{ "cells": [ { "cell_type": "markdown", "id": "7995a5d9", "metadata": {}, "source": [ "# Getting started with `specsr-roman`\n", "\n", "Roman's grism will deliver spectra at R ≈ 461, where the diagnostic complexes\n", "stay blended: Hα+[N II], [O III]+Hβ. `specsr-roman` super-resolves them with a\n", "three-stage network — and, more importantly, is calibrated so that lines the\n", "data could not have revealed are **not drawn**.\n", "\n", "This notebook takes you from a fresh install to the numbers in the README:\n", "\n", "1. download a small held-out subset of the training data,\n", "2. load the published checkpoints,\n", "3. super-resolve one spectrum and read what comes back,\n", "4. see why the redshift is a **PDF** rather than a number,\n", "5. measure what photometry buys, and why it must be noisy,\n", "6. measure line recovery **split by recoverability** — the only honest way to\n", " score a model like this.\n", "\n", "Everything runs on a laptop CPU in about two minutes and downloads roughly\n", "17 MB (3.8 MB of data, 13 MB of weights)." ] }, { "cell_type": "markdown", "id": "23e0af7f", "metadata": {}, "source": [ "## Setup\n", "\n", "```bash\n", "pip install \"specsr-roman[hub]\"\n", "```\n", "\n", "The `hub` extra pulls in `huggingface_hub`, which is what fetches the published\n", "checkpoints and the tutorial data below." ] }, { "cell_type": "code", "execution_count": 1, "id": "2cadae79", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:02.589467Z", "iopub.status.busy": "2026-08-27T03:09:02.589380Z", "iopub.status.idle": "2026-08-27T03:09:03.762039Z", "shell.execute_reply": "2026-08-27T03:09:03.761104Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "specsr-roman 0.1.0 | torch 2.11.0+cu128\n", "device: cpu\n" ] } ], "source": [ "import numpy as np\n", "import torch\n", "import matplotlib.pyplot as plt\n", "from huggingface_hub import hf_hub_download\n", "\n", "import specsr_roman\n", "from specsr_roman import LINE_LIST_REST_AA, RomanPipeline\n", "from specsr_roman.data import RomanFixedGridDataset, apply_phot_noise, normalize\n", "from specsr_roman.evaluation import line_amplitude_recovery, redshift_summary\n", "from specsr_roman.grids import ROMAN_MEDIUM_BANDS, WAVE_HR\n", "from specsr_roman.lines import LABEL_LINES_AA\n", "from specsr_roman.models import topk_modes\n", "\n", "print(\"specsr-roman\", specsr_roman.__version__, \"| torch\", torch.__version__)\n", "\n", "# Everything here fits comfortably on a CPU; a GPU only makes it faster.\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(\"device:\", device)" ] }, { "cell_type": "code", "execution_count": 2, "id": "a1cdcdc3", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:03.765029Z", "iopub.status.busy": "2026-08-27T03:09:03.764877Z", "iopub.status.idle": "2026-08-27T03:09:03.767332Z", "shell.execute_reply": "2026-08-27T03:09:03.766894Z" } }, "outputs": [], "source": [ "# Plot styling: recessive axes, one accent per model stage, truth always black.\n", "INK, MUTED = \"#0b0b0b\", \"#52514e\"\n", "LR_GREY, SR1_BLUE, SR2_ORANGE = \"#9a9a94\", \"#2a78d6\", \"#eb6834\"\n", "\n", "plt.rcParams.update({\n", " \"figure.dpi\": 100, \"savefig.dpi\": 100,\n", " \"font.size\": 9, \"axes.titlesize\": 10, \"axes.labelsize\": 9,\n", " \"axes.edgecolor\": \"#c9c8c2\", \"axes.labelcolor\": MUTED,\n", " \"axes.spines.top\": False, \"axes.spines.right\": False,\n", " \"xtick.color\": MUTED, \"ytick.color\": MUTED,\n", " \"grid.color\": \"#e8e7e2\", \"grid.linewidth\": 0.8,\n", " \"legend.frameon\": False, \"figure.facecolor\": \"white\",\n", "})" ] }, { "cell_type": "markdown", "id": "d58fc0d4", "metadata": {}, "source": [ "## 1. The tutorial dataset\n", "\n", "The full training set is 271 MB and 36,404 spectra. This notebook uses a 512-row\n", "subset of it, published alongside it on the Hub.\n", "\n", "Two things about how that subset was drawn matter more than its size:\n", "\n", "- **It comes from the held-out side of the canonical object-id split**, so every\n", " number you compute below is an honest out-of-sample number. (The same galaxy\n", " appears in several visits as independent noise realisations, so a row-wise\n", " split would have measured memorisation instead.)\n", "- **It is a uniform random draw within that split** — no cherry-picking on\n", " brightness or line strength. It carries the population's real mix of\n", " recoverable and undetectable lines, which is exactly what section 6 needs.\n", "\n", "Rebuild it yourself with `python scripts/make_tutorial_dataset.py`." ] }, { "cell_type": "code", "execution_count": 3, "id": "0f2fed39", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:03.768514Z", "iopub.status.busy": "2026-08-27T03:09:03.768416Z", "iopub.status.idle": "2026-08-27T03:09:04.002075Z", "shell.execute_reply": "2026-08-27T03:09:04.001659Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flux_low (512, 864) float32\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "flux_low_err (512, 864) float32\n", "flux_high (512, 2500) float32\n", "redshift (512,) float64\n", "phot (512, 14) float64\n", "ids (512,) int64\n", "snr (512,) float64\n", "ab_h158 (512,) float64\n", "wavelength_low (864,) float64\n", "wavelength_high (2500,) float64\n" ] } ], "source": [ "path = hf_hub_download(\"aryana-haghjoo/romansr-data\",\n", " \"tutorial/ou2024_h10307_tutorial.npz\",\n", " repo_type=\"dataset\")\n", "data = np.load(path, allow_pickle=True)\n", "\n", "for key in (\"flux_low\", \"flux_low_err\", \"flux_high\", \"redshift\", \"phot\",\n", " \"ids\", \"snr\", \"ab_h158\", \"wavelength_low\", \"wavelength_high\"):\n", " print(f\"{key:16s} {str(data[key].shape):12s} {data[key].dtype}\")" ] }, { "cell_type": "markdown", "id": "92a5fdf8", "metadata": {}, "source": [ "| Key | Meaning |\n", "|---|---|\n", "| `flux_low`, `flux_low_err` | the extracted grism spectrum and its 1σ error, 864 px on the native ~10.76 Å sampling |\n", "| `flux_high` | the noiseless ground-truth SED, 2500 px — the super-resolution target |\n", "| `redshift` | the true redshift |\n", "| `phot` | catalogue fluxes; column order is fixed by `grids.PHOT_BANDS` |\n", "| `ids` | OU2024 `object_id` — the thing to split on |\n", "| `snr`, `ab_h158` | median extraction S/N and H158 magnitude |\n", "\n", "⚠️ `flux_low` and `flux_high` are **not calibrated to each other** — they differ\n", "by roughly twenty orders of magnitude. Only the *shape* is meaningful, which is\n", "why every stage of the pipeline normalises each spectrum individually." ] }, { "cell_type": "code", "execution_count": 4, "id": "dfc8db03", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.005442Z", "iopub.status.busy": "2026-08-27T03:09:04.005336Z", "iopub.status.idle": "2026-08-27T03:09:04.008847Z", "shell.execute_reply": "2026-08-27T03:09:04.008361Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "512 spectra of 476 galaxies\n", "redshift 0.11 - 2.52 (median 0.76, 30% above z = 1)\n", "AB(H158) 17.9 - 22.5\n", "median extraction S/N 0.46\n" ] } ], "source": [ "z_all = data[\"redshift\"]\n", "print(f\"{len(z_all)} spectra of {len(np.unique(data['ids']))} galaxies\")\n", "print(f\"redshift {z_all.min():.2f} - {z_all.max():.2f} \"\n", " f\"(median {np.median(z_all):.2f}, {(z_all > 1).mean():.0%} above z = 1)\")\n", "print(f\"AB(H158) {data['ab_h158'].min():.1f} - {data['ab_h158'].max():.1f}\")\n", "print(f\"median extraction S/N {np.median(data['snr']):.2f}\")" ] }, { "cell_type": "markdown", "id": "4a99e0b7", "metadata": {}, "source": [ "A median extraction S/N of about 0.5 is not a typo. These are single 301 s grism\n", "exposures, and most of these galaxies are *faint*. That is the regime the model\n", "has to be trustworthy in." ] }, { "cell_type": "markdown", "id": "80bf1bfc", "metadata": {}, "source": [ "## 2. Load the published chain\n", "\n", "`from_pretrained()` downloads the three canonical checkpoints once into the\n", "standard Hugging Face cache and reuses them afterwards." ] }, { "cell_type": "code", "execution_count": 5, "id": "2b263f02", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.010226Z", "iopub.status.busy": "2026-08-27T03:09:04.010129Z", "iopub.status.idle": "2026-08-27T03:09:04.343797Z", "shell.execute_reply": "2026-08-27T03:09:04.342621Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sr1 sr1_ou2024_v6\n", "zhead zhead_ou2024_roman_med3_noisy\n", "sr2 sr2_ou2024_v5_romanonly\n" ] } ], "source": [ "from specsr_roman.checkpoints import CANONICAL_CHAIN\n", "\n", "pipe = RomanPipeline.from_pretrained(device=device)\n", "for stage, name in CANONICAL_CHAIN.items():\n", " print(f\"{stage:6s} {name}\")" ] }, { "cell_type": "markdown", "id": "b60bfc92", "metadata": {}, "source": [ "The three stages each do one job they can be held to:\n", "\n", "- **SR1** — a conservative residual CNN. It sees `[flux, err]` scaled by a\n", " *shared* factor, so the channel ratio literally is the per-pixel S/N and the\n", " network can matched-filter rather than guess which bumps are noise. It emits a\n", " mean and a log-variance.\n", "- **ZHead** — a softmax over a redshift grid, conditioned on the coarse spectrum\n", " *and* the Roman imaging that ships with the grism. It returns a whole P(z).\n", "- **SR2** — attention with one token per rest-frame feature, run once per\n", " redshift hypothesis and combined by mode mass. That is what makes it robust\n", " when the top redshift mode is wrong." ] }, { "cell_type": "markdown", "id": "324b8ac7", "metadata": {}, "source": [ "## 3. Super-resolve one spectrum\n", "\n", "`RomanFixedGridDataset` is what the training loops consume. We use it here for\n", "something the raw npz does not carry: the **per-line integrated S/N labels**,\n", "which record whether each line was recoverable from the low-resolution data at\n", "all. Those labels drive everything in section 6.\n", "\n", "It also applies the dataset's quality cuts, so index the raw arrays through\n", "`ds.keep_mask` before pairing them with anything the dataset returns — mixing\n", "the two indexings is a silent, and very confusing, off-by-N." ] }, { "cell_type": "code", "execution_count": 6, "id": "51579b74", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.346061Z", "iopub.status.busy": "2026-08-27T03:09:04.345953Z", "iopub.status.idle": "2026-08-27T03:09:04.819223Z", "shell.execute_reply": "2026-08-27T03:09:04.816850Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "quality cuts: kept 512 / 512 rows\n", "phot tier 'medium': 3 bands ['roman_flux_Y106', 'roman_flux_J129', 'roman_flux_H158']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "line-S/N labels: 17.4% of rows have a >3sigma recoverable line (median best-line S/N 1.04)\n", "512 of 512 rows pass the quality cuts\n" ] } ], "source": [ "ds = RomanFixedGridDataset(path, with_phot=True, phot_tier=\"medium\")\n", "\n", "keep = ds.keep_mask\n", "flux_low = data[\"flux_low\"][keep]\n", "flux_low_err = data[\"flux_low_err\"][keep]\n", "phot_true = data[\"phot\"][keep][:, list(ROMAN_MEDIUM_BANDS)]\n", "z_true = data[\"redshift\"][keep]\n", "ab_h158 = data[\"ab_h158\"][keep]\n", "wave_low = data[\"wavelength_low\"]\n", "\n", "print(f\"{len(ds)} of {len(z_all)} rows pass the quality cuts\")" ] }, { "cell_type": "markdown", "id": "c15c67fd", "metadata": {}, "source": [ "`phot_tier=\"medium\"` selects Roman F106/F129/F158 — and only those. That is the\n", "imaging that actually ships with the HLWAS grism, and feeding the redshift head\n", "anything more is how you accidentally measure the catalogue instead of the\n", "instrument." ] }, { "cell_type": "code", "execution_count": 7, "id": "e1ffbe6e", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.821801Z", "iopub.status.busy": "2026-08-27T03:09:04.821561Z", "iopub.status.idle": "2026-08-27T03:09:04.825507Z", "shell.execute_reply": "2026-08-27T03:09:04.824881Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "row 237: z = 1.498, AB(H158) = 21.2, best line S/N = 5.4\n" ] } ], "source": [ "# Recoverability of a row = the S/N of the best line the grism data actually shows.\n", "best_line_snr = ds.line_snr.max(dim=1).values.numpy()\n", "\n", "# A clearly-but-not-trivially recoverable example: the median row among those\n", "# whose best line sits between 4 and 8 sigma.\n", "band = np.where((best_line_snr > 4) & (best_line_snr < 8))[0]\n", "i = int(band[np.argsort(best_line_snr[band])[len(band) // 2]])\n", "print(f\"row {i}: z = {z_true[i]:.3f}, AB(H158) = {ab_h158[i]:.1f}, \"\n", " f\"best line S/N = {best_line_snr[i]:.1f}\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "1cdf2da5", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.826958Z", "iopub.status.busy": "2026-08-27T03:09:04.826834Z", "iopub.status.idle": "2026-08-27T03:09:04.871853Z", "shell.execute_reply": "2026-08-27T03:09:04.870374Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flux_sr (2500,) super-resolved spectrum\n", "flux_sr_err (2500,) per-pixel uncertainty\n", "flux_sr1 (2500,) the SR1 stage alone\n", "pz / z_grid (310,) the whole redshift PDF\n", "presence (98,) per-line presence probability\n", "\n", "z = 1.5097 +/- 0.0339 (true 1.4983)\n" ] } ], "source": [ "out = pipe.predict(\n", " flux_low[i], # (864,) on the native grism grid\n", " flux_low_err[i],\n", " phot=phot_true[i], # F106, F129, F158 — in that order\n", ")\n", "\n", "print(f\"flux_sr {out.flux_sr.shape} super-resolved spectrum\")\n", "print(f\"flux_sr_err {out.flux_sr_err.shape} per-pixel uncertainty\")\n", "print(f\"flux_sr1 {out.flux_sr1.shape} the SR1 stage alone\")\n", "print(f\"pz / z_grid {out.pz.shape} the whole redshift PDF\")\n", "print(f\"presence {out.presence.shape} per-line presence probability\")\n", "print()\n", "print(f\"z = {out.z:.4f} +/- {out.z_err:.4f} (true {z_true[i]:.4f})\")" ] }, { "cell_type": "markdown", "id": "8da33a2e", "metadata": {}, "source": [ "### A note on flux scales\n", "\n", "`predict()` returns values on **your input's** flux scale, so they plot straight\n", "against the spectrum you handed it. The noiseless target lives on the\n", "simulation's own scale instead, so to compare all four curves we put everything\n", "back in the shared normalised space the model works in.\n", "\n", "Inverting that de-normalisation takes both the mean and the standard deviation.\n", "Dropping the mean leaves the output offset from the input by the continuum\n", "level — which looks exactly like a broken model and is not one." ] }, { "cell_type": "code", "execution_count": 9, "id": "faac8669", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.874417Z", "iopub.status.busy": "2026-08-27T03:09:04.874290Z", "iopub.status.idle": "2026-08-27T03:09:04.878369Z", "shell.execute_reply": "2026-08-27T03:09:04.877934Z" } }, "outputs": [], "source": [ "def lr_scale(f_low):\n", " # The per-row (mean, std) predict() used to put its output on your scale.