From 717ef1e8352dfc38acd3ced8a5ff7878d4dc8c56 Mon Sep 17 00:00:00 2001 From: rozyczko Date: Tue, 17 Mar 2026 11:48:07 +0100 Subject: [PATCH 1/5] initial implementation of the mighell algorithm for data with zero variances --- CHANGELOG.md | 9 + notebooks/zero_variance_fitting.ipynb | 828 ++++++++++++++++++++++++++ src/easyreflectometry/fitting.py | 201 +++++-- tests/test_fitting.py | 383 +++++++++++- 4 files changed, 1354 insertions(+), 67 deletions(-) create mode 100644 notebooks/zero_variance_fitting.ipynb diff --git a/CHANGELOG.md b/CHANGELOG.md index bb6a1617..b786ccd6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,12 @@ +# Version 1.4.0 (unreleased) + +Add Mighell-based handling of zero-variance points in fitting (issue #256). +Zero-variance data points are no longer forcibly discarded; instead, a hybrid +objective applies a Mighell substitution for zero-variance points while using +standard weighted least squares for the rest. The previous masking behavior is +available via `objective='legacy_mask'`. New `objective` parameter on +`MultiFitter`, `fit()`, and `fit_single_data_set_1d()`. + # Version 1.3.3 (17 June 2025) Added Chi^2 and fit status to fitting results. diff --git a/notebooks/zero_variance_fitting.ipynb b/notebooks/zero_variance_fitting.ipynb new file mode 100644 index 00000000..3fdfc09e --- /dev/null +++ b/notebooks/zero_variance_fitting.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a8a52ac", + "metadata": {}, + "source": [ + "# Zero-Variance Handling in Reflectometry Fitting\n", + "\n", + "This notebook demonstrates how `easyreflectometry` handles data points with **zero variance**\n", + "during fitting. We compare three objective strategies:\n", + "\n", + "| Objective | Behaviour |\n", + "|---|---|\n", + "| `legacy_mask` | Drops zero-variance points entirely (old default) |\n", + "| `hybrid` | Keeps all points; applies Mighell substitution only to zero-variance entries (**new default**) |\n", + "| `mighell` | Applies the Mighell (1999) transform to every point |\n", + "\n", + "The experimental data comes from `tests/_static/ref_zero_var.txt`, which contains 192 data points,\n", + "6 of which have zero error (= zero variance)." + ] + }, + { + "cell_type": "markdown", + "id": "5c6429ac", + "metadata": {}, + "source": [ + "## 1. Import Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4983acb8", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipp as sc\n", + "\n", + "from easyreflectometry.calculators import CalculatorFactory\n", + "from easyreflectometry.data.measurement import load\n", + "from easyreflectometry.fitting import MultiFitter\n", + "from easyreflectometry.model import Model\n", + "from easyreflectometry.model import PercentageFwhm\n", + "from easyreflectometry.sample import Layer\n", + "from easyreflectometry.sample import Material\n", + "from easyreflectometry.sample import Multilayer\n", + "from easyreflectometry.sample import Sample\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "96e174d4", + "metadata": {}, + "source": [ + "## 2. Load Experimental Data\n", + "\n", + "The file `ref_zero_var.txt` is a comma-separated file with columns: `q (Å⁻¹)`, `R`, `error`.\n", + "Several points near the high-Q end have `error = 0.0`, meaning zero variance." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "47fb3264", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total data points : 192\n", + "Q range : [7.6135e-03, 3.3436e-01] Å⁻¹\n", + "R range : [3.1715e-07, 1.0429e+00]\n" + ] + } + ], + "source": [ + "DATA_PATH = os.path.join('..', 'tests', '_static', 'ref_zero_var.txt')\n", + "\n", + "# Load raw data for inspection\n", + "raw = np.loadtxt(DATA_PATH, delimiter=',', comments='#')\n", + "q_raw, r_raw, err_raw = raw[:, 0], raw[:, 1], raw[:, 2]\n", + "\n", + "# Load through easyreflectometry (produces a scipp DataGroup)\n", + "data = load(DATA_PATH)\n", + "\n", + "print(f'Total data points : {len(q_raw)}')\n", + "print(f'Q range : [{q_raw.min():.4e}, {q_raw.max():.4e}] Å⁻¹')\n", + "print(f'R range : [{r_raw.min():.4e}, {r_raw.max():.4e}]')" + ] + }, + { + "cell_type": "markdown", + "id": "c4495939", + "metadata": {}, + "source": [ + "## 3. Inspect Data and Identify Zero-Variance Points" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1be2ca78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of zero-variance points: 6\n", + "Indices: [170 179 183 189 190 191]\n", + "Q-values with zero variance:\n", + " [170] Q = 2.2061e-01 Å⁻¹, R = 7.8023e-07\n", + " [179] Q = 2.6364e-01 Å⁻¹, R = 6.5027e-07\n", + " [183] Q = 2.8538e-01 Å⁻¹, R = 4.6847e-07\n", + " [189] Q = 3.2138e-01 Å⁻¹, R = 3.1715e-07\n", + " [190] Q = 3.2781e-01 Å⁻¹, R = 3.4336e-07\n", + " [191] Q = 3.3436e-01 Å⁻¹, R = 3.2231e-07\n" + ] + } + ], + "source": [ + "zero_mask = err_raw == 0.0\n", + "zero_indices = np.where(zero_mask)[0]\n", + "print(f'Number of zero-variance points: {zero_mask.sum()}')\n", + "print(f'Indices: {zero_indices}')\n", + "print(f'Q-values with zero variance:')\n", + "for idx in zero_indices:\n", + " print(f' [{idx:3d}] Q = {q_raw[idx]:.4e} Å⁻¹, R = {r_raw[idx]:.4e}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "373cdc62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkL9JREFUeJzt3Ql4VOXVwPEzkx2SDDsBZBNQDIIoIqKiCFjF1rq0ClYU0GK1SLFoq3RBrdatFrE0lWpVxLrgVuxXlaooiooKKIiAQBAVBULYMknIOnO/57x408kyySSZff4/nzGZmzszd+6dGebcc97zOizLsgQAAAAAAASdM/h3CQAAAAAACLoBAAAAAAghMt0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBNwAAAAAAIULQDQAAAABAiBB0A0CMmDJlivTp00fi3cKFC8XhcMiXX37Zotsnyn5Cffq6ufXWW9k1deg+0X0TLXRbrrvuupB+Fti3Xb16dZPrjh492lxs+nh6W72P5rJve99990kk6WegfhYCiA4E3QCCxv6SY1+Sk5OlR48e5h/+b7/9lj2dAA4dOmS+4C9fvlxi0Z133ilLliyJ9GYAUeP999837+mDBw9GelPiziuvvMJJIiBBEHQDCLo//OEP8sQTT8iCBQtk/Pjx8s9//lPOOOMMKS8vZ2+3wsMPPyybN2+O+qD7tttuI+hGRJSVlcnvfvc79n4duk9037Q06Nb3dCSC7ssvv9xsd+/evcP6uPp4+rj6+KEOunXfAoh/yZHeAADxRwPtE0880fz+05/+VDp16iT33HOP/Pvf/5ZLLrkk0psXc0pLS6Vt27aSkpIS6U1BAtGTZKmpqeJ0Rvf5ea/XK5WVlZKenm4uqE+rjvQSa5KSkswl3LRSi9cSgGCK7n9JAcSFUaNGmZ/btm2rWaZfkufMmSPDhg0Tl8tlgkpd76233qp12xNOOEEuuuiiWssGDx5svhR9+umnNcsWL15slm3atKnRbamoqJBbbrlF+vfvL2lpadKzZ0/59a9/bZbbJk+ebL5w1b2vs88+W9q3by87d+6sVU7/zjvvyM9+9jPp2LGjZGdnyxVXXCEHDhyo99ivvvqqeY76XLOysuT73/++bNiwodY6WoqfmZlp9tW5555r1rvssssaHKvsO3YwLy9PjjzySGnTpo1873vfkx07dohlWXL77bfLEUccIRkZGXL++efL/v37W7VdOkzgggsuML937txZbrzxRvF4PDXbo8uUZm/sYQb2GFs9Xno/up26f3NycuTKK6+Uffv2SUtpKfixxx5r7k9//utf/2pwPd1Hp5xyijlGui/0dff888/XWke3VU9wPP744zXbbo+J/Oqrr+TnP/+5HH300eb2ej8XX3xxi8edB4O+FnyHc/hefMv79Zjpfu7atat5zQ8aNEgeffTRWvel6+vtnnnmGZMV1WEh+lpyu93m788995zZZ/rc9STapEmTAhoyosfkzDPPbDBQ1sf48Y9/3Kxj5Dse+MknnzTPRZ/T0qVLGxzTHehxs9/L7733nsyaNcu8jvX9cOGFF0phYWGD7xmt3tH3i77nhw8fLk899VStdT788EM555xzzOeb7ktdX++/Kfax0M+03/zmN+Z9otvywx/+0Lyv6wrk2DQ0ptvej/Z7yH5t2PvSvt2vfvUr83vfvn1rXl/2/nv99dfltNNOk3bt2pnPBN3Pus2Bauyx/Y3p1teOblf37t3NftXX18aNG/2OYdbP9kCOqS9/Y7p1X+fm5tb6vGmsh8RDDz0k/fr1M89PXyOrVq2q+ZveTj+3le971/d5zps3z+wXfTx9/+q/M3X/bdHP+TvuuMN8ztv7o+7nN4DIi73TngBijv2FSQNWm36Z/8c//iGXXnqpTJs2TYqLi+WRRx4xge1HH30kQ4cONetpMPj000/X3E6DRv1Codm3FStWyJAhQ8xy/V2/VB1zzDF+t0O/xOgX13fffVeuvvpqs+769evl/vvvly1bttSM5X3ggQfkzTffNMH3ypUrTabl73//u7z22mumbF6/7PnSL676pVO/CGr594MPPmi+7NtfnpXeTu9Pn59m/bUMW9fTL6yffPJJrS9t1dXVZj39mwYi+kWqMRp86EmMGTNmmP1z7733moqCMWPGmG246aabJD8/X+bPn2+CZN+AqznbpcG1rjdixAizXW+88Yb8+c9/Nl8qr732WrP/9bb6u36xtU+W2MdIv6B/8cUXMnXqVBNI6HHUL6X684MPPmh2oyc9Hj/60Y/Ml+C77rrLBO963/rlsy49pnrs9QSG7isNLjX4+s9//mNOMtj7QiszTjrpJPP6UPrclH5Z1jLbiRMnmvvX17Q+V22+pF/4mzpGoaBfyEtKSmot09fy2rVrTXCpCgoK5OSTT64JsPQYacB41VVXmffg9ddfX+v2epJGs9v6OtFgRX/XwEP3qwYNup/1PnV/agCprxF97fszYcIE877YvXu3OeY2fQ/qySvdn805RjZ9fz777LPmOWmg6S/oae5x0/eQfk7piTldV/exPoYGwDbdH3oSQ4Oh2bNnm+ev+0EDxp/85Cc126cVPxoM633p59Vjjz1m3pP6WaWvsab88Y9/NMdN37979uwx2zJu3DhzfDXAtrelpcfGPg4vvviiOTGhJxD+8pe/mPfU119/bV5D+h7Wz0b9DNbXlu5rpa8jfd/+4Ac/MO9vHVKkgaV+zgRyYiGQx/ZH97l+xp133nnm82jdunXmp7/hS4Ec00C8/PLL5vWsJ311X2vwq+8jPXnUED0Jo/+uaaCsx1G3WfenfgZq1ZIu1/eAfi7qZ09d+nf7+P7iF7+Q7du3y1//+ldzXHUf25VPevJag249SauXjz/+2Jx41fcQgChiAUCQPPbYY5Z+rLzxxhtWYWGhtWPHDuv555+3OnfubKWlpZnrturqaquioqLW7Q8cOGB17drVuvLKK2uWPffcc+Y+N27caK7/+9//Nvf1wx/+0JowYULNekOGDLEuvPDCRrfviSeesJxOp7VixYpayxcsWGAe47333qtZ9t///tcsu+OOO6wvvvjCyszMtC644IIGn++wYcOsysrKmuX33nuvWf7SSy+Z68XFxVa7du2sadOm1br97t27LZfLVWv55MmTzW1vvvnmetuvf+vdu3fN9e3bt5t1df8ePHiwZvns2bPN8uOOO86qqqqqWX7ppZdaqampVnl5eYu36w9/+EOtdY8//njz/G163HW9W265pd72Hzp0qN6yp59+2qz/zjvv1Nuv+vwaM3ToUKtbt261nvtrr71mbuu7nxp6bD1exx57rDVmzJhay9u2bWueayDbvnLlSvNYixYtsqLBs88+W+8YXXXVVWYf7d27t9a6EydONMfYfl5vvfWWue2RRx5Z67nqfurSpYvZV2VlZTXL//Of/5j158yZ0+g2bd682aw3f/78Wst//vOfm/eU72MFeoz0/vR9vGHDhnqPV/e1F+hxs19z48aNs7xeb83yX/7yl1ZSUlLNa0x/ZmVlWSNGjKi1P5R9O/05YMAA6+yzz651X7otffv2tc4666xG95l9LHr06GG53e56x/eBBx5o9rHRfVL3K59e18+D/Pz8mmXr1q2rd7z+9Kc/Nfh+vP/++81yfc83V6CPXfezQD+bkpOT630W33rrrWY93/duoMdUnXHGGeZS97NV78M2ePBg64gjjjCfm7bly5fX+7yxb9uxY0dr//79Ncv13wNd/n//9381y6ZPn17vuCj9N0qXP/nkk7WWL126tNbyPXv2mP34/e9/v9Zz/M1vflNvfwCILMrLAQSdZmM0E6Kl21o+qiV9Op7bNwOp2WPNotkZaM3QaoZXx4Lrmfq6pelawq00S6RZnbPOOsv8rrTBz2effVazrj9aGqjZ7YEDB8revXtrLpp9Ur6l7Zop0EyDZnA0O6HlfZrtbohmRX3HW2umV8dPapMcpZkM3UbN6vs+ru4DzRrXLam37yNQmg3UElab3qfSMlPfcZy6XLMfdulpS7brmmuuqXVd97lmbgJhZ+eUZqX0sTQLq3yPeSB27dplMn6apfd97vq60Mx3Y4+tGaqioiKz7YE+ru/tq6qqTFZdhyhoJrG52x4KmrXV7KsOIbAbiWls88ILL5iMoP7ue4w1M6j7oO626/70fa463ZJmWTUb6TvGVTPP+j7S7F9jjjrqKFO14ptV1IoJLRvX7fJ9rOYcIy3Vbug4t/a46XvZt+JCH1+3VytX7PeMZi9vvvnmemN+7dvp63Lr1q0m662PZ+9zHbowduxY81mmn3lN0WEqmgG26Wdpt27daj5XWnts7M9qu5pDadZay+UDeU/bWfSXXnopoOcTjMdetmyZ+XdCn3PdbLY/TR3TQGhGWqui9JhoGb3v61Az3w3RrLhvdZf971Mg+1b/rdLPNf08833fauWEPr792azVRnaVk+9zrFvBAiDyKC8HEHQ6Tk2/bOuXZi1l1i+ZWnpYl46d1fLkzz//3HwhtunYQZuOYxswYIAJsDUI1p86Zu300083XzT0C4yOvdYvfU0F3fpFWNe1xx3XpV9gfWkJtX6h1C/RWirYpUuXBm+n2+dLvxTpl2O7rF4fV9nBfV36RdOXBsoNlUj706tXr1rX7SBUT3o0tNweE9jc7dIv9nX3nX6pbGj8ekP0xIqO9day4br7Wl8rzWF/Ya6775WOK60bUGmJspZg6rH0Hb8faEm7djLWklItEdaTFoeTdYFtu44ftce9N5fu76YaSWmZuJ4Y0jLXRYsW1TwnfVw9qaIl/HppSN3j4Pve893Puk/r0sBOS4SVPr+642Q7dOhgTqxp8KHjfHW/6TbqkAd9XF3e0mNUdzuDddzqvpfsoMl+jdt9KXQ8rz/2+0pPYPijj+0bkDWk7mtb94OeMLA/VwI9No2p+3yb857W46dDhHRIhp6E0BMK+jrUkwOBNN9ryWPbz1n3Q93Xmr/92dQxDYS/x7WXNXQCpzWPq68hfY34+zfHft/6+xzUz42mXl8AwougG0DQ6XhFu3u5Nt3S8cGa9dHxznaWQKcR00Yy+ndt1KNfLjS40C/Ivg3XlN5eMxz6BXrNmjVmDJt+6dVMiwbhGkjr/R5//PGNbpcG5pqVmDt3boN/rxuk6tg5+8uNZjk0I9wSdhZIx+35jmu11e0qrCcomtMx2l9Q5m+5HXg0d7ta20VYx5nr+Fo93pr91GOm26DNplqSKQuUvkZ0rLCeqPnb3/5mTohoZYIGYnWbX/mjJ3h0fc0gjRw50pzA0CBIxwo3te1amdGcrJovHcfpb7yyTd9HmonTXgi+J0rs7dKKB38BoD3evqHMcHNog6+6gbBm43TstAZnOg5Xs3e6/3Qstu4/Pe4tPUaBbmdzj1tT75lA2Pf7pz/9qaY3RV2+2dJIas3z1WOgJ1T1OGtWXce0a0WDnsTTfgtNfV4EY18HIlyPE8zH1deQ/puo/Toa4u/EMYDoRdANIKTsQFqz09oERjMiSstLtYu1NtLxzWRps5u6NIOtX5w1Q6oZNe1wrEGpBuN20K3LmvqSp6WM2nRHMzJNZTi1FFQb2GgJq963NsHR5mAaQDWUlfDt0KzNrbT8WZva2I+r9EuUllRGi1Bsl7/9qtkdPXGimW49aVI3K9hc9ry9Dd2+7lzmWmKtWfr//ve/tSou9DUV6Pbr61UDV63M8C2RD2TuYv3i3NI5khs6GeLr7rvvNg0A9X2k2c26X8y1PFnfMy09vvZ+1n1atyJCl9l/1+3U0mtfxx13nPmpwbieiNOATBtY6bbqyTbfY9GcY9QcrTlujb1ndDhLQ1lP33X0BEhr3ld1X9sarGmjMvtESaDHprUa+6zUz2H9PNWLnsy888475be//a0JxEPxWWc/J90Pvid5tIy/OZnr1jxuXQ0ta+2+1deQlo6feuqpjZ5g8v0c1H9PbVp1Esr9AaD5GNMNIOQ026VfurVrrN1h1g6Qfc/66xQ72i28LrtsXLtr6xdOu0xal2sgp2MbmyottzOtWmL68MMP1/ubBkUaaNu0Y7B20dUSeP0yqdlG/fLuW/Zq09Jd3/J47Y6s4w61e7HS8bP6BVy/kPquZ2tq+ppQCcV22d2g6wY1DR1vpa+JltBMqGYR9fj4lglr4Kfjm+s+tn659S3x1hJdu1u9L+0/0FBApvdRd9u1G3wgZeP6xVkDkJZcGpsrWL+U6/htDXI0iG1om7UbtAa0GiS25PhqxYqelFmwYEGt1752QNeTXXZXcd3OutvuW96q2W7tUK/DTXRsat3S8uYco+ZozXFriPZ60BMZeiKxbrds+3F03K0GTTo8pW53+ea8r3SogI4f9z2BoCfz7M+VQI9Na+l7QtV9XzQ0/aCd2W/oczIYNLjXChz9jPWlJ3RDSWes0OoqPSa+x/Ttt982VVDB3rf6b5W+RnU2gbr03xZ7fX2faUWIvqZ9X+ct/VwFEDpkugGEhZYUa8MvnQJFm3HpVDOa8dLssX451DJa/fKomeW6X1Q1o6SZNM3e+DbM0VJUDY5VIEH35Zdfbkpb9fE1E6PBkH6x0THlulyzbPpFVqf70RJXzbrrPOF2xk1PHvz+9783WW9f2shGvwzqFyXdRr2tZuG1XFZpYKtfEvXx9f60tFWzkBrUa1mmbkeovzQ2JBTbpVkZPYaa1dRx/TrWUr+s6kWPl+47DfB1bK+WoOpxbykNfPS1o/tam4hpEKBfPnUqJ9/XkK6jJ060nFmHOeiQAe07oK8r37ne7YBJg1ldX79oazZNm8rp61XL8PWEjz4/PTmk6zU2tVGo6XAHPV46nlOHa/jSBkzaD0Ez4fpa1+egU/Pptut+0jGouv0NBU6+9Au9nuzSqg9tGqWPaU9LpSeifvnLXwa0rfre0GnI9KKvibpZ0OYco+YI9nHT94xOnaXjmLXqRbdVTy5oBY1Ot6cngTT7q2OdNTjW16LuO3296wk/PRZ6H//3f//X5GPpftLXtt5e97kGUro/9DgG89g0Rd8TSk/u6GeEPq42wdMmk1persdOM656zPSzT/tR6HaHgr6mZ86caSoX9PNVXy+67/VEg05n1txpB5tDT05qo0L9XNR9rplk/XzUz7aGTq40Z9/qlGB6ElRPEuk+1uOpPUz0M057HOjJHt3vmtHWYRp6jHXsvL7/9T2l6+lrXaurdFiUvT8ARJEId08HEEfsKVpWrVpV728ej8fq16+fueh0YTq9yZ133mmmWtEpwHTqKZ3qpu60WLaLL77Y3PfixYtrlumUOW3atDFTptSdvscfvc0999xjDRo0yDxu+/btzZRXt912m1VUVGSm6NHHP+GEE2pNt2VPNaNTFemUQ77P9+2337auvvpqc186DdJll11m7du3r8GpgHQaIZ2qKT093eyLKVOmWKtXr65ZR5+/TlvVEH9ThumUPnUfR5frdGuBHJ/WbFdDUxG9//77Zp/qcfGdwumbb74x07rpNGX6WHpMd+7cWW+ap0CnDFMvvPCCdcwxx5hjmZuba7344osNvoYeeeQRM42Trjdw4EDzGA1t++eff26dfvrpVkZGRq0pd3Q6u6lTp1qdOnUyx1j3l66rjxOpaXl0+/xd9JjaCgoKzNREPXv2tFJSUqycnBxr7Nix1kMPPdTka8am7zt9j+r+69Chg3mN6/FsjlNPPdU8xk9/+tMG/x7oMdLr+nz87RPf11Kgx62x90bd/WlPXXjKKaeY10l2drZ10kknmenvfH3yySfWRRddZKaO0uekj3nJJZdYy5Yta3Q/2Y+p96fT/+m0YPo4Oi3UV1991aJj05z92NBr+vbbbzdTmOnnn/3e1Odx/vnnW927dzfvdf2p0xJu2bKl0efXnMdu6LNA//34/e9/b17Hul90SrlNmzaZ/XzNNde06JgGMmWYeuaZZ8xrU/e1TtWmr4Mf/ehHZllTn8sNvT71ucyYMcNM++hwOOodI32P6mepPk+dqk6nLfv1r39tPjd9/23Vf790akBdb/To0dZnn30W0c8mAPU59H+RDvwBIBZp1l4zHqtWrappHAcAraHd3bVHhGY0NZuJpmm5tVYcaPd7zciHk5bUa8a5bk8DAPDFmG4AAADEhIaaEtpjmHUIUKjosBgdT133BImWt4fycQHEB8Z0AwAAICZovwitMtLxyzr1ms5H/vTTT5txzzreOlR0TL72ItAp+LTfg/YC0T4k2m9E+4QAQGMIugEAABATdAYL7WCuTRndbndNczUtLQ8lLV/XxmfaJE870GvncW0ip80KI9lQEUBsYEw3AAAAAAAhwphuAAAAAABChKAbAAAAAIAQYUx3E7xer+zcuVOysrLE4XCE6jgAAAAAACJAZ9EuLi42jRKdzuDnpQm6m6ABd8+ePYO+4wEAAAAA0WPHjh1yxBFHBP1+CbqboBlu+wBkZ2cH/QAAAAAAACJHZ0PQRKsd+wUbQXcT7JJyDbgJugEAAAAgPjlCNJyYRmoAAAAAAIQIQbcfeXl5kpubK8OHDw/VvgcAAAAAxDmHpa3a0Gh9v8vlkqKiIsrLAQAAACDOuEMc8zGmGwAAAIgSHo9HqqqqIr0ZQFxJSUmRpKSkiD0+QTcAAAAQYVp8unv3bjl48GCkNwWIS+3atZOcnJyQNUtrDEE3AAAAEGF2wN2lSxdp06ZNRAIDIF5PaB06dEj27Nljrnfr1i3s20DQDQAAAES4pNwOuDt27MixAIIsIyPD/NTAW99n4S41p3s5AAAAEEH2GG7NcAMIDfv9FYmeCQTdAAAAQBRoUUn5u++Gdn0gTjgiOGSDoBsAAACIRbfeKjJqlMg99wS2vq6n6+vtAIQNQTcAAAAQazRjfdtth3+/+eamA2/9u66n9HZkvJtt4cKFpgN2Ili+fLnJDEeim35lZaX0799f3n///YBvs3HjRjniiCOktLRUohFBNwAAABBrTjtN5O67/3e9scDbN+BWeju9fZACM3+XM888U+LJhAkTZMuWLZIITjnlFNm1a5e4XK6AbzNlyhS54IILWv3YCxYskL59+5pt8PXyyy/LiBEjTFO09u3b13qs3NxcOfnkk2Xu3LkSjQi6AQAAgFh0001NB94NBdx6uyAGZnUvf//7303Q/fOf/7xV2c5oos23NNjTzteJIDU1NSJzWluWJX/961/lqquuqrX8hRdekMsvv1ymTp0q69atk/fee09+8pOf1FpH//bggw9KdXW1RBuCbgAAACAeA+8QBty+gZnv5cCBA3LjjTfKb37zG7n44otr1v3ss89k/PjxkpmZKV27djUB1N69e2v+Pnr0aLnuuuvk+uuvl06dOsnZZ59tlr/99tty0kknSVpamplf+eabb240qNJATDPSdQNmvc9FixaZ60uXLpXTTjvNlIrrFG0/+MEPZNu2bTXrf/nllybYXLx4sZxxxhmSnp4uTz75ZL3ycr3N+eefb56PPq/hw4fLG2+8Ueux+/TpI3feeadceeWVkpWVJb169ZKHHnqo1jrffPONXHrppdKhQwdp27atnHjiifLhhx/W/P2ll16SE044wWzHkUceKbfddluj+8DOOOt6nTt3luzsbLnmmmtqncioqKiQX/ziF+Ykgt6v7o9Vq1b5LS+3n/t///tfOeaYY8zzPeecc8xJFnXrrbfK448/brbVrnTQ+9DHvO6668yx08fp3bu33HXXXX63fc2aNWa/fv/7369Zps915syZ8qc//ck8j6OOOspkti+55JJatz3rrLNk//795jUTbQi6AQAAgHgLvHW+7xAG3A3RAE2DUA2gb7/99lrLx4wZI8cff7ysXr3aBL0FBQX1giYN2jSQ1yymlhh/++23cu6555pgVrObmsV85JFH5I477vC7DZdddpn83//9n5SUlNQs00Dx0KFDcuGFF5rrOu531qxZZluWLVsmTqfT/M3r9da6Lw3wNdjbtGlTzUkAX/oYun16H5988okJQs877zz5+uuva6335z//2QTSuo5m/6+99lrZvHlzzX1oYK/P9d///rd5nr/+9a9rtmXFihVyxRVXmO3QcctaRaAB8B//+MdGj4Vuk263Br5PP/20vPjiiyYIt+ljaPZY9/nHH39sxlDrc9Sg1R/dh/fdd5888cQT8s4775jnqSdYlP7U42kH4nrRSoi//OUv5nk9++yz5jnryQs9EeGPPl8NqvUEhU23T/ePHid9DWkArydw9ESOL33tDB061NxH1LHQqKKiIkt3k/4EAAAAgq2srMzauHGj+dkqd99tWfr1vu5Fl4eYx+Oxxo8fbx1zzDGW2+2u9bfbb7/d+t73vldr2Y4dO8x37M2bN5vrZ5xxhnX88cfXWuc3v/mNdfTRR1ter7dmWV5enpWZmWkeryFVVVVWp06drEWLFtUsu/TSS60JEyb43fbCwkKzLevXrzfXt2/fbq7Pmzev1nqPPfaY5XK5Gt0PgwYNsubPn19zvXfv3takSZNqrutz6dKli/Xggw+a63//+9+trKwsa9++fQ3e39ixY60777yz1rInnnjC6tatm99tmDx5stWhQwertLS0Zpk+nr3fSkpKrJSUFOvJJ5+s+XtlZaXVvXt369577zXX33rrLbMPDhw4UPPc9Xp+fn6tY9G1a9daj3v++efX2pYZM2ZYY8aMqXUMGzNz5kyzvq+nn37aPHavXr2s559/3lq9erU5ph07dqy33y688EJrypQpzX6fhTrmS4hM93/+8x85+uijZcCAAfKPf/wj0psDAAAABJ9msjt0qL1Mr4c4w620nHzlypWmvNg3S6k0e/vWW2+ZkmT7MnDgQPM337LuYcOG1bqdZmpHjhxZa1zxqaeearLDWpKtmVbf+9Qy7uTkZJNx1YyqndXWbdIMuG3r1q2mnFtLtbX02s681s1Qa3a6MbodmuHVcmstvdZt0G2uez9Dhgyp+V2fi5bh79mzx1xfu3atyd5qaXlDdN/94Q9/qPU8p02bZjLJmnn257jjjpM2bdrUXNf9qNu7Y8cOs8+15F73pS0lJcWU8ev2+6P3169fv5rrmnG2n0djpe5r1641sZiWs7/22muNrl9WVmbK0H3ZWf/f/va38qMf/ci8Th577DGzL5977rla6+q4+8b2S6QkS5zTMQBaPqJvdO2+pwdJy0d0/AYAAAAQN3QMd93yYL2uy0MYeD/zzDOm7Fi7S2uSqy4N9rTs+p4Guqtr4GbT8czN0b17dxPQ2ezAVQNsLdnWgPD11183gZiWPdt0W3Rs8cMPP2zuQ4O6Y489tl7ztqa2RwNuvX997lqerY/z4x//uN79aEDrS4NFO5DU2zRG952WhV900UX1/lY3OA21hp6HNj5rjI5F3759u7z66qtmvLueEBk3bpw8//zzDa6vY+/Xr1/f4GtEx3HbdIy/njSpe4JDy+N9TwxEi7gPuj/66CMZNGiQ9OjRw1zX+n89w6Jnt+LF/tJKKXCXS2qyUyqrvdI1O106tE2N9GYBAAAgXOo2TdMA1A7A7eUhCLw16NVO03fffXeD457twEvHD2tGWTPRgdIMst5OAzs7263jvTWTrnMy6xhfDXbr0rHEPXv2NI3QNNjThm52wLhv3z4ztlgD7lGjRpll77ZwznLdFs3k2mPFNUDWJmzNoVlwrcTVYLGhbLfuO93ehp5nYzRDrlljO6j/4IMPTJZc94sGtvbYeT35oDTzrY3UtJFdS+l9ejyeesuzs7NNczu96EkJPQHi7/lq1l/H7vsec02aapCt+0Ebvtnbq/va3n6bjvPWx4g2UV9eroP09WyUnoXSHb9kyZJ66+Tl5Zk3sZ7t0bnbNNC27dy5sybgVvq7DsSPFyu2FspVj6+SqY+tkvPmr5ArHvlQLlnwvjz0zjYTjAMAACDONdSlfN++wOfxbiHtPq5dsrVx2qRJk2T37t21LoWFhWa96dOnmyBLk14a2Gl5szY30ymeGgrSbNp0TMuhZ8yYIZ9//rkpE7/llltMFasG3I3RLubajE0z0b6l5Tq/s1a8agfx/Px8efPNN839tYRm9bVBmZ540CBXH7NuM7am6D7RcnPdjxoEf/HFF+ZEg5bqqzlz5piu65rt3rBhgyn/1sqC3/3ud43er2bb9WSINl975ZVXzH7TLuK63zSDr83cfvWrX5mmdrqOlqxrWXbdqbqaQ+OxTz/91ATH+trQwFjnzX766afN8dM5zrUcXJ+vbxd4Xzq3u5680Odqs7uv63PQ5Knev26/8u2Qr0G4xnmaSY82UR906zgMHZOggXVD9AyWvlH0IGhnO11Xz7I1Nb4gHmhQfetLG+STrw/Kbne5HKr0SmFJpeQXlsqdr3wuF+S9Z4JyAAAAxKnGpgULZB7vVtBy8q+++soEdVoCXPeiXceVJs80oNQA+3vf+54MHjzYZFQ18GoseNZkmd63JtT0O74GXhoUNhVwKg20NZjU+/Adu6yPp0GrTk2lJeW//OUvzVRULaEBpQbxmlnXJKHGIJqZbm52WANJnbpLO6HrvtGqgaSkJPN3vU/tT6Xr6P48+eST5f7776+X4a1r7Nix5qTA6aefbjLMP/zhD820XjZ9DB0frVO36TbrCQg9EaLPp6U0cNex2zoWXqcqs6sS7r33XrNMt18DYz2m/o67nhDRygF7TL5Nj9HEiRPN9ur96OtOT5j4bq8G9/r6amrfRIJDu6lJjNBM97/+9S9zJsimmW3d8TqJutKzS1o2oWfEtM3/+++/bw6S3k7pG1ybBNSdTN13zjq92Nxut7m/oqIic5Ylmmg2++5XPxdvI0ewa3aavDrzdMrNAQAAolR5ebkZ99q3b9/mjdMNdB7uEM/XjeiiJe86TVtDFcKx4NNPPzVzbmtFhJbEB0Iz+3qS4amnnqp1kiXQ95nGfNr/K1QxX9RnupvauXqWyreEQM+a6HW7JEMDbK3t11IDLVXQcR3+xpsonaxdd7h90YA7WrPcz6/Z0WjArQrcFfLEyuaNLQEAAECU03HIgQbSDWW8WziOGQi1IUOGmKZ7GiAHShuqaQd9fwF3pMV00K1jBbRMpWvXrrWW63UdR6K0WYNOSK/jA3Sy9BtuuKHRzuWzZ882Zzjsi44jiUbaOM0T4JCRJWt3Mr4bAAAgnmhDqVtuCTxz7Rt46+2+a0gFRGu2fvDgwQGvr43mfvazn0m0ivvu5UrHMOglENoZTy/RTjuUd2ybJkWHKmVvaZXf9RzfXfYUl1NiDgAAEE90jK5WfAYaQGvgrZlAAu64tnDhwkhvAuIp063t7rXJQEFBQa3lel274rWGNm7TueDsBhDRRqcEmzG2vxzZJUuy0g83WqhLg+2OmanSKStNumSFdx4/AAAAhEFzA2gCbiDsYjrTrd3+dN62ZcuW1TRX00Zqel1b4reGTm2gF3tQfTQaNaCzDOruMlns3QfLJb+wRPp3zpR13xw0JeUadGvAPWNMf7LcAAAAABABUR90a/MzbWFv0wH1OheeTqbeq1cvM13Y5MmTTRt6bZo2b948M82YzvuXCDTjrZeBOdkyemAXs0x/Xj6yjwnGNcOtfwcAAAAAhF/UB92rV682TdBs9uT1GmjreAWdd66wsNBMHK/N07RZmk7yXre5WiKKncngAAAAACA+RX3QPXr0aGlqKnEtJW9tOXlDY7r1ot3RY82KrYUyf1m+FJdXSVZ6ihn7raXoAAAAAIDwiulGaqGk47k3btwoq1atklii83drwH2wrNKUlevP+W/mM2UYAABAAiiv8shVC1eZi/4OIPIIuuOMzt9dVFYlhe4K2by7WDq2TZXisiozvhsAAACIxsrW66+/vuZ6nz59TJ+mxjgcDlmyZElIt0uHsrZr1y6kj4HEQNAdZ3T+bldGinTOTpOjc7JkX2mlZGWkMGUYAAAAguq8886Tc845p8G/rVixwgTGn376abPvVytNr776aok07R21ZcuWVp1AABRBd4zO093U/N3t2qSaknL9yZRhAAAACLarrrpKXn/9dfnmm2/q/e2xxx4zswsNGTKk2ffbuXNnadOmjURaRkaGdOlyeHYgoDUIuuNsTLfSpmkLJg2T+ycONT9pogYAAJA4Kqu9UlxeLQdKK0P6OD/4wQ9MgKxl2HWn/H3uuedMUL5v3z659NJLpUePHiaQHjx4sDz99NON3m/d8vKtW7fK6aefLunp6SYppoF+IBlnu9myy+WSTp06ye9///taDZoPHDggV1xxhbRv395s2/jx481j+Ssvv/XWW81MSU888YTZRr3fiRMnSnFxsfn7lClT5O2335YHHnjAZPn18uWXX5rHueyyy8y+0kB+wIAB5qQEEgdBd5yy5+5mjm4AAIDE8V7+Xln/bZFs3FUkM57+xMxqEyrJyckmaNXg1DeY1YBbZwDSYLu8vFyGDRsmL7/8snz22WembPzyyy+Xjz76KKDH8Hq9ctFFF0lqaqp8+OGHsmDBArnpppsCuu3jjz9utlEfSwPhuXPnyj/+8Y+av2uQrNMT//vf/5aVK1ea53DuuedKVVWV3/vctm2bGUv+n//8x1w0yL777rvN3/QxRo4cKdOmTZNdu3aZS8+ePU2wr8m8V199VTZt2iQPPvigOQmAxBH1U4YBAAAAaJoOLXxw+Tap8nglNckpB8uqzCw2g7q7QpaIufLKK+VPf/qTCT41u6w0i/ujH/3IZIL1cuONN9asP2PGDPnvf/8rzz77rJx00klN3v8bb7whn3/+ublN9+7dzbI777zTZKWbogHv/fffbzLORx99tKxfv95c16BYM9oabL/33ntyyimnmPWffPJJcxsNqi+++GK/JwH0JENWVpa5ricQli1bJn/84x/Nc9WTA5o1z8nJqbnN119/Lccff7wpt1eaJUdiIdMNAAAAxMksNlpWrgF3ktMhnTNDP4vNwIEDTdD66KOPmuv5+fmmiZqWlivNeN9+++2mrLxDhw6SmZlpAmgNRAOhmWENhO2AW2k2ORAnn3yyCbh9b6fBtm6T3q9mwUeMGFHz944dO5rgXP/mjwbMdsCtunXrJnv27Gl0O6699lp55plnTGn6r3/9a3n//fcD2n7ED4LuOGukVvds56ZdbuboBgAASJBZbLLSk6XS4xWP15LCkvDMYqMB9gsvvGDGNmuWu1+/fnLGGWeYv2kWXMuutST8rbfekrVr18rZZ58tlZWhHW8eKikpKbWua1Cv2e/GaFb+q6++kl/+8peyc+dOGTt2bK3sP+IfQXccNlJTOn7nmifWyKzFa83PUI7nAQAAQORpCfm1o/tJSpJTqrxeadcmJSyz2FxyySXidDrlqaeekkWLFpmSczvDrOXb559/vkyaNEmOO+44OfLII5s1DdcxxxwjO3bsMOOjbR988EFAt9Ux4L70dtrELCkpydxvdXV1rXW06dvmzZtN4q2ltLxcM+l1aRO1yZMnyz//+U/TJO6hhx5q8WMg9hB0xyHNcM9fli8HyyrNh6z+1PE8uhwAAADx69T+nWRwD5cM6uaS+ROPD8ssNloyrnNaz5492wTH2qDMpkGudhvXkmot2/7Zz34mBQUFAd/3uHHj5KijjjIB67p160zp+m9/+9uAbqsl7LNmzTKBtHZMnz9/vsycObNmu/RkgI7vfvfdd81964kB7bKuy1tKy881kNeu5Xv37jVZ8Dlz5shLL71kSu83bNhgGrBp0I/EQdAdp+N5isqqpNBdIZt3F0vHtqEfzwMAAIDokJrslMz0ZGkf4gx33RJznRpLS8d9x1//7ne/kxNOOMEs10Zr2mDsggsuCPh+NYP+r3/9S8rKykzjtZ/+9KemaVkgtLO6fTutYtWAW7un27QUXjur69RnOt5bu5e/8sor9UrIm0PLxjWTrtlyzW5r4K/Zbz0hoXOW69Rn+ncd443E4bB8+/ujHrfbbToRFhUVSXZ2dkzsIc1oa0m5Zrg7Z6ZJYUmFtGuTaubsZgoxAACA6KLTam3fvl369u1r5qJu1X1VeWT6kx+b3/MuO0HSU5IkEWmAr43LfOf7RmIrb+R9FuqYj0x3HDZS08B6xtj+JtDWAFx/hmM8DwAAAACgNubp9kNLUPRin/WINTp+R+dk1JJy7VhJwA0AABD/NLP9yJTYSxoB8YygO45poE2wDQAAgESzfPnySG8CUIPycgAAAAAAQoSgGwAAAACAECHoBgAAAKKAzukMIP7eX4zpBgAAACJI53HW+ah37txp5nbW6w6Hg2MCBIHOkF1ZWSmFhYXmfabvr3Aj6G5kyjC9eDye8B4RAAAAJBQNBHTu4F27dpnAG0DwtWnTRnr16mXeb+HmsDT0h1+hnigdAAAAUPq1vLq6mqQPEGRJSUmSnJzst4Ik1DEfme44tr+0Ugrc5dI1m3m6AQAAop0GBCkpKeYCIH4QdMepFVsLZf6yfCkur5Ks9BSZMba/jBrQOdKbBQAAAAAJhe7lcZrhfuCNrbJhZ5HsLiqX/YcqZf6b+WY5AAAAACB8CLrjkJaUF5dXS2qSU5KcDumcmSrFZVWyp7g80psGAAAAAAmF8vI4pGO4XRkpYoklnTPTpLCkQtq1SZUuWemR3jQAAAAASChkuuNQh7apZgy3BtpaUq4/Z4zpb5YDAAAAAMKHTHec0qZpg7q7TEm5ZrgJuAEAAAAg/Ai6/cjLyzMXj8cjsUoDbYJtAAAAAIgch2VZVgQfP+qFeqJ0AAAAAED8xnyM6QYAAAAAIEQIugEAAAAACBGCbgAAAAAAQoSgGwAAAACAECHoBgAAAAAgRAi6AQAAAAAIEYLuOLe/tFI27XKbnwAAAACA8EoO8+MhjFZsLZT5y/KluLxKstJTZMbY/jJqQGeOAQAAAACECZnuOKWZ7Qfe2CobdhbJ7qJy2X+oUua/mU/GGwAAAADCiEx3nCpwl0tpRbUM7dlO0lOSpLzKYwLuPcXl0qFtaqQ3DwAAAAASApluP/Ly8iQ3N1eGDx8usahrdropKS8sqTABt/7MykiRLlnpkd40AAAAAEgYDsuyrEhvRDRzu93icrmkqKhIsrOzJebGdL+ZL8VlVSbgnjGGMd0AAAAAEM6Yj/LyOKZN0wZ1d5mScs1wU1YOAAAAAOFF0B3nNNAm2AYAAACAyGBMd5xjnm4AAAAAiBwy3XGMeboBAAAAILLIdMcp5ukGAAAAgMgj6I7jebqLy6slNckpSU6HdM5MNV3MtakaAAAAACA8KC+PUzpPtysjRSyxpHNmmpmnu12bVObpBgAAAIAwItMdp7Rj+Yyx/U2graXm+lPn6aaTOQAAAACED5nuOMY83QAAAAAQWQTdcY55ugEAAAAgcigvTwDM1Q0AAAAAkUGmO84xVzcAAAAARA6Z7jjGXN0AAAAAEFkJEXRfeOGF0r59e/nxj38siYS5ugEAAAAgshIi6J45c6YsWrRIEnWu7s7ZaXJ0TpbsK62UrIwU5uoGAAAAgDBJiKB79OjRkpWVJYmGuboBAAAAIMGD7nfeeUfOO+886d69uzgcDlmyZEm9dfLy8qRPnz6Snp4uI0aMkI8++igi2xqrc3UvmDRMbj1/kMw66ygZ1N0V6U0CAAAAgIQR8e7lpaWlctxxx8mVV14pF110Ub2/L168WGbNmiULFiwwAfe8efPk7LPPls2bN0uXLl3MOkOHDpXq6up6t33ttddMMJ/oNuwskvnL8qW4vEqy0lNkxtj+JhgHAAAAAMR50D1+/Hhz8Wfu3Lkybdo0mTp1qrmuwffLL78sjz76qNx8881m2dq1a4O2PRUVFeZic7vdEg8dzDftcktqklOqvJbMfzPfZLy1/BwAAAAAEMfl5Y2prKyUNWvWyLhx42qWOZ1Oc33lypUhecy77rpLXC5XzaVnz54Sy+hgDgAAAACRE9VB9969e8Xj8UjXrl1rLdfru3fvDvh+NEi/+OKL5ZVXXpEjjjii0YB99uzZUlRUVHPZsWOHxDI6mAMAAABAApeXh8Mbb7wR8LppaWnmEm8dzLWkXEvN27VJlRlj+lNaDgAAAACJHnR36tRJkpKSpKCgoNZyvZ6TkxOx7Yo12jRNx3DvKS43c3QzlhsAAAAAwiOqy8tTU1Nl2LBhsmzZspplXq/XXB85cmRIH1unKcvNzZXhw4dLPNBAe2BONgE3AAAAACRSprukpETy8/Nrrm/fvt10I+/QoYP06tXLTBc2efJkOfHEE+Wkk04yU4bpNGN2N/NQmT59urlo93JtqAYAAAAAQMwF3atXr5Yzzzyz5roG2UoD7YULF8qECROksLBQ5syZY5qn6ZzcS5curddcDQAAAACAaOOwLMuK9EZEIy0v14t2T9+yZYvpZJ6dnR3pzQIAAAAABJFd3RyqmI+gO8IHAAAAAAAQvzFfVDdSQ/DodGGbdrnNTwAAAABAgozpRuit2Foo85flS3F5lWSlp5h5u3UaMQAAAABAaJHpjvMpwzSz/cAbW2XDziLZXVQu+w9Vyvw388l4AwAAAEAYEHT7odOFbdy4UVatWiWxrMBdLsXl1ZKa5BSHQ6RNilMOlFbInuLySG8aAAAAAMQ9gu441zU7XVwZKdImLUmqvZZ8XlAiu9wV8kVhaaQ3DQAAAADiHkF3nOvQNlWmnNpbisqqpbiiWjwer7jSk2Xh+19SYg4AAAAAIUbQnQD6dsqUnOw0yUpLNlnvXh3aSHFZFSXmAAAAABBidC9vpJGaXjwej8RDiXmHtmnidDqkc2aaFJZUSLs2qdIlKz3SmwYAAAAAcc1hWZYV6Y1I5InSwzpt2Jv5JsOdlZEiM8YwbRgAAAAAuEMc85HpThA6L/eg7i5TUq4Zbh3rDQAAAAAILcZ0JxjqGgAAAAAgfMh0JwhTXr4sX4rLqyQrPUVmjKW8HAAAAABCjUy3H9pELTc3V4YPHy6xbn9ppTzwxlbZsLNIdheVy/5DlWZ8ty4HAAAAAIQOQbcf06dPl40bN8qqVask1hW4y6W4vFpSk5ySZDqYpzJlGAAAAACEAUF3AtApw7LSk6XS4xWP15LCkkrTwZwpwwAAAAAgtAi6E4B2Kp85boAM6uGSTlmpkp7ilCmn9KaDOQAAAACEGEF3Ak0ZNuWUPpKWkiQV1V5Z+N5XprkaAAAAACB0CLoThDZNe/Td7bK9sFT2uitopgYAAAAAYUDQnSBopgYAAAAA4UfQnQBThimaqQEAAABA+Dksy7Ii8Lgxw+12i8vlkqKiIsnOzpZYpmO4dX7u4rIq0718xpj+Zqw3AAAAACQqd4hjvuSg3yOilgbYg7q7ZE9xuZkuTLuaAwAAAABCh6A7wdiBto7x9r0OAAAAAAg+gu4EY0rMl+VLcXmVZKWnyIyxlJgDAAAAQKjQSC3Bpg174I2tsmFnkewuKmfaMAAAAAAIMYLuBMK0YQAAAAAQXgTdCYRpwwAAAAAgvAi6E4g2TZs5boAM6uGSHNfh7uU6bRjN1AAAAAAgNGik5kdeXp65eDweibdpw7q3y5DNu4vl6Jws6dc5M9KbBAAAAABxy2FZlhXpjUjkidLDbdmmArnh2XVS7fXKwJxsk/nWQBwAAAAAEpE7xDEf5eUJ1r38weXbpMrjlRSnUw6WVcn8N/PNcgAAAABA8BF0JxC6lwMAAABAeBF0JxC6lwMAAABAeBF0J2j38k5ZqZKe4pQpp/SmezkAAAAAhAhBd4LRpmlTTukjaSlJUlHtlYXvfSUrthZGerMAAAAAIC4RdCcYbZr26LvbZXthqex1V8j+Q5U0UwMAAACAECHoTjA0UwMAAACA8CHoTjA0UwMAAACA8CHoTuBmajmudHN9xpj+NFMDAAAAgBBIDsWdIvqbqQ3q7pI9xeXSJetw4A0AAAAACD6Cbj/y8vLMxePxSDzSQJtgGwAAAABCy2FZlhXix4hpbrdbXC6XFBUVSXZ2tsRTF3NtqqZjvAm+AQAAACQqd4hjPjLdCWjZpgK54dl1Uu31ysCcbDPGW0vOAQAAAADBRSO1BKMZ7geXb5Mqj1dSnE45WFbFPN0AAAAAECIE3QmGeboBAAAAIHwIuhMM83QDAAAAQPgQdCcY5ukGAAAAgPChkVoCYp5uAAAAAAgPgu4ExTzdAAAAABB6lJcDAAAAABAiBN0AAAAAAIQIQXeCz9m9aZfb/AQAAAAABB9juhPUsk0FcsOz66TK45HeHdvKdWP6y7mDu0d6swAAAAAgrhB0JyDNbD+4fJuUVXmkotorm3YVy6+fXy8iDjl3cLdIbx4AAAAAxA3KyxNQgbtcDhyqlGqv11x3OkQqqz3y4PJ8Ss0BAAAAIIjiPujesWOHjB49WnJzc2XIkCHy3HPPSaLrmp0uaclO+S7mFktE0lKSpLzaK3uKyyO9eQAAAAAQN+I+6E5OTpZ58+bJxo0b5bXXXpPrr79eSktLJdHn6L769CPF+d3Rd4hIdnqytG+TKl2y0iO9eQAAAAAQN+J+THe3bt3MReXk5EinTp1k//790rZtW0lkFxx/hKQmJ8n8ZVuktNIjHTPTZMaY/iYgBwAAAADESab7nXfekfPOO0+6d+8uDodDlixZUm+dvLw86dOnj6Snp8uIESPko48+atFjrVmzRjwej/Ts2TMIWx77tMT824PlUlhcKdZ3peYAAAAAgDgKurXU+7jjjjOBdUMWL14ss2bNkltuuUU+/vhjs+7ZZ58te/bsqVln6NChcuyxx9a77Ny5s2YdzW5fccUV8tBDD4XlecVKB3OP15L0ZKeUVFbL/DdppAYAAAAAcVVePn78eHPxZ+7cuTJt2jSZOnWqub5gwQJ5+eWX5dFHH5Wbb77ZLFu7dm2jj1FRUSEXXHCBWf+UU05pcl292Nxut8RrB/Pi8mpJTXJKktMhnTNT5eChKtNIjRJzAAAAAIiTTHdjKisrTUn4uHHjapY5nU5zfeXKlQHdh2VZMmXKFBkzZoxcfvnlTa5/1113icvlqrnEaym6djDPSk+WSo/XZLsLSyolKyOFRmoAAAAAkChB9969e80Y7K5du9Zartd3794d0H289957pkRdx4prGbpe1q9f73f92bNnS1FRUc1FpxyLR5rNvnZ0P0lJckqV1yvt2qTQSA0AAAAA4q28PNROO+008doTUgcgLS3NXBLB2GO6yps3jjYl5TpVGGXlAAAAAJBAQbdO75WUlCQFBQW1lut1nf4LraeBNsE2AAAAACRgeXlqaqoMGzZMli1bVrNMs9Z6feTIkSF9bO2mnpubK8OHD5dE6GT+wRf7zEV/BwAAAADESaa7pKRE8vPza65v377ddCPv0KGD9OrVy0wXNnnyZDnxxBPlpJNOknnz5plpxuxu5qEyffp0c9Hu5dpQLV4t21QgM576WA5VecXpEOnXOVPmnJcrowZ0jvSmAQAAAEDMi3jQvXr1ajnzzDNrrmuQrTTQXrhwoUyYMEEKCwtlzpw5pnmaNkJbunRpveZqaD7Nav9l2VYprz485t2yRHYcOCT3v75FBnV3UXYOAAAAALEedI8ePdpM69WY6667zlzCScvL9aLd0+OVztWtc3PbdL5uh4gcYL5uAAAAAIj/Md2RpKXlGzdulFWrVkm80rm6daowm87Xrac/2rdhvm4AAAAACAaC7gSmXct/MXaApKccfhk4HCK9OrSRX551FKXlAAAAABAP5eWI/Fzd7940VrbuKRZNcw/omkXADQAAAABBQtCdwGO6fTPeI/p2jPRmAAAAAEDccVhNdTFLcPaUYUVFRZKdnR3pzQEAAAAAxFDMx5huAAAAAABChKAbAAAAAIAQIeiGUV7lkcv/8aFcsmCl7DpYxl4BAAAAgCAg6PZDm6jl5ubK8OHDJRG8l79X1n9bJBt3FcmMpz+RFVsLI71JAAAAABDzaKTWhERopLa/tFKuXrRaNu1yS2qSUzpmpZmO5gsmDWP6MAAAAABxzU0jNYRagbtcisurTcCd5HRI58xUKS6rkj3F5ex8AAAAAGgFysshXbPTJSs9WSo9XvF4LSksqZSsjBTpkpXO3gEAAACAViDohikhv3Z0P0lJckqV1yvt2qTIjDH9KS0HAAAAgFZKbu0dxHMjNb14PB5JBKf27ySDe7ikstor8yYOlW7tMiK9SQAAAAAQ88h0+zF9+nTZuHGjrFq1ShJFarJTMtOTpX3b1EhvCgAAAADEBYJu1NAstzZUO1BayV4BAAAAgCAg6IbBPN0AAAAAEHwE3TDzdD+4fJtUebyS4nTKwbIqmf9mvlkOAAAAAGg5GqmhZp5u7V5eUlktVrFIksNh5unWzuYAAAAAgJYh041a83R7vZaUV3kkPdXJPN0AAAAA0EoE3X7odGG5ubkyfPhwSZR5ukUs8Vhigu9DFR7ZsLMo0psGAAAAADGNoNuPRJsyLLdbtiQ5neJ0HH5RfHuwTB5YtpVx3QAAAADQCgTdMLR5WndXhmSnp4g4Do/p1qnDdFw3AAAAAKBlCLpRa1x3WZVHqr0iJRXVUuCukC8KS9lDAAAAANBCBN2oGdc96eReUu31Hn5hOERcGcmy8P0vKTEHAAAAgBYi6EaNvp0yJS05SZIcItkZKdKrQxspLquixBwAAAAAWoigGzW6ZKVJitMhlvYxt0QKSyolKyOFqcMAAAAAoIUIulGjW7sMmTthqGmmpmXm7dqkyIwx/U3pOQAAAACg+ZJbcBvEsVP7d5LBPVxSWe2VeROHmkAcAAAAANAyZLr9yMvLk9zcXBk+fLgkmtRkp2SmJ0t7MtwAAAAA0CoOy9LRu/DH7XaLy+WSoqIiyc7OZkcBAAAAQBxxhzjmI9MNAAAAAECIEHQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBN+opr/LIVQtXmYv+DgAAAABoGYJuAAAAAABChKAbAAAAAIAQIej2Iy8vT3Jzc2X48OGSiCqrvVJcXi0HSisjvSkAAAAAELMclmVZkd6IaOZ2u8XlcklRUZFkZ2dLIli6fpdc9/Qnoi+N43q2k1+edZSMGtA50psFAAAAADEX87Uo0/3FF18EfUMQHfaXVspDK74Qr2WJwyFSVFYl89/MN8sBAAAAAGEIuvv37y9nnnmm/POf/5Ty8vKW3AWiVIG7XEoqqsXpEHE4HNIpM1WKy6pkTzHHGQAAAADCEnR//PHHMmTIEJk1a5bk5OTIz372M/noo49acleIMl2z0yUzLVm8lpjy8r0llZKVkSJdstIjvWkAAAAAkBhB99ChQ+WBBx6QnTt3yqOPPiq7du2S0047TY499liZO3euFBYWBn9LERYd2qbK1aOONJluDbw1AJ8xpr9ZDgAAAAAIY/fy5ORkueiii+S5556Te+65R/Lz8+XGG2+Unj17yhVXXGGCccQm017vu2w3AAAAACACQffq1avl5z//uXTr1s1kuDXg3rZtm7z++usmC37++ee35u4RwUZqGmo7nSKllR4aqQEAAABACyW35EYaYD/22GOyefNmOffcc2XRokXmp1OjNBHp27evLFy4UPr06dPS7UI0NFITh+wrqTDLduw/RIk5AAAAAIQj6H7wwQflyiuvlClTppgsd0O6dOkijzzySEvuHlHSSM0pllR6vJKalCRdstI4LgAAAAAQjqBby8d79epVk9m26fjfHTt2mL+lpqbK5MmTW3L3iCBtmDb9zP5yw7PrpMrjleQkh/Tp1Fba00gNAAAAAMITdPfr1880SdNstq/9+/eb0nKPx9OSu0WUOLV/JxncwyVlVR5xfFdmfqC0Urq1y4j0pgEAAABA/DdS89fRuqSkRNLTmc85HqQmO6Wy2itbCopl464imfH0J7JiK1PBAQAAAEDIMt2zZs0yPx0Oh8yZM0fatGlT8zfNbn/44YdmDm/EPg24v9pXKlVeS9KSnHKwrMp0MR/U3UVDNQAAAAAIRdD9ySef1GS6169fb8Zt2/T34447zkwbhthXUe2Vaq8lqUkOKamsFqtYJMnhkD3F5QTdAAAAABCKoPutt94yP6dOnSoPPPCAZGdnS7Q7ePCgjBs3Tqqrq81l5syZMm3atEhvVtRLS3ZKstNhupfrSRb9mZWRLF2yGD4AAAAAAIFyWP4GaMcJLXuvqKgwpfClpaVy7LHHyurVq6Vjx44B3d7tdovL5ZKioqKYOMkQTMs2FcisZ9dKSXm1ZKYnyz0XDZHn1nxj/pZ32QmSnpIU6U0EAAAAgFYJdcwXcKb7oosukoULF5qN0N8b8+KLL0q0SEpKqhl7rsG3nmOI8/MMQe1irmO4N+4sktzuLhnZr2NN0A0AAAAACGL3co38tYGa0sBbr/u7NMc777wj5513nnTv3t3c/5IlS+qtk5eXJ3369DGd0UeMGCEfffRRs0vMdbz5EUccIb/61a+kU6dOzbp9Iiqv8sg1T6yRzbvc5rhoY7UDhyojvVkAAAAAEFMCznQ/9thjNb9rxjtYtORbA+Irr7yywQz64sWLTdf0BQsWmIB73rx5cvbZZ8vmzZtr5gnXjuk6Xruu1157zQTz7dq1k3Xr1klBQYF5jB//+MfStWvXoD2HeKZjucuqvLJpt1tmPbtOvJZIx7b/a6AHAAAAAAhSIzXbHXfcIZdddpn07dtXWmv8+PHm4s/cuXNN4zNt3qY0+H755Zfl0UcflZtvvtksW7t2bUCPpYG2BvgrVqwwgXdDtARdL771/YkdcHtMoJ3idEpRWZUUFleIUzLlQGmldGuXEelNBAAAAID4KC/39dxzz0n//v3llFNOkb/97W+yd+/e0AR9lZWyZs0a033c5nQ6zfWVK1cGdB+a3S4uLja/68B4LWc/+uij/a5/11131SqV79mzpyQqLSnX4e9Oh0iS0yEpOn1YRbVs2l0sM57+RFZsLYz0JgIAAABA/AXdWqr96aefyujRo+W+++4zJdzf//735amnnpJDhw4FbeM0mNfu43VLwfX67t27A7qPr776SkaNGmUy3PpzxowZMnjwYL/rz5492wTn9mXHjh2SqFKTnaLD+DXTXeXxyJaCEvO7zt19sKxK5r+ZL/tLGecNAAAAAEENutWgQYPkzjvvlC+++MLM362Nzq6//nrJycmRaHLSSSeZ8nP7RMHPfvazRtdPS0szjeJ8L4kqIyVJcru5JCs9WaqqD3d8T3KIJCc5pXNmqhSXVcme4vJIbyYAAAAAxF/Q7att27aSkZEhqampUlVVJcGiXcZ1yi8tEfel16MtuI/X8nLNdh/VNUuOysmStmnJoqG3x2tJYUmlZGWkSJes9EhvJgAAAADEX9C9fft2+eMf/2gy3ieeeKJ88skncttttwVc9h0IDeKHDRsmy5Ytq1nm9XrN9ZEjR0oo6TRlubm5Mnz4cElE7+XvlfXfFsmmXW7ZUlBsxnb37tBGnA6HVHm90q5NiswY0186BKmTuZap62NRrg4AAABAEr17+cknnyyrVq2SIUOGmK7il156qfTo0aNFG1BSUiL5+fm1gnktB+/QoYP06tXLTBc2efJkE9hrqbhOGabTjNndzENl+vTp5qLdy5s793is08D3weXbpMrjlZQkp+li/tX+UjmmW7ZkpyfLkV0yZf7E41vcvVzvXwN5pVn0DTuLZP6yfDlwqELSkpPkipG9pVfHtjV/twN7vV2Bu1y6ZqcHLdgHAAAAgKgLuseOHWum7NJMcGutXr1azjzzzJrrGmQrDbR1PvAJEyZIYWGhzJkzx2TRdU7upUuXMs92CGlgW1xeLalJTnE6HZIqThOAH6qoFq9lmeXtWxj0asfzO/6zSb7ef0gsy5JOWamSmpQkOw+WSbXXKyIOuemF9aZTepLTKd3bp8uEEw93kH/1s91SXumRrPQUmTG2v4wa0DnIzxwAAAAAgsthaeSDBsvL9aLd07ds2WI6mSdKUzXNKF+9aLUp9z6c6fZIeZXXdDLXQd2Z6cky95KhMvaYrs2+36mPfWTK1rULuq/v7tov/btqk+qUDplp0ikzTR6ZPJyMNwAAAIBWsaubQxXzBRx0awb69ttvN03T7Gy0P3PnzpV4EeoDEK2WbSqQG55dZzLcDqdIWYXHBMU6Z3dqcpLkds+Why4/sVlBrwbx1zyx5nCWuxXblpGirQgc8vsfHCM/GdG7FfcEAAAAING5QxzzBVxero3S7M7k+jvi26n9O8ngHi4pq/JIRbVHNu50m657DofDlJcXl1Wb6cKaE3TrWGxtwPb1/qbXbSzzrVn31GSHLFr5lYw4sqP065wZ+BMDAAAAgDAKOOjWubgb+h3xS6cL08vvfzBEzv/re1JcUS1OyzKN1bIykps9XZgG6D87o5/c9com+fZAmRwewX2Y/kxPSTKl7DqWW8eOV3ssE3hrWbvOD16tN6i57pTte0tl5jOfyE3nDGR8NwAAAID4mTLsyiuvlOLiw92nfWlXcf1bPEj0KcN8tW+TKr07tjXThaWlHC4tnzl2QMBZbns6sJc++VZ+8+J62V9aId3apUv3dumSluKU9BSnDOyWbZbpz6ennSz//OkI+c25A6V/57aSnpwkyc7D63VumyptUpNMBr6q2isHSitl7utbmGoMAAAAQPw0UktKSpJdu3ZJly5dai3fu3ev5OTkSHV1tcSLRB3TXV7lkelPfmx+n3vJcTLzmbXy6TcHmz1d2P/GhntMabpmrzNSkqRjVppkpSfLucfmmK7kZdqVPOPw3N++Xck1YN+6p9jUmu8rrZS/Lc+XrQUlJuDWjLdOMaYvYMZ3AwAAAIjpMd32xmiMrhfNdKen/6+8WLt8v/LKK/UCccQHLSlv7nRh2wpL5M+vbZHKao/JkldUHb4PzVR3zkyVg4eqZNRRneVHw3qa8eFarl43e67XR/TtWHP96Jwsmf7kGvly7yGzTXpyIOW78d0Dc7IlIzWJebwBAAAARI1mBd3t2rUz2Uq9HHXUUfX+rstvu+22YG4fosDKbftkw7dFUlJRLRt2Fsl7+XubnC5MM9zXP7PW3EZZPmO3NWjXrHW7Nqk1gXagperaNO3ykX3kjv9srLlDHd+9bU+JTHrkQ+mYmSrdsjOYxxsAAABA7AXd2kBNs9xjxoyRF154QTp06FDzt9TUVOndu7d07949FNuJCKms9spDK76QKq9lyrmrPJY8+PY2Ob5Xe7+Bsp3hLq+qbrAD+bcHyyS3W7YpJW9O93Pb+GO7yfNrvpHC4go5eKhSSio8Znl1pUd2HiiTFKdD5r+ZL4O6u5jHGwAAAEDsBN1nnHGG+bl9+3bp1auXyWzHK22kphctm09E2kn8kSnDTQM0zVhrWbnO2d3UdGErthbKPa9+LlsL3FL1XbdxX0d2biteryU3jR9Yq2y8OfRxZ511lNyz9HMTeJtugI7/TTOmTdcOlFY0e0ozAAAAAIiK7uVvvvmmPP/88/WWP/fcc/L4449LPJg+fbps3LhRVq1aJYlM59bWhmc6flqrHHRKr93uMvnjfzaZ8dS+tOnZA29slS0FxfUC7sNTgjml2mtJ5+x0GdAlq1Xbpc3WHph4vAzomim9OraRzLRkk4nXx/lyX6nsLCqXd7YU0tUcAAAAQOwF3XfddZd06tSp3nJtonbnnXcGY7sQJTRTfO3ofpKS5BDtc5+c5DTTh+n83XUVuMvlwKFK8TTQED85ySH6X8e2qS0uK29ofLfO0d0lO90E3V5LzEU7pGt38/tf3ypXPb7KZN8BAAAAIOrLy21ff/219O3bt95yHdOtf0N8ObV/JxnSo50Z333PjwfL75dskOLyajNHtu/UYZoVT0t2itcny+3URmdOh5lvWxugnXNst6CWfGvGW8duf/DFPvnTfw+Xm2sGXjPq1V6P7CupYHw3AAAAgNjKdGtG+9NPP623fN26ddKxY8vG6SK6aWY7Mz1Z1n9bJCu/2Cdrvtov05/6uCaLrKXlmumeckpfaZueLKnJDslOTzbdyXWar79eNkx+MqJ3SMZY632efGRH6ZyZbjLpqUlJZnC3TlPWJStNisuqzFzfOj5dtxMAAAAAojrTfemll8ovfvELycrKktNPP90se/vtt2XmzJkyceLEYG8jooRmuh9590sz17ZmsIvKqkwW+et9pXLXq5vNmO9jumXL1FN6y/LNhVJe7ZX2bQ6Xk2speChp4D1jbH+Z+/oW2VNcaQZ36xjyg2XVJtN+96ufS3mlR7LSU5hODAAAAEB0B9233367fPnllzJ27FhJTj58F16vV6644oq4GdOd6N3LG1JR7ZXSiipTHlHtFdnjLpeS8mr5/UsbzFjqJIfIrqIy+XD7AZk38XjT7dyehzsc7FLzpZ/tMlOKlVV6JD01yWzjlt3Fhzuwey3KzQEAAACEjcPS9GQLbdmyxZSUZ2RkyODBg82Y7njjdrvF5XJJUVGRZGdnSyLSMdLTn/zYZLqLK6pl3Y6DDc6/rdnvtmnJkuNKl/mXHi8DcyK3v7SMXKcM+3r/Ibnz5U1ysLTSNIHTbucHD1XJ/ROHRnT7AAAAACRGzNeiTLetT58+pqS4X79+NRlvxPe47glDjpC1Ow42+Hedrqu8stq8JlKSWtQuIGg0u75hZ5E8+NY2KXBXSEW1R9qkJslud4W0TUuK+PYBAAAASAwtijwOHTokV111lbRp00YGDRpU07F8xowZcvfddwd7GxFFSsurTXDdEI8lZn7ub/aXyU3PfxrRqbp85wzXsnc9YVBZbUlhcbkZiz77hfVMJQYAAAAgOoPu2bNnm7Ly5cuXS3p6es3ycePGyeLFi4O5fYgiWl7+73U7G3wRaWCbnuw0U4ZlpCTJwe+arEWqW7h2UtdpzXQcd3pKkhzdNVMcjsNzeJeUVcv+Q5UR3T4AAAAAiaFFNeFLliwxwfXJJ58sDo1kvqNZ723btgVz+xBljdS0I3mbFKeUakr7O/pbWpJT2rVJlYoqj+kW3jkz1Yyd1nHV4Wqk5kvnDHdlpIgllnTOTJNvDhwyY851O6Nh+wAAAAAkhhZlugsLC81c3XWVlpbWCsJjmXYuz83NleHDh0ui00zxI1OGy4LLh0l2eopoyjgj2VFTZq5Z7vZtUqSkotqMnfZ4LSksqZSsjBTTvTwS7CnE9ESAZrM7ZKZJr45tpIsrXY7s3FZ2Hiwznc0jtX0AAAAAEkOLgu4TTzxRXn755ZrrdqD9j3/8Q0aOHCnxYPr06bJx40ZZtWpVpDclamgge+3ofoebkDkc35WSO01GuW+ntibw7tcl03QvN0HvmP4RzSLrFGILJg0zncofmTxcfvf9XPNaXf+t28zlfaiy2jRbAwAAAICoKi/XubjHjx9vgtLq6mp54IEHzO/vv/++vP3228HfSkSNU/t3ksE9XFJaUS3VliXb9pSITjqnme1u7TLkrosGh31+7sboNtjboXN4ZyQniYhlysw9XmHObgAAAADRl+k+7bTTZO3atSbg1vm5X3vtNVNuvnLlShk2bFjwtxJRRTuBt2+bKj8/43DWu8rrlXZtUkxmu1/nTDP/dTQE3A01Vyur8kh6cpKZs1vHdReXHR7XDQAAAACh0OLJtXVu7ocffji4W4OYMnpgF3nzxtEmaI2WzHZTzdWy0pPlmwNeSRWnyc7rNjOuGwAAAEDEg2632x3wnWZnZ7d0exBjfMu3o51u58xxA0xJuWa4tdFbpMedAwAAAIhvAQfd7dq1a7IzuWVZZh2PxxOMbQNC0lxNx3Zrdl5L43XucdPdnMAbAAAAQCSD7rfeeisUjw+EnQbY2rV8/rJ8KS6vkqz0FDO9mAbkAAAAABCRoFs7lC9cuNCUji9atEgmTJggaWlpQd0YIBw0s/3AG1tl0y63pJpGcBZdzAEAAABEtnv5f/7zHyktLTW/T506VYqK4nt+47y8PMnNzZXhw4dHelMQgi7mxeXVJuBOcjqkXUayFLrLZeueYvY1AAAAgMhkugcOHCizZ8+WM88804zdfvbZZ/02TLviiisk1k2fPt1ctIGcy+WK9OYgRF3MHR6Rz3a6xSEOufvVz2XWWUdRZg4AAAAgaByWRtABeP/992XWrFmybds22b9/v2RlZTXYWE2X6d/jhR10a2afruwi5VUemf7kx2bf5F12gqSnJEksWrG1UOa+vkU+31UsFdUeaZOaJDmuDDPee8GkYTRWAwAAABKEO8QxX8CZ7lNOOUU++OAD87vT6ZQtW7ZIly5dgr5BQDho0zTtXn7zC5/KwdJKSU5ySufMVDl4qMp0NqebOQAAAICwBt2+tm/fLp070+k5EWlm+5Ep8THO/aiuWdIpM00KiyvE4bCksOTw1GFdstIjvWkAAAAAEq2Rmq/evXvLu+++K5MmTZKRI0fKt99+a5Y/8cQTZjkQCzTAnjlugAzq4ZIcV7q5PmNMf7LcAAAAACKb6X7hhRfk8ssvl8suu0w++eQTqaioMMu1Bv7OO++UV155JXhbCIS4zHxQd5cpKdcMN2XlAAAAACKe6b7jjjtkwYIF8vDDD0tKSkrN8lNPPVU+/vhwky0gVmigPTDncMMEnbtb5/EGAAAAgIhlujdv3iynn356veXa8e3gwYPB2C4grJZtKpAbnl0n1V6vCcC17Fyz4AAAAAAQ9kx3Tk6O5Ofn11uu47mPPPLIVm0QEG6a2X5w+Tap8nglxemUg2VVMv/NfDLeAAAAACITdE+bNk1mzpwpH374oZmXe+fOnfLkk0/KDTfcINdee23rtwoIowJ3uRSXV0tqklOSnA4zdVhx2eGpwwAAAAAg7OXlN998s3i9Xhk7dqwcOnTIlJqnpaXJr371K/npT3/aqg0Cwq1rdrq4MlLEEks66xRiJRXSrg1ThwEAAACIUKZbs9u//e1vZf/+/fLZZ5/JBx98IIWFhWZMd9++fSUe5OXlSW5urgwfHh9zUsM/M1XY2P4m0NZSc/3J1GEAAAAAgsFhWZYV6Mo6Nditt94qr7/+ek1m+4ILLpDHHntMfve730lSUpJMnz5dbrrpJokXbrfbnEzQ6dCysw93uEZ80oCbqcMAAACAxOIOcczXrPLyOXPmyN///ncZN26cvP/++3LxxRfL1KlTTab7z3/+s7mugTcQqxlv5ukGAAAAELGg+7nnnpNFixbJD3/4Q1NWPmTIEKmurpZ169aZknMAAAAAANDCMd3ffPONDBs2zPx+7LHHmhLzX/7ylwTciLsy80273EwZBgAAACC8mW6PxyOpqan/u3FysmRmZrZ+K4AosWJrocxfli/F5VWSlZ5iGqyNGtA50psFAAAAIBGCbu25NmXKFJPhVuXl5XLNNddI27Zta6334osvBncrgTBluB94Y6vJcuuc3eXVXrln6efSvV2G9OvMySUAAAAAIQ66J0+eXOv6pEmTWvCQQHQqcJdLcXm1Cbg9liV7Sypk50GvzHzmE7npnIFkvAEAAACENujWqcGAeNU1O12y0pNlx36PVHi84vWKOB0ihyqqZf6b+TKou4vu5gAAAABC10gNiGc6XdjMcQPkyC6Zkux0moC7bVqyCcYPlFbIB1/so7kaAAAAgNBluoF4p03TdAy3lpSXVXokJztdvtxXKkXl1XL/G1ukfUYqzdUAAAAABIxMN1CHNk3TMdwdM9NkT3G5FJVVS3W1V/a6K2T/oUpTaq5N1wAAAACgKWS6AT8Zbx3DrSXlc1/bLPtKKiXJ6ZDOmaly8FCVCca1HB0AAAAAGkPQDfihQfXJR3aUDm3TxGkC7jQpLKmQdm1SpUtWOvsNAAAAQJMoLweaCLxnjO1vAm0tKdefM8b0J8sNAAAAICAJk+k+dOiQHHPMMXLxxRfLfffdF+nNQQyxS821pFwz3JSVAwAAAAhUwmS6//jHP8rJJ58c6c1AjNJAe2BOtvl90y43jdQAAAAABCQhMt1bt26Vzz//XM477zz57LPPIr05iFErthbK/GX5UlxeJVnpKUwdBgAAACD6M93vvPOOCYa7d+8uDodDlixZUm+dvLw86dOnj6Snp8uIESPko48+atZj3HjjjXLXXXcFcauRaHQ89wNvbJUNO4tkd1E5U4cBAAAAiI2gu7S0VI477jgTWDdk8eLFMmvWLLnlllvk448/NuueffbZsmfPnpp1hg4dKscee2y9y86dO+Wll16So446ylyAlipwl0txebWkJjlrpg4rLjs8dRgAAAAARG15+fjx483Fn7lz58q0adNk6tSp5vqCBQvk5ZdflkcffVRuvvlms2zt2rV+b//BBx/IM888I88995yUlJRIVVWVZGdny5w5c0LwbBCvumaniysjRSyxmDoMAAAAQOxkuhtTWVkpa9askXHjxtUsczqd5vrKlSsDug8tK9+xY4d8+eWXpmu5BvCNBdwVFRXidrtrXQCmDgMAAAAQk5nuxuzdu1c8Ho907dq11nK9ro3RQkGD9Ntuuy0k943YxtRhAAAAAOIq6A62KVOmNLnO7NmzzRhym2a6e/bsGeItQyxlvJmnGwAAAEBcBN2dOnWSpKQkKSgoqLVcr+fk5ITkMdPS0swFaKqbuTZX07HeBOEAAAAAYnJMd2pqqgwbNkyWLVtWs8zr9ZrrI0eODOljazf13NxcGT58eEgfB7Fn2aYCGXPfcrl4wfty9aLVZv5uAAAAAIjKTLd2FM/Pz6+5vn37dtONvEOHDtKrVy9T6j158mQ58cQT5aSTTpJ58+aZacbsbuahMn36dHPR8nKXyxXSx0JsZbgfXL5NqjxeM33YwbIqmf9mvgzq7iLjDQAAACD6gu7Vq1fLmWeeWXPdHk+tgfbChQtlwoQJUlhYaDqO796928zJvXTp0nrN1YBIzdd98NDh+bopMwcAAAAQdUH36NGjxbKsRte57rrrzCWctLxcL9o9HbAxXzcAAACA5nBYTUW8Cc4uLy8qKpLs7OxIbw6igI7h1pLy4rIqycpIkRlj+pvpxAAAAADEHneIY76IZ7qBWMN83QAAAADions5EK10/PbAnMNnwTbtcpsGawAAAABQF5luPxjTjYDKzJflS3F5lWSlp8iMsZSZAwAAAKiNMd1NYEw3GqKZbZ2jW7Pc2sm8Y1aayX4vmDSMLuYAAABADHGHeEw35eVAkKYO08ZqOnUYAAAAANgIuoEWTh2WlZ4slR6veLyWFJZUmk7mXbLS2Z8AAAAAahB0Ay2gpeQzxw2QQT1ckuNKN9d16jD9CQAAAAA2Gqn5QSM1NIWpwwAAAAA0hUZqTaCRGgAAAADELzeN1AAAAAAAiE2M6QYAAAAAIEQIuoEgzdutc3brTwAAAACw0UgNaKVlmwrkhmfXSbXXKwNzsk1Xc22yBgAAAABkuhvpXp6bmyvDhw/nVQK/NLP94PJtUuXxSorTKQfLqmT+m/lkvAEAAAAYBN1+TJ8+XTZu3CirVq3ytwogBe5yKS6vltQkpyQ5HdI5M1WKy6pkT3E5ewcAAAAAQTfQGl2z0yUrPVkqPV7xeC0pLKmUrIwU6ZKVzo4FAAAAQNANtEaHtqlmDPegHi7JcaWb6zPG9Dc/AQAAAIBGakAradO0Qd1dpqRcM9yxFnDruHQtk9esfaxtOwAAABDtCLqBINBg1Q5YozWIbWi7VmwtlPnL8qW4vEqy0lNkxtj+5gSCvZ6KxucCAAAAxAqC7ka6l+vF4/GE94ggpkXr9GF1g+spp/aWDm3T5M//3SJb9xSbRnBVXkvueHmTtElNkvJKj3gtEXFY4hRHTUAeDc8FAAAAiCUOy7L0qzX8cLvd4nK5pKioSLKzs9lPaDSTfPWi1bJpl9sEsR2z0kx2eMGkYRHNEtfdroy0JHGXV0uHNilSWFxhuq6nJydJn05tZMPOYhGxzHoV1V7TIC47LVk6fdcw7qZzBspRXbPIegMAACBuuEMc8zFlGBDH04dpwP3Btn1y8FCl2S6HQ8w2llZUm7/pGTftun5kl7YmANeAO+279fRiOByS4nTI57uKZfaL6+WaJ9aYzDkAAACAplFeDgSJjnt2ZaSIJZZ0zkyTwpIKadcmNWLTh9ml7lUejzgcTrNteiJg465ic7ZNg/Ce7dPly32HpLC4Ujpkpkl6apJo7Uu7jBT59NsiSUt2St/ObWTTrmKprPbKgZLDmfH5b+absd+M8wYAAAAaR9ANBImZLmxsfxOQahZZA+5ITR+mj//g8m1S5fFKalLSdyXlVZKW4pTUFKe40tOkT8c25sTAwG5ZcvM5A2VA1yzZsLPIbL9m6Ht3bGvu62BplWjSW8d6Jyc5TeB+8NDhDD5BNwAAANA4gm4gDqcPq1vq3rtDG9lbUiGzvne0yWQvfP/LWicGRhzZscHtV9po7e5XPzcl6b4Z/JQkpxknTmdzAAAAwD+CbiCE04dFU6l7+7ZpMqJvR7NtJx/Z0e+Jgbrbr7eZddZRtTL4o4/uLLNfWF9rqjE6mwMAAAD1EXQDccaej3vKqX3qZbTtYLq5JwZ8M+Ca4b7p+U9ruqHrVGOM8QYAAAAaRtANxJGG5uM+snNmUErd7UBdg+26XdoZ4w0AAAA0jCnD/MjLy5Pc3FwZPny4v1WAqKIZ7Qfe2Gqaoe0uKpf9hypl4ftfBX1suV263jk7TY7OyZJ9pZWSlZESsS7tAAAAQDQj6PZj+vTpsnHjRlm1alV4jwgQ5fOE213atWQ90l3aAQAAgGhHeTkQJzQDnZWeLN8c8EqqOKWwpNIEwqHIQDfU5ZxO5gAAAEB9BN1AHDVPu/K0vqZ5mma4teQ7lBloe4z3sk0FcsOz66Ta65WBOdkyc9wAOpkDAAAA3yHoBkIcCId6HutQNk8L5Dk+uHybVHm8pqz9YFkVncwBAAAAHwTdQAiEK/trN0/znb5Lm6ctmDQsLGOsGxpHTidzAAAA4H9opAaEMPub4vxf9leXx2rzNH/oZA4AAAA0jqAbiOFA2G6eVunxisdrmeZp4Zy+y18nc6XZ91CcaAAAAABiCeXlQAx3Edf71dJ1zaSHo3laIJ3MdZ7wa55YUzPGXIPyUJTWAwAAALGAoBuI8UC4btAbifmy7U7mDY0x1/2g28c83gAAAEhEBN1AHATCdtAbaTRWAwAAAGoj6AbiPBAOJ7uxmiWWdM5Mk8KSCjPOO1xjzAEAAIBoQyM1IEZpKXe0NSujsRoAAABQG5luP/Ly8szF4/H4WwWImBVbC2X+svyobFZGYzUAAADgfxyWZVk+11GH2+0Wl8slRUVFkp2dzf5BxGlm++pFq2ualXXMSjMZ5gWThkVdOXssbSsAAAASkzvEMR/l5UCMCec84Im0rQAAAEAoEHQDMToPeKXHKx6vZeYB12nJorFZWSxtKwAAABAKBN1AjM4DPqiHS3Jch6cjC+U84ImyrQAAAEAoMKa7CYzpRrSNkdaSbc0gq3DNAx6M7Y6VbQUAAEBicYd4TDfdy4EYsWxTgdzw7Dqp9nplYE62ySBHS8fypiTinOUAAACAorwciAGaKX5w+Tap8nglxemUg2VVMv/N/KiaoxsAAABAfQTdQAygCzgAAAAQmwi6gRgQT13ANTuv83aTpQcAAEAiYEw3EOYGaC0Z22x3AdeScp3nWgPuWOwCvmJrocxfli/F5VWSlZ4iM8b2j5lx6QAAAEBLEHQDMdIATW8zqLsrZruA64mHB97YarLcqUlOqfJa5iSCPqdYey4AAABAoCgvB2KoAZoGpxq4x2KQyrh0AAAAJCKCbiCECDTjc1w6AAAAEKiEKC/v06ePmeTc6XRK+/bt5a233or0JiHBAs1vDnglVZwm0NQsdSIGmvEyLh0AAABojoQIutX7778vmZmZkd4MJBgNKK8d3c+M6a7yeqVdm+YHmq1twhZNYn1cOgAAANBcCRN0A5Ey9piu8uaNo1sUaAarCVs00edPsA0AAIBEEfEx3e+8846cd9550r17d3E4HLJkyZJ66+Tl5ZkS8fT0dBkxYoR89NFHzXoMvd8zzjhDhg8fLk8++WQQtx4IXQO0YDdhAwAAAJCAme7S0lI57rjj5Morr5SLLrqo3t8XL14ss2bNkgULFpiAe968eXL22WfL5s2bpUuXLmadoUOHSnV1db3bvvbaayaYf/fdd6VHjx6ya9cuGTdunAwePFiGDBkSlucH2MqrPDLt8dVSUe2VByYOlW7tMprdhO3goSqTMY+nTHE8lc8DAAAAURd0jx8/3lz8mTt3rkybNk2mTp1qrmvw/fLLL8ujjz4qN998s1m2du3aRh9DA27VrVs3Offcc+Xjjz8m6EbYvZe/V9Z/W2RKxWc8/UmTpeKJ0IQtHsvn/Z1I4OQCAABAYop40N2YyspKWbNmjcyePbtmmXYg12z1ypUrA86ke71eycrKkpKSEnnzzTflkksu8bt+RUWFudjcbncrnwVQu1RcM9d2qbg2FfOX3Q1GE7Z42yexeiKhstoblycXAAAAEONB9969e8Xj8UjXrl1rLdfrn3/+eUD3UVBQIBdeeKH5Xe9Ls+Y6ttufu+66S2677bZWbjkQnFLx1jRhi3bxUj7vm8FWdU8kzH19i1heibuTCwAAAIiDoDsYjjzySFm3bl3A62tWXceQ+2a6e/bsGaKtQ6JoTal4vHb7jofy+bpZ7QtP6FHvRMLuonKxRBo8uaAYzw4AABDfojro7tSpkyQlJZlstS+9npOTE5LHTEtLMxcgmOK9VLwl9LlrmbVmfYvLqiQrI7b2SUPl8c+v+UbapCVJ5+w06ZyZJoUlFdLuu+eTmuz837I2qbJ5V7FM/PsHlJwDAADEuagOulNTU2XYsGGybNkyueCCC8wyHZ+t16+77rqQPrZOU6YXLUkHgiGeS8VbSsc1a5l1LO4Tf+Xxl4/sLUvW7jRBuQbXeiJB2dO96bIpp/SWR9/9kpJzAACABBDxoFubm+Xn59dc3759u+lG3qFDB+nVq5cp9Z48ebKceOKJctJJJ5kpw7Q5mt3NPFSmT59uLlpe7nK5QvpYSBzxWiqeiPvEX3n8Ocd2M5e6JxJ8Ty7Ey3h2AAAAxEDQvXr1ajnzzDNrrtvjqTXQXrhwoUyYMEEKCwtlzpw5snv3bjMn99KlS+s1VwOAUKs77Vdj5fF1g+e6JxdcGSliiVWr5FwDcqYWAwAAiC8Oy7K0xw8aKS/fsmWLFBUVSXZ2NvsJSFD+5hTXILkl5fErthbWC9iZWgwAACD87OrmUMV8BN0RPgAAop8G1lcvWi2bdrlNSXjHrDQTYC+YNKxV5eC+AbsKxWMAAAAgsjGfM+j3CABxpqEx2Jqhtqf9aikNpjVrrj9D9RgAAACILIJuAAiwaVqlxyser2WapmlJeDDnFA/HYwAAACD8CLr90PHcubm5Mnz48PAeEQBRx26aNqiHS3Jch8duB3tO8XA8BgAAAMKPMd1NYEw3EF7R0r27oe1oadO05j5uLM5bDgAAEKvcIR7THfEpwwDUV17lkWmPr5aKaq88MHGodGuXkdAdwqNlO8Ixp3iszlsOAACAhlFeDkSh9/L3yvpvi2TjriKZ8fQnZnqpeKcZ3geXb5Mqj1dSnE45WFZlptTS5Ym4HQAAAIgPBN1AhDLZl//jQ7lkwUrZdbCs1t8SNeiLlu7d0bIdAAAAiA8E3X7QSA2RymQnatAXLd27o2U7AAAAEB8Iuv2YPn26bNy4UVatWhXeI4K411QmO1GDvkh27/atPKio8kRdF3F9bWza5Y77agcAAIB4RCM1IMwaymQfPHQ4k2030bp2dD/TyKvK65V2bVIiHvSFizYrG9TdFfbu3XblgTZO08oDDboXTBoWFV3Eo6W5HAAAAFqGoBsIMzuT/c0Br6SK02SyNajzzWSPPaarvHnj6KgI+sIt3N27fSsP9ESIXXmgQbcGuZHkb9v0xEQivSYAAABiGeXlQJjZmeyUJKcpIS8sLpfKaq+0SU2qt54GfQRXoRXNY+ijedsAAAAQGDLdQASc2r+TDO7hkrIqjzgcIg5xyO6iMrn9P5vM3/MuO0HSU2oH4Yhc5UGkRPO2AQAAIDBkuv2gezlCLTXZaTLcWwqKTRfzWc+uq8lsHqBhVkI0cIvlbQMAAEBgHJZlWQGum5Dcbre4XC4pKiqS7OzIju9EfNHxulcvWm26Umv5cHpqkikbTk9OkmO60TArEscjWsfQR/O2AQAAxDp3iGM+ysuBKBivqyXmJRXV4rXEjN2lYVb8N3CLl20DAABA4ygvByLEdz7uKo9l5ofWN2QyDbMAAACAuEHQDURBF3Ov5TVjvPV3HfChDbOyMlJomIUmy851eIL+BAAAQHSivByIgi7m2lDt4hN7yB0vfy5VXq+0a5NCwyw0atmmArnh2XVS7fWaqeW04dqoAZ3jeq/pyQUdlqFVIpTbAwCAWEHQ3Uj3cr14PJ7wHhEkHM1w6+Ws3Bx5ae0uE4DPmzhUurXLiPSmIYqDzweXb5Mqj9f0BIinHgD+AutEPMkAAADiA93Lm0D3cgDRRkvKr39mrexxl5vGewO6ZsrBQ1Vy/8ShJiCNVf4C67qd/jtmpZmAfMGkYeYnGXAAANAadC8HADTYhO+bA15JFafpAaDBp04pFo/Ze99O/3qSoXNmqjnJoNOoffL1ATLgAAAgqtFIDQBiuAlfvPQAaCiwLi47HFj7dvr3eK2aRoP6/O1APcX5v0CdxnIAACCaMKYbAGLQ2GO6yps3jjZBqWa4Yzngbip7rz+11FwDag3ENeDWkwza/8BfBjzW9wcAAIgfBN0AEkp5lUemP/mx+T3vshMkPSVJYpUGlrEaXNYdh+0vsLafn47t1lJz35MMeh/xVmYPAADiD0E3gKgOkKc9vloqqr3yQBA7umuGVO/zQGll3HWJj4WmYv4apjUUWDd2kqGpQB0AACAaEHQDiFrv5e+V9d8WmeBsxtOfBGWaqFDcZ7SIhWm1mprurLnZ+6YCdQAAgEijkRqAqOQbnAWrSVYo7jNaxMpza6xhWktpoK0nGRoKuPX561Rj0bYfAABA4iDo9iMvL09yc3Nl+PDh4T0iAEIWnIXiPqNFrDw3f53IQzEOWzP/Y+5bLhcveN/M871ia2HQHwMAAKApBN1+TJ8+XTZu3CirVq1qcicCiI3gLJwBX7jFynOzx2EP6uGSHNfhcvBQjMOOlcw/AACIfwTdABJmLup4nN86Fp+bjsNeMGmY3D9xqPkZinHnsZL5BwAA8Y9GagCi1qn9O8ngHi7TbXxekLqXx9v81rH63EI93Vlj834DAACEk8OyLCusjxhj3G63uFwuKSoqkuzs7EhvDoAomXYM0U/HcNedTizaurkDAID4j/nIdAOIa/E8RRgax3RiAAAgGjCmG0DcopkWGptODAAAIBwIugHEVKn4VQtXmYv+3hSaaUWOHp/L//GhXLJgpew6WBbBLQEAAIgsgm4AMUWbqmlX6gMBTP0UK9NoxXNZ/8ZdRaasPxrnyNZKiE273HE1jVg8PicAAGIdQTeAuA3kYmkarXgSC2X9yzYVyJj7lsvFC96XqxetjsqTAs2lz+GaJ9bIrMVrzc94eE4AAMQDgm4AcR3I2dOODermkvkTj6eJWhhEe1l/LJwUaC7d9gfe2CobdhbJ7qJy2X+oMuafEwAA8YKgG0DcB3KpyU7JTE+W9mS4g66hsdvRXtYf7ScFWiIenxMAAPGCoBtATGhpIJeekiSPTBluLvo7Ql/yr+X7OjXboB4uyXGlm+vRVNYf7ScFQvmcGPMNAED4MU+3H3l5eebi8TTdIRlA6Nnjs294dh3js6OEb5m2ZljtMu1B3V1RPUe2fVJAt1WzwRqcRtNJgVA9Jx3Hru8fnbNep1FjznoAAMLDYVmWFabHiklut1tcLpcUFRVJdnZ2pDcHkEQvZZ72+GrTwXzexKHSrV1GpDcpoWmX7OufWSt73OWmpHlA10w5eKhK7p841AR1sXDSIBpPCoTiOelybRinx0xPkHTMSjN/XzBpWNw8dwAAojXmI9MNIKbo+Gy9MD67+ScrKqq98kAQT1bYJc3fHPBKqjhNSbMGcLFSpq3bGm8Bp7/n1NCYbz1BogF63eBc19VjG2/7BgCASCHoBhAz7PHZaNm4ay0r1nHXwSorpuQ/dgRygoTycwAAQoOgGwASdNx1MDKZY4/pKm/eODruyrTjTVNjvkP9OgEAIJERdANAHAu0rLg14rFMOx411twuHK8TAAASFVOGAYjpscpXLVxlLvo7EmN6rETW2im/NIDWJnd1A+nmvE6YdgwAgOYh6AYQ07STuWboDrQwCIl39rjrlCQnU601QzQGljrmesx9y+XiBe+bTuQ6J3qwBDq3eii3AQCAeEV5OYCYFaoGYfHm1P6dZHAPF1OtBSgaG4qFY8x1U3OrM+4bAICWIegGEJMIAJrX9f2Jn44I4dGIH9H6ugrXmOvGxucz7hsAgJahvBxATGooANCuzBqEAPH2uoqGsfnRsA2Rpr0jLv/Hh3LJgpWy62BZpDcHABAjCLoBxCR/AYArPYXmaojJwLKxgC7QMdehRH+A/w1p2biryAxpYUw7ACAQlJcDiEl2AKBjb6u8XmnX5vC8w+2Z3ggheF2FI7htqkdBU2OuwyGR52WP1qEHAIDoR9ANIK4ahDF1GGIxsAw0oIuGOdGjYRsigTHtAICWSojy8u3bt8uZZ54pubm5MnjwYCktLY30JgEIktRkp2SmJ9fKcDONWPAl2pzo/uazTrSx5ImkqWniGNMOAGiphAi6p0yZIn/4wx9k48aN8vbbb0taWlqkNwlAiDDmMnQ4mRE68RLQxWqjsUDmH4+GcfUAgNgU9+XlGzZskJSUFBk1apS53qFDh0hvEoAgToX1yJThNdcZcxk6zIkeWnZApyXlmuHWgDsWA7pYfJ0053MjGsbVAwBiT8Qz3e+8846cd9550r17d3E4HLJkyZJ66+Tl5UmfPn0kPT1dRowYIR999FHA979161bJzMw0j3HCCSfInXfeGeRnACBaUKIb+qAkxfm/oMRfGW48C2UmVwO6BZOGyf0Th5qf0R6sxsrrpKlj1tzPjXAPPQAAxL6IB906vvq4444zgXVDFi9eLLNmzZJbbrlFPv74Y7Pu2WefLXv27KlZZ+jQoXLsscfWu+zcuVOqq6tlxYoV8re//U1Wrlwpr7/+urkAiD/xUqIbbTiZEb7hC7Ec0EXr66SpY8bnRmTGyANAIol40D1+/Hi544475MILL2zw73PnzpVp06bJ1KlTTSO0BQsWSJs2beTRRx+tWWft2rXy2Wef1bto9rxHjx5y4oknSs+ePc1Y7nPPPdesDyD+MI9waBCUBCeTG6vjnWP5dRLIMeNzIzJj5AEgkUQ86G5MZWWlrFmzRsaNG1ezzOl0muuatQ7E8OHDTVb8wIED4vV6TTn7Mccc43f9iooKcbvdtS4AYm8asUHdXDJ/4vExV6IbjQhKgpPJjfcmf9HYaCzQY2ZPE/f8tafIQ5efGL+fG+++G9r1o3iYAQBEUlQH3Xv37hWPxyNdu3attVyv7969O6D7SE5ONuO4Tz/9dBkyZIgMGDBAfvCDH/hd/6677hKXy1Vz0Qw5gNifRgyBTQHmbzknM1qXyU2UQCTaxqU355jFcml/QG69VUSbyt5zT2Dr63q6vt4uDoYZAEAkRXXQHcwS9vXr15uScy1Xb8zs2bOlqKio5rJjx46wbScARMMUYA0t107xT/x0hCy+ZqR0a5eRkAeqNRn/RApEoil4jVSVRtQNJdCM9W23Hf795pubDrz177qe0ts1I+MdjcMMACDSonrKsE6dOklSUpIUFBTUWq7Xc3JyQvKYOu6bebyB+JlGDM2b2ikWp3wKJ7sMublTRtmByDcHvJIqThOI6G0JRKL3mLVG1L2PTjtN5O67/xdI2z9vuqnxgFvp7fT231Vs6AkkfT3724/2iY4bnl0X1hMdABDNojrTnZqaKsOGDZNly5bVLNNx2Xp95MiRIX1s7aaujdt0TDgAxBN/pc7bCksSogQ6EplcxsVHNpsczux71A4l0ABbA2hbQxnvhgLu7wLz5jRHC+UY+airIgCAWMh0l5SUSH5+fs317du3m+7iHTp0kF69epnpwiZPnmw6kJ900kkyb948M82YdjMPpenTp5uLNlLTsd0AEC8aKnU+eKhKNu8ubnC5ZgjJUsVmxjWeRV02uYn3V1S8j+zMdkMZ70YCbt8TCfq87BMJg7q7Gs14h+L