From e45f2c6830e4c44078ab3183df7dfcbdf1f6fcc1 Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 2 Apr 2026 11:29:38 +0200 Subject: [PATCH 1/2] Add files via upload Full notebook for the MD (and non MD part). psd_noise_model is used to generate random noise from an ensemble of blank files. --- ...ulation_of_DNA_chains_imaged_via_AFM.ipynb | 2807 +++++++++++++++++ tutorial/psd_noise_model.npz | Bin 0 -> 1054252 bytes 2 files changed, 2807 insertions(+) create mode 100644 tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb create mode 100644 tutorial/psd_noise_model.npz diff --git a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb new file mode 100644 index 0000000..e9ea75b --- /dev/null +++ b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb @@ -0,0 +1,2807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell_md_title", + "metadata": { + "id": "cell_md_title" + }, + "source": [ + "# Simulation of 'DNA chains on a substrate imaged via AFM'\n", + "**This notebook provides a complete example of generating a dataset of simulated DNA chains relxed on a substrate imaged via AFM.**\n", + "\n", + "- **Output1:** synthetic AFM-like height images (optionally with `blank.spm` noise texture or simulated noise texture)\n", + "- **Output2:** DNA vs background mask (polyline from final bead coordinates, lightly dilated for better training)\n", + "- **Output3:** crossing mask (polyline intersection detection + Gaussian/chain-biased target)\n", + "\n", + "Outputs are written to `OUT_DIR/` as `.npy` plus `manifest.csv` (which documents the user inputs used to create the images and masks).\n", + "\n", + ">\n", + "---\n", + "**Cell execution order:** run cells 1 → 12 in sequence. \n", + "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github.com/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_01", + "metadata": { + "id": "cell_md_01" + }, + "source": [ + "# 1. **Imports and global settings**\n", + "## 1.1. Imports\n", + "\n", + "First we import all the libraries that will be necessary for simulation of the images. If you plan to use molecular dynamics for the generation of the DNA chains, then please import the molecular dynamics libraries as well." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "cell_code_01", + "metadata": { + "id": "cell_code_01" + }, + "outputs": [], + "source": [ + "# Install dependencies (uncomment in a fresh environment / Colab)\n", + "# !pip cache purge\n", + "# Chain generation mode\n", + "USE_MD = False # True → OpenMM Langevin dynamics\n", + " # False → fast 2-D persistent-walk (no OpenMM needed)\n", + "# Molecular dynamics\n", + "!pip install -q scipy matplotlib pillow\n", + "if USE_MD:\n", + " !pip install openmm[cuda13] #change the cuda version depending on the CUDA version available on your system.\n", + " import openmm\n", + " import openmm.unit as unit\n", + "\n", + "import os\n", + "from pathlib import Path\n", + "import re\n", + "import csv\n", + "import traceback\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import animation\n", + "from IPython.display import HTML, display\n", + "\n", + "\n", + "\n", + "\n", + "# Image processing\n", + "from scipy.ndimage import (\n", + " gaussian_filter, grey_dilation, distance_transform_edt,\n", + " binary_dilation, median_filter, affine_transform\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#1.2 Defining the variables\n", + "Here we define the variables that will be used for the generation of the images and the masks. The variables control how the images and their masks will appear at the end. For training a Machine learning network like a U-Net, all images need to be of a standard size and need reproducible properties. To generate the likeness of DNA chains from simulated data, we start with building a chain by using a random walk in 3D with some set parameters like number of beads, bond length and persistence bonds and to simulate chain relaxation on substrate we raise the chain to a certain height." + ], + "metadata": { + "id": "1oW9llw-imr1" + }, + "id": "1oW9llw-imr1" + }, + { + "cell_type": "code", + "source": [ + "# ─────────────────────────────────────────────\n", + "# Global settings (these are the variables that need to be changed to change the appearance of the resulting images)\n", + "# ─────────────────────────────────────────────\n", + "\n", + "# Dataset\n", + "OUT_DIR = \"dna_dataset_1000_4lengths\" # Name of the output directory that you would like to store your images and masks in\n", + "N_SAMPLES = 1000 # Number of samples that will be generated\n", + "BASE_SEED = 0 # Seed for reproducibility\n", + "\n", + "# Image resolution (keep fixed for U-Net)\n", + "NX = 512\n", + "NY = 512\n", + "\n", + "# Ring + MD\n", + "N_BEADS = 90 # default bead count for vizualization\n", + "BEAD_COUNTS = [70, 80, 90, 100] # four chain lengths (1 bead corresponds to 10 base pairs on DNA approximately)\n", + "BOND_LENGTH = 1.0 # distance between beads\n", + "PERSISTENCE_BONDS = 23.0 # used to determine the range of angles which the chain is allowed to turn\n", + "BASE_Z = 5.0 # Height above the substrate that the chain starts at before relaxing\n", + "\n", + "ANGLE_STIFNESS_MULT = 0.6 # If USE_MD is False, then angle stiffness multiplier control chain propogation through space\n", + "\n", + "# MD recording\n", + "N_FRAMES = 200\n", + "STEPS_PER_FRAME = 200 # Between every frame local energy minimization happens for the given number of steps\n", + "\n", + "# AFM rendering\n", + "AFM_KW = dict(\n", + " nx=NX, ny=NY,\n", + " dna_diameter_nm=2.0, # mapping to physical values\n", + " tip_radius_nm=0.01, # Radius of tip (modelled as hemishpere for convolution)\n", + " max_height_nm=6.0, # Maximum height for the colorbar\n", + " target_nm_per_px=0.08, # Matching pixels to size of chain\n", + " max_radius_px=96, # following parameters are used for improving the visualization of the chain\n", + " radius_shrink_px=2.0,\n", + " final_blur_sigma_px=0.20,\n", + " apply_edge_taper=True,\n", + " taper_sigma_nm=0.45,\n", + " taper_floor=0.10,\n", + " add_center_ridge=True,\n", + " ridge_sigma_nm=0.25,\n", + " ridge_amp_nm=0.25,\n", + " grain_nm=0.0,\n", + " grain_sigma_px=0.6,\n", + " enable_crossing_boost=True, # To boost the height of the crossings\n", + " min_separation_beads=12, # Distance to other chain segments to not boost indiscriminately\n", + " boost_window_beads=2, # How many beads to boost the height for\n", + " guaranteed_offset_nm=1.0, # How much to boost\n", + " boost_method=\"additive\", # add to the height of beads being boosted\n", + " boost_profile=\"gaussian\", # Height increase profile\n", + " boost_sigma_beads=None,\n", + " far_clip_nm=2.5, # Clip bead heights for beads that are not part of a crossing\n", + " far_clip_window_beads=3, # How many beads to clip the height for\n", + " return_crossing_info=True, # To ensure that mask information is passed to other functions. Change to False if masks are not needed\n", + " return_masks=True,\n", + ")\n", + "\n", + "# DNA mask\n", + "DNA_MASK_DILATE_PX = 3 # Dilation of mask for better training\n", + "\n", + "# Crossing mask\n", + "CROSS_MIN_SEP_BEADS = 12 # Where to stop for the crossing calculation\n", + "CROSS_SIGMA_CENTER_PX = 5.0 # How many pixels make us the center of a crossing\n", + "CROSS_SIGMA_PERP_PX = 1.8 # Crossing coloring parameter (how many pixels to modulate for the crossing)\n", + "CROSS_CHAIN_EXTENT = 5.0 # For the chain weighting of the mask\n", + "CROSS_CENTER_WEIGHT = 1.7 # For the chain weighting of the mask (chain-weighted guassian)\n", + "CROSS_CHAIN_WEIGHT = 0.9\n", + "CROSS_CLIP_TO_DNA_MASK = True" + ], + "metadata": { + "id": "sSqadS02hzPe" + }, + "id": "sSqadS02hzPe", + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#1.3 Noise\n", + "To add realistic noise to the images so that they closely resemble real AFM images, 2 options have been provided in this notebook.\n", + "\n", + "\n", + "1. For users that have access to AFM imaging setups and substrates, a **blank ``.spm``** file can be loaded into this enviroment. The notebook will parse through the file and add the noise that would appear as if a DNA chain were imaged on that substrate, thus making the output dataset closer to real DNA if it were to be imaged on that setup and substrate.\n", + "2. For users that do not have access to AFM imaging setups or available blanks, noise can be generated through an ensemble of blanks that were imaged on nickel susbtrates and processed. The information of that noise is available as a Power spectral density noise model that is available in this repository. There are 3 noise synthesis models offered but *mean_full2d* is preferred and chosen as the default.\n", + "\n" + ], + "metadata": { + "id": "-e3QvQzGlKYY" + }, + "id": "-e3QvQzGlKYY" + }, + { + "cell_type": "code", + "source": [ + "#Noise\n", + "TARGET_NOISE_RMS_NM = 0.19 # This variable controls the height of the noise in nanometers and this is added to the final image\n", + "\n", + "\n", + "# Blank SPM noise\n", + "USE_BLANK_SPM_NOISE = True\n", + "BLANK_SPM_PATH = \"/content/20240411_blank_water.0_00000.spm\" # optional; if missing/invalid, PSD fallback noise is used\n", + "\n", + "\n", + "# PSD-model fallback noise (used when no blank .spm file is available)\n", + "USE_PSD_NOISE_FALLBACK = True\n", + "MODEL_PATH = \"/content/psd_noise_model.npz\"\n", + "PSD_NOISE_METHOD = \"mean_full2d\" # or \"lognormal_full2d\" / \"empirical_radial\"\n", + "PSD_NOISE_STD_SCALE = 2.0\n", + "\n", + "#Checking if output directory will be created\n", + "os.makedirs(OUT_DIR, exist_ok=True)\n", + "for _sub in [\"images\", \"dna_masks\", \"cross_masks\", \"meta\"]:\n", + " os.makedirs(os.path.join(OUT_DIR, _sub), exist_ok=True)\n", + "\n", + "print(\"Output folder:\", os.path.abspath(OUT_DIR))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "47h81smulGxd", + "outputId": "ba1026d2-8f61-4ce2-f7fe-9b6aae526059" + }, + "id": "47h81smulGxd", + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output folder: /content/dna_dataset_1000_4lengths\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_02", + "metadata": { + "id": "cell_md_02" + }, + "source": [ + "## 2. Chain Creation (3-D Persistent Random Walk)\n", + "Now we start with creating the DNA chain.\n", + "\n", + "The function `make_tangled_ring_initial` builds a closed polymer ring via a persistent random walk and enforces ring-closure.\n", + "\n", + "A 3-D plot is shown at the end of the cell so you can inspect the initial geometry immediately." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "cell_code_02", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "cell_code_02", + "outputId": "3e2e58d9-df87-4b6e-d23e-829c3c60e822" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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T4cCBA3juuecwNjbGbdLd3d3417/+teaaL7nkkqCi4rOf/SzOPvtsfOlLX8KePXu42++++2785Cc/wXe/+11cd911Ub0/kWK323HrrbeioKDAR3xdeOGF+MlPfoJf/epXnN2A0+nEI488gj179qzZqQasphEfeugh3HDDDQBWOwwfeugh5OTkoLGxEQBw8cUX44UXXsCvf/1rH9sAsjaGYTifLsA3omk2mwMMMU8//XQoFAr89Kc/xZlnnsmd82A1XouLiz7p6/T0dFRXV0dsU0ChrBdUHFE2HOSi/7//+7+45JJLoFAocO655wbUXgDApz/9adxxxx246qqr0NzcjPb2djzxxBM+v3jFwM0334zHH38cZ5xxBr7+9a9zrfylpaUwGo3rMlPs1VdfxbXXXouLLroItbW18Hg8+P3vfw+ZTMbV01RVVeHOO+/Ed77zHYyMjOD8889HRkYGhoeH8eyzz+Kaa67BTTfdBKlUit/85jc466yz0NDQgKuuugpFRUWYnJzEa6+9Bo1Gg7///e8AgIMHD+Kf//wnTjzxRHz1q1+Fx+PBT3/6UzQ0NKCtrS3smuvq6lBXVxf0bxUVFT4Ro2effRY333wzampqUF9fj8cff9zn/meccQZnHzAyMoKKioo105DAqhgpLCzE5s2bsby8jIcffhhDQ0N4/vnnkZGRwd1vz549uOiii/Cd73wHc3NzqK6uxu9+9zuMjIwERDpDUVhYiB//+McYGRlBbW0tnnzySRgMBvzqV7/i6r0uu+wyPPXUU/jKV76C1157Dfv27YPX60VPTw+eeuop/Otf/8Jxxx2HM888EykpKTj33HPx5S9/GRaLBb/+9a+Rm5vLpfoAcH5kd911Fz796U/j7LPPRktLC1588cWAaOzmzZtx8skno7GxEXq9HocPH8bTTz8dMMqFQhEd69kqRzk2CNXKn5aWFnDf2267LaBdG36t/CzLst///vfZoqIiViqV+rS6B2vlv/HGG9mCggJWrVaz+/btYw8dOsTu37+f3b9/P3e/aFv5+a/F/2/+rfzBWpn9j8+yq63zJ554IqtUKtni4mL2rrvuYv/f//t/LAB2ZmYm7LoSwdDQEPuFL3yBraqqYlUqFavX69lTTjmFffnllwPu+5e//IU94YQT2LS0NDYtLY2tq6tjv/a1r7G9vb0+92tpaWEvuOACNisri1UqlWxZWRl78cUXs6+88orP/d544w22sbGRTUlJYSsrK9kHH3ww6GcjUhCklZ88X6j/XnvtNe6+7e3tLAD2f/7nf9Y81o9//GO2rq6OValUbGZmJnveeef5WDTwsdvt7E033cTm5+ezSqWSPf7449l//vOfEb2m/fv3sw0NDezhw4fZpqYmVqVSsWVlZezPfvazgPu6XC72xz/+MdvQ0MAqlUo2MzOTbWxsZA8ePMiazWbufn/729/Ybdu2sSqVii0vL2d//OMfsw8//HDA59rr9bIHDx7kvlcnn3wy29HREfD9u/POO9ndu3ezOp2OVavVbF1dHfuDH/yAsxmgUMSKhGUFqgqlUCiCc/311+Ohhx6CxWKho0nWkV/84he4+eabMTg4GJUZJYVC2ZhQnyMKRST4+9osLi7i97//PU444QQqjNaZ1157Dd/4xjeoMKJQjhFo5IhCEQk7duzAySefjPr6eszOzuK3v/0tpqam8Morr+Ckk05a7+VRKBTKMQMtyKZQRMLZZ5+Np59+Gr/61a8gkUiwa9cu/Pa3v6XCiEKhUJIMjRxRKBQKhUKh8KA1RxQKhUKhUCg8qDiiUCgUCoVC4UHFEYVCoVAoFAoPKo4oFAqFQqFQeFBxRKFQKBQKhcKDiiMKhUKhUCgUHlQcUSgUCoVCofCg4ohCoVAoFAqFBxVHFAqFQqFQKDzo+BAKhUKhUPzwer1wu93rvYxjDoVCIYpB21QcUSgUCoXyMSzLYmZmBktLS+u9lGMWnU6H/Px8SCSSdVsDFUcUCoVCoXwMEUa5ublITU1d1w36WINlWdhsNszNzQEACgoK1m0tVBxRKBQKhYLVVBoRRllZWeu9nGMStVoNAJibm0Nubu66pdhoQTaFQqFQKABXY5SamrrOKzm2Ie//etZ8UXFEoVAoFAoPmkpbX8Tw/lNxRKFQKBQKhcKDiiMKhUKhUDYwJ598Mq6//vpj7tiJhIojCoVCoVCEpL0dOHgQ+OY3V/+3vX29V7SuvP7669i1axeUSiWqq6vx6KOPrveS1oR2q1EoFAqFIgQDA8AVVwDvvgvIZIBUCjAMcPvtwL59wKOPAtXV673KpDI8PIxzzjkHX/nKV/DEE0/glVdewdVXX42CggIcOHBgvZcXEho5olAoFAolXgYGgD17gPffX/231wu43av/CwDvvbf694GBhBze4/Hg2muvhVarRXZ2Nm699VawLMv93el04qabbkJRURHS0tKwZ88evP7669zfFxcXcemll6KoqAipqanYunUr/vjHP/ocw2q14vLLL0d6ejoKCgpwzz33rLmuBx98EBUVFbjnnntQX1+Pa6+9FhdeeCHuu+8+wV57IqDiiEKhUCiUeLniCsBs/kQM+eP1rv79yisTcvjf/e53kMvl+OCDD/DAAw/g3nvvxW9+8xvu79deey0OHTqEP/3pT2hra8NFF12ET33qU+jv7wcAOBwONDY24vnnn0dHRweuueYaXHbZZfjggw+45/jWt76FN954A3/961/x73//G6+//jqOHDkSdl2HDh3C6aef7nPbgQMHcOjQIQFffQJgKRQKhUKhsHa7ne3q6mLtdnt0D2xrY1kg8v/a2gRd9/79+9n6+nqWYRjutm9/+9tsfX09y7IsOzo6yspkMnZyctLncaeddhr7ne98J+TznnPOOeyNN97IsizLrqyssCkpKexTTz3F/X1xcZFVq9XsddddF/I5ampq2B/+8Ic+tz3//PMsANZmswV9TMznQUBozRGFQqFQKPHwzDOrNUahokZ8ZDLg2WeBrVsFXcLevXt9/IGamppwzz33wOv1or29HV6vF7W1tT6PcTqdnBO41+vFD3/4Qzz11FOYnJyEy+WC0+nkDBkHBwfhcrmwZ88e7vF6vR6bNm0S9HWIBSqOKBQKhUKJh6Wl1eLrSMSRVAqYTAlfEh+LxQKZTIaPPvooYBxHeno6AODuu+/GAw88gPvvvx9bt25FWloarr/+erhcrriOnZ+fj9nZWZ/bZmdnodFouFEhYoSKIwqFQqFQ4kGnW+1KiwSGATIzBV/C+6QQ/GPee+891NTUQCaTYefOnfB6vZibm8OJJ54Y9PHvvPMOPvOZz+Dzn//8x8tk0NfXh82bNwMAqqqqoFAo8P7776O0tBQAYDKZ0NfXh/3794dcV1NTE1544QWf21566SU0NTXF/FqTAS3IplAoFAolHi64ILKoEbB6vwsuEHwJY2NjuOGGG9Db24s//vGP+OlPf4rrrrsOAFBbW4vPfe5zuPzyy/HMM89geHgYH3zwAe666y48//zzAICamhq89NJLePfdd9Hd3Y0vf/nLPhGf9PR0fPGLX8S3vvUtvPrqq+jo6MCVV14JqTS8jPjKV76CoaEh3Hzzzejp6cEvfvELPPXUU/jmN78p+HsgJDRyRKFQKBRKPGzdCjQ3r7bxhxNJMhmwdy+wZYvgS7j88stht9uxe/duyGQyXHfddbjmmmu4vz/yyCO48847ceONN2JychLZ2dnYu3cvPv3pTwMAbrnlFgwNDeHAgQNITU3FNddcg/PPPx9ms5l7jrvvvhsWiwXnnnsuMjIycOONN/r8PRgVFRV4/vnn8c1vfhMPPPAAiouL8Zvf/EbUHkcAIGFZnhEChUKhUCjHKA6HA8PDw6ioqIBKpYruwcTnKFQ7v0wGaLWrAuoYM4KMlrjOg0DQtBqFQqFQKPFSXb0qfPbuXf23TAYoFKv/C6zeToXRhoGm1SgUCoVCEYLqauDtt1dnqT377GpXWmbmao1RAlJplMRBxRGFQqFQKEKydavgPkaU5ELTahQKhUKhUCg8qDiiUCgUCoVC4UHFEYVCoVAoPJhIDR0pCUEM7z+tOaJQKBQKBUBKSgqkUimmpqaQk5ODlJQUn3lllMTCsixcLhfm5+chlUqRkpKybmuhPkcUCoVCoXyMy+XC9PQ0bDbbei/lmCU1NRUFBQVUHFEoFAqFIhZYloXH44E30pEgFMGQyWSQy+XrHrGj4ohCoVAoFAqFBy3IplAoFAqFQuFBxRGFQqFQKBQKDyqOKBQKhUKhUHhQcUShUCgUCoXCg4ojCoVCoVAoFB5UHFEoFAqFQqHwoOKIQqFQKBQKhQcdH0KhiAiGYeBwOMAwDBQKBWQyGWQy2bobolEoFMqxBDWBpFBEAMuy8Hq98Hg8cDqdnDPvxMQE8vLykJ6eDrlcTsUShUKhJAEaOaJQ1hmWZeF2uzlBJJPJAAASiQRTU1PQaDRQKBRwuVwAAKlUCrlcTsUShUKhJAgqjiiUdcTr9cLtdoNhGEilUkgkEk4kSSQSSCQSSKVSKBQKsCzL/ed0On3EEknByeVy7nkoFAqFEhtUHFEo6wAZbOnxeAAgpKCRSCQgmW8iloDV6BJfLDkcDu4+RCyRyBIVSxQKhRIdVBxRKEmGYRguWgT4ih5/wokaKpYoFAolMVBxRKEkCZZlfYRRuGiR/+MiIVKxRNJvVCxRKBRKcKg4olCSgH/RdThBwhdD/LRatIQSSwzDwOl0wuFwQCqVBhR4U7FEoVCOdag4olASDIkWeb3eqIVHPOIo2HPxj03EktfrhdfrhdPp5NJwVCxRKJRjGSqOKJQEQYTH6OgoLBYLNm3aFLXISKQo4XfDAeHFEr8bLlyNFIVCoRwNUHFEoSQAfhrN5XLBbrfHLCiS5dMaTix5PB7u7/41S1QsUSiUow0qjigUgfH3LiJiIxaETKvFcuxgYsnj8WB8fBwulwvl5eWQyWQ+o07ieb0UCoUiBqg4olAEgu9dxLIsV6sTb1G1WCb88MUSGXMikUjg8Xjgdrt9IktULFEolI0MFUcUigAwDAOPxxO0G01MAkdo+JExfmTJ7XZzf/cfdULFEoVCETtUHFEoccD3LmJZNmj9TbTiiP94sQqrUP5MwdJwbrcbLpeL+zsVSxQKRexQcUShxIj/CJBQhclHS1rNn7XWFUwsESFJIkv+Yol0w1EoFMp6QsURhRIDfO8ivgAIhpgFTjIh9UgEvlgKFlnid8NRKBRKMqHiiEKJAn5re7gRIHyOxrSaEEQilqRSaUCBNxVLFAol0VBxRKFESDQjQPgcjWm1RAiUSMWSf80SFUsUCkVoqDiiUCKAYRi4XK6Io0V84hVHYiXRoo0vlsixyHkINeqEiiUKhSIEVBxRKGEgaTTSjRbLnLFoxRHp8uL/+1iHP0AXCBRLLpcLQHDrACqWKBRKtFBxRKGEINY0mj80rZa44/PFEvmvu7sbcrkcZWVlVCxRKJSYoOKIQgkCP1oU7+ywo1EciQ3+OSIiSSqVgmVZOJ3OkEN0YxW8FArl6IaKIwqFR6TeRdFwtIojsa4LgI+9gkwm84ksORwOn/soFAouskTFEoVCAag4olA4SGcUwzAAIJhzczQCx+PxoKenBzabDXq9noteUeKDL3LDiSUSUaJiiUI5tqHiiHLMw28Zj6UbbS0iFUcrKyswGAxQKBTQ6/VYWlqCyWTC0tISLBYLMjMzkZmZidTU1HXfsNf7+OEgqdBwhBJLDMNwYonMjaNiiUI59qDiiHJMI1TRdTjWEkcsy2JiYgI9PT0oLy9HWVkZvF4vZDIZurq6IJFIoFarsbCwgIGBAcjlck4oZWZmQqVS0Q07TsKJJafTCYfDQcUShXIMQcUR5ZiFPwIkkZtcOHHk8XjQ0dEBo9GIXbt2ISsrC16vlxNrEokESqUS5eXl3JqXl5dhMpkwMzOD3t5eKJVKH7GkVCoT8jr8OZrTff61ZkQskXMTqsBbiBo1CoWy/lBxRDnmIJvcwMAACgoKoFQqE7qhhRJHZrMZra2tUKvV2LdvX1BR4/9YqVQKnU4HnU6HiooKeL1emM1mmEwmTExMoKurC6mpqZxQ0ul0SElJSdhrO1YINkSXP0qG/N2/ZomKJQplY0LFEeWYgp9GGxgYQHZ2NlQqVUKP6S9wWJbF2NgY+vr6UFlZicrKypAb6FopOZlMBr1eD71eD2A1EkVqlUZGRmCxWJCenu4jluTy+L/2Yt7wI6k5ipdQYsnj8cDtdocUS0IV+VMolMRCxRHlmIF4FyWi6DocfIHjdrvR3t6O5eVlHHfcccjMzAx6/2D/PxLkcjmys7ORnZ0NAHC5XJxYGhgYgN1uR0ZGBieWtFqtzzwzSmxEI5b4Q3SpWKJQxAkVR5SjHn/vIiKMJBIJ17afSIg4MplMaG1tRUZGBpqbmyNOd8VT25OSkoLc3Fzk5uYCAJxOJ0wmE0wmE7q7u+FyuaDVaqHT6TixFOmGfTTXHMVLKLG0vLyM9vZ27N69O6h7NxVLFIo4oOKIclTj713ErwEhDsrJWseHH36I2tpalJWVRdRqTv5XyDUqlUrk5+cjPz+f8/ghYmlqagoejwdarRaZmZnQ6/VIT0+nG7YAkM+dRCKB1+uFXC7nUrwul4v7GxVLFIo4oOKIclTC9y4KNQIkGZEjl8uFzs5OsCyLPXv2QKfTRfX4RDpkE4sAtVqNwsJCsCwLm83GiaWxsTGwLOvTCZeWlkaLjOOEL5T4kSXyeXW73dz9+GKJdMNRKJTEQ8UR5ajD37so1Gae6MiR0Wjk0mgAohZGQHLHh0gkEqSlpSEtLQ3FxcVgWRYWiwUmkwlGoxFDQ0OQSqXIzMzkIiDJKH6OFjGuiRBqbaQeiX8/IpaCRZb43XAUCkV4qDiiHFVE412UKOHBsiwGBwcxPDyMTZs2IScnB2+88UbMm/Z61fZIJBJkZGQgIyMDpaWlYBgGKysrnMeSzWbDu+++y9UrZWZmQq1Wr8taNwqRnstIxJJUKg0o8KZiiUIRBiqOKEcFfM+ZSLvREpFWczgcaGtrg8PhwJ49e6DRaOB0Ork1Rrt5iWnwrFQqhVarhVarhUKhwNzcHMrLy2EymTA9Pb2uhpQbiVgETKRiyb9miYolCiU2qDiibHhiHQEidFptfn4e7e3tyM7Oxq5duzg/IbKWWI4lJnHkj0Qi4UQQsOqxRAwpx8fH0dXVhbS0NB+PJYVCsc6rXl+ESvnxxRL5fDAMA5fL5ePeTcUShRIbVBxRNjTxeBcJJTwYhkF/fz/GxsZQX1+PoqKioF5FYhU5sRDsfZbL5cjKykJWVhaAVU8n4rE0NDQEq9Ua4LEkhCGlP2KvORIa/kw4/jGoWKJQYoeKI8qGhO9dxLJsTKaOQqTV7HY7Wltb4fF40NTUhPT09KDHIWuOFjFHjtZal0KhQE5ODnJycgCseiwRsdTX1weHw3HMGVImy70b8BVL5D+n0wmXywUAVCxRKGGg4oiy4WAYBh6PJ+o0mj/xCo+5uTm0t7cjLy8P9fX1ITf2o1UcRYtSqUReXh7y8vIArApLIpa6u7vhdruh0Wg4j6WMjIyj0ucn2QKE360pk8kCxFKoIbrJdJGnUMQGFUeUDUMk3kXREGvNEcMw6O3txeTkJBoaGlBQUBD2/vGKo6MV4rFUUFAAlmVht9s5j6WJiQkwDOPTCZeenr7h3w8xpPzCiaWFhQWMjIxg69atnFjiz4Vb77VTKMmCiiPKhsB/BIgQRoSxpNVsNhsMBgMAoKmpCWlpaREdB4i93kSMkSOhN0mJRILU1FSkpqaiqKgILMvCarVyYml4eNinAFyn03GGlMlYn1CI9Vzy3y+73c79cHA4HNx9gg3RFev7TKHECxVHFNHD9y7iuwrHS7SRo+npaXR2dqKoqAibNm2KeB1Ha1otkeuSSCRIT09Heno6SkpKwDAMZ0i5sLCAgYEByOXyAI8lMb9fgDgiR+Eg9Xvks82PLDEMQ8US5ZiBiiOKaInFuygaIt1IvV4venp6MD09ja1bt3I1M4k4VrDHUVaFrEajgUajQVlZGRiGwfLyMkwmE2ZnZ9HX14eUlBRkZmbC4XBEFNFbL8R8ToOJt1BpOIZh4HQ64XA4OEFFxRLlaIGKI4ooidW7KBoiSatZLBYYDAbIZDLs27cvZgfoeCIaYo6ErBdSqRQ6nQ46nQ4VFRXwer2cxxKpm5mbm/NJw6WkpKz3skV/LiOJbPmn4YhY8nq98Hq9Ia0DqFiibCSoOKKIDuLPkohoEZ+10mqTk5Po6upCaWkpampq4krnxRM5EuOGKrZNTiaTQa/XQ6/Xw2azISMjA+np6TCZTBgZGYHFYkF6ejonlNbLkFLsaTWGYWKyxPAfosuP+pK/+6fh6ABjipih4ogiGsgFlXSjJfqXZqjIkcfjQVdXF+bn57Fjxw7OpyfeYx1N4ggQbxSEZVnIZDJkZ2cjOzsbAOByuTjbgMHBQU5A8SNLyfBYErs4EmJ9ocSSx+OB2+2mYomyIaDiiCIKWJbFysoKBgYGUF9fn5QQfDDhsbKyAoPBgJSUFOzbtw8qlSphx4r0cZT4SUlJQW5uLnJzcwGsGlKSTrienh64XC7OY4kYUibKY0nM55T8KBGSaMQSf4ju0ehxRdk4UHFEWXdItMjlcnHeQcmAn1ZjWRbj4+Po7e1FeXk5qqur16X4OxhijdBsZJRKJfLz85Gfn8+1rBOxNDU1BY/HA61WyxlSpqenC7JZi/1cJsvBO5xYAoK7d1OxREkmVBxR1g1/7yKZTAaGYZKWeiBpNbfbjc7OTphMJuzatYubDSb0sY6mtJqYox9AdOuTSCScIWVhYSFYloXNZuPE0tjYGFiW5dJv8RhSij2tFkvNUbyEEkvkBxNAxRIl+VBxRFkXiCghNT98b5VkiiO73Y53330XqampaG5uhlKpTNixIhU5NpsN09PT0Gq1AMQbbRDruuJFIpEgLS0NaWlpKC4uBsuynMcSMaQk3XIkDZeamhrxZ1bM4kgM4i2YWCLXC7fbjampKS4FSsUSJVFQcURJKvwLnX83Gv9imIx1rKyswGg0oqamBhUVFQkv/o7kdc3MzKCjowPp6ekYGxuD1+uFVCrF2NgY9Hp9WFdoyipCf34kEgkyMjKQkZGB0tJSMAyDlZUVmEwmzM/Pc4aURCgRQ8pQaxPz+UtEzVG8kHokwvz8PBQKBdLT0+FyuTgxxY8syeVyUb/PFPFDxRElaazlXUQuygzDJLRzyOVyoaOjA2azGVlZWaisrEzYsQhriSOGYdDT04OpqSk0NDRAr9cDAMbGxjAxMeETsSAbsF6vj9l3KV6O5Y1HKpVCq9VCq9WivLwcXq+XM6Scnp5Gb28vlEqlj1giEUmxR9vELt6AT64Pcvnq9sX/wUXEklQqDSjwFvvroogLKo4oSYE/AiRUJxpfHCUKk8mE1tZWaDQalJSUwOl0JuxYfMKJI5vNhtbWVrAsi+bmZqhUKu4in5qaCoVCge3bt/u4Qs/MzKCvr4/bhPV6PTIzM0VhdHisIZPJOBEErFpBEEPK8fFxdHV1IS0tDZmZmfB6vaIWSOtRcxQt/tEt/8hSKLHkX7Mk9tdJWV+oOKIklGhGgMQ7oHWtdQwPD2NwcBA1NTUoKyvD8PAwNysq0YQSR7Ozs2hvb0dhYSE2bdrEFaXzH0fwd4Umm7DRaMTo6Cg6OzuRlpbGCSWdTsf9uk4EYt7k13Pjk8vlyMrK4gr73W4357G0uLgIl8uFDz74wMdjKZHnKRo2SuQoXOqPL5bIZ5QYy4Zy76ZiieKPOL6RlKOSaEeAkNoBoSNHTqcTbW1tsNls2L17N1fonIhjhcJfHDEMg76+PoyPj2PLli0oKCgI+dhQIsR/EyZGh0ajEf39/XA4HMjIyODEUiK9e8SE2ESbQqFATk4OcnJyoFKpYDKZkJ+fD5PJ5HOe+B5LyTCkDMZGEUfRFr9TsUSJFiqOKAmBeBdFOwJEKpUKKlgWFxfR1taGzMxMNDc3+4yMSGabPP9Ydrsdra2t8Hq9aG5uDjskNZo1+hsd2u32AO8efodVRkZGzBsA3Thig7h35+XlcQOM+R5L3d3dcLvdXDeWXq9HRkZG0kStGAuy/VkrchSOcGLJ6XSGtQ6gn/ljCyqOKILi710UrdO1UOKIZVkMDAxgZGQEdXV1KC4uDljHWrPVhISInPn5ebS1tSEvLw/19fVrRgjiuSD7e/dYrVZuEx4ZGYFEIgko7qYbQGIJFplRqVQoKChAQUEBWJb1EbUTExNgGMZH1MbqsRTr+sSGkAKOL5ZkMhnnscSybIBYIsXdcrmcDtE9BqDiiCIY/t5FscxKEiKa43A40NraCpfLhb179yIjIyPksZKVVmNZFlNTU1hcXERDQwMKCwtD3pf/ngkV3ZJIJEhPT0d6ejpKSkp82tHn5uYwMDAAhULhI5bW8nwSW/qKj5g3rrVSy6mpqUhNTUVRUVGAqB0eHoZEIvERS0LaO2wEcRRP5Ggt+Ncsf7FE6hNJGk6hUHCRJSqWjj6oOKLEDb87hFxcY71QxBs5IpGZnJwcNDY2hi10TVZazeFwwGazwel0oqmpCenp6Ws+hv/+JWKNwdrRSYfVxMQEuru7kZqaygml9ZpiHwtiFm3Rri2YqCWGlIuLixgcHIRcLvcRS/FEABNtoyEEyeyoo2Lp2IWKI0pc+BddxztZO1ZxxDAM+vv7MTY2hs2bN6OoqCiiYyV6I11YWEBbWxukUimqq6sjEkZ8kiXgZDIZ9Ho99Ho9qqqqfDqs+FPsSXG3mAWImIk3MiOVSqHRaKDRaFBWVuZj7zA7O4u+vj6kpKT4eCxFMzxZ7JEjIkzWqy4qUrHk8XigVCqhUqmoWNqgUHFEiZlIvIuiJRZxZLfbYTAY4PV6I47MAIlNq/Frnurr6zE9PR3T+7Nes9X4HVbAasef0WjkioZdLhekUilGRka44m6xF/KKBSE3SX97B34EcHJyEj09PVCr1T5z4cJ5YYm9IJt8F8SyxlBiqaurC7m5ucjLy+NGI/ELvKlYEj9UHFGiJhrvomiJVgzMzs6io6MD+fn5qKuriyolkKjIkdPpRGtrK5xOJ1fzND09HfPgWTGgVCp9ioanpqYwPDyMlZUVn8GsiaiDOZpIdGSGHwEEVg0pSQSQ74XF91jip0vFHjniz2IUI0QssSzLiSFyvfR6vSGtA6hYEh9UHFGiIlrvomiJNHLkP24jnE9QKBIRlVlcXERrayuysrKwa9curuYpHiEmthSWRCKBUqmEQqHA1q1buTl1/nUwkcwaSwRi3uCTfS7lcjmys7ORnZ0N4BMvLP90KTlPXq9XtO8dAJ9mDzFDareCDdHl/7gkfyddcPy5cGJ/jUc7VBxRIiZW76JoiEQcWa1WtLa2AgCam5uRmpoa07GETKuxLIvBwUEMDw9j06ZNKCkpCXh/Yo0ciU0c+SORSALqYEhqh8waU6lUPsXdx/KYk/Xc9Py9sJxOJ9cJ19vbC4fDgZWVFXi9XlEah4otrRaKUB11ocSSx+OB2+0OEEtEMFGxlHyoOKKsCd+7iNQkJOqLupZgmZ6eRkdHB4qLi7Fp06a4LpJCpdVcLhdaW1tht9uxZ88eaDQawY4l1gtiuHXxh+MCvqmd4eFhWK1WpKenc2JJq9WKZnxGohFbVEupVCI/Px/5+fkAgJaWFqSkpMBut3PGoVqt1sc4dD2FCelUE9N7GAxSh7kW0Ygl/hBdsYvDo4Fj44pEiRmGYeDxeBKWRvMnlIjwer3o7u7G7Owstm/fzv3yjQchIkdGoxGtra3Q6XRoamoK2+5+tKTVCJGuK1hqx2QywWg0ore3F06nExqNhuuE02g0R+3FX2ziyB9S4E08lmw2GxdZIrVlyTKkDMZGGIwLxO7FFKlY8nfvPlq/L+sJFUeUoJAv5MLCAhdaT8ZFKVhazWKxwGAwQC6Xo7m5WbD6lXhSVmSQ7cDAADZt2oTS0tI1zf2O1rRatKSkpPiMz7Db7VwnnBCO0GIWIGLvBuOLD4lEgrS0NKSlpaG4uBgsy3IeSyQKSMQUOVepqakJfe8TaQApJEL5RYUSS263O+yok43wHokdKo4oARBhZLPZ8MEHH+D0009P2mbDF0csy2JychLd3d0oKytDdXW1oF/6WFNdLpcL7e3tsFgs2LNnDzfINhHHEusmLyRqtRpFRUU+jtBELJEN2L+4eyO/L2JeezhhKZFIkJGRgYyMDJSWlvq4rM/Pz2NgYCDhhfhiF5eERIm4YGKJWKq4XC4aWRIQKo4oPvh7FwHJTeuQSInH40FXVxcWFhawY8cOzm8nEceKBpPJhNbWVmg0moBBtmtxNKXVEllzRhyhyQbsb3KoVCp9NuC1xpyICTGeSz7RRN2CuayTc0UK8YU+VxshckQESzLWSeqR/I/tdrvhdru5+/DFEumGo4SHiiMKgPDeRcmaPwasXnDtdjveffddqFQqNDc3R+XwGw3R1ByxLIuRkRH09/ejpqYG5eXlUV1goo0cEeF2NKbVoiGYySEp7h4fH0dXVxfn20M64cSMmFN+QHyRGZlMFlCIT7oWybkiI2nIf9GOpNkINUfr6cUUTiyRyJJUKg3aDUfxhYojSkjvIvKFIbcnYx02mw1LS0uoqqpCVVXVuhR/++N2u9He3o7l5WUcf/zx3MU/WuJJq4lxU10P0SaTyZCVlYWsrCwAq+eG1MD09/fD4XBAKpVibm4OSqUSGo1GVLPCxHge+Qi5PrlcHnCu+F2LHR0dXNciMaRcq2txI0SOxGRUGU4sWSwWjI2NYdOmTQHdcGL+jCYLKo6OcRiGgcvlCuldJJPJkhI5crvd6OjowPLyMvLy8lBdXZ3wY0YSlTGbzTAYDEhPT0dzc3PM/jy05igxKBQKH98eh8OBI0eOwO12o7Ozk2tFJ51wGRkZ6/6ervfxw5HIyIz/SBrStcgXtnxDSq1WGyBsN0LNEbleikmUE/hiyeFwYG5uDrW1tQGRJf+aJTF/ZhMFFUfHKCSN5na7w3oXxToINhqWlpbQ2tqKtLQ0FBYWJu3iR8RRsF/LLMtibGwMfX19qKqqQkVFRdwXiKMpciSmtfBRqVRISUlBcXExcnNzYbPZuOLu0dFRAPBJ6yS6u8ofsadIk/k58+9adDgcnFjq7u6G2+2GRqPhzpVGo9kQaTXiMi72dZIfxEQskc8m+cEcatTJsSKWqDg6BolmBEgixVGwOp7+/n6ukDDR8AvO+a+fRLGWlpbQ2NjIzamKh3hrh8S4qYpxTXz4reglJSVgGAYWiwVGo5HrrlIoFDFPsI8FsYlcf9ZzfSqVymd+n91u58QSsXhQqVRc4bcYooDB2AipP2BVxPGjW+S9DCaWnE5nWOsAMZ6HeKHi6BiDHy2K5NdNosQRaYdfWVnxqeMRcqTHWvALzsnFbHl5GQaDAWq1Gs3NzYJ1QsX6uviRI0rkhIqCkjEnpLuKP8G+u7s77oLhWNcmFsSStpJIJEhNTUVqaqqPxcPIyAhMJhMMBgMA+HgsiWXY8UYVR/7wxZJMJuOi7CzLBoglUq8kkUigUChEcR7ihYqjYwT+CBAAEYd9EyGO+O3w+/bt89mAhBrpEQn+xmrj4+Po7e1FZWUlKisrBf2Cx/pcR8NFJtlE+vnxn2AfrGCY1MCQMSfx1pGIPXIk1rQVsXjQ6XRgGAZbt24NGHbM75ZbTz8soQwgE0206+TvGf5iyeFwAAA+9alP4Zvf/CYuvfTShKw5mVBxdAxAuhNi6aIQUhyxLIuhoSEMDQ2htrY2qKt0MmqcCOTYbrcbXV1dWFxcxK5du7juGqGPFenrYhgGk5OTUCqVyMjIACC+yJEYN9B48S8Y5g9l7e7uhsvl4uaM6fX6mOaMie08+rNRxJtEEjjs2N8PKyUlJakpU0Kkc9XWm3jXGUwsWSyWmAeBiw0qjo5i+G2bobrR1kIoseJ0OtHW1ga73Y7du3eHdJVeD3H04YcfQq1WY9++fQkzFIz0fbfb7TAYDNw5czqdAIDR0VHk5OSs++BPPmLf6OOFP5SVXwNjNBoxPj7OzRkjnXCRpHXELj7Evr5QKatgflj8lGlPTw9UKpWPWIq18zTWNYqNtdJqsWCz2ZCeni7oc64XVBwdpURTdB0OIcTKwsIC2traoNfrsXPnzrBeJsk0PZyamgIA5OTkoL6+PqGbQiSRo/n5ebS1tSEvL4/zeLLb7Xj//fdhtVq59ZINWa/Xb/hRGolC6PckWA0MmTNG0jqRjs4Q8/kSS81RKCJdn3/K1OPxcCnT0dFRdHZ2cuahxGNJqPqyY1kcWa1WKo4o4sV/BEg8F+N4xBHDMBgcHMTIyAjq6upQXFy8bgXgfPijSQBE7XYdC+Gen2VZDAwMYGRkBJs3b0ZhYSEXOSIbbF1dHVJSUrCyshLQbUUiF3q9PmG/hqN5PccCweaMkUgFf3QGOTckUiH2aNtGjRythVwuR3Z2NrKzswGsNoQQsTQ4OAibzebjsaTT6WIWDhul5igR6T8qjiiiJNwIkFiRSqUxOWQ7HA60trbC5XJh7969XO1MJMdLpDiyWCwwGAxQKBRobm7Gm2++mZQ0XqjIkcvlQmtrK+x2O/c+8TdQf5+jYN1WJM3T1dXFOQ6TURob4SItNOshQPjDcYHgkYr09HS43W6kpqbC4/Gs6Qa9HmwEcSTE+lJSUnzMQ/n1Zb29vXA6nT4eS1qtNmIhsVEiR0KLOK/XC7vdTsURRVwIlUbzJxaH7Lm5ObS3tyM3NxeNjY1RbQKJFEdTU1Po7OxEaWkpampquPcoGZtpsHNBuva0Wi2am5uDvk/hWvn9Uwd8x2Fygee7Q2s0GkE3PrFHQdaTYJEK4gQ9OzuLycnJmDffRBHKEFVMJCrtx68vA+DjsTQ1NcU5rZPzFa72byMVZAspjqxWKwBQcUQRD8S7SKhoEZ9oxArDMOjr68P4+DgaGhpQWFgY9fESIVa8Xi+6u7sxOzuLHTt2cN1IQPKsA/ivi2VZjI6OcuaXZWVlYc9ZpOeT7zjsX0A8NjYG4BN3aFqvlFzIuZmcnERhYSG0Wi23+U5OTsLr9foUd6enpyf93JDPp5g3doZhkhJxU6vVUKvVKCws5GY+kvM1NjbGFeOT7xP/fG2UyJHX6xU0DU/FEUU0+HsXCS2MyHNGIo5sNhtaW1vBMAyamppi/oIIHTmyWq0wGAyQyWRobm4OKJJNlukkEUcejwcdHR0wmUw47rjjIh5iG62AC1ZAzK9X6u/vR0pKSsz1SmIXVWJdHzmP/puv1WrlhOzw8DDXfUXOTzKELFmbWN87YH18mPhO68XFxT7F+MQTi5yvzMxMOBwOUb+HhEREjpRKZUKMU9cDKo42KP7eRYma5ROJWJmZmUFHRwcKCwu5Cc+JPF6kTE9Po6OjAyUlJaitrQ3ZApysyJHb7cahQ4egUqmict8WIpoWrl5pbGzsqKpXEnO6L1jaihgcpqenc2NOiJDle/bwi7sTYTmxUcTRekdlghXjE0PK+fl5LC0tcd/ZtToX1xOh30ur1Zr0WYWJhIqjDQbfuyjSESDxEE6seL1e9Pb2YmpqClu2bOHy9Yk6XqR4vV709PRgenoa27Zt4wZbBiNZNUdmsxlLS0uorKxEdXV1VOcsEWsMVa9kNBoD6pWI4WGw4byU6ImkY1Or1UKr1fp49vAL7/lt6JmZmYKkmvg/tMSKGK0G+OervLwcPT098Hg8UKvVPp2L/POVKD+1aBA6cmSxWJCWlibY8603VBxtIPyLrpMx+VkqlQYdBEvSVVKpFM3NzYK5osYrBGw2GwwGAyQSSUTrSnRajWEYdHd3Y2ZmBunp6aipqVnzMf7nNRmbVbT1SlQYxUYsBc/BxpyQlM7g4CDsdrtPG3qsY05o5EgYWJZFamoqKisrAax2LhKbByJukzHDby0SkVZbj1q5REHF0QaBYRjMzc0hJSUlqaHLYJEc0vUVLl0l5PEihZ/eq6uri2hdiUyrEbdrlmVRXV2N+fn5mJ8rmWIkknolhUIBj8eD2dnZhLoNx4pYL9BCnEeFQuHThu5wODix1NXV5dNZFSrqF25tYn3vAPHOfuPjL+DkcjmysrK4sUTBZviRlDbxWEpG0bnQrfw2m41GjijJg+9d1NfXh6KioqR+APk+R/yur+3bt3MXZ6GPF604YhgGvb29mJycjDq9l6i0GnG7zs/PR319PWZmZmI+TjJdw0Md379eaXp6GgMDAz4ePqQmZiPXKyUDoTd3lUqFgoICFBQUBO2sAuBT3B3qx1Uy0vTxshEiR2uJDv8ZfnwLjv7+fjgcDkEigWshtOUATatRkoZ/Gi0Wz6F4IZGVlZUVtLa2cuaJiSowjDbN5d8lF+2XU+i0Gt/tmm9nEI/AEdtmJZPJOF+e3bt3x1SvlEjEnPJLtI9QsM4qfrHwwMAAN+aEiCUykHUjRGXEWHPkT7Sig5/SBnwjgf4Dj4lfmRDvQSLSalQcURJOMO+i9RBHEokEVqsV7733HsrKylBdXZ3QixMRY5FsIsRsMj8/H3V1dTF90YVMq5Hhug6HI8AVPN7oj5g3fOqvFDnJPo/+0+u9Xi+Wl5dhNBp9BrKS8yJ2NkrkKJ41+kcC+YaUExMTYBgmwJAylu9TomqOjhaoOBIZfO8i8iuJfPBjHeURKx6PB1NTU7BarWhsbOTcfhMJuaiEE0dCmE0ShEpZmUwmGAwGZGZmBh2uG+1x/AuyxSaOQp2bcPVKc3Nz6O/v5zp3SORCbPVKiWS9HahlMlnQMSdELDEMgw8//FCQGWOJYCNEt4Ss5Qn2fSKeWGQ0DQAfQ8q0tLQ13yPS9UwjR6Gh4khEMAwDj8cTcgRIMiNHy8vLXDdaRkZGUoQR8Ik4CvXry263o7W1FR6PJy6zSUK8abVI3a6jFTj+89XEJo4iJVi9UrCZY0LXK4l1A11vceQPf8zJ8vIyWltbUVJSEjCChl/cvZ6Rm2MhchQOf08sftp0cXERg4ODPgI4lIEoueZRcRQaKo5EQKTeRcmIHLEsi7GxMfT19aGyshJpaWkYGhpK6DH5hJslRoqc8/LyUF9fL8gXO560msfjQXt7O8xm85pu1xtZ4IQiltcjk8l8Onf49Uo9PT1cfUU89UpH2/ucLEikmswYC5XSIVEKvV4fUZQiEWsUM8kUcP5pU4ZhsLy8DJPJ5GMgyhdLKpXK5we4UFitVhQUFAj2fOsNFUfrjP8IkHDdIjKZLKHiyO12o6OjA0tLS2hsbIRer8f8/HxS65z4kSMCwzBcZ9TmzZtRVFQk2PFijRytrKygpaUFarUazc3Na6aG4i3IPlo3/GD1Skaj0afT6miqVxJb5IiPf8oqWEon2NgM//OT6DWKXRyt5+BZMsZEp9P5GIiSGX6kxkyj0QBY/YEnVPSI1hxRBINEi7xeLyQSyZpfKKlUCpfLlZC1LC0twWAwICMjA/v27eM2e6Fnna0Ff3gjsNq50draCpfLJUgazZ9YIkeTk5Po6upCeXl5xG7XVBytDX8z5ndaHU31SmIWR2utLdjYDBKlmJmZQV9fX8LPz7FWcxQv/gaipMZsbm4OAPDOO+/4uK3rdLqYDSmpzxElbvjeRfxutLVIhFBhWRYjIyNczUx5ebnPWtZDHBExsLCwgLa2NmRnZ6OxsTEhxmjRCA8ylmRmZgY7duzgfEqEPk6wx4qNZKwpnnolMb5nBLGuLdqUlX+UgjhBG41G7vykpaX5nJ94v8M0rRYfpMYsJSUFRqMRu3fv5r5Tg4ODsNlsPh5L0dQAWiwWGjmixI6/d1GkwggQPq3mcrnQ3t4Oi8WC3bt3Q6fTBdwn2eIIWN08hoeHMTU1hfr6ehQVFSVsQ4k0reY/liTa9MHRGDlK9poiqVfS6XRwu92wWq3Q6XSiEyJiPI+EeKNa/k7QLpeL64Qj5oYajcbH3DAaEUFqM8UqPAgbYY0k9ZeSkuLjtu50Orm0KSnIj/ScWa1WH/uSjQ4VR0mEYRi4XK6ookV8hBQqRqMRra2t0Ol0aG5uDhlKTbZ9gNPpBMuymJ+fD/AKSgSRCA/ip1RQUBDxWJJYjhMOMW+q60WoeiWz2YzBwUEMDQ35pHjEUK+0kdNq0eK/8fKLu6empuDxeHyKu9eay7VRxptshOhWKI8jpVLJFeQDwc+Zv8cSea2xpNXefPNN3H333fjoo48wPT2NZ599Fueffz6A1RrYW265BS+88AKGhoag1Wpx+umn40c/+lFY+5bbb78dBw8e9Llt06ZN6OnpiWptVBwlAZJGI91osQgjQJjIEcuyGBoawtDQEDZt2oSSkpKwa0mmfcDi4iLa2togkUiwdevWpPwKCVdzxLIs+vv7MTo6mnQ/JbH7HIkNfr3S2NgY6urqIJfLYTQaua4dsdQriXVzT3Q9j1qthlqtRmFhYYBfz8jICCQSSdjie3IdErPw2AhrBCI3gPQ/Z/6jaV555RW899572LdvH4xGY9QDyK1WK7Zv344vfOELuOCCC3z+ZrPZcOTIEdx6663Yvn07TCYTrrvuOpx33nk4fPhw2OdtaGjAyy+/zP07lnQuFUcJJp40mj/xChXi4Gy327Fnzx6uYyEc0ThWx4q/YBscHExqK2yw99TpdKK1tRVOp1MwPyVac5Q8QtUr8eth1mMe3LEUOQqHv18PwzCcX8/c3BwGBgagUCh8xBK5JohZeCTCPygRxOKOHWw0TWZmJuRyOd5++23Mzc3hggsuwBlnnIFTTz0Vp556KjZt2hT2M3XWWWfhrLPOCvo3rVaLl156yee2n/3sZ9i9ezfGxsZQWloa8nnlcnlUMzaDPkdcj6aEhR8tEmKgYzwpLlLcnJWVFdTBOdwxgcR1YLhcLrS1tcFqtWL37t3QarUYGRlJWrQqmGjhu13v2rVLkEJwmlZLHsE2+bXqldxud0yT7GNZm1hZz3SQVCqFVquFVqvlxCxpQZ+YmEB3dzdX57ewsAC9Xh9zV1UiIddnsQpgghDXcxLh37p1K1iWRU5ODh566CEMDQ3hmWeewY033ojMzEw8/fTTaG5uFmTdZrMZEokkaH0sn/7+fhQWFkKlUqGpqQl33XVXWDEVDCqOEkA03kXREEvkiO8RFEtxcyLFEREh/nVPySwC56fVSOfewMAAamtrUVpaKthF7mhMq4lxTZESqb+S0PVKNHIUGfwW9KqqKrjdbszNzaG3txfDw8Po7OxERkYGd34SNbk+WmKtJ002QnsxeTweuFwuNDc345JLLsF3v/tdOBwOvPfee6irqxPkGA6HA9/+9rdx6aWXhs167NmzB48++ig2bdqE6elpHDx4ECeeeCI6OjqiKtWg4khgiHdRInLP0UaO+KM2Yi1uDmbKGC8sy2J4eBiDg4NBRYiQw2DXQiKRcBG+jo4OmM1mHH/88Wv+MonlOJG+JoZhuO6erKwsLq1JSQz+/kr8FE8i6pXEunGK2UNIoVBAp9NBKpVi7969cDqdnJjt7u6G2+2GRqPhzs96jTnZCJ1qgPBDZy0WCwD47DEqlQonn3yyIM/vdrtx8cUXg2VZ/PKXvwx7X36abtu2bdizZw/Kysrw1FNP4Ytf/GLEx6TiSCD4I0AS9eshmsgR6bCKd9SGvyljvBD7gJWVlZAiJN55Z9EglUrhdDpx6NAhpKamRuR2HQuRiiO73Q6DwQCWZaHVajE6OgqLxQKXywWr1Qq9Xs9tEuuJWDdRQjzrC5biCVWvpNfro4paiFnkiilyFAx+2k+pVAZMrudH/kg9TDTDWIVATAaQ4RBaHFmtVgBIiM8REUajo6N49dVXI6qV5aPT6VBbW4uBgYGoHkfFkQAIWXQdjkgiRwzDoLe3FxMTE3F3WAHgnLuFECvEhVuj0WDfvn1h7QOSJY5WVlYwPz+PqqoqVFVVJdRPaa2NcXFxEQaDAXl5eaipqeEutB999BFSU1PhcrnQ1dXFtUCTzTnZ863EjtACJFi9kn/UIpJ6JbG3