-    "    (0.3, 1.5, 0.1),\n",
-    "]\n",
-    "\n",
-    "print(f\"  {'α (deg)':>8}  {'β':>6}  {'r₀ (deg)':>9}  {'∫p dΩ':>9}  {'error':>8}\")\n",
-    "print(\"  \" + \"-\" * 50)\n",
-    "for alpha_d, beta_v, ext_d in cases:\n",
-    "    alpha_v = np.radians(alpha_d)\n",
-    "    ext_v = np.radians(ext_d)\n",
-    "    p = ext_pdf.pdf(\n",
-    "        psi_fine,\n",
-    "        np.full_like(psi_fine, alpha_v),\n",
-    "        np.full_like(psi_fine, beta_v),\n",
-    "        np.full_like(psi_fine, ext_v),\n",
-    "    )\n",
-    "    integral = np.sum(p * 2.0 * np.pi * psi_fine) * dpsi\n",
-    "    print(\n",
-    "        f\"  {alpha_d:>8.1f}  {beta_v:>6.1f}  {ext_d:>9.1f}  {integral:>9.5f}  {(integral - 1) * 100:>+7.3f}%\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3b4c5d6",
-   "metadata": {},
-   "source": [
-    "## 6. Multi-source evaluation with `evaluate()`\n",
-    "\n",
-    "`ExtendedSourceKingPDF.evaluate()` computes the PDF for all (event, source) pairs and returns a **sparse matrix** of shape `(n_events, n_sources)`. Only pairs within `angular_cutoff` are evaluated; the rest are treated as zero.\n",
-    "\n",
-    "Signature:\n",
-    "```python\n",
-    "result = ext_pdf.evaluate(\n",
-    "    source_ras,        # (n_sources,)\n",
-    "    source_decs,       # (n_sources,)\n",
-    "    source_extensions, # (n_sources,)  ← per-source extension\n",
-    "    event_ras,         # (n_events,)\n",
-    "    event_decs,        # (n_events,)\n",
-    "    alpha,             # (n_events,)\n",
-    "    beta,              # (n_events,)\n",
-    "    mask=None,         # pass previous result to reuse sparsity pattern\n",
-    ")\n",
-    "```\n",
-    "\n",
-    "The optional `mask` argument lets you reuse the sparsity pattern computed on a previous call — useful when the source and event positions are fixed but the source extension is being scanned."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b4c5d6e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Events  : 10080  (including 80 signal events near source 0)\n",
-      "Sources : 8\n",
-      "Extensions: [1.5 0.5 1.  2.  0.3 1.5 0.8 2.5] degrees\n"
-     ]
-    }
-   ],
-   "source": [
-    "# --- Simulate a realistic event sample ---\n",
-    "n_events = 10_000\n",
-    "n_sources = 8\n",
-    "\n",
-    "# Events: random sky positions with per-event King parameters\n",
-    "event_ras = rng.uniform(0, 2 * np.pi, n_events)\n",
-    "event_decs = np.arcsin(rng.uniform(-0.8, 0.8, n_events))  # |sin dec| < 0.8\n",
-    "alpha_ev = np.radians(rng.uniform(0.2, 5.0, n_events))\n",
-    "beta_ev = rng.uniform(1.5, 6.0, n_events)\n",
-    "\n",
-    "# Seed a handful of signal events near source 0 (first source below)\n",
-    "n_signal = 80\n",
-    "src0_ra, src0_dec = 1.2, 0.3  # radians\n",
-    "src0_ext = np.radians(1.5)  # 1.5 degree extension\n",
-    "alpha_sig = np.radians(rng.uniform(0.3, 1.5, n_signal))\n",
-    "beta_sig = rng.uniform(2.0, 4.0, n_signal)\n",
-    "psi_sig = KingPDF().sample(n_signal, np.radians(1.5), 2.5, rng=rng)  # approx draw\n",
-    "phi_sig = rng.uniform(0, 2 * np.pi, n_signal)\n",
-    "event_ras = np.append(event_ras, src0_ra + psi_sig * np.cos(phi_sig) / np.cos(src0_dec))\n",
-    "event_decs = np.append(event_decs, src0_dec + psi_sig * np.sin(phi_sig))\n",
-    "alpha_ev = np.append(alpha_ev, alpha_sig)\n",
-    "beta_ev = np.append(beta_ev, beta_sig)\n",
-    "n_events += n_signal\n",
-    "\n",
-    "# Sources: random positions with varying extensions\n",
-    "src_ras = np.array([src0_ra] + list(rng.uniform(0, 2 * np.pi, n_sources - 1)))\n",
-    "src_decs = np.array([src0_dec] + list(np.arcsin(rng.uniform(-0.7, 0.7, n_sources - 1))))\n",
-    "src_exts = np.radians(np.array([1.5, 0.5, 1.0, 2.0, 0.3, 1.5, 0.8, 2.5]))\n",
-    "\n",
-    "print(f\"Events  : {n_events}  (including {n_signal} signal events near source 0)\")\n",
-    "print(f\"Sources : {n_sources}\")\n",
-    "print(f\"Extensions: {np.degrees(src_exts).round(1)} degrees\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c5d6e7f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Result shape : (10080, 8)\n",
-      "Non-zeros    : 688  (0.1 per event on average)\n",
-      "Sparsity     : 0.0085\n",
-      "Eval time    : 4 ms\n",
-      "\n",
-      "PDF value range: [9.684e-05, 1.587e+02] sr⁻¹\n"
-     ]
-    }
-   ],
-   "source": [
-    "# --- First call: builds sparsity pattern ---\n",
-    "t0 = time.perf_counter()\n",
-    "result = ext_pdf.evaluate(\n",
-    "    src_ras,\n",
-    "    src_decs,\n",
-    "    src_exts,\n",
-    "    event_ras,\n",
-    "    event_decs,\n",
-    "    alpha_ev,\n",
-    "    beta_ev,\n",
-    ")\n",
-    "t1 = time.perf_counter()\n",
-    "\n",
-    "print(f\"Result shape : {result.shape}\")\n",
-    "print(f\"Non-zeros    : {result.nnz}  ({result.nnz / result.shape[0]:.1f} per event on average)\")\n",
-    "print(f\"Sparsity     : {result.nnz / (result.shape[0] * result.shape[1]):.4f}\")\n",
-    "print(f\"Eval time    : {(t1 - t0) * 1e3:.0f} ms\")\n",
-    "print()\n",
-    "print(f\"PDF value range: [{result.data.min():.3e}, {result.data.max():.3e}] sr⁻¹\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d6e7f8a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/4030331970.py:41: UserWarning: Glyph 8315 (\\N{SUPERSCRIPT MINUS}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n",
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/4030331970.py:41: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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n0IBgV/L7JDTrU/pZWl9a6+tK30cCkdLHkgxfCfRKgFL6ShKslOBrKOknyxVJ8riD0X57yCBdkm0tAXvp60o/TFt2ONsQia7bq/2GOOKII4L9Pzn5L322nJwc1ceTfrv2uZCr2LTkEul3ypVd0g+VEh49/V7S+p1ENDD4K4woDklnQTJRpUlHQYI0EtyRbFKt/pIEoUL/CcsZ4dBLX7TLroRkGgjpFEoQSi77kSw8uRy7u4CeRjoPoa8hnQ3JVpVLsiVYJE2CXNKxOPPMM1WA8lDB24OR4J4EbiVYK51QuSRJGzCia/2vg5HOY2hgKrQ8gLx/6YRKFqpcpiQlKCQTM/TMeXfvPxxaMFs6tN39OOiudtShOpiyzzWhx7O7LOtDCQ30Skc79MeKRi67kv0m5HIrqb8lQT7JXpXjqo1CK+tJJoDsRxnYQPadZIFKYFzefzjr9ST0fctnTk5OSIBVe9+hZTC0gdZC70vgVtaVbQ+9XK6n55Vt7ZoVLB1oOUEiJOj/s5/9TH0epYMvWRLacQk9kdFdR15eR35shQ6+0fU7JaUbtBMz3b0vjbx/yfyO5PsXetzls84ByoiIKFHJ/0sJHEr/WJIfupaqOhTtJG13tfslAKllRobqrv5/KDlZK4Fb6YdIH1j6v5JEIH0ErcRZb/ru4eg62G5of0auCupufQl6Sv1dWS5Xs8n2SJN+h+xT6aNIgDM0GBourY8nQeDQ8mJy5ZsEhKUkRGg5ASG/EeR3SU/k5L0ExmUfS5P9q9UXvuSSS4LHqTfbEMkgxt1tr1Yiq+tnUfrjGumXa1ne0jfV+qddf2scLHArySZCgu2a0NuhfUUiih4GboniiGQOSratBIvkLLd0kOTsumRvSpMMOgmCScdOAnOhJQRC/wl3JZ0UyRCQ55Sz5tJxlct1tEzX0MvjQ4VmLoZeri6BVgnWSR1TyYSUgKs0yUCUDMFIybZIhqJ0TuUSNm0wLhm8qaeObzjvXzpgEgCXwKJsr1yWJdkM8v7lUqiu2aKR6ClQLSUIwtmvmtAOb2+C4N0JfR65rF4TmvkQWotVOoihg2LdeOON6oeKNKmBK5kIElyXfaoNetDTeod6z6JrR7/rez9UtmjoD5euGQU9PW9Pnyk5QaLVm5MMdq3GW0/bdLDj33U/RPq+Ivn+he4LZtsSEVGikVr23Q1CFSn5X6slSXTtV0gfU8scjYScZJZSVhIMlHJY2pVfMiCW1lfrTd+9O10DvF37zJH0ZySoKP1zKTkgpRokk1Wyb6XJ+5ASTl3HdujJofpmWjC0a/8u3ICjBDTlcyD9fclMlUxl2WZp8h6kJERPVw0ebBu67ttD/Rbpbnu15z3UIHPh9FsPVcJBPpvyXrXPV9e+qTbwHhFFFwO3RHFEzorKZS/yj1wyIyVw2x3pOHQNAMnl/qGDe0nhfY10ACWwIx0/rd6sls0XOhBWV107cvKPXv75S7kBqVUlHTMJIEuJBun4yCVEEtw6WJBO6/B0PSMtnVHpBMqZe8kslgzjSMokCAkgulyuYIc6NKAol5TJpWhax1sylrVOcXc1yA62nd3N155HXiN0QAM5cy+XzXeX5dgfQbTQoGxopohkfMpnS7JB5VhqQgO1cnlWaMaurKsF0CVrObSD2rVO1sHWO1yhWavy+ZAMCI32uZZtkUHHIiHH72Aj6spnWbKGhWS6apcuhj5WI5exSRkFbaAHyeiV+107+V2PvTyHzJP15TsqWfZd35eQ8h+Rfv+0DA3ZhnBPgBAREdF+EuiTxApx3HHH9emukcxaKb8k/V+t7EFoea3e9N1DaX200H5g6IBV3fVnpOyBNqivZHtK30MyRaWvIifipR8i/Wm5VF8u/Ze+htRqffTRR4OlnaTPEmkfT8ZJkIC0BEK1wK+8T60P2fU5w+lLS5asbK9sk4yDIVeDSf9U+kzSX5L+lPQpjz/++Ii2IbTvK/tWy2rtul972l7pJ8q+DP3NomUJy76W7ZJxJKRfJ/elDJ5WKk+CvVLiQsYz0F6/O5LNK0kx8rtEK5sV2u/Vrrwjouji4GREcUSCfFqwVs5ay6BF8k9ZOhzSkdIuj5F1Qutdav/AJeNRArZyRllGodWCcFIPKbT4vAQxpbMiHRft8q5wLsWXM+tS++r2229Xl0nJZekS8NOCfHJW+FA1lLTC+FoAKvTSLMkiFJL9Kp1ACcCGO7CVdrZZLsuXzogEsP75z38GMxokMzm00ypn2OUSdal3qg3sEPr+te2Uy9dk32sjrmrzZbulAyTvW7IftMC5ZA1LB0yeUy4Xk2D0bbfdhoEgAXytYxnakZT3oG2zZB9IHWU5FlLvS0j2h3TgJaNDe78yUIKsI+9L+wxKJ14uDwx3vcMl26XV9ZJ6s7Ldsq+ffPJJdbJDq/3VU2ZvuCSTRat3Jp8hyTCR/ag16UAfeeSRwUsmf/Ob36gOv3xf5YeB1DGWEw89XcYoA55Ix1zI+5D3I+9L3p/8CNKybyTgHun3T35waccg0uwXIiKiRB3MTJr09aQvLmMACOmXykncviQnhOV/t1wyLxmRcmL9pJNOCi4/3L67ljwgj5OrcuT9SR8q9GS9kKuJtBPk0r+Q0gzS/5USA9KXkd8d0u+RcglSGkr6ttIXkfvyXKFjBfQmOUHKPQl5TbmySPrycsWWNjir9LWkFEOkpB8k4w7IWAzyXBKEl2MbWm5C295ItiE0KUP6i9LXk6sFDzVIc3ekryjWrFmDv/zlL6pfJ88n/XNJCNAGHdP67dK3lgQX+WzK4G4y3oOWgX0w2m8p+e0l/Ur5/SLjW4iJEycGB2kmouhixi1RnJHOkAxyJZdnS7BWWijJ4pQgUVcyX84KyxlljQRxJDNPOiXyj13WkX/c//rXv1QLJR3G0DPM3ZGOgwSzJAOgu22TztyhLl2S4JvWEZXOqnSmZBRbIaUgtExQLashkrpL0omSzox0bDUSuNQ6XEcffbTKapBOq2QtSAsVmmEq2yln4GWgBsmAlIC4DCgl8yVAKzXGJEgoo7xKh1Y62dK5l9qj0jQSsNQGG+hvEkyVQKdkjEogUPavRn6EyGX20tmWzmno/tI+W7Ltsp4MyiABw9CRi+VzpQ3qEe56fUFKL0gJBslKCd1uIcdGPut95fHHHw/elg516P7TagJLGQV575K18e2336qsCI38EJNMlHACphLwlh8TcqJA6i9L08iJF3nfvfn+afXapGNOREREvRvMTP6nS6BSu7Kmr8j/eDnJr5UIk0Be6P/xw+27y/O99tprah0puSD9MllXsizlRLNG5kl/Tfo2Mr9r3X7J9NQGE5YApyRAvPnmm6qFWrhwYaexBcIlrydZodI3lcB06NVw0jeVflFvrhySQVwlM1iC1TLGhdQVDiXjYchJ+Ei3QY6Z9ptFAq4y/oFcjSf9rdArHnsivy3k94Mkk8hzSAsNpkuJN/HjH/9YJbZIkoqMu9A1MDtz5syDvoaUiZDfLHKsZFulacdcft8QUf9gCg1RnJHsOOlkSYBKzn5LAE7O8sttKaIvnaXuLgeXx8mlSvKPXjoYcumMBCe1cgMSYJNglPxzl86G1MuVTD8tQCVlBrQSBQcjzytna6+//nr1/PI8kvkrHUAJpGlB0oORM8PymtL5k46oNuq9kMt8pMOnCb1kPBySlSiBVG2fSZakZC9qnRkZEEyykKVTJftTaj5JR0YCjlo2gjZIwFVXXaUGqNL2k2yrkM6SDMAg71kerw30JQFhCdJJZ1VeWx4j+1062FIPbaDIexBdz8RLNq4M5iXvXzIIZH/I/vvHP/4R7MBq+0E+H7JMOqtyjOS21FkOPVbhrne45LMtn/8LLrhA1eSSz6NkClx55ZUqQ2EgygFIR1+yHmSAMPk8SAat7HfJZtAyKXoix0O+8/I+5PMi70tORMj7lCxc7YdipN8/rRRG6DElIiKinknSg/wvlpPz0p8LTYzoS6HPG1omoS/67tIHk5PD0leS/oKUmZK+mfTRupIAoPSbpc8g/XR5PenzSt86NNNYTiZLkon0p6XPI/1e6f/KyWxZtzckMC79JunLyPNKP1K7Ikn2fdeT55GQE+zST5d+mvTnpS8l/Sq5kkr6vVrGbSTbIP22v/3tb2o9ef/yG0z2iQRYIyH7WZ5b+pLalXKybfIbUK4K1H5nyJVwcuJe+u1yFaG8pvzekfcWztgicsyuueYa9RpaYods/1FHHRXx/iSi3tEFDjV8IRHFPfnnLp22vhqAYSBJTScJYElHRc6MhzMyrXSkJAtWAqW97TDGK7mETUpQSEBTMkJ5uXzikMvrpEadZNdI9kjXUbCJiIiIiIgo+phxS0SDmlz2LrWxpC6tdsmVZCqGE7SlQ5NBFCRbQi6lk8v9KXFInWspCyInNhi0JSIiIiIiGhgM3BLRoCbZgFIrSi7/lkEW5DIhuWSc+oYMlCUkk5kShwxcIhnWUpeOiIiIiIiIBgYDt0Q0qEktJ6k5JbWkJDtUBhCQWqbUNyTjUmqZSW3YlpYW7tYEILWaZSRkqYUmWddEREREREQ0MFjjloiIiIiIiIiIiCjGMOOWiIiIiIiIiIiIKMbEZOC2vb1dXaK5ZMmSTpduXnHFFZg2bZoa/V0u4wz19ddfq8dMnToVl112mVo/1D/+8Q8cffTRmD59Om655Ra0tbUFl7ndbjVv1qxZqlbmk08+2Q/vkoiIiIiIiIiIiGiQBG4liPqLX/wCW7ZsCc4LBAJqgBSHw4FXXnkFZ511Fq699lrs3btXLZepLD/77LPx8ssvw263qwF15HHivffewyOPPILf/e53ePrpp7F69Wr88Y9/DD7//fffj3Xr1qllt99+u1r33XffHYB3T0RERERERERERAQYY2knbN26FTfccEMw4Kr59ttvVQbtCy+8gJSUFIwaNQrffPONCuJed911eOmllzBp0qTgSPL33nsv5s2bh6VLl2LOnDl45plncPnll+P4449Xy++8805cddVV+OUvf6leSx7/xBNPYOLEiapJ0Pj555/HySef3O12Vlc3IRb9f099hUa3D2lmA/72g3kDvTmDgt2eitpaDrjEfcbPWSzh95L7jJ+z2BPL38vsbNtAb0JM8/n8MXvsKDG+o9S3eKwTB491YuBxThzZveyzxlTGrRZoffHFFzvNlwzZCRMmqKCtZubMmVi1alVwuZQ50Mjo8hKAleU+nw9r167ttFzKLXg8HmzatEk1r9erSiiEPrc8p9/vx2AiQdvQKR2aTgcYDHo1pfBwn0WO+4z7K9r4GeM+42eMDoV9nfjH/wOJg8c6cfBYJwYe58Sh08VJxu3FF1/c7fzq6mrk5OR0mpeVlYWKiooelzc2NqryC6HLjUYjMjIy1HK9Xo/MzEyYTKbgcinJII+pr69XZReIiIiIiIiIiIiI+lNMBW4PRgYSCw2sCrkvg5j1tNzlcgXvd7dcSiV0t0xoz9+dWM/SjPXti6V9xH3FfcbPWezg95L7rD+0tnuxpbwB5oAfKaZB0RUaUNxfREREREQDY1D8WjGbzSr7NZQEVS0WS3B51yCr3E9LS1PLtPtdl0tJBSml0N0yoT1/dzVI5LKzWJOfZkZ5o1tNHQ7WewtXVhb3VaS4zyLHfcb9FW38jIVPgrardzoxdbgDhfx/2aM1pTX4enM5jhqXi8Ih/J9JRERERNRfBkXgNjc3Vw1cFsrpdAbLH8hyud91eXFxsSqJIMFbuS+DmgmpaSuB4OzsbJVxW1dXp+ZJCQWt9IIEbSXw2x0p/B+LWZpXHT8W68ubMDHfBqczNgdQiyVyDCXQUVPThC7j4RH3WZ9mqrl1emb28XsZNfxbFjnJtJWgrUz5/7JnDQ0tkH+UMnU6O1+lFAt4spqIiIiI4tWgCNxOnToVjz/+uCp7oGXBLl++XA0ipi2X+xopnbBhwwZce+21qobt5MmT1XIZ+EzIoGUSpB0/fry6L7dlnjaAmawrj5HHHkwsBvoKs2zIy06Hye+Pye2LVbKvuL+4z6KlsqENpfVtKMpIxnBm9vF7GUX8WxY+KY8gmbYStOXf/545rMkYO8QAR6qJ+4uIiIiIqB/F3vX+3TjiiCOQn5+Pm2++GVu2bFFB3DVr1uDcc89Vy8855xysWLFCzZflst7QoUODgVoZ9Oz//u//8OGHH6rH3XHHHTj//PNVqQRpixcvVvNkmazz5JNP4rLLLsNgk2o2Ykx+upoSUWzISUtWmX0ypZ61uDtqj8qUwsN9RtHW7PagttmlpkRERERE1H8GRYTPYDDgb3/7G2699VacffbZKCoqwl//+lcMGTJELZcg7V/+8hfcc889av706dPVVLevnsFpp52GsrIy3Hbbbap+7aJFi/DLX/4y+PwS6JXA7eWXXw6r1YrrrrtOrUNEdLjkREoRM/vCVtXIDOVIcZ/1LthdV96grlDh4GQ9kxNPGRmpan8REREREVH/0QWkyCtFpLo6NuvHSpxa6rzx0k/uL37GYge/l5HXBG7X6xlQ4z6Lqp3OJpYwiaO/Y9nZHDCtJ7F67CgxvqPUd3isEwePdWLgcU68Yx23GbdERJQYmKHMfdYfmEEaGWYoExERERENjEFR45aIiIiIBq4cx+qdTjUlIiIiIqL+w4xbIiIiSiisCxwZZigTEREREQ0MBm4pYfHST+4zIkpMDERGhiVMiIiIiIgGBkslUMLipZ/cZ/2hutGFj9eWqSlRtE5CbSlvUFMKPxA5Jj9dTYmIiIiIiGIVf7FQwmLGFfdZfyipaMCKXbWYUWiHw2bpl9ekxMLL/omIiIiIiOITA7eUsHjpJ/dZfxiblw6bLRn5qaZ+eT1KPFZzEuzWgJoSERERERFR/GDglogoilLMRgxLNsHk93M/U1Q0uz2obXbBZtQxq5uigjXhB7d6Vz2WVi2DDnoY9AbodXoYdEYY1NSAbEsOMs12tW67rx0N7fUwGcywGCww6U3Q6XQD/RaIiIiIEhYDt3GEP6yIYg8vY6doY9kXijb+HRvcNtZuxK+W/eKgy388/jqcN/IidXtrYwmu/ebqTssleGs2mGHSm3HRqEtw9vDz1fyqtko8vP5BpCZZkWqUltrR1P1UjLCNwgjbSLWuP+CH1++FycCrT4iIiIgiwcBtHOEPK6LYo9fp0O71qylRNLDsC0UbTw4MbsnGZIxJGwtfwAdfwK+mftU6bkugVeMJeFQWrszXtPvbVQOa4Pa5g/Nr3TX4uurLg77u90ddjqvG/UjdLm/di0s/Ox8pxhSkJ2UgzZSOdFMG0mWalI5Z2XNwRPZcta7P70W1qxp2s11l/hIRERElMgZu4wh/WEWGGcrUH/bUtmBzWR1SDToUZu3/cUxERNQfpuVMw+NH/wOBQM/rTrVPxwenfKGCp26/WwVqZdouU58bdnNWcN2c5Dz8YtJNaPa2oMXTjBZvC1rltrcZLZ4WDEstDK7b6GlQ01Zvq2rlbXs7va7ZYAkGbitdlbjk0/PUbVuSDVlmR0ezONTrz3TMVk0EAgEVhDbo+ZOGiIiI4hN7OXGEWVeRYYYy9QcOTkZEgx3/XyYeCYSmSDOmHnQdyYg9vXBxWM83Lr0Y/z3xXTS0N6gausGppwGN7fUqYKxpbG9Akt4Ej78dTZ4m1XY279i/bTpDMHBb0VaOSz+7AA6zA7nJechNzlUB5RxLrrovpRpyknMPa18QERERDSQGbuMIM0gjwwxl6g/ZaRYUj8yG09kUVrYTEVGs4f9LOlwyIJotKU21oanDDrnu+IwJePekT1TAtsbtVCUZZFrjcqLGXYMp9qnBdaXOrmTcVrkqVVtb1/m5vj/qMlw17hp1W57nrxseQkHqUAxJKQg2yeblAGxEREQUqxi4jSPMiIkMM5SJKB7wpB1FG/9fUn+TQGqaKU01bYCz7ky2T8W/T/gvKtsqVBBXplJqQbs9LLUouO6u5lJ8Uv7hAc9hMViQnzwE54+8GCcNPVXNa/e1o9nbjExTJoO6RERENKAYuI0jzIghIko8PGlHRImcyeuwZKs2MXPyIdfNS87Hj8Zfi70te7C3tUw1Ce66fC7saN4Oj98TXHdzw0b87Nsfw2q0odBaqALAhdaOJrclU9fIurpERETUDxi4jSPMiKFoY2YfUezhSTsiop7lpeTjgpEXd5rn9XtV8LasdQ+GW0cE50vZBT30aPY2YUP9etVC/WziDTir6JyOddsqsaZ2FUbYRqnAbpI+iYeDiIiI+gwDt0QUNmb2ERERUbyQrFmpeSst1IIhi3B07rHY07IHu1pKsbu5VE2l3MLullIUWocH111Zsxz3rbkrOHDasNRCFcQdaRulppMyp6iSD0RERES9wcAtEYWNmX1EsafU2YwNlU2YkGtD8ZCMgd6cQYFXD3B/EfXEZDBjZNoo1UL5A34EsH+00WRDsgrO7mjahhZvC3Y271BNq6f7h9l/whHZc9XtTfUbVPbu6LQxqqUYU3kgiIiI6JAYuCWisLEcB1EsCgABCSLsDyTQofHqgcjw5ABR57q6oY7JP161QCCgSixIAHdH03Y13d60XWXdar6u+hLPbf1H8P7QlGEYnT4WY9LGYnTaWDXYmgyWRkRERKRh4JaIiGgQK3LYkJ+TDpPfP9CbMmjw6oFI8eQAUU90Oh1yk/NUm5szr9t1ilKH48ic+djaWIJqVxX2tO5W7dPyj9TyZ459EUNTh6nb62rXoMHToIK62ZYc9fxERESUeBi4JSKimMFL2CPHTHjus2jLtiVjrN6A7FRT1F+LKJ4tKFikmqh312FLYwm2NG7G1sYt2NOyC0NSCoLr/qf0JXyyL6CbbspQAdxx6cUYnzEB49OLkWVxDNj7ICIiov7DwC0REcUMXsJOFHua3R7UNrtgM+rgsPEybqK+kGHOxOzsOap1Z0jKUIy0jUZp8w40tNfjO+dS1bRB0N5c9CHMBrO6v6dlNyxphTwwREREcYiBWyIiihm8hJ0o9ljNSbBbA2pKRP3jqnE/Uq3d51Y1c0saN2Nz/UZsrF8Pk8EUDNqKu1bejpJPN6PQWtSRlZs+AeMzilXgV9YlIiKiwYuBWyIKGy9jj1x1owtryhuRn2piploYeNk/Uexhxi3RwDEZzBiXUazaGYWL1Tyf3xtc7g/40eptRQABlDbvVO39snfUsiR9Eo7Inovfz7xvwLafiIiIDg8Dt0QUNl7GHrk1u2uxdGcNjhiehRMmDOGnjYgGHWbcEsUWg37/Tzi9To9njnsButR2fL19GTbVb8Smho3YVL8BjZ4GGHX7M+UDgQCu+epKDE0diomZkzExYzJGpY2BMeT5iIiIKLbwvzQRhY2XsUcuM9WEbJtFTalnzOomij27a1uwYlcdZhRm8soBohiVlZyFI3PnYW7OvGCQtrxtL7wh2bllrXvUYGjStIHPzPqOjF4J4h6ZMw+T7FMG7D0QERHRgRi4JSKKonH5GRhRYIfJ7+d+DgOzuqk/sIRJZOpb3HA2tqK+JSVKR4SI+ppOp8OQlIJO83IsOXhwzl+wvm6tahvq16HJ04Q1tatUk3ILWuC2xdOCT8o/wKTMqSiyDlfPR0RERP2PgVtKWMzsixyDapFjzdbIMKub+kNJRQNW7KrFjEI7M0jDMCo3DV69HqOyrdE/OEQU1Xq507NmqqbVx93Tsgvr69apQO7s7DnBdTfUr8Wf1t2vbqebMjA5cyqm2Kdhin0qRtlGdyrXQERERNHD/7hxhIHIyDAIGTkG1SjaGOim/jA2Lx02W7IaNJB65g8EYDLq1ZSI4ofUxy20DlftlGGnd1pm0BkxzT5DZeU2tNfjy8rPVBMpxhTcMvUOHJU7f4C2nIiIKHEwcBtHGIiMDAdbiRyDakQUD7LTLCgemQ2nswmMRfaMJ+2IEs8MxyzVPH4PSho2qVIKa2tXY23dGrR4m5Gfkh9c953db+LdPW8FM3Jl4LMUY+qAbj8REVG8YOA2jvCHVWSa3R7UNrtgM+p4qSwREdFB8KQd0eDh8vjwZUklyupaUZCZgvljc2FJMvT6+ZL0SSoQK+2iUZfCF/BhZ9N2FFlHBNf5rmoFtu2yoHRbLf6T9CLc1lswOmN4sLzC7Oy5MBvMffQOiYiIEgsDt3GEP6wiw0A3UexhyRei2MPvJVFsB2ePHpcbXCbzvyqpgsmow87qZjVv4cQhfRbsNegMGJU2ptO8Yt05qEEl2vSNaG4bgQYAm3VLsLlhE17d+RJeO/GdYOB2d/MuZJrtsCaxZjYREVE4GLilhMVAN1HsYckXotjD7yVRbOkanNUBuDA/Qy2T4KvMlwQF+e7K/Z4Cs70J9oZqaTUjNyULOWlD1Wtm2iZgzJhTsKZuNZo8jbAm2YLr/nHtPdhQtw5j0sdiWtZMVUd3sn0KSysQEREdBAO3REQUM5gJTxR7+L2kRCsPEOu6C85q5P1L8FXmt3sD6n5PgdlDPV84ur7m6OxsLCiYggUFizqtJ2UWmj1N8MOvsnGlvbj9eeh1BoxPH495uceocgxERES0HwO3REQUM5gJTxR7+L2kWHe4GaN9GfztjyByd8FZjbyeCH39F5fsOGRg9lDPF47uXvNgZRaePOZ5VLdVYVXtCqyq6WgVLdXYudsKX0UA2a696vFmox4vbH8O49KLVX1d1sglIqJExcAtEVEUsTYkUezh95IovhxuxmhfBX/78nkiCpSG1LiVIHHX1+spMBtu4PVgunvNQ8lOzsGJBSerJl5duRFflJTDEkhS+04UD/fjic1/V7eT9CZMyJiIGVmzMMMxC+PTi2HQ82csERElBv7HIyKKItaGpGhjEDJy/F7yMzaYud1u3HTTTaipqUF7eztuueUWTJs2DYnscDNG+yr425fPE0mgVKcDXO1efLhuL/Z0k+nbXWA2lspLNLUkIUfVyN2/z8YWJWHhkEVYWbMCNW4nVteuVO2pLU8g1ZiKq8f/BGcULg7r+bu+19kjHFi2wxkT752IiKgnDNzGEf545/6i2GM1J8FuDagpUTQwCBk51mzlZ2wwe/nllzFy5Eg89NBD2L59O26++Wa8+OKLSGSHmzHaV8HfvnyeSH24Zo/KVk3qJtO3u4zYD9fvjXpm8OHss0LrENwy7Q4EAgHsadmNlTXLsaLmO6yqWY5GTyPsZnvw8Rvr1+O10lcwM2s2pjtmIduSfcgs6E3lDahpcsfEeyciIuoJA7dxpNTZjA2VTZiQa0PxkI6RZengGOyg/tDs9qC22QWbUQeHzcKdTn2OJwcix5qtkWGgO7acddZZ0EmKpQz25PMhKamHE4NVG5G0eysCOr3kZgJqKjf1KlUzgI5pp/naep2W6/bP1xsR0BnUVN2W6b776rZ2f992Rlukl+pHK/jbl8/Tm98BSRFk+vZHZnBf7DP5rA+zFqp2ZtH31ABn2xq3YFhqYXCdb6u+xgdl76omiqzD95VVmI2p9ukHvtfaFqQlJ8XEeyciIuoJA7dxRC4Dqm9xw+XpnzP7gx1/iFJ/YFCNoo0nByjaGOiOLVarVU1ra2tVyQRph/S3uUjHwAh0E8wN6JMA/b75ankSAoYkwGBCwGACDOaOdULvB2/L1Nwx1Wv3Q5ftW9+YjIDRoqbYN9XmyfLuAsp9Efzty+eJVJHDio276sLO9B2ozODD3WcywNnY9PGd5h2ZMw9ev1dl5JY0bEJp807V/lP6MvQ6A36Y81e0Vwf2v1d7qsq4jYX3TkRE1BMGbilh8Yco9QcG1SjaeBKKKPHs2LEDP/3pT3H99dfjyCOPRCxywYwPMR+l/gIU+cuwMPAlLHAP9GYhIFnDEsRNSkbAYOmYqgDv/uCuFvTtFPhNSkEgKXV/M1n3zbPum5ei5qkAdTd1VaNdR3XhlKFobnZ3qnEba5nBh9onh7O/xmdMUE00tjdiVe0KrHAuw4qa5Whor8MZk8cjy9JR03aL62vsTN6IbONcpPgLMD4nv8/eeyzVDSYiovjBwG0csSTpkZFiUlMiig3MuCUior5UXl6OH//4x7j//vsxZcqUnh9wxsNordiuaoVCmoQuA341hUxD5qv7nZZrrfP6uoAP8HtV0wVk2nFfJ/MCMvXhvZYJ+MQ1ASa0YwsmwGd04HTjtx2PU+t0PF49zueBzt/eLx8UKf4Abyt03uhcHi8ZwRLIfRMnYIl/Fkx6P3bDguT1O3FyVnlIsNcKv9mGgCkNAXMaAiYb/GracVsFgbXyFWGwmIxYOGlIx6GM0czgrrVmhbYNh1oWiTRTGo7JO041LZCbYjKp55LvwEWf/BJVDZUAPlPLMyoysNR7BGY5OlqWxRGV90dERNRbDNzGkSKHDfk56TD5pXNN1PeqG11YU96I/FQT67WGu8+aXCipbIIx18Z9RlHBet1Eg1t7ezvOPvts/Pa3v8WcOXPUPLfbjTvvvBPvv/8+LBYLrrzyStXE3/72N7S2tuKPf/yjup+ZmYmHH3744C8w83K0OpvCDuj1lS1fboW+rgX2fXVEt2TORe38Bw/+ANlAfzt0vnbAJ1P3vmnIPAnuBu+7Q9btsr7XBXjb1FSnph0NwfuukHX2NQki9wHZDmm7dSmwoAH5qEI5clDmroel+qWIMoM7Arg2BMw2+E3patpxPw3+YIA3DQFLGlCXC6PbCL8pA35LBgLm9GD2b6w4VF3daNXclUBuqHtmPYDlNctURu7q2lWob6/HR3vfV01q4z51zD+D6/r8Xhgi2IcHew/MxCUiosMRW//N6bDw0n+KtpKKBqzYVYsZhXYGIcPE2tMUbczqpv4Y9Oj9jRUYl21FYVZHfVXqGxKgveGGG7Bly5ZO8yWbdt26dXj66aexd+9e/OpXv8KQIUNw8skn4/e//33Er9NPY4R1MrRLDVW5f8jtkIV6M2A0q7tanLnf4s2S9etzAR4tmNsR5A0Gdz2tHa29ed9tmbZA197S5XZHK3TVY6tvFMr9OXDrTCgKlEWcGaxrbwSkdSRv9qjr0MR+FeTNQMCiBXP3T9W87pZZMjrqAPfzZyLiz0svyEBno9JHqXb+yAvh8Xuwvm4dljuXYln1Eky2Tw2+ZruvHed9dBbGpo/D7Ow5mJ19BIZbRwYHBozk/X1VUomvS6rU4HGyXJ5BsqN79x46Tyl+8VgnBh7nxKE7jL/bDNwSUdjG5qXDZktWGbcUHpYwiUyL24u68gZ15UCKif+iwsE6yhRtK3fWYMkOJ1pHOBi47UNbt25VQVtVwiCEZNO+9NJLeOKJJzBx4kTVJLD7/PPPq8Btb2Rl2dDfFh81ClarWQX+ZeAsqcEql/MnihPbvdCt2YPS6iYUZRixcMy5QKANaG8G3NKaAHcj4JLWsO92yFTmhy6XjOII6dubAGlNuyN7YFIKkJzZ0VLsQEoWkOIAUh37bu9rofdlgLnD+EwM1OclP+cYLBx3jLot30UtMLusYhkaPQ34zrlUtb9vBLKTs3FE7jykt82FyZuHcXlZYb2HGpcHqSlJyM9MRXldi7rvcBzed3IgvtM0MHisEwOPMx1K4vSeiOiwZadZUDwyG84BuORysGIJk8jwsv/IcXAyirbpw7OQkmpWGbfUd5YuXapKI8gAY9OmTQvO37RpE7xeL6ZPnx6cN3PmTDz66KPw+/3Q6yMfy6CmZmD+b88dnqWaaG5sCzdxNG50ev/SdJmAJLP2JqFVSjC4m1QWrn7fVOduhK69SQVoUw0utNVXQ+dqgN5VD527HjpXPfT7pqoucbg8rR2tMfwsYVW6IdkOv8UOf3JWx+1kOwL77qt5lkwcZc/G3KE5ajC4rp+JWPq8DDeMw1PHPK+CtpKNu7pmJarbqvHJ2nIkt+zEEGsbduxtRV1DG3T6AMrr2jDMbsX8cbkHvIcsSxI2tnpQ2t6gMnHlvvSle0PiyhLgGajvNPUfHuvEwOOceMe6Nxi4jSPMVCOKPSxhEhle9s/PGMUeyRybOT6fJ+362MUXX9zt/OrqalW31mTaf3WLw+FQZRXq6+tht9sjfq3g+GM0eOnNCCSbgWQHfN38GEx12NBysBPrkkkq5RxCArlacLe7IO/+derCHjROv6+sg6FhZ1jr+2VwthQHAinZ8Cc74A9Oc9T8/fezOzKA+50ORdYRqp0z/AK0+9xYW7cGL3yzGxXtbgy32+Fq0+G7nTWobavFnrZSWHemY2WNEefPmIJhqYXB7N15Y3NVyQ+peVuQmdJxP8Lvo1Ynd29dK8YNy8S0ggyYjYbovHWKKfz7nRh4nOlQGLiNI3JZzobKJkzItaF4SNcqV0REsY+X/VN/4IlOimVtbW2dgrZCuy8DmRFFTLd/oDM/hoX/OBXwbYHOVQt9Ww30bbX7b7tqoVPz6qB31UDXVqvm6d0NYT213tMMfUMzEEagN2BMUQHc1uQ8fOQ7ArsC+SiwBnDMMD1Mtmz4U3PhT5GWHVbJht4wGcyY6ZiNupEF+KqkSgVtJXvWoNehzd8Ej7EajRJc3VWFt5vvRY4lV9XGneU4AnNyjsLCib2raauRoK28rgx+tqe+Dc3Nbizo8pyhg6DlpFnUvKpGlwoWzx+bC0sSA71ERIMRA7dxRUunYEoFEQ1OvOyf+gNLclAsM5vNBwRotfsWS0cwhqj/Ar5W1fxpheEP8ubqCOZKoLdzsLcG+lYndK1O6Nuq1W2VqdvTZnhbYWgsxWdNo/GZzg5zoAk76kww7/oSpwc+Cq4XkCHdpCxDSg58EsxNzYE/NW9fYDenTwK8EgBFSPasx+eHd5sfDmSj3tUEr60JbfokVLkq8dbu11V7ecEbsBg6vrt17lqkmdJh0EUWRJXXk6Btbnoy6to86v6hgrurS2vUL0IJ4MqAaOJwg8dERDQwGLiNI6ylSdHGLDWKNpaWoP7AEwQUy3Jzc1FXV6fq3BqNxmD5BAnapqWlDfTmJYzQ7EVmLEbAkIRAqgROcw4o6dAtr6sjgKsFcvdNda3V0Lc5oQ+ZlrYXwBxoRz6qUB7IQSkKOj2VTkK3sm6bE8aaDQd9yUMGeFPz4bfmw2fNV/V5uw4DLlmroQFQ+ZwkGfT7PidFmD92AQK6H2FN7SpVH7faVQm7uaPmrbhn9Z3Y0rAZMx1H4IjsuSorN3T5wchnUAKwlQ1tCOj06v7BgrvyP25vfRtky+W2nKzsLtBLRESDAwO3cYQBD4o2ZqlFjsFu7i+KPfx/SbGsuLhYBWxXrVqFWbNmqXnLly/H5MmTezUwGfVOaPYiMxajyGiBP22oaj2xr92NjSV7sQtetHs8yHUMQ0tGHvQtldC3VEDfWtVxu7UaOr/noM8TdoDXYFYBXQniSjBXBXRTO9+2pDi6yWRNxpycI1UL5fN7sb1xGxo9jfik/EPVxOi0MZjtmIu5OUdhsn1qt9sye4QDm8obUFbbglF56Zg90nHQ4K7015P08i47+u5S0qG7QC8REQ0ODNzGEQaIKNqYpRY5Bru5v6KtutGFNeWNyE81wWHjZdTh4P9LimXJyclYvHgx7rjjDtxzzz2oqqrCk08+iXvvvXegNy2hhGYvMmMxNswfPwTQG4JZ0HPG5qK1u7qtAX9HuQYV0K2EQQVztaBuxzx9S5Wad8gAr8+tSjRI07hgxoe6+SrbtwhlWKD7FqZUezBLV2XuBm/vC/Km5KjtNuiN+PcJr2Fj/QYsdX6LZdXfYnPDJmxt3LKvleC+I/4cfK1qVzWyLdnq9rIdTtQ0uZGWnKTq1i7b7jygxm1oGYdpRVkH1LglIqLBiYHbOMIAEUUbs9Qix2A391e0lVQ0YMWuWswotDNwGyb+v6RYd/PNN6vA7eWXXw6r1YrrrrsOixYtGujNSiih2YvMWIwNXcsUHJROr0oh+KQ5JuCgodkDArwVHZm7zeWqGVo6pqEDrknQ9iPdfFWyYatuhBpa5PSmj2Bo2o2DVc0N6I0qiOuzFcBvG4ojbAWYZRsKX+EVqDHbsMS1B8tql2Ny+ix8uH6vCrymW/34y54fYphtCGZnz0Xr3pkwGqyHrHEb9v6JEMuGEBENLAZu4wgDRESxh8Fu7q9oG5uXDpstWWXcUnj4/5JizebNmw/Iur3vvvtUo4HRdRAqZizGoXADvO0tHUHd5nJsWdUMQ6MX2YZ6VLYZsDMwGf7ACuhddQd/Gb9XBXaldZUBYBSAC5Oz8YbRg499LpiMRqyDHimmmdil/xa7WkqR0rQTqa0zsb3JgrQkO0blDomJsiEM6hIRRR8Dt3GEASIiosSTnWZB8chsOJ1NCEhBO+oR/19GhqUlKBFFK3uRBiFTKnymUfBljkJe415sK6lCmXEY2s0BZI09FjUTbwS8bdA3V+zP0tUydpvkdhkMTXs6Ze52JYOy7dEFkIxKNfiaATmYpEuFv3EC1hsLUWZuQFPyKri9WagMVOGsJjPMW46EzzYUDcl26FKykZJkPay3ebAg7KHKhrAWNBFR9DFwS0QURQx4ENFgx9ISRESHzsR2BUz4ssyMsrocFGQOx/wpHUHPULr2Zuib9sDQVLY/mNsk0zI1v7B1ryq/UB7IgVtngieQgirvHOR72mF35WOy7hsYUt/Cl0YLTlxXg7Q1z6nnfTk9DX/LTMd0nwFzDXbMSR2FkRkT4E8vgi9tGHy2QhV87ilAe7Ag7KHKhoRTC5pZuUREh4eBWyKiKGLAg4gGO5aWICI6dCZ2OJmnAZMVvqzxqnVnmqsNLeu2odxZh6FJDdhVlwljC5Cjq0aFy4A2fwF+0tiEyxubOj2uxJQEr06HZUY/lsGJv7Q6kdP4FY5qc2FemwsLWlphkHIQtmHwpRfhC/csfNM0FElJZuysMKtavwsnDT1oEPZQZUO6BnVz0izBOr3aup9sLMf7a8vg8wewqlQHj8+PU6YM5UeKiChMDNwSEUWR1ZwEuzWgpkREREQUf8LJPO2JxZKMBbMmBe9LAHRHSRX2GMeooKh9zEmoGXkdsgx1aNyzBfrGPTA07sbvG0txTcMufOurxVeWJCyzmFFlNOI1mxXvp6aowK2+rUa1mpq12OtPhwUueHUG1MGBFdUrcebyf2K0cSF2eybD2WiCV2dBocMFnSsLFkvmQcuGdA3qSlC2awB7+Y4aNLu9sBj1aHb71H0GbomIwsfALRFRFDW7PahtdsFm1MFhs3BfE9Ggs7m8ASt21WFGYSamF2UN9OYQEcWcQ5UT6K3uMl0DJgPgGIV2y7hOde3TACwK+HFySyU89duxvupbLG1Yg4C7EYH8AvgaS6FvqcQP83LQ1NYCW1MOAv5UGAMBVMGOT1pG4OTA0zDq5qMUBShCGRau+BKWFW74zenwpQ8PaSOCty3JWZ2Cuk9/ubX7ALbaVl3HVHfYu4aIKKEwcEsJi7VHuc/6Ay8xjgy/l0Sxp77FDWdjK+pbDj8QQUQUjw5VTqDfBsjT6eG35sNgzceUofMwZd9sbUi0+rZKVH9+MVqNqxBomwqj1wifsQ7uQACv68dguPcLnN7+0QFPK4Oq6atWI6lq9QHL/EnWYBDXnz4cw10TUNqaiSpfO9oDRrUvpHyCJDF4/AHYLEbMHH7wE4Ch9XDlcaKq0dWpFi8RUaJh4DaOMOARGdYejRz3GUUbP2NEsWfyMDscdivyU00DvSlERDEp4iDrAMhIzsV/TnwPG+rX4T8rN2Hbnna0+n1oQxJWp9Tgn+N+gF8WLIa+oRRo2Im6hq0Y0lQBQ8NONYiariNtthO9pxl65zokOdep+6fDDMu+rN1CfRWOd5XBl1YEa9os7PLnYYg9DfOG+gG/D9AbDlkreHVpjXpFCeAerG4wEVEiYOA2jjDgERnWHo2cXqdDu9evphQefi8jwwxlIiIiougw6o2YYp+GscdMVkHS7c5auI3lqLfsxLyCE+HNngxkT8b6urW47pt/oihzBI4YezGOyJqFqYYMpDTtVYFcQ8OOjmm9BHX3QBfwqee3wI3TA/uydmVWrbS1OBtvdszbA2ANEDCYOzJ1M0bClzEKXplmjsLeakuw1MLe+jZVVeFw6gYTEcUDBm7jCAMekWHt0cjtqW3B5rI6pBp0KMyy9uIZEg+/l5FJNRtR5LDB6WzqVLuNDo5XW1C0lVRIjdtazCi0s1Y3EVFcZQhLkwHRTuy0fHvTNuihR2nzDtVe2vECLAYLptlnYHb2XBw7/gbYzfaOlX0eFbztCOh2aY27oPN7Dnh9nc8NY+1m1UKN0y3Abv1xqK0ywBRwIKA3wlnRCo/egoK0jOjuFCKiGMXAbRxhwCMyDKhFbmxeOmy2ZF4uGwF+LynamNUdOQa7I8O//UREieWMwsU4Nu8ErKhZhqXV32JZ9RLUuJ34tvpr1camjwsGbmu9TUi25iE5YwQOCNH6fdA3a1m6+1r9jmDGbteg7sLAl4AfKHUX4EhUqnl7XblqsLQFVV/BtCobvsyR8KZ3ZOhqGbtS21dq/BIRxSMGbilhMaAWuRSzEcOSTTD5/VE4IkTUGzwJFTkGuyOTnWZB8chsZsITESWQNFMajstfoFogEFBZuMuqv8WautUYn14cXO8fJU/gvbK3MSVzGmZnz1EZucOtI6CT0mp6A/xpw1TzDDu68wv4vSpT11i3rSOQK9P67Tilfj0MLQcOkqY07YahaTdM+KzT7IDRAl/6CBXI9WbsC+hmjlYtYLL2OBAaBz8joljGwC0RhY3BDqLYw5NQkWOwOzLMUCYiSmwShB2VNlq1C3FJp2WlzTvh8XuwvGaZao9uegQ5ltxgEPfo3GM7grhd6Y3wpw9He/rwA5e1t8Aowdz67TDUdwR0VavbpgZEO2D7vC4YazaqZu6yzGfNhy9zDLwqkDtGBXO9mWPw5fZ2fLWlYyC0cAY/Y6CXiAYKA7dEFDYGO4hiD4NqkWOwOzI8aUdERAfzP3P/ht0tu1Q27jLnEqyqWYEqVyXe2v06VteuwjF5xwXX3dtahrzkfOi7lDXoNiiaPQnebKm/GyIQgK61GsaGjiBuMKArrbEUOr/3gO0zNJerZtr9eaf5NcYfIsU4ErkpQHkgCxWl9dAP88FvK+i27IJs31cl4Qd6iYj6CgO3cYQ/3inaGOwgij0MqlG08aQdEREdjGTTFlqLVDtnxAVw+9xYU7sSS6uXIMucFVxPsnJ/+MXlsBjMmJU9B0c45mJW9hFIN2WEHxTV6RBIzYFH2pC5nZf5PKqMgpaZa6jbAmPdVjXVuxsOeKrhvu3Y5h+Canc7fDoXxtR/iawdHyFgTN6XnTu6U6ZuWU1AbZ/8T5S+lwSZiYj6w6AK3JaXl+OOO+7AsmXLkJGRgcsuuwxXXHGFWrZhwwbcfvvtKCkpwejRo3HnnXdi0qT9Z+jefPNN/M///A+qq6sxf/58/P73v4fd3lFQXWr2PPjgg3j55Zfh9/tx7rnn4sYbb4ReP7gKnPPHO0UbTw4QxR6rOQl2a0BNiaKBJ+2IiChcZoNZlUiQFmpPyy41rWuvwwdl76qmgw5j08cjve4MJCEPQ9PsvQ+KGpL2DVY2Ehi+sHOWbpsTxrotMEggt7YjoHt87RagDShFAYoCZR0DowFwe/340JmDUqcNRViOhYGHYIEb4/QnYo9xAWqdRniNVhSl6GCsalVlF5CUzA8IEUXNoArc/vznP8eQIUPw6quvYuvWrSq4WlBQgHnz5uHqq6/GGWecgT/84Q/417/+hR/96Ef44IMPkJKSgjVr1uDWW29Vwdzx48fj7rvvxs0334zHHntMPe9TTz2lAruPPPIIvF4vfvnLXyIrKwtXXXUVBhP+eKdoK3U2Y0NlEybk2lA8JIM7nCgGVDe5UFLZBGOuDQ6bZaA3Z1DgSSgiIqL+NcI2Cq+d+A7W1a1RZRUkI3d701ZsbtiIlJY0DPefgpTGZLR7A8hJS0KNy4ksi+PwX1iydFOy4ZFWcFSnRXPbmzBPZeVuhb9uHNy1W/FeZSY+dY+HOdCOrboRar3TAx/hRP/n0Hn8KPUUoAhlWLjpS1g2uRGADv60Qnjt4+DNGgefTO1j4cscBRi6VtwlIorjwG1DQwNWrVqlMmWHDx+u2tFHH41vvvlGLTObzbjpppvUpRoSpP3888/x7rvv4uyzz8Zzzz2HU045BYsXL1bPdf/99+P444/H7t27MWzYMDzzzDP46U9/ilmzZqnlEhB+6KGHBl3gttntQW2zCzajjj/eKUoC6qy1mhJRTJC6cPUtbrg8KQO9KYMGr1AhIiLqf0n6JEzPmqna1eN/gmpXNb6rXoIllcswDjlod6WqGrc+23qc9/HtGGUbowY5OyJ7LiZmTlaP70sBkw3e3OmquffN2/LlVuhqm2G3tKOqrhnbjBlw2aww1pbgtPovofO54YIZH+rmd2TrShC38UtYGkth3vn+/ufWGeDLGLEvkLsvmJs1Hj4ZjE0/aMIwRBQDBs1fDIvFguTkZJVte8MNN6ig64oVK1QW7urVqzFz5szgaJUynTFjhgr0SuBWlv/whz8MPld+fr7K3JX5JpNJlWCYPXt2cLk8V1lZGaqqqpCTk4PBgjXoKNqKHDbk56TD5PdzZxPFCEuSHhkpJjWl8PD/ZWSYoUxERNGQbcnGKcNOVy3U01veUWUUtjVtUe2F7c8h2ZCCGY6ZmO2YixOGLIQ1yRaVbZLAsdTZrXCb0W4yIWfseDRNPK9jod8HfdNufLx6O77Y5YbZ14It7ZMQ8Jlwhu+dTs+jC/hUSQZp5m1vBee36a14P+UslCaNQUFGMuaPyYYxZ6zK2u1uUDQiokETuJWM2ttuu01l3EqGrM/nU0HZ8847Dx999JGqaxtKSh1s2bJF3e4uACvLKyoqVM1bEbrc4ei4JEOWD6bALWvQET9jRImHJ1Qix/+XkWGZHCIi6k+Xj7kKi4vOwXfOpVhWvUSVVpDauF9VfqHakbnzg4HbyrYKZJgyVW3dvjB/bK6aSp1dCeJq9xW9Af704SjVe2FMb0HWvoHKNmfMxoyJt+LrddtR5qxHkX8XFno/hLV+o8rQDfVRYA4+bR0Nc8CNHQ0BmHc+r0oxqEHRJCvXPlZl6PqyxgJJM4FAmoSB++S9EdHgNGgCt2Lbtm2qxMEPfvADFZSVIO6RRx6JtrY2lTkbSu63t7er2y6X66DLZZl2P3SZ0B7fnX3JvTFF26ZY3LZYxP3FfdYfWtu92FLeAHPAjxTToPqTOyD4vYyc1WJEVpYNNTVNHZVMiJ+zPqbTdZTJkSn7GERE1B/STRlYMGSRav6AH1sbt6gA7p6W3SpTV/Ontfdhde1KTLVPVyUVZFC0YamFwatxI2VJMmDhxCFhZeVK0FZq8hbYU/F5OfBVjR2mpCxs9Y6Eq/h8LCzOgaFxFww1m2Cs3QxDbQl27hkJU7sH+ahCeSBHlVsIDopW7UBpdTKK8A0WBv4oc2E32fYHc1Ud3fHwOiYgYMns1fsjosFn0EQRpJbtyy+/jM8++0yVTZg8eTIqKyvx97//XdWp7Rpklfuynpat291yKb0QGqSV9bTbQpZ3x25PhcEQu5cxyA944v7iZyw2SNB29U4npg53oNDB72a4+Hcsctxn3GfRMt1qQV52OvIzU2C19G19QSIiop7odXqMTR+nWigJ6Ja37kW7vx3LnEtUw8aHkJecrwK4R+bMw9yczgOSRSsr98UlO2Ay6lQ5JgnoyjKpZevLGKla+6hT1WOy1u/Fps0V2AU3PO425NlS4dafhPcq0vGpu/iAQdH07U3QVyxHUsXyTtvgS82FL6sYXq05iuHLkAHROiesEdHgN2gCt+vWrUNRUVEwGCsmTJiARx99VA0q5nQ6O60v97UyB7m5ud0uz87OVsuElEwYOnRo8LaQ5d2prW2JyYwT2SZmXXF/RTt71K3TM3s0ApJpK0FbmTqdTdE7OHGCf8e4z/g5i83v5Zj8dJXV7WruuFIpljh4UoyIKGEDuk8f+wJKm3eqbNyl1d9iTd0qVLSV441d/8He1j2dAre7mndiWGpRr7NxD5WVe0AWbmb3g8Z2DfoeMfZYNCZd0TEoWk0T7BZ3x6BoBiuQboCvYgMMTXsOeB5DS6Vqpl2fBucF9EnwZY7aH8zNKobPUQx/Si4vyyUaxAZN4FaCsKWlpSobVsuS3b59uwq2Tp06FU888QQC6jI+nZrKwGXXXHONWk+WL1++XNXEFTIYmTSZL4FbGahMlmuBW7kt8w5V3zaWL0eVbYvl7Ys13F/hq2xoQ2l9G4oykjGcP5TDIuURJNNWgrb8XvJ7GU38W8Z9Fs3ByWrLG9TAlCz5QkREsUR+/w+3jVDtvJEXoc3bhtW1K7C0egnGpY8PrlfjcuKKzy9GltmBWY4jVFmFmY4jkGaSGrJRro0bRimG4KBo7clwmcyoSyvAXzOPQlZBEuYPT0Fq07aOcgvOjTDWdDS9u6HzvvB7YJSyDDWbAPwnON9vydwXyB2/P0vXPg5I6v4KYyKKLYMmcHvCCSfgj3/8I37zm9/gxz/+MXbs2KGyba+//nqcfPLJePDBB3H33XfjwgsvxAsvvKDq3p5yyinqsRdddBEuvfRSTJs2TZVYkPWOO+44VWJBW/7AAw8gLy9P3ZfnuvLKKwf0/VL0cZTsyFnNSbBbA2pKRESJQbKHeNKOiIgGg2RjMuaqEgnzOs3f0bQdFoMFNW4n3it7WzU99BifMUEFcY/PX4hh1sJev244tXHDDfw2uTyobnLB5fNjY6sHAeRg4cSZ8ObN3P+AQAD6lnIYnRth2BfIlYCtoX4bdH5vp+fWu+pgKvtateDDoYMvY8S+QO74YIauP20YoIvdspBEiUgXkPTUQWLr1q0q6LpmzRrY7XZ8//vfx+WXX67Ossm822+/XQ1gNm7cONx5552qlILm1VdfxcMPP4yGhgbMmzdPDWyWmdlR0Nvn8+H+++9X6xgMBpx77rm44YYbDnoJRXV1bF7uLJsrlwsysy88O51N/CEaIe6zyPF7yf0VbfyMcZ/1R5mcdr0+ZjNus7NZP7wn7BvGN/4fSBw81oen3efG2ro1qqSClFbY2bwjuOyWqbdjYcFJ6nZDewN8AR/sZjsGwtNfbsXeuhYU5aajtLIBQzJTcfn80eE92OeGoW4bjDUbVFBXBXNrNsLQWhXWw/1JVvgcE9QAaF7HxI5mHwsY95esPBiXx4cvSyo7ZR1LQJsOjt/pxDvWcR+4jRWxGriN9R9WsYb7i/uMn7PYw84L9xk/Z7En1r+XDNz2LFaPHSXGd5T6Do9136pqq1QDmi2t+hY/n3QjMvcFav+17Vk8sfnvGJ02RpVVmOWYg0mZU2Dqp4G/Ply/F1+VVMGakoTmVg/mjc05rGxeoWt1dpRRqN20v9xC7WbofO4eHxvQGeDLHN05mOuYiECyvVOwVmUKN7pgSdKrOr99sd3xjt/pxKE7jMAto3txhJcyRibVbEQRO7kUZfxeEhERERHFnpzkXJw27EzVQpW37lXTrY1bVHth+/OqzMIU+3QVyJX1pSRDtEimqlz7W+PyIMuShHkHqZcbcfbrsPnwDJu/fyW/F4aGnZ3LLTg3wNBc1um5dAGfCvJKQ8n+2rm+1Dy8YTkH33gmw2RKwa42E1LMJhRmZajfQPLah9q2Xc5mtHp8SDEZUJhlZYYu0UEwcBtHctKSkZGRqjJuiaKBQcjI8XtJRERERDR4/GLyr3DF2B9ihXMZvnMuVa3WXYOl1d9gTe0qnFn4veC6Wxo2Iyc5D+mm9D57fVUvd9KQw8qkl8CoZO2ajDo16Jk4IPtVb1SZtNIw5ozgbJ2rTgVwO9p61Qx1Ww6onWtoqUBZaxNSUIp8VKFONx4trlTUtm6Gz5iG4ckBGCub4c0aB4QEurVta3Z5UNfajsyUJOyu6QjyMkOX6EAM3MYRZpBStDEIGTl+LynaONAiERERUd+S+rZS81aaVJfc3rRNBXBbvM2dSibcveoO7G7ZhbHp4zE7e47KyJ2QMQlG/cCGWiTbVYK28vvtUNmv3QlYMuEZOk+1IJ8bxtotMIQEcyWwW+Qpw1bdCJQHcmBFK0b7dyLN3YQidxnmb16KL0uOQKluKIZaPDg23w1jzjiUV06ESW+FXq+DQQc1lW2NZBuJEgkDt0QUNgYhIyd1ntaUNyI/1QSHreei/omOQcjIMROeiIiIKHpk0PJRaaNVC9XmbYVBZ0AAAWxu2Kjac1v/gRRjCqZnzcRxeQuwoGDRgBwaKY8gmbbST5R6s3L/sBjM8GZPUi1YFTcQwPS6XWhbU4K9NfUo8m7Hia53kNq8XS1+U7cAH+nmwxxox1aXCYbtX+L0bfdgrG4BduvmA7oM+HSZCLQ2wxMwYWiBST2nKgZKREEM3MYRBjyIYk9JRQNW7KrFjEI7A7dhYBAycsyEJyIiIup/ycYU/N8xz8HpqsZy5zIsq16C5TXL0NBej68qv0C6KSMYuPUFfPi68ktMz5oBa1LvBiiKhNS0FaE1bvucTgeTvQjHHVcUnNWKX6HNVa/q5W5dVoWkBg9yfXtQ4dahFAVqnYWBL9V0e6AQLUhBSlsrRrXtwsJvvoRplRXNWdPwof44lOqHIj83H/OmjIeFg69TAmPgNo4w4EEUe4baU9HiC6gp9YxByMgxEz5yPNEZmVJnM97fWIFx2VY1eAgRERHt57Bk46Shp6rmD/jVgGbfVS/BhMxJwXVKGjbh9hU3Q68zoDhjgiqpMNsxB+PSx8MQhbIKqk5u15q2/SRgyYCn4Ejk1u/F1pIq7DaOQbvHB0dBAI3pM2B0rsOi6nUwOl+Gvr2p84Pb3Pi8DPhC54U5UIJdu3YidflfcFJuLbyOyfuyfifDlzES0BsG5P0R9TcGbuMIAx4UbQx29G6fNbZ51JR6xiAk9Qee6IzMyp01WLLDidYRDgZuiYiIDkGv02Ns+jjVQjV5mjAstVDVw11ft1a1p7f8H6xGG2Y4ZuKikZdiXEZx1Pety+NTg4OFZuJKkLc/sn6PGpsLd9JMuHFuxwoBP/QNpUhSQdx1MMq0eg1K3QWqvIIMeCa1c3f5smAqewumsm+Czx0wJsPrmKgCuZ7syfA6JsFnHwOE1B/u7/dLFC0M3MYRBjwo2hjsiJx0Fupb3HB5DrOuFBH1GZ7ojMz04VlISTWrjFsiIiKK3BHZc3HEsS+goq0c31UvVQOdrXB+h2ZvEz6v+BTnDL8guO62xi2odlVhQfoxfb6rJYj5VUmVGgxMauCKaGXm9pj1q9PDnzECbmljzuiYFwgga8UmbCqpwG5fITztLgzzb8L+wrr7HuptQ1LFd6ol75sX0JvgzRofzMqV6ZeVdny1ra5f3i9RtDBwSwmL2aORs5qTYLcG1JTCY0nSIyPFpKZEFBt4ojMyMrBiWnoKTH5/lI4IERFRYshLzsfphWepJnVvpYSC1MctzpgYXOeNXa/h9V3/gXGFEZMypqiyCjMdszE6fawaDO1wSOapBDHlJLYk5cj9mKLTYd6UsQhY0oNZstPHng2n5zaVkRuanWtoLIULZnyom6/q5xYFyrCw+kskV68JPl2t/gqkGEcjL1WHvchF+Z4WYGwGkHTwpBpm6VKsYeCWEhazRyNX3eRCSWUTjLk2DrQVpiKHDfk56Qx4ENGgxf+XREREfU+CsBKwDQ3aCrs5C/nJQ1Detheraleo9r8ljyItKQ3Tsmbi5qm3wWww9+o1JRAqmafyv73dG1D3Y013mbqBpGx4io5XTaNz1ePj79biy50tsHgbscUzAQGPDmcEPgyuUxTYja3eIlTVt8Ona8fY+i/h2PYJfJlj4M2Z2lFmIWcKvI4JgDG537OSicLBwC0lLF4q2xsBdfmKmhIRDVK84iIy/H9JRETx6mDZlQOZdXnZmCtx+dgr0Waqx/tbPsay6iVYVbMCjZ5GbG0s6RS0/W/pK0g3ZWJ61kykm9Ijrjur3R+MZBC0XciHMb0F9rSRKhi9OW0m5o+9FsbqtSor97iqTQjUf63WUxm5gS+hgx/G2s2qWTb9u+O5dAb47OPgyZmMyqbjYfZnItuWhaomd+xlJVPCYeCWEhYvlY0cs0cjx0w1otjD72Vk+P+SiIji1cGyK2Mh63JY2jCcVXQ2ziw8Gz6/F5sbNqHR0xBc7vV78cTmv6PV2woddBiTNg4zHLNUaYVJmZNh6iYrt8e6s4PMARnEjnR484vhzZ8VXGeu14X5EqitWotAdS48VWtgrN0EnX//4NG6gA/Gmg2qjdY5Uaqbj7oqD/zGdIz01MGyPkNl6Hrt4w4YAI0o2hi4JaKw8cd75JipRhR7+L0kIiKiQ9V8jbVasB6fDnv32lFWZ0Fz9V6VKeuFC6cMPQMrnMuwo3k7Sho3qfbC9udg0puwuOhcXFN8bVwf6LAyiI2WjqBrztT987wuGGs2wihB3Oo1SKpaA0NtiQrgSlauUHVzPUuwsPxLWMrd+wdAcxTDmz1FlVjw5ExVZRdg2D8GzEBla7M2b/xi4JaIKIoY7CaKPfxeEhER0aFqvsZaLdiDZQD/ZMLP1O0alxMrar7DCud3+M65FDVuJ1KTUoOPb2hvwMPrH8AMx2zMzJqNvJR8xINeZxBLMDd3umpBnraOrNuqNVhYvQbGqiUw1G1RpRU0On87kqpWq4b1HfMCBrOqkasCudlT8XHjaHy1VweTUd+v2dqHkyXOoG9sY+CWiCiKWEuT+4uIiIiIDk+0AksHy9iMtVqwPWUAZ1kcOLHgZNUCgQB2tZQixbg/cLuyZjk+Kf9INVGQMnRfEHcWpjtmwpaU1u/vKeYkJcObN1M1lzbP0wqjc70K1GrZuYa6bdCFjPmi87mRVLlSNRnezKm7DKn6Yciz+LDXUIDK7RUw5DfDlzEK0Ecv8/ZwssRjoTQIHRwDt5SwGFCj/sBamtxfRIMd/18SEdFAi1Zg6WAZm7FWCzaSDGCdToci6/BO80bZRuOy0Vdiec0ybKzfgLLWPSjbtQdv7PoP9NDjjhl3Y37esf3wTgaZpBR482erptG1N8PoXNcRyNUCug07gsuLUIatgRGobPXDq3NidNOXsP/rIwSMKWrwM2/ONHgk2zdnKgJpQ/tsUw8nSzzWSoNQZwzcxhH+sIoMA2rUH1hLk/uLaLArdTZjQ2UTJuTaUDwkY6A3h4iIElCiB5YONwN4mLUQV4z9f7gC/w8tnhasql2hauMudy5T2blj08cH13299D/4qvJzzJSMXMdsjLCNgl6n7/P3NFgFTFZ4hsxVTaNzN8JYvVYFcedXroe/fCN2uywoCpQFa+bqvK0w7V2imsafnAUMnYnkzEnwZk+FJ3caAjKvnz8jsVYahDpj4DaO8IdVZBhQo/7AWprcX0SDXwAIyCWB+y8LJCKigZdIdSkTPbDUlxnAUvt2Xu7Rqmn1caXUgubbqq+wzLlENZFpysS0rJmY4ZiF6VkzMSSloE+2I54EzGnwDJ2nmjgSwFGuOhir18FbNRtuVTN3DQxNezo9Tt9WA2x5H6l4PzjPZxumArhqQLXcafA4JgOm/WUvovEZibXSINQZA7dxhT+sItHq9qK8pR35qSakmPhVICIi6k6Rw4b8nHSY/PsH5yAiooGXSHUpGViKntCgrbh6/E8w3TFLZeSurl2FuvY6fFL+oWpJehNeP/E9mA1mta7b5w7eps4Clkx4hh2tWtu+ebpWZ0e9XKmJW7VKlVrQu+o6Pc7QtFs1bH2j43l0evgyx+wL5k5TwVyvfTxgSOqzXR5rpUES8eTUoTBaFUeybckYqzcgO9U00JsyKJRUNGDFrlrMKLTDYbMM9OYQERERERGFLZHKB8RqYCkeDbeNUO28ERfC4/dgQ/06rHQuVwOcSZA2NFB77ddXwxvwqkzcGVkzMTVrOgc6O4RAigPtwxeoJmSQM4ehBo2bv4axoiOQm1S9BjqvFuoFdAE/jLWbVcPGFzuex2CG1zExJDN3Onzpw4E4K2mRSCenDoWB2zjS7PagttkFm1HHQGQYxualw2ZLVhm3RESDFeubc59FG2vCExHFpkQvH0DRl6RPwlT7dNWkPq4/sP/qm2ZPE3Y0bYMffpQ278BrpS+rgc7GpI9Vgdy5OfMwxT6Nh+lQdDrAPgLtYxxwjz6zY57fC0Ntyb7M3I5grrFmI3QB3/6H+dxIqlyhmsZvSusI4uZ01MqVqd+aP6izWBPp5NShMHAbR1izNTLZaRYUj8yG09nUUbqPiAYcg5CRY1CN+yzarOYk2K0BNSUiotjB8gHU30IHKbMm2fDKwrewunYlVjq/Uxm5MtDZ5oZNqtW4a4KBWwn4rq9bi/EZE1QwmA61k43wOSaohgkXdczztsHo3ICkypX7g7kNOzo/rL0Rpj1fqKbxpeZ2lFfImbYvmDsFX25tGTRZrDw51YGB2zjCQZCIaLBjEDJyPGnHfRZtvKKHiCg2sXwADbR0UzqOyTtONVHtqsbKmu9UaYWj9g1+JiQz92ff/hgWQzKm2Kd2DHaWNQuj0kbDoIvNbM/eikpGqzEZ3ryZqmmv8dX6ndhbXoaiwG4s9H8Oa/UyGFqrOj3M0FIJz45P8clOL0pRjiL8LypNk5CSVIQcWzLKA5koq5GykbEZuOXJqQ4M3BIRRREzSCPDICT1B57ojAwzbomIiCgc2ZZsLCo4RbVQVW1VSDdloKG9Hkurv1VN2JJsmGafiQtGXowJmZPiYif3R11W9Ro7mmAypmO7Nw3tY4/HwtPyoW8p76iTq2XlVq3Gh54j8JFuPsyBdmzVjUBOuxMeTwOcLdXw6yowruprZJSXdwx6psosTFeDoUFvGHQnp1yDqAxEJBi4JSKKImaQRoYBNX7GKPYw45aIiIgOx5G58/BKzpvY0bQdKyQjt2Y51tSuRJOnCV9Ufoqzis4OrrulYTO2NW1VdXJzk/MOe8f3dzCvP+qydvsaOh381iFolzZyX+A84Mfmj1fC4KxDdqASlS0+pLS7sCDwJUpRgKJAGRYGvkSS040k5zpg/XPqYf6k1H2Dns1QgVxpgdScPn8ffX1svozTwcwYuCUiiiJmkFK08TMWOWbC8zNGRERE/V8fV0ojSDtvxIXw+r0oadikArkTMycH1/ug7F28vPNFdbsgZSimZc3oaPYZyLI4Yj6Y1x91WcN+DZ0eQ/LysaPRgDJjNtrNAeSMPh7zcutwbNUqJFW6YKwajkBtCXTYP/CP3tMCU9nXqml8tqEqgOvdF8j1Zk9SJRwOR18fm7I4HcyMgVsioihiBilR7GEmfGT4d4yIiIj6mlFvVOURupZIKEgdhuKMidhcvxFlrXtUe2v362rZsNRCPHzkY6q2bqwG8/qjLmskr9Hdut6kYWqgMteky9QyXXszjNVrYaxciaSqVTBWrIChpaLT8xia9qiGrW+o+wG9Ed6sCfDm7Qvk5s6AL32EyvwdiGPj8vjQ5PJgT20rapvbkWI2RiVoPhAYuCUiiiJm9lG0MQgZOWYpU7z48MMP8dFHH+Hee+8d6E0hIiLqE1I2QVqzpxlraldhde0KrKpZia2NJWj1tiItKS247t83Pgy3z60GO5tqn4ZMs31AMmD7e9DASF4jnHUDJis8BUeq1rZvnr65vCOQW7liX0B3DXRebSmg83uRVL1GteS1T6t5fnOGqpUbmpkbsGQe9HX78th8WVKJ6kYXUkwGtLZ7UZRtjUrQfCAwcEsJiwE16g8MqlG0MQgZOWaQRkY6wWvKG5GfaoLDJiMPUyy477778Mknn2DatGkDvSlERER9zppkxVG581UTTZ5GlLfuhW5fRqc/4Md7e95Bo6cBr+/6j5o33DpCBXGn2adjatZ0NSBaf2TARttADLrlt+ajXdqoU/fN8MJQsxlJlSv3B3TrtnR6jN5dD9OuT1XTeNNHdARx82aoqTerGDCY1LK+PDZlda2wJOlRmJWhfoPbLElxMTCZYOCWEhYDatQfOBo7RRuDkBRtJRUNWLGrFjMK7QzcxpApU6bg2GOPxWuvvTbQm0JERBR1tqQ02NL3Z9sGAn7cOPnXWLUvI3d701bsbN6h2mulL2NCxiQ8ctTjwYzTFk8LUpNSB+WRilad3ogCwnojfNkTVcOkS9QsnbsRxqrV+4K5K9RU31bT6WHGhh2qWUpeVfcDBjO82ZNVNq45dzoWDZsO/4RREZVY6E5/Z1b3JwZuKWExS436w+7aFqzYVYcZhZkMeFBUMBuSom1sXjpstmSVcUux45RTTsGSJUsGejOIiAZNBiHFF4PeiPl5x6omGtrrsbp2FVbVSCB3OaZnzQiuK2UXvvfhKRhuHblvsLPpmGKfpoLBg0G06vQebkA4YE6DZ9jRqnXMCEDftBtJFfvKK0hAt3oddP724GN0PjeSKr5TTeNPzt5fXkEyc3OmqvINkZgfB5nVB8PALSUsZqlRf6hoaMWumiYMSTcDyOJOpz7HbEiKtuw0C4pHZsPpbJL+OBERUcxmEFLikrIIx+Qdp5pWSkFT0rAJvoAP25q2qPbKzhehgw6j08aqIO6CIYswNn08YlW0skn7PCCs08GfVgi3tLGLO+b53DA6NwQDuRLUNTSWdnqYvq0a5p3vqyYC0KElcyLet5yGXYaRyM/Lw1HTJsNiNg1obeGBwsAtEVEU5aUnozDLqqZE0cBsSCIiIhpsopVBSKTR6/TB2zMcs/DygjexWrJxa1eqjNzdLbuwpXGzagUpw4KBW6erWg2ENjlzWsyUVohWNmm/lBeQ0gj7smld+2bp2mpDyiusgrFqFfTuhuBDdAjg0/pcfKGzwBzYidK9e5Gy8jGclFcXHPTMmzsD/tTchMjuZ+CWiCiKhtmtMJpNvMSYoobZkESDX3t7O84++2z89re/xZw5c9Q8t9uNO++8E++//z4sFguuvPJK1YiI4kE816Ok2GQ323H8kIWqiRqXU9XHXV2zEjMds4PrfVX5BR5a/wD00GNM+lhVUmGKfTomZ05FmmlgSitEK5t0oMoLBJLtaB++QLWOGX4Y6rfvL69QsQKltcNgDrQjH1UoD+RgV8ABU9nbMJV9E3wen3XIvkDujI5gbvZkICl5wLP7uwsWJ5t6Hyxm4DaOtLi9qCtvgMnvR4qJh5YoFjS7PahtdsFm1LHGLRERHUACtDfccAO2bOk8MvP999+PdevW4emnn8bevXvxq1/9CkOGDMHJJ58cXEeCvFqgl4hoMInnepQ0OGRZHKpEgrRQBp0eQ1IKsLe1DJsbNqn20o4XVGmFEbZR+O3036HIOrzPA31fbO7/rNCYKS+g08OXOVo19/jz1Cz7mlJs2rgLu31N8LhbMcxXArg7P8zQvFc187a31P2AzgCvY0KnrNyyWn+/Z/d3Fyw+cVLv9zOje3Gk1NmMDZVNmJBrQ/GQjIHeHCLiIHjUD3jSjmjw2rp1qwraBroUD25tbcVLL72EJ554AhMnTlRNArvPP/98p8BtuA5zoGaKcdrx5XGOf/F0rCX77HACGfEuno71YHNG0WLVqtuq1GBna2pXqazcXS2l2Nm0HdmW7OBxeWXHv1HavFNl5U7Nmq6WRUJ7Hgn0fV1ShaR9gT6ZvTDBvx9HTxgKnSEpGMyeMe57qHXfrLJytZZUtRo6z/5ArC7gQ1L1WtWS1z2j5o0zno7dxhNQ05gMj8GGocNSo/692ruvFExuejIqG9rU/cN5TQZu40pAjeKnpkQUEzgIHkWbnDkurW9DUUYyhjts3OHU53hyIHqWLl2qMmavv/56TJs2LTh/06ZN8Hq9mD59enDezJkz8eijj8Lv90Ov31+3LxxZWfzbkAh4nBMHj3Xi4LEeOA7YUDxsFC7EOeq+s82JkroSFOXnBdf5fOnHWFO9Bm/sek3dH2odill5szAzdyZm5c5CgbUAujAidjVtHqSmJCE/MxXldS2ocXngYL8eF+Z3TUjMAIaPAXB+x12/D6jaCJR9B+zZ16o3dYqJnej9ADpfG0rdBShCGRZ+9SUsGwuAglnA0Fkd07zJgPHgA59FatywTOypb0NdmwcBnV7dP5zvMgO3caTIYUN+TroqlUA94w9RIooHVnMS7NaAmhJFA08ORM/FF1/c7fzq6mpkZmbCZNr/I8LhcKiyCvX19bDb7RG9Tk1NU8e5fYpLEhOQH4Q8zvGPxzpx8FjHIjPGmibD6WwKzrmw6FKMsy5XmblbG0qwp3kP9mzdg9e2voZMUyZeWfhmMHBb665V80IDudpxzkpOwsZWD0rbG1TN5yxLUqfXoUMwFgFF0joC7Lr2Jhir1qg6uZKVa6pcidNbP+r8mNrtHW3tv9XdgAyg5pgIb15HvVxv3gz4bUN7nfI+rSADzc3uYLaw3Jf/0b0N3jJwG0eY2RcZ/hCNHIPd3GcUe1hHmaJNaoJlZKTyxHA/amtr6xS0Fdp9GcgsUhK0ZeA2/vE4Jw4e68TBYx3b5ubMU020eFqwrm5NR2mF2pXIS86XMOK+YxjA1V9cAX/A31FWwT4dU7OmYbhthHqs1LSV9bRA37x998MZ9Ko/auEOJoEkG9oL5qnWMSMAfdOejkHP1OBnK2CsXgedb3/BXLkt86Ul4//UPH9yNjx5+wY9k2n2VMCUGtY2mI0GLOhSO/hw+mEM3FLC4g/RyDHYzX1GsYcZtxRtPDHc/8xm8wEBWu2+xWIZgC0iIiKiQ0lNSsWcnCNVE6H1651uJxo9jfD42/FZxceqibSkdMzKm4l5juOwcOLCXg16FRODi8UynQ7+tGFwSxtzZsc8XzuMNRs7Arn7MnONDTs6PUzfVg3zjvdUE1LywGcfvy+Y25GV68sYqQZWizYGbilh8Ydo5Bjs5j6j2MOMW6L4k5ubi7q6OlXn1mg0BssnSNA2LS1toDePiIiIehBaEkEGLXvjxPewqWGjysaVrNz1dWvR6GnAx7s/hsOYi+PzOwK3bd5WvLrzJUyyT0Fx+gSYDObg80imrQRt5Xe5JFXJfeoFgwnenKmquSZfoWbpXHWds3IrV0Hvbgg+RBfww1izQbXk9c+peX5zOry501QgVwVzc6cjYOlal/fwMXAbR3gZO0Ubg91EsYcnVIjiT3FxsQrYrlq1CrNmzVLzli9fjsmTJ0c8MBkRERENPAnASpkEacLr92JL42ZsdW/AaHNxcL0N9evxfyWPqdtJ+iSMSy/G5MypmGyfAkdaPnZWB1TQVmrhSrmEROLqRamIcB8TsGSivegE1Tpm+GGo365q5aoyChUrYKjdpAK4GgnsmnZ9pprGmzGqo7TCvmCuL2scoD+80CsDt3GEl7ETxR5+LynaeEKFoo0nhvtfcnIyFi9ejDvuuAP33HMPqqqq8OSTT+Lee+8dgK0hIiKivmbUGzEhcyKOccxVA5FplRWSDck4Ju94rK1djbr2WlU3V9q/tgN6vwmLc34DW2B4Ry3cMdkJdWB6Uyqi1+UlpDRC5mjV3MXnd8xrb0FS9ep9wVwps7Ac+jZnp4cZ67epZtn0krofMCbDI9m9eTOAM3vXj2PgNo4w64oo9rD+KBENdjwBNTBuvvlmFbi9/PLLYbVacd1112HRokUDtDVERETUHyZkTsIdmXerGrl7W8tUWQUJ3Eogd0/rbpwyuQij0kardd/a/Tqe3/o0JtunYnLmFEy2T0NhahHcXn9cDmJWFmapiNAs2901LTDq+6i8hCkVnoKjVGvrNPDZChgrlnfUy3Wuh87vCT5E522Dae+3qjFwS8y6IopBrD8aGWb2EcUenoDqH5s3bz4g6/a+++5TjYiIiBKvRm5B6lDVThl2uppX665Buml/DdV1tWtQ0VaOirJyfFD2bnDAsyLv6fDXj0VOSlbUBjHrTdmCw1WQmaLeT0+lIkKzbGuaXZBqw1Jpqs/LS3Qa+Oysjnlelwredgx6tq/EQnPZYb0MM26JiKKImfCRYWYfUeypbnKhpLIJxlwbHDbLQG8OERERUUKym7M63b9u4vU4YciJwYzcDfXr1IBnpbWNMHp2YWLeENQ1e1VwdYXzOwQQwISMiUg2Hn7wstclCA7D/LG5ahoaLO4pM9fvl3etw5DM1EM+ps8YLfDmzVRNo2+pUKUVeju8LAO3RBQ2ZkNGjvVHI8NAN1HskYyK+hY3XJ7EGgCDiIiIKJalGFMxO3uOasLj92BLw2a8sXobdpQZVdBWyzJ9esuDWFu3GnqdAWPSxuwb8GwaJmVORqbZHrWyBX3JkmQIKzgcmpnr9QPzxmZHPah8KP7UPLSPOqXXj2fglojCxmxIijYGuolijyVJj4wUk5rGs8rKSnzxxRdYvnw59uzZg7q6Ouj1ejgcDuTn5+Ooo45SLTMzc6A3lYiIiOgASfokVSN35LziA8oYrNpYiMq2ClS5KrG5YZNqL+98UT1uQsYkPHLU41EpWzAQpRjmh5mZO1gwcEtEYWM2ZOSYpUxEg12Rw4b8nHSY/H7EIwnUPv300/j444/h9XpVbdmCggI1IJjf71dB3JUrV+KVV16BwWDAggULcNVVV2Hq1KkDvelEREREYWWm3jjlZjWV4K3UxpXsWymvsKN5e6e6ueKHX1yObEs2JmZOxqTMKRiXUQyLwdKvwdHDKcVgCTMzN1Zq9/aEgVsiChuzISPHLGWKNp4coGiL17/91dXVuPvuu/HBBx/gyCOPxO233445c+agsLDwgHUlgFtSUoLvvvsOb731Fi644AIVwL3tttuQmzu4sziIiIgoceQm5yG3IA8LChap+02eRjR5moLLJbC7rWmLat9Wf63mGVR5hXEqkHtkzjzMcMw6rOBorJZiGKjavT1h4JaIKIqYpUzRxpMDkWOwm/tLnH766Tj11FNV4HbIkEN3yKVkwvjx41W75JJLUFpaiscffxxnnnkmlixZ0qvvLhEREdFAsyWlqaZxmB3421H/i3V1a9WgZ+vr1qLG7cSmhg2qBeBXgVvh8rnw3p63VVbucNsIFeDtK9EuxRBrAeNDYeCWiCiK4jVTjWIHTw5ErtTZjA2VTZiQa0PxkM6XhlHi7K+XX34Zw4YN69Vji4qKVLbuNddc0+fbRURERDRQDHojxmdMUO3cERcgEAh0lFfYF8Sdmz0vuO7m+o14aP0D6naqMRXFGRNVEFfa+IxiNXhabw1UndqCAQoYHwoDt0RERIMYTw70RgAdZ1J4NiWR91dvg7Z9/RxEREREsUqn0yEvJV+1hQUnHbBsZtZsbKhfjxZvC75zLlVN6KHHTVNvxaKCU9R9X8Cn5sljwhHtUgyDaWAzBm6JiKKoutGFNeWNyE81wWHrXNCdiAZGvA+21de4v4iIiIioqyn2afjjnIfg83vVIGfrajvKK0irclWiMLUouO77e97BU1ue2JeROxkTMyZjdNoYleEbSywDFDA+lNjaQ0REcaakogErdtViRqGdgVuiGMEsZdJcdtllB90ZzzzzDHcUERERUQ8k+Do6baxqJxcsVgN8ba12YteeNAxP9alg6Pr6tXC6qvFp+UeqCYvBgnHpxZiQMQnnjLgAdrM9Lva1y+NT+yA0azfZ1Pv6vwzcEhFF0di8dNhsySrjligaONAWRVs8D4CXl5eHt99+G1dddRWSkpIGenOIiIiIBjUJWH5VUqUG+Pqmzgm9Tq8yWH9S/HMsHHJSsFbu+rp1aPY2YXXtStXOH3lR8Dk+K/8Yde46TMichJG2UTDGWFZuJPtA6uWKEyf1Pot3cL17IqJBJsVsxLBkEy/JpqiJ56AaxYZ4HgDv3nvvxe7du5Gamoqrr756oDeHiIiIaFCTLFMJWEr/UX6nyH2RbEzGtKwZqgl/wI9dzaXYUL8Oe1p2I920fwDc13f9Bytrlgezcsemj1dZuRMzJ6E4Y1LMZ+YebB/0FgO3cYRZV0SxJ15HY6fYYTUnwW4NqClRNMRzaQmDwYC77roLa9euHehNISIiIhr0pDSAZJlKwLLdG1D3uyOZuMNtI1Tr6ojsI2HUGbGxfoPKyl1Tu0o1YTXa8NqJ76jHi4rWcjgs2TGVlRvuPghX7LwzOmzMuiKKRfE5GjvFjma3B7XNLtiMOtZRJuqFUaNGqUZEREREh0fquYrQ+q6RumDkxapJVu7ull3YULdO1ciVaU5yXjBoK36x5FrUuWtVVq5k5EpmrpRYsJuzBvU+CMXAbRyJ50sZiQYrjsZO0ca//RRt1Y0urClvVLW6HTYLdzgRERERdUsGIpOatn1BArRF1uGqnTLsdDXPF/AFlzd7mtHibYbb78bautWqafKS87FgyCJcNe5Hg3ofCAZu40g8X8pINFjxe0n8jNFgV1LRgBW7ajGj0B6XgVspk9Dc3DFwRE9kvRNPPDHq20REREREBzLoDMHb1iQr/rPwHexp2aUGO9tYv17VzN3RtB0VbeVo8jQG1233ufGrZb/AuPRilZE7IWOiKrEwGDBwS0QURaw9TUSD3di8dNhsySrjNh69+eabuPnmm8MezIyBWyIiIqLYoNfpUWgdrpqWldviacHmho2dBjzb0liC1bUrVcOOjnnZlhwUZ0zA+PQJmJNzFEbYRiIWMXBLRBRFrD1N0caTAxRtKWYjhiWb4rYUUyCCy5QiWZeIiIiI+l9qUipmOGZ1mleQMhS/mvKbffVy12Fn03ZUu6pQXVGFzys+VQFgLXDrdFVjafW3KqgrAeHQLN+BwMBtHOGPd6LYw/qjFG08OUDRVupsxobKJkzItaF4yP7MhXih0+misi4RERERxYYMcyZOGnqqaqLN24qShs2qvMLG+g2YYp8WXHdlzXI8sPZedTvFmKIGPitOn6gCucUZE5FlcfTrtjNwG0f4450o9rDGLUUbTw5EjoNtRaa+tR27q5swxGbuxd4mIiIiIootycYUTM2arlpXKcZUTLVPx+aGTWj1tmJVzQrVNPfMegBzc45St5s9TTDojEg2JkdtWxm4jSN6nQ7tXr+aEhFRYuDJgcjF+2Bbfc3V7oXb41VTIiIiIqJ4Ni/3aNV8fi92Nu9UWbmb6jeo6c7mHRiTNja47is7/41nt/4DI6wjgxm54zMmoMg6XJVfiOnArdQA4+Vk/WtPbQs2l9Uh1aBDYZa1n1+diIhocIj3wbb62pDMFBS1pKkpEREREVEiMOiNGJU2WrXTC88KlliQbF3Nnpbd8Ad82Na0RbU3d/83WGJhXHoxfjvtd6pMw+HoVfj3z3/+M7zeg2ddbNu2DRdddNHhbBf18ofosRML1JQoWnWUt5Q3qCkRP2M0WGWnWXDC5AI1pZ6V1bViZ3WTmhIRERERJarkkKCtuHXaHXjx+Ndwx/S7ccHI76tauRaDRZVY2FS/ETZT2sBk3D722GP44osvcP/992P06NHB+X6/H//7v/+Lv/71rxx1dwDID9DikdlwOpvAQY8pGlhHmaKNn7HIcWBKirZkkxGWJIOaxiO5SoyIiIiIqDeyk3NUOyb/eHW/o8TCDlS0VcCgM+Bw9aoH/sc//hF33XUXzj77bPzsZz/DVVddhZKSEtx8881Yv3495syZg9tvvx19rb29Hffeey/efPNNJCUl4dxzz8X111+vSjJs2LBBvaZshwST77zzTkyaNCn4WHnM//zP/6C6uhrz58/H73//e9jt9mCH/cEHH8TLL7+sgs/yvDfeeCP0+r6pR0GxicGOyHEQJH7Ooo2fscgx2E3RlpGShGFZVjWNR1OmTME333wT1roOR/+OIkxEREREg7HEwhjV+oIu0Ms0AwmASnD0o48+wpgxY7Bjxw6kp6fjpptuwplnnolouO2227BkyRIVOG5paVFB25///Ofq9RYtWoQzzjhDBV3/9a9/4Z133sEHH3yAlJQUrFmzBpdeeqna3vHjx+Puu+9W8yVzWDz55JN45pln8MADD6gSEL/85S9xxRVXqIB09++9CbFIxiRzOGzMuA3TTmcTSuvbUJSRjOEOW3QPTpzgZyxy/JxFprXdi3a9Hia/Hylxmt3X15xNLpS3tKt6rRxoKzz8WxZf38vsbP4P7wmvxopv/JuWOHisEwePdWLgcU68Y90bvU4pzc7Oxi9+8QsMHTpUZbl6PB6cc845OO200xAN9fX1eOWVV1SmrGRGHHnkkbjyyiuxevVqvP322zCbzSpoPGrUKNx6661ITU3Fu+++qx773HPP4ZRTTsHixYtV4FZKPHz22WfYvXu3Wi5B25/+9KeYNWsW5s6dq7Jtn3/++ai8D4qtzL6pwx1qSsTPWexkj67e6VRTCk+z24PaZpeaEkVDqtmIMfnpahrPtmzZwrIJRERERBRTehW4dbvdaoCys846SwVUf/Ob3+Ckk05SGaxSPmHVqlV9vqHLly+H1WrFEUccEZx39dVXq9IJErydOXOmKpkgZDpjxozgdshyCcpq8vPzMWTIEDW/srIS5eXlmD17dnC5PFdZWRmqqqr6/H1Q7EiUH6I0sPg5iwxPqESO+yxyHGgxMtWNLny8tkxN45lcbSWls4iIiIiIYkWvIlaSVbtnzx4cffTRKgM2Ly8Pl1xyiSpNIOUILr74YlWy4He/+12fbahkxxYUFOC1117Do48+qjJ8JUj84x//WJVtCB0kTWRlZanMCSEB2JycnAOWV1RUqMeK0OVa/TJZ3vVxmn0x4piibVMsblss4v7iPuuvS4y3lDfAHIjNS4xjjdViRFaWDTU1HGSxN3/L+Pc/PNVNbdhZ14bhmSyVE46SygasKK3FjCK7Ggg1XrW2tqoryYiIiIiIYkWvoghNTU34wx/+oEoPhDrxxBPVwGRSQ/all17q08CtdKZLS0vxwgsvqCxbCbhKzdvk5GS0tbXBZDJ1Wl/uy2BmwuVyHXS5LNPuhy4T2uO7sttTYTDE7sBlEvQg7q9oaHZ5VBAyPzMFVkt8DlLT12R/yaX/UpajkLWUw8a/Y/yMRZPFakF6eiv/loVprtEAmzUZE4dlwpGRgnh1+eWX46mnnsLEiRMxefLkgd4cIiIiIqLeBW7ffPNNVeO2O2lpabjvvvtw+umn9+nuNRqNaG5uVpewSeat2Lt3rxqIrKio6IAgq9y3WDqyQqT+bXfLJegbGqSV9bTbQpZ3p7a2JSazmmSbmKnG/RVNK3fWYPmuOswszMT04VlRfa14IZm2ErSVqQwOQz1nKLt1emYo8zMW9f+XUipHMrtdzfF9+X9fMOqAEyYXqP0Vi3/HejvQQ1fr1q1TV2mdf/75qg+ZkZEBvb7ziXopx/Xhhx/2yesREREREUUlcKsFbWtra/H111+rAOqpp56KlJQU1NXVqQHCpIxCX5LXlMCqFrQVI0aMUPVppe6t0+nstL7c18oc5ObmdrtcnlOWCcng1S6P08onHCw4LQIBxCzZtljevljD/RW+uhY3nI2tqGtJ4WcsTFIeQTJtOaJ3eCob2lBa34aiDF7Czs9YdGvc1pY3wORnCZNIxPv/SxnDYdKkSQO9GUREREREQb0uuPjkk0/ioYceUp1cyT6QS8paWlpw3XXX4cILL1RlDLTBwvrC1KlT1Wvt2LFDBWzF9u3bVSBXlj3xxBNqJGB5TZmuWLEC11xzTfCxMriZ1MQVEuyVJvMlcCsDlclyLXArt2XewerbEiWqycPscNityE/tXHqEqC8H2srISFUBNaJoqWrkCYJIA911CRDofvbZZwd6E4iIiIiIOulVodY33ngD999/PxYuXKiCtxIoFVITTOrcSh3avu78jhw5EscddxxuvvlmbNq0CV988QUef/xxXHTRRTj55JPR2Nioautu3bpVTaXu7SmnnKIeK+v897//VXV35bE33XSTeq5hw4YFlz/wwANYsmSJalKO4bLLLuvT7SeKBzIojVwuG8+D0/Q1jl4fmVSzUV3CLlOiaJ4gkBImMqXwAt1Sq1umiUYGw/30009Vv9Pr9Q705hARERFRgjH2Ntt23rx5KsAppRE0+fn5ePjhh1WmqwRJ+zr4KcHV3//+9yrQKvVnv//97+PSSy9VWbaPPfYYbr/9dvz73//GuHHjVFBXSjeI6dOnq4HSZNsaGhrUtsvzaK666irU1NTg2muvhcFgwLnnnosrrriiT7ediBITM/uIYo+cGChiCZOwJUomvIxxcNddd2HPnj2qryv3L7jgAnXSX0gpsKeffhpZWazxTkREREQxHLjdtm2bCm4ezPHHH497770Xfc1ms6lM3+5MmTIF//nPfw76WCmToJVK6EqCtZLJK20wq250YU15o7qM3WFjRiRRLEiUgAcRxa9WtxflLe2qfxHPpRIeeeQRlQBwzjnnqPuvvfYaNm7cqBIRiouL8Yc//EFdaSbJAERERERE/aFXve/U1FQ0NR18VGEZrEzLdqX+U1LRgBW7ajGj0M7ALVGMYGYfEQ12idK/eOedd1RigmTdivfee08lDUiJLaPRiN27d6sryoiIiIiIYjpwe/TRR+Of//wnzjvvPOj1ncvkyuVkzz//vKohS/1rbF46bLZkDhxFFEOYCU9Eg12i9C8qKiowbdo0dVvGSli2bJnqz0rQVisJJmMqEBERERHFdOD2hhtuUBkJp512GmbPnq1qzL744osqYCsDOFitVvzsZz/r+62lQ5IBo4pHZsPpbMK+8eKIaIAlSqYaDewAeHXlDaocRzxfxk4DJ1H6Fw6HA06nU92Wwcikxm1oIsLmzZuRk5MzgFtIRERERImmV7/wcnNz8corr+BPf/oTPvroIwQCAbz77rtqwLAFCxbgxhtvxLBhw/p+a4loQDFAFLlEyVSjgcMB8CLHv2XUnTlz5qjBx8xms0pGkH7twoULVZat9Hul/u2FF17InUdERERE/abXqTmScSCDNEjQtq6uDj6fD3a7XQ30RUTxiQGiyCVKphoNHA6AFzn+LaPu3HLLLaisrMR9992nxmqQWrdpaWlYvny5midXmV177bXceURERETUbw77mkopkyABWyKKfwwQEcUeDoAXOas5CXZrQE2JNBKkfeqpp1BbW6vKfplMHVdKFBcXq5JgU6dO5c4iIiIiotgL3I4fP14FaCO1cePG3mwT9RIv/eT+Ihrs+HeM+kOz24PaZhdsRh1rTyfw97KsrAwFBQUHzO+akCDZtwcL2u7evZvlwYiIiIgoasLqfS9evLhT4FbKI7z//vvwer2YP38+Ro4cCb/frzqvn332mcpSOO+886K31dQtXvoZGe6vyHGfRa660YU15Y2qxi0HJ+NnLBriNagWTbx6IDLx+rf/nHPOwemnn44f/vCHavyGSEif9/HHH1f94SVLlkRtG4mIiIgosYX1C09q2YaSjqoM3CADNYwYMaLTsj179uDiiy/uVYYuHR7+EOX+ijZ+xiJXUtGAFbtqMaPQzsAtP2NRUepsxobKJkzItaF4SEZ0XiTOsLxEZPQ6Hdq9fjWNJ//9739x9913q4F1Z82ahRNPPBHHHHNMtxm0krRQUlKC7777Dm+//TZWrFiB448/Xj0HEREREVG09Co157nnnsPll19+QNBWDB06FJdccokalffnP/95X2wjhYk/RCPD/RU57rPIjc1Lh82WrDJuiZ+x6AhIVKljShQFe2pbsLmsDqkGHQqzrHGzjyXL9uGHH8ayZcvwj3/8A/fcc48akCw5OVmVUJAryLRBeKuqquByuVRiwgknnIDnn38eM2bMGOi3QERERERxrleB2+bm5uCADd2Rsgnt7e2Hs13UC7xclij2ZKdZUDwyG05nU0dsjQ6Jf8ciV+SwIT8nXZVKIIqGeD8BNXv2bNUqKirw+eefq2xaKYVQX18PvV6P/Px8zJw5E3PnzlUlwjgoLxERERHFdOB22rRpePbZZ3HaaacdUBNs27ZtKmvhiCOO6KttpASvQUc0mDEQGRn+HYscM+Ep2hLlBFReXh7OP/981YiIiIiIBm3g9he/+AUuvfRSnHrqqTj22GNVLTC3242dO3fiyy+/hM1mw0033dT3W0uHxPqjRLGHgcjI8O8YERERERER0WEEbidNmoSXXnpJ1QX79NNP0draquZLLbAzzjgDP/vZz1TWAvUvZl0RxR4GIolosOOVA0REREREgyhwK0aPHq0Ct9qgDTJYQ2ZmZt9uHRHRIMcTKpFhhjL1BwYiI8PvJRERERHRIAvcaiRgy0EaiIioLzBDmfoDA5GRsZqTYLcG1JSIiIiIiGI8cNve3q6ybd944w04nU74uxnJWgK6GzZs6IttpDAxgygy3F+R4z6jaGOGMvUHniCITLPbg9pmF2xGHRw2S5SOChERERER9Ung9v7778dzzz2HUaNGYdasWTCZTL15GupjzCDi/oq2UmczNlQ2YUKuDcVDMqL+ekRENPASMeO2qqoK5eXlGDlyJMxmM4xGI/R6/UBvFhERERElmF4Fbt955x0sWrRIZd1S7GAGUWQS8Yfo4QsAgUDHlIhokOKJzshUN7lQUtkEY64t7jNuly9fjrvvvhsbN25U95988kn4fD7ccsst+PWvf41TTz11oDdRXel26623YseOHUhNTVUJFVlZWQO9WUREREQUBb1KHWhpacHRRx/d91tDh32J8Zj8dDWl8C/9lCmFJ9uWjLFDMtSUiGJDdaMLH68tU1MK/0Tn1OEONaVwJMZJuzVr1uAHP/iB6udefvnlwfnp6ekq4/bGG2/EZ599hoH2wQcfwGKx4IUXXsA555yDxx57bKA3iYiIiIhiKXA7adIkrFu3ru+3hg67/uiW8gY1pZ7xh3vkGOwmij0lFQ34bH2ZmlJ4eKIzMkUOGxZMGaqm8eyhhx7C0KFD8d///hdXX301AipYDUyePBmvv/66KhEWC0HSFStWYN68eeq2JFIsXbp0oDeJiIiIiGIpcPurX/0K7777Lp5//nnU1tb2/VZRry/9XL3TqabUM/5wjxyD3USxZ2xeOo6dWKCmFB6e6IxMovy/XLlyJc4++2yVzSqD7IayWq04//zzsWXLFgy05uZmtT1CSiVIhjARERERxade9cBvuukmNb3rrrtU6450eDds2HB4W0cRYY1bijb50S4ZV05nU8dVs0Q04LLTLCgemc3vZQRY45YO5lAD7rrdblVfdqBJ0FYL1srUZovvTGgiIiKiRNarwO3UqVMPyEQgIiIiGgx4ojPyDOW68gaY/H6kmOI361b6t2+++SYuu+yyA5a1trbipZdeUmUTBtq0adPw1VdfYcGCBfj8888xffr0gd4kIiIiIoqSXvW+//CHP/T9ltBhK3U2Y0NlEybk2lA8JIN7lIiIqBu8eiAyiZKh/NOf/hSXXnopLrnkEhUUlSQFGbBMyiM8++yz2Lt3L+68884+f9329nZVouG3v/0t5syZE8zuldd6//33VemGK6+8UjWxaNEiFbC98MILkZSUhD/96U99vk1EREREFBviN20iISXGqM80cBIl64qIiPazmpNgtwbUNJ5J5qoMPnb77bfjvvvuU/P+/Oc/q2l2drYKkM6dO7dPX1MCtDfccMMBtXPvv/9+NRDw008/rQLGMr7EkCFDcPLJJ8NgMODee+/t0+0gIiIiotgUVuSluLhYdSDPOOMMdX/8+PE9lkpgjdv+J7VH83PSVVCNesYgZOQSJeuKaDDh3zKKtuomF0oqm2DMtcFhs8T1Dp83bx4++OADrF+/Hrt371Y1bQsKCjBp0iQYjX17wnLr1q0qaBvoUjReK8vwxBNPYOLEiapJYFcGBZbAbW+wwll8044vj3P847FOHDzWiYHHOXHoDqPabFg90MWLF6OwsLDTfda4jT289DMyDEJGjnUhiWIP/5ZRtLk8PtS3uOHypCTEzpY+rgRqpUXT0qVLVWmE66+/XtWt1WzatAler7dT7dqZM2fi0UcfVYFkvV4f8WtlZfFkayLgcU4cPNaJg8c6MfA402EHbrtejsUatxQPGISMHE8OEMUe/i2jaLMk6ZGRYlLTeNbdoGTdeeaZZ/rk9S6++OJu51dXVyMzMxMmkyk4z+FwqLIK9fX1sNvtEb9WTU1TRzUtitssHvnRz+Mc/3isEwePdWLgcU68Y90bLFJJREQxg5f9R44nVCjasm3JGKs3IDt1fyAxHu3Zs+eAeZLhWldXp4KmUjJhzJgxUd+Otra2TkFbod2Xgcx6Q4K2DNzGPx7nxMFjnTh4rBMDjzMdCgO3lLB4eTFR7OH3kij2JEqN248//rjb+T6fDx999BF+85vf4Kqrror6dpjN5gMCtNp9iyV+9z8RERERHYiBW0pYvLyYKPbwe0n9gZndkdLSNRPzWnuDwYBFixZh9erVeOCBB/Diiy9G9fVyc3NVlq/UudUGRJPyCRK0TUtLi+prExEREVFsie9iZUQ9XF48Jj9dTYmipbrRhY/Xlqkp9YzfS+oPpc5mfLS2TE2pZ0UOGxZMGaqmiWz48OFq4LBoKy4uVgHbVatWBectX74ckydP7tXAZEREREQ0eLH3R0QURSUVDfhsfZmaElGsSOwM0kjxhEpHqYLXX38dWVlZUd/fycnJWLx4Me644w6sWbMGH374IZ588smwB08jIiIiovjRq1RD6Tj++Mc/xpFHHnnQGmEPPvgg3nrrrcPdPiKiQW1sXjpstmTkx/mgPkSDiWSO5uekw+T3D/SmUAw5WGBUgrY7duxAY2Mjrrvuun7ZlptvvlkFbi+//HJYrVb1ulKugYiIiIgSizHc0W2l1pZm6dKlOPHEE1FUVNTt6Luff/55tyPzEhElmhSzEcOSTQwQEcVYBqkEb53Opo7EWyLgoH1XqXE7cuRInH766bj44oujsq82b958QNbtfffdpxoRERERJa6wA7dyyVZTU5O6r9PpcM8996jWnUAggHnz5vXtlhLRgOOAPpGramxDaX0bijKSMTzB60NSdPB7yX1GfUOuGCMiIiIiGnSBW7vdjj/+8Y9Yu3atCsr+9a9/VRm348aNO2BdGTRB1j/ttNOisb1ENIAYhIxcTloyMjJSmXFLUcPvJfdZtPHkABERERFRjNe4PfbYY1UTe/fuxYUXXoipU6dGc9uIKMYwCBk5XpJN0cbvJfdZtMXryYHeDPYlV509/fTTUdkeIiIiIqI+GZzs3nvv7c3DKMqYEUPRxiBk5Pi9pGjj95L7LNri9eQAx2MgIiIiorgM3AoZgOyNN96A0+mEz+c7YDkzEvpfvGbEUOxgEDJy/F4SxR7+LYtMvJ4cYE1bIiIiIorLwO3zzz+Pu+66S93OysqCyWTq6+2iXtDrdGj3+tWUKBoYhIxcvGaqEQ1m/FtGvVVbW6vGciAiIiIiitnA7TPPPIPx48fjiSeegMPh6Putol7ZU9uCzWV1SDXoUJhl5V6kPscgZOTiNVONaDDj37LIJFKG8r/+9S988cUXaG1thT/khJtcXdbS0oKtW7di3bp1A7qNRERERJQ4etX7Li8vxy233MKgbYwZak9Fiy+gpkTRwCAkUexJpKBaX+HfssgkSoayJCQ8+OCD6koyq9WKuro65OXlob6+Hm1tbbBYLLj00ksHejOJiIiIKIHoe/OgwsJCVduWYos/EIDJqFdTIiJKnKDa6p1ONSWKBqs5CXarRU3j2auvvori4mJ8/fXXePHFFxEIBNRVZt999x1uu+02uN1uTJ06daA3k4iIiIgSSK8Ct1dffTWeffZZbNmype+3iA7r0s+pwx1qSkQ0WLNHt5Q3qCmFh3/7Kdqqm1woKW9Q03hWVlaGs846S2XbDhs2DOnp6SpoazAYcPHFF+PUU0/F008/PdCbSUREREQJpFfXVC5fvhypqamqcztixAg1SIOuy4BYcp+d2/7FSz8jw8uLiWJPolyS3Zf4t5+izeXxob7FDZcnJa53ttFoVP1bTVFRETZv3hy8P2fOHPz5z38eoK0jIiIiokTUq8CtDNogpO6X1PySDAUaeAxERoYBIqLYw0GjiGKPJUmPjBSTmsazUaNGYeXKlTjvvPPUfUlOCB2IrKGhAe3t7QO4hURERESUaHoVuP3444/7fkvosJU6m7GhsgkTcm0oHpLBPdoDBoiIYg+zR4liT5HDhvycdDUAXjw7++yzceedd6rg7O9+9zuccMIJ+NnPfoZHHnkEI0eOVFeSjR8/fqA3k4iIiIgSyGEPP11VVYXy8nLVoTWbzeoyM70+vjMyYlcAUAOTcXCycDBARP2hutGFNeWNyE81wWGzcKcT0aCTKP8vL7roIlRUVOD5559X/dlFixbhuOOOU4FbIbVvb7zxxoHeTCIiIiJKIL0O3Eqd27vvvhsbN25U95988kn4fD7ccsst+PWvf60GcKD+lSgZMUSDSUlFA1bsqsWMQjsDt0REMe7666/HddddpwK34tFHH1UDlNXX12P69OnIysoa6E0kIiIiogTSq9TYNWvW4Ac/+AFaWlpw+eWXB+fL6LvS0ZVshM8++6wvt5PCzIgZk5+upkQUG8bmpePYiQVqSkREsevWW2/FkiVLgkFbzaxZs7Bw4UIGbYmIiIhocARuH3roIQwdOhT//e9/cfXVVyOw77q5yZMn4/XXX1eDOzz22GN9va1ERINOdpoFJ0wuUFMiIopdb731Fq644goce+yxuP/++7Fhw4aB3iQiIiIiSnC9CtzKiLsygIPFYoFOp+u0TOp/nX/++diyZUtfbSMRESWIFrcXW8ob1JSIYqdW98dry9Q0nn3zzTd48MEHVSKC1Lk955xzVOkvKZewe/fugd48IiIiIkpAvb6m3mQyHXSZ2+2Gn3VWieKOBNPqyhtUHeUUE0tyUN+ramxDaX0bijKSMdxh4y6mqODfssgkSq3u5ORkFaiV1tzcjA8//BDvvPMO/vrXv6qrzaZOnYozzjgD3//+9wd6U4mIiIgoQfQq41Y6rm+++Wa3y1pbW/HSSy+pbAUiir+g2uqdTjUlioactGRMHe5QU6Jo4d+yyCRirW65gmzx4sWq9Nfbb7+tyiesWrUKd91110BvGhERERElkF6lzP30pz/FpZdeiksuuQQLFixQ5RJkwDIpj/Dss89i7969uPPOO/t+a4loQEkwLSMjVWXcEkWDDK5Y5LDB6WzCvvLpRH2Of8siIzW6i0dmJ9T3sra2Fh988IHKuP3uu+/g8/kwe/ZsnHnmmQO9aURERESUQHQBbWSxCH311Ve4/fbbsWfPnk7zs7Oz8Zvf/AYnnXQS4lV1dRNikbPJhfKWduSnmuL6Usa+IuWZHQwQcZ/xcxZT+L2MHP/283MWba3tXrTr9TFbJic7u2/KqtTV1eH999/Hu+++i2XLlsHr9WLcuHGqPMLpp5+OvLw8DFaJFHRPRPzfmTh4rBMHj3Vi4HFOvGPdG73ufc+bN09lIsiIu7t27VI1bQsKCjBp0iQYjbHXqU8Ea3bXYunOGhwxPAsnTBgy0JtDRET94LsdTnyzzYkjRzlw8pSh3OfU5zaXS43bOswozMT0oqy43cPz589X/dn8/HxcddVVKmA7evTogd4sIiIiIkpgvYqw3nrrrepSsTlz5mDixImq0cDLTDUh22ZRUyKKDRwEiaLN7fUGG1E01Le44WxsRX1LSlzv4PPOO08Fa2fOnDnQm0JERERE1PvA7VtvvYVXX30VOTk5OO2001Qnt7i4uDdPRX1oXH4GRhTYWX+UKMYGQSqtb0NRRjKG9/LSCKJDKcyywtniUVOiaJg8zA6H3apKMcWzO+64Y6A3gYiIiIjo8AO333zzDT755BM1yu7zzz+Pp556CiNGjFBZuBLIHTZsWG+elg4TB/WJDDMhqT9YzUmwWwNqShQNPGlHREREREQUn/S9eVBycjJOPfVUPPLII2qQsnvvvVcFa//6179i0aJFuPDCC1VAlyiWSSbk6p1ONSWKlma3B7XNLjUlitZJuzH56WpKFK0a+m+tKFVTIiIiIiLqP4f9K89qtWLx4sWq7d69G3fffTc+/fRTrF69Gt///vf7ZispLMwgjUxOWjIyMlJZWoKiihm3FG3820/Rxhr6RERERESDNHBbW1uLDz74AO+88w6+++47+Hw+zJ49W5VNoP5V6mzGhsomTMi1oXhIBnd/D1hagvoz49Zm1MFhs3CnU59jHWWKNpbjICIiIiIaRIHburo6vP/++3j33XexbNkyeL1ejBs3Dj//+c9x+umnIy8vr++3lMIQAAKBjikRxQRmdhM/YzTYxeuJzk2bNqGgoAA2GweOJCIiIqI4CtzOnz8ffr8f+fn5uOqqq3DGGWdg9OjRfb91FBH5UZWfk85L/4liSLwGPCh28DNG1Dvf+973cP/996t+rEaSEVauXInx48czoEtEREREgzNwe95556lO7syZM/t+i6jX+OOdiIiI+lp1owtryhuRn2qKq5IvgW7OpjU1NeGyyy7Dk08+iSOPPHJAtouIiIiISKNHL9xxxx0DHrS9+uqr8etf/zp4f8OGDSqgPHXqVJxzzjlYt25dp/XffPNNLFy4UC3/yU9+omrzhnbcH3jgAcydOxdHHHGEyr6QjGIior4IeHy8tkxNiYgGo5KKBny2vkxNE0F3AV0iIiIiopgN3F577bVq4LFQEtiU2mBtbW0HrP/666+juLgY0fLWW2/hs88+C95vbW1VgdxZs2bh1VdfxfTp0/GjH/1IzRdr1qzBrbfeqt7Hiy++iMbGRtx8883Bxz/11FMqsPvII4/g4YcfxhtvvKHmDcaRxbeUN6gpET9jsSHRAh7U//i3n6JtbF46jp1YoKZERERERBRjgdsPP/wQ5eXlneY1NDSo2mCrVq1Cf6qvr1cZsZMnTw7Oe/vtt2E2m3HTTTdh1KhRKkibmpqqBk8Tzz33HE455RQsXrxY1SyTx0vgd/fu3Wr5M888g5/+9Kcq8CtZtzfeeCOef/55DDalzmZ8tLZMTYmiNXr96p1ONaXwMOBB0cbvJUVbdpoFJ0wuUFMiIiIiIorxUgkDeSnZfffdh7POOqvTYGirV69WpRt0Op26L9MZM2YEg8qyXIKyGhlUbciQIWp+ZWWlCkrPnj07uFyeq6ysDFVVVRhcAnJQOqbUI2apRS4nLRlThzvUlMLDgAdFG7+XFG38f0lERERENIgGJxso33zzjSrZIKUMpM6uprq6ulMgV2RlZWHLli3qtgRgc3JyDlheUVGhHitClzscDjWV5V0fF8tk5Pr8nHSYWJ837Cy10vo2FGUkY7jDFu3DExc4AB5R7OH3kqItnv9fbt++HcuWLes0OJnYvHkzjMbuu8mhJ/uJiIiIiKJp0ARu3W43br/9dtx2222wWDpfqid1dk0mU6d5cr+9vV3ddrlcB10uy7T7ocuE9vju7EvujSlWixFZWTbU1DR1JN7SIeWmJyMjIxXmgD8mj2cs0vYT9xf3GT9jsYPfy8i1tnfUhJe//ymmQdMVGjDx/P/y0UcfVa27K7wOZuPGjVHeKiIiIiKiDoPm14oMHDZp0iQcffTRByyT+rZdg6xyXwvwHmx5cnJypyCtrKfdFrK8O3Z7KgyGw6oyEVUSvCXiZyy28HvJ/cXPWOyQoK3U65bSL4VxlkFK4ZNBa4mIiIiIYtmgCdy+9dZbcDqdmD59eqfg6nvvvYfTTz9dLQsl97UyB7m5ud0uz87OVsuElEwYOnRo8LaQ5d2prW2JyYwTySBy6/TMIAqTHENmKEeG+yxy3GfcX9HGz1jkJHNUgrYydTo7Lo2nwdu/cPQy+M7ALRERERHFurB73/X19di7d2/wfkNDg5rW1tZ2mi/q6urQ15599ll4vd7g/QceeEBNb7zxRlWb7IknnlCDpcnAZDJdsWIFrrnmGrXO1KlTsXz5cpx99tnqvgxGJk3mS+BWBiqT5VrgVm7LvEPVt43FUgQ7q5uxobIJE3JtKB6SMdCbMygGW6ktb1A1gWPxh2gsk89/LH4HYhE/Z73Dzxj3WTTJ33zJtJWgLf+W9Yz9CyIiIiKigRF2tOqee+5RrSsJnPaHgoKCTvdTU1PVtKioSA009uCDD+Luu+/GhRdeiBdeeEHVvT3llFPUOhdddBEuvfRSTJs2DZMnT1brHXfccRg2bFhwuQSC8/Ly1H15riuvvBKDjxZNY0Qt0QdbodhR6uQJFSIa7OK7fyF9xldeeQVffPEFNm3apJIVJBHAbrdj3LhxWLhwIc4444wDxksgIiIiIoqJwO33vvc9xDKr1YrHHntMDV7273//W3WyH3/8caSkpKjlUl7hd7/7HR5++GGVKTxv3jz8/ve/Dz7+qquuQk1NjbpkzmAw4Nxzz8UVV1yBwabIYUN+TrrKIKWe5aR1DLbC/UXRFd8BDyKKf9m2ZIzVG5CdGn+BS7lq6+c//7nqB0pgtrCwUCULyFVeEsD99P9v707Aoiq/B44fEFkEBBE01NIsNdNc0lLLyiXNzFKz0szMyqzMXSu13DI1LfWXS26paW6VZvumtm9Wmnuae6a44AoKuMD/Oa//Ow0wwAwyMMx8P88zz2XubHfee4H3nnve8377rXzzzTdmroUJEybYSnYBAAAA+cEvTesKwCVHjiR4bJ1DrfPG0E/ai2PMs2pDnvX3pySHk/g75jrajDZztz/3HpW1/xyX668oIbXLlxRPExOTu1EzO3bskHbt2pkEgOeff15atGiRKas2MTFRvvjiC3PxX39evny5Ge1V2NA39G78H/Ad7Gvfwb72Dexn39vXueGfq1cBXlJ7VGcW1yVoM3cJDQqQSrERZgkAhdGJ0ykSf+qMWXoTHZ0VEhIi77//vtxzzz0OSyFoUFdHYi1dulSCgoLkzTffLJBtBQAAgG8ikgCfRY1b2iw/6IWB40yCB6AQu+7yKImOCpNYLyuV8Ntvv5mgrE5UmxOdsLZNmzaycuXKfNk2AAAAQBG4hc+ixi1tlh+4QACgsIspHixVK8Z43XB7rWurNW2dVbFiRTl06JBbtwkAAACwR+DWi5DZ5xoduq4Tunnbiag70Wau4wIB3I2//bQZx1junDt3zpRKcJaWStDXAAAAAPmFGrdeltm3fk+8WQLwDNS4hbvtjU+UVRv3myWcw/9LjjEAAACgMCDj1ouQ2QcAvihNLg4bYOiAs8KCikpUWJpZwrePsRMnTsiBAweceu7x48fdvj0AAACAPQK3XoRh7ADge7TkS2ypCAlMTS3oTSk0ElPOybHEZAkP8JPo8OCC3hyP583H2OjRo80NAAAA8EQEbgHAjag/Snu5GxftXMcIFY4x1bZt2zz+bQQAAADyFoFbAE4jCJm7Wpp7TyRJ+cgQqRAdztFGe8EDEOyGGjNmDA0BAAAAj8bkZACcxoQ+ucvsq1kh2ixBewHwPOfOnZO//vpLNm7cKElJhW+C15UrV8qgQYMKejMAAADgBmTcAnAaw4tdt+9oovz57wmpXS5SqsRGcrTlgExIAPnprbfekqlTp0piYqK5HxgYKB07dpT+/ftLQIDnd5PHjh0r33zzjdSqVaugNwUAAABu4Pk9UgAeg6Ca677belDW7j0mpxKTCdwCgAf54IMP5JVXXpGyZctK69atxd/fX1avXm2CuRcuXJDBgweLp6tRo4bcdttt5rsAAADA+xC4BQA3qnxZcTmSeNYsAQCeY9GiRSZTdd68eRIUFGTWpaWlSd++feWdd96RAQMGmAzcgqbb8vbbb6dbN3v2bCldurTceeedJtgMAAAA70TgFoDTmJzMddXLRUnJEmESG1rwJ/8AkBve+rd/586d0q9fP1vQVvn5+UmXLl3kyy+/lF27dsk111wjBa19+/bmBgAAAN/D5GQAnMbkZK47kpAsf8edNEsAKIy89W+/TkQWHh6eaX25cuVM5u2pU6cKZLsAAAAAC4FbAC5NTlazQrRZwllpOvb24hIACiFv/dufmppqMmwzKlKkiFlqnVt3OHv2rLRq1SpdiYOUlBRTU7du3brSsGFDmTNnjls+GwAAAIWL94x3A+B2TE7mupjwEKnsX0RiKJUAoJDib3/e0QBt//79Zfv27enWjxs3TjZt2mTq7R44cECef/55KVOmjLRo0SLH96xXr565AQAAwPsQuAUAN0pMOSfHEpMlPMBPosODaWsA8CAnTpwwgVJ7J0+eNMtjx45lekxpQDU3duzYYYK2WobB3pkzZ+S9996TWbNmSbVq1cxNA7sLFy50KnDrDAeJxfAi1v5lP3s/9rXvYF/7Bvaz7/C7hL4YgVsv4q2Th7gL7YX8oEOLIyNDze8lABRGR04ly4a4U2aSRW+7ADV69Ghzc2TAgAGZ1mlphS1btuTqs3777TeTGdu3b1+pVauWbf3WrVvl/PnzUrt2bdu6OnXqyPTp0005B3//S69sVrJk5lq+8D7sZ9/BvvYd7GvfwH5GdojueRGdNGTviSQpHxkiFaLpoNNe8AQMMXYNF1QAz/P3wZOy9p9jcv0VUV4VuG3btm2+fl7Hjh0drj9y5IiUKFFCAgMDbeuio6NNWQXNCI6Kirrkzz56NOFiuXV4bRaPnvSzn70f+9p3sK99A/vZ9/Z1bhC49SJk9tFeQGHHBSjXEeyGu5WLCpXTF9LM0puMGTNGPEFSUlK6oK2y7utEZnlBg7YEbr0f+9l3sK99B/vaN7CfkR0Ct16EzD7aCyjsuADlOoLdyI+LA6eSzpkl8l5QUFCmAK11PzjYezKcAQAA4DoCt16ErCsAhR0XoFxHsBvuZ6VrMtbeHUqXLi3Hjx83dW4DAgJs5RM0aFu8eHG3fCYAAAAKh0uf7QAelXW1fk+8WQIAfCfYXSk2wiwBdygfHS5Na5QzS+S9qlWrmoDtunXrbOvWrFkj1113XZ5MTAYAAIDCi96gl2Vd1awQbZaAu7K6t8edZLgsbQYUavwtcw0XB9wrJCRE2rRpI8OHD5cNGzbIypUrZc6cOdK5c2c3fzIAAAA8HYFbL8KJFdyNrG7aDPAG/C2Dpxk0aJBUq1ZNHnnkERkxYoT07NlTmjdvXtCbBQAAgALGuEoATgsLKipRYWlmCedQfxTwPPxeoqBt27YtU9bt2LFjzQ0AAACwkHELwGmJKefkWGKyWQJAYcUIFQAAAACFARm3AJxGllruhmTvPZEk5SNDpAIT+wAopDWBj8edlMDUVCkWSNcRAAAAyC/0vgG4lKWms4rHxydIWhoN5wyC3YDnIRDpGi5AAQAAAAWDwC0ApxHscB3BbsDz7I1PlC2HEuTa0uFStUxkQW+Ox+MCFAAAAFAwqHELwGnMxJ67YPf2uJNmCcBTpMnFYQMMHQAAAADguci4BeC0sKCiEhWWZpZwDkOM4W5kwrtOS77EloowNVuRM/6OAQAAAAWDwC0ApyWmnJNjickSHuAn0eHBtJwTCHbD3Rj27zpKmLiGUgkAAABAwSBwC8BpnLy7jmA33I9h/3AvAt0AAABAwSBwC8BpnLy7jmA33I1h/66jvAQAAACAwoDJyQA4jYm2chfsrhQbYZaAO3CMuY6JFgEAAAAUBgRu4bMIQrqOYAcA76k9HcxEiwAAAAA8Gilg8FnMku06hv0D8AbUngYAAABQGBC4hc8iCOk6atwC8Ab8/XfNkVPJsiHulMSGBkp0eLCb9goAAACAjCiVAJ9FXcjcnbx/vXG/WQJAYcXff9ds2HdMPl271ywBAAAA5B8CtwCc9vfBk/Ld5v1mCQDwDSVCAyUmPNgsAQAAAOQfSiXApycnOx53UgJTU6VYIL8Kzqh8WYSEh4eY4bLgOAPgG6rERsqVZaPM/0sAAAAA+YeMW/j05GTr98SbJZwTUzxYmlxX1izBcQbAN1BaAgAAACgYpBnCZ4UFFZWosDSzBNyFSZAAAAAAAEBukHELn5WYck6OJSabJeAuZKohP8q+bI87aZagzTjGAAAAAO9Bxi18FpmQrqMuMG3mbhxjrtsbnyhbDiXItaXDpWqZSDfsFe+jJXL2nkiS8pEhUiE6vKA3x+Ntizspa/85LtdfUUJqly9Z0JsDAAAA+AwybuGzyITMXYBo1cb9ZgnazB2oPZ0baSJpaReXcPrCXc0K0WaJnJ04nSLxp86YJQAAAID8Q8YtABcQIHIdbeYKMuFdVz46XGJLRUhgamouXu27F+603eLjEy7GvJGt6y6PkuioMIkNDaSlAAAAgHxE4BY+iyHZriNARJu5GwE12gyep1hQgFweEsjFAQAAACCfUSrBizBBjWsYku06ykvQZgB8D/8vAQAAgIJBxq0XYbIV1zAkGwAA/l8CAAAAnorArRchEOkahmQjP1CSA0Bhx/9LAAAAoGBQKsGLMIwd8DwMMQYAAAAAALlBxi0AuFFYUFGJCkszS+SMDGUAAAAAAC4i4xaA05gAz3WJKefkWGKyWSJnZCgDAAAAAHARGbcAnMYEeK6j9jTt5W5kKQMAAACAdyJwC8BpBCFdx6Q+tJe7cUEF7sbFAQAAAKBgELiFz+JEFBxn8AZcUIG77Y1PlC2HEuTa0uFStUwkDQ4AAADkE2rcwmdRS5M2y6+Ax6qN+80ScFdWd6XYCLME3CNNJC3t4hIAAABAvuEsDz6LLDXaLH8Q8AA8DSMuXFM+OlxiS0VIYGqqm/YIAAAAAEcI3MJnUXuUNssPBDxcQ0AN+YG6wK45k3Je4k6fldjQQCkWSNcRAAAAyC/0vr3IkVPJsiHulDmxig4PLujNAcAFApcRUEN+YMSFa/4+eFLW/nNMrr8iiv4FAAAAkI8I3HoRTqwAFHYE1JAfGHHhmsqXRUh4eIi5MAwAAAAg/xC49SLlokLl9IU0s0TOGJINeB4CaoDniSkeLFUrxkh8fMLFOcoAAAAA5Av//PkY5IfUtDQJDPA3Szg3JHv9nnizBIDCfBFqe9xJswQ4xgAAAADvQcatF2GIMe0Fz0NmN9yNusDgGAMAAAC8E4FbL8IQY9oLnoegGtyNi3bgGAMAAAC8E4FbAHAjgmpwNy7agWMMAAAA8E6FqsbtoUOHpFevXnLjjTfKLbfcImPGjJGUlBTz2L59+6RLly5Sq1Ytadmypfz444/pXvvzzz9Lq1atpGbNmtK5c2fzfHtvvfWWec/atWvL4MGDJSmp8NU9pc4hAAAAAAAA4B0KTeA2LS3NBG01oLpw4UKZOHGifPPNN/K///3PPPbMM89IdHS0LFu2TFq3bi09evSQAwcOmNfqUh+/9957ZenSpRIVFSXdu3c3r1NffvmlTJkyRV566SWZN2+erF+/Xl599VUpbPbGJ8qqjfvNEoBnYBI813ABCgAAAACAQha43bVrl6xbt85k2VaqVEnq1q1rArmffPKJ/PrrryaDVgOvV111lTz55JMm81aDuOq9996T6tWry2OPPWZeq++xf/9++e2338zj8+fPl0ceeUQaN24sNWrUkBEjRpjXFr6s2zSNcF9cAvCYUgk1K0SbJXJGoBvwPFxQAQAAAApGoQncxsTEyJtvvmmyau0lJiaaDNlrr71WihUrZltfp04dE+hV+rgGei0hISFSrVo18/iFCxdk48aN6R7XoO+5c+dk69atUpiUjw6XpjXKmSUAz6k/Wik2wiyRMwLdgOfhggoAAABQMApNJKF48eKmBq0lNTVVFixYIPXr15cjR45IqVKl0j2/ZMmScvDgQfNzdo+fOnXK1Mm1fzwgIEAiIyNtr3fEz088TlhwgJQsGS5HjyZcTLxFtqx96In70lPRZrSZu/F3jGMsP/C3zDWlI0IkMjJUgtJS+Z8JAAAA5KNCE7jNSGvQbtmyxdSs1YnFAgMD0z2u98+ePWt+1pIHWT2enJxsu5/V6zOKigqVIkU8N1lZg7egvdzh0Ikz8vXG/VLt8hJSOvK/DHfwe5nX+DtGm+UHjjMAAAAAniygsAZtdRIxnaCscuXKEhQUJCdOnEj3HA26BgcHm5/18YxBWL2vWbz6mHU/4+NaUsGRY8dOe2TGiW4TGbe0lzv9uv2QrN17TBISk+TmSqXd+lnegt9L2otjzPPwe+ld7RVNiSgAAAB4qUIXuB05cqQsXrzYBG/vuOMOs6506dKyY8eOdM+Lj4+3lT/Qx/V+xserVq1qSiJo8Fbv68Rm6vz58yYQrHV1s+KJJy722+bJ2+dpaC/nVS4dIeFhIRIbGsgxxnHmVvxe0mb5gePMOUdOJcv6A6fM3/7o8IsXxQEAAAC4n+eO93dgypQpsmTJEpkwYYLcddddtvU1a9aUzZs328oeqDVr1pj11uN636KlE7TMgq739/eX6667Lt3jOmmZ1rm95ppr8u27AYVBTPFgaXJdWbOEc5iNHfA8/F665u+DJ+W7zfvNEgAAAED+KTSB2507d8obb7whTzzxhNSpU8dMOGbdbrzxRomNjZVBgwbJ9u3bZebMmbJhwwa57777zGvbtWsna9euNev1cX1euXLlpF69eubxjh07yuzZs2XlypXmdcOHD5cHHnggy1IJAOCsvfGJsmrjfrME4BkOn0qS9XvizRI5q3xZhNxWraxZAgAAAMg/haZUwqpVq+TChQsybdo0c7O3bds2E9R94YUX5N5775Xy5cvL1KlTpUyZMuZxDdJOnjxZRo8ebdbXrl3bLP3+v1CtZu/u379fhg4damrbNm/eXJ599tkC+Z4AvI1Vu4T6JYCnKFU8RCIjQyUwNbWgN6VQ0FEWVSvGSHy8Z9a4BQAAALyVX1oaXXBXHTmSIJ5I49A6QQcnVrQXx5jnOHP2vJz19zcBomKBheZaWYHh7xhtxnHmeTz971hMTHhBb4LHo2/o3fjf6TvY176Dfe0b2M++t69zw/N630A+1jg8HnfSY09EPRFt5rrQoAApzwUVAIWYlpTYeyJJykeGSIVcdjiRd1JSUuS5556To0ePmpFigwcPllq1atHEAAAAXqjQ1LhFzphsxTXUHnUddSEBwPeEBRWVqLBgs0TBW7p0qVSsWFEWLFggr7zyiowZM6agNwkAAABuQpqhFyEjxlXUHnUVdSHhbmR102bwPIkp5+RYYrKEB/hJdHhwQW+Oz2vdurVtngad/6FoUQLqAAAA3orArRchqOYaHb4eWyqCyWlcwLB/uBsXoGgzeB76FwXjnXfekbfffjvdutmzZ0vp0qXNz8eOHTMlE/Tm63r06Cbr1q3N8vFPPlkpkZGRefI5sbFl5IUXhkte2bBhnZn0r2bN3Je7ePnl4XL06GGZOPEN8VRJSUny2WcfS7t2DxT0pgAAUKgQuIXPIgjpOrIh4W4EiGgzeB7+XxaM9u3bm5sju3fvll69eknfvn2lQYMG+b5tnqhJk2bSu3d/h49FRESIp+revasMHjzskgK3hcHixW8TuAUAIBcI3HoRMtVcQxCSYwyehwCR686knJe402clNjSQiRbhFvy/9CxxcXHy9NNPy7hx46RGjRoFvTkeIygoSEqWjC7ozUAW0jStGAAAuIzJybwIk4e4hom2cpcNWbNCtFnCOUdOJcvXG/ebJeAOfx88Kd9t3m+WcA6TebqG/5d54+zZs9KqVStZvXq1bV1KSooMHjxY6tatKw0bNpQ5c+bk+D5vvPGGnDlzRl599VV5+OGHTeYtcjZ79gxp3bqFpKam2tYlJydLs2a3yscff2Duf//9t/LEE4/I7bc3lCZNbpLHHuskq1f/4vD91q79Qxo2rCtxcQeyXHfq1CkZO/ZladPmTrnttnrSqlUzc18/V+lz1ejRI2TUqIvlF44cOSzDhg2SFi0aScuWTeX55/vKvn3/pAuAvvXWm9K2bUuznfras2dTsv3u586dkzfemGS2o1mzW6Rbty7y22+/msf0WNJ1y5cvTfeauXNnyb333mXaSz9z4cJ5cv/9raVp05ulS5eO8tVXn6f73vr9fvnlR3n44QekceMG0rFjO/nhh29tba/vd/BgnK19jh8/Ji+++LzcdVdTadLkZnn66cfkzz/XcCgDAJABgVsvciQhWf6OO2mWyBlByNxlQ1aKjTBLOIegGtytXFSoVClbwizhHAKRruH/5aXTAG2/fv1k+/bt6dZr1uymTZtk3rx5MmzYMJkyZYp88cUX2b7XyJEj5fvvvzf1b/U2adKkPNhC73fnna3k2LGjJsho0UBtWlqqNGlyu2zd+pe8+OJz0qzZHTJ//jsyY8ZcKVEiSkaOHGoCn7kxevRw+fvvbTJq1KuyZMly6dWrn3zxxafy0Ufvm8c//PDivu7Vq7/07j3A1IHt2fNJs27y5JkyZcoMiYiINIFWDeiqBQvekkWL3pbu3XvJnDkLJDw8XFatWpHtdmhQ+Pfff5WhQ0fKnDkLzfd97rk+8vPPP0qxYsWkcePbZcWK9MedBmZbtLhL/P39ZebMN+SDD5ZJ377Pmra5//4O8tprr8j7779ne75OlKfB4T59Lj6nYsWr5OWXh5nA8IMPPiwdOnSSUqVKm++sy9deG2MCzvo9589fIpdfXl4GDepv2gAAAPyH6ItXSdPL8BeXyBFDsl2nWaMb4k6ZIdnMLO6cypdFSHh4iGkzwB1S09IkMMDfLOEcaim7hv+Xl2bHjh3Sv3//TEPFNaD13nvvyaxZs6RatWrmpoHdhQsXSosWLSQv+flJvkg+d0F+3HZI9h8/I2VLFJOGVUpLcNEibv9c/X4aaPz221WZHrv11kYmYFm2bFmpVet6WbHic7nhhhvNY/rzrbc2lrCwMClSxF/69XtO2ra9z/baBx7oIP3795Ljx49K6dKX2dpRl1n9bL/uhhvqSe3adeSqq64268uUKSNLl74ju3btMI9HR18s7aCfHx4eZjJ/ExMTzPYGBFw8TRs0aIjJRP344+Xy2GPdzOs1cNq8+cVjRIPBVjDa0X7+9999snLllzJ37kKpXLmKWXfFFZ1kx47tsnjxfLn55obSsmUr6dnzKTl0KE4uuyxW/vprs8nyveuuuyU5OUneeWeRjBgxyjxXlStXzmTPLlo0X9q1u9/2ud26dZe6dW8wP3fp0lW+/fZr2b17h1SvXkOKFQsxQWDrO+/fv1+uuuoqKVeurAQFBUufPv3Nd9L9kF/Ha2GU8ViD92Jf+wb2s+/wu4S/2wRuvUj56HCJLRUhgXZDwIC8zh5d+88xuf6KKAK3TioWFCCXhwTyewm3IQjpOgKRyE+//fab1KtXz0wkVqvWfxNQbd26Vc6fPy+1a9e2ratTp45Mnz7dDE/XIFdeKVkyXPLDJ3/skdW7j0pQUX/590SShIUFSau6Fdz+uUWLBkjTpk1lwIABmR7TjNLo6Ivfv0OHB0zG8pgxo+T06dPy+++rTeBcH4+OrisVKpSR999fLLt27ZK9e/eafaQiIkLMc/RzgoOLmp8jIoqZx0qUCLW9f8Z13bo9Jl9//bW8+eaXsmfPHhPE//fff6VKlUq216jw8GBz/59/dpnyCnfe2SRTxnZc3L9SpMh5OXo0XurVq5Pu9XXr1pGdO3c63M+//77XLJ955ol06zWLuHjx4uZ9br/9NhOM/emnb+TJJ5+U775bKddff73UqnWtbNiwwWTGjhjxYrpjUo9dLf8RFlbU9r1r1apm267k5NL/3/4X26tYsSATlLUe79Onlzz77LMmuKvHvZYK0VIipUtTp9iTfqdR8NjXvoH9jOwQuPUinIjC3cgedR2TBgIo7Jic7NJ07NjR4fojR45IiRIlJDDwvxEZmo2oQboTJ05IVFSU5JWjRxMuDspys237jotfWqqUCAmSQ2eTzP36FUq6/XPPnTsvRYoESmio4zaLj08wyzp1bpILF1Llo48+k6NHj0rJkiXl6qurmcc1q7Vfv57SoMHNUqNGLbn11qaSkpIsAwcOkOPHT0twcIL5nOTkc+b5J0+eMe9pPaaOHUuwrQsMPCnPPttHdu/eKc2atZBbbmkiXbt2l7FjR9new5KQkGzunzmTIldcUV7Gjp2Q6TuEhISY91X62favP38+Lcv9bG3n1KkzpVix9CV1NBBrvU/z5i1l+fIPpE2b9vLpp5/JE088bR6zPvOll8ZI+fKZg/CnTqXYPuP06f++l/W6EydO276btr31eO3a9eWDDz6XX3/9Wf744zeZPXuOTJ48RWbOnGvKLCDrjC0N8OTX7zQKDvvaN7CffW9f5waBWwBOiykeLFUrxphON51F55ANCXfj4oDrKPvimr3xibLlUIJcWzpcqpaJzEWLwxGt5WkftFXWfc1kzEv6Pzs//m+XKVFMdh9JlEMnk+Ts+TRzPz8+1/qMnD4rODjE1Hf95puv5fDhQ3LHHXeJn5+/ed3ixQukdu26ph6tZenSJWaZmpqWrg11GRBQ1PycmHjatn7fvn22x//++28TlJwx4y2pVq26LUt1//59UqZM2UzbqvevvPIqUwM3NDRcIiMjba8ZPnywNG7cTJo2bWbqw27YsF5uuaWR7bVbt26RkJBgh/tZ31PFx8dLgwbX2NbPmDFVihQpIl27PmWrATxnzkxZvnyZnDlz2rSTvtcVV1Qwzzt48KDcdNMttte/994S2bNnlzz77OB07ZJxX/y37uIYUf1Zj+8ZM6bIHXe0lKZNm5ubBsnvuecOU3fX2mYU/O80Ch772jewn5EdJicDADdiQje4W1hQUYkKCzZLOIdJA11FDX13CAoKyhSgte4HBwdLYdSwcmm5uXIpKVMi1Cz1fn7RTGUtI+DoZt/OLVveLT///INs3rzR/GwpVeoy2blzu6xfv07i4g7Ip59+JG++Od085mhyMq1bGxJSTN5+e67s3/+vrF79iyxZssD2uGbzasDz669XyIED+01wdciQgSbT99y5/7ZH32PPnt1y8uQJE8gsXjzCTJK2efMm2bt3j5ngSwPAVp3cTp26yLJl78onn3wg//yzV2bNmiZbtmzOsl00e1UDrq++OkZ+/PF7s60LF84zk5yVLVvO9jytbXv99XVNQFfr/oaGhtnq77Zp0858zpdffmZe/8knH8q0aZOkZEnnyxro90xIOGW2WTN9//pri4wbN1o2bdpo2vuzzz4xFzO0Hi4AAPgPGbcAABRiiSnn5FhisoQH+FF72kmUfXFNaFBRKV4s0CyRd0qXLi3Hjx83GZXWRFRaPkGDtlp7tDDSichur1amQD5bA6R6c2TkyFekcePbzc81a9Y2AccSJaKkXLnLbc/p2vVJOXYsXp5/vo+5X6FCRRk0aKi89NIQM1lXxjIBWnZgyJCXZPr0ydKp0/1y9dWVpEePPjJo0MU6u9HRMfLCCyNkzpwZsnz5exIVVVJuuqmhtG/f0QRQLR06PGQm+dq7d7eMHTtRpkyZKVOn/k/69+9hSgtUqXKNTJw4VSpUuNI8/95775fU1Asyb94cEwSuV6+BtGrV2tTAzYqWOZg5c6q8+upoEzwtU6acDBw4xGTZ2tNA9po1v2da37NnP4mMLGEC2fHxR0zW7+OPPykdO3Z2ev80atTETLDWpcuDMnnyTLNNkyZNkIED+8np04kms1cnZdP9AwAA/uOXlnGKW+ToyJH/akp5kjNnz8tZf38zCVKxQGLyObk4m284w/5dQJu5jjajvdyNv/20mbv9vP2QbWLKmyrlXwals2JiCs8kPVWqVJH58+ebyco0u1CXc+bMkbp165rHp06dKr/88ossWPBf5mZeoMSRd6Ov4TvY176Dfe0b2M++t69zg1IJXlbncP2eeLMEAPgGynHkrmbrqo37zRI5KxcVKlXKljBL5B2dbKpNmzYyfPhw2bBhg6xcudIEcTt3dj6LEQAAAN6NtEyvq3OYRp1DAACyRc1WV5xOOS+nks6ZJfLWoEGDTOD2kUceMbVEe/bsKc2bN6eZAQAAYBC49SLUOXSNnoAejztJaQkA8DHlo8MltlSE+fsPZxDozivbtm3LlHU7duxYcwMAAAAyInDrRci4dY2WlNh7IknKR4ZIhVzWGvE1BLsBeEt5CQ3eUvfTOTHhIVLZv4jEhAa6ec8AAAAAsEeNWy/MuNUlclaqeIjUrBBtlnAOdZQBwPfQvwAAAAAKBhm3XkQDkJGRoQz9dBIZV64jqxuAN2D0gGvoXwAAAAAFg4xbL8LM4nA3sq4AeANGDwAAAAAoDMi4BeA0sq5cR2Yf4Hn4W+aavfGJsuVQglxbOlyqlol0014BAAAAkBEZt17kyKlk+XrjfrME3IGsbteR2Qd4Hv6WuSpNJC3t4hIAAABAviFw60X+PnhSvtu83ywBeAYmwQNQ2JWPDpemNcqZJeCJevToJg0b1s3yduLECafeZ8OGdbJ+/Tq3bmtc3AGzTWvX/uH299i6dYt07dpZUlNTpSBoe9566405Pu/IkcMO99tnn31sHr9w4YKMHTtKWre+Q1588Tk5c+aMFKRx40bJqFHD063TbdJt023UbdVtdtb58+flrbfelPvuu1uaNbtFnnnmCdm5c4fL27Vr10559tne0rJlU2nVqpm8+OLzcvDgwWxf06dP90ztrr9PKjk5WTp1uj/H9wAAuBelErxIuahQOX0hzSwBeAYmwQM8DyVMXMPfMRQGTZo0k969+zt8LCIiwqn36N69qwwePExq1qwlhZ0GA0ePHiF9+jwr/v5Z5+ps375NVq78KsvH77mnrZQtWy5XQduBA/s7FTTesWO7BAYGybvvfih+fv+tDwsLM8sVK76QY8fiZfz4KbJs2Tvy7ruLpEuXrpLf9LvMmjVNPvpoudx5Z6t0j+k2hYdHmG2cOXOqrFz5pdxxR0un3nfatMnm+YMGDTVt/fbbc2XAgF6yaNEyCQkJceo9Tp48IX37dpfrrqslU6bMkLNnz8mUKRNlwICeMnv2AgkKCnL4Og0QDxgwUG65pZFtXdGiRc0yODhYOnbsLGPHjpSJE6c6tR0AgLxH4NaLpKalSWCAv1kCAADHqNkKeB8NTJUsGV3Qm+ExvvzyMxMMvf76utk+78yZJHn66Z5ZPq4ZyK4EbjVgPG3aJFm27F2pWPFqOXUq55GAu3btkMsvv0Kiox3vP81mveyyWLnqqqulXLnLTZAyv+3Zs9sEMPft2yelS1/mYBtPm++g26jbevr0aaffW4O27dt3lPr1bzL3n3vuBbnzzsaybt0aadCgoVPv8f3330pSUrIMGTJCgoKCzbohQ16Sdu1ayaZNG6ROnRsyveb48WPmdu211bP83dHg8/TpU2TNmt8dvgcAwP0I3HoRJluBu2n95A1xpyQ2NFCiwy92CpE9MvsAT0TNVlcD3V/9dVCqxITJFSUvZsABhc3mzZuke/fH5amnesqDD3Yy62bMmCrvv/+uzJ27SB54oLVZp1mqf/65Rl54YbgZwq9Zi6tX/yL+/kXkuutqSI8efU2ATlnD5SMiIuWLLz6VpKQzJrilgbfo6BhbUPJ//3tNtmzZZIJjDz/cJdO2ffrpR7Jo0XyJi4uT2NhYad26ndx3X3tbpqwz75HR4sULpGXL/7JCtezAvHmzTSDw888/NgHdMWPGu9SGOpT/4ME4h49ppnLLlndLUlKSrFv3p0yYMMU8V9szJ5r1WaFChSwfb9GipfTu3V0aNaovsbFlZdKkaU5vs5bJmDhxrPz8808SEBAgrVq1lqZNm0vXrg/LvHlLTBC4V6+nsnz9jz9eLEehZSnKl79SRo9+TYYMGZjpeffd10F69XpaZsyYIpUrVzHHmbM0eKrHkMXPz0/STCKOn23fZdWOGiReuvRjqVv3RnnllfG2oK2yjp+EhFNZZjrrZ11xRdZtX6RIEWnUqIksWbKAwC0AFBACt16EoYxwN62fvPafY3L9FVEEbp1EZh/gebRWa2ypCAksoLqPhc2fe47K6t3xcubKaAK3KLSqVasuDz/8qMyePV1uvbWRxMfHy8KF80xWYpkyZeXDD7+Q1q1bSK9e/W0ByJ49n5QqVa6RyZNnSpEi/rJkyULp1q2LzJ+/RGJiStmyJZs1ayFTp86SY8eOyvDhg2XmzDdMIDMxMdEEHKtXryEzZ86To0ePmPqn9j788H0TQO7X7zmpWrWaKV0wceI4iY8/LN2793bqPTLat+8f2bNnV6Zszf37/5X4+CMyZ85CSUlJcbkNZ82aL6mpjmu3WmUNwsPDZfbst83PVo1aZwK3kZGRprbrP//sNVm1jzzyuC0DtVixUJk1a55p38jIEtmWfsho5MihsnPndnn55bFmn+n+11q0JUuWNNmx586dM/s+J/fee3+2j5cqVVoWLVoqJ04cl6iokuJqCQYNoCoNJL/55gyJjS1jC5Q2bdpM6tVrYH7Wp0VFhcmxY4lmzki9oKD0+Xqzt2DBWyYTvWbN6x1+rl4QCA0NkwkTxsrvv682ZRkaN77dtH1gYKDteTfddIsMGtTf1LzV8gkAgPxF4BaA0ypfFiHh4SEm4xbOIrMP8DRc6HRNhZgwOZiYYpbwfoE7PpHQ314Tv7OJBfL5aYFhcvrGAXL26vQ1RHPy1Vefy7ffrsq0XoO0Q4aMND9rXdRff/1Zxo592Uzw1aLFXSboqqyh4hqA1Nsnn3wgiYkJ5rWaqakGDhxisnG1xunjjz9p1mngSzNs9Tnly1cw2Zy//PKTLaibnJxksnf1PStWvMoEhgcPHmDbPs2C7dLlcbn99jvMfS1LoMPsx48fK48//pRT75HR5s0bTZ3SK64on+kxbYPc1KxVJUqUkLympRX++WeP+PtfKT179jNBWv3OOsmW1lXVTFKLqwHRvXv3yOrVP5v9ZgU+NaDepk1LqVevvrmv7ZRXJTY0oOzqNtp7552FMnnyRHMhYfjwUbbgqWbRWpm0GriNjg4XP79gE7jNytKlS0y5ij59BmS533Qys7Nnz8q111aTDh0eku3b/5apU183mdJ6QcOix5wGuLdt+0tq1qyd6+8HAMgdArcAnBZTPFiqVoyR+PiEbDuLsGuz8BCp7F9EYgh2AyikjiamyPHTKWYJ71fsz2kScNz1Ge3zzGndhukuB24bNrxVnn66V6b19pM7aXB16NCXpHPnDibA1rfvc1m+37Zt2+TUqVOm1qg9DXRpQNCiQVArsGsFcjUYaV+71cpGVVpuwXL8+HE5fPiQTJ8+1Ux6ZZ+BefZsigku5/QejmhmavHixc0w94wuv/xyya1OnR6QQ4ccl0p49tnB0rz5nS6/p7bdp5+uMhnNVnDymmuqyu7du0y5B/vAras0IKw0k9l+/2h7WuvWr//TTASWlRUrfpD80rLlPVKrVh1Zs+Y36d37aVub6kWJV18d7aCUgkjp0rGyYMG7tsd0/ZtvTjcXBDRzVks4ZEXf/5ln+phjRWlNYt0fw4bp+t62ILRmOaujR4+67bsDALJG4BaA06hx67rElHNyLDFZwgP8KC8BoFDy9/OTCxfSzBLe70ztpws84/ZM7axrjmZFMzV1iL0zw/I1uHX0aLwZQq8lCBxuR1qqyVh95ZUJ2QaDNWMz82vTbAG21NT0V7qLFAlI9xmqV6++UrduvUzvo5Ng5fQejvj5+cuFC45LwdjXQHXVa6+9bgtKZxQVFZXr9y1WrFimdZrlqdmyl8IqP5DRhQvnbSUGNEisNY4Lmpbm0DITWppDbydPnjQlNDRwqxcldAIxpV+pRIlQOX78tEmisL9ooPtGa+GuWPGF9OrVTx54oGO2n6mvtYK2liuvvMosDx8+bAvc6oUE5e/P/wAAKAgEbgE4jRq3rmPSQLgbE+DB3ZLOnZezFy6YJbyfZrq6mu1aWGhd21dfHSOdOz8mBw7sl5dfHmaCdvaBWPsAlk44FhYWbuqvWoExrWHbuHEzU3c0J5UqVZZPPvnQTJBlvce2bVtsj5coEWWyGXVb7IPOq1Z9Jd9//4288MKIHN/DER36rxNSacDNlXqwOdGJsPKaDtd/6qnHzMRaOmGa5a+/NsuVV1a8pPfWycSs99J6ttZEYFoD+O+/t9oC2c4E/N1NyxDYi4iIkJSUZNtFCb3Zl0oIDc08+m3kyCHy3XffyLBhL9tKb2SnR49upiyDlo+wbN26xVyMsM/M1jZT1oR7AID8lXf/yYFCGOzYHnfSLOF8jdvbqpU1SzhfS7NSbIRZAu5w+FSSrN8Tb5aAO0QWC5ISoUFmCXgqnWxLs2gd3bS8gRoz5iWJiYkxgdvevfvLmTNnZPLk/zJqQ0KKyZ49u80EUXfc0VKKF48wE1lt3rzJlEfQQK/WyLWCgDlp2vQOk7WowV6tH6r1cV9/fXy6jNCHHnpEli59R5Yte8dMHqaBt9dee8UEFLXGaU7vkdVEbBcuXJAdO/4WT6QlInTSNVWhwpVSvnx5mTBhnClboO2s+2TLlk1mqH9OdP/qfnRESyJoturs2TNkzZrfTfuNGPGiyWTetGmj5AfdD7qNVhA2Kxqs18nE9Bj4+ecfzUR4zZo5X3pCJ4JbtWqFdOv2jNSuXSfd8W99traT3rc0btxUvvzyM/ngg6Xmc3Ub3njjdenQoZMpKWHRIHdgYJBcdVWlXLUBAODSEEmAz9obnyhbDiXItaXDpWqZixkMyB41bgHPQ1Y33C2yWFG5vGSYWQKe6uuvV5ibIyNHviLHjh2TP/5YLTNmzDUZhUWLRkjfvs/KkCEDpWHD2+SmmxqaCZoWLZove/fulrFjJ8qUKTNl6tT/Sf/+PUzpAR3CrhNmabDRGZrJ+/rr02TixHHSvfvjEh5eXLp2fcoMZ7c8+GAnCQoKMpNJ6cRUGqS95562tsnPnHmPjLTurpYaWLPmD6lc+Zpst1Ezc6dNm5zl4w0a3Cx57YknOpvgok64phnB2tbTp0+RoUMHSkJColSpUsW0s9ZczUnr1i3k0UefsLVXRppNOn78KzJwYD8JCCgqzZu3MPVbH330IZN5q8Fdd9Iaxvfff4/ZjpYt787yefoddFK5uXNnmQsGOmlet27dnf4cLY+gNPCqN3vWZy9e/LZ5/x9//MOsb9euvSmroRcOJk2aYDK1tbxCp05d0r1+7do/TK1hR5npAAD380uzijDBaUeOJHhka1lDZ5g4yjl/HTguWw4myLWXaeA272fJ9UYcY647c/a8nPX3l8DUVCkWyLUyjrG8xzHmOv6WuSY+IVniTp+V2NBAj6zVHRMTXtCb4PHoG/re37SPPlpugsHz578j3uzrr1fKgQP/Zgo2ehK9GKBZvk2bNnf4eMOGdXMM7BbU/y/NVm/b9k4ZPny03HBD5jrMcB/6Kr6B/ex7+zo3KJUAn1U+Olya1ihnloC7MIwd7sYxhvyaZFGXAAoHDQKePXtOfv/9V/FWWsP33XcXya23NhZPdfp0oilhoBmrhZHWedbMZ4K2AFBwSP/yIkxQ4xqtOapBW7JQ4E4MY4e7cYzB3cKCikpUWJpZAigcAgIC5MUXR8iECWOlTp0b83SSMk+h32ny5Bmm9IWn0lqx06fP8ehtzEpSUpIprzB+fNalNAAA7kfg1suyrvaeSJLykSFSgSxSAPAJZ1LO24axU47DOVzozF3GbXiAn0eWSgDgWPXq18mcOQu8unkKQ0A0p220as56Gq1pu3jx+wW9GQDg8wjcehGyrgDPwwUVuNsfu+Pll53x0uCqaGlRoxwN7gR+L11D/wIAAAAoGARuvQhD/+FuZKm5joAH3C9N/CTVLOEcfi9dQ/8CAAAAKBgEbgE4jSw11xHwcA0XB1xX98oYKVsqwpRKgHP4vQQAAABQGBC4hc8iQOQ6stQ4ztyNiwOuiykeLFUrxjDRItyG/5cAAABAwfC+6UUBFwJE6/fEmyWcz1KrFBthluA4c9fFgZoVos0SgGfYG58oqzbuN0sAAAAA+YfoC3wW2aPgOPM8DGEHPFGaSJrWUKaOMgAAAJCfyLiFzyJ7FBxnAODM/8uiUrxYoFkCAAAAyD9k3AKAG1EbEkBh9++x07Jt/3EJLeInV5QMK+jNATK577675eDBONt9Pz8/CQkpJpUrV5GuXZ+SWrWud6rVPvvsYxk9eoT8+OMfTj0/LS1NvvjiU6lf/yYpUSLKJ/bMoEH9pUWLVnLbbY3d8v7jxo2Sc+fOyQsvDLetO3PmjIwePVw2blwvN910qwwYMFCKFCni1PudP39eFix4Sz755EM5efKEVK58jfTr97xcddXVLm3Xrl07Zdq0SbJ58ybx9/c3x1SPHn3lsssuy/I1ffp0lz/++C3dOn3dlCkzJTk5Wbp2fVhee21ytu8BAAAZtwBcCkJujztplnAOtZQBFHaVL4uQ26qVNUvAU3Xo0Ek+/PALc1u+/HOZPn22hIaGSv/+PeXgwYNOvUfTps3M6521bt1aGTVquAnC+YIVK76QhIQEtwRtU1NTZcaMqfLRR8szPfbuu4skPDxCxo+fIkePHpGVK790+n2nTZssy5cvlQEDBsmcOQulbNlyMmBAL0lKcn6OCw349u3bXYKCgmXKlBny2muT5MSJ4zJgQE9JSUnJ8nU7d+4wQWbruNTb6NGvmseCg4OlY8fOMnbsSKe3AwDgmwjcAnAaQUjXMdkW3O3IqWT5euN+swTcIaZ4sDS5rqxZAp4qJCRESpaMNrfo6GipWPFqefbZwSaw9v333zj1HhqY09c7SzNufcWFCxdk1qxpJtiY1/bs2S3PPNNVPv74AyldOnP26Zkzp+Xyy68wWbKXXRYrp0+fdvq9Ncjbvn1HkxWt7/Hccy9IYmKCrFu3xun3+P77byUpKVmGDBlhjqtrrqkqQ4a8ZLZ706YNDl9z/Pgxc7v22uq241JvxYv/dwHsjjtamuDumjW/O70tAADfQ6kEL8KQbLgbE7q5jsm24G5/Hzwpa/85JtdfESXR4QTWkPfoX/iepPNZZyMW8fOXwCJBTj3X389fgnL53LxgDacPDLxYnzklJVnmz58rX331hcncvOKKCtKly+PSqFFTh6USGjasKwMHDpEVK740w/TDw8OkTZv75NFHn5C1a/+QXr2eMs+7//57ZPDgYdKy5d2ZtuHzzz+RhQvny4ED/5qgXePGt8vTT/eUwMBA87gG/mbOfEO2bftLAgIC5Oabb5VnnuktERGRtjIQd97ZSh5//Enbe9qv022eN2+2NGnSWN5//325/vq6MmbMePn3330yZcpE+fPPNVKkSIDccEM96dNngK2kw6effiSLFs2XuLg4iY2Nldat28l997U3ZQAc+e67b+TUqVPmfSwnTpyQiRPHys8//2S2vVWr1tK0aXNTAmDevCUmU9VqI0esdta2LF/+Shk9+jUZMmRgpufdd18H6dXraZkxY4opf/HUUz3FWRo8tdrSKqNxMeDul26fO6JB4qVLP5a6dW+UV14ZbwL7FqudEhJOOXztjh3bzWfpMZbd8dmoURNZsmSB1Klzg9PfCQDgWwjcelk25N4TSVI+MkQqRIcX9ObACxGEdB0BD7ibDl8PDw+R2NCLQQAgr+2NT5QthxLk2tLhUrXMfwEQeK+7vroYyHSkXkwDGXPDeNv9dqvukuQLjjP+a0bVlon1p9rud/y2nZw8e8Lhc6tEXCPTbp4jeeXIkcMyadIEk4lbv35Ds2748Bdk27atZth8uXKXm6H/GigcNepVufXWRg7fZ8qU/0nfvs/K88+/YLI3Nchau3Ydue66mjJq1Dh54YXnZNaseVKx4lUOg3das3Xo0JFStWp12bt3t9mGiIgI6dKlq2zZskl69nxS7rmnram7euzYUZkwYaz07dvDvKezdVz37/9XDh8+LHPnLpTk5BRTzuCZZ54wGaqvvz5d/P395NVXR5vvqvVVP/zwfVOWoF+/56Rq1Wqyffs2mThxnMTHH5bu3Xs7/IwffvjWBG2LFv1vksKRI4fKzp3b5eWXx0pMTCmZPXu6vPjic1KyZEnz2Vqr1pnSE/fee3+2j5cqVVoWLVpqyhNERZUUV0swaABVaSD5zTdnSGxsGVugVMtj1KvXwOFr/f0vtr8+X2/2tG5uUFCQ1KzpuH7yrl07JDQ0zOzP339fbY5DDdo/8sjjtqC9uummW0zdYC23oeUTAADIiMCtFyEbEvA8XFCBu+nw9aoVYyQ+PkF8aNTuJeGCimuSz12QE6dTJPlcMTftEeDSvf32XJO5aA3rP3v2rFSocKW89NIrZvInHdb+ww/fydixE+Wmmy4GcjVjVYOrb789J8vArWa26pB21bnzY7Jo0dsm+1YnmQoPL27WR0aWSJeNaTlwYL8JGmrQT7dBbxMnTpFixULN40uWLJSrrqokffs+Z+7r9g4bNkoefbSj/PbbL9KgwcXtdEb37t0lOrqs+T/wwQfLTHmB4cNHS/HiF7fx+eeHmMCztotm6Gqm8e2332Ee07qvWn5g/Pix8vjjT5mAZEabN29Ml1G8d+8eWb36Z5ORbAU+Neu4TZuWUq9efXNfg7yulJ7Ijma4uhq0tffOOwtl8uSJUqZMWRk+fJQteKr7zdG+y87SpUtk2bJ3/z+DuUSWk5lpW197bTXp0OEh2b79b5k69XUziZ6WWbBowF8D3JpxXbNm7Vx/PwCA9yJw60XIhgQ8DxdU4G4EIV3HBRXXBBf1l8higWYJ3/Bp81XZlkqwt6zpp9mWP7C3qNEyp5/rqjZtdKh/h4vv5e9vyhKEhYXZHtdaoqpGjVrpXle79vUyffp/WcEZlS+ffqi7vqcG2pyhAc3q1WtI166dJTa2rNx4Yz1p2PA2qVKlqi0r84YbLgY5LZUqVTafodvrSuC2QoUKkph4zva+Ws/VCtqqq6+uZG7Hjx+Xw4cPme+sNWvtM1PPnk2RuLgDJoCckWYDa4Da8s8/e8xSM3YtmmGqn2utW7/+TzMRWFZWrPhB8kvLlvdIrVp1ZM2a36R376dN/ePmze+Ur7763GQjO1K6dKwsWPCu7b6WWHjzzekm8K2Zs9bx5oi+/zPP9LHtA62Nq+Ukhg3T9b1tQWirTY8ePZrH3xgA4C0I3AKAG3FBxTUEIV1HENJ1XFBxTUx4iFT2LyIxlOPwGSEBIQX+XFdp9quWP8ia4yEJGrDUgFpW7Ie1297JyeENmrk6adJ0+fvvrbJ69a/y+++/yvPP95UWLe4y2alZvY+uz26bNKM4Ix1mbwVus3ttWlqqWfbq1Vfq1v2vXq3F0eRgys/P37TVf/f9sti287YSAzqJ19y5i6SgJSUlSXh4uFSpco25nTx50pSK0MBtw4a3mgnEHLFvx/Pnz5tauFpeo1evfvLAAx2z/Ux9rX3gXF155cVyGlrWwgrcWm2q5SwAAHCE1An4dIBoe9xJswQ4zjwnCLl+T7xZwvkgZM0K0WYJuENiyjk5lphslkBhpSUJ1IYN69KtX79+ncMMU2dkFby0/PLLTzJ37iypXPkaefjhLiaIq+UZVq36yrZNGbdHh9Rr2YIKFSqa+wEBRU3ZA8vp04km+zU7+tp9+/6RxMRE2zqt7duqVTOTLaxZnlrGQQPd1k2H6s+a9UaWwWQteaA1Zi06mZj666/N6SYC08/VQLXSEgT2n5Hxll/0u9nTGsM6UZ3SshVZbZ9OTmYZOXKIfP31Chk27OUcg7aqR49umSY927p1iykfcfnll6drMxUdHXPJ3xMA4J3IuPUiZKq5hiw15AeOM9eQCek6srpdx++la/i9hDfQ4KxOBDV+/Csm4KqBOQ2g/vjjd/LSS2Ny9Z4hIcVswdaIiEgpVqxYpqxLDdzq+ltuaSSnTp2Sn3/+UapXr2keb9/+Iene/XEzMVjbtvebgKz+XLlyFalb90bznOrVr5NVq1ZIo0ZNJSws3EwAVqRI9qdwmkn61ltvmmDjE090N9mi48ePMROGaUbtQw89YoK0+nP9+jebOr+vvfaK3HLLbQ4zjFW1atVsAVmlJRE0W3X27IuTfWlpiqlT/2fec9OmjZIfNPNYg8laWiK7OrW6n6+4oryZHExr82pt4WbN7nT6cz777GOzD3TiNp2Y7ujReNtj1mefOXNGkpLO2Gr6Nm7c1EyOpzVutRyGBm3feON16dChkykpYdE2DQwMsl1YAAAgIwK3XoQTUddwIor8wHHmGoKQyA/8XrqG30t4ixEjRpsh8q+8MlISExNM3dGXXx4nt93WOFfvp4HQBg1ulmHDBkm3bs/Igw92Svf4DTfUM5N3LV78tsyc+YYpZ6CB0h49+prHq1WrLuPHTza1Zh977CGT/akB3qef7mEbpv/kk8/IqVMnpU+f7iZwq4G/hIT/Mmkd0c+ZMGGKmYzrqaceNfe1Xm6PHn3M47qdWsZBJ9nS5+iw/XvuaWuygbOi2zV27CgTBLa2Tcs9aCB84MB+JjO4efMWpn7ro48+ZDJvNbjrTlqr9/777zHbYT9xWkaPPvqEmVxNg+gaYG7WrIV069bd6c/R8ghKA696s2d9tu5jff8ff/zDrG/Xrr0pL7F06TsmgKsBXc3U7dSpS7rXr137hwnSh4QwagYA4JhfmrNFmmBz5EiCR7bGmbPn5ay/vwSmpkqxQGLyOdHRbdHR4czE7gLazHW0Ge3lbhxjtJm7xSckS9zpsxIbGijR4a7Nvp4fYmLCC3oTPF58fILQ4/de+fF/QAO2Dz54r3Tv3stkrnqKRYvmmyzfpk2bO3y8YcO6OQZ2C8rZs2elbds7Zfjw0SbI7wz+5/sO9rVvYD/73r7ODWrcellGTKXYCLMEAADIC38fPCnfbd5vlgB8k2bZPvZYN3n33YKfbMy+3q+WMLDKShQ2X3zxqcn6djZoCwDwTQRu4bOYnAwcZ/AG/C2Du1W+LEJuq1bWLAH4rjvvbCXh4cXlm29WiifQWrHTp88x9YULm6SkJFNeYdCgoQW9KQAAD0dqJnwWNYHBcQZvwN8yuFtM8WCpWjGG4fYAZNy4/3lUKxQtWjTbx62as55Ga9ouXvx+QW8GAKAQIHALn8XkNMgPYUFFJSoszSwBd+BvWe6ylI/HnaQmPAAAAACPRqkE+CxqAruOIdmuS0w5J8cSk80SgGfYFndSlv2yyyyRM/72AwAAAAWDwC0Al4Zkr98Tb5ZwPhuyZoVoswTcgd9L1504nSLxp86YJXJGoBsAAAAoGJRKgM9iqKzrGPafu8zu8tHh1IaE21AqwXXXXR4l0VFhEhsa6IY94s2B7mIFvSkAAACATyFw60UIRLqGCX1yP+w/PMBPosODc/EOAPIaFwdcx2RbriHQDQAAABQMArdehECka8hScx1tBngeLtrB3Qh0AwAAAAWDwK0XIajmGrLUXEebuY6gGtyNi3au4/cSAAAAQGHA5GT/LyUlRQYPHix169aVhg0bypw5c6QwBtUqxUaYJQDPwMRRcDcmwHMdv5cAAAAACgMifP9v3LhxsmnTJpk3b54cOHBAnn/+eSlTpoy0aNGiYPcQgEKNTHi4G5nwruP30jVkKAMAAAAFg8CtiJw5c0bee+89mTVrllSrVs3ctm/fLgsXLiRw68U4EUV+IKgGeB5+L12zNz5RthxKkGtLh0vVMpFu2isAAAAAMqJUgohs3bpVzp8/L7Vr17Y1TJ06dWT9+vWSmpqaqdHgPSeiqzbuN0vAnRcItsedNEsAnoHfS1eliaSlXVwCAAAAyDcEbkXkyJEjUqJECQkMDLQ1THR0tKl7e+LEifzbG8hnnIjC/bhAAHgeaty6JjSoqBQvFmiWAAAAAPIPpRJEJCkpKV3QVln3z54967Dh/PzE41jb5Inb5okqxIRLbKkICUpLpc2cxDHmOj+/ixcIdMnvJseYO/B76brSESESGRnK338n/Xv8tGzbf1xCA/ykfHRYLlocAAAAQG4QuBWRoKCgTAFa635wcHCmRouJCRdPVrKkZ28fCj+OMefVDguWy2IiJLZEMQkLJluNY8x9+L2Eu9QPKCLhYSFS7fISEh1ZjIYuhKKj6Rv6Avaz72Bf+w72tW9gPyM7BG4186Z0aTl+/LipcxsQEGArn6BB2+LFi2fbgACQHQ3WVoqNoJEAFFqlI4uZGwAAAID8RY1bEalataoJ2K5bt87WMGvWrJHrrrtO/P1pIgAAAAAAAAD5i6ikiISEhEibNm1k+PDhsmHDBlm5cqXMmTNHOnfunM+7AwAAAAAAAAAI3NoMGjRIqlWrJo888oiMGDFCevbsKc2bNy8Ux0hKSooMHjxY6tatKw0bNjRBZzhHaxm3atVKVq9eTZPl4NChQ9KrVy+58cYb5ZZbbpExY8aYYw+O7d27Vx5//HGpXbu2NGrUSN58802aygXdunWTgQMH0mY5WLFihVSpUiXdTX9PkfXffP0ff8MNN8hNN90kEyZMkLS0NJorC++//36m40tv11xzDW3m4egb+h76tN6Pvrhv4BzCN3Hu491WXOI5GzVu7bJux44da26Fzbhx42TTpk0yb948OXDggDz//PNSpkwZadGiRUFvmsef1PTv31+2b99e0Jvi8TSwoX9YtObzwoUL5eTJk+ZigZYS0eMN6aWmppp/vlpuZfny5aYD1q9fP1NP++6776a5cvDpp5/Kd999J23btqWtcrBjxw5p3LixjBw5Mt2Em3Ds5ZdfNhfqZs+eLadPn5a+ffua/5cdOnSgyRxo2bKluVBn0bkA9AK3XoyCZ6Nv6Fvo03o/+uK+gXMI38S5j/fbcYnnbARuC7kzZ87Ie++9J7NmzTIZw3rTQKQG1wjcZv+Lo0FbMq2cs2vXLlMD+qeffpLo6GizTgO5eqGDwG1m8fHxpna2ll8JCwuTChUqSIMGDUztbAK32Ttx4oQJOGjQGznbuXOnVK5cWWJiYmguJ46tZcuWydy5c6VGjRpm3WOPPSbr168ncJsFnaRVb5YZM2aY/5sDBgzgePNg9A19C31a30Bf3DdwDuF7OPfxDTsv8ZyNGreF3NatW00GjA7HttSpU8eciOoVOzj222+/Sb169eSdd96hiZygf2B0qL8VtLUkJibSfg6UKlVK/ve//5mgrQY5NGD7+++/mzITyJ5eDGjdurVcffXVNJWTnQC9MICc6e+h/k7a/x5qZryWfYFzJxZ6kVgvegYGBtJkHoy+oW+hT+sb6Iv7Bs4hfA/nPr5h5yWes5FxW8gdOXJESpQoke4kSoNrOmRKT7KioqIKdPs8VceOHQt6EwoVLZFgP1xWLwosWLBA6tevX6DbVRg0adLElDDRoRF33HFHQW+OR/vll1/kjz/+kI8//thkKyN7elFg9+7d8uOPP5pMyAsXLpiRFpoNT2Ats3379knZsmXlgw8+kOnTp8u5c+fk3nvvlaefftqUfUH2Fi9ebE4oGc3j+egb+hb6tL6Bvrjv4RzC+3Hu4xvS8uCcjTOVQi4pKSnTzrbu6yQFgDu8+uqrsmXLFlMfEtmbNGmSCRL99ddfZPZlQy82DRs2TIYOHZpuaDayphcErP8BmuGtZUs06K2lJuB4+LjWm16yZIn5XdT2evvtt+Wtt96iuZzocGpZpk6dOtFWhQB9Q8D70Rf3fpxDeDfOfXzHgTw4ZyPjtpDTgsYZA7TWfYIfcFdHUSfCmzhxoqnTguxZtVr1n7PWhXzuuefIhnRgypQpUr169XSZ3cieZo/qRFsRERHi5+dn6iprNvyzzz4rgwYNkiJFitCEdgI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-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# --- Visualise the sparse result matrix ---\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "\n",