5RutxB4CoznSvXr1ajj/+eHNRGmTr73PmzDHXJ0yYIPfdd5+5rvNxa0C+dOnSes3VAACtG3N5dE4WYzETaLxzLIvabHIsjGluKOPdsaPfgDuaehJE83GPxU7zABIo6B49erRYllXvsnDhwpp1rrvuOvnqq6/MdF4ffvihjBgxIuTbRXk5gHjlr9S5X+fMiDSdAporWoLAmB1KUDfw3r/fb8AdTScSovm4N5eeKNi0y93wCYMwdZoHED4OSyNc+GWXl2sn8+zsbPYUgLig4yKnPb7adCqfN3FoTUdyf8uBaKKBio4r1qBFA7COWWkmqNVpyqIluNVtbGoogf1+q6j2ygOReL9phts34O7QQWTfvgZX1THd2hxNy7oHdsuWmWPDX9YdC8c9ELX2ZU527RJ5zVhrAN3ISZBa6g4LWLGipvFdxF9fQAxxhzjmi3imGwAQXfOZM895y9HkKTxiIZscyFACe3zyxl1FZnxyY83Jgk6DNd+AW+l1P9nVUDVHa25DvGg/7q0ukbc7zdsam+KtkU7zEX99AYiuRmoAgOiZWo0p11qHJk+BBVnTn/zY/J532QnmNdcSp/bvJIN7uGK2KqMlzcmCpm6wphluOwBvZDqxUDRHa+57JtYbEgbUaK+xhne2Rhrfteb1RXYcCA0y3QAABEFCNXlqJQ2UNfA40Ip9o8H6Ez8dIYuvGRlzAXdExyc3FKxpSXmg2dUoeM/EckPCgMfHNzbFWxMBd2teX2THgdAg6PaDRmoAgERt8hRKfKmPYHOyxoK1QObxDrJEfM80q0S+BZ3mW/P6as2JQ4bWAI0j6PZD5+jeuHGjrFq1qoldCABA9HR4jmaBfqlPhC/wYR+fHEB2NNyBdzS+Z8Lx2mvW+PhmdppvzeurNSdBOJkGNI6gGwCAIIiHJk+hFuiX+kT5Ah+K5mQaNF61cJW56O81HbEDyI76DbxDNAd0NL5nwvXaa1aJvB4THXdf6w46NN7VvAWvr5aeBGFoDdA0gm4AAILEbu41qJtL5k88PuxTKkW7QL7UJ9oX+FCMT643Zl47Wt9yS2BTUNUNvPV2Ph2xgy1UXdFbImpfe83sNN/S11dLT4Ik4jABoLnoXt7ImG69eDzfnSUGACDA5l5o/Eu9zlHs70t9QN2dE1CgXd/9dgO/9VaRceMCD6A18D711JAG3KHsit6SbtxR+dprYaf5lmpJd3j7ZNo3B7ySKk5zMk1v12CGXKsmmvOaau76QJQi0+0HY7oBAAh/ZjMax/nGStf3gOaAbo44CHaaUy4eda+9CHWab271RcAZcj3xM2pU4Nur6+n6ejsgxhF0AwCAsGrsS300jvONleCRMt/WlYu35LUXssZrUdZpvtXDBDRjfdttgW+v7/PX29XpK5AIzRYRXygvBwAAUTk2XjO785ooCU604FFLn+3gcVB3V62AsFllvgmgJeXizS2v9lvOH45O88peLwSl5s3V6DABrZrQ5xHI9jb0/OtUXYRkvwMhRKYbAABE5dj4xdeMTPiAuzkZbKoEglMuHmh5dUgar0Vpp/mgCCRDH8AJh6hteAc0gqAbAAAgToLHaOoGHmmhPgkRknL+KO40H/LAO5AMfyv3O2XpiBTKy/2gezkAAIiVru9110/0MfCt6cYdqJCV80dxp/mgaKg0/t57a0+L1sgJh9bsd8rSESkOy7KsiD16DHC73eJyuaSoqEiys7MjvTkAACBBaflsKIJHtNyyTQXmZIiOLR7YLVtmjmVsccDqZrZtAWT4W7Lf9f1z9aLVsmmX22TJO2almffRgknDeD9BQh3zkekGAACIAWSwow9N/1pBA+u6GW6dhzyAZnAt2e9ROQ87EgZBNwAAANCKpn9oYabbN+BWel2XNxF4t2S/R7K7v44ln/b4aqmo9soD0T4jgzbja85Qheaun6BopAYAAAAgcqXlmuEO8bzjkezub48l37iryExxtmJroUQl7ScwalTg+1/X0/X1dmgUQTcAAACA8GioS/m+fU1PJxYEdln6oG4umT/x+GZ199ds9VULV5mL/h6oYExx1tLHbhbNWN92W+D73/c46u18p6tr7tR170bxVHdBQtANAAAAIPQamxYskHm8W8kuS198zcgWlXjrGHIdF36gGQFzsKaWC/ixWxrwaol4oPu/oeNol5iTLW8QQXcjU4bl5ubK8OHD/a0CAAAAIBCBzMMdhsA73CXi9ljySo9XPF7LjCXPykhp1ljygB+7tQFvIPu/seMYzGx5nCHo9mP69OmyceNGWbVqVXiPCAAAABBPNJhqKuC2NRT4RTgYa02JeGvHkgf82MEKeBsLvJs6cRKsbHkcIugGAAAAEDoaTN1yS8DzcNcK/PR2EQ7GWlsi3pqx5AE/djAD3oYC744dAztx0tpseZxiyjAAAAAAoaUlzOPGBR5AaxB26qkRD7iDNd1YarLTXNo3s1t6sx7bDlztgNb+6RvQBhrw1r0v3+ndmgqSG9uOexIv4FYOy7KsSG9ENHO73eJyuaSoqEiys7MjvTkAAAAAwmzZpgK54dl1Uu31ysBu2TJz7IBmZazD+tj+AtuWBLya4fYNuHV6N+0239Kp4fY3I3iPo5iPTDcAAAAABFAirl3E500c2qLu52F77IYyzffe2/yAV4Nm39sova7LAwmWW5MtjzOM6QYAAACAJmh5eGZ6crNLxCPy2HXHVrck4K6bpW5JV3l9nA4+t7XvK4ECbkV5eRMoLwcAAAAQk1pSHh7M8vS6t4nSTHeoYz4y3X4wTzcAAACAmNVYeXhjt/EXWDd3HvVgZcvjAEG3H8zTDQAAACAmtSTgDSSTHWjg3dB97dvXvKA9jhB0AwAAAEC8aEnA++67gZeONxR46+1DkS2PEwTdAAAAABAPWhrw6nzot9xS/zb++N6X3s6eTz2Y2fI4QiO1JtBIDQAAAEDUC7TRWWPracbaDqAD4bu+/j5qVNOP7287Vqxo3mMHEY3UAAAAAAD+Bas8vLlBr+/6wcqWxyEy3U0g0w0AAAAg6t16q8httwU+HZedadaAV28bLK3JlsdpzEfQHeEDAAAAAACJGvBGA8rLAQAAAABNa015OEKG7uUAAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3X7k5eVJbm6uDB8+PFT7HgAAAAAQ55gyrAlMGQYAAAAA8csd4mmiyXQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhEhyqO44XliWVdNGHgAAAAAQX9zfxXp27BdsBN1NKC4uNj979uwZkgMAAAAAAIiO2E/n6w42hxWqcD5OeL1e2blzp2RlZYnD4QjamRQN4nfs2BGSydcRfTjmiYXjnVg43omHY55YON6JheOdmMf766+/NrFe9+7dxekM/ghsMt1N0J1+xBFHSChowE3QnVg45omF451YON6Jh2OeWDjeiYXjnVhcLldI4zIaqQEAAAAAECIE3QAAAAAAhAhBdwSkpaXJLbfcYn4iMXDMEwvHO7FwvBMPxzyxcLwTC8c7saSFKS6jkRoAAAAAACFCphsAAAAAgBAh6AYAAAAAIEQIugEAAAAACBGCbgAAAAAAQoSgO0jy8vKkT58+kp6eLiNGjJCPPvqo0fWfe+45GThwoFl/8ODB8sorr9T6u2VZMmfOHOnWrZtkZGTIuHHjZOvWrcHaXETZ8Z4yZYo4HI5al3POOYfjFKPHfMOGDfKjH/3IrK/Hct68ea2+T8T28b711lvrvcf1MwGxd7wffvhhGTVqlLRv395c9N/nuuvzb3hiHW/+DY+vY/7iiy/KiSeeKO3atZO2bdvK0KFD5Yknnqi1Du/xxDreU4LxPd1Cqz3zzDNWamqq9eijj1obNmywpk2bZrVr184qKChocP333nvPSkpKsu69915r48aN1u9+9zsrJSXFWr9+fc06d999t+VyuawlS5ZY69ats374wx9affv2tcrKyjhicXi8J0+ebJ1zzjnWrl27ai779+8P47NCMI/5Rx99ZN14443W008/beXk5Fj3339/q+8TsX28b7nlFmvQoEG13uOFhYVheDYI9vH+yU9+YuXl5VmffPKJtWnTJmvKlCnm3+tvvvmmZh3+DU+s482/4fF1zN966y3rxRdfNN/Z8vPzrXnz5pnvcUuXLq1Zh/d4Yh3vyUH4nk7QHQQnnXSSNX369JrrHo/H6t69u3XXXXc1uP4ll1xiff/736+1bMSIEdbPfvYz87vX6zVf3P70pz/V/P3gwYNWWlqa+VKH+Dre9pv5/PPPD+FWI5zH3Ffv3r0bDMJac5+IveOtQfdxxx0X9G1F67X2vVhdXW1lZWVZjz/+uLnOv+GJdbwV/4ZHt2D8e3v88cebpIniPZ5YxztY73HKy1upsrJS1qxZY8qNbE6n01xfuXJlg7fR5b7rq7PPPrtm/e3bt8vu3btrreNyuUx5hL/7ROweb9vy5culS5cucvTRR8u1114r+/btC9GzQKiPeSTuE8ERymOjQ4S6d+8uRx55pFx22WXy9ddfB2GLEenjfejQIamqqpIOHTqY6/wbnljH28a/4fF5zDVBuWzZMtm8ebOcfvrpZhnv8cQ63sF6jxN0t9LevXvF4/FI165day3X6xo4N0SXN7a+/bM594nYPd5Kx4UsWrTIvNHvueceefvtt2X8+PHmsRB7xzwS94ngCNWx0ZOmCxculKVLl8qDDz5ovrTpONHi4uIgbDUiebxvuukmczLF/pLHv+GJdbwV/4bH3zEvKiqSzMxMSU1Nle9///syf/58Oeuss8zfeI8n1vEO1ns8OeA1AYTMxIkTa37XRmtDhgyRfv36mbNqY8eOZc8DMU7/cbbp+1uD8N69e8uzzz4rV111VUS3DS139913yzPPPGM+q7VhDxLzePNvePzJysqStWvXSklJiQm0Zs2aZaqURo8eHelNQwSOdzDe42S6W6lTp06SlJQkBQUFtZbr9ZycnAZvo8sbW9/+2Zz7ROwe74boG10fKz8/P0hbjnAe80jcJ4IjXMdGu6QeddRRvMdj+Hjfd999Jgh77bXXzBcwG/+GJ9bxbgj/hsf+MdeS5P79+5tO1jfccIP8+Mc/lrvuusv8jfd4Yh3vYL3HCbpbScsQhg0bZs6K2Lxer7k+cuTIBm+jy33XV6+//nrN+n379jUvDN913G63fPjhh37vE7F7vBvyzTffmLEiOmUcYu+YR+I+ERzhOjZ6Nn3btm28x2P0eN97771y++23m+ECOtWML/4NT6zj3RD+DY+/z3S9TUVFhfmd93hiHe+gvcdb1YYNNa3ptbP4woULTbv5q6++2rSm3717t/n75Zdfbt188821ppBKTk627rvvPjP9hHa1bWjKML2Pl156yfr0009NxzymDIvP411cXGymG1q5cqW1fft264033rBOOOEEa8CAAVZ5eXnEnidafswrKirM9DJ66datmzm++vvWrVsDvk/E1/G+4YYbrOXLl5v3uH4mjBs3zurUqZO1Z8+eiDxHtPx467/POh3N888/X2v6GP0s912Hf8MT43jzb3j8HfM777zTeu2116xt27aZ9fX7m36Pe/jhh2vW4T2eOMe7OEjf0wm6g2T+/PlWr169zAeztqr/4IMPav52xhlnmFbzvp599lnrqKOOMuvr3K0vv/xyrb/rdAS///3vra5du5oXztixY63NmzcHa3MRRcf70KFD1ve+9z2rc+fOJhjXKYd0TkGCr9g95vqhrOc06150vUDvE/F1vCdMmGACcr2/Hj16mOs6Hyhi73jrZ3RDx1tPqNr4Nzxxjjf/hsffMf/tb39r9e/f30pPT7fat29vjRw50gRyvniPJ87xPhSk7+kO/V/geXEAAAAAABAoxnQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBNwAAqOH1euXqq6+Wbt26mZ/MLAoAQOsQdAMAgBr//e9/ZcuWLfLqq6/K559/LkuXLmXvAADQCgTdAAAkqNNPP12eeuqpWstcLpe0b99e+vfvLx06dDCX1lqwYIGcd955rb4fAABiEUE3AAAxaseOHXLllVdK9+7dJTU1VXr37i0zZ86Uffv2NXnbf//731JQUCATJ06stfyUU06RyspKE3x7PB4ZMWJEwNuzbNkyufjii6Vt27by5Zdf1izXbfz4449lxYoVzXyGAADEPoJuAABi0BdffCEnnniibN26VZ5++mnJz883GWUNfEeOHCn79+9v9PZ/+ctfZOrUqeJ01v4qUFVVJatWrZJf//rX5md1dXXA2zR27Fh57rnnpHPnzrWW6wmBn/zkJ+YxAQBINA6LDikAAMSc8ePHy2effWbGX2dkZNQs3717t/Tr10+uuOIKefDBBxu8bWFhoXTt2lXWr18vgwYNqvW3JUuWyHXXXSfbt2+XPn36mPv44Q9/WPP3X/3qVybD7mvUqFEyffr0mut6u+XLl5uftnfeeUfOOussOXjwYK3tBQAg3iVHegMAAEDzaBZbG5798Y9/rBfA5uTkyGWXXSaLFy+Wv/3tb+JwOOrd/t1335U2bdrIMcccU+9vjz32mFx66aWSkpJifup136D7T3/6U4sOl2blNWv+4YcfyujRo1t0HwAAxCLKywEAiDFaUq6Fag0FzUqXHzhwwGS0G/LVV1+ZTHfd0nId4/3KK6/IpEmTzHX9+fLLL/u9n7rWrl1rAmrNtutY8UceeaTmbxrk6zhxfWwAABIJmW4AAGJUUyPEdCx1Q8rKyiQ9Pb3e8n/+858ycOBAOe6448z1oUOHylFHHSVPPvmkXH/99U1uj66vZeX+aFb+0KFDTd4PAADxhEw3AAAxRqfz0rLxTZs2Nfh3Xa7NzNq1a9fg3zt16mQy4XVpKfmGDRskOTm55rJx40ZZuHBh0Mri6zZZAwAg3hF0AwAQYzp27GiakumYbc1a+9LSbs1MT5kyxe/tjz/+eLOeb+Ctnco1wNZMtZaJ2xdtgPbpp5/KJ5980qpt3rZtm5SXl5vHBgAgkRB0AwAQg/76179KRUWFnH322SYw1o7iS5cuNcG4loTPmTPH72018NVs93vvvVcry33SSSfJ6aefLscee2zN5bTTTjNTkOnfW0Pn6D7yyCNNZ3UAABIJQTcAADFowIABJjutgewll1wivXv3NtOIacCtwXRmZqbf2yYlJZk5ujUjrjQDrXN9/+hHP2pwfV3+1FNPSWVlZYu3V+9/2rRpLb49AACxinm6AQCIE7fccovMnTtXXn/9dTn55JMbXVfLy3WO7o8//tgE7KGk48THjBlj5hTXDuYAACQSgm4AAOKIloEXFRXJL37xi3pTgtW1ZMkSMz581KhRId2mN954QzwejymFBwAg0RB0AwAAAAAQIozpBgAAAAAgRAi6AQAAAAAIEYJuAAAAAABChKAbAAAAAIAQIegGAAAAACBECLoBAAAAAAgRgm4AAAAAAEKEoBsAAAAAgBAh6AYAAAAAIEQIugEAAAAAkND4f1NBeL0UCDnHAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Plot all data\n", + "valid = ~zero_mask\n", + "ax.errorbar(q_raw[valid], r_raw[valid], yerr=err_raw[valid],\n", + " fmt='o', ms=3, color='C0', alpha=0.7, label='Valid points')\n", + "ax.plot(q_raw[zero_mask], r_raw[zero_mask],\n", + " 'rx', ms=10, mew=2, label=f'Zero-variance points ({zero_mask.sum()})')\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel('Q (Å⁻¹)')\n", + "ax.set_ylabel('Reflectivity')\n", + "ax.set_title('Raw experimental data — zero-variance points highlighted')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "428759d3", + "metadata": {}, + "source": [ + "## 4. Define Materials\n", + "\n", + "A simple film-on-substrate structure: **Si** substrate / **SiO₂** native oxide / **Film** / **D₂O** superphase." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "006fb3cf", + "metadata": {}, + "outputs": [], + "source": [ + "si = Material(2.07, 0, 'Si')\n", + "sio2 = Material(3.47, 0, 'SiO2')\n", + "film = Material(2.0, 0, 'Film')\n", + "d2o = Material(6.36, 0, 'D2O')" + ] + }, + { + "cell_type": "markdown", + "id": "6031b9ad", + "metadata": {}, + "source": [ + "## 5. Define Layers and Sample Structure" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2643141f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Film Structure:\n", + "- EasyMultilayer:\n", + " Si layer:\n", + " - Si layer:\n", + " material:\n", + " Si:\n", + " sld: 2.070e-6 1/Å^2\n", + " isld: 0.000e-6 1/Å^2\n", + " thickness: 0.000 Å\n", + " roughness: 0.000 Å\n", + "- EasyMultilayer:\n", + " SiO2 layer:\n", + " - SiO2 layer:\n", + " material:\n", + " SiO2:\n", + " sld: 3.470e-6 1/Å^2\n", + " isld: 0.000e-6 1/Å^2\n", + " thickness: 30.000 Å\n", + " roughness: 3.000 Å\n", + "- EasyMultilayer:\n", + " Film layer:\n", + " - Film layer:\n", + " material:\n", + " Film:\n", + " sld: 2.000e-6 1/Å^2\n", + " isld: 0.000e-6 1/Å^2\n", + " thickness: 250.000 Å\n", + " roughness: 3.000 Å\n", + "- EasyMultilayer:\n", + " D2O superphase:\n", + " - D2O superphase:\n", + " material:\n", + " D2O:\n", + " sld: 6.360e-6 1/Å^2\n", + " isld: 0.000e-6 1/Å^2\n", + " thickness: 0.000 Å\n", + " roughness: 3.000 Å\n", + "\n" + ] + } + ], + "source": [ + "si_layer = Layer(si, 0, 0, 'Si layer')\n", + "sio2_layer = Layer(sio2, 30, 3, 'SiO2 layer')\n", + "film_layer = Layer(film, 250, 3, 'Film layer')\n", + "superphase = Layer(d2o, 0, 3, 'D2O superphase')\n", + "\n", + "sample = Sample(\n", + " Multilayer(si_layer),\n", + " Multilayer(sio2_layer),\n", + " Multilayer(film_layer),\n", + " Multilayer(superphase),\n", + " name='Film Structure',\n", + ")\n", + "print(sample)" + ] + }, + { + "cell_type": "markdown", + "id": "51bcedeb", + "metadata": {}, + "source": [ + "## 6. Create the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "22e6fbd9", + "metadata": {}, + "outputs": [], + "source": [ + "resolution_function = PercentageFwhm(0.02)\n", + "model = Model(sample, 1, 1e-6, resolution_function, 'Film Model')" + ] + }, + { + "cell_type": "markdown", + "id": "7eaf6993", + "metadata": {}, + "source": [ + "## 7. Set Calculator Backend and Compute Initial Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "175afdcc", + "metadata": {}, + "outputs": [], + "source": [ + "interface = CalculatorFactory()\n", + "model.interface = interface\n", + "\n", + "# Compute initial model reflectivity\n", + "r_init = interface.fit_func(q_raw, model.unique_name)" + ] + }, + { + "cell_type": "markdown", + "id": "fff782bb", + "metadata": {}, + "source": [ + "## 8. Plot Initial Model vs Experimental Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a7656b8b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "ax.errorbar(q_raw[valid], r_raw[valid], yerr=err_raw[valid],\n", + " fmt='o', ms=3, color='C0', alpha=0.6, label='Experiment (valid)')\n", + "ax.plot(q_raw[zero_mask], r_raw[zero_mask],\n", + " 'rx', ms=10, mew=2, label='Experiment (zero variance)')\n", + "ax.plot(q_raw, r_init, '-', color='C1', lw=1.5, label='Initial model')\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel('Q (Å⁻¹)')\n", + "ax.set_ylabel('Reflectivity')\n", + "ax.set_title('Experimental data vs initial model (before fitting)')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6c916d41", + "metadata": {}, + "source": [ + "## 9. Set Fitting Parameters and Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a85ab7e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Free parameters:\n", + " thickness = 30 bounds=(15, 50)\n", + " sld = 2 bounds=(0.1, 3)\n", + " thickness = 250 bounds=(200, 300)\n", + " scale = 1 bounds=(0.5, 1.5)\n", + " background = 1e-06 bounds=(1e-07, 1e-05)\n" + ] + } + ], + "source": [ + "sio2_layer.thickness.fixed = False\n", + "sio2_layer.thickness.bounds = (15, 50)\n", + "\n", + "film_layer.thickness.fixed = False\n", + "film_layer.thickness.bounds = (200, 300)\n", + "\n", + "film.sld.fixed = False\n", + "film.sld.bounds = (0.1, 3)\n", + "\n", + "model.background.fixed = False\n", + "model.background.bounds = (1e-7, 1e-5)\n", + "\n", + "model.scale.fixed = False\n", + "model.scale.bounds = (0.5, 1.5)\n", + "\n", + "print('Free parameters:')\n", + "for p in model.get_fit_parameters():\n", + " print(f' {p.name:20s} = {float(p.value):.4g} bounds={p.bounds}')" + ] + }, + { + "cell_type": "markdown", + "id": "b83af903", + "metadata": {}, + "source": [ + "## 10. Zero-Variance Handling — Three Objective Modes\n", + "\n", + "We now fit the same data with each objective mode and compare the results.\n", + "\n", + "### How each mode handles zero-variance points\n", + "\n", + "- **`legacy_mask`** — zero-variance points are dropped before fitting. The fitter sees fewer\n", + " data points, and those Q-values have no influence on the result.\n", + "- **`hybrid`** (default) — valid points use standard weighted least-squares ($w = 1/\\sigma$).\n", + " Zero-variance points get the **Mighell (1999)** substitution:\n", + " $y_\\text{eff} = y + \\min(y, 1)$, $\\sigma = \\sqrt{\\max(y + 1,\\, \\varepsilon)}$.\n", + " This keeps all data in the fit while giving zero-variance points reasonable weight.\n", + "- **`mighell`** — applies the Mighell transform to *every* point, not just zero-variance ones.\n", + " This changes the chi-square landscape for the entire dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8182f0a9", + "metadata": {}, + "outputs": [], + "source": [ + "def build_fresh_model():\n", + " \"\"\"Build a fresh model+interface so each fit starts from the same initial state.\"\"\"\n", + " _si = Material(2.07, 0, 'Si')\n", + " _sio2 = Material(3.47, 0, 'SiO2')\n", + " _film = Material(2.0, 0, 'Film')\n", + " _d2o = Material(6.36, 0, 'D2O')\n", + "\n", + " _si_layer = Layer(_si, 0, 0, 'Si layer')\n", + " _sio2_layer = Layer(_sio2, 30, 3, 'SiO2 layer')\n", + " _film_layer = Layer(_film, 250, 3, 'Film layer')\n", + " _superphase = Layer(_d2o, 0, 3, 'D2O superphase')\n", + "\n", + " _sample = Sample(\n", + " Multilayer(_si_layer),\n", + " Multilayer(_sio2_layer),\n", + " Multilayer(_film_layer),\n", + " Multilayer(_superphase),\n", + " name='Film Structure',\n", + " )\n", + " _resolution = PercentageFwhm(0.02)\n", + " _model = Model(_sample, 1, 1e-6, _resolution, 'Film Model')\n", + "\n", + " _sio2_layer.thickness.fixed = False\n", + " _sio2_layer.thickness.bounds = (15, 50)\n", + " _film_layer.thickness.fixed = False\n", + " _film_layer.thickness.bounds = (200, 300)\n", + " _film.sld.fixed = False\n", + " _film.sld.bounds = (0.1, 3)\n", + " _model.background.fixed = False\n", + " _model.background.bounds = (1e-7, 1e-5)\n", + " _model.scale.fixed = False\n", + " _model.scale.bounds = (0.5, 1.5)\n", + "\n", + " _model.interface = CalculatorFactory()\n", + " return _model" + ] + }, + { + "cell_type": "markdown", + "id": "fd6f4833", + "metadata": {}, + "source": [ + "### 11. Fit with `legacy_mask`\n", + "\n", + "Zero-variance points are simply dropped." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e856e00f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WARNING] Masked 6 data point(s) in reflectivity ref_zero_var due to zero variance during fitting.\n", + "Success : True\n", + "Reduced chi² : 286.8691\n" + ] + } + ], + "source": [ + "model_mask = build_fresh_model()\n", + "fitter_mask = MultiFitter(model_mask, objective='legacy_mask')\n", + "\n", + "with warnings.catch_warnings(record=True) as w_mask:\n", + " warnings.simplefilter('always')\n", + " result_mask = fitter_mask.fit(data)\n", + "\n", + "for w in w_mask:\n", + " print(f'[WARNING] {w.message}')\n", + "print(f'Success : {result_mask[\"success\"]}')\n", + "print(f'Reduced chi² : {result_mask[\"reduced_chi\"]:.4f}')\n", + "\n", + "r_fit_mask = model_mask.interface.fit_func(q_raw, model_mask.unique_name)" + ] + }, + { + "cell_type": "markdown", + "id": "24cb6fe0", + "metadata": {}, + "source": [ + "### 12. Fit with `hybrid` (new default)\n", + "\n", + "All points kept; Mighell substitution applied only to zero-variance entries." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "20c034ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WARNING] Applied Mighell substitution to 6 zero-variance point(s) in reflectivity ref_zero_var during fitting.\n", + "Success : True\n", + "Reduced chi² : 277.6647\n" + ] + } + ], + "source": [ + "model_hybrid = build_fresh_model()\n", + "fitter_hybrid = MultiFitter(model_hybrid, objective='hybrid')\n", + "\n", + "with warnings.catch_warnings(record=True) as w_hybrid:\n", + " warnings.simplefilter('always')\n", + " result_hybrid = fitter_hybrid.fit(data)\n", + "\n", + "for w in w_hybrid:\n", + " print(f'[WARNING] {w.message}')\n", + "print(f'Success : {result_hybrid[\"success\"]}')\n", + "print(f'Reduced chi² : {result_hybrid[\"reduced_chi\"]:.4f}')\n", + "\n", + "r_fit_hybrid = model_hybrid.interface.fit_func(q_raw, model_hybrid.unique_name)" + ] + }, + { + "cell_type": "markdown", + "id": "79258c5f", + "metadata": {}, + "source": [ + "### 13. Fit with `mighell`\n", + "\n", + "Mighell transform applied to *all* points — the chi² landscape changes entirely." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "90b08fdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WARNING] Applied Mighell substitution to 192 zero-variance point(s) in reflectivity ref_zero_var during fitting.\n", + "Success : True\n", + "Reduced chi² : 0.095375\n" + ] + } + ], + "source": [ + "model_mighell = build_fresh_model()\n", + "fitter_mighell = MultiFitter(model_mighell, objective='mighell')\n", + "\n", + "with warnings.catch_warnings(record=True) as w_mighell:\n", + " warnings.simplefilter('always')\n", + " result_mighell = fitter_mighell.fit(data)\n", + "\n", + "for w in w_mighell:\n", + " print(f'[WARNING] {w.message}')\n", + "print(f'Success : {result_mighell[\"success\"]}')\n", + "print(f'Reduced chi² : {result_mighell[\"reduced_chi\"]:.6f}')\n", + "\n", + "r_fit_mighell = model_mighell.interface.fit_func(q_raw, model_mighell.unique_name)" + ] + }, + { + "cell_type": "markdown", + "id": "6136693d", + "metadata": {}, + "source": [ + "## 14. Compare Fit Results\n", + "\n", + "### Parameter comparison table" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "77c23990", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameter legacy_mask hybrid mighell\n", + "-----------------------------------------------------------------\n", + "thickness_1 0.002002 0.001911 593.4\n", + "sld -11.06 -11.06 5.516\n", + "thickness_2 211.4 211.4 364.7\n", + "scale 0.007455 0.007455 0.7049\n", + "background 5.389e-07 5.389e-07 7.115e-08\n", + "-----------------------------------------------------------------\n", + "reduced chi² 286.8691 277.6647 0.095375\n", + "success True True True\n" + ] + } + ], + "source": [ + "models = {\n", + " 'legacy_mask': model_mask,\n", + " 'hybrid': model_hybrid,\n", + " 'mighell': model_mighell,\n", + "}\n", + "results = {\n", + " 'legacy_mask': result_mask,\n", + " 'hybrid': result_hybrid,\n", + " 'mighell': result_mighell,\n", + "}\n", + "\n", + "# Build descriptive labels to disambiguate duplicates (e.g. two \"thickness\" params)\n", + "ref_params = model_mask.get_fit_parameters()\n", + "seen = {}\n", + "labels = []\n", + "for p in ref_params:\n", + " n = p.name\n", + " seen[n] = seen.get(n, 0) + 1\n", + " labels.append(f'{n} ({seen[n]})')\n", + "# Simplify if name appears only once\n", + "name_counts = {}\n", + "for p in ref_params:\n", + " name_counts[p.name] = name_counts.get(p.name, 0) + 1\n", + "labels_clean = []\n", + "seen2 = {}\n", + "for p in ref_params:\n", + " n = p.name\n", + " seen2[n] = seen2.get(n, 0) + 1\n", + " if name_counts[n] > 1:\n", + " labels_clean.append(f'{n}_{seen2[n]}')\n", + " else:\n", + " labels_clean.append(n)\n", + "\n", + "obj_keys = ['legacy_mask', 'hybrid', 'mighell']\n", + "\n", + "header = f'{\"Parameter\":<20s} {\"legacy_mask\":>14s} {\"hybrid\":>14s} {\"mighell\":>14s}'\n", + "print(header)\n", + "print('-' * len(header))\n", + "\n", + "for i, label in enumerate(labels_clean):\n", + " vals = []\n", + " for obj in obj_keys:\n", + " params = models[obj].get_fit_parameters()\n", + " vals.append(float(params[i].value))\n", + " print(f'{label:<20s} {vals[0]:>14.4g} {vals[1]:>14.4g} {vals[2]:>14.4g}')\n", + "\n", + "print('-' * len(header))\n", + "print(f'{\"reduced chi²\":<20s} {result_mask[\"reduced_chi\"]:>14.4f} {result_hybrid[\"reduced_chi\"]:>14.4f} {result_mighell[\"reduced_chi\"]:>14.6f}')\n", + "print(f'{\"success\":<20s} {str(result_mask[\"success\"]):>14s} {str(result_hybrid[\"success\"]):>14s} {str(result_mighell[\"success\"]):>14s}')" + ] + }, + { + "cell_type": "markdown", + "id": "906b9cdb", + "metadata": {}, + "source": [ + "### Fitted reflectivity curves — all three objectives" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a4f8f3a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Experimental data\n", + "ax.errorbar(q_raw[valid], r_raw[valid], yerr=err_raw[valid],\n", + " fmt='o', ms=2, color='grey', alpha=0.5, label='Experiment')\n", + "ax.plot(q_raw[zero_mask], r_raw[zero_mask],\n", + " 'rx', ms=10, mew=2, label='Zero-variance points')\n", + "\n", + "# Fitted curves\n", + "ax.plot(q_raw, r_fit_mask, '-', lw=1.5, label=f'legacy_mask (χ²={result_mask[\"reduced_chi\"]:.2f})')\n", + "ax.plot(q_raw, r_fit_hybrid, '--', lw=1.5, label=f'hybrid (χ²={result_hybrid[\"reduced_chi\"]:.2f})')\n", + "ax.plot(q_raw, r_fit_mighell, ':', lw=1.5, label=f'mighell (χ²={result_mighell[\"reduced_chi\"]:.4f})')\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel('Q (Å⁻¹)')\n", + "ax.set_ylabel('Reflectivity')\n", + "ax.set_title('Fitted reflectivity — comparison of objective modes')\n", + "ax.legend(fontsize=9)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "52821f2d", + "metadata": {}, + "source": [ + "## 15. Examine Residuals\n", + "\n", + "We compute normalised residuals $(R_\\text{exp} - R_\\text{model}) / \\sigma$ for each objective.\n", + "Zero-variance points are shown separately — for `legacy_mask` they were excluded from\n", + "the fit, while `hybrid` and `mighell` included them with transformed weights." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0292ef11", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n", + "\n", + "fits = {\n", + " 'legacy_mask': r_fit_mask,\n", + " 'hybrid': r_fit_hybrid,\n", + " 'mighell': r_fit_mighell,\n", + "}\n", + "\n", + "for ax, (obj_name, r_fit) in zip(axes, fits.items()):\n", + " # Normalised residuals for valid points\n", + " residuals_valid = (r_raw[valid] - r_fit[valid]) / err_raw[valid]\n", + " ax.plot(q_raw[valid], residuals_valid, 'o', ms=2, alpha=0.6, color='C0', label='Valid points')\n", + "\n", + " # Un-normalised residuals at zero-variance points (no sigma to normalise by)\n", + " residuals_zero = r_raw[zero_mask] - r_fit[zero_mask]\n", + " ax.plot(q_raw[zero_mask], np.zeros_like(residuals_zero), 'rx', ms=10, mew=2,\n", + " label='Zero-var Q positions')\n", + "\n", + " ax.axhline(0, color='k', lw=0.5)\n", + " ax.set_ylabel('Normalised residual')\n", + " ax.set_title(f'{obj_name}')\n", + " ax.legend(fontsize=8, loc='upper right')\n", + "\n", + "axes[-1].set_xlabel('Q (Å⁻¹)')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a2f39948", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| Objective | Zero-var handling | Chi² interpretation |\n", + "|---|---|---|\n", + "| `legacy_mask` | Dropped from fit | Classical reduced χ² |\n", + "| `hybrid` | Mighell substitution for zero-var only | Approximately classical when zero-var fraction is small |\n", + "| `mighell` | Mighell transform for **all** points | **Not** a classical χ² — values are not directly comparable |\n", + "\n", + "The `hybrid` mode (new default) is recommended: it keeps all data points in the fit while\n", + "producing chi² values that remain approximately classical. The `mighell` mode is useful when\n", + "the entire dataset has low counts and Poisson-like statistics are desired." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "era", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/easyreflectometry/fitting.py b/src/easyreflectometry/fitting.py index 86df41e3..01fbcf43 100644 --- a/src/easyreflectometry/fitting.py +++ b/src/easyreflectometry/fitting.py @@ -11,14 +11,110 @@ from easyreflectometry.data import DataSet1D from easyreflectometry.model import Model +_VALID_OBJECTIVES = ('legacy_mask', 'mighell', 'hybrid', 'auto') +_EPS = 1e-30 + + +def _validate_objective(objective: str) -> str: + """Validate and resolve the objective string. + + :param objective: The objective mode string. + :type objective: str + :return: Resolved objective string ('auto' becomes 'hybrid'). + :rtype: str + :raises ValueError: If the objective is not one of the valid options. + """ + if objective not in _VALID_OBJECTIVES: + raise ValueError(f'Unknown objective {objective!r}. Valid options: {_VALID_OBJECTIVES}') + if objective == 'auto': + return 'hybrid' + return objective + + +def _prepare_fit_arrays( + x_vals: np.ndarray, + y_vals: np.ndarray, + variances: np.ndarray, + objective: str, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, dict]: + """Prepare x, y_eff, and weights arrays for fitting based on the objective mode. + + For ``legacy_mask``, zero-variance points are removed from all arrays. + For ``hybrid``, valid-variance points use standard WLS while zero-variance + points use Mighell-transformed y and weights. + For ``mighell``, all points use the Mighell transform. + + Note: ``variances`` here means σ² (the scipp convention), not σ. + + :param x_vals: Independent variable values. + :type x_vals: np.ndarray + :param y_vals: Observed dependent variable values. + :type y_vals: np.ndarray + :param variances: Variance (σ²) of each observed point. + :type variances: np.ndarray + :param objective: One of 'legacy_mask', 'hybrid', 'mighell'. + :type objective: str + :return: Tuple of (x_out, y_eff, weights, stats) where stats is a dict + with keys 'valid', 'mighell_substituted', 'masked'. + :rtype: tuple[np.ndarray, np.ndarray, np.ndarray, dict] + """ + n = len(y_vals) + zero_mask = variances <= 0.0 + n_zero = int(np.sum(zero_mask)) + n_valid = n - n_zero + + if objective == 'legacy_mask': + valid = ~zero_mask + x_out = x_vals[valid] + y_eff = y_vals[valid] + if n_valid > 0: + weights = 1.0 / np.sqrt(variances[valid]) + else: + weights = np.array([]) + stats = {'valid': n_valid, 'mighell_substituted': 0, 'masked': n_zero} + return x_out, y_eff, weights, stats + + # hybrid or mighell + y_eff = np.copy(y_vals) + sigma = np.empty(n) + + if objective == 'mighell': + apply_mighell = np.ones(n, dtype=bool) + else: + # hybrid: apply Mighell only to zero-variance points + apply_mighell = zero_mask + + # Standard WLS for non-Mighell points + standard = ~apply_mighell + if np.any(standard): + sigma[standard] = np.sqrt(variances[standard]) + + # Mighell transform for selected points + if np.any(apply_mighell): + y_m = y_vals[apply_mighell] + delta = np.minimum(y_m, 1.0) + y_eff[apply_mighell] = y_m + delta + sigma[apply_mighell] = np.sqrt(np.maximum(y_m + 1.0, _EPS)) + + weights = 1.0 / sigma + n_mighell = int(np.sum(apply_mighell)) + stats = {'valid': n - n_mighell, 'mighell_substituted': n_mighell, 'masked': 0} + return x_vals, y_eff, weights, stats + class MultiFitter: - def __init__(self, *args: Model): - r"""A convinence class for the :py:class:`easyscience.Fitting.Fitting` + def __init__(self, *args: Model, objective: str = 'hybrid'): + r"""A convenience class for the :py:class:`easyscience.Fitting.Fitting` which will populate the :py:class:`sc.DataGroup` appropriately after the fitting is performed. - :param args: Reflectometry model + :param args: Reflectometry model(s). + :param objective: Zero-variance handling strategy. One of + ``'hybrid'`` (default, Mighell for zero-variance, WLS otherwise), + ``'mighell'`` (Mighell transform for all points), + ``'legacy_mask'`` (drop zero-variance points), + ``'auto'`` (alias for ``'hybrid'``). + :type objective: str """ # This lets the unique_name be passed with the fit_func. @@ -32,18 +128,29 @@ def wrapped(*args, **kwargs): self._models = args self.easy_science_multi_fitter = EasyScienceMultiFitter(args, self._fit_func) self._fit_results: list[FitResults] | None = None - - def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup: + self._objective = _validate_objective(objective) + + def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> sc.DataGroup: + """Perform the fitting and populate the DataGroups with the result. + + :param data: DataGroup to be fitted to and populated. + :type data: sc.DataGroup + :param id: Unused parameter kept for backward compatibility. + :type id: int + :param objective: Per-call override for the zero-variance objective. + If ``None``, uses the instance default set at construction. + :type objective: str or None + :return: A new DataGroup with fitted model curves, SLD profiles, and fit statistics. + :rtype: sc.DataGroup + + :note: Under the ``mighell`` objective all points are transformed, + so ``reduced_chi`` is not a classical chi-square statistic. + Under ``hybrid``, only zero-variance points are transformed; + when they are a small fraction of the data the chi-square + remains approximately classical. """ - Perform the fitting and populate the DataGroups with the result. + obj = _validate_objective(objective) if objective is not None else self._objective - :param data: DataGroup to be fitted to and populated - :param method: Optimisation method - - :note: Points with zero variance in the data will be automatically masked - out during fitting. A warning will be issued if any such points - are found, indicating the number of points masked per reflectivity. - """ refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]] x = [] y = [] @@ -55,25 +162,24 @@ def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup: y_vals = data['data'][f'R_{i}'].values variances = data['data'][f'R_{i}'].variances - # Find points with non-zero variance - zero_variance_mask = variances == 0.0 - num_zero_variance = np.sum(zero_variance_mask) + x_out, y_eff, weights, stats = _prepare_fit_arrays(x_vals, y_vals, variances, obj) - if num_zero_variance > 0: + if stats['masked'] > 0: warnings.warn( - f'Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.', + f'Masked {stats["masked"]} data point(s) in reflectivity {i} ' + 'due to zero variance during fitting.', + UserWarning, + ) + if stats['mighell_substituted'] > 0: + warnings.warn( + f'Applied Mighell substitution to {stats["mighell_substituted"]} ' + f'zero-variance point(s) in reflectivity {i} during fitting.', UserWarning, ) - # Keep only points with non-zero variances - valid_mask = ~zero_variance_mask - x_vals_masked = x_vals[valid_mask] - y_vals_masked = y_vals[valid_mask] - variances_masked = variances[valid_mask] - - x.append(x_vals_masked) - y.append(y_vals_masked) - dy.append(1 / np.sqrt(variances_masked)) + x.append(x_out) + y.append(y_eff) + dy.append(weights) result = self.easy_science_multi_fitter.fit(x, y, weights=dy) self._fit_results = result @@ -94,36 +200,43 @@ def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup: new_data['success'] = result[i].success return new_data - def fit_single_data_set_1d(self, data: DataSet1D) -> FitResults: + def fit_single_data_set_1d(self, data: DataSet1D, objective: str | None = None) -> FitResults: + """Perform fitting on a single 1D dataset. + + :param data: The 1D dataset to fit. Note that ``data.ye`` stores + variances (σ²), not standard deviations. + :type data: DataSet1D + :param objective: Per-call override for the zero-variance objective. + If ``None``, uses the instance default set at construction. + :type objective: str or None + :return: Fit results from the minimizer. + :rtype: FitResults """ - Perform the fitting and populate the DataGroups with the result. + obj = _validate_objective(objective) if objective is not None else self._objective - :param data: DataGroup to be fitted to and populated - :param method: Optimisation method - """ x_vals = np.asarray(data.x) y_vals = np.asarray(data.y) variances = np.asarray(data.ye) - zero_variance_mask = variances == 0.0 - num_zero_variance = int(np.sum(zero_variance_mask)) + x_out, y_eff, weights, stats = _prepare_fit_arrays(x_vals, y_vals, variances, obj) - if num_zero_variance > 0: + if stats['masked'] > 0: warnings.warn( - f'Masked {num_zero_variance} data point(s) in single-dataset fit due to zero variance during fitting.', + f'Masked {stats["masked"]} data point(s) in single-dataset fit ' + 'due to zero variance during fitting.', + UserWarning, + ) + if stats['mighell_substituted'] > 0: + warnings.warn( + f'Applied Mighell substitution to {stats["mighell_substituted"]} ' + 'zero-variance point(s) in single-dataset fit during fitting.', UserWarning, ) - valid_mask = ~zero_variance_mask - if not np.any(valid_mask): + if obj == 'legacy_mask' and len(x_out) == 0: raise ValueError('Cannot fit single dataset: all points have zero variance.') - x_vals_masked = x_vals[valid_mask] - y_vals_masked = y_vals[valid_mask] - variances_masked = variances[valid_mask] - - weights = 1.0 / np.sqrt(variances_masked) - result = self.easy_science_multi_fitter.fit(x=[x_vals_masked], y=[y_vals_masked], weights=[weights])[0] + result = self.easy_science_multi_fitter.fit(x=[x_out], y=[y_eff], weights=[weights])[0] self._fit_results = [result] return result diff --git a/tests/test_fitting.py b/tests/test_fitting.py index 446f10b9..12783545 100644 --- a/tests/test_fitting.py +++ b/tests/test_fitting.py @@ -12,6 +12,8 @@ from easyreflectometry.data import DataSet1D from easyreflectometry.data.measurement import load from easyreflectometry.fitting import MultiFitter +from easyreflectometry.fitting import _prepare_fit_arrays +from easyreflectometry.fitting import _validate_objective from easyreflectometry.model import Model from easyreflectometry.model import PercentageFwhm from easyreflectometry.sample import Layer @@ -76,7 +78,7 @@ def test_fitting(minimizer): def test_fitting_with_zero_variance(): - """Test that zero variance points are properly detected and masked during fitting when present in the data.""" + """Test that zero variance points are handled via Mighell substitution (hybrid default).""" import warnings import numpy as np @@ -129,26 +131,24 @@ def test_fitting_with_zero_variance(): model.interface = interface fitter = MultiFitter(model) - # Capture warnings during fitting - check if zero variance points still exist in the data - # and are properly handled by the fitting method + # Capture warnings during fitting with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') analysed = fitter.fit(data) - # Check if any zero variance warnings were issued during fitting - fitting_warnings = [str(warning.message) for warning in w if 'zero variance during fitting' in str(warning.message)] + # Under hybrid default, zero variance points trigger Mighell substitution warnings + mighell_warnings = [str(warning.message) for warning in w if 'Mighell substitution' in str(warning.message)] + mask_warnings = [str(warning.message) for warning in w if 'Masked' in str(warning.message)] + + # Hybrid mode should NOT produce mask warnings + assert len(mask_warnings) == 0, f'Unexpected mask warnings under hybrid: {mask_warnings}' - # The fitting method should handle zero variance points gracefully - # If there are any zero variance points remaining in the data, they should be masked - # and a warning should be issued - if len(fitting_warnings) > 0: - # Verify the warning message format and that it mentions masking points - for warning_msg in fitting_warnings: - assert 'Masked' in warning_msg and 'zero variance during fitting' in warning_msg - print(f'Info: {warning_msg}') # Log for debugging + # If there are zero-variance points in the loaded data, Mighell warnings should appear + if len(mighell_warnings) > 0: + for warning_msg in mighell_warnings: + assert 'zero-variance point(s)' in warning_msg # Basic checks that fitting completed - # The keys will be based on the filename, not just '0' model_keys = [k for k in analysed.keys() if k.endswith('_model')] sld_keys = [k for k in analysed.keys() if k.startswith('SLD_')] assert len(model_keys) > 0, f'No model keys found in {list(analysed.keys())}' @@ -157,7 +157,7 @@ def test_fitting_with_zero_variance(): def test_fitting_with_manual_zero_variance(): - """Test the fit method with manually created zero variance points.""" + """Test the fit method with manually created zero variance points using hybrid (default).""" import warnings import numpy as np @@ -217,12 +217,12 @@ def test_fitting_with_manual_zero_variance(): warnings.simplefilter('always') analysed = fitter.fit(data) - # Check that warnings were issued about zero variance points - fitting_warnings = [str(warning.message) for warning in w if 'zero variance during fitting' in str(warning.message)] + # Under hybrid default, should get Mighell substitution warning, not masking + mighell_warnings = [str(warning.message) for warning in w if 'Mighell substitution' in str(warning.message)] + + assert len(mighell_warnings) == 1, f'Expected 1 Mighell warning, got {len(mighell_warnings)}: {mighell_warnings}' + assert '7 zero-variance point(s)' in mighell_warnings[0], f'Unexpected warning content: {mighell_warnings[0]}' - # Should have one warning about the 7 zero variance points (5 + 2) - assert len(fitting_warnings) == 1, f'Expected 1 warning, got {len(fitting_warnings)}: {fitting_warnings}' - assert 'Masked 7 data point(s)' in fitting_warnings[0], f'Unexpected warning content: {fitting_warnings[0]}' # Basic checks that fitting completed despite zero variance points assert 'R_0_model' in analysed.keys() assert 'SLD_0' in analysed.keys() @@ -230,9 +230,10 @@ def test_fitting_with_manual_zero_variance(): def test_fit_single_data_set_1d_masks_zero_variance_points(): + """Legacy mask mode: zero-variance points are dropped.""" model = Model() model.interface = CalculatorFactory() - fitter = MultiFitter(model) + fitter = MultiFitter(model, objective='legacy_mask') captured = {} mock_result = MagicMock() @@ -255,7 +256,7 @@ def _fake_fit(*, x, y, weights): ye=np.array([0.01, 0.0, 0.04]), ) - with pytest.warns(UserWarning, match='Masked 1 data point\(s\) in single-dataset fit'): + with pytest.warns(UserWarning, match='Masked 1 data point\\(s\\) in single-dataset fit'): result = fitter.fit_single_data_set_1d(data) assert result is mock_result @@ -286,9 +287,10 @@ def test_reduced_chi_uses_global_dof_across_fit_results(): def test_fit_single_data_set_1d_all_zero_variance_raises(): + """Legacy mask mode raises when all points have zero variance.""" model = Model() model.interface = CalculatorFactory() - fitter = MultiFitter(model) + fitter = MultiFitter(model, objective='legacy_mask') data = DataSet1D( name='all_zero', @@ -376,3 +378,338 @@ def _fake_fit(*, x, y, weights): assert result is mock_result assert np.allclose(captured['x'][0], np.array([0.01, 0.02, 0.03])) assert np.allclose(captured['y'][0], np.array([1.0, 0.8, 0.6])) + + +# --- New tests for objective-based zero-variance handling --- + + +def test_objective_validation_rejects_unknown_value(): + with pytest.raises(ValueError, match='Unknown objective'): + _validate_objective('bad_value') + + +def test_objective_validation_resolves_auto(): + assert _validate_objective('auto') == 'hybrid' + assert _validate_objective('hybrid') == 'hybrid' + assert _validate_objective('legacy_mask') == 'legacy_mask' + assert _validate_objective('mighell') == 'mighell' + + +def test_prepare_fit_arrays_weights_always_positive_and_finite(): + """Weights must be strictly positive and finite for all inputs and objectives.""" + test_cases = [ + # (y_vals, variances, description) + (np.array([0.0]), np.array([0.0]), 'y=0, var=0'), + (np.array([-0.5]), np.array([0.0]), 'y=-0.5, var=0'), + (np.array([-1.0]), np.array([0.0]), 'y=-1, var=0'), + (np.array([1e6]), np.array([0.0]), 'y=1e6, var=0'), + (np.array([0.5, 0.3, 0.1]), np.array([0.0, 0.0, 0.0]), 'all-zero variances'), + (np.array([0.5, 0.3, 0.1]), np.array([0.01, 0.0, 0.04]), 'mixed variances'), + (np.array([0.0, -0.5, -1.0, 1e6]), np.array([0.0, 0.0, 0.0, 0.0]), 'edge y values'), + ] + + for objective in ('hybrid', 'mighell'): + for y_vals, variances, desc in test_cases: + x = np.arange(len(y_vals), dtype=float) + _, _, weights, _ = _prepare_fit_arrays(x, y_vals, variances, objective) + assert len(weights) == len(y_vals), f'Wrong length for {desc}, {objective}' + assert np.all(weights > 0), f'Non-positive weight for {desc}, {objective}: {weights}' + assert np.all(np.isfinite(weights)), f'Non-finite weight for {desc}, {objective}: {weights}' + + +def test_prepare_fit_arrays_legacy_mask_drops_zero_variance(): + x = np.array([0.01, 0.02, 0.03]) + y = np.array([1.0, 0.8, 0.6]) + var = np.array([0.01, 0.0, 0.04]) + + x_out, y_eff, weights, stats = _prepare_fit_arrays(x, y, var, 'legacy_mask') + + assert np.allclose(x_out, [0.01, 0.03]) + assert np.allclose(y_eff, [1.0, 0.6]) + assert np.allclose(weights, [1.0 / np.sqrt(0.01), 1.0 / np.sqrt(0.04)]) + assert stats == {'valid': 2, 'mighell_substituted': 0, 'masked': 1} + + +def test_prepare_fit_arrays_hybrid_transforms_zero_variance(): + x = np.array([0.01, 0.02, 0.03]) + y = np.array([1.0, 0.8, 0.6]) + var = np.array([0.01, 0.0, 0.04]) + + x_out, y_eff, weights, stats = _prepare_fit_arrays(x, y, var, 'hybrid') + + # x unchanged + assert np.allclose(x_out, x) + # Index 0 and 2: standard WLS (unchanged y) + assert y_eff[0] == pytest.approx(1.0) + assert y_eff[2] == pytest.approx(0.6) + assert weights[0] == pytest.approx(1.0 / np.sqrt(0.01)) + assert weights[2] == pytest.approx(1.0 / np.sqrt(0.04)) + # Index 1: Mighell transform — y_eff = y + min(y, 1) = 0.8 + 0.8 = 1.6 + assert y_eff[1] == pytest.approx(0.8 + 0.8) + # sigma = sqrt(y + 1) = sqrt(1.8) + assert weights[1] == pytest.approx(1.0 / np.sqrt(1.8)) + assert stats == {'valid': 2, 'mighell_substituted': 1, 'masked': 0} + + +def test_prepare_fit_arrays_mighell_transforms_all(): + x = np.array([0.01, 0.02]) + y = np.array([0.5, 0.3]) + var = np.array([0.01, 0.04]) # All valid, but mighell transforms everything + + x_out, y_eff, weights, stats = _prepare_fit_arrays(x, y, var, 'mighell') + + assert np.allclose(x_out, x) + # y_eff = y + min(y, 1) = y + y (since y < 1) + assert y_eff[0] == pytest.approx(0.5 + 0.5) + assert y_eff[1] == pytest.approx(0.3 + 0.3) + # sigma = sqrt(y + 1) + assert weights[0] == pytest.approx(1.0 / np.sqrt(1.5)) + assert weights[1] == pytest.approx(1.0 / np.sqrt(1.3)) + assert stats == {'valid': 0, 'mighell_substituted': 2, 'masked': 0} + + +def test_fit_single_data_set_1d_hybrid_keeps_zero_variance_points(): + """Hybrid mode keeps all points (transforms zero-variance ones).""" + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model) # default objective='hybrid' + + captured = {} + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.n_pars = 1 + + def _fake_fit(*, x, y, weights): + captured['x'] = x + captured['y'] = y + captured['weights'] = weights + return [mock_result] + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(side_effect=_fake_fit) + + data = DataSet1D( + name='hybrid_test', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.01, 0.0, 0.04]), + ) + + with pytest.warns(UserWarning, match='Mighell substitution'): + result = fitter.fit_single_data_set_1d(data) + + assert result is mock_result + # All 3 points should be passed through (not masked) + assert len(captured['x'][0]) == 3 + assert len(captured['y'][0]) == 3 + assert len(captured['weights'][0]) == 3 + + +def test_fit_single_data_set_1d_all_zero_variance_hybrid_does_not_raise(): + """Hybrid mode handles all-zero-variance data without raising.""" + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model) # default objective='hybrid' + + captured = {} + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.n_pars = 1 + + def _fake_fit(*, x, y, weights): + captured['x'] = x + captured['y'] = y + captured['weights'] = weights + return [mock_result] + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(side_effect=_fake_fit) + + data = DataSet1D( + name='all_zero_hybrid', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.0, 0.0, 0.0]), + ) + + with pytest.warns(UserWarning, match='Mighell substitution'): + result = fitter.fit_single_data_set_1d(data) + + assert result is mock_result + assert len(captured['x'][0]) == 3 + + +def test_fit_single_data_set_1d_legacy_mask_preserves_old_behavior(): + """Legacy mask mode drops zero-variance points and warns with old message.""" + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model, objective='legacy_mask') + + captured = {} + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.n_pars = 1 + + def _fake_fit(*, x, y, weights): + captured['x'] = x + captured['y'] = y + captured['weights'] = weights + return [mock_result] + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(side_effect=_fake_fit) + + data = DataSet1D( + name='legacy_test', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.01, 0.0, 0.04]), + ) + + with pytest.warns(UserWarning, match='Masked 1 data point'): + result = fitter.fit_single_data_set_1d(data) + + assert result is mock_result + assert np.allclose(captured['x'][0], np.array([0.01, 0.03])) + assert np.allclose(captured['y'][0], np.array([1.0, 0.6])) + + +def test_fit_multi_dataset_hybrid_uses_transformed_y_and_weights(): + """Multi-dataset fit with hybrid objective transforms zero-variance points.""" + import scipp as sc + + qz_values = np.linspace(0.01, 0.3, 10) + r_values = np.exp(-qz_values * 50) + variances = np.ones_like(r_values) * 0.01 + variances[3:5] = 0.0 # 2 zero-variance points + + data = sc.DataGroup( + { + 'coords': {'Qz_0': sc.array(dims=['Qz_0'], values=qz_values)}, + 'data': {'R_0': sc.array(dims=['Qz_0'], values=r_values, variances=variances)}, + } + ) + + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model) + + captured = {} + + def _fake_fit(x, y, weights): + captured['x'] = x + captured['y'] = y + captured['weights'] = weights + mock_r = MagicMock() + mock_r.reduced_chi = 1.0 + mock_r.success = True + mock_r.chi2 = 1.0 + mock_r.n_pars = 1 + mock_r.x = x[0] + return [mock_r] + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(side_effect=_fake_fit) + fitter.easy_science_multi_fitter._fit_objects = [MagicMock()] + fitter.easy_science_multi_fitter._fit_objects[0].interface.sld_profile.return_value = ( + np.linspace(0, 100, 5), + np.ones(5), + ) + + import warnings + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + fitter.fit(data) + + # All 10 