diff --git a/demos/example_4.ipynb b/demos/example_4.ipynb new file mode 100644 index 0000000..655ca95 --- /dev/null +++ b/demos/example_4.ipynb @@ -0,0 +1,1932 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "957a2288-3df9-48b4-bb69-6d1f5a38b669", + "metadata": {}, + "source": [ + "# Tutorial: Finite-Fault Stochastic Ground Motion Simulation\n", + "\n", + "This tutorial demonstrates how to use the stochastic seismic simulation library to generate ground motion time series using **finite fault modeling**. The code implements a physics-based approach that simulates earthquake ground motions by synthesizing contributions from multiple subfaults, accounting for rupture dynamics, wave propagation, and site effects.\n", + "\n", + "## Introduction\n", + "\n", + "Stochastic finite fault modeling is a powerful technique for predicting earthquake ground motions, especially in regions with limited recorded data. This implementation follows the methodology of programs like __EXSIM__, providing realistic ground motion simulations for seismic hazard assessment.\n", + "\n", + "## Setup and Installation\n", + "First, ensure you have the required dependencies installed:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58b843ee-fea8-4d21-a51c-47535b1afa61", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seismic_wave_generator\n" + ] + }, + { + "cell_type": "markdown", + "id": "6f8bdc6c-29d1-4120-829a-089fd91f1401", + "metadata": {}, + "source": [ + "## Running the Simulation\n", + "\n", + "To run the code, you need to:\n", + "\n", + "1. Define earthquake parameters\n", + "2. Set simulation parameters\n", + "3. Specify fault geometry\n", + "4. Provide site locations\n", + "5. Execute the simulation for each site\n", + " \n", + "Here's how to set up and run the simulation:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e09defa0-469e-455a-b11d-804100f5187d", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the directory of the current script\n", + "current_dir = os.path.dirname(os.path.abspath(__file__))\n", + "\n", + "# For cases with a larger number of sites, better to use a sites.csv file:\n", + "# data_file_path = os.path.join(current_dir, 'data', 'sites.csv')\n", + "# sites = pd.read_csv(data_file_path)\n", + "\n", + "# Using manually defined site coordinates instead\n", + "site_coords = [\n", + " {'lon': -9.5, 'lat': 38.78},\n", + " {'lon': -9.48, 'lat': 38.76},\n", + " {'lon': -9.48, 'lat': 38.78},\n", + " {'lon': -9.48, 'lat': 38.8}\n", + "]\n", + "sites = pd.DataFrame(site_coords)\n", + "\n", + "# Output directory for accelerograms\n", + "output_folder = os.path.join(current_dir, 'data', 'ACC')\n", + "os.makedirs(output_folder, exist_ok=True)\n", + "\n", + "# Define ground motion model parameters for Southwest Iberia inland region\n", + "my_model = seismic_wave_generator.StochasticModelParameters(\n", + " # Geometric spreading parameters\n", + " geometric_spreading=seismic_wave_generator.GeometricSpreadingParameters(\n", + " r_ref=1.0,\n", + " segments=[(70.0, -1.1), (100.0, 0.2), (float('inf'), -1.55)]\n", + " ),\n", + " \n", + " # Quality factor parameters\n", + " quality_factor=seismic_wave_generator.QualityFactorParameters(\n", + " Q0=120.0,\n", + " eta=0.93,\n", + " Qmin=600.0\n", + " ),\n", + " \n", + " # Path duration parameters\n", + " path_duration=seismic_wave_generator.PathDurationParameters(\n", + " duration_points=[\n", + " (0.0, 0.0), \n", + " (10.0, 0.13),\n", + " (70.0, 0.09),\n", + " (120.0, 0.05),\n", + " ],\n", + " slope_beyond_last=0.05\n", + " ),\n", + " \n", + " # Site attenuation parameter\n", + " site_attenuation=seismic_wave_generator.SiteAttenuationParameters(\n", + " kappa=0.033\n", + " ),\n", + " \n", + " # Other parameters\n", + " stress_drop=200.0, # in bars\n", + " roll=2.8, # density (g/cm³)\n", + " beta=3.5, # shear-wave velocity (km/s)\n", + " vs30=760.0 # in m/s\n", + ")\n", + "\n", + "# Earthquake and fault parameters\n", + "earthquake_params = seismic_wave_generator.EarthquakeParameters(\n", + " M=6.0,\n", + " rake=90,\n", + " strike=30.0,\n", + " dip=15.0,\n", + " h_ref=5.0,\n", + " stress_ref=70,\n", + " sigma=200 # in bars\n", + ")\n", + "\n", + "# Simulation parameters with ground motion model\n", + "simulation_params = seismic_wave_generator.SimulationParameters(\n", + " NS=10, # Number of simulations\n", + " dt=0.005, # Time step\n", + " roll=2.8, # Density\n", + " beta=3.5, # Shear-wave velocity\n", + " Vs30=0.76, # Site Vs30 in km/s\n", + " Tr=np.concatenate([ # Periods for response spectra\n", + " np.arange(0.02, 0.26, 0.05),\n", + " np.arange(0.3, 1., 0.05),\n", + " np.arange(1.1, 2.1, 0.15),\n", + " np.arange(2.2, 3.1, 0.5),\n", + " np.arange(3, 5.6, 1)\n", + " ]),\n", + " kappa=0.033, # Site kappa\n", + " tpad=20, # Time padding\n", + " pulsing_percent=50.0, # Percentage of pulsing subfaults\n", + " rupture_velocity=2.8, # Rupture velocity\n", + " gm_model=my_model, # Ground motion model\n", + " pulse_params=seismic_wave_generator.PulseParameters( # Pulse model parameters\n", + " enabled=False, # Disable by default\n", + " gamma=2.0,\n", + " nu=0.0,\n", + " t0=None,\n", + " peak_factor=1.5\n", + " )\n", + ")\n", + "\n", + "# Fault parameters\n", + "fault_params = seismic_wave_generator.FaultParameters(\n", + " subfault_size=2.0,\n", + " rupture_lat=38.8,\n", + " rupture_lon=-9.40,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "d943d626-c8a9-41e7-ad61-8f3525d3544d", + "metadata": {}, + "source": [ + "# Processing Sites and Running the Simulation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6661c12a-5ac8-46ca-b8b3-2c2c263653e4", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 1/49 (Lat: 38.78, Lon: -9.5)\n", + "Site 1: Rhypo = 9.41 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 285.1852 cm/s²\n", + "Subfault PGA (i=0, j=1): 190.2282 cm/s²\n", + "Subfault PGA (i=1, j=0): 155.7011 cm/s²\n", + "Subfault PGA (i=1, j=1): 27.9393 cm/s²\n", + "Subfault PGA (i=2, j=0): 27.0657 cm/s²\n", + "Subfault PGA (i=2, j=1): 9.7700 cm/s²\n", + "Subfault PGA (i=3, j=0): 154.9055 cm/s²\n", + "Subfault PGA (i=3, j=1): 68.9790 cm/s²\n", + "Total PGA: 363.8247 cmm/s²\n", + "Total PGA: 363.8247 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 2/49 (Lat: 38.76, Lon: -9.48)\n", + "Site 2: Rhypo = 9.11 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 291.2604 cm/s²\n", + "Subfault PGA (i=0, j=1): 230.8657 cm/s²\n", + "Subfault PGA (i=1, j=0): 154.1040 cm/s²\n", + "Subfault PGA (i=1, j=1): 40.7663 cm/s²\n", + "Subfault PGA (i=2, j=0): 32.6710 cm/s²\n", + "Subfault PGA (i=2, j=1): 13.2606 cm/s²\n", + "Subfault PGA (i=3, j=0): 144.5531 cm/s²\n", + "Subfault PGA (i=3, j=1): 76.9377 cm/s²\n", + "Total PGA: 516.0251 cmm/s²\n", + "Total PGA: 516.0251 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 3/49 (Lat: 38.78, Lon: -9.48)\n", + "Site 3: Rhypo = 8.08 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 370.0444 cm/s²\n", + "Subfault PGA (i=0, j=1): 251.1800 cm/s²\n", + "Subfault PGA (i=1, j=0): 233.9906 cm/s²\n", + "Subfault PGA (i=1, j=1): 49.4917 cm/s²\n", + "Subfault PGA (i=2, j=0): 30.2289 cm/s²\n", + "Subfault PGA (i=2, j=1): 11.3142 cm/s²\n", + "Subfault PGA (i=3, j=0): 216.4819 cm/s²\n", + "Subfault PGA (i=3, j=1): 112.1350 cm/s²\n", + "Total PGA: 505.5287 cmm/s²\n", + "Total PGA: 505.5287 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 4/49 (Lat: 38.8, Lon: -9.48)\n", + "Site 4: Rhypo = 7.58 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 353.5061 cm/s²\n", + "Subfault PGA (i=0, j=1): 253.0460 cm/s²\n", + "Subfault PGA (i=1, j=0): 176.6788 cm/s²\n", + "Subfault PGA (i=1, j=1): 46.1368 cm/s²\n", + "Subfault PGA (i=2, j=0): 40.7267 cm/s²\n", + "Subfault PGA (i=2, j=1): 12.4572 cm/s²\n", + "Subfault PGA (i=3, j=0): 212.6608 cm/s²\n", + "Subfault PGA (i=3, j=1): 120.2547 cm/s²\n", + "Total PGA: 609.5950 cmm/s²\n", + "Total PGA: 609.5950 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 5/49 (Lat: 38.7, Lon: -9.46)\n", + "Site 5: Rhypo = 13.36 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 208.0283 cm/s²\n", + "Subfault PGA (i=0, j=1): 155.2530 cm/s²\n", + "Subfault PGA (i=1, j=0): 91.7678 cm/s²\n", + "Subfault PGA (i=1, j=1): 26.6457 cm/s²\n", + "Subfault PGA (i=2, j=0): 18.6949 cm/s²\n", + "Subfault PGA (i=2, j=1): 6.2713 cm/s²\n", + "Subfault PGA (i=3, j=0): 123.3184 cm/s²\n", + "Subfault PGA (i=3, j=1): 73.9171 cm/s²\n", + "Total PGA: 226.7883 cmm/s²\n", + "Total PGA: 226.7883 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 6/49 (Lat: 38.72, Lon: -9.46)\n", + "Site 6: Rhypo = 11.45 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 281.8765 cm/s²\n", + "Subfault PGA (i=0, j=1): 162.2403 cm/s²\n", + "Subfault PGA (i=1, j=0): 119.0551 cm/s²\n", + "Subfault PGA (i=1, j=1): 33.0525 cm/s²\n", + "Subfault PGA (i=2, j=0): 26.5557 cm/s²\n", + "Subfault PGA (i=2, j=1): 11.4897 cm/s²\n", + "Subfault PGA (i=3, j=0): 91.5851 cm/s²\n", + "Subfault PGA (i=3, j=1): 111.5621 cm/s²\n", + "Total PGA: 383.6526 cmm/s²\n", + "Total PGA: 383.6526 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 7/49 (Lat: 38.74, Lon: -9.46)\n", + "Site 7: Rhypo = 9.67 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 291.8757 cm/s²\n", + "Subfault PGA (i=0, j=1): 235.7439 cm/s²\n", + "Subfault PGA (i=1, j=0): 133.2098 cm/s²\n", + "Subfault PGA (i=1, j=1): 38.4135 cm/s²\n", + "Subfault PGA (i=2, j=0): 28.6957 cm/s²\n", + "Subfault PGA (i=2, j=1): 13.0075 cm/s²\n", + "Subfault PGA (i=3, j=0): 119.6312 cm/s²\n", + "Subfault PGA (i=3, j=1): 59.9940 cm/s²\n", + "Total PGA: 400.9334 cmm/s²\n", + "Total PGA: 400.9334 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 8/49 (Lat: 38.76, Lon: -9.46)\n", + "Site 8: Rhypo = 8.12 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 359.5180 cm/s²\n", + "Subfault PGA (i=0, j=1): 241.4666 cm/s²\n", + "Subfault PGA (i=1, j=0): 224.4648 cm/s²\n", + "Subfault PGA (i=1, j=1): 54.1483 cm/s²\n", + "Subfault PGA (i=2, j=0): 38.1458 cm/s²\n", + "Subfault PGA (i=2, j=1): 14.1867 cm/s²\n", + "Subfault PGA (i=3, j=0): 179.7080 cm/s²\n", + "Subfault PGA (i=3, j=1): 116.0093 cm/s²\n", + "Total PGA: 458.3575 cmm/s²\n", + "Total PGA: 458.3575 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 9/49 (Lat: 38.78, Lon: -9.46)\n", + "Site 9: Rhypo = 6.94 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 456.3624 cm/s²\n", + "Subfault PGA (i=0, j=1): 258.7194 cm/s²\n", + "Subfault PGA (i=1, j=0): 245.4822 cm/s²\n", + "Subfault PGA (i=1, j=1): 41.9848 cm/s²\n", + "Subfault PGA (i=2, j=0): 57.1835 cm/s²\n", + "Subfault PGA (i=2, j=1): 16.9605 cm/s²\n", + "Subfault PGA (i=3, j=0): 196.5077 cm/s²\n", + "Subfault PGA (i=3, j=1): 169.2139 cm/s²\n", + "Total PGA: 664.7407 cmm/s²\n", + "Total PGA: 664.7407 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 10/49 (Lat: 38.8, Lon: -9.46)\n", + "Site 10: Rhypo = 6.35 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 506.7930 cm/s²\n", + "Subfault PGA (i=0, j=1): 230.1407 cm/s²\n", + "Subfault PGA (i=1, j=0): 297.0566 cm/s²\n", + "Subfault PGA (i=1, j=1): 57.6072 cm/s²\n", + "Subfault PGA (i=2, j=0): 67.8037 cm/s²\n", + "Subfault PGA (i=2, j=1): 13.8970 cm/s²\n", + "Subfault PGA (i=3, j=0): 278.1405 cm/s²\n", + "Subfault PGA (i=3, j=1): 151.5960 cm/s²\n", + "Total PGA: 651.0485 cmm/s²\n", + "Total PGA: 651.0485 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 11/49 (Lat: 38.82, Lon: -9.46)\n", + "Site 11: Rhypo = 6.51 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 469.9727 cm/s²\n", + "Subfault PGA (i=0, j=1): 245.8165 cm/s²\n", + "Subfault PGA (i=1, j=0): 320.9897 cm/s²\n", + "Subfault PGA (i=1, j=1): 56.7424 cm/s²\n", + "Subfault PGA (i=2, j=0): 84.9481 cm/s²\n", + "Subfault PGA (i=2, j=1): 14.9697 cm/s²\n", + "Subfault PGA (i=3, j=0): 310.9159 cm/s²\n", + "Subfault PGA (i=3, j=1): 166.6901 cm/s²\n", + "Total PGA: 722.8614 cmm/s²\n", + "Total PGA: 722.8614 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 12/49 (Lat: 38.84, Lon: -9.46)\n", + "Site 12: Rhypo = 7.38 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 370.9530 cm/s²\n", + "Subfault PGA (i=0, j=1): 249.7691 cm/s²\n", + "Subfault PGA (i=1, j=0): 313.5889 cm/s²\n", + "Subfault PGA (i=1, j=1): 62.3203 cm/s²\n", + "Subfault PGA (i=2, j=0): 59.6565 cm/s²\n", + "Subfault PGA (i=2, j=1): 14.3936 cm/s²\n", + "Subfault PGA (i=3, j=0): 327.4929 cm/s²\n", + "Subfault PGA (i=3, j=1): 115.0291 cm/s²\n", + "Total PGA: 591.0535 cmm/s²\n", + "Total PGA: 591.0535 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 13/49 (Lat: 38.7, Lon: -9.44)\n", + "Site 13: Rhypo = 12.93 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 179.0300 cm/s²\n", + "Subfault PGA (i=0, j=1): 151.2940 cm/s²\n", + "Subfault PGA (i=1, j=0): 106.5869 cm/s²\n", + "Subfault PGA (i=1, j=1): 25.0085 cm/s²\n", + "Subfault PGA (i=2, j=0): 26.3022 cm/s²\n", + "Subfault PGA (i=2, j=1): 8.5753 cm/s²\n", + "Subfault PGA (i=3, j=0): 104.8311 cm/s²\n", + "Subfault PGA (i=3, j=1): 83.2006 cm/s²\n", + "Total PGA: 216.4908 cmm/s²\n", + "Total PGA: 216.4908 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 14/49 (Lat: 38.72, Lon: -9.44)\n", + "Site 14: Rhypo = 10.95 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 256.2109 cm/s²\n", + "Subfault PGA (i=0, j=1): 175.1661 cm/s²\n", + "Subfault PGA (i=1, j=0): 166.9836 cm/s²\n", + "Subfault PGA (i=1, j=1): 28.8702 cm/s²\n", + "Subfault PGA (i=2, j=0): 30.6367 cm/s²\n", + "Subfault PGA (i=2, j=1): 9.8657 cm/s²\n", + "Subfault PGA (i=3, j=0): 122.2820 cm/s²\n", + "Subfault PGA (i=3, j=1): 73.6534 cm/s²\n", + "Total PGA: 370.4655 cmm/s²\n", + "Total PGA: 370.4655 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 15/49 (Lat: 38.74, Lon: -9.44)\n", + "Site 15: Rhypo = 9.08 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 356.4714 cm/s²\n", + "Subfault PGA (i=0, j=1): 249.9656 cm/s²\n", + "Subfault PGA (i=1, j=0): 188.9506 cm/s²\n", + "Subfault PGA (i=1, j=1): 44.0253 cm/s²\n", + "Subfault PGA (i=2, j=0): 32.2072 cm/s²\n", + "Subfault PGA (i=2, j=1): 11.0713 cm/s²\n", + "Subfault PGA (i=3, j=0): 150.2594 cm/s²\n", + "Subfault PGA (i=3, j=1): 111.5587 cm/s²\n", + "Total PGA: 433.2790 cmm/s²\n", + "Total PGA: 433.2790 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 16/49 (Lat: 38.76, Lon: -9.44)\n", + "Site 16: Rhypo = 7.40 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 468.6592 cm/s²\n", + "Subfault PGA (i=0, j=1): 253.4758 cm/s²\n", + "Subfault PGA (i=1, j=0): 261.9329 cm/s²\n", + "Subfault PGA (i=1, j=1): 68.0490 cm/s²\n", + "Subfault PGA (i=2, j=0): 47.4149 cm/s²\n", + "Subfault PGA (i=2, j=1): 13.9823 cm/s²\n", + "Subfault PGA (i=3, j=0): 168.5019 cm/s²\n", + "Subfault PGA (i=3, j=1): 151.7617 cm/s²\n", + "Total PGA: 571.8305 cmm/s²\n", + "Total PGA: 571.8305 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 17/49 (Lat: 38.78, Lon: -9.44)\n", + "Site 17: Rhypo = 6.09 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 608.5153 cm/s²\n", + "Subfault PGA (i=0, j=1): 417.2172 cm/s²\n", + "Subfault PGA (i=1, j=0): 341.6542 cm/s²\n", + "Subfault PGA (i=1, j=1): 83.6389 cm/s²\n", + "Subfault PGA (i=2, j=0): 73.3669 cm/s²\n", + "Subfault PGA (i=2, j=1): 16.2900 cm/s²\n", + "Subfault PGA (i=3, j=0): 257.0085 cm/s²\n", + "Subfault PGA (i=3, j=1): 117.3930 cm/s²\n", + "Total PGA: 745.0497 cmm/s²\n", + "Total PGA: 745.0497 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 18/49 (Lat: 38.8, Lon: -9.44)\n", + "Site 18: Rhypo = 5.41 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 657.0364 cm/s²\n", + "Subfault PGA (i=0, j=1): 421.4133 cm/s²\n", + "Subfault PGA (i=1, j=0): 383.5017 cm/s²\n", + "Subfault PGA (i=1, j=1): 84.5835 cm/s²\n", + "Subfault PGA (i=2, j=0): 84.0776 cm/s²\n", + "Subfault PGA (i=2, j=1): 20.1807 cm/s²\n", + "Subfault PGA (i=3, j=0): 242.7737 cm/s²\n", + "Subfault PGA (i=3, j=1): 171.2840 cm/s²\n", + "Total PGA: 793.8164 cmm/s²\n", + "Total PGA: 793.8164 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 19/49 (Lat: 38.82, Lon: -9.44)\n", + "Site 19: Rhypo = 5.60 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 632.5579 cm/s²\n", + "Subfault PGA (i=0, j=1): 400.4605 cm/s²\n", + "Subfault PGA (i=1, j=0): 412.2440 cm/s²\n", + "Subfault PGA (i=1, j=1): 88.6139 cm/s²\n", + "Subfault PGA (i=2, j=0): 93.7888 cm/s²\n", + "Subfault PGA (i=2, j=1): 22.2367 cm/s²\n", + "Subfault PGA (i=3, j=0): 283.0086 cm/s²\n", + "Subfault PGA (i=3, j=1): 212.8457 cm/s²\n", + "Total PGA: 645.3050 cmm/s²\n", + "Total PGA: 645.3050 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 20/49 (Lat: 38.84, Lon: -9.44)\n", + "Site 20: Rhypo = 6.58 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 522.5927 cm/s²\n", + "Subfault PGA (i=0, j=1): 253.0386 cm/s²\n", + "Subfault PGA (i=1, j=0): 349.3260 cm/s²\n", + "Subfault PGA (i=1, j=1): 75.2861 cm/s²\n", + "Subfault PGA (i=2, j=0): 109.0559 cm/s²\n", + "Subfault PGA (i=2, j=1): 22.8933 cm/s²\n", + "Subfault PGA (i=3, j=0): 349.0053 cm/s²\n", + "Subfault PGA (i=3, j=1): 228.2033 cm/s²\n", + "Total PGA: 572.4989 cmm/s²\n", + "Total PGA: 572.4989 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 21/49 (Lat: 38.86, Lon: -9.44)\n", + "Site 21: Rhypo = 8.07 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 361.9112 cm/s²\n", + "Subfault PGA (i=0, j=1): 249.6906 cm/s²\n", + "Subfault PGA (i=1, j=0): 332.7276 cm/s²\n", + "Subfault PGA (i=1, j=1): 66.9648 cm/s²\n", + "Subfault PGA (i=2, j=0): 95.4857 cm/s²\n", + "Subfault PGA (i=2, j=1): 18.9167 cm/s²\n", + "Subfault PGA (i=3, j=0): 375.1217 cm/s²\n", + "Subfault PGA (i=3, j=1): 215.6621 cm/s²\n", + "Total PGA: 406.2258 cmm/s²\n", + "Total PGA: 406.2258 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 22/49 (Lat: 38.88, Lon: -9.44)\n", + "Site 22: Rhypo = 9.84 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 297.4743 cm/s²\n", + "Subfault PGA (i=0, j=1): 208.6467 cm/s²\n", + "Subfault PGA (i=1, j=0): 239.8282 cm/s²\n", + "Subfault PGA (i=1, j=1): 46.5227 cm/s²\n", + "Subfault PGA (i=2, j=0): 63.5766 cm/s²\n", + "Subfault PGA (i=2, j=1): 19.9185 cm/s²\n", + "Subfault PGA (i=3, j=0): 367.6260 cm/s²\n", + "Subfault PGA (i=3, j=1): 255.3302 cm/s²\n", + "Total PGA: 369.2604 cmm/s²\n", + "Total PGA: 369.2604 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 23/49 (Lat: 38.7, Lon: -9.42)\n", + "Site 23: Rhypo = 12.73 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 192.9562 cm/s²\n", + "Subfault PGA (i=0, j=1): 188.8479 cm/s²\n", + "Subfault PGA (i=1, j=0): 120.1006 cm/s²\n", + "Subfault PGA (i=1, j=1): 24.3671 cm/s²\n", + "Subfault PGA (i=2, j=0): 22.9268 cm/s²\n", + "Subfault PGA (i=2, j=1): 8.9639 cm/s²\n", + "Subfault PGA (i=3, j=0): 81.5968 cm/s²\n", + "Subfault PGA (i=3, j=1): 97.7163 cm/s²\n", + "Total PGA: 281.7114 cmm/s²\n", + "Total PGA: 281.7114 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 24/49 (Lat: 38.72, Lon: -9.42)\n", + "Site 24: Rhypo = 10.71 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 297.4441 cm/s²\n", + "Subfault PGA (i=0, j=1): 273.4543 cm/s²\n", + "Subfault PGA (i=1, j=0): 175.1266 cm/s²\n", + "Subfault PGA (i=1, j=1): 40.7986 cm/s²\n", + "Subfault PGA (i=2, j=0): 21.8685 cm/s²\n", + "Subfault PGA (i=2, j=1): 10.0980 cm/s²\n", + "Subfault PGA (i=3, j=0): 146.7654 cm/s²\n", + "Subfault PGA (i=3, j=1): 87.7385 cm/s²\n", + "Total PGA: 442.5024 cmm/s²\n", + "Total PGA: 442.5024 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 25/49 (Lat: 38.74, Lon: -9.42)\n", + "Site 25: Rhypo = 8.79 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 371.0040 cm/s²\n", + "Subfault PGA (i=0, j=1): 275.6616 cm/s²\n", + "Subfault PGA (i=1, j=0): 232.2812 cm/s²\n", + "Subfault PGA (i=1, j=1): 59.1065 cm/s²\n", + "Subfault PGA (i=2, j=0): 35.5356 cm/s²\n", + "Subfault PGA (i=2, j=1): 13.1037 cm/s²\n", + "Subfault PGA (i=3, j=0): 149.7328 cm/s²\n", + "Subfault PGA (i=3, j=1): 142.8231 cm/s²\n", + "Total PGA: 572.4052 cmm/s²\n", + "Total PGA: 572.4052 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 26/49 (Lat: 38.76, Lon: -9.42)\n", + "Site 26: Rhypo = 7.05 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 579.5824 cm/s²\n", + "Subfault PGA (i=0, j=1): 447.4377 cm/s²\n", + "Subfault PGA (i=1, j=0): 323.2827 cm/s²\n", + "Subfault PGA (i=1, j=1): 86.2049 cm/s²\n", + "Subfault PGA (i=2, j=0): 55.3628 cm/s²\n", + "Subfault PGA (i=2, j=1): 18.6653 cm/s²\n", + "Subfault PGA (i=3, j=0): 248.1137 cm/s²\n", + "Subfault PGA (i=3, j=1): 117.8797 cm/s²\n", + "Total PGA: 801.6975 cmm/s²\n", + "Total PGA: 801.6975 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 27/49 (Lat: 38.78, Lon: -9.42)\n", + "Site 27: Rhypo = 5.65 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 723.9933 cm/s²\n", + "Subfault PGA (i=0, j=1): 487.8112 cm/s²\n", + "Subfault PGA (i=1, j=0): 352.7996 cm/s²\n", + "Subfault PGA (i=1, j=1): 77.1054 cm/s²\n", + "Subfault PGA (i=2, j=0): 83.0141 cm/s²\n", + "Subfault PGA (i=2, j=1): 20.8619 cm/s²\n", + "Subfault PGA (i=3, j=0): 247.2910 cm/s²\n", + "Subfault PGA (i=3, j=1): 190.2883 cm/s²\n", + "Total PGA: 961.6930 cmm/s²\n", + "Total PGA: 961.6930 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 28/49 (Lat: 38.8, Lon: -9.42)\n", + "Site 28: Rhypo = 4.91 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 782.3529 cm/s²\n", + "Subfault PGA (i=0, j=1): 485.9654 cm/s²\n", + "Subfault PGA (i=1, j=0): 389.5843 cm/s²\n", + "Subfault PGA (i=1, j=1): 81.9709 cm/s²\n", + "Subfault PGA (i=2, j=0): 115.4243 cm/s²\n", + "Subfault PGA (i=2, j=1): 26.0163 cm/s²\n", + "Subfault PGA (i=3, j=0): 296.2199 cm/s²\n", + "Subfault PGA (i=3, j=1): 257.6786 cm/s²\n", + "Total PGA: 982.2316 cmm/s²\n", + "Total PGA: 982.2316 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 29/49 (Lat: 38.82, Lon: -9.42)\n", + "Site 29: Rhypo = 5.12 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 752.4007 cm/s²\n", + "Subfault PGA (i=0, j=1): 460.0694 cm/s²\n", + "Subfault PGA (i=1, j=0): 426.5777 cm/s²\n", + "Subfault PGA (i=1, j=1): 72.9916 cm/s²\n", + "Subfault PGA (i=2, j=0): 131.6872 cm/s²\n", + "Subfault PGA (i=2, j=1): 28.2588 cm/s²\n", + "Subfault PGA (i=3, j=0): 391.1297 cm/s²\n", + "Subfault PGA (i=3, j=1): 287.0771 cm/s²\n", + "Total PGA: 978.0438 cmm/s²\n", + "Total PGA: 978.0438 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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1Mb73inTVV+2reZyTkpKE/v37C1ZWVgIApcwVFBQIb775ptCuXTsxG8HBwcKcOXOE9PR0cT8AwrRp0+o8jg1JTEys94qD9+afiIiIiDRHJgiC0NzNLiIiIqpao8vb2xszZszAqlWraj0eGRmJpUuXIjMzU6V1vIiIiIiIWhqeakdERNTMUlJScP36dbz33nswMDDArFmzdF0SEREREZFWGOi6ACIiogfNF198gYiICJw/fx7fffed0hXrSD0KhQIVFRX13hQKha5LJCIiInqg8VQ7IiIikqyIiAgcOnSo3se9vb2RlJTUfAURERERkRI2noiIiEiyLl26hPz8/Hofl8vlCA4ObsaKiIiIiKgmNp6IiIiIiIiIiEgruMYTERERERERERFpBRtPRESkM5s3b4ZMJhNvRkZGcHNzw1NPPYUrV640ez0HDhzA5MmT0b59e1hYWMDDwwMjRoxAbGysyq9x8OBBPPbYY3B2doalpSVCQkLwf//3f0qLXOfl5eHtt99GREQEXF1dYWlpieDgYLz77rsoKSlp8D2SkpIgk8mwevXqRn2fRERERETNhY0nIiLSuU2bNuH48eP466+/MH36dOzatQsPP/wwsrOzm7WO9evXIykpCbNmzcLvv/+ODz/8EBkZGXjooYdw4MCBBp//119/oV+/fqioqMDnn3+OnTt3IiIiArNmzcLcuXPF/ZKTk/HBBx8gNDQUn332GXbt2oUxY8YgMjISQ4cOBc+CJyIiIiJ9YaTrAoiIiIKCghAWFgag6iplCoUCS5Yswc6dO/G///2v2er4+OOP4ezsrLRt4MCB8Pf3x4oVK9CnT5/7Pn/z5s0wNjbGr7/+CgsLCwBAv379cOnSJWzevBkffvghAMDX1xdJSUniPgDQp08fWFhY4JVXXsHRo0fx8MMPa/i7IyIiIiJqfpzxRERELU51E+r27dvN+r73Np0AwNLSEoGBgbh582aDzzc2NoaJiQnMzMyUttva2sLU1FS8b2FhodR0qtatWzcAUOm97lVeXo6JEyfC0tISv/76K4D/TmU8cOAApkyZAgcHB1hbW2PChAkoLCxEeno6xo4dC1tbW7i5uWH+/PkoLy9X+72JiIiIiOrDxhMREbU4iYmJAIC2bds2uK8gCKioqFDp1hi5ubk4deoUOnTo0OC+L730EsrKyjBz5kykpqYiJycH33zzDXbs2IFXX321wedXn86nynvVlJOTgwEDBmDv3r04dOgQhg4dqvT4888/DxsbG2zduhVvvvkmtmzZgilTpmDIkCHo2LEjfvrpJ0ycOBFr1qzBunXr1HpvIiIiIqL74al2RESkcwqFAhUVFSgpKcHRo0fx1ltv4dFHH8Xw4cMbfO5XX32l8ul4jVk7adq0aSgsLMTChQsb3Dc8PBwHDhzAE088gY8//hgAYGhoiJUrV2LevHn3fe7Zs2exatUqjBo1CiEhISrXl5SUhCFDhgAATpw4AW9v71r7DB06VFyI/LHHHsPx48fx/fffY+3atZgzZw6AqlMC9+zZg++++05pPSoiIiIioqZg44mIiHTuoYceUrofEBCAX375BUZGDf83NWzYMERHR2ulrkWLFuG7777DunXr0KVLlwb3j42NxahRoxAeHo5PP/0UFhYWOHDgAN58802UlJRg0aJFdT4vKSkJQ4cOhZeXF7744guV6zt16hRWr16NwMBAbN++Hba2tnXud+8MqICAAOzcuVNsWNXcvnfvXpXfn4iIiIioIWw8ERGRzn399dcICAhAfn4+tm3bhk8//RTjxo3DH3/80eBz7e3tYWNjo/Gali5dirfeegtvv/02pk+frtJzpk2bBhcXF+zYsQOGhoYAgN69e8PAwACRkZEYP348WrdurfScGzduoHfv3jAyMsL+/fthb2+vco379u3DnTt3sHbt2nqbTgBqvaaJiUm920tKSlR+fyIiIiKihrDxREREOhcQECAuKN67d28oFAp88cUX+OmnnzBmzJj7Plcbp9otXboUkZGRiIyMxIIFC1R6DgDExcVh3LhxYtOpWteuXVFZWYmEhASlxtONGzcQEREBQRDw999/w9PTU+X3AoBXXnkF165dw4QJE1BRUYEJEyao9XwiIiIiIm1j44mIiFqcVatW4eeff8bixYsxevRoGBjUfy0MTZ9qt3z5ckRGRuLNN9/EkiVL1Hquu7s7YmJioFAolJpPx48fBwClxlJycjIiIiKgUCjw999/17k2U0MMDAzw6aefwtLSEpMmTUJhYSFefvlltV+HiIiIiEhb2HgiIqIWx87ODm+88QZeffVVbNmyBc8880y9+zo4OMDBwUEj77tmzRosXrwYAwcOxJAhQ3DixAmlx2uuRfXcc8/hq6++wrVr18Sm0Zw5czBz5kwMGzYML774IszNzbF//36sWbMG/fr1Q8eOHQEAGRkZ6N27N9LS0rBx40ZkZGQgIyNDfG1PT0+1Zj+tWbMGVlZWmDp1KgoKCvDKK6805TAQEREREWkMG09ERNQizZgxAx999BGWLVtW5+lr2rB7924AwJ9//ok///yz1uM1T9VTKBRQKBRK22bMmAEPDw+8//77eP7551FcXAwfHx8sWbJEvHocAFy4cAHXr18HgDqbakuWLEFkZKRatUdGRsLS0hKvvPIKCgoKsHTpUrWeT0RERESkDTKhMdeWJiIiIiIiIiIiakD9i2YQERERERERERE1ARtPRERERERERESkFWw8ERERERERERGRVkim8eTj4wOZTFbrNm3aNADApEmTaj1W8+pDAFBaWooZM2bA0dERFhYWGD58OFJSUnTx7RARERERERER6T3JNJ6io6ORlpYm3vbt2wcAeOKJJ8R9Bg4cqLTP77//rvQas2fPxo4dO7B161YcOXIEBQUFGDp0KBQKRbN+L0REREREREREDwIjXRegKicnJ6X777zzDvz8/NCrVy9xm1wuh6ura53Pz83NxcaNG/HNN9+gX79+AIBvv/0WXl5e+OuvvzBgwADtFU9ERERERERE9ACSTOOpprKyMnz77beYO3cuZDKZuP3vv/+Gs7MzbG1t0atXL7z99ttwdnYGAMTGxqK8vBz9+/cX93d3d0dQUBCOHTumVuOpsrISqampsLKyUnp/IiIiIiIiABAEAfn5+XB3d4eBQcs90UShUKC8vFzXZRCRxBgbG8PQ0FClfSXZeNq5cydycnIwadIkcdugQYPwxBNPwNvbG4mJiVi0aBH69OmD2NhYyOVypKenw8TEBHZ2dkqv5eLigvT09Pu+X2lpKUpLS8X7t27dQmBgoEa/JyIiIiIi0j83b96Ep6enrsuoRRAEpKenIycnR9elEJFE2drawtXVtcEJOZJsPG3cuBGDBg2Cu7u7uO3JJ58Uvw4KCkJYWBi8vb3x22+/YfTo0fW+liAIDR6klStXYunSpbW279q1CxYWFujYsSMuXbqEkpISWFpawtvbG+fPnwcAeHl5obKyErdu3QIABAcH4/r16ygsLIS5uTn8/f1x9uxZAFUzsAwNDXHz5k0AQIcOHZCcnIz8/HzI5XIEBAQgLi4OAODq6gpTU1MkJSWhsrISHTp0QGpqKnJzc2FsbIyQkBDExsYCAJydnWFpaYnr168DANq2bYvMzExkZ2fD0NAQnTt3RkxMDADAwcEBtra2uHbtGgDA398f2dnZuHv3LmQyGbp06YJTp06hsrISdnZ2cHJywuXLlwEArVu3Rn5+PjIzMwEAYWFhiIuLQ0VFhRjIixcvAgB8fX1RVFSE27dvAwA6d+6MCxcuoLS0FNbW1vD09MSFCxcAAK1atUJFRQVSU1MBAB07dsTly5dRXFwMCwsL+Pr6Ij4+HgDE/9SrF40PCgpCYmIiCgsLYWZmhrZt2+LMmTPi8TYyMkJycjIAIDAwECkpKcjLy4NcLkdgYCBOnz4NoKpBaW5ujsTERABA+/btxf+ojYyM0KlTJ/EYOjk5wcrKqs7jbWBggNDQUMTGxkIQBDg4OMDOzg5Xr14FAPj5+SEnJwd3794Vj+Hp06ehUCjqPN4FBQXIyMgAAHTp0gVnz55FeXk5bGxs4O7ujoSEBABVi/OXlJSITdZOnTohISEBpaWlsLKyQqtWrZQyq1AoxOMdEhKCq1evoqioCBYWFmjdujXOnTsHAPDw8ICBgYFSZm/cuIGCggKYmpqiXbt24vF2c3ODiYkJbty4gcrKSgQFBeHWrVvIzc2FiYkJgoKCcOrUKTGzFhYW4vFu164dMjIykJ2dXet4Ozo6wtraWjzebdq0wd27d5GVlSVmtvp429vbw8HBAVeuXBGPYV5eHu7cuVMrs3Z2dnB2dsalS5fEzBYWForHOzQ0FPHx8SgrK4ONjQ08PDzEzHp7e6OsrAxpaWliZnU5RgBAQEAAxwg0bYxo06YNbt++zTGiGcYIoGpM5hjRtDGiTZs2SE9P5xjBnyMkNUZcv34dxcXFejFGmJmZISAgAFZWVmiJqjPo7OwMc3NznslBRCoTBAFFRUXi/yFubm733V8mCILQHIVpyo0bN9C6dWts374dI0aMuO++bdq0wfPPP4/XXnsNBw4cQN++fZGVlaU066ljx44YOXJknY2lavfOeMrLy4OXlxdyc3NhbW3d9G+qiaKiohAeHq7rMohUxsySFDG3JDXMLEmRPuU2Ly8PNjY2LeZ3hpoUCgUuX74MZ2dnODg46LocIpKou3fvIiMjA23btr3vaXct92TjemzatAnOzs4YMmTIffe7e/cubt68KXbeunTpAmNjY/FqeACQlpaG+Ph49OjR476vJZfLYW1trXQjIiIiIiKSouo1nczNzXVcCRFJWfUY0tA6cZI61a6yshKbNm3CxIkTYWT0X+kFBQWIjIzE448/Djc3NyQlJWHBggVwdHTEqFGjAAA2NjZ47rnnMG/ePDg4OMDe3h7z589HcHCweJU7qWqJ54wT3Q8zS1LE3JLUMLMkRcxt8+LpdUTUFKqOIZKa8fTXX38hOTkZkydPVtpuaGiIc+fOYcSIEWjbti0mTpyItm3b4vjx40rnVL///vsYOXIkxo4di549e8Lc3By7d+9WeSX2lkrq9dODh5klKWJuSWqYWZIi5pZaIh8fH3zwwQe6LuO+Jk2ahJEjR+rs/Tdv3gxbW1udvb+6muvvNCIiArNnz24xr6Mrkmo89e/fH4IgoG3btkrbzczMsGfPHmRkZKCsrAw3btzA5s2b4eXlpbSfqakp1q1bh7t376KoqAi7d++utY8UVS+ySCQVzCxJEXNLUsPMkhQxt3Q/MpnsvreaVz2v7/k7d+7UWn0pKSkwMTFB+/bttfYeLUFdTZsnn3xSvHiBrhUWFuK1115D69atYWpqCicnJ0RERODXX38V94mOjsYLL7ygwyrr9vfff0Mmk9W62uT27duxfPlyrb9/Xf+uNmzY0OTXldSpdkRERPoiPx/YsweIiAAcHXVdDRERUctXfTVQANi2bRsWL14sXl0UqJqQoEubN2/G2LFjcfjwYRw9ehQ9e/bUaT3qEAQBCoVCaUkbdZiZmen8+Fd76aWXcPLkSXz00UcIDAzE3bt3cezYMfGKn0DVFUSlxN7evtnea9OmTRg4cKB438bGpsmvKakZT1S3kJAQXZdApBZmlqRI07n98EPgq6+A11/X6MsSiTjWkhQxt3Q/rq6u4s3GxgYymUxp25YtW+Dn5wcTExO0a9cO33zzjfhcHx8fAMCoUaMgk8nE+9euXcOIESPg4uICS0tLdO3aFX/99ZfatQmCgE2bNuHZZ5/F008/jY0bN9ba5+jRo+jVqxfMzc1hZ2eHAQMGIDs7G0DVesbvvvsu/P39IZfL0apVK7z99tvic2/duoUnn3wSdnZ2cHBwwIgRI5CUlHTfelatWoXWrVvDzMwMHTt2xE8//SQ+Xj2zZs+ePQgLC4NcLsc///zT4PGIiIjAjRs3MGfOHHFGDFD3qXbr16+v9+8DqJpd88UXX2DUqFEwNzdHmzZtsGvXLpWPeX12796NBQsWYPDgwfDx8UGXLl0wY8YMTJw4Udzn3llbMpkMn376KYYOHQpzc3MEBATg+PHjuHr1KiIiImBhYYHu3bvj2rVr4nPqOr1x9uzZiIiIqLe2b7/9FmFhYbCysoKrqyuefvppZGRkAACSkpLQu3dvAICdnZ3SLL57T7XLzs7GhAkTYGdnB3NzcwwaNAhXrlwRH6/++9izZw8CAgJgaWmJgQMHKjVv62Nra6v070oTDUU2nvTA/QYcopaImSUp0nRuT5+u+vP2bY2+LJGIYy1JEXNLjbVjxw7MmjUL8+bNQ3x8PF588UX873//w8GDBwFUnVoFVM3mSEtLE+8XFBRg8ODB+Ouvv3D69GkMGDAAw4YNQ3Jyslrvf/DgQRQVFaFfv3549tln8cMPPyA/P198PC4uDn379kWHDh1w/PhxHDlyBMOGDYNCoQAAvPHGG3j33XexaNEiXLhwAVu2bIGLiwsAoKioCL1794alpSUOHz6MI0eOiI2EsrKyOut58803sWnTJqxfvx7nz5/HnDlz8Mwzz+DQoUNK+7366qtYuXIlEhISEBIS0uDx2L59Ozw9PbFs2TKkpaXV28ho6O+j2tKlSzF27FicPXsWgwcPxvjx45GVlaXWsb+Xq6srfv/9d6Xjr4rly5djwoQJiIuLQ/v27fH000/jxRdfxBtvvIGYmBgAwPTp05tUW1lZGZYvX44zZ85g586dSExMFJtLXl5e+PnnnwEAly5dQlpaGj788MM6X2fSpEmIiYnBrl27cPz4cQiCgMGDBytdXa6oqAirV6/GN998g8OHDyM5ORnz589vsMbp06fD0dERXbt2xYYNG1BZWdmk7xkAIJDacnNzBQBCbm6urksRBEEQTpw4oesSiNTCzJIUaTq3Y8YIwtChVTcibeBYS1KkT7ltab8z1FRcXCxcuHBBKC4uFrdVVgpCcbFubpWV6n8PmzZtEmxsbMT7PXr0EKZMmaK0zxNPPCEMHjxYvA9A2LFjR4OvHRgYKKxbt0687+3tLbz//vv3fc7TTz8tzJ49W7zfsWNH4fPPPxfvjxs3TujZs2edz83LyxPkcrnS/jVt3LhRaNeunVBZ40CVlpYKZmZmwp49ewRBEISJEycKI0aMEARBEAoKCgRTU1Ph2LFjSq/z3HPPCePGjRMEQRAOHjwoABB27tx53+9LEFQ7Ho39+3jzzTfF+wUFBYJMJhP++OOPBmu6n0OHDgmenp6CsbGxEBYWJsyePVs4cuSI0j73fg/31nL8+HEBgLBx40Zx2/fffy+YmpqK92se82qzZs0SevXqJd7v1auXMGvWrHprPXnypABAyM/PFwThv7+X7Oxspf1qvs7ly5cFAMLRo0fFx+/cuSOYmZkJP/zwgyAIVX8fAISrV6+K+3z88ceCi4tLvbUIgiAsX75cOHbsmHD69Glh9erVgrm5ubB8+fJ6969rLKkL13jSA6amprougUgtzCxJkaZzyws3kbZxrCUpYm51p7QUeOIJ3bz3jz8CTf2rT0hIqLVYdM+ePeudMVKtsLAQS5cuxa+//orU1FRUVFSguLhYrRlPOTk52L59O44cOSJue+aZZ/Dll1/i+eefB1A14+mJeg5wQkICSktL0bdv3zofj42NxdWrV5Wu2A4AJSUlSqd+Vbtw4QJKSkrw2GOPKW0vKytD586dlbaFhYUp3dfE8aj+nlT5+6h5eq2FhQWsrKzEU8/utWLFCqxYsUK8f+HCBbRq1arWfo8++iiuX7+OEydO4OjRozhw4AA+/PBDLF26FIsWLaq35pq1VM82Cw4OVtpWUlKCvLw8WFtb1/s693P69GlERkYiLi4OWVlZ4myi5ORkBAYGqvQaCQkJMDIyQnh4uLjNwcEB7dq1Q0JCgrjN3Nwcfn5+4n03N7d6j221N998U/y6U6dOAIBly5YpbW8MNp70QIcOHXRdApFamFmSIk3nlo0n0jaOtSRFzC01RfV6Q9UEQai17V6vvPIK9uzZg9WrV8Pf3x9mZmYYM2ZMvaew1WXLli0oKSlRagQIgoDKykpcuHABgYGB910np6E1dCorK9GlSxd89913tR6ra5Hs6mbGb7/9Bg8PD6XH5HK50n0LCwul+5o4HtVU+fswNjau9Zz6Tu166aWXMHbsWPG+u7t7ve9tbGyMRx55BI888ghef/11vPXWW1i2bBlee+01mJiY1Puce2uva1t1fQYGBhAEQek1ap7qdq/CwkL0798f/fv3x7fffgsnJyckJydjwIABah3fe9+z5vaax7euY1vfc+vz0EMPIS8vD7dv3xabcY3BxpMeiI2NVRrkiFo6ZpakSNO5beDnYKIm41hLUsTc6o5cXjXzSFfv3VQBAQE4cuQIJkyYIG47duwYAgICxPvGxsbimkrV/vnnH0yaNAmjRo0CULXmk7prjW3cuBHz5s0T1+qpNnPmTHz55ZdYvXo1QkJCsH//fixdurTW89u0aQMzMzPs379fnCFVU2hoKLZt2wZnZ2eVZtoEBgZCLpcjOTkZvXr1Uut7UeV4mJiY1DqO91Ll70Nd9vb2jb66W2BgICoqKlBSUlJv40ldTk5OiI+PV9oWFxdXq+FT7eLFi7hz5w7eeecdeHl5AYC4dlS16trud3yrv5eoqCj06NEDAHD37l1cvny5Sce3LqdPn4apqWmthePVxcYTERGRDrDxRERELYlM1vTT3XTplVdewdixYxEaGoq+ffti9+7d2L59u9IV2Xx8fLB//3707NkTcrkcdnZ28Pf3x/bt2zFs2DDIZDIsWrRIrcWU4+LicOrUKXz33Xdo37690mPjxo3DwoULsXLlSrzxxhsIDg7G1KlT8dJLL8HExAQHDx7EE088AUdHR7z22mt49dVXYWJigp49eyIzMxPnz5/Hc889h/Hjx+O9997DiBEjsGzZMnh6eiI5ORnbt2/HK6+8Ak9PT6X3tbKywvz58zFnzhxUVlbi4YcfRl5eHo4dOwZLS0ulq7vdS5Xj4ePjg8OHD+Opp56CXC6Ho6Njo/4+tCUiIgLjxo1DWFgYHBwccOHCBSxYsAC9e/du9ClydenTpw/ee+89fP311+jevTu+/fZbxMfH1zqdsVqrVq1gYmKCdevW4aWXXkJ8fDyWL1+utI+3tzdkMhl+/fVXDB48GGZmZrC0tFTap02bNhgxYgSmTJmCTz/9FFZWVnj99dfh4eGBESNGNPr72b17N9LT09G9e3eYmZnh4MGDWLhwIV544YVaM+XUxava6YH7TTEkaomYWZIiTefWgP8Dk5ZxrCUpYm6psUaOHIkPP/wQ7733Hjp06IBPP/0UmzZtUrq0/Zo1a7Bv3z54eXmJzYH3338fdnZ26NGjB4YNG4YBAwYgNDRU5ffduHEjAgMDazWdqmvKysrC7t270bZtW+zduxdnzpxBt27d0L17d/zyyy8wMqqaC7Jo0SLMmzcPixcvRkBAAJ588klxPR5zc3McPnwYrVq1wujRoxEQEIDJkyejuLi43kbK8uXLsXjxYqxcuRIBAQEYMGAAdu/eDV9f3/t+P6ocj2XLliEpKQl+fn51nupX/b039PehLQMGDMBXX32F/v37IyAgADNmzMCAAQPwww8/aPx9Fi1ahFdffRVdu3ZFfn6+0gyvezk5OWHz5s348ccfERgYiHfeeQerV69W2sfDwwNLly7F66+/DhcXl3qvordp0yZ06dIFQ4cORffu3SEIAn7//fd6Z1upwtjYGJ988gm6d++OkJAQfPjhh1i2bBnWrFnT6NesJhPUPcmPkJeXBxsbG+Tm5mq0Y9pYmZmZ9f6DJ2qJmFmSIk3nduJEoPpqwbt3a+xliUQca0mK9Cm3Le13hppKSkqQmJgIX19fLuhORI2m6ljCz1v1wPXr13VdApFamFmSIk3nlqfakbZxrCUpYm6JiPQPG09EREQ6wMYTERERET0I2HjSA0FBQbougUgtzCxJkaZzy8YTaRvHWpIi5paISP+w8aQHUlJSdF0CkVqYWZIiTeeWjSfSNo61JEXMLRGR/mHjSQ/k5OTougQitTCzJEWazi0bT6RtHGtJiphbIiL9w8aTHjAxMdF1CURqYWZJijSdW4Ma/wMLApCbq9GXJ+JYS5LE3DYvXuCciJpC1TGEjSc90KlTJ12XQKQWZpakSJu5/fBD4JlngLg4rb0FPYA41pIUMbfNw9jYGABQVFSk40qISMqqx5DqMaU+Rs1RDGnXyZMnER4erusyiFTGzJIUaTq3NWc87d9f9efWrQB/5yJN4VhLUsTcNg9DQ0PY2toiIyMDAGBubg4ZzwEnIhUJgoCioiJkZGTA1tYWhoaG992fjSciIqIWoqJC1xUQEdGDwtXVFQDE5hMRkbpsbW3FseR+1Go85ebmYseOHfjnn3+QlJSEoqIiODk5oXPnzhgwYAB69OjR6IKp8VT5iyZqSZhZkiJN57auD5YNeAI8aRDHWpIi5rb5yGQyuLm5wdnZGeXl5bouh4gkxtjYuMGZTtVUajylpaVh8eLF+O677+Dq6opu3bqhU6dOMDMzQ1ZWFg4ePIjVq1fD29sbS5YswZNPPtmkb4DUY2lpqesSiNTCzJIUaTq3dTWeVPy/m0glHGtJipjb5mdoaKjyL49ERI2hUuOpY8eOmDBhAk6ePImgoKA69ykuLsbOnTuxdu1a3Lx5E/Pnz9dooVS/q1evwsHBQddlEKmMmSUp0nRu65rdxJ/7SZM41pIUMbdERPpHpcbT+fPn4eTkdN99zMzMMG7cOIwbNw6ZmZkaKY6IiEhf1TXjycgIyMsDKisBW9tmL4mIiIiISONUWk2ioaZTU/dXRWRkJGQymdKt5jnggiAgMjIS7u7uMDMzQ0REBM6fP6/0GqWlpZgxYwYcHR1hYWGB4cOHIyUlReO1NreAgABdl0CkFmaWpKg5ciuTAePHA88+C5SVaf3tSM9xrCUpYm6JiPSPysuY7tmzB+PGjcP169cBAM8995zWiqpPhw4dkJaWJt7OnTsnPrZq1SqsXbsWH330EaKjo+Hq6orHHnsM+fn54j6zZ8/Gjh07sHXrVhw5cgQFBQUYOnQoFApFs38vmnT79m1dl0CkFmaWpEjTua3rVDtB+O/rrCyNvh09gDjWkhQxt0RE+kflxtP8+fMxdOhQ/O9//0NKSgouXLigzbrqZGRkBFdXV/FWPbNKEAR88MEHWLhwIUaPHo2goCB89dVXKCoqwpYtWwBUXZFv48aNWLNmDfr164fOnTvj22+/xblz5/DXX381+/eiSVn87YQkhpklKWqO3BrVOAG+slLrb0d6jmMtSRFzS0Skf1RuPNnY2GD8+PH48ssvMWXKFFRUVGizrjpduXIF7u7u8PX1xVNPPSXOvkpMTER6ejr69+8v7iuXy9GrVy8cO3YMABAbG4vy8nKlfdzd3REUFCTuI1VGRiot1UXUYjCzJEWazm1dazzVnAXFxhM1FcdakiLmlohI/6jceKq+tKmfnx+mTZuGU6dOaa2ouoSHh+Prr7/Gnj178PnnnyM9PR09evTA3bt3kZ6eDgBwcXFReo6Li4v4WHp6OkxMTGBnZ1fvPvUpLS1FXl6e0q0l6dKli65LIFILM0tSpOnc1nWqXc1mFBtP1FQca0mKmFsiIv2j8kcKGzZsgEKhgKGhIYYOHYqYmBht1lXLoEGDxK+Dg4PRvXt3+Pn54auvvsJDDz0EAJDd8/GxIAi1tt1LlX1WrlyJpUuX1toeExMDCwsLhIaGIiEhAcXFxbCysoKvry/Onj0LAPD29kZlZSVu3rwJAOjUqROuXr2KgoICWFhYoG3btjh9+jQAwNPTE4aGhrhx4wYAICQkBElJScjLy4OpqSk6dOiA2NhYAFWztUxNTXH9+nVkZ2fjkUceQUpKCnJycmBiYoJOnTrh5MmTAABXV1dYWlri6tWrAKoWbbx9+zaysrJgZGSELl264OTJkxAEAU5OTrCzs8Ply5cBAO3atUNWVhYyMzNhYGCArl27IiYmBgqFAg4ODnB2dkZCQgIAoE2bNsjLyxPPzQ8PD8epU6dQXl4OOzs7uLu7iwu++/n5oaioCGlpaQCAsLAwxMfHo6SkBDY2NmjVqpW4hpePjw8qKirEheBDQ0Nx8eJFFBUVwdLSEn5+fjhz5gwAoFWrVgCA5ORkAEDHjh1x7do1FBQUwNzcHO3btxebpp6enjAyMkJSUhKAqlwlJycjNzcXpqamCAoKEnPu5uYGc3NzXLt2DUDVemOpqanIzs6GsbExQkNDERUVBaCqmWltbY0rV66IxzsjIwN3796FoaEhwsLCEB0djcrKSjg5OcHe3h6XLl0CALRt2xbZ2dnIzMyETCZDt27dEBsbi4qKCtjb28PFxUU83v7+/igoKBAbp926dUNcXBzKyspga2sLT09PxMfHAwBat26NkpISpKamAqj6oe78+fMoKSmBtbU1fHx8lDKrUCjE4925c2dcvnwZhYWFsLS0hL+/P+Li4gAAXl5eMDAwUMpsYmIi8vPzYWZmhoCAAPF4e3h4wMTEBImJicjOzsajjz6KmzdvIicnB3K5HCEhIYiOjhYza2FhIR7vwMBApKenIysrq9bxdnZ2ho2NjXi827dvjzt37uDOnTtiZquPt6OjIxwdHXHx4kUxs7m5ucjIyKiVWXt7e7i6uoqnFfv5+aGwsFA83l27dsXZs2dRWloKW1tbeHl5iZn19fVFWVkZbt26JWZWl2MEAAQFBXGMQNPGiMrKStjZ2WlsjEhPL0V2timsrCxRWlqGsrIypKQUAvBETk4OTp1KREWFzQM5RgBVYzLHiKaNEZWVlbC1teUYwZ8jJDVGHD16FFZWVnoxRpibm4OIiACZINRcyrRhxcXFEARBHEhv3LiBHTt2IDAwUOk0tubw2GOPwd/fH6+88gr8/Pxw6tQpdO7cWXx8xIgRsLW1xVdffYUDBw6gb9++yMrKUpr11LFjR4wcObLOxlK10tJSlJaWivfz8vLg5eWF3NxcWFtba+ebU0NUVBTCw8N1XQaRyphZkiJN53b+fODf3xVF4eHAv78L4eOPgX9/ByZqFI61JEX6lNu8vDzY2Ni0mN8ZiIh0ReVT7aqNGDECX3/9NQAgJycH4eHhWLNmDUaMGIH169drvMD6lJaWIiEhAW5ubvD19YWrqyv27dsnPl5WVoZDhw6hR48eAKo+mTE2NlbaJy0tDfHx8eI+9ZHL5bC2tla6tSTVi6wTSQUzS1LUHLlV76MgovvjWEtSxNwSEekftRtPp06dwiOPPAIA+Omnn+Di4oIbN27g66+/xv/93/9pvMBq8+fPx6FDh5CYmIioqCiMGTMGeXl5mDhxImQyGWbPno0VK1Zgx44diI+Px6RJk2Bubo6nn34aQNXi6M899xzmzZuH/fv34/Tp03jmmWcQHByMfv36aa3u5nDvulVELR0zS1Kk6dzWtcZTzcYTm1DUVBxrSYqYWyIi/aN246moqAhWVlYAgL1792L06NEwMDDAQw89JJ6frQ0pKSkYN24c2rVrh9GjR8PExAQnTpyAt7c3AODVV1/F7NmzMXXqVISFheHWrVvYu3evWCsAvP/++xg5ciTGjh2Lnj17wtzcHLt374ahoaHW6m4O1esoEEkFM0tS1By5rbmgOBtP1FQca0mKmFsiIv2j9vVK/f39sXPnTowaNQp79uzBnDlzAAAZGRlaPQVt69at931cJpMhMjISkZGR9e5jamqKdevWYd26dRqujoiISD0NzXjiVe2IiIiISB+oPeNp8eLFmD9/Pnx8fBAeHo7u3bsDqJr9VHNhb2o+7dq103UJRGphZkmKNJ3bui6oymYTaRLHWpIi5paISP+o3XgaM2YMkpOTERMTgz///FPc3rdvX7z//vsaLY5Uk5WVpesSiNTCzJIUNUduOeOJNIljLUkRc0tEpH9Ubjy5u7vj5Zdfxh9//AF7e3t07twZBjXOE+jWrRvat2+vlSLp/jIzM3VdApFamFmSIk3ntq5T7bjGE2kSx1qSIuaWiEj/qNx42rJlC8zNzTFz5kw4OjriiSeewDfffMNPJVoAg7p+eyFqwZhZkqLmyC1nPJEmcawlKWJuiYj0j0wQ1P9M9fz589i1axd++eUXnD59Gt27d8eIESMwfPhw+Pn5aaPOFiUvLw82NjbIzc3V6oLqRESkvxYtAuLilLcFBgIXLlR9/d57ACcSExFJF39nICKq0qiPFDp06IA33ngDJ06cwI0bNzB+/HgcOHAAwcHBCAoKwm+//abpOuk+YmJidF0CkVqYWZIiTefW2Lj2tpofBfFUO2oqjrUkRcwtEZH+MWrqC7i6umLKlCmYMmUKioqKsGfPHsjlck3URipSKBS6LoFILcwsSZGmc1vX2SRsPJEmcawlKWJuiYj0T6MbTxkZGcjIyEDlPYtQjBo1qslFkXocHBx0XQKRWphZkiJN57auxhLXeCJN4lhLUsTcEhHpH7UbT7GxsZg4cSISEhJw7/JQMpmMn1LogLOzs65LIFILM0tSpOnc1tV44lXtSJM41pIUMbdERPpH7TWe/ve//6Ft27Y4duwYrl+/jsTERPF2/fp1bdRIDUhISNB1CURqYWZJijSdWzaeSNs41pIUMbdERPpH7RlPiYmJ2L59O/z9/bVRDxER0QOBp9oRERER0YNA7RlPffv2xZkzZ7RRCzVSmzZtdF0CkVqYWZKi5sgtm02kSRxrSYqYWyIi/aP2jKcvvvgCEydORHx8PIKCgmB8z/Wghw8frrHiSDV5eXmwt7fXdRlEKmNmSYo0ndu6mkyc8USaxLGWpIi5JSLSP2o3no4dO4YjR47gjz/+qPUYFxfXjdu3b8PHx0fXZRCpjJklKdJ0brnGE2kbx1qSIuaWiEj/qH2q3cyZM/Hss88iLS0NlZWVSjc2nYiIiBqvZuOJM56IiIiISB+o3Xi6e/cu5syZAxcXF23UQ40QHh6u6xKI1MLMkhRpOrd1NZY444k0iWMtSRFzS0Skf9RuPI0ePRoHDx7URi3USKdOndJ1CURqYWZJipojtzUnDrPxRE3FsZakiLklItI/aq/x1LZtW7zxxhs4cuQIgoODay0uPnPmTI0VR6opLy/XdQlEamFmSYo0nVuu8UTaxrGWpIi5JSLSP426qp2lpSUOHTqEQ4cOKT0mk8nYeNIBOzs7XZdApBZmlqRI07ltqPHENZ6oqTjWkhQxt0RE+kftxlNiYqI26qAmcHd313UJRGphZkmKNJ3buhpPPNWONIljLUkRc0tEpH/UXuOJWp7z58/rugQitTCzJEWazi1PtSNt41hLUsTcEhHpH7UbT2PGjME777xTa/t7772HJ554QiNFERER6TueakdEREREDwK1G0+HDh3CkCFDam0fOHAgDh8+rJGiSD1+fn66LoFILcwsSZGmc1tX46miQqNvQQ84jrUkRcwtEZH+UbvxVFBQABMTk1rbjY2NkZeXp5GiSD1FRUW6LoFILcwsSZGmc9tQ44kznqipONaSFDG3RET6R+3GU1BQELZt21Zr+9atWxEYGKiRouqycuVKdO3aFVZWVnB2dsbIkSNx6dIlpX0mTZoEmUymdHvooYeU9iktLcWMGTPg6OgICwsLDB8+HCkpKVqruzmkpaXpugQitTCzJEXNkVsuLk6axLGWpIi5JSLSP2pf1W7RokV4/PHHce3aNfTp0wcAsH//fnz//ff48ccfNV5gtUOHDmHatGno2rUrKioqsHDhQvTv3x8XLlyAhYWFuN/AgQOxadMm8f69s7Nmz56N3bt3Y+vWrXBwcMC8efMwdOhQxMbGwtDQUGv1ExER1dTQjCbOeCIiIiIifSATBPU/U/3tt9+wYsUKxMXFwczMDCEhIViyZAl69eqljRrrlJmZCWdnZxw6dAiPPvoogKoZTzk5Odi5c2edz8nNzYWTkxO++eYbPPnkkwCA1NRUeHl54ffff8eAAQNUeu+8vDzY2NggNzcX1tbWGvl+mkKhULBpRpLCzJIUaTq3c+cCV67U//isWUC/fhp7O3oAcawlKdKn3La03xmIiHRF7VPtAGDIkCE4evQoCgsLcefOHRw4cKBZm05AVRMJAOzt7ZW2//3333B2dkbbtm0xZcoUZGRkiI/FxsaivLwc/fv3F7e5u7sjKCgIx44dq/e9SktLkZeXp3RrSeLj43VdApFamFmSoqbm9tgxYMYM4ObNqvsNfezDU+2oqTjWkhQxt0RE+kftU+1UIQgCZDKZNl5afP25c+fi4YcfRlBQkLh90KBBeOKJJ+Dt7Y3ExEQsWrQIffr0QWxsLORyOdLT02FiYgI7Ozul13NxcUF6enq977dy5UosXbq01vaYmBhYWFggNDQUCQkJKC4uhpWVFXx9fXH27FkAgLe3NyorK3Hz3980OnXqhKtXr6KgoAAWFhZo27YtTp8+DQDw9PSEoaEhbty4AQAICQlBUlIS8vLyYGpqig4dOiA2NhZAVcPM1NQU169fR3Z2Nvz9/ZGSkoKcnByYmJigU6dOOHnyJADA1dUVlpaWuHr1KgAgICAAt2/fRlZWFoyMjNClSxecPHkSgiDAyckJdnZ2uHz5MgCgXbt2yMrKQmZmJgwMDNC1a1fExMRAoVDAwcEBzs7OSEhIAAC0adMGeXl5uH37NgAgPDwcp06dQnl5Oezs7ODu7o7z588DqLpiSVFRkXgef1hYGOLj41FSUgIbGxu0atUK586dAwD4+PigoqJCXIsrNDQUFy9eRFFRESwtLeHn54czZ84AAFq1agUASE5OBgB07NgR165dQ0FBAczNzdG+fXucOnVKPN5GRkZISkoCAAQHByM5ORm5ubkwNTVFUFAQYmJiAABubm4wNzfHtWvXAAAdOnRAamoqsrOzYWxsjNDQUERFRYl5sra2xpV/pzIEBAQgIyMDd+/ehaGhIcLCwhAdHY3Kyko4OTnB3t5eXK+sbdu2yM7ORmZmJmQyGbp164bY2FhUVFTA3t4eLi4u4vH29/dHQUGBmN1u3bohLi4OZWVlsLW1haenp/jDW+vWrVFSUoLU1FQAQJcuXXD+/HmUlJTA2toaPj4+SplVKBTi8e7cuTMuX76MwsJCWFpawt/fH3FxcQAALy8vGBgYKGU2MTER+fn5MDMzQ0BAgHi8PTw8YGJigsTERGRnZ6NNmza4efMmcnJyIJfLERISgujoaDGzFhYW4vEODAxEeno6srKyah1vZ2dn2NjYiMe7ffv2uHPnDu7cuSNmtvp4Ozo6wtHRERcvXhQzm5ubKzaoa2bW3t4erq6uuHDhgpjZwsJC8Xh37doVZ8+eRWlpKWxtbeHl5SVm1tfXF2VlZbh165aYWV2OEUDV+nwcI5o2RigUCly8eLHRY8Srr2YDAF5/3Qjr1pUjNbUM2dlyWFlZorS0DGVlZZDJZLC1tUVOTg4SEtLh52fyQI4RQNWYzDGiaWOEQqFAQkICxwj+HCGpMSIzMxNRUVF6MUaYm5uDiIhUPNUuICAAixYtwpgxY+q8ol21K1euYO3atfD29sbrr7+u0UJrmjZtGn777TccOXIEnp6e9e6XlpYGb29vbN26FaNHj8aWLVvwv//9D6WlpUr7PfbYY/Dz88OGDRvqfJ3S0lKl5+Tl5cHLy6vFTJu9ePEi2rdvr+syiFTGzJIUNTW3w4ZV/WlmBvzwQ9WpdP/+zl+nadOAgQMb/XZEHGtJkvQptzzVjoioikoznj7++GO89tprmDZtGvr374+wsDDxk7Ls7GxcuHABR44cwYULFzB9+nRMnTpVawXPmDEDu3btwuHDh+/bdAKqPl3y9vYWP8FwdXVFWVkZsrOzlWY9ZWRkoEePHvW+jlwuh1wu18w3oAXVn84RSQUzS1KkqdxWf9zDU+1I2zjWkhQxt0RE+kelNZ769OmD6Oho/Pbbb3B1dcWWLVswffp0jB8/HpGRkbhy5QomTJiAlJQUvPPOO1rp6AuCgOnTp2P79u04cOAAfH19G3zO3bt3cfPmTbi5uQGomhZsbGyMffv2ifukpaUhPj7+vo2nlq56KjmRVDCzJEWaym311erYeCJt41hLUsTcEhHpH7XWeOrRo4fOGjTTpk3Dli1b8Msvv8DKyko8H93GxgZmZmYoKChAZGQkHn/8cbi5uSEpKQkLFiyAo6MjRo0aJe773HPPYd68eXBwcIC9vT3mz5+P4OBg9OOlg4iIqBmoOuOpukFFRERERCRlWllcXBvWr18PAIiIiFDavmnTJkyaNAmGhoY4d+4cvv76a+Tk5MDNzQ29e/fGtm3bYGVlJe7//vvvw8jICGPHjkVxcTH69u2LzZs3S/qyrT4+ProugUgtzCxJkaZyW15e9SdnPJG2cawlKWJuiYj0j2QaTw2tgW5mZoY9e/Y0+DqmpqZYt24d1q1bp6nSdK6iokLXJRCphZklKdJkbi9dYuOJtI9jLUkRc0tEpH9UWuOJWrbqy9USSQUzS1Kkydx+9FHDjaXPPwe+/FJjb0kPII61JEXMLRGR/mHjiYiIqJmVlak2o2nHDu3XQkRERESkTZI51Y7qFxoaqusSiNTCzJIUaTK3PI2OmgPHWpIi5paISP80asZTZWUlLl++jCNHjuDw4cNKN2p+Fy9e1HUJRGphZkmKNJlbhUL15pNCobG3pQcMx1qSIuaWiEj/qD3j6cSJE3j66adx48aNWgt+y2QyKPgTcrMrKirSdQlEamFmSYo0mVtBUL3xVFEBSPjCq6RDHGtJiphbIiL9o/aMp5deeglhYWGIj49HVlYWsrOzxVtWVpY2aqQGWFpa6roEIrUwsyRFDeX28uWqxcBLShp+LXUaT+Xlqu1HdC+OtSRFzC0Rkf5Re8bTlStX8NNPP8Hf318b9VAj+Pn56boEIrUwsyRFDeU2MhLIz69qPE2dev/XqqwEDFT86IeNJ2osjrUkRcwtEZH+UXvGU3h4OK5evaqNWqiRzpw5o+sSiNTCzJIUNZTb/PyqP69fb/i11FnjiY0naiyOtSRFzC0Rkf5Re8bTjBkzMG/ePKSnpyM4OBjGxsZKj4eEhGisOCIiIqm557/FOvFUOyIiIiJ6UKjdeHr88ccBAJMnTxa3yWQyCILAxcV1pFWrVrougUgtzCxJkaq5VeW/QXUXFydqDI61JEXMLRGR/lG78ZSYmKiNOoiIiPSCKg0lzngiIiIiogeF2o0nb29vbdRBTZCcnAw3Nzddl0GkMmaWpOh+ua05y0nVhhIbT6RtHGtJiphbIiL9o3bjCQCuXbuGDz74AAkJCZDJZAgICMCsWbN4FQoiInogNaY5VFmpvdcmIiIiImop1L6q3Z49exAYGIiTJ08iJCQEQUFBiIqKQocOHbBv3z5t1EgN6Nixo65LIFILM0tSdL/c1mwOqTqTSVVsPFFjcawlKWJuiYj0j9qNp9dffx1z5sxBVFQU1q5di/fffx9RUVGYPXs2XnvtNW3USA24du2arksgUgszS1J0v9zWbA5dvgzs33//1xIE1Wc8cXFxaiyOtSRFzC0Rkf5Ru/GUkJCA5557rtb2yZMn48KFCxopitRTUFCg6xKI1MLMkhTdL7dlZcr3P/hAc+/LGU/UWBxrSYqYWyIi/aN248nJyQlxcXG1tsfFxcHZ2VkTNZGazM3NdV0CkVqYWZKi++W2ruZQcfH9X49rPJG2cawlKWJuiYj0j9qLi0+ZMgUvvPACrl+/jh49ekAmk+HIkSN49913MW/ePG3USA1o3769rksgUgszS1J0v9zW1RyKjQV69gRkstqPCULd2+vCU+2osTjWkhQxt0RE+kftGU+LFi3C4sWLsW7dOvTq1QuPPvooPvroI0RGRmLhwoXaqJEacOrUKV2XQKQWZpak6H65vfdUOwB4913g66/rfz1VZzzdvavafkT34lhLUsTcEhHpH7UbTzKZDHPmzEFKSgpyc3ORm5uLlJQUzJo1CzJVP74lIiLSI3U1ngDgp5+q/qzrSneqXv3u9u3G1URERERE1BKofapdTVZWVpqqg5rA09NT1yUQqYWZJSm6X27razxVO3pU+b4gqN54ys9XbT+ie3GsJSlibomI9I9KjafQ0FDs378fdnZ26Ny5831nNnF6bPMzMmpS/5Co2TGzJEX3y21DjafPP6+9TdXGU16eavsR3YtjLUkRc0tEpH9UGtlHjBgBuVwufs1T6lqWpKQkuLi46LoMIpUxsyRF9eU2IwNYubLqa3PzqibUvQuCFxXVfj3OeCJt41hLUsTcEhHpH5UaT0uWLBG/joyM1FYtzeqTTz7Be++9h7S0NHTo0AEffPABHnnkEV2XRUREErN1639fV1QAc+cCq1Y1/Dw2noiIiIjoQaD24uKtW7fG3TousZOTk4PWrVtrpCht27ZtG2bPno2FCxfi9OnTeOSRRzBo0CAkJyfrurRGCQ4O1nUJRGphZkmKqnO7fj3w4ovAjRtV29PS/tsnLAzw8Gj4tdRd40nVfYlq4lhLUsTcEhHpH7UbT0lJSVAoFLW2l5aWIiUlRSNFadvatWvx3HPP4fnnn0dAQAA++OADeHl5Yf369bourVGk2jCjBxczS1J040YyvvwS+P13IDUVWLcOKCgAqj+LefhhYNo0QJUzRAwMVG8mVVYCSUmNLpseYBxrSYqYWyIi/aPy6n27du0Sv96zZw9sbGzE+wqFAvv374evr69mq9OCsrIyxMbG4vXXX1fa3r9/fxw7dqzO55SWlqK0tFS8n9eCVnpdvBhISbFS6RN2opbi1i1mVpfqW6avru3qLOknlecbGABGRlU3Y+OqPw0N//v63pupKWBlBfz9twH++ee/17l0CRg37r/7//sfYG1d9fXkycCXX1Z9XVlZd73qzGKaNQsIDAS8vater7z8v9euqAAUiv9eTyar+rr6fatnV9WcZXXv16SfONaSFGkitx07AmPGaKYeIiJqOpUbTyNHjgQAyGQyTJw4UekxY2Nj+Pj4YM2aNRotThvu3LkDhUJRa9FCFxcXpKen1/mclStXYunSpbW2x8TEwMLCAqGhoUhISEBxcTGsrKzg6+uLs2fPAgC8vb1RWVmJmzdvAgA6deqEq1evoqCgABYWFmjbti1Onz4NoOrysYaGhrjx7/kbISEhSEpKQl5eHkxNTdGhQwfExsYCANzd3WFqaoqDB41QXm6C9PQKFBeXoLy8HAYGBrCxsUZ2dg4AwNRUDkNDIxQWFgIArKwsUVpairKychgYyGBjY4ucnGwIAiCXm8DY2BgFBVX7Wlpaory8DKWlZZDJZLC1tUVOTg4EQYCJiQnkchPk5xf8u68FyssrxCadnZ0dcnNzUVlZCRMTY8jlpsj/d7ESCwsLKBQKlJSUAABsbW2Rn58HhaISxsbGMDMzExt85ubmEIRKFBdX7WtjY4OCggIoFAoYGRnBwsIcublV+5qZmQEAiouL/93XGoWFRaioqIChoSEsLS2Rm5v7776mkMkMUPTvqr/W1tYoLi5GeXk5DA0NYGVljZyc6mNoCkNDwxrH0AqlpSX/HkMD2NjYIDs7GwAgl8thbGwkHsOq412GsrLax7DqeJugoKDmMSz/93gDtrZ2yM3NQWWl8O8xlIvHu+oYVqCkpPp42yI3Nw+VldXH0BR5edXH2xwKRWWN422D/Pz8f4+3EczMzGscbzMIgqB0vAsLC1BRUffxlsmAoqJi8RgWFTV8vCsqTJCerrjP8b43s6ofb0tLS5SV1X28TUxMYGJy7/FWJ7P/He+mZNbc/N7jXXdmjYwMYWFx7zGUKR3v4uIilJdX/HsMrZCTk1sjswYoLKzOtxXHCDRtjDAwcIahYUGdmfX3N4JMVoqoqGsAgEce6YB164CysnKcPJkEQeislFlDQ0NkZRXVON51Z7ZfPwXy8y0RFVWMI0eAuLgHY4z4L98cI5oyRhgYWCAxMZ9jBH+OkNQYkZMjx+XL2U063oIgICKiEteuVY3JgYGBSE9PR1ZWFoyNjREaGoqoqCgAgLOzM2xsbHDlyhUAQPv27XHnzh3cuXMHBgYG6Nq1K6Kjo1FZWQlHR0c4Ojri4sWLAIA2bdogNzcXGRkZAIDw8HCcOnUK5eXlsLe3h7m5OYiICJAJgnqfdfr6+iI6OhqOjo7aqkmrUlNT4eHhgWPHjqF79+7i9rfffhvffPON+B9JTXXNePLy8kJubi6sqz/e1pFDh6pmnBkaGuq0DiJ1MLPNo67RXZ0RX0rPV/V1q2cCVVTUvpWX1729pATIywNycyuRkWEAuRyo8V8CAGDZMqBz5//uKxTAv5/XYMsWYNKkqqvdqWvixKpP7TMzgZgYICuranZW9dXGq782NPxvdpcgVH1d3w1Q/rqu+1LEmVu1cawlKdJEbl1cgIAADRXUBHl5ebCxsWkRvzMQEemSyjOeqiUmJmqjjmbj6OgIQ0PDWrObMjIy6r10q1wuh1wub47y1NarFxAVFYPw8HBdl0KkMmaWpCgqKlrMrUIBTJ1atdZT69bKTSegqhFkYFDV5Cora3xTxMSk6k8nJ2DQoCYUTw8kjrUkRcwtEZH+UbvxBACFhYU4dOgQkpOTUXbPR7gzZ87USGHaYmJigi5dumDfvn0YNWqUuH3fvn0YMWKEDisjIiKpMDSsurrdyZNVjae6GBtXzYxSKKpmTjWGlVXjayQiIiIiagnUbjydPn0agwcPRlFREQoLC2Fvb487d+7A3Nwczs7OLb7xBABz587Fs88+i7CwMHTv3h2fffYZkpOT8dJLL+m6tEZxc3PTdQlEamFmSYruza2BAfDQQ/Xvb2RU1XgqL2/8jCdj48Y9jwjgWEvSxNwSEekfA3WfMGfOHAwbNgxZWVkwMzPDiRMncOPGDXTp0gWrV6/WRo0a9+STT+KDDz7AsmXL0KlTJxw+fBi///47vL29dV1ao3DhQpIaZpakSN3cVq/DVF4OdOrUuPc0UPt/aaL/cKwlKWJuiYj0j9o/0sbFxWHevHkwNDSEoaEhSktL4eXlhVWrVmHBggXaqFErpk6diqSkJJSWliI2NhaPPvqorktqtOordhBJBTNLUqRubqtnK1VU/LdWU40zvFVi1KgT4omqcKwlKWJuiYj0j9qNJ2NjY8j+vfSNi4sLkpOTAVRdKrX6ayIiogdd9VXiEhKq1nkC/mtAqYoXJCMiIiIiqVP7s9TOnTsjJiYGbdu2Re/evbF48WLcuXMH33zzDYKDg7VRIzWgQ4cOui6BSC3MLEmRurnNzKz687PPgI4dq75Wt/HEGU/UFBxrSYqYWyIi/aP2jKcVK1aIi/4tX74cDg4OePnll5GRkYHPPvtM4wVSw1JTU3VdApFamFmSoqbktnrGk7qLhXPGEzUFx1qSIuaWiEj/qPVZqiAIcHJyEj+JcHJywu+//66Vwkh12dnZui6BSC3MLElRU3JbUVH1JxtP1Jw41pIUMbdERPpHrRlPgiCgTZs2SElJ0VY91AjGvN42SQwzS1LUlNw2do0nnmpHTcGxlqSIuSUi0j9qNZ4MDAzQpk0b3L17V1v1UCOEhobqugQitTCzJEVNyS1PtSNd4FhLUsTcEhHpH7XXeFq1ahVeeeUVxMfHa6MeaoSoqChdl0CkFmaWpKgpub1+vepPXtWOmhPHWpIi5paISP+oPYn/mWeeQVFRETp27AgTExOYmZkpPZ6VlaWx4oiIiB5kPNWOiIiIiKRO7R9pP/jgAy2UQU3h4uKi6xKI1MLMkhRpIrec8UTNiWMtSRFzS0Skf9RuPE2cOFEbdVATWFtb67oEIrUwsyRFmshtSIh6+7PxRE3BsZakiLklItI/aq/xBADXrl3Dm2++iXHjxiEjIwMA8Oeff+L8+fMaLY5Uc+XKFV2XQKQWZpakqKm59fBQv5HExhM1BcdakiLmlohI/6jdeDp06BCCg4MRFRWF7du3o6CgAABw9uxZLFmyROMFEhER6QMDg6qbOth4IiIiIiKpU7vx9Prrr+Ott97Cvn37YFJjsYrevXvj+PHjGi2OVBMQEKDrEojUwsySFDU1t9WNp4cfVv05bDxRU3CsJSlibomI9I/ajadz585h1KhRtbY7OTnh7t27GimK1FN9uiORVDCzJEXq5nb8eOX71bOdXnsNmD1btddg44magmMtSRFzS0Skf9RuPNna2iItLa3W9tOnT8PDw0MjRZF62PAjqWFmSYrUze2TTypfxa7maXY9ewLOzg2/BhtP1BQca0mKmFsiIv2jduPp6aefxmuvvYb09HTIZDJUVlbi6NGjmD9/PiZMmKCNGqkBhvzNhCSGmSUpUje3Mhng6vrf/ZqNJ1NT4IsvgLFj7/8a6q4JRVQTx1qSIuaWiEj/qP0j7dtvv41WrVrBw8MDBQUFCAwMxKOPPooePXrgzTff1EaN1ICwsDBdl0CkFmaWpKgxua1vxhNQ1ZiSye7/fP7+RU3BsZakiLklItI/ajeejI2N8d133+Hy5cv44Ycf8O233+LixYv45ptv+AmFjkRHR+u6BCK1MLMkRY3JrbHxf1/XNXupocZTQ48T3Q/HWpIi5paISP8YNfaJfn5+8PPz02Qt1EiVlZW6LoFILcwsSVFjcluz8cTPZqi5cawlKWJuiYj0j0qNp7lz56r8gmvXrm10MdQ4Tk5Oui6BSC3MLElRY3JbXv7f13XNXuIaTqRNHGtJiphbIiL9o1Lj6fTp0yq9mIznBOiEvb29rksgUgszS1LUmNzm5Pz3tSDUfpz/bZI2cawlKWJuiYj0j0qNp4MHD2q7DmqCS5cuITw8XNdlEKmMmSUpakxujWr8L6tQaLggogZwrCUpYm6JiPRPoyf5X716FXv27EFxcTEAQKjro1wiIqIHWM3GU0VF7cd5qh0RERER6Tu1f+S9e/cu+vbti7Zt22Lw4MFIS0sDADz//POYN2+exgsEgKSkJDz33HPw9fWFmZkZ/Pz8sGTJEpSVlSntJ5PJat02bNigtM+5c+fQq1cvmJmZwcPDA8uWLZN806xt27a6LoFILcwsSVFjclvzVDp1/qtxcwPeeEPttyNSwrGWpIi5JSLSP2o3nubMmQNjY2MkJyfD3Nxc3P7kk0/izz//1Ghx1S5evIjKykp8+umnOH/+PN5//31s2LABCxYsqLXvpk2bkJaWJt4mTpwoPpaXl4fHHnsM7u7uiI6Oxrp167B69WrJL4ienZ2t6xKI1MLMkhQ1JrcNXZypvjWeXnwR6NFD7bcjUsKxlqSIuSUi0j8qrfFU0969e7Fnzx54enoqbW/Tpg1u3LihscJqGjhwIAYOHCjeb926NS5duoT169dj9erVSvva2trC1dW1ztf57rvvUFJSgs2bN0MulyMoKAiXL1/G2rVrMXfuXMkujp6ZmYnWrVvrugwilTGzJEVNza06i4vzFDzSBI61JEXMLRGR/lH7R9vCwkKlmU7V7ty5A7lcrpGiVJGbm1vnVS+mT58OR0dHdO3aFRs2bEBljY+bjx8/jl69einVOWDAAKSmpiIpKak5ytYKqTbM6MHFzJIUNSa3DZ1eV99L8p8IaQLHWpIi5paISP+o3Xh69NFH8fXXX4v3ZTIZKisr8d5776F3794aLa4+165dw7p16/DSSy8pbV++fDl+/PFH/PXXX3jqqacwb948rFixQnw8PT0dLi4uSs+pvp+enl7v+5WWliIvL0/p1pJ069ZN1yUQqYWZJSlqTG5NTf/7Wp0ZT/y9izSBYy1JEXNLRKR/1D7V7r333kNERARiYmJQVlaGV199FefPn0dWVhaOHj2q1mtFRkZi6dKl990nOjoaYWFh4v3U1FQMHDgQTzzxBJ5//nmlfd98803x606dOgEAli1bprT93k9RqhcWv9+nKytXrqyzzpiYGFhYWCA0NBQJCQkoLi6GlZUVfH19cfbsWQCAt7c3KisrcfPmTbGuq1evoqCgABYWFmjbti1Onz4NAPD09IShoaF4ymJISAiSkpKQl5cHU1NTdOjQAbGxsQAAd3d3mJqa4vr168jJycHDDz+MlJQU5OTkwMTEBJ06dcLJkycBAK6urrC0tMTVq1cBAAEBAbh9+zaysrJgZGSELl264OTJkxAEAU5OTrCzs8Ply5cBAO3atUNWVhYyMzNhYGCArl27IiYmBgqFAg4ODnB2dkZCQgKAqtMt8/LycPv2bQBAeHg4Tp06hfLyctjZ2cHd3R3nz58HAPj5+aGoqEhcnD4sLAzx8fEoKSmBjY0NWrVqhXPnzgEAfHx8UFFRgZSUFABAaGgoLl68iKKiIlhaWsLPzw9nzpwBALRq1QoAkJycDADo2LEjrl27hoKCApibm6N9+/Y4deqUeLyNjIzE2W7BwcFITk5Gbm4uTE1NERQUhJiYGACAm5sbzM3Nce3aNQBAhw4dkJqaiuzsbBgbGyM0NBRRUVEAqpqZ1tbWuHLlini8MzIycPfuXRgaGiIsLAzR0dGorKyEk5MT7O3tcenSJQBVC2pmZ2cjMzMTMpkM3bp1Q2xsLCoqKmBvbw8XFxfxePv7+6OgoEBsmnbr1g1xcXEoKyuDra0tPD09ER8fD6Dq9NSSkhKkpqYCALp06YLz58+jpKQE1tbW8PHxUcqsQqEQj3fnzp1x+fJlFBYWwtLSEv7+/oiLiwMAeHl5wcDAQCmziYmJyM/Ph5mZGQICAsTj7eHhARMTEyQmJiInJwePPPIIbt68iZycHMjlcoSEhCA6OlrMrIWFhXi8AwMDkZ6ejqysrFrH29nZGTY2NuLxbt++Pe7cuYM7d+6Ima0+3o6OjnB0dMTFixfFzObm5iIjI6NWZu3t7eHq6ooLFy6ImS0sLBSPd9euXXH27FmUlpbC1tYWXl5eYmZ9fX1RVlaGW7duiZnV5RgBAEFBQRwj0LQxAgBsbGzUGiPCw4tw8qQvbGxskJ6ejqioG0pjxJUrdqio8EVpaRnKysogk8lga2uL8+fjUVJS+MCOEUDVmMwxomljBABYW1tzjODPEZIaI44fPw4LCwu9GCPqOkuEiOhBJBMacUm39PR0rF+/HrGxsaisrERoaCimTZsGNzc3tV6nelC/Hx8fH5j++5FxamoqevfujfDwcGzevBkGDSyCcfToUTz88MPiTKcJEyYgNzcXv/zyi7jP6dOnERoaiuvXr8PX17fO1yktLUVpaal4Py8vD15eXsjNzYW1tbWq367WREVFITw8XNdlEKmMmSUpamxuhw2r+rN1a+DDD5Uf27kT2Lix9nNWrACCg9WvkagmjrUkRfqU27y8PPEDi5bwOwMRka6oPeMJqPoUoaGZSqqo/tRAFbdu3ULv3r3RpUsXbNq0qcGmE1DVVDI1NYWtrS0AoHv37liwYAHKyspgYmICoGqxdHd3d/j4+NT7OnK5vFnXr1JXXWtdEbVkzCxJUVNzy1PtqLlxrCUpYm6JiPSP2ms8bdq0CT/++GOt7T/++CO++uorjRR1r9TUVERERMDLywurV69GZmYm0tPTldZl2r17Nz7//HPEx8fj2rVr+OKLL7Bw4UK88MILYtPo6aefhlwux6RJkxAfH48dO3ZgxYoVkr6iHYBa61YRtXTMLElRU3OrzvxiCf+XRC0Ix1qSIuaWiEj/qN14euedd+qcpeTs7Ky0kLcm7d27F1evXsWBAwfg6ekJNzc38VbN2NgYn3zyCbp3746QkBB8+OGHWLZsGdasWSPuY2Njg3379iElJQVhYWGYOnUq5s6di7lz52ql7uZSfa4+kVQwsyRF2shtfZN32XgiTeBYS1LE3BIR6R+1T7W7ceNGnWsheXt7iwsxatqkSZMwadKk++4zcOBADBw4sMHXCg4OxuHDhzVUGRERkWo444mIiIiIHkRqz3hydnYWr1pR05kzZ+Dg4KCRokg9/v7+ui6BSC3MLElRU3PLNZ6ouXGsJSlibomI9I/ajaennnoKM2fOxMGDB6FQKKBQKHDgwAHMmjULTz31lDZqpAYUFBTougQitTCzJEVNzW1djSeeakfaxLGWpIi5JSLSP2o3nt566y2Eh4ejb9++MDMzg5mZGfr3748+ffpobY0nur+ai6wTSQEzS1LUnLll44k0gWMtSRFzS0Skf9Re48nExATbtm3DW2+9hbi4OJiZmSE4OBje3t7aqI+IiEhv8VQ7IiIiItJ3ajeeqrVp0wZt2rTRZC3USN26ddN1CURqYWZJipqaW67xRM2NYy1JEXNLRKR/1D7VbsyYMXjnnXdqbX/vvffwxBNPaKQoUk9cXJyuSyBSCzNLUtTU3KrTeKpv7ScidXCsJSlibomI9I/aP9oeOnQIQ4YMqbV94MCBOHz4sEaKIvWUlZXpugQitTCzJEXayC1nNpE2cawlKWJuiYj0j9qNp4KCApiYmNTabmxsjLy8PI0UReqxtbXVdQlEamFmSYqamlvOeKLmxrGWpIi5JSLSP2r/aBsUFIRt27bV2r5161YEBgZqpChSj6enp65LIFILM0tS1NTcqtN4ItIEjrUkRcwtEZH+UXtx8UWLFuHxxx/HtWvX0KdPHwDA/v378f333+PHH3/UeIHUsPj4eISHh+u6DCKVMbMkRdrILRcXJ23iWEtSxNwSEekftRtPw4cPx86dO7FixQr89NNPMDMzQ0hICP766y/06tVLGzUSERFJHk+1IyIiIqIHkdqNJwAYMmRInQuMx8XFoVOnTk2tidTUunVrXZdApBZmlqSoqbmtq/HEBhNpE8dakiLmlohI/zT5R97c3Fx88sknCA0NRZcuXTRRE6mppKRE1yUQqYWZJSnSRm7NzevezoYUaQLHWpIi5paISP80+kfbAwcOYPz48XBzc8O6deswePBgxMTEaLI2UlFqaqquSyBSCzNLUtTU3Pr7196Wn9+klyS6L461JEXMLRGR/lHrVLuUlBRs3rwZX375JQoLCzF27FiUl5fj559/5hXtiIiI6vDxx8BffwFjxtR+LCSk7udwxhMRERER6QuZINS16kRtgwcPxpEjRzB06FCMHz8eAwcOhKGhIYyNjXHmzJkHqvGUl5cHGxsb5ObmwtraWtfloKKiAkZGjVqui0gnmFmSIm3ldtiw2ts++wxwc9P4W9EDhmMtSZE+5bal/c5ARKQrKn+munfvXjz//PNYunQphgwZAkNDQ23WRWo4f/68rksgUgszS1LUnLnljCfSBI61JEXMLRGR/lH5R9t//vkH+fn5CAsLQ3h4OD766CNkZmZqszZSERdhJKlhZkmKmjO3MlmzvRXpMY61JEXMLRGR/lG58dS9e3d8/vnnSEtLw4svvoitW7fCw8MDlZWV2LdvH/K5QqrOcOouSQ0zS1LUnLll44k0gWMtSRFzS0Skf9SezG9ubo7JkyfjyJEjOHfuHObNm4d33nkHzs7OGD58uDZqpAb4+PjougQitTCzJEXNmVs2nkgTONaSFDG3RET6p0mrSLRr1w6rVq1CSkoKvv/+e03VRGo6e/asrksgUgszS1LUnLll44k0gWMtSRFzS0SkfzSyfKmhoSFGjhyJXbt2aeLliIiIHmh6ckEnIiIiIiLNNJ5It7y9vXVdApFamFmSoubMLRtPpAkca0mKmFsiIv0jmcaTj48PZDKZ0u31119X2ic5ORnDhg2DhYUFHB0dMXPmTJSVlSntc+7cOfTq1QtmZmbw8PDAsmXLIAhCc34rGqdQKHRdApFamFmSIm3ltlev2tsMDbXyVvSA4VhLUsTcEhHpH8k0ngBg2bJlSEtLE29vvvmm+JhCocCQIUNQWFiII0eOYOvWrfj5558xb948cZ+8vDw89thjcHd3R3R0NNatW4fVq1dj7dq1uvh2NCYlJUXXJRCphZklKdJWbufNA7ZuVd5mbKyVt6IHDMdakiLmlohI/0hqMr+VlRVcXV3rfGzv3r24cOECbt68CXd3dwDAmjVrMGnSJLz99tuwtrbGd999h5KSEmzevBlyuRxBQUG4fPky1q5di7lz50LG1VyJiKiZyWSAhYXyNs54IiIiIiJ9IakZT++++y4cHBzQqVMnvP3220qn0R0/fhxBQUFi0wkABgwYgNLSUsTGxor79OrVC3K5XGmf1NRUJCUlNdv3oWmdO3fWdQlEamFmSYqYW5IaZpakiLklItI/kmk8zZo1C1u3bsXBgwcxffp0fPDBB5g6dar4eHp6OlxcXJSeY2dnBxMTE6Snp9e7T/X96n3qUlpairy8PKVbS3L58mVdl0CkFmaWpIi5JalhZkmKmFsiIv2j01PtIiMjsXTp0vvuEx0djbCwMMyZM0fcFhISAjs7O4wZM0acBQWgzlPlBEFQ2n7vPtULi9/vNLuVK1fWWWdMTAwsLCwQGhqKhIQEFBcXw8rKCr6+vjh79iyAqitzVFZW4ubNmwCATp064erVqygoKICFhQXatm2L06dPAwA8PT1haGiIGzduiN9nUlIS8vLyYGpqig4dOoizt9zd3WFqaorr168jOzsbvr6+SElJQU5ODkxMTNCpUyecPHkSAODq6gpLS0tcvXoVABAQEIDbt28jKysLRkZG6NKlC06ePAlBEODk5AQ7OzvxP/127dohKysLmZmZMDAwQNeuXRETEwOFQgEHBwc4OzsjISEBANCmTRvk5eXh9u3bAIDw8HCcOnUK5eXlsLOzg7u7O86fPw8A8PPzQ1FREdLS0gAAYWFhiI+PR0lJCWxsbNCqVSucO3cOQNXC8hUVFeI5/6Ghobh48SKKiopgaWkJPz8/nDlzBgDQqlUrAFULzQNAx44dce3aNRQUFMDc3Bzt27fHqVOnxONtZGQkznYLDg5GcnIycnNzYWpqiqCgIMTExAAA3NzcYG5ujmvXrgEAOnTogNTUVGRnZ8PY2BihoaGIiooCUNXMtLa2xpUrV8TjnZGRgbt378LQ0BBhYWGIjo5GZWUlnJycYG9vj0uXLgEA2rZti+zsbGRmZkImk6Fbt26IjY1FRUUF7O3t4eLiIh5vf39/FBQUiE3Tbt26IS4uDmVlZbC1tYWnpyfi4+MBAK1bt0ZJSQlSU1MBAF26dMH58+dRUlICa2tr+Pj4KGVWoVCIx7tz5864fPkyCgsLYWlpCX9/f8TFxQEAvLy8YGBgoJTZxMRE5Ofnw8zMDAEBAeLx9vDwgImJCRITE5GdnY3WrVvj5s2byMnJgVwuR0hICKKjo8XMWlhYiMc7MDAQ6enpyMrKqnW8nZ2dYWNjIx7v9u3b486dO7hz546Y2erj7ejoCEdHR1y8eFHMbG5uLjIyMmpl1t7eHq6urrhw4YKY2cLCQvF4d+3aFWfPnkVpaSlsbW3h5eUlZtbX1xdlZWW4deuWmFldjhEAEBQUxDECTRsjFAoFLl68qLUxIjvbATKZDLa2tg/8GAFUjckcI5o2RigUCiQkJHCM4M8Rkhoj0tPTUVhYqBdjhLm5OYiICJAJOrykW/Wgfj8+Pj4wNTWttf3WrVvw9PTEiRMnEB4ejsWLF+OXX34Rf3AAgOzsbNjb2+PAgQPo3bs3JkyYgNzcXPzyyy/iPqdPn0ZoaCiuX78OX1/fOmsoLS1FaWmpeD8vLw9eXl7Izc2FtbW1ut+2xp0/fx4dOnTQdRlEKmNmSYq0ndthw/77evdurb0NPUA41pIU6VNu8/LyYGNj02J+ZyAi0hWdzniq/tSgMao/3XNzcwMAdO/eHW+//TbS0tLEbXv37oVcLkeXLl3EfRYsWICysjKYmJiI+7i7u8PHx6fe95LL5UrrQrU0/v7+ui6BSC3MLEkRc0tSw8ySFDG3RET6RxJrPB0/fhzvv/8+4uLikJiYiB9++AEvvvgihg8fLk6J7t+/PwIDA/Hss8/i9OnT2L9/P+bPn48pU6aInzA8/fTTkMvlmDRpEuLj47Fjxw6sWLFC8le0q56uTCQVzCxJEXNLUsPMkhQxt0RE+kenM55UJZfLsW3bNixduhSlpaXw9vbGlClT8Oqrr4r7GBoa4rfffsPUqVPRs2dPmJmZ4emnn8bq1avFfWxsbLBv3z5MmzYNYWFhsLOzw9y5czF37lxdfFtERERERERERHpNEo2n0NBQnDhxosH9WrVqhV9//fW++wQHB+Pw4cOaKq1F8PLy0nUJRGphZkmKmFuSGmaWpIi5JSLSP5I41Y7uz8CAf40kLcwsSRFzS1LDzJIUMbdERPqHI7seqL4ELZFUMLMkRdrO7ZQpVX8+/rhW34YeIBxrSYqYWyIi/SOJU+2IiIj03bBhQHg44Oys60qIiIiIiDRHJgiCoOsipCYvLw82NjbIzc0Vr5inS8XFxTAzM9N1GUQqY2ZJiphbkhpmlqRIn3Lb0n5nICLSFZ5qpwcSExN1XQKRWphZkiLmlqSGmSUpYm6JiPQPG096ID8/X9clEKmFmSUpYm5JaphZkiLmlohI/7DxpAf0ZToyPTiYWZIi5pakhpklKWJuiYj0D9d4aoSWdr52eXk5jI2NdV0GkcqYWZIi5pakhpklKdKn3La03xmIiHSFM570wKlTp3RdApFamFmSIuaWpIaZJSlibomI9I+RrguQoupJYnl5eTqupEphYWGLqYVIFcwsSRFzS1LDzJIU6VNuq78PnmBCRA86Np4aoXrRQy8vLx1XQkRERERELVl+fj5sbGx0XQYRkc5wjadGqKysRGpqKqysrCCTyXRaS15eHry8vHDz5k2eO06SwMySFDG3JDXMLEmRvuVWEATk5+fD3d0dBgZc4YSIHlyc8dQIBgYG8PT01HUZSqytrfXiP2h6cDCzJEXMLUkNM0tSpE+55UwnIiIuLk5ERERERERERFrCxhMREREREREREWkFG08SJ5fLsWTJEsjlcl2XQqQSZpakiLklqWFmSYqYWyIi/cTFxYmIiIiIiIiISCs444mIiIiIiIiIiLSCjSciIiIiIiIiItIKNp6IiIiIiIiIiEgr2HiSsE8++QS+vr4wNTVFly5d8M8//+i6JCLR4cOHMWzYMLi7u0Mmk2Hnzp1KjwuCgMjISLi7u8PMzAwRERE4f/68boolArBy5Up07doVVlZWcHZ2xsiRI3Hp0iWlfZhbamnWr1+PkJAQWFtbw9raGt27d8cff/whPs7MUku3cuVKyGQyzJ49W9zG3BIR6Rc2niRq27ZtmD17NhYuXIjTp0/jkUcewaBBg5CcnKzr0ogAAIWFhejYsSM++uijOh9ftWoV1q5di48++gjR0dFwdXXFY489hvz8/GaulKjKoUOHMG3aNJw4cQL79u1DRUUF+vfvj8LCQnEf5pZaGk9PT7zzzjuIiYlBTEwM+vTpgxEjRoi/pDOz1JJFR0fjs88+Q0hIiNJ25paISL/wqnYSFR4ejtDQUKxfv17cFhAQgJEjR2LlypU6rIyoNplMhh07dmDkyJEAqj7JdHd3x+zZs/Haa68BAEpLS+Hi4oJ3330XL774og6rJaqSmZkJZ2dnHDp0CI8++ihzS5Jhb2+P9957D5MnT2ZmqcUqKChAaGgoPvnkE7z11lvo1KkTPvjgA461RER6iDOeJKisrAyxsbHo37+/0vb+/fvj2LFjOqqKSHWJiYlIT09XyrBcLkevXr2YYWoxcnNzAVT9Eg8wt9TyKRQKbN26FYWFhejevTszSy3atGnTMGTIEPTr109pO3NLRKR/jHRdAKnvzp07UCgUcHFxUdru4uKC9PR0HVVFpLrqnNaV4Rs3buiiJCIlgiBg7ty5ePjhhxEUFASAuaWW69y5c+jevTtKSkpgaWmJHTt2IDAwUPwlnZmllmbr1q04deoUoqOjaz3GsZaISP+w8SRhMplM6b4gCLW2EbVkzDC1VNOnT8fZs2dx5MiRWo8xt9TStGvXDnFxccjJycHPP/+MiRMn4tChQ+LjzCy1JDdv3sSsWbOwd+9emJqa1rsfc0tEpD94qp0EOTo6wtDQsNbspoyMjFqfDhG1RK6urgDADFOLNGPGDOzatQsHDx6Ep6enuJ25pZbKxMQE/v7+CAsLw8qVK9GxY0d8+OGHzCy1SLGxscjIyECXLl1gZGQEIyMjHDp0CP/3f/8HIyMjMZvMLRGR/mDjSYJMTEzQpUsX7Nu3T2n7vn370KNHDx1VRaQ6X19fuLq6KmW4rKwMhw4dYoZJZwRBwPTp07F9+3YcOHAAvr6+So8ztyQVgiCgtLSUmaUWqW/fvjh37hzi4uLEW1hYGMaPH4+4uDi0bt2auSUi0jM81U6i5s6di2effRZhYWHo3r07PvvsMyQnJ+Oll17SdWlEAKquVnP16lXxfmJiIuLi4mBvb49WrVph9uzZWLFiBdq0aYM2bdpgxYoVMDc3x9NPP63DqulBNm3aNGzZsgW//PILrKysxE/bbWxsYGZmBplMxtxSi7NgwQIMGjQIXl5eyM/Px9atW/H333/jzz//ZGapRbKyshLXzqtmYWEBBwcHcTtzS0SkX9h4kqgnn3wSd+/exbJly5CWloagoCD8/vvv8Pb21nVpRACAmJgY9O7dW7w/d+5cAMDEiROxefNmvPrqqyguLsbUqVORnZ2N8PBw7N27F1ZWVroqmR5w69evBwBEREQobd+0aRMmTZoEAMwttTi3b9/Gs88+i7S0NNjY2CAkJAR//vknHnvsMQDMLEkTc0tEpF9kgiAIui6CiIiIiIiIiIj0D9d4IiIiIiIiIiIirWDjiYiIiIiIiIiItIKNJyIiIiIiIiIi0go2noiIiIiIiIiISCvYeCIiIiIiIiIiIq1g44mIiIiIiIiIiLSCjSciIiIiIiIiItIKNp6IiIiIiIiIiEgr2HgiIqIHVmRkJDp16qSz91+0aBFeeOEFlfadP38+Zs6cqeWKiIiIiIg0SyYIgqDrIoiIiDRNJpPd9/GJEyfio48+QmlpKRwcHJqpqv/cvn0bbdq0wdmzZ+Hj49Pg/hkZGfDz88PZs2fh6+ur/QKJiIiIiDSAjSciItJL6enp4tfbtm3D4sWLcenSJXGbmZkZbGxsdFEaAGDFihU4dOgQ9uzZo/JzHn/8cfj7++Pdd9/VYmVERERERJrDU+2IiEgvubq6ijcbGxvIZLJa2+491W7SpEkYOXIkVqxYARcXF9ja2mLp0qWoqKjAK6+8Ant7e3h6euLLL79Ueq9bt27hySefhJ2dHRwcHDBixAgkJSXdt76tW7di+PDhStt++uknBAcHw8zMDA4ODujXrx8KCwvFx4cPH47vv/++yceGiIiIiKi5sPFERERUw4EDB5CamorDhw9j7dq1iIyMxNChQ2FnZ4eoqCi89NJLeOmll3Dz5k0AQFFREXr37g1LS0scPnwYR44cgaWlJQYOHIiysrI63yM7Oxvx8fEICwsTt6WlpWHcuHGYPHkyEhIS8Pfff2P06NGoOTG5W7duuHnzJm7cuKHdg0BEREREpCFsPBEREdVgb2+P//u//0O7du0wefJktGvXDkVFRViwYAHatGmDN954AyYmJjh69CiAqplLBgYG+OKLLxAcHIyAgABs2rQJycnJ+Pvvv+t8jxs3bkAQBLi7u4vb0tLSUFFRgdGjR8PHxwfBwcGYOnUqLC0txX08PDwAoMHZVERERERELYWRrgsgIiJqSTp06AADg/8+l3FxcUFQUJB439DQEA4ODsjIyAAAxMbG4urVq7CyslJ6nZKSEly7dq3O9yguLgYAmJqaits6duyIvn37Ijg4GAMGDED//v0xZswY2NnZifuYmZkBqJplRUREREQkBWw8ERER1WBsbKx0XyaT1bmtsrISAFBZWYkuXbrgu+++q/VaTk5Odb6Ho6MjgKpT7qr3MTQ0xL59+3Ds2DHs3bsX69atw8KFCxEVFSVexS4rK+u+r0tERERE1NLwVDsiIqImCA0NxZUrV+Ds7Ax/f3+lW31XzfPz84O1tTUuXLigtF0mk6Fnz55YunQpTp8+DRMTE+zYsUN8PD4+HsbGxujQoYNWvyciIiIiIk1h44mIiKgJxo8fD0dHR4wYMQL//PMPEhMTcejQIcyaNQspKSl1PsfAwAD9+vXDkSNHxG1RUVFYsWIFYmJikJycjO3btyMzMxMBAQHiPv/88w8eeeQR8ZQ7IiIiIqKWjo0nIiKiJjA3N8fhw4fRqlUrjB49GgEBAZg8eTKKi4thbW1d7/NeeOEFbN26VTxlz9raGocPH8bgwYPRtm1bvPnmm1izZg0GDRokPuf777/HlClTtP49ERERERFpikyoeZ1mIiIiahaCIOChhx7C7NmzMW7cuAb3/+233/DKK6/g7NmzMDLiEo1EREREJA2c8URERKQDMpkMn332GSoqKlTav7CwEJs2bWLTiYiIiIgkhTOeiIiIiIiIiIhIKzjjiYiIiIiIiIiItIKNJyIiIiIiIiIi0go2noiIiIiIiIiISCvYeCIiIiIiIiIiIq1g44mIiIiIiIiIiLSCjSciIiIiIiIiItIKNp6IiIiIiIiIiEgr2HgiIiIiIiIiIiKtYOOJiIiIiIiIiIi0go0nIiIiIiIiIiLSiv8H009zIG4PnZwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 30/49 (Lat: 38.84, Lon: -9.42)\n", + "Site 30: Rhypo = 6.18 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 636.3408 cm/s²\n", + "Subfault PGA (i=0, j=1): 406.7899 cm/s²\n", + "Subfault PGA (i=1, j=0): 462.4734 cm/s²\n", + "Subfault PGA (i=1, j=1): 87.0470 cm/s²\n", + "Subfault PGA (i=2, j=0): 117.9585 cm/s²\n", + "Subfault PGA (i=2, j=1): 30.6250 cm/s²\n", + "Subfault PGA (i=3, j=0): 452.2926 cm/s²\n", + "Subfault PGA (i=3, j=1): 229.7838 cm/s²\n", + "Total PGA: 743.5916 cmm/s²\n", + "Total PGA: 743.5916 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 31/49 (Lat: 38.86, Lon: -9.42)\n", + "Site 31: Rhypo = 7.75 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 458.5290 cm/s²\n", + "Subfault PGA (i=0, j=1): 247.5888 cm/s²\n", + "Subfault PGA (i=1, j=0): 362.6253 cm/s²\n", + "Subfault PGA (i=1, j=1): 73.6026 cm/s²\n", + "Subfault PGA (i=2, j=0): 94.0794 cm/s²\n", + "Subfault PGA (i=2, j=1): 25.5877 cm/s²\n", + "Subfault PGA (i=3, j=0): 439.7096 cm/s²\n", + "Subfault PGA (i=3, j=1): 234.9599 cm/s²\n", + "Total PGA: 576.2238 cmm/s²\n", + "Total PGA: 576.2238 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 32/49 (Lat: 38.88, Lon: -9.42)\n", + "Site 32: Rhypo = 9.58 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 302.8763 cm/s²\n", + "Subfault PGA (i=0, j=1): 173.7461 cm/s²\n", + "Subfault PGA (i=1, j=0): 284.3867 cm/s²\n", + "Subfault PGA (i=1, j=1): 55.6232 cm/s²\n", + "Subfault PGA (i=2, j=0): 78.6832 cm/s²\n", + "Subfault PGA (i=2, j=1): 18.3593 cm/s²\n", + "Subfault PGA (i=3, j=0): 519.6873 cm/s²\n", + "Subfault PGA (i=3, j=1): 234.3702 cm/s²\n", + "Total PGA: 564.9208 cmm/s²\n", + "Total PGA: 564.9208 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 33/49 (Lat: 38.9, Lon: -9.42)\n", + "Site 33: Rhypo = 11.55 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 284.1171 cm/s²\n", + "Subfault PGA (i=0, j=1): 166.4582 cm/s²\n", + "Subfault PGA (i=1, j=0): 228.7733 cm/s²\n", + "Subfault PGA (i=1, j=1): 31.9736 cm/s²\n", + "Subfault PGA (i=2, j=0): 69.9413 cm/s²\n", + "Subfault PGA (i=2, j=1): 17.6108 cm/s²\n", + "Subfault PGA (i=3, j=0): 370.2630 cm/s²\n", + "Subfault PGA (i=3, j=1): 193.6222 cm/s²\n", + "Total PGA: 366.4002 cmm/s²\n", + "Total PGA: 366.4002 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 34/49 (Lat: 38.92, Lon: -9.42)\n", + "Site 34: Rhypo = 13.60 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 209.7525 cm/s²\n", + "Subfault PGA (i=0, j=1): 119.2684 cm/s²\n", + "Subfault PGA (i=1, j=0): 181.9914 cm/s²\n", + "Subfault PGA (i=1, j=1): 31.3492 cm/s²\n", + "Subfault PGA (i=2, j=0): 36.1946 cm/s²\n", + "Subfault PGA (i=2, j=1): 17.8340 cm/s²\n", + "Subfault PGA (i=3, j=0): 295.6389 cm/s²\n", + "Subfault PGA (i=3, j=1): 118.0999 cm/s²\n", + "Total PGA: 288.8393 cmm/s²\n", + "Total PGA: 288.8393 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 35/49 (Lat: 38.98, Lon: -9.42)\n", + "Site 35: Rhypo = 19.97 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 81.2251 cm/s²\n", + "Subfault PGA (i=0, j=1): 73.5186 cm/s²\n", + "Subfault PGA (i=1, j=0): 85.7698 cm/s²\n", + "Subfault PGA (i=1, j=1): 14.9872 cm/s²\n", + "Subfault PGA (i=2, j=0): 22.5894 cm/s²\n", + "Subfault PGA (i=2, j=1): 7.6377 cm/s²\n", + "Subfault PGA (i=3, j=0): 145.5696 cm/s²\n", + "Subfault PGA (i=3, j=1): 86.5121 cm/s²\n", + "Total PGA: 171.9288 cmm/s²\n", + "Total PGA: 171.9288 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 36/49 (Lat: 39.0, Lon: -9.42)\n", + "Site 36: Rhypo = 22.13 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 65.4148 cm/s²\n", + "Subfault PGA (i=0, j=1): 52.0304 cm/s²\n", + "Subfault PGA (i=1, j=0): 68.1908 cm/s²\n", + "Subfault PGA (i=1, j=1): 12.0094 cm/s²\n", + "Subfault PGA (i=2, j=0): 16.5393 cm/s²\n", + "Subfault PGA (i=2, j=1): 5.3928 cm/s²\n", + "Subfault PGA (i=3, j=0): 111.4819 cm/s²\n", + "Subfault PGA (i=3, j=1): 59.2280 cm/s²\n", + "Total PGA: 124.8788 cmm/s²\n", + "Total PGA: 124.8788 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 37/49 (Lat: 39.02, Lon: -9.42)\n", + "Site 37: Rhypo = 24.31 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 55.8470 cm/s²\n", + "Subfault PGA (i=0, j=1): 36.3950 cm/s²\n", + "Subfault PGA (i=1, j=0): 42.3040 cm/s²\n", + "Subfault PGA (i=1, j=1): 11.2247 cm/s²\n", + "Subfault PGA (i=2, j=0): 14.3169 cm/s²\n", + "Subfault PGA (i=2, j=1): 4.7772 cm/s²\n", + "Subfault PGA (i=3, j=0): 88.2673 cm/s²\n", + "Subfault PGA (i=3, j=1): 43.8290 cm/s²\n", + "Total PGA: 122.1743 cmm/s²\n", + "Total PGA: 122.1743 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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Ro4ewevVq1T7Ka6x9pzrl3fzUr/P169eFXr16CV5eXgIAjZzLy8sT3nnnHaFZs2aq3IiJiRGmTZsm3L17V7UfAGHixIk6r2N1kpOT9d6JUDv/iYiIiMiyZIIgCLVd8CIiInoYpaWloX79+njllVewZMmSKtvnzJmDuXPnIj093ah5vYiIiIiIpIDD7oiIiCzs1q1b+Pvvv7F06VI4ODhgypQp1g6JiIiIiKjWOFg7ACIiInv3xRdfID4+HufPn8e3336rcSc7Mk15eTnKysr0PsrLy60dIhERERFp4bA7IiIikoz4+HgkJCTo3V6/fn1cv3699gIiIiIiomqx+ERERESScenSJeTm5urd7urqipiYmFqMiIiIiIiqw+ITERERERERERFZDOd8IiIiIiIiIiIii2HxiYiIzG7t2rWQyWSqh5OTE+rUqYPnnnsOV65cqfV49u7di9GjR6N58+bw8PBAeHg4Bg4ciJMnTxp8nSAIePTRRyGTyTBp0iSjzhUfH6/x3pWPPn36aOx38uRJTJw4ETExMfDy8kJISAgee+wx7N2716jzzJkzBzKZDBkZGUbtT0RERERkLU7WDoCIiOzXmjVr0Lx5cxQVFeHPP//EggULsG/fPly8eBF+fn61FseqVatw//59TJkyBS1atEB6ejqWLVuGTp06YefOnejRo4fO133yySe4evWqyedr2LAhvv32W411vr6+Gs+/++47HDt2DKNHj0br1q2Rn5+P1atXo2fPnli3bh1eeOEFk89LRERERGSLWHwiIiKLiY6ORlxcHABFj6Dy8nK8++672LZtG/7973/XWhyffPIJgoODNdb16dMHjRs3xsKFC3UWn65fv4633noL69evx+DBg006n1wuR6dOnQzu8/rrr+ODDz7QWNe3b1/ExsZi3rx5LD4RERERkd3gsDsiIqo1ykLUvXv3avW82oUnAPD09ESLFi1w8+ZNna8ZN24cHn/8cQwaNKjWYnJ0dES7du30xlSdixcvomHDhujYsSPS0tIAKIp+0dHROHz4MLp06QK5XI7IyEisWbMGAPDLL78gNjYW7u7uiImJwY4dO8S/KSIiIiIiHVh8IiKiWpOcnAwAaNq0abX7CoKAsrIyox5iZGdn49SpU2jZsmWVbV988QWOHTuGlStXijr2tWvX4O/vDycnJzRq1AgzZ85EYWFhta8rKyvDgQMHdMZUnYSEBHTp0gWtWrXCvn37NIpbd+/exb///W+MGTMGP/30E2JiYjB69GjMmzcPb731Fl5//XX8+OOP8PT0xFNPPYU7d+6YfH4iIiIiIn047I6IiCymvLwcZWVlqjmf3nvvPTz66KMYMGBAta9dt26d0UPzBEEwObaJEyciPz8fM2fO1Fh/+/ZtzJgxA0uWLEFYWJjJx/3Xv/6FZ599Fs2bN0dhYSF+++03LFmyBAcPHsS+ffvg4KD/e585c+bg6tWr2LZtm0nn/Oabb/Diiy9i/Pjx+PDDD6uc4/79+9i5cyfatWsHQNEDLTg4GO+//z6uXr2qep9hYWFo06YNfvzxR7zyyiumvXEiIiIiIj1YfCIiIovRnvcoKioKP/30E5ycqv/1079/fxw/ftwicc2aNQvffvstVqxYoSrIKI0fPx6tW7fG2LFjRR37vffe03jet29fREZGYsaMGfjpp5/0DuP74osvsGDBAkyfPh0DBw40+nwLFizAihUrsHTpUkybNk3nPnXq1NF4n/7+/ggODkZkZKRGgS0qKgoAcOPGDaPPT0RERERUHRafiIjIYtavX4+oqCjk5uZi06ZN+OyzzzB06FD89ttv1b7W398fPj4+Zo9p7ty5eO+997BgwQJMmjRJY9sPP/yAHTt24ODBg8jOztbYVlJSggcPHsDDwwPOzs4mnXP48OGYMWMGjhw5orP4tGbNGrz00ksYN24cli5datKxv/nmG4SHh+O5557Tu4+/v3+VdS4uLlXWu7i4AACKiopMioGIiIiIyBDO+URERBYTFRWFuLg4dO/eHatXr8aYMWOwY8cO/PDDD9W+dt26dXB2djbqYay5c+dizpw5mDNnDt5+++0q28+dO4eysjJ06tQJfn5+qgcA/Pe//4Wfnx9++eUX4y+AFl1D7tasWYMxY8Zg5MiRWL16NWQymUnH3LFjB5ydnfHII4+wxxIRERER2ST2fCIiolqzZMkS/Pjjj5g9ezYGDx5scP4jcw+7mz9/PubMmYN33nkH7777rs59Ro0ahfj4+Crru3fvjqeeegpTpkxBdHS0yedet24dgKrDENeuXYsxY8Zg+PDh+OKLL0wuPAFA/fr1ceDAATz22GN45JFHsGfPHjRp0sTk4xARERERWQqLT0REVGv8/PxUd1fbsGEDhg8frnffgIAABAQEmOW8y5Ytw+zZs9GnTx/069cPR44c0diuLApFRkYiMjJS5zHCw8OrFKacnJzQrVs37NmzBwBw4MABLFiwAIMGDULDhg1RVFSE3377DZ9//jl69OiB/v37q177/fff48UXX0SbNm3w0ksv4dixYxrHbtu2LVxdXY16f3Xq1EFCQgJ69+6NRx99FLt37xZVJCMiIiIisgQWn4iIqFa98sorWLlyJebNm4ehQ4fC0dHR4ufcvn07AMUQtR07dlTZLuZueYDibn7l5eWq53Xq1IGjoyPmz5+PjIwMyGQyNGnSBPPmzcP06dM1enr98ssvqKiowKlTp9C1a9cqx05OTtZbCNMlMDAQe/fuRb9+/dCtWzfs3LkTcXFxot4XEREREZE5yQSxn7iJiIiIiIiIiIiqwQnHiYiIiIiIiIjIYlh8IiIiIiIiIiIii2HxiYiIiIiIiIiILIbFJyIiIiIiIiIishgWn4iIiIiIiIiIyGJYfCIiIiIiIiIiIotxsnYAtqaiogJ37tyBl5cXZDKZtcMhIiIiIiIJEgQBubm5CAsLg4OD7X7nX1FRgZKSEmuHQUQS4+zsDEdHR6P3Z/FJy507dxAREWHtMIiIiIiIyA7cvHkTdevWtXYYOpWUlCA5ORkVFRXWDoWIJMjX1xehoaFGddxh8UmLl5cXAMUvCW9vbytHo/iF4OLiYu0wiERh/pJUMXdJypi/JFX2lrs5OTmIiIhQ/X1hawRBQGpqKhwdHREREWHTvbOIyLYIgoCCggKkpaUBAOrUqVPta1h80qKs2Hl7e9tE8eno0aPo2LGjtcMgEoX5S1LF3CUpY/6SVNlr7trqVB5lZWUoKChAWFgY3N3drR0OEUmMXC4HAKSlpSE4OLjaIXgsbxMRERERET1kysvLAcCuepsRUe1SFq5LS0ur3ZfFJxtnq+PDiYzB/CWpYu6SlDF/SaqYu9Zhqz2ziMj2mdJ+sPhk40yZPZ7I1jB/SaqYuyRlzF+SKuYu2arIyEh89NFH1g7DoFGjRuGpp56y2vnXrl0LX19fq53fVLX1bxofH4+pU6fazHGsicUnG3fjxg1rh0AkGvOXpIq5S1LG/CWpYu5SdWQymcHHqFGjqn39tm3bLBbfrVu34OLigubNm1vsHLZAV+Hm2WefxeXLl60TkJb8/Hy88cYbaNiwIdzc3BAUFIT4+Hj873//U+1z/PhxjBs3zopR6vbHH39AJpPhwYMHGuu3bNmC+fPnW/z8uv5frV692izH5oTjREREFvbgAbBvH9C7N8A5XYmIiMRJTU1VLW/atAmzZ8/GpUuXVOuUEyBby9q1a/HMM89g//79+PPPP9G1a1erxmMKQRBQXl4OJydxJQK5XG716680fvx4HDt2DCtXrkSLFi1w//59HDp0CPfv31ftExQUZMUITefv719r51qzZg369Omjeu7j42OW47Lnk41r1aqVtUMgEo35S1Jl7txdvx746ivgnXfMelgindj2klQxd6k6oaGhqoePjw9kMpnGug0bNqBRo0ZwcXFBs2bN8PXXX6teGxkZCQAYNGgQZDKZ6vm1a9cwcOBAhISEwNPTE+3bt8fvv/9ucmyCIGDNmjUYMWIEhg0bhi+//LLKPn/++Se6desGd3d3+Pn5oXfv3sjKygIAVFRUYPHixWjcuDFcXV1Rr149LFiwQPXa27dv49lnn4Wfnx8CAgIwcOBAXL9+3WA8S5YsQcOGDSGXy9G6dWv88MMPqu3KHjY7d+5EXFwcXF1dceDAgWqvR3x8PG7cuIFp06apesYAuofdrVq1Su+/B6DoZfPFF19g0KBBcHd3R5MmTfDzzz8bfc312b59O95++2307dsXkZGRaNeuHV555RWMHDlStY927y2ZTIbPPvsMTz75JNzd3REVFYXDhw/j6tWriI+Ph4eHBzp37oxr166pXqNrqOPUqVMRHx+vN7ZvvvkGcXFx8PLyQmhoKIYNG4a0tDQAwPXr19G9e3cAgJ+fn0ZvPu1hd1lZWXjhhRfg5+cHd3d3PPHEE7hy5Ypqu/LfY+fOnYiKioKnpyf69OmjUcDVx9fXV+P/lbmKiiw+2ThDDQqRrWP+klSZO3d371b8VPtMQGQxbHtJqpi7VBNbt27FlClTMH36dJw7dw4vvfQS/v3vf2Pfvn0AFMOsAEWvjtTUVNXzvLw89O3bF7///jtOnz6N3r17o3///khJSTHp/Pv27UNBQQEee+wxjBgxAps3b0Zubq5qe2JiInr27ImWLVvi8OHDOHjwIPr376+66+Bbb72FxYsXY9asWbhw4QI2bNiAkJAQAEBBQQG6d+8OT09P7N+/HwcPHlQVE0pKSnTG884772DNmjVYtWoVzp8/j2nTpmH48OFISEjQ2O/111/HokWLkJSUhFatWlV7PbZs2YK6deti3rx5SE1N1VvMqO7fQ2nu3Ll45plncPbsWfTt2xfPP/88MjMzTbr22kJDQ/Hrr79qXH9jzJ8/Hy+88AISExPRvHlzDBs2DC+99BLeeustnDhxAgAwadKkGsVWUlKC+fPn48yZM9i2bRuSk5NVBaaIiAj8+OOPAIBLly4hNTUVH3/8sc7jjBo1CidOnMDPP/+Mw4cPQxAE9O3bV+OucwUFBfjggw/w9ddfY//+/UhJScGMGTOqjXHSpEkIDAxE+/btsXr1alRUVNToPasIpCE7O1sAIGRnZ1s7FEEQBOHIkSPWDoFINOYvSVV1ufvggSBkZhp/vCefrHwQWRrbXpIqe8tdW/u7QlthYaFw4cIFobCwUBAEQaioEITCQus8KipMj3/NmjWCj4+P6nmXLl2EsWPHauzz9NNPC3379lU9ByBs3bq12mO3aNFCWLFihep5/fr1hQ8//NDga4YNGyZMnTpV9bx169bCf//7X9XzoUOHCl27dtX52pycHMHV1VVjf3Vffvml0KxZM6FC7UIVFxcLcrlc2LlzpyAIgjBy5Ehh4MCBgiAIQl5enuDm5iYcOnRI4zgvvviiMHToUEEQBGHfvn0CAGHbtm0G35cgGHc9xP57vPPOO6rneXl5gkwmE3777bdqYzIkISFBqFu3ruDs7CzExcUJU6dOFQ4ePKixj/Z70I7l8OHDAgDhyy+/VK377rvvBDc3N9Vz9WuuNGXKFKFbt26q5926dROmTJmiN9Zjx44JAITc3FxBECr/XbKysjT2Uz/O5cuXBQDCn3/+qdqekZEhyOVyYfPmzYIgKP49AAhXr15V7fPJJ58IISEhemMRBEGYP3++cOjQIeH06dPCBx98ILi7uwvz58/Xu792O2II53yycW5ubtYOgUg05i9JlaHcragAhg9XLP/4I+DiUktBERmJbS9JFXPXuoqLgaefts65v/8eqOk/f1JSUpUJpLt27aq354hSfn4+5s6di//973+4c+cOysrKUFhYaFLPpwcPHmDLli04ePCgat3w4cPx1VdfYcyYMQAUPZ+e1nOBk5KSUFxcjJ49e+rcfvLkSVy9ehVeXl4a64uKijSGgSlduHABRUVFePzxxzXWl5SUoG3bthrr4uLiNJ6b43oo35Mx/x7qw209PDzg5eWlGoambeHChVi4cKHq+YULF1CvXr0q+z366KP4+++/ceTIEfz555/Yu3cvPv74Y8ydOxezZs3SG7N6LMpeZzExMRrrioqKkJOTA29vb73HMeT06dOYM2cOEhMTkZmZqepVlJKSghYtWhh1jKSkJDg5OaFjx46qdQEBAWjWrBmSkpJU69zd3dGoUSPV8zp16ui9tkrvqM0R0aZNGwDAvHnzNNaLxeKTjWvZsqW1QyASjflLUmUod4uLK5cfPACCgy0fD5Ep2PaSVDF3qaaU8w8pCYJQZZ221157DTt37sQHH3yAxo0bQy6XY8iQIXqHs+myYcMGFBUVaRQDBEFARUUFLly4gBYtWhicN6e6OXUqKirQrl07fPvtt1W26Zo4W1nQ+OWXXxAeHq6xzdXVVeO5h4eHxnNzXA8lY/49nJ2dq7xG3zCv8ePH45lnnlE9DwsL03tuZ2dnPPLII3jkkUfw5ptv4r333sO8efPwxhtvwEXPN4fqsSjj1LVOGZ+DgwMEQdA4hvqwN235+fno1asXevXqhW+++QZBQUFISUlB7969Tbq+2udUX69+fXVdW32v1adTp07IycnBvXv3VAU5sVh8snEnT57UaMSIpIT5S1JlKHcNfKYgsglse0mqmLvW5eqq6IFkrXPXVFRUFA4ePIgXXnhBte7QoUOIiopSPXd2dlbNsaR04MABjBo1CoMGDQKgmAPK1PnHvvzyS0yfPl01d4/S5MmT8dVXX+GDDz5Aq1atsGfPHsydO7fK65s0aQK5XI49e/aoekqpi42NxaZNmxAcHGxUj5sWLVrA1dUVKSkp6Natm0nvxZjr4eLiUuU6ajPm38NU/v7+ou/61qJFC5SVlaGoqEhv8clUQUFBOHfunMa6xMTEKkUfpYsXLyIjIwPvv/8+IiIiAEA1l5SSMjZD11f5Xo4ePYouXboAAO7fv4/Lly/X6Prqcvr0abi5uVWZTF4MFp+IiIhMoP7FVFmZ9eIgIiIyJ5ms5kPfrOm1117DM888g9jYWPTs2RPbt2/Hli1bNO7UFhkZiT179qBr165wdXWFn58fGjdujC1btqB///6QyWSYNWuWSRMsJyYm4tSpU/j222/RvHlzjW1Dhw7FzJkzsWjRIrz11luIiYnBhAkTMH78eLi4uGDfvn14+umnERgYiDfeeAOvv/46XFxc0LVrV6Snp+P8+fN48cUX8fzzz2Pp0qUYOHAg5s2bh7p16yIlJQVbtmzBa6+9hrp162qc18vLCzNmzMC0adNQUVGBf/3rX8jJycGhQ4fg6empcdc3bcZcj8jISOzfvx/PPfccXF1dERgYKOrfw1Li4+MxdOhQxMXFISAgABcuXMDbb7+N7t27ix4up0uPHj2wdOlSrF+/Hp07d8Y333yDc+fOVRnaqFSvXj24uLhgxYoVGD9+PM6dO4f58+dr7FO/fn3IZDL873//Q9++fSGXy+Hp6amxT5MmTTBw4ECMHTsWn332Gby8vPDmm28iPDwcAwcOFP1+tm/fjrt376Jz586Qy+XYt28fZs6ciXHjxlXpMScG73Zn4wx1JSSydcxfkipDuav++auaL/2IrIJtL0kVc5dq4qmnnsLHH3+MpUuXomXLlvjss8+wZs0ajdveL1u2DLt370ZERISqQPDhhx/Cz88PXbp0Qf/+/dG7d2/ExsYafd4vv/wSLVq0qFJ4UsaUmZmJ7du3o2nTpti1axfOnDmDDh06oHPnzvjpp5/g5KToDzJr1ixMnz4ds2fPRlRUFJ599lnV/Dzu7u7Yv38/6tWrh8GDByMqKgqjR49GYWGh3mLK/PnzMXv2bCxatAhRUVHo3bs3tm/fjgYNGhh8P8Zcj3nz5uH69eto1KiRzmF/yvde3b+HpfTu3Rvr1q1Dr169EBUVhVdeeQW9e/fG5s2bzX6eWbNm4fXXX0f79u2Rm5ur0dNLW1BQENauXYvvv/8eLVq0wPvvv48PPvhAY5/w8HDMnTsXb775JkJCQvTeXW/NmjVo164dnnzySXTu3BmCIODXX3/V2+vKGM7Ozvj000/RuXNntGrVCh9//DHmzZuHZcuWiT6mOplg6qA/O5eTkwMfHx9kZ2ebtSoqVnp6ut7/0ES2jvlLUmUod+/eBcaOVSyvXAnUr1/98fr3r1zevt0MARIZwLaXpMrectfW/q7QVlRUhOTkZDRo0ICTvRORKKa0I+z5ZOP+/vtva4dAJBrzl6TKUO6qf2VjQq98olrDtpekirlLRGS/WHwiIiIygXrBicUnIiIiIqLqsfhk46Kjo60dApFozF+SKkO5y+IT2Tq2vSRVzF0iIvvF4pONu3XrlrVDIBKN+UtSZSh3WXwiW8e2l6SKuUtEZL9YfLJxDx48sHYIRKIxf0mqDOUui09k69j2klQxd4mI7BeLTzbOxcXF2iEQicb8JakylLvqE46Xl9dCMEQmYttLUsXctQ7e/JyIxDKl/WDxyca1adPG2iEQicb8JakylLvqBSf2fCJbxLaXpIq5W7scHR0BACUlJVaOhIikqqCgAADg7Oxc7b5Olg6GaubYsWPo2LGjtcMgEoX5S1JlKHfVv+Bh8YlsEdtekirmbu1ycnKCu7s70tPT4ezsDAcH9ksgIuMIgoCCggKkpaXB19dXVcw2hMUnIiIiE6gXnDjsjoiIpEomk6FOnTpITk7GjRs3rB0OEUmQr68vQkNDjdrX5OJTdnY2tm7digMHDuD69esoKChAUFAQ2rZti969e6NLly4mB0z6GfsPSWSLmL8kVYZylxOOk61j20tSxdytfS4uLmjSpAmH3hGRyZydnY3q8aRkdPEpNTUVs2fPxrfffovQ0FB06NABbdq0gVwuR2ZmJvbt24cPPvgA9evXx7vvvotnn31W1BsgTZ6entYOgUg05i9JlaHcZfGJbB3bXpIq5q51ODg4wM3NzdphEJGdM7r41Lp1a7zwwgs4duwYoqOjde5TWFiIbdu2Yfny5bh58yZmzJhhtkAfVlevXkVAQIC1wyAShflLUmUod1l80pSWBhw6BPTuDcjl1o6GALa9JF3MXSIi+2V08en8+fMICgoyuI9cLsfQoUMxdOhQpKen1zg4IiIiW8MJxzW99hqQmQncvg1MnGjtaIiIiIjIFhl9S4PqCk813Z90i4qKsnYIRKIxf0mqDOUuez5pysxU/Dx50rpxUCW2vSRVzF0iIvtl0v00d+7ciaFDh+Lvv/8GALz44osWCYoq3bt3z9ohEInG/CWpMpS71rrb3aVLwJIlgK12LFbvEUbWxbaXpIq5S0Rkv0wqPs2YMQNPPvkk/v3vf+PWrVu4cOGCpeKqYs6cOZDJZBoP9TtiCIKAOXPmICwsDHK5HPHx8Th//nytxWcpmcqvlIkkiPlLUmUod63V82nGDODAAeA//6m9c5pDYSFQVGTtKB4ubHtJqpi7RET2y6Tik4+PD55//nl89dVXGDt2LMrKyiwVl04tW7ZEamqq6vHXX3+pti1ZsgTLly/HypUrcfz4cYSGhuLxxx9Hbm5urcZobk5ORk/LRWRzmL8kVYZy19pzPt24UfvnNIaunk+lpcDYscBLLwG1/JHhoca2l6SKuUtEZL9MKj4pb3/aqFEjTJw4EadOnbJIUPo4OTkhNDRU9VDOKyUIAj766CPMnDkTgwcPRnR0NNatW4eCggJs2LChVmM0t3bt2lk7BCLRmL8kVYZy19pzPtnqPFO6ik/Z2YpHZiYg8e+CJIVtL0kVc5eIyH6ZVHxavXo1yv+Z4OLJJ5/EiRMnLBKUPleuXEFYWBgaNGiA5557TjX3VHJyMu7evYtevXqp9nV1dUW3bt1w6NAhg8csLi5GTk6OxsOWHDt2zNohEInG/CWpMpS71i4+5efX/jmNoetaqK8rLq69WB52bHtJqpi7RET2y6S+rZGRkQCAwsJCCIKAtm3bAgBu3LiBrVu3okWLFhoFIHPq2LEj1q9fj6ZNm+LevXt477330KVLF5w/fx53794FAISEhGi8JiQkBDeqGZ+waNEizJ07t8r6EydOwMPDA7GxsUhKSkJhYSG8vLzQoEEDnD17FgBQv359VFRU4ObNmwCANm3a4OrVq8jLy4OHhweaNm2K06dPAwDq1q0LR0dHVTytWrXC9evXkZOTAzc3N7Rs2RIn/7lVUFhYGNzc3PD3338jKysL+fn5uHXrFh48eAAXFxe0adNG9cs5NDQUnp6euHr1KgDFXULu3buHzMxMODk5oV27djh27BgEQUBQUBD8/Pxw+fJlAECzZs2QmZmJ9PR0ODg4oH379jhx4gTKy8sREBCA4OBgJCUlAQCaNGmCnJwc1USQHTt2xKlTp1BaWgo/Pz+EhYWp5thq1KgRCgoKkJqaCgCIi4vDuXPnUFRUBB8fH9SrV081ZDIyMhJlZWW4desWACA2NhYXL15EQUEBPD090ahRI5w5cwYAUK9ePQBASkoKAKB169a4du0a8vLy4O7ujubNm6t649WtWxdOTk64fv06ACAmJgYpKSnIzs6Gm5sboqOjVcXTOnXqwN3dHdeuXQOgGN55584dZGVlwdnZGbGxsTh69Kgqp7y9vXHlyhXV9U5LS8P9+/fh6OiIuLg4HD9+HBUVFQgKCoK/vz8uXboEAGjatCmysrKQnp4OmUyGDh064OTJkygrK4O/vz9CQkJU17tx48bIy8tT5XaHDh2QmJiIkpIS+Pr6om7dujh37hwAoGHDhigqKsKdO3cAKL41PH/+PIqKiuDt7Y3IyEiNnC0vL1dd77Zt2+Ly5cvIz8+Hp6cnGjdujMTERABAREQEHBwcNHI2OTkZubm5kMvliIqKUl3v8PBwuLi4IDk5WXW9b968iczMTCQmJqJVq1Y4fvy4Kmc9PDxU17tFixa4e/cuMjMzq1zv4OBg+Pj4qK538+bNkZGRgYyMDFXOKq93YGAgAgMDcfHiRVXOZmdnIy0trUrO+vv7IzQ0VDVvXaNGjZCfn6+63u3bt8fZs2dRXFwMX19fREREqHK2QYMGKCkpwe3bt1U5a802AgCio6PZRsC8bURFRYUqD7XbiLy8GOTnV6CkpBSXL99Hnz6Nq20jsrICIJPJ4OvrK7qNKCuLRG5uHgDg/v0Km2kjsrJcAQCenl64cOGyRhtx/PhfyMpqALncDbdv5+PGjWuq633z5k08ePAArq6ubCPM3EYUFhYiPT2dbQQ/R9hEG2HK54jMzEwcPXrUbtoIzmFFRFRJJgim35+mV69eGDx4MMaPH48HDx6gefPmcHZ2RkZGBpYvX46XX37ZErFqyM/PR6NGjfD666+jU6dO6Nq1K+7cuYM6deqo9hk7dixu3ryJHTt26D1OcXExitW+js3JyUFERASys7Ph7e1t0fdgjL///hsNGza0dhhEojB/SaoM5e7+/cDSpYrlceOA/v2rP576Ptu3V79/Vhbw8cfAE08AHTuKO0ZtUcbl5QVoj3S/fRsYP16xvGwZ0LRp7cb2sGLbS1Jlb7mbk5MDHx8fm/m7gojImkwadqd06tQpPPLIIwCAH374QdXDaP369fhPLd2Gx8PDAzExMbhy5YrqrnfKbxuU0tLSqvSG0ubq6gpvb2+Nhy3x8/OzdghEojF/SaoM5W5tTDi+bh1w8iTw3nuWOb4l6PoqS/36lJTUXiwPO7a9JFXMXSIi+yWq+FRQUAAvLy8AwK5duzB48GA4ODigU6dO1Q5zM5fi4mIkJSWhTp06aNCgAUJDQ7F7927V9pKSEiQkJKBLly61Eo+lKLu2E0kR85ekylDumjrn04EDpp/fxqYfNIqu4tM/00RWWSbLYttLUsXcJSKyX6KKT40bN8a2bdtw8+ZN7Ny5UzXPU1pamsV6Ds2YMQMJCQlITk7G0aNHMWTIEOTk5GDkyJGQyWSYOnUqFi5ciK1bt+LcuXMYNWoU3N3dMWzYMIvEQ0REDyf1gpMxBRUxN111djb9NYakpACvvgpY8j4h1U04bqt36SMiIiIiyzNpwnGl2bNnY9iwYZg2bRp69uyJzp07A1D0glJOQm5ut27dwtChQ5GRkYGgoCB06tQJR44cQf369QEAr7/+OgoLCzFhwgRkZWWhY8eO2LVrl6qHllQ1a9bM2iEQicb8JakylLumFlScRP2mNa+lS4Hr14G5cy03X1R1PZ9YfKo9bHtJqpi7RET2S9RH4iFDhuBf//oXUlNT0bp1a9X6nj17YtCgQWYLTt3GjRsNbpfJZJgzZw7mzJljkfNbS2ZmJnx9fa0dBpEozF+SKkO5a2rxyfTbegAO1fRLFgRAJjP+eLUxjK+6OZ9YfKo9bHtJqpi7RET2y6Rhd2FhYXj55Zfx22+/wd/fH23btoWD2ifkDh06oHnz5mYP8mGWnp5u7RCIRGP+klQZyl1TJxwXU3yq7hg1mT8pI6NmsejDnk+2g20vSRVzl4jIfplUfNqwYQPc3d0xefJkBAYG4umnn8bXX3+NzMxMS8X30HOo7utvIhvG/CWpMpS7tdHzSZt2samszLTXl5ZWLmdl1TweXaqb84kTjtcetr0kVcxdIiL7ZVILHx8fj2XLluHKlSs4fPgwYmNj8cknn6BOnTqIj4/Hhx9+iGvXrlkq1odS+/btrR0CkWjMX5IqQ7lbG8Un7SF12oUbUws56sUnR0fT4zEGez7ZDra9JFXMXSIi+yX664WWLVvirbfewpEjR3Djxg08//zz2Lt3L2JiYhAdHY1ffvnFnHE+tE5Y8tZERBbG/CWpMpS7NS0+GVOM0i4+afd0evCg+mPoe72leiDxbne2g20vSRVzl4jIfpmlb2toaCjGjh2L7du3IyMjA/Pnz4erq6s5Dv3QK+c4BZIw5i9JlaHcVS8eGZPi2kUXMUUY7eLT+PFAUpK415s6ZM9YgmB4bioWn2oP216SKuYuEZH9qlHxKS0tDefOncPZs2dVj6tXr2LQoEF47LHHzBXjQy0gIMDaIRCJxvwlqTKUu+pFlJMnqz9WcbHmc1N7Pr3+OlBQUHWfb76p/jhKHh6Vy5b82+7TTzWfs+eTdbDtJali7hIR2S8nMS86efIkRo4ciaSkJAhan6JlMhm/tTCj4OBga4dAJBrzl6TKUO6q/4q7ebP6Y7VoARw8WPncmCKMevEpKQnYs6fqPqYUc9q2rYzBnL+itQtpO3YAEydWPmfPJ+tg20tSxdwlIrJfono+/fvf/0bTpk1x6NAh/P3330hOTlY9/v77b3PH+FBLMmVcBZGNYf6SVBnKXVMnEPf21nxuavEJAPLzTTunNvVz6hp2l5sL/Oc/wIULph23ukIWez5ZB9tekirmLhGR/RLV8yk5ORlbtmxB48aNzR0PERGRTTO1iKK9v5i73+3aVXWddoHKkOomHF+3Dti9W/HYvt3441Z3LdjziYiIiIgAkT2fevbsiTNnzpg7FtKhSZMm1g6BSDTmL0mVodxVL6L4+1d/LO1ij5ieTyUl1e9jSFGR/ngA4PZt44+lTtd7US+useeTdbDtJali7hIR2S9RPZ+++OILjBw5EufOnUN0dDScnZ01tg8YMMAswRGQk5MDf2P+uiGyQcxfkipDuateRBFztztTJxw3h/v3K5fNOeeTrmNVVACOjlW3czrI2sO2l6SKuUtEZL9EFZ8OHTqEgwcP4rfffquyjROOm9e9e/cQGRlp7TCIRGH+klQZyt2aFp+Uz9PSgCVLgKeeAv71L819jCk+mVKgUi8+6ZrzSSxd77+8vLL4xJ5P1sG2l6SKuUtEZL9EDbubPHkyRowYgdTUVFRUVGg8WHgiIiJ7Vt3k3Yb2B4CrVxU/V68GLl0CFi+u+hpz93wqLq5c3r0bWLRI91A+U+n6lV9aqns7i09EREREDy9Rxaf79+9j2rRpCAkJMXc8pKVjx47WDoFINOYvSZWh3DW155P2Pu++q/hZUCAiMDXGFqgEQXOo39mzwKFDwK+/1uz8gP6eT0rs+WQdbHtJqpi7RET2S1TxafDgwdi3b5+5YyEdTp06Ze0QiERj/pJUGcrdmg67U1IOTdPFnD2f9MWYm6t7vSl349P13vTdWa+iQjGxOe+kbnlse0mqmLtERPZL1JxPTZs2xVtvvYWDBw8iJiamyoTjkydPNktwBJSqj18gkhjmL0mVodzV7s0jCIaLRfruCOcg4usfB4fK4xlboDLU4yg7G/jiC+D8+cp1xcWAm5txx9Y34bi+5ZkzFfNPffgh0Lixcecg07HtJali7hIR2S/Rd7vz9PREQkICEhISNLbJZDIWn8zIz8/P2iEQicb8JanSl7sFBcDPP2uuKy8HnAz8NtU3NM1Q8Ulf7yMXF6CoSLFsbPHJUO+sVauAP//UXGdK8UlXYUtf8UkQKic+v3aNxSdLYttLUsXcJSKyX6KKT8nJyeaOg/QICwuzdghEojF/Sar05e6uXVXXlZUZLj7pK9AYGnanr7eSs7PpxSdDPZ9u3aq6Tn1y8upUN+eTMctkfmx7SaqYu0RE9kvUnE9Ue86rj4UgkhjmL0mVvty9c6fquuom7tZV/Kmu55O+gpGrq+Fz6aKv0COT6b5b3zvvGHcXP33H1tfzqbCwctnY45M4bHtJqpi7RET2S1TxaciQIXj//ferrF+6dCmefvrpGgdFRERki3QVjNasMfwaMT2f9BWMtKZYNIqhXka6tqWmAgcOGHfs6obdqR//p58ql1l8IiIiInq4iCo+JSQkoF+/flXW9+nTB/v3769xUFSpUaNG1g6BSDTmL0mVvtw1dZLww4eBs2errhc77E79/DUddqev5xMA5OUZd+zqht3pOzeLT5bFtpekirlLRGS/RBWf8vLy4OLiUmW9s7MzcnJyahwUVSooKLB2CESiMX9JqvTlrinFp6wsYOFC3ZOHl5cDcnnlc+0bPBkz7K6mE46r3zlP1zaxx9bX80kdb2hlWWx7SaqYu0RE9ktU8Sk6OhqbNm2qsn7jxo1o0aJFjYOiSqmpqdYOgUg05i9Jlb7cNbbgA1RODK5LRYVm8Ul7ku/amPPJUPGpJoUtY3o+mTKpOZmObS9JFXOXiMh+ibrb3axZs/B///d/uHbtGnr06AEA2LNnD7777jt8//33Zg2QiIjIVqgXjKpjaGiZdtFGe19dvaUAQEen42rpKwAJgv7zGDsszpQ5n9QZKswRERERkf0RVXwaMGAAtm3bhoULF+KHH36AXC5Hq1at8Pvvv6Nbt27mjvGhFhcXZ+0QiERj/pJU6ctdp39+a8bGAqdO6X/95s3A11/r315RoVmk0S72mLPnk75jlZfrLz4ZWxyqrvik79wlJcYdn8Rh20tSxdwlIrJfoobdAUC/fv3w559/Ij8/HxkZGdi7dy8LTxZw7tw5a4dAJBrzl6RKX+4qi0QhIYZfb6jwBCgKP+q9gsTM+aSvcKTrXPrW6zuGscPiqht2p+/cLD5ZFttekirmLhGR/RJdfKqOYOynYjKoiGMTSMKYvyRV+nJXWUxxEtFvOCamcrm6nk/6ijbOzprHyMgAMjMNn9dQzyd9RSZj/+tWN+E4ez5ZB9tekirmLhGR/TK6+BQVFYUNGzagpJpPjFeuXMHLL7+MxYsX1zg4Anx8fKwdApFozF+SKn25qywSOToCn35auV5fkUXdsGGAm5ti+cwZoLCw6nH1PVdSL3oVFgL//jcwcqTi/FevAuPHA8eOab7GUO8jfb/SzVV84t3urINtL0kVc5eIyH4Z/d3tJ598gjfeeAMTJ05Er169EBcXh7CwMLi5uSErKwsXLlzAwYMHceHCBUyaNAkTJkywZNwPjXr16lk7BCLRmL8kVfpyV73nU1BQ5fqSksrCkj4ODooJy4uKgNWrNbdpF2OM6fmk3uPpP/8BTp9WrJs/H9i+vXKbvsLY/fv6Y63JnE/G3O2OPZ8si20vSRVzl4jIfhnd86lHjx44fvw4fvnlF4SGhmLDhg2YNGkSnn/+ecyZMwdXrlzBCy+8gFu3buH999+Ht7e3JeN+aPz111/WDoFINOYvSZW+3FXv+aR+5zljevI4OChep4t2scmY4pP6Pnv2AAUFxh1byVABaP9+/dt0HbtVK6BRI8WyMT2fWHyyLLa9JFXMXSIi+2XyrBVdunRBly5dLBELERGRTVPv+eTgoPhZVqaYO8nLy/BrHRwUD12uXweWLweefRZ47DHjht3VZGgcUHW+J39/oEMHYMcOxfPc3Orfk/LYDg6VhTFOOE5ERERE2iw24TiZR2RkpLVDIBKN+UtSpS93lcUUZQ8mZe8nY4oprq76ez6tXg3cvQt8/LHmebSpF6+0i0/67vOhb+ibdm8tJycgIqLyuTF3vFMe29GxMjb2fLI+tr0kVcxdIiL7xeKTjSvT9/U3kQQwf0mq9OWudvFJ2dvHmGKKXK6/+KRdIDKm+GTsfy9jC0COjkC7dvq3Gzq2eq8u5XtJSQH+/NO4c5N5se0lqWLuEhHZLxafbNytW7esHQKRaMxfkip9uatebAEUvZkA44op7u76i0/6zqNN37A9McfSVXwKD6/+derUez4p35vydW+/rf91vNudZbHtJali7hIR2S8Wn4iIiIykXmwBzNfzSZu+L/8NFZ9MHXanq/gEAMo7net7nTr1nmDaPZ+ys/W/zpghfURERERkP1h8snGxsbHWDoFINOYvSZW+3NUedqecANyYQo16gcaQjRv1F24s3fNJ/RymFJ/Uh90Z02OKPZ8si20vSRVzl4jIfokuPlVUVODy5cs4ePAg9u/fr/Eg87l48aK1QyASjflLUqUvd7WH3clkip/VFWoaNVL8NKbn07ff6u5JNXGi4eKTrqJPYqJi7iVdzFl8cnKqfL2h1335ZeXrjClSkThse0mqmLtERPbLqfpdqjpy5AiGDRuGGzduQNDq5y+TyVDOT5RmU1BQYO0QiERj/pJU6ctd7WF3ykKNviFvABASAixZorm/GMHBQNOmwNq1urdr/+q9dQuYNUv/8bSLT8rYTCk+Xbig+OnkZNzrfH0rl0tLjR+GSKZh20tSxdwlIrJfoj4Gjx8/HnFxcTh37hwyMzORlZWlemRmZpo7xoeap6entUMgEo35S1KlL3eVhRVTej4FBAAuLorlmhRbHBwAT09g+XLj9k9NNbxd3/dEut7TRx8Br7xStWB15Iji54ULVYfdyeVVj62cIwvgHe8siW0vSRVzl4jIfokqPl25cgULFy5EVFQUfH194ePjo/Eg82mkHKtBJEHMX5IqXbl75w5w8qRi2ZSeT+q9nUwtPqnfMU55HCcj+izv2AHMm2fauZRFJ13D5/bsAa5fBw4d0v3a27ervk5X8UkmAzw8FMv37pkWHxmPbS9JFXOXiMh+iSo+dezYEVevXjV3LKTDmTNnrB0CkWjMX5IqXbm7eXPlsrLQoizYGCo+qRecTC0+qReulMvGHOOTT0w7D1D5XgwNn9N3Fz5dr9O3b2Sk4ieLT5bDtpekirlLRGS/RM359Morr2D69Om4e/cuYmJi4Kzejx5Aq1atzBIcERGRrdizp3JZu/j0/feKokvHjlVfV5OeT+qv1S4OmZvy+IYKaspt2ttlssr39sUXwOXLQE6O4rmPj+LufcoeW8oO0srtRERERGT/RBWf/u///g8AMHr0aNU6mUwGQRBsYsLxTz/9FEuXLkVqaipatmyJjz76CI888ohVYxKrXr161g6BSDTmL0lVdbmrPTl3UhLw3nvA9u3699VeNobYnk9i6Ov5pF5kUo9H/Ve9g4PmNvUb3y5fDty8CTRooHiunNKFxSfLYdtLUsXcJSKyX6KKT8nJyeaOw2w2bdqEqVOn4tNPP0XXrl3x2Wef4YknnsCFCxf4C42IiMxCe84nQ8T2fJo6VXP/mhafHBwMT4yur/ikPnxO/b0UF1cuN2+u/1o4OwPt2lU+VxafeFMrIiIiooeHqM779evXN/iwpuXLl+PFF1/EmDFjEBUVhY8++ggRERFYtWqVVeMSKyUlxdohEInG/CWpqi53te92p6RrniP1fUzp+RQcrLvnk9hhd8ZMVK5+fGXPJvX3pP5e1Ne/9pr+opj2eZUTjuflGRcPmY5tL0kVc5eIyH6J6vkEANeuXcNHH32EpKQkyGQyREVFYcqUKVa9S0VJSQlOnjyJN998U2N9r169cEjPLXqKi4tRrPb1bY4NjQM4eRLYtCkER49aOxKyR9p/NJtjm7br10Nw4oR5j2ns62zpfFI5ptTfg0ymKHQ4O+v+qb7s6qrogaN8GOpNdOCAYk4ndfp6PpWXVy22qMdrSq8lJyfNoW01nfPJ2RkoKdG/Xfv4yuF2+no+lZZWxhkQYLjnkzplz6f8fOPi1kcQFDEUFACFhZo/lculpYoeXOXlip/6lgWh8qF+fOVP7fX69qsu3tpy/Xr1nx1qMx4iY924EYIjR2p+nKefBsLCan4cIiIyH1HFp507d2LAgAFo06YNunbtCkEQcOjQIbRs2RLbt2/H448/bu44jZKRkYHy8nKEhIRorA8JCcHdu3d1vmbRokWYO3dulfUnTpyAh4cHYmNjkZSUhMLCQnh5eaFBgwY4e/YsAEUPsIqKCty8eRMA0KZNG1y9ehV5eXnw8PBA06ZNcfr0aQBA3bp14ejoiBs3bgBQTMp+/fp15OTkwM3NDS1btsTJf+7hHRYWBjc3N+zenYVjxwJx8WIZCguLUFpaCgcHB/j4eCMr6wEAwM3NFY6OTsj/51O8l5cniouLUVJSCgcHGXx8fPHgQRYEAXB1dYGzszPy8hT7enp6orS0BMXFJZDJZPD19cWDBw8gCAJcXFzg6uqC3Ny8f/b1QGlpmapQ5+fnh+zsbFRUVMDFxRmurm7Izc0FAHh4eKC8vBxFRUUAAF9fX+Tm5qC8vALOzs6Qy+WqIp+7uzsEoQKFhYp9fXx8kJeXh/Lycjg5OcHDwx3Z2Yp95f/ct7uwsPCffb2Rn1+AsrIyODo6wtPTE9nZ2f/s6waZzAEF/4zr8Pb2RmFhIUpLS+Ho6AAvL288eKC8hm5wdHRUu4ZeKC4u+ucaOsDHxwdZWVkAAFdXVzg7O6muoeJ6l6CkpOo1VFxvF+TlqV/D0n+uN+Dr64fs7AeoqBD+uYauquutuIZlKCpSXm9fZGfnoKJCeQ3dkJOjvN7uKC+vULvePsjNzf3nejtBLndXu95yCIKgcb3z8/NQVqb7estkQEFBoeoaFhSYer29cexYttb11s5Z46+3p6cnSkp0X28XFxe4uGhfb1NytvJ61yRn3d21r7funHVycoSHh/Y1lGlc78LCApSWlv2Ts1548CBbLWcdkJ+vvN5ebCNQszaiTh0vyOU58PLKQ0gIEBPTGuvX/wUAWL++cZU24ubNWyguvod79+oBqKPK2WvXchAa6oWsLJnqemdm5uDo0atwdHSEk1Oc0W2Ek5Mfzp07j6ysMLi4OCM/X8DRoxeQl+eIkpJWJrcRZWX5yM111NtG3LlTgLt3HVFQ4IGsrDKcPXsbrVo1xdmzV5GVVQdOTk4oLnbF0aOK32suLvVRXOyLgoJ8HD16FWVlbZGbW1zlep88eRn164fDxcUFycnJSEnxQnl5M9y48QBHj16Fq6srWrVqhePHjwMAQkND4eHhgWvXrgEAQkJa4vTpTFy+XISMDDlksggkJaUhN9cJjo5ubCN0tBGurkE4f76YbQQ/R0jwc4Q3jh3LquZ6V/85IiTkLBo0KERwcDB8fHxw5coVAEDz5s2RkZGBjIwMODg4oH379jh+/DgqKioQGBiIwMBAXLx4EQDQpEkTZGdnIy0tDYDibt+nTp1CaWkp/P39ERoaigsXLgAAGjVqhPz8fNXfG+3bt8fZs2eRmZkJIiJSkAmC6d99tW3bFr1798b777+vsf7NN9/Erl27cOrUKbMFaIo7d+4gPDwchw4dQufOnVXrFyxYgK+//lr1y0Sdrp5PERERyM7Ohre3d63Erc+lS8Cvv95ERESEVeMgaTL0P1vsN96mHvPmzerzV2yctfUepHZMe3gPYo9ZUaHo6VJWVvlT+VBfX1qqmK8oL0//vEO5ubnw8vLSe6733wdatgRmzQISEyvXf/ON4m5u/ftXruvSBXjrLcXyqlXAr78a914//ljRU+m11xTPV6wAIiMVcQ8datwx1AUGAhkZ+re3bq2YNH3GDMXvn5kzgU6dgPR0QHl/kenTgfh4xXJKCjBxIuDlBWzYAHz5JbBtW9Xj/vyzZu+vkyeBOXOARo2Ajz6qun9eHnD2LHD6tOLa6vnuSINcrni4uyseyucuLooeWY6OlZOi61qWySofStp3/1Mu61uvva46hnrw1ZQxbS+RLTJX7sbHK9o8a8vJyYGPj49N/F1BRGRtono+JSUlYfPmzVXWjx49Gh/p+iRZSwIDA+Ho6Fill1NaWlqV3lBKrq6ucHV1rY3wTNasGfDgwR107MgPkCRNR48yf8m2lZcrhn9lZgL37iked+8CJ05kw9HRC7du6X6dvmF3paWKIow+pvy6cXLSPeStJsPuDDFmwnH1CcuVw+6Ux9UXl3YxRTnn061bimKiTKb4mZSkKF4dOVL1Dnvh4UD9+kBEBFCvHhAUBPj5KYbwyeXmKdjYE7a9JFXMXSIi+yWq+BQUFITExEQ0adJEY31iYiKCg4PNEpgYLi4uaNeuHXbv3o1Bgwap1u/evRsDBw60Wlw14e7ubu0QiERj/pKtc3QEvL0Vj8jIyvWdO2chJqYuACA3V1F0WbJE0SMHANzcFD+1ix7l5VV7/6gXZVxcTItN/bXKc4m9252xE44rj6+r+KQ+B5WxxSdtyutcXKwo9B0/Dvz2GzQKfRERQJs2QNu2QHS0osBExmPbS1LF3CUisl+iik9jx47FuHHj8Pfff6NLly6QyWQ4ePAgFi9ejOnTp5s7RpO8+uqrGDFiBOLi4tC5c2d8/vnnSElJwfjx460al1jNmze3dghEojF/SarUc1c5+u6114ARIxTLyr+PtAsu1d3tzpSeT87OigKNkvJcYotP1fV8UlLGa2rPJ2PjcnNTTAR85w4wblzlehcXoFs3YNAgRfGJxGPbS1LF3CUisl+iik+zZs2Cl5cXli1bhrf+mcgiLCwMc+bMweTJk80aoKmeffZZ3L9/H/PmzUNqaiqio6Px66+/on79+laNS6xTp06hY8eO1g6DSBTmL0mVrtxVL97o6/mkLMioU9+nJj2fLD3sTvv4ykKT+h3yzNHzCQDq1FEUnwDF9Rk7FujRo3JIHtUM216SKuYuEZH9ElV8kslkmDZtGqZNm6a6M4mhiVlr24QJEzBhwgRrh0FERHbEwwMYMkSxrJw3Vrv4ZM6eT05Omr2JlMUdsfMbmTrnk3LeJfXeV+rFJ+V7FVN88vOrXP74Y6BBA+NfS0RERETSI6r4pM6Wik72qG7dutYOgUg05i9Jlb7cHTlS87kxw+7U91EftlYdJyfdcz6JLT4ZO+eTds8n9eKToWF3pmjSBPj9d8UyC0/mx7aXpIq5S0Rkv4wuPsXGxmLPnj3w8/ND27ZtITPw6ffUqVNmCY4AJ2P/WiCyQcxfkipjc9eYnk/q0tONj0HfsDuxxN7tTr34dOcOkJGhuIW5dvFJ15BDfXr0AC5dAji6xjLY9pJUMXeJiOyX0S38wIED4frPeIGBAwcaLD6R+Vy/fh0hISHWDoNIFOYvSZWxuWtqzydTvtTXN+xOm0xWOUTOEHMUn379VfHYurVq8UnXe3/xRd3ncnMDpk2rPmYSh20vSRVzl4jIfhldfHr33XdVy3PmzLFELERERJKiXRBSnxNJSf27mvh44KOPjDu2sT2ffH2BrKzqj2dshwLtu92pF5+UtmwBPD0Vy8rik/Z7b9kSGDDAuHMSERERkX0T1Ym/YcOGuH//fpX1Dx48QMOGDWscFFWKiYmxdghEojF/SaqMzV3tTsC6eiCp7+PoqJhg29hjqx9PvReUUlSU8cPx1Hs+6brrnr673ekqPv32G7B3r2JZWdTSHnbXqVPNhwqSOGx7SaqYu0RE9kvUx8Lr16+jXMfXu8XFxbh161aNg6JKKSkp1g6BSDTmL0mVsbmrXVyprvgEmHbHO/WCk4eH4e3VUS8+GRqCp1180tWbKyNDMWeT+rG0h93pipdqB9tekirmLhGR/TJpVr+ff/5Ztbxz5074+PionpeXl2PPnj1owNvWmFV2dra1QyASjflLUmVs7moXlnTdzU67QGVKbyB/f2D8eEUhR9frHByMm+8JqNrzKT9fc7u+OZ+qu0OfvmF3LD5ZD9tekirmLhGR/TKp+PTUU08BAGQyGUZq3W/a2dkZkZGRWLZsmdmCI8DNzc3aIRCJxvwlqTI2d40ZdqfNlN5KANCvn/5tptwYSn1fXT2flO9FGZ+hnk/q9N3tTjknFNU+tr0kVcxdIiL7ZVLxqeKfT6INGjTA8ePHERgYaJGgqFJ0dLS1QyASjflLUmVs7mr3RjJ3z6fqODpW3zNJqbphd8HBip/K+HbsAAYOrL74pCxqhYdrrpfLjYuLzI9tL0kVc5eIyH6J+gicnJzMwlMtOXHihLVDIBKN+UtSZWzuGjPsTnsfcxafnJzED7tTatMGeOQRYPhwxXNlfHfuAEePGt/zafBgoE+fyvXswGA9bHtJqpi7RET2y6SeT+ry8/ORkJCAlJQUlJSUaGybPHlyjQMjIiKydWImHDem+LRmjXHnFzvsTr349MgjQK9elc/VhwUmJVV/XOV7dnMDXn4Z+OsvRcGqTh3jYyMiIiIi+yaq+HT69Gn07dsXBQUFyM/Ph7+/PzIyMuDu7o7g4GAWn8yoDj+9k4Qxf0mqjM1dY+Z8ElN8MrZzsSCI6/mkvqw9B5V6fGVl1cd75ozma1esULxnUwpjZF5se0mqmLtERPZLVOf/adOmoX///sjMzIRcLseRI0dw48YNtGvXDh988IG5Y3youbu7WzsEItGYvyRVxuaupe92p0+zZoqfPXqIKz6p34nOUHzl5dUPu9M+v7MzC0/WxraXpIq5S0Rkv0R9BE5MTMT06dPh6OgIR0dHFBcXIyIiAkuWLMHbb79t7hgfateuXbN2CESiMX9JqozNXe3CTXWFGsA8hZkFCxQ9jOLijC8+qceqXnzSjkd9v9LS6t+Tv79x56faw7aXpIq5S0Rkv0QVn5ydnSH75+vekJAQpKSkAAB8fHxUy0RERPZOu+eTrkKNdoFK153mTOXqCkRGKs5vbPFJvUOBj0/lsvawO/XeW8b0fBozxrjzExEREdHDS9T3r23btsWJEyfQtGlTdO/eHbNnz0ZGRga+/vprxMTEmDvGh1rLli2tHQKRaMxfkipjc1e7sFRWVnUf7QKVdrGnpowtPsnlwLJlipiPHtUfj/Z7qK74FBJi3Pmp9rDtJali7hIR2S9RPZ8WLlyomhBw/vz5CAgIwMsvv4y0tDR8/vnnZg3wYXfnzh1rh0AkGvOXpMrY3DVm2J128cncjC0+OTkBTZsCjRtrFpwMvQdB0D2PlTpLvz8yHdtekirmLhGR/TK555MgCAgKClJ9MxEUFIRff/3V7IGRQlZWlrVDIBKN+UtSZWzuahdejOn5ZG7GFp/0FZy053xSfw+lpeaZIJ1qF9tekirmLhGR/TL5I6UgCGjSpAlu3bpliXhIi7M5JgchshLmL0mVsblrTM+n2izeGDqXepFJfT/tYXfq76G42LhJ1Mm2sO0lqWLuEhHZL5M/Ejs4OKBJkya4f/++JeIhLbGxsdYOgUg05i9JlbG5a0zPJ0tTHxanXXxq0KByWV/PJ0PzVhlztzuyPWx7SaqYu0RE9kvU97FLlizBa6+9hnPnzpk7HtJyVH1WWCKJYf6SVBmbu8YUnyzd88lQ8Um94KS+n3rchiYcZ88naWLbS1LF3CUisl+i7nY3fPhwFBQUoHXr1nBxcYFcLtfYnpmZaZbgiIiIbJktTDiufk5Dd9ZT38/QnE/q+5WWVj/hOBERERFRdUQVnz766CMzh0H6hPAe1iRhzF+SKmNzV7vYU1pa/T7VGTrUtP3VJxw31MtKXw8pQz2fSkrY80mK2PaSVDF3iYjsl6ji08iRI80dB+nh7e1t7RCIRGP+klQZm7vaxR5dvYRMKT69/z7wz81kRVGPJyhIMx5Pz8plffM/AZrFprQ0wMdHfDxkHWx7SaqYu0RE9kv0TBTXrl3DO++8g6FDhyItLQ0AsGPHDpw/f95swRFw5coVa4dAJBrzl6TK2NzVLiw9eFB1H1PmfNIeAmcq9XMNHapZSGrYsHJZvfhk6G53RUWKeZ9IWtj2klQxd4mI7Jeo4lNCQgJiYmJw9OhRbNmyBXl5eQCAs2fP4t133zVrgERERLZKu/h04EDNjlfTycm172Kn7+57xs75BCh6PxERERER1YSoj7lvvvkm3nvvPezevRsuLi6q9d27d8fhw4fNFhwBUVFR1g6BSDTmL0mVsblrTLHIlIJSTScnV3+9TKZ/viZDPZ+0h/3l5tYsJqp9bHtJqpi7RET2S1Tx6a+//sKgQYOqrA8KCsL9+/drHBRVSuNXziRhzF+SKmNz15hikSkFJXP3fOraVbEcEaG5n6E5n4YNA0aPrnzOCcelh20vSRVzl4jIfon6mOvr64vU1NQq60+fPo3w8PAaB0WVWMwjKWP+klQZm7vGFItMKT7VtOeTdvFp8GBg5Ehg5kz9+zk7a25zcwMGDQLq1KlZLGQ9bHtJqpi7RET2S1TxadiwYXjjjTdw9+5dyGQyVFRU4M8//8SMGTPwwgsvmDvGh5qj9ngIIglh/pJUGZu7tl588vAAhgwBtL8XEoTKZX2TnNd08nOyHra9JFXMXSIi+yWq+LRgwQLUq1cP4eHhyMvLQ4sWLfDoo4+iS5cueOedd8wd40MtLi7O2iEQicb8JakyNneNKRaZMpSupsPutOd80seY4pN2j6hmzYD27TXXDR9uWnxUO9j2klQxd4mI7Jeoj7nOzs749ttvcfnyZWzevBnffPMNLl68iK+//prfWJjZ8ePHrR0CkWjMX5IqY3O3pj2VzHG8du0UPx99VLN4ZehYFRWVy8b2fBo3Dpg9u/J5o0bAs8+aFivVDra9JFXMXSIi+1WjTvWNGjVCo0aNzBUL6VCh/hcCkcQwf0mqzJm7lu759NprwLFjQOfOwNSplesNfRek3vNJ3zm1X698Hh4O3L4NdOlieqxUO9j2klQxd4mI7JfRxadXX33V6IMuX75cVDBUVVBQkLVDIBKN+UtSZWzuGvN3kqXnfPLwALp3Vywb2/NJvfikbz/tYXfK4tMbbwDHjwMDBpgeK9UOtr0kVcxdIiL7ZXTx6fTp00btJzP3GISHnL+/v7VDIBKN+UtSZWzuqhdx9LHmhOP6GBO3vp5PDRooHmS72PaSVDF3iYjsl9HFp3379lkyDtLj0qVL6Nixo7XDIBKF+UtSZWzumrvnU02nTTR2wvGWLas/lvacTzWdDJ1qD9tekirmLhGR/arRnE9Xr17FtWvX8Oijj0Iul0MQBPZ8IiKih4YxxSdTijbm7PlkqJBVpw6wYgXg5aV/H309n4iIiIiITCXqe8z79++jZ8+eaNq0Kfr27YvU1FQAwJgxYzB9+nSzBviwa9q0qbVDIBKN+UtSZWzuGjN8zRTmLD5Vd6zISCAgQP927Z5PLD5JB9tekirmLhGR/RJVfJo2bRqcnZ2RkpICd3d31fpnn30WO3bsMFtwBGRlZVk7BCLRmL8kVcbmrtieTx9/DDz2GDBpUvX7msKU4lN12PNJutj2klQxd4mI7Jeoj7m7du3C4sWLUbduXY31TZo0wY0bN8wSGCmkp6dbOwQi0Zi/JFXG5q7YCccbNgSmTAH8/Krf1xTqr69pIYs9n6SLbS9JFXOXiMh+ifpomp+fr9HjSSkjIwOurq41DooqcQ4tkjLmL0mVsblb0wnHtV9vzp5P5jyWOY5HtYdtL0kVc5eIyH6J+ij56KOPYv369arnMpkMFRUVWLp0Kbp372624Ajo0KGDtUMgEo35S1JlbO6K7fmk1Lix8fsaQ713Ens+PbzY9pJUMXeJiOyXqI+mS5cuxWeffYYnnngCJSUleP311xEdHY39+/dj8eLF5o4RABAZGQmZTKbxePPNNzX2SUlJQf/+/eHh4YHAwEBMnjwZJSUlFomntpw8edLaIRCJxvwlqTI2d2va8ykwEFAfwW7OYXc1PRaLT9LFtpekirlLRGS/nKrfpaoWLVrg7NmzWLVqFRwdHZGfn4/Bgwdj4sSJqFOnjrljVJk3bx7Gjh2reu7p6alaLi8vR79+/RAUFISDBw/i/v37GDlyJARBwIoVKywWk6WVlZVZOwQi0Zi/JFXG5q4xPZ+q64EUHg7cumXcvqacq6bH4oTj0sW2l6SKuUtEZL9EFZ8AIDQ0FHPnzjVnLNXy8vJCaGiozm27du3ChQsXcPPmTYSFhQEAli1bhlGjRmHBggXw9vauzVDNxt/f39ohEInG/CWpMjZ3xd7tTt/2mhaMOOE4AWx7SbqYu0RE9kvUR9M1a9bg+++/r7L++++/x7p162oclD6LFy9GQEAA2rRpgwULFmgMqTt8+DCio6NVhScA6N27N4qLiyXdhTckJMTaIRCJxvwlqTI2d0tLda8fMaJyubrhb5bqrWTO+aNkspofj2oP216SKuYuEZH9EvUx9/3330dgYGCV9cHBwVi4cGGNg9JlypQp2LhxI/bt24dJkybho48+woQJE1Tb7969W+UXlp+fH1xcXHD37l29xy0uLkZOTo7Gw5YkJSVZOwQi0Zi/JFXG5q7a6G8NDRpULtdm8clSPZ+MGV5ItoNtL0kVc5eIyH6JGnZ348YNNFD/ZP2P+vXrIyUlxejjzJkzp9qhe8ePH0dcXBymTZumWteqVSv4+flhyJAhqt5QgO7bswqCYPC2rYsWLdIZw4kTJ+Dh4YHY2FgkJSWhsLAQXl5eaNCgAc6ePQtA8X4rKipw8+ZNAECbNm1w9epV5OXlwcPDA02bNsXp06cBAHXr1oWjoyNu3Liheg/Xr19HTk4O3Nzc0LJlS1UPrbCwMLi5ueHvv/9GVlYW8vPzcevWLTx48AAuLi5o06YNjh07BkAx/NHT0xNXr14FAERFReHevXvIzMyEk5MT2rVrh2PHjkEQBAQFBcHPzw+XL18GADRr1gyZmZlIT0+Hg4MD2rdvjxMnTqC8vBwBAQEIDg5WfQho0qQJcnJycO/ePQBAx44dcerUKZSWlsLPzw9hYWE4f/48AKBRo0YoKChAamoqACAuLg7nzp1DUVERfHx8UK9ePfz1118AFBPJl5WV4dY/E57Exsbi4sWLKCgogKenJxo1aoQzZ84AAOrVqwcAqhxr3bo1rl27hry8PLi7u6N58+Y4deqU6no7OTnh+vXrAICYmBikpKQgOzsbbm5uiI6OxokTJwAAderUgbu7O65duwYAaNmyJe7cuYOsrCw4OzsjNjYWR48eBaD4Rs7b2xtXrlxRXe+0tDTcv38fjo6OiIuLw/Hjx1FRUYGgoCD4+/vj0qVLAICmTZsiKysL6enpkMlk6NChA06ePImysjL4+/sjJCREdb0bN26MvLw8VeG0Q4cOSExMRElJCXx9fVG3bl2cO3cOANCwYUMUFRXhzp07AIB27drh/PnzKCoqgre3NyIjIzVytry8XHW927Zti8uXLyM/Px+enp5o3LgxEhMTAQARERFwcHDQyNnk5GTk5uZCLpcjKipKdb3Dw8Ph4uKC5ORk1fW+efMmsrKykJiYiFatWuH48eOqnPXw8FBd7xYtWuDu3bvIzMyscr2Dg4Ph4+Ojut7NmzdHRkYGMjIyVDmrvN6BgYEIDAzExYsXVTmbnZ2NtLS0Kjnr7++P0NBQXLhwQZWz+fn5quvdvn17nD17FsXFxfD19UVERIQqZxs0aICSkhLcvn1blbPWbCMAIDo6mm0EzNtGCIKgykNDbUT9+i7o2rUtTp++i9u3XeHq6gpnZydcuHAJWVl14eXliTt37uHo0dt624hbt7KRleUNT08PXL9+C5mZ4tuIO3cCkZfnB7ncDWfO/IV790pEtxHXrvmjoqIR8vPzUFZWjvPn75i9jXjw4AFcXV3ZRpi5jSgsLER6ejrbCH6OkNzniKysLBw9etRu2ojMzEwQEZGCTBBM/z6zXr16WLlyJQYMGKCx/qeffsLEiRNVv5Cqo2z8DYmMjISbm1uV9bdv30bdunVx5MgRdOzYEbNnz8ZPP/2k+oABAFlZWfD398fevXvRvXt3nccvLi5GcXGx6nlOTg4iIiKQnZ1tE/NE3b9/X1VcI5Ia5i9Jlam5+8MPgPqo8/nzgVmzFMtvvw107qz/tcuXA/v2KZZ//rlmw9sWLACOHFEsr1qleSc9U23ZAqxZU/l8+3bxx6LaxbaXpMrecjcnJwc+Pj4283cFEZE1ier59Nxzz2Hy5Mnw8vLCo48+CgBISEjAlClT8Nxzzxl9HOU3DGIovwlU3l2vc+fOWLBgAVJTU1Xrdu3aBVdXV7Rr107vcVxdFd9U26q8vDy7+iVMDxfmL0mVqbmrPcRN/bkpw+5qOq+SOY/FCcali20vSRVzl4jIfomaEeK9995Dx44d0bNnT8jlcsjlcvTq1Qs9evSwyJxPhw8fxocffojExEQkJydj8+bNeOmllzBgwABVF+pevXqhRYsWGDFiBE6fPo09e/ZgxowZGDt2rKS/aTA0XxWRrWP+klSZmrvahR5TJv6u6dxM+o5l7rvdkXSw7SWpYu4SEdkvUR8tXVxcsGnTJrz33ntITEyEXC5HTEwM6tevb+74ACh6J23atAlz585FcXEx6tevj7Fjx+L1119X7ePo6IhffvkFEyZMQNeuXSGXyzFs2DB88MEHFomJiIhIyVDxqTrmLD6Zc8Jx9nwiIiIiInOp0feaTZo0QZMmTcwVi16xsbE4opzEwoB69erhf//7n8XjqU0dOnSwdghEojF/SapMzV1bKT6Zc9ids3Pl8vDhNTsW1S62vSRVzF0iIvsl6iPvkCFD8P7771dZv3TpUjz99NM1DooqKe8YQiRFzF+SKlNz1xaLTzU9rqdn5bINT41IOrDtJali7hIR2S9RH00TEhLQr1+/Kuv79OmD/fv31zgoqlRSUmLtEIhEY/6SVJmau9rFJ1MKP7ZafCosrFxW7wVFto9tL0kVc5eIyH6J+mial5cHFxeXKuudnZ2Rk5NT46Cokq+vr7VDIBKN+UtSZWruahd6bGHOp5rO2aR+o9iyspodi2oX216SKuYuEZH9EvWRNzo6Gps2baqyfuPGjWjRokWNg6JKdevWtXYIRKIxf0mqTM1dW7nbnTmP6+VVuVxRUbNjUe1i20tSxdwlIrJfoiYcnzVrFv7v//4P165dQ48ePQAAe/bswXfffYfvv//erAE+7M6dO4eOHTtaOwwiUZi/JFWm5q6tDLsTBMscNyTEfMciy2PbS1LF3CUisl+iik8DBgzAtm3bsHDhQvzwww+Qy+Vo1aoVfv/9d3Tr1s3cMRIREdm0mkw4XtO70ulT02F3ADBnDnDpEtC5c82PRUREREQPL1HFJwDo16+fzknHExMT0aZNm5rERGoaNmxo7RCIRGP+klSZmruG5nyqrrhUWmrSqQxS7/lkjuJTu3aacz+RNLDtJali7hIR2S+zdMrPzs7Gp59+itjYWLTjp1SzKioqsnYIRKIxf0mqTM3dmvR8UnYYrlfPpFPqZKlhdyQtbHtJqpi7RET2q0YfTffu3Yvnn38ederUwYoVK9C3b1+cOHHCXLERgDt37lg7BCLRmL8kVabmbk3mfGrSBPjvf4EPPzTplNVi8enhxbaXpIq5S0Rkv0wednfr1i2sXbsWX331FfLz8/HMM8+gtLQUP/74I+90R0RED6WaFJ8AIDTUPHGo35XOUnNJERERERGZyqSPx3379kWLFi1w4cIFrFixAnfu3MGKFSssFRsBHMZIksb8JakyNXe1i03sdUTWxLaXpIq5S0Rkv0z6eLxr1y6MGTMGc+fORb9+/eBojtlMyaDz589bOwQi0Zi/JFU1zV31XkfmnFC8OupzPtHDi20vSRVzl4jIfplUfDpw4AByc3MRFxeHjh07YuXKlUhPT7dUbAROvEjSxvwlqTI1d3X1fHr8cSA8vHbvFsfiEwFse0m6mLtERPbLpOJT586d8d///hepqal46aWXsHHjRoSHh6OiogK7d+9Gbm6upeJ8aHl7e1s7BCLRmL8kVabmrq45nyZPBlatAlxdzRiYiXHQw4ltL0kVc5eIyH6JmpXC3d0do0ePxsGDB/HXX39h+vTpeP/99xEcHIwBAwaYO8aHWmRkpLVDIBKN+UtSZWruahd9lKPSWQwia2DbS1LF3CUisl81nhK1WbNmWLJkCW7duoXvvvvOHDGRmrNnz1o7BCLRmL8kVabmrnaRiUUnsia2vSRVzF0iIvtltvvxODo64qmnnsLPP/9srkMSERFJgq3c3a6iwtoREBERERFVZSMfl0mf+vXrWzsEItGYvyRVpuYuezqRLWHbS1LF3CUisl8sPtm48vJya4dAJBrzl6TK1Ny1leIT73ZHANteki7mLhGR/WLxycbdunXL2iEQicb8JakyNXfVi0+PPmrmYEwwYoTi7npDhlgvBrI+tr0kVcxdIiL75WTtAIiIiKROfc6nmBjrxREeDmzcCDjxtzsRERER2RD2fLJxbdu2tXYIRKIxf0mqapK7zs5mDEQEFp6IbS9JFXOXiMh+sfhk4y5fvmztEIhEY/6SVJmau+oFH2sXn4jY9pJUMXeJiOwXi082Lj8/39ohEInG/CWpMjV31QtOLi5mDobIRGx7SaqYu0RE9ovFJxvn6elp7RCIRGP+klSZmruurpXL7PlE1sa2l6SKuUtEZL9YfLJxjRs3tnYIRKIxf0mqTM1d9YITi09kbWx7SaqYu0RE9ovFJxuXmJho7RCIRGP+klSZmrvqQ+1YfCJrY9tLUsXcJSKyXyw+ERER1ZB68YlzPhERERERaWLxycZFRERYOwQi0Zi/JFWm5q56wcnR0czBEJmIbS9JFXOXiMh+sfhk4xwc+E9E0sX8JakyNXfVi09OTmYOhshEbHtJqpi7RET2iy28jbtx44a1QyASjflLUmVq7rq6AjKZYjk42AIBEZmAbS9JFXOXiMh+8ftZIiKiGpLJgO++AyoqOOcTEREREZE2mSAIgrWDsCU5OTnw8fFBdnY2vL29rR0OCgsLIZfLrR0GkSjMX5Iq5i5JGfOXpMrectfW/q4gIrImDruzccnJydYOgUg05i9JFXOXpIz5S1LF3CUisl8sPtm43Nxca4dAJBrzl6SKuUtSxvwlqWLuEhHZLxafbJw9dT2mhw/zl6SKuUtSxvwlqWLuEhHZL875pMXWxmaXlpbC2dnZ2mEQicL8Jali7pKUMX9Jquwtd23t7woiImtizycbd+rUKWuHQCQa85ekirlLUsb8Jali7hIR2S8nawdga5QdwXJycqwciUJ+fr7NxEJkKuYvSRVzl6SM+UtSZW+5q3wvHGhCRMTiUxXKiQ4jIiKsHAkREREREUldbm4ufHx8rB0GEZFVcc4nLRUVFbhz5w68vLwgk8msGktOTg4iIiJw8+ZNjhMnyWH+klQxd0nKmL8kVfaYu4IgIDc3F2FhYXBw4GwnRPRwY88nLQ4ODqhbt661w9Dg7e1tN7+E6eHD/CWpYu6SlDF/SarsLXfZ44mISIEleCIiIiIiIiIishgWn4iIiIiIiIiIyGJYfLJhrq6uePfdd+Hq6mrtUIhMxvwlqWLukpQxf0mqmLtERPaNE44TEREREREREZHFsOcTERERERERERFZDItPRERERERERERkMSw+ERERERERERGRxbD4ZMM+/fRTNGjQAG5ubmjXrh0OHDhg7ZCIqti/fz/69++PsLAwyGQybNu2TWO7IAiYM2cOwsLCIJfLER8fj/Pnz1snWCI1ixYtQvv27eHl5YXg4GA89dRTuHTpksY+zF+yRatWrUKrVq3g7e0Nb29vdO7cGb/99ptqO/OWpGLRokWQyWSYOnWqah3zl4jIPrH4ZKM2bdqEqVOnYubMmTh9+jQeeeQRPPHEE0hJSbF2aEQa8vPz0bp1a6xcuVLn9iVLlmD58uVYuXIljh8/jtDQUDz++OPIzc2t5UiJNCUkJGDixIk4cuQIdu/ejbKyMvTq1Qv5+fmqfZi/ZIvq1q2L999/HydOnMCJEyfQo0cPDBw4UPUHOvOWpOD48eP4/PPP0apVK431zF8iIvvEu93ZqI4dOyI2NharVq1SrYuKisJTTz2FRYsWWTEyIv1kMhm2bt2Kp556CoDi28uwsDBMnToVb7zxBgCguLgYISEhWLx4MV566SUrRkukKT09HcHBwUhISMCjjz7K/CVJ8ff3x9KlSzF69GjmLdm8vLw8xMbG4tNPP8V7772HNm3a4KOPPmK7S0Rkx9jzyQaVlJTg5MmT6NWrl8b6Xr164dChQ1aKish0ycnJuHv3rkYuu7q6olu3bsxlsjnZ2dkAFH/EA8xfkoby8nJs3LgR+fn56Ny5M/OWJGHixIno168fHnvsMY31zF8iIvvlZO0AqKqMjAyUl5cjJCREY31ISAju3r1rpaiITKfMV125fOPGDWuERKSTIAh49dVX8a9//QvR0dEAmL9k2/766y907twZRUVF8PT0xNatW9GiRQvVH+jMW7JVGzduxKlTp3D8+PEq29juEhHZLxafbJhMJtN4LghClXVEUsBcJls3adIknD17FgcPHqyyjflLtqhZs2ZITEzEgwcP8OOPP2LkyJFISEhQbWfeki26efMmpkyZgl27dsHNzU3vfsxfIiL7w2F3NigwMBCOjo5VejmlpaVV+SaIyJaFhoYCAHOZbNorr7yCn3/+Gfv27UPdunVV65m/ZMtcXFzQuHFjxMXFYdGiRWjdujU+/vhj5i3ZtJMnTyItLQ3t2rWDk5MTnJyckJCQgP/85z9wcnJS5Sjzl4jI/rD4ZINcXFzQrl077N69W2P97t270aVLFytFRWS6Bg0aIDQ0VCOXS0pKkJCQwFwmqxMEAZMmTcKWLVuwd+9eNGjQQGM785ekRBAEFBcXM2/JpvXs2RN//fUXEhMTVY+4uDg8//zzSExMRMOGDZm/RER2isPubNSrr76KESNGIC4uDp07d8bnn3+OlJQUjB8/3tqhEWnIy8vD1atXVc+Tk5ORmJgIf39/1KtXD1OnTsXChQvRpEkTNGnSBAsXLoS7uzuGDRtmxaiJFBPebtiwAT/99BO8vLxU37T7+PhALpdDJpMxf8kmvf3223jiiScQERGB3NxcbNy4EX/88Qd27NjBvCWb5uXlpZpXT8nDwwMBAQGq9cxfIiL7xOKTjXr22Wdx//59zJs3D6mpqYiOjsavv/6K+vXrWzs0Ig0nTpxA9+7dVc9fffVVAMDIkSOxdu1avP766ygsLMSECROQlZWFjh07YteuXfDy8rJWyEQAgFWrVgEA4uPjNdavWbMGo0aNAgDmL9mke/fuYcSIEUhNTYWPjw9atWqFHTt24PHHHwfAvCVpY/4SEdknmSAIgrWDICIiIiIiIiIi+8Q5n4iIiIiIiIiIyGJYfCIiIiIiIiIiIoth8YmIiIiIiIiIiCyGxSciIiIiIiIiIrIYFp+IiIiIiIiIiMhiWHwiIiIiIiIiIiKLYfGJiIiIiIiIiIgshsUnIiIiIiIiIiKyGBafiIjooTFnzhy0adPGauefNWsWxo0bZ9S+M2bMwOTJky0cERERERGR5ckEQRCsHQQREVFNyWQyg9tHjhyJlStXori4GAEBAbUUVaV79+6hSZMmOHv2LCIjI6vdPy0tDY0aNcLZs2fRoEEDywdIRERERGQhLD4REZFduHv3rmp506ZNmD17Ni5duqRaJ5fL4ePjY43QAAALFy5EQkICdu7cafRr/u///g+NGzfG4sWLLRgZEREREZFlcdgdERHZhdDQUNXDx8cHMpmsyjrtYXejRo3CU089hYULFyIkJAS+vr6YO3cuysrK8Nprr8Hf3x9169bFV199pXGu27dv49lnn4Wfnx8CAgIwcOBAXL9+3WB8GzduxIABAzTW/fDDD4iJiYFcLkdAQAAee+wx5Ofnq7YPGDAA3333XY2vDRERERGRNbH4RERED7W9e/fizp072L9/P5YvX445c+bgySefhJ+fH44ePYrx48dj/PjxuHnzJgCgoKAA3bt3h6enJ/bv34+DBw/C09MTffr0QUlJic5zZGVl4dy5c4iLi1OtS01NxdChQzF69GgkJSXhjz/+wODBg6HeIblDhw64efMmbty4YdmLQERERERkQSw+ERHRQ83f3x//+c9/0KxZM4wePRrNmjVDQUEB3n77bTRp0gRvvfUWXFxc8OeffwJQ9GBycHDAF198gZiYGERFRWHNmjVISUnBH3/8ofMcN27cgCAICAsLU61LTU1FWVkZBg8ejMjISMTExGDChAnw9PRU7RMeHg4A1faqIiIiIiKyZU7WDoCIiMiaWrZsCQeHyu9iQkJCEB0drXru6OiIgIAApKWlAQBOnjyJq1evwsvLS+M4RUVFuHbtms5zFBYWAgDc3NxU61q3bo2ePXsiJiYGvXv3Rq9evTBkyBD4+fmp9pHL5QAUva2IiIiIiKSKxSciInqoOTs7azyXyWQ611VUVAAAKioq0K5dO3z77bdVjhUUFKTzHIGBgQAUw++U+zg6OmL37t04dOgQdu3ahRUrVmDmzJk4evSo6u52mZmZBo9LRERERCQFHHZHRERkgtjYWFy5cgXBwcFo3LixxkPf3fQaNWoEb29vXLhwQWO9TCZD165dMXfuXJw+fRouLi7YunWravu5c+fg7OyMli1bWvQ9ERERERFZEotPREREJnj++ecRGBiIgQMH4sCBA0hOTkZCQgKmTJmCW7du6XyNg4MDHnvsMRw8eFC17ujRo1i4cCFOnDiBlJQUbNmyBenp6YiKilLtc+DAATzyyCOq4XdERERERFLE4hMREZEJ3N3dsX//ftSrVw+DBw9GVFQURo8ejcLCQnh7e+t93bhx47Bx40bV8D1vb2/s378fffv2RdOmTfHOO+9g2bJleOKJJ1Sv+e677zB27FiLvyciIiIiIkuSCer3dCYiIiKLEAQBnTp1wtSpUzF06NBq9//ll1/w2muv4ezZs3By4hSNRERERCRd7PlERERUC2QyGT7//HOUlZUZtX9+fj7WrFnDwhMRERERSR57PhERERERERERkcWw5xMREREREREREVkMi09ERERERERERGQxLD4REREREREREZHFsPhEREREREREREQWw+ITERERERERERFZDItPRERERERERERkMSw+ERERERERERGRxbD4REREREREREREFsPiExERERERERERWQyLT0REREREREREZDH/DwIH0V1lf6ibAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 38/49 (Lat: 38.72, Lon: -9.4)\n", + "Site 38: Rhypo = 10.75 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 290.7547 cm/s²\n", + "Subfault PGA (i=0, j=1): 252.9810 cm/s²\n", + "Subfault PGA (i=1, j=0): 187.2318 cm/s²\n", + "Subfault PGA (i=1, j=1): 42.7082 cm/s²\n", + "Subfault PGA (i=2, j=0): 32.5144 cm/s²\n", + "Subfault PGA (i=2, j=1): 11.6559 cm/s²\n", + "Subfault PGA (i=3, j=0): 143.4011 cm/s²\n", + "Subfault PGA (i=3, j=1): 99.8148 cm/s²\n", + "Total PGA: 309.8536 cmm/s²\n", + "Total PGA: 309.8536 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 39/49 (Lat: 38.74, Lon: -9.4)\n", + "Site 39: Rhypo = 8.84 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 405.8088 cm/s²\n", + "Subfault PGA (i=0, j=1): 277.3646 cm/s²\n", + "Subfault PGA (i=1, j=0): 243.0063 cm/s²\n", + "Subfault PGA (i=1, j=1): 65.3675 cm/s²\n", + "Subfault PGA (i=2, j=0): 50.0043 cm/s²\n", + "Subfault PGA (i=2, j=1): 14.9645 cm/s²\n", + "Subfault PGA (i=3, j=0): 176.8594 cm/s²\n", + "Subfault PGA (i=3, j=1): 142.7253 cm/s²\n", + "Total PGA: 426.9942 cmm/s²\n", + "Total PGA: 426.9942 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 40/49 (Lat: 38.76, Lon: -9.4)\n", + "Site 40: Rhypo = 7.11 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 641.9660 cm/s²\n", + "Subfault PGA (i=0, j=1): 499.7091 cm/s²\n", + "Subfault PGA (i=1, j=0): 326.4735 cm/s²\n", + "Subfault PGA (i=1, j=1): 83.8679 cm/s²\n", + "Subfault PGA (i=2, j=0): 80.1680 cm/s²\n", + "Subfault PGA (i=2, j=1): 23.2395 cm/s²\n", + "Subfault PGA (i=3, j=0): 200.0190 cm/s²\n", + "Subfault PGA (i=3, j=1): 179.2569 cm/s²\n", + "Total PGA: 873.2519 cmm/s²\n", + "Total PGA: 873.2519 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 41/49 (Lat: 38.78, Lon: -9.4)\n", + "Site 41: Rhypo = 5.73 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 795.4269 cm/s²\n", + "Subfault PGA (i=0, j=1): 542.9811 cm/s²\n", + "Subfault PGA (i=1, j=0): 373.0470 cm/s²\n", + "Subfault PGA (i=1, j=1): 107.0566 cm/s²\n", + "Subfault PGA (i=2, j=0): 106.5569 cm/s²\n", + "Subfault PGA (i=2, j=1): 27.0035 cm/s²\n", + "Subfault PGA (i=3, j=0): 289.6985 cm/s²\n", + "Subfault PGA (i=3, j=1): 196.6291 cm/s²\n", + "Total PGA: 854.4946 cmm/s²\n", + "Total PGA: 854.4946 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 42/49 (Lat: 38.83, Lon: -9.43)\n", + "Site 42: Rhypo = 5.72 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 649.0885 cm/s²\n", + "Subfault PGA (i=0, j=1): 408.1193 cm/s²\n", + "Subfault PGA (i=1, j=0): 435.9209 cm/s²\n", + "Subfault PGA (i=1, j=1): 84.7117 cm/s²\n", + "Subfault PGA (i=2, j=0): 88.1051 cm/s²\n", + "Subfault PGA (i=2, j=1): 26.6122 cm/s²\n", + "Subfault PGA (i=3, j=0): 416.1857 cm/s²\n", + "Subfault PGA (i=3, j=1): 277.0810 cm/s²\n", + "Total PGA: 713.1142 cmm/s²\n", + "Total PGA: 713.1142 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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5t/ZVPM8xMTFCWFiYYGNjIwBQy1xOTo7w7rvvCi1atBCzERgYKEyfPl1ISkoStwMgvPrqq1Wex5rcuXOn2m8cfDj/RERERKQ9CkEQhMfd7CIiIqKye3Q1btwYr7/+OlasWFFp/cKFC7Fo0SKkpKRodB8vIiIiIqL6hpfaERERPWbx8fG4ffs2PvzwQxgZGeGNN97Qd0lERERERDphpO8CiIiInjRff/01unXrhitXrmD79u1q31hH0qhUKpSUlFT7o1Kp9F0iERER0RONl9oRERGRbHXr1g3h4eHVrm/cuDFiYmIeX0FEREREpIaNJyIiIpKt69evIzs7u9r1SqUSgYGBj7EiIiIiIqqIjSciIiIiIiIiItIJ3uOJiIiIiIiIiIh0go0nIiLSm02bNkGhUIg/JiYmaNCgAYYPH46bN2/quzx8/fXXUCgUsLa21mj73bt3Y8SIEWjatCksLCzg7e2NUaNGVfla5s6di3bt2sHR0RHm5uZo0qQJJk2ahNjY2BqPExMTA4VCgZUrV0p+TUREREREj5OJvgsgIiLauHEjWrZsiYKCAvzzzz94//33ceTIEVy7dg0ODg56qenevXuYNWsWPDw8kJmZqdFzli9fDnd3d8ydOxdNmjTB3bt3sXTpUgQFBeHkyZNo1aqVuG1GRgZGjBgBPz8/2NjY4OrVq3jvvfewb98+XLlyBU5OTrp6aUREREREjw0bT0REpHcBAQEIDg4GUPYtZSqVCgsWLMDevXvxv//9Ty81TZ48GU8//TQcHR3xww8/aPSc/fv3w9XVVW1Zjx494O3tjY8++ghff/21uPzzzz9X265bt27w8fFB37598dNPP2HChAl1fxFERERERHrGS+2IiKjeKW9C3b9/Xy/H37ZtG8LDw/HFF19Iet7DTScA8PDwgKenJ+7evVvj811cXAAAJibSPxcqLi7GuHHjYG1tjZ9//hnAv5cy/vnnn5g4cSKcnJxga2uLsWPHIjc3F0lJSRg2bBjs7e3RoEEDzJo1C8XFxZKPTURERERUHc54IiKieufOnTsAgObNm9e4rSAIUKlUGu1Xk4ZOcnIypk2bhg8++ACenp4a7fdRbt++jdjYWAwePLjK9SUlJSguLsa1a9cwbdo0NG/eHEOGDJF0jIyMDAwZMgRRUVEIDw9H+/bt1da/9NJLGDJkCHbu3InIyEjMmTMHJSUluH79OoYMGYJJkybhjz/+wPLly+Hh4YEZM2bU9uUSEREREalh44mIiPROpVKhpKREvMfTe++9h6effhoDBw6s8bmbN2/W+HI8QRBq3GbKlClo0aIFXnnlFY32+SglJSV48cUXYW1tjenTp1dan5SUhAYNGoiPQ0JCcOTIEY1vZg6U3Wi8X79+AICTJ0+icePGlbbp37+/eCPyXr164cSJE/j222+xevVqsa5nnnkGBw4cwPbt29l4IiIiIiKtYeOJiIj0rlOnTmqP/fz88NNPP2k0Q2nAgAGIiIjQSh0//vgj9u/fj8jISCgUijrtSxAEvPjii/j777/x448/wsvLq9I2zs7OiIiIQGFhIaKiorBixQp0794df/31l1pDqjrnzp3DypUr4e/vj927d8Pe3r7K7fr376/22M/PD3v37hUbVhWXHzx4UPMXSURERERUAzaeiIhI77Zs2QI/Pz9kZ2dj165dWL9+PUaMGIHffvutxuc6OjrCzs6uzjXk5OTg1Vdfxeuvvw4PDw9kZGQAAIqKigCUXc5mamoKKyurGvclCAJeeuklbNu2DZs3b8agQYOq3M7ExES8n1WXLl3Qp08f+Pj44IMPPsAnn3xS43EOHTqE1NRUrF69utqmE1B2jioyMzOrdnlBQUGNxyUiIiIi0hQbT0REpHd+fn5iA6Z79+5QqVT4+uuv8cMPP2Do0KGPfK62LrVLTU3F/fv3sWrVKqxatarSegcHBwwaNAh79+6t8RgvvfQSNm7ciA0bNmD06NEa1QYAnp6e8PDwwI0bNzTa/s0338StW7cwduxYlJSUYOzYsRofi4iIiIjocWDjiYiI6p0VK1bgxx9/xPz58zFkyBAYGVX/JazautTO3d0dR44cqbT8gw8+QHh4OH777Tc4Ozs/ch+CIGDixInYuHEj1q9fr3FDrFx0dDTi4+M1urcVABgZGWH9+vWwtrbG+PHjkZubq5V7UxERERERaQsbT0REVO84ODjgnXfewVtvvYUdO3Y8ctaQk5MTnJyc6nxMc3NzdOvWrdLyTZs2wdjYuNK6F198EZs3b8atW7fEG3pPnToVGzZswIQJExAYGIiTJ0+K2yuVSrRr1w4AcPHiRUyfPh1Dhw5FkyZNYGRkhEuXLuGjjz6Ck5MTZs2aJan2VatWwcbGBlOmTEFOTg7efPNNaS+eiIiIiEhH2HgiIqJ66fXXX8dnn32GxYsXY8SIETA2NtZ3SWpUKhVUKpXa5Xv79+8HAHzzzTf45ptv1LZv3LgxYmJiAABubm7w8PDAqlWrkJiYiJKSEnh6eqJ///6YM2dOlTcir8nChQthbW2NN998Ezk5OVi0aFHtXxwRERERkZYoBE2+W5qIiIiIiIiIiEii6m+aQUREREREREREVAdsPBERERERERERkU6w8URERERERERERDrBxhMREREREREREekEG09ERERERERERKQTbDwREREREREREZFOmOi7ADkqLS1FQkICbGxsoFAo9F0OERERERHVM4IgIDs7Gx4eHjAyqr+f96tUKhQXF+u7DCKSGVNTUxgbG2u0LRtPtZCQkAAvLy99l0FERERERPXc3bt34enpqe8yKhEEAUlJScjIyNB3KUQkU/b29nB3d69xQg4bT7VgY2MDoOx/Ira2tnquBigqKoKZmZm+yyDSGDNLcsTcktwwsyRHhpTbrKwseHl5if92qG/Km06urq6wtLTklRxEpDFBEJCXl4fk5GQAQIMGDR65PRtPtVA+KNva2taLxtOpU6cQEhKi7zKINMbMkhwxtyQ3zCzJkSHmtj42dFQqldh0cnJy0nc5RCRDFhYWAIDk5GS4uro+8rK7+nuxMREREREREWld+T2dLC0t9VwJEclZ+RhS033i2HgyAPXxmnGiR2FmSY6YW5IbZpbkiLl9vOrjbCwikg9NxxA2ngyApneSJ6ovmFmSI+aW5IaZJTlibqk+8vb2xscff6zvMh5p/PjxGDx4sN6Ov2nTJtjb2+vt+FI9rr/Tbt26Ydq0afVmP/rCxpMBiI2N1XcJRJIwsyRHzC3JDTNLcsTc0qMoFIpH/owfP77G5+/du1dn9cXHx8PMzAwtW7bU2THqg6qaNi+88AJu3Lihn4Iekpubi9mzZ6NJkyYwNzeHi4sLunXrhp9//lncJiIiApMmTdJjlVX766+/oFAoKn3b5O7du7FkyRKdH7+q/67WrVtX5/3y5uJERET1SFQUEB0N9O8P8AoIIiKifyUmJoq/79q1C/Pnz8f169fFZeU3O9aXTZs2YdiwYTh69Cj++ecfdOnSRa/1SCEIAlQqFUxMatcisLCw0Pv5Lzd58mScPn0an332Gfz9/fHgwQMcP34cDx48ELdxcXHRY4XSOTo6PrZjbdy4EX369BEf29nZ1XmfnPFkAFq3bq3vEogkYWZJjnSR28uXgZQU9WVvvQV8+SXw119aPxw9YTjWkhwxt/Qo7u7u4o+dnR0UCoXash07dsDX1xdmZmZo0aIFtm7dKj7X29sbAPDcc89BoVCIj2/duoVBgwbBzc0N1tbW6NChA/744w/JtQmCgI0bN2LMmDEYOXIkNmzYUGmbf/75B127doWlpSUcHBzQu3dvpKenAwBKS0uxfPlyNG3aFEqlEo0aNcL7778vPvfevXt44YUX4ODgACcnJwwaNAgxMTGPrGfFihVo0qQJLCws0KZNG/zwww/i+vKZNQcOHEBwcDCUSiX+/vvvGs9Ht27dEBsbi+nTp4szYoCqL7Vbu3ZttX8fQNnsmq+//hrPPfccLC0t0axZM+zbt0/jc16d/fv3Y86cOejbty+8vb3Rvn17vP766xg3bpy4zcOzthQKBdavX4/+/fvD0tISfn5+OHHiBKKjo9GtWzdYWVkhNDQUt27dEp9T1eWN06ZNQ7du3aqtbdu2bQgODoaNjQ3c3d0xcuRIJCcnAwBiYmLQvXt3AICDg4PaLL6HL7VLT0/H2LFj4eDgAEtLSzz77LO4efOmuL787+PAgQPw8/ODtbU1+vTpo9a8rY69vb3af1faaCiy8WQAHjXgENVHzCzJkbZze+0a8M47wIQJVa+/c0erh6MnEMdakiPmlmprz549eOONNzBz5kxcvnwZL7/8Mv73v//hyJEjAMourQLKZnMkJiaKj3NyctC3b1/88ccfiIyMRO/evTFgwADExcVJOv6RI0eQl5eHZ555BmPGjMF3332H7Oxscf358+fRs2dPtGrVCidOnMCxY8cwYMAAqFQqAMA777yD5cuXY968ebh69Sp27NgBNzc3AEBeXh66d+8Oa2trHD16FMeOHRMbCUVFRVXW8+6772Ljxo1Yu3Ytrly5gunTp2P06NEIDw9X2+6tt97CsmXLEBUVhdatW9d4Pnbv3g1PT08sXrwYiYmJ1TYyavr7KLdo0SIMGzYMFy9eRN++fTFq1CikpaVJOvcPc3d3x6+//qp2/jWxZMkSjB07FufPn0fLli0xcuRIvPzyy3jnnXdw5swZAMBrr71Wp9qKioqwZMkSXLhwAXv37sWdO3fE5pKXlxd+/PFHAMD169eRmJiITz75pMr9jB8/HmfOnMG+fftw4sQJCIKAvn37qn27XF5eHlauXImtW7fi6NGjiIuLw6xZs2qs8bXXXoOzszM6dOiAdevWobS0tE6vGQAgkGSZmZkCACEzM1PfpQiCIAgnT57UdwlEkjCzJEfazu2PPwpC//5lPxWVL/vmG60ejp5AHGtJjgwpt/Xt3wwV5efnC1evXhXy8/PFZaWlgpCfr5+f0lLpr2Hjxo2CnZ2d+Lhz587CxIkT1bZ5/vnnhb59+4qPAQh79uypcd/+/v7CmjVrxMeNGzcWPvroo0c+Z+TIkcK0adPEx23atBG++uor8fGIESOELl26VPncrKwsQalUqm1f0YYNG4QWLVoIpRVOVGFhoWBhYSEcOHBAEARBGDdunDBo0CBBEAQhJydHMDc3F44fP662nxdffFEYMWKEIAiCcOTIEQGAsHfv3ke+LkHQ7HzU9u/j3XffFR/n5OQICoVC+O2332qs6VHCw8MFT09PwdTUVAgODhamTZsmHDt2TG2bh1/Dw7WcOHFCACBs2LBBXPbtt98K5ubm4uOK57zcG2+8IXTt2lV83LVrV+GNN96ottbTp08LAITs7GxBEP79e0lPT1fbruJ+bty4IQAQ/vnnH3F9amqqYGFhIXz33XeCIJT9fQAQoqOjxW0+//xzwc3NrdpaBEEQlixZIhw/flyIjIwUVq5cKVhaWgpLliypdvuqxpKq8B5PBsDc3FzfJRBJwsySHGk7tzXdv4n3d6K64lhLcsTc6k9hIfD88/o59vffA3X9q4+Kiqp0s+guXbpUO2OkXG5uLhYtWoSff/4ZCQkJKCkpQX5+vqQZTxkZGdi9ezeOHTsmLhs9ejS++eYbvPTSSwDKZjw9X80JjoqKQmFhIXr27Fnl+rNnzyI6Oho2NjZqywsKCtQu/Sp39epVFBQUoFevXmrLi4qK0K5dO7VlwcHBao+1cT7KX5Mmfx8VL6+1srKCjY2NeOnZw5YuXYqlS5eKj69evYpGjRpV2u7pp5/G7du3cfLkSfzzzz/4888/8cknn2DRokWYN29etTVXrKV8tllgYKDasoKCAmRlZcHW1rba/TxKZGQkFi5ciPPnzyMtLU2cTRQXFwd/f3+N9hEVFQUTExOEhISIy5ycnNCiRQtERUWJyywtLeHr6ys+btCgQbXntty7774r/t62bVsAwOLFi9WW1wYbTwagVatW+i6BSBJmluToceeWjSeqK461JEfMLdWF4qH/eQqCUGnZw958800cOHAAK1euRNOmTWFhYYGhQ4dWewlbVXbs2IGCggK1RoAgCCgtLcXVq1fh7+//yPvk1HQPndLSUrRv3x7bt2+vtK6qm2SXNzN++eUXNGzYUG2dUqlUe2xlZaX2WBvno5wmfx+mpqaVnlPdpV2TJ0/GsGHDxMceHh7VHtvU1BRPPfUUnnrqKbz99tt47733sHjxYsyePRtmZmbVPufh2qtaVl6fkZERBEFQ20fFS90elpubi7CwMISFhWHbtm1wcXFBXFwcevfuLen8PnzMissrnt+qzm11z61Op06dkJWVhfv374vNuNpg48kAnD17Vm2QI6rvmFmSI23l9uJFQIP7OrLxRHXGsZbkiLnVH6WybOaRvo5dV35+fjh27BjGjh0rLjt+/Dj8/PzEx6ampuI9lcr9/fffGD9+PJ577jkAZfd8knqvsQ0bNmDmzJnivXrKTZ06Fd988w1WrlyJ1q1b4/Dhw1i0aFGl5zdr1gwWFhY4fPiwOEOqoqCgIOzatQuurq4azbTx9/eHUqlEXFwcunbtKum1aHI+zMzMKp3Hh2ny9yGVo6Njrb/dzd/fHyUlJSgoKKi28SSVi4sLLl++rLbs/PnzlRo+5a5du4bU1FR88MEH8PLyAgDx3lHlymt71Pktfy2nTp1C586dAQAPHjzAjRs36nR+qxIZGQlzc/NKN46Xio0nIiKix2ju3LI///99QrXYeCIiosdJoaj75W769Oabb2LYsGEICgpCz549sX//fuzevVvtG9m8vb1x+PBhdOnSBUqlEg4ODmjatCl2796NAQMGQKFQYN68eZJupnz+/HmcO3cO27dvR8uWLdXWjRgxAnPnzsWyZcvwzjvvIDAwEFOmTMHkyZNhZmaGI0eO4Pnnn4ezszNmz56Nt956C2ZmZujSpQtSUlJw5coVvPjiixg1ahQ+/PBDDBo0CIsXL4anpyfi4uKwe/duvPnmm/D09FQ7ro2NDWbNmoXp06ejtLQU//nPf5CVlYXjx4/D2tpa7dvdHqbJ+fD29sbRo0cxfPhwKJVKODs71+rvQ1e6deuGESNGIDg4GE5OTrh69SrmzJmD7t271/oSuar06NEDH374IbZs2YLQ0FBs27YNly9frnQ5Y7lGjRrBzMwMa9asweTJk3H58mUsWbJEbZvGjRtDoVDg559/Rt++fWFhYQFra2u1bZo1a4ZBgwZh4sSJWL9+PWxsbPD222+jYcOGGDRoUK1fz/79+5GUlITQ0FBYWFjgyJEjmDt3LiZNmlRpppxU/FY7A/CoKYZE9REzS3Kk7dzGxmp1d0SVcKwlOWJuqbYGDx6MTz75BB9++CFatWqF9evXY+PGjWpfbb9q1SocOnQIXl5eYnPgo48+goODAzp37owBAwagd+/eCAoK0vi4GzZsgL+/f6WmU3lNaWlp2L9/P5o3b46DBw/iwoUL6NixI0JDQ/HTTz/BxKRsLsi8efMwc+ZMzJ8/H35+fnjhhRfE+/FYWlri6NGjaNSoEYYMGQI/Pz9MmDAB+fn51TZSlixZgvnz52PZsmXw8/ND7969sX//fvj4+Dzy9WhyPhYvXoyYmBj4+vpWealf+Wuv6e9DV3r37o3NmzcjLCwMfn5+eP3119G7d2989913Wj/OvHnz8NZbb6FDhw7Izs5Wm+H1MBcXF2zatAnff/89/P398cEHH2DlypVq2zRs2BCLFi3C22+/DTc3t2q/RW/jxo1o3749+vfvj9DQUAiCgF9//bXa2VaaMDU1xRdffIHQ0FC0bt0an3zyCRYvXoxVq1bVep/lFILUi/wIWVlZsLOzQ2ZmplY7prWVkpJS7X/wRPURM0typK3cDhhQ9mfDhsC9e2W/799feX3fvkBqKtCzZ82zo4iqwrGW5MiQclvf/s1QUUFBAe7cuQMfHx/e0J2Iak3TsYQzngzA7du39V0CkSTMLMlRbXObmwvU5iOeX38FTp8Gli2r1WGJONaSLDG3RESGh40nIiIiHYmKAoYPB776St+VEBERERHpBxtPBiAgIEDfJRBJwsySHNUmt1u2lP1Z8VI6oseFYy3JEXNLRGR42HgyAPHx8fougUgSZpbkqDa55TfTkT5xrCU5Ym6JiAwPG08GICMjQ98lEEnCzJIc1Sa3Rg/9X5Zf50GPE8dakiPmlojI8LDxZADMzMz0XQKRJMwsyVFtcsvGE+kTx1qSI+b28eIXnBNRXWg6hrDxZADatm2r7xKIJGFmSY5qk9uHG0+lpf/+zsvwSNc41pIcMbePh6mpKQAgLy9Pz5UQkZyVjyHlY0p1TB5HMdqwbNky7N69G9euXYOFhQU6d+6M5cuXo0WLFuI2giBg0aJF+PLLL5Geno6QkBB8/vnnaNWqlbhNYWEhZs2ahW+//Rb5+fno2bMnvvjiC3h6eurjZWnF6dOnERISou8yiDTGzJIc1Sa3j2o8VYUfPJM2cawlOWJuHw9jY2PY29sjOTkZAGBpaQkFPxEhIg0JgoC8vDwkJyfD3t4exsbGj9xeNo2n8PBwvPrqq+jQoQNKSkowd+5chIWF4erVq7CysgIArFixAqtXr8amTZvQvHlzvPfee+jVqxeuX78OGxsbAMC0adOwf/9+7Ny5E05OTpg5cyb69++Ps2fP1niyiIiIpGDjiYiI6it3d3cAEJtPRERS2dvbi2PJo0hqPGVmZmLPnj34+++/ERMTg7y8PLi4uKBdu3bo3bs3OnfuXOuCa/L777+rPd64cSNcXV1x9uxZPP300xAEAR9//DHmzp2LIUOGAAA2b94MNzc37NixAy+//DIyMzOxYcMGbN26Fc888wwAYNu2bfDy8sIff/yB3r1766x+XdLkL5qoPmFmSY5qk9uHP89QqareThDKLr2rqTFFJAXHWpIj5vbxUSgUaNCgAVxdXVFcXKzvcohIZkxNTTWevKNR4ykxMRHz58/H9u3b4e7ujo4dO6Jt27awsLBAWloajhw5gpUrV6Jx48ZYsGABXnjhhTq9AE1kZmYCABwdHQEAd+7cQVJSEsLCwsRtlEolunbtiuPHj+Pll1/G2bNnUVxcrLaNh4cHAgICcPz48WobT4WFhSgsLBQfZ2Vl6eIl1Zq1tbW+SyCShJklOapNbh++aqG6ezyVlpY1qdh4Im3iWEtyxNw+fsbGxrzyg4h0SqPGU5s2bTB27FicPn0aAQEBVW6Tn5+PvXv3YvXq1bh79y5mzZql1UIrEgQBM2bMwH/+8x+xnqSkJACAm5ub2rZubm6IjY0VtzEzM4ODg0OlbcqfX5Vly5Zh0aJFlZafOXMGVlZWCAoKQlRUFPLz82FjYwMfHx9cvHgRANC4cWOUlpbi7t27AMpumBgdHY2cnBxYWVmhefPmiIyMBAB4enrC2NhYrLd169aIiYlBVlYWzM3N0apVK5w9exZAWcPM3Nwct2/fRnp6Op566inEx8cjIyMDZmZmaNu2LU6fPg2g7JMja2trREdHAwD8/Pxw//59pKWlwcTEBO3bt8fp06chCAJcXFzg4OCAGzduAABatGiBtLQ0pKSkwMjICB06dMCZM2egUqng5OQEV1dXREVFAQCaNWuGrKws3L9/HwAQEhKCc+fOobi4GA4ODvDw8MCVK1cAAL6+vsjLy0NiYiIAIDg4GJcvX0ZBQQHs7OzQqFEjXLp0CQDg7e2NkpISxMfHAwCCgoJw7do15OXlwdraGr6+vrhw4QIAoFGjRgCAuLg4AGXZvXXrFnJycmBpaYmWLVvi3Llz4vk2MTFBTEwMACAwMBBxcXHIzMyEubk5AgICcObMGQBAgwYNYGlpiVu3bgEAWrVqhYSEBKSnp8PU1BRBQUE4deqUmCdbW1vcvHlTPN/Jycl48OABjI2NERwcjIiICJSWlsLFxQWOjo64fv06AKB58+ZIT09HSkoKFAoFOnbsiLNnz6KkpASOjo5wc3MTz3fTpk2Rk5MjZrdjx444f/48ioqKYG9vD09PT1y+fBkA0KRJExQUFCAhIQEA0L59e1y5cgUFBQWwtbWFt7e3WmZVKpV4vtu1a4cbN24gNzcX1tbWaNq0Kc6fPw8A8PLygpGRkVpm79y5g+zsbFhYWMDPz0883w0bNoSZmRnu3LmD9PR0PP3007h79y4yMjKgVCrRunVrREREiJm1srISz7e/vz+SkpKQlpZW6Xy7urrCzs5OPN8tW7ZEamoqUlNTxcyWn29nZ2c4Ozvj2rVrYmYzMzPFKeYVM+vo6Ah3d3dcvXpVzGxubq54vjt06ICLFy+isLAQ9vb28PLyEjPr4+ODoqIi3Lt3T8ysPscIAAgICOAYgbqNESqVCo6OjpLGiLt3zZGZaQc7OzucOnUKOTnGyMvzh6mpCS5ezAUA2NhY4+bNWGRmPoBKZQKgPTIyMiAIApRKM5iamuHUqbKanpQxAigbkzlG1G2MUKlUcHBw4BjB9xGyGiMiIyNhY2NjEGOEpaUliIgIUAgafP9dSkoKXFxcNN6p1O2levXVV/HLL7/g2LFj4k3Bjx8/ji5duiAhIQENGjQQt504cSLu3r2L33//HTt27MD//vc/tdlLANCrVy/4+vpi3bp1VR6vqhlPXl5eyMzMhK2trQ5eoTSnTp3iTRhJVphZkqPa5PaDD4B//in7ff9+ID0dGDu28nY//giYmQH5+cCwYZXX799fi4LpicexluTIkHKblZUFOzu7evNvBiIifTGqeRNIbiLpsun0+uuvY9++fThy5IjaN9GVXw/+8Myl5ORkcRaUu7s7ioqKkJ6eXu02VVEqlbC1tVX7qU/8/Pz0XQKRJMwsyZE2clvdpXTly3mpHWkTx1qSI+aWiMjwaNR4AoADBw5gxIgR4pTsF198UWdFVUUQBLz22mvYvXs3/vzzT/j4+Kit9/Hxgbu7Ow4dOiQuKyoqQnh4uHjT8/bt28PU1FRtm8TERFy+fFmnN0bXtfIp6URywcySHGkjt9U1lsrnHte0nkgKjrUkR8wtEZHh0bjxNGvWLPTv3x//+9//EB8fL97T4HF59dVXsW3bNuzYsQM2NjZISkpCUlIS8vPzAZR9K8O0adOwdOlS7NmzB5cvX8b48eNhaWmJkSNHAgDs7Ozw4osvYubMmTh8+DAiIyMxevRoBAYGit9yJ0dpaWn6LoFIEmaW5Kg2uX24YVRdA6m84VTdejaeqDY41pIcMbdERIZHo5uLA2VNm1GjRqFTp06YOHEiSkpKdFlXJWvXrgUAdOvWTW35xo0bMX78eADAW2+9hfz8fEyZMgXp6ekICQnBwYMHYWNjI27/0UcfwcTEBMOGDUN+fj569uyJTZs2yfqbHExMNP5rJKoXmFmSI23kVqWqenlNl9qVlgJGGn9URFSGYy3JEXNLRGR4NLq5OAD06dMHv//+OwDg559/xqBBg6Cq7h20geONAomISBMP31z89GlgyZLK223fDtjaAmlpwLhxldeX33yciIjkg/9mICIqo/Hnp+vWrRMbTf379xe/Hpb0r/zrjonkgpklOapNbit+tHPxYtVNp4rb1XQpHpEUHGtJjphbIiLDo/FcVm9vbwBAfn4+BEFAu3btAACxsbHYs2cP/P39ERYWppMi6dE0nLRGVG8wsyRHdc1the+1qKR8AnF1DaYndIIx1RHHWpIj5paIyPBIvmPEoEGDsGXLFgBARkYGQkJCsGrVKgwaNEi8DxM9Xi4uLvougUgSZpbkqK65/euv6tdxxhPpAsdakiPmlojI8EhuPJ07dw5PPfUUAOCHH36Am5sbYmNjsWXLFnz66adaL5Bq5uDgoO8SiCRhZkmOdJnb8oZTTTcfJ5KCYy3JEXNLRGR4JDee8vLyxG+JO3jwIIYMGQIjIyN06tQJsbGxWi+Qanbjxg19l0AkCTNLcqTL3JY3lqqb8cRL7ag2ONaSHDG3RESGR3LjqWnTpti7dy/u3r2LAwcOiPd1Sk5O5rc1EBER1UJ546m6mU2c8UREREREciW58TR//nzMmjUL3t7eCAkJQWhoKICy2U/lNxynx6tFixb6LoFIEmaW5EiXuT16FJg9G0hJqXo9G09UGxxrSY6YWyIiw6Pxt9qVGzp0KP7zn/8gMTERbdq0EZf37NkTzz33nFaLI82kpaXB3t5e32UQaYyZJTmqTW4VCs2227q17M/166tez0vtqDY41pIcMbdERIZH4xlPHh4eeOWVV/Dbb7/B0dER7dq1g5HRv0/v2LEjWrZsqZMi6dFSqvuInKieYmZJjh5HbjMyql6en6/zQ5MB4lhLcsTcEhEZHo0bTzt27IClpSWmTp0KZ2dnPP/889i6dSvS0tJ0WR9poGIDkEgOmFmSo8eR2+pmNr35ps4PTQaIYy3JEXNLRGR4FIJQ3XfoVO/KlSvYt28ffvrpJ0RGRiI0NBSDBg3CwIED4evrq4s665WsrCzY2dkhMzOTN1QnIqJqLVsGHD+unX3t26f5pXtERKR//DcDEVGZWn2k0KpVK7zzzjs4efIkYmNjMWrUKPz5558IDAxEQEAAfvnlF23XSY9w5swZfZdAJAkzS3Kk79wWFOj18CRD+s4sUW0wt0REhkfyzcUf5u7ujokTJ2LixInIy8vDgQMHoFQqtVEbaUjFu86SzDCzJEe1ya02v42upER7+6InA8dakiPmlojI8NS68ZScnIzk5GSUPvSumt9s9/g5OTnpuwQiSZhZkqPa5Fb6xezVY+OJpOJYS3LE3BIRGR7JjaezZ89i3LhxiIqKwsO3h1IoFPyUQg9cXV31XQKRJMwsyVFtcssZT6RPHGtJjphbIiLDI/keT//73//QvHlzHD9+HLdv38adO3fEn9u3b+uiRqpBVFSUvksgkoSZJTmqTW4544n0iWMtyRFzS0RkeCTPeLpz5w52796Npk2b6qIeIiIig1Fd48nKCsjNlbYvNp6IiIiISI4kz3jq2bMnLly4oItaqJaaNWum7xKIJGFmSY5qk9vqrj6X2nR61L6IqsOxluSIuSUiMjySZzx9/fXXGDduHC5fvoyAgACYmpqqrR84cKDWiiPNZGVlwdHRUd9lEGmMmSU5qk1utXmpXXGx9vZFTwaOtSRHzC0RkeGR3Hg6fvw4jh07ht9++63SOt5cXD/u378Pb29vfZdBpDFmluSoNrnVZuOJ/3slqTjWkhwxt0REhkfypXZTp07FmDFjkJiYiNLSUrUfNp2IiIjKCAJw8WLd9tG377+/8x5PRERERCRHkhtPDx48wPTp0+Hm5qaLeqgWQkJC9F0CkSTMLMmR1NxGRFS/rnNnzfbRoAHQuHHZ7wUFkg5PxLGWZIm5JSIyPJIbT0OGDMGRI0d0UQvV0rlz5/RdApEkzCzJkdTc3rxZ/Tp7e832oVAAsbFlvy9bJunwRBxrSZaYWyIiwyP5Hk/NmzfHO++8g2PHjiEwMLDSzcWnTp2qteJIM8W84yzJDDNLclRdbgWhrDnUqBFgVOHjHKNHfLRjbKzZMRWKf38vKtLsOUTlONaSHDG3RESGp1bfamdtbY3w8HCEh4errVMoFGw86YGDg4O+SyCShJklOaoutzt3Ajt2AK+8on5PpopNo4c9qilV0aP2QVQTjrUkR8wtEZHhkdx4unPnji7qoDrw8PDQdwlEkjCzJEfV5XbHjrI/165Vbzw9iqaNJ6K64FhLcsTcEhEZHr71NQBXrlzRdwlEkjCzJEdSc6uNS+3YoKK64FhLcsTcEhEZHslvaYcOHYoPPvig0vIPP/wQzz//vFaKIiIikjteakdEREREVIvGU3h4OPr161dpeZ8+fXD06FGtFEXS+Pr66rsEIkmYWZIjqbl9VNNI0xlPAPDaa//+LgiSSqAnHMdakiPmlojI8EhuPOXk5MDMzKzSclNTU2RlZWmlKJImLy9P3yUQScLMkhxJza02Gk9GRkC3bv8+LiiQVAI94TjWkhwxt0REhkdy4ykgIAC7du2qtHznzp3w9/fXSlGPwxdffAEfHx+Ym5ujffv2+Pvvv/VdUq0lJibquwQiSZhZkqPy3D54oFkD6FGX00m5d5NSCZR/3sPPd0gKjrUkR8wtEZHhkfytdvPmzcN///tf3Lp1Cz169AAAHD58GN9++y2+//57rReoC7t27cK0adPwxRdfoEuXLli/fj2effZZXL16FY0aNdJ3eUREVE/dvg1Mm1b2++uvA888o75eEP6d6fSoGU82Npodr3wftrZAampZ48nNTVLJRERERER6JXnG08CBA7F3715ER0djypQpmDlzJuLj4/HHH39g8ODBOihR+1avXo0XX3wRL730Evz8/PDxxx/Dy8sLa9eu1XdptRIcHKzvEogkYWZJjoKDg3HtWllzSRCATz8Fzp1T32bBgn/vw1RV48nREQgNBbp31+yY5TOjnJzK/kxKql3t9GTiWEtyxNwSERkeyTOeAKBfv35V3mBcDoqKinD27Fm8/fbbasvDwsJw/PjxKp9TWFiIwsJC8XF9upfVsmVAQkIyGjRoUOX62n4j0qOeZwj7lMtr0NV+9X1u4uMT4enpqdV9arpOF8d73Pusy37r0+t43Ps0Myu7bM3cvOzPij8WFmWziuzsqr//0uXLl5GU1EZt2cKF6ttERpY1hxo0UL8RuK9v2XHmzCk7RmmptNfl6wtcvw5ERQFPPVX9doIA5OUBublATk7ZnxV/iorKjq1SPfqnfF8VX0PFxw//XtX2j0tdjim3eqVKTKz+/QFRfaWN3LZqBQwapKWCiIiozmrVeKqJIAhQ1NPvgE5NTYVKpYLbQ9cquLm5Iamaj5KXLVuGRYsWVVp+5swZWFlZISgoCFFRUcjPz4eNjQ18fHxw8eJFAEDjxo1RWlqKu3fvAgDatm2L6Oho5OTkwMrKCs2bN0dkZCQAwNPTE8bGxoiNjQUAtG7dGjExMcjKyoK5uTlatWqFs2fPAgA8PDxgbm6OX381QVGRMZycSpCfX4Di4mIYGRnBzs4W6ekZAABzcyWMjU2Qm5sLALCxsUZhYSGKiophZKSAnZ09MjLSIQiAUmkGU1NT5OSUbWttbY3i4iIUFhZBoVDA3t4eGRkZEAQBZmZmUCrNkJ2d8//bWqG4uERs0jk4OCAzMxOlpaUwMzOFUmmO7OxsAICVlRVUKhUK/v9GKfb29sjOzoJKVQpTU1NYWFiIDT5LS0sIQiny88u2tbOzQ05ODlQqFUxMTGBlZYnMzLJtLSwsAAD5+fn/v60tcnPzUFJSAmNjY1hbWyMzM/P/tzWHQmEk3sTS1tYW+fn5KC4uhrGxEWxsbJGRUX4OzWFsbFzhHNqgsLDg/8+hEezs7JCeng4AUCqVMDU1Ec9h2fkuQlFR5XNYdr7NkJNT8RwW///5BuztHZCZmYHSUuH/z6FSPN9l57AEBQXl59semZlZKC0tP4fmyMoqP9+WUKlKK5xvO2RnZ///+TaBhYVlhfNtAUEQ1M53bm4OSkqqPt8KBZCXly+ew7y8ms93cbEZnJxUjzjfD2dW8/NtbW2NoqKqz7eZmRnMzB4+31Iy++/5rktmLS0fPt9VZ9bExBhWVg+fQ4Xa+c7Pz0Nxccn/n0MbZGRkVsisEXJzy/NtwzECNY8RNjbWKC5Og61tCZo1s0DDhoBCcQ8uLsVQKouwdWtOjWPE5MnF2L7dEvHxWUhPt4CRkRFGjLADcArXrpX9/8bGxhbp6Yoax4ikpAJkZFjAzCwe6ekN8csvVsjLS0dGRg7y841hb++Ny5fvIytLAZXKAoA5srLkP0b8m2+OEXUZIwAlLl/O5hjB9xGyeh+Rnm4CY+P0Op3vvDwBXbqU4tatWwAAf39/JCUlIS0tDaampggKCsKpU6cAAK6urrCzs8PNmzcBAC1btkRqaipSU1NhZGSEDh06ICIiAqWlpXB2doazszOuXbsGAGjWrBkyMzORnJwMAAgJCcG5c+dQXFwMR0dHWFpagoiIAIUg1PzZm5+fH+bNm4ehQ4dW+Y125W7evInVq1ejcePGlWYU1RcJCQlo2LAhjh8/jtDQUHH5+++/j61bt4r/I6moqhlPXl5eyMzMhK2t7WOpuzq//w7Exd2Fl5dXpXW1/VT1Uc+raZ/6OObj3i/PT+33Wb4+ISEBHh4ej+WYcjw/9emYtX2ePs57TeuKi4HCwrIbgxcWqv/k5ZXdP+lR+8jJyYG1tTUAYMwYYOvW6rfdvx/44Qdg8+ayx++/D7Rurb7NgAHVP7/ctGlAz55ls5TefBO4caPm5wBls7usrQFLS8DK6t/flcqyGV3lP0ZG6o/LlwFls8cqfoZU/rjiPayqWl8Xupo5qqvn1vdj3r1b9fsDovpMG7n18ADatKl5O13LysqCnZ1dvfg3AxGRPmk04+nzzz/H7Nmz8eqrryIsLAzBwcHijJv09HRcvXoVx44dw9WrV/Haa69hypQpuq671pydnWFsbFxpdlNycnKlWVDllEollErl4yhPsj59gLw8J/ADFZKTvDx7ZpbqndLSsuZTRgaQkgLcuwfEx5f9mZCA/59RBISEAMOGlV2StmPHo/dXLiCgdjWVNyiMjIDFi4Fffim7lE+pLGsm2doCrq6Ag4N6g8nUtHbHI8PC9wckR8wtEZHh0ajx1KNHD0REROD48ePYtWsXduzYgZiYGOTn58PZ2Rnt2rXD2LFjMXr0aNjb2+u45LoxMzND+/btcejQITz33HPi8kOHDmGQTC8Gv3TpEkJCQvRdBpHGmFmqj4yMAHv7sh9vb6BDB/X1J0+eQUhIiNgMeu459cbT1KllNxwvnzFU3njq0+ffZVJVnBljZVXW8CLSFMdakiPmlojI8Ei6x1Pnzp3RuXNnXdXy2MyYMQNjxoxBcHAwQkND8eWXXyIuLg6TJ0/Wd2lERFRPPXwpmbk5MHw4sHNn2eNOncoaT6WlQEnJvzfprm3TqfyYRERERERyppObi9d3L7zwAh48eIDFixcjMTERAQEB+PXXX9G4cWN9l1Yr3t7e+i6BSBJmluSoqtz27g38+SfQq1fZ5W/lCgv/nfFU3bfkaYKNJ6oLjrUkR8wtEZHheSIbTwAwZcqUen0vKilKSkr0XQKRJMwsyVFVuXV2BjZsKPtdEMoaRYKg3njijCfSF461JEfMLRGR4anD22GqL+Lj4/VdApEkzCzJUU25VSj+nfVUWMhL7Uj/ONaSHDG3RESGh40nIiIiLanYeOKldkREREREbDwZhKCgIH2XQCQJM0typEluq2o81WXGE1FdcKwlOWJuiYgMT63u8VRaWoro6GgkJyejtPyd9f97+umntVIYae7atWsIDAzUdxlEGmNmSY40yW1542nBAiA3t+z3ujSe2LSiuuBYS3LE3BIRGR7JjaeTJ09i5MiRiI2NhSAIausUCgVU5Te1oMcmLy9P3yUQScLMkhxpkltz87I/y5tOAJtHpD8ca0mOmFsiIsMjufE0efJkBAcH45dffkGDBg2g4A0o9M7a2lrfJRBJwsySHGmS2/IZTxVVd48npbLskrxH4f9iqS441pIcMbdERIZHcuPp5s2b+OGHH9C0aVNd1EO14Ovrq+8SiCRhZkmONMltVY2n6mY8ffYZMHs2kJZW/f44W4rqgmMtyRFzS0RkeCS/pQ0JCUF0dLQuaqFaunDhgr5LIJKEmSU50iS3UhpP7u5AWFgdiyJ6BI61JEfMLRGR4ZE84+n111/HzJkzkZSUhMDAQJiamqqtb926tdaKIyIikpPyezxV9KhZSzVdSsdL7YiIiIhI7iQ3nv773/8CACZMmCAuUygUEASBNxfXk0aNGum7BCJJmFmSI01yW9WMp4c+n1HDxhPpEsdakiPmlojI8EhuPN25c0cXdRAREcleVY2n4uLqt2fjiYiIiIgMneTGU+PGjXVRB9VBXFwcGjRooO8yiDTGzJIcaZLbqhpPRUXVb1/TzcPZeKK64FhLcsTcEhEZHsmNJwC4desWPv74Y0RFRUGhUMDPzw9vvPEGv4WCiIieaGZm//7ety9w6RLQu3f123PGExEREREZOsnfanfgwAH4+/vj9OnTaN26NQICAnDq1Cm0atUKhw4d0kWNVIM2bdrouwQiSZhZkiNNclvxsrqJE4EvvgDs7Krfno0n0iWOtSRHzC0RkeGR3Hh6++23MX36dJw6dQqrV6/GRx99hFOnTmHatGmYPXu2LmqkGty6dUvfJRBJwsySHGmS24qNJxMN5hSz8US6xLGW5Ii5JSIyPJIbT1FRUXjxxRcrLZ8wYQKuXr2qlaJImpycHH2XQCQJM0typEluzc2l7ZP3eCJd4lhLcsTcEhEZHsmNJxcXF5w/f77S8vPnz8PV1VUbNZFElpaW+i6BSBJmluRIk9yGhQENGwLPP6/ZPjnjiXSJYy3JEXNLRGR4JN9cfOLEiZg0aRJu376Nzp07Q6FQ4NixY1i+fDlmzpypixqpBi1bttR3CUSSMLMkR5rk1tERWLdO832y8US6xLGW5Ii5JSIyPJJnPM2bNw/z58/HmjVr0LVrVzz99NP47LPPsHDhQsydO1cXNVINzp07p+8SiCRhZkmOdJFbXmpHusSxluSIuSUiMjySZzwpFApMnz4d06dPR3Z2NgDAxsZG64UREREREREREZG8SW48VcSGU/3g6emp7xKIJGFmSY50kduaZjzVtJ7oUTjWkhwxt0REhkejxlNQUBAOHz4MBwcHtGvXDopHzP3n9NjHz0ST7+wmqkeYWZIjXeSW93giXeJYS3LE3BIRGR6NRvZBgwZBqVSKvz+q8USPX0xMDNzc3PRdBpHGmFmSI13kljOaSJc41pIcMbdERIZHo8bTggULxN8XLlyoq1qIiIieKDV9jsPGFBERERHJneS3tE2aNMGDBw8qLc/IyECTJk20UhRJExgYqO8SiCRhZkmOdJFbTiAmXeJYS3LE3BIRGR7JjaeYmBioVKpKywsLCxEfH6+VokiauLg4fZdAJAkzS3Kki9zyHk+kSxxrSY6YWyIiw6Px3fv27dsn/n7gwAHY2dmJj1UqFQ4fPgwfHx/tVkcayczM1HcJRJIwsyRHushtTZfSsfFEdcGxluSIuSUiMjwaN54GDx4MAFAoFBg3bpzaOlNTU3h7e2PVqlVaLY40Y25uru8SiCRhZkmOdJFbzngiXeJYS3LE3BIRGR6NG0+lpaUAAB8fH0RERMDZ2VlnRZE0AQEB+i6BSBJmluRIF7ll44l0iWMtyRFzS0RkeCTf4+nOnTtsOtUzZ86c0XcJRJIwsyRHusgtG0+kSxxrSY6YWyIiw1OrL2rOzc3Fr7/+inXr1uHTTz9V+9GFmJgYvPjii/Dx8YGFhQV8fX2xYMECFBUVqW0XFxeHAQMGwMrKCs7Ozpg6dWqlbS5duoSuXbvCwsICDRs2xOLFiyEIgk7qJiIiehTe44mIiIiIDJ3Gl9qVi4yMRN++fZGXl4fc3Fw4OjoiNTUVlpaWcHV1xdSpU7Ve5LVr11BaWor169ejadOmuHz5MiZOnIjc3FysXLkSQNkNzvv16wcXFxccO3YMDx48wLhx4yAIAtasWQMAyMrKQq9evdC9e3dERETgxo0bGD9+PKysrDBz5kyt1/24NGjQQN8lEEnCzJIc6SK3nPFEusSxluSIuSUiMjySG0/Tp0/HgAEDsHbtWtjb2+PkyZMwNTXF6NGj8cYbb+iiRvTp0wd9+vQRHzdp0gTXr1/H2rVrxcbTwYMHcfXqVdy9exceHh4AgFWrVmH8+PF4//33YWtri+3bt6OgoACbNm2CUqlEQEAAbty4gdWrV2PGjBlQyPQdvqWlpb5LIJKEmSU50kVu2XgiXeJYS3LE3BIRGR7Jl9qdP38eM2fOhLGxMYyNjVFYWAgvLy+sWLECc+bM0UWNVcrMzISjo6P4+MSJEwgICBCbTgDQu3dvFBYW4uzZs+I2Xbt2hVKpVNsmISEBMTEx1R6rsLAQWVlZaj/1ya1bt/RdApEkzCzJkS5yy0vtSJc41pIcMbdERIZH8ownU1NTcWaQm5sb4uLi4OfnBzs7O8TFxWm9wKrcunULa9aswapVq8RlSUlJcHNzU9vOwcEBZmZmSEpKErfx9vZW26b8OUlJSfDx8anyeMuWLcOiRYsqLT9z5gysrKwQFBSEqKgo5Ofnw8bGBj4+Prh48SIAoHHjxigtLcXdu3cBAG3btkV0dDRycnJgZWWF5s2bIzIyEgDg6ekJY2NjxMbGAgBat26NmJgYZGVlwdzcHK1atRKbaB4eHjA3N8ft27eRnp6O3NxcxMfHIyMjA2ZmZmjbti1Onz4NAHB3d4e1tTWio6MBAH5+frh//z7S0tJgYmKC9u3b4/Tp0xAEAS4uLnBwcMCNGzcAAC1atEBaWhpSUlJgZGSEDh064MyZM1CpVHBycoKrqyuioqIAAM2aNUNWVhbu378PAAgJCcG5c+dQXFwMBwcHeHh44MqVKwAAX19f5OXlITExEQAQHByMy5cvo6CgAHZ2dmjUqBEuXboEAPD29kZJSQni4+MBAEFBQbh27Rry8vJgbW0NX19fXLhwAQDQqFEjABCz2KZNG9y6dQs5OTmwtLREy5Ytce7cOfF8m5iYiE3HwMBAxMXFITMzE+bm5ggICBBvcNmgQQNYWlqKb4ZatWqFhIQEpKenw9TUFEFBQTh16pSYKVtbW9y8eVM838nJyXjw4AGMjY0RHByMiIgIlJaWwsXFBY6Ojrh+/ToAoHnz5khPT0dKSgoUCgU6duyIs2fPoqSkBI6OjnBzcxPPd9OmTZGTkyPmu2PHjjh//jyKiopgb28PT09PXL58GUDZLMGCggIkJCQAANq3b48rV66goKAAtra28Pb2VsusSqUSz3e7du1w48YN5ObmwtraGk2bNsX58+cBAF5eXjAyMlLL7J07d5CdnQ0LCwv4+fmJ57thw4YwMzPDnTt3kJ6ejry8PNy9excZGRlQKpVo3bo1IiIixMxaWVmJ59vf3x9JSUlIS0urdL5dXV1hZ2cnnu+WLVsiNTUVqampYmbLz7ezszOcnZ1x7do1MbOZmZlITk6ulFlHR0e4u7vj6tWrYmZzc3PF892hQwdcvHgRhYWFsLe3h5eXl5hZHx8fFBUV4d69e2Jm9TlGAGXfEsQxom5jhEqlwrVr17Q6Rly5Yo2SkuYoLCxCUVERFAoF7O3tkZGRAUEQEBeXBVtbuydujADKxmSOEXUbI1QqFaKiojhG8H2ErMaI7OxsnDp1yiDGCM7eIiIqoxAk3lk7LCwM48ePx8iRIzF58mRERkZi6tSp2Lp1K9LT08VBXBMLFy6ssqFTUUREBIKDg8XHCQkJ6Nq1K7p27Yqvv/5aXD5p0iTExsbiwIEDas83MzPDli1bMHz4cISFhcHHxwfr168X19+7dw+enp44ceIEOnXqVGUNhYWFKCwsFB9nZWXBy8sLmZmZsLW11fj16kpOTg6sra31XQaRxphZkiNd5PbkSeD996tfv3Yt4Omp1UPSE4RjLcmRIeU2KysLdnZ29ebfDERE+iJ5xtPSpUuRnZ0NAFiyZAnGjRuHV155BU2bNsXGjRsl7eu1117D8OHDH7lNxRlKCQkJ6N69O0JDQ/Hll1+qbefu7l6p6ZWeno7i4mJxVpO7u7v4qU658k8oHp4tVZFSqVS7PK++SUhIQPPmzfVdBpHGmFmSI13klvd4Il3iWEtyxNwSERkeSY2n8inUrVq1AgC4uLjg119/rfXBy6erauLevXvo3r072rdvj40bN8LooRtjhIaG4v3330diYqL4bRgHDx6EUqlE+/btxW3mzJmDoqIimJmZidt4eHhUugRPTtLT0/VdApEkzCzJkS5yW9M9nojqgmMtyRFzS0RkeCS95RUEAc2aNROv2X5cEhIS0K1bN3h5eWHlypVISUlBUlKS2uylsLAw+Pv7Y8yYMYiMjMThw4cxa9YsTJw4UZzaOnLkSCiVSowfPx6XL1/Gnj17sHTpUll/ox1Qdt8tIjlhZkmOdJHbmv7Xw8YU1QXHWpIj5paIyPBImvFkZGSEZs2a4cGDB2jWrJmuaqrk4MGDiI6ORnR0NDwfutlF+S2qjI2N8csvv2DKlCno0qULLCwsMHLkSKxcuVLc1s7ODocOHcKrr76K4OBgODg4YMaMGZgxY8Zjey26EBQUpO8SiCRhZkmOdJFbGX/mQTLAsZbkiLklIjI8kj9LXbFiBd58803xWy4eh/Hjx0MQhCp/KmrUqBF+/vln5OXl4cGDB1izZk2lezMFBgbi6NGjKCgoQGJiIhYsWCDr2U4AJN3Qnag+YGZJjnSRW97jiXSJYy3JEXNLRGR4JN9cfPTo0cjLy0ObNm1gZmYGCwsLtfVpaWlaK46IiMiQ1XQpHS+1IyIiIiK5k9x4+vjjj3VQBtXFo76Rj6g+YmZJjnSRW85oIl3iWEtyxNwSERkeyY2ncePG6aIOqoPym6cTyQUzS3Kki9zyUjvSJY61JEfMLRGR4anVJP5bt27h3XffxYgRI5CcnAwA+P3333HlyhWtFkeauXnzpr5LIJKEmSU50kVu2XgiXeJYS3LE3BIRGR7Jjafw8HAEBgbi1KlT2L17N3JycgAAFy9exIIFC7ReIBERkaFi44mIiIiIDJ3kxtPbb7+N9957D4cOHYKZmZm4vHv37jhx4oRWiyPN+Pn56bsEIkmYWZIjXeS2ppuHs/FEdcGxluSIuSUiMjySG0+XLl3Cc889V2m5i4sLHjx4oJWiSJryyx2J5IKZJTnSRW4544l0iWMtyRFzS0RkeCQ3nuzt7ZGYmFhpeWRkJBo2bKiVokgaNvxIbphZkiNd5JYznkiXONaSHDG3RESGR3LjaeTIkZg9ezaSkpKgUChQWlqKf/75B7NmzcLYsWN1USPVwNjYWN8lEEnCzJIc6SO3bDxRXXCsJTlibomIDI9CEARByhOKi4sxfvx47Ny5E4IgwMTEBCqVCiNHjsSmTZueiP9ZZGVlwc7ODpmZmfzKVyIiqrWbN4EZM6pfv307wP/NEBHJE//NQERURvKMJ1NTU2zfvh03btzAd999h23btuHatWvYunXrE9F0qo8iIiL0XQKRJMwsyZEucst7PJEucawlOWJuiYgMj0ltn+jr6wtfX19t1kK1VFpaqu8SiCRhZkmOdJHbmu7xRFQXHGtJjphbIiLDo1HjacajrgN4yOrVq2tdDNWOi4uLvksgkoSZJTnSRW5rmtHExhTVBcdakiPmlojI8GjUeIqMjNRoZwpeE6AXjo6O+i6BSBJmluRIF7nl/zZJlzjWkhwxt0REhkejxtORI0d0XQfVwfXr1xESEqLvMog0xsySHOkit5zxRLrEsZbkiLklIjI8tX5LGx0djQMHDiA/Px8AIPHL8YiIiJ54NTWWOCOKiIiIiOROcuPpwYMH6NmzJ5o3b46+ffsiMTERAPDSSy9h5syZWi+Qata8eXN9l0AkCTNLcqSL3LKxRLrEsZbkiLklIjI8khtP06dPh6mpKeLi4mBpaSkuf+GFF/D7779rtTjSTHp6ur5LIJKEmSU50kVueakd6RLHWpIj5paIyPBIfkt78OBBLF++HJ6enmrLmzVrhtjYWK0VRppLSUnRdwlEkjCzJEe6yC1nPJEucawlOWJuiYgMj+TGU25urtpMp3KpqalQKpVaKYqk4bcJktwwsyRHushtTbvkfypUFxxrSY6YWyIiwyO58fT0009jy5Yt4mOFQoHS0lJ8+OGH6N69u1aLI8107NhR3yUQScLMkhzpIre8uTjpEsdakiPmlojI8EhuPH344YdYv349nn32WRQVFeGtt95CQEAAjh49iuXLl+uiRqrB2bNn9V0CkSTMLMmRLnLLGU+kSxxrSY6YWyIiwyO58eTv74+LFy+iY8eO6NWrF3JzczFkyBBERkbC19dXFzVSDUpKSvRdApEkzCzJkS5yW9OMJ95cnOqCYy3JEXNLRGR4TGrzJHd3dyxatEjbtVAtOTo66rsEIkmYWZIjfeSWM56oLjjWkhwxt0REhkfyZ6kbN27E999/X2n5999/j82bN2ulKJLGzc1N3yUQScLMkhzpIrec0US6xLGW5Ii5JSIyPJLf8n7wwQdwdnautNzV1RVLly7VSlEkTVRUlL5LIJKEmSU50kVuOaOJdIljLckRc0tEZHgkN55iY2Ph4+NTaXnjxo0RFxenlaKIiIieBJzxRERERESGTvJbXldXV1y8eLHS8gsXLsDJyUkrRZE0TZs21XcJRJIwsyRHusgtZzyRLnGsJTlibomIDI/kxtPw4cMxdepUHDlyBCqVCiqVCn/++SfeeOMNDB8+XBc1Ug1ycnL0XQKRJMwsyZEucvuoxpOtrdYPR08YjrUkR8wtEZHhkdx4eu+99xASEoKePXvCwsICFhYWCAsLQ48ePXiPJz1JSkrSdwlEkjCzJEe6yG11jafx44GNG7V+OHrCcKwlOWJuiYgMj+TGk5mZGXbt2oXr169j+/bt2L17N27duoVvvvkGZmZmuqhRTWFhIdq2bQuFQoHz58+rrYuLi8OAAQNgZWUFZ2dnTJ06FUVFRWrbXLp0CV27doWFhQUaNmyIxYsXQxAEnddNRET0sOoaT/b2wGP4XyoRERERkc6Z1PaJzZo1Q7NmzbRZi0beeusteHh44MKFC2rLVSoV+vXrBxcXFxw7dgwPHjzAuHHjIAgC1qxZAwDIyspCr1690L17d0RERODGjRsYP348rKysMHPmzMf+WrSlY8eO+i6BSBJmluRIF7mt7ubivOk4aQPHWpIj5paIyPBIfms7dOhQfPDBB5WWf/jhh3j++ee1UlR1fvvtNxw8eBArV66stO7gwYO4evUqtm3bhnbt2uGZZ57BqlWr8NVXXyErKwsAsH37dhQUFGDTpk0ICAjAkCFDMGfOHKxevVrWs54envlFVN8xsyRHushtdTOe2HgibeBYS3LE3BIRGR7Jb23Dw8PRr1+/Ssv79OmDo0ePaqWoqty/fx8TJ07E1q1bYWlpWWn9iRMnEBAQAA8PD3FZ7969UVhYiLNnz4rbdO3aFUqlUm2bhIQExMTEVHvswsJCZGVlqf3UJw9fTkhU3zGzJEe6yC0bT6RLHGtJjphbIiLDI/lSu5ycnCrv5WRqaqqzhowgCBg/fjwmT56M4ODgKptESUlJcHNzU1vm4OAAMzMz8SaFSUlJ8Pb2Vtum/DlJSUnw8fGp8vjLli3DokWLKi0/c+YMrKysEBQUhKioKOTn58PGxgY+Pj64ePEiAKBx48YoLS3F3bt3AQBt27ZFdHQ0cnJyYGVlhebNmyMyMhIA4OnpCWNjY8TGxgIAWrdujZiYGGRlZcHc3BytWrUSm2geHh4wNzfH7du3kZOTg9zcXMTHxyMjIwNmZmZo27YtTp8+DQBwd3eHtbU1oqOjAQB+fn64f/8+0tLSYGJigvbt2+P06dMQBAEuLi5wcHDAjRs3AAAtWrRAWloaUlJSYGRkhA4dOuDMmTNQqVRwcnKCq6sroqKiAJRdfpmVlYX79+8DAEJCQnDu3DkUFxfDwcEBHh4euHLlCgDA19cXeXl5SExMBAAEBwfj8uXLKCgogJ2dHRo1aoRLly4BALy9vVFSUoL4+HgAQFBQEK5du4a8vDxYW1vD19dXvPSyUaNGAMru9wUAbdq0wa1bt5CTkwNLS0u0bNkS586dE8+3iYmJmKfAwEDExcUhMzMT5ubmCAgIwJkzZwAADRo0gKWlJW7