\n", " ok = np.isfinite(f_low)\n", " _, mean, std = normalize(np.interp(WAVE_HR, wave_low[ok], f_low[ok]))\n", " return mean, std\n", "\n", "\n", "def to_model_space(flux, f_low):\n", " # Undo predict()'s de-normalisation: back to the shared normalised space.\n", " mean, std = lr_scale(f_low)\n", " return (flux - mean) / std\n", "\n", "\n", "lr_n = ds[i][0][0].numpy() # LR input, on the HR grid, normalised\n", "truth_n = ds[i][1].numpy() # noiseless target, normalised\n", "sr1_n = to_model_space(out.flux_sr1, flux_low[i])\n", "sr2_n = to_model_space(out.flux_sr, flux_low[i])\n", "# An error bar takes the scale, not the offset.\n", "sigma_n = out.flux_sr_err / lr_scale(flux_low[i])[1]" ] }, { "cell_type": "code", "execution_count": 10, "id": "f4e86095", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:04.881277Z", "iopub.status.busy": "2026-08-27T03:09:04.881168Z", "iopub.status.idle": "2026-08-27T03:09:05.192666Z", "shell.execute_reply": "2026-08-27T03:09:05.191954Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wave_um = WAVE_HR * 1e-4\n", "peak = wave_um[np.argmax(truth_n)]\n", "zoom = (peak - 0.05, peak + 0.05)\n", "\n", "\n", "def mark_lines(ax, lo, hi):\n", " # Stagger the labels: at grism redshifts these complexes sit close together.\n", " for n, (name, rest) in enumerate(LABEL_LINES_AA):\n", " lam = rest * (1 + z_true[i]) * 1e-4\n", " if lo < lam < hi:\n", " ax.axvline(lam, color=MUTED, lw=0.7, ls=\":\", alpha=0.7, zorder=0)\n", " ax.annotate(name, (lam, 0.96 - 0.13 * (n % 2)),\n", " xycoords=(\"data\", \"axes fraction\"), ha=\"center\",\n", " va=\"top\", fontsize=8, color=MUTED)\n", "\n", "\n", "fig, axes = plt.subplots(3, 1, figsize=(9, 8.4), constrained_layout=True)\n", "full = (wave_um >= 1.0) & (wave_um <= 1.93)\n", "\n", "# 1. What the instrument gives you.\n", "axes[0].plot(wave_um[full], lr_n[full], color=LR_GREY, lw=1.2)\n", "axes[0].set_title(f\"What the grism gives you — row {i}, z = {z_true[i]:.3f}\",\n", " color=INK, loc=\"left\")\n", "\n", "# 2. What the chain produces, against the noiseless truth.\n", "axes[1].plot(wave_um[full], truth_n[full], color=INK, lw=1.5, label=\"truth\")\n", "axes[1].plot(wave_um[full], sr1_n[full], color=SR1_BLUE, lw=1.3, ls=\"--\",\n", " label=\"SR1\")\n", "axes[1].plot(wave_um[full], sr2_n[full], color=SR2_ORANGE, lw=1.5, label=\"SR2\")\n", "axes[1].set_title(\"What the chain produces\", color=INK, loc=\"left\")\n", "axes[1].legend(loc=\"upper center\", ncols=3)\n", "\n", "# 3. The strongest complex, with SR2's own uncertainty.\n", "m = (wave_um >= zoom[0]) & (wave_um <= zoom[1])\n", "axes[2].fill_between(wave_um[m], (sr2_n - sigma_n)[m], (sr2_n + sigma_n)[m],\n", " color=SR2_ORANGE, alpha=0.20, lw=0, zorder=1,\n", " label=\"SR2 $\\\\pm 1\\\\sigma$\")\n", "axes[2].plot(wave_um[m], lr_n[m], color=LR_GREY, lw=1.2, zorder=2,\n", " label=\"grism input\")\n", "axes[2].plot(wave_um[m], truth_n[m], color=INK, lw=1.6, zorder=5, label=\"truth\")\n", "axes[2].plot(wave_um[m], sr1_n[m], color=SR1_BLUE, lw=1.4, ls=\"--\", zorder=3,\n", " label=\"SR1\")\n", "axes[2].plot(wave_um[m], sr2_n[m], color=SR2_ORANGE, lw=1.8, zorder=4,\n", " label=\"SR2\")\n", "axes[2].set_title(\"Zoom on the strongest complex\", color=INK, loc=\"left\")\n", "axes[2].set_xlim(*zoom)\n", "axes[2].legend(loc=\"upper left\", ncols=2)\n", "axes[2].set_xlabel(\"observed wavelength [µm]\")\n", "\n", "for ax, (lo, hi) in zip(axes, [(1.0, 1.93), (1.0, 1.93), zoom]):\n", " ax.set_xlim(lo, hi)\n", " ax.set_ylabel(\"normalised flux\")\n", " ax.grid(axis=\"y\", alpha=0.7)\n", " ax.set_axisbelow(True)\n", " mark_lines(ax, lo, hi)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b400b0b8", "metadata": {}, "source": [ "The three panels have **different y-scales, on purpose**. Each spectrum is\n", "normalised by its own mean and standard deviation, and the input's standard\n", "deviation is set by its noise while the target's is set by its continuum. So a\n", "line that is a modest bump in panel 1 is a tall spike in panel 2 even though\n", "nothing was added — putting them on one axis would imply a calibration between\n", "them that does not exist.\n", "\n", "Read panel 3. SR1 (blue) is deliberately cautious and under-shoots the\n", "amplitude; SR2 (orange) sharpens it toward the truth. The shaded band is SR2's\n", "own per-pixel uncertainty, and it is *wide* here — the model is telling you it\n", "knows the line is there but not exactly how bright. That is the intended\n", "behaviour: the alternative is a confident wrong amplitude.\n", "\n", "What neither stage does is add lines elsewhere in the band. That restraint is\n", "the whole design, and section 6 is where it gets measured." ] }, { "cell_type": "markdown", "id": "63919603", "metadata": {}, "source": [ "## 4. The redshift is a PDF, not a number\n", "\n", "Redshift from a grism is a line-*identification* problem. A single observed line\n", "is consistent with Hα, [O III], [O II] or Lyα, and a model that returns one\n", "number has to average between those alternatives — which is how you get a\n", "confidently wrong answer. Two earlier designs of this head did exactly that, and\n", "both sat at a ~40 % catastrophic floor.\n", "\n", "The ZHead returns a distribution over a grid instead. `out.z` is its **mode**;\n", "`out.pz` is the whole thing. Keep the PDF." ] }, { "cell_type": "code", "execution_count": 11, "id": "8be13add", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:05.198178Z", "iopub.status.busy": "2026-08-27T03:09:05.198062Z", "iopub.status.idle": "2026-08-27T03:09:05.325125Z", "shell.execute_reply": "2026-08-27T03:09:05.324742Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mode 1: z = 1.510 mass = 1.000\n", "mode 2: z = 1.427 mass = 0.000\n", "mode 3: z = 0.491 mass = 0.000\n", "\n", "true z = 1.498\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def top_modes(result, n=3):\n", " # The package's own mode finder -- the same hypotheses SR2 is run on.\n", " # It suppresses +/-15 grid bins around each pick, so the modes are distinct\n", " # line-alias candidates rather than adjacent bins of one peak, and weights\n", " # each by the probability *mass* in its window rather than by a bin height.\n", " zs, ws = topk_modes(torch.tensor(result.pz)[None, :],\n", " torch.tensor(result.z_grid), n)\n", " return list(zip(zs[0].tolist(), ws[0].tolist()))\n", "\n", "\n", "def plot_pz(result, truth, title):\n", " fig, ax = plt.subplots(figsize=(9, 3.6), constrained_layout=True)\n", " ax.fill_between(result.z_grid, result.pz, color=SR1_BLUE, alpha=0.30, lw=0)\n", " ax.plot(result.z_grid, result.pz, color=SR1_BLUE, lw=1.6, label=\"P(z)\")\n", " ax.axvline(truth, color=INK, lw=1.4, ls=\"--\", label=\"true redshift\")\n", " # Stagger the mode labels by rank with a leader line: adjacent aliases sit\n", " # close enough in z that centred labels would overlap.\n", " for rank, (zm, mass) in enumerate(top_modes(result), start=1):\n", " height = result.pz[np.argmin(np.abs(result.z_grid - zm))]\n", " ax.plot([zm], [height], marker=\"o\", ms=5, color=SR1_BLUE, zorder=3)\n", " ax.annotate(f\"mode {rank} · {mass:.0%}\", xy=(zm, height),\n", " xytext=(zm, 1.05 - 0.09 * rank),\n", " textcoords=(\"data\", \"axes fraction\"), ha=\"center\",\n", " fontsize=8, color=MUTED,\n", " arrowprops=dict(arrowstyle=\"-\", lw=0.7, color=\"#c9c8c2\"))\n", " ax.set_xlim(0, 2.6)\n", " ax.set_ylim(0, result.pz.max() * 1.5) # headroom for the mode labels\n", " ax.set_xlabel(\"redshift\")\n", " ax.set_ylabel(\"probability\")\n", " ax.set_title(title, color=INK, loc=\"left\")\n", " ax.legend(loc=\"upper right\")\n", " ax.grid(axis=\"y\", alpha=0.7)\n", " ax.set_axisbelow(True)\n", " plt.show()\n", "\n", "\n", "for rank, (zm, mass) in enumerate(top_modes(out), start=1):\n", " print(f\"mode {rank}: z = {zm:.3f} mass = {mass:.3f}\")\n", "print(f\"\\ntrue z = {z_true[i]:.3f}\")\n", "\n", "plot_pz(out, z_true[i], f\"row {i}: unambiguous — one mode holds nearly all the mass\")" ] }, { "cell_type": "markdown", "id": "ffff8397", "metadata": {}, "source": [ "That galaxy is the easy case: one mode carries almost all the probability mass,\n", "it sits on the truth, and `z_err` is correspondingly small. The two remaining\n", "modes are the leftovers after suppression — real but negligible.\n", "\n", "Section 5 finds the interesting case." ] }, { "cell_type": "markdown", "id": "b2528bc5", "metadata": {}, "source": [ "## 5. Photometry, and why it must be noisy\n", "\n", "Three broadband colours break most of the alias degeneracy, which is the entire\n", "reason the redshift head takes photometry at all. But the catalogue photometry\n", "in this dataset is **noiseless truth**, and a metric measured on noiseless truth\n", "photometry is not a metric. `apply_phot_noise` adds the same 0.05 mag jitter the\n", "head was trained with.\n", "\n", "We run the whole subset three ways: with realistic photometry, with the\n", "noiseless catalogue values, and with none." ] }, { "cell_type": "code", "execution_count": 12, "id": "0e32c8b4", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:09:05.328144Z", "iopub.status.busy": "2026-08-27T03:09:05.328038Z", "iopub.status.idle": "2026-08-27T03:10:13.836649Z", "shell.execute_reply": "2026-08-27T03:10:13.836027Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "512 spectra through the chain, three ways\n" ] } ], "source": [ "gen = torch.Generator().manual_seed(0) # seeded, so the run reproduces\n", "phot_noisy = apply_phot_noise(torch.tensor(phot_true, dtype=torch.float32),\n", " 0.05, gen).numpy()\n", "\n", "\n", "def run_chain(phot, batch=64):\n", " results = []\n", " for s in range(0, len(flux_low), batch):\n", " p = None if phot is None else phot[s:s + batch]\n", " results += pipe.predict(flux_low[s:s + batch],\n", " flux_low_err[s:s + batch], phot=p)\n", " return results\n", "\n", "\n", "outs = run_chain(phot_noisy)\n", "outs_clean = run_chain(phot_true)\n", "outs_nophot = run_chain(None)\n", "print(f\"{len(outs)} spectra through the chain, three ways\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "b53586e0", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:13.838619Z", "iopub.status.busy": "2026-08-27T03:10:13.838493Z", "iopub.status.idle": "2026-08-27T03:10:13.842104Z", "shell.execute_reply": "2026-08-27T03:10:13.841541Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "configuration NMAD med |dz| catastrophic\n", "grism + Roman imaging, 0.05 mag noise 0.0068 0.0046 6.1%\n", "grism + noiseless catalogue photometry 0.0064 0.0046 4.7%\n", "grism only (photometry mean-imputed) 0.0219 0.0152 27.5%\n" ] } ], "source": [ "configs = {\n", " \"grism + Roman imaging, 0.05 mag noise\": outs,\n", " \"grism + noiseless catalogue photometry\": outs_clean,\n", " \"grism only (photometry mean-imputed)\": outs_nophot,\n", "}\n", "\n", "print(f\"{'configuration':40s} {'NMAD':>8s} {'med |dz|':>9s} {'catastrophic':>13s}\")\n", "for label, res in configs.items():\n", " m = redshift_summary(np.array([r.z for r in res]), z_true)\n", " print(f\"{label:40s} {m['nmad']:8.4f} {m['median_abs_dz']:9.4f} \"\n", " f\"{m['catastrophic_frac']:12.1%}\")" ] }, { "cell_type": "markdown", "id": "ca6494b3", "metadata": {}, "source": [ "The first row is the deployable configuration, and it lands on the published\n", "numbers (NMAD 0.0065 and 5.1 % catastrophic on the full 7,334-row test split;\n", "this is a 512-row draw from it, so expect a little scatter).\n", "\n", "Two cautions on the other two rows.\n", "\n", "**The noiseless row is barely better than the noisy one.** That is the *point*\n", "of restricting to three bands: three broadband colours are not a complete SED,\n", "so there is very little for the head to cheat with. Hand a redshift head a\n", "catalogue's every filter with no noise and it stops needing the spectrum at\n", "all, and the score stops describing the instrument.\n", "\n", "**`phot=None` is not a grism-only model.** The head was trained with photometry,\n", "so passing `None` feeds it its training-mean colours: an out-of-distribution\n", "input, not a clean ablation of the information. Read that row as \"the colour\n", "prior is carrying most of the redshift accuracy\", not as a measurement of the\n", "grism-only information floor. That floor is set by physics — with one line in\n", "band the identification is genuinely alias-degenerate — and pinning it down\n", "would take a head trained without colours, which is a separate experiment." ] }, { "cell_type": "code", "execution_count": 14, "id": "b662395c", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:13.843252Z", "iopub.status.busy": "2026-08-27T03:10:13.843138Z", "iopub.status.idle": "2026-08-27T03:10:13.979614Z", "shell.execute_reply": "2026-08-27T03:10:13.978737Z" } }, "outputs": [ { "data": { "image/png": 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uHP7++2/s3LkTPXv2xLBhwzB79mzbMoUBDYBth//gdQ8OYXnQ/v37bX8fPXoUU6dOxZ9//mm77mFDcQpFRESgbt26qFu3LsxmM1555RUcPny4SCB5mPvrLqy9uOdX+Hw2btyIDz74ALNnz0bbtm2hUCiwYMGCIkMTi1tvadukvLUVXsfunht06NAhDB48GB988AG6desGpVKJTZs24euvvy7X45a1lvI+nwcFBARUaPoNnU6HN954AyNHjixyW2RkZKVqepjg4GD06NEDq1atQq1atfD333/bvWYXLFiARYsWYd68eWjcuDF8fHzwzjvvVKrhhV6vR2hoqN3jFFKpVHaXs7OzERgYWOHHIsSZVcVnUGUe+2GfxTqdDs2bN8eKFSuKrKss79Hi9tOvv/46li5dikmTJmHVqlV49dVX7QJ3Rep+cP+m1+vRo0cPfPzxx0XWVRgA+/bti5o1a+Lrr79GWFgYOI5DmzZtYDQaS3zsBx/n/v3/w/b15d3uZSEUCossX5bP6PJ8FhNCXJfHhMeoqChIJBIcO3bM9uVZq9XiypUr6NixY7nXFxQUhP79+6N///7o0KEDpkyZYhceHaFOnTq2v1NTUyEWi+2uK6/nnnsOM2fOxLJlyzBmzBjExMTg119/BWPMtuM6cOAAfH19i5wsXx4HDx5E27ZtMWzYMNt1Dx4B4suhQ4dQs2ZNuyZJDx71qlevHo4dO4ZXXnnFdt2xY8cq9bj169fHjRs3cOfOHdvRx7Kss1mzZli3bp3ddQ0aNMDvv/9ud92D5/HFxcXhwoULpb5eHrzfkSNHEBMTU+yyMTExWL16NfR6ve2L28GDByEUCu2O4A8cOBCDBg1CjRo1EB0dbdfg4eDBg+jZs6et2RDHcbhy5QoaNGhQal116tSxO1J9/3O9c+cOxGLxQ6czuXr1KgoKCtCsWbMSlyHEWZXnPVGcsrx/JRJJkaP7ZX3fl1dcXBy2bNmCoKAgKJXKYpcprp6HeemllzB16lQsWrQIFy5cQP/+/StcX0maNWuGX3/9FbVq1Sq2A3tmZiYuX76MhQsX2r5b3B8Ey6oy+/qyOHLkiO2orNlsxsmTJzF8+HAA1vCem5tr92/+4DzXUqm0yL9NWT6LY2JicOTIEbv9K52HTohr8Zhhq76+vujfvz+mTJmCPXv24Ny5cxg1ahSEQmGpv0w+6OOPP8a2bduQmJiIc+fOYfv27SV+4XYmAoEAI0eOxGeffYa8vDwMGzYMqampmDRpEi5evIht27Zh1qxZGDNmjN0wmPKqU6cOTpw4gX///ReXL1/GRx99hOPHjzvwmVRcnTp1kJKSgo0bN+Lq1atYtGhRkW6nI0aMwI8//ojVq1fjypUr+OSTT3D27Nlyv07u9/jjjyM6OhrDhw/HmTNncODAAdsv1w9bb9euXXH+/HlkZ2fbrhs8eDASExPxwQcf4NKlS9iwYUORRhETJ07EoUOHMHHiRCQkJODKlSvYtm0bJk6caLfcwYMH8cUXX+Dy5ctYsmQJfv75Z4waNarYWvr16weZTIZhw4bh7Nmz2LNnDyZPnoyXX37Zbjhut27d4Ovri3nz5uG1116zW0edOnWwc+dOHDx4EBcuXMDYsWORlpZW5LFu3LiBd9991/b8Fi9eXGJdXbp0QZs2bfDSSy9hx44dSE5OxsGDBzF9+nS7193+/fsRHR2N2rVrl7C1CXFe5XlPFKcs799atWrhzJkzuHTpEjIyMmAymcr8vi+vfv36ISAgAP369UN8fDySkpKwd+9eTJ482XbaRHH1PIyfnx969eqFKVOmoGvXrpX6EbQkw4cPR3Z2NgYOHIhjx47h6tWr+PfffzFixAhYLBb4+fnB398f33//PRITE7F792689957Dq+jspYuXYqtW7fi4sWLmDBhAjQaje3zulWrVvDx8cH06dNx9epVbNiwwTYcuFCtWrWQnJyMhIQEZGRkwGAwlOmzeOTIkVi1ahVWrVqFy5cvY+bMmTh//ny1P39CSMV5THgEgDlz5qBNmzbo27cv/ve//6F9+/aIiYmBl5dXudYjlUoxbdo0tGvXDk899RREItFDO0E6k/79+8NsNmPJkiUIDw/H5s2bcezYMbRv3x7jxo3D66+/XmyH2fIYPHgwevXqhddffx1dunRBVlYWhg4d6qBnUDnPPPMMxowZg0mTJqFDhw44dOhQkefbr18/TJo0Ce+//z46deqE