points should be present (not masked) + assert len(captured['x'][0]) == 10 + assert len(captured['y'][0]) == 10 + assert len(captured['weights'][0]) == 10 + + # Zero-variance points should have Mighell-transformed y + for idx in [3, 4]: + y_orig = r_values[idx] + expected_y_eff = y_orig + min(y_orig, 1.0) + assert captured['y'][0][idx] == pytest.approx(expected_y_eff) + + # Check that Mighell warning was emitted + mighell_warnings = [str(ww.message) for ww in w if 'Mighell substitution' in str(ww.message)] + assert len(mighell_warnings) == 1 + assert '2 zero-variance point(s)' in mighell_warnings[0] + + +def test_fit_warnings_objective_specific(): + """Verify that each objective mode produces the correct warning type.""" + import warnings + + model = Model() + model.interface = CalculatorFactory() + + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.n_pars = 1 + + data = DataSet1D( + name='warn_test', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.01, 0.0, 0.04]), + ) + + for obj, expected_fragment in [ + ('legacy_mask', 'Masked 1 data point(s)'), + ('hybrid', 'Mighell substitution'), + ('mighell', 'Mighell substitution'), + ]: + fitter = MultiFitter(model, objective=obj) + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(return_value=[mock_result]) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + fitter.fit_single_data_set_1d(data) + + matching = [str(ww.message) for ww in w if expected_fragment in str(ww.message)] + assert len(matching) > 0, f'No warning containing {expected_fragment!r} for objective={obj}' + + +def test_multifitter_constructor_rejects_bad_objective(): + model = Model() + model.interface = CalculatorFactory() + with pytest.raises(ValueError, match='Unknown objective'): + MultiFitter(model, objective='nonsense') + + +def test_fit_per_call_objective_override(): + """Per-call objective override in fit_single_data_set_1d works.""" + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model, objective='hybrid') # default + + captured = {} + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.n_pars = 1 + + def _fake_fit(*, x, y, weights): + captured['x'] = x + captured['y'] = y + captured['weights'] = weights + return [mock_result] + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(side_effect=_fake_fit) + + data = DataSet1D( + name='override_test', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.01, 0.0, 0.04]), + ) + + # Override to legacy_mask — should drop the zero-variance point + with pytest.warns(UserWarning, match='Masked 1 data point'): + fitter.fit_single_data_set_1d(data, objective='legacy_mask') + + assert len(captured['x'][0]) == 2 # one point dropped From f65c8420e1deab26a8dc89da9d216adc1cfa499b Mon Sep 17 00:00:00 2001 From: rozyczko Date: Tue, 17 Mar 2026 12:09:00 +0100 Subject: [PATCH 2/5] ruff on notebook --- notebooks/zero_variance_fitting.ipynb | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/notebooks/zero_variance_fitting.ipynb b/notebooks/zero_variance_fitting.ipynb index 3fdfc09e..1a84cc9d 100644 --- a/notebooks/zero_variance_fitting.ipynb +++ b/notebooks/zero_variance_fitting.ipynb @@ -40,7 +40,6 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import scipp as sc\n", "\n", "from easyreflectometry.calculators import CalculatorFactory\n", "from easyreflectometry.data.measurement import load\n", @@ -132,7 +131,7 @@ "zero_indices = np.where(zero_mask)[0]\n", "print(f'Number of zero-variance points: {zero_mask.sum()}')\n", "print(f'Indices: {zero_indices}')\n", - "print(f'Q-values with zero variance:')\n", + "print('Q-values with zero variance:')\n", "for idx in zero_indices:\n", " print(f' [{idx:3d}] Q = {q_raw[idx]:.4e} Å⁻¹, R = {r_raw[idx]:.4e}')" ] @@ -606,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "77c23990", "metadata": {}, "outputs": [ @@ -675,8 +674,10 @@ " print(f'{label:<20s} {vals[0]:>14.4g} {vals[1]:>14.4g} {vals[2]:>14.4g}')\n", "\n", "print('-' * len(header))\n", - "print(f'{\"reduced chi²\":<20s} {result_mask[\"reduced_chi\"]:>14.4f} {result_hybrid[\"reduced_chi\"]:>14.4f} {result_mighell[\"reduced_chi\"]:>14.6f}')\n", - "print(f'{\"success\":<20s} {str(result_mask[\"success\"]):>14s} {str(result_hybrid[\"success\"]):>14s} {str(result_mighell[\"success\"]):>14s}')" + "print(f'{\"reduced chi²\":<20s} {result_mask[\"reduced_chi\"]:>14.4f} \\\n", + "{result_hybrid[\"reduced_chi\"]:>14.4f} {result_mighell[\"reduced_chi\"]:>14.6f}')\n", + "print(f'{\"success\":<20s} {str(result_mask[\"success\"]):>14s} \\\n", + " {str(result_hybrid[\"success\"]):>14s} {str(result_mighell[\"success\"]):>14s}')" ] }, { From eb733d4877638e28f1f862ed76983fbb4c5b3bf3 Mon Sep 17 00:00:00 2001 From: rozyczko Date: Tue, 17 Mar 2026 13:36:26 +0100 Subject: [PATCH 3/5] don't fail on codecov upload --- .github/workflows/python-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml index 90967cd9..1d33313f 100644 --- a/.github/workflows/python-ci.yml +++ b/.github/workflows/python-ci.yml @@ -61,7 +61,7 @@ jobs: name: unit-tests-job flags: unittests files: ./coverage-unit.xml - fail_ci_if_error: true + fail_ci_if_error: false verbose: true token: ${{ secrets.CODECOV_TOKEN }} slug: EasyScience/EasyReflectometryLib From f65cf9ba0b9d0640f7de34b180fe700626c1a138 Mon Sep 17 00:00:00 2001 From: rozyczko Date: Mon, 23 Mar 2026 09:26:22 +0100 Subject: [PATCH 4/5] fixed chi2 reporting, added MD file with description --- MIGHELL_IMPLEMENTATION.md | 113 ++++++++ notebooks/zero_variance_fitting.ipynb | 363 +++++++++----------------- src/easyreflectometry/fitting.py | 114 +++++++- tests/test_fitting.py | 68 +++++ zero_variances_example.py | 222 ++++++++++++++++ 5 files changed, 639 insertions(+), 241 deletions(-) create mode 100644 MIGHELL_IMPLEMENTATION.md create mode 100644 zero_variances_example.py diff --git a/MIGHELL_IMPLEMENTATION.md b/MIGHELL_IMPLEMENTATION.md new file mode 100644 index 00000000..26a61a9d --- /dev/null +++ b/MIGHELL_IMPLEMENTATION.md @@ -0,0 +1,113 @@ +# Mighell Implementation Notes + +## Summary + +The `mighell` objective implemented in `easyreflectometry.fitting` is mathematically consistent with the +`chi^2_gamma` statistic described in `mighell_fitting.pdf`. + +However, the paper derives this statistic for Poisson-distributed count data, whereas this library applies it to +reflectometry values that are typically already normalized and are not raw counts. That distinction is important for +interpreting the fit behavior. + +## Formula From The PDF + +Section 3 of `mighell_fitting.pdf` defines the statistic + +$$ +\chi^2_\gamma = \sum_i \frac{[n_i + \min(n_i, 1) - m_i]^2}{n_i + 1} +$$ + +where: + +- `n_i` are Poisson-distributed observed counts +- `m_i` are model values + +The paper proposes this in preference to the modified Neyman statistic for Poisson data. + +## Implemented Form + +The code in `src/easyreflectometry/fitting.py` implements the same objective in weighted least-squares form. + +For `objective='mighell'`, it constructs: + +$$ +y_{\mathrm{eff},i} = y_i + \min(y_i, 1) +$$ + +and + +$$ +\sigma_i = \sqrt{y_i + 1} +$$ + +with weights + +$$ +w_i = \frac{1}{\sigma_i} +$$ + +so the minimized least-squares objective is + +$$ +\sum_i \left(\frac{y_{\mathrm{eff},i} - f_i}{\sigma_i}\right)^2 += +\sum_i \frac{[y_i + \min(y_i,1) - f_i]^2}{y_i + 1} +$$ + +which is exactly the same algebraic form as the paper's `chi^2_gamma` statistic. + + +This has been implemented: +- shifting the target by `min(y, 1)` +- using `sqrt(y + 1)` as the effective uncertainty +- minimizing the resulting weighted residual sum of squares + +## Important Scope Limitation + +The PDF is explicitly about Poisson-distributed count data. + +In `reflectometry-lib`, `mighell` is being applied to reflectometry intensities / reflectivities, which are generally: + +- normalized values rather than raw counts +- potentially processed quantities rather than direct Poisson observations +- not guaranteed to satisfy the assumptions used in the paper's derivation + + +## The Full Mighell Fit Can Look Worse + +The poor visual agreement of the full `mighell` fit against the measured reflectivity is expected under this +implementation (see results of running `zero_variances_example.py or +notebooks\zero_variance_fitting.ipynb) + +That is because `mighell` here is not only a reweighting of the residuals. It also changes the fitted target: + +$$ +y \rightarrow y + \min(y,1) +$$ + +For any point with `0 < y < 1`, this doubles the target value. + +At low `Q`, where reflectivity values can still be relatively large, the transformed target can be substantially higher +than the measured reflectivity. The optimizer is therefore solving a different problem than “fit the plotted +reflectivity values as closely as possible”. + +The effect: + +- `legacy_mask` and `hybrid` often nearly overlap +- full `mighell` can visibly deviate from the experimental curve +- the `mighell` objective value can be small while the classical chi-square is poor + +## Hybrid mode + +The `hybrid` mode is not taken directly from the PDF. + +It is a project-specific adaptation that: + +- uses standard weighted least squares where variances are valid +- applies the Mighell substitution only where variance is zero or invalid + +This makes `hybrid` a better (?) reflectometry-oriented compromise rather than a direct reproduction of the paper. + + +I would propose using `hybrid` as default, but give users an option of both masking the zero variance points and applying proper Mighell algorithm. + diff --git a/notebooks/zero_variance_fitting.ipynb b/notebooks/zero_variance_fitting.ipynb index 1a84cc9d..ea3ede96 100644 --- a/notebooks/zero_variance_fitting.ipynb +++ b/notebooks/zero_variance_fitting.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "4983acb8", "metadata": {}, "outputs": [], @@ -67,20 +67,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "47fb3264", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total data points : 192\n", - "Q range : [7.6135e-03, 3.3436e-01] Å⁻¹\n", - "R range : [3.1715e-07, 1.0429e+00]\n" - ] - } - ], + "outputs": [], "source": [ "DATA_PATH = os.path.join('..', 'tests', '_static', 'ref_zero_var.txt')\n", "\n", @@ -90,10 +80,16 @@ "\n", "# Load through easyreflectometry (produces a scipp DataGroup)\n", "data = load(DATA_PATH)\n", + "data_key = next(iter(data['data'].keys()))\n", + "coord_key = next(iter(data['coords'].keys()))\n", + "result_model_key = f'{data_key}_model'\n", "\n", "print(f'Total data points : {len(q_raw)}')\n", "print(f'Q range : [{q_raw.min():.4e}, {q_raw.max():.4e}] Å⁻¹')\n", - "print(f'R range : [{r_raw.min():.4e}, {r_raw.max():.4e}]')" + "print(f'R range : [{r_raw.min():.4e}, {r_raw.max():.4e}]')\n", + "print(f'Data key : {data_key}')\n", + "print(f'Coord key : {coord_key}')\n", + "print(f'Model key : {result_model_key}')" ] }, { @@ -106,26 +102,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "1be2ca78", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of zero-variance points: 6\n", - "Indices: [170 179 183 189 190 191]\n", - "Q-values with zero variance:\n", - " [170] Q = 2.2061e-01 Å⁻¹, R = 7.8023e-07\n", - " [179] Q = 2.6364e-01 Å⁻¹, R = 6.5027e-07\n", - " [183] Q = 2.8538e-01 Å⁻¹, R = 4.6847e-07\n", - " [189] Q = 3.2138e-01 Å⁻¹, R = 3.1715e-07\n", - " [190] Q = 3.2781e-01 Å⁻¹, R = 3.4336e-07\n", - " [191] Q = 3.3436e-01 Å⁻¹, R = 3.2231e-07\n" - ] - } - ], + "outputs": [], "source": [ "zero_mask = err_raw == 0.0\n", "zero_indices = np.where(zero_mask)[0]\n", @@ -138,21 +118,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "373cdc62", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkL9JREFUeJzt3Ql4VOXVwPEzkx2SDDsBZBNQDIIoIqKiCFjF1rq0ClYU0GK1SLFoq3RBrdatFrE0lWpVxLrgVuxXlaooiooKKIiAQBAVBULYMknIOnO/57x408kyySSZff4/nzGZmzszd+6dGebcc97zOizLsgQAAAAAAASdM/h3CQAAAAAACLoBAAAAAAghMt0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBNwAAAAAAIULQDQAAAABAiBB0A0CMmDJlivTp00fi3cKFC8XhcMiXX37Zotsnyn5Cffq6ufXWW9k1deg+0X0TLXRbrrvuupB+Fti3Xb16dZPrjh492lxs+nh6W72P5rJve99990kk6WegfhYCiA4E3QCCxv6SY1+Sk5OlR48e5h/+b7/9lj2dAA4dOmS+4C9fvlxi0Z133ilLliyJ9GYAUeP999837+mDBw9GelPiziuvvMJJIiBBEHQDCLo//OEP8sQTT8iCBQtk/Pjx8s9//lPOOOMMKS8vZ2+3wsMPPyybN2+O+qD7tttuI+hGRJSVlcnvfvc79n4duk9037Q06Nb3dCSC7ssvv9xsd+/evcP6uPp4+rj6+KEOunXfAoh/yZHeAADxRwPtE0880fz+05/+VDp16iT33HOP/Pvf/5ZLLrkk0psXc0pLS6Vt27aSkpIS6U1BAtGTZKmpqeJ0Rvf5ea/XK5WVlZKenm4uqE+rjvQSa5KSkswl3LRSi9cSgGCK7n9JAcSFUaNGmZ/btm2rWaZfkufMmSPDhg0Tl8tlgkpd76233qp12xNOOEEuuuiiWssGDx5svhR9+umnNcsWL15slm3atKnRbamoqJBbbrlF+vfvL2lpadKzZ0/59a9/bZbbJk+ebL5w1b2vs88+W9q3by87d+6sVU7/zjvvyM9+9jPp2LGjZGdnyxVXXCEHDhyo99ivvvqqeY76XLOysuT73/++bNiwodY6WoqfmZlp9tW5555r1rvssssaHKvsO3YwLy9PjjzySGnTpo1873vfkx07dohlWXL77bfLEUccIRkZGXL++efL/v37W7VdOkzgggsuML937txZbrzxRvF4PDXbo8uUZm/sYQb2GFs9Xno/up26f3NycuTKK6+Uffv2SUtpKfixxx5r7k9//utf/2pwPd1Hp5xyijlGui/0dff888/XWke3VU9wPP744zXbbo+J/Oqrr+TnP/+5HH300eb2ej8XX3xxi8edB4O+FnyHc/hefMv79Zjpfu7atat5zQ8aNEgeffTRWvel6+vtnnnmGZMV1WEh+lpyu93m788995zZZ/rc9STapEmTAhoyosfkzDPPbDBQ1sf48Y9/3Kxj5Dse+MknnzTPRZ/T0qVLGxzTHehxs9/L7733nsyaNcu8jvX9cOGFF0phYWGD7xmt3tH3i77nhw8fLk899VStdT788EM555xzzOeb7ktdX++/Kfax0M+03/zmN+Z9otvywx/+0Lyv6wrk2DQ0ptvej/Z7yH5t2PvSvt2vfvUr83vfvn1rXl/2/nv99dfltNNOk3bt2pnPBN3Pus2Bauyx/Y3p1teOblf37t3NftXX18aNG/2OYdbP9kCOqS9/Y7p1X+fm5tb6vGmsh8RDDz0k/fr1M89PXyOrVq2q+ZveTj+3le971/d5zps3z+wXfTx9/+q/M3X/bdHP+TvuuMN8ztv7o+7nN4DIi73TngBijv2FSQNWm36Z/8c//iGXXnqpTJs2TYqLi+WRRx4xge1HH30kQ4cONetpMPj000/X3E6DRv1Codm3FStWyJAhQ8xy/V2/VB1zzDF+t0O/xOgX13fffVeuvvpqs+769evl/vvvly1bttSM5X3ggQfkzTffNMH3ypUrTabl73//u7z22mumbF6/7PnSL676pVO/CGr594MPPmi+7NtfnpXeTu9Pn59m/bUMW9fTL6yffPJJrS9t1dXVZj39mwYi+kWqMRp86EmMGTNmmP1z7733moqCMWPGmG246aabJD8/X+bPn2+CZN+AqznbpcG1rjdixAizXW+88Yb8+c9/Nl8qr732WrP/9bb6u36xtU+W2MdIv6B/8cUXMnXqVBNI6HHUL6X684MPPmh2oyc9Hj/60Y/Ml+C77rrLBO963/rlsy49pnrs9QSG7isNLjX4+s9//mNOMtj7QiszTjrpJPP6UPrclH5Z1jLbiRMnmvvX17Q+V22+pF/4mzpGoaBfyEtKSmot09fy2rVrTXCpCgoK5OSTT64JsPQYacB41VVXmffg9ddfX+v2epJGs9v6OtFgRX/XwEP3qwYNup/1PnV/agCprxF97fszYcIE877YvXu3OeY2fQ/qySvdn805RjZ9fz777LPmOWmg6S/oae5x0/eQfk7piTldV/exPoYGwDbdH3oSQ4Oh2bNnm+ev+0EDxp/85Cc126cVPxoM633p59Vjjz1m3pP6WaWvsab88Y9/NMdN37979uwx2zJu3DhzfDXAtrelpcfGPg4vvviiOTGhJxD+8pe/mPfU119/bV5D+h7Wz0b9DNbXlu5rpa8jfd/+4Ac/MO9vHVKkgaV+zgRyYiGQx/ZH97l+xp133nnm82jdunXmp7/hS4Ec00C8/PLL5vWsJ311X2vwq+8jPXnUED0Jo/+uaaCsx1G3WfenfgZq1ZIu1/eAfi7qZ09d+nf7+P7iF7+Q7du3y1//+ldzXHUf25VPevJag249SauXjz/+2Jx41fcQgChiAUCQPPbYY5Z+rLzxxhtWYWGhtWPHDuv555+3OnfubKWlpZnrturqaquioqLW7Q8cOGB17drVuvLKK2uWPffcc+Y+N27caK7/+9//Nvf1wx/+0JowYULNekOGDLEuvPDCRrfviSeesJxOp7VixYpayxcsWGAe47333qtZ9t///tcsu+OOO6wvvvjCyszMtC644IIGn++wYcOsysrKmuX33nuvWf7SSy+Z68XFxVa7du2sadOm1br97t27LZfLVWv55MmTzW1vvvnmetuvf+vdu3fN9e3bt5t1df8ePHiwZvns2bPN8uOOO86qqqqqWX7ppZdaqampVnl5eYu36w9/+EOtdY8//njz/G163HW9W265pd72Hzp0qN6yp59+2qz/zjvv1Nuv+vwaM3ToUKtbt261nvtrr71mbuu7nxp6bD1exx57rDVmzJhay9u2bWueayDbvnLlSvNYixYtsqLBs88+W+8YXXXVVWYf7d27t9a6EydONMfYfl5vvfWWue2RRx5Z67nqfurSpYvZV2VlZTXL//Of/5j158yZ0+g2bd682aw3f/78Wst//vOfm/eU72MFeoz0/vR9vGHDhnqPV/e1F+hxs19z48aNs7xeb83yX/7yl1ZSUlLNa0x/ZmVlWSNGjKi1P5R9O/05YMAA6+yzz651X7otffv2tc4666xG95l9LHr06GG53e56x/eBBx5o9rHRfVL3K59e18+D/Pz8mmXr1q2rd7z+9Kc/Nfh+vP/++81yfc83V6CPXfezQD+bkpOT630W33rrrWY93/duoMdUnXHGGeZS97NV78M2ePBg64gjjjCfm7bly5fX+7yxb9uxY0dr//79Ncv13wNd/n//9381y6ZPn17vuCj9N0qXP/nkk7WWL126tNbyPXv2mP34/e9/v9Zz/M1vflNvfwCILMrLAQSdZmM0E6Kl21o+qiV9Op7bNwOp2WPNotkZaM3QaoZXx4Lrmfq6pelawq00S6RZnbPOOsv8rrTBz2effVazrj9aGqjZ7YEDB8revXtrLpp9Ur6l7Zop0EyDZnA0O6HlfZrtbohmRX3HW2umV8dPapMcpZkM3UbN6vs+ru4DzRrXLam37yNQmg3UElab3qfSMlPfcZy6XLMfdulpS7brmmuuqXVd97lmbgJhZ+eUZqX0sTQLq3yPeSB27dplMn6apfd97vq60Mx3Y4+tGaqioiKz7YE+ru/tq6qqTFZdhyhoJrG52x4KmrXV7KsOIbAbiWls88ILL5iMoP7ue4w1M6j7oO626/70fa463ZJmWTUb6TvGVTPP+j7S7F9jjjrqKFO14ptV1IoJLRvX7fJ9rOYcIy3Vbug4t/a46XvZt+JCH1+3VytX7PeMZi9vvvnmemN+7dvp63Lr1q0m662PZ+9zHbowduxY81mmn3lN0WEqmgG26Wdpt27daj5XWnts7M9qu5pDadZay+UDeU/bWfSXXnopoOcTjMdetmyZ+XdCn3PdbLY/TR3TQGhGWqui9JhoGb3v61Az3w3RrLhvdZf971Mg+1b/rdLPNf08833fauWEPr792azVRnaVk+9zrFvBAiDyKC8HEHQ6Tk2/bOuXZi1l1i+ZWnpYl46d1fLkzz//3HwhtunYQZuOYxswYIAJsDUI1p86Zu300083XzT0C4yOvdYvfU0F3fpFWNe1xx3XpV9gfWkJtX6h1C/RWirYpUuXBm+n2+dLvxTpl2O7rF4fV9nBfV36RdOXBsoNlUj706tXr1rX7SBUT3o0tNweE9jc7dIv9nX3nX6pbGj8ekP0xIqO9day4br7Wl8rzWF/Ya6775WOK60bUGmJspZg6rH0Hb8faEm7djLWklItEdaTFoeTdYFtu44ftce9N5fu76YaSWmZuJ4Y0jLXRYsW1TwnfVw9qaIl/HppSN3j4Pve893Puk/r0sBOS4SVPr+642Q7dOhgTqxp8KHjfHW/6TbqkAd9XF3e0mNUdzuDddzqvpfsoMl+jdt9KXQ8rz/2+0pPYPijj+0bkDWk7mtb94OeMLA/VwI9No2p+3yb857W46dDhHRIhp6E0BMK+jrUkwOBNN9ryWPbz1n3Q93Xmr/92dQxDYS/x7WXNXQCpzWPq68hfY34+zfHft/6+xzUz42mXl8AwougG0DQ6XhFu3u5Nt3S8cGa9dHxznaWQKcR00Yy+ndt1KNfLjS40C/Ivg3XlN5eMxz6BXrNmjVmDJt+6dVMiwbhGkjr/R5//PGNbpcG5pqVmDt3boN/rxuk6tg5+8uNZjk0I9wSdhZIx+35jmu11e0qrCcomtMx2l9Q5m+5HXg0d7ta20VYx5nr+Fo93pr91GOm26DNplqSKQuUvkZ0rLCeqPnb3/5mTohoZYIGYnWbX/mjJ3h0fc0gjRw50pzA0CBIxwo3te1amdGcrJovHcfpb7yyTd9HmonTXgi+J0rs7dKKB38BoD3evqHMcHNog6+6gbBm43TstAZnOg5Xs3e6/3Qstu4/Pe4tPUaBbmdzj1tT75lA2Pf7pz/9qaY3RV2+2dJIas3z1WOgJ1T1OGtWXce0a0WDnsTTfgtNfV4EY18HIlyPE8zH1deQ/puo/Toa4u/EMYDoRdANIKTsQFqz09oERjMiSstLtYu1NtLxzWRps5u6NIOtX5w1Q6oZNe1wrEGpBuN20K3LmvqSp6WM2nRHMzJNZTi1FFQb2GgJq963NsHR5mAaQDWUlfDt0KzNrbT8WZva2I+r9EuUllRGi1Bsl7/9qtkdPXGimW49aVI3K9hc9ry9Dd2+7lzmWmKtWfr//ve/tSou9DUV6Pbr61UDV63M8C2RD2TuYv3i3NI5khs6GeLr7rvvNg0A9X2k2c26X8y1PFnfMy09vvZ+1n1atyJCl9l/1+3U0mtfxx13nPmpwbieiNOATBtY6bbqyTbfY9GcY9QcrTlujb1ndDhLQ1lP33X0BEhr3ld1X9sarGmjMvtESaDHprUa+6zUz2H9PNWLnsy888475be//a0JxEPxWWc/J90Pvid5tIy/OZnr1jxuXQ0ta+2+1deQlo6feuqpjZ5g8v0c1H9PbVp1Esr9AaD5GNMNIOQ026VfurVrrN1h1g6Qfc/66xQ72i28LrtsXLtr6xdOu0xal2sgp2MbmyottzOtWmL68MMP1/ubBkUaaNu0Y7B20dUSeP0yqdlG/fLuW/Zq09Jd3/J47Y6s4w61e7HS8bP6BVy/kPquZ2tq+ppQCcV22d2g6wY1DR1vpa+JltBMqGYR9fj4lglr4Kfjm+s+tn659S3x1hJdu1u9L+0/0FBApvdRd9u1G3wgZeP6xVkDkJZcGpsrWL+U6/htDXI0iG1om7UbtAa0GiS25PhqxYqelFmwYEGt1752QNeTXXZXcd3OutvuW96q2W7tUK/DTXRsat3S8uYco+ZozXFriPZ60BMZeiKxbrds+3F03K0GTTo8pW53+ea8r3SogI4f9z2BoCfz7M+VQI9Na+l7QtV9XzQ0/aCd2W/oczIYNLjXChz9jPWlJ3RDSWes0OoqPSa+x/Ttt982VVDB3rf6b5W+RnU2gbr03xZ7fX2faUWIvqZ9X+ct/VwFEDpkugGEhZYUa8MvnQJFm3HpVDOa8dLssX451DJa/fKomeW6X1Q1o6SZNM3e+DbM0VJUDY5VIEH35Zdfbkpb9fE1E6PBkH6x0THlulyzbPpFVqf70RJXzbrrPOF2xk1PHvz+9783WW9f2shGvwzqFyXdRr2tZuG1XFZpYKtfEvXx9f60tFWzkBrUa1mmbkeovzQ2JBTbpVkZPYaa1dRx/TrWUr+s6kWPl+47DfB1bK+WoOpxbykNfPS1o/tam4hpEKBfPnUqJ9/XkK6jJ060nFmHOeiQAe07oK8r37ne7YBJg1ldX79oazZNm8rp61XL8PWEjz4/PTmk6zU2tVGo6XAHPV46nlOHa/jSBkzaD0Ez4fpa1+egU/Pptut+0jGouv0NBU6+9Au9nuzSqg9tGqWPaU9LpSeifvnLXwa0rfre0GnI9KKvibpZ0OYco+YI9nHT94xOnaXjmLXqRbdVTy5oBY1Ot6cngTT7q2OdNTjW16LuO3296wk/PRZ6H//3f//X5GPpftLXtt5e97kGUro/9DgG89g0Rd8TSk/u6GeEPq42wdMmk1persdOM656zPSzT/tR6HaHgr6mZ86caSoX9PNVXy+67/VEg05n1txpB5tDT05qo0L9XNR9rplk/XzUz7aGTq40Z9/qlGB6ElRPEuk+1uOpPUz0M057HOjJHt3vmtHWYRp6jHXsvL7/9T2l6+lrXaurdFiUvT8ARJEId08HEEfsKVpWrVpV728ej8fq16+fueh0YTq9yZ133mmmWtEpwHTqKZ3qpu60WLaLL77Y3PfixYtrlumUOW3atDFTptSdvscfvc0999xjDRo0yDxu+/btzZRXt912m1VUVGSm6NHHP+GEE2pNt2VPNaNTFemUQ77P9+2337auvvpqc186DdJll11m7du3r8GpgHQaIZ2qKT093eyLKVOmWKtXr65ZR5+/TlvVEH9ThumUPnUfR5frdGuBHJ/WbFdDUxG9//77Zp/qcfGdwumbb74x07rpNGX6WHpMd+7cWW+ap0CnDFMvvPCCdcwxx5hjmZuba7344osNvoYeeeQRM42Trjdw4EDzGA1t++eff26dfvrpVkZGRq0pd3Q6u6lTp1qdOnUyx1j3l66rjxOpaXl0+/xd9JjaCgoKzNREPXv2tFJSUqycnBxr7Nix1kMPPdTka8am7zt9j+r+69Chg3mN6/FsjlNPPdU8xk9/+tMG/x7oMdLr+nz87RPf11Kgx62x90bd/WlPXXjKKaeY10l2drZ10kknmenvfH3yySfWRRddZKaO0uekj3nJJZdYy5Yta3Q/2Y+p96fT/+m0YPo4Oi3UV1991aJj05z92NBr+vbbbzdTmOnnn/3e1Odx/vnnW927dzfvdf2p0xJu2bKl0efXnMdu6LNA//34/e9/b17Hul90SrlNmzaZ/XzNNde06JgGMmWYeuaZZ8xrU/e1TtWmr4Mf/ehHZllTn8sNvT71ucyYMcNM++hwOOodI32P6mepPk+dqk6nLfv1r39tPjd9/23Vf790akBdb/To0dZnn30W0c8mAPU59H+RDvwBIBZp1l4zHqtWrappHAcAraHd3bVHhGY0NZuJpmm5tVYcaPd7zciHk5bUa8a5bk8DAPDFmG4AAADEhIaaEtpjmHUIUKjosBgdT133BImWt4fycQHEB8Z0AwAAICZovwitMtLxyzr1ms5H/vTTT5txzzreOlR0TL72ItAp+LTfg/YC0T4k2m9E+4QAQGMIugEAABATdAYL7WCuTRndbndNczUtLQ8lLV/XxmfaJE870GvncW0ip80KI9lQEUBsYEw3AAAAAAAhwphuAAAAAABChKAbAAAAAIAQYUx3E7xer+zcuVOysrLE4XCE6jgAAAAAACJAZ9EuLi42jRKdzuDnpQm6m6ABd8+ePYO+4wEAAAAA0WPHjh1yxBFHBP1+CbqboBlu+wBkZ2cH/QAAAAAAACJHZ0PQRKsd+wUbQXcT7JJyDbgJugEAAAAgPjlCNJyYRmoAAAAAAIQIQbcfeXl5kpubK8OHDw/VvgcAAAAAxDmHpa3a0Gh9v8vlkqKiIsrLAQAAACDOuEMc8zGmGwAAAIgSHo9HqqqqIr0ZQFxJSUmRpKSkiD0+QTcAAAAQYVp8unv3bjl48GCkNwWIS+3atZOcnJyQNUtrDEE3AAAAEGF2wN2lSxdp06ZNRAIDIF5PaB06dEj27Nljrnfr1i3s20DQDQAAAES4pNwOuDt27MixAIIsIyPD/NTAW99n4S41p3s5AAAAEEH2GG7NcAMIDfv9FYmeCQTdAAAAQBRoUUn5u++Gdn0gTjgiOGSDoBsAAACIRbfeKjJqlMg99wS2vq6n6+vtAIQNQTcAAAAQazRjfdtth3+/+eamA2/9u66n9HZkvJtt4cKFpgN2Ili+fLnJDEeim35lZaX0799f3n///YBvs3HjRjniiCOktLRUohFBNwAAABBrTjtN5O67/3e9scDbN+BWeju9fZACM3+XM888U+LJhAkTZMuWLZIITjnlFNm1a5e4XK6AbzNlyhS54IILWv3YCxYskL59+5pt8PXyyy/LiBEjTFO09u3b13qs3NxcOfnkk2Xu3LkSjQi6AQAAgFh0001NB94NBdx6uyAGZnUvf//7303Q/fOf/7xV2c5oos23NNjTzteJIDU1NSJzWluWJX/961/lqquuqrX8hRdekMsvv1ymTp0q69atk/fee09+8pOf1FpH//bggw9KdXW1RBuCbgAAACAeA+8QBty+gZnv5cCBA3LjjTfKb37zG7n44otr1v3ss89k/PjxkpmZKV27djUB1N69e2v+Pnr0aLnuuuvk+uuvl06dOsnZZ59tlr/99tty0kknSVpamplf+eabb240qNJATDPSdQNmvc9FixaZ60uXLpXTTjvNlIrrFG0/+MEPZNu2bTXrf/nllybYXLx4sZxxxhmSnp4uTz75ZL3ycr3N+eefb56PPq/hw4fLG2+8Ueux+/TpI3feeadceeWVkpWVJb169ZKHHnqo1jrffPONXHrppdKhQwdp27atnHjiifLhhx/W/P2ll16SE044wWzHkUceKbfddluj+8DOOOt6nTt3luzsbLnmmmtqncioqKiQX/ziF+Ykgt6v7o9Vq1b5LS+3n/t///tfOeaYY8zzPeecc8xJFnXrrbfK448/brbVrnTQ+9DHvO6668yx08fp3bu33HXXXX63fc2aNWa/fv/7369Zps915syZ8qc//ck8j6OOOspkti+55JJatz3rrLNk//795jUTbQi6AQAAgHgLvHW+7xAG3A3RAE2DUA2gb7/99lrLx4wZI8cff7ysXr3aBL0FBQX1giYN2jSQ1yymlhh/++23cu6555pgVrObmsV85JFH5I477vC7DZdddpn83//9n5SUlNQs00Dx0KFDcuGFF5rrOu531qxZZluWLVsmTqfT/M3r9da6Lw3wNdjbtGlTzUkAX/oYun16H5988okJQs877zz5+uuva6335z//2QTSuo5m/6+99lrZvHlzzX1oYK/P9d///rd5nr/+9a9rtmXFihVyxRVXmO3QcctaRaAB8B//+MdGj4Vuk263Br5PP/20vPjiiyYIt+ljaPZY9/nHH39sxlDrc9Sg1R/dh/fdd5888cQT8s4775jnqSdYlP7U42kH4nrRSoi//OUv5nk9++yz5jnryQs9EeGPPl8NqvUEhU23T/ePHid9DWkArydw9ESOL33tDB061NxH1LHQqKKiIkt3k/4EAAAAgq2srMzauHGj+dkqd99tWfr1vu5Fl4eYx+Oxxo8fbx1zzDGW2+2u9bfbb7/d+t73vldr2Y4dO8x37M2bN5vrZ5xxhnX88cfXWuc3v/mNdfTRR1ter7dmWV5enpWZmWkeryFVVVVWp06drEWLFtUsu/TSS60JEyb43fbCwkKzLevXrzfXt2/fbq7Pmzev1nqPPfaY5XK5Gt0PgwYNsubPn19zvXfv3takSZNqrutz6dKli/Xggw+a63//+9+trKwsa9++fQ3e39ixY60777yz1rInnnjC6tatm99tmDx5stWhQwertLS0Zpk+nr3fSkpKrJSUFOvJJ5+s+XtlZaXVvXt369577zXX33rrLbMPDhw4UPPc9Xp+fn6tY9G1a9daj3v++efX2pYZM2ZYY8aMqXUMGzNz5kyzvq+nn37aPHavXr2s559/3lq9erU5ph07dqy33y688EJrypQpzX6fhTrmS4hM93/+8x85+uijZcCAAfKPf/wj0psDAAAABJ9msjt0qL1Mr4c4w620nHzlypWmvNg3S6k0e/vWW2+ZkmT7MnDgQPM337LuYcOG1bqdZmpHjhxZa1zxqaeearLDWpKtmVbf+9Qy7uTkZJNx1YyqndXWbdIMuG3r1q2mnFtLtbX02s681s1Qa3a6MbodmuHVcmstvdZt0G2uez9Dhgyp+V2fi5bh79mzx1xfu3atyd5qaXlDdN/94Q9/qPU8p02bZjLJmnn257jjjpM2bdrUXNf9qNu7Y8cOs8+15F73pS0lJcWU8ev2+6P3169fv5rrmnG2n0djpe5r1641sZiWs7/22muNrl9WVmbK0H3ZWf/f/va38qMf/ci8Th577DGzL5977rla6+q4+8b2S6QkS5zTMQBaPqJvdO2+pwdJy0d0/AYAAAAQN3QMd93yYL2uy0MYeD/zzDOm7Fi7S2uSqy4N9rTs+p4Guqtr4GbT8czN0b17dxPQ2ezAVQNsLdnWgPD11183gZiWPdt0W3Rs8cMPP2zuQ4O6Y489tl7ztqa2RwNuvX997lqerY/z4x//uN79aEDrS4NFO5DU2zRG952WhV900UX1/lY3OA21hp6HNj5rjI5F3759u7z66qtmvLueEBk3bpw8//zzDa6vY+/Xr1/f4GtEx3HbdIy/njSpe4JDy+N9TwxEi7gPuj/66CMZNGiQ9OjRw1zX+n89w6Jnt+LF/tJKKXCXS2qyUyqrvdI1O106tE2N9GYBAAAgXOo2TdMA1A7A7eUhCLw16NVO03fffXeD457twEvHD2tGWTPRgdIMst5OAzs7263jvTWTrnMy6xhfDXbr0rHEPXv2NI3QNNjThm52wLhv3z4ztlgD7lGjRpll77ZwznLdFs3k2mPFNUDWJmzNoVlwrcTVYLGhbLfuO93ehp5nYzRDrlljO6j/4IMPTJZc94sGtvbYeT35oDTzrY3UtJFdS+l9ejyeesuzs7NNczu96EkJPQHi7/lq1l/H7vsec02aapCt+0Ebvtnbq/va3n6bjvPWx4g2UV9eroP09WyUnoXSHb9kyZJ66+Tl5Zk3sZ7t0bnbNNC27dy5sybgVvq7DsSPFyu2FspVj6+SqY+tkvPmr5ArHvlQLlnwvjz0zjYTjAMAACDONdSlfN++wOfxbiHtPq5dsrVx2qRJk2T37t21LoWFhWa96dOnmyBLk14a2Gl5szY30ymeGgrSbNp0TMuhZ8yYIZ9//rkpE7/llltMFasG3I3RLubajE0z0b6l5Tq/s1a8agfx/Px8efPNN839tYRm9bVBmZ540CBXH7NuM7am6D7RcnPdjxoEf/HFF+ZEg5bqqzlz5piu65rt3rBhgyn/1sqC3/3ud43