oou9BT1UZCuUszp/GKtcLvcp7lapVAlZ43qODokGoUWc1WqFWq0WXBgSYdTf34/XXnuN+w5Gg8ViweDgIC677LKoHkfFUZzwR4AkOtdMIkehfuHZbDa0traCYRg0NzcLZuUer1ghk+v7+vqCunALfbxI8Hq96O7uxsLCAjIzM1FdXZ3Q4/EH6vq/dn6asb6+HsXFxZx7OrB63jUaDTffirRAG41GDA0NQS6Xc0IpMzMTSqUyoa+Fv+5jkZSUlIDhrEajEUajMWi9Emlv3gjiSKxrAyJPWQUbxkqKu6enp9Hb2wuVSsWdI51OJ1i0eCOl1YSMkFutVqSmpkb9+bFYLD4RneHhYRgMBuj1ehQUFODCCy/EkSNH8I9//ANerxczMzMAAL1ez63/tNNOw2c/+1lce+21AICbbroJ5557LsrKyjA1NYXbbrsNMpkMl156aVRro+IoRmIdARIP4YqjZ2Zm0NHRgcLCQmzatElQBR+PhYB/LU+4yfWERKfV+G7X5eXlsNlsCTsWIZQ48ng8aG9vh9ls5rr1Qj2W/H//FmgysmF8fBxdXV3r0qJ+rBJpvRLZhMljxIiYa46A2IUHfzgu8Ml8MTI8lwxM5YulWL8zG0kcCV1zFMuP8cOHD+OUU07h/n3DDTcAAK644grcfvvt+Nvf/gYA2LFjh8/jXnvtNa6eaXBwEAsLC9zfJiYmcOmll2JxcRE5OTk44YQT8N5770U17gmg4igmkpVG84d8mPniiMz7mp6expYtW+L2dggGKVqOFrPZDIPBgLS0tKhqeRJZkD03N4e2tjYUFhairq4OY2NjXL48kfDFEWFlZQUtLS1Qq9Vrvj+h3g/+hb+qqipkizqJLK3lQhzt6xEr67W+teqVAKClpQVZWVlR1yslGrFHjoRK+5H5YtnZ2QB8bR34IzPIDwyNRhPxcY/lmqNYri0nn3xy2Gt9JPvAyMiIz7//9Kc/RbWGUFBxFCVer5f7Eh133HFJvZiQL6jX64VCoYDFYkFrayukUimam5ujdieNlGgjRyzLYmxsDH19faiqqkJFRUXU9gFCR45IB9jY2Bi2bNmCgoICAMkr/vYXR9PT0+jo6EB5eTmqq6vXnIYeqVj0b1EnjrZGoxEjIyOQSqWcUNLr9UlLwR2r8OuVPB4P3nzzTRQXF2N5eTmqeqVksFFrjuKF/50B4FPcPTExAYZhuDEnmZmZYUXARqo5EnKdNpstYfvPekHFUYTwvYtYluXMqJIJiVAxDIPJyUl0dXWhtLQUNTU1Cf1CRiNWPB4POjo6YDKZ0NjYCL1en9DjRQJxu3a5XAFu18nyECKfFa/Xi/7+fkxOTmL79u3c3Km1HhvLGv0dbRmGwfLyMoxGIyYnJ9HT04PU1FTuF3JmZmbUvybFGG0Qey1UXl4eVz8Wrl6JjNBIFmI8l3ySlbJSq9UoKiryqfEjYml4eNgnWuvvgXUsp9US0am2nlBxFAH+3kVCzDiLFYlEgp6eHphMJuzYsSPqPGosRCpWlpeXYTAYuDRRrFEJIQULGbCr1+uDul0ny1OJXDyPHDkCr9eLpqamiHP0Qm1YUqkUOp0OOp0OlZWVcLvdXFSpr68PTqfTJwUXLooh5k1UrPgXZIeqV/KfB8cvtk+kK7TYxdF6RLb4NX6lpaXcD4xgHliZmZlwuVzHpDiyWq2CNQCJBSqOwsD3LuKPAJHL5dzfkvlFWFlZAcMwsNvt2LdvX8LaUf1ZSxyxLIuJiQn09PSgoqIi7pZ4ISJHfLfrcAN2k5VWW1paArBqjLZt27Y1L0zBOtqERqFQ+ExN56fgSBSDvzEnM4pxNLLWOeTXK1VUVPjUK42MjHAOvySyJHS9ktjrZcRQMM7/gcE/RyaTCePj41hZWYFcLkdfXx9X3C3E+CGhSVTN0dGE+M6aSPAvuuaPAOEXRidDHPHFh0wmQ11dXdKEERBerHg8HnR2dmJxcRG7du2KyYcimuNFgtvtRnt7O5aXl0N2gPGPlcjIEbEx6O/vBwDU1dXFNPAxGdEtEsUoKiryiWKQ9me1Wh20PX29N6xgiHFNhEjXFqm/Et8VOp7XLdZzSRBjysr/HPX19cFms4FlWc7l3l/QiuE1CC2EbTYbjRwdC6zlXcTvFEv0rwJSw2M0GrFr1y50dXUlvaYilFhZWVmBwWCAUqnEvn37BCvujSeaQ1J7kbpdJ1J4EOFoNBpx3HHH4f3334/pedZjtpp/FMPj8XBRpcHBQdjtdgCrnSJZWVnrNq4hGGKtOYrX58jfX8m/2F4ikXBCKZZ6JbGLI7EXjBPS0tJQU1MDYHUeGDlHnZ2d8Hg8ggraWBG6cJzWHB3lROpdRG5PdN2R2WxGa2sr1Go1Jz6ina8mBFKpFLMrLkwNG1GuT0W+VsVNyo6k2yqW48XyGsmaKisrUVlZGdGaEpVWs1qtaGlpgUKh4OqvohE5Ytuk5HI5cnJyuBq35eVlHD58GFarFRMTEwDWr5B4oyCkaAtWbB9vvZLYxYcYI0f+MAzj8z6rVCqfMSc2m42L/hFrB35xdyxGitFCSkKETqvF0nwjZqg4+phovYsSWZTNT8X4b/TxGDJGwozZgRGjjRNBAPDSsBMPty6CxQgkEuBLOzOwI92CnTt3cl4h4R4fLdGm1bxeL7q6ujA3Nxd0TWsdS+hIw+zsLNrb21FcXIza2lrugh5P15nYoiEkrdvQ0ACJRBJ0Y87KyuIu+oksJN4oJNIhO1ikj5iDDg8PR1SvJIaannCIfX1AeAHHF7TEwNViscBoNGJ+fh4DAwNQKBQ+Y04SYbVB9i1akB0eKo4Abip7NE7XiRJHLpcLHR0dWF5exnHHHRfgKC1E5CiUgHnq8CRu+0c3GBaQAKgvSIfZ7sHkkoO7D8sCvzqygiKdCptmJ1CRbUJldioqstPQMWnGj/7VD4YFpBLgjnPrcVFjUdTri0Yc2Ww2tLS0QCaTxVSkLmTkiNQZjI6OYuvWrQGGnNGKHH5Xk9jEEYFEG8i4hvLycs6BmGzMnZ2dyMjI4KIY0ZjqxYoYN9Fkpq3kcrlPLYzT6eTSO/71SsQcVOxptY0SOYpUdPh/b7xeLzfmZHJyEt3d3UhNTfWJLAnxI4PfdS0UNpsNGRkZgj2fGDimxZG/d1E0TteJEEcmkwmtra3IyMgIWS8Tb+Tozx9N4nt//0QAHdici9QUGTqml9E3+4lTNAuga9qC3PTgNTtehsWb/Yt4tXch6N8ZFrj1b92AhMWukkyUZKqRIv/kwhYuwhSpGCARmqKiImzatCmmC6dQwsPlcqG1tRUOhyPASyneY4l5wwqGvwMxv+6ivb0dDMMEpOA22muMlfV6nUqlMmy9Ern2SSQS2O12UaZFxZ72A+Kr5ZHJZNx3AlhtLOGPORGqW9Hr9fo0GAkBjRwdRTAMA4/HE/MIECFrf8jg0YGBAdTW1qKsrCzkWuI57ozZgVv/1g2yPbMA/tk1BwDQqoN/FP7nQDVu/EsX+Fu6VAI8efXxyEpPwbjJjuEFG17vm8dTH035vi4At/y1BwAgk0pQkqlGRVYq3F4G7wwawSJ4hIlEjkIJKL7bdbAITTQIkVYzm81oaWmBVqtFU1NTyCL9eISY2CJH0XxX/OsugqUS+K7d8f46Ftt7RRDLukLVK3V1dWFlZQXvvfdeUv2VImWjRI6EWqNCofCp8yPRP9Kt6HK5AtzVIzl2IoakW61WGjna6ITyLooWoSJHTqcT7e3tsFqt2L17NzecMtxxY4kcWZwefOevnQh2ef75pduwpUCDU+57GwzvDlIJwMwP4fObJHiij/VJlxGxUpmdhsrsNDQUZODpI1MBj//WmTUwWl0YXrBheNGGtwYW4OEtn2GBW/7WjT9/NIm6ggxUZadBAztaR2148m9vB6ToHA4HWltb4Xa7Q0ZooiHeyNH4+Dh6enpQXV2N8vJywcaACLlGMSGRSJCRkYGMjAyUlZUFePn4p+DE0vosBGJNW5F6JbVazfleJSJiES9i92ECEivg/KN/drudi/6Nj4+DZVmfFFxaWlrQz5vQHkfAqjii40M2MPw0GoC4QotCiKPFxUW0tbVBp9Ohubk5ol9nkUaO+FGXeYsLNz7djlGjPfD5JMCWAg3ytSrccW49l3KTSoBLq4GaomzUlcpwQpUV6txSlIUotA72+GA1R+8MLOILv28JeHz/nAWtk8tBXwvDAt/7eze2ZsswNdiF7OxsNDY2CmKjEGvNkdfrRXd3N+bm5iL2dzoa02rxijZ/nxin08l183R2dsLr9UKn03FiKRndPIlEzGsnBc/+adFI6pWS8bo2SuQoGQKO765OxpxYLBaYTCYsLi5icHAQcrk8YMwJILw4IiNWaORog8L3LpJIJHF/yeIRRyzLYnBwEMPDw2Hdm2M9rn9dkUSymtb67qdqkZoiCxAwROxc1FiEfVWZeNvQC4l1EScdtxV5eXkYGhpCpkqC7RXhWzUvaizCidVZGDXaQoqoqpy01YiUX4TphWubIJdJMTBvxV8PD+PZTpPP4xgW+PXL7fjG6bUoLS0VdKRGtBu8zWaDwWCARCJBc3NzxEXg8YijZLh4iwGlUumTgiNzrcgFn3TzkI05lI+VGEWI2KN/oSJbkdQr8TusElWvdLTXHMUDPyJLxpyQ4m5i4kpSpXK5XPA10pqjDUik3kXREqs4cjgcaGtrg8PhwJ49e6DRaKJ6vFQq5SJfwZgxOzjxA6zW/bAs8KtLt+PEmtVfgqEEjM1mw0iXAcUpwI7dzVyYNJrusXytKmwLf6gIU4Fu9YKak6FEGmPFc52mgBTgP8YkGHpxBl87WY3TNuVAIpHEbR0QrfCYn59HW1sbCgoKUFdXF9VF5miqOUoG/nOtSDcPGW/S1dWF9PR0nxScTCYT7Xsl1rQaIRLxEc5faWZmJqHz4DZK5EgMa+QLVgBcB6nJZMLc3BwcDgc++OAD7j7xjjmhkaMNRrTeRdEQizian59He3s7srOzgw5BjfS4Tqcz5N9HjDafqAyB3ykWTMCQzq/CwsKATV+IWWd81oow5WuUuGKzAo91uzkBdcOplViwefDHDyfwtT+2YXNBBnaWaPHHDyfisg6IVLCwLIuhoSEMDQ1h8+bNKCqK3qLgaKo5Wo9N3r+bJ9g4DZ1Ox0U3xJiCE9t6+MQi3oL5KwWrVyJCKZ56pY3icyTGuih+qjQtLQ3T09MoLi6GyWTihk5rNBou+heN3YbL5YLb7aYO2RsFhmHgcrkEjRbxiaYwmt9dVV9fj6KiopjXs5ZQ6QhStyOVAGX64MVyDMOgt7cXk5OT2LJlS9DOL6HFEbB2hGlvLoOqNBaKzALsbajiIktX7yvDb94ZxR8+GEfX9Monr+PjuqQTq7OiiiCRtFq4jcHtdqOtrQ0WiyWmaB/haBJHhPVcV7BxGkajkRvVQGouiKBKhKFeNGyEyFG860tkvZJYojLh2Ahr9Hq9UCgUyMvLQ15eHgBwxd0mk4mz2+CPOQl3nqzWVQsYKo5EDkmjkW60RAgjIPLIkd1uR2trKzwejyDdVaFEmcvD4M4Xe/Hk4Uno1HIsOzxB64r812YwGMAwDJqamkLmjJM5ssTr9WJsbAxOpxOnNR0fUOick6HEdz5Vi10lWnzjqXafvzEsMLJoi0ockc9GqI1hZWUFLS0tSEtLi7hoPtyxxCpyNjr8dM/g4CCOO+44uN1uGI1GbqxMWloatynrdLqk/8IX+7lPRGQmWL0SifZFW6+0EWqONoo48v/sq9VqqNVqFBYWcrV+RNQODw9DKpVyjRGkuJt8ViwWCwBEXXP05ptv4u6778ZHH32E6elpPPvsszj//PO5v7Msi9tuuw2//vWvsbS0hH379uGXv/wlN7cuFD//+c9x9913Y2ZmBtu3b8dPf/pT7N69O6q1AUeZOEpkGs0fmUwGl8sV9j6zs7Po6OhAXl4e6uvrBbkY84UKqbdJS5HjBy/2omXcjKZKPe69cAtcHiZsYfTc3Bza29uRn5+/5qT4RI8sIVitVhgMBrAsy42fCMX2Ym1AYTcA3PfKAG6W16KxVBfRMcmFLNjGNTU1hc7OTlRUVKCqqiruz9LRGDkSK/xNt6qqCm63m7vY9/b2wul0+nTBJaPj6liIHIUj2PgM/3ollUrFCSX/eiWxp9WITYzYxdFaa+TX+vmfJzIeKCUlBe+88w60Wi2qq6uRmpoa9eu2Wq3Yvn07vvCFL+CCCy4I+Pv//d//4f/9v/+H3/3ud6ioqMCtt96KAwcOoKurK2QTzJNPPokbbrgBDz74IPbs2YP7778fBw4cQG9vL3Jzc6Na31EjjmIZARIP4SJH/FRVQ0MDCgoKBD0uwzA+HWmEL+4rww2nVUEuW/2QBhNF/BRfQ0MDCgsL1zxmItJq/szMzKCjowPFxcXIy8uDwWAIe/9ghd07S7QwTCzjv357GCdWZ+H606qQnZYStmCbHzkiMAyDnp4eTE9PY8eOHZwJW7wcTeJI7JuUPwqFgvPwIR4xJAXHj2AQsRTtGJpIEfv7lsz1RVuvtF6dYJFCrpFiXiMQfSu//3kijRH//ve/8dRTT6G/vx9SqRTXXXcdTj/9dJx88snQarVrPu9ZZ52Fs846K+jfWJbF/fffj1tuuQWf+cxnAACPPfYY8vLy8Nxzz+GSSy4J+rh7770XX/rSl3DVVVcBAB588EE8//zzePjhh/E///M/Eb9m4CgQR/7eRckQRkBocUTavAGETVXFilQqxbzVje+96SuMJBLg8j0lnDAKhsPhgMFgiDrFl0hxxDAM+vr6MDExwdU8LS8vR3S8YIXdwwtW/Oz1ITzfMYu3BhY/eQ0hCrbJZ4Ucj7xHXq8XTU1NghqbRSNyvF4vVlZWRN8BIjbRFgl8jxjScbW8vAyj0YipqSn09vZCrVb7pOCE8NTaCJGj9dzYw9UrdXV1weVyYXh4GDabLan+SpGSiJlliYDUHMUKaYw4ePAgDh48iOeeew433ngjvF4vvv3tb2NgYACNjY04/fTTcccdd8T0fgwPD2NmZgann346d5tWq8WePXtw6NChoOLI5XLho48+wne+8x3uNqlUitNPPx2HDh2Keg0bWhwR76L1UOzBxNH09DQ6OzvjmvW1FrNWDx7vcgWkk1gWGDWGrrchLei5ubnYvHlz1L8cEiGO/N2uiZCMxzqgIjsN91y4FRfsLMQXHvvEbJIUbDdX6VGk+6SugZ9WMxqNMBgMyM7ORkNDQ0IucpGICavViiNHjsBms0Emk0GpVEIqlcLhcCQsonG0Ee0oIJ1OB51Oh8rKSm6mldFoRH9/PxwOh08RcUZGRkybstiFpNjSVv71Su+++y50Oh3MZnNAtI9vcrhebKTIkZDXERKV/cUvfgEAmJiYwKuvvor29vaYr6EzMzMAwBWME/Ly8ri/+bOwsACv1xv0MT09PVGvYUOKI5LbHRsbg8PhQEVFRdK/1HxxRNySZ2ZmsHXr1oCTIwTd0yv4zTsjeLFjFt4g19hQHWkMw2BgYACjo6Mxt6AnQhwtLi6itbUV2dnZOO6443y+REIcTyYN/DwwLPCfv/4QV+8rwwU7C6FRK7jPzdjYGEZGRqI25YyGSAwn5+bm0NbWhqKiIpSVlcFms2FoaAjLy8s4dOiQT0QjMzNT9L9SNyL+M634KbixsTEACBicGwkbIXIk1vWRdeXm5kKn0/nUwRCTQ5VKxX0v1mMeXCIGuiYCoeuibDabT4akuLgYl19+uWDPv15sOHHEL7p2OBxYXl5elw8jKYy2WCwwGAyQyWTYt2+fIL9eSKF1WaYaI0Y7fvP2CN4eNAIAdpdq0KSzIKd8U0inawKJzLhcLuzduzfmFI2Q4ojvF1RXV4fi4uKA80fST/FcrMv1qQEF2xKsdvXd9a9+PPDaEM7fXoBLj1utBxsfH8fxxx+/5my7eAkljviu6Q0NDcjPz4fL5YJGo0FWVhZkMhnq6+u58QDEmyTZRcUEMW8AQkdo1Go1ioqKuDENJAXHLyLmC9ZwKTixv29iXh9/U0+mv1Is6xMzQo8PsVgsgpePEEuZ2dlZn5rd2dlZ7NixI+hjsrOzIZPJMDs763P77OxsTMPJN5Q44o8AkUqlkMvlSWsx94eYMR46dAilpaWoqakR5IsRrNBaKgHO2ZKHL+4rQ2mGBO+//z5OX8NIkR+ZiXcOmVDiiO8XtHv37pBFe/xUV6wX61BO3Odty8fzHbP4/fvj+MOHE/jDhxOo00pxzcmV0GjWLiKMh1CRI4/Hg7a2NqysrHA+Svz3m4hFuVzORTSCFRXzTRLDjdagxI5EIgm6KRuNRgwODsJut0Oj0fik4MJ1RIqJ9a45Wotw61urXsnj8UCn03ERv0T8kBCrAaQ/Qosjq9UquMdRRUUF8vPz8corr3BiaHl5Ge+//z7++7//O+hjUlJS0NjYiFdeeYWzBGAYBq+88gquvfbaqNewIcRRqBEgMpks7CiNROHxeDA8PAy3243GxkbBupn8R38Aq9GOJ65qxK6yVRt4q9XKCcJgRoosy2JgYAAjIyMhIzPRQsRRPGLFbDbDYDAgPT19Tb8gcgGM95dYKCfuC3YWoilfgr++24kPltQ4NGbDDX8dwANvTeLze0pwwY5CpKvkcY8mCYb/BmmxWNDS0gKVSoWmpqaQgsb/ccGKislojfHxcZ/RGllZWQmbbi/2DT/R+G/KDoeDE6wTExNgWZaL7on9vdoIkaNI1xepv5KQ9Upi76YjJEIcxRI5slgsGBgY4P49PDwMg8EAvV6P0tJSXH/99bjzzjtRU1PDtfIXFhb6eCGddtpp+OxnP8uJnxtuuAFXXHEFjjvuOOzevRv3338/rFYr170WDaIXR+G8i9YjcrS8vIzW1lbIZDJIpVLBhBEADC9aAwutAbh5N5LZUcEuZE6nE21tbbDb7XGl0fwhX6RYLp4sy2JiYgI9PT2oqqqKqD6ML47ixV9AEiuD8fFxfPaErfhKXh7+/PzLGEABnutYwA9e7MN9rwxiW5EG74+YwMYxmsQf/8gRGdlCIo+h3pdIutz8fX3IaA3iFn20Tbdfi/V6bSqVCoWFhZyZHqmLmZubg9lsBgD09PQIPncsXuJNYyeDWH8shfNXErJeaaOk1RJRcxRL5Ojw4cM45ZRTuH/fcMMNAIArrrgCjz76KG6++WZYrVZcc801WFpawgknnIB//vOfPsXkg4ODWFhY4P79n//5n5ifn8f3vvc9zMzMYMeOHfjnP/8ZUx2wqMURy7JwOp0hvYtiHf4a61rGx8fR29uL8vJyFBYW4q233hL0gnJoyBRwm3+hNflQe71en1SZ0WhEa2srMjMzsXPnTkFaj4MdM5ovldfrRWdnJxYWFrBr166wpo58gnkPCYHT6fSpwSJf6Cy1FGc2FuObZ9bhH+0z+O07o3hv+JNzEetokmCQTYhE97Zu3bpmPjyWz5f/aA0y3X5hYQGDg4NISUmJaziomDdRsSCRSKDRaKDRaFBeXo7p6WmMjo5CJpNxdTEkBUfqYtZrcyXfNbGeVyE7wULVKxE3aH69EhloHMlxN4o4SkTNUSzi6OSTTw57jZdIJLjjjjtwxx13hLzPyMhIwG3XXnttTGk0f0QtjsgXNZR3UbLSam63G52dnTCZTNwmT4a/CiWO/tE+g4feGkGeRon5FWfIQmv/qAq/wDlRnVaxRHKsVitaWlqgUCjQ3NwcVeuov/eQECwtLaGlpQWZmZkBQ38lEgkYhoFKIcOFu4pQpFPhyt+1+DyeYYH/ea4TXzqhHHsr9EG74dZCKpXC4/Hgo48+gs1miyq6F49QDDbdnr8ZdHZ2IiMjA1lZWQF1MhsNMaeuJBIJFAoFN/7A6XRy0b3JyUkwDLNu0T3yvon1vCdyfcHqlUgKrrOzk6tXWmvO2LFcc5SIDu31RtTiCAg/uiIZaTVSK5Oamorm5mZueCX5cHk8nrgLXz8YMeF/nu1EnkaJp64+HgBCFlqT45LBum1tbbBarWELnOMlWrHCd7uura2N+oImkUgEKwLnR/xqampQVlYWsjuOUJGVFnQ0yaEhEw4NmZCnUeK8bfn47I5CVOVEnmv3eDwYGRmBTqdDU1PTmtEasi6hHbJlMhmysrK4SB6/TmZ8fBzAJ63qWVlZYYWtWMWIGCMg/j+klEolCgoKUFBQAJZlYbFYfKJ7CoXCJ7qXyAL7jRI5Ssb6/M8Lv16JzBkLVq+0UWqOhBZxVqtVULNcsSB6cRQOklZLRK6cZVmMjo6iv78/aK0MX6TEyozZgXeHjPjBi71IkUvxq8/t4MRQqPQN8dEgM6J0Ol3cA1HXIlKxwh+bEq/fE4nmxAM/rdfY2Ai9Xh/0fv61QKE63Y4vz8RfW6fx19Zp/PrtUfz67VFsLdLgs9sLcPbWPGSmht68pqenYTQauchVJJ9X8rlO9PiQYHUyi4uLXKt6MG8lsW6iYidcXVlGRgYyMjJQVlbGjWgwGo0YHR3lonvRpnoiZaOIo2SLj2D1SsvLyzCZTAH1SsmYPxkvxCNQSHFks9lE7+QfCxtaHMnl8oScbJfLhY6ODiwvL+O4445DZmZmwH2IYIg1cuXfsn9lUynq8tf+gJENs6OjA7W1tUEjIYlgLXHkP3YjXt+LSAwTw2Gz2dDS0gKZTLZmWi+YEAvV6XbdqVX4+smV+HDUhOcM0/hn1xzueKEXd/2rDyfXZuP8HQXYX5MNhUyKGbMDwwtWMMszsC9OQ6fTISsrS7QbEOBbJ0PqMUhLtL+3EiC+yJHY1sMnmrXxbRkABBTYezwen2n2aWlpcX2ukhmZiQWxrI/vpu5fr7S4uAiXy4XDhw9z52U968iCwW9sEopYu9XEjujFUbgvAxFEQuZQTSYTWltbodFo0NzcHDaUHWtBeLCW/cfeG8NVTaVhC35dLhfa29vBMAy2bNmC4uLiqI8dK+HEEfFUysnJiXo0SSzHWwsyKqWwsDCiMS6hIjPBrBJW1ybBngo99lToces5dfh39xz+apjGyz3zeKl7HpmpCmzKS+e63SQAbjlQiVy1ZcMNng3nrQSsdpyQWiXqrRSeeCLcoQrsjUYjhoaGIJfL4/K4EnvkiHgciW19/HqllJQULC8vIycnJ+p6pWRB9iux+xyJAdGLo3CQTU+Iuh9S2Dw4OBhxRCZWcTRitAXUszBrzEZbWlqCwWBARkYGVCpV0ucIBRMr/GLw+vp6QcVaLOKI3wXW0NCAwsLCiI8Vq/hITZHh/O0FOH97AabNDvytdRp/PjLp0+3GAvjBv4fwy7OyoVZvLHHkvw6+t9Krr76KmpoaWK3WpHorbVSESv/7F9iH87gi0Yu1NkN+CleMiG3uWzAYhkFKSkrQeiXS/MCvV9Lr9UmflUhGnAj1vSSvkUaORIZEIhGkKJv4A9lsNuzZsyfiwuZYxVGw0RahZqOR2qe+vj7U1NSgvLwc77zzTtLz2/5ihUSxLBYL5+osJNEKAlKcHm0XWCzHCkWBVoUvn1SBIrUbN/5jzOdvDAvMWL0ozIxNHIkR4hadn58vOm8lMb9nQhPM44qkQru7u+F2u30G5waLXhytHkfJxL8gO1S90nrOg0tER53FYqE1R2IkXq+jxcVFtLW1ITMzM+rC5liP7V/wCwBZaSkYWrByfwdWLQQ6OjpgNptx/PHHc7VP8dQ6xQr/mGazGS0tLVzqMRFf6GgiR8vLy2hpaUFGRkZEXWD+CFH8DXxSkO6Ynwza7Tax7MXOCEVYsM1L7CTaWykSxPw+JWttKSkpyMvLQ15eXkD0grhD81NwSqVS9JEZsY82AdYWHvx6JQBB/ZU0Gk1C65US0VFH02rrRKJSWwzDYHBwMK4xG/EIM37B789eH8IHI0u46rEWrjPqzOp0GAwGpKWlBdQ+hbM3SBTktY6NjaG3tzdit+tYiVQcTU5OoqurC5WVlaisrIxpPfEWfwOr0UeDwQC3242zT24Gm2fy6XZLS5Hj1y3LYCUSfGtTZM9JXotY0mrBCLWuY8lbKVLWIzoTLHphNpthMpkwOTmJ7u5upKWlIT09nRvTJEavHrGLN2B1jdEIfqH8laJB6PNLfgTRtJoIkcvlURtBCjmtPp4IDokQHR5d4m4jbszscSwa64Nv+OsROZJIJBgdHYXdbg/bFi/k8cKJI4Zh0N3djZmZGezcuZO7wCTiWGtBImk6nY4b8ntRY6pPt5uXZXHZb97Hb46YIUsbwDdPq4r4Qif2TSEShPRW2sis97nkp+AqKyvhdrthMpkwMzMDhmHw5ptv+qTgMjIy1n3NwMZIq8WbsvL3V7JarVx6lNQrEaEUa72S0OLI6XTC6/XStJoYiTZ6QzqZcnJy4p5WL0S9U6jibH1pLaqqyoI+JtmRI4vFgpWVFSiVSjQ1NSVl4woXzbHb7TAYDGBZFs3NzXEXp8cTOZqYmEB3dzeqq6tRXl7us5H4d7v96IwcfP/1BTz01ggWLC7ccW4d5LK1L/hijRzFs2nG4q2UrLUlCjGeQ4VCgdzcXCiVSiwvL2PXrl2caB0dHV33AmLCRkirCZmy4kde16pX0uv10Ol0EUWtEuGODYCm1daDSNJqkUSOyMDRsbExbN68GUVF8Q0RBYSJ4IQqzt5SFtpAMZmRo+npaXR0dCAlJQVlZWVJuziGSqstLi7CYDAgLy8P9fX1gnzRYxEf/MhVpHPjtCo5Dp6ShV+2ufGXlikYrS7cd9FWmO1ujBhtKA/iiE4Q48YqFNF4K4UqKN4IiLnomURm+N2IwQa08kWrTqcTdIZjJOsTM4lcY7h6pcHBQdjtdmRkZKxZryT0Gi0WC9fFerQhenG0FpFEb2w2G1pbW8EwDJqamgRTuUIMvs3TKPHNE/Jw31szYLB64dyUl44Row1AcKdsoUZrhIPvdr1t2zZMTU0ldYP2f40sy2J4eBiDg4OC2wZEm1YjhpcMw0QVuZJIJEiRAr+4dDtu+Ws3nmudxnm/eA8TS3YfJ+6LGosCHidWcZSIdYXzVhoZGfExSPT39BHr+wSIWxwFW1uwAa1EtPb398PhcHCDc/V6PTQaTcJe30apOUqWgAtVr8Q3CQ1Wr5SIyFG8BqRiZcOLo7UEyuzsLNrb21FQUIC6ujpBPxjxiiOPx4Ouri5USxfwzFVbsMwqcdvfu9E9Y8EVjx4JuVkKIcrCwXe7bm5uRmpqKleTkCz44sjj8aC9vR1mszkhM+SiER8mkwkGgwFZWVloaGiI6vNELiAKmRQ/+uxmqBVS/PHwJPd3Um92YnUW8jTKgMcdi/h7K4Xz9MnKyhJ9YahYz2Ukwo0vWgH4iFb/ujG9Xi+oF9tGiRytVzF7pPVKQu8bVBytI7Gm1bxeL3p7ezE1NYWGhgYUFBQIvjaZTAaXyxXTY1dWVmAwGKBUKrnxFjNmB0aMdu4+/M2SH0FKZORoYWEBra2tAWmrZESr+BDBsrKygpaWFqjV6jUdy2Mlkpoj/gDb2tpalJaWRn1B4EeoJBIJPrUlz0ccAZ+YgfqLIzFHRJJJME8f/1/MwGoXY05OTlK9ldZCzOcwlpoetVqNoqIiFBUVcd9Vo9GI2dlZ9PX1xVQTI+T6ko1YBs+uVa/kdDrx3nvvcUI2nnNztHaqARtAHK2FXC6H2+32uc1qtaK1tRUAuMhHIoi1MJq0n5eXl6Oqqor7Qo0YbfC/fgZzzo5HlIWCZVkMDg5ieHg4aNoq2R1yUqkUZrMZAwMDKC8vR3V1dcI2ubXSal6vF11dXZifn4+rU89//dGagYoNMYgOf2+l5eVlfPTRR1wKLhneSpGy0dJq0cCvGysvLw9ZE8NPwUUjJDZK5EiMa+TXK3m9XjAMA71eD5PJFFW9UjAsFouofoAIyYYXRzKZDA6Hg/v31NQUOjs7UVxcHNFcrXiPHY1gIJvs3NwcduzYwYWnCcE2S0mQzVLobjW+u3Qot+tkRo7ILx2bzYYdO3YgNzc3occLF5khnXEA1hxgG8lx+O9hMDPQ1BQZVpwe5GFjRI7EtC5+Yei2bdsgkUhE560k1k1E6Joe/5oYvnXD5OQkGIYJSMGFO/5GqTkSo0cUH6/XC4VC4ZMedTgcXArOv15praHGNpvtqOxUAzaAOIo0reb1etHd3Y3Z2Vls37494RsqOXak4shiscBgMEChUGDfvn1BN9lgm6VMIkH3zIpPN5OQURwys02j0YR1l5ZKpVH7ScUCMVN0Op0oLCxMynkMlVYzGo0wGAzIzc3F5s2b495Ig32W+WagI4s2fP+FXlz2yEf47WU7UZ2lDPk4ytqIzVvpaI4crYW/dYPFYoHRaMT8/Dz6+/uhVCrDRvjEGpXhsxHW6PV6Az7nKpUqZn8li8USd1qtvLwco6OjAbd/9atfxc9//vOA2x999FFcddVVPrcplUqfIIkQiF4crYVcLofT6cShQ4cgl8sF8b2JlEjFEYlmlZaWoqamJuwXiL9Z2lxeXPunNnzlD6spQlKgvScn/sgRv4YmmEePP4lI5flDip1Ju3ayfoX5R3TIPLv+/n7U1dWhpKREsOMEE2HED2lPhR55GhW+/mQbrvzdEfz84gZsL9Zwa6LER6K9ldZCzOcwmcJNIpEgIyMDGRkZKCsrC3BP7+jo8EnBabXaDVFztFHEUbjPdaT+Sn/4wx+wd+9ezM3NxR05+vDDD3320Y6ODpxxxhm46KKLQj5Go9Ggt7fXZ91CsyHEUahNhWVZmM1mmM1mVFZWorq6OqkfzrXEUazRLLJZzpgd8PJybKRA+w+XVMXdJdfZ2Qmj0RhxDU0i02osy2JsbAx9fX1csXNfX1/Sapz4kTiv14vOzk4sLi7iuOOO4+bZCUEk6bGTa7Px4KXb8d9/MODqxw24fpcSjaVaMAwjuouvWKMgwNprWw9vJbFHjtbrs+Uf4fNvSyepIKVSCavVKsoaFzJ6RUzfz2BE28ofzF9penoaDocDP/zhDzExMQG9Xo9bb70Vp59+OpqamqJumvEvL/nRj36Eqqoq7N+/P+RjJBIJ8vPzozpOtGwIcRQMssHPz89DpVKhtrY26WsIJ46sVisMBgOkUmnM0awRow3+WynDAv/oNeO03NXjrna4hTcQ5EPSeykpKajc0og+swflMseaj02UOOILNb4YEWoYbCSQY9lsNu6cJcIJPBJxxDAMtI5pfG0L8MsuGR4wuPFNuQ0qF4Pf/v0tVOdrUVucI3irdKyIORoSDfF4K0WD2DZ1gpiEW7C29N7eXjidTnz44YdQKBQ+aZ5EdLBGC/keiF0cxfsDSy6Xo6SkBL/+9a8BAN/4xjcwPDyM4eFh/Od//idWVlawf/9+XHbZZbj00kujfn6Xy4XHH38cN9xwQ9jPo8ViQVlZGRiGwa5du/DDH/4QDQ0NMb+uYGxIcbS8vAyDwQCVSoUtW7b4hNeSSShxNDMzg46ODhQVFcVVFB6sQBsAHj+ygJdSJThhthPPGqbDGgjyIW7XZWVlMCyn4oqffxDxYxMhjqxWK1paWqBQKNDc3Ayl8pMiZCGGwUaKRCKB3W7HoUOHOD+sRFzk1hJHfHPJyz7VhMadNnzlTx34v/etYFgZWEgg6VjGVVuc2KH5JA2UlZUFnU4n+mLQZCDEZyZab6VIu3vELCTFWvDMT/PodDqUl5dz52JsbMznXJAU3Hp8D8i1UezfQaFNIGUyGRobG3HPPfeAZVl0dnbipZdegtPpjOn5nnvuOSwtLeHKK68MeZ9Nmzbh4YcfxrZt22A2m/GTn/wEzc3NXCOWUGwIcUQ2FX6dTEVFBaqqqrC8vJz0IawE/8JohmHQ09ODqakpbN26FXl5oUeARIJ/gbZUAnznQC2mFs3445FZ/KVl+pNjs8Ctf+tG64QZcpkUTo8XLg8Dp4eBw83AtLwCq90FuVINZ+ccxkwOn8cG81Pyf61CiqO5uTm0tbWhuLgYtbW1AZtLsrrjSGrWaDSioaFB0C+XP+HE0dLSElpaWjhzSalUim1Fcvz4M5vw1Sc7gY/d01kAj3Y68cJXdiPFa4PRaERvby9cLhe0Wi2XnhBj6mGjspa3ktfr9UnBhXrvxRSd8UfMawM+iXjwI3gAfM5FV1dXVJ1WQq8PEH/kSGhxZLPZuFFcEokEW7ZswZYtW2J+vt/+9rc466yzUFhYGPI+TU1NaGpq4v7d3NyM+vp6PPTQQ/j+978f87H92RDiCADcbjc6OjqwtLTkM8sq0tlqiYAfOSIpGUBYbyV+gXbZx6kzo9EItX0ev+jwFQ8sgD8fmfK5TQJAIQMUUiBVmQKVVwImyAbNsMDvDo3h66dWITUl8MsjlFhhWRb9/f0YHR3Fli1bQppzJkMceTwe7jOl1+sTKoyA0OKI+F7xC+PJ/VKCDKZlWGBqxY3jy3zTQIuLizAajRgaGuJSD1lZWQnz9xHzZprItfl7K1mtVhiNRiwsLGBwcDCkt5KYBYjYC55DRbb8z4XNZuPE0tDQEORyuU8Kjh+dFhKyD4j1/BKEthsgNWBCMDo6ipdffhnPPPNMVI9TKBTYuXMnBgYGBFkHYUOII/KrOi0tDfv27fPJMcvlcjAMsy4XHuI3RNJohYWFCUnJ+E93l8lkKE5ngxoI/unq41GSqUaKXIoVkxGdHW3Iz8/3cbueMTtwyn1vB6TrHj40hqdbpvAfOwvxud3FKOH5Ky3YvOicd6PMvHZ9UihcLhdaW1vhcDjWnHGXaG8fktIjA3UtFkvCjkXwf01kft3U1BR27tzJecLwKdWrg57nksxP6o34aaCSkhJ4vV6YzWYsLi5y/j5kBlZWVhYyMjIE+66IOVWUDPhpn9LS0oDOK763ksvlStjmHC9iFm5AZLUyEokEaWlpSEtL4zqtSFR4YmIC3d3dSEtL83HtFkookPWJ+T0EhHfxtlqtgvkcPfLII8jNzcU555wT1eO8Xi/a29tx9tlnC7IOwoYQR+Pj4ygtLQ3abk4+3F6vN2kToglkLe3t7di6dWvCq+cJUqkUGjmDO87d7JNyu+PcemwvXm17HRgYwMjICDZv3syFPQnB0nX/e9YmSCTAEx9M4JFDY3j0vTHsr8nG5/eUYHrJgdv+0QuGBe4+/Paa9UnBMJvNaGlpgVarRVNT05rnKpGRo/n5ebS2tnI1YUd6htA950JBHMIvEvjiyOVywWAwwOVyoampKeSvr3yNEt89swI/+NcQ2I9TaxkqOeTS8LYL/NQD8fdZXFzE2NgY51tCxJIYClqFYr3FWjhvJaPRCJPJBKvVmjRvpUgRa80RIZbIln861O12c+ehp6cHbrcbWq1WkI7EjWAACQifVhNKHDEMg0ceeQRXXHFFwN5w+eWXo6ioCHfddRcA4I477sDevXtRXV2NpaUl3H333RgdHcXVV18d9zr4bAhxtHXr1pAbJTnRHo8nqeKI75wsdMv3WpCIVbCUG4nO2O127N27FxkZGUGfI9hjAeC/ji/Ge8Mm/P79cbzWO4/X+xZ8HhdJfZI/4+Pj6OnpichPiRCNOIq0Y49lWQwNDWFoaAgNDQ3Q6HPxyzdH8NPXRsAC+OGh2IRfpBBxtLy8jJaWFmg0GuzatWvNz+35W3OhmO9HUd0OdM9acc8rw/jakx145LLtQVOg/vD9ffi+JeTXtL+njJjTKxsN/nvf0dEBuVwOpVKZNG+lSNkIkaN416dQKJCXl4e8vLygHYnhzA4jWZ/YvzckwyK0OBJittrLL7+MsbExfOELXwj4G/lBRzCZTPjSl76EmZkZZGZmorGxEe+++y42b94c9zr4bAhxFO5LIZVKkz73a25uDu3t7cjLy4PZbE56qJx0cjEM45NyI27XJDqzVp2Jf7oOWH2vmyr1aKrUY8Jkx70vD+D5jlmf+zAs0DW9sqY4Ij5Pc3NzPnViwfAXOJG28v/5o8mA6NmFuwqx4vBgZtmJmWUHZsxOTC3Z0D06jTmLG3aoMfdhL6zOroDXFa3wiwaJRAKXy4X3338flZWVqKysjOiCL5FIoFMCx5VqsadCD6vTiwffHsPNz3bj/osawkaR/OH7llRWVvoUtHZ0dHBzlyKZrC7mzVSMSCQSqNVqlJWVJc1bKVI2Qs2RkOsL1pFIfjRMTU2hp6cHqampPim4cD9iNoLHkdAddaTGK9QP8Gg488wzQ0Z9X3/9dZ9/33fffbjvvvviPuZabHhxBCSvKJthGPT392NsbAwNDQ0oLCzE9PR0UqfVA598uMkFg2+iGE10Zi2KM9W4+cwavNg5G1Cf9PUnW3FCdTbO2pKH0zflIF21+lEiIic/VYqpwS5IJJI1Z5L5C5xbztqEnTkSzFq96JlZgd3thd3lhc3thd3FcP9/btmJh98d5bygGBa45W/duPPFXjjcwc+JWiFFvlaKbUVayCTA24NGn7+TwvQbz6iGPEgxdKywLIupqSnY7Xbs2rUrYkPQYL/ov3pSGabMDvytfQ4/+vcA/vdA7EN5/QtaLRYLFhcXucnqarWam0UWrEZjvdNYoRCjcPM/l5F4K2VmZnLvfyLTnxshcpRI8eH/o8HtdgcIV34Kzr9ubyNEjkgAQch1WiwWOltNzMjl8oRHjhwOB1pbW+F2u32KiaMdPisE5MNNjtvR0QGTyZSQ9F6w+qTztxdgzuLCWwOLeL1vASlyKU6qzoI+TYGnj0yBYVe75L64U4OLT2xA34ITJrsFJpsbS1YXTHb36v+3uTG77IRhwswdj2GBO17g+Va9+X7Ua67OScOmvAzka5RIlbhgnZ9EXVke9mzdBK1awV3UwhWmv96/gBtPr8ZpdTlxbxputxttbW1YXl5GampqzPPiiBCRSCS4/ZxazK648ORH0yjWqfCpzbkYM9pRqlcjXxNbJJM/1oFMVjeZTFhcXORqNHQ6HbdZi1UYiZVw71c03kpEqAq5yYk9cpTs9SkUCuTm5nLfVZvNxomlsbExAPBJh26EmiOv1wuJRCLagmyxcVSIo0QLlPn5ebS1tXEDSPlfgvUURysrK+ju7oZSqQwwURSSixqL0FikxotvHcZ/nHkSl3IyWl14qXsOL3TM4pWeeR83bxbAb1qW8ZuWQyGfVyIBUhXBLygnVmiQxlhRU1mGVIUM6pTV/7j/r5DB5vLgy08YAjq5fn7JduRplB8XpU9g60nBi+VDFaYbrS48cmgMX/tTG3aVavGtM2qwq1QXwzu3+svqyJEjSE1NRX19Pfr7+6N+DiLO+JurQibFvf+xGVc8ZsA9rwzj3leGwX78+m87uwYX7AhukRAN/pEN0ia9uLiIwcFBMAyD0dFRFBQUIDMzM+kNEcEQs2CLJjoTzluJ+Pnwp9rH62sl9s19vQvGiXAtKioCwzBYWVnxmTdGShjm5+dF813wR+hibGJjIUTNkRgR3xkMwnql1dbq+iLHTrY4kkgkkEgkaGlpQVlZGWpqahJ+4SjQqlGVwSCPF5XQp6XgP48rxn8eV4wX2qbwzb90BTzu7C152FyQAZ1agcw0BTJTU1b/f6oCWrUC8yvOgOiNVALceHIJ5sf6cdJJlWHX5S9u7ji3HlmpMhw5cgRWqzVsUTqwKvzqdCw+7B7G2Scezwm/S48vxs9eH8Kfj0zh0t8exul1Objh9GpU5aRFXABOjC5LSkpQW1sLo9EY0+Yd6txqVHIcPKcWn3vU4JNaPPhCP5or9TFHkEKtgd8m7fV68e6770IqlWJwcBB2ux0ajYbr1EpmvUyo9YoRodKfkXorRcqxnlaLBqlUCq1WC61Wy9WODQ4OcueCfBf4KTgxrF1oAWy328GyrCA1R2JkQ4ijtUhEWs3hcKCtrQ1OpzPsBptscURcuFmWRU1NDcrLy5NyXPLlDnYRXVlZgWumHxLAJ3oklQDfPrMmrIAIFr2549x65GtVmI2glsu/6y5N6sahQ4eQlpYWUVE6AORlpKBWJ/FZZ06GEgfPrccVTaW4/5VB/KtrDq/1LWBXiRYfjS2FHbvC74rjG13G690U7LHBaqsYFhg32QUVR/7IZDJIpVKUlJRAq9X61MuMjo5ynT/8epmZZWfcqT8Agj1PshEqqhWNt1KkmzMVR7Ejl8uRmpqKjIwMbNu2DXa7nUvBjY+PAwAX5cvMzBTMNDFaEtHGD4Cm1cSM0JGjxcVFtLa2Iisra81W62SKI2IfwLIsUlJSoNVqk3Jc4BNx5H+RmpqaQmdnJ7ZUVuD7WeqgImctgtkKmM3miAvdSdfdzMwM3mtvR3l5OaqrIy9SDidaKrPT8P/+cxsM42b84MVefDi6xP2NdLftLtehLGs1tOzxeNDe3g6z2Yw9e/ZAo9FEdJy11gd8srmyLItFrxcSALk6JSQSgPWLvPFNIhMJWZNarUZRURGXdvCvl2kxq/G7Lnfcqb9nDNM4+EI/9xnzf56jJa0WDeG8lfw351DeSrTmKD7410W1Wg21Wo3CwkKwLMul4EiTg1KpjCvKFytCd9RZLBZIpVLReHUJzVEhjoSKHLEsi8HBQQwPD6Ourg7FxcURpfSSIY5I3VN+fj7q6urwzjvvJLVLjt8hR/63p6cH09PT2LFjB3JyclANBPVOigR/W4FofI5YlkVfXx/Gx8exbdu2qGfaRTLkdkeJFjecXoUrf9ficzvDAmf97BC2FmrRkJ8KjWsB9TlKnNLc7NNdNGN2oGNiBcv26M8Z/zPoYVlMu91wkPWqpfjvsyvw01dGIPGwkHpXBcN6RlT49TKVlZVonzDhd491+KT+bn++H4bhOegyUgGJDB6GhZdhff6X/H8vw8LtZWFzefGBnzhNRAoxUSQrOsP3ViKb8+LiYlhvpfWu6VkLsa8vVFRGIpFAo9FAo9FwTQ7BonzkfGg0moSJwETMVUtLSxO1aI2HDSGOkiFQnE4n2traYLfbA37xJ/rY4QhV95RsbydyDrxeLzc93uv1Brg7B/NOivV4kUQB+CNJ9u7dG1OIN1JPpYqstIBRHhIAx5fq0Dm9wuu6c6Pw8AfYWaLFzhItFm1uPPTmMNfF582djMpokrz3dq8XJpcL/jHS0+tzUZeXjq/9uRM1uelo3JyFBY8HqVIp1B/Xp8VLsFQWeV6H24spsxMTJjvGlxyYWHJgwuTAxJIdk0sO2IOk/lgAz3aZAZgD/hYMuVSCYK+CYYEXO+dw5d61f8gci/A353DeSk6nE2q1WrTpNTGn1YDI1yeXy5Gdnc2NCnI4HNz5aG9vB8MwASk4oc6H0OLoaG7jBzaIOFqLeAWK0WhEa2srMjMzsXPnzqg6DRIpUsK5XZNfe8mCtICSKfDZ2dloaGhIWIdLJJEjvtN0JCNJQhGpEAtVH3Wc3oW+fhMyCisx5VLBMG5Gy7gZz3fMBhhosojMaFLiJ2rsEgkm3W6khHi/i/WpOK5Yi8PjZizaXHCpFTB9nHpLlUqRKpUiTSqFIoYL7V9apnHHi/2cuNtfo4dGJUfXmAdLH3RhwRqY0pZLJSjUKrGzRItMtQIvdM4F1KP96tKtyEqVwbqyjGXzElbMS2C8HugzdcjRZyInOwvpaamc39TMshMHfvZ+gPXCva8O440BI76+vxybc1JEubkD4qjr4XcgAuA6EIeHhzE+Ps65DifDWykaNlJaLRpUKhUKCgpQUFDA+YwZjUbMz89jYGCAGyBN/osnBZeIobNHa6cacBSJI4fDEfXj+IWzmzZtQklJSdQXr0RFjkwmEwwGA3Q6HZqbmwM2/mRHjoh46OzsRF1dXUzvVTSQVFeoDYXUOkXjNL3WsSKBXx9VrFNicXwQIyOL2LP7eOh0OgDAhbtWo0JmuxtPHp7APS8P+jwHwwJX/O4jfGZ7AU6ry0VtblrY9S96PDBJpShdY41NVZn4YMyM90eXcHrd6ubHArAyDEaXHZhecqBEp0K5RsUJJh8jO5bF5JIDwws2DC3aMLRgQ8+sFd0znwzlZQG83r9qnJmuAEoyFTi+LBPFOhWKM1Uo1qlRnKlCbobSx7l7d7k2oFZoT8XHnlx5GgDFnF3A4uIiFhcXMTk2jJSUFG6jzs7MxG1n1/g8z3WnVGDMaMdzrTO48vet2Fumwb7Igr5JRwziyB/Sor6wsICcnBykpqYmzVspUsh1QOziKN7aIb7PWFlZmU+h/ejoaEAKLtpRP4kYOitkZEtsbAhxtNabH0vNkcvlQltbG6xWK3bv3h1zcbPQ4ohlWYyOjqK/vx81NTUoKysL+vqTWQju8Xi40RJbtmxBcXFxwo8ZqjuOTLKfnJzkap3iJdK0GiFfq4I2hUVLSwukUimampqCFiVq1Qqct60A970yGJCKW7C48MCrQ3jg1SGUZKpxel0OTqvLwa5SHWQfiwqGZdFmtGDI7MSKe+0L0J4yHaQS4NCgiRNHAPBS9xx+9sYoJyi+ckIpNhdkYGLJgWmjAzOLdozP2zG2aIfT4/s+pCuD/9L8+cUNkM/3or6+OiLj0Qt2FKC5Uo9xkx0lmcG7zPh2AfwuLOKrZLfbUa7V4rfnF8IqSUVtoR4FH0ffvtBUgl+8NYoXOubwHmQ44uzEtfvLUZsrrl+2Yt1ISOQjmd5K0awNEO97ByTGJ8q/0N7pdHIpuM7OTp/zkZmZibS08D+yxDp0VqxsCHG0FtEKBZPJhNbWVmi1WjQ3N8el+GUyGdxud8yP50NESCRu14mcWs/HYrGgpaUFSqUSSqUyaWFU8iXnh6udTicMBgPcbjeam5sFa4mNJnIErH5+WlpaOFPQcL/G/FNxErD4/nmbcf6OAhweXcIrPfN4uWcOjxwawyOHxpCZqsApm7Kxf1MOupdW8PO3Rz9+nAROjRHnbA+sVfJ4GVidHtjdDNJSZPhgzIzX+xbAssDcigtPfDjpUwz9i7fGAp4jJz0FdVUaVOjUqNWnoi4rDdU5aXB6mIBUllQC1OalY3ghus0qX6OMqnDaf3MgdgGLi4uwmiYxuCiD6eONOl+vx48+U4fP78zBj57vwGt9i3i9bxGf2pyDr55UhvKs1c/KetoAbLROukR7K0UKuc6JOXKUjNlqSqUy6PkgPx7kcrlPCs4/JUrFUXRsGHEUri5ELpdH1MrPsixGRkbQ39+P2trakFGZaBAqgrOysgKDwQCVShWR23Uyao5mZmbQ0dGBkpIS1NTU4O23305anRPfOgAAzGYzWlpaoNPp0NjYKKgDbTQt9mNjY+jt7Y0qDUtScX3TJkz1tXHF2GTA7/+eVYuu6RW83DOPV3rm8XTrNJ7snQW/ApmFBA++O4U3Bs1we1lYP54vZ3N64fYvwgFwzyvDYdd0crUeu0q1KPk4FaZK8b1oSgBYJSxSU2T47tnV+OELAz4psXyNEsNI7oYfyi5gbGwMXV1d0Gg0yMjIwBfrGOTUNuLnb43ixa55/Kt7HudtzUN5Vir+3+vDIW0AEo0Y02qEtdaWCG+laNYGiFscJbtgPNj58P8+8FOiWq0WXq9X0CkKFouF1hyJnUgEitvtRnt7O5aXl7F7926uPiQZx14LUj8TjT9PImuOyIDd8fFxbNmyhRu9kaxoFTkWWcvExAS6u7sFHarLJ5K0GsMw6OrqwtzcHBobG6HX66M6xmoqToc3RgLFhEQiQUOhBg2FGlx+UhnaF1bw9JEp/MOvmBsAhhdt0KoVUKfIkJmqQBoZq/LxfwNzVrRNraw+L4AD9dn4V89CgA/SFU0lyE4PXWzLArCzLOxeL3bVZ+HXJRkwLbtQlalGhTY5Hkrh8E8BOZ1OGI1GzM3NgWVZGAcN+NoWPS7YVIQn2pfxXJvve7leNgBiFkfRbO5CeCtFykZJq62neJPJZJwQAlbLRkgKrru7G263G3K5HOnp6VhZWRHEwZ4WZG8A1jKBXFpagsFgQEZGBpr9/GeEOHasgsHr9aKnpwczMzNR188kKnLkdDrR2toKl8sV0BqfTHFEvri9vb1YWFjAzp07ufZXoQnn/g2svictLS1gGAZNTU1Qq2MTB3wzR//jsCyLWY8HK14vCnRqXHJ8MV7onA1IZ/3y0m0hRc2CxYUvPt76yXMC+Ff3AvbX6PHmgJGLmFy7vwwA0D6xjAKdKqxIImSmpyAzPQUsgCGXCyqJBCsSySd+S+uMUqlEQUEBNBoNjEYjduzYgcXFReiNRlxevIxKVQr+3O/7Y4Jhgb5ZS9LE0UaOHK1FLN5KkUI8jsT63gHim02XkpKCvLw85OXlcY0OHR0dcDgcOHLkCGQymU/9WCwRJZvNRtNqYmCttFqwKAq/uDlRUYdYI0c2mw0GgwESiQTNzc1Rb7iJiBwREanT6YI6gyezQ450Hy4vLwd4KQlNONFiNptx5MgR6PV6bNmyJa4LYKjjeFgWUy4XnLzPd06GEtfuL8fP3hhZFTUArt6TH1bITC85Atrc+d1lGUoZqnNS8eGoGT99fZRzq752fxnOqM+N6rU4WBbLEgmmPB44nE4fuwDZOm9i/t4+brcbeeNzeLp/EP5XkG/+pQun12XjM9vysKc8kyuGTwQbreYoViL1ViIb81pRDLF3qgHJqTmKFdLoQARTfn4+l4IjUfm0tDSfrsRIrnMWi4WLHB5NjIyMoKKiYuOIo3AEEyhutxsdHR1YWlpas7hZ6GOvxdzcHNrb21FQUIC6urqYvlRCjkxhWRbj4+Po7e1ds0MuGZEjYmMgkUiwdevWhM8iClb8DQCTk5Po6uoSTFj7jwEBAAfDYMrlQrBP0Bl12dhWkIY5qwcLo304vloX9vkLdKoAk0qpBLhybzGmzE70zVpgmFjxEQgMC/z0jVFsLshAkS76iBjLsvACWGEYrHz82VBKJEj7WCyp1uEXv//xFAoFGiqLcPs5Uh8bgOZCOaaW3Xihcx4vdM4jO02OT2/Jw2d3FKAyW/jPnJgjR4l0oA7lrWQ0GjEyMsJFMUJ5K613yioSNsIaSUG2f0ra7XZz4rW3t5cTr+SchBKvVqsVpaWl6/BKEktJSQmmp6ePHnHk8Xi4i4/ZbIbBYEBaWhr27duXUCOzaMQRy7Lo7+/H6OgoGhoaUFhYGPNxhYrieL1edHZ2YmFhYc1amkSn1fgirba2Fv39/UnZTPhpNeATu4CpqSnB7AKAQHG07PVizu0OiGYAqxGrrq4usCwLrVaLDAW7ZldkdnoKrt1f5tO27x8VOjxqwsEXBnwex7LAdU914oRqPU6tzcKWQg2kUgkWLC5MLzkiTr0RnCwLp9cLo9cLKXxNKOXrKA6C2Ql4vV50jMzir23TeHXIikffn8Sj70+iJisF523Nw2d2FCEzbfW1C9HpJlZxlEzhRryViouLg87h8/dWEvvoEGBjiKNQa1QoFMjNzUVubi5Ylg06RJqfgiP1Y2R8SDzcfvvtOHjwoM9tmzZtQk9PT8jH/PnPf8att96KkZER1NTU4Mc//jHOPvvsuNbBRyaTIT8/f+OIo3BfDpL+8Xq9mJqaQm9vryDmgJEQqTgitTxOpzPA7TrW48YrVGw2G1paWiCTydDc3Lxm0WQixZHX60V3d7dPwfPQ0FBSUhF80UJcycl5ErLgkH+cebcbSyE+NzMzMxgcHERpWRlS1Wpu8+jt7UVaWhp0Oh10Ot3qLzq/i90Z9bnYWaLDtNmBAm2gqCkPMQKlQKfEK72LeKV3EVlpCpTp1WiZWAYbQmRFCgPAwjCw8KJKRCwJNdqEz1qfF387AZlMhu1VhdheVYhbWBbv9s/hmZZJvDVswT2vj+P+N8bRWJCCAl0q/t69FFenm9jTauuxuUfirZSeng6GYURtOii2mqNgRNLKL5FIAsTr8vIyjEYjpqam8Nxzz+HnP/85mpubMTQ0hFNPPTXudTU0NODll1/m/h2uE/ndd9/FpZdeirvuuguf/vSn8Yc//AHnn38+jhw5gi1btsS9Fj4bRhyFg5zwtrY2mM1m7Nq1K2m50EjEEUkTZWZmBq3liYV4I0dkkG1hYSE2bdoU0YUxUeLI4XCgpWV1oCtfpCWrAJxcbJeXl7mW5L179wpqF0COwwCYdLngDnKRYhgGw8PDmJubw+bNm7lNIUOjwfz8PMrKysAwDJaWltDX18dFlbRa7arPzMcR0uz0lJCRnnDRpQmjHa/1L+CV3kUcGV/+ZF0s8LM3RrGzROfzvLFsUiSqZPo4qqSOc7SJkEglEpxQm4cTavNgc3nxcs88nmmZxAcTVmDKxd2PdLo1VWRyJpSRIOa0mljWFsxbaWJiAhaLBR9++GHSvJWiRcw1R4RYfI6kUin3Y6yyshJVVVXQarV45ZVXMDAwgG9961t47rnncMYZZ+DMM89EY2Nj1MeQy+VcR/RaPPDAA/jUpz6Fb33rWwCA73//+3jppZfws5/9DA8++GBUx11zXYI+2zphtVoBrLYvRuIRJCThxBG/ILy2thalpaWCXYBiLQTnD7KNNrWXCLFiNBphMBiQm5uL+vp6ny9WssVRS0sLKioqUFVVlZCNws2ymJdKYfV6A2akudxu9PT0wO1yYceOHVCr1T5pNIlEstqum5WF7Jwc4OONw2QyYW5uDsPDw0hNTYVOp1tNw2VkBESVCKGiS8V6NS7bU4JthRrc8o8+n8cwLDBtdgSIrniiIQxWR5tYGQbzAFIkEqTyIkvruVmnpshw3rZ8nLctH//smsO3nvUN8zMs8MyrH2B/jZ5rV1/ruiMWARIMMaauiJdPdnY2lpeX0djYmDRvpWjZCGk1IUwgMzMz8bnPfQ6f+9zn0NTUhK997WuQyWT497//jXvuuQcSiQSPP/54VGmu/v5+FBYWQqVSoampCXfddVfIWqZDhw7hhhtu8LntwIEDeO655+J5WQH85S9/2TjiKNgXl2VZTExMoKenB1KpFJs3b06qMAI+cVf2/3J4PB60t7fDbDbj+OOPF8xXiRBLWs3lcqG9vR1WqzWm1J6QYoUvHEMZKkZjzhjPOvr7+wGs5roTVWBo9Xox7XbDg0BBYbVa0dXVhdTUVGzevp2LWPHfj4DPv0SCtPR0pKWno7ikBB63G0tLSzCbzejv7wfDMNBqtVxhpcKv7i5cdKkoUx2QegOANEViL/4uloWLZbHEMNzAXPXHxd0pUWw8Qm/yO4q1Qd+Px/olWJF7sM8yiZ6eHq5WJisrK+TcK7EJEMJGEG7J9FaKho0w+w0QVsARe4Dq6mqcccYZuOaaa+D1enH48GGUl5dH/Dx79uzBo48+ik2bNmF6ehoHDx7EiSeeiI6OjqD708zMDPLy8nxuy8vLw8zMTLwviaOjowOXX375xhFH/ng8HnR1dXEeOO3t7UmdUk/g1zuRD97KygpaWlqgVqsF91UiRJtWIxPsMzIy0NTUFFM4WihxRIrAFxcXw3YSJjpy5Ha7ufl6EokkamPHSDF5PFjweLjCa744WlxcRG9vLwoKCsJ2xK0lFOUKBbJzcgKiSvPz81xUiYilcFElIDD1JsGqJcCPXhrC98+tRZ4m8ZsOGZhrBbDg9UKBTwq7U6VSSJO4kedrlAEDb8/flocPx8x4om0Zz6vluHJPFU4vSoHVbEJnZye8Xm/AHDKx1xyJWRwF29QT6a0U7foAiLrmiGEYsCyb0PEhMpkMe/bsieo5zjrrLO7/b9u2DXv27EFZWRmeeuopfPGLXxRsrdFw+PBh2Gy2jSmOyKiNlJQUrkZFyNb2aCBfWq/XC4VCwbV/R+N2HQvRRI7ImuItUheiQ474O4Ub2Mo/XqLEEZkZp1ar0dTUhDfeeEPwzYtv7AiAM7IjvzQnJiYwNjaGmpoa5OauXewc8fqCRJXMZjOWlpZ8okpanQ6ZOh1SgkRb/VNvH46a8Ms3x3Dzsz2449O1SFPKMWRmkJ7lQWZiNKUPbgBmhoH546iSWiKB+uNaJSVv40yUAAnW6eb2Mvhb2ywefHsM978+isfTUnD1vhJcuGcT3E47FhcXMTc3h/7+fqhUKng8HpjNZqSnp4tuIxVz5COStQntrRQNG2X2GyCsgEuEQ7ZOp0NtbS0GBgaC/j0/Px+zs75u97OzsxHXLEXClVdeiSuvvHLjiCPyQSYbfVlZGaqrq7kPZCgjyEQj/bg2wu12Y2BgALOzs4K2f4c77lqvl2EYdHd3Y2ZmRhCHaalUGteQ3cXFRRgMhoj9nSIZ6xELc3NzaGtrQ0lJCWpraznRIuSxghk7Aquvyev1ore3F2azGdu2bQuZ3gybVosCuUKBrOxsZGVnc1GlpaUlLMzPY2R4GGq1mqtV0mg0XFSJn3o7qyEP6UoF7nllCDf+pQsuhl3tZOsaxbX7EVMnW6ywAGwsC5vXi0WvF3IAaVIp1FIpmARGZ/w73RQyKf5jZwHO3ZqHp1um8et3xvGjfw/i0fcmcM2+Upy/vQRlZWWYNFrRNb4Ay/IwxsfHMTg4CJ1Ox9XKrDVNPdEQsS7myFG0a4vXWykayHVYzOJI6OgW6R4U2iHbYrFgcHAQl112WdC/NzU14ZVXXsH111/P3fbSSy+hqalJ0HUAG6ggm9TwzM3NBRUfQg2AjQWpVAqDwQC5XB6T23Wsxwy3mZMOMJZlBVtTrJEjMvB3YGAA9fX1KC4ujvh4QkYCWJbF0NAQhoaGAorRhTxWOGNHYHUkilQqxc6dOxPqwRUUXlSpqLgYHrcby8vLWFpawsDAABiGgUajgS4zMyCqdGK1Hl4vg3te/WSobahOtmTiwSdRJavbjQWpFEaPZ9WEMgkbVopciv86vgif3ZGPpz6axm8PjeOOF/vx8KFxHF+mxV/bZj9OT0rxP6cV4fw9udw09aGhISgUCq5OZj06sMjnXsziKF7hEa23UjTH2wjjTUjZh1BrtNlsABC3Jc1NN92Ec889F2VlZZiamsJtt90GmUyGSy+9FABw+eWXo6ioCHfddRcA4LrrrsP+/ftxzz334JxzzsGf/vQnHD58GL/61a/ie0FB2DDiaGlpCTabLeRGL5fL1yWtNjc3B6/Xi/T0dGzfvj1pvx7CpdUWFxfR2toatAMsUccMhcfj4ZzKd+/eDa1WG/FjhUyr8Qvk9+zZA41G4/N3oYq/wxk7Li8vg2EYqFQq1NfXR/1ZSUTKSK5QQJ+VBX1WVsioEqlV0mg0yEoLFEChOtnWC5dEgsWPo0oyfBJVSvRoE7VChiv2FuPCnfl44sMpPPzeOJ5t/SQFwAL48atjOHVzAYqLi7mN2r8DS6PRcGIpIyMj4Zuu2MWR0Cm/SLyV/OvFwr03G8XjSMj3kHSIxxs5mpiYwKWXXorFxUXk5OTghBNOwHvvvccFP8bGxnzW3dzcjD/84Q+45ZZb8N3vfhc1NTV47rnnBPc4AjaQOMrJyVkN+Yf4kCY7ckQm14+NjSElJQWlpaVJDauSKA4/HM6yLIaHhzE4OIi6ujqUlJQIfsxoxIrNZsORI0egUCjQ1NQUdSehUOKIv45QBfJCpNXCGTvOzs5iYGAAUqk04s8Kl+5Acjr3IokqMcoMSCSrrtoEqQRR+f0kEy+AZYbB8sfnVsU3oUzQ9zVNKcc1J5SiJjcN3/hzp8/fGBZ47L1xfKG5FNnpKZBKpdwmXF1dHdCBRRoF4hkQuhbkcyXWtFCi2+SDeSsZjUYsLCxgcHBwTW+lY6WNn4/VaoVcLo/78/inP/0p7N9ff/31gNsuuugiXHTRRXEdNxI2jDgCwv+ySWZBNt/tuqmpCa2trUlP6ZEPOhFH/MhItBGaSIlGrMRiMumPEIIg0jqneI7FsCym3W7Ygrw3RLDOzMygvr4evb29MR0jKeLID/+oks1mw9LSEi6oduGZfidWZRuglEvg9qxPSjtaHCwLx8ejTWTw7YATerRJfX56UAuA3384hcc/nMKOYg1Or8vGaZuyUaRbFZf8DiyGYbgOrMnJSXR3dyM9PZ2rkwllFxAtYo8cJdODiXgrpaeno7S0FF6vd01vpaPVADIcpBhbrJ8ZIdhQ4igcySrINhqNaG1thV6v59yu16PeiXzQvV4vFxlJpHUAEJk4ClfXk4jjhVsH8VGKpM4p1pojF8Ngyu2GO8hj3W43ent74XA4sGPHDi48L+aW7pBIJEhNS0NqWhquLCrCWcfZ8I6hC+b/z95/xzmSltfi+KlSDq3YOeecZmZ3ZnvILMsuLNhgjAPXxnDx2tcmXFi+vgbbYBuHNRcwYLDB3B8mGBOMSQYMeNlEWhZ2uns6qKVW56huZbVyqPr90f3WlNTKKqmlYc/nsx/oHrWqJJXe99TznOecmARfW4viHV9dxJvvUKOj8VSzIauQx0wpyBSYqxAo2iTVAoACiwef34VGnRLfNzvwo3UX5vZ8eN/3NzDSrMY9Z0SJhN7SNI0QJYeDNqBzsA0GOcVVlfh2AWSjLlZXSL5j1brRXWRlJh9vJZVKBYZhEA6HK+atVCiEbv35/X7BJ9WqDbcNORKJRCVNUuUCX1Sc6nZ9EeSILBaHh4ewWCxltw4gx8xGVkj1yufzpdX1CH28TOD7KOVrwFlMWy2QSMAWiyHdXwWDQZhMJshkMkxNTXGl+GLJUbWRqiadEgMGKdra2tDUHMfHfrSDTy9F8T/HHdja2oJcLk/SKtFVrskAbkWbQMDAXL4FwK5pFi+53ASFQoGXjjUiHEvgJxtufN/iwONWF/7h8S38w+Nb6K1X4kVD9aAp4BM/3jmX50baP36/H06nE0dHR0m+PkajETqdLu/NsNorR9VkM5DOW2l3dxd+vx9PPvlkxbyVCkU5NEdC2iFUI2qGHOX6EEQiEUKhUFmOHYvFsLS0lNHt+iIn5SwWC6ampvLyySkV2cgK8Q0iFvBCVK+KIUf8nLZcPkqpxyqEfBBjx7T/5nbDbDajqakJPT09567d24EcAbe+ky+daIInHMMXnj7EV7fk+F/PHsf2sQeJYAgu1wbi8Tg0Wi30Z3YB8gpMc5aK1MBc6VlVqZjA3GaNDE11UpysJ69jcokILxyqxwuH6hFLMPj5tgffNzvxyKoDn/jxTvL5nOW5Xe81oFkjA0VRqKurQ11dHbq7u5N8fSwWC6LRKLRaLVf1yCYqJq35at3oqlXwTLyVjEYjwuEwpqamKuatVCjK0VZTKpWCPV81ombIEZB9gyhXW83n82F+fh5KpTJjy6rS5CgSiWB+fh4AKkaMgMxkJZ1vkBAotJrjdrsxNzeHhoYGjI2NFXSnlC/5SDV2TP23/f19bG9vo7+//5zNPVB8+65aNy7yWl5zRxs8wTi+Y7LjD760AuC02vHG53bh2V0qbnR6a2sLMpmMC7MUuqpULgIZZVlEzwJzSbSJkqahpChBok0kIhrXew243mvAn97Xjy88fYD3Prye9BiGBbacwSSvJQK+rw/LsgiFTk0oXS5XTruAavY4Ak7JUbUEzKYDaftV0lupUJSDHAntcVRtqClylA3lICh7e3tYWVnJGUZaSXLkdrsxPz8Pg8EAiURS0Sy5VJ8jfojt+Pg4Wlpaynq8bNjd3YXZbC464DcfIpbJ2BE4XSDX1tbgcrkwMTFRcksxzQmmtQeoGlAUfvVSC75jsnO/YljgH3+wjcu/NYWW1la0tLYiEY9zE3Cbm5uIxWKnvkpnZKkWqkpctMnZ9SIBblWVMkSbFELaRDSFFw3X433fXz8n5v7b763hT+/rx7Xu9JE7wOm1THx9Ojo6kEgk4PV64XQ609oFCOl/Uw5U+zRYpvMrp7dSoSiXIPt2xm1DjoT0OUokElhZWcHR0VFeztJCxGrkAsuy2NnZwerqKkcAnnjiiYpWrEQiEbfI83PJigmxzQf5OHLzXcAvX77MCSeLOVa2DSybsWM0GoXJZALDMLh06VJWwlpI5ejchlVlbbVUHPki536X6oEkEouhNxigNxiAswqHx+OB2+3G9vY2V1XSarWn01hFLOiV3uhjADwMwwXmKs7sAviBuYXqetLluT2rV4+ntjz43X9bxAsGjXj73b3oMuQmkyKRiNuEgVuiYqfTiZ2dHe7G4PDwEEajsfLGpDlQTZqjdMiHvKXzVnK73XA6nUV5KxVzjs8IsgtDTZGjbK0Poao3JPuLoqi8naXLbSMQj8exvLwMl8uVFNRajCljKSAkkATrqlSqokNs8z1ettdH2ovxeLxkF/BslaNsxo5+vx/Ly8vQarUYGBjIawEqqq1W8F+UH6mLd4tOfm50PasHEkVBoVRCoVSeqyptbW0lVZW0Wu3p51vFFQ4gOdrEwYs2kbEsGBRG3NLlue17wvjgo5v43oodP1xz4TV3tuL3n90FjTz/pTzVLuDw8BBWq5WrlNfV1XFVJY1Gc+HEpJKj/MWgmKqMVCpFU1MTmpqakryVnE5nXt5KxZxjOQTZtzNqihxlgxDkiGhn2traCvLmEYlEiETO3zULgUAggLm5Oc7AkF+VqETFig9yvJ/+9KcVmY7LRoa9Xi/m5uag0+lwxx13lHxXlOlY2Ywd7XY7VldX0dnZifb29rzei9tlWo2Af071aine9LwufPSJbY4g1clEUEjy/B6lqSqRwNydnR1IJBKu/ZapqlRt7xGJNknE47DRNPZjMWjOxN2yPNaX1Dy3Np0c7/+VEbxmtxX/9+F1fPapfXxz8RhvfG4XXnWpBQ5/FDuuEDoNirTapFTQNA21Wg2JRII777yTc4t2Op1YXFwEwzBJJpSViEZKRa221fJFvt5KfNf0Qo+XSCQElWAEg8FnKke1glKqN3y362K0M+XSHBGy1t7ejsHBwXNfiEpWjoiVAcuymJiYEDQFORMyVY4ODg6wvLyMvr6+tNNgxR6Lv7HmMnbc3t7GwcEBhoeHC2rlFUJyhJhyqzTuGWnEpQ4dDr1hLB2e4PM/P8CHHt3En97XX1jVh1dVam5pAXOmmyFVpWg0mqRVqvaqEsuyYHFqQhnjBebyTSgLiTa53KHF519/Cd9aPMaHHtvEX393DZ/40Q7s/ihYJI/+53Nu5FpLdYtOZxdABMWF2AWUgtudHKUim7fS3t4eACS14PIhrOXQHJU7XP2icduQo2Kn1UhrJhaLYWZmpqhSodDkiGVZWK1WbG9vZyVrlaocRaNR3Lx5k7NKuKjpOIZhsLq6ir29vbThw6WAT1qyGTvG43Gsrq7C7/djamqqqLun26lylA71ainq1VJMtNZhzx3GD9Zc+Oq8Db9yqXjBPi0SJVWVwuEwPB4PPB4Pdnd3uaqSVCqt6veJT3jjSB9tosozMJemKPzSZBNeNFyPf3h8E//28wPu31JH/7MhU9sqk12A0+mE2WxGLBaDTqfjyJLQOhmCWpimKydJTOet5HQ6YbPZkvytsnkrlUNz1NPTI9jzVSNqihzlig9hGKYgFs93u75y5QrE4uLeDiHJESEi4XA4J1mrROXI5/Nhbm4OdXV1uPPOO/HEE09U7E6OrwNKfV+ELumSY2UzdgyFQjCZTJBIJJi+dAnSInQAhY7yk2u+GslRzg2LovDm53VjyxnEZ57aQ2OdFFq5BC06eWkhtRQFuUKBZoWCqyoRrdLR0RFisRhWTKaqqirlI8jOFG2SKzBXKRXhhYP1SeQIOCVI3zfb8VtXs7vD50s+Uu0CyKh6qk6G2AUUu56mohYqR5USsRNvJY1Gg56eniR/q2zeSkJrjkKh0DNttVoB+SLmcxHwA1qHhobQ0dFR0p2JUCSF6Gi0Wi1mZmZyLi7lrhyR9lVvby96e3u511ipVh4hEicnJ5idnUVdXV1e70uxx/IkEmAyTMd5PB6srKygoaEBvb29Vb1YVxK5CJtcKsI7X9yPt3x5Ge99eAPAacvnTc/rwj0jwlQgaZEIOr0eurNWg9VqhV6vh8frxe7uLsRiMXQ6HfR6PTQaDURluH6ERqZoEyVNQ57GsLHToEib4/behzfwsNmB33tWJ6736tOuc8VMg1EUBZVKBZVKxdkFEJ3M+vo6QqEQtFotR5ZKMUCsdnJ0kdlq+XorhUIhQW+u/H7/M4LsWgE/ayybsj8Wi3ERF0IFtApROSKTIv39/eju7s5rISlX5YhhGFgsFuzv7ye1r8gCUElyFA6Hyy4AZ1kWTgAxhkE6d6LDw0NsbGygt7e3ZC8nqgQTyGqrHOULuVSEOMPXcwEffWIblzp0pVWQMoCmaTS3tJyrKu3s7CAcDqOuro6rKimVyopUlUqN6CDRJq4M0SbpRv/f9Lxu7LnD+M/FI/yvLy5htFmN33t2J14waEzyYhKibcXXyQwMDCAUCnGb9Pb2Nmia5tpvhRog3g6j/JVCJm8lu93OrelCeCs9M61WZcj2BaYoCjRNZxVl5+N2XQxKIUfEU+n4+Lhgn55yVI4ikQhu3ryJaDR6rn1FIgYqQY5YloXNZkMwGMSlS5fSuk0LAWLsGAQgSnldDMNgY2MDdrsdY2NjeWW05YN8SU4oFILP54NOp6tqzUUuHHrC52wQUv2PygV+VQkAImdaJbfHg729Pa6qRCbgylVV4j5zAT7HTNEm9002YaZHjz1PmBv9B4D/9ZxOfOqne/jK3CHe+h8m9Dco8cCzOnHvSANENFUWTY9CoUBbWxva2trSGiDyk+1z2QVU+yh/NZEjPvjeSsfHx+jv7wfDMIJ4Kz1jAlljyCbKJpUZ0iIS8stWLDkKhUKYm5sDRVGYmZkpeExW6MoRfzz+8uXLadtXlRCBx+Nx3Lx5Ez6fD0qlsmzEiG/smLq4RWMxmFdWEIvFMD09LdgIM51nBcjtdmNlZQUikQhra2sQi8VQKpUIkkyjKtgs8v0OFex/VMbzksnlaGpuRlNzM5gzzy4i6rZarWWtKpVrg+dHm9ByCm2tKshpGjGWhYSi0KKV40/u7cfvPasTn31qD1+8cYA//roZ//SDbbxhpgN3NLBwR1j8bMuTtwVAOth8kbQ2AukMEEm0yeLiIliWTYrVSM1DrFbyQVCt2W98JBIJSKVSaLXajN5KEomE+wyyeSsRvVk5jH+rCbcVOUpHUhKJBEwmE+x2e15u10IdNxccDgdu3ryJ5uZmjIyMFPXlz1UpKwT5tvXKLQIPBAKYnZ2FXC7HyMgIrFZrWY6TauzIr4gFAgEsLy9DrVZjdHRUcI1TNnLEsiwODg6wtbWFvr4+6PV6xGIxWK1WRCIRLC0tVZWGJh+il+p/ROFUc1SOqlEhrUdaJIJWp4NWp0MXblWVPGWoKlWqJcrgVrSJHadVJeXZFJxRJcGDd/fif8504HM/38fnf76Pd397FXVSGv4oAxYLBVkA8PHV+cOkll6255BKpWhpaUFLS0vS9NXh4SEsFguUSiWnVdJqtVVPji5Sc5QvUkf5S/VWKrWt9tBDD+GrX/0qzGYzFAoFrl+/jve+970YGhrK+Def/vSn8frXvz7pdzKZDOFwuOjzyIaaIke57rxSvY4CgQDm5+chEolw/fr1vBPaCwUhR/mUp1mWxcbGBjY2NjA6Ooq2traSjhuNRov+e+D0rsdsNuPw8DDvqJRykSO73Y6bN29yvk4ej6csx8pk7MiyLDem3NbWhq6uLsHv9rNNqzEMg/X1dTgcDoyPj6Ourg6xWAxSmQxKpRIikQjt7e1ctYNoaDQaDfR6/Wk2mVxeFVWlVNwz0ojpdi3e+h8mqKQiwcTYQoJfVWIZhtMq8atKWq0WOp3utKVQwPt8UePoUZZFlGWTo02kNH7vOZ34nWvt+P/9eAf/8tM97vEMC/z5t6344KObkIpoiEUUxDQFsYiGiD79/xKaglhEcT8nGOBn256k5/iLb1vBssBwsxrNGhkMSklGuwD+9NWey4/lHTtivgCOj08rtwzD4Pj4GCKRCAqFoupabNVO3liWzelzlI+30tbWFpxOJ+6///6S22pPPPEE3vjGN+LOO+9EPB7Hn/zJn+DFL34xTCZT1ufVaDSwWCzcz+W8FmqKHOUCv612dHSExcXFgt2uiwG56HKVV4kY/OTkBNeuXSs5nLTUFlckEsHc3BwSiQRmZmZO2wh5HFNowsKfHhwbG0NrayuA/MJgC0HizNgxlOY5KYrCyckJjo+PMTg4WFaDs3TUiN/Gu3TpEuRyedJrJ4tAarUjfJZNRsiSVCotW+J9qWiok+GOLi0etTjx4zUnhprrylI9EmLBpGj6fFXJ64XH7cbBwQHXKiLvszgPW4eL3tRTo00kFDDaowXz8z1QieSImga1FAqpCPEEizhz+l80ziCYYBA7+znBsIgnTn+f7lh/8V+3qr5SEcW5fTdrZGji/r+c+93DK3a85zu3qk/vfskA7hvU4KmnnuJIqlQqTWr9lGNytVDUAjkCUFDrL5230vr6Or7yla/gPe95DxKJBD7wgQ/gV3/1V/H85z+/YKL03e9+N+nnT3/602hsbMSNGzfw3Oc+N+PfURRVEQNi4DYjR6RyZDabsbu7WzEnZ/6kXKYLkJ9Hdv36dUHyyEppcbndbszPz8NgMGB8fDzvL47Q5Cgej2NpaQkej+fc9GChnkDZkM3YMZFIwOVyIRwOY2pqqqxTGBRFnQuQDQQCMJlMUCqVGdt4mabVUv1+iIs0l3iv1UJ/RpZkZaqcFgLR2Q78dw9vCD7SX07I5HI0yeWneo2zqpLX68Xe3h5CoRDUajVHStNVlapx0jAGQKOVglHQYFmASrCgEoCYYfFPvzGRt/bI5ovg3o8+dU5T9v/d3YtQjIHNF4HNF8GhL4wVmx8/2/bmfE6GBd7zHSuu914FAIyNjUEikcDj8XAaGSHtAkpBtWuOyA10sQSOVPde//rX4/Wvfz22trYwOTkJiqLwlre8BXt7e3j2s5+NF7/4xXjjG99Y1Prp9Z5eEyQcORP8fj+6urrAMAwuX76Mv/3bv8XY2FhRrysXaooc5brwKYrCxsYGRCJR0W7XxYBcdJmqOMQvqKenB319fYJ9gYutHO3u7sJsNmNgYKDg1pGQ5CgYDGJubg5isRgzMzPnsn+EOlY2Y8dIJILl5WXE43HodLqyXzOpJIcIr5ubm3NGoeTaYFNdpEniPfE8kcvlSVUlqsS7XYqi0lbBMsHhj+IRi5P7uRwj/ZUgIfyqUmdXF6KRCNweT1JVia9VEkskVevyXK+W4rcndfjcggeMiAIlBv7X87oRkgFHsRhnF0BnOfd0NgLZNEeBSJwjTOS/xYMT/HjDnfQ4hgV2XEEAp2tBauuH2AU4nU5sb29DJBIl5cBVypix2jVHZI8QisCR9/VjH/sYZDIZ1tfX8b3vfQ+PPvpoUTf9DMPgrW99K571rGdhfHw84+OGhobwL//yL5icnITX68X73/9+XL9+HcvLy2hvz250WgxqihxlA5l+UKlUuHbtWkXLrRRFpRVl8/U8QsddAIVXjhiGgclkKso2gEAowuJ0OjE/P4+WlhYMDw+nXVyEaKu54nE4M4jWvV4vTCYTjEYjFAoF/H5/ScfKF2QD39/fx9bWFvr7+3NO5BX8XmRIvHe73VhbWwPDMJx+Rq/XQ1KBjeTQEz5nUlipkf5yQiqT3UpYZxhOE7a/v4+1tTWo1epbLWuWrTpN2LO6lOhWJyA3tqJFe+penhptoqAoKLJEm/zKdAuu9xqw6w4l2Qikg0omRl+DGH0Nt1ox6apPAPC41YnLovRVj0x2ATs7OzCZTNBoNCWFteaLam+rEfImFDkPBAKQSqUcEerr68Mf/uEf4g//8A+Ler43vvGNWFpawo9+9KOsj5uZmcHMzAz38/Xr1zEyMoJ//ud/xl/91V8VdexsqHlyxNerkMX+IvrQqVWccDiM+fn5gvQ8pR4zG8LhMObm5sCybFG2AfxjlkJYSGir1WrF8PAwOjo6sh6r2EoAy7KwxWKcF0wqjo6OsLa2hu7ubrS2tmJ/f78yVYczkmO1WjnhdT5GpCWb9KUk3gcCAXg8HtjtdmxubkKpVHLVDrVaXXJVKR0qPdJ/EaBoGhqtFhqtlqsqeTweOBwOxONx3Lhxg6so6XS6vLRK5QbLsjAoxehry6yBDLEsQrxoExVNc2SJRJsQ7VAxSFd9qldL8bmfH+KHahH6JsPobchc1U21C4hEIpygeGFh4fQ1nlWUjEajYAn1LMtWPTkSuu1HxNhCkK03velN+Na3voUf/OAHBVd/JBIJLl26hLW1tZLPIx1qihylfhixWAwLCwvw+/24evUqDg8PKxLEmg78ypHL5cL8/Dzq6+sxNjZWtn50vpUjcj4NDQ0YHR0t6XxKEYETWwWHw4E77rgD+jNjvmzHYlm24JYEMXaMpCE7DMNga2sLR0dHGB0d5c6hkuaWdrsdIpGIE15nA8MwoHl/KwgoCiq1Giq1Gm3t7YjHYpxWaXV1FSzLcpu3TqfLXFUqcHGs1Eh/NbWvpDIZGpuaoFQqYbFYMDAwAK/Xi8PDQ6yvr0OlUnHVu0In4IRCod+vBNIH5irPCFOxSK0+GZQSfOjRdXzu5wf49X+Zx9vv7sWvX2nJ61xlMllOuwAi7C7FKboYsXOlIXTbz+/3l2wAybIs3vzmN+NrX/saHn/88aJCbBOJBBYXF/HSl760pHPJhJoiR3x4vV7Mz89DrVZjZmYGUqkUdrudS46vNIgYfGtrC1arVZDMtlzIRVRYlsXOzg5WV1cFO59iReCkcgWclkfzsVXgx5Xku/jwjR1TEYvFYDabEYlEMDU1lVTNE1L8nQnEdE0ikWBqaiprhZPckfL/Y1kWLMMIXtURSyQw1tfDWF/PVZXcbjeOjo6wsbEBpVLJ3ZWf28ALfM/uGWnERIsGf/ilJfTUK2tCjC0ECAEhVaWOzk7EolG43W54PB7YbDZQFMWRUq1WW5FWJzm3UpAuMJdUlcQFrjep1ac3P6cd+uAe/mNPjr/53hoetzrxnpcNorEu/8pPql1ALBaD2+2G0+nEytmEKN+EspAqP1kLq7lylGuMv1AIUTl64xvfiM9//vP4xje+gbq6OthsNgCAVqvluhqvfe1r0dbWhoceeggA8J73vAd33XUX+vv74fF48L73vQ/b29v43d/93dJfVBrUJDkiguJUt2shMs6KBU3T2NjYQDgcxp133ilY1EQ2ZCMqiUQCy8vLcDqdeVVp8kUxFRYyGVdfX19Q5YosOPku3qnGjnyQiTCFQoHp6elzxKTc2WVEeC2Xy09FujmIEfHNkkil8J61v1pbWzkfLxKXQ1GUsGSJV1Vq7+hALBrlrALIBk4qSmyRlbZmnRyXOjR4eseLQCQOlUy4Zagap8IIUjcTiVSKxqYmNJ5plfx+PzweDw4PDzmtEr/VWa6qkpBi8UyBuQqahiJNYG4uMAyDQT2Nr7zkCt773+v4+sIRXvmJG3jXS/px32hjRlfubJBIJGhsbERjY2OSU7TdbofVaoVcLufab7lkGqVOglUC5SJHpeBjH/sYAOD5z39+0u8/9alP4XWvex0AYGdnJ+l9dbvdeOCBB2Cz2aDX63HlyhX85Cc/wejoaEnnkgk1RY4YhsHi4iLsdntaQXGqCWSl4Pf7EQgEoFAo0k5dlQuZKkckloSm6byrNPmi0MoRcd4uZjKOPDaf42UydgRO24pmsznrRFg5yRFfeJ1N9E1aiAzDgGFZ0BQF+/ExNjY20NPTg8bGRq6CxDBM0mdP0/QpWRJ4kZZIpWhobERDY2PSBn5wcIBAIIBwOMxN+hXSFrqjU4ufbXsxv+fDs/qyj+/eDsh1bVE0jTqNBnUaDVdVSiWl5aoqlTPYlQTmIiUwV0nTkORxrZBcNbVMjL96+RCeP2jEX/6XFX/0NTM++9Qelg/9eU3IZUI6p2i32w2XywWr1YpwOAydTseRpdSKCVmbqqmVm4pykKNSp3rzWWsff/zxpJ8/+MEP4oMf/GBJxy0ENUWOKIriQmPTbfjZstXKBZvNhqWlJchkMnR1dVWMGAHpiQqZAislliQb8hVk8yf1SpmMI8+VCdmMHVmWxd7+Pna2t3NOhJWDHKU6Xmu1WgQCgbSvh99GA071ONvb25w2ioi2+e8JC4A5qzAl/W2ZqkqpG/iKyQSJRIJAIHDOGDFX3MaVLh3wwx08bLZjqEktqO6oGjeqQqszmUgpqSqpVCruvS61qlSp1PtMgbmKM81Suvcn9dzuHqrHVJsG7/yGGT/d8tx6bhb4y/+y4nqvoWhROHC6ptbX13NJAcFgkBN2b21tcXYCRNxNWv7VeM0RlEuQfbuj5sjRwMBAxs2ykm01MnG0u7uL8fFxHB4eVkTQywchKmRT39rawtraGkZGRsri+8A/ZjZEIhHMz88jHo+XNKlHnZXhMx0vyjDYj8UQz2DsaLVa4fF4MDk5mTMkUWhBdvRM3xSLRnMKr/ltNPKzxWJBOBzG5ORk2slCsmGIeGQpXVWJvIflqCrRNA21Wo3mlpaMcRtkA1coFEkb+M29U9O3Gzs+vOFzN/HG53bhxaO3r/6olNZVrqoSgPwE9GU4t1LABebi9GaAqypRFKS86zqVuNWrpfjd6x1J5Ag4JUhbzmBJ5CgVSqUSSqUS7e3tYBiG8w3b3t7G8vIyVCoVWJaF1+s99Q6rQpIktCD7GXJUgxCLxRVpq0UiEdy8eRPRaBR33XUX1Go1jo+PK161IncDsVgMJpMJbre77HqnXCJwr9eLubk56HQ6XLlypWRbhUykJZexo8lkAgBcunQpr2qekILsJMfrFOF16utJbaNFIhGYzWZIJBJMTk7m/f6RthqQXFXiC7rJ4whhErqqlC7E1X1GliQSCbd5x0QKfPSJbe5vGRb4yBPb+L7FicFGFXqMCvQYlejQKyARnz9Hhz+KQ08YLTp5TfkjCbVx8qtKYFmuqmSz2bgJOEJKVSpVzs+5GgwqWdwKzAUACU7tAiLxONg059ZlVJ6zhQCAd31rFW+Y6cAvTzVBIRF2goymaa5iBJyuM3t7e9jd3cXNmzcBgGu/GQyGinYRskHotprf76+YwfJF4rYiR5WoHHk8HszPz0On0+Hy5cvc5nURYnCyGT711FOQSqW4fv162b+QNE0jFoul/TfiBN7X15fT7bmQ46WSlmzGjicnJ1heXoZOp8PAwEDei4JQm0Mux2v+z6RiRIjRyckJzGYzDAYDent7i77by1RV4leWyLmURdSN5BBXJpHgqkpbW1uwOmNg2PPHWzsOYMV2S5Mloim062ToNijRU69Et1GBXVcIn/rpHqczqZX4kbIJxSkK6ro6qOvqkgT0Xq8XZrMZQO6qUjWK2GMAPAwDdywGm0iE/WiUqyzJaDqtL9Jz+w2Y3/Phb763hn/64TZec0crfuNKK8JxpmDRdj6QyWTQ6/U4Pj7GXXfdBZ/PB5fLhf39fZjNZqhUKo4sabXaCxNtC02OgsHgM5WjakS2TaycgmyWZbG7u8t5laSKiy+CHLlcLgCATqfD2NhYRb586dpqLMtidXUVu7u7gjuB84+Xy9jx+PgYVqsVnV1daG9rK1j8XWpbLR/Ha3Ic7r8zYuRwOLC2toau7m60NDcLeiefrapUiqg733OkRSLo9Hro9Hp0d3ejwXmCz61bklwAaAp43/2dkClV2HFHsOkMYtMZwpYziCfWXHhizXXueRkW+Mjj26iTiTHRpsk69VYN1aZKVGcyVZWOjo6SfJX4Zp/VUDnKBoqiuMBcJBIQ47Sq9OKJRtzVo8e+J8y5cgejCXztpg2ffWoP//iDbXzixzuIJU4vtGJF29nA1xxptVpotVrOLoBEmywvLyORSECv13NkqVgT3lLOUSgEAoGKhb9eJGqOHGUDEWQL/WUnY/EOhwNXrlxJG44nEokQiUQEO2Y2sCyLjY0NbGxsAEBJVYZCkUqOYrEY5ufnEQ6HMTMzI/gdBTlenGWxH40imuYul2VZbG1t4fDwECMjIznDC9OhFEE2wzDY2NiA3W7P6XhNrktOE4RTa4rDw0MMDw8LZrmQCbmqSoVaBRT8nlEU2us1eDPPDJKmgF8bUcF7tIt4PA6jVou+Lj10k/WQyeUIROLYdoXwozUXvrl0nHx8AH/zvXUAp1qUVrUIOlEcDpkDXQYF2nVyPLHmTDpWrVSbSkaaqlI6s89wOCzoRKuQINNqfMQBeBkGXoYBJafQ0qKElKYRYRgopSL8jzvb8OtXWvHvswd46OzaAIQTbaeeX7q1VyKR3IqUObMLcDqd3A2cXC7n2m96vb6sJpKJRELQ1AghptVqAbcVOSIXmJBMmYSjikSijFNyQGnO0YUgHo9jcXERXq8X165dw89+9rOKCsH5r/Pk5ARzc3NQqVSYmZkpS2wLRVEIJhJwRSJp9UXxeBxmsxmhUAhTU1NFk7NiyVEsFsNKAcJrAJxoWa/X4+DgAH6/HxMTE2WJmMmFc1Ul3uRbOa0C7hlpxKUOHQ69YS7PCyyLYDB4mrzucGBrcxMKhQI6nQ7tOh1eOdWEby8fn4sf+ZWpZjgDUWy5QjAdhxFnWPxgfxPAKfnkf6rlCLvNB9XQupJIpahvaEB9Q0OS2afX68Xe3h7cbjc3aVhXV1eWCJlCkSuag8WtaBMnL9pESdPoMZ7/PjEssOsOCUaO8hE78+0Curq6EI/HT69xpxOrq6uIRCLQ6XQcWRIqmoN/jkK31Z4hRzUGsjnH43FBLga73Y6FhYWs4agElWir+f1+zM3NQS6X4/r165BKpRVv5xH7gKOjIywsLKC7uxv9/f1lK8uHaBr7sRjS1VNCoRBMJhMkUimmp6eLSoQmKKatFgwGsby8nFZ4nQpCOhoaGiAWi+F0OrG3tweKolBfX49gMAiZTHahMQRcVensM2YBsFmqSqWiXi1NJigUBaVKBaVKhda2NsRjMU6rZLVawTAMfnVIgf+whDNWgex2BxY39iGr78C2K4iFvRMs25K9pU43yGDFyVFVta54Zp/+QACaujpIpVJ4vV7uva50MHE6MAXaDCRFm2jEYBQU2DhAJ1hQzOk106EXrqVVTK6aWCxOaxfgdDqxsbEBiUTCtd/0en1J6xrwjCC7WNQcOcq2wJApnFLJAsuyWF9fx+bmJsbGxtDa2przb8pNUo6Pj7GwsICOjg4MDAxwX8hKVawIKIpCIBDAwsICJiYmytZ7ZlkW9ngcHpqGLg1pcbvdMJvNaGxsRE9PT8mbdaF/n0t4zQdfeC2RSKBSqbCzs4OGhgY0NDTA6/Vid3cXq6ur0Gq1XFRHJXUJqeDejyxWAbFYDIlEAol4vCxWAWKJBAajEQajkat0tLjd6K1zY98dRItOgW5VGP6TE87rR0RTaFBQmOgz4Fl9Brx4JIo3fO7muammD3x/E6+61Iz7RhugkJZ/Gaw6csQHy0IkFqetKh0fH3PBxIQsVbSqVML7Vq+W4o0v7MZHn9hGnKUAFnjpYD2UKjESLMsF5pYCIUJnM9kFbG5uYnl5GRqNhiNLdXV1Bb8f1WgCWQuoOXKUDRRFlUxSotEoFhcXEQgEcNddd+X0xyEoFzliWRZra2vY2trC+Pg4WlqSxYTFZp0Vg3g8jp2dHUSjUczMzOT93hQKvrFjutH3w8NDbG5uoq+vTzByVkjl6ODgAJubmzmNJQGepudMeE2cd9vb29F2JhrXnwmVw+EwXG433G43tre3IZVKORGnRqO50IgCfvstkUhgbW0NkUgEer2+IlYB6WJNiH7GYrGAZdm0UQ+pYbc0Bcz06rFy6Me/PLmHL80e4mVjjXj5RCNiDMoq2q5WcsSeXZscUt5rfjAxv6qk1emg1+kgLeOEbDrNUSEg7dtNRwD/9+F1LDv9OIrFAIriok2UNA15EdEmgPDEg28X0N/ff7omnJlQ7u7ugqIo7t/ztQt4xgSyONxW5AgobWLN5/Nhbm6OC7MtpJxZDpISi8WwsLCQlajl61hdKgKBAObm5jiX8nIRo1RjR/4ofzrHaSGRSxdSiPCaRIHwhdf7+/vY29tDf38/V1LnQy6Xo7WlBa0tLUgwDDxnRMlqtSKRSCRVlS7KQyUej2NlZQUMw2BqagpSqfRCrAJS9TNkKstutyMSiWBpcZFrCd0z3HBO3xSLM3h01YGvzNnwpdlD/Me8DQnm1lST0KLtatAcZUIuApIumNjj8cBhtyfpwspRVRKiMkPaty8Za8LXbtrw5dlDvGCoHvVqKSJngbn8aJNCAnOFOL9skMvlaG1tRWtrKxiGwcnJCZxOJ/b397GysgK1Ws1plTLZBQhpAsme6QLLtf5XE2qOHOVi98VGiOzv78NkMp0Ls80XQtsI+P1+zM7OQqlUZiVqlWir2e123Lx5E+3t7TAajZx/itDwJxI4SjF2JOQoGothxWRCIpHIKXwuBrlMIPnC6+np6awtr3OO1wA21tfh8XgwPj6eV0laRNMwGo0wGo3cguR2u2G327GxsQGlUskRpWJK7cUgHA5z4b2DQ0PcxFuhVgEABK8qkakshUKBg4MDNDU1nQvLbdXpoJWdfm4SMY17Rxtxz3ADvms6xsd+uMM9HRFtT7dr0VBA+ns2VHNbraBz41WV2trb01aVNBoNdHq9IFUlIaNNdIrT7e6zP9vH536+n0SAM0WbKOnsgbnlJkd80DTN2QX09vYiGo1yVSW+XQAhS2SNqsbg2VpAzZGjXCi0vcUwDFZWVmCz2XDp0qW0d/T5HleoCo7NZsPi4mJeYudyttVYlsXm5ibW19c57ZXL5SrL8TIaO1IUQqEQtmZnodFoMDAwULapOFLtSX2/ifBaoVBgKk/hNWmjkWk6hmEwOTlZVMWHoiioVCqoVCq0t7cjFouduk+f6a4AcEQpV4p4sfD5fDCbzWhoaEB3d3fGa1Joq4BiQFFUUi7ZyckJPB4P9vf3k9Lu9Xo9lEolOnTniS7DAn/6nxb8xpVWPKffwDl1F+uXxKK622rFnls+VSWiVdJoNAV/1kKRSoc/is88tcf9nGtqkYs2OasqKXhVJX5gbiXJUSqkUimam5vR3NwM9qx66nQ6cXR0hNXVVSgUChgMhoymvcXiGc1RjaKQCk44HMbc3BxYlsX169dLEsAKoTliWRZWqxU7OzuYnJzMqWcBylc5SiQSWFpagtvtxtWrV5OCT4UkR7mMHeOxGPb29tDZ2YmOjo6ybTDkeVMX42KF1/QZqSOl7/6BAY4wlAqJRMKJuZmzUFK32429vT2srq5ymWbk7rHU94wzqOzqOqd5y4WLsgogoGgaGq0WGq0WnV1diEYicHs88LjdODg4OL2jlmvORVFQAFyBKD742CY+9dNdvGSsAUqJGJ/66W7xfklVSo4AgYhbmqoSmTZcW1u7VVU6a8HJ8qj+lqo5Ijj0hM+J8hkWOPSGc5JcBreiTey4FW2iPFt7qyEmhKIo1NXVoa6uDt3d3YjH43C73XC5XEgkElyqA6lGK5XKot7XeDyOSCTyDDmqRgjVVnM6nbh58yYaGxsxMjJSctlRCCH4wsICQqEQl9eW73GFruSEQiHO22lmZibpyy8kOcpl7Lizs4NAIIDGxkZ0dnYKcsxMIJsyv7VWivCaGO01Nzejs7OzbKSOpmloNBpoNBp0dXUhEolwiyLJNCNVJa1OVxBBY1mW00kNDg4WZa6Zeq5A8jVbjqpStvdaKpPdMufjheX+Ui+Nb6wnwIICTQG/N9OK5w024GGLE99aOsYXnj5Mep6C/ZKqWHNUrpZfumlD4u+ztbUFuVzOEaVMVSWhxMQtOvk5AkxTQIu28PY8iTbxMAy2EwnoAOjjcahomgvMvWiIxWI0NDSgvr4e+/v7mJqaQiAQgMvlKskuwO8/tcV4RnNUg8hFUvitouHhYXR0dAh2XLLIF7rQECF4XV1dwWaKQleOXC4X5ubm0NzcjJGRkXMlY6HIUYhhcBCNZjR2tFqtODk5gU6nq4g5IvnESGWjFOH1oc2G7bMYESGjVPKBTCbjSu0JhoHP64Xb7cbm5iai0WiSqDubbouI3z0eDyYmJsrifM7/X6GqSoUIn/lhuW/o7sa9Dh/WD12QJYKgw3uwrhxhWqfDc1/Sge+tB/CFG7akv2dY4DNP7uIFQ0aMNKvPWQLwW3Asy6Ja60YV0UOlVJUS8TinVVpfX0c8HodGq4U+paoklOaITC1+5IltsOxpEe9Nz+sqeSoxwTCIUhQciQQciQQkuCXsVtJ08hTgBYCs1Wq1GgaDAR0dHUgkEvB6vXA6nUl2AUSrlE3DGAgEuOe73XHbkSOxWJyxrcZ3l+a3ioQAubsp1KqdhLX29PSgr6+vKCG4EOSIVGpWV1ezkkYhyJg3Hoc9Hke6bSwcDmN5eRlisRjTly5hY329ItN45H2PRqNYW18vSHjNnG18LICtzU04nU6MjY1Bo9GU/byzQUTTHBFiWRahUAhut5tbFIkmgUwZkU0oHo/DYrEgFosVrZMqFOkMKJkzUXultErt9Rq0159+ZszZBuLxeLCzvYXGSAwU6HPX7ONrLjy+5gJNAb31Soy31mGsWY1jfxSf/MmtFtxvTekxZahOeiRU66oQiMTipKoS54zOqypptVpEIpGSTRAJ7hlpRDTO4uM/2sEfPqdTkGnEVJPKGHjRJgDkFMVplWQXUFUiazW/+iYSiTgrAACcXYDT6cT29jZnJ0DIkpRnABoIBCCXywWp5v3jP/4j3ve+98Fms2Fqagof+chHcPXq1YyP//KXv4x3vetd2NrawsDAAN773vfipS99acnnkQk1R45yfYkzkYV07tJColByxDAMVldXsbe3h6mpKTQ2FvdFFaKSwzAMlpeXYbfbcccdd2TN9+JHtBR6R0eMHb0ZyJXX64XJZEJ9fT36+vq4ikElxqDJa1laWoJSqcxLeM0nRgzDJBGKasuqIhYMSqUSbW1tXISB2+1O8glSq9U4OjqCXC7HxMTEhTh2ZxJ18w0o+Y8th1aJFomgNxigNxgAQixF+/jcTQ8YADSAXxtTY7zdgE0fA5PNj+WDE3zdHsTXbx4lPRfDAp+76UbX9epsRQg5EVYUUpzRE/E4fD4f3G43Tk5O4PP5EAgGoT+LNpGXoA3VKE6JllouDOFis6yDqdEmYiRXlYQwocwFvv9YJqTaBfh8Pq4tbzKZUFdXh+XlZbS0tECr1QoSb/KlL30JDz74ID7+8Y/j2rVr+NCHPoR7770XFosl7V74k5/8BL/5m7+Jhx56CC972cvw+c9/Hq94xSswOzuL8fHxks4lE2qOHOWCSCRCNBpN+t3h4SGWlpbQ1dWFgYGBstwlFeLOHY1GMT8/z5kpltKySPd6CwERpQPImh1HwG+DFGTrzzN2TAebzYb19XX09PQkOZJXysfJ6/UCOJ36yjUhmCq8DofDWFlZuVBCUSj4EQZk0sVms2F7exssy0IkEuHg4AB6vV7wrKdCkc0qINWAsizXCkVBoVTi1dcH8ILJKPbdAagRBRX1w+PeQ1sigZFuDf7ndCsClAKPWT34xmJyQC7DAh+f9cMU2MVUuwajzWrIJKfXSbETcEKh2mwGRGIxR0yjsRiUCgUkEglcLhe2trYgk8mStEp0Ad830dnLZFLV2UWiEA+hOHjRJkiuKsnLRE7J+eX7+dI0zb23fLuAz3zmM/jGN76BcDgMiqLwiU98Avfddx+6urqKOq+///u/xwMPPIDXv/71AICPf/zj+Pa3v41/+Zd/wTve8Y5zj//whz+M++67D3/0R38EAPirv/orPPzww/joRz+Kj3/840WdQy7UJDnKFhIqFosRCoUACFedyfec8mlxeb1ezM3NQavV4vLlyyWPXZciyPZ4PJibm4PRaMTY2Fhemzp/k8oXEYbBAc/YkQ+GYbC5uYnj42OMjo6eq1oVGwhbCIjwGgDa29sLIkY+n4+72+nq6qqqTSZfUBSFaDQKp9OJ7u5uGI1Grqq0v78P+qw9R1pwVZH/lsEqgHz347FYWdpvyXlwTVxLyO12w+lwwO/3Y1AqPxd4CwAnURZfmbfhK/M2iGkKI81qqKQ0ntr2gi12Au4XADK5HE1NTWhpbeWqSh6PB5ubm4jFYkkTcLmqSuTryQi0ppRSdQuzLMJnJpQiJJtQClVVKtXjiNgFfPSjH8WHP/xhfPjDH8ZHP/pR/Nu//Rve+MY3YmBgAPfddx/uv/9+vOhFL8rrOaPRKG7cuIF3vvOd3O9omsaLXvQiPPnkk2n/5sknn8SDDz6Y9Lt7770XX//614t+bblQk+QoGwhBiUQimJ+fRywWK7k6U+ixM4EYTfb19eUcC88XxWqA9vb2sLKygoGBgYI29ULJUTpjR4JoLAZzDmPFYgJh80Wq8HpxcTEjEUsVXtMUhaOjI2xubqKnp6dsGXPlBsuyODg4wO7uLgYGBmA0GgGAm+girrwulwvb29uwWCxVk/8GIKn1urOzA7vdjoGBAQBI234DhDegJC0hvinir4eO8O+m4GkLjgJ+dUiOO1skkBjasXBwgvk9H5YPThBJ3LreGBb4yOPb6DIoMdhUvOC10ErURWiO8gV7pt0h4FeVSLuTEPnt7W2uqkTMElOrSkQgLdSSItR7lwBwwjA4SakqKWkaihKuVyENIEUiETo7O9Hd3Y0f/OAH8Hq9ePTRR/Hd734Xn/3sZ/MmRw6HA4lE4twEcFNTU0aDYZvNlvbxNpst7eOFwG1JjkKhEH7yk5/AYDDgypUrZTHFS4dMRIVhGJjNZhweHpZkNJkOhVaO+Ody+fJlbjPMF6R9mM8xnfE4XBnE8YFAACaTKWeiPU3TgjqPE6RzvM5UpeJPURFsbW1x1S6hY0wqBVK1IwLyTPE0ZKPp6elJyn8jwllClC4q/41M1nm9XkxMTHDTjRdhQElMEf/H8+rx4ssRbB55oGBDiPmc8Lj9UEWjuNOoxz19rVh1x/Hn315L+nsWwNu/uoIuowJ3dGhxR5cWw01qiEX5mVA+vHKclCOXTyWq2tpqfGRt35+1OxVK5bmq0tbWVlJVSavVQqFQcG21bVcIDn+05DZmuUwg+VUlGqe+Sgq6sGgTcn7lCp3VarV45StfiVe+8pWCPX81oSbJUbZNjIj4RkZGyuovkw7pKkekghWPxzEzMyP4WHohlaNUrVOx55JL25HL2NHpdMJisaClpSWr23I+xyoGmRyv04m/z0WBsCwsFgvC4TAmJycvvHJSLBKJBMwWC6KRSEEC8qT8t0SCu2sn+W/EeVqv1ws+9JAOiUTitPoYi2FiYuKcJ9dFGlA21MnQUHd6t7u3e3rTptfr4fF4YDab4YvhXPuNooDrPTosHfq5FpxSSuNSuxZyMY3HrE6O+Pz6lRZMtGhwEonjJBLHoTeCr87buOfL14vpwgXZWVAI+UhXVeImDnd2IJFI8MTh6Xf9qzdt+PqCreQ2ptDkI+0xkFxVkvG0SrmqSkLmqgGn5KjUPay+vh4ikQhHR8mDC0dHRxkr8M3NzQU9XgjUJDlKh3g8juXlZTgcDigUiqKFYqUglRwRTY/BYMD4+HhZvkT5kgefz4fZ2VnodLqStU7ZjpnL2HFvbw87OzsYGBjISwNGCTytRiI3mpqa0rY2+cdK1RdFIhGYzWZIJBJMTk5WrCIpNCKRCFZWViCRSDAxMVH06xCJROfy31wuF46OjrC+vs7lvxkMBqjVasFvVLjXIZXmFMKnswpgK1xVEolEXFguyzAIBAJ4DbOPLyyecBNw/2NSi/snW6BQKrFhD+LpXS+e3vHixxvupOdiWOALTx/iCzhMeyz+49aP/ahXZzDwzBCZU00o6tx4VaXmlhYwiQS2Dp144qlt7iGEPI7Uy04tHIo4zkW0JCMsi0giASlFIdetWTly1Ur1OJJKpbhy5QoeeeQRvOIVrwBw+j4+8sgjeNOb3pT2b2ZmZvDII4/grW99K/e7hx9+GDMzMyWdSzbU5uqeApIYL5FIMDY2VrZg1Fzgk6Pd3V2YzeaCNT3FHDMXOSLTesWG6qYiU7Uqm7FjIpGA1WqF1+vF5ORk3g6rFIRLNCfC676+vrR3HPx2YSoxOjk5gdlshtFoRE9PT9XeaeeC3+/HysoK9Ho9ent7BXsd/Py3jo6OpPw3k8kEiqIEzX8LBoMwmUzQarWc7UO+4B5bQauAVAJC0TTUdXX4jWcP40XTUew4/FAiDDpyApPJxIngX9yjw6unm/CzHR/+9nvr55735RONGGpSQyMXIZFg8Z7vrp0z4/7AY5t4jS+K+8cbuYw4/nkB1Zv7JhT5oEUiBCA/J5JnWODJm2b0GyRJE3CiPK/Pi8hWowA0icWoy4P0CE2OgsGgIAaQDz74IH7nd34Hd9xxB65evYoPfehDCAQC3PTaa1/7WrS1teGhhx4CAPzv//2/8bznPQ8f+MAHcP/99+OLX/winn76aXziE58o+VwyoSbJEf/Lcnx8jIWFBbS3t2NwcBB+v7/sKfWZQHLdlpaWcHx8XJSmp1Bka6uxLIvV1VXs7u4KOq2XjpBlM3aMRCLcBnnp0qWC2i1C+Tjl43hNyFFqFAiXLdbdjZbm5qrdSHLB5XJhdXUVHR0daG1tLevryCf/zWAwcKLuQs7F5/NhZWUFLS0tguTtZbIKENKAMhvBP52Au1XZ4Yfl7u3twWq1AhLVuRYcTQG/Mt2S1DJ78/O6kjRH9wzXY27Ph08+uYtvLR3jd+5qx3P69FyVpBIeYqVASPLRpDlvZkpTwPPunISUCcPj8WB7exuRSCRpAk6hUKSvKp1dH5Wc3iyEGAHCt/38fr8g5OjXf/3XYbfb8e53vxs2mw3T09P47ne/y4mud3Z2kj7369ev4/Of/zz+7M/+DH/yJ3+CgYEBfP3rXy+bxxFQo+QIuBXSur29jfHxcS4QUyjH6GKxtbUFqVSKmZmZiuhRMr3eWCyGmzdvFpzVlg/4hCWXsaPP54PJZIJer8fAwEDBCx1N0yWN3aYTXmc9Fq9yQOH0S2qz2TA8PJzVHLPacXB4iJ3tbfT39ws6EJAPUvPfwuEw3GeibqIFyTf/zeFwwGq1lm1CsFwGlIW0rlLDciPhMDxeL17tO8KXzWEu/+31dzTAoEje+O4ZacSlDh0OvWG0aE9F27E4g28uHuHfZw/wfx9exzcWVPjd6x0Ybq7jyFG1VkKF1ENtOYMAbum8iGC9UasAoIDu7PsdPpuA83i92N3dhVgs5rR0/KoSt05U6GaJAtAsFkNdANkph+ZIqHXwTW96U8Y22uOPP37ud69+9avx6le/WpBj54OaJEfRaBSzs7MIhUKYmZlJ2vhJVaPS5U4Sy6BSqXDt2rWK3U2kq+L4/X7Mzs5CpVLhrrvuEsx+n4AjETmMHY+Ojrg097a2tqIWEYqiwBZZOcokvE4Hsnm53W7I5XJIpFJYV1cRCASSJqBqDSzLYmtrC3a7HaOjoxceaQKcirpbWlrQ0tKSlP+2sbGBWCyWNv+NZVkcHh5iZ2cHQ0NDJYfg5otCDCjJJGcmslTsJiqTy9Ekl+O3m5pw7+UQ1g/dULAhIOzGz39ug1arTfL5SfZiAiRiGr9yqQUvGq7HF54+wHdMdvzR18x4Vp8ev3WlGSdRYOnwBG161YWYUGaDkHqoby4dgaaA979yBOE4w5HHVMgVCjQrFJxWiUzA7ezsIBwOo66ujnOTBypDLCkALWIxVAXuK9WoOaoV1CQ52tjYgFgsxvXr189teGIeq6/ERcvPJCMjz5Uss6a21Y6OjrC4uIjOzs6yuYHTNI1QIoGdaDStsSMJ97XZbBgZGSlpIyvWBDKX8Dr1fBOJBFpbW2G32zE7OwuKoiAWi9HX11e7E2kMg1XeZF21RZoAyflvPT095/LflEoldDodwuEwfD5fRsuBSiCXASUhSunab0Jt8o1axVml4xRcleNs85ZKpRndozUKCX7/OV142XgjPv3TPfx43Y0nN9xgWBFgWqtKE0qhNEe7riDm907wnH4DBgrwkKJFIuj0eq6qFAmftt/cZy1P4LRbQOwC8tUqFQIKQKtEAmUR+1k5NEcX9f2rNGqSHA0NDSGRSKT90vAzzoSumKQikUhgeXkZTqcTd9xxB46Pjyve0uOmb1gW6+vr2NzcxMTERFlHHMM0jf1YDI1pSEssFuNG3aenp0uuuBSjOcolvOaDrylpbGyESqWCyWSCQqGATCaD1WpNEhPr9fqaiAeJRqMwmUwQi8UlTaRVEuny31wuF3Z2dhCJRCAWi3FwcMA5dZf7+50L2apKqe03hmHK8hmkVjnI6PrGxkbGpPs2vQJ/+pIB/GjNifc+vAGc2SzmO/pfSQjVVvvmWZzLL02URvxkcjmampvR1NyMYCCAxcVFiEQi7O7uwmq1clUlnU53uvaVSOxKIUbA6R4lpKWGEKP8tYLqXzHTINuGSWI8ymEcyEcoFMLc3BxomsbMzAzkcjmcTmdJOWfFgHjzzM3N4eTkBHfddVdZmb0zHoeLolCf5v0nE0QymQxTU1OCbF6FBM/mK7zmP55sajRFwel0Ym1tDe3t7VwbkC8m3t3d5SqEZES9GqsxgUAAKysrRU1yVRNYlsXR0RGkUikmJycRiUTgcrmwv78Pq9UKtVrNEdZqyH8D0leVGIZBOByGSqUqW6wJkByW25Mm6V6hUHAbd11dHbRpwlcZFvjwoxv4n9c70FNf/lSBrDirxpV6/frDMTy26kB/gxLDJTiPp4NIJEJXdze6cKuqRIT0RKtUbFWJxikxKsUhuxwmkM9UjqoYuRbBcouynU4n5ufn0dzcjJGRkSQPlUpXjiKRCABwMSnlMt5jWBZHZ8aO6chpIW2sQpBvW60Q4XWq4zWF02iXvb29pAgNIL2Y2OV2w30WgqlQKLgNuq6u7sKJiNvtxurqKlpbW3PmxFUzwuEw56A+MDgIEU1DKpWirq4OXV1diEajnKh7f38fIpEoySrgoqt7pKqUYBismc2Ix+PcJE4lDCjTJd2TqpLVaj29KZBr0mbAze+f4C1fNuFKpwavmm7BRGtdyRWQYiCUzcDDZgfCcRa/NNEk6OtIbfnxq0rsWbq9x+NJqioRfZhKpcp6LkIQI0B4eYnf769IFFc1oCbJUS6Uq3JExK1ra2sYHh5GR0fHueNWkhw5HA7Mz88DACYnJ8tGjGJnwbFR3mQLmSBjWRb7+/vYPpuESs2/KRX5RJWQipVcLs9LeM13vAaA9fV1eDwejI+P5xQbpjpEkw3aYrGAZdmk9lulW1k2mw2bm5vo7+9HQ0NDRY8tJPx+P0wmE+rr6zMSbalUmpT/5vP5uHwtLv/NYICBJ+quNOLxOFZWVgCAa21emAGlWAyD0QiD0QiwLAKBADweD17ZH8TX1uLcBNwbrjZjuE2Hr948wpMbbtzY8WGgQYVfvdSMu3r0oGmq4Oy2YsHXcBX/HCy+tXQMrUKMZ/cJK+LPVtWiaBpanQ5ane5WVcnrhcftxsHBAedjRbRhYl6VnQbQJpFALsA1IKTmiBi9PlM5qmGIxWLBSQrxL3K73bjzzjuh0+nOPabYENhCwSdpIyMjWFpaKtux0hk7kgkyhmGwtrYGl8uFiYmJskxC5dIcFSq85vsXxeNxmM1mMAyDycnJpOiJfCASiVBfX4/6+nqwLAu/35/U9inFy6cQsCyL7e1tHB8fY2xsrCom0ooFIZqdnZ1oaWnJ6z2jaZprXyTlv7lc2NrcvJD8N+LtJZfLMTg0xLXbLsKA8hwoCiq1Giq1Gr+m06FDYYLc0AwFEwTCR/Dt2PCqbi1e1t+Kx7fDeHTVjYf+ex0tGhmGm1R4Ys1VUHZbsRDCZuCpLTeOT6L4jSst5wwwS0UhLSsycdjU1MRVlbxeL/b29hAKhaBWq6HT6WDU6zGg1QpCjIDyTKs9ozmqYQhdwQkGg5ibm+Mm5DJtouUgZalIJBJYWlqCy+XC1atXodVqYTKZynLcTMaONE0jGo1iYWEBDMPg0qVLBROLfJGtrVao8JrveB0KhWAymVBXV4f+gYGs3jr5nmddXR3X9olEInC73ZygWCqVcjolITfoBMMkWQ7U6mQdcDppSSpfpXgxXXT+WygUwvLycl6ar2wGlIVaBRQLvZzGldGzKvhZVcntdiPsduIOeQBX7lBhziXB49sBPGaNcH9XbgG3EOToW4vHENEUXjoqPIErdgKRX1Xq7OpCNBKB2+PBiduNo709eEUiGAwGGI1GGAyGkrSbz2iOikdNkqN8NEdCtdXsdjsWFhbQ2tqKoaGhnAtdOckRXwTOJ2n5RIgUApZlcRyPw5fhtTAMA7vdDqPRiIGBgbLqO9IJsosVXhNi5PF4sLq6KpjDcjrIZDI0NzejubkZCYaB1+OBy+VK2qBJVanYxS8Wi2FlZQUURWFycvLCp7eKBcuy2N3dxeHhoeBeTKn5b2Tj5+e/kc9BiPw3n8/HVTILDb4uxSqgWJy78eBVldo7OhCLRuHxeFBf54EOLL6wlvxwhgUOveGykCNuTSvkPbRaIX70UeDkBG6RHCfBNjz76iT0ZTg/ofQ8UpkMrU1NaG9vhxiA1+vlRPQmkwkajYYjS3V1dQVdU0JqjqLRKGKx2DPkqJYhRAWHZVlsbGxgY2MDo6OjaGtry/k35dQcuVwuzM/Po7GxEaOjo0kXvJCkLMGyOIhGEc5QrbHb7bDb7VCr1RgaGiq74DdVc1So8JpUjIBT4fWhzYbtra2K6nJENA2DwQCDwZC0QR8eHmJtbY2bujIYDFAqlXm9p0RnpdFo0NffX3Ll66LAMAzW19fh9XrLbrZJURTUajXUajWX/0Y0Y4eHhyXnv5F4ls6uLrSeOfaXgkKsAsjjiok1ybZ5SqRSNDQ2oqGxEbrmML60vggmZWl4eHEXHepO6DRqwQXP+W7s1M4OZO9+N8QLC2BpGqBp1CcY/AvLwG8aBzX012A7OwU7t0LPLxvEONUYSc+ei1yD/f39py1ilwtOp5OL1CAVJaPRmPOGSMi2WiAQAIBnTCBrGaWSlHg8jsXFRXi9Xq51le9xhazgALfuqi0WC4aGhtCZ5gsu1HEjZ8LrTMaO29vbODg4QH19PUQiUUUmofjkqBThNQtgc2MDLpfrQnU5qRt0uqkrUsnIFKXh9XphNpvR3NxccHWimpBIJGC2WBCLRjExMVG21mwmSCQSNDY2orGxMW3+m0aj4TaqXJqx4+NjrK+vY2BgoCzxLLmqSsWKugsxWWzQyPGmlOw2rYzGY5tBLB2u4BW9NMbbb2m7SjVEzNfjiNrZgfK1rwX8/tOfGQZgGBBKoLKYgNe+FsHPflZQgsSybMmVu1RilAq5XI7W1la0traCYRh4vV6uVb+ysoK6ujquMpquqiQkOfKfvb/PTKtVMcrZVgsEApibm4NMJsP169cL0iQIPSXHMAxMJhOOj49x5cqVjE7TQlSO/IkEjmIxpKNY8Xgcq6ur8Pv9mJqagtPpRDAYLOl4+YIsjqUIrxOJBCwWC2KxWNU5RaebunK5XElRGoQsyWQybhPu7e0VfDKwkiAmlRKJBBMTE1Uxep8r/418DhqtliMpLMvi4OAAe3t7GBkZSTuoUa7zLaSqlGkTL1Q3k5rdplOI8ZW5Q3zhxiE+ucLihaEQntXgQyIWASNVIyxWYqDFiPZ6TcFVpXzPTfbudwN+/ykpSgOKYcD6/ZD9+Z8j/KlPFXQO2cAwTEkVWwmANqkUkjzfFzLhptfr0dfXh0gkAqfTCafTid3dXVAUdU6rJKTmiIixL9qupFKoSXKUC8W21Y6Pj7GwsID29nYMDg4WfBHw3apLvZsPh8OYn58HwzA5Q2xLrRw543G4MpA6IlyWSCSYvnQJUokELre7YmneZFFfXl5Gf39/wcLrcDiMlZUVyOXyqtiEsyF16ioUCsHlcsFut3OROfF4HD09PWhsrJ6Ih0JBKoDVbFKZmv/mPRN1r6+vJ+W/kWrT2NjYhbUb+FUlsg7kW1UqZq1KzW779TvacFePHh9+bAvf3wxgxSXDnZ1N+Ma8AywbAgUHfqmHxt2DBs4Qkc7je5hPVYu2WiFeWMj5XBTDQHzzJmirFczAQO4XmQdK0fMUSozSQSaTJVWVfD4f134jwybAKamRyWQl70mBQODCzVYriZolR9mmmEQiUUFO1fzojfHxcbQUqRcQiUSczqWUC8jj8WBubg5GoxFjY2M5N/RiK0cMy8IWiyGQgVh5PB6srKygoaEBvb293EJA5+E9JAQYhsH21hYAYGRkJMmcMdPj+RUjvji2q6urpr7U/CiNltZWWFdX4fP5oNXpsLOzg93dXU6nVA2mh/nC5/NhZWWlplqCqZoxQlp3d3cRi8WgUCjgdDrBMAzUavWFkj3uO8qvKvEm39LFmgjxGXQZlXjfr4zg6zdt+NzP9vD1xVtTbSwofHOLxR1dLDxbW4jFYtBoNElhuemQj6ZH/OijYGk6Y9WID5amIX70UUQFIkfFrvNSikKbRAKxgNc+/8aKVJWOj49xcnKC5eXlc1WlYqY0f5HG+IEaJkfZUEh7KxaLYWFhAX6/v+ToDX6uW7EL5P7+PkwmE/r7+9Hd3Z3Xl6+YylGqsWMqyJh8b2/vObJYTN5ZoSDC6+iZA3i2zyVVeE1TFDcWXuvtp1gsxnkxTU9PQyqVgmEYnJycwOVy3TI91OlgSEmyrzY4HA6sra2hu7u7rNl/5QRFUZDJZPD6fJBKpRgfH0cwGOTavnwj0GrJfwN4VW3csgpgWRbhcBgABIk1EdEUXnWpBTqFGB96bCvp3xgWEGkaMDHci9BZWC4x7ZTL5UlhufyqVs519OTk1DMqn/WIpk8fLxCKEWSXgxilg0wmQ319PaxWK571rGfh5OSEa7+RqhLRKmk0mrz2GeKOXQs3NELgtiRH+bbV/H4/ZmdnoVQqBYneKCX0lmEYWCwWHBwc4NKlSwWJOgutHAUTCRxm0Bfxx+THxsYyml2WkxylCq+ffPLJjFXCdI7XW1tbsNvtGB0dzVtMX40gLU2VSsVFaACn779Wq4VWq82YZM+PNKmGxezg4AA7OzsYHBzMqJ2rBcRisaRAX5FIBKVSyRmBnpyccOJ6kv9GtEr5TiKWC6mibrvdjv39ffT29gIQLtZkql0LmkLSVBtNAS1aOUBRUCiVUJxVRBPxOOduvr6+jkQicVpV0usB5OGOXVeXHzECTh8n4Bh6oWHCUopCu0QCUYWuAaI3EolE56pKZAJub28PALiqktFozLgPBgKBX5hJNaCGyVGutlousmCz2bC4uIiuri4MDAwIsmiRnn6hLa5oNIr5+XlEo1HMzMwUXLospHLkicfhSGPsCADRWAzmlRXEYrGsY/LlJEfphNeZIkRS9UWEYEYikaoTXhcK0hJsbGzM2RJUKBRQKBRobW1FPB7n7srNZjMAlDSeXiqImzsh27XskULy3lQqFQYGBs5VDSiKShJ1EyNQMgGXlP+m11+o/YLNZsPW1haGhoZgMBjOibpLMaCsV0uTptoAQCGhcRKOnfNDEonFXFgu+GG5Dgd8Ph9omsbO9jYXlpt6/PgLXwjpP/9zXq+ZYhjE7747r8fmg0JakrKzilGliBGQuYMhk8k4PR2pQhOilG4CjjwH0RwJia2tLfzVX/0VHn30UdhsNrS2tuK3fuu38Kd/+qdZixXPf/7z8cQTTyT97vd///fx8Y9/XLBzq1lylA1EuJoOLMvCarVie3sbExMTgpf3C7UR8Pl8mJubg0ajweXLl4vavPIhZLmMHQOBAJaXl6FWqzE6Opr1PMpFjjI5Xqc7XioxikQiMJvN3PRTpUmAkLDb7VhbW0NPT0/B16dYLE6KNCGVDDKeToTEZDy9nCDu3cFgsObJKvl+ZMt7SwXfCJSf/7a1tYXIWStUr9dXPP+NhCzzDTeFNqDkT7XtukP4fz/exTu+Ycaf3TeAibYMNhopYblHNhtsNhtisRgXlkuCW/V6PSRSKZiBAcQnJyFaWsqqO2JpGomJCTD9/cW8ZWmRb1vtIogRkN8YP78K3dvbi2g0CqfTCZfLhZs3bwIA/vVf/xXT09Nwu92CkyMiGfjnf/5n9Pf3Y2lpCQ888AACgQDe//73Z/3bBx54AO95z3u4n4XWQ9XuDpIFmQgKibwIBoOYmZkpS4mwEHJ0eHiIpaUl9Pb2ore3t+jqVa7KUS5jR6fTCbPZjLa2tryEy+lcq0tBrlZe6vmkCq9PTk5gNpthNBrR09NTldNP+YBlWezt7eHg4ADDw8PQn7UWikVqJYPLHDvboEnmmMFgSLpDFAJEK8WyLCYmJi5ce1MKiK9UW1sb2traivqepptEdKfJfyvHZ0HAsix2dnZwdHSUc7pOCANKMtU20aZBh06Bv/6eFe/+1ioevLsXI83qnOG1FEVBKpWir78/Kdbk+Pg4qX1s+OM/Rv0f/EHGcX6WpgG1GpG//MtC37KsyIccyc+IEX0B7dRiPI6kUilXVWJZFm63GzqdDp/5zGe4QOc//dM/xUte8hLcddddJd+E3nfffbjvvvu4n3t7e2GxWPCxj30sJzlSKpVl1S7WLDnKtkClIygnJyeYnZ2FWq3GzMxM2RbrfMgRqV7t7Oxgamqq5LHsbJWjXMaOu7u72N3dxeDgYN6O0ZnaXMUgH8drcrx0wmsi8u3q7hbElfiiwHeKHh8fL4vRWrrMMZfLBYvFApZluUiTUoXEpP2kVCqTtFK1iHKJyPmt0NTPgmEYwfPfyESux+MpOINPCAPKiXYN/u6Xh/EX37bi/z68DgqnxqzZwmuTTBbTxJp4vV54PB6Y/H7I/+iPMP6v/wr16ioSFHVqFcAwoBgGiYkJRP7yLwV3yM5lAqmgKLReEDECSs9VIxNu73vf+wAADz74INbW1rC9vY1XvvKViMfjuOeee/DAAw/gnnvuEeq04fV689Il/tu//Rs+97nPobm5GS9/+cvxrne9S9DqUc2So2xIbauRCk1PTw/6+vrKKorMRY7IdFwgEMBdd90lSPUq03TeSSKB4wzC60QigdWz8fCpqamCzkOotlq+jteE/PF1EACws7MDm80mSJXlIhGPx2E2mxGPxyvmFJ2aOUb8eoiQuK6ujqtk5HKH5sPv98NkMhXUfqpWEF3OwMBAThuJUpAr/02lUnFEqZj8N4ZhYLVaEQwGBbm+zlWVslgF8EXdPfUqvPO+fvzRV1c4zWO28FqGYTISC4lUivqGBtQ3NABn1+8POobxH99cwP9wzGFUFoXYaAR1772QT0wIGmtCkEgkMhL/iyZGgLC5asApWZqamsKHPvQhJBIJ3LhxA9/5znfg8XgEO8ba2ho+8pGP5KwaveY1r0FXVxdaW1uxsLCAP/7jP4bFYsFXv/pVwc7ltiRHhKAkEglYrVbs7e0JUqHJB9mqOGQ6TqVSCVq9SnfMbMaOkUgEy8vLEIlEuHTpUsF3pkK01QpxvKbOXK4TDMPdcfKT6GvZe4OYVMpkMkxMTl5IlYWiKNTV1aGurg6dnZ2ckJh4+UilUo4oaTSajAuu2+2GxWJBR0cHWltba5YYlTMINxeEzn9LMAzMZ5XZ8fFxwSvm6awC2CxVpUj0/JqUKbw271F5ioK6rg626Ak2De2Q/tbzEJLH4PF44PV6Qd+4wVXiNBoNxAK9B5nOT3lGjC76+hcyOgQ41d2R7oJIJMLVq1dx9erVtI99xzvegfe+971Zn29lZQXDw8Pcz/v7+7jvvvvw6le/Gg888EDWv/293/s97v9PTEygpaUFd999N9bX19HX15fvS8qKmiVHudpqAPD0008jFothZmamYnkwmSpHxH27s7NTsOk4/jE5Z9wcxo5erxcmkwlGoxH9/f1F3VmU2lbLJLxOB+J1cnBwgMZ4HCqVChaLBSKRqKaT6IFbE2kNDQ15e1pVAnwhMd8d2mq1IpFIpG35HB0dYWNjo2zZYpUCCZx2uVxVQbxT89+IwH53dzdn/ls8HofJZAJN0xVxh+fWkixVpSa1+NyYPwCoJOfXoUJNFhcOfJCJKIy36SAR02hsagJ79p55PB7s7e0hFAqhrq6O038plcqiq0rpzq9aiBFQHnKUb4fh7W9/O173utdlfQyxkABO94QXvOAFuH79Oj7xiU8UfG7Xrl0DcFp5+oUnR9lAcr/EYjGuXLlS0cmlVHJEFtuNjY2S3LezgVSOchk7Hh0dcfqJUu7si22rMQyDzc1NHB8fZ/RQ4oPoi/r6+jgPn2g0yuWRVcKlu1wgWhahEtzLBb47dG9vL4LBIFwuV1LLRyQSwe/3VzRbrBwg03WhUAgTExNVN13Hnyzq7u7m8t9cZ/lvpMJHiJLZbIZUKsXQ8PCFVCTTVZUaRSL8wbM78bEf7YBhwVWC//p763jXS/rQU39r8y2EHEVjCZgO/Rhvq4NEfOu1UjQNjVYLjVaLzq4uRCMRuD0eeM4mOMVicVJVqZCw3NTKkYqm0SIWVwUxAoQnR8FgMG8rjoaGhrw1rPv7+3jBC16AK1eu4FOf+lRRN+zz8/MAIOj+etuRI+IwDQDDw8MVH+nmk6N4PI7FxUV4vV5cu3atbOV5kUiEIMNgJxrNaOy4tbWFo6MjjI6OlqzPIW21QhxiyfRSNIvwmoAIr8lEWl1dHaLRKGw2G9ra2iAWi+FwOLC5uQmVSsVt3hdtspcPSFApEcHXkiEiRVFQqVRQqVTo6OhAOByG1WrFyZnrsNVq5dpvWp2upoTYRPfFMExZ2k/lQKb8t7W1Ne4morGpCfFYDKIK6NiygS/qvn+yBXd267HvDqJZI8PNfR/+8Ye7+OOvW/D/vbAbVzq13A1YvuvLis2PGMNisjX7GiuVybigZ/bMXsHj8WBnZwfhcDgp1kShUGStKvHJh5qm0VxFxAgoXZCdinLEh+zv7+P5z38+urq68P73vx92u537N9JV2N/fx913343PfvazuHr1KtbX1/H5z38eL33pS2E0GrGwsIC3ve1teO5zn4vJyUnBzq1myVG68W6z2YzDw0NMT0/j5s2bF1JZIHdJwWAQs7OzkEqluH79uiATJ5ngB3CUSCAdZyakJBKJYGpqSpCLm+KVzfNZvPIVXgNIKsMDp3eWxJdlcHCQE8a2t7dzegyXy4X9/X2IxeJbm7NWW3Uj/cSywOVyYXx8vKbdZhOJBNY3NpBIJHD58mVIJBL4fD64XC6uwqfVajl36EqIzItFNBqFyWSCVCrFyOhoTZE6AlLhk8vlcDqdqK+vh0qlgtPhwFYVuqY3auRo1JxW5pq0CjRrFfjb763hb/57A78704Y7OzVYsYfRrJaASSRy+iot7PsAANNt+ZuMUjQNrU4HrU6HLgDhs1gTj8eD3d1dSCQSTtul0WjOheWSylYdTaOpyogRUJ62mtAmrg8//DDW1tawtraG9vb2pH8jutZYLAaLxcJ1hKRSKb7//e/jQx/6EAKBADo6OvCqV70Kf/ZnfyboudUsOeIjEolgfn4e8Xicc5jOZgRZTohEIpycnODJJ59Ea2srhoaGyrZJE2NHN05bAqkIBAIwmUxQKBSYnp4WrIrGjfTmIcouRHidLgqEP36cqhtL1WN4vV4uhiAej3Oj6Xq9/sIrAfF4HBaLBbFYDFNTU1VNFnKBkAliuEkWYHLHTcJZ3W437HY7NjY2bnnSGAxFTVyVC6FQCMvLy9Bqtejr66s6Ql0ITk5OuFDfjo4OUBSF9vb2tK7pfN3YRRum0jSN6Q4dPvir43j3t8z4xE/28Ymf7J/+GwX8r7gKLxoyZLUKuLl/ApVMhN6G4rWlcoUCzQoFmltawCQSnFXA5ubmaViuVgv92TUuk8vBMAw0YjGaq7TKmEgkBLspJ1OUQmt3X/e61+XUJnV3dyftCR0dHefcscuBmidHJMHeYDBgbGyM+6IX6lQtBPiOxGNjY+eYsJDgGzum0wC5XC6YzWY0NzcLPlJNNhA2R2Xu8PAQGxsbeQuv+Y7X8XgcKysrYFkWU1NTOb/kNE1zi31PTw+njTk8PMTa2hrq6uo4olTIaLoQCIfDp+7dUmlFhLHlBKkCZiMTFEVBqVRCqVSira0N8Xicq/CZTKakiSu9Xn9h7wchE01NTejs7KwawlYMvF4vVlZW0mrYMrmmp9o2XHT+W5tOjnfdN4g//NIi9zuGBT7+411c6dTCoBSntQoIxhhY7QFc7dKBpoU5d1okSoo1IWG5LpeLM1CVJxJQhkJgymTaWSpqoXJUzahZckRRFHZ3d2E2mzEwMHDO2TmT90+5kEgksLy8DK/XC6PRWFZiFGEY7EejIMsEnxyxLIu9/X3sbG+jv7+/LIn02fLOgOKF1+wZMSKBq3V1degfGCi4zZGqjeGPphPhKtEplcuNmID4/tS6ezdwOl1HKhOFkAmxWMwJNNNNXF1EjIbH44HZbEZnZydaW1srcsxywel0wmq1ore3N6ddSb75bxelG9u22c/9jmEBezCBFr0qrQHlzV0vGBaYbKsDyzBFheVmRZqw3ITXiyOzGWtra7BYLNDr9ZxXVbUI+WtBc1TNqFly5PF4YLVacfny5bQGbWKxuGKVo1AohLm5OdA0jZ6eHk6gWg6cJBI4isWSgmMJOSK+Th6PB5OTk2Vl+Zkm1koRXtMUxY2Mt7S0cK2BUpE6mu45I0rEGZpsBkIHs5JNq9Z9fwDhnKLTTVy5XC4uRkOhUHAVPrVaXRYySbLr+vv7856oqVYcHx9zFgrFGFWmy3/j68Y0Z1l8lSCuNpsNYdchKFDJ6xsFtGpP29DpDCgXDwMAgPFmFXdDnGpAKSQMUin0DQ04Mptx7do1hMNhOJ1O2Gw2rK6uQqVScUQpmy9YuSGkCSRpqz1TOaoB6HQ6PPe5z824mVWqreZ2uzE3N4fGxkaMjo5if39fUMdQPhyxGNxpXhMZ5V9YWAAAXLp0qeyalnRGkKUKrw8PD7Gzs4P+/v6yeeWIaPqcM7TL5UoOZjUYStoMWJatyGupFA4ODrCzs1OW6Tq5XI7W1la0trZm1MYISVzJa6l1V3Xg1msZGRmBVqst+fn4+W8sy3JWAXziyhd1C7npkwnOmelR/NCzj9ndU4E1TQFvfn4P6tXn1zNy/GdSP6wAAK1sSURBVMVDPwxKCXoa1Ny6km+sSaHQ0TQaJBJEIhEAp/sMMVDt7u5GLBaDy+WC0+nE4uIiWJaFwWDg1pxyDuakQsi2WjQaRSKReIYc1QqyLZaVaKvt7OzAYrFgaGiIq3KUg5TlMnYMhUJgGAYKhQIDAwMV0XCkttVKFV6TKa6xsbGKfQH5ztBJwaxpqhj5TvgQ2wSHw1HR11IOsCyLra0tLhC43K8lkzaGEFdieEgiTQpBauhqrX8u5X4tFEWdy38j7TdScSXTiDqdruhNn4QtEzdytVqNQ28EBqUEf3RPH9p08rTEiMAdjGHbGcILBo1J6x6/9ZYr1iRf6EUi1J/tOQzDcKSLD4lEcssq4Owadjqd2N/fh9lshlqtTqoqlbOaLCQ58vv9AFAxM+VqQM2So1wXVTnbagzDwGQy4fj4GFeuXEm6mxaaHOUydjw+PobVagWAihEjILmtVorwOpFIcFNck5OTF9qvTw1mJZvBysoKJyI2GAzQ6fVptRjktUQikQt/LaUiwTBYs1oRCAQu5LWkamNSDQ9lMlneKfb8UN9CQ1erDSzLYnNzE06nE+Pj4xXTgIhEoiTiSrL4Dg8PYbVaoVarC85/SyV5KpUKm44gDn0RvHyyCVPtuathP1pzAgB665M37XPtNwDM2Q0Zv2LNVZRyVJUMIhGMvJvxfGxM+NdwT08PotEonE4nnE4n9vb2uGBXQpaEnqgVmhyRQYtfFNQsOQJOL75MGV/lqhxFIhHMzc2BYRjMzMycW2izZasVimAigcMMwbHkrv7w8BCDg4NYWVkRXICXDeR1rq+vFyy8JsSI5IopFIqqm+LibwYMw3Dtt+3t7Vvtt7PNWSaTIRKJYGVlhRtvv+jx6FLAnxScmJi4cBsEIMXw8CzFnl/F4I+m8883wTCwnGngKhXqWy4wDIO1tTWcnJxcqIN3ahZfNBrlPo/U/LdM04h8kscnrD/ecAEArvfkbt9+z3SMj/1wGwDwqSd3oJaJcO/oeUE634ASOF9V4lr7ZyQptaqUSoyA4vQ8UqmUu4aJvsvpdGJnZwcrKyvQaDQcURLC7kLI/YCM8dfyQEmhqN0VPAfEYjHXFxYKXq8Xs7OzMBgMGB8fT3vhCVWx8sTjcMTjSEf9iJtvKBTC1NQUt7BU0vSSoihsb2+DZdmcwmvg1oJEiBHJFWtqajo3aVhtoGmauwPs7u5GKBSCy+XiXLoVCgUikQg0Gs2FRTUIBUJY5XI5BoeGqvK1pEuxd7lcODg4wNraGtRqNReSu7m5CZqmMT4+XtOENZXkVVK7kgtSqTRr/hu5kdDzdHxra2vw+XznSN6TGy5oZGKMt2Y3SHX4I/jI45vczwwLfOTxTVzp1GZtwwGZq0r8STjyuHqxGPo034FC3LsznQPRd/X19SESiXBVpe3t7aRr3GAwFHXtCinIJuSomtdpoVG7q0UOCN3eIrEk/f39WUNCS60cEWNHX4bnIGPuEqkU09PT3F1yqWGwhSAUCiEUCkGhUOQlvCYVIwCgKQpHR0fY3NxEb29vWawGyg2FQoG2tja0tbXBbrfDarVCqVTC5/Nh9sYNziZAo9VWJbnIBGI7UF9fL7g3VrnAT7Hv7OzkRtPJJkPTNBoaGnByclJznwcBqeQBqHqSl3Ya8aw9vb29zZE6hmHOEaPlQx82nSE8p9+Q83Pa90TOhdcyLHDgjeQkR6nnC5yvKjEMA4NIBM2Z5xpf1E0kBUJWumUyGTeYQAxtSZ4kMSk1Go2or6/Py4uKVMWErBz9IrXUgBonR5VoqzEMA4vFgoODA0xPT+cc/S2FlPGNHdOBiJ4bGxvPeeYUGwZbKMg5iMViLucsE1KF1xRFJQl8y5U1VykcHh5ie3sbg4ODXPuNjEJXo0t3NpAWVa3bDshkMmg0Guzu7qKxsRFGozHJNZ0faVJN1ZdMiMViWF5ehkQqxXCVVyUd/gj2PRG06WQcQeHr+GyeIH5y0wI1HYVeTmN+fp6rKt04ZvBPP9wFAPxozYXvdRynbZERtOlkXGgtAX/kv1iQqlK9WAzt2URu4qyqxF/XY7FY2b4jfEPb/v5+hEIhrqq0ubkJqVTKVZUytS3JuQqpOXqmcnSbQIjKUTQaxc2bNxGJRHDXXXflpdTnEqgLSJQGzhs78kFGwzc3NzOKnitBjvjCa7vdnpGYArxR/bM2Gsm+ux3EyvwprtHRUY7k8Uvl6Vy6SbuHTFtVy0JzdHSEjY2N28L3hxhV8n2yDAYDWJZFMBiE2+3G0dER1tfXoVKpON1YNS784XAYJpMJKpUKAwMDVa33+J7pGB95fBMMe5rV+qrpFlzt1oFhAYZh8dSWG/+5cAQWp7Yd940aMWCUYdvtx/HmAb6zGT37l1PCk6tFVq+Woa9eiTXHad5WtpH/QtEgFkPHu+kjazpwSjoSiQScTifEYjGi0ei5qpLQUCgUaG9vR3t7O6e3Ix5qkUgEOp2OI0ukukPOV8jKUS1nQRaD25Yclar9OTk5wezsLDQaDS5dupR3KZtcjIWUNNMZOxKQSRuHw4Hx8fGMfiblJEfpHK+dTmfG46UKryORSFJ8RjW3BXIhwTBYtVgQCoWykrxUl+5oNAqXywWXy4Xd3d2KunRnAsuy2N3d5caohfDKuUi4XC6srq6iq6sLLSkRGvzPIzW0+ODggHOG1uv1GacRKwmS+abX69Hb21t1xI0Pov8hbS6WBf5j7hD/MXeY9vEsgO+Y7PhO0m9TgsRZ4KmlNVzta0yb/5ZgWNhOIug1KvB7z+lGq1YmCDFqlEigTbNuk+8nRVFc3uP09DRHnNJZBZTjO83XIgGn3nJOp5MzaZXL5TAajZy9g1DXzTNttdsIpVSObDYbFhcX0dPTg76+voIuMEKI8h2jzGTsCADRWAwrJhMSiQQuXbqUtdpSLnKUyfGapum0wbOpwuuTkxOYzWbU19eju7u7qu9+cyEajWJlZQU0TWNycrIgkieVSpNcur1nOU38aSvS7qkEeSSkm4T61vrCR3Rs+ZpupoYW+3w+uN1ubG1tIZpBRFwpEO1XrWS+7XnC5/Q/APDyiSa0aWRY3NzDjw/Or02/fbUNfQ0q+CNxfOD7G+daZG06ecb8N+uxH/5IAi8b12OyTZj2fJNEAk2WNZtlWaysrMDlcuGOO+5IGoThT75lMqAsx9pHMgw7OjqSMgzX19cBAIuLi5youxQLi2cqRzWGbIuGWCwuWHPEsiysVit2dnYwOTlZlFiYfAFyEbNcxo6BQABLS0vQaDQYHBzMSbTKQY7I3atMJsPk5GSSZoaiqKTg2XTCa7vdjvX1dXR3d5+7k6815BO4mi9ENM1Vjfgu3WQj0Gg03L+XY2NOJBIwWyyIRaOYnJys6fF2lmWxv7+P/f39op2i+e1QfqQJ0XgolcokZ+hykhXSFmxvb0dbW1vZjiMk1o4D535HU8ArJhpwtG1FQ48KTx6eJBEomgLuGWngqj2xBMtVn0iLbHr4VHPEz3/b3d2FRCLBz5ynfzfZJowBZj7EyGQywe1248qVK0lEgxN187oGRGtZyaoSP8OwqakJCwsL0Gq1ODo6wurqKpRKJVd10mq1BZ3DM+ToNkKhlaNYLIaFhQUEAgHcddddRV8I+bhkxxgG+7EYYhk0O3a7Haurq+jo6Mg7X0xIfyUgWfydrqyfGnab6ni9s7MDm82GkZGRnP5H1Q6PxwOLxSJo3htBOpduYna4tbVVlEt3NkSj0dNpR4mk6iefcoF45ZCWsxDuvcQZmkwjkrvxVDPQbB4+xYK0BXt6empmivPQF8bnn96HWiZCMJrgyM0fPLsDh5sWqNVqTPb3481Sxznyw2+D3TvaiCudWhx4I+daZKnZiD6vF5+1bkJKA/6dFSyfaGEosspH4ZQY1eVBjDweD+64446cx0iX/5bIYBXA91YSEizLQiKRoKurC11dXYjH4xzhX15eRiKRSDKgzHWD9Aw5uo1QyLSa3+/H3NwcFAoFZmZmSp4qykaOchk77uzsYG9vD0NDQwVlcvFFg6UiH8drQo7OCa9ZFtbVVQQCgdumXUPei1yJ50Ig1eyQbMwka6yUjZlUAoWofl00GIaBtQIO3vy7cb4ZaJKHT4lZfMCtMNyBgYGayeJLMCz+/vsbCMcY/M0vDaNDL8eBNwKjHLBtWVGn0aC/vx8URWUlPwT16ty6IRFNQ67WYNMTx+V2La5c7kqq8hWS/0YBaJZIoM7yPSJpCD6fD3fccUfBVdZMVSX+2gkI335L9TgSi8VcG5lUq51OJw4PD2GxWLiw3Pr6+rSxJoFAoOaniwtFTZOjXG01cvFlu9iOj4+xsLCAjo4ODA4OClIVyESOchk7rq6uwu/3Y3p6uuC7YCHaaumE17mOxxdeE02OSCQ614arNRCiarPZLkysnBrZcHJykrwx63Qw8Fy6s4FvulkLOpZsSCQSMJvNiMfjFXXwTjUDTZfFR6bf1Gp13pscsYSotSrr1xdsWD48wcsmmnCp4/T7oRIxWFpagtFoPOeVlQ/5yQeL+z4kGBaXOrTnqnz5OqdTAFokEqhyEKOlpSX4/X5cuXJFkPZztqpSuvYb+f+FIpvmlV+tJmG5xCpgYWEBLMtyOiUAaGlpQTAYRGtra8HnkQvd3d3Y3t5O+t1DDz2Ed7zjHRn/JhwO4+1vfzu++MUvIhKJ4N5778U//dM/CV5trWlylA18YXS6i4tlWWxsbGBjYwPj4+OCamJSyVEuY8dwOIzl5WWIxWJMX7oEaRGLPU3TSJRAjjIJrzOBoiiEQiFEo1FIpVIEAgGsrKxwjq+1XJUguWJ+vx+Tk5NVkcXFz2niu3TzdTGk/ZYaPUAmWdJNcdUaON+fs7bgRUbOZMriM5vNYFk2qcqXrn1JQlcPDg6SLCFqAdvOID7z5C7adDL8z+udAE6rC8vLy2Un4HN7PgDApQ5d0u9Tg4sz5b8ZDQb063Q5idHi4iKCwSDuuOOOsnhi5aoqlSLqLiRXTSKRcG1LlmW5WJOf/exn+J3f+R0MDQ0hHA7DaDSW7AyeDu95z3vwwAMPcD/nClJ+29vehm9/+9v48pe/DK1Wize96U34lV/5Ffz4xz8W9Lxue3IUj8fP3VnG43EsLS3B4/Hg2rVrgi9KfHKUy9jR6/VyrsSlkAqappME0oUgm/A6FUR4rdFo4HK5cOPGDSgUCoRCIbS1tdV8VSIWi3FuxNVc/UqniyGeSsREzmAwIBQKYXd3F4ODg0kBybUIchNRV1eH/v7+qiLg6ap8brc7adoqVWRPvLKE0ktVCjGGwfu/vw6GZfH2u/sgF9M4OTlJ8pcqJ+Z2PTCqJOg0ZG5jpst/c7vd8LjdONjbg5f3eRmNxiTyyidGV65cqZhZaGpVif9foaLuYt2xKYriHM57e3uxsLCAb33rW3jf+96HT37yk/j3f/93vOQlL8FLX/pS3HPPPdDr9YW/0BTU1dXlDCwn8Hq9+OQnP4nPf/7zeOELXwgA+NSnPoWRkRH89Kc/xV133VXy+RBQbDYnvyoHwzCIxWIZ//3hhx/GzMxMkpAsGAxibm4OEokE09PTZbnwn3rqKbS3t6O+pSWjsSNwahmwvr6Onp6ekkuWq6urkEgk6OnpKejvPB4PVlZWMgqv+UgnvN7e3sbh4SGUSiWCwSA32UNaC7VElEg0i1qtRv/AwIV73RQDvkv38fExEokENBoNGhsba8YVOh3IeHtDQ0PW+J5qBBHZu91ueL1eSKVS0DSNWCyG8fHxmtLlOfwRfOrJXTy26sRv3NGK117r4CbsiLt6eY8fxWs/M4e7h+rx9hf1FfS3NIAWqRRynK57DocDDocDwWAQer2eI0rEXPHKlStVcXOUziqAnzqQrqq0vb2Nk5MTjI+PC3IOL37xi/GGN7wBQ0ND+K//+i98+9vfhslkwre//W3ce++9RT8vaU/HYjF0dnbiNa95Dd72trdlHBR59NFHcffdd8Ptdie1oLu6uvDWt74Vb3vb24o+l1TctpUj4Hx7y+l0Yn5+Hi0tLRgeHi7bnadYLIY3FkMoGs1o7Ei0PaOjo4Kwb5FIlNWxOh2I8Lq3tzdnu4Vv7Ehs+9fPwiOnpqagUqmSKhgmkwk0b2S90NHRSsPr9cJsNqO5ubmmq180TaNOo8HR0RHEYjEGBwcRDAY5V+hqdenOBvLZtLe312S0CV9kH4vFYDKZEA6HQVEUFhYWaiZi5numY/zD45sgy0y9WgqPxwOz2Yzu7u687/5LwfyeFwA4jVO+oAG0SqVQnK1B5DtAvh8Oh4ObEqZpGi0tLfD5fNDr9Re+bqVrv+WqKhXSVssHgUAAOp0Oz3nOc/Cc5zwHDz30EHZ3d0veu97ylrfg8uXLMBgM+MlPfoJ3vvOdODw8xN///d+nfbzNZoNUKj2nzWtqaoLNZivpXFJxW5Mj4nXEsiy2t7dhtVoxMjKC9vb2sh7XR1HwxuNIV1yOnml7Ynlqe/JFIcGzRG+VLzlLdbwmQZgsy2JycpKrRqRO9pAKxtraGhKJBFdRqpTRYb44Pj7G+vp6zQbh8sH/bKampiCRSGAwGNDe3s61Foiom/wbSbC/6E0gHRwOB6xWa8WmBcuJRCKB1dVVUBSFK1euQCQSIRAIpI2YIWaH1UIEiQs2//7rn57YgmicxeWRvorFzsztnpKj6Y78pRA0gDapFPIM17dSqURbWxscDgc0Gg06OzvhdruxvLyMeDwOg8HAteCqIfYok6ibVJTi8TgXa5JIJEqegGNZNu0of6b26Tve8Q68973vzfqcKysrGB4exoMPPsj9juwlv//7v4+HHnrowv3XqmeHKgK5Fg6RSIRYLIbFxUU4nU7ceeedZZ0GYVgWh7EY/DQNRRrxdSAQgMlkglKpxGiONPtCQV5rLhDhdSQSyYucpRKjYDCIlZWVU91HltZTas4Y2QSIBkOoEehSwI/PqLVJoXQIh8NYWVmBXC7H4NDQuc9GKpWiqakJTU1NSS7dVqsVDMNU3KU7F8gU19DQUM3rpUjFSCwWY2xsjLurV6vVUKvVSboYl8uFvb09iMViTtCt1ekutM2774mcc8FmWEBV31YxYsSyLOZ2feg2KmBQ5tceFlEUWiWSjMQIOCWt8/PzYBgGV65cgVgsRktLCyfqdjgcODg4gNlshlqt5oiSVqu9cPKarqrkcDhgs9kwPDwsmAFlIT5Hb3/72/G6170u62N6e3vT/v7atWuIx+PY2trC0NDQuX9vbm5GNBqFx+NJWq+Pjo4Er1xe/ApYRlAUBYvFAqlUipmZmbJuwnxjx3Sj/E6nkzMSLIdmIp9Rfr7wmlQVMiGd47Xb7cbq6ipaW1sLMkOkKCppE0gdgSaTVpUMACXxGV6v97bwYyKaHKPRmFcWVzqXbr6AWKPRcJW+Sk/rERuFo6OjmpviSodIJAKTyQSFQoHBwcGMm1MqefV5vXC73djY2EAsFksaS6/0XXWbTsa10wloChjurFw1b27XB08ohms9urweL6IotEkkkOUgRnNzc2BZ9lyGJl/U3dPTg2g0yuWYzc3NgaKoJFF3NbREPR4PlpaWMDIywk2fCWFAWQg5It2DYjA/Pw+apjNWiYkO7JFHHsGrXvUqAIDFYsHOzg5mZmaKOmYm3LbkyO124+TkBFqtFlevXi1ryyDV2JFvyEjGdXd2djAwMFC21kCutlqhwmv+Fwk4vYvf2dnJO7sqG/gj0Hyd0tLSEkQiEefcWq5WD2k9MQyT1BasVRDdB4mcKJRcpk72RCKR05Bctxvb29uQy+VJIbnlJK8Mw2BjYwMej6fmxMrpEA6HsbS0VLDxpuhs4lCv16Onp4ezbjg+PsbGxkZW64ZyoF4tw0hLHUyHJwDSu1yXE98zHeMfHtsEAPy3yY7hJjXuHc28luZDjOLxOEdyLl++nFOjI5VKOe0YwzDwer1wOBzY3NzkPuP6+no0NDRU7CaPD5fLhfn5eQwPDycJ40s1oMzUVisVTz75JJ566im84AUvQF1dHZ588km87W1vw2/91m9xUo/9/X3cfffd+OxnP4urV69Cq9XiDW94Ax588EFODvDmN78ZMzMzgk6qAbcpOdrd3eVKoE1NTWUlRu4zY0c+aJpGPB5HIpGA1WqF1+vF5ORkTv+GUiASiTL6HBUjvOaLuzc2NuByuTA2Nib4a0jVKXm93rK2esLhMNfaHBgcrMmJND6IXqq/v1+w9oZMJiubS3c2JBgGqxYLwuEwJiYmLlxzUCqI709jYyO6urqK3iwpiuICRtvb2xGLxbjP5PDwEBRFcd8RnV5ftms6EAxDJQbe/sJO9LcYKkaMOL3T2c8sgI88vokrndq05yA+I0bSHMRodnYWIpEI09PTBV/HNI+8DgwMIBQKcdNvGxsbkEqlXFXJYDCU3Y/L7XZjfn4eQ0NDGScGCzWgJI8NhUJgWVbwtV8mk+GLX/wi/uIv/gKRSAQ9PT1429velqRDisVisFgsCAaD3O8++MEPgqZpvOpVr0oygRQaNT3KD5yWrAkYhsHKygpsNhump6exv78PlUqFvr7CRj7zBcOy8CQS8CcSiPDext3dXfh8Pk4UNzo6WvbqxNHREWw2G6amprjf8YXXw8PDBQuv+U7EIyMjFd2syN2K0+mE2+1GMBgsWadEXKJL3ayqAXwDweHh4Yo4ePP9e1wuF0KhEOfSXWp6PV9IPjo6WhWap1JAxtuJF1W5rjWGYTjndJfLhWg0Co22+KyxdGBZFpvbO3jLf9kw3VaHv/7lUQHOPH/c3PPhnd9YOff7v3vFCCbbkluu+RCjWCyGubk5iMViTE1NCU5cyA0FIUuRSCRJ1C10m9rj8WB2dhaDg4NFDxulVpX4VgFOpxODg4M4OTn5hcpXq+0VCGfp8CyLSCSC+fl5xONxXL9+HQqFAkdHR3nnqxUDmqJgEIthEIsRZ1n4Ewn4z7yX3G43GhoaMDAwUJEpoNTg2VKF10Tcq1AoMDIyUnEnYr5OiQSyutxuuJzOonRKt5NLNGk9ud3uihoI8l26uc8kTXp9oR5XkUgEKysrkEqlGBoervlqHomvqMS1RtM0Z9rHb7+lfibFBheTSd+lrWMwLDDYXL7qdya06WSgKCRNytEU0KpNvlkTUxTapVJIsrzGWCyG2dlZSKVSTE5OlmVdSzUEDQQCcDgcODo6gsVigVKpRENDAyfqLmV/8Hg8mJubw8DAQElT2NkMKJ988knu979IqHlyBJz6oMzNzUGn0+GOO+7gLvhsAbBCQ0xR0InFCB4cILG7i2aFApPDwwizbNqQWaHB9zkqRnjND4/1er2wWCxV5fmTTackFoszjqSzLIv9/X0uzFcIT6mLRCKRgMViQTQaxeTk5IW2nuRyOVpbW9Ha2nrO44q0egwGQ9ZJq9spDBe4ZT1wUQGy6bLGXC7XuZaoTqfLWZ0jlWe32w1a3wZgD/0NlXfyrlfL8Ow+A3645gKQXu8koSi05SBG0WgUs7OzkMvlmJycrMi1xr/JIzlmLpcLdrsdN2/e5HLMCJkqpMNA9r3+/n5BXcn5RGl2dhZvectb8MADD9T8YEShqHlytL+/j+XlZfT19Z0LOhSJREltt3KCZVmsrq5id3cXfd3dcLvdaJPJwLIsggwDP8PAn0iUjSgRTwu+8LqnpyfrApAqvKYpCkdHR9jc3KxqX5l8dEpGoxFarRZbW1vweDyYmJioqYiGdIhGo9w4+Pj4eFW1nlI/E9Lq2dzcRDQahVar5cgS2QBI5MTtEIYLnBrUkRHkarAe4GeNMQzDTSRywcVaLVfpS22/kWlOn8+H8fFx/OxnpwZ7ffUXI5CPJVjQAP78ZYPoMSrPEaN2qRTiHMToxo0bUCqVmJiYuDASLpFIuIlEkmPmcDiws7PD3SSQzyxbpc/r9WJ2dhZ9fX1li2tZXFzEL//yL+P//J//g3e+851lOUY1o3pW1yLAsiwODw8xPT2dVowqFosrUjmKxWJYWFhAMBjEzMwMV0YFTkmLSiSCSiRCk0SC0BlJ8jMM4gLKvUQiEeLxOJaXl4sWXpOsp7GxsZq5S+ALI3t7e+H3+zmTQ4vFApFIhLa2tqoiEsWARJtUY65YKvitHn5ILpm0UqlUUCqVcDgc6OrqKnvkRCWwt7eH/f39qrUeoGn6fEv0TNS9tbXFTSSS6be1tTUEg0FMTExAKpXCfHgCuZgCfQH8lWFZmA5O0N+owp1dyZVf6VnFKB9ipFKpMD4+XjXfHX6OWV9fHyKRCKdT2tra4tpzDQ0NMBgM3Brm8/kwOzuL3t5edHZ2luXcTCYTXv7yl+PNb34z3vnOd9b8jUsxqOkdg6Io3HnnnRl7oZVoqwUCAczOzkKhUOCuu+6CRCJBOBzOeFwFTUNB02gAEOFVlKIlECWWZXFwcIB4PI6JiYmihNeW1VVEIxFMTk5WhQtsMSAj6RKJBC6XC1qtFjqdDh6PB7u7u5xOyWg0VpX7cC4QIXktVljSTVptbW3h+PgYFEXh4OAA4XC4ql26s4HvyVRLAbL8NjWpOLtcLlgsFsTjcYhEIu5a++7yMdYcp9NCr//Xm3jz83uyjtELjV1XGL5IHC9qSW5T5kOMIpEIbty4gbq6OoyNjVX19SWTybiWKMMwnKjbarUiFApxxHV/fx+9vb3o6uoqy3msrq7i5S9/Od7whjfgz//8z2tqvRESNU2OcoFUU8oFh8OBmzdvoq2tDUNDQ9xFlC8pk9E0ZDQNY4qgO8wwaTPZ0iEej8NsNiMYDIKiqLyIEXlPaIq6JYaVyTAxMVHzFRbSqqmvr0d3dzdomk4af3a5XDg4OMiqU6om3E5CcuDUesDpdJ7aQmg08PFaoolEomZyxoDT79L6+jrXtq20WaZQEIlEMBqN0Ol0WFlZQSwWg16vx9HREW5aNvGR5VuiZYbNPkZfDiwd+gAA47zJNNkZMRJl2bjD4TBu3LgBrVaLsbGxmtrkaZqG0WiE0WjE0NAQgsEg55cHnMpJIpEI6uvrBc1/W19fx8te9jK85jWvwd/8zd/U1HsmNGp7J0T2CJFytdX4WW2jo6Noa2tL+vdiKlZE0K0DkGBZBM4qSsEsRIm0WqRSKcbGxnDjxg2wLJv2PeELr1mcEqOTkxOYzeYkIlHLIESis6sLrSlEQiKRoLGxEY2NjUnRGaurq2BZNin3rdKTeZlwcHiIne1tDAwMwGg0XvTplATynTk+Psb4+Dg3EsxviZKImYODA6ytraGuri4pJLeawDAMrFYr13qqdU+meDzOhUXzp7ie3nSAXV5PeizDAqv7ThgGmiuyZiwfnJKj0ZbTSblCiJFOp8Po6GjNb/KJRAIHBwecxsjlcsHhcHD5b3xRd7HX4tbWFl72spfhFa94Bd73vvfV/H5QKmqeHGVDOdpqDMPAZDLBbrdnzGpLHasvFCKKgkYkguZsAi1w1n4L8ATdqcLr+NnxGIY5t7mnE17b7Xasr6+ju7u75isSpK24u7ubF5FIF51BdEqrq6ucd4/BYLiQTY9PJKpVw1II+OLeTG3b1IgZ4tLtdruxs7MDmUzGfWZqtfpCF+4Ew5z6f8ViGB8fr/oKVy7EYjEsLy+ntVLobqhLGxsSsu/hZ+6dpEiTcni5sSyLxQM/uowKaORiyGkarRJJVmIUCoVw48YNGAwGjIyM1Dwx8vv9uHHjBjo7O9HT0wMA3I0eWb/sdjv29/exsrLC5b81NDRAo9Hk9fr39/dx//3347777sM//MM//MITI+A2J0disVjQtlokEsHc3BwYhsma1UbG6hmGKfkioygKapEIapEIOBN0r+7uwry2hm6e8Jo+Iz6JFHKUKrymKAo7Ozuw2Wy3Rdgq8fxxuVxJFYl8wY/OyOTdYzQaYTAYKqJTIhUJv99f0/ovgkQiAbPFglg0yol780GqS3cpI+lCgl9hGR8fr5oqY7EgE5ByuTxt7lu9WoZXX27Bv88eArg1Rv/CkQYEg0G4XC7YbDasr69DrVZzFVihviu2kwicgShmepogp2m0SSSgcxCjp59+GvX19RgeHr5tiFFHR0fasFb++tXb28vlv9ntdszOzuaV/3Z4eIiXvvSleMELXoB/+qd/eoYYneG2JkdCVo7IhIBer8+5KPKzbIS80FiWxfbqKhz7+3jp9DTUej0n6GbPjsnyxOmp/kUMy2LVYkEgEMDk5GTVtSoKBX/jFYpI8L17+Dql/f19TqdkNBpRV1cn+CJC9GOJRAKTk5O3RUXCZDJBJBJhYmKiaCJBNDFGozHJpTtpJL0E5/R8QYiETCbD4NBQzZtVRiIRLC8vo66uLqvH1PMG6vHvs4e4Z7gev32tndMaqVQqqFQqdHR0IBqNcpEm+/v7XEaiXq/P6nOVC8sHp1lul9vqchKjYDCIGzduoKGhIUkDWqsIBAK4ceMG2tra8k55yCf/ze12w2Aw4PLly7Db7XjZy16Ga9eu4f/9v/9X82RfSNQ8Ocr2BSDkKJMOJ1/YbDYsLi6m9VLKdFzgdLMT6q42Ho/j5s2bCAaDuOuuu7ipGCLojjEMFgFIzypEqRNp0WgUKysrEIvFt8XGS4TkEqm0pI03GzLplCwWi+A6JeJILpPJMDI6WvMbLz/DLlsSfaFI69LtdsPtcnHO6cW4dOdCOBzG8vIyNBrNbWFWSQJxdTod+vr6sr5Pcunpa9UqJBlF2FKplPPvYRgGPp8PLpcLGxsbiMVinM+VXq8vqFW9dHACKsHink5jVmJEiERTUxMGBwdvC2L09NNPF0SMUpEp/+0LX/gCPvWpT0Gr1SJ21hr+2Mc+9gwxSkHNk6NsIB92IpEoiqSwLIu1tTVsb29jamoqb1NEknAslN16MBjknF2JXUAqJDQNLU2jRSSCXCLBSTyOE4ZBCMCJ34+VlRXo9frbYmH3+/0wmUwwGAzo7e2tyOspp06JBJQajcacxp21gEAgAJPJxL2ecm5Uqc7ppP1GXLoJUSolkDUYDGJpaQn19fVlfz2VAHk9DQ0N6O7uzvl6FJLTdTQcy289o2kaOp0OOp0uKdLEbrdjY2OjIAJr2vOhWyFHsyZzRZAQiZaWFgwMDNT850NeT2tra07iWggUCgU6Ojrw93//93jwwQfx4he/GMBpFmhTUxNe+MIX4v7778f9999fNv+kWsJtTY4IISqGHMXjcSwuLsLn8+HatWsFJxIL1dJzuVyYm5tDa2srhoaGsm6cNE2faqwYBnU0DY1MhqOjI6yaTBjs7oahtbUiUSblBJkw6+joQGtr64UshOl0Sk6nkytfq1Qqjkjl0l54PB5YLJayB5RWCiRwtbW1Fe3t7RV9PamO0MSle2trC9Gz9luh1QtiDdHc3IyOjo6a/3wIES/EM0t+Ro5CscLXs3Q+VxkJrE6XVL0Ih+Kw2UL4lenmjM9PNDmkwlLrnw9pDba0tKC/v78sr8fr9eI1r3kNxsbG8NWvfhVSqRQmkwnf/va38aUvfQkf/OAHYbFYav69LBU1T46yfYCkglMoSSGVGqlUipmZmaKmMIQgR3t7e1hZWcHw8HBOi3iWZUHTNJxOJxQKBcRiMTY3N7G1tYWrExOcg3iQZxEQE9ChuxIgo+39/f0XkluVCXK5nCM3fJ3S3t4epFIpR5RSdUrHx8dYX19Hf39/Wof3WoPT6YTVakV3dzeamzNvaJVAOpdut9vNVS/yIbAejwdmszmtNUQt4uTkBCaTCW1tbQWFlMpEFCjkXznKBolEci5mxu12Y3t7GxaLBdqz6bcOoxG7+yFQAK506tI+l9/vx9NPP82JlWt9Mw8Gg3j66afR1NRUtgrYyckJXvnKV8JgMOArX/kKd5MwNjaGsbEx/J//838Qi8Vq/r0UAjVPjnKh0Ik1UqlpaWnB8PBw0S2OUsgRy7KwWCzY39/H5cuXc46mE+F1R0cH9vf3sbGxAYlEgkQigenp6aScJyVNQ3n2miI8i4BIFRMllmWxubkJh8NR9aPtuXRKpHIRCARum4lB4FauWDV6MvGrF6kElgjtSfWCpKQTZ+JqzhgsBF6vFysrK0URPYqiIJPQCMWENdTNRGDDLhfW19fx7Z1T+cCgjj6nGz05OeGmuIrV5FQTiP1AOTVTgUAAv/qrvwqFQoGvfe1rGYcXal2PKhRue3JUCEnZ2dmBxWLJq1Ij5HH5yCS8zgROeM0w6OrqQnNzM+bm5hCLxaBQKDA7OwudToeGhgY0NjYmTajxHbpjZw7dAYZBSCCtlBAgKfSRGow2SdUpkTbP+vo64vE4NBoNwuEwIpFIzZoIsiyLvb09HBwcVD1xJUglsMSle21tDYlEAnK5HMFgEP39/bcFMXK73bBYLOjp6UFTU1NRz6GQiASpHGU9hkKBBrUazT09iMfj+PA//xx6eQz2TROe2FpJSq5fWFhAV1cX5/tTyyD2Aw0NDWUjRqFQCL/2a78GAPjmN79ZMzE3F4maJ0f5TI7lqhwxZ6Zuh4eHuHLliiCJ2sWQo3yE13wQY0diGeD3+zE/P8+5wopEIoTDYdjtdhwfH8NqtUKtVnNEiS+GlFAU9GIx9Dh16CYVpWwO3eUGmUgTi8U1H21CURRUajX29vYgk8kwPDzMmbfx2zxGoxEKhaImytosyyZ5TNXigitKCS7e2tqCzWaDXC7H2toajo6OuGpfrXwufDidTqyurmJgYKCkVrRERMMdjMHhj5QtNqROJELz2Zq36Y5gwxXB8weNeP7zJ+D1emG327G2toZQKASFQgGaphEIBGryuiMgFaP6+vqy2Q+Ew2H85m/+JsLhML773e8W7AX3i4ra3W3yRK4IkWg0ivn5ecRiMczMzECpVApy3ELJUSHCaxIFQp6ftAEWFxfR3d2dNFEjl8vR0dGBjo4OxGIxOBwOHB8fY2trCzKZjCNKOp3uVjYcRUErEkErEoFh2VOdEsMgyDBIVKj9FggEsLKywiVW1/oEF7FSEIlEGB8fh1gshlar5do8LpcrL51StSDBMLCurnLxGbVU0UsHEiBrt9sxPj6Ouro6RCIRrv22s7NTE58LH4RMDA4OltTq/J7pGMcnEQDA6z47X5bgWT4x+srcAd79TQsA4IlVJ746f4hXXTodvtjb20NPTw9kMhnsdjusVisUCgXnCK3T6ar+cyEgEScGg6FshpWRSAS//du/DZfLhYcffhharVbwY9yuoFi2isUmeYBlWUSj0Yz//rOf/YwTy6bi5OQEs7Oz0Gg0glcmZmdnYTQa80pOLlR4zXe8Bk5HMdfX1zE6Opq3EDaRSMDlcuH4+Bh2ux0AOKJkMBjSel6wLIsQr/0WL9Ol43a7sbq6eiETT+UAycCrq6tDf39/1sU7wTDwnG3IbrcbwC036GrJfeObVY6Ojta8RoFfARsbG0t7g5T6ufB9rirt0p0PiAZseHi4JE2bwx/B6z47D4b3Vacp4NOvnRasgqQRidB0dg3ZfGG86MNPnjveV183hv21ZfT19SWNmcfjcc4mwOFwgGGYpJyxckSaCIFwOIynn366rBEnsVgMr33ta7G9vY1HHnmk6rSA1Y7q+kYXgWLbasfHx7h58ya6u7vLMjKZT75aMcJroi+i6VORotlsht1ux5UrVwq6KxCJRNzUCMuy8Hg8OD4+5vQ99fX1aGxsRH19Pbf5URQFJUVxgu4wqSgJKOi22WzY3Ny8bSa4fD4fzGZz3qPTIl4aN98NemdnB1arlRtHNxgMF7LwE5doiVSK8dvArJJhGKytrcHv92etgKV+LsTnam9vr6Iu3fng4PAQuzs7gmjA9j2RJKICnAbPHniFaa9pRSI08sj1tjOU9niP/nwJ9985cO7mUSwWJ+WM+Xw+OBwO7OzswGQyQavVclUllUpVFTdakUgEN27cgF6vLxsxisfjeMMb3oD19XU89thjzxCjIlDz5CgXUttq5C5xY2MDExMTZRs5ztXOK0V4TdM0YrEYFhYWEI/Hce3atZIWZOI1otfrMTg4CL/fj+PjY2xvb2N5eRl6vR6NjY1oaGhIOo6cpiGnaUAAQTc/bHVsbKwmhL25QEbbu7q6igr3TXWDTjXTU6vVSan15V74bzeX6ATDwGI2IxqNYnx8PG+ymTaPj+fSrVAoOJ1SXV1dRTfkvb097O/vY3R0tGBvtnRo08lAUQCbUslp1ZZOjHQiERpSqo5dRgVoCsmVI7C4NtaTs6pOURQ3/dbX14dwOAyHw8F9X6RSKRoaGlBfXw+DwXAh128kEsHTTz8NrVaL0dHRslwbiUQCf/AHf4Dl5WU89thjt8VN5kWg5ttqwOndbKaXsbS0BJlMhoGBASQSCSwuLsLj8eDy5ctl3YBJOOXw8PC5f+MLr6empgoWXgeDQczNzUGtVpctOoMgFApxgm6Px4O6ujqu/ZbpTqxQQTfRrwQCAYyOjtZ85htwGua4vb1dttF2vk7J4/Fwehij0ViW1HriSt7Y2Iiurq6quAMvBfF4HCsrKwCAkZERQWN+iMmh2+0WzKU7F4hm6ujoCGNjY4KKlP/iWxb8bNsD4FbwbKmao3TEiOArcwd415nmiAaLtz67Gb/7wtGSjkdkBA6HAw6HA7FYLKn9VolpUVIx0mg0GBsbKxsxevOb34wf//jHePzxx9PKSZ5BfrjtyRFZALu7uzE3NweRSITp6emyfxksFgunyeCjFOE1RVFwuVxYWFhAe3t72RxUMyEajXKCbqfTCblczhElrVab9lwYlkWAYU7/SyTOOXQToTIhkreDfoVsUsPDwxWpgGXSKQm1IRMzxI6OjttisY3FYlheXoZEKsXw8HDZCAvf5NDlciESiZy234qMmckElmWxtbUFh8OB8fFxQW8uWJbF739+Ab5QDO+4dwDtennJ7TS9WIz6HGT0eR/4IZCI4R9e2Yepgdy6zUJA2qJEp+Tz+VBXV8e138pR7YtGo3j66adRV1eH8fHxsqzbDMPgbW97Gx555BE89thjeeldn0Fm3PbkyGq1wufzwev1orGxEaOjoxUpp66trSEYDGJycpL7HRFeDw0N5cyuIcaOJJ+NTGqsrq5iZGQEra2tZT3/XEgkEnA6nZygm6bpJEF3uvc4VdDtO8vg0mg0OYXKtQCGYWC1WuH3+y+sAsb3U+JvyMXqlIgZ4u2iASOBuCqVCgMDAxW95khb1OVy4eTkBEqlkvtcitXDsCyL9fV1eDwejI+PC653WrMH8JZ/X8LLJprwh8/tLvn5DGIxjDmIkd1ux32fWMRIkwqf+91rJR8zFyKRCJxOJ+x2O5xOJxdD09DQkHE4pRBEo1HcuHEDKpUK4+PjZbnmGIbBH//xH+Ob3/wmHn/8cfT29gp+jF803BaaI4qiMpIjv98Ph8OB4eHhvLOEhIBIJOKITSnCa3K+FosFNpsNly9fhl6vL/v554JIJOKEkAzDcILulZUVxGIxTtBtNBrTCrqdTidWFxbQ39aGhs5OxC749ZQK/gTX5OTkhVXA+Dol4jpMSGyhOiUS1zI8PFwV11ypCIVCWFpa4gKLK90aVCgUaWNmDg4OIBKJuM9Fo9XmVc1KFZOXoxr+A6sTAPC8gdK934xiMQx5EKP5mwsIJ2jUayvjXySTydDa2orW1lYwDMPFzJDhFIPBwJGlQslnpYjRu971Lnz9619/hhgJiNuCHKUDwzBYXV2F3W7nBK2VBPE5KlV4TQJww+Ewrl69KpgPk5CgeU7QQ0NDODk5wfHxMTY3N7nNiAi6ZTIZ9vf3YTabMcGrgEVJ663KHLrzQTgcxsrKCmQyGUaqbIJLoVCgvb0d7e3tiEajcLvdcDqd2N3dhUwmS/LtIWQhVb8ihLD3okE0U4UErpYTfJduhmHg8/mS3NN1ZxljBoMhLdEm61s4HC5ITF4IGJbFE2tO1KulGGku7RqoF4uhz0GMjo+Psbi4iN7BEbA/sUAtq/z2RKdMJQYCATgcDthsNlgsFqhUKm7KV6PRZL2OYrEYZmdnoVQqy0aMWJbFX//1X+MLX/gCHnvsMQwMDAh+jF9U3JbkKBaL4ebNmwiFQujv74fD4aj4OYhEIkSjUfz0pz+FTCbLy/E6lRiFQiHMz89DLpfjzjvvrAk9Dr9y0d/fj2AwiOPjYxweHsJsNkMqlSIWi51rDUppGlKariqH7nwQOGsNEoflam4NSqVSNDU1oampKUmnZDabAdzyU3K5XPD5fJiYmLgtxPFerxdmsxnt7e1VqZmiaRo6nQ46nQ49PT0IBoNwuVyw2WxYX18/V+1jziw84rEYxsbGyrYumG1+2E+ieNV0M+gSyGQ+xOjo6AhLS0uYmJhATHqq06uTX+z2RFEU1Go11Go1uru7ORNdh8OB2dlZ0DTNCbqNRmOSqD8Wi+HGjRuQy+WYmJgoGzF673vfi09+8pN49NFHMTIyIvgxfpFxW5AjPnv3+/2YnZ2FSqXCzMwMHA5H0QGwpSAUCsHn86GjoyNngC0RXvMn0jweD27evInm5mYMDg5W9aabDUqlEt3d3ejo6MDCwgK8Xi80Gg1MJhO2tra4ihL/LizVoTvA81OqppqSx+OBxWLhWiUXXY0oBOn8lIj1AMMw0Ol08Pl8EIlEVWuklw9cLhdWV1dLyhWrJCiKgkqlgkqlQkdHB6LRKKdT2t3dhUQiAcuyEIvFZSVGAPA4aakNFh870iAWQ5eDGNlsNphMJkxOTqKhoQFmmx8AoLlgcpQKiUSClpYWtLS0cFICh8OB9fV1LC4uQq/Xcy7dKysrkMvlmJycLBsx+uAHP4h//Md/xCOPPIKJiQnBj/GLjuq6+kqE3W7HzZs30dHRwQX45ZOtJjT29vY4X43UabVUpAqvaZrG4eEhVlZWMDg4WHIAbjUgGo3i5s2bYFkW169fh1QqRTwe57Qws7OznCllY2Mj9Ho9t6DQFIU6kQh1IhFYsbgiDt354Pj4GOvr67dFajtJrd/a2oJKpUJ3dzd8Ph+Ojo6SKhck961WQOIzSs0Vu0hIpVI0NzejubkZkUgEy8vLSCQSiMVimJubg06n4zyVhHTpTjAMfrTmRJtOhr764lr5jRIJtDnEzGStm5yc5D6jk/CpAvGiK0fZwJcSDA4OIhgMwm634+joCBaLBSKRCHq9Hl6vF1qtVlCCxLIsPvrRj+IDH/gA/vu//xvT09OCPfczuIXqvfoKAMuy2NzcxNraGsbGxpLaNbnMGIU+DyK8HhwcxPb2ds7H86NAKIrC2toa9vb2MD09fVu4mgYCAczNzXEjrGTyQywWcy0eIoI8Pj7G0tISGIZJEnSTRT+TQ3cgkUC0QkSJZVns7+9jf38fIyMjJUUzVAsikQhMJhPkcjkGh4YgomloNBpOp8SvXGTSKVUbiJj8dvmMYrEYV40YGh4GTVGcS/f+/j6sVis0Gg332ZQ6tbawfwJPKI6XjjcV9Rk3SSTQ5CBGBwcHMJvNmJqaSlrrfJHTm9lqqxxlg1KpRFtbG46OjqDX69HW1gan08ndFBJBN39ApRiwLItPfOITeOihh/Cd73wHd955p4CvIn987GMfw8c+9jFsbW0BAMbGxvDud78bL3nJSy7kfMqB2rn6smBtbQ3b29u4evXquQiNQgNgi0Wq8Doej2NjYyPj4/nEiKIoMAyDhYUF+P1+XL16taaTpgncbjdu3ryJtra2rJ5MfBHk8PAwfD4fV5lJFXTzWzzEobteLEaUECWGQbhMgm6GYbCxsQG3212zKfSpCAaDnAt6Os0Uv3KRSCTgdrvhdruTdErlNjgsBCzLYnd3Fzab7bYRkxPyqlQqk+wH0rp0u93Y2toq2aX7CW5KrfAbtGaJBHU5iNH+/j4sFgump6dhMCRPwp2ET8nRRQiyi0U8Hsfs7CzEYjGmpqYgEonQ0tIClmXh9XrhcDi4ARWdTseRJaVSmfdnw7IsPv3pT+PP//zP8a1vfQszMzNlflWZ0d7ejr/7u7/DwMAAWJbFZz7zGfzyL/8y5ubmMDY2dmHnJSRuC5+jk5MTMAyT9m4pGAzihz/8Ie69996yHZ84XstkMkxPT0MikcDv9+PJJ5/EPffcc+7xqcQoEolgfn4eYrEYk5OTNa3xIDg8PITJZMLQ0BDa29uLfp5AIMB5Kfl8Pmi1Wo4oZZrcixPjSQEF3SRqIhKJYHR0tCKOuuWGz+fDysoKWlpa0NHRUdAGyjAM/H4/nE4nXC4XotEo1+LJNGFVbhAzRLvdjvHx8aqc7CwU/MiWfE1f+SS2mPDiQ28Yb/ziAho1Mnz8N6fyPlcKpxWjXMSI+LVdunQprUXEvz61h4e+Z8Vnf+cS7ujS5X38i0I8Hsfc3Bxomsb09HTW9zcUCnGRJm63GzKZjIs04csJUsGyLP7t3/4Nb3/72/Gf//mfeMELXlCul1M0DAYD3ve+9+ENb3jDRZ+KIKgdap4FSqUyo65IJBJxup5yCOPcbjdmZ2fR0tKSJLwmFStCgID0wmufz4f5+Xk0NDTkFG7XAkh23c7OjiCtQZVKhZ6eHvT09CAcDsNut8Nut8NqtUKlUnFEiX93LBZY0B2LxWAymSASiTAxMVF1CezFgAiVi819o89ab3w/JZfLxemU6urqkiasyg2GYbC+vg6fz4fJyckLD38VAqFQKKmqly95FYlE3BQVIbEulws7OzunIbk6HQwZXLq/ZzrGPzy2CRbAriuM75mO84oKoXBaMVLnIEa7u7tYW1vD5cuXM7Y7a0FzRJBIJPImRsCptUZHRwc6Ojo4I12Hw4GlpSUkEgkYjUaOLJGbZJZl8eUvfxkPPvggvvKVr1QdMUokEv//9s47PqoyffvXzKSQkF5JSAIkQEICSSaFqhQRQ80MKrqKC+taWFdw0VXs7qvs2rCwC66Cu4sF+YlmEnoVAogIkt57bzOTnplMP+f9A89xJiSkzWQKz/fz8Q+TkPNMzsw517mf+74ufP/995DL5WatZhkby3/3jRLmzarVao1ekbmV4zVzXIqiDASafuO1WCxGUVERwsLCLMJ7ZbRQFIWioiJ0dHQgISHB6Fsa48aNYy8szFitVCpFbW0t6xvDTIsM1NDdywilITZ0KxQKFBUVwcXFZcwdlU2FWCxGVVWV0RqVmYZuZ2fnm/qU6urqMG7cOJMGseooCmW/GvbNmjXLJiqvvb29KCgoGHWW3UAiltnmYVy6PT09oYQ9dl2oZiutNIBdF6oRH+J+y8gQDoAAe3uMH0QY1NXVobKyEnw+/5Z9YMy2mqWLI0YYcTicIQmjvugb6TITo1KpFHV1dfjwww/x448/YunSpfDy8sL777+PgwcPmnQHZLjk5+dj3rx5UCqVcHFxQVpa2qADSNaEZb/7hsitLhzMG9aYfUdDcbxmbqI6nQ5cLvemxuuqqirU1tZi1qxZNhHLwHhLabVazJ492+TbTvpjtUyopFQqRX5+PmiaZo3avL292fcAh8PBeB6PvYgP1tDd3d2NkpISizEOHC36zeSRkZE39ecZi6H0KXl7e8Pdw2PUfUo6nQ7FxcWgKAozZ860iaoeY1g5YcKEYW93DkZfl24mJLepqQl1ci6oPh8DigaaulQDiqOhCqOamhpUV1cjLi5u0PeduEcFAFCox96CZajodDrk5OSApmnExcWNOmJE3x8uLCwMgYGBCAoKwoEDB1BUVAQfHx+cPHkSPB4PS5YssYjKaHh4OHJyctDV1YWUlBRs3LgRFy9etBmBZP1XkkFgxvmNJY6G6nitX7HicrnsNhpFUSgsLERnZycSExNtomG0t7cX2dnZGD9+PPh8/qgvFMOFsQHw9fVlGyAlEgnKysqgUqng7e0NPz8/g1I1cOuGbsbzJ2TSJASOYNvJ0ujbjzNWzeR9t3iY3Lfq6mq2T8nb2xuenp7D7lNitjvt7OwQNXOmRTSEjxamD2wsDCvt7e3Zzw1FUfBtacOXJVUGPXpcDuDj1P/flQMg0MGBnR4diOrqatTW1iI+Pn7QIGZRdhNOFUkBAILPfsGbq8NxH9+8OZJ9YYQRRVEmu96FhIRgxowZqKmpwddffw0fHx8cO3YMf/rTn9Da2oply5bhv//9r1knmh0cHDB16lQAQHx8PK5fv45//vOf2LNnj9nWZExsXhwBN8bGjeF1pN94PZjjNYfDAZfLhUajgZ2dHbhcLuv3A2BMqitjQWdnJ3JychAQEMB6S5kTDofDug1PmzaNbeiuq6tDUVERPDw82O03/V4YBy4XXlwuvABU19VBUlmJ6PBwjPfysmiH7qHAZHD19PSYtR+Hy+XC3d0d7u7uBrlvzc3NqKioGFafEjPB5eTkZNUmqfp0dXWhuLh4xH1go4HL5SIs0BcTPRrR0HmjcsPlAMKp9qgpyUNrH5duHoeDQAcHOA3yd2f6D+Pj4wd9EGzpVuJvx0rZ/6do4G/HSrEgzAsT3MxfKQFuCKPc3FzodDrExcWZrFJ5/vx5bNiwAXv27MH69evB4XCwfPly7Nq1CwUFBTh9+rTF5R1SFAWVSmXuZRgNmxBHg92QjVE5GqjxeiCY6bnCwkL4+/tj/PjxKCkpgYeHByIjI8e8umIKxGIxCgsLMXXq1Jt6riwBffv/0NBQKBQKtqG7rKwMLi4u7J4/U0mpqKhAY2MjFv7aF2HJDt1DQafToaSkBBqNxqyBuH3R71MKDg6GSqW6kfvW3o7a2tpbjqIzjcru7u5DnuCydJgG+dDQULOZikp6VGjsVCExxB33xQUi0N0RPi6ObCYf63Vlb49wb2/0+vnBcYAJK2Ywo76+fkjCCABq2xT9buvVtSssQhwxditardakwujSpUt46KGHsGvXLlYYMXA4HMyaNcvsjtgvv/wyVqxYgZCQEPT09ODAgQO4cOECTp8+bdZ1GRObEEeDMVpxdKvG674wE2nMk0VraysaGxvR09MDBwcHODs7Q6FQYPz48VZ7UWe2aKqrq62qZ8rJyQkhISEICQmBRqNhhVJ1dTUcHR3B4XCg1WqRkJAAFxcXAAM3dMspCjoLd8HQaDQoLCyEvb09Zs2aZdGC3NHRsd8+peLiYnA4HAOLgOLi4lE3KlsSra2tKC8vN7uT9/nSVtAAVs70R/TE37a/9DP5aIqCk0yGnl8nrCiKMpiwYuJNKisr0djYaPBZGoxJ3k7gcmAgkLgcIMTL/K7sFEUhNzcXarXapMLoypUreOCBB7Bjxw784Q9/sNj3t0QiwYYNG9Dc3Ax3d3dER0fj9OnT/VrXWCs24XNE0zTUavWA37969SpCQkIMnLOH+nvLysqG7Fjdn+N1bW0tqqqqEB4eDi6XC4lEgtbWVowbN46tWgyW7mxJUBSFkpIStLa2IjY2dtAeAmtAqVQiKysLarUaNE2Dy+WyvRheXl4DigoFRUH2q5fSWDl0DxXGH8fap+z0+5RaW1uhVqtZN+KR9ClZGhKJBFVVVZg+ffpNZohjCU3T2HQgDz1KLb5+lA+7ft4vPA4Hgfb2GPfr92iaRnd3N6RSKVpbWyGTyeDu7g7Or+7diYmJw+5tE2U34fWjN7bWuBxYRM8RUzFSqVSIi4sz2Xvu+vXrEAgE2L59OzZv3mw19wRbxSbEEYBb7nVmZGTA399/WDll+o3XcXFxg37ImVF9ZjqNpmkUFxezIkJ/QoPxt2DMDfVHOvXH0C0NjUaDvLw8qNVq8Pl8i5iYGC1KpRLZ2dlsSCSHw0FnZyekUikkEgk0Gg3rZss8GffHWDh0DxW5XI7CwkL4+PhgypQpNnGRZabdAgMDweVy0d7eDrlcDldXV3h7exslMmOsaW5uRm1tLSIiIswecVLS0oPnREUQxEzApjsm3fR9HoeDifb2cLzFtYmxvWCMJ52cnNiHjOHki939r5/BBfDVH/hm306jKAr5+flQKBSIj483mTDKzs7G6tWr8dprr+G5556zic+stXPbbKsNpyF7OI3XwG8VI2YijRERWq0Wc+bMuemirS+GKIpCe3s7JBKJwRi6n5/fLasWY41CoWBFRGJiok2MTPf09CA7Oxs+Pj4GfWT6gZIymQwSiQQ1NTUoLCyEl5cXe8HXP6/6Dd1avXBchZEcuodKV1cXSkpK2HFtW7jI9rftxPQptbe3o71PZIaXlxdcXFws+rU3NTWhvr4ekZGRFlF9PV96Iy5kafjN23pDEUY0TaOurg69vb1suHRbWxsbBg7AIF/sVtcPDgA3J7vbRhjl5+cjOTkZ27ZtI8LIgrD+O9yvcDgcDFQEG07PUUdHB7KzszFhwoQhN17rO14zY+2urq5DGvPkcrnsqDNN0+js7IREImGbaJkAVh8fH7MJkq6uLuTk5MDPz4/dHrR22trakJeXh8mTJ2Py5Mn9XpA4HA6bXxUWFsYmb7e0tKC0tBSurq43NXQDNxy6Pezs4AGAomm2omTqhm5GRISGhsLf39+ERxo7WlpaUFNTg4iIiJumcxwdHQ28rpim4aKiIoM+JWP4KRkLmqbR0NCA5uZmREVFDbkfx5RodBQulrdikpcTwnwMI1fsfhVGDoMII2arPSEhgZ00ZPuUfrXXkEqlqKysRH5+Pry8vFix1HcyUauj+93WG0soikJBQQF6e3tNKoyKioqwZs0aPPPMM3jppZeIMLIgbEYc3Qo7O7shiaPGxkY2D2w4jdcA2FJ/Xl4egoKCRjRFw+Fw2Pyj6dOno6enh+1JYKoW/QWwmhKJRIKCggKbcfEGfst9mzFjxrD60JydnTFp0iRMmjQJarWa3XqrqqoasIeMy+HAjceDm4kbupktmvDwcLP2rhiLvoaVg1VX+vNTamtrQ1VVFbRaLZv7Zs4+Jaa6IhaLERUVZTHBxb/UdKJHpcO6eF+Dz/dQhVFxcTHa29sNhJE+fe01mIcMxots/PjxbDXWzc0NWoqCHdd81xlGGMnlcsTHx5vsWltaWorVq1fj8ccfxxtvvGET11ZbwmZ6jphm2v4oLS2FTqcb0LnTGI3XTJjicG+4Q4Xx65FIJOjp6WH9evz8/EzSa8FcyCsrKzFz5kyzjRcbE2bKrqamBtHR0UYzUNNqtewWglQqBZfLZc/NrcIkmYZuOUVBM8KPIZNC39zcjBkzZljEFs1ooWkatbW1kEgkoxYRNE2jt7eXDcjt7e2Fm5sbW1Uaqz4lmqZRXV2NtrY2zJw5c0zy5obKWyfK8EtNB77cyIf3+BtCwI7DQZCDA+xvccOmadogLmgkf0v9GKC2tjZwuVy8cIXGNF9nfPVo/Ji3FdA0jYKCAvT09CAhIcFkwqiyshLLly/HQw89hPfff98mqvG2hs2II41Gw+aW9aWyshJyuRzR0dE3fY9pvGaeEkbSeF1WVoaWlhbExMSMiTGXUqlkhVJnZyfc3NzYPiVjPI1SFIWysjKIxeKbmsmtFYqiUFpaCqlUCj6fbzJncoqi0NHRwT4Z63Q6g4bugbZGVb9Wk4bT0M2MTHd0dCAqKsomUuhpmkZFRQW6uroQFRVldBGh36fU1dkJZ2dneP4awmqqPiXmNXV3dyMqKsqiGse7lRqs35eNmImu+HvyDACAPYeDiUMQRoWFhejq6kJ8fLxRXhPz2Vm2Jx+hbsBTM3QGPX6mNs1lXlN3dzfi4+NNdryamhqsWLECycnJ+Oc//0mEkYVyW4ijmpoadHR0gM/nG3xdv/E6NjZ22I3XWq0W+fn5UCqV4PP5Znka1N/eaWtrY0vU/v7+I7rYM69JoVCY7TUZG51Oh7y8vDF/TfqjzhKJBL29vawnzK0u9kNp6NZRFMrLyqBQKBAZGWkTbuuMKB+r16TVatlssY6ODnC53N9y34YxXXUrKIpCeXk5ent7LfI8HclrwWc/1uKFZWFYMt0H9r9WjOxucd1gIpB6enpMIiJi/3ER80I9sGNNGFuN7e7uhqurK/vZMbaQHSth1NDQgKSkJCQlJeHf//43EUYWzG0hjurr69HS0oLExET2a6NtvFYoFMjJyWFHwC1hekur1aK1tZX1UnJwcGC3dxj/kVvBjLU7ODhYlJvyaFCr1cjOzgaPx0NMTIxZX5NcLmeFUnd3N9zd3dmK30BVH92vDt3yX/2UKNw4z8XFxaBpGpGRkRbx3hstjJO3VqtFZGTkmJ8niqLQ3d19o6rU3s72KTG5byP5G+soCqUlJVCr1YiKirK4z1OrTIWXDxejVabG/z2WADd7HiYOQRgVFBRAJpOZTEREvZWO2GA3fHhfFDuxxjwEMttvDg4OrFC61db1UGC2Bzs7O5GQkGAyYdTc3Izly5fjzjvvxOeff24xk8iE/rEZcaTVagdsumaaVefOnQtgdI3XjA9Obm4uAgICLNZgj0mqZ7yUOBzOLftgmLF2b29vzJgxwyJf03BhKoNubm6YOXOmRb0mlUrFCqX29na24ufn53dTXAYDTdNoVyhwLTcXlKMjpkZEWMwU1mjQarUoKioCl8vFjBkzzH7TMEafko6iUFJczPY6WpqAPV0kwb8uVIO5+v91yRQ8zg8eVBjl5+ezE1ym6Mf5PquJzVcbyASSubYxvUpardbApXs46+rbUG6qLU+xWIwVK1YgISEBX375pdnf44TBuS3EkUQiQXl5OebPn882XsfExAxq1d9f43VzczMbJRIUFGT012EKKIpiLQKYPhjmRuzt7Y329nbk5+djypQpA461WxtdXV3Izs5GYGAgpk2bZtGvqW/Fj0lL72sKKpfLkZWVBS8vL8yYMQMqYNQN3eZGrVajqKgIjo6OmB4ebpFiT6lUsjYBXV1dg/Yp6XQ6FBUVAYBF5ii2ylT4w1c5bEwHh6Jhr6Rx7i/zBvQWYlyilUol4uLiTCKMWrqVuPufP98UH/LDLdZF0zR6enrYqhLj0s1UlW7VgzlWwqi1tRUrV65EVFQUvvnmG4sTyoT+uS3OEo/Hg0ajQVZWFuRyOebOnTukxmumv4jD4YDD4aCiomLIE22WBJfLZZ96w8PD0d3dzY7RKpVK0DSNoKAgBAUFWbSIGCqM/YClBuL2xc7Ojs0V62sKSlEUe5GvqakxsIlwAuDE5cIXNxq6Zb9uv6msRCgxESdubm4ICwuzqMqePuPGjWP9lJg+pba2Nrbaxfopubuz/Th29vaIsNDKXmOnykAY8RQ0aAwc8MrkiqlUKpN6/lS1yocdPMvhcODm5sa+h5RKJSuUKisrMW7cuH5duhlvJlMLo/b2diQnJ2PatGnYv38/EUZWhM2cqVvd1LVaLVQqFcaPH4958+YNu/Fap9OxvhezZ8+2GH+SkcDhcODu7g43NzfodDo0NzfD398fXV1duHjxooGXkqU1jw6F+vp6lJeXW639QF9T0K6uLtTW1qKiogIcDgdyuRzNzc03bR84crlw5HLhbWcHza8N3b0UhV4zR5kMRG9vLwoKCuDr62tV1Uo7OzsDPyWmT6myshJarRYcDgeOjo6YNnWqRQojAJjo4QguB6C1NHhKGhwMHPCq0+mQm5sLjUZjUmEEAOeKW2/62nCDZ8eNG4fg4GAEBwdDq9Wivb2ddelm0gd8fHzQ3t6OtrY2kwqjrq4uCIVCBAUF4dtvv7W4njPCrbGZbTWdTtdvREhHRweysrKg1WqxbNmyQZ9O+wojlUqF7Oxs2Nvbm72h11jodDrk5+dDLpeDz+ezzcC9vb1sj1JXVxfc3d3ZPiVLn1pjxqUbGxsRGxtr9qwqY6FvWOnm5sb2KTFeV8z220DnR7+hWz7GUSYD0d3djeLiYgQGBtpMtVKpVKKgoAA8Hg8cDge9vb1wd3eHp5cXvDw9LWp8HwD2XKjG8V9aWGE0UG9Pbm4utFot+Hy+Sa99F8pa8edv8zHR3RHN3TcqW8YMntV36W5sbIRGo4GHhwf8/f37dekeLT09PRAIBHB3d8fhw4ct7vwTBsemxRHTeB0aGory8nIkJSUNeCHur/G6u7sbOTk58PX1HdJEmzWgUqmQk5Mz6PSWSqVihVJ7eztcXFwMojIs6YbGbGV0dXWBz+dbdWVPn9raWlRWViImJuambVxm+0AikaCjowMuLi6sUBpozJn+VSjJfq0oGdOhe6h0dnaipKQEIZMmITAgYMyPbwoYYeTh4YGwsDBwOBwolUq0d3Sgo0+fkre3t9k/P05cLvadq8b/XW/Cu8IZmD3Z46ZtK51Oh5ycHFAUBT6fb7LtoJZuJTJrO/HmiTJwORyInkwEj3tjKy3Ey8mo+WqMJ51EIkFkZCRkMhlaW1vR0dFxk0v3aM6PXC7HvffeC3t7exw7dsxs/mPvvPMOUlNTUVJSAicnJ8yfPx/vvfcewsPDzbIea8NmxBFFUdBoNAAMHa9jYmLg7u6Oc+fO4e677+73Q84YOzJWABwOB2KxGEVFRTYVmyGTyZCdnQ1PT09ERkYOWexpNBoDL6WBojLMARPyq9FowOfzrXIrsC80TaO8vBxNTU3g8/mDmnAyLsPM+WHGnJmG7oHOjzEcuocDk/02depU+Pr6mvx4Y4FCoUBBQQG8vb0xZcqUfv/WWq2Wbeju6OgAj8eDl5cXvL294ebmNqYPXc5cLgLs7SH87Dp6lFqc3zrvpjVrtVrk5OQAAGJjY00mjETZNybTmD6jRxIn4pUV001yLOYz1dLSgoSEBAPBotFoWIf71tZWdmubCckdTkN9b28v1q1bB51OhxMnTpg1O2/58uX43e9+h8TERGi1WrzyyisoKChAUVGRzTxAmhKbE0f9OV5TFIUzZ85g8eLFN5U3+5tIq6qqQm1tLWbNmmUzF3EmaDUkJAShoaEjFjQ6nc5gssrOzq7fyaqxgPFlGjduHGbNmmUTzY4URbGeKyOpgvW1cADAnh8vL68BL/SmbugWi8Worq7G9OnTbSL7DbhRISgsLIS/v/+QH6CYPiXGJkCn07GTbyP1UxoqzlwuAu3t0d6rwZ0f/oTVs/zx/lrDSCWtVovs7GxwuVzExsaabNJuJJNpI4XZcm9ubh40BYGZ7GWaulUqFevS7ePjc8vtMaVSiQcffBByuRynTp2yuCgfqVQKPz8/XLx4EQsXLjT3ciwe67+b6KHveK3feM3lcsHhcG4a9e/bX8QYnHV1dSExMdFkERNjTUNDA0pLSxEZGYmAUW5l8Hg8Nm2772QV0/DIWASYUigxvkw+Pj42s+XJ9HioVCokJiaOqArG4/HY7QGaplkLh9LSUqhUKvj4+MDPzw8+Pj4GW6r9NXQzDt2jpbGxEQ0NDZgxY4ZNRNEAN95/RUVFmDhx4rAsPbhcLhvCGhoaCrlcjra2NjQ2NqK8vNxkfUrjf60YcTgcZNR2AgBmT/Iw+BmtVousrCzweDyTCiMAqG1TDHsybSQwETtNTU1ISEgY9GFDf/pw+vTprHFrc3MzSkpK4Orqyn6G9LevVSoVHnnkEXR2duLs2bMWJ4yAGw3iAGzm4cTU2Iw46u7uxtWrVwd0vLazszMQR32FkVqtRm5uLgBgzpw5Y5Z6b0qYJ6aGhgbExcUZPfet72QVcyMuKSmBRqMxuBEb84m4vb0dubm5mDRp0oBbGdaGWq1GTk4OuFwuEhISjNL8yuFw4OnpCU9PT0yfPh0ymQwSiQS1tbUoLCyEp6cnO5mofyO253DgaWcHT4yuobtvCr05txiMCdNQHhwcPKqQaQ6HAxcXF7i4uGDSpEkGfUo11dVwdnZmb9Sj6VNy4XIx4VdhBADXfxVHiZM92J9hrE6YwRNTezPx+nmWGe5k2lCoqqpCY2PjkIRRX/TPz5QpU6BWq1njyZqaGrz88suIjIzEihUrIBKJ0NTUhHPnzlnkMAhFUdi6dSsWLFiAmTNnmns5VoHNbKup1Wo0NjYOeLG6cOECYmJi4OHhYdBjxOVyIZPJkJOTAw8PD4s0