5ORk9O/fv9yvk/uJRCKsW7cOer0enTt3xujRo/HWW28BwEPXGxsba/uFvlBkZKSthX379u2xfPlyTJ8+vcj9tm/fjitXruDJJ59Ex44dMXPmTISFhdkt9+abb+L48ePo2LEj5s2bhzlz5qBbt27F1uLj44NffvkF2dnZePTRR/Haa6/h0UcfxWeffWa3nFAoRP/+/WGxWOx+XQas03bExcWhd+/e6NGjB4KDg9GzZ88ij/Xyyy8jPz8fjz32GCZNmoSRI0fijTfeKLYugUCALVu2oGPHjhgxYgTi4uIwcOBApKSk2M5XAqzDqQcOHFjsOghxduV5TxSnLO/fwml0OnfujKioKBw4cKDM7/vy8vHxwV9//YXIyEi88soraNmyJUaPHo2CggLbMM3i6inNgAEDYDQai/xw5ShhYWH4999/YbFY8Oyzz6Jt27Z4++23oVKpIBQKIRQKsXLlSpw4cQJt2rTBu+++W62d1ctqxowZ+Pzzz9G+fXscOHAA69evtw0X9vf3x/Lly/H333+jbdu22LhxI95//327+z/77LPo1q0bnn76aURFRWHjxo1l+ix+4YUX8M4772DKlCl45JFHkJKSgiFDhlT78yeEVJyAlWcgvJvR6/WoX78+Zs+ejddff53vcogT+9///oeQkJAyTdlRVgcOHMATTzyBhISEhx4N2759Oz744AMcOXKkUkeEH9SoUSOMHj0ao0ePdtg6C40aNQoZGRnFzuHIh3PnzuGZZ57ByZMn6dwb4nKeeuopNG3aFPPmzeO7FKe3du1avPPOO7hy5YptWipyT3JyMho3boz9+/ejadOmfJdDCHFBHnPOIwCcOnUKFy9eRKtWrZCTk4O5c+cCsB6NIqRQXl4eVqxYga5du0IkEmHjxo3YtWtXkeGt5bV161YoFArUqVMHV69exVtvvYX27duXOozyqaeeQmJiIm7evImIiIhK1VDVtFotzp49iw0bNjhNcASAO3fuYNmyZRQcCXFTeXl5uH37Nj777DO88cYbFBwJIaSKeFR4BICvvvoKly9fhlQqRVxcHP766y/qvkjsCAQC/PXXX5g/fz4KCgpQr149rF69Gl26dKnUenU6HT788EOkpKQgICAAXbp0KXOTpao4OlgV+vXrh2PHjmHw4MF4/PHH+S7HprL/doQQ5/bFF19g/vz56NixIyZPnsx3OYQQ4rY8etgqIYQQQgghhJCy8aiGOYQQQgghhBBCKobCI3Eqe/fuhUKhgEajKXGZWbNm2c0dSAghhDi7p556ym6O4eIoFIpKn19PCCFVyePC482bNzF48GDUrFkTgYGBaNOmzUPnILx9+zYGDRqEuLg4+Pr6FvvBv3PnTsTFxSEsLAxDhgyB0Wi03abVahEXF4fr169XyfO5X6NGjfDNN984dJ3Dhw+3TejuLMaNG4dt27bxXQYhhFSLTz/9FJ07d0ZoaCiioqLw0ksv4dKlS7bbk5OToVAoiv3v/ml+HjR8+PAiy/fu3dt2u8FgwJAhQxAWFoa4uDjs2rXL7v4LFizApEmTHP58H/TTTz85fM7Gwm2WkJDg0PVWVmJiIp588km+yyCEkBJ5VHjMzs5Gt27dIJFIsGXLFhw9ehRz5syBWq0u8T4GgwGBgYF4++230aRJkyK3cxyHQYMGYfDgwdixYweOHz+O7777znb7hx9+aAur7qy0yZsdSaFQICAgoNoejxBC+PTff/9h2LBh2LlzJ3777TeYTCY8++yz0Ov1AICIiAgkJiba/ffBBx9AoVCUGkSeeOIJu/t9//33ttu+++47nDhxAjt27MCgQYMwaNAgFLZJSEpKwg8//IBp06ZV3RN3Avf/GFwdQkJCIJPJqvUxCSGkXJgHmTp1KuvWrVuF79+9e3f21ltv2V13584dJpfLWX5+PmOMsSlTprDx48czxhg7cOAA69SpEzObzWVa/40bN9jrr7/OIiIiWFBQEOvUqRM7fPgwY4yxxMRE9uKLL7KoqCgWHBzMHnnkEbZz50672uRyud1/jDGWkZHBXn/9dVa3bl0WGBjIWrduzdavX2/3uFu2bGGtW7dmAQEBLDIykj3zzDNMp9OxmTNnFlnnnj17WFJSEpPL5Wzjxo3sySefZP7+/mzVqlXMYrGw2bNns3r16jE/Pz/Wrl079vfff9sep/B+GzZsYI8//jjz9/dnrVq1Ynv37rUts2fPHiaXy9nOnTtZp06dWGBgIHv88cfZxYsXbcvMnDmTtWvXzu45rFy5krVs2ZL5+fmx2rVrswkTJpRpmxNCiKtJS0tjcrmc7du3r8Rl2rdvz0aOHPnQ9QwbNoz169evxNvHjRvHpk6dyhhjLC8vj8nlcpaWlsYYY+zZZ59lv/76a5lrfthn9FdffcVat27NgoKCWP369dm4ceNYbm4uY+zePuH+/2bOnMkYY2zNmjWsU6dOLCQkhEVHR7OBAweyO3fu2NablZXFBg0axGrWrMkCAgJY06ZN2Y8//sgYY0XW2b17d7tt8sknn7A6deqwRo0aMcYYO336NOvRo4dtPzl69Ghbjfffb9asWaxmzZosNDSUvfnmm8xgMNiW6d69O5s0aRL74IMPWEREBIuOjrY9l0JyuZxt3brVdvlh3wsIIYQPHnXk8ffff0eLFi3w6quvIioqCh06dLD7lbUigoKCEBoaih07diAvLw/79+9HbGwsTCYTxo8fj6+++goikajU9eh0Ojz11FO4efMm1q9fjwMHDmD8+PHgOA4AoNfr8eSTT2Lbtm2Ij49Ht27d0LdvX6SkpAAA1qxZgxo1amDKlCm2X5AB65HT5s2bY/PmzTh8+DAGDRqEoUOH4ujRowDuDcsdMGAAjh07hj/++AO9evUCYwzjxo3D888/b/fLdLt27Ww1T5s2DaNGjcKxY8fQrVs3LFq0CF9//TVmzZqFgwcPolu3bnjxxRdx5coVu+c6ZcoUvPnmm4iPj0fbtm3x4osvIjMz026Zjz76CLNnz8bevXshEokwatSoErfdsmXLMHHiRAwaNAiHDh3Chg0bUKdOnTL86xFCiOvJyckBAPj5+RV7+4kTJ5CQkIABAwaUuq59+/YhKioKzZs3x7hx4+w+i5s0aYIDBw4gPz8f//77L0JDQxEYGIj169fDy8sLvXr1KlO9pX1GC4VCzJ8/H0eOHMGSJUuwZ88eTJkyBQDQrl07fPLJJ1Aqlbb90Lhx4wBYR7xMnToVBw4cwLp163D9+nWMGDHCtt6PP/4YFy5cwM8//4xjx45hwYIFtlEre/bsAQD89ttvSExMxJo1a2z32717Ny5fvoytW7di48aN0Ov16N27N9RqNfbs2YMff/wRu3fvLjJkd/fu3bh48SL+/PNPfP/999i6dWuR6ZjWrFkDHx8f7Nq1CzNnzsTcuXOxc+fOYrdbad8LCCGEF3yn1+rk7+/P/P392bRp09jJkyfZihUrWEBAAPvpp5/KdP/ijjwyxlh8fDx75JFHWKNGjdj48eOZ0Whkc+bMYW+99RY7e/Ys69q1K4uLi2PffvttietesWIFCw0NZZmZmWV+Pq1atbJbZ8OGDdnChQtLvV+fPn3Ye++9xxhj7MSJE0wul7Pk5ORily3ul+nCI4gPPlbdunXZvHnz7K7r3Lmz7Uhs4f0+++wz2+0mk4nVr1+fff7554wx+yOPhbZv3253dPfBI49169Zl06dPL/V5E0KIq7NYLKxPnz4PHUUzbtw41qJFi1LXtWHDBrZt2zZ2+vRptnXrVtaiRQvWuXNn22gZo9HIxo8fzxo1asQeeeQRFh8fzzIzM1lsbCxLSUlh06dPZ02aNGG9evViqampJT5OeT+jt2zZwiIjI22XV61axcLDw0u937Fjx5hcLrcdEezbty8bMWJEscsW7o9OnTpld/2wYcNYdHS03RHD7777jkVERDCdTme7bvv27czX15fdvn3bdr/IyEim1+ttyyxbtoyFhIQwi8XCGLN+h3jw361z5862o7uM2R95rMj3AkIIqWpivsNrdeI4Di1atMD06dMBAM2aNcO5c+ewYsUK9O/fv8Lr7dChA/bu3Wu7fPnyZaxduxbx8fHo3r07Ro0ahSeeeAJt2rRBp06dEBsbW2QdCQkJaNq0Kfz9/Yt9DJ1Oh9mzZ2P79u24c+cOzGYz8vPzcePGjYfWZrFYMH/+fGzZsgW3bt2C0WiEwWCAt7c3AOsvy4899hjatm2Lrl27omvXrujdu3eJv2jfr0WLFra/c3JycOvWLbsjk4D1V+PTp0/bXdemTRvb32KxGM2bN8fFixftlrl/G4WGhgIA0tPTERkZabdcWloabt26hccee6zUegkhxNVNmDAB586dwz///FPs7fn5+di4cSPeeeedUtfVt29f29+xsbGIjY1FkyZNsHfvXnTp0gUSiQRffPGF3X1GjBiBESNG4NSpU9i2bRsOHDiAL774ApMnT7Y7eleoLJ/Ru3btwqeffopLly4hNzcXZrMZBQUFyMvLg4+PT4n3O3HiBGbPno3Tp09Do9HYjsilpKSgYcOGGDJkCPr374+TJ0+ia9eu6NmzZ5F9VHEaN24MqVRqu3zx4kXExsZCLpfbrmvXrh04jsPly5cREhJi24b319u2bVvodDrcuHHD1vfgwf1/aGgo0tPTi62jtO8FhBDCB48athoaGooGDRrYXRcTE2Mb+ukoY8eOxezZs8FxHE6dOoXnnnsOwcHB6NSpE/bt21fsfQrDXEnef/99/Pbbb5g+fTr++usv7N+/H40bNy71ZP4FCxZg0aJFmDhxIv744w/s378f3bp1szW4EYlE+O2337BlyxY0aNAAixcvRvPmzZGUlFTq87x/R+poEonE9rdAIACAYofqlLbdCCHEXUycOBHbt2/HH3/8UWL30V9++QV5eXl4+eWXy73+6OhoBAQE4OrVq8XevmfPHpw/fx4jRozAvn370L17d8jlcjz//PP477//ir1PaZ/RycnJeOGFFxAbG4vVq1dj3759+PzzzwE8vFmNXq/Hs88+C19fX6xYsQJ79uzB2rVrAdxr4Pbkk0/i3LlzGDNmDG7duoWePXvi/fffL3U7VNe+DbDu30oahkr7N0KIM/Ko8NiuXTu79uYAcOXKFYd2Ql25ciX8/PzwzDPPwGKxALi3IzObzSXuJGJjY3H69GlkZWUVe/vBgwfRv39/9OrVC7GxsQgJCSky/YdUKrU95v3369mzJ1566SU0adIE0dHRRc5BFAgEaN++PaZMmYL9+/dDKpXa5pmSSCRF1lkcpVKJsLAwHDx4sMjjPxjYjxw5YvvbbDbj5MmTiImJKfUxiuPr64tatWph9+7dFbo/IYQ4O8YYJk6ciN9++w2///47oqKiSlx25cqVePrppxEUFFTux0lNTUVWVpZttMf9CgoKMHHiRNt5/BaLxW7fVtJ+orTP6BMnToDjOMyZMwdt2rRBvXr1cOvWLbtlitu3Xbp0CVlZWfjoo4/QsWNHxMTEFHsELygoCP3798eKFSvwySef2PocFB5ZLMv+LSYmBmfOnLF1twWs+zahUIh69erZrjtz5gzy8/Ntlw8fPgyFQoGIiIhSH6M4pX0vIIQQPnhUeBwzZgyOHDmC+fPnIzExERs2bMD333+PYcOG2ZaZNm0ahg4dane/hIQEJCQkQK/XIyMjAwkJCTh//nyR9aelpWHevHn47LPPAFibGcTExOCbb77BoUOHsHv37hKHzPTt2xchISF46aWXcODAAVy7dg2//PILDh06BACoU6cOtm7dioSEBJw+fRpvvPFGkSBas2ZNxMfH4+bNm8jIyLDdb+fOnTh48CAuXLiAsWPHIi0tzXafwu1x/PhxpKSk4Ndff0VGRoYtzNWqVQtnzpzBpUuXkJGR8dApOcaPH48vvvgCmzZtwqVLl/Dhhx8iISGhSLObpUuXYuvWrbh48SImTJgAjUaD1157rcT1lub999/H119/jUWLFuHKlSs4efIkvv322wqvjxBCnMmECROwfv16fPfdd/D19cWdO3dw584du6ACWOcIjI+Px8CBA4tdT/PmzbF161YA1lMhPvjgAxw+fBjJycnYtWsX+vXrhzp16qBbt25F7jt37lx0794dzZo1A2D9MXbr1q04c+YMlixZ8tDhoA/7jK5Tpw5MJhO+/fZbXLt2DWvXrsWKFSvs7l+zZk3odDrs2rULGRkZyMvLQ0REBKRSKRYvXoxr167h999/xyeffGJ3v48//hjbtm1DYmIizp07h+3bt9v2bUFBQfD29sY///yDO3fuQKvVllh/v379IJPJMGzYMJw9exZ79uzB5MmT8fLLL9uGrALWI6WjRo3C+fPn8ddff2HWrFkYPnw4hMKKfdUq7XsBIYTwgu+TLqvbH3/8wVq3bs38/f1Z8+bN2XfffWd3+7Bhw2wtuws92NJbLpezhg0bFln366+/XqQpzpEjR1jz5s1ZZGQkmz179kNrS05OZq+88goLCwtjQUFB7JFHHmFHjhxhjFlP7u/RowcLDAxkMTExbPHixUUa+Bw6dIi1bduW+fv726bqyMzMZP369WMhISEsKiqKzZgxgw0dOtTWBOf8+fPs2WefZbVq1WL+/v5FGvukpaWx//3vfywkJKTIVB0PNhqwWCxs1qxZrF69ekytVpc4Vcf69evZo48+yvz8/FjLli3Z7t27bcsUNszJzs62XXfq1Ckml8tZUlISY6z4qTqWL1/O4uLimFqtZnXq1GGTJk166LYmhBBXUdw+SC6Xs1WrVtktN23aNBYTE2Nr0FLcegrvk5eXx3r16sVq1arF1Go1a9iwIRs9erStAcz9zpw5w5o2bWrXMMZisbBx48axsLAw1rlzZ3blypWHPoeHfUZ//fXXrE6dOiwwMJA9++yzbM2aNUX2A2PHjmWRkZF2U3WsX7+eNWrUiPn7+7PHH3+c/f7773b7prlz57IWLVqwwMBAFhkZyfr168euXbtmW+f333/PYmJimK+vb5GpOh5U1qk6Pv74YxYZGclCQkLY6NGjWUFBgW2Z4pru9evXjw0bNsx2+cGpOh72vYAQQvggYOzujL+EVLHk5GQ0btwY+/fvR9OmTfkuhxBCCHGI4cOHQ6vVYt26dXyXQgghVcqjhq0SQgghhBBCCKkYCo+EEEIIIYQQQkpFw1YJIYQQQgghhJSKjjwSQgghhBBCCCkVhUdCCCGEEEIIIaWi8EgIIYQQQgghpFQeER4ZY7CYzaDTOwkhhLgK2ncRQghxNmK+C6gOnMWCY8cOoEXLthCLpXyX43IY46DRZEKtDoBA4BG/NzgMbbuKo21XcVWx7RhjyM3JRn6+3iHrcxVJmRxuajmEq4SICnj4tvzll614970puHA+ASGhkZV+bNp3VQ59hlQcbbuKo21XOVWx/fLycpGbo3HIutzR8eMnMHzEGPj6KnDq1CmIxZKHLk+vakIIIQ/FGEOOhwbHP8+ZcCrVgj/PmZCUyRW7HGMMX331DSZNfge9evWs5ioJIYSUJE9PwfFh/vhjO/q/OhC1a0dj86Z1ZboPhUdCCCElKgyOBR4WHAHgppZ76GUAMBiMmPzWu/jyq4WYOHEcPpk7q7rKI4QQ8hB5+lzk5mr4LsMpMcawZMkyvDl2Ap566kn8uPI7+Pn5lem+HjFslRBCSPkxxpCjzUJBQR7fpfAiXCXEqVSL3eX7aTQajBo1FidOnMQXX3yKXv97prpLJIQQUgy9Phc6Co7FMplM+HDaR9iwYRPGjBmJ8ePehEAgKPP9KTwSQggpwtODIwBEBQjRo5Gk2HMek5OvY8jQEcjOzsaqVd+jVauWPFZKCCGkkF6fA12ulu8ynFJubi7GjBmPQ4ePYN68OejzfO9yr4PCIyGEEDsUHO+JCijaKOfY8RMYMXw0lColNm5ci+ioKH6KI4QQYkevy4FOR8GxOKmpqRg8ZATu3EnD998tQ/v2bSu0HjrnkRBCiA0Fx4f7/fc/8eqrA1G7Tm1souBICCFOg4JjyRJOn0GfF15CQUEBNm5YU+HgCFB4JIQQchdjDFptJgXHYjDGsHjxMowdN7HczQUIIYRULZ1OS8GxBP/8swMvv/waImrUwKZN61C3bp1KrY+GrRJCCLEFR0NBPt+lOJ37mwu8OWYUxo0bU67mAoQQQqqOTqeFXpfDdxlOhzGGH374EbNmf4Knuj+JTz+dCy8vr0qvl8IjIYR4OMYYtJpMGAwUHB+Um5uL0WPG4fDhoxVuLkAIIaRq6HK10OspOD7IbDZj5sw5WPXTGgwfNgSTJ0+AUOiYAae8hUeTyYTly5YgIeEUcnJy4B8QgN69n0fXrt2KXf7DqR/g4sULEIvvlfz1wkXw9w+orpIJIcTtUHAsmaOaCxBCCHE8Co7F0+n0GDd+Ivbti8esmTPw0ksvOnT9vIVHi8UCtZ8fpk3/CCEhobh86RJmzpyBgIAAxMU1L/Y+r732Onr+r1c1V0oIIe6JMQaNJgNGQwHfpTidhITTGDpsFLy9vbBxw5pKnyNCCCHEcXJzNcjT5/JdhtO5ffsOhg4dievXr2PF8iV45JGODn8M3hrmeHl54eWX+yM0NAwCgQD1Y2IQG9sE58+f46skQgjxGBQcS/b3P//i5VcGIDLCMc0FCCGEOA4Fx+KdP38Bffr0Q7ZGgw0b1lRJcASc6JxHo9GIy5cv45FHOpe4zKZNG7Bx43oEBQWhZ89eeKzL48UuZzKZYDKZbJctFgsAgDEOjHGOLdwDWLcbo21XAbTtKo62XcWVtu2swTETJqOhmitzbowxfP/9SsyeM8+hzQXKivZdjkWfIRVH267iaNtVTmnbLzdHi/x8XTVX5fx27d6DceMmIjo6GsuWLkJwcHCVPZZThEfGGL5dtBBh4WFo2659scv0f/U1REREQiaT4fTpBHz+2Xx4e3sXu/yWzZuwYcM622WpVIoJE8ZCq9FAJBZV2fNwWwwwm03QarIBajBYPrTtKo62XcU9bNsxwGQy0hebB5jNZnz88Wz8tHqtw5sLlBXtuxyMPkMqjrZdxdG2q5yHbD+zyQSOs/BTlxP76ac1mPHRLDze5TF88cV8+Pj4VOnjCRhjrEofoRSMMSxduhiJV65g2vSPIJfLy3S/H3/8ARnp6Zg46a0itxX36+35cyfRvEVriMVSh9XuKRjjoNFkQa32h0BAU4OWB227iqNtV3ElbTvGOGiys2Ay0RHH+93fXOCjGR86pLlASGhkue9D+y7Hos+QiqNtV3G07SqnpO2Xm6NBfr6ex8qcj8ViwSeffIoV3/2AQQMH4L333oZIVLkfGgMCQyEWSx66DK9HHhljWLZ0CS5fuoTpMz4uc3AE8NA5tiQSCSSSe0/cYjbfvY+Q3sgVJBAIaPtVEG27iqNtV3EPbjvGOGg1FBwfdOvWbQwdNhIp11OqrLlAWdG+y/HoM6TiaNtVHG27ynlw++XkZFNwfEBeXh4mTnobO3bswrQPp2DAgP7V9ti8vqqXL1uCCxfOY9r0GVAoFCUup9frcOzYURgMBlgsFiQknMLff/2Fdu07VGO1hBDimqxHHDNgpHMc7Zw7dx59XugHjUZbpc0FCCGElB9jzBoc8+gcx/ulp6fjlf6vIz7+AJYsXlitwRHg8chjWloatm//ExKJBCOGD7Vd37nzoxg+YhRmfjwDDRs2Qp8X+sJstmDjhnX44vNPAQBBwcEYOOgNdOhAO3pCCHkYxnHI1mRQc5wH7Nq9B2PHTkTt2lXfXIAQQkj5MMagy6Whqg+6dOkyBg8ZDovFgnVrV6Fx40bVXgNv4TE4OBibt/xa4u1Tpk6z/a1SqTD3k0+royxCCHEbjLOeO0JDVe3Zmgs83gVffD6vypsLEEIIKQdmPcexoCCP70qcSnz8fowaPQ4RETWwfNlihIWF8lIHDcYmhBB3xIDs7EwKjvexWCyYNWsupk3/GK8PeBWLvvmSgiMhhDgRxhjMZhMFxwds2LAJbwwejpYtm2P9utW8BUfASabqIIQQ4jgcx8FkMoDnZtpOJS8vDxMmvo2dO6u/uQAhhJDSWc9x1NB0HPfhOA6ffbYAi5csQ/9XXsKHH34AsZjf+EbhkRBC3AjHWZvjUHC8Jz09HUOHjUJi4lUsWbwQjz/ehe+SCCGE3IcxhhxtFgx0xNGmoKAAb739Hv788y+8/947eOON1x8620R1ofBICCFuwhoc02E2m0pf2ENcvHQJQ4aM4LW5ACGEkJIVBkcaqnpPZmYWho8YjfPnL+Cbb75E9yef4LskGzrnkRBC3ADHccjOTofJZOS7FKfx33/xePHF/lAqldi8af1Dg2NSJof9V81IyuSqsUJCCPFsFByLunr1Gl544SVcv56CNatXOlVwBCg8EkKIy+M4DtlZaTBTcLRZv35jmZsLJGVy+POcCadSLfjznIkCJCGEVAMKjkUdOnQYL/R9GTKZFFs2r0ezZk35LqkICo+EEOLCOM5iDY40VBWANUjPn/853v/gQ7zUry+WLlkEhUL+0Pvc1HIPvUwIIcSxGGPQajMpON7n559/xesDh6Bx44bYsGENIiJq8F1SseicR0IIcVHW4EjnOBYqKCjAW2+9hz+3l6+5QLhKiFOpFrvLhBBCqkZhcDQU5PNdilNgjOGrr77BV19/g74vPI+PP54OiUTCd1klovBICCEuiIKjvco0F4gKEKJHIwluajmEq4SICqDwSAghVYExBq0mEwYDBUcAMBiMeP/9Kfjl198wedIEjBgx1Ck6qj4MhUdCCHExFBztJSZexZAhI6DPy8Oa1SsrdI5IVACFRkIIqUoUHO1pNBqMHPkmTp5KwJdffoaezzzNd0llQuGREEJcCAVHewcPHsao0WMRHBSIVau+d9pzRAghxJNZg2MGDIYCvktxCklJyRgydAQ0Gg1WrfoerVq24LukMqOfWQkhxEVwFguyqDmOzZaff8HAQUMQG9vIqZsLEEKIJ2OMQUPB0ebY8RPo2/dlAMCmjetcKjgCFB4JIcQlWO4GR4vZzHcpvGOMYcGCr/HWW++h97P/w4rlS6BUKvkuixBCyAMKg6ORgiMAYNvvf+DVVweibt062LRxLaKiavFdUrnRsFVCCHFyFot1Og6LhYKjwWDEe+9Nwa9bXae5ACGEeCLGGDTZGTAaKTgyxvDtt0vx2ecL0PvZ/2H27JmQyaR8l1UhFB4JIcSJUXC8x1WbCxBCiKeh4HiPyWTChx/OwIaNmzH2zdEYO3a0S//oSeGREEKclMViRnZWOgVHFN9cICmTo+k1CCHEyTDG3Q2OBr5L4V1OTg5GjxmHI0eO4dP5c/Hcc8/yXVKlUXgkhBAnRMHxnqPHjmPEiNFQq9XYtHEdoqJqISmTw5/nrI2DTqVa0KORhAIkIYTwjILjPTdupGLIkOG4k5aOH75fjnbt2vBdkkPQnpYQQpwMBcd7tv3+B157bRDq1a1r11zgppazW+7By4QQQqoXYxyyKTgCABISTqPPCy/BYDBi08a1bhMcAQqPhBDiVKzBkc5xZIxh0aIlGDduEp7u0R0//LACarXadnu4yn739eBlQggh1YdxHLKzMmCi4Ii//v4HL78yAJGREdi0aR3q1KnNd0kORcNWCSHESVjMZmRlp4GzWPguhVcmkwlTp07Hxk1bMG7sGLz55qgizQWiAoTo0UhC5zwSQgjPOM46VNVk8uzgyBjDihU/YO4n89GjR3fMnzcHXl5efJflcBQeCSHECVjMZmRlpYHjPDs4lqe5QFQAhUZCCOGTNTimw2Qy8l0Kr8xmMz76aBZWr1mHkSOGYeLEcRAK3XP/ROGREEJ4ZjZbh6p6enAsbC6Qlp6BlT8sR9u27nOOCCGEuBsKjlY6nR5jx03Af//tx+xZH6Ffv758l1SlKDwSQgiPzGYTsrPSPT44njqVgKHDRkHu44NNG9eidu1ovksihBBSAgqOVrdu3caQoSNw40YqvluxBJ06deS7pCrnnsdTCSHEBVBwtPrrr7/xSv/XUbNmJDZtWkfBkRBCnBgFR6uzZ8+hzwv9kJOTg40b1nhEcAQoPBJCCC8oOFqbC3y2dBMmf7oJHbr3xU+rvkdAgD/fZRFCCCkBx3HIpuCInTt34aWXX0NwcDC2bF6P+vXr8V1StaFhq4QQUs2swTENHOeZcxMmZXI4ft2IX/7ciwvJJrTpMRAtWrXAbb0UUe7XmI4QQtwCx3HIzkqD2WziuxRe/fjjanw8cza6du2Czz+bBx8fH75LqlYUHgkhpBpRcOTw08E8/JdwHXpTBCKbxKJ+fX9AIMBNLUfdUwkhxAlxnAXZWekeHRwtFgvmzJmH73/4EYPfGIh33pkMkUjEd1nVjsIjIYRUE7PJhOxszw2OAHDmWgZ2x5+DQaRAeEQtSGQ+0BkYlN4ChKsoOBJCiLOh4Ajk5eVhwoS3sHPXbsyYPhWvvvoK3yXxhsIjIYRUA5PJiOzsdDAPDo5nz57DnKmzIW7wAuo0bQovLy8EKQRoWVOEuAgxHXUkhBAnQ8ERSEtLw9Bho3Dt2jUsXboIXR57lO+SeEXhkRBCqpjJZER2VjoY89zguHPnLowbPxl16tTGtNGdkKxXAACFRkIIcVIUHIGLFy9h8JARYByHdWt/QqNGDfkuiXcUHgkhpAp5YnBMyuRwU8shXCVEVIAQK3/8CXO/WYeW3d/A1AmDEBPug+Z8F0kIIaREnMWCrOw0WMxmvkvhzb598RgzZhxq1qyJpUsXISwslO+SnAKFR0IIqSKeGhz/PGf9lfpkihnX43/ALz9vRbt+76FN69bYmSiATEaNcQghxFlZLBZke3hwXLduAz6c9hEeeaQjvlzwORQKOd8lOQ0Kj4QQUgU8MTgCwE2t9fmaTSbs2r0Xlw+ewICRk+BXr43dMhQeCSHE+VgsFmRnpcFi8czgyHEc5n/6OZYuXYHXXn0FU6a8B7GY4tL9aGsQQoiDWYNjGhhjfJdS7cJVQhy4nIu///4XWq0WUye+gTatW9uORhYuQwghxLl4enAsKCjA5MnvYvtff2PKB+9i4MABEAgEfJfldCg8EkKIA5mMBmtXVQ8MjgBgyLiCn7+cBYFPCGa9PRRd29QDAPRoJLE7D5IQQojzsFjMyM5K99jgmJGZiRHDR+P8hYtYtOgrPPlEN75LcloUHgkhxEGMRgM0Hhwc9+79D2++Od7aXODrT+yaC0QFUGgkhBBn5OnB8cqVRAwZOgL5+QVYu3YVmjaJ5bskp0Z7ckIIcQBPD45r123AkKEj0Kp1S3yy6Edcyw9EUqZnne9JCCGuxtOD44EDh9D3xVfg5eWFLZvXUXAsAzrySAghleSJwbFwOo4wJbDhuwW25gKvjXoHf19kACw4lWpBj0YSOuJICCFOyBoc02CxWPguhRebt/yCDz74EG3btMbChQvg6+vLd0kugcIjIYRUgqcGxz/PmWAxm7F7916c+XOXrbnAgWsWAPe+iFBnVUIIcT4WsxlZ2WngPDA4Msaw4MuvsXDht3jxxRfw0YwPIZFI+C7LZVB4JISQCjIaC6DJzvCo4AhYA2F+fj7++XsHsrKz8Oa7MzDo+VYArJ1UT6Xe+zJCnVUJIcS5WMxmZGWlgeM8LzgaDEa8+94H2Lp1G95+ayKGDRtCHVXLicIjIYRUgNFQgGxNBuBhwREAzNob+G3rfzBbLOj5zNN45tEw221RAULqrEoIIU7KbLYOVfXE4JidnY0RI99EQsJpfP3VF3j66af4LsklUXgkhJBy8uTgeODAIYwbPRbBtZtj4jsz0KR2UJGASJ1VCSHE+XhycExKSsbgIcORo83B6p9+QIsWzfkuyWXR3p0QQsrBYChAdrZnBsfNW37BwEFD0LRJLDZ9Px//ax1CIZEQQlyA2Wzy2OB49OgxvND3JQiFQmzevJ6CYyXRXp8QQsrIYLCe4wh4VnBkjOGLBV/h7bffw/PP98by5YupKx0hhLgIa3BM98jguPW33/Haa4NQv149bNywBjVrRvJdksujYauEEFIGBkM+NNmZ8LTgSM0FCCHEdd074uhZ8+4yxrDo2yX4/PMv8dxzz2L2rI8glUr5LsstUHgkhJBSWINjBt9lVLusrGyMHFV8c4HCeR6pKQ4hhDgnTw2ORqMRU6ZOx+bNP2P8uDEYM2YU/ejpQBQeCSHkIQwF+dBoPC84XktKwuDBw5Gbk1ukuUDhPI8AcCrVgh6NJBQgCSHEiZhNJmRne15wzMnJwajR43Ds6DF89ukn6N27F98luR0Kj4QQUoKCgnxoPSg4Fh5NzL5xHh9OHAq/mk0wZfbH8K8VbLfcTS1X5DKFR0IIcQ5mkxHZ2ekeFxxTUm5gyJARyMjMwMqVK9CmTWu+S3JLFB4JIaQYBQV50Goy+S6j2hQeTUxMvIq9ew6gVps+ePT54bhplOHmOZPd0cVwlRCnUu81XghXUXAkhBBnYLobHJmHBceTJ09h6LBRUCgU2LRxLaKjo/kuyW1ReCSEkAd4WnAEgFSNBSdPnMTRYydQr14ddH20I8wQ2W6//+hiVIAQPRpJ6JxHQghxIiaTEdlZ6WDMs4Lj9u1/Y+KktxEb2xiLv10If38/vktyaxQeCSHkPp4WHJMyOVzPNGLF2m24mClDkxZt0bZ5QzQKFyPhZslHF6MCKDQSQoiz8MTgyBjDsuXfYd68z/DMMz0w75PZkMlkfJfl9ig8EkLIXQX5edBqPSs4/npCh793H4KW1ULj2ACEh4WhaQ0xOtYRo4ZaSEcXCSHEyXlicDSbzZg+YybWrl2PkSOHY+KEsRAKaT9VHSg8EkIIPC84AkBCYhq2/rYLRrEfohtGITxEgXC1EIUdzenoIiGEODdPDI65uTq8OXYCDhw4iLlzZqJv3z58l+RR6FsBIcTj5efrPS44njx5CrPenwCO4/BYx5aQy+VQyKypkRrgEEKI8zMZDcjOSvOo4Hjz5i30e6k/Tp48he++W0rBkQd05JEQ4