er2bb9WSINl975ZVXzH7TLuK63zSDr83cfvWrX5mmdrqOlqxrWXbdqbqaQ+OxTz/91ATH+trQwFjnzX766afN8dM5zrUcXJ+vbxd4Xzq3u5680Odqs7uv63PQ5Knev26/8u2Qr0G4xnmaSY82UR906zgMHZOggXVD9AyWvlH0IGhnO11Xz7I1Nb4gHmhQfetLG+STrw/Kbne5HKr0SmFJpeQXlsqdr3wuF+S9Z4JyAAAAxKnGpgULZB7vVtBy8q+++soEdVoCXPeiXceVJs80oNQA+3vf+54MHjzYZFQ18GoseNZkmd63JtT0O74GXhoUNhVwKg20NZjU+/Adu6yPp0GrTk2lJeW//OUvzVRULaEBpQbxmlnXJKHGIJqZbm52WANJnbpLO6HrvtGqgaSkJPN3vU/tT6Xr6P48+eST5f7776+X4a1r7Nix5qTA6aefbjLMP/zhD820XjZ9DB0frVO36TbrCQg9EaLPp6U0cNex2zoWXqcqs6sS7r33XrNMt18DYz2m/o67nhDRygF7TL5Nj9HEiRPN9ur96OtOT5j4bq8G9/r6amrfRIJDu6lJjNBM97/+9S9zJsimmW3d8TqJutKzS1o2oWfEtM3/+++/bw6S3k7pG1ybBNSdTN13zjq92Nxut7m/oqIic5Ylmmg2++5XPxdvI0ewa3aavDrzdMrNAQAAolR5ebkZ99q3b9/mjdMNdB7uEM/XjeiiJe86TVtDFcKx4NNPPzVzbmtFhJbEB0Iz+3qS4amnnqp1kiXQ95nGfNr/K1QxX9RnupvauXqWyreEQM+a6HW7JEMDbK3t11IDLVXQcR3+xpsonaxdd7h90YA7WrPcz6/Z0WjArQrcFfLEyuaNLQEAAECU03HIgQbSDWW8WziOGQi1IUOGmKZ7GiAHShuqaQd9fwF3pMV00K1jBbRMpWvXrrWW63UdR6K0WYNOSK/jA3Sy9BtuuKHRzuWzZ882Zzjsi44jiUbaOM0T4JCRJWt3Mr4bAAAgnmhDqVtuCTxz7Rt46+2+a0gFRGu2fvDgwQGvr43mfvazn0m0ivvu5UrHMOglENoZTy/RTjuUd2ybJkWHKmVvaZXf9RzfXfYUl1NiDgAAEE90jK5WfAYaQGvgrZlAAu64tnDhwkhvAuIp063t7rXJQEFBQa3lel274rWGNm7TueDsBhDRRqcEmzG2vxzZJUuy0g83WqhLg+2OmanSKStNumSFdx4/AAAAhEFzA2gCbiDsYjrTrd3+dN62ZcuW1TRX00Zqel1b4reGTm2gF3tQfTQaNaCzDOruMlns3QfLJb+wRPp3zpR13xw0JeUadGvAPWNMf7LcAAAAABABUR90a/MzbWFv0wH1OheeTqbeq1cvM13Y5MmTTRt6bZo2b948M82YzvuXCDTjrZeBOdkyemAXs0x/Xj6yjwnGNcOtfwcAAAAAhF/UB92rV682TdBs9uT1GmjreAWdd66wsNBMHK/N07RZmk7yXre5WiKKncngAAAAACA+RX3QPXr0aGlqKnEtJW9tOXlDY7r1ot3RY82KrYUyf1m+FJdXSVZ6ihn7raXoAAAAAIDwiulGaqGk47k3btwoq1atklii83drwH2wrNKUlevP+W/mM2UYAABAAiiv8shVC1eZi/4OIPIIuuOMzt9dVFYlhe4K2by7WDq2TZXisiozvhsAAACIxsrW66+/vuZ6nz59TJ+mxjgcDlmyZElIt0uHsrZr1y6kj4HEQNAdZ3T+bldGinTOTpOjc7JkX2mlZGWkMGUYAAAAguq8886Tc845p8G/rVixwgTGn376abPvVytNr776aok07R21ZcuWVp1AABRBd4zO093U/N3t2qSaknL9yZRhAAAACLarrrpKXn/9dfnmm2/q/e2xxx4zswsNGTKk2ffbuXNnadOmjURaRkaGdOlyeHYgoDUIuuNsTLfSpmkLJg2T+ycONT9pogYAAJA4Kqu9UlxeLQdKK0P6OD/4wQ9MgKxl2HWn/H3uuedMUL5v3z659NJLpUePHiaQHjx4sDz99NON3m/d8vKtW7fK6aefLunp6SYppoF+IBlnu9myy+WSTp06ye9///taDZoPHDggV1xxhbRv395s2/jx481j+Ssvv/XWW81MSU888YTZRr3fiRMnSnFxsfn7lClT5O2335YHHnjAZPn18uWXX5rHueyyy8y+0kB+wIAB5qQEEgdBd5yy5+5mjm4AAIDE8V7+Xln/bZFs3FUkM57+xMxqEyrJyckmaNXg1DeY1YBbZwDSYLu8vFyGDRsmL7/8snz22WembPzyyy+Xjz76KKDH8Hq9ctFFF0lqaqp8+OGHsmDBArnpppsCuu3jjz9utlEfSwPhuXPnyj/+8Y+av2uQrNMT//vf/5aVK1ea53DuuedKVVWV3/vctm2bGUv+n//8x1w0yL777rvN3/QxRo4cKdOmTZNdu3aZS8+ePU2wr8m8V199VTZt2iQPPvigOQmAxBH1U4YBAAAAaJoOLXxw+Tap8nglNckpB8uqzCw2g7q7QpaIufLKK+VPf/qTCT41u6w0i/ujH/3IZIL1cuONN9asP2PGDPnvf/8rzz77rJx00klN3v8bb7whn3/+ublN9+7dzbI777zTZKWbogHv/fffbzLORx99tKxfv95c16BYM9oabL/33ntyyimnmPWffPJJcxsNqi+++GK/JwH0JENWVpa5ricQli1bJn/84x/Nc9WTA5o1z8nJqbnN119/Lccff7wpt1eaJUdiIdMNAAAAxMksNlpWrgF3ktMhnTNDP4vNwIEDTdD66KOPmuv5+fmmiZqWlivNeN9+++2mrLxDhw6SmZlpAmgNRAOhmWENhO2AW2k2ORAnn3yyCbh9b6fBtm6T3q9mwUeMGFHz944dO5rgXP/mjwbMdsCtunXrJnv27Gl0O6699lp55plnTGn6r3/9a3n//fcD2n7ED4LuOGukVvds56ZdbuboBgAASJBZbLLSk6XS4xWP15LCkvDMYqMB9gsvvGDGNmuWu1+/fnLGGWeYv2kWXMuutST8rbfekrVr18rZZ58tlZWhHW8eKikpKbWua1Cv2e/GaFb+q6++kl/+8peyc+dOGTt2bK3sP+IfQXccNlJTOn7nmifWyKzFa83PUI7nAQAAQORpCfm1o/tJSpJTqrxeadcmJSyz2FxyySXidDrlqaeekkWLFpmSczvDrOXb559/vkyaNEmOO+44OfLII5s1DdcxxxwjO3bsMOOjbR988EFAt9Ux4L70dtrELCkpydxvdXV1rXW06dvmzZtN4q2ltLxcM+l1aRO1yZMnyz//+U/TJO6hhx5q8WMg9hB0xyHNcM9fli8HyyrNh6z+1PE8uhwAAADx69T+nWRwD5cM6uaS+ROPD8ssNloyrnNaz5492wTH2qDMpkGudhvXkmot2/7Zz34mBQUFAd/3uHHj5KijjjIB67p160zp+m9/+9uAbqsl7LNmzTKBtHZMnz9/vsycObNmu/RkgI7vfvfdd81964kB7bKuy1tKy881kNeu5Xv37jVZ8Dlz5shLL71kSu83bNhgGrBp0I/EQdAdp+N5isqqpNBdIZt3F0vHtqEfzwMAAIDokJrslMz0ZGkf4gx33RJznRpLS8d9x1//7ne/kxNOOMEs10Zr2mDsggsuCPh+NYP+r3/9S8rKykzjtZ/+9KemaVkgtLO6fTutYtWAW7un27QUXjur69RnOt5bu5e/8sor9UrIm0PLxjWTrtlyzW5r4K/Zbz0hoXOW69Rn+ncd443E4bB8+/ujHrfbbToRFhUVSXZ2dkzsIc1oa0m5Zrg7Z6ZJYUmFtGuTaubsZgoxAACA6KLTam3fvl369u1r5qJu1X1VeWT6kx+b3/MuO0HSU5IkEWmAr43LfOf7RmIrb+R9FuqYj0x3HDZS08B6xtj+JtDWAFx/hmM8DwAAAACgNubp9kNLUPRin/WINTp+R+dk1JJy7VhJwA0AABD/NLP9yJTYSxoB8YygO45poE2wDQAAgESzfPnySG8CUIPycgAAAAAAQoSgGwAAAACAECHoBgAAAKKAzukMIP7eX4zpBgAAACJI53HW+ah37txp5nbW6w6Hg2MCBIHOkF1ZWSmFhYXmfabvr3Aj6G5kyjC9eDye8B4RAAAAJBQNBHTu4F27dpnAG0DwtWnTRnr16mXeb+HmsDT0h1+hnigdAAAAUPq1vLq6mqQPEGRJSUmSnJzst4Ik1DEfme44tr+0Ugrc5dI1m3m6AQAAop0GBCkpKeYCIH4QdMepFVsLZf6yfCkur5Ks9BSZMba/jBrQOdKbBQAAAAAJhe7lcZrhfuCNrbJhZ5HsLiqX/YcqZf6b+WY5AAAAACB8CLrjkJaUF5dXS2qSU5KcDumcmSrFZVWyp7g80psGAAAAAAmF8vI4pGO4XRkpYoklnTPTpLCkQtq1SZUuWemR3jQAAAAASChkuuNQh7apZgy3BtpaUq4/Z4zpb5YDAAAAAMKHTHec0qZpg7q7TEm5ZrgJuAEAAAAg/Ai6/cjLyzMXj8cjsUoDbYJtAAAAAIgch2VZVgQfP+qFeqJ0AAAAAED8xnyM6QYAAAAAIEQIugEAAAAACBGCbgAAAAAAQoSgGwAAAACAECHoBgAAAAAgRAi6AQAAAAAIEYLuOLe/tFI27XKbnwAAAACA8EoO8+MhjFZsLZT5y/KluLxKstJTZMbY/jJqQGeOAQAAAACECZnuOKWZ7Qfe2CobdhbJ7qJy2X+oUua/mU/GGwAAAADCiEx3nCpwl0tpRbUM7dlO0lOSpLzKYwLuPcXl0qFtaqQ3DwAAAAASApluP/Ly8iQ3N1eGDx8usahrdropKS8sqTABt/7MykiRLlnpkd40AAAAAEgYDsuyrEhvRDRzu93icrmkqKhIsrOzJebGdL+ZL8VlVSbgnjGGMd0AAAAAEM6Yj/LyOKZN0wZ1d5mScs1wU1YOAAAAAOFF0B3nNNAm2AYAAACAyGBMd5xjnm4AAAAAiBwy3XGMeboBAAAAILLIdMcp5ukGAAAAgMgj6I7jebqLy6slNckpSU6HdM5MNV3MtakaAAAAACA8KC+PUzpPtysjRSyxpHNmmpmnu12bVObpBgAAAIAwItMdp7Rj+Yyx/U2graXm+lPn6aaTOQAAAACED5nuOMY83QAAAAAQWQTdcY55ugEAAAAgcigvTwDM1Q0AAAAAkUGmO84xVzcAAAAARA6Z7jjGXN0AAAAAEFkJEXRfeOGF0r59e/nxj38siYS5ugEAAAAgshIi6J45c6YsWrRIEnWu7s7ZaXJ0TpbsK62UrIwU5uoGAAAAgDBJiKB79OjRkpWVJYmGuboBAAAAIMGD7nfeeUfOO+886d69uzgcDlmyZEm9dfLy8qRPnz6Snp4uI0aMkI8++igi2xqrc3UvmDRMbj1/kMw66ygZ1N0V6U0CAAAAgIQR8e7lpaWlctxxx8mVV14pF110Ub2/L168WGbNmiULFiwwAfe8efPk7LPPls2bN0uXLl3MOkOHDpXq6up6t33ttddMMJ/oNuwskvnL8qW4vEqy0lNkxtj+JhgHAAAAAMR50D1+/Hhz8Wfu3Lkybdo0mTp1qrmuwffLL78sjz76qNx8881m2dq1a4O2PRUVFeZic7vdEg8dzDftcktqklOqvJbMfzPfZLy1/BwAAAAAEMfl5Y2prKyUNWvWyLhx42qWOZ1Oc33lypUhecy77rpLXC5XzaVnz54Sy+hgDgAAAACRE9VB9969e8Xj8UjXrl1rLdfru3fvDvh+NEi/+OKL5ZVXXpEjjjii0YB99uzZUlRUVHPZsWOHxDI6mAMAAABAApeXh8Mbb7wR8LppaWnmEm8dzLWkXEvN27VJlRlj+lNaDgAAAACJHnR36tRJkpKSpKCgoNZyvZ6TkxOx7Yo12jRNx3DvKS43c3QzlhsAAAAAwiOqy8tTU1Nl2LBhsmzZspplXq/XXB85cmRIH1unKcvNzZXhw4dLPNBAe2BONgE3AAAAACRSprukpETy8/Nrrm/fvt10I+/QoYP06tXLTBc2efJkOfHEE+Wkk04yU4bpNGN2N/NQmT59urlo93JtqAYAAAAAQMwF3atXr5Yzzzyz5roG2UoD7YULF8qECROksLBQ5syZY5qn6ZzcS5curddcDQAAAACAaOOwLMuK9EZEIy0v14t2T9+yZYvpZJ6dnR3pzQIAAAAABJFd3RyqmI+gO8IHAAAAAAAQvzFfVDdSQ/DodGGbdrnNTwAAAABAgozpRuit2Foo85flS3F5lWSlp5h5u3UaMQAAAABAaJHpjvMpwzSz/cAbW2XDziLZXVQu+w9Vyvw388l4AwAAAEAYEHT7odOFbdy4UVatWiWxrMBdLsXl1ZKa5BSHQ6RNilMOlFbInuLySG8aAAAAAMQ9gu441zU7XVwZKdImLUmqvZZ8XlAiu9wV8kVhaaQ3DQAAAADiHkF3nOvQNlWmnNpbisqqpbiiWjwer7jSk2Xh+19SYg4AAAAAIUbQnQD6dsqUnOw0yUpLNlnvXh3aSHFZFSXmAAAAABBidC9vpJGaXjwej8RDiXmHtmnidDqkc2aaFJZUSLs2qdIlKz3SmwYAAAAAcc1hWZYV6Y1I5InSwzpt2Jv5JsOdlZEiM8YwbRgAAAAAuEMc85HpThA6L/eg7i5TUq4Zbh3rDQAAAAAILcZ0JxjqGgAAAAAgfMh0JwhTXr4sX4rLqyQrPUVmjKW8HAAAAABCjUy3H9pELTc3V4YPHy6xbn9ppTzwxlbZsLNIdheVy/5DlWZ8ty4HAAAAAIQOQbcf06dPl40bN8qqVask1hW4y6W4vFpSk5ySZDqYpzJlGAAAAACEAUF3AtApw7LSk6XS4xWP15LCkkrTwZwpwwAAAAAgtAi6E4B2Kp85boAM6uGSTlmpkp7ilCmn9KaDOQAAAACEGEF3Ak0ZNuWUPpKWkiQV1V5Z+N5XprkaAAAAACB0CLoThDZNe/Td7bK9sFT2uitopgYAAAAAYUDQnSBopgYAAAAA4UfQnQBThimaqQEAAABA+Dksy7Ii8Lgxw+12i8vlkqKiIsnOzpZYpmO4dX7u4rIq0718xpj+Zqw3AAAAACQqd4hjvuSg3yOilgbYg7q7ZE9xuZkuTLuaAwAAAABCh6A7wdiBto7x9r0OAAAAAAg+gu4EY0rMl+VLcXmVZKWnyIyxlJgDAAAAQKjQSC3Bpg174I2tsmFnkewuKmfaMAAAAAAIMYLuBMK0YQAAAAAQXgTdCYRpwwAAAAAgvAi6E4g2TZs5boAM6uGSHNfh7uU6bRjN1AAAAAAgNGik5kdeXp65eDweibdpw7q3y5DNu4vl6Jws6dc5M9KbBAAAAABxy2FZlhXpjUjkidLDbdmmArnh2XVS7fXKwJxsk/nWQBwAAAAAEpE7xDEf5eUJ1r38weXbpMrjlRSnUw6WVcn8N/PNcgAAAABA8BF0JxC6lwMAAABAeBF0JxC6lwMAAABAeBF0J2j38k5ZqZKe4pQpp/SmezkAAAAAhAhBd4LRpmlTTukjaSlJUlHtlYXvfSUrthZGerMAAAAAIC4RdCcYbZr26LvbZXthqex1V8j+Q5U0UwMAAACAECHoTjA0UwMAAACA8CHoTjA0UwMAAACA8CHoTuBmajmudHN9xpj+NFMDAAAAgBBIDsWdIvqbqQ3q7pI9xeXSJetw4A0AAAAACD6Cbj/y8vLMxePxSDzSQJtgGwAAAABCy2FZlhXix4hpbrdbXC6XFBUVSXZ2tsRTF3NtqqZjvAm+AQAAACQqd4hjPjLdCWjZpgK54dl1Uu31ysCcbDPGW0vOAQAAAADBRSO1BKMZ7geXb5Mqj1dSnE45WFbFPN0AAAAAECIE3QmGeboBAAAAIHwIuhMM83QDAAAAQPgQdCcY5ukGAAAAgPChkVoCYp5uAAAAAAgPgu4ExTzdAAAAABB6lJcDAAAAABAiBN0AAAAAAIQIQXeCz9m9aZfb/AQAAAAABB9juhPUsk0FcsOz66TK45HeHdvKdWP6y7mDu0d6swAAAAAgrhB0JyDNbD+4fJuUVXmkotorm3YVy6+fXy8iDjl3cLdIbx4AAAAAxA3KyxNQgbtcDhyqlGqv11x3OkQqqz3y4PJ8Ss0BAAAAIIjiPujesWOHjB49WnJzc2XIkCHy3HPPSaLrmp0uaclO+S7mFktE0lKSpLzaK3uKyyO9eQAAAAAQN+I+6E5OTpZ58+bJxo0b5bXXXpPrr79eSktLJdHn6L769CPF+d3Rd4hIdnqytG+TKl2y0iO9eQAAAAAQN+J+THe3bt3MReXk5EinTp1k//790rZtW0lkFxx/hKQmJ8n8ZVuktNIjHTPTZMaY/iYgBwAAAADESab7nXfekfPOO0+6d+8uDodDlixZUm+dvLw86dOnj6Snp8uIESPko48+atFjrVmzRjwej/Ts2TMIWx77tMT824PlUlhcKdZ3peYAAAAAgDgKurXU+7jjjjOBdUMWL14ss2bNkltuuUU+/vhjs+7ZZ58te/bsqVln6NChcuyxx9a77Ny5s2YdzW5fccUV8tBDD4XlecVKB3OP15L0ZKeUVFbL/DdppAYAAAAAcVVePn78eHPxZ+7cuTJt2jSZOnWqub5gwQJ5+eWX5dFHH5Wbb77ZLFu7dm2jj1FRUSEXXHCBWf+UU05pcl292Nxut8RrB/Pi8mpJTXJKktMhnTNT5eChKtNIjRJzAAAAAIiTTHdjKisrTUn4uHHjapY5nU5zfeXKlQHdh2VZMmXKFBkzZoxcfvnlTa5/1113icvlqrnEaym6djDPSk+WSo/XZLsLSyolKyOFRmoAAAAAkChB9969e80Y7K5du9Zartd3794d0H289957pkRdx4prGbpe1q9f73f92bNnS1FRUc1FpxyLR5rNvnZ0P0lJckqV1yvt2qTQSA0AAAAA4q28PNROO+008doTUgcgLS3NXBLB2GO6yps3jjYl5TpVGGXlAAAAAJBAQbdO75WUlCQFBQW1lut1nf4LraeBNsE2AAAAACRgeXlqaqoMGzZMli1bVrNMs9Z6feTIkSF9bO2mnpubK8OHD5dE6GT+wRf7zEV/BwAAAADESaa7pKRE8vPza65v377ddCPv0KGD9OrVy0wXNnnyZDnxxBPlpJNOknnz5plpxuxu5qEyffp0c9Hu5dpQLV4t21QgM576WA5VecXpEOnXOVPmnJcrowZ0jvSmAQAAAEDMi3jQvXr1ajnzzDNrrmuQrTTQXrhwoUyYMEEKCwtlzpw5pnmaNkJbunRpveZqaD7Nav9l2VYprz485t2yRHYcOCT3v75FBnV3UXYOAAAAALEedI8ePdpM69WY6667zlzCScvL9aLd0+OVztWtc3PbdL5uh4gcYL5uAAAAAIj/Md2RpKXlGzdulFWrVkm80rm6daowm87Xrac/2rdhvm4AAAAACAaC7gSmXct/MXaApKccfhk4HCK9OrSRX551FKXlAAAAABAP5eWI/Fzd7940VrbuKRZNcw/omkXADQAAAABBQtCdwGO6fTPeI/p2jPRmAAAAAEDccVhNdTFLcPaUYUVFRZKdnR3pzQEAAAAAxFDMx5huAAAAAABChKAbAAAAAIAQIeiGUV7lkcv/8aFcsmCl7DpYxl4BAAAAgCAg6PZDm6jl5ubK8OHDJRG8l79X1n9bJBt3FcmMpz+RFVsLI71JAAAAABDzaKTWhERopLa/tFKuXrRaNu1yS2qSUzpmpZmO5gsmDWP6MAAAAABxzU0jNYRagbtcisurTcCd5HRI58xUKS6rkj3F5ex8AAAAAGgFysshXbPTJSs9WSo9XvF4LSksqZSsjBTpkpXO3gEAAACAViDohikhv3Z0P0lJckqV1yvt2qTIjDH9KS0HAAAAgFZKbu0dxHMjNb14PB5JBKf27ySDe7ikstor8yYOlW7tMiK9SQAAAAAQ88h0+zF9+nTZuHGjrFq1ShJFarJTMtOTpX3b1EhvCgAAAADEBYJu1NAstzZUO1BayV4BAAAAgCAg6IbBPN0AAAAAEHwE3TDzdD+4fJtUebyS4nTKwbIqmf9mvlkOAAAAAGg5GqmhZp5u7V5eUlktVrFIksNh5unWzuYAAAAAgJYh041a83R7vZaUV3kkPdXJPN0AAAAA0EoE3X7odGG5ubkyfPhwSZR5ukUs8Vhigu9DFR7ZsLMo0psGAAAAADGNoNuPRJsyLLdbtiQ5neJ0HH5RfHuwTB5YtpVx3QAAAADQCgTdMLR5WndXhmSnp4g4Do/p1qnDdFw3AAAAAKBlCLpRa1x3WZVHqr0iJRXVUuCukC8KS9lDAAAAANBCBN2oGdc96eReUu31Hn5hOERcGcmy8P0vKTEHAAAAgBYi6EaNvp0yJS05SZIcItkZKdKrQxspLquixBwAAAAAWoigGzW6ZKVJitMhlvYxt0QKSyolKyOFqcMAAAAAoIUIulGjW7sMmTthqGmmpmXm7dqkyIwx/U3pOQAAAACg+ZJbcBvEsVP7d5LBPVxSWe2VeROHmkAcAAAAANAyZLr9yMvLk9zcXBk+fLgkmtRkp2SmJ0t7MtwAAAAA0CoOy9LRu/DH7XaLy+WSoqIiyc7OZkcBAAAAQBxxhzjmI9MNAAAAAECIEHQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBN+opr/LIVQtXmYv+DgAAAABoGYJuAAAAAABChKAbAAAAAIAQIej2Iy8vT3Jzc2X48OGSiCqrvVJcXi0HSisjvSkAAAAAELMclmVZkd6IaOZ2u8XlcklRUZFkZ2dLIli6fpdc9/Qnoi+N43q2k1+edZSMGtA50psFAAAAADEX87Uo0/3FF18EfUMQHfaXVspDK74Qr2WJwyFSVFYl89/MN8sBAAAAAGEIuvv37y9nnnmm/POf/5Ty8vKW3AWiVIG7XEoqqsXpEHE4HNIpM1WKy6pkTzHHGQAAAADCEnR//PHHMmTIEJk1a5bk5OTIz372M/noo49acleIMl2z0yUzLVm8lpjy8r0llZKVkSJdstIjvWkAAAAAkBhB99ChQ+WBBx6QnTt3yqOPPiq7du2S0047TY499liZO3euFBYWBn9LERYd2qbK1aOONJluDbw1AJ8xpr9ZDgAAAAAIY/fy5ORkueiii+S5556Te+65R/Lz8+XGG2+Unj17yhVXXGGCccQm017vu2w3AAAAACACQffq1avl5z//uXTr1s1kuDXg3rZtm7z++usmC37++ee35u4RwUZqGmo7nSKllR4aqQEAAABACyW35EYaYD/22GOyefNmOffcc2XRokXmp1OjNBHp27evLFy4UPr06dPS7UI0NFITh+wrqTDLduw/RIk5AAAAAIQj6H7wwQflyiuvlClTppgsd0O6dOkijzzySEvuHlHSSM0pllR6vJKalCRdstI4LgAAAAAQjqBby8d79epVk9m26fjfHTt2mL+lpqbK5MmTW3L3iCBtmDb9zP5yw7PrpMrjleQkh/Tp1Fba00gNAAAAAMITdPfr1880SdNstq/9+/eb0nKPx9OSu0WUOLV/JxncwyVlVR5xfFdmfqC0Urq1y4j0pgEAAABA/DdS89fRuqSkRNLTmc85HqQmO6Wy2itbCopl464imfH0J7JiK1PBAQAAAEDIMt2zZs0yPx0Oh8yZM0fatGlT8zfNbn/44YdmDm/EPg24v9pXKlVeS9KSnHKwrMp0MR/U3UVDNQAAAAAIRdD9ySef1GS6169fb8Zt2/T34447zkwbhthXUe2Vaq8lqUkOKamsFqtYJMnhkD3F5QTdAAAAABCKoPutt94yP6dOnSoPPPCAZGdnS7Q7ePCgjBs3Tqqrq81l5syZMm3atEhvVtRLS3ZKstNhupfrSRb9mZWRLF2yGD4AAAAAAIFyWP4GaMcJLXuvqKgwpfClpaVy7LHHyurVq6Vjx44B3d7tdovL5ZKioqKYOMkQTMs2FcisZ9dKSXm1ZKYnyz0XDZHn1nxj/pZ32QmSnpIU6U0EAAAAgFYJdcwXcKb7oosukoULF5qN0N8b8+KLL0q0SEpKqhl7rsG3nmOI8/MMQe1irmO4N+4sktzuLhnZr2NN0A0AAAAACGL3co38tYGa0sBbr/u7NMc777wj5513nnTv3t3c/5IlS+qtk5eXJ3369DGd0UeMGCEfffRRs0vMdbz5EUccIb/61a+kU6dOzbp9Iiqv8sg1T6yRzbvc5rhoY7UDhyojvVkAAAAAEFMCznQ/9thjNb9rxjtYtORbA+Irr7yywQz64sWLTdf0BQsWmIB73rx5cvbZZ8vmzZtr5gnXjuk6Xruu1157zQTz7dq1k3Xr1klBQYF5jB//+MfStWvXoD2HeKZjucuqvLJpt1tmPbtOvJZIx7b/a6AHAAAAAAhSIzXbHXfcIZdddpn07dtXWmv8+PHm4s/cuXNN4zNt3qY0+H755Zfl0UcflZtvvtksW7t2bUCPpYG2BvgrVqwwgXdDtARdL771/YkdcHtMoJ3idEpRWZUUFleIUzLlQGmldGuXEelNBAAAAID4KC/39dxzz0n//v3llFNOkb/97W+yd+/e0AR9lZWyZs0a033c5nQ6zfWVK1cGdB+a3S4uLja/68B4LWc/+uij/a5/11131SqV79mzpyQqLSnX4e9Oh0iS0yEpOn1YRbVs2l0sM57+RFZsLYz0JgIAAABA/AXdWqr96aefyujRo+W+++4zJdzf//735amnnpJDhw4FbeM0mNfu43VLwfX67t27A7qPr776SkaNGmUy3PpzxowZMnjwYL/rz5492wTn9mXHjh2SqFKTnaLD+DXTXeXxyJaCEvO7zt19sKxK5r+ZL/tLGecNAAAAAEENutWgQYPkzjvvlC+++MLM362Nzq6//nrJycmRaHLSSSeZ8nP7RMHPfvazRtdPS0szjeJ8L4kqIyVJcru5JCs9WaqqD3d8T3KIJCc5pXNmqhSXVcme4vJIbyYAAAAAxF/Q7att27aSkZEhqampUlVVJcGiXcZ1yi8tEfel16MtuI/X8nLNdh/VNUuOysmStmnJoqG3x2tJYUmlZGWkSJes9EhvJgAAAADEX9C9fft2+eMf/2gy3ieeeKJ88skncttttwVc9h0IDeKHDRsmy5Ytq1nm9XrN9ZEjR0oo6TRlubm5Mnz4cElE7+XvlfXfFsmmXW7ZUlBsxnb37tBGnA6HVHm90q5NiswY0186BKmTuZap62NRrg4AAABAEr17+cknnyyrVq2SIUOGmK7il156qfTo0aNFG1BSUiL5+fm1gnktB+/QoYP06tXLTBc2efJkE9hrqbhOGabTjNndzENl+vTp5qLdy5s793is08D3weXbpMrjlZQkp+li/tX+UjmmW7ZkpyfLkV0yZf7E41vcvVzvXwN5pVn0DTuLZP6yfDlwqELSkpPkipG9pVfHtjV/twN7vV2Bu1y6ZqcHLdgHAAAAgKgLuseOHWum7NJMcGutXr1azjzzzJrrGmQrDbR1PvAJEyZIYWGhzJkzx2TRdU7upUuXMs92CGlgW1xeLalJTnE6HZIqThOAH6qoFq9lmeXtWxj0asfzO/6zSb7ef0gsy5JOWamSmpQkOw+WSbXXKyIOuemF9aZTepLTKd3bp8uEEw93kH/1s91SXumRrPQUmTG2v4wa0DnIzxwAAAAAgsthaeSDBsvL9aLd07ds2WI6mSdKUzXNKF+9aLUp9z6c6fZIeZXXdDLXQd2Z6cky95KhMvaYrs2+36mPfWTK1rULuq/v7tov/btqk+qUDplp0ikzTR6ZPJyMNwAAAIBWsaubQxXzBRx0awb69ttvN03T7Gy0P3PnzpV4EeoDEK2WbSqQG55dZzLcDqdIWYXHBMU6Z3dqcpLkds+Why4/sVlBrwbx1zyx5nCWuxXblpGirQgc8vsfHCM/GdG7FfcEAAAAING5QxzzBVxero3S7M7k+jvi26n9O8ngHi4pq/JIRbVHNu50m657DofDlJcXl1Wb6cKaE3TrWGxtwPb1/qbXbSzzrVn31GSHLFr5lYw4sqP065wZ+BMDAAAAgDAKOOjWubgb+h3xS6cL08vvfzBEzv/re1JcUS1OyzKN1bIykps9XZgG6D87o5/c9com+fZAmRwewX2Y/kxPSTKl7DqWW8eOV3ssE3hrWbvOD16tN6i57pTte0tl5jOfyE3nDGR8NwAAAID4mTLsyiuvlOLiw92nfWlXcf1bPEj0KcN8tW+TKr07tjXThaWlHC4tnzl2QMBZbns6sJc++VZ+8+J62V9aId3apUv3dumSluKU9BSnDOyWbZbpz6ennSz//OkI+c25A6V/57aSnpwkyc7D63VumyptUpNMBr6q2isHSitl7utbmGoMAAAAQPw0UktKSpJdu3ZJly5dai3fu3ev5OTkSHV1tcSLRB3TXV7lkelPfmx+n3vJcTLzmbXy6TcHmz1d2P/GhntMabpmrzNSkqRjVppkpSfLucfmmK7kZdqVPOPw3N++Xck1YN+6p9jUmu8rrZS/Lc+XrQUlJuDWjLdOMaYvYMZ3AwAAAIjpMd32xmiMrhfNdKen/6+8WLt8v/LKK/UCccQHLSlv7nRh2wpL5M+vbZHKao/JkldUHb4PzVR3zkyVg4eqZNRRneVHw3qa8eFarl43e67XR/TtWHP96Jwsmf7kGvly7yGzTXpyIOW78d0Dc7IlIzWJebwBAAAARI1mBd3t2rUz2Uq9HHXUUfX+rstvu+22YG4fosDKbftkw7dFUlJRLRt2Fsl7+XubnC5MM9zXP7PW3EZZPmO3NWjXrHW7Nqk1gXagperaNO3ykX3kjv9srLlDHd+9bU+JTHrkQ+mYmSrdsjOYxxsAAABA7AXd2kBNs9xjxoyRF154QTp06FDzt9TUVOndu7d07949FNuJCKms9spDK76QKq9lyrmrPJY8+PY2Ob5Xe7+Bsp3hLq+qbrAD+bcHyyS3W7YpJW9O93Pb+GO7yfNrvpHC4go5eKhSSio8Znl1pUd2HiiTFKdD5r+ZL4O6u5jHGwAAAEDsBN1nnHGG+bl9+3bp1auXyWzHK22kphctm09E2kn8kSnDTQM0zVhrWbnO2d3UdGErthbKPa9+LlsL3FL1XbdxX0d2biteryU3jR9Yq2y8OfRxZ511lNyz9HMTeJtugI7/TTOmTdcOlFY0e0ozAAAAAIiK7uVvvvmmPP/88/WWP/fcc/L4449LPJg+fbps3LhRVq1aJYlM59bWhmc6flqrHHRKr93uMvnjfzaZ8dS+tOnZA29slS0FxfUC7sNTgjml2mtJ5+x0GdAlq1Xbpc3WHph4vAzomim9OraRzLRkk4nXx/lyX6nsLCqXd7YU0tUcAAAAQOwF3XfddZd06tSp3nJtonbnnXcGY7sQJTRTfO3ofpKS5BDtc5+c5DTTh+n83XUVuMvlwKFK8TTQED85ySH6X8e2qS0uK29ofLfO0d0lO90E3V5LzEU7pGt38/tf3ypXPb7KZN8BAAAAIOrLy21ff/219O3bt95yHdOtf0N8ObV/JxnSo50Z333PjwfL75dskOLyajNHtu/UYZoVT0t2itcny+3URmdOh5lvWxugnXNst6CWfGvGW8duf/DFPvnTfw+Xm2sGXjPq1V6P7CupYHw3AAAAgNjKdGtG+9NPP623fN26ddKxY8vG6SK6aWY7Mz1Z1n9bJCu/2Cdrvtov05/6uCaLrKXlmumeckpfaZueLKnJDslOTzbdyXWar79eNkx+MqJ3SMZY632efGRH6ZyZbjLpqUlJZnC3TlPWJStNisuqzFzfOj5dtxMAAAAAojrTfemll8ovfvELycrKktNPP90se/vtt2XmzJkyceLEYG8jooRmuh9590sz17ZmsIvKqkwW+et9pXLXq5vNmO9jumXL1FN6y/LNhVJe7ZX2bQ6Xk2speChp4D1jbH+Z+/oW2VNcaQZ36xjyg2XVJtN+96ufS3mlR7LSU5hODAAAAEB0B9233367fPnllzJ27FhJTj58F16vV6644oq4GdOd6N3LG1JR7ZXSiipTHlHtFdnjLpeS8mr5/UsbzFjqJIfIrqIy+XD7AZk38XjT7dyehzsc7FLzpZ/tMlOKlVV6JD01yWzjlt3Fhzuwey3KzQEAAACEjcPS9GQLbdmyxZSUZ2RkyODBg82Y7njjdrvF5XJJUVGRZGdnSyLSMdLTn/zYZLqLK6pl3Y6DDc6/rdnvtmnJkuNKl/mXHi8DcyK3v7SMXKcM+3r/Ibnz5U1ysLTSNIHTbucHD1XJ/ROHRnT7AAAAACRGzNeiTLetT58+pqS4X79+NRlvxPe47glDjpC1Ow42+Hedrqu8stq8JlKSWtQuIGg0u75hZ5E8+NY2KXBXSEW1R9qkJslud4W0TUuK+PYBAAAASAwtijwOHTokV111lbRp00YGDRpU07F8xowZcvfddwd7GxFFSsurTXDdEI8lZn7ub/aXyU3PfxrRqbp85wzXsnc9YVBZbUlhcbkZiz77hfVMJQYAAAAgOoPu2bNnm7Ly5cuXS3p6es3ycePGyeLFi4O5fYgiWl7+73U7G3wRaWCbnuw0U4ZlpCTJwe+arEWqW7h2UtdpzXQcd3pKkhzdNVMcjsNzeJeUVcv+Q5UR3T4AAAAAiaFFNeFLliwxwfXJJ58sDo1kvqNZ723btgVz+xBljdS0I3mbFKeUakr7O/pbWpJT2rVJlYoqj+kW3jkz1Yyd1nHV4Wqk5kvnDHdlpIgllnTOTJNvDhwyY851O6Nh+wAAAAAkhhZlugsLC81c3XWVlpbWCsJjmXYuz83NleHDh0ui00zxI1OGy4LLh0l2eopoyjgj2VFTZq5Z7vZtUqSkotqMnfZ4LSksqZSsjBTTvTwS7CnE9ESAZrM7ZKZJr45tpIsrXY7s3FZ2Hiwznc0jtX0AAAAAEkOLgu4TTzxRXn755ZrrdqD9j3/8Q0aOHCnxYPr06bJx40ZZtWpVpDclamgge+3ofoebkDkc35WSO01GuW+ntibw7tcl03QvN0HvmP4RzSLrFGILJg0zncofmTxcfvf9XPNaXf+t28zlfaiy2jRbAwAAAICoKi/XubjHjx9vgtLq6mp54IEHzO/vv/++vP3228HfSkSNU/t3ksE9XFJaUS3VliXb9pSITjqnme1u7TLkrosGh31+7sboNtjboXN4ZyQniYhlysw9XmHObgAAAADRl+k+7bTTZO3atSbg1vm5X3vtNVNuvnLlShk2bFjwtxJRRTuBt2+bKj8/43DWu8rrlXZtUkxmu1/nTDP/dTQE3A01Vyur8kh6cpKZs1vHdReXHR7XDQAAAACh0OLJtXVu7ocffji4W4OYMnpgF3nzxtEmaI2WzHZTzdWy0pPlmwNeSRWnyc7rNjOuGwAAAEDEg2632x3wnWZnZ7d0exBjfMu3o51u58xxA0xJuWa4tdFbpMedAwAAAIhvAQfd7dq1a7IzuWVZZh2PxxOMbQNC0lxNx3Zrdl5L43XucdPdnMAbAAAAQCSD7rfeeisUjw+EnQbY2rV8/rJ8KS6vkqz0FDO9mAbkAAAAABCRoFs7lC9cuNCUji9atEgmTJggaWlpQd0YIBw0s/3AG1tl0y63pJpGcBZdzAEAAABEtnv5f/7zHyktLTW/T506VYqK4nt+47y8PMnNzZXhw4dHelMQgi7mxeXVJuBOcjqkXUayFLrLZeueYvY1AAAAgMhkugcOHCizZ8+WM88804zdfvbZZ/02TLviiisk1k2fPt1ctIGcy+WK9OYgRF3MHR6Rz3a6xSEOufvVz2XWWUdRZg4AAAAgaByWRtABeP/992XWrFmybds22b9/v2RlZTXYWE2X6d/jhR10a2afruwi5VUemf7kx2bf5F12gqSnJEksWrG1UOa+vkU+31UsFdUeaZOaJDmuDDPee8GkYTRWAwAAABKEO8QxX8CZ7lNOOUU++OAD87vT6ZQtW7ZIly5dgr5BQDho0zTtXn7zC5/KwdJKSU5ySufMVDl4qMp0NqebOQAAAICwBt2+tm/fLp070+k5EWlm+5Ep8THO/aiuWdIpM00KiyvE4bCksOTw1GFdstIjvWkAAAAAEq2Rmq/evXvLu+++K5MmTZKRI0fKt99+a5Y/8cQTZjkQCzTAnjlugAzq4ZIcV7q5PmNMf7LcAAAAACKb6X7hhRfk8ssvl8suu0w++eQTqaioMMu1Bv7OO++UV155JXhbCIS4zHxQd5cpKdcMN2XlAAAAACKe6b7jjjtkwYIF8vDDD0tKSkrN8lNPPVU+/vhwky0gVmigPTDncMMEnbtb5/EGAAAAgIhlujdv3iynn356veXa8e3gwYPB2C4grJZtKpAbnl0n1V6vCcC17Fyz4AAAAAAQ9kx3Tk6O5Ofn11uu47mPPPLIVm0QEG6a2X5w+Tap8nglxemUg2VVMv/NfDLeAAAAACITdE+bNk1mzpwpH374oZmXe+fOnfLkk0/KDTfcINdee23rtwoIowJ3uRSXV0tqklOSnA4zdVhx2eGpwwAAAAAg7OXlN998s3i9Xhk7dqwcOnTIlJqnpaXJr371K/npT3/aqg0Cwq1rdrq4MlLEEks66xRiJRXSrg1ThwEAAACIUKZbs9u//e1vZf/+/fLZZ5/JBx98IIWFhWZMd9++fSUe5OXlSW5urgwfHh9zUsM/M1XY2P4m0NZSc/3J1GEAAAAAgsFhWZYV6Mo6Nditt94qr7/+ek1m+4ILLpDHHntMfve730lSUpJMnz5dbrrpJokXbrfbnEzQ6dCysw93uEZ80oCbqcMAAACAxOIOcczXrPLyOXPmyN///ncZN26cvP/++3LxxRfL1KlTTab7z3/+s7mugTcQqxlv5ukGAAAAELGg+7nnnpNFixbJD3/4Q1NWPmTIEKmurpZ169aZknMAAAAAANDCMd3ffPONDBs2zPx+7LHHmhLzX/7ylwTciLsy80273EwZBgAAACC8mW6PxyOpqan/u3FysmRmZrZ+K4AosWJrocxfli/F5VWSlZ5iGqyNGtA50psFAAAAIBGCbu25NmXKFJPhVuXl5XLNNddI27Zta6334osvBncrgTBluB94Y6vJcuuc3eXVXrln6efSvV2G9OvMySUAAAAAIQ66J0+eXOv6pEmTWvCQQHQqcJdLcXm1Cbg9liV7Sypk50GvzHzmE7npnIFkvAEAAACENujWqcGAeNU1O12y0pNlx36PVHi84vWKOB0ihyqqZf6b+TKou4vu5gAAAABC10gNiGc6XdjMcQPkyC6Zkux0moC7bVqyCcYPlFbIB1/so7kaAAAAgNBluoF4p03TdAy3lpSXVXokJztdvtxXKkXl1XL/G1ukfUYqzdUAAAAABIxMN1CHNk3TMdwdM9NkT3G5FJVVS3W1V/a6K2T/oUpTaq5N1wAAAACgKWS6AT8Zbx3DrSXlc1/bLPtKKiXJ6ZDOmaly8FCVCca1HB0AAAAAGkPQDfihQfXJR3aUDm3TxGkC7jQpLKmQdm1SpUtWOvsNAAAAQJMoLweaCLxnjO1vAm0tKdefM8b0J8sNAAAAICAJk+k+dOiQHHPMMXLxxRfLfffdF+nNQQyxS821pFwz3JSVAwAAAAhUwmS6//jHP8rJJ58c6c1AjNJAe2BOtvl90y43jdQAAAAABCQhMt1bt26Vzz//XM477zz57LPPIr05iFErthbK/GX5UlxeJVnpKUwdBgAAACD6M93vvPOOCYa7d+8uDodDlixZUm+dvLw86dOnj6Snp8uIESPko48+atZj3HjjjXLXXXcFcauRaHQ89wNvbJUNO4tkd1E5U4cBAAAAiI2gu7S0VI477jgTWDdk8eLFMmvWLLnlllvk448/NuueffbZsmfPnpp1hg4dKscee2y9y86dO+Wll16So446ylyAlipwl0txebWkJjlrpg4rLjs8dRgAAAAARG15+fjx483Fn7lz58q0adNk6tSp5vqCBQvk5ZdflkcffVRuvvlms2zt2rV+b//BBx/IM888I88995yUlJRIVVWVZGdny5w5c0LwbBCvumaniysjRSyxmDoMAAAAQOxkuhtTWVkpa9askXHjxtUsczqd5vrKlSsDug8tK9+xY4d8+eWXpmu5BvCNBdwVFRXidrtrXQCmDgMAAAAQk5nuxuzdu1c8Ho907dq11nK9ro3RQkGD9Ntuuy0k943YxtRhAAAAAOIq6A62KVOmNLnO7NmzzRhym2a6e/bsGeItQyxlvJmnGwAAAEBcBN2dOnWSpKQkKSgoqLVcr+fk5ITkMdPS0swFaKqbuTZX07HeBOEAAAAAYnJMd2pqqgwbNkyWLVtWs8zr9ZrrI0eODOljazf13NxcGT58eEgfB7Fn2aYCGXPfcrl4wfty9aLVZv5uAAAAAIjKTLd2FM/Pz6+5vn37dtONvEOHDtKrVy9T6j158mQ58cQT5aSTTpJ58+aZacbsbuahMn36dHPR8nKXyxXSx0JsZbgfXL5NqjxeM33YwbIqmf9mvgzq7iLjDQAAACD6gu7Vq1fLmWeeWXPdHk+tgfbChQtlwoQJUlhYaDqO796928zJvXTp0nrN1YBIzdd98NDh+bopMwcAAAAQdUH36NGjxbKsRte57rrrzCWctLxcL9o9HbAxXzcAAACA5nBYTUW8Cc4uLy8qKpLs7OxIbw6igI7h1pLy4rIqycpIkRlj+pvpxAAAAADEHneIY76IZ7qBWMN83QAAAADions5EK10/PbAnMNnwTbtcpsGawAAAABQF5luPxjTjYDKzJflS3F5lWSlp8iMsZSZAwAAAKiNMd1NYEw3GqKZbZ2jW7Pc2sm8Y1aayX4vmDSMLuYAAABADHGHeEw35eVAkKYO08ZqOnUYAAAAANgIuoEWTh2WlZ4slR6veLyWFJZUmk7mXbLS2Z8AAAAAahB0Ay2gpeQzxw2QQT1ckuNKN9d16jD9CQAAAAA2Gqn5QSM1NIWpwwAAAAA0hUZqTaCRGgAAAADELzeN1AAAAAAAiE2M6QYAAAAAIEQIuoEgzdutc3brTwAAAACw0UgNaKVlmwrkhmfXSbXXKwNzsk1Xc22yBgAAAABkuhvpXp6bmyvDhw/nVQK/NLP94PJtUuXxSorTKQfLqmT+m/lkvAEAAAAYBN1+TJ8+XTZu3CirVq3ytwogBe5yKS6vltQkpyQ5HdI5M1WKy6pkT3E5ewcAAAAAQTfQGl2z0yUrPVkqPV7xeC0pLKmUrIwU6ZKVzo4FAAAAQNANtEaHtqlmDPegHi7JcaWb6zPG9Dc/AQAAAIBGakAradO0Qd1dpqRcM9yxFnDruHQtk9esfaxtOwAAABDtCLqBINBg1Q5YozWIbWi7VmwtlPnL8qW4vEqy0lNkxtj+5gSCvZ6KxucCAAAAxAqC7ka6l+vF4/GE94ggpkXr9GF1g+spp/aWDm3T5M//3SJb9xSbRnBVXkvueHmTtElNkvJKj3gtEXFY4hRHTUAeDc8FAAAAiCUOy7L0qzX8cLvd4nK5pKioSLKzs9lPaDSTfPWi1bJpl9sEsR2z0kx2eMGkYRHNEtfdroy0JHGXV0uHNilSWFxhuq6nJydJn05tZMPOYhGxzHoV1V7TIC47LVk6fdcw7qZzBspRXbPIegMAACBuuEMc8zFlGBDH04dpwP3Btn1y8FCl2S6HQ8w2llZUm7/pGTftun5kl7YmANeAO+279fRiOByS4nTI57uKZfaL6+WaJ9aYzDkAAACAplFeDgSJjnt2ZaSIJZZ0zkyTwpIKadcmNWLTh9ml7lUejzgcTrNteiJg465ic7ZNg/Ce7dPly32HpLC4Ujpkpkl6apJo7Uu7jBT59NsiSUt2St/ObWTTrmKprPbKgZLDmfH5b+absd+M8wYAAAAaR9ANBImZLmxsfxOQahZZA+5ITR+mj//g8m1S5fFKalLSdyXlVZKW4pTUFKe40tOkT8c25sTAwG5ZcvM5A2VA1yzZsLPIbL9m6Ht3bGvu62BplWjSW8d6Jyc5TeB+8NDhDD5BNwAAANA4gm4gDqcPq1vq3rtDG9lbUiGzvne0yWQvfP/LWicGRhzZscHtV9po7e5XPzcl6b4Z/JQkpxknTmdzAAAAwD+CbiCE04dFU6l7+7ZpMqJvR7NtJx/Z0e+Jgbrbr7eZddZRtTL4o4/uLLNfWF9rqjE6mwMAAAD1EXQDccaej3vKqX3qZbTtYLq5JwZ8M+Ca4b7p+U9ruqHrVGOM8QYAAAAaRtANxJGG5uM+snNmUErd7UBdg+26XdoZ4w0AAAA0jCnD/MjLy5Pc3FwZPny4v1WAqKIZ7Qfe2Gqaoe0uKpf9hypl4ftfBX1suV263jk7TY7OyZJ9pZWSlZESsS7tAAAAQDQj6PZj+vTpsnHjRlm1alV4jwgQ5fOE213atWQ90l3aAQAAgGhHeTkQJzQDnZWeLN8c8EqqOKWwpNIEwqHIQDfU5ZxO5gAAAEB9BN1AHDVPu/K0vqZ5mma4teQ7lBloe4z3sk0FcsOz66Ta65WBOdkyc9wAOpkDAAAA3yHoBkIcCId6HutQNk8L5Dk+uHybVHm8pqz9YFkVncwBAAAAHwTdQAiEK/trN0/znb5Lm6ctmDQsLGOsGxpHTidzAAAA4H9opAaEMPub4vxf9leXx2rzNH/oZA4AAAA0jqAbiOFA2G6eVunxisdrmeZp4Zy+y18nc6XZ91CcaAAAAABiCeXlQAx3Edf71dJ1zaSHo3laIJ3MdZ7wa55YUzPGXIPyUJTWAwAAALGAoBuI8UC4btAbifmy7U7mDY0x1/2g28c83gAAAEhEBN1AHATCdtAbaTRWAwAAAGoj6AbiPBAOJ7uxmiWWdM5Mk8KSCjPOO1xjzAEAAIBoQyM1IEZpKXe0NSujsRoAAABQG5luP/Ly8szF4/H4WwWImBVbC2X+svyobFZGYzUAAADgfxyWZVk+11GH2+0Wl8slRUVFkp2dzf5BxGlm++pFq2ualXXMSjMZ5gWThkVdOXssbSsAAAASkzvEMR/l5UCMCec84Im0rQAAAEAoEHQDMToPeKXHKx6vZeYB12nJorFZWSxtKwAAABAKBN1AjM4DPqiHS3Jch6cjC+U84ImyrQAAAEAoMKa7CYzpRrSNkdaSbc0gq3DNAx6M7Y6VbQUAAEBicYd4TDfdy4EYsWxTgdzw7Dqp9nplYE62ySBHS8fypiTinOUAAACAorwciAGaKX5w+Tap8nglxemUg2VVMv/N/KiaoxsAAABAfQTdQAygCzgAAAAQmwi6gRgQT13ANTuv83aTpQcAAEAiYEw3EOYGaC0Z22x3AdeScp3nWgPuWOwCvmJrocxfli/F5VWSlZ4iM8b2j5lx6QAAAEBLEHQDMdIATW8zqLsrZruA64mHB97YarLcqUlOqfJa5iSCPqdYey4AAABAoCgvB2KoAZoGpxq4x2KQyrh0AAAAJCKCbiCECDTjc1w6AAAAEKiEKC/v06ePmeTc6XRK+/bt5a233or0JiHBAs1vDnglVZwm0NQsdSIGmvEyLh0AAABojoQIutX7778