From 925d9000a8e8c60742c5d6e2e511e6838b3b6f08 Mon Sep 17 00:00:00 2001 From: AarynnCarter <23636747+AarynnCarter@users.noreply.github.com> Date: Mon, 31 Aug 2026 15:49:28 -0400 Subject: [PATCH 1/4] Add exoearth spectroscopy tutorial and update related configurations --- .gitignore | 1 + docs/source/conf.py | 4 +- docs/source/index.md | 1 + .../exoearth_spectroscopy_tutorial.ipynb | 1583 +++++++++++++++++ 4 files changed, 1588 insertions(+), 1 deletion(-) create mode 100644 tutorials/exoearth_spectroscopy_tutorial.ipynb diff --git a/.gitignore b/.gitignore index 1c515b1..a039eb4 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ src/pyEDITH/__pycache__ build docs/source/imaging_tutorial.ipynb docs/source/spectroscopy_tutorial.ipynb +docs/source/exoearth_spectroscopy_tutorial.ipynb src/pyEDITH/components/__pycache__ src/pyEDITH/components/__pycache__/coronagraphs.cpython-312.pyc src/pyEDITH/components/__pycache__/telescopes.cpython-312.pyc diff --git a/docs/source/conf.py b/docs/source/conf.py index e817dfd..91540fc 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -95,7 +95,9 @@ def setup(app): tutorials_src = source_dir / "../../tutorials" # Copy specific notebooks directly to source folder - notebooks_to_copy = ["imaging_tutorial.ipynb", "spectroscopy_tutorial.ipynb"] + notebooks_to_copy = ["imaging_tutorial.ipynb", + "spectroscopy_tutorial.ipynb", + "exoearth_spectroscopy_tutorial.ipynb"] for notebook in notebooks_to_copy: src = tutorials_src / notebook diff --git a/docs/source/index.md b/docs/source/index.md index 678bd7c..116698c 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -45,6 +45,7 @@ installation run_pyedith imaging_tutorial spectroscopy_tutorial +exoearth_spectroscopy_tutorial yippy_guide glossary validation diff --git a/tutorials/exoearth_spectroscopy_tutorial.ipynb b/tutorials/exoearth_spectroscopy_tutorial.ipynb new file mode 100644 index 0000000..3e4dd87 --- /dev/null +++ b/tutorials/exoearth_spectroscopy_tutorial.ipynb @@ -0,0 +1,1583 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# pyEDITH Tutorial: ExoEarth Spectroscopy Case Study\n", + "\n", + "This notebook walks through assessing the feasibility of distinguishing between different Earth-like spectra for a given Habitable Worlds Observatory (HWO) Early Achitecture Design (EAD) concept." + ], + "id": "c3142ca8c91dd016" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Start by importing the necessary packages, set the verbosity to pyEDITH to \"info\" for full information, and set the default style for HWO plots.", + "id": "463c891fcfec9c0a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:21.986178Z", + "start_time": "2026-08-31T19:43:20.186508Z" + } + }, + "cell_type": "code", + "source": [ + "import os\n", + "import glob\n", + "import copy\n", + "import itertools\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.interpolate import interp1d\n", + "from scipy.optimize import minimize, NonlinearConstraint\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "from astropy import units as u\n", + "from synphot import SourceSpectrum, BlackBodyNorm1D\n", + "from pyEDITH import Filter, set_verbosity, parse_input\n", + "from pyEDITH import AstrophysicalScene, Observatory, Observation\n", + "from pyEDITH import calculate_exposure_time_or_snr\n", + "import hwostyle\n", + "\n", + "# pyEDITH verbosity\n", + "set_verbosity(level=\"warning\")\n", + "\n", + "# Plot styling\n", + "hwostyle.use(\"light\")\n", + "colors = hwostyle.palette" + ], + "id": "d88f2ac1cd5aa658", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 1: Initial Setup", + "id": "5c7e1e2e0b64c8b9" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We need to provide a quantitative metric for how to distinguish between any two spectra. Let us assume a 5$\\sigma$ detection threshold, which corresponds to a $\\chi^2$ value of 25 for Gaussian uncertainties.\n", + "\n", + "We will also define the spectral channels of interest for the calculation, covering 0.4-1.8 $\\mu$m. In pyEDITH these are defined as `Filter` objects, each with its own wavelength bounds and spectral resolution, which are later passed to the calculation via the `filter_list` parameter.\n", + "\n", + "Note that if you need to conduct a more complex test such as comparing multiple models simultaneously, you may need to adopt a different metric." + ], + "id": "6b3581c6d592d309" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:22.006285Z", + "start_time": "2026-08-31T19:43:21.991065Z" + } + }, + "cell_type": "code", + "source": [ + "SIGMA_TARGET = 5.0\n", + "CHI2_TARGET = SIGMA_TARGET ** 2\n", + "\n", + "# Define the spectral channels as pyEDITH Filter objects\n", + "FILTERS = [\n", + " Filter(\"VIS\", low=0.4, high=0.85, resolution=140, type=\"IFS\"),\n", + " Filter(\"Bridge\", low=0.85, high=1.1, resolution=140, type=\"IFS\"),\n", + " Filter(\"NIR\", low=1.1, high=1.8, resolution=70, type=\"IFS\"),\n", + "]\n", + "\n", + "channel_names = [f.name for f in FILTERS]\n", + "n_channels = len(FILTERS)" + ], + "id": "5b72139ff573a075", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Next, we need to provide all of the spectra we would like to compare, and extract the relevant information from them to provide to future calculations. Let's define a dictionary to do this, using the keys as the more readable names, and the values as the paths to the files.", + "id": "8fe6f00d6f6607e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:22.074855Z", + "start_time": "2026-08-31T19:43:22.010248Z" + } + }, + "cell_type": "code", + "source": [ + "# Assemble spectra\n", + "SPEC_PATH = \"/Users/aacarter/Documents/SOFTWARE/HWO/hwo-tools/coron_model/planets/\"\n", + "\n", + "# List Earth spectra files in dictionary\n", + "EARTH_SPECTRA_FILES = {\n", + " \"Archean Earth\": os.path.join(SPEC_PATH, \"ArcheanEarth_geo_albedo.txt\"),\n", + " \"Hazy Archean Earth\": os.path.join(SPEC_PATH, \"Hazy_ArcheanEarth_geo_albedo.txt\"),\n", + " \"Modern Earth\": os.path.join(SPEC_PATH, \"Earth_geo_albedo.txt\"),\n", + " \"Modern Earth 2\": os.path.join(SPEC_PATH, \"Earth2_geo_albedo.txt\"),\n", + " \"Proterozoic Earth (Low O2)\": os.path.join(SPEC_PATH, \"proterozoic_low_o2_geo_albedo.txt\"),\n", + " \"Proterozoic Earth (High O2)\": os.path.join(SPEC_PATH, \"proterozoic_hi_o2_geo_albedo.txt\"),\n", + "}\n", + "\n", + "# Create a dictionary to store the extracted model spectra\n", + "models = {}\n", + "for label, path in EARTH_SPECTRA_FILES.items():\n", + " arr = np.loadtxt(path)\n", + " wl = np.asarray(arr[:, 0], dtype=float)\n", + " albedo = np.asarray(arr[:, 1], dtype=float)\n", + " order = np.argsort(wl)\n", + " models[label] = {\n", + " \"path\": path,\n", + " \"wavelength_um\": wl[order],\n", + " \"albedo\": albedo[order],\n", + " }\n", + "\n", + "# And also extra the names from the dictionary keys\n", + "model_names = sorted(models.keys())\n", + "if len(model_names) < 2:\n", + " raise ValueError(\"Need at least two Earth models for pairwise comparison.\")" + ], + "id": "7aa7e5e9faf99d6c", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's take a pause here and plot all of our spectra to see that things are working correctly.", + "id": "7d8a836405b3806" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:23.351718Z", + "start_time": "2026-08-31T19:43:22.077916Z" + } + }, + "cell_type": "code", + "source": [ + "for name, spectrum in models.items():\n", + " plt.plot(spectrum[\"wavelength_um\"], spectrum[\"albedo\"], label=name)\n", + "plt.xlabel(\"Wavelength (um)\")\n", + "plt.ylabel(\"Geometric Albedo\")\n", + "plt.xlim([0, 2.5])\n", + "plt.ylim([0, 0.5])\n", + "plt.legend()\n", + "plt.show()" + ], + "id": "e31bee91dfae8a5d", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Looks good! Now lets prepare our calculations. Specifically, we need to define the parameters for the simulations, including system specific parameters, instrument setup, and the input wavelength grid.\n", + "\n", + "Note that the input wavelength grid is simply the grid on which we provide the input spectra (stellar flux, planet contrast, target SNR). The actual resolved wavelength grid of each observation is determined by the `Filter` objects we defined above. pyEDITH will automatically rebin things during the actual calculations for a given filter, we just need to make sure the input grid covers the wavelength range of every filter.\n" + ], + "id": "fcec20a8ee30d0fc" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:23.456940Z", + "start_time": "2026-08-31T19:43:23.401607Z" + } + }, + "cell_type": "code", + "source": [ + "# Define the system parameters, let's use Earth at 10 parsecs.\n", + "ap = 1 * u.au # Semi-major axis of planet\n", + "rp = 1 * u.earthRad # Radius of planet\n", + "phase_angle = np.pi/2\n", + "\n", + "# And parameters for star\n", + "ra = 236.00757736823\n", + "dec = 2.51516683165\n", + "dist = 10 * u.pc # Distance to the system\n", + "Tstar = 5800 * u.K\n", + "Rsol = 1.0 * u.Rsun\n", + "\n", + "# And noise parameters\n", + "CRb_multiplier = 2.0 # Background multiplier\n", + "nzodis = 1.0 # Number of zodis\n", + "\n", + "# Define the input wavelength grid on which all input spectra are provided.\n", + "# It must fully cover the wavelength range of every filter.\n", + "wl_input = np.linspace(0.2, 1.9, 1000)\n", + "\n", + "# Define helper functions for stellar spectrum\n", + "def compute_blackbody_photon_flux(temp, wavelengths):\n", + " \"\"\"Generate photon flux density (photon/s/cm^2/um) for a blackbody.\"\"\"\n", + " bb = SourceSpectrum(BlackBodyNorm1D, temperature=temp)\n", + " flux_photlam = bb(wavelengths)\n", + " return flux_photlam\n", + "\n", + "# Calculate the observed Fstar at 10 pc\n", + "wl_input_u = wl_input * u.um\n", + "Fstar = compute_blackbody_photon_flux(Tstar, wl_input_u)\n", + "Fstar = Fstar.to(u.photon / (u.s * u.cm**2 * u.nm)) # Convert to pyEDITH units\n", + "Fstar_obs_10pc = Fstar * (1000*u.pc / dist)**2 # Scale from 1 kpc (synphot default) to 10 pc\n", + "\n", + "# Define the base parameters for the simulations. The spectral channels are\n", + "# specified through the filter_list parameter, and pyEDITH will rebin all\n", + "# input spectra onto each filter's resolved wavelength grid.\n", + "base_params = {\n", + " # --- Observation setup ---\n", + " \"observing_mode\": \"IFS\",\n", + " \"wavelength\": wl_input, # input grid; rebinned per filter\n", + " \"filter_list\": FILTERS, # spectral channels\n", + " \"snr\": np.ones_like(wl_input), # placeholder\n", + " \"CRb_multiplier\": CRb_multiplier,\n", + "\n", + " # --- Astrophysical scene: star ---\n", + " \"distance\": dist.value, # pc\n", + " \"stellar_radius\": Rsol.value, # solar radii\n", + " \"Fstar_10pc\": Fstar_obs_10pc.value, # photon/s/cm^2/nm\n", + " \"ra\": ra, # deg\n", + " \"dec\": dec, # deg\n", + "\n", + " # --- Astrophysical scene: planet ---\n", + " \"Fp/Fs\": 1e-10 * np.ones_like(wl_input), # placeholder\n", + " \"separation\": ap.value / dist.value, # arcsec\n", + "\n", + " # --- Astrophysical scene: zodi ---\n", + " \"nzodis\": nzodis,\n", + "\n", + " # --- Observatory / instrument ---\n", + " \"observatory_preset\": \"EAC1\",\n", + " \"psf_trunc_ratio\": 0.3,\n", + " \"IFS_eff\": 1.0,\n", + " \"noisefloor_PPF\": 30,\n", + "}" + ], + "id": "4ee0778a4ce6754b", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Now we have defined some base settings for the calculation, we can parse the filter list to confirm which filters are active for our input wavelength range, and extract the resolved wavelength grid of each channel." + ], + "id": "c95e32bb261fc415" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:23.482083Z", + "start_time": "2026-08-31T19:43:23.467261Z" + } + }, + "cell_type": "code", + "source": [ + "# Parse the filter list to validate it against the input wavelength range.\n", + "parsed_filters = parse_input.parse_filters(base_params)\n", + "for f in parsed_filters:\n", + " print(f)\n", + "\n", + "# Each Filter carries its own resolved wavelength grid.\n", + "wl_ch = {f.name: np.asarray(f.wavelength.to_value(u.um), dtype=float) for f in parsed_filters}" + ], + "id": "fa161b066900f6bf", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filter(name='VIS', type='IFS', range=0.400 um-0.850 um, center=0.625 um, R=140)\n", + "Filter(name='Bridge', type='IFS', range=0.850 um-1.100 um, center=0.975 um, R=140)\n", + "Filter(name='NIR', type='IFS', range=1.100 um-1.800 um, center=1.450 um, R=70)\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "The calculations we perform will be based on the contrast ratio of the planet to its host star, which is a function of the geometric albedo, the phase angle, the radius of the planet, and the semi-major axis of the orbit.", + "id": "fdc8e69412ec49ce" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:23.497887Z", + "start_time": "2026-08-31T19:43:23.486433Z" + } + }, + "cell_type": "code", + "source": [ + "# Helper function for computing contrast_ratio\n", + "def contrast_ratio(geometric_albedo, phase_angle, planet_radius, separation):\n", + " phase_function = (np.sin(phase_angle) + (np.pi - phase_angle) * np.cos(phase_angle)) / np.pi\n", + " return geometric_albedo * phase_function * (planet_radius / separation).decompose().value ** 2\n", + "\n", + "fpfs_input_all = {} # Contrast ratio on the input grid\n", + "# Loop over each model, compute the contrast ratio and interpolate onto the input grid\n", + "for name, model in models.items():\n", + " fpfs_native = contrast_ratio(model[\"albedo\"], phase_angle, rp, ap)\n", + " fpfs_input_all[name] = np.interp(wl_input, model[\"wavelength_um\"], fpfs_native)" + ], + "id": "6fa25698d89dce4a", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 2: Running The SNR Calculations", + "id": "f2ea572264cd13ca" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We're now in a position to start conducting calculations. We'll start by defining a function that uses the base parameters and the contrast ratio to produce all the relevant setups for a pyEDITH calculation, and then returns a second function that can be used to calculate the SNR for a given input exposure time.", + "id": "a6a4bdba6938d261" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:23.511465Z", + "start_time": "2026-08-31T19:43:23.501199Z" + } + }, + "cell_type": "code", + "source": [ + "def make_snr_runner(base_params, fpfs_input):\n", + " \"\"\"\n", + " Build pyEDITH's observation, scene, and observatory setups\n", + " and return a function that can call the SNR calculation\n", + " for a given exposure time.\n", + "\n", + " Parameters\n", + " ----------\n", + " base_params : dict\n", + " Base parameters for the pyEDITH calculation, including system and instrument setup.\n", + " fpfs_input : dict\n", + " Contrast ratio for the planet to star, keyed by channel name.\n", + "\n", + " Returns\n", + " -------\n", + " run : function\n", + " A function that takes a reference exposure time (in hours) and returns the SNR results for each channel.\n", + " fpfs_resolved : dict\n", + " The contrast ratio rebinned onto the resolved wavelength grid of each channel, keyed by channel name.\n", + " \"\"\"\n", + " params = copy.deepcopy(base_params)\n", + " params[\"Fp/Fs\"] = np.asarray(fpfs_input, dtype=float)\n", + "\n", + " setups = []\n", + " fpfs_resolved = {} #pyEDITH rebinned Fp/FS\n", + " for f in parse_input.parse_filters(params):\n", + " observation = Observation()\n", + " observation.load_configuration(params, filter=f)\n", + " observation.set_output_arrays()\n", + " observation.validate_configuration()\n", + "\n", + " scene = AstrophysicalScene()\n", + " scene.load_configuration(params)\n", + " scene.calculate_zodi_exozodi(params)\n", + " scene.regrid_spectra(observation)\n", + "\n", + " fpfs = scene.Fp_over_Fs\n", + " fpfs_resolved[f.name] = np.asarray(getattr(fpfs, \"value\", fpfs), dtype=float)\n", + "\n", + " observatory = Observatory()\n", + " observatory.create_observatory(parse_input.get_observatory_config(params))\n", + " observatory.load_configuration(params, observation, scene)\n", + " observatory.validate_configuration()\n", + "\n", + " setups.append((f, observation, scene, observatory))\n", + "\n", + " def run(t_ref_hr):\n", + " results = {}\n", + " for f, observation, scene, observatory in setups:\n", + " observation.set_output_arrays() # reset outputs between runs\n", + " observation.obstime = t_ref_hr * u.hr\n", + " calculate_exposure_time_or_snr(observation, scene, observatory,\n", + " mode=\"signal_to_noise\")\n", + " results[f.name] = {\"wavelength\": observation.wavelength,\n", + " \"snr\": observation.fullsnr}\n", + " return results\n", + "\n", + " return run, fpfs_resolved" + ], + "id": "c64d20fa7253abff", + "outputs": [], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Now we can produce a measure of the SNR for any model and filter, however, we cannot simply scale a single reference exposure time to get the SNR for an arbitrary exposure. As pyEDITH accounts for multiple noise sources, the scaling is not linear. For example, if the reference exposure time is high we will be dominated by photon noise, but if it is low then we will be dominated by read noise. Therefore, instead of scaling a single reference exposure, we will build a grid calculations for a range of exposure times and then interpolate between them to get an appropriate noise estimate for any trial exposure time.\n", + "\n", + "Note this cell can take ~20 seconds to run with the default grid spacing." + ], + "id": "7dc673d7146f4897" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:43.776589Z", + "start_time": "2026-08-31T19:43:23.514677Z" + } + }, + "cell_type": "code", + "source": [ + "# Create a grid of reference exposure times in log-log space, from 1 to 100 hr\n", + "t_noise_grid = np.logspace(np.log10(1.0), np.log10(100), 30)\n", + "\n", + "# Determine the per-channel noise templates for each model at every grid time.\n", + "sigma_grid_ch = {}\n", + "fpfs_ch = {}\n", + "for name in model_names:\n", + " runner, fpfs_ch[name] = make_snr_runner(base_params, fpfs_input_all[name])\n", + " rows = {cn: [] for cn in channel_names}\n", + " for t in t_noise_grid:\n", + " results = runner(t)\n", + " for cn in channel_names:\n", + " snr = np.asarray(getattr(results[cn][\"snr\"], \"value\", results[cn][\"snr\"]), dtype=float)\n", + " rows[cn].append(fpfs_ch[name][cn] / np.clip(snr, 1e-30, np.inf))\n", + " sigma_grid_ch[name] = {cn: np.vstack(rows[cn]) for cn in channel_names}\n", + "\n", + "# log times for interpolation routine\n", + "_log_t_grid = np.log(t_noise_grid)\n", + "# Set a noise floor to avoid logging zero or negative values\n", + "_SIGMA_FLOOR = 1e-30\n", + "\n", + "# Define function to return an interpolation function/interpolator for a given noise grid\n", + "def _make_sigma_interp(sigma_grid_2d, kind=\"slinear\"):\n", + " \"\"\"\n", + " Returns a interpolation function that can determine a noise template for any given exposure time\n", + " \"\"\"\n", + " # Operate in log space\n", + " log_sigma = np.log(np.clip(sigma_grid_2d, _SIGMA_FLOOR, None))\n", + " #Build interpolation function\n", + " f = interp1d(_log_t_grid, log_sigma, kind=kind, axis=0,\n", + " bounds_error=False, fill_value=\"extrapolate\")\n", + " lo, hi = t_noise_grid[0], t_noise_grid[-1]\n", + " return lambda t: np.exp(f(np.log(np.clip(t, lo, hi))))\n", + "\n", + "# Create interpolator for each individual channel of every model\n", + "sigma_interp_ch = {}\n", + "for name in model_names:\n", + " # Dict comprehension over channels\n", + " sigma_interp_ch[name] = {\n", + " cn: _make_sigma_interp(sigma_grid_ch[name][cn])\n", + " for cn in channel_names\n", + " }" + ], + "id": "3d30eed6b6dc6aa2", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,519]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,522]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,525]\u001B[0m `FstarV_10pc` not specified in parameters. 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Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,864]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,864]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,905]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,026]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:25,030] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:25,031] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:25,031] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,210]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,221]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,222]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,224]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "/Users/aacarter/Documents/SOFTWARE/HWO/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", + " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,721]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,723]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,725]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,726]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,726]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,769]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,888]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,891] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,892] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,892] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,055]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,066]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,067]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,070]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,070]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,073]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,075]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,076]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,076]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,111]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,253]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,256] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,257] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,258] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,406]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,416]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,417]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,421]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,423]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,424]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,424]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,460]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,570]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,573] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,574] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,574] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,734]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,743]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,745]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,746]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,124]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,125]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,128]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,129]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,129]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,174]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,293]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,441]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,451]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,452]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,454]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,454]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,457]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,458]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,459]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,459]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,487]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,592]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,594] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,595] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,595] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,748]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,758]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,759]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,761]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,762]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,765]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,767]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,767]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,768]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,807]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,925]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,928] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,928] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,929] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,075]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,083]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,084]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,086]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,254]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,255]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,257]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,258]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,258]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,302]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,417]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,420] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,420] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,421] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,560]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,569]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,570]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,572]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,573]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,575]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,577]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,577]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,578]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,607]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,715]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,718] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,719] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,719] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,869]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,882]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,884]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,885]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,885]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,888]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,890]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,890]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,891]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,930]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,036]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,039] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,039] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,040] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,178]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,190]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,191]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,193]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,525]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,527]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,529]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,530]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,530]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,572]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,689]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,692] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,692] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,693] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,841]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,849]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,850]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,852]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,852]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,854]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,856]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,857]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,857]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,885]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,004]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,007] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,007] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,008] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,154]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,163]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,164]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,166]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,166]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,169]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,171]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,172]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,172]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,214]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,328]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,331] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,332] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,332] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,475]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,486]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,488]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,489]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,494]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,496]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,498]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,499]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,499]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,540]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,648]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,651] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,652] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,652] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,803]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,811]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,812]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,814]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,815]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,816]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,819]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,819]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,820]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,851]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,963]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,966] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,966] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,967] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,104]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,114]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,115]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,116]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,117]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,119]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,121]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,122]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,122]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,155]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,268]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,271] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,271] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,272] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,408]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,416]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,418]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 3: Optimizing The Exposure Time", + "id": "cbcfc4f752cea686" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Now we need to take our individual model estimates and compare them in a pairwise fashion to identify how well we can distinguish between any two models. As observations in different channels can have different exposure times, we need to optimize the exposure time in each channel simultaneously to find the minimum required exposure time to reach the desired $\\chi^2$ threshold.\n", + "\n", + "There are two pieces to this that we need to produce. First, a function that determines the $\\chi^2$ value for a given model pair and exposure time, and second, a function that optimizes the exposure times across the different channels together to reach the desired $\\chi^2$ threshold in the minimum amount of time.\n", + "\n", + "For the first, it's simply a case of calculating the sum of the squared differences between the two models, divided by the noise for the model we're assuming to be true." + ], + "id": "c09d27150463b4d7" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:43.807336Z", + "start_time": "2026-08-31T19:43:43.799776Z" + } + }, + "cell_type": "code", + "source": [ + "def chi2_multichannel(times_vector, fpfs_ch_a, fpfs_ch_b, sigma_interp_a, channel_names, t_min=0.01):\n", + " \"\"\"\n", + " Compute chi2 for a comparison between two models on a channel by channel basis\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " times_vector : 1D array\n", + " Exposure times for each channel (hours), ordered as channel_names\n", + " fpfs_ch_a : dict\n", + " Contrast ratios for model A in each channel, keyed by channel name\n", + " fpfs_ch_b : dict\n", + " Contrast ratios for model B in each channel, keyed by channel name\n", + " sigma_interp_a : dict\n", + " Noise interpolators for model A in each channel, keyed by channel name\n", + " channel_names : list of str\n", + " Names of the channels (filter names)\n", + " t_min : float, optional\n", + " Minimum exposure time to avoid division by zero (default is 0.01 hours)\n", + "\n", + " Returns\n", + " -------\n", + " chi2 : float\n", + " Chi-squared value for the comparison between the two models\n", + " \"\"\"\n", + "\n", + " delta_all = []\n", + " sigma_all = []\n", + " for i, cn in enumerate(channel_names):\n", + " # Calculate the delta contrast ratio\n", + " delta_all.append(fpfs_ch_a[cn] - fpfs_ch_b[cn])\n", + " # Determine noise for this exposure time using interpolator\n", + " sigma_all.append(sigma_interp_a[cn](times_vector[i]))\n", + "\n", + " delta_all = np.concatenate(delta_all)\n", + " sigma_all = np.concatenate(sigma_all)\n", + "\n", + " valid = np.isfinite(delta_all) & np.isfinite(sigma_all) & (sigma_all > 0)\n", + " if not np.any(valid):\n", + " return np.inf\n", + "\n", + " # Calculate chi2\n", + " chi2 = np.sum((delta_all[valid] / sigma_all[valid]) ** 2)\n", + " return float(chi2)" + ], + "id": "80656de75913ecf", + "outputs": [], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "And for the second, we can use `scipy.optimize.minimize` to find the optimal exposure times for each channel that minimize the total exposure time while still reaching the desired $\\chi^2$ threshold. To ensure we don't get caught in a local minimum, we'll repeat the optimization with multiple random starting points and keep the best result.", + "id": "1272f9202701e4b9" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:43.819475Z", + "start_time": "2026-08-31T19:43:43.813114Z" + } + }, + "cell_type": "code", + "source": [ + "def required_times_for_pair_multichannel(fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n", + " channel_names, chi2_target=CHI2_TARGET,\n", + " t_min=0.01, t_max=100.0, n_starts=10):\n", + " \"\"\"\n", + " Find optimal per channel exposure times to distinguish two models that also\n", + " minimizes the total exposure time across all channels.\n", + "\n", + " Parameters\n", + " ----------\n", + " fpfs_ch_a : dict\n", + " Contrast ratios for model A in each channel, keyed by channel name\n", + " fpfs_ch_b : dict\n", + " Contrast ratios for model B in each channel, keyed by channel name\n", + " sigma_interp_a : dict\n", + " Noise interpolators for model A in each channel, keyed by channel name\n", + " channel_names : list of str\n", + " Names of the channels (filter names)\n", + " chi2_target : float, optional\n", + " Required chi2 value to distinguish the models\n", + " t_min : float, optional\n", + " Minimum exposure time to avoid division by zero (default is 0.01 hours)\n", + " t_max : float, optional\n", + " Maximum exposure time to avoid unbounded optimization (default is 100 hours)\n", + " n_starts : int, optional\n", + " Number of random starting points to try for optimization (default is 10)\n", + "\n", + " Returns\n", + " -------\n", + " times_opt : 1D array\n", + " Optimal exposure times for each channel (hours), ordered as channel_names\n", + " chi2_final : float\n", + " Final chi2 value for the optimized exposure times\n", + " \"\"\"\n", + "\n", + " n_channels = len(channel_names)\n", + "\n", + " # Define the objective function we are trying to minimize (sum of times)\n", + " def objective(times):\n", + " return np.sum(times)\n", + "\n", + " # Define our constraint function, i.e. the chi2 for a given set of exposure times.\n", + " def constraint_chi2(times):\n", + " return chi2_multichannel(times, fpfs_ch_a, fpfs_ch_b, sigma_interp_a, channel_names, t_min)\n", + "\n", + " # Set the bounds on the optimization\n", + " bounds = [(t_min, t_max) for _ in range(n_channels)]\n", + "\n", + " # And set the constraint for our chi2 to be above the target\n", + " constraint = NonlinearConstraint(constraint_chi2, chi2_target, np.inf)\n", + "\n", + " best_result = None\n", + " best_total_time = np.inf\n", + " # Conduct multiple optimizations to avoid local minima\n", + " for attempt in range(n_starts):\n", + " if attempt == 0:\n", + " # First attempt is at geometric mean as time is in log space\n", + " x0 = np.full(n_channels, np.sqrt(t_min * t_max))\n", + " else:\n", + " # And the others are random in log space\n", + " x0 = np.exp(np.random.uniform(np.log(t_min), np.log(t_max), n_channels))\n", + "\n", + " # Run minimizer, choice of SLSQP is intentional here to enable constraint.