-    "# Sparsity pattern\n",
-    "rows, cols = result.nonzero()\n",
-    "axes[0].scatter(cols, rows, s=0.5, alpha=0.3, color=\"steelblue\", rasterized=True)\n",
-    "axes[0].set_xlabel(\"Source index\", fontsize=13)\n",
-    "axes[0].set_ylabel(\"Event index\", fontsize=13)\n",
-    "axes[0].set_title(f\"Sparsity pattern  ({result.nnz} non-zeros)\", fontsize=13, fontweight=\"bold\")\n",
-    "axes[0].set_xlim(-0.5, n_sources - 0.5)\n",
-    "axes[0].set_ylim(-0.5, n_events - 0.5)\n",
-    "\n",
-    "# PDF values for source 0 as a function of angular distance\n",
-    "# Extract column 0 as a dense array to avoid fancy-indexing quirks with csr_array\n",
-    "s0_col = result[:, 0].toarray().ravel()  # shape (n_events,)\n",
-    "s0_events = np.flatnonzero(s0_col > 0)\n",
-    "s0_vals = s0_col[s0_events]\n",
-    "psi_s0 = angular_distance(src_ras[0], src_decs[0], event_ras[s0_events], event_decs[s0_events])\n",
-    "\n",
-    "# Reference curves\n",
-    "psi_ref = np.radians(np.linspace(0.01, np.degrees(ext_pdf.angular_cutoff), 400))\n",
-    "p_ref_ext = ext_pdf.pdf(\n",
-    "    psi_ref,\n",
-    "    np.full_like(psi_ref, np.radians(1.0)),\n",
-    "    np.full_like(psi_ref, 2.5),\n",
-    "    np.full_like(psi_ref, src_exts[0]),\n",
-    ")\n",
-    "p_ref_pt = king.pdf(psi_ref, np.full_like(psi_ref, np.radians(1.0)), np.full_like(psi_ref, 2.5))\n",
-    "\n",
-    "axes[1].scatter(\n",
-    "    np.degrees(psi_s0),\n",
-    "    s0_vals,\n",
-    "    s=6,\n",
-    "    alpha=0.6,\n",
-    "    color=\"steelblue\",\n",
-    "    label=\"Evaluated events\",\n",
-    "    zorder=3,\n",
-    ")\n",
-    "axes[1].semilogy(\n",
-    "    np.degrees(psi_ref),\n",
-    "    p_ref_ext,\n",
-    "    \"C1-\",\n",
-    "    lw=2,\n",
-    "    label=f\"Extended (r₀={np.degrees(src_exts[0]):.1f}°, α=1°, β=2.5)\",\n",
-    ")\n",
-    "axes[1].semilogy(np.degrees(psi_ref), p_ref_pt, \"C2--\", lw=1.5, label=\"Point source (α=1°, β=2.5)\")\n",
-    "axes[1].set_xlabel(\"Angular separation ψ (degrees)\", fontsize=13)\n",
-    "axes[1].set_ylabel(\"PDF value (sr⁻¹)\", fontsize=13)\n",
-    "axes[1].set_title(\"PDF values for source 0\", fontsize=13, fontweight=\"bold\")\n",
-    "axes[1].legend(fontsize=11)\n",
-    "axes[1].set_xlim(0, np.degrees(ext_pdf.angular_cutoff))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7f8a9b0",
-   "metadata": {},
-   "source": [
-    "### Mask reuse\n",
-    "\n",
-    "Pass a previous result as `mask=` to skip the geometric masking loop. This is the recommended pattern when scanning over extension hypotheses for a fixed set of source and event positions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f8a9b0c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/xz/hwb936c16hv507dyhyngvbhm0000gn/T/ipykernel_65368/4207960206.py:25: UserWarning: Glyph 8320 (\\N{SUBSCRIPT ZERO}) missing from font(s) Arial.\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best-fit extension (coarse scan): 2.50°   (true: 1.5°)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Scan over extension hypotheses for source 0, reusing the mask\n",
-    "ext_scan_deg = np.linspace(0.1, 4.5, 12)\n",
-    "src_exts_scan = src_exts.copy()\n",
-    "\n",
-    "total_log_pdf = []\n",
-    "for ext_d in ext_scan_deg:\n",
-    "    src_exts_scan[0] = np.radians(ext_d)\n",
-    "    r = ext_pdf.evaluate(\n",
-    "        src_ras,\n",
-    "        src_decs,\n",
-    "        src_exts_scan,\n",
-    "        event_ras,\n",
-    "        event_decs,\n",
-    "        alpha_ev,\n",
-    "        beta_ev,\n",
-    "        mask=result,  # reuse sparsity pattern\n",
-    "    )\n",
-    "    s0_col = np.asarray(r[:, 0].todense()).ravel()\n",
-    "    total_log_pdf.append(np.sum(np.log(s0_col[s0_col > 0])))\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "log_arr = np.array(total_log_pdf)\n",
-    "ax.plot(ext_scan_deg, log_arr - log_arr.max(), \"o-\", lw=2, ms=6, color=\"steelblue\")\n",
-    "ax.axvline(\n",
-    "    np.degrees(src_exts[0]),\n",
-    "    color=\"C1\",\n",
-    "    ls=\"--\",\n",
-    "    lw=1.5,\n",
-    "    label=f\"True r₀ = {np.degrees(src_exts[0]):.1f}°\",\n",
-    ")\n",
-    "ax.set_xlabel(\"Hypothesized extension r₀ (degrees)\", fontsize=13)\n",
-    "ax.set_ylabel(\"Δ log L  (relative)\", fontsize=13)\n",
-    "ax.set_title(\"Extension scan — log-likelihood profile for source 0\", fontsize=13, fontweight=\"bold\")\n",
-    "ax.legend(fontsize=12)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "best_ext = ext_scan_deg[np.argmax(log_arr)]\n",
-    "print(f\"Best-fit extension (coarse scan): {best_ext:.2f}°   (true: {np.degrees(src_exts[0]):.1f}°)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9b0c1d2",
-   "metadata": {},
-   "source": [
-    "## 7. Performance benchmarks\n",
-    "\n",
-    "### 7a. `pdf()` speed vs number of events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b0c1d2e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Y2HCkpeWUW3kVbVPWekeX+wJ3ls3ZY6vZ3959nL0O1KzjdaCOo/XmyPZq/9btWV/aOl++BtxZPlcc113XAT8TnKtnR/AzwXN17SxeB+rqJdYP7w04QR4REREREXkMcyyIiIiIiMhpDCyIiIiIiHyUyWxGToFBfvd1zLEgIiIiIvIx+5Oy8MXWU1iyNwmFRjOCdBoMbV4NN3eshabVIuGL2GJBbsHBxoiIiIjUWbInCbd/thW/7z4ngwpBfBevxXKx3hexxYKkadNewO+//1JmbSQkJOB//1tSYW1lZmbirbdexVVXjUD79h09UrujR98Jg8GI996b59Rxzpw5jVGjrsbkyc/j8suvUpb/9tvP+OmnH3D48CEYjQbUqFEL/fsPwC233I7w8Aj4E/G7TJ/+Ir755ifUqFHTZt3ixQvx/vvvYsiQ4XjmmRfwyScf4uOP52Pt2n9cWgZLPVsTwzyHhoaiTp16GDlyFK688hplXe/enW221el0iIiIRLNmzXH99TehV68+Nuuvv/4qnD17psz3/+WXZYiNjXXJ7/LRRx/IOtq5c7d8bTAYMHPmVKxc+Zf8nV555XV07FhU/jffnCXLfe+9D+CXX/6HVav+wquvvu2SchARUWC1VDz3+16YSun5dDHGkOsbJIT7XMsFAwsfIPrM5RtMiC7tCvIgETxMm/Zaqevi46PsOsaBA/uwZMlvuOIK2xtHfzVnzmzMm/cBbr75dtx551jo9Xrs3bsHn3++CJs2rcf77y9AIPj8809lUCECqqeeehZarVYGh9269XTbe4r67NGj98VXZmg0Rnz55Vd45ZWX5Q36iBHXKduKQOPKK0fInw2GQly4cAG//vo/TJo0AY888jhGjbrJ5tg9evTCnXfeXer7Rka675/wpk0bZIA+evTd6Ny5K5o2bX5x+UasXr0SX3zxvXwt/j6+//5rGWDcdtvNbisPERH5ny+2nkLRrGplE+u/3HoKzw1rBl/CwMKH+s4F67QY0ryq1/rOBQUFo3XrNqWu8/b4yd5QWFiIBQs+kkHFffc9qCzv0qUb6tevj6efflzeLI4c6d9B1JdffoY5c97G1VdfiyeemKxMElmtWqL8cpdatWrbXG/iGmvRoh0OHNiPr7/+3CawqFq12iXX5sCBg/HMM09g9uy30Lt3X8TGNrY6VlyZ17I7ZWSky+8iQKtZs5ayfObMmbjhhltkq4wg6vj22+/CG2/MwvXXX+vxchIRkW8ymczyvtDSMlEWsf6PvUl4dmhT5XPbFzDHwof6zhUYTT7dd048qe/Xr5vsNmWRmpqCK68cjEcffQBbt/6Dhx8eJ5eL7+PH36tst2bNSowdezsGDOiJq68eirfeeg25ubk2XUpuvHEE1q9fizvvvAmXXdYDN900En/88atNGc6ePYvJk5/A0KH95HHETXFpfv75R9x22w3yOCNHXiGPbzQabbYRXVHuvPNmDBjQC2PG3CpvaK1lZWUhLy+v1HwR8aRddGmxvnkU27/77hu44YZr5O95++034Pvvv7PZT3TrEWWxJl5bd/d55pnJeOSR+/HaazMwZEg/3HbbKFl2EejMn/8+Ro0Sx+8lj1+y+1pF9VySuIF/7723cN11N9gEFaWVS5zP5557Fp999omsU/Ee998/Brt377Q5pjiHRWXoJc/hn3/+Ic9tyd+7NKKlpGnTZvI820MEfKJ1Qzz5V0tct+L33Lx5Ix588J6L5b4WP/zwrc12+fn58vyKeh08uI/sUlZQUKCsF38Xlr8NcQ1Yrn9RH+LaGjRoiM3xevbsg4KCfPzwQ1ErBhERUZ7BqNwXVkRsJ3q8+BK2WHjBnjMZPtt3TtyklUbcXDdv3gK33TYaCxd+hGHDrkCnTl0wa9Z0mEwm2Sc/PDwcjz02CW+8MVN+79Chk9x36dI/8NJLU2Tf/XvuuR9nz57GBx/MwZEjh/HWW7OV97hw4bzsh37nnWOQmFgDX3zxKaZOfR4tWrRCvXr15Q3y+PH3yO5ITz45BVqtRt6snjx5Aq1bt1WO8+mnH2PevDm47rob8fDDj8nuWR99NA9JSefw9NPPyW1WrlyBKVMmYciQYXjggYexf/8+vPzysza/c1xcHNq2bSvLcf58Mvr2vQxt27aX/fNFGe64Y4yybX5+Hh54YCxSU1Mxdux9Mn9B3OSLG/GTJ8/YbGuP7du3Ijg4GNOnv4q8vFyZV/D885OxYYMIvMaiZcvW2LBhnbyRFWUZPHhYhfVc8onG119/gXfeeQPXX38jHn30CbvK9eefS1G3bn1MmPCEfKoiWgtEPYqcDVFGcZP+9NMT5U3z3XePk+dGBEjWN+AVOX78GGrVKg7YyiPKkphYHTt2bL/kei3tWhZ1VZbnnnsaw4dfKc/VmjWr8Prrr8jl1157vfwurg/RpUkElLVr18FPP32PpUt/V/YX3Z9EC4/4+5g27VXUrVtPLhddA9u2bSdbXayFhISgV6+++OWXXzB8eFE3LyIiqtxMZjO0GvG94m3FKFEhet9qI2Bg4QWfbDjqk33nRMJr//7dS103ceLjuPbam3DXXfdg7drV8qZLBBni5vnll19Rbprq12+gfG/QoKG8wZs7913ZV/+5515Wjle7dl3ZyiFuji+/vOhJrmgdmDRpiuybLohE3uuvv1I+8RWBhUg8PnfuLBYt+koeWxA32OLpsnWrgUg6vuaakXj00cflsq5duyMmJgavvDIVN954Kxo2bIT3339fBizPPltUpm7dekDcd8+d+57N7/3GG2/hySeflC0n4kvcnIv37tdvAEaNuhnR0dFyu99++0Umd8+du0AJcsQxtVrgk08+kt16oqNj7D4XooVCtCBYuiIdPnwQK1cux8MPT8QNNxT1yRf1JIKHrVu3YNCgoRXWc8+elnwG4LvvvpatPeL3SUtLs7tc4mb9jTfelUnIQk5OtgxuxBN5EXiKQK9Bg0YyILIEMiJAe+GFZy45lghILTf/4ucTJy7g448/wcGD+/H440/ZXab4+ASkpFywWWY5XyXNnftxmV2k+vW7DI88MlE5dxcuJMtrSZy7gwcPyIRsUa4RI65Xtrnjjptw9OhhpWuX+BJEq4slOV4EW1dccUWp79m8eUssX75U1qO/DQRARESuteVEGqb9ecCuoEKnAYY1r+ZT3aAEBhYusGxfMj5YfxQ5BbZdbTRaDcwlrg5xo52cXVjhMUXLxc+7zmHj0RS7L5rwYB3G9aqPgU2rQo2EhCqYOfONUtc1blxfeeI7ZcoLuOeeO2WSrXjCe9llg8p9+ixaCkR/cusnyGLEqIiICPz99yYlsBCsWx6qVSsKVsQTe2HHjm3yxs0SVAjiaXW7du1guNgUuHPnDtllRTwJtn4/8Vr4559N8mn47t275BN1awMGDL4ksKhevTreeWeufOq/ceN6bNv2D7Zv3yZHAhJPrN97bz5iY5tj27Yt8kbSuvzClVdeJbtD7dr1n1WicsVEIGSd32B5Ii9ufq2JJ+PCsWNHK6xn68BCBBXi9xd1JVp4RN6IPQn3jRo1VoIKwVJGcY5Eq4SofxF8Wl+z4vp4+eWiliJr4voRXyUTq0WLzNVXj4T9zJf8jYgWkzFj7rlky3r1igLf0ohr2ZoIHkXLxYkTx7Bnzw6b68jSbeuyywbi44+LAovSiFY20V3QEnCUVKNGDRlEnjt3zua6JiKiyiO7wIB3Vx/Bd/+WPaJhSeLu8qaO9rXuexIDCysxMWHKDYpOp5XJpOWxbPPFttM4mlJ2P3Zn2BOEWPt822lc17WoC4YjgoP1CAkJRvfutkN7Wv+uRmPRzXuXLh3kUJ+7du3EkCGDbOopMjJU+S6WHz6cJ1+LFg5L1xJrGRkp8tihoUHydfXq8aWUTSePJZ7qJiTEX3JeRGtJcnKyXG4wFJ2HJ554pNTfIzMzDRqNQQZ4NWpUszlWWFhd+T08PFhZbjnHHTq0ll/AvTLX4ccff8C0aVOxYMFcvPPOO8jJyUK1alUvKVtiYlFwZDIVKOvE7yp+thzb8rtb1otLUAQD1sfKzy9KnG/QoLbsIlWSPfUsjid+N+Ghhx7GffeNk7/Lli2b5BDBPXt2Q4MGxTfeJcul1+sQFFR0LiyiosKUOtNoCuVNcs2aiZfUg+g+Zvm9s7OL9rn//gfQr19/5SY9JiZaBmfi55Is+5ZGdFNr3LiJUp+ii1zVqgllXssWlu0t12zDhnVt3qNOnRrKucvMzJA/16tX06b+a9euYXMsS/1GR4fJ1wUFWfJ1RER4qeWvUqVo2FtxTVqvt+f/jze5q3yuOK6jx7B3e0c+Exxd7+hyX+DOsjl7bDX7e+o6ULOO14H/XQc6B9avPXgeU/63E6fSij7Hhc714jC4RTXMXLJP3pcarR5S68RDa7MZr13fFl2b2nax9QUMLKykp+c6NAqSZZtbOtZERm6BS1ssLKpGBDnUYnFLh5qqRm8qEFPFm8xl7mtdH//73/cyqGjcuCmmT5+O5s3bISqqaDjarKw85XvR9kU3pw888Ag6dizKubAWFRUtA5a8vKI6Ke39xTqxPCIiCkePHrtkG5HXIOaxEMu12hC57LnnpqJu3aJAwVpcXDzM5iB583rq1FmbYyUnFyXM5+QUyOUiB+HTTxfgm29+VkbzsRg8+Er89dcKHDhwQJY/PDwSx45dWjbxJFoICiquv5ycfPmzpU5TU4tuWi3rRa54yXOh1xe9/9Gjp2xaMkRLRXp6GiIjoyqsZ3E88bsJffsOUo4/efKLGDPmNjz22AR88MEnyo1zyXMi6lgEF9blsj7fWm2obNEqWa+im5PobmU5jxkZRX9ncXFVUbt2Q7v+5iz7liRakkRQKbonifMgthF1J65ne/9+Lb/DiRNnER1dRVl//Php5dzFxBQFAIcPn5StWBZnzybL75b3ttSv+B0jIsT7F9Vlenp6qeU5c+a8/K7Thdqs9/VR2NxVPlcc19Fj2Lu9I58Jjq53dLkvcGfZnD22mv09dR2oWcfrwP+ug1g71p88l4G3Vh3G//4rHqwkVK/F40Oa4qrmVaHVaNC6aoTsFv+H1czbovuTaKkQObie/P9Qtap90w4wsHAB0fWotO5HZV1YM/46iP9tP13uUGKi79zlLRN9bnxikYcxe/bbcl4B0eVFjLz09tuvYcqUF+V6kcBrTeRGiJt5MSla8+a3K8vPnz8vu8eMGDESrVo1teu9O3fugmXLlmDv3t2yb7ogblh37PhXmS+gVavWCAoKwvnzSTIx20IkcItyiwRb0X2qffv2ss+8eG0J3NatW23zfuLpvQhavv32S5lPYk08mT916iQaNmysdDn6668/ZVcg6+5QP//8syyPyOcQREuEJYCx+O+/fyv83UXSeFEZ1yjJxML7778jb6znzfukwnouqzuOyIe5//6H5HmcM+cdJTfFUeLci3KKvBtxbViInJySI3K5isjpEEnQJbsxqSHKLa4fC5HTUr16DVlvUVFFAeuKFctw8823KduI81EeEaSJ+WHKGuUqOfmcrLeSid1ERBS4Vu5PxjM//IekrOKBTTrVicGUIU3Run6Ccu8oggdxHzhlaFOERoQgPzvf53IqSmJg4QWje9THj9uLnob6Ut+5wkLRR/6/UtdFRYWiWrXamDHjZfn0XjwZF4nLYvShd955Hf37D5RzCVienItkYfGUvEmTpnIUnVdfnS6b/sQsyZmZWVi48EMkJSWhWbMWdpdv6NAr8M03X8rhZsUxxU36woUL5BNxC/Fk+ZZb7sCHH85Fdna2HJlK3MiL1+KPUbSyCI888ijGjLlLHkskeotckEWLbCe769KlOy6//Ap88MFsHDp0UM62LeZHELkMotVGlP/ll2cq8xZ8//03cm4LMSqUGIZ27dpVcihRcZNtadERff+XLVsqk86bN2+Mb775FqdOnajwdxf1KHIVxHwTIsldvBY5H+LGVuRZiJtTZ+pZjAwlAisRRIl8i5KzWdtL/O4PPXSfHClKBJ8iEBV1L5TWxcle4hxark0x+7l4/fvvv2Lz5g02Se72EOdP1EnXrh1sln/11WIZCIjAUAQVom6ff36qXCdGeBLzfMyfP0fmsIjkbDHa06FDByp8P3Edbdu2tdR1InemU6fOl7SIERFR4MnIK8QbKw7h193FDxjDg3R4qG8DjGxXQ7ZSlEYsDw/Wo+Biq7gvY2DhBS1qROOl4c3lkLLiErJuuRAtFeKlWO/poWbFbMbjxt1V5npx47ply2a89NIrymhI4ob0zz9/lze0YkhNkYAqRigSow5t3LgOn376tZzBWYx4I2ar/umnHxAWFoY2bdrJ7krW80BURDz5f/ttkdPwupyfQQQK4mavXr26OHeu+I9UBDsiEV3c6Iv3FAGOGEHp3nsfVGZdFjdzr732DubNmy2Di5o1a8qhaMVMztZeeWUmWrVqJ4cVFaNK5ebmyJYBcfM9efLzSvnFjeF7782TIzOJG2mRDyKGQn3ppZcxYMBw5XgPPTRB3piK1pOgIL1MGB83brw8dkXEaE8LFszDN998IVtqxCR9U6fORN++RXkKztSzqEsxZLAY5WjGjBfxySdfQI127Tpg6tRZ+OijuXLYWfHEf8KEJ/H880/L8qgl5qmwzFUhAhQxwpZoXXjrrTly2GNHiDlORPL9kiV/IiIiTlkuRtwSI4+JeTrEuRN1KwJmi4kTn7p4XX0tJ8ITI3CJoWnF3CLlEccQE/mJXJAqVYpbNkXivBjRSwS5REQU2FYdPI8Zyw7iQnZxcNCtXiyeGdIUNaID5+GSxlza7F+VVHJypkf704qZt637zomZt4c2r6r0nfMl7E+rrm4qW39a0UpTtWqiTO63EMPw3nHHjXjlldfRu3c/1cd2tmxlbW+Z2FGM/tWxY2eX96cV/2LFBIxiHhTrLmJiOFzRlU0EOHl5Jr+5BgTmWKirF+ZYeOb6Yo6F5/DeoOJ6ScspxGsrDmLJ3qKcPCEyRI9H+zXA1a2rX9K1yVfvDZhj4Qes+86JmROrV4m0SSAn8jdiAjkxL8MDDzwk5yERXZbEhHEi10Z0CaqMxIfGxIkT5YzqN954i2xVEt33vvjiM9x1172ytSsvz3eDCCIiUmf5/mTMWn4QKTnFg/b0ahCPGde1RZjZt2bMdhV2hfIBou9cWJDO5xNyiCoyfvwjMpla5L6Irj+iy5zoMiS6e4nllVXv3n3Qp09/LFr0sayLX3/9SXarEpPvERFRYLmQlY9nft6N5fuLRv4TokL0mHhZI1zeshriYmxHAgwkDCyIyGVCQkIxfvyj8stfiO5Pa9f+4/b3efzxp5WfRT6M+CIiosBhNpuxdG8yXl95CKlWrRT9GiXgqUGNUSUy8B+wMbAgIiIiInLC+ax8vLLsIFYduqAsiwnV48mBjTG4WdVK0yuFgQURERERkcpWil93n8MbKw4jM9+gLB/UtAqeGNgY8eFFE6VWFgwsiIiIiIgcdC4zHzP+PIB1R1KUZfHhQXjx6lboXqtoWP7KhoEFEREREZEDrRQ/7jiDt1YdRnaBUVk+tHlVPH5ZY9SvGROwydkVYWBBRERERGSHMxl5ePTHXVhnlUtRJSIYTw1qgn6NEyp9HTKwICIiIiIqh8lsxvf/nsG7q48gp7C4leKKVol4rH9DRIcGsf7YYkFEREREVLaTabmYunQ/tpxIV5ZViwzG5CFN5YR3VElaLPLz8/Hkk0/iwoULKCgowOTJk9G+fXtvF4uIiIiI/KCV4uttpzF7zRHkGYpnyr6xc22M614XkSEBfRutSkDXyLfffouGDRvi7bffxuHDh/H000/jq6++8naxgPST0CefunR5bij0WXnKS1NoPExRtdxenPHj75Xf33tvns3y7OwsTJgwHgcO7MPrr7+Bjh174Prrr0KHDp3wzDMvuLQMH330AT7+eL7NMp1Oh8jISLRt2wH33HM/GjZsJJf/9tvPmD79RZttg4ODER+fgC5duuOuu+5GtWqJyrqtW//Bww+PK/O9e/bsjVmz3nLZ71Kyjg4dOoipU5/D0aNHUKtWbXz22TdyeWZmJkaNGolXX30L9es3kOfh2mtH4brrrlH93pMmTUDv3v2cnnxNnPuXXnoW//yzGUFBwZg/fyHq1KmLQHD8+FFMnPgwvvvu+0D/F0hERCodS8nBy0v249/TGcqyGtEheGZIUwxtV6vSJmdXJKA/Va+55hplQhKj0YigIO/3f9NmnoJ+cV/EGfNLXR9n9bNZF4KUW1d7JLgoKScnW958HTx4ANOnv4YBAwbKP6Lp019FRESk29537tyPlZ9NJiPOnj2L+fPn4MEH78Fnn32NhIQqyvpp016Vr6OiQpGcnIojRw7js88+wZo1K/HBBx/Lm3hrjz02CU2bNpc/i30yM4uCuOjoKLjTxx/Pk7+HqLu4uOIm01demYHevfvKoEJ4+OHH8NhjD6F//97Q6cIcfh8RcCUnJ+GKK652usx//PEr1q1bI+usQYOGqFGjJgJF3br1ZfA1Y8Z0TJr0nLeLQ0REPsRoMuPzLSfxwfpjyLdqpbi+XQ2M79sAEcEBfevstICuHfG0W0hJSZFdosSXt2nzUqApI6goSWwntvd0YFEUVDwkWypmznxdtgJYWG7M3aV16zY2r9u2BRITE2Vg8dtvv+D220dblaWZvOGNjQ2XQU/nzl3ljfqYMbfh1Ven46235tgcS9zAW45v2ccT0tPT0ahRY/To0VtZtm/fXvz66y/4/vtfrX6f5mjZshU++GAuHnhggkPvkZ+fh/fffxcTJ06CVqt1SZmFa6+9PiBnC73ttjtx3XVXYsSIG9CsmXuvaSIi8g8HkrLw5Lf/YueZTGVZrZhQPDu0KTrVifVq2fxFQAcWwpEjR/Dwww9jwoQJ6NGjh7eL4/NycnKUlopXX30bHTt2LrObz5kzpzFq1NV46aVX8NdfS7Fp00bo9Xr07z8ADz88EWFhRU/dDQYD5s9/H0uX/i5vWNu374ghQ4Zh6tTn8c03PyE2tnG5ZWrevIX8fu7cmQrLLwKNa64ZKVsuTp06eUmrhb2mTSv6/UaOvBazZ89GRkY6WrZsjfHjJ6BJk6bKdqKe5s59B//+ux3R0TG4774HbY7TunVL5efevTtj8uTncfnlV+HTTz9G167dZPcta4MHD8OsWdNw882jERdn3X5Vvl9++QkFBfno2bOPze+QlHROdmFauvQPVKtWDQsXfikDhcWLF+GXX36U6xMTa+D662/A9dffJPcTXbK2b98qf+7TpwuGD79Snm+Rs/T663Pw66+/IjU1BXXr1sMdd4zBwIFDbK6Pvn37y+5f//23Q57np556Vtbf3LnvYc2aVbKbVePGTXHvvQ/IYNC6rkQLyf79e7Fq1Qp53XTv3hMTJjxhU0+iNeXrrz/HsWNHERMTiyFDhmPs2PuU9YcPH5TvtX37Nvm6U6cuGD/+UZtrQbR0ifoX52Hq1Jl21zMREQUeg8mMT/8+gfkbjqHQaJbLxCO1GzvWwgO96yMsSOftIvoN5x9t+rAzZ87g/vvvx7Rp0zBgwABvF8fn5ebm4oknHpE3dq+99u4lQUVZROtA9eo1MWPGa7jlltvxyy//w8KFHynrxY3yN998geuuu0FuEx8fL5fZ6/jxY/J7zZr2BQlduxa1sOzYsd1muclkkjer1l+ii1xZDh7cj7fffgtjxtyLZ599GWlpaXjooXtx/vx5uV50Oxo//h5kZWXhuedext13j5OtBmK5xeLFX8iWFfElunmJVgsRvK1duwqDBw++5D1FFx1RptWrV8ARImgTxxa5JtZEgHDuXFE3rHHjxsu8lddem4GPPporb8hnznwTl102EO+88wY++eRDuc/EiU/hyiuL8jxEmUePvltOBjR58hP4+uuvcOONt+CVV95A69Zt8fzzk/H777/YvOd3332N5s1b4pVXXpfHEQHJww/fj7VrV8tgYtq0WTLIEa1iW7b8bbPvvHmz5Xl68cXpePDBh2V3rLffft3m2CIgbdasheyid/vtd+Hbb7/Em2/OUq6VcePGysBHBEMiqDl9+hQeeKBombUhQ4bK8yDOBxERVU4Hk7Mx5vNtmLP2qBJU1I0Lw/yb2mHiZY0YVARyi4UY2WnkyJF49tln0a1bN7lM3LS8+OKLWLp0KUJDQzFmzBj5JcyZM0feNLz66qvytXgC/M4777ilbGHb58kva1qtBvGmoovUwhhdz6HjRv90G6Aryg3JbX+v/LLQFGQh7vP+yuuS6x2Rl5cngwrLzXhurv03Wz179pJPhAXxBPrvvzdh/fo18kZWtBqIG88HH3wEN910m9ymW7cesnva5s0bLjmWuNm37t5z8OBBvP32a7Jb29Chw+0qj+Xp9oULRQGAxaOPPnDJtuKp++eff1fqcUTAMHv2HDRqVNTqIFosbrjhGhkk3X//Q/j66y9kEPD++3Oh0YRcPF593HdfcXetdu3aITw8Qv5s6Ya1YcM6+Xu2aWPb7UsQrTxiwAFxwy1aXuzturZ3724MHHhpoCLK98QTk5VkdnHj/fPPP8qWldtuG60EYqL71KJFH8uuTyKnomrVajZl/vvvjdi0aT1effV19OjRTzmPeXm5snVAtLSI1iqhevUasn4sfvrpBxmkffDBJ2jVqrVc1r17Lzz00H0yEPvww0XKtg0bNpatOha7d+/CihXL5c8i4BDBT58+/TFp0hSbgHjZsiUoLCyUAwCI/wOiG5wlF6hz5y7yvH3++afyOrRo3bq1PA///rsNPXr0squuiYgoMBiMJny8+QQWbDwuWywErQa4pVNt3NezHkLZShHYgYUIICZOnIgDBw7YLJ81axZ27tyJhQsX4vTp05g0aRJq1qyJYcOG4eWXX/ZY+TQFmdBln71kecnGM1OIY330dHkXbN7DltnmPS9dbz9xYxoTE4PZs+dj+vSXZDeaTz753CZZuiytWrW1eS1uSkVrkWVEJvG0+7LLBtlsM3jw0FIDi/79i/M5LMSNrng6bU9ZBPF+QsncgMcff1rpVhUZGYqsrDwEBxcFBKWpUaMWOnXqrORiVKlSBW3atFW6CYkb0lat2siA1bKNuHFOTKxebvnEE3RBdM0prcGkZs1aOHOmlFHDyiASw0UAUVqCtTintiNk/S3rp1evvjZBnMhNEa1MokuX6MpU0j///C3rs1+/vsjJKd6vV69+WLLkdxw5cghNmjSTy6y7iglbtmxGQkKCzGWwfk/RbWvOnLeRkZGB6OjoUnNsRNlF8CKcOHFctjr063eZzTailUx8icEZREDWoUNHhISEKu8lAjsxspgIeK3VqlWUuyS6vBERUeWx71wWXlyyDweSs5VlDRLCMeu6tqgfZdvyTwEYWIin1iKosNwwWojWiG+++Qbz589Hq1at5JcIPBYvXiwDC0fFxIQpN6M6nVYm+JbHehttTDzMUTVKbCGOZVtmXUQcYNsjo1zm8CpKi0VoTDyCrcuUb7R5z0vW20mv18kbuwULPkGzZs0QGzsTd9xxO1555SXMmzdf1onldxWtMMHBevlzdnZRDkV8fLRNXYWGBkOjMctleXlZclmDBrVtuunUqVNU7ujoMHns0IszVn755dfKNuJGUdzMi5tSa+Hhwcq+4j1KnqucnKLE4/r168jlIogQWrZshq5di7p3iX2MxuLRHkoSv2ONGomXHLtatarYs2f3xd8/E/Xq1b1km8TEakodiXWifgXLNsaLyfuRkRHQaC7tjRgRES5bjCq6/iw0mqIb6CpVYm32EWWIiIiwWZafXxQA3X77DaUeS9Sd2N5yPiz75uVly7+/rl27lLpfbm6Gcn3ExNheDyKnQswlU1rQKBQWZiE2tigYi4mJstk3LCxYvq9YdvhwnnLtlFY3oq5FLsfy5X/Kr5JEFzzr/cxmk3I+HPlbd/X60tbZ8//Hm9xVPlcc19Fj2Lu9o58Jjqx3dLkvcGfZnD22mv09dR2oWcfrQJ3S6k2M8jR75UHMW3NEjv4kt9NqcG/vBnjwssYID9GXe2/grc8EX78O/C6w2Lx5s+z6JBKwrSe427t3r3wq2aFDB2VZp06dMHfuXNltwtHRcdLTi56M2jtqkM02ze4q+ipr/UX65P8Qd8K+Lj1C2pWfwlDV6imuzfF0wB22fdRt19vHYDDKLiiJiXVkeevWbSL7rotuJXPmzMWtt96p/C4mkxkFBQb5c0ZGUX3l5BTY/J5ivdhOLIuKKkpAPnz4JKpXL36Sf+JEUYuGOIb4I87LK5Sva9dueGkdlPidxPtZ9o2IKLoBt95m1arVMhhq3LilXC5aJgTx3bJdRedX/A4XLqTIsllvd+5cEqKjYy/+bjHydcltUlJSUatWUR2J9xH1a/17hIQUdY1KS0tXulDZ/r7piIqKtnvUKp2uKHA6e/Z8mefBQq8v2vadd+YiPPzSf1CitUVsbzkfln2Dg8MQFhaOjz/+RKlPa7Vq1bnk+rAIDQ1H7dp18cILU0stf2RkvLK9eF/rfW3LURTsnDx51mab9PQ0mRfUq1d32WWuU6euuPnm20qpJ53NfiZTnnI+HPpbd/H60tZ5ctQyNdxVPlcc19Fj2Lu9w58JDqx3dLkvcGfZnD22mv09dR2oWcfrQJ2S9bbrbCZe+mMfDl8oXta4SgSeG9YULRKjkJuVhxC9a66DWBd/JthzTHerWjUqcJK3b7nlFjlrtmWUIYvk5GTZDcX6Sbh4wi26TYlEW1LvzjvHokWLlnI0p927d6o+Tps27eQNXclkZEeTk+0lRjn66acf5aR3FXVJqojoenPo0CHl9fnzydi5c4ccZUgQ38Xrc+fOKduIuTQsXZ3KInIQhLNnSx/lShxPjNRkL9H1TNSx+N0rIkbkEsTfh0iwtnylpqZi/vy5yjCzpe0nWlFE64H1fmL0pwUL5pebBC+6JomyxcbG2+y7efNGOTqVTmff84169eojNjZWJnRbE6NEPf74IzLHQpRTTEQoRp2yvI9I9P7yy8VYvXqlzX4iqd36fBARUeARrRTvrj4sE7QtQYVopbinR10suq2DDCqokrVYlEUkbZYcBcfyWiR6k3oiEVeMhDRmzK144YVn8P33P6iKQ0UeweWXX40PPngPBkOhvOETQYXl5rC0rkD22r9/n+xiIya7O38+TSYIf/XV5zJ5d8KESQ4ea6+cYVrkc1iIm+jx4x/A3XffL2/cFyyYJ1sSRo0qGpb1hhtuliNg3XffPRg9+h55cz1v3hzo9eVPxNiuXQeEhIRg69atuOKK+pckjIshbEeNutnusouAWwRwIvH+xhtvLXdbMZ+GSIKfNWsqzp49LW+8xUzUH3wwR+ZolDW7tkhuFjftDz88HnfcMVbe5O/Zs0vOmC6SuMUNf1nE+RejOU2Y8IAcnlYEfCLfYfHihbjuuhuVpO+KiHMwZsx9eOONmfKBgsgLEcnoH300T444JvJJxHkYN+4uPPnkBFx77XUyh+Z///teTppYclhZUf/iWmnXrrgVlIiIAse/p9Ll7NnHUot7pDSrFonnhjZF02rum+y3MvPrwELcnJUMICyvxQ2DJ3Ms7F6vqQWzPgQaQ8WT5ImZtyOr1QJi3NunrmQOgEVsbAs8/viTmDr1Jbz88ouYOfPVUnMsRM5Dyb79YjvLshdffB5xcdHyqbHob9+tW3fce+99mDv3fdSoEW+TY2FP/0FLjsUzzzxhk48hkvaHDBmCu+++G1WqVFXWWXIsxHfL8UuemylTnpRJ0598slD5HWrUqIExY8bivffelEFs9+498MQTT6JWraIn3GL/zz5bjFmzXsH06S/KrkVi+z/++L3cHAsgHH369MXatWtw6622XXY2blwlf5fLLx+CGAfO+/DhwzB79nsIC9PJv4vSzoPFzJkz8eGH8/Hjjz/IQEgkxV9++eVyvheR4yCUdj7mzZuH2bPflXOEpKRckBMX3nHHnbj//geU97S+PizEz59++hneeutNOQpUVlamTJx+9NEJGD36Lpsui+J9bfN1bMsxZsydSEiIwccfL5CjTYkgZezYsRg79m5Z1507t8OiRZ/hnXfeksPSiuCwSZMmeOedd3HZZbZDTq9duxb9+vVHYmLxbOhlYY6FY/WhFnMsPFPPrsAcC3V1E2h96331OsgtMGL6H3vxyfqjsKTnBuk0eOiyxri7dwME6bRuzbXRVeIcC425ZEa0jxPJxYsWLZI5F+KJ42233YYdO3YoTz03btyI++67D9u2bXM4xyI5OdMz/Wk1KchKurS7jGWkIgtTaLzHZ90ui9q+fSKZduPG9fKptpjMzGL27Lfx668/4bfflvtkf1oxKta2bVuwbNlyt/SnFaNw3XffXfjyyx9sRnN65JH70aJFc4wbVzwsqr3DBYshVcUwr2JCO3cJlH7VohvajTeOwPz5C+2aTZ45Furr2hHMsfBMPbtCoPwvcHQf5lg4X9euPiclbTmRhqlL9+NkWvH9VKvqUXL27EZVIlzynsyxCNAWixYtWsiAYvv27ejcuWi0ny1btsj5ARwNKjwqpjYM5lKekopEXx/9EFFLDPv51luvyaFIRdch0W1n167/8N13XynzKFRGoguSmKBNzK0wceIkJdg4cGA/Xn/9NYePVzSHy7348svP5MR3otsQle2LLz6V9W9PUEFERL4vp8CI99YcwTfbi4cQD9ZpMK5XfdzcqTb0YpIKcjsfvvuumLhJHTFiBF544QXZarFs2TIsWLAAd9xxh7eLRheJLjJvvz0HISHBmD79BZlkK2aJFhOV3XXXPZW6nqZMmYING9bKhG9BzH49YcITNt24HDFixHUykVtMgEdlO3bsqJwFXNQ/ERH5v83HUnHzwn9sgoqOdWOx+I5OuL1LHQYVHuTXXaEE0fddBBZi5m0x1KTobz16tLon4WKYTOsci4rGMq5om7LWO7rcF7izbM4eW83+9u7j7HWgZh2vA3UcrTdHtlf7t27P+tLW+fI14M7yueK47roO+JngXD07gp8Jnqtrf78OMvMMmLlkL77656SyLDRIi4mDm2J0zwZiFBaXv6c3PhN84ToIsnMmcr8LLNzJYzkWPjpGsb/1o3Rmf/an9Vxde+rY7pq/wJ5tOY+FazDHwvX14S6B9L/AkX2YY+F8XbvqnKw/koJpS/cjKat4EJ+OtWNkLkXt2DCvXgexlXgeC7/OsSAiIiKiyiMjrxBvrjyMX3YVz90UFqTFQ30b4rp2NaC92POEvIOBBRERERH5vNWHLmDGnwdwPru4laJr3Vg8M6QpasY4Ps0AuR4DCyIiIiLyWWm5hXh9xSH8sSdJWRYRrMMj/RpiRJvqSn4seR9zLKwwedt3E7RcvT+Ttz1X1546NpO3PYfJ2+rqhQN6eOb64mdCYH0mLNl1Fi/8shvnrXIp+japgqnXtEKNmDCXlo3J22Vj8rYKTN4uGxP11NWNPyZoVZbrgMnb6nGCPHX1wgE9PHN9MXk7MD4TjEF6TPn+Pyzbn6wsiwrRY0L/hriyVWKFrRRM3nYtJm8TERERkV8Rg5X+uS8Zr604hNScQmV5n4bxeHpwE1SNDPFq+ah8zLEgIiIiIq8TSdkzlx3AyoMXlGUxoXo8PqAxhjavylwKP8DAgoiIiIi82krx+54kmaCdkWdQlg9oUgVPDmyMhIhgnh0/weRtK0zerhxJu47sw5m3na9rV58TV+3PmbfVY/K2unph8rZnri9+JvjX/4KzGXl47qddWLGvOJciPiIYL13dCkNbJnq0bEzedj55my0WVtLTc5WfOfN24CbtOrIPZ1l1vq5dfU5ctT+Tt9Vj8ra6emHytmeuL34meI4z50q0Uvy88xzeXHUIWflGZfmQZlXx+IBGaFAz1m+vg1jOvE1ERERE5H6ilWLanwew8Wiqsiw+PAhPD2qC/k2q8BT4MbZYEBEREZHbiVaKH3acwTurjyC7oLiV4vKW1fBY/0aICQviWfBzDCyIiIiIyK1Opedi6tID+Od4mrKsamQwJg9ugt4NE1j7AYKBBRERERG5hclsxrfbT+O9NUeQW1icTH1N6+p4pF9DRIXyVjSQ8GwSERERkcsdT83F1CX7sO1UhrKselQInhnSBN3rx7PGAxCHm7XC4WYrxzCjjuzD4Wadr2tXnxNX7c/hZtXjcLPq6oXDzXrm+uJngueUVddGkxkLNxzFm8sPIM+qleLmLnXwxJBmdrVS+PN1oHNivaP/J3xtuFkGFlaSkzOVnzncbOAOM+rIPhxu1vm6dvU5cdX+HG5WPQ43q65eONysZ64vfiZ4Tml1ffRCDl5ash//nSlupagZE4opQ5qgS904p47tbNlctQ+Hmy0bu0IRERERkVMMJjMW/3MS89YfRYHRLJdpANzQoSYe7NMAYXY+8Sb/xsCCiIiIiFQ7eD4bLy/Zj91ni3t+1I0Lw7NDmqJ97RjWbCXCwIKIiIiIHFZoNOGjjcfw4YbjssVC0GqAmzvWxrhe9RDKVopKh4EFERERETlkX1IWpi3ehj1WrRT148Pw3NBmaFMzmrVZSTGwICIiIiIHWimO45PNJ+ToT4JOA9zWpQ7u6VEPIXota7ISY2BBRERERBUSORQvLdmHQ+eLR05qVCVctlK0rB7FGiQON2uN81hUjvkLHNmH81g4X9euPieu2p/zWKjHeSzU1QvnsfDM9cXPBNfLLzTi3RUH8eG6o0orhV6rwf39G2Fcn4YIdkMrhT9fB7pKPI8FWyyspKfnKj9zHovAnb/AkX04j4Xzde3qc+Kq/TmPhXqcx0JdvXAeC89cX/xMcK3/TmfIEZ+OpBSfk6ZVI/DcsGbo1rSaPFfu+FTw5+sg1on1jv6f8JSqVe1rkWJgQUREREQ28gqNeH/dUXyx5RSK2iiKWinu7lEXd3apA72OuRTEwIKIiIiIyrHtZDpeXrIPJ9LylGUtEiNlK0XjKhGsOyoTWyyIiIiICLmFRsxecwRfbzuttFIE6zS4t2d93Nq5tmyxICoPAwsiIiKiSu6f42l4eel+nE4vbqVoUyNKjvhUPyHcq2Uj/8HAgoiIiKiSyi4w4N3VR/Ddv2eUZWIuigd618eNHWpBx1YKcgADCyIiIqJKaOPRFExbegBnM/OVZR1qRWPK0GaoGxfm1bKRf2JgQURERFSJZOYZ8NaqQ/hp5zllWViQFuP7NMD17WtCq2EuBanDwIKIiIioklh7+AJm/HkASVkFyrLOdWMxZUgT1IphKwU5R2M2my2J/5UeZ96uHDMuO7IPZ952vq5dfU5ctT9n3laPM2+rqxfOvO2Z64ufCaVLyynAtN/24sd/TyvLIkJ0mDS0GW7qXAcaFa0UgfSZ4Mg+nHm7bAwsrCQnZyo/c+btwJ1x2ZF9OPO283Xt6nPiqv0587Z6nHlbXb1w5m3PXF/8TLjUygPn8cryg7iQXdxK0b1eHJ4Z0gTVo0M9WteeOjZn3nYtzrxNREREVIml5RTi1b8OYum+ZGVZZIgOE/o1wlWtE1W1UpD7aTNPQZuXYrswNxT6rOKhgAVTaDxMUbV86pQwx4KIiIgowCzbl4xZyw8iNbdQWda7YTyeHtQE1aJCvFo2Kkf6ScQv7guNsXikLou4Eq/NuhCk3Lrap4ILBhZEREREAUJ0dxIBxV8HzivLokP1mHhZIwxvUY2tFL4u50KpQUVpxHaiZYOBBRERERG5jBiL54+9SXj9r0NIzzMoy/s3TsCkQU1QJSKYtU1uxxYLIiIiIj+WnJUvh5Bdc7i4X35sWBCeGNAIg5tVZSsFeQwDCyIiIiI/baX4Zdc5vLnyMDLzi1spRDAhgoq4cLZSkGcxsCAiIiLyM2cz8jBj2QGsP5KqLIsPD8JTg5rgsiZVvFo2qrwYWBARERH5IJPZjJwCg/yuvTg0rGil+PG/s3h71WFkFxiVbUVi9mOXNZJdoMiP5RfPqeaPGFgQERER+ZD9SVn4YuspLNmbhEKjGUE6DYY2r4ZBTavi8y0nsfl4mrJt1chgOYRsn0YJXi0zOclsRsjBn6BfPcWvq5KBBREREZGPWLInCc/9vheifcJoLlomgotfd5+T+RTWrmqViAn9GyEqlLdz/kybcRxRq55G8PFV8He8EomIiIh8pKVCBBWmiwGFNbPVMpFL8fywZujZIN6j5SMXMxYi7N95iPj7TWgMtrNq+yuttwtARERERJDdn4oyKcom1netG8ugws/pz25B3DfDEblhhhJUGCNrwHD5W3JGbXuI7UyhvhVcaswiC4ikggKDMtazTqeF0Wgqt2Yq2qas9Y4u9wXuLJuzx1azv737OHsdqFnH60AdR+vNke3V/q3bs760db58DbizfK44rruuA34mOFfPjqisnwni/qPl80tQYEc5gnVa7Hx+sNfnp+B1oK5edIZsaN5uDU1eunxt1mhh6nwPTP0mQxceA2PKcTkDt+0xNTBa+sZZhCcAMbXhCUFBOru2Y1coK+npucrPsbHhSEvLKbfyKtqmrPWOLvcF7iybs8dWs7+9+zh7HahZx+tAHUfrzZHt1f6t27O+tHW+fA24s3yuOK67rgN+JjhXz46orJ8JweHBdgUVgtju3PkshNp5s+cuvA7U1UtsbATyuj6BqNVTUFilNbIumwlDtXZALhAbYkKaOR4Ii6/4mCLO8NBnRdWqUXZtx8CCiIiIyMt2nc6we1sxSlSInr3Z/YU24wTMIdEwh8Qoy/Ja3Q5zUCTym44AtIFzO86rkoiIiMhLsvINmLnsAG75aLNd2+s0wLDm1bzeDYrsYDIgbNtcxH8xABEbZtiu0+qQ3/z6gAoqhMD6bYiIiIj8xOpDF2RQkZRVYPc+ovfLTR1rubVc5Dz9uW2IWjEJ+gu75euwXZ8hr9n1MNToHNDVy8CCiIiIyIMuZBfgtb8OYdn+ZGVZWJAO43rVQ1xYEF74Y5/NPBaWlgrx8qXhzdG0WiTPl4/SFGRCu+RFxP7zITTyjInzpkFemzthTGiGQMfAgoiIiMgDxECcP+08i7dXHUZGnkFZ3r1+HGaMbIPIi72bGlWJwJdbT+EPq5m3Rfcn0VLBoMJHmc0IPvw7Itc8C1128USGhoSWyBTJ2YkdUBkwsCAiIiJys5NpuXjkh11Yf7h4GNGYUD0eu6wRhreohri44lF/RPDw3LBmmDK0KUIjQpCfnc+cCh+mzTyFyNVTEHL0T2WZWR+G7K6PI7fd2IDLoyhP5flNiYiIiDzMYDLjiy0n8cH6Y8g3FA8nO6xFNTzWvyHiwoPL3Fer0SA8WI+CHPtzMMjzgo+tsAkqTI0GI7XnSzBF16l0p4OBBREREZEb7DuXhalL92NvUpayrHpUCJ4a3AS9GvjWjMmkXl6rWxC67xtoM04iq8+LCO80CiarudEqEwYWRERERC6UV2jE/A3HsfifE0oCtkifuL17PYztUhvhwd6d2I7U0xRkQbP7D6DmMKuFWmQMfk/OUyHmqwivxEMBM7AgIiIicpGNhy9g8g//4URanrKsYUI4pgxpij4tq7ttxnJyv+DDSxC5Zgp0WWcQdO13KKzZTVlXGbs9lYaBBREREZGTMvIK8c6qI/jfzrPKMjGa05hudXFn1zoI0nFOYn+lzTwtR3sKObJEWRax9kWkjfoVqMStE6VhYEFERETkxBCyKw6cx6y/Dsn5KSza1YzGM0OaokFCOOvWX5mMCPvvY4RvehXawuzixQ0HIqPXywwqSsHAgoiIiEiFpMx8vPrXQaw8WDyEbESIDuN7N8DIdjXkqE7kn/TJ/yFyxSQEJe9QlpnCqsrk7LDON1ba5OyKMLAgIiIicoDJbMaPO87gndVHkF1gVJb3aRiPaSPbIsxcPKws+RljPiI2zETYjg+hsTqPua1uQ3aPp2WCdhgDxjJVmsBi2bJlWL58OWbMmOHtohAREZGfOpqSg+lL92PbqQxlWXx4EJ4Y0BgDm1ZBXEwoE7T9mTYIQUnblaDCEN8Mmf1nwlCjs7dL5hcqRWAxc+ZMrFixAu3bt/d2UYiIiMgPFRpNmLPyEGavPIgCyxiyAK5unYiH+zZETFiQV8tHLqLRIrP/K4j97hrkdHwQue3vBXRlT2JIlTCwaNu2Lfr164cff/zR20UhIiIiP7PrTAamLj2Ag+eLE3hrxYRi8uAm6FovzqtlIyeYjAjduQjGhOYorNVDWWyMb4qUOzfDHBzF6nVQpQgshg8fjk2bNnm7GERERORHcguNmLvuKL7cegqmi40UWg1wa6fauLdnPYQGcaI7f6VL3oWolZNktydDbEOk3vQngOIRvBhUqFMpAgsiIiIiR2w4moJX/jyA0xn5yrIW1aMweVBjNE/kk2y/VZiDiM2vI+xfkZxdlHivTzuM4OOrgIQR3i6d32NgQURERHRRWk4h3lx1CL/tTlLqJESvxb096uH+gU2QnVk8ozb5l+CjyxG5+hnoMk8qywxxTZDV/xU5izZnHHGe300DWVBQgCuvvNKma1N+fj4mT56Mzp07o3fv3liwYIFXy0hERET+N9HdH3uSMOqTf2yCis51YvDFHZ1wB2fP9lva7HOI+mMcYn69UwkqzLoQZHd7Aqk3LpFBBVXCFgsRQEycOBEHDhywWT5r1izs3LkTCxcuxOnTpzFp0iTUrFkTw4YNU7bp1q2b/CIiIiKydiotF5N/2In1R1KVZVEhejzaryGuap0IDect8FvBR/5E1LKHoS3IVJYV1OqFrP4zYIxt6NWyBSKNWYTofuDgwYMyqBDF3bdvHxYtWiQDhZycHHTv3h3z589XAoc5c+Zgw4YN+PTTTx16j4ICg/LPQ6fTwmgsf4KbirYpa72jy32BO8vm7LHV7G/vPs5eB2rW8TpQx9F6c2R7tX/r9qwvbZ0vXwPuLJ8rjuuu64CfCc7Vs69+JhhNZny26RjeWHYAOVYT3Q1rlYhnr2iBalGhDh+bnwk+dh2c3w/9h32hMRbAHJ4A46CpMLe+ASglWHTV/wOdiz8THCmbuwTZOVCB37RYbN68WQYOEyZMsJmPYu/evTAYDOjQoYOyrFOnTpg7dy5MJhO0Wvt7e6VbTc8eGxte4QQ3FW1T1npHl/sCd5bN2WOr2d/efZy9DtSs43WgjqP15sj2av/W7Vlf2jpfvgbcWT5XHNdd1wE/E5yrZ1/8TBBDx05buh87zxQ/ya4aGYwnBzRG/yZVRNSh6m+Tnwk+dh3oayO844PQZp5Gds9nYA6LB6zu99S8p6c/E3zhc6Fq1Sj3BRYiz2HLli3y6+TJk0hNTZU38FWqVEGNGjXQo0cPefPvyqbDW265pdTlycnJiIuLQ3Bw8eQlohyi21RaWhri4+NdVgYiIiLybwUGExZsOo6Fm0/AYBlDFsB17WpgfJ8GiAzxm2euVELQ8ZUI3z4f6Zd/ZDN0bE6Xx0ptoSDXc+ivRwQRixcvxnfffYfMzEzZLSksLAwRERHy5/T0dNl68O677yI6OhojR47EnXfeierVq8NdcnNzbYIKwfJaBEBEREREwr+n0jF16X4cTSl+Yl0vLgwzRrZBk1jbbk/kPzTZSYhc9yJCD/xPvg7f8i4w9HmrDRhU+FRgkZeXJ4MFkRxdt25d2XoguiU1bdoUCQkJNtueP38e27dvl60Zv//+u8xzENs/+uijCA93/UBeISEhlwQQltehoY79k4iJCbPJsRDNTuWpaJuy1ju63Be4s2zOHlvN/vbu4+x1oGYdrwN1HK03R7ZX+7duz/rS1vnyNeDO8rniuO66DviZ4Fw9e/szITPPgNf/3I/Fm48ry/RaDe7t0xAP9GuI8NAgu/vW8zPBdefK2bqG2QTNtkXQrXgRmrx0ZXFYyg457qm37g10Lv5McKRsfhFYiJmrmzRpIgMLkb9QHtENadCgQfLrySefxMaNGzFv3jxcccUVWLFiBVwtMTFRdsUSLSV6vV7pHiWCCtFq4gjmWAR2f1o1+7A/rfN17epz4qr9mWOhHnMs1NUL8+48c32V3H/1oQuYuewAkrKKH0K2qh6FKUOaonHVCORm5yMkSMfPBBfUtSePrbuwD1GrnoL+zN/KMlNILLJ6PYf85qMQa4LX7g1imWNRvtdee63CgKI04um/yLcQX3//XXziXalFixYyoBCtJGIeC0G0lrRp08ahxG0iIiIKHBeyC/DaX4ewbH+ysixUr8X9vevjxg61oNOye4xfMuQi/J93EL7tfWhMBmVxXvNRyOo5BeYw25405IMtFmqCipK6dOkCdxA5HiNGjMALL7yA6dOnIykpSU6QN2PGDLe8HxEREfkukfP5086zeHvVYWTkFd94dq8fh6cHNUHNGOZS+C2TAXHfXAl9yj5lkSGmQdHM2bV7ebVo5OQ8FmI3kcxdp04d+frIkSP4+uuvZeuBSNpu0KAB3KVZs2bKPBaWBG4RWCxduhSRkZEYO3YsRo8e7fBxOY9F4I9Z7ug+HLPc+bp29Tlx1f6cx0I9zmOhrl44t5H7r69jKTl47qddWH/ogrIsLjwIzwxvgavb1ShztEp+JvjPZ4J2zavQrZ4BszYIpp6PwtRrAqAP9al7A10lnsdCVWBx9uxZefMuRl/64YcfZMK2yMMQI0VZWhHE6FEtW7aEP0lOLh7LmmOWB27fekf2YY6F83Xt6nPiqv2ZY6EecyzU1QtzLNx3fYlhY7/YchIfrD+GfEPxzdewFtXwWP+GiAsPdsl78jPB+XPl0LFTswCTEdAFFa8w5iNy5WTkdhgHY3wTp86V2n2YY1E2VUkIb7zxBs6cOYObb75ZvhYtFSKoeOutt7B8+XI5l8U777yj5tBEREREdtt3Lgt3Ld6Gd1YfUYKK6lEheGtka7x8efMKgwryUef3IeaHUYjY/Lrtcl0Isga+Xm5QQd6jahaYdevWyfkpbrjhBvn6r7/+ksHEsGHD5GuxfM6cOa4tKREREdFFeYVGzN9wHIv/OQHjxb4XoqPT7d3rYWyX2ggPtq/rBvkYQ56ch0K/dQ40pkIEnduCvKYjYExo7u2SkbsCC9E6Ubt2bfnzhQsXsGvXLowaNUpZL7pCieFfiYiIiFztn+NpmP7nfpxIy1OWNUwIl0PI9mlZ3W3dc8i9gk6uQ+TKp6BPP6IsM0XWgqYwm1UfyIFFzZo1sX//fvnzr7/+Kr9fdtllyvo1a9YogYc/4QR5PjwJjov35wR5nqtrTx2bE+R5DifIU1cvnDTV+esrPbcQM5fswzdbTirLgnQaPNCvkZzsLliv5WeCP/4vyD4P3fLnoP3vS2WRWauHqccjMPV6DJFBYR4pGyfI81JgceWVV8quTseOHcOmTZtkN6g+ffrg+PHjcsjXVatW4amnnoK/4QR5lSNp15F9mKjnfF27+py4an8mb6vH5G119cLkbfXXlxhnZsWB85j11yE5P4VFu5rReGZIUzRICEdOVh7EXvxM8KP/BWYzQvZ+g8j1L0Obl6osLqzRBbjqLaQF1QOyRT83x9+DyduuVbVqlPsCi/Hjx0On0+GXX35Bx44d5QzbYpjZrKws/PPPPxg3bpzMwSAiIiJyRlJmPl796yBWHiweQjYiWIfxfRpgZLsa0JYxhCz5vpB93yH6r8eU16aQGGT3mIy8ljcjNi4SYJc2v6MqsBDuv/9++WWtefPm2LBhA4KCrIYFIyIiInKQyWzGjzvOyNGesguMyvI+DeMxaVATJEaFsE79XH6Tq2HY9r6c8C6vyTXI6vU8zBHVvF0s8kZgIRQWFsrkbZPJVGYuBhEREZEjjqbkYPrS/dh2KkNZFh8ehCcGNMbAplXKnOiOfJs24wRM0UUTK0u6YGRe9io0+ekorFecq0v+S9UEeadOncLkyZPx999/y36PZdmzZw/8CWferhwzLjuyD2fedr6uXX1OXLU/Z95WjzNvq6sXzrxdsQKDCQvWH8W7Kw7Kny2u71gLk4Y2Q6wdc1LwM8Fz7K7rnBSZnK3Z+TUMY/4CElu77tgu3J8zbzs/87aqFovnn38eW7ZswRVXXCFHfxL5FoGAyduVI2nXkX2YvO18Xbv6nLhqfyZvq8fkbXX1wuTt8u06k4GpSw/g4PnioUVrxYRi8uAm6FovTkQdSCuoeCh7fiZ4ToV1LZKz932HyHUvQZuXUrTs50eQNvJHQKsL2HuDWCfWO/p/IiCSt7du3Yp77rkHjzzyiJrdiYiIiKTcQiPmrjuKL7eeguliJwitBri1U23c27MeQu18Ukq+RZd2GJErn0bwqXXKMlNwNPKa3wCwK1vAUhVYxMTEIC4uzvWlISIiokpjw9EUvPLnAZzOyFeWtagehcmDGqN5on1PSMnHGPMRvvV9OXu2xlh8XvMaX4Xs3i/AFJHo1eKRDwYWt912G7788ktcffXViI2NdX2piIiIKGCl5RTizVWH8NvuJGVZiF6Le3vUw/0DmyA7s3hGbfIfQac3Fc2cnXpAWWaMqo2svtNQUH+gV8tGPhxYjB07Vg4rO2jQIDmPRUJCwiXbiBEbxGR5RERERIIY8GXJ3mS8vuIQ0nILlUrpXCcGkwc3RZ24MATptKwsf2Q2IXL1s0pQYdbokNv+HmR3eQwIcsHs3BS4gcWHH36ItWvXyp9Xr15d6jYMLIiIiMjiTEYeXll2AOuPFM+wHBWix6P9GuKq1okcQtbfabTI7D8Dsd+NgKFaO2ReNgvGKi29XSryh+Fm+/bti+rVq2PGjBmoX79+wIwKxeFmK8cwo47sw+Fmna9rV58TV+3P4WbV43Cz6uqlsg43azSZ8dmmY3hj2QHkWE10N6xVIp69ogWqRYWqPrazZXN0H34mWEk9Ap25EMb4pjZ1pDm2FuY6PSoc9clV58SV+3O4WS8NN5uZmYkHH3wQjRo1QiDhcLOVY5hRR/bhcLPO17Wrz4mr9udws+pxuFl19VIZh5sVQ8dOW7ofO89kKsuqRgbjyQGN0b9JFRF1XHIcfiaoq2uPMRYgbPs8RPz9JszVWiJtRImhY2M6AlbJ+Gr583UQy+FmHdO+fXvs3bvXwb2IiIioMpAT3W06joWbT8BgGUMWwHXtamB8nwaIDFH1XJO8TH/mb0SJ5OyUffK15sw2hO7+Anmtb/N20chHqPrLnjJlCu68806Eh4fLBO4qVaqU2h2qZs2arigjERER+Yl/T6Vj6tL9OJqSqyyrFxeGZ4Y0RYfaMV4tG6mjyUtDxIYZCNu9WFlm1mhh6joOeU2vZbWSc4HF9ddfj8LCQnz00UdYsGBBmdvt2bNHzeGJiIjIz2TlGzB7zRF8++8ZZZlOq8GdXetgTLe6cjhZ8jNi5uyDPyFyzQvQ5iYriwurtkXWZTMR2aSbGDvYq0WkAAgsxowZw9EbiIiISFp96AJmLjuApKwCpUZaVY/ClCFN0bhqBGvJD2kzTiBq1dMIPr5SWWYKikBOtyeR22a008nZFJhUBRYPPfSQ60tCREREfuVCdgFe++sQlu0vfpodqtfi/t71cWOHWrLFgvyTNvcCgo6vUl7nNxiKrD4vwxTFbu5UNrvaJT/44APk5amfBTM7Oxtz5sxRvT8RERH5DjFS/U87z+KGT/6xCSq614/DV6M745ZOtRlU+DlDYnvZMmGMqI704R8i4/KPGFSQa+axEC0U27ZtkwnbV111lZzDwh4nTpzADz/8gM8//xydOnXC7Nmz4cs4j0XlmL/AkX04Zrnzde3qc+Kq/TmPhXqcx6Jyz2NxLCUHz/20C+sPXVCWxYUH4ZnhLXB1uxpOd5XmZ4Jr68MueRnQbvkQph4PA1qrziz5F4cJDokK+M8ER/ZR+7duz3p/n8fC7gnyVq5cKSfEO378OFq0aIH+/fujWbNmqF27NiIjI2EyibGo03DmzBns2LED//zzD3bt2iUn0HvssccwePBg+Lrk5EyXzV9QWccs98axOY+F5wTSdcB5LNTjPBaV8zNBDBv7xZaT+GD9MeQbim9whrWohsf6N0RceLBL3oefCa6tj3KZzQg+9Csi1zwPXc45ZPV+Abnt7q6UnwmO7MN5LFyQYyECiX79+mH58uWyBWLevHkwGAyXPJkQcUpYWBi6deuG9957DwMHDrT3LYiIiMgH7TuXJYeQ3ZuUpSyrHhWCpwY3Qa8G8V4tG6lPzo5cPQUhx5Yry8K3zEZuq9sAve1s6ERuSd4WQYSYt0J8iZyLf//9V3Z3Ei0VWq0WCQkJqFWrFtq2bYvgYNc8uSAiIiLvyCs0Yv6G41j8zwkYL/ZvEI8Tb+9eD2O71EZ4MEcG8jsmA8L+/RARm1+HxlA810h+/UHI6jOVQQU5RfXUl6GhobJVQnwRERFRYPnneBqm/7kfJ9KKB29pmBAuh5Dt07K6T3XTIvvoz21D5MqnEHR+l7LMGJEoR3sqaDhcPEFmVZJ3AgsiIiIKPBl5hXhn1RH8b+dZZVmQTiMnuROT3QXpONGd3zGbEbHuRYT9+xE0KGp6MkODvDZ3Irv7JJiDS0/OJnIUAwsiIiKSOZIrDpzHrL8OyfkpLNrVjMYzQ5qiQUI4a8lfXWyJsAQVhoSWyLxsJgyJHbxcMAo0DCyIiIgquaTMfLz610GsPFg8hGxEsA7j+zTAyHY1oGUXGb+X3fUJOYt2XoubkdturO2wskQuwquKiIiokjKZzfhxxxm8s/oIsguMyvI+DeMxaVATJEaFeLV8pDI5+79PYNbokNf2ruLlwRFIvWkZAwpyKwYWREREldDRlBxMX7of205lKMviw4PwxIDGGNi0itMT3ZHn6ZN2IHLlJAQl/wezPgwF9QfBFF2neAO2UpCb2TVB3unTp1UdvGbNmvAnnHm7bJxdU13d+OPsmpXlOuDM2+px5m3/nnm7wGDCh2uPYPaqQ/Jni+s71sKkoc0Qa8dEd4H0v8CRfXz2MyE/E9rVM6D9ex40ZpOSnG0a/jpMHUfDXXgdqKsXXWWfebt58+aqnlzs2bMH/oQzb5eNs2uqqxs163xttt1AvQ4487Z6nHnbf2fe3nUmA1OXHsDB89nKsloxoZg8uAm61ourlP8LHNnHFz8Tgg8vQeSaKdBlnVGWGRKaI7P/TBiqd4I78TpQVy+xfnhvULVqlOu6Qj344INsEiUiIvJTuYVGzF13FF9uPQXTxceJWg1wa6fauLdnPYTa+TSSfIc26zQiVz+LkCNLlGVmfSiyu0xAbrt7AV2QV8tHlZNdgcVDDz3k/pIQERGRy204moJX/jyA0xn5yrKmVSPw7NCmaJ7I+Qt8iTbzFLR5KbYLc0OhzyqepFDQpB9H9F+PQVtY3PJUULcfMvtOhymmnqeKS6QusKgsORZERESBIi2nEG+uOoTfdicpy0L0Wtzbox5u6VQLek5051vSTyJ+cV9ojMUBoEXJTmpmXTBMIXFAYTZMYVWR1ecF5De+mjNnk38EFgMGDKgUORZERET+TqROLtmbjNdXHEJabqGyvHOdGEwe3BR14sK8Wj4qQ86FUoOK0miMBcjp+AD0KfuR3eNpmENjWa3kE5hjQUREFCDOZOThlWUHsP5IqrIsKkSPR/s1xFWtE5kvGUAMNbsiT0x0R+RDmGNBRETk54wmM77Zfhpz1h5BbmHxkJRiPorHL2uEKpGc6I6IfHyCvIyMDOTk5MBkKv4nZjQakZ2djY0bN2L0aPeNnUxERESQQ8dOW7ofO89kKtVRNTIYTw5ojP5NqrCKiMi3A4tz587JkaL++++/crdjYEFEROQeYnK7BZuOY+HmEzBYxpAFcF27GhjfpwEiQ5x6dkhE5DBV/3VmzZolg4rLL78cwcHB+OGHH3DfffchJSUFS5cuRX5+Pj755BM1hyYiIqIK/HsqHVOX7sfRlFxlWb24MDwzpCk61I5h/fkZTV4atOvf8HYxiLwTWGzYsAEjRozAjBkzkJWVhR9//BF9+vRB586d8cADD+C6667Dn3/+ifbt2ztfQiIiokrGZDYjp8Agv2utRmXMyjdg9poj+Pbf4lmWdVoN7uxaB2O61ZXDyZL/ifl1NHRn//F2MYi8E1iI3IqOHTvKnyMjI+V8FTt37pSBRY0aNTBq1CjZcvHEE084X0IiIqJKYn9SFr7YegpL9iah0GhGkE6Doc2r4eaOtXA2Mx8zlx1AUlaBsn2r6lGYMqQpGleN8Gq5yTnZXR5F7M+3sRqpcgYWMTExyM0tbn6tW7cu9u3bp7yuU6cOzp4965oSEhERVQJL9iThud/3QrRPGC+mTIjg4vfd5/DLrnM224bqtbi/d33c2KGWbLEgP2LIhTY/A6aIRGVRYd3+MHZ7ALpNc7xaNCJnacxiJh0HicTtEydO4NNPP0VUVJTsEiW6Pv3+++8ICQnB888/j7/++gtr1qyBPykoMChjfOt0WhiNxaNdlaaibcpa7+hyX+DOsjl7bDX727uPs9eBmnW8DtRxtN4c2V7t37o960tb58vXgDvL54rjuus6cPdnws6TaRjx/npY5WCXqU/jKnjp6paoHRcOb+Fngrq60R35C5rfHoc5ti6Mt/xgM1O2LusUNO91tmuSPLMuBIb7/wZiasObeB2oqxedH94bBAXp3BdY7N69GzfffDN0Oh2WL18uk7avvvpq1K9fX3aLWrt2La6//nq8/PLL8CfJycVD9cXGhiMtLafc7Svapqz1ji73Be4sm7PHVrO/vfs4ex2oWcfrQB1H682R7dX+rduzvrR1vnwNuLN8rjiuu64Dd38mTPhym2yZsLRUlKV9rWjMu7Gd1ye642eCY3WjzTqDiLUvIvTQL8qyjMHvIr/ptTb7ZZw4AG1eis2+kZGhyMrKs1lmCo2HKaoWvI3Xgbp6ifXDe4OqVaPc1xWqZcuW+Prrr/HFF18gLi5Ofr366qt4/fXXsW3bNgwfPhxPPvmkmkMTERFVKiaTWeZUVBRUCLvOFj8AIz9gMiDsv08Qvuk1aAuzlMUFNbvDUKX1pZtH1bo0YIgNh8GHHzQQWVM9yHWzZs3wwgsvKK/F0LPdunVDfHy815+kEBER+Ys8g1HmUthDbJdvMCHUzm4J5D36c9sQufJpBJ3fqSwzh1dBZo8pyG92nU03KKJAoXpcOpFfIYaYFbkWFqLVomfPnnL4WSIiIipfodGE77aetLuaxChRHFLWx+WlI3LVZMR+e7VNUJHb8lYYxm1CfvPrGVRQwFLVYvHNN99g2rRpcnhZvV5v02qRlJSEp59+GuHh4RgyZIgry0pERBQQxPwUS/cmY+66oziVbtt/viw6DTCseTX2CvBxmrP/ImznIuW1IaEFMvvNgKFGZ8SGhQP57NZEgUtVYLFo0SIMHDgQs2fPtlnet29f+TVu3Dh88MEHDCyIiIisiPFSNhxNlZPc7U/OdqhuRGepmzp6P2GXymeu3xd5Ta9FyOElyO72OHLbjgG0qnueEwV+VyjR/alfv35lrr/ssstw+PBhZ8pFREQUULafSMP93+zAI9/vtAkqejZMwCe3dsDUy5tDTEkhWiasiddi+UvDm6NptUjPF5zKZshF6K7FImK0WZzV63mk3LISue3vZVBBlYqqEFqMAmU9IV5JR44ckTNyExERVXZHL+RgzrqjWHHgvM3y5tUiMb5PAwxtX0sOIylm0W6QEI4vt57CH1Yzb4vuT6KlgkGFbwk6vhJRq56BLuMYzLpg5DcfZZOk7fBY/kSVNbAYPHgwPv/8c3Tq1EnmVVhbtmyZXCfmsSAiIqqskjLzMW/DMfyy86zNULJ1YkMxrld9DGpWFdoSIwOJ4OG5Yc0wZWhThEaEID87nzkVPkabfbZoToqDPyvLIja+gvwm1wC6YK+WjcgvA4uHH34YmzZtwsSJE2USd506deQ/PtFF6sKFC2jSpAkeffRR15eWiIjIx2XkFWLh5hP4attpOTSsRdXIEIzpVgcj2lSHXld+T2QRcIQH61GQU+CBEpNdTAZo//4AcSumlZiTohuy+s1gUEGkNrAQ3Zy+/fZbOTrUqlWrcOrUKRiNRjm3hUjevummmxASEsIKJiKiSiO3wCgDCvGVmW9QlkcE63BHlzoYN6AxAwU/pT+3HZGrnoYu+T+b2a+zej2L/GYcPpZI+VtRWxVBQUG45ZZb5BcREVFlZTCZ8fPOs/ho43Gcy8xXlgfrNBjVvhZGd6uD2LAgtkD4I7MJkWueReh/i6CxyprIbXkLsns8DXNonFeLR+RrOP4ZERGRyqFj/zpwHnPWHsXx1FxluRjB6YqWibi3Zz1Ujw5l3fozjRaawlwlqDBXa4m03tPlnBREdCkGFkRERA76+3gq3ltzFLvPZtos79coAff3ro9GVSJYpwEiq+czCDq5Ts5HEdJ3PAyZhd4uEpHPYmBBRERkp33nsvDemiPYeCzVZnmHWtF46vIWaBjN/EK/ZchD+Jb3YIyui/wWNyiLzWEJSLltjUzODtEFAWBgQVQpAwuTyYRnnnlGzqsRERGBWbNmISEhwdvFIiIiP3MsJQezftuDpfuSbZY3rhKBB/vUR68G8YiLi5DzUZD/0Rz+C3G/PQ59+lGYQmJQUH+gDCgUHEaWyH2BxXfffSfnsKhfvz582Z9//onQ0FB8+eWX+O233/DBBx9g8uTJ3i4WERH5ifPZBfhowzH8+N9ZmaRtUSM6RM5FMbR5NehEUgX58ZwUL0F/8CdlmaYwG0GnNqCg8ZVeLRtRpQksxNwVY8eOxYMPPghftnXrVvTq1Uv+3KdPH8ybN8/bRSIiIj+QlW/Ap/+cxOf/nESe1VwUYnSnMd3r4rq2NRCsL38uCvJhJiNCdy5ExMZZtnNS1BBzUkyHMaGZV4tHVKkCi/DwcL+YpyIrK0vOuSGIrlDZ2dneLhIREfkwMaHdd/+exoKNx5GeVzwXRXiwDrd2qoVbO9dGRHBA9yIOePqkfxG58mkEJe9QlpnD4pHZYwrym48CSsyGTkT2U/Xf8YUXXsDzzz8vZ9vu3Lkz4uPjodPpLtmuZs2a8CYRVFiCCfE9KirKq+UhIiLfZDSZ8dvuc5i3/hjOWs1FoddqcF27Gnh0SDPoDUavlpGcF3Lgf4haOr7EnBQ3Qz/0ZeQXcGhgIq8EFo899hgMBgNee+21crfbs2cPvKl9+/ZYt24dBg4ciNWrV6NDhw5eLQ8REfneXBSrDl7AnLVHcPhCceK1eGY9tEU13NezHmrHhiE2MoSJ2QGgoE5fOamdJi8FhvhmyOz/Cgw1uiA2PBwoYOI9kVcCi3vuuUe2VnhaQUEBRo4ciWeffRbdunWTy/Lz8/Hiiy9i6dKlMlF7zJgx8ksYMmSIDChuuukmOVP4G2+84fEyExGRb9p+Mh1zv9mBLcfTbJb3bBCHB3o3QLNqRV1pyY8V5gJBYcpLEVRk9XlJJm3nth0LyOFjicirgcVDDz0ETxMBxMSJE3HgwAGb5WII2Z07d2LhwoU4ffo0Jk2aJLtgDRs2THbPmjFjhsfLSkREvutgcjZmrz2CtYdTbJa3rhGF8X0aoFOdWK+VjVw4J8XWOQjbuQgpNy6FOaKasiq/6QhWM5GbaMyiHVilffv2YeXKlfKG/o477pBJ3fv370e/fv1cWsiDBw/KoEIUVbznokWLZItFTk4Ounfvjvnz5ystGHPmzMGGDRvw6aefOvw+BQUGpSVGp9PCaCweCaQ0FW1T1npHl/sCd5bN2WOr2d/efZy9DtSs43WgjqP15sj2av/W7Vlf2jpfvgbcWT5XHLe8Y5xKy8Xbyw/gx39Pw/qTr2GVCEwc3BSDW1QrszWenwmuP1du+0w4ugqa3yZCk3pYvja1uh7GEeWPCsnPhAC8Drx4b6Bz8WeCI2Vzl6CgS3OpS6N6aIuXX34Zn3/+ubzZF/+IRQtBRkYGHnnkEfTv3x9vv/22y0aO2rx5swwcJkyYIPMmLPbu3StzPaxzJ8T8GnPnzpWT42m1jg0FmJ6eq/wcGxteYX/airYpa72jy32BO8vm7LHV7G/vPs5eB2rW8TpQx9F6c2R7tX/r9qwvbZ0vXwPuLJ8rjlvaMdJyCrFg03F8++9pFBqLI4pqkcF4dFBTXNYgTiZpW38GqCkbPxO8ex1os88hYt1LCDrwP2WZWatHXkg1ZKdmAZqy7wn4meDZcxXo9waxLv5McKRs7lK1apT7AgvRYrB48WLcd999MjH6hhtukMt79OiB0aNH45NPPpGtCOPHj4cr3HLLLaUuT05ORlxcHIKDg5VlVapUkd2m0tLS5GhVRERUOeUUGPH5lpP47J+TyC4oHtEpOlSP0V3rYFT7mqheNcqngziyd06KRYjYNAvagkxlcWGNrsjsN4NzUhB5kKrAQsxkLVooRAtCamqqsjw6OhpPPfUUUlJS8Msvv7gssChLbm6uTVAhWF6LRG8iIqp8Co0m/LDjDD7aeBwpOYXK8hC9Fjd1rIU7u9RBVCjnogjsOSmeuTgnBScxJPIkVf9ZT5w4IXMqyiLmtliyZAncTXS1KhlAWF6LEaIcFRMTZpNjIZqdylPRNmWtd3S5L3Bn2Zw9tpr97d3H2etAzTpeB+o4Wm+ObK/2b92e9aWt8+VrwJ3lc/a4JpMZv/x3Bm/8uR8nUou7Nem0GozqVBvj+zdCYnSoV/4XlLee/wscr2vJWAD9wruhyTqjLDK1uw0Y/BLCQmJRPBZUxfiZoA7vDdTViy7A7g2cDixE96OzZ8+WuV6M3BQTEwN3S0xMlC0mIs9Cr9cr3aNEUCFaTxzFHIuysR+lurrxx36UleU6YI5F4ORYiFy/DUdTMXvNEexPLpoU1WJQ0yoY16s+6sWHi8hDdT4Lcyxcc65cfeyQHs8g+s/xRXNS9JsBQ82uiA1h3l1luw5ctT9zLLyUYzF48GCZuH3llVciISFBLrM86V+1ahW++uorXHvttXC3Fi1ayIBi+/btspVE2LJlC9q0aeNw4jYREfmfnWcy8N6aI9hyIt1meZe6sXiwTwO0qm7fhyH5Pl3aYZiDImCKSFSW5Te5BhkwI7/RlZyTgsgHqAosxMhPYqQmMVldkyZNZFDx3nvvYebMmXKkplq1aslt3C0sLAwjRozACy+8gOnTpyMpKQkLFizg3BVERAHu6