vmZmZkd4MJBgNKK8d3c+M6a7yeqVdm+YHmq1twhZNYn1cOgAAANBcCRN0A5Ey9piu8uaNo1sUaAarCVs00edPsA0AAIBEEfEx3e+8846cd9550r17d3E4HLJkyZJ66+Tl5ZkS8fT0dBkxYoR89NFHzXoMvd8zzjhDhg8fLk8++WQQtx4IXQO0YDdhAwAAAJCAme7S0lI57rjj5Morr5SLLrqo3t8XL14ss2bNkgULFpiAe968eXL22WfL5s2bpUuXLmadoUOHSnV1db3bvvbaayaYf/fdd6VHjx6ya9cuGTdunAwePFiGDBkSlucH2MqrPDLt8dVSUe2VByYOlW7tMprdhO3goSqTMY+nTHE8lc8DAAAAURd0jx8/3lz8mTt3rkybNk2mTp1qrmvw/fLLL8ujjz4qN998s1m2du3aRh9DA27VrVs3Offcc+Xjjz8m6EbYvZe/V9Z/W2RKxWc8/UmTpeKJ0IQtHsvn/Z1I4OQCAABAYop40N2YyspKWbNmjcyePbtmmXYg12z1ypUrA86ke71eycrKkpKSEnnzzTflkksu8bt+RUWFudjcbncrnwVQu1RcM9d2qbg2FfOX3Q1GE7Z42yexeiKhstoblycXAAAAEONB9969e8Xj8UjXrl1rLdfrn3/+eUD3UVBQIBdeeKH5Xe9Ls+Y6ttufu+66S2677bZWbjkQnFLx1jRhi3bxUj7vm8FWdU8kzH19i1heibuTCwAAAIiDoDsYjjzySFm3bl3A62tWXceQ+2a6e/bsGaKtQ6JoTal4vHb7jofy+bpZ7QtP6FHvRMLuonKxRBo8uaAYzw4AABDfojro7tSpkyQlJZlstS+9npOTE5LHTEtLMxcgmOK9VLwl9LlrmbVmfYvLqiQrI7b2SUPl8c+v+UbapCVJ5+w06ZyZJoUlFdLuu+eTmuz837I2qbJ5V7FM/PsHlJwDAADEuagOulNTU2XYsGGybNkyueCCC8wyHZ+t16+77rqQPrZOU6YXLUkHgiGeS8VbSsc1a5l1LO4Tf+Xxl4/sLUvW7jRBuQbXeiJB2dO96bIpp/SWR9/9kpJzAACABBDxoFubm+Xn59dc3759u+lG3qFDB+nVq5cp9Z48ebKceOKJctJJJ5kpw7Q5mt3NPFSmT59uLlpe7nK5QvpYSBzxWiqeiPvEX3n8Ocd2M5e6JxJ8Ty7Ey3h2AAAAxEDQvXr1ajnzzDNrrtvjqTXQXrhwoUyYMEEKCwtlzpw5snv3bjMn99KlS+s1VwOAUKs77Vdj5fF1g+e6JxdcGSliiVWr5FwDcqYWAwAAiC8Oy7K0xw8aKS/fsmWLFBUVSXZ2NvsJSFD+5hTXILkl5fErthbWC9iZWgwAACD87OrmUMV8BN0RPgAAop8G1lcvWi2bdrlNSXjHrDQTYC+YNKxV5eC+AbsKxWMAAAAgsjGfM+j3CABxpqEx2Jqhtqf9aikNpjVrrj9D9RgAAACILIJuAAiwaVqlxyser2WapmlJeDDnFA/HYwAAACD8CLr90PHcubm5Mnz48PAeEQBRx26aNqiHS3Jch8duB3tO8XA8BgAAAMKPMd1NYEw3EF7R0r27oe1oadO05j5uLM5bDgAAEKvcIR7THfEpwwDUV17lkWmPr5aKaq88MHGodGuXkdAdwqNlO8Ixp3iszlsOAACAhlFeDkSh9/L3yvpvi2TjriKZ8fQnZnqpeKcZ3geXb5Mqj1dSnE45WFZlptTS5Ym4HQAAAIgPBN1AhDLZl//jQ7lkwUrZdbCs1t8SNeiLlu7d0bIdAAAAiA8E3X7QSA2RymQnatAXLd27o2U7AAAAEB8Iuv2YPn26bNy4UVatWhXeI4K411QmO1GDvkh27/atPKio8kRdF3F9bWza5Y77agcAAIB4RCM1IMwaymQfPHQ4k2030bp2dD/TyKvK65V2bVIiHvSFizYrG9TdFfbu3XblgTZO08oDDboXTBoWFV3Eo6W5HAAAAFqGoBsIMzuT/c0Br6SK02SyNajzzWSPPaarvHnj6KgI+sIt3N27fSsP9ESIXXmgQbcGuZHkb9v0xEQivSYAAABiGeXlQJjZmeyUJKcpIS8sLpfKaq+0SU2qt54GfQRXoRXNY+ijedsAAAAQGDLdQASc2r+TDO7hkrIqjzgcIg5xyO6iMrn9P5vM3/MuO0HSU2oH4Yhc5UGkRPO2AQAAIDBkuv2gezlCLTXZaTLcWwqKTRfzWc+uq8lsHqBhVkI0cIvlbQMAAEBgHJZlWQGum5Dcbre4XC4pKiqS7OzIju9EfNHxulcvWm26Umv5cHpqkikbTk9OkmO60TArEscjWsfQR/O2AQAAxDp3iGM+ysuBKBivqyXmJRXV4rXEjN2lYVb8N3CLl20DAABA4ygvByLEdz7uKo9l5ofWN2QyDbMAAACAuEHQDURBF3Ov5TVjvPV3HfChDbOyMlJomIUmy851eIL+BAAAQHSivByIgi7m2lDt4hN7yB0vfy5VXq+0a5NCwyw0atmmArnh2XVS7fWaqeW04dqoAZ3jeq/pyQUdlqFVIpTbAwCAWEHQ3Uj3cr14PJ7wHhEkHM1w6+Ws3Bx5ae0uE4DPmzhUurXLiPSmIYqDzweXb5Mqj9f0BIinHgD+AutEPMkAAADiA93Lm0D3cgDRRkvKr39mrexxl5vGewO6ZsrBQ1Vy/8ShJiCNVf4C67qd/jtmpZmAfMGkYeYnGXAAANAadC8HADTYhO+bA15JFafpAaDBp04pFo/Ze99O/3qSoXNmqjnJoNOoffL1ATLgAAAgqtFIDQBiuAlfvPQAaCiwLi47HFj7dvr3eK2aRoP6/O1APcX5v0CdxnIAACCaMKYbAGLQ2GO6yps3jjZBqWa4Yzngbip7rz+11FwDag3ENeDWkwza/8BfBjzW9wcAAIgfBN0AEkp5lUemP/mx+T3vshMkPSVJYpUGlrEaXNYdh+0vsLafn47t1lJz35MMeh/xVmYPAADiD0E3gKgOkKc9vloqqr3yQBA7umuGVO/zQGll3HWJj4WmYv4apjUUWDd2kqGpQB0AACAaEHQDiFrv5e+V9d8WmeBsxtOfBGWaqFDcZ7SIhWm1mprurLnZ+6YCdQAAgEijkRqAqOQbnAWrSVYo7jNaxMpza6xhWktpoK0nGRoKuPX561Rj0bYfAABA4iDo9iMvL09yc3Nl+PDh4T0iAEIWnIXiPqNFrDw3f53IQzEOWzP/Y+5bLhcveN/M871ia2HQHwMAAKApBN1+TJ8+XTZu3CirVq1qcicCiI3gLJwBX7jFynOzx2EP6uGSHNfhcvBQjMOOlcw/AACIfwTdABJmLup4nN86Fp+bjsNeMGmY3D9xqPkZinHnsZL5BwAA8Y9GagCi1qn9O8ngHi7TbXxekLqXx9v81rH63EI93Vlj834DAACEk8OyLCusjxhj3G63uFwuKSoqkuzs7EhvDoAomXYM0U/HcNedTizaurkDAID4j/nIdAOIa/E8RRgax3RiAAAgGjCmG0DcopkWGptODAAAIBwIugHEVKn4VQtXmYv+3hSaaUWOHp/L//GhXLJgpew6WBbBLQEAAIgsgm4AMUWbqmlX6gMBTP0UK9NoxXNZ/8ZdRaasPxrnyNZKiE273HE1jVg8PicAAGIdQTeAuA3kYmkarXgSC2X9yzYVyJj7lsvFC96XqxetjsqTAs2lz+GaJ9bIrMVrzc94eE4AAMQDgm4AcR3I2dOODermkvkTj6eJWhhEe1l/LJwUaC7d9gfe2CobdhbJ7qJy2X+oMuafEwAA8YKgG0DcB3KpyU7JTE+W9mS4g66hsdvRXtYf7ScFWiIenxMAAPGCoBtATGhpIJeekiSPTBluLvo7Ql/yr+X7OjXboB4uyXGlm+vRVNYf7ScFQvmcGPMNAED4MU+3H3l5eebi8TTdIRlA6Nnjs294dh3js6OEb5m2ZljtMu1B3V1RPUe2fVJAt1WzwRqcRtNJgVA9Jx3Hru8fnbNep1FjznoAAMLDYVmWFabHiklut1tcLpcUFRVJdnZ2pDcHkEQvZZ72+GrTwXzexKHSrV1GpDcpoWmX7OufWSt73OWmpHlA10w5eKhK7p841AR1sXDSIBpPCoTiOelybRinx0xPkHTMSjN/XzBpWNw8dwAAojXmI9MNIKbo+Gy9MD67+ScrKqq98kAQT1bYJc3fHPBKqjhNSbMGcLFSpq3bGm8Bp7/n1NCYbz1BogF63eBc19VjG2/7BgCASCHoBhAz7PHZaNm4ay0r1nHXwSorpuQ/dgRygoTycwAAQoOgGwASdNx1MDKZY4/pKm/eODruyrTjTVNjvkP9OgEAIJERdANAHAu0rLg14rFMOx411twuHK8TAAASFVOGAYjpscpXLVxlLvo7EmN6rETW2im/NIDWJnd1A+nmvE6YdgwAgOYh6AYQ07STuWboDrQwCIl39rjrlCQnU601QzQGljrmesx9y+XiBe+bTuQ6J3qwBDq3eii3AQCAeEV5OYCYFaoGYfHm1P6dZHAPF1OtBSgaG4qFY8x1U3OrM+4bAICWIegGEJMIAJrX9f2Jn44I4dGIH9H6ugrXmOvGxucz7hsAgJahvBxATGooANCuzBqEAPH2uoqGsfnRsA2Rpr0jLv/Hh3LJgpWy62BZpDcHABAjCLoBxCR/AYArPYXmaojJwLKxgC7QMdehRH+A/w1p2biryAxpYUw7ACAQlJcDiEl2AKBjb6u8XmnX5vC8w+2Z3ggheF2FI7htqkdBU2OuwyGR52WP1qEHAIDoR9ANIK4ahDF1GGIxsAw0oIuGOdGjYRsigTHtAICWSojy8u3bt8uZZ54pubm5MnjwYCktLY30JgEIktRkp2SmJ9fKcDONWPAl2pzo/uazTrSx5ImkqWniGNMOAGiphAi6p0yZIn/4wx9k48aN8vbbb0taWlqkNwlAiDDmMnQ4mRE68RLQxWqjsUDmH4+GcfUAgNgU9+XlGzZskJSUFBk1apS53qFDh0hvEoAgToX1yJThNdcZcxk6zIkeWnZApyXlmuHWgDsWA7pYfJ0053MjGsbVAwBiT8Qz3e+8846cd9550r17d3E4HLJkyZJ66+Tl5UmfPn0kPT1dRowYIR999FHA979161bJzMw0j3HCCSfInXfeGeRnACBaUKIb+qAkxfm/oMRfGW48C2UmVwO6BZOGyf0Th5qf0R6sxsrrpKlj1tzPjXAPPQAAxL6IB906vvq4444zgXVDFi9eLLNmzZJbbrlFPv74Y7Pu2WefLXv27KlZZ+jQoXLsscfWu+zcuVOqq6tlxYoV8re//U1Wrlwpr7/+urkAiD/xUqIbbTiZEb7hC7Ec0EXr66SpY8bnRmTGyANAIol40D1+/Hi544475MILL2zw73PnzpVp06bJ1KlTTSO0BQsWSJs2beTRRx+tWWft2rXy2Wef1bto9rxHjx5y4oknSs+ePc1Y7nPPPdesDyD+MI9waBCUBCeTG6vjnWP5dRLIMeNzIzJj5AEgkUQ86G5MZWWlrFmzRsaNG1ezzOl0muuatQ7E8OHDTVb8wIED4vV6TTn7Mccc43f9iooKcbvdtS4AYm8asUHdXDJ/4vExV6IbjQhKgpPJjfcmf9HYaCzQY2ZPE/f8tafIQ5efGL+fG+++G9r1o3iYAQBEUlQH3Xv37hWPxyNdu3attVyv7969O6D7SE5ONuO4Tz/9dBkyZIgMGDBAfvCDH/hd/6677hKXy1Vz0Qw5gNifRgyBTQHmbzknM1qXyU2UQCTaxqU355jFcml/QG69VUSbyt5zT2Dr63q6vt4uDoYZAEAkRXXQHcwS9vXr15uScy1Xb8zs2bOlqKio5rJjx46wbScARMMUYA0t107xT/x0hCy+ZqR0a5eRkAeqNRn/RApEoil4jVSVRtQNJdCM9W23Hf795pubDrz177qe0ts1I+MdjcMMACDSonrKsE6dOklSUpIUFBTUWq7Xc3JyQvKYOu6bebyB+JlGDM2b2ikWp3wKJ7sMublTRtmByDcHvJIqThOI6G0JRKL3mLVG1L2PTjtN5O67/xdI2z9vuqnxgFvp7fT231Vs6AkkfT3724/2iY4bnl0X1hMdABDNojrTnZqaKsOGDZNly5bVLNNx2Xp95MiRIX1s7aaujdt0TDgAxBN/pc7bCksSogQ6EplcxsVHNpsczux71A4l0ABbA2hbQxnvhgLu7wLz5jRHC+UY+airIgCAWMh0l5SUSH5+fs317du3m+7iHTp0kF69epnpwiZPnmw6kJ900kkyb948M82YdjMPpenTp5uLNlLTsd0AEC8aKnU+eKhKNu8ubnC5ZgjJUsVmxjWeRV02uYn3V1S8j+zMdkMZ70YCbt8TCfq87BMJg7q7Gs14h+L5RutxB4CoznSvXr1ajj/+eHNRGmTr73PmzDHXJ0yYIPfdd5+5rvNxa0C+dOnSes3VAACtG3N5dE4WYzETaLxzLIvabHIsjGluKOPdsaPfgDuaehJE83GPxU7zABIo6B49erRYllXvsnDhwpp1rrvuOvnqq6/MdF4ffvihjBgxIuTbRXk5gHjlr9S5X+fMiDSdAporWoLAmB1KUDfw3r/fb8AdTScSovm4N5eeKNi0y93wCYMwdZoHED4OSyNc+GWXl2sn8+zsbPYUgLig4yKnPb7adCqfN3FoTUdyf8uBaKKBio4r1qBFA7COWWkmqNVpyqIluNVtbGoogf1+q6j2ygOReL9phts34O7QQWTfvgZX1THd2hxNy7oHdsuWmWPDX9YdC8c9ELX2ZU527RJ5zVhrAN3ISZBa6g4LWLGipvFdxF9fQAxxhzjmi3imGwAQXfOZM895y9HkKTxiIZscyFACe3zyxl1FZnxyY83Jgk6DNd+AW+l1P9nVUDVHa25DvGg/7q0ukbc7zdsam+KtkU7zEX99AYiuRmoAgOiZWo0p11qHJk+BBVnTn/zY/J532QnmNdcSp/bvJIN7uGK2KqMlzcmCpm6wphluOwBvZDqxUDRHa+57JtYbEgbUaK+xhne2Rhrfteb1RXYcCA0y3QAABEFCNXlqJQ2UNfA40Ip9o8H6Ez8dIYuvGRlzAXdExyc3FKxpSXmg2dUoeM/EckPCgMfHNzbFWxMBd2teX2THgdAg6PaDRmoAgERt8hRKfKmPYHOyxoK1QObxDrJEfM80q0S+BZ3mW/P6as2JQ4bWAI0j6PZD5+jeuHGjrFq1qoldCABA9HR4jmaBfqlPhC/wYR+fHEB2NNyBdzS+Z8Lx2mvW+PhmdppvzeurNSdBOJkGNI6gGwCAIIiHJk+hFuiX+kT5Ah+K5mQaNF61cJW56O81HbEDyI76DbxDNAd0NL5nwvXaa1aJvB4THXdf6w46NN7VvAWvr5aeBGFoDdA0gm4AAILEbu41qJtL5k88PuxTKkW7QL7UJ9oX+FCMT643Zl47Wt9yS2BTUNUNvPV2Ph2xgy1UXdFbImpfe83sNN/S11dLT4Ik4jABoLnoXt7ImG69eDzfnSUGACDA5l5o/Eu9zlHs70t9QN2dE1CgXd/9dgO/9VaRceMCD6A18D711JAG3KHsit6SbtxR+dprYaf5lmpJd3j7ZNo3B7ySKk5zMk1v12CGXKsmmvOaau76QJQi0+0HY7oBAAh/ZjMax/nGStf3gOaAbo44CHaaUy4eda+9CHWab271RcAZcj3xM2pU4Nur6+n6ejsgxhF0AwCAsGrsS300jvONleCRMt/WlYu35LUXssZrUdZpvtXDBDRjfdttgW+v7/PX29XpK5AIzRYRXygvBwAAUTk2XjO785ooCU604FFLn+3gcVB3V62AsFllvgmgJeXizS2v9lvOH45O88peLwSl5s3V6DABrZrQ5xHI9jb0/OtUXYRkvwMhRKYbAABE5dj4xdeMTPiAuzkZbKoEglMuHmh5dUgar0Vpp/mgCCRDH8AJh6hteAc0gqAbAAAgToLHaOoGHmmhPgkRknL+KO40H/LAO5AMfyv3O2XpiBTKy/2gezkAAIiVru9110/0MfCt6cYdqJCV80dxp/mgaKg0/t57a0+L1sgJh9bsd8rSESkOy7KsiD16DHC73eJyuaSoqEiys7MjvTkAACBBaflsKIJHtNyyTQXmZIiOLR7YLVtmjmVsccDqZrZtAWT4W7Lf9f1z9aLVsmmX22TJO2almffRgknDeD9BQh3zkekGAACIAWSwow9N/1pBA+u6GW6dhzyAZnAt2e9ROQ87EgZBNwAAANCKpn9oYabbN+BWel2XNxF4t2S/R7K7v44ln/b4aqmo9soD0T4jgzbja85Qheaun6BopAYAAAAgcqXlmuEO8bzjkezub48l37iryExxtmJroUQl7ScwalTg+1/X0/X1dmgUQTcAAACA8GioS/m+fU1PJxYEdln6oG4umT/x+GZ199ds9VULV5mL/h6oYExx1tLHbhbNWN92W+D73/c46u18p6tr7tR170bxVHdBQtANAAAAIPQamxYskHm8W8kuS198zcgWlXjrGHIdF36gGQFzsKaWC/ixWxrwaol4oPu/oeNol5iTLW8QQXcjU4bl5ubK8OHD/a0CAAAAIBCBzMMdhsA73CXi9ljySo9XPF7LjCXPykhp1ljygB+7tQFvIPu/seMYzGx5nCHo9mP69OmyceNGWbVqVXiPCAAAABBPNJhqKuC2NRT4RTgYa02JeGvHkgf82MEKeBsLvJs6cRKsbHkcIugGAAAAEDoaTN1yS8DzcNcK/PR2EQ7GWlsi3pqx5AE/djAD3oYC744dAztx0tpseZxiyjAAAAAAoaUlzOPGBR5AaxB26qkRD7iDNd1YarLTXNo3s1t6sx7bDlztgNb+6RvQBhrw1r0v3+ndmgqSG9uOexIv4FYOy7KsSG9ENHO73eJyuaSoqEiys7MjvTkAAAAAwmzZpgK54dl1Uu31ysBu2TJz7IBmZazD+tj+AtuWBLya4fYNuHV6N+0239Kp4fY3I3iPo5iPTDcAAAAABFAirl3E500c2qLu52F77IYyzffe2/yAV4Nm39sova7LAwmWW5MtjzOM6QYAAACAJmh5eGZ6crNLxCPy2HXHVrck4K6bpW5JV3l9nA4+t7XvK4ECbkV5eRMoLwcAAAAQk1pSHh7M8vS6t4nSTHeoYz4y3X4wTzcAAACAmNVYeXhjt/EXWDd3HvVgZcvjAEG3H8zTDQAAACAmtSTgDSSTHWjg3dB97dvXvKA9jhB0AwAAAEC8aEnA++67gZeONxR46+1DkS2PEwTdAAAAABAPWhrw6nzot9xS/zb++N6X3s6eTz2Y2fI4QiO1JtBIDQAAAEDUC7TRWWPracbaDqAD4bu+/j5qVNOP7287Vqxo3mMHEY3UAAAAAAD+Bas8vLlBr+/6wcqWxyEy3U0g0w0AAAAg6t16q8httwU+HZedadaAV28bLK3JlsdpzEfQHeEDAAAAAACJGvBGA8rLAQAAAABNa015OEKG7uUAAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3X7k5eVJbm6uDB8+PFT7HgAAAAAQ55gyrAlMGQYAAAAA8csd4mmiyXQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhEhyqO44XliWVdNGHgAAAAAQX9zfxXp27BdsBN1NKC4uNj979uwZkgMAAAAAAIiO2E/n6w42hxWqcD5OeL1e2blzp2RlZYnD4QjamRQN4nfs2BGSydcRfTjmiYXjnVg43omHY55YON6JheOdmMf766+/NrFe9+7dxekM/ghsMt1N0J1+xBFHSChowE3QnVg45omF451YON6Jh2OeWDjeiYXjnVhcLldI4zIaqQEAAAAAECIE3QAAAAAAhAhBdwSkpaXJLbfcYn4iMXDMEwvHO7FwvBMPxzyxcLwTC8c7saSFKS6jkRoAAAAAACFCphsAAAAAgBAh6AYAAAAAIEQIugEAAAAACBGCbgAAAAAAQoSgO0jy8vKkT58+kp6eLiNGjJCPPvqo0fWfe+45GThwoFl/8ODB8sorr9T6u2VZMmfOHOnWrZtkZGTIuHHjZOvWrcHaXETZ8Z4yZYo4HI5al3POOYfjFKPHfMOGDfKjH/3IrK/Hct68ea2+T8T28b711lvrvcf1MwGxd7wffvhhGTVqlLRv395c9N/nuuvzb3hiHW/+DY+vY/7iiy/KiSeeKO3atZO2bdvK0KFD5Yknnqi1Du/xxDreU4LxPd1Cqz3zzDNWamqq9eijj1obNmywpk2bZrVr184qKChocP333nvPSkpKsu69915r48aN1u9+9zsrJSXFWr9+fc06d999t+VyuawlS5ZY69ats374wx9affv2tcrKyjhicXi8J0+ebJ1zzjnWrl27ai779+8P47NCMI/5Rx99ZN14443W008/beXk5Fj3339/q+8TsX28b7nlFmvQoEG13uOFhYVheDYI9vH+yU9+YuXl5VmffPKJtWnTJmvKlCnm3+tvvvmmZh3+DU+s482/4fF1zN966y3rxRdfNN/Z8vPzrXnz5pnvcUuXLq1Zh/d4Yh3vyUH4nk7QHQQnnXSSNX369JrrHo/H6t69u3XXXXc1uP4ll1xiff/736+1bMSIEdbPfvYz87vX6zVf3P70pz/V/P3gwYNWWlqa+VKH+Dre9pv5/PPPD+FWI5zH3Ffv3r0bDMJac5+IveOtQfdxxx0X9G1F67X2vVhdXW1lZWVZjz/+uLnOv+GJdbwV/4ZHt2D8e3v88cebpIniPZ5YxztY73HKy1upsrJS1qxZY8qNbE6n01xfuXJlg7fR5b7rq7PPPrtm/e3bt8vu3btrreNyuUx5hL/7ROweb9vy5culS5cucvTRR8u1114r+/btC9GzQKiPeSTuE8ERymOjQ4S6d+8uRx55pFx22WXy9ddfB2GLEenjfejQIamqqpIOHTqY6/wbnljH28a/4fF5zDVBuWzZMtm8ebOcfvrpZhnv8cQ63sF6jxN0t9LevXvF4/FI165day3X6xo4N0SXN7a+/bM594nYPd5Kx4UsWrTIvNHvueceefvtt2X8+PHmsRB7xzwS94ngCNWx0ZOmCxculKVLl8qDDz5ovrTpONHi4uIgbDUiebxvuukmczLF/pLHv+GJdbwV/4bH3zEvKiqSzMxMSU1Nle9///syf/58Oeuss8zfeI8n1vEO1ns8OeA1AYTMxIkTa37XRmtDhgyRfv36mbNqY8eOZc8DMU7/cbbp+1uD8N69e8uzzz4rV111VUS3DS139913yzPPPGM+q7VhDxLzePNvePzJysqStWvXSklJiQm0Zs2aZaqURo8eHelNQwSOdzDe42S6W6lTp06SlJQkBQUFtZbr9ZycnAZvo8sbW9/+2Zz7ROwe74boG10fKz8/P0hbjnAe80jcJ4IjXMdGu6QeddRRvMdj+Hjfd999Jgh77bXXzBcwG/+GJ9bxbgj/hsf+MdeS5P79+5tO1jfccIP8+Mc/lrvuusv8jfd4Yh3vYL3HCbpbScsQhg0bZs6K2Lxer7k+cuTIBm+jy33XV6+//nrN+n379jUvDN913G63fPjhh37vE7F7vBvyzTffmLEiOmUcYu+YR+I+ERzhOjZ6Nn3btm28x2P0eN97771y++23m+ECOtWML/4NT6zj3RD+DY+/z3S9TUVFhfmd93hiHe+gvcdb1YYNNa3ptbP4woULTbv5q6++2rSm3717t/n75Zdfbt188821ppBKTk627rvvPjP9hHa1bWjKML2Pl156yfr0009NxzymDIvP411cXGymG1q5cqW1fft264033rBOOOEEa8CAAVZ5eXnEnidafswrKirM9DJ66datmzm++vvWrVsDvk/E1/G+4YYbrOXLl5v3uH4mjBs3zurUqZO1Z8+eiDxHtPx467/POh3N888/X2v6GP0s912Hf8MT43jzb3j8HfM777zTeu2116xt27aZ9fX7m36Pe/jhh2vW4T2eOMe7OEjf0wm6g2T+/PlWr169zAeztqr/4IMPav52xhlnmFbzvp599lnrqKOOMuvr3K0vv/xyrb/rdAS///3vra5du5oXztixY63NmzcHa3MRRcf70KFD1ve+9z2rc+fOJhjXKYd0TkGCr9g95vqhrOc06150vUDvE/F1vCdMmGACcr2/Hj16mOs6Hyhi73jrZ3RDx1tPqNr4Nzxxjjf/hsffMf/tb39r9e/f30pPT7fat29vjRw50gRyvniPJ87xPhSk7+kO/V/geXEAAAAAABAoxnQDAAAAABAiBN0AAAAAAIQIQTcAAAAAACFC0A0AAAAAQIgQdAMAAAAAECIE3QAAAAAAhAhBNwAAqOH1euXqq6+Wbt26mZ/MLAoAQOsQdAMAgBr//e9/ZcuWLfLqq6/K559/LkuXLmXvAADQCgTdAAAkqNNPP12eeuqpWstcLpe0b99e+vfvLx06dDCX1lqwYIGcd955rb4fAABiEUE3AAAxaseOHXLllVdK9+7dJTU1VXr37i0zZ86Uffv2NXnbf//731JQUCATJ06stfyUU06RyspKE3x7PB4ZMWJEwNuzbNkyufjii6Vt27by5Zdf1izXbfz4449lxYoVzXyGAADEPoJuAABi0BdffCEnnniibN26VZ5++mnJz883GWUNfEeOHCn79+9v9PZ/+ctfZOrUqeJ01v4qUFVVJatWrZJf//rX5md1dXXA2zR27Fh57rnnpHPnzrWW6wmBn/zkJ+YxAQBINA6LDikAAMSc8ePHy2effWbGX2dkZNQs3717t/Tr10+uuOIKefDBBxu8bWFhoXTt2lXWr18vgwYNqvW3JUuWyHXXXSfbt2+XPn36mPv44Q9/WPP3X/3qVybD7mvUqFEyffr0mut6u+XLl5uftnfeeUfOOussOXjwYK3tBQAg3iVHegMAAEDzaBZbG5798Y9/rBfA5uTkyGWXXSaLFy+Wv/3tb+JwOOrd/t1335U2bdrIMcccU+9vjz32mFx66aWSkpJifup136D7T3/6U4sOl2blNWv+4YcfyujRo1t0HwAAxCLKywEAiDFaUq6Fag0FzUqXHzhwwGS0G/LVV1+ZTHfd0nId4/3KK6/IpEmTzHX9+fLLL/u9n7rWrl1rAmrNtutY8UceeaTmbxrk6zhxfWwAABIJmW4AAGJUUyPEdCx1Q8rKyiQ9Pb3e8n/+858ycOBAOe6448z1oUOHylFHHSVPPvmkXH/99U1uj66vZeX+aFb+0KFDTd4PAADxhEw3AAAxRqfz0rLxTZs2Nfh3Xa7NzNq1a9fg3zt16mQy4XVpKfmGDRskOTm55rJx40ZZuHBh0Mri6zZZAwAg3hF0AwAQYzp27GiakumYbc1a+9LSbs1MT5kyxe/tjz/+eLOeb+Ctnco1wNZMtZaJ2xdtgPbpp5/KJ5980qpt3rZtm5SXl5vHBgAgkRB0AwAQg/76179KRUWFnH322SYw1o7iS5cuNcG4loTPmTPH72018NVs93vvvVcry33SSSfJ6aefLscee2zN5bTTTjNTkOnfW0Pn6D7yyCNNZ3UAABIJQTcAADFowIABJjutgewll1wivXv3NtOIacCtwXRmZqbf2yYlJZk5ujUjrjQDrXN9/+hHP2pwfV3+1FNPSWVlZYu3V+9/2rRpLb49AACxinm6AQCIE7fccovMnTtXXn/9dTn55JMbXVfLy3WO7o8//tgE7KGk48THjBlj5hTXDuYAACQSgm4AAOKIloEXFRXJL37xi3pTgtW1ZMkSMz581KhRId2mN954QzwejymFBwAg0RB0AwAAAAAQIozpBgAAAAAgRAi6AQAAAAAIEYJuAAAAAABChKAbAAAAAIAQIegGAAAAACBECLoBAAAAAAgRgm4AAAAAAEKEoBsAAAAAgBAh6AYAAAAAIEQIugEAAAAAkND4f1NBeL0UCDnHAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", @@ -184,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "006fb3cf", "metadata": {}, "outputs": [], @@ -205,55 +174,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2643141f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Film Structure:\n", - "- EasyMultilayer:\n", - " Si layer:\n", - " - Si layer:\n", - " material:\n", - " Si:\n", - " sld: 2.070e-6 1/Å^2\n", - " isld: 0.000e-6 1/Å^2\n", - " thickness: 0.000 Å\n", - " roughness: 0.000 Å\n", - "- EasyMultilayer:\n", - " SiO2 layer:\n", - " - SiO2 layer:\n", - " material:\n", - " SiO2:\n", - " sld: 3.470e-6 1/Å^2\n", - " isld: 0.000e-6 1/Å^2\n", - " thickness: 30.000 Å\n", - " roughness: 3.000 Å\n", - "- EasyMultilayer:\n", - " Film layer:\n", - " - Film layer:\n", - " material:\n", - " Film:\n", - " sld: 2.000e-6 1/Å^2\n", - " isld: 0.000e-6 1/Å^2\n", - " thickness: 250.000 Å\n", - " roughness: 3.000 Å\n", - "- EasyMultilayer:\n", - " D2O superphase:\n", - " - D2O superphase:\n", - " material:\n", - " D2O:\n", - " sld: 6.360e-6 1/Å^2\n", - " isld: 0.000e-6 1/Å^2\n", - " thickness: 0.000 Å\n", - " roughness: 3.000 Å\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "si_layer = Layer(si, 0, 0, 'Si layer')\n", "sio2_layer = Layer(sio2, 30, 3, 'SiO2 layer')\n", @@ -280,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "22e6fbd9", "metadata": {}, "outputs": [], @@ -299,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "175afdcc", "metadata": {}, "outputs": [], @@ -321,21 +245,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a7656b8b", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", @@ -364,23 +277,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "a85ab7e8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Free parameters:\n", - " thickness = 30 bounds=(15, 50)\n", - " sld = 2 bounds=(0.1, 3)\n", - " thickness = 250 bounds=(200, 300)\n", - " scale = 1 bounds=(0.5, 1.5)\n", - " background = 1e-06 bounds=(1e-07, 1e-05)\n" - ] - } - ], + "outputs": [], "source": [ "sio2_layer.thickness.fixed = False\n", "sio2_layer.thickness.bounds = (15, 50)\n", @@ -420,12 +320,18 @@ " $y_\\text{eff} = y + \\min(y, 1)$, $\\sigma = \\sqrt{\\max(y + 1,\\, \\varepsilon)}$.\n", " This keeps all data in the fit while giving zero-variance points reasonable weight.\n", "- **`mighell`** — applies the Mighell transform to *every* point, not just zero-variance ones.\n", - " This changes the chi-square landscape for the entire dataset." + " This changes both the weighting and the fitted target for the entire dataset.\n", + "\n", + "Below we report two metric families:\n", + "\n", + "- **objective chi²** — the quantity actually minimized by the selected objective\n", + "- **classical chi²** — a standard variance-weighted chi² computed only on the original\n", + " positive-variance points, for comparison" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "8182f0a9", "metadata": {}, "outputs": [], @@ -479,20 +385,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e856e00f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WARNING] Masked 6 data point(s) in reflectivity ref_zero_var due to zero variance during fitting.\n", - "Success : True\n", - "Reduced chi² : 286.8691\n" - ] - } - ], + "outputs": [], "source": [ "model_mask = build_fresh_model()\n", "fitter_mask = MultiFitter(model_mask, objective='legacy_mask')\n", @@ -503,10 +399,23 @@ "\n", "for w in w_mask:\n", " print(f'[WARNING] {w.message}')\n", - "print(f'Success : {result_mask[\"success\"]}')\n", - "print(f'Reduced chi² : {result_mask[\"reduced_chi\"]:.4f}')\n", + "print(f'Success : {result_mask[\"success\"]}')\n", + "print(f'Objective reduced chi² : {result_mask[\"objective_reduced_chi\"]:.6f}')\n", + "print(f'Objective total chi² : {fitter_mask.objective_chi2:.6f}')\n", + "print(f'Classical reduced chi² : {result_mask[\"classical_reduced_chi\"]:.6f}')\n", + "print(f'Classical total chi² : {fitter_mask.classical_chi2:.6f}')\n", "\n", - "r_fit_mask = model_mask.interface.fit_func(q_raw, model_mask.unique_name)" + "r_fit_mask = result_mask[result_model_key].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79755052", + "metadata": {}, + "outputs": [], + "source": [ + "result_mask\n" ] }, { @@ -521,20 +430,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "20c034ba", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WARNING] Applied Mighell substitution to 6 zero-variance point(s) in reflectivity ref_zero_var during fitting.\n", - "Success : True\n", - "Reduced chi² : 277.6647\n" - ] - } - ], + "outputs": [], "source": [ "model_hybrid = build_fresh_model()\n", "fitter_hybrid = MultiFitter(model_hybrid, objective='hybrid')\n", @@ -545,10 +444,13 @@ "\n", "for w in w_hybrid:\n", " print(f'[WARNING] {w.message}')\n", - "print(f'Success : {result_hybrid[\"success\"]}')\n", - "print(f'Reduced chi² : {result_hybrid[\"reduced_chi\"]:.4f}')\n", + "print(f'Success : {result_hybrid[\"success\"]}')\n", + "print(f'Objective reduced chi² : {result_hybrid[\"objective_reduced_chi\"]:.6f}')\n", + "print(f'Objective total chi² : {fitter_hybrid.objective_chi2:.6f}')\n", + "print(f'Classical reduced chi² : {result_hybrid[\"classical_reduced_chi\"]:.6f}')\n", + "print(f'Classical total chi² : {fitter_hybrid.classical_chi2:.6f}')\n", "\n", - "r_fit_hybrid = model_hybrid.interface.fit_func(q_raw, model_hybrid.unique_name)" + "r_fit_hybrid = result_hybrid[result_model_key].values" ] }, { @@ -563,20 +465,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "90b08fdd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WARNING] Applied Mighell substitution to 192 zero-variance point(s) in reflectivity ref_zero_var during fitting.\n", - "Success : True\n", - "Reduced chi² : 0.095375\n" - ] - } - ], + "outputs": [], "source": [ "model_mighell = build_fresh_model()\n", "fitter_mighell = MultiFitter(model_mighell, objective='mighell')\n", @@ -587,10 +479,13 @@ "\n", "for w in w_mighell:\n", " print(f'[WARNING] {w.message}')\n", - "print(f'Success : {result_mighell[\"success\"]}')\n", - "print(f'Reduced chi² : {result_mighell[\"reduced_chi\"]:.6f}')\n", + "print(f'Success : {result_mighell[\"success\"]}')\n", + "print(f'Objective reduced chi² : {result_mighell[\"objective_reduced_chi\"]:.6f}')\n", + "print(f'Objective total chi² : {fitter_mighell.objective_chi2:.6f}')\n", + "print(f'Classical reduced chi² : {result_mighell[\"classical_reduced_chi\"]:.6f}')\n", + "print(f'Classical total chi² : {fitter_mighell.classical_chi2:.6f}')\n", "\n", - "r_fit_mighell = model_mighell.interface.fit_func(q_raw, model_mighell.unique_name)" + "r_fit_mighell = result_mighell[result_model_key].values" ] }, { @@ -608,24 +503,7 @@ "execution_count": null, "id": "77c23990", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter legacy_mask hybrid mighell\n", - "-----------------------------------------------------------------\n", - "thickness_1 0.002002 0.001911 593.4\n", - "sld -11.06 -11.06 5.516\n", - "thickness_2 211.4 211.4 364.7\n", - "scale 0.007455 0.007455 0.7049\n", - "background 5.389e-07 5.389e-07 7.115e-08\n", - "-----------------------------------------------------------------\n", - "reduced chi² 286.8691 277.6647 0.095375\n", - "success True True True\n" - ] - } - ], + "outputs": [], "source": [ "models = {\n", " 'legacy_mask': model_mask,\n", @@ -662,7 +540,7 @@ "\n", "obj_keys = ['legacy_mask', 'hybrid', 'mighell']\n", "\n", - "header = f'{\"Parameter\":<20s} {\"legacy_mask\":>14s} {\"hybrid\":>14s} {\"mighell\":>14s}'\n", + "header = f'{\"Parameter\":<24s} {\"legacy_mask\":>14s} {\"hybrid\":>14s} {\"mighell\":>14s}'\n", "print(header)\n", "print('-' * len(header))\n", "\n", @@ -671,13 +549,15 @@ " for obj in obj_keys:\n", " params = models[obj].get_fit_parameters()\n", " vals.append(float(params[i].value))\n", - " print(f'{label:<20s} {vals[0]:>14.4g} {vals[1]:>14.4g} {vals[2]:>14.4g}')\n", + " print(f'{label:<24s} {vals[0]:>14.4g} {vals[1]:>14.4g} {vals[2]:>14.4g}')\n", "\n", "print('-' * len(header))\n", - "print(f'{\"reduced chi²\":<20s} {result_mask[\"reduced_chi\"]:>14.4f} \\\n", - "{result_hybrid[\"reduced_chi\"]:>14.4f} {result_mighell[\"reduced_chi\"]:>14.6f}')\n", - "print(f'{\"success\":<20s} {str(result_mask[\"success\"]):>14s} \\\n", - " {str(result_hybrid[\"success\"]):>14s} {str(result_mighell[\"success\"]):>14s}')" + "print(f'{\"objective red. chi²\":<24s} {result_mask[\"objective_reduced_chi\"]:>14.4f} '\n", + " f'{result_hybrid[\"objective_reduced_chi\"]:>14.4f} {result_mighell[\"objective_reduced_chi\"]:>14.6f}')\n", + "print(f'{\"classical red. chi²\":<24s} {result_mask[\"classical_reduced_chi\"]:>14.4f} '\n", + " f'{result_hybrid[\"classical_reduced_chi\"]:>14.4f} {result_mighell[\"classical_reduced_chi\"]:>14.4f}')\n", + "print(f'{\"success\":<24s} {str(result_mask[\"success\"]):>14s} '\n", + " f'{str(result_hybrid[\"success\"]):>14s} {str(result_mighell[\"success\"]):>14s}')" ] }, { @@ -690,34 +570,57 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "a4f8f3a7", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", "# Experimental data\n", - "ax.errorbar(q_raw[valid], r_raw[valid], yerr=err_raw[valid],\n", - " fmt='o', ms=2, color='grey', alpha=0.5, label='Experiment')\n", - "ax.plot(q_raw[zero_mask], r_raw[zero_mask],\n", - " 'rx', ms=10, mew=2, label='Zero-variance points')\n", + "ax.errorbar(\n", + " q_raw[valid],\n", + " r_raw[valid],\n", + " yerr=err_raw[valid],\n", + " fmt='o',\n", + " ms=2,\n", + " color='grey',\n", + " alpha=0.5,\n", + " label='Experiment',\n", + ")\n", + "ax.plot(q_raw[zero_mask], r_raw[zero_mask], 'rx', ms=10, mew=2, label='Zero-variance points')\n", "\n", "# Fitted curves\n", - "ax.plot(q_raw, r_fit_mask, '-', lw=1.5, label=f'legacy_mask (χ²={result_mask[\"reduced_chi\"]:.2f})')\n", - "ax.plot(q_raw, r_fit_hybrid, '--', lw=1.5, label=f'hybrid (χ²={result_hybrid[\"reduced_chi\"]:.2f})')\n", - "ax.plot(q_raw, r_fit_mighell, ':', lw=1.5, label=f'mighell (χ²={result_mighell[\"reduced_chi\"]:.4f})')\n", + "ax.plot(\n", + " q_raw,\n", + " r_fit_mask,\n", + " '-',\n", + " lw=1.5,\n", + " label=(\n", + " f'legacy_mask (obj={result_mask[\"objective_reduced_chi\"]:.3g}, '\n", + " f'class={result_mask[\"classical_reduced_chi\"]:.3g})'\n", + " ),\n", + ")\n", + "ax.plot(\n", + " q_raw,\n", + " r_fit_hybrid,\n", + " '--',\n", + " lw=1.5,\n", + " label=(\n", + " f'hybrid (obj={result_hybrid[\"objective_reduced_chi\"]:.3g}, '\n", + " f'class={result_hybrid[\"classical_reduced_chi\"]:.3g})'\n", + " ),\n", + ")\n", + "ax.plot(\n", + " q_raw,\n", + " r_fit_mighell,\n", + " ':',\n", + " lw=1.5,\n", + " label=(\n", + " f'mighell (obj={result_mighell[\"objective_reduced_chi\"]:.3g}, '\n", + " f'class={result_mighell[\"classical_reduced_chi\"]:.3g})'\n", + " ),\n", + ")\n", "\n", "ax.set_yscale('log')\n", "ax.set_xlabel('Q (Å⁻¹)')\n", @@ -742,21 +645,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "0292ef11", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n", "\n", @@ -793,15 +685,18 @@ "source": [ "## Summary\n", "\n", - "| Objective | Zero-var handling | Chi² interpretation |\n", - "|---|---|---|\n", - "| `legacy_mask` | Dropped from fit | Classical reduced χ² |\n", - "| `hybrid` | Mighell substitution for zero-var only | Approximately classical when zero-var fraction is small |\n", - "| `mighell` | Mighell transform for **all** points | **Not** a classical χ² — values are not directly comparable |\n", + "| Objective | Zero-var handling | Objective chi² interpretation | Classical chi² comparison |\n", + "|---|---|---|---|\n", + "| `legacy_mask` | Dropped from fit | Same as classical chi² on retained points | Directly comparable |\n", + "| `hybrid` | Mighell substitution for zero-var only | Slightly modified objective | Usually close to classical when zero-var fraction is small |\n", + "| `mighell` | Mighell transform for **all** points | **Not** a classical chi²; target and weights both change | Can look visibly worse against plotted reflectivity even when the objective is minimized well |\n", "\n", "The `hybrid` mode (new default) is recommended: it keeps all data points in the fit while\n", - "producing chi² values that remain approximately classical. The `mighell` mode is useful when\n", - "the entire dataset has low counts and Poisson-like statistics are desired." + "preserving a classical chi² comparison on the original positive-variance points.