tHy8/XI0WbxXUa1ScrksG3XMSz5YhYaRsjxwYSnoYcvGAMEAtAQ1UoTwFep5rsIQoibMxkN1q6qjPFdSrU5feYshg0bCYlEgo0b1qBevbp8l+RWfHx8IRKVHg0pPBJCPJanBcdrGRbMW38Chw8fQ8PuIzFz6KOoH+bl0MconCvSE0OoSCyGShUAiUTKdymEEDdmNBqg8bDguGPHLowbPwn169fD0iXfIDAwkO+S3IZAKIRK5Q+ZzLtMy1N4JIR4pPw8PXJyPCc4mkwmzFu0FocT8xAX1xStWrZARr4Y9R34GIVzRQLAqVSL3dyQ7s7bWw5fpRoCgWc8X0IIPzwxOP6wchVmzZqLJ57ois8+/QTe3mULOaR0UqkMSlUARCJR6QvfReGREOJx8vN0yMnJ5ruMapGUySHxdh6++3oOTp5KwJODZ6N+jDUyOvrcxptarshldw+PAoEASqU/vLx9+C6FEOLmPC04WiwWzJw1Fz/++BOGDHkD77w9iTqqOpBCoYKP3BeCwi55ZcRbeDSZTFi+bAkSEk4hJycH/gEB6N37eXTt2q3Y5fPy8rBkybc4dvQIpFIpevR4Bn1f7FfNVRNCXF1eng65HhQcNx3W4K+//4HOUgczZ/ZB87jGVTasNFwlxKnUkueGdDcSiRQqVQBEYvodlhBStYzGAmiyMzwmOOr1ekyY8BZ279mLjz+ahldeeYnvktyGUCiCSh0AqbRic2LytsezWCxQ+/lh2vSPEBISisuXLmHmzBkICAhAXFzzIsuvWL4UutxcLFm6HFqtFjOmf4igoCA81uVxHqonhLgiTwqOAHAgIQm/bt0HkUiEXv97BiHRAVU6/UZUgBA9Gkk84pxHH7kvFApVuX+xJYSQ8jIaCqDReE5wvHMnDUOHjkBScjKWLf0Wjz76CN8luQ2ZzBtKlX+ljuDyFh69vLzw8sv9bZfrx8QgNrYJzp8/VyQ8GgwG/PffPsyaPRdyuQJyuQI9nn4GO3b8S+GREFImeXm5yM3R8F1GlUvK5HDyhhkXL17C+h++RUijx/HEk13h7e1dLUcC3X1uSKFQCKUqADKZYxsNEUJIcYyGAmRrMgAPCY4XLlzE4CEjAADr161Gw4YNeK7IXVg7gfv4KCq9JqcZa2M0GnH58mU88kjnIrelpqbCbDYjOrq27broqNrYsnlTsesymUwwmUy2yxaLdRgVY5xHzYXjKNbtxmjbVQBtu4pz5LbL0+ug02kdUJXzik8049+LJqRkcUjPykVWthAR7QdgYt9YSKUytz8SWB0kEhmUKj+IRKKHvi4r2jSH9l2ORZ+/FUfbruIcue0MBgO0mgwHVOUa9uzZh7FjJ6BWrZpYtmwxQkKC+S7JLQhFIqhVARBLJKW+Lsuy/3KK8MgYw7eLFiIsPAxt27UvcntBQT68vLzsOgHJ5XLk5+cXu74tmzdhw4Z1tstSqRQTJoyFVqOBSFz2bkLkLgaYzSZoNdkAjdAqH9p2FeegbWcxm2GxmB1XlxOKTzTj230GZOoZDCYLOLME3sogKP1qQmOQoHcDp/iod2kikRgCgQC5uZpSl/XzC6rQY9C+y8Ho87fiaNtVnIO2HcdxMJuMjqvLya1Zux7Tp3+MRzs/ggULPoVcLue7JLcgFIogFkmg0+eUafmy7L94/0bBGMPSpYuRmpqKadM/KnYMrpeXNwwGAywWiy1A6vP0Jbbqfb7PC/hfr2dtly0WC86fOwmVWg2xmObfKi/GOGg0WVCp/agNfTnRtqs4R2w7vV4Ho6HAwZU5n0NJZmjzGQwGI5hABJFEDJlUBImIvvVVllAotM7dKK36fQftuxyLPn8rjrZdxTli2xkMBdBqMh1cmXPiOA6fzPsMy5d/hwEDXsWUD94t17QRpGTWTuCOn9aE1/DIGMOypUtw+dIlTJ/xcYm/MtSoUQMikQhJSddQp05dAEDStWuoWatWsctLJBJIJBLbZYvZetRBIBDSh2AFCQQC2n4VRNuu4iqz7fT6HOjdfKhqIYklD3pdATiBBBKJCDKJCJF+AtTyFyEugvffCF2WVOYFlSqg2lrD077L8ejzt+Jo21VcZbadwZDvMcExPz8fkye/i7/+/gdTp76Pga+/xndJbkEslkCtDqyyTuC8fiIsX7YEFy6cx7TpM6BQlHwCp0wmQ8eOnbB27Rro9XrcvHkTf/zxO7p1faIaqyWEuAq9Lge6XM8IjhcuXMT6L8bDknYWEX4i1AsRY3AHKV5tI0O/llI6z7GCrDvf6guOhBBiKMiHJtszznHMyMhA/1cHYs/efVj87UIKjg4iFIng5x9UpVNI8faTdFpaGrZv/xMSiQQjhg+1Xd+586MYPmIUZn48Aw0bNkKfF/oCAIYMHY7Fixdh2NA3IJXK0OPpp6nTKiGkCL0ux+2b4xTas2cf3hw7HlG1amHa0DgYxEpqjFNJYrEESpW/x7yGCCHOoaAg32Oa41y+fAVDho6AwWDA2rWr0CS2Md8luTyhUAhfpT/y83VVPoUUb+ExODgYm7f8WuLtU6ZOs7vs4+ODiRMnV3VZhBAXptNpodeV7aRwV7dmzTpMnzGTmgs4kLePAr6+agCe0RKfEOIcCgryPGaoavz+Axg9ehzCw8Owds2PCA8P57sklyeVekGl8odAKEB+vq7KH49OhiGEuAVPCI5JmRxSNWb8vnEl1i//nJoLOIhAIIRS5QcvLx8A8JiJuAkh/POk4Lhp0xZ8MGUa2rdri6+/XgBf38rPOejpFL4q+Pj4QiAQVNvUOhQeCSEuT5erhb6MbahdVVImh98S8rFn914kJVvw5vtzMP6N3nyX5fIkEilU6gCIRLQ7JIRUL08JjhzH4YsFX2HRoiV46aUXMX3aFLvmYKT8RCLR3U7gsmp/bNpbEkJcmicERwC4kKLBH7/vQnZ2Np7o1hVtOkTzXZLLk8uVkCuUVX5+CCGEPKggPw9arfsHR4PBgLffeR/btv2Bd96ZjKFD3qDP3EqSeflAqfTjraEbhUdCiMvKzdUgT5/LdxlV7vLlK5g9ZSZYZBc807MHAgMDEa6ipjgVJRSKoFL5Qyrz4rsUQogH8pTgmJWVjREjx+DMmbNY+PUC9OjRne+SXJtAAKWvGt4+/A73pfBICHFJnhAckzI57Dh0Dt/Mm4FQuREzxnWHSRpIHVUrwTp3oz+EQjpPlBBS/fLz9cjRZvFdRpW7du0aBg8ZgdxcHVb/9AOaN4/juySXJhZLoFIHQCzmf7gvhUdCiMtx9+CYlMnh5A0zth64jvPnk1Gj8wjMHdIWjWtSc4HKUPiqIZf78l0GIcRDeUpwPHLkKEaMHIOAgABs2bwekZERfJfk0go7gTvLcF/66ZoQ4lJyc9w/OP551oifdl7DlSwJ/CMbIbxBO1zOlvJdmssSicTwDwih4EgI4Y2nBMdff/0NAwa8gQYNGmDjhjX3gmN6CnDxiPX/pEwEAiFU6gAolX5OExwBOvJICHEhuTnZyMur+jmM+JScYcCu3XuRqhVDXbMp/FTeTrXTcDVeXj7w5bGxACGE5OfpkJOTzXcZVYoxhoXffIsFC75Gnz7PYebH0yGV3v3RMz0FghM7rH8nnwVr3hUIiuSvWBfgzJ3Ana8iQggpRk5ONvLdPDhmZWXjyzmzkCqMQZtHHodO7AN/HwGCfYWIi6CP6/IQCATwVfrB21vOdymEEA/mCcHRaDTigynTkLDjD3w16mU8/fRTEFw7BfiHWkNi1m37O2TdpvD4EM7eCZy+jRBCnBpjDLm57h8cr127hjcGj4BOp8NHnw+CKrwWGAMEAlCDnHJypsYChBDPlZenQ66bB0etVouRo8Yi58oZfDvkWdQJ84Ngx2owhRpQBYJ17G0Nkcln793JP5Svcp2aq3QCp/BICHFezHqOY0FBHt+VVImkTA43tRwyr5/F9MnDqbmAA/j4KKBwosYChBDPlJenhy5Xw3cZVer69RQMHjIcWVlZWD9jMupIDMCta0B+DgQWE2DIA7t2BmjTwzpUNev2vaORxI4rdQKn8EgIcUqMMZjNJnCche9SqkRSJoc/z5mQeCURe/ceQp24x7Dss/egUqn4Ls0lCQRCqFT+kHl5810KIcTDWSxmtw+OJ06cxLDho+Hrq8CmTesQLRcBhec1AkDhyI/C3/GCIik0lsDXVw0fF2roRuGREOJ0GGPIzdG4bXAEgFSNBSeOn8Sx4ydQv35djOw7ACqVcw9VcVYSqQwqVQBEIuf/xZYQ4t7y9DpYzGa+y6hSf/yxHZPfehdNmsTi20Vfw9/fDwCsRxfVZwDZ3R/xvORAVCyPlTo3kUgMlToAEolrdVOn8EgIcSqMMeTkZLvtUFXA2lxgzfKvcOymAq1atUBcs6aoGeBaOw9nIVcoIZc7b2MBQojn0OtzodNp+S6jyjDGsHTpcsyb/zn+979n8MncWZDJZPcWuHt0kUXH0hDVUrhyJ3AKj4QQp8EYQ442y62DY2FzgRPHT+DtmV8ipnkLaohTAUKhCCp1AKRSWekLE0JIFdPrc916qKrJZMK06R9j/fqNGDNmJMaPe7PkH+1oiGqJ3KETOIVHQohT8ITgeH9zgVWrvkerVi35LsklyWTeUKr8XfIXW0KI+9Hrc6DLdd8jjrm5uRgzZjwOHjqMTz6ZjRf6PMd3SS7JXTqBU3gkhPDOE4Lj8eMnMHzEmHvNBaKi+C7JBQngq1TBx8d1GgsQQtybXpfj1kNVb968icFDRuDWrdv4/vtl6NC+Hd8luSR36gRO4ZEQwit3Do6FU3FcORWPOR+MRdOmTbD426/h5+fHd2kux1UbCxBC3Je7B8eE02cwbNhIyGQybNq4FnXr1uG7JJcjEAihUvtDJnOfTuAUHgkhvGGMQavNhKEgn+9SHC4pk8OfZ41ISDiNw0fOo13v0Rj4+mvQcjJQdCwfb285fJVqCAQ0TJUQ4hx0uhzo3Tg4/vPPDoyfMBndmtbDxxNHQKmiH+7KSyqVQemGncApPBJCeOEuwbHw6OKDTW+OJRvw74HzSLl6BY2bt0FEnYY4lybEuTQTejSSUIOcMhAIBFAq/eHl7cN3KYQQYqPTaaHX5fBdRpVgjOGXZQuxfc0qvPlEawx7oi1EWdeBrOvWqTioEU6ZyBUqyOW+bjFM9UEUHgkh1Y4xBq0mEwaDawfH+EQztp8zQSETQOktsIXCs9dz8f3Ph6DhVIhu1hlBIX5QyO7tQG5qOQqPpRBLpFCrAiAS026KEOI8dLla6PXuGRzNZjOWzJoGr7P/YcgTbdC6Tg0IjHmA+O555lm3KTyWwhM6gdNemRBSrdwlOCZlcth+zoR0HUO6jiE6QIibWg6SgluYMGUpdF610P7RJ+Cj9EdUgBBGy737hqsoOD6Mj9wXCoXKLX+xJYS4LrcJjukpReZh1Ov1GDd+EsSJJzCh9+No0CAGyMsFCvRAYZMy/1Aei3Z+ntIJnMIjIaTaWINjBgyGAr5LqbSbWg4KmQDpOgYA0BkYdGmJ6DNhCLyC6uJ/Q0ZB7acGAHSNkdjuQ3M6lkwoFEKpCoBM5sV3KYQQYkeXq4Fen8t3GZWXngLBiR3Wv5PPgjXvitsWKYYOHYnr169jxdypaCDKtt7u4wvW8G531fuCJnmQAL5KNXx8FHwXUi0oPBJCqgVjDBpNBoxuEBwB69FDpbcA0QFC6AwMYaZzmDBkCBrE1MeSpZ9CB78iYZFCY8mkUi8oVf5u11iAEOL6cnM1yHOH4AhYjzje50bCUfT78EsIhEJs2LAGMTH1wYo5MkmKJxKLoVJ5VidwCo+EkCrnbsERsAbBHo0kOJFixsGDh/DVd3PR7bFH8emnc+Hl5YVAUFgsK4VCBR83bSxACHFtbhUcAWsgTD4L5OUi40Yy5v12EAGBIVi29FuEhARblwmKpNBYBp7aCZzCIyGkSjHGoMnOgNHoPsGxkMVixs9/7cfZcxfQod97mPR6O3h50cdqWYlEIusvtm7cWIAQ4rpyczTIy3Oj4AgAQZFgtRrjxm8rcfjUOfRqGo1Ob34Ir8LgSErl6Z3A6VsOIaTKuHNw1Ov1mDJ3Ba5ky9CpUwc0aBCDWzlAdCDflbkGmZc3lEr3byxACHFNuTnZyMvT8V2Gw1ksFmzZuBk5CacRG9sIbdu2AfI1fJflMiQSKVQe3gncc585IaRKMcbdDY4GvktxuNu372Do0JFIzRGgx4j5iIioAYC6qJaJQAClrxreHtJYgBDienJyspHvhsExPz8fEye9jaTD/+Gz/k+iceOGAABGXVTLhDqBW1F4JIQ4nDsHx/PnL2DIkBEQCIVYu3wxZIFR1EW1jMRiCVTqAIjFEr5LIYSQYrlrcExPT8ew4aNx5UoivlzwORo1qQtGTXHKhDqB26PwSAhxKMY4ZGdnwOSGwXHX7j0YN24ioqOjsWzpIgQHW88RodBYOm8fBXx91R7/iy0hxDkxxpCbk438fD3fpTjcpUuXMXjIcJjNZqxd8yNiYxtbb6DQWCqp1AsqlT+E1AnchsIjIcRhGHc3OJrcKzgmZXJY8+surFryBTq3a4cvvpgPHx/PPFG+vAQCIZQqP3h50fYihDgntw2O6Sk4H78b73+2CL7KYCxfthjh4WF8V+UyFL4q+PhQJ/AHUXgkhDgEx1mHqrpbcLyabsJHKw/i9OnLaPPC23hnYHv4+NCwy7KQSKRQqQMgEtGuhhDinNw5OF5a9y0Oxh/AiNa10Wnsh5BTcCwT6gT+cLRHJ4RUmjU4psNkMvJdikPl5eXhw3nf40K6GO3bt0Xjxo1wO1eA2kF8V+b85HIl5Aol/WJLCHFajDHk5GSjwM2CI8dx+HXFEmQdj0fDhjFo374dBIYcvstyCTIvHyiVftQJ/CEoPBJCKsVdg2N6ejqGDhuFpEwLnh4+D5E1reeGUEfVhxMKRVCp/CGlxgKEECdmDY5ZKMjP47sUhzIYDHjr7fdwKX4nPn25G5rExgIC6qhaKuoEXmYUHgkhFeauwfH+5gI/Lf0WirDa1FG1DKSyu40FhNRYgBDivBhjyNFmoaDAvYJjZmYWRowcg3PnzuPzz+YhtkVD6qhaBtQJvHwoPBJCKsQdg2NSJocdB8/im3kzEKZUYtnSb23NBSg0PpzCVw253JfvMggh5KHcLjimpwBZt3FDb8Zrk6ZBn5eHNatXolmzptbbKTQ+FHUCL79yh8dX+7+En1avK3L96wP6Y+WPqx1SFCHEuXEch+zsdJjdIDgmZXI4ecOMqxkcTly8jUunz6Jmp9cxe0QXhIfT8JXSiERiqNQBkEikfJdCCCEP5XbB8eIRCE7sQLouHyf2H8QLNWV4YfJshDRpyndlTo86gVdcucMjY6zIdRaLxSHFEEKcH8dxyM5Kg9ls4ruUSkvK5LD+mBEX7piRmmmAoUCMwLrt0C42GBoThaHSeHn5wJcaCxBCXABjDFptJgwF+XyX4hgXj0CwbxNyM9KguZOGlqG+eLpRHMS3zoCFhtARx4egTuCVU+atNu+TOQAAs9ls+7tQeno6akVFObQwQojz4TgLsrPS3SI4AsBNLYecfDNu3s5AgVkMbx8f+Klk0BsF1BjnIQQCAXyVfvD2lvNdCiGElMrtgmN6CgQndiDzViqYTosgXznUAQGAQmW9Pes2hccSUCfwyitzeIyKigYAHDt21PY3YP0SEde8BTp06Oj46gghTsPdgiMA+LAcHDt4BHnSUKiCIqFWSBHiK8BTjSR0jmMJqLEAIcSVMMag1WTCYHCT4AjAfOcGTp4+D6+cTNQICbQGR79QwOfueefUWbUI6gTuOGUKj+++8xbmfjIfAHD48EG82O+lKi2KEOJc3DE4Xr16DROGDEeeOBADJsyCl1qOALkAcRFiCo4l8PFRQEGNBQghLsIdg6NGo8HM+QsRa7yFbh1awz/ID6x5V2tgpM6qxaJO4I5VpvB482YqLBYLRCIRbt++XdU1EUKciLsFx6RMDruPXMDCedMRKJNi1fL5iIiowXdZTk0gEEKl8ofMy5vvUggPzLeuwpJ+A6KgCIjDavNdDiFl4o7BMTn5OgYPGQ6NRoNB86cjIszfOn9jYVik0FiEr68aPtQJ3KHKFB4bN47FpInjER4eDpPJVOScx0Jvv/OeQ4sjhPCLs1iQlZ0Gi9nMdykOEZ9oxvc7ruPMiQSEt+2POSO7ICJCyXdZTk0ilUGlCoBI5NhfbK/cMSAly4hIfynqhsgcum7iOOZbV2HYv9X69+XjQIdeFCCJ02OMQaPJgNFQwHcpDnPlj3XYu/o7tPMTY8iydYiKqsV3SU6NOoFXnTKFx4mT3sKB/fFIS0srcs4jIcQ9WSwWZLtBcCyciiNTx/DX0WSk3tEgsHZLtG9SA1oz7VQeRq5QQi53fGOBK3cM+PmYBgBw9FoenmuppgDppCzpN4pcpvBICjnjUWm3Co5353A8E78bwpO70CXSFwNr1YTQkAaAwmNJqBN41SpTeJRIJOj86GMAAL1eT+c8EuLmLBYLsrPSYLG4dnCMTzTj+wMGZOgY8vQ5yNOZERQSjtBgP+SZqKNqSYRCEVTqAEilVRPoUrKMRS5TeHROoqAI6xHH+y4TAjjnUWnGGDTZGTAa3SA47lgDwbl4pOQaYLl5A0F+KoREREAoEIJLvQzEtOa7QqdDncCrR7knOHl94KCqqIMQ4iTcJTgmZXJYc8SI69kczGYLOCaD2j8YdcJ94COljqolkcm8oVT5V+kvtpH+Uhy9lmd3mTgncVhtoEMvpzu6RPjnbEel3S04Cg9uhclkRJDZAu/gQASplYDg7udyjXr81ueEqBN49SlTeBw+bDCWLF0BABjw2islDmFa+eNqx1VGCKl27hIcAescjrn5BpiNHJhACIlUDH9fER6pK6aOqsUSwFepho+PosofqW6IDM+1VNM5jy5CHFabQiMpwpmOSjPG3Q2OBt5qcJj0FLAz+2A0GiFkDBKJBIF+KnBtewK6bGtwpKOOdqgTePUqU3gcP2GS7e933n2/yoohhPDHYjEjOyvdLYIjAGhSL+LamRRIQprAW+4Lb6kQz8VJ0bsZHeV6kEgshkpVvY0F6obIKDQS4sKc5ai0WwVHAJmXzyIl6SYaKgQQS6QQioTgGnUE2vTguzSnIxDe7QQu89xO4PefdywKjaqWxyxTeGzYsJHt78aNY6usGEIIP9wtOK78eTe+WLoB0VG10K1VOxiYFG2jxOhYp9wj9d2el7ccSqUaAgEdiSWElA/fR6XdLThe+m8Hfv3qMzT1l6FBTCMITHngGnUAur7Cd2lOp6o6gbuSB887lrbvCXirqvxxy/1NymKxYN/ePUi8moiCfPu5c0aPGeuwwggh1cMaHNNgsVj4LqVSkjI5pGos2L79L/xx+AZi2j+HRzp3xDNNvGiIajEEAgGUSn94efvwXQohhJQb4zhkazJgcpPgGP/LelzZsBQNgv3Q5ZF2kEY3BouOpbkbi1FVncBdzYPnHXPpqUBNJwyP3y5aiNOnExDXvAV8fOhLByGuzGI2Iys7DZwbBMffTxsQv38/Em8zxLZoi3bNGwICAW5qOQqPD5BIpNZfbMV0JJYQ4no4znrE0WRy/eDI0q7j3w1rEP/nVjwaWx+PPvoIRGIRmCqQguMDqroTuKt58LxjYVCNannccn9zOHLkMBZ8uRB+fn5VUQ8hpJpYzGZkZaWB41w7OMYnmrH9TB6OnjiNzOREtOrwGAJCI4G7v0jSdBz2fOS+UChUHv+LLSHENVmDYzpMJmPpCzuz9BRwiaeQ8OfPuH7+Cvq2b4rGsY0gEN8dhukfym99TqY6OoG7mgfPOxaFRgGazKp/3PLeQeHrS0ccCXFxZrN1qKo7BMfv/tPh2vVb4GSRaP1YFGJrB6JpuAgCgTU40lFHK6FQCKUqADKZF9+lEEJIhbhTcDQf2o4z+3ZAkq9F1/atEN24CZg6xHrE0T+UjjraVF8ncFd0/3nHjHHV85hlWSgv7958XC+++BIWf/sN+r74EtRqtd1yFCoJcX7uEhwBYO/pW7iSpIVQLEVIoC9CgrzQg+ZvLEIq9YJS5e/RjQUIIa7NbYIjgOzE8zi+/Q/4sgLEREXAJyzIegOd42iHj07gpHRlCo/3z+3IGAMA7Nu31+46gUCAjZt+rqIyCSGOYDabkJ2V7tLBMSmTw00th2tnD2LTTzugbjsESqUSZiZAk3ARBccHKBQq+Mh9aZgqIcRluUVwTE8Bsm7jSpYeS+fOw6h6PogIqwGpRAIuuCbQ5BEKjvfx8vKBUuVHncCdUJnC46Jvl1Z1HYSQKubqwTEpk8PJG2ZcuMPh+uWzOHzkDBrE1Eet+ipo84XwlwsQoqSdzP38/IIgpWGqhBAXxnEcsrPTYXbl4HjxCAQnduC2Jhf79x1EqzAVatYMgxgMEEsAhR8Fx/uIxRIKjk6sTOExODi4xNu0Wi1EIhEUChqLTIizsgbHNHBc9YyHd7SkTA7rjxmRnGVBcmo60i9dQrOmTdDz8Va4nQsU7nKpOY6VVOYFMEAipaE+hBDXxXEcsrPSYDab+C6l4g7/CcGhbcjNyUHGnQzUiwhFm3btIU5PvrcMDQwBYO0ErlT6I1en4bsU8hDlbpizbOliPPpoF9SPicH+/fFY8MVnEAgEGD9hEtq371AVNRJCKsFVgmPhcNTimtycvGHG+ZtmpKZnwWgRIaZZe7RqEobmkRIAKPF+HkcggK+vGt7ePtBUQ8c1QgipKhxnQXZWumsHx4tHIDi4FfnZGZCYjKgRoEbj+rFAo7Zg5xlQoAe85EBULN+V8q6wEzjA+C6FlKLc4fHgwYMY8PogAMDPWzZj0qS34COX47sVyyk8EuJkzCYTsrNdIzj+ec76BeFUqqVI05vrd3JxPS0fFguDwtcXdaPsG+N4fGiEdZiPSh0AsVhSbR3XCCGkKrhFcARgTjqH9LRMyMwFUPj4wM/PH6xFNyCmNZh/KJB12+M7qz7YCbywtwpxXuUOjwZDAWQyGXJycpCWloa27doDADIy0su1nj/++B27d+1AcnIymrdoiXfffb/EZT+c+gEuXrwA8X0TWn+9cBH8/QPKWz4hHsNsMiI7O93pgyNgPXL44OVUDYeLdyzwNqRi408bIarbE6Fh4ZB7SdGsBjXGuZ+3jwK+vmpqikMIcXkuHRzTU4CEfUB+DrRBdfD16l/Rx8+AqIgweMmk4Nr2BGJaW5cNivTo0AhYO4GrVP4QUidwl1Lu8BgcHIJ9e/fg1q1biI21HmbX63V2wa4s/P390eeFF5GQcAqZmaUPr3rttdfR83+9ylsuIR7JdDc4MhcIjoD1XMVTqfca+Zy/ZcHfF0woKDAgK00HhcIX7eJqwCyQQSETIC6i3B9dbkkgEEKp8oOXF02TRAhxfa4eHAU7VkNw/RwsZjN0mj+Qo82DYsAoeAsLwNWody84EuoE7sLK/Q3s9dcHYeHCLyEWi/H2O+8BAI4ePYo6deuVaz3t7h6xTLp2rUzhsTxMJhNMpnsfPBaL9UspYxwN56oA63ZjtO0qgI9tZzJZu6q60nkDUQFC9GgkwU0thzs5HNYcMSIrzwKLiUGmDEGXDgPwYisfOrfxPoXDVEUiUZHXF71nK6ey26+iHQJp3+VY9D6oOD62HWexICs7HZzFNTuC49oZIPUSuPw8mM1m+MlEmPHK0/Du9jzflTkVoVBonbtRKgXAigxTpfdtxTli25Vl/1Xu8NgsLg7Lln9vd13Hjp3QsWOn8q6qXDZt2oCNG9cjKCgIPXv2wmNdHi9x2S2bN2HDhnW2y1KpFBMmjIVWo4FITIfGy41Zm65oNdnUEay8qnnbMY5zyXmwCpvlMAbsu2JCts4ADmIIxRLIpF4I8LWe30ih0UokEkMoFCE3V1P8AvSerZxKbj8/v6AKPSztuxyM3gcVV93bjjEYTUbAFc93S08Bks5AcP4gLLpcCDkLJCIhpDJvsKBQvqtzKkKhEGKxFPq8XCCvhIXofVtxDth2Zdl/lSk8pqeX7XzGoKCK7TBL0//V1xAREQmZTIbTpxPw+Wfz4e3tbTvf8kHP93kB/+v1rO2yxWLB+XMnoVKrIRZT6/ryYoyDRpMFldrxc+4k3jEgJcuESH8J6oTIHLpuZ1CV2+5B9444upbCZjk5+QxJGWZcTb4JI3wg9fGFSChCmFKArjESvst0CgKBECqVP6Syh79XqvN154742n6073Iseh9UXHVuO4vFOlTVVYOjIP4XIOMGclKTocvNR5BCBqnMCywoAmjyCN8VOg1fXzW8feSlLucq71vzravg0lMhDKoBcVhtvssBUH3brkzhceSIoRAIBGCM2Y1NfvDyxk0/O75CADExDWx/N2/eAk882R3x8f+VGB4lEgkkkntfNi1mMwDrFy9nfiE6M4FA4PDtd+WOAT8fzwEAHE3Kx3Mt1ajrhgGyKrbdg0wmIzTZGXCloaqFbmo55OQzXLpjQNL1WzAxMYL8ZRDLRAiUC/FKaykdcYR17kaVyh9CYdmOQFXH686d8bH9aN/lePQ+qLjq2HYWiwWa7AxwnOsOVWW3k3AnNQVe5gKoVEpI/YPAFGqwdj09viEOYN8JvKyc/X1rvnUVxgPbrBeunICgQy+nCZDVse3KFB5X/rja9veBA/tx6OABvNjvJQQFBSM9PQ0bN25A27btqqzIB9HJte4hJctY5LI7hseqZjIarM1xXPFXW1ib5dzOzEXilRsAGFrERKBmiC9qBwoRFyGm4AhA4auGj4+CPvsIIW7DesQxDRaLme9SKkyv1+POtWsoKDAgqkYofKIagotubJ23kYKj23YCt6TfKHLZWcJjdSjTtzK5XG77b8vmTZgwcTLq1asPtVqNevXqY9y48di0aUO5HthiscBoNMLCWcA4Dkaj0a5RQCG9Xodjx47CYDDAYrEgIeEU/v7rL7SjOSVdXqS/9KGXSemMLh4cASDl3AHsW/0RxGYtHm0RjZhIFZ6Pk6J3MzriKBKJ4R8QAjl1pCOEuBGLxezywfHKlUSM/nQZzmflI7JxM8jrxwFd+gGte3h8cBQIhFCpA6BU+rnlvksUFPHQy+6u3A1z9Ho9jEYjfHzutYY3GozI05d05mvxNm3cYNcY4OWX+qJx41h89PEszPx4Bho2bIQ+L/SF2WzBxg3r8MXnnwIAgoKDMXDQG+jQoWN5SydOpm6IDM+1VCMly4hIfykddSwno9EAjYsHx3XrNuDDaR/hkUc6YtKIVtCYvKib6l1eXj7wVfpBKKRtQQhxH9bgmO7SwfHAgUMYNXosQkKC0WrcNPhKGJh/qMeHRgCQSKR3O4G775Ra4rDaQIdesKTfgCgowqOOOgKAgJXzm+eSJd/i8qVL6PNCXwQFBiE9PQ0//7wZderUxfARo6qqzkqxmM04duwAWrRsS00HKsB6Am4m1OoApx1/7qyqatu5enC8lmHGNz/8gm3rv0O/p9th6tT3yz1XrLsSCATwVfrB27v0xgIlofds5TjL9qN9V+U4y7+jK6qqbXfviKOLnuMI4J81P+DnFYuhio7B+18shK+vL98lOQ25XAm5Qlmpo418vm/Nt666dCCsrm1X7m9rb7wxBJs2bcBPq1YiKysL/v7+6NSpM17o+2JV1EcIeYCrB8dLt/Lx/pI9SErKQNdBMzHwxWYQ0zQEACrWWIAQQlyBxWxGVnaay87jyBjDqs8/genQn3ilQyw6duwAQYEGoPAIoVB0txO4F9+lVJj51lUY9m+1/n35OOBETXCcTbnDo0Qiwcsv98fLL/evinoIIQ9hNBZAk53hcsGxcB5HH5aDDz9ZgjuWQHTr+jhqRdXCrRyG6EC+K+Sfj48CCjdsLEAIIRazGVlZaa7XVTU9Bci6DZPJhB9//AnXz5xAny4d0KxpE0AAsKzbHj9UtbydwJ2VpzfBKY8KjRO7efMm9u//D9nZ2Rg6dDhSU2/AZDIjKirKweURQgoZDQXI1mS41FxY8Ylm/JpgxJU0DkazGXpNFoxcAHo+8zQCg6yJMVzl2cPJCudulHl5810KIYQ4nNlsHarqUsExPQVI2AfB6d1geToY8woQlaVD96c7IzIi4t4E7P6hfFbJO4WvGnK5exx5FQVFWI843neZFK/c4fHIkcP46ssFaNWqFY4cOYyhQ4dDr9Nj3bo1+HDajKqokRCP56rB8cvdBcjUAQwMjIkgkAcjoGU/xNb2RohS6PHNcSQSGVRqf7duLECIo7n6eUnVie9t5XLBMT0FuHYGglO7Ibh9DeDMEDAGhRjoEh0AUUQEWHhdQBVoDY4eetRRJBJDpQ6AROI+52J7ehOc8ij3N5Y1q3/CB1OmokGDhhjw2isAgOjatXHt2jWHF0cIAQwG61BVwHWCY1Imh60JRmjyCoPj3R9qBQJwTIjsPIbn4jw7MMkVSsjllWssQIinofOSyo7vbWU2m5Cdle5SwVFwYgdw5SQEGTcAxtl+sBUIBBCDAyvQA9GePYejO3cCF4fVps+TMij3v3xmZgYaNGgIALYvPSKRCBzHObYyQojLBscV+w24nM7BbOHsgiMggEIGxIS49rkRlSEUiuDnHwyFQkXBkZByKu68JFI8PreVywVHAMi6DeTlQpCTCca4u70FBHf3XQBTBoE17+qxwVEgEECp8odKHeDSwdF86yoMCXthvnWV71JcVrn/9cPDw3H27Bm7686dO4uICBobTIgjGQz5LhccAWDHRRPO3rJAqysAYwwCAQeBQACJEGgcJsSbj3mhYx3PPOook3kjIDAUUinNaUpIRXj65Nzlwde2sgZHFxqqWsg/FNBmwGgoABiDAAJrUxyRGFztOLCX3gFiWvNdJS/EYgn8A0IqNYWUMyg8Gm++fNz6fwqQFVLub3Avv/Iq5n0yB126dIXJZMLqn1Zh584dmDBxUlXUR4hHuhccXU+apgBarQ5mC4PEWwKxSARvCVA70BocPfMcRwF8lSr4+LhHYwFC+ELnJZUdH9vqXnB0vdFoRlUI/rt8E01NBfDzkUEiACCRAeF1gacGeewRR3fqBE4dVR2j3N/imjWLw4yPZsFkNiE2tgl0eh2mTP0QsbFNqqI+QjyOocB1g2N8wg38s20zzAW5CPbzga+XCHIZoPYRQG8ETt4w811itROJxPAPCKbgSNwOX8O/xGG1IWvamb70lUF1biuzyXWDo+7aeSx88w1sOnAa5tDakCiUgJccUAZam+Nk3ea7xGonEAihVgfCV+nnFsERoJELjlKuI48WiwUfvP8OPvp4NoYOHV5VNRHisQoK8qHVuGZw/H3vacxefRReXt5oFROEGgE+AIBLafZfJArnfPSETqte3nIolWoIBO79PInn4bsZC3EuZpMR2dnpLhkcbyccQfzCmQjOz0f/13sjpG1XcKmXIUi5aA2OPr4eNyWHRCqDShUAkci9+hPQyAXHKFd4FIlEyM7WVFEphHi2goI8aDWZfJdRLkmZHHZcNCHhQjLid+9CaHhNdOv6OGReMjSrIUK4Soj1x4zQGRgUMgGCFEL8ec4EADiVakGPRhK3DJACgQBKpT+8vH34LoWQKkHDv0gh093gyFwtOB7+E5pT8Yg/cAScUIJevZ6BSqWydhl4/BWw9BTrEUcPm5LDVTuB3z8tDYASAyJ1VK28cp/z+HyfPvjh++/w2oAB8KYvRoQ4hCsGx/hEM1YfMeDqnXzk6WUIbPosmtULgMxLAgC2I4v9WkptRxpvau2/XNzUcm4XHsUSKVSqAIjFntkUiHgGmlCbAHeDY1Y6GHOh4JieAhz8HeaEPeBy9eheQw6vWg0gUamstxceZQyK9KjQKBSKoFIHuGRDt/tHQhhP7AIACBUqGhVRRcr97Wb1T6uQn5+Pf/75C15e3hAK7/0ysfLH1Q4tjhBP4KrB8acjBbhyUw8TJ4S3XI5AlRcCFGLbEcfCUBgVYD889VTqvQ584Sr3Co4+cl+agoN4BBr+RVwyOF48AsGJHdBdPg2xIQ/eMgl8FL5gXl5gtRp73FHGQjKZN5Qqf5edguP+kRBcfq51ejCFynYbfT45VrnD4zvvvl8VdRDikQry86DVuk5wjE8041CSGQk3TEi5kwUzZJDKpJBKRZCKBGgbJUaH2iV/rEQFCNGjkcTtznkUCoVQqgIgk3nxXQoh1YaGf3kulwuOF48Al48ByeeRmX4H5jwdAuUyiGXegEAAVr+1h07DIYCvUg0fHwXfhZTb/cNU7x8JIfS2b05HoyIcr9zhsXHj2KqogxCP42rB8eeTRmw+aYK+wAyt3gwOUvj6yKDwEaFhqAjdYiRlmr/xwSORrk4qlUHpho0FCCGkOC4XHA//CeHB38AZ8mHWaSE1WaBQKiFS+oHzDwHqtwba9OC7ymonEomhUgdAIpHyXUq5PdiwS1yvBQQB4RBAAFmHXgBKPueRVB6dlEMID/Lz9cjRZvFdRpklZXL494IZOXkm5OgNEAjFUCp8oPQRo0t9MQZ3cL1zJBxBoVDBR+5Lw1QJIR7BZDLcDY6M71LKJj0FgoTdsBToYcjNgQgM3nIFJL4qcLEdgcdf4btCXrh6J3C7Yao6LQz7t0IUFGFtdoSWNCqiirnmq4YQF+ZqwRGwNrYx6dKg1WohBAdvLyki/SVoEi5C1xgJ3+VVO5FIBH//YMgVrteRjhBCKsJodLHgCABZt5HDiaDVaGDiGCReXhCHRIDVbAg0eYTv6qqdQCCAShUAlcrfZYMjYD8UlcvPheC+oaoPdoImjkdHHgmpRq4WHJMyOaRqLPj7n39x/GAywuu1QI2IRmhVU4KGYSK3Om+xrGRe3lAqXbexACGElJfRaIAm24WC491pNk6dOo2Tew6hYbAKLetHA/VbgkXU88jGOJK7ncBFbtAJ/P6GXeJ6LajzczVz/VcQcagrdwxIyTIi0l+KuiGeORSxquTn6ZCTk813GWWSlMnh5A0zLtw248zxQzh/IRldmgbhud5xqKEWe1xgBAAIBPD1dc3GAoQQUlGuGBwFJ3bg7NnzOHbwEAxhdRH3xiCIQmt6XGAs5A6dwO9vkFM4LLVwaKooKILOcaxGZQqPGzasK9PKXnzxpUoVQ/h15Y4BPx/TAACOXsvDcy3VFCAdJD9Pj9xcDd9llElSJoc/z5lwPcOE4xeuQ3P9Djo/0hHPP97Qbq5GTwqQYrEEKnUAxGLPG6JLCPFcRqMRWk2G6wRHAFxiAi4f2IvzV5IQG9sYrV4YAGHDtnyXxQt36QT+YIOcB+dupHMcq1eZwuO1q1dtf1s4DidPHEdYWBiCgoKRnpGOWzdvonnzFlVWJKkeKVnGIpcpPFaexWKp1uCYlMlVaiqMHRdNOJ6Uj6uXzsMIKVq1ewT1aweCMeDPcyYA1rkaezSSeESA9PaWw1fp59K/2BJCSHkxjoMmO53vMsql4NQ+JP/6I0QFOjzeOBr+DRqABYbzXRYvXL0TuPnWVXAZN21HFe9Hczfyq0zh8f65Hb/99hsMGTIMT3Z/ynbdP//8jStXLju+OlKtIv2lOHotz+4yqZy8PD0sZlO1PV7hUUOg/AEvKZPDlhNG7LmUD21uHgTyCMTW8MYTTZWIixDbjjgWuqnl3Do8CgRCKFV+8PLy4bsUQnjx4DAxZ+ZKtboCo9EAk8lY+oLO4O75jfqLJ6Ddvx0+ZhMCwsMgD64BFl7XcUNV7z6OK5wv6YqdwAvfw8LAcLAcLQxn9kIgENim4rgfndfIr3Kf83hgfzyGDx9pd93jj3fFqh9/wMiRox1WGKl+dUNkeK6lms55dJC8vFzoqnmoakUDXnyiGVtOGnHhlgH6AjOEQjEC1N6oFy5F72b3fkQ4lWqx/R2uct/gKJFIoVIHQCSi08KJZyptmNiDy/IZ3MpTKymd0VAATXYG32WUzcUjEJzYgbzsDCD1ClQiBrnCB0KZFEzmDUQ7aG7yu+dRAgCSz4I17+qUAVIkEkGlCoBE6lrf33S/LoLxxA4I1cEQ+oWAs3DghEKIfNW2ZWR3G+TQD0T8K/c3I1+lEidOHEfLlq1s1506dRK+vr4PuRdxFXVDZE4XGivbxKeqmwAVt/48fW6VDFUtbUhquEpYroBX2Bhn3xUzrqTmQJfPQST1htxLAplEgLZR9h8RYUrrr5hxEe7bNEcuV9IUHMTjlXWYWFmCW1WHSxrS5jgGVwmO6SlA0hkIzh2ELjMNBWmp8JaI4SP3hlAkAhMIHBvwsm4Xvexk4dFVO4Hrfl2Egp1rAY4Dl3kbAoUf4B8Ki9kAgaAuhAqVXZMcwr9yh8dXXx2A+fPmomnTZggKCkJ6ejpOn07A2LHjq6A84ukq28SnqpsAFbf+cIWxyoJjaUNSowKE6NFIUqZzHguPNmryGW7dyUJubi7kqkAoFBKovQV4JlaCjnXERR4bAOIc/uz4JxQKoVIFQOrijQUIcQRRUESZ2t9b0m+A02nB7s619mBwq46jgmWtlTzcveDo5M1x0lMgiP8FyLiB3Ds3cScjE0q5D+RyLwh8lAAA1rYnENPacY/pHwokn7W/7CwEAih91fB2wU7g5ltXYTy+A+A4gLMAAgGYIQ9QB0EklUHoFwxZ6+4UGp1MucNj+/YdUKtWLeyPj0dWVhbq14/BwEFvIDy8RlXURzxcZZv4OKIJ0MOOXD64/is3tfANM5Rr/WVV1iGpUQGlN8pJyuSw5aQR1zI55OTqYTIJEBakQGSoAo3CROgaYx9M3f18R6nMCyqVP4RC12wsQIij3T+PWmlHDC03E61/ZKcBzbvY31YNRwXLU6u7cPTRXIMhH5rsTDh9cASAa2eAm5eRm54GS0EeZGp/BNVvDBZcC0wAoEY9xwZHAAiKtB7JdLJzHl29E7gl/QaE6mBw2WmAEAAEEAVFgvNRQijzouDopCp0Qk94eA280PdFR9dCSBGVbeJT2fuXduTy/vWbTEb4STnc/QR0uPIOSS1JUiaHnRdNyMkzQqPRwWKxwE+lQLsYOZ6PkzpkOKwrUdydu5GGqRJir6zDxEThdcDl50LoXfT0leo6KuhJQ9ocfTTXGhxdYKgqAKSngLuagILbqTAajVD4+EDVIA7o0q/qA11QpNOERgDw9lHA11ft0vsuUVAEJDVjAACcJg3SFl0ha/0kcpMuQBbVwGPe066m3OHRbDZj06YN2Ld3D3JycrDqp7U4cfw4bt+5hR49nqmKGokHq2wTn8rev7Qjl4Xrv5KqgZ+sao/GlWdIakmSMjms2G9AUlo+Uu5oAM6EkOBgxIR5lRgcHfXYzkYkEkOlDoBEQl2FCakoUVAEhAoVhAqV7fL9PPGoYFVz5NFcQ0E+NBrXCY6Gnetw58Qh+IstkKvU8FL5g4tq7FShrqq5Uyfwws8Hcb17nw+McRB4qyBWB/BdHilBucPjqh9/QHJyMoYNH4lP588DANSIiMCPq36g8EiqRGWb+FTm/pH+Uvx7Jhe5BRb4eomKPXIZJjfAN9yIqjrieL+yDEktSVImh5UHDTieXABdbi5EEi80ifRDl4byMjXAqcxjOxsvLx/4Kv1crrEAIc6mLOHQk44KVgdHHc11meB4d4oM7ZGd0J/eDxHj4CNXQqQOBAur7biOqi7AHTuB0+eD6yn3q2///v34YsFXUCgUEAqth8qDg4ORke4CH0CEVFBJZ4HodDnQ67TVWktFFDa8SbiWBX2BFBKpDEpfBWoFie2m4nB3AoEAvko/eHvL+S6FELdBX/6qlyOO5hYU5EPrIsFRcGIH0pKugks8AaVEDLlSDZGXHFxEDNDuGY856kidwImzKHd4ZIyDTGZ/FCc/Px/e3tShkLiflCwj1HIR1HKR7XLhUUydTgu9LofP8sosVWPG4UNHcCM5G6rabaFUKKD0KjoVhztz9cYChBBSqDKBvaAgD1pNpoMrqiJZt3Hp0mVcP3kEDUOUUPirIRRLwVRBHhMchUIRVCp/6gROnEa5vzk2adIUa9euxoABA23X/fLzFjRrFufAsghxDiU13NHlaqHXO3dwLJzD8fJtI/47fAq307PRunEE/Gr4ws/HGhwLp+Jwdz4+CihcvLEAIYRUlssEx/QU4OppJPy7DbrrVxAeHooaNYIBVSAY4Ng5HJ0YdQInzqjc3xwHDhqMuXNmYdDA15CXl4ehQwYhICAQ738wtSrqI4RXxTXccfbgWBga910x43KaGboCDpx3PYTFiNC8qaLINBzuTCAQQqXyh8zLm+9SCCGEVwX5edBqXSA4Hv4TOLANBZm3UddiBosIhKJmNFjDdoAq0KmmyqhKCl815PKiHYwJ4Vu5w6NKpcKcufNw5cplpKelISAwEHXr1qPGE8Rt3d9wR5ergV6fy3NFJYtPNGP7OROy9AzXMszIN3KAUAihxBsmgQDafOYxwVEikUGl9nerxgKEEFIRTh8c01Os8zfeSQYuHAKn08JLwCCQiiAQC6zzN6oCHT9/oxOiTuDE2ZX7W+TSJYsBAHXr1kP7Dh1Rv34MhEIhli1b4vDiCHEmuU4eHJMyOWw/Z8INDYfkDAPyCgwQgEEgEEAA63BNlbdnDNuUK5Tw8w+i4EgI8Xj5+XqnD46C+F8gPLId7Mw+GHM0YIxBKCzcc93lH8pXhdXGy8sH/gEhFByJUyt3eNy7d3ex1/+3b29layHEaeXmapDn5MFx50UT8owMaZoC5Or0EAoF8PGSQCISQC4FGoQI0TXGvZvFCIUi+PkHQ6FQ0fmNhLgo862rMCTshfnWVb5LcXn5+XrkaLP4LqNk6SnA6X1A+g2YcjKRp9NBLAREEikgFAEyH7CgCLC2Pd16qKpAIIBS5Q+VOoBG8hGnV+af5Y8cPgQA4DgOR44cBti9yQtu37kNHzm1vifuKTdHg7w85w6O648ZkZbL4XyKFjnabPgqFGhQ0xuRfiKovAUIkAvKNJejK5PJvKFU+VFjAUJcmPnWVRj2b7X+ffm4dQJxmgakQlwhOArifwFyMmBIu4F8XS6EYjGESn9AHQwuuCYQUss6j6MbB0fqBE5cTZnD43ffLQcAmEwmfLdime16gUAItVqFwYOHOr46QniWm5ONvDwd32UUKymTw00th/O3LbiaYcaNG6nIzbMgyN8XbRsEQeUtQI9GntAcRwBfXxV8qLEAIS7Pkn6jyGUKj+WXn6dDTk4232UULz0FyLoNpF6GIO067qSlIT9LA2+FAkG160OgDgbr2NutA2MhV+0Ebr51tVLzjBLXVubw+O1ia2D89NN5mDz57SoriBBnkZOTjXwnDo5/njMBAC6kGnA16RYK9DmIiIxAt6b+aBgqQrhK6PbBkRoLEOJeREER1iOO910m5ePswVFwYgcAgEtNxO0bKUjL1iIkJBhB7Z8EIuqDeUA3VVfuBE6jA0i5u0kMGjQYubm58PW99yu/TqeD0WiAv3+AQ4sjhC/OHBwB4KaWAwBotVqcPnIQJrEacU3qIyJY7TFTcXh5y6FUqiEQuP9zJcRTiMNqAx160VGNCsrL0yHXWYMjYD3iCMBgMODg6YuQ5msRVb8hgqPrgDXt7PahEQAkUhlUqgCIRK55igWNDiDlDo/z583BiBGj7cJjenoali5ZjDlz5zm0OEKqG2MMubkapw6OSZnWoarHrmTjwvH/IBOaMPnFNggKCvSIo40CgQBKpT+8vH34LoUQUgXEYbXpy2gFOHVwTE8Bks4At5NRcPMa9p46jzu6ArTtOxCh9et5xNFGwNoJXC5Xutww1fvR6ABS7vCYmpqKqOhou+uioqJx48aNEu5BiGtgjCE3Jxv5+Xq+SylRfKIZW04akXwnB3du34EiKAqTnquHJ5qo+C6tWoglUqhUARCLaQoOQggplJeXi9wcDd9lFO9uYxzBzSswa7OQnqVBoFSApkPeREjn//FdXbUQCkVQqQMglcr4LqXSaHQAKfc3MLlcAY1GA7VabbtOo9HAy8vLkXURUq1cITgWzuN46UY2tHozfH190TwmBHK56++MysJH7ktTcBDCA3dojuEOz6Ekefpc5OZq+C6jeIVTceRkQJ+jRb5WC4lEgiZNm0Mc4v7zNgKFncD93WoKDhod4NnK/Upu2aoVFi78CpmZ1glnMzMz8e2336B16zYOL46Q6sAYs57j6MTBEQCuZxpx/vRxZGdmQC73hn9QMJRe1sY47kwgEEDtFwRfF+xIR4irK2yOYb583Pp/F5x70R2eQ0l0iaeRe/Qfa0h7UHoKcPFI8bdVh7vNcQTaTGSnXEN6WjqkMilCatSAxFcN+Lt/ePRVqqH2C3Sr4EhIuY88vvrqa/j6qy8xfNhgSCQSmM1mtG7dBq8NGFAV9RFSpazBMQsXbuhwU8s57TmDZ5K1+OzbjbhToECrVi0hUyvQKEzk9s1xBAIh/AOCaf4rQnjiDs0xuPRUu8uu+ByKk5t4Gvnxv0AAAMlnwZp3vXfe4H1dTYvcVh3uHnHkdFrsP3kWt6/dQL0GDRDZriOg9AeLcu+5GwEB/PyD3GKYKiEPKnd49Pb2wdvvvAeNRoOM9HQEBQVBdd8QVkJcBWMMOdosXEjV2aa9OJVqcaq5EZMyOexMSMdPvx+GQS9Ep8c6oVPjYMRFiJ2mxqoilythMhldtiMdIe7AHZpjCINqAFdO2C674nN4kF6fg7wbl2E3FiPr9r1AdrerabG3VbW7wdWkzULqiQNIu5GFpm3bo16/EW4eGK28vHzAcRwkEvrRk7inCnedUKvVduc9EuJKCoNjQUGebdqLQje1nFMEs6RMDit3p+LfA+cgUUSiS4d2qBGkRLCvcx4ddRSRSGRtiiORQKPJ5LscQjyaOzTHEIfVhsDFn8P99Poc6HK11mGfyWfv3XD/MNCH3VbVsm5Dp9Phrx17ITbmo3W3pxDR4yW3D46FncBlXl607yJurUzh8YP338Ws2XMBAJMnjQdQ/HlHn372haPqIqTK3B8cASBcJcSpVIvtdr7PIYxPNOPiHQsuX76CnXv2wz+0FmrUqQ3B3V8x+a6vKsm8vKFUWhsLMMaVfgdCSJVzh+YY7vAcAECvy4FOp7VeCIq0DkfNum0Nh/eHs4fdVlUuHgFSLyM1pwB7t26DSCTC4917QP14H7cPjpK7ncBFYjHtu4jbK1N4fOqpHra/n+nZq8qKIaSqMcag1WbCUJBvuy4qQIgejSROcc5jfKIZqw4bkJ6egTsZZoTVjUO3Do2hNwoREyJ04+GqAvgq1fDxUfBdCCGEOCW74FgoKLLkYPaw2yorPcU+mB7+E8KDv0FXYIQ29RZ0EgX6DRsJZVR9tw+O1AmceJoyhcdHOj9q+7tLl8errBhCqlJxwbFQVEDFQ2NSJuew4Hn+lhGpqTeRlZWN4OAgPBIbihY1JbyH2qokFkugUgdQUxxCCCmBTqeFXpfDdxlWDzbjqdUYgoTdyNdmo0Cvg7+vHIOe6Qlx66781lnFhEIhlKoAyGQ0VR3xLGUKj7t37SzTyh6jYEmcFGMMWk0mDIaiwbEykjI5hzXbyc3NxR8/b4RG2R6hNSIR5K9C22gJOtSu8KnJTs/bWw5fpR/9YksIISVwquAIFGnGw1Iu4mJqGgLzdPDx8UaAXwBYrYY8FVc9pFIZlKoAauhGPFKZvpVu27bV7vL169chk3nBz88P2dnZMBoNiIysSeGROKWqCo4AHNZsZ9vRO/hi7X4UsDA0i1JCIFOhWwMxOtZxz+AoEAigVPnDy8uH71IIIcRp6XK10OurMTg+OBy1OPc14zHnZGH7/uMwZtxC66YNEaiWgzV9DIhpXX01VzOFQgUfuS/96Ek8Vpm+mX762QLb32vW/IQWLVuhX7+XIRKJYDabsXHD+qqqj5BKqcrgCDim2c7anVexeLcGQv/GCPIPQYC/FOFqIUKU7jlMVSKRQqUOgEjknsGYEEIcgY/gWKa5Ie8248k9tR9H9vyHrKwcPNm5PUJadHTr+RsLO4FLaO5G4uHK/e30n7//sgVHABCLxej7Yj/8/fd2hxdHSGUwxqDRZFRZcATuNdtpVkNkG7KalMlh/1UzkjJL77j2zz878MWPf0IqkSCyRihEIhGy9AyAe3ZVlcuV8PMPpuBICCEPkZurqd7gCBQ/N+SD0lOAi0dwNfEqZi5cjjRdAXr+7xmERtcFlIFuGxxlXt7wDwil4EgIKjDPo1QqxdWriahXr77tuqRr12gyVOJUCoOj0VBQ5Y91f7OdspwDmZTJ4USKCQcPHcavKz5Hm8eegXftaAiEQohEDC1qitA1puLnTjojoVAIlSoAUmosQAghD5Wbq0GePrf6H7i0uSHTUyCI/wUZN5Jx+tgp+Pr64tlnu8HHx6f45d2BQABfX+oETsj9yh0en+39PD6aMR2dH30UQUFBSE9Px769e/DSy/2roj5Cyo0xBk12BozGqg+ODyrtHMikTA7rjhbgvzN3oM0NQLN+czDj9Ua4qQUu3rEgJkTkduc5SmVeUKn8IRRSYwFC3I351lVY0m9AFBThFvMo8i03R4O8PB6CI/DwuSHTU4CDvyP37GHos7LQJlyNHj36QxrTEqw655KsRtQJnJDilftb6tNPP4PIyEjs27sHZ86cgb+fHya/9Q6aNm1WFfURUi58Bkeg9HMgDyfmYdeJVBTAB0r/YEiU3jiVyqF3M6nbhUYAUNz9xZYaCxDifsy3rsKw39pQz3z5ONChFwXISsjNyUZeno7fIoqbGzI9BTj+L24ej0eAKQfBSh/4yBXgjHlVO5ekI5WlEdB9qBM4ISWr0LfVJk2aokmTpo6uhZBKYYy7GxwNvNVQeA7k/fM+Fs4DmZ2djeWbj8DgWx8ypRISmQhSNz0YJxKJoVIHQCKR8l0KIaSKWNJvFLlM4bFinCI4lsB4+zr27dgFn5w0BIar4S2XA96+gK8f36WVTVkbAQEQCIRQqvyoEzghD1Gh8Lhzx7/Yt28vNFoNvvjiK5w9ewYajQYdO3Yq8zr++ON37N61A8nJyWjeoiXefff9EpfNy8vDkiXf4tjRI5BKpejR4xn0fbFfRUonbsoZgmOh4s6BzMrMwt/xpwGTEc2bKXE7XwSFTIB6wSLERbjXEUcvLx/4Kv0gFLrPOZuEkKJEQRHWI473XSbll5OTjXwnDY5ZF09h01efI8SUg8ZtW8OL6cEUamtznKhYvssrm+IaARUTHqkTOCFlU+53yKZNGxAf/x+eebonVq78AQDg5+eHlT98X67w6O/vjz4vvIiEhFPIzMx86LIrli+FLjcXS5Yuh1arxYzpHyIoKIjmlSQAKhccC48KFh4ldLSbWg4pKTewc+cuKANqIK7TY4gO8UZYPkNMiBBBCqHtPElXb5AjEAjgq/SDt7ec71IIIdVAHFYb6NCLznmsIMYYcnM1zhcc7w7xTE29if9WL4WvxYJHH+0MdWwrcMpA6zKudI5jaY2AYO0ELlcoaZgqIWVQ7vC4499/MHPmHAQEBuLHH38AAISGhuHOnWJaOj9Eu3btAVg7tT4sPBoMBvz33z7Mmj0XcrkCcrkCPZ5+Bjt2/FtieDSZTDCZTLbLFov1HDTGODBW+vQJxJ51uzGn3HaM45CtyYTZZCz3fcvSGbWikjI5nLxhxp5DpxF//BIiwsLQpcsjaFHLCwLBvXMhq+rxq1vhMFWxWOyw14kzv+6cHW27yqns9hMIKvY+dsV9lyg0CqLQKABwuhqd+X3AGMP/27vv8Eaqe//j75lRtaole73r3WVrWMqGtin0BEiBhJDQQwikUNN7vbkkJCQh7ZdCCgkJpAKBBe7lJiGFToBQlr67mG3e5l33Jlt15vz+GEvutiRLlsv39Tw8WNJoZnYs6+ijc8739PZ0kUj0V/pUBrXuhhcfQdvdQEvSYsuGp4h6PRxz8tvw+32oYA2seW1h+5sJxXQmKASkaTqhUASX2w0olFJTPtxMft3NBnL9ileKa5dP+1VweEwkkoSrqwcOYH9DY5pm2apR7d27l0wmw4oVg99orli+kjvvWD/uc+68Yz233XZr7rbL5eJTn/o43V1dGI45OsmsnBRkMmm6uzphJn0ppyCdThX9RzJZZdRi/b97Ezy0NUNfPEGyN8CS1Yfz3lNWcdQBw8PhY9szZTn+dNN1A8NwEIt1l3bHM/V1NxvItZuaKV6/6uraog4rbVeJlfjvQDXvgo59EFmEVnfAlM/LsszJty21sQJd627Y8RLaCw+ite4hk0pCX5K66iAHrj0Mh39gNEkhS3EUMM9wWoxR2EfTdZwOJ/3xGP3xEvb+yvvv1Mj1K14Jrl0+7VfB4fHAAw/kH3+/h7e9/fTcfffddy8HHXRQobvKSyIRx+PxYBiDDafP5yMeH3/h97POPod3nPHO3G3TNNm86TlC4TAOhxTwKJRSFl1dHYTC1UV/o15qlmXR1dnOjrZM0cNOJ6uMWozfPJrkbxszWEqhNDeuUB1Ov4Gua6POrxzHn14awVAEj6c8azfOxNfdbCHXbmoqdf2k7SqtUv4eM/u2k3zpYftG0xbcwXcUNUxXKUVPT1fFguOoQAf2fbs2ozVtQykLh1LUVjlZEAqhDlyHCtUU3nuY5zzDSvH5Q1RV+coyTFXef6dGrl/xpuvaFRweP/DBS7j6a1fxwAP3k0gk+PKXPk9XVzdf/drXy3F+eDxekskkpmnmAmRffx9er3fc5zidTpzOwZ5QM2P38GiaLi/EImmaNmOun2VZdHe1s7U5MaVhn2NVRs3HWPMkG9st7nw2xT9fTmIpDYWGpoHCGPfLn2KPPxM4nW5C4UjZCwvMpNfdbCPXbmoqcf2k7Sq9Uv0erbamYUHDamtCq19d0D6ycxyTif7KDOkcK9B1t8GuzdCyB2VZaCjQNHQ0lNcPoQKHqmblMc9wWg1cb6NmMaEDjyx7JXB5/50auX7Fm45rV/Anv3C4mh/9+Kc8s+FpWlpaqKmpYd1rXlu23ofFixdjGAaNjTtYtcp+o27csYMDli0ry/FE+W1tTrK7I8XSiIvVde6Cnmv3OLaSTqdKMux0aGXUfIw1TxLgugcTvNSUIZVWoOvYnzE0HDoTVlQt9Pgzgc8fxOeTwgJCiPljqpVllVL0dHfYcxwrNaRzZKADtJf/A807MVMpDE2hNM3+wtPhBG+w+NA3wTzDaTdwvR0OJ67WnWiRWpDiTkIUraDwaJomH3j/Rfzhj7dwzLHHTenApmna/1kmyrJIpVJomjbsW1cAt9vNcccdzy233MynPvUZuru7+dvf/soFF7xnSscXlbG1OcldG7oAeHpHP2euC+cdIIcGR6jMsM9sYO2JK2JJxX0NaTRge3OcZNJC1w3QDDQ0PE44ZY2Ds45wTSkglrsibL503SAUjuJyFRb4p/JlgRBCzARTqSw7LDhC5YZ01i5FLTsU9m4BfzVs2UCmeQ+JeAId8Lqc6IYOTg+qeiHq6NOndl5jzDOsBK2zGZfbg3OgNoesRyrE1BQUHg3DYOHCRcRiMcLh8JQOvP7224YVBrjg3edy6KFr+fo3vsk137iagw8+hLPPOReASy+7guuv/zmXX/ZBXC43p73tbbJMxyy1uyM16nY+gcKyLDo7W4dVVa3EsM/6kM4jWzM0NJv0pRQ72i20eCsd3TqG24/b5ULXYe0inTMOc3HcqqkN6yxnRdhCuN1egqFqOxwXYCpfFgghxEziWLSy4NAxKjhC5YZ0tu5G2/wf6G5D624lnkiQ7m7Hbeg4q3zo3iDWysOgbhmsWDsjgt9UORxO/CsOJdO6M3efrEcqxNQU/Mn2LW99K9/77rW868yzqKmpGTamdvny5Xnv5/x3X8D5775gzMe+8t9fHXa7qqqKT3/6s4WeqpiBlkZcPL2jf9jtyViWRWdHC5lMetRj0z3sc3lUx6lDZ7/CtKAzmSDemybodRAMuagNaJx0oJMzjyjNfIpyVYQtRCAQpsoXKOq5xX5ZIIQQs92YwREqN6Sz8SW0ll2Q6CPd3U5nbz+Ww82iaC1EF2G99rTi5jfOUN4qP4FAGE1biOFwyHqkQpRIweHxN7++AYDvXPutYfdrmsbt6+8qzVmJOWt1nZsz14XzHsZoWSadHa1jBsdKyK7fGEuCaaaxLI1AMMTSBT5WRA1OWO3g2JWlKyJTyYqs2bUbp1JYoJgvC4QQYrZTStHd3U4yMU5l+EoM6dy/E7pa6Y/HySTjeL1eQvVL0eqWo45715zoaYTBtRvdnsHCisX0Ggshxlbwp9z1d/xPGU5DzCer69x5DlWdWcER4L6GNG19CtM0UUrD7VDUR+3gGPRq1If0ks5RrFRFVo/XRzAQRtOndrxCvywQQojZTilFd1c7yeT4S4pNu4anYPvzpPt7cJgmyu3Ff9QboW45ao4MUQVwutyEQuWvBC7EfFb0X1dHRzsd7R1Eo1GqI5FSnpMQMy443vVcihebTHa3xon3JcBw43A6WRBy8IFj3GjaYK9gqecoTufQXE3TCASr8Xp9Jdtnvl8WCCHEbDfjgmPDU7BlA2bTNrpbmzHMDP6qKqpqF6EOfM2cGqYqlcCFmB4Fh8fOjg5+/OMf8tJLL+J0OslkMhx8yCF88pOfJhKJluMcxTwz04Lj/7s3wb8aMqAyxPuT6FaGSNiHw9A56/DhRXEe254Z9txKzFEslsPpIhSK4nDIN7ZCCFGoGRccn7wH/dG7MBP9JPv68CgLTyCAw+VChesqv/ZiiRRbCVwIUZyCPyX+7GfXEa2p4Te/+S2hcJjuri7+8Iff8bOfXsd/X/W1MpyimE8s06SjsyW3OHalPbotw70NGZIZhbJA13UOWhLmNcvdrKkzRlVTreQcxamoqgrgD4TkG1shhCiCUoqurjZSyUSlT8XW8BTaY3dh9XZgZUxA4YrWodcuwVq8Gl59wpwYqlpsJXAhRPEKDo8NDS/zmxt/h8tlF74IhcNcdvmVXHrJB0p+cmJ+MU2TzhkUHAFufTpJImOilI6mGTjcPt58iGvcaqqVmqNYLF3XCYYiuN3eyTcWQggxyowKjq277aqqzz2I2dOFYZk4dA2nw0BFF6FO/cCcCI2gEQiEiq4ELoQoXsHhceHCRbS1tVFfX5+7r729jYUL58bwB1EZpmnS2dGCac6M4NjYbnHb03E27k2gNAf2ijQayyJMugzHdC8fUiyXy00wFMUw5BtbIYQohlKKrs42UqmZERy1Z++DfTtINu3AYaZA19E0wHCglqyZE8GxFJXAhRDFKzg8Hn30MVzzjas59dTTqKmtpbWlhX/+6x+ccvKbeOrJJ3LbvfZ1ry/piYq5ayYGxz88HuPBzT1Yuhfd0NDQqHLB+4/2VPr0SsLvt7+xlWGqQghRnBkVHAE69mPFumnb3oAvk8TlMtAADAdUBSFYXekznDKP10cwGB62xrgQYnoVHB7vvfefANxzz1+H3f+vf/1j8IamSXgUo2xtTo5asmEmBse7nmzjgf9sxXRH8IQDGLqOQ4d3HuYcNcdxttENg1BICgsIIUS+Mvu2j1pgXimLrs72mREcW3fDjpdIdexn2xP/xor3sWJhLboDsCxwe1GLVsLytZU+06JpmkYwGMHjrar0qQgx7xX8SfgX199QjvMQc9zW5iR3begC4Okd/Zy5LsyKGoPOjtYZExyv/kucJ3cm6WlrxekOsXTxQtAdeBzw9rXOSYerznRuj5dgMII+xbUbhRBivsjs207ysbvtn7c8A8eegbFw+UCPY7LCZ4ddGOefv0V1txGPJzHjKQ541Rp8S1eiABWI2j2Oy2fvWo5SCVyImUX+EsW02N2RGnZ7Z1ucsB6fMcHx83f28/RuE2XpOENLWRRUHLXMzcoanSOWOGbFHMbxaQSCYaqq/JU+ESGEmFXM1j3Db7fspsftJz0TgmPrbrR7fo3W3YqmFGGnhn9RBOOQ16KWvMpeimOWBsasKl8Av18qgQsxk0h4FNNiacTF0zv6AbAsi5DRi2lO8qRp0NhucedzSZ7enR6oqKqBptGdgLOOcOUdGhvbrRlZYdXhcNrf