bRsJOp2OzQoa6yZlxpiNMZ1UKBTw8vKCv7//sB1s+6I/vTWaG5MloVAokJWVBRcXF8ycOXNM3n8KhYLtI+vs7ISrqysrlAa6EQ+noZumaVRVVaG9vd3iUuhHA9NQPnnyZEyYMMFkx+nbp2RnZ8cKpeH0KfUVRi3dSjyyLwsaHY0Lz84Hh8OBRqNBZmYmHB0dERMTY/IqrFKjw0P/y0KpWAYuB0afTGOorKxEQ0MD4uPjjS7MNRoNjhw5gsOHD+PYsWPQaDRYuXIl7r//fqxatWpQg+Gx5qmnnsLJkydx+fJlqzEvNjc2I45omoZarR7w+5cvX8b06dPh7e1t0HgtlUpRUFBgU+7QTBUCuNFQae4qmFwuZ4VST0/PgBWLW0HTNGpqalBdXd3v9Ja1IpPJkJWVxU5EmuP9xzwR6zfcM9ujA2Xy0TQNhZ6fkn5DN0VRqKiogEwmQ2RkpM2MMbe1taGsrGzMG8opikJXVxeb+0ZRFJv75uHhMWBV1oXHwwQ7O/b89W2A3r4mHGuifJCVlcVmRJpSGLV0K1HbpkBKdhOOF0iwedFk3MsPMMlkWlVVFerq6pCQkGCyiqVOp8OmTZuQlZWF3bt348qVKzh69Ciys7Mxd+5cPP744/jDH/5gkmMPh82bN+Pw4cO4dOkSpkyZYu7lWA23jTj6+eefERISwhoDcjgc1NbWoqqqClFRUfD39x+rpZoUuVyO7OxsuLm5ISoqyuKqYEqlkhVKnZ2dcHNzM6hY9AfjZiuRSBAXF2czvWAdHR3IycmxqO1BnU6HtrY2tqGby+UaNHQPdPNkGrq7NRrkFRdbbNjqSGEm7aZNm2bWqgBN05DJZGxFSd9PydvLi+1Tc+XxMEHvbz9QA/R7dzgiyGs8Zs2aZVJh1FeYhfk449CfZoPHNf57vrq6GrW1tYiPjzfZtUKn02HLli24cuUK0tPTMXHiRPZ7TU1NOHbsGOzt7fHoo4+a5PhDgaZpbNmyBWlpabhw4QKmTZtmtrVYIzYjjoAbTXH9QdM0MjIyoNPpEBQUBG9vb5SXl6OtrQ2xsbEW2Tw3EphenODgYNZvxZJhkraZisX48eNZiwCm2ZExrOzt7QWfz7eZ7Rkm4mT69OkWW+bWz+STSqUGfWTe3t43CR+NRoOcnBxoAUydNQsqLtcoDd3mhpm0Cw8PN3rf3mhh+pTa29rQ3d0NZ2dnhHh7I3LCBIOG4WvVHXj065yb/v3rC9zw4BK+yStGfYUZB8AH90WCH+xu1IpRTU0NampqTCqMKIrCs88+i3PnzuHChQsWG1H05z//GQcOHMDhw4cNvI3c3d1t5jpqSmxKHKnVavR9OTRNQ6vVQqlUoqmpCRKJBHK5HDweD2FhYQgMDLSJp9umpiYUFxcjIiLC4CnGWugbvurg4ABvb2+238LUDr1jSUNDA8rKyqwq4kS/j0wqlUIulxtEzQBAdnY2HB0dER0dzVYsdTTNWgT0WohD93Bobm5GbW2tVUzaabVaaDs7QbW1oa2tjQ0w9vX1hYrnjHt2Xb2pcnRmy1wEepj2RjmQMGPWYKxeI2YnID4+3mQPvBRF4cUXX8SxY8eQnp6O0NBQkxzHGAz0cLxv3z6L2O6zdGxWHDGO1/qN13K5HDk5OXBycoKHhweb4sxc5P38/MzenzNcmMbXuro6REdH20QvDpP5VlZWBoqiYG9vz54fT09Pqx3bp2maLfnHxsZaXBViOOhHzXR2doLL5cLZ2RmzZs0asMeDomn0mtmhezjoWxBYQ3XZnceD368PEIzNBuPXo9PpkCdzwec5MlYgvbU6HPfHmX6oob/KkT7G8DcaK2H02muvISUlBenp6WSbysaxSXHUn+N1e3s78vLybtpyUigUEIvFkEgk6O7uhoeHB3sjtvRGUiY2gzEMtJVR6a6uLmRnZyMwMBBhYWHo6upi+5R0Op2Bl5Kl9VQNBE3TKC0thVgstqm+KZlMhszMTIwfPx48Hg9tbW1wdnZmK0oDOajrN3TLKQpaC7oM6VsQREZGWsXnyoPHg+8AlVWaptHd3Y3m5mbkVzbgYCUHRZ1c/O++EMSGTRyT61zfnqO+fLEhFrMnj+xhoa6uDpWVlYiLizNZdY+mabz11lv46quvkJ6ejoiICJMch2A52Jw4oijKwPGay+Wivr4eZWVlg5ogDtQs7O/vb3F7tIwvE0VRiI2NtYnYDOC3XpypU6fetJfPXOQlEgnEYvEtTQ0tCcZctKenB3FxcRb3XhopjIgNCgpiHzi0Wi3b0N3a2sqaUg5W9VPqOXSrzXhJYqYipVIpZs6cabZcrOHgaWcHn0F8xBQKBTIzM+Hl5YU6rRueO1SOhyIcMc+rd0i5fKOlR6nFu2fKkZbTctP3RlM5qq+vR0VFhcmF0bvvvos9e/bg/PnzxCfoNsHmxJFWq4VOpwOXy4VGo0F5eTmkUumwk9pVKhWkUinEYjEb7Onv788Gr5qT3t5eZGdnj6kvzlhQX1+P8vLyIU0P0jQNuVzOVv369sBYilhk4myYZHNr27YdCKb5PzQ0FJMmTer3ZyiKQkdHB/vAQVGUQUP3QCPoxnboHirMFnVHRwdmzpxp8ZVjYOjCKCMjg3WTV2kpLPjgMqb6jsczi0LgAgXQ22nQp2SsOCCapnG8QIL3z1agVaYGP8gN88O88OmlmlH7GzU0NKC8vBx8Pt9kxos0TePjjz/Gxx9/jHPnziE2NtYkxyFYHjYjjpqbm/Hmm29i9erVuPPOO9HZ2Yl169YhNjYW77zzzqie1pngVbFYbDBV5e/vP+YJ2x0dHcjNzUVgYCCmTZtm8RNpQ4Fx8m5sbBy2iGVgemCY7VF3d3d2e9RclRqVSoXs7Gw4ODggOjraJrLfgN+qe+Hh4UNu/tev+kmlUtYYlBGzA4nGsWropigKlZWV6O7uRlRUlFUIIy87O3gP8p7q7e1FZmYmfH19ER4ezl4v7tt7HcUtMgC/CRRhtL9BnxIjZplcsaG+fxk/Iw4H+PRSDa7VdMLDyR7PLwuDMGYCuBwOWrqVo/I3YoYa4uLiTCqMdu/ejffeew+nT59GYmKiSY5DsExsRhzV1dXhrbfewuHDh0FRFNRqNYKDg3H8+HGjehjpJ6C3trayCfX+/v5wdXU1qVhpbm5GcXExpk2bhuDgYJMdZyxhglY7OjoQFxdnlKqcSqVihRJT9dO3CBgLmJw/xnXdWpvI+8JMRc6aNWtUk3aMMahUKjUQs76+vgNuZVH6Dt06HYxVU6IoCmVlZVAoFIiKirKK6p63nR28BhErcrkcmZmZ8Pf3x/Tp0w1cspfu/NlAaPbd2uorZnt7e9kA1luZt/bXW7QuLgDP3hUGD2fjbHs3NjaitLQUfD7fZEMNNE1j7969ePPNN3Hy5EnMmzfPJMchWC42I44Y0tPTIRQKERwcjNbWVqjVaqxatQpCoRBLliwx6hMhk1AvFovR2toKe3t7duttIGfhkcBMOdXU1CA6OtrirOlHikajQV5eHjQaDfh8vkm2wpiqn777MyOUBmoWHi3d3d3Izs5GQECAzVT3gBsPIBUVFUZ3KFcqlWy1or29na3M+vr6DvjAYayGbh1FobSkxKpMK33s7OA5BGGUkZGBwMBATJ061eBvONBo/a2aont7e9nPUVdXF1xdXVmhxPQp1XcosHzX1VuKrtHS1NSEkpISxMbGmixAlaZpfPHFF3j55Zdx7NgxkmB/m2JT4mjfvn3YvHkzPv74Yzz55JPQ6XS4fPkyRCIR0tLS0N3djRUrVkAoFOLuu+82arNlX2dhHo9nMH4+0hskRVEoLi5GW1sb+Hy+zUw5KZVKA1+csdhyYsQsU/Wzs7MzaBY2hohpa2tDXl4eG0djCzC9OPX19eDz+Sb1+2Eqs1KplH3gYITSrXpgRtLQrdPpUFJSAp1Oh8jISKvY9vS1s4PHIOtkJggnTpzYrxnsQKaM57YOTcToB7C2traCZ++AAtl4/F+hDB0K3U0/P5pJNH3GShh98803+Otf/4ojR45gyZIlJjkOwfKxGXGUlpaGxx57DCkpKbjrrrtu+j5FUbh69SorlCQSCZKSkiAQCLB8+XKjbrcwHiPM1g6Hw4Gvry/8/f2H5dOjX1mJjY21ij6IocDkiTENoubYctI/R1KpFDRNG1gEjGRNLS0tKCwstKlQXMaCgIluGcuxdp1OZ9ADw5wjX1/fW9o4qBmhRFFQDtDQrdVqUVRUBB6Ph4iICKsYavCzt4f7IOvs6elBZmYmgoODERoaOqDg72/767H5IXg4MRB17UpM8h68F0hLUTiS24x/X6xGU7cGTjygrzYyVuWIaSkwZa4iTdP4/vvvsXnzZohEIiQlJZnkOATrwGbEkUajQUNDw5CC9SiKQlZWFlJSUpCamoqGhgbcfffdEAgEWLlypVG3W/QjGMRiMSiKYitKt7oJKxQKZGdnw8nJCbNmzbKKp9qhwEw5WVKeGE3T7DmSSCRsTIa/v/8tp6r0YbacbGnbk+kH6+zsRHx8vFktCGiaNvC7UqlU8Pb2Zm0cBuoT0v7ap6Tf0K3RaFBYWAgHBweER0SAZwX9YMMRRiEhIUNybmaaoj2c7fH/jpUip6Gb/V7fKTKmyXqStxN8XRxxqkiCTy5Wo6ZNAbdxdvjD3GA8Mnsi0jLr8N75OlA0wAGNx6KdsS5+4rBCpm9aZ0sLioqKTB44nZaWhk2bNuHgwYNYtWqVyY5DsA5sRhyNFIqikJ+fj5SUFKSlpaG8vBxLly5FcnIyVq9ebbTtFuC3Czwzfq7RaNhqhY+PD/v0yvjHTJgwwWDCxNppbm5GUVGRRVdW9GMyJBIJFArFLW/CNE2jsrISDQ0NI560s0SYTDuFQoG4uDiLsUYAfrNxYM6RTCZjzVt9fX0HFHEUTaNdocDPOTngOjtj6vTpVtEo729vD7dBhFF3dzeysrLYh47hUtPWi5WfXDP4GocD/Gd9DBo6lXjzeOmvggfwdXGARKbGeAceNswNxsa5QXAbZxhyW9eugM84gKvsglQqNehTYuxQhnJdY4SRqR86jh07hkcffRTffPMNhEKhyY5DsB5ue3GkD03TKC4uZoVSYWEhFi5cCKFQiDVr1sDHx8eoQqk/Q0NHR0c0NjZi2rRpFhtoOFxommbt/a2tsqJ/E+7p6YGnp6fB+HlxcTHa29uNNmlnCWi1WuTk5ICiKKvItFMoFOzWW9/pRP2bMGOE6OHhgRkzZkAJsFEmluTQzcDBDWHkOogw6urqQlZW1qj63G6Vf9YfDyUEYsvi0CFPoDF9SsxghKOjI7tFOlAvmVgsRkFBAWJiYkx6zTh16hQ2bNiAffv2Yd26dSY7DsG6IOJoABjvHUYoZWdnY/78+RAKhUhOTsaECROMKpR6enpQXl6O9vZ2cDgceHt7w9/fH76+vhZ/c7oV+rEZfD7fKjKqBoK5CTMO6jweD1wuF9HR0Vadk6aPWq1GdnY27O3tERMTYxW9OPow04lMs7CjoyM7mVhWVgZfX19ERETc9Nm1FIduhqEKo87OTmRnZyMsLGxUD1P9NmlzgEXTvHGhrO2mnx9NkzXTS8YMRtA0zfopMdvYEokE+fn5iI6OZoONTcH58+fxu9/9Dnv27MHDDz9sM1V6wugh4mgIMJUPkUiE1NRUXLt2DXPmzIFAIIBAIEBQUNCoPlQURaGkpARSqRR8Ph88Ho+tKFlzMC6zNdPb2ws+n28zsRkajQZZWVnQaDRwcnJCR0cHO35uyggGU6NUKpGVlcU6r1vDltOtYCZIm5qaIJVKweVyMWHCBPj5+cHLy2tUDd2mhANggr09XIYojKZOnWoU3zP9Jm2m52hBmNdNosmY4/lMqwEjaBUKBcaPHw+ZTIYZM2YM2WR0JFy6dAnr1q3Drl27sHHjRqv8zBJMBxFHw4SmaTQ2NiI1NRUikQhXrlwBn8+HUCiEQCDA5MmTh/Uh02q1yMvLg0qlQmxs7E0Coq/zs7UE46rVauTk5IDD4SA2Ntaqq1/6MAKCSaDn8XjQarUGFgEODg7sOTKm35UpkcvlyMrKgre3N2bMmGEVax4KTC9OUFAQvLy82Mof03TPuD8P9P7sr6HblHAABNjbY/wgwqijowPZ2dmYPn06goKCjHb8/pyr+xNNI4n7GAr19fUoLS2Fk5MTFAoFXF1d2W1sY6YRXLlyBffeey927NiBJ5980mzv90uXLmHHjh3IzMxEc3Mz0tLSSM+ThUDE0SigaRpisRhpaWkQiUS4ePEiZs6cCYFAAKFQOKgB4HC9fphgXLFYjK6uLri5ubGmk5ZUlWGy31xdXREVFWV1WzMDIZPJkJ2dDW9v7wEtCJhqBfMkzOFwDPyuLLEa09PTg6ysrH4NA60ZprIyefJkgyZlmqYhk8nYhw4ml28w92dTOXQzcAAEOjjAeZD3SHt7O3JycoYV3zJaRhv3MRSkUiny8vIwc+ZM+Pv7Q61Ws58j/T4l5qFjpJ+lX375BUKhEH//+9/x9NNPm/X9fvLkSfz000+Ij4/HvffeS8SRBUHEkZGgaRptbW04fPgwRCIRzp07h+nTpyM5ORlr16696Wn8559/RldXF0JCQkbk9dNfRIYlBOMyk3YBAQEGkQXWTmdnJ3JycgwS6AeDCV5lqhU6nc7AS8kSRGNHRwdycnJuEhDWDiMgpk6dOmgvTn/uz/oN3f1B0zR6GaFkhIbuoQqjtrY25ObmIiIiwmInPkdCa2srcnNzWWHUl74PHQAMQoyH+lnKysrCmjVr8Prrr+PZZ5+1qOsTh8Mh4siCIOLIBDC+OUePHoVIJMKZM2cQEhICgUCAtWvXoqCgAH/5y1/wzjvv4PHHHx/1B5R5wmImQcwVjCuVSpGfn4+wsLABk9qtEeZ1DeVGOxD9+fQwF/dbbeuYEuZ1GXtrxty0trYiLy9vRJUV/c9Se3v7kONmRtPQzcUNYeQ0iDBiXteMGTMQEBAwrGNYMozgi4yMxIQJEwb9ef3PklQqhVKpNKj8DWQ7kZ+fj5UrV+KFF17Aiy++aFHCCCDiyNIg4mgM6O7uxvHjxyESiXD06FFoNBosXboUr732GuLj44261dJfMC5TUTJlMC6Tkh0VFWXUoF9zwwStRkVFDenCPRQG2tYZy6Z7xnPKmK/LEmCmnIzxurRarUG1gsvlDmmLdDgN3UMVRoyQHaqAsBYYYTQawSeXy1lB293dDTc3N/j6+qK3txczZswAl8tFUVERVqxYgc2bN+ONN96wOGEEEHFkaRBxNEbodDo8++yz+Pbbb7F161bk5+fj+PHj8PT0RHJyMgQCAebMmWPUrZa+wbimaBRmTBDr6+sRGxtrMyPtzIRidXU1YmJiTJblBNzcdM8k1Juql6y+vh7l5eVW5zk1GIzgmzVrFvz8/Iz6u/vbItVv6B6oX1CrF46r6NPQzQUw0cEB4wYRRozgG2jLyVphtj6NWQlTqVRobW1FTU0NkpOT4eXlhcTERFy6dAmbNm3C22+/bZHCCCDiyNIg4mgMkMlkeOihh1BZWYnjx4+zvR0KhQJnzpyBSCTCsWPHMG7cOCQnJ0MoFGL+/PlGjQwxRTAuEy/R0dFhUyaINE2jrKwMLS0tY+7NxCTU6/eS6VsEjAaaplFTU4Oamhrw+XybcfMGfqtcmjpiAvjNwJU5T729vfD29h50W0e/oVtFUQiwt4fjEIWRKQSfOWGEkSl7p7q7u/Gf//wH//jHP8DhcDB+/HisXr0aAoEAy5Yts7jrFRFHlgURR2PAl19+ia+++gopKSkDVlbUajV++OEHiEQiHDlyBBwOB6tXr8batWuxcOFCo/akGCMYV6vVIjc3FxqNBnw+36LiJUYDRVEoLCxEV1cX4uLi4OzsbLa1MIaGTC+Zk5MTO9Y83Pw/mqZRXl6O5uZmxMXFwdXV1YQrH1vq6upQWVlptspl320dd3d3tvF+NO8fxiHa1oQRY0Ng6mm7mpoaLF++HEKhEB9++CGuXbuGw4cP4/Dhw6ivr8cLL7yAt956y2THHy5EHFkWRByNATRNQ6fTDbkSpNFocPHiRaSkpODQoUPQaDRYtWoVhEIhlixZYlQhwgTjMnlvQ0mnH64FgbXAeE6p1WqLE3zMFinTS2ZnZ8cKpcEqfzRNo6ioCO3t7YiPjzer4DM21dXVqKmpQVxcHNzd3c29HKhUKoOG7vHjx7Ofp+H0/DEp9LNmzTKpQ/RY09nZiaysLJMPATQ0NCApKQlJSUn497//bXAdo2kaJSUlkMvlSEhIMNkahoJMJkNFRQUAgM/n46OPPsKSJUvg5eVlM/FR1goRRxaOTqfD5cuXWaHU09ODFStWQCgU4u677zZqT0rfYFytVmswUcXj8VivHy8vL7bZ0RZgYjPs7OwQExNj0YKvb+UPALv15uXlZXBOmGBluVyOuLg4izYOHQ5MvE9TU5PFVsL6moPa29uzQmmgPDHgN2Fkaz1hjO/UtGnTTCqMmpubkZSUhEWLFmHv3r0WYZkxEBcuXMCSJUtu+vrGjRvxxRdfjP2CCCxEHFkRFEXh6tWrrFCSSqVISkqCQCBAUlLSqHtS9OkvGNfNzQ3d3d0ICQmxKbNAhUKBrKwsuLq6Wl1sBmMbwQgljUZjYJRXWFgIrVYLPp9vVdEzt4LJ65NIJIiPj7e43pH+0Be0UqkUFEX163nV1NSEkpKSMemdGkuYcFxjRZ0MhFgsxooVK5CYmIgvvvjCooURwbIh4shKoSgKmZmZbDBuQ0MDli1bBoFAgBUrVhh1i4Fp5K2oqICjoyM0Gg28vb3ZbR1rjgZh3KH9/f0RHh5u1YKPCTBmBG1vby/s7e0RFhaGCRMmWPV5YqBpGsXFxWhra0NCQoJFOcMPFf08MYlEAqVSyQauMgHNppyOHGsYYTTacNzBaG1txcqVKzFz5kzs37/foqu/BMuHiCMbgKIo5OXlsUKpsrISd911FwQCAVavXg0PD48R3/SZkfaqqiq2zC+Xy9mtN2sOxm1vb0dubi4mT5487Ew8S0alUiErKwv29vbw9PREa2srenp64OnpyQpaa9xeY5rlu7u7ER8fb5WvoS80TUMul6OiooJ1fvb09GSrStYo/vTp7u5GZmamyYVRe3s7Vq1ahbCwMBw8eNAmHgQI5oWIIxuDebJOSUlBamoqioqKsGjRIgiFQqxevRo+Pj5DFgHM9gXzNNvfSHt/wbj+/v4WfwMWi8UoLCwc03yqsaC3txdZWVnw9PQ06AlTKBRspaKzsxNubm6soLWGBm3mAUChUCAuLs6imuVHCzNtx+fzMW7cuJusHBih5OLiYlUCnhFGoaGhJnXM7+zsxJo1axAQEIDU1FSrekAjWC5EHNkwTNMqI5RycnKwYMECCIVCJCcnw9/ff8CLrU6nM2jkHcoTbN9gXFObGY4UxgRx5syZNjUizWwRTpgw4Za5dmq1mhW0zESVvpeSpd2AdTodaxsRFxdnU1UBpirb37Sdvtt9W1sbHBwcDBq6Le086dPT04PMzEy2Kmsquru7IRQK4e7ujsOHD1v0AxnBurBacVRWVoYXXngBP/30E9RqNaKjo7F9+/Z+O/8Jv/UNiUQipKam4pdffsHcuXMhEAggEAgwceJE9mLb3NyMb7/9FnfccQdiY2NHdDPqG4w7lDBPU0PTNKqqqlBXV2dzJojMJNCkSZMwZcqUId84+7sBG9tFfTRotVrk5OSApmnw+Xyb6iOpqalBdXX1kGwIdDqdQUM3AFYoeXl5WVTjMSOMmPeiqZDJZLj33nvh4OCA48ePW9QDGMH6sVpxNH36dEybNg3vvPMOnJycsHPnTnzxxReorKy0qewhU0DTNBoaGpCamorU1FT89NNPiI+Ph0AgQFRUFJ5++mnMmDEDIpHIKDcjSwjGZbYbW1tbERcXZ9TJPnPD5FNNmzZtVJNAfV3Uh5olZio0Gg2ys7PB4/EQGxtrUQJgtFRXV6O2thZxcXHDdmDXn1CUSqVQq9XsgIS5QowZZDIZMjIyEBISgtDQUJMdp7e3F/fffz9omsbx48dt6vNMsAysUhy1trbC19cXly5dwp133gngxtOKm5sbzp49i7vvvtvMK7QeaJpGS0sL0tLS8MUXX+D69evw8vLC5s2bsXbtWkybNs2o4oWpVIjFYrS1tY1JMK5Op0NBQQHkcjn4fL5NPWEyLsqRkZFGTWpnssSY6t9Ao+emQq1WIzMzE+PGjUN0dLRNCSOmehkfHz9qfyb9EGOpVAqZTGa2xntGGAUHByMsLMxkx1EqlXjwwQchl8tx6tSpMY33Idw+WKU4omkaM2bMwJ133omdO3fC0dERO3fuxI4dO1BSUmIz4adjyfHjx/G73/0OL774IgICApCSkoLz589j+vTpEAgEEAqFmDFjhlHFC5N63jcY19/ff9jxGAOh0WiQm5sLiqIQGxtrU82ajY2NKC0tNbmLMjN6zgglU1cqlEolsrKy4OLiYnW+U7eC2datr683ijDqj76N98x2tq+vr0mrtHK5HBkZGQgKCjKpMFKpVFi/fj1aW1tx5swZm9oaJ1gWVimOgBv28EKhEFlZWWz5//jx4+Dz+eZemtWxd+9ePPfcc9i3bx/WrVsH4LfS/ZEjRyASiXD27FlMmjQJAoEAa9euNfpNa6BgXH9//xE3nzIj7bZYfWD6VWJiYsbUE0e/UiGRSCCXy41q5aBQKJCZmQlPT09ERkaavefJWNA0jcrKSjQ2NiI+Pn5MtoHUarVBP9m4ceMMDEKN9bdlhNHEiRMRFhZmsnOmVquxYcMG1NfX49y5czblBUWwPCxKHL300kt47733bvkzxcXFCA8Ph1AohEajwauvvgonJyf85z//wZEjR3D9+nWjbi/YOkqlEkuXLsW7777LblH2R3d3N44dOwaRSIRTp05hwoQJbEUpLi7OqEKJcRMWi8WQSqXgcDjD7n2Ry+XsSHtkZKRNVR8qKirQ2Ng4on4VY9PXysHd3Z21chju9iVzznx9fa3ekFMf/aiThIQEswwk6D98tLa2smHT/UXODAdGGAUGBprUNV+r1eKPf/wjSktLcf78eZvKmyNYJhYljqRSKdra2m75M6Ghofjxxx9xzz33oKOjw+DmMG3aNDz22GN46aWXTL1Um4Km6WFd1GQyGU6ePAmRSIQTJ07A09MTycnJEAqFmD17tlErNCMJxu3q6kJ2djYmTpxoUzEnTGAm01RuabEZSqXyJo8epp9ssLXKZDJkZmaa/CY71tA0jbKyMojFYouJOmE+U0yVVqPRsBmK3t7eQ94m7e3tRUZGBgICAkx6znQ6HTZt2oScnBykp6fD39/fJMchEPSxKHE0VI4ePQqhUIiuri6D8nR4eDg2btyIV155xYyru71QKBQ4ffo0UlNTcfToUTg7O2PNmjUQCoWYP3++UUev++aIMcG4/v7+bJMwM7kVFhZmUuO5sYaiKBQUFKCnp2fIvlPmRKPRGEwoOjk5sdW/vo333d3dyMrKQkhIyLBsCCwdxkRVKpUiPj7eIs029SNnpFKpwTapr6/vgGabjDCaMGGC0Yc29NHpdNiyZQuuXLmCCxcuIDAw0CTHIRD6YpXiqLW1FREREVi0aBHeeOMNODk54fPPP8c///lPXL9+HTExMeZe4m2JUqnEuXPnkJqaisOHD4PH42H16tVYu3Yt7rzzTqM27vYXjOvi4oKenh5ERESYNPV7rGFMENVqNeLi4qyuqZxpvGe2dOzs7FihBAA5OTkmd1Eea/SrfNaUAcdsk0qlUnR1dbFO6kxDN3DjgSgjIwN+fn63NBsdLRRFYevWrUhPT0d6erpJ40cIhL5YpTgCgIyMDLz66qvIyMiARqNBVFQU3njjDaxYscLcSyPgRuXg4sWLSElJwaFDh6DRaLB69WoIhUIsXrzYqPEPNE2jvLwcdXV1cHR0NJimsvZgXI1Gg5ycHAAYsSGnJaGfTi8Wi6HVauHu7o7Q0NBR9b5YEoynVnt7O+Lj461GGPVFpVJBKpVCKpWivb0dTk5O8PT0hEQigZ+fHyIiIkwqjLZt24bjx4/jwoULJjWTJBD6w2rFEcF60Gq1uHz5MiuUZDIZVq5cCaFQiKVLl47q5qHfoMzn8+Hu7m4wTWXNwbi2PG0nlUqRl5eHkJAQUBRlsE3K9L5Yoxs2TdMoKipCR0cHEhISbCbOQqvVorm5GeXl5aAoyiDKxNgGoRRF4bXXXkNKSgrS09Mxbdo0o/3ukfLJJ59gx44daGlpQUxMDHbt2oXZs2ebe1kEE0LEEWFM0el0uHr1KiuUWltbsXz5cggEAiQlJQ2rYZWiKPZGNFCDMrNNIBaL0dPTwwbj+vn5WXR4KTPS7u7ujqioKJuoqDAwxpUzZ85km2v1e18kEgkUCoXVVf9omkZhYSG6uroQHx9vM8IIuLFlnpGRAW9vb0yfPt2goVun0xlN1NI0jbfeegtfffUV0tPTERERYcRXMTIOHjyIDRs24LPPPsOcOXOwc+dOfP/99ygtLbWpbEaCIUQcEcwGRVHIyMhASkoK0tLS0NTUhGXLlkEgEGDFihW3HFPX6XTIy8uDUqkcckq7tQTjymQyZGVlwc/Pz6ZG2gGgqakJJSUlgxpXymQySKVSiMVis7o+DxWKolBYWIienh7Ex8dbtPAeLoww8vLyuskIVr/3TyqVQqFQGDR0D6dSS9M03n33XezZswfnz5/HzJkzTfFyhs2cOXOQmJiI3bt3A7hxroODg7FlyxYyGW3DEHFkIo4fP4633noLeXl5GDduHBYtWoRDhw6Ze1kWC0VRyM3NZYNxq6qqsHTpUggEAqxatcrACJKJO0lMTERMTIxRg3H9/f3NOlXE2BAwTsO2JIzq6+tRXl6O2NjYYRn4Ma7PjKhlmoT9/PwsYgKMmSSUy+WIj4+3qq3bwVAqlcjMzISHh8eQTDnlcjkrlBjfK0Yo3epc0TSNjz76CDt37sT58+ctZqhGrVbD2dkZKSkpEAqF7Nc3btyIzs5OHD582HyLI5gUIo5MgEgkwhNPPIG3334bd911F7RaLQoKCvDAAw+Ye2lWAdO3kZKSgtTUVBQXF2Px4sUQCoWIiorC73//e8yYMQMpKSlG6cNhgnHFYjHa29sxfvx4duttLAMt29vbkZOTY3M2BMBvjt58Pn9UkQ9Mk7BEImHPFSOUXFxcxlxMUhSF/Px89Pb22pwwUqlUyMjIGLIw6gvje8U0dDPnytfX18DOgaZp7Nq1C++//z7OnDmDhIQEU7ycEdHU1ISJEyfiypUrmDdvHvv1bdu24eLFi7h27ZoZV0cwJUQcGRmtVovJkyfjzTffxGOPPWbu5Vg9zCRaSkoKvvnmGxQVFcHPzw8vvvgihEIh/P39TRqMy/jz+Pv7m/TmK5FIUFBQgIiICJvyctHPEzO2ozdzrhiLAEdHR1YoGTMeYyAoijLY2rU1YZSZmQk3NzdERUWN+m/JnCupVIrW1lbs2rUL3t7eEAgEqK2txdtvv41Tp05h7ty5RnoFxoGIo9sX6xsHsXCysrLQ2NgILpcLPp+PlpYWxMbGYseOHRazh25NcDgcTJ8+HQsXLsSOHTuwdetWNhh327ZtmDdvHgQCAZKTkzFx4sRRX8Tt7e0REBCAgIAAaLVa9uZ7/fp1kwTjAjcuwMXFxZg1a5ZNNXgywra5uRkJCQlGr8Lpnyv9eIzs7Gw2b9EU01TAb9vAarUa8fHxVtEwPlTUajUyMzPh6upqFGEEGJ4rxs4hLS0Njz76KBQKBZYvX46Wlhb09vZaxFYpg4+PD3g8HsRiscHXxWIxJkyYYKZVEcYCUjkyMt9++y0eeughhISE4KOPPsLkyZPx4Ycf4syZMygrKyNhiSPgyJEjePjhh/Hhhx9i06ZNAG7ceBsaGpCamorU1FT89NNPSEhIgEAggEAgwKRJk4xaOegvGJfZehtpMC4A1NXVoaKiAjExMfD29jbaes2Nvjv0WEedUBSFjo4OtqeMoiiDyJnRbsUyppxarRZ8Pt8mhdH48eONHi6tD03T2L9/P/7617/i7bffRn19PTuUkZSUBKFQiPXr11uEncOcOXMwe/Zs7Nq1C8CN91dISAg2b95MGrJtGCKOhshQQ3GzsrKwfv167NmzB08++SSAGyXqoKAg/P3vf2dv7oShoVKpEBcXh+3bt+Pee+/t92dommabtEUiES5duoTo6GhWKBk798kYwbj6202MP5OtoO/1Y24TRJqm0dXVxQqlvgahw7356nQ65OTkQKfTIS4uziJu3sZiLIXRd999hy1btiA1NRX33HMP+/WioiKkpaXhl19+weHDhy1iIOHgwYPYuHEj9uzZg9mzZ2Pnzp347rvvUFJSQnLebBgijobIUENxf/rpJ9x111348ccfcccdd7DfmzNnDu6++2784x//MPVSbQ6NRjPkp3OaptHa2opDhw5BJBLh/PnziIiIYIVS31Hk0dK3SjGUYFymqiKRSBAXFzemTd+mhpnckslkiIuLs6ixe5qmDQxC9XPEhmIQyggjiqLA5/NtShhpNBpkZmbCyckJs2bNMqmvVmpqKv70pz/h4MGDWLVqlcmOY0x2797NmkDGxsbiX//6F+bMmWPuZRFMCBFHRqa7uxt+fn745JNP2IZsjUaDoKAgbN++na0mEUwPTdPo6OjAkSNHIBKJcPbsWUyZMgUCgQBCodDoT8f9BeP23c5hjCs7OzvNXlUxNoz3FFPts/QGZcYgVCKRoLu7Gx4eHmxFqe950Wq1yM7OBofDAZ/Ptym38rEURseOHcOjjz6Kb775xmA0nkCwNIg4MgFbt25FSkoK/ve//2HSpEnYsWMHjh49ipKSEnh6epp7ebctXV1dOHbsGFJTU3Hq1CkEBASwQonP5xtdKOkH4zLbOQqFAhRF2ZxRoP52kzX24TBj5319rxgndabJOzY21iaFERNRY0phdOrUKWzYsAH79u3DunXrTHYcAsEYEHFkAjQaDV5++WV8/fXXUCgUrOV8VFSUuZdG+BWZTIYTJ04gNTUVJ06cgJeXF5KTkyEUCpGYmGjUGyBTUSooKIBarQYAq4vGuBX6VZXY2Fir325Sq9UGFgEcDgeOjo6IiooaVfO9paHRaJCVlQUHBwfExMSYVBidO3cODz30EPbu3YuHHnrIZv6GBNuFiCPCbU9vby/OnDkDkUiEY8eOYfz48VizZg2EQiHmzZs36pu9Wq02uAkpFAq2osT0vfj7+w87bsESYG6w9vb2iImJscmqCk3TGD9+PFpbW2Fvb89WlKxZKGm1WoPzZkphdOnSJaxbtw67du3Cxo0brfZvRri9IOKIQNBDqVTi3LlzEIlEOHLkCHg8HtasWYO1a9fijjvuGHaVR6lUIisrCy4uLv32OPUNxmUyxCw9GBe4MUmYlZUFZ2dnk/eqjDWMMHJ0dGTFA0VRaGtrY7ffALDnysvLy2pePyOM7OzsTC5of/rpJ9x333344IMP8MQTTxBh9CtfffUVnn32WTQ1NRl8zoVCIVxdXfH111+bcXUEgIgjAmFANBoNLly4gJSUFBw6dAg6nQ6rV6+GQCDA4sWLBxUvcrkcWVlZ8Pb2HtKUHFNRkkgkFh2MC/yWucU4KFuLMBgKTKXvVn04FEUZJNNrtVo2mZ4xDrREmC3Qseif+uWXXyAQCPCPf/wDTz/9NBFGeigUCgQEBODzzz9n+68kEgkmTpyIM2fOYMmSJWZeIYGIo9sYlUqFOXPmIDc3F9nZ2YiNjTX3kiwWrVaLy5cv4/vvv8ehQ4cgl8uxatUqCAQCLF269CbxUlhYCIlEgqCgoBH5LPUXjMuYTprbQbi3txeZmZlDFn3WBOP1M5xqmH7zvUQigVKptMieMp1Oh6ysrDERRllZWVizZg3eeOMNbN261abeI8biz3/+M2pqanDixAkAwEcffYRPPvkEFRUV5O9lARBxdBvzl7/8BeXl5Th58iQRR8NAp9Ph559/hkgkQlpaGtrb27F8+XIIBALcc889+PHHH7Fhwwbs3r3bKGHDlhKMC9yohmVmZsLf3x/Tp0+3qYs4kyc20BboUNH3UpLJZBaxVarT6ZCdnQ0AJrciyMvLw6pVq7Bt2zZs27bNpt4jxiQ7OxuJiYmora3FxIkTER0djXXr1uH1118399IIIOLotuXkyZN47rnnIBKJEBUVRcTRCKEoCtevX2eFUn19PTQaDe677z7s2rULrq6uRj2eRqNhe17GMhgXAHp6epCZmYmgoCCEhYXZ1E2PEUZMnpixtgn7bpW6ubmxQmmsKoCMzQJN0yYXRkVFRVixYgW2bNmC119/3abeI6YgPj4e999/P+655x7Mnj0bNTU1CA4ONveyCCDi6LZELBYjPj4ehw4dgo+PD6ZMmULEkRH45ptv8Pjjj2P58uUoKSlBTU0Nli5dCoFAgFWrVhk9KV4/GLe1tRUODg5sRcmYwbjADY+orKwsTJ48GVOmTDHa77UEmP4pd3d3owWt9odKpWKFLVMBZISSqYTtWLp6l5aWYsWKFXj88cexfft2IoyGwKeffoqdO3di2bJlKC8vx+nTp829JMKvEHF0m0HTNFauXIkFCxbgtddeQ01NDRFHRuDTTz/Ftm3bkJKSgqSkJNA0jcLCQqSkpCAtLQ3FxcVYsmQJhEIhVq1aBW9vb5MG49rZ2Rlt5LyjowM5OTkICwtDSEiI0dZsCSiVSmRkZMDT0xORkZFjdkPXaDQGwtbR0ZE9X8YS0UxALmPMaUphVFFRgRUrVuDhhx/Ge++9Z1MN+qakq6sLgYGB0Gq1+Oqrr/Dggw+ae0mEXyHiyEYYajDumTNn8N133+HixYvg8XhEHBmBtLQ0/PGPf8Tx48cxf/78m75P0zTKysogEomQmpqK3Nxc3HnnnRAIBEhOToafn5/R8970hdJIgnEZ2trakJubi+nTpyMoKMhoa7QEFAoFMjMz4eXlZdbG8r7Clsfjsc3cwz1fDBRFIScnB1qt1uQBuTU1NVi+fDmEQiF27txJhNEw2bBhA44fP37TWD/BvBBxZCMMNRj3gQcewNGjRw1uBDqdDjweD+vXr8eXX35p6qXaHGq1GjU1NZg+ffqgP0vTNKqrq1mhdP36dcyfPx/JyckQCAQIDAwck2Bcf3//Qb15JBIJ8vPzERkZiYCAAKOtyRJgJu58fX0RHh5uMVtAA50vX19fNp9vKL8jNzcXarUacXFxJp2Wq6+vR1JSEpYvX45///vfRBiNgKVLlyIqKgr/+te/zL0Ugh5EHN1m1NXVobu7m/3/pqYmJCUlISUlBXPmzLG56oAlQ9M06uvrkZqaitTUVFy5cgWJiYlsjElISIhRb9r6wbhisRg6ne6mYFyG5uZmFBUVYdasWfDz8zPaGiwBRhj5+flZ9MQdTdPo6upihZJarTbwUuqvGjSWwqi5uRlJSUlYtGgR9u7da7HeTpZKR0cHLly4gPvvvx9FRUUIDw8395IIehBxdJtDttUsA5qm0dzcjLS0NIhEIvz444+Ijo6GUCiEQCAw+nQY480jFotvuvFqNBqUl5cjOjoaPj4+RjumJcBYEUyYMAHTpk2zWGHUF5qmDSwC5HK5gZeSg4MDKIpCXl4elEol4uPjTSqMxGIxVqxYgdmzZ2Pfvn1EGI2AyZMno6OjA6+//jqef/55cy+H0Acijm5ziDiyPGiaRmtrKyuU0tPTERERwQqliIgIowslmUwGsViMxsZGqNVqeHh4ICgoCD4+PhZjYjha5HI5MjIyEBgYOCJjTktCLpezk2/d3d1wd3eHVqsFTdNITEw06TlrbW3FypUrMXPmTOzfv9/qg4YJhP4g4ohAsGBomkZHRwcOHz6M1NRUnD17FqGhoRAIBBAKhUb15KmurkZNTQ0iIiKgUChsIhiXQSaTITMzExMnTrQ5j6be3l7k5eWht7cXFEXB1dWVbcAfP368UY/V3t6OVatWISwsDAcPHrRI4fyPf/wDx48fR05ODhwcHNDZ2WnuJRGsECKOCAQroqurC0ePHkVqaipOnz6NwMBAVijFxsaOSCjRNI3Kyko0NDQgPj7ewLiyt7eX3XqztmBcBsa8Mjg4GGFhYeZejlGhKAoFBQWQy+WIj48HcKOyw7ipMyahfn5+cHV1HZUo7OzsxJo1axAYGAiRSGSxQvlvf/sbPDw80NDQgP/+979EHBFGBBFHBIKVIpPJcOLECYhEIpw4cQI+Pj5Ys2YN1q5di8TExCHngpWVlaGlpQXx8fG3jCPpLxiXMZ0cN26cMV+a0WCEUUhICEJDQ829HKNC0zQKCgogk8kQHx9/k1jRarVoa2uDWCxGa2sr7O3tR+x91d3dDaFQCA8PDxw6dMhiz7c+X3zxBbZu3UrEEWFEEHFEsChqamqwfft2nD9/Hi0tLQgMDMQjjzyCV1991WKfVC2B3t5enD59GiKRCMeOHYOrqyvWrFkDoVCIefPm9dswS9M0iouL0dbWhvj4+GHFWVhyMC5Dd3c3srKyMGnSJJtz9WZMRru7u5GQkDDoZ6M/7ytmUnEwSweZTIZ7770Xjo6OOHbs2E0hy5YKEUeE0UA66QgWRUlJCSiKwp49ezB16lQUFBTgiSeegFwuxwcffGDu5Vkszs7OWLt2LdauXQulUokffvgBIpEIDz30EOzt7dmK0oIFC2Bvbw+1Wo2NGzdiwYIFeOyxx4Z9w3N0dERwcDCCg4OhVqtZoVRRUQEXFxeDWAxzwMSdhIaGYtKkSWZZg6nQF0b9VYz6g8vlsn5JFEWxlg5FRUXQ6XQGFgH6Qrq3txcPPPAAeDweDh8+bDXCiEAYLaRyRLB4duzYgU8//RRVVVXmXorVodFokJ6ejpSUFBw+fBg6nQ4rVqxAXl4euru7cfLkSaNGgpgzGJehs7MT2dnZNhl3QtM0ioqK0NnZiYSEhFH3fTGWDoy4ra6uxjfffIM1a9ZgzZo12Lx5M+RyOU6dOgU3NzcjvYrhM9QEgIiICPb/SeWIMBqIOCJYPK+99hpOnTqFjIwMcy/FqtFqtfjhhx+wadMmNDU1wdnZGWvWrEFycjLuvvtuo/eR6AfjSqVSODo6miwYl4HJgZs6darNpZsz26AdHR2Ij483+vli3Nv37NmDkydPoqqqCq6urvjb3/6Ghx9+GBMmTDDq8YbDUBMA9KtoRBwRRgMRRwSLpqKiAvHx8fjggw/wxBNPmHs5Vo1MJkNycjJUKhWOHj2KwsJCiEQipKWlobOzE0lJSRAKhbjnnnuM3jfE5IcxzcHGDMZlaG9vR05Ojk3mwDHCqL29HQkJCSZtiFar1diwYQMqKiqwfv16nDp1Cj///DPmzp2LtWvX4r777sPkyZNNdnxjQcQRYTQQcUQYE0ZSFm9sbMSiRYuwePFi/Oc//zH1Em2azs5OrFy5Ek5OTjh8+LBBLxBFUbh+/TpSUlKQlpaGlpYWLFu2DEKhEMuXLzcY7TcGAwXj+vv7w8PDY0R2BIwwCg8Px8SJE426XnND0zRKSkrQ1tZmcmGk0Wjw2GOPobS0FOnp6axDektLC44cOYLU1FTw+Xy88847JlvDaKmrq0N7ezuOHDmCHTt24McffwQATJ061Ww9cATrg4gjwpgw3LJ4U1MTFi9ejLlz5+KLL74ggZajpLq6Gn//+9+xe/fuWzbVMmnujFCqqanB3XffDYFAgJUrV8Ld3d1ignEZ2trakJubi4iICAQGBhptbZYATdMoLS2FVCpFQkKCSRuitVotNm3ahNzcXKSnp8Pf33/ANVmyieYf/vCHfgO009PTsXjx4rFfEMEqIeKIYHE0NjZiyZIliI+Px/79+0luk5lgpqJSUlKQmpqK0tJSLFmyBEKhEKtWrYKXl5dJgnEZ08lbBeMytLa2Ii8vDzNmzEBAQIDR1mIJjKUw0ul02Lx5M37++WdcuHDB5kQmgTBciDgiWBSNjY1YvHgxJk2ahC+//NLghmjOhtDbHcYsUiQSQSQSIS8vDwsXLoRAIMCaNWvg5+c3JsG4/v7+8Pb2hp2dHaRSKfLz8xEZGWlz7w3m7y2RSEwujCiKwtatW5Geno709HSbm/AjEEYCEUcEi+KLL77Ao48+2u/3yFvVMqBpGlVVVRCJREhNTUVmZibmzZsHgUCA5ORkBAYGGl0o9fT0sFtvCoUCLi4u6OnpQWRkpM1VOWiaRnl5OVpaWpCQkGBSU02KorBt2zacOHEC6enpNmeWSSCMFCKOCATCiKFpGnV1dUhNTUVqaip+/vlnJCYmQiAQQCAQICQkxOj9KbW1tSgvL4ejoyNUKhW8vb3h5+dn1cG4DDRNo6KiAs3NzWMijF599VWIRCJcuHABU6dONdmxCARrg4gjAoFgFGiaRlNTE9LS0pCamooff/wRMTExEAqFEAgECA0NHbVQamlpQVFREWbNmgVfX1+bCMZlYAKAGxsbkZCQgPHjx5v0WG+++Sa+/vprXLhwAeHh4SY7FoFgjRBxRCAQjA5N05BIJDh06BBSU1ORnp6OGTNmsEIpPDx82EKpubkZxcXFiI6OZkfM9bHGYFx9KioqxkwYvfPOO9i7dy/S09MRFRVlsmMRCNYKEUcEwiB88skn2LFjB1paWhATE4Ndu3Zh9uzZ5l6W1UDTNDo6OnD48GGIRCL88MMPCAsLg0AggFAoRGRk5KAj+01NTSgpKUFMTAy8vb0HPaZSqYRUKoVYLEZnZyfc3NzYipKlBOPqU1lZiYaGBsTHx5vUi4emaXz00Uf45z//iXPnziEmJsZkxyIQrBkijgiEW3Dw4EFs2LABn332GebMmYOdO3fi+++/R2lpKfz8/My9PKukq6sLR48ehUgkwunTpxEUFMQKpZiYmJuE0rVr1yCXy4csjPqiH4zb3t7OBuP6+/ubtEIzVKqqqlBXV4eEhASTC6Ndu3bh/fffx5kzZ5CQkGCyYxEI1g4RRwTCLZgzZw4SExOxe/duADeaWIODg7Flyxa89NJLZl6d9dPT04MTJ05AJBLh5MmT8PHxQXJyMtauXYuEhATs3LkT7777Ln7++WeEhYWN+nj9BeMyW29jFYyrT3V1NWpraxEfH290J3J9aJrGnj178NZbb+HUqVOYO3euyY5FINgCRBwRCAOgVqvh7OyMlJQUCIVC9usbN25EZ2cnDh8+bL7F2SC9vb04deoURCIRjh8/DgCQy+X429/+hr/85S9GNwNlgnGZvLexCMbVp6amBjU1NWMijPbt24dXX30Vx48fxx133GGyYxEItgIRR4RhQ1HUbRHn0dTUhIkTJ+LKlSuYN28e+/Vt27bh4sWLuHbtmhlXZ9t89NFHeP3113HnnXfil19+gaOjI9asWYO1a9diwYIFsLOzM+rx9INxpVIp7O3t2a03Y0emAGMrjPbv34/nn38eR48eJfEZBMIQMe4VhmDT0DQNmUx208X8dhFLhLHh448/xvbt23H+/HnMmTMHarUa6enpEIlE2LhxI2iaxurVqyEUCrFo0SKjeBvxeDy2YVs/GDcnJwccDoetKI00GFef2tpaVFdXj4kw+u677/DXv/4VqampRBgRCMOA3NEIQyYvLw+TJk3CpUuXDL5uq8LIx8cHPB4PYrHY4Otisdjm4ioshf/+97/Yvn07zp49izlz5gAAHBwckJSUhL1796KpqQnfffcdnJyc8NRTT2HKlCnYtGkTTpw4AaVSaZQ1cLlc+Pr6IioqCgsXLsTMmTNBURTy8/Nx6dIlFBUVobW1FRRFDft319XVoaqqCnFxcXBzczPKegciLS0NW7ZswXfffYd77rnHpMciEGwNsq1GGBbJycnw9/fH559/Dq1Wi9OnT6O2thabNm26ZU9IfX09GhoaDLanrIE5c+Zg9uzZ2LVrF4AbVbKQkBBs3ryZNGSbgKamJkgkEsTGxg76szqdDleuXEFKSgoOHTqEzs5OLF++HEKhEMuWLTP6yP5IgnH1qa+vR0VFBeLi4uDu7m7UtfXl6NGj+OMf/4gDBw5AIBCY9FgEgi1CxBFhWLz99ts4ffo09u/fj/feew9Hjx7FkiVL8Mknnww4Ft3c3Iz169fDw8MDKSkpVlVpOnjwIDZu3Ig9e/Zg9uzZ2LlzJ7777juUlJTA39/f3Msj/ApFUfjll19YodTS0oJ77rkHQqEQSUlJRt++omkaXV1drEWAfjAuU3HUhxFGfD4fHh4eRl1LX06ePImNGzfiiy++wP3332/SYxEItgoRR4QhQdM0OBwOGhoaMHv2bISGhqKlpQV79+7FXXfdddPPM31IFRUV+Nvf/ga5XI5Dhw4Z/C5rYffu3awJZGxsLP71r3+xWz4Ey4OiKGRnZ0MkEkEkEqGurg533303hEIhVq5cafRJNP1gXLFYDKVSCR8fH/j5+cHHxwdisRhlZWWIi4szuTA6d+4cHnroIXz++ed46KGHTHosAsGWIeKIMCiM0FEqldi/fz+efPJJzJ49G2lpaQgICOi3IZsRQI8//jjEYjH+/ve/IyYm5qaf1Wq14HK57Nc0Gg0oirK6XCyCZULTNAoKCpCSkoLU1FSUlZXhrrvugkAgwOrVq+Hp6Wl0oSSXy9mtN7lcDpqmMXnyZEyePBn29vZGO1ZfLl26hHXr1mH37t3YsGGDxT2A1NTUsI32LS0tCAwMxCOPPIJXX33V6gODCbaH9exvEMwGl8tFd3c3HnjgAXz++eeIiIhAeHg4fHx8BpxU43A4KC8vx8GDB/HII4+wMQVcLhdqtRpNTU0AADs7O4N/X1paiqVLl7KmiwTCaOBwOJg1axbefPNN5OXlITc3FwsWLMDevXsRGhoKgUCA//73v5BIJDDGcyKHw4GLiwvCwsIwadIkcDgcTJw4Ee3t7bh48SIyMzPR0NAAlUplhFf3Gz/99BMeeOABfPTRRxYpjACgpKQEFEVhz549KCwsxMcff4zPPvsMr7zyirmXRiDcBKkcEQbl0KFDePHFF+Hg4ICDBw9CoVBg6dKlyM/PR3Bw8E0/z1SNnnnmGfz44484d+4cvLy8AADl5eV48803ce3aNSiVStx///146qmnMH36dPbfNzc3A8CAVSkCYbTQNI3KykqIRCKkpqYiKysL8+fPh0AgQHJyMgICAkYlMJgsuNjYWPa9zwTjisVidHd3Gy0Y95dffoFAIMDbb7+NP//5zxYpjAZix44d+PTTT1FVVWXupRAIBpC7DmFQnJyckJycjMuXLyMyMhJTpkxBcHAwjh07dst/l5qait/97nfszQEAXn31VWRlZeF///sfdu/ejcLCQrz99tugKAoqlQr19fUICAhAQEAAgBuVppGMTBMIt4LD4WDq1Kl48cUXcfXqVVRUVEAgECAtLQ0zZszAsmXLsGvXLtTV1Q27otTc3HyTMAJufI4mTZqE2bNn44477sCECRMgkUhw+fJl/PLLL6ipqYFCoRjWsbKysrB27Vq8+eabVieMgBs5e/p/IwLBUiCVI8Kw0Ol04PF4eOKJJ5Cbm4srV64Y9AwxNDY2IjQ0FOfOnTOIKxAKhXBxccH//vc/ODg4oKGhAfX19Zg3bx6uX7+Oxx57DA888ABee+01KJXKm56odToduFyu1d0ETMk777yD1NRUlJSUwMnJCfPnz8d7772H8PBwcy/N6qBpGk1NTUhNTUVqaiouX76M2NhYCIVCCAQCTJky5ZbvvebmZhQXFw8rJLe/YFymonSrYNy8vDysXLkSL774IrZt22Z1n4mKigrEx8fjgw8+wBNPPGHu5RAIBpDKEWFQ9PUzM6L86aefYvfu3VCpVAbCiPnZjIwMxMbGso3VzNf/8pe/4Nq1a3jooYeQnp6OoKAg1vuopKQEOp2OFVP79u3D/fffj7Nnz6K0tJQ9PnMToCgKOp3OlC/dKrh48SKefvppXL16FWfPnoVGo8E999wDuVxu7qVZHUyP0JYtW3D+/Hk0NDTg8ccfx8WLFxEXF4cFCxbgvffeQ2lp6U0VpZSUFGRlZQ1LGAE3TC6DgoIQFxeHRYsWISQkBJ2dnbh69SquXLmCyspKdHV1GVRQi4qKsGbNGjz77LNmF0YvvfQSOBzOLf8rKSkx+DeNjY1Yvnw51q1bR4QRwSIhlSOCSfjpp5/w8ssv41//+hdiY2MNeodKSkrw8ccfIzMzE9u3b8eKFSsgl8vxyiuv4Nq1a7h69SoA4LnnnsO+fftw1113oaWlBbm5ufj444/JxXQQpFIp/Pz8cPHiRSxcuNDcy7EJaJpGe3s7Dh8+DJFIhB9++AHTpk1DcnIy1q5di2vXruH555/H/v37sXLlSqMcUz8YNyUlBQcPHkRSUhLmzp2L1157DU888QS2b99u9oqRVCpFW1vbLX8mNDSUnUhramrC4sWLMXfuXHzxxRekp5BgkRBxRDAJ9fX1iIiIQHt7OxwdHSGRSCASiXDffffBz88PAJCUlAQ3Nzd8//33yM3NxdatW8Hn8/HRRx+hoqICjz76KGQyGXbv3g0+n4/33nsPKSkp7M2JqUAZ62ZkK1RUVGDatGnIz8/HzJkzzb0cm4MxgDx69ChEIhFOnjwJjUaDlStX4pVXXkF0dLTRb/g9PT1ITU3FgQMH8NNPP8HV1RWPPfYY1q1bhzlz5liNwGhsbMSSJUsQHx+P/fv3D+oqTiCYC+v4RBGsDpVKhbvuugs5OTkAgPb2dnz55Zd4/vnn8cMPP6CiogItLS3saH9xcTGam5uxevVqAMDVq1ehUqnw17/+FQsWLICzszNmz56N4uJivPTSS+jt7YWbmxuefPJJpKam3nItt5P+pygKW7duxYIFC4gwMhEcDgceHh74/e9/j0cffRR2dnZ47rnn4OTkhKSkJERHR+OVV17B9evXjTZM4OrqikWLFqG2thZPPfUUvv76a3R0dGDVqlUIDg7Gli1bkJWVZZRjmYrGxkYsXrwYISEh+OCDDyCVStHS0oKWlhZzL41AuAk7cy+AYJtMnToV9957L/v/ERER2LVrF3bt2oUHH3wQgYGBmDp1Kl544QXweDxkZGTAw8ODddvOyMiAi4sLFi1axP6OjIwMTJ06FU899RSSkpIAANXV1di3bx/uvffeAZ23zb3tMJY8/fTTKCgowOXLl829FJvn6NGjePjhh/HNN99AKBQCAORyOU6dOoXU1FQIBAK4ubkhOTkZQqEQc+bMGXGlpL6+HitXrsSqVauwa9cucLlcJCcnQ6PR4MKFCxCJRMjLy0NcXJwRX6FxOXv2LCoqKlBRUYGgoCCD791ODzAE64BsqxFMykCCpbKyEj4+PnB3d0d2djYeffRRzJkzB3v27EFVVRWeeeYZBAUF4bPPPmP/TVJSEiZNmoQPPviATTRftmwZQkJC8J///MfgOMxUXW5uLhoaGrBy5UqD71MUxTaL2gqbN2/G4cOHcenSJUyZMsXcy7FpqqqqEB0dja+++srgIUAfhUKBs2fPIjU1FUeOHMG4ceOwZs0aCIVCLFiwAHZ2Q3s2bW5uRlJSEhYvXoy9e/dazRYagWDNkMoRwaToiw+apkFRFHg8HsLCwtivT548GevXr2fzyi5cuID6+nqDbKhr165BIpFg9erVrDDq6elBeXk5Vq5cOaAIe//991FYWIjw8HBMnToVwG/CSR+tVjvkm5WlQdM0tmzZgrS0NFy4cIEIozEgNDQUmZmZt7RLYPzBkpOToVarkZ6ejpSUFGzcuBEAsGrVKqxduxYLFy4cMD6jpaUFK1euxPz587Fnzx4ijAiEMYJ80ghjBofD6XdbwdPTEy+88AI7WTVhwgRERUVh/vz57M+cPXsWnp6eSExMZL924sQJ2NnZ9dsAyxwnNzcXAoHAoIz/pz/9CS+99BI6OzvZrzHCiKZpaLXa0b/YMeTpp5/G/v37ceDAAbi6urJ9HMM1FCQMj+H4SDk4OCApKQmff/45mpqa8O2332LcuHH405/+hNDQUGzatAknT540iBWRSqVYs2YN4uLi8L///Y80LxMIYwgRRwSLQH93d+XKlThw4IBBBeTMmTNwc3PDjBkz2K8dOnQIUVFRbPQI8zuYJtiLFy+iu7sbfD6fNZNUq9XIyMiAQqFgPZiEQiEKCgrQ29sLDodzUwVJp9Nh8+bNePLJJw3Wayk70p9++im6urqwePFi1l08ICAABw8eNPfSCP1gZ2eHu+66C59++inq6+tx6NAheHp64tlnn8WUKVPwxz/+EQcOHMDq1asRERGBr776ymqrmgSCtUI+cQSLoG8/UN9K0IEDB9Dc3Ax3d3cAgFKpxNmzZ7Ft2zY2aoT5HYxoSUlJwdSpUxEZGcn+nnPnzkGtVmP+/PlwcnJCdXU1jhw5Al9fX4jFYlRXV2Pz5s147LHH2BsSj8fDpk2bIBaL2d9vSb1KliLSCMOHx+Nh4cKFWLhwIXbu3Ilr164hJSUFzz//PHx8fPB///d/sLe3N/cyCYTbDlI5Ilgc/fVVBAUFGWypNTQ0YN68eZg1a9ZNT9XM9kNmZiZmz55tEI577NgxTJw4ka1AffnllwBuPM3/v//3/7Bu3Tp8+OGHuHjxIvtvVCoVZs2ahbvvvhvAjYrUM888g/b2diO9YgLhxvt+3rx5+PDDD9Hc3IyMjIwBe5EIBIJpIeKIYJVMnToVR48eZUf6+9LS0gIejwe5XA4nJyf26xcuXEBsbCzbnP3111/j8ccfx4cffoi4uDi88cYb4PF4OHToEIAb23BbtmzBfffdB+BGH0hmZiY+//xzNjCTBOMSjI2TkxM8PDzMvQwC4baFiCOCVcIIkv6qTDRNY8KECUhISMD169fR2NiI7OxsbN68GbW1tYiPj4ezszNqampQXV2N+++/H87Ozuy/7+npYatNXV1dOH/+PLs1t3XrVrzwwguIiIhAc3PzTWvQ6XRELBEIBIKVQ8QRwSq51Ugz0w/06KOPwtXVFdOnT8f+/fvx/fffY+7cueyW2r59+xAaGmrQk5SXl4eenh7WXbqwsBBVVVV49NFHAQC///3v4eTkBGdnZ0ycOBFCodAgV4rH47FrY0SSRCLBX//6V9TX1xvxL2DdvPvuu+BwONi6dau5l0IgEAg3QRqyCTZLdHQ0zp49i56eHshkMtbfaPLkyQCAb775BqtXr4avry/7bw4cOIDQ0FBEREQAAI4cOYJp06YhNDQUSqUSOTk5cHd3x08//QSVSoWKigo4ODhgz549OHbsGAICAvD4449j9uzZ4HK5oCgKfn5+qKurw5EjR/D000+b409hUVy/fh179uxBdHS0uZdCIBAI/UIqRwSbhaIo6HQ6uLq6IiAgADt37sSOHTvg5uaG1tZWVFVVYeHChexIPwAcP34cS5YsQWBgILq7u3Hs2DE88MADAG70G507dw5LliwBcMO7JjIyEp999hm2bNmCO+64Ax0dHbj//vvxyCOPQK1Ws1UkqVQKjUbDrut2RSaTYf369fj888/h6elp7uUQCARCvxBxRLBZuFwuO7nGuHMz1RwfHx+oVCqsWLGC/fmCggIUFhZi4cKFGDduHPLz81FZWYn169cDuBEZkZOTg4cffhjADVdtDoeDjIwMPPzww3jxxRfx/fffQyQSYc6cOeykkVqtRkhICFpbW0HT9G3tcvz0009j1apV7OQfgUAgWCJkW41wW6Cfo8YIJHt7ewMPGQcHBzz33HPsds+BAwcQFBSEiIgI6HQ6FBQUgMPhYNWqVQDA/tv4+Hi89dZbmDBhAp588kkkJiYiKiqK/b0ajQY6nQ5NTU3gcDgW55M0Vnz77bfIysrC9evXzb0UAoFAuCW37yMs4bamv+rN9OnT8cEHHyA0NBQURWH8+PFsIzaPx0NpaelNaeIAsG3bNnzzzTcoLy/H9u3b0d7ebjD9BgBXrlxBbGysSV6LNVBfX4+//OUv+Oabb1i3cgKBQLBUODSx1yUQAAzufP3111/jjTfewOLFi/H666/Dzs4OLS0tiI6OBo/HQ15eHh577DGEhoYiNTWV/XdFRUWIjo5GZmYmYmJixuKlWByHDh3C2rVrDfLBdDodOBwOuFwuVCoVyQ4jEAgWA6kcEQi/0lcY9W2c/v3vf499+/ahtbUVn376Kaqrq7F792589dVX4PF4iI+PR1hYGGQyGXp7e9l/d+DAAUyaNImdgLsdWbp0KfLz85GTk8P+l5CQgPXr1yMnJ4cIIwKBYFGQniMCYQD623pbvHgxFi9eDOCGWeTly5exfft2vPbaa4iOjkZ+fj7+/e9/s1tHGRkZOHjwIJ566imDqbjbDVdXV9Y7imH8+PHw9va+6esEAoFgbkjliEAYBszUG3Djhv/qq6+yyeoPPvgg0tPTcd9994HL5UKn0+G///0vAgIC8Kc//cnMKycQBiY5ORkhISEYN24cAgIC8Pvf/x5NTU3mXhaBYDZIzxGBMAoYewAGpm+Jpmns3LkTn3/+Ofbs2YM777zztp1SI1g+H3/8MebNm4eAgAA0Njbi+eefB3BjkIBAuB0h4ohAMALMx4gRP//3f/+Hb7/9Fhs2bGBDawkEa+HIkSMQCoVQqVQGdhcEwu0CEUcEggkoLS2Fk5MTQkJCzL0UAmFYtLe346mnnkJjYyMuX75s7uUQCGaB9BwRCCYgPDycCCOCVfHiiy+yTfJ1dXU4fPiwuZdEIJgNIo4IBAKhD42NjXjkkUfg7e0NJycnzJo1CxkZGeZe1rB46aWXWGf4gf4rKSlhf/6FF15AdnY2zpw5Ax6Phw0bNoBsLBBuV8i2GoFAIOjR0dEBPp+PJUuW4KmnnoKvry/Ky8sRFhaGsLAwcy9vyEilUrS1td3yZ0JDQ9kMQH0aGhoQHByMK1euYN68eaZaIoFgsRCfIwKBQNDjvffeQ3BwMPbt28d+bcqUKWZc0cjw9fWFr6/viP4tY1ehUqmMuSQCwWoglSMCgUDQIzIyEklJSWhoaMDFixcxceJE/PnPf8YTTzxh7qWZhGvXruH69eu444474OnpicrKSrz++usQi8UoLCy8rc1LCbcvpOeIQCAQ9KiqqsKnn36KadOm4fTp03jqqafwzDPP4MsvvzT30kyCs7MzUlNTsXTpUoSHh+Oxxx5DdHQ0Ll68SIQR4baFVI4IBAJBDwcHByQkJBgYID7zzDO4fv06fv75ZzOujEAgjBWkckQgEAh6BAQEIDIy0uBrM2bMQF1dnZlWRCAQxhoijggEAkGPBQsWoLS01OBrZWVlmDRpkplWRCAQxhoijggEAkGPZ599FlevXsXbb7+NiooKHDhwAHv37sXTTz9t7qURCIQxgvQcEQgEQh+OHTuGl19+GeXl5ZgyZQqee+45m51WIxAIN0PEEYFAIBAIBIIeZFuNQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0IOKIQCAQCAQCQQ8ijggEAoFAIBD0+P+iwBgEXJfUaQAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "def make_tangled_ring_initial(\n", + " seed,\n", + " n_beads=N_BEADS,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " base_z=BASE_Z,\n", + "):\n", + " \"\"\"\n", + " Generates a closed (ring) polymer with a persistent random-walk tangent.\n", + " Each step rotates the current direction by a small random angle around a\n", + " perpendicular axis. Ring closure is enforced by linearly distributing\n", + " the end-to-end gap across all beads. The chain is then lifted to base_z\n", + " so it can relax down to the z=0 substrate during MD.