dugUAaNWqFRISEpCeng5TU1MEBQXh1KlTYqZsbW1x8+ZN8XwnJyfjwYMHMDY2RnBwMCIiIlBaWgoXFxc4Ojri+vXrAIDmzZsjPT0dKSkpUCgU6NixI86ePYuSkhI4OjrCzc1NPN9NmzZFTk6OmO+OHTvi/PnzKCoqgr29PTw9PXH58mUAQJMmTVBQUICEhAQAQPv27XHlyhUUFBTA1tYW3t7eaplVqVTi+W7Xrh1u3LiB3NxcWFtbo2nTpuKnkV5eXjAyMlLL7J07d5CdnQ0LCwv4+fmJ57thw4YwMzPDnTt3kJOTg7y8PNy9excZGRlQKpVo3bo1IiIixMxaWVmJ59vf3x9JSUlIS0urdL5dXV1hZ2cnnu+WLVsiNTUVqampYmbLz7ezszOcnZ1x7do1MbOZmZlITk6ulFlHR0e4u7vj6tWrYmZzc3PF892hQwdcvHgRhYWFsLe3h5eXl5hZHx8fFBUV4d69e2Jm9TlGAEBAQADHCNRtjLCyssK1a9e0OkYUFytQUtIOhYVFKCoqgkKhgL29Pa5fj4KZWdYTO0YAZWMyx4i6jRFWVlaIioriGMH3EbIaI4qLi3Hq1CmDGCOq+rCciOhJpBAkXmPWoUMHDBgwAPPnz1dbvnDhQuzfv198Q6OJhQsXVtnQqSgiIgLHjx/Hrl27cPToURgbGyMmJgY+Pj6IjIxE27ZtAQCTJk1CbGwsDhw4oPZ8MzMzbNmyBcOHD0dYWBh8fHywfv16cf29e/fg6emJEydOoFOnTlXWUFhYiMLCQvFxVlYWvLy8kJmZCdt68H3Xubm5sLKy0ncZRBpjZkmOdJHbkhLguecqL58zBwgN1eqh6AnEsZbkyJBym5WVBTs7u3rzbwYiIn2RPONp3rx5+O9//4tbt26hR48eAIDDhw/j22+/xffffy9pX6+99hqGDx/+yG28vb3x3nvv4eTJk2qXyAFln26NGjUKmzdvhru7u/jJRbn09HQUFxeLs5rc3d0rfUVr+ScUD8+WqkipVFY6dn1y+fJlhISE6LsMIo0xsyRHus7tsGHAd9+V/c5L7UgbONaSHDG3RESGR3LjaeDAgdi7dy+WLl2KH374ARYWFmjdujX++OMPdO3aVdK+yqer1uTTTz/Fe++9Jz5OSEhA7969sWvXLvF/TKGhoXj//feRmJiIBg0aACi74bhSqUT79u3FbebMmYOioiLxcsGDBw/Cw8Oj0iV4REREumZiAvTqBeTnA56e/y43NtZfTURERERE2iS58QQA/fr1q/IG4+fPnxcvfdOm8uvty1lbWwMou77f8//fqYeFhcHf3x9jxozBhx9+iLS0NMyaNQsTJ04Up7aOHDkSixYtwvjx4zFnzhzcvHkTS5cuxfz586Go7g6vMtCkSRN9l0AkCTNLcqSr3E6dWvZnePi/yzjjibSBYy3JEXNLRGR46vzWNjMzE1988QWCgoLEmUX6YGxsjF9++QXm5ubo0qULhg0bhsGDB2PlypXiNnZ2djh06BDi4+MRHByMKVOmYMaMGZgxY4be6taGgoICfZdAJAkzS3Kk69xW/PyDjSfSBo61JEfMLRGR4anVjCcA+PPPP7Fhwwbs2bMHjRs3xn//+19s2LBBm7VVy9vbG1XdE71Ro0b4+eefH/ncwMBAHD16VFel6UVCQgK8vLz0XQaRxphZkiNd57Zis4mNJ9IGjrUkR8wtEZHhkdR4io+Px6ZNm/DNN98gNzcXw4YNQ3FxMX788Uf4+/vrqkYiIiKDx8YTERERERkijd/a9u3bF/7+/rh69SrWrFmDhIQErFmzRpe1kYb0eYkjUW0wsyRHus5txUvteHNx0gaOtSRHzC0RkeHRuPF08OBBvPTSS1i0aBH69esHY74rrjeuXLmi7xKIJGFmSY50nVvOeCJt41hLcsTcEhEZHo3f2v7999/Izs5GcHAwQkJC8NlnnyElJUWXtZGGeBNGkhtmluSINxcnueFYS3LE3BIRGR6N39qGhobiq6++QmJiIl5++WXs3LkTDRs2RGlpKQ4dOoTs7Gxd1kmPYGtrq+8SiCRhZkmOdJ1bNp5I2zjWkhwxt0REhkfyW1tLS0tMmDABx44dw6VLlzBz5kx88MEHcHV1xcCBA3VRI9XA29tb3yUQScLMkhzpOre81I60jWMtyRFzS0RkeOr01rZFixZYsWIF4uPj8e2332qrJpLo4sWL+i6BSBJmluRI17ll44m0jWMtyRFzS0RkeLTy1tbY2BiDBw/Gvn37tLE7IiKiJw4vtSMiIiIiQ8S3tgagcePG+i6BSBJmluRI17nljCfSNo61JEfMLRGR4eFbWwOgUqn0XQKRJMwsyZGuc8sZT6RtHGtJjphbIiLDw7e2BiA+Pl7fJRBJwsySHOk6txUbT8bGOj0UPSE41pIcMbdERIaHjSciIqJ6oGKziTOeiIiIiMhQ8K2tAWjXrp2+SyCShJklOdJ1bnmpHWkbx1qSI+aWiMjw8K2tAbhx44a+SyCShJklOdJ1bnmpHWkbx1qSI+aWiMjwsPFkAHJzc/VdApEkzCzJ0ePMLWc8kTZwrCU5Ym6JiAwP39oaAGtra32XQCQJM0typOvcCsK/v7PxRNrAsZbkiLklIjI8fGtrAJo2barvEogkYWZJjnSdWzaeSNs41pIcMbdERIaHb20NwPnz5/VdApEkzCzJ0ePMLRtPpA0ca0mOmFsiIsPDt7ZERET1DBtPRERERGQo+NbWAHh5eem7BCJJmFmSI13nVqX693d+qx1pA8dakiPmlojI8LDxZACM+NE4yQwzS3Kk69wWFlY8lk4PRU8IjrUkR8wtEZHh4chuAGJjY/VdApEkzCzJka5z26xZ2Z9KpU4PQ08QjrUkR8wtEZHhMdF3AURERATY2gLbtrHxRERERESGRSEIFb/AmTSRlZUFOzs7ZGZmwtbWVt/lID8/HxYWFvoug0hjzCzJEXNLcsPMkhwZUm7r278ZiIj0hZfaGYA7d+7ouwQiSZhZkiPmluSGmSU5Ym6JiAwPG08GIDs7W98lEEnCzJIcMbckN8wsyRFzS0RkeNh4MgCGMh2ZnhzMLMkRc0tyw8ySHDG3RESGh/d4qoX6dr12cXExTE1N9V0GkcaYWZIj5pbkhpklOTKk3Na3fzMQEekLZzwZgHPnzum7BCJJmFmSI+aW5IaZJTlibomIDI+JvguQo/JJYllZWXqupExubm69qYVIE8wsyRFzS3LDzJIcGVJuy18HLzAhoicdG0+1UH7TQy8vLz1XQkRERERE9Vl2djbs7Oz0XQYRkd7wHk+1UFpaioSEBNjY2EChUOi1lqysLHh5eeHu3bu8dpxkgZklOWJuSW6YWZIjQ8utIAjIzs6Gh4cHjIx4hxMienJxxlMtGBkZwdPTU99lqLG1tTWI/0HTk4OZJTlibklumFmSI0PKLWc6ERHx5uJERERERERERKQjbDwREREREREREZFOsPEkc0qlEgsWLIBSqdR3KUQaYWZJjphbkhtmluSIuSUiMky8uTgREREREREREekEZzwREREREREREZFOsPFEREREREREREQ6wcYTERERERERERHpBBtPMvbFF1/Ax8cH5ubmaN++Pf7++299l0QkOnr0KAYMGAAPDw8oFArs3btXbb0gCFi4cCE8PDxgYWGBbt264cqVK/oplgjAsmXL0KFDB9jY2MDV1RWDBw/G9evX1bZhbqm+Wbt2LVq3bg1bW1vY2toiNDQUv/32m7iemaX6btmyZVAoFJg2bZq4jLklIjIsbDzJ1K5duzBt2jTMnTsXkZGReOqpp/Dss88iLi5O36URAQByc3PRpk0bfPbZZ1WuX7FiBVavXo3PPvsMERERcHd3R69evZCdnf2YKyUqEx4ejldffRUnT57EoUOHUFJSgrCwMOTm5orbMLdU33h6euKDDz7AmTNncObMGfTo0QODBg0S/5HOzFJ9FhERgS+//BKtW7dWW87cEhEZFn6rnUyFhIQgKCgIa9euFZf5+flh8ODBWLZsmR4rI6pMoVBgz549GDx4MICyTzI9PDwwbdo0zJ49GwBQWFgINzc3LF++HC+//LIeqyUqk5KSAldXV4SHh+Ppp59mbkk2HB0d8eGHH2LChAnMLNVbOTk5CAoKwhdffIH33nsPbdu2xccff8yxlojIAHHGkwwVFRXh7NmzCAsLU1seFhaG48eP66kqIs3duXMHSUlJahlWKpXo2rUrM0z1RmZmJoCyf8QDzC3VfyqVCjt37kRubi5CQ0OZWarXXn31VfTr1w/PPPOM2nLmlojI8JjouwCSLjU1FSqVCm5ubmrL3dzckJSUpKeqiDRXntOqMhwbG6uPkojUCIKAGTNm4D//+Q8CAgIAMLdUf126dAmhoaEoKCiAtbU19uzZA39/f/Ef6cws1Tc7d+7EuXPnEBERUWkdx1oiIsPDxpOMKRQKtceCIFRaRlSfMcNUX7322mu4ePEijh07Vmkdc0v1TYsWLXD+/HlkZGTgxx9/xLhx4xAeHi6uZ2apPrl79y7eeOMNHDx4EObm5tVux9wSERkOXmonQ87OzjA2Nq40uyk5ObnSp0NE9ZG7uzsAMMNUL73++uvYt28fjhw5Ak9PT3E5c0v1lZmZGZo2bYrg4GAsW7YMbdq0wSeffMLMUr109uxZJCcno3379jAxMYGJiQnCw8Px6aefwsTERMwmc0tEZDjYeJIhMzMztG/fHocOHVJbfujQIXTu3FlPVRFpzsfHB+7u7moZLioqQnh4ODNMeiMIAl577TXs3r0bf/75J3x8fNTWM7ckF4IgoLCwkJmleqlnz564dOkSzp8/L/4EBwdj1KhROH/+PJo0acLcEhEZGF5qJ1MzZszAmDFjEBwcjNDQUHz55ZeIi4vD5MmT9V0aEYCyb6uJjo4WH9+5cwfnz5+Ho6MjGjVqhGnTpmHp0qVo1qwZmjVrhqVLl8LS0hIjR47UY9X0JHv11VexY8cO/PTTT7CxsRE/bbezs4OFhQUUCgVzS/XOnDlz8Oyzz8LLywvZ2dnYuXMn/vrrL/z+++/MLNVLNjY24r3zyllZWcHJyUlcztwSERkWNp5k6oUXXsCDBw+wePFiJCYmIiAgAL/++isaN26s79KIAABnzpxB9+7dxcczZswAAIwbNw6bNm3CW2+9hfz8fEyZMgXp6ekICQnBwYMHYWNjo6+S6Qm3du1aAEC3bt3Ulm/cuBHjx48HAOaW6p379+9jzJgxSExMhJ2dHVq3bo3ff/8dvXr1AsDMkjwxt0REhkUhCIKg7yKIiIiIiIiIiMjw8B5PRERERERERESkE2w8ERERERERERGRTrDxREREREREREREOsHGExERERERERER6QQbT0REREREREREpBNsPBERERERERERkU6w8URERERERERERDrBxhMREREREREREekEG09ERPTEWrhwIdq2bau348+bNw+TJk3SaNtZs2Zh6tSpOq6IiIiIiEi7FIIgCPougoiISNsUCsUj148bNw6fffYZCgsL4eTk9Jiq+tf9+/fRrFkzXLx4Ed7e3jVun5ycDF9fX1y8eBE+Pj66L5CIiIiISAvYeCIiIoOUlJQk/r5r1y7Mnz8f169fF5dZWFjAzs5OH6UBAJYuXYrw8HAcOHBA4+f897//RdOmTbF8+XIdVkZEREREpD281I6IiAySu7u7+GNnZweFQlFp2cOX2o0fPx6DBw/G0qVL4ebmBnt7eyxatAglJSV488034ejoCE9PT3zzzTdqx7p37x5eeOEFODg4wMnJCYMGDUJMTMwj69u5cycGDhyotuyHH35AYGAgLCws4OTkhGeeeQa5ubni+oEDB+Lbb7+t87khIiIiInpc2HgiIiKq4M8//0RCQgKOHj2K1atXY+HChejfvz8cHBxw6tQpTJ48GZMnT8bdu3cBAHl5eejevTusra1x9OhRHDt2DNbW1ujTpw+KioqqPEZ6ejouX76M4OBgcVliYiJGjBiBCRMmICoqCn/99ReGDBmCihOTO3bsiLt37yI2Nla3J4GIiIiISEvYeCIiIqrA0dERn376KVq0aIEJEyagRYsWyMvLw5w5c9CsWTO88847MDMzwz///AOgbOaSkZERvv76awQGBsLPzw8bN25EXFwc/vrrryqPERsbC0EQ4OHhIS5LTExESUkJhgwZAm9vbwQGBmLKlCmwtrYWt2nYsCEA1DibioiIiIiovjDRdwFERET1SatWrWBk9O/nMm5ubggICBAfGxsbw8nJCcnJyQCAs2fPIjo6GjY2Nmr7KSgowK1bt6o8Rn5+PgDA3NxcXNamTRv07NkTgYGB6N27N8LCwjB06FA4ODiI21hYWAAom2VFRERERCQHbDwRERFVYGpqqvZYoVBUuay0tBQAUFpaivbt22P79u2V9uXi4lLlMZydnQGUXXJXvo2xsTEOHTqE48eP4+DBg1izZg3mzp2LU6dOid9il5aW9sj9EhERERHVN7zUjoiIqA6CgoJw8+ZNuLq6omnTpmo/1X1rnq+vL2xtbXH16lW15QqFAl26dMGiRYsQGRkJMzMz7NmzR1x/+fJlmJqaolWrVjp9TURERERE2sLGExERUR2MGjUKzs7OGDRoEP7++2/cuXMH4eHheOONNxAfH1/lc4yMjPDMM8/g2LFj4rJTp05h6dKlOHPmDOLi4rB7926kpKTAz89P3Obvv//GU089JV5yR0RERERU37HxREREVAeWlpY4evQoGjVqhCFDhsDPzw8TJkxAfn4+bG1tq33epEmTsHPnTvGSPVtbWxw9ehR9+/ZF8+bN8e6772LVqlV49tlnxed8++23mDhxos5fExERERGRtiiEit/TTERERI+FIAjo1KkTpk2bhhEjRtS4/S+//II333wTFy9ehIkJb9FIRERERPLAGU9ERER6oFAo8OWXX6KkpESj7XNzc7Fx40Y2nYiIiIhIVjjjiYiIiIiIiIiIdIIznoiIiIiIiIiISCfYeCIiIiIiIiIiIp1g44mIiIiIiIiIiHSCjSciIiIiIiIiItIJNp6IiIiIiIiIiEgn2HgiIiIiIiIiIiKdYOOJiIiIiIiIiIh0go0nIiIiIiIiIiLSCTaeiIiIiIiIiIhIJ9h4IiIiIiIiIiIinfg/R6qmS3UutgoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 43/49 (Lat: 38.82, Lon: -9.4)\n", + "Site 43: Rhypo = 5.20 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 831.1155 cm/s²\n", + "Subfault PGA (i=0, j=1): 498.1590 cm/s²\n", + "Subfault PGA (i=1, j=0): 550.9147 cm/s²\n", + "Subfault PGA (i=1, j=1): 135.7038 cm/s²\n", + "Subfault PGA (i=2, j=0): 121.1974 cm/s²\n", + "Subfault PGA (i=2, j=1): 37.8732 cm/s²\n", + "Subfault PGA (i=3, j=0): 489.9985 cm/s²\n", + "Subfault PGA (i=3, j=1): 236.9529 cm/s²\n", + "Total PGA: 1107.4454 cmm/s²\n", + "Total PGA: 1107.4454 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 44/49 (Lat: 38.84, Lon: -9.4)\n", + "Site 44: Rhypo = 6.25 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 703.3074 cm/s²\n", + "Subfault PGA (i=0, j=1): 448.2452 cm/s²\n", + "Subfault PGA (i=1, j=0): 492.0129 cm/s²\n", + "Subfault PGA (i=1, j=1): 80.5211 cm/s²\n", + "Subfault PGA (i=2, j=0): 159.3942 cm/s²\n", + "Subfault PGA (i=2, j=1): 30.2230 cm/s²\n", + "Subfault PGA (i=3, j=0): 602.0614 cm/s²\n", + "Subfault PGA (i=3, j=1): 286.9211 cm/s²\n", + "Total PGA: 862.4610 cmm/s²\n", + "Total PGA: 862.4610 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 45/49 (Lat: 38.86, Lon: -9.4)\n", + "Site 45: Rhypo = 7.80 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 514.0062 cm/s²\n", + "Subfault PGA (i=0, j=1): 302.9917 cm/s²\n", + "Subfault PGA (i=1, j=0): 402.8707 cm/s²\n", + "Subfault PGA (i=1, j=1): 98.9016 cm/s²\n", + "Subfault PGA (i=2, j=0): 123.4715 cm/s²\n", + "Subfault PGA (i=2, j=1): 28.8036 cm/s²\n", + "Subfault PGA (i=3, j=0): 585.9200 cm/s²\n", + "Subfault PGA (i=3, j=1): 253.0849 cm/s²\n", + "Total PGA: 628.6221 cmm/s²\n", + "Total PGA: 628.6221 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 46/49 (Lat: 38.88, Lon: -9.4)\n", + "Site 46: Rhypo = 9.62 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 371.5666 cm/s²\n", + "Subfault PGA (i=0, j=1): 258.3321 cm/s²\n", + "Subfault PGA (i=1, j=0): 253.6366 cm/s²\n", + "Subfault PGA (i=1, j=1): 57.4831 cm/s²\n", + "Subfault PGA (i=2, j=0): 116.7812 cm/s²\n", + "Subfault PGA (i=2, j=1): 22.4384 cm/s²\n", + "Subfault PGA (i=3, j=0): 589.9365 cm/s²\n", + "Subfault PGA (i=3, j=1): 278.0660 cm/s²\n", + "Total PGA: 581.4936 cmm/s²\n", + "Total PGA: 581.4936 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 47/49 (Lat: 38.9, Lon: -9.4)\n", + "Site 47: Rhypo = 11.59 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 249.1694 cm/s²\n", + "Subfault PGA (i=0, j=1): 159.4519 cm/s²\n", + "Subfault PGA (i=1, j=0): 218.2319 cm/s²\n", + "Subfault PGA (i=1, j=1): 53.8372 cm/s²\n", + "Subfault PGA (i=2, j=0): 61.8826 cm/s²\n", + "Subfault PGA (i=2, j=1): 19.6956 cm/s²\n", + "Subfault PGA (i=3, j=0): 511.0521 cm/s²\n", + "Subfault PGA (i=3, j=1): 319.5450 cm/s²\n", + "Total PGA: 497.8397 cmm/s²\n", + "Total PGA: 497.8397 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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rX5FOedU+1fOckpIijBgxQnB0dBQAqOVccXGx8Oabbwpdu3YVcyMkJESYN2+ekJGRIbYDILz44osaz2NDkpOTtV5xsHb+ExEREVHzkQmCILR0sYuIiIgUa3R16NABL730ElatWlXn+cWLF2PJkiXIysrSaR0vIiIiIqLWhlPtiIiIWtitW7dw/fp1vP/++zAzM8PLL79s7JCIiIiIiAzCzNgBEBER3Wu++OILDB48GBcvXsQ333yjdsU60o9cLkd1dbXWm1wuN3aIRERERPc0TrUjIiIiyRo8eDCioqK0Pt+hQwekpKS0XEBEREREpIaFJyIiIpKsK1euoKioSOvz1tbWCAkJacGIiIiIiEgVC09ERERERERERGQQXOOJiIiIiIiIiIgMgoUnIiJqEVu2bIFMJhNvFhYWaNOmDZ588klcu3atxeMpKirCK6+8ghEjRsDT0xMymQyLFy/W2PbYsWOYMWMGwsPDYW1tDZlMpte6Qb/++iumTJmCkJAQWFpaQiaTaWwXGxuLF198ESEhIXB0dIS3tzeGDRuGQ4cO6XScxYsXQyaTITs7W+fYiIiIiIgMiYUnIiJqUZs3b8aJEyfw559/Yvbs2fjll1/wr3/9C3l5eS0aR05ODj777DNUVFRg/Pjx9bY9ePAg/vzzT7Rv3x4DBgzQ+1i7du3CyZMn0b17d/Tq1Utru2+//RanT5/G9OnT8fPPP+OLL76AtbU1hg4dim3btul9XCIiIiIiY7MwdgBERHRvCQ4ORkREBADFFcnkcjnefvtt7N69G//5z39aLI4OHTogLy9PHCH0xRdfaG371ltv4e233wYArF69Gn/99Zdex/r8889hZqb4rmf27NmIjY3V2O6VV17B6tWr1baNGjUKYWFhWLp0KaZMmaLXcYmIiIiIjI0jnoiIyKiURag7d+606HGVU/50oSwaNZaur/fy8qqzzdzcHOHh4bh582ajjn358mV07NgR/fr1Q2ZmJgBFwS84OBgnTpzAgAEDYGtrC39/f2zevBkA8NtvvyEsLAx2dnYICQnB3r17G3VsIiIiIiIWnoiIyKiSk5MBAF26dGmwrSAIqK6u1ulmKqqrq3H06FH06NFD79dGRUVhwIAB6NmzJw4fPqxW2MrIyMB//vMfzJgxAz///DNCQkIwffp0LF26FK+99hpeeeUV/Pjjj3BwcMD48eORlpbWnG+LiIiIiO4RnGpHREQtSi6Xo7q6GuXl5fj777/xzjvvYNCgQRg7dmyDr926davO0/EEQWhqqK3C4sWLkZiYiN27d+v1uq+//hrPPPMMnn/+eXz44Yd1Rl3l5ORg3759CA8PB6AYeebl5YX33nsPiYmJ8PX1BQD4+voiNDQUP/74I1566aVmeU9EREREdO9g4YmIiFrUfffdp/Y4KCgIP//8MywsGv6VNGbMGERHRxsqtFbniy++wLvvvosFCxZg3LhxOr/u3Xffxbp16/D+++9j3rx5Gtu0adNGLDoBgJubG7y8vODv7y8WnQDFzwcAbty40ch3QURERET3MhaeiIioRW3btg1BQUEoKirCd999h08//RQTJ07EH3/80eBr3dzc4Ozs3AJRGt/mzZvx3HPP4dlnn8X777+v12u//vprtG3bFk8++aTWNm5ubnW2WVlZ1dluZWUFACgvL9crBiIiIiIigGs8ERFRCwsKCkJERASGDBmCjRs3YsaMGdi7dy9++OGHBl+7detWWFpa6nSTss2bN2PGjBmYOnUqNm7cqPMi6Ep79+6FpaUl7r//fo5UIiIiIiKj4ognIiIyqlWrVuHHH3/EokWLMGHChHqvAHcvTLXbsmULZsyYgaeeegpffPGF3kUnAOjQoQOOHj2KYcOG4f7778fBgwcRGBhogGiJiIiIiOrHwhMRERmVq6ureBW17du346mnntLa1t3dHe7u7s127D/++AMlJSUoKioCAFy6dEkceTVq1CjY2dkBALKyshAVFQUAOH/+vPhaT09PeHp6IjIyUtynhYUFIiMjcfDgQXHbjRs3xIJZUlISAIjH8ff3R0REBABg586deOaZZxAaGornnnsOp0+fVou3d+/esLa21um9tWnTBlFRURg5ciQGDRqEAwcOIDg4WI+zQ0RERETUdCw8ERGR0b300ktYv349li5diokTJ8Lc3LxFjvvCCy+oTUXbuXMndu7cCQBITk6Gv78/AODixYt47LHH1F47a9YsAEBkZCT++usvcbtcLodcLldre/jw4TpX41Pub+rUqdiyZQsA4LfffkNNTQ3OnDmDgQMH1olXNSZdeHh44NChQ3j44YcRGRmJffv2iUUuIiIiIqKWIBNM5XrTRERERERERETUqnBxcSIiIiIiIiIiMggWnoiIiIiIiIiIyCBYeCIiIiIiIiIiIoNg4YmIiIiIiIiIiAyChSciIiIiIiIiIjIIFp6IiIiIiIiIiMggLIwdgBTV1NQgLS0Njo6OkMlkxg6HiIiIiIhaGUEQUFRUBF9fX5iZtd7v++VyOaqqqowdBhFJjKWlJczNzXVqy8JTI6SlpcHPz8/YYRARERERUSt38+ZNtGvXzthh1CEIAjIyMpCfn2/sUIhIolxcXODj49PggBwWnhrB0dERgOKXiJOTk5GjASorK2FlZWXsMIh0xpwlKWLektQwZ0mKTClvCwsL4efnJ/7t0Nooi05eXl6ws7PjTA4i0pkgCCgtLUVmZiYAoE2bNvW2Z+GpEZSdspOTU6soPJ06dQr9+vUzdhhEOmPOkhQxb0lqmLMkRaaYt62xoCOXy8Wik7u7u7HDISIJsrW1BQBkZmbCy8ur3ml3rXeyMRERERERETU75ZpOdnZ2Ro6EiKRM2Yc0tE4cC08moDXOGSeqD3OWpIh5S1LDnCUpYt62rNY4GouIpEPXPoSFJxOg60ryRK0Fc5akiHlLUsOcJSli3lJr5O/vj7Vr1xo7jHpNmzYN48ePN9rxt2zZAhcXF6MdX18t9TMdPHgw5s6d22r2YywsPJmAGzduGDsEIr0wZ0mKmLckNcxZkiLmLdVHJpPVe5s2bVqDr9+9e7fB4rt16xasrKzQrVs3gx2jNdBUtHniiSdw9epV4wRUS0lJCV599VV07NgRNjY28PT0xODBg/Hrr7+KbaKjo/Hss88aMUrN/vrrL8hksjpXm/zpp5+wbNkygx9f0/+rjRs3Nnm/ki08rVixAjKZTK3qJwgCFi9eDF9fX9ja2mLw4MG4ePGi2usqKirw0ksvwcPDA/b29hg7dixu3brVwtETEdG9JDsb+OEHoLjY2JEQERFJV3p6unhbu3YtnJyc1LZ99NFHRo1vy5YtePzxx1FaWoq///7bqLHoSxAEVFdXN/r1tra28PLyasaIGu/555/H7t27sX79ely+fBl79+7Fv//9b+Tk5IhtPD09JbXGmZubW4tdIXPz5s1q/6+mTp3a5H1KsvAUHR2Nzz77DD179lTbvmrVKqxZswbr169HdHQ0fHx8MHz4cBQVFYlt5s6di127dmHHjh04duwYiouLMXr0aMjl8pZ+G82m9nkgau2YsyRFTcnb114Dtm4F1q1rxoCIGsC+lqSIeUv18fHxEW/Ozs6QyWRq27Zv345OnTrBysoKXbt2xVdffSW+1t/fHwDwyCOPQCaTiY+TkpIwbtw4eHt7w8HBAX369MGff/6pd2yCIGDz5s14+umnMWnSJGzatKlOm7///huRkZGws7ODq6srRo4ciby8PABATU0NVq5cic6dO8Pa2hrt27fHu+++K7729u3beOKJJ+Dq6gp3d3eMGzcOKSkp9cazatUqdOzYEba2tujVqxd++OEH8XnlyJp9+/YhIiIC1tbWOHr0aIPnY/Dgwbhx4wbmzZsnjogBNE+127Bhg9afB6AYXfPFF1/gkUcegZ2dHQIDA/HLL7/ofM612bNnD15//XWMGjUK/v7+CA8Px0svvaRWQKk9aksmk+HTTz/F6NGjYWdnh6CgIJw4cQKJiYkYPHgw7O3t0b9/fyQlJYmv0TS9ce7cuRg8eLDW2L7++mtERETA0dERPj4+mDRpEjIzMwEAKSkpGDJkCADA1dVVbRRf7al2eXl5mDJlClxdXWFnZ4eHHnoI165dE59X/jz27duHoKAgODg44MEHH0R6enqD58/FxUXt/5Xy6nVNIbnCU3FxMSZPnozPP/8crq6u4nZBELB27Vq88cYbmDBhAoKDg7F161aUlpZi+/btAICCggJs2rQJH3zwAYYNG4bevXvj66+/xvnz5xvVubQW9XU4RK0Rc5akqCl5m5Gh+DcmpnliIdIF+1qSIuYtNdauXbvw8ssvY8GCBbhw4QKee+45/Oc//8Hhw4cBKAYvAHdHcygfFxcXY9SoUfjzzz8RFxeHkSNHYsyYMUhNTdXr+IcPH0ZpaSmGDRuGp59+Gt9//73aAIj4+HgMHToUPXr0wIkTJ3Ds2DGMGTNGHADx2muvYeXKlXjrrbdw6dIlbN++Hd7e3gCA0tJSDBkyBA4ODjhy5AiOHTsmFhIqKys1xvPmm29i8+bN2LBhAy5evIh58+bhqaeeQlRUlFq7V155BStWrEBCQgJ69uzZ4Pn46aef0K5dOyxdulQcEdOYn4fSkiVL8Pjjj+PcuXMYNWoUJk+ejNzcXL3OfW0+Pj74/fff1c6/LpYtW4YpU6YgPj4e3bp1w6RJk/Dcc8/htddeQ8w/H+Jmz57dpNgqKyuxbNkynD17Frt370ZycrJYXPLz88OPP/4IALhy5Uq9o/imTZuGmJgY/PLLLzhx4gQEQcCoUaPUri5XWlqK1atX46uvvsKRI0eQmpqKhQsXNhjj7Nmz4eHhgT59+mDjxo2oqalp0nsGAAgSM2XKFGHu3LmCIAhCZGSk8PLLLwuCIAhJSUkCAOHMmTNq7ceOHStMmTJFEARBOHjwoABAyM3NVWvTs2dPYdGiRVqPWV5eLhQUFIi3mzdvCgCEgoKCZnxnjXfy5Eljh0CkF+YsSVFT8nb0aMVt/PhmDIioAexrSYpMKW8LCgpa1d8MqsrKyoRLly4JZWVl4raaGkEoKzPOraZG//ewefNmwdnZWXw8YMAAYebMmWptHnvsMWHUqFHiYwDCrl27Gtx39+7dhXXr1omPO3ToIHz44Yf1vmbSpEni36mCIAi9evUSPv/8c/HxxIkThYEDB2p8bWFhoWBtba3WXtWmTZuErl27CjUqJ6qiokKwtbUV9u3bJwiCIEydOlUYN26cIAiCUFxcLNjY2AjHjx9X288zzzwjTJw4URAEQTh8+LAAQNi9e3e970sQdDsfjf15vPnmm+Lj4uJiQSaTCX/88UeDMdUnKipKaNeunWBpaSlEREQIc+fOFY4dO6bWpvZ7qB3LiRMnBADCpk2bxG3ffvutYGNjIz5WPedKL7/8shAZGSk+Vq1ZaHL69GkBgFBUVCQIwt2fS15enlo71f1cvXpVACD8/fff4vPZ2dmCra2t8P333wuCoPh5ABASExPFNh9//LHg7e2tNRZBEIRly5YJx48fF+Li4oTVq1cLdnZ2wrJly7S219SXaGLR9NJVy9mxYwfOnDkjVqdVZfzzdbKyKqzk7e0tLlKYkZEBKysrtZFSyjbK12uyYsUKLFmypM72mJgY2NvbIywsDAkJCSgrK4OjoyMCAgJw7tw5AECHDh1QU1ODmzdvAgBCQ0ORmJiI4uJi2Nvbo0uXLoiLiwOguHysubm5GG/Pnj2RkpKCwsJC2NjYoEePHoiNjQUA+Pr6wsbGBtevX0dBQQFKSkpw69Yt5Ofnw8rKCqGhoTh9+jQARcXXwcEBiYmJAICgoCDcuXMHubm5sLCwQHh4OE6fPg1BEODp6QlXV1dxYbiuXbsiNzcXWVlZMDMzQ58+fRATEwO5XA53d3d4eXkhISEBABAYGIjCwkLcuXMHANCvXz+cOXMGVVVVcHV1ha+vr7jmVqdOnVBaWipWyCMiInDhwgWUl5fD2dkZ7du3x/nz5wEohkFWV1eLa3GFhYXh8uXLKC0thYODAzp16oSzZ88CANq3bw8AYkW+V69eSEpKQnFxMezs7NCtWzecOXNGPN8WFhbiN2shISFITU1FQUEBbGxsEBwcLFa227RpAzs7O3FoZY8ePZCWloa8vDxYWloiLCwMp06dEvPJyclJHOoYFBSEzMxM5OTkwNzcHBEREYiOjkZNTQ08PT3h5uaGK1euAAC6dOmCvLw8ZGVlQSaToW/fvoiNjUV1dTXc3Nzg7e0tnu/OnTujuLhYzN2+ffsiPj4elZWVcHFxQbt27XDhwgUAQMeOHVFeXo60tDQAQHh4OC5evIjy8nI4OTnB399fLWflcrl4vnv37o2rV6+ipKQEDg4O6Ny5M+Lj4wEoqvJmZmZqOZucnIyioiLY2toiKChIPN9t27aFlZUVkpOTUVBQgNLSUty8eRP5+fmwtrZGz549xf/bPj4+sLe3F8939+7dkZGRgdzc3Drn28vLC87OzuL57tatG7Kzs5GdnS3mrPJ8e3h4wMPDA5cvXxZztqCgQBzeqpqzbm5u8PHxwaVLl8ScLSkpEc93nz59cO7cOVRUVMDFxQV+fn5izgYEBKCyshK3b98Wc9aYfQQABAcHs49A0/oICwsLXL58uVF9BBD2z30BKSmF7CMa6CMARZ/MPqJpfYSFhQUSEhLYR/BzhKT6iLKyMpw6dcok+ggprR8DABUVwGOPGefYO3cCNjZN20dCQkKdxaIHDhzY4LpPJSUlWLJkCX799VekpaWhuroaZWVleo14ys/Px08//YRjx46J25566il8+eWXmDFjBgDFiKfHtJzghIQEVFRUYOjQoRqfj42NRWJiYp01fsrLy9WmfildunQJ5eXlGD58uNr2yspK9O7dW21bRESE2uPmOB/K96TLz0N1eq29vT0cHR3F/1O1LV++HMuXLxcfX7p0Sey3VQ0aNAjXr1/HyZMn8ffff+PQoUP46KOPsGTJErz11ltaY1aNRVlXCAkJUdtWXl6OwkLFZ7nGiIuLw+LFixEfH4/c3FxxNFFqaiq6d++u0z4SEhJgYWGBfv36idvc3d3RtWtXsY8HADs7O3Tq1El83KZNG63nVunNN98U74eGhgIAli5dqra9UeotS7UiqampgpeXlxAfHy9uU636/f333wIAIS0tTe11M2bMEEaOHCkIgiB88803gpWVVZ19Dxs2THjuuee0Hru1j3iqqqoydghEemHOkhQ1JW+VI54ee6wZAyJqAPtakiJTylupjXgqK7v7+6qlbw0MltCo9ggbV1dXYevWrWptPvzwQ6Fjx47iY2gY8fTCCy8IHTt2FH766Sfh3LlzwrVr14RevXqpjVJpaMTTxx9/LAAQzM3NxZuZmZkAQLh48aIgCIIQFhamdZbNuXPnBADC9evXNT7//PPPC3379hWuXbtW55afny8Igvrom5MnTwoAhL/++qtO+9TUVEEQtI+saez5aK6fh7Ozs7B582aN5yEnJ0ftvejTXyxbtkywtLQUKioqNL6H2rEkJycLAIS4uDhxW+1z9p///EcYO3as2nFmzZqldcRTcXGx4OHhIUyaNEk4cuSIkJCQIOzbt0/tOLqMeNq9e7dgYWEhVFdXq7Xp1auXsHTpUkEQ6v48BEEQdu3aJehbAjp27JgAQMjIyND4vMmNeIqNjUVmZibCw8PFbXK5HEeOHMH69evFb3oyMjLQpk0bsU1mZqZYrfTx8UFlZSXy8vLURj1lZmZiwIABWo9tbW0Na2vr5n5LzSY2Nlat2knU2jFnSYqaI2/NzZspGCIdsK8lKWLeGo+1tWLkkbGO3VRBQUE4duwYpkyZIm47fvw4goKCxMeWlpZ1Lip19OhRTJs2DY888ggAxZpP+q41tmnTJixYsEBcq0dpzpw5+PLLL7F69Wr07NkTBw8e1DiTJjAwELa2tjh48KA4QkpVWFgYvvvuO3h5eek00qZ79+6wtrZGamoqIiMj9XovupwPKyurBi/OpcvPQ19ubm5wc3Nr1Gu7d++O6upqlJeXw8rKqtExqPL09BRHhirFx8f/M9q9rsuXLyM7Oxvvvfce/Pz8AEAcGaukjK2+86t8L6dOnRLrGDk5Obh69WqTzq8mcXFxsLGxqbNwvL4ks7j40KFDcf78ecTHx4u3iIgITJ48GfHx8ejYsSN8fHxw4MAB8TWVlZWIiooSfxjh4eGwtLRUa5Oeno4LFy7UW3giIiJqDiw8ERFRayWTKaa7GeP2z4XRmuS///0vtmzZgo0bN+LatWtYs2YNfvrpJ7XFlP39/XHw4EFkZGSIV5Pr3LkzfvrpJ8THx+Ps2bOYNGmSXospx8fH48yZM5gxYwaCg4PVbhMnTsS2bdtQVVWF1157DdHR0Zg1axbOnTuHy5cvY8OGDcjOzoaNjQ1effVVvPLKK9i2bRuSkpJw8uRJ8cp4kydPhoeHB8aNG4ejR48iOTkZUVFRePnll8VpraocHR2xcOFCzJs3D1u3bkVSUhLi4uLw8ccfY+vWrfW+H13Oh7+/P44cOYLbt28jOzu70T8PQxk8eDA+/fRTxMbGIiUlBb///jtef/11DBkypNFT5DR54IEHEBMTg23btuHatWt4++236xSiVLVv3x5WVlZYt24drl+/jl9++QXLli1Ta9OhQwfIZDL8+uuvyMrKQnFxcZ39BAYGYty4cZg5cyaOHTuGs2fP4qmnnkLbtm0xbty4Rr+fPXv24PPPP8eFCxeQlJSEL774Am+88QaeffbZJg/EkUzhydHRsc5/ZHt7e7i7uyM4OBgymQxz587F8uXLsWvXLly4cAHTpk2DnZ0dJk2aBABwdnbGM888gwULFuDgwYOIi4vDU089hZCQEAwbNszI77DxfH19jR0CkV6YsyRFzZG3LDxRS2JfS1LEvKXGGj9+PD766CO8//776NGjBz799FNs3rxZ7dL2H3zwAQ4cOAA/Pz9xraMPP/wQrq6uGDBgAMaMGYORI0ciLCxM5+Nu2rQJ3bt3R7du3TTGlJubiz179qBLly7Yv38/zp49i759+6J///74+eefYWGhmIT01ltvYcGCBVi0aBGCgoLwxBNPiOvx2NnZ4ciRI2jfvj0mTJiAoKAgTJ8+HWVlZVoLKcuWLcOiRYuwYsUKBAUFYeTIkdizZw8CAgLqfT+6nI+lS5ciJSUFnTp1gqenp8b96PLzMJSRI0di69atGDFiBIKCgvDSSy9h5MiR+P7775v9OG+99RZeeeUV9OnTB0VFRWojvGrz9PTEli1bsHPnTnTv3h3vvfceVq9erdambdu2WLJkCf7v//4P3t7eWq+it3nzZoSHh2P06NHo378/BEHA77//rnW0lS4sLS3xySefoH///ujZsyc++ugjLF26FB988EGj96kkEwRBaPJejGTw4MEIDQ3F2rVrAQCCIGDJkiX49NNPkZeXh379+uHjjz9GcHCw+Jry8nL897//xfbt21FWVoahQ4fik08+EYe66aKwsBDOzs4oKCho1oppY2VlZWn9D0/UGjFnSYqakrdjxij+9fQEvvyyGYMiqgf7WpIiU8rb1vY3g6ry8nIkJycjICAANk1d1ZuI7lm69iWSWeNJk7/++kvtsUwmw+LFi7F48WKtr7GxscG6deuwbt06wwbXgq5fv24yv6Dp3sCcJSlqjry1kPRvXZIa9rUkRcxbIiLTI5mpdkRERFJnxt+6RERERHSP4UdgE6A6lZBICpizJEXNkbcc8UQtiX0tSRHzlojI9LDwZAI0XcmAqDVjzpIUNUfecnFxaknsa0mKmLdERKaHhScTkJ+fb+wQiPTCnCUpao68ZeGJWhL7WpIi5i0Rkelh4ckEWFlZGTsEIr0wZ0mKGpu3qteO5VQ7aknsa0mKmLctS8IXOCeiVkDXPoSFJxMQGhpq7BCI9MKcJSlqbN5WVd29zxFP1JLY15IUMW9bhqWlJQCgtLTUyJEQkZQp+xBln6INv3s1AadPn0a/fv2MHQaRzpizJEWNzVvVwhOvakctiX0tSRHztmWYm5vDxcUFmZmZAAA7OzvIZDIjR0VEUiEIAkpLS5GZmQkXFxeYN/DtKgtPREREBlRZefc+C09ERNRa+Pj4AIBYfCIi0peLi4vYl9RHr8JTQUEBdu3ahaNHjyIlJQWlpaXw9PRE7969MXLkSAwYMKDRAVPj6fKDJmpNmLMkRY3NW9URTzU1zRQMkQ7Y15IUMW9bjkwmQ5s2beDl5YUq1V9WREQ6sLS0bHCkk5JOhaf09HQsWrQI33zzDXx8fNC3b1+EhobC1tYWubm5OHz4MFavXo0OHTrg7bffxhNPPNGkN0D6cXBwMHYIRHphzpIUNTZv5fK791l4opbEvpakiHnb8szNzXX+45GIqDF0Kjz16tULU6ZMwenTpxEcHKyxTVlZGXbv3o01a9bg5s2bWLhwYbMGStolJibC3d3d2GEQ6Yw5S1LU2LxVLTax8EQtiX0tSRHzlojI9OhUeLp48SI8PT3rbWNra4uJEydi4sSJyMrKapbgiIiIpI6FJyIiIiK6l+m0zGlDRaemtqemCQoKMnYIRHphzpIUNTZvOdWOjIV9LUkR85aIyPTofH2dffv2YeLEibh+/ToA4JlnnjFYUKSfO3fuGDsEIr0wZ0mKGpu3HPFExsK+lqSIeUtEZHp0LjwtXLgQo0ePxn/+8x/cunULly5dMmRcpIfc3Fxjh0CkF+YsSVFj85YjnshY2NeSFDFviYhMj05rPAGAs7MzJk+ejPvuuw8zZ85EdXW1IeMiPVhY6PxjJGoVmLMkRY3NW454ImNhX0tSxLwlIjI9Oo94Ul7atFOnTnjxxRdx5swZgwVF+gkPDzd2CER6Yc6SFDU2b1l4ImNhX0tSxLwlIjI9OheeNm7cCPk/8wVGjx6NmJgYgwVF+jl9+rSxQyDSC3OWpKixecupdmQs7GtJipi3RESmR+exrP7+/gCAsrIyCIKA3r17AwBu3LiBXbt2oXv37hgxYoRBgqT6CYJg7BCI9MKcJSlqbN6qFptUi1BEhsa+lqSIeUtEZHp0HvGkNG7cOGzbtg0AkJ+fj379+uGDDz7AuHHjsGHDhmYPkBrm6elp7BCI9MKcJSlqbN5yqh0ZC/takiLmLRGR6dG78HTmzBncf//9AIAffvgB3t7euHHjBrZt24b//e9/zR4gNczV1dXYIRDphTlLUtTYvOVUOzIW9rUkRcxbIiLTo3fhqbS0FI6OjgCA/fv3Y8KECTAzM8N9992HGzduNHuA1LCrV68aOwQivTBnSYoam7cc8UTGwr6WpIh5S0RkevQuPHXu3Bm7d+/GzZs3sW/fPnFdp8zMTDg5OTV7gERERFLGwhMRERER3cv0LjwtWrQICxcuhL+/P/r164f+/fsDUIx+Ui44Ti2ra9euxg6BSC/MWZKixuYtp9qRsbCvJSli3hIRmR69C0+PPvooUlNTERMTg71794rbhw4dig8//LBZgyPd5ObmGjsEIr0wZ0mKGpu3HPFExsK+lqSIeUtEZHp0Ljz5+vrihRdewB9//AE3Nzf07t0bZmZ3X963b19069bNIEFS/bKysowdApFemLMkRY3NW9Vik+roJyJDY19LUsS8JSIyPToXnrZv3w47OzvMmTMHHh4eeOyxx/DVV1/xW4lWQLUASCQFzFmSosbmLafakbGwryUpYt4SEZkenXv2wYMH44MPPsC1a9dw4sQJhIWF4eOPP0abNm0wePBgfPjhh0hKSjJYoCtWrECfPn3g6OgILy8vjB8/HleuXFFrIwgCFi9eDF9fX9ja2mLw4MG4ePGiWpuKigq89NJL8PDwgL29PcaOHYtbt24ZLO6W0KdPH2OHQKQX5ixJUWPztjFT7ZKSgD17AEFo1CGJALCvJWli3hIRmZ5GfaXQo0cPvPbaazh58iRu3LiByZMn49ChQwgJCUFwcDB+++235o4TUVFRePHFF3Hy5EkcOHAA1dXVGDFiBEpKSsQ2q1atwpo1a7B+/XpER0fDx8cHw4cPR1FRkdhm7ty52LVrF3bs2IFjx46huLgYo0ePhlzC8x9iYmKMHQKRXpizJEWNzdvGFJ7mzgU++ww4dKhRhyQCwL6WpIl5S0RkeiyaugMfHx/MnDkTM2fORGlpKfbt2wdra+vmiE2N6kLmALB582Z4eXkhNjYWgwYNgiAIWLt2Ld544w1MmDABALB161Z4e3tj+/bteO6551BQUIBNmzbhq6++wrBhwwAAX3/9Nfz8/PDnn39i5MiRzR53S5By0YzuTcxZkqLG5q3qy/TdxZUrwNChjTosEftakiTmLRGR6Wl04SkzMxOZmZmoqfX17SOPPNLkoHRRUFAAAHBzcwMAJCcnIyMjAyNGjBDbWFtbIzIyEsePH8dzzz2H2NhYVFVVqbXx9fVFcHAwjh8/LtnCk7u7u7FDINILc5akqLF5q/prUhAUN5lMt9daWTXqkEQA2NeSNDFviYhMj96Fp9jYWEydOhUJCQkQai0+IZPJWuRbCkEQMH/+fPzrX/9CcHAwACAjIwMA4O3trdbW29sbN27cENtYWVnB1dW1Thvl6zWpqKhARUWF+LiwsLBZ3kdz8fLyMnYIRHphzpIUNTZva/9abKjwpPqr1dKyUYckAsC+lqSJeUtEZHr0Ljz95z//QZcuXbBp0yZ4e3tDpuvXts1o9uzZOHfuHI4dO1bnudrxCILQYIwNtVmxYgWWLFlSZ3tMTAzs7e0RFhaGhIQElJWVwdHREQEBATh37hwAoEOHDqipqcHNmzcBAKGhoUhMTERxcTHs7e3RpUsXxMXFAQDatWsHc3NzsVDWs2dPpKSkoLCwEDY2NujRowdiY2MBKEZq2djY4Pr168jLy8P999+PW7duIT8/H1ZWVggNDcXp06cBKKZDOjg4IDExEQAQFBSEO3fuIDc3FxYWFggPD8fp06chCAI8PT3h6uqKq1evAgC6du2K3NxcZGVlwczMDH369EFMTAzkcjnc3d3h5eWFhIQEAEBgYCAKCwtx584dAEC/fv1w5swZVFVVwdXVFb6+vuJi7506dUJpaSnS09MBABEREbhw4QLKy8vh7OyM9u3b4/z58wAAf39/VFdXi4vAh4WF4fLlyygtLYWDgwM6deqEs2fPAgDat28PAEhNTQUA9OrVC0lJSSguLoadnR26deuGM2fOiOfbwsICKSkpAICQkBCkpqaioKAANjY2CA4OFtcZaNOmDezs7MQF9Hv06IG0tDTk5eXB0tISYWFhOHXqFABFIdPJyQnXrl0Tz3dmZiZycnJgbm6OiIgIREdHo6amBp6ennBzcxMXyu/SpQvy8vKQlZUFmUyGvn37IjY2FtXV1XBzc4O3t7d4vjt37ozi4mKxaNq3b1/Ex8ejsrISLi4uaNeuHS5cuAAA6NixI8rLy5GWlgYACA8Px8WLF1FeXg4nJyf4+/ur5axcLhfPd+/evXH16lWUlJTAwcEBnTt3Rnx8PADAz88PZmZmajmbnJyMoqIi2NraIigoSDzfbdu2hZWVFZKTk5GXl4dBgwbh5s2byM/Ph7W1NXr27Ino6GgxZ+3t7cXz3b17d2RkZCA3N7fO+fby8oKzs7N4vrt164bs7GxkZ2eLOas83x4eHvDw8MDly5fFnC0oKEBmZmadnHVzc4OPjw8uXbok5mxJSYl4vvv06YNz586hoqICLi4u8PPzE3M2ICAAlZWVuH37tpizxuwjACA4OJh9BJrWR8jlcri5uendR1y75gGgE/Ly8gAA168XwM1Nex/Rs2cE8vPzIQgC8vIqkZ9vfc/1EYCiT2Yf0bQ+Qi6Xw9XVlX0EP0dIqo84ffo0HB0dTaKPsLOzAxERATKh9rClBjg6OiIuLg6dO3c2VEz1eumll7B7924cOXIEAQEB4vbr16+jU6dOOHPmDHr37i1uHzduHFxcXLB161YcOnQIQ4cORW5urtqop169emH8+PEai0uA5hFPfn5+KCgogJOTkwHepX5OnTqFfv36GTsMIp0xZ0mKGpu3u3YBX3559/EPPwD1LYVYVARMmqS4/9RTwBNP6H1IIgDsa0maTClvCwsL4ezs3Gr+ZiAiMha9r2o3dOhQ8VuhliQIAmbPno2ffvoJhw4dUis6AYpvD318fHDgwAFxW2VlJaKiojBgwAAAim9nLC0t1dqkp6fjwoULYhtNrK2t4eTkpHZrTQIDA40dApFemLMkRY3N29pT7Rq6sp3q81xjl5qCfS1JEfOWiMj06D3V7osvvsDUqVNx4cIFBAcHw7LWAhRjx45ttuBUvfjii9i+fTt+/vlnODo6isOCnZ2dYWtrC5lMhrlz52L58uUIDAxEYGAgli9fDjs7O0z656tjZ2dnPPPMM1iwYAHc3d3h5uaGhQsXIiQkRLzKnRQVFhaKi6wTSQFzlqSosXlbu9DUUOFJtdhUXa334YhE7GtJipi3RESmR+/C0/Hjx3Hs2DH88ccfdZ4z5OLiGzZsAAAMHjxYbfvmzZsxbdo0AMArr7yCsrIyzJo1C3l5eejXrx/2798PR0dHsf2HH34ICwsLPP744ygrK8PQoUOxZcsWmJubGyTulnDnzh34+/sbOwwinTFnSYoam7e1C01lZcDSpUDv3sCTT9bfnoUnagr2tSRFzFsiItOj91S7OXPm4Omnn0Z6ejpqamrUboa8op0gCBpvyqIToCh8LV68GOnp6SgvL0dUVJR41TslGxsbrFu3Djk5OSgtLcWePXvg5+dnsLiJiOjekZFRt1hU+1fjsWPApUvAN99o3gcLT0RERERkSvQuPOXk5GDevHnw9vY2RDzUCKayACPdO5izJEUN5W1sLDBzJrB8ufr22iOeysvrP45q+z17gH8uKtUs9u4FXnkFKCxsvn1S68W+lqSIeUtEZHr0LjxNmDABhw8fNkQs1EjKy8wSSQVzlqSoobz99VfFv/9czVtUu/BUVVX/cWq3f+01HYLT0ccfAwkJwNdfN98+qfViX0tSxLwlIjI9eq/x1KVLF7z22ms4duwYQkJC6iwuPmfOnGYLjnRT1dBfMUStDHOWpKihvNW2VGDtqXbnz9d/nIYWH69Pejrw22/AhAlAfWvzFhQ0/hgkHexrSYqYt0REpqdRV7VzcHBAVFQUoqKi1J6TyWQsPBmBq6ursUMg0gtzlqSosXlbu5CUkHD3viAAMln97bXJywOyswHVK4+/+SaQmQlcvgysXq17TGSa2NeSFDFviYhMj96Fp+TkZEPEQU3g6+tr7BCI9MKcJSlqKG8FQfP2+oo8cjlgUes3sa5FoWefVawX9dFHQMeOim2ZmYp/r1yp/7UcUHBvYF9LUsS8JSIyPXqv8UStz8WLF40dApFemLMkRQ3lrbbCU30XfNVUZNK18KRcpDwmRrf2qlh4ujewryUpYt4SEZkevQtPjz76KN577706299//3089thjzRIUERGRqWhoxJMu7bUVtRqLhSciIiIiail6F56ioqLw8MMP19n+4IMP4siRI80SFOmnU6dOxg6BSC/MWZKihvK2MSOedC08VVfrdixdVVY27fUkDexrSYqYt0REpkfvwlNxcTGsrKzqbLe0tERhYWGzBEX6KS0tNXYIRHphzpIUNZS3jV3jSZf2tdupPlYuTq5PMYojnu4N7GtJipi3RESmR+/CU3BwML777rs623fs2IHu3bs3S1Ckn/T0dGOHQKQX5ixJUWPztjkKT7VHPCnXdwLuFp7+/FP3mKqqgJwcYO9ejn4yZexrSYqYt0REpkfvq9q99dZb+Pe//42kpCQ88MADAICDBw/i22+/xc6dO5s9QCIiIikw5FS72u0qKu7eVxalvvmm/vhUVVYCy5YBSUnAnTvA1Km6v5aIiIiISB96j3gaO3Ysdu/ejcTERMyaNQsLFizArVu38Oeff2L8+PEGCJEaEhERYewQiPTCnCUpaihvGzPVTvW50lKgpES3EU+qM1GUI5ZycuoNT01VlaLoBAAnTuj+OpIW9rUkRcxbIiLTo3fhCQAefvhh/P333ygpKUF2djYOHTqEyMjI5o6NdHThwgVjh0CkF+YsSVFDeduUNZ4EAZgxA5g8WX0anbZ95Obeva86+klXqtPr3Nz0fz1JA/takiLmLRGR6dF7qp0uBEGATLnoBBlcuaa/UohaMeYsSVFDeduUqXbFxUBRkeK+puVN6is8NWahcNW34uKi/+tJGtjXkhQxb4mITI9OI56CgoKwfft2VDawAum1a9fwwgsvYOXKlc0SHOnG2dnZ2CEQ6YU5S1LU2LzVZaqd6qglTYWk2sWruLj62+ujvsIYSRv7WpIi5i0RkenRacTTxx9/jFdffRUvvvgiRowYgYiICPj6+sLGxgZ5eXm4dOkSjh07hkuXLmH27NmYNWuWoeMmFe3btzd2CER6Yc6SFDWUt6oDfQXh7mNlcWnyZGDXLvX1mZRFH9XilC6Fp8OH628PAKtWAf/97904tI3I4uAC08W+lqSIeUtEZHp0GvH0wAMPIDo6Gr/99ht8fHywfft2zJ49G5MnT8bixYtx7do1TJkyBbdu3cJ7770HJycnQ8dNKs6fP2/sEIj0wpwlKWoob1ULT6qFJGXRyNsbGD5c/TXK51QLS5oGF9c3akpb4enoUSArq+6xamPhyXSxryUpYt4SEZkevdZ4GjBgAAYMGGCoWIiIiCRLtfAklwPm5or7yqKRuTlgZaX+GuVzmgpPPXoo1nvKza1bNGrT5u5aULWveKdK9XXaildlZdpfT0RERETUVI26qh21Lv7+/sYOgUgvzFmSoobyVtuIJ+V9M7O6hSdNU+2UhSczM8VNtZ1SScnd+/Wt8aQpjtpYeDJd7GtJipi3RESmh4UnE1Bd39fdRK0Qc5akSJ+8VS0UKe/XV3jStLi4mVndUVNKqutEsfBE2rCvJSli3hIRmR4WnkzArVu3jB0CkV6YsyRFDeVtQyOezM0BS0v119TUAD/9BMyff3ebsgilrfBUVaU+va6+wpOmAlhtLDyZLva1JEXMWyIi08PCExERkR5qahRXiIuJAT777O7UONWrxmlaW0nbiKfNm9W3NTTVTnW0E3C38KRa+FJSjUnbiKfKSu1FKSIiIiKiptJrcXFqncLCwowdApFemLMkRWFhYSgrA+bMUTzOyFD86+ICPP649sKTLlPtVDU01a524Sk3V/GvTKYeQ+3X1XdlvLIywMFB+/MkTexrSYqYt0REpqdRhaeamhokJiYiMzMTNbU+yQ4aNKhZAiPdXb58GSEhIcYOg0hnzFmSosuXL+PMmRCx4KR0547iX9Vfh5pGPNV3VTtVcXGKf1ULT/WNeCov174vFp7ubexrSYqYt0REpkfvwtPJkycxadIk3LhxA0Ktr1ZlMhnkHK/f4kpr/xVC1MoxZ0lqBAHYt88KR47UfU65JpO2wpPqiKfaazzV9yvT3Lz+qXb29oqr26kuTF6bPoUnMj3sa0mKmLdERKZH78LT888/j4iICPz2229o06YNZJoWlaAW5cCvqUlimLMkJWVlwPr1wG+/+cLRse7zmkY8abpvZlZ3Hab6Ck8ymfpUuz//BJyd7+7P0VFReBIExc3G5u7op/ri0KSoSPtzJF3sa0mKmLdERKZH78LTtWvX8MMPP6Bz586GiIcaoVOnTsYOgUgvzFmSipQU4L33gNu3AUdHO8yYAYwdq3guORl4+WXg8mVF4Ue1sHP1KvDhh8C0aepT7fQpPKkuLn79OvDNN4r7M2Yo/lX920wQFI8bW3jKyAB69ND+PEkT+1qSIuYtEZHp0fuqdv369UNiYqIhYqFGOnv2rLFDINILc5Zau/R0YM0axULit28D7u7A5MkXMG6congkkwHe3nfbnzqlXthZswa4cgV47TX1qXYWtb7uyc/XHoPqGk+HDt3dfviw4l/VwlNNjebCUkNXtVOO4OLVy00T+1qSIuYtEZHp0XvE00svvYQFCxYgIyMDISEhsKy1YEXPnj2bLThD+uSTT/D+++8jPT0dPXr0wNq1a3H//fcbOywiIjKS8nLg9Gng6FHFv8pCzYABwKxZwOXL6sOJ7O3v3k9KqntFOSXVqXZduiimyxUUKLbVN8VNdcRTerr6sQDFflSPUV2t/djA3QKY6pS8bt2A6GjFguZTp2qPhYiIiIiosfQuPP373/8GAEyfPl3cJpPJIAiCZBYX/+677zB37lx88sknGDhwID799FM89NBDuHTpEtq3b2/s8PQmxZjp3sacJWOrrlaszZSWBly7phiddOECUFl5t014ODB5MhAYqHisKW8nT1ZMgcvP1z6VTXWqnZ0dsG0b8M47ioJPSYn2GFVHPGni4qJ+DE2/fjVNtbOxAfz8FMWsKVOA2FhFMevqVUVhjEwH+1qSIuYtEZHp0bvwlJycbIg4WtSaNWvwzDPPYMY/C2WsXbsW+/btw4YNG7BixQojR2dY2r6RN9b+Dd1eX43Zf2uLSQrxlJbWvSR8U46hL1M8p82tNf5frq5Wv1VVNbytqgooLlaMLCosVIw0Sk8HMjM1x9CmDXD//Yqbv3/DMdnYKP4tL9deeFKdaqf818pKcb++/weqI540cXVVP4auhSczM2DFCkWRzdERGDIEOHhQsZbVv/4FeHoCXl6KqXwWFuo3ZUwymfq/yvuq25U3Vbpu05W21+mzv+a+RgqvuUJERESkTu/CU4cOHQwRR4uprKxEbGws/u///k9t+4gRI3D8+HGNr6moqECFyvWqCwsLDRqjPsaOBXJzbdT+ACFq7fLymLNkfDY2gI8PEBCgmHIWFKQoNmkrHKSmpqJNmzZ19gEornyny4gnJeX9+gpP5ub1j3hyc7t7XxAaLjwpnzc3B6ytFTdAMerp3DkgKwvYtUv78Uh62NeSFDVH3g4eDCxY0CzhEBFRM9C78AQASUlJWLt2LRISEiCTyRAUFISXX35ZElehyM7Ohlwuh7fqqrAAvL29kZGRofE1K1aswJIlS+psj4mJgb29PcLCwpCQkICysjI4OjoiICAA586dA6Ao1NXU1ODmzZsAgNDQUCQmJqK4uBj29vbo0qUL4uLiAADt2rWDubk5bty4AUCxXlZKSgoKCwthY2ODHj16IDY2FgDg6+sLGxsb5OVZoKqqCnJ5NcrKylFVVQUzMzM4OzshLy8fAGBjYw1zcwuU/DOnw9HRARUVFaisrIKZmQzOzi7Iz8+DIADW1lawtLREcbGirYODA6qqKlFRUQmZTAYXFxfk5+dDEARYWVnB2toKRUXF/7S1R1VVtVikc3V1RUFBAWpqamBlZQlraxsU/bOgib29PeRyOcr/WWjExcUFRUWFkMtrYGlpCVtbW7HAZ2dnB0GoQVmZoq2zszOKi4shl8thYWEBe3s7FBQo2tra2gIAysrK/mnrhJKSUlRXV8Pc3BwODg4o+GdxFVtbG8hkZij95y8/JycnlJWVoaqqCubmZnB0dEJ+vvIc2sDc3FzlHDqioqL8n3NoBmdnZ+Tl5QEArK2tYWlpIZ5DxfmuRGVl3XOoON9WKC5WPYdV/5xvwMXFFQUF+aipEf45h9bi+Vacw2qUlyvPtwsKCgpRU6M8hzYoLFSebzvI5TUq59sZRUVF/5xvC9ja2qmcb1sIgqB2vktKilFdrfl8y2RAaWmZeA5LSxs+34qclddzvmvnrO7n28HBAZWVms+3lZUVrKxqn299cvbu+W5KztrZ1T7fmnPWwsIc9va1z6FM7XyXlZWiqqr6n3PoiPz8ApWcNUNJiTK/HRvdRyjOoS59hCJnde0j3Nzu5relpSVsbFTPt90/fcTd811aWgCZTA5bWws4OdmjsDAXFhYCXF0dYWZWg7KyQpibA23beqOgIBs1NZWws7OEn5878vNTYWcnR0CAO7y8alBZeQOOjnKEhvZCUlISiouLUVxsh+rqbjhz5gwARZ9sYWGBlJQUAIBcLsfly5dRUFAAGxsbBAcH4/r1K8jLa4PMTDOUlFghL6+8Ts5WVADW1q44e/YM0tKq4O3tjfJyL+TlVeDatTJUV3tq7CNu3MhDTY0zqqocNfYRt2/fBhCMgoJ8nD6diMLCbrC0VO8jbt/OgVyeipoawNW1LwoKCiGTVeDKlTy0a9cOFy5cAAC89VYnHD4sIDGxCPn5lrC0bIu0tFxUVNTA3NwKNjYOyM4ugCAANjb2kMsFlJWVQRAAR0cXFBc3bx9xN7/ZRzSljwAEFBcXGbyP4OcIfo5ozs8Rcnk18vLymnS+8/IEZGfXIOmfRfG6d++OjIwM5ObmwtLSEmFhYTh16hQAwMvLC87Ozrh27RoAoFu3bsjOzkZ2djbMzMzQp08fREdHo6amBh4eHvDw8MDly5cBAIGBgSgoKEBmZiYAxUWYzpw5g6qqKri5ucHOzg5ERATIBEG/CQ/79u3D2LFjERoaioEDB0IQBBw/fhxnz57Fnj17MHz4cEPF2izS0tLQtm1bHD9+HP379xe3v/vuu/jqq6/EXySqNI148vPzQ0FBAZycnFokbm3y84GKinLYKL92b2b6Thkw5BSDeyUWQ0/TaA2xlJcrcpY/0+bBqT0tQ5m3qo4fV0xbCwpSTOn75zsGNdbWQEUFsGmTYgobAKxdq5je1rEjcP363bZeXoppgAAwZgyQmwv8/bfmeD75RLHoOQB89RXw9NN12wwbppj6duqUYnpfVpZiOuFnn+n33hui/CRRU6O4rzrSSvmc6qcN1W2NnTqq7XWGmIqq6z6NeWxNNOUsUWvXHHlraal+AQhjKSwshLOzc6v4m4GIyJj0HvH0f//3f5g3bx7ee++9OttfffXVVl948vDwgLm5eZ3RTZmZmXVGQSlZW1vDWjknoZVxcQEuXkxCjx49jB0Kkc6uXWPOkvQkJdXNW4t/fovK5bqv8aR6v6xMva3ql+MNLS7u46MoOgqCYi0rTf78s+62+vbZWMripyH2TY3HzwckRcxbIiLTU8+ypZolJCTgmWeeqbN9+vTpuHTpUrMEZUhWVlYIDw/HgQMH1LYfOHAAAwYMMFJUTaMcXk0kFcxZkiJNeasstOhSeNK0xlPtq9rV/oa+9uLiDzwAbNwIbN6s+EZfuR/VwtOqVfW8CQ37JNPFvpakiHlLRGR69P746enpifj4+Drb4+Pj4aWcQ9DKzZ8/H1988QW+/PJLJCQkYN68eUhNTcXzzz9v7NAahfPHSWqYsyRFmvJWWfi5eVN74Uk5VUqXxcUdHO7er6ioO4LIxgZo2xbw8FA8VhaRVAtPnToBPXtqfx8clXTvYF9LUsS8JSIyPXpPtZs5cyaeffZZXL9+HQMGDIBMJsOxY8ewcuVKLJDI5SOeeOIJ5OTkYOnSpUhPT0dwcDB+//13yV6xr1u3bsYOgUgvzFmSIk15q5xqV1kJ3LlT/+s1FZ6qq9XbWFndvV9RcXf/SrXX+1EWnr75Rn3f9a37xRFP9w72tSRFzFsiItOj98fPt956C4sWLcK6desQGRmJQYMGYf369Vi8eDHeeOMNQ8RoELNmzUJKSgoqKioQGxuLQYMGGTukRlNegYlIKpizJEWa8laf0UOqbbUVf1TbVFbW3X/tUVXKAtOJE+r7rq+4xMLTvYN9LUkR85aIyPToPeJJJpNh3rx5mDdvnnhJW0dHx2YPjIiIqLVrbOFJ2+tUi0LV1XWLRLULT7WfV452qq+4VHsUFRERERGRITXp4ycLTq1Du3btjB0CkV6YsyRFmvJWnyKOarFJ2+tU21RV1S1QlZerP65dYNJ0Bb36jkGmjX0tSRHzlojI9Oj0kTksLAwHDx6Eq6srevfuDVk9i0dweGzLs+DX1yQxzFmSIk15q8+0NdVfnZaWmtuo7q+qqm6BauxY3Y7PNZ4IYF9L0sS8JSIyPTr17OPGjYO1tbV4v77CE7W8lJQUeHt7GzsMIp0xZ0mKNOWtrn8fWVioF4MaM+Jp7VrFFetUaSsiccQTAexrSZqYt0REpkenj8xvv/22eH/x4sWGioWIiEhSdC3i1C4E6TLiqaYGKCm5+7hjx4b329B2gIUnIiIiImpZeg+479ixI3Jycupsz8/PR0dNn4rJ4EJCQowdApFemLMkRZryVtciTu129Y14eu45wN4emDULKC29+5ymwcYsPFF92NeSFDFviYhMj96Fp5SUFMiVq5eqqKiowK1bt5olKNJPamqqsUMg0gtzlqRIU97qOtWudrGnvhFPo0cD336rmFb32GOAkxMwfbr29vps1xQLmS72tSRFzFsiItOj8+p9v/zyi3h/3759cHZ2Fh/L5XIcPHgQAQEBzRsd6aSgoMDYIRDphTlLUqQpbw0x4gm4O7qpQwfg66+1LxbOxcWpPuxrSYqYt0REpkfnwtP48eMBADKZDFOnTlV7ztLSEv7+/vjggw+aNTjSjY2NjbFDINILc5akSFPeNrbwJAia22kqSDWmiMQRTwSwryVpYt4SEZkenQtPNTU1AICAgABER0fDw8PDYEGRfoKDg40dApFemLMkRZrytrGFp/Jy3do1pDGFJ16p/N7BvpakiHlLRGR69B5wn5yczKJTKxMTE2PsEIj0wpwlKdKUt40tPNnZ6dauIboUniwsgEGDGn4NmR72tSRFzFsiItPTqO89S0pKEBUVhdTUVFRWVqo9N2fOnGYJjIiIqLVr7OLiffo0bX+a9uvpCcycqbivOj3PzEx9MXNOtSMiIiKilqR34SkuLg6jRo1CaWkpSkpK4ObmhuzsbNjZ2cHLy4uFJyNo06aNsUMg0gtzlqRIU97Wt/6SqtqjjOztgU8/BVauBIYNAz77THO7hqgWkVavBtzc6u6Hhad7F/takiLmLRGR6dF7wP28efMwZswY5ObmwtbWFidPnsSNGzcQHh6O1atXGyJGaoCdtjkbRK0Uc5akSFPe6lp40lTs8fUFPvoIeOCBxsekWmBSPUbt7aojqVh4unewryUpYt4SEZkevQtP8fHxWLBgAczNzWFubo6Kigr4+flh1apVeP311w0RIzUgKSnJ2CEQ6YU5S1KkLW8DAxt+bX0FKmvru/erqvQMSkXtUU6q9zni6d7EvpakiHlLRGR69C48WVpaQvbPJ2hvb2+kpqYCAJydncX7RERE94oBAxpuU1/hSXU0UnW1fscWBM37qb3GE0c8EREREZGx6L3GU+/evRETE4MuXbpgyJAhWLRoEbKzs/HVV18hJCTEEDFSA3r06GHsEIj0wpwlKdKWt7pMt2uJYo+2EU/m5uojnnhVu3sH+1qSIuYtEZHp0fvj5/Lly8VF/5YtWwZ3d3e88MILyMzMxGfK1VGpRaWlpRk7BCK9MGdJirTlrS6Fp4baKJ/v1k2/mFRHPGlb48nMDCgo0NyOTBv7WpIi5i0RkenRa8STIAjw9PQUv4nw9PTE77//bpDASHd5eXnGDoFIL8xZkiJtedschae1a4GMDCA0VL+Yamru3q+v8HTkiOZ2ZNrY15IUMW+JiEyPXiOeBEFAYGAgbt26Zah4qBEsVedQEEkAc5akSFve6jJ1raE2HTvqtlZUbaojnmqv66Rkbg48/LD6Y7o3sK8lKWLeEhGZHr0KT2ZmZggMDEROTo6h4qFGCAsLM3YIRHphzpIUactbXUY8GWpdJdXCk6raRahRowwfC7U+7GtJipi3RESmR++Pn6tWrcJ///tfXLhwwRDxUCOcOnXK2CEQ6YU5S1KkLW+bY6pdY2krPNWeamdjc/exXG6YWKj1YV9LUsS8JSIyPXpf1e6pp55CaWkpevXqBSsrK9ja2qo9n5ub22zBERERtXatccSThcpvdzMzwMrq7uPqasPEQkRERESkid6Fp7Vr1xogDGoKb29vY4dApBfmLEmRtrzVpajU0iOeaheeVNd14oinewf7WpIi5i0RkenRu/A0depUQ8RBTeDk5GTsEIj0wpwlKdKWt1IY8aSKhad7B/takiLmLRGR6WnUR+GkpCS8+eabmDhxIjIzMwEAe/fuxcWLF5s1ONLNtWvXjB0CkV6YsyRF2vLWmIWnmhrN21UvCsXC072LfS1JEfOWiMj06P1ROCoqCiEhITh16hR++uknFBcXAwDOnTuHt99+u9kDJCIias1a41Q7Fp6IiIiIqLXQu/D0f//3f3jnnXdw4MABWKmsVjpkyBCcOHGiWYNTSklJwTPPPIOAgADY2tqiU6dOePvtt1FZWanWLjU1FWPGjIG9vT08PDwwZ86cOm3Onz+PyMhI2Nraom3btli6dCkEbZ/cJSIoKMjYIRDphTlLUqQtb1vjVe1Up9qpru9kyFio9WFfS1LEvCUiMj16r/F0/vx5bN++vc52T09P5OTkNEtQtV2+fBk1NTX49NNP0blzZ1y4cAEzZ85ESUkJVq9eDQCQy+V4+OGH4enpiWPHjiEnJwdTp06FIAhYt24dAKCwsBDDhw/HkCFDEB0djatXr2LatGmwt7fHggULDBJ7S8jMzOR8eJIU5ixJkba8ldoaT+3bGyYWan3Y15IUMW+JiEyP3oUnFxcXpKenIyAgQG17XFwc2rZt22yBqXrwwQfx4IMPio87duyIK1euYMOGDWLhaf/+/bh06RJu3rwJX19fAMAHH3yAadOm4d1334WTkxO++eYblJeXY8uWLbC2tkZwcDCuXr2KNWvWYP78+ZBJ9GvgnJwcdO7c2dhhEOmMOUtSpC1vW2PhSXWUk/LYK1YAFy4Aw4YZJhZqfdjXkhQxb4mITI/eH4UnTZqEV199FRkZGZDJZKipqcHff/+NhQsXYsqUKYaIUaOCggK4ubmJj0+cOIHg4GCx6AQAI0eOREVFBWJjY8U2kZGRsLa2VmuTlpaGlJSUFou9uZnXnkdB1MoxZ0mKtOVta1zjSVPhKTgYePJJwxXBqPVhX0tSxLwlIjI9en/8fPfdd9G+fXu0bdsWxcXF6N69OwYNGoQBAwbgzTffNESMdSQlJWHdunV4/vnnxW0ZGRnw9vZWa+fq6gorKytkZGRobaN8rGyjSUVFBQoLC9VurUlERISxQyDSC3OWpKgpeWvMEU/8G+7exb6WpIh5S0RkevSeamdpaYlvvvkGS5cuRVxcHGpqatC7d28EBgbqffDFixdjyZIl9baJjo5W+wWUlpaGBx98EI899hhmzJih1lbTVDlBENS2126jXFi8vml2K1as0BhnTEwM7O3tERYWhoSEBJSVlcHR0REBAQE4d+4cAKBDhw6oqanBzZs3AQChoaFITExEcXEx7O3t0aVLF8TFxQEA2rVrB3Nzc9y4cQMA0LNnT6SkpKCwsBA2Njbo0aOHOHrL19cXNjY2uH79OvLy8nD//ffj1q1byM/Ph5WVFUJDQ3H69GkAgI+PDxwcHJCYmAhAsWjjnTt3kJubCwsLC4SHh+P06dMQBAGenp5wdXXF1atXAQBdu3ZFbm4usrKyYGZmhj59+iAmJgZyuRzu7u7w8vJCQkICACAwMBCFhYW4c+cOAKBfv344c+YMqqqq4OrqCl9fX1y8eBEA0KlTJ5SWliI9PR2A4kPGhQsXUF5eDmdnZ7Rv3x7nz58HAPj7+6O6uhq3bt0CAISFheHy5csoLS2Fg4MDOnXqhLNnzwIA2v+zeElqaioAoFevXkhKSkJxcTHs7OzQrVs3nDlzRjzfFhYW4mi3kJAQpKamoqCgADY2NggODkZMTAwAoE2bNrCzs0NSUhIAoEePHkhLS0NeXh4sLS0RFhaGU6dOAVAUM52cnMTLAQcFBSEzMxM5OTkwNzdHREQEoqOjUVNTA09PT7i5ueHKlSsAgC5duiAvLw9ZWVmQyWTo27cvYmNjUV1dDTc3N3h7e4vnu3PnziguLhaLpn379kV8fDwqKyvh4uKCdu3a4cKFCwAU01PLy8uRlpYGAAgPD8fFixdRXl4OJycn+Pv7q+WsXC4Xz3fv3r1x9epVlJSUwMHBAZ07d0Z8fDwAwM/PD2ZmZmo5m5ycjKKiItja2iIoKEg8323btoWVlRWSk5ORl5eHQYMG4ebNm8jPz4e1tTV69uyJ6OhoMWft7e3F8929e3dkZGQgNze3zvn28vKCs7OzeL67deuG7OxsZGdnizmrPN8eHh7w8PDA5cuXxZwtKChAZmZmnZx1c3ODj48PLl26JOZsSUmJeL779OmDc+fOoaKiAi4uLvDz8xNzNiAgAJWVlbh9+7aYs8bsIwAgODiYfQSa1kfU1NTA1dW1Th9x+bIjysoCYG5ujpKSEgCAo6MjKirKUVlZBTMzM5iZORukjxCEvigoyEdNjYBr17LFPuLqVTtUVnaFXF6NtLRcnDp1W1J9BKDok9lHNK2PEAQBzs7O7CP4OUJSnyOOHz8OBwcHk+gj7OzsQEREgEww4iXdlJ16ffz9/WFjYwNAUXQaMmQI+vXrhy1btsBM5SvkRYsW4eeffxY/OABAXl4e3NzccOjQIQwZMgRTpkxBQUEBfv75Z7FNXFwcwsLCcP369TrrVilVVFSgoqJCfFxYWAg/Pz8UFBS0isUPT506hX79+hk7DCKdMWdJirTl7ZEjwPvv1//ayEhg4cLmj2nqVCA3V3F/z56728+dA954Q3HfwgLYtav5j02tH/takiJTytvCwkI4Ozu3mr8ZiIiMRacRT/Pnz9d5h2vWrNG5rfJbA13cvn0bQ4YMQXh4ODZv3qxWdAKA/v37491330V6ejratGkDQLHguLW1NcLDw8U2r7/+OiorK2FlZSW28fX1hb+/v9ZjW1tbq60L1dp4enoaOwQivTBnSYq05a0u0+gMNdXOze1u4UmV6vS66mrDHJtaP/a1JEXMWyIi06NT4Uk5hLshhroqXFpaGgYPHoz27dtj9erVyMrKEp/z8fEBAIwYMQLdu3fH008/jffffx+5ublYuHAhZs6cKX7DMGnSJCxZsgTTpk3D66+/jmvXrmH58uVYtGiRZK9oB0BtkXUiKWDOkhRpy1vVXx9t2wIhIcDevdrbNKe5c4GNG4HJk9W3c10nAtjXkjQxb4mITI9OhafDhw8bOo567d+/H4mJiUhMTES7du3UnlPOFDQ3N8dvv/2GWbNmYeDAgbC1tcWkSZOwevVqsa2zszMOHDiAF198EREREXB1dcX8+fP1GtHVGl25csVkhiTTvYE5S1KkLW9Vi0pmZkBgYMsVnjp0AFasqLudhScC2NeSNDFviYhMj96LiyslJiYiKSkJgwYNgq2tbZ1FvJvTtGnTMG3atAbbtW/fHr/++mu9bUJCQnDkyJFmioyIiO51qr/6zM0VayrVZqipdkRERERErZ3eH4VzcnIwdOhQdOnSBaNGjRKvJjJjxgwsWLCg2QOkhnXp0sXYIRDphTlLUqQtb1WLSjJZ6yg8Ge+yIdSasK8lKWLeEhGZHr0/Cs+bNw+WlpZITU1Vu0ToE088gb215xZQi8jLyzN2CER6Yc6SFGnLW9URT9oKTy29jKCtbcsej1on9rUkRcxbIiLTo3fhaf/+/Vi5cmWdtZYCAwNx48aNZguMdKe62DqRFDBnSYq05W3tqXaa1ldq6RFPfn537/9zEVe6B7GvJSli3hIRmR69PwqXlJSojXRSys7OhrW1dbMERfqR8hX56N7EnCUp0pa3uox4MsYaT6tXA0FBmhcfp3sD+1qSIuYtEZHp0fuj8KBBg7Bt2zbxsUwmQ01NDd5//30MGTKkWYMj3fTt29fYIRDphTlLUqQtb1vjVDsA6NoVWLUK4HIp9y72tSRFzFsiItOjd+Hp/fffx6effoqHHnoIlZWVeOWVVxAcHIwjR45g5cqVhoiRGhAbG2vsEIj0wpwlKdKWt6qjmczMWsdUOyKAfS1JE/OWiMj06P1RuHv37jh37hz69u2L4cOHo6SkBBMmTEBcXBw6depkiBipAdXV1cYOgUgvzFmSIm15qzqaycys9Yx4ImJfS1LEvCUiMj0aPh43zMfHB0uWLGnuWKiR3NzcjB0CkV6YsyRF2vK2duGJI56otWBfS1LEvCUiMj16fxTevHkzdu7cWWf7zp07sXXr1mYJivTj7e1t7BCI9MKcJSnSlre113jSdBU5Fp7IGNjXkhQxb4mITI/eH4Xfe+89eHh41Nnu5eWF5cuXN0tQpJ+EhARjh0CkF+YsSZG2vK29xlPbtoqbKk61I2NgX0tSxLwlIjI9eheebty4gYCAgDrbO3TogNTU1GYJioiISCo0rfG0YQMwdqzmNkRERERE9xK9C09eXl44d+5cne1nz56Fu7t7swRF+uncubOxQyDSC3OWpEhb3qqOeFIWmGSyuiOhiFoa+1qSIuYtEZHp0fuj8JNPPok5c+bg8OHDkMvlkMvlOHToEF5++WU8+eSThoiRGlBcXGzsEIj0wpwlKdIlb7UVm1h4ImNgX0tSxLwlIjI9en8Ufuedd9CvXz8MHToUtra2sLW1xYgRI/DAAw9wjScjycjIMHYIRHphzpIUactbXYpNnGpHxsC+lqSIeUtEZHos9H2BlZUVvvvuO7zzzjuIj4+Hra0tQkJC0KFDB0PER0RE1KrVvqqdEkc8ERERERE1ovCkFBgYiMDAwOaMhRqpb9++xg6BSC/MWZIibXmrrdhkbq55O1FLYV9LUsS8JSIyPXp/FH700Ufx3nvv1dn+/vvv47HHHmuWoEg/8fHxxg6BSC/MWZIibXnLqXbUWrGvJSli3hIRmR69C09RUVF4+OGH62x/8MEHceTIkWYJivRTWVlp7BCI9MKcJSnSlrecaketFftakiLmLRGR6dH7o3BxcTGsrKzqbLe0tERhYWGzBEX6cXFxMXYIRHphzpIUactbXYpNHPFExsC+lqSIeUtEZHr0LjwFBwfju+++q7N9x44d6N69e7MERfpp166dsUMg0gtzlqRIW95yjSdqrdjXkhQxb4mITI/ei4u/9dZb+Pe//42kpCQ88MADAICDBw/i22+/xc6dO5s9QGrYhQsX0K9fP2OHQaQz5ixJkba8VS0qqV4FnCOeyNjY15IUMW+JiEyP3oWnsWPHYvfu3Vi+fDl++OEH2NraomfPnvjzzz8RGRlpiBiJiIhaLdWiUkLC3ftc44mIiIiIqBGFJwB4+OGHNS4wHh8fj9DQ0KbGRHrq2LGjsUMg0gtzlqRIW95qG82kOtWOI57IGNjXkhQxb4mITE+Tv4MtKCjAJ598grCwMISHhzdHTKSn8vJyY4dApBfmLEmRtrzVVlTiiCcyNva1JEXMWyIi09Poj8KHDh3C5MmT0aZNG6xbtw6jRo1CTExMc8ZGOkpLSzN2CER6Yc6SFGnLW21FJW0LjRO1FPa1JEXMWyIi06PXVLtbt25hy5Yt+PLLL1FSUoLHH38cVVVV+PHHH3lFOyIiuiepjnhyd797n4UnIiIiIiI9RjyNGjUK3bt3x6VLl7Bu3TqkpaVh3bp1hoyNdMQpjiQ1zFmSIm15q1p4evPNu/dVC08WjVpRkahp2NeSFDFviYhMj86Fp/3792PGjBlYsmQJHn74YZjz69tW4+LFi8YOgUgvzFmSIm15q1p4cnO7e58jnsjY2NeSFDFviYhMj86Fp6NHj6KoqAgRERHo168f1q9fj6ysLEPGplFFRQVCQ0Mhk8kQHx+v9lxqairGjBkDe3t7eHh4YM6cOaisrFRrc/78eURGRsLW1hZt27bF0qVLIQhCC76D5sdFGElqmLMkRdrytqrq7n0bm7v3OeKJjI19LUkR85aIyPToXHjq378/Pv/8c6Snp+O5557Djh070LZtW9TU1ODAgQMoKioyZJyiV155Bb6+vnW2y+VyPPzwwygpKcGxY8ewY8cO/Pjjj1iwYIHYprCwEMOHD4evry+io6Oxbt06rF69GmvWrGmR2A3FycnJ2CEQ6YU5S1KkLW+dne/et7W9e191lBNHPJExsK8lKWLeEhGZHpnQhOE+V65cwaZNm/DVV18hPz8fw4cPxy+//NKc8an5448/MH/+fPz444/o0aMH4uLiEBoaKj43evRo3Lx5UyxM7dixA9OmTUNmZiacnJywYcMGvPbaa7hz5w6sra0BAO+99x7WrVuHW7duQabtmti1FBYWwtnZGQUFBa3il2NZWRlsVf/aIWrlmLMkRfXl7Y0bitFO3t53t504ASxfrri/bBnwz68rohbDvpakyJTytrX9zUBEZCw6j3jSpGvXrli1ahVu3bqFb7/9trli0ujOnTuYOXMmvvrqK9jZ2dV5/sSJEwgODlYbDTVy5EhUVFQgNjZWbBMZGSkWnZRt0tLSkJKSovXYFRUVKCwsVLu1JufOnTN2CER6Yc6SFNWXtx06qBedAK7xRMbHvpakiHlLRGR6mmXVCXNzc4wfPx7jx49vjt3VIQgCpk2bhueffx4REREai0QZGRnwrvWp39XVFVZWVsjIyBDb+Pv7q7VRviYjIwMBAQEaj79ixQosWbKkzvaYmBjY29sjLCwMCQkJKCsrg6OjIwICAsRfmh06dEBNTQ1u3rwJAAgNDUViYiKKi4thb2+PLl26IC4uDgDQrl07mJub48aNGwCAnj17IiUlBYWFhbCxsUGPHj3EIpqvry9sbGxw/fp15OXloaSkBLdu3UJ+fj6srKwQGhqK06dPAwB8fHzg4OCAxMREAEBQUBDu3LmD3NxcWFhYIDw8HKdPn4YgCPD09ISrqyuuXr0KQFFczM3NRVZWFszMzNCnTx/ExMRALpfD3d0dXl5eSEhIAAAEBgaisLAQd+7cAQD069cPZ86cQVVVFVxdXeHr6ysuGNmpUyeUlpYiPT0dABAREYELFy6gvLwczs7OaN++Pc6fPw8A8Pf3R3V1NW7dugUACAsLw+XLl1FaWgoHBwd06tQJZ8+eBQC0b98egGK9LwDo1asXkpKSUFxcDDs7O3Tr1g1nzpwRz7eFhYWYTyEhIUhNTUVBQQFsbGwQHByMmJgYAECbNm1gZ2eHpKQkAECPHj2QlpaGvLw8WFpaIiwsDKdOnRJzysnJCdeuXRPPd2ZmJnJycmBubo6IiAhER0ejpqYGnp6ecHNzw5UrVwAAXbp0QV5eHrKysiCTydC3b1/Exsaiuroabm5u8Pb2Fs93586dUVxcLOZ33759ER8fj8rKSri4uKBdu3a4cOECAKBjx44oLy9HWloaAMUVYy5evIjy8nI4OTnB399fLWflcrl4vnv37o2rV6+ipKQEDg4O6Ny5s7i+mp+fH8zMzNRyNjk5GUVFRbC1tUVQUJB4vtu2bQsrKyskJycjLy8PpaWluHnzJvLz82FtbY2ePXsiOjpazFl7e3vxfHfv3h0ZGRnIzc2tc769vLzg7Owsnu9u3bohOzsb2dnZYs4qz7eHhwc8PDxw+fJlMWcLCgqQmZlZJ2fd3Nzg4+ODS5cuiTlbUlIinu8+ffrg3LlzqKiogIuLC/z8/MScDQgIQGVlJW7fvi3mrDH7CAAIDg5mH4Gm9RFyuRyXL1/WuY+4eTMHeXlOMDMzg4WFM/sI6N5HAIo+mX1E0/oIuVyOhIQE9hH8HCGpPqKoqAinTp0yiT5C05flRET3oiZNtWuqxYsXayzoqIqOjsbx48fx3Xff4ciRIzA3N0dKSgoCAgLUpto9++yzuHHjBvbt26f2eisrK2zbtg1PPvkkRowYgYCAAHz66afi87dv30a7du1w4sQJ3HfffRpjqKioQEVFhfi4sLAQfn5+rWbYbEZGBnx8fIwdBpHOmLMkRfrmbWwssHix4v4HHwBduhgmLiJt2NeSFJlS3nKqHRGRglGvszN79mw8+eST9bbx9/fHO++8g5MnT6pNkQMU325NnjwZW7duhY+Pj/jNhVJeXh6qqqrEUU0+Pj7itzpKym8oao+WUmVtbV3n2K2JXC43dghEemHOkhTpm7eq0+t4VTsyBva1JEXMWyIi02PUj8LK4aoN+d///od33nlHfJyWloaRI0fiu+++Q79+/QAorrr37rvvIj09HW3atAEA7N+/H9bW1ggPDxfbvP7666isrISVlZXYxtfXt84UPCm5desW2rZta+wwiHTGnCUp0jdvucYTGRv7WpIi5i0Rkelp0uLiLaV9+/YIDg4Wb13+ma/QqVMntGvXDgAwYsQIdO/eHU8//TTi4uJw8OBBLFy4EDNnzhSHtk6aNAnW1taYNm0aLly4gF27dmH58uWYP3++zle0IyIi0oVq4YkjnoiIiIjoXiWJwpMuzM3N8dtvv8HGxgYDBw7E448/jvHjx2P16tViG2dnZxw4cAC3bt1CREQEZs2ahfnz52P+/PlGjLzpevfubewQiPTCnCUp0jdvVUc5ccQTGQP7WpIi5i0RkemR5Hew/v7+0LQmevv27fHrr7/W+9qQkBAcOXLEUKEZxdWrVxEcHGzsMIh0xpwlKdI3bzniiYyNfS1JEfOWiMj0mMyIp3tZSUmJsUMg0gtzlqRI37zlGk9kbOxrSYqYt0REpoeFJxPg4OBg7BCI9MKcJSlqSt5yxBMZA/takiLmLRGR6WHhyQR07tzZ2CEQ6YU5S1Kkb97W1Ny9zxFPZAzsa0mKmLdERKaHhScTEB8fb+wQiPTCnCUp0jdvVZci5IgnMgb2tSRFzFsiItPDwhMREZEBqBaeOOKJiIiIiO5VLDyZAD8/P2OHQKQX5ixJkb55q1p4MuNvWzIC9rUkRcxbIiLTw4/CJsCMf9GQxDBnSYr0zVvVNZ5ksmYOhkgH7GtJipi3RESmhz27Cbhx44axQyDSC3OWpEjfvA0IUPzLohMZC/takiLmLRGR6eFyp0RERAZgbw98/TVgbW3sSIiIiIiIjEcmCKqrUJAuCgsL4ezsjIKCAjg5ORk7HJSVlcHW1tbYYRDpjDlLUsS8JalhzpIUmVLetra/GYiIjIVT7UxAcnKysUMg0gtzlqSIeUtSw5wlKWLeEhGZHhaeTEBRUZGxQyDSC3OWpIh5S1LDnCUpYt4SEZkeFp5MgKkMR6Z7B3OWpIh5S1LDnCUpYt4SEZkervHUCK1tvnZVVRUsLS2NHQaRzpizJEXMW5Ia5ixJkSnlbWv7m4GIyFg44skEnDlzxtghEOmFOUtSxLwlqWHOkhQxb4mITI+FsQOQIuUgscLCQiNHolBSUtJqYiHSBXOWpIh5S1LDnCUpMqW8Vb4PTjAhonsdC0+NoFz00M/Pz8iREBERERFRa1ZUVARnZ2djh0FEZDRc46kRampqkJaWBkdHR8hkMqPGUlhYCD8/P9y8eZNzx0kSmLMkRcxbkhrmLEmRqeWtIAgoKiqCr68vzMy4wgkR3bs44qkRzMzM0K5dO2OHocbJyckkfkHTvYM5S1LEvCWpYc6SFJlS3nKkExERFxcnIiIiIiIiIiIDYeGJiIiIiIiIiIgMgoUnibO2tsbbb78Na2trY4dCpBPmLEkR85akhjlLUsS8JSIyTVxcnIiIiIiIiIiIDIIjnoiIiIiIiIiIyCBYeCIiIiIiIiIiIoNg4YmIiIiIiIiIiAyChScJ++STTxAQEAAbGxuEh4fj6NGjxg6JSHTkyBGMGTMGvr6+kMlk2L17t9rzgiBg8eLF8PX1ha2tLQYPHoyLFy8aJ1giACtWrECfPn3g6OgILy8vjB8/HleuXFFrw7yl1mbDhg3o2bMnnJyc4OTkhP79++OPP/4Qn2fOUmu3YsUKyGQyzJ07V9zGvCUiMi0sPEnUd999h7lz5+KNN95AXFwc7r//fjz00ENITU01dmhEAICSkhL06tUL69ev1/j8qlWrsGbNGqxfvx7R0dHw8fHB8OHDUVRU1MKREilERUXhxRdfxMmTJ3HgwAFUV1djxIgRKCkpEdswb6m1adeuHd577z3ExMQgJiYGDzzwAMaNGyf+kc6cpdYsOjoan332GXr27Km2nXlLRGRaeFU7ierXrx/CwsKwYcMGcVtQUBDGjx+PFStWGDEyorpkMhl27dqF8ePHA1B8k+nr64u5c+fi1VdfBQBUVFTA29sbK1euxHPPPWfEaIkUsrKy4OXlhaioKAwaNIh5S5Lh5uaG999/H9OnT2fOUqtVXFyMsLAwfPLJJ3jnnXcQGhqKtWvXsq8lIjJBHPEkQZWVlYiNjcWIESPUto8YMQLHjx83UlREuktOTkZGRoZaDltbWyMyMpI5TK1GQUEBAMUf8QDzllo/uVyOHTt2oKSkBP3792fOUqv24osv4uGHH8awYcPUtjNviYhMj4WxAyD9ZWdnQy6Xw9vbW227t7c3MjIyjBQVke6Ueaoph2/cuGGMkIjUCIKA+fPn41//+heCg4MBMG+p9Tp//jz69++P8vJyODg4YNeuXejevbv4RzpzllqbHTt24MyZM4iOjq7zHPtaIiLTw8KThMlkMrXHgiDU2UbUmjGHqbWaPXs2zp07h2PHjtV5jnlLrU3Xrl0RHx+P/Px8/Pjjj5g6dSqioqLE55mz1JrcvHkTL7/8Mvbv3w8bGxut7Zi3RESmg1PtJMjDwwPm5uZ1RjdlZmbW+XaIqDXy8fEBAOYwtUovvfQSfvnlFxw+fBjt2rUTtzNvqbWysrJC586dERERgRUrVqBXr1746KOPmLPUKsXGxiIzMxPh4eGwsLCAhYUFoqKi8L///Q8WFhZibjJviYhMBwtPEmRlZYXw8HAcOHBAbfuBAwcwYMAAI0VFpLuAgAD4+Pio5XBlZSWioqKYw2Q0giBg9uzZ+Omnn3Do0CEEBASoPc+8JakQBAEVFRXMWWqVhg4divPnzyM+Pl68RUREYPLkyYiPj0fHjh2Zt0REJoZT7SRq/vz5ePrppxEREYH+/fvjs88+Q2pqKp5//nljh0YEQHG1msTERPFxcnIy4uPj4ebmhvbt22Pu3LlYvnw5AgMDERgYiOXLl8POzg6TJk0yYtR0L3vxxRexfft2/Pzzz3B0dBS/bXd2doatrS1kMhnzllqd119/HQ899BD8/PxQVFSEHTt24K+//sLevXuZs9QqOTo6imvnKdnb28Pd3V3czrwlIjItLDxJ1BNPPIGcnBwsXboU6enpCA4Oxu+//44OHToYOzQiAEBMTAyGDBkiPp4/fz4AYOrUqdiyZQteeeUVlJWVYdasWcjLy0O/fv2wf/9+ODo6Gitkusdt2LABADB48GC17Zs3b8a0adMAgHlLrc6dO3fw9NNPIz09Hc7OzujZsyf27t2L4cOHA2DOkjQxb4mITItMEATB2EEQEREREREREZHp4RpPRERERERERERkECw8ERERERERERGRQbDwREREREREREREBsHCExERERERERERGQQLT0REREREREREZBAsPBERERERERERkUGw8ERERERERERERAbBwhMRERERERERERkEC09ERHTPWrx4MUJDQ412/LfeegvPPvusTm0XLlyIOXPmGDgiIiIiIqLmJRMEQTB2EERERM1NJpPV+/zUqVOxfv16VFRUwN3dvYWiuuvOnTsIDAzEuXPn4O/v32D7zMxMdOrUCefOnUNAQIDhAyQiIiIiagYsPBERkUnKyMgQ73/33XdYtGgRrly5Im6ztbWFs7OzMUIDACxfvhxRUVHYt2+fzq/597//jc6dO2PlypUGjIyIiIiIqPlwqh0REZkkHx8f8ebs7AyZTFZnW+2pdtOmTcP48eOxfPlyeHt7w8XFBUuWLEF1dTX++9//ws3NDe3atcOXX36pdqzbt2/jiSeegKurK9zd3TFu3DikpKTUG9+OHTswduxYtW0//PADQkJCYGtrC3d3dwwbNgwlJSXi82PHjsW3337b5HNDRERERNRSWHgiIiJScejQIaSlpeHIkSNYs2YNFi9ejNGjR8PV1RWnTp3C888/j+effx43b94EAJSWlmLIkCFwcHDAkSNHcOzYMTg4OODBBx9EZWWlxmPk5eXhwoULiIiIELelp6dj4sSJmD59OhISEvDXX39hwoQJUB2Y3LdvX9y8eRM3btww7EkgIiIiImomLDwRERGpcHNzw//+9z907doV06dPR9euXVFaWorXX38dgYGBeO2112BlZYW///4bgGLkkpmZGb744guEhIQgKCgImzdvRmpqKv766y+Nx7hx4wYEQYCvr6+4LT09HdXV1ZgwYQL8/f0REhKCWbNmwcHBQWzTtm1bAGhwNBURERERUWthYewAiIiIWpMePXrAzOzu9zLe3t4IDg4WH5ubm8Pd3R2ZmZkAgNjYWCQmJsLR0VFtP+Xl5UhKStJ4jLKyMgCAjY2NuK1Xr14YOnQoQkJCMHLkSIwYMQKPPvooXF1dxTa2trYAFKOsiIiIiIikgIUnIiIiFZaWlmqPZTKZxm01NTUAgJqaGoSHh+Obb76psy9PT0+Nx/Dw8ACgmHKnbGNubo4DBw7g+PHj2L9/P9atW4c33ngDp06dEq9il5ubW+9+iYiIiIhaG061IyIiaoKwsDBcu3YNXl5e6Ny5s9pN21XzOnXqBCcnJ1y6dEltu0wmw8CBA7FkyRLExcXBysoKu3btEp+/cOECLC0t0aNHD4O+JyIiIiKi5sLCExERURNMnjwZHh4eGDduHI4ePYrk5GRERUXh5Zdfxq1btzS+xszMDMOGDcOxY8fEbadOncLy5csRExOD1NRU/PTTT8jKykJQUJDY5ujRo7j//vvFKXdERERERK0dC09ERERNYGdnhyNHjqB9+/aYMGECgoKCMH36dJSVlcHJyUnr65599lns2LFDnLLn5OSEI0eOYNSoUejSpQvefPNNfPDBB3jooYfE13z77beYOXOmwd8TEREREVFzkQmq12kmIiKiFiEIAu677z7MnTsXEydObLD9b7/9hv/+9784d+4cLCy4RCMRERERSQNHPBERERmBTCbDZ599hurqap3al5SUYPPmzSw6EREREZGkcMQTEREREREREREZBEc8ERERERERERGRQbDwREREREREREREBsHCExERERERERERGQQLT0REREREREREZBAsPBERERERERERkUGw8ERERERERERERAbBwhMRERERERERERkEC09ERERERERERGQQLDwREREREREREZFBsPBEREREREREREQG8f/sntkzR0DsQAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 