2DqdU97XWMOOhRBz11jDNecbo3aJ3eOIPccx5vaRmSHBkbt/Ad2toBSgoWngNNNYwWpY89pKn+GUSCVwIWYuCY9iWqyuc3PmujA72+KEjF6Whit7PnZoTPFUY4aW7uSwiqoAi0JaQcHxnk1pAJ7fa3LaIc4ZESC9Xh+BEhUWGGvYsQRIIeausYZrThQg52rQdCxaCceeQaZlN30uH5lwXaVPCZ68B+2Jv2B27MOwFGhatukCpwuCNRU9vamSSuBCzGwSHsW0WRE1CGn9bG+1eGz79PbSDe0ZBLjuwQQb95mkMtZAcNTJtr4uA1bXGjS2W3mdX1O3Nep2JcOjpmkEQxE8ntIVFhg57Hh3R0rCoxBz2Kjhmq17xg2FhQbN2UavW06/y0cmnZp843J78h60h28nE+tGVwqla7nciMuLWnZoJc9uyqQSuBAzn4RHMS0ymQydHS1sb00X1Us3lWGhI3sG4ynFxn1pUmlQmp7rcXTo4HPB2nqDoFfLOwTWh3Se32sOu10pTqeLUDiKYZT2T3vosOPsbSHE3DV0uGb29niyQdOKdWPFe9EbNsyZ8GhZFl2draQrFRxbd0PHfnv+Ysd+ePh2Mr1daMpCN3Q0wwG6AxWIwNI1UBWwt51lpBK4ELOHhEdRdtngaFlmUb10Ux0WOvSYPXHF41vjJNM6aPrAaB97DceDF+pEfTpBr/2NZzYEThZcl0d1TjvEWfE5jz5fAF+ZCgtkhx3LnEch5ofscM18hqIatUtIPfsAZtM2ADI7N5HZt33WB8iZEBy1Z++zf978HzK7G6CnE0MHTdfRnG6sAw6G1542GC5nYZEcqQQuxOwi4VGUVSaTprOjFcuye+aK6aWb6rBQpeClvSbJjKKnt5fOXgvD5RsYpaoR9MLRyx0cv8pBa8w+VrbCar7BdXm0cqFR13X7G1u3p6zHWV3nltAoxDziWLQyrwDoWLQSY9nBqHgvmjeA7g9NOMx1Nqh4cAQ7DPb3QqKPvo42evbsIuIycGgGmgbWwhXw3qsGt59loVEqgQsxO0l4FGUzMjhCcb10SkFTl4XfrRH0agUNC21st7jp8SQ7OxSmypBJargNHd1p2GsnO+CEVQ5ev9zBC02D53nEwP9n2nzGkVwuD6FwBF2XwgJCiMpxrXkNqr0pd3uiYa4znWVZdHa2Vn6OY3cb2qbHsdIpzHiCHsvFQq8XzUqDw4VWFUS17p6FoXGgEng4isMx9UrgQojpJeFRlIUdHFuwLGvUY4X00jW2W7zQZOJ3a8SSiuNXFbbm4u/+k2R7uwKlUBg4XB7cLoNIFfQmYWWNjtel5Xocs7IhcTrnMxY6r9MfCFFVJYUFhBCVV8gw11IoV3VXy7Lo7Gghk0mXbJ9FaXgK7b4/QjqBrhQhj4uq2gNgwRJU216orhscqjrLwqNdCbxa2i4hZikJj/NcOdbumyg4Firb8xf02r2OhbQ1je0WT+00B4IjA881cBqwKKRz6JD5jSNlQ+J0zWcsZF6nYTgIhaM4nVK0Rggxc+Q7zHWqylXd1bJMOjtaKx8cW3fDw+shFbeH3gw0fI6eZtSbLoSdGwe3nUXFcTRNJxiqLmklcCHE9JPwOI+VY+2+TDpNZ2dpgiNMrZLpxsZ2+vstlKNqoO21G+BwlcaBdQapwd1yxBIHR8CYIXE65jPmOzzW46kiEKyWwgJCiIqq5LqOhSwjkq8ZExyB5L6dtO3cQb1uYRhD3uu9AQDUkafMuuI45aoELoSYfvJXPI9NZe2+sXosM+kUnZ2tJQuOUFjP39Bhn1u3buXbP7sLY+VpOKpXoAaCY10ADqozOGWNPc9i5H4rNZ9xspCsaRqBYDVer2+6T00IIYap9LqOhSwjko+ZFBw7Gp7n9p/8kCNd/SyqD4GZBhQ4vbD81YOBcZaERgCfL4jPH5RhqkLMERIe57Fi1+4bq8dyWUSjs6MVpUoXHLPy6fnLDvvsiSsadrXz8satBGpXcsxRS4mbBp39ioPqDI5e6ZgRYXGkiUKyFBYQQswk5ej5K0Qp51fOpODY/H+/peP+OzjEZbHmtcei19RixWPg9UPdMlixtrDQOHSNyAqEzemqBC6EmF4SHuexYtfuG9lj2djST5D+sgTHfDV1WzR1WTy7o5fePhNf/VpOPDRCJODk8MUGx66c+S/1sUKyt8pPIBCWb2yFEDNGqXv+ilGK+ZUzJji27mb3X2/Gtfnf1PscHLwoiOZ1og5cB2teW/Q+c2tE7txoD3WdxgDpcnsIhaQSuBBz0cz/RC3Kqpi1+4b2WFqmSdDoRanKhZvGdotNTRme2BojrZw4vC7cLgcdcZ1IoLwVUsvFLiwQwePxVvpUhBBimOmurFoOpmnS2dmCmclU7iQanoJXNtCx8Wncnc0E3U68Pj+arqNinVMrhtOxf/TtaQqP/oC9dqN86SnE3CThURQs22PZ2NJP0OhlWaRyDcSj2zL88YkEW5p6yOhedMNAQ8dpwOKwPmHV0pnK6XQTCkeksIAQYsaarsqq5WCaJp0dLZhmZYOjdu8fSLTtI2ymsbwuXLpmV1Z1eVGHvXFqYS+ycNqrskolcCHmB/l0KoqyLKINDFWtbI/jbx/vY1tzBkv3ohsOdE1D1yDghrOOcM264OjzB/H5pLCAEEKUw4wIjoC5+QnizU24rTSG08BhGOBwYQVr4KR3Fz9cNat26bRWZZVK4ELMHxIeRcHS6eRAcRxV0fN4atNutmztRvMvwuFwYqHh0CHkhTNnWXDUdYNQOIrLVZq1NoUQQgw3U4JjW3s7D/3zQd4USuHwuNAYqBdQFSxNcMyahqqsUglciPlHwqMoSDqVpLOzcsExuxzHvq3P8e1vXkvk+I/iqVpOytRx6VDj1zjzcCdnHjF7hs243V6CoWopLCCEEGVimhk6O1orHhy3bdvOJZdewWsCJu9YuQpDpUHTUf4Q6sDXli44TgOpBC7E/CThUeQtlUrR3dVW0eB4z6Y0z2/ewXMb93PgUSdw1ZWv4fYXDXZ3WCwK6dSHdeqCs6fHMRAIU+ULTLjNWGtqCiGEyI9pmnR1tlUuOLbuhhcfob3hJR54+AneUOflc+eeijsdg5ZdqAUH2ENLX31CZc6vCEMrgWf2bZ/VxZOEEIWR8CjyoiyLrs7Wih3/0W0Z7n4hxYs7OulLBQgtW0ek/o280uHiTWt0Xmgyc9vOhuqq+RYWGLmm5rrlVWgaEiSFECIPSik6O1qwrAotJdW6G+2eX2M2biaQTnHB6hDeJSsxDAWBhSiPDxWM2sGxAmsxFkrTdEKhCO6BSuCZfdtJPna3/fOWZ+DYM3IBUkKlEHOThEcxqVQqRTqdmnzDMmhst/jd40kebzRJpU2UHsDwamTQ2NkBj2zNsCCgU+PXiPo0jljimLFzHbNDblfU+Th8RQ1aHoUFhq6p2dVncteGLg6I2kulnLkuLAFSCCHGYZom6VSyPDtv3T1xMZrs4/++C2vPy2BaOA0dj9uDUiYk+qAqYP83S4Kj0+UmFIpiGINTLMzWPcO2MVv34Fi0csJQKYSY3SQ8zmGlGO6YSiUq1uPY2G5x3YMJnttjohQoTccuQqqhaWApiKcVO9otYkmNlKlzREXOdHKN7RZ/35TG5fKwtUvh96fz+p0MXVOzN2ES9Aw22rs7UhIehRAVVY7epUL3Odb2ZiZDR3tLSc5nlNbdaM/eZ/+8c6Nd1XRo+Bt4XG18DL27DYVCMwa+LFRAuM5+DkxLJdRS8PZ14m7uQiWXwJDfiVG7xA6HQ27D2KEy+3/piRRidpPwOEeNHO5YTC+VHRzbynB2E8v20G3eb7KlOY2lNLKBEewlLFwGBDzgdWr0pcDvtu9v6rZmZM/j/piGx+vLlTHPN/hl19Tc3ZFi3fIqNjT25x5bGpk9RYGyZP6mEHNH8oWHSTx2N7o3gO4PFdy7NFboG9pjlXr2AYxlB+Na85px9ztWDxe1B9DZ0YJSZRqq2rF/9O1sAByY32hufwm9uw1QuaWXlOFEvfpEOPrtpQ2Mk/WCToGm6QSSvVjP3U8GyGwd3ovoWLQSjj1j1O9xZKgEpCdSiDlCwuMcNXS4Y/Z2IR/WU8kEnV1tMM3FcR7dluHO51LE04rWrjg9vSl0lw/0gQAJLA3DCa9yclCdQWvMoqHZIui1G+eh8x2zIbQ+pFc0UOq6wSHLwrzS3pu7r5Dgt7rOnfvdHRB1zdrwVYovNIQQM0M2tKnOFszOFqhflRuyWMjzYXiYyPZQWbFuzKZtqHgvqr1p3LAxsocrvX8nMcODZZmjti2ZyELYudEObLFOCNfZ9zc8hfbsffT392Pu2UqVDrox2Paow0+Cd1xZ2nOZrBd0CnRdJxJdQGbjNobG8JG/Z8eilaN+NyND5XjDW4UQs4+Exzlq6HDH7O18JZPZHsfpDY6N7RZ3PpfilRaLeMoknczg0DX8Xp1kxg6HK2s0Xr3YwSlrnLlAOFZIzFZmBXh+r8lphzinPUDquk4gWE083sfqsIcz1xlTDn5Dg+RsM9UvNIQQM4fZugfNG0C17EZlUtDZnBuyONR4Q1DHCxPZHisrbn/ZpnkDwx4faWgPl2VZ9Du9o4Njw1OwdwssflVplsKoXYoK16FvfMw+x6f+jgVoLzxIpq2JjvYuUkpjRcgDlgkolL/a7nHMx3g9iWPdP1EvaNE0/P4gqXQSXTfGHZo6mZGhsph9CCFmHgmPc9TQ4Y6FhJVKBUeA5/ZkaOq26EuaKEthON0sr3FyaL39Mk2b5HoYhw5PXR4d3bPY1G2Nuj1ym3L2TLpcboKhKLquEY/3AbM7+JXCVL7QEELMLMM+/CvQ3FWjtsns2078n3/AiveiewN433LRuMMas/vL9ljpDRvI7NxkD4cdebwhstun9++0g2PN4uEbNDyF/tBtkE7BS//G6m6D151W3FDPoc9p3glmGiwLkv3w2P9i9nZgplIs8Dox6leCLwhNW8DlRfOFUI0vTX6s8XoSB3o18figKjB4f7YXNCuyML9/yziylcAdDgepLrvY0HhDUwtRin0IIWYGCY9zWKFhJZmM09XZznQGx8Z2i+f2ZHh+j8nm/SYt3Sm7MI6h43XqhKt0zjrCDhnZnkSYfDmO+pDO83vHX76jsd3izxtSxJIKv1vj/HWukoVLvz9ElS+Apmnlm3MzCxX7hYYQYuZxLFqJsexgVLwXbWDO48jewXTDBsymbQCYnS2kGzZMOlcu+1i2YufQQiu55zG8R5PapcOHqjY8Ba9ssMNbX48dHOM9gN07qEI1aNnAtXMjatmh9s8TBcmRoQ7s4JhOgrJQ3a2Y6QyGw8Dp8mBFFsLyQyE+OF0hr6Z1rJ5EQHv2PrSeduhpt9eFzPYw1i61g2QJ5jx6vD6CwTCapo9qu8YamlqoUuyjUmTZESEGSXgUQDY4Tm9xnMZ2i988luSpnSYpU4FlYSlwGuBx6dQFtFxwbOq2OKzeQNPIK8wtj+qcdohz3PD33J4MDc0mKdMuvvPcngzLo4M9YcUMe9UNg1AoissloWg88733VYi5xLXmNfZ8xAEjewfViLQ08vZkYSL72KiCOEPuSzU8TeLgY7GUZQeo7jb0p/6eC4sqvABS8cGd+qvtIaxZ/b12KBzowRtzzuBAERz6e+2lNQDqlqG2PYuW6MOyLFIZE5fTwHB5wOOHw98AkYWopq32shweH6xYO+6/NWesnsSO/fbze9rt+xJ9w3sYB0JksTRNIxiM4PGO7j0WE69lKcR8JOFRkEzE6eqa/uD4uyeSPNFokrEUDCzFoTt0/B6NlTV2j+PisD6sx7GQuYtjDWfNau9TdMftDzLxgdtD5TPsdSi3x0swGMlVUxVCiLlusqGIrjWvwdy5Odc76VrzmmGPZ/ZtJ9XwNBoazjXrcr2NQ+8bb8kHsOc4JhL9WNtfQOtqtu/ctXkwLFomxGNYiw9E62m1g2NkoT33MRvQssEua+ScwWyPY38vWssuu9evKgC9nRDrRFkWOgqP07DngHr9qNefnptbqY57V2G9guP1JFYF7GMn+kpaFMfhdBEK2cNUxdik2I8Qw8m7xTxXieCYrai6vc0iYymUshfg0DTQ0ThhtYOzjrCHkT62PTPsuaVaiiPq0wh6NdKm3dMZ9WnDHp9s2OsgjUAwTFWVf8rnJIQQs81EvYeORSvxvuWiMcNldj5kdlhrZucmXEeeROrZB0bdN1S2dzPV8DSJRD8q24Bk+auhp9MOjukUmkeBy4064RzoabOHjkYWorI9essOHRzCCqPnDGaHkQ6ENxWMwqtPILX+x7jS6YFC4HYlcOXyoN7y/uFFeYrpFRz5nBIOTR2qyhfA7w/llhIRYyu2YJAQc5WEx3kskYjTXYEexzufS7Gzw6IvkcYyQdcdA42/xlFLdT59iie3ff4hrjBHLHHQ0Gzl5jwesWT4n8Jkw14BDIeDcKgGh9NZknMSQoi5ZrxwabbuQQ2ZD2jFe8k0bhx1H4B7RO9mOp0ifvCx9pDZbNjrHOh5jCzEWnm4XWAm2Qc1Ax/0t2xAy6TtXsNnm+0wlu0dzAbJsYLZ0GGkiT5IJ2n/y+/o29bAAT4HZIOXbqBWHVmaaq5jmeLQ1KF0XScYiuB2e0uyv7lOiv0IMZyEx3kqkeinu6t92o97X0OaLS0WfSkTM6PQNXAYGoYGRy83+OrpwxuzfEJcMZZHdc5f55pwv9lhr43tFo9tzwzbzuv1ERgoLCCEEKIwRu0SexmOzhYAdG8Ax/JDsTpbht2X/bCe/cCe2NVAT+MmVHXdsKA2qmduxVoYMtwUTxVaon9w2OnQ4akTBbNsr98Lj6DvfQWrL4YvlQSXE6r89hBZ3UAtWZP/UhwVlK0EbhhGpU9lVpnNxX6EKDUJj/NQpYLjo9sy3LMxTSxpodDQHQ78Lp1XLdBZELDD3Fgmmrs4Ffnsd2ThnLcd6uSwlbV4PFJYQAghipUd0jpyzqNRu2TUfVmJXQ30Png7oNAaXxo+92+8oZ4vPsKQHdi9h1WBwpe0aHwRK9YFpoXHYeBxV6EtXIEC1NI18OoTStYzWC5DK4ELIUSxJDzOM4l4P93d0xscs8tx/O9zSTp6MyjdMTDSR8PlhNUL7G9ASzWfsZDzmqxHc2jhHEM36FUhCY5CCFECY/XmjNfDk06n6GncBCi76mmiDyZbN7F2qR3qBpbYUAsOQNWvtnsl8wl6rbuh8SXUS4+R3PUKXk2hG3ZboVwu1CFHw/I891VBUglcCFFKEh7nkUoFx3s2pXlmez872yx7DceB4OjQITQ4vbFk8xkLOS+YeCmO7JxLp9OF0+lmWY3MERFCiOmUTqfo7GhFVdehbXrcHoYKsHcravnuSQNkUcVmBqqsZnZsxGrahmGaaM6BoZ66gVp6CLz2tKn9w6bBZJXAZf1CIUShJDzOE/F4Hz3dHdN6zGxxnBd29rJjfwzdHcTtNMhYGm4HHLTQXo4j37UbSynfpThW1jo4/+hq9sc0WdheCCFKKJ/gYgfHFruqau1Su+cwu7zGyLmL4ymm2MwLj5B++Wn6mvdSpSscLhcoCxxOCERg+aGF7a+UWndPHoY1jUBg4krgsn6hEKIYEh7ngUoFxz9vSPHE1l46+sFw+nC4XPg9Gh6nxkkHOjhlzfhrNuYzpHQq8qni6nJ5CIUj1OoGB5f8DIQQYv7KBhcr1o2K9+I+9gzch504bJt0KklnZ6sdHLNWrIXsmo5Q+NzFybTuhhceIbPhX1i9nfgMDafhQPP4wMqgahbbFVxXrC3tcQs4P+3R/8kFaHXcu0YFSIfDSSgcxeGYuBK4rF8ohCiGhMc5rtzBcbyQ9+zuDE+/3ExrdwpPVZBg0Ee4CpZFDE5Z4+S4VeO/9PIdUjoVk1Vx9QdCVFVJYQEhhCgHs3UPVqw7t6Zj4rG7h/VAplJJukYGRyjbmodAbqhq94tPoHd3YOgGbq8XPD6sJQfCskMhVFP64xai8aXBYbs97agR8z7tSuDVebVd82n9QhmeK0TpSHicw+L9ffT0lDc4jhXytrWkuPUfG9jXV4UvUo/X6yXo1ThwgcH561yTBsF8h5RO1VjVVg3DQSgcxekcu/KrEEKIqTNqlwxb01H3BnI9X+MGx6wSrnk4TMd+Nm7czN6NWzhuSYiqKi+aw4latBLeeP7MKIwz8pIM3NY0nWCouqCCbvNl/UIZnitEaUl4nKPi/TF6ejrLtv/Gdov7G9L0xBVBr/0NZ1O3RdTVz5e/9Wu2d1fxmmNOpCrqI+jROHyJwRFLHMPC2ni9lvkMKS0Hj6eKQLB63MICQgghSsOxaCXuY88g8djd6N4Auj9kL9MxWXAsE9M0+dWf78b10n949avXUrV6CcrhREXq4bAZtAzHirWopq2D8z5XrMXpdBEKRzGMwj/SzYf1C2V4rhClJeFxDurvj9Fb5uB4zyY7OO5ot1gR1Ql6NZypdt59wRXs63Xw9g99j/rF9QBjDjudaGjqZENKS03TNALBarxeX1mPI4QQs1mph/65DzsRo3ZJbp9WdPH0BseBwjMJb5hPfPNH3P/Ag/zwSx/ndcceVdmhqROpXWrPcxwYtutbfig+f1CmWExgPg3PFWI6SHicY8odHGH4sFKfCxJpRcBs5pPf+i+M/l5uveF6XNFlo8Lf0J7GyYamjjWktBzyLSwghBDz2XhD/6YaKLM9X6lUYnqCY8NT8MoGADQzTT8G//znvbRsbOJXv/o5J73xDeU9finULkWvW44/3o2+43nMPK/9fJ33N1+G5woxXSQ8ziH9/b309nSV/Tj1IZ07nk3xSouFUmCoBC2bniB06Lv49pVv5MDVNQCjhqgO7Wk8rN7IPdYTV7T0WjS2l2du43i8VX4CgbB8YyuEEJMYa+gfUJK5ZKlkgs6uNigkOOazXMVIDU+h3/dHiHVCOkXacLGpqRNnxuQnX/s8S2dDcARcbg/+/m5ST/8Di/Gv/dCwCKX5Xc1W82F4rhDTRcLjHNHf10tvb1fJl7gYa3/P7s7Q0GwRT4PCwkprhJccyvFHLOPZFifV1aNDYLansSeuiCUVi4Iapx3i5Lk9GXriin09in2b0mWprDqSXVgggsfjBWBrc5LdHSlZx1EIIcaQfOFhUi8+itXTjmOBHUSM2iWkGzaQ3v0KoDCqFxY1l2zc4DheOBxYSkN75SnIpMDrR51wDqx57fgHad0NO16yex172yGVxLIstGSSZX4nR6xZhbF0Bg5RhVHXwT+wdmNq9+Zhm4289iN7ivXo4gm3F0KIfEl4nAOGBsepLHExMihm12qMJRV+t8b56+wKpPe+nCFjgVIWSlnoDge+0DJebIKmbpPWmBpVVbU+pPPI1gw72u0Q2dBsccQSWBDQ2dcz+KGhXJVVs5xON6FwJFdYYGtzkrs2dAHw9I5+zlwXlgAphBAD+h+8jcT9t6A57Pd/Vb8Sz+tOBSC1+YncUhuqpwPXkSeNev5EQyWTyQRdnW2MKiE63lqGA/drW5+F/h572+42eGQ9amjIHBq4wH7O3q0Q64BEv300S2FpGtGFC9HdbtSWDcODajE9m2OZyn4Glg4B0Hdtxv+Gc/D4Bvdhtu5BG1JsaKiRPcVqxDUea97f0N+VsXB5YecqZp3x/jbn6/Dm8Yy8HnJ9JDzOen19vcR6u4CpLXExtAhOLKk49RAnrTErF/b2dlnc+VyKlTU6QbdFOtmH0txoho5T1+lNgKZBImMRTyue25NheXRwuYvlUZ01dXouiAa9Wi6oTldlVZ8/iM83vLDA7o7UsG12d6QkPAohBPaHptQTf8OKdYGZQfOF0DQNx6KVJF94GDTQ3FWoZBzN7R3z+eMNlezfuZnexpchUmdvvOMl0IDla2HHS2hNWyGTBocTtWNgLcOO/XagtKzBnkplQqwLXnwEXn0CgB24+nsh0YdacAC07rF7HM0MFmCaFpqu43J7wcxA2160/l5UJm0H1ew+AHZutNeVHNn7mU8gHBL+xtzPZPtrfAk69mP4Qrira9G6muGANSRfeJjkY3ejFKh4L64jTxr1IXZkkRjXmtcAjPshGIYPa3Udczp4Q+P/2+aB7PXJKjQsZPZtx2prmpEhY6I5zPN5ePNII69HKlpP5uUnc1/azNfrI+FxmpRjaOTQ4AhTW+KiqdvKVU8F+PumNNVVGrGk3UDHkorndmd4fleGPbt2kEwmCS5ag9OpY1qQzNjtOUB/aux5K0cscQzrZcz2cJa7sqquG4TCUVyu0dd9acTF0zv6h92eyWSIrRBiupite+yMlowDoHo7cgVtjNoloMDq6wIzg9nVlnte8oWHyTRuHNWhmB0qGdtwL/2P/A9adk3CRB9aT7t9jL1bUamk3aMIYDjsOYpghyuPDzxVkOwDywRLQ+vrgd0NkEmhDBfs24HW1QpWBvZsQUsnIJ1EWRYoC93QMRwulMuNluizv/lMxaGnzQ5xI3XsH9YjmXewHLmvgTCYC4kDw2m1pq1QFRi+v9bd6Hu3ovd1YcR7US63HQj3bSfx2N2ozhb78tSvGvN3N16RmOz/xxrWasW6UfFeNG8Aq3UvHDB/w2P2+lixbsymbRj1qwoKC6p5F8mXHkbTtBkZwsZbvqTSy5pk9m3HbNmNcvshHC3fMfLsORx6PaxYN+a251GJflQmhbFoFY55Ovy7YuExk8lw002/4ZGHH0LTNE444Q184IOXYBjGqG2vu+7H/PuRh3E4Bk/3qq9ezZo1B03nKRetHEMj+/p6iPV2D7tvKkGsPqTngmLWtlaLWFLRn7Q/A/QnTdIZwL+MQNSgPuyguVdhKvtxQ7Pb4IhP44gl9u9q5FDYsc6vnJVV3W4PwVAEXR/9ugJYXefmzHXhWRHIZIitEKJcRvZCZT80aRrgdIOZhip75EZ2WyvWYQdLTUPFe0k+cQ/pHS+RevpfaE77izi9dmluniRAz703k9hwL1qyH3pAeaoglRg8kZ42tI59kE4xKn3WLkUdfDSqfR9arBNN1wcCpIm2bztq4Hy1jn3Q123frxQKhaYApQbW8dUADc007R5My4RsGzEw1JWdGwePm70PRgfCiYLlskMHt+vvRevvhc7m3GPa5v9A2x60VAJVv9oOkAP7M7rbcEcXgteHivdiLDs41+OrewOYA+FRxXtHDUEd+rt0H3bimL9v+/fXjRXvRfcGUA5XbggynS2oI0pbPGi2DfXLvv5VvBfAvk7+UP5hqmPfqP3NpH/3eMuXVHJZk2xgV0qhkgkywRDO+tVlOQbk17M69HqoeC+4PFhtTQCDfy9Fnsds+nsYqWLhcf3623h582Z+/OOfAnDNNV/njjtu57zz3j3m9m9962l88JJLp/MUS6bUQyP7Yj3EYt1jPlZsEFse1Tn1ECd/35TG79Zo6bXY32vRlwRTASiU0tF0QDNImhotMYXPZfc6OjRwGLAopHHha925OZNjzcGcroqqgUCYKl9g0u1W17lnRQibzUNspcdUiMqY7ENKZt920g0byOzcBIDZ2YzmrsoFPn3JgdC8E81RhcpkyDTvxPrnH7D6ezF3vWwHLwAzQ+rlJ2HT43Yg03V0XxgtGMHxqqMASGx+kuS+HWixLju/DcyjxOmB7lZwOCGlo6WSoAaGsqgh0zEGwpnWuss+rmliB0wLkiba/u2oZYeiPFVovR2552oDvaWanp2yoOxeSVMHtwcUKH8I9frTc0FQHXnK2ENJIwvtYNmx3+4RDdcNPjZGr2VuP3u3oLXssu+sCsArG+zb6RTEe+xez6oARBZSVeXHs/JQUs3bwR8Cfyg37DT3Yd7rAzTcIz78FvLhOPvh1+xswVm9AKN+VS5MapSuCvlsHAqZDQ2aNwCdLejeQO7+vEQWQdOWYfubSSbsma7QsiYjez2t1r1Q4vBYaM/q0OvheNVRJB67G90fzvU8FmO6/h7KGVArFh7vv+9ePvCBS6iORAA4+5xz+d1vbxo3PBYinU6TTqdzt03TbtyyBV6m29JqB09tV8NuF3sefbFe+vp6pnxOY1VRXRzWWXeA/e1rIq3oTQwJjpay5woOXdZCgcPQeNUCnf6U4pBFBqesGSzSM5U5mFORHabqdDor8vseyn7NqZKcRylfR9NpW3OSOzfYX3Y8tb2Ps9aFWJVHgCzltZtv5NpNzVSvn6YV9z5X6rbL/pDyf/a+X9mA+9h3jBE0/g+zbS9myy47h2XSKDONSvShV9ehOV3o4Tqs/m40zUB17CdjmahMenSF1HjM/r+ywOHE6uvCUb8Kvaaevv/8jdS+HfbwUA2ULwxuL2rl4WhNW3N9jCqyCK230+6NVApcXvBX2w9mA5tlDQmOgxQaxDrR0qncuWWH2Q5fkmngZ00DTwDMNOqotwyv2Fq7dPRw1MaX7KBpuND3bbd38fTfsUI19nOzwTJrSPDUnr3PDouGE7V4NQQHhuQ5XUAQFVkER76J0OrDcXs8EAjBMadjte5Fr12MsXB57nWg1MBwH6xRrw2zZfewtTLNlt1jF79RCn3RytwwVS0QQUslMHxBAPSa+pK9h+R9TjOIsXA5xuojoHETxgEHYUQWDvs9jJTZt33Y74oFS3EfczpWW9OEz6skY+Hy3O9h6LmNd3+56TX1qFc2YJd3UgOvwdIef/AYQ25PcoyR1yP52P/l5jwWc47l/HvIvj9kmraRfPwvwNjv/RPJp/2qSHiMxWK0t7ezfMXgP2T58hW0tbXS19eHz+cb9ZyHHnqAhx56gHB1Naec/CZOf8cZA8NPRrvzjvXcdtutudsul4tPferjdHd1YTjGHr5YTlE3vOlAxd4uk8Vhg6g7RldXrOD9mJkMppkp6DmN7RbP7bGfc8QSx4RVVLO9hPa6iwqnARnTXstR0xgeHIEFAY23r3VSF9THHCY7ncVwsnRdx+FwliRgl4SCTCZNd1cnU/0it1Svo+m2eXeSZDIz5HaGqDuP3scSXrt5R67d1Ezx+lVX1xZ12FK3XarxZVRycEhoqvFltCFFULKPK8MJQ7Yj0U+mbS90tYDLYxebyaRRmk5Gd9i9ZboDrBHhMfshStPscBNdTDzeT//9f8Zqa0Lb0wBVdjhRS9fYBW469kNXs93r1m8PEVSRRbnLrhathBVr7RuRhXYvpZVh1JBWFFo6iUqnUC43ccONM9mPpusYDgcYTjAHRm+4faDrqHCtHUw9PgjVjH8hs1VeB3oOVbIf0knQdehPwysb7PBYu9Qeqrp3Cyx+1eC8xv/81R5K63Da1zEQhVefgOppz1WU1V7/DhyLVxJP9BFP9NnH9YYG5x12ted+Z1Y6aQ/LBWIP30m/O4BWd4D9uNs//Hfu9tM/8NxhV8vtRznd9pBkILNoNSxabQ+3jCwi7Q2TyaRK8h6S7znNJKp5F+qlx+0bsW60Ravtv52xrmXzLtSGf+Zua+veQiaykJg3DAeE7Ttn+L93RvCGUGtPhPZ9WKEa+/qV+rplj5F7nQ/+TlXzrtz92b+nUQ44FOUOjPn8fJX172Gg7eptbJjwvX8i+bRfFQmPiYQ9+X5oSMz+nIjHR4XHt7/tdC6++P34/X62bd3KD37wXTRd4x3veOeY+z/r7HN4xxmDj5mmyeZNzxEKh3E4KlMQ5cgwHDmF58diPaSGNu55yIbEoctjnL/OxXN7BpfMaI2pXLhs6rLwu+0iOS6Hhdm7n7TpwuPzUxNw0R6DzEB77XfBMSsdnHnE+NdzOorhDBUIVuP1VpX1GIVSyqKrq4NQuLro3oihpvo6qoSDlybZ3NI95HaIcDi/nsdSXrv5RK7d1FTq+pW67cosP4jkkKFz7uUH4RhShCL3uNuDmUmhEv2ggdXTjh6I2j2EyTjK68fq70UzHDgCYbQFS9BDtST7u1FDh2rqBqCBrqOFa3FEF4LTSTJbTc0bRLk8ULNkMFx17M8N99R62mHBARCqwVq6xg52K9YO9gAOzHmkrxutc79djXVoiFR2FdZd7d10N3dSHwkSidZAqAbl9kLnfjssZXsyPT47tMLweY0jZau8DmWZdngEGOito3U3WrbncedGew7mzo32B83+Hjs4e3wQrLb/Lce9Czr241m8isDKtSN6R8eWWX4Q/c8/gDVQH8IIRnAnY7iyv9dwlEwwlOsFG7e3Ydzt7BampH8DA8dKv7IBFDiDoWGvw5kotWsTabcnd9s59BpPsq0j2UO/Y6m8/xYjHEWpw8v7/huOMvKTVGbfdpIvPWzfaNqCOzhBT90Yzy/0+Hn9jRYh+3cbWL6G5L7x3/unqiLhMbs4e39/H8FgcOBnu+Klxzu63PfKVYPjig9cs4YzzzybBx98YNzw6HQ6cTqdudtmxg5HmqbPyj/kWG83/X29udtjDTkdS1O3NawITiypRg0lBdjeZrG3y6KjX+F2aIRcaTZveJzunh4OW3c0r1vjozuheGRrhljSnttY49eI+iZv6KZjjqPD4SQUrhlWUGkm0TRt1r72SmH1Qi9nvUYvas6jpmlsb0mzuzMj8yULNN9fd1NVietX6rbLWb8a7bh3jjvvZejjnuPs9jTV8DTmzs3o/pBdXbBjP1b7PjTdQHN5cdSvwv3at2K27sHcv4OMUqh4DDQdPVCNSicxapeie33or3krfW4f2r4ddmhyuuzgODC3Lxe2PD5o24MKRgfD3OJXDR9GmrViLWx/3g5jqs/uhcxWytF1mvbt5+sPb+V973wra487Dla+GtWx3y5kMzBHSS1ebS8JAvktuZGt8jpQEZbapahgDaQTdhAdWCJk1JzHvQMf3kI10Nc1GJwHjq3XLSe0Zt2YlcDH46xfjee4d9pzr7JrPC5YOuz14axfnddcscm2K+XfgKbpqHa7iEzq8b+gzfB5j8aCpWS2Pjvs9njXYdS2tUvl/XeKpvv6WW1NaJqWqzicfuWZkhfrGSrfv9FiaJqGo37VhO/9U1WRT9t+v59oNMqOHTtYuHARAI07tlNTUzPmkNWRBie9zy7FFA2J9XYPG