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + "\n", + " steps = np.zeros((n_beads, 3), dtype=np.float32)\n", + " vec = np.array([1.0, 0.0, 0.0], dtype=np.float32) #Initialization of vector for chain propogation\n", + "\n", + " for k in range(n_beads):\n", + " dtheta = rng.normal(scale=np.sqrt(1.0 / persistence_bonds))\n", + " axis = rng.normal(size=3).astype(np.float32) #choose an axis at random for chain propogation in 3D\n", + " axis -= axis.dot(vec) * vec # remove component along vec\n", + " axis_norm = np.linalg.norm(axis)\n", + " if axis_norm < 1e-8:\n", + " axis = np.array([0.0, 0.0, 1.0], dtype=np.float32)\n", + " axis_norm = 1.0\n", + " axis /= axis_norm\n", + " vec = vec * np.cos(dtheta) + np.cross(axis, vec) * np.sin(dtheta) # Calculating vector for chain propogation\n", + " vec /= np.linalg.norm(vec)\n", + " steps[k] = bond_length * vec\n", + "\n", + " coords = np.cumsum(steps, axis=0) # Coordinates for open chain\n", + "\n", + " # Enforce ring closure\n", + " closure_error = coords[-1] - coords[0]\n", + " t = np.linspace(0, 1, n_beads, dtype=np.float32).reshape(-1, 1) # Based on the distance between first and last bead a closure error is propogated to all bead positions to form a closed chain\n", + " coords -= t * closure_error\n", + " coords -= coords.mean(axis=0) # This method works better than directing the chain to go away and then come back to original position\n", + "\n", + " # Lift above substrate\n", + " coords[:, 2] += float(base_z) # Changing z values to lift the chain off the substrate\n", + " return coords.astype(np.float32)\n", + "\n", + "\n", + "# ── Quick visualisation ──────────────────────────────────────────────────────\n", + "_demo_coords = make_tangled_ring_initial(seed=43)\n", + "_cc = np.vstack([_demo_coords, _demo_coords[0]]) # close the ring for plotting\n", + "\n", + "fig = plt.figure(figsize=(7, 6))\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.plot(_cc[:, 0], _cc[:, 1], _cc[:, 2], \"-o\", markersize=3, linewidth=1.2)\n", + "ax.scatter(_cc[0, 0], _cc[0, 1], _cc[0, 2], color=\"red\", s=60, zorder=5, label=\"bead 0\")\n", + "\n", + "# Draw substrate plane\n", + "_xs = np.linspace(_cc[:, 0].min(), _cc[:, 0].max(), 2)\n", + "_ys = np.linspace(_cc[:, 1].min(), _cc[:, 1].max(), 2)\n", + "_X, _Y = np.meshgrid(_xs, _ys)\n", + "ax.plot_surface(_X, _Y, np.zeros_like(_X), alpha=0.15, color=\"cyan\")\n", + "\n", + "ax.set_xlabel(\"x (sim units)\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z\")\n", + "ax.set_title(f\"Initial ring — seed 42, {len(_demo_coords)} beads\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_03", + "metadata": { + "id": "cell_md_03" + }, + "source": [ + "## 3. Molecular Dynamics Relaxation\n", + "To accurately mimic chain relaxation on a susbstrate a coarse grained molecular dynamics simulation is performed using OpenMM (no water or other molecules simulated). If Molecular dynamics is not needed, please skip this cell.\n", + "\n", + "The function `run_md_relaxation` runs a Langevin-dynamics simulation in the overdampled regime via OpenMM.\n", + "Here we have added:\n", + "\n", + "1. Harmonic bonds\n", + "2. Bending-angle stiffness\n", + "3. A surface-attraction to cause chain relaxation\n", + "4. A hard-wall force to keep chain above substrate\n", + "5. A short-range WCA repulsion to ensure chain does not take non physical configurations\n", + "\n", + "The function then records `n_frames` snapshots which can be used for visualization." + ] + }, + { + "cell_type": "markdown", + "source": [ + "We start with testing the OpenMM installation (can be tricky at times)" + ], + "metadata": { + "id": "wFFhsVZhqU8a" + }, + "id": "wFFhsVZhqU8a" + }, + { + "cell_type": "code", + "source": [ + "## Test the OpenMM installation before running the following cell to ensure that GPU acceleration will work\n", + "import openmm.testInstallation\n", + "\n", + "# Run the standard OpenMM installation test to check for CUDA/GPU support\n", + "try:\n", + " openmm.testInstallation.main()\n", + "except Exception as e:\n", + " print(f\"Test failed: {e}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3NjBSwfAe4Mj", + "outputId": "05dd0ee7-e21b-4b54-c39b-cfacc339576f" + }, + "id": "3NjBSwfAe4Mj", + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "OpenMM Version: 8.5\n", + "Git Revision: b55e60882dffcddb2532cceda4201c0fdc9ec2d5\n", + "\n", + "There are 4 Platforms available:\n", + "\n", + "1 Reference - Successfully computed forces\n", + "2 CPU - Successfully computed forces\n", + "3 CUDA - Error computing forces with CUDA platform\n", + "4 OpenCL - Successfully computed forces\n", + "\n", + "CUDA platform error: Error loading CUDA module: CUDA_ERROR_UNSUPPORTED_PTX_VERSION (222)\n", + "\n", + "Median difference in forces between platforms:\n", + "\n", + "Reference vs. CPU: 6.29228e-06\n", + "Reference vs. OpenCL: 6.74321e-06\n", + "CPU vs. OpenCL: 7.92217e-07\n", + "\n", + "All differences are within tolerance.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "And then once we are sure that OpenMM is correctly installed we run the MD functions" + ], + "metadata": { + "id": "9XbRNS3TqbKH" + }, + "id": "9XbRNS3TqbKH" + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cell_code_03", + "metadata": { + "id": "cell_code_03" + }, + "outputs": [], + "source": [ + "def run_md_relaxation(coords0, seed,\n", + " n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME):\n", + " \"\"\"\n", + " Takes initial bead coordinates and runs Langevin dynamics (pure OpenMM).\n", + "\n", + " Forces applied:\n", + " - Harmonic bonds + angle stiffness along the ring\n", + " - Surface attraction (linear in z) + hard wall at z = 0\n", + " - Short-range WCA repulsion between non-bonded beads\n", + "\n", + " Returns a list of (n_beads, 3) coordinate arrays (n_frames + 1 entries;\n", + " frame 0 is the pre-minimisation geometry).\n", + " \"\"\"\n", + " coords0 = np.asarray(coords0, dtype=np.float32)\n", + " n = int(coords0.shape[0])\n", + "\n", + " extent = (coords0.max(axis=0) - coords0.min(axis=0)).max() # how big the bounding box for the molecular dynamics needs to be\n", + " box = float(extent + 10.0)\n", + "\n", + " # ── Reduced-unit constants (matches polychrom temperature=1 convention) ──\n", + " # All lengths in nm, energies in kJ/mol.\n", + " BOLTZ_kJmol = 0.00831445 # kJ / (mol · K)\n", + " temperature = 1.0 # K (reduced; sets energy scale kT ≈ 0.0083 kJ/mol)\n", + " kT = BOLTZ_kJmol * temperature # kJ/mol\n", + " conlen = 1.0 # nm\n", + " mass_amu = 100.0 # Da per bead\n", + " collision_rate = 0.6 # ps⁻¹\n", + " error_tol = 0.005\n", + "\n", + " # ── Build system ─────────────────────────────────────────────────────────\n", + " system = openmm.System()\n", + " system.setDefaultPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + " for _ in range(n):\n", + " system.addParticle(mass_amu)\n", + "\n", + " # Remove COM drift — applied every 50 steps\n", + " system.addForce(openmm.CMMotionRemover(50)) # To ensure CPU and GPU calculation are consistent (since GPU minimizations calculations can suffer from drift)\n", + "\n", + " # ── 1. Harmonic bonds ────────────────────────────────────────────────────\n", + "\n", + " # OpenMM HarmonicBondForce: E = (k/2)·(r − r0)² → k = kT / wiggle²\n", + " bond_L = 1.0 # nm\n", + " wiggle = 0.1 # nm (bondWiggleDistance)\n", + " k_bond = kT / (wiggle ** 2) # kJ/mol/nm²\n", + "\n", + " bond_force = openmm.HarmonicBondForce()\n", + " bond_force.setUsesPeriodicBoundaryConditions(True)\n", + " for i in range(n):\n", + " bond_force.addBond(i, (i + 1) % n, bond_L, k_bond)\n", + " system.addForce(bond_force)\n", + "\n", + " # ── 2. Bending (angle) stiffness ─────────────────────────────────────────\n", + " # E = kT · k_angle · (1 − cos(θ − π)) — equilibrium at θ = π (straight)\n", + " k_angle = 12.0\n", + " angle_force = openmm.CustomAngleForce(\n", + " \"kT * kangle * (1 - cos(theta - 3.141592653589793))\"\n", + " )\n", + " angle_force.addGlobalParameter(\"kT\", kT)\n", + " angle_force.addGlobalParameter(\"kangle\", k_angle)\n", + " for i in range(n):\n", + " angle_force.addAngle((i - 1) % n, i, (i + 1) % n, [])\n", + " system.addForce(angle_force)\n", + "\n", + " # ── 3. Surface: linear attraction (z > 0) + harmonic hard wall (z < 0) ──\n", + " wall_expr = (\n", + " \"kT * (\"\n", + " \" F_pull * z * step(z) + \"\n", + " \" 0.5 * wallK * (z^2) * step(-z)\"\n", + " \")\"\n", + " )\n", + " wall_force = openmm.CustomExternalForce(wall_expr)\n", + " wall_force.addGlobalParameter(\"kT\", kT)\n", + " wall_force.addGlobalParameter(\"F_pull\", 9.0)\n", + " wall_force.addGlobalParameter(\"wallK\", 100.0)\n", + " for i in range(n):\n", + " wall_force.addParticle(i, [])\n", + " system.addForce(wall_force)\n", + "\n", + " # ── 4. WCA repulsion (repulsion between non-bonded pairs only) ──────────────────────────────\n", + " rep_sigma = 1.4 * conlen # nm\n", + " rep_cutoff = 2.3 * conlen # nm\n", + " rep_expr = (\n", + " \"4*eps*((sig/r)^12 - (sig/r)^6 + 0.25) * step(cutoff - r); \"\n", + " \"cutoff = 2^(1.0/6.0) * sig\"\n", + " )\n", + " rep_force = openmm.CustomNonbondedForce(rep_expr)\n", + " rep_force.setNonbondedMethod(openmm.CustomNonbondedForce.CutoffPeriodic)\n", + " rep_force.setCutoffDistance(rep_cutoff)\n", + " rep_force.addGlobalParameter(\"eps\", 1.7 * kT)\n", + " rep_force.addGlobalParameter(\"sig\", rep_sigma)\n", + " for i in range(n):\n", + " rep_force.addParticle([])\n", + " for i in range(n): # exclude bonded neighbours\n", + " rep_force.addExclusion(i, (i + 1) % n)\n", + " system.addForce(rep_force)\n", + "\n", + " # ── Integrator ────────────────────────────────────────────────────────────\n", + " integrator = openmm.VariableLangevinIntegrator(temperature, collision_rate, error_tol)\n", + " integrator.setRandomNumberSeed(int(seed))\n", + "\n", + " # ── Platform selection: OpenCL → CPU fallback ─────────────────────────────\n", + " context = None\n", + " for platform_name in (\"OpenCL\", \"CPU\"): # CUDA did not work on google colab, so using OpenCL here\n", + " try:\n", + " platform = openmm.Platform.getPlatformByName(platform_name)\n", + " context = openmm.Context(system, integrator, platform)\n", + " print(f\"Using {platform_name} platform.\")\n", + " break\n", + " except openmm.OpenMMException as e:\n", + " print(f\" {platform_name} unavailable: {e}\")\n", + " except Exception as e:\n", + " print(f\" {platform_name} failed unexpectedly: {e}\")\n", + "\n", + " if context is None:\n", + " raise RuntimeError(\"No OpenMM platform could be initialised.\")\n", + "\n", + " # ── Set initial positions and box ─────────────────────────────────────────\n", + " positions = [\n", + " openmm.Vec3(float(coords0[i, 0]), float(coords0[i, 1]), float(coords0[i, 2]))\n", + " for i in range(n)\n", + " ]\n", + " context.setPositions(positions)\n", + " context.setPeriodicBoxVectors(\n", + " openmm.Vec3(box, 0, 0),\n", + " openmm.Vec3(0, box, 0),\n", + " openmm.Vec3(0, 0, box),\n", + " )\n", + "\n", + " # Capture the true initial geometry before any minimisation (just in case the chain reaches substrate even before the 1st frame)\n", + " frames = []\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " # Cap iterations so that extra compute is not used once chain relaxation is complete\n", + " openmm.LocalEnergyMinimizer.minimize(context, tolerance=10, maxIterations=200)\n", + "\n", + " # ── Record frames ─────────────────────────────────────────────────────────\n", + " for _ in range(int(n_frames)):\n", + " integrator.step(int(steps_per_frame))\n", + " state = context.getState(getPositions=True)\n", + " pos = state.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n", + " frames.append(pos.copy())\n", + "\n", + " return frames # n_frames + 1 entries (frame 0 = initial geometry)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##4. Non-MD chain generation\n", + "In case you would not like to use molecular dynamics to generate the chains, it is also possible to skip the molecular dynamics and just use a simple 2D persistent walk to generate the DNA chains. The chains generated by this method might have some non physical configurations but the propoerties of the function can be modulated to achieve desired results." + ], + "metadata": { + "id": "Bx7_OzWRr-l3" + }, + "id": "Bx7_OzWRr-l3" + }, + { + "cell_type": "code", + "source": [ + "def make_ring_2d_persistent_initial(\n", + " seed,\n", + " n_beads,\n", + " bond_length=BOND_LENGTH, # From Cell 1\n", + " persistence_bonds=PERSISTENCE_BONDS, # From Cell 1\n", + " angle_stiffness_mult=ANGLE_STIFNESS_MULT, # New parameter (only in Non-MD, defined in Cell 1)\n", + " z_std=0.08, # Small Gaussian noise for Z\n", + " base_z=0.2, # Low altitude for \"deposited\" look\n", + " angle_limit=np.pi/2 # Constraint to keep walk exploring\n", + "):\n", + " \"\"\"\n", + " Generates a 2D persistent ring and returns it as a list of frames [ (N,3) ].\n", + " Replaces both the initial generation and the MD relaxation cells.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + " n = int(n_beads)\n", + "\n", + " # 1. Angular exploration\n", + " sigma_dtheta = np.sqrt(2.0 / max(persistence_bonds, 1.0)) / max(angle_stiffness_mult, 1e-6) #Which angles are permissible for placements of the next bead\n", + "\n", + " theta0 = rng.uniform(0.0, 2.0 * np.pi)\n", + " # Clipping the normal distribution implements the \"angle constraint\"\n", + " dtheta = rng.normal(0.0, sigma_dtheta, size=n).astype(np.float64)\n", + " dtheta = np.clip(dtheta, -angle_limit, angle_limit)\n", + " thetas = theta0 + np.cumsum(dtheta)\n", + "\n", + " # 2. XY Coordinates\n", + " bonds = np.stack([np.cos(thetas), np.sin(thetas)], axis=1) * float(bond_length)\n", + " xy = np.zeros((n, 2), dtype=np.float64)\n", + " xy[1:] = np.cumsum(bonds[:-1], axis=0)\n", + "\n", + " # 3. Seamless Closure Correction\n", + " # Ensures the last bead connects back to the first with bond_length consistency\n", + " end_error = (xy[-1] + bonds[-1]) - xy[0]\n", + " t = (np.arange(n, dtype=np.float64) / n)[:, None]\n", + " xy = xy - t * end_error\n", + " xy -= xy.mean(axis=0) # Center at origin\n", + "\n", + " # 4. Assembly with Gaussian Z-noise\n", + " coords = np.zeros((n, 3), dtype=np.float32)\n", + " coords[:, 0] = xy[:, 0].astype(np.float32)\n", + " coords[:, 1] = xy[:, 1].astype(np.float32)\n", + " # Assign random small z values to simulate a deposited state\n", + " coords[:, 2] = rng.normal(loc=base_z, scale=z_std, size=n).astype(np.float32)\n", + "\n", + " # Return as a list containing one frame to mimic run_md_relaxation output\n", + " return [coords]" + ], + "metadata": { + "id": "yXs0I0akr948" + }, + "id": "yXs0I0akr948", + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "cell_md_04", + "metadata": { + "id": "cell_md_04" + }, + "source": [ + "## 5. MD Animation\n", + "Now we can visulize the Molecular dynamics simulation to see how the relaxation has worked.\n", + "\n", + "The function `show_md_animation` renders an interactive 3-D animation of MD frames in the notebook.\n", + "Running this cell will automatically generate and display the animation for a single deterministic chain (seed 0, default bead count)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cell_code_04", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 228 + }, + "id": "cell_code_04", + "outputId": "21c1cd4a-654f-4fc1-d966-057f8bc2acbc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generating initial ring and running MD (seed=0)…\n" + ] + }, + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'make_tangled_ring_initial' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_10425/24671148.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# ── Run animation automatically (deterministic seed) ─────────────────────────\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generating initial ring and running MD (seed=0)…\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0m_anim_coords0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_tangled_ring_initial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m _anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n\u001b[1;32m 61\u001b[0m \u001b[0mn_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_FRAMES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'make_tangled_ring_initial' is not defined" + ] + } + ], + "source": [ + "def show_md_animation(frames, interval_ms=150):\n", + " \"\"\"\n", + " Renders an in-notebook HTML5 animation of MD frames.\n", + " Pure visualisation — does not mutate any arrays.\n", + " \"\"\"\n", + " frames = [np.asarray(f, dtype=np.float32) for f in frames]\n", + "\n", + " # Use only the first frame for x/y limits — avoids COM drift blowing the scale\n", + " first = frames[0]\n", + " xy_pad = 5.0\n", + " x_min, x_max = first[:, 0].min() - xy_pad, first[:, 0].max() + xy_pad\n", + " y_min, y_max = first[:, 1].min() - xy_pad, first[:, 1].max() + xy_pad\n", + "\n", + " # Clamp z: floor at -1 (just below substrate), ceiling from initial max + padding\n", + " z_min = -1.0\n", + " z_max = float(first[:, 2].max()) + 3.0\n", + "\n", + " x_plane = np.linspace(x_min, x_max, 2)\n", + " y_plane = np.linspace(y_min, y_max, 2)\n", + " X_plane, Y_plane = np.meshgrid(x_plane, y_plane)\n", + " Z_plane = np.zeros_like(X_plane)\n", + "\n", + " fig = plt.figure(figsize=(6, 6))\n", + " ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + " def _set_axes():\n", + " ax.set_xlim(x_min, x_max)\n", + " ax.set_ylim(y_min, y_max)\n", + " ax.set_zlim(z_min, z_max)\n", + " ax.set_xlabel(\"x\"); ax.set_ylabel(\"y\"); ax.set_zlabel(\"z (nm)\")\n", + "\n", + " def init():\n", + " _set_axes()\n", + " return []\n", + "\n", + " def update(i):\n", + " ax.cla()\n", + " c = frames[i]\n", + " # Clip beads to visible z range for plotting so outliers don't distort\n", + " visible = (c[:, 2] >= z_min) & (c[:, 2] <= z_max)\n", + " cc = np.vstack([c, c[0]])\n", + " ax.plot(cc[:, 0], cc[:, 1], cc[:, 2], \"-\", linewidth=1)\n", + " ax.scatter(c[visible, 0], c[visible, 1], c[visible, 2], s=5)\n", + " ax.plot_surface(X_plane, Y_plane, Z_plane, alpha=0.2, color=\"cyan\")\n", + " _set_axes()\n", + " ax.set_title(f\"Frame {i}/{len(frames)-1} | z ∈ [{c[:,2].min():.1f}, {c[:,2].max():.1f}] nm\")\n", + " return []\n", + "\n", + " ani = animation.FuncAnimation(\n", + " fig, update, frames=len(frames), init_func=init,\n", + " interval=int(interval_ms), blit=False\n", + " )\n", + " plt.close(fig)\n", + " display(HTML(ani.to_jshtml()))\n", + "\n", + "\n", + "# ── Run animation automatically (deterministic seed) ─────────────────────────\n", + "print(\"Generating initial ring and running MD (seed=0)…\")\n", + "_anim_coords0 = make_tangled_ring_initial(seed=0)\n", + "_anim_frames = run_md_relaxation(_anim_coords0, seed=0,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + "print(f\"MD done — {len(_anim_frames)} frames. Rendering animation…\")\n", + "show_md_animation(_anim_frames)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_05", + "metadata": { + "id": "cell_md_05" + }, + "source": [ + "## 6. Height based AFM Rendering\n", + "Once we have the coordinates of the relaxed chain either via Molecular Dynamics or without, now we can start to convert the coordinates into a DNA chain as if it were imaged via AFM. We mimic tip convolution using a spherical tip to get the desired appearance. \n", + "\n", + "Here we define all the AFM rendering utilities. All functions described here are essential for getting the 'look' of the DNA chains. Function descriptions are added to the functions themselves. Information necessary to build the masks later on is also preserved in these functions." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cell_code_05", + "metadata": { + "id": "cell_code_05" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Low-level geometry helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _seg_intersect_2d(p1, p2, q1, q2, eps=1e-12):\n", + " \"\"\"\n", + " Strict 2-D segment intersection.\n", + " Returns (hit, pt, t, u) where pt = p1 + t*(p2-p1) = q1 + u*(q2-q1).\n", + " Returns (False, None, None, None) for parallel / collinear segments.\n", + " \"\"\"\n", + " p1 = np.asarray(p1, dtype=float); p2 = np.asarray(p2, dtype=float)\n", + " q1 = np.asarray(q1, dtype=float); q2 = np.asarray(q2, dtype=float)\n", + " r = p2 - p1; s = q2 - q1\n", + " rxs = r[0]*s[1] - r[1]*s[0]\n", + " if abs(rxs) < eps:\n", + " return False, None, None, None\n", + " qp = q1 - p1\n", + " t = (qp[0]*s[1] - qp[1]*s[0]) / rxs\n", + " u = (qp[0]*r[1] - qp[1]*r[0]) / rxs\n", + " if 0.0 <= t <= 1.0 and 0.0 <= u <= 1.0:\n", + " return True, p1 + t*r, t, u\n", + " return False, None, None, None\n", + "\n", + "\n", + "def _circ_sep(n, i, j):\n", + " \"\"\"Circular distance between bead indices on a ring of n beads.\"\"\"\n", + " d = abs(int(i) - int(j))\n", + " return min(d, n - d)\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Structuring-element / grid helpers\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def disk_footprint(r_px: float):\n", + " \"\"\"Boolean circular footprint of radius r_px pixels.\"\"\"\n", + " if r_px < 0.5:\n", + " return np.ones((1, 1), dtype=bool)\n", + " r = int(np.ceil(r_px))\n", + " y, x = np.ogrid[-r:r+1, -r:r+1]\n", + " return (x*x + y*y) <= r*r\n", + "\n", + "\n", + "def upsample_nn(a, out_shape):\n", + " \"\"\"Nearest-neighbour upsample array a to out_shape.\"\"\"\n", + " ny_out, nx_out = out_shape\n", + " ny_in, nx_in = a.shape\n", + " if (ny_out, nx_out) == (ny_in, nx_in):\n", + " return a\n", + " sy = max(ny_out // ny_in, 1)\n", + " sx = max(nx_out // nx_in, 1)\n", + " a_up = a.repeat(sy, axis=0).repeat(sx, axis=1)\n", + " return a_up[:ny_out, :nx_out]\n", + "\n", + "\n", + "def choose_effective_grid(xmin, xmax, ymin, ymax, nx, ny,\n", + " target_nm_per_px=0.10):\n", + " \"\"\"\n", + " If the physical pixel size is already coarser than target_nm_per_px,\n", + " use the full grid directly. Otherwise return a reduced grid that\n", + " will be upsampled after rendering.\n", + " \"\"\"\n", + " px = (xmax - xmin) / max(nx - 1, 1)\n", + " py = (ymax - ymin) / max(ny - 1, 1)\n", + " p = 0.5 * (px + py)\n", + " if p >= target_nm_per_px:\n", + " return nx, ny, p\n", + " factor = int(np.ceil(target_nm_per_px / max(p, 1e-12)))\n", + " nx_eff = max(128, nx // factor)\n", + " ny_eff = max(128, ny // factor)\n", + " px_eff = (xmax - xmin) / max(nx_eff - 1, 1)\n", + " return nx_eff, ny_eff, px_eff\n", + "\n", + "\n", + "def disk_cap_structure(radius_nm: float, p_nm_per_px: float,\n", + " max_radius_px=96):\n", + " \"\"\"\n", + " Hemispherical-cap structuring element representing the DNA cross-section.\n", + " Each pixel inside the disk gets a height equal to the spherical-cap height\n", + " at that radial distance.\n", + " \"\"\"\n", + " r_px = radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(r_px), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (r_px * r_px)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = np.sqrt(\n", + " np.maximum(radius_nm**2 - d_nm[inside]**2, 0.0)\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "def disk_sphere_structure(tip_radius_nm: float, p_nm_per_px: float,\n", + " max_radius_px=128):\n", + " \"\"\"\n", + " Spherical-cap structuring element representing the AFM tip geometry.\n", + " Used to convolve the surface with the tip shape.\n", + " \"\"\"\n", + " Rpx = tip_radius_nm / max(p_nm_per_px, 1e-12)\n", + " R = int(np.clip(np.ceil(Rpx), 1, max_radius_px))\n", + " y, x = np.ogrid[-R:R+1, -R:R+1]\n", + " d2 = x*x + y*y\n", + " inside = d2 <= (Rpx * Rpx)\n", + " d_nm = np.sqrt(d2).astype(np.float32) * np.float32(p_nm_per_px)\n", + " structure = np.full((2*R+1, 2*R+1), -1e9, dtype=np.float32)\n", + " structure[inside] = (\n", + " np.sqrt(np.maximum(tip_radius_nm**2 - d_nm[inside]**2, 0.0))\n", + " - tip_radius_nm\n", + " ).astype(np.float32)\n", + " return inside.astype(bool), structure\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Crossing boost\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def _taper_weight(idx_off, w, profile=\"gaussian\", sigma=None):\n", + " \"\"\"\n", + " Smooth weight in [0, 1] used to fade the crossing height boost along\n", + " the bead-index window of half-width w.\n", + "\n", + " profile:\n", + " 'gaussian' — smooth dome (recommended)\n", + " 'hemisphere' — half-circle taper\n", + " 'linear' — simple linear ramp\n", + " \"\"\"\n", + " if w <= 0:\n", + " return 1.0\n", + " a = abs(idx_off)\n", + " if profile == \"linear\":\n", + " return 1.0 - (a / (w + 1.0))\n", + " if profile == \"hemisphere\":\n", + " x = a / (w + 1.0)\n", + " return float(np.sqrt(max(0.0, 1.0 - x*x)))\n", + " # gaussian (default)\n", + " if sigma is None:\n", + " sigma = max(1e-6, 0.5 * (w + 0.5))\n", + " g = float(np.exp(-(idx_off**2) / (2.0 * sigma**2)))\n", + " g_end = float(np.exp(-(w**2) / (2.0 * sigma**2)))\n", + " if g_end < 0.999:\n", + " g = float(np.clip((g - g_end) / (1.0 - g_end), 0.0, 1.0))\n", + " return g\n", + "\n", + "\n", + "def _collect_intersections(xy_nm, min_separation_beads=12,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Brute-force all segment pairs of a closed ring polyline to find crossings.\n", + " Skips adjacent/shared-vertex pairs and contour-nearby pairs.\n", + " Clusters nearby raw hits via _merge_crossing_clusters.\n", + "\n", + " Returns list of dicts:\n", + " {pt_nm, seg_i, seg_j, t, u, n_hits}\n", + " \"\"\"\n", + " xy = np.asarray(xy_nm, dtype=float)\n", + " n = int(xy.shape[0])\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if hit:\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " clusters = _merge_crossing_clusters(raw, merge_radius_nm=merge_radius_nm)\n", + " return clusters\n", + "\n", + "\n", + "def _merge_crossing_clusters(raw_list, merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Greedy proximity-based clustering of raw crossing dicts.\n", + " Each dict must have key 'pt' (2-D float array).\n", + " Returns averaged clusters with representative metadata.\n", + " \"\"\"\n", + " clusters = []\n", + " for r in raw_list:\n", + " pt = r[\"pt\"].astype(float)\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1, \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt = (c[\"pt_sum\"] / c[\"n\"]).astype(np.float32)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = pt,\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def boost_crossings_intersections(\n", + " x_nm, y_nm, zc_nm,\n", + " crossings=None,\n", + " min_separation_beads=12,\n", + " boost_window_beads=2,\n", + " guaranteed_offset_nm=1.0,\n", + " boost_method=\"additive\",\n", + " boost_profile=\"gaussian\",\n", + " boost_sigma_beads=None,\n", + " far_clip_nm=2.5,\n", + " far_clip_window_beads=12,\n", + " merge_radius_nm=0.5,\n", + "):\n", + " \"\"\"\n", + " Boost z-height at strand crossings so they are visually distinct in\n", + " the AFM image.\n", + "\n", + " For each crossing:\n", + " - Determines which strand is on top (higher z)\n", + " - Raises top-strand beads in a tapered window to at least\n", + " (bottom_max + guaranteed_offset_nm)\n", + " - Optionally clips non-crossing beads to far_clip_nm\n", + "\n", + " Returns (modified_z, crossing_info_list).\n", + " \"\"\"\n", + " x = np.asarray(x_nm, dtype=np.float32)\n", + " y = np.asarray(y_nm, dtype=np.float32)\n", + " z = np.asarray(zc_nm, dtype=np.float32).copy()\n", + " n = int(z.shape[0])\n", + " if n < 5:\n", + " return z.astype(np.float32), []\n", + "\n", + " if crossings is None:\n", + " xy = np.stack([x, y], axis=1)\n", + " crossings = _collect_intersections(\n", + " xy,\n", + " min_separation_beads=int(min_separation_beads),\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + "\n", + " def get_segment_indices(center, w):\n", + " return np.array([(center + k) % n for k in range(-w, w+1)],\n", + " dtype=np.int32)\n", + "\n", + " crossing_info = []\n", + " crossing_centers = []\n", + " w = int(boost_window_beads)\n", + "\n", + " for c in crossings:\n", + " i = int(c[\"seg_i\"]); j = int(c[\"seg_j\"])\n", + " i2 = (i + 1) % n; j2 = (j + 1) % n\n", + " ti = float(c.get(\"t\", 0.5)); uj = float(c.get(\"u\", 0.5))\n", + "\n", + " bead_i = i if ti < 0.5 else i2\n", + " bead_j = j if uj < 0.5 else j2\n", + " zi = float((1.0 - ti) * z[i] + ti * z[i2])\n", + " zj = float((1.0 - uj) * z[j] + uj * z[j2])\n", + "\n", + " top_center, bottom_center = (bead_i, bead_j) if zi >= zj else (bead_j, bead_i)\n", + " top_seg = get_segment_indices(top_center, w)\n", + " bottom_seg = get_segment_indices(bottom_center, w)\n", + "\n", + " bottom_local_max = float(z[bottom_seg].max())\n", + " top_local_max = float(z[top_seg].max())\n", + "\n", + " if boost_method == \"absolute\":\n", + " target_height = bottom_local_max + float(guaranteed_offset_nm)\n", + " else:\n", + " target_height = max(top_local_max, bottom_local_max) + float(guaranteed_offset_nm)\n", + "\n", + " for off in range(-w, w+1):\n", + " k = (top_center + off) % n\n", + " wt = float(_taper_weight(off, w,\n", + " profile=boost_profile,\n", + " sigma=boost_sigma_beads))\n", + " z[k] = float((1.0 - wt) * z[k] + wt * max(z[k], target_height))\n", + "\n", + " crossing_centers.extend([int(top_center), int(bottom_center)])\n", + " crossing_info.append(dict(\n", + " pt_nm = np.asarray(c[\"pt_nm\"], dtype=np.float32),\n", + " seg_i = int(i),\n", + " seg_j = int(j),\n", + " top_center = int(top_center),\n", + " bottom_center= int(bottom_center),\n", + " ))\n", + "\n", + " # Clip beads far from any crossing\n", + " if crossing_centers and far_clip_nm is not None:\n", + " centers = np.array(crossing_centers, dtype=np.int32)\n", + " r = int(far_clip_window_beads)\n", + " for k in range(n):\n", + " d = int(np.min(np.minimum(np.abs(k - centers),\n", + " n - np.abs(k - centers))))\n", + " if d > r:\n", + " z[k] = min(z[k], float(far_clip_nm))\n", + "\n", + " return z.astype(np.float32), crossing_info\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Main AFM renderer\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def create_z_based_afm(\n", + " coords,\n", + " nx=800, ny=800,\n", + " dna_diameter_nm=2.0,\n", + " tip_radius_nm=0.01,\n", + " max_height_nm=6.0,\n", + " target_nm_per_px=0.08,\n", + " max_radius_px=96,\n", + " radius_shrink_px=2.0,\n", + " final_blur_sigma_px=0.20,\n", + " apply_edge_taper=True,\n", + " taper_sigma_nm=0.45,\n", + " taper_floor=0.10,\n", + " add_center_ridge=True,\n", + " ridge_sigma_nm=0.25,\n", + " ridge_amp_nm=0.25,\n", + " grain_nm=0.0,\n", + " grain_sigma_px=0.6,\n", + " grain_seed=1,\n", + " enable_crossing_boost=True,\n", + " min_separation_beads=12,\n", + " boost_window_beads=2,\n", + " guaranteed_offset_nm=1.0,\n", + " boost_method=\"additive\",\n", + " boost_profile=\"gaussian\",\n", + " boost_sigma_beads=None,\n", + " crossings_precomputed=None,\n", + " far_clip_nm=2.5,\n", + " far_clip_window_beads=12,\n", + " return_crossing_info=False,\n", + " return_masks=True,\n", + " extent=None,\n", + "):\n", + " \"\"\"\n", + " Master AFM renderer. Pipeline:\n", + "\n", + " 1. Project 3-D bead coords onto a 2-D effective grid\n", + " 2. Optionally boost crossing heights (boost_crossings_intersections)\n", + " 3. Rasterise closed polyline with z-interpolation\n", + " 4. Apply disk_cap_structure (DNA cylindrical cross-section)\n", + " 5. Apply disk_sphere_structure (tip-sample convolution)\n", + " 6. Clip, blur, edge-taper, center-ridge\n", + " 7. Optional synthetic grain noise\n", + " 8. Upsample to requested (nx, ny)\n", + "\n", + " Returns (afm_image, debug_dict) when return_masks=True.\n", + " \"\"\"\n", + " x, y, z = coords.T\n", + " if extent is None:\n", + " xmin, xmax = float(x.min() - 2), float(x.max() + 2)\n", + " ymin, ymax = float(y.min() - 2), float(y.max() + 2)\n", + " else:\n", + " xmin, xmax, ymin, ymax = extent\n", + "\n", + " nx_eff, ny_eff, p_eff = choose_effective_grid(\n", + " xmin, xmax, ymin, ymax, nx, ny, target_nm_per_px)\n", + "\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx_eff - 1)).astype(np.int32),\n", + " 0, nx_eff - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny_eff - 1)).astype(np.int32),\n", + " 0, ny_eff - 1)\n", + "\n", + " zc = (z - z.min()).astype(np.float32)\n", + "\n", + " # ── Crossing boost ───────────────────────────────────────────────────────\n", + " crossing_info = []\n", + " if enable_crossing_boost:\n", + " zc, crossing_info = boost_crossings_intersections(\n", + " x.astype(np.float32), y.astype(np.float32), zc,\n", + " crossings=crossings_precomputed,\n", + " min_separation_beads=int(min_separation_beads),\n", + " boost_window_beads=int(boost_window_beads),\n", + " guaranteed_offset_nm=float(guaranteed_offset_nm),\n", + " boost_method=str(boost_method),\n", + " boost_profile=str(boost_profile),\n", + " boost_sigma_beads=boost_sigma_beads,\n", + " far_clip_nm=far_clip_nm,\n", + " far_clip_window_beads=int(far_clip_window_beads),\n", + " )\n", + "\n", + " # ── Rasterise polyline ───────────────────────────────────────────────────\n", + " z_line = np.zeros((ny_eff, nx_eff), dtype=np.float32)\n", + " line_mask = np.zeros((ny_eff, nx_eff), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0, z0 = int(ix[k]), int(iy[k]), float(zc[k])\n", + " x1, y1, z1 = int(ix[k2]), int(iy[k2]), float(zc[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " z_line[y0, x0] = max(z_line[y0, x0], z0)\n", + " line_mask[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " zs = np.linspace(z0, z1, n_seg).astype(np.float32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys, zs = xs[keep], ys[keep], zs[keep]\n", + " z_line[ys, xs] = np.maximum(z_line[ys, xs], zs)\n", + " line_mask[ys, xs] = True\n", + "\n", + " # ── DNA cap dilation ─────────────────────────────────────────────────────\n", + " r_eff = max(0.5*float(dna_diameter_nm) - float(radius_shrink_px)*p_eff, 0.2)\n", + " fp_dna, cap = disk_cap_structure(r_eff, p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(z_line, footprint=fp_dna,\n", + " structure=cap).astype(np.float32)\n", + " dna_region = grey_dilation(line_mask.astype(np.uint8), footprint=fp_dna) > 0\n", + " surface[~dna_region] = 0.0\n", + "\n", + " # ── Tip convolution ──────────────────────────────────────────────────────\n", + " r_tip_px = float(tip_radius_nm) / max(p_eff, 1e-12)\n", + " if r_tip_px >= 0.5:\n", + " fp_tip, tip_struct = disk_sphere_structure(\n", + " float(tip_radius_nm), p_eff, max_radius_px=max_radius_px)\n", + " surface = grey_dilation(surface, footprint=fp_tip,\n", + " structure=tip_struct).astype(np.float32)\n", + "\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + " if final_blur_sigma_px > 0:\n", + " surface = gaussian_filter(surface,\n", + " sigma=float(final_blur_sigma_px)).astype(np.float32)\n", + "\n", + " # ── Edge taper + center ridge ────────────────────────────────────────────\n", + " if (apply_edge_taper or add_center_ridge) and np.any(line_mask):\n", + " dist_px = distance_transform_edt(~line_mask).astype(np.float32)\n", + " dist_nm = dist_px * np.float32(p_eff)\n", + " if apply_edge_taper:\n", + " sig = max(float(taper_sigma_nm), 1e-6)\n", + " factor = taper_floor + (1.0 - taper_floor) * np.exp(\n", + " -(dist_nm**2) / (2.0 * sig**2))\n", + " surface[dna_region] *= factor[dna_region]\n", + " if add_center_ridge and ridge_amp_nm > 0:\n", + " rs = max(float(ridge_sigma_nm), 1e-6)\n", + " ridge = np.exp(-(dist_nm**2) / (2.0 * rs**2))\n", + " surface[dna_region] += ridge_amp_nm * ridge[dna_region]\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " # ── Synthetic grain ──────────────────────────────────────────────────────\n", + " if grain_nm and grain_nm > 0:\n", + " rng = np.random.default_rng(int(grain_seed))\n", + " n = rng.normal(0.0, 1.0, size=surface.shape).astype(np.float32)\n", + " if grain_sigma_px and grain_sigma_px > 0:\n", + " n = gaussian_filter(n, sigma=float(grain_sigma_px)).astype(np.float32)\n", + " std = float(n[dna_region].std()) if np.any(dna_region) else float(n.std())\n", + " if std > 1e-9:\n", + " n /= np.float32(std)\n", + " surface[dna_region] *= (1.0 + np.float32(grain_nm) * n[dna_region])\n", + " np.clip(surface, 0, max_height_nm, out=surface)\n", + "\n", + " # ── Upsample ─────────────────────────────────────────────────────────────\n", + " if (nx_eff, ny_eff) != (nx, ny):\n", + " surface_up = upsample_nn(surface, (ny, nx)).astype(np.float32)\n", + " line_mask_up = upsample_nn(line_mask.astype(np.uint8), (ny, nx)) > 0\n", + " dna_region_up = upsample_nn(dna_region.astype(np.uint8), (ny, nx)) > 0\n", + " else:\n", + " surface_up = surface\n", + " line_mask_up = line_mask\n", + " dna_region_up = dna_region\n", + "\n", + " if return_masks:\n", + " debug = {\n", + " \"line_mask\": line_mask_up,\n", + " \"dna_region\": dna_region_up,\n", + " \"extent\": (xmin, xmax, ymin, ymax),\n", + " \"p_nm_per_px\": float((xmax - xmin) / max(nx - 1, 1)),\n", + " }\n", + " if return_crossing_info:\n", + " debug[\"crossings\"] = crossing_info\n", + " return surface_up, debug\n", + "\n", + " return surface_up, (xmin, xmax, ymin, ymax)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_06", + "metadata": { + "id": "cell_md_06" + }, + "source": [ + "## 7. Noise Functions\n", + "Here we describe all the functions and utilities needed to extract and add the noise to our generated AFM_img. Depending on the choise of the user either:\n", + "\n", + "1. Bruker/Nanoscope `.spm` file parser and TopoStats-like noise extraction pipeline is used or,\n", + "2. If no blank `.spm` file is supplied, the notebook falls back to a PSD-based synthetic noise model.\n", + "\n", + "Note: Topostats is an AFM imaging software package developed at the University of Sheffield which is used for filtering of AFM data. We desribe functions that perform operations similar to Topostats to clean the noise data from the blank files before adding it to the AFM_img.\n", + "\n", + "### Mathematics of PSD-Matched Noise Synthesis\n", + "\n", + "All three methods in the code utilize **Frequency-Domain Synthesis**. The core principle is that the height field $z(x, y)$ can be generated from a target Power Spectral Density (PSD) using the inverse Fourier Transform.\n", + "\n", + "#### 1. General Synthesis Workflow\n", + "For an output image of shape $(N_y, N_x)$:\n", + "1. **Define a PSD**: Let $S(f_x, f_y)$ be the target power spectrum.\n", + "2. **Generate Complex Spectrum**: A complex field $Z(f_x, f_y)$ is created in the frequency domain:\n", + " $$Z(f_x, f_y) = \\sqrt{S(f_x, f_y)} \\cdot e^{i \\phi(f_x, f_y)}$$\n", + " where $\\phi$ is a random phase drawn from a uniform distribution $U(0, 2\\pi)$.\n", + "3. **Inverse Transform**: The spatial noise $z(x, y)$ is the real part of the Inverse Fast Fourier Transform (IFFT) of $Z$.\n", + "4. **RMS Scaling**: The image is normalized such that its standard deviation matches the target Root Mean Square (RMS) roughness." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "cell_code_06", + "metadata": { + "id": "cell_code_06" + }, + "outputs": [], + "source": [ + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# Parsing the .spm file for height data\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def robust_range(a):\n", + " \"\"\"Return (p1, p99, p99-p1) of finite values in array a.\"\"\"\n", + " a = np.asarray(a, dtype=np.float32)\n", + " p1, p99 = np.percentile(a[np.isfinite(a)], [1, 99])\n", + " return float(p1), float(p99), float(p99 - p1)\n", + "\n", + "\n", + "def looks_like_afm_height(img):\n", + " \"\"\"True if the image has a finite, positive, sane dynamic range.\"\"\"\n", + " p1, p99, rng = robust_range(img)\n", + " return np.isfinite(rng) and 0 < rng <= 1e9\n", + "\n", + "\n", + "def maybe_to_nm(img):\n", + " \"\"\"Auto-convert from metres to nm if values look metre-scale.\"\"\"\n", + " p1, p99, rng = robust_range(img)\n", + " mx = max(abs(p1), abs(p99))\n", + " if mx < 1e-3:\n", + " return img.astype(np.float32) * 1e9, \"meters->nm (auto)\"\n", + " return img.astype(np.float32), \"as-is (nm or counts)\"\n", + "\n", + "\n", + "def parse_blocks(filename, max_blocks=128):\n", + " \"\"\"\n", + " Read a Bruker .spm file and parse binary data-block metadata.\n", + " Returns (raw_bytes, header_string, block_list).\n", + " \"\"\"\n", + " raw = open(filename, \"rb\").read()\n", + " i_end = raw.find(b\"\\x1a\")\n", + " if i_end == -1:\n", + " i_end = min(len(raw), 400_000)\n", + " header = raw[:i_end].decode(\"latin1\", errors=\"ignore\")\n", + "\n", + " matches = [m.start() for m in re.finditer(r\"Data offset\", header)]\n", + " if not matches:\n", + " raise RuntimeError(\"No 'Data offset' found in header.\")\n", + "\n", + " blocks = []\n", + " for k, pos in enumerate(matches[:max_blocks]):\n", + " win = header[max(0, pos-2500): min(len(header), pos+2500)]\n", + "\n", + " def grab_int(key):\n", + " m = re.search(rf\"{re.escape(key)}\\s*:\\s*([0-9]+)\", win)\n", + " return int(m.group(1)) if m else None\n", + "\n", + " def grab_float(key):\n", + " m = re.search(\n", + " rf\"{re.escape(key)}\\s*:\\s*([+-]?[0-9]*\\.?[0-9]+(?:[Ee][+-]?[0-9]+)?)\",\n", + " win)\n", + " return float(m.group(1)) if m else None\n", + "\n", + " name_candidates = re.findall(r\"@[^:\\n\\r]{0,40}:\\s*([^\\n\\r]{1,80})\", win)\n", + " name = name_candidates[-1].strip() if name_candidates else None\n", + "\n", + " off = grab_int(\"Data offset\")\n", + " length = grab_int(\"Data length\")\n", + " bpp = grab_int(\"Bytes/pixel\")\n", + " nx_b = grab_int(\"Samps/line\")\n", + " ny_b = grab_int(\"Number of lines\")\n", + " zscale = grab_float(\"Z scale\")\n", + " zoff = grab_float(\"Z offset\")\n", + "\n", + " if None in (off, length, bpp, nx_b, ny_b):\n", + " continue\n", + " blocks.append(dict(name=name or f\"block_{k}\",\n", + " off=off, length=length, bpp=bpp,\n", + " nx=nx_b, ny=ny_b,\n", + " zscale=zscale, zoff=zoff))\n", + "\n", + " uniq, seen = [], set()\n", + " for b in blocks:\n", + " if b[\"off\"] not in seen:\n", + " uniq.append(b); seen.add(b[\"off\"])\n", + " if not uniq:\n", + " raise RuntimeError(\"Found 'Data offset' strings but no parseable blocks.\")\n", + " return raw, header, uniq\n", + "\n", + "\n", + "def read_block_candidates(raw, b):\n", + " \"\"\"\n", + " Decode one binary data block, trying all plausible dtypes.\n", + " Applies z-scale / z-offset if present.\n", + " Returns list of (dtype_str, image_array).\n", + " \"\"\"\n", + " off, length, bpp = b[\"off\"], b[\"length\"], b[\"bpp\"]\n", + " nx_b, ny_b = b[\"nx\"], b[\"ny\"]\n", + " blob = raw[off:off+length]\n", + " if len(blob) < length:\n", + " raise RuntimeError(\"Truncated block.\")\n", + "\n", + " n = nx_b * ny_b\n", + " cands = []\n", + " dtype_map = {2: [\"i2\", \"u2\"],\n", + " 4: [\"i4\", \"f4\"],\n", + " 1: [\"u1\"]}\n", + " for dt in dtype_map.get(bpp, []):\n", + " arr = np.frombuffer(blob, dtype=np.dtype(dt), count=n)\n", + " if arr.size != n:\n", + " continue\n", + " cands.append((dt, arr.reshape(ny_b, nx_b).astype(np.float32)))\n", + "\n", + " out = []\n", + " for dt, img in cands:\n", + " z = img\n", + " if b.get(\"zscale\") is not None:\n", + " z = z * np.float32(b[\"zscale\"])\n", + " if b.get(\"zoff\") is not None:\n", + " z = z + np.float32(b[\"zoff\"])\n", + " out.append((dt, z))\n", + " return out\n", + "\n", + "\n", + "def choose_best_height_image(raw, blocks, verbose=True):\n", + " \"\"\"\n", + " Iterate all blocks and dtype candidates, pick the one that:\n", + " - passes looks_like_afm_height\n", + " - has the highest log-variance score\n", + " \"\"\"\n", + " best, best_score = None, -np.inf\n", + " for b in blocks:\n", + " try:\n", + " for dt, img in read_block_candidates(raw, b):\n", + " if not looks_like_afm_height(img):\n", + " continue\n", + " p1, p99, rng = robust_range(img)\n", + " var = float(np.var(img))\n", + " score = np.log10(var + 1e-9) - 0.001 * max(rng - 1e6, 0)\n", + " if score > best_score:\n", + " best_score = score\n", + " best = (b, dt, img, (p1, p99, rng, var))\n", + " except Exception:\n", + " continue\n", + "\n", + " if best is None:\n", + " raise RuntimeError(\"Could not find a plausible AFM height image decode.\")\n", + " b, dt, img, stats = best\n", + " if verbose:\n", + " p1, p99, rng, var = stats\n", + " print(f\"Chosen block: {b['name']!r} {b['ny']}x{b['nx']} \"\n", + " f\"bpp={b['bpp']} decode={dt}\")\n", + " print(f\"Robust p1={p1:.3g}, p99={p99:.3g}, range={rng:.3g}, var={var:.3g}\")\n", + " return b, img\n", + "\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# TopoStats-like background / noise extraction\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "def plane_remove(z, mask=None):\n", + " \"\"\"Fit and subtract a tilted plane from z (optionally background-only).\"\"\"\n", + " ny, nx = z.shape\n", + " Y, X = np.mgrid[0:ny, 0:nx]\n", + " A = np.c_[X.ravel(), Y.ravel(), np.ones(X.size)]\n", + " b = z.ravel()\n", + " if mask is not None:\n", + " m = mask.ravel().astype(bool)\n", + " A, b = A[m], b[m]\n", + " C, *_ = np.linalg.lstsq(A, b, rcond=None)\n", + " plane = (C[0]*X + C[1]*Y + C[2]).astype(np.float32)\n", + " return (z - plane).astype(np.float32)\n", + "\n", + "\n", + "def line_flatten_median(z, mask=None):\n", + " \"\"\"Subtract per-row median (background pixels only if mask given).\"\"\"\n", + " out = z.astype(np.float32, copy=True)\n", + " for i in range(out.shape[0]):\n", + " if mask is None or not np.any(mask[i]):\n", + " med = np.median(out[i])\n", + " else:\n", + " med = np.median(out[i, mask[i]])\n", + " out[i] -= np.float32(med)\n", + " return out\n", + "\n", + "\n", + "def feature_mask(z, smooth_sigma_px=3.0, thresh_sigma=3.0, dilate_px=4):\n", + " \"\"\"\n", + " Detect features (DNA, particles) as pixels deviating by more than\n", + " thresh_sigma MADs from the smooth-residual background.\n", + " \"\"\"\n", + " z_s = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " resid = (z - z_s).astype(np.float32)\n", + " med = float(np.median(resid))\n", + " mad = float(np.median(np.abs(resid - med)))\n", + " sig = 1.4826 * mad if mad > 1e-12 else float(np.std(resid) + 1e-12)\n", + " feat = np.abs(resid - med) > (float(thresh_sigma) * sig)\n", + " if dilate_px and dilate_px > 0:\n", + " r = int(dilate_px)\n", + " yy, xx = np.ogrid[-r:r+1, -r:r+1]\n", + " fp = (xx*xx + yy*yy) <= r*r\n", + " feat = grey_dilation(feat.astype(np.uint8), footprint=fp) > 0\n", + " return feat\n", + "\n", + "\n", + "def inpaint_simple(z, mask, smooth_sigma_px=8.0):\n", + " \"\"\"Replace masked pixels with a heavily blurred background estimate.\"\"\"\n", + " bg = gaussian_filter(z, sigma=float(smooth_sigma_px)).astype(np.float32)\n", + " out = z.copy()\n", + " out[mask] = bg[mask]\n", + " return out\n", + "\n", + "\n", + "def bandpass_noise(z, low_sigma_px=1.0, high_sigma_px=30.0):\n", + " \"\"\"\n", + " Bandpass filter: subtract large-scale trend from small-scale smoothed\n", + " image to isolate mid-frequency noise texture.\n", + " \"\"\"\n", + " low = gaussian_filter(z, sigma=float(low_sigma_px)).astype(np.float32)\n", + " high = gaussian_filter(z, sigma=float(high_sigma_px)).astype(np.float32)\n", + " return (low - high).astype(np.float32)\n", + "\n", + "\n", + "def extract_topostats_like_noise(\n", + " z_in,\n", + " median_size=3,\n", + " bg_quantile=40,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=4,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.8,\n", + " band_high_sigma_px=35.0,\n", + " target_rms_nm=None,\n", + "):\n", + " \"\"\"\n", + " Full noise-extraction pipeline:\n", + " plane-flatten → line-flatten → detect features → inpaint → bandpass.\n", + " Optionally rescale to target RMS amplitude.\n", + " Returns (rms_nm, noise_texture, z_flattened, feature_mask).\n", + " \"\"\"\n", + " z = z_in.astype(np.float32)\n", + " if median_size and median_size > 1:\n", + " z = median_filter(z, size=int(median_size)).astype(np.float32)\n", + "\n", + " q = np.percentile(z, float(bg_quantile))\n", + " bg0 = z <= q\n", + "\n", + " z_flat = plane_remove(z, mask=bg0)\n", + " z_flat = line_flatten_median(z_flat, mask=bg0)\n", + "\n", + " feat = feature_mask(z_flat,\n", + " smooth_sigma_px=feature_sigma_px,\n", + " thresh_sigma=feature_thresh_sigma,\n", + " dilate_px=feature_dilate_px)\n", + " z_bg = inpaint_simple(z_flat, feat, smooth_sigma_px=inpaint_sigma_px)\n", + " tex = bandpass_noise(z_bg,\n", + " low_sigma_px=band_low_sigma_px,\n", + " high_sigma_px=band_high_sigma_px)\n", + "\n", + " bg = ~feat\n", + " rms = float(np.std(tex[bg])) if np.any(bg) else float(np.std(tex))\n", + " if target_rms_nm is not None:\n", + " target = float(target_rms_nm)\n", + " if rms > 1e-12:\n", + " tex *= target / rms\n", + " rms = target\n", + "\n", + " return rms, tex.astype(np.float32), z_flat.astype(np.float32), feat.astype(bool)\n", + "\n", + "\n", + "def tile_or_crop(tex, out_shape, seed=0):\n", + " \"\"\"\n", + " Tile tex to at least out_shape, then take a random (deterministic) crop.\n", + " \"\"\"\n", + " ty, tx = tex.shape\n", + " oy, ox = out_shape\n", + " if (ty, tx) == (oy, ox):\n", + " return tex\n", + " tiled = np.tile(tex, (int(np.ceil(oy / ty)), int(np.ceil(ox / tx))))\n", + " rng = np.random.default_rng(seed)\n", + " y0 = int(rng.integers(0, tiled.shape[0] - oy + 1))\n", + " x0 = int(rng.integers(0, tiled.shape[1] - ox + 1))\n", + " return tiled[y0:y0+oy, x0:x0+ox].astype(np.float32)\n", + "\n", + "\n", + "def load_blank_spm_noise(blank_path, target_rms_nm=0.06, verbose=True):\n", + " \"\"\"\n", + " High-level loader: parse SPM file → select best image → convert to nm\n", + " → extract background noise texture.\n", + " Returns (rms_nm, noise_texture, diagnostics_dict).\n", + " \"\"\"\n", + " raw, header, blocks = parse_blocks(blank_path)\n", + " b, img0 = choose_best_height_image(raw, blocks, verbose=verbose)\n", + " img_nm, unit_note = maybe_to_nm(img0)\n", + " if verbose:\n", + " print(\"Loaded via parser:\", img0.shape, \"| units:\", unit_note)\n", + "\n", + " rms_nm, noise_tex, z_flat, feat = extract_topostats_like_noise(\n", + " img_nm,\n", + " median_size=3,\n", + " bg_quantile=45,\n", + " feature_sigma_px=3.0,\n", + " feature_thresh_sigma=3.0,\n", + " feature_dilate_px=6,\n", + " inpaint_sigma_px=10.0,\n", + " band_low_sigma_px=0.7,\n", + " band_high_sigma_px=40.0,\n", + " target_rms_nm=target_rms_nm,\n", + " )\n", + " diag = {\"flattened\": z_flat, \"feature_mask\": feat,\n", + " \"height_nm\": img_nm, \"unit_note\": unit_note}\n", + " if verbose:\n", + " print(f\"Extracted noise RMS (bg): {rms_nm:.4f} nm | \"\n", + " f\"tex shape: {noise_tex.shape}\")\n", + " return rms_nm, noise_tex, diag\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# PSD-based fallback noise synthesis\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "TARGET_SHAPE = (NY, NX) #To match the shape of the AFM_img\n", + "EPS = 1e-12 #Safety buffer to ensure no division by 0 or square roots producing NaN values\n", + "\n", + "\n", + "def load_model(path=MODEL_PATH):\n", + " \"\"\"Load the PSD noise model from a .npz file.\"\"\"\n", + " data = np.load(path)\n", + " return {\n", + " \"log_psd_mean\": data[\"log_psd_mean\"].astype(np.float32),\n", + " \"log_psd_std\": data[\"log_psd_std\"].astype(np.float32),\n", + " \"radial_freq\": data[\"radial_freq\"].astype(np.float32),\n", + " \"rms_values_nm\": data[\"rms_values_nm\"].astype(np.float32),\n", + " }\n", + "\n", + "\n", + "def generate_noise(\n", + " seed,\n", + " target_shape=TARGET_SHAPE,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " method=\"mean_full2d\",\n", + " std_scale=1.0,\n", + " model=None,\n", + " model_path=MODEL_PATH,\n", + "):\n", + " \"\"\"\n", + " Generate a single random AFM-like background noise image from the PSD model.\n", + "\n", + " Parameters\n", + " ----------\n", + " seed : int\n", + " RNG seed for reproducibility.\n", + " target_shape : (rows, cols)\n", + " Output image shape. The PSD is resized automatically if it differs\n", + " from the shape used when building the model.\n", + " target_rms_nm : float or None\n", + " RMS amplitude in nm. None -> draw from the blank-file ensemble.\n", + " method : str\n", + " 'mean_full2d' -- mean log-PSD; randomness from phase only (recommended).\n", + " 'lognormal_full2d' -- draw a random PSD from the fitted log-normal\n", + " distribution (more sample-to-sample variation).\n", + " 'empirical_radial' -- isotropic synthesis from the mean radial PSD.\n", + " std_scale : float\n", + " Multiplier on log_psd_std for lognormal method (>1 -> more variation).\n", + " model : dict or None\n", + " Pre-loaded model dict. If None, loaded from model_path on each call.\n", + " model_path : path-like\n", + " Path to the .npz file (used only when model is None).\n", + "\n", + " Returns\n", + " -------\n", + " z : np.ndarray, shape target_shape, dtype float32\n", + " Height noise field in nm.\n", + " \"\"\"\n", + " if model is None:\n", + " model = load_model(model_path)\n", + "\n", + " rng = np.random.default_rng(int(seed))\n", + " out_shape = tuple(target_shape)\n", + "\n", + " if method == \"mean_full2d\":\n", + " psd_sample = np.exp(model[\"log_psd_mean\"])\n", + "\n", + " elif method == \"lognormal_full2d\":\n", + " noise = rng.standard_normal(model[\"log_psd_mean\"].shape).astype(np.float32)\n", + " psd_sample = np.exp(model[\"log_psd_mean\"] + std_scale * model[\"log_psd_std\"] * noise)\n", + "\n", + " elif method == \"empirical_radial\":\n", + " radial_psd = np.exp(model[\"log_psd_mean\"].mean(axis=1))\n", + " radial_freq_for_interp = np.abs(np.fft.fftfreq(model[\"log_psd_mean\"].shape[0], d=1.0))\n", + "\n", + " fy = np.fft.fftfreq(out_shape[0], d=1.0)\n", + " fx = np.fft.rfftfreq(out_shape[1], d=1.0)\n", + " fy2d, fx2d = np.meshgrid(fy, fx, indexing=\"ij\")\n", + " kr = np.sqrt(fy2d ** 2 + fx2d ** 2)\n", + "\n", + " psd_sample = np.interp(\n", + " kr.ravel(),\n", + " radial_freq_for_interp,\n", + " radial_psd,\n", + " left=float(radial_psd[0]),\n", + " right=float(radial_psd[-1]),\n", + " ).reshape(kr.shape).astype(np.float32)\n", + "\n", + " else:\n", + " raise ValueError(\n", + " f\"Unknown method {method!r}. \"\n", + " \"Choose 'mean_full2d', 'lognormal_full2d', or 'empirical_radial'.\"\n", + " )\n", + "\n", + " psd_ny, psd_nx = psd_sample.shape\n", + " out_ny, out_nx = out_shape\n", + " rfft_nx = out_nx // 2 + 1\n", + "\n", + " if (psd_ny, psd_nx) != (out_ny, rfft_nx):\n", + " from scipy.ndimage import zoom\n", + " psd_sample = zoom(psd_sample, (out_ny / psd_ny, rfft_nx / psd_nx), order=1)\n", + "\n", + " psd_sample = psd_sample.astype(np.float32)\n", + "\n", + " phase = rng.uniform(0.0, 2.0 * np.pi, size=psd_sample.shape)\n", + " spec = np.sqrt(np.maximum(psd_sample, EPS)) * np.exp(1j * phase)\n", + "\n", + " z = np.fft.irfft2(spec, s=out_shape).real.astype(np.float32)\n", + " z -= np.float32(np.mean(z))\n", + "\n", + " target = float(rng.choice(model[\"rms_values_nm\"])) if target_rms_nm is None else float(target_rms_nm)\n", + " cur = float(np.std(z))\n", + " if cur > EPS:\n", + " z *= target / cur\n", + "\n", + " return z\n", + "\n", + "\n", + "def sample_noise_image(noise_source, noise_asset, seed, out_shape, target_rms_nm):\n", + " \"\"\"Return a per-sample noise image from either blank.spm or PSD fallback.\"\"\"\n", + " if not USE_BLANK_SPM_NOISE or noise_asset is None:\n", + " return None\n", + "\n", + " if noise_source == \"blank_spm\":\n", + " noise_tex = random_transform_noise_texture(noise_asset, seed=seed)\n", + " return tile_or_crop(noise_tex, out_shape, seed=seed).astype(np.float32)\n", + "\n", + " if noise_source == \"psd_model\":\n", + " return generate_noise(\n", + " seed=seed,\n", + " target_shape=out_shape,\n", + " target_rms_nm=target_rms_nm,\n", + " method=PSD_NOISE_METHOD,\n", + " std_scale=PSD_NOISE_STD_SCALE,\n", + " model=noise_asset,\n", + " ).astype(np.float32)\n", + "\n", + " return None\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_07", + "metadata": { + "id": "cell_md_07" + }, + "source": [ + "##8. DNA Ground-Truth Mask\n", + "Now that we have produced the AFM_img and added noise to it, we can generate the ground truth mask for the DNA chain, which is useful for training of machine learning models for classification and segmentation.\n", + "\n", + "The functions described here perform rasterization of the closed bead polyline into a binary mask using the same coordinate mapping as the AFM renderer, then dilates with a disk footprint. So, it takes the coordinates that are being used to generate the chain, and uses those to create the mask, thus removing any influence of the noise on the mask.\n", + "\n", + "We also dilate the mask by a value of **DNA_MASK_DILATE_PX** to ensure that the mask has enough width for optimal training." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "cell_code_07", + "metadata": { + "id": "cell_code_07" + }, + "outputs": [], + "source": [ + "def nm_to_px(coords_xy_nm, extent, nx, ny):\n", + " \"\"\"Map (x, y) in nm to integer pixel indices (ix, iy).\"\"\"\n", + " x = coords_xy_nm[:, 0]; y = coords_xy_nm[:, 1]\n", + " xmin, xmax, ymin, ymax = extent\n", + " ix = np.clip(((x - xmin) / (xmax - xmin) * (nx - 1)).astype(np.int32),\n", + " 0, nx - 1)\n", + " iy = np.clip(((y - ymin) / (ymax - ymin) * (ny - 1)).astype(np.int32),\n", + " 0, ny - 1)\n", + " return ix, iy\n", + "\n", + "\n", + "def rasterize_closed_polyline_mask(coords, extent, nx, ny):\n", + " \"\"\"\n", + " Return a 1-px-wide binary raster of the closed bead polyline.\n", + " Each consecutive bead pair is drawn by dense linear interpolation;\n", + " duplicate pixels are de-duplicated.\n", + " \"\"\"\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " ix, iy = nm_to_px(coords[:, :2], extent, nx, ny)\n", + " out = np.zeros((ny, nx), dtype=bool)\n", + " npts = len(ix)\n", + " for k in range(npts):\n", + " k2 = (k + 1) % npts\n", + " x0, y0 = int(ix[k]), int(iy[k])\n", + " x1, y1 = int(ix[k2]), int(iy[k2])\n", + " n_seg = int(max(abs(x1 - x0), abs(y1 - y0)) + 1)\n", + " if n_seg <= 1:\n", + " out[y0, x0] = True\n", + " continue\n", + " xs = np.linspace(x0, x1, n_seg).astype(np.int32)\n", + " ys = np.linspace(y0, y1, n_seg).astype(np.int32)\n", + " if xs.size > 1:\n", + " keep = np.ones(xs.shape[0], dtype=bool)\n", + " keep[1:] = (xs[1:] != xs[:-1]) | (ys[1:] != ys[:-1])\n", + " xs, ys = xs[keep], ys[keep]\n", + " out[ys, xs] = True\n", + " return out\n", + "\n", + "\n", + "def make_dna_mask_from_beads(coords, extent, nx, ny, dilate_px=3):\n", + " \"\"\"\n", + " Rasterise the polyline and dilate by dilate_px pixels.\n", + " Returns a uint8 mask (0/1).\n", + " \"\"\"\n", + " line = rasterize_closed_polyline_mask(coords, extent, nx, ny)\n", + " if dilate_px and dilate_px > 0:\n", + " line = binary_dilation(line, structure=disk_footprint(float(dilate_px)))\n", + " return line.astype(np.uint8)" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_08", + "metadata": { + "id": "cell_md_08" + }, + "source": [ + "## 9. Crossing Detection & Crossing Mask\n", + "\n", + "The functions in this cell decribe how crossings are detected. Crossings are detected using polyline intersections for different chain segments and the height information of the beads in those segments is used to assign top chain segment and bottom chain segment (also used for boosting crossings).\n", + "\n", + "We have set the masks for the crossings as chain weighted gaussians so that the machine learning models can have more information about the crossings and thus have better predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "cell_code_08", + "metadata": { + "id": "cell_code_08" + }, + "outputs": [], + "source": [ + "def _canon_axis(u):\n", + " \"\"\"\n", + " Normalise a 2-D vector and canonicalise its sign so that +v and -v\n", + " are treated as the same axis direction.\n", + " Returns None if the vector is degenerate.\n", + " \"\"\"\n", + " u = np.asarray(u, dtype=float)\n", + " nrm = float(np.linalg.norm(u))\n", + " if nrm < 1e-12:\n", + " return None\n", + " u = u / nrm\n", + " if (u[0] < 0) or (abs(u[0]) < 1e-12 and u[1] < 0):\n", + " u = -u\n", + " return u\n", + "\n", + "\n", + "def find_polyline_crossings(coords_xy, min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Detect all strand crossings of a closed ring polyline in XY.\n", + "\n", + " Skips adjacent / shared-vertex segment pairs and pairs whose circular\n", + " contour separation is less than min_separation_beads.\n", + " Nearby raw hits are clustered via _merge_crossing_clusters.\n", + "\n", + " Returns list of dicts:\n", + " {\n", + " pt_nm : (2,) float32 — crossing position in nm\n", + " axes_nm : list of unit vectors — chain directions at the crossing\n", + " seg_i : int — first segment index\n", + " seg_j : int — second segment index\n", + " t : float — parametric position along segment i\n", + " u : float — parametric position along segment j\n", + " n_hits : int — raw intersection count merged into this cluster\n", + " }\n", + " \"\"\"\n", + " xy = np.asarray(coords_xy, dtype=float)\n", + " n = int(xy.shape[0])\n", + "\n", + " raw = []\n", + " for i in range(n):\n", + " i2 = (i + 1) % n\n", + " p1, p2 = xy[i], xy[i2]\n", + " for j in range(i + 1, n):\n", + " j2 = (j + 1) % n\n", + " if i == j or i2 == j or j2 == i:\n", + " continue\n", + " if _circ_sep(n, i, j) < int(min_separation_beads):\n", + " continue\n", + " q1, q2 = xy[j], xy[j2]\n", + " hit, pt, t, u = _seg_intersect_2d(p1, p2, q1, q2)\n", + " if not hit:\n", + " continue\n", + " ua = _canon_axis(p2 - p1)\n", + " ub = _canon_axis(q2 - q1)\n", + " if ua is None or ub is None:\n", + " continue\n", + " raw.append(dict(pt=np.array(pt, float),\n", + " axes=[ua, ub],\n", + " seg_i=int(i), seg_j=int(j),\n", + " t=float(t), u=float(u)))\n", + "\n", + " # Cluster nearby raw intersections\n", + " clusters = []\n", + " for r in raw:\n", + " pt = r[\"pt\"]\n", + " placed = False\n", + " for c in clusters:\n", + " if np.linalg.norm(pt - c[\"pt_sum\"] / c[\"n\"]) <= float(merge_radius_nm):\n", + " c[\"pt_sum\"] += pt\n", + " c[\"n\"] += 1\n", + " c[\"axes\"].extend(r[\"axes\"])\n", + " c[\"items\"].append(r)\n", + " placed = True\n", + " break\n", + " if not placed:\n", + " clusters.append({\"pt_sum\": pt.copy(), \"n\": 1,\n", + " \"axes\": list(r[\"axes\"]), \"items\": [r]})\n", + "\n", + " out = []\n", + " for c in clusters:\n", + " pt_mean = c[\"pt_sum\"] / c[\"n\"]\n", + " axes = []\n", + " for uvec in c[\"axes\"]:\n", + " uvec = _canon_axis(uvec)\n", + " if uvec is None:\n", + " continue\n", + " if any(abs(np.dot(uvec, v)) > 0.95 for v in axes): # ~18°\n", + " continue\n", + " axes.append(uvec)\n", + " rep = c[\"items\"][0]\n", + " out.append(dict(\n", + " pt_nm = np.asarray(pt_mean, dtype=np.float32),\n", + " axes_nm = [np.asarray(v, dtype=np.float32) for v in axes]\n", + " if axes else [np.array([1.0, 0.0], np.float32)],\n", + " seg_i = int(rep[\"seg_i\"]),\n", + " seg_j = int(rep[\"seg_j\"]),\n", + " t = float(rep[\"t\"]),\n", + " u = float(rep[\"u\"]),\n", + " n_hits = int(c[\"n\"]),\n", + " ))\n", + " return out\n", + "\n", + "\n", + "def _add_crossing_patch(mask, pt_px, axes_px,\n", + " sigma_center=2.0,\n", + " chain_extent=5.0,\n", + " sigma_perp=1.8,\n", + " center_weight=1.0,\n", + " chain_weight=0.8):\n", + " \"\"\"\n", + " Paint one crossing onto mask as:\n", + " center_weight * isotropic_Gaussian\n", + " + chain_weight * max(anisotropic Gaussians aligned to chain axes)\n", + "\n", + " Uses np.maximum so multiple crossings compose correctly.