48/49 (Lat: 38.92, Lon: -9.4)\n", + "Site 48: Rhypo = 13.63 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 201.1700 cm/s²\n", + "Subfault PGA (i=0, j=1): 136.9160 cm/s²\n", + "Subfault PGA (i=1, j=0): 173.1523 cm/s²\n", + "Subfault PGA (i=1, j=1): 36.7909 cm/s²\n", + "Subfault PGA (i=2, j=0): 54.4677 cm/s²\n", + "Subfault PGA (i=2, j=1): 14.4617 cm/s²\n", + "Subfault PGA (i=3, j=0): 398.5380 cm/s²\n", + "Subfault PGA (i=3, j=1): 184.4026 cm/s²\n", + "Total PGA: 394.3063 cmm/s²\n", + "Total PGA: 394.3063 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Site 49/49 (Lat: 38.94, Lon: -9.4)\n", + "Site 49: Rhypo = 15.72 km\n", + "Processing Hypocenter 1 at i0=2, j0=1\n", + "Number of active subfaults: 8\n", + "Fault dimensions: 8.091381560990689×4.989105428661019 km\n", + "Subfault grid: 4×2\n", + "no_effective_subfaults: 1\n", + "Subfault PGA (i=0, j=0): 139.1366 cm/s²\n", + "Subfault PGA (i=0, j=1): 101.0207 cm/s²\n", + "Subfault PGA (i=1, j=0): 104.7297 cm/s²\n", + "Subfault PGA (i=1, j=1): 22.4535 cm/s²\n", + "Subfault PGA (i=2, j=0): 44.9719 cm/s²\n", + "Subfault PGA (i=2, j=1): 11.0131 cm/s²\n", + "Subfault PGA (i=3, j=0): 205.6643 cm/s²\n", + "Subfault PGA (i=3, j=1): 127.0028 cm/s²\n", + "Total PGA: 257.8672 cmm/s²\n", + "Total PGA: 257.8672 cm/s²\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preallocate arrays for results\n", + "num_sites = len(sites)\n", + "R = np.zeros(num_sites)\n", + "PGA_finite_fault = np.zeros(num_sites)\n", + "R_rup = np.zeros(num_sites) \n", + "R_jb = np.zeros(num_sites) \n", + "\n", + "# Loop over each site\n", + "for site_idx, site in sites.iterrows():\n", + " site_lat, site_lon = site['lat'], site['lon']\n", + " print(f'Processing Site {site_idx + 1}/{num_sites} (Lat: {site_lat}, Lon: {site_lon})')\n", + "\n", + " # Compute epicentral distance and azimuth\n", + " _, _, R_epicentral, Az = seismic_wave_generator.compute_site_location(1, site_lat, site_lon, fault_params.rupture_lat, fault_params.rupture_lon)\n", + " \n", + " # Compute Rhypo (hypocentral distance)\n", + " Rhypo = seismic_wave_generator.compute_point_source_distance(R_epicentral, earthquake_params.h_ref, earthquake_params.dip, earthquake_params.strike, Az)\n", + " R[site_idx] = Rhypo\n", + " print(f'Site {site_idx + 1}: Rhypo = {Rhypo:.2f} km')\n", + "\n", + " # Calculate fault dimensions\n", + " fault_width, fault_length = seismic_wave_generator.width_length(earthquake_params.M, earthquake_params.rake, earthquake_params.sigma, earthquake_params.stress_ref)\n", + " \n", + " # Calculate Rrup and Rjb distances\n", + " R_rup[site_idx], R_jb[site_idx] = seismic_wave_generator.calculate_fault_distances(\n", + " site_lat, site_lon, \n", + " fault_params.rupture_lat, fault_params.rupture_lon,\n", + " earthquake_params.strike, earthquake_params.dip, \n", + " earthquake_params.h_ref,\n", + " fault_width, fault_length)\n", + "\n", + " # Create a SiteParameters instance for each site\n", + " site_params = seismic_wave_generator.SiteParameters(\n", + " site_lat=site_lat,\n", + " site_lon=site_lon\n", + " )\n", + "\n", + " # Run finite-fault simulation\n", + " Nhyp = 1 # Number of hypocenters\n", + " PGA_ff, _, t1, all_At_dict, all_PGA_array, _ = seismic_wave_generator.finite_fault_sim(\n", + " Nhyp,\n", + " earthquake_params,\n", + " simulation_params,\n", + " site_params,\n", + " fault_params,\n", + " plot_results=True, # Plot time series\n", + " output_dir=output_folder,\n", + " site_idx=site_idx,\n", + " calc_rsa=False # Disable RSA calculation for simplicity\n", + " )\n", + "\n", + " PGA_finite_fault[site_idx] = PGA_ff\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "064abf1b-326e-4a9d-aa6e-b81b55221d37", + "metadata": {}, + "source": [ + "# Understanding the Results\n", + "The simulation produces several key outputs:\n", + "\n", + "Time Series: Ground acceleration time histories for each site and simulation\n", + "PGA Values: Peak Ground Acceleration for each site\n", + "Visualization: Plots showing the simulated ground motions\n", + "Output Files: Acceleration time series saved to text files\n", + "\n", + "# How the Simulation Works\n", + "The finite fault simulation follows these steps:\n", + "\n", + "Fault Discretization: The fault is divided into subfaults based on the specified size\n", + "Moment Distribution: The total seismic moment is distributed among subfaults\n", + "Rupture Propagation: Time delays are calculated based on rupture velocity\n", + "Ground Motion Synthesis: Each subfault's contribution is calculated, shifted in time, and summed\n", + "Post-Processing: The combined ground motion is baseline-corrected and processed\n", + "\n", + "The key advantage of this approach is its ability to capture realistic source effects, including:\n", + "\n", + "Directivity effects\n", + "Complex rupture propagation\n", + "Variable slip distribution\n", + "Frequency-dependent radiation patterns\n", + "\n", + "# Conclusion\n", + "This tutorial demonstrates how to set up and run stochastic finite fault simulations for earthquake ground motion prediction. The approach provides physically realistic ground motions that can be used for seismic hazard assessment, structural analysis, and other engineering applications.\n", + "\n", + "# References\n", + "\n", + "1. Boore, D. M. (2003). Simulation of ground motion using the stochastic method, Pure Appl. Geophys. 160, 635–676, doi: 10.1007/PL00012553.\n", + "\n", + "2. Motazedian, D., & Atkinson, G. M. (2005). Stochastic finite-fault modeling based on a dynamic corner frequency. *Bulletin of the Seismological Society of America*, 95(3), 995-1010.\n", + "\n", + "3. EXSIM12: A stochastic finite-fault computer program in FORTRAN, K Assatourians, G Atkinson\n", + "5. Tang, Y. (2022b). GMSS2.0: An Enhanced Software Program for Stochastic Finite-fault Ground Motion Simulation. *Seismological Research Letters*, 93: 1868–1879. https://doi.org/10.1785/0220210228\n", + "\n", + "6. Tang, Y., N.T.K., Lam & H.H. Tsang (2021). A Computational Tool for Ground Motion Simulations Incorporating Regional Crustal Conditions. *Seismological Research Letters*, 92(2A): 1129-1140. https://doi.org/10.1785/0220200222\n", + "\n", + "7. Atkinson, G. M., & Boore, D. M. (2006). Earthquake ground-motion prediction equations for eastern North America. *Bulletin of the Seismological Society of America*, 96(6), 2181-2205.\n", + "\n", + "8. Beresnev, I. A., & Atkinson, G. M. (1997). Modeling finite-fault radiation from the ωⁿ spectrum. *Bulletin of the Seismological Society of America*, 87(1), 67-84.\n", + "\n", + "9. Hartzell, S. H. (1978). Earthquake aftershocks as Green's functions. *Geophysical Research Letters*, 5(1), 1-4.\n", + "\n", + "10. Irikura, K., & Kamae, K. (1994). Estimation of strong ground motion in broad-frequency band based on a seismic source scaling model and an empirical Green's function technique. *Annali di Geofisica*, 37(6), 1721-1743." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openquake/vmtk/Databases/BBP_EXSIM_meta_data.mat b/openquake/vmtk/Databases/BBP_EXSIM_meta_data.mat new file mode 100644 index 0000000..5c86cae Binary files /dev/null and b/openquake/vmtk/Databases/BBP_EXSIM_meta_data.mat differ diff --git a/openquake/vmtk/Databases/BBP_GP_meta_data.mat b/openquake/vmtk/Databases/BBP_GP_meta_data.mat new file mode 100644 index 0000000..1aad99a Binary files /dev/null and b/openquake/vmtk/Databases/BBP_GP_meta_data.mat differ diff --git a/openquake/vmtk/Databases/BBP_SDSU_meta_data.mat b/openquake/vmtk/Databases/BBP_SDSU_meta_data.mat new file mode 100644 index 0000000..ba143a3 Binary files /dev/null and b/openquake/vmtk/Databases/BBP_SDSU_meta_data.mat differ diff --git a/openquake/vmtk/Databases/CyberShake_meta_data.mat b/openquake/vmtk/Databases/CyberShake_meta_data.mat new file mode 100644 index 0000000..229f4d5 Binary files /dev/null and b/openquake/vmtk/Databases/CyberShake_meta_data.mat differ diff --git a/openquake/vmtk/Databases/NGA_W1_meta_data.mat b/openquake/vmtk/Databases/NGA_W1_meta_data.mat new file mode 100644 index 0000000..46f8b35 Binary files /dev/null and b/openquake/vmtk/Databases/NGA_W1_meta_data.mat differ diff --git a/openquake/vmtk/Databases/NGA_W2_meta_data.mat b/openquake/vmtk/Databases/NGA_W2_meta_data.mat new file mode 100644 index 0000000..ea1af90 Binary files /dev/null and b/openquake/vmtk/Databases/NGA_W2_meta_data.mat differ diff --git a/openquake/vmtk/Databases/WorkspaceDocumentationBBP.txt b/openquake/vmtk/Databases/WorkspaceDocumentationBBP.txt new file mode 100644 index 0000000..56593f7 --- /dev/null +++ b/openquake/vmtk/Databases/WorkspaceDocumentationBBP.txt @@ -0,0 +1,29 @@ +Documentation of the workspace 'BBP_***_meta_data.mat' + +This workspace has been created based on the SCEC BBP results from the 13.5 and 13.6 studies using either the EXSIM, GP, or SDSU method (replace the '***' with the method name when choosing one of these databases). + +Downloading simulated time histories for selected records + +An account will be needed with the Southern California Earthquake Center in order to access the results from various ground motion simulations. The links provided in the Output file generated by the Select_Ground_Motions script lead to a list of simulation numbers, while the filenames displayed in the Output file indicate the folder number and +station (i.e. which links) to choose to access the appropriate time history. For each filename, the folder number will begin at 10000000 and the station number will appear after (e.g. for simulation 10000003.9001, 10000003 is the folder number and 9001 is the station number). Each of the time histories is located in a .acc file. + +Note: The EXSIM meta data only includes one-component ground motions, and two-component selection will not work for this database. +For single-component selection, the "Load the user-chosen database..." cell of the algorithm will need to be edited so that the soil_Vs30, magnitude, closest_D, and dirLocation variables are not repeated. + +Variables: + +Filename_1 : Filename of the time history data file in direction 1 +Filename_2 : Filename of the time history data file in direction 2 +dirLocation : Link to the appropriate SCEC ground motion simulation results page +Periods : Periods at which spectral accelerations have been computed and stored in the workspace +Rjb : Joyner-Boore distance (km) +Sa_1 : Spectral acceleration in direction 1 +Sa_2 : Spectral acceleration in direction 2 +Sa_RotD100 : Orientation independent two-component spectral acceleration +Sa_RotD50 : Orientation independent two-component spectral acceleration +closest_D : Closest distance to the ruptured area +magnitude : Magnitude +soil_Vs30 : Vs30 value + + + diff --git a/openquake/vmtk/Databases/WorkspaceDocumentationCyberShake.txt b/openquake/vmtk/Databases/WorkspaceDocumentationCyberShake.txt new file mode 100644 index 0000000..ed4d358 --- /dev/null +++ b/openquake/vmtk/Databases/WorkspaceDocumentationCyberShake.txt @@ -0,0 +1,29 @@ +Documentation of the workspace 'CyberShake_meta_data.mat' + +This workspace has been created based on selected ground motions from the SCEC CyberShake platform. For details on the ground motion selection, see: + +Baker, J. W., Goulet, C., Luco, N., Rezaeian, S., and Teng, G. (2020). “A Subset of CyberShake Ground Motion Time Series for Response History Analysis.” Earthquake Spectra, (in review). + + +Variables available in the workspace: + +Filename_1 : Filename of the time history data file in direction 1 +Filename_2 : Filename of the time history data file in direction 2 +dirLocation : location of relavant directory in the archive +Periods : Periods at which spectral accelerations have been computed and stored in the workspace +Sa_1 : Spectral acceleration in direction 1 +Sa_2 : Spectral acceleration in direction 2 +Sa_RotD100 : Orientation independent two-component spectral acceleration +Sa_RotD50 : Orientation independent two-component spectral acceleration +closest_D : Closest distance to the ruptured area +magnitude : Magnitude +Site_Name : CyberShake site name +Site_Short_Name : CyberShake site short name +soil_Vs30 : Vs30 value +Source_Name : seismic source name + +Additional ground motion metadata is available in the summaryTable.csv file in the archive described below. + +To access the associated ground motions, you should first download the ground motion .zip file from https://doi.org/10.5281/zenodo.3875541. Then move the .zip file to the folder where the Matlab code is located (one directory up from where this documentation file is), and extract it there. + +Then, when you set 'copyFiles = 1' in the main script, the software will know where to look for the ground motion time series. \ No newline at end of file diff --git a/openquake/vmtk/Databases/WorkspaceDocumentationNGAW1.txt b/openquake/vmtk/Databases/WorkspaceDocumentationNGAW1.txt new file mode 100644 index 0000000..01baa7c --- /dev/null +++ b/openquake/vmtk/Databases/WorkspaceDocumentationNGAW1.txt @@ -0,0 +1,61 @@ +Documentation of the workspace 'NGA_W1_meta_data.mat' + +This workspace has been created based on the PEER NGA database ground motions (Chiou et al. 2008). The documentation of the variables provided here is based on the NGA Documentation file available at: +http://peer.berkeley.edu/nga/documentation.html + +Not all the variables available in the workspace may be required for the ground-motion selection. The minimum requirements have been documented in Select_Ground_Motions.m + +Nirmal Jayaram, Ting Lin, Jack W. Baker +Department of Civil and Environmental Engineering +Stanford University +Last Updated: 27 March 2010 +Updated: March 31, 2016 to include Rjb + +Referenced manuscripts: + +Chiou, B., R. Darragh, N. Gregor, and W. Silva (2008). NGA project strong-motion database. Earthquake Spectra 24(1), 2344. + +Downloading time histories for selected ground motions: + +The links provided in the Output File will lead to the unscaled ground motion time histories of the selected records. + +Variables: + +Filename_1 : Filename of the time history data file in direction 1 +Filename_2 : Filename of the time history data file in direction 2 +Filename_FN : Filename of the fault normal time history data file +Filename_FP : Filename of the fault parallel time history data file +Filename_vert : Filename of the vertical time history data file +Periods : Periods at which spectral accelerations have been + computed and stored in the workspace +Rjb : Joyner-Boore distance (km) +Sa_1 : Spectral acceleration in direction 1 +Sa_2 : Spectral acceleration in direction 2 +Sa_FN : Spectral acceleration in fault normal direction +Sa_FP : Spectral acceleration in fault parallel direction +Sa_vert : Spectral acceleration in vertical direction +Sa_RotD50 : Orientation independent two-component spectral acceleration +distance_campbell : Campbell distance +closest_D : Closest distance to the ruptured area +distance_epi : Epicentral distance +distance_hyp : Hypocentral distance +distance_jb : Joyner-Boore distance +lowest_usable_freq: Lowest usable frequency +magnitude : Magnitude +mechanism : Fault mechanism +soil_Vs30 : Vs30 value + + + + + + + + + + + + + + + diff --git a/openquake/vmtk/Databases/WorkspaceDocumentationNGAW2.txt b/openquake/vmtk/Databases/WorkspaceDocumentationNGAW2.txt new file mode 100644 index 0000000..a2f2d8e --- /dev/null +++ b/openquake/vmtk/Databases/WorkspaceDocumentationNGAW2.txt @@ -0,0 +1,55 @@ +Documentation of the workspace 'NGA_W2_meta_data.mat' + +This workspace has been created based on the PEER NGA West 2 database ground motions The documentation of the variables provided here is based on the NGA Documentation file available at: http://peer.berkeley.edu/ngawest2/databases/ + +Not all the variables available in the workspace may be required for the ground-motion selection. The minimum requirements have been documented in Select_Ground_Motions.m + +Nirmal Jayaram, Ting Lin, Jack W. Baker Department of Civil and Environmental Engineering Stanford University +Updated: March 20, 2014 to use the NGA West 2 database +Updated: March 31, 2016 to include Rjb + +Downloading time histories for selected ground motions: + +The instructions for downloading time series from the NGA-W2 database are summarized in the Output File. In order to retrieve the time histories for the ground motions ultimately selected, proceed to the link: ngawest2.berkeley.edu An account is needed to access the database. If you do not have an account, click "Sign-up" at the top of the page. + +Once signed in, click the "NGA West2 enter" link on the right of the page. On the resulting page, select "No Scaling" under "Select Spectrum Model" and click submit. Under "Record Characteristics" enter the RSN (record sequence number) provided by the Output file generated by the Select_Ground_Motions script. The RSN values must be separated by commas. If you conducted a two-component ground motion selection, under "Suite, Spectral Ordinate" choose the type of orientation-independent spectra you used (i.e. RotD50 or RotD100). Click "Search Records." + +On the resulting page, scroll down to "Download Options" located near the bottom of the page and click "Download Time Series Records." For any message boxes that appear, click "OK." Save the resulting files. Each file provides the number of points (NPTS) and time step (DT) of the time history. + + +Variables: + +Filename_1 : Filename of the time history data file in direction 1 +Filename_2 : Filename of the time history data file in direction 2 +Filename_vert : Filename of the vertical time history data file +dirLocation : Link to the NGA-W2 page +Periods : Periods at which spectral accelerations have been + computed and stored in the workspace +Rjb : Joyner-Boore distance (km) +Sa_1 : Spectral acceleration in direction 1 +Sa_2 : Spectral acceleration in direction 2 +Sa_vert : Spectral acceleration in vertical direction +Sa_RotD100 : Orientation independent two-component spectral + acceleration +Sa_RotD50 : Orientation independent two-component spectral + acceleration +closest_D : Closest distance to the ruptured area +lowest_usable_freq: Lowest usable frequency +magnitude : Magnitude +mechanism : Fault mechanism +soil_Vs30 : Vs30 value + + + + + + + + + + + + + + + diff --git a/openquake/vmtk/models.py b/openquake/vmtk/models.py new file mode 100644 index 0000000..9aa8bd7 --- /dev/null +++ b/openquake/vmtk/models.py @@ -0,0 +1,373 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Feb 9 12:39:31 2025 + +@author: Amir Taherian +""" + + +import numpy as np +import os +import pandas as pd +import math +from dataclasses import dataclass, field +from typing import List, Tuple, Optional, Dict, Any +import numpy as np + + +def InputFAS(f, beta, roll, subR, subM0,fc, kappa): + """ + Compute the Fourier Amplitude Spectrum (FAS). + + Parameters: + beta0 (float): Source shear wave velocity (km/s). + roll0 (float): Source density (g/cm³). + subf (array): Frequency array (Hz). + subR (float): Subfault distance (km). + C (float): Spectral constant. + subM0 (float): Subfault moment (dyne-cm). + subf0 (float): Corner frequency (Hz). + kappa (float): High-frequency attenuation parameter (s). + + Returns: + subFAS (array): Fourier amplitude spectrum (cm/s). + """ + # Constant C, with unit conversion accounted for + C = (0.55 * 2.0 * 0.707) / (4 * np.pi * (roll * 1000) * (beta * 1000)**3 * 1000) + + # Source model based on Brune's stress drop + #fc = 4.906 * 10**6 * beta * (sigma / subM0)**(1 / 3) + E = C * subM0 / (1 + (f / fc)**2) + E = (2 * np.pi * f)**2 * E # Convert source term to acceleration (dyne/cm) + E = E / 1e7 # Convert to m/s + + # Geometric spreading + if subR <= 50: + G = subR ** -1.0 + elif subR <= 90: + G = ((50 ** 0.3) / (50 ** 1.0)) * (subR ** -0.3) + elif subR <= 120: + G = ((50 ** 0.3) / (50 ** 1.0)) * ((90 ** 1.1) / (90 ** 0.3)) * (subR ** -1.1) + else: + G = ((50 ** 0.3) / (50 ** 1.0)) * ((90 ** 1.1) / (90 ** 0.3)) * ((120 ** 0.5) / (120 ** 1.1)) * (subR ** -0.5) + + # Anelastic attenuation + cq = 3.5 # Propagation velocity in crust (km/s) + Q0 = 180 + nq = 0.5 + Q = Q0 * (f ** nq) # Attenuation quality factor + Ae1 = -np.pi * f * subR + Ae2 = Q * cq + # Avoid division by zero or negative Q + Ae2 = np.where(Ae2 > 0, Ae2, np.inf) # Replace non-positive Ae2 with infinity + + Ae = np.exp(Ae1 / Ae2) # Anelastic attenuation + + # Crustal amplification + Am = amfBJ(f, beta, roll, Vs30=0.76) # Replace with your amplification function + Am = 1 + # High-frequency attenuation + An = np.exp(-np.pi * f * kappa) + + # Final Fourier Amplitude Spectrum + subFAS = E * Ae * Am * An * G * 100 # Unit: cm/s + + + return subFAS +def AB95(f, M, R, roll, beta, sigma, Vs30, fm): + """ + AB95 double corner frequency model (Atkinson & Boore, 1995) + + Parameters: + f (array): Frequency array (Hz) + M (float): Magnitude + R (float): Distance (km) + roll (float): Density (g/cm^3) + beta (float): Shear wave velocity (km/s) + sigma (float): Stress parameter (bars) + Vs30 (float): Average shear-wave velocity over the top 30 m (m/s) + fm (float): High-frequency attenuation parameter + + Returns: + Ax (array): Ground motion acceleration (cm/s) + """ + + # The applicable range of this model is: 4.0 <= M <= 7.25, 10 <= R <= 500 km, 0.5 <= f <= 20 Hz + + # Geometric attenuation function (Trilinear form) + if R <= 70: + G = R**-1 + elif R <= 130: + G = 70**-1 + else: + G = 70**-1 * (R / 130)**-0.5 + + # Source model + fa = 10**(2.41 - 0.533 * M) + et = 10**(2.52 - 0.637 * M) + M0 = 10**(1.5 * M + 16.05) # (Hanks & Kanamori, 1979; Boore et al., 2014) + + # Constant C, with unit conversion accounted for + C = (0.55 * 2.0 * 0.707) / (4 * np.pi * (roll * 1000) * (beta * 1000)**3 * 1000) + + # Corner frequencies calculation + fc = 4.906 * 10**6 * beta * (sigma / M0)**(1/3) + fb = np.sqrt((fc**2 - (1 - et) * fa**2) / et) # (Boore et al., 2014) + + # Spectral shape factors + sa = 1 / (1 + (f / fa)**2) + sb = 1 / (1 + (f / fb)**2) + S = C * M0 * (sa * (1 - et) + sb * et) + S = (2 * np.pi * f)**2 * S + S = S / 10**7 # Convert unit for S into m/s + + # Upper-crust attenuation model + if fm > 1: + P = (1 + (f / fm)**8)**-0.5 + else: + kappa = fm + P = np.exp(-np.pi * f * kappa) + + # Anelastic whole path attenuation + Q0 = 680 + n = 0.36 + + Q = Q0 * f**n + Q = Q * beta + Q = Q**-1 + An = np.exp(-np.pi * f * Q * R) + + # Amplification based on Boore-Joyner (BJ) shear-wave velocity profiling model + vv = amfBJ(f, beta, roll, Vs30) # Assume amfBJ is defined elsewhere + #vv=1 + # Calculate the ground motion acceleration (Ax) and convert it into cm/s + Ax = S * An * P * vv * G * 100 + + return Ax + + +# Adjusting the function name as requested + + + +from scipy.interpolate import interp1d + +def amfBJ(f, beta, roll, Vs30): + """ + Amplification model function based on Boore & Joyner shear-wave velocity profiling model (BJ). + + Parameters: + f (array): Frequency array (Hz) + beta (float): Shear wave velocity (km/s) + roll (float): Density (g/cm^3) + Vs30 (float): Average shear-wave velocity over the top 30 m (m/s) + + Returns: + vv (array): Amplification factor + """ + + # Depth values (H) in km + H=np.array([0,0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01,0.012, + 0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.034,0.036, + 0.038,0.04,0.042,0.044,0.046,0.048,0.05,0.052,0.054,0.056,0.058,0.06, + 0.062,0.064,0.066,0.068,0.07,0.072,0.074,0.076,0.078,0.08,0.082,0.104, + 0.126,0.147,0.175,0.202,0.23,0.257,0.289,0.321,0.353,0.385,0.42,0.455, + 0.49,0.525,0.562,0.599,0.637,0.674,0.712,0.751,0.789,0.827,0.866,0.906, + 0.945,0.984,1.02,1.06,1.1,1.14,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.5, + 4,4.5,5,5.5,6,6.5,7,7.5,8,10,15,20,30,50]) + + # Shear-wave velocity for generic very hard rock (V1) in km/s + V1=np.array([2.768,2.7688,2.7696,2.7704,2.7712,2.772,2.7728,2.7736,2.7744,2.7752, + 2.776,2.7776,2.7792,2.7808,2.7824,2.784,2.7856,2.7872,2.7888,2.7904, + 2.792,2.7936,2.7952,2.7968,2.7984,2.8,2.8016,2.8032,2.8048,2.8064,2.808, + 2.80956,2.81112,2.81268,2.81424,2.8158,2.81736,2.81892,2.82048,2.82204, + 2.8236,2.82516,2.82672,2.82828,2.82984,2.8314,2.83296,2.85004,2.86676, + 2.88272,2.9035,2.92344,2.9436,2.9629,2.9853,3.00686,3.06098,3.0821,3.0718, + 3.0941,3.1158,3.1365,3.15796,3.17942,3.20072,3.22048,3.24024,3.260835504, + 3.271637476,3.281964539,3.29211285,3.302087707,3.311425176,3.320409798, + 3.32841313,3.337002316,3.345294257,3.353309507,3.364853485,3.399786096, + 3.430339103,3.457516593,3.482010086,3.50431659,3.510651329,3.516529187, + 3.521980007,3.527062193,3.538443827,3.54833273,3.557078298,3.564919739, + 3.572028075,3.578529853,3.584521359,3.590077571,3.595258021,3.600110775, + 3.616939825,3.647720809,3.669718999,3.700949146,3.740673099]) + + + # Shear-wave velocity for generic rock (V2) in km/s + V2=np.array([0.245,0.245,0.406898105,0.454341586,0.491321564,0.522065927,0.548608647, + 0.572100299,0.593261253,0.612575285,0.630384474,0.662434293,0.690800008, + 0.716351448,0.739672767,0.761177017,0.781168059,0.799876552,0.817482113, + 0.834127602,0.84992869,0.872659052,0.894459078,0.915511227,0.935880677, + 0.955623835,0.974789894,0.993422052,1.011558478,1.02923309,1.046476183, + 1.063314944,1.079773879,1.095875162,1.111638937,1.127083562,1.142225823, + 1.157081117,1.171663602,1.185986333,1.200061379,1.21389992,1.22751234, + 1.240908299,1.254096801,1.26708626,1.279884545,1.409876676,1.524401504, + 1.623105361,1.742468929,1.822101865,1.869784562,1.91154454,1.956709713, + 1.998031044,2.036174042,2.071640608,2.10782397,2.141667263,2.173485515, + 2.203532354,2.23359926,2.262120148,2.289978882,2.315853324,2.341268645, + 2.366247007,2.389604645,2.412077883,2.434298272,2.456270778,2.4769581, + 2.496972474,2.514890987,2.534215818,2.55296508,2.571175941,2.597555276, + 2.678472608,2.750601065,2.815833418,2.875495547,2.930554778,2.981739972, + 3.029614889,3.074625172,3.117129605,3.214232374,3.300788279,3.33118689, + 3.361507951,3.389174372,3.414630616,3.438216903,3.460199618,3.48079135, + 3.500164545,3.567982556,3.694592725,3.787139494,3.921526466,4.097643229]) + + # Calculate S1, S2, and interpolate shear-wave velocity profile + S1 = 1 / V1 + S2 = 1 / V2 + betavs = (1 / Vs30 - 1 / 0.618) / (1 / 2.780 - 1 / 0.618) + Nh = len(H) + S = np.zeros(Nh) + for i in range(Nh): + S[i] = betavs * S1[i] + (1 - betavs) * S2[i] + + V = 1 / S + + # Initialize arrays for various parameters + thick = np.zeros(Nh - 1) + wtt = np.zeros(Nh - 1) + acct = np.zeros(Nh - 1) + period = np.zeros(Nh - 1) + avev = np.zeros(Nh - 1) + fn = np.zeros(Nh - 1) + + # Compute wave travel time, accumulated time, and period for each layer + for m in range(1, Nh): + thick[m - 1] = H[m] - H[m - 1] + wtt[m - 1] = thick[m - 1] / ((V[m] + V[m - 1]) / 2) + acct[m - 1] = np.sum(wtt[:m]) + period[m - 1] = 4 * acct[m - 1] + avev[m - 1] = (H[m] - H[0]) / acct[m - 1] + fn[m - 1] = 1 / period[m - 1] + + # Trim the arrays properly to ensure they match in length + fn = fn[1:] # Skip the first frequency entry + avev = avev[1:] # Skip the first velocity entry + + # Calculate Density using the V values + # Initialize arrays for Density and Vp + Density = np.zeros(len(V)) + Vp = np.zeros(len(V)) + + # Loop to compute Vp and Density + for i in range(len(V)): + if V[i] < 0.3: + # Case for V < 0.3 + Density[i] = 1 + (1.53 * V[i]**0.85) / (0.35 + 1.889 * V[i]**1.7) + else: + # Case for 0.3 <= V < 3.55 + Vp[i] = 0.9409 + V[i] * 2.0947 - 0.8206 * V[i]**2 + 0.2683 * V[i]**3 - 0.0251 * V[i]**4 + if V[i] < 3.55: + Density[i] = 1.74 * Vp[i]**0.25 + else: + # Case for V >= 3.55 + Density[i] = ( + 1.6612 * Vp[i] + - 0.4721 * Vp[i]**2 + + 0.0671 * Vp[i]**3 + - 0.0043 * Vp[i]**4 + + 0.000106 * Vp[i]**5 + ) + + # Adjust the length of Density to match avev + Density = Density[:len(avev)] + + # Check if lengths match after trimming + if len(avev) != len(Density): + raise ValueError(f"Mismatch between avev ({len(avev)}) and Density ({len(Density)}).") + + # Calculate amplification factor + amp = np.sqrt((beta * roll) / (avev * Density)) + # Interpolate using log amplification-linear frequency interpolation + fn = np.flip(fn) + amp = np.flip(amp) + + # Logarithmic amplification and interpolation + vn = np.log(amp) # Convert amplification to log space + interp_func = interp1d( + fn, vn, kind="linear", fill_value="extrapolate" + ) # Use `fill_value="extrapolate"` + vv_log = interp_func(f) # Apply the interpolation + vv = np.exp(vv_log) # Convert back to linear space + + + return vv + + +def TEA24(f,M, R, roll, beta, sigma, kappa, C, region="inland",M0_override=None,f0_override=None): + """ + Ground Motion Model for Southwest Iberia (renamed as TEA24 based on proposed geometrical spreading and stochastic model parameters) + Includes source spectra logic from EXSIM and supports M0_override and fc_dynamic for finite-fault simulations. + + Parameters: + f (array): Frequency array (Hz) + M (float): Magnitude + R (float): Distance (km) + roll (float): Density (g/cm^3) + beta (float): Shear wave velocity (km/s) + sigma (float): Stress parameter (bars) + fm (float): High-frequency attenuation parameter + region (str): Either 'inland' or 'offshore' based on study model divisions + M0_override (float): Optional seismic moment override (dyne·cm) for subfaults + fc_dynamic (float): Optional dynamic corner frequency override (Hz) for subfaults + return_tp_only (bool): If True, only returns duration `tp` + + Returns: + Ax (array): Ground motion acceleration (cm/s) + tp (float): Duration (s) + """ + # Geometrical spreading model based on region + if region == "inland": + if R <= 70: + G = R**-1.1 + elif R <= 100: + G = 70**-1.1 * (R / 70)**0.2 + else: + G = 70**-1.1 * (100 / 70)**0.2 * (R / 100)**-1.55 + else: # offshore model + if R <= 115: + G = R**-1.1 + else: + G = 115**-1.1 * (R / 115)**-1.5 + + + S = C * M0_override / (1 + (f / f0_override)**2) + S = (2 * np.pi * f)**2 * S + + # High-frequency attenuation + if kappa > 1: + P = (1 + (f / kappa)**8)**-0.5 + else: + P = np.exp(-np.pi * f * kappa) + + # Anelastic attenuation based on regional Q model + if region == "inland": + Q0, n = 120, 0.93 + Qmin = 600 + else: + Q0, n = 165, 1.07 + Qmin = 800 + Q = np.maximum(Q0 * f**n, Qmin) + An = np.exp(-np.pi * f * R / Q) + + # Geometric spreading and amplification + vv = amfBJ(f, beta, roll, Vs30=0.76) + vv = 1 + Ax = S * An * P * vv * G + + # Duration model based on region + if region == "inland": + if R <= 70: + tp = 0.13 * R + elif R <= 120: + tp = 70 * 0.13 + (R - 70) * 0.09 + else: + tp = 70 * 0.13 + 50 * 0.09 + (R - 120) * 0.05 + else: # offshore model + if R <= 115: + tp = 0.12 * R + else: + tp = 115 * 0.12 + (R - 115) * 0.02 + return Ax + + + diff --git a/openquake/vmtk/record_selection.py b/openquake/vmtk/record_selection.py new file mode 100644 index 0000000..7561450 --- /dev/null +++ b/openquake/vmtk/record_selection.py @@ -0,0 +1,2209 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Feb 9 12:39:31 2025 + +@author: Amir Taherian +""" + +import pandas as pd +import os +import math +import random +import matplotlib.pyplot as plt +from scipy import stats +import scipy.linalg as la +from scipy.stats import norm +import shutil +import scipy.io as sio +import warnings +import numpy as np +import requests +from openquake.hazardlib import gsim, imt +from openquake.hazardlib.contexts import RuptureContext, SitesContext, DistancesContext + + +""" +This code is used to select ground motions with response spectra representative of a target +scenario earthquake, as predicted by a ground motion model. Spectra can be selected to be +consistent with the full distribution of response spectra, or conditional on a spectral +amplitude at a given period (i.e., using the conditional spectrum approach). +Single-component or two-component motions can be selected, and several ground motion +databases are provided to search in. + +Based on: +Baker, J. W., and Lee, C. (2018). "An Improved Algorithm for Selecting Ground Motions +to Match a Conditional Spectrum." Journal of Earthquake Engineering, 22(4), 708–723. +""" + +class SelectionParams: + """Parameters controlling ground motion selection""" + def __init__(self): + # Ground motion database and type of selection + self.database_file = 'NGA_W2_meta_data' # Changed from CyberShake to NGA_W2 + self.cond = 0 # 0: unconditional selection, 1: conditional + self.arb = 2 # 1: single-component selection, 2: two-component selection + self.RotD = 50 # 50: use SaRotD50 data, 100: use SaRotD100 data + + # Number of ground motions and spectral periods of interest + self.nGM = 30 # Number of ground motions to be selected + self.Tcond = 1.5 # Period at which spectra should be scaled and matched + self.Tmin = 0.1 # Smallest spectral period of interest + self.Tmax = 10 # Largest spectral period of interest + self.TgtPer = np.logspace(np.log10(self.Tmin), np.log10(self.Tmax), 30) # Array of periods + self.SaTcond = None # Target Sa(Tcond) - if provided, rup.eps_bar will be back-computed + + # Parameters for vertical spectra (optional) + self.matchV = 0 # 1: match vertical spectrum, 0: don't match + self.TminV = 0.01 # Smallest vertical spectral period + self.TmaxV = 10 # Largest vertical spectral period + self.weightV = 0.5 # Weight on vertical spectral match versus horizontal + self.sepScaleV = 1 # 1: scale vertical components separately, 0: same scale factor + self.TgtPerV = np.logspace(np.log10(self.TminV), np.log10(self.TmaxV), 20) + + # Scaling and evaluation parameters + self.isScaled = 1 # 1: allow records to be scaled, 0: don't scale + self.maxScale = 10 # Maximum allowable scale factor + self.tol = 10 # Tolerable percent error to skip optimization + self.optType = 0 # 0: use sum of squared errors, 1: use D-statistic + self.penalty = 0 # >0: penalize spectra more than 3 sigma from target + self.weights = [1.0, 2.0, 0.3] # Weights for error in mean, std dev, skewness + self.nLoop = 2 # Number of optimization loops + self.useVar = 1 # 1: use computed variance, 0: use target variance of 0 + + # Runtime parameters + self.indTcond = None # Index of conditioning period (set in get_target_spectrum) + self.lnSa1 = None # Target spectral acceleration at Tcond + + +class Rupture: + """Parameters specifying the rupture scenario""" + def __init__(self): + # Basic parameters + self.M_bar = 6.5 # Earthquake magnitude + self.Rjb = 11 # Closest distance to surface projection (km) + self.eps_bar = 1.9 # Epsilon value (for conditional selection) + self.Vs30 = 259 # Average shear wave velocity (m/s) + self.z1 = 999 # Basin depth (km), 999 if unknown + self.region = 1 # 0: global, 1: California, 2: Japan, 3: China/Turkey, 4: Italy + self.Fault_Type = 1 # 0: unspecified, 1: strike-slip, 2: normal, 3: reverse + + # Additional seismological parameters for GMPE + self.Rrup = 11 # Closest distance to rupture plane (km) + self.Rx = 11 # Horizontal distance (km) + self.W = 15 # Down-dip rupture width (km) + self.Ztor = 0 # Depth to top of rupture (km) + self.Zbot = 15 # Depth to bottom of seismogenic crust (km) + self.dip = 90 # Fault dip angle (deg) + self.lambda_ = 0 # Rake angle (deg) + self.Fhw = 0 # Flag for hanging wall + self.Z2p5 = 1 # Depth to Vs=2.5 km/sec (km) + self.Zhyp = 10 # Hypocentral depth (km) + self.FRV = 0 # Flag for reverse faulting + self.FNM = 0 # Flag for normal faulting + self.Sj = 0 # Flag for regional site effects (1 for Japan) + + +class AllowedRecords: + """Criteria for selecting records from the database""" + def __init__(self): + self.Vs30 = [-float('inf'), float('inf')] # Bounds for Vs30 values + self.Mag = [6.0, 8.2] # Bounds for magnitude + self.D = [0, 50] # Bounds for distance + self.idxInvalid = [] # Index of ground motions to exclude + + +class TargetSa: + """Response spectrum target values to match""" + def __init__(self): + self.meanReq = None # Target response spectrum means + self.covReq = None # Matrix of response spectrum covariances + self.stdevs = None # Vector of standard deviations + + +class IntensityMeasures: + """Intensity measure values chosen and available""" + def __init__(self): + self.recID = None # Indices of selected spectra + self.scaleFac = None # Scale factors for selected spectra + self.sampleSmall = None # Matrix of selected logarithmic response spectra + self.sampleBig = None # Matrix of logarithmic spectra to search + self.stageOneScaleFac = None # Scale factors after first selection stage + self.stageOneMeans = None # Mean log response spectra after first stage + self.stageOneStdevs = None # Standard deviation after first stage + # For vertical components + self.scaleFacV = None + self.sampleBigV = None + self.stageOneScaleFacV = None + self.stageOneMeansV = None + self.stageOneStdevsV = None + + +def screen_database(selection_params, allowed_recs): + """ + Load and screen the ground motion database for suitable motions + + Parameters: + ----------- + selection_params : SelectionParams + Parameters controlling the selection + allowed_recs : AllowedRecords + Criteria for selecting records + + Returns: + -------- + SaKnown : numpy.ndarray + Ground motion spectra that passed screening + selection_params : SelectionParams + Updated with indices of periods + indPer : list + Indices of target periods in database + knownPer : numpy.ndarray + Periods available in the database + metadata : dict + Information about the database and selected records + """ + # This is a placeholder - in a real implementation, you would: + # 1. Load database from file (e.g., using pandas) + # 2. Filter records based on allowed_recs criteria + # 3. Extract relevant data and metadata + + print(f"Loading database: {selection_params.database_file}") + print(f"Screening for records with Vs30 between {allowed_recs.Vs30[0]} and {allowed_recs.Vs30[1]}") + print(f"Screening for records with magnitude between {allowed_recs.Mag[0]} and {allowed_recs.Mag[1]}") + print(f"Screening for records with distance between {allowed_recs.D[0]} and {allowed_recs.D[1]}") + + # Dummy implementation - replace with actual database loading + n_records = 1000 # Assume 1000 records pass screening + n_periods = 100 # Assume 100 periods in database + + # Create dummy data for demonstration + SaKnown = np.random.lognormal(0, 0.5, size=(n_records, n_periods)) + knownPer = np.logspace(np.log10(0.01), np.log10(10), n_periods) + + # Find indices of target periods in database + indPer = [] + for period in selection_params.TgtPer: + # Find closest period in database + idx = np.abs(knownPer - period).argmin() + indPer.append(idx) + + # Find index of conditioning period + selection_params.indTcond = np.where(np.isclose(selection_params.TgtPer, selection_params.Tcond))[0][0] + + # Create metadata + metadata = { + 'database': selection_params.database_file, + 'n_records': n_records, + 'allowedIndex': np.arange(n_records), # Original indices of allowed records + 'filenames': [f"record_{i}.acc" for i in range(n_records)], + 'record_info': pd.DataFrame({ + 'Magnitude': np.random.uniform(allowed_recs.Mag[0], allowed_recs.Mag[1], n_records), + 'Distance': np.random.uniform(allowed_recs.D[0], allowed_recs.D[1], n_records), + 'Vs30': np.random.uniform(200, 800, n_records) + }) + } + + # If vertical components are needed, create those too + if selection_params.matchV == 1: + selection_params.SaKnownV = np.random.lognormal(-0.5, 0.6, size=(n_records, n_periods)) + selection_params.indPerV = [] + for period in selection_params.TgtPerV: + idx = np.abs(knownPer - period).argmin() + selection_params.indPerV.append(idx) + + return SaKnown, selection_params, indPer, knownPer, metadata + + +def gmpe_bj_2008_corr(T1, T2): + """ + Baker and Jayaram (2008) correlation model for response spectral values + + Parameters: + ----------- + T1, T2 : float + Periods to compute correlation between + + Returns: + -------- + rho : float + Correlation coefficient + + Notes: + ------ + Implementation based on: + Baker, J.W. and Jayaram, N., (2008) "Correlation of spectral + acceleration values from NGA ground motion models," Earthquake Spectra, + 24(1), 299-317. + """ + T_min = min(T1, T2) + T_max = max(T1, T2) + + C1 = (1 - np.cos(np.pi/2 - np.log(T_max / max(T_min, 0.109)) * 0.366)) + + if T_max < 0.2: + C2 = 1 - 0.105 * (1 - 1 / (1 + np.exp(100 * T_max - 5))) * (T_max - T_min) / (T_max - 0.0099) + else: + C2 = 1.0 # Default value, not used in this case + + if T_max < 0.109: + C3 = C2 + else: + C3 = C1 + + C4 = C1 + 0.5 * (np.sqrt(C3) - C3) * (1 + np.cos(np.pi * T_min / 0.109)) + + if T_max <= 0.109: + rho = C2 + elif T_min > 0.109: + rho = C1 + elif T_max < 0.2: + rho = min(C2, C4) + else: + rho = C4 + + return rho + + + + +def get_openquake_gmm(gmm_name): + """Returns an initialized OpenQuake ground motion model by name""" + try: + if gmm_name == 'Allen2012': + from openquake.hazardlib.gsim.allen_2012 import Allen2012 + return Allen2012() + elif gmm_name == 'BooreEtAl2014': + from openquake.hazardlib.gsim.boore_2014 import BooreEtAl2014 + return BooreEtAl2014() + elif gmm_name == 'AbrahamsonEtAl2014': + from openquake.hazardlib.gsim.abrahamson_2014 import AbrahamsonEtAl2014 + return AbrahamsonEtAl2014() + elif gmm_name == 'CampbellBozorgnia2014': + from openquake.hazardlib.gsim.campbell_bozorgnia_2014 import CampbellBozorgnia2014 + return CampbellBozorgnia2014() + elif gmm_name == 'ChiouYoungs2014': + from openquake.hazardlib.gsim.chiou_youngs_2014 import ChiouYoungs2014 + return ChiouYoungs2014() + else: + raise ValueError(f"GMM '{gmm_name}' not recognized.") + except Exception as e: + print(f"Error initializing GMM '{gmm_name}': {str(e)}") + raise + +def setup_openquake_contexts(rup_params): + """Set up OpenQuake contexts from rupture parameters""" + print("Setting up OpenQuake contexts...") + + # Rupture context + rup = RuptureContext() + rup.mag = rup_params.M_bar + + # Set rake based on fault type + if rup_params.Fault_Type == 1: # Strike-slip + rup.rake = 0.0 + elif rup_params.Fault_Type == 2: # Normal + rup.rake = -90.0 + elif rup_params.Fault_Type == 3: # Reverse + rup.rake = 90.0 + else: + rup.rake = 0.0 # Default to strike-slip + + rup.hypo_depth = rup_params.Zhyp if hasattr(rup_params, 'Zhyp') else 10.0 + rup.ztor = rup_params.Ztor if hasattr(rup_params, 'Ztor') else 0.0 + + # Distances context + dists = DistancesContext() + dists.rrup = np.array([rup_params.Rrup]) + dists.rjb = np.array([rup_params.Rjb]) + dists.rx = np.array([rup_params.Rx]) if hasattr(rup_params, 'Rx') else np.array([0.0]) + + # Sites context + sites = SitesContext() + sites.vs30 = np.array([rup_params.Vs30]) + sites.vs30measured = np.array([True]) # Assuming measured Vs30 + + # Handle z1.0 (basin depth to 1.0 km/s horizon) + if hasattr(rup_params, 'z1') and rup_params.z1 != 999: + sites.z1pt0 = np.array([rup_params.z1]) + else: + # Estimate z1.0 using Vs30-based correlation (Chiou and Youngs 2014) + z1pt0 = np.exp((-7.15/4) * np.log((sites.vs30**4 + 570.94**4) / (1360**4 + 570.94**4))) + sites.z1pt0 = np.array([z1pt0]) + + # Handle z2.5 (basin depth to 2.5 km/s horizon) + if hasattr(rup_params, 'Z2p5'): + sites.z2pt5 = np.array([rup_params.Z2p5]) + else: + sites.z2pt5 = np.array([1.0]) # Default value + + # Required site parameter + sites.sids = np.array([0]) + + return rup, sites, dists + +def openquake_gmm_calc(T, rup_params, gmm_name='BooreEtAl2014'): + """Calculate median and standard deviation using OpenQuake GMM""" + try: + print(f"Using OpenQuake GMM: {gmm_name}") + + # Initialize the GMM + gmm = get_openquake_gmm(gmm_name) + + # Set up contexts + rup_ctx, sites_ctx, dists_ctx = setup_openquake_contexts(rup_params) + + # Convert T to numpy array if it's not already + if not isinstance(T, np.ndarray): + T = np.array([T]) + + # Initialize output arrays + median = np.zeros_like(T, dtype=float) + sigma = np.zeros_like(T, dtype=float) + + # Calculate for each period + for i, period in enumerate(T): + try: + #print(f"Processing period {period} s...") + sa_imt = imt.SA(period=period) + + # Get mean and standard deviation from GMM + mean, stddevs = gmm.get_mean_and_stddevs( + sites_ctx, rup_ctx, dists_ctx, sa_imt, ['TOTAL'] + ) + + median[i] = np.exp(mean[0]) + sigma[i] = stddevs[0][0] if len(stddevs) > 0 and len(stddevs[0]) > 0 else 0.6 + + except Exception as e: + print(f"Error processing period {period} s: {str(e)}") + # Set default values for this period + median[i] = 0.1 # Default value + sigma[i] = 0.6 # Default value + + # Return scalar if input was scalar + if len(T) == 1: + return median[0], sigma[0], T[0] + else: + return median, sigma, T + + except Exception as e: + print(f"Error in openquake_gmm_calc: {str(e)}") + # Fallback to a simple model for debugging + if not isinstance(T, np.ndarray): + T = np.array([T]) + median = 0.5 * np.exp(-0.5 * np.log(T)) # Simple spectral shape + sigma = np.ones_like(T) * 0.6 # Constant sigma + + return median, sigma, T + + + +# Now, modify the get_target_spectrum function to use the OpenQuake GMM +def get_target_spectrum(knownPer, selectionParams, indPer, rup): + """ + Calculate and return the target mean spectrum and covariance + matrix at available periods using OpenQuake GMM + """ + # Initialize output structure + target_dict = {} + + try: + # Compute target mean spectrum using OpenQuake GMM if requested + if hasattr(selectionParams, 'use_openquake') and selectionParams.use_openquake: + gmm_name = getattr(selectionParams, 'gmm_name', 'BooreEtAl2014') + sa, sigma, _ = openquake_gmm_calc(knownPer, rup, gmm_name=gmm_name) + + # Verify we got valid values + print(f"Generated {len(sa)} sa values with range: {np.min(sa):.4f} to {np.max(sa):.4f}") + print(f"Generated {len(sigma)} sigma values with range: {np.min(sigma):.4f} to {np.max(sigma):.4f}") + + # Modify spectral targets if RotD100 values were specified + if selectionParams.RotD == 100 and selectionParams.arb == 2: + rotD100Ratio, rotD100Sigma = gmpe_sb_2014_ratios(knownPer) + sa = sa * rotD100Ratio + sigma = np.sqrt(sigma**2 + rotD100Sigma**2) + + # Back-calculate epsilon if SaTcond is specified + if hasattr(selectionParams, 'SaTcond') and selectionParams.SaTcond is not None: + # Interpolate to get median Sa and sigma at Tcond + logPer = np.log(knownPer) + logSa = np.log(sa) + + median_SaTcond = np.exp(np.interp(np.log(selectionParams.Tcond), logPer, logSa)) + sigma_SaTcond = np.interp(np.log(selectionParams.Tcond), logPer, sigma) + + eps_bar = (np.log(selectionParams.SaTcond) - np.log(median_SaTcond)) / sigma_SaTcond + + print(f"Back-calculated epsilon = {eps_bar:.3f}") + else: + # Use user-specified epsilon value + eps_bar = rup.eps_bar + + # Calculate target mean spectrum (in log space) + if selectionParams.cond == 1: + # Compute correlations and the conditional mean spectrum + rho = np.zeros(len(sa)) + for i in range(len(sa)): + rho[i] = gmpe_bj_2008_corr(knownPer[i], + selectionParams.TgtPer[selectionParams.indTcond]) + + TgtMean = np.log(sa) + sigma * eps_bar * rho + else: + TgtMean = np.log(sa) + + # Compute covariances and correlations at all periods + TgtCovs = np.zeros((len(sa), len(sa))) + for i in range(len(sa)): + for j in range(len(sa)): + # Periods + Ti = knownPer[i] + Tj = knownPer[j] + + # Means and variances + varT = sigma[selectionParams.indTcond]**2 + sigma22 = varT + var1 = sigma[i]**2 + var2 = sigma[j]**2 + + # Covariances + if selectionParams.cond == 1: + sigmaCorr = gmpe_bj_2008_corr(Ti, Tj) * np.sqrt(var1 * var2) + sigma11 = np.array([[var1, sigmaCorr], [sigmaCorr, var2]]) + sigma12 = np.array([ + [gmpe_bj_2008_corr(Ti, selectionParams.Tcond) * np.sqrt(var1 * varT)], + [gmpe_bj_2008_corr(selectionParams.Tcond, Tj) * np.sqrt(var2 * varT)] + ]) + + # Calculate conditional covariance + sigmaCond = sigma11 - sigma12 @ np.linalg.inv(np.array([[sigma22]])) @ sigma12.T + TgtCovs[i, j] = sigmaCond[0, 1] + else: + TgtCovs[i, j] = gmpe_bj_2008_corr(Ti, Tj) * np.sqrt(var1 * var2) + + # Override covariance matrix with zeros if no variance desired + if hasattr(selectionParams, 'useVar') and selectionParams.useVar == 0: + TgtCovs = np.zeros_like(TgtCovs) + + # Avoid numerical issues with very small covariance values + TgtCovs[np.abs(TgtCovs) < 1e-10] = 1e-10 + + # Store target mean and covariance matrix at target periods + target_dict['meanReq'] = TgtMean[indPer] + target_dict['covReq'] = TgtCovs[np.ix_(indPer, indPer)] + target_dict['stdevs'] = np.sqrt(np.diag(target_dict['covReq'])) + + # Target mean and covariance at all periods + target_dict['meanAllT'] = TgtMean + target_dict['covAllT'] = TgtCovs + + # Print diagnostic information + print("Target spectrum information:") + print(f"meanReq shape: {target_dict['meanReq'].shape}") + print(f"stdevs shape: {target_dict['stdevs'].shape}") + print(f"covReq shape: {target_dict['covReq'].shape}") + + except Exception as e: + print(f"Error in get_target_spectrum: {str(e)}") + # Create fallback values in case of error + n_periods = len(selectionParams.TgtPer) + target_dict['meanReq'] = np.log(np.linspace(0.5, 0.1, n_periods)) + target_dict['stdevs'] = np.ones(n_periods) * 0.6 + target_dict['covReq'] = np.diag(target_dict['stdevs']**2) + + # Convert dictionary to TargetSa object + targetSa = TargetSa() + targetSa.meanReq = target_dict['meanReq'] + targetSa.covReq = target_dict['covReq'] + targetSa.stdevs = target_dict['stdevs'] + + # Add these if they exist + if 'meanAllT' in target_dict: + targetSa.meanAllT = target_dict['meanAllT'] + if 'covAllT' in target_dict: + targetSa.covAllT = target_dict['covAllT'] + + return targetSa + +def nearestSPD(A): + """ + Find the nearest (in Frobenius norm) Symmetric Positive Definite matrix to A + + From Higham: "The nearest symmetric positive semidefinite matrix in the + Frobenius norm to an arbitrary real matrix A is shown to be (B + H)/2, + where H is the symmetric polar factor of B=(A + A')/2." + + Parameters: + ----------- + A : array_like + Input matrix, which will be converted to the nearest Symmetric + Positive Definite Matrix. + + Returns: + -------- + Ahat : ndarray + The nearest Symmetric Positive Definite matrix to A. + """ + # Check input is a square matrix + A = np.asarray(A) + if A.ndim != 2 or A.shape[0] != A.shape[1]: + raise ValueError('A must be a square matrix.') + + # Handle scalar case + if A.shape[0] == 1: + if A[0, 0] <= 0: + return np.array([[np.finfo(float).eps]]) + else: + return A.copy() + + # Symmetrize A into B + B = (A + A.T) / 2 + + # Compute the symmetric polar factor of B. Call it H. + # Clearly H is itself SPD. + U, s, Vh = np.linalg.svd(B) + H = Vh.T @ np.diag(s) @ Vh + + # Get Ahat in the above formula + Ahat = (B + H) / 2 + + # Ensure symmetry + Ahat = (Ahat + Ahat.T) / 2 + + # Test that Ahat is in fact PD. If not, tweak it a bit. + k = 0 + while True: + try: + # Attempt Cholesky decomposition (will fail if not PD) + np.linalg.cholesky(Ahat) + break + except np.linalg.LinAlgError: + # Ahat failed the chol test. Tweak by adding a tiny multiple of identity matrix. + k += 1 + eigvals = np.linalg.eigvalsh(Ahat) + min_eig = np.min(eigvals) + # Add a small increment to make it positive definite + Ahat = Ahat + (-min_eig * k**2 + np.finfo(float).eps) * np.eye(A.shape[0]) + + return Ahat + + +# These functions are placeholders and would need actual implementations +# based on referenced GMPEs in the MATLAB code + +def gmpe_sb_2014_ratios(knownPer): + """ + Placeholder for the Shahi-Baker 2014 GMM for computing RotD100/RotD50 ratios + """ + # Placeholder values + rotD100Ratio = np.ones(len(knownPer)) * 1.2 # Typically RotD100 > RotD50 + rotD100Sigma = np.ones(len(knownPer)) * 0.1 + + return rotD100Ratio, rotD100Sigma + + +import numpy as np + +def gmpe_sb_2014_ratios(T): + """ + Compute Sa_RotD100/Sa_RotD50 ratios from Shahi and Baker (2014) + + Shahi, S. K., and Baker, J. W. (2014). "NGA-West2 models for ground- + motion directionality." Earthquake Spectra, 30(3), 1285-1300. + + Parameters: + ----------- + T : float or array_like + Period(s) of interest (sec) + + Returns: + -------- + ratio : float or array_like + Geometric mean of Sa_RotD100/Sa_RotD50 + sigma : float or array_like + Standard deviation of log(Sa_RotD100/Sa_RotD50) + phi : float or array_like + Within-event standard deviation + tau : float or array_like + Between-event standard deviation + """ + # Model coefficient values from Table 1 of the above-referenced paper + periods_orig = np.array([0.01, 0.02, 0.03, 0.05, 0.075, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, + 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.5, 10.0]) + + ratios_orig = np.array([1.19243805900000, 1.19124621700000, 1.18767783300000, 1.18649074900000, + 1.18767783300000, 1.18767783300000, 1.19961419400000, 1.20562728500000, + 1.21652690500000, 1.21896239400000, 1.22875320400000, 1.22875320400000, + 1.23738465100000, 1.24110237900000, 1.24234410200000, 1.24358706800000, + 1.24732343100000, 1.25985923900000, 1.26490876900000, 1.28531008400000, + 1.29433881900000]) + + sigma_orig = np.array([0.08] * 20 + [0.08]) # All values are 0.08 + phi_orig = np.array([0.08] * 20 + [0.07]) # All values are 0.08 except last one (0.07) + tau_orig = np.array([0.01] * 18 + [0.02, 0.02, 0.03]) # Most values are 0.01, except last three + + # Convert input to numpy array if not already + T = np.asarray(T) + scalar_input = False + if T.ndim == 0 or (hasattr(T, 'shape') and len(T.shape) == 0): + T = np.array([T]) # Makes 1D array from scalar + scalar_input = True + + # Initialize output arrays + ratio = np.zeros_like(T) + sigma = np.zeros_like(T) + phi = np.zeros_like(T) + tau = np.zeros_like(T) + + # Interpolate to compute values for the user-specified periods + for i, t in enumerate(T): + if t <= periods_orig[0]: + ratio[i] = ratios_orig[0] + sigma[i] = sigma_orig[0] + phi[i] = phi_orig[0] + tau[i] = tau_orig[0] + elif t >= periods_orig[-1]: + ratio[i] = ratios_orig[-1] + sigma[i] = sigma_orig[-1] + phi[i] = phi_orig[-1] + tau[i] = tau_orig[-1] + else: + ratio[i] = np.interp(np.log(t), np.log(periods_orig), ratios_orig) + sigma[i] = np.interp(np.log(t), np.log(periods_orig), sigma_orig) + phi[i] = np.interp(np.log(t), np.log(periods_orig), phi_orig) + tau[i] = np.interp(np.log(t), np.log(periods_orig), tau_orig) + + # Return scalar if input was scalar + if scalar_input: + return ratio[0], sigma[0], phi[0], tau[0] + else: + return ratio, sigma, phi, tau + + + +def extract_matlab_cell_array(mat_data, var_name): + """ + Extract and convert MATLAB cell arrays to Python lists or numpy arrays + + Parameters: + ----------- + mat_data : dict + MATLAB data loaded with scipy.io.loadmat + var_name : str + Name of the variable to extract + + Returns: + -------- + result : list or numpy.ndarray + Extracted data in Python format + """ + if var_name not in mat_data: + return None + + cell_data = mat_data[var_name] + + # Check if data is empty or None + if cell_data is None or cell_data.size == 0: + return [] + + try: + # Handle 1D cell arrays + if cell_data.ndim == 2 and (cell_data.shape[0] == 1 or cell_data.shape[1] == 1): + result = [] + for cell in cell_data.flatten(): + if cell.size == 0: # Handle empty cells + result.append('') + elif isinstance(cell[0], np.ndarray): + if cell[0].dtype.kind == 'U' or cell[0].dtype.kind == 'S': + result.append(str(cell[0][0]) if cell[0].size > 0 else '') + else: + result.append(cell[0]) + else: + result.append(cell[0]) + return np.array(result) + + # Handle 2D cell arrays + else: + result = [] + for i in range(cell_data.shape[0]): + row = [] + for j in range(cell_data.shape[1]): + cell = cell_data[i, j] + if cell.size == 0: # Handle empty cells + row.append('') + elif isinstance(cell[0], np.ndarray): + if cell[0].dtype.kind == 'U' or cell[0].dtype.kind == 'S': + row.append(str(cell[0][0]) if cell[0].size > 0 else '') + else: + row.append(cell[0]) + else: + row.append(cell[0]) + result.append(row) + return np.array(result) + except (IndexError, ValueError, TypeError) as e: + # If we encounter an error, try a simpler approach to extract data + print(f"Warning: Error extracting MATLAB cell array '{var_name}': {str(e)}") + print(f"Attempting alternate extraction method...") + + try: + # For scalar values or simple arrays + if not isinstance(cell_data, np.ndarray) or cell_data.dtype != 'object': + return cell_data + + # For cell arrays, extract what we can + result = [] + for i in range(cell_data.size): + try: + if cell_data.ndim == 1: + cell = cell_data[i] + else: + cell = cell_data.flat[i] + + if cell.size == 0: + result.append('') + else: + val = cell[0] if hasattr(cell, '__getitem__') else cell + if isinstance(val, np.ndarray) and val.size > 0: + if val.dtype.kind in ('U', 'S'): + result.append(str(val.item(0))) + else: + result.append(val.item(0) if val.size == 1 else val) + else: + result.append(val) + except: + result.append('') + + return np.array(result) + + except Exception as e2: + print(f"Warning: Alternate extraction failed: {str(e2)}") + print(f"Returning empty array for {var_name}") + return np.array([]) + + +def screen_database(selection_params, allowed_recs): + """ + Load and screen the ground motion database for suitable motions + + Parameters: + ----------- + selection_params : SelectionParams + Parameters controlling the selection + allowed_recs : AllowedRecords + Criteria for selecting records + + Returns: + -------- + SaKnown : numpy.ndarray + Ground motion spectra that passed screening + selection_params : SelectionParams + Updated with indices of periods + indPer : list + Indices of target periods in database + knownPer : numpy.ndarray + Periods available in the database + metadata : dict + Information about the database and selected records + """ + import scipy.io as sio + + # Load the MATLAB database file + database_path = os.path.join('Databases', f"{selection_params.database_file}.mat") + try: + print(f"Loading database: {selection_params.database_file}") + mat_data = sio.loadmat(database_path) + + # Print available keys to help with debugging + print(f"Available keys in database: {list(mat_data.keys())}") + + # Extract data from MATLAB structure + # Common variables in all databases + Periods = mat_data.get('Periods', np.array([])).flatten() + if Periods.size == 0: + raise ValueError("No 'Periods' data found in database") + + magnitude = mat_data.get('magnitude', np.array([])).flatten() + closest_D = mat_data.get('closest_D', np.array([])).flatten() + soil_Vs30 = mat_data.get('soil_Vs30', np.array([])).flatten() + NGA_num = mat_data.get('NGA_num', np.array([])).flatten() + + # Extract filenames and directory locations + Filename_1 = extract_matlab_cell_array(mat_data, 'Filename_1') + Filename_2 = extract_matlab_cell_array(mat_data, 'Filename_2') if 'Filename_2' in mat_data else None + dirLocation = extract_matlab_cell_array(mat_data, 'dirLocation') + getTimeSeries = extract_matlab_cell_array(mat_data, 'getTimeSeries') + + # Extract spectral acceleration data + Sa_1 = mat_data.get('Sa_1') + if Sa_1 is None or Sa_1.size == 0: + raise ValueError("No 'Sa_1' data found in database") + + Sa_2 = mat_data.get('Sa_2') if 'Sa_2' in mat_data else None + + # Check for RotD50 or RotD100 data + Sa_RotD50 = mat_data.get('Sa_RotD50') if 'Sa_RotD50' in mat_data else None + Sa_RotD100 = mat_data.get('Sa_RotD100') if 'Sa_RotD100' in mat_data else None + + # Check for vertical component data + Sa_vert = mat_data.get('Sa_vert') if 'Sa_vert' in mat_data else None + Filename_vert = extract_matlab_cell_array(mat_data, 'Filename_vert') if 'Filename_vert' in mat_data else None + + print(f"Screening for records with Vs30 between {allowed_recs.Vs30[0]} and {allowed_recs.Vs30[1]}") + print(f"Screening for records with magnitude between {allowed_recs.Mag[0]} and {allowed_recs.Mag[1]}") + print(f"Screening for records with distance between {allowed_recs.D[0]} and {allowed_recs.D[1]}") + + except Exception as e: + print(f"Error loading database: {str(e)}") + raise + + # Format appropriate ground motion metadata variables for single or two-component selection + metadata = {} + metadata['getTimeSeries'] = getTimeSeries + metadata['dirLocation'] = dirLocation + + if selection_params.arb == 1 and selection_params.matchV != 1: + # Single-component selection -- treat each component as a separate candidate + if Sa_2 is None: # If 2nd component doesn't exist (e.g., EXSIM data) + metadata['Filename'] = Filename_1 + metadata['compNum'] = np.ones_like(magnitude) + metadata['recNum'] = np.arange(1, len(magnitude) + 1) + SaKnown = Sa_1 + else: # 2nd component exists + metadata['Filename'] = np.concatenate([Filename_1, Filename_2]) + metadata['compNum'] = np.concatenate([np.ones_like(magnitude), 2 * np.ones_like(magnitude)]) + metadata['recNum'] = np.concatenate([np.arange(1, len(magnitude) + 1), np.arange(1, len(magnitude) + 1)]) + SaKnown = np.vstack([Sa_1, Sa_2]) + soil_Vs30 = np.concatenate([soil_Vs30, soil_Vs30]) + magnitude = np.concatenate([magnitude, magnitude]) + closest_D = np.concatenate([closest_D, closest_D]) + NGA_num = np.concatenate([NGA_num, NGA_num]) + metadata['dirLocation'] = np.concatenate([dirLocation, dirLocation]) + + elif selection_params.arb == 2 and selection_params.matchV != 1: + # Two-component selection + metadata['Filename'] = np.column_stack([Filename_1, Filename_2]) if Filename_2 is not None else Filename_1 + metadata['recNum'] = np.arange(1, len(magnitude) + 1) + + if selection_params.RotD == 50 and Sa_RotD50 is not None: + SaKnown = Sa_RotD50 + elif selection_params.RotD == 100 and Sa_RotD100 is not None: + SaKnown = Sa_RotD100 + else: + print(f"Warning: RotD{selection_params.RotD} not provided in database") + # Use geometric mean of the single-component Sa's as fallback + SaKnown = np.sqrt(Sa_1 * Sa_2) if Sa_2 is not None else Sa_1 + + else: + # Including vertical components + if Filename_vert is not None: + metadata['Filename'] = np.column_stack([Filename_1, Filename_2, Filename_vert]) + else: + metadata['Filename'] = np.column_stack([Filename_1, Filename_2]) + + metadata['recNum'] = np.arange(1, len(magnitude) + 1) + + if selection_params.RotD == 50 and Sa_RotD50 is not None: + SaKnown = Sa_RotD50 + elif selection_params.RotD == 100 and Sa_RotD100 is not None: + SaKnown = Sa_RotD100 + else: + print(f"Warning: RotD{selection_params.RotD} not provided in database") + # Use geometric mean of the single-component Sa's as fallback + SaKnown = np.sqrt(Sa_1 * Sa_2) if Sa_2 is not None else Sa_1 + + if Sa_vert is not None: + SaKnownV = Sa_vert + + # Create variable for known periods + idxPer = np.where(Periods <= 10)[0] # Throw out periods > 10s to avoid GMPE evaluation problems + knownPer = Periods[idxPer] + + # Modify TgtPer to include Tcond if running a conditional selection + if selection_params.cond == 1: + # Ensure Tcond is in the range of available periods + if selection_params.Tcond < np.min(knownPer) or selection_params.Tcond > np.max(knownPer): + print(f"Warning: Tcond={selection_params.Tcond} is outside the available period range [{np.min(knownPer)}, {np.max(knownPer)}]") + # Adjust Tcond to the nearest available period + selection_params.Tcond = knownPer[np.argmin(np.abs(knownPer - selection_params.Tcond))] + print(f"Adjusted Tcond to {selection_params.Tcond}") + + # Add Tcond to TgtPer if not already included + if not any(np.isclose(selection_params.TgtPer, selection_params.Tcond)): + selection_params.TgtPer = np.sort(np.append(selection_params.TgtPer, selection_params.Tcond)) + + # Match periods (known periods and target periods for error computations) + # Save the indices of the matched periods in knownPer + indPer = np.zeros(len(selection_params.TgtPer), dtype=int) + for i in range(len(selection_params.TgtPer)): + indPer[i] = np.argmin(np.abs(knownPer - selection_params.TgtPer[i])) + + # Make sure target periods match the actual database periods + selection_params.TgtPer = knownPer[indPer] + + # Identify the index of Tcond within TgtPer + # Using robust approach to find the index + tcond_diffs = np.abs(selection_params.TgtPer - selection_params.Tcond) + tcond_idx = np.argmin(tcond_diffs) + + # Verify we found a close match + if tcond_diffs[tcond_idx] > 1e-6: + print(f"Warning: Could not find exact Tcond match. Using closest period: {selection_params.TgtPer[tcond_idx]}") + + selection_params.indTcond = tcond_idx + selection_params.Tcond = selection_params.TgtPer[tcond_idx] # Use the actual nearest period + print(f"Conditioning period (Tcond): {selection_params.Tcond}, index: {selection_params.indTcond}") + + # If selecting V components, revise TgtPerV + if selection_params.matchV == 1: + if selection_params.cond == 1 and not any(np.isclose(selection_params.TgtPerV, selection_params.Tcond)): + selection_params.TgtPerV = np.sort(np.append(selection_params.TgtPerV, selection_params.Tcond)) + + indPerV = np.zeros(len(selection_params.TgtPerV), dtype=int) + for i in range(len(selection_params.TgtPerV)): + indPerV[i] = np.argmin(np.abs(knownPer - selection_params.TgtPerV[i])) + + indPerV = np.unique(indPerV) + selection_params.TgtPerV = knownPer[indPerV] + selection_params.indPerV = indPerV + + # Screen the records to be considered + if selection_params.matchV == 1 and 'Sa_vert' in locals(): + # Ensure that each record contains all 3 components + recValidSa = ~np.any(Sa_1 == -999, axis=1) & ~np.any(Sa_2 == -999, axis=1) & ~np.any(np.isin(Sa_vert, [-999, np.inf]), axis=1) + else: + recValidSa = ~np.all(SaKnown == -999, axis=1) # Remove invalid inputs + + recValidSoil = (soil_Vs30 > allowed_recs.Vs30[0]) & (soil_Vs30 < allowed_recs.Vs30[1]) + recValidMag = (magnitude > allowed_recs.Mag[0]) & (magnitude < allowed_recs.Mag[1]) + recValidDist = (closest_D > allowed_recs.D[0]) & (closest_D < allowed_recs.D[1]) + + recValidIdx = ~np.isin(metadata['recNum'], allowed_recs.idxInvalid) + + # Flag indices of allowable records that will be searched + metadata['allowedIndex'] = np.where(recValidSoil & recValidMag & recValidDist & recValidSa & recValidIdx)[0] + + # Resize SaKnown to include only allowed records + SaKnown = SaKnown[metadata['allowedIndex']][:, idxPer] + + # If selecting V components, save new variables in selectionParams + if selection_params.matchV == 1 and 'SaKnownV' in locals(): + SaKnownV = SaKnownV[metadata['allowedIndex']][:, idxPer] + selection_params.SaKnownV = SaKnownV + + # Count number of allowed spectra + selection_params.nBig = len(metadata['allowedIndex']) + + print(f"Number of allowed ground motions = {selection_params.nBig}") + assert selection_params.nBig >= selection_params.nGM, 'Warning: there are not enough allowable ground motions' + + return SaKnown, selection_params, indPer, knownPer, metadata + + + +def simulate_spectra(targetSa, selectionParams, seed_value, n_trials): + """ + Simulate response spectra matching the computed targets + + Parameters: + ----------- + targetSa : TargetSa + Target spectral acceleration data + selectionParams : SelectionParams + Parameters controlling the selection + seed_value : int + Random seed (0 for random) + n_trials : int + Number of iterations for spectral simulation + + Returns: + -------- + best_spectra : numpy.ndarray + Best set of simulated spectra + """ + # Set random seed if provided + if seed_value != 0: + np.random.seed(seed_value) + + n_gm = selectionParams.nGM + n_periods = len(targetSa.meanReq) + + # Initialize storage for best simulation + best_err = float('inf') + best_spectra = None + + print(f"Simulating {n_trials} sets of response spectra") + + # First ensure the covariance matrix is positive definite + cov_matrix = targetSa.covReq.copy() + + # Check if the matrix is positive definite + try: + L_check = la.cholesky(cov_matrix, lower=True) + print("Covariance matrix is positive definite, proceeding with simulation") + except la.LinAlgError: + # Not positive definite, fix it + print("Covariance matrix is not positive definite, applying correction") + cov_matrix = nearestSPD(cov_matrix) + + # Verify we fixed it + try: + L_check = la.cholesky(cov_matrix, lower=True) + print("Covariance matrix has been corrected to be positive definite") + except: + # If still fails, use a simpler approach + print("Simplifying covariance matrix due to persistent issues") + n = cov_matrix.shape[0] + std_devs = np.sqrt(np.diag(cov_matrix)) + # Create a simplified correlation matrix with exponential decay + simplified_corr = np.zeros((n, n)) + for i in range(n): + for j in range(n): + # Correlation decreases with distance between periods + simplified_corr[i,j] = np.exp(-0.3 * np.abs(i-j)) + + # Convert back to covariance + cov_matrix = np.diag(std_devs) @ simplified_corr @ np.diag(std_devs) + + # Perform multiple trials and select the best one + for trial in range(n_trials): + try: + # Generate multivariate normal samples + z = np.random.normal(0, 1, size=(n_gm, n_periods)) + + # Cholesky decomposition of covariance matrix + try: + L = la.cholesky(cov_matrix, lower=True) + # Apply correlation structure to random samples + spectra = targetSa.meanReq + np.dot(z, L.T) + except: + # If Cholesky still fails, use eigenvalue decomposition + print(f"Warning: Cholesky decomposition failed on trial {trial}, using eigenvalue method") + eigvals, eigvecs = la.eigh(cov_matrix) + # Handle small negative eigenvalues due to numerical errors + eigvals = np.maximum(eigvals, 0) + L = eigvecs @ np.diag(np.sqrt(eigvals)) + spectra = targetSa.meanReq + np.dot(z, L.T) + + # Calculate error in mean and standard deviation + sim_mean = np.mean(spectra, axis=0) + sim_std = np.std(spectra, axis=0) + sim_skew = stats.skew(spectra, axis=0) + + mean_err = np.mean(np.abs(sim_mean - targetSa.meanReq) / np.abs(targetSa.meanReq)) + std_err = np.mean(np.abs(sim_std - targetSa.stdevs) / targetSa.stdevs) + skew_err = np.mean(np.abs(sim_skew)) + + total_err = (selectionParams.weights[0] * mean_err + + selectionParams.weights[1] * std_err + + selectionParams.weights[2] * skew_err) + + # Save if this is the best simulation so far + if total_err < best_err: + best_err = total_err + best_spectra = np.exp(spectra) + except Exception as e: + print(f"Warning: Error during simulation trial {trial}: {str(e)}") + continue + + # If all trials failed, create a simple set of spectra based on the mean and stddev + if best_spectra is None: + print("Warning: All simulation trials failed. Creating simplified spectra.") + # Create simplified spectra based on lognormal distribution + best_spectra = np.zeros((n_gm, n_periods)) + std_devs = targetSa.stdevs + + for i in range(n_gm): + # Randomize around the target mean with the target standard deviation + # But without correlation structure + log_values = targetSa.meanReq + np.random.normal(0, std_devs, n_periods) + best_spectra[i] = np.exp(log_values) + + return best_spectra +def find_ground_motions(selection_params, simulated_spectra, IMs): + """ + Find best matches to the simulated spectra from ground-motion database + + Parameters: + ----------- + selection_params : SelectionParams + Parameters controlling the selection + simulated_spectra : numpy.ndarray + Simulated target spectra + IMs : IntensityMeasures + Structure to store results + + Returns: + -------- + IMs : IntensityMeasures + Updated with selected ground motions + """ + # Determine index for scaling - conditioning period or all periods + if selection_params.cond == 1: + scale_fac_index = selection_params.indTcond + else: + scale_fac_index = np.arange(len(selection_params.TgtPer)) + + # Initialize vectors + IMs.recID = np.zeros(selection_params.nGM, dtype=int) + IMs.sampleSmall = np.zeros((0, len(selection_params.TgtPer))) # Empty array to stack onto + IMs.scaleFac = np.ones(selection_params.nGM) + + # Find database spectra most similar to each simulated spectrum + for i in range(selection_params.nGM): # For each simulated spectrum + err = np.zeros(selection_params.nBig) # Initialize error matrix + scale_fac = np.ones(selection_params.nBig) # Initialize scale factors to 1 + + # Compute scale factors and errors for each candidate ground motion + for j in range(selection_params.nBig): + if selection_params.isScaled: # If scaling is allowed + # Calculate scale factor using equation from MATLAB code + exp_sample = np.exp(IMs.sampleBig[j, scale_fac_index]) + numerator = np.sum(exp_sample * simulated_spectra[i, scale_fac_index]) + denominator = np.sum(exp_sample**2) + scale_fac[j] = numerator / denominator + + # Compute error - sum of squared log differences + err[j] = np.sum((np.log(np.exp(IMs.sampleBig[j, :]) * scale_fac[j]) - np.log(simulated_spectra[i, :]))**2) + + # Exclude previously-selected ground motions + if i > 0: + err[IMs.recID[:i]] = 1000000 + + # Exclude ground motions requiring too large of a scale factor + err[scale_fac > selection_params.maxScale] = 1000000 + + # Find minimum-error ground motion + min_err = np.min(err) + if min_err >= 1000000: + raise ValueError('Warning: problem with simulated spectrum. No good matches found') + + min_idx = np.argmin(err) + IMs.recID[i] = min_idx + IMs.scaleFac[i] = scale_fac[min_idx] # Store scale factor + + # Store scaled log spectrum + scaled_spectrum = np.log(np.exp(IMs.sampleBig[min_idx, :]) * scale_fac[min_idx]) + IMs.sampleSmall = np.vstack([IMs.sampleSmall, scaled_spectrum]) + + return IMs + +def find_ground_motionsV(selection_params, simulated_spectra, IMs): + """ + Find best matches to the simulated spectra including vertical components + + This is similar to find_ground_motions but includes matching of vertical components. + The implementation would be more complex in a full version. + """ + # This is a placeholder for the vertical component matching + # Full implementation would be similar to find_ground_motions but with + # additional consideration of vertical spectra + + # First call regular ground motion selection + IMs = find_ground_motions(selection_params, simulated_spectra, IMs) + + # Then add vertical scale factors if needed + if selection_params.matchV == 1 and selection_params.sepScaleV == 1: + n_gm = selection_params.nGM + IMs.scaleFacV = np.ones(n_gm) + + # Here would compute separate scale factors for vertical components + # For simplicity in this example, use same scale factors + IMs.scaleFacV = IMs.scaleFac.copy() + + return IMs + +def plot_target_spectrum(target_sa, selection_params): + """ + Plot target response spectrum to visualize its properties + + Parameters: + ----------- + target_sa : dict or TargetSa + Target spectral acceleration data + selection_params : SelectionParams + Parameters controlling the selection + """ + import matplotlib.pyplot as plt + + # Check if target_sa is a dictionary and access attributes accordingly + if isinstance(target_sa, dict): + mean_req = target_sa.get('meanReq') + stdevs = target_sa.get('stdevs') + cov_req = target_sa.get('covReq') + else: + mean_req = getattr(target_sa, 'meanReq', None) + stdevs = getattr(target_sa, 'stdevs', None) + cov_req = getattr(target_sa, 'covReq', None) + + # Check if required attributes are valid + if mean_req is None: + print("Error: mean_req is None") + return + + # Print some basic information about the target_sa object + print(f"Target spectrum information:") + print(f"meanReq shape: {np.shape(mean_req) if mean_req is not None else 'None'}") + print(f"stdevs shape: {np.shape(stdevs) if stdevs is not None else 'None'}") + print(f"covReq shape: {np.shape(cov_req) if cov_req is not None else 'None'}") + + try: + plt.figure(figsize=(10, 6)) + + # Plot target mean spectrum + plt.loglog(selection_params.TgtPer, np.exp(mean_req), 'r-', linewidth=2, label='Target Mean') + + # Plot confidence intervals (mean ± 1 standard deviation) + plt.loglog(selection_params.TgtPer, np.exp(mean_req + stdevs), 'r--', linewidth=1.5, label='Mean + 1σ') + plt.loglog(selection_params.TgtPer, np.exp(mean_req - stdevs), 'r--', linewidth=1.5, label='Mean - 1σ') + + # Add plot details + plt.grid(True, which="both", ls="-", alpha=0.3) + plt.xlabel('Period (s)') + plt.ylabel('Spectral Acceleration (g)') + plt.title('Target Response Spectrum') + plt.legend(loc='upper right') + + # Add conditioning period marker + plt.axvline(x=selection_params.Tcond, color='k', linestyle=':', label=f'Tcond = {selection_params.Tcond}s') + + # Save the plot + plt.savefig('target_spectrum.png', dpi=300) + plt.close() + + print(f"Target spectrum plot saved to 'target_spectrum.png'") + print(f"Mean values range: {np.min(np.exp(mean_req)):.4f} - {np.max(np.exp(mean_req)):.4f}") + print(f"Standard deviation range: {np.min(stdevs):.4f} - {np.max(stdevs):.4f}") + + # Also print covariance matrix properties if available + if cov_req is not None: + try: + eigenvalues = np.linalg.eigvalsh(cov_req) + print(f"Covariance matrix eigenvalues range: {np.min(eigenvalues):.6f} - {np.max(eigenvalues):.6f}") + print(f"Condition number of covariance matrix: {np.max(eigenvalues)/np.min(eigenvalues):.2f}") + except Exception as e: + print(f"Error calculating covariance matrix properties: {str(e)}") + + except Exception as e: + print(f"Error plotting target spectrum: {str(e)}") + + + + +def within_tolerance(sample_small, target_sa, selection_params): + """ + Check if errors are within tolerance to potentially skip optimization + + Parameters: + ----------- + sample_small : numpy.ndarray + Selected spectra + target_sa : TargetSa + Target spectral data + selection_params : SelectionParams + Parameters controlling the selection + + Returns: + -------- + bool + True if within tolerance, False otherwise + """ + # Calculate sample mean and standard deviation + sample_mean = np.mean(sample_small, axis=0) + sample_std = np.std(sample_small, axis=0) + + # Calculate errors + mean_err = np.mean(np.abs((sample_mean - target_sa.meanReq) / target_sa.meanReq)) * 100 + std_err = np.mean(np.abs((sample_std - target_sa.stdevs) / target_sa.stdevs)) * 100 + + # Calculate total error using weights + total_err = selection_params.weights[0] * mean_err + selection_params.weights[1] * std_err + + print(f"Mean error: {mean_err:.2f}%, Std dev error: {std_err:.2f}%, Total: {total_err:.2f}%") + + # Check if within tolerance + return total_err < selection_params.tol + + +def within_toleranceV(IMs, target_sa, selection_params): + """ + Check if errors are within tolerance for both horizontal and vertical components + + Parameters are similar to within_tolerance but include vertical components + """ + # Check horizontal components + h_within = within_tolerance(IMs.sampleSmall, target_sa, selection_params) + + # For vertical components, simplified here + if selection_params.matchV == 1: + # Calculate some dummy errors for demonstration + IMs.medianErr = 5.0 + IMs.stdErr = 7.0 + v_within = (IMs.medianErr + IMs.stdErr) < selection_params.tol + else: + v_within = True + + return h_within and v_within, IMs + + +def optimize_ground_motions(selection_params, target_sa, IMs): + """ + Further optimize the ground motion selection + + Parameters: + ----------- + selection_params : SelectionParams + Parameters controlling the selection + target_sa : TargetSa + Target spectral data + IMs : IntensityMeasures + Current set of selected motions + + Returns: + -------- + IMs : IntensityMeasures + Optimized set of selected motions + """ + n_gm = selection_params.nGM + n_big = IMs.sampleBig.shape[0] # Number of records in database + n_loops = selection_params.nLoop + + print(f"Optimizing selection with {n_loops} iterations") + + # Store current selection + best_rec_id = IMs.recID.copy() + best_scale_fac = IMs.scaleFac.copy() + best_sample_small = IMs.sampleSmall.copy() + + # Calculate initial error + best_mean = np.mean(best_sample_small, axis=0) + best_std = np.std(best_sample_small, axis=0) + + mean_err = np.mean(np.abs((best_mean - target_sa.meanReq) / target_sa.meanReq)) * 100 + std_err = np.mean(np.abs((best_std - target_sa.stdevs) / target_sa.stdevs)) * 100 + + best_err = selection_params.weights[0] * mean_err + selection_params.weights[1] * std_err + + # Optimization loops + for loop in range(n_loops): + print(f"Optimization loop {loop+1}/{n_loops}, current error: {best_err:.2f}%") + + # Try replacing each ground motion + for i in range(n_gm): + # Remove one ground motion from the set + temp_set = np.delete(best_sample_small, i, axis=0) + + # Calculate the target properties with one fewer ground motion + temp_mean = np.mean(temp_set, axis=0) + temp_std = np.std(temp_set, axis=0) + + # Find a replacement that minimizes error + best_replacement_err = float('inf') + best_replacement_id = -1 + best_replacement_scale = 1.0 + + # Search through database for a better replacement + for j in range(n_big): + # Skip if already in the selected set + if j in best_rec_id: + continue + + db_spectrum = IMs.sampleBig[j, :] + + # Compute scale factor to match at conditioning period + if selection_params.isScaled == 1: + scale_factor = np.exp(target_sa.meanReq[selection_params.indTcond] - db_spectrum[selection_params.indTcond]) + + # Check if scale factor is within limits + if scale_factor > selection_params.maxScale or scale_factor < 1.0/selection_params.maxScale: + continue + else: + scale_factor = 1.0 + + # Scale the spectrum + scaled_spectrum = db_spectrum + np.log(scale_factor) + + # Add to temporary set and calculate new statistics + new_set = np.vstack([temp_set, scaled_spectrum]) + new_mean = np.mean(new_set, axis=0) + new_std = np.std(new_set, axis=0) + + # Calculate error + new_mean_err = np.mean(np.abs((new_mean - target_sa.meanReq) / target_sa.meanReq)) * 100 + new_std_err = np.mean(np.abs((new_std - target_sa.stdevs) / target_sa.stdevs)) * 100 + + new_err = selection_params.weights[0] * new_mean_err + selection_params.weights[1] * new_std_err + + # Save if this is the best replacement so far + if new_err < best_replacement_err: + best_replacement_err = new_err + best_replacement_id = j + best_replacement_scale = scale_factor + + # Check if the replacement improves the overall error + if best_replacement_err < best_err: + best_err = best_replacement_err + best_rec_id[i] = best_replacement_id + best_scale_fac[i] = best_replacement_scale + best_sample_small[i, :] = IMs.sampleBig[best_replacement_id, :] + np.log(best_replacement_scale) + + print(f" Replaced ground motion {i+1}, new error: {best_err:.2f}%") + + # Update the results + IMs.recID = best_rec_id + IMs.scaleFac = best_scale_fac + IMs.sampleSmall = best_sample_small + + return IMs + + +def optimize_ground_motionsV(selection_params, target_sa, IMs): + """ + Optimize ground motion selection including vertical components + + Similar to optimize_ground_motions but includes vertical components + """ + # This is a placeholder - full implementation would be more complex + # First optimize horizontal components + IMs = optimize_ground_motions(selection_params, target_sa, IMs) + + # Then optimize vertical components if needed + if selection_params.matchV == 1 and selection_params.sepScaleV == 1: + # Here would optimize vertical scale factors + # For simplicity, just use the horizontal scale factors + IMs.scaleFacV = IMs.scaleFac.copy() + + return IMs + +def plot_results(selection_params, target_sa, IMs, simulated_spectra, SaKnown, known_per): + """ + Plot results of the ground motion selection + + Parameters: + ----------- + selection_params : SelectionParams + Parameters controlling the selection + target_sa : TargetSa + Target spectral data + IMs : IntensityMeasures + Selected intensity measures + simulated_spectra : numpy.ndarray + Simulated target spectra + SaKnown : numpy.ndarray + Ground motion spectra from database + known_per : numpy.ndarray + Periods available in the database + """ + target_periods = selection_params.TgtPer + + plt.figure(figsize=(12, 8)) + + # Plot individual selected spectra + for i in range(selection_params.nGM): + # Extract original record and apply scale factor + original_spectrum = SaKnown[IMs.recID[i], :] + scaled_spectrum = original_spectrum * IMs.scaleFac[i] + + # Plot on log-log scale + plt.loglog(known_per, scaled_spectrum, color='lightgray', linewidth=0.5) + + # Plot target spectrum with confidence intervals + plt.loglog(target_periods, np.exp(target_sa.meanReq), 'r-', linewidth=2, label='Target Median') + plt.loglog(target_periods, np.exp(target_sa.meanReq + target_sa.stdevs), 'r--', linewidth=1.5, label='Target 84th Percentile') + plt.loglog(target_periods, np.exp(target_sa.meanReq - target_sa.stdevs), 'r--', linewidth=1.5, label='Target 16th Percentile') + + # Calculate mean and confidence intervals of selected spectra + # Extract values at target periods for each selected ground motion + selected_spectra = np.zeros((len(IMs.recID), len(selection_params.TgtPer))) + + # For each selected ground motion, extract values at target periods + for i, rec_id in enumerate(IMs.recID): + # Find the indices in known_per that correspond to target periods + for j, period in enumerate(selection_params.TgtPer): + idx = np.argmin(np.abs(known_per - period)) + selected_spectra[i, j] = np.log(SaKnown[rec_id, idx] * IMs.scaleFac[i]) + + # Calculate statistics + selected_means = np.mean(selected_spectra, axis=0) + selected_std = np.std(selected_spectra, axis=0) + + # Plot selected spectra statistics + plt.loglog(target_periods, np.exp(selected_means), 'b-', linewidth=2, label='Selected Median') + plt.loglog(target_periods, np.exp(selected_means + selected_std), 'b--', linewidth=1.5, label='Selected 84th Percentile') + plt.loglog(target_periods, np.exp(selected_means - selected_std), 'b--', linewidth=1.5, label='Selected 16th Percentile') + + # Add details to plot + plt.grid(True, which="both", ls="-", alpha=0.3) + plt.xlabel('Period (s)') + plt.ylabel('Spectral Acceleration (g)') + plt.title(f'Response Spectra for M{selection_params.rup.M_bar}, R{selection_params.rup.Rjb}km') + plt.legend(loc='upper right') + + # Mark conditioning period if conditional selection + if selection_params.cond == 1: + plt.axvline(x=selection_params.Tcond, color='k', linestyle=':', label=f'Tcond = {selection_params.Tcond}s') + + plt.tight_layout() + plt.savefig('selected_spectra.png', dpi=300) + plt.show() + + + +def plot_resultsV(selection_params, target_sa, IMs, simulated_spectra, SaKnown, known_per): + """ + Plot results of ground motion selection including vertical components + + Similar to plot_results but includes vertical component plots + """ + # First plot horizontal components + plot_results(selection_params, target_sa, IMs, simulated_spectra, SaKnown, known_per) + + # Then plot vertical components if needed + if selection_params.matchV == 1: + plt.figure(figsize=(12, 8)) + plt.title('Vertical Component Spectra') + # Implementation would be similar to plot_results + # but for vertical components + plt.tight_layout() + plt.savefig('selected_spectra_vertical.png', dpi=300) + plt.show() + + + +def download_time_series(output_dir, rec_idx, metadata): + """ + Extract selected time series from NGA-West2 zip file to the working directory + """ + import zipfile + import os + import re + + # Create output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + print(f"Extracting time series files to {output_dir}") + + # Read RSN numbers from the output file + output_file_path = os.path.join(output_dir, "M6p5_R10_rock_Output.dat") + rsn_numbers = [] + + if os.path.exists(output_file_path): + with open(output_file_path, 'r') as f: + lines = f.readlines() + found_table = False + + for line in lines: + # Skip until we find the table header + if "Record Number\t" in line: + found_table = True + continue + + # Skip empty lines and header + if not found_table or not line.strip(): + continue + + # Parse the tab-separated line + parts = line.strip().split('\t') + if len(parts) >= 3: + try: + # The RSN is in the second column + rsn = int(parts[1]) + rsn_numbers.append(rsn) + except (ValueError, IndexError): + pass + + if rsn_numbers: + print(f"Found {len(rsn_numbers)} RSN numbers: {rsn_numbers[:5]}...") + else: + print("WARNING: Could not read RSN numbers from the output file") + # Fallback to using NGA_num if available + if 'NGA_num' in metadata and len(metadata['NGA_num']) > 0: + for idx in rec_idx: + try: + rsn = metadata['NGA_num'][idx] + rsn_numbers.append(rsn) + except (IndexError, TypeError): + pass + + # Path to the NGA_W2.zip file - adjust as needed + zip_path = r"C:\Users\35191\Documents\NGA_W2.zip" # Update this path + + if not os.path.exists(zip_path): + print(f"ERROR: Zip file not found at {zip_path}") + return + + print(f"Looking for RSN numbers: {rsn_numbers}") + + extracted_files = [] + + # Extract files from zip + try: + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + # List all files in the zip + file_list = zip_ref.namelist() + + # Filter out macOS metadata files + file_list = [f for f in file_list if not f.startswith('__MACOSX/') and not os.path.basename(f).startswith('._')] + + # For each selected ground motion + for i, rsn in enumerate(rsn_numbers): + # Find files that match the RSN pattern + rsn_pattern = f"RSN{rsn}_" + rsn_files = [f for f in file_list if rsn_pattern in f and f.endswith('.AT2')] + + if not rsn_files: + # Try with subdirectory + rsn_files = [f for f in file_list if f"NGA_W2/RSN{rsn}_" in f and f.endswith('.AT2')] + + if not rsn_files: + # Try more permissive patterns + rsn_files = [f for f in file_list if f"RSN{rsn}" in f and f.endswith('.AT2')] + rsn_files.extend([f for f in file_list if f"NGA_W2/RSN{rsn}" in f and f.endswith('.AT2')]) + + # Remove any macOS metadata files that might have slipped through + rsn_files = [f for f in rsn_files if not os.path.basename(f).startswith('._')] + + if rsn_files: + # Extract each matching file (up to 2 for horizontal components) + for j, rsn_file in enumerate(rsn_files[:2]): + # Check file size in zip to avoid extracting tiny metadata files + file_info = zip_ref.getinfo(rsn_file) + if file_info.file_size < 5000: # Skip suspiciously small files + print(f" WARNING: File {rsn_file} is too small ({file_info.file_size} bytes), skipping") + continue + + # Determine output filename + base_name = os.path.basename(rsn_file) + out_path = os.path.join(output_dir, f"GM{i+1}_comp{j+1}.AT2") + + # Extract the file + with zip_ref.open(rsn_file) as source, open(out_path, 'wb') as target: + target.write(source.read()) + + print(f" Extracted {base_name} to {out_path}") + extracted_files.append(out_path) + else: + print(f" WARNING: No files found for RSN {rsn}") + + # Summary + if extracted_files: + print(f"Successfully extracted {len(extracted_files)} files to {output_dir}") + else: + print("No files were extracted") + + # Print some example filenames to help troubleshoot + valid_files = [f for f in file_list if f.endswith('.AT2') and not os.path.basename(f).startswith('._')][:10] + print("\nExample valid filenames in the zip:") + for f in valid_files: + print(f" {f}") + + except zipfile.BadZipFile: + print(f"Error: {zip_path} is not a valid zip file") + except Exception as e: + print(f"Error extracting files: {str(e)}") + +def write_output(rec_idx, IMs, output_dir, output_file, metadata): + """ + Write a tab-delimited file with selected ground motions and scale factors + + Parameters: + ----------- + rec_idx : numpy.ndarray + Indices of selected motions in original database + IMs : IntensityMeasures + Selected intensity measures + output_dir : str + Directory for output files + output_file : str + Name of output file + metadata : dict + Information about the database and selected records + """ + # Create directory for outputs, if it doesn't yet exist + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + with open(os.path.join(output_dir, output_file), 'w') as f: + # Print header information if available + if 'getTimeSeries' in metadata and metadata['getTimeSeries'] is not None: + for i in range(min(3, len(metadata['getTimeSeries']))): + if metadata['getTimeSeries'][i]: + f.write(f"{metadata['getTimeSeries'][i]}\n") + f.write("\n") + + # Write column headers based on number of components + if isinstance(metadata['Filename'], list) or isinstance(metadata['Filename'], np.ndarray): + if len(np.shape(metadata['Filename'])) == 1 or np.shape(metadata['Filename'])[1] == 1: + # Only one ground motion component + f.write("Record Number\tRecord Sequence Number\tScale Factor\tComponent Number\tFile Name\tURL\n") + elif np.shape(metadata['Filename'])[1] == 2: + # Two components + f.write("Record Number\tRecord Sequence Number\tScale Factor\tFile Name Dir. 1\tFile Name Dir. 2\tURL 1\tURL 2\n") + else: + # Three components (including vertical) + f.write("Record Number\tRecord Sequence Number\tScale Factor (H)\tScale Factor (V)\tFile Name Dir. 1\tFile Name Dir. 2\tFile Name Dir. 3\tURL 1\tURL 2\tURL 3\n") + + # Write record data + for i, idx in enumerate(rec_idx): + try: + idx_int = int(idx) # Convert to integer for indexing + + if isinstance(metadata['Filename'], list) or isinstance(metadata['Filename'], np.ndarray): + # Handle different array shapes + filename_shape = np.shape(metadata['Filename']) + + if len(filename_shape) == 1 or filename_shape[1] == 1: + # Only one ground motion component + comp_num = metadata.get('compNum', [1])[idx_int] if 'compNum' in metadata else 1 + filename = metadata['Filename'][idx_int] + dir_location = metadata.get('dirLocation', [''])[idx_int] if 'dirLocation' in metadata else '' + f.write(f"{i+1}\t{idx_int+1}\t{IMs.scaleFac[i]:.2f}\t{int(comp_num)}\t{filename}\t{os.path.join(dir_location, filename)}\n") + + elif filename_shape[1] == 2: + # Two components + filename1 = metadata['Filename'][idx_int, 0] if filename_shape[1] > 0 else '' + filename2 = metadata['Filename'][idx_int, 1] if filename_shape[1] > 1 else '' + dir_location = metadata.get('dirLocation', [''])[idx_int] if 'dirLocation' in metadata else '' + url1 = os.path.join(dir_location, filename1) + url2 = os.path.join(dir_location, filename2) + f.write(f"{i+1}\t{idx_int+1}\t{IMs.scaleFac[i]:.2f}\t{filename1}\t{filename2}\t{url1}\t{url2}\n") + + else: + # Three components (including vertical) + filename1 = metadata['Filename'][idx_int, 0] if filename_shape[1] > 0 else '' + filename2 = metadata['Filename'][idx_int, 1] if filename_shape[1] > 1 else '' + filename3 = metadata['Filename'][idx_int, 2] if filename_shape[1] > 2 else '' + dir_location = metadata.get('dirLocation', [''])[idx_int] if 'dirLocation' in metadata else '' + url1 = os.path.join(dir_location, filename1) + url2 = os.path.join(dir_location, filename2) + url3 = os.path.join(dir_location, filename3) + + # Get vertical scale factor if available + scale_v = IMs.scaleFacV[i] if hasattr(IMs, 'scaleFacV') else IMs.scaleFac[i] + + f.write(f"{i+1}\t{idx_int+1}\t{IMs.scaleFac[i]:.2f}\t{scale_v:.2f}\t{filename1}\t{filename2}\t{filename3}\t{url1}\t{url2}\t{url3}\n") + + except Exception as e: + print(f"Warning: Error writing record {i+1} (index {idx}): {str(e)}") + # Write a placeholder line + f.write(f"{i+1}\t{idx}\t{IMs.scaleFac[i]:.2f}\tError writing record\n") + + print(f"Output written to {os.path.join(output_dir, output_file)}") + + + +def get_default_parameters(): + """ + Get default parameters for ground motion selection + + Returns: + -------- + dict + Dictionary containing all default parameters + """ + params = { + # Ground motion database and selection + "database_file": "NGA_W2_meta_data", + "conditional": False, # Use conditional spectrum? (False: unconditional) + "components": 2, # 1: single-component, 2: two-component selection + "rotD": 50, # 50: use SaRotD50, 100: use SaRotD100 + + # Spectral periods and scaling + "num_motions": 30, # Number of ground motions to select + "cond_period": 1.5, # Conditioning period (s) + "period_min": 0.1, # Minimum period of interest (s) + "period_max": 10.0, # Maximum period of interest (s) + "num_periods": 30, # Number of periods between min and max + "sa_cond": None, # Target Sa at conditioning period (optional) + + # Vertical component + "match_vertical": False, # Match vertical spectrum? + "period_min_v": 0.01, # Minimum vertical period (s) + "period_max_v": 10.0, # Maximum vertical period (s) + "weight_v": 0.5, # Weight for vertical spectrum matching + "scale_v_separate": True, # Scale vertical components separately? + + # Scaling and error options + "allow_scaling": True, # Allow records to be scaled? + "max_scale_factor": 10.0, # Maximum scale factor + "tolerance": 10, # Tolerable percent error to skip optimization + "error_metric": "SSE", # "SSE": sum squared error, "KS": K-S statistic + "error_penalty": 0, # Penalty for spectra far from target + "error_weights": [1.0, 2.0, 0.3], # Weights for [mean, stdev, skewness] errors + "optimization_loops": 2, # Number of optimization loops + "use_variance": True, # Use computed variance vs. target of 0 + + # Rupture scenario + "magnitude": 6.5, # Earthquake magnitude + "distance_jb": 11, # Joyner-Boore distance (km) + "epsilon": 1.9, # Target epsilon (for conditional selection) + "vs30": 259, # Shear wave velocity (m/s) + "z1": 999, # Depth to Vs=1.0 km/s horizon (km), 999=unknown + "region": 1, # 0: global, 1: California, 2: Japan, 3: China/Turkey + "fault_type": 1, # 0: unspecified, 1: strike-slip, 2: normal, 3: reverse + + # Additional rupture parameters + "distance_rup": 11, # Rupture distance (km) + "distance_x": 11, # Horizontal distance (km) + "width": 15, # Down-dip rupture width (km) + "depth_tor": 0, # Depth to top of rupture (km) + "depth_bor": 15, # Depth to bottom of rupture (km) + "dip": 90, # Fault dip angle (deg) + "rake": 0, # Rake angle (deg) + "hanging_wall": 0, # Hanging wall flag + "z2p5": 1, # Depth to Vs=2.5 km/s (km) + "hypo_depth": 10, # Hypocentral depth (km) + + # Ground motion database filtering + "vs30_min": float('-inf'), # Minimum Vs30 to consider (m/s) + "vs30_max": float('inf'), # Maximum Vs30 to consider (m/s) + "mag_min": 6.0, # Minimum magnitude to consider + "mag_max": 8.2, # Maximum magnitude to consider + "dist_min": 0, # Minimum distance to consider (km) + "dist_max": 50, # Maximum distance to consider (km) + "exclude_records": [], # List of record IDs to exclude + + # OpenQuake integration + "use_openquake": True, # Use OpenQuake hazardlib GMMs? + "gmm_name": "BooreEtAl2014", # OpenQuake GMM to use + + # Runtime options + "show_plots": True, # Generate plots? + "copy_files": True, # Extract time series files? + "random_seed": 0, # Random seed (0 for random) + "num_trials": 20, # Number of Monte Carlo trials + "output_dir": "Data", # Output directory + "output_file": "Output_File.dat" # Output filename + } + + return params + + +def create_selection_params(params): + """ + Create a SelectionParams object with custom parameters + + Parameters: + ----------- + params : dict + Dictionary with parameter values + + Returns: + -------- + SelectionParams + Initialized object + """ + sp = SelectionParams() + + # Ground motion database and selection + sp.database_file = params["database_file"] + sp.cond = 1 if params["conditional"] else 0 + sp.arb = params["components"] + sp.RotD = params["rotD"] + + # Spectral periods and scaling + sp.nGM = params["num_motions"] + sp.Tcond = params["cond_period"] + sp.Tmin = params["period_min"] + sp.Tmax = params["period_max"] + sp.TgtPer = np.logspace(np.log10(sp.Tmin), np.log10(sp.Tmax), params["num_periods"]) + sp.SaTcond = params["sa_cond"] + + # Vertical component + sp.matchV = 1 if params["match_vertical"] else 0 + sp.TminV = params["period_min_v"] + sp.TmaxV = params["period_max_v"] + sp.weightV = params["weight_v"] + sp.sepScaleV = 1 if params["scale_v_separate"] else 0 + sp.TgtPerV = np.logspace(np.log10(sp.TminV), np.log10(sp.TmaxV), 20) + + # Scaling and error options + sp.isScaled = 1 if params["allow_scaling"] else 0 + sp.maxScale = params["max_scale_factor"] + sp.tol = params["tolerance"] + sp.optType = 0 if params["error_metric"] == "SSE" else 1 + sp.penalty = params["error_penalty"] + sp.weights = params["error_weights"] + sp.nLoop = params["optimization_loops"] + sp.useVar = 1 if params["use_variance"] else 0 + + # OpenQuake integration + sp.use_openquake = params["use_openquake"] + sp.gmm_name = params["gmm_name"] + + return sp + + +def create_rupture(params): + """ + Create a Rupture object with custom parameters + + Parameters: + ----------- + params : dict + Dictionary with parameter values + + Returns: + -------- + Rupture + Initialized object + """ + rup = Rupture() + + # Basic parameters + rup.M_bar = params["magnitude"] + rup.Rjb = params["distance_jb"] + rup.eps_bar = params["epsilon"] + rup.Vs30 = params["vs30"] + rup.z1 = params["z1"] + rup.region = params["region"] + rup.Fault_Type = params["fault_type"] + + # Additional parameters + rup.Rrup = params["distance_rup"] + rup.Rx = params["distance_x"] + rup.W = params["width"] + rup.Ztor = params["depth_tor"] + rup.Zbot = params["depth_bor"] + rup.dip = params["dip"] + rup.lambda_ = params["rake"] + rup.Fhw = params["hanging_wall"] + rup.Z2p5 = params["z2p5"] + rup.Zhyp = params["hypo_depth"] + + # Set fault mechanism flags + if rup.Fault_Type == 3: # Reverse + rup.FRV = 1 + rup.FNM = 0 + elif rup.Fault_Type == 2: # Normal + rup.FRV = 0 + rup.FNM = 1 + else: # Strike-slip or unspecified + rup.FRV = 0 + rup.FNM = 0 + + return rup + + +def create_allowed_records(params): + """ + Create an AllowedRecords object with custom parameters + + Parameters: + ----------- + params : dict + Dictionary with parameter values + + Returns: + -------- + AllowedRecords + Initialized object + """ + ar = AllowedRecords() + + ar.Vs30 = [params["vs30_min"], params["vs30_max"]] + ar.Mag = [params["mag_min"], params["mag_max"]] + ar.D = [params["dist_min"], params["dist_max"]] + ar.idxInvalid = params["exclude_records"] + + return ar + +def export_scale_factors(rec_idx, scale_factors, output_dir): + """ + Export scale factors to a separate file for easy use in other applications + + Parameters: + ----------- + rec_idx : numpy.ndarray + Indices of selected motions (Record Sequence Numbers) + scale_factors : numpy.ndarray + Scale factors for each selected motion + output_dir : str + Directory for output files + """ + # Create output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + # Define output file path + sf_file_path = os.path.join(output_dir, "SF.txt") + + # Write scale factors to file + with open(sf_file_path, 'w') as f: + f.write("# Ground Motion Scale Factors\n") + f.write("# GM_ID\tRSN\tScale_Factor\n") + f.write("#---------------------------\n") + + for i, (rsn, sf) in enumerate(zip(rec_idx, scale_factors)): + gm_id = f"GM{i+1}" + f.write(f"{gm_id}\t{rsn}\t{sf:.4f}\n") + + print(f"Scale factors exported to {sf_file_path}") + + +def select_ground_motions(custom_params=None): + """ + Select earthquake ground motions with response spectra matching a target scenario. + + Parameters: + ----------- + custom_params : dict, optional + Dictionary of parameters to override defaults + + Returns: + -------- + dict + Results of the ground motion selection process + """ + script_dir = os.path.dirname(os.path.abspath(__file__)) + os.chdir(script_dir) + + # Get default parameters and update with custom ones + params = get_default_parameters() + if custom_params: + params.update(custom_params) + + print("Starting ground motion selection process") + print(f"Scenario: M{params['magnitude']}, R={params['distance_jb']}km, Vs30={params['vs30']}m/s") + print(f"{'Conditional' if params['conditional'] else 'Unconditional'} spectrum with {params['num_motions']} ground motions") + + # Create parameter objects with custom values + selection_params = create_selection_params(params) + rup = create_rupture(params) + allowed_recs = create_allowed_records(params) + IMs = IntensityMeasures() + + # Assign rupture parameters to selection_params for reference + selection_params.rup = rup + + # Results dictionary + results = { + 'success': False, + 'target_spectrum': None, + 'selected_records': None, + 'scale_factors': None, + 'error': None + } + + # Load and screen the database + try: + SaKnown, selection_params, ind_per, known_per, metadata = screen_database( + selection_params, allowed_recs + ) + + # Save logarithmic spectral accelerations at target periods + IMs.sampleBig = np.log(SaKnown[:, ind_per]) + if selection_params.matchV == 1: + IMs.sampleBigV = np.log(selection_params.SaKnownV[:, selection_params.indPerV]) + + # Compute target means and covariances + gmm_name = params["gmm_name"] if params["use_openquake"] else "built-in BSSA 2014" + print(f"Computing target spectrum using {gmm_name} GMM") + target_sa = get_target_spectrum(known_per, selection_params, ind_per, rup) + results['target_spectrum'] = target_sa + + if params["show_plots"]: + plot_target_spectrum(target_sa, selection_params) + + # Simulate response spectra + simulated_spectra = simulate_spectra(target_sa, selection_params, + params["random_seed"], params["num_trials"]) + + # Find best matches + if selection_params.matchV == 1: + IMs = find_ground_motionsV(selection_params, simulated_spectra, IMs) + else: + IMs = find_ground_motions(selection_params, simulated_spectra, IMs) + + # Store first stage results + IMs.stageOneScaleFac = IMs.scaleFac.copy() + IMs.stageOneMeans = np.mean(np.log(SaKnown[IMs.recID, :] * IMs.stageOneScaleFac[:, np.newaxis]), axis=0) + IMs.stageOneStdevs = np.std(np.log(SaKnown[IMs.recID, :] * IMs.stageOneScaleFac[:, np.newaxis]), axis=0) + if selection_params.matchV == 1: + IMs.stageOneScaleFacV = IMs.scaleFacV.copy() + IMs.stageOneMeansV = np.mean(np.log(selection_params.SaKnownV[IMs.recID, :] * IMs.stageOneScaleFacV[:, np.newaxis]), axis=0) + IMs.stageOneStdevsV = np.std(np.log(selection_params.SaKnownV[IMs.recID, :] * IMs.stageOneScaleFacV[:, np.newaxis]), axis=0) + + # Optimize if needed + if selection_params.matchV == 1: + within_tol, IMs = within_toleranceV(IMs, target_sa, selection_params) + if within_tol: + print(f"Optimization skipped - errors within tolerance") + else: + IMs = optimize_ground_motionsV(selection_params, target_sa, IMs) + else: + if within_tolerance(IMs.sampleSmall, target_sa, selection_params): + print(f"Optimization skipped - errors within tolerance") + else: + IMs = optimize_ground_motions(selection_params, target_sa, IMs) + + # Plot results + if params["show_plots"]: + if selection_params.matchV == 1: + plot_resultsV(selection_params, target_sa, IMs, simulated_spectra, SaKnown, known_per) + else: + plot_results(selection_params, target_sa, IMs, simulated_spectra, SaKnown, known_per) + + # Output results + rec_idx = metadata['allowedIndex'][IMs.recID] + write_output(rec_idx, IMs, params["output_dir"], params["output_file"], metadata) + + # Export scale factors to separate file + export_scale_factors(rec_idx, IMs.scaleFac, params["output_dir"]) + + + # Copy time series if requested + if params["copy_files"]: + download_time_series(params["output_dir"], rec_idx, metadata) + + # Store results + results['success'] = True + results['selected_records'] = rec_idx + results['scale_factors'] = IMs.scaleFac + results['record_info'] = { + 'magnitude': params['magnitude'], + 'distance': params['distance_jb'], + 'vs30': params['vs30'], + 'conditional': params['conditional'], + 'cond_period': params['cond_period'] if params['conditional'] else None + } + + print("Ground motion selection complete") + + except Exception as e: + print(f"Error during ground motion selection: {str(e)}") + results['error'] = str(e) + import traceback + traceback.print_exc() + + return results + + +if __name__ == "__main__": + # Example usage with parameter overrides + + # Example 1: M6.5 strike-slip earthquake at 10km, rock site + rock_site_params = { + "magnitude": 7.5, + "distance_jb": 10, + "vs30": 760, + "vs30_min": 200, # Minimum Vs30 to consider (m/s) + "vs30_max": 900, # Maximum Vs30 to consider (m/s) + + "conditional": True, + "cond_period": 1.0, + "num_motions": 20, + + # Key improvements: + "optimization_loops": 5, # Increase from 2 to 5 + "max_scale_factor": 3.0, # Increase from 10 to 15 + "error_weights": [1.0, 1.0, 0.1], # Adjust to balance mean/stddev errors + + # Filter database to more relevant records + "mag_min": 6, # Target larger earthquakes + "dist_max": 100, # Allow slightly larger distances + + "output_file": "M6p5_R10_rock_Output.dat", + + # GMM specification + "use_openquake": True, # Use OpenQuake hazardlib + "gmm_name": "BooreEtAl2014" + } + select_ground_motions(rock_site_params) \ No newline at end of file diff --git a/openquake/vmtk/seismic_wave_generator.py b/openquake/vmtk/seismic_wave_generator.py new file mode 100644 index 0000000..add6cfa --- /dev/null +++ b/openquake/vmtk/seismic_wave_generator.py @@ -0,0 +1,1650 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Feb 9 12:39:31 2025 + +@author: Amir Taherian +""" +import os +from math import ceil +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from dataclasses import dataclass, field +from typing import Tuple, Dict, Any, List, Optional +from scipy.fft import fft, ifft +from scipy import signal +from scipy import linalg +from scipy.signal import butter, sosfilt, savgol_filter +from models import amfBJ + + +@dataclass +class GeometricSpreadingParameters: + r_ref: float = 1.0 + segments: List[Tuple[float, float]] = field(default_factory=lambda: [(1.0, -1.0)]) + + def get_spreading(self, r: float) -> float: + if r <= self.r_ref: + return (r/self.r_ref)**self.segments[0][1] + + g = (self.r_ref/self.r_ref)**self.segments[0][1] + prev_r = self.r_ref + + for i, (dist, slope) in enumerate(self.segments): + if r <= dist: + g *= (r/prev_r)**slope + return g + elif i < len(self.segments) - 1: + g *= (dist/prev_r)**slope + prev_r = dist + else: + g *= (r/prev_r)**slope + return g + return g + +@dataclass +class QualityFactorParameters: + Q0: float = 100.0 + eta: float = 0.5 + Qmin: float = 100.0 + + def get_Q(self, f: np.ndarray) -> np.ndarray: + return np.maximum(self.Q0 * f**self.eta, self.Qmin) + +@dataclass +class PathDurationParameters: + """Parameters defining the path duration model.""" + duration_points: List[Tuple[float, float]] = field(default_factory=lambda: [(0.0, 0.0), (10.0, 0.5)]) + slope_beyond_last: float = 0.05 # Duration increase per km beyond last point + + def get_duration(self, r: float) -> float: + """Compute path duration for distance r.""" + if r <= self.duration_points[0][0]: + return self.duration_points[0][1] + + for i in range(1, len(self.duration_points)): + r1, d1 = self.duration_points[i-1] + r2, d2 = self.duration_points[i] + + if r <= r2: + # Linear interpolation between defined duration points + return d1 + (d2 - d1) * (r - r1) / (r2 - r1) + + # Beyond the last point, use the specified slope + last_r, last_d = self.duration_points[-1] + return last_d + self.slope_beyond_last * (r - last_r) + +@dataclass +class SiteAttenuationParameters: + kappa: float = 0.03 + + def get_diminution(self, f: np.ndarray) -> np.ndarray: + return np.exp(-np.pi * f * self.kappa) + +@dataclass +class PulseParameters: + """Parameters for the Mavroeidis and Papageorgiou pulse model.""" + enabled: bool = False + gamma: float = 0.5 + nu: float = 0.0 + t0: Optional[float] = None + peak_factor: float = 1.0 + +@dataclass +class StochasticModelParameters: + """Complete set of parameters for stochastic ground motion simulation.""" + # Basic simulation parameters + dt: float = 0.005 # Time step (s) + ns: int = 5 # Number of simulations + + # Ground motion model components + geometric_spreading: GeometricSpreadingParameters = field(default_factory=GeometricSpreadingParameters) + quality_factor: QualityFactorParameters = field(default_factory=QualityFactorParameters) + path_duration: PathDurationParameters = field(default_factory=PathDurationParameters) + site_attenuation: SiteAttenuationParameters = field(default_factory=SiteAttenuationParameters) + + # Source parameters + stress_drop: float = 200.0 # Stress parameter (bars) + + # Crustal parameters + roll: float = 2.8 # Density (g/cm³) + beta: float = 3.5 # Shear-wave velocity (km/s) + + # Site parameters + vs30: float = 760.0 # Average shear-wave velocity in the top 30m (m/s) + + # Additional control parameters + taper_percentage: float = 0.05 # Taper percentage for time window + high_cut_freq: float = 50.0 # High-cut filter frequency (Hz) + high_cut_order: int = 8 # High-cut filter order + +@dataclass +class EarthquakeParameters: + M: float + rake: float + strike: float + dip: float + h_ref: float + stress_ref: float + sigma: float + +@dataclass +class SimulationParameters: + # Original parameters + NS: int + dt: float + roll: float + beta: float + Vs30: float + Tr: np.ndarray + kappa: float + tpad: float + #iflagscalefactor: int + pulsing_percent: float + rupture_velocity: float + pulse_params: PulseParameters = field(default_factory=PulseParameters) + + + # New parameter to hold ground motion model settings + gm_model: StochasticModelParameters = None # Default to None, user must provide + +@dataclass +class SiteParameters: + site_lat: float + site_lon: float + +@dataclass +class FaultParameters: + subfault_size: float + rupture_lat: float + rupture_lon: float + + + + + +SCALING_ACCELERATION = 1 # iflagscalefactor value for acceleration scaling +SCALING_BOORE = 2 # iflagscalefactor value for David Boore's scaling +MOMENT_CONSTANT = 16.05 # Constant in seismic moment calculation +FREQUENCY_CONSTANT = 4.906e6 # Constant in static corner frequency calc + +# 1. Fault Grid Initialization +def initialize_fault_grid(earthquake_params: EarthquakeParameters, fault_params: FaultParameters) -> Tuple[float, float, int, int, float]: + """Initializes parameters related to the fault grid.""" + fault_width, fault_length = width_length(earthquake_params.M, earthquake_params.rake, earthquake_params.sigma, earthquake_params.stress_ref) + num_subfaults_length = max(1, int(fault_length / fault_params.subfault_size)) + num_subfaults_width = max(1, int(fault_width / fault_params.subfault_size)) + subRadius = np.sqrt((fault_params.subfault_size**2) / np.pi) # Subfault radius + return fault_width, fault_length, num_subfaults_length, num_subfaults_width, subRadius + +# 2. Generate Hypocenters +def generate_hypocenters(Nhyp: int, fault_params: FaultParameters, earthquake_params: EarthquakeParameters, + fault_width: float, fault_length: float, num_subfaults_length: int, + num_subfaults_width: int, site_params: SiteParameters) -> np.ndarray: + """Generates multiple hypocenter locations.""" + return calculate_random_hypocenters( + Nhyp, fault_params.rupture_lat, fault_params.rupture_lon, earthquake_params.h_ref, earthquake_params.strike, earthquake_params.dip, fault_length, + fault_width, num_subfaults_length, num_subfaults_width, site_params.site_lat, site_params.site_lon + ) + +# 3. Compute Seismic Moment +def compute_seismic_moment(earthquake_params: EarthquakeParameters,simulation_params: SimulationParameters, num_subfaults: int) -> Tuple[float, float, float, float]: + """Computes seismic moment-related parameters.""" + M0 = 10 ** (1.5 * earthquake_params.M + MOMENT_CONSTANT) # Total seismic moment (dyne·cm) + averageMoment = M0 / num_subfaults + fc_static = FREQUENCY_CONSTANT * simulation_params.beta * ((earthquake_params.sigma / M0) ** (1 / 3)) + fc_st = FREQUENCY_CONSTANT * simulation_params.beta * ((earthquake_params.sigma / averageMoment) ** (1 / 3)) + return M0, averageMoment, fc_static, fc_st + +def initialize_time_frequency(simulation_params: SimulationParameters, earthquake_params: EarthquakeParameters, subTrise, subTarrive, subTend) -> Tuple[np.ndarray, int, float, float, float]: + """ + Initializes time and frequency-related parameters to match GMSS2SS.m. + """ + #Constants and Moment Calculation: + C = (0.55 * 2.0 * 0.707) * 1e-20 / (4 * np.pi * simulation_params.roll * simulation_params.beta**3) + + M0=10**(1.5*earthquake_params.M+16.05) # total seismic moment + + # Find maximum Trise, minimum Tarrive and maximum Tend + Trise_max = np.max(subTrise) + Tarrive_min = np.min(subTarrive) + Tend_max = np.max(subTend) + + tpadl, tpadt = simulation_params.tpad, simulation_params.tpad + # Calculate NtotalWave + NtotalWave = (Tend_max - Tarrive_min + tpadl + tpadt + Trise_max) / simulation_params.dt + + # Calculate NT (nearest power of 2) + nseed = round(np.log2(NtotalWave)) + if 2**nseed >= NtotalWave: + NT = 2**nseed + else: + NT = 2**(nseed + 1) + + # Calculate T, df, fmax + T = NT * simulation_params.dt + df = 1 / T + fmax = 1 / (2 * simulation_params.dt) + + # Create time vector + t = np.arange(simulation_params.dt, T + simulation_params.dt, simulation_params.dt) + + return t, NT, df, fmax,C,M0 + + +# 5. Initialize Rupture Variables +class RuptureParameters: + """A simple class to encapsulate rupture parameters.""" + def __init__(self, R_epicentral: float, h_ref: float, dip: float, strike: float, Az: float): + """Initializes rupture parameters.""" + self.R_epicentral = R_epicentral + self.h_ref = h_ref + self.dip = dip + self.strike = strike + self.Az = Az + +def process_single_subfault(i: int, j: int, i0: int, j0: int, dl: float, dw: float, + simulation_params: SimulationParameters, beta: float, subRadius: float, + rpathdur: list, pathdur: list, durslope: float, no_effective_subfaults: int, + rupture_params: RuptureParameters, active_subfaults: np.ndarray, + num_subfaults_length: int, num_subfaults_width: int) -> Tuple[ + int, float, float, float, float, float, float]: + + # Convert 0-based indices to 1-based indices for use in utils functions + i_onebased = i + 1 + j_onebased = j + 1 + i0_onebased = i0 + 1 + j0_onebased = j0 + 1 + + # Calculate n_pulsing_subs but don't use it to determine activity + n_pulsing_subs = number_of_pulsing_subs( + i_onebased, j_onebased, i0_onebased, j0_onebased, num_subfaults_length, num_subfaults_width, no_effective_subfaults + ) + + # ALL subfaults are active + active_subfaults[i, j] = 1 + + # Store n_pulsing_subs for later use in corner frequency calculation + # You might need to return it or store it in a global array + + # Calculate subfault parameters normally + R_subfault = compute_subfault_distance( + rupture_params.R_epicentral, rupture_params.h_ref, rupture_params.dip, rupture_params.strike, rupture_params.Az, dl, dw, i_onebased, j_onebased + ) + + delay_val = np.sqrt((dl * (i_onebased - i0_onebased)) ** 2 + (dw * (j_onebased - j0_onebased)) ** 2) / simulation_params.rupture_velocity if (i_onebased != i0_onebased or j_onebased != j0_onebased) else 0.0 + t_arrive_val = delay_val + R_subfault / simulation_params.beta + rise_time_val = subRadius / simulation_params.rupture_velocity + dur_path_val = compute_t_path(R_subfault, rpathdur, pathdur, durslope) + dur_sub_val = dur_path_val + rise_time_val + + return active_subfaults[i, j], R_subfault, delay_val, t_arrive_val, rise_time_val, dur_path_val, dur_sub_val + + + +def process_subfaults(num_subfaults_length: int, num_subfaults_width: int, i0: int, j0: int, dl: float, dw: float, + simulation_params: SimulationParameters, beta: float, subRadius: float, rpathdur: list, pathdur: list, durslope: float, + no_effective_subfaults: int, rupture_params: RuptureParameters) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, float]: + """Processes all subfaults in the grid.""" + active_subfaults = np.zeros((num_subfaults_length, num_subfaults_width), dtype=int) + R_subfaults = np.zeros((num_subfaults_length, num_subfaults_width)) + delay = np.zeros((num_subfaults_length, num_subfaults_width)) + t_arrive = np.zeros((num_subfaults_length, num_subfaults_width)) + rise_time = np.zeros((num_subfaults_length, num_subfaults_width)) + dur_path = np.zeros((num_subfaults_length, num_subfaults_width)) + dur_sub = np.zeros((num_subfaults_length, num_subfaults_width)) + + t_arrive_min = float("inf") + t_end_max = 0 + + active_subfaults[i0, j0] = 1 + + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + active, R_subfault, delay_val, t_arrive_val, rise_time_val, dur_path_val, dur_sub_val = process_single_subfault( + i, j, i0, j0, dl, dw, simulation_params, beta, subRadius, + rpathdur, pathdur, durslope, no_effective_subfaults, # PASSED ARGUMENTS + rupture_params, active_subfaults, num_subfaults_length, num_subfaults_width #PASSED ARGUMENTS + ) + + + active_subfaults[i, j] = active + R_subfaults[i, j] = R_subfault + delay[i, j] = delay_val + t_arrive[i, j] = t_arrive_val #if (np.isnan(t_arrive_val) == False and active == True) else 0 + rise_time[i, j] = rise_time_val + dur_path[i, j] = dur_path_val + dur_sub[i, j] = dur_sub_val + + if active == True and np.isnan(t_arrive_val) == False: + t_arrive_min = min(t_arrive_min, t_arrive[i, j]) + t_end_max = max(t_arrive[i, j] + dur_sub[i, j], t_end_max) + else: + t_arrive[i, j] = 0 + rise_time[i, j] = 0 #To ensure to prevent error + dur_path[i,j] = 0 #To ensure to prevent error + dur_sub[i,j] = 0 #To ensure to prevent error + + # Validate active subfaults + assert np.any(active_subfaults), "No active subfaults found. Check pulsing logic or input parameters." + + if t_arrive_min == float("inf"): + raise ValueError("t_arrive_min was not updated. Ensure at least one subfault is active and arrival times are calculated.") + + return ( + active_subfaults, R_subfaults, delay, t_arrive, rise_time, + dur_path, dur_sub, t_arrive_min, t_end_max + ) + +# Distance calculation functions + +def width_length(mag, rake,stress,stress_ref): + assert -180 <= rake <= 180, 'Rake must be between -180 and 180.' + + if rake is None: + faultwidth = 10 ** (-1.01 + 0.32 * mag)*(stress_ref/stress)**(1/3) + faultlength = 10 ** (-2.44 + 0.59 * mag)*(stress_ref/stress)**(1/3) + elif (-45 <= rake <= 45) or rake >= 135 or rake <= -135: + faultwidth = 10 ** (-0.76 + 0.27 * mag)*(stress_ref/stress)**(1/3) + faultlength = 10 ** (-2.57 + 0.62 * mag)*(stress_ref/stress)**(1/3) + elif rake > 0: + faultwidth = 10 ** (-1.61 + 0.41 * mag)*(stress_ref/stress)**(1/3) + faultlength = 10 ** (-2.42 + 0.58 * mag)*(stress_ref/stress)**(1/3) + else: + faultwidth = 10 ** (-1.14 + 0.35 * mag)*(stress_ref/stress)**(1/3) + faultlength = 10 ** (-1.88 + 0.50 * mag)*(stress_ref/stress)**(1/3) + + return faultwidth, faultlength + + + +def haversine_distance(lat1, lon1, lat2, lon2): + radius_earth = 6371 # Earth radius in km + lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2]) + dlat = lat2 - lat1 + dlon = lon2 - lon1 + a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 + return 2 * radius_earth * np.arcsin(np.sqrt(a)) + +def compute_subfault_distance(R_hypo, h_ref, Fdip, Fstrike, Az, dl, dw, i, j): + """ + Python equivalent of FUNsubR: computes the distance between a site and a subfault. + + Parameters: + - R_hypo: Epicentral distance (km) + - h_ref: Depth of fault reference point (km) + - Fdip: Fault dip angle (degrees) + - Fstrike: Fault strike angle (degrees) + - Az: Azimuth to the site (degrees) + - dl: Along-strike subfault length (km) + - dw: Down-dip subfault width (km) + - i, j: Subfault indices (along strike and dip) + + Returns: + - subR: Distance to subfault (km) + """ + + # Convert angles to radians + Fstrike_radians = np.radians(Az - Fstrike) + Fdip_radians = np.radians(90 - Fdip) + + # Compute offsets + t1 = R_hypo * np.cos(Fstrike_radians) - ((2 * i - 1) * dl / 2) + t2 = R_hypo * np.sin(Fstrike_radians) - ((2 * j - 1) * dw / 2) * np.sin(Fdip_radians) + t3 = -h_ref - ((2 * j - 1) * dw / 2) * np.cos(Fdip_radians) + + # Euclidean distance + subR = np.sqrt(t1**2 + t2**2 + t3**2) + return subR + +def compute_site_location(SLfactor, SL1, SL2, FaultLat, FaultLon): + """ + Computes the site location's epicentral distance and azimuth relative to the fault origin. + + Parameters: + - SLfactor: Determines input type (1 for lat/lon; 2 for distance/azimuth). + - SL1, SL2: Inputs (latitude/longitude or distance/azimuth). + - FaultLat, FaultLon: Latitude and longitude of the fault origin. + + Returns: + - SiteLat: Latitude of the site (if applicable). + - SiteLon: Longitude of the site (if applicable). + - R: Epicentral distance (km). + - Az: Azimuth (degrees). + """ + + if SLfactor == 1: + # Input is in lat/lon + site_lat = SL1 + site_lon = SL2 + + # Calculate epicentral distance (R) + # Haversine distance for epicentral distance + R = haversine_distance(site_lat, site_lon, FaultLat, FaultLon) + + # Calculate azimuth (Az) + delta_lon = np.radians(site_lon - FaultLon) + lat1, lat2 = np.radians(FaultLat), np.radians(site_lat) + + x = np.sin(delta_lon) * np.cos(lat2) + y = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(delta_lon) + Az = (np.degrees(np.arctan2(x, y)) + 360) % 360 # Normalize to [0, 360) + + elif SLfactor == 2: + # Input is R (distance) and Az (azimuth) + R = SL1 + Az = SL2 + + # Convert back to lat/lon if needed + earth_radius = 6371 # Earth's radius in km + angular_distance = R / earth_radius + azimuth_rad = np.radians(Az) + + FaultLat_rad = np.radians(FaultLat) + FaultLon_rad = np.radians(FaultLon) + + site_lat = np.degrees(np.arcsin(np.sin(FaultLat_rad) * np.cos(angular_distance) + + np.cos(FaultLat_rad) * np.sin(angular_distance) * np.cos(azimuth_rad))) + site_lon = np.degrees(FaultLon_rad + np.arctan2(np.sin(azimuth_rad) * np.sin(angular_distance) * np.cos(FaultLat_rad), + np.cos(angular_distance) - np.sin(FaultLat_rad) * np.sin(np.radians(site_lat)))) + else: + raise ValueError("SLfactor must be 1 (lat/lon input) or 2 (distance/azimuth input).") + + return site_lat, site_lon, R, Az + +# First, add these two functions to your imports section +def calculate_FUNh(rx, rz, w1, w2, s1, s2): + """Calculate distance to fault rupture (helper function)""" + if rx <= s1: + if rz <= w1: + h = np.sqrt((rx - s1)**2 + (rz - w1)**2) + icase = 1 + elif rz > w1 and rz < w2: + h = np.abs(rx - s1) + icase = 4 + else: # rz >= w2 + h = np.sqrt((rx - s1)**2 + (rz - w2)**2) + icase = 7 + elif rx > s1 and rx < s2: + if rz <= w1: + h = np.abs(rz - w1) + icase = 2 + elif rz > w1 and rz < w2: + h = 0 # Inside the rupture plane + icase = 5 + else: # rz >= w2 + h = np.abs(rz - w2) + icase = 8 + else: # rx >= s2 + if rz <= w1: + h = np.sqrt((rx - s2)**2 + (rz - w1)**2) + icase = 3 + elif rz > w1 and rz < w2: + h = np.abs(rx - s2) + icase = 6 + else: # rz >= w2 + h = np.sqrt((rx - s2)**2 + (rz - w2)**2) + icase = 9 + return h, icase + +def calculate_fault_distances(site_lat, site_lon, ref_lat, ref_lon, f_strike, f_dip, h_ref, + fault_length, fault_width): + """Calculate Rrup, Rjb and other fault distances""" + # Define fault boundaries for distance calculations + s1 = -fault_length/2 + s2 = fault_length/2 + w1 = 0 # Top of fault + w2 = fault_width + h_min = 0 # Minimum seismogenic depth + + d2r = np.pi / 180.0 + fstrike = f_strike * d2r + fdip = f_dip * d2r + + # Compute unit vectors + ix_n = np.cos(fstrike) + ix_e = np.sin(fstrike) + ix_d = 0 + iy_n = -np.sin(fstrike) * np.sin(fdip) + iy_e = np.cos(fstrike) * np.sin(fdip) + iy_d = -np.cos(fdip) + iz_n = -np.sin(fstrike) * np.cos(fdip) + iz_e = np.cos(fstrike) * np.cos(fdip) + iz_d = np.sin(fdip) + + # Convert site lat/lon to northern and eastern distances + dist_n = (site_lat - ref_lat) * d2r * 6371 + dist_e = (site_lon - ref_lon) * d2r * np.cos(0.5 * d2r * (site_lat + ref_lat)) * 6371 + dist_d = -h_ref + + # Convert coordinates to fault reference frame + rx = dist_n * ix_n + dist_e * ix_e + dist_d * ix_d + ry = dist_n * iy_n + dist_e * iy_e + dist_d * iy_d + rz = dist_n * iz_n + dist_e * iz_e + dist_d * iz_d + + # Find Rrup + hrup, _ = calculate_FUNh(rx, rz, w1, w2, s1, s2) + Rrup = np.sqrt(hrup**2 + ry**2) + + # Find Rjb + s1jb = s1 + s2jb = s2 + w1jb = w1 * np.cos(fdip) + w2jb = w2 * np.cos(fdip) + rxjb = rx + rzjb = -np.sin(fstrike) * dist_n + np.cos(fstrike) * dist_e + + hjb, _ = calculate_FUNh(rxjb, rzjb, w1jb, w2jb, s1jb, s2jb) + Rjb = hjb + + return Rrup, Rjb + + +def compute_point_source_distance(R, h_ref, Fdip, Fstrike, Az): + """ + Computes the distance between a site and a point source (hypocenter). + + Parameters: + - R: Epicentral distance (km). + - h_ref: Depth of the point source (km). + - Fdip: Fault dip angle (degrees). + - Fstrike: Fault strike angle (degrees). + - Az: Azimuth to the site (degrees). + + Returns: + - pointR: Distance to the point source (km). + """ + + # Convert angles to radians + Fstrike_radians = np.radians(Az - Fstrike) + Fdip_radians = np.radians(90 - Fdip) + + # Hypocenter position (simplified for a point source) + t1 = R * np.cos(Fstrike_radians) + t2 = R * np.sin(Fstrike_radians) * np.sin(Fdip_radians) + t3 = -h_ref - R * np.sin(Fstrike_radians) * np.cos(Fdip_radians) + + # Euclidean distance + pointR = np.sqrt(t1**2 + t2**2 + t3**2) + return pointR + + +def number_of_pulsing_subs(i, j, i0, j0, num_subfaults_length, num_subfaults_width, no_effective_subfaults): + """ + Determines the number of pulsing subfaults around the given subfault (i, j). + . + """ + # Compute Rmax and Rmin + r_max = max(abs(i - i0) + 1, abs(j - j0) + 1) + r_min = max(0, r_max - no_effective_subfaults) + + n = 0 # Counter for active subfaults + + # Iterate over the entire fault grid + for ii in range(1, num_subfaults_length + 1): + for jj in range(1, num_subfaults_width + 1): + r = max(abs(ii - (i0 + 1)) + 1, abs(jj - (j0 + 1)) + 1) + if r_min < r < r_max: + n += 1 + + return n + +def locate_hypocenter_randomly(fault_length, fault_width, num_length, num_width): + """ + Randomly selects a hypocenter on the fault plane based on subfault dimensions. + + Parameters: + fault_length (float): Total fault length (km). + fault_width (float): Total fault width (km). + num_length (int): Number of subfaults along the length. + num_width (int): Number of subfaults along the width. + + Returns: + tuple: Hypocenter coordinates (x, y) in fault plane indices. + """ + # Randomly select indices within the valid range + i0 = np.random.randint(0, num_length) # Python indexing: 0 <= i0 < num_length + j0 = np.random.randint(0, num_width) # Python indexing: 0 <= j0 < num_width + + return i0, j0 + + +def calculate_random_hypocenters( + Nhyp, rupture_lat, rupture_lon, h_ref, strike, dip, fault_length, fault_width, + num_length, num_width, site_lat=None, site_lon=None +): + """ + Calculates multiple random hypocenters and their distances and azimuths relative to the fault's top edge. + + Parameters: + - Nhyp (int): Number of hypocenters to generate. + - rupture_lat (float): Fault's top-center latitude. + - rupture_lon (float): Fault's top-center longitude. + - h_ref (float): Fault's top depth (km). + - strike (float): Fault strike angle (degrees). + - dip (float): Fault dip angle (degrees). + - fault_length (float): Total fault length (km). + - fault_width (float): Total fault width (km). + - num_length (int): Number of subfaults along the length. + - num_width (int): Number of subfaults along the width. + - site_lat (float, optional): Site latitude (degrees). + - site_lon (float, optional): Site longitude (degrees). + + Returns: + - list: A list of dictionaries, each representing a hypocenter's properties: + - 'latitude': Latitude of the hypocenter. + - 'longitude': Longitude of the hypocenter. + - 'depth': Depth of the hypocenter (km). + - 'R': Distance to the site (if site location is provided). + - 'Az': Azimuth to the site (if site location is provided). + - 'i0': Hypocenter's subfault index along the length. + - 'j0': Hypocenter's subfault index along the width. + """ + hypocenters = [] + + for _ in range(Nhyp): + # Generate a single random hypocenter + i0, j0 = locate_hypocenter_randomly(fault_length, fault_width, num_length, num_width) + + # Calculate subfault dimensions + subfault_length = fault_length / num_length + subfault_width = fault_width / num_width + + # Hypocenter's local coordinates on the fault plane + x_offset = (i0 - 0.5) * subfault_length # Middle of subfault in the x-direction + z_offset = (j0 - 0.5) * subfault_width # Middle of subfault in the z-direction (down-dip) + + # Convert to geographical coordinates + dx = x_offset * np.cos(np.radians(strike)) + dy = x_offset * np.sin(np.radians(strike)) + dz = z_offset * np.sin(np.radians(dip)) + + hypo_lat = rupture_lat + (dy / 111) # Approximate conversion of km to degrees latitude + hypo_lon = rupture_lon + (dx / (111 * np.cos(np.radians(rupture_lat)))) # Adjust for longitude convergence + hypo_depth = h_ref + dz + + # Calculate site distance and azimuth (if site location is provided) + if site_lat is not None and site_lon is not None: + delta_lat = np.radians(site_lat - rupture_lat) + delta_lon = np.radians(site_lon - rupture_lon) + avg_lat = np.radians((site_lat + rupture_lat) / 2) + + R = np.sqrt((delta_lat * 6371) ** 2 + (delta_lon * 6371 * np.cos(avg_lat)) ** 2) + Az = np.degrees(np.arctan2(delta_lon, delta_lat)) % 360 + else: + R, Az = None, None + + # Store hypocenter properties + hypocenters.append({ + "latitude": hypo_lat, + "longitude": hypo_lon, + "depth": hypo_depth, + "R": R, + "Az": Az, + "i0": i0, + "j0": j0, + }) + + return hypocenters + +# Central Difference Method +def calc_psa_cdm(at, dt, Tr, psi=0.05): + """ + Implementation of the Central Difference Method (CDM) for PSA calculation. + + Parameters: + at : numpy array + Acceleration time series + dt : float + Time step + Tr : numpy array + Natural periods + psi : float, optional + Damping ratio (default is 0.05) + + Returns: + RSD, RSV, RSA : numpy arrays + Spectral displacement, velocity, and acceleration + """ + # Calculate frequencies and parameters + fr = 1.0 / Tr + wn = fr * (2 * np.pi) + A = -wn**2 + 2/(dt**2) + B = psi * wn / dt - 1/(dt**2) + C = 1/(dt**2) + psi * wn / dt + + # Initialize + Nr = len(Tr) + NT = len(at) + u = np.zeros((Nr, NT+1)) # +1 for the extra step in CDM + + # Set initial displacement + u[:, 0] = np.zeros(Nr) # u at t=-dt + u[:, 1] = -at[0] * dt**2 / 2 # u at t=0 + + # Main integration loop + for i in range(Nr): + # Skip very short periods that might cause numerical issues + if Tr[i] < dt*2: + continue + + for j in range(1, NT): + u[i, j+1] = (-(at[j]) + A[i] * u[i, j] + B[i] * u[i, j-1]) / C[i] + + # Calculate response spectra + Sd = np.max(np.abs(u), axis=1) + Sv = wn * Sd + Sa = wn * Sv / 980 # Convert to g + + return Sd, Sv, Sa + +def compute_t_path(R, rpathdur, pathdur, durslope): + """ + Computes the path duration (Tpath) based on the hinge distances, durations, and slope. + + Parameters: + R (float): Distance to the site (km). + rpathdur (list): Hinge distances [km] (e.g., [0.0, 10.0]). + pathdur (list): Corresponding durations at hinge distances [s] (e.g., [0.00, 0.13]). + durslope (float): Slope for extrapolation beyond hinge distances. + + Returns: + float: Path duration (Tpath) in seconds. + """ + if R <= rpathdur[0]: + # Below the first hinge point + Tpath = pathdur[0] + elif R <= rpathdur[1]: + # Between the first and second hinge points + Tpath = pathdur[0] + (pathdur[1] - pathdur[0]) * (R - rpathdur[0]) / (rpathdur[1] - rpathdur[0]) + else: + # Beyond the last hinge point + Tpath = pathdur[1] + durslope * (R - rpathdur[1]) + return Tpath + + +def compute_stochastic_wave( + NS: int, + dt: float, + roll: float, + beta: float, + M: float, + R_subfault: float, + params: StochasticModelParameters, + C, + F0_main: Optional[float] = None, + NT_main: Optional[int] = None, + fc_subfault: Optional[float] = None, + subdur: Optional[float] = None, + subM0: Optional[float] = None, + npadl: int = 0, + n_subs: Optional[int] = None +) -> Tuple[float, np.ndarray]: + """ + Modified compute_stochastic_wave function using parameterized approach for ground motion model. + + Args: + NS (int): Number of simulations. + dt (float): Time step (seconds). + roll (float): Density (g/cm³). + beta (float): Shear-wave velocity (km/s). + M (float): Moment magnitude. + R_subfault (float): Hypocentral distance to subfault (km). + params (StochasticModelParameters): Model parameters. + F0_main (float, optional): Main corner frequency (Hz). + NT_main (int, optional): Original number of time steps. + fc_subfault (float, optional): Corner frequency of the subfault (Hz). + subdur (float, optional): Duration of the subfault (seconds). + subM0 (float, optional): Seismic moment of subfault (dyne-cm). + npadl (int, optional): Left padding samples. + n_subs (int, optional): Number of subfaults. + + Returns: + tuple: (mean_pga, at_padded) + mean_pga (float): Mean Peak Ground Acceleration (g). + at_padded (numpy.ndarray): Simulated acceleration time series (m/s²). + """ + # Default duration if not specified (identical to original) + if subdur is None: + fa = 10**(2.41 - 0.533 * M) + et = 10**(2.52 - 0.637 * M) + fc = F0_main if F0_main is not None else fc_subfault + fb = np.sqrt((fc**2 - (1 - et) * fa**2) / et) if fc is not None else fa + subdur = 1 / (2 * fa) + 1 / (2 * fb) + 0.05 * R_subfault + + # Default seismic moment if not specified (identical to original) + if subM0 is None: + subM0 = 10**(1.5 * M + 16.05) + + # Default corner frequency if not specified (identical to original) + if fc_subfault is None: + sigma_bars = params.stress_drop + fc_subfault = 4.906e6 * beta * (sigma_bars / subM0)**(1/3) + + # Time parameters - identical to original approach + nseed = round(np.log2(subdur / dt)) + 1 + subN = 2**nseed if NT_main is None else NT_main + taper = 0.05 + tmax = subN * dt + subdf = 1 / tmax + subNf = subN // 2 + subf = np.arange(1, subNf + 1) * subdf + ndur = round(subdur / dt) + ntaper = round(taper * ndur) + nstop = ndur + 2 * ntaper + + # Initialize arrays for all simulations + all_seismograms = np.zeros((NS, subN)) + + # Loop through each simulation + for sim in range(NS): + # Gaussian white noise (equivalent to np.random.randn) + nt0 = np.random.normal(0, 1, nstop) + + # Sargoni & Hart window function parameters - identical to original + eps = 0.2 + eta = 0.2 + nstart = 1 + + b = -eps * np.log10(eta) / (1 + eps * (np.log10(eps) - 1)) + c = b / (eps * subdur) + a = (np.e / (eps * subdur))**b + + # Initialize arrays for window function + win = np.zeros(nstop) + twin = np.zeros(nstop) + twin1 = np.zeros(nstop) + wf = np.zeros(nstop) + st = np.zeros(subN) + + # Apply window function to noise - identical to original + for k in range(1, nstop + 1): + k_idx = k - 1 # Adjust for 0-based indexing + if k < nstart or k > nstop: + win[k_idx] = 1 + else: + if k > (nstart + ntaper) and k < (nstop - ntaper): + twin[k_idx] = (subdur / (nstop - nstart - 2 * ntaper)) * (k - nstart - ntaper + 1) + win[k_idx] = a * (twin[k_idx]**b) * np.exp(-c * twin[k_idx]) + else: + if k <= (nstart + ntaper): + twin1[k_idx] = subdur / (nstop - nstart - 2 * ntaper) + wf[k_idx] = a * (twin1[k_idx])**b * np.exp(-c * twin1[k_idx]) + win[k_idx] = abs(np.sin((k - nstart) / ntaper * np.pi / 2)) * wf[k_idx] + else: + if k >= (nstop - ntaper): + wf[k_idx] = a * (subdur)**b * np.exp(-c * subdur) + win[k_idx] = abs(np.sin((nstop - k) / ntaper * np.pi / 2)) * wf[k_idx] + + st_idx = k_idx + npadl + if st_idx < subN: + st[st_idx] = win[k_idx] * nt0[k_idx] + + # Fourier transform - identical to original + As = fft(st) + Angle = np.angle(As[:subNf]) + Asf = np.abs(As[:subNf]) + Asf = Asf * dt + + # Normalize to unit amplitude - identical to original + Asfsum = np.sum(Asf**2) + AveAsf = np.sqrt(Asfsum / subNf) + Adj = Asf / AveAsf + + # THIS IS WHERE WE USE THE PARAMETERIZED APPROACH INSTEAD OF TEA24 + # ------------------------------------------------------------------- + # Compute source spectrum (omega-squared model) + #S = (2 * np.pi * subf)**2 * (subM0 / (1 + (subf / fc_subfault)**2)) + S = C * subM0 / (1 + (subf / fc_subfault)**2) + S = (2 * np.pi * subf)**2 * S + + + # Get geometric spreading + G = params.geometric_spreading.get_spreading(R_subfault) + + # Get anelastic attenuation + Q = params.quality_factor.get_Q(subf) + An = np.exp(-np.pi * subf * R_subfault / Q) + + # Get high-frequency diminution + P = params.site_attenuation.get_diminution(subf) + + # Get site amplification (if available) + if hasattr(params, 'vs30'): + vv = amfBJ(subf, beta, roll, Vs30=params.vs30) + else: + vv = 1.0 + + # Combine all factors + Ax = S * G * An * P * vv + # ------------------------------------------------------------------- + + # Apply a low-cut filter (high-pass) - identical to original + fcut = 0.05 + norder = 8 + blcf = 1 / (1 + (fcut / subf)**(2 * norder)) + Ax = blcf * Ax + + # Apply scaling for finite fault effects - identical to original + if F0_main is not None and n_subs is not None: + NF = n_subs + ScH = np.sqrt(NF) * (F0_main / fc_subfault)**2 + # Low frequency scaling + Csc = np.sqrt(NF) / ScH + f0eff = fc_subfault / np.sqrt(Csc) + L1 = 1 + (subf / fc_subfault)**2 + L2 = 1 + (subf / f0eff)**2 + ScL = Csc * L1 / L2 + + # Total scaling factor + Sc = ScL * ScH + Ax = Ax * Sc + + # Combine normalized noise and target spectrum - identical to original + Aaf = Adj * Ax + + # Inverse Fourier transform with proper symmetry - identical to original + complex_spectrum = np.zeros(subN, dtype=complex) + complex_spectrum[:subNf] = Aaf * np.exp(1j * Angle) + + # Handle Nyquist frequency separately - identical to original + if subN % 2 == 0: # Even number of points + complex_spectrum[subNf] = 0 # Set Nyquist frequency to 0 + complex_spectrum[subNf+1:] = np.conj(complex_spectrum[1:subNf][::-1]) + else: # Odd number of points + complex_spectrum[subNf:] = np.conj(complex_spectrum[1:subNf+1][::-1]) + + # Inverse FFT and scaling - identical to original + subAt0 = ifft(complex_spectrum) * subN + subAt = np.real(subAt0) * 2 * subdf + + # Apply baseline correction - identical to original + subAt = apply_baseline_correction(subAt, dt, 1) + + # Store the result + all_seismograms[sim, :] = subAt + + # Define padding parameters - identical to original + tpad = 1.0 # Padding duration in seconds + npad = int(tpad / dt) # Number of padding samples + N = subN + 2 * npad # Total length after padding + + # Apply pre- and post-padding - identical to original + at_padded = np.zeros((NS, N)) + for m in range(NS): + # Pre-pad with npad zeros + at_pre_padded = np.pad(all_seismograms[m, :], (npad, 0), mode='constant', constant_values=0) + # Post-pad with npad zeros + at_padded[m, :] = np.pad(at_pre_padded, (0, npad), mode='constant', constant_values=0) + + # Create the filter - identical to original + filterts = np.zeros(N) + filterts[:npad + subN] = 1 # 1 for pre-padding and original signal, 0 for post-padding + + # Apply filter - identical to original + for m in range(NS): + at_padded[m, :] *= filterts + + # Adjust to NT_main if necessary - identical to original + current_length = at_padded.shape[1] + if NT_main is not None: + if current_length < NT_main: + padding_size = NT_main - current_length + at_final = np.pad(at_padded, ((0, 0), (0, padding_size)), mode='constant', constant_values=0) + elif current_length > NT_main: + at_final = at_padded[:, :NT_main] + else: + at_final = at_padded + else: + at_final = at_padded + + # Calculate PGA in g - identical to original + PGA_ss = np.max(np.abs(at_final), axis=1) / 9.8 # Convert m/s² to g + mean_pga = np.mean(PGA_ss) + + # Return results + return mean_pga, at_final + + +def apply_baseline_correction(acc, dt, BLfactor=1): + """ + Apply baseline correction to acceleration time series. + + Args: + acc (numpy.ndarray): Acceleration time series. + dt (float): Time step (seconds). + BLfactor (int, optional): Correction factor. Defaults to 1. + + Returns: + numpy.ndarray: Baseline-corrected acceleration time series. + """ + if BLfactor == 0: + return acc + + N = len(acc) + vel = np.zeros(N) + + # Calculate velocity by integrating acceleration + for i in range(1, N): + vel[i] = vel[i-1] + acc[i] * dt + + # Apply baseline correction + t = np.arange(0, N) * dt + a = np.polyfit(t, vel, BLfactor) + + # Calculate correction term + vel_corr = np.zeros(N) + for i in range(BLfactor + 1): + vel_corr += a[i] * t**(BLfactor - i) + + # Calculate acceleration correction + acc_corr = np.zeros(N) + acc_corr[1:] = (vel_corr[1:] - vel_corr[:-1]) / dt + + # Apply correction + acc_corrected = acc - acc_corr + + return acc_corrected + +def mavro_papa_pulse(acceleration, dt, magnitude, + gamma=0.5, nu=0.0, t0=None, peak_factor=1.0): + """ + Apply the Mavroeidis and Papageorgiou (2003) pulse model to a ground motion time series. + + Parameters: + ----------- + acceleration : numpy.ndarray + Input acceleration time series + dt : float + Time step (seconds) + magnitude : float + Earthquake magnitude (used for period scaling) + gamma : float, optional + Parameter controlling the oscillatory character (default: 0.5) + nu : float, optional + Phase of the harmonic (default: 0.0) + t0 : float, optional + Time shift parameter (seconds). If None, automatically determined. + peak_factor : float, optional + Factor to control the pulse amplitude (default: 1.0) + + Returns: + -------- + numpy.ndarray + Modified acceleration time series with pulse component + """ + # Calculate pulse period based on magnitude (Mavroeidis & Papageorgiou, 2003) + pulse_period = 10**(-2.9 + 0.5 * magnitude) # in seconds + pulse_freq = 1.0 / pulse_period + + # Find the peak value of the input motion (to scale the pulse) + peak_value = np.max(np.abs(acceleration)) + + # Number of points + n_points = len(acceleration) + + # Time vector + time = np.arange(0, n_points) * dt + + # Set t0 to 1/4 of the total duration if not specified + if t0 is None: + t0 = 0.25 * n_points * dt + + # Create the pulse in time domain + pulse = np.zeros(n_points) + + # PI constant + pi = np.pi + + # Calculate duration of the pulse + pulse_duration = 2.0 * gamma / pulse_freq + + # Filter the time points where the pulse will be applied + t_pulse_indices = np.where((time >= t0 - pulse_duration) & (time <= t0 + pulse_duration))[0] + + # Apply the pulse only within the relevant time window + for i in t_pulse_indices: + t = time[i] + + # Normalized time + t_norm = (t - t0) * pulse_freq + + if np.abs(t_norm) <= gamma: + # Velocity pulse component (this is the key equation from the paper) + pulse[i] = 0.5 * peak_value * peak_factor * ( + 1.0 + np.cos(pi * t_norm / gamma) + ) * np.cos(2.0 * pi * t_norm + nu) + + # Convert velocity pulse to acceleration by differentiation + acc_pulse = np.zeros(n_points) + acc_pulse[1:-1] = (pulse[2:] - pulse[:-2]) / (2.0 * dt) + + # Add the pulse to the original acceleration + modified_acc = acceleration + acc_pulse + + return modified_acc + + + +# 7. Compute Tapering Factor ( Newly added) + +def simulate_subfault_wave(simulation_params, earthquake_params, R_subfault, + subfault_M0, f0, C, F0_main, dur_sub, NT, npadl, + scaling_factor=None, n_subs=None): + """Simulates the wave contribution from a single subfault using the parameterized approach.""" + + # Check if a ground motion model is provided + if hasattr(simulation_params, 'gm_model') and simulation_params.gm_model is not None: + # Use the ground motion model from simulation parameters + stochastic_params = simulation_params.gm_model + + # Ensure stress_drop is set correctly (from earthquake_params.sigma) + stochastic_params.stress_drop = earthquake_params.sigma * 10 # Convert MPa to bars + else: + # Create a new StochasticModelParameters with default values + stochastic_params = StochasticModelParameters( + dt=simulation_params.dt, + ns=simulation_params.NS, + roll=simulation_params.roll, + beta=simulation_params.beta, + vs30=simulation_params.Vs30 * 1000 if simulation_params.Vs30 < 10 else simulation_params.Vs30, + stress_drop=earthquake_params.sigma * 10, # Convert MPa to bars + + # Default Southwest Iberia inland parameters if no model provided + geometric_spreading=GeometricSpreadingParameters( + r_ref=1.0, + segments=[(70.0, -1.1), (100.0, 0.2), (float('inf'), -1.55)] + ), + + quality_factor=QualityFactorParameters( + Q0=120.0, + eta=0.93, + Qmin=600.0 + ), + + path_duration=PathDurationParameters( + duration_points=[(0.0, 0.0), (10.0, 0.13)], + slope_beyond_last=0.13 + ), + + site_attenuation=SiteAttenuationParameters( + kappa=simulation_params.kappa + ) + ) + + # Call the new function with the prepared parameters + return compute_stochastic_wave( + simulation_params.NS, simulation_params.dt, simulation_params.roll, + simulation_params.beta, earthquake_params.M, R_subfault, + stochastic_params, C, F0_main=F0_main, NT_main=NT, + fc_subfault=f0, subdur=dur_sub, subM0=subfault_M0, npadl=npadl, + n_subs=n_subs + ) +# 12. Finalize Results +def finalize_results(all_PGA: np.ndarray) -> Tuple[float, float]: + """Computes geometric and arithmetic means of PGA values.""" + all_PGA_np = np.array(all_PGA) #enforce to make consistent + all_PGA_non_zero = all_PGA_np[all_PGA_np > 0] # Filter values > 0 + if all_PGA_non_zero.size > 0: + mean_PGA_geometric = np.exp(np.mean(np.log(all_PGA_non_zero))) + else: + mean_PGA_geometric = 0.0 # Or some other appropriate default value + + mean_PGA_arithmetic = np.mean(all_PGA_np) + return mean_PGA_geometric, mean_PGA_arithmetic + +import os + +def export_acc_to_txt( + output_dir: str, + site_idx: int, + hypo_idx: int, + earthquake_params: EarthquakeParameters, + site_params: SiteParameters, + fault_params: FaultParameters, + simulation_params: SimulationParameters, + time_vector: np.ndarray, + acc_data: np.ndarray, + pga: float, + R: float, + sim_idx: int # Add sim_idx as a parameter + +) -> None: + """ + Exports acceleration time series to a text file for a given site and hypocenter. + + Parameters: + - output_dir: Directory where the text file will be saved. + - site_idx: Index of the site (1-based in filename for readability). + - hypo_idx: Index of the hypocenter (1-based in filename). + - earthquake_params: Earthquake parameters (magnitude, etc.). + - site_params: Site parameters (latitude, longitude). + - fault_params: Fault parameters (rupture coordinates). + - simulation_params: Simulation parameters (dt, etc.). + - time_vector: Array of time points (s). + - acc_data: Acceleration time series (m/s²). + - pga: Peak Ground Acceleration (g). + - R: Distance to fault (km). + """ + # Ensure output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Define filename + #txt_filename = os.path.join(output_dir, f'acc_site_{site_idx + 1}_hypo_{hypo_idx + 1}.txt') + txt_filename = os.path.join(output_dir, f'acc_site_{site_idx + 1}_hypo_{hypo_idx + 1}_{sim_idx + 1}.txt') + + # Open file and write metadata + with open(txt_filename, 'w') as f: + f.write(f'Earthquake Magnitude: {earthquake_params.M}\n') + f.write(f'Rupture Coordinates: Latitude = {fault_params.rupture_lat}, Longitude = {fault_params.rupture_lon}\n') + f.write(f'Site Coordinates: Latitude = {site_params.site_lat}, Longitude = {site_params.site_lon}\n') + f.write(f'Distance to Fault (km): {R:.2f}\n') + f.write(f'Time Step (dt): {simulation_params.dt:.5f} s\n') + f.write(f'Peak Ground Acceleration (PGA): {pga:.6f} g\n') + f.write('Time (s)\tAcceleration (m/s^2)\n') # Assuming m/s²; adjust if cm/s² + + # Write time and acceleration data + for t, acc in zip(time_vector, acc_data): + f.write(f'{t:.5f}\t{acc:.6f}\n') + + print(f"Saved acceleration data to {txt_filename}") + + +def finite_fault_sim(Nhyp: int, earthquake_params: EarthquakeParameters, simulation_params: SimulationParameters, + site_params: SiteParameters, fault_params: FaultParameters, + plot_results: bool = False, output_dir: str = "acceleration_files", + site_idx: int = 0, calc_rsa: bool = False) -> Tuple[float, float, np.ndarray, Dict[str, Dict[str, Any]], np.ndarray, Optional[Dict]]: + """ + Finite-fault stochastic simulation function with properly independent simulations + """ + # Seed the random number generator for reproducibility + np.random.seed(42) + + # 1️ Initialize fault grid (as before) + fault_width, fault_length, num_subfaults_length, num_subfaults_width, subRadius = initialize_fault_grid( + earthquake_params, fault_params + ) + + # 2️ Generate multiple hypocenters (as before) + hypocenters = generate_hypocenters( + Nhyp, fault_params, earthquake_params, + fault_width, fault_length, num_subfaults_length, num_subfaults_width, site_params + ) + + # 3️ Compute seismic moment (as before) + M0, averageMoment, fc_static, fc_st = compute_seismic_moment( + earthquake_params, simulation_params, num_subfaults_length * num_subfaults_width + ) + + # Initialize rpathdur, pathdur, durslope (assuming these are constant) + if hasattr(simulation_params, 'gm_model') and simulation_params.gm_model is not None: + # Extract path duration parameters from the model + duration_points = simulation_params.gm_model.path_duration.duration_points + durslope = simulation_params.gm_model.path_duration.slope_beyond_last + + # Convert to separate arrays for distances and durations + rpathdur = [point[0] for point in duration_points] + pathdur = [point[1] for point in duration_points] + + print(f"Using parameterized path duration model: {len(rpathdur)} points, slope={durslope}") + else: + # Fallback to default values if no model is provided + rpathdur = [0.0, 10.0] # Default hinge distances (km) + pathdur = [0.00, 0.13] # Default path durations (seconds) + durslope = 0.130 # Default slope + print("WARNING: Using default path duration parameters") + + # 5️ Loop over hypocenters and process subfaults + all_PGA = [] + all_At = [] + all_At_dict = {} + + # Initialize RSA results dictionary if needed + rsa_results = None + if calc_rsa: + rsa_results = { + "periods": simulation_params.Tr, + "all_rsa": [], # Store RSA for all hypocenters and simulations + "mean_rsa": None # Will store the mean RSA at the end + } + + + for idx, hypo_data in enumerate(hypocenters, 1): + print(f"Processing Hypocenter {idx} at i0={hypo_data['i0']}, j0={hypo_data['j0']}") + + # Initialize i0, j0 properly + i0, j0 = hypo_data["i0"], hypo_data["j0"] + R_hypo = hypo_data["R"] # Call parameter from generate_hypocenter + + # Calculate number of effective subfaults + no_effective_subfaults = max(1, round(num_subfaults_length * (simulation_params.pulsing_percent / 100.0) / 2)) + + # Process each subfault within the fault grid + # Compute Epicentral Distance and Azimuth + _, _, R_epicentral, Az = compute_site_location(1, site_params.site_lat, site_params.site_lon, + fault_params.rupture_lat, fault_params.rupture_lon) + # Initialize rupture variables for each hypocenter + rupture_parameters = RuptureParameters(R_hypo, earthquake_params.h_ref, earthquake_params.dip, + earthquake_params.strike, Az) # creating object + + # Call process_subfaults *before* the nested loops + active_subfaults, R_subfaults, delay, t_arrive, rise_time, dur_path, dur_sub, t_arrive_min, t_end_max = process_subfaults( + num_subfaults_length, num_subfaults_width, i0, j0, fault_params.subfault_size, + fault_params.subfault_size, + simulation_params, simulation_params.beta, subRadius, rpathdur, pathdur, durslope, + no_effective_subfaults, rupture_parameters + ) + + # 4️ Initialize time and frequency parameters - AFTER subfault processing + t1, NT, df, fmax, C, M0 = initialize_time_frequency(simulation_params, earthquake_params, rise_time, + t_arrive, dur_sub) + + # Calculate slip weights and moments + slip_weights = np.zeros((num_subfaults_length, num_subfaults_width)) + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + slip_weights[i, j] = np.random.rand() + + total_weight = np.sum(slip_weights) + subfault_moments = M0 * slip_weights / total_weight + + # Calculate dynamic corner frequency based on number of active subfaults + dynamic_corner_frequencies = np.zeros((num_subfaults_length, num_subfaults_width)) + + # Active Subfaults Logic + NR = np.zeros((num_subfaults_length, num_subfaults_width)) + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + NR[i, j] = number_of_pulsing_subs(i + 1, j + 1, i0 + 1, j0 + 1, num_subfaults_length, + num_subfaults_width, no_effective_subfaults) + if NR[i, j] == 0: + NR[i, j] = 1 # Ensure minimum value of 1 + + # Initialize other parameters + subf0 = np.zeros((num_subfaults_length, num_subfaults_width)) + subTrise = np.zeros((num_subfaults_length, num_subfaults_width)) + subTpath = np.zeros((num_subfaults_length, num_subfaults_width)) + subR = np.zeros((num_subfaults_length, num_subfaults_width)) + subdur = np.zeros((num_subfaults_length, num_subfaults_width)) + subdelay = np.zeros((num_subfaults_length, num_subfaults_width)) + subTarrive = np.zeros((num_subfaults_length, num_subfaults_width)) + subTend = np.zeros((num_subfaults_length, num_subfaults_width)) + + # Subf0 calculations + firstf0 = fc_st + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + subf0[i, j] = firstf0 * NR[i, j] ** (-1 / 3) + # Correct the subfault size + subR[i, j] = compute_subfault_distance(R_epicentral, earthquake_params.h_ref, + earthquake_params.dip, earthquake_params.strike, Az, + fault_params.subfault_size, fault_params.subfault_size, i + 1, + j + 1) + + subTpath[i, j] = compute_t_path(subR[i, j], rpathdur, pathdur, durslope) + subTrise[i, j] = subRadius / simulation_params.rupture_velocity + + subdur[i, j] = subTrise[i, j] + subTpath[i, j] + + subdelay[i, j] = np.sqrt( + (fault_params.subfault_size * (i - i0)) ** 2 + ( + fault_params.subfault_size * (j - j0)) ** 2) / simulation_params.rupture_velocity if ( + i != i0 or j != j0) else 0.0 + subTarrive[i, j] = subdelay[i, j] + subR[i, j] / simulation_params.beta + + subTend[i, j] = subTarrive[i, j] + subdur[i, j] + + stutter = np.zeros((num_subfaults_length, num_subfaults_width)) + + # Generate random stutter delays for complexity in the rupture process + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + stutter[i, j] = np.random.rand() * subTrise[i, j] + + t_arrive_min = np.min(subTarrive) + + nshift = np.zeros((num_subfaults_length, num_subfaults_width)) + # Check conditionals + if (np.isnan(subTarrive).any() == False) and (np.isnan(t_arrive_min) == False) and ( + np.isnan(stutter).any() == False): + nshift = np.round((subTarrive - t_arrive_min + stutter) / simulation_params.dt).astype(int) + else: + nshift = np.zeros_like(subTarrive) + + npadl = int(10/simulation_params.dt) + df = 1/(NT * simulation_params.dt) + f_array = np.arange(0, fmax, df) + n_subs = num_subfaults_length * num_subfaults_width + + # NEW APPROACH: Process each simulation as an independent entity + # Store all simulation results + all_sim_accel = [] + all_sim_pga = [] + + # Loop over all simulations + for sim_idx in range(simulation_params.NS): + # Set a unique random seed for this simulation + np.random.seed(42 + sim_idx) + + # Initialize acceleration array for this simulation + acc_total_sim = np.zeros(NT) + + # Process all subfaults for this single simulation + for i in range(num_subfaults_length): + for j in range(num_subfaults_width): + # Only process active subfaults + if active_subfaults[i, j] == 1: + # Calculate parameters for this subfault + f0 = subf0[i, j] + subfault_M0 = subfault_moments[i, j] + + # Generate a SINGLE subfault contribution + _, acc_single = simulate_subfault_wave_single( + simulation_params, earthquake_params, R_subfaults[i, j], + subfault_M0, f0, C, fc_static, + dur_sub[i, j], NT, npadl, + scaling_factor=None, n_subs=n_subs + ) + + # Ensure acc_single is the right length + if len(acc_single) > NT: + acc_single = acc_single[:NT] + elif len(acc_single) < NT: + acc_single = np.pad(acc_single, (0, NT - len(acc_single)), mode="constant") + + # Apply shift and add to total + delay_steps = nshift[i, j] + acc_total_sim += np.roll(acc_single, delay_steps) + + # Apply pulse model if enabled + if simulation_params.pulse_params.enabled: + acc_total_sim = mavro_papa_pulse( + acc_total_sim, + simulation_params.dt, + earthquake_params.M, + gamma=simulation_params.pulse_params.gamma, + nu=simulation_params.pulse_params.nu, + t0=simulation_params.pulse_params.t0, + peak_factor=simulation_params.pulse_params.peak_factor + ) + + # Store this simulation's complete time series + all_sim_accel.append(acc_total_sim) + + # Calculate PGA for this complete simulation + pga_sim = np.max(np.abs(acc_total_sim)) / 981 # Convert to g + all_sim_pga.append(pga_sim) + + # Export acceleration data to text file for each simulation + export_acc_to_txt( + output_dir=output_dir, + site_idx=site_idx, + hypo_idx=idx - 1, # Adjust to 0-based index + earthquake_params=earthquake_params, + site_params=site_params, + fault_params=fault_params, + simulation_params=simulation_params, + time_vector=t1, + acc_data=acc_total_sim, + pga=pga_sim, + R=R_epicentral, + sim_idx=sim_idx + ) + + # Convert lists to arrays + all_sim_accel = np.array(all_sim_accel) + all_sim_pga = np.array(all_sim_pga) + + # Store in output structures + At_hypo = all_sim_accel + PGA_hypo = all_sim_pga + all_PGA.append(PGA_hypo) + + # Calculate RSA for each simulation if requested + if calc_rsa: + hypo_rsa = np.zeros((simulation_params.NS, len(simulation_params.Tr))) + for sim_idx in range(simulation_params.NS): + _, _, rsa_sim = calc_psa_cdm(all_sim_accel[sim_idx, :], simulation_params.dt, simulation_params.Tr) + hypo_rsa[sim_idx, :] = rsa_sim + + # Store RSA for this hypocenter in the results dictionary + rsa_results[f"Hypocenter_{idx}_RSA"] = hypo_rsa + rsa_results["all_rsa"].append(hypo_rsa) # Store for averaging later + + # Add hypocenter results to dictionary + all_At_dict[f"Hypocenter_{idx}"] = { + "Acceleration": At_hypo, + "Time(s)": t1 + } + + # Print some statistics + print(f"Mean PGA: {np.mean(PGA_hypo):.4f} g") + + # Compute final PGA results + mean_PGA_geometric, mean_PGA_arithmetic = finalize_results(np.array(all_PGA)) + + # Compute average RSA across all hypocenters and simulations + if calc_rsa: + # Stack all RSA arrays + all_rsa_stack = np.vstack([rsa for hypo_rsa in rsa_results["all_rsa"] for rsa in hypo_rsa]) + + # Calculate mean across all simulations and hypocenters + rsa_results["mean_rsa"] = np.mean(all_rsa_stack, axis=0) + + # Plot results if requested + if plot_results: + plot_idx = 0 + num_plots = max(1, len(all_At_dict)) + num_rows = num_plots + num_cols = 1 + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 3 * num_rows), dpi=100) + + # Ensure axes is a NumPy array, even if only one plot + if num_plots == 1: + axes = np.array([axes]) + axes = axes.reshape(num_rows, num_cols) + + # Plot each hypocenter's first simulation + for idx, (hypo_name, hypo_data) in enumerate(list(all_At_dict.items())[:num_plots]): + if hypo_name == "Uniform_Time(s)": + continue + + # Extract acceleration time series and time array for the current hypocenter + At_hypo = hypo_data["Acceleration"] + t1 = hypo_data["Time(s)"] + + # Calculate Epicentral Distance and Azimuth + _, _, R_epicentral, _ = compute_site_location(1, site_params.site_lat, site_params.site_lon, + fault_params.rupture_lat, fault_params.rupture_lon) + + # Plot ONLY the first simulation + sim = 0 # Index of the first simulation + + axes[idx, 0].plot( + t1, + At_hypo[sim, :], + label=f"Total Acceleration - Simulation {sim + 1}", + alpha=0.7, + color='blue' + ) + + # Add title, labels, and legend + title = f"Total Accel. - {hypo_name}\nR = {R_epicentral:.2f} km" + axes[idx, 0].set_title(title) + axes[idx, 0].set_xlabel("Time (s)") + axes[idx, 0].set_ylabel("Acceleration (cm/s²)") + axes[idx, 0].legend(loc='upper left', bbox_to_anchor=(1.05, 1), borderaxespad=0., ncol=1) + axes[idx, 0].grid(True, which='both', linestyle='--', linewidth=0.5) + + plt.tight_layout() + plt.show() + + if calc_rsa: + return mean_PGA_geometric, mean_PGA_arithmetic, t1, all_At_dict, np.array(all_PGA), rsa_results + else: + return mean_PGA_geometric, mean_PGA_arithmetic, t1, all_At_dict, np.array(all_PGA), None + + +def simulate_subfault_wave_single(simulation_params, earthquake_params, R_subfault, + subfault_M0, f0, C, F0_main, dur_sub, NT, npadl, + scaling_factor=None, n_subs=None): + """ + Simulates a single subfault wave contribution (for a single simulation). + This is a modified version of simulate_subfault_wave that returns just one time series. + + Returns: + tuple: (mean_pga, accel_single) + mean_pga (float): Peak Ground Acceleration (g). + accel_single (numpy.ndarray): Single acceleration time series. + """ + # Check if a ground motion model is provided + if hasattr(simulation_params, 'gm_model') and simulation_params.gm_model is not None: + # Use the ground motion model from simulation parameters + stochastic_params = simulation_params.gm_model + + # Ensure stress_drop is set correctly (from earthquake_params.sigma) + stochastic_params.stress_drop = earthquake_params.sigma * 10 # Convert MPa to bars + else: + # Create a new StochasticModelParameters with default values + stochastic_params = StochasticModelParameters( + dt=simulation_params.dt, + ns=1, # Always use 1 here since we want a single realization + roll=simulation_params.roll, + beta=simulation_params.beta, + vs30=simulation_params.Vs30 if simulation_params.Vs30 < 10 else simulation_params.Vs30, + stress_drop=earthquake_params.sigma, # Convert MPa to bars + + # Default Southwest Iberia inland parameters if no model provided + geometric_spreading=GeometricSpreadingParameters( + r_ref=1.0, + segments=[(70.0, -1.1), (100.0, 0.2), (float('inf'), -1.55)] + ), + + quality_factor=QualityFactorParameters( + Q0=120.0, + eta=0.93, + Qmin=600.0 + ), + + path_duration=PathDurationParameters( + duration_points=[(0.0, 0.0), (10.0, 0.13)], + slope_beyond_last=0.13 + ), + + site_attenuation=SiteAttenuationParameters( + kappa=simulation_params.kappa + ) + ) + + # Call compute_stochastic_wave with NS=1 to get a single time series + pga, acc_array = compute_stochastic_wave( + 1, simulation_params.dt, simulation_params.roll, + simulation_params.beta, earthquake_params.M, R_subfault, + stochastic_params, C, F0_main=F0_main, NT_main=NT, + fc_subfault=f0, subdur=dur_sub, subM0=subfault_M0, npadl=npadl, + n_subs=n_subs + ) + + # Extract the single time series + return pga, acc_array[0, :] \ No newline at end of file