4I5XhGakRrbLVp6LfpT9rqNTgP8bnt4aX1Ip6HZDpbtfRaP78iQHJhmE3Qn2fzPm0g1PcPHvvh1lh1QzwtNJltbTeJpu33OmNCbUGxvs3h0W4bjVk3tZZRvGB5LVVUAfyCU1ze2onKKLUq0oy3Dva90o2maVJgVogiORSsnLQgx9PGR6/+lGp4mrdnzCY3qOhzLD81tr3sD6B4fyuFEjyzEdcRJGAM9eGaght6BIiPZojHq4KPtg2TDWsNT9u2qgB2qhvbuTdQTmIrbw2ldHpSmofX3YOkOOrq6+fWLLZz5hWs4/m2nDm6fDYhZwZphvZmTGuglVANzHnPDaEcGz5FzHhe/yr5dFUDVrx4MrbVLJ60EPhH3YSdi1C6ZVdUaK70ERKEKKRwzcltj4XIZpjrLGLVLSD37wGARqZ2byezbPqNfo5OZ7L1/Svsuy17zcNLJp3DH+ts56KCDAbjjjvWc8qY3j7nto4/+myOPPAqv18u2bVu56847OfW006bzdKesmGUWenu7RvU43t+QpieuCHrtsDRWEZpswGzqsnsU/W6NkFfj+FWO3LbHr3Jw9wspGtsVyUx24I+ip8/CUb2S73/hQl570EIe225/853KTlkb6MjsTsBLTSavtNg9mcUGyHzD8EiarhMKRXCPsTC1mDv2dpkMfZuaTRVmhZitRn7oUO1NuZ+zaw2arXswlh2M2dmMjkKvXohrzWtwLFpJPN5Hb3fH4A5HFp/JGhq2qgKjw+VYOvbb4S/Rb8+9dHvoWHwIf/nLPTzT0sd7v34dRx01YkjZWIVsCjXWv2GM2yOrtKrseo9D/k35VgKfSDk/GJZDJZeAKFYh13jotjOtMI6YnGPRShzLDslVGy5oWZZ5qGLh8dxzzyfW28snPv4RAE488Y2cffa5APzy+p8DcMWVHwbgnnv+yvW/+DmWZRKJRDj11NM444x3VeS8xzNZr2KhyyyMFRzv2WQHxx3tFiuiOkGvNmYRmqZuK7dd2oREBl4V0HP1bhrbLf6+Kc3GfRaJXB0Tu6KqbhgsO+JNPN3qorbWyhW9WRTS2ddjYuhgWuA0IGWCywENzWbR4bGYiqwul5tgKDrmmqBiblkcNtjcMnh7aaQyc5aFmK9G9qoAg2XmW/dgVNfZi6dj9yalw3X0DA2OExkjbE0qstAOmsEoWssuOtMe/vI//8tDPS7+++e/54ADBtdcHBbiCj1OsYaGzBHnYBiOgUrg8+99LJ+evNm+9p2Y3Zxr1mG1783dng1fcFRKxcKjw+Hgssuv5LLLrxz1WDY0Zl1zzben67SKkk+v4tKIi6d39A+7PZ7eni76+3uH3ZcNWUGvxoqoTq1f4+Q1Y/fS1Yf03FDVmIKMqWjptXJB87k9Gba0mOQKYCqVmzLiMJx4XIO9mseudOSK3iyP6Dyz20ShaO5RuAay25q64kNcoRVZ/f4QVb6ADFOdJ1bUODgr4Jc5j0JU0NBeleQLD+fu170Be+HsgfCYCUSJ5Rscs8brlZxge3XkKfDiI+xu7eDeh59kwYIF/PhTH6ZqSHDUnr3P/nnnRnv7Qo8zVSPOwXX06YRWH442TpX4+WCinrzZuBakmFsqucblbDMzK4zMMvn0Kq6uc3PmuvCkcx57ezrp7x+9/MLQkBX0jh8cwS5Sc+ohTu58LkXaVIBGlWswbLX3KXqTA2s4DqzjpOkKr9OBxwnLIkbumNn9LY/qHLvSwSkD8xObeyw6+xVr6owpzXnMtyJrdu3GQgoLiLlhVZ2b1QtleLIQM8HQ4Ye6P4TjyJOAgeDoDU7LOaiaJfztuS24Nz7D2lXLOOq4E9CXDPmgN7JwTcf+6Q2OQ85BQ8PpduNN9M7r4DiZ2TYnUsxNs204eKVIeCyBfHsVJysaMl5whMKXvThulYPWmMWGXSZ+t0bQq+WGhEZ9Gg5NgZnBQsMwNBYEHKyuNThqqTFs3caRxWzKUT11sn263d6BwgLS8AohRCWN9e18vD9GrKdzWo6fSqX4yX9/keodGzjuyLUcvGIpauVhw8NhKeY4TlVkIfruzbjdXnRdlyFwk5iNcyKFmK8kPJZAvr2KE+np6SQ+TnDMKjS4HbHEwb6ewaU6sj2JtVozvXt2YVW/CofTg9uhE6myeyuH9iIWW8ymdDQCwTBVVf5pPKYQQoiJDP12Pt4fo2eagmN3dzcf/sgnCO17mc+c/WZWr1419obTOcdxHFXLDsETrcNq2ytD4PIgQwaFmD0kPJZIsUsRKKXo7e2aNDgWY6zeyqeeepqPf+ijVB/8ZupWHErC1PG5NQ6IDBbUySqmmE2pGA4H4VAUxzwsLCCEELNBf3+M3mkKjrt27ebSy66kvb2d337nq6w2mwcfHKtncbrnOA7QdZ1gthJ4MAz14wRcMYoMGRRidpDwWEFKKXp7OonH+ybfuEhDeyvvvvsvfOELX+aodUfxgS98lg1Nbna02wExllSjitUUWsymVLxeH4FgWBbXFUKIGWo6g+Ozzz7H5Vd8hEDAz/rbb2HFihWoEZVMZwKpBC6EmA8kPFbIdATHocf62c+v54c//AlnnfUuvnnN1TT1OtjSmWZF1K7MeuoYQ1ILnWc5VZqmEQxF8HiqynocIYQQxevv66W3t2tajnXPPf/gM5/9AmvXHsr1v/gpkUi1/UCFehbHI5XAhRDzhYTHClBK0dPTSWIagmMqleK/vvJV7rzzf/jgJ77CG992Hk29Rt7BsBwFcsbidLoIhaMYhrwkhRBippqu4KiU4lc3/IbvfvcHnH762/jud76F2z3zqm3rhkEoJJXAhRDzh3xSn2Z2cOwgEe+ffOM8jKyGOlS2uMAzG57hv779U5ILj+eFJosXmqxc8ZvpLYAztipfAL8/JN/YCiHEDNbX10tsGoJjOp3ma1dfw6233saHP3wFn/rkx2dktW23x0swKJXAhRDzi4THaaSUoqe7g0SidMExWw31ka0Z1tTpHLHEwfKoPqy4wO9/fyPp6BHD5i/mW/xmonA6VXZhgShut6ek+xVCCFFa0xUce3t7+djHPsXj/3mC71z7Tc4556yxN6zonEepBC6EmL8kPE6TUgdHGKyG2tRlsaXFZEuLRkOzxWFV27jq05cPKy7Q2G4VXPymnEt1uFweQqEIuhQWEEKIGa2vr4dYb3fhTyww4DU1NXHJpVeyb99+brzxVxx37DHj7ld79j77550b7WU5Ru6/TOHS4XASCkVxOJ0l26cQQswmEh6nQTY4vrw3VtJevPqQziNbM2xpMelOQCKt6O7p5rb717PskGO48lNfQQsGgeKK35RrqQ5/IERVlRQWEEKIma4v1kMsVlxwnDTgDfHiSxu57LIrcbvd3H7bzbzqVavH33fH/tG3h+67wGPnSyqBCyEEyDtgmSml6O5u5+W9Me7ZlOb5vSb3bErT2G5N/uRJLI/qrKnTcTk0dA0SyQT7W9pZvXYdJ7//62zr8Q471vKozrErHXkHwLGW7pgKw3AQiS7A5wtKcBRCiBmu6OAIYwe8cdx77/1ccMFF1NfXc8f6WycOjjB6XceRtws4dj40TSMUjhIMRSQ4CiHmPXkXLKNscEwm4mP24g3V2G7x2PZMwaGy1q9jaIpUIk48ZVETdvPud7152DpTI4+Vr2xv5eGLjSkPWfV4qohE63A6pSKdEELMdLFYd/HBESYPeANu+u3vufJDH+XEE4/nT3/8LTU1NZPvu3Yp6shTUMsOHbtXMc9j58PpdBGtWShLSAkhxAAZtlomSim6u9pJJuOA3Ws33pzDYucWNrZbvLA7wb7Gl0nqIcKhECvrF7EgYLC/t7D5jeOZakVWTdMIBKrxVvmK3ocQQojpE4t10xfrmdpOBgLeePMOTdPkmm9ey+9//0cuvfSDfOHznymsaulE6zxOcux8+XwBfFIJXAghhpHwWAYjgyNMPOewmLmFje0W65/s5O8P/IdUYBX1C6oJB6pYENDRNAqe35jdZynnZDocTkLhKA6HFBYQQojZINbbTV/fFINj1jgBr6+vj09+8rM89PAjfOPrX+U973l3aY6Xx7HzIZXAhRBifBIeS2ys4Jg1Xi/eRL2S43n8hUb+9+5HMAyDdUdGSWlVHBDRCXq1XPgrJACWurKqt8pPIBCWb2yFEGKWKGlwHEdzcwuXXXYlO3fu4oZf/YI3vOGEsh6vUC73QCVwXSqBCyHEWCQ8lpBSiq6uNlLJRMHPXRS0Q1Z2ncaJ3Hvv/Vx79XXUv/69vPktp+D1ejms3kDTGNZrWEhPYqkqq2qaTjAUwePxFvxcIYQQldHb20V/X29Zj7F588tcetmHAPjzn//EQQetKevxCuUP2Gs3ypeeQggxPgmPJVJscBza4wdwxCTb3/Tb3/PNb17LW9/yZj7+6dNoT7jGDIeF9iQW0/s5ktPpJhSOYBjyshJCiNliOoLjgw89zMc//imWL1vGDTdcT13dgrIerxCG4SAUjuJ0uip9KkIIMeNJtdUSUErR1Vlcj+NkVVizTNPk6q9/k2uu+TaXXvoBrrvuhxPOx8h3v1lTrazq8wWpjtRKcBRCiFmkt6f8wfHmm2/l8ss/zOtf9zpuueUPMyo4DlYCl+AohBD5kE/6U5QLjqnCgyPk1+M3rLjAN77Gey44f1TP4shhq8X0JBZTWVXXDULhCC6XFBYQQojZpLenk/7+WNn2b1kW137n+/zmNzdx8cXv5Sv/9cVhy0hVklQCF0KI4kh4nAKlLLo624sOjjBxFVYYLC6wp1vj8m/eTtWrVuXmMmb1xBV/35SmPqwPG6J62iFOntuTKfrcJiOFBYQQYnYqd3CMx+N8+jOf59577+e///vLvP99F5XtWIWSSuBCCFE8CY9FsoNjG6lUcsr7GqvHr7Hd4smXdvGTa68C/Jz60e+xNR1g66YMDc0Wx68a/NXFkgq/e3CC/9BiN/t6lP3/TemihqOOV3QnEAhT5QsU+k8VQghRYT09ncTLGBzb2tq4/IqP8MorW7j+Fz/llFNOKtuxCiWVwIUQYmokPBZBKYvOzjbSJQiOY2lst7jhHzu47/4HCB18Ohee+Saeb/EQSypchh0Wh67leFi9wQtNo4eoNnVb9MRVLlwWWkH10W0Z/r4pjd+tEfRqnHaIk1ULXFJYQAghZiGlFL29XWUNjlu2bOWSS68glUpxyy1/4NVrDy3bsQohlcCFEKI0JDwWSFkWnV2lD46N7Rb3NaTZ02mxa9cuNm5+hdpla3n9urWkDSed/RniKUUcWBxm1FqOi8N6rocQ4LHtGTbvM3mxycRlgMuhcfyq0cfMDmsduURIY7vF3zelaY0pWmOKFVGd1riD10Xr0HSpsySEELOJUorenk7i8b6yHePRxx7nIx/5BPX1i/j1Db+gvr6+bMcqhMPhIlwdlYJuQghRAvJOWgDLsoeqptOlD47f/Vec7W2KVCaDmYniW3k8oZCTPd06i8J2ONQ1C5cD1h1gjOpBzAbJbCGdnrjixSYThSJlaiwOawwdpdPYbvHnDSl2tNtzJxuaLc5f58rtt6nbwu/WaI3Zw177UnDQkogERyGEmGWmIzjefvsdfOW/v8YxxxzNdT/5IYGAv2zHKoRhOKiO1MjcfCGEKBFJAnmyg2Nr0cGxsd3ise0ZGtutUff/4L44Dc2KVMZCYWA4XWiak644+AZGh7bGFA5Dw1Iatf7xf23ZQjrZIa4aWm4+5NCKq03dFrGkIpWxh7W29A4vwlMf0gl6NVbVOlgc8XD6YV5W1bmL+rcLIYSojHIHR8uy+P73f8gXv/QVzjnnLG741c9nRHDUdYNwdQ2GwyHzG4UQooSk5zEPg8Exldf2I4vMjFxWI1u4Jtvj+HKzBQoU2d5BHQUooLlXccgiWBHVc3MXJ2oHs0t0+N0aLofd4whw6ohiOfUhnf6UPSTV0MFlwOZ9Ji/vN1lTZ3DcKgdnrQvRnnCzNOIk6i7fHBkhhBClp5Sip6eTRJmCYzKZ5HOf/xJ//es9fPGLn+PSSz4wI4Ka2+0lGKpG07SyVpQVQoj5SMLjJIoJjiOD4niFa+5rSLO11UQpu7G121z7Z4cBXqcdGqM+jZQJQe/oHsSRx84W0NE0OH4Vw9Z+HEuVyz6ixwkPbMngd2s8ucvE7w/wlsNrgIHKsl12A7y1OcnujhRLIy5WS0+kEELMSEopero7SCT6y7L/9vYOrvzQR9m4cRM/++mPOfXUt5TlOIUaWglcKWuSrYUQQhRKwuMEigmO9zfY8w2DXo2mLovfP5FkcUjPzS1sjalc4ZpXduwnlfCiOz0DydEOhx4HuB0Q9NhDTo9Y4uAIGHctyOyxs6EVmHRZjqZuiwUBnb6Bf1osqVAo+lIaHqfBy80WIz8KbGtOctczPQA8vaOfM9eFJUAKIcQMU+7guH37Di659EpisRh/+uNvOfLII8pynEIYhkMqgQshxDSQ8DgOy7Lo7GwlU2CPY09csaPdwuey7wt5NV5psagLDA7laY1Z3H77Hdzz+39Rd8qXMbWq3GNVTghXaayI2sNKnQNz/IdWVh1r7cWh8xWztycKj9k5jdnhsNVencd2WKBrJDIWUf/ol8bujvSI2ykJj0IIMYOUOzg++eRTXPmhj1FTE+XOO/7M0qVLynKcQni8PoKBsBR0E0KIaSDvtGOwLIvOjpa8gyMMhrdsIEubEPLa8w5dBrTELJp7FR39Frfe38C379rNa088leMOiVLlBKduD1ONVNlDVf1ujY5+xd4uuypqttBONqQ+v9fknk3p3P0jh7KON7R1qEVBjTV1Om9b62Zh1M+rFnlYUu3k8AO81Fc7R22/NOIccVu+4RVCiJlCKUV3GYPj//zP3Vz8vks4+OCDuP22myseHDVNIxiKEApJJXAhhJgu0vM4gmWZdHa0ksmkJ994iGyhGrAD5IELHPxjs72GYl9KkTZB0xStre309eksOfp8nLVB9nTZ8xs1DUwLVtUavO9oN3c+l0LDXiJjx8B6jMujrnF7GJdH9dz8yonmOMLwIa77e6ArYaFpCfqTFmuXeAn7jDGD4ao6N2euC8ucRyGEmGHs4NhOMhEvy76vu+7n/PgnP+Xss8/kmm98DZersl8eOpwuQqEoDod8jBFCiOkk77pDFBscgTHD24KAzr0vp2mLQcZUxOIZTDwEIgFqqt2ARipjYSmwFBg67O602NtlsbJG55WW0ZP9h4bU7O2h5zBRaMzKBtC46WRra4pYMoXLoVHl0nE6tAnnMq6uc0toFEKIGUQpRXdXO8lk6YNjKpXiy/91FXfd9b98+tOf4MMfuqLiFVWrqgL4A6GKn4cQQsxHEh4HTCU4Zo0Mb8etctDQbLKlOUF7dxoMN06XF92hkzIVKRMSaTA0yMZBS8HfN6U59RBnbj5iVmO7VVAP43iWRp280u6mvcs+qj6k/T1woYRDIYSYLcoZHLu6uvjwRz7Bs888yw9/+H3OeMfbS36MQui6TjAUwe32VvQ8hBBiPpPwSGmC43j0nm3s2mXiCNSj6XbPntcJXqeGUuA0NPpTdpB0O8Dl0HJrOZ6/zsVzezI0NFvs61Hs25TOVVEtJjQCeL0+1tWFCYfTPLm9DxTsbE+RzihW17l53Upfia+AEEKIcihncNy5cxeXXnYlnZ2d/OEPN/Ga16wr+TEK4XK5CYaiGIZR0fMQQoj5bt6Hx3IGx//93//jO1/8L1afcRXBA5bTldCwFCQz4DI0Ij6NPV0WGdMuluPQodavEfRquV7Fpm6dfT2DvY+TVVEdT7awgMdjV3bNDj993UqfHSKB1630Sa+jEELMAuUMjhueeZYrr/gIwVCQ22+/hRXLl5f8GIXw+0NU+QIyTFUIIWaAeR0eLdOko7MFM5Mp6X6VUvz0pz/nRz+2iwtc9ql3cO8WeGmvSXOvxbKIPR8yllREqjTS5mCF1Vq/xslrBtdonGiOY76cThehcBTDGP3rljmMQggxuyil6OpqI5VMlHzff/3rPXz2c1/ksMNezfW/uI7q6uqSHyNfumEQCkVxuaSNEkKImWLehkfTNOnsaME0SxschxYX+MynP8mHPnQ5mqbhdFosCmo0NFsEvfa3p8evctAas4bdNzQ4wtiFeApR5Qvg90thASGEmAvKFRyVUvzyl7/me9//f5xxxulc++1v4nZXrqKq2+0lGIqgyxIcQggxo8zL8Fiu4DhRcQF7nqKLxnZrVBAc676hipnjaBcWiOJ2e6b2jxJCCDEjKKXo6mwjlSptcEyn01z11a9z223r+ehHP8QnP/GxCn7hqBEIhqmq8lfo+EIIISYy78JjscFxsoCXb3GBsYLgVArgjMXl8hAKRdClsIAQQswJ5QqOvb29fOSjn+CJJ57iO9/5FuecfWZJ918Ih8Npr93odFbsHIQQQkxsXoVH0zTp7WkvKjjes8kuqPP8XjNX8TTrmWee5YoZUlzAHwhRVSWFBYQQYq5QyqKrs5VUKlnS/e7du5dLLr2S5uYWbrrpBo495uiS7r8QXq+PQDCMpskwVSGEmMnm1bt0V2fhwRHsCqfj3f7rX+/hwve+n1WrV7K+gsHRMBxEogvw+YISHIUQYg7p7uooeXB84YUXOevsd5NIJLj9tpsrFhw1TSMUjhIMRSQ4CiHELDCv3qkty5x8ozGMrHBaH9JRSnH99Tfw8U98mtNOfSu/++2NFatK5/FUEYnW4XRKRTohhJhr0unSLiX1z3/dywXvuZilSxazfv2trF69qqT7z5fT6SJaszC3hJQQQoiZb14NWy3WyIqni4MmX/4vu7jAxz76YT7xiY9WpLdP0zQCgTBeKSwghBBiEkopbrrpd3zr29/l1Le+he9//1o8nsoUVfP5AvikErgQQsw6Eh7zlC1q09vby6WXfpInnnyK737325x91rsqcj4Oh5NQOIrDIYUFhBBCTCyTyfCNb3yLP/7pFi6//BI+99lPV2QZDF3X7bUbpRK4EELMShIeCzCsuMCNN3DMMa/P+7mTVWsthLfKTyAQlm9shRBCTCoW6+MTn/w0jzzyKNdcczUXvPu8ipyHyz1QCVyXSuBCCDFbSXjM0wsvvsTll38Ij8fD+ttvYdWqlXk/d7JqrfnSNJ1gKILH4y34uUIIIeafffv2c9nlH2L3rt385te/5IQTjqvIefgD9tqN8qWnEELMbvOqYE6x/vmve7nggotYstguLlBIcISJq7Xmy+l0E62pk+AohBAiL5s2bebsc86nq6ub2267uSLB0a4EXofPJ0tICSHEXCDhcQJKKW688bd8+MMf5+ST3sgf//hbaqLRgvczVrXWQvh8QaojtRiGdBQLIYSY3AMPPsT5734vtbW13HnHraxZc+C0n8NgJXDXtB9bCCFEeUgaGUcmk+Gaa77NH/54M1dcfimf/eynii4uMLJaa75DVnXdIBSO4HJJYQEhhBD5+eMfb+bqr3+Tk096Iz/84feoqprepTDsSuDVeKt803pcIYQQ5SfhcQxDiwt885qreXcJigtkq7XmSwoLCCGEKIRpmlx77fe48abf8f73X8SXv/QFDGN62xCpBC6EEHObhMcR9u9v5rLLPsSuXbsqVlwgEAhT5QtM+3GFEELMTv39/Xz6M5/nvvse4Kqr/ov3XfzeaT8HqQQuhBBzn4THITZt2sxll30ITde57fabWXPg9M4RMQwHoXBU5ocIIYTIW2trK5dd/mG2bdvOL6//KSeffNK0Hl8qgQshxPwh4XHAAw8+xMc//mlWrlzBDb/6OQsWLJjW43u8VQQD1WgVWLRZCCHE7NTwyitceumVmKbJrbf8gUMPPWRaj+90ugmFI1LQTQgh5glJKtjFBS6//MMce+wx3HLz76c1OGqaRjAUIRSKSnAUQgiRt3//+1HOO+9CgsEgd6z/87QHR59fKoELIcR8M6/TimmafOtb3+GrX/sG77v4vfz8Zz+e1qp0DoeTSLQOr1cq0gkhhMjfn/98Ox+85AqOOupI/nzrn1i0aOG0HVvXDaojC/D7QzK/UQgh5pl5+3Xh0OICX73qK1x88YXTevyqKj9+KSwghBCiAJZl8YMf/Ijrf3kD73nP+Xz1qq/gcExfU+52ewmGqqUSuBBCzFPzMjwOLS7wq1/+jJNOeuO0HVvTdUKhCG63FBYQQgiRv0Qiwec+9yXu+fs/+PKXvsAHP/i+af0CUiqBCyGEqFh4zGQy3HTTb3jk4YfQNI0TTngDH/jgJWOuSVXItpOpZHEBl8tNMBSd9nW3hBBCzG7t7R1cceVH2Lz5ZX720x/x1re+ZdqOLZXAhRBCZFVszuP69bfx8ubN/PjHP+VHP7qOzZs3cccdt09524k8/vh/OO+8CwmFQtNeXMDvDxGurpXgKIQQoiA7djRyzjnvZteu3dz8p99Na3D0eKuIRuskOAohhAAqGB7vv+9ezjnnXKojEaojEc4+51zuu/feKW87kY9+7JOsW3ckt97yx2krLpAtLODzB2V+oxBCiIJd/L5LcLtd3HnHnzn88MOm5ZhSCVwIIcRYKjJsNRaL0d7ezvIVK3P3LV++gra2Vvr6+vD5fEVtm5VOp0mn07nbmUwGgPPPO4dPf/qTOBwOLMsqxz9tGKfTTTAURtc1MplU2Y9XLkpZmJkMmUwKTZMPEYWQa1c8uXbFk2s3NVO9fho6umEU/IXheG3X4Yev5dvfuoZAIDAtbZdhOAiGIjgchrRd85Rcu+LJtZsauX7FK8W1y6f9qkh4TCTiAMOCX/bnRDw+7P5Cts2684713Hbbrbnbfr+fj3zkSi644Dz27Wsq4b9ECCGEGG3dumMwCqyCOl7b9YXPf4aurk66ujpLfZoT2D6NxxJCCDFTTNZ+VSQ8ejx2pdH+/j6CweDAz/32Y15v0dtmnXX2ObzjjHfmbvfFYnz2s5/iup/+At80ruM4V/TH41x+2Qf51Q03UjXONRdjk2tXPLl2xZNrNzWluH56EfPbpe0qLfk7KJ5cu+LJtZsauX7FK9W1m6z9qkh49Pv9RKNRduzYwcKFiwBo3LGdmpqaUT2JhWyb5XQ6cTqdw+6LxWI4HI6CvwkWYBgGqVQKwzDk+hVIrl3x5NoVT67d1FTq+knbVVryd1A8uXbFk2s3NXL9ijdd165ig4lPOvkU7lh/O52dnXR2dnLHHes55U1vnvK2QgghhBBCCCFKr2KR/txzzyfW28snPv4RAE488Y2cffa5APzy+p8DcMWVH550WyGEEEIIIYQQ5Vex8OhwOLjs8iu57PIrRz2WDY35bJsPp9PJeee9e9RwIJEfuX7Fk2tXPLl2xZNrNzUz5frNlPOYreT6FU+uXfHk2k2NXL/iTde105RSqqxHEEIIIYQQQggx68kCKkIIIYQQQgghJiXhUQghhBBCCCHEpCQ8CiGEEEIIIYSYlIRHIYQQQgghhBCTmjOrb2YyGW666Tc88vBDaJrGCSe8gQ988BIMw5jStvNBIdfjuut+zL8feRjHkMVHr/rq1axZc9B0nvKM8be//ZUHH7iPnTt3cuRR6/jiF7887rb9/f388pe/YMPTT+FyuTjttLdz7nnnT+PZziyFXLur/vu/aGh4edjr7rqf/pxIJDodpzrjpNNpfn3DL3nhhefp6ekhEo3yrnedxSmnvGnM7eW1N1yh16+crz9pu6ZG2q/iSNs1NdJ+FUfarqmZKW3XnAmP69ffxsubN/PjH/8UgGuu+Tp33HE755337iltOx8Uej3e+tbT+OAll07nKc5YkUiEs885jxdeeJ729vYJt/3Nr39FrLeXX/7q13R3d3P1166itraWN5508jSd7cxSyLUDuOii93H6O86YhjOb+UzTJFxdzVe/9nXq6hay5ZVXuOaaq4lGoxxxxJGjtpfX3nCFXj8o3+tP2q6pkfarONJ2TY20X8WRtmtqZkrbNWeGrd5/372cc865VEciVEcinH3Oudx3771T3nY+kOtRvKOPPobXv/5ogoHghNslk0n+/e9HuOA9F+Lz+amvX8xpb3s79903f69zvtdOjObxeLjgggtZuHARmqZx4Jo1rF37ajZv3jRqW3ntjVbI9Ss3abumRq5JcaTtmhppv4ojbdfUzJS2a06Ex1gsRnt7O8tXrMzdt3z5CtraWunr6yt62/mgmOvx0EMP8L6LL+QTn/god//v/2BZ1nSd7qy1d+9eMpkMK4Zc5xXLV7JzZ2PlTmqWWb/+Nt538YV89jOf5MEH7q/06cwoqVSKLVu2sHzZ8lGPyWtvchNdv6xyvP6k7Zoaab/KT94/SkPar7FJ2zU1lWq75sSw1UQiDoDP58vdl/05EY8Pu7+QbeeDQq/H2992Ohdf/H78fj/btm7lBz/4Lpqu8Y53vHP6TnoWSiTieDyeYfNwfD4f8Xi8gmc1e1z43otYsmQpbrebF198gf/3g+/h9Xp5/dHHVPrUKk4pxS9+/lMW1S8a83rIa29ik10/KN/rT9quqZH2q/zk/WPqpP0am7RdU1PJtmtO9Dx6PF4A+vsHv2ns7++3H/N6i952Pij0eqxctYpQKIRhGBy4Zg1nnnk2j/7739NzsrOYx+MlmUximmbuvr7+Przz8DVXjDVrDsLn8+FwODjyyKN481veyqOPyutOKcWvfnU9e/fu5Qtf+DK6PvotXV5748vn+kH5Xn/Sdk2NtF/lJ+8fUyft12jSdk1NpduuOREe/X4/0WiUHTt25O5r3LGdmpqaUd88FrLtfDDV66HpWjlPb85YvHgxhmHQ2Dj0Ou/ggGXLKnhWs5emyetOKcUNv/olW155hau+evW4f6/y2htbvtdvLKV6/UnbNTXSfpWfvH+U3nxvv6TtmpqZ0HbNifAIcNLJp3DH+tvp7Oyks7OTO+5YzylvevOUt50PCrkejz76b/r7+1FKsXXrFu66806OPmb+Dr0wTZNUKoVpmSjLIpVKkU6nR23ndrs57rjjueWWm+nr66OpqYm//e2vvOmU+fu6y/fa9fXF2LDh6dw3kC+88Dz//Mc/OPqYYytw1jPHr2/4JS+/vJmvfu1q/H7/uNvJa29s+V6/cr/+pO2aGmm/iiNt19RI+1U8abumZia0XZpSSk15LzNAJpPhpht/zSOPPAzAiSe+MbfW0y+v/zkAV1z54Um3nY8KuXZf+cqX2Nm4E8syiUQinHLKmznjne8at8t8rvvzrbdw2223Drvv0EPX8vVvfJNrvnE1Bx98CGefcy5gD6e6/vqfD6xX5Oa0t71tXpfYz/fadXd38+1vfYM9e/YAULtgAaeffsa46xrNBy0tLXzoystwOp3D3rdOPPENXHHlh+W1N4lCrl+5X3/Sdk2NtF/FkbZraqT9Ko60XVMzU9quORMehRBCCCGEEEKUz/z7uk0IIYQQQgghRMEkPAohhBBCCCGEmJSERyGEEEIIIYQQk5LwKIQQQgghhBBiUhIehRBCCCGEEEJMSsKjEEIIIYQQQohJSXgUQgghhBBCCDEpCY9CCCGEEEIIISYl4VEIMSV9fTHOPuudtLQ0T7hdS0szZ5/1Tvr6YmM+vmnTRi679IO526lUiu9c+y0ueu97+MLnP1vScxZCCDG/SdslRHEkPAoxQ1x5xWU88cR/Kn0aFXPIIYdyw69vzN1+/PHHaGray403/Y7vfPf7/PnWW7j22m9V8AyFEEKMJG2XtF1ifpHwKMQsYZomSqmyHiOTyZR1/4VoaW5mUf1inE5npU9FCCFEkaTtEmJucVT6BIQQ8P3vfYe2tlZ+9MMfoOs6J574Bq648sOcfdY7ueTSy/nnP/7Ovn1N/PZ3f+C9F17A93/wQ1asWAnAX/7vbp588gm+/o1vAtDd1cVNN/2GF198EU2DY489nosuft+YDdlLL73Id679Nhe+9yLuunM9oVCY737vBzz//HPc/Kc/0tTURCQa4b0XXsRrX/d6ANLpNDfe+Gsee/TfVFVVcfbZ5w7b5/PPPcdvf3cjLc3NuN1uXn/0MVxxxYdyjz/11FPcftut9PT08rrXv54PfegjOByO3Ln84Y8389ubbuSee/6KZVlc+J7zqatbyN69e3K3Af5085/L8rsQQgiRH2m7pO0S84+ERyFmgM9+7gtcecVlfOCDl/D61x897LF/P/IQV331awQCQQzDmHA/Sim+/e1vctBBB/PzX/ySVCrJ9773HdbffhsXvOfCMZ+TSMTZ2biDn1z3cwAaGxv5wfe/y+c+/0UOPXQtDQ0v861vfoNrv/M9Fi9ewvr1t/FKQwM//NFPcLvd/PCH/2/Y/q677ke896L38cY3nkQikaCxccewx5999hm+/4MfEo/H+eIXPsfDDz/EySefMmyb93/gg3i9XnY07uCLX/wyAH++9ZZht4UQQlSWtF3Sdon5R4atCjHDvfNdZxGJRHE6nej6xH+y27ZuZd++fVz8vvfjdrsJBIKcfda5PPLIw+M+x7Is3nvRxbjdbtxuN//659856aSTefWrD0PXdQ4++BDWrXsNjz36KACPPPwwZ519DpFIFJ/Pz3nnnT9sf4bhYP/+fXR3d+PxeDjooIOHPX7uuefj9VYRiUQ58sij2L5tW5FXRgghxEwlbZcQc5P0PAoxw9XW1Oa9bUtrC/39fbzv4vcOuVdhWda4z/F6vfh8/sF9tLTw0ksvcv/99+fusywTb1UVAJ2dHdTWDp5Tbe2CYfv7whe+xPo7bufjH/swtbW1nHnWORx33PG5x6urw7mf3W4Pff19ef/7hBBCzA7SdgkxN0l4FGKG0HQtr/s9Hg/JZDJ3u7OzM/dzTbSGYDDEb278bf7H1YZ/I1xTU8Pb3n46F130vjG3r66O0NrayoEHrgGgra112OMrV63i85//IpZl8eSTT/CD73+XQw9dm/f5jHue41wfIYQQlSNt1yTnKW2XmGNk2KoQM0Q4FKZ5//5Jt1uxciUPPfQgpmmyY8d2Hnrogdxjq1avpqamhptv/iPxeD9KKVpaWnjmmQ15n8eb33IqD9x/Hy+++AKmaZJOp2loeJk9e3YDcPwJJ3DXnXfQ0dFOX1+M228bnPyfTqd58MEHiMVi6LqOz+cDwDCm/lYTDoVpa23BNM0p70sIIURpSNs1MWm7xFwjPY9CzBBnnX0ON/7mBm6//TZOOOFELr/iyjG3u/SSy7nuuh9z8UXvYc1BB/HGk07mlYYGAAzD4Mtf/gp/+MPv+PjHPko83k9NTS1vectb8z6PlStX8qlPf5Zbbv4Te/fuQdM0lq9Ywfve9wEAzjnnPLq7u/nUJz+O11vFOeecy4YNT+ee/+9HHuamG39DJpOmpraWT37qMwQCQeLx+BSuDhxz7HE88sjDfOD9F6EU/OGPN09pf0IIIaZO2q6JSdsl5hpNlXvxHSGEEEIIIYQQs54MWxVCCCGEEEIIMSkJj0IIIYQQQgghJiXhUQghhBBCCCHEpCQ8CiGEEEIIIYSYlIRHIYQQQgghhBCTkvAohBBCCCGEEGJSEh6FEEIIIYQQQkxKwqMQQgghhBBCiElJeBRCCCGEEEIIMSkJj0IIIYQQQgghJiXhUQghhBBCCCHEpP4/kqsA3vcqYX8AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(9, 4.4), constrained_layout=True,\n", " sharex=True, sharey=True)\n", "zline = np.linspace(0, 2.6, 200)\n", "\n", "for ax, (label, res, colour) in zip(axes, [\n", " (\"grism + Roman imaging (deployable)\", outs, SR1_BLUE),\n", " (\"photometry mean-imputed\", outs_nophot, SR2_ORANGE)]):\n", " zp_i = np.array([r.z for r in res])\n", " frac = np.mean(np.abs(zp_i - z_true) / (1 + z_true) > 0.15)\n", " ax.fill_between(zline, zline - 0.15 * (1 + zline), zline + 0.15 * (1 + zline),\n", " color=\"#e8e7e2\", lw=0, zorder=0)\n", " ax.plot(zline, zline, color=INK, lw=1.0, zorder=1)\n", " ax.scatter(z_true, zp_i, s=9, color=colour, alpha=0.55, lw=0, zorder=2)\n", " ax.set_title(f\"{label}\\n{frac:.1%} catastrophic\", color=INK, loc=\"left\")\n", " ax.set_xlabel(\"true redshift\")\n", " ax.set_xlim(0, 2.6)\n", " ax.set_ylim(0, 2.6)\n", " ax.grid(alpha=0.7)\n", " ax.set_axisbelow(True)\n", "\n", "axes[0].set_ylabel(\"predicted redshift\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d99b7920", "metadata": {}, "source": [ "The shaded corridor is the ±0.15 catastrophic boundary, and the two panels fail\n", "in visibly different ways.