\n", + " result = minimize(objective, x0, method='SLSQP', bounds=bounds, constraints=constraint,\n", + " options={'ftol': 1e-6, 'maxiter': 1000})\n", + "\n", + " # Check if the total time is better than the current one\n", + " if result.success:\n", + " total_time = np.sum(result.x)\n", + " if total_time < best_total_time:\n", + " best_total_time = total_time\n", + " best_result = result\n", + "\n", + " #Return best result\n", + " if best_result is not None and best_result.success:\n", + " times_opt = np.maximum(best_result.x, t_min)\n", + " chi2_final = constraint_chi2(times_opt)\n", + " return times_opt, chi2_final\n", + " else:\n", + " return np.full(n_channels, np.inf), 0.0" + ], + "id": "7e8175a12e7bf313", + "outputs": [], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Great! We're ready to run the optimization. We'll set up a loop over all possible pairs of models that we're investigating and run the optimization for each pair.\n", + "\n", + "Notice that we account for the bi-directionality of the comparison. The ability to distinguish between two scenarios depends on which one is assumed to be true, so we need to run the optimization twice for each direction and store the results.\n", + "\n", + "\n", + "Notice also that we define a pruning function to remove any contributions from channels at the lower edge of the time grid that contribute very little to the overall $\\chi^2$ value. These are likely unphysical, and an artifact of pyEDITH SNR constraints at very low exposure times." + ], + "id": "a228b068596aeb06" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:43.825945Z", + "start_time": "2026-08-31T19:43:43.820756Z" + } + }, + "cell_type": "code", + "source": [ + "# Helper function to prune channels that are stuck at the lower bound and are not needed\n", + "def prune_unused_channels(times_opt, fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n", + " channel_names, chi2_target, t_min):\n", + " \"\"\"Because the initial time grid starts at a non-zero value, need to make sure\n", + " we set results at the lower end of the grid (which are typically set to NaN) to zero\n", + " if they have very little impact on the chi2.\n", + " \"\"\"\n", + " times = np.asarray(times_opt, dtype=float).copy()\n", + "\n", + " # Gather per channel chi2 contributions\n", + " contrib = np.zeros(len(channel_names))\n", + " for i, cn in enumerate(channel_names):\n", + " delta = fpfs_ch_a[cn] - fpfs_ch_b[cn]\n", + " sigma = sigma_interp_a[cn](times[i])\n", + " valid = np.isfinite(delta) & np.isfinite(sigma) & (sigma > 0)\n", + " contrib[i] = np.sum((delta[valid] / sigma[valid]) ** 2)\n", + "\n", + " total = contrib.sum()\n", + " # Find channels close to the lower time bound of the grid\n", + " pinned = [i for i in np.argsort(contrib) if times[i] <= t_min * 1.01]\n", + " for i in pinned:\n", + " # Check if removal of channel affects chi2\n", + " # Scale by slightly <1 to account for optimizer tolerance.\n", + " if total - contrib[i] >= chi2_target * 0.999:\n", + " total -= contrib[i]\n", + " times[i] = 0.0 # Force time to zero\n", + " return times" + ], + "id": "e1720248467fd099", + "outputs": [], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:44.921434Z", + "start_time": "2026-08-31T19:43:43.826911Z" + } + }, + "cell_type": "code", + "source": [ + "# Need to define a minimum and maximum exposure time, let's just use the limits of the interpolation grid\n", + "t_min_per_channel = t_noise_grid[0] # 1 hr\n", + "t_max_per_channel = t_noise_grid[-1] # 100 hr\n", + "nstarts = 10 #Number of starting positions for the optimizer\n", + "\n", + "# Create an empty matrix to store the required exposure times\n", + "n = len(model_names)\n", + "t_required_matrix = np.full((n, n), np.nan, dtype=float)\n", + "\n", + "# Fill diagonals with zeros as you cannot distinguish a model from itself.\n", + "np.fill_diagonal(t_required_matrix, 0.0)\n", + "\n", + "pair_rows = []\n", + "# Loop over all possible pairs of models\n", + "for a, b in itertools.combinations(model_names, 2):\n", + " # Case 1: Model A is true, distinguish from B (noise from A's interpolators)\n", + " times_a_truth, chi2_a = required_times_for_pair_multichannel(\n", + " fpfs_ch_a=fpfs_ch[a],\n", + " fpfs_ch_b=fpfs_ch[b],\n", + " sigma_interp_a=sigma_interp_ch[a],\n", + " channel_names=channel_names,\n", + " chi2_target=CHI2_TARGET,\n", + " t_min=t_min_per_channel,\n", + " t_max=t_max_per_channel,\n", + " n_starts=nstarts\n", + " )\n", + " # Conduct pruning of low chi2 contributors\n", + " times_a_truth = prune_unused_channels(times_a_truth, fpfs_ch[a], fpfs_ch[b],\n", + " sigma_interp_ch[a], channel_names,\n", + " CHI2_TARGET, t_min_per_channel)\n", + "\n", + "\n", + " # Case 2: Model B is true, distinguish from A (noise from B's interpolators)\n", + " times_b_truth, chi2_b = required_times_for_pair_multichannel(\n", + " fpfs_ch_a=fpfs_ch[b],\n", + " fpfs_ch_b=fpfs_ch[a],\n", + " sigma_interp_a=sigma_interp_ch[b],\n", + " channel_names=channel_names,\n", + " chi2_target=CHI2_TARGET,\n", + " t_min=t_min_per_channel,\n", + " t_max=t_max_per_channel,\n", + " n_starts=nstarts\n", + " )\n", + " # Conduct pruning of low chi2 contributors\n", + " times_b_truth = prune_unused_channels(times_b_truth, fpfs_ch[b], fpfs_ch[a],\n", + " sigma_interp_ch[b], channel_names,\n", + " CHI2_TARGET, t_min_per_channel)\n", + "\n", + " # Store total times in matrix\n", + " ia = model_names.index(a)\n", + " ib = model_names.index(b)\n", + " t_required_matrix[ia, ib] = np.sum(times_a_truth)\n", + " t_required_matrix[ib, ia] = np.sum(times_b_truth)\n", + "\n", + " # Store per-channel breakdown\n", + " pair_rows.append({\n", + " \"model_a\": a,\n", + " \"model_b\": b,\n", + " \"t_required_a_truth\": np.sum(times_a_truth),\n", + " \"t_required_b_truth\": np.sum(times_b_truth),\n", + " \"max_required_hours\": max(np.sum(times_a_truth), np.sum(times_b_truth)),\n", + " \"chi2_a_truth\": chi2_a,\n", + " \"chi2_b_truth\": chi2_b,\n", + " # Per-channel times for A truth\n", + " **{f\"t_{channel_names[ch]}_a_truth\": times_a_truth[ch] for ch in range(n_channels)},\n", + " # Per-channel times for B truth\n", + " **{f\"t_{channel_names[ch]}_b_truth\": times_b_truth[ch] for ch in range(n_channels)},\n", + " })\n", + "\n", + "# Convert to a dataframe to make the visualisation a little easier.\n", + "t_required_matrix_df = pd.DataFrame(t_required_matrix, index=model_names, columns=model_names)\n", + "ranked_pairs = pd.DataFrame(pair_rows).sort_values(\"max_required_hours\", ascending=True).reset_index(drop=True)" + ], + "id": "42fd79c2d40d94f3", + "outputs": [], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "As you can see, the optimization itself is quite fast compared to the generation of the noise templates. The results are now stored in a dataframe, and we can display the required total exposure times for each model pair as follows:", + "id": "99713e30c8713d23" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:44.998632Z", + "start_time": "2026-08-31T19:43:44.955691Z" + } + }, + "cell_type": "code", + "source": [ + "print(\"Directional matrix: rows are the assumed true model; columns are the competing model.\")\n", + "display(t_required_matrix_df.round(2))" + ], + "id": "37ea1be13ea79ce6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directional matrix: rows are the assumed true model; columns are the competing model.\n" + ] + }, + { + "data": { + "text/plain": [ + " Archean Earth Hazy Archean Earth Modern Earth \\\n", + "Archean Earth 0.00 8.80 14.70 \n", + "Hazy Archean Earth 8.25 0.00 9.37 \n", + "Modern Earth 14.31 9.51 0.00 \n", + "Modern Earth 2 23.67 5.68 8.39 \n", + "Proterozoic Earth (High O2) 75.76 6.73 12.61 \n", + "Proterozoic Earth (Low O2) 79.12 6.74 11.63 \n", + "\n", + " Modern Earth 2 Proterozoic Earth (High O2) \\\n", + "Archean Earth 22.93 73.44 \n", + "Hazy Archean Earth 5.24 6.21 \n", + "Modern Earth 8.05 12.17 \n", + "Modern Earth 2 0.00 59.19 \n", + "Proterozoic Earth (High O2) 58.41 0.00 \n", + "Proterozoic Earth (Low O2) 63.07 inf \n", + "\n", + " Proterozoic Earth (Low O2) \n", + "Archean Earth 76.57 \n", + "Hazy Archean Earth 6.22 \n", + "Modern Earth 11.22 \n", + "Modern Earth 2 63.82 \n", + "Proterozoic Earth (High O2) inf \n", + "Proterozoic Earth (Low O2) 0.00 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 4: Visualizing the Results", + "id": "d0878aabb0907baa" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "The table output is useful, but it would be nice to visualize things a little more clearly, and also to see the per-channel breakdown of the required exposure times so we know which mode is the most important.\n", + "\n", + "Let's make a heatmap figure instead:" + ], + "id": "89ac78af3bc94bfa" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:45.402332Z", + "start_time": "2026-08-31T19:43:45.026401Z" + } + }, + "cell_type": "code", + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "# Heatmap data\n", + "data = t_required_matrix_df.values.astype(float)\n", + "\n", + "# Determine max finite value of required times for scaling of the colorbar\n", + "finite_mask = np.isfinite(data)\n", + "theoretical_max = n_channels * t_max_per_channel # i.e. 3 channels x 100 hrs = 300 hr max\n", + "inf_display_value = theoretical_max # Place inf cells at theoretical max for coloring\n", + "data_display = np.copy(data)\n", + "data_display[~finite_mask] = inf_display_value\n", + "\n", + "# Create colormap\n", + "cmap = plt.cm.inferno_r.copy()\n", + "cmap.set_bad(\"grey\")\n", + "im = ax.imshow(data_display, origin=\"upper\", norm=LogNorm(), cmap=cmap)\n", + "\n", + "# Formatting\n", + "ax.set_xticks(np.arange(len(t_required_matrix_df.columns)))\n", + "ax.set_yticks(np.arange(len(t_required_matrix_df.index)))\n", + "ax.set_xticklabels(t_required_matrix_df.columns, rotation=45, ha=\"left\")\n", + "ax.set_yticklabels(t_required_matrix_df.index)\n", + "ax.xaxis.tick_top()\n", + "ax.xaxis.set_label_position(\"top\")\n", + "ax.tick_params(top=True, labeltop=True, bottom=False, labelbottom=False)\n", + "ax.set_xlabel(\"Competing model\")\n", + "ax.set_ylabel(\"Assumed true model\")\n", + "\n", + "# Colorbar\n", + "cbar = plt.colorbar(im, ax=ax)\n", + "cbar.set_label(f\"Required Hours To Distinguish at {int(SIGMA_TARGET)}$\\\\sigma$\")\n", + "\n", + "# Build per-channel times lookup from ranked_pairs\n", + "model_names_list = list(t_required_matrix_df.index)\n", + "\n", + "# For each cell, extract per-channel times\n", + "per_channel_times = {}\n", + "for idx, row in ranked_pairs.iterrows():\n", + " a, b = row[\"model_a\"], row[\"model_b\"]\n", + " ia, ib = model_names_list.index(a), model_names_list.index(b)\n", + " # A as model truth\n", + " per_channel_times[(ia, ib)] = [row[f\"t_{channel_names[ch]}_a_truth\"] for ch in range(n_channels)]\n", + " # B as model truth\n", + " per_channel_times[(ib, ia)] = [row[f\"t_{channel_names[ch]}_b_truth\"] for ch in range(n_channels)]\n", + "\n", + "# Small function to format per channel times\n", + "def fmt_time(t, t_max=t_noise_grid[-1]):\n", + " return f\">{t_max:.0f}\" if (not np.isfinite(t) or t >= t_max) else f\"{t:.1f}\"\n", + "\n", + "# Annotate cells with required times.\n", + "for i in range(t_required_matrix_df.shape[0]):\n", + " for j in range(t_required_matrix_df.shape[1]):\n", + " val = t_required_matrix_df.values[i, j]\n", + " \n", + " if i == j:\n", + " # Diagonals are self comparisons - skip\n", + " txt_total = \"\"\n", + " txt_channel = \"\"\n", + " elif not np.isfinite(val):\n", + " # If no valid exposure time, assume it's above the theoretical maximum\n", + " txt_total = f\">{theoretical_max:.0f}\"\n", + " txt_channel = \"\\n\".join(f\"{name}: {fmt_time(t)}\" for name, t in zip(channel_names, per_channel_times[(i, j)]))\n", + " else:\n", + " # Time is just the total required time\n", + " txt_total = f\"{val:.1f}\"\n", + " txt_channel = \"\\n\".join([f\"{name}: {t:.1f}\" for name, t in zip(channel_names, per_channel_times[(i, j)])])\n", + "\n", + " # Determine text color based on cell brightness\n", + " if np.isfinite(val) and val > 0:\n", + " rgba = im.cmap(im.norm(val))\n", + " brightness = 0.2126*rgba[0] + 0.7152*rgba[1] + 0.0722*rgba[2]\n", + " text_color = \"black\" if brightness > 0.5 else \"white\"\n", + " else:\n", + " # For inf cells, use the color at inf_display_value\n", + " rgba = im.cmap(im.norm(inf_display_value))\n", + " brightness = 0.2126*rgba[0] + 0.7152*rgba[1] + 0.0722*rgba[2]\n", + " text_color = \"black\" if brightness > 0.5 else \"white\"\n", + " \n", + " # Add text for total and per channel times\n", + " ax.text(j, i - 0.25, txt_total, ha=\"center\", va=\"center\", \n", + " fontsize=12, fontweight=\"bold\", color=text_color)\n", + " if txt_channel:\n", + " ax.text(j, i + 0.15, txt_channel, ha=\"center\", va=\"center\", \n", + " fontsize=7, color=text_color, linespacing=1.2)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "id": "eb20bb5ffc297e58", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 15 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Nice! It looks like we can constrain between most of the models in ~5-15 hours of exposure time. However, there are certainly some comparisons that are more challenging. We can also see that every comparison strongly prefers a single observation in the VIS channel, with the Bridge and NIR channels being disfavoured for this simple model comparison.\n", + "\n", + "Let's finish by plotting the spectra and noise templates some of the comparisons to see what's going on.\n", + "\n", + "The easiest model pair to distinguish is Hazy Archean Earth vs Modern Earth 2, which takes ~5 hours in the VIS channel to distinguish between. We can make another figure from the simulation results to see what's happening:" + ], + "id": "1be71cccc4fd7ceb" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:45.425048Z", + "start_time": "2026-08-31T19:43:45.407995Z" + } + }, + "cell_type": "code", + "source": [ + "def plot_spectra_comparison(model_a, model_b):\n", + " '''\n", + " Plot the spectra comparison between two models with their respective noise templates.\n", + " '''\n", + "\n", + " # Find the pair regardless of stored order\n", + " pair_row = ranked_pairs[ranked_pairs[['model_a', 'model_b']].apply(frozenset, axis=1) == frozenset({model_a, model_b})]\n", + "\n", + " if pair_row.empty:\n", + " print(f\"Pair ({model_a}, {model_b}) not found in results\")\n", + " else:\n", + " r = pair_row.iloc[0]\n", + " # Because of the way the data is stored, we need to check if the provided order of model_a and model_b\n", + " # is consistent with the stored order. If not, we can swap things round.\n", + " a_is_stored_a = (r[\"model_a\"] == model_a)\n", + " suf_a = \"a_truth\" if a_is_stored_a else \"b_truth\"\n", + " suf_b = \"b_truth\" if a_is_stored_a else \"a_truth\"\n", + "\n", + " # Use the per-channel noise interpolators to get the errors for each channel\n", + " sigma_scaled_a = {cn: sigma_interp_ch[model_a][cn](r[f\"t_{cn}_{suf_a}\"]) for cn in channel_names}\n", + " sigma_scaled_b = {cn: sigma_interp_ch[model_b][cn](r[f\"t_{cn}_{suf_b}\"]) for cn in channel_names}\n", + "\n", + " # Build the labels for the legend\n", + " def label_fmt(t):\n", + " return f\">{t_noise_grid[-1]:.0f}\" if (not np.isfinite(t) or t >= t_noise_grid[-1]) else f\"{t:.1f}\"\n", + " label_a = ', '.join(f\"{cn}={label_fmt(r[f't_{cn}_{suf_a}'])}\" for cn in channel_names) + ' hr'\n", + " label_b = ', '.join(f\"{cn}={label_fmt(r[f't_{cn}_{suf_b}'])}\" for cn in channel_names) + ' hr'\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + " # Plot each channel separately, only labelling the first to avoid duplicate legend entries\n", + " for i, cn in enumerate(channel_names):\n", + " wl = wl_ch[cn]\n", + "\n", + " # Plot model A with errors\n", + " ax.plot(wl, fpfs_ch[model_a][cn], 'o-', linewidth=2.5, markersize=5,\n", + " label=f'{model_a} ({label_a})' if i == 0 else None, color='C0', zorder=3)\n", + " ax.errorbar(wl, fpfs_ch[model_a][cn], yerr=sigma_scaled_a[cn],\n", + " fmt='none', ecolor='C0', alpha=0.5, capsize=3, linewidth=1.5, zorder=2)\n", + "\n", + " # Plot model B with errors\n", + " ax.plot(wl, fpfs_ch[model_b][cn], 'o-', linewidth=2.5, markersize=5,\n", + " label=f'{model_b} ({label_b})' if i == 0 else None, color='C1', zorder=3)\n", + " ax.errorbar(wl, fpfs_ch[model_b][cn], yerr=sigma_scaled_b[cn],\n", + " fmt='none', ecolor='C1', alpha=0.5, capsize=3, linewidth=1.5, zorder=2)\n", + "\n", + " # Formatting\n", + " ax.set_xlabel(\"Wavelength (µm)\", fontsize=14)\n", + " ax.set_ylabel(\"Fp/Fs\", fontsize=14)\n", + " ax.set_xlim([0.35, 1.85])\n", + " ax.set_ylim([0, 5e-10])\n", + " ax.set_title(f\"Spectral Comparison: {model_a} vs {model_b}\", fontsize=12)\n", + " ax.tick_params(which='both', direction='in', top=True, right=True)\n", + " ax.grid(True, alpha=0.3)\n", + " ax.legend(fontsize=10, loc='best')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "id": "647f1e333d18a850", + "outputs": [], + "execution_count": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:45.548317Z", + "start_time": "2026-08-31T19:43:45.426325Z" + } + }, + "cell_type": "code", + "source": [ + "# Choose the models we'd like to compare and run the plotting function\n", + "model_a = \"Hazy Archean Earth\"\n", + "model_b = \"Modern Earth 2\"\n", + "plot_spectra_comparison(model_a, model_b)" + ], + "id": "b489282218f9ffbe", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "For this comparison we can see that the VIS channel was deemed the most important likely due to the large divergence in the models between 0.4 and 0.6 microns, in combination with the resolution available at the VIS wavelengths. While there are divergences in the Bridge and NIR channels, the resolution and SNR were likely not sufficient to make them as important.\n", + "\n", + "We can also see that the required observing time is a little longer if we assume the Modern Earth 2 model as the truth. This may seem counterintuitive, but likely results from the brighter model having a greater photon noise contribution, which makes it harder to distinguish - the noise from a brighter model can more easily mimic a fainter model than the noise from a fainter model can mimic a brighter one." + ], + "id": "79a609a5edc299c1" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's look at one more example between the two Proterozoic Earth models, which indicate that an exposure time greater than 100 hours for any channel would be needed to distinguish between them.", + "id": "9618707ba049daa4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-08-31T19:43:45.660840Z", + "start_time": "2026-08-31T19:43:45.556482Z" + } + }, + "cell_type": "code", + "source": [ + "model_a = \"Proterozoic Earth (Low O2)\"\n", + "model_b = \"Proterozoic Earth (High O2)\"\n", + "plot_spectra_comparison(model_a, model_b)" + ], + "id": "7d4a282367c2f7f0", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Here we can see that the two models are extremely similar, with only small divergences in the VIS channel. Even with the long observation, we cannot reach sufficient SNR across the three channels to distinguish between them. Nevertheless, if needed, we could calculate the require time to distinguish between the two by extending the noise template grid to longer exposure times.", + "id": "9a451f44f945a02b" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "This marks the end of the notebook, congratulations on making it to the end! Hopefully you can see how the tools we've built can be used to distinguish between different atmospheric scenarios. More complex investigations could be conducted that build on this framework and explore more complex features such as atmospheric abundances and processes, across a wider range of planetary and stellar parameters, for a wider range of models.", + "id": "c2b07a8ba1a8e727" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 0b978fd746e358dbf12fdc88a297617cea9ca0ff Mon Sep 17 00:00:00 2001 From: AarynnCarter <23636747+AarynnCarter@users.noreply.github.com> Date: Thu, 3 Sep 2026 13:41:27 -0400 Subject: [PATCH 2/4] Add phase angle to figures and reduce warning print outs --- .../exoearth_spectroscopy_tutorial.ipynb | 500 ++++++------------ 1 file changed, 161 insertions(+), 339 deletions(-) diff --git a/tutorials/exoearth_spectroscopy_tutorial.ipynb b/tutorials/exoearth_spectroscopy_tutorial.ipynb index 3e4dd87..82a83e1 100644 --- a/tutorials/exoearth_spectroscopy_tutorial.ipynb +++ b/tutorials/exoearth_spectroscopy_tutorial.ipynb @@ -19,8 +19,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:21.986178Z", - "start_time": "2026-08-31T19:43:20.186508Z" + "end_time": "2026-09-03T17:40:29.177263Z", + "start_time": "2026-09-03T17:40:27.747176Z" } }, "cell_type": "code", @@ -74,8 +74,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:22.006285Z", - "start_time": "2026-08-31T19:43:21.991065Z" + "end_time": "2026-09-03T17:40:29.192385Z", + "start_time": "2026-09-03T17:40:29.179104Z" } }, "cell_type": "code", @@ -106,8 +106,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:22.074855Z", - "start_time": "2026-08-31T19:43:22.010248Z" + "end_time": "2026-09-03T17:40:29.274651Z", + "start_time": "2026-09-03T17:40:29.195227Z" } }, "cell_type": "code", @@ -156,8 +156,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:23.351718Z", - "start_time": "2026-08-31T19:43:22.077916Z" + "end_time": "2026-09-03T17:40:29.455120Z", + "start_time": "2026-09-03T17:40:29.276227Z" } }, "cell_type": "code", @@ -199,8 +199,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:23.456940Z", - "start_time": "2026-08-31T19:43:23.401607Z" + "end_time": "2026-09-03T17:40:29.475524Z", + "start_time": "2026-09-03T17:40:29.456681Z" } }, "cell_type": "code", @@ -266,8 +266,9 @@ " # --- Observatory / instrument ---\n", " \"observatory_preset\": \"EAC1\",\n", " \"psf_trunc_ratio\": 0.3,\n", - " \"IFS_eff\": 1.0,\n", + " \"IFS_eff\": [1.0]*len(wl_input),\n", " \"noisefloor_PPF\": 30,\n", + " \"ez_PPF\": [np.inf]*len(wl_input),\n", "}" ], "id": "4ee0778a4ce6754b", @@ -285,8 +286,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:23.482083Z", - "start_time": "2026-08-31T19:43:23.467261Z" + "end_time": "2026-09-03T17:40:29.510857Z", + "start_time": "2026-09-03T17:40:29.476496Z" } }, "cell_type": "code", @@ -322,8 +323,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:23.497887Z", - "start_time": "2026-08-31T19:43:23.486433Z" + "end_time": "2026-09-03T17:40:29.529983Z", + "start_time": "2026-09-03T17:40:29.519561Z" } }, "cell_type": "code", @@ -358,8 +359,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:23.511465Z", - "start_time": "2026-08-31T19:43:23.501199Z" + "end_time": "2026-09-03T17:40:29.538733Z", + "start_time": "2026-09-03T17:40:29.531533Z" } }, "cell_type": "code", @@ -440,8 +441,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:43.776589Z", - "start_time": "2026-08-31T19:43:23.514677Z" + "end_time": "2026-09-03T17:40:52.779581Z", + "start_time": "2026-09-03T17:40:29.540115Z" } }, "cell_type": "code", @@ -495,304 +496,116 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,519]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,522]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,525]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,525]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,526]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,567]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:23,683]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:23,700] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:23,701] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:23,701] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/obs_config/CI/CI.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/ProtectedAg_refl.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/dichroic_refl.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/dichroic_transmission.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/ProtectedAl_refl.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/UVFusedSilica_trans.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,500]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,513]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,515]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,518]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,519]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,522]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,524]\u001B[0m `FstarV_10pc` not specified in parameters. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/obs_config/Tel/EAC1.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/reflectivities/XeLiF_refl.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/Detectors/Teledyne_e2v_EMCCD_QE.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n", - "/obs_config/Detectors/SaphiraLMAPDwGain_QE.yaml\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/Sci-Eng-Interface/hwo_sci_eng\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,845]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,854]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,856]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,858]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,858]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,861]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:24,863]\u001B[0m `FstarV_10pc` not specified in parameters. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,210]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,221]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,222]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:25,224]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:29,549]\u001B[0m `FstarV_10pc` not specified in parameters. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,513]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,593]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,620]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,015] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,016] \u001B[0mUsing default unit for D: m. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:31,725]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", "/Users/aacarter/Documents/SOFTWARE/HWO/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,721]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,723]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,725]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,726]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,726]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,769]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:27,888]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,891] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,892] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:27,892] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,055]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,066]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,067]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,070]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,070]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,073]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,075]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,076]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,076]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,111]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,253]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,256] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,257] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,258] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,406]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,416]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,417]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,421]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,423]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,424]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,424]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,460]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,570]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,573] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,574] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:28,574] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,734]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,743]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,745]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:28,746]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,124]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,125]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,128]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,129]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,129]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,174]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,293]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,296] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,441]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,451]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,452]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,454]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,454]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,457]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,458]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,459]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,459]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,487]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,592]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,594] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,595] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,595] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,748]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,758]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,759]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,761]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,762]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,765]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,767]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,767]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,768]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,807]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:31,925]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,928] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,928] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:31,929] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,075]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,083]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,084]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:32,086]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,254]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,255]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,257]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,258]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,258]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,302]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,417]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,420] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,420] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,421] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,560]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,569]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,570]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,572]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,573]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,575]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,577]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,577]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,578]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,607]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,715]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,718] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,719] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:34,719] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,869]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,882]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,884]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,885]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,885]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,888]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,890]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,890]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,891]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:34,930]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,036]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,039] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,039] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:35,040] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,178]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,190]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,191]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:35,193]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,525]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,527]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,529]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,530]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,530]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,572]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,689]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,692] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,692] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:37,693] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,841]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,849]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,850]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,852]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,852]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,854]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,856]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,857]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,857]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:37,885]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,004]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,007] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,007] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,008] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,154]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,163]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,164]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,166]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,166]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,169]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,171]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,172]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,172]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,214]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,328]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,331] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,332] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:38,332] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,475]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,486]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,488]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:38,489]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,494]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,496]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,498]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,499]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,499]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,540]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,648]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,651] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,652] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,652] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,803]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,811]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,812]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,814]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,815]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,816]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,819]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,819]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,820]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,851]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:40,963]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,966] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,966] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:40,967] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,104]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,114]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,115]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,116]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,117]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,119]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,121]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,122]\u001B[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,122]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,155]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,268]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,271] \u001B[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,271] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-08-31 15:43:41,272] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,408]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,416]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,418]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-08-31 15:43:41,419]\u001B[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:33,800]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:33,844]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,166] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,166] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,167] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,314]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,397]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,428]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,745] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,746] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,746] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,889]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,972]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:35,003]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,321] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,322] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,323] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:35,469]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:37,544]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:37,582]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,905] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,905] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,906] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,044]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,125]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,154]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,477] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,477] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,478] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,614]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,694]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,726]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,050] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,050] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,051] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:39,192]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,389]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,431]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,752] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,752] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,753] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,898]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,978]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,007]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,404] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,404] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,405] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,541]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,621]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,655]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,982] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,983] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,983] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:43,118]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,219]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,261]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,593] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,594] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,594] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,735]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,818]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,848]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,170] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,171] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,171] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,310]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,392]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,426]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,745] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,746] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,746] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,890]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:48,963]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,004]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,328] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,328] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,329] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,480]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,561]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,590]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,913] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,913] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,914] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,056]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,137]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,170]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,498] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,498] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", + "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,499] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", + "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,638]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n" ] } ], @@ -819,8 +632,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:43.807336Z", - "start_time": "2026-08-31T19:43:43.799776Z" + "end_time": "2026-09-03T17:40:52.819760Z", + "start_time": "2026-09-03T17:40:52.812078Z" } }, "cell_type": "code", @@ -883,8 +696,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:43.819475Z", - "start_time": "2026-08-31T19:43:43.813114Z" + "end_time": "2026-09-03T17:40:52.827925Z", + "start_time": "2026-09-03T17:40:52.821196Z" } }, "cell_type": "code", @@ -989,8 +802,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:43.825945Z", - "start_time": "2026-08-31T19:43:43.820756Z" + "end_time": "2026-09-03T17:40:52.834542Z", + "start_time": "2026-09-03T17:40:52.829337Z" } }, "cell_type": "code", @@ -1030,8 +843,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:44.921434Z", - "start_time": "2026-08-31T19:43:43.826911Z" + "end_time": "2026-09-03T17:40:53.901992Z", + "start_time": "2026-09-03T17:40:52.835788Z" } }, "cell_type": "code", @@ -1110,7 +923,16 @@ "ranked_pairs = pd.DataFrame(pair_rows).sort_values(\"max_required_hours\", ascending=True).reset_index(drop=True)" ], "id": "42fd79c2d40d94f3", - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aacarter/miniconda3/envs/hwo/lib/python3.14/site-packages/scipy/optimize/_numdiff.py:710: RuntimeWarning: invalid value encountered in subtract\n", + " df = [f_eval - f0 for f_eval in f_evals]\n" + ] + } + ], "execution_count": 13 }, { @@ -1122,8 +944,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:44.998632Z", - "start_time": "2026-08-31T19:43:44.955691Z" + "end_time": "2026-09-03T17:40:53.949358Z", + "start_time": "2026-09-03T17:40:53.926396Z" } }, "cell_type": "code", @@ -1279,8 +1101,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:45.402332Z", - "start_time": "2026-08-31T19:43:45.026401Z" + "end_time": "2026-09-03T17:40:54.485604Z", + "start_time": "2026-09-03T17:40:54.020008Z" } }, "cell_type": "code", @@ -1315,7 +1137,7 @@ "\n", "# Colorbar\n", "cbar = plt.colorbar(im, ax=ax)\n", - "cbar.set_label(f\"Required Hours To Distinguish at {int(SIGMA_TARGET)}$\\\\sigma$\")\n", + "cbar.set_label(f\"Required Hours To Distinguish at {int(SIGMA_TARGET)}$\\\\sigma$ ($\\\\alpha=\\\\pi / 2$)\")\n", "\n", "# Build per-channel times lookup from ranked_pairs\n", "model_names_list = list(t_required_matrix_df.index)\n", @@ -1380,7 +1202,7 @@ "text/plain": [ "
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wYcPo8OHDdPz4ccnSGuIAl4NZbnrEc7dyGfOLFy+ks3J0dLRkbmMHufDhfvjhB/nJnTu3TBt07do1Wf7NN9/Q+PHj9etxYzC+OMGNqPjCgy7I5eWczWWcmecuzTzH7scOpcsAAAAAAJBkXKK8ZMkSydrGDnIlwFCrZa5We3t7+u2332SZp6enTB+EIDdlcVfluXPn0t69eyWryxnzR48eSWMpnkaIy8p1MmbMqM+6xxfkMu6UbQlBLkNGFwAAAAAAklW27O7uLnOyNm3aNMH1eFobzvpevnwZe9cEOGPeoEEDafTFJeSxDRo0SMZKr1+/Xsbecqadx09zdt3DwyNOkGtpkNEFAAAAAIAkc3Z2JicnJwmwEsMdluObygZSxpw5cySAjS/IZdOnT6cSJUpIWbMu0z5r1izJrlt6kMsQ6AIAAAAAQJJZWVlR8+bNZZwtZwcTwmNFOdAC0+Cpgnx8fBJ83NbWloYMGSIXJHRTDNWtW1fmNLb0IJch0AUAAAAAgGT57rvvKDg4WOZqDQoKivP4lStXpGx2+PDh2LMmwmNueT8npl69evpycx07O7t0cUwQ6AIAAAAAQLIUKFBAGk5xh96KFSvS9u3bZboaHgfK09PUqlWLPvvsM/kNpsGdrXm/c4Y2IQ8fPpQyc+6ynN6gGRUAAAAAAHyQQ4cOSSdlLqN1dHSUcaDclXnixInI5poYZ9ILFy4s3a256RfPg2soOjpaMrpcPj5jxgxKbxDoAgAAAADAB+MsLge8V69elXLa+vXr66eyAdM6ceKEBLPcRZmbUzVq1EiWP336lD755BPJ6B49epRcXFzS3aFAoAsAAAAAAAkGscuXL6cHDx5Qly5dKHfu3NhTaQyXLvO8uHyhgUuUvby8ZOxu9erVadmyZdJlOT1CoAsAAAAAAPTkyRNq164d/fnnn1SkSBEpQe7QoQNt2bJFGhhxKeyqVav0WUNIXWfPnqUyZcrE+xiPj962bRsdO3aMrK2tZWx07dq10/UhQqALAAAAAAAUFhZGLVq0oEuXLtHevXvp5s2bNGLECClL5qxgnz59aM2aNdJsqkmTJthjqcjPz49y5cpFBw8elPmJ4f0Q6AIAAAAAQJxgt2HDhpJBHDZsmL6MuVevXrRy5UoEu2bQs2dPunXrloy5hffD9EIAAAAAACAcHBxo48aNVLx4cVq8eLHRHLncUXnRokVSztymTRvaunUr9pqJ8bhbnZ9++knG3vKFBng/BLoAAAAAAEARERGSveXgloNd7uY7depUunz5crzB7p49e7DXTGjnzp0yNVDNmjUlwOXpg7755hsaM2aMHCtIHEqXAQAAAABAOitzoykrKysZo8tT0hiO2eUGVTpcxsxBL5h2XG7BggVlqqbHjx/LvMRfffUVValSRcZLjxw5Ers/EfjXCQAAAACQjkVFRUngxOXKtra2dOPGDapTp44+s8vL+b5hGS2CXNMJDw+X35zB/f7778nR0ZG2b99Ou3btkgwvd1T+8ccf6eXLlybcio8fAl0AAAAAgHTC398/zrL//e9/dODAAbp27RpduHBBGh5xwBtfsMtTEIHpcFdrngt31qxZkjUfMmQI2djY0Llz5+jMmTOS0eUxum/evKFffvkFhyIRKF0GAAAAAEgHIiMjqVixYjLOs2vXrvrlFSpUkDG3hqWwHBBz9jBDhgz6MuaFCxfSoEGDzLT16UNgYKBka3/77Tc5VnPmzJGMO5eUX79+nbJmzUqvX7+WsdN8LHLkyGHuTU6zEOgCAAAAAKQTx44do4oVK1JMTIxkChk3O+KMLWcRDU2cOFGCYg64Dh8+LMEumAaXIXPzKT4uPK1TaGioNJ1avXo1ffrpp/T06VOys7Ojf//9F4cgiVC6DAAAAABgwXx9fen333+X25UrV5bxte3ataPPPvtMlnXu3Jn+/PNPOnv2rNHzuHS2X79+FBISQl988YVZtj29lCt7e3vT+PHjpVQ5b968dPz4cVq+fLlcYOCS5S1bttDSpUvp4MGD5t7cjwYCXQAAAAAACzZ//nwJoLgkVoeD2z/++EOCXQ5mOavboEEDGY+rKAqdOnVKymebN29O06dPp7///luyjZCyeNqg7t2707x58+ju3bsyBprLyLt16ybNpypVqkRHjx6V/V+3bl3y8fHBIUgi66SuCAAAAAAAHx/u3MtT1XDGkI0bN446deoktzmgYuvXr5fOyy1btiQHBwcZz8vrN23alA4dOoQuyybC4555/C1feGBOTk6SXefjNXjwYOmArVKpJPjlH0g6BLoAAAAAABaMAyXOGLLEgt1ly5ZJQyruuly+fHkpoQ0ICJBlHATz/LqQsnj/RkdHxzlePD66TJkydOfOHcqXLx92+wdAoAsAAJAMz58/Jy8vL/394OBg+vrrr2Xqh7Zt20p5IJ+kAACkxWCXg6rEgt0ZM2ZQ2bJlafHixTRw4ECZO7dIkSL0888/m3X7LVX16tWpb9++dPnyZWn6pZMpUyb5rWsYBsmHMboAAADJmPaBT0R049y4UUuzZs1o69atEvx+/vnncrKIcWwAkBYaT8X26tUruSjH87RysKv7LONg959//pExu9wYiXl6elLhwoVljt3t27dLOTMkD491njlzJoWHhye4TpcuXahkyZIyFprnMGbc/IvHTvOY3Ny5c2O3fyBkdAEAAJLI1dVV5i7kEj5WtWpVevz4MZ0/f17GVe3YsYNatWolATCfNKLMDwDM0XiKy17fvHkjGVvDeXE5cOJ5V7mj79ChQ+NkdrlEtly5crKscePG8gMf7uLFi1L2vWnTJmnyZW9vH2cda2tr2rx5swS6pUuXplKlSklDKg8PD5m/GD4c5tEFAABIJu5+ycEuB7pVqlSRjIcOd8ls0aKFBLwIdgEgNTO5mTNnJltbW+rfv780NOKAl4NYwyCXM7Y8HytnG2OvBymD9y13UOYxzjwtUJs2baSrdULBLuNKoLVr10rGnZ/XtWvXBNeFpEFGFwAA4D14TFvPnj2pd+/eku3gE5YFCxZIsMtX4w3Vr19fTmY42OUyZp73EGN2AcDUuNqkYcOGMpzCsPEUl8Hy8ArDIDd2gyru8Asph+cjnjx5Mq1evVq6VnMAy8Eufy8kFOwGBQXJz6RJk3AoUggyugAAAO/Bpci9evWSkxaew5C7kfKUELrM7oQJE+ibb74xeg5ndnl+xOHDh2P/AoDJ8UU1LoHl38wwY1uwYEEZ/6kLcg3xergYl7KioqIoW7ZsdPv2bXJzc5NliWV2ucycL5JyH4gzZ86Qs7NzCm9R+oRAFwAAIAm49E831QafpPB4KqYLdnmeSpT+AYC5vHjxgvLkyUP37t3Td4ZHebL58HcEB6/cVEonvmDXMMjdt2+fBMiQMtB1GQAA4D24nIwzuHwy0r17d+rYsSPt3r1bHuOSZi5j5qyuroMpAEBq4y7JlStXpjFjxuiX6cqTefoawy7LYHr8PcFjn/kiqY6ujPnAgQNSxvz06VMEuSaEjC4AAMB7cKb24MGDcnLCdGXMfEW+Xr16RpndI0eOUKVKlbBPASDV8ecPz8u6YcMGfdUJQ2bXPL0dSpQoQUWLFqWVK1calYfrMrtqtZpy5cqFTK6JINAFAABIAJeUcWOQy5cvS7DLXZZjj9nlE0oeg8XZlLCwMCpSpAj2JwCYzSeffEIrVqyg/fv366cKMgx2uVyW53YF0+NSZK4E4imGDLvz64LdsWPHyhzFKFc2DQS6AAAACeBGIjVq1KBnz57JyQifsOhwsMtdmHkKIb4q/9dff1GPHj2wLwHArCIjI2UqoUuXLkmZbJ06dfSPofFU6vvjjz9o8ODBNGzYMJo2bZp8XxhOKYT51k0HgS4AAEAibt68SbVq1ZL5Kbl82cXFxeikkTMnGTNmpAYNGmA/AkCa6Sug6yXAHeFHjRol8+uCecyePVsCXf4umTt3Lnl7e+NQpAIEugAAALGmheDpOI4ePSoZWs7i6oJdPjnhzK5hsAsAkBbxhTgOsL799lvKkCEDffHFFxL86qa7gdR17tw5Gjp0qMyxO3DgQGkQhqEupoWuywAAAG8FBwdLc6nJkyfTw4cPqVGjRrRkyRIqUKCAjHfjaTt4GWdLAABSA38WPX/+3Kg0ecSIEZQlSxZq2LAh3blzJ97ncfOjQYMG0YMHDySry830KlSogM7L//HiAQeqhrgpYeHCheV74t9//03wuaVLl6bDhw/LuF0uX+Y+Dzw0Bt8npoOMLgAAwFt8hf3q1au0Y8cOydpWqVKFTpw4QYsWLZJphQwzuxz42tjYYN8BgEk1a9aM7t+/T3v37pX5cT/99FPauXOndHnnoRM8Rc2ePXukw29SYJzuh+MGhF26dJEuyq1atZIu11z1069fP5ljnbvv85RCSZ1THcfCtBDoAgBAunPhwgXpPsoni7oyvtDQUCnv4yCXg1nOfnCzqQ4dOtDUqVNlHl3dtEF8UsknmwAApsbN8LihFGdot23bJtPVXLlyhXLmzClVKDw3K9/nQDipwS58OO6gPGPGDAl2161bJ8dj9OjR8hh3s/7ss8+SFeyC6Vib8LUBAADSZJDLHUk5UDUcq8bZWUdHR8mc+Pn5UdeuXSW45av1HBD37NmTrK2t6dChQwhyASBVcFdeboTHQSwHu/zZlT9/fglymbOzM23dupWaNGkijyPYNe28uPwd8PPPP8t9vgiaPXt2CXx1eAwu42CXIdg1L4zRBQCAdBnk/vDDD0aPcaDLc+J27tyZvvzySzmJ0U0nxI/xNB18QqnL6gIAmBJ3Su7WrZtUm3CJLAex/FnE83objst1cnKSzybOLHKwy2N6IWWdPHlS9u+tW7do8+bNMuaZg1m+MLp+/XqjdTnY5Yzv+PHjpRkYmA9KlwEAgNJ7kMvNXQyn3uBxcFOmTJFmIVyaxtNC+Pr6mmGrASC94uETnKnlrG3WrFmlX8CbN28kmOV5vPlx/qzSCQkJoQULFuizipByAgMDpREhB7q8n7n7PjeS4mzu9OnTadWqVdS6dWuj5/B4Xb5Ymi1bNhwKM0FGFwAA0nWQe/fuXZniwbCTZsWKFSWb0qlTJylh/t///meGrQaA9Kxy5cpUtmxZ6bjMHX25bFZXxsxdezngNezGzJldBLmm4erqKtn1ly9fym2+8MC4jHn48OEybVPszC4Pd0GQa14IdAEAwOLHuHEZspWVlZyQxA5ya9euLSeMPPWD4ZV4DnIjIiKkqyl32QSwNJyZ4jmjuczy+vXr5t4ciNWNl0tfOdBds2YNbdmyRQItwzG7vE7sYBdMg6cN8vf3p2XLlpGPj480LOTsri7Y5TJm/p6JHeyCeaF0GQAALN758+dlflxu4LJ7927y8PDQB7k8D+XcuXOlo2lCpcwAlub27dvUoEEDmRKFM4WcqeJqh6+++srcmwZEEtjyHLinTp2S8mRdGXOLFi3on3/+kQt3um7MPXr0kL4CYBp8QYEvLnBHZc7c6sqYeX5inmaOm4MxLmPmkmb+vuH3FJgfAl0AAEh3we68efOoXbt28Qa5x44dk2yu7gQTwNJwVpCnoeHGanPmzJGgiadH+eWXX+jgwYNUvXp1c29iuhUVFSWBq4ODA+XNm9eoa68u2G3evDl98cUXdObMGWmeZ9g9HlLWkiVLZFgLN/u6ceOGfrlhsMt9HLgzP/dy4LHTOB5pB0qXAQAgXShVqpRkc7kjaYUKFSQTEl+QyycvPM4NQS5YqsOHD9PNmzelZJm7+PJ4z59++olcXFykoyyYD2cCufkUT2127949o8dq1qwpARfPpcufYRxkIagyrSxZssjFIJ5yjrtf6/A4Xc7echkz93RYunSpXEDC8UhbEOgCAEC6C3a5dJkzvK9evYoT5H799ddG8yICWOJ8oJx54jG6OpzV5WZG/BiYD19444qTvn370qJFi6SEOXawe/XqVblYMXnyZLNtZ3rBXZM3btwow1n69esn7xvDYJfHSu/atUumH3J3dzfrtkJcCHQBACDdZna5lJkbjCDIhfSES5O5G6xhczaetoazVsWLFzfrtqXXMaA8/pO7LA8cOFAyhxzs9u7dm9q2bStZXEPZs2enqlWrmm17LR1P48Tl4dypn8uWdcEuz6Xep08fo2CXKyL4e4Sz8JD2INAFAIB0HezyXIjI5EJ6wo3WFi9ebNRpnDOEzHB87rlz52Q+aTAdDpq4W+/UqVMleOVgqmnTphQWFibBbvfu3alNmzZxgl0wDe5wzU0KeegKHxu+zRdCdcEud+GPHexC2oWWYAAAKSQ4OFgaV3DpGU8DgSu85nXx4kUZX5XQWFtdsMtX41GuDOkNn8Dzj87p06cly8sNkHRBLme0uMEOpAzO1Do6Ohot4/GfXPbK+58/q3iM54QJE6TpFAe3HOwyDnZ5nWLFiuFwpFAWnaePs7e31y/jaZp47tsFCxZIQ0K+EJorVy65EMrjcXXBLne+5qz6jz/+iGORxiGjCwDwATj7wVNzBAUF6cv+uIMpdy7lckCe+zB2IxFIHXxMuNFUyZIlpcPytGnTElyXg12ePxRjciG948ZGumlSDIPcb775xtybZhF8fX2lLJzHcxrieVm5yzIHuTzl2e+//y7B1KVLlyTYPXLkCI0dO1bG6yLITbkgd8CAATIO2tCmTZsoQ4YMEuTyOtyUkP/987hoDnb52HEml5uBDR48OIW2BkwJgS4AwAfgzAcHSPzlx4HVpEmTKEeOHDLGjeen5KvEPKE8gt3UN2bMGGkcwlNx8NySHMRyhiQhmTJlStXtA0iLsmbNSo8fP5YxiQhyUz7I5ex5tWrVZN8aKliwoHxP8LRCPB6Xq0t4fOjnn38uQS6XknM5MwdfkHJB7o4dO+j777+Pcyxev34tF65//vlnevHihUzvNHv2bMnG88XtUaNGSeDL5wCQ9mEeXQCAD8RX3zmY5awhN6TgL8Zy5crJY9zgiLOK/IXJE8p7e3tjP5sY7+srV65Qr169JOOeOXNmWc6lf9zgZfz48XFObABA6/LlyzK3Ll+k4wtEyOSmfJDLUwbxVE6GuPM7L/vnn39k/OehQ4dkOQe6RYoUkaoT3fcKpFyQu2/fPpkaKLY7d+5IN+U8efLQhQsXKF++fDIMhi+Y8hRcfJ+n4YKPAzK6AADJwNNxzJo1S25zBpeDWB7Hw9lDw6k6ePoannaApxtAZjd1LF++XMZQPXv2zGiah/79+8s4uIkTJyaa2YUPp2tkpMNjDrnREWfL+QIDzy8JqYMrTfhCm2HvAB53yGM/OSN1//79eJ/HZbFffPEFgtxUCnI5wOXKk4wZM8rnFXf65QCLcWDFc3zz5xmC3NQJcrkai/EYdd7/PH6XA1o+RlzCzFlc/kxDkPuRUQAAIMn27t2r2NjYKH379lUKFSqk7N+/X7lz546SM2dOpUSJEkpAQIDR+i9fvlRKly6tLF++HHs5FYwbN07hr7aZM2fGeWzu3LmKSqVSDh06hGORgq5fv65YWVkpX3/9tdx/+PCh4u7urvTp00eOh52dndKmTRslMjIS+z0VVKtWTSlVqpR89rC2bdvKZ9PPP/+sFClSRMmcObNy5cqVeJ+r0WhwjFLIgwcPFB8fH6VHjx5KTEyM0WN8bEqWLKlMnDhRv2zlypXy2VWsWDHFwcFBmTBhAo5FCuF/1/369VNy5col39exjR8/XilYsKASHR0t99+8eaNky5ZN3iv83V60aFElMDAQx+MjhEAXACCZZs+eLSckefPmVaKiomSZLtitUqVKnC9E3TpgmhOYbdu2KfPnz1dCQkJkGQdXHHitWLEizvoXL17EYTCBZcuW6YPdX375RU7udfbt26c4Ojoi2E0ljx8/VvLnzy/B7o0bN5QMGTIo/v7+8hhfiKtcuXKiwS6kjBEjRsj3xNKlS+MNcps2baqEh4cbPbZ582Zl5MiRysaNG3EYUtDp06flAjXv89gX3DjI9fDwUM6fPx/nQsVXX32lTJ06VQkKCsLx+Egh0AUASAbOVvXs2VOpUaOGfHEOHTpU/1hiwS6kPD5pb9iwoWQ/OJDiq+7Pnj17b7ALKSciIiJOsOvt7a0sXrzYaD2ufECwa1qcjdJlDnXBLh+LOnXqGK2HYDf1jkeHDh2MPocSC3KfPn2aSluWPq1Zs0a+sw2rSxIKcvnYvXjxwkxbCikJgS4AQDLLAn/88Uf5ItR9ccYX7PJ6scvVIGW1aNFCqV69upw8Pnr0SAJeBLup58mTJxJIbdmyRTl48KBy8+ZNfbDLxyY2XbD7ySefpOJWph98AY5/Xr9+LWWwumDXyclJ8fX1TTDYxQl96gS7c+bMSTDI5cw7l8quXr3ahFsDhsHu2LFjEwxyO3bsqDRr1gw7zAIg0AUASAI+aTx+/Lji5eWlL5FlsYNdPz8/CXaRSTStS5cuSVkgHxfGwVbx4sXlxF4X7IaFhUlml8cmgmnKxnmsOo/B5WCKy8eZLtjlsr/4gt3YJ5aQMnbu3KnY29srGTNmVGrVqiUn7Lpgl8fo6sbsGga7CxcuxO5PpWCXP6/KlSuXYJA7ZMgQHItUoPvOVqvVyqlTp+INcgsUKKD/boGPGwJdAIAkGDZsmFz95WYuCX1xchbXxcUFJWipgBtKcWOpu3fvKvfu3ZPM1NGjR5Xbt29LkJU7d27JrKOE3LR4/C2fMPK/f77YEN+YXUgd/G+dM4YcUHXv3j1OGbNhgyowfxkzQ5CbdsqYEeRaJgS6AABJwF+GrVq1kuBq165dcR7nZVyStm7dOuzPVMom/vDDD5IdqVu3rjJ58mT9cm4S9uWXX8YZJwop68iRIxLIcnM2XWYXwa75xkoPHjxY/t3zZxBndrmMGcFu2g12EeSmnWCXq3+QybVMCHQBABLAQROXA/LJO9/WBbuurq6SPYTUwwEtd/Nt0KCB0rlzZ+miqWsOxhms+/fvy/158+ZJ4AumV7t2bf0UKIZlzLGDXc62o9GOaf39998yLY1uejNdGXN8wS6/R8D8wa6npyfKldNIsJslSxaUK1soFf+fuefyBQBIa549e0Zt2rShkydPUkxMjNxetmyZPNahQwfau3cvbd++nSpXrmzuTbV4YWFhVK9ePQoMDKTOnTvTvn376MCBA3I8eHm2bNmofv36VK5cOfrpp59o5cqV1LhxY3NvtsU6fPgw/fLLL3Tp0iW6ePEiOTo6ynI+nejfvz8tWbKEFi9eTPfv36fWrVtT1qxZydnZ2dybbZFCQkLk88jFxYVq1KhBgwYN0j+2a9cuatGiBbVv35569epFt2/fpi5duuBYmFB0dLT8tra2TnAd/j7h4+Dl5UUzZ8405eake/zd4eDgkOh+WLt2LU2YMIF27twp3yVgWdTm3gAAgLR4ssIniJkyZaKXL1/KF+CaNWuoU6dO8jgHUnXq1KFGjRrRsWPHzL25Fu/XX3+loKAgOnHiBI0dO5a8vb0leCpdurScyHDAy0HXwoUL6Y8//kCQa2J8MnjmzBm6c+cO3bp1S79cpVLRvHnzqE+fPtSxY0cJhjn4RZBrOnwS7+HhQStWrDA6Fowv/mzcuJHWrVsnF4RCQ0NxLEz4nTFmzBjKmDEjubm50eTJkxNc18rKipYuXYog14T4O7tgwYJyEY6/q9+8eZPgunwR+/z58whyLRQyugCQrnFmlrO3nCnUWb9+PX3yySf08OFD+aLkk/cFCxbQ1atX5eRx+fLlcmLTvXt3atWqFXXr1s2s/w2Wjk9U+GRk8ODBNGDAANqxY4dkdfmHTxj37Nlj7k1Md+7evUu1atWSIIuPg7u7u9Hjp06dojx58pCnp6fZtjG90Gg0krHlz6WtW7dKUGvo8ePHcsGuZMmSZttGS9ejRw+p/uGKkkePHlHFihWlwgRSH1cytGzZkr799lsqUqQI+fr6GlU6QDpj7tppAABz2bNnj0yL8scffxgt5+Y6PI1QaGiocvbsWRlLxXOEcpMX/tgsW7asUqlSJbNtd3rDTb4+//xzpV+/fkquXLlk+ia2YcMGmUoFTI8b57Rv31469/IcrYZzRpcvX17mboXUwb0CvvvuO5kyaODAgdJIh8fhcqdlnks6vmZ5kDIePHggTfAM8XcDNynUfS4ZQnNC0+G+AN9++63M522oSpUq+t4Bhniub8wZnf6gdBkA0m0ml8uTp06dSp9++qnRY1yi3Lt3b1Kr1dS1a1eaPn065c+fn8qXL0/FihWjwoUL0+jRo8227enBhQsX6MmTJ3KbxxhOmzaNtmzZItlDHx8fWX79+nXKnTu3mbfU8nF2tkKFClKazFlBLkv+888/5Tjs37+f/Pz8qEGDBomWB0LKiIyMpIYNG0q5frNmzWjVqlXyOcbLFy1aRO3atZP7u3fvxi43gbNnz0o1A2fRdfg+lyPnyJEjzvuGK1G4xB9S3uvXr+nQoUMyfMUQHw+uJolt4MCBNGPGDByK9MbckTYAQFrJ5Ma3nqOjo1w5ZnyVuH///qm0lenD+fPnlYULF+rvBwUFSTdf/nqytrZWvvnmG8lWNWvWTLpj8v3Lly8rCxYsUJydnSWrCylH17VXh/c9T9fE+13XNZYrHDiDNX/+fKPMbqNGjXAoTHgsGGdyixYtKu8TNmLECDkW9evXj5PZ5TmmwXRd4HXdq319feUY/Pnnn3HW4+8ZZHVNb9WqVfrMbr169ZTq1avrv7d1Bg0apLRs2TIVtgbSEgS6AJCuJBbkcpkyfxnqviDPnDkjAdfo0aOViRMnStB77tw5M2y15ZoxY4ZR0DRgwAApPTt58qTy888/yzQcQ4cOlXlCP/vsM8XW1laOSYYMGeI9sYQPN3XqVJmiRjcdDTt16pTsb39/f7nP87Ty9E4c+PJx+/3336WU+fbt28qFCxew+1PItWvXlKxZsyqHDx82Ws5BLk+zxfiziKdu4qmE3NzcJNjdvn27TLWFC0CmtXfvXv1nE+vVq5d8rxw6dEi/Dk+BxuvwtE5gOvzdUKhQIZkeiINdPjb82cQXgXTf5VFRUUrp0qWV//3vfzgU6QwCXQBIN/jEg4NVDmbjC3I5eOIxP4Z0AS6PDcXJo2lMmTJFH+z6+Pjo58TVzcNqeEIZGBgoY+J4nCKkbJAb3/zQPKaNM+d84WHz5s1K7ty5ZRmfQHIgxll3zvA+f/4chyMFg1ye13P48OFxHuM5WIcMGSIZXX6v6MZL88U43UUgwwoJMB3Dz6aQkBClRo0aUnXSo0cPZeTIkXLx4fvvv8chSAW6OaJ1wa7uAir3D/j666+VcuXKyUU8Pk6QviDQBYB0g0v7GjZsqNjb20vm431Brk7sEigwXbDr4uIiV+gNxQ52IXWCXB0uzWTe3t7K+vXr9WW1uosSaPCSOkEu4wsK3CSPP6tatWqlX87lyly+efXq1RTcGoiNqxaWLFmiHDhwQL4XDD+b+OLb9OnTZehFnTp1cMHBxF6+fCn/5levXq28efMmTrDLzae4gR43juT3k64qBdIXBLoAkK6D3cSCXM7gckYXUi/Y5YxU7K6mTHdCuXv3bhyOVApyucOs4UklH5tt27bJCX6fPn2Ujh074likUpD79OlToyoGHrOu2//79u2TrDtO5E2H9323bt0ka87VPfxe4AwuX+TBhbjUt2LFCvk3nz17dvku5+77W7dujRPsAiDQBYB0Hey6u7snGORyQ5d//vnHLNuYnk7uT5w4oc+aG5Yxx3b9+nUzbGH6DHL//vtvya4/e/ZMv6xWrVpSxp8nTx4ZG4pmR6kT5D58+FDJly+fZAt1Fi1aJMEWn9BzueysWbNScGsgNi5FLlKkiP7iD4+dzpYtm5TGchCMYDf18Nh0/u7mfc74Ak+bNm0UOzs75fjx40bBLsZHAwJdAEjXwS6PMTQsY2YIck2PS19btGghJ+v807p1a/1jiQW7kDL45JDnii5YsKBRMKsLcvkiD4/JNRQcHKxMmzZNmTx5smQYIeXwfLj8PuBSzPiC3K5du0rHa0N8fMaPHy9ltGA6vN85e8hNv2J3jOfgSneRgQOvYcOG4VCkwnulS5cuRsu42VS1atVkLC7jAJfnX0egCwh0ASDdim/MLoLc1MFBLp+Y8PjPK1euKPfu3TN6HMGu6fE0TRzscidfXbCbUJBrOFYXUh6fqLdt21YuvPGYw/cFuTgWqYfHRPNFiPiGTfTs2RPTaqUyLtnnMvLYuISfjxMuwoEhtbnn8QUAMBd7e3tav3491axZk1q1akVff/01derUiebPn09du3bFgTGRR48e0caNG2nBggWUM2dOKlKkCOXJk4eioqJozJgxFB0dTaNGjaKffvqJ3rx5g+NgIkWLFqW9e/fSixcvqE6dOvTbb7/RwIEDadWqVdS0aVOjdfm9we8TPkaQ8qytrWn58uXUsmVL+Qz6/fffqXbt2lSxYkX6+++/ycrKSr/uuXPnqGTJkrRjxw4cChOJiIigw4cPy20HBwcqU6YM/frrr3HWy5o1K6nVOJU2tf3791NMTIzcrlq1Kq1bt47u3bsX51gww/cKADK6AJDu6TK73OwIY3JNhxsa8VjcS5cuyZV3nibIEDcP4WYvGzduTPf/Js2R2eVjwmM/Y+PyWA8PDynVhNTJ7PKxqFmzZpxM7vs6xMN/Fx4eLmWvPPaZy/XZrl27FLVarQwePFjfFIzHinITJN0UT2AaXGXC+37u3Ln6DDsPueAx0zx/N+PjxA3amjRpgsMARnAZCgAslp+fHzVp0oQyZMhAvXv3puDg4EQzu9u3b0cm14S6d+9O06dPp4IFC5KnpydNmjQpzhX5HDly0PPnz025GZBAZtfLy4umTp1qtP85kzt79mzas2ePZBEhdTK7bdu2pSNHjsjnkmEmt27dujRs2DD65ptvcChMlMnlfX/x4kWpXvD19ZXl9erVo4ULF9Kff/4pVSjVq1eXbHvfvn2pffv2OBYmsnjxYqky8fDwoDt37ugz7PxdzTPHFC5cmKpVq0b58+en+/fv06JFi3AswIiKo13jRQAAHz/+aCtfvjy5uLhIqdPcuXMpd+7ctGvXLgl8IfVw+TFfdODjcPv2bdn/fELCFx/GjRtH33//vZT/7dy5U0o3b9y4Qbly5cIhSmVXrlyREma+CMGB78yZMxHkmgmX73MJ84YNGyTw9fHxQZCbSkEuD63gCwze3t5SHt6gQQP9Olwu+++//1JQUJBcROVyfjBtkMtDKXiYi52dHS1dutToePH9S5cuycXTHj16SBAMYAiBLgBYHH9/fzkh+eSTT+j06dNkY2MjAVatWrUoc+bMtHv3bgS7qWjIkCEyJtfd3V0yJTqTJ0+m8ePHS1DLJ5WHDh2iP/74g/r165eamwfxBLsajUYuFiGTmzaCXScnJxoxYgQyuSbUunVr+d7gf/OcQeTPq19++UW+RyB1rV27lrp166bvFzB8+HA6c+aMfEcAJAdKlwHAovDJOV+B//TTT6W0jINcli9fPmlo8ezZMylDe/36tbk31eIkVCDETaU4kOUg6tSpU/rlY8eOpfPnz0tJc4UKFaT5C4Jc03n69KlkRnifv6+MmbO6CHJNhxvrcNZw9erVEtC+r0EVglzT4+yhLshlPIxCV7rMbt68KQ3bwPS4+mfTpk36pnixjwW/f7h8H80K4X2Q0QUAi3PixAkJdnnM4eXLl6XkSUeX2c2WLRsdO3YMHRpTyIULF6hFixaSFRk5cqScmBgKCQmRUj8uS+ZAijstQ+q+Jxo1aqQ/MeSTxG+//TbB9Tmji26yphEZGSnvBQ6qWLly5Wjz5s1SbYJjkfr433poaCg5OzsbLW/cuLH0DeCLQxzkchfsNm3aSEk/mA730nB0dDT6/FmxYoVkeLlcmS+o8qwIPGadL17rui0DxAcZXQCwGHzFlzO1nMnl8Z7cVIe/EA0zJrrM7ujRoxHkpiDOPPH+55PAvHnzSpOWW7du6R/n0sutW7fKWCoujb169WpK/nl4D86UcxMjvuDADcF++OEHeQ8kBEGu6cyZM0cuOPD7hU/WX716JUEUj2PHsUg93GyKpzHjEmXu5cDN1gxLY/li3cOHDxHkphL+fihUqJAcC86q82cUX4TQHQv+HufjoQty9+3bhyAX3s+4CTMAwMfn+fPnSqNGjWRKDldXV2XTpk2y/Pjx43K/ffv2Mm0HmM7jx49laqCRI0fKFBx8m6eE6NChg0zDocPTQNSoUUPJnDmzcuXKFRwSE9u/f7+yatUqxdvb22g5TyPEx2fUqFE4BqkkKChImTRpktKxY0dlz549+uUPHz5U8ubNqxQuXFh5+vQpjkcq6dq1q+zz7du3y/ukbt26MqXQzp075fHvvvtOyZIli5ItWzZlyJAhOC4mxMfAwcFBmTZtmkyhNWHCBMXa2lrp1auXPP7gwQP5fi9TpoxSoEAB+b4BSAoEugDwUePAqWjRokrz5s2VM2fOKD169DCaixXBburp06ePnBjyPIf3799XevfuLXMT8wkKz2946NAho2B31qxZqbh16Q/Pwfq///1PThizZ88e53EEu6l7LK5du6ZkypRJjse2bduMHkewm7r44ht/T/j6+hodI56LlS/C8WfUggUL5LMLQa7plSxZUvnpp5+Mli1fvlz2/+rVq+VCNV+YQ5ALyYVAFwA+alOmTFGKFSumREZG6q/C58qVK06wmyFDBrlqD6bDJ/IqlUqZPn26ftn8+fPlZIWPB/+uXr26XL1Hht20rl69qlSqVEkJCwtTvv/+e9n3f/zxR7zBrqenp/Lo0SMTb1H6pdFolAYNGsjnz8WLFyXYrVq1qhyb+IJdXAAyvX/++Sfeiz+cUbezs1OWLVsmwe6MGTNSYWuAL/7s3r07zo5o2bKl0rBhQ/1nFTK5kFwYowsAHzUeq8Pjeri7Mo+vmj17Nh0/flw6lbZr144+++wzGctz9+5dzHloYnwcuCHVlClTpGnIgQMHZFoIHmvFc1N+9913Mt6N5z3kjrJgGoGBgfTXX39JJ2t7e3v6+uuvZa5inuZpyZIlRuv27NlTGrRlz54dh8MEeEw0/3vnMemlS5em4sWLSzM2bsrGjdv4faLD4xDPnj1LgwcPxrEwEf73zlOccR+Bx48fS5M2Q1myZJFGedydn/sKDB06FMfCRFauXEmTJk2S23w8uAN5bNzPgY+F7thxE0mAZEl2aAwAkIZs3rxZ6d+/vxIQECBX6DlbyNatW6cUL15cMiRr164192amG0ePHpXsIY+tcnJyUn744Qejx7k8EEzn7t27SsaMGZVSpUrp3ws6nNnl8r/FixfjEKSSxo0bKxUrVlQ6depktFyX2eXeAuHh4TgeJsafOxcuXJCy5GfPnsmyypUrSynskydPjI4LV5/wumDa41G6dGnJ0rI5c+ZINdDKlSv163DVT7Vq1ZTPP/8chwI+GKYXAoCPCs+fx1NxcEakSpUqVK1aNVnO3X658++RI0f0XWY5e8IZXUhdfEz4OHAWkbOJkLrGjx9PP/74o8y9Om3aNKPHOLvO0wpxZrdLly44NCbG80bzVGc8dc3169clS6jDmV7OWOmmF7KyssLxMBHOnvN3B3fzXbhwoSzjDr41a9aUrHv//v1JpVLJPLmcUddlGiHlrV27Vqp+uMqHO4/rOrzzdzZXonTu3JnKli0r3+c81dDRo0elMzbAh0DpMgB8NLiEicsxBw0aROvWraMaNWpQ7969ZdqBoKAgCX65FG3u3Lm0dOlSKV2G1Pfll1/K7/z582P3pyI+YeeT+YkTJ9K4cePkpD12qTJfeOCTzKJFi+LYmHjqmrCwMCpfvrxMdcYn7B07dpQ5dHV0Zcwc7CLINa3u3bvT9u3b6fTp0/IeYTlz5pQLEXxcuIyWp7f55ZdfEOSaWNWqVeX7mkvH+XjozJ8/XwLdO3fuyBAkDnYPHz6MIBf+E2R0AeCj0aRJEzkhXLVqlfyuVauWzEfJY0H5qnD16tVlLBzPw7d48WJq1aqVuTfZIvFcxT169KA1a9aQra1tnMe50WGxYsUoQ4YMcqICpsXjOnn87bFjx8jNzY3GjBkjFxs4qJ08eTItWrRITvTB9EJDQ2VuVs4a8vhb/syaN2+eZK84s8sn+ZzRiu99A6bF+71Tp06SvZ01axZ2t5kvWvMFHr4gxN/fWbNmxfEAk0BGFwA+Ci9fvpQr8nyll4NcvgrPQS43muJSTW4wwo2pzp8/L1eKEeSajr+/vxyL2NlCHS4B5JN9Ll/m0jQwHa5i4As+XN3AF3k+/fRTGjt2LP3555+S2eXbvXr1SvBYQcqXyJ45c4Z2794tmVw+iefKEl1ml98Tbdq0McrsQsrjKh8uP+YmR9xojcv4GzVqJOWwfOEBQ1pS1/79+6VMPGPGjFS/fn3y8/OTagZuIsmfX0+fPk3lLYJ048OH9wIApB7dhPE8/UPr1q2VIkWK6JuK1KlTRxkzZgwORyriOXK5kUtMTEy8j/N0T3/++SeOSQriJlKvX782WsbzRvMP46k3+Jh07NhRefHihbJz505ZPm7cOMXLy0satkHKuHLlSpzpUPbu3SvTmPn7+8t9nn81W7Zsyo0bN5QVK1bIspMnTyru7u4yfQ2YTocOHeS9wPt53rx5iqOjozT+YmvWrFFsbGyUoUOH4hCkgk2bNin29vbSDI9vc3M2blR4584dxc/PT77LYzcFA0gpCHQB4KOZi9Lb21tO2A2DXFavXj1lwoQJZt2+9IZPUnjuQ8MumWA6oaGh8u++fPnyRsFuuXLllL///tsoyOWOptztumzZsvr1+AIRpByer9vBwUHZtWuXfhnPf1u7du04QS7Pj8sX6S5duiSP4YTetA4ePKi4ubnp51zl9wJfXDh16pR0hTcMdlevXm3irQEfHx/93NDnz59XPDw8lPHjx+sDXV2w26xZM+wsSHEIdAEgTTh79qwSERGhv8+BbPPmzWWqlK5du8rJPU8ZxFMQtGnTRnn16pU+y8UnnLdu3TLj1ls23tc8FcegQYPkpF3nk08+kUALUofuhNAw2G3fvr1MYWMY5DI+gedAC0zniy++MAp2t2zZori6usrUWrogV8fFxUWfYYeUpfsu0Pnll1/02VvDIJcDK55ei6uD2Llz53AoUpiumsHwPl/k4c8uwyCX1ahRQz/9HD+uuzABkJIQ6AKA2XGAmytXLqVp06Zym7O3HEDxF+HEiROVrFmzKkWLFpXgd8GCBXJyaWdnp+TIkUNKopBVNK3AwEBlypQpchx4v+sC3nv37klWhEs2wTzB7oEDB+TiT758+YwuFHHQy0EwpF6wy+X6BQsWlBN7w/fEtm3bZJ2XL1/icKSwyZMny0Ue/izi7wp+D/z7779yoYEDKl2Qy/h7hefI5fcMpLzbt29LJp3L9PkY8FzEPBcul41z2bJhkMv69esnQ2AATAmBLgCkCXx1nb8IOdg9dOiQnLzoTtz5CjyXLeuCXS79mz9/vvw8evTI3Jtu0ZkSLsW8ePGi3A8PD1fmzp0rQZUu4OXMCR8zML0zZ85ImT6fxHMwpQt2f/75Z/39H3/8UWnSpIni6empz1xBygsJCZHy5EKFCsmFBl2wy2N3M2fOLJ9lo0aN0gfCs2fPxmEw4YUfDmBLliwpGUS+MMdDXPg9sX//fqOSZv7cev78OY6FifC/dysrK/k3v3XrVlk2cOBAORaGQS1/t/N757fffsOxAJNCoAsAaS7YzZs3r9K/f3+jx2IHu2D6ILd06dJygvLTTz8ZPcYNqPiqfZkyZeRxPtE3LNME02RLuPyVm675+voq27dvlwsOumCX7zds2FBO+rk51d27d3EYTIgvJnDVCQ+5uHr1quxzXbDLJZgDBgyQzyq+UMTjQcE0OEvLpftckmzY0IgbhXG1DwdTfJGBAyr+buELQWA63PyLjwUHu7oGbHzhgfsF8EWGL7/8UrK9NWvWlAsT3HsAwJQwjy4ApCk8PVC9evXI2dmZrly5Qk5OTvrHeKoanoqAH+MpPHhqAjDNPLl169YlLy8vCgwMpLJly9LMmTPjXXfHjh00fPhwmbrj119/xeEwkZEjR8p0HDxnruE0T+XKlSNPT0+Zusbd3R37PxVcvnyZihcvThcuXKASJUrol/OUNTyt08aNG+UzDFLHv//+S6VLl6b27dvLtEI8lQ3Py8rvldGjR9Px48cpS5Ys8jnF802D6dy8eZPu3r0r02tNnz6dli5dSh06dKDg4GCZ15vv89y5TZs2le+LTJky4XCASSHQBYA0G+xWqFCB1q9fT7a2tkbB7okTJ+SkBkwb5G7YsIH69u0rwS7fTsiaNWtk/laeG1GtxvTspjBw4EC6desW7dmzx2j5ypUrZU5p3TytCHZNjwOnypUry2dRzpw59ct5btzMmTNTREQEgt1UwPOm88UGPgZ16tSh58+fy2/DYBdSb557Dm5ZgwYNZL5cvjhnGOwCmAPOSADA7IFVjx49JEvLWZJDhw5RqVKl5Evz5MmT1KZNGzmB1MmVKxeCXBPasmWLnKxzYGtnZycnkQ8fPjRa59WrV0b3vb296cWLFxQUFGTKTUvX+OSR3xt8Ym+ocOHCktXlY/b48WOzbV96wvvbw8MjTpUDX5Dz8fGhfv36ScUJmAZnBLt27UqVKlWiH374QS6KVqlShYfiSdWDtbW1VP6sXbtWjgUvB9Phi238HTBmzBi5MMrvAf7++PnnnyWL3qVLF/r999/lYui1a9dwKCB1mbQwGgDgPQ1deBxb/fr1ZTzPyJEjjcbfGjaoMuwoC6bFY3B1fv/9dyVTpkz6+9wojDuZXr9+Xb+sTp060kETUs79+/elmRFPpcVzRPP7gqd44u7khtOi8H7n6WzAdLibMo/z7NChg9K3b1/l2LFj8r7gsek8lQ2PE2XczZfHUfPnGpgOvy94LDqPVWcnT56U9wWP+eSGedygStdfgMeEgunwlEE8Fnr58uVy/82bN0qnTp2kGz83/2JfffWVHItixYopL168wOGAVIVAFwDMhk8euaEOn0gaCg4OVk6cOCG3+aSeO8hiDkrT4YsIM2fOVLp166bs2bPH6LGNGzfKSQo3DeEg19nZWfn111/jNK4yDI7hv+FAiqfp4IZHn376qVzsyZkzpwRSfLLIzV50zVz4vYHGU6YTFhYm+9rHx0f57LPPpPkX739+v4wYMULeG9w8jxuBcedfXg6mw58zPFcxT9lk6PLlyxJwTZs2Te7zfNJ8sQhMa/DgwTKVmSHe99yErUSJEvplPB0dX4QASG0YowsAZjNixAi6fv06bdu2zWg5l6MtX75cmlExHiPq6upqpq20bFxu3KRJEylPzp07Nx05coQWL14s5Wa68dLc6OWvv/6iYcOGybHhcjQwDY1GQ/nz56eePXvShAkTZBmPfW7YsCGFh4dLOf/q1aulNJbLlbk0M1u2bDgcJvLtt9/SunXr6PDhw+Ti4iJlsGPHjqUpU6bImGhHR0cpkeVy2pYtW8oYUUh53MyImw9yDwAeUrF161ZpgGeI3wv37t3TjxUF047J5UZS3bp1kzHR/H1tiL9HqlWrJt8rOXLkwKEA80n10BoA0rWgoCCZe5XLyzijy1NyxJ4LlzNX/PHE2RQwrZ49eyrVqlWTLDrjjBVPDcFTQDAuNdNNIRQ7kwv/zV9//RVnqiyes5j3N79PDN25c0fKAfk5kPJ4/luuXoiNp9CKL0vL8xlXr14dhyKVDB06VObtZhUrVpThLrqScZ1x48ZJZh1Mi0vFs2fPLtMG8Xc4Z9Jv3rxptM6tW7fkc+zp06c4HGBWaEYFAKmGsx7coZcztNzMhRuKcHfGdu3a0Zs3b/TrnT59mooUKUL29vY4OimEmxjNmzcvTjaXO2JOnjxZpnHiRi7caIoz7dwgjB/j+9zwaNq0acjkpqDZs2fTJ598ou8Wq8PZKha7aQs3eOHMO2d0IWXdv3+fateuTW3btpUu74b4eMTXQGfw4ME4FqmAGxHeuHFDqhj4c4lxNn3fvn00YMAA6XCt68DM76k+ffqkxmalW5cuXZIKht69e0uFA//Oly8fNWvWTKqzWEhICH3++efUuHFjmdYJwKzMG2cDQHrBDVq4gUjdunWV6dOn65efOnVKmhvlzp1b+f777+XKPU8sjzG5KWvAgAFyhX3WrFn6ZTxmise7LVmyRHn8+LGSNWtWZe/evZIpKV68uGRxra2tZWwupCzOmPPxcHR0lIZshpldHttWo0YNJSoqyug5nMn68ccfcShSGGemeBw0HwvOmq9bt07/GDeb4qoTbrpj6I8//lDy58+PY5HCeKwtZwx1Y/65iiRjxoyKl5eXUQaXmx/xceHjxk3a+LiNHj0axyMFcTXP/v37pcEUe/Lkiexz7hfAzSMNx9/yZxZXAlWoUEHJkiWL9BJ4/vw5jgeYHQJdAEg1U6ZMkZP79u3bGy3nZjq9e/dWChcuLE0sdu/ejaOSwrhBCHeNjR3s8okMN6Pq0qWLdDM1LNncsGGDsmvXLhwLE+ATef73zu+FbNmyGQW7fKLPJ5TczfrChQtygtm/f39Zz9/fH8fDBCZNmqTkyJFDadu2rVGwy++NKlWqSEC1bNky2f98ks8XiJYuXYpjkUJ4+AoPoeDPJ/7hgInfD/y5xZ9NvEzX2VeHuy7/73//U8aOHavv8Av/HV9Q+OabbyRw5f3O/9b5u4Dxb35/NG7c2OjCAzeU5Aum/B0yf/58DDuCNAOBLgCkKO7aO3DgQBlTmFiwi+6kaSfYZRkyZJATFbZy5UrF29vbDFuYvnCgxNPRHD16VLLphsHukSNHpJuv7sS/YMGCku0C0+Ax0RzMcqaWu8gaBruc8dW9b/iHL0Lg8yvlPHjwQLpat27dWsZ68lhprvJp3ry5PK4LdjnjzhUnYDocvPKUZXxRbceOHcq1a9fkgpuTk5McJ8Ngl8dMxx4nDZDWINAFgBTDJyQ85yqfDPLVYD45uXTpUrzBLpfFxg62IHWOUcuWLeMEu9wgjKeq4RN6w3kRwfRZXc5I3bhxI06wy49zaf/p06fluIHps7o8hIKn0uL3QewyZj7R50Z5PJ0WpGyQ26NHD6MpyubMmSPTOL1+/VruI9hNvSCX5yQ2vFDN/9754s5vv/2mX4ZgFz4WCHQBIEUNGTJErrwPHz5crgJzQNuiRQvl+PHjRush2DUdHk84d+7ceB/jExg+keGMrWGw+/LlS7lCz1mVzZs3m3Dr0h8e58nvh/g6kHI5LGd1ef/HF+xCyndX5gs9XLIfX1aXL9Txe0dX/RA72AXTB7m69wUHuoYXFRDsmj7I5fG3sauxdL0cDANdhmAXPgYIdAEgRd27d0+yudxwik/W+QSfM4QcVPH4W8Mxnxzs8rrXr1/HUTBx4ynDIJdPaHhMVUJlzJByuAzTsOR1xIgRMrWWDp/gc5O2MWPGyH0Eu6bDmVqeFkV3PPjziDO0hiZPnizvEX5/INg1rX/++UcuhHbq1MmoYoEv+nDZPo9Lj00X7PL7CFIOfyZxkMsly7GnCho2bJhcgIuvPwAHu/z5xccMIC1CoAsAKa5z587yhclXgnWNRipVqmTUaISzJHwVmTMsYPqxuIZBrm5cVWJjdiFljwVnBnnOYu5GygHv559/rg94uUzc2dlZf7LIwS53veY5dSFlrVmzRo4FfwbxWFzOGvIYRF0zI11Wl0tnDY8ful2bBmfPDYNdfg+ULFlSadq0qTQCiw9fHMLY0JTH3xGxg93x48fL2PXYXccNxe4OD5CWINAFgBR37tw5o4ZTq1atUmxtbZUZM2YoEydOlKvDnMndtm0b9r6JGAaxX3/9dZwg13A9PuGfOnUqjoWJjwW/B9auXSvNvjjg5RL/L774QkqauVxZl9VlOJE3fbDLmUFu8MW/OeDlqc94Ki3u5MvvF12ghWOROsEudyDXBbm6i6RgvmD3008/fW+QC5DWIdAFAJNo0KCBTNfB5Wl8gr9gwQL9Y3wCiS/P1A12q1atmuAJO07kU+9Y6MZ88j7nC0CcueWAl+ehNMzqQuoEu7pMIg+f6Nq1q1yA01Wf6LK6kHrBLs+Xy3Oug/mDXX4PrF69GocCPmpqAgAwgdGjR9OjR4+oR48eNGfOHOrdu7f+MVtbWypZsiT2u4lZWVnR0qVLqUOHDnTkyBH6448/4l1PpVLhWKTSsWjdurUcjw0bNlC7du3owoULtHjxYlnHzs6O/P39cSxSQZs2bWj58uW0Zs0a6tatG+XLl4/++ecfunr1qtzm4xUcHIxjkUr69+8v3xMvXrygTz75hGJiYrDvzcTHx4f2799POXPmpM8++4xu3bqFYwEfLRVHu+beCACwTOXKlZMT97t37yKYMiM+aezSpQutXLmSZs2aRYMHDzbn5qRrumOxbt06OR6tWrWS5fxVzCf5Xl5e5t7EdGXt2rXUqVMnatu2rQS6HOAyPz8/ypIli7k3L92ZN28eDRw4kDp27Gh0PCD18fd2rVq15DOLA9/8+fPjMMBHBxldADBpVvf+/fu0c+dO7OU0ktkdMmQI/f777zgeaSSzu379en1WHUGu+TO7ukwiglzzZnZXrFhhdDzAfJld/szigBeZXfgYIdAFAJPhLEnevHkRWKWhAIszJWFhYebenHTNMNgdNGgQhYaGmnuT0jXDYHfjxo3m3px0Txfsenp6IqObRoLdwoULk5OTk7k3ByDZULoMACYvReMvyK5du2JPpwFcIosxuWkDZ6u44oEvBoH53bhxgwoWLGjuzQAAgBSCQBcAAAAAAAAsCkqXAQAAAAAAwKIg0AUAAAAAAACLgkAXAAAAAAAALAoCXQAAAAAAALAoCHQBAAAAAADAoiDQBQAAAAAAAIuCQBcA/pOIiAj69ttv5TeYF45F2oFjkbbgeKQdOBZpB44FWDrMowsA/0lgYCC5ublRQEAAubq6Ym+aEY5F2oFjkbbgeKQdOBZpB44FWDpkdAEAAAAAAMCiINAFAAAAAAAAi2Jt7g0ASK80Gg29ePGCHB0dSaVS0ccqJCSE1Gq1/jfgWADeF2kNPqfSDhyLtMNSjoWiKBQaGkqenp5p7r+Dx0FHRUWl+t+1sbEhOzs7Su8wRhfATJ49e0ZNmzbF/gcAAAD4j7Zs2UKZM2dOU0FuhQoVJOhMbQ4ODuTv7y+3Bw8eLD/pETK6AGbCmVxWr149srbGW9HcPq+/1dybAIaO3Mb+SCNeX/Y29ybAW38eqYJ9kYZolI+3GsuSRKti6IDnGf15VVrBmVwOci9evEoxMTGp9netrKyoRIkidPLkSXJ2dqb0DGfXAGaiK1fmINccV/vAmJMD9kiaYqMx9xbAWxFW2BVphY2C07a0JAaBbpqSVoeBaWI0pGiU1Pt7hO9PnbRVyA4AAAAAAADwH+HSIAAAAAAAgClwpjk1s81pNLNtDsjoAgAAAAAAgEVBoAsAAAAAAAAWBaXLAAAAAAAAFpFXRB5TB3sCAAAAAAAALAoyugAAAAAAACagIjWpUvnvgRb2BAAAAAAAAFgUZHQBAAAAAABMQaXmNGvq/j0Q2BMAAAAAAABgURDoAgAAAAAAgEVB6TIAAAAAAIBJYHohc0FGFwAAAAAAACwKMroAAAAAAAAmoJL/pV5ukf8aaCHQBQD4SOw8Fkinr4ZSZJSGiuZ1oNa13cnaWmWy54Gxm8819MfRKPLJqKbPatjEu3uiYhSasC2KwqIVyuSkovH1beNdz/e1hqYdiEp0F/evZENFsqDwKjab/KXIrlhFUtk7U4z/Uwo/vYc0r54le5342PgUI7uS1UhlZ0/RTx9Q+ImdpISHvPd56Y1P1QJUuGHJRNfZ/sNaiomKIQc3RyrVriK5ZnWn4BeBdPfoTXp66WGS/1aGXB5UpW8due175i5d2nDmP2+/JbGxt6FG41omus7lLefp3vHbcjtjnkxUrGlpcnB1IP/7L+jSpnMUERz+3r/zoc8DMCcEugAAaVxMjEJtR96l7UcDjZaXyO9AO2fnI3cX6xR9HsSzLzUKfb09kjZcjqHKeRIOdP+3J4p+PagNYH08Eg50nwQqNOdodKK7ukEBKwS6sbh0GUnOjbsbLXPtOJzezJ9A4ce3J3md+Lh0Gk7OTXsbLYtpN5he/TSQoh9pgwTQyl4qN1XsVSPR3bHrfxsoS5Ec1G3RAHLycDF67OSSQ7R53Mok7c6WP3WRwJpZ21kj0I3FxtGOKr3nWPjffymBbqm25an1lM5kbfvus7/BmOa0oNMs8rv2JMHnf+jzQEulUpNKpaTa7lCpcCFbB2c5AABp3IIN/hKsWlkRDWzrSe6uVjRr+Qu6eCuMvp3zlKaPypmiz4N3tl2LpmXnoumUr4buvUr8ROWKn4am7osiazVRtCbxvZg7g4p+bRk3CN55I4a2XY+hrC4qKpUd2dzY2VZdABt+Zi9F3r5EDhUbkk2eQuTe7zt6fv0MWWXM/N51NG9exNnvdqVq6IPcsJO7KOr+VXKs1oKss3mT+6eT6OW4DnhbGLhz6DpFBEfE2ScccGXKm5nuHrlJUWFR1OrnLhLkPrnkS5c3n6O81QpS3uqFqEL36nRt+wW6c+hGovu1bJcqEuRyZtjKxgrHIB6RoRG0afyqOMuzlchJZTtUksc5i+6ZL7M+WL20+Rw9ufSQynWuTB55PGX57Oa/xLt/P/R5AGkBAl0AgDTu6IVg+d2ypjv98kUOuW1ro6JvZj+lI+eDU/x58M7phxpaeT4mSRnfgasiiM/Fu5ezfm+2Nqurmj6tahzI3niuofHbIkmtIlrc1Y6yuSHQNcQlxSzq0W16PX2E3A47spkyz9hFKlt7sitemawyZXvvOmGHNsY5Hg5Vm2qfd/86vZk5Uvu8o1vJa9o2sslVkGy8i1LUvSt4a7z15OJD+TFUonU5CXIDnwXQysELySGDE2UuqD0eS/vOp8Cnb+jYX/vpq8s/kY29LWUulD3RQNclsxs1/KoVPb38kIJfBlH+WkWw/+MRHR5Fx/8+ZPzv2d2RagyuL7c3jlslWdfmP7STYPXu0Vu0/NOF8ti1nZcob9UCpCgJX8Sr1LP6Bz0PIC1AoAsAkMZ5ZtB+VAeGvAu4gt7edkuk/PhDnwfvNCpkJWNt2eLT0XTucfyp2pmHounUQw1Na2lLEYnHuPHSaBTqtzKCQiKJhtewpuo+yF7FFnntNAX8PZliXjzWL1PCQ0nRaEilVhNZWSdpnfhYeWSV39FP7rw7Jv5+pIQFk8rJlWwKlEKgmwiXzK7U9Pv2cnvT2OUU+iqYnDycafPXq0iJ0UiQyxwzOJKVjfYYPL/5NNH3RPNJHcnGwZbWj15Kdb7QXoiApOGg1i2rO13dcZHOrT4py7wr55PfN/ZeoRIty1D2Erko+GUwXdp4ht48fp3ga33o88AQX7RMzYsCKF3WwZkOAEAaN6CdJy3e/Ip2nwiiZp/dpgwuVrRu3xvi8/bPOnum+PPgnfK5rOSHHb0fQ+fexU96d/w19N3OSKqUW00DK1vTb4eSH+kuOBlNJ301UrKc0Lje9C7y+mn5MeTcsq82gOVs7J1LFP3w1nvXiY8mJEB+W+cqxAPciBSFrHPmJ7WTqyy3cslgkv8mS9FwXGtpOnV91yW6sfuyLAvxD6aTfx+U214FslK1QfXIp2pBUlup6fSyo3T7wLUEX694y7JUqH5xOvj7Tnp6+VGq/XdYAp8q+alkq3IUFR5Fmyes0S93z+Ehv6v2rUWuWdz1y+sMb0j/9P0zwez6hz4PIC1AXRQAQBqXxcOaqpd2ltsctK7a/YaiY7RNpSqXcErx50HScenep6siSKMQzW1vR2quO06myGiFJu/RNrD6orYNOdvhavx7WVmTa7fR+nG1oQfWSZCb7HXeCj+9V37b5MhLmb5bSm59v6OMY+a9W8HGLtnHNb3gILZYizJye+8vW+Jdxz1nRirVpgK5ZnaT+znL5JHy2vg4ZnSmJt+2pZd3ntH+6dtMuOWWqd6oZvKbLyYEPHlt1J2ZOXu5SqnznmnbKORVMNk62lH76d3JyqDRlKEPfR68o1JZpfoPaCHQBQBI4wZNfkibDgaQi6OaRvbwom8GZKUcmW3o/I0waj/6Xoo/D5Lu7CMNHbyroexuKpp9NIqGr4+gjVe0GV3/EEXu33+VeGeqpeei6XGAQk62RL3K46TxfdTunuQx7i9yathV7oceWE8BCycmex1DYQfWUfCWRaTERJONdxFyrNmKKDKCYgJfyeNKqHHncnin2qf1SK1W0/3jt8jvajwlD1ymfOMp7Zy8gW7s0WZ7eexuvdHN4123dLsK0sCKpyLiTDGXRHvl15aW5yzrTY0mtMHuT0CeinkpdzlvuX1sgTabrhMeFCa/T/1zRJpX7f11G635/F9Z5uLlStlLxN+c8EOfB5AW4BsVACAN47lvV+/WXpUf2ycLfd49s9wuX9SRmn12h45fDKGrd8OoiI9DijwPkkfXXfmuf9zpggLCSZZ1Lm1NeTIm/BqLT2mf16SwFbK572GVORd5jJ1PVh5ZSIkMp8B/f6bQvauSvU58gpb/KuvZ5C3Oc3NRxOVj5DVdOx1R9LOkz/uantg62VGRxtr5dC/Gmt82T8V8VLRpaQmU9kzdTIdn75afXsuGSAlzjlK5431NtbU2G5WnUn75iZ09zpArE23/fq3J/ps+ZmU6VJTfjy48kLluDfE4aUd3J3p200+/zPDChLVd/FOmfejz4B0VqUmVimN0VRijq4dAFwAgDYuIVPicO05TqYDgd7eDQzUp9jxInjwZ1XGmCdp3O4Y2XomhTE5E4+rZyjoJ4azvsQfa49CgIMrNEmVlTRk/nyEBbMyrZ/Rq2mcU/eB68teJh23RimRfto40rgpaNUPG6NqVrE5qB2fJ8kZe1Tb0AWM8VRCXsLJb+68aPWbv5ihz7Wo0Gjq74hi99vWX+T1164cHajOFsd0+eI0iQoynLirfrapkgX1P36Xza3EsElKoXrG3xyLu+Oeb+65RlsLZKX+NQnTibZfm3BV89I+/uP0ukE2J5wGkBQh0AQDSMBcnK6pZ1pkOnAmmnxc/o6t3w8nRQU2bDmib5+TKakulCznSruOBtOWQdtnPI3Ik+Xnw32R2UdGnVY0zGuHRJIGuq/27x5afi6bjD2LIxU5FPzR+FxhzgyvdDB2VciPQTQzPh8vz2rJovwfkWLO18X4/d0CaRr1vnRi/B+TUuIfcD929nKKf3CMlNIgc63aQplW2BUpT9POHZF+urnadA+tJ87aEGYzlrpBXfvOUQgGxOvDyXLuhb0IkG/jJmhF09/ANmZM1e0ltJvfMimPyu9bwxuSU0Vnm2j236oQ0n4rdgCp/rcIS6L645Uen/zmCwxAPrwJZZD8y3zP34zx+8p/DVKl3DSrcoDj1XzeCXvu+1GfjL285T0HPAilv9YJUpEFxWbb1+3Uyf3FSngeQViHQBQBI4+ZPyE09vr4v5cYb3waqrHRBB3nMxlpFp6+G0pzVL2X5/z7LTtbWqiQ9D1LH/tsxtOhUNGV2Ng50b7/URrk8/26+TDgeibErWfXd7SIV5MeQJuClPshNbJ2IiDByqt9J7kecOyCBbtS9qxS45Cdy7TyCbAuWlh9dYBz479T/dOwtmYe3tns7B6CxRYVF0spBC6n9zJ7ShKpUW+2xiImOoSNz99DFddru2CXblCePPJ4yXQ0HuvDfjgV7Hs/xeP3wFa0etoTaTe8m43h1Y3mfXn1Mm79ZLbezF89JlXrVkNs7Jm+UQDcpz4PEqVRqUqlSsXSZO8eDQKALAJDG5cpiS/vnF6DTV0Polq+2pC9fTjsqV8RR/4XWoLIrubtoM4K6ADYpz4Ok61HemqrksaKsronvuzr5teXMbg7v1utU2ppKZlOTY6yZg6rk0a7LjU1xTBIXdmInRd66mODjUXcuUhRPL/SedWJePZe5duX+k7v6xzi7G35yJ9kWKksqW3t5raSUPadn51YelzLZV7HGg+pwFnda5W/Iu0oBcsniRpHBEVJ+bNgNeN+vW8ne1ZH87z5P8O+cWnJY/s6L98y9m569vPNcmkWxoGfvLmwaurLtAt09dovyVS9Ids725H/vBd0/eYcUbhsvZePXKfJt2Xh0ZHSSnweQVqkUnhsBAFJdcHAw1apVixo1akQ2NmjmYG5jGm8w9yaAoQM3sT/SiFcXteWpYH6zD2izbZA2xCi4YJgWRKuiaY/XSdq/fz85O2vLt9PSed61yy9Ik4oXBXiau8LFPNPc/jAHTC8EAAAAAAAAFgWlywAAAAAAACaAMbrmg4wuAAAAAAAAWBQEugAAAAAAAGBRULoMAAAAAABgstLl1Nu1mFThHWR0AQAAAAAAwKIgowsAAAAAAGACKlJTak5EhUmv3kFGFwAAAAAAACwKMroAAAAAAAAmgDG65oOMLgAAAAAAAFgUBLoAAAAAAABgUVC6DAAAAAAAYAIqskIzKjNBRhcAAAAAAAAsCgLddKZfv35UqFAhOn36dIq8XsOGDWnhwoWUVkyZMkX++xL62b9/f4r9rbT23w4AAAAAabEZVer+gBZKl9ORly9f0t9//01RUVG0ZMkSKleu3H9+zTt37pC/vz+lFc+fP6fXr1/TX3/9Fe/jRYoU+eDX/vbbb8nX15cWLFiQJv/bAQAAAABAC4FuOrJs2TJSqVTUrFkzWrFiBU2bNo2srKzI0jg4OMh/Y0rz8/OTQBcAAAAAIClUpE7VElpVKv6ttA657XRk8eLFUm7bu3dvevbsGe3evdvo8RYtWkjGd9y4cVSsWDFZpigKzZ49m6pXr07ly5engQMH0qNHj4yeFx0dTd9//708XrVq1TjZ1PXr11OjRo2oVKlS1LlzZ6OyaY1GQ7/++ivVqlWLSpcuTe3ataO9e/fqH+ftXb58OU2dOpUqVqxIlStXlu35r973d2Pviy+++EIuDhw7dky/b5Ly3w4AAAAAAKkPgW46ce3aNQkwO3ToIEEnZz3//fdfo3Xu3r1Lo0ePpocPH9KECRP05bpjx46VQPDzzz+nixcvUoMGDSgmJkb/PA5Cb9y4IcFgyZIlqW/fvnT06FF5jIPSXr16UY0aNWjMmDHk5OQkAaEu2J04cSJNnjyZ2rZtK0ElbxcHt1wWzPi37u/y65coUYIGDRpEhw4d+k/7431/N/a+6NatmwTZ+fPnl3HASflvjy0iIoICAwP1P0FBQf/pvwEAAAAAAOKH0uV0grOTdnZ2kql0dHSUoG7dunUUGhoq93UKFy4smV/deFcO6n7//Xfq06ePLOOAtVKlSnT58mUJ7BhnM3VBc6dOneR1uekTZ0o5SJ45cyZ1795d//irV68km8rP4eBv1qxZEoCzli1byvILFy5Q3rx59dvEY4oZr7d582YJdDnLHJ8nT54YZV11uBnV6tWr5XZS/65uX7BcuXJRZGQkNWnSRL8sof/2KlWqxPn7HFh/9913+vtqtVr2EcCOowFUII89eWez0++MPScDKYeXLXlnt6XVu99Ql8YZZXlMjEL7TgfRq4AYKlfUkXyyv3tOQh4/j6Qj50MoV1ZbqlTc6YPXsVR3/DV096WG6hd895Xo+1pDl55qqGkRa9pzK4YKeKoop7v22vC1Zxq6+ERDWV1VVN2HG38krVBs69VoalIk7tfuXX8NvQpVqFxO46EkTwM1dMpXY7TMx0NNxbJa9jVqh+otKez4NqKoyHfLqjaj8LP7Se3oQlaZc1Lk1ZOyXOXoQraFud+EiiKvnSIl9P0XEK2zeZN17kIU7XuToh/fee9yo+fmKijrRd44S5rXz8nS5SrvQxFB4fTs+hP9shylcktV1JOLD6lkm/J0acMZ0sRo/53mrpCXXLO607NrT+j5zaeJvrZXwayUMben0bJH5+9T8PNAcvZ0oVzl81J4QCjdPXIz8W0s501vHr2mQL83ZMkcMzqTT+V8dHnLef0ye1cHKlinKF1Yf5pylMpFkaGR9PymnzzmniMj5Sydm8ICw2QfaqKNP0sM2TrZUd5qBYyWBT8Poofn7sttrwJZKHPBrOR7+h4FPI1/PydlnfRKpbJK1XriJH4lpQuW/W0Jgr+QOBjjTK6rq6ssa926NQUHB9PGjRuN9lK1atX0tznoCw8PNwrssmfPLllOXZAb+znMw8NDspVnz56lgIAAKe3lwFP3c/jwYXlt9uOPP1KBAgVo7ty5kvHlsbVcLs3brFO3bl2j1/f09Ew0G+ru7i4Z29g/gwcP1q+TlL8b+78rPgn9t8eHg37eH7qf2CXgkH4dvxRKc1e9NFrW/wdfiopWKDRcQ6N+1f5biY5WqPGQ27R+3xu6fj+c2n5xlxZsMH5ebHceRlDDQbfppm84jZv1mH5a5PdB61gyPif4ZEUEaTSKftlfJ6Lp4B1t5cqco1F06Yn2s2HesSgasjZCguM/jkRRk/nhFB3z7nkJOXo/hj7f+C5w0+G/2WtZBK2/9K5KRudNGNFJX43+58fdUXTg7TZZMocaLcmuaCX9fSuvHOTS+XNSwkPJOk8hcqyrvUCp9shCHl//TbY+xcg2f0nK9ONKCUQTY1u8CmUY9itZZ/Mh9yFTyL5c3USXG21X9Zbk1mucBLoZv5xDVl45ydJ5eHtRreGNjJY1/Lo1uXi5ye0m37UjazvtxZt2M3tRua5V5TmtpnahOp+/O3eIjwRiZfLof1r8rxM5e7qSW7YM1HvFMMpaJDtV6lOLWk7pkuBruGR2pa4LB0rQbOkiQyOo9ZTO5OBukJxoWJyKN9deMC/WrDTlq15IbheoXYS6zvuEMvlkphLNy9DgbaPJzsU+wde2cbClnKXz6H8q965JJVqVlccK1StGXeZ+QlmKZKe+qz4j1yzucZ6flHUAzAEZ3XSAx55yUMVBK2c1dRlNxgEwZyJ1rK2tjbo0MxcXl0Rf394+7ocnB40hISFye9iwYZQjRw6jx52dneU3jxfmMbycUeXAc+TIkbRz506jdW1tbeN9/YRwhrpVq1aJbnNS/q7hvkjuf3t8OKPOP4YZXQDWpo47dfnqHv1vWHa5f+FmKDnaq6lYPgd6ExSt30lHLwZTJndrmjUml9z/pJUHNRl6m/q0zCT3/936itrVcyc723f/trYeDqCuTTLS2D5ZqGODDNRj/H36slcWox2flHUsGWdJs7mq6eRDDVXKrc2qbrsWQzPaxP3s+fVAFJ0a4UDOdtpL5k3nh9GZRxqqmNuKTjyIISdbVZyM66erI2jtxWhyc4h7mf33I9GU0MdZ4cxqmthEuw0P32jo+IMYGljF8r+2w0/tJrvSNSni/EG5z7fDz+wjUowzUo41WlLonhUUunuF3I96cJ0c67SnwEUTSeXkSnaFy1P46T1Gz3Gq257e/PktRd06TxEXDpFLh89knYSWGz23SU96NbkvaQJfUbTfAwm4g5b9Qpbs+o6L1HB8K7KysaKYqBgJsjzzZ6HbB68breeWPQN55MlEc5v/LPcPz95FI458S/t+3SbfiYUblqAHJ+9Q6GvteQG7ueeK/LBCDUpIwOx35RFV7FWDTv1ziI4vOEBWttY0/JB2KFV8ONB+7Zv4xT5LER0eRbcOXJcM7vm1p2RZobrFjDK8OlX71qI1X/xLfte0mfgWP7aXYPTCutNy/BzcHCTzqhPyMoh2/m+TPujts3wI7Z66Re7XGdGYln26ULL6QX4BlKdSXrq4/ozR30vKOum9GVXq/j3QsfxvTJDyWzc3Nykhjr18x44dMkUOZyJj05Xw8vhe3VREHPw2b95cmi69b6oe3fO9vLyMAk8e18rBc86cOWnRokW0fft2KaXWvb6p8Zhac/xdgIQUz+8gwc7NB+FUILc9bTkUSK3rxL0i7uxgRSevhNCu44FUo4wzZfeypQsr3r0PA0NiyKAoQVQp5UxD/+dLubPa0rYjAdS0hjYTk9x1LF2r4la0+WqMBLocVPqHKlQxV9yTE0dbkkxuj/LWlMVFTVv6OegfC4sislbHjVpnt7OjyU1tqcL0MKPl919paMu1aBpQxZquP0s8K/zdjij6vKYNWalV6SLQzdS0FwW+vW9fuiYFb9ZO62ZICQ+TzGvkrQsU/eA6hR/bJj9MpbYilUPcEvywk7so2veGdh0bO1KprRNdbkhlZ0+asGDtncgIssmZnyxdWEAoPb30kLwr55fgtkCdonRr31WKiXx3AU4XhGXIlYmKNCklj0eFRdLUcuP1j9s625PKKv6TfbWVWrK/i7rMkvsnFmkvcOSplI+KNi1NV+IJ5FjRpqXota8/hQcYv68s2ZVt56lok1IS6PLFB+9K+WjtqKXxZn/Ld6lCh+ftpdcPX9HGcav0j1nbWJGNvU2Cf6Nqv1p0aeNZiggOl6CXL24EPQsgnyr56cKGMxT2JtRo/aSsA2AuSClZOC5PXrt2LfXs2VMyt4Y/X375pcypu2rVuw9AQzz+tGzZsjRq1CjJCL9584aGDh0qY2ALFky8PEwX6HLjKm7mdOnSJRkPzMH1V199RT4+PtIAinEZM3cv5gCUM62c6eTsc2JZ2//iQ/+ujY2NlBwbljcDpJRWtd1py6EAuc3BJmd5YytT2FEyreNmPaGs9S9R06G36cj54HeZw/ae5GBv/LHuZK+m8EiFTlwKodu+EeTmFHdKsaSsY+naFLeWMbS6bG6rYlbxjr39u7M9HX+goaI/hVGZX0Jp6r535ci18llR2VjjbBMzYn0k/dLCjtTvGVDFY3Wv+GniHd9riXjsa4y/H9l4FyGVvRNZZ/ehyKvaDJahkF3LKOrBDcow/Ffy+n0fuQ+cROqMmbWvEfSawg4ZD81h4Ue3khIRJmNx3fp+SyG7lye63Oi5Z/aSW58JMl7YqWkvIpu4GX9LdGXreSpYr7jcLlC3GF3dGjfwDPEPpg1fLqOq/erQl+cn0yerh1Oh+trnsAtrTkrWMD6FG5Wg+yduUeird59lzDN/VvIqkFVKnGOzd3Ogyp/Upn3TtlJ6cmPPFfKumFeCXL4Q8Oj8AxlDHdumr9dI8Dlo6yj64vAEajSupWTH2dOrj+nO4fjHPfPrlmxZjk79e0TuO2dylkx7p9m9qVjTUjR46yjKlNfL6DlJWSe94894tUqdij+Wf0E0qRDoWrg1a9ZICTFPCxRbzZo1KV++fHG6L+vwSd7SpUslwOXsa4YMGejKlStS8pvU+Xf5+VwazGN6ueMyd1CeMWOGBMDc3InHynLXYw4ia9euLQE4j5fl5lW6cbzJ9eDBAyk7ju9n3rx5H/x3eawwjzs2bN4FkFI4g7vlcCA9fxVFrwNjqFTBuP/OwiM01KuFB538pxDd2liUWtZyow6j71JEZMIXX35Z8oyGd/Wi30bnpJ2z89OkBX4y1je561i6Al7cVIro3isNbb0WQ61LWMc7ntbbQ0Vre9uT33eO9GsrO1pxLlp+kmvj5WiKiFHozksNnXsUQ7dfaujy0/iP49+noqlT6fQR5OqEn9aWL9uVqEIRFw4TaeKOTVZZWUvp8IsRjcn/u+6kCQ0i90++fe9rc5CaceQsClrxG4Uf3/7e5TpBS6dR5OXjpHbPRCE7l0ownR5c236B8tcpQmprtTR+urX/WrwB0s29V2h+62k0pcxXdHLxIWo5tYs0pnqfcl2q0rlVJ/T3uREVB06nlhyiRZ1mSoOjjHmMm1bVHt5Ygry8NQprx/qW9SZnL20PEkvGzaZ8z96XILdQ3aJ0OZ6LDiw8KIxWDl1Mk0p8RcsHLaRsxXJQjU/jjjuPrUijEnTv+C2KCo/SLlCpyM7Znpb0nidZ4UNz90qm2EhS1gEwk/T1zZkOcQOqW7duSUAbn4MHD+qbJ23atCnOeFwOUs+dO0ePHz+WsbLcCMoQj2vl5k+G+HV0Y3C5JJobXnGnZc4uczMrwyCZs7s8hpfLp3WPdezYURpe5cmTJ97X50Cbg+b4cJaap/hJSJYsWZL0d+PbF1x+zfMPc2Y6Kf/tAMlRtrAjPXkRSUu3vZIANj4zlj2niCiFvu6XlTzcrKl/W0+avvQ5vQmKocwe8V+3DI/UyLhe5uJkRQ52atJI1YIqWeukB62KWdPqC9F047mGquSOuz+fBStU6/dwujHWkWysVFQzrxW1LWktpc7J5eWiojLZraTJ1L1XCr0IVqTTc3wdlVeej6Z1fRJuJGOJwk/upgzDplFUpmzxBp0sw2fTKHjTXxR5/TTFPH8ktz2+nJvo6/IYXvsytejluA4y1vZ9yw05tx5IIdsWkxIWTE5NelDUncuUHnC2NuDxa6rYs4aM64yOeBsEGSjSuBTlq1mY1n3xD0WGRNCljWeoTKfK5JrZjQIT6cDr5OFMGXJ50NPL75oz1vmiqZRJc+aYK6wigiPi/M3HF3wpc6Fs0sTKMYMTZcqbmRzcHKVjs6XjMbmF6xejvNUK0t7p8b83Rh37lqZU/EZKyB9ffEinlh2jvFWNuyrHp2SrcnT0r/36+4F+ARQRFCavw3iMtUesiw5JWQfAXBDoWrjMmTPLT0KyZs0qP8zb2zvB9TgYjA+XIMcW3+tkzJhRfuLDQath4MqZV91rxPf6HIgmhAPx2MF4QhL7uwnti0yZtE1/Etq2xPYhwPs0r+FGk/7yo62z4r8w1b5BBqrX/xZ5ultT3px2tPdkkExBlNlDO96KS485YLa2fhegtq2bgX780494eNzh8yFUtaQT2dqoKSQshm75RkjmOKF10pvWxa2pzuww6lzamtTxjIXN6qqmIpnVNGBVBLUpbkVPAhVaeiaaVvTQBqGcDba1Isru9v59x2OBdY2vOJDl6Yp0pckXnsRQXg+1NLx6HapQaBRR7gzp63jEvHxCikYjUwcF/PVuWjZDoQfWkmvPryh4wzwpO3as3ZbCTmhP/FV2DmSVJbeM3TXkWKcdBW9ZJI/xD3dy5rG5CS1Xu3uSysaWYl48JrWTK7l0HEaR105LYOz/Qy9KL65uO091RzajDV/GHQ/Kbu2/SnVHNaXqg+vLmN5sJXJJAOp37bE8zt14X91/IRlJQznL+tD9E8ZTOV3edJYajmslwW22YjkpPDBMgmWVWiVTGz08e58urj9tNMXOtR0X6cWt9NEt/vruy9RkQmt6euVRgmNhz605RZ1+70Wnlh4lWydbqjm4Pm37cYM85uLlKuNqXz2I25ske8lc5HvmXZMqHovN5dJNvmkjFx+qD6hLG8dpm79xeXLoqxAJbBNaB7RUZJWqF45VxBeqLb9Df1Ig0AUASCN6NPOgqGiickXeXYCxs1Hr59DleXb3zi9A/2zxp5u+EdLEakL/d9NqrNj5mormtSdnayujsb9ODmraeSyQcmaxpfF9tVUNrwJjaOOBAAl0E1onveFsaq/y1tSljPFXY938VpTzbaC5sqcd/XMmmnbeiCEPJxWt/8ReglJ22ldDPPNHfIEuz8DSoWT8X7k+HiqyNWjUs/lKDHUrp5JA92WIQkOqJdw4xpKFbPpLAk2KeVcaHvPiCUVe0Za5hp/cRTGvX0izKrKyotA9q6RjMuOuy/Zla1NwrEA38uZ5sitS4d3r+ftRsO+NBJfz+GC1a0YJdAOX/UJO9TuTTd7i9Hr6CNIE+FN6weN0MxfOTjf3XjVafmHtSYqJ1kgAO6/FL5LF5XG8/vee08KOMyg6QnvseLzuudUn4gS6fDGDA1tDPOfrrv9tlAwxZwv/7T1HllvZWFOJVuUl0DX04NQdCniaPsrIGY/JPTJ/Hz25YjxF4aNzD6R5GNv63VqZbsinan7Z56tH/CuBMePst2sWtziBLl8wOLvyuP6Y6Wz4aqWUIvNrbf5mNT067yvLfSrnp8eXHkqgm9A6AOamUkzV8QcAEsWl3LVq1ZLych4rDOY1prH2ajekEQfib5YCqe/VRW0HfTC/2QdqmHsTwECMkr6Gd6RV0apo2uN1kvbv35+mho/pzvMe3bQhRZOKGV21QjkKRKW5/WEOyOgCAKSSsTMe0/0n2oyGs6OamlZ3k2xqbIMn+9LvY7Vz5eqs2/uGPDNYU7XSKfelFRQSQ1MXPyO/l1HUr00mKl/U6YPW+RhtuxZNi09rMxfWaqJCXmoaUdOGHG2NT0aWnY2mnBlUVM37XZb8VahCs49E0bj6Kdt1d9X5aNp2PYbK51TLfLnxdX1OyjofHVt7ch8wUX9XE+hPofvW6qf70eHsrmOtNhS83ngcrnOrAdKBWQlJufGZVl45Zd5cioqQkmbNmxcftM7HqNrAupS9VG65zU2J7h+7RWdXHI+zXr3RzenI3D36LCLLXSGvjLu9uu3DmknGh5tgVR1QVzKRl9afjjOHb1LX+RhlK55Tyo6ZolHo9UN/afYUu0O1T9UCUip+efM5o+VNv21DW75dm6LbxH+rdNvykhE+OHtPnKmmkroOmF6FCtoqlcGDB8tPepS+Bv0AAJjRnpNB1KtFRhrfLwt1a5qRvvztMR2/FBJnvWFd4k7NcP1+OPn6GZf9/VefTXko3ZXb189AXcfdp2f+UR+0zsfo9kuFMjqqaHx9W/qilg3df63QqE1x92+d/FZUPIvxV2VopEKH76Xs+Ke9t2Jo2oEo6lnemvbdjqG5x6I/aJ2PkcrahmwLlKbgdXPkJ/LGOfIYMzfO9D0yZdDhTXGeb1uwDKlsU7BZl9pKui9H3TxH0U/uUYbh0z9snY8Uj5vlzsr7pm2jU4sPUfnu1al4i7Jx1juz/KjMtWrILXsG8syXskMfag1rLAHs+dUnqfE3baUJ1Yes8zHi8bS2jra059dttH/mDoqJjqFOv/eMs96Tyw/p3rFbcZbnr1k4RbeHm0y1+qmTzOPr7OlKDcc2/6B10hu1yirVf9jJkyfp6tWr6TbIZcjoAgCkonw57aWRFOPGUS9fa4OV0dMfUa1yLtIg6uaDcMnoclDJU//ExBDZcIOpt8NxX7yOoimLnpG1lYqK5rMnn+x2VKWks0w/xF2YH/pFUpu67lS3gna6jW2HA8jvVTT1buGh3w5el5tZ3dtSTBovta7tRpsOBlDf1pmStc7HLJOTioq+DWLbFFdo0SntseDOy5zl3XEjhirlVlPeTGrJ6M4/FkUnfDVUv4Dx9Gp/Htcu71DSis480tCYutoAjefl3XglRrLFg6tZS6dmDpIHr42ghZ2MA7MlZ6JoTF0b6eTs4aiiIWsjaGAVm2Sv89HSxFD0o9tyk3+7dh1JKhs7mUfXoVZrouhoignwJ2uvHJLRtfEuSo6121D0s4cyvYkOz4PrVLcDRT26TWoHJwrZtZyU0CCZX9epUXfpB8NjeWP8HuinFIq8foai7lzSv4ZtobISvIYd3SL3uckVN6jSPSep63zM3jx6Rc9vPpXb947eJEcPbSVJ7c+bkO+puzK9DVcTcEY3OjKaagyuL+sEPHo3VtbG3oZqDGlA9m6OEoRpojV0fedFeV6FntUpS+HsdPvgNbqyRTtFDjew4k7Ch/7YZbQtpdpWoNlNfpLGSyeXHJKg+9n1J8le52PFUwU9v6E9Fjx+ljsus9wVfChDDg/KWiwHPbn0kGKiYiSjW7RJKSpUryhd33U5ztRBheoXk/3tXTk/bX/bnCpXWW8q3a4CBT0LoCN/7tdfvGjzcxfa+v06aQamU7JVWTq55LDMw/vw3AMacWA8bf1undHfSco6AKkFGV0AgFT03bynNOR/vtTpy7v05GUUNaqiDUY3HwqQZlDNarjR9iPaEsyOX96TILZ+JReZdkiHl+fNYUfVyzjT+FlP6NEzbZa1/0RfKW/mbPHE+X50+Jy2vK1CcSf939G5/TCCvLPb6bsLcwB+73FEstf5mHFDqSFrIqSL8rD1kfRlHW3QeO2ZhibuipTuyzzVkO9rheYfj6Lt12OoT0VrWn3xXSb1rxNREhBzlnXe8Wg6ck+jL41eejaaPqloTUERCn22Tpsttrcm+rJ23JLn688U6ejM8nuq6K6/5oPW+VipHF3Itdd4cu39NXmMX0RhR7dKgMrdk11a9peuyjyFkGRvnd3JffBPFH5mvyznoFNew9mNMgz9mcLP7JX7zi37S8dkzgxnGPwTRV46SpGXT1DGEb+Ryl47T3XY8e0U/fiu0bZYZ89L0Y/fdQKO9nsgAXZy1/mYVe5Tk5pP6kjtZvSkAnWK0vnV2gZgeSrmozIdK9GlDWfIu0p+6d7bckoX4m4vlzeepVLt3jX0avFTZym35TJm7tjsmU87A0T9sS3I1smOziw/RsVblqMSrcrJ8pd3nhl1U2a8ntrGSt9d2P/uc8qQO1Oy1/mYZS+Ri1pO6iBZ0u4L+9PuX7bKcvfsGanBmOZ059ANsne2J6/8WShvtQIyX+5pnk6oekGZ35j5VMlP1QfWpbOrTkpjMA54mVeBLFT3iyZ0bvVJCnwWQN3+6qf/uwdn75apogx5FshCz29qu1vzY5oYjez/5K4DkFqQ0QUASEWNq7pSNk8bevkmmn5a+Iw2HHgj0/tExxB9/2k2cnXWnpjc9tVeVR/YXjtdVseGGeT33ccRksDSLW9dRzvG901QtGRfXZ3UdO56KFlZqWjbkQAZ08tz7sYWFqEhR4d31zrt7VQUHqEke52PWaHMKmpfypoioxXpivzDziha10e7/3tXsKEaea0kGGb/nomm+R3sKL+nmpxtib7crA1cOZjl5T4eanK0Ifpmu/aiw8KT2qzw36eiKUbh7K72dfiiAf/d2MKiFHJ8G//aWasoVuPTJK/zsVIiw/Xz5UZlyUUurQZQ6K7lcj/66X0K3buKrLJqp5azL1eHwk/s0HdYdqzZRru8TG0JkCMuHNYur6Vdble8imSG7crW0f4xtVpKpSMuHiGNf9wpaVS2dhJA67ctKpLIxi7Z63zM7h27TU8u+ZK9i4OUn5brXIWOzNNeQDg4a6c+22tla025ynnT6qGL5D5nU+2c7GXMbM6y3rRm2GJZfmbZUX3QxdlXng6odPuKsqxg/eIS4HJQFDuw4qxwVOi7ZZzRtOY3azLX+Zi9efyaLm48K/uUs651P29CN3ZfkceubLtAN/ddpXKdKsv9Um3K055p2+jBqbvSZTn/zjFGy+8fvy3LB20ZKcvLtK8ocxXzeFqWvUROCUr5OLy88zzOtvC+jnw7Xy7jKaCs7WyMjltS1klvVJJXTO3phYBZzicBAMBHoEJRJ33pslqlotW7X0ugy3RBLnv+Opq8Mr77iM74Nlh98Spa5tHV0T3n+ato8nC3krG0OjzHbkL49QKC3o0zDQyOoUwZrJO9zscsl7tayoBZvQJWlPmbUAqJ1J4guMQa8vkiWCEvZ+2JCo/tNVzu6aS972b/bvmzIIX6VrKmXG+nJepWNvH9xq8ZEMZTExEFhisyddGHrPPRio6iyOtvs3nXT5NNzgJkV7KaBK2aMOPGO1Y83Y/B1D6a4Dfym6cB4nG8Okqo9nlWbpkk+6oLpPk3lzYnRBMcQNZvg2p5XQdno9dN6jofM57/loMiFuD3hpr/2FEf6BqOy3Vwc6Cw1+/6DPBUMxzocmOkcIMmVVx+65TRWQJj/rm08cy757yK26dAhxtd2bk46O/budpTqH9wstf5mIX4B9G9t8eCy4E5I+uZXzsOOvYYaedMLhT8Mkhu87RCXFbOuEFYyNt9EhUaSQpffeP1PV2lrNz3tHbuXA6oOTBNCB9fB9d3+9rW0c7oOCd1HYDUgtJlAAAz8fOPIncX4/GeOlwyfPlOGGk02hOSCze0JwrZvWzo1sN3V8aPX9SeJObIbEuvA2IkkK5Z1oVOXg6hS7ffZZzivH42W3rxJpoCgrWB7NELIVSphFOy17EUgW/PF+3iPxwyTvfcY22p8IUn70qGs7up6OYL7f1jD95dFPDJpJIxuRxIZ3dV0bxjiTfxqpjLio7cf7uf7/HYYKsPWsdSqN0zkSaBLso8Ltcmz9smO1bWZJ1DO/2R5tUzffDJpcm65dHPfEnt7C6BNP84NeiiLWlOQNTti5Lx1b9+rvwUdf9astexFNwQKaFAhYMn57cNk1jWojkNlrvps7g8DpRx912eB/bV/ZcSSLtmdSfvqgUS/Ns8rjfg8Svy8NE26MtdPi/5nrmX7HUsBY9v5qDVcNysIf/7LyhbMW0JPZeKc3aVBT59Q5nyavdPjlK5JTvMXt1/QWorKwmkn1x+RLU+ayj7MyE8h3Hu8j5y28Pbk14/fCWlycldJ71Rk1Wq/4CW5VyaBwD4CMxY9pzcXa0kO7r7RBCt+Vl7QhBb1kw2VL+iKzUZeltu334UoQ9oi+VzoPaj7sq4uMfPo8jKisjRXk2DO3lS4yG3qYiPPZ2+Gkr7/9SeQHJn51cB0dSkmpv+9bmE9rNOntRy+B3Kk91WSqlrldU2nFm+4xWVyO9ARXwcElzHEhy6G0PfbI8kPgfbfTOGvmlgKw2+4jOqto2M5W1Y0IpuvA1s2bDqNtR3ZYQ0q+IAWJftHVnLljouDqcTD2KkQ/LPLbRZ/PAoRTonf1XPONDqX8Wams0Pp6t+Ghnzu7KHNqV8/ZmGzj7WUJcy1gmuYwlUDk7k3E7bGdTaM7tkYXmsLf+OLfz0HnJu2ovc+v9AajcPUsK1QVjY6T3k1LyPjPO1zpZHyqFJo6HIqydJadqL3AdPkZJjzsZqXmvLMu0rN6Zo35vG420f3yFNgL+MA+aAO+zIFiJ+Lc6AtehLwdv/SXQdS1C6XUXyrpSPbBztZIzu+pH/xLsej8E9On8f9V7xmXT+zV4iN13bfkGWn1t5nHosGSQZ4ezFc9GL28/kOXt+3kzdFg2kByfvyDjSBR1nyPJM+TLLdDoX1xmP0z34+y7qOLsPPTx7j3KW8aZ907RjVPPXLkIRQWGSjUxoHUuQuWA2qjeqqQxZyVXOh65sPU8BT+KvHji24CD1/OdTylokuwT+EW/LhY8vPkxd5vWhPOV9JADWBZ4nlhymvqs+IxcvFwmATy45on8tbjB2fOEByQzrXNxwlgasHyEXNrihFTerYhx8l2hZlo4tOJDgOgDmoFK4OB8AzDaReKNGjcjGxkI6p37ExjTWdqA0pT0nA/WlwG4uVlSuiBO5vS093nE0gBpW0Qai248EUKOq2ttHLwTLOva2arKxUVF2Txu6ejec3gTHkLuzFc1c/px6t/SgyiW0AeilW2GSKa5e2pns7bRX7e88jKDgsBgqWUDbgMfQ+RuhbwNYF7Lmzs5vA+OcmW0o+9vS5/jWMbkDN0368ndeavSZWU46cfdlHmera0bFCarcGdR0xU9DLnYkJcgPXmvo/iuFymRX04WnGglu7/hrKCRCIf9QoqgYhdZfiqE/2tnpy5rPPoqhQpnV8lqM19l2LYZaFIt7nfl5sEKnfWOodA41ZXXVrv8kQCNTH1XJY5XgOqb26qI2M2oyVtZkX6aW9raiUMybFxR17ypRTLTMsWvrU1Q6I0uWNldBmdKHx8Pa5i8pnY+tMmWlqAc3JEtrlTGzBJ48rjfjF7Po5fiO0tGZVDwutxQpmhiKuvVujlebfCUoxt9PH/jq8fqFy5ESFkJR97TjIZld6Zoytle2LYF1TGn2gRom/xs5y+Qhlyzasf88rvLJRV8pR9U1o3p84YHMr8tBDGfvuNSVO/+q1CoKehZIds720lgqc+HsZPN2rGy+WkXI/95zaWLFMubORB7eXuR75q5keJlLZlfKkNNDX0ZriKcO4sfuH78lf5vx60eHR5L/vRcJrmNqMYppPw+dPV0kQ80UjYZe3nuh78Dsli0D2TnbSeMn9xwZpTHYi1t+EnRmKZyNHp33lTG3d4/eItcs7uTgrh1vHegXQM1/aEcLOs2S1+ExuZxx5+BZdzGCFapXjG4dvB5nDlxeP0+FvPTy7nOZJ5fxMed/N7r5i+Nbx5SiVdG0x+sk7d+/n5ydndPced7zW26kaFJxjK5aIa/8AWluf5gDAl0AM0Ggm/4C3ZRStdcNKlnAQZoocRC7e05+aT5lUUwc6KaUP45E0crz0VQ+p5p23YyhPzvaUbmcllU2ZvJAN4XwFEIeX/1F4Wf3k02OfBTle4OClv9KliQ1At2U0nHOJ6SJjpExtBys/dnm1zhjSj92pg50U0rBOkWo4Vct6NaB65S3agHaO307Xd1+kSwFAl1jCHTfQekyAMBHZu+8/HT4fLCM16pWysnygtyPyKCqNlTDx0qyvZ/Xskm1LCvExWN0X37bjWzzFqfwY1u1WWEwm5WDFkipLWcad0xcT1EGnXghdd3Ye5Ve3HkuUxCdWHw4VbKsAGkBAl0AgI+Mna2a6lYwnhcXzKdYVrX8gPkpwW/00w6BefE4XR6HC2kDB7cIcM1DTWpSML2QWSDQBQAws72ngqjH+Pt0aGEB8s6mHd/5v4V+0oSqVW13qtTjOl1bW5Su3wunegNvUUY3bWlsVLRC9Su50rTPcyQ6dvbbOU9oxc7XlDOzLf31bW75/SHrpBfFp4RSrwrW9EUt7T6490pDg9dE0NZ+DtRzabhMFVS/oDU1mBNGjwIUmS+Xe7tkdlHR723tqHDmhIPeI/diaPj6SIqMUWhcPVvqUMr6g9ZJL5xbDyC7UjXJ/9tuHDnJsgwjf6fAJT+RdZbcZF+xAQXM+5ocarUhl9afkhL+dqoalYpCdvxDoXtWJfjaKjsHch/wI1nnKUSRV09RwMIftGNvk7lOepExjycN2DSS/uk1hx6+7Wpcsk15ypArE+2fvo0+P/odzaz7oyz/4sT3FPLy7fRONmp6fMGXNny5LNG5VMt3q0ZV+tWmsIAw2jB6KT27/uSD1kkvei4eSG8evaINX63UL/v88ASaVu17qjeyCQU+C6STSw5T66mdyadKAe1YW5V2eqEt36/TTx8Vn0w+XtR2WldyzOgkDaqOzN/3QesAmBsuQQMAmFl0tCLNoib8/u6kLSxCQ5FRCnG/QO7QzGI0CuXKYkMXVxbR/3Dwu3JXwvN37j4RSIfOBdOFFYWpR/OMNOa3xx+0TnrCUw39djBKGj8xjYYo+O35eWgUN5TS3uZlq3ra08VRjnTlS0fqWtaaPt+Q8Ik8H7/+KyPor462tHugA43fFkmvQpVkr5OeqGzsySZHXnKs3Va/TG3vxG3DiaytJRCV9axtKez4NnrxZSv58f+xDzm3GURqD+18o/Fxbv4JRfs9oBdfNJMg2rFWmw9aJ71QW6nJ2s6aGo1vrV/Gc+LqprCxc7GXzsD8w9ncmXUmys9vNX6gmKgYqtznbcOxeHCH4Ep9atGcZlNpx8R11HJKlw9aJz3hhk98oSFzoWz6ZfZvJwC3trcha1vtBTLufrz1+7U0vfaPNL3Wj7T1h/XUblrXRF+71U+d6MCsXTSzwU9Uum15aW71IeuAlorUpCKrVPxBeKeDPQEAkAY0q+5G52+Gyfy3SWVjraJqpZ3p/hPt2LdqvW/QmyDjbNPWw4HUo5kH2dqoqUP9DLTvVFCc10nKOulN7wo2NHFX8sYU1slvJV2Z2ZS9kfT3KePOrxefaCirq4pKZLMiT2ftHLv7b8cke530JmTXMpnWh7suJxVP/RP95C5ZeWQlq6x5KMOI3+Kswx2UQ/etlgA27MhmsitZ/YPWSU+eXHpI0ZHRVLTZ2zmEk4Av1t07epPcc3rI/U7z+pJnAeMLEAXrFKXLm89KB2Yud3bK6EyOGZ2TvU56c/Sv/dT461bJes69Y9ouzNzjoWSrclRnRGOjx/mCRYZcHnR992WKDo+iy1vOU4HaRZK9DkBagEAXACAN4NLjyUOz05fJyKZyJpinAipVUJvVOrywILm7GJe53n8SQbmyaktwOZB1sFfrM8TJWSe9+aKWDe24HiPz2CZnXt5S2bVfq6Pr2FLP8sbThvE0QTxNkU52NxU9DVSSvU56E/30AYWd3EXOzfok+TkqZ3eyypSNYvweUMzT+/T612Fx1rHyzEYxL7VTtcS8ekZWGTw/aJ30Ztt3a6jeqGZkxfNyJVHuCnnp6ZVHcnt5/z/pxU0/o8c5COYyXJ1Avzfk4uWa7HXSmwvrTsvUPgVqFU7yc3KX96HnN5/KBYgL60/T3l+3GT2eIUdGCnjyRn8/4Okbcsnslux14B3OsKbm/5DRfSf9DvwBAEhjmtVwoz9WvaB1e9+dQMR29V44leig7Sb74nU0FcxtT42qJH6yZ2swfpc7NHNp4Yesk5442hB929CWvtoaSVObJzxeue2icLK1IgqP5jlvFToxXHvRISG8rg43y473WCRhnfQmeP1cyvTjSgrZ8248YmyO1VuSfakassN4Pt2QXctJE/guMIqDa9LfjvvV3lY+bJ10xu/qY7p3/DZV7FWTwoPC4l3H3tWBhu4dL7cd3BwoKiyKNo9PeLw0i4l6V42iidFIIPYh66Qn/N+/9du11GpKJ5nzNiHNf2hPDce0ILW1WubcXdrvr0RfN/Z+ju/ffVLWATA3BLoAAGnI1OHZqfPYe9S8RvxXx4t429PRvwvJ7dBwDdUfeIu2HQmkptXjXz+Lhw35+WtLaDUahSIjNeTiZJXsddKjTqWtaM7RKNqXSOnwml72VDSLWk44fz0QRT/sjKTlPbTj5GLL4qIiv6B3J4PPghQqkU2d7HXSIyU0iEI2LSCXDp8luE7ooQ0UtGya3OZsbqZJqyh401/y3PhoAl+T2iUDaYJek9rNQzK2H7JOerRnyibqv3EknVoSf4fr8MAwGZ/LOLjqOPsTKtu5Mp1YdDDe9YOfB5KL17vPMOdMLhT0LCDZ66RHD8/dl4sP5TpVTnCdTV+voms7LsntIo1KUL1RTaXsOD5Bsp/fXTzl25yxTe46AGkBvj0BANKQonkdqHY5F/pnSyKZqLcc7dVUsbiTPkiNT72KLrR+n/YEZMuhAKpayvmD1kmPeAzbzy1s6cddUUlat3Y+K6MgNbZyOdV09ZmGXoYoFBiu0MG7MVTdxyrZ66RXofvWkE3O/GSVJdd71415+YRiXjyRIDUhEZePkX25unLbvlJDirh09IPWSY9C/IPp5OJDVLlvnfeuq4nW0IMTt8nZM+HKk1sHr1HhhiXkfZSlSHbJFIe9CU32OunVjsmbqPqgekla987hm+Ti6ZLoseXOzF4Fs0oDsiINS9DtWNnipKwD76jN8D/Qwp4AAEhjJgzIShFRSSsDc3O2oqt3wuV2k6G3KSDW2Fqenogryoq3v0rfzn1KPwzWdsa87RtOPSfcT3QdICqfy4pq5U/aV6Wbg0qaUYVFKTTzUBQtP2fcGMzGSkUTG9tStZlhVP7XMBpWw4YyOmrrkvssj6AbzzWJrpPuKRoKXPoLWblpmxq9jyY0iKxz5COrzLnI/dNJcR4P3jBfujl7/rSerDPnptAD62S5fcWG5NS4R6LrANHxBfspMjThLuOGwgLDJChibX7tTpnyZjZ6/Omlh/To3H36bP946ji7D22ZsFrfMbjXsqGJrgPa8crnV59M0q7gKZ7sXBwkC1u0SSmqPlB7IcfQ5m/XULc/+9KIA+Pp7tGb5HdNOyNA/S+bUd5qBRJdByAtUSnpfYADgJkEBwdTrVq1qFGjRmRjY9y0BlLfmMYbzLbbualUZLQiGVqdkLAY6arMP0EhGnJ1tpKyYi5XdnZ8l+GLjNJOQ8TLOMh1dVJLxiO2iEgN2dm+e/2YGO1rGZYox17HrA7cNNuf5kyqq/27fRgVo1BENJGznYpCIhWysyKytlJRcIQiY3nV6nfrvglTyNWOKCKGiBfbxTO/MU8hxN+8/Bo6QeEKOdoSWb19rfjWMZdXF/Oa74/b2GrHxhrMXatycNbOl6u2IrKyJooM1/7mKYeiDDpl29prx9dGR5PKzp6U8ASyf9Y2RNFRxvdV/FoRCa9jJrMP1DDb3+bPFRsHG4oMfbePre34u0uh6Iho6cTLHZEZN0iKCNbeZpz1s3GwlWU8LU5UWKRMQRQbN7jiqYgMGb5uQuuYS4xivvcnXwTgsc+603iVWkW2jnayj3kaKF7Mc+fy9E8x0TGSWdfh4xMVHinHVGWlls7J8eGyc8Pnxfdasdcxh2hVNO3xOkn79+8nZ2fnNHeeF3ArG5EmFb9b1Rpyy/8kze0Pc8AYXQCANNBxmX8MOTm8C0A5yGUcUBkGubouybY277K7CYkdwHLDqdjjcNNMkGtmhkEu4yyrrsGsk+27xzjwjc3dQbvMIZFdqQtmDbnE+pvxrZMuGQaubylhwdobHPzqAmC5HWtFDoB1z0koyGWxA9j4Ato0EOSaGwdUhkEui454t18Mg1HDIFfXrEi3jDOKCYkvgDV83YTWSY9iHwu+cKDbx3zhQScqniDW6Pgksj9jB7DxvZa5g1yAxCDQBQAAAAAAMAHtmFlcSDYH7HUAAAAAAACwKAh0AQAAAAAAwKKgdBkAAAAAAMAE1IqaiH9SC9oM6yGjCwAAAAAAABYFGV0AAAAAAAATUKEZldkgowsAAAAAAAAWBRldAAAAAAAAE8D0QuaDjC4AAAAAAABYFAS6AAAAAAAAYFFQugwAAAAAAGACalKlcm4R8wvpIKMLAAAAAAAAFgUZXQAAAAAAABNQK2oi/kktCjK6OsjoAgAAAAAAgEVBRhcAAAAAAMBkY3T5J7Wk5t9K25DRBQAAAAAAAIuCQBcAAAAAAAAsCkqXAQAAAAAATEAleUVML2QOCHQBzGxE8/3k5IDxFOb2v3Utzb0JYOCr2suxP9IIr5Gjzb0J8NbI4BvYFwCxhIRE0Z5m2C0QFwJdAAAAAAAAE1C9/V9qUdCMSg9jdAEAAAAAAMCiIKMLAAAAAABgAmpFTSol9XKLiqKQJtX+WtqGjC4AAAAAAABYFAS6AAAAAAAAYFFQugwAAAAAAGACajM0o0LpshYyugAAAAAAAGBRkNEFAAAAAACwgOmF+C+CFjK6AAAAAAAAYFGQ0QUAAAAAALCQMbqghYwuAAAAAAAAWBQEugAAAAAAAGBRULoMAAAAAABgAihdNh9kdAEAAAAAAMCiIKMLAAAAAABgApheyHyQ0QUAAAAAAACLgowuACQqIlJD9x+HU0Y3G/LMaJPkvfU6MJoeP4sgF0cryp3dHnsZLM7jF1H0OkhDuTJbk6uTVbKe+yowhu49iZTb3tlsKaNr8p4PWhERUXTp0sN4d0e2bBnkJzHBweH04MFLsrOzJh8fL1Krcf0/Jfj7h9KLFyGUI4cbOTvbJuk5Go1C9++/pvDwaMqVyz3Jz4PE8f588OA1ubnZU5YsLkneXS9fhpCfXzBlyOBA2bO7Yjf/B2qVilSqVJxeKBX/VlqHQBcA4hUSGkNjp92jBWv9KCZGu6xYfkf645v8VL54wl+WbwKjadD3t2jjXn/983Jns6Mpo3yoRR0P7G346N18GEmfTHpCJ66Gy30rK6I2NV1o9sgsSQp4FUWhjl8/pgPnQ+X+wnFZqWsDN5NvtyW6cMGXqlT6Jt7Hvp7Qmr75tm28j0VGRtPwzxbTokUH5Tbz8nKl775vR/361zHpNluyV6/CaOiQTbRlyw1SFCI+327ZqjD9Oq0JZfRwTPB5Gzdeo9GjdtDTp0Fy39paTW3bFqUpUxuRuzsulH6ouXNO0o+TDlDAG+1nVd68GennXxpTnTo+CT6Hj8GnAzfS/v135RiyQoUy0bRfm1DVqrk/eFsAzAGXLgEgXmN+uUfzV2mD3AJ5HMjV2You3wqlNkOv0HN/bSYqPsMm3aF1u/xJoyHyyWlP7i5W9OBJBHUZeY0OnwnA3oaPWkBwDDUa4StBrp2tigrmsiW+dr5qbxB1mvA4Sa8xf+MbfZAL/43vg5f6ILVsWW+jn8Syud99u4bmzdsrQW7u3JnI09OVnj8PpE8HLqCl/x7BYfkAMTEa6tB+GW3efINsba2oYMFMpFaraP26a9Sv3/oEn3ft2nPq03utBFiurnaUL19Gio7W0IoVl6hnz9U4Fh9o4cKzNHr0Dglyvb0zyAWDO3deUdcuK+nObf8En8fHYt++u3Ls+Fg4OdnQ9esvqW2bpXTzpvb9BvCxQKALAPFmnFZseyG3Jw7PQxc2lKXLm8pRBldrevk6mrYfep1gNnftLu0X4YxxeenK5nJ0fVt58s5hLwHzrH+fYG/DR23J9gB69CJagtxjc/PQpSU+tPu3XMQVr7tPh9Lxy2GJPv/R8yj6au4LKpXfjhzsUF72X/n6ak/Yh49oTCdO/WD0k1hm9q8/98vv0V82pzv3ptN939+ocpX8smz6r9v+83alR5s2XadTpx5LyfGxYwPo5KlPafGSdlSqdFbJ9AYFRcT7vH/+uUBRURoJqq5cHUZnzg6mKVMaymP7992jK1eepfJ/yccvIiKafpyo/Tc+blxNOn9hCF2+8hlVr56bChTMRGfOxP9dfOvWSzp61FduL1vWUY4F/3CQHBYWTX/9eSZV/zssaXqh1P4BLZQuA0Ac0dEKhYZr647LF3OW3zw+N2dWOxl7+yogKt699sw/kkoUcJLbretnkt9uLtZUsYQL3XsUTg+fxn+iA/CxOHtTWwJY3MeOivnYye0qxR2pSB47unw3gnadDqFKxRwSfP6QaX4UGq6heV9mpdpDH6Tadlt6RrdQoWzk7x9E/v7BMtbW2jrhEvLAwFDJ4vJPl65VZJmdnQ3VqlWEjh29JWN2Ifm2brkpv5s1K0i5crvT1avPqVSprHTgQN9En6doFAmGW7QoJBldVq9+PiLaIbcf+gZQ0aKZcUiSgS848BhpGxs1DR5SiR4+DKDg4Ahat74r2dgk/N54/jxEjoWNtZoaNORjQJQ1qwsVK5aZDh9+QA8fvsFxgI8KAl0AiIO/HBvXyEib972iH+doG71cvBlCl26GyHjE+lXiLwks6O1IR5aVMloWGhZDR88Hyu3c2bUnMQAfK92YtaBQTZzmUszXL/6LQOzfnQG09VgIfdnNg0rlx7jDlPDAVxuUfjNhtb4plYuLvYzN5SxvfFxdHSXja0ij0dCePZfltre3Z4psW3qjy7z6+4dRkcK/SdDEKlbMQX8taEM5c8Y/Dn3S5AZxlu3dc0d/O3ced5Nts6W6cll7LDw8HKl7t9W05+3+5MZSnC3v0LF4vM/jMbixL0xwwHzpkp/czp078eZukHD5bGrmWN9+TQFKlwEgIbO+zkeeGWzo4OkAatj3Mo2ack9O8n8YloeK5tdmbd8nKCSauo66Tr5PtJncQZ2zYYfDR61CEW229oZvJE348wUdvhBKg3/xoycvtQ2N3gS/7cAWy4s30TRy1nMZ0zu+J5qypXRG9/LlR1SgQBZydXWgoKBwGvnFvzRzhjYj+D5RUdE0oN9fdPKENhgYPCRu4AXvFxik7d2wa9dt6fTr45NR7p848UjG7vIY3qTYtu0mTZiwR25Xr5GHChf2wu5PpsC3ZeLcNXnv3jtUoIAHOThY0+vXYTRgwAY6dkxbnvw+HOR26riCAgIi5AJ4n0/K4FjARwVjdAEgjpgYhXqNuUEvXkfJ2MNCPg4yPpf9b95Dyey+z5VbIVStywUZz8udN6eM8qZqZdFZFj5uPRu7UdmC2mzs/5b4U53PfKW5lL2t9nq9k338X6sT5r8g/4AYGtDSXUqcz1wPk4Zt7P7TKLr1KOEGb5Awj0wu0njq78UD6er1n+nu/ekS8LK5c7TBUmIePvSnWjUn0sKFB+T+sOGNqHuP6tjlH8BKrdJ39r14aSidOz+Ypk5tJMuuXn1BBw/ef+/0QhN/2EedO62g0NAoaWb111+tcSw+5FhYvfsc+ndpBzp1ehCdPjNIMry8n+fNPfXe1zh+/CHVqD6fTp9+LEHunLktKX9+7ZAkSB6VGf4HWgh0ASCOnUde0/6T2g7JG/8oSufWlaU7u8pTmSLOFBgcQ9/NSnxs4Zb9/lSz+wW6eT9Mui6vnF6YhnbLjj0NHz1HezUd+D03TRnkRY0rOVGdso40Z1QWKl1AG/xm8Yh/RNDD59qM7+czn1PlAQ/kJyxCW2D23YKXNOI3NNxJrjdvQmjS5I70++ze1KlzZVnm7u5ELVuVk9vvG2t74sRtqlDuazpx/DbZ29vQnLmf0C/TuiV7O4D0YzlZ02YFpUSW9exVWi50Ml/fhLvuc2Dbof1ymjr1sFQO8Tjf3Xv6UObM2h4RkDxZs2j3GzeRatq0oNzmOY0bNMj33mPBFi8+R82aLqYnT4LkuG7a3J3atSuGwwAfHYzRBYA4bj141zm2SmntRPEO9lZUqrATnb0aTNfuJjw1yqHTAdT5i+sUFa1QmaLOtHRqIcqdHeMRwTK8fBNND/yiqGZpRxreUVuaGRKmoc9nagPVqiXib0SVP4ctvQwwLms+fytcsrreWW3kcUiee/deUKUKE+Q2j7nlzC57/jxAP+VQQq5ff0KNG/5EgYFhlDevF61YNYxKlcIcof9FyZJZpGPvM79g/bI3b8L149pz5HBNsMt/t66rZBwpz5/7ww/1aNDgiv9pW9K7kqWy6i8gcLdrFxdtfwzufs1y5Ez4vbFmzRX6bOhmOW61antLVj1TpqQNVwJIaxDoAkAchX0c9bc/+/EO9Wydme4/CqfVO7QZkiJ5HfXlyeGRGsroZiNTCLHhk+5IkJvF05Z++sKbXr6Jkh9mb6tO8vhegLTo3tMoqjpQW9Hwfd9MVK2EI81c85pCwxXK7mlN9co5yb//i7e13ZlzZbEhT3dr+nVY3K6xGRrdoJAwhSb0yURdG6CsP7mKF89JWbK4kZ9fAA0ftpi++74dXb/2hJYtPSaPt2lbQX7fvPlUAlpnZ3vpzsxGjfxXv+zX33pQdHQMnT59Vx5TqVT6oBmSrnuPUjRnzkkJlIoVzywdl2f8pj0WGTM6UPnyOSTQun9fOz1dkSJeZG9vTevXX9M3S+IAt1LlnHT27Lvpb/LkySDPh6TjfVuuXHYpO+Z5cYcMrUSXLz2T8dOsYQPtVFrnzz+VUmbOnGfP7irTEo0etV2CXC4d/3p8bcn+6jLALi62KF/+AFzVoNaVNqQCDSqX9VQKX0oDgFQXHBxMtWrVoh2/25OTQ9r6VOKPhQ7Dr9Hm/a/iPOaV0YZ2LSxOBfI4UtFmp+nuw3Bq1zATLZlSiM5fC6bKnc4n+Lo+Oe1lbt206Kd19cy9CWDgq9rL0+T+kOzT909o1d4go+W2NipaNTE7Na7kLHPl+rTXnrjP+jwz9W8Zf6dSXaC7cFzWNB3oqquPp7Rq8+Zz1Lb1r3EaHRUtmoMOHZkgHZYb1J9Me/dcoQoV89LRY9/JNERZMw+SE/yExjdGRC2mtCgk+AalZePH76aZM7TBraGZs5pRjx6lacXyi9S//wZZdur0p1SgQCYZk7t1q3ZqovjMm9eSOnYqYdLttkTnzj2lpk3+ppAQ407wlSrlpG3be5JarSIvz0kUEREjgfCPP9aXRmDcfCoh1arlpi1be1Baw/+NzZvtov3795Ozs3OaO8/LeLMsqTWpl1vUqKPpVYEzaW5/mAMyugAQB2c0eFztim0vpJkUz3/r4W5NZYu60MBOWWVuXFY0nyO5u1hLAMv830TJON6EZM+M8kz4+N8bi8dno4YVAmjD4WB6ExQjZceftslAJfPZ64PeMm8bVnlmSPhrtnR+ewqNUMjDNeF5LSFxzZqVpoOHJ9Cc2bvp1q1n5OxsRzVrFqbBQ+pLkMsKFshKAW9CqXBhbTb36dM3VLp0ngRf08oa7Us+1MSJ9ahkicyS1eXsLY8L7dqtJNWtm1cez5jRUeZpZZzNZTa2Vvpl8eHnQPKVLp2V9h/oS7NmHacb11+So6ONlCL361deglzGWfeIyBjKkV1byhwYEJ7osciXHx3jPwR/ouBTxTyQ0QUwk7Sc0U2PkNFNW9JqRjc9SssZ3fQmrWd0AcwhrWd0M5kho/sSGV2BjC4AAAAAAICJKoH4J7Wk5t9K65BJBwAAAAAAAIuCjC4AAAAAAIAJYIyu+SCjCwAAAAAAABYFgS4AAAAAAABYFJQuAwAAAAAAmACaUZkPMroAAAAAAABgUZDRBQAAAAAAMAE0ozIfZHQBAAAAAADAoiCjCwAAAAAAYAJqlfYn1aTm30rjkNEFAAAAAAAAi4JAFwAAAAAAACwKSpcBAAAAAABMQPX2f6klNf9WWodANwX99ddf5OnpSS1atIjz2NKlS8na2po6dPg/e2cBHcXZheF3Je5CcHd3d3d3p4VSrJSiLdCWAsUpLRQo+uO0aHEo7u4aHBKIu2ftP/cbNskmu5AsEBJyn3PmZHfm7szszGZm3u9aN3xKdu/ejXv37mHgwIFiX9+XZcuWoWLFiqhWrRoyAsePH8fFixdNLu/atSsKFy78QbaV0b47wzAMwzAMwzASLHQ/IAsWLECZMmWMCt3ly5fD2tr6kwpdjUaDr7/+Gr6+vrCxscG333773uucO3cuhgwZkmHE3r59+7B06VK0bNnS6PKmTZuave5Dhw4hJCQEPXr0yJDfnWEYhmEYhsmA7YXYyfpJYKGbhSChRiK3QIEC2Lhx4wcRuhkR8lRv27btg693586dePz4cYLQZRiGYRiGYRgmY8JC9xPi6emJkydPIj4+HqVLl0b9+vUhl8vx7Nkz/PPPPynsc+bMif79+wuRamFhYeAdfv36NdatW4c+ffogT548RrdHy8nj/OWXX2L06NF49OgRihYtahB6TaG45Hneu3cvxo8fL+b7+fmJ97GxsahcuTJq1KiRYt0kAElIW1paCo929uzZE5ZFRkaKz5PILlmyJJo0aQKFQpGw3MfHR3w2LCxMhBU3a9ZMrEcfHlyzZk04OzsLby0dnzZt2iB37txmH/fUbDf5sShRogRu3LiBoKAgzJkzJ+HYvOu7MwzDMAzDMFkXcuZyd6FPA1dd/kQsWbJEiFsSrXv27BHirVWrVmIZCa8rV64kTGfOnMEPP/wg8nz1wopCkEkg69mwYQNmzZoFd3d3o9sLDw8X+bndu3dHp06dxDzadvLQ68mTJ6Nx48Y4deqUmEdCnMTwn3/+KT7foEEDfPPNNwafO3DgAGrXri2+xy+//CLENIlIgvKBSSROmTIFBw8eRN++fYVwpRBg4ujRo8LDTOs/fPgwBgwYgAoVKiAiIiIhPPjnn39GrVq1xPqnTZuGsmXL4tWrV+91/N+13eTH4unTpwgICBDH8erVq6n67gzDMAzDMAzDfBrYo/sRvLTz5s1LMd/Ly8vAezp79mz89ttvGDlypHhPYos8it7e3kJwJQ29JRHm4uIick8J8tqScDxy5EiCON66dSu6dOkiPJDG2LJlC2JiYoQXOH/+/MJbSUKX1pMU8lrevHkTHh4eUKlU+Oqrr0SoLuUYE+S5bNGiBcaOHSvWQ5AIvHPnjggZDg4ORt68ebFjxw4MHz4cvXv3Rtu2bYWwl8lkQuCSMFy4cKEQsHQMyEutXz8JWPr82bNnxXaIS5cu4fr162KfSGySQKUw4hEjRhj9riRGjZ0D+ny/fv3E69RsN+mxIB48eCAGGZJ629/23ZMTFxcnJj1RUVH4lGg0OigUhmOMarUOSqUMOp0OOh0g56QSJoth7LdP87RaiP8XrVYHmQziepYe0Pay8v+hWq2BUpkYAURoNFooFPI354qOD4/ZpwfGfvs0j6DfqP68MB8f6ZqkS3G89ecgva9TDJNR4SvSB4aEGHlgk0/kpU3KrVu3hBgiUUavL1y4kBDmm5RFixZh/fr1wptbqFAhMY/CbCl8mMQt8fz5c+H5JVFpirVr16J8+fIoVqyYeE9eXRJtJCKTQh5fvbAjzyWFNyfN5SUxTgLb3t7e4DP6Cs6urq7Ily+fCKUmby6JRQqznj9/vhCfFBJMXmcKByb+/vtv4VUlEU5C8sSJE+ICnvQ4kNDX7xNth47D27ymFGJt7ByQWNaTmu0mPRamMPXdjTFz5kw4OTklTKZCzNODqGgNcta9gOAwVcI8z2fRKNz0khDAf258jYkLnov54ZFq9Bp7H5U6XkOp1lcwfu7ThIcbU1y5E4Ea3a6jXLur6P7dfcTGaVPY3H8SjapdrqFM2ytoO+QOQsLVH+GbMkzaOHk9GtW/kn77elbsDkWvX6T/664/vsL+89Ig1d1ncWg44gXK9n0qpnUHQt+5/r92hqBU7yco1esJ1u5/u/34xX6YvCIgS5/C4kXH4ObNFwnvIyJi4Or8FUJCorB711X06L4oQRAP/XoVypYej+JFR2NA/78QHZ04sGiMc+ceokC+kShdcpyY1q87ncLGxycEdev8gmJFRqNO7V/w4kUgsiqzZp7ExB8OG8wb+OUOrFxxRbwuWGAeIiOlSLM9ex6gVs1lqFxpMRo3Wo0rl98dhbV1y22ULbMQFcr/ib833zJqM2XKUZQu9QcqVlhs0iYr4O0djkIF50OtTry3Hj/+FPXrrRSvf/zxCJYukZ7vfH0j0LnTJlSvtlQct7lzU/7Ok+PjE4EmjVejfLk/0afPVqhUmhQ2Fy54ifNL6xwwYDtiY/kebgq5TJbuEyPBQvcDU6dOHfz7778pJgppTQp5BUl0Ut5tr169cO3atRTrOn36NMaMGSPCdfVeRj3k1d21a5fwupLgJdFEOb7GoJxf8lQ6ODiI8GaayHNsLHyZBFjSzxEFCxZMmEejgxTO6+bmljAvebg05d9ShWfyYhMkJJMKThKE+krFtF/Vq1cXHmvKb92/f3+K/adlxtZvChKnxs4BhSPrSc12kx4LU5j67sag8HMa8NBP+nPwKbCzVaBhdWccOi2FkBP7TwWjXSO3FF7e39a8QpUyDri2sxJu7aqMF6/jsHGPv8GocnJ++O0ZFv1YBLd2V4aDnQJ/75PskzJxwTNMHVkAd/ZUQeUyDvh97ac7Hgyjp255W/gEqeHlnzgItP98JDrVd0hxkIbP98Xc4R64vb4Qji3Kh1nrg/D0tfSgT/8X9P+RFG9/Ff7aFYLLKwvi8qqC+H1LCAJCjT8cXrkfg/WHwrP8ienQsQr27kkcpPzv0G3UrlMMLi52Bsdm7ZpTsLBQ4vbdOfB89BucnWwxd440oKr3dCXn2VN/TJrcAXfvzxVT3351U9jMmL4LPXvWwsPHv+GLL+ph8qQtWfactGtfEgcOPEx4TyLr+PFnaNuuhIFdeHgcfv7pKPbt74er14ZjzpzmGDhw51vPRWBgFH755TiOHvtSTL/8cizFveXq1dc4c/oFbt76Bv8dHiBswsJikRXJm9cJhQq54vz5lwnzDh18hPbtS6aw/WXKMfTsWQ4XLw3F2XODceTwEyGK33YPn/jDf+jbryJu3hoBG2sldu9+kMJm7JgDWP2/Trh+Y7iw2bDhxgf/ngzzvrDQ/QRQn1fKsaXQXcoJpdDXn376ycCGRBD1fKXcXRJIxjyJ5H0k7yoJXfLmmgrfoiJUhJ2dnfBc0kRe4Fy5cgnBbUqY6cUsheTqUavVokBUanJk9QKVvltSwUmezcGDB4vCTh07dhTh1+TZfvjwIdasWYOPzafarpWVFRwdHRMmGnj4lHRs6oa9JxPP7YFTwejYNGWO9/NXscjmYiFeU1jzjO8KoEwx6SHz5KUwjJ4l3TCTolTIhLeYQqHDItVwdEiZJaFS6dCirvQbqV7OAZ7PYj7o92MYc6CBnra17bHvrBTdEROnxYW7MWhdMzGKRc8LXxXcnKWw2mzOSqz4PiesLaWBovl/B2P9QcNInvsv4lG5uDXsbORiKlnAEufvpPzdq9Q6TFjqj7E9XbP8SezcpRr27EkcCN637zo6d07Z0u3ZswC4uSeeo+8ntkPDRqXFa0/P1+jW9Y8Un3n5Mgh58iYO2hojPCIGnTpXFa9r1CwKzwfGI3ayAmXKZBchyg8eSFEGJLKKF3dHzpyG97IA/0hhZ2Mj3TcqV8mNX6Y2ShBU5P27f99w8PPokSdo1aoYPDzs4eZmi+07eqUQYF5eoejZqzyUSjmyZbND9uz28PY2/B/LSrTvUBIH9icOPBw69FjMS86LF6Fwc7cVr21tLTBvfgvkzu0o3m/fdhfzknl4aSDi/Hkv9OpVTryfMbMZatXKZ2BD54bOQfnyOcX7qtXy4NHDoI/wLT8PZPoWQ+k0ZSZ/7tWrV0X0Kj2bfwxY6H4Cnjx5Iv62bt06QZzqxShBeZydO3cWQpFCjo3lWJAnkby8FA58+fLlhNxTY1DoM/WPpWJQSScqnkQVlakwkzHI60pezdWrVxuE/FLINfXhfReUB0weaxLGSUO7GzVqJLZPYpnCjJNWO6bv+7Exd7t0HpJ7aDIzreq54vTlMKhUWoSGq/HoeQzqVUnpxR7YOQfGzX2Kur1v4OdFzxEUqkbFktIDZYPqzvh9YuEUnxk3MC86j7yHfA0v4uGzGLGt5OxdVkYc0+gYDRZteIVaFaUbL8N8ajrWd8C+c5LQPXY1GrXL2cLeNuXt8psuLqj0xTO0HeeF3/4OQu5sSuRylx7ux/VyQ7+Wzgb2xfJa4vL9WIREaOAbpMaFOzEICks50Dh3UxB6N3NCDjcuo1GzZlH4+oTC1zcUWq0WRw7fQfsOVVIcs959amPF8mOoWP4HjB2zEXfueKNePcnTWLx4Lmzf8V2Kz3i9DMKsmbtE2HKXzr8jICClB33tuqHIkcNZhEbPmb0HtWtL6T9ZlXbtSyR4dQ+a8CAWLOSK4iXcUbLE7yK0ed2662jYsFBCrvn69V1RsqRhWtCzZyHCO9us6f9QtcoS3L3jJwRtUjp0KIWBAyuL14f/eyzCawsXfvtAxedMu3Z0Lh6J1w8fBsLKSoFixVIOVn81uCr69N6KVi3XYvasUyKHV2/XpWsZjJ9Qz8Dezy8Sjo5WGDZ0tzgXP04+AhcXw2c+Opc7/5XS5YKDorF61VXUqJn3I35b5nPl3r17ol7R7du3P8r6Weh+Aqi9DoXXUvVhqkJM1ZfJs0uia9SoUZg6darInSWR+N1332HQoEFi0heuShq+fOzYMRGCW6pUKaPbohBdEtZDhw416hUmL2/y8GU91NKHcmtJENM+d+jQQbQmmjFjhgg/fheUm0sCkoozValSRWyPQrgpx5i+J+0ziWGaT9+Fjgd5p2k5bffFi8S8rLRAo0L6Y5Z8oirS5m6XCk2RN57W8zngaK9E1bL2OH01HP+dDUGLuq7CY5uculWc8PRwNUwakg9x8Tp0/fYeVm41nSNNgwHfTH+MI/8rh1enqgvxvGCN8bDky7cjULPHDeTPZY1hPXN90O/HMObSqJIdbj2JQ2S01mTYMjG6hxsebCqMXs0cRb5u1UHPcfOx6VDK/DksMKqbKxqPfIm+U1+jXBEr2Fob3oYfvIjDudsx+KL1u1MnsgI0GNa+fWXs23sdly49RYmSueDunvJ8lCyZG0+f/4Hffu8LW1tLDBm8ChN/SNmmLylVqhbCnLm9RNhyuXL5MG6s1NkgOQ8f+qBe3akiL3jq9K7IyrRvXwoH34grEpskfJNDImjjxm4ivLhGzXzYueMeGtRfhaioxE4RyYmJVePJk2Ahnvbu64effjoqwpmTEx+vweTJRzBy5F6sW08FOLPuYBCFLtvZWwiRa2rQgejYsRTu3f8WQ4dVR0BAFFq0WIuDSULQkxMTo8ajR0H46ququHR5qIhyWbHislHbI0eeoG7dFWjarIjYDmMcqTBY+k6ZBd0bBxJ1WaHnc2ofSo62cePG4f79+++9/qx7hfgIkAAy1UOV8nBJ+BEkcqkA1ebNm0XlXfKQUnEpCqclFz6145kwYUKKdST3orZs2VI8BFBlZFNER0dj0qRJovJxcih0lgoy6Ysn0f4nzyUeOHAgKlWqJHJYSYj/+OOPopeuniFDhgihnfw40HcgyJNMObpURZrEfM+ePUU4tlKpTMhDJqFNPXYp/Jq81OShpp65VPDK2PpJbOuLaiWHvMX6dRuDqlLT8ndt19ixoKJcdA70Bave9d0zAx2auGPviSCEhKnRs43xwlvDfnmEueMKCSEspjoumLfaG4O6SiFLyQmP1CAmVosa5SUPbYcmbvjfDr8UdkfOheDrnx/hj0mF0aZB1h2VZzIeNODTpKotDl+JwrGrUZjxtVRwLil+wWrheZ03Ijt6NnUS05RVAThwPhLlixivfh8RrUH10jb4so3k6W0zzgsl8ktRJXou3o3BI694lO33DBFRWsSptHCyU2BCn6z7P9Kxc1X8vuAAnj8PNBq2TFDu7MBBDdCwYSkxDfiiPlq3nIMZM7ubXG/bdpWQLZt0nerRsyb69l6SwoYKYbVtPQ8/T+mEgYMaIqtTsWJO4fG7dNFbeP3y5Ek5IHPixDO8fBmKfv0qomhRd3z1VRW0bLEWd+74oXp1414/8hg2alQIdnaWYqIw6Zcvw+DubmcQLtuly2a4u9ni7Lmv4er67siyz5327Upi//6HYtBh7jzDWi56ETH4q3+xfEUHtG1bQkyVK+cSxcJatCxm8lxQWHi16lLBTLI7elSKREwKFQObMfMkVqzsmCK0mclavHr1Sjz/UucZSnukaFNTdYNMQTrD1tZWCF0qhEvP5PruNLQ+qoFjDix0PyDkpTQF5aQmhQRxcnsKZaaJoDZA74LCf8nrSuLRFCQ0aTIFVTR+1/6T95MmY1CboeQkXw95QskzbQzyKCc/NjSSoy9WZWz9yT3bSaG8W33Lpbfxru0aOxb0maTfIzXfPaPTpqEbZq/0Eu1Ulk9NbH+VlLBIDZZsfi3CkemmefpqGIoWMP2A4eSghLWVHDcfRKJ8CXucuBSGCm9CnZPy/fxn2PlnKZQrnnIZw3xqOtV3xNT/BaB4Pks42ae8wbo6KvDvqQi0r+sgClhRLu+VB7H4so1pT2xEtBZdJ3vj6uqCeOgVLwpelS1kZWDTv5WzmIh/joYLD3FWFrkEhSAP+nIFnj0NwH9HUtasIKgq7Lw5+7BocX+REnTyxH0UK258ME5P86azsHTZl6hevQh27rgswqST8+PkrVi89Au0bVvpg32fzE7rNsUxdtwBdOliOBisJ1s2W3w3aj8aNy4sckEpj9b7VTgKFDAsLJmU+vULYvy4gxg9pg6io+KFd7doUcPf/e7d94W4pgJIjATl5H4xYLvollCqVMrBanKGPH8eKkQp5TeL/NsLXiiS7NgmhQYQsuewF8W/SBSTyK1UyTDiigqRTZ9+Aof+G5CQ78tkXUaNGoV69eoJsUpdZMhxR9GRVJsmtR5der6mGj70GZpHka0kdH///XdRwDVpGmVa4NDlTAh5YKm3LXmCqSozjYAwjDm4OCpRJJ8N6lRyhIVF4uWAStPr2/Mt+L4Qbj6IEq2FqF3QK794TPu2gFh2/GIoRk5/nGK9/5tZDMOmPkaFDlfh5RuHYT2lB865q7yw7l8/REZr8PhlDPqO90T59lfFNGpGyhFjhvlUNKlii+c+KnRqYPgQR/8XVFrBQinDP1Nz46cVAaJVULVBz1G/oq0QyMTsDUEp2gdR/u6gts6oOOAZBkz3wcrvcybkLXaZ5C3Cn5NCixRZuIeuHhKuzZuXg0d2R2TPnjiQQMdO30f0p587iof4EsXGokSxMTh48Cb+WvalWPbgwWt0aD8/xXqX/PUlhg1ZLXJ0r1x5hp9/6SzmU5uhX6f/K15fufwU34/fnNCCqHOnxOr9WZUO7Uvi7h3/FKGydC4oZLJ06ez44Yd66NRxIypVXCzyQ+fOaS68hETvXltSFKMiT3G3bmVQt85ytG27Hr/OaAoHBytERMShXr0VwoaE1+VL3qhSeUnCRGG7WRnKtSWdQJ7apFC4sf7aQt7c7dvviVZBVasshb2dJUaMqCGWbfnnNmbOOJlivcuXt8cP3x9CtapLxftevcqLvxPGH8J/hx7h6dNgBAdHo327DQnnYu6cd7ctyqrQqUjvKT3x8fER0aU0sELpgFS4Vt95JTWQt5Y6zOiFMa2HIiapYO4ff/yB//3vfyIS0xxkus+puk4WITQ0VIxyUIgseXO5IXjmhEKgGzRogEOLqQorP8x+ambvbPKpd4FJwsSGf/PxyCDI607+1LvAvCEq0pOPBcMkIypKhbZtDouuIpR+ltGe84o9rw6FLv2CaDUyNR4WuJjm43HgwAFRHIqcaMk5fPgw9uzZI7yt1JbTVLToihUrRK2hp0+fJqRsvo3t27cL5x2lN1J6oTEqVKgg2rdSumVaYY9uJoTClelHRHm/LHKZtxEeqcb+k8FiovZBFE5sbGwrIFiF2w9TFv44ddnQI/UhoJZDZ66G4eHz6PeyYZj34ZlPvKisTNOhi5F47mO8UA4Vh3oVkNhTl4iN0+LSvQ/fDovSBI5fixL5v+9jk9mgisZ7915PmC5ffiLmJScqKhYXL6aMIKF5sbGmCx2Zy4ULj3Hjxov3tslsXLv2WhQroonybamqrjEuXPBCbKzh7/D163ARdvyh8fIKw6lTzxETo3ovm8wGFY/SnwvypHp6Sq2dkkPh4Y8fpWzPcvr08w++T3TOab3Utuh9bLISsk8wpRWVSiXanh4/fjzFsoULF4rUSqr7Qy1JqTgtFY9KHm1KaZcUfkytRFMjcgmqh0MeYBK8pihZsiSePXtmxrdiocswnzXPvGNFBeRTV8JEmPGomU8wdErKB8WQcBUePk/54P7N9A8bTkzFRFp9fRuLN71G/wme+Ovv12bZMMz7sudMJGatD8SpG9E4dDEKLUZ7Yd2BlA9lz3xU8A8xFF2BYRr8tNL4A6e5kJiuOfg5NhwKQ8MRL3DlfoxZNpmRyMhY9Om1WOTVnjh+Dz//tA2tWs5JMSgXHR2PWzdfGs2jDQyM+KD7NKD/X5jy8zZ8N2o9JozfbLZNZoT6qm7++xZOn3mBnTvvoXbt5aKQVHJu3vBBXJyh0D196jl2bL/7QfeHKvu2a7sBGzfcQKOGq0QbInNsMiNXr7wSVabpXBw//gxffrEDM349kcLOzzcSL16mvH5RrvSHJDpahcaNVmHd2uvo2mWzyJs2x4bJOPj5+Ykir0WLFhUFoJITEhIiitpSJOnKlSuxZMkSEU5MxWkpd5a4du2a6K5ChWLv3LljULT2XRQvXhy1a9fGsGHDRJeW5MTHx+PKlSuigK45sEeXYT5zqHXPrDEFMWdcIez7qwy2HpQe0INCVbj3OEr0z1WpdSj2psAU9bU9cy0MfkGGHpKYWI3wstLnaLkeKoJx5U5ECu8r2YRFGD4EHT0fCgdbBTbPL4l9y8uI6s0kbNNqwzAfgoaV7TB7mAd+G5kdf4zKjm3HJbFEhaJ8gtQ4dzsa+TyU8HCRilH5h6hx8noUYuINf480nwQztSO6nER8RsVocfZWtOiZmzRagTzIyVmyMwRftXfGqh9yYc5wD8z/O9gsm8yKk5MN5s7rhXnze2P/gQm4e8db9LWNiYkXHl4vryD4+YWhXHmpumt8vBrnzj3EixeGOZoqlRpnzz7E69chuHTpCeLiEr17t269FFWUkwroK1eeih69SaGcXhLU+w+Mx6H/vsfWLRcQGhqVZpvMzIABlfDrr03xxx+t0a17WSEkCaq2THmzlCtbvkJOWFlJ4ZhPHgeJZckDhsjLSLm11POW8jr1+PtH4vz5l4iMjDeYR7bJmTXzpChAtWx5BzRqVBhbttwxyyazUrZsdnEuZs5qhnXru2LbtrsJ3nM6prdv+8LG1gL580lF7Ejkkzc1NNRQ7IeHx+HMmRfimJ8799KgZROdu+TeVypClXwgY9u2O6hWPa+otLx+QxfMnXsmxf6mxiarkZFzdHU6nWgZ2qVLFxQqVCjF8kOHDiEmJsageC15dEmAUqcSgoq7kvidPn26WXWDNmzYAHd3d3Tu3FkIZmprSnm5tE4KWX78+LHojmIOXHWZYT5zIqPVuHgrHFothSKHoXUDqQfy3UdR+G7mUxTMY43mdVxw7V4kZo8tiMYDbolqyS99YkXbIWkdGjT78hZKFLLFa/94PPGKxaNDVYXIpb667i4W8AtSiXX9PrGw+My1u5EokNtaVGHWc/5GOBpUl27Grk4WcHVS4uXrOBTIY50mG4b5ELwOUIt2PjTQs/ZAGNrUlnKZ/j4ShgPno1C6oBXcnOQoV8QapQpYoefPr9Cgoi3uPY+HrbX0JHHjUSz6TH2NWmVsRJVma0s59s7Ni5d+KvSe8goVilrj8oNY0T+3RxNHqDU6nLkVg+bVDfOmzt+OEYKbqF3OFqP+SOlBS41NZoUetikMmB667tz2QtFiOUTrHxKy1PanUGEP9O5dG2vXnsaBgxPQvNkseHg4Ijg4Eg89pb7eWq1WeILd3OwRFBQpqjSfOfczcuRwxuBBKxAZFScKVpH43fnvaJH6Q4LayspC2Og5f+4RGjQoKYpgWVrKUb5Cfly//kK0LUqLTWbmoWegKFoUHhGHM6dfYP5vLcX8X345BktLBerUzY/9+x6KXraHDz/Gsr8uoXz5nLh69RW6dSsrbNevvyHmly7tgbt3/dGpc2mMHl0bRw4/xuzZp1G6jAfGjjmAlas6omRJDwQFRePePX9R6Tfp74JCoalYFVGjZl7s2+spWhalxSYzExwSIwYWNFod/t15D61aF0/wni9ceB5Fi7mjYoWcwpPar39FtG2zHrVq58OcOafFPZqggYYO7TegcuXcmDv3tPAAX7g4RIje7t3+RuEirnj0MEj0w6VzRFy84IVq1fIkDGbo55ENUbx4Nnh7hYnjT7+JtNgw6UNERIS4LuqhYk/JKyHnyJFDtO8hqB1ocq5fv478+fMb5PqSKM2WLRuePHmC8PBw0Rp19uzZYtJDocj0udRQoEAB4RWmtMxVq1ZhypQpBsv79esnJnNgocswnzl+gSqs3OorvKJ3HkWjYpJWP7Y2cmxbWAr/nQ0RQnfjHn+0qu+KX74pIDy3xVtIYSw0v1kdV0wZkV/k85Zuc0XM//doENxcLDCwaw7x/osfHsInIB45s1liZN/cKfaF1lk0f2JrIhdHC4REqFEgjTYM8yG4/igWy3eHIC5eh+sPY9GkSmLPzgaVbDFziAcmLpOqw87aEIQ/R+dAk6p2Iq930bbghOrKf3ybHY2r2GHX6Qis2C15ReZtCkL3xo6oWtIGrWrZY8ISfyF0qfXWtK9S9uWlcGh3J+lB0NlejtBIrVk2mZWoqDgsX3ZUvH70yBeOjjaiZZA+ZHn3nrF4/NhPEroHbiJPHles3zBMeHALFZDauh08eAs5czpj3fphwuNbIJ/Uik54hL2D8fMUqary2DEbhbeX2gr1H1Avxb5QGLSbe2KYnKurHUJDotJsk5k5ePARbtzwEV7AkJAYaNSJv7Vhw6oLIUNCl+4r8+efxenTX8HZ2RqTJh1OsJs39wxOnBwo+rJ+P+FQwvwpvxwTHkpbGwvRmmb58itYsKCVELs0JYW8k05OiYOctK7Q0Jg022Rmnj4NwerVV0VLn+vXfdCxY+JgSsFCrlizpjPWrr0uhO7y5ZcxbHh1DBpURVSk7txpk7Cj+YO/roaBAyuLwYRePbeI+WvXXEOlyrnQrm0JaHU6USGbKjKTKJ04qUGKfaHBCDe3RI+ds7ONOP7ZstmlyYZJH/LkyWMgdCkHN7mIfBdBQUHC42usXlBgYKAIKSZBbawlZ1pwcnLC/PnzMWvWLCGcqZcuUalSJZQuXRrmwkKXYT5zCuezwYppUmN4eiip3fMGLtwMF+/JQ5uUu4+i0ayO1OvQzdkCeXJII3/3n0SjaS3J45HN1UIIWYIKWFGbIBLSRO1KjlAleSBKjo21QnjP9MSrtbC3UaTZhmE+BK1r2WPqIEl0BoSqUbLXU/RvKbWvKZlf+o3rufM0DlVKSA/TFYomjojfex6HaqWk+eWLJM6/9SQOvsFqIaaJ+hXeHs5lay2H6o33heow2VnLzbLJrLi42GL1/75OeN+p4wLs2H4JNWoWRZGi2aFUJl4DyONbpYoUYmdhoUSp0nnE63t3vVGtmuRJsrRUokRJyTNIIcZ+vmEJQrpYsRywsDB9TbG1tRTCWw95o+zsrdNsk5kZ+W1NNGwoHeO9ex+Inqk7/+0t3hcr7p5gFxQYDSdHKyFyifLlcogQ2MDAKDg5WwnRSZQtlwN+fpHiOD19Eiz6uuohT7ApbGwshMAzOM52lmm2ycxUqZwLS/9qn1DkiVo2keeWKF4s8VwQ9+4mtn2i1kPW1tJj/oP7AejQQZpP/Xb17QQp9/q1TwQCA6RBmiZNCotwZVPeVzrWKlXisabBKGPn4102WQ062ul5tdY/QXl7exsIztT0tU0OFZWiAlQptqHTiTBlioz5kJWuaXs1atQQ04eAhS7DZCGorx6JVApJtrNJedm1t1MIj6peFAeHSqHLNtZyhEdJFzq6gfkGSnlV9rYKNK3pgolDpLy5FVt8kN3N9A2tSD5reD6LTrhI+gbEI18uqzTbMMyHxs1RIfILY+KM54M72MqFR9XZQWFQnMrWiv43tCKv3Ms/MZ/NwUaOb7q4om55W5Gru+WYNLhkiiK5LeD5Ih4Fc1rikXc8SiQT2qm1+VzIk9sVwcHGPaQODtbw9U2sExDgLx1bW1srhIYm1grweilVoXVwsEGlSgWwcvVg8f6fv88Lj7ApihTNgTNnEtv4kCe55BvRnBabz4VcuRyFV9cYtnYWCA6OMagSrBc7EeGJ+bcUvmphqRACiwYZ/ljYRoipBw8CRFitKeztLcW9iEJs6TWFKBdPIrRTa/O5QMLV1dW0x5q+v75KNuVSx8ZpEs9HhHQ+6Hjrhai9vRW6dMmPvn0riPcUak79i01RuDCFOAeiUaNCwoOsVMpha2uRZhsmfXBwcHhvEZo9e3YEBKQsvkjeXFpmDhTyHBsba+Cppee95cuXY/Xq1Xj+/Lnw8NatWxejR49+L4/u5zMczDCMUeLitXjyMkZMOw4H4v7TaOF5NUbHpm74c8NrkdM7c7kXomOlm2TTWi5Ysuk1rt6NwIT5z0S+L9GhsRs27PEXhadWb/fF1kMBsLKULitevnFi20lp28gNO48E4ey1MPz610vUruQk7OkhhSpEv82GYT40oREaPPaOFy2EKDS5Zhkb2Nsa/611buAgKi1f84zF3E2JbTyaVrPDr2uDRLuhX9cGJrR16NrIATPXBQp7ClumnF2CfutPX6dshdOzqRPmbQ4S9pOXB6BXU+l/lEQyFcZ6m83nAOXOPn7sK6bDh2/jwIEbaNGyvFHbtu0qYdPGszh+/B5WrjiOhw+lHN1GjUtjw/rTOH/+EWb8ugve3sHC29C0WVlhSyHPO3dcxswZu+DiInk5qBAVtS1KCuXZXrv6DAcP3hReYBIJefO6iWXPnvmLUMC32XwO+PpECMFIhY4WLDiLdm+8hMkhT13JktmEDRU0orxc/XwPDzusWXNNtCii0FqZDOJ8tGpVDNOmHRdtjEZ+s1cMwBJRUfHw9U0pejt2KoVfp58Q61++7BI6dSqdECKrL7hkyuZz6RFL54IKfq1bd114SJOHeOvp0LEU5s07I47ttKnHoVRI17PGTQpj4R/nxfyffzqa0JqSjtvSJRdFdWf63OXLrxLW9fx5iPi/TErnLqVFGLWoBj3pMDp1LpXgRaf2Tm+zycpIv/30nT4U1apVE55hHx/pOktQqx+qxly/fn2z1kkh1AMHDjSYN2jQIAwZMgQ3b96Eh4eH8CJTQaqKFSti0yYpBN8c+OmRYT5jnOyVyO5mgXFznmL83KeiavKepWXgaK+Eq7MFyhW3SwhHLlvMDtXLOeLHYfnw12Yf5M1hhS86S7m3jWo4Y1jPXFi4/jWqlHZA7uySJ6lIfhssn1oUq7f54u7jKGyYUyJh2wvWeMPbNzG0jyBv7/JpRfHX3z6IjtUmFK6KidVi0u/P3mrDMB+SgrkshPgc+6efEI0ULv/3L1JeedE8lsjjIXkgSuS3Qp5sSozp4YpKxayxeEewCG+uVkoKyZzUzw25symxdGeIEMMOdtJttV9LZ3Rt5CiEad7sFpjUT/Iwkcd44l8BRsOoezdzwuyNQSJXuH8r54SQ6VV7Qt9qk9khD1+lygUx+rsNGDN6I3Zsu4TN/3yDQoU8RIhwjRpFE7xPlFebL587Vq/5Gmv+dxIRETH4/od2sLGxRIkSuTDvtz5Ysvgw7OysULZsXuH9JVG7c9dobN96EYcO3sK2HaNEyDOxetVJ3Lhh2LKIwp637fgOf28+j9u3vLD5728Slk2auEW0Q3qbTWanYqVc2LnzPn74/j/8Nv+sCGHWFyiqWi13gneOij7ZWCtFdV2f1xHYsuU2xk+oi0KFJW/52nWdceumL3btuo9u3cskeAp/W9BKfI4KKQ0ZWg316xdMCLtdu+Z6iv358ceGsLFVYsnSi5g9pwWKFJUGFPbseZDQJ9aUTWYnm4fkjaNzQfnPnp6B2LW7j/CS5srtiGLFpO+ZJ48jihRxFWHLvftUEOK1br0CaNFC+t/p1aucCEtesvgimrcoCgdH6R5eq1Y+TP6xAZYsuSg8wAt+b5Ww7enTTiR4gfWQwJ7yS2MsXHReDGT88IMkdCgs/bffzr7VhsmcNGvWTBSsokJR5HUlATphwgSULVtWiNAPwZEjR4Qnl3r1kqi+ffu28PpSISxqe0RVncmDbA4yXfJGdQzDpAuRkZFo0KABDi22hp3NBxx++whQsap/jwRiWK9coh/vlTuRWDtLqvz4uTB7Z5NPvQtMEiY2/DtTHA/y4lIF5kaV7bBwazBqlrbFoHafhwDVI687GZmB27e9MPWXHfjxp464feslVq06gWPHM8e+p5aoyMRw6YxOzx7/4MsvK4s83XHjDuKvZe1EFV4m/dm08SbuPwhAjx5lsX37XRG6PG1ak8/K6922zWGcOHHig+aLfqjnvHJe1aHQpV+2qEamxq28F9N8PNq0aSP+7t2712D+0aNH0bFjR7i5uYnvpFQqcfDgQZQvbzzq5l306dNHtAy6cOGCeD9q1CisWLECr1+/FiHLSbl165bYzsaNG9GrV680b4tzdBmGeSdNajrjzqMoEUpMLYP+YC8rwwiGdHDB7A2BQvBSPu6XbQxv0kz6QR7cFi3K4dfp/yK7hxPWrR/Kh/8TMnFSffz112VER8Xj++/rscj9hFBrp9/mn8HMmadQupQHxo9PWW2cYcaOHWv0IDRu3BgvXrzA6dOnRbEoClk2p1+uKUg8FylSJIXIJchzbGlpKUSwObDQZRjmnVAO1egBUmVThmEScXNSYM5w8wpyMB+egYMaion59JQtmwOLF7f91LvBvCliZaxdEMMkhbzPpnBxcUG7du3wMSAxu23bNlGgytrasHo9FaaKj49HrlzmFftjocswWZSNe/wgl8nQs01iUYvpS1/g+6/y4fyNcIRFqtGmgRv2ngjCiUtSkQkqcEBVm/u1zw53F9NVFCkjYvPeAHg+j0bHJu6okKR3b1psGOZTMGVVAFrWsEf10lIebmCoGpsOh2NkV1f8b1+omF+qgBV+/ycYXv5SkSmlQiaqIPdt7gSl0nQqQkS0Biv3hIpc3S9bOyOHm9Ism6zC3r3X8eypP74Z2Txh3qKFh9C9Rw34+YWLglDUC5cqIFNur0Amg5ubPfr0rYP8+d9efXfP7qu4cOGxKFjVoEEps22yAlRRmXJ2x46rk9An9fIlb1EUqkXLYpgy5SgmTpQelH/68UjC5yytFKhXtwCaNJVaP5ni2bMQEWJLeZ39B1Qy2uImNTZZhb+WXkKBAs7i2OuhAlQ//tQQ+/d7wtHRGnXq5MfmTTdx86ZvwqB1vnzO6NO3gqjQbAoqeEWFxSj3msKdCxdxM8uGkaA7QnomqGXsZDgJKjpFnuGSJUsib968iI6OxldffYU1a9ZAoZD+rz09PdG7d2/RIqlRo0YwBy5GxTBZlIOnQzB82mN4+yUWjPrfDj+oNTrceBCJM1ellh30V63WoVltFxHCTC2HWg2+/dZ1U9GqLQcDUKyADXqPeyAqPptjwzCfglV7QzH8N19RIZkIjUxsD7T3XCSevZbE7d9Hw4W4bVbNDnXK22DP2UiMW+z/1nX3m+YDn0C1aFfUYsxLaN70xU2rTVbh7BlPUaTqypWnCfO2br0oWg89feKHffuk4kU3b7yA96tgNGtRDs2al4WVlRL16vyC6GjDgnhJoQrMM2bsRqlSufHdt+tx+vQDs2yyCtRiiIoWzZp5KmHevfsBOH/BS7xeveqqqL5L08aNN0XxI5qoT+7EiYexe/d9k+um1kDt229AjpwOuHPXH2PGHDDLJiuxc+c9jBixVxSR0kPVjomzZ1+KQmDEf/89hqOjlTgXDRoUxL37/hjQf/tb102Vmc+dfYl8+Z3RoeMmgxZSabFhGGMMGzYMP/74IwoVKoQ7d+5g0aJFUKlU2LBhQ0J1Z8oBLlGiBK5evYqZM2eKgljmkHWHiRmGQc0Kjpiy6AVWTk8cETZG8YI2QugSzeu4Cm/wK784hISrcelWBL58U51Zz4qtPvhvVVnk8rCCX5AKf+8PwKQ3vXbTYsMwn4psTgqsPxj2zsrGNcvYokwhqZpssbyW6Pi9N9UOx7+nIuBsL0eDSlJlc8LbX4WHXvHYOVNKAzhyOQpnb8egXgXbNNlkNaht0Lixm3D8xNsLS+XPnw0tWkjFUejvzp1XcP36CxQo4I6NG85i/ATDMNq//jqK3xb0Qc2aRWFlZSGqONetWyLNNlmJ0qU9sHfvA3z9ddW3VjamqsBJPbhhYbE4fPgJ2rUriT/+OIfOnUsjT57EfLz9+zxFdeeBAyuLljYlS/yBmBiVaNuUFpusRukyHqK1008/vd3bVa58joTz0aBhIeTONVsM5F286AVf30h07JgYqaBWa7Flyx3cvTdSDBjdue0nznm/fhXTZMMkQh200tOzqMvgLt1atWqJKSkRERF49OgRsmWTCta5urqKasvk0a1Xz/yccvboMkwWZkTvXLhwMxw37kem+jNUrZFasTjYKeDhaoGKpQxDjqOiNYiM0ggBS5QuYgvPZ9FptmGYT8mc4R6YvjZQtLhKLZHRWjjZSyFX5OktkNPwAfzWkzhUKCr95olSBa3g+TIuzTZZjW7dqgths3uX5K1KLZERsXB2toWjow1q10lZJZ68wBUr5hevS5fJgwcPfMyyyUpQi6AJ39fDzz8fTdPnyBvr5CTl3tWokVeE1Sbl1m0/lCsnDZgqFHLRLsfbOyzNNlmNiRPrY/OmW2k6DtSvmFpEURgzDTZQH+SkPHsWjNx5HIWAJUqWyoaHD4PSbMMwacHBwQGVKlWClZVVQv/eZcuWvZfIJdijyzBZGCtLOaZ/WwDfz3+GgyvLmrQ7ci4EYRFqUDOys9fCMaRHTtGL19Ee8HAzzPOJjNHA3i4xb8rWWoHoGG2abRjmU1IyvxXa1HLAb38Ho0cTR5N21OM2m4sC8Sod/j0dgfkjsif0301OZIwWDjaJ48u21nJExejSbJMVIa/qgH5/oVXrCiZtLl58LCouE7duvUTlKgVRurTkGa9dO2XUSmysCtbW0vXL1tYK0VFxZtlkNchrt3LlFZw9+8KkTWysGnPnnBavw8LjcOTwY2zfIbUGqV49r1Hx5ZAkZ5SEGLWMSatNVoNaN337bS2Rm7tseQeTdv/uvI/79wLEgNGRI08w9U1robx5U1a5jYxUGR5nGwtROTutNkwidEVPV48uH/wE2KPLMFmcDk3chYDdfzLYpI2LkwXy57IWU9ECNjh52fTosYujUnhs9URGa+DipEyzDcN8an78wh1r9ofCL1ht0ianuxL5c1igUG5L5HJT4swt05EJLg4KRCQZ0ImK0cLVUZ5mm6xIlSqFUL1GYSz7y7Qn0cnJRoQp00Q5tefPPYJKZfrcOThYIzZWejiPjIyFi6udWTZZDfIEzprVHJMnHRFFBU3Z5MvnJKaiRd1EqKunZ6DJdTo7WyMiMt7AA0wiLq02WZFBX1URxaZu3DAdbZAtm504FwUKuKBgQRecPvXcpG2K4xyVinNhxIZhMgL8ZMkwDOaOL4gvfnhosuhN5dL2CdWZu7fKBvea50V+Dz3MJMfSQg43Zwt4+cYhbw4rXL8XiSplHNJswzCfGldHBUb3cMOUVaYf0FvVtE/I0S2cywI/rQwwaVu+iBVuPEr0CF7zjEW/lk5ptsmqTP+1G+rWngp7+5TecqJEidzo3adOwvtNG8/C2zsYBQsmVpZPSsVKBXD58lORc0vVm6tWLWyWTVaEqvnmyeuIbVvvoHKV3CmWUzXk7j3KJbz394vEhfMv0ahRIaPrK18+h8jhpfzb6GgVgkNihDBLq01WhPKhp09vgsmTEytdJ6d2nXxo00bKLW/YqBDq11tp0paOaWBAlDjG5DUnAd2iRdE02zBMRoCHiRmGQbni9qhRwUEUhXoXCoUMjnYKUYjqxatYHDqT0hM8vHcufPGDJ2Yue4nN+/zRvaWUA0Rti255Rr7VhmEyEoPbOcM/1LRXMLkwDg6XvLHn70Tj5uNYg+UeLkpUKWGNr2b5YMR8X9hYyUSbImL7iXAEhKrfapPVyZnTBQMHNcCdO1Tw6924ujmI6sxBQRHYuuVCiuUjvmmOb79Zh9mzdouQ58FfSwV9bt/2Eu2K3mbDANOmNcGlS6k7Fy6uNggJkf4f/v33HgIDowyWN29eFFcue2PmjJPo2fMfIWZlMpmo4Lxu3fW32jAQhaas3+TLvvP/wtVGFAejwer79/1x7txLg+WU/9ynTwV8MWA7fvnlGK5de40mTaRCVseOPcXTp8FvtWGMIJPaM6bXpO8vVK1aNZQqVQqLFy/OsqeFPboMk0Xp085DhCHrmTKiAEoUsoWFUoa6lZ1EHi3RpqGrELZJmTG6AIJCVaJ3KBWmSs4XnXKgUB5rPPGKxZE15eDsKF1qqE2RRvt2G4b51Ez5MhvetPETPXHXTMqFR95SmN4XrZxQqqCUmzaqmytyJulxSyHMX7ZxEkWpKGJWayTtfOX3ObHzVISInuhUP9HTGKfSiRSCt9lkRdq0rSQKSun5bnRL2NlZIXt2J1hYKEQPXaJO3eIoV96wavv337eFVqsVD/Tx8SkHK5o1KwtXVzvhqd1/cAIKFZKOtVqtEdPbbLIiFP46ZGi1hPcUBrt2XZeEkNWffm4EKyvpH4d6uSalQf2CIo+TiI/TJPzW9VDl5AMH+mPPXk+MHFkTjRtLnnMKjY6L07zVJqtC54LOiZ5581sIIUq0bl0cdnbS8e7VqzwKF3FNsKP/G8rR9fWNECHl1A83OT9MrI+DBx4iMCgao0b1S+hXrIrXQPsm8suUDZNxuHTpEuztDQuGZjRevnwJX19fIcpNcfHiRdGKSF+ROS3IdKYSLBiG+ahERkaiQYMGOLTYGnY2PCr9qZm9UyrOwWQMJjb8+1PvAvMGed23t/Vh0o+oSMnTzDBMkv+LKBXatjmMEydOZChhp3/Oq/q6OpS69BvMV8vUuJzrYoY7HsaYPHkyVq5cKcSuKZycnPDTTz9hzJgxaV4/u1AYhmEYhmEYhmGYjw5F2jRr1ky8fvLkCUJCQtCkiXFnAy0LDw+Ho6Pp7gdvg4UuwzAMwzAMwzDMRyBJ2my6kNFjBHU6HZ4/lyp/h4WFQaPRJLxPjqWlJfr374/evXubtS0WugzDMAzDMAzDMMxHR6FQ4PHjxwahy/r3HxquuswwDMMwDMMwDMOkK6NHj8aFCymr4ifl7NmzCAgw3brvbbBHl2EYhmEYhmEY5iMgl6WvZ5G2l1lwdXUVk4+PD/z9/UVYc1Kio6NFQa9Zs2ZxMSqGYRiGYRiGYRgmc0DVlH/77TeTy6lfdvny5c1aN4cuMwzDMAzDMAzDfATkn2DKLDx69AgLFixA8+bNsWLFCpQsWVK8XrZsGYYPHy6KUe3YscNkVeZ3waHLDMMwDMMwDMMwTLpy9OhR4bHdtGmTCGEOCgrCzp07MXjwYLE8b968mDRpEtq0aQOlMu2yNTOJfoZhGIZhGIZhmEyDTJb+U2YhMDBQ9MglkUsUKFAAd+/eTVhOgvfevXs4fPiwWetnocswDMMwDMMwDMOkKzly5EBoaKgoREUUKlQIkZGR8PLyEu+dnZ1F+PL9+/fNWj8LXYZhGIZhGIZhGCZdad26tRCynTp1Em2EqOiUjY0NZsyYIQQv5erGx8cjZ86cZq2fhS7DMAzDMAzDMMxHgItRmYYE7KJFi3Djxg0sXbpUiN7vvvsOf/31FxwcHDB06FAUKVIEHTp0gDlwMSqGYRiGYRiGYRgm3aE83L59+4oQZmL69OkoWrQoTp06hdy5c2PUqFHCy2sOLHQZhmEYhmEYhmE+AqJAVDoe2cxUjEoPCVm9mKUqzAMGDBDT+8KhywzDMAzDMAzDMJ8R1apVQ6lSpbB48WJkVdijyzAMwzAMwzAM8xGQy3SQQ5eu2yMuXboEe3t7ZGXYo8swDMMwDMMwDMN8VrDQZRiGYRiGYRiGYT4rOHSZYT4x8pPXIbfQfurdyPJMrO+X5Y9BRmLG8d6feheYN4x3XcHHIoNge/XCp94FJim2Vnw8MgC6OPLbFUZGhWpDpWsxqnTcVkaHhS7DMAzDMAzDMAzzSXj06BHu3LmD8PBw6HQp85krVaqEcuXKpXm9LHQZhmEYhmEYhmE+AnJZ+uaK0vYyCzqdTrQRWrdu3Vvtpk2bxkKXYRiGYRiGYRiGyfjs379fiNzGjRtj2LBhcHV1NWpXsGBBs9bPHl2GYRiGYRiGYZiPADlY09Ojm4kcurhx4wbkcjl27twJBweHD75+rrrMMAzDMAzDMAzDpCtOTk6wtLSEra3tR1k/C12GYRiGYRiGYRgmXenUqRMUCgX+/vvvj7J+Dl1mGIZhGIZhGIb5CMhk6dxeKIPHLnt6eooqy3qGDh2KQYMG4fLly6hRowbs7e1TfKZEiRIoUqRImrfFQpdhGIZhGIZhGIb56Kxfvx6//vprivl//PGHmExVXZ48eXKat8VCl2EYhmEYhmEY5iMgh05M6UV6bssc+vbtKzy3aYE8uubAQpdhGIZhGIZhGIb56BQvXlxM6QEXo2IYhmEYhmEYhvlYObrpPGUmNBqNKEZ19uxZ8T4kJAT9+/dHpUqVMGTIEAQHB5u9bha6DMMwDMMwDMMwTLrToUMH9OzZE5cuXRLvv/76a2zcuFG0HKJ83ubNm0OnMy8cm4UuwzAMwzAMwzAMk64cO3YMe/fuxezZszFq1ChERERg165dmDBhAs6cOSM8vVeuXBF25sBCl2EYhmEYhmEY5iMgl6X/lFm4dOkSlEolRo8eDZlMJsRtfHw8unXrJpa3bt0aFhYWoiWRObDQZRiGYRiGYRiGYdIVhUIBOzs7IXaJ48ePw83NDeXKlRPvSfSq1WrExMSYtX4WugzDMAzDMAzDMB+xvVB6TkS1atVQqlQpLF68OMOeVxK0YWFhOHToELy8vERObrNmzYR3l9i2bZvIzzW3SjO3F2IYhmEYhmEYhvmMuHTpEuzt7ZGRIVFbt25dtGjRQrwngTt8+HDxeuDAgUL4Ug9dKkj1UYXuzZs3cf78+TStvEKFCmluCMwwzKclNEaHxn/GQ6sDroy1hMJIsgeNrvVap8IDP2nU8OBQS2R3MJ0UEhmnw7xjahx/pBXvi2WTY3hdBSrk4aCS1PI6UI3f/gnB+TsxsFDKUKWENUZ2cUa+7BZv/VyDb7wQGikd96RUKWGFlRNypHr7DJNRuf8kCgvXecPzaTTsbBSoXsERowbkhb2t4q2fu/soCn+s9cL9J9Hif6pGBUeM/jIf3F3e/j/FpGT2XjU2n9diUAMFRjQ1PO77b2jwv9NavAjUwcEaaFBSjm+aKuBs9/ZEQnM/xwBDlsXgwkMNfu1lhdaVU/6eJ2+Kxd6ranzX1hL9G1i+85Btv6DCxlMqeAdpxfFvXFaJ4S0tYW/N5yI10FFKz5Y/memsyGQy/Pfff/jrr79w7949tG3bFrVr1xbLXr9+LUTvTz/9JPJ0P6rQPXz4MMaNG5emlY8ZM4aFLsNkIsJidPh2uwp3fCQBa6qa+/JzGuy4mSieVBrT64yK06HeH/G465u4sgvPNVh/RYN1fSzQreLbH0YZ4LmvCnWHecEvJPFAn7sTi81HInB8YR4Uy2v8QSUmTivsjOHuxMedyfxcuBGG5l/chEqdeH05ej4E/x4OwOnNlWBjbfx3ftszEg36XEdMbOJ17MKNcOw9HoSz/1SCgx0HvKWWE/e1mLVXg8hYwD/c8Kax7JgGI9apDead8tRg4zkNTk+2RDZH44/k5n4uq6PV6vD3WTVWH1OJ+3doVMqb+IFrKizYG494NRCU7HwZY86/cZi0Kc5g3vE7Gmw+o8KpaXZwtOVzwbwf1tbWouJycg4cOPCea06D0P3iiy8S3Mqpxd3d3Zx9YhgmnbnyUotea+PxKgxQp3T+GeAVosOkvWq42gLB0e9e97rLGiFy3e2Apd0t4GAFjNimxuMAHX45qGahmwp++CtQiFwSp8vHe8DWSo4xiwNw91k8xi8NxL8zchn93As/6UGxYlErrPohu8EyO2v2pjOZnykLnwmRW76kPeaOLwy/wHh8/ZMn7j2OxtqdvhjSM7fRz01b/FyI3Dw5rPDnz8XgGxCPEVMf4vGLGPxvmw9G9s+b7t8ls9F/mQoHb2kRHGV8uVqjw9R/pWtQmwpyjGmlwFN/HYatUeOJPzBnnwZzeyo/2OeyOnUnR+GulwYRJmr2dJoTjdP31Qg1cb6MERWrw8wdksjtXkuJYS0tceelFiNXxeKulxaL9sdjUherD/QNmKzCkydPhLeWQpbp9bNnz975mSJFiqBAgQJp3laqrxRUAYsmhmE+P6LidXgRkjrb4VtViFEBc9srMWSL4Yi7MS6/lJRz36oKtCsjeVe+rK7DxL1qBESY1wA8q3HwovRkQqHKbWpJ+TZzhrqj9fjXOHw5CpExWtjbpBSuL31V4m+5wlYoU5AfRpjPD89n0lP98N65UaeKs3i95YA/9hwLEiHJxohXaXHknHTBG/9VPjSr4ypeHzkXjG0HA7DvRBAL3VTgHawzKXKJh746+IdLr0mYFskuQ51iEOJ46yUtzr5JZflQn8vqPPPTmhS5xIsAbZpELnHzheSpJ37/0hrujnLUKg78e0mFwzc1OOtJzwB8b3kXdHdOz6HljD6M/b///Q8rV66Er6+veP3rr7++8zPTpk3D5MmT07yt9xoSO336tNjRu3fvwsPDA/v378ekSZPQpUsXVKxY8X1WzTBMOlIlrxzXxknhr9tuaDDjsPFY5I1XNDj0QIvvmyhQLnfqLqU/tVBiTEMk5PDGqXU49uZBpWHRjH45/vTExWvFqDrh5pgYhql/rVID957Fo1op6xSfffnGoxsdp0XfaT54+lqFgrksMLyjM2qWsUm378AwH4syRe2EF/fc9TD0aZ8DEVFqkXtLFMln/Df+1CsGsXHSNahUEbuE+dLrANx7nEY1kEVZOchCpKYQTWapEBRpuLyAuwzXp0t5dYWyJc73C5M+Q3m3xjD3c1mdo7/YivtBRKwO9SanHOTZMsYWMfHSMaz+fZQIXX4XZfMpcH2encgvJZGrxy9Ufy44bJlJO927d0fVqlUTXpcpU+adn9G3G0o3obtixQp8/fXXsLW1hY2NDSIjpSvcrl27MHfuXCxZsgSDBg0yd/UMw6QjdlYylM4p3bDOPjM+Wu4focPYf1UokV2Gic2UCXm876KAa+LNsdv/4nHEU4uoeCCXI7CwCxd9eRdWlnJkd1GI0OUdpyLQv6UjLJTA6v1vXB6gPCvjAxMv/SWP7tbjiU+glx/EifcLv82Gr9tLHjCGyaz8OqYQbn51C2u2++LQ6WBERWsQHqlB1XIO+LJLTqOfCQqR/i8IZ8fExyAnB+l1cJhaFNzTt7dgjFMwmyixI14rjYxZ2lrJUCaP4TFcc1qDU57SvaNleeMDneZ+LqtTPJc0+GksL5conCPxuKX2p+1gI0OZfIZ57r/vjcOtF9JzQstKHEKeGmQyHWRvWv6kB+m5LXMoW7asmJK//hiYdbUIDg4WScOUs0tuZ1Ljek6ePClKRQ8bNgwvXrz4kPvKMMwnZNQOFUJjgGXdLWClNO8BkPJ7SeQSr8OB73cnPnAypvm6vZP4e/RqDPJ0eooCXZ9h+e6wdx4yvUe3WF4L7J+bC3tn50LhXBaiSMnE5UGIjecQQCZz4+2bWCTHxz9eiFxCqZCJYmzG0CSZbazgHs3TvKXAHpN2NFodftymxuDV0jWpVlEZhjZSfLTPMR8eyoUftToW49ZJ/3PNyivQtz4PVjPvR2BgIB48ePDOKSgoKP2ELjX1jY6OxsKFC1P0Z6I83rVr10KtVmPPnj1m7RTDMBmPvXe1sFAAQ7eoUHFOHPqsSxSpLf+Kx/8uvDsOak0fC+wZbIG6hSShvOmqFrdfs9h6FxP7umL6V25wsJWLVkFB4VoMauOYsDxpSHNSfvnSDddW58OJRXnRpIodmlWzw48DpHzEiGgt7r94M+rAMJmQ4DAV+o27h8AQFVo1cMPx9RWwdVFp5MtlhfPXwzFq+iOjn0vadog8wMlf21jLoTRzMI9JSVCkDi3nqkRlZhpE6FxFjv1jLWBlIfson2M+PD4hWjT8ORqLD0r3jH4NLLBjvK3R9oOM6Rzd9JwyC7///jtKliz5zmnp0qVmrV9prvqmkJ78+fMbXU5i19XVVXh7GYb5fIijfNAkbYL0PPTXIdBEWluTP+MQFA1MaKxEj8oKFPeQ8nWrzZdumFR9uazxosHMG+RyGcb3csWY7i546a8W1ZevPIjFyr3hUCqAkgUsjY6+d/nRR/wlkawvYuWaRBTLOTSTycRQO6DoGGmg7NfvCqF4IVvxmiou//zHMxw4ZdwDUDCPtUG+brXy0qDRE6+YFMuZ9yMkSofGM1W4+0onBkpndlPg2+bKj/Y55uOI3AY/ReGpnw42lsCCL6wxsPG7e+8yTGqgvrk5cuQwmEfOUtKaJ06cwIULF0Qf3U6dOsEczLpqFCtWTOSv0MapNHRyHj58KFzMuXMbL+vPMEzm4+JoS2iTaFwSvH3WS17dA0MsUC6X6TFEsl15Xo2O5eUi7JnydPUUducR4XcxbnEADl+JFm2CVk7ILrJv/tgWKpY1qGgrPL3JsVDKRB7WvefxWLQtFM2rSQVFVu6VQp5dHOQoZUQgM0xmwS5JpfGTl0OF0FWptDh7VfqN25pooeXiZIGyxexw+2EUth8KQLdWHoiI0uDom0rMdd9Ub2ben5Hr1UKs2lkB+8ZaoHYqCxCa+znmwzP4r1ghcp3tgP9+skPFghw6znw4qlevLiZTzJs3T0zfffdd+gndxo0bCzfygAEDRNVllUp62KWCVKS+x4wZA0dHR3Tu3NmsnWIYJuNRMkkhCyI2ScPdYh5yuNvLsO+uBpP3SSHMOwZaoKCbHEPqKHH6qQqnn+qQc3IcnG0g+vUSLUvJU129OStTu5yNELYkWknwarVAYJhGFKWi8GRi5Z4w/LlDEr+XlueDpYUMP/Z3RefJPjh+PQb5uzyDVqdDcLh03ib3cxVimGEyK7UrOaNkYVvRRojClOeveonIKA1CwqVr0JBe0mD7sCmeuHQzHGWL2eN/s0uKecP75MGQnzxFK6EiTS4gJlaDsAj6n5JhcA8OMfkQUJVkagdEKOTA8DWG6S153WTYM9oCbX9TwStIh+Zl5ZjdQ5nqzzEfHvLcUjGrrjUtRH/cx75aHLwuHX+6W3zxp2H/otJ55dg4SoqkYExDEd7ydCwQ9Tk9VY0aNUp09Nm9ezd69+6dPkJXqVSKDbZu3RqNGjUS8yiU2cHBQbymv1u3bhUthxiGyTqExSSGNlOYM9GlggKBUTrM/E8N3wjq2QtYKoDuleRY0JEfVlJDh7r2WD7OAz+tCoJvsJRHWDSvBRaMyIaqJaUwS/9QjRDCBAlaeixpW9sei0Zlw9Q1wQgIlT7n6ijHhN6u+KaLy8f4CTBMukF5tLv+Kocf5j3BrqOB8PKRiuTk9LDE2IH5MKSnJFhfeMeKcGa7JLm5/TrmwCu/OPy+xgu+AdL/Te7sVpj/QxGULJzYcogxn4tPtAmFv8JjIDy0SYlVSe8f+WrxxB8onVuXps8xH54Hr7QIitChVnHpBJx7kDjIEBJFk2FNDSu+hTMfmdjYWGg0GrPTYWU6ikF+j41v2bIFx44dg5+fH+zs7FCpUiXh6c2Vi0dEGeZtUAREgwYNcLjbfdhZZJyCTCHROrx+06+wdE7T44L0sPEkULIr7iETVU7DYnTwftNfr2g2GSyTeAy1Wh28w6Q+unmcZLCxzFjeRFnFvMjo0OXaO0AtvE45XA3HKQNC1aIFEVG6gKVBaxQ69s991SJ0OZ+HEgpFxjr2xphxMu0jt8zHYXzn4xn+0KrVOnj7xsLBXgk3Z8On7xevYhEZrRGhzAXzGvbWjVdpReVm+p/Kk8Mqw7cUsrh6ARkRTx8tVBrAw1EmJoI8g94hph8xrZRA0RxyIXRpYNTZVoY8rrJUfy5DYGuFjAZd7+95S88Ued3lcLJN+Zu+560RkUE5XWRwc0g8lg9eaaDWAK72MuRylSMoQguft5wLuo8nbVv0qYiKk6Px74VFVGnyIrkZ4Tmva2hlWCD9Qr5V0GCr89UMdzyM8fr1azEZg7TlsmXLRHHjffv2oVWrVmle/3tl9ltbW6Nfv35iYhjm88DFViamd2Ftkdh7V4+TjUxMpgoq5RNOxIz9IJmRoYfwvB7Gh9CzOSvFZOrYF8rFQ+/M5+3dLZDHUMTqyZ/bdHEpSws5CiUTv0zaKW5kUNTZTiamd5FctKb2cwxMXu+T975NTqk8xpeXyG04n0SwmxSsyTAfhSVLluDXX399Z8EqamlrDqkWuleuXBEjA2mhatWqqF+/vjn7xTAMwzAMwzAMk6mhQJH09HvLMnnVZT02NjaiJlTNmjXNjrZJtdAlkTtu3LhUr9jKykpUyGKhyzAMwzAMwzAMw6Sl6vL7kmqh+80332DQoEEJ76dMmYKNGzdixowZaN68ObJnzy4ShQ8ePIiff/4ZQ4YMETYMwzAMwzAMwzAMk9ocXT0KhQKurq6i/pOFhcXHEbrkoaWJuHHjBv744w8cOnQIzZo1S7DJnz8/vv76a1SuXFmELVM8dY0aNdK0QwzDMAzDMAzDMJ8DMpkOsnRsL5Se20qPHF091Lq2W7dumDNnDlxcUtc5wqxiVOfPnxex0lRJzBhVqlSBu7s7/vvvPxa6DMMwDMMwDMMwjAFffPEFAgMDsWrVKtEnt2HDhsiWLZuIEt65cyf27t2LESNGoGLFisLRumLFCty/fx+nT59OVd6uWUKXkoOpzcXNmzeF5zY5AQEBCA0NFa5mhmEYhmEYhmGYrAgVokrPYlSfvuFT6lEqlVi9erUQusm7+Hz55ZeYPXs2pk2bhu+//168r1evHrp27SqELr3+KMeCcnJJ7NIOXbp0yWDZ3bt30aVLFyGE27dvb87qGYZhGIZhGIZhmM+Yf//9V4hd8uYa49tvv0VsbKzw7BIdO3YU9uRsTQ1mCd2cOXNi3bp1ePHihaiURWWhyaWcN29elClTBmfOnMHvv/8uXjMMwzAMwzAMw2TZHN10nohq1aqhVKlSWLx4MTIq0dHRUKvViIuLM7qclhERERHiLzlSidS2GzLbu01eW09PT/zwww8oXLgwIiMj4ebmhoEDBwovL8VTMwzDMAzDMAzDMOnLpUuXcO/ePQwfPjzDHvqmTZtCpVJhzJgx4m9SNBoNfvzxR/FX34JozZo1QvyWK1cuVes3K0dXD3lwqb0QwzAMwzAMwzAMw6QWKmA8btw4zJ07V4Qx161bVxSjolpPVPz42bNnIhWW5i9atAgjR44Ur1OTn/ve+cq0Az169EDRokVFb6Py5cuLUQMaPWAYhmEYhmEYhsnKyD/BlJmYM2cOtm3bhmLFimH37t2i5dCmTZtEUeMpU6Zgy5YtCamz9P7AgQOpXrfZHt0NGzZgwIAB4nXp0qVFnu6rV6/Ezi1fvhzLli0T1bEYhmEYhmEYhmEYxhidO3cWE+XgUt6utbV1iu49lDZLU1owS+gGBwdj6NChwnW8efNmIXL1kDeX3MrDhg1DkyZNkC9fPnM2wXxkQkJCROK3s7Oz+DElJyoqSiR+29raigbN77MdCwsL2NvbIz2gfaZ9NwU1mLaysvog26K+X3R8aGIYhmEYhmGY5MhlOsghFVFKD9JzWx+K+Ph4eHt7i+d4fcGppJA3N3v27Gler1ne7f/++08Un1q5cqWByCWoutc///wjEof37NljzuqZdKBnz57iRzNx4kSTDZxp+fjx499rO9Travr06UgvfvnlF7HfpqbDhw+bve7w8HAh3PXUqFFDRDAwDMMwDMMwDJN2SCdQQWMqblyhQgXRySf5tGLFCjPWbKZH19/fH3K5XOyQMWhnyXP2+vVrs3aKSR/oHFJM/Pz58w3KdFPIwP79+8XyzAhFEVA/Z2NQ/2dzIdH/+PFjHDly5D32jmEYhmEYhskq0BO2LJ23l1k4deqUqKycP39+jB49WhSiMgY5l9JN6FK1Za1Wi9u3b6Ns2bIplpPADQgIEIKXybjUqVMHp0+fxsWLFw1+QHqRS8XFTIUX0GQqHJlChymu3lhINEHefgpNoLDppAQFBQkhSqHAfn5+IkSBwoPt7OzEfFov7de7xCqJ9tSGSlOJclqvk5PTW/eFvktMTIz43vp9S8t3ZhiGYRiGYRgmkbNnz4rndtIjpC8/NGa57Jo1ayYe9Cn89cyZMwbLyONF4apKpRJt27b9UPvJfAQolLdmzZrYunWrwXzy8rZr1w6WlpYpQnf79OkDBwcHMRUpUkSEqScd4GjcuLEQmSROe/fuLYShHuqPNWrUKCEqyeNPlbqpVLie+vXri3ZVtWvXRp48ecQ8EuALFixA69atxTZp3d27d09oIG0ulGdO5cpJmJLgdnd3x7Rp00zuy9dff41169aJf8TcuXMn2JHobdSokdgvmvr16yeEPMMwDMMwDMMwpqG6OeRU+hgi12yhSyKGSj1TlWUqSEV5utQHiYRP8eLFceHCBSFOqO0Qk3EhQdatWzchbPWQ13Lfvn1isCK5YBs8eDCuXLmCEydOiPD1CRMmCDG7fft2sZyqcPv6+orzT32v6IdLwlDPN998I3JkKcfbx8dHlBP//vvvDbb/22+/YeDAgSIhXQ8J0I4dO4p1U7Vv+u3t2LHD5PeiaAPyBCefqCdX0jBkLy8vXL9+XUQf/Prrr/jpp59Ec21j+7J27VrxukGDBggLC0uwWbx4sRjwoe9DpdBp/6gPmDGo+BcNFugn8mozDMMwDMMwn3kxqnSeMgtt2rQRz8fJHacfCrOTMKlRL1VYJnFQokSJhPBPKmJEobAjRoz4sHvKfHCoqhkJWhJ8dM4I6k1FIQQtWrQwqHpGnnry3v7111/CC0wx9F999RU6deokvLJ37twRIpaWV69eXeTJkgjUFysjoUnFy6hYFPXJIo8/bYM+T/P1kJeV2lIlDQ0mMT5o0CB4eHgIUUkDKvfv3zf5vej70P4ln0ik6mnatKlIbKfQe/LmkmCn7/3y5Uuj+0IjTrTPFJ5MAz1J942OA31Pel2wYEFxLIwxc+ZM8T+in/Re6/QiKk6HrzaroNYkntewGB0G/62CRqvDfw80+Oda4uDG/nsajNyuwvhdKlzz0qZ6O4tOqREYmfIi6x2qw/RDxj3x4bE6zDysxvCtKvx7K+t5xP/YGoIzt2IM5s3bHIxzt2MQHavFiAX+CfNf+Krw48pADP/ND+sOhhucT1PsOhMp7P/cHgqt1rj9938FYMyf0vS/fYmDOQzzKRnxy0MEhqgS3tN9achPnggOU+Ha3Qgs2Zg4KHr+ehjGzX6MsbMe4/iFxMKBplCrdVi66ZXYxoZdvibt1u70wfApD/HvkQBkNTac1WDnFcNr8prTGuy6Ks0bukaFOJV0TQmK1GH6LjWG/E+FJUc0iIlP3cO2V5AOK04YbuPKMy2+WafCxC1qvAoxvp7U2HxuTN4Ui/vehsfq+w2x8HytwctALab8E5sw/85LDSasj8U3K2Ow82Li/9C7OHxTjTP3De/VvqFaLDmYGKGXnBvPNRi5KkbsC+0Hw6QWeqZfuHCh0CPr16/Ho0ePhIMp+WSuc0j+vqGvJFzIw0fC4+rVq0K0kHeXyRxQ+DCF5+rDl/Vhy8lb8JB4o/xYsk0K9VAm7+2tW7eEUKxWrVrCMgp9LlOmjHhNy8lDTD/kpOKTPKAvXrxI+AyJ4OTQNpJCQpM8z6ag8Aeqjpx8SupdplBoGj0i4Uzh0RR9kLycubF9SU7JkiUN3lNer6l9++GHH4Q3WD8l9VqnB3ZWMtz20eLcs8Tv+d8DLV6H6aCQy/DAT4dr3tINaut1Df48pUHn8nJUzy9Hn/UqXH7x9ptXaIwOO29qMOWAGpFGHnC+2abCofvGRWz/DSrIZRDb+/U/NU4/yVo3SqVChjUHEsWlSq3DnE0hKJDTAvFqHbafkC7wYZEatP3+NcoUskLXhg44dCkK4xa//eF777lILPgnRNjffxGHX9cHp7AJidDgwt1YNKtmK6YKxT5MCy6GeV+CQlU4eCoo4f3l2xG4cD0Mrk4W8PaNw5mr0v/NxRthQuA2r+uKxrVc8MP8p+8UpnNXvsQtz0j0aO2BTXv8jIrdjbt98c8+f7Rv4o7fVnvh8NmU/z+fM862wOIjhtft+fs1yO4klbvZelELlQZisLTlXBVyOMnQq6YCD3116LXk3SlGt720mPCPGucfJV7z/cJ06P6nCo1KyZHfXYZWc6XB2KSkxuZzJE4NbDuvNhCgq47Gozx9zCwAAQAASURBVKCHHMGROhy4Li175q9F799jUKekAu2rWWDpoXgs2Bv31nXTvYYE7rh1sXjil3g+7nppMH5dHM4+MH4+abud50SjXikliuaUo8W0aHEPYxKRyXTpPmUWfvrpJ9GSlqI2Kf2Pnr3pOT759Mcff6RfMSqCRAEJBapuS95cYz2PqlatKnIdmYwNeSKp8jKV9967d68Qn8bCgY1Bgo08rSRy6TeQ3C656KOkc2pBlZSkDaGTVn/Wk9bqz2SfvNBVUihXuFatWkKIU+QB/VORZzd5v2Bj+5Kc5M2s3wYNHiQdQPgUVa07llNg710N6hWRJ3htO5VPuR977mgwuqEC9YtI349EqKe/DlXzQ3h3Tz7R4rsGhpePE4+02HhVI2yTQ55iZxsgMNL4frnaAhOaSOtrUlyLxwFa1C2cOat+m0OHuvaYtSFYeFvlchlO34xBqQKWyOWuRGhk4kPmpfuxqFDECt0bOYj3VUtYY8g8v4Tlw+b7Yc6wbLC3STx2O09FYmJfVzSoaIvKxa1R9auX+LG/YaHAl35qlC9ihebVEqMVGCYj0KGJO/49Eog+7aXooAMng9ChacqqnPtOBmFg11xoUstVvHd2UAoPL+HlE4sV/7zG1FGFDD5z/V4EvumbB3WqOKN9kyjce5yyB/vaHb6YNa4wKpV2QGycFpv3+KFpbWkbWYGmZeQYuFKNkCgdXOxkeOKvE9FB1QsbXuhpvrUFMKiBdM+oW1yGbn+qE65pP25T48v6ChTMZvi5Gbs1eBGkQ4mcifOP3NWiW3UFOlaR1rX6lAaePjqUyp02m8+RjtWVGLU6Fj92lZ4l9l9To2VFC1gqDb/3kVtqtKuqRNsqFuI9CWESu0R0nA6j/heL5UMMC3s+99diwZ74FJ74WTvj8Nxfh3zuxo/t8Tu0LQt0qSlta90JFW6/1KJSodQ/HzFZl6ZNm6aqkGvSqMyPLnSpEBAVmjp48OBb7caMGcNCNxPQpUsXUSRq6tSpQtw1b948hQ0JQRKxx44dS1hOwvbQoUPix6ev0Eytd/RFyChc+caNG6K6c7ly5UToL+X4kshMGh5MwpfCetMLij6giuGUS0xh1vp5WYGO5eTouEqFOe2lEfijD7WY2166OSWlXC45xlEIWm0dGhWTo2P5xBtWIXcZHK1TitAO5RRiqjrPcNQ4KEqHv86qsaKHBb7YaDx8alUvSzz012L2ETWue+swoYlhIbTPndzZlCiYywIX78WiZhkb7DsfhU71U1YOL5rXEv9djsKkFYFoUc0O1UtZY/2POROWD27vDGtLw4cRGys5QiOlASi/EDWe+6rE/27SgRwvfxUiY7SYtjYI2V0UGNDSCZYWn/cDI5M5aNXADeNmP0FcvBZWlnLsPxmE5dNLpLArV9wek357isgoNRrVdEGNCo6oWVGqpu/uYoEebQwr5RMj+ubBdzMeoXp5J5y5Gopti6QIpKSQ+C1dVBoAKl7QFo9fmo4m+hyxspChWVk5Dt7SomdNBfZe16BDZUWKgeBczjI89ddh5HoV2lZUoHZRGbZ+k3hv6VlTjuyGY8mCzcMtRBj0rmtaA3Hd5E0gFwlsCm12szfcXmpsPkdqFlMgIFyHV8Fa5HaVY98VNfo1SHkPL5NPjp//joONpQxNyytRqZAc8/pLYsLKAhjaPOU9tlguBbaPt8XoNYnhz8T6kbY4dEONdSeMhy7XL6VAreLSM0J4tA5P/bRwd/z8z0VaoCem9By6z0xugvr1639UrWiW0CWBSxPlJlIOoyklTp4+JuNDIegkRufOnYsePXqkCFsmKLSX8lip4BQVh6KcVH3YMRV2ov5XVA2ZcmmpsBOd+9mzZyf8NigPlryn1CuLbpAUk0/h0iSMqSjVh+RtSe20XQqBoPBnKphG+/vkyRMsXbpUVE2m8HtjQp+g4lrPnz8XIdBUhC0zUsxDDisFeWe1wrtaKocc7kYeDsibm9tZJjy70w6p4Worw98DLIS9s41MTKll3C4VfmlpAet3CCc3OxlalFTg5is1dt3WoH81swNOMiUd69lj7/koIXQPXIzCofmJ1b31FMhhgYPz82D9wXB8u9AfL3zVmPKFK77p4iKWk7c3OV+1dULPKT44di0a957Hw8ku5S0wIEQDnyANWlS3E+HQhy5FY/v0XB/pmzJM6nGwU6JqOQecuhyKkoXtEB2jRfkSKQeBurTwgI21HP8eDsSfG16JeStnlEC9qs6wsVagVJGU0QpHzgYL8dqklgsePY/GyUuhKFrA1sCGQjBJYBO2NnLExWWttAqiY2U5dlyRhO7+m1r82CHltdneWoaTky2x+qQGP21X494rHYY2VmBWd8m2VO7UP3p7vBFJ119oMWCZ5AnWh0qnxeZzhJ6fyFO776oa/RtY4NJjDTaOStlysWYxJf4ZbYNNZ1TovyhGhBcvGmiNrrUsRKpSxYIfztvq7ihPyAnutygG3WtbIJ97ZpJaTHpDxWEp55aex+l10mKxpqBuLclbgaYGs54kKSeT/tkoeZj7hmZOXF1dDYoq9e3bF56eniJnVQ+J06Q/KireRDnZFOZMIcnkxaVGzyRyidWrV2PixInCO0utgCjm/tq1a+K1voox/agpj5t+4OTlpc9TpW799pL3v6U83qT7qbfTrzM5FH5M3iryUhuDRDXlAOzcuRM///yzqCRNIfabN28WwnvVqlVi8MbYvvTv3x9Hjx4VyylZPq37llHoVF6BvXe0CI7WGQ1bJg57atGjkhw9KytE6Nm8Y1Lu7ZYv0uZpfRKoxbGHWrjYaBAVD7wM0WH+MTXGNFIa5Paee6ZFq1IKdK2ogK0lsOEKCV1kKTrVs0f7H16jb3NHuDookNcj5Sj93WdxcHWQY/4IKXTz/ot41Bvuhb4tHOFsb/zBhUKSzyzJiwcv4lEkjwXqDPNK4Y35orWTmIjO9e2Rp9MzRMVoYZckBJphPhUdm2bDvuNBeP4qVoQyG+P05VA0rO6C1g2k5X/v9cOEuU9wfktlk+tds8MXd/ZXg6O9EoXz22Dk1IcY1M1wgMfBToHoGA1sbRQIi1DDzSXl/+XnTotycny3US3E0hM/nfDWJudZgE4MCvzaVbq2U3GoBr/Go1ctOcrlTft1ZOUJDab+q8acHkr0qKEw2+ZzpHMNC8zbFY/82eSoV0qRIoqHuPxYg5J55Fg6WBLBlHvbZV6MELofg/Un4zFxYxxm9LZC3/pZKyKLSTvz5s0TWoDycuk1OcjeBTnZJk+enD5CV1+4h3qI6kUOk7mgVjhJIc8mTUlJHppOhZZmzZolJlMez99//11MxqCH69GjR4vJGFTULDnnz59PMY+8wG9LaqcpNTkBNJn6rLF9IWF/8+bNt+6bsc9lxPBlqqYcFgPsG2L8hkSi1snGAjUKyER+VYU8Mlz1Svu2sjvIsKy7dGMNjYEQtLUKpnzoGbVdhRYl5GJbgVE65MqCYU/5c1jAxkqGxdtDjYYtEydvxODO0zgsGSOFYRbPawEHWznedrRmbwxGoVwWohjVnrORqFU25ej/b/+EoFYZa9QobYOoWB2UCgp5znrngMmYtG7ghlnLXuClTywmDStg1Gbp5lfo0CQburWSIskop/Zdv2ASseGRGiF0Y2I0cHFKKQKqlnPEhRvhIhz64s1w8T6rYWslQ40icvy0Q41WFeRG61fce6UVlZb3jZXuKbldZMLDas5V5JaXFr8d0OD8z5ZiPebafK7UKaHAl4s12HpOhU41jAtXWubmIMOEjlKUT4WCClh8pLEAqvg8fVsczvxqJ8Q3kxLR8gfpVyAqPbdlDvT8TY6hDJujSzvVsGFD4dkiRU4tVRiGyRyUzilHUBTgYS8TQtQYv7RSikrIrUvLQYUsD93X4n+9pRsq5dLefKUT3td3YW8lQ/OSkp1vuA5/ngJqvhG6Rzw1cLCSoXoBOVqUUqDNchXK5pJh/z0tdn2V9bwm+vDln1cF4f5G4w/z/Vo4otG33ugxxQeFc1vg1I0YdGvkAKc33lwSrEM7OIm8XD1Nq9iizzRfXL4fK9oM/TtTCon2C1Zjz9koDGrrJEKeB8zwE9unQljje7mKQQeGyQiQAC2UzwYPn0WjcmnjETMTBudHj1F3cfpKKOxsFKJS85RvpWcTv8B47D0eKIpVJWXcV/nQdeQdNK3lgoOngjHvhyJi/uXb4QgNV4uiU1SsiloLtajnir3Hg3BglVSPIqvRqYoc/Zap8d94C5Ne36VHNWg1Lx4V88tx9bkWBdxlKP2mONSqkxq0Ki9HTud3X1d2X9Mivzuw9nRiIb6B9RWiAvSiwxqMbaU0aZMVwpfp2tysvBJ/n1Vh4UDjAuHbNpZoMiUanq+18HCS4b+b6gTRS+2gFu2Px9j25lfXpxofc/+Nx/edrLDnslqEKm88lViDo39DC5FDzDDvysvNkDm6VGmWvHJUSKhQoUIiVJO8ecmh0NXUeNcYhklf/upuAZtkzyvNSsgR8+Y+1ayEAqe/lePySy2UcmBaKyUcrKUHCFtLGTwcTI8WTm2lRDa7lA8bVHX5xxaJlxzK+7V541Be2NlCeHtDonX4vokSLraf/8OKMQa0dESR3BYiF1ePnbUcf46WvFRUTfn80rw4dycWgWEafNGK7BO98nk9lCmqXlcqbo3983LjxqM4jOrmIio5E1RsKqe7JJAbVbYVOcHXH8Whb3MHlCrA7YWYjMWM0YWE9zUp5LV1c5Z+z5S3e2FrZeF1pd7SYwflE0WoEn7rHil/0wM65UTtSk64/yRahCznyyWJBkc7JfROy1qVnLDtzzK47RmJ0V/mRTbXrBmW2aaiHP/7SimqKSdl6RdKUW2Z8j73jrHExSdavArWoUcNJcomCVnO5QxYmXjirFxQLsSYHip+lbw6s6VSqv6fz032VpusAonUDtUshLddT353OX7pLv3OSWRem2eH854aRMToMKKlJfK4SedDHMe3eF5717MQFbaTUi6/HENbJP72aWl+D2kdDcsqkTOZV93Uuc6q0NFJz6eazPQENXv2bGzcuFG0ITUFFa0dN24cvvjiizSvX6Yz1hcoFTm6JUqUgIWFhahaayonsUOHDqJ4EcMwKYmMjBShGIe73YedRdYrcJLRkFXM+6l3gUnCjJO9+XhkEMZ3Pv6pd4F5g8XVC3wsMhK2PCiYEYiKk6Px74VF+ljy+ioZ4Tnv65iysET65ZHHQ4NlNrcz3PHQQ9Jz2bJl4vXu3btF61ESvMYIDg4WublUH+i7775L87bMGnOhHMn4+HicO3cOlSubLvTAMMyn5Z6vFm2WxcPOivocA47WwI/NlWhZyvCCe/apFoc9NZjS0tDN22hRHI5982Fv5GsuqrHghAZutsDibhYomV1ulk1mpMUYb7z0V4sRdQo/a1HNFjO/dodCYTj+2mSUN478nsdg3sJtIciTTYlO9T9csbNH3vEYMtcf/qFqfNPZGYPbOZtlwzDvQ3ikGhXaXYajnUJklllbyTG8dx706yj1ztXz3DsGM5e9xLJpUgFDPX3G3MPc7wsjZ7YPd62iNkZT/ngGS0s55owvLDy75thkRgauVOHsQ4rmka5L1YvIsLCvUtxHktJ7iQq/9VYahAvvuKyBV7AO3zb/cC49aqdDvXwf+5GnWI6fOirNssmMHL2lxldLY4Tnlu7hbo4yzOlrhRrFDL/f9gsq+IbqMDyJ15Vo8FMUTkz9sP3R5/wbh7XHVcjvIcOKoTZGQ5RTY5OVkKVzjq4sg+foajQaDB061GBe8vdJcXNzQ6tWrczalllXAhodoGIE5EpmGCbjotIA+V1lOP5GrF7z0qLt8ni8mmYodGsXkospOQFRH3Z/HgdoMfeYBqdGWuK6txZD/lHh5EirNNtkVijcePesXCLcOCZOKwQttfJpVdPwQSS5yCUiY3SIif+wN6+v5/phZBcXNKpkg7rDvVGvgi1K5LNMsw3DvA9arRROeWOPVGrdyycW1TpfQfsm7nBySHxMKZDHJoXIJULCVNAYRjW/F1Rd+btfH+HgqvKIjtWi28g7uL2vmkHeempsMiuhUcDi/hZoXFouwsB7LFZj7WkthjUxvG9sHJYyX5fSX6IMW6m/N+P+VqNxaRn+GaFE+wUqHL6jFX1002qTGYlV6VCzuAIbR0npgXuuqDBiZSyuzLFPUYnZGIHhH/aecfqeGv9eUuHSbDtsPa/C2DWx2DzaNs02TNZGqVTi+vXr4vXixYtFJxRThWYtLS1Fmqy5XX7MErrt2rVDnjx5sGbNmrcqcIZhMhZUzMPxTa7t+WdarLmowfMQHZqXkME3HJjT3gIzD6vxzzUNquSTQZ3k4XH2ETU2XdWgTiE5Hvnr8N9wSexsuqLBn6fVsFZSESsL1C0sPVx0XR0vilpR7109VGiqawW56JnbpLgCw7aoEBipM+jlmxqbzwFqCUFFoxzf9LUl0Vu7nA1CwjW4cC8Wl5bnw8V7MRi9KEAsL5DTAvlzSJdsKiw18g9/WFnIRPugxpVt0a6OPZ75qPDtH/54HahGh7r2mNhXKiq1/lA4vPzV4r2ekAgNXvpJdgRVej5wIcpAxKbGhmE+Ru9cKihFubXevrEYN/uJ8GZVK+eAa3cjsWF+Kaze5oO/Nr1CvtzWiIhOvFCt3emDP9e9QrGCNohX6bDkl2Iir/bIuWBMX/xcFNf7bkBedGwmtegisdq6oRua1Er836B+unUqO6FgXqlCee7sVrhxP1LkBKfF5nNAqZDB3hpwfFOsvesiFSoXkOHyU50YSF32pRKRsToM+Z8aIdFAlYIyUYSKIO/q16tViIgFGpWSI6+bDMObKBAYocPI9Wrcf61Dg5JyzOwqtcg5dk+L7Zc1QmQnDXEk0bp0gCVsLGXoVUuBvdc1BiI2NTafC862Mji+6WO/44IKp+9rcOWJBu2qKKGQS0Woxq+LE8WnWlZUGhSP+n5DHA5eV6NjdSUuPtLg0I/SAOsf++Kw/qQK7g4yzOlnjXL5pQGNOpOi8O8Em4Q+ucSeK2oMaGgJO2sZete1wIT1ceL4J63EnRobhqlQoYI4CNTyk7qa6N9/aMwSut7e3vjqq68watQonDlzBjVq1DCap1umTBlUqVLlQ+wnwzBmcuOVDhXnxIkHvBfBOsxqJ/3bx2t0OHBfg3PfWeGuj1ZUU6bw5ROPtDj/nSVOPNZi81WpOtXRhxqcfKzFuVGWOPpIi/WXpfl3Xmux/aYGx0ZYIjAKaLYkHtfGWcLaQoatX6YUQ8+CdCiXK/Fml9tZBp9wQxGbGpvMTLvvX8NSKUNwuEaI15qlpVHKl34q9PRwwC9fuqFg12fCk/LFTD9s/jmnKDJVb4SX8PzS/EGz/bBucg7kzqYUfXT1LYOGzPXD7996oGgeC2Gz9UQkujdyEL15k/PcRyXWq4de338en2YbhvkQ+AerUKXjZSFoX/nFYWDXnLCxVkCjUeHQ6WCc2lQRFko5jp4LEdWXl2z0xn9rKsDLJw71el4T63j0PBpLNrzCf2vK45l3LBr0uQ6NFvAPisfcFS/x79Jy4oG72YCbqFbeUYjTBZOKpvzf8I5JKExF5Mlhhdf+cQYiNjU2mZkh/1OJUOXwGJ0o8rSor3QdoF66MfEybBupRJ1pKnF8v1ypxuR2StQrIUPr+aoEofvVKhV+aKtEnWIytJynguubQb1v1qmF4K1VVIafd2iw8D8NxrdRCjFMU1JCoqQiUyRgibyuMuy7gTTbZGYO3VCjwphIcayf+mnx92jpek8RPkduqXFqmh12XFThVZAW606o4BeqxbW5dlhzQoXYi5JHd8MplQjvvjLHDquOqvDCX5Ww7nteWlyYaYeHPlr0/C0GN3+TBjapXVBynvlr0aSc9FuwUMrgYCMdf1f7tNlkxdDl9Awnzuihy0mpW7eumJ4+fSoGQ6iTj1arFTm8t2/fRrVq1dC/f3+zB0rMErp79uxJqKZM/ViT92TVM2bMGBa6DPOJqZBbhsPDLIXQpbZA3dfEo/WbHN0GReTI4yzDXR/J9r8HWnxRXSEeGCiPN7eTdDM84inNpwefdmUUyOGgSvC+XvXSouaC+IQWQo8CdKJNkDFoH6yUicuoorM5NpmZnb/mEq2BAkI1GLUwAAu2hGBsT1fxgJ9UkN57Hi+qL5PHltD31n3oFY982ZUJ8zvUk+b7BKmFJ7jnFOlkhkdpkc1JIYSuMcRxtkhynJPlCafWhmE+BB6uFriwVRoYf/wyGt1G3kX7JtmQ3c1SVFQuU8wenk+jxXLyzlK/XFcnCzFVKSv9xo9dCBHzqR0RTRVLSf8bJy6GisrKTfpLoXI+/vG4fi9CCF1jiN+9pfzd/xvvsMnMLBlggYYlZaLf+rR/Nfhhqxp/9pM8rb1rJ/bSjYjVISgCaF5OOha9ayngF6ZDdJwOgRFSdWSiV00FwqJ1YqDh4G2t8OYS0fE6lM9n+iIv3Q+QinvG220yM9RKaN1IGxFhdeaBBoOWxKBhGekLt66shEuSQeD/bqgxrIWlEJhfNrLA/F1SHPmRm2oMaW4prudfNbHAnweke/beKyohlquMl/KUHvtqxWCGq4mBZUozsLJ4x/lIhQ3DJGXKlCmYNm2aKEg1duxYfP/995g7d64IW166dKkIa96wYQPSTej27t0bderUeaddrlyGPesYhvk06B/CquaXoUo+OW680orCVMnbMUTHGz4wUMsIIire8MalflMkOlqlw5hGSgytLQnn4GjA7S11L3I4yuAbkTjS6B8J5HSUpdkmM6NQSOcjp5sSfZo5YN2h8IRlFKqph3J4k77Xn8PoWB2s3nguCI3mzQNjrBbF81ni3FKpenRM3NtHdLO7KuAbnBjy6R+iRq5syjTbMMyHQvlmgKtEITu0rOeKK7fD0bqBu8H/AREdozUqMmk+FbLSo1br/zc06NHaAzPGFBbvwyLVcLAzXQGVxPWlW+EG3uZcydoTpcYmM0OXfjqubvbAl/XlotCTHsskoj42nq5bKUUN5ekau2eQWKN2RFemSgvj1UBc4qpT4GoHhEZL4ckkrv0jdKJVUVptMjOyN+dCqYDwlFIrn4evpQNq7B6uPx/6oof680Hh4QSFnMuS2FO4cvuq0ooorNyUyCWyO8tE0auk26OUqLTaZDWoEFV6FqNKz229L+S1nTp1Knr27Ik+ffpApVJh+fLl6NWrF9avXy+qLY8fP160ta1UqVKa12/WOEvOnDlFuPK7pnz58pmzeoZhPhIU6nTrtQ5Fk/Uf1FM5rwz77mkSwpK93tysSnjIcPqJNqFCM4UpS/Zy4QWm3KCQGKD+wnjhmTRFk2Jy7L+rEQ8kFC5tZ4kUIcmpsflcIA9ssbzG810pD/b2kzgR4kxi9r9LkjerUC4LMZ+EcFy8FgcvSvPz57BAQIhG5OfSQ9Gw+X44fk1aZow82SwSqirT+vecjRK5vmm1YZgPDf3WrtyJQNECxn9rFB588FQwtFod/ALjce1uhJhfvJAtTl8JE6+fvIzBvcdRCfbk1aVrSrxKi6b9byA03LS6alDDWXiHydY3MF6si7zKabX5XLjwWIfiOYxfg90dgKhYncjHJQ7clO4T2RxlwjNI3l067nuuS/cV8jTmcZXhyjOduE7N2KPB+rOmK4mRUKtaSI4T96X177ySsshUamw+F+iYvgzUIb+pe3ghOfZdlX7b/93UIP7NYE+J3HKcuqdOyKGlnF2iSmGFCF+mc0Fh0W1mmL5nEE3LK4UXmBCe4MKKFAXYUmPDpA/VqlUThYOp4FNGRV+Eijy3OXLkwKVLlxAWFobBgwdDLpfjm2++EX/1xavSCg/NM8xnzh0fHZoulsKXfMKBwbUUKJFdDr+IlA8XvasosOu2FpXmxImQ5sJvcq0GVFegwwoVqs6LQ3YHmfC6Em1Ky7H3jhYV58QjVg3MbqdM8K5QteTvGipQ/E1TeaJqfjlK5pCjwpx4MZK/upckpCLjdOi5VoU9gy1N2nwu9J/uK4pQRURr4e6kwMafDVuo6HGyV2ByP1dU/eolcrgqkN1Vuly7OiowvJMzKn35Uni6yOtK4Wh03Bd9lw3NRr8SI/cVi1qhbW3Jvb79ZAReB6jxTRcXg23MG+6OthNeQy6HKDhVrrDkkfppVSCaVLYVFZZN2TDMhyQwRIXmX0iJlQHBKjSp5SKml69TlvBtVNMFWw/6o2L7y8jhbomSRaTfOXmBN+32E7m+9rYKkUNL/xvlitujbWN3VO54RQzEjeibGx5u0gDTlIXP0LimC+pWTXQBUpuiPu1zoGqnK1CpdJgxtlBCC7BOw2/jf7NKvtXmc4CqGLvYSt44GsjcbKTCMkEeVGo91HxOvGgzlN8t8RjM7qFE3enxsLUkcZvoafyznxIDV6iFxze7E7BjpLTgzEOtEMq/djV8NJ3eRYFeS9TCW1w+vwytK0j3lMVHNMjmAHSrrjBp8zlw/I4GjadEid+uT4hOtBdyczD+/b5pZYX2s6NRdUIkiuWUJ4Q1f9vaEu1mRWPjKRWK5JAnpKRQeHPPBWpUGheJmDhgxbDEvPNOc6KxcpiNgYe3XVUlNp1WoeLYSKjUwLZxNgl5ub9sicOaETYmbbIy5JVPz6uDfluXLl3KkH10kxITEwMnJyc4OkqpWydPnoStrS1q1aqVUKFZoVAgKCjIrPXLdDTUxjDMJ2skfrjbfdhZvInr+sBQ4SK995UutNnsEkOZaKSXWkE42cjEaxKq+orM5PmlPN2gKJ2ofkzeXfLYkseXwpN7ronH6VGJgofyrCzfhOTqoc86WRvPXaO8LlvLxNBougwFRAIeDjKTNh8bWUUp5PdjEhSmgepNqLGjrRy21okPKwGhamRzVqZ4TV5b8oLExuuE2LRQyLB6Xzj6tnAQrztMfI2ZQ9xRpqB0PsjLRWHLdjaJ66awZipk4mCb8uFIpSYvl6F9aKRGnH99eKgxm4/NjJO9021bzNsZ3/n4Rz1E9P/vFyR5gOi/3c3ZIiGMmby7FGpMubj0mnruUv4tEROrEQWryDvraK+At28c7jyKQoNqzuLa16jvdVzeUSUhn5T+l+ilpUWS33q4GjbWcoNQaD3RMRqxH0ntqbBVNleLhHUas/mYWFy98NG3ERqlQ6x0OmBnBTi8qfKr9yg62UrXZXpNIal0T6HjTdB1hsKUqZ7DqpMadK8uF+kw325QC/HZukJiyHhEjM5g3XEqKnQFONulvOaTBzI6znBf6PMkwqnHrCmbj47txx34o2MSEiUdW7oVkqdc/9ujewIda3trmbhna968NriHR2iFKD7/UC1qX5Bn9563FjO3x2H7+MSICfLK21gmPh8Q/mFaUYnZmDc2PFonqnHrl9H5p99N0grNyW0+JlFxcjT+vTBOnDiRoYSd/jlvRHwpWMF0usSHJg4a/Gl5L8MdD2P8888/Imz58ePHoqMPhSfnzZsXBw4cEMtPnTqF+vXri3pQZJdW2KPLMJ8xJDJzpCy4K6DKv/r8nqSvCX31ShK5BInhARvjRW4VCd3FyUbcacQ+OfrPGsPhzc1YD924PRzebvM54OZk+kanF7bJX+sfwG2THI8bj2Ox+OtQ8XDRrKpdgsgl6KHCLtmDXlJBnRwS0TQlxdle8U4bhvlQ0P8/eWaNQV5SErn613qRS5DIFb9XR2XC33krX2LmXy8QHKrCTyMKGFTqNCZm9Z81hq1Nyv9XvSf4bTaZHWNCU09S717S1/oBTcoj1V+N/MN0qDFFBaUSKJFDhhZvClbpSS5IycuYNK83KSSsqXrv2z5vzCazQ8ckh7Px86HPuU16z07+Xu/5pYGHXgti4GhLPdmB9d8aHihqBZQcDyfT9w1HW0N7Ov/uyWppJLdhGGO0b99eCFwKsyav7rNnzzB58mSx7M8//8TMmTPh4uKCli1bwhxY6DIM807yushwZZwVQmkE3ir9vKyMcRaPzi68tO8SsQyTlXC0V+LY+ooIi1CL/wuLdPKyMsb5oZ0So1vqRApKunpZmRRQb9zbC+wQGkUDGdLgEpN+yGVcjMoU1tbWwmtLlZbv3bsnRG6PHj3Est9++03Ue1qyZAmcnc2rMMdCl2GyKNRTl7y4SSsa3/fVokR2mQh3psqMuZxkeB1GYcVS6BRZZneUIXsqWkVS3i31vy3inhhqZY5NVoDydamPbukknlnfYLUITXa2l+P+y3jhtY2N1+LBS1WSqDmZ6Jn7rmNH4cxPfVTI4aqEvYnw49TYMEx64xMQJ3Jgk/aspcJP1Lc2Nk4rQo/z57YWOb7Uf5egfwfyApPNu6B1ePnEomAem4RwaXNssgIURvvAR4dyeROv1xS+TFWTczrLcNtLi9K5pfm3vBKz4shLW8RCigx5F0/9dXCxA1ze4lVOjU1WgFoBUeh40nDhOy81KJNPgdfBlPJCoc5yvAjQGoQ/k+RKTU9bCkWm81sou/y9bBjmXRQoUAB///13ivl3796Fjc37hWm8l9A9ffo0Vq5cKXbEw8MD+/fvx6RJk9ClSxdUrFjxvXaMYZiPy6S9Ktx8rcP1cZYJYWetl8Xj3kQrbL6qwaswHWa3s8AfJ9U4+VgrxCjdKqkXb7sycsxoa7pI1KUXWvRZr0JBVxniNTocGGIJ62QtQlJjk1U4dydGFHw6+nse1C0vXdSX7AxFnmxKdGvkgKajvOGzqzAev1Kh+WhvNKlim5Dz6xeiwZEFeUyGRVNubatxr8TDv7e/GismZEedcjZptmGYT8GSDa+wZocPbu+rnhBmPHjyAyyZUhyez6Lx9z4/bPqtNLYe8Meyza9Q7k3lY+q5W6KwLdbOKWVy3c+8YtBuyG3ky2klhPKe5eVShCanxiar8CxAhyo/qfC/r5To86al3M6rWjzx02FGNyUazlDh+QLp2NSepkKHSpL4CYvR4ZGvDvvGWqJIdtPX+N5LVHgRpENQJDC1swJdqynMsskqDFwcI3KUj/2S2NOvyS/R8F3lgN/3xiO3mwzftrbCxI2xeBWsQ25Xmehxe+WJBt+0tsTIVqYHgqhq8ug1scjtJhcDCtvH2aQYUE2NDSNBT0/SE1T6kJ7bMofY2FjEx8eLUGV6TZMp4uKkAUwSvFZWac+JN3sIZsWKFSI5ePv27Xjx4gWePn0q5u/atQvVq1cXAphhmIwNFXxafs50awc9/aoqsKGfJTb2s8T57yzFZ+jhhaZnQSkLaf24T43l3ZU4NMwSJbPL8fc1jVk2WYmcbgpM+CtAFOZ5GwVzWmDjTznFdHB+HlQoYoV/jkntVW4/jUvoq6uHluXLrsThBXnwv4nZRUXl5KTGhmE+FRSCPHv5i3fatazvhnVzS4np7D+VcflWBO4+ihJFqx4+S9k2Zdbylxg3KB/2rSyPLi098NfmV2bZZCWyOwJTdqhFsaO34WANbBxmIaa9YyzRs6YCy49L13hPH22Kz5/21MI3TIczP1ri8AQLTNqasv1Tamyyold316XEKB9TfNfGEhtH2WLzaFsc/cUOU7dI4iEwXAtvI/fwCevjsHeiLY7/Yid6Hx+5pTHLhmGMMX36dBQrVizhNeXgvmuaO3cuzMEsoRscHIxRo0ahRYsW8PX1Rffu3ROWUVnoZs2aYdiwYUIAMwyTcZncXIl5x9QIj0396B8ViXKyobBj4HGgDtvf9E3UQwWSbvtoUa+wdHlpUlyOc890abbJajSoaAsnOzn+PiqJ1tSSL4cFwqKkc7BoWyii4wyP46kbMWhdSxrxr17KGneexosw5bTaMMynYmS/PMJj+9w7JtWfoRDjXNktRZVmaldEXuHknL4cilYN3BJaFp2/Hm6WTVaicHYZGpWW449DaRM1+dxkCIuWrilrT2vhE2q4/OQDbUJLIOqzSwUOXwbp0myT1ZjW0wqTNsUlVL1ODXTsqKo2XeOvP9Piv5uGAwYU9kyO2WK5JG9543IKnH2QdhsmEQoZT+8pI1OnTh2hE/Wvx4wZ886pZs2a6Re6fOjQIURHR2PhwoUpyla7ublh7dq1yJYtG/bs2YMRI0aYtWMMw3x8CrrJ0KOSArOPqPFrG9OhyGGxgHeoTvTxO/FYi7zOMuR+M1XOazheRlWZHawSWxK42UmthtJqkxWZOzwbukz2Qcd6phOoqNWPd4A0gu8frMH+81Ei1JhYPl76mxS/EDU83lRxprAyEtMkjF0cFGmyYZhPhZODEuMH58dPfzwT3lpTREZrRIsh4vbDSISEqVGhpL2ozjxjTOEU9hSK7Pbmd+/ubCEqNZtjk9WY1lmJ6lPi8WV909cHGifzDpau6RT5s+GcBuNbS/YU5pwc8tRWfzPwSbjZA0GROiGQ02KT1aheVIGKheRYdliF4S1Mh9TTcSLPLbUg2npOhTaVleL+27S8kXMRqkM2p8RjSi2GHvto02zDMKYgRylNyV9/DMwSuoGBgeJhKH/+/EaXk9h1dXUV3l6GYTI2PzRVovLcOAyuZfpysOmqRuTp0mPLkwAtvqhu2pZaECUNv6U+fzYWabfJilDBqaZVbLFoezJ3RxKe+6oxaJafeB0eLY2qF8xh+nxYWcqgTXqsNYDNm76TabFhmE/JwC45sWrLa1y6Zdqj+t+ZYDx9KXl9vXzjULOiU0ILImPQ714PecSsjVQwT41NViO7kwwjmirwy041KhUwfjwiY4GBK6VBAfIe+oTqUCaP6WNH9Rkof/Tt941322RFZva2Rv0fo9CnnumD8eeBePxzViUGq+96aTG9p+lcR2tL8vYmO87J2helxoZhUktQUBCuXbuGgIAAODg4IHfu3ChfvjwUCsWnEboUV00PqRcuXEDdunVTLH/48KHYadpRhmEyNhSKTGL3p/2mPRVDayswrK50uSDPa+GpcZjU3Pjlw9FaJqpwxqp04sHkZbAOhd1labbJqvz8pRtqfv0SzarZimJUyaEqy5Sbq6fTpNc4eCka3RsZL4VdILsFnr1WoX4FiKrNlhb0kCJPsw3DfEqoh+6scYXxw7wnJm06NcuGmWMlz228SosijS/ANzDeZI/ePDmt8dw7FgXz2uDl61gUzmtjlk1WZGQzBSpNVsHWUgcLI8+ijjbAofGJx/27jWpsIq9uG+P3jfzuUrErPX5hKT21qbHJiuRxk2NAQ0vM2iFFMxjj565WaF9NEsK3X2rQdW40vmhk4v/CVW6Qt/syQIfCOeRptmES4fZCxvH09MTYsWNFMWNt0pETihJwd0fPnj0xZcoU4Tw1F7N+lY0bN0bJkiUxYMAAHD9+HCqV9IAcGRmJvXv3om3btqKSVufOnc3eMYZh0o/+1RTw9Kc2Qu+2dbOTwUopiVTK8VEZyQ1qXVqBP09p4BWiw19nNehUXpHgEdG8yf00ZZPVcXdSYERnZ6w7mLpc3UK5LBAWKeXLxcWnDB3r3MAeK/eGwydIjVkbQtC2thQWTYOVVG35bTYMk5FoWMNFtA26dvfd/xuWFnLkym6FsHD1m9+61qgwXrjOW4jhBWu80LFZNjGfCrqp1bq32mR1LJUyTO+qwNKjqcvVLZgNCH1TDyxeTWkwhveN9pUU2HpJi+cBOqw4oRHeX2qfpm9r9C6brM6Ydpb491LqcmQLecgTzgXdj5Pn9zraylA6rwIbTsXj4WsN/j6jQpsqyoTUGbrvv82GYVLDuXPnULVqVaEbq1SpIgTvggULMG/ePJH2SimwixYtQqlSpYQDNV2FrlKpxO7du8XfRo0aYfny5WInyN1MItfHxwdbtmwRLYcYhsmY5HeViTxZgnJ15nWwED10KW02m71M9NAl6C+9T0rNgnLc89Xh1BMtxu9KeXOd0VaJ+35a9F0fj5H1lSifW7rU/HFSg81XtW+1yYo42spF1WM9wzo6izZD1DJIIZehTCEpzIxCw4rlNRyFL5HPEj5B0sNmi7GvEPpG9OqpUdoGfZs7oPvPPvAPUWPqQKmwzkMvFfpO932rDcN8anJ6WMLVOTEkk7y6xQvZwdpKDicHBQrklvrrZnO1QC4Pw3DMyqUd8PhlNB49j8GACfeNFrmia1+Pb++gUQ0XtG3kLuZT4avf13i91SYrQtE3SdsDdaisQMcqcuR0kd6XySsT7W5oKpPH8J5R2EMm8kSJAcvVuP/aUFyRZ5baBfVfrsKpB1osGSBdDyNidGgyW/VWm6xKkZzyhJZ8JPin97JCmXzSfTSXq0z00CXyZ5PDyTbxfNhZy1AouwwvA7XYfkGNubviU6x7yWBr7LmsxpBlsZjb3wq5XaV1Td4cl1Bd2ZQNY7q9UHpOGZn4+Hj07t0blpaWOHbsGC5evCiqKlOhYyo8RQL33r17+OeffxAREYFOnTq9syOFKWQ6cz/5pg8SCVraST8/P9jZ2aFSpUrC05srVy5zV8swWQKKgGjQoAEOd7sPOwsu4vCpkVXM+6l3gUnCjJO9+XhkEMZ3Pv6pd4F5g8XVC3wsMhK2ae/ryXx4ouLkaPx7YZw4cSJFkdyM8Jw3VlMMVki/qLU4aDBP8TDDHQ89+/btQ5s2bYSQ7dat21ttV69ejYEDB+LUqVNG02XfxXsNh1lbW6Nfv35iYhiGYRiGYRiGYRLhHF1D7ty5I6KCO3TogHfRq1cvfPXVV+Iz6SZ0KUz51q1b77QrXrw4ypYta84mGIZhGIZhGIZhmM+IyMhIUWCKQpdT41Qlr3RwcLBZ2zJL6FJ+7rhx495pR3HWlFTMMAzDMAzDMAzDZG10Op1oU5tayNbcTFuzhG737t1FhaykaDQa0VLozJkzIp56woQJGDx4sFk7xTAMwzAMwzAMk9mRydK3QFRGL0aVnpgldPPmzSsmY1BScfv27dGyZUv06NED2bNnf999ZBiGYRiGYRiGYT4DoqOjUx31Gxdnukf0u/gotdmpzy7FXlOIM4UvMwzDMAzDMAzDZDUoSDc9my9lhu7SERERqUqDfV+UH2vnKWk4PDz8Y6yeYRiGYRiGYRiGyWT07dsXNWrUSNNnSpQokX5C9+7du7h8+bLRZYGBgaIvkkqlQrVq1czaKYZhGIZhGIZhmMwO5+im7MpDU3pgltA9cODAW93Ncrkcw4YNQ+vWrd9n3xiGYRiGYRiGYRjm01VdTtrvqEiRInB3dzdn1QzDMAzDMAzDMAyT/kL31atX8PT0xKBBg6BQKN5vDxiGYRiGYRiGYT5D5NBBLku/lj9yM3vOfo6YVQTs/v37GDp0qCgNzTAMwzAMwzAMwzCZXuhSn1wPDw+sWLHiw+8RwzAMwzAMwzDMZ4BM9qYgVbpNn/obZ/LQZaqsTMWmfvjhB5w+fRr16tWDjY1NCrsKFSqkuXw0wzAMwzAMwzAMkzXQ6XQ4c+YMbty4gZiYGOTIkQN169ZFwYIF01/o7t69Gz///LN4/e+//4rJGGPGjGGhyzAMwzAMwzBM1s3RRTrm6Kbjtj4Ez58/R6dOnXD9+nWD+TKZTMxfvnw5XF1d00/ofvHFF2jRosU77bjyMsMwDMMwDMMwDJMcjUaDtm3b4sWLF/jll1/QoEED2NrawsvLC7t27cLGjRvh4+MjvL0kfNNF6KrVasTHx6NSpUombe7cuSP66TIMwzAMwzAMwzBMUvbv3y8048GDB9G8efOE+dTGtmPHjmjZsiV69OiBU6dOoX79+kgrZinR9evXo1atWm+1adOmDRYtWmTO6hmGYRiGYRiGYTI96VuISpoyCzdv3oS9vb2ByE1K165dYWFhIXJ3zSFNHl3aGCUIP336FCqVSohZY0RERAgXtIODg1k7xTAMwzAMwzAMw5hHtWrVxN/hw4eLKSNibW0tIoVJV5KgNaYpablWq/34QvfWrVuIiopCZGSk2KApdU072qFDBwwcONCsnWKYrETgteKIVnzqvWBchg3gg5CBmOD8z6feBeYNs7c342ORQXDbWOJT7wKThFgNp+hlBFTQAC5XkVGRy3RiSu9iVJcuXRLe0owMde6JjY3F77//jnHjxqVYTtHBVJG5cuXKH1/oenp6ir/z5s3D5MmT4e3tbdZGGYZhGIZhGIZhmKztde7VqxfGjx8vuvi0b98euXPnRlBQEPbu3YvDhw+jWbNmQhCna9XlVq1ambVBhmEYhmEYhmGYrEB6583KMll7oTVr1qBw4cJYuHAhzp07lzDf0tISgwcPxvz5881et1lC183NTUwMwzAMwzAMwzAMYw6U8jp16lQRLUwVmAMCAkTIddmyZeHo6Ij3wSyhyzAMwzAMwzAMwzBpYebMmdiwYQPu3r1rMJ88uG9rXWsOLHQZhmEYhmEYhmE+o2JUGRUqbEw5uOkBl4tjGIZhGIZhGIZhPivYo8swDMMwDMMwDPMR4GJUmUDoXrlyBSdOnEjTyqtWrYr69eubs18MwzAMwzAMwzDMZ0ZERARGjBiRavu2bduiefPmH0/oksg11sjXFFZWVvjuu+9Y6DIMwzAMwzAMkyWhdj/p2fInM7QXio6OxuLFi1NtnyNHjo8rdL/55hsMGjQo4f2UKVOwceNGzJgxQ2w4e/bs8PX1xcGDB/Hzzz9jyJAhwoZhGIZhGIZhGIZhCGpTS5oxteTOnRvmoEyLh5Ym4saNG/jjjz9w6NAhNGvWLMEmf/78+Prrr1G5cmURttyiRQvUqFHDrB1jGIZhGIZhGIZhPi+USiWqVKmSMasunz9/HjKZDA0aNDC6nHbc3d0d//333/vuH8MwDMMwDMMwTKZuL5SeE/MeQtfGxgY6nQ43b940ujwgIAChoaFQKBTmrJ5hGIZhGIZhGIZh0re9EOXkktjt168f1q5di2rVqiUsu3v3LoYNGyaEcPv27c3fM4ZhGIZhGIZhmEwMtxcypE6dOrC0tESGFbo5c+bEunXrhNCtXr26KERF8wIDA+Ht7Q25XC5yeMuUKfPh95hhGIZhGIZhGIbJdLRo0UJMGVboEl26dBEid+nSpTh58iT8/f1FBS3y9g4dOlQUpGIYhmEYhmEYhsnKHt30zJvNDO2F0guzhS6RN29e0V6IYZjPA2XOArAsVh4yS2uo/bwQd/cSoFEns8kPyxLSQJba5zniH1x76zrtmveETGH6UhP/9O4718EAK1ecQWRkfIpDkSePM7p0rZRifkxMPJb9death65ho2IoXz4PH14zCAlX4+y1cLzyj0O+nFaoW9kJ9rapr0tx7V4kTl4JE69b1nFBiUK2fB6YTIVDbjcUaZ7y2kO8OHUHwY99xGu5hRL56pSEYx43RAdFwOvsfcSFRb9z/QorC+StXRKOuV0RHxmH11ceIdwr8IN/j88JS3trFGpYBlYONgh9EYCX5z2h0xqKnmwl8yBXxYLQqDQIfPgavjefp2rdroVzIHuZfFBaKcXnfG6k7nMMk2mF7unTp7Fy5UqRl+vh4YH9+/dj0qRJwttbsWLFD7eXDMN8dBx7fweHVn0N5qn9vRH022iovR6L9w6dvoZDh4EGwjX++QMELxgDTaD0UJMcpx4jIbOUWpMZI2LfOha670Cr1WLs6B1QqTQpltWtW8So0A0Li8WkH3a9db3zF3RmoWsG/50NwZeTHyEoNHEQKJeHJTbNLY7q5Rze+fmoGA16jfPEi9dx4n0ON0sWukymI1eVIqj9fWejy2JCIoXQdSmcA22WDYdTvmwJy1Qx8Tg2cR0e7btict0uhXOi3apv4JDLNWGeVqPFzbVHcXbW9g/8TT4P8tcugbaLBsHaKXHQLOixL7Z/8SfCXweL902m9kCF3vUMPvfywkPsHr4CsaFRJtfd8MeuqNSvPmTyxBq2z07exa6hy6GOU32U78Mwn1TorlixQvTMtbW1FYWpIiMjxfxdu3Zh7ty5WLJkCQYNGvRBdpJhmI+LVdmaCSI37t5lqH1ewqZ6Eyg98sDtu/nwG9sJVqWrwrHz18Im5vIx6GKjYVOzOSwLlIDzl5MQNGeE0XVHHtoMJLk5EgoHF9jWayteq57c5dP7DnxehwuRmz2HI7r3MEwLKVTI3ehnbG0tMXJUwxTznz4JxN49t6FUylGhQl4+9mkkIkqDLyY9QnCYGsUK2KBBVUccuRCGp16x6Pf9Q9zdXQlKpeyt6/hx4YsEkcswmRW9CPU6dx+B970NlgU/egWZXIaWf34tRG5McASeHLoO9xJ5kKNiITSZ3R8B970Q+tTP6Lobz+gr1k+C+emRG3DKmw15ahRHxS+bIviRD+5vP5cu3zGz4JDTBe2XDBYe3dfXn8H/vjeKtagItyI50Hx2H2ztuxBlutQUIpcGDB7svQKFhRLFWlRAvhrFUG98B/w3caPRdRdvVQmVB0j3kidHb4tzUqJtFRSsXxq1vm2NU3P+Tedvm/ngYlSZTOgGBwdj1KhRIpF4y5Yt+P7773HkyBGxjPJ1+/fvLyovN23aFPnz5//Q+8wwzAfGplpj8Tfu/lUE/vpGzF45DvcJf0KZPS8si5SBdYU6Yn70+UMI+fMH8VoTGgiHtgNEuLMpwv9eaDhDJof75OUJIjjm4mE+n+/g5UtpNL5GjQKYOatDqo6Xo6N1CtvIyDjUrTVPvJ72azvUqFmQj30auXE/UohcmQz4b2VpZHezxOMXMSjb4Tpe+sTB83k0ShexM/n58zfCsWyLLzo3c8PB0yGIitHyOWAybegycXPtMTw/fjvF8tzVi8G1cE7x+sCIZXh95TE98aPr1gnIXq4ASnWpjXNzdqT4nJ2HkxDDxMGRy/Hq4kPxuss/48X8om2qstBNRsW+9YXI9b3zEpu7zRPhynd3XBBiV6uWIoEopJm4+r9jODlTOu6tfhuAUu2rIXflwibPc/46JcTf56fvY+fgpeI1eYhrjWyNAnVLstBlPljkWmxsrHCgEpcuXcKdO3dQpUoVlCtXLn376B46dAjR0dFYuHAh7O3tDZZRQSpqOaRWq7Fnzx6zd4xhmPQj/skdEUIceSBxRDdpKLLM2g6ql4+ETfTxnSk+rwl8nept2bfuC6sSlaB6/Rxhm37/AHufdYRu4SLZcOTwA6xYfgaHD983Gsr8NiZP3IWHD/1Rr14RfDOywUfa288bGxvptklC18pSem1llXgrVSpMe3Pj4rUYOvUJXByV+G0CDzIwn4dHNy48GkVbV0HJzrXgWjRXwnLnAh4Jr31vPJVe6HTwufo4QQgbQ6aQ4/qqw2Lyu/ksYX6Ej3QdtLQznQqTVclXWxKjD/dfQ84KBVGxXwM45nLFuYX7cHqulMLy6soTXF55BA/2XE7x+fBXQSbXrYqRQpOThiir46S0DfIOM6n36KbnlJnYvn276N5D0cD6qGEqeDxw4EBUqFABs2fPTl+PLrURkslkJr21JHZdXV3h6+tr9o4xDJN+RJ9IGXpk16SL+KvTqKHyfoy4W4mhYrb12sGyWDnY1m0LbWwMwjb8lqrtKNxywKHjYPE6bM0sQM25Panh5csQ8Xfp4lP4bd7RhPklS+XAhk1foESJHO9cx5XLL7ByxTkRsvz7om7iGs6knUol7VG1rD0u345E++H30Ly2C/aelB7Aa1ZwQPGCpotKzVjuBc9nMVg9vSg8XNOnhyDDfGyh2271t7CwkX7POq0W97aexYkpm6GKSgzPdy6QPaE4lVsJqQCerbuj0fVG+oTg7OztKbzH+eqWFq+Th0kzgEt+KQe6VIdqIgw54Vj6h2HHwMXwv+ctPLl6Kg1oiBzl8gtvLoUin55nup7D7S3nUL5nHRRuVAbNZvRCTHAUKvSpl7CMYd4HLy8v9O7dG+XLl0eTJk2g0+kwdepU0Wt30aJFol3tjz/+iK5du6JQISnS46N7dIsVKyZ25MKFC0aXP3z4EEFBQcidO7c5q2cY5lMik8Ox5yjYN+8p3kYd3Q5tsL+BiV3TbrBr2AkypQWgUUFmnbqKsQ4dB0FubSPygEVFZyZVeL3x6JIHt1v3yqL4lFwuw/17vujXZ02q1jHl573iut2jZxUUL56dj7yZ0HH/flAe4dG9dDsS0/7ywvX7USIvd8aoAiY/d9MzCr+tfY0WdZzRs3ViYR6GyexCNyYoArc3nYT/7ReiWFHp7nVRvn8jeF/0TPACtlo6FNW+aYMWCwcjb03J+2hhkzrPrFuJ3Oi47jtRSTg+MhZXVxz6iN8qc2JhKx1L92K58PT4HdzZfl4ce3sPJ7T9c5DIl05K9SHNhMglqPqy/vPGCHr0Gp77r4lzW657HVQf2lycC787L0V4NPNuqLVQek+ZhX379iEuLg7btm0T3ttbt27B29sb48aNE+/nz58PlUqFc+fMG1QxS+g2btwYJUuWxIABA3D8+HGxAwQVpNq7dy/atm0LR0dHdO5svBofwzAZE7mjK9wn/gWHNv3E++gz+xG2XsrpTErM+YOIPncQuvhYyO0c4TJkKuSOLm9ft7O78AATkfuNF71gjFO1WgFRWGr9xgH439p+WLu+P36Y2Fwsu3vHRxSYehvXrr7E8WNSntsIDll+Ly7fiUCPMZ4UgYnKpe3xdbccKFfMFmq1Dh1G3MOL17EmvblkUzifDRaseyUmlVp6GDl4JgQ7j5gOHWSYjIbCUonbG0+K8OIdvebh5JTN2NptNkKfScWlCjUpj+iAcBybtB7xUbFwzu8hhG6RFpUSWgTFRby7xVCJjjXR5Z8JcMzrLjyPe75ahAhv/l9Jjipa8p7f330ZOwYtwcHx63Ho+w1inkt+D9EWKCm3/jmLh4duiPxdIYYXDRLn1Bj1JnRE6Y7VhSB+sO8qbv1zBrHh0WKd9DmGeR8CAgLg5OQkWtYSp06dglKpRMOGUgE0FxcXWFpawsfHeGePjxK6TDuwe/dutG7dGo0aNRLzKAzOwUFqq0B/t27dKloOMQyTOVDmyAe3H5ZC6Z4TOrUKYZv/QNTBTQnL7Zp2h8zCErG3zyNyv3QDjalYF25j/4Dcxg6Whcsg9vppk+u3rdNaeIC1EaGIvXk2Xb7T5wB5cYODo5A9uyNKl0nMf6teIzHHMySE2kIYr75MrF0jjbqXKZsLZctypM37sHq7nxCoBfNY4cSassKTGxunRfFWV+EfrML/dvpjynDDh0oiJlbKZVu8KeXNesvBQNGXt2MTqbgPw2R0qG0QCVmNSoVIXym1QqfRwu/2CzgXzA4rJ6kg28Pdl/D60iMUbFwONm6O8L32RIQgV/iiMSJeSZEqpqg5tgMqD24hXvvdeo6D365AxFtySbMykX5hwstKebh6vC9LudCEtbMdqgxqIl4/PHANZxfsFa/L96yLptN7CrFLfXIDkoeFy2QJ7YgurziMM/N3i9ePj9xGpxVDUbhRWbgWyo5gE9WzGf1hTN+8WRkyj0c3T548wlEaGhoKZ2dn7Ny5E9WqVUvQlJ6enoiPjxdpsenaXqhIkSK4efOmqLp87Ngx+Pn5wc7ODpUqVRKe3ly5Eh/IGIbJ2MisrOE2fpEQuZpgfwQtGAPVU8O2P3ZNu8IidyEoDmZP8PJqo6W2YoROFZ+qys6xdy5SBYuP8j0+RywsFFi86CRevQpFQEAEfp3RXsy/efOV+CvqJRR4+w1g166b4m+z5iXTYY8/b4LCpCIsVhbyhDZCFkpZwmv/IOP/Bx0bu6FUYcMQ/yWbfYRopnDmdg1Z5DKZBxJO+h663uc9E/Jv3UtIA2nhLwNEOGyZHpJIenzwKiJeB4sQ2jqTur753AOT6y/Ts16CyL218QTOzNgKbRqL72UlvC48FK2EclYogBsbT4l57sUTn8ODn/ii7cKBQgxT2sXlFVKnlLjImAQbTXxiX3A9FtYWCfnX1P9Yjzo28TXlWrPQZcylTZs2otJys2bNhHakSOEFCxYkdPKhEGZysFL+broKXcLa2hr9+vUTE8MwmRfbhp1EGyEi9s4FURWZJj2xN84g9upJIXTtmnWHzMoGmrAg2NWXRBeJ4zjP61DmKpjQhijq5C7oosLFa7K3KCDlZcU/vPEJvmHmpku3SvhjwTEheOPeVLtcvVLKV+nYqTzc3e1x6eJznDsnVTalisoKhZSZ4unphwB/aUCiZs20F3JgDKlfxQl7jgfjwbMYdP72PqqWscepK+F47S89+DWu6YzIaA1WbJOKMTau7oxyxe3wRaeUedErt/kKodutRTbO22UyFb7Xn4pKvY653dBq8RA83HsZOSoVhlsxSeje+fu0CKct26cBHPO4oUTnmnj0f/bOAryps4vj/yR1VyqUUihS3F2LDHd3GxsbYzC2MRiwMXTfsAFjDAYMd3cY7u4ORVtoqbsm+Z7zXpI2bVraDir0/J7nkuS950pvQnL/77E9l+BaU2o5RLm2VLSKKNO1LkyszBH6xB/Pjt+C3ECOml+3FeuiA8IQ+TIYlfpJ0YNEXEQ07m3hIkgpub72BCr2rIdynWtDbqhA2IsgVOop/RY/P3sfEX4hogcuFauq90070XeXKidX7FFP2FDfXRLDVKDKrWZJMXZ1xVEhbl/feAaXSh4ir9fU1ly8r+W71BE2FE5OuboMk10cHR2xbds2DBs2DJcuXRKta+k5Qe1qIyIisHr1ari7p42U+qBCl4qanD59Gnfu3EF0dLR4nZoaNWqgUaNG2T0EwzA5hGm15P+n5g3bp1lPojZy5zIYkQAuVQnm3p2061RR4QhZ8AOQmADDYmVg3ecbMU5hzElvhS6FRcsU0tcNtSlissbEn1rh/LknuHD+mai8rKFipcKY87vkHTl29AEm/7JPPB/2RYNkoXs/ufp9+RShz0z2+KybE87fiMCWQ8HYdzJULAR5dL/q7YIuzR3gGxCPH+c+F+Pzf1QIocswHxNJcYk4/P0/aLN4uAhVpvxbTdXlK4sP4vmJ2+L1oW+Xoe2S4bAv6Qr7b6SJURJPh75bjpgg6feh2uctRQ7vw72XhNB1ruIJM3upIrO5k43Wc6wh7PkbFrqpCHr4GsembYX3hK4o066GdjwqIAxHft4onp/4dZvIqyXPb9UBUv4jEfE6FHtHLRfP3euU1lZtvrHuFFRJ8Tg4djU6/PW5yPWtPripzr73fbdSmx/MpA+HLmcMeWsfP34sQpQpH1cDpcFSTajUrWw/uNClHrlUcOrAgQMZ2n377bcsdBkmHxB38xwSnt5Ld32S3xOo42IQNHkIjCvWhlHxcpAZGCHpjS9iLx2FOiZSa0e9dglVVJh2e1VcjHY86bUkAJjMY2pqhH+PjMTBA3dx5fILEXpWqbIbWrUuBwMDhbCpWUsqWEVQCyENLi7W2nEnZ/3tPJjMY2gox+r/lcaIvpG4fDsKgSGJcHM2QuMa1qLQFGFppsDIftKkQkYid3hvF8QnqOFVXNqOYfITry4/xupmE0WBKarAHBcWjRen7yL4gZRWofH8rmoyQWsTHRgBn4NXRaVmDeTZNbW1QODdl+J1YnScKHKVHuRFZNJybdVxvDj3AJ7e5WFkaYrQZ2/w6OB14T0n6NqvajcdxRuXh6NXYcgM5Ah57I/H/97QhiWT95Z67RLKxCStiP6nxRSUaFoRtsUKibzdsOeB8DlyU0x4MMx/4ddff8WGDRtw/fp1HZGrcZj+V2Rqfa7Yd6CprDx06FAMHjxYhDDrg4pRca4uw+iHku8bN26M9SVUMJO0CpOL2C4fyNc/DyG/J3khmNznfzs/ye1TYN5ivzZ7lUeZD0OcMlvNS5j3TCKU2Gp7BcePH/9P3r8PdZ8339oOprKc+6zEqlX4Ojwkz10PfVDxqc6dO4uqys7Ozu99/9ny6D59+lQUQJk/f366IpdhGIZhGIZhGIZh9NGxY0fRjrZXr15YuXJltnNx36vQLVmypMjJpUrLRYsWfa8nxDAMwzAMwzAM8zHAObrps2TJErx8+RKXL1+Gh4eHiATW50QdPXo0vvzyS+SI0G3evLlo5Ethy0uXLkWxYsn9HBmGYRiGYRiGYRgmI0xNTWFrayu0ZUaQTXbIltBVKBRCWXfo0AHFixcXTX2pB1JqSHn/9NNP2ToxhmEYhmEYhmEY5uOk/wduU5vtHF2KpyYVXqtWLSF09fG+46wZhmEYhmEYhmHyC9QpgcKXc+x4OXakvE+2hO7hw4dFr6OzZ8+iWrVq7/+sGIZhGIZhGIZhmI+WZcuWYfXq1e+0GzJkCPr165czQpdKVVPV5bJly2Znc4ZhGIZhGIZhmI8eOdSQ56CbVZ71zrG5RkxMDIKCgnTGkpKSxFhwcLBIl23QoIHQndkhW02d2rdvDzc3N6xYsSJbB2UYhmEYhmEYhmEKLiNGjMDt27d1lvv37wuhe+vWLZEiS/11+/btm639Z8uj6+vri6FDh2LUqFE4ffo0ateurTdPt3z58qhevXq2ToxhGIZhGIZhGCb/txfK2eN9DJQvXx6bNm0SztVhw4ahUaNGOSN0d+/era2mvG7dOrHo49tvv2WhyzAMwzAMwzAMw2QJTV/dq1ev5pzQ7dOnD+rXr5+pk2MYhmEYhmEYhmFyjpo1a4rH4cOHiyU/smXLFsTFxcHKyipb22dL6Lq4uIiFYRiGYRiGYRiGyVuhyxcvXhQFhPMyixYtEpWX9REQECDSZW1sbNCuXbucE7oPHz7EzZs332lXunRpVKhQITuHYBiGYRiGYRiGYT5SkpKShMdWH1SEytvbG2PGjEGhQoVyTuju2rUL33///TvtKEd31qxZ2TkEk0UmTJiAy5cvY+DAgejZs2ea9X/88Qf27NmDNm3aiApn2WXcuHEoVaoUBg0alCPv0dKlS0XYQnpMnjxZG5rxXxk8eLC4Pl26dMn0NmfOnMHKlSvh7++PIkWK4PPPP0fFihXfy/kwDMMwDMMw+RuZXA2ZPAePh/xTjGrEiBH/SZd8EKHbo0ePNEWmlEql6HdEVZiXL1+OH374AZ999tn7Ok/mHZDIPXjwIKKjo/UK3Tlz5uDp06fw8PD4T9fy0qVLUOdgfy4qMU7H/OWXX/Suz+4MD7FgwQK8evUKM2bMEK9PnjyZpd7Q27ZtQ7du3UTJc8pZ37t3ryiDTqEiHMnAMAzDMAzDMJkjLCxMtBSKjY0V3ly6JzcwyJZU1ZKtrclzRYs+unfvjg4dOqBVq1ZCcDk5Of2nE2QyT7FixYSHkcRbykJgJIKfPXuGokWL5svLSa2rvvrqq/e+3zt37uDx48fZ3n7SpEkYMmQIlixZoo1gqFKlCqZMmSLKoTMMwzAMwzAFG5lMBXkOJunmt/ZCUVFRwqu7evVq4TjV4ODggJEjR2Ls2LHZFrz/TSanQ9OmTWFnZydCnOnmn8kZKIQ3MTERW7du1QkD2Lx5M+rWravz4dHw77//ivZQ9CGrVKmSEJSU9K2BPnTUToo+YAMGDEizPYloCi+m0N0yZcrgyy+/1E6CUM+r1q1bi2TyHTt2CI8nhQfTZEhkZKQISZbL5cIjSiHD/5Xt27dj586dCA8Ph6enpziWxkOb+lwaNGiAffv2ISYmRhybzk3DqlWrxN9sZGQk/uZPPvkkzbFotolmnaZOnaodUygUwqNLUQ0MwzAMwzAMw2QMRUdSVGrHjh3RuHFjmJmZ4eXLl+KefuLEiXjw4IHQI9nhg0SMk4gJCQlBRETEh9g9kw4kZLt27SqEbUpI+NKHiBK+U0KeSPK8k6AjkXzo0CEhVp88eSLWU7gwiUV7e3t4eXkJsXjt2jUdYdmwYUOoVCrUrl1biF4K2aUPJEGC77vvvsPatWvFB1cTHjx69GgsXLgQ1apVE6K0ffv2OvvNDpSDTIKZzrVevXriXEh0+vn56T2XqlWrCg83zRalFNm0n7///lt7biSO9RVeMzQ0FOdMkzoaaJKBjpNetAPDMAzDMAzDMBKnTp3CgQMHRPVlSgn8+uuv8emnnwoNcv36dVGLZ82aNbhx4wZyzKNLIZ+UN6mPoKAgbNy4Udz0v68iQUzmoNxZ8pbOnz8fr1+/Fi2gqMEyCVcSwClnQ0jEUR41FQsbNWqUGCMBSgKQQnIpp/e3337D3LlztWHD5N2kStoEvb9ffPGFsNGsp9CCZs2aYfbs2dpwXgo7Pnr0qPDcaqCZmiNHjggPKHn8KQ6fPMsU9quPN2/eoG3btmnGixcvLv5WgsQonYumTxidEx2HPqeFCxfWey70H4pCl8kLrYFCvkmMy2QyIeApB5j+A6YuMEUe7sqVK+t87un6kMgnsayP+Ph4sWigfOocxdAIRiUqIOHeleQxmQzGZWsg/u5lKGwdhY0y4KXW3rBICagTE5D0MnMh3nIbBxg4uCLRzwfq2OS/T25XCAo7JyS9eAR1QqrqejI5DN1L6AypE+KR9Po5CgrXrr6EWxFbODomtwG4ffsVbG3M4ORsifPnnqJ+g+Rr9PjxG4QEx8CrjDOsrEzeuX/al1KpEs+trUzhUcxeZ/2zp8EIj4jVGStVqhBMTY1Q0HgdmIDAkERULG2uHQsOS8QT3zjUKG+JWw+j4eJoBAdbQ7EuJDwRD5/FwcXREEVd3/1eEHd9YhCfoEKl0uaQy9MPZwsKTUR4ZBI83U3fw1/GMFnH1tMFiTFxiHodqh2zdncUvx3hz9/AtUZJvL7yGGqVFCpp6WoHcycbhD0NQFzYu3/jbIo5wcBE+r+UFJcotiPkhgZw8CqMmMAIRPknHzslMrkMjuXckRAdh7An0nYfM8aWprDzdMLr68+0Y3TtnCt6wPfiI9h4OEIZn4TIt++VoZkx7Eu6ID4iBqFP32TqGKa25uL9DX70Gokxyfcr1kXsYWprgTf3fKFKTBsdmFmbgkputRfKD5w9exYmJiYiFVAfVPyYRC+lZlLkaY4I3f3792dYdZmEBImH9xGOymQN8qy6ubkJLy6JPQoPprBljdjTcOXKFZH0nTIcmYQneTBpm3Pnzomw3j59+mjXkwdUI0bJm0lhwJSLSkJQA+UCBwYGal9T2G9KkUtQaAIdS3NMEpehofp/yAj6D0ACOjWOjo7a5ySsqRgahT5Q0S0S+CT8U3qx9Z1Lalq0aCFELkG2lGOe0bkRK1asEP8f6Fg0yZPSy5sSKnqVsqgW7T89cf9BSEqE3YhfEfjzQCgDJU+3UcmKsB7wA96M6QKTmk2hsCuEiHW/Q+FUBPbfzELC03tQWNpCbm2PoBlfQB0Tme7uTao2hHWfb5Hw6AaMSlVC8KxRSHr1FOYtesGsUXshlul4gVOHQhWS/KMrMzSCRbvkKt4kuNWJ8Qj+NX82N88OmzZdgZmpESb+3Fo71qfncixd3hfmFkbo3XM5XvhNF+MD+69EaGgMChWSBPD/ZnZC6zbl0913QkISOndYjDp1i4nXVau5Y+SoJjo2O3fewNUrL7Sv9++7g8tXx8G9qB0KGiRcu4y6h0f7kwsuLt0SAL+AeCF0Jy96iUGdCqF1QzvsPBqMaX+9RNWyFrj9OAblSphh8STdSZvUfPHLY/i9SYCRgQyx8SrsWlgWCoX+O6ChPz1CuRLmmDoyf9ZXYPI/bnVKw6miBw6PWaEda/RLb9zdfFoI3TaLvsCKhuOEKKrzXUe4VPVE+PNAOFcpjuv/HMadjRmn8rT643MEP5R+jyL9gnFu1g4YWZig09pvEfLoNexKOOP6P0fwYOcFne1kCjk6rRmN+LBomNhYIPDeS5ycvAEfM3RP03XFCPxV70ckRksi1LNpRVToXhdbLj5CpV4NhMi9uuIYnCt5oMWMPvC/+RzWbvZQKVXYPnQRlAm6kX0pca1aHG3mDMSra0/hVN4d67rNEpMVjcZ2gms1T0S+CoFDKVcxnhClO2Fd8/NP4NmsIiL8gmFfwgUbes5JY8Mw+iCnEjmPNLpAH3RfTpokV6supxQlJUqUECGhTM5DHwZN+LJG6Oor5EQ5tfTBsrW11RnXfNhIrJIQs7a21llPXlFCI/4o1zWl4CRBmnKfxsbGej8jqc85o0rOVlZWWq9zepCA/PXXX0W4MrU/qlGjhghBTom+c0mNubl5ps+N/tPRRAFdY5oQmDlzpvCiZ9SaibzmKT262W2AnS3UasRdPg6Tao0QfWCdGDKp0gCxFw+nMbVs0w8RWxcj7tJR8dqq59cwb9QeUfvXQm5pA7mto/DO6mzTYQiCZ41E0utnMGvcERYteyNs+TTxGDCmK5AYD6teI2FSoQ5iTuxMPq2EOIT+MU772v77+QhfNxcFiY4dK2Hk15u1QvfhwwDExSWieo2iCA9P9rRevPAMUVHx2Ln7C/H6/n1/9O+7Qit0T518jNp1isHQMPkHw/dlGLyblMLiv5MnrVKTUviuW3sJxYs7FkiRS5CwNDNR4MaDaOFxJfadDMGk4e5pbH/+4wX+XVoejnaG4nui8YBbuHYvClXKWODR81gYG8nh7pL8vRMRlYSjF8LxYF818bp+35t48CwWZT3N0ux7/b5APPGNF+fDMLnFk0PXUGN4a+E9Ja+tobkxnCoUxf7hf+nYmTlaoWjD8tjQXqpbYWxtht77ftYKXRLLIT6vtQKNkBvIERMUgUPfLNPZl1en2nh65CYuzt8NQ3MT9Nj+YxqhW6Sul/D00rZ0bv3+nYIrhawR/SYcHyskHP0u+6BYg7J4eEBK9yruXV77PCV1vmqFg+PWwv+G5P1tPWcgSjSriAf7rsLC2QZGZsYISeUFb/B9B+z7bqU4Rr1RbVHyk0q4v/uyELDLm08W9xBt5g6Ca5VieHbqns62VQc0xt+NfxJCmmyK1C4Fn8Np074KKuzRTR+KJKU6QZSPS46w1JDjjhxJmojSXK+6zOQ+FL78+++/C887hebq6wtL3ln64Pj4+IjCTRooHp6EIlVwplkWau+jKeikeU0fSs37X6dOHZ2wYips9V9LgWcV8iJTuDV5U+lv1+SJf+j2VpTzS9eYilpRrvO7IKGdUmy/y7v8ISBRS97TlEI35M8JaQ3lChiXror42xdECHLk9iWAQgovk9s4Cs9saqEbd+u8ELmEOi4GMjMpDDdwYj/IFAoYlqwO49JVEHs2OQIgNSTClaFvkOT3FAWJmrU8EBIcjZcvQlDE3U54VDt0qqSNLtCQlKTEE58gIYRLlXKCl5cztm3/XLv+wIE7qFqtiI7QffEiBIUL2+DF8xBY25jC2jr9MNjY2ATMnXMER49nPLH0sdOxqR32nQgRQpfCmJ/5xaNhdd1JPyIxSY2jF8LQpbkDDAxk2Da/DMxMpP/Xtx/FwMZSoSN0zU0VsDCTY/PBIJgay0VYchFnY70hy39v9sc3/V3x+AV7RZjcg4Rj+LNAOFf1xOvLj+Feryx8LzzUCWsl5AoFzBysUKi8O97cfoH48Bhs6iy17iMK1y6N6MBwHaFr4WKH6IAwEeqsTEhEXKgU6mxqb4WIl0HieWJ0nBDNpvaWiA1Ojiii49/bclY8JwGelJAIQwsT4CMWusTD/Vfh2bSCJG5lMnjU98KJGVvT2NEkgnud0gi674ek+EQc/inZ221TxAHmhax1hK7CyEB4fl9dfSJCkM8u2Ac1pbvIZFjXZSZMrEyFl9i+hDOCH/unOV7Qo9eo2LM+gh+/RqEybjjz+54PeBWYj4nmzZsLLUGdeih8mTr3UBQqRWnu2bNHtAKl+kEtW7bM1v6zfacdFxcn8iOpIJEmj5LCXimnkUJESRAxuQN5NUmIfv7556IwU+qwZYLyp2l2hHJkqYIwQYKNwpCp6BRtR575MWPGaMMFKJ+XqqARJH7Jq09VhzXeXcptpQ9pTvbZ1eQLEwkJCVqRq/GckjhPD/Jcp8yZzQqU47xs2TKRw5wZkZtXoFxcQ7fikJlaQOHgApmBURrBSkRu/xsGbsXh8ue/cPh5Ocyb99Dm1ia9fISYI2l/WCO3LBKPBoWLw6rHV4g9JwlaVVSYOJZl2wEih1etSj93x6LtAETuXomCBgna9h0qYv/+O+L1gX130KlTcg64hrr1PNG2XQU0afQ7ynlNxldfbkBMrPS5J6ZN7wBzc13h9PJlKFatuoCRX29C7Rq/YdXK8+mex9o1F9GhQ0VYWmYu1/RjpWNTe+w9KX2vHTwdijaNbPWGFy+c4ImZy/3g2vgiOn99D0fOh8PURJpk6NTMHt61kivYE7SP+lWtMGeFH2b944eSRU2F4E3NmNnPMHlEUeERZpjcxufQVRTzlupUeHhXgM+Bq2lsyLt6+a8DaLPoSww89SuazRwoBKyGq0sO6uT5EpYudijasByaTu+Hrpt+QPUvpN9S33P3Ub53Q7hWL4HqX7aGkbmJyDdNyesrPnh55p7w5tYe3QFJsQkIy2Qean7m8eGbKFq/jBCgrlWLIdgnADHBUWnsTszYLgTx8Csz0X3NSJTtWBMJbycZfC89xoO9KWp10CScg5WYMOi5YTSaTuqBgfsmwMLJWooEC4+BQ+nCqPlZcyiMpeiV1Dw9cQdV+jcSIcyJsQmIDUl7TgUZyaObs0t+QS6Xiy49pB3//PNPkUJYvnx5NGrUSERKUl4uOZWoCGy29p/dEyPFTb2NNH1I6fnhw4fRpEkT3L59W4SwZjeemvnvUPgyiVKqtqwP+sBQFTPK1aXwZAo1p+rH5KWkCQuqxKxZT6HI1C6KwgponQYKj6YJD5rcoFxWyk2lamkU2v4+ocrJJLr1LevXr0fJkiWF97Zfv34iP5kW8khTRWgap+Jp+iChTlWSafusQteFhPW0adPSnBP9J82zqJSIu34GJpXqSmHLl47oNVMG+yP41y/x+svmiNr1j7C16pZctCs9KB/XYfxiKez5yglpUCZDkq8Pgn8bgag9K2HRUn8IrUGRklDHxyUXwypgdOxUCXv33EZYWAyePQsRIcj6mDy1HV74TcOa9YNgY2uG1i0XCk9vetStVxwHDn2F7TuH4eiJURg/bqe2MFVq/ll+DgMH1UFBh0KPQyOSRGGqfadChWjVR+Oa1ri8uTKub6sibCbMe4b9p0LS3e/FW5G4fDsKZ9dVxPGVFYTw3X1c1/7YhTAEhybC0lyBl/7xCAxNhN+b7E3IMcz7wOfgNXg0qSC+y8kz+/So/pDUm6uO4p8GY7Fz4DyEPn6Ndn9/BVM7Kd1JH2HPArBj4O/YNWSBCHku37uRCIH2u/AQVxYfQOmOtREXGiUKVJGHODVU+KrLhu/hWNYduz/9Q4iyjx0SnUEPXqFw1eLw9K4gPLz6CHr4Cuu7zcLi+uNxfc1J4W0lIZoeMgM5zBwssWPYYmwb8ifubDuPyn3f2stkotjVpr7z8OjgdVTqWV9nWzN7C1Qf0gyr2kzHlgEL8FLkC+vaMExGkAYhsUtOUnIiUSoiFXY9f/68WCgKNbtkK8aUKl9RK5p58+YJYUNih0QQeQep4A71F6UqtdSvtHfv3tk+OSbzkOBKmchNxZFosoHCATSQ+z9lzi0JParITIWbyKtLMygpc6vJM0zrqVUP5ebSrApVNzY1lUIfPTw8RFEqCncmLyp5eVNuv3jxYiGAU/LPP/+kCXunGZz0crqHDh2qtxCVhnLlymmPRZ8/yj2mFkckzqkqNJ0biU995zJo0CBROZnOPb1zo+1S5iBroL+VZpj0YWGRXDk3L0Li1qxuS8gtbBCxaaFeG8epaxE882uowoMRd+0UVFHhsOySHCKrD4tWfWBaqzneTOijLTZFnly7UbMQOEESt0mBfjDy1F84yax+m3SFd0GABOmD+wHYse0GWrcplyZsmaCwYgMDOUZ87Y0qVYqIZffOmwh8EwUX17ShtcSbgEiUKFlIPHdxsYahkYEoUJW6ovLdu69FyDNVf2aA9t5Ssakrd6KwcnrayTASn92/uY8zayvBtZAR+rUvJITxrYcxaNVAf34zVW+mCsqa97ZMcVMhqFNCcxBWFgrMWu6Ll/4JIrz5+IVw9GknvYcMk9NQESIKOS7dviaCH/iJcOLUlGhdDc6ViuP0jM0I9XmNKz6vUbhWaVgXdURsiP4ihhSOHP+22jt5ZCmM2cjCFFaF7RETFIljE9aIwlSVBzYVlYNTQuMdV3+Dq0sO4c7GUyhIPDx4TXhrPRqWxdZB+rs8fHpsMpZ/8osoJkVhzuT5JvuMQtTjwmK0ntjQZ2/gXrsUXKsVR83PPsGOz6Wc7HDfYBHinBJjKzOxPYVIE1Sx2a647v0Ww2QGijTNbi7uexW6ml5G1GOVILVNQkmTRExCg8SBxtvLfHio72tKSNSljmdPKXpTenZJ0KYHiVoqOKUhdWlvumFLr3IwhT+nJuW+NFBV6PfxoafcYlo0kNj19vZO91yIlOeu79zS245aImU3XyC3ib91HjYDx4oWPonP7um3uX0BNgN/QOSOZaL9D4UUx988J600NILc2FSI35SYt+yN4NmjtO2EkBAPZdBrikuBab3WSHz+AJZtByLqoJQrROHT5GFWx0s3OsZeVRB9NG1IdEGBwndatCyLyZP3YeWq5GroKWnW3AvduyxFyZKFUNzTAceOPoSJqaFW5L5+FS7aEaXM/z5zxgeL/zqFsT+2wL69d1ClipsQuYmJSoSERMPJyUqyO+2Dxo2T//8UdMhD22P0fTSvawNDw7TBT66ORsIjO33xS3Rubi9E7qYDQVg4Uap5QAKV1luYJU9AUtXmUTOe4sj5MNhYGmDXsRDsWFBGrHsTkgBbSwM0q2MjFoL2d/NBNItcJtfxOXgVdb/vjHOzpXS11Ly69Aj1xnRBwM2nCLz7EoXKFxWCVVNRmTy78RHRUCUlR5PYFndG2e71cWrqJjiWKwK5oQJhz96gcM2SqDO6I45NXINKA5vi3vbkdAvKLSVRVbJtDfief4Bnx29pQ6RjgyN09v+x8vjQdfTbOQ5hL4MQHRih1+bVtSdo8lN3XF99QhQQq9yvkfDsEoamRpAbKBAfmVzokFo7+d98hmqDmuDZqbuo0r8xLiw6iIBbL+BQ0kUUvYp6E44q/Rrh0Pi3NT6szaSWUC+CxMRD6bbVEPzwFSr2qIeTM5MLTjJ0G6WmW6kcQ4a8Hd2wY8cOkX+bFTp16pStbj7ZErpUNZcEjqawDvUmJY+fphIz5UVGRERAqeQ+WgyT51AmIebkbqiidYWqMshfhA4TERv/gHmTzrDsOARqpRKxl44i9vResc7IwwvGlesjcvOf2m1lxqZI8n8B6z7JVaUTHt5A5Na/EDJntNiPafXGiD62DXGXpUrO5FVWRoYhjqo+y2RQhgUV2LBlDb1618CrV+GoV7+4doy8rPUbSOKpQoXCWLVmAJYtPYvg4GhUqOCK3XuTQ8pHjdyMv5f11emtO+qbJpg7+wh+mrAbpUo74Z+Vkoh++SIUs2cdxsJFPcVrqvLc2JuFroYa5S1Qs6Il+qXypFYsZSZ66NJv4M4/ymL+6leYOP857G0MMe/H4qhVUQrV3Lg/CPa2BqJQlQbabtNcLyxY80r00Z3/Y3F4FJbeqyl/vsTX/VxF3q4GZwdDxMQW7HxpJm/w+MBVuNUtgyeHJUeHBr+LD6FKUop+tzsHz0Ol/k1Qql1NEZa8c/B8bfGpWiPbiXZDJGQ1PNp7GcbW5qj9TQdEvQ7B7k8XiPBjCl2+t+0sao1qj8A7L3B1iVTvwcDUCI0n98HeYX9CJpcLId3sf8mTgscmrtUWsfqYoZxcytX1u+KjMx76NACxIVJBr4Nj16DqQG/U+6YtEmMScHnpYfgcuSXWudfzEl5ZakOUkn2jV6D2V61ExeUb60/h+WlpIpzCmWtQfq6hAqdm7dRWcibRS/m+Ly88wpaBC1BzWAuUblkFl5cfEaHODJMeFClKIcpZgaJIsyN0ZepsVA6i8FXy7FEhHsqDpLxN8nxRCWhNX1EKC6UcTsoVZRgmLVROvXHjxlhfQoUUTh8ml7BdPpCvfR5Cfm9jbp8C85b/7fyEr0UewX7t69w+BSYFcUouGJcXSIQSW22v4Pjx43kqfUxzn7fCzQhmcv390j8EMSo1Bvom5LnroSEoKEgsKaHity9evMCGDfr7YVOksL29/noZ792jS/m3FLb8zTffiNcUJqepcturVy/R5oVsqLgRwzC5B1U5tmjR6+0rNZQhgYg5vRfqWN2KiAbO7jBwK6H1tqbMu6Xeue8ThZ0TTOu3Efm/Maf2iPDl7NjkR/5ceAKvX0uedEsLEzT/pAyqVE3bqm3mb//i+zHNdcZOHH8EG1tTVKrk9t7Oh4pYUd/cAP8I9OhZXW/v3MzY5EfOXo/AvhNSFVhqDeRV3BTdWzhAnupm5NCZUDg5GGl76mr64W77NxgDO73fPLQLNyNx5FwYqpe3wCf1bLNtwzD/hdIdasGupNQTnkJTX118JDy3qaHiUdTfNmXOrkMZN5GzSV7Z94lny6oi1Jn6+oY8fp1tm/wG5bqW61JbPKeqyJQje3f7BdGvNiXOFYrCyNIEL84+0BmvNrgJriw/+t7PqVSrKiJkmfrsZteGKbg4ODikqc1jZWUlIoa9vLze67GyPRVFLmcqQPXLL7+IGQNNLiOpbWo5Q3m7VLmXYZjcQ2FtJ3rTJjy+hQSfuzBwKgKHH9IWr1DFREIZ6Jtm3KxJ5/d6PjIjEziMXyJaFRl5VYF1/++zZZNfWbXyAooWtUPNmh4oVMgS3br+jdu3X6Wxq1wlrZg9f+4J7t55vzdvE8bvwuF/78PK2hStW/6B8PDYbNnkR6jI1FO/ONSsaIEKpcywbk8gfv7jRRo76nNrb6M7JxwRrRT5s++Ta/ei8OnER3BxNML0Jb7YcigoWzYM81/xbFFFFIfyv/5UhAJ7T+0L9wZpCxmFPPSDKlFXcNmVdIVLVSnV4n1RsV9jlO/ZQOTgtvnrS1imKoaUWZv8iE1RRxSu5onX158h4PYLuNUogdaz00YfxQRHIso/LM04VVt+n5g7WqHLP1+JEGmvttVR64sW2bIpaMhkqhxfmP/g0dVAHtvUXlsqB80wTN6BikbFXTkunsfdOA2XRVJlY4VjYRi6l4DczBLKiBDIDE2Q+PwhDAoXg0m1xkj00W3LZODiAZMaTZDw4BoMPUoj+m1RKbmVHUzrthJe4thzB7X9ds1b9ETsxSNQhQZq90GiO/7eZUQfWAfIFXCevw/ha+cAiQlZssnPeDcpDU9PqZL3sWMP8ejhG5Qv74o/FhxHg4YlcP+eP168CEXz5mUQE5OANasvQKlUIyEh2atN+bQrV5yHgUKOYsUd4FjIQuTvUibK1i3XRO/c1m3Ko3RpyeN47uwTUXiqTdsKOp7aDesv48GjX2BsbCBENLU36t2nRpZs8jOUD9veW7ohtjJXYNEGf/H86PkwmJspcOpKOMoUN4ObszHcnIxFEalLtyJRu5JUwCtlS6CLt6JEv92rd6PQv4N03amQ1MEzofAqZoZ23pInPC5ehZnLfTHxC3edfSzbGoAfPyuCXm0cUa6kmRDdXT9xyLINw7wPXl97At+z98VzuxIusC9ZGC9O3UW5HvXhf+0JnKt6ijzZwHu+Ike3dIfaMLWzQFJciu9pmQxeHWvD2NoMATeeigJIVLSKcG9YDg5ebvA9ew9vbksTTLaeLnCqWBT3UxSfIir0aSxaFlGvXvIWl25fC5f/3Jdlm/xKVEAYHv8r5Ua/ueeLzkulugyOZdxgamsOu2JOCPcLFl7ekCcBcK5YFB4NyuDZKd1ik04V3OHRoCyeHLsNt5olcG2ldF9A+brkfY0KCMf9vVegftt+jvJ1yRucGJPc3oz68d7Zel7k8D48cBV9d4wVRatSkhkbhskpsu3RpZZC8+fPx/btUgW+N2/eiFxd6qlKTX+pFxLDMLmPwrYQLFr3E5WT7b7+DZG7/pHGHZxhM2Qi5LaFIDe3gknleqIdkP3ouVBFhol2P7Stpoqy/bdzhWg29+4Ey05DxbjMxAz2o+cIkUth0vbfz9ceN/HlY21FZQ2GHl5IfPr2x1elhDIkAAb2zlm2yc+s/Oc8fp97VHhKfV+G4pMWUtXdvxefxpxZR1CkiC2WLJbaZQzsvxKPHwcKAbt06RntPgYNWAUfn0DExiVi6KdrcP9egBgf8/12XL/uK7zFZHPrllTxlCorpw45fvQoEEWL2gsBS5Qr74IH9/2zbJOfuXgzEnNX+WHmMl9MWvgCI/u5asOah016DAcbQ5y5GoG7j2NEqyGyIcE74+/komm7j4WIcRdHQ4yZ9QxbDgVrQ4zHznmGIi7G2HVM2pZQyIFq5dLmTF2/H40qZaTw6PIlzHD/SUy2bBjmfVCiZVVUGdwcNb9uhyJ1y+DhnotivFTbmqjzXSfRcsirYy1Rbbf+uG4oUtdLVPGlbTTUH9sFbnVLIyk2Hs3+NxDOVaQie5UGNBFeYxKl9X/spvUWU1Xm0CfSd5kGAxNDIVzJlqCqvnaezlm2yc/YFnNC9U+boeZnzdFyRl+taHQo5YqWv/UXPXDJ8+tWvQScyhdBq5kDEOkfJuwVBlIBkEJl3dDqt/6iSnPtL1uicu+GYtzSxRat5w5CdFAkHL0Ko82cZG9xwK3nYhIjJYXKFoH/24mJ2NBoUaCKrn9WbRgmzwvdDh06YOTIkdoWQvT88OHDaNKkCW7fvi16n8bE8I8ww+Q6KqUITVZFR0IZ7A/TWs0AhSRcEp/eRdSu5VBFSS0KSNxG/bsJMce2I3TpFCBJ6otnVr+taAsUc3QrQpdN1e6aPLkkfkkok8dV4eQGhZOUc5pw9zLUMbq5wCSMVbFSVUhCHRcrxrJqk5+xtDKBjbUprK1N8eZNJK5clm4IyGM7c3Zn1K0nhf2RCCbP7m8zO+PL4Y3QoUNFMf7KL0x4bGn8qxGN0aKFdJMYFRWPHduvw8HBAoGBUXB3t8W2LdfFOmpFRB7flERFxsHSUqqcT5ibGyM6OiHLNvkZE2M5bCwMYGmhEKJ2a4pQ4L7tC4kcXE3O7l8b/fHHBE8xPnlEcvP6xZte48+fPIUXd/yw5Hzrheteo4ynmWg7VMLdFGt2S9VmqVVR64Zp85yjYpTiPAgzUwVi4lTZsmGY9wF58eIjY5AQFQuFkQEK10yuyH7xj714cVKK+JEp5Cj+SWUcHrsSdzedxu23PW2pb2vx5lVw5IeVuLPxNO5vf9uejoTuwKYI8wmAuYM1wp6+Ed5Xgio3k+c3JYbmJkiISs4BpnxgQzPjLNvkZ0hsUh/h+Mg4RAaECY+phqcn7gjPLIWaa0KVz8zdLTyq/05cL94HMd6jPk7P2Y3bm8/iyC+bIH/bMq1iz3oIfxkEM3tLxIREiV67GlH69MTdNLnARqmudUJMPIzMjbNsU9CQydQ5vjD/IXT5zJkzOHToEObNm4evv/5aeHcpX/fbb7/FjBkzcOvWLVGMivok9e7dOzuHYBjmPaGkgk7Hd2hf2/+wEMZlq0OdlChCllNCXt7Eqye1bYiUEW+L9Tg4I+6adANDglb9VhgbOLpAFR0hhDQRuW0J1NH6+/oR6phIHdEqMzEV22fVJj/TuUtlbegy5esuX3YWDRuVFK/JE6uBxGyxYsl5Zm5uUuEhX98wsZ0GFxeph+6rV2EwNjIQIppo2bIcypRN36thbWOqI1qjo+NhY2OaZZv8TMXS5hjUWQoz/rSLM1waXcBv3xUTrwvZ6XogXr6OR7HC0s2am3Ny/YmX/gko6mqi7a2r4fmrOJT1NBVCmvhluG6ocmqsLRSIjpGEa0ysUrzOjg3DvA+en7yjDV32v+qDemO74uGeS+J1bLD0fU+Y2logNiRKG+4a9ToUVm72om9uXGiUKKBEUO9bU3tLyA0NhPeVRLTY9/UniPTT/R1KSXxErOj7mlLUkujLqk1+hoTorU1nxXMKBx64fwLsSzhrc3NTYuVqJ/rrarypSfGSULV0tRWFrDTbqBKl94taNMWFRWuv14kZ27TvmT7iImJglGISwdDUGHERsVm2YQo2ISEhYkldoTo+Pl7rQE0N1YCytbXNGY/ujRtSrgBVXiao8FRsbCw6duwoXleoUEGUgU7vZBmGyT1kBoaiN64+qCqzgbPkrZIZm0BhK+X/keClSshi3NxKhDtr7FURoUJI02JUqjJUcenfYCQ+ewCjYlKoLgyNILe0gTLwVZZtPhbi45Ng8Da0LDWurtZ49ChFz8m3z8ljS2JXg6aYlbOzNaJjEtBvQC0MHFwHhkYKhEckz6qnxsPDXniH6RyIG9f9UK26e5ZtPhaUKjW18KS0Qr24FjLCo+fS9Xz89pFwsDWAr7+Uw3brUbSOffmSkpBu2cAWNx8mr9NHZS9zXL0nRUBcfxCtN7w5MzYM875RGBlC9VbIpoZEknkha8gNpNtJm2LSb0NcWBTMHK20/6HsS0sRJVS8ivJ4fQ5ew93NZ4QwNneSJuv0QfYknOgYhEOZIiKkNqs2HxMKYwOokvS/HxSybPv2PaCwZI13liYj6DXhUNJZ69Gl/F8SwCSk72y/gMLVPdN4cVPy5u5LER5NmDlYvRXNyizbFDRkcnWOL3mZOXPmoGTJkjoLOUyvX7+eZlyzLFy4MOc8ulT+WSaTwdhYmrE5evQoLC0tUb16dfFapVIhIiICynRuphmGyTkUVnYwa9hOPDd0Lw2ZoRES7l+FUenKaWxjTu4SFY8Jo5IVoE6QbuBjzx6A/be/C+FpUqE21InSjX7MmX0oNGU1lJGhMHApKrzAmnBnk6oNkfDwhght1kBeYcsuw2DRbiCMS1dB9LG3nmaFAUxrNUfs2f3p23wk7NxxE46OFoiIiMXSJWewZGkfvXZFPexRvLgDvhy2Hm5uNjhz2gdNmpYWYchmpoYYN3aHyN29ePEZuveoBisrE7RvX1Hk5lauUgSrV57H4WOjxL7u3fMXYcg1anpo929kZIBuParh08Gr4eXljCtXXmDuPKnv+fFjD8WxKa83PZuPgdsPo7F61xskJamx50QIPu3qDGMj/fO/X/ZywfApPujb3hH7T0qRDsSADoXw5RQftPe2E/m5Dm8rNA/v7YoRU33w7FUcth8OFh5jIiFRha3/BqNXa8mrr2FwF2f0H/sQQaGJWLH9DX4fK3mWX7yOh8+LWHjXsknXhmHeN+71ysLCyRaGZkYo270+Li7Yk25Y7eN9V9Di96GiSFWJVtXwcNdFIcR8z91H0+n9EOEXDM9PquDmGqn40Y0VR9Bi3lA8P34L5Xs1xN4vFolxCxdb2HgUgu853RY5t9YcR7OZg0S4dJnOdbC5269ivFBFDyTFxItWQunZfAxQsShqMURTBm41SyLo4WuEPnsD50rJ3+cayOPb9vfBMHewgnsdyo+WInLubDuPppN6wMbdAcUbl9d6bW9uOI1uK78Wz2nfb+4k1x8o3bYafP69iaR46TeduLfzEnpt/laEI5dsURlXV0iti4wtTVGkTik8PnQjXRuG0VC1alUMGDAAWaFSpUrIDjI13SllkZs3b4oDzp07Fw0bNhRFqKi90NatW8X6FStWYNCgQdi8eTO6dv14booY5kM0El9fQgWzDxSBKLeyhVmjDtILtVp4RklIUmVkhb0zDIqUQPz101A4usLA1QPxN85CUcgNxhVqIeH+NRgVLyv1sTUwhGHhYjAqWQnxD67Dbvg0vBnb/e0x7GBS3Vuq7nzpKDX7E+NmDdsj7sYZ0QtX95zshKhVBr9GnCZM2sAQFs27a3v26rX5wNguT9uy4X2zauV5kZdLUAhwY+9SKFFCmn3/e8lpDP1MagVBxag++7yB8KRu3nRV2Do7W8HM3AhlyjiLfRzYf1eMHzv6AJ06V0GjxiWF8N296yb8/SPQrl1FuLhKHo6rV14gNDQGTZvp9qcj++3briM4OBpdulaBnZ1U6Gjnjhsi7LlUKad0bT408nsbP3h7oaMXJM+4oYEMFUqZo2ltG/H6zLUIERZMHtlTl8Nhb2uIsp5mYvzh01g0rWOD8zci0b2lA6JjleL5M784ODsYCVG7dIoUin7ncTROXIpAxVJmqF9Nei/iE1Qi31dT+Coltx5G4/ilcNStbKX11j5+Hiu8wZ2bO6Rr86H5385PcuQ4zLuxX/vh+8NSzq2Nh5M2V/fV5UcIvi8VtivVtgaeHb8lcjCp3+6Tw9eRGB0vBC7lg1IhKCpQRaKXqi0XbVBOeHUpnDk6IAz3tkm5ulTgytbTCc9P3kX4cylaxbpoIVGJ2efg1TTnRFWaqeCSz6HrYj+EW53SSIiOx5ubz9K1+dDEKbNd6iZTWLs7oHSrquI5fReHPn0Dn6O3RKi4fUkXmFibwe+yDxxKF4axhQn8rkjPC1ctjmen74p2RHe2XYCBsSEcyxQWhaICbr2A98SuWN99ttivVWE7IX4jXoWIiswaqgxojNubziLxrVjWQPaezSoi+NFrbd9eqv5c4pPKuLXxTLo2H5JEKLHV9opod2phYZHn7vPWFJPBLFWP9g9JjEqNvk/Vee565AbZErrEkCFDsHz5cvFcLpfj5MmTQuz26tULGzduFOHLly5d4l66DJOLQvd9YVyuJqx6j0LM0W0wKlsdSv+XiNicvTCSvEpOCN33AUXMVKk4HQMG1UFiQhK2br2GU2e+01ZG/lj40EL3fTH5zxe4/zQWDatbYe2eQPz0RRE0r5v1PKK8DAvdgiV03xed1n6L15cfIy48GuW618e2PrN18ns/Bj600H1flO1UCxW61hHtg0o0ryTaFd1Y97buxkcAC11dWOgmk+07o2XLlonKyxRP7e3tLUSuJll46tSp+Oabb1jkMsxHQvydiwhf8SsMPctL4cVXTuT2KRVYaGLx4OGvsWf3Tchggn0HvvroRG5+YvznRUTY8/NX8Zg7thiql0suKMYwBZm9ny8UIcsGZsbYOWjeRydy8xN3t18QBcGoJdGlv//NES8rw+QF/tPdUfv27cWSkj/++EM8hoeH4+nTpyhbVmp9wTBM/ibh0U2xMLkPhTF/OlQKc2ZyF4VChg5NkqtjMwwjQeHNmlBlJvd5fua+WJicR2r5k7PHYyTea8xFQkKCqJrVrVs3ODs7a0ObGYbJe1h1Gw7rvt/qjNl/N08qDFWnBcxb9BJj9Og4ZY20TF4Nu9FzYFiiQob7lhmbwmbweDhM+Btm3p2ybVNQuHD+KbwbzUVQUHLfYcrZpVxZ6o/bppUUJv7EJwj1687SLp80nY/58469c/+rV11Ay+YLMPyLDQgPj822TUGh/fC7WL83UPvaNyAen/38SDwfO+eZyNUlhkx4hHq9b4ilYf+bwob65mbEw2ex6D76vjjG6asR2bZhmJzG0tUO3baOha1nctuyYk0roULfxuJ5x1XfQGFsKJbuW8dply7rv0etUe21lZnTo2jj8ui4chQ+mTME5k422bYpKHwyvQ9qDmuhM9ZtlVRYqupAb3i1kwrE1v+2Pfru+EEsfbaNQbv5Q2CX4j3UB/XVbTVrALqvGYmSn1TOtg3DfBRCl/rqfvHFF3BxcREthrZs2SKEbp06dd7H7hmG+QAonNxg3rw7DIsnR10YFi1FsbGQW9tDYS8VI6FHClcOmjoUQdM/R8yx7bD/ZjYgTz+x2Krn11BFhyN08SSYe3eCUalK2bIpKFAboMuXnmP61APasVd+4QgMjERSkhK3bkqFYGLjEqBSqXHg0Aix/L2sD9avu4Q9u2+lu+8rl59jwfxj+POvXihSxBbjx+3Mlk1B4uaDaEyY/xxRMVLngPh4lcjDJXxexiE8Umq/cf9JDOaNL46DS8tjx4IycHMyxsgZT9LdL5XE6PfDA/Rp64hJw92FUKZiVlm1YZjcQGFiCMey7qg3prN2zNTOAhbOkuB0LFsEcoVcLJZu9tjeb45YDoz6G47l3FG+tySI9UGitdHEnjg5dRNenr6LpjP6Z8umIGHn6YQ6w1uK3rkaHMu4iUdqJURClKBKy5eWHsbG3r9jc//5eHrqHtotGJLhvptP642A2y/w74T1aDCmgyiKlR0bRoK8uTnaXigHvccfrdB98OABJk6ciOLFi6N+/fr466+/RHGdYcOG4dSpU3jy5Am6dOnyfs+WYZj3SsyJXbDuM/qdduqkJKjjY6GOi5GqNiclQmHjACOvqmm8woRpdW9E7lwOZaCfaA9kUqNJtmwKEq1alxOC9eHDgAzt5HIZLCyMxUItiFq3Lo/bt6Reuq0+WZDGG7t163VRzZnaEo0Y2Rh79qQVxZmxKWi0amCLOSukCYaMMDWWw8JMATtrQ/RrXwi33/bSnbf6lY5XmLj3JBZGRnIR6ly1rAVqV7LE8YvhWbZhmNyCKhxbuNihcO3S77Slis20UAVknwNXRTVgovmswTpeYaJ4s0p4tP8KQh69EuHOtN7I0jTLNgWNuzsvosH3bzsrZIAyPkm8FxROfnvLOdgVdxbqq3Trqmm8wtR716WSB66uOCbaGD3YcwUlmlbMsg3D5Lsc3YCAAGzYsAFr1qzB5cuXtT11O3fuDD8/P/j7+2PRIqkfGsMweZ/YC//CsvNnoj1Q3OV3h8AScgtryAyNRe9cZWggEnyS2xEQMlMqZa8WophQBr2CScXaWbYpaJBwHT+xFSb8uAubtgzN9HYkjNu0lULJt2z/DObmUn9zDU98AtHsbVshWqdQyEU4NB0vKzYFjYlfFEHd3jcxpKsU2ZAZHjyNRbHCJuL5Fz2dRd/LlPi8iEMJd2k9UdTVWPTJzaoNw+Rm1fdT0zej3g9dsKnzjExvZ1vcGeEvg8Tzo+NXQ5kgRUVosHYvhJDH0oQdEfU6FBbOtgiJjM2STUHj2qoTaLfgUzhXLAr/m88ztY1tUUdE+YeKloOPDl2HTK7r87Iu4oAIvxDt63DfYDh6Fc6yDZOMTKbK4Rxd+jfvVgRftWoV1q1bl6VtqO8udfb5YEKXTmrw4MFQKpUwNjZGu3bt0KNHD1GMytLSEp9++qkQugzD5C/CV82E7VfThac2Pcybd4NJlQbi29PQvSQiti4CEt/mImoe3yIzMIA6IfnGXE3rU4U5Z8amINKvf03RQ/fkCSkfVB8+jwPRoZ00oUj9dE1MDNGjZzXxOrXIJRISlDA1NdS+NjYyEOHQWbUpaNhbG2JUf1f8svAFfhgihQPqY8S0J8Kjm5CowtW70Ti2orwYNzJMe5ORkKQSHmANxkZyJCnVWbZhmNzE7/wDRL0OgVfHWunaGJmboN3SEeK5saUpTB0ssa7VL+K1Mj4xjb3cSIGkuORxEsKpc3ozY1PQUCUpceLXbWj8Yxds6DknXbs6X7VCpd71hah1Ku+O/d+tfLs99b2nJRmFoQJJ8amus0KeZRuGSY/o6GgEBUkTX0RMTAzu3bsHQ0NDESns6OiI169fi+hg6jTRvHlz8ZgdMi1037x5I0SutbU15syZgz59+gjByzBM/ibx+QMkPLwB82Zd07WJPXcQMaf2CKFrVLIirHqMQMyRrXptVVERkJklt1iRW9hAFR6cZZuCCH2R/zazM8b+sB1Nm0oe1tQUcbfF/AXdxfOIyDj077MCJ44/QmPvUnrtHR0tEBwshdMSMbEJsLY2zbJNQWRYd2fU6nkDNx4kX5vU/PhZEZQsagKlEvhzw2vMXfkKS6eU1GtbyNYQQWHJnqzgsESULGqaZRuGyW1Oz9iCDv+MxI2VR/SupzDZ4z9LHhuFsQG8p/RFqbY10q3CTK2HTGwp0kfCxMYcMYERWbYpiDw5dhtV+jfOsCDUjfWn8OzUPchkMtFHt9YXLcR2+ogJjoSprbn2tYmtOaKDIrJswzDpQXWdaNEUMm7YsCEaN26MFStWoGjRolq727dvo3///kJvdu8u3fdklUzLY/LcDhkyRBTKoEcnJyfhxT1+/LgYYxgm/xKx8Q9YtOwNmVFyyGRqYaoMeg1l4CvEnj0ghGm6Hli1Ckm+PjAsVka8NKlYB/F3L2fdpoBSr74nPDzssX3bdb3rjYwMRG4uLRUqFBYC9/mz9CcJ6jcogaNHHmirO1euXETc7GTVpiBiaCjH9FEe+PmPF+nauDgaoqirCYoXMUHP1g546heXrm21cha4cT8asXFKJCaqcPpKBOpXtcqyDcPkNhEvg+Bz8Boq9vXWu57uCyP9gsUS9iQAz47ehFWR9IsVvbr4CO71pMk9Cxdb8f0Tk0o4ZcamoHJ82hbUH90u3fUxwVEi1JhCjG9vPSdCj9MjKiAccgOFtshYsQZl8eL8wyzbMMnkaCGqt0t+Yfv27bhw4YIIZU4pcony5ctj8+bNoqPP0aNHP6zQLVWqFJYuXSrCkylHt0aNGvjnn3/g7e0Nd3d3FrwMk49RRYQg+shWkX+bGdQxkVA4uMDQowwsWvdNsz5i61+w+2oG7H9YCANnd8RelGb9Teu1hnGlehnaMMDUae3h6xuWqUtBntfnz6VcqRHDN4rc2pR0614VV6++QOeOi/Hp4DX4eVIbMf7yRQh+mrA7QxsGaNnAFsXd9E8ApcbGwgC+/glQKtWiENWBU6E6681MFRjW0xmNB9xCw/630KaRHVwLGYl1P//xHM/84jK0YZi8xKU/92W6GFR8ZKyoxEzU/qaD9rkGv4sPoVKq0WHFSHRc+Q3Ozt6uLXrkPa1vhjYMEPzYH8/PSZOV74IKUplYm8HQ3BhF63mhfJe09THOzNktWhX1WP8N4qNi8eqKVE2+yoDGcKnskaENw2SFR48ewdTUVDhQ9eHp6QkjIyNcv65/8v9dyNT/wR3r6+srcndXrlyJhw+lmZwyZcpg0KBB6Nevn2gxxDCMfqhKOYVqrC+hglkupKcqHAtDFRmqLQhF/XMp/zbx6T3IrWwBA0OoQt5AbldI5OGqIpOFl0HhYlBFhEKtTILc0hbKgJdp9i8zs4DCxhFJr54mH9POSVRsJmGdnk1uYbt8YK4dOyIiDiHB0fAolnzz9+jRG9jYmMHOzgx37rxGxYqFEReXiGfPguHllfzdSnm6ISHRYuzGDV+UK+cCAwNFmgIyDx68gZubDSwtJdEWG5uAp0+DUbasS7o2uYn83sZcbS9UsbS5TvhwUGgiShczw5OXcbC3MYC1pYFoL+RR2AQmb3NqSeBevx+NciXMEBiaCCNDGZzs04pU6surUgHuLsnpP/d8YkThKRK66dnkFv/b+UlunwLzFvu1r3PtWiiMDGBZ2B5hT5Mrw9NryhOlysoOZdwQdF+qVG5f2hXBb58TJIgtC9uJMVtPF0T6Benk22qwKe4kQpTjw6XfJZlcBvvSbgi69zJdm9wkTpl7eam2xQoJL62msJehqRFsijoi8L6f8LSqEpOEJ9e6iD3iwmLEZEPKNkQRvsGQGypgYGyIyNe6k3Ka1lHGVqYIe5ZcPV7sKzwG8RGx6drkBolQYqvtFeF0s7BIDm/PK/d5G0sn5eh9HnXG6/HAIM9dD30sW7ZMRAjv378fLVu2TLP+2LFjaNKkidCaFMaco0I3JefOnROx1Rs3bkR4eDgUCoXI5f36a6l5NcMweUvoMnlH6DJ5S+gyurDQzTvkptBl8pbQZZJhoZt/hW5oaCi8vLyQmJiICRMmCLFL3l2qDbV79278+uuvwuNLxaqsrLKexvPe/ofWqVMHixcvFqHN69evR7NmzfDqVXIZeIZhGIZhGIZhmIKETKbO8SW/YGtri0OHDsHFxQXffvstypUrBwcHB5QtWxY//PCDWH/gwIFsidws99HNDNRXt2fPnmJJStLtk8YwDMMwDMMwDMMwRKVKlXDz5k0cPnwYp0+fRmBgoBC2NWvWFMWQKUc3u7x3oauzc4MPunuGYRiGYRiGYRgmH6NQKNCiRQuR0kcthywtk1tQ/hc4uYBhGIZhGIZhGOYDwO2FMoYigKdMmSIqLFNkcPPmzcU4iV5qO/RfYKHLMAzDMAzDMAzD5DiDBw/GTz/9hMKFC4t2thri4+PRp08f/Pjjj9neNwtdhmEYhmEYhmGYD4BMpsrxJb9w/fp1rF69GtOmTcPJkydRo0YN7bozZ85g2LBhovKyj49PtvbPQpdhGIZhGIZhGIbJUY4ePQq5XI6RI0emWUfjJICpEy71080OXC2KYRiGYRiGYRjmQyBXQ5aTrsX8010IcXFxIi/XzMxM73rqA0zFjcPCwrK1f/boMgzDMAzDMAzDMDlK9erVERMTI3rl6mPnzp2iWJWXl1e29s8eXYZhGIZhGIZhGCZHoQrL9erVQ69evURBqoCAACF8Dx06JPrqLly4EBUrVkTLli2ztX8WugzDMAzDMAzDMB8AmUwNmSznLm1OHuu/IpPJsGvXLgwYMADffvutdpx66hI1a9bEli1bRPhydmChyzAMwzAMwzAMw+Q4dnZ22L17N27fvo0TJ04gKCgIlpaWogJzgwYN/tO+WegyDMMwDMMwDMN8COTqnK2KlI+KUa1btw7nzp3DggULUL58ebG8T7gYFcMwDMMwDMMwDJOjREVFYdGiRYiNjf0g+2ehyzAMwzAMwzAM8wGQyVQ5vuQX+vTpgxIlSmD8+PFQqd7/eXPoMsMwDMMwDMMwDJOjnDlzRlRdnjdvHvbs2SOKT1Ff3dR06tQJbdq0yfL+WegyDMMwDMMwDMMwOQrl565duxaGhoZ48eKFWPRBXl8WugzDMAzDMAzDMHkEmVwNWQ4mi8ryUTGqn3/+WSwfCvboMkwus+xcLRio+b9ibjMm/F5unwKTArOrl/l65BEc173fKphM9gns48qXLw8xadKU3D4FhgoOyeWoYluRrwWTBr67ZhiGYRiGYRiG+QDIoIZMlnOXNgcPlS0oF/fs2bOYPn26eH7gwIF3btOuXTu0aNEiy8diocswDMMwDMMwDPMRUbNmTfE4fPhwseQVzp8/j+XLlwuhS88XLlz4zm2cnZ1Z6DIMwzAMwzAMwxTYHN23x7p48SIsLCyQ15g0aRImTpyofT5hwoR3bkPFqrIDe3QZhmEYhmEYhmGYD46BgYFYUj//IMf6YHtmGIZhGIZhGIZhGD2cOnVK9NJ9Fw0bNkTdunWRVVjoMgzDMAzDMAzDfAjkKiAHQ5dz9Fj/kYMHD2LatGnvtJsyZQoLXYZhGIZhGIZhGCbvM2bMGAwbNkxnLCkpCUFBQTh+/DhmzpyJn376KY1NZmGPLsMwDMMwDMMwzAeAWgvJZOocu7aynOxl9B+xsrISS2o8PDxQvXp11K5dG97e3mjevDlKlSr1MTu3GYZhGIZhGIZhmIJA/fr1RcXlI0eOZGt79ugyDMMwDMMwDMN8sPZCOejR/YjcmA8ePEBsbCwSExOztT0LXYZhGIZhGIZhGCZHOXz4sMjF1UdAQAD27NkjQrEpfDk7sNBlGIZhGIZhGIZhchQSuRlVXXZycsLixYtRoUKFbO2fhS7DMAzDMAzDMMwHay+Uc6HLkMvyddVlDaamprCzs/tPxbVY6DIMwzAMwzAMwzB5oury+4KFLsMwDMMwDMMwzAeAWgvlbHsh5BsOHDgg8nSzSqtWrdC0adN32rHQZRiGYRiGYRiGYXKUS5cu4a+//kJ0dLR2TKFQQKlUZridjY0NC12GYTKHqY1ZhjkQibEJSIxLhLGlCRQGijTrVSoV4sJjM3UsA2MDJMUn8VuTTVQqNeLjlTA1zXieUq1WIyQkLkMbc3NDmJjwfGdmSVKqERYDGCoAazP9/19iE2jmHjAxzNqUulKlRpISMM7idgUNAzNj8R2ij7jQ5BslDQpjQyjjs96WQmFkAENzY/E8PiIWaqUqG2fLMHkL+p03NzdHVFRUhnYWFhZCeNDvyH+xYd5ed24vlC4TJ05ESEgI9u3bh9mzZ4vqyvQZpbF///0X33//Pdq0aYO5c+fqbEe9dTMD3+EwDIMxF6fA0MQo3StxevERHJi2A0O3jIJzmcJp1oe+DMbsepPS3d6ptAta/9wFRaoVEzep4b6hOPXXYVxcc5qvfiYJC4/H2AlnsG2njxC6ri7mGDq4PEZ9VRlyPYUnAgNjUbLCqgz3OXN6fXw2pDy/B5lkxWkVvlilRN0SMpwYp/sju/yUEv/bq8STQOl1WVcZxrSWo0+dtBNDKfENUePrtUnYf0sN0lLFHYHvWykwpGHG2xVUms8egmJNKuld92fpz8Wjhasd6o3thiL1ysDIwhTRb8Jwe/0JXFtyAKqkdwtWmVyGjmu+g1OlYuL1tt6/wf+Kz3v+Sxgm56CiPlOnTsFnn30qBGp4eDj27NmLcePG4+XLl8LGwMAA06ZNxZAhg2Bvb4+EhAQcOHAQX331dZZsGCYrnDp1Cr///jvu3bsHLy8v7TgVoerRowdKlCiB6tWro3///qhTpw6yCgtdhmEQGx6LhJiENFfC3M5CPCbExItHm8J2wnsbS26tFKR+nRILR0sM3jAC5vaWYltlQhJs3e3RfnoPyBRyXFh5kt+Bd0DCtkO3Pbh+I1DyFpoo8Op1NH6ZdgExMYmYMLZmmm1ojt3OzkSvRzgsTHo/zc35J+BdRMer4RcKnH2swvit+kOptl1W4fMV0jqFXCp4efeVGgOXKhGXiHRFa3CUGo1/TcTzYGkbIwXg8wYYtlIp3ufBDVjspsbS1U48xoVGic94agxMDNFu+UjYFnPWRqOYF7JBrZEdYGxhirO/bX3ne15xQFOtyGWYvEqPHt1x6NC/CA0NfaftP/8sE/ZETEwMrK2t0adPb9StWwflylVEbGwsZsyYju++Gy1sSMAaGRmhfft2KFbMA5UrVxO/35mxYZiscObMGVGMKqXITUm1atVgbGyM8+fPZ0voyrO8BcMwHx2/1ZiAGZXH6Sxn/z4m1r2+64dTiw7DxMpULKEvgtPY/tnmt3T3Xb5tVSFyY0KjMbfBZEwu/R0urZU8uXUGNcqxvzE/s3bjAyFybW2Ncf5Ed/g/+1R4cknI7tzzRO82ToXM8PTewDRLh7bFxfqm3kXQq3vpHP5L8h+zDyhRbnwihv6jREjayFjB/MOSyG1eTobgPwwRuMAQ3l6Sl33KrvTzjOYcVAqRW6IQ8GSmIcL+NETv2nLYWwCbL/INoz4sXe2hTEjEP3W/wz+1v9VZCLe6ZYTIpQm1zV2m4+/KI3Dh951iXbnejYW3NiOsijig5sgOCH7ol7kPCMPkEpMm/QQ/vxdYvXolGjRokK6di4uLVuS2bdsB5uZW6N9/oHhdrFgxNGnSRISKDh/+hRibO3eesGnTpr0QrtS/tE2b1pmyYTJqL5TDSz7B0tISERERuHPnjt71T548QXx8PMzMzLK1fxa6DMOkoUhVDzT9rg0S4xKwcfg/Ij/X2tVWrAv0CRCP6eXJpcbCwVI8+t/1EyHOlM/z4Kj0hWblYsNXPxPs2CWFTfbt5QWv0naIik7Ez+NrCeF66XTPTF/DbTsfY+Wae3CwN8HiBU30hjwzupgZyYTwpMUonY/86zC1WP9pQwXMjWVi6Vlb+nklb3BUnP4ctq2XpZuREc0VKGwrQ2wCsOJTBfznGWH/t5nLPypIGJqbwNjKDGHP3kCtUos82tSY2kttKqICQhF4+7l4/uzoDWl7UyOY2EhRKunReGo/IYZPTFr7Qf4GhnlfHD9+QoQS9+3bBydPHsOdOzcxatRI2NpKv9UaSpcujaCgINy6dQt79+4VY3v37tPJ261Xr54IbyZmz56DpKQkkTNJ4aREs2ZNM2XDMFmlc+fOQux26tRJfJ4oUoCgCRTy9tJ6Ernt27dHdmChyzCM7peCgRwdf+sNuUKOE38cQtBbYWvjJoUMWjlZY/TpnzHp0VyMuz4DLSd0EjeQ6eFz6r54dKvqgZKNyoiw5eq964mxZ+cf89XPBDdvB4nHiIgEVKm9Dm6ey+HmuQzfjzstwpozQ2hYPMb8eEY8n/ZLXTg6SjcsTMZ810oSnrR0rKL/J/PBr9L6ztWT11/wkcStlSlgYZJ2QiEyVi3ClAmfN2q4j06A3VeJcPo6EVN3KUWIOaOLZWHpO4gEbo/dP+GzGwsw5PLv8J4+ACa25mKd34UHUCYqYVXYHqXa1xL5uhX6ekv/B3xeIzYkMt3LWrZHA7jV9sLlhXsR9sSfLz+Tp/nii+EoWrQ4fv75F/j6+qJs2bKYO3c2Xr16iTVrVmm9vMePH4ejozMqVqwCExMTlCxZEr/88rNY9/z5cxw9ehQlS5YQr8lz5ueXHM3g4yNFDNE2mbFhMm4vlJNLfsHFxQU7duwQIfhUdIo+o5T/TZMq9evXx/3797FixQphlx04QYthGB2q96oHp1IuiAgIF0WoNFB+LuFavghUShWS4hNFDm/9z5qIYlMr+/2p90o+Pf9YFJ5qMKwZBqz+Ujse5huCXT9u4KufCTQ5teSNJS+skZEc0TFJWLL8NiKjEvDXgibv3Mec+VcRGBSLihUc0LNbKb7uH5B/Tinxz2nJWzugnn5xHJoirX3+vypQMXNaaPyXnUpRhfnnjvwTnTpsmbDxcIKa8v0TlTC2NEWZLnVRqHxRbO48FREvAnFm+iY0/LkXms0crN02PjIWh0YvTfc9M3eyQZ3vuyDw7gtcW3oQxjRDwTB5nNevX2Py5CmYOnWayJX94othwrNK+be0XLlyBdWr19LaDxjQH3/9lfxbPWXKNG3OLpG6GrPmNa3PjA3DZAcKn3/06BHWrl0rvLiBgYHCi0sh8QMGDPhPkyjs0WUYRovCUIFGw5uL52f+PqrTBsjM1hzRIVF4duGxyMudXOY7XNtyQawjT62+asyEa4UiqNZTKiBAAjmB4jPfeoir9sh6YYGCBoV6a0KMy3rZ4eHN/vB9PATdu0hf/Bs2P4SvX8atIoKDY7HsHylc/NuRVXLgrAsmCUlSBeXPVihBHTeoOvOUzu8uKNWkjAxv5hkiYF5ybu/cQyrEJ+afWfmcwNTOErGhUSJ/drX3j1hSaQQu/ymFYtqXLgz3RhVgWdgeVT79RLtN4ttCeiSIK/ZPP7Sy/vgeMDI3xvlZ24TINbaWPMRiWwtTGGQQtcIwuQ2Fee7YsROtW7fF2LE/anuQenp66tjFxcWJRQOJ3pQFflK3bNG8TtlCKDM2jP72Qjm55DdsbGwwfPhwrFu3TrQV2rlzJ6ZOnfqfIwVY6DIMo6V0s/IiF5c8Jde2XNS5MudXnMA876n4p89CxIbHiDYdp/5K9vjaU18UPbSa2AlmNubwu/UCv1b9EZNLf4u9v0iVT5uMagUHTyd+BzKA8qcKvQ0zHtC3jAg5NjZW4LtRVcUY3Vs8ehyW4TVct+mh8ADb25ugTUsPvt4fgMBINZr8LwmLjiZ7cg99ZyDydfXhbE3vrfT8q2YKWJrKYGUqw+iWkjCOjgdehvBblZIn/17D+lY/YWu3GYjyDxUf/qt/H9SuJ09vjRHthNiNfBWCVd7j8HeVr7F/+CKxnjy/ReqX1XtR7Uq6QiaXo93yURh8fg76HJyiXddmyQjUG9uV3wwmz+Lk5IQJE8bj6dPH+O23X6FQKITYPXz4iPCMUSgo5UGuXLkKpqYWaNKkueiBSzm+1HKIepYS1HpILk+WBhovLa3PjA3DZJft27ejQ4cOKFWqlDYf94svvhBRCf8FFroMw2ip1LGGeHx59SliQnS9hJ9tG40fb/yKpqOTKyumzM2Ni4jVeyU1nt7HJ++LysvErV3JX1yFKxThd+AdUAEqIjomUTsWn6DUqbCcEZu2PhKPzZu4w9CQW9a8b6jYVPOZSbjwRA0zI2DpIAWWDjaAsWH6xb6MDGSi2rKmhZGG+OS3GE5SXSXmLZ/MHSpEKOXkakjZ/zs+IgYOXm7iuf/Vx4h6Jd14Pz18HUlxUiQJhTjrIz48RniLNQu1L0q530SaeWCYPAbl4a5fvxYvXjzFlCm/oEiRIqKf7S+/TIGHhye6deuB77//DkFBAbh06bx2u2PHjuHWrdtakXzvnlRLgwRsmTJltHblykkTQ/fvP8iUDcNkh8mTJ4uiU9RCiHo8v3kjFbDYv3+/iDigHN3swkKXYRit57B4XSlE5MnZh2muytPzkliiMOSiNT2FJ7bFOGnWjTy8Ly4/FUViKMSZFk0bjzAqOwugQtsqcKviAZvCtmj0VQvtfsP8eBb4XfTpIbUBWrTkFo6eeAmfp+GYPF0KG3d1MUfJEtaIjU0SIcq0pCQkJA633hazaljflT/tH4AZe5W446cWPXTXfG6ANpXkCIpUaxcqLEVimJ6HRCWL2v71pEmHKTuVOO+jwh0/FabvkSYwqheTCS8vkwwVmiKKN6+Mot4VRSug+uOltikUhfLy9B1EvgoWr4vULwe3Ol6wcLZF1c9awuCtICZPL/XvpuJVtFDxPWJbz//ptCpa1+on7XH3DvsjU/13GSYnOXhwv6i23LNnDyE+d+3aLdoHkcCdNEkqUEVcuHBRW32ZqjIXLVpUVGquWlVKYzlz5izOnTun7cc7fvw4IZiHDfschQtLE9X79u3PlA2TDrIcbi1Ex8snPH36VAjdgQMHiiJnzZtL6XPE1atX0bRpUwwbNkwrfrMKV7pgGEZgV9QBptaSZ9Dv1ss0V+XInH0o16qS6Ik7dMsonXX7ftmKxNgEVOpUHd3mSd6W372niorNR+fsQ68lQ2BX1BHDdkq9LjXc2X8Dzy/p7wPLJNO5oyeWr7qLs+dfo1N3KSdRwy8Tagkv7e8LrmDq/y6JsYDnn8LERPp6v34rUIQ3E5Ur6g8vZ/4bq85INxVKFdB5QXJeu4anMw0xeZcS/5xSCS+t71xJdH3VVI7VZ5R4GAA0mJ68HRWl+q0be95Tc3PVUXh1rgvb4s5o89dwnXVXFu1F1OtQXFm0X4hcExtztF/xjY6N//UneLzvEpwqF0PndWPE2O5P5+PlKf39GxkmL+PuXgTPnj3D8uUrsGzZcrx69Uqv3aFDh4SYrVevrqjKTIuGBw8eYMGCP0RLl2nTZmDWrN/Qq1dPsWg4duy4qNxMZMaGYbICtRSiMPvZs2eLUPqU2NnZYfHixWJy5sCBA+jfvz+yCgtdhmEE5g4WotgUEfZS8oqkJDooEos7zoH3qFbwqOEpPCGBj/1xdtlxPDx2V9hQ8SrNPlRJkmfq3qGbWNx+Nhp82QyFSjrD0NgIIS+DcH3rJVzfqpsHzOhHoZBjy7rWmDHrMg7++xxRUYnwLGaNzz4tj/ZtigsbUzMD2NmZaL3zGkJC4rXjhQtn3EOUyRhLU6mfrk2KSPHgKDUSldJ4epCn19JEsrG3SH5vqO3Q8XGG+Gm7EsfuqZCQBJQrLMN3LRVoUJoDrlKjjE/Ejj6zUGNEW7jVLSMKREW8CMK9zafxYKcUlvnm1jNsbD8Z1b9oDYcyRWBkYSq8uJTfe3vdCVFbQJWoFOHJhCox7cQEQX16k20y18KLYXKSAQMG49KlS+8sAkWFqlq0aIUffhiDdu3aoFChQsIz+++/hzF16nRERERoe+OGhYVhyJDBKFrUXTzfu3c/pkyZqt1XZmyYtOR0y58UtwB5HvosUt43iVp9uLm5iYJnAQFSq8usIlNzmTSGyRWoJH/jxo3RLLAGDNQ855TbjLms/4aXyR3M9iznS59HWPK/gbl9CsxbAntz+kFeYtKk5KJlTO5BodtVqlQUXmUSTXntPu/AJy9gbphzQjc6UYaWh9zz3PXQx5YtW9CtWzdcvnwZ1apVQ9++ffH48WORr0ucPn1a5KJv2LABPXr0yPL+ecqYYRiGYRiGYRjmQ0DtfnJ6ySe0a9dOtMEiEUth9vHxUuE/CsVftWqVGHd1dUXbtm2ztX92IzEMwzAMwzAMwzA5irGxscjTpZZCLVokFyrVFDmjquB79uyBuXlyb/OswEKXYRiGYRiGYRiGyXGod+7Nmzexbds2nDhxAkFBQaLvc40aNdCnTx9YWWW/1x4LXYZhGIZhGIZhmA+ATK4WS04hy4eJqUZGRujZs6dY3icsdBmGYRiGYRiGYZgc4/79+zhz5oxoK9SsWTNtuDIV8aLWWfQYHR2NGzduQKFQYOTIkVk+BgtdhmEYhmEYhmGYD4FclbMFouR5v7/Q7Nmz8f3332vbY5FHd926dQgMDMSoUaO0Rak0TJmSvQrnLHQZhmEYhmEYhmGYD87z588xduxYuLu7Y9iwYSIHd82aNRg0aBASExNhbW2NNm3aCA8v5erSevL4ZgcWugzDMAzDMAzDMB8AmUwtlpxClscduocPH0ZSUhL279+PMmXKiLHBgwcL4UueXOqhW6xYsfdyLBa6DMMwDMMwDMMwzAeHeuTa2tpqRS5hYmKCWrVqCW/v+xK5RD6sy8UwDMMwDMMwDMPkN+Lj40VObmooZNnMzOy9Hos9ugzDMAzDMAzDMB8CcityMapcgT26DMMwDMMwDMMwzEcFe3QZhmEYhmEYhmE+WHshVc5dW3Zj5i2he/36ddE0OCXm5uaoUKECPDw88LGzd+9ekXhdtmzZ93LtUtKwYUO4urrifZ1niRIlULp06Sxt9/jxY8TGxor389y5cwgPD0fLli3T2F29ehW+vr5o3759lq9Lds+NePjwoVhkMhmKFy+ukxyfEqVSiQsXLsDf3x9FihRB9erVxTbU8+vRo0eoW7dulo/NMAzDMAzDMAWJN2/ewMLCIk3urkqlSjNO/PTTTxgzZkz+FLrUO2nevHmoVKmSdiw4OBgvXrwQf9SMGTOyve/Lly8jKioKjRs3Rl5l6dKlaNeuXbaELl27BQsWCBGpDzc3t2wL3dTXbuTIkaLfVVbEJJUP79q1K1asWCFe0/t8+/ZtvUJ3+fLl2LFjh1boZuW6ZOfcbt26hSFDhoi/k0SyQqEQgrVUqVL466+/xCSBBvos0jmTEKdrSuK9Zs2aOHDggEiep/1QmfSCMDHDMAzDMAzDZA6ZXC2WnCInj5UdqD9u1apVs7SNg4NDto6VJ4QuYW9vLwRHSv744w+MGDFCiJ3sestILJEoyctCd/v27f9pexcXlzTX7n3wPq7d4sWLhTCsXLlyjl+XjHj69Cm8vb1Rvnx5+Pj4aEuZv379Gp9++ilatGiB48ePi1LnxKhRo2BqaoqXL18KYXvv3j3Url0b//vf/zBlyhQMHz4cX3/9NXbt2vXBzplhGIZhGIZh8jNffPGFWFDQo7gHDRokHjUijrxn5HGjUNFNmzZp7RISEnDixAkRvkqeNw0UJksiJiAgQMee3OLUjJi8hw8ePNA5ZlaPQSG5GzZsSLNs3rxZZ7/pbU/Q2N27d3Vc90eOHBHj5EF8X8TFxQnxtnPnTuFVzejvTu/aERR6TOf277//in2mB/0dJAJpsiI7pL4u5F3et28fjh07JjzFdD1T/x2ZPbexY8eKEuZ79uzR6ddFkwb099rZ2WH06NHazwsdl/4OErkEhTd37NhRHIsYMGAAjh49ikuXLmXrb2UYhmEYhmEY5v2RZzy6+tCIPEdHR/H43XffoUqVKjh48CAKFSqE7t27CyHUtm1bBAUFibxJEmsURkrhvCRibty4IQTPnDlzhL2fn5/wED958gTOzs7ikV5TCDB57LJ6jMjISMyaNStNSCzlGHfr1k28zmh7AwMDEXY7cOBAEaJL25ItCWhqpkyex2nTpuH777//T9fy2rVr2nBhup4k8Ck0l4QaNWlO/Xd36NAhzbUjaILgt99+E0KQBDt5ay9evAgbG5s0xyRBGhERgSZNmmTrnFNeF5rsaNOmjciJpffJ0tJS9OCia0Ve2aycG11b8haPGzdObx4AvXc00zRx4kSRQ0CvKSy6Xr16OnYhISGQy6W5Ijqf+vXrY926dahRo0a2/l6GYRiGYRjmI0OmlpacPB6Tt4Quef+2bNmifU0hpAsXLhTCkESXBhKv5G308vKCWq0WnjQSOrQtCR8KKaUwZ3d3dyEQKdeXwm8PHz4stu/Xr5/w4J0+fVp49EhINmrUCIsWLdJ68LJyDBJLKcOG165di759+2L16tXidWa210C25MWmuHXyCJMIXrlypRjr378/nJyc9F67mJgYnWungQRes2bNxHPyrJIQI28l5aKSJ5Tyes+cOYOmTZum+buJ1NeOOHXqlPCkkkfz2bNnIieWjk3hvqkhryr9LYaGhjrjJH71nS95kPVBHtXBgwejVatWWLZsmTh/er++/PJLIXSzem5UeCoxMTHDcGpNXjB9Pih8+ffff9dZT+8zTRLQddWgEboZfcZp0RAdHZ2uLcMwDMMwDMMwH4HQpbDUqVOnal+TF41yQ6nKFglSDb1799YKMfJSksi8c+eOEJAEiRzyPm7dulVHRGqEFHkZSZxQKKoGjQjVCN3sHoNshw4dKryvGg9oVre/cuWKyGslkUuQaKYwXVrSIywsTOfaaSChpxG6v/76q/B0kkgkjyaF+BKhoaF6r2169OnTR1uVmAov0aTB8+fP9dqSSNRXCIvCo/WdL3nbjY2N9VaWJk83eWHp/AnyuFJ+bHbOTSM2NddYH+Q5Tvmogbzy9P5ScS0S2Sk97fS3kogmYa7x9KaEiqr98ssv2tdkQ170nMLQ1AjD9/+AP1v/DwkxCWLM2tUW/VcOwx8tfkXVbrVgWcgaxxcchMJQgWbft0WJhmWgSlLi2taLOP/PiXT3bWRujKFbRumMBfoEYNNXK1CqSTk0/z55QmLf5G14eu6Rjq29hyPaTO4KaxcbPD55Hwem7hATPwWF78adQrUqhdCre3IxtX5DDqF/by/UqumMlu124Oxx6Ttl/8FnmLvgGsIjEsQ2k8bXQqFCyd+R+ti64zHmzLsGCwtD/Da9PipV0C3qMG7iGZw680pnbMXfzVHCM22kxsfIyQcqzDmoxI6vkyflNl1U4tBtNZYONsBnK5LQs6YcTcrK4ReqxpiNStzyVaGQlQw/tFagefl3ZwElJqnRbl4SDnybfIxTD1QYt0WJ2ESgbx05vmkhfb+l5HmQGsNXJ8E3FOhfV47RLdPafGx03foj/h29FOHP34jXCiMD9Nj9E7Z2/xWFKnigePMqOPHzWrGu6mct4dmyGmRyGZ4evYHLf+yBWpX+d4dDmSJoMmOA9vW1ZYfwaPdFmNpZouHPvWDr6YKAm89wasp6JMVK35OpsXJzQPM5n4rzYZicYOHCBbh16zb++muxdmz37p34+++lOHz4CO7evQUPD08x3qVLZ4wb94OIWLt9+w6++eZbUV8ku3Ts2EFM/k+fnlwY9ueff0K/fn3w8qUvBgwYpE3JS2+8wEPFoXKyQFQeL0ZVIIUuFaMiQfMuNGHMBIUAk1hJLc6omheF4KbnMVy/fn0aL2PKPM3sHIO8n507dxae2pRVorNyjuQ9JVJWDiZhR2HOGUEC613XjryHP/zwgwjvJWGsr9pZyr87PSiHNSX0t5F3VB90TUqWLJlm3NPTU+/5fvXVVyJvOjUUXk6ikFr/pCR1hePMnhtde40QTw9634iUx9y9e7fwrlOboT///FOEM6cUwhRqTutoEoGep4YmNTSTKZr3hMLmc4rE2ASEvAhCiUZlcHf/DTFWuml5vL7jK24MjSxMYGJlKsbrfuoNmVyORW1+E2O9/hqCCP8w7XapSYiOx+KOs7WvG3/dEiHPg8RzZy9XHJmzD49P3hOvlQnKNNu3+aUrzq84KWx6Lf4UFdpXxc2dV1BQqF3TBZu3PtIK3fCIeBw/4YtF872RlKTC64AYMf7SNxJTZlzEto1tYG9ngr+X38GI0SewcU2rdPf97HkEJk+/iH072uPpswiMHnMSR/Z31rGZ/FNtKJXSD+MjnzD8NPk8iheT8tELArU9ZTj7SI3ASDUcLaX/09uvqNG8vPQ8OFItxCjx+YokDG2kwJrPFbjtp0bnBUk4PtYQhW11J8VSsv+mCouOKvE4IPnmQ6lSY+iKJKwbZgBPRxmazUwS51GnhK5oHvpPEvrXk6NDFTka/pqEVhXlKOOa/rE+BgJvP4NHk4q48Y8UTVS4thdigiIQHx4DA2NDmNiYi/ESrarD3ssN23r+D3JDBRpP6YeKA5pqt9OHTTEn3Nt6Bnc3nhKvaSKPqPF1O/heeICDo/5Gw596omL/pri6eL/efTSY2BMWLmm/4xnmQ3Ho0L/48ssvtEKX0qUaNKiPnj17i/sQTf0QioKcOHE8vL2bCUfGwIEDsHz5UjRv3iLLxyTnzLhxY/HZZ59i+XKpcwZBTqjmzZuhbNkKQlTPnTsbXbp0S3ecYXKTPF2M6l1YWVkJT2fqEFDK7aX/7KkhLzHxzz//CKGlWSjElsKks3sMEjc9e/YUHigqRKXxOmb1HDXnl9KW9kmCmDyJ2YVChSk8mcQXhSZTqPPJkyezta/UHs53fUm+j/BcOiZ5SckTnZLU1ySz50ZClzy/GzduTNeGQs9p0kJTzpw8/hRCTx5y8tqSRzn18ej8aIx+gPRB3mr6PGiW9Ow+JCRUvZolt6Iq3bQcbu9NO+ngUtYNzy/5QKVUISY0GvunbkdcuHT9i9crhXbTJO9iSpLik8RCXmGPWiVwddN5rdeYRK9mvT5PLYnpqKAIqJJUiA2Lgdzg4/dapaRFc3ecv+SP2FgpcuPIsZdoWN8VFua6E3IPHoWhqLsVnJ3MYWiowOeflhd2GrxbbEVYeHJ4PLFr7xP07VUahV0tUL+uK74blXaSi/ZlYmIgFhK50ybVhVz+cYuplBgZyNCighz7bqi03tdj91VCXKbm5ks16peSif/rFdzk+F83A8S/Dbj5314lVpxOO5GTkATUK6m7r+fBQIlCMlQtKoe1mQxNy8pw5bnu/w0S3r6havStq4ClqQzLBivwdi7qo8bn4DV4eFfUvvbwroAnB6+msXMo44aAG0+hTEhCYnQ8LszdgUjfYLHOprgTWi36Ms02Fq52iHgZJLahReP9NbY0RWxwJP3oike5gf7bozJd68H/xpMMvcYM8745cOAgqlWrqr1vaNmyBY4ePZbmHoucCwEBb7TReitWrMSWLVu168+ePS0cSylJ7fhJfa967NhxnbF27dqK/VKR1U2bNqNRo4YZjjN0fyq1F8qxhXN0Pw6hW61aNVGYaNWqVdox+s9NVYUpnzM15MWkWS/y6GqgYkt16tRJV+hm5hjkKaWc323btqXp85SVc6xevbooDEUhzSmrIZNdel7TzECeSzrmZ599JrygdINGPV8/NCQmKUz5v6IJ703ZuufChQsiHDy7UEj82bNnsWTJkjTr5s+fLwqITZ8+XbwmkU2FsSg0miYy0vN8kweb3v+MQqJzm7sHbqBkIy/xGTA0MUThiu5aT2tKbu+9hi5z+qLHwkGo3qsuogIj8eTsQ7HuyZmH2D1etxJ3Shp/3QKnFx/RvrYpbIcGw5ri0y2j4D2yJeSKtF87R2bvxZCNIzH61E8oUsUDt3anvan9mLG0MELdWi44flIqwHfg0HN0aKsbwUBQqPLd+yFo1no7/jf7Mi5ffYPhw5L7jx872AU21rrh/w8fhQkB3a3PPvTstx9ubmkLsGk4c+4VzEwNULaMHQoaXarLsfet0D31UI3K7jLYW6QV+91rylHlp0QRTrz1sgrNy8lQ3FGy+6GNAgPrp52k6VBVjs+9dT/3tM2eb6QbzNBoNfZcV6GSm+7xHvir4eEgw1erk+D9ayLO+6gz9Bx/LPhdeADb4s4wtpJC8t0bloePHqH79MgN1BjRTgjaCn2lKJQn/14T68KeBGD/F3+m2cbS1R6lO9ZGx7Xfoe7YbjAwlVKKLi/aB+9p/dH7wGSU79MYt9fp3twTpg5WKN2xDq4tOfAB/mqGSR9KuaIQ5RYtPhGv27ZtoyNgNVABThcXZ9y6dR2//fY/NG/eXIQ3a6hbt764V0nJoUMH8Oeff6RJpSLBumzZcpw7J01aayhWzAPPn7/QOnrImUKOlPTGGSY3ybt35JmAqgNPmjRJiBDyVFIVZQovpSJM1NNUY0OFnSgndMKECaI3L1XypbyBEiVKiAJM5IEdM2ZMto5Bub6zZ88WYpmep8z9pW0yc44aqNjU+PHjRc/Wq1evivwK8j5TSG/qsNyUUKisvpxXonXr1iK3ggQuFXSi8GoK4abQXAp5JgGeuppwyr895bXLKlTVmapZkwcvK57g1FD4MOU+0/lTwSl6v8jDStcyu/slDzyFRFNBK7oGVEiK9kvil1oEUREwKlJGkNf/1atXwuOe+jqTsKUQZoLyqzXb5FXIOxv4KABFqnnAzNZCiFbysuoTxK9uvUCpJuVRopEXWk3shK2j14jxjCDPbOFKRbH9++SiXIGP/fHo5D0E3H+FDjN6os7gxjjz91Gd7Vr82AE7xqwTYrr99J6o2a8+zi1Le6P5MdO+bTHsO/gMLZoXFfmys35tkMbG1sYY5090Fx7fk6f9MGTYYVSu5Ig1y9MPS4uOTsTF5xFYsrAJ7t4LQe8BB3HzkhTulpql/9zBl58ne9IKEi3Kk6BUIz5Rjb03VehcTf888KyeBuhVW4V/76jx51ElRqxR4/R4Q63YzSrH76tEeDIJ6AaldY8ZHQecfazG2DYKsf/OfyTBy1kmcoU/ZtRKFZ6fvA33RuUR8vAVYgIjEB0QlsaOvLnrPpmIoo0roHDt0qg5sj0uzt+FW6uPpbvvqNcheH3pEXzP30etUe1R+9vOOD11A2p90xFX/tyL+9vPosZX7VDtizY4M113Qq/BhB44+9sWEXnCMDnN1q3bhNeUHps08cZXX+neQ2rq3VSpUl3ci5DN77/Phq+vH1q0SD+9pUmTZkIQjxnznYg0XL16DdatWy+6iqQHiWANJGo1kVrpjTNMbpEnfi1pFol6kr4L8mxqWsloIIFKYpXCOSiUlUQHFX/StI2h3qdU0Imq8BL0nAQntZ4hbyMJKBI2mvyGrB6DvH09evQQFZSpknHKReOFfdc5UlGjcuXKieckKElc0k0obU9Cd968eRleuxYtWqQ5tmahFjjkJabqynSNSbCRACUP9F9//aWdbdP3d6e+dnSeqXONabtKlZI9SimhcGkK56UiWxooHFift13j/U5ZYTvldSHPK4UT0xenxtNNIlPTOiir50b8+OOPogI22VG/YGol1L59exGaTH+3BjomvceU25z6+mpyeQmq+Jzy/PMqt/ddF+HLXs3K4/a+5PcmJU2+aYXIwEhcXH0KG4Ytx6avV6J677rv3HelTjXw8Kiup/3AtB3wOfVAeIUvrDolRHZKjC1NYOFgKby40cFRuLTmNNyrJefMFxRatfDAsRO+uHDJHxXLO8DKUvI0peTQ4ee4fjMQbVsXE0WlrpzthctXAhDwRsrh1YelpRF6dC0FVxcLNGviDjMzAwQHp+0xHRISJzzEdWunP6n2MWNqJEODUnIcv6/GodsqdKya9ucxKFKNuQeVqOYhF+LzyBhD9Kkjx5ZL2RM+y04q8enyJPw1wADTuqSdd7Y0Baq4y9CwtBxudjL0qCnHtRcF48bR58BVEb5MYcs+B/Xn61fo5w3IZbi/7SyOjPkH2/vMRPnejTPc77W/D+Lx/suIC43CjRVH4FxFipxwrVESN1cfRVxotHjUjGsgzy+dT+PJfdF9xwRRvIq8wgyTU+zduw/e3o3FxPy1a9f1ClESwt7e3jh69CgmTJiIihWroFSpkuKeNz1IjB46dAi9evUROb/9+/fD4cOH0rWnrijkNdZABWPp/ii9ceZtcaicXpi849GlkFBa3sXMmTP1jlN/WE2P2NRQmGlqoUh9TtPrdZrVY5DAStniJj0yOsfUrWtILGVWMGX22pH3VhOKq4EKIWmKIen7u1Nfu9Tnmd52GkhEkxd26dKlongTkdqLnRIq9ERL6uNRePk333wj1pE4J+gL9f79+9oWQVk9t5T5LORFzwj6rFDIckZQnjdNTpAgzuvcPXgDg9YOh8LQAHt/TtvmiXCr7IGK7avh2pYL4rWVszUiA6RK3RlBYdEn//xX+5rClL85+RMWtv6fyPEtWr04Au6/1tkmPjJOm8sb/ioUTl6uCPKRqq0WJCjkuHQpW8yYeRk9u5XSaxMZlYg/l9zC5rWtRF5teHi8KCKVOlw5JbVrOgvx3K+3F4KDYxEXp4SdnUkau1NnX6Fxw8L/Kfoiv9OpmkxUX3aykomKyqmxNAFmH1CidSU5SjvLxA3i6zCgYtqSEO8kLEaNabuVODvBEM7W+q95pSIyUeU5PEYt8nivPlMLYV0Q8D13T1RBtnZ3xIGvkyvNpoQqJJfv2RCX/tgjXps72SL6TcY31t13TsCBEYsR8SIQzlU9EfzAT4xT3q5jWXfhJbYvVRhhTwN0tqMKzMtqfKN9TSHOuwam/d1hmA8F1Ve5fPkKpk2bgqVLl6Vr99NPE4Qzg+6dKPKNnB00kZ8RDRo0wKBBA0TK2YYNG7FqldQiM73CWH369BZ2FDV4/vyFDMcZBgVd6DIfL9TKiXKj6UuWQqGzA31JU0GETp06iRZIdCNO/YCbNGkivpzzAtTqaNasWRkWdcgrRL2JQGx4LKICI5AYpz/3e89Pm9H19/5S9WUZhKd188iVYl2RasVQ2rssDs/am2a7whWL4tUtKc+UoGJWJxYewvB9PyA6JApxEbFYP0z6ga7Ztz6iQ6NwZ+917PxxIwat/wqxodFQJqmwdkja3OmCAOXljv7hFFYtk/KwUtO5gycuXPRH1Trr4VTIHBGRCZjzW0MYG0t5ob0HHMBffzTR8QZ371JSeIobNNsicnV/nSIVmnryLBxz51/DgjmSB+ziJX+RA1yQaV1RjiHLlfhfd/3F0IwNZaLdUKf5ibAxkwmx6l1Gjt61JfH51zElHCxk6Frj3WKU8m0pa2DA38mpA8O8FehUTY4vVyVh1CcKlHKWYXpXA9SdmggzY5kQvq0qFoyJCFWiEq+vPBa5ulGvQvTaXJy3C81nDUHPvT8jKS5RbHPsR+l7yqqIg2g9dHziGp1tLvy+C+3/GSUVnqKIkxHS5OmpyevhPb0/EiLjoDA20I5TKyOzQta4vfa4KF6lgSY5VInpt/1jmA8B5eX+888ytGun3xmye/ceMTl///4dkSNLUYeDBw/VhhTv378XvXv31WktSd5bitxbsmSp6MzxLnbt2o3u3buJY1DkYceOXTIcZ97Gz+bkHGXBmA/NFDI1B9AzHxjKSSb+SysdKsRA+b5UhIo+spQT3a9fvzwhLP39/YVHmXoVZwXKpaFy/M0Ca8BAnbNzTpRLSyKU2gJpMDIzgkwh13pYNWMqpRpJ8cmC2MDYQPTkperIqbEoZCWEdGqoLy8tmv69mpBlysXTjNEEhpGFsc7xc5Ixl3P/pjUxUYmQ0Hg4peiLS593Ck2mSssaqOUQiVw7W13P7Gv/aLGtvorJUdGJotCUZh0dKzQ0XtuDNyQ0Tqynyst5AbM9y3PluAHhatiaS5WYNQRHqWFuDJgYJo9RASny8BooksfI80q11ixM0l5/8T5GQOu9jU1QIzhK18bGTNr2TYQatmaA4dtzSFKqReVmEru5wZL/DcyV4xqYGUNhoEB8RPJ3DfXUpTBiajWkHTM2FN9dSTHJ32dUNdnY2lwraFNCPXcNzYyREJX2u8bI0hQJkbE650CRKSnHNIWpYoPSftd9aAJ7p+1Nz+QekyZNydHj0e8k1Quh+4eUUCcHErapO1+kzJklKIUudcizPruU0H0WtXik+7DMbPeu/X0I6PyqVKmI48ePa9MC8wKa+7zDfe7C3CjncvujE+RotrZsnrseuUHeuKNhPmreR69Yas1D/YTf1VM4N6DQoKyK3JyCbgpti7xtJaBWIyIgXIhb8qymhISrkbmxyKNNibm9JUJf6lZo1LQJ0oc+kUsoE5Vi0WDpZCUEbUrhS0IgpcjVZ5Pfef4iAvHx0nWwsDAUebMpoXBkEqrkbS3uYa29sSGRSzm0BoZy4a01MJCnEbmEi3OyGE5N6lZFdCwSuSSAIyMTMtw2Mzb5DRKqJDwJ6iTjbi+JW6dUYcTU4sfYQFfkkvCMigdszXVtKbw4PcT7aK2bE+yWqri1SqXGi2A1XKyTRa44P4UMmo5bZOMbijQ2+RqZDDbFkqMJSJySkCXhmvKbhnrlUm5slH+yN4owtbVAdKBuaoVoVaZH5BLUGkifyCVSCloTWwsxGZdSaGvP8a3IzciGYbIK1XuhTh0akeTn56f72VWrxTgV6aSCmho0FY411Y7FZ1mP2NSX1/suUZpe1w/ajto1pj5H6jxBLYwoxSwllLNL9XBSjxcENG1/cvJ4jAQLXYb5iHEs4YTB60fg2UUf4cVwLu2Kq5vP49g83fYYDsULiQJQVCwqJf1XfoF5TfRX9M4unWf3hXMZV5hYmmD/lB24d+hmtmzyI30GHoS9vQnMzQwRHBKH2Lgk7N7aDtZWujm2c+Zdwx9zdYvqLFtxB+7ulqKw1Pvi4L/P8d046qtoAs9i1lj2V7Ns2eRHVp1VYckxJbxcZKA5mBsv1Vgy0ED0003JwVsquNtTQahkUekfDlFE6uB37y+ihMKgW8xKgokhEBKtxvYRhijhJMuyTX7EyMIE3bdPwIvTd0WqhI2HE/yv+eDYeN08QVNbS5TpVh+XFkhRQhq8ZwzA0bEr9FZmzi7UtsizZVXRsoiqOOtrN5QZG4bJClu3bkJYWLgQsyQWLSzM0aRJ8zRFncaN+wFDh36uM/bZZ0NFhNnKlcntLD8k5IA4f/4MPDw8tYVCqTjqokV/IDAwSIRD9+jRK8NxhvnQcBQ3w3zkUHufdUP/Fnmvf7b5DfWHSUKF+uiaO1jC2MIEkW/CcWf/dZ0QZAr9Sw15WSmMzyaVK8rU2gwGxro3/WRDYc4pca9eDHZFHfBn69+wvOcCtJyQttp6ZmzyM5RTu25lSxzc3RGFXSxw9LiU0/zsOeVTqeEfEI3RI5P7GUZGJYiw4tSER8SLhdaRjQYKSQ4M1PXYh4bF442eysw//nwW2ze2wfGDXRAYFIvTZ19lyyY/987dOsIQu0YZYl4fAyw8otRWV46MpUJTatEnl3rqar2pIWlnypUqtSgapdSz3j9cGtdA+3jon3Yfi46q8El5GU6MMxQVnWfsVWbLJr8SFxaNA8MXYf+Xi7Cx/WR4NKkkvKX0PWTuZCPCk1VKJe5tPq3dhry7NJ4aU3tLyA0NxHY0wafB0NwYhua6kRDmhWzEeEosXGxRsk0NbOo4DVu6zkDVz1umOU5mbBgmO1DboE6duqBhw8Z48uQpWraU2sdRm0gRGeLsjBkz/pf8GTY319uvlsKUyYNK6+i5BmqlSMVGU0J2WamjMnDgANy5czNNNec5c2aidet2qFWrjginploqGY0zzIeGPboM85FDwtTM1lzc8BWvWwp+N56LcbfKRdFsTDvxw3lz1xU4e7li98TN6L9iGAxMDEV4n7GFdANIN5t9l38OE0tTUfTFxs0WcxpMFus6/NoTjp5OIu/32paL2h651C/3wsqTCHkepD2X4nVK4f7hW+J5mF8olIlJsHaxQTiVrs2CTX4mLCxeVD8OCo7Di5eRKF9WCi3v3nc/Snpao3JFRyxfdRf3rvcTj/P+uA4HexOKPMfnn0otwFatvYfZ867B0cEURkZyDB5QDl07lcDhoy/w85QLsLU1hkIhE0WtyFt8/sJrkec7sF9Z7Xn4vYqCQiFHCU+pRVeTxm44e/416td1zZJNfiY2URK15NE9+UCFGsWkyR3qj3vhiRqxCUB1DxkquctQp4Qc7eYmwsVGJvJwNTwLUqPDvCTYWwDWpkCCEtj7jSFCotTo/mcSDBXAqzBgXh8FGnvJxTEn71RizecGafrpTuksxSc3LyfH5J1pwwUzY5Nfoe8nE0qOhgz2pQu/DV+OhoWLHVr9+QUSo+PxYNcFeLaoit2DfkfT3wbBroSrEL/Glsk57d7TB8DByw3K+EQYWphg16DfRZhxtS9ai/ZAdBzyFp+eJvXILd2xNvwuPkTA9eQwUNcapUTVZyo2RUvwA18UKl9UFMfKig3DZAdqm0jeXGqhWLx4Mdy4IUU07dmzE/fvP8DNm7cwdOgQFCnigcGDB2H8+HF48yZQ2Pz1l1ShfMCA/qL6sr9/gAgxpirNa9euQ7NmzTBr1v8QHBwiwqC7du0uvMXU+tHdvQgWL04uBElhyeHhknc5NQcOHMSlS5dx6lRyFIOTk5MIW6aOGJoqzA0bNsCdO3f0jlMLpAIDzbflZPBN/g/0eW+w0GWYjxzHEs7os3SoELT2xQvh4upkjwiJ04UtfoVn/dJC6FJLoeBngdj140aR2/v1kR+FXcUO1YVg3TNxs/C2fnVwrBgv2aiMyP1d99lSUQxm6JZRuLrpPGLDY7B/8rY050KeYo3QJqKDomBmZ6EjYjNjk5+hqsomxgoEBcfC3NwQtjbSZEJsbCK+Hl4ZtWo4C4FLebEkck8d6QpLC0N06Ca1UKHx31OMt+28S4wrlSpMnnERG9e0gqmJAr8vvI4Nmx7i808riB69qfEPiEEhBykXjCDRfPNWUJZt8jNbL6tw8YlU/djnjRqrhib/JBaxk4n+tj9ukbJEp+5S4qcOCvSopcCas0qsPqvSjk9srxCVllecVmLz2566sw4oMaC+HK0qyPE8WI0vVylx4Sc5zI1laUSuJhya2hoRDhZUAAvZssmvmNiYo9XCL7UVk1+euScmdzRtg9Y0/VF4aEnoutYsJby9mztPE2HPfY9IrfNcqpeEmb2lGDe2MkPfI9PEOFVudq5cHHs/XyBet/rzS9iVckXIw1e4ukQ3jYMwc7RGTFByLiOJbjpeVm0YJjssXDgfsbFxcHCwR2xsLIKCgrShwvPmLcCpU6eE0KVcXhK5VapUF3m5VFGZoPEffxyrHf/334NinO4Bfv11Gtq16yhaFX377Wjhmf3993nYv39/mvMgDzKdCwnav/9ehosXL2rXUYg0LZqQZcLFxQUBAcltjKjbhqdn8XTHGSYnYKHLMB85AQ9e4e8uv2vDlb/c/wNu770mXr+6+UJUX9ZAXt7HJ6VZVypCRR5VonDFIvA59UA8J8EbQXfcb1sNFatbSghpIjo4UniPSejqI2VBKkJUSk1R0TmzNvmZ5YubiVxXYsqMi5gz/xqmT64rXtes7qS1e/AgBGVK22pbBWm8qI99wuBVKnm8bm1p/KVvFJ4+jcCQYYe1+yjcPv0bb/IEp4T68RqnqricGZv8TN86ckzuLP09jwLUaDg9Ea9+l8JPa3vqTolffqrCrB7SukZecq3QvfJcjVk9JduGpeVaoUvtg47cVWPpCem1Q3LkoF7I86uB9J2+KNjM2ORXYkOisL231PucvK7tV3wDD+8Kos9tyKNXwqOroVBFD/ieuSeeU1GpoHsvpfHyRfHyrDROxaE0PXKdKheHXanCWiFNFy+lFzg1om2QiZHOd5AyITHLNgyTHfr06Y8HD6Tf259//gkTJvyIkSOlHs7nzp3T2nl5eQkPr6b41IkTJ8VjiRIl8PDhI+34yZOntB7akiVLYt265JZb27ZtT/c8zpw5g8qVq6FRo0b48sth+P33OVi3bj1WrFip18ubuqgVhUjHxcWnO16g4PZCucbHc8fCMMw7ob65gY8DRP4s3SBqPCYaKEzY0DT57tn4be4atRiSG8q1s8IkZsV4ohJXNpzDqUWSuCrR0Athfvp7XhJhvsHJVaAph6iQlVZMZ8XmY6FKJUds2PJQ+5qurQZDIwViYpNrzpInl6CQ5MSk5MkJypstXcoWhoZyODubidxfjSBO/f6mpLCrBV74JnukfP2iUNzDKss2HwslnWSgy6opxps68osqHFMBcMpOT1mwl7oLaeZmqC2Q1l4BzO+rQK3iciQmqXH0XsZVMD0cpDDoog4yvAgGPAvJsmXzMUBpE0H3fWHpai+J1VQfZPreofZAGjTP1SqVyM1Nmasr7JOUeH7iFk5OWideO1fzROij9Cu/RvgFo3izItrXFD4d/jwwyzYM81+5fPmK8N5qSNkRlAQkVTLWfgbftpEhL2vK1ouFCjmKCs1UPTkgIAANGjQS456eniKk+F2cOHEC169fF8WuJk+ehOvXb+D06eTIMA2+vr4oUsRNp4L048eP0x1nmJyAi1ExTAHCuWxh4Z31vZ4cGpwS8ubW7FtfCNnKXWpqBe2Ly09QtWttEepcZ0hjrRh+eOIuKneuIcKNi9UpiU/Gtk/jkU3J/X9vo3zbqrBwtBT7f/PIH4mUCJlFm48BKiS1fvNDNEgn35W8ts9fROLchdcil3f7Th8xXqK4De4/CMXdeyG4czcYu/c+EZVqXV3MYWSowP6Dz0QxqmEjjoleu+lB7Yk8ilph194nou3Rtp0++KSZe5ZtPgaoXdD680oUc5TB0lS/eKSiVL8fUopeuZqiVUTdkjL8fVwleu0u+FelFcgtK8jx5xGVsJ97SIV9NzPuodipmhyLjkr7n7lfiU5V5dmy+RiwdLNH0Ybl8eryI73rX565i1Lta8HC2RautUrBoaz0maTc2xKtq4sQ4hKtqsO6aCEhkv3OP4B7g3LiNeX/ek/tn2GUiO/ZeyI82srdEYVre8HI3AThz99k2YZh/gtUtGnAgH44fvyE3vXk9XVzK4wGDRqIolA9enQT4yQiS5YsgQoVKqBs2bLo2rWL1Mc7IEC0F+rQob3I/1258h+RD5weNPFKOb3kAd67d7fw4lKFZX0ilyAPMnmYu3XriiJFiqB3757Ys2dvuuMF0qObkwsjYI8uw3zEUL9bM1sLjDw6QfzQUTjyphErER0UiYTCdqLaMpEQE4/INxF4eOwuCpVyQb9/PofP6Qd4cOSOWH9n33U4lXbBwDXD8fDYHYRRI08R+vwSp/46gt6LP0VsWAw2f71Se+zeSz7F4Vl78Oahv3aMjn98/gH0+2eY8NLuHLdBjFMP3yGbvsaiNjPTtfkYKErtgfpKuVDWVkYid/azIVKBKY+iyU1WixezgpGRQoQ5/zz5PKytjdC3V2kRrmxqaoBF873x7dhTYh9NvYvAzNRA3JSsWtYcEyadx29zrmBQ/7KoVkWqorl+0wP4+kbh+9HVdM5n/uxG+P7H0/g9OA6/TKil7ev73bhT4tyaNi6Srk1+x84cQqBuu5Ig+tRWcJNhxwjpJ9HeQqbtietoKYO1qQwT28vx7QYl2s9LQt+6csS91Um/dFRg9AYlOs5PQovyctx8KXlcvmoqx6SdSjSflYRKRWSY01OKO6Zqzk1nJuHiT7pxx71qyXH3lRpNfktCo9IyjGgm3akcvavC1isqLOxnkK5Nfoc8sYkx8ei1/xchTKPfhOPczK0Ivu8rqiJHvZaiRJQJSeJ56OPXuPrXfnzy+2cIuvcCD7afFV7bN7ee497WM2iz+Cu8ufVMbJ8UlyDCnk/+sg7e0/qL4lXHflwpilURDSb2xLOjN0ROsIak2AQcn7gGzf43CAnRcTj83TLtui6bx2L34Hmi3256NgyTXZ49e44dO7aK5+HhEdi9e4/IoSV8fJ5oPbqPH/sgKSkJ3bv3wqxZvwkBu3LlalE8ijy9AwYMxoIF84TIPHjwkMjJJbp164nZs2fixx/HiQJVFy5cEOO9e/cSYc2//CIVmSQ6d+6Epk2bYNasObh69are83306LGOl/nzz7/AwoULRP7v+PEThTc3o3GG+dDI1Ck/oQzD5Bg0O9q4cWM0C6wBA3XennMqVrsE3Kp44NzyE/CsXwo1+zbA6kF/4WNizOXkMOG8DBWd+nLkcUz5qbYIYe7Z/wA2rWkFF2fJ+/6xYLZnOfID5N2lwlVNy8rw614lbMxk+LZlimTaj4Al/xuI/ICtpwtKd6qDy3/shn1pNyFiqfXPx0Rg74+j4vnHwqRJU3L7FNJAk54rVizHmDFjRT7szp3b0LlzN7x8KeWyf4zI5XJUqVIRx48f14Zw56X7vCOD78DcKOOonvdJdIIcTZeXy3PXIzfI23fXDMPkCV5ee46KHarh8x2jhcd194SNuX1KBRZq90NtfnoNOCBuaL4ZUfmjE7n5CfLijtmUhEk7gAalZBjf7uPwsuZHQp/4C49wp/VjRBXkY+NX5/YpMUyOQ/4rav+zfbvkGZ49e+5HLXLzBdTPO0VP7xw5HiNgocswzDuhfLad41jc5hV6dC0lFib3KeEkw7YRH1H54/yMWo3zs7eLhWEKMuvXbxALwxR0WOgyTAGlRAMvUWX56fnk6odVutbC9W0X4VTaFUbmRnhx+SncqxeDS7m31UXVakQEhItcXsqXy4giVT3g6OmEhyfuIepNRLZtCgIvfSPx79GXGNi3DORvZ2IvXwkQfXY9i1tjw+aH6N+nDEJC47BlW/L7Rfm6jRsWRhG3jHvXBLyJweGjL8S+atd0ybZNQYH64dYrKReVmAkq/nT4rhpdqsux/6YKZVxl8HCQYfMlJTQfWwMFUMZFJloMZYRSpcbeG2rEJqjRvoocpkaybNkUFKg1kKmdBZ4dvakdK968CnzP34extRlsijnj5ak7oi+ua423kz9qIC4sSlRaTtmWSB9UnMqxfFG8uvQIES8Cs23DMB8SqlRMxZwoX1fTu7ZevXoi9/bWrVsYPvxLzJs3H7a2tqI6sgZaTzm6Dx8+/E/HpiVlESoqKtW+fTuRN3zgwIF3jhd45NQ2LQevAgcWaeFLwTAFlKo9aqPX4k9hYm2qHWv2fRsoDBUoVrckyraoJMbo0aOWp7a/ZaWO1UWxqowgm3ZTuos2QZ9uHikqKGfHpqDw4GEovvn+pBC0GvYfeo6zF16LFkM/T5UKhvj7x2D+n9e1Nr5+kWjScptoJZQeISFx+KTtDjx9FiEKVa1aey9bNgWJCVuV+HJV8kROYCQw56B0c7n0hBJ3/KTSFrP2q+AfLj2PiQd+2KzEjD3pVx0nvlylxOqzSpx9rEbrOUk6hVyyYlNQKNa0ElrM+1xUN9ZQaVAzmDlawaG0G8p2rSfGSOQWa1ZZMpABhWuVRpeNYyE3SP82x6V6SXwy7zNYutih7ZIRQixnx4ZhPjRUKIqKTg0d+ql2rFmzpqhbtw6MjY0xadJPYqxQoUKi562/v79YqM3Q8eNHRAXm7EDti3755We0aPGJdowqNtM+Kffzu+9GY+TIrzMcZ5jchIUuwxRgQn2D0XhEy3faPbvggwsrT+L8ipPY8MVyuJYvAnMHSyFSSzUpl8a+/rBm2PjVPzgyZx9u7ryCSp1qZMumIFGhnD1mzLyE2BS9c/Vhb28qKjXTMu77GujWuSR273sq1q1YfRdxcbrbU8Xlzh08MWFsTSz7qyn+XJzsGcuKTUEjPBbYc/3dxUN61JRjeFMFvmmhwOIBCqw7Lwnd049UuP5Cd/ugSOqnq8KGLwwwrw9VygauPFdn2aagEfzQD3W+6/xuu3svcXvtcbGc+HmtiDqh1kMmtuYo0Sbt90vlwc1wesoGXPpjD64uOYByPRpmy4ZhcoIrV65g/PhxsLTMeFI4JCQEK1euEsucOXOxYsUq4WUlRoz4Sqf37rvaG125chFt2rTWGe/btw/Wr9+I//3vN/Tt2x9fffVlhuOMqBCWnKebEwsdjxGw0GWYAsyJPw6hQtsqsHW3z9J21MqDFn0YGBvAspAVgp9KIX5+N1/Apaxblm0KGmW87NCkcRH88deNLG0nV8hgaJj+V/nV64GoVcNZPC/qboWAwFgkpup1nBmbgsbsHgr8uCVJ9NjNLAo5hTCnf4Nx7bkaVYvKoHgbnl7NQ4YbL9RZtilo3Nt8GjbFnOBcTYosyUrbovS+p4hCFTzw+toT8fzN7WdwKFMkWzYMkxNQK59Nm7Zg7NgfsrQdVV6Oi4sTz9+8eQOVSncCbtSokfDw8EizHbUmKlOmPH75RbeydLVqVXHx4kXxnLzG5ME1MTFJd5xhchPO0WWYAkx8ZByOLziIT8a2x8Yv/0nXzrW8Gyq0qyqq/BavVwr3D98WfXNpoSrMKTG2NEV8VJzOMUysTLJsUxCZOLYmGjTbggF9yqRrExYWjy3bpTzdwMAYHD/hiy3rpRn3gf3KprUPjxd9eDVYmBsiMjIRdnaKLNkUNOqUkKFiETn+PqFC83LpTyQcuq3GHT8lEpTAytMqfNtCsq1fMu02oTFqbX9ewspEhojYrNsUNEisnv11M+r90A1bu/+arh3l65ZoXV08d/ByQ1xYNILu+YraAo/3Xkpjb2hugiSKOade4lFxMLIwzZYNw+QUkydPwc2b17BoUfrt/ezs7DBkyGBtKHPz5k3RsmUb8Xrjxk1p7F+/fo2lS5eIvrzUW3fnzl1ITHzbKFwP1tbWosevBurhS17m9MY1IpthcgP26DJMAefyurNwKFZIFIZKDwtHKziWcIJDCSeRS2tokn6VWRKt5LHVQLZxEXFZtimIODiY4ovPKmD6zMvp2lBo8qPHoWJ56RuF6JhExMen77WytDBCXFzyerK1tDTMsk1BZEY3BWYfUCIiLn2P6pNANR74q/E4QI2wGCAmIf39WZnKEJfi/jE2UQ0r06zbFERenrmH2JBIrZDVh4mNOWyLO4vFwsUOBqbJkzf6IAErIzc8zfqbGCIhKjZbNgyTU4SHh2PGjP9h+vSpGXpwbWxsxEL5uyRA6Xl6kPht1uwTDBv2JcqXL48zZ05m6DWmczA1Tf5Souc0lt44k6K9UE4ujIA9ugxTwKFCN/smb0OrCZ3StaEqy5SjS1Cxqgl3ZgIjV6Xbiohy40xtzITH17GkM17f9c2yTUFl2KcVUK/JZpQqaQtX17T9cZ2dzUVurgaqxHz2/GsRcqyPMl62uHsvGN6N3BAcHAsHexMYGiqybFMQKWInQ7+6cvy2N/2JhGHecpR3k4SQdxkVpu1W4rPG+q8dVWW+7Zssmu+9UqNzdXmWbQoqZ37djDZ/fZWu2PS/5iNyaTX0OzodFs42iHodqtc+xOc17EsVRtC9l7DzdBWP2bFhmJxkyZK/MWzYZ0K8UkXl1AQGBmL27Dna166uLqhfv947Ky+TKKaFat/Fx6dfrfzWrduoXLmSqKpsb2+P0NBQJCQkpDvOMLkJ/3oyDIOn5x4hMjAS1i6277waykQlEqLjYGxhAjM7CziXKZzG5sqG82g3tTsqtq+Gmv3qi2JThH0xR1i72mZoU9AxMlLglwm1sedtganMeIHDw6WbidNnX6XJre3WpSSWrriD7bt8MHzUcdGmiIiKTsSlywEZ2jDA960UuPAkczmyVDicvLrEQ381XgTrblfUQQYnK4jKzHMPKhEQAVQrKs28X3iiQmSsOkObgk74szd4dvyWyJvNDOQBNrYyg4GZMZwqFdOb+1t3bFcUb1EVNb5uh3tbz4hxC2dbkROckQ3D5BaUYzt69Pdo165tpuyDgoK1Ht2WLVvCyEg30qF//344ceIYfv11Ok6fPoM6deph7tzf093f2rXrMGTIIAwaNBBr167GokWLMxxn2KObm7BHl2EKKI9P3EP4q2RPx4Gp2xEZEA5Vkgr+d3wR9jJEjD+/9ASxYdE621L1ZZvCtqKyX5FqHvC/56ez/sTCQ6jeqy6cyxXG+s+Wiv0STl6uiIuIFcdNz6YgQn1wmzROLsbVuqUHfhxTHWW97GBspED/3l5i3M7OWFRHTknjhm7w9Y0Uzw8efo4qlRx1vLHFPayxdFEz7Njtg47tPdGzm9RrNCIiHidO+6FGdad0bQoqA+vLtZFfZsYyLBlogNtvWwq1qiiHh4O0rntNOewtkkWom60MDUrJEBWnFl5ZGzPA3V5XpK7/wgCLjqpA7V13jqSqytL6E/fVcLGWwdI0fZuCCHlpY0OitK8vL9wDuYEC8eExiPANwotTd8R48AM/RPvrem7vbz8HY2tzGIdGo3BtLwTc0J08erTnElRKlRDBJyetQ9BdyVtLrYyod2/Y04B0bRgmJ3n27JnIndVw9OhRjBo1GleuXBVe07lz54nx4OBgLF++Qmfb/fsPwNOzuHhevXo1nDp1SsfTSl7c7t17IiBAmvhMDR0jZaVnPz8/dOjQGV26dBbe5W3btmc4zjC5iUxdkBv0MUwuEhUVhcaNG6NZYA0YqHnOKbcZcznjtj5MzmK2Zzlf8jzCkv8NzO1TYN4S2Jv7+OYlJk3SrUjM5A5yuRxVqlTE8ePHRbXnvHafd+SrR7AwVuXccePlaPpHyTx3PXIDDl1mGIZhGIZhGIZhPipY6DIMwzAMwzAMwzAfFRwvyTAMwzAMwzAM8yHI6ZY/3F5IC3t0GYZhGIZhGIZhmI8K9ugyDMMwDMMwDMN8MI9uDvoW2aObfCly7qozDMMwDMMwDMMwzIeHPboMwzAMwzAMwzAfAvLmynOwmyt7dLWwR5dhGIZhGIZhGIb5qGChyzAMwzAMwzAMw3xUcOgywzAMwzAMwzDMBytGlYOXlkOXtbBHl2EYhmEYhmEYhvmoYI8uwzAMwzAMwzDMh4CLUeUa7NFlGIZhGIZhGIZhPirYo8swDMMwDMMwDPMhYI9ursEeXYZhGIZhGIZhGOajgoUuwzAMwzAMwzAMk23++OMPrFmzBnkJFroMwzAMwzAMwzAfApn8bfhyDi10vBzkwYMH+Pnnn/Hjjz8iLCwMeQkWugzDMAzDMAzDMEyWiY2NhaWlJcqUKYO8BhejYhiGYRiGYRiG+RDI5VDL1Tl3beWybG124sQJPHz4EEOHDk2z7sqVK9i7dy/UajXatm2LatWqaddVrlxZLPfv30deg4Uuw+QyiSoZ1OrsfSkxzEeLqWFunwHzljilgq9FHmHSpCm5fQpMCiZNmsjXIw+QmJiIAwcO5PZp5GtUKhV++ukn4ZlNLXRXrVqFwYMHC4FrZGSE6dOni3xcfYI4r8GhywzDMAzDMAzDMB9EbSlyfskkwcHBGDNmjPDQnjx5Ms36yMhIjBw5ElOnTsWOHTuwadMm/Pbbb/jmm28QHR2NvA4LXYZhGIZhGIZhmAJGXFwc3rx5g0qVKsHNzS3N+kOHDiEiIgLDhg3TjvXo0UOI3H379iGvw6HLDMMwDMMwDMMwHxGRkZEiJFmDsbGxWFJSuHBhrFixQjyn0OTUXL58Ge7u7rCxsdGOOTs7w9HRUeTz5nVY6DIMwzAMwzAMw3wIRNufnC9G5ebmpiN0qQXQpEmTsrSroKAg2Nvbpxm3tbVFYGCgztiMGTNEDm9egoUuwzAMwzAMwzDMR4Svry/Mzc21r1N7czODnCpGq9OKdBozMTHRGSMvb16DhS7DMAzDMAzDMMyH8rBms+VP9o4nPVhaWsLCwuI/7crJyUkUrEpNSEgIChUqhLwOF6NiGIZhGIZhGIZhdKhSpQpevHghQpg1+Pn5CfFbr1495HVY6DIMwzAMwzAMw3ywHN0cXt4TrVu3Fvm4M2fO1Mn1LVGiBGrWrIm8DocuMwzDMAzDMAzDMDpQXu/SpUvRq1cvHDlyRLQaCggIwP79+yGT5WA4djZhocswDMMwDMMwDPMBUMsVUOdgDG12j/XZZ5/pHe/UqZNoJXT48GEYGhqiVatWcHBwQH6AhS7DMAzDMAzDMEwBpn379umuo166gwcPRn6Dc3QZhmEYhmEYhmGYjwr26DIMwzAMwzAMw3wIRIGoHLy07MbUwpeCYRiGYRiGYRiG+ahgjy7DMAzDMAzDMMyHgD26uQZ7dBmGYRiGYRiGYZiPCvboMgyDvosHw8A4/a+D2/tv4vLG85AbyFGta00Ur1sScrkcr++9EuNRQZGZ+8IxNkCfRYMgk8uwctDfUKvVfPWzwMnTftiy/TEiIxNQvJg1BvQtA/cilnptwyPiMfSLIxnub8jAcmjRvCi/B1lg+LJ4+Iao8VsfI5R2TTtXPGFDAm69VGFcR0PULql45/4CwtVYcjgRd31VsLeUoXkFBTrU4J/mjLAp6oiKPevDys0eUf5heLj/Kl5dfaJjU7JlFXg2rQiFkQHCXgTizpZzCHsemOn3udXsgTC2NMW/49ciOjAi09sxyVCPzUGDBqJp0ybi9YMHD/H330vx+vVrHZvevXuhWbOmsLCwgI/PEyxdugyPHz/Okg3D5Glkipx1Leb99rY5Bv+aMgyDMs3KZyh0Ax8HiPVD1n6J4rVLaMcrd6yG+kMaYUmPP/DmkX+GV5JuVlqMaYuyn1SQXstlUCtZ6GaW3+ZcwbT/XdIZW7DoBrZtaIP6dV3T2MfGJuHg4RcZ7rNZE/dMH58B1p1OwpIjSeJSjOuY9rN7+JYSM3cnQqUGBjd+98/rPT8VPpkWB/+w5H0tPpyE3vWTsPJLE77keijaoAzaL/wMhmbG2rGqg5rg1MwduLT4kHjdcuYAlO1US2e76p82x6Fxq3Fvx8V3Xtcq/RujTIea4nnK4xR0TExMkJCQAJVK9U5bAwMDHDiwTytyNfzww/do164jjh49Kl5v2LAO3bt307EZMWI4vL2b4eLFi5m2YRiG0QcLXYZhsGroUiFEU+JWyR3NR7dCQkw8ru+8ioafNxEiV6VU4dCsfQjzC0HjL5vB2csVnf/XA391nqf3Sjp7uaD9lK4o5OkEy0JWfLWzwcVL/kLk0lv09fDKqFjeQYjc6zcC8cP4MzhzTPcmkLCxNsbG1a3SjB88/BzLV96Fo4MpWn7C3tx3EZegRoeZcXgWqMaTN/onZjrNisNjfxXuv8raxM3AP+OFyK1WXI5vWhvi+nMVZu1OxLrTSgxtokR9r3d7hAsSJDpbzx4kHh8fvoF7Oy+iZPPK8GpfA/VGt8etTWfgUMpViFxVkhLnFuxD+Msg1P6qNeyKO6Hx+K64v+sS1DQTkQpTOwu0+X2w8BZbFbbPlb8vr1OtWjVs2LAWy5evEB7Vly9fpmvbp09vIXKjoqIwfvxEREZGYtq0KXBxccGcOTNRuXI1NGnSRCtgFy36C5cuXcb48ePg6fn/9s4DKoqzC8MvSy/Sm6KgIiL2ir333rsxJiYmdqPGmMSuMdEYY4n5rbHX2HvvimLB3hCxgaKAgPRl2f/cb9hlF1gEoohwn3Pm7Oy3d2Z2Z2F23u82d8ydOwf16jXIkg3DMBnj7S1N2A0dOlQsBREWugzD4P7xO1pnwdTKFJ1nSjcXuydvR9DNZ+g6u5d4fm3XFZxYKHlOQu6/xMhD41DC2x3WRWwQEfwm3dk0szGHe20PPsv/gRWrpe+nXZsSmDaxllivXdMZN2+FCfGbESYmBumEbODjKHw95JjYZsmiJjrDnplUkpKB47cz92CduK1ATEL2zprvQwWuBiZDpgdsG20MF1sZetYBGpWVIUkBFLXj2LO0lG5TVQjS6JAI7B2+HMlyBQKP38LdPSmRDkrApZq79Ld++g4uLjogDSuS0Xb+QJjaWIhw5PjI2HT7NjQ1gmudMvynnwkKhQJFixbFpEkThNg8dOgwlixZir1796Xz8tarV1c8rly5CgsWLBTrjo6O+O23mShRooR43rVrZ/F4/fp1DBkyTKxHRERg+/atqFu3jhDFWbHRDIVmmLyIUk8GZS5e0lXH8vX1FeH+BRkWugzDpKPdpM6wdrHFg5N3cWmjjxhTeWMjnoer7d48D1OvF6vilqHQfXk3GCs/XyLWPRuXRZ0B9fmMZxOfi9KNnHd1J/z+5xVcvxkKRwdT9O3piWpVnbK8n+GjTyLqbSIGfOaFpo2K8feQBUyNgJ1jpfDVhyFKjF2bmM5m8yhjIU7DopUYuDj96xlx7r4kDLxc9HDrWTJ+3iSHPEmJBmX1RdizoQEL3bSoRGzQ5QCUaVcdJRqVhyIxCQHHb8D/gJ94LeDoDby6+xxRGtcmx3JSiP6bx68yFLlEbOhb7Pj6b7HuVK4Y6oxqn6XvsSBx4cIF1KhRC4MHf4NevXqiTZvWYgkKClJ7eZ8+ldIlli1bgZ07d+Pu3bvq7atUqSweL16Uwo0rVaokHq9elb67tOsVK1bMkg0LXYZhdMFCl2EYLVyrFkf1nrWgkCuwc+JW9fjb129h6WQF9zqlIdM/IEKYy7WsqH7dzNoswzMZGxGLe8dui3WrwtZ8tnNA8IsY8fj73KtCqKpYtfYulv7VBN26vNtjTkWszpwLhpWVEaZMkLzCzLvRl+mhbVXpp/LyI0WGNi0rSa8/D3t37qKKoHDJ9mmYEu1mpbqDt1xQYPvFJOz9wYTFbhosCtuIx+INysKzbTX1OIUqX1t/Gscnb0Log2CxEDWHtELpNtXgUMYFCW/jcGD0Sp3fR1KCHIEnbqk9wEzGXL58GQMHXsaYMd9jwIDP8e23g+Dp6YmJE3/GTz+NF17eadNm4OLFi+ptFi6cjxYtmqN06dJCFH/zzWAx7uTkKB7DwlInT8PCNCYoHB2yZMMweR5ZLhej4p46avhUMAyjRYuxbcSj347LCAtMrVLqt00KD3SrXgJjT03AN1tHoEtKOLO4mBhyPuGHgCpTy+XSjXeiXIEZU2pj0bxGIuxYoVBizI9nERsrz3QfyclKzPrjslgf/HUF2FhzgZ2PTYJU0wpv44D21fSxfrgx+tST/ocoVHr16RQDRo2BkTShQOHH19adwr6RK/D0/D0xVrlvAxSuIoXEqnAoW1SIXNU2VQdoF0Zicg6FD8+bNx9lypRD06YtcO7ceejr6wsPb7duXbVsa9WqKUQu4eLigq++GijWDQ0NxaNm2LPmupGRUZZsGIZhdMFCl2EYNc5eReDRQMpTO7/qtNaZObPsBI7OO4ikhCTYFbcXhakCzj4Qnl8i9o3kdWTeL1QkjLywxNBvKmL44Ero17sMFvzRUIxFRCSIUObMOHj4CR74R8DAQIYv+pflrygPYGsuhSbbFwI2jzRGj9oGWDXYWIQyEyduZew9LsjER0jXmKArATg+ZTPu77uCXYOXQB4nRTm41vbUsr+05AgOjV+LYD+p9RAVrXKto23D/DdatGiBESOGoWZNqeiNKpdXk7FjfxBeXFUY8/jx41CqVCnExEjfp4WFudpWM5+QXs+KDcPkeWSyFK9ubi0s71Rw6DLDMGqqd5dacoQ9CUXQjfQVNY/M2Y+zy0/C2bMw4qPiEPMmBj9fni5ee+UfwmfyA1HMpRAiI8Pg4pJ6g1fcLbWCta6CVCrWb76vLmDl7JR6w8h8PIrZS1+ak5WeOkSZJjXc7GW4G6R453daEIlKqQHw9kVqLQB5TALi3kTD0NQW1Ja74c9dYePmCP9Dfri97QJCbj7Bo+M3Mdh3trC3L+OCp+el/wcmZ1hbW4v+uJSr6+HhoRa3FLa8dOky7Nq1G+vWrYGVlRX+/nsxDhw4gFOnTuHKlau4fPmi6MFeoUJ5PH78BGXLloWbW2rRPFfX1JZn9HpWbBiGYXTBkp9hGDVezcuJxwenpHBATbz71sGAVYNEL9zAiwF4cTcY3n3qiNciX0bgxZ0gPpMfiHZtiovHHbsCIE/xoB849Fg8kpfWs7Stzm3J/vgJadKiaWMuQJVXaFdVHwb6EC2JVLm/ryKVuPhQWq/gyj/PaXl45Jp4JK+smb000eNYrhgKOUu5/6H3g2DhaI2STSqgUr+G0E8JdabWQipiQiJz4dvNn9jb22P58qUICnoqWvuQyKWc2xkzZsLdvTRatWqD7dt3CNFLHtt27dqKHF4VXl6pVa2Dg1+IcGeiTp3aQhQTFPpMxMbGws/PL0s2DMMwumCPLsMwAnM7C9iXkAp/PPOTRJQm4U9C4dWsvFgvWdMd8dHxcKsm5cQd/fOgNF67lOi3S+z4cQsiX0Tw2X0PULjx/5bdFMWkqtbehCKFzXHx0kvx2uf9yoic2207H2Lzvw/E2PpVLWGYkjN942YoYuOkfM/qVaXvl8ldYuKV6LNQKjj1RSMDdKphAGdrGT6rb4CVJ5PQeGo8vEuRJzcZlAFAdd2+aiLlJjKpPL/oL8KQi1QpiQEHJ+L1/SA4lXeFnkwmRG7gyVuify4VqnKu4IYvjk4RfXRVubu0TlWZbUs5o8E4qW3N+fl78eq27n6wTCpUdGrgwC+FkN2//4Dw3lJrobShysSqVatFOHOHDu1x//4dvHr1GrVrS0XwfHwuiGJVz58/x4QJP8HGxgY3b17Do0eB6rZEtH1CQgJWr17zThuGyesoZTIoqZdcrh2PeoVz+gvBU8YMwwhsXe3UZ+J1wKt0Z+Xh2QfYPn4z4iJj4eRZWIjcxNgEHJy1F77rz6urKpMYpsXIjIuEvC8o3HjDqlYo7GyGp8/e4oLvSxGm2bqFG6am9NV99CgSh44+FQsVqVLx+Olb9bpHKa56/TEgJ/x+P4VYHoWkfjdz+xuhcw19xMuB03eT8ToKKGyth40jTWBXiGOXM4L65764FggTa3MUq1kaRuYmCPUPxt4RK6BMVuLx6Ts4MeNfyGMTYFnEVtgYGBni1Z1n2D5wkaiubGJlLry+tJjZcS/prBIZGSkqKpcoUQpt27YXIcoZiVxi8eIlmD17DhITE0UhKhKnVKzqxImT6NKlm7Ahb3Dv3n0RHh6OYsWKoWHDBsKGRPS4ceOzbMMwDKMLPSWV9GQYJteJjo5Go0aN0DDEGwbKjx9cYW5rIXrhEo98/JEYm3E/UBKwzmWKQKYvw8v7L0SurgpLZysUKVdUrAec91cXiVFhXcRGFLwiVC2H8go/X828cnFegMKQr98IxdtoOdxLWonKyyoCAiPh7y950Fs0c4UsZfaYhPGdu1JrjuZNi0Ff/9OY3zQ7thZ5jchYJc7ek27s63rqwzqloJSK+EQljqUUkaruri/yb4kkhRKHrkvjXi4ylHTS/g4CXyUjIEQJKzOgSnEZDPTzlshdMLE/8hp2HoVh4WSNmFeR6nZCmhiYGMLBqxiMzI1FTm94gBQBQRhbmqJISk/el9cfIy48WmtbU1sLOFeS0gWe+dxHEs1E5BHGBCzFp4SlpaXohWtsbIyAgAAEBgams6HKydWqVYO5ufl/svkYTJky8WO/BUb8Nspx8OBBnDx5UqtYWV65zzu80Ajmprl3XY+JU6LF8MQ8dz4+Bh//7pphmDxBTHh0lsQnCeCnV9OHNhNRLyPFoouI4DdiYXIGhSNXr5aab6iJewkrsaSFxLCmIGZyjpVZak/djDAxyvh1Eq6ZbVfCUYaUrAEmi4T5vxCLLkicvkiptpyWhKg4dc/cjCDhm9nrTNaJiorCmTNnMrUhr6+Pj89/tmEYhkkLC12GYRiGYRiGYZgPgFKm/xFydBni04hhYxiGYRiGYRiGYZgswkKXYRiGYRiGYRiGyVdw6DLDMAzDMAzDMMyHQKYP5GLoMjh0WQ17dBmGYRiGYRiGYZh8BXt0GYZhGIZhGIZhPgBcjOrjwR5dhmEYhmEYhmGYfIS3tzfKli2LRYsWoaDCHl2GYRiGYRiGYZgPgZ4MkOWib1EvWTz4+vrCwsICBRn26DIMwzAMwzAMwzD5Cha6DMMwDMMwDMMwTL6CQ5cZhmEYhmEYhmE+WDGq3PMtKnOzlVEehz26DMMwDMMwDMMwTL6CPboMwzAMwzAMwzAfApl+7hajYo9u3vXo/vzzz6JCmObi5OSEFi1a4MqVK8jvVKpUCbNnz35v505zOXjw4Ht9nwsWLMj2dsOGDcOGDRvEeu/evcX3+rFITk7G0qVL0aBBAzg7O6Nw4cKoW7cu5s6di4SEhHS2v//+uyjTbmtrKz7/ypUrxWtBQUGoU6cOkpKSPtInYRiGYRiGYRgmT3t0SWCYmJjg6NGj6rGwsDD88ssvaNasGe7cuSMESU4YNWoUAgICsGfPHuRVNm3aJIRUTs+dpaUl9u/fn+HrJUuWzPH7SnvuYmJikJiYmK19XL58GQcOHMC8efPE87i4OMTGxuJjQKK0S5cuOHbsGL777jvMmDED+vr6OHXqFKZNmya+B/obpPNJzJo1CzNnzhQiuFy5cti9eze+/PJLODg4oF27dihfvrx4bdy4cR/l8zAMwzAMwzB5D87R/XjkOaFLGBgYoHLlylpjnp6eKFasmPBKfvHFF1qvKRQKIVLeRXx8vBBXOSGrx/iveHl5/aftjYyM0p27/4Lqc/+Xc6fi+++/x4gRI8T3+7H5448/xN/SiRMnhBdXBa2Tl7lWrVr46aef8Ndff4lxarY9duxYfP311+I5eXBJuM+fP18IXZoIqFmzJgYOHAg7O7uP9rkYhmEYhmEYhsmDocu6UHnWVGLL29tbeNA+//xzODo6qr10U6ZMgYeHh/D6tmrVChcvXhSvDRgwACtWrMDJkydhbW2t3i8JGfLG2dvbo379+ti3b5/6teweIzg4OMOQYdq3isy2Txu6LJfLRThyiRIlhC0Jqtu3b//nc0midcyYMShVqpTwSJKoU4UTZ/S5dZ078uoOGjRIvDc3NzfhCdUFvW/avk+fPll+n5mdK/KcVqxYUW27Y8cOca7XrVunHiOP7ejRo9PtV6lUirDrXr16aYlcFdWrV0f//v1FaDJ5rd++fYuIiIh0tq6urnj+/LlYp5Bmd3d3rF27Nsufj2EYhmEYhmGYAix0SVBNnjxZeBabNGkixijkderUqXBxcVGH05J4+/vvvzFp0iRs375diEYSJxSOSradOnUSIoaeEzRG4agkmnbu3ImmTZuic+fOOH78eI6OQaLx7Nmz6mX9+vVCrJJAU5HZ9mlDgimkdtWqVZgzZw62bNkCPT09sa/shgynhcQzHZtyTulzNWzYEH379lWL6LSfO6NzR9D2NjY2+Pfff/HNN9+I7+jw4cMZHpNClkuXLi3OUVbJ7FzRe7558yZevnwpbOk7o3On+u5IJB85cgSNGjVKt99Hjx6JSQkKhdcFTXrQebh37x4KFSqE6OhoLXs67t69e8V7UkG5vrrCxlWh5VFRUeqFBDTDMAzDMAyTj5EZ5P7CCPLkmXj16pWW55BEAeXtLl68GGXKlNESIyRUVQWBSBRt3boVHTt2FGO1a9fG9evXRX4lCRAKKX3z5o0QJ7RPGidPJok4ol69enj48KHw8qoEdXaPoQobJs8z5XBSKDIVPMrK9iTeVJDtkiVLhFju2rWrGKPc0LZt2+LBgwfCC50RT58+1Tp3KipUqIAzZ86I9aJFi4p9qwpBVa1aVYhWEnV0jLSfm9A8dypIdNP7Vp27//3vf/D19c2wwJSfn5/Wd/cu3nWuaNzY2FhMKHTr1g2nT59G+/bt1UKc3gdNCKi+R01CQ0PFY2aim4pTESRw00LnkTy+tH/K7VVB3/U///yjc5+//vqrmDRQIZPJUKVKFeQWhqZGqP91IxxfkDoZYWBiiIbfNMHxhYdRrLIbjC1M4H/6nnjNqbQzPBqUQVJCEm4dvI7o17qFub6hPrz71tEaiwmLxo09fmKCpkK7yrAqbI3bB28g/GmYzv1UaFsZ/mfuIz7qv4XJf2ps2foA5craiUXF1u3+8PCwgaeHNRYvv4lRw6S/legYOXbuDkD4m3h4V3dCLe931ywIC4vDv9sfolAhQ/To6gFDQ+00jMNHn+DxE+3vt2snd9jZmaIgQFEev+6UY2RrQ5ibpPYf/G1nIga3MERQuBJ3g5LRtab0kxn4Khn7/BQgy9aV9VHSKWtzxkduJMGziAyu9qn2j0KS8SRUicblMk6NCXurxIZzSShkAvSpZwAjg/zfH9F7cEv4rTkJeUxqUcAa37bEjQ2nYWZvCYcyLniw/6oYt3SxRckmFQDoIfDULUQ+la7vmWHnURjFG5RDmH8wHp++ox4v2bQCbEs44cX1xwi69FDn9q51PBHx5DWigsKRXzEzM8PEiRPw008/i/8P1dikSRPFGKXvWFlZqaPgaCK8VauWImLs33+34smTJ//p+H379sG+fftFNJWKXr16ivuIbdu2i4nud40zDMPkSY8uFWOiQkCqhTyNdLH76quvtOxIoKkgAURevJYtW2rZUN4kFbBKy7Vr14QYJcFCwlC1kIC6f//+fz4GhfTShZ5CaunHIbvb37hxQ9g2btxY67z4+PjoFLkqgaZ57lQLCVsVI0eOFJ7miRMnol+/fuIHin7IVD9maT+3LtLa0I8eeVV1TV6Q9zervOtc0TklDyqJTvrboL8RCp0mby2JZMq9JfFN4cxpIQ8tERISovP4qpBkTTFMkyP0N0gTEjR+4cIFEf6tORlAnz8jcUz8+OOPiIyMVC+qY+QW8rhEVOlSAy4Vi6nHPOp5CjGrTFbCtUpxlG7gKcbd65ZGj3mfISEmASaWphi2ZwzsS0rh+1nBtWpxeDYuK9Y7TO+GMk3KQh4nx4BV36CQo5SGkJbCXkXQ6ZfuMLUsGOJKk2dB0Vi5NvUakJysxA8TzglhGp+gwPxF18V4fHwSWnfchRcvYmBZyAiTp1/EnHnSDb8uaJtWHXchMioB5y+8xOgfpAkvXSQkKjB3Qeb7zG/QZMzJOwocvalQj/k9VmDFiSRYmekJkbv9olRV/dazZHT6PR4GMkCuAFrOjMe5+6nb6eLhy2SMXJWIp6Gp19k30UpM/jcRJ25nvH2CXIkm0+KEnY9/MgYv/2/RPJ8KRb09ULy+dP0g7EsXQaU+9ZEQFQfbks4o3Ub67bEt5YzOK4aK65eeTA9dV40Q275L5HZaOhiKRDm8B7dCmQ41xHjNoa1RvmttxEfGosmkHiniOT1WrvZoMrknLIvm71oMFNHUvn1bkcqkonnzZqhZ01t0IahWrSoaNZIm51u3bo2FC+fD399fCN2TJ4+pJ82zC0XukYieP/9PcU+hYsSI4Rgw4POU+6qtIqUrs3GGyXvthXJ5YfKuR5eKFZH4ehfkEVNB4oIukIaGhlo2dNHNyMOpah+zfPnydNWIyXv8X45BVYXJU0zhupoX3ey8RxJChKmpabaLUWV27ugHin6U7t69K0Q+eV8nTJiQrgiW5ufO7Fhp0RTLmmQ33Dor54o+B+XEktil2VzyplPRMvLqUggzvZ4RFEJNYpeEKuUfZwRNDlBbK5WQffbsmQhtJ28weZppIiPtOXpXsTLyQNOSnXP8vrm1/xrKtqiAoBvPxHOvZuVwc/+1dHbVe9TEwd/2qL27cZGxKN2wDEIfvYKDuyNcKhTDtZ2p7b4UcgV8Vp1Re3erdq2BlZ8tFgKiXMsK+NV7svjbMDQ1RJUu1XF68fF0x6s/qDGMLVLPT0GiU/uS6Nh9L+b8Wl88v3wlBIULm6NkcStERKZ6tS74vkTpUtb4fnQ18bxD25Lo+8UhjB0l3fjPXeCHb78qDzOz1P+bPfsC4V3dGT+MqQ65XIFvh59Id/wWzdzU67/MuoSJP3oXGG+uii7eBthzVYGONaSfxf1XFWIsLRvPJWFUW0N80Ug6xyUd9XDAT4G6nvoIfpOMPVcU+KaZ9nXrgF8SftyYiJeRqdfHyFglGk+PQ/hboIRjxteC3VcUqFpSH5O6GSFJocSAv7XbnuVX/A/6wb1pRfFIlGhSAf6HpHVNyrSrjqsrj+Pm5nPiOXlZSzQqj+e+/sLz69GyMq6vP621TZXPG+P8/L24u9MXzy89hHuKoC1cuQT2jVwBeWyCEM30/NFxbe+gW30vNPyxK8ydUgVYfmbr1u1o376dujZGu3ZtsXXrtnR2ffr0wqRJU0S6EEFpOfXq1RUT0FS/gsTxypWrsnTM3r17YejQIVr3YcTQoYPRuHEzkXZEdU/69euL6dNn6BxnGIbJsx7dnEBChyoEX7p0SWucRI9m0SIVKmFHs5YkDFULCVRdeZZZOQatU3VhaoeUNoQ3O+9RFeZLIb8qyANIladv3bqFnEICl/JoqWgThd2S2M3I6/m+KVKkCMLDsx7mlZVzRaHT5PmlXFmV55tClenznT9/Xis3Ou1EClXupvznwMDAdK+TR59ygqlCNAk1gjy55GGm43377bcZilQK7aaJidw4nznl5v7r8GqWGhHg2dgLt/ZL3kJNIl9GoOG3TeBR3xMGxga4uO4czq+UbhjJyxsR/EbnMWp+Vhf3jt1BbEQsRRNKSwr6hgZwcHdKt83lLRfxZ7Pf8DrgFQoi7iWthYf25m0p7PLgkSfo2C59OzBnJzOcOhOENevv4sXLGNjamuAnCemuAAAsuUlEQVTALim0nyjlbgUDcjVqcPlqCOrUcsae/YE4cuwZlv3dVOf7CHkVi/0HH6NX99IoaHSqoY9D1xXCm05QaHKXmuknr4rY6GHJ0STsvZKEt3FKdKhugBm9pEk/U0M9lHBIH1rcuooBrs02Q/0yqfsjT/G1WWb4oaO2KNbE96EC9cvIsOtSEvb7KbB6aMGYCHp45Drc6pURgpMo2bg8/A+kF7rRIZGo2Lu+CDk2NDdG4MlbODN7h3hNkSBH5LP0YcyFKxXH63tBwpNraGKIi39L/eV3fv23OF6Nb1qgQo+6uL/3crptn5y5izVtZuCZzwMUBCgUuEOHdurnFJq8fbt0fjUJDHyMSZMmoFOnjsILu3r1GixZIqVs0eTwzZva9yw0KT1y5IgMWymuW7cetWvXFd5hFfS7SgVJScwSt27dhpdXGZ3jDJM32wsZ5OLCHt18J3RJ/DRv3lwIEqruS/mm1B6GWsCQ8CTIm0YhtHRRpDzVnj17Cm8meV7poko5qQsXLhQhsTk5Bnn9unfvLvJbyVNIBYtUC3lSs/IeNX8ISLwNHz5ceB5J3JI4ox+RzEKCSBxqHldzoVBtCq8lzyP1gX38+LHoI0vVh8k7SyG/Kk93WjTPXU6gIlKaP1ya4cCaBbxUC3nC33WuaLKChP/q1avVRafI67px40Yxs5tZiPf06dOF95e2I8FLudkkeiknmo5L+1Edh84LzVRTfi15mDXPqSrfl6B9ZDSpkpcIvvUcJhYmIl+2SLmiiHwZicgXqTlQKo7+eRCPLz1C6586YPKt3/D5ykGwdJa8GFEvI/HY95HOY3j3ro3zK6VcaQopvH/iLnr/1R9NR7VC9Z41IdPPN5ed90rH9iVx8JCU13b46FN0au+ezqaMpy0Wzm2IvfsD4V1vM7zrbxYCVgV5eI2MtH/gQsPisfDv67h9Jwybtz3AgEGS1yUj/ll9G199UQ76BfA7craWoZSzHnwDkvEqUikWb/f052FwcwP0rG2AWbvlcBkci+Yz4nAvKFm8ZmOhhxaV3l+g1OsoYN5+OW48Tcbm80noOa9geHTjwqMR9vAlClcpCRNrcxQqbIPgq+mvOTc2ncXdXb6oMagFvr0wC93XjxLhzETC2zit/FsVpjYWaDC+i9hn44k9UPmz1NoYqmuWgZkRilRP//9X0KB8V/rtp64K5Ah4/PgJXrx4kc6OPKg7duzEd9+NQnDwMxw/flRsQ9B9A/1uaxIQECAe9+/fi/Xr12ZYNFITisDSLN5IKULm5uY6xxmGYVTkq7sZ8lKSJ5DyOkkE7dq1S3jmqBATQUWnSMyowompfUyHDh2EOKXtyJtLFYSp3U5OjkGVkV+/fi3yQ6kdjuaiEojveo+akOgiIUe5pjVq1BAiiwSqysuYEeT1TXts1UKCkHJ4ly1bhjVr1oiQbao4TQKOcnWpynNGYjSjc5ddSLSTWKUfvbQ/pDQxkHahkKesnCtVFWpVIS86DnledXlzVdAsMAlq+tz0+SmcmdoY0WQH5TBTtWlV2DR5cSnslrzfac8pfTcqzp07pzNcOi9BhaW8mpeXwpb3pQ9bJmxcbITYXdD6d/xaYxIigt6g9Y8d3rlvyvd95R+CuMjUYlLbx23C1e2X8fphCM4sPYHoUK42nREkUvcfeoznQdHCq+hRyjpDj2vtWoWxZX0bBN4bgKkTamLoyBOIiZHr/E5kMj3061MG48dWx6qlzXHpcgii3qZPJVAokrF5qz+6dCq4N/idaxhg7xUF9vsloWMN/QyvtYGvlRjeygBnppoi6H9maOClj2ErP4wApcCR/g0MMLGrEdYNN4bf42SRr1sQoFBlCl8u0agcAo7eyNDGqpgdrq09ic09/8DiWj/gydl7aDY98xZ2Sihx9veduLTkMPaPWYVyXWunhCoXR2J0PC4vO4JD369BuS667wMKEtu27RDhy7rClgn6TZ479080bNgYDg7OuHLlKn799Red+yRxOn/+AtSqVQdz5sxFly6dcOWKr/jtzwiqw6EZKUViliLEdI0zDMPk2RxdEhqZ9WNVQSGtaXMiqecrFZMi7yktFKKqCYkgyn0lrydBYS+Ub7lo0SIxltY+u8egUFdqs5MRqlnGd71HKkKlElgkpEhw0XujUNnMBK7q3FHfWV2ocl7IM0wLCULV8cmDSVWTybOb0edOe+4032dm50sFeaGpajJNBgwbNkyMbdq0SbyHjKBiU/SZMztXBHng//jjD/X5pVAo8hJnlD+cFpoNphBzWuh90PEyCkmmEHRdrYBU9uTNv3LlilY/4rwKidvmo1vD1MoMG4ZknDfVb8mXWP/tSrx6GCJCkG/s9ROhzO+iYvsq6UKhey/6HBuHrhaTBd3m9MHtQxnftBZ0vMrYIiY2CavX3UWHDMKWic1bHwgP7bSJtUSIcuuWxWFlZSwKTpmbZxwCS4JZlnLtoGsIbWegn/5a4ns5RIQ+W1kWjPDYjOjirY/2s+Ph4SzDyDYZn88RKxPxXVtDNKugj0KmeqIS8s7L7y5GlRM8C+tpf3cywKCARKQ9PHQNXdeMEFWV0+bZqmg8oTuurTslPLdUoZm8u2XaZ17f482jEOG1JZTJyVDIk8TzDou/xbJ6PyE5KZlONhKiYj/I5/rUIHH7228zYW1thS5dumdos3LlcvTt219EnlE6GFVL/u67ke/cN/1uV6pUUUxkP3oUqBUhpQlNZoeFhYmWhxRhRYWwLl26rHOcYfIcuV0gKl+5MfOZ0CWBkhWRklmRJl2CRddr0s2fwX8+Bgm/tOIvu+9RVaE5O0WOsnvuVKT9zKptdX1uzfec0ft8V+Gs2bNni1DkIUOGiP2kLTaRk+8zo3Oek9CljL5/zdfelXc7f/588bnIA5/Xeeb3BHYlHBD7JgZvnmc8+31q8XEhdi+sOye8HTX71MGB36Re0hTCbOtql2H4cvHqJXD4d6ndhGahqs6/9UBUSBSKlHPB9h82iXEKnU6MSxQFrhiJ9m1KYMHf13DykNRSLC29upVG0zbbhfgpWcISJ08HoUplB3XhqN37HqFVczet8OXePUqjW5/9MDSU4c69cDRuWFQUq6IiV1T0qlkTV3Whq9o1392qKD9T1E4Gc2M9XH+SjDqlM77mjGxtgCHLE0TbIRMjYM2pJAxqKl0/yNt6KUDxn8KXqUjV+fsKkdfbt54B2v4WD2ND4F5wMup7SeK6IBDzOgrxEbFwrlQc+0Zl3LbtysrjaDGzL66tOw15XILwwt7YKBXFMy5kisJVSqQLX766+gSaTuuFW/+eF8Wsrq2V0izu77mMNvO+FHm4FfvUx/l5e8W4dXFHGJkb49VtqYBfQePq1atwc3NFaGiYzk4BM2bMxLZtW7By5WrxW92/fz+MGPGdeM3V1VWkCakKValqdsyYMQ0VK1bApk1b0KdPPxENlxkLFvyFtWtX4/DhI/jss76oWbNOpuMMw0BdNX3o0KFiKYjkOaHL5F8ozJfyn8mT26dP5uFlnxL0A02e97SFs/Iy+3/ZhcQY7fDVp36PYewvTT5c3eqLF3eC4F7bQ3jxVw9chrDH0my7sbkxrIukbxVFE0ZXtvri7asorfGtYzegSufq4mZxWa+/kKyQ8hktHAoh4W08NOfwL6w9h7gC1kNXk369y4iWQuTdVWFirI+RQ6X+1Y6OZjhzrLuopBwWHoc+vTzRtFFRte3DgEgkNU7WErrFihbC9k1txTb16hRB5w5SaHJCggKBT1K/q1Lu1vBwLxjVZDNjancjRMQqtSJovFxk6JLSQ5dE7N7xMhy+oUBiErBkkDEqukqiOE6uFKHNuuhVxwCu9tpCtYa7DBrtYpEgBx69UqqF9/4fTbDdV4F6nvroXquAuHNT8Jm/F6a2FlTOXz0W/uiluofuM5/72DZgIYo3KAt9IwMcmbABofeCxGv6xoawKmafbp+Pjt1ETEgkitb0gM+CfQi6LOWLnpy5DR6tKsOyiC2O/LReLWxNrc1gYqU9eUqFqqKe6+4Hnt8YOXJ0utaBp0+fUbfqo765165dR4MG9UV0VJMmzdXpWjQRbW6uPTFOk9wkiqljgi7mzp2nFYa8bNlyUfSqVCl3NGrUVN1fV9c4w+QllHoGUOrl3vVbmfL75evrm6cLpOYGekpd/WAY5gNAool+CDXb7BTUz0QtlKgIR8MQbxgoec7pY/PzVd15rkzuY3ZsLZ/2PMKCif0/9ltgUhgTIFUzZvIGU6ZM/NhvgQEgl8tx8OBBUbw0Lwk71X3e3s3lYG6We0I3JlaBdj1v57nz8THgKG4mV6Ew7PwkcvP6Z6IQ5aG7R4tlyK7vRDgy9cBNi2vV4mg0tHm68T6LPn/v76l0Iy8MXD8EPed/hkJOljm2+RT5ZthxNG29XSwdu+/BkuXafTpV9PrsQLqx9Zvu4eiJ9xs++epVLL4dfhydeuzFwcNPcmzzKbLFJwl1J8WJpeGUOHy7LAGvo9LP+/7viByHr2vXEiC7UavffwGqP/fJRRXnnzYmQp6kzLHNp4ahmTF6b/1evXRY/A3c6mn3dicKFbFF44np80RpTHh+3yNOFdzQaelgtPvrK9i6O+fYJq9DveJ9fM6J5fz5s/j3382oWlXqy61JzZo1MX78D+nGN25cj9yECkdOmPCz1hgVrDx06ADWrFklapu8a5xhch2ZQe4vjICFLsPkY4xMjUQRqBX9/oeVny+B387L6L98YDq759ef4uyKk+nGC5dLDYt9H5Bo7fxrDxE6HXjxIbr/0TdHNp8q1OJn7uwG2LaprXj8Z80d0Rc3LUsz6HUbHByDsLD3G9Y9YswpVCxvj1+n1cH4iefw9NnbHNl8iryIUIpw4H0/mGDraBPR1/b7demrUX/R0ACNy2nPxCfIlbj7XArBf1/s8KVeuUn431fGCItW4s/98hzZfIpQy7FCzjbY/sVf2P7lX7i87Cjazh8IA1PtmhNvX7zBmTm70m1vW6qwCF1+XxgYG4rjX15xFLe3XkC7hV/lyOZTgGpa0G9Eq1Zt0LZte2zYsBE7dmxNZ0ctgubNm59uvHJlKa0iN+jYsQOWL18Kd/fUYn0kYBcvXoRx48aLcOp//lme6TjDMAULlvwMk8+hnNj4lLzXxxcfQZZS3KxI+aIo26KCaCVE+bmmVuY4uegIqnStgaqdq+PJ1cda+6ncuTqqdfNGwPkHcPRwxpZR68R4yToeqDewERKi43F8wSG8DpCKS/WY1w8nFh5WPyfKtawoKjhT/i8tzb5rDZNCJoh/G58tm08ZCwtDWFsZi8WtWCHIqcorgK8GH0X9ui548jQKd+6GY9Pa1gh4FIFpv/pCoVDCwd4Urq5STlzg4yhMnnEBBvoyVKpoL4QoFZkKfxOP6b/6ihZFXTq6o3cPT2G/bedDBL+IwfDBqTel0TFy+F1/jY1rWol8VOrbSx7bQQPLZ8vmU4aKPFmbS7lM3qVkQkgSK07IIU8C9l5VoHlFGbyKyNC0gj6mbpXjor8Cbaum/nQqkpWYvk2OC/4KUTzq2pNk/PGZFOGx9Jgcuy4pUMZFD5O7GsHSTA/R8Up0nhOPIxNM03mYR7c1RClnmXjsvygB49J09MqKzacKtf2h3rdEyM0nSIpPFIXwzB0sRZ9cej3i8WvYlnTCien/okTDcqIHbsST19A3TJ2IcKvvhaoDmiDsQbDI06U83/jIWNh7uqDmUOnv+Mo/x/DCT+o/Xf/7Tnh85g6eXXig3kexWqURcusJnl+U2u3RdnYehRHm/yJbNp9S+g11VSDOnDmrLs5YpUoVdOjQXhSjunDBF7a2Nvjtt1no16+vWHx8Lmjtp0+f3vj88/44fvwEypb1wueffyHGKXRz1KgRoiPCL7/8ivv374vx1atXYubM39TP3wV5lanKsiadOnXEli1bRRtAWiZPnijaB+oap/fAMEzBgT26DJPPcSrtjAGrBuGLNd9g5KFxODrvoBgn8Vj3ywbw3eCDyJdRsCtuj2JV3IRo3Tlxq+h3a+cmFXMpWskV9QY2xK6JW0Wl5AptK4txm2K2aDqyJfZO24ELa8+i/4qvhXeG2PnzvwgN1K6kaV/CAWEaY9Sf16qwdbZtPmVGjjmFHn33o0mr7XgTkaAuJnXpyisEv4jG8CGVcf1mqPCyfPblYfTo4oHxY6rj0BEpbFj0dP7qMHp29cCo4ZWxeNlNhIVLkwBDRpxAm5bFMXdWfSFuVdu0a10CXw0op/U+Ah9Hws21kLroEq0/ff422zafMv/6JKHj7/Fo82s8hq5IwPcdpAruQWFKrD6VhPkDjPAyQonXb4G5e+UIfqPE0kHGQsyqmLdPGicv67FbybgXJIUTbzibhFtPk7HsGyOUKSLDoKVSqLO5MYQHOS3+L5VCwBIlHPXwLCw5RzafKqbW5iIMuNOyIei/fwLu7LwoWgaRp7Zi3/p4ev4+gq8ECO+thbM1Gk3sLry7T33uo0g1qcAajTeZ1ANnZu/Ei2uBqNSnPmSGBjCyMBGVli8s3I8Liw6g5az+MLGWCkz5/LUfQZcfar0XqrRMolpF1LMwWLrYZdvmU4FE6Z49u7Bv3x74+V3GtGkzxDgJwxEjhmHZshUIDg4SnlSq4kqiddiwEXj16hXc3aVzX716dTE+fPhI0WKoWzepcnzx4sUxceLPGD16LBYvXoqdO7epO0kMHToc/v7SRIEKCjP+8cfxcHZOHwr+008/Y9Wq1VpjHh4eWvt4+vQZihYtqnOcYT4KerkctkzHYwR8JhgmnxP+JAx7pmwXYoVa+rSb3Fnd69b/9H08uRIocmKJ8m0q4fzKU0Jo0tJkeAsxXq5VRZz755RoBURLo8HNxHiFNpVhYWeBDlO7iOfmdhZwKOWEkPsvkKhZSjYFfUMDJMalhlsmJSZBlqYxaFZsPmW+/64aiha1QFhoHKbOvIhVa+/ii/5lRa/oUcOqwNRUuizff/AGNjbGaNu6hHjerYuHeAx4FAkb69Rx8twSFNZ8/sILJKf0CH35MhanzwWjZXM3GBunP3+JicnqYxFkkyRPzrbNp0yzivoY2doQCUnA7stJGPZPIs5MlTytnzc0gLuTDHqQRD5VPt440hhuDjJ8394Qo1ZJf9/UQ3fjCGNRIXlMO0OM3yCFP2/2ScLbOCUGL08URYOp7RBB/4dW6buziXxbs5RUfyMDPSQpcmbzqUJRJyd/kUJmrd0c0Gx6b9zZcRFJcYl4fec5Ak/cgk1JJ/G6e9OKuLf7kqiwTAt5gAny8tI2ofeDxPI6pQIz5fuaO1gJ7y1haGokWg/RPmn/aSEPsVxjPOPr1LttPhUePXqEUaNGi9ZAFIo8d+4cbNu2Xbx25MhR+Pj4iHxXomvXLli4cBEePnwolp9+Gi/GO3fuhPnzF+LBgwdiGTdurNre0dEB8+f/KZ47ODigTJkyuH37tijUk5YhQ4ahZ88e2LRpg6i6vHz5P6LIEF0fM8LIyFAIaxUJCQmi0rOucYZhChYsdBkmnyNPkKtbA5GHtXKnaqL4VEJMPOTx2jl+ppamWu19EmOlm3lTKzPER6WGDitS7rCp9cbN/dfhtz21tVJEsO72DjHh0TC3TW3VYWZjJjzH2bX5lCGR617CSiyDv66ITVsfCKFLaIrKyKhEWFmmFjmzMJdu0iIiEmBpmZq7aGAgefjIO1zUxQK/zqirfs1awy4t9nYmak8wQWHP1L4ouzafMjbmemoPabmiRpi7N0aIUyJNeqjob2ttJoleCw2HbESMUuT3EhoRtAiPVuLnzlKYMSF7R/tbe0s9hL5VwsVW2tbRUi9HNp8qyuRkEYZM0OPDw9fhWrsMHh2/gSTquaSBsaUp4qNSRYw85TplYmUmWpapSE65TtH4M597uLQ0tZdr9CspVDcjYsOj4eDlon5uamOO2NDIbNt8KsTHJyAgQGqzRF5QCkGuVasW3r59i7g47boA1tZWWi18VG2HbGysERmZGhZMnQgIW1tbbN26HevWpRatevZMd1E9Er8rVvwjFio89eeff2D27F9RvnzGucCvX4fC3j61jZSdnS1CQkJ0jjPMx0Ap04cyFwtEKTleVw2fCoYpQJA3iUKUI19mLEZDHrxEyZqlxDqFC1u7SP1yw5+GipxeVSh0IQcpV5Q8tw4lHYWQjnoZic//GZTp8QMvPESpuqXV+6f3k7bvblZs8gv+AREo4qzdo1NFqZJWuHk7FPHx0g2jz0Up98+1WCHcvRcuPLe0nDkXrO6X+zo0DrY2JkJEL1txCyczKHSlws3VEjExcoS8kgTDiVPPUa924Wzb5BdevEmGTCaFFmdE2aIynLknCadz91O9SyUcZbj2WHp+7Faqi7VcURmehkmhxiSSv1uT3nOoSf0y+jh+S9rP0ZsK1PfSz5FNfoG8t9EhGV+nKA+2aA0pwoEKVjl4SdemiGehcCwrrZvZFRI5s0Togxew8ygiBHTks1C0/mMATCy1c6Q1Cbrkj2I1S6srQtN+QlL66mbH5lOErrfUkzYoKONrx+3bd9CgQQOxTqHArq6uYv3Ro0BUqSKltJQtWxZOTpL3/datW/D0LC2ENPXW3b17xzvfg42NDYYOHYIVK5YhNDQU3347VKctFZpq2rSJWHdxcRFe6ZcvX+ocZximYMEeXYbJ51DOa/e5UuVi5zJFEODjj+Bbz1GytiRoNbm06QK+3TpC5PQamRsj8mWk1rhL+aIwI8/FG0n43Njjh+o9amLgusGwdLYS4c2KREmYNR/bBpc3XcCb5+Hq/Qec90f9b5rgy3WDYetqh33Td4pxQxNDEVK948ctOm3yC5OnX0AhC0NERSXi8ZO3+HdD6wzt7OxM8VkfLzRqsQ1OTmZISJBEFHlUW7UojmZtdoBSZ5MUyTA0lImw4gnjvdGi3Q6RSxsXl4QZU2qLbY6dfIbQ0Dj07CbdmKv4cWx1tO+6B472pmK/3jWkvDjK+/Wu4YSqlR112uQHqNjU8/AEJCmU8H2YjPkDjCHT4Xqd0MUQPf5MELm7miHD4zsZYuD/EuBZRIaoOCUsTfXU4x1mx+PwdQVuP0vGmmGSgo5NUGLsukT8PVBbUQ9uYSjaBp27r8DdoGTs/UFyG18NVODMvWQRYq3LJj9gYmmGlrM+E+tWxewR9yYGAcduoJBz+vz8gGM3UalfQ3RbM0Lk4MakeGcDjlwXhagoz9fM1kLsg7y6L/we4U1gCHpuGi1SI56cv4foEGmbKv0bIfjqI4Tceqref+SzMFGsqseG70Qu7+XlR5Esl770JlN74cysHZnafGp4eJTCypUrxHqFCuVx4sRJ+Pn5oWHDhuls//lnJU6dOi5yeqk/p0oQkwf29OkTqFq1Cuzs7NRFo7Zs+RdffDEABw/uh4tLESxY8BcSE6VJn6lTp4j9PXmS2rZszpzfUbduHeEBbteug7pIli5OnDiBMWO+w4ED+1CyZAmMHTsu03GG+Sjo5XLebP4J9vnP6CmpsgnDMB+tkXjDEG8YKD/MBZAEZOGyUngd/atTvi6FBhPGFiYipzb8SagoTEUCNvxpmCgmRV7fsCehcHCX8m2p6JRJIVMo5Aqx/ZdrB2Nhm9/Vx7Ev6Yj4t3GIpqo9KTiXKYywJ2FaeWwqnDwLC1vVe6HqqtTfl9oc6bL50Px8VZ4r7YViY6WJACsrI5Ryt1YLKypAVamCfbp1audDYctUndlAXw/W1sY4cTpIeG0tCxlizPizGDG0khClBAna0LA4eJa2UReRIo8seYbJQ5uWlyExiIxMFPYqHgZEwNbWRHiHddl8aMyOrf2g+38ZkYzHr5XqkGMqGGVuIp2v52HJMDbUg4OlVPDJ1FBPhA1TWHNIpBLFHfREYSgvF5nIvaWQZtpTwMtkHLimwIIvjNVtiB68kOwLpQhgqtJ8NTAZNdzTe2PjE5W4F5yM0oVlMDOW7N9EK/EqSimEtC6bD82Cif0/6P7p/9+5optYpzsSCiuOfvFGPKdiVFau9gh/+FJaL2aP8ICX5HqETXEHRAWFi/66UUFhQixbFrVDYnS8EL89N4/Bmra/SDsVAtoOyUnJok2RZlGp+DfRojJzWui1pLgEtSgmnMq74tXd51AqknXafEjGBCx9r/szNTVFxYoV1b8RlK9LXlSiUKFCIqeWxqgwFQnYwMBAUUyK+u+Sl9bT01Pk21LRKSsrKyFiafsDB/aievWa6uNQGDKJVs3w4fLly4t9a+bSurm5aQnftFhbW4twaNpOk3Llyol9q977u8bfF1OmTPwg+2Wyh1wuF7ncJ0+eFBMwee0+b8+OOjA3zz2hGxOThPadz+e58/ExYKHLMPlY6L4vHNwdMWD1Nwi68QyFvYrgyNwDwpubn8gNofu+6Np7n/DmUrEoczNDbFjdUi1q8wsfWui+L6i68qxdiUL03nqWLCoql3HJX1lBH1rovi9MbS3QY8NohD4Igk1xR9zddQlXVhxFfuJ9C933BQnevXt34cqVq6hYsQKmTJkmvLn5HRa6eQMWutqw0E0lb99dMwyTJ6BeuPOazxJh0JEvIhD7RipAwnwctm1siwf+b4S49SiVf1ovfYr0qWeA5hX18Tw8WXiFTY3y14TDp0RceDTWtv8Ftu7OYl0V0sx8eKgXbqVKVUVbn+fPn6frd8swBRpV259cO17uHSqvw6eCYQooFAYoS6nYqxnqTNA4tc8Qdob6MDIzEh5EqtqcHZFLIYnvwya/o1AkIzZW26OcmKhAUpIUHkkFoQgqPhUdIxdLkSIWKFHcMlvHeB82BQHKo1W1aVKFdMYlKtWhwxR+TNBYdLxSVGjOjsil/WnuP6c2BQGZob64VmliYGyovnaoXqNrFhWFohxcKjqVHZGrl9L7+7/a5DcoXNncXLtYnqqSMYU8q0Ii6ZGKT1Go84sXLxAV9d+LB9K+qIBUWnSFYRb08EyGYTKGPboMU0Dp+Wdf2BSzw6IOc9Vj35+ZiNn1pqFWv3qwKmyFfTN2odUP7VChXRXEhEWLvDgLewucWHgEF9ae1blvuxIO+Hz5V6Jf5ePLj7Bl5Dpx455dm4ICVTPu0e8Azh7rjrJetmJs1h9XUKSIObp2KoUqtTYi8O4A3Lv/Bk1bb0fplFzZN2/iUbaMLVYuba7VmkgTEkvDvjuJM+eCYGpqiDXLm6OMp222bQoSpUfF4bMGBvi1t9RjKCBEic//TsC5aabouzABXzY2QNuqBmg0NV7k4VI+b2KSEvGJwIrBxqhTWnc15F2XkjB6baKoFTKitaFYcmJTUKgzqh3KtKuOlc2nqgvddV09HId/Wg/bks7w6lQDe4ctR8Xe9VFzSCu8Takob+5gidtbfXB+3l6d+zaxMUdnKlxlVwhvHr/C7iFL0/XVzYpNfmXJkv+hdGkPVKvmrR7z87uMUqU8MWjQ16Lq8vffj8PkyZPQsWN7BAUFiygTKv40b94CzJ0r9c7NCYcPH0SvXn3U+bpUyXnnzm0iB9jf/yG6desBhUKhc5xh8hTs0f1oFLwpSoZh1DiWckKlDlXfeUZOLT6GhW3niAJUf7X7A61/6iAEKnlUDIzTC6y2P3cUebyz6kyFgaEByreplCObggTl2k6Y6vNOOxK5pw53FcuNS1I17S3b/LU8v5rsP/hYFKi6ebkfZkyuhYnTLuTIpqCx5pQcT16/28O9brgJLv5iCr9ZZpjZ2wg/bpBEEAlfeZL2xA2NjVmbiBOTTHBzjikWHZKLtkbZtSloGFuaoeoXUquYzKCc3A2dZ4mFhHH57nVg7eYgJuhUXmBNag1pjQcH/LCi8SS8vhuEyn0b5MgmP0PFoT77rN877ebPX4jGjZuiUaMmqFKlOiZPnii8wUZGRqJIVVZp3rw5zp07g1q1UgtZEbNn/4bRo78X/XSpvkX37t0yHWcYhiFY6DJMAeb4wsNoMa5tutDAzKCetnGRsTCzNkNxb3e0Gt9e63Wa0XerURK3D90Qz+nRo75ntm0KGi2auYo+uNQKKDtUrGCPVyl9bnv2O4DIqASt14+eeIZunaVWUk0bF8PFSy/Tec6zYlPQGNPOCBM2Z89zV7m4TFRlJhYdSsKWC9qeJaq2XMpZD672Uphz43L6OHU3Ods2BY0r/xwTbYCo2FRWIa8rhTCb2VsKsdsipXWRJsUblMX9vZfE+sMj1+Fat0yObPIzM2bMxLRpU2BikvVWVpSfGxERISok16hRAz/8oN3ah8bNzMwy3PbOnTsYN248/P2lyTuCQpi9vWtg//794vnevfvQpEljneMMk9fQ09OHnp5BLi75t8d6dmGhyzAFmKCbzxB4IQB1v8i6l8KtegnRMijqZSQCLzzE3qk7tF43tTZDYmyCaOOhEsYW9oWybVPQoDZDv02vg0lTL2Q5NzMiMkGEPdeuVVg837ujA6wstfuzBgdHo3BhKc9OX1+GQhZGiIxKzLZNQWNwCwNce5wM34dZC4Ok72zbxSTU95JuML5ra4i+9bQnkILClShim/qz62ytpxbG2bEpaESHRODGxjOoPbxNlrexKeEIC2cbhD0IRsTjV9g/6p90NuZOVoh+JeWTxoZGivDknNjkZ27evIUDBw5i9OjvMrUj0UlthwwMDNCiRQshdKnH7rlz5/DTTz9r2RYrVgwXLpzD33//hSpVqmi9ptomJia1FoSNjY14npws/V6EhLyCo6OjznGGYRgVnKPLMAWcg7P2YPjeMbi8WXe4atMRLVFnQAMRqmxdxBoHft2j0+NH48o0Qk2RpMi2TUGkbu0icHe3wrqN93Ta3Lsfjup1N4n18DfxKOFmiRrVnDIV0KpevSoM0xQhy4pNQYNqsc3qa4QfNiRi2SDtyQNNusyJBwVEUPro83AlzkzV7fmiU5y29lpKzbds2RRELi87iv4HJggBq4tyXWuhZOPyUi0BJyvc3HIOCW/jdNorFUp1j11CNfGWXZv8zsSJk3H16iUsX75Cp8348eMwaNBXIlqHcnenTZuh0/bmzZuiQnPTpk0xduxouLq6Yt269Vi/foMIP05LUlKSWswSdIyEhASd4wzDMCpY6DJMASf69Vv4rDmLpt+10mlzbMEh+Kw6I9ZtXe0w8uA4nFtxKkPbuIhYGFuYQKYvQ7IiGZbOVoh4Hp5tm4LK9Em10KHbXrRp6Zbh61QkivJzVRMG7brsxsEjT9CxXckM7V1cLBAcLHlHqIozeR7NzQ2zbVMQaVPFQIQgU3EoXWwfa4LyxaRJgVUn5Zi+TY6d32esTIva6SEoPPXGPPiNEjXcZdm2KYgkJchx7o/dqP9DZ502t7ddwOnftot140KmGHhqOi7+fRDxOirFR7+KgLmjlajQbO5ojajgsBzZ5HcoFPnPP+dj6tTJOm1mzvwNixb9LdZJ6N65czPTYlR07Tp69ChOnDiBPn16Y8GCeXj79i02bNiYzjYyMlJUVSZvMYnbokVdEBj4WOc4w+Q5ZIbSkmvHy71D5XX4VDAMgzPLTqJ0Qy+YWpm+82yEP5Vu9FTthzIi4NwDVGhXWbT8qN6jJu4cvZ0jm4KIm6slunZyx/pN999pSx6M8mXtEB4er9OmVXM3bN72QLQO2rjlARo3LJojm4LKnM+MMGdv+iJfGVGpuAxh0brDjKuVkOFRiBKPQpJFgamz9xRokBLqnB2bgsr9fVdgam0Ou9JF3mlLnty3QeEwsdJuj6NJ4PFbKNullliv0LMuHh27mSObgsBffy1C/fr1YGv77mrs1EeXKh9TISpdULXk33+fDV9fH3h6eqJq1RoZilwVhw4dRv/+n4m2Rl9//RV27dqd6TjDMAzBQpdhCijyhCThTSWobcfB3ygcWYrSS5InISmllQc9KuTaYcWRLyJQuJwL3OuWRqeZPdLte/+MXajdvz7GnpyAQN9HIpeXaDS0uRC1mdkURCgv1tg4VcyMHlkV1tbGInyY+hdbmEvBN/r6ejBL00bI0dEMd+9J3vDGLbeJvF1Nmjd1haeHDarW3iSqM0/5WTr/D/zfoNdnBzK1KaiYG4voV4GXi0zk2pql3LNTz1yDlK/KzBjQbK9qZ6EnPLDUW3fWrkTh4dWEwsP/HmiMLn/Eo/mMePzWxwiFTKUDdZsbj9vPkzO1KYjQtSlZ4/pzcsZWkQ9KqQ/JCgWS4uVqO1X7Ic3cXgcvF9iUdELHJd+m27fv4kMoWt0dXx6fJvZzd5evGPfqVFO0KsrMpiBAebaqUGDymH7//XjRI5e8sZQbq+qXS4+aObXEgwf+qFixIpo0aSJycTWpX78+Zs6cgdOnT6NGjVqYMGEiAgMDtWxCQ8O02gRRgaoePbqLEGoSsz4+PpmOM0xeIncLUUkLI6GnLOilNRnmI0G5SI0aNULDEG8YKPmi9LH5+WrWvHZM7mB2bC2f6jzCgon9P/ZbYFIYE7CUz0UeYsqUiR/7LTA0cS+X4+DBgzh58qQIZ89r93n7DnTI1XQgajXYtvVuxMZKHRmGDh0qloII310zDMMwDMMwDMN8CMjDmpteVj3Jh+nr65unhP/HgEOXGYZhGIZhGIZhmHwFC12GYRiGYRiGYRgmX8GhywzDMAzDMAzDMB8APZmBWHILPRmXX1LBHl2GYRiGYRiGYRgmX8EeXYZhGIZhGIZhmA+Anp5+rrb80dOTWkcy7NFlGIZhGIZhGIZh8hns0WUYhmEYhmEYhvkAkDeXPbofB87RZRiGYRiGYRiGYfIVLHQZhmEYhmEYhmGYfAWHLjMMwzAMwzAMw3wAZHoGYsktZFyMSg17dBmGYRiGYRiGYZh8BXt0GYZhGIZhGIZhPgB6oPZC+rl6PEaCPboMwzAMwzAMwzBMvoI9ugzDMAzDMAzDMB8A8ubmbnshRa4dK6/DHl2GYRiGYRiGYRgmX8FCl2EYhmEYhmEYhslXcOgywzAMwzAMwzDMB0BPpg+ZLBeLUeXisfI6LHQZ5iOhVCrFYxLnUuQJYmI5pyUvoUzggKO8ghz8v5FXkMn4/yIvIZfLP/ZbYOg+KilJ674qrxETk5ivj5eX0VPm1b8KhsnnhISEoG3bth/7bTAMwzAMw3zy7Nu3D05OTsgrJCQkoEOHDggLC8v1Y9vZ2WH37t0wNjZGQYaFLsN8JJKTk/H69WuYmZlBT0+PvweGYRiGYZhsQj672NhYODg45LmoBxK7H8Pzb2hoWOBFLsFCl2EYhmEYhmEYhslX5K1pD4ZhGIZhGIZhGIb5j7DQZRiGYRiGYRiGYfIVLHQZhmEYhmEYhmGYfAULXYZhGIZhGIZhGCZfwUKXYRiGYRiGYRiGyVew0GUYhmEYhmEYhmHyFSx0GYZhGIZhGIZhGOQn/g8dmSxB1qC5GQAAAABJRU5ErkJggg==" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -1403,8 +1225,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:45.425048Z", - "start_time": "2026-08-31T19:43:45.407995Z" + "end_time": "2026-09-03T17:40:54.537825Z", + "start_time": "2026-09-03T17:40:54.516957Z" } }, "cell_type": "code", @@ -1458,7 +1280,7 @@ "\n", " # Formatting\n", " ax.set_xlabel(\"Wavelength (µm)\", fontsize=14)\n", - " ax.set_ylabel(\"Fp/Fs\", fontsize=14)\n", + " ax.set_ylabel(f\"Contrast Ratio ($\\\\alpha={phase_angle/np.pi:.2f}\\\\pi$)\", fontsize=14)\n", " ax.set_xlim([0.35, 1.85])\n", " ax.set_ylim([0, 5e-10])\n", " ax.set_title(f\"Spectral Comparison: {model_a} vs {model_b}\", fontsize=12)\n", @@ -1476,8 +1298,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:45.548317Z", - "start_time": "2026-08-31T19:43:45.426325Z" + "end_time": "2026-09-03T17:40:54.649959Z", + "start_time": "2026-09-03T17:40:54.538640Z" } }, "cell_type": "code", @@ -1494,7 +1316,7 @@ 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -1521,8 +1343,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-08-31T19:43:45.660840Z", - "start_time": "2026-08-31T19:43:45.556482Z" + "end_time": "2026-09-03T17:40:54.754789Z", + "start_time": "2026-09-03T17:40:54.651559Z" } }, "cell_type": "code", @@ -1538,7 +1360,7 @@ "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" From ed1960ba8fdd42af5435b05e77b9efd9e2bb5606 Mon Sep 17 00:00:00 2001 From: eleonoraalei Date: Tue, 8 Sep 2026 18:04:49 -0400 Subject: [PATCH 3/4] Added minor edits to the tutorial. --- .../exoearth_spectroscopy_tutorial.ipynb | 1614 +++++++++++++---- 1 file changed, 1247 insertions(+), 367 deletions(-) diff --git a/tutorials/exoearth_spectroscopy_tutorial.ipynb b/tutorials/exoearth_spectroscopy_tutorial.ipynb index 82a83e1..54d6f80 100644 --- a/tutorials/exoearth_spectroscopy_tutorial.ipynb +++ b/tutorials/exoearth_spectroscopy_tutorial.ipynb @@ -1,29 +1,52 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "id": "c3142ca8c91dd016", + "metadata": {}, "source": [ "# pyEDITH Tutorial: ExoEarth Spectroscopy Case Study\n", "\n", + "\n", + "Curator: [Aaryn Carter](https://www.stsci.edu/stsci-research/research-directory/aarynn-carter) (STScI)\n", + "\n", "This notebook walks through assessing the feasibility of distinguishing between different Earth-like spectra for a given Habitable Worlds Observatory (HWO) Early Achitecture Design (EAD) concept." - ], - "id": "c3142ca8c91dd016" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Start by importing the necessary packages, set the verbosity to pyEDITH to \"info\" for full information, and set the default style for HWO plots.", - "id": "463c891fcfec9c0a" + "id": "463c891fcfec9c0a", + "metadata": {}, + "source": [ + "Start by importing the necessary packages, set the verbosity to pyEDITH to \"info\" for full information, and set the default style for HWO plots." + ] }, { + "cell_type": "code", + "execution_count": 1, + "id": "d88f2ac1cd5aa658", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.177263Z", "start_time": "2026-09-03T17:40:27.747176Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:51.332188Z", + "iopub.status.busy": "2026-09-08T22:04:51.331917Z", + "iopub.status.idle": "2026-09-08T22:04:54.065442Z", + "shell.execute_reply": "2026-09-08T22:04:54.065117Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ealei/Coding/pyEDITH/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import os\n", "import glob\n", @@ -48,37 +71,45 @@ "# Plot styling\n", "hwostyle.use(\"light\")\n", "colors = hwostyle.palette" - ], - "id": "d88f2ac1cd5aa658", - "outputs": [], - "execution_count": 1 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## 1: Initial Setup", - "id": "5c7e1e2e0b64c8b9" + "id": "5c7e1e2e0b64c8b9", + "metadata": {}, + "source": [ + "## 1: Initial Setup" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "6b3581c6d592d309", + "metadata": {}, "source": [ "We need to provide a quantitative metric for how to distinguish between any two spectra. Let us assume a 5$\\sigma$ detection threshold, which corresponds to a $\\chi^2$ value of 25 for Gaussian uncertainties.\n", "\n", "We will also define the spectral channels of interest for the calculation, covering 0.4-1.8 $\\mu$m. In pyEDITH these are defined as `Filter` objects, each with its own wavelength bounds and spectral resolution, which are later passed to the calculation via the `filter_list` parameter.\n", "\n", "Note that if you need to conduct a more complex test such as comparing multiple models simultaneously, you may need to adopt a different metric." - ], - "id": "6b3581c6d592d309" + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "5b72139ff573a075", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.192385Z", "start_time": "2026-09-03T17:40:29.179104Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:54.066956Z", + "iopub.status.busy": "2026-09-08T22:04:54.066803Z", + "iopub.status.idle": "2026-09-08T22:04:54.069170Z", + "shell.execute_reply": "2026-09-08T22:04:54.068921Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "SIGMA_TARGET = 5.0\n", "CHI2_TARGET = SIGMA_TARGET ** 2\n", @@ -92,43 +123,103 @@ "\n", "channel_names = [f.name for f in FILTERS]\n", "n_channels = len(FILTERS)" - ], - "id": "5b72139ff573a075", - "outputs": [], - "execution_count": 2 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Next, we need to provide all of the spectra we would like to compare, and extract the relevant information from them to provide to future calculations. Let's define a dictionary to do this, using the keys as the more readable names, and the values as the paths to the files.", - "id": "8fe6f00d6f6607e" + "id": "8fe6f00d6f6607e", + "metadata": {}, + "source": [ + "Next, we need to provide all of the spectra we would like to compare, and extract the relevant information from them to provide to future calculations. Let's define a dictionary to do this, using the keys as the more readable names, and the values as the paths to the files." + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "7aa7e5e9faf99d6c", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.274651Z", "start_time": "2026-09-03T17:40:29.195227Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:54.070488Z", + "iopub.status.busy": "2026-09-08T22:04:54.070394Z", + "iopub.status.idle": "2026-09-08T22:04:57.315422Z", + "shell.execute_reply": "2026-09-08T22:04:57.315101Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/ArcheanEarth_geo_albedo.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Hazy_ArcheanEarth_geo_albedo.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Earth_geo_albedo.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Earth2_geo_albedo.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/proterozoic_low_o2_geo_albedo.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/proterozoic_hi_o2_geo_albedo.txt\n" + ] + } + ], "source": [ "# Assemble spectra\n", - "SPEC_PATH = \"/Users/aacarter/Documents/SOFTWARE/HWO/hwo-tools/coron_model/planets/\"\n", + "import requests\n", + "import io\n", + "import numpy as np\n", + "\n", + "def load_spectrum(url):\n", + " response = requests.get(url)\n", + " print(response.status_code, url) # debug line — remove later\n", + " response.raise_for_status() # raises a clear HTTPError if something's wrong\n", + " return np.loadtxt(io.StringIO(response.text))\n", + "\n", + "BASE_URL = \"https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/\"\n", "\n", - "# List Earth spectra files in dictionary\n", "EARTH_SPECTRA_FILES = {\n", - " \"Archean Earth\": os.path.join(SPEC_PATH, \"ArcheanEarth_geo_albedo.txt\"),\n", - " \"Hazy Archean Earth\": os.path.join(SPEC_PATH, \"Hazy_ArcheanEarth_geo_albedo.txt\"),\n", - " \"Modern Earth\": os.path.join(SPEC_PATH, \"Earth_geo_albedo.txt\"),\n", - " \"Modern Earth 2\": os.path.join(SPEC_PATH, \"Earth2_geo_albedo.txt\"),\n", - " \"Proterozoic Earth (Low O2)\": os.path.join(SPEC_PATH, \"proterozoic_low_o2_geo_albedo.txt\"),\n", - " \"Proterozoic Earth (High O2)\": os.path.join(SPEC_PATH, \"proterozoic_hi_o2_geo_albedo.txt\"),\n", + " \"Archean Earth\": BASE_URL + \"ArcheanEarth_geo_albedo.txt\",\n", + " \"Hazy Archean Earth\": BASE_URL + \"Hazy_ArcheanEarth_geo_albedo.txt\",\n", + " \"Modern Earth\": BASE_URL + \"Earth_geo_albedo.txt\",\n", + " \"Modern Earth 2\": BASE_URL + \"Earth2_geo_albedo.txt\",\n", + " \"Proterozoic Earth (Low O2)\": BASE_URL + \"proterozoic_low_o2_geo_albedo.txt\",\n", + " \"Proterozoic Earth (High O2)\": BASE_URL + \"proterozoic_hi_o2_geo_albedo.txt\",\n", "}\n", "\n", "# Create a dictionary to store the extracted model spectra\n", "models = {}\n", "for label, path in EARTH_SPECTRA_FILES.items():\n", - " arr = np.loadtxt(path)\n", + " arr = load_spectrum(path)\n", " wl = np.asarray(arr[:, 0], dtype=float)\n", " albedo = np.asarray(arr[:, 1], dtype=float)\n", " order = np.argsort(wl)\n", @@ -142,68 +233,83 @@ "model_names = sorted(models.keys())\n", "if len(model_names) < 2:\n", " raise ValueError(\"Need at least two Earth models for pairwise comparison.\")" - ], - "id": "7aa7e5e9faf99d6c", - "outputs": [], - "execution_count": 3 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Let's take a pause here and plot all of our spectra to see that things are working correctly.", - "id": "7d8a836405b3806" + "id": "7d8a836405b3806", + "metadata": {}, + "source": [ + "Let's take a pause here and plot all of our spectra to see that things are working correctly." + ] }, { + "cell_type": "code", + "execution_count": 4, + "id": "e31bee91dfae8a5d", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.455120Z", "start_time": "2026-09-03T17:40:29.276227Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.317111Z", + "iopub.status.busy": "2026-09-08T22:04:57.316983Z", + "iopub.status.idle": "2026-09-08T22:04:57.520332Z", + "shell.execute_reply": "2026-09-08T22:04:57.520064Z" } }, - "cell_type": "code", - "source": [ - "for name, spectrum in models.items():\n", - " plt.plot(spectrum[\"wavelength_um\"], spectrum[\"albedo\"], label=name)\n", - "plt.xlabel(\"Wavelength (um)\")\n", - "plt.ylabel(\"Geometric Albedo\")\n", - "plt.xlim([0, 2.5])\n", - "plt.ylim([0, 0.5])\n", - "plt.legend()\n", - "plt.show()" - ], - "id": "e31bee91dfae8a5d", "outputs": [ { "data": { + "image/png": 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", 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" + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 4 + "source": [ + "for name, spectrum in models.items():\n", + " plt.plot(spectrum[\"wavelength_um\"], spectrum[\"albedo\"], label=name)\n", + "plt.xlabel(\"Wavelength (um)\")\n", + "plt.ylabel(\"Geometric Albedo\")\n", + "plt.xlim([0, 2.5])\n", + "plt.ylim([0, 0.5])\n", + "plt.legend()\n", + "plt.show()" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "fcec20a8ee30d0fc", + "metadata": {}, "source": [ "Looks good! Now lets prepare our calculations. Specifically, we need to define the parameters for the simulations, including system specific parameters, instrument setup, and the input wavelength grid.\n", "\n", + ".. important::\n", "Note that the input wavelength grid is simply the grid on which we provide the input spectra (stellar flux, planet contrast, target SNR). The actual resolved wavelength grid of each observation is determined by the `Filter` objects we defined above. pyEDITH will automatically rebin things during the actual calculations for a given filter, we just need to make sure the input grid covers the wavelength range of every filter.\n" - ], - "id": "fcec20a8ee30d0fc" + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "4ee0778a4ce6754b", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.475524Z", "start_time": "2026-09-03T17:40:29.456681Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.521694Z", + "iopub.status.busy": "2026-09-08T22:04:57.521564Z", + "iopub.status.idle": "2026-09-08T22:04:57.535904Z", + "shell.execute_reply": "2026-09-08T22:04:57.535692Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "# Define the system parameters, let's use Earth at 10 parsecs.\n", "ap = 1 * u.au # Semi-major axis of planet\n", @@ -270,37 +376,32 @@ " \"noisefloor_PPF\": 30,\n", " \"ez_PPF\": [np.inf]*len(wl_input),\n", "}" - ], - "id": "4ee0778a4ce6754b", - "outputs": [], - "execution_count": 5 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "c95e32bb261fc415", + "metadata": {}, "source": [ "Now we have defined some base settings for the calculation, we can parse the filter list to confirm which filters are active for our input wavelength range, and extract the resolved wavelength grid of each channel." - ], - "id": "c95e32bb261fc415" + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "fa161b066900f6bf", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.510857Z", "start_time": "2026-09-03T17:40:29.476496Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.537225Z", + "iopub.status.busy": "2026-09-08T22:04:57.537120Z", + "iopub.status.idle": "2026-09-08T22:04:57.540223Z", + "shell.execute_reply": "2026-09-08T22:04:57.540005Z" } }, - "cell_type": "code", - "source": [ - "# Parse the filter list to validate it against the input wavelength range.\n", - "parsed_filters = parse_input.parse_filters(base_params)\n", - "for f in parsed_filters:\n", - " print(f)\n", - "\n", - "# Each Filter carries its own resolved wavelength grid.\n", - "wl_ch = {f.name: np.asarray(f.wavelength.to_value(u.um), dtype=float) for f in parsed_filters}" - ], - "id": "fa161b066900f6bf", "outputs": [ { "name": "stdout", @@ -312,22 +413,41 @@ ] } ], - "execution_count": 6 + "source": [ + "# Parse the filter list to validate it against the input wavelength range.\n", + "parsed_filters = parse_input.parse_filters(base_params)\n", + "for f in parsed_filters:\n", + " print(f)\n", + "\n", + "# Each Filter carries its own resolved wavelength grid.\n", + "wl_ch = {f.name: np.asarray(f.wavelength.to_value(u.um), dtype=float) for f in parsed_filters}" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "The calculations we perform will be based on the contrast ratio of the planet to its host star, which is a function of the geometric albedo, the phase angle, the radius of the planet, and the semi-major axis of the orbit.", - "id": "fdc8e69412ec49ce" + "id": "fdc8e69412ec49ce", + "metadata": {}, + "source": [ + "The calculations we perform will be based on the contrast ratio of the planet to its host star, which is a function of the geometric albedo, the phase angle, the radius of the planet, and the semi-major axis of the orbit." + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "6fa25698d89dce4a", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.529983Z", "start_time": "2026-09-03T17:40:29.519561Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.541399Z", + "iopub.status.busy": "2026-09-08T22:04:57.541314Z", + "iopub.status.idle": "2026-09-08T22:04:57.545662Z", + "shell.execute_reply": "2026-09-08T22:04:57.545461Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "# Helper function for computing contrast_ratio\n", "def contrast_ratio(geometric_albedo, phase_angle, planet_radius, separation):\n", @@ -339,31 +459,41 @@ "for name, model in models.items():\n", " fpfs_native = contrast_ratio(model[\"albedo\"], phase_angle, rp, ap)\n", " fpfs_input_all[name] = np.interp(wl_input, model[\"wavelength_um\"], fpfs_native)" - ], - "id": "6fa25698d89dce4a", - "outputs": [], - "execution_count": 7 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## 2: Running The SNR Calculations", - "id": "f2ea572264cd13ca" + "id": "f2ea572264cd13ca", + "metadata": {}, + "source": [ + "## 2: Running The SNR Calculations" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We're now in a position to start conducting calculations. We'll start by defining a function that uses the base parameters and the contrast ratio to produce all the relevant setups for a pyEDITH calculation, and then returns a second function that can be used to calculate the SNR for a given input exposure time.", - "id": "a6a4bdba6938d261" + "id": "a6a4bdba6938d261", + "metadata": {}, + "source": [ + "We're now in a position to start conducting calculations. We'll start by defining a function that uses the base parameters and the contrast ratio to produce all the relevant setups for a pyEDITH calculation, and then returns a second function that can be used to calculate the SNR for a given input exposure time." + ] }, { + "cell_type": "code", + "execution_count": 8, + "id": "c64d20fa7253abff", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:29.538733Z", "start_time": "2026-09-03T17:40:29.531533Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.546919Z", + "iopub.status.busy": "2026-09-08T22:04:57.546842Z", + "iopub.status.idle": "2026-09-08T22:04:57.549681Z", + "shell.execute_reply": "2026-09-08T22:04:57.549474Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "def make_snr_runner(base_params, fpfs_input):\n", " \"\"\"\n", @@ -423,29 +553,840 @@ " return results\n", "\n", " return run, fpfs_resolved" - ], - "id": "c64d20fa7253abff", - "outputs": [], - "execution_count": 8 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "7dc673d7146f4897", + "metadata": {}, "source": [ "Now we can produce a measure of the SNR for any model and filter, however, we cannot simply scale a single reference exposure time to get the SNR for an arbitrary exposure. As pyEDITH accounts for multiple noise sources, the scaling is not linear. For example, if the reference exposure time is high we will be dominated by photon noise, but if it is low then we will be dominated by read noise. Therefore, instead of scaling a single reference exposure, we will build a grid calculations for a range of exposure times and then interpolate between them to get an appropriate noise estimate for any trial exposure time.\n", "\n", "Note this cell can take ~20 seconds to run with the default grid spacing." - ], - "id": "7dc673d7146f4897" + ] }, { + "cell_type": "code", + "execution_count": 9, + "id": "3d30eed6b6dc6aa2", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:52.779581Z", "start_time": "2026-09-03T17:40:29.540115Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:04:57.550989Z", + "iopub.status.busy": "2026-09-08T22:04:57.550901Z", + "iopub.status.idle": "2026-09-08T22:05:18.658532Z", + "shell.execute_reply": "2026-09-08T22:05:18.658241Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:57,559]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:57,599]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:57,745] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:57,746] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:57,746] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,592]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,614]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,642]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:58,762] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:58,763] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:58,763] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,903]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,918]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:58,947]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:59,066] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:04:59,067] \u001b[0mUsing default unit for D: m. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:04:59,204]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ealei/Coding/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", + " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:01,766]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:01,805]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:09,094]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:09,107]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:09,138]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:09,258] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:09,259] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:09,259] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:09,400]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ealei/Coding/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", + " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:11,760]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:11,799]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:11,918] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:11,919] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:11,919] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,054]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,067]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,095]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,214] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,214] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,215] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,347]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,361]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,391]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,510] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,511] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:12,511] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:12,648]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ealei/Coding/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", + " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,250]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,292]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,417] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,418] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,418] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,569]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,584]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,614]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,736] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,736] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:15,737] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,872]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,885]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:15,916]\u001b[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:16,036] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:16,036] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-09-08 18:05:16,037] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-09-08 18:05:16,162]\u001b[0m No nrolls in YIPs, setting nrolls = 1.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ealei/Coding/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", + " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n" + ] } - }, - "cell_type": "code", + ], "source": [ "# Create a grid of reference exposure times in log-log space, from 1 to 100 hr\n", "t_noise_grid = np.logspace(np.log10(1.0), np.log10(100), 30)\n", @@ -489,154 +1430,45 @@ " cn: _make_sigma_interp(sigma_grid_ch[name][cn])\n", " for cn in channel_names\n", " }" - ], - "id": "3d30eed6b6dc6aa2", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:29,549]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:29,591]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:29,926] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:29,927] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:29,927] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,513]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,593]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:30,620]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,015] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,016] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,016] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:31,157]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:31,238]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:31,272]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,593] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,593] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:31,593] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:31,725]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "/Users/aacarter/Documents/SOFTWARE/HWO/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n", - " np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:33,800]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:33,844]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,166] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,166] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,167] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,314]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,397]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,428]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,745] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,746] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:34,746] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,889]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:34,972]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:35,003]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,321] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,322] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:35,323] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:35,469]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:37,544]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:37,582]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,905] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,905] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:37,906] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,044]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,125]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,154]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,477] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,477] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:38,478] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,614]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,694]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:38,726]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,050] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,050] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:39,051] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:39,192]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,389]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,431]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,752] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,752] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:41,753] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,898]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:41,978]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,007]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,404] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,404] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,405] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,541]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,621]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:42,655]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,982] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,983] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:42,983] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:43,118]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,219]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,261]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,593] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,594] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:45,594] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,735]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,818]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:45,848]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,170] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,171] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,171] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,310]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,392]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,426]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,745] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,746] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:46,746] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:46,890]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:48,963]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,004]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,328] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,328] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,329] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,480]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,561]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:49,590]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,913] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,913] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:49,914] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,056]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,137]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,170]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,498] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,498] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n", - "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-03 13:40:50,499] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n", - "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-03 13:40:50,638]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n" - ] - } - ], - "execution_count": 9 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## 3: Optimizing The Exposure Time", - "id": "cbcfc4f752cea686" + "id": "cbcfc4f752cea686", + "metadata": {}, + "source": [ + "## 3: Optimizing The Exposure Time" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "c09d27150463b4d7", + "metadata": {}, "source": [ "Now we need to take our individual model estimates and compare them in a pairwise fashion to identify how well we can distinguish between any two models. As observations in different channels can have different exposure times, we need to optimize the exposure time in each channel simultaneously to find the minimum required exposure time to reach the desired $\\chi^2$ threshold.\n", "\n", "There are two pieces to this that we need to produce. First, a function that determines the $\\chi^2$ value for a given model pair and exposure time, and second, a function that optimizes the exposure times across the different channels together to reach the desired $\\chi^2$ threshold in the minimum amount of time.\n", "\n", "For the first, it's simply a case of calculating the sum of the squared differences between the two models, divided by the noise for the model we're assuming to be true." - ], - "id": "c09d27150463b4d7" + ] }, { + "cell_type": "code", + "execution_count": 10, + "id": "80656de75913ecf", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:52.819760Z", "start_time": "2026-09-03T17:40:52.812078Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:05:18.660215Z", + "iopub.status.busy": "2026-09-08T22:05:18.660132Z", + "iopub.status.idle": "2026-09-08T22:05:18.662635Z", + "shell.execute_reply": "2026-09-08T22:05:18.662414Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "def chi2_multichannel(times_vector, fpfs_ch_a, fpfs_ch_b, sigma_interp_a, channel_names, t_min=0.01):\n", " \"\"\"\n", @@ -682,25 +1514,33 @@ " # Calculate chi2\n", " chi2 = np.sum((delta_all[valid] / sigma_all[valid]) ** 2)\n", " return float(chi2)" - ], - "id": "80656de75913ecf", - "outputs": [], - "execution_count": 10 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "And for the second, we can use `scipy.optimize.minimize` to find the optimal exposure times for each channel that minimize the total exposure time while still reaching the desired $\\chi^2$ threshold. To ensure we don't get caught in a local minimum, we'll repeat the optimization with multiple random starting points and keep the best result.", - "id": "1272f9202701e4b9" + "id": "1272f9202701e4b9", + "metadata": {}, + "source": [ + "And for the second, we can use `scipy.optimize.minimize` to find the optimal exposure times for each channel that minimize the total exposure time while still reaching the desired $\\chi^2$ threshold. To ensure we don't get caught in a local minimum, we'll repeat the optimization with multiple random starting points and keep the best result." + ] }, { + "cell_type": "code", + "execution_count": 11, + "id": "7e8175a12e7bf313", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:52.827925Z", "start_time": "2026-09-03T17:40:52.821196Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:05:18.663939Z", + "iopub.status.busy": "2026-09-08T22:05:18.663845Z", + "iopub.status.idle": "2026-09-08T22:05:18.667102Z", + "shell.execute_reply": "2026-09-08T22:05:18.666820Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "def required_times_for_pair_multichannel(fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n", " channel_names, chi2_target=CHI2_TARGET,\n", @@ -781,14 +1621,12 @@ " return times_opt, chi2_final\n", " else:\n", " return np.full(n_channels, np.inf), 0.0" - ], - "id": "7e8175a12e7bf313", - "outputs": [], - "execution_count": 11 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "a228b068596aeb06", + "metadata": {}, "source": [ "Great! We're ready to run the optimization. We'll set up a loop over all possible pairs of models that we're investigating and run the optimization for each pair.\n", "\n", @@ -796,17 +1634,25 @@ "\n", "\n", "Notice also that we define a pruning function to remove any contributions from channels at the lower edge of the time grid that contribute very little to the overall $\\chi^2$ value. These are likely unphysical, and an artifact of pyEDITH SNR constraints at very low exposure times." - ], - "id": "a228b068596aeb06" + ] }, { + "cell_type": "code", + "execution_count": 12, + "id": "e1720248467fd099", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:52.834542Z", "start_time": "2026-09-03T17:40:52.829337Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:05:18.668216Z", + "iopub.status.busy": "2026-09-08T22:05:18.668142Z", + "iopub.status.idle": "2026-09-08T22:05:18.670492Z", + "shell.execute_reply": "2026-09-08T22:05:18.670243Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "# Helper function to prune channels that are stuck at the lower bound and are not needed\n", "def prune_unused_channels(times_opt, fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n", @@ -835,19 +1681,25 @@ " total -= contrib[i]\n", " times[i] = 0.0 # Force time to zero\n", " return times" - ], - "id": "e1720248467fd099", - "outputs": [], - "execution_count": 12 + ] }, { + "cell_type": "code", + "execution_count": 13, + "id": "42fd79c2d40d94f3", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:53.901992Z", "start_time": "2026-09-03T17:40:52.835788Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:05:18.671743Z", + "iopub.status.busy": "2026-09-08T22:05:18.671675Z", + "iopub.status.idle": "2026-09-08T22:05:19.666118Z", + "shell.execute_reply": "2026-09-08T22:05:19.665767Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "# Need to define a minimum and maximum exposure time, let's just use the limits of the interpolation grid\n", "t_min_per_channel = t_noise_grid[0] # 1 hr\n", @@ -921,39 +1773,32 @@ "# Convert to a dataframe to make the visualisation a little easier.\n", "t_required_matrix_df = pd.DataFrame(t_required_matrix, index=model_names, columns=model_names)\n", "ranked_pairs = pd.DataFrame(pair_rows).sort_values(\"max_required_hours\", ascending=True).reset_index(drop=True)" - ], - "id": "42fd79c2d40d94f3", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/aacarter/miniconda3/envs/hwo/lib/python3.14/site-packages/scipy/optimize/_numdiff.py:710: RuntimeWarning: invalid value encountered in subtract\n", - " df = [f_eval - f0 for f_eval in f_evals]\n" - ] - } - ], - "execution_count": 13 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "As you can see, the optimization itself is quite fast compared to the generation of the noise templates. The results are now stored in a dataframe, and we can display the required total exposure times for each model pair as follows:", - "id": "99713e30c8713d23" + "id": "99713e30c8713d23", + "metadata": {}, + "source": [ + "As you can see, the optimization itself is quite fast compared to the generation of the noise templates. The results are now stored in a dataframe, and we can display the required total exposure times for each model pair as follows:" + ] }, { + "cell_type": "code", + "execution_count": 14, + "id": "37ea1be13ea79ce6", "metadata": { "ExecuteTime": { "end_time": "2026-09-03T17:40:53.949358Z", "start_time": "2026-09-03T17:40:53.926396Z" + }, + "execution": { + "iopub.execute_input": "2026-09-08T22:05:19.667828Z", + "iopub.status.busy": "2026-09-08T22:05:19.667711Z", + "iopub.status.idle": "2026-09-08T22:05:19.678017Z", + "shell.execute_reply": "2026-09-08T22:05:19.677780Z" } }, - "cell_type": "code", - "source": [ - "print(\"Directional matrix: rows are the assumed true model; columns are the competing model.\")\n", - "display(t_required_matrix_df.round(2))" - ], - "id": "37ea1be13ea79ce6", "outputs": [ { "name": "stdout", @@ -964,31 +1809,6 @@ }, { "data": { - "text/plain": [ - " Archean Earth Hazy Archean Earth Modern Earth \\\n", - "Archean Earth 0.00 8.80 14.70 \n", - "Hazy Archean Earth 8.25 0.00 9.37 \n", - "Modern Earth 14.31 9.51 0.00 \n", - "Modern Earth 2 23.67 5.68 8.39 \n", - "Proterozoic Earth (High O2) 75.76 6.73 12.61 \n", - "Proterozoic Earth (Low O2) 79.12 6.74 11.63 \n", - "\n", - " Modern Earth 2 Proterozoic Earth (High O2) \\\n", - "Archean Earth 22.93 73.44 \n", - "Hazy Archean Earth 5.24 6.21 \n", - "Modern Earth 8.05 12.17 \n", - "Modern Earth 2 0.00 59.19 \n", - "Proterozoic Earth (High O2) 58.41 0.00 \n", - "Proterozoic Earth (Low O2) 63.07 inf \n", - "\n", - " Proterozoic Earth (Low O2) \n", - "Archean Earth 76.57 \n", - "Hazy Archean Earth 6.22 \n", - "Modern Earth 11.22 \n", - "Modern Earth 2 63.82 \n", - "Proterozoic Earth (High O2) inf \n", - "Proterozoic Earth (Low O2) 0.00 " - ], "text/html": [ "
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