\n", + "\n", + "The full `mighell` mode matches the Mighell paper's objective mathematically, but that paper is\n", + "derived for Poisson count data. Applied to normalized reflectivity, it should be interpreted as a\n", + "Poisson-style objective rather than a visually comparable reduced chi² fit." ] } ], diff --git a/src/easyreflectometry/fitting.py b/src/easyreflectometry/fitting.py index 01fbcf43..ffcfec03 100644 --- a/src/easyreflectometry/fitting.py +++ b/src/easyreflectometry/fitting.py @@ -71,7 +71,7 @@ def _prepare_fit_arrays( weights = 1.0 / np.sqrt(variances[valid]) else: weights = np.array([]) - stats = {'valid': n_valid, 'mighell_substituted': 0, 'masked': n_zero} + stats = {'valid': n_valid, 'mighell_substituted': 0, 'masked': n_zero, 'transformed_all_points': False} return x_out, y_eff, weights, stats # hybrid or mighell @@ -98,10 +98,32 @@ def _prepare_fit_arrays( weights = 1.0 / sigma n_mighell = int(np.sum(apply_mighell)) - stats = {'valid': n - n_mighell, 'mighell_substituted': n_mighell, 'masked': 0} + stats = { + 'valid': n - n_mighell, + 'mighell_substituted': n_mighell, + 'masked': 0, + 'transformed_all_points': bool(objective == 'mighell'), + } return x_vals, y_eff, weights, stats +def _compute_weighted_chi2(y_obs: np.ndarray, y_calc: np.ndarray, sigma: np.ndarray) -> float: + """Return weighted chi-square for finite, strictly positive uncertainties.""" + valid = np.isfinite(y_obs) & np.isfinite(y_calc) & np.isfinite(sigma) & (sigma > 0.0) + if not np.any(valid): + return 0.0 + residual = (y_obs[valid] - y_calc[valid]) / sigma[valid] + return float(np.sum(residual**2)) + + +def _compute_reduced_chi2(chi2: float, n_points: int, n_params: int) -> float | None: + """Return reduced chi-square or None when degrees of freedom are not positive.""" + dof = int(n_points) - int(n_params) + if dof <= 0: + return None + return float(chi2 / dof) + + class MultiFitter: def __init__(self, *args: Model, objective: str = 'hybrid'): r"""A convenience class for the :py:class:`easyscience.Fitting.Fitting` @@ -128,6 +150,7 @@ def wrapped(*args, **kwargs): self._models = args self.easy_science_multi_fitter = EasyScienceMultiFitter(args, self._fit_func) self._fit_results: list[FitResults] | None = None + self._classical_fit_metrics: list[dict] | None = None self._objective = _validate_objective(objective) def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> sc.DataGroup: @@ -155,6 +178,7 @@ def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> x = [] y = [] dy = [] + original_arrays = [] # Process each reflectivity dataset for i in refl_nums: @@ -170,7 +194,13 @@ def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> 'due to zero variance during fitting.', UserWarning, ) - if stats['mighell_substituted'] > 0: + if stats.get('transformed_all_points'): + warnings.warn( + f'Applied Mighell transform to all {len(y_vals)} point(s) ' + f'in reflectivity {i} during fitting.', + UserWarning, + ) + elif stats['mighell_substituted'] > 0: warnings.warn( f'Applied Mighell substitution to {stats["mighell_substituted"]} ' f'zero-variance point(s) in reflectivity {i} during fitting.', @@ -180,15 +210,16 @@ def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> x.append(x_out) y.append(y_eff) dy.append(weights) + original_arrays.append({'x': x_vals, 'y': y_vals, 'variances': variances}) result = self.easy_science_multi_fitter.fit(x, y, weights=dy) self._fit_results = result + self._classical_fit_metrics = [] new_data = data.copy() for i, _ in enumerate(result): id = refl_nums[i] - new_data[f'R_{id}_model'] = sc.array( - dims=[f'Qz_{id}'], values=self._fit_func[i](data['coords'][f'Qz_{id}'].values) - ) + model_curve = self._fit_func[i](data['coords'][f'Qz_{id}'].values) + new_data[f'R_{id}_model'] = sc.array(dims=[f'Qz_{id}'], values=model_curve) sld_profile = self.easy_science_multi_fitter._fit_objects[i].interface.sld_profile(self._models[i].unique_name) new_data[f'SLD_{id}'] = sc.array(dims=[f'z_{id}'], values=sld_profile[1] * 1e-6, unit=sc.Unit('1/angstrom') ** 2) if 'attrs' in new_data: @@ -196,6 +227,28 @@ def fit(self, data: sc.DataGroup, id: int = 0, objective: str | None = None) -> new_data['coords'][f'z_{id}'] = sc.array( dims=[f'z_{id}'], values=sld_profile[0], unit=(1 / new_data['coords'][f'Qz_{id}'].unit).unit ) + original = original_arrays[i] + sigma_classical = np.sqrt(np.clip(original['variances'], 0.0, None)) + n_classical_points = int(np.sum(original['variances'] > 0.0)) + classical_chi2 = _compute_weighted_chi2(original['y'], model_curve, sigma_classical) + classical_reduced_chi = _compute_reduced_chi2(classical_chi2, n_classical_points, result[i].n_pars) + objective_chi2 = float(result[i].chi2) + objective_reduced_chi = float(result[i].reduced_chi) + + self._classical_fit_metrics.append( + { + 'classical_chi2': classical_chi2, + 'classical_reduced_chi': classical_reduced_chi, + 'objective_chi2': objective_chi2, + 'objective_reduced_chi': objective_reduced_chi, + 'n_classical_points': n_classical_points, + } + ) + + new_data['objective_chi2'] = objective_chi2 + new_data['objective_reduced_chi'] = objective_reduced_chi + new_data['classical_chi2'] = classical_chi2 + new_data['classical_reduced_chi'] = classical_reduced_chi new_data['reduced_chi'] = float(result[i].reduced_chi) new_data['success'] = result[i].success return new_data @@ -226,7 +279,13 @@ def fit_single_data_set_1d(self, data: DataSet1D, objective: str | None = None) 'due to zero variance during fitting.', UserWarning, ) - if stats['mighell_substituted'] > 0: + if stats.get('transformed_all_points'): + warnings.warn( + f'Applied Mighell transform to all {len(y_vals)} point(s) ' + 'in single-dataset fit during fitting.', + UserWarning, + ) + elif stats['mighell_substituted'] > 0: warnings.warn( f'Applied Mighell substitution to {stats["mighell_substituted"]} ' 'zero-variance point(s) in single-dataset fit during fitting.', @@ -238,6 +297,20 @@ def fit_single_data_set_1d(self, data: DataSet1D, objective: str | None = None) result = self.easy_science_multi_fitter.fit(x=[x_out], y=[y_eff], weights=[weights])[0] self._fit_results = [result] + sigma_classical = np.sqrt(np.clip(variances, 0.0, None)) + model_curve = self._fit_func[0](x_vals) + n_classical_points = int(np.sum(variances > 0.0)) + classical_chi2 = _compute_weighted_chi2(y_vals, model_curve, sigma_classical) + classical_reduced_chi = _compute_reduced_chi2(classical_chi2, n_classical_points, result.n_pars) + self._classical_fit_metrics = [ + { + 'classical_chi2': classical_chi2, + 'classical_reduced_chi': classical_reduced_chi, + 'objective_chi2': float(result.chi2), + 'objective_reduced_chi': float(result.reduced_chi), + 'n_classical_points': n_classical_points, + } + ] return result @property @@ -262,6 +335,33 @@ def reduced_chi(self) -> float | None: return total_chi2 / total_dof + @property + def classical_chi2(self) -> float | None: + """Classical chi-squared using only points with positive variances.""" + if self._classical_fit_metrics is None: + return None + return float(sum(metric['classical_chi2'] for metric in self._classical_fit_metrics)) + + @property + def classical_reduced_chi(self) -> float | None: + """Reduced classical chi-squared using only points with positive variances.""" + if self._classical_fit_metrics is None or self._fit_results is None: + return None + total_chi2 = self.classical_chi2 + total_points = sum(metric['n_classical_points'] for metric in self._classical_fit_metrics) + n_params = self._fit_results[0].n_pars + return _compute_reduced_chi2(total_chi2, total_points, n_params) + + @property + def objective_chi2(self) -> float | None: + """Objective-space chi-squared returned by the minimizer.""" + return self.chi2 + + @property + def objective_reduced_chi(self) -> float | None: + """Objective-space reduced chi-squared returned by the minimizer.""" + return self.reduced_chi + def switch_minimizer(self, minimizer: AvailableMinimizers) -> None: """ Switch the minimizer for the fitting. diff --git a/tests/test_fitting.py b/tests/test_fitting.py index 12783545..4747e459 100644 --- a/tests/test_fitting.py +++ b/tests/test_fitting.py @@ -5,6 +5,7 @@ import numpy as np import pytest +import scipp as sc from easyscience.fitting.minimizers.factory import AvailableMinimizers import easyreflectometry @@ -505,6 +506,73 @@ def _fake_fit(*, x, y, weights): assert len(captured['weights'][0]) == 3 +def test_fit_single_data_set_1d_mighell_warning_mentions_all_points(): + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model, objective='mighell') + + mock_result = MagicMock() + mock_result.chi2 = 1.0 + mock_result.reduced_chi = 0.5 + mock_result.n_pars = 1 + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(return_value=[mock_result]) + fitter._fit_func = [lambda x: np.zeros_like(x)] + + data = DataSet1D( + name='mighell_warning', + x=np.array([0.01, 0.02, 0.03]), + y=np.array([1.0, 0.8, 0.6]), + ye=np.array([0.01, 0.02, 0.04]), + ) + + with pytest.warns(UserWarning, match=r'Applied Mighell transform to all 3 point\(s\)'): + fitter.fit_single_data_set_1d(data) + + +def test_classical_and_objective_chi_are_split_for_fit_results(): + model = Model() + model.interface = CalculatorFactory() + fitter = MultiFitter(model, objective='mighell') + + fit_result = MagicMock() + fit_result.chi2 = 0.25 + fit_result.reduced_chi = 0.125 + fit_result.n_pars = 1 + fit_result.x = np.array([0.01, 0.02, 0.03]) + + fitter.easy_science_multi_fitter = MagicMock() + fitter.easy_science_multi_fitter.fit = MagicMock(return_value=[fit_result]) + fitter.easy_science_multi_fitter._fit_objects = [MagicMock(interface=MagicMock())] + fitter.easy_science_multi_fitter._fit_objects[0].interface.sld_profile.return_value = (np.array([0.0, 1.0]), np.array([1.0, 2.0])) + + fitter._models = [MagicMock(unique_name='model_0', as_dict=MagicMock(return_value={'name': 'model_0'}))] + fitter._fit_func = [lambda x: np.array([0.8, 0.75, 0.7])] + + data = sc.DataGroup( + { + 'coords': {'Qz_0': sc.array(dims=['Qz_0'], values=np.array([0.01, 0.02, 0.03]), unit=sc.Unit('1/angstrom'))}, + 'data': {'R_0': sc.array(dims=['Qz_0'], values=np.array([1.0, 0.9, 0.7]), variances=np.array([0.01, 0.0, 0.04]))}, + 'attrs': {}, + } + ) + + analysed = fitter.fit(data) + + expected_classical_chi2 = ((1.0 - 0.8) / 0.1) ** 2 + ((0.7 - 0.7) / 0.2) ** 2 + expected_classical_reduced = expected_classical_chi2 / (2 - fit_result.n_pars) + + assert analysed['objective_chi2'] == pytest.approx(0.25) + assert analysed['objective_reduced_chi'] == pytest.approx(0.125) + assert analysed['classical_chi2'] == pytest.approx(expected_classical_chi2) + assert analysed['classical_reduced_chi'] == pytest.approx(expected_classical_reduced) + assert fitter.objective_chi2 == pytest.approx(0.25) + assert fitter.objective_reduced_chi == pytest.approx(0.125) + assert fitter.classical_chi2 == pytest.approx(expected_classical_chi2) + assert fitter.classical_reduced_chi == pytest.approx(expected_classical_reduced) + + def test_fit_single_data_set_1d_all_zero_variance_hybrid_does_not_raise(): """Hybrid mode handles all-zero-variance data without raising.""" model = Model() diff --git a/zero_variances_example.py b/zero_variances_example.py new file mode 100644 index 00000000..fb2b42c6 --- /dev/null +++ b/zero_variances_example.py @@ -0,0 +1,222 @@ +""" +Example: Zero-variance handling with the three objective modes. + +This script demonstrates how MultiFitter handles data points that have +zero variance under each of the three objective strategies: + + - legacy_mask : drops zero-variance points (old behaviour) + - hybrid : keeps all points; applies Mighell substitution only to + zero-variance entries (new default) + - mighell : applies the Mighell transform to every point + +The example builds a simple film-on-substrate reflectometry model, +creates synthetic data with a few zero-variance points injected, and +fits with each mode so you can compare results side-by-side. +""" + +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import scipp as sc + +from easyreflectometry.calculators import CalculatorFactory +from easyreflectometry.fitting import MultiFitter +from easyreflectometry.model import Model +from easyreflectometry.model import PercentageFwhm +from easyreflectometry.sample import Layer +from easyreflectometry.sample import Material +from easyreflectometry.sample import Multilayer +from easyreflectometry.sample import Sample + + +def build_sample_and_model(): + """Return a fresh (sample, model) pair with fittable parameters.""" + si = Material(2.07, 0, 'Si') + sio2 = Material(3.47, 0, 'SiO2') + film = Material(2.0, 0, 'Film') + d2o = Material(6.36, 0, 'D2O') + + si_layer = Layer(si, 0, 0, 'Si layer') + sio2_layer = Layer(sio2, 30, 3, 'SiO2 layer') + film_layer = Layer(film, 250, 3, 'Film Layer') + superphase = Layer(d2o, 0, 3, 'D2O Subphase') + + sample = Sample( + Multilayer(si_layer), + Multilayer(sio2_layer), + Multilayer(film_layer), + Multilayer(superphase), + name='Film Structure', + ) + + resolution_function = PercentageFwhm(0.02) + model = Model(sample, 1, 1e-6, resolution_function, 'Film Model') + + # Free the parameters we want to fit + sio2_layer.thickness.fixed = False + sio2_layer.thickness.bounds = (15, 50) + + film_layer.thickness.fixed = False + film_layer.thickness.bounds = (200, 300) + + film.sld.fixed = False + film.sld.bounds = (0.1, 3) + + model.background.fixed = False + model.background.bounds = (1e-7, 1e-5) + + model.scale.fixed = False + model.scale.bounds = (0.5, 1.5) + + model.interface = CalculatorFactory() + + return sample, model + + +def make_synthetic_data_with_zero_variances(model): + """Generate a DataGroup from the model, then inject zero variances.""" + qz = np.linspace(0.01, 0.30, 80) + r_true = model.interface.fit_func(qz, model.unique_name) + + # Add a little Gaussian noise for realism + rng = np.random.default_rng(42) + noise_sigma = 0.02 * r_true + 1e-4 + r_noisy = r_true + rng.normal(0, noise_sigma) + + # Variance = sigma^2 + variances = noise_sigma**2 + + # Inject 6 points with zero variance (simulating detector dead-spots, etc.) + zero_indices = [5, 15, 30, 45, 60, 75] + variances[zero_indices] = 0.0 + + data = sc.DataGroup( + { + 'coords': { + 'Qz_0': sc.array(dims=['Qz_0'], values=qz, unit=sc.Unit('1/angstrom')), + }, + 'data': { + 'R_0': sc.array(dims=['Qz_0'], values=r_noisy, variances=variances), + }, + } + ) + return data, zero_indices + + +def run_fit(objective, data): + """Build a fresh model and fit *data* using the given objective.""" + _, model = build_sample_and_model() + fitter = MultiFitter(model, objective=objective) + + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + result = fitter.fit(data) + + return result, fitter, caught + + +def plot_fit_comparison(data, fit_summaries, zero_indices): + """Plot the experiment and all fitted curves on a single matplotlib chart.""" + qz = data['coords']['Qz_0'].values + reflectivity = data['data']['R_0'].values + variances = data['data']['R_0'].variances + yerr = np.sqrt(np.clip(variances, 0.0, None)) if variances is not None else None + + fig, ax = plt.subplots(figsize=(10, 6)) + ax.errorbar( + qz, + reflectivity, + yerr=yerr, + fmt='o', + ms=4, + color='black', + alpha=0.65, + capsize=2, + label='Experiment', + ) + ax.scatter( + qz[zero_indices], + reflectivity[zero_indices], + s=80, + facecolors='none', + edgecolors='crimson', + linewidths=1.5, + label='Zero-variance points', + zorder=4, + ) + + for summary in fit_summaries: + ax.plot( + qz, + summary['model_curve'], + linewidth=2, + label=( + f"{summary['objective']} fit " + f"(obj={summary['objective_reduced_chi']:.3g}, class={summary['classical_reduced_chi']:.3g})" + ), + ) + + ax.set_title('Zero-Variance Objective Comparison') + ax.set_xlabel('Qz (1/angstrom)') + ax.set_ylabel('Reflectivity') + ax.set_yscale('log') + ax.grid(True, which='both', alpha=0.25) + ax.legend() + fig.tight_layout() + plt.show() + + +def main(): + # --- setup --------------------------------------------------------------- + _, seed_model = build_sample_and_model() + data, zero_idx = make_synthetic_data_with_zero_variances(seed_model) + + total_points = len(data['data']['R_0'].values) + print(f'Dataset: {total_points} points, {len(zero_idx)} with zero variance\n') + + # --- fit with each objective -------------------------------------------- + objectives = ['legacy_mask', 'hybrid', 'mighell'] + fit_summaries = [] + + for obj in objectives: + print(f'{"=" * 60}') + print(f'Objective: {obj}') + print(f'{"=" * 60}') + + result, fitter, caught = run_fit(obj, data) + + # Show warnings emitted during fitting + for w in caught: + print(f' [WARNING] {w.message}') + + print(f' Success : {result["success"]}') + print(f' Objective reduced chi2 : {result["objective_reduced_chi"]:.6f}') + print(f' Objective total chi2 : {fitter.objective_chi2:.6f}') + print(f' Classical reduced chi2 : {result["classical_reduced_chi"]:.6f}') + print(f' Classical total chi2 : {fitter.classical_chi2:.6f}') + print() + + fit_summaries.append( + { + 'objective': obj, + 'model_curve': result['R_0_model'].values, + 'objective_reduced_chi': float(result['objective_reduced_chi']), + 'classical_reduced_chi': float(result['classical_reduced_chi']), + } + ) + + # --- interpretation note ------------------------------------------------- + print('-' * 60) + print('NOTE: Under the mighell objective ALL points are transformed,') + print('so objective chi2 is not a classical chi-square statistic.') + print('Under hybrid, only zero-variance points are transformed;') + print('the classical chi2 is still computed from the original') + print('positive-variance points for comparison.') + print('-' * 60) + + plot_fit_comparison(data, fit_summaries, zero_idx) + + +if __name__ == '__main__': + main() From e1b5d78cb664f7730f23881e9c0959c8c54496c0 Mon Sep 17 00:00:00 2001 From: rozyczko Date: Mon, 23 Mar 2026 10:47:41 +0100 Subject: [PATCH 5/5] unit test update --- tests/test_fitting.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/tests/test_fitting.py b/tests/test_fitting.py index 4747e459..e4145f45 100644 --- a/tests/test_fitting.py +++ b/tests/test_fitting.py @@ -428,7 +428,7 @@ def test_prepare_fit_arrays_legacy_mask_drops_zero_variance(): assert np.allclose(x_out, [0.01, 0.03]) assert np.allclose(y_eff, [1.0, 0.6]) assert np.allclose(weights, [1.0 / np.sqrt(0.01), 1.0 / np.sqrt(0.04)]) - assert stats == {'valid': 2, 'mighell_substituted': 0, 'masked': 1} + assert stats == {'valid': 2, 'mighell_substituted': 0, 'masked': 1, 'transformed_all_points': False} def test_prepare_fit_arrays_hybrid_transforms_zero_variance(): @@ -449,7 +449,7 @@ def test_prepare_fit_arrays_hybrid_transforms_zero_variance(): assert y_eff[1] == pytest.approx(0.8 + 0.8) # sigma = sqrt(y + 1) = sqrt(1.8) assert weights[1] == pytest.approx(1.0 / np.sqrt(1.8)) - assert stats == {'valid': 2, 'mighell_substituted': 1, 'masked': 0} + assert stats == {'valid': 2, 'mighell_substituted': 1, 'masked': 0, 'transformed_all_points': False} def test_prepare_fit_arrays_mighell_transforms_all(): @@ -466,7 +466,7 @@ def test_prepare_fit_arrays_mighell_transforms_all(): # sigma = sqrt(y + 1) assert weights[0] == pytest.approx(1.0 / np.sqrt(1.5)) assert weights[1] == pytest.approx(1.0 / np.sqrt(1.3)) - assert stats == {'valid': 0, 'mighell_substituted': 2, 'masked': 0} + assert stats == {'valid': 0, 'mighell_substituted': 2, 'masked': 0, 'transformed_all_points': True} def test_fit_single_data_set_1d_hybrid_keeps_zero_variance_points(): @@ -545,9 +545,11 @@ def test_classical_and_objective_chi_are_split_for_fit_results(): fitter.easy_science_multi_fitter = MagicMock() fitter.easy_science_multi_fitter.fit = MagicMock(return_value=[fit_result]) fitter.easy_science_multi_fitter._fit_objects = [MagicMock(interface=MagicMock())] - fitter.easy_science_multi_fitter._fit_objects[0].interface.sld_profile.return_value = (np.array([0.0, 1.0]), np.array([1.0, 2.0])) + fitter.easy_science_multi_fitter._fit_objects[0].interface.sld_profile.return_value = \ + (np.array([0.0, 1.0]), np.array([1.0, 2.0])) - fitter._models = [MagicMock(unique_name='model_0', as_dict=MagicMock(return_value={'name': 'model_0'}))] + fitter._models = [MagicMock(unique_name='model_0', + as_dict=MagicMock(return_value={'name': 'model_0'}))] fitter._fit_func = [lambda x: np.array([0.8, 0.75, 0.7])] data = sc.DataGroup( @@ -728,7 +730,7 @@ def test_fit_warnings_objective_specific(): for obj, expected_fragment in [ ('legacy_mask', 'Masked 1 data point(s)'), ('hybrid', 'Mighell substitution'), - ('mighell', 'Mighell substitution'), + ('mighell', 'Applied Mighell transform to all 3 point(s)'), ]: fitter = MultiFitter(model, objective=obj) fitter.easy_science_multi_fitter = MagicMock()