IUczFl3FCsOnLdZ3qpmNO7vUQ/d6sd5rWzknjkpwrfORn79wcgcNrd4nUaD/Kbuf5BJRG6ex0IkTn/44YdyxmuRc2E0GmVAMWDAADlalLu6QjVr1kyZx8JSDhFYiHJERkZi7NixcmQqNTiPRdk4VrW6uvHHsaory3XAeSz8cx6LM+l5eG/FQXy79SRMVp9eoqvThEFNcGXbmrJrlCvf097tOLeRa1jXo+bISuj+eAKalEPKesPN38LccIBT50rtPpzbyPm6dvU5ceX+nMfC+XksnJogL9AkJxcPU8f+tLbYj7JszLFwDeZYeObvzV9zLNJzC7Fw8wl8vf008g3FNwsJEcG4u3tdjGhTHfqLyY3uyLXhZ4K6elN77PRTRxG57kWElpiTIrfdPcjuMgEIKjuJlXMbeQ7vDdTVS6wf5l+6NcfC4vDhw3Lm7VOnTsmchvr168sWixo1ajhzWCIiIimv0Igvt57Cor9PIjPfoNRKRLAOd3Spg5s71UKYnU/SyA+YjND+8yHiV7xcypwU02FMaO7V4hGRGwILMav1iy++iG+++Ub+bG3GjBkYN26c2+ewICKiwGUwmfHTzrP4cMMxJGcVDyserNNgVPtaGN2tDmLDgrxaRnItffJORK6cBF3Sv8oyU2gcsns8g7wWN3BOCqJADSw++OADOfLTVVddhbvuugv16tWTAcaRI0fw8ccfY/bs2ahevTquv/5615eYiIgCluid+9eB85iz9iiOW81FodUAV7RMxL0966F6ibkoKDBoM44hyCqoyG1xowwqxIR3ROQfVOVYiO5OrVu3xjvvvFPqetFicfLkSTn7tj9h8nbZmKClrm6YvG0/Jm975u/NFdxVvk1HUzDrj33Yccp26NhBzavhsUFN0CQxymtJ/Ezedq6e7WI2Q/fVjdBknIJx+Osw1+mu6jBM3vYc3huoqxddACdvq2qxSElJUUZlKk3fvn3l0LP+hhPklY0JWurqxh8TtCrLdcAJ8nznOth3LkvORbHxWKrN8g61ouVcFO1qFY0y6KokazXbM3nbuXoubU6KkIM/I6ez7dD0mn5vIKZaItIyCwGVx2fytucE0meCI/s4k5ztr/cGbk3eFhPQiYnwbr311lLXi0nqxOR1REREZTmRmou5645i6b5km+WNq0TgwT710atBvDL5KgUIY37RnBRb3oPGmA9DfHMUNByqrJbdnnQid6bQq8UkInVUBRYvvfSSzK14+OGHcc8996BRo0ZyBmwxn8Vnn32G5cuXywnzTp8+bbNfzZo1VRaTiIgCxfnsAny04Rh++O8sjFaTUdSKDcW9PephaPNq0ImkCgooQSfWIHLVZOjTjyjLwrd/YBNYEFElDCyuvPJKmWAnJqX7888/bdZZUjZEwFHSnj171JaTiIj8XFa+AZ/+fQKfbzmFPKu5KMToTmO618WYvo2Qm5Xn1TKS62myky6dk0KjQ277e5DdeQKrnKiyJ2+/++67qpqnfX0IWiZvl40JWurqxh8TtCrLdcCZt+Gxus4vNGLx5uOYu/owUnOKu7iEB+swtld9jOnVAJEhepdcX0ze9gy76lnMSbH1Y2hXToUmP6N4ce1uMjkb1VqqP7azZVO5D2fedr6uXX1OXLk/Z94uG2feVoEzb5eNCVrq6sYfE7Qqy3XA5G317K070c3pt93nMG/9MZzNzFeW67UaXNeuBu7qVlfOnK3mnDhbNke3Z/K2Y/WhKchEzP9ushk+1hQSi+yeYk6KG8udk8Kfk3Yr2oafCa4/J67cn8nbXp55m4iIqCTREL76UArmrD2CwxeKP9hFO/fQFtVwX896qB0bxooLYObgKJjCE5XXuc1vlEEF56QgCmwMLIiIyGW2n0yXQ8f+e7q464vQs0EcHujdAM2qRbK2A5GlV7VVN+msPi9Bm5OE7F7PorBm2UPUE1HgYGBBREROO5icjdlrj2Dt4RSb5a1rRGF8nwboVCeWtRygtGlHELV6CnJb3YKCRlcoy03RtZF2/c82wQYRBTYGFkREpNqZjDx8sO4oftudBOuRQOrHh8kWiv6NEzgXRSWZk0KXsg+pdfrBHGzVKsWggqhSYWBBREQOS80pwIJNJ/Ddv6dRaCwOKapFBuPenvVwRavqMkmbApPmyCrE/TYR+rTDVgu10GYch7FK6aM9EVHgUzXcrMW+ffuwcuVKORHeHXfcgfDwcOzfvx/9+vWDP+Jws2XjkHLq6obDzdqPw8165u/NWdn5Bnyy4Rjmrz2M7HyjsjwmLAjj+jbEbd3qIjRIp+rYHG7W9fXhcllJ0C1/Ftqd39jMSWHqdj9MfZ4ErFsrKuEwoxVtw88E158TV+7P4WadH25WdYvFyy+/jM8//1yO/iHmtBg2bBgyMjLwyCOPoH///nj77bcREhICf5Kenqv8zKEFA3eYUUf24dCCzte1q8+Jq/bncLOOKTSa8MOOM/ho43GkWM1FEaLX4uaOtXBHlzqICtUjLzsfaqe443Czrq8PlzEZEbp7MSI2vAJtQXFifmH1zsjsN72olUIUNcf58vIzwbX14U6B9JngyD4VbRfrxPpKOdzsokWLsHjxYtx3330YOHAgbrjhBrm8R48eGD16ND755BPMnz/f5yfEIyKi8pnMZizdm4y5647iVHpxyKDTANe0qYG7e9RF1Uj/eohEjgvf/BoitryrvDaHxSGr++QK56QgospFVWDx5ZdfyhaKCRMmIDU1VVkeHR2Np556CikpKfjll18YWBAR+SnRGr3haCpmrzmC/cnZNuuGt6qOsV1ro158uNfKR56V1/oOhO1YAG1hNvKa3wDdsKnIK+T5JyIXBBYnTpyQORVl6dy5M5YsWaLm0ERE5GU7z2TIuSi2nEi3Wd6lbiwe7NMAvZon+mzXDHIBsxna7DMwRdZUFpkiayCr/yvye2HN7oiNCAd4DRCRKwKLuLg4nD17tsz1Bw4cQExMjJpDExGRlxy9kIM5645ixYHzNsubV4uUc1F0qx/HcxPgtOlHEbX6GejO70HqLStgDin+LM9veq1Xy0ZEARpYDB48WCZuX3nllUhISJDLRAK3sGrVKnz11Ve49lr+AyIi8gfnMvMxf8Mx/LzzLExW4wTWiQ3FuF71MahZVWg5H0ElmJPifYRveVfOSSFEbJqFrL7TvF0yIgr0wEKM/LR582aMHDkSTZo0kUHFe++9h5kzZ2Lv3r2oVauW3IaIiHxXem4hFm4+ga+3n0a+oXhYxoSIYNzdvS5GtKkOvY6JuYEu6OQ6RK6aDH3aIWWZMbIGCmr39mq5iKgSzWORm5uLDz/8EEuXLpU5F0ajUQYUAwYMkKNF+WNXKM5jUTbOY6Gubjhmuf04j4Vn/t6E3AIjFm08hnlrDiMjz6AsjwzR457eDTC6Zz2EB5f93Mld5eM8Fp6p5wrnpOg6Dqa+k8qdk4KfCWXjZ4JrcB4L/5zHwqkJ8gJNcnKm8jPnsbDFsarh0vGofXms6spyHVTGeSwMJjN+2nkWH244huSsAmV5sE6DUe1rYXS3OogNC6rwOO4qH+ex8Ew9w2xC6C4xJ8WMEnNSdEJmvxl2zZwdSP8LHNmHcxs5X9euPieu3J/zWHhpHgvBZDLh5MmTSEpKksMSlqZLly5qD09ERC4i/kf/deA85qw9iuOpxROBajXAFS0TcW/PeqgeHcr6riQ0uRcQsWE6tAVFD9NMITHI7jEZeS1v5pwUROQUVYGFyKMQORTHjx8vdb1lNu49e/Y4VzoiInLK38dT8d6ao9h9trhFVujXKAH3966PRlUiWMOVjDm8KrK7T0LU6inIaz4KWT2nwBxWNBALEZHHA4sXX3wRycnJMpeidu3a0Ons63dFRESesfdcJmavOYqNx4onMRU61IqWc1G0q+V/eXCkgtmM4MO/obBWT5hDi4cLzmt1OwxV28JQvSOrlYi832LxwAMP4J577nFdSYiIyGknUnPx/rqj+HNfss3yxlUi8GCf+ujVIF4ZHpwqw5wUUxB8fCVyW90mJ7grXqljUEFEvhFYJCYmspWCiMiHnM8uwEcbjuGH/87CaDUZRY3oEDkXxdDm1aATSRVUOeak2DYX4f+8o8xJEbbrM+S2HQNjfFNvl46IApiqwEJ0gXrzzTfRr18/NGrUyPWlIiIiu2TlG/Dp3yfw+ZZTyLOai0KM7jSme11c17YGgvWci6Kyz0mR1ftFGOOaeLVsRBT4VAUW11xzDX799VdcffXVqF+/PuLjL21aF68XLlzoqnISEZEVMaHdt9tP4+NNx5FuNRdFWJAWt3WujVs710ZEOXNRUGDR5CQjct3LCN3/vc2cFLltxyKn62MwlzMnBRGRq6j61Hnttdewdu1a6PV6ZGVlycnyiIjIeSazGTkFBvldW0ouhOjm9Nvuc5i3/hjOZhZ1cxH0Wg2ua1cDd3WrK2fOpsojZN+3iFzzPLT56cqywsSOyOz/il1zUhARuYqqCfJ69OiB1q1by+5QkZGB8xSEM2+XjbOsqqsbzrxtv8o+8/aeMxn4ZMNR/LLjLAqMJgTrtLiybXWM7lEfLWpEF81FsTcZry/bjwNJWcr+Iva4qk0NPDKwCerGh8MTOPO2unopa72jy0vSbFsI/W8T5M/m0FgYBzwPc/vb3TonBT8T1NUNPxM8d42p2d/efTzxmeCvM2+rarHIz8/HkCFDAiqoENLTi1teOPO2Lc6uWTbOvO0alXnm7a82HMVzv++FaJ8wXnzUI4KL/20/jR+3n8aYbnWx+XgadpwuniVZ6NkgDg/2boCm1Yr+F3tqxnbOvK2uXspa7+jyS9S/DrHVF8MYU79oTorwKkB6HtyJnwnq6kbNOnfWtbN4Hairl1g/vA7cOvN27969sXHjRowaNUrN7kREdJFoqRBBhdVATgpLkPHhRtvJSFvXiML4Pg3QqU4s6zFAaDNPQZuXYrswNxT6LKsAwWyG7tx26LNOIbvH08XLNVqkXfMloOfs6UTkXaoCCzGHhRgZ6tFHH8WgQYOQkJAg8y1K6tKliyvKSEQUsET3J3sHga0fH4YHejdA/8YJnIsikKSfRPzivsrQsNaKp7SzVVBvAAprditewKCCiPw1sBgxYoT8/scff2DJkiWXrBd9gcWoUHv27HG+hEREAUokaIucCkvLRHnEHBSf39EJQToOHRtwci6UGlSUJ/jQr7aBBRGRvwYW06dP59MyIiIVDCYzDp/Pxs4zGdh2KkPmUthDjAYlvuzMn6MAltnjGeR1GOftYhARuSawGDlypJrdiIgq5YzY/5zJxMaD52UwsftsJnILHR/ZI0inQQgnuiMRnNbpXTQcGBGRPwYWf//9t5xhW0yEZ3ltD+ZYEFFlUmAwYX9yFv47k4mdpzNkIHE6o/wuLuL2sKKeUDoNMKx5NbYUExGR/wcWt99+O1599VVcddVVyuuSM21bY44FEQU68X9OTFD33+kMHEjJxZajKdiblIXCChImEqNC0KZGFFrXiJajO8VGh+GG+RtLHRVKeS8AN3Ws5fpfgoiIyNOBxYwZM9ChQweb10RElUluoVF2Y9opWiPOZMhWiQvZBeXuI7outawehc7149EkPkwGElUjQy4Zm/yl4c0vmcfC0lIhXor1lrkqKICYzQg6vRGIT/B2SYiIPBdYbN68GQ0bNkTt2rXl62uvvdY1705E5KOjNR1PzZUBhAgkdp/Lwr5zmeW2Kgh148KU1og2NaLRqEo49DpthRMbDW1RDQ0SwvHl1lP4Y2+SbPUQORWi+5NoqWBQEXj0Z7cicv1UBJ3ZDMOQV7xdHCIizwUWP/zwA3r27Il27dq55l2JiHxIRl4hdonWiNOZ+O9Mhvw5I89Q7j6RITq0rl7Unal7k6qoFxWM2LAg1WUQwcNzw5phytCmCI0IQX52PnMqApA2/RgiNs5E6MGflGW6zXO9WiYiIq+OCkVE5M/DvR46n43DB85j86ELslXiaEpuuftoNUCjKhEyiLC0RtSLD4P2Yq5ZRS0SjhDHDA/WoyCn/G5W5F80eanQLpuO+L/nQ2MqVJYbYhsCne6CfvmzXi0fEZErMLAgooAf7nXXmQzsOF2UGyHyJPIM5Q/3GhcWhDY1i1ojRBDRvVk1GHJ5o08qGPMR9t9ChP/zNrT56cpiU2g8srtORF7LWxCrz4R55VS7Jskz60LkvkREvkhjFkObVKB58+a48cYbbRK4HZmh218UFBiUrgc6nRbGCiauqmibstY7utwXuLNszh5bzf727uPsdaBmHa8DdUS95eQbsPtMBrafSMO/J9Kw/WQaTqXllbufyGVoUSMa7WvHon2dGLSvE4vasWE23ZDU/q3bs760db58DbizfK44rqPHcOv/gtxU6BcMgCbtmLLIrA+Fqev9MPV4BAiNLt4v5bicgdv2eBoYS44yFp4AxBTlO3oTPxPU1Q0/EyrHvYHOxZ8JjpTNXYLsnJ3V7sCivOFlA2W42eTkTOVne7o2VLRNWesdXe4L3Fk2Z4+tZn9793H2OlCzjteB/f9nzmTkKyM07UnKkkFFRcO9Vo8Kkd2ZujZKQKPYUDSrFlnhxHNq/9btWV/aOl++BtxZPlcc19FjuPt/QfSvoxFydBnM0MDc5kakdngMpqiadh3bl68Dfiaoqxt+JlSOe4NYF38mOFI2d6laNcq1XaFuuOEGtG/f3pkyERGpllNgxJ5zmXLeCDFSk0iyTskp7qte3nCv1vNGWIZ79fY/aQpAaccBc4LNrNjZPSZDYyxEdo+nEdmkK0y85ogogNkdWHTu3FmZII+IyO3DvabkyuDBEkSIhGu1w70SuZMm9wIi/n4T+l2fIXjweyhofKWyzhjfFOlXL+YJIKJKgcnbROR16bmF2JGcjI0HkmW3pl1nMpGZb/9wr61rRqNns2rQFJS/D5FLGXIR9u9HCN86G9qCoq60kRtmIKXBEEAXzMomokqHgQUReWW4V0tuxM7TGTiW6txwr0JseDDSGFiQJ5hNCNn/PSI2zoIu63Tx4qAI5DW7DjAbeR6IqFKyK7AQM23XrVvX/aUhooAc7lUEDzKIsHO41/jwICUnQgQRLapHIiKYz0HI+4JOrkPEupcRdH6nssys0SKvxc3QD3oGOcaikZ6IiCojuz6pZ8yY4f6SEJHfKzCYsC8pS8mNEIGEGLmpPHqtRo7M1Kl+PJrGh6F1zSjUjA7lrNPkW4yFyghP1vLrDUB2j2dgTGiG2KhwgMnZRFSJ8REgEalScrhX8V0EFRUN91ojOgStqkejTc2ibk2W4V45ShP5NF0QzPpw5WVhlVbI7vksCuv09mqxiIh8CQMLIrJ7uNd9Ry5gw/5ku4d7Db043GtRXoT4HoUqF4d7JfJphTmALgTQFk8Kld3jKeiTdyCny6PIbzoS0HDEMSIiawwsiMhlw73WixNdmSxBhBjuNUJ2dSLyGyYjQvd+hfBNryO7+yTkt7iheFV0XaTeupoBBRFRGRhYEJEc7nXX2aLuTPYO9xoVokcrmVxdFES0qh6FmLAg1ib5J7MZwcdXIGL9NOhT9slFEZtmIb/xVUBQWPF2bKUgIioTAwuiSsaZ4V4tCdZipKa6JYZ7JfJXuuRdiFw/FcEn19gsN1RrB01hFszWgQUREZWJgQVRgDuflX+xO5Njw722sQz3WjMaLRKjEB6sY4I1BRRt1mno1ryJuB1fQoPifn6F1dohu9dzKKzZzavlIyLyNwwsiHwstyGnwCC/q2kNUDvca/PESKsE62g5cpOGrREUqMxmhG9+DeHb5kJjLP77MEbVkQnasvsTuzwRETmMgQWRD9iflIUvtp7Ckr1JcrjWIJ0GQ5tXw80da6FptUiXD/dqPflc04vDvRJVGhoNtDlJSlBhColBTqeHkdt2dNFIUEREpEqlCiyWLVuG5cuXc8I/8ilL9iThud/3QrRPWGICERz8vvscftt9Di8Nb46hLarJ4V73nMvEf6eLR2ricK9EdjCLPyyzTStETtfHEXLoN5jb3YLUNg/AHBrHqiQiclKlCSxmzpyJFStWoH379t4uCpFNS4UIKkobxtUSZDz7217M23AUJ9PyONwrkYP057YjYv3LyG90JfLa3qUsN0UkIuWOTYipVg1mzpZNROQSlSawaNu2Lfr164cff/wRgdi3nrxPnDuR41BgNMnv+UYTLhSacD41p3i5ZZ3BBH1IED7bcNQqZbR0Yv3x1LxLlnO4V6KyaTNOIGLjKwg98D/5Wp+yH/nNroM5JLr4byu49G6GRESkTkAFFl999RU+/fRTm2UfffQREhMTMXz4cGzatAmB0LeeSs83KDCalRt66xt88T1feS22M0EfrEdqZh4KS6wX3wutjmPWaJCdV3jJccQ28mfxZTIhv9Akh3F1p8ZVwtG2ZoySG8HhXolKkZuGiHUzEbbjY2hMBcpiU0gstFmnYLQKLIiIyLUCKrC48cYb5Veg9a33h5t6eTNucwNv++S+6HXxE/2iG/ii79ogPTKy8pBvEMexDQws2xUdx7ZFoOSNfqD7+JYOCA3SebsYRL7JWICwnYug3/I2gnJTlcWm0Hhkd5mAvFa3ATpO4EhE5E4BFVgEWt96sb5BQni5LRfipl48Kbe9oS/x5N6qG47ylP1iIGC9neVG37q7jnwybzTBKB4E5htsAgLr96ssxERxwTqtHEUpWK9FkE6LsGAd9BqNXB6s1xSv12kRGR4MGE1y26LlGrmPWB8VGYJpv+2F0Y6WDtGSxZGbiEoXfOg3RK6fBl3GMWWZWReC3HZ3I6fjgzbdn4iIqJIGFgUFBRg5ciSeffZZdOtWNFFRfn4+XnzxRSxduhShoaEYM2aM/PI3ovtTRZkU4n7zoe/+Q82YUCUgKHqib/vkvrLc1ov6Ejfolpv2opt1DcKC9dCLG37Lcss6sa1Oq9yUW5aLn2MiQ2EsMCjbieNYBwyW41SJj0B+dr6yjU6ruWR+h9jYcKSVkfxZ0bqtR1JkC1V5sZlOAwxrXo3zShCVIejMZpugIq/Zdcju9iRMUbVYZ0REHuSzgYUIICZOnIgDBw7YLJ81axZ27tyJhQsX4vTp05g0aRJq1qyJYcOGVXhMEZxYAhRvMpnMMqfCngf9YjjRioYU9QRxL2190x4ibsQvPrG3uWmXN/JFT+ZL3uiXDAgs28fFhKEgr1D+XGpAcPF1aTf1Fd28l8XefWJjQpFmLn+WameIXBrR7a084jK5qSNvkIjKktP5EYTu+RqGqm2gGToVmWFNWFlERF6gMYu+ND7m4MGDMqgQRdu3bx8WLVokA4KcnBx0794d8+fPVwKEOXPmYMOGDZckbatRUGBQblx1Oi2MxvJvKCvapqz1omtR6xeWOlQ2cYOt3GTLL13xTbjVMsvNe0iQZZ3tMmVfy81+iWUhVsuKtinaPzRYJwMid7Cnrl29v737OHsd2LPu5x2n8fi3O+S1Z90tSgRS4m/gtevb4qq2NeELnD1X7jy2o/s7sr3av3V71pe2zp317AruKl+Fx825AO3aV4HoWjB1f8h2XfoJILo2dHqdW64Dd34mOLrcFwTS/wJf+0xQWzZv4HWgrl50fngdBNmZ4+mTLRabN2+WgcOECRNs5p3Yu3cvDAYDOnTooCzr1KkT5s6dC5PJBK3WudmD09NzHXqiXdE2Za2Pjg6TT+PtSTgW2619uJfTv5vDRLxpMMJoMEL8BsEqWgXspabFwWMtFk5eB/as61M3Fp/e1hFfbj2FP6xGBxPdn0RLhcixcVfde/pcufPYju7vyPZq/9btWV/aOnfWsyu4q3xlHteQh7AdCxC+5V1oCzJhCopEet1rYA6vYrVRApCe67brwJ2fCY4u9wWB9L/A1z4T1JbNG3gdqKuXWD+8DqpWjfLfwOKWW24pdXlycjLi4uIQHBysLKtSpYrsNpWWlob4+Hj4A622aEhZe/vWezyoII8TwcNzw5phytCmCI0IkXkdpXX7IqpUzCaE7P8RERtnQpd1SlmsMRsRlPQvCuoP9GrxiIjIDwKLsuTm5toEFYLltUj09ifsW0+lEZMjhgfrUZDjX9czkasFnVyHiPVTEZT8n7LMrNEir8WNyOk6EaaI6qx0IiIf41eBRUhIyCUBhOW1GCHKWTExYTY5FqLZqTwVbVPWerG8a9Nqsu98RX3rxXa+wJ768Nax1exv7z7OXgdq1rmzrp0VSNeBI9ur/Vu3Z31p63z5GnBn+eRxDSegW/4CtAeX2KwzNRoE44AXoK/WEtFeuA7c/ZnA/wWOnxNnzpXaffiZ4Hxdu/qc+OJ1oHPxZ4IjZfM2vwosxAzaqampMs9Cr9cr3aNEUBEd7fw45Z7KsWDfesfqsSLsT+s5gdSfljkWvpljUbDlS0RYBRWGhJbI6vUsCuv0KVrg5P9ltdszx8K5enYEPxM8V9fO4nWgrl5imWPhG1q0aCEDiu3bt6Nz585y2ZYtW9CmTRu/zUNg33oiomK5He5H2K7FMGv1yO4+CflNRwJazjhPROQP/KrFIiwsDCNGjMALL7yA6dOnIykpCQsWLMCMGTPg79i3nogqFZMRoXu/gaYgA7nt71UWm4MjkX7VpzDENgaCwrxaRCIiCoB5LKw1a9ZMmcfCksAtAgsx83ZkZCTGjh2L0aNHu+S9PDWPha+OUVwejlWtrm78cazqynIdcB4L750rzaHl0P31PDRJu2HWh8Jw/99ybgpXXF/uug74meBcPTuC81h4rq6dxetAXb3oAngeC58PLDwpOTlT+Zn9aW2xH2XZOGa5azDHwjN/b66gtny687sRuX4qgk+stlme1ftF5LYb65LfmzkWnsHPBHV1449968vD60BdvcT64XXg1/NYEBFR4NBmnUbEptcQIro+ofhZVmG1dsgWidk1u3u1fERE5BoMLIiIyC00BVkI2zoH4f/Og8aQpyw3RtVGdvenkN/kakDjnwNvEBHRpRhYEBGRW4Tu/hwRW95RXptCYpDT6SHkthkN6J2fe4iIiHwLcyysMHm7bEzQUlc3/pigVVmuAyZvq2d33RXmQj+3K5CVBFPnsTD1ehwIj3fJOXHVMZi87Zl69uSx1ezvqeuAnwmuPye+eB3omLxNApO3y8YELXV1448JWpXlOuAEeeqVVnf6pB3QJ21HXus7bJYHndoAY2QNmGLqu/ScuOoYnCDPM/XsyWNz0lTP4XWgrl5i/fDegMnbRETkdtqMk4jYNBOh+3+Qk9oV1u4NY2xDZX1hrR48C0RElQSz5oiIyGGa/HRErJ+G+M/7yaBCLjMZELbjI9YmEVElxeRtIiKyn7EA2r8/RfzqWdDmpSqLTaFxyO4yAXmtbmNtEhFVUkzetsLk7bIxUU9d3TBRz35M3vbM35tqZjM0+36G7q+XoEk9XLxYFwJT13Ew9XwUCI1x6i2YvO36+nAXfiaoqxt+JnjuGmPytmtx5m0VmLxdNiZoqasbf0zQqizXAZO3HRNy4GdEL73fZlle05HI7vYkTNG1HTya8+fEVcdg8rZn6tmTx2bytufwOlBXL7F+eG/A5G0iInKZ/IbDYIhtCH3aYZjq9UZ618kwVGvLGiYiIgVzLIiIyIYmLxXBJ1Yjv8k1xQt1QcjqOx0aYx7C210FQ3oua42IiGwwsCAioiKGPITt+BjhW96FpiAThrgmMFZpqdROYZ3e8nu4RsMaIyKiS3C4WSKiys5sQsj+HxD/eX9EbpgGbUEGNDAjYtNr3i4ZERH5EbZYEBFVYkGn1iNi3VQEJe9QlpmhQV6LG5DT9XGvlo2IiPwLh5u1wuFmy8ahBdXVDYcWtB+Hm/XM35vi/D7o/noR2gN/2Cw2NRwA44AXgcRWZe7qrvJxuFnP1LMr8DNBXd3wM8Fz1xiHm3UtDjerAoebLRuHlFNXN/44pFxluQ4q83Cz+rNbEPv9SGjMRmWZIaE5snpOQWHd/hXu767ycbhZz9SzKwTS/wJH9rFnO34muIY/XwexHG6WiIgqC0NiBxlIBJ3fBWNEInK6PoG85qMArc7bRSMiIj/GHAsiokBmMiLo9EYU1u5VvEyjRXav5xB05m/ktL8XCAr3ZgmJiChAMLAgIgpQQSdWI3LdVOgv7Ebqdf+DoXonZZ0INGyCDSIiIidxuFkiogCju7AHMT/fhtifbpFBhRC5fipgNnu7aEREFMDYYkFEFCC02WcRvuk1hO79Ghpz8WgqhVXbIrvbEwAntiMiIjdiYEFE5O8KshG+7X2Eb/8AGkOustgYWQvZPZ5CfpNrZF4FERGRO3EeCyucx6JsHLNcXd1wzHL7cR4LlXWRfhL6jwdBk52kLDKHRMPU6zGYutwL6EN97ly587iOHsPe7e3ZrqJtylrv6HJfwM8EdXXDzwTPXWOcx8I781iwxcJKenquy8aqLm895y9wrB4rwjHLPSeQxq4PmHkszHGIja6HoOwkmLV65La+AzmdH4U5LB7IEh/KvjnfhLuO667rgJ8JztWzI/iZ4Lm6dhavA3X1EuuHc1xVrRpl13YMLIiIvEm0OCSfsl2WGwp9Vp7NIlNoPExRtaBLOwxjbMPiFRqNnNhOdIPK7v6U7ToiIiIPYmBBROQl2sxT0C/uizhj/iXr4kq8NmuDkV9vAEKP/IG0q79EYZ3eyjoxjGzGsHkeKDEREVHZmM1HROQl2rwUaEoJKkqjMRXIoEKIWP8yYDXqExERkS9gYEFE5EdMIbHIbz6KgQUREfkcdoUiIvITuc2uQ3afl2AOifF2UYiIiC7BFgsiIj+R1+5uBhVEROSzGFgQEXmDsRDatCOseyIiChjsCkVE5CmGPASfWIOQw78j+MhSm1myiYiI/B1n3rbCmbfLxllW1dUNZ1m1X8DOvJ2fAd3h5cCen6E5+Cc0hdlQq3DMCqBGO3gbZ95WVy+ceTvwZ1yuaBt+Jrj+nPjidaBzYr2j/yc8hTNvq8CZt8vG2TXV1Y0/zq5ZWa4Dt868bTYhdM/XCBYtEyfWyKFiSzIFRaAwsRNCTq62u8xZWXkw+MC1wZm31dWLo3/z/F/guevTUzOw8zPB9efEF6+DWM68TURELqPRImz7XOhTD9osNoXGIb/BEBQ0vBwFtXtBn3oAIV/bH1gQERH5MuZYEBGpJJKvRb6ELusw0Pc1m3X5DYdDv+VdGCOqAy2uQmatQSis2Q3QFv/bNYXGw6wLsWuSPLGd2J6IiMhXMbAgIrKX2QzdhT0ymBBf+gt7lVXaduNhiqmvvM5reSsK6g+GIbE9YuMiUVhKs7kpqhYM9/+NrKRTNssjI0NltyebbUPj5fZERES+ioEFEVF5zCZoTv2NiH9/QMih36HLOFbqZsGnNiDPKrAwRdeWXxWKqQ2DuURLRGy4T+RSEBEROYKBBRFRWUxGxC/uK4OJ0v5ZFiZ2lF2eQtpfizxtddYjERFVagwsiIgEQ55MtjZUbV1cH1odDPFNlVYKs0aHwprdkd9oOAoaDIUpsoZcHhIbDrCFgYiIKjkGFkRUaWkKshB8bEXRsLDHlgMaHS6M2Q7ogpVt8ptcjSC9Fll1h8icCXMYE6iJiIhKw8CCiCoVTV4qgo/8Cd2JpUg4vOKSEZmCTq1HYd3+yuv8ptcirOutyGeLBBERUbkYWBBR4DMWInT353Ikp6BTG6AxGy/ZxBQSi4IGQ2AKq+qVIhIREfk7BhZEFPi0eoRvex+6zJM2i43hiShoOEwmYMs5JnRBXisiERGRv2NgQUQBNMfE3qIJ6zKOI3Pgm8XrNBoZPIT/Ox/G6Hoy+Tq47QikRrSUs2QTERGR8xhYEJH/MpugP7e9aLK6Y0sRn3JIWZXd7Ull1CYht+1dyGs+CsaEFjLQCOJITkRERC6lMZvNZtce0n8VFBig0WjkzzqdFkajqdztK9qmrPWOLvcF7iybs8dWs7+9+zh7HahZx+ugAiYDNMc3QLPvF2j3/QJN5plSNzOMmA9zq+tUnTdHt3VmfWnrfPkacGf5XHFcR4/hqf8F5a3n/wJ158SZc6V2H34mOF/Xrj4nvngd6Fz8meBI2dwlKEhn13YMLKwkJ2cqP8fGhiOtglFgKtqmrPWOLvcF7iybs8dWs7+9+zh7HahZx+ugHMZ8xC/qAV1O0iWrzBpt0RwTDYejoKGYY6Km6vPm6LbOrC9tnS9fA+4snyuO6+gxPPW/oLz1/F+g7pw4c67U7sPPBOfr2tXnxBevg1gXfyY4UjZ3qVo1yq7t2BWKiHxTQTb0aYdgqNa2eJkuBEYxYd3FwMKsDUZBnT4oaDgcoe2uRnphuPfKS0REVMkxsCAi35pj4ugyhBz6HcEnVsEcHIULo7fIGbAt8ppeWzQ0rJj9ut4AuY0QGsHZr4mIiLyJgQUReZU2+xyCjyyB7vgSJBxdazPHhCY3H0Fn/ykaCvai/BY3yi8iIiLyLQwsiMjzDHkI+++Tognrzm4pdRPLHBOm0HiPF4+IiIgcx8CCiDxPG4TwbR9Am5tss1jOMSEmrGt0OQyJHTjHBBERkR9hYEFE7mE2Q59UNMeEJucCsga+XrxOq0N+w6EI2/UZDPHN5EhOwe1HIi24gZxjgoiIiPwPAwsich2TAUFnNiP40O8IOfIHdFlnlKFgs3s+A3NYcbemnA7jkNv+XhhjG8rXwZywjoiIyK8xsCAi5xjzEXxiLYIPi2BiKbR5KaVuFnRuKwrqD1Jem2Lqs+aJiIgCCAMLIlJNU5CF+IVdoS3IuGSdWRukzDGR32AIzGEJrGkiIqIAxsCCiOyeY0Jzei8Q3lxZZg6OlBPWac/+U/RaHybnlpCzX4s5JkKiWbtERESVBAMLIqpgjomlRcPCnloPRFQDbt9kk2Cd1+x6GGPqFwUTdfsC+jDWKBERUSXEwIKIbGgzjiPk8B8ymNCf+QcamItXZp6GPulfGBLbK4vyWt8mv4iIiKhyY2BBREBBNsJ3fChHcwo6v7PUGjFG1QFaXgVTaBxrjIiIiC7BwIKIAF0wwrbPgzY/3aY2DHFNkd9ouEzANlRphdi4CJjSclhjREREVPkCi/z8fDz55JO4cOECCgoKMHnyZLRvX9yNg6jSMBkRdPZv2SqhMeQg67JXi9fpglDQYAhC936DwmrtivIlGg6DMa6xN0tMREREfiTgA4tvv/0WDRs2xNtvv43Dhw/j6aefxldffeXtYhF5bo6Jk+sQLHImjiyBNveCXGzWBiO757M2ozZld34E2V0fhymqFs8OEREROSzgA4trrrkGmosj2BiNRgQFBXm7SETuVZiD4OMrEHLodwQfWw5tQWYpG5mhT/4PhbV7KUs4YR0RERE5I2ACC9EK8emnn9os++ijj5CYmCh/TklJkV2ixBdRoNLkpiBhUVdoDHmXrCuaY+Kyi3NMDOQcE0RERORSARNY3HjjjfKrNEeOHMHDDz+MCRMmoEePHh4vG1FZtJmnoM1LsV2YGwp9lm1gYAqNv6SLkiY7CbrMkzBU76gsM4fFwxDbWBnZyRQSg4L6g4qCiTr9gCDOMUFERETuETCBRVnOnDmD+++/H7NmzULbtm29XRyiYuknEb+4LzTG/EtqpeSArmZdCFJuXQ2YTVZzTPwNY0w9pN66xnbCupY3wXBhrwwmCmv1kCM+EREREVXawEKM4DRy5Eg8++yz6NatmzLC04svvoilS5ciNDQUY8aMkV/lmTNnDnJycvDqq0Uj4MTFxeGdd97xyO9AVK6cC6UGFaUR20X/dCuC0g7aLNenH4UuZR+MCc2VZXltRrPiiYiIyON8MrAQAcTEiRNx4MABm+Wi1WHnzp1YuHAhTp8+jUmTJqFmzZoYNmxYmcd6+eWXPVBiIvcrGVQY4prIVglzcPHITkRERETeojGbzWb4kIMHD8qgQhRr3759WLRokWyxEK0O3bt3x/z585UWDNEasWHDhkuSttUqKDAoI0jpdFoYjaZyt69om7LWO7rcF7izbM4eW83+9u7j7HVgs078qYkRmvIygLw06M5shfbXRx0qt6lGB5ibXQlTsyuAKk3haYF0HTiyvdq/dXvWl7bOl/8XuLN8rjiuu64DfiY4V8+OqDSfCXau8+X/B7wO1NWLzg+vg6AgnX+2WGzevFkGDiLR2noiu71798JgMKBDhw7Ksk6dOmHu3LkwmUzQarVOv3d6eq7yc2xsONIqmGG4om3KWu/ocl/gzrI5e2w1+9u7zyXbGfOhyUuHtiADmvx0OVN1hD4feSlJ0OZnILfNnTajLcUd/wnaVTPlthqxj1n9P4W0yz9BYYNBVgs8f60E0nXgyPZq/9btWV/aOl/+X+DO8rniuO66DviZ4Fw9B+xngoPbqFnny/8PeB2oq5dYP7wOqlaN8s/A4pZbbil1eXJyssyPCA4uTkStUqWK7DaVlpaG+Ph4D5aS/JbJCI1oNUhNgv78OWjyRYCQJoMCefOfn4GCuv1hqNlV2UWbcRKx310tg4iyciIsf275jYbDaBVYiEBEl3HMJUU3RxYNnUxERETki3wusChLbm6uTVAhWF6LRG+qJER3IkNu0U3+xUBAk5yPkJSiIKFoeQZMkTWQ2/5em11jv7kSQUnbyxx5SXmLoHCbwMIcFAZdTpJdxRNlshEWD1NILMwhMXLoV9GaUfRzNIJ1Juj+4yzwREREFBj8JrAICQm5JICwvBYjRLlCTEyYTY6FaHYqT0XblLVeLtekyFGBbJYXaBBrLJHyEp4AxNSGt9lTH3YzGYHcVEAEAblp0KZlIi4nVXYxgvgSN+e5afIm3XjVHEAfouyqXTkNunWvX3LIkunLppqdENLfNn9BVyIwLUuYJhchF39X8XvHVKsOc1RNIKwoQECo+IqFOTRafteExcIkEqhDYxFZtzUQVlxP2oRrYGx+lfJac/FLdNzTJO0AHAgsIiNDAVedA1+4Dlx8bEf3d2R7tX/r9qwvbZ0769kV3FU+VxzXXdeB2z8THFjuCwLpf4EnrwM163gd+N91oHPxZ4IjZfM2vwksxAzaqampMs9Cr9cr3aNEUBEd7ZpRcTyWY6FJgf79LqV2q9GWMX9BycnRPK2030WbefpiNyIRBGRYtRgU5RNYWg9y292Nwtq9lP3057Yh7turbI9VxvumdHsO5vCqyuswUygi7SivKSftkvJGxjSFvrBQthroI+ORr4mUPystCcExMIXGwBhTH6aL+8rfO7MQuGOzfXUjTml+jn39KI3mMn/v0mRl5cHg5X62gdSfljkW6jHHQl29MO/OM9cXcyw8J5A+ExzZx9N5dwJzLFysRYsWMqDYvn07OnfuLJdt2bIFbdq0cUniti/PXyBmZnZXYKHNOg1t1hmbrkWlBQfaul2BDo/Z7Bv77VXQ5Zyr8D0K6g20CSzkU397y5efAaNVYGGMrY+CWj2LA4LgGITEVkGOKexiN6NY2c3IHJZwybGy+r9SXPbYcGT5aDIcERERkT/ymxaLsLAwjBgxAi+88AKmT5+OpKQkLFiwADNmzEBlp8lLhT55V1HrgRh5KM86KCgauUi2KBjykHrTUpt9w7e8h7Cdiyp8D1NwcXckmwDBjsBCU2Cbd2AKjUd+vYEXA4FoBEdXQa45XMk9EN8tOQmmiOo2+xY0HC6/rAXFhiPPH4OE8ATZImVPkCm2E/VGRERE5Kt8bh4La82aNVPmsbAkcIvAQsy8HRkZibFjx2L0aNfNMuyxeSySdkA7v7/d5TLVLXrar8lLkzkIxqtmw1y/j7Jec/gv6L+43q5jFU46Y5uzsOJl6Na/WeF+5trdYLjzd5tlIt8BOeeVnAOIgOBi3oFYZr74Xb7WBZV57Eo9ZnnK8UtzbXQaGH041yZQxq7nPBbqcR4LdfXCuY08c3359WeCD85fUFk+ExzZx9NzG/nTPBY+HVh4WnJypmdyLHIPIGjBZarLmTHkfeQ3uarcnIXSmIIikXL7epjDip98Bx9djqCTa+TszaWNXCS/B8cgtmoVpFnloLiSP/ejrGgbf+xHWVn60zLHQj3mWKirF+ZYeOb64meC5wTSZ4Ij+zDHIgC6QhFg1gZfzE+wjViNUbWR3emhouAgVAQC1t2JLv4cHAVoLz3dBfUHyq8KXWzJISIiIiIqDQMLP5B21WcorNkN0IeVul6MmpTTfZLHy0VEREREZOFnwylVTnKEozKCCiIiIiIiX8AcCz9I3i4cswKo0Q7exAQtdXXjjwlaleU6YPK2ekzeVlcvTN72zPXF5G3PCaTPBEf2YfJ22dgVyhsT5HFiNIfqsSJM1POcQErUY/K2ekzeVlcvTN72zPXFzwTPCaTPBEf2YfJ22dgVyovzF9iD8xcQERERkT9gi4U3xNRGyq2r5Yza1iIjQ5GVlWezTEyK5q5Zt4mIiIiIXIWBhZeIYOGSgCE2HAYfnb+AiIiIiKg8TN72RvI2k3YdqseKMFHPcwIpUY/J2+oxeVtdvfAzwTPXFz8TPCeQPhMc2YfJ22Vji4U3krc547JD9VgRJup5TiAl6jF5Wz0mb6urF34meOb64meC5wTSZ4Ij+zB5u2xM3iYiIiIiIqcxsCAiIiIiIqcxsCAiIiIiIgYWRERERETkfWyxICIiIiIip3G4WSscbrZsHFJOXd2oWefOunZWIF0HHG5WPQ43q65eONysZ64vDjfrOYH0meDIPhxutmwcbtYKh5stG4eUU1c3ata5s66dFUjXAYebVY/DzaqrFw4365nri8PNek4gfSY4sg+Hmy0bu0IREREREZHT2BWKiIiIiIicxhYLIiIiIiJyGgMLIiIiIiJyGgMLIiIiIiJyGgMLIiIiIiJyGoeb9RPLli3D8uXLMWPGDG8XhTwsPz8fTz75JC5cuICCggJMnjwZ7du353moZAoLCzFp0iScPXsWYWFhePXVVxEfH+/tYpGXHDlyBNdddx22bt3Kc1BJXX755UhISJA/d+zYERMmTPB2kcjD3nvvPaxdu1beGzz44IMYOHAgvI2BhR+YOXMmVqxYwZvJSurbb79Fw4YN8fbbb+Pw4cN4+umn8dVXX3m7WORhv/32GxITE/HGG2/g+++/x/z582WgQZVPbm6u/FwICQnxdlHISzIzMxEXF4dPP/2U56CS2rBhA/bv348vvvgCKSkp+Pnnn+ELGFj4gbZt26Jfv3748ccfvV0U8oJrrrkGGo1G/mw0GhEUFMTzUEmvgyuuuEL+LFotYmJivF0k8pJp06bJp5OPPPIIz0EltXv3bvy/vTuBjaJ84zj+gshZERXFoFFQLJRaCm0RQUSsgCBVq1CRUkAjAUEolwpVFAJeoKJSNUgqiAoqiEY8SRBjVBS8AFGqoBarIIcFlEOuzj+/N85kt90e2+XfbbvfT9K0O52ZfWfmze77zPu87+zbt88MHTrU1K1b19x7772mRYsW4S4WKtHq1atNy5YtzciRI22P9pQpU0xVwBiLaqBPnz5ewxKRJyoqyjRq1MjekVBKlBoUiEx16tQxw4cPt3cpu3fvHu7iIAzUW9mmTRsTFxfH+Y/w74Vhw4aZhQsXmhEjRtiebESWgoICs2nTJpsOlZmZWWUCC3osgGqST60PDuXQdu7cOdzFQRjNmzfPbN261QYYK1as4FpEmOXLl5vatWvba79r1y7buMzJyQl3sVDJWrVqZX8kKSnJ7Nixg2sQYZo0aWKio6PtDaf4+Hizfft2UxUQWABVnD4s1NU5a9YsmxaHyL1Tre7ujIwM07BhQ9u4RORZtGiR93dycjJBRYRavHixvWM9ceJEk5uba5o3bx7uIqGSJSQkmFdffdWmw2n8pcbcVAV8M1UyjdxPSUkxa9as8Zv1RzP96K5D165dzfz58yu7WKjCdeDZZ581Bw8etLMADR482PZcIPLqgVIiNfuHAgvl1s+YMSNMJceJxHcCKlIPBg4caPLy8sygQYPMQw89ZKZPn86JjLA6kJycbCd2SUtLs2nSU6dONVUBPRaVSBVEdxc2b97st1x3ojdu3GhzJbdt22ZnetHdh969e3vrdOrUyf4g8uoADciapyL1oHHjxmbu3LlhKzOq1neCrFq1issSofWgfv36Jjs7O2xlRvjrQK1atark2BoCi0qyZcsWW2kcx/FbrjvRS5cutVNHxsbG2h9VLHV3F/0SQfVGHQD1AHwegO8F1OS2AalQlWTt2rW2x6Ho8weUG3ns2DHToUMHb1liYqJZv369KSwsrKzioRJQB0A9AJ8H4HsBNbltQI9FJUlPTw+4XLN6aMCN5qF2NW3a1HaL7d27lyfr1iDUAVAPwOcB+F5ATW4b0GNRBZ6g6ltpxH2tgTyo+agDoB6AzwPwvYCa0DYgsAizevXqFasg7msNzkLNRx0A9QB8HoDvBdSEtgGBRZg1a9bM7Nmzx+bR+XZ/qdJoFhjUfNQBUA/A5wH4XkBNaBsQWIRZTEyMfWriunXrvGVff/21iYuL4wFYEYI6AOoB+DwA3wuoCW2DqluyCNGgQQOTmppqpk2bZjZs2GBWrlxpH4AyZMiQcBcNlYQ6AOoB+DwA3wuoCW0DZoWqAvSAE1UcPZY9KirKjBkzxvTq1SvcxUIlog6AegA+D8D3Aqp726CWU/SJHAAAAAAQJFKhAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAIAAABAyAgsAAAAAISMwAJAxJs8ebJp3bq1WbRoUcBz8fvvv9v/Z2dnV+q50nuqbFXZkSNHTFZWlklISLA/q1atMtVRfn5+uItQpQwePNgkJyeX+BoAAiGwAID/PPnkk2b37t2cjyAsWbLEvPHGG6ZHjx42wLj44our3flbtmyZ6du3b7iLAQDVHoEFAPzn77//Ng8//DDnIwg//vij/X3//febtLQ0c9ZZZ1W78/fll1+aw4cPh7sYAFDtEVgAwH+U6vHOO++Yzz//nHNSTkePHrW/o6KiOGcAEOEILADgP1OmTDENGjQw06ZNs2MHygpClHde1nK9nj59ulm6dKm5+uqrTbt27Uy/fv3Mhg0bzK5du8zYsWNNhw4dzOWXX25mz55tCgsLi+1z7ty59v/x8fFmyJAhdtuiPvroI3PzzTfbdTp27GjGjBljfv3112JjNpTudfvtt9uUJaX/HDt2rMRjXLlypd2nypyUlGS3y83N9dvfm2++6f0d6HwEU8Zhw4aZTp06FSuTO8bl6aefDvp4582bZxYsWGBTtXTM1157rXn//fe9dVRm32Nwx7Ts27fP/t29e3e7nbZ//PHHy+zZ0P5uueUWO9bkmmuusecuNTXVrFixoti6W7ZsMXfccYc9tzoOHc8nn3xSbH+33XabeeKJJ2w96dy5s9dLFMjPP/9s65TOY2Jiot3+q6++8ltHx5+RkWH/r2NTHZ01a1aZdR4AykJgAQD/Oeecc8yoUaNMXl6ebZCeKGqgP/XUU6Z///5m9OjR5pdffrEN4VtvvdXUrl3bNmCjo6PNc889Z9566y2/bdUgVcNYjU41QrWtgovNmzd762iMw8iRI21QdNddd9mG7bfffmtuuummYo3thQsX2l4GBVFKXapTp07AMmsgu95P606YMMHuUwHNwIEDvcBGjVE1it2/FXiUpDxlVKN/7969ZvXq1X7bvvfee97/gz3eV155xbzwwgv2f3fffbc5ePCgGT9+vPnpp5/s/1Vm32MYMGCA/XvcuHE2eNE5mjp1qrnkkktsnXjggQdMWRQwZGZm2oDnzjvvtNdYr99++21vHQUHei+tO2LECFsmBVTDhw/3jtf1zTff2GBAx3rDDTeYVq1aBXxf1Vsd5xdffGEDB103nU/VM/eaKcDVsZ1yyim2bDonqvfPP/+8DToBICQOAES4SZMmOdHR0fbvI0eOOH379nXi4uKcvLw8uyw/P9/+f86cOd42V155pZORkVFsX0WX63Xr1q2d3Nxcb9nMmTPt/saNG+ctO3DggBMbG+tMmDDBW6Z1YmJi/LZVmdq2beuMHj3avv7nn3+chIQEZ/z48X7l2Llzp9OxY0dn1KhRfvtLSkpyDh06VOr5KCgocOLj453+/fs7hw8f9pbrPGh5v379Ap67kpS3jPv377f7z8rK8lsvNTXVSUtLq9Dxtm/f3v7PtW7dOrt89uzZJR7D7t277eucnBy/95g8ebIzdOjQUo9V117bLliwwFum892zZ0+na9euzvHjx731evToYa+76+jRo056errTpUsX77y7+1O5yzJ27FinXbt2Xr11r2ViYqKTmZlpX/fu3dsZMGCAU1hY6Pe+3bp1c1JSUvyOQ3W3pNcAEAg9FgDg4+STT/ZSoZTCdCKcd955Ns3G1bJlS/u7Z8+e3rKGDRuaM844w6ZH+VIKlO+2559/vunWrZv59NNPzfHjx81nn31m9u/fb1N1CgoKvJ+TTjrJXHrppXY939QipebUr1+/1PJqjMmhQ4fsne66det6y88991xz3XXXme+++87s3Lmz3Mdf3jI2atTIXHXVVebDDz/0xm6oB+KHH37weiuCPV6l+5x55pne65iYGPu76Hn2pbv5uh6LFy+2PUbq5RAN7FfvR1m0fXp6uvda51s9PTpnGzduNHv27DFr1641V1xxhfn333+9Y9DkAaoTmplM59h3+7i4uFLfUyl0H3/8sd2n6ojrtNNOs8ehHipZvny57XmpVauWt85ff/1lGjdu7B0nAFRU4D5wAIhgSo1RyolSbt59912b/x4KBQy+1AiW008/vdhyx3H8ll1wwQUBAxXl8Ksx+ttvv9llSqUpidZzZ2sq+p6BaExDSe994YUX2t/btm0r9wxQwZRRAYQ7gF4BlNKCdF40XiHYfQU6XjdQCjSWxXcdBZX33XefTWHSa6VC9erVy46XqFevXqnHq+vjG5CJ29j/448/vGUvvfSS/Qlk+/bt3t9NmjSx6VSlUcqTAgPfoMKlNDvfwFmzYOkcK61O51OBhSglCgBCQWABAAEon12Nd92lzsnJKfc5Ui9CsQ/aEsYx+N41DoYbfKjB7TaQZ8yYYXsUAjn11FOLBTUV5b63GqjlFUwZL7vsMtuQ1pgCBRb6rQHLbnAW7PGW1SAviQIc9RZpfIx6AjTuQ70huvuvcQpFAwdfgc6NW26df7eODBo0yPa8BOI7jqI818zdZ1l1Suft5ZdfNm3btjXt27c3119/vR0UruW+wQwAVASBBQAEoDvdGtyqFJJAg1rVYC06i45ScJTmojvWJ4rvHW7fQbpKt1Gai3uXWeXt0qWL33pr1qyxDdrSGsGBuPvUHe02bdr4/U/L5Oyzzw56f+Upoxrlffr0MR988IEdYK1B6potqiL7qqgDBw6YTZs2mYsuusgOuNePrvWjjz5qXnzxRRtglPYUavX4KADzbeTrmol6FHTd3ICh6DFoMLe218D0YGifSplye3R8aWC2Ur80Q5SCCgUTGqjuiwdDAjgRGGMBACVQgzIhIcHODlRU06ZNbf6/cuRd6uE40Q9a0/SjO3bs8F6rse02bNVwVcNUqTnqVXHHJYi20QxXjz32WNA9I+4+NRuVb/D0559/2pmNNE6jaHpXefZX3jKqt0ABmqbfVQPbdyzK/+N43V4Nt1dBwYx6E15//XVvHQUrustfnh4ENdJ9p7RVipJmp2rRooUdL6M0LU3zqmlufa+tjueee+6x6VelTQMciHrF1Nuj3hXfngdNm6vAIj8/3/4tRWeV0jYKfIJ9TwAoih4LACiBGqgayH3jjTcWa3SlpKTY9BHdTdeA5q1bt5olS5ac8Dx1NWg1EFh3m9VA1XSxGmirKUPdO/eaVlQpW5q+VGVRWZWyoyBn0qRJQb+n7n67+9SgYzX0dRdfjWM1vt2BwOUVbBkVzOk8KqDTszY0qLui+ypv+WTOnDn2+Q8aBK5xNnp2hBrpCgb0W3f7Ne5EqVmlUa9LVlaW+f77720QsWzZMhtA6HkkLp3DoUOH2mea6Bwr/UvjedavX28mTpzo9WoEQ9tpelz9KDDSQwtVJ1VvVF/UW9K8eXNbDp0r9TppGloFOArWdI0BIBQEFgBQCjUq9dyI+fPn+y1XY18DZnVXWwGGUob0ADetdyJn11HjWQGO2xhUw1fPvVAD0aXnODRr1sz2MKgxrJSY2NhYm7qjWZEqQvtUo1jH4/YcaACznsPhO0tVMPsrbxl1vApmdMwK4ELZV3moYa9nP6gXRLMxKXB45pln7PVUcPPaa6/ZcRsavK2Hz5WVaqXzpp6HmTNn2hQklU1l1XMtXBrXoEAtOzvb/k/BkWYLe+SRR+zEARWhgfUqq66XjkU9MepdUjmU1iWaEUrvoZQupWspbU9l1fs/+OCDdtYq9aYAQEXU0pyzFdoSAAD4Uc+SxsUoLQ4AIg1jLAAAAACEjMACAAAAQMgILAAAAACEjDEWAAAAAEJGjwUAAACAkBFYAAAAAAgZgQUAAACAkBFYAAAAAAgZgQUAAACAkBFYAAAAAAgZgQUAAACAkBFYAAAAAAgZgQUAAAAAE6r/AZC48922Pyt0AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overhead vs KingPDF.pdf():  9.8×  (mean across sizes)\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_list = [10, 100, 1_000, 10_000, 100_000, 1_000_000]\n",
-    "times_pdf = []\n",
-    "times_king = []\n",
-    "\n",
-    "for n in n_list:\n",
-    "    psi_t = np.radians(rng.uniform(0.01, 5.0, n))\n",
-    "    alpha_t = np.radians(rng.uniform(0.2, 4.0, n))\n",
-    "    beta_t = rng.uniform(1.5, 6.0, n)\n",
-    "    ext_t = np.radians(rng.uniform(0.1, 3.0, n))\n",
-    "\n",
-    "    n_rep = max(1, int(3e6 // n))\n",
-    "    times_pdf.append(\n",
-    "        timeit(lambda: ext_pdf.pdf(psi_t, alpha_t, beta_t, ext_t), number=n_rep) / n_rep * 1e3\n",
-    "    )\n",
-    "    times_king.append(timeit(lambda: king.pdf(psi_t, alpha_t, beta_t), number=n_rep) / n_rep * 1e3)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "ax.loglog(n_list, times_pdf, \"o-\", lw=2, ms=7, label=\"ExtendedSourceKingPDF.pdf()\")\n",
-    "ax.loglog(n_list, times_king, \"s--\", lw=2, ms=7, label=\"KingPDF.pdf()  (reference)\")\n",
-    "ax.set_xlabel(\"Number of events per call\", fontsize=13)\n",
-    "ax.set_ylabel(\"Time per call (ms)\", fontsize=13)\n",
-    "ax.set_title(\"pdf() evaluation speed\", fontsize=13, fontweight=\"bold\")\n",
-    "ax.legend(fontsize=12)\n",
-    "ax.grid(which=\"both\", alpha=0.8)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "overhead = np.array(times_pdf) / np.array(times_king)\n",
-    "print(f\"Overhead vs KingPDF.pdf():  {overhead.mean():.1f}×  (mean across sizes)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1d2e3f4",
-   "metadata": {},
-   "source": [
-    "### 7b. `evaluate()` speed vs number of sources"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d2e3f4a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    1 sources:  first =    0.6 ms,  mask =    0.2 ms,  speedup = 2.2×\n",
-      "    2 sources:  first =    1.0 ms,  mask =    0.4 ms,  speedup = 2.8×\n",
-      "    5 sources:  first =    2.3 ms,  mask =    0.6 ms,  speedup = 4.0×\n",
-      "   10 sources:  first =    4.6 ms,  mask =    1.1 ms,  speedup = 4.2×\n",
-      "   20 sources:  first =    9.0 ms,  mask =    2.0 ms,  speedup = 4.4×\n",
-      "   50 sources:  first =   23.0 ms,  mask =    5.2 ms,  speedup = 4.4×\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_ev_bench = 50_000\n",
-    "ev_ras_b = rng.uniform(0, 2 * np.pi, n_ev_bench)\n",
-    "ev_decs_b = np.arcsin(rng.uniform(-0.8, 0.8, n_ev_bench))\n",
-    "alpha_b = np.radians(rng.uniform(0.2, 5.0, n_ev_bench))\n",
-    "beta_b = rng.uniform(1.5, 6.0, n_ev_bench)\n",
-    "\n",
-    "n_src_list = [1, 2, 5, 10, 20, 50]\n",
-    "times_first = []  # first call (build mask)\n",
-    "times_mask = []  # subsequent call (reuse mask)\n",
-    "\n",
-    "for n_src in n_src_list:\n",
-    "    s_ras = rng.uniform(0, 2 * np.pi, n_src)\n",
-    "    s_decs = np.arcsin(rng.uniform(-0.7, 0.7, n_src))\n",
-    "    s_exts = np.radians(rng.uniform(0.2, 3.0, n_src))\n",
-    "\n",
-    "    t0 = time.perf_counter()\n",
-    "    mask_r = ext_pdf.evaluate(s_ras, s_decs, s_exts, ev_ras_b, ev_decs_b, alpha_b, beta_b)\n",
-    "    times_first.append((time.perf_counter() - t0) * 1e3)\n",
-    "\n",
-    "    n_rep = max(1, int(5 // (times_first[-1] / 1e3 + 0.01)))\n",
-    "    times_mask.append(\n",
-    "        timeit(\n",
-    "            lambda: ext_pdf.evaluate(\n",
-    "                s_ras, s_decs, s_exts, ev_ras_b, ev_decs_b, alpha_b, beta_b, mask=mask_r\n",
-    "            ),\n",
-    "            number=n_rep,\n",
-    "        )\n",
-    "        / n_rep\n",
-    "        * 1e3\n",
-    "    )\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "ax.plot(n_src_list, times_first, \"o-\", lw=2, ms=7, label=\"First call (builds mask)\")\n",
-    "ax.plot(n_src_list, times_mask, \"s--\", lw=2, ms=7, label=\"Subsequent call (mask reuse)\")\n",
-    "ax.set_xlabel(\"Number of sources\", fontsize=13)\n",
-    "ax.set_ylabel(\"Time (ms)\", fontsize=13)\n",
-    "ax.set_title(f\"evaluate() speed  ({n_ev_bench:,} events)\", fontsize=13, fontweight=\"bold\")\n",
-    "ax.legend(fontsize=12)\n",
-    "ax.grid(alpha=0.8)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "speedup = np.array(times_first) / np.array(times_mask)\n",
-    "for ns, sf, sm, sp in zip(n_src_list, times_first, times_mask, speedup):\n",
-    "    print(f\"  {ns:>3} sources:  first = {sf:6.1f} ms,  mask = {sm:6.1f} ms,  speedup = {sp:.1f}×\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1c2d3e4",
-   "metadata": {},
-   "source": [
-    "### 7c. `evaluate()` speed vs number of events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c2d3e4f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       500 events:  first =     1.1 ms,  mask =    0.2 ms,  speedup = 6.7x\n",
-      "     1,000 events:  first =     0.7 ms,  mask =    0.2 ms,  speedup = 4.2x\n",
-      "     5,000 events:  first =     0.8 ms,  mask =    0.2 ms,  speedup = 4.2x\n",
-      "    10,000 events:  first =     1.0 ms,  mask =    0.2 ms,  speedup = 4.2x\n",
-      "    50,000 events:  first =     2.4 ms,  mask =    0.6 ms,  speedup = 3.9x\n",
-      "   100,000 events:  first =     4.3 ms,  mask =    1.1 ms,  speedup = 3.8x\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_src_fixed = 5\n",
-    "sr_f = rng.uniform(0, 2 * np.pi, n_src_fixed)\n",
-    "sd_f = np.arcsin(rng.uniform(-0.7, 0.7, n_src_fixed))\n",
-    "se_f = np.radians(rng.uniform(0.2, 3.0, n_src_fixed))\n",
-    "\n",
-    "n_ev_list = [500, 1_000, 5_000, 10_000, 50_000, 100_000]\n",
-    "times_first_ev = []\n",
-    "times_mask_ev = []\n",
-    "\n",
-    "for n_ev in n_ev_list:\n",
-    "    ev_r = rng.uniform(0, 2 * np.pi, n_ev)\n",
-    "    ev_d = np.arcsin(rng.uniform(-0.8, 0.8, n_ev))\n",
-    "    al = np.radians(rng.uniform(0.2, 5.0, n_ev))\n",
-    "    be = rng.uniform(1.5, 6.0, n_ev)\n",
-    "\n",
-    "    t0 = time.perf_counter()\n",
-    "    mask_ev = ext_pdf.evaluate(sr_f, sd_f, se_f, ev_r, ev_d, al, be)\n",
-    "    times_first_ev.append((time.perf_counter() - t0) * 1e3)\n",
-    "\n",
-    "    n_rep = max(1, int(3_000 // (times_first_ev[-1] + 1)))\n",
-    "    times_mask_ev.append(\n",
-    "        timeit(\n",
-    "            lambda: ext_pdf.evaluate(sr_f, sd_f, se_f, ev_r, ev_d, al, be, mask=mask_ev),\n",
-    "            number=n_rep,\n",
-    "        )\n",
-    "        / n_rep\n",
-    "        * 1e3\n",
-    "    )\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "ax.loglog(n_ev_list, times_first_ev, \"o-\", lw=2, ms=7, label=f\"First call ({n_src_fixed} sources)\")\n",
-    "ax.loglog(n_ev_list, times_mask_ev, \"s--\", lw=2, ms=7, label=f\"Mask reuse ({n_src_fixed} sources)\")\n",
-    "ax.set_xlabel(\"Number of events\", fontsize=13)\n",
-    "ax.set_ylabel(\"Time (ms)\", fontsize=13)\n",
-    "ax.set_title(\"evaluate() speed vs number of events\", fontsize=13, fontweight=\"bold\")\n",
-    "ax.legend(fontsize=12)\n",
-    "ax.grid(which=\"both\", alpha=0.8)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "for ne, tf, tm in zip(n_ev_list, times_first_ev, times_mask_ev):\n",
-    "    print(\n",
-    "        f\"  {ne:>8,} events:  first = {tf:7.1f} ms,  mask = {tm:6.1f} ms,  speedup = {tf / tm:.1f}x\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3f4a5b6",
-   "metadata": {},
-   "source": [
-    "### 7d. Table build time vs grid density"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "f4a5b6c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Label        nα   nβ  next    nψ  shape                       MB   build (s)\n",
-      "  ------------------------------------------------------------------------\n",
-      "  coarse       10    8     8   200  (10, 8, 8, 201)            1.0        0.03\n",
-      "  medium       20   12    12   350  (20, 12, 12, 351)          8.1        0.23\n",
-      "  default      30   20    20   500  (30, 20, 20, 501)         48.1        1.36\n",
-      "  fine         50   30    30   600  (50, 30, 30, 601)        216.4        6.18\n"
-     ]
-    }
-   ],
-   "source": [
-    "configs = [\n",
-    "    (\"coarse\", 10, 8, 8, 200),\n",
-    "    (\"medium\", 20, 12, 12, 350),\n",
-    "    (\"default\", 30, 20, 20, 500),\n",
-    "    (\"fine\", 50, 30, 30, 600),\n",
-    "]\n",
-    "\n",
-    "print(\n",
-    "    f\"  {'Label':<10} {'nα':>4} {'nβ':>4} {'next':>5} {'nψ':>5}  {'shape':<22}  {'MB':>6}  {'build (s)':>10}\"\n",
-    ")\n",
-    "print(\"  \" + \"-\" * 72)\n",
-    "for label, na, nb, ne, np_ in configs:\n",
-    "    t0 = time.perf_counter()\n",
-    "    e = ExtendedSourceKingPDF(\n",
-    "        points_alpha=np.radians(np.logspace(-1, 1, na)),\n",
-    "        points_beta=np.logspace(np.log10(1.01), np.log10(10.0), nb),\n",
-    "        points_extension=np.radians(np.logspace(-2, np.log10(4.9), ne)),\n",
-    "        points_psi=np.concatenate([[0.0], np.logspace(-5, np.log10(np.pi), np_)]),\n",
-    "    )\n",
-    "    elapsed = time.perf_counter() - t0\n",
-    "    shape_str = str(e._table.shape)\n",
-    "    mb = e._table.nbytes / 1e6\n",
-    "    print(\n",
-    "        f\"  {label:<10} {na:>4} {nb:>4} {ne:>5} {np_:>5}  {shape_str:<22}  {mb:>6.1f}  {elapsed:>10.2f}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5b6c7d8",
-   "metadata": {},
-   "source": [
-    "## 8. Summary\n",
-    "\n",
-    "`ExtendedSourceKingPDF` provides an efficient implementation of the King PSF convolved with a Rayleigh source extension:\n",
-    "\n",
-    "1. **Construction** — builds a 4D lookup table over (log α, log β, log extension, ψ) using 32-point Gauss-Laguerre quadrature. One-time cost paid at startup.\n",
-    "\n",
-    "2. **`pdf(ψ, α, β, extension)`** — evaluates the convolved PDF via linear interpolation from the table. Accepts any broadcastable combination of scalar or array inputs. Returns probability per steradian.\n",
-    "\n",
-    "3. **Physical behavior** — for r₀ < α the source looks point-like; for r₀ ~ α the peak is suppressed and there is a probability enhancement near ψ ≈ r₀ (the \"ring effect\"); normalization is preserved.\n",
-    "\n",
-    "4. **`evaluate()`** — returns a sparse `csr_array` of shape `(n_events, n_sources)`. Pass a previous result as `mask=` to reuse the sparsity pattern and skip the geometric masking loop on repeated calls, e.g., when scanning extension hypotheses.\n",
-    "\n",
-    "5. **Limits** — the flat-sky (Rayleigh) approximation is valid for r₀ ≲ 5°; a `ValueError` is raised for larger extensions. Interpolation error is ~0.9% with the default grid and ~0.3% with the fine grid.\n",
-    "\n",
-    "### Quick-reference: custom grid\n",
-    "```python\n",
-    "ext_pdf = ExtendedSourceKingPDF(\n",
-    "    points_alpha=np.radians(np.logspace(-1, 1, 40)),    # finer α resolution\n",
-    "    points_beta=np.logspace(np.log10(1.01), 1, 25),     # finer β resolution\n",
-    "    points_extension=np.radians(np.logspace(-2, np.log10(4.9), 25)),\n",
-    "    points_psi=np.concatenate([[0.], np.logspace(-5, np.log10(np.pi), 600)]),\n",
-    "    n_quad=48,   # more quadrature nodes for extreme parameter values\n",
-    "    angular_cutoff=np.radians(20.),\n",
-    ")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54979e0e-76be-48af-86d5-7315a2e6ac3a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/fitting_demo.ipynb b/examples/fitting_demo.ipynb
index 9e1e8ad..9c977d3 100644
--- a/examples/fitting_demo.ipynb
+++ b/examples/fitting_demo.ipynb
@@ -569,8 +569,8 @@
     "bin_idx = (6, 7, 2)  # Mid energy, mid dec, mid sigma\n",
     "gamma_idx = 0\n",
     "\n",
-    "alpha = results['alpha'][gamma_idx][bin_idx]\n",
-    "beta = results['beta'][gamma_idx][bin_idx]\n",
+    "alpha = results['alpha'][0, gamma_idx][bin_idx]\n",
+    "beta = results['beta'][0, gamma_idx][bin_idx]\n",
     "\n",
     "print(f\"Fitted parameters for bin {bin_idx}:\")\n",
     "print(f\"  alpha = {np.degrees(alpha):.4f} degrees\")\n",
diff --git a/examples/likelihood_demo.ipynb b/examples/likelihood_demo.ipynb
index 0762bc3..b86637d 100644
--- a/examples/likelihood_demo.ipynb
+++ b/examples/likelihood_demo.ipynb
@@ -272,10 +272,11 @@
     "    spectral_indices=[2.0, 3.0],\n",
     "    angular_cutoff=np.pi,\n",
     "    cache_parameters=False,  # disable caching for this demo\n",
+    "    true_energy_name=\"true_energy\",\n",
     ")\n",
     "\n",
     "print(\"KingSpatialLikelihood initialised successfully.\")\n",
-    "print(f\"  alpha_values shape : {wrapper.alpha_values.shape}  (n_spectral_indices × n_energy_bins)\")\n",
+    "print(f\"  alpha_values shape : {wrapper.alpha_values.shape}  (n_extensions × n_spectral_indices × n_energy_bins)\")\n",
     "print(f\"  beta_values  shape : {wrapper.beta_values.shape}\")\n",
     "print(f\"  spectral_indices   : {wrapper.spectral_indices}\")"
    ]
@@ -339,7 +340,7 @@
     "for g_idx, (gamma, color) in enumerate(zip(wrapper.spectral_indices, colors)):\n",
     "    ax1.plot(\n",
     "        bin_centers,\n",
-    "        np.degrees(wrapper.alpha_values[g_idx]),\n",
+    "        np.degrees(wrapper.alpha_values[0, g_idx]),\n",
     "        \"o-\",\n",
     "        markersize=9,\n",
     "        linewidth=2,\n",
@@ -348,7 +349,7 @@
     "    )\n",
     "    ax2.plot(\n",
     "        bin_centers,\n",
-    "        wrapper.beta_values[g_idx],\n",
+    "        wrapper.beta_values[0, g_idx],\n",
     "        \"o-\",\n",
     "        markersize=9,\n",
     "        linewidth=2,\n",
@@ -380,11 +381,11 @@
     "\n",
     "print(\"Fitted α values (degrees):\")\n",
     "for g_idx, gamma in enumerate(wrapper.spectral_indices):\n",
-    "    fitted = np.degrees(wrapper.alpha_values[g_idx])\n",
+    "    fitted = np.degrees(wrapper.alpha_values[0, g_idx])\n",
     "    print(f\"  γ={gamma:.1f}: {fitted}  (true: {true_alphas_deg})\")\n",
     "print(\"\\nFitted β values:\")\n",
     "for g_idx, gamma in enumerate(wrapper.spectral_indices):\n",
-    "    fitted = wrapper.beta_values[g_idx]\n",
+    "    fitted = wrapper.beta_values[0, g_idx]\n",
     "    print(f\"  γ={gamma:.1f}: {fitted}  (true: {true_betas})\")"
    ]
   },
@@ -533,8 +534,8 @@
     "\n",
     "# Fitted parameters at the test energy (interpolated between bin centers)\n",
     "def fitted_params_at(loge, g_idx):\n",
-    "    alpha = np.interp(loge, bin_centers, wrapper.alpha_values[g_idx])\n",
-    "    beta = np.interp(loge, bin_centers, wrapper.beta_values[g_idx])\n",
+    "    alpha = np.interp(loge, bin_centers, wrapper.alpha_values[0, g_idx])\n",
+    "    beta = np.interp(loge, bin_centers, wrapper.beta_values[0, g_idx])\n",
     "    return alpha, beta\n",
     "\n",
     "\n",
diff --git a/kingmaker/__init__.py b/kingmaker/__init__.py
index 0ba216a..49ee51a 100644
--- a/kingmaker/__init__.py
+++ b/kingmaker/__init__.py
@@ -1,12 +1,12 @@
 # Import main classes for convenience
-from .pdf import KingPDF, MarginalizedKingPDF, TemplateSmearedKingPDF
 from .fitting import KingPSFFitter
+from .pdf import KingPDF, MarginalizedKingPDF, TemplateSmearedKingPDF
 from .wrapper import KingSpatialLikelihood
 
 __all__ = [
     "KingPDF",
-    "MarginalizedKingPDF",
-    "TemplateSmearedKingPDF",
     "KingPSFFitter",
     "KingSpatialLikelihood",
+    "MarginalizedKingPDF",
+    "TemplateSmearedKingPDF",
 ]
diff --git a/kingmaker/distribution.py b/kingmaker/distribution.py
index 98ee180..9c554e9 100644
--- a/kingmaker/distribution.py
+++ b/kingmaker/distribution.py
@@ -1,7 +1,6 @@
-from typing import Union
 import numpy as np
 import numpy.typing as npt
-from numba import njit, vectorize, float32, float64
+from numba import float32, float64, njit, vectorize
 
 _log10pi: float = np.log10(np.pi)
 
@@ -12,10 +11,10 @@
     cache=True,
 )
 def _unnormalized_pdf(
-    x: Union[float, npt.NDArray[np.floating]],
-    alpha: Union[float, npt.NDArray[np.floating]],
-    beta: Union[float, npt.NDArray[np.floating]],
-) -> Union[float, npt.NDArray[np.floating]]:
+    x: float | npt.NDArray[np.floating],
+    alpha: float | npt.NDArray[np.floating],
+    beta: float | npt.NDArray[np.floating],
+) -> float | npt.NDArray[np.floating]:
     """
     Evaluate the unnormalized spherical King function (without solid angle Jacobian):
         f(x) = [1 + (1 - cos x) / (alpha² * beta)]^(-beta)
@@ -43,8 +42,8 @@ def _unnormalized_pdf(
     cache=True,
 )
 def _unnormalized_cdf(
-    x: Union[float, npt.NDArray[np.floating]], alpha: float, beta: float
-) -> Union[float, npt.NDArray[np.floating]]:
+    x: float | npt.NDArray[np.floating], alpha: float, beta: float
+) -> float | npt.NDArray[np.floating]:
     """
     Evaluate the CDF of the radial King function (without solid angle Jacobian).
 
@@ -78,10 +77,10 @@ def _unnormalized_cdf(
     cache=True,
 )
 def _norm(
-    alpha: Union[float, npt.NDArray[np.floating]],
-    beta: Union[float, npt.NDArray[np.floating]],
+    alpha: float | npt.NDArray[np.floating],
+    beta: float | npt.NDArray[np.floating],
     maximum: float,
-) -> Union[float, npt.NDArray[np.floating]]:
+) -> float | npt.NDArray[np.floating]:
     """
     Compute the normalization constant for the King PDF over the sphere.
 
diff --git a/kingmaker/fitting.py b/kingmaker/fitting.py
index d20008b..05d3022 100644
--- a/kingmaker/fitting.py
+++ b/kingmaker/fitting.py
@@ -1,12 +1,13 @@
-from typing import Any, Dict, List, Optional, Tuple, Union, cast
-from tqdm import tqdm
+from typing import Any, cast
+
 import numpy as np
 import numpy.typing as npt
 from scipy.optimize import minimize
+from tqdm import tqdm
 
 from .distribution import _cdf_and_gradient
 from .pdf import KingPDF
-from .utils import angular_distance
+from .utils import angular_distance, offset_position
 
 
 class KingPSFFitter:
@@ -42,7 +43,7 @@ class KingPSFFitter:
         The percentiles (ranging from 0-100) defining the range of weights to accept
         for the per-parametrization bin histogramming and fitting. Note that these are
         applied based on the weights based on sorted index value and not cumulative
-        weight value like np.percentile. Default is [0, 95],
+        weight value like np.percentile. Default is (0, 95).
     weight_field : str, optional
         Field name for oneweight. If None, equal weights are used.
     true_ra_name : str
@@ -55,13 +56,17 @@ class KingPSFFitter:
         Spectral indices (gamma) for reweighting. Default is [2.0].
     angular_cutoff : float, optional
         Maximum angular separation for King PDF. Default is pi.
+    extension_grid : array-like, optional
+        Source extension widths (Gaussian sigma) in radians, non-negative.
+        Each event's true position is displaced by a Rayleigh(extension)-magnitude
+        offset before computing dpsi. Default is [0.0], the point-source case.
 
     Attributes
     ----------
     fit_alpha : ndarray
-        Fitted alpha parameters for each bin.
+        Fitted alpha parameters, shape ``(n_extension, n_gamma, *bins)``.
     fit_beta : ndarray
-        Fitted beta parameters for each bin.
+        Fitted beta parameters, shape ``(n_extension, n_gamma, *bins)``.
     histograms : ndarray
         Histogram values for each bin.
     uncertainties : ndarray
@@ -75,17 +80,18 @@ class KingPSFFitter:
     def __init__(
         self,
         signal_events: npt.NDArray[Any],
-        parametrization_bins: Dict[str, Union[int, List, Tuple, npt.NDArray]],
+        parametrization_bins: dict[str, int | list | tuple | npt.NDArray],
         dpsi_nbins: int = 101,
         minimum_counts: int = 100,
         remove_weight_outliers=True,
-        weight_outlier_percentiles=[0, 95],
-        weight_field: Optional[str] = "ow",
+        weight_outlier_percentiles=(0, 95),
+        weight_field: str | None = "ow",
         true_ra_name: str = "trueRa",
         true_dec_name: str = "trueDec",
         true_energy_name: str = "trueE",
-        spectral_indices: Optional[Union[List[float], npt.NDArray[np.floating]]] = None,
+        spectral_indices: list[float] | npt.NDArray[np.floating] | None = None,
         angular_cutoff: float = np.pi,
+        extension_grid: list[float] | npt.NDArray[np.floating] | None = None,
     ) -> None:
         """Initialize the KingPSFFitter."""
         self.signal_events = signal_events
@@ -103,6 +109,18 @@ def __init__(
         )
         self.angular_cutoff = angular_cutoff
 
+        self.extension_grid = np.atleast_1d(
+            np.asarray(extension_grid if extension_grid is not None else [0.0], dtype=np.float64)
+        )
+        if (
+            self.extension_grid.ndim != 1
+            or self.extension_grid.size == 0
+            or not np.all(np.isfinite(self.extension_grid))
+            or np.any(self.extension_grid < 0)
+        ):
+            raise ValueError("extension_grid must be a 1-D, finite, non-empty, non-negative array.")
+        self.extension_grid = np.sort(self.extension_grid)
+
         # Initialize King PDF
         self.king_pdf = KingPDF(angular_cutoff=angular_cutoff)
 
@@ -112,12 +130,16 @@ def __init__(
         self.bin_names = list(self.parametrization_bins.keys())
         self.parametrization_shape = [len(bins) - 1 for bins in self.parametrization_bins.values()]
 
-        # Calculate angular distances
-        self.dpsi = angular_distance(
-            self.signal_events["ra"],
-            self.signal_events["dec"],
-            self.signal_events[self.true_ra_name],
-            self.signal_events[self.true_dec_name],
+        # Find default alpha value for failing bins.
+        self._alpha_guess = float(
+            np.median(
+                angular_distance(
+                    self.signal_events["ra"],
+                    self.signal_events["dec"],
+                    self.signal_events[self.true_ra_name],
+                    self.signal_events[self.true_dec_name],
+                )
+            )
         )
 
         # Bin events
@@ -140,7 +162,7 @@ def __init__(
         self._initialize_storage()
 
     def _validate_fields(
-        self, parametrization_bins: Dict[str, Union[int, List, Tuple, npt.NDArray]]
+        self, parametrization_bins: dict[str, int | list | tuple | npt.NDArray]
     ) -> None:
         """
         Validate that required and parameterization fields exist in signal events.
@@ -156,6 +178,8 @@ def _validate_fields(
             If required fields are missing.
         """
         required_fields = ["ra", "dec", self.true_ra_name, self.true_dec_name]
+        if self.weight_field is not None:
+            required_fields.append(self.true_energy_name)
         if hasattr(self.signal_events, "dtype"):
             names = self.signal_events.dtype.names or ()
         else:
@@ -164,7 +188,7 @@ def _validate_fields(
         if missing_required:
             raise ValueError(f"Signal events missing required fields: {missing_required}")
 
-        missing_params = [key for key in parametrization_bins.keys() if key not in names]
+        missing_params = [key for key in parametrization_bins if key not in names]
         if missing_params:
             raise ValueError(
                 f"Parametrization fields {missing_params} not found in signal events. "
@@ -175,8 +199,8 @@ def _validate_fields(
             raise ValueError(f"Weight field '{self.weight_field}' not found in signal events.")
 
     def _setup_bins(
-        self, parametrization_bins: Dict[str, Union[int, List, Tuple, npt.NDArray]]
-    ) -> Dict[str, npt.NDArray[np.floating]]:
+        self, parametrization_bins: dict[str, int | list | tuple | npt.NDArray]
+    ) -> dict[str, npt.NDArray[np.floating]]:
         """
         Convert binning specifications to explicit bin edges.
 
@@ -198,7 +222,7 @@ def _setup_bins(
             elif isinstance(val, (tuple, list, np.ndarray)):
                 bins_dict[key] = np.asarray(val)
             else:
-                raise ValueError(
+                raise TypeError(
                     f"Unknown binning specification for '{key}': {val}. "
                     "Use int for number of bins or array-like for bin edges."
                 )
@@ -208,7 +232,7 @@ def _get_percentile_bins(
         self,
         nbins: int,
         values: npt.NDArray[np.floating],
-        weights: Optional[npt.NDArray[np.floating]] = None,
+        weights: npt.NDArray[np.floating] | None = None,
     ) -> npt.NDArray[np.floating]:
         """
         Create bins with approximately equal number of (weighted) events.
@@ -249,7 +273,7 @@ def _get_percentile_bins(
 
         return bin_edges
 
-    def _bin_events(self) -> Dict[str, npt.NDArray[np.integer]]:
+    def _bin_events(self) -> dict[str, npt.NDArray[np.integer]]:
         """
         Assign each event to a bin index for each parameterization dimension.
 
@@ -265,30 +289,39 @@ def _bin_events(self) -> Dict[str, npt.NDArray[np.integer]]:
 
     def _initialize_storage(self) -> None:
         """Initialize arrays to store fit results and diagnostics."""
-        shape_with_gamma = [len(self.spectral_indices)] + self.parametrization_shape
+        shape = [len(self.extension_grid), len(self.spectral_indices)] + self.parametrization_shape
 
         # Fit parameters
-        self.fit_alpha = np.full(shape_with_gamma, np.median(self.dpsi))
-        self.fit_beta = np.full(shape_with_gamma, 2.25)
+        rayleigh_median = self.extension_grid * np.sqrt(2 * np.log(2))
+        alpha_fallback = np.hypot(self._alpha_guess, rayleigh_median)
+        self.fit_alpha = np.empty(shape)
+        self.fit_alpha[...] = alpha_fallback.reshape(-1, *[1] * (len(shape) - 1))
+        self.fit_beta = np.full(shape, 2.25)
 
         # Diagnostics
-        self.histograms = np.zeros(shape_with_gamma + [self.dpsi_nbins], dtype=float)
-        self.uncertainties = np.zeros(shape_with_gamma + [self.dpsi_nbins], dtype=float)
-        self.dpsi_bins = np.zeros(shape_with_gamma + [self.dpsi_nbins + 1], dtype=float)
-        self.fit_quality = np.zeros(shape_with_gamma, dtype=float)
-        self.event_counts = np.zeros(shape_with_gamma, dtype=int)
-
-    def fit_all_bins(self, verbose: bool = True) -> Dict[str, npt.NDArray]:
+        self.histograms = np.zeros(shape + [self.dpsi_nbins], dtype=float)
+        self.uncertainties = np.zeros(shape + [self.dpsi_nbins], dtype=float)
+        self.dpsi_bins = np.zeros(shape + [self.dpsi_nbins + 1], dtype=float)
+        self.fit_quality = np.zeros(shape, dtype=float)
+        self.event_counts = np.zeros(shape, dtype=int)
+
+    def fit_all_bins(
+        self, verbose: bool = True, rng: np.random.Generator | None = None
+    ) -> dict[str, npt.NDArray]:
         """
         Fit King PSF parameters in all bins.
 
-        Iterates over all bins defined by parametrization_bins and spectral_indices,
-        fitting King distribution parameters to the angular error distribution.
+        Iterates over all bins defined by parametrization_bins, spectral_indices,
+        and extension_grid, fitting King distribution parameters to the angular
+        error distribution.
 
         Parameters
         ----------
         verbose : bool, optional
             Print progress information. Default is True.
+        rng : np.random.Generator, optional
+            Random number generator for the extension smearing draws. Defaults
+            to a fixed seed so repeated fits reproduce the same result.
 
         Returns
         -------
@@ -301,65 +334,92 @@ def fit_all_bins(self, verbose: bool = True) -> Dict[str, npt.NDArray]:
             - 'dpsi_bins': angular error bin edges
             - 'fit_quality': chi-square values
             - 'event_counts': number of events per bin
+            - 'parametrization_bins': bin edges
+            - 'extension_grid': the extension values fit
         """
+        if rng is None:
+            rng = np.random.default_rng(0)
+
         if verbose:
             print(f"Fitting King PSF in {np.prod(self.parametrization_shape)} bins...")
             print(f"  Spectral indices: {self.spectral_indices}")
+            print(f"  Extensions: {self.extension_grid}")
             print(f"  Binning dimensions: {self.bin_names}")
 
-        # Iterate over spectral indices
-        for g_idx, gamma in enumerate(self.spectral_indices):
-            if verbose:
-                print(f"\n  Spectral index γ = {gamma:.2f}")
-
-            # Calculate event weights
-            if self.weight_field is not None:
-                weights = self.signal_events[self.weight_field] * self.signal_events[
-                    self.true_energy_name
-                ] ** (-gamma)
-            else:
-                weights = np.ones(len(self.signal_events))
-
-            # Iterate over all bin combinations
-            n_fitted = 0
-            n_skipped = 0
-
-            total_bins = np.prod(self.parametrization_shape)
-            for bin_indices in tqdm(np.ndindex(*self.parametrization_shape), total=total_bins):
-                flat_idx = int(np.ravel_multi_index(bin_indices, tuple(self.parametrization_shape)))
-                event_idx = self._event_sort_order[
-                    self._bin_boundaries[flat_idx] : self._bin_boundaries[flat_idx + 1]
-                ]
-
-                if self.remove_weight_outliers and len(event_idx) > 0:
-                    bin_weights = weights[event_idx]
-                    idx_range = [
-                        int(len(bin_weights) * self.weight_outlier_percentiles[0] / 100),
-                        int(len(bin_weights) * self.weight_outlier_percentiles[1] / 100),
-                    ]
-                    idx = np.digitize(bin_weights, np.unique(bin_weights))
-                    event_idx = event_idx[(idx_range[0] <= idx) & (idx <= idx_range[1])]
-
-                n_events = len(event_idx)
-                param_idx = tuple([g_idx] + list(bin_indices))
-                self.event_counts[param_idx] = n_events
-
-                # Skip if insufficient events
-                if n_events < self.minimum_counts:
-                    n_skipped += 1
-                    continue
-
-                # Fit this bin
-                success = self._fit_single_bin(event_idx, weights, param_idx)
-                if success:
-                    n_fitted += 1
+        reco_ra = self.signal_events["ra"]
+        reco_dec = self.signal_events["dec"]
+        true_ra = self.signal_events[self.true_ra_name]
+        true_dec = self.signal_events[self.true_dec_name]
+        trueE = self.signal_events[self.true_energy_name] if self.weight_field is not None else None
+        ow = self.signal_events[self.weight_field] if self.weight_field is not None else None
+
+        n_fitted = 0
+        n_skipped = 0
+        total_bins = np.prod(self.parametrization_shape)
+        for bin_indices in tqdm(np.ndindex(*self.parametrization_shape), total=total_bins):
+            flat_idx = int(np.ravel_multi_index(bin_indices, tuple(self.parametrization_shape)))
+            event_idx = self._event_sort_order[
+                self._bin_boundaries[flat_idx] : self._bin_boundaries[flat_idx + 1]
+            ]
+            if len(event_idx) == 0:
+                n_skipped += len(self.extension_grid) * len(self.spectral_indices)
+                continue
+
+            bin_reco_ra = reco_ra[event_idx]
+            bin_reco_dec = reco_dec[event_idx]
+            bin_true_ra = true_ra[event_idx]
+            bin_true_dec = true_dec[event_idx]
+            bin_trueE = trueE[event_idx] if trueE is not None else None
+            bin_ow = ow[event_idx] if ow is not None else None
+            unit_offset = rng.rayleigh(1.0, size=len(event_idx))
+            bearing = rng.uniform(0, 2 * np.pi, size=len(event_idx))
+
+            for ext_idx, extension in enumerate(self.extension_grid):
+                if extension == 0.0:
+                    bin_dpsi = angular_distance(
+                        bin_reco_ra, bin_reco_dec, bin_true_ra, bin_true_dec
+                    )
                 else:
-                    n_skipped += 1
-
-            if verbose:
-                print(f"    Fitted {n_fitted} bins, skipped {n_skipped} bins")
+                    smeared_ra, smeared_dec = offset_position(
+                        bin_true_ra, bin_true_dec, extension * unit_offset, bearing
+                    )
+                    bin_dpsi = angular_distance(bin_reco_ra, bin_reco_dec, smeared_ra, smeared_dec)
+
+                for g_idx, gamma in enumerate(self.spectral_indices):
+                    if bin_ow is not None:
+                        bin_weights = bin_ow * bin_trueE ** (-gamma)
+                    else:
+                        bin_weights = np.ones(len(event_idx))
+
+                    local_idx = np.arange(len(event_idx))
+                    if self.remove_weight_outliers and len(local_idx) > 0:
+                        idx_range = [
+                            int(len(local_idx) * self.weight_outlier_percentiles[0] / 100),
+                            int(len(local_idx) * self.weight_outlier_percentiles[1] / 100),
+                        ]
+                        idx = np.digitize(bin_weights, np.unique(bin_weights))
+                        local_idx = local_idx[(idx_range[0] <= idx) & (idx <= idx_range[1])]
+
+                    n_events = len(local_idx)
+                    param_idx = (ext_idx, g_idx) + tuple(bin_indices)
+                    self.event_counts[param_idx] = n_events
+
+                    # Skip if insufficient events
+                    if n_events < self.minimum_counts:
+                        n_skipped += 1
+                        continue
+
+                    # Fit this bin
+                    success = self._fit_single_bin(
+                        bin_dpsi[local_idx], bin_weights[local_idx], param_idx
+                    )
+                    if success:
+                        n_fitted += 1
+                    else:
+                        n_skipped += 1
 
         if verbose:
+            print(f"\nFitted {n_fitted} bins, skipped {n_skipped} bins")
             print("\nFitting complete!")
 
         return {
@@ -371,6 +431,7 @@ def fit_all_bins(self, verbose: bool = True) -> Dict[str, npt.NDArray]:
             "fit_quality": self.fit_quality,
             "event_counts": self.event_counts,
             "parametrization_bins": self.parametrization_bins,  # type: ignore[dict-item]
+            "extension_grid": self.extension_grid,
         }
 
     def _cdf_chi2(self, cdf_hist, cdf_variance, bins, alpha, beta):
@@ -404,19 +465,19 @@ def _cdf_chi2(self, cdf_hist, cdf_variance, bins, alpha, beta):
 
     def _fit_single_bin(
         self,
-        event_idx: npt.NDArray[np.intp],
-        weights: npt.NDArray[np.floating],
-        param_idx: Tuple[int, ...],
+        masked_dpsi: npt.NDArray[np.floating],
+        masked_weights: npt.NDArray[np.floating],
+        param_idx: tuple[int, ...],
     ) -> bool:
         """
         Fit King parameters for a single bin.
 
         Parameters
         ----------
-        event_idx : ndarray
-            Integer indices of events in this bin.
-        weights : ndarray
-            Event weights.
+        masked_dpsi : ndarray
+            Angular errors for events in this bin.
+        masked_weights : ndarray
+            Event weights for events in this bin, not yet normalized.
         param_idx : tuple
             Index tuple for storing results.
 
@@ -425,10 +486,7 @@ def _fit_single_bin(
         bool
             True if fit succeeded, False otherwise.
         """
-        # Extract events in this bin
-        masked_dpsi = self.dpsi[event_idx]
-        masked_weights = weights[event_idx]
-        masked_weights /= masked_weights.sum()  # Normalize
+        masked_weights = masked_weights / masked_weights.sum()  # Normalize
 
         # Create bins for this subset. Also calculate the
         # phase space parameter while we're here. We'll need
@@ -451,12 +509,24 @@ def _fit_single_bin(
         cdf_hist = np.cumsum(hist)
         cdf_variance = np.cumsum(hist2) / np.sum(hist) ** 2
 
+        bounds = [
+            (np.nextafter(1e-4, np.pi), np.nextafter(self.angular_cutoff, 0)),
+            (1.01, 1000),
+        ]
+
+        def fit(alpha0, beta0):
+            return minimize(
+                lambda params: self._cdf_chi2(cdf_hist, cdf_variance, dpsi_bins, *params),
+                [alpha0, beta0],
+                method="L-BFGS-B",
+                jac=True,
+                bounds=bounds,
+            )
+
         # Get initial guess by doing a rough scan over alpha and beta.
         alpha_median_guess = bin_centers[np.searchsorted(cdf_hist, 0.5)]
         alpha_candidates = np.clip(
-            alpha_median_guess * np.array([0.5, 0.75, 1.0, 1.5, 2.0]),
-            np.nextafter(1e-4, np.pi),
-            np.nextafter(self.angular_cutoff, 0),
+            alpha_median_guess * np.array([0.5, 0.75, 1.0, 1.5, 2.0]), *bounds[0]
         )
         beta_candidates = [1.25, 1.75, 2, 2.5, 4, 7, 9]
         best_prescan, alpha_guess, beta_guess = None, alpha_median_guess, 2
@@ -465,44 +535,24 @@ def _fit_single_bin(
                 val = self._cdf_chi2(cdf_hist, cdf_variance, dpsi_bins, alpha, beta)[0]
                 if best_prescan is None or val < best_prescan:
                     best_prescan, alpha_guess, beta_guess = val, alpha, beta
-        result = minimize(
-            lambda params: self._cdf_chi2(cdf_hist, cdf_variance, dpsi_bins, *params),
-            [alpha_guess, beta_guess],
-            method="L-BFGS-B",
-            jac=True,
-            bounds=[
-                (np.nextafter(1e-4, np.pi), np.nextafter(self.angular_cutoff, 0)),
-                (1.01, 1000),
-            ],
-        )
+        result = fit(alpha_guess, beta_guess)
 
         # If the fit doesn't succeed, try manually seeding with other beta values.
         if not result.success:
             best = None
             for beta in beta_candidates:
-                result = minimize(
-                    lambda params: self._cdf_chi2(cdf_hist, cdf_variance, dpsi_bins, *params),
-                    [alpha_guess, beta],
-                    method="L-BFGS-B",
-                    jac=True,
-                    bounds=[
-                        (np.nextafter(1e-4, np.pi), np.nextafter(self.angular_cutoff, 0)),
-                        (1.01, 1000),
-                    ],
-                )
+                result = fit(alpha_guess, beta)
+                if result.success and (best is None or best.fun > result.fun):
+                    best = result
 
-                if result.success:
-                    if (best is None) or (best.fun > result.fun):
-                        best = result
-
-            result = best
+            if best is not None:
+                result = best
 
         # Store histogram data (pad/truncate to match storage size).
         # Make sure to rescale by the phase space to get densities.
         n_store = min(len(hist), self.dpsi_nbins)
         self.histograms[param_idx][:n_store] = hist[:n_store] / delta
         self.uncertainties[param_idx][:n_store] = np.sqrt(hist2[:n_store]) / delta
-        self.dpsi_bins[param_idx][: len(dpsi_bins)] = dpsi_bins
 
         # Store results if we found a solution
         if result.success:
@@ -513,14 +563,17 @@ def _fit_single_bin(
 
         return False
 
-    def get_interpolator(self, gamma_index: int = 0) -> Tuple[Any, Any]:
+    def get_interpolator(self, gamma_index: int = 0, extension_index: int = 0) -> tuple[Any, Any]:
         """
-        Get an interpolator for fitted parameters at a given spectral index.
+        Get an interpolator for fitted parameters at a given spectral index
+        and extension.
 
         Parameters
         ----------
         gamma_index : int, optional
             Index of the spectral index to use. Default is 0.
+        extension_index : int, optional
+            Index into extension_grid to use. Default is 0.
 
         Returns
         -------
@@ -540,27 +593,29 @@ def get_interpolator(self, gamma_index: int = 0) -> Tuple[Any, Any]:
         # Create interpolators
         alpha_interp = RegularGridInterpolator(
             tuple(bin_centers),
-            self.fit_alpha[gamma_index],
+            self.fit_alpha[extension_index, gamma_index],
             method="linear",
             bounds_error=False,
-            fill_value=self.fit_alpha[gamma_index].mean(),
+            fill_value=self.fit_alpha[extension_index, gamma_index].mean(),
         )
 
         beta_interp = RegularGridInterpolator(
             tuple(bin_centers),
-            self.fit_beta[gamma_index],
+            self.fit_beta[extension_index, gamma_index],
             method="linear",
             bounds_error=False,
-            fill_value=self.fit_beta[gamma_index].mean(),
+            fill_value=self.fit_beta[extension_index, gamma_index].mean(),
         )
 
         return alpha_interp, beta_interp
 
     def plot_fit(
         self,
-        bin_indices: Union[Tuple[int, ...], Dict[str, int]],
+        bin_indices: tuple[int, ...] | dict[str, int],
         gamma_index: int = 0,
-        ax: Optional[Any] = None,
+        ax: Any | None = None,
+        *,
+        extension_index: int = 0,
     ) -> Any:
         """
         Plot the fitted King PDF for a specific bin.
@@ -574,6 +629,8 @@ def plot_fit(
             Index of spectral index. Default is 0.
         ax : matplotlib.axes.Axes, optional
             Axes to plot on. If None, creates new figure.
+        extension_index : int, optional
+            Index into extension_grid to use. Default is 0.
 
         Returns
         -------
@@ -588,13 +645,13 @@ def plot_fit(
         import matplotlib.pyplot as plt
 
         if ax is None:
-            fig, ax = plt.subplots(figsize=(8, 6))
+            _, ax = plt.subplots(figsize=(8, 6))
 
         # Convert dict to tuple if needed
         if isinstance(bin_indices, dict):
             bin_indices = tuple(bin_indices[key] for key in self.bin_names)
 
-        param_idx = tuple([gamma_index] + list(bin_indices))
+        param_idx = (extension_index, gamma_index) + tuple(bin_indices)
 
         # Get histogram data
         hist = self.histograms[param_idx]
diff --git a/kingmaker/pdf.py b/kingmaker/pdf.py
index ff0c6f8..62e34a3 100644
--- a/kingmaker/pdf.py
+++ b/kingmaker/pdf.py
@@ -1,14 +1,13 @@
-from typing import Optional, Tuple, Union, cast
+from typing import cast
+
+import healpy as hp
 import numpy as np
 import numpy.typing as npt
-import healpy as hp
 from scipy.interpolate import interpn
 from scipy.sparse import csr_array
-from scipy.special import legendre_p_all, sph_harm_y_all, gammaln
-from numpy.polynomial.laguerre import laggauss
+from scipy.special import legendre_p_all, sph_harm_y_all
 
-from .distribution import _log10pi
-from .distribution import _norm, _unnormalized_pdf, _unnormalized_cdf
+from .distribution import _log10pi, _norm, _unnormalized_cdf, _unnormalized_pdf
 from .utils import _build_marginalized_grid, angular_distance
 
 
@@ -35,9 +34,9 @@ def __init__(
 
     def norm(
         self,
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
-    ) -> Union[float, npt.NDArray[np.floating]]:
+        alpha: float | npt.NDArray[np.floating],
+        beta: float | npt.NDArray[np.floating],
+    ) -> float | npt.NDArray[np.floating]:
         """
         Compute the normalization constant for given King parameters.
 
@@ -57,11 +56,11 @@ def norm(
 
     def pdf_from_norm(
         self,
-        x: Union[float, npt.NDArray[np.floating]],
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
-        norm: Union[float, npt.NDArray[np.floating]],
-    ) -> Union[float, npt.NDArray[np.floating]]:
+        x: float | npt.NDArray[np.floating],
+        alpha: float | npt.NDArray[np.floating],
+        beta: float | npt.NDArray[np.floating],
+        norm: float | npt.NDArray[np.floating],
+    ) -> float | npt.NDArray[np.floating]:
         """
         Evaluate the King kernel given a precomputed normalization constant.
 
@@ -93,10 +92,10 @@ def pdf_from_norm(
 
     def pdf(
         self,
-        x: Union[float, npt.NDArray[np.floating]],
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
-    ) -> Union[float, npt.NDArray[np.floating]]:
+        x: float | npt.NDArray[np.floating],
+        alpha: float | npt.NDArray[np.floating],
+        beta: float | npt.NDArray[np.floating],
+    ) -> float | npt.NDArray[np.floating]:
         """
         Evaluate the normalized King PDF at given angular separation(s).
 
@@ -118,11 +117,10 @@ def pdf(
             Normalized PDF value(s) with units of probability/steradian.
         """
         # Scalar-like: check if we can shortcut using the angular cutoff.
-        if np.isscalar(x) and (x > self.angular_cutoff):  # type: ignore[operator]
+        if (np.isscalar(x) and (x > self.angular_cutoff)) or (  # type: ignore[operator]
+            isinstance(x, np.ndarray) and x.size == 1 and float(x.flat[0]) > self.angular_cutoff
+        ):
             return 0
-        elif isinstance(x, np.ndarray) and x.size == 1:
-            if float(x.flat[0]) > self.angular_cutoff:
-                return 0
 
         if np.any(alpha <= 0):
             raise ValueError("Received alpha <= 0. The King distribution is not defined here.")
@@ -146,10 +144,10 @@ def pdf(
 
     def cdf(
         self,
-        x: Union[float, npt.NDArray[np.floating]],
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
-    ) -> Union[float, npt.NDArray[np.floating]]:
+        x: float | npt.NDArray[np.floating],
+        alpha: float | npt.NDArray[np.floating],
+        beta: float | npt.NDArray[np.floating],
+    ) -> float | npt.NDArray[np.floating]:
         """
         Evaluate the normalized King CDF at given angular separation(s).
 
@@ -174,9 +172,8 @@ def cdf(
         if np.isscalar(x):
             if x > self.angular_cutoff:  # type: ignore[operator]
                 return 1
-        elif isinstance(x, np.ndarray) and x.size == 1:
-            if float(x.flat[0]) > self.angular_cutoff:
-                return 1
+        elif isinstance(x, np.ndarray) and x.size == 1 and float(x.flat[0]) > self.angular_cutoff:
+            return 1
 
         if np.any(alpha <= 0):
             raise ValueError(
@@ -207,7 +204,7 @@ def sample(
         n: int,
         alpha: float,
         beta: float,
-        rng: Optional[np.random.Generator] = None,
+        rng: np.random.Generator | None = None,
         n_grid: int = 10000,
     ) -> npt.NDArray[np.floating]:
         """
@@ -254,7 +251,7 @@ def evaluate(
         alpha: npt.NDArray[np.floating],
         beta: npt.NDArray[np.floating],
         *,
-        mask: Optional[csr_array] = None,
+        mask: csr_array | None = None,
     ) -> csr_array:
         """
         Evaluate the King PDF for all (event, source) pairs and return a sparse matrix.
@@ -397,10 +394,10 @@ class MarginalizedKingPDF:
     def __init__(
         self,
         *,
-        source_declination: Union[list, npt.NDArray[np.floating]],
+        source_declination: list | npt.NDArray[np.floating],
         angular_cutoff: float = np.pi,
-        points_alpha: Optional[npt.NDArray[np.floating]] = None,
-        points_beta: Optional[npt.NDArray[np.floating]] = None,
+        points_alpha: npt.NDArray[np.floating] | None = None,
+        points_beta: npt.NDArray[np.floating] | None = None,
         n_signed_delta_dec: int = 200,
         n_ra_bins: int = 100,
     ) -> None:
@@ -481,9 +478,9 @@ def _build_cache(self) -> None:
 
     def pdf(
         self,
-        x: Union[float, npt.NDArray[np.floating]],
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
+        x: float | npt.NDArray[np.floating],
+        alpha: float | npt.NDArray[np.floating],
+        beta: float | npt.NDArray[np.floating],
         source_dec: float,
     ) -> npt.NDArray[np.floating]:
         """
@@ -558,7 +555,7 @@ def evaluate(
         alpha: npt.NDArray[np.floating],
         beta: npt.NDArray[np.floating],
         *,
-        mask: Optional[csr_array] = None,
+        mask: csr_array | None = None,
     ) -> csr_array:
         """
         Evaluate the RA-marginalized King PDF for every (event, source) pair.
@@ -727,12 +724,12 @@ def __init__(
         self,
         skymap: npt.NDArray[np.floating],
         *,
-        eval_decs: Optional[Union[float, npt.NDArray[np.floating]]] = None,
-        eval_ras: Optional[Union[float, npt.NDArray[np.floating]]] = None,
+        eval_decs: float | npt.NDArray[np.floating] | None = None,
+        eval_ras: float | npt.NDArray[np.floating] | None = None,
         angular_cutoff: float = np.pi,
-        points_alpha: npt.NDArray[np.floating] = np.logspace(-4, _log10pi + 1e-2, 100),
-        points_beta: npt.NDArray[np.floating] = np.nextafter(np.logspace(0, 1, 100), np.inf),
-        lmax: Optional[int] = None,
+        points_alpha: npt.NDArray[np.floating] | None = None,
+        points_beta: npt.NDArray[np.floating] | None = None,
+        lmax: int | None = None,
         interpolation_method: str = "nearest",
         memory_limit_gb: float = 1.0,
     ) -> None:
@@ -745,6 +742,10 @@ def __init__(
 
         super().__init__(angular_cutoff=angular_cutoff)
 
+        if points_alpha is None:
+            points_alpha = np.logspace(-4, _log10pi + 1e-2, 100)
+        if points_beta is None:
+            points_beta = np.nextafter(np.logspace(0, 1, 100), np.inf)
         if np.any(points_alpha <= 0):
             raise ValueError(
                 "Received points_alpha containing at least one point <= 0. The"
@@ -806,8 +807,8 @@ def __init__(
 
     def set_coordinates(
         self,
-        eval_decs: Union[float, npt.NDArray[np.floating]],
-        eval_ras: Union[float, npt.NDArray[np.floating]],
+        eval_decs: float | npt.NDArray[np.floating],
+        eval_ras: float | npt.NDArray[np.floating],
     ) -> None:
         """
         Set evaluation coordinates and pre-compute spherical harmonics.
@@ -1016,9 +1017,7 @@ def convolve_map(self, alpha: float, beta: float) -> npt.NDArray[np.floating]:
         harmonic_convolution = hp.almxfl(alm=self.skymap_alm, fl=b_l, mmax=self.mmax, inplace=False)
         return hp.alm2map(harmonic_convolution, nside=self.nside, lmax=self.lmax, mmax=self.mmax)
 
-    def convolve_at_grid_point(
-        self, alpha: float, beta: float
-    ) -> Union[float, npt.NDArray[np.floating]]:
+    def convolve_at_grid_point(self, alpha: float, beta: float) -> float | npt.NDArray[np.floating]:
         """
         Evaluate convolved PDF only at pre-set grid points (eval_decs, eval_ras).
 
@@ -1056,9 +1055,9 @@ def sample(
         n: int,
         alpha: float,
         beta: float,
-        rng: Optional[np.random.Generator] = None,
+        rng: np.random.Generator | None = None,
         n_grid: int = 10000,
-    ) -> Tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
+    ) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
         """
         Sample reconstructed positions from the PSF-convolved template skymap.
 
@@ -1116,460 +1115,3 @@ def sample(
 
         colatitude, longitude = hp.pix2ang(self.nside, pixel_indices)
         return longitude, np.pi / 2 - colatitude  # reco_ra, reco_dec
-
-
-class ExtendedSourceKingPDF:
-    """
-    King PSF convolved with a Rayleigh (Gaussian) source extension.
-
-    Precomputes a 4D lookup table of convolved PDF values over
-    (log10(alpha), log10(beta), log10(extension), psi) using Gauss-Laguerre
-    quadrature on the inverse-gamma scale mixture representation of the King
-    distribution. At runtime, evaluates each event via quadrilinear
-    interpolation into this table.
-
-    Both the King PSF and the Rayleigh extension are axially symmetric, so the
-    convolution depends only on the scalar angular distance psi between the
-    event and the source — not on the full sky coordinates of either.
-
-    Parameters
-    ----------
-    angular_cutoff : float, optional
-        Maximum angular separation in radians. Default is pi.
-    maximum_sigma : float, optional
-        Number of source-extension radii beyond the King angular_cutoff
-        that ``evaluate()`` considers for each source. For a source with
-        extension r₀, events at angular distance greater than
-        ``maximum_sigma * r₀ + angular_cutoff`` are skipped. Default is 3.
-    points_alpha : ndarray, optional
-        Alpha grid points in radians. Default: 30 log-spaced values from
-        0.05 degrees to pi.
-    points_beta : ndarray, optional
-        Beta grid points. Default: 20 log-spaced values from ~1.023 to 10.
-    points_extension : ndarray, optional
-        Extension radius grid points in radians. Default: 20 log-spaced values
-        from 0.05 degrees to 5 degrees.
-    points_psi : ndarray, optional
-        Angular separation grid points in radians. Default: 0 followed by 500
-        log-spaced values from 1e-4 rad to angular_cutoff.
-    n_quad : int, optional
-        Number of Gauss-Laguerre quadrature nodes for the scale mixture
-        integral. Default is 32.
-    """
-
-    def __init__(
-        self,
-        *,
-        angular_cutoff: float = np.pi,
-        maximum_sigma: float = 3.0,
-        points_alpha: Optional[npt.NDArray[np.floating]] = None,
-        points_beta: Optional[npt.NDArray[np.floating]] = None,
-        points_extension: Optional[npt.NDArray[np.floating]] = None,
-        points_psi: Optional[npt.NDArray[np.floating]] = None,
-        n_quad: int = 32,
-    ) -> None:
-        self.angular_cutoff = float(angular_cutoff)
-        self.maximum_sigma = float(maximum_sigma)
-
-        self.n_quad = n_quad
-
-        self._points_alpha = np.sort(
-            np.asarray(
-                points_alpha
-                if points_alpha is not None
-                else np.logspace(np.log10(np.radians(0.05)), _log10pi, 30),
-                dtype=np.float64,
-            )
-        )
-        self._points_beta = np.sort(
-            np.asarray(
-                points_beta if points_beta is not None else np.logspace(0.01, 1, 20),
-                dtype=np.float64,
-            )
-        )
-        self._points_extension = np.sort(
-            np.asarray(
-                points_extension
-                if points_extension is not None
-                else np.logspace(np.log10(np.radians(0.05)), np.log10(np.radians(5.0)), 20),
-                dtype=np.float64,
-            )
-        )
-        # The psi table must reach the widest possible search window so that
-        # interpn stays in-bounds for all events accepted by evaluate().
-        # That window is maximum_sigma * max_ext + angular_cutoff, capped at π.
-        _psi_max = min(
-            self.maximum_sigma * self._points_extension[-1] + angular_cutoff,
-            np.pi,
-        )
-        self._points_psi = np.sort(
-            np.asarray(
-                points_psi
-                if points_psi is not None
-                else np.concatenate([[0.0], np.logspace(-4, np.log10(_psi_max), 500)]),
-                dtype=np.float64,
-            )
-        )
-
-        if np.any(self._points_alpha <= 0):
-            raise ValueError(
-                "points_alpha contains values <= 0. The King distribution is not defined here."
-            )
-        if np.any(self._points_beta <= 1):
-            raise ValueError(
-                "points_beta contains values <= 1. The King distribution is not defined here."
-            )
-        if np.any(self._points_extension <= 0):
-            raise ValueError(
-                "points_extension contains values <= 0. Extension radius must be positive."
-            )
-        if np.any(self._points_extension > np.radians(5.0) + 1e-12):
-            raise ValueError(
-                "points_extension contains values > 5 degrees. The flat-sky (Rayleigh) "
-                "approximation error exceeds ~0.5% at this scale; use a vMF extension instead."
-            )
-
-        self._log10_points_alpha = np.log10(self._points_alpha)
-        self._log10_points_beta = np.log10(self._points_beta)
-        self._log10_points_extension = np.log10(self._points_extension)
-
-        # Gauss-Laguerre nodes and weights for the scale mixture integral.
-        # The substitution t = beta*alpha^2 / v^2 maps the InvGamma weight
-        # exp(-beta*alpha^2/v^2) to the standard Gauss-Laguerre form e^{-t}.
-        self._quad_nodes, self._quad_weights = laggauss(n_quad)
-
-        self._build_table()
-
-    def _scale_mixture_pdf(
-        self,
-        psi: npt.NDArray[np.floating],
-        alpha: npt.NDArray[np.floating],
-        beta: npt.NDArray[np.floating],
-        extension: float,
-    ) -> npt.NDArray[np.floating]:
-        """
-        Evaluate the King–Rayleigh convolution via Gauss-Laguerre quadrature.
-
-        Derivation
-        ----------
-        Consider integrating a Rayleigh PDF over an unknown scale v^2, weighted
-        by an InvGamma(kappa, c) density:
-
-            integral_0^inf  [psi/v^2 * exp(-psi^2/(2v^2))]   <- Rayleigh
-                            * [c^kappa/Gamma(kappa) * (v^2)^{-kappa-1} * exp(-c/v^2)]
-                            dv^2
-
-        The two exponentials combine: exp(-psi^2/(2v^2)) * exp(-c/v^2)
-        = exp(-(psi^2/2 + c)/v^2). Collecting powers of v^2 gives an integrand
-        proportional to (v^2)^{-(kappa+2)} * exp(-A/v^2) with A = c + psi^2/2.
-        That integral is a standard gamma-function result: Gamma(kappa+1)/A^{kappa+1}.
-        Substituting back:
-
-            result = psi * kappa * c^kappa / (c + psi^2/2)^{kappa+1}
-                   = psi/c * kappa / (1 + psi^2/(2c))^{kappa+1}
-
-        Matching to the flat-sky King PDF psi/alpha^2 * (1-1/beta)
-        * (1 + psi^2/(2*beta*alpha^2))^{-beta} requires kappa = beta-1 and
-        c = beta*alpha^2. The King distribution is therefore exactly equal
-        to an InvGamma-weighted integral of Rayleigh distributions.
-
-        For the convolution with a Rayleigh source extension of radius r0: the
-        event position is the vector sum of an independent PSF displacement
-        (Rayleigh with scale v^2) and a source-extent displacement (Rayleigh
-        with scale r0^2). Adding two independent 2D Gaussian displacements
-        produces a 2D Gaussian with combined scale v^2 + r0^2. Replacing v^2
-        with v^2 + r0^2 inside the integral above gives the convolution.
-
-        The substitution t = c/v^2 (so v^2 = c/t) maps exp(-c/v^2) to exp(-t),
-        putting the integral in standard Gauss-Laguerre form
-        integral_0^inf f(t) * e^{-t} dt, evaluated here with fixed nodes and
-        weights from laggauss(n_quad). After the substitution the integral
-        becomes:
-
-            p_conv = psi / Gamma(beta-1)
-                     * integral_0^inf  t^(beta-1) / (c + r0^2 * t)
-                                       * exp(-psi^2 * t / (2*(c + r0^2*t)))
-                                       * e^{-t} dt
-
-        Setting r0 = 0 recovers the flat-sky King PDF exactly. Uses psi^2/2
-        throughout rather than 1 - cos(psi); error is below 0.5% for psi and
-        r0 below 5 degrees.
-
-        Parameters
-        ----------
-        psi : ndarray
-            Angular separation(s) from the source in radians.
-        alpha : ndarray
-            King alpha parameter(s) in radians.
-        beta : ndarray
-            King beta parameter(s). Must be > 1.
-        extension : float
-            Rayleigh source extension radius in radians. Must be <= 5 degrees.
-
-        Returns
-        -------
-        ndarray
-            Convolved PDF values, same shape as the broadcast of inputs.
-            Zero wherever psi = 0.
-
-        Raises
-        ------
-        NotImplementedError
-            If extension exceeds 5 degrees (np.radians(5)), where the
-            flat-sky approximation error exceeds ~0.5% and a vMF extension
-            should be used instead.
-        """
-        if float(extension) > np.radians(5.0) + 1e-12:
-            raise NotImplementedError(
-                f"extension={np.degrees(float(extension)):.2f} deg exceeds the 5-degree "
-                "limit for the flat-sky (Rayleigh) approximation. "
-                "A von Mises-Fisher extension needs to be implemented "
-                " for larger angular scales."
-            )
-
-        psi, alpha, beta = np.broadcast_arrays(
-            np.asarray(psi, dtype=np.float64),
-            np.asarray(alpha, dtype=np.float64),
-            np.asarray(beta, dtype=np.float64),
-        )
-
-        r0_sq = float(extension) ** 2
-        c = beta * alpha**2  # InvGamma scale: beta * alpha^2
-
-        # Broadcast data arrays against the quadrature axis
-        t = self._quad_nodes  # (n_quad,)
-        w = self._quad_weights  # (n_quad,)
-        c_q = c[..., np.newaxis]  # (..., 1)
-        psi_q = psi[..., np.newaxis]  # (..., 1)
-        beta_q = beta[..., np.newaxis]  # (..., 1)
-
-        denom = c_q + r0_sq * t  # (..., n_quad); always positive
-
-        # t^(beta-1) via log to stay in float64 range for large beta or t
-        t_pow = np.exp((beta_q - 1.0) * np.log(t))  # (..., n_quad)
-
-        integrand = psi_q * t_pow / denom * np.exp(-(psi_q**2) * t / (2.0 * denom))
-        # (..., n_quad); naturally zero when psi = 0
-
-        quad_sum = (w * integrand).sum(axis=-1)  # (...)
-
-        return quad_sum / np.exp(gammaln(beta - 1.0))
-
-    def _build_table(self) -> None:
-        """
-        Precompute the convolved PDF on the (alpha, beta, extension, psi) grid.
-
-        Evaluates _scale_mixture_pdf over the full four-dimensional parameter
-        space by looping over extension values (keeping one (n_alpha, n_beta,
-        n_psi) slice in memory at a time) and stacking the results. Stores the
-        result as self._table with shape (n_alpha, n_beta, n_extension, n_psi)
-        for use by interpn at runtime.
-        """
-        # Broadcast axes over (alpha, beta, psi) for each extension slice.
-        # Shape annotations assume n_alpha, n_beta, n_psi grid sizes.
-        alpha_g = self._points_alpha[:, np.newaxis, np.newaxis]  # (n_alpha, 1, 1)
-        beta_g = self._points_beta[np.newaxis, :, np.newaxis]  # (1, n_beta, 1)
-        psi_g = self._points_psi[np.newaxis, np.newaxis, :]  # (1, 1, n_psi)
-
-        slices = [
-            self._scale_mixture_pdf(psi_g, alpha_g, beta_g, float(ext))
-            for ext in self._points_extension
-        ]  # each element: (n_alpha, n_beta, n_psi)
-
-        self._table = np.stack(slices, axis=2)  # (n_alpha, n_beta, n_extension, n_psi)
-
-    def pdf(
-        self,
-        x: Union[float, npt.NDArray[np.floating]],
-        alpha: Union[float, npt.NDArray[np.floating]],
-        beta: Union[float, npt.NDArray[np.floating]],
-        extension: Union[float, npt.NDArray[np.floating]],
-    ) -> npt.NDArray[np.floating]:
-        """
-        Evaluate the convolved PDF at angular separation(s) x.
-
-        Parameters
-        ----------
-        x : float or ndarray
-            Angular separation(s) from the source in radians.
-        alpha : float or ndarray
-            Per-event King alpha parameter in radians.
-        beta : float or ndarray
-            Per-event King beta parameter.
-        extension : float or ndarray
-            Source extension radius in radians.
-
-        Returns
-        -------
-        ndarray
-            Convolved PDF values in probability/steradian.
-        """
-        x = np.asarray(x, dtype=np.float64)
-        alpha = np.asarray(alpha, dtype=np.float64)
-        beta = np.asarray(beta, dtype=np.float64)
-        extension = np.asarray(extension, dtype=np.float64)
-        x, alpha, beta, extension = np.broadcast_arrays(x, alpha, beta, extension)
-        shape = x.shape
-        x = np.atleast_1d(x).ravel()
-        alpha = np.atleast_1d(alpha).ravel()
-        beta = np.atleast_1d(beta).ravel()
-        extension = np.atleast_1d(extension).ravel()
-
-        result = np.zeros(len(x))
-        in_bounds = x <= self._points_psi[-1]
-
-        if np.any(in_bounds):
-            queries = np.column_stack(
-                [
-                    np.log10(alpha[in_bounds]),
-                    np.log10(beta[in_bounds]),
-                    np.log10(extension[in_bounds]),
-                    x[in_bounds],
-                ]
-            )
-
-            # _table stores the 1D radial density f(ψ): ∫₀^∞ f dψ = 1.
-            # Per-steradian density: p(ψ) = f(ψ) / (2π ψ)  [flat-sky: dΩ = 2π ψ dψ].
-            f_psi = interpn(
-                (
-                    self._log10_points_alpha,
-                    self._log10_points_beta,
-                    self._log10_points_extension,
-                    self._points_psi,
-                ),
-                self._table,
-                queries,
-                method="linear",
-                bounds_error=True,
-            )
-
-            x_in = x[in_bounds]
-            with np.errstate(invalid="ignore", divide="ignore"):
-                result[in_bounds] = np.where(x_in > 0.0, f_psi / (2.0 * np.pi * x_in), 0.0)
-
-        return result.reshape(shape)
-
-    def evaluate(
-        self,
-        source_ras: npt.NDArray[np.floating],
-        source_decs: npt.NDArray[np.floating],
-        source_extensions: npt.NDArray[np.floating],
-        event_ras: npt.NDArray[np.floating],
-        event_decs: npt.NDArray[np.floating],
-        alpha: npt.NDArray[np.floating],
-        beta: npt.NDArray[np.floating],
-        *,
-        mask: Optional[csr_array] = None,
-    ) -> csr_array:
-        """
-        Evaluate the extended-source convolved PDF for all (event, source) pairs.
-
-        Iterates over sources, applies a declination pre-filter and a full
-        great-circle distance check to identify pairs within ``angular_cutoff``,
-        then evaluates the convolved PDF for those pairs using ``pdf()``. On
-        repeated calls where the source and event positions are unchanged, pass
-        the result of a previous call as ``mask`` to skip the masking loop and
-        go straight to vectorized PDF evaluation.
-
-        Parameters
-        ----------
-        source_ras : ndarray, shape (n_sources,)
-            Source right ascensions in radians.
-        source_decs : ndarray, shape (n_sources,)
-            Source declinations in radians.
-        source_extensions : ndarray, shape (n_sources,)
-            Per-source angular extension radii in radians. Must be within the
-            range covered by ``points_extension`` provided at construction.
-        event_ras : ndarray, shape (n_events,)
-            Reconstructed event right ascensions in radians.
-        event_decs : ndarray, shape (n_events,)
-            Reconstructed event declinations in radians.
-        alpha : ndarray, shape (n_events,)
-            Per-event King alpha parameter in radians.
-        beta : ndarray, shape (n_events,)
-            Per-event King beta parameter.
-        mask : csr_array, optional
-            Sparse array whose nonzero structure encodes the valid
-            (event, source) pairs. When provided, the masking loop is skipped
-            and only the indexed pairs are evaluated. Pass the result of a
-            previous :meth:`evaluate` call to reuse the geometry.
-
-        Returns
-        -------
-        csr_array, shape (n_events, n_sources)
-            Sparse array of convolved PDF values in probability/steradian,
-            indexed ``[event_index, source_index]``.
-        """
-        source_ras = np.atleast_1d(np.asarray(source_ras, dtype=np.float64))
-        source_decs = np.atleast_1d(np.asarray(source_decs, dtype=np.float64))
-        source_extensions = np.atleast_1d(np.asarray(source_extensions, dtype=np.float64))
-        event_ras = np.asarray(event_ras, dtype=np.float64)
-        event_decs = np.asarray(event_decs, dtype=np.float64)
-        alpha = np.asarray(alpha, dtype=np.float64)
-        beta = np.asarray(beta, dtype=np.float64)
-
-        n_events = len(event_ras)
-        n_sources = len(source_ras)
-
-        if mask is not None:
-            rows, cols = mask.nonzero()
-            psi = angular_distance(
-                source_ras[cols],
-                source_decs[cols],
-                event_ras[rows],
-                event_decs[rows],
-            )
-            vals = self.pdf(psi, alpha[rows], beta[rows], source_extensions[cols])
-            nonzero = vals > 0.0
-            return csr_array(
-                (vals[nonzero], (rows[nonzero], cols[nonzero])),
-                shape=(n_events, n_sources),
-                dtype=np.float64,
-            )
-
-        row_chunks, col_chunks, val_chunks = [], [], []
-        for source_index, (src_ra, src_dec, src_ext) in enumerate(
-            zip(source_ras, source_decs, source_extensions)
-        ):
-            radius = min(
-                self.maximum_sigma * src_ext + self.angular_cutoff,
-                self._points_psi[-1],
-            )
-            dec_mask = np.abs(event_decs - src_dec) <= radius
-            candidate_indices = np.flatnonzero(dec_mask)
-            if len(candidate_indices) == 0:
-                continue
-
-            psi = angular_distance(
-                src_ra,
-                src_dec,
-                event_ras[candidate_indices],
-                event_decs[candidate_indices],
-            )
-            within_cutoff = psi <= radius
-            event_indices = candidate_indices[within_cutoff]
-            if len(event_indices) == 0:
-                continue
-
-            vals = self.pdf(
-                psi[within_cutoff],
-                alpha[event_indices],
-                beta[event_indices],
-                np.full(int(within_cutoff.sum()), src_ext),
-            )
-            nonzero = vals > 0.0
-            row_chunks.append(event_indices[nonzero])
-            col_chunks.append(np.full(int(nonzero.sum()), source_index, dtype=np.intp))
-            val_chunks.append(vals[nonzero])
-
-        if not row_chunks:
-            return csr_array((n_events, n_sources), dtype=np.float64)
-
-        return csr_array(
-            (
-                np.concatenate(val_chunks),
-                (np.concatenate(row_chunks), np.concatenate(col_chunks)),
-            ),
-            shape=(n_events, n_sources),
-            dtype=np.float64,
-        )
diff --git a/kingmaker/utils.py b/kingmaker/utils.py
index 07ea426..f7e065c 100644
--- a/kingmaker/utils.py
+++ b/kingmaker/utils.py
@@ -1,4 +1,3 @@
-from typing import Tuple, Union
 import numpy as np
 import numpy.typing as npt
 from numba import njit, prange
@@ -31,10 +30,11 @@ def _interp1d(x: float, xlow: float, xhigh: float, ylow: float, yhigh: float) ->
     """
     return ylow + (yhigh - ylow) / (xhigh - xlow) * (x - xlow)
 
+
 @njit(cache=True)
-def _interp1d_order2(x: float,
-                     xlow: float, xnearest: float, xhigh: float,
-                     ylow: float, ynearest: float, yhigh: float) -> float:
+def _interp1d_order2(
+    x: float, xlow: float, xnearest: float, xhigh: float, ylow: float, ynearest: float, yhigh: float
+) -> float:
     """
     Perform 1D order-2 interpolation.
 
@@ -62,21 +62,20 @@ def _interp1d_order2(x: float,
     """
     # np.linalg.solve solves Ax = B. We'll use that
     # to get the coefficients in y = a x**2 + b * x + c.
-    A = np.array([[xlow**2, xnearest**2, xhigh**2],
-                  [xlow,    xnearest,    xhigh],
-                  [1,       1,           1]])
+    A = np.array([[xlow**2, xnearest**2, xhigh**2], [xlow, xnearest, xhigh], [1, 1, 1]])
     B = np.array([ylow, ynearest, yhigh])
     coeffs = np.linalg.solve(A, B)
 
     return np.dot(coeffs, np.array([x**2, x, 1]))
 
+
 @njit(cache=True)
 def angular_distance(
-    src_ra: Union[float, npt.NDArray[np.floating]],
-    src_dec: Union[float, npt.NDArray[np.floating]],
-    ra: Union[float, npt.NDArray[np.floating]],
-    dec: Union[float, npt.NDArray[np.floating]],
-) -> Union[float, npt.NDArray[np.floating]]:
+    src_ra: float | npt.NDArray[np.floating],
+    src_dec: float | npt.NDArray[np.floating],
+    ra: float | npt.NDArray[np.floating],
+    dec: float | npt.NDArray[np.floating],
+) -> float | npt.NDArray[np.floating]:
     """
     Calculate angular distance on the sphere using the haversine formula.
 
@@ -103,6 +102,76 @@ def angular_distance(
     return np.arccos(np.minimum(np.maximum(cosDist, -1.0), 1.0))  # type: ignore[no-any-return]
 
 
+def offset_position(
+    ra: float | npt.NDArray[np.floating],
+    dec: float | npt.NDArray[np.floating],
+    distance: float | npt.NDArray[np.floating],
+    bearing: float | npt.NDArray[np.floating],
+) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
+    """
+    Move (ra, dec) by an angular distance along a bearing on the sphere.
+
+    Parameters
+    ----------
+    ra, dec : float or ndarray
+        Starting position(s) in radians.
+    distance : float or ndarray
+        Angular distance(s) to move, in radians.
+    bearing : float or ndarray
+        Bearing(s) in radians, measured from north towards increasing ra.
+
+    Returns
+    -------
+    ra, dec : ndarray
+        Offset positions in radians.
+    """
+    sin_dec = np.sin(dec)
+    cos_dec = np.cos(dec)
+    sin_d = np.sin(distance)
+    cos_d = np.cos(distance)
+
+    sin_dec2 = np.clip(sin_dec * cos_d + cos_dec * sin_d * np.cos(bearing), -1.0, 1.0)
+    new_dec = np.arcsin(sin_dec2)
+    new_ra = np.mod(
+        ra + np.arctan2(np.sin(bearing) * sin_d * cos_dec, cos_d - sin_dec * sin_dec2),
+        2 * np.pi,
+    )
+    return new_ra, new_dec
+
+
+def sample_with_extension(
+    true_ra: float | npt.NDArray[np.floating],
+    true_dec: float | npt.NDArray[np.floating],
+    extension: float | npt.NDArray[np.floating],
+    rng: np.random.Generator | None = None,
+) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
+    """
+    Sample a position offset from (true_ra, true_dec) by a Rayleigh(extension)
+    magnitude at a uniformly random bearing, simulating a source's angular extent.
+
+    Parameters
+    ----------
+    true_ra, true_dec : float or ndarray
+        True source position(s) in radians.
+    extension : float or ndarray
+        Rayleigh scale of the angular offset, in radians.
+    rng : np.random.Generator, optional
+        Random number generator. If None, uses np.random.default_rng().
+
+    Returns
+    -------
+    ra, dec : ndarray
+        Sampled positions in radians.
+    """
+    if rng is None:
+        rng = np.random.default_rng()
+
+    true_ra, true_dec, extension = np.broadcast_arrays(true_ra, true_dec, extension)
+    d = rng.rayleigh(extension)
+    theta = rng.uniform(0, 2 * np.pi, size=np.shape(d))
+    return offset_position(true_ra, true_dec, d, theta)
+
+
 @njit(cache=True)
 def _premask_events(
     ra_i: float,
@@ -135,7 +204,7 @@ def _pre_mask_and_distance(
     src_ra: npt.NDArray[np.floating],
     src_dec: npt.NDArray[np.floating],
     cutoff: float,
-) -> Tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.float64]]:
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.float64]]:
     """Rectangular pre-filter and haversine for one or more sources, returned ready
     for input into a sparse array.
 
@@ -351,7 +420,7 @@ def _build_marginalized_grid(
 @njit(cache=True)
 def meshgrid2d(
     a: npt.NDArray[np.floating], b: npt.NDArray[np.floating]
-) -> Tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
+) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
     """
     Create a 2D meshgrid from 1D coordinate arrays, compatible with numba JIT compilation.
 
diff --git a/kingmaker/wrapper.py b/kingmaker/wrapper.py
index 2928c70..48a5514 100644
--- a/kingmaker/wrapper.py
+++ b/kingmaker/wrapper.py
@@ -1,15 +1,25 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
-import numpy.typing as npt
-
-from os.path import exists
 import logging
-import numpy as np
+from collections.abc import Sequence
+from os.path import exists
+from typing import Any
 
+import numpy as np
+import numpy.typing as npt
 from scipy.sparse import csr_array
 
-from .pdf import KingPDF, MarginalizedKingPDF
 from .fitting import KingPSFFitter
-from .utils import _pre_mask_and_distance, _interp1d
+from .pdf import KingPDF, MarginalizedKingPDF
+from .utils import _interp1d, _pre_mask_and_distance
+
+logger = logging.getLogger(__name__)
+
+
+def _nearest_index(centers, values):
+    """Index of the nearest center for each value."""
+    if len(centers) == 1:
+        return np.zeros(np.shape(values), dtype=np.intp)
+    i = np.searchsorted(centers, values).clip(1, len(centers) - 1)
+    return np.where(values - centers[i - 1] < centers[i] - values, i - 1, i)
 
 
 class KingSpatialLikelihood:
@@ -26,7 +36,7 @@ class then fits King distribution parameters using the requested parameter binni
     """
 
     # Configuration parameters
-    parametrization_bins: Dict[str, npt.NDArray[np.floating]]
+    parametrization_bins: dict[str, npt.NDArray[np.floating]]
     spectral_indices: npt.NDArray[np.floating]
     angular_cutoff: float
     cache_parameters: bool = True
@@ -35,20 +45,21 @@ class then fits King distribution parameters using the requested parameter binni
     king_pdf: KingPDF
 
     # Marginalized PDF (optional — enabled by passing marg_source_decs to __init__)
-    mkpdf: Optional[MarginalizedKingPDF]
-    _marg_source_decs: Optional[npt.NDArray[np.floating]]
-    _marg_matrices: List[csr_array]
+    mkpdf: MarginalizedKingPDF | None
+    _marg_source_decs: npt.NDArray[np.floating] | None
+    _marg_matrices: list[csr_array]
 
     # Source-level information
-    source_ras: Optional[npt.NDArray[Any]] = None
-    source_decs: Optional[npt.NDArray[Any]] = None
+    source_ras: npt.NDArray[Any] | None = None
+    source_decs: npt.NDArray[Any] | None = None
+    source_extensions: npt.NDArray[Any] | None = None
 
     # Have some place to cache the per-event information so we don't need to
     # recalculate it every time we evaluate the PDF.
-    events: Optional[Any] = None
-    event_distances: Union[npt.NDArray[np.floating], List[float]]
-    map_index: Union[npt.NDArray[np.integer], List[int]]
-    _pdf_matrices: List[csr_array]
+    events: Any | None = None
+    event_distances: npt.NDArray[np.floating] | list[float]
+    map_index: npt.NDArray[np.integer] | list[int]
+    _pdf_matrices: list[csr_array]
 
     # General warning flags
     multiple_source_warning_logged: bool = False
@@ -56,15 +67,10 @@ class then fits King distribution parameters using the requested parameter binni
     def __init__(
         self,
         signal_events: npt.NDArray[Any],
-        parametrization_bins: Dict[str, Union[int, List, Tuple, npt.NDArray]],
+        parametrization_bins: dict[str, int | list | tuple | npt.NDArray],
         dpsi_nbins: int = 101,
         minimum_counts: int = 100,
-        spectral_indices: Union[List[float], npt.NDArray[np.floating]] = [
-            1.0,
-            2.0,
-            3.0,
-            4.0,
-        ],
+        spectral_indices: Sequence[float] | npt.NDArray[np.floating] = (1.0, 2.0, 3.0, 4.0),
         angular_cutoff: float = np.pi,
         cache_parameters: bool = True,
         cache_name: str = "./king_parameters_cache.npz",
@@ -75,12 +81,13 @@ def __init__(
         true_dec_name: str = "trueDec",
         true_energy_name: str = "trueE",
         enable_marginalization: bool = False,
-        marginalization_source_decs: Optional[npt.NDArray[np.floating]] = None,
-        marginalization_angular_cutoff: Optional[float] = None,
-        marginalization_points_alpha: Optional[npt.NDArray[np.floating]] = None,
-        marginalization_points_beta: Optional[npt.NDArray[np.floating]] = None,
+        marginalization_source_decs: npt.NDArray[np.floating] | None = None,
+        marginalization_angular_cutoff: float | None = None,
+        marginalization_points_alpha: npt.NDArray[np.floating] | None = None,
+        marginalization_points_beta: npt.NDArray[np.floating] | None = None,
         marginalization_n_signed_delta_dec: int = 200,
         marginalization_n_ra_bins: int = 100,
+        extension_grid: npt.NDArray[np.floating] | None = None,
     ):
         # Store some of the configuration parameters for this instance.
         # Note that we don't need to store the signal events, dpsi_nbins,
@@ -92,13 +99,13 @@ def __init__(
 
         # Set some default values for the event-level parameters.
         self.event_distances, self.map_index = [], []
-        self._pdf_matrices: List[csr_array] = []
+        self._pdf_matrices: list[csr_array] = []
 
         # Obtain the King distribution parameters for all bins. If we're caching parameters
         # and a cache file exists, load from the cache instead of fitting. Otherwise,
         # run the fitter and potentially cache the results. When running the fitter,
         # angular_cutoff is set to pi so every bin's full angular error distribution is fit.
-        fitted_parameters: Dict[str, npt.NDArray[np.floating]] = {}
+        fitted_parameters: dict[str, npt.NDArray[np.floating]] = {}
         if cache_parameters and (cache_name is not None) and exists(cache_name):
             fitted_parameters_npz = np.load(cache_name, allow_pickle=True)
             for key in fitted_parameters_npz.files:
@@ -117,6 +124,7 @@ def __init__(
                 true_ra_name=true_ra_name,
                 true_dec_name=true_dec_name,
                 true_energy_name=true_energy_name,
+                extension_grid=extension_grid,
             )
             fitted_parameters = fitter.fit_all_bins(verbose=True)
             if cache_parameters and (cache_name is not None):
@@ -136,9 +144,45 @@ def __init__(
             self.keys.append(key)
             self.bin_centers.append((edges[:-1] + edges[1:]) / 2)
 
-        # And grab the fitted alpha/beta arrays
+        # Extension grid (bin-center-style values, nearest-snapped per source).
+        if "extension_grid" not in fitted_parameters:
+            raise ValueError(f"Cache {cache_name!r} has no extension_grid. Delete it and refit.")
+        self.extension_grid = np.atleast_1d(fitted_parameters["extension_grid"]).astype(np.float64)
+        if (
+            self.extension_grid.ndim != 1
+            or self.extension_grid.size == 0
+            or not np.all(np.isfinite(self.extension_grid))
+            or np.any(self.extension_grid < 0)
+        ):
+            raise ValueError(
+                "extension_grid must be a 1-D, finite, non-empty, non-negative array of radians."
+            )
+        if np.any(np.diff(self.extension_grid) < 0):
+            raise ValueError(f"Cache {cache_name!r} extension_grid is not sorted.")
+        if extension_grid is not None:
+            requested = np.sort(np.atleast_1d(np.asarray(extension_grid, dtype=np.float64)))
+            if requested.shape != self.extension_grid.shape or not np.allclose(
+                requested, self.extension_grid
+            ):
+                raise ValueError(
+                    f"Cache {cache_name!r} has extension_grid {self.extension_grid}, "
+                    f"not {requested}. Delete it and refit."
+                )
+
+        # And grab the fitted alpha/beta arrays, shape (n_extension, n_gamma, *bins).
         self.alpha_values = fitted_parameters["alpha"]
         self.beta_values = fitted_parameters["beta"]
+        n_ext = len(self.extension_grid)
+        if self.alpha_values.shape[0] != n_ext or self.beta_values.shape[0] != n_ext:
+            raise ValueError(
+                "Cached alpha/beta first axis must match extension_grid length. Delete the cache and refit."
+            )
+        expected_ndim = 2 + len(self.parametrization_bins)
+        if self.alpha_values.ndim != expected_ndim or self.beta_values.ndim != expected_ndim:
+            raise ValueError(
+                f"Cached alpha/beta have {self.alpha_values.ndim} dimensions, expected "
+                f"{expected_ndim} (extension, gamma, *bins). Delete {cache_name!r} and refit."
+            )
 
         # Instantiate the PDF object.
         self.king_pdf = KingPDF(angular_cutoff=angular_cutoff)
@@ -163,7 +207,7 @@ def __init__(
                 if marginalization_angular_cutoff is not None
                 else angular_cutoff
             )
-            kwargs: Dict[str, Any] = {}
+            kwargs: dict[str, Any] = {}
             if marginalization_points_alpha is not None:
                 kwargs["points_alpha"] = marginalization_points_alpha
             if marginalization_points_beta is not None:
@@ -175,7 +219,6 @@ def __init__(
                 n_ra_bins=marginalization_n_ra_bins,
                 **kwargs,
             )
-        return
 
     def _events_match(self, events: npt.NDArray[Any]) -> bool:
         if self.events is None:
@@ -188,7 +231,12 @@ def _events_match(self, events: npt.NDArray[Any]) -> bool:
         result &= np.array_equal(self.events["dec"][::10], events["dec"][::10])
         return result
 
-    def _sources_match(self, source_ras: npt.NDArray[Any], source_decs: npt.NDArray[Any]) -> bool:
+    def _sources_match(
+        self,
+        source_ras: npt.NDArray[Any],
+        source_decs: npt.NDArray[Any],
+        source_extensions: npt.NDArray[Any] | None = None,
+    ) -> bool:
         if self.source_ras is None:
             return False
         if self.source_decs is None:
@@ -201,6 +249,15 @@ def _sources_match(self, source_ras: npt.NDArray[Any], source_decs: npt.NDArray[
             return False
         if len(self.source_decs) != len(source_decs):
             return False
+        expected_ext = (
+            np.zeros(len(source_ras), dtype=np.float64)
+            if source_extensions is None
+            else np.asarray(source_extensions, dtype=np.float64)
+        )
+        if self.source_extensions is None or not np.array_equal(
+            self.source_extensions, expected_ext
+        ):
+            return False
         return np.array_equal(self.source_ras, source_ras) and np.array_equal(
             self.source_decs, source_decs
         )
@@ -208,8 +265,9 @@ def _sources_match(self, source_ras: npt.NDArray[Any], source_decs: npt.NDArray[
     def set_events(
         self,
         events: npt.NDArray[Any],
-        source_ras: Optional[npt.NDArray[np.floating]],
-        source_decs: Optional[npt.NDArray[np.floating]],
+        source_ras: npt.NDArray[np.floating] | None,
+        source_decs: npt.NDArray[np.floating] | None,
+        source_extensions: npt.NDArray[np.floating] | None = None,
     ) -> None:
         """
         Cache per-event King PDF values for each spectral index ahead of a call
@@ -220,8 +278,9 @@ def set_events(
         computed, and the King PDF is evaluated and cached for every spectral
         index in ``spectral_indices``. This must be called before
         :meth:`evaluate_pdf`. Calling it again with the same ``events``,
-        ``source_ras``, and ``source_decs`` as the previous call is a cheap
-        no-op, so it is safe to call once per trial without checking first.
+        ``source_ras``, ``source_decs``, and ``source_extensions`` as the
+        previous call is a cheap no-op, so it is safe to call once per trial
+        without checking first.
 
         Parameters
         ----------
@@ -234,25 +293,29 @@ def set_events(
         source_decs : ndarray
             Source declination(s) in radians. Must have the same length as
             ``source_ras``.
+        source_extensions : ndarray, optional
+            Source extension widths (Gaussian sigma) in radians, nearest-snapped
+            to the fitted ``extension_grid`` and within its range. Defaults to
+            zero (point source) for every source.
 
         Raises
         ------
         ValueError
-            If ``source_ras``/``source_decs`` are not provided, or their
-            lengths do not match.
+            If ``source_ras``/``source_decs`` are missing or differ in length,
+            ``source_extensions`` has the wrong length or lies outside
+            ``extension_grid``, or ``source_decs`` differ from
+            ``marginalization_source_decs``.
 
         Notes
         -----
         Support for multiple simultaneous sources is experimental and logs a
         one-time warning; results should be checked carefully in that case.
         """
-        if self._events_match(events) and self._sources_match(source_ras, source_decs):
+        if self._events_match(events) and self._sources_match(
+            source_ras, source_decs, source_extensions
+        ):
             return
 
-        self.events = events
-        self.source_ras = source_ras
-        self.source_decs = source_decs
-
         # Make sure we have a matching number of source_ras and source_decs if we're given multiple sources.
         if (source_ras is None) and (source_decs is None):
             raise ValueError(
@@ -265,12 +328,42 @@ def set_events(
             )
 
         if (not self.multiple_source_warning_logged) and (len(source_ras) > 1):
-            logging.warning(
+            logger.warning(
                 "Multiple source positions provided. This has not been tested and"
                 " may not work as expected. Please check the results carefully!"
             )
             self.multiple_source_warning_logged = True
 
+        source_extensions = (
+            np.zeros(len(source_ras))
+            if source_extensions is None
+            else np.asarray(source_extensions, dtype=np.float64)
+        )
+        if len(source_extensions) != len(source_ras):
+            raise ValueError(
+                "source_extensions must have the same length as source_ras and source_decs."
+            )
+        lo, hi = self.extension_grid[[0, -1]]
+        if np.any(~np.isfinite(source_extensions)) or np.any(
+            (source_extensions < lo - 1e-9) | (source_extensions > hi + 1e-9)
+        ):
+            raise ValueError(
+                f"source_extensions must be finite and lie within extension_grid [{lo}, {hi}]."
+            )
+        if self.mkpdf is not None and (
+            self._marg_source_decs.shape != np.shape(source_decs)
+            or not np.allclose(self._marg_source_decs, source_decs)
+        ):
+            raise ValueError(
+                "marginalization_source_decs must match the source_decs passed to set_events."
+            )
+
+        self.events = None
+        self.source_ras = source_ras
+        self.source_decs = source_decs
+        self.source_extensions = source_extensions
+        ext_idx_per_source = _nearest_index(self.extension_grid, source_extensions)
+
         # Calculate the (event, source) angular distances via a single compiled
         # pass that pre-filters on a dec/RA bounding box before the haversine,
         # returning only the (event, source) pairs within the cutoff.
@@ -279,6 +372,7 @@ def set_events(
         event_rows, event_cols, self.event_distances = _pre_mask_and_distance(
             events["ra"], events["dec"], source_ras, source_decs, cutoff
         )
+        ext_idx_pairs = ext_idx_per_source[event_cols]
 
         # alpha/beta/norm are looked up once per event (they don't depend on
         # source), so event_mask is the per-event OR across sources, and each
@@ -304,9 +398,9 @@ def set_events(
             # event_distances are already within angular_cutoff by construction.
             values = self.king_pdf.pdf_from_norm(
                 self.event_distances,
-                all_alpha[i][pair_position],
-                all_beta[i][pair_position],
-                all_norm[i][pair_position],
+                all_alpha[ext_idx_pairs, i, pair_position],
+                all_beta[ext_idx_pairs, i, pair_position],
+                all_norm[ext_idx_pairs, i, pair_position],
             )
             self._pdf_matrices.append(
                 csr_array(
@@ -316,55 +410,64 @@ def set_events(
                 )
             )
 
-        # Marginalized path: precompute one sparse (n_events, n_sources) matrix per
-        # spectral index. The first call establishes the sparsity structure; all
-        # subsequent calls pass that result as mask= so interpn operates on the same
-        # fixed (event, source) pairs. The mask path in MarginalizedKingPDF.evaluate()
-        # does not filter values > 0, so every matrix in _marg_matrices is guaranteed to
-        # share identical .indices and .indptr — a requirement for the scalar lerp
-        # on .data in evaluate_marginalized_pdf.
         if self.mkpdf is not None:
             all_alpha_full, all_beta_full = self._lookup_all_events_grid(events)
+
+            unique_ext = np.unique(ext_idx_per_source)
+            group_source_idx = [np.flatnonzero(ext_idx_per_source == e) for e in unique_ext]
+            # Same sparsity for every gamma.
+            group_masks: list[csr_array | None] = [None] * len(unique_ext)
+
             self._marg_matrices = []
-            mask_marg: Optional[csr_array] = None
             for i in range(len(self.spectral_indices)):
-                mat = self.mkpdf.evaluate(
-                    self._marg_source_decs,
-                    events["dec"],
-                    all_alpha_full[i],
-                    all_beta_full[i],
-                    mask=mask_marg,
+                data_parts, row_parts, col_parts = [], [], []
+                for g, (ext_idx, source_idx) in enumerate(zip(unique_ext, group_source_idx)):
+                    mat = self.mkpdf.evaluate(
+                        self._marg_source_decs[source_idx],
+                        events["dec"],
+                        all_alpha_full[ext_idx, i],
+                        all_beta_full[ext_idx, i],
+                        mask=group_masks[g],
+                    )
+                    if group_masks[g] is None:
+                        group_masks[g] = mat
+                    coo = mat.tocoo()
+                    data_parts.append(coo.data)
+                    row_parts.append(coo.row)
+                    col_parts.append(source_idx[coo.col])
+                self._marg_matrices.append(
+                    csr_array(
+                        (
+                            np.concatenate(data_parts),
+                            (np.concatenate(row_parts), np.concatenate(col_parts)),
+                        ),
+                        shape=(len(events), len(self._marg_source_decs)),
+                        dtype=np.float64,
+                    )
                 )
-                if mask_marg is None:
-                    mask_marg = mat
-                self._marg_matrices.append(mat)
+        self.events = events
         return
 
     def _lookup_event_grid(self, events):
         """
-        Nearest-bin lookup of alpha, beta, and norm for each (unmasked) event.
+        Nearest-bin lookup of alpha, beta, and norm for each (unmasked) event,
+        for every extension in extension_grid.
 
         Shared by :meth:`get_alpha_beta` and :meth:`set_events` so the
         nearest-bin index computation is only ever done once per call.
         """
-
-        # Nearest-bin lookup. Extracts each field individually after masking.
-        def index(centers, values):
-            i = np.searchsorted(centers, values).clip(1, len(centers) - 1)
-            return np.where(values - centers[i - 1] < centers[i] - values, i - 1, i)
-
         event_indices = tuple(
-            index(self.bin_centers[i], events[key][self.event_mask])
+            _nearest_index(self.bin_centers[i], events[key][self.event_mask])
             for i, key in enumerate(self.keys)
         )
 
-        idx = (slice(None), *event_indices)
+        idx = (slice(None), slice(None), *event_indices)
         return self.alpha_values[idx], self.beta_values[idx], self.norm_values[idx]
 
     def _lookup_all_events_grid(self, events):
         """
         Nearest-bin lookup of alpha and beta for every event, without applying
-        ``event_mask``.
+        ``event_mask``, for every extension in extension_grid.
 
         Used by :meth:`set_events` to supply per-event PSF parameters for
         :meth:`MarginalizedKingPDF.evaluate`, which performs its own angular
@@ -372,21 +475,16 @@ def _lookup_all_events_grid(self, events):
 
         Returns
         -------
-        alpha : ndarray, shape (n_gamma, n_events)
-        beta  : ndarray, shape (n_gamma, n_events)
+        alpha : ndarray, shape (n_extension, n_gamma, n_events)
+        beta  : ndarray, shape (n_extension, n_gamma, n_events)
         """
-
-        def index(centers, values):
-            i = np.searchsorted(centers, values).clip(1, len(centers) - 1)
-            return np.where(values - centers[i - 1] < centers[i] - values, i - 1, i)
-
         event_indices = tuple(
-            index(self.bin_centers[i], events[key]) for i, key in enumerate(self.keys)
+            _nearest_index(self.bin_centers[i], events[key]) for i, key in enumerate(self.keys)
         )
-        idx = (slice(None), *event_indices)
+        idx = (slice(None), slice(None), *event_indices)
         return self.alpha_values[idx], self.beta_values[idx]
 
-    def get_alpha_beta(self, events):
+    def get_alpha_beta(self, events, extension_index: int = 0):
         """
         Look up fitted alpha/beta parameters for each event via nearest-bin lookup.
 
@@ -399,6 +497,8 @@ def get_alpha_beta(self, events):
             Events to look up. Must contain the fields referenced by
             ``parametrization_bins``. Only events selected by the mask set in
             the most recent :meth:`set_events` call are returned.
+        extension_index : int, optional
+            Index into ``extension_grid``. Default is 0.
         Returns
         -------
         alpha : ndarray, shape (n_gamma, n_masked_events)
@@ -407,9 +507,9 @@ def get_alpha_beta(self, events):
             Fitted beta values for each spectral index and event.
         """
         alpha, beta, _ = self._lookup_event_grid(events)
-        return alpha, beta
+        return alpha[extension_index], beta[extension_index]
 
-    def get_alpha_beta_gamma(self, gamma, events=None, alpha=None, beta=None):
+    def get_alpha_beta_gamma(self, gamma, events=None, alpha=None, beta=None, extension_index=0):
         """
         Get alpha/beta at a given spectral index, interpolating if necessary.
 
@@ -433,6 +533,8 @@ def get_alpha_beta_gamma(self, gamma, events=None, alpha=None, beta=None):
         beta : ndarray, optional
             Pre-computed beta values for all spectral indices, matching
             ``alpha``.
+        extension_index : int, optional
+            Index into ``extension_grid`` used with ``events``. Default is 0.
 
         Returns
         -------
@@ -443,7 +545,7 @@ def get_alpha_beta_gamma(self, gamma, events=None, alpha=None, beta=None):
         """
         if alpha is None:
             assert events is not None
-            alpha, beta = self.get_alpha_beta(events)
+            alpha, beta = self.get_alpha_beta(events, extension_index)
         assert len(alpha) == len(beta)
 
         # If we have this gamma, just return it. Make sure to use copy()
diff --git a/pyproject.toml b/pyproject.toml
index b3dd252..40c837c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,7 @@ dynamic = ["version"]
 [project.optional-dependencies]
 dev = [
   "pre-commit",
-  "ruff",
+  "ruff==0.16.10",
   "mypy",
   "pytest",
   "pytest-cov",
diff --git a/tests/test_basic.py b/tests/test_basic.py
index 048ab1d..99700fa 100644
--- a/tests/test_basic.py
+++ b/tests/test_basic.py
@@ -1,7 +1,7 @@
 """Smoke tests: verify all public classes can be imported."""
 
-from kingmaker.pdf import KingPDF, TemplateSmearedKingPDF
 from kingmaker.fitting import KingPSFFitter
+from kingmaker.pdf import KingPDF, TemplateSmearedKingPDF
 from kingmaker.wrapper import KingSpatialLikelihood
 
 
diff --git a/tests/test_extended_source_king_pdf.py b/tests/test_extended_source_king_pdf.py
deleted file mode 100644
index 9b4cf17..0000000
--- a/tests/test_extended_source_king_pdf.py
+++ /dev/null
@@ -1,310 +0,0 @@
-"""
-Unit tests for ExtendedSourceKingPDF.
-
-Covers initialization (composition, not inheritance), pdf() correctness
-(normalization, boundary behaviour, shape), and evaluate() correctness
-(sparse output, geometry screening, mask reuse).
-"""
-
-import numpy as np
-import pytest
-from numpy.testing import assert_allclose
-from scipy.sparse import csr_array
-
-from kingmaker.pdf import ExtendedSourceKingPDF, KingPDF
-
-
-# ---------------------------------------------------------------------------
-# Shared fixtures
-# ---------------------------------------------------------------------------
-
-
-# Minimal grid shared by most pdf() tests; built once per module.
-@pytest.fixture(scope="module")
-def ext_pdf():
-    return ExtendedSourceKingPDF(
-        points_alpha=np.radians(np.logspace(-1, 1, 10)),
-        points_beta=np.logspace(np.log10(1.01), 1, 8),
-        points_extension=np.radians(np.logspace(-1.5, np.log10(4.9), 8)),
-        points_psi=np.concatenate([[0.0], np.logspace(-4, np.log10(np.pi), 200)]),
-        n_quad=16,
-    )
-
-
-# Fixture for evaluate() tests: small angular_cutoff so far events are screened.
-@pytest.fixture(scope="module")
-def ext_eval():
-    return ExtendedSourceKingPDF(
-        angular_cutoff=np.radians(10.0),
-        points_alpha=np.radians(np.logspace(-1, 1, 10)),
-        points_beta=np.logspace(np.log10(1.01), 1, 8),
-        points_extension=np.radians(np.logspace(-1.5, np.log10(4.9), 8)),
-        points_psi=np.concatenate([[0.0], np.logspace(-4, np.log10(np.pi), 200)]),
-        n_quad=16,
-    )
-
-
-PARAM_CASES = [
-    pytest.param(np.radians(0.5), 2.0, np.radians(0.5), id="narrow-moderate-small-ext"),
-    pytest.param(np.radians(1.0), 2.5, np.radians(1.0), id="moderate-moderate-med-ext"),
-    pytest.param(np.radians(2.0), 4.0, np.radians(1.5), id="wide-heavy-med-ext"),
-]
-
-
-# ---------------------------------------------------------------------------
-# Initialization
-# ---------------------------------------------------------------------------
-
-
-class TestExtendedSourceKingPDFInit:
-    def test_not_instance_of_king_pdf(self, ext_pdf):
-        assert not isinstance(ext_pdf, KingPDF)
-
-    def test_default_angular_cutoff(self, ext_pdf):
-        assert ext_pdf.angular_cutoff == pytest.approx(np.pi)
-
-    def test_custom_angular_cutoff(self):
-        cutoff = np.radians(5.0)
-        ext = ExtendedSourceKingPDF(
-            angular_cutoff=cutoff,
-            points_alpha=np.radians([0.5, 1.0]),
-            points_beta=np.array([1.5, 3.0]),
-            points_extension=np.radians([0.5, 1.0]),
-            n_quad=4,
-        )
-        assert ext.angular_cutoff == pytest.approx(cutoff)
-
-    def test_default_maximum_sigma(self, ext_pdf):
-        assert ext_pdf.maximum_sigma == pytest.approx(3.0)
-
-    def test_custom_maximum_sigma(self):
-        ext = ExtendedSourceKingPDF(
-            maximum_sigma=5.0,
-            points_alpha=np.radians([0.5, 1.0]),
-            points_beta=np.array([1.5, 3.0]),
-            points_extension=np.radians([0.5, 1.0]),
-            n_quad=4,
-        )
-        assert ext.maximum_sigma == pytest.approx(5.0)
-
-    def test_table_shape(self, ext_pdf):
-        expected = (
-            len(ext_pdf._log10_points_alpha),
-            len(ext_pdf._log10_points_beta),
-            len(ext_pdf._log10_points_extension),
-            len(ext_pdf._points_psi),
-        )
-        assert ext_pdf._table.shape == expected
-
-    def test_table_finite(self, ext_pdf):
-        assert np.all(np.isfinite(ext_pdf._table))
-
-    def test_table_nonneg(self, ext_pdf):
-        assert np.all(ext_pdf._table >= 0.0)
-
-
-# ---------------------------------------------------------------------------
-# pdf()
-# ---------------------------------------------------------------------------
-
-
-class TestExtendedSourceKingPDFPdf:
-    @pytest.mark.parametrize("alpha, beta, extension", PARAM_CASES)
-    def test_pdf_valid(self, ext_pdf, alpha, beta, extension):
-        psi = np.linspace(0, np.radians(5), 50)
-        vals = ext_pdf.pdf(psi, np.full_like(psi, alpha), np.full_like(psi, beta), extension)
-        assert np.all(vals >= 0)
-        assert np.all(np.isfinite(vals))
-
-    def test_zero_at_psi_zero(self, ext_pdf):
-        val = ext_pdf.pdf(0.0, np.radians(1.0), 2.5, np.radians(1.0))
-        assert val == 0.0
-
-    def test_zero_beyond_psi_max(self):
-        """Angles above _points_psi[-1] (but still ≤ π) must return 0.
-
-        Use angular_cutoff=10° without a custom points_psi so the default
-        upper bound is max_sigma*max_ext + angular_cutoff ≈ 16° < π, leaving
-        room for test points on the sphere that are out-of-table.
-        """
-        small = ExtendedSourceKingPDF(
-            angular_cutoff=np.radians(10.0),
-            points_alpha=np.radians([0.5, 1.0, 2.0]),
-            points_beta=np.array([1.5, 2.5, 5.0]),
-            points_extension=np.radians([0.5, 1.0, 2.0]),
-            n_quad=4,
-        )
-        psi_max = small._points_psi[-1]
-        assert psi_max < np.pi, "fixture must end before π for this test to be meaningful"
-        beyond = np.array([psi_max + np.radians(5.0), psi_max + np.radians(20.0)])
-        beyond = beyond[beyond <= np.pi]
-        alpha = np.full(len(beyond), np.radians(1.0))
-        beta = np.full(len(beyond), 2.5)
-        ext = np.full(len(beyond), np.radians(1.0))
-        assert np.all(small.pdf(beyond, alpha, beta, ext) == 0.0)
-
-    def test_output_shape_array(self, ext_pdf):
-        psi = np.linspace(0.01, np.radians(5), 20)
-        vals = ext_pdf.pdf(psi, np.radians(1.0), 2.5, np.radians(1.0))
-        assert vals.shape == psi.shape
-
-    def test_scalar_input_finite(self, ext_pdf):
-        val = ext_pdf.pdf(np.radians(1.0), np.radians(1.0), 2.5, np.radians(1.0))
-        assert np.isfinite(val)
-
-    def test_oob_alpha_raises(self, ext_pdf):
-        with pytest.raises(ValueError):
-            ext_pdf.pdf(np.radians(1.0), np.radians(0.001), 2.5, np.radians(1.0))
-
-    def test_oob_extension_raises(self, ext_pdf):
-        with pytest.raises(ValueError):
-            ext_pdf.pdf(np.radians(1.0), np.radians(1.0), 2.5, np.radians(10.0))
-
-    @pytest.mark.parametrize("alpha, beta, extension", PARAM_CASES)
-    def test_normalization(self, ext_pdf, alpha, beta, extension):
-        """∫ pdf(ψ) 2π ψ dψ ≈ 1 (flat-sky)."""
-        psi = np.linspace(1e-4, ext_pdf._points_psi[-1], 30_000)
-        dpsi = psi[1] - psi[0]
-        vals = ext_pdf.pdf(
-            psi,
-            np.full_like(psi, alpha),
-            np.full_like(psi, beta),
-            np.full_like(psi, extension),
-        )
-        integral = np.sum(vals * 2.0 * np.pi * psi) * dpsi
-        assert_allclose(integral, 1.0, rtol=0.02)
-
-    @pytest.mark.parametrize("alpha, beta, extension", PARAM_CASES)
-    def test_small_extension_approaches_king(self, ext_pdf, alpha, beta, extension):
-        """Convolved PDF with the smallest grid extension should be close to flat-sky King."""
-        tiny_ext = ext_pdf._points_extension[0]
-        psi = np.radians([0.5, 1.0, 2.0])
-        psi = psi[psi < alpha * 3]  # stay in the PSF core where flat-sky is accurate
-        if len(psi) == 0:
-            pytest.skip("no test angles within PSF core for this alpha")
-
-        flat_norm = (beta - 1.0) / (2.0 * np.pi * beta * alpha**2)
-        flat_king = flat_norm * (1.0 + psi**2 / (2.0 * beta * alpha**2)) ** (-beta)
-        conv = ext_pdf.pdf(
-            psi,
-            np.full_like(psi, alpha),
-            np.full_like(psi, beta),
-            np.full_like(psi, tiny_ext),
-        )
-        assert_allclose(conv, flat_king, rtol=0.15)
-
-    def test_larger_extension_broader(self, ext_pdf):
-        """Larger extension shifts probability outward, reducing the PDF near psi=0."""
-        alpha = np.radians(1.0)
-        beta = 2.5
-        psi_near = np.radians(0.1)
-        val_small = ext_pdf.pdf(psi_near, alpha, beta, ext_pdf._points_extension[0])
-        val_large = ext_pdf.pdf(psi_near, alpha, beta, ext_pdf._points_extension[-1])
-        assert val_small > val_large
-
-
-# ---------------------------------------------------------------------------
-# evaluate()
-# ---------------------------------------------------------------------------
-
-
-class TestExtendedSourceKingPDFEvaluate:
-    def test_returns_csr_array(self, ext_eval):
-        result = ext_eval.evaluate(
-            np.array([0.0]),
-            np.array([0.0]),
-            np.array([np.radians(1.0)]),
-            np.array([0.0]),
-            np.array([0.0]),
-            np.array([np.radians(1.0)]),
-            np.array([2.5]),
-        )
-        assert isinstance(result, csr_array)
-
-    def test_output_shape(self, ext_eval):
-        src_ras = np.radians([0.0, 45.0])
-        src_decs = np.radians([0.0, 10.0])
-        src_exts = np.radians([1.0, 1.0])
-        ev_ras = np.radians(np.linspace(0, 5, 8))
-        ev_decs = np.zeros(8)
-        alpha = np.full(8, np.radians(1.0))
-        beta = np.full(8, 2.5)
-        result = ext_eval.evaluate(src_ras, src_decs, src_exts, ev_ras, ev_decs, alpha, beta)
-        assert result.shape == (8, 2)
-
-    def test_nonneg(self, ext_eval):
-        rng = np.random.default_rng(0)
-        ev_ras = rng.uniform(0, 2 * np.pi, 30)
-        ev_decs = np.arcsin(rng.uniform(-1, 1, 30))
-        alpha = np.full(30, np.radians(1.0))
-        beta = np.full(30, 2.5)
-        result = ext_eval.evaluate(
-            np.array([0.0]),
-            np.array([0.0]),
-            np.array([np.radians(1.0)]),
-            ev_ras,
-            ev_decs,
-            alpha,
-            beta,
-        )
-        assert np.all(result.toarray() >= 0)
-
-    def test_near_source_positive(self, ext_eval):
-        """Events close to a source should get a positive PDF value."""
-        result = ext_eval.evaluate(
-            np.array([0.0]),
-            np.array([0.0]),
-            np.array([np.radians(1.0)]),
-            np.array([np.radians(0.1)]),
-            np.array([0.0]),
-            np.array([np.radians(1.0)]),
-            np.array([2.5]),
-        )
-        assert result.toarray()[0, 0] > 0
-
-    def test_zero_beyond_search_radius(self, ext_eval):
-        """Events beyond maximum_sigma * ext + angular_cutoff should be zero."""
-        src_ext = np.radians(1.0)
-        radius = ext_eval.maximum_sigma * src_ext + ext_eval.angular_cutoff
-        # Place one event just inside and one well outside
-        psi_far = min(radius + np.radians(5.0), np.pi)
-        result = ext_eval.evaluate(
-            np.array([0.0]),
-            np.array([0.0]),
-            np.array([src_ext]),
-            np.array([np.radians(0.5), psi_far]),
-            np.array([0.0, 0.0]),
-            np.array([np.radians(1.0), np.radians(1.0)]),
-            np.array([2.5, 2.5]),
-        ).toarray()
-        assert result[0, 0] > 0
-        assert result[1, 0] == 0.0
-
-    def test_mask_gives_same_result(self, ext_eval):
-        rng = np.random.default_rng(42)
-        src_ras = np.radians([0.0, 45.0])
-        src_decs = np.radians([0.0, 10.0])
-        src_exts = np.radians([1.0, 2.0])
-        ev_ras = rng.uniform(0, 2 * np.pi, 30)
-        ev_decs = np.arcsin(rng.uniform(-1, 1, 30))
-        alpha = np.full(30, np.radians(1.0))
-        beta = np.full(30, 2.5)
-        first = ext_eval.evaluate(src_ras, src_decs, src_exts, ev_ras, ev_decs, alpha, beta)
-        second = ext_eval.evaluate(
-            src_ras, src_decs, src_exts, ev_ras, ev_decs, alpha, beta, mask=first
-        )
-        assert_allclose(first.toarray(), second.toarray(), rtol=1e-12)
-
-    def test_two_sources_prefer_nearest(self, ext_eval):
-        """An event near source 0 should get a higher PDF for source 0 than source 1."""
-        src_ras = np.radians([0.0, 90.0])
-        src_decs = np.radians([0.0, 0.0])
-        src_exts = np.radians([1.0, 1.0])
-        ev_ras = np.radians([1.0])
-        ev_decs = np.radians([0.0])
-        alpha = np.array([np.radians(1.0)])
-        beta = np.array([2.5])
-        result = ext_eval.evaluate(
-            src_ras, src_decs, src_exts, ev_ras, ev_decs, alpha, beta
-        ).toarray()
-        assert result[0, 0] > result[0, 1]
diff --git a/tests/test_fitting.py b/tests/test_fitting.py
index 13547d8..ec98ed2 100644
--- a/tests/test_fitting.py
+++ b/tests/test_fitting.py
@@ -7,14 +7,16 @@
 - Correlated auxiliary parameter (log10_energy) → monotonic trend in alpha/beta
 """
 
+from types import SimpleNamespace
+
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose
 
+import kingmaker.fitting as fitting_module
 from kingmaker.fitting import KingPSFFitter
 from kingmaker.pdf import KingPDF
 
-
 RNG_SEED = 42
 
 # One shared PDF instance to avoid re-building the 200×200 norm grid per test.
@@ -81,11 +83,11 @@ def test_bin_names(self, fitter):
         assert fitter.bin_names == ["aux"]
 
     def test_fit_alpha_shape_before_fitting(self, fitter):
-        # Shape should be (n_spectral_indices=1, n_bins=3).
-        assert fitter.fit_alpha.shape == (1, 3)
+        # Shape should be (n_extension=1, n_spectral_indices=1, n_bins=3).
+        assert fitter.fit_alpha.shape == (1, 1, 3)
 
     def test_fit_beta_shape_before_fitting(self, fitter):
-        assert fitter.fit_beta.shape == (1, 3)
+        assert fitter.fit_beta.shape == (1, 1, 3)
 
     def test_explicit_bin_edges_count(self):
         """Passing k explicit edges should produce k-1 bins."""
@@ -101,6 +103,25 @@ def test_explicit_bin_edges_count(self):
         )
         assert fitter.parametrization_shape == [4]
 
+    def test_missing_energy_field_raises(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "ow", np.ones(500), rng)
+        with pytest.raises(ValueError, match="trueE"):
+            KingPSFFitter(events, parametrization_bins={"ow": [0.0, 2.0]}, weight_field="ow")
+
+    def test_empty_bins_counted_as_skipped(self, capsys):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(2000, np.radians(1.0), 2.5, "aux", rng.uniform(-1, 1, 2000), rng)
+        fitter = KingPSFFitter(
+            events,
+            parametrization_bins={"aux": [-1.0, 0.0, 1.0, 2.0]},
+            minimum_counts=100,
+            weight_field=None,
+            extension_grid=[0.0, np.radians(1.0)],
+        )
+        fitter.fit_all_bins(verbose=True)
+        assert "Fitted 4 bins, skipped 2 bins" in capsys.readouterr().out
+
 
 # ---------------------------------------------------------------------------
 # Uncorrelated auxiliary parameter
@@ -135,29 +156,29 @@ def result(self):
 
     def test_all_bins_have_enough_events(self, result):
         fitter, _, _ = result
-        assert np.all(fitter.event_counts[0] >= 100)
+        assert np.all(fitter.event_counts[0, 0] >= 100)
 
     def test_alpha_consistent_across_bins(self, result):
         """Coefficient of variation of fitted alpha should be small (<30%)."""
         fitter, _, _ = result
-        alphas = fitter.fit_alpha[0]
+        alphas = fitter.fit_alpha[0, 0]
         assert alphas.std() / alphas.mean() < 0.30
 
     def test_beta_consistent_across_bins(self, result):
         """Coefficient of variation of fitted beta should be small (<30%)."""
         fitter, _, _ = result
-        betas = fitter.fit_beta[0]
+        betas = fitter.fit_beta[0, 0]
         assert betas.std() / betas.mean() < 0.30
 
     def test_mean_alpha_roughly_accurate(self, result):
         """Mean fitted alpha should be within 30% of the true value."""
         fitter, alpha_true, _ = result
-        assert_allclose(fitter.fit_alpha[0].mean(), alpha_true, rtol=0.30)
+        assert_allclose(fitter.fit_alpha[0, 0].mean(), alpha_true, rtol=0.30)
 
     def test_mean_beta_roughly_accurate(self, result):
         """Mean fitted beta should be within 40% of the true value."""
         fitter, _, beta_true = result
-        assert_allclose(fitter.fit_beta[0].mean(), beta_true, rtol=0.40)
+        assert_allclose(fitter.fit_beta[0, 0].mean(), beta_true, rtol=0.40)
 
 
 # ---------------------------------------------------------------------------
@@ -172,11 +193,11 @@ class TestKingPSFFitterCorrelated:
     """
 
     # (alpha_true, beta_true, (log10_E_lo, log10_E_hi))
-    _GROUP_PARAMS = [
+    _GROUP_PARAMS = (
         (np.radians(2.0), 2.0, (3.0, 4.0)),  # low energy:  broad PSF
         (np.radians(1.0), 3.0, (4.0, 5.0)),  # mid energy
         (np.radians(0.5), 4.0, (5.0, 6.0)),  # high energy: narrow PSF
-    ]
+    )
 
     @pytest.fixture(scope="class")
     def result(self):
@@ -204,22 +225,160 @@ def test_parametrization_shape(self, result):
 
     def test_alpha_decreases_with_energy(self, result):
         """Higher-energy bins should produce a smaller fitted alpha."""
-        alphas = result.fit_alpha[0]
+        alphas = result.fit_alpha[0, 0]
         assert alphas[0] > alphas[1] > alphas[2]
 
     def test_beta_increases_with_energy(self, result):
         """Higher-energy bins should produce a larger fitted beta."""
-        betas = result.fit_beta[0]
+        betas = result.fit_beta[0, 0]
         assert betas[0] < betas[1] < betas[2]
 
     def test_alpha_values_per_bin(self, result):
         """Fitted alpha per bin should be within 40% of the true value."""
-        alphas = result.fit_alpha[0]
+        alphas = result.fit_alpha[0, 0]
         for i, (alpha_true, _, _) in enumerate(self._GROUP_PARAMS):
             assert_allclose(alphas[i], alpha_true, rtol=0.40, err_msg=f"bin {i}: alpha mismatch")
 
     def test_beta_values_per_bin(self, result):
         """Fitted beta per bin should be within 50% of the true value."""
-        betas = result.fit_beta[0]
+        betas = result.fit_beta[0, 0]
         for i, (_, beta_true, _) in enumerate(self._GROUP_PARAMS):
             assert_allclose(betas[i], beta_true, rtol=0.50, err_msg=f"bin {i}: beta mismatch")
+
+
+# ---------------------------------------------------------------------------
+# extension_grid
+# ---------------------------------------------------------------------------
+
+
+class TestKingPSFFitterExtensionGrid:
+    def test_default_is_point_source(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", np.zeros(500), rng)
+        fitter = KingPSFFitter(
+            events, parametrization_bins={"aux": [-1.0, 1.0]}, minimum_counts=100, weight_field=None
+        )
+        assert_allclose(fitter.extension_grid, [0.0])
+
+    def test_negative_extension_raises(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", np.zeros(500), rng)
+        with pytest.raises(ValueError):
+            KingPSFFitter(
+                events,
+                parametrization_bins={"aux": [-1.0, 1.0]},
+                minimum_counts=100,
+                weight_field=None,
+                extension_grid=[-0.1, 0.0],
+            )
+
+    def test_extension_grid_is_sorted(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", np.zeros(500), rng)
+        fitter = KingPSFFitter(
+            events,
+            parametrization_bins={"aux": [-1.0, 1.0]},
+            minimum_counts=100,
+            weight_field=None,
+            extension_grid=[np.radians(2.0), 0.0, np.radians(1.0)],
+        )
+        assert_allclose(fitter.extension_grid, [0.0, np.radians(1.0), np.radians(2.0)])
+
+    def test_get_interpolator_fill_value_indices(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", rng.uniform(-1, 1, 500), rng)
+        fitter = KingPSFFitter(
+            events,
+            parametrization_bins={"aux": [-1.0, 0.0, 1.0]},
+            minimum_counts=100,
+            weight_field=None,
+            spectral_indices=[2.0, 3.0],
+            extension_grid=[0.0, np.radians(1.0)],
+        )
+        fitter.fit_beta = np.arange(8.0).reshape(2, 2, 2)
+        _, beta_interp = fitter.get_interpolator(gamma_index=1, extension_index=1)
+        assert_allclose(beta_interp([5.0]), fitter.fit_beta[1, 1].mean())
+
+    def test_fallback_alpha_grows_with_extension(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", np.zeros(500), rng)
+        fitter = KingPSFFitter(
+            events,
+            parametrization_bins={"aux": [-1.0, 1.0]},
+            minimum_counts=100,
+            weight_field=None,
+            extension_grid=[0.0, np.radians(2.0)],
+        )
+        assert_allclose(fitter.fit_alpha[0], fitter._alpha_guess)
+        assert np.all(fitter.fit_alpha[1] > fitter.fit_alpha[0])
+
+    def test_all_fits_failing_keeps_fallback(self, monkeypatch):
+        failed = SimpleNamespace(success=False, x=np.array([1.0, 2.0]), fun=1.0)
+        monkeypatch.setattr(fitting_module, "minimize", lambda *a, **k: failed)
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(500, np.radians(1.0), 2.5, "aux", np.zeros(500), rng)
+        fitter = KingPSFFitter(
+            events, parametrization_bins={"aux": [-1.0, 1.0]}, minimum_counts=100, weight_field=None
+        )
+        result = fitter.fit_all_bins(verbose=False)
+        assert_allclose(result["alpha"], fitter._alpha_guess)
+        assert np.all(result["histograms"].sum(axis=-1) > 0)
+
+    def test_extension_fit_independent_of_grid(self):
+        rng = np.random.default_rng(RNG_SEED)
+        events = _make_events(5000, np.radians(1.0), 2.5, "aux", np.zeros(5000), rng)
+        results = []
+        for grid in ([0.0, np.radians(2.0)], [0.0, np.radians(1.0), np.radians(2.0)]):
+            fitter = KingPSFFitter(
+                events,
+                parametrization_bins={"aux": [-1.0, 1.0]},
+                minimum_counts=100,
+                weight_field=None,
+                extension_grid=grid,
+            )
+            results.append(fitter.fit_all_bins(verbose=False))
+        np.testing.assert_array_equal(results[0]["alpha"][-1], results[1]["alpha"][-1])
+        np.testing.assert_array_equal(results[0]["beta"][-1], results[1]["beta"][-1])
+
+    @pytest.fixture(scope="class")
+    def multi_ext_result(self):
+        rng = np.random.default_rng(RNG_SEED)
+        alpha_true, beta_true = np.radians(1.0), 2.5
+        n = 100_000
+        events = _make_events(n, alpha_true, beta_true, "aux", np.zeros(n), rng)
+        extension_grid = np.radians([0.0, 1.0, 2.0])
+        fitter = KingPSFFitter(
+            events,
+            parametrization_bins={"aux": [-1.0, 1.0]},
+            dpsi_nbins=100,
+            minimum_counts=100,
+            weight_field=None,
+            extension_grid=extension_grid,
+        )
+        result = fitter.fit_all_bins(verbose=False)
+        return result, alpha_true, beta_true, extension_grid
+
+    def test_shape_matches_extension_grid(self, multi_ext_result):
+        result, _, _, extension_grid = multi_ext_result
+        assert result["alpha"].shape == (len(extension_grid), 1, 1)
+        assert result["beta"].shape == (len(extension_grid), 1, 1)
+        assert_allclose(result["extension_grid"], extension_grid)
+
+    def test_fitted_values_finite_and_valid(self, multi_ext_result):
+        result, _, _, _ = multi_ext_result
+        assert np.all(np.isfinite(result["alpha"]))
+        assert np.all(np.isfinite(result["beta"]))
+        assert np.all(result["alpha"] > 0)
+        assert np.all(result["beta"] > 1)
+
+    def test_zero_extension_recovers_point_source_fit(self, multi_ext_result):
+        """extension=0 should reproduce the un-smeared point-source fit."""
+        result, alpha_true, beta_true, _ = multi_ext_result
+        assert_allclose(result["alpha"][0, 0, 0], alpha_true, rtol=0.1)
+        assert_allclose(result["beta"][0, 0, 0], beta_true, rtol=0.2)
+
+    def test_alpha_increases_with_extension(self, multi_ext_result):
+        """A wider source extension should widen the fitted PSF."""
+        result, _, _, _ = multi_ext_result
+        alphas = result["alpha"][:, 0, 0]
+        assert alphas[0] < alphas[1] < alphas[2]
diff --git a/tests/test_king_pdf.py b/tests/test_king_pdf.py
index 939d836..9803867 100644
--- a/tests/test_king_pdf.py
+++ b/tests/test_king_pdf.py
@@ -11,7 +11,6 @@
 
 from kingmaker.pdf import KingPDF, MarginalizedKingPDF
 
-
 # ---------------------------------------------------------------------------
 # Shared fixtures and helpers
 # ---------------------------------------------------------------------------
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 4f94e6f..d5b7adc 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,15 +1,13 @@
 """
 Unit tests for kingmaker.utils.
 
-Covers angular_distance (known angles, symmetry, self-distance) and
-meshgrid2d (shape, values, dtype preservation).
+Covers angular_distance, meshgrid2d, offset_position, and sample_with_extension.
 """
 
 import numpy as np
 from numpy.testing import assert_allclose
 
-from kingmaker.utils import angular_distance, meshgrid2d
-
+from kingmaker.utils import angular_distance, meshgrid2d, offset_position, sample_with_extension
 
 # ---------------------------------------------------------------------------
 # angular_distance
@@ -113,3 +111,95 @@ def test_dtype_preserved(self):
         ga, gb = meshgrid2d(a, b)
         assert ga.dtype == np.float32
         assert gb.dtype == np.float32
+
+
+# ---------------------------------------------------------------------------
+# sample_with_extension
+# ---------------------------------------------------------------------------
+
+
+class TestSampleWithExtension:
+    def test_zero_extension_is_noop(self):
+        rng = np.random.default_rng(0)
+        ra, dec = sample_with_extension(1.0, 0.5, 0.0, rng)
+        assert_allclose(ra, 1.0)
+        assert_allclose(dec, 0.5)
+
+    def test_output_shape_matches_input(self):
+        rng = np.random.default_rng(1)
+        true_ra = np.linspace(0, 2 * np.pi, 50)
+        true_dec = np.linspace(-1.0, 1.0, 50)
+        ra, dec = sample_with_extension(true_ra, true_dec, np.radians(2.0), rng)
+        assert ra.shape == (50,)
+        assert dec.shape == (50,)
+
+    def test_finite_and_in_range(self):
+        rng = np.random.default_rng(2)
+        n = 5000
+        true_ra = rng.uniform(0, 2 * np.pi, n)
+        true_dec = np.arcsin(rng.uniform(-1, 1, n))
+        ra, dec = sample_with_extension(true_ra, true_dec, np.radians(3.0), rng)
+        assert np.all(np.isfinite(ra))
+        assert np.all(np.isfinite(dec))
+        assert np.all(dec >= -np.pi / 2 - 1e-9)
+        assert np.all(dec <= np.pi / 2 + 1e-9)
+        assert np.all(ra >= 0.0)
+        assert np.all(ra < 2 * np.pi)
+
+    def test_rayleigh_scale_recovered(self):
+        """Offset magnitude follows Rayleigh(extension): mean ~ scale*sqrt(pi/2)."""
+        rng = np.random.default_rng(3)
+        n = 200_000
+        extension = np.radians(2.0)
+        true_ra = np.full(n, 1.0)
+        true_dec = np.full(n, 0.3)
+        ra, dec = sample_with_extension(true_ra, true_dec, extension, rng)
+        offset = angular_distance(true_ra, true_dec, ra, dec)
+        expected_mean = extension * np.sqrt(np.pi / 2)
+        assert_allclose(offset.mean(), expected_mean, rtol=0.02)
+
+    def test_no_pole_crash(self):
+        rng = np.random.default_rng(4)
+        for dec0 in [np.pi / 2, -np.pi / 2, np.radians(89.9), np.radians(-89.9)]:
+            ra, dec = sample_with_extension(0.0, dec0, np.radians(3.0), rng)
+            assert np.isfinite(ra)
+            assert np.isfinite(dec)
+            assert -np.pi / 2 - 1e-9 <= dec <= np.pi / 2 + 1e-9
+
+    def test_seeded_rng_is_reproducible(self):
+        true_ra, true_dec, extension = 0.5, 0.2, np.radians(1.5)
+        ra1, dec1 = sample_with_extension(true_ra, true_dec, extension, np.random.default_rng(7))
+        ra2, dec2 = sample_with_extension(true_ra, true_dec, extension, np.random.default_rng(7))
+        assert_allclose(ra1, ra2)
+        assert_allclose(dec1, dec2)
+
+    def test_default_rng_when_none(self):
+        ra, dec = sample_with_extension(0.0, 0.0, np.radians(1.0))
+        assert np.isfinite(ra)
+        assert np.isfinite(dec)
+
+
+# ---------------------------------------------------------------------------
+# offset_position
+# ---------------------------------------------------------------------------
+
+
+class TestOffsetPosition:
+    def test_distance_recovered(self):
+        rng = np.random.default_rng(5)
+        n = 1000
+        ra0 = rng.uniform(0, 2 * np.pi, n)
+        dec0 = np.arcsin(rng.uniform(-0.99, 0.99, n))
+        distance = rng.uniform(0, np.radians(10.0), n)
+        ra, dec = offset_position(ra0, dec0, distance, rng.uniform(0, 2 * np.pi, n))
+        assert_allclose(angular_distance(ra0, dec0, ra, dec), distance, atol=1e-7)
+
+    def test_north_bearing_increases_dec(self):
+        ra, dec = offset_position(1.0, 0.2, 0.1, 0.0)
+        assert_allclose(ra, 1.0)
+        assert_allclose(dec, 0.3)
+
+    def test_east_bearing_on_equator_increases_ra(self):
+        ra, dec = offset_position(1.0, 0.0, 0.1, np.pi / 2)
+        assert_allclose(ra, 1.1)
+        assert_allclose(dec, 0.0, atol=1e-12)
diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py
index 3757ca6..e2abca4 100644
--- a/tests/test_wrapper.py
+++ b/tests/test_wrapper.py
@@ -12,35 +12,68 @@
 import pytest
 
 from kingmaker.pdf import KingPDF
-from kingmaker.utils import angular_distance, _interp1d
-from kingmaker.wrapper import KingSpatialLikelihood
-
+from kingmaker.utils import _interp1d, angular_distance
+from kingmaker.wrapper import KingSpatialLikelihood, _nearest_index
 
 SPECTRAL_INDICES = np.array([1.0, 2.0, 3.0])
 BIN_EDGES = np.array([0.0, 1.0, 2.0, 3.0])  # bin centers: 0.5, 1.5, 2.5
+EXTENSION_GRID = np.array([0.0])  # single point-source extension
+# shape (n_extension=1, n_gamma=3, n_bins=3)
 ALPHA_VALUES = np.array(
     [
-        [np.radians(0.5), np.radians(1.0), np.radians(1.5)],
-        [np.radians(0.6), np.radians(1.1), np.radians(1.6)],
-        [np.radians(0.7), np.radians(1.2), np.radians(1.7)],
+        [
+            [np.radians(0.5), np.radians(1.0), np.radians(1.5)],
+            [np.radians(0.6), np.radians(1.1), np.radians(1.6)],
+            [np.radians(0.7), np.radians(1.2), np.radians(1.7)],
+        ]
     ]
 )
 BETA_VALUES = np.array(
     [
-        [2.0, 2.5, 3.0],
-        [2.1, 2.6, 3.1],
-        [2.2, 2.7, 3.2],
+        [
+            [2.0, 2.5, 3.0],
+            [2.1, 2.6, 3.1],
+            [2.2, 2.7, 3.2],
+        ]
     ]
 )
 
 
-def _make_likelihood(tmp_path, angular_cutoff=np.pi):
+def _make_likelihood(tmp_path, angular_cutoff=np.pi, **kwargs):
     cache_path = tmp_path / "king_cache.npz"
     np.savez(
         cache_path,
         parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
         alpha=ALPHA_VALUES,
         beta=BETA_VALUES,
+        extension_grid=EXTENSION_GRID,
+    )
+    return KingSpatialLikelihood(
+        signal_events=np.empty(0),
+        parametrization_bins={"aux": 3},
+        spectral_indices=SPECTRAL_INDICES,
+        cache_parameters=True,
+        cache_name=str(cache_path),
+        angular_cutoff=angular_cutoff,
+        **kwargs,
+    )
+
+
+# Three extensions; alpha grows with extension index so per-source
+# differentiation is directly observable.
+MULTI_EXTENSION_GRID = np.radians([0.0, 1.0, 3.0])
+MULTI_EXT_ALPHA_VALUES = np.stack([ALPHA_VALUES[0] * scale for scale in (1.0, 2.0, 4.0)])
+MULTI_EXT_BETA_VALUES = np.stack([BETA_VALUES[0] for _ in range(3)])
+
+
+def _make_multi_ext_likelihood(tmp_path, angular_cutoff=np.pi, extension_grid=None):
+    cache_path = tmp_path / "king_cache_multi_ext.npz"
+    np.savez(
+        cache_path,
+        parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
+        alpha=MULTI_EXT_ALPHA_VALUES,
+        beta=MULTI_EXT_BETA_VALUES,
+        extension_grid=MULTI_EXTENSION_GRID,
     )
     return KingSpatialLikelihood(
         signal_events=np.empty(0),
@@ -49,10 +82,14 @@ def _make_likelihood(tmp_path, angular_cutoff=np.pi):
         cache_parameters=True,
         cache_name=str(cache_path),
         angular_cutoff=angular_cutoff,
+        extension_grid=extension_grid,
     )
 
 
-def _make_events(n_per_bin, rng, offset_scale=np.radians(2.0)):
+OFFSET_SCALE = np.radians(2.0)
+
+
+def _make_events(n_per_bin, rng, offset_scale=OFFSET_SCALE):
     """n_per_bin events at each of the 3 known bin centers (0.5, 1.5, 2.5),
     at small random offsets from a source at (ra=0, dec=0)."""
     aux_centers = [0.5, 1.5, 2.5]
@@ -95,8 +132,8 @@ def test_matches_direct_pdf_computation(self, likelihood):
         for gamma_idx, gamma in enumerate(SPECTRAL_INDICES):
             result = likelihood.evaluate_pdf(events, gamma=gamma).toarray().ravel()
 
-            alpha = ALPHA_VALUES[gamma_idx][bin_idx]
-            beta = BETA_VALUES[gamma_idx][bin_idx]
+            alpha = ALPHA_VALUES[0][gamma_idx][bin_idx]
+            beta = BETA_VALUES[0][gamma_idx][bin_idx]
             expected = king_pdf.pdf(dist, alpha, beta)
 
             np.testing.assert_allclose(result, expected, rtol=1e-10)
@@ -145,8 +182,8 @@ def test_matches_direct_pdf_computation_per_source(self, likelihood):
 
         bin_idx = np.array([_bin_index(a) for a in events["aux"]])
         gamma_idx = int(np.searchsorted(SPECTRAL_INDICES, 2.0))
-        alpha = ALPHA_VALUES[gamma_idx][bin_idx]
-        beta = BETA_VALUES[gamma_idx][bin_idx]
+        alpha = ALPHA_VALUES[0][gamma_idx][bin_idx]
+        beta = BETA_VALUES[0][gamma_idx][bin_idx]
         king_pdf = KingPDF(angular_cutoff=likelihood.king_pdf.angular_cutoff)
 
         dense = result.toarray()
@@ -178,8 +215,8 @@ def test_sparse_structure_with_partial_and_overlapping_coverage(self, tmp_path):
 
         bin_idx = np.array([_bin_index(a) for a in events["aux"]])
         gamma_idx = int(np.searchsorted(SPECTRAL_INDICES, 2.0))
-        alpha = ALPHA_VALUES[gamma_idx][bin_idx]
-        beta = BETA_VALUES[gamma_idx][bin_idx]
+        alpha = ALPHA_VALUES[0][gamma_idx][bin_idx]
+        beta = BETA_VALUES[0][gamma_idx][bin_idx]
         king_pdf = KingPDF(angular_cutoff=cutoff)
 
         for j, (src_ra, src_dec) in enumerate(zip(src_ras, src_decs)):
@@ -319,3 +356,199 @@ def test_shape_matches_event_count(self, likelihood):
 
         result = likelihood.evaluate_pdf(events_10, gamma=2.0)
         assert result.shape == (len(events_10), 1)
+
+
+class TestNearestIndex:
+    def test_snaps_to_nearest(self):
+        idx = _nearest_index(MULTI_EXTENSION_GRID, np.radians([0.4, 1.6, 2.9]))
+        np.testing.assert_array_equal(idx, [0, 1, 2])
+
+    def test_single_center(self):
+        np.testing.assert_array_equal(_nearest_index(np.array([1.0]), [0.5, 1.5]), [0, 0])
+
+
+class TestSourceExtensions:
+    def test_default_is_zero(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        rng = np.random.default_rng(10)
+        events = _make_events(5, rng)
+        likelihood.set_events(events, source_ras=np.array([0.0]), source_decs=np.array([0.0]))
+        np.testing.assert_array_equal(likelihood.source_extensions, [0.0])
+
+    def test_narrower_extension_source_has_higher_nearby_pdf(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        src_ras = np.array([0.0, 0.0])
+        src_decs = np.array([0.0, 0.0])
+        src_exts = np.array([MULTI_EXTENSION_GRID[0], MULTI_EXTENSION_GRID[2]])
+
+        dtype = [("ra", float), ("dec", float), ("aux", float)]
+        events = np.zeros(1, dtype=dtype)
+        events["ra"], events["dec"], events["aux"] = np.radians(0.1), 0.0, 0.5
+
+        likelihood.set_events(
+            events, source_ras=src_ras, source_decs=src_decs, source_extensions=src_exts
+        )
+        result = likelihood.evaluate_pdf(events, gamma=2.0).toarray()
+        assert result[0, 0] > result[0, 1]
+
+    def test_none_after_extended_resets_to_point_source(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        src_ras, src_decs = np.array([0.0]), np.array([0.0])
+        events = _make_events(5, np.random.default_rng(11))
+
+        likelihood.set_events(
+            events,
+            source_ras=src_ras,
+            source_decs=src_decs,
+            source_extensions=MULTI_EXTENSION_GRID[2:],
+        )
+        likelihood.set_events(events, source_ras=src_ras, source_decs=src_decs)
+        result = likelihood.evaluate_pdf(events, gamma=2.0).toarray()
+
+        fresh = _make_multi_ext_likelihood(tmp_path)
+        fresh.set_events(events, source_ras=src_ras, source_decs=src_decs)
+        expected = fresh.evaluate_pdf(events, gamma=2.0).toarray()
+
+        np.testing.assert_array_equal(likelihood.source_extensions, [0.0])
+        np.testing.assert_allclose(result, expected, rtol=1e-12)
+
+    def test_mismatched_extension_grid_raises(self, tmp_path):
+        with pytest.raises(ValueError):
+            _make_multi_ext_likelihood(tmp_path, extension_grid=np.radians([0.0, 2.0]))
+
+    def test_matching_extension_grid_loads(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path, extension_grid=MULTI_EXTENSION_GRID[::-1])
+        np.testing.assert_allclose(likelihood.extension_grid, MULTI_EXTENSION_GRID)
+
+    def test_extension_outside_grid_raises(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        events = _make_events(5, np.random.default_rng(13))
+        with pytest.raises(ValueError):
+            likelihood.set_events(
+                events,
+                source_ras=np.array([0.0]),
+                source_decs=np.array([0.0]),
+                source_extensions=np.radians([5.0]),
+            )
+
+    def test_failed_set_events_keeps_previous_state(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        rng = np.random.default_rng(15)
+        events_a, events_b = _make_events(5, rng), _make_events(6, rng)
+        src_ras, src_decs = np.array([0.0]), np.array([0.0])
+        likelihood.set_events(events_a, source_ras=src_ras, source_decs=src_decs)
+        with pytest.raises(ValueError):
+            likelihood.set_events(
+                events_b,
+                source_ras=src_ras,
+                source_decs=src_decs,
+                source_extensions=np.radians([5.0]),
+            )
+        with pytest.raises(RuntimeError):
+            likelihood.evaluate_pdf(events_b, gamma=2.0)
+
+    def test_get_alpha_beta_gamma_extension_index(self, tmp_path):
+        likelihood = _make_multi_ext_likelihood(tmp_path)
+        events = _make_events(5, np.random.default_rng(14))
+        likelihood.set_events(events, source_ras=np.array([0.0]), source_decs=np.array([0.0]))
+        alpha, _ = likelihood.get_alpha_beta_gamma(2.0, events, extension_index=2)
+        bin_idx = [_bin_index(a) for a in events["aux"][likelihood.event_mask]]
+        np.testing.assert_allclose(alpha, MULTI_EXT_ALPHA_VALUES[2, 1, bin_idx])
+
+    def test_missing_extension_grid_key_raises(self, tmp_path):
+        cache_path = tmp_path / "stale_no_key.npz"
+        np.savez(
+            cache_path,
+            parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
+            alpha=ALPHA_VALUES[0],
+            beta=BETA_VALUES[0],
+        )
+        with pytest.raises(ValueError):
+            KingSpatialLikelihood(
+                signal_events=np.empty(0),
+                parametrization_bins={"aux": 3},
+                spectral_indices=SPECTRAL_INDICES,
+                cache_parameters=True,
+                cache_name=str(cache_path),
+            )
+
+    def test_wrong_ndim_raises(self, tmp_path):
+        cache_path = tmp_path / "stale_wrong_ndim.npz"
+        np.savez(
+            cache_path,
+            parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
+            alpha=ALPHA_VALUES[0],
+            beta=BETA_VALUES[0],
+            extension_grid=np.array([0.0]),
+        )
+        with pytest.raises(ValueError):
+            KingSpatialLikelihood(
+                signal_events=np.empty(0),
+                parametrization_bins={"aux": 3},
+                spectral_indices=SPECTRAL_INDICES,
+                cache_parameters=True,
+                cache_name=str(cache_path),
+            )
+
+    def test_unsorted_extension_grid_raises(self, tmp_path):
+        cache_path = tmp_path / "unsorted_ext.npz"
+        np.savez(
+            cache_path,
+            parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
+            alpha=MULTI_EXT_ALPHA_VALUES,
+            beta=MULTI_EXT_BETA_VALUES,
+            extension_grid=MULTI_EXTENSION_GRID[::-1],
+        )
+        with pytest.raises(ValueError):
+            KingSpatialLikelihood(
+                signal_events=np.empty(0),
+                parametrization_bins={"aux": 3},
+                spectral_indices=SPECTRAL_INDICES,
+                cache_parameters=True,
+                cache_name=str(cache_path),
+            )
+
+    def test_empty_extension_grid_raises(self, tmp_path):
+        cache_path = tmp_path / "empty_ext.npz"
+        np.savez(
+            cache_path,
+            parametrization_bins=np.array({"aux": BIN_EDGES}, dtype=object),
+            alpha=MULTI_EXT_ALPHA_VALUES[:0],
+            beta=MULTI_EXT_BETA_VALUES[:0],
+            extension_grid=np.array([]),
+        )
+        with pytest.raises(ValueError, match="non-empty"):
+            KingSpatialLikelihood(
+                signal_events=np.empty(0),
+                parametrization_bins={"aux": 3},
+                spectral_indices=SPECTRAL_INDICES,
+                cache_parameters=True,
+                cache_name=str(cache_path),
+            )
+
+
+MARG_KWARGS = {
+    "enable_marginalization": True,
+    "marginalization_points_alpha": np.radians([0.5, 2.0]),
+    "marginalization_points_beta": np.array([1.5, 3.5]),
+    "marginalization_n_signed_delta_dec": 10,
+    "marginalization_n_ra_bins": 10,
+}
+
+
+class TestMarginalizationSourceDecs:
+    def test_mismatched_decs_raise(self, tmp_path):
+        likelihood = _make_likelihood(
+            tmp_path, marginalization_source_decs=np.array([0.2]), **MARG_KWARGS
+        )
+        events = _make_events(5, np.random.default_rng(12))
+        with pytest.raises(ValueError):
+            likelihood.set_events(events, source_ras=np.array([0.0]), source_decs=np.array([0.3]))
+
+    def test_matching_decs_ok(self, tmp_path):
+        likelihood = _make_likelihood(
+            tmp_path, marginalization_source_decs=np.array([0.2]), **MARG_KWARGS
+        )
+        events = _make_events(5, np.random.default_rng(12))
+        likelihood.set_events(events, source_ras=np.array([0.0]), source_decs=np.array([0.2]))
+        assert likelihood.evaluate_marginalized_pdf(events, gamma=2.0).shape == (len(events), 1)