\n", + " \"\"\"\n", + " H, W = mask.shape\n", + " cx, cy = float(pt_px[0]), float(pt_px[1])\n", + " sigma_parallel = max(1e-6, float(chain_extent) * float(sigma_center))\n", + " R = int(np.ceil(3.0 * max(sigma_center, sigma_parallel, sigma_perp)))\n", + " x0 = max(0, int(np.floor(cx - R))); x1 = min(W-1, int(np.ceil(cx + R)))\n", + " y0 = max(0, int(np.floor(cy - R))); y1 = min(H-1, int(np.ceil(cy + R)))\n", + "\n", + " xs = np.arange(x0, x1+1, dtype=float)\n", + " ys = np.arange(y0, y1+1, dtype=float)\n", + " X, Y = np.meshgrid(xs, ys)\n", + " dx = X - cx; dy = Y - cy\n", + "\n", + " gc = np.exp(-0.5 * (dx*dx + dy*dy) / (sigma_center**2))\n", + "\n", + " g_chain = np.zeros_like(gc)\n", + " for v in axes_px:\n", + " vx, vy = float(v[0]), float(v[1])\n", + " u_par = dx*vx + dy*vy\n", + " u_perp = -dx*vy + dy*vx\n", + " g = np.exp(-0.5 * (u_par**2 / sigma_parallel**2\n", + " + u_perp**2 / sigma_perp**2))\n", + " g_chain = np.maximum(g_chain, g)\n", + "\n", + " patch = center_weight * gc + chain_weight * g_chain\n", + " mask[y0:y1+1, x0:x1+1] = np.maximum(mask[y0:y1+1, x0:x1+1], patch)\n", + "\n", + "\n", + "def make_crossing_mask(coords, extent, nx, ny,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=None,\n", + " merge_radius_nm=0.5):\n", + " \"\"\"\n", + " Build the crossing ground-truth mask (float32, normalised to [0, 1]).\n", + "\n", + " Uses find_polyline_crossings unless crossings_precomputed is supplied\n", + " (recommended: reuse the same crossing list that was used for the\n", + " height boost so the mask and the AFM image are perfectly aligned).\n", + " \"\"\"\n", + " coords = np.asarray(coords, dtype=np.float32)\n", + " xy_nm = coords[:, :2].astype(float)\n", + "\n", + " if crossings_precomputed is None:\n", + " crossings = find_polyline_crossings(\n", + " xy_nm,\n", + " min_separation_beads=min_separation_beads,\n", + " merge_radius_nm=float(merge_radius_nm),\n", + " )\n", + " else:\n", + " crossings = crossings_precomputed\n", + "\n", + " xmin, xmax, ymin, ymax = extent\n", + " sx = (nx - 1) / (xmax - xmin)\n", + " sy = (ny - 1) / (ymax - ymin)\n", + "\n", + " mask = np.zeros((ny, nx), dtype=np.float32)\n", + "\n", + " for c in crossings:\n", + " x_nm, y_nm = c[\"pt_nm\"]\n", + " px = (x_nm - xmin) * sx\n", + " py = (y_nm - ymin) * sy\n", + "\n", + " axes_px = []\n", + " for v in c[\"axes_nm\"]:\n", + " vx_px = v[0] * sx; vy_px = v[1] * sy\n", + " nrm = float(np.hypot(vx_px, vy_px))\n", + " if nrm < 1e-12:\n", + " continue\n", + " axes_px.append(np.array([vx_px/nrm, vy_px/nrm], dtype=float))\n", + " if not axes_px:\n", + " axes_px = [np.array([1.0, 0.0], dtype=float)]\n", + "\n", + " _add_crossing_patch(\n", + " mask,\n", + " pt_px=(px, py),\n", + " axes_px=axes_px,\n", + " sigma_center=float(sigma_center_px),\n", + " chain_extent=float(chain_extent),\n", + " sigma_perp=float(sigma_perp_px),\n", + " center_weight=float(center_weight),\n", + " chain_weight=float(chain_weight),\n", + " )\n", + "\n", + " m = float(mask.max())\n", + " if m > 1e-12:\n", + " mask /= m\n", + " return mask.astype(np.float32), crossings" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_09", + "metadata": { + "id": "cell_md_09" + }, + "source": [ + "## 10. Decoy Chain Functions\n", + "\n", + "real AFM images often contain small chain segments that are not part of the main chain and thus constitute the noise in that sample, but since those segments are broken off DNA, they cannot be neatly integrated into the noise model.\n", + "Therefore, we offer the possibility of addition of small DNA chains to every every sample in the dataset where ``add_decoy = True`` which would then contain a small 2–4 bead \"decoy\" fragment placed in a corner of the image.\n", + "\n", + "The decoy appears in the AFM image but is **excluded** from both\n", + "ground-truth masks, training the model to ignore isolated unconnected fragments." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "cell_code_09", + "metadata": { + "id": "cell_code_09" + }, + "outputs": [], + "source": [ + "def make_decoy_chain(seed, n_beads_range=(2, 4), bond_length=BOND_LENGTH):\n", + " \"\"\"\n", + " Generate a small (2–4 bead) slightly curved chain without MD relaxation.\n", + " Z heights are set to mimic surface-deposited DNA.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + " n_b = rng.integers(n_beads_range[0], n_beads_range[1] + 1)\n", + " coords = np.zeros((n_b, 3), dtype=np.float32)\n", + " angle = rng.uniform(0, 2 * np.pi)\n", + " dirn = np.array([np.cos(angle), np.sin(angle), 0.0], dtype=np.float32)\n", + "\n", + " for i in range(1, n_b):\n", + " da = rng.normal(0, 0.3)\n", + " c, s = np.cos(da), np.sin(da)\n", + " R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)\n", + " dirn = R @ dirn\n", + " dirn = dirn / np.linalg.norm(dirn)\n", + " coords[i] = coords[i-1] + bond_length * dirn\n", + "\n", + " coords[:, 2] = rng.uniform(2.5, 4.0, n_b)\n", + " return coords\n", + "\n", + "\n", + "def place_decoy_in_corner(decoy_coords, global_extent,\n", + " corner_margin_nm=2.0, seed=None):\n", + " \"\"\"\n", + " Translate decoy_coords so its centroid lands in a randomly chosen\n", + " corner of global_extent = (xmin, xmax, ymin, ymax).\n", + " \"\"\"\n", + " if seed is None:\n", + " seed = int(np.sum(decoy_coords))\n", + " rng = np.random.default_rng(int(seed))\n", + " xmin, xmax, ymin, ymax = global_extent\n", + " corner = rng.integers(0, 4)\n", + " targets = [\n", + " (xmin + corner_margin_nm, ymax - corner_margin_nm), # top-left\n", + " (xmax - corner_margin_nm, ymax - corner_margin_nm), # top-right\n", + " (xmin + corner_margin_nm, ymin + corner_margin_nm), # bottom-left\n", + " (xmax - corner_margin_nm, ymin + corner_margin_nm), # bottom-right\n", + " ]\n", + " tx, ty = targets[corner]\n", + " centroid = decoy_coords.mean(axis=0)\n", + " offset = np.array([tx, ty, 0.0]) - centroid\n", + " return decoy_coords + offset" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_10", + "metadata": { + "id": "cell_md_10" + }, + "source": [ + "## 11. Chain Generation Pipeline\n", + "\n", + "Now, we are finally ready to generate on sample in our dataset. The following functions are used for building the final image and masks:\n", + "- `generate_chain_with_beads` with `n_beads` defaulting to `N_BEADS`\n", + "- `generate_one_sample_multilength` generates the image and mask (this is what iterates to produce the dataset)\n", + "- `random_transform_noise_texture` applies a per-sample random affine transform to the noise texture for variety (in case of blank .spm files)\n", + "- Noise injection uses `blank.spm` when available and the PSD model fallback otherwise\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "cell_code_10", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cell_code_10", + "outputId": "f21a85c7-5c3e-432a-8c70-76f58292c879" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "No blank .spm file found; trying PSD fallback noise model.\n", + "Loaded PSD fallback noise model from: /content/psd_noise_model.npz\n" + ] + } + ], + "source": [ + "def random_transform_noise_texture(tex, seed):\n", + " \"\"\"\n", + " Apply a deterministic random affine transform (rotation, anisotropic\n", + " scaling, translation, optional flips, amplitude jitter) to the blank.spm\n", + " noise texture. Ensures every sample gets a unique-looking noise pattern\n", + " even though only one blank SPM scan was acquired.\n", + " \"\"\"\n", + " rng = np.random.default_rng(int(seed))\n", + " tex = np.asarray(tex, dtype=np.float32)\n", + "\n", + " theta = np.deg2rad(float(rng.uniform(0.0, 360.0)))\n", + " base_scale = float(rng.uniform(0.85, 1.15))\n", + " anis = float(rng.uniform(0.85, 1.15))\n", + " sx, sy = base_scale * anis, base_scale / anis\n", + "\n", + " c, s = float(np.cos(theta)), float(np.sin(theta))\n", + " A = np.array([[c*sx, -s*sy], [s*sx, c*sy]], dtype=np.float32)\n", + " M = np.linalg.inv(A).astype(np.float32)\n", + "\n", + " h, w = tex.shape\n", + " center = np.array([(h-1)/2.0, (w-1)/2.0], dtype=np.float32)\n", + " t = np.array([float(rng.uniform(-0.15*h, 0.15*h)),\n", + " float(rng.uniform(-0.15*w, 0.15*w))], dtype=np.float32)\n", + " offset = center - M @ (center + t)\n", + "\n", + " out = affine_transform(tex, matrix=M, offset=offset,\n", + " output_shape=tex.shape, order=1,\n", + " mode=\"wrap\", prefilter=False).astype(np.float32)\n", + "\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[::-1, :]\n", + " if bool(rng.integers(0, 2)):\n", + " out = out[:, ::-1]\n", + " out *= float(rng.uniform(0.90, 1.10))\n", + " return out.astype(np.float32)\n", + "\n", + "\n", + "def generate_chain_with_beads(seed, n_beads=N_BEADS, add_decoy=False):\n", + " \"\"\"\n", + " Build a tangled ring with n_beads, run MD relaxation, detect crossings,\n", + " and optionally generate a corner-placed decoy fragment.\n", + "\n", + " Returns dict:\n", + " seed, n_beads, coords (N,3), crossings (list), decoy_coords or None\n", + " \"\"\"\n", + " seed = int(seed)\n", + " n_beads = int(n_beads)\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " base_z=BASE_Z,\n", + " )\n", + " frames = run_md_relaxation(coords0, seed=seed,\n", + " n_frames=N_FRAMES,\n", + " steps_per_frame=STEPS_PER_FRAME)\n", + " else:\n", + " frames = make_ring_2d_persistent_initial(\n", + " seed,\n", + " n_beads=n_beads,\n", + " bond_length=BOND_LENGTH,\n", + " persistence_bonds=PERSISTENCE_BONDS,\n", + " angle_stiffness_mult=ANGLE_STIFNESS_MULT,\n", + " )\n", + "\n", + " last_coords = frames[-1].astype(np.float32)\n", + "\n", + " crossings = find_polyline_crossings(\n", + " last_coords[:, :2],\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " decoy_coords = None\n", + " if add_decoy:\n", + " xmin, xmax = last_coords[:, 0].min(), last_coords[:, 0].max()\n", + " ymin, ymax = last_coords[:, 1].min(), last_coords[:, 1].max()\n", + " decoy_raw = make_decoy_chain(seed + 9999)\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_raw, (xmin, xmax, ymin, ymax),\n", + " corner_margin_nm=15.0, seed=seed)\n", + "\n", + " return dict(seed=seed, n_beads=n_beads,\n", + " coords=last_coords, crossings=crossings,\n", + " decoy_coords=decoy_coords)\n", + "\n", + "\n", + "def render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None):\n", + " \"\"\"\n", + " Render AFM image + DNA mask + crossing mask for a chain dict.\n", + "\n", + " Decoys are rendered into the AFM image via np.maximum compositing\n", + " but are deliberately excluded from both ground-truth masks.\n", + " \"\"\"\n", + " seed = int(chain[\"seed\"])\n", + " coords = chain[\"coords\"]\n", + " crossings = chain[\"crossings\"]\n", + " decoy_coords = chain.get(\"decoy_coords\", None)\n", + "\n", + " padding = 10.0\n", + " xmin = coords[:, 0].min() - padding\n", + " xmax = coords[:, 0].max() + padding\n", + " ymin = coords[:, 1].min() - padding\n", + " ymax = coords[:, 1].max() + padding\n", + " global_extent = (xmin, xmax, ymin, ymax)\n", + "\n", + " afm_img, dbg = create_z_based_afm(\n", + " coords, grain_seed=seed,\n", + " crossings_precomputed=crossings,\n", + " extent=global_extent,\n", + " **AFM_KW\n", + " )\n", + "\n", + " if decoy_coords is not None:\n", + " decoy_coords = place_decoy_in_corner(\n", + " decoy_coords, global_extent,\n", + " corner_margin_nm=8.0, seed=seed)\n", + " decoy_kw = {**AFM_KW,\n", + " \"apply_edge_taper\": False,\n", + " \"enable_crossing_boost\": False,\n", + " \"add_center_ridge\": False}\n", + " decoy_afm, _ = create_z_based_afm(\n", + " decoy_coords, extent=global_extent, **decoy_kw)\n", + " afm_img = np.maximum(afm_img, decoy_afm)\n", + "\n", + " actual_extent = dbg[\"extent\"]\n", + "\n", + " noise_img = sample_noise_image(\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " seed=seed,\n", + " out_shape=afm_img.shape,\n", + " target_rms_nm=TARGET_NOISE_RMS_NM,\n", + " )\n", + " if noise_img is not None:\n", + " afm_img = np.clip((afm_img + noise_img).astype(np.float32),\n", + " 0, AFM_KW[\"max_height_nm\"])\n", + "\n", + " dna_mask = make_dna_mask_from_beads(\n", + " coords, actual_extent, NX, NY, dilate_px=DNA_MASK_DILATE_PX)\n", + "\n", + " cross_mask, crossings_out = make_crossing_mask(\n", + " coords, actual_extent, NX, NY,\n", + " sigma_center_px=CROSS_SIGMA_CENTER_PX,\n", + " sigma_perp_px=CROSS_SIGMA_PERP_PX,\n", + " chain_extent=CROSS_CHAIN_EXTENT,\n", + " center_weight=CROSS_CENTER_WEIGHT,\n", + " chain_weight=CROSS_CHAIN_WEIGHT,\n", + " min_separation_beads=CROSS_MIN_SEP_BEADS,\n", + " crossings_precomputed=crossings,\n", + " merge_radius_nm=0.5,\n", + " )\n", + "\n", + " if CROSS_CLIP_TO_DNA_MASK:\n", + " cross_mask = cross_mask * dna_mask.astype(np.float32)\n", + "\n", + " return dict(\n", + " seed=seed,\n", + " afm_img=afm_img.astype(np.float32),\n", + " dna_mask=dna_mask.astype(np.uint8),\n", + " cross_mask=cross_mask.astype(np.float32),\n", + " extent=np.array(actual_extent, dtype=np.float32),\n", + " n_crossings=int(len(crossings_out)),\n", + " decoy_coords=decoy_coords,\n", + " )\n", + "\n", + "\n", + "def generate_one_sample_multilength(seed, n_beads, noise_source=\"none\",\n", + " noise_asset=None, add_decoy=False):\n", + " \"\"\"\n", + " Generate one complete sample at a specified bead count.\n", + " Uses blank.spm-derived noise when available and otherwise falls back\n", + " to PSD-based synthetic noise generation.\n", + " \"\"\"\n", + " chain = generate_chain_with_beads(seed, n_beads, add_decoy=add_decoy)\n", + "\n", + " s = render_chain_and_masks(\n", + " chain,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " )\n", + " s[\"n_beads\"] = int(n_beads)\n", + " return s\n", + "\n", + "\n", + "# ── Load noise asset once; prefer blank.spm, fall back to PSD model ───────────\n", + "noise_source = \"none\"\n", + "noise_asset = None\n", + "noise_diag = {\"source\": \"none\"}\n", + "\n", + "if USE_BLANK_SPM_NOISE:\n", + " blank_path = str(BLANK_SPM_PATH) if BLANK_SPM_PATH else \"\"\n", + " if blank_path and os.path.isfile(blank_path):\n", + " try:\n", + " noise_rms_nm, noise_asset, noise_diag = load_blank_spm_noise(\n", + " blank_path, target_rms_nm=TARGET_NOISE_RMS_NM, verbose=True)\n", + " noise_source = \"blank_spm\"\n", + " print(f\"Loaded blank .spm noise texture RMS ≈ {noise_rms_nm:.3f} nm\")\n", + " except Exception as e:\n", + " print(\"blank .spm noise failed; trying PSD fallback. Error:\", e)\n", + " else:\n", + " print(\"No blank .spm file found; trying PSD fallback noise model.\")\n", + "\n", + " if noise_source == \"none\" and USE_PSD_NOISE_FALLBACK:\n", + " try:\n", + " noise_asset = load_model(MODEL_PATH)\n", + " noise_source = \"psd_model\"\n", + " noise_diag = {\n", + " \"source\": \"psd_model\",\n", + " \"model_path\": str(MODEL_PATH),\n", + " \"method\": PSD_NOISE_METHOD,\n", + " \"target_rms_nm\": float(TARGET_NOISE_RMS_NM),\n", + " }\n", + " print(f\"Loaded PSD fallback noise model from: {MODEL_PATH}\")\n", + " except Exception as e:\n", + " print(\"PSD fallback noise model failed; proceeding without noise. Error:\", e)\n", + " noise_asset = None\n", + "else:\n", + " print(\"Noise injection disabled.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_11", + "metadata": { + "id": "cell_md_11" + }, + "source": [ + "## 12. Sanity Check\n", + "\n", + "It is always a good idea to check if the pipeline is working correctly and producing the inteded resutls and so we have added a sanity check that can be performed before starting the generation of the entire dataset.\n", + "Here we render one sample per bead count for the bead counts provided in the global variables and by default we have set `add_decoy=True` here.\n", + "\n", + "This check displays the AFM image, DNA mask, and crossing mask side-by-side.\n", + "We also print the extent / decoy-placement diagnostics for verification and adjustment." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "cell_code_11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cell_code_11", + "outputId": "9b890f12-a0d1-4eea-de35-ad3b13fb6e05" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "==================== SAMPLE 1 (Seed: 0, 70 beads) ====================\n", + "Image window (nm): X=[-20.6, 19.4], Y=[-18.8, 16.6]\n", + "Decoy XY bounds (nm): X=[11.4, 11.5], Y=[-11.3, -10.3]\n", + "Decoy Z range (nm): -0.41 – 0.41\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 2 (Seed: 1, 80 beads) ====================\n", + "Image window (nm): X=[-18.0, 21.3], Y=[-19.3, 21.2]\n", + "Decoy XY bounds (nm): X=[13.0, 13.7], Y=[12.8, 13.6]\n", + "Decoy Z range (nm): -0.03 – 0.03\n", + "Decoy inside extent.\n", + " Decoy height ≈ 0\n", + "\n", + "==================== SAMPLE 3 (Seed: 2, 90 beads) ====================\n", + "Image window (nm): X=[-20.0, 16.0], Y=[-18.0, 20.6]\n", + "Decoy XY bounds (nm): X=[7.8, 8.3], Y=[-11.0, -9.1]\n", + "Decoy Z range (nm): -0.26 – 0.15\n", + "Decoy inside extent.\n", + "\n", + "==================== SAMPLE 4 (Seed: 3, 100 beads) ====================\n", + "Image window (nm): X=[-22.7, 27.1], Y=[-16.9, 19.5]\n", + "Decoy XY bounds (nm): X=[18.8, 19.4], Y=[-10.3, -7.4]\n", + "Decoy Z range (nm): -0.39 – 0.44\n", + "Decoy inside extent.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "bead_counts = list(BEAD_COUNTS)\n", + "seeds = [int(BASE_SEED + k) for k in range(len(bead_counts))]\n", + "\n", + "samples = [\n", + " generate_one_sample_multilength(\n", + " seeds[i], n_beads=bead_counts[i],\n", + " noise_source=noise_source, noise_asset=noise_asset, add_decoy=True\n", + " )\n", + " for i in range(len(bead_counts))\n", + "]\n", + "\n", + "fig, axes = plt.subplots(len(samples), 3, figsize=(16, 5 * len(samples)))\n", + "\n", + "for r, s in enumerate(samples):\n", + " extent = s[\"extent\"]\n", + " img = s[\"afm_img\"]\n", + "\n", + " print(f\"\\n{'='*20} SAMPLE {r+1} (Seed: {s['seed']}, \"\n", + " f\"{s['n_beads']} beads) {'='*20}\")\n", + " print(f\"Image window (nm): X=[{extent[0]:.1f}, {extent[1]:.1f}], \"\n", + " f\"Y=[{extent[2]:.1f}, {extent[3]:.1f}]\")\n", + "\n", + " if s.get(\"decoy_coords\") is not None:\n", + " d = s[\"decoy_coords\"]\n", + " print(f\"Decoy XY bounds (nm): \"\n", + " f\"X=[{d[:,0].min():.1f}, {d[:,0].max():.1f}], \"\n", + " f\"Y=[{d[:,1].min():.1f}, {d[:,1].max():.1f}]\")\n", + " print(f\"Decoy Z range (nm): {d[:,2].min():.2f} – {d[:,2].max():.2f}\")\n", + " oob = (d[:,0].min() < extent[0] or d[:,0].max() > extent[1] or\n", + " d[:,1].min() < extent[2] or d[:,1].max() > extent[3])\n", + " print(\" Decoy out-of-bounds!\" if oob else \"Decoy inside extent.\")\n", + " if d[:,2].max() < 0.1:\n", + " print(\" Decoy height ≈ 0\")\n", + "\n", + " ax1, ax2, ax3 = axes[r]\n", + " ext_list = list(extent)\n", + "\n", + " ax1.imshow(img, cmap=\"afmhot\", origin=\"lower\", extent=ext_list)\n", + " ax1.set_title(f\"AFM image\\n(seed {s['seed']}, {s['n_beads']} beads)\")\n", + " ax1.axis(\"off\")\n", + "\n", + " ax2.imshow(s[\"dna_mask\"], cmap=\"gray\", origin=\"lower\", extent=ext_list)\n", + " ax2.set_title(\"DNA mask (decoy excluded)\")\n", + " ax2.axis(\"off\")\n", + "\n", + " ax3.imshow(s[\"cross_mask\"], cmap=\"magma\", origin=\"lower\", extent=ext_list)\n", + " ax3.set_title(f\"Crossing mask (n={s['n_crossings']})\")\n", + " ax3.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##13. Benchmarking\n", + "\n", + "We also provide the option to benchmark the performance of the pipeline and to see estimates on how long it might take to generate the required amount of samples. This can be very useful to compare which system might be optimal for production of the dataset and how GPU acceleration can be used to improve the time needed for dataset generation." + ], + "metadata": { + "id": "3YoKkN3e5dIo" + }, + "id": "3YoKkN3e5dIo" + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "WLAYkyIQlhuX", + "metadata": { + "id": "WLAYkyIQlhuX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2efe41b8-37bd-4d1b-a01e-d50eed42cac6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==============================================================\n", + " SYSTEM PROFILE\n", + "==============================================================\n", + " OS : Linux 6.6.113+\n", + " CPU (logical) : 2 cores\n", + " RAM available : 11.5 GB\n", + " Mode : Non-MD (2D Walk)\n", + "\n", + "==============================================================\n", + " RUNNING BENCHMARK\n", + "==============================================================\n", + " Rep 1/3: 0.05s\n", + " Rep 2/3: 0.06s\n", + " Rep 3/3: 0.06s\n", + "==============================================================\n", + " Median Wall Time: 0.06 s\n", + " Peak RAM (RSS) : 611.8 MB\n", + " Projected Total : 0.1 hours for 4000 samples\n", + "==============================================================\n" + ] + } + ], + "source": [ + "import time\n", + "import tracemalloc\n", + "import os\n", + "import platform\n", + "import psutil\n", + "import threading\n", + "import statistics\n", + "\n", + "# ── Helper: peak RSS tracker ──────\n", + "class _PeakMemTracker:\n", + " def __init__(self):\n", + " self._proc = psutil.Process(os.getpid())\n", + " self.peak_mb = 0.0\n", + " self._stop = threading.Event()\n", + "\n", + " def _run(self):\n", + " while not self._stop.is_set():\n", + " try:\n", + " rss = self._proc.memory_info().rss / 1e6\n", + " if rss > self.peak_mb:\n", + " self.peak_mb = rss\n", + " except Exception: pass\n", + " self._stop.wait(0.05)\n", + "\n", + " def start(self):\n", + " self._t = threading.Thread(target=self._run, daemon=True)\n", + " self._t.start()\n", + "\n", + " def stop(self):\n", + " self._stop.set(); self._t.join()\n", + "\n", + "# ── System Info ──\n", + "cpu_logical = psutil.cpu_count(logical=True)\n", + "if USE_MD:\n", + " platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]\n", + " active_platform = \"OpenCL\" if \"OpenCL\" in platforms else \"CPU\"\n", + "\n", + "print(\"=\" * 62)\n", + "print(\" SYSTEM PROFILE\")\n", + "print(\"=\" * 62)\n", + "print(f\" OS : {platform.system()} {platform.release()}\")\n", + "print(f\" CPU (logical) : {cpu_logical} cores\")\n", + "print(f\" RAM available : {psutil.virtual_memory().available / 1e9:.1f} GB\")\n", + "if USE_MD:\n", + " print(f\" OpenMM Platforms: {platforms}\")\n", + "print(f\" Mode : {'MD (OpenMM)' if USE_MD else 'Non-MD (2D Walk)'}\")\n", + "print()\n", + "\n", + "BENCH_SEED = int(BASE_SEED)\n", + "BENCH_N_BEADS = int(BEAD_COUNTS[0])\n", + "BENCH_REPS = 3\n", + "\n", + "def _bench_stages(seed, n_beads):\n", + " t = {}\n", + " t0 = time.perf_counter()\n", + "\n", + " if USE_MD:\n", + " coords0 = make_tangled_ring_initial(seed, n_beads=n_beads)\n", + " t[\"1_chain_init\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " frames = run_md_relaxation(coords0, seed=seed, n_frames=N_FRAMES, steps_per_frame=STEPS_PER_FRAME)\n", + " t[\"2_md_relax\"] = time.perf_counter() - t0\n", + " else:\n", + " # Skip initial 3D step and run 2D persistent walk\n", + " t[\"1_chain_init\"] = 0.0\n", + " t0 = time.perf_counter()\n", + " frames = make_ring_2d_persistent_initial(seed, n_beads=n_beads)\n", + " t[\"2_md_relax\"] = time.perf_counter() - t0\n", + "\n", + " last_coords = frames[-1]\n", + " t0 = time.perf_counter()\n", + " crossings = find_polyline_crossings(last_coords[:, :2])\n", + " t[\"3_crossing_detect\"] = time.perf_counter() - t0\n", + "\n", + " t0 = time.perf_counter()\n", + " chain = dict(seed=seed, n_beads=n_beads, coords=last_coords, crossings=crossings)\n", + " render_chain_and_masks(chain, noise_source=\"none\", noise_asset=None)\n", + " t[\"4_afm_render_masks\"] = time.perf_counter() - t0\n", + " return t\n", + "\n", + "print(\"=\" * 62)\n", + "print(\" RUNNING BENCHMARK\")\n", + "print(\"=\" * 62)\n", + "\n", + "wall_times = []\n", + "mem_tracker = _PeakMemTracker()\n", + "mem_tracker.start()\n", + "\n", + "for rep in range(BENCH_REPS):\n", + " t0 = time.perf_counter()\n", + " _ = _bench_stages(BENCH_SEED + rep, BENCH_N_BEADS)\n", + " elapsed = time.perf_counter() - t0\n", + " wall_times.append(elapsed)\n", + " print(f\" Rep {rep+1}/{BENCH_REPS}: {elapsed:.2f}s\")\n", + "\n", + "mem_tracker.stop()\n", + "med_wall = statistics.median(wall_times)\n", + "total_samples = int(N_SAMPLES) * len(BEAD_COUNTS)\n", + "est_hours = (med_wall * total_samples) / 3600\n", + "\n", + "print(\"=\" * 62)\n", + "print(f\" Median Wall Time: {med_wall:.2f} s\")\n", + "print(f\" Peak RAM (RSS) : {mem_tracker.peak_mb:.1f} MB\")\n", + "print(f\" Projected Total : {est_hours:.1f} hours for {total_samples} samples\")\n", + "print(\"=\" * 62)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell_md_12", + "metadata": { + "id": "cell_md_12" + }, + "source": [ + "## 14.Dataset Generation\n", + "\n", + "Finally, we are ready to generate the full dataset. The amount of samples and the lenghts of the chains is defined in the global variables and will be referenced here.\n", + "This notebook generates the full dataset (default: 1000 samples × 4 chain lengths).\n", + "\n", + "\n", + "Progress is printed every 10 samples and a `manifest.csv` tracks all file paths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell_code_12", + "metadata": { + "id": "cell_code_12" + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "manifest_path = os.path.join(OUT_DIR, \"manifest.csv\")\n", + "print(f\"Starting dataset generation: {N_SAMPLES} samples…\")\n", + "\n", + "# Define the header including global parameters\n", + "header = [\n", + " \"index\", \"seed\", \"n_beads\", \"bond_length\", \"persistence_bonds\",\n", + " \"image_npy\", \"dna_mask_npy\", \"cross_mask_npy\", \"meta_npz\",\n", + " \"n_crossings\", \"has_decoy\", \"afm_params_json\"\n", + "]\n", + "\n", + "with open(manifest_path, \"w\", newline=\"\") as f:\n", + " w = csv.writer(f)\n", + " w.writerow(header)\n", + "\n", + " for idx in range(int(N_SAMPLES)):\n", + " # Seed override for a known-problematic sample\n", + " if idx == 832:\n", + " seed = int(BASE_SEED + idx + 9997)\n", + " print(f\"Special override for index 832: seed = {seed}\")\n", + " else:\n", + " seed = int(BASE_SEED + idx)\n", + "\n", + " n_beads = int(BEAD_COUNTS[idx % len(BEAD_COUNTS)])\n", + " add_decoy = (idx % 5 == 0)\n", + "\n", + " s = generate_one_sample_multilength(\n", + " seed, n_beads=n_beads,\n", + " noise_source=noise_source,\n", + " noise_asset=noise_asset,\n", + " add_decoy=add_decoy,\n", + " )\n", + "\n", + " img_path = os.path.join(\"images\", f\"img_{idx:04d}.npy\")\n", + " dna_path = os.path.join(\"dna_masks\", f\"dna_{idx:04d}.npy\")\n", + " cross_path = os.path.join(\"cross_masks\", f\"cross_{idx:04d}.npy\")\n", + " meta_path = os.path.join(\"meta\", f\"meta_{idx:04d}.npz\")\n", + "\n", + " np.save(os.path.join(OUT_DIR, img_path), s[\"afm_img\"])\n", + " np.save(os.path.join(OUT_DIR, dna_path), s[\"dna_mask\"])\n", + " np.save(os.path.join(OUT_DIR, cross_path), s[\"cross_mask\"])\n", + "\n", + " # Save detailed metadata\n", + " np.savez_compressed(\n", + " os.path.join(OUT_DIR, meta_path),\n", + " seed = s[\"seed\"],\n", + " n_beads = int(s[\"n_beads\"]),\n", + " extent = s[\"extent\"],\n", + " n_crossings= s[\"n_crossings\"],\n", + " has_decoy = add_decoy,\n", + " bond_length = BOND_LENGTH,\n", + " persistence = PERSISTENCE_BONDS\n", + " )\n", + "\n", + " # Write to manifest including JSON-serialized AFM parameters\n", + " w.writerow([\n", + " idx, seed, n_beads, BOND_LENGTH, PERSISTENCE_BONDS,\n", + " img_path, dna_path, cross_path, meta_path,\n", + " s[\"n_crossings\"], add_decoy, json.dumps(AFM_KW)\n", + " ])\n", + "\n", + " if (idx + 1) % 10 == 0:\n", + " print(f\"Progress: {idx+1}/{N_SAMPLES}\")\n", + "\n", + "print(\"\\nDone. Dataset written to:\", OUT_DIR)\n", + "print(\"Manifest:\", manifest_path)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/tutorial/psd_noise_model.npz b/tutorial/psd_noise_model.npz new file mode 100644 index 0000000000000000000000000000000000000000..9971a4d0a701f9d439b020d81ea8ca873809d268 GIT binary patch literal 1054252 zcmbTdiC+!)AOBk_?fbr|v}@O{(s@d|w5w=WDix)o#8|T>N+rn_Qc3o8Fqrt-2ZO<6 z9Sk!V3fvOgt@K% zBoA9hbK6N%rMp{QHRZ3I1TsVz+6K9Zmq>6smDo#ij@gk~-(@hneZOG-OcLlDO^SCp7 z3bNY@dGn%-8pR5#L-IJMn8Uku*))#NMC#ilmcN_A)9+K5@ozTT`I%VvrJ=Doi<$?S z>}tv&#y6FgtaLW5E5Q1E0bPZK98H_V@jEF5S;q5p_&7>C6L@+#h=e66xUC+~i{%-V zEX?Hj>S&sNj75K3Dz_secyT-i>mSDA;vJ3QnX&YYjX@?ZihCN79Pmo!=)MHz6^vu9 zc`Exh$7Ax>2#kBY_*N8-%)1zF+>Jr*Nfdb_f;s)2C)%D8(u!h;SsjVgyjT(sjiqaX 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zeVaSFBZRv$^FH^q|06D{?g^LqEsQI_@{()c^oBE@@{SwSBa)jS|H#e$^o4UjAI)uB z8_ONDkK?WyCUVat$=uiQG%ou@CRgE>!*z_#<4m**xgq(*+@vrGH|MaF^KdEWwhyc1 zjtgqI>lw9N*!>1BdT$e#+s35_4wR3`n<&4fUo+~kZ)=*;stIdyzY;#e7Di2eE;j+c`L1+{HQIx_{n*_dBi~6&2@d`&C>?QznE^xKW7Ds9`tN5acttMz`)tEsC;ao_uM$Q7-`{6% z-RAj!o&0NJ_;tts`uf+C1B{LSeVX|9Xa03jetp)zzKs8VS~~ixOzlP||NA9!nx&!n R>#x-5UlSe8On<%E{{j_gpkx35 literal 0 HcmV?d00001 From e0d75ab1fdf6d9d162b0130f2db068d38992a21a Mon Sep 17 00:00:00 2001 From: Prakhar-Dutta Date: Thu, 2 Apr 2026 11:33:16 +0200 Subject: [PATCH 2/2] Update Simulation_of_DNA_chains_imaged_via_AFM.ipynb tried to fix colab link --- tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb index e9ea75b..b0a9769 100644 --- a/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb +++ b/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb @@ -21,7 +21,7 @@ "**Cell execution order:** run cells 1 → 12 in sequence. \n", "Cells 1–9 define functions and globals. Cell 10 is the sanity check. Cell 11 is used for benchmarking the time for execution and generation of the dataset. Cell 12 generates the full dataset.\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github.com/Prakhar-Dutta/ASAP/blob/1b5cfc5173e1bed4e59575e6dd4724cb20682620/tutorial/Not_final_version_DNA_notebook%20(1).ipynb)" + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/Prakhar-Dutta/ASAP/blob/e45f2c6830e4c44078ab3183df7dfcbdf1f6fcc1/tutorial/Simulation_of_DNA_chains_imaged_via_AFM.ipynb)" ] }, { @@ -2804,4 +2804,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +}

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