\n", "\n", "On the left the failures are not scattered noise: they cluster at particular\n", "(true, predicted) pairs — systematic line misidentifications, each cluster one\n", "alias. On the right a **horizontal track** appears, where the head, deprived of\n", "colour, falls back on its prior and returns nearly the same redshift regardless\n", "of the input. Both are structure a point estimate hides and a PDF exposes." ] }, { "cell_type": "markdown", "id": "7ca19595", "metadata": {}, "source": [ "### When the point estimate is wrong\n", "\n", "Below we take one of those catastrophic outliers and ask what its PDF was\n", "actually saying." ] }, { "cell_type": "code", "execution_count": 15, "id": "b838123f", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:13.982517Z", "iopub.status.busy": "2026-08-27T03:10:13.982421Z", "iopub.status.idle": "2026-08-27T03:10:14.174430Z", "shell.execute_reply": "2026-08-27T03:10:14.173646Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31 catastrophic outliers out of 512\n", "18 of them have the truth as their second mode\n", "\n", "row 302: true z = 0.904, point estimate = 1.230 (z_err = 0.201)\n", " mode 1: z = 1.230 mass = 0.456\n", " mode 2: z = 0.841 mass = 0.483 <- the truth\n", " mode 3: z = 0.968 mass = 0.038 <- the truth\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "zp = np.array([r.z for r in outs])\n", "bad = np.where(np.abs(zp - z_true) / (1 + z_true) > 0.15)[0]\n", "print(f\"{len(bad)} catastrophic outliers out of {len(zp)}\")\n", "\n", "\n", "def truth_rank(row):\n", " # Which mode, if any, landed on the true redshift.\n", " for rank, (zm, _) in enumerate(top_modes(outs[row])):\n", " if abs(zm - z_true[row]) / (1 + z_true[row]) < 0.05:\n", " return rank\n", " return None\n", "\n", "\n", "# Of the failures whose *second* mode is the right answer, show the one where\n", "# that runner-up carries the most mass -- the clearest instance of the pattern.\n", "runners_up = [r for r in bad if truth_rank(r) == 1]\n", "k = max(runners_up, key=lambda r: top_modes(outs[r])[1][1])\n", "modes = top_modes(outs[k])\n", "\n", "print(f\"{len(runners_up)} of them have the truth as their second mode\\n\")\n", "print(f\"row {k}: true z = {z_true[k]:.3f}, point estimate = {zp[k]:.3f} \"\n", " f\"(z_err = {outs[k].z_err:.3f})\")\n", "for rank, (zm, mass) in enumerate(modes, start=1):\n", " flag = (\" <- the truth\"\n", " if abs(zm - z_true[k]) / (1 + z_true[k]) < 0.05 else \"\")\n", " print(f\" mode {rank}: z = {zm:.3f} mass = {mass:.3f}{flag}\")\n", "\n", "plot_pz(outs[k], z_true[k],\n", " f\"row {k}: the point estimate is wrong, the PDF is not\")" ] }, { "cell_type": "markdown", "id": "ded75310", "metadata": {}, "source": [ "This is why `out.pz` is worth keeping. The point estimate is the **tallest\n", "bin**, but the runner-up mode carries *more probability mass* than it does: the\n", "model is not confidently wrong here, it is genuinely split between two readings\n", "of the same line, and `z_err` is wide because of it.\n", "\n", "A pipeline that propagates the PDF can act on that — deprioritise the source,\n", "or carry both hypotheses forward. One that stores only `z` cannot tell this\n", "source apart from a confident, narrow, correct one.\n", "\n", "SR2 uses the same information: it runs once per redshift hypothesis and combines\n", "by mode mass, so when the top mode is wrong the right lines still get drawn, at\n", "reduced weight." ] }, { "cell_type": "markdown", "id": "1800eced", "metadata": {}, "source": [ "## 6. Line recovery, split by recoverability\n", "\n", "Here is the metric that matters, and the reason it is shaped the way it is.\n", "\n", "A single averaged \"how much line flux did you recover\" number can be improved\n", "two ways: by getting better, or by hallucinating harder. Those are opposite\n", "behaviours, and no average distinguishes them. So the score is **binned by\n", "whether the line was recoverable from the low-resolution data at all**, using\n", "the per-line integrated S/N labels loaded in section 3:\n", "\n", "| bin | integrated line S/N | what it tests |\n", "|---|---|---|\n", "| `unrecoverable` | < 1 | **the control** — a good model scores ≈ 0 here |\n", "| `marginal` | 1 – 3 | the hard, interesting regime |\n", "| `good` | 3 – 6 | should be recovered |\n", "| `strong` | > 6 | should be recovered well |\n", "\n", "A model scoring 0.85 in `strong` *and* 0.85 in `unrecoverable` would be\n", "worthless, and a single averaged metric would call it the better model. Read the\n", "first row first." ] }, { "cell_type": "code", "execution_count": 16, "id": "3cb1b504", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:14.178339Z", "iopub.status.busy": "2026-08-27T03:10:14.178241Z", "iopub.status.idle": "2026-08-27T03:10:14.317431Z", "shell.execute_reply": "2026-08-27T03:10:14.316236Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recoverability n SR1 SR2\n", "unrecoverable 71 -0.04 0.00\n", "marginal 154 0.13 0.44\n", "good 58 0.65 0.82\n", "strong 30 0.63 0.75\n" ] } ], "source": [ "truth_all = np.stack([ds[j][1].numpy() for j in range(len(ds))])\n", "sr1_all = np.stack([to_model_space(r.flux_sr1, flux_low[j])\n", " for j, r in enumerate(outs)])\n", "sr2_all = np.stack([to_model_space(r.flux_sr, flux_low[j])\n", " for j, r in enumerate(outs)])\n", "\n", "rec = {stage: line_amplitude_recovery(pred, truth_all, ds.line_snr.numpy())\n", " for stage, pred in ((\"SR1\", sr1_all), (\"SR2\", sr2_all))}\n", "\n", "bins = list(rec[\"SR1\"])\n", "print(f\"{'recoverability':16s} {'n':>5s} {'SR1':>8s} {'SR2':>8s}\")\n", "for b in bins:\n", " print(f\"{b:16s} {rec['SR1'][b]['n']:5d} \"\n", " f\"{rec['SR1'][b]['median']:8.2f} {rec['SR2'][b]['median']:8.2f}\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "88155d94", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:14.319566Z", "iopub.status.busy": "2026-08-27T03:10:14.319459Z", "iopub.status.idle": "2026-08-27T03:10:14.413925Z", "shell.execute_reply": "2026-08-27T03:10:14.412675Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(7.4, 4.2), constrained_layout=True)\n", "x = np.arange(len(bins))\n", "w = 0.36\n", "\n", "for off, stage, colour in ((-w / 2 - 0.01, \"SR1\", SR1_BLUE),\n", " (w / 2 + 0.01, \"SR2\", SR2_ORANGE)):\n", " vals = [rec[stage][b][\"median\"] for b in bins]\n", " ax.bar(x + off, vals, w, color=colour, label=stage, zorder=2)\n", " for xi, v in zip(x + off, vals):\n", " ax.annotate(f\"{v:.2f}\", (xi, v), textcoords=\"offset points\",\n", " xytext=(0, 4 if v >= 0 else -12), ha=\"center\",\n", " fontsize=8, color=MUTED)\n", "\n", "ax.axhline(1.0, color=INK, lw=1.0, ls=\"--\", zorder=1)\n", "ax.annotate(\"true amplitude\", (len(bins) - 0.45, 1.0),\n", " textcoords=\"offset points\", xytext=(0, 4), ha=\"right\",\n", " fontsize=8, color=MUTED)\n", "ax.axhline(0, color=\"#c9c8c2\", lw=0.8, zorder=1)\n", "ax.set_ylim(top=1.28)\n", "ax.set_xticks(x)\n", "ax.set_xticklabels([f\"{b}\\n(n = {rec['SR1'][b]['n']})\" for b in bins])\n", "ax.set_ylabel(\"median recovered flux fraction\")\n", "ax.set_title(\"Line amplitude recovery, binned by what the data could show\",\n", " color=INK, loc=\"left\")\n", "ax.legend(loc=\"upper left\")\n", "ax.grid(axis=\"y\", alpha=0.7)\n", "ax.set_axisbelow(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "482a3b08", "metadata": {}, "source": [ "This reproduces the published pattern on a 512-row draw. SR2 sharpens\n", "recoverable lines substantially toward truth — and leaves the undetectable ones\n", "at essentially zero. The leftmost pair is the one to check on any model you\n", "train yourself: a bar that climbs *there* is a model inventing lines, however\n", "good the rightmost pair looks.\n", "\n", "SR1's `unrecoverable` bar sits slightly below zero. That is a conservative model\n", "declining to draw, not an error." ] }, { "cell_type": "markdown", "id": "a47dff28", "metadata": {}, "source": [ "### Per-line presence probabilities\n", "\n", "SR2 also emits a presence probability for each of its 98 rest-frame features.\n", "For the example galaxy from section 3:" ] }, { "cell_type": "code", "execution_count": 18, "id": "0f837c36", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:14.416015Z", "iopub.status.busy": "2026-08-27T03:10:14.415919Z", "iopub.status.idle": "2026-08-27T03:10:14.418736Z", "shell.execute_reply": "2026-08-27T03:10:14.418243Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "feature p observed\n", "[NII]_6548 0.93 1.636 µm\n", "Halpha_6563 0.89 1.640 µm\n", "[OIII]_5007 0.73 1.251 µm\n", "[OIII]_4959 0.69 1.239 µm\n", "[NII]_6583 0.66 1.645 µm\n", "Hbeta_4861 0.61 1.215 µm\n", "[SIII]_9531 0.23 out of band\n", "[FeII]_16435 0.21 out of band\n" ] } ], "source": [ "rest_aa = dict(LINE_LIST_REST_AA)\n", "\n", "print(f\"{'feature':22s} {'p':>5s} observed\")\n", "for j in np.argsort(out.presence)[::-1][:8]:\n", " name = out.line_names[j]\n", " lam_obs = rest_aa[name] * (1 + z_true[i])\n", " where = (f\"{lam_obs * 1e-4:.3f} µm\" if 10000 < lam_obs < 19300\n", " else \"out of band\")\n", " print(f\"{name:22s} {out.presence[j]:5.2f} {where}\")" ] }, { "cell_type": "markdown", "id": "c9f149c8", "metadata": {}, "source": [ "Features falling outside the grism band are gated off in SR2's line branch, so a\n", "high presence probability there costs nothing and means nothing — read this list\n", "together with the observed wavelength." ] }, { "cell_type": "markdown", "id": "a39f2df9", "metadata": {}, "source": [ "## 7. From the command line\n", "\n", "Everything above has a CLI equivalent, which is usually what you want for a\n", "catalogue rather than for a notebook." ] }, { "cell_type": "code", "execution_count": 19, "id": "d4680478", "metadata": { "execution": { "iopub.execute_input": "2026-08-27T03:10:14.419950Z", "iopub.status.busy": "2026-08-27T03:10:14.419856Z", "iopub.status.idle": "2026-08-27T03:10:16.779079Z", "shell.execute_reply": "2026-08-27T03:10:16.777501Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "specsr-roman 0.1.0\r\n", "torch 2.11.0+cu128 | cuda available: False\r\n", "hub repo: aryana-haghjoo/roman-spectral-superresolution\r\n", "canonical chain:\r\n", " sr1 sr1_ou2024_v6\r\n", " zhead zhead_ou2024_roman_med3_noisy\r\n", " sr2 sr2_ou2024_v5_romanonly\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " extra [extract]: ok\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " extra [train]: ok\r\n", " extra [hub]: ok\r\n" ] } ], "source": [ "!specsr-roman info" ] }, { "cell_type": "markdown", "id": "06524133", "metadata": {}, "source": [ "```bash\n", "# run the chain over an npz holding flux_low[, flux_low_err, phot, wavelength_low]\n", "specsr-roman predict spectra.npz --out predictions.npz --phot-tier medium\n", "\n", "# reproduce the published metrics and figures from a frozen prediction cache\n", "specsr-roman evaluate cache --out pred_cache.npz\n", "specsr-roman evaluate metrics --cache pred_cache.npz\n", "specsr-roman evaluate figures --outdir figures/\n", "\n", "# the audit that keeps the line-recovery claims honest\n", "specsr-roman evaluate prior\n", "```\n", "\n", "Training each stage is one command and one config file:\n", "\n", "```bash\n", "specsr-roman train sr1 --config configs/sr1.yaml\n", "specsr-roman train zhead --config configs/zhead.yaml # needs SR1\n", "specsr-roman train sr2 --config configs/sr2.yaml # needs SR1 + ZHead\n", "```" ] }, { "cell_type": "markdown", "id": "95fe8c4f", "metadata": {}, "source": [ "## 8. Where to go next\n", "\n", "- **[Quickstart](https://aryana-haghjoo.github.io/specsr-roman/guides/quickstart.html)**\n", " — the same API in reference form.\n", "- **[Data guide](https://aryana-haghjoo.github.io/specsr-roman/guides/data.html)**\n", " — the full dataset, the splitting rule, and the photometric tiers.\n", "- **[Training guide](https://aryana-haghjoo.github.io/specsr-roman/guides/training.html)**\n", " — including why SR2's best epoch is 4, which is the design working rather than\n", " a truncated run.\n", "- **[Evaluation guide](https://aryana-haghjoo.github.io/specsr-roman/guides/evaluation.html)**\n", " — the metrics above, plus the prior-dominance audit.\n", "- **`ARCHITECTURE.md`** — the full design, the losses, and why each one is\n", " shaped the way it is.\n", "\n", "### Before you use this on anything real\n", "\n", "- **These results are on a simulation's manifold.** Targets are Diffsky model\n", " SEDs with that simulation's line physics. A model can score well by learning\n", " the manifold rather than by measuring anything, and no reconstruction metric\n", " distinguishes the two — which is why the package ships a prior-dominance\n", " audit. The published SR1 sits at r ≈ 0.45: it reads the data about half as\n", " much as it could.\n", "- **Trained on simulations, not sky.** Real Roman spectra will differ, and\n", " domain adaptation is open work.\n", "- **The catastrophic rate is physics, not a bug to tune away.** With a single\n", " line in band the identification is genuinely ambiguous. Photometry breaks most\n", " of that degeneracy and cannot break all of it — so propagate the PDF." ] } ], "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }