From 1c32f6fa1ec5aadf944898200d6cb76d4825c5f2 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:38:35 +0000 Subject: [PATCH 01/20] feat: implement DragerEitParser.parse() with full calibration pipeline - parse() reads preamble via np.memmap, resolves spec by frame-size divisibility, memory-maps binary frames with FRAME_EIT_DTYPE - calibration pipeline (EIDORS read_draeger_file.m, Adler 2016): vv = FT_A * trans_A - FT_B * trans_B [calibrated transimpedance] I_real = injection_current / FC_CURRENT [A] V_diff = (voltage_A - voltage_B) / FV_VOLTAGE [V] - aux_signals: timestamp, trans_A/B raw, I_real, V_diff, frame_counter, medibus - warns if Framerate [Hz] missing from header before defaulting to 50 Hz - RawImpedanceData: add aux_signals field; clarify measurements docstring --- src/fasteit/models/raw_impedance_data.py | 6 +- src/fasteit/parsers/draeger/eit/eit_parser.py | 69 +++++++++++-------- 2 files changed, 43 insertions(+), 32 deletions(-) diff --git a/src/fasteit/models/raw_impedance_data.py b/src/fasteit/models/raw_impedance_data.py index a002f4e..1de6103 100644 --- a/src/fasteit/models/raw_impedance_data.py +++ b/src/fasteit/models/raw_impedance_data.py @@ -21,9 +21,11 @@ class RawImpedanceData(BaseData): before any image reconstruction. The intended consumer is pyEIT. Attributes: - measurements: Raw transimpedance array, shape (N_frames, 208). + measurements: Calibrated transimpedance array, shape (N_frames, 208). + Values are gain-corrected (e.g. ``vv = FT_A*trans_A - FT_B*trans_B`` + for Dräger), not raw ADC counts and not yet image-reconstructed. 208 = 16 electrodes × 13 measurement pairs - (adjacent drive, standard Dräger/Timpel pattern). + (adjacent drive, standard Dräger pattern). aux_signals: Optional dict of auxiliary waveforms keyed by signal name, e.g. ``{"timestamp": ..., "frame_counter": ..., "medibus_Paw": ...}``. Each value is a 1-D array of diff --git a/src/fasteit/parsers/draeger/eit/eit_parser.py b/src/fasteit/parsers/draeger/eit/eit_parser.py index baec1de..31595c3 100644 --- a/src/fasteit/parsers/draeger/eit/eit_parser.py +++ b/src/fasteit/parsers/draeger/eit/eit_parser.py @@ -24,7 +24,7 @@ from fasteit.parsers.header_formats import get_eit_specs from .eit_dtypes import FRAME_EIT_DTYPE, PREAMBLE_DTYPE, PREAMBLE_N_FIELDS -from .eit_utils import FC_CURRENT, FV_VOLTAGE, SEPARATOR, parse_eit_header +from .eit_utils import FC_CURRENT, FT_A, FT_B, FV_VOLTAGE, SEPARATOR, parse_eit_header class DragerEitParser(BaseParser): @@ -69,7 +69,9 @@ def parse(self, path: Path) -> RawImpedanceData: if path.stat().st_size < preamble_size: raise ValueError(f"'{path}' is too small to be a valid .eit file.") - preamble = np.memmap(path, dtype=PREAMBLE_DTYPE, mode="r", shape=(PREAMBLE_N_FIELDS,)) + preamble = np.memmap( + path, dtype=PREAMBLE_DTYPE, mode="r", shape=(PREAMBLE_N_FIELDS,) + ) _, sep_offset, _ = int(preamble[0]), int(preamble[1]), int(preamble[2]) with path.open("rb") as f: @@ -79,8 +81,7 @@ def parse(self, path: Path) -> RawImpedanceData: binary_start = sep_offset + len(SEPARATOR) data_size = path.stat().st_size - binary_start candidates = [ - s for s in get_eit_specs("draeger") - if data_size % s.frame_size_bytes == 0 + s for s in get_eit_specs("draeger") if data_size % s.frame_size_bytes == 0 ] if not candidates: known = [s.frame_size_bytes for s in get_eit_specs("draeger")] @@ -111,38 +112,46 @@ def parse(self, path: Path) -> RawImpedanceData: ) # ── 5. Extract fields and compute calibrated transimpedance ────────── - ft = metadata.get("calibration_factor") - if not isinstance(ft, list) or len(ft) != 2: - raise ValueError( - f"'Calibration Factor' in header must be two floats, got: {ft!r}" - ) - # Calibration pipeline — EIDORS read_draeger_file.m (GPL, understanding - # only, no code copied). Empirical constants estimated 2016-04-07 A. Adler. - trans_A = np.array(frames["trans_A"]) # (n_frames, 208) ADC counts - trans_B = np.array(frames["trans_B"]) # (n_frames, 208) ADC counts - inj_curr = np.array(frames["injection_current"]) # (n_frames, 16) ADC counts - voltage_A = np.array(frames["voltage_A"]) # (n_frames, 16) ADC counts - voltage_B = np.array(frames["voltage_B"]) # (n_frames, 16) ADC counts - - vv = ft[0] * trans_A - ft[1] * trans_B # (n_frames, 208) calibrated transimpedance - I_real = inj_curr / FC_CURRENT # (n_frames, 16) actual injected current [A] - V_diff = (voltage_A - voltage_B) / FV_VOLTAGE # (n_frames, 16) differential voltage [V] + # only, no code copied). Empirical constants hardcoded in EIDORS by + # A. Adler, estimated 2016-04-07. Not present in the .eit header. + trans_A = np.array(frames["trans_A"]) # (n_frames, 208) ADC counts + trans_B = np.array(frames["trans_B"]) # (n_frames, 208) ADC counts + inj_curr = np.array(frames["injection_current"]) # (n_frames, 16) ADC counts + voltage_A = np.array(frames["voltage_A"]) # (n_frames, 16) ADC counts + voltage_B = np.array(frames["voltage_B"]) # (n_frames, 16) ADC counts + + vv = ( + FT_A * trans_A - FT_B * trans_B + ) # (n_frames, 208) calibrated transimpedance + I_real = inj_curr / FC_CURRENT # (n_frames, 16) actual injected current [A] + V_diff = ( + voltage_A - voltage_B + ) / FV_VOLTAGE # (n_frames, 16) differential voltage [V] aux_signals: dict[str, np.ndarray] = { - "timestamp": np.array(frames["timestamp"]), # fraction of day - "trans_A": trans_A, # raw, for audit - "trans_B": trans_B, # raw, for audit - "injection_current": inj_curr, # raw ADC counts - "I_real": I_real, # [A] actual injected current - "voltage_A": voltage_A, # raw ADC counts - "voltage_B": voltage_B, # raw ADC counts - "V_diff": V_diff, # [V] differential voltage - "frame_counter": np.array(frames["frame_counter"]), - "medibus": np.array(frames["medibus"]), # (n_frames, 67) + "timestamp": np.array(frames["timestamp"]), # fraction of day + "trans_A": trans_A, # raw, for audit + "trans_B": trans_B, # raw, for audit + "injection_current": inj_curr, # raw ADC counts + "I_real": I_real, # [A] actual injected current + "voltage_A": voltage_A, # raw ADC counts + "voltage_B": voltage_B, # raw ADC counts + "V_diff": V_diff, # [V] differential voltage + "frame_counter": np.array(frames["frame_counter"]), + "medibus": np.array(frames["medibus"]), # (n_frames, 67) } # ── 6. Assemble result ──────────────────────────────────────────────── + if "fs" not in metadata: + import warnings + + warnings.warn( + f"'Framerate [Hz]' not found in header of '{path}'. " + "Defaulting to fs=50.0 Hz.", + UserWarning, + stacklevel=2, + ) fs: float = metadata.get("fs", 50.0) metadata["n_frames"] = n_frames metadata["frame_format"] = spec.name From ec2564030f6459800b17e78fac226780e6256d6f Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:38:58 +0000 Subject: [PATCH 02/20] docs: update README and data_model for DragerEitParser implementation - README: DragerEitParser status Scaffold -> Implemented; add .eit quick-start example; update RawImpedanceData table entry - docs/data_model.md: document aux_signals field in RawImpedanceData with all keys, shapes, and units --- README.md | 10 ++++++++-- docs/data_model.md | 20 ++++++++++++++++++-- docs/reverse_eng/eit_format.ipynb | 2 +- 3 files changed, 27 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index fa43f03..1fbb3c6 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,12 @@ print(data.n_frames, data.fs) # e.g. 11500, 50.0 print(data.pixels.shape) # (11500, 32, 32) print(data.global_signal[:10]) # first 10 frames global EIT signal +# Dräger .eit — raw transimpedances for pyEIT reconstruction +data = load_data("patient01.eit") +print(data.n_frames, data.fs) # e.g. 11500, 50.0 +print(data.measurements.shape) # (11500, 208) — 208 = 16 inj × 13 meas +print(list(data.aux_signals.keys())) # timestamp, I_real, V_diff, medibus, ... + # Dräger .asc — continuous frame-by-frame signal export data = load_data("patient01.asc") print(data.n_frames, data.fs) # e.g. 11500, 50.0 @@ -57,7 +63,7 @@ Pre-alpha. Data model and parsing layer implemented. |-------|---------|-------------| | `ReconstructedFrameData` | 32×32 pixel matrices + synchronized signals | `.bin`, `.txt` | | `ContinuousSignalData` | Signal table, one row per frame | `.asc` | -| `RawImpedanceData` | Raw transimpedances for pyEIT | `.eit` (scaffold) | +| `RawImpedanceData` | Calibrated transimpedances + aux signals for pyEIT | `.eit` | **Parsers** (`parsers/`): @@ -65,7 +71,7 @@ Pre-alpha. Data model and parsing layer implemented. |--------|--------|---------| | `DragerBinParser` | Implemented | `.bin` — base frame (4358 b) and PressurePod frame (4382 b); registry-driven, new frame sizes require only a dtype + one entry | | `DragerAscParser` | Implemented | `.asc`, `.txt`, `.csv` — continuous waveform export | -| `DragerEitParser` | Scaffold | `.eit` (Fase 2) | +| `DragerEitParser` | Implemented | `.eit` — ASCII header + binary frames (5495 b/frame); 208 calibrated transimpedances + Medibus aux signals | | `TimpelTabularParser` | Implemented | `.csv`, `.txt` — reconstructed frame export | All parsers are accessible via the single entry point `load_data(path)`, which diff --git a/docs/data_model.md b/docs/data_model.md index 22c8450..5a2c129 100644 --- a/docs/data_model.md +++ b/docs/data_model.md @@ -100,12 +100,28 @@ table pd.DataFrame One row per EIT frame. Snake_case column names. ## 5. `RawImpedanceData` **File:** `models/raw_impedance_data.py` -**Produced by:** `DragerEitParser` (`.eit`), `TimpelRawParser` (`.x`) — both scaffold +**Produced by:** `DragerEitParser` (`.eit`); `TimpelRawParser` (`.x`) — scaffold ``` measurements np.ndarray shape (N_frames, 208) + Calibrated transimpedances (not raw ADC counts, not + yet image-reconstructed). For Dräger: + vv = FT_A*trans_A - FT_B*trans_B (EIDORS, Adler 2016) 208 = 16 electrodes × 13 measurement pairs - Adjacent drive pattern (Dräger and Timpel) + (adjacent drive, standard Dräger pattern) + +aux_signals dict|None Named per-frame arrays. Populated by DragerEitParser: + "timestamp" float64 (N,) fraction of day + "trans_A" float64 (N,208) raw ADC — primary + "trans_B" float64 (N,208) raw ADC — reference + "injection_current" float64 (N,16) raw ADC counts + "I_real" float64 (N,16) injected current [A] + "voltage_A" float64 (N,16) raw ADC counts + "voltage_B" float64 (N,16) raw ADC counts + "V_diff" float64 (N,16) differential voltage [V] + "frame_counter" uint16 (N,) + "medibus" float32 (N,67) ventilator channels + None if the source file carries no auxiliary signals. ``` Intended consumer: **pyEIT** or any reconstruction library. diff --git a/docs/reverse_eng/eit_format.ipynb b/docs/reverse_eng/eit_format.ipynb index 8bbacda..cdbf1ce 100644 --- a/docs/reverse_eng/eit_format.ipynb +++ b/docs/reverse_eng/eit_format.ipynb @@ -971,7 +971,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "fasteit", "language": "python", "name": "python3" }, From 2f4816b453f31d558a1be9dfdc111d0267da56cd Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:39:10 +0000 Subject: [PATCH 03/20] feat: add EIT parser validation notebook - Parse patient01.eit and .bin, verify measurements shape and sanity - Check timestamps monotonic, print calibration constants - Compare frame count between .eit and .bin --- notebooks/03_eit_parser_validation.ipynb | 242 +++++++++++++++++++++++ 1 file changed, 242 insertions(+) create mode 100644 notebooks/03_eit_parser_validation.ipynb diff --git a/notebooks/03_eit_parser_validation.ipynb b/notebooks/03_eit_parser_validation.ipynb new file mode 100644 index 0000000..bfabc06 --- /dev/null +++ b/notebooks/03_eit_parser_validation.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EIT Parser Validation\n", + "\n", + "Verifica che `DragerEitParser` legga correttamente i file `.eit` e che la calibrazione produca valori sensati.\n", + "\n", + "Pipeline di calibrazione (costanti EIDORS, A. Adler 2016 — hardcoded, non nell'header):\n", + "- `vv = FT_A * trans_A - FT_B * trans_B` → transimpedenza calibrata [Ω]\n", + "- `I_real = injection_current / FC_CURRENT` → corrente reale iniettata [A]\n", + "- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → tensione differenziale [V]\n", + "\n", + "Confronto con il file `.bin` corrispondente per validazione incrociata." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from fasteit.parsers.draeger.eit.eit_parser import DragerEitParser\n", + "from fasteit.parsers.draeger.bin.bin_parser import DragerBinParser" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "EIT_FILE = Path(\"../src/fasteit/test_files/patient01.eit\")\n", + "BIN_FILE = Path(\"../src/fasteit/test_files/patient01.bin\")\n", + "\n", + "eit = DragerEitParser().parse(EIT_FILE)\n", + "bin_ = DragerBinParser().parse(BIN_FILE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Struttura del risultato" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== .eit parser output ===\n", + "measurements (vv) shape : (11500, 208)\n", + "fs : 50.0 Hz\n", + "n_frames : 11500\n", + "n_electrodes : 16\n", + "n_measurements : 208\n", + "detected_spec : Draeger_EIT_v51\n", + "date / time : 04.01.2024 18:10:54.015\n", + "frequency : 101.501 kHz\n", + "amplitude : 9100.0 µA\n", + "\n", + "calibration constants (EIDORS hardcoded, A. Adler 2016):\n", + " FT_A=0.00098242, FT_B=0.00019607, FC_CURRENT=194326.3536, FV_VOLTAGE=0.11771\n", + "\n", + "aux_signals keys: ['timestamp', 'trans_A', 'trans_B', 'injection_current', 'I_real', 'voltage_A', 'voltage_B', 'V_diff', 'frame_counter', 'medibus']\n" + ] + } + ], + "source": [ + "from fasteit.parsers.draeger.eit.eit_utils import FT_A, FT_B, FC_CURRENT, FV_VOLTAGE\n", + "\n", + "print(\"=== .eit parser output ===\")\n", + "print(f\"measurements (vv) shape : {eit.measurements.shape}\")\n", + "print(f\"fs : {eit.fs} Hz\")\n", + "print(f\"n_frames : {eit.metadata['n_frames']}\")\n", + "print(f\"n_electrodes : {eit.metadata['n_electrodes']}\")\n", + "print(f\"n_measurements : {eit.metadata['n_measurements']}\")\n", + "print(f\"detected_spec : {eit.metadata['detected_spec']}\")\n", + "print(f\"date / time : {eit.metadata.get('date')} {eit.metadata.get('time')}\")\n", + "print(f\"frequency : {eit.metadata.get('frequency_khz')} kHz\")\n", + "print(f\"amplitude : {eit.metadata.get('amplitude_ua')} µA\")\n", + "print()\n", + "print(\"calibration constants (EIDORS hardcoded, A. Adler 2016):\")\n", + "print(f\" FT_A={FT_A}, FT_B={FT_B}, FC_CURRENT={FC_CURRENT}, FV_VOLTAGE={FV_VOLTAGE}\")\n", + "print()\n", + "print(\"aux_signals keys:\", list(eit.aux_signals.keys()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Sanity check: vv non è zero, non ha NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN count : 0\n", + "Zero rows : 0\n", + "min / max : -0.0000 / 0.1597\n", + "mean : 0.0125\n" + ] + } + ], + "source": [ + "vv = eit.measurements\n", + "\n", + "print(f\"NaN count : {np.isnan(vv).sum()}\")\n", + "print(f\"Zero rows : {(vv == 0).all(axis=1).sum()}\")\n", + "print(f\"min / max : {vv.min():.4f} / {vv.max():.4f}\")\n", + "print(f\"mean : {vv.mean():.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Timestamps monotoni" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestamp range : 0.757570 – 0.760232 (fraction of day)\n", + "Monotonically increasing: True\n", + "Duration approx : 3.8 min\n" + ] + } + ], + "source": [ + "ts = eit.aux_signals[\"timestamp\"]\n", + "print(f\"Timestamp range : {ts.min():.6f} – {ts.max():.6f} (fraction of day)\")\n", + "print(f\"Monotonically increasing: {bool(np.all(np.diff(ts) >= 0))}\")\n", + "print(f\"Duration approx : {(ts.max() - ts.min()) * 24 * 60:.1f} min\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Waveform vv — primo elettrodo, primi 500 frame" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 3))\n", + "ax.plot(vv[:500, 0], lw=0.8)\n", + "ax.set_xlabel(\"Frame\")\n", + "ax.set_ylabel(\"vv [Ω]\")\n", + "ax.set_title(\"Calibrated transimpedance — measurement 0, first 500 frames\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Confronto frame count: .eit vs .bin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".eit n_frames : 11500\n", + ".bin n_frames : 11500\n", + ".eit fs : 50.0 Hz\n", + ".bin fs : 50.0 Hz\n", + "\n", + "Frame count difference: 0 (0.0 s)\n" + ] + } + ], + "source": [ + "print(f\".eit n_frames : {eit.metadata['n_frames']}\")\n", + "print(f\".bin n_frames : {bin_.n_frames}\")\n", + "print(f\".eit fs : {eit.fs} Hz\")\n", + "print(f\".bin fs : {bin_.fs} Hz\")\n", + "\n", + "delta = abs(eit.metadata['n_frames'] - bin_.n_frames)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fasteit", + "language": "python", + "name": "fasteit" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 876a4a67bf1297cd15b84a5733292c9dcf1b0753 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:42:11 +0000 Subject: [PATCH 04/20] fix: move warnings import to module level in eit_parser --- src/fasteit/parsers/draeger/eit/eit_parser.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/fasteit/parsers/draeger/eit/eit_parser.py b/src/fasteit/parsers/draeger/eit/eit_parser.py index 31595c3..42e5fc9 100644 --- a/src/fasteit/parsers/draeger/eit/eit_parser.py +++ b/src/fasteit/parsers/draeger/eit/eit_parser.py @@ -14,6 +14,7 @@ from __future__ import annotations +import warnings from pathlib import Path import numpy as np @@ -144,8 +145,6 @@ def parse(self, path: Path) -> RawImpedanceData: # ── 6. Assemble result ──────────────────────────────────────────────── if "fs" not in metadata: - import warnings - warnings.warn( f"'Framerate [Hz]' not found in header of '{path}'. " "Defaulting to fs=50.0 Hz.", From 463f37858df7afd7f016d862c87c3c2939a8f65f Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:47:40 +0000 Subject: [PATCH 05/20] test: add full test suite for .eit parser (84 tests) - test_header_formats.py: registry contents, get_eit_specs(), frozen spec - test_eit_dtypes.py: itemsize 5495, field offsets trans_A=16 trans_B=2592, frombuffer round-trip - test_eit_utils.py: parse_eit_header() valid/error cases, field map keys, calibration constants, Time colon edge case - test_eit_parser.py: validate(), parse() shape/metadata/calibration/ aux_signals, fs warning, truncated file error, 5 real-file tests --- tests/test_eit_dtypes.py | 98 +++++++++++ tests/test_eit_parser.py | 303 +++++++++++++++++++++++++++++++++++ tests/test_eit_utils.py | 190 ++++++++++++++++++++++ tests/test_header_formats.py | 84 ++++++++++ 4 files changed, 675 insertions(+) create mode 100644 tests/test_eit_dtypes.py create mode 100644 tests/test_eit_parser.py create mode 100644 tests/test_eit_utils.py create mode 100644 tests/test_header_formats.py diff --git a/tests/test_eit_dtypes.py b/tests/test_eit_dtypes.py new file mode 100644 index 0000000..9481d5d --- /dev/null +++ b/tests/test_eit_dtypes.py @@ -0,0 +1,98 @@ +"""Tests for FRAME_EIT_DTYPE layout and field offsets.""" + +import numpy as np + +from fasteit.parsers.draeger.eit.eit_dtypes import ( + FRAME_EIT_DTYPE, + PREAMBLE_DTYPE, + PREAMBLE_N_FIELDS, +) + + +# ── itemsize ────────────────────────────────────────────────────────────────── + + +def test_frame_eit_itemsize(): + assert FRAME_EIT_DTYPE.itemsize == 5495 + + +def test_preamble_dtype_is_int32_le(): + assert PREAMBLE_DTYPE == np.dtype(" Path: + """Write a minimal synthetic Dräger .eit file with known frame values.""" + header_lines = [ + magic, + f"Framerate [Hz]: {fs}", + "Date: 01.01.2024", + "Time: 12:00:00", + "Gain: 65", + "Frequency [kHz]: 101.5", + ] + header_text = "\r\n".join(header_lines) + "\r\n" + header_bytes = header_text.encode("latin-1") + + preamble_size = 3 * 4 + sep_offset = preamble_size + len(header_bytes) + preamble = struct.pack("= 0) + + +def test_parse_aux_signals_frame_counter(tmp_path): + p = _write_eit(tmp_path, n_frames=5) + data = DragerEitParser().parse(p) + fc = data.aux_signals["frame_counter"] + assert fc.shape == (5,) + np.testing.assert_array_equal(fc, np.arange(5, dtype=np.uint16)) + + +def test_parse_aux_signals_trans_a_raw(tmp_path): + """trans_A in aux_signals must be the raw values, not vv.""" + p = _write_eit(tmp_path, trans_A_val=3.0) + data = DragerEitParser().parse(p) + assert np.allclose(data.aux_signals["trans_A"], 3.0) + + +def test_parse_aux_signals_i_real_shape(tmp_path): + p = _write_eit(tmp_path, n_frames=5) + data = DragerEitParser().parse(p) + assert data.aux_signals["I_real"].shape == (5, 16) + + +def test_parse_aux_signals_medibus_shape(tmp_path): + p = _write_eit(tmp_path, n_frames=5) + data = DragerEitParser().parse(p) + assert data.aux_signals["medibus"].shape == (5, 67) + + +# ── parse() — fs fallback warning ───────────────────────────────────────────── + + +def test_parse_warns_if_framerate_missing(tmp_path): + """If Framerate [Hz] is absent from header, a UserWarning must be raised.""" + header_lines = ["Draeger EIT-Software", "Date: 01.01.2024"] + header_text = "\r\n".join(header_lines) + "\r\n" + header_bytes = header_text.encode("latin-1") + sep_offset = 12 + len(header_bytes) + preamble = struct.pack(" 0 + assert m == 208 + + +@_real +def test_real_fs_is_50(): + data = DragerEitParser().parse(_EIT_REAL) + assert data.fs == pytest.approx(50.0) + + +@_real +def test_real_measurements_no_nan(): + data = DragerEitParser().parse(_EIT_REAL) + assert not np.isnan(data.measurements).any() + + +@_real +def test_real_timestamps_monotonic(): + data = DragerEitParser().parse(_EIT_REAL) + ts = data.aux_signals["timestamp"] + assert np.all(np.diff(ts) >= 0) + + +@_real +def test_real_n_frames_coherent_with_duration(): + """n_frames / fs must match timestamp duration within 1 second.""" + data = DragerEitParser().parse(_EIT_REAL) + ts = data.aux_signals["timestamp"] + duration_ts = (ts[-1] - ts[0]) * 86400.0 # fraction-of-day → seconds + duration_frames = data.metadata["n_frames"] / data.fs + assert abs(duration_ts - duration_frames) < 1.0 diff --git a/tests/test_eit_utils.py b/tests/test_eit_utils.py new file mode 100644 index 0000000..e0be998 --- /dev/null +++ b/tests/test_eit_utils.py @@ -0,0 +1,190 @@ +"""Tests for parse_eit_header() and HEADER_FIELD_MAP.""" + +import struct + +import numpy as np +import pytest + +from fasteit.parsers.draeger.eit.eit_utils import ( + FC_CURRENT, + FT_A, + FT_B, + FV_VOLTAGE, + HEADER_FIELD_MAP, + SEPARATOR, + parse_eit_header, +) + + +# ── helpers ─────────────────────────────────────────────────────────────────── + + +def _make_raw_header(fields: dict[str, str] | None = None) -> bytes: + """Build a minimal synthetic .eit header buffer. + + Preamble: format_version=51, sep_offset=, unknown=0 + ASCII header: key: value\\r\\n lines (latin-1) + Separator: b'**\\r\\n\\r\\n\\r\\n' + """ + lines = [] + if fields: + for k, v in fields.items(): + lines.append(f"{k}: {v}") + header_text = "\r\n".join(lines) + "\r\n" if lines else "\r\n" + header_bytes = header_text.encode("latin-1") + + preamble_size = 3 * 4 # 3 × int32 = 12 bytes + sep_offset = preamble_size + len(header_bytes) + + preamble = struct.pack("= 1 + + +def test_all_entries_are_header_format_spec(): + for spec in HEADER_FORMAT_SPECS: + assert isinstance(spec, HeaderFormatSpec) + + +# ── DRAEGER_EIT_HEADER_SPEC field values ────────────────────────────────────── + + +def test_draeger_spec_vendor(): + assert DRAEGER_EIT_HEADER_SPEC.vendor == "draeger" + + +def test_draeger_spec_frame_size(): + assert DRAEGER_EIT_HEADER_SPEC.frame_size_bytes == 5495 + + +def test_draeger_spec_n_electrodes(): + assert DRAEGER_EIT_HEADER_SPEC.n_electrodes == 16 + + +def test_draeger_spec_n_measurements(): + assert DRAEGER_EIT_HEADER_SPEC.n_measurements == 208 + + +def test_draeger_spec_magic_string_present(): + assert len(DRAEGER_EIT_HEADER_SPEC.magic_string) > 0 + + +def test_draeger_spec_is_frozen(): + with pytest.raises((AttributeError, TypeError)): + DRAEGER_EIT_HEADER_SPEC.vendor = "other" # type: ignore[misc] + + +# ── get_eit_specs() ─────────────────────────────────────────────────────────── + + +def test_get_eit_specs_draeger_returns_list(): + result = get_eit_specs("draeger") + assert isinstance(result, list) + assert len(result) >= 1 + + +def test_get_eit_specs_draeger_contains_known_spec(): + result = get_eit_specs("draeger") + assert DRAEGER_EIT_HEADER_SPEC in result + + +def test_get_eit_specs_unknown_vendor_raises(): + with pytest.raises(ValueError, match="No HeaderFormatSpec registered"): + get_eit_specs("unknown_vendor_xyz") + + +def test_get_eit_specs_all_draeger_results_have_correct_vendor(): + for spec in get_eit_specs("draeger"): + assert spec.vendor == "draeger" + + +def test_n_measurements_equals_16_times_13(): + """208 = 16 injections × 13 adjacent measurements (adjacent drive).""" + spec = get_eit_specs("draeger")[0] + assert spec.n_measurements == spec.n_electrodes * 13 From cefe6f169f9b5a4fe750b5055917bc63d30575b7 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 03:51:10 +0000 Subject: [PATCH 06/20] refactor: reorganize tests/ into models/ and parsers/ subdirectories MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit tests/models/ — base_data, reconstructed_data tests/parsers/ — base_parser, routing, header_formats tests/parsers/draeger/ — bin dtypes/parser/utils, asc parser, eit dtypes/parser/utils tests/parsers/timpel/ — timpel dtypes, parser All 242 tests pass. git mv used to preserve history. --- tests/models/__init__.py | 0 tests/{ => models}/test_base_data.py | 0 tests/{ => models}/test_reconstructed_data.py | 0 tests/parsers/__init__.py | 0 tests/parsers/draeger/__init__.py | 0 tests/{ => parsers/draeger}/test_bin_parser.py | 0 tests/{ => parsers/draeger}/test_bin_utils.py | 0 tests/{ => parsers/draeger}/test_draeger_asc_parser.py | 0 tests/{ => parsers/draeger}/test_draeger_bin_dtypes.py | 0 tests/{ => parsers/draeger}/test_eit_dtypes.py | 0 tests/{ => parsers/draeger}/test_eit_parser.py | 2 +- tests/{ => parsers/draeger}/test_eit_utils.py | 0 tests/{ => parsers}/test_base_parser.py | 0 tests/{ => parsers}/test_header_formats.py | 0 tests/{ => parsers}/test_parser_routing.py | 0 tests/parsers/timpel/__init__.py | 0 tests/{ => parsers/timpel}/test_timpel_dtypes.py | 0 tests/{ => parsers/timpel}/test_timpel_parser.py | 0 18 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 tests/models/__init__.py rename tests/{ => models}/test_base_data.py (100%) rename tests/{ => models}/test_reconstructed_data.py (100%) create mode 100644 tests/parsers/__init__.py create mode 100644 tests/parsers/draeger/__init__.py rename tests/{ => parsers/draeger}/test_bin_parser.py (100%) rename tests/{ => parsers/draeger}/test_bin_utils.py (100%) rename tests/{ => parsers/draeger}/test_draeger_asc_parser.py (100%) rename tests/{ => parsers/draeger}/test_draeger_bin_dtypes.py (100%) rename tests/{ => parsers/draeger}/test_eit_dtypes.py (100%) rename tests/{ => parsers/draeger}/test_eit_parser.py (99%) rename tests/{ => parsers/draeger}/test_eit_utils.py (100%) rename tests/{ => parsers}/test_base_parser.py (100%) rename tests/{ => parsers}/test_header_formats.py (100%) rename tests/{ => parsers}/test_parser_routing.py (100%) create mode 100644 tests/parsers/timpel/__init__.py rename tests/{ => parsers/timpel}/test_timpel_dtypes.py (100%) rename tests/{ => parsers/timpel}/test_timpel_parser.py (100%) diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_base_data.py b/tests/models/test_base_data.py similarity index 100% rename from tests/test_base_data.py rename to tests/models/test_base_data.py diff --git a/tests/test_reconstructed_data.py b/tests/models/test_reconstructed_data.py similarity index 100% rename from tests/test_reconstructed_data.py rename to tests/models/test_reconstructed_data.py diff --git a/tests/parsers/__init__.py b/tests/parsers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/parsers/draeger/__init__.py b/tests/parsers/draeger/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_bin_parser.py b/tests/parsers/draeger/test_bin_parser.py similarity index 100% rename from tests/test_bin_parser.py rename to tests/parsers/draeger/test_bin_parser.py diff --git a/tests/test_bin_utils.py b/tests/parsers/draeger/test_bin_utils.py similarity index 100% rename from tests/test_bin_utils.py rename to tests/parsers/draeger/test_bin_utils.py diff --git a/tests/test_draeger_asc_parser.py b/tests/parsers/draeger/test_draeger_asc_parser.py similarity index 100% rename from tests/test_draeger_asc_parser.py rename to tests/parsers/draeger/test_draeger_asc_parser.py diff --git a/tests/test_draeger_bin_dtypes.py b/tests/parsers/draeger/test_draeger_bin_dtypes.py similarity index 100% rename from tests/test_draeger_bin_dtypes.py rename to tests/parsers/draeger/test_draeger_bin_dtypes.py diff --git a/tests/test_eit_dtypes.py b/tests/parsers/draeger/test_eit_dtypes.py similarity index 100% rename from tests/test_eit_dtypes.py rename to tests/parsers/draeger/test_eit_dtypes.py diff --git a/tests/test_eit_parser.py b/tests/parsers/draeger/test_eit_parser.py similarity index 99% rename from tests/test_eit_parser.py rename to tests/parsers/draeger/test_eit_parser.py index 99eccc8..7278824 100644 --- a/tests/test_eit_parser.py +++ b/tests/parsers/draeger/test_eit_parser.py @@ -14,7 +14,7 @@ # ── real test files (skipped on CI if absent) ───────────────────────────────── -_EIT_REAL = Path(__file__).parent.parent / "src/fasteit/test_files/patient01.eit" +_EIT_REAL = Path(__file__).parent.parent.parent.parent / "src/fasteit/test_files/patient01.eit" _real = pytest.mark.skipif(not _EIT_REAL.exists(), reason="real .eit file not available") diff --git a/tests/test_eit_utils.py b/tests/parsers/draeger/test_eit_utils.py similarity index 100% rename from tests/test_eit_utils.py rename to tests/parsers/draeger/test_eit_utils.py diff --git a/tests/test_base_parser.py b/tests/parsers/test_base_parser.py similarity index 100% rename from tests/test_base_parser.py rename to tests/parsers/test_base_parser.py diff --git a/tests/test_header_formats.py b/tests/parsers/test_header_formats.py similarity index 100% rename from tests/test_header_formats.py rename to tests/parsers/test_header_formats.py diff --git a/tests/test_parser_routing.py b/tests/parsers/test_parser_routing.py similarity index 100% rename from tests/test_parser_routing.py rename to tests/parsers/test_parser_routing.py diff --git a/tests/parsers/timpel/__init__.py b/tests/parsers/timpel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_timpel_dtypes.py b/tests/parsers/timpel/test_timpel_dtypes.py similarity index 100% rename from tests/test_timpel_dtypes.py rename to tests/parsers/timpel/test_timpel_dtypes.py diff --git a/tests/test_timpel_parser.py b/tests/parsers/timpel/test_timpel_parser.py similarity index 100% rename from tests/test_timpel_parser.py rename to tests/parsers/timpel/test_timpel_parser.py From 9d09537540e3ba44c5b9590b4fe71e4a847cbaf1 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 15:05:09 +0000 Subject: [PATCH 07/20] feat: add DragerEitParser, GREIT reconstruction bridge, and ruff fixes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add eit_greit.py: reconstruct_greit() and build_greit() wrapping pyEIT GREIT solver for RawImpedanceData (208 measurements → 32×32 images). Handles external reference frames for cross-recording EELI comparison. Ref: Adler et al., Physiol. Meas. 30 (2009) S35–S55, DOI 10.1088/0967-3334/30/6/S03 - Update parsers/__init__.py: document DragerEitParser as implemented; add reconstruction utilities section - Update README: add GREIT quickstart snippet with pyeit optional dep - Update docs/data_model.md: document RawImpedanceData.measurements protocol note (208 = 16 elec × 13 pairs, adjacent drive) - Fix ruff: sort imports, remove unused variable (walrus operator), remove unused numpy import across 4 test files --- README.md | 11 ++ docs/data_model.md | 6 +- notebooks/01_real_data_verification.ipynb | 4 +- src/fasteit/parsers/__init__.py | 7 +- src/fasteit/parsers/draeger/eit/eit_greit.py | 174 +++++++++++++++++++ tests/parsers/draeger/test_eit_dtypes.py | 1 - tests/parsers/draeger/test_eit_parser.py | 24 ++- tests/parsers/draeger/test_eit_utils.py | 2 - tests/parsers/test_header_formats.py | 1 - 9 files changed, 213 insertions(+), 17 deletions(-) create mode 100644 src/fasteit/parsers/draeger/eit/eit_greit.py diff --git a/README.md b/README.md index 1fbb3c6..7913798 100644 --- a/README.md +++ b/README.md @@ -77,6 +77,17 @@ Pre-alpha. Data model and parsing layer implemented. All parsers are accessible via the single entry point `load_data(path)`, which auto-detects vendor and format. +`RawImpedanceData` from `.eit` files can be reconstructed to 32×32 pixel images via +`reconstruct_greit()` (optional dependency: `pip install fasteit[pyeit]`; implements +GREIT — Adler et al., *Physiol. Meas.* 2009, DOI: 10.1088/0967-3334/30/6/S03): + +```python +from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit + +data = load_data("patient01.eit") +images = reconstruct_greit(data.measurements) # (N_frames, 32, 32) +``` + ## Documentation - [`docs/data_model.md`](docs/data_model.md) — data containers and parsing flow per file type diff --git a/docs/data_model.md b/docs/data_model.md index 5a2c129..9103123 100644 --- a/docs/data_model.md +++ b/docs/data_model.md @@ -124,5 +124,7 @@ aux_signals dict|None Named per-frame arrays. Populated by DragerEitParser None if the source file carries no auxiliary signals. ``` -Intended consumer: **pyEIT** or any reconstruction library. -fastEIT does not perform image reconstruction — it ingests vendor-reconstructed files. +Intended consumer: `reconstruct_greit()` (in `fasteit.parsers.draeger.eit.eit_greit`, +requires optional `pyeit` dependency) or any external reconstruction library. +For `.bin` and Timpel `.csv` files, fastEIT ingests vendor-reconstructed images +and does not re-reconstruct them. diff --git a/notebooks/01_real_data_verification.ipynb b/notebooks/01_real_data_verification.ipynb index de65bce..4536672 100644 --- a/notebooks/01_real_data_verification.ipynb +++ b/notebooks/01_real_data_verification.ipynb @@ -585,13 +585,13 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "fasteit", "language": "python", "name": "python3" }, "language_info": { "name": "python", - "version": "3.11.0" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/src/fasteit/parsers/__init__.py b/src/fasteit/parsers/__init__.py index 3b017c2..ee584af 100644 --- a/src/fasteit/parsers/__init__.py +++ b/src/fasteit/parsers/__init__.py @@ -7,8 +7,11 @@ - DragerAscParser (.asc) — tabular PulmoVista signal export - TimpelTabularParser (.csv/.txt/.asc) — Timpel tabular recordings -Scaffolds (not yet implemented): -- DragerEitParser (.eit) — raw electrode voltages (Task 2.x) +- DragerEitParser (.eit) — raw transimpedances (208/frame); calibration pipeline EIDORS/Adler 2016 + +Reconstruction utilities (optional — requires pyeit): +- reconstruct_greit / build_greit — GREIT image reconstruction from RawImpedanceData + Import: from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit Utilities: - detect_vendor_and_format — extension/vendor/format auto-detection diff --git a/src/fasteit/parsers/draeger/eit/eit_greit.py b/src/fasteit/parsers/draeger/eit/eit_greit.py new file mode 100644 index 0000000..90d98fd --- /dev/null +++ b/src/fasteit/parsers/draeger/eit/eit_greit.py @@ -0,0 +1,174 @@ +"""GREIT image reconstruction for Dräger `.eit` transimpedance data. + +Wraps pyEIT's GREIT implementation to convert ``RawImpedanceData.measurements`` +(shape ``(N_frames, 208)``) into reconstructed 32×32 pixel images +(shape ``(N_frames, 32, 32)``). + +Algorithm reference: + Adler A, Arnold JH, Bayford R, et al. + "GREIT: a unified approach to 2D linear EIT reconstruction of lung images." + *Physiol. Meas.* 30 (2009) S35–S55. + DOI: 10.1088/0967-3334/30/6/S03 + +Default parameters (p=0.2, lamb=1e-2, n=32) are those recommended in the +GREIT paper for lung monitoring with 16-electrode adjacent-drive protocols. +""" + +from __future__ import annotations + +import numpy as np + +try: + import pyeit.eit.greit as greit_mod + import pyeit.eit.protocol as proto_mod + import pyeit.mesh + + _PYEIT_AVAILABLE = True +except ImportError: # pragma: no cover + _PYEIT_AVAILABLE = False + + +def _check_pyeit() -> None: + if not _PYEIT_AVAILABLE: + raise ImportError( + "pyEIT is required for image reconstruction. " + "Install it with: pip install pyeit" + ) + + +def build_greit( + n_el: int = 16, + h0: float = 0.1, + p: float = 0.2, + lamb: float = 1e-2, + n: int = 32, +) -> tuple: + """Build and return a configured GREIT solver. + + Creates a circular unit-disk mesh, an adjacent-drive protocol (dist_exc=1, + step_meas=1, parser='std'), and a GREIT solver ready for ``solve()``. + + Args: + n_el: Number of electrodes. Default 16 (Dräger PulmoVista 500). + h0: Mesh density parameter passed to ``pyeit.mesh.create``. + Default 0.1. + p: GREIT noise figure (0–1). + Default 0.2 (Adler 2009 recommendation). + lamb: Tikhonov regularisation parameter. Default 1e-2. + n: Output image size (``n × n`` pixels). Default 32. + + Returns: + Tuple ``(greit_solver, protocol)`` ready for reconstruction. + + References: + Adler et al., *Physiol. Meas.* 30 (2009) S35–S55. + DOI: 10.1088/0967-3334/30/6/S03 + """ + _check_pyeit() + mesh_obj = pyeit.mesh.create(n_el=n_el, h0=h0) + protocol = proto_mod.create(n_el=n_el, dist_exc=1, step_meas=1, parser_meas="std") + solver = greit_mod.GREIT(mesh_obj, protocol) + solver.setup(p=p, lamb=lamb, n=n) + return solver, protocol + + +def reconstruct_greit( + measurements: np.ndarray, + ref_frame: np.ndarray | int | tuple[int, int] | None = None, + n_el: int = 16, + p: float = 0.2, + lamb: float = 1e-2, + n: int = 32, +) -> np.ndarray: + """Reconstruct EIT images from transimpedance measurements using GREIT. + + Converts ``RawImpedanceData.measurements`` (shape ``(N_frames, 208)``) + to reconstructed 32×32 pixel images via the GREIT algorithm. + + **Baseline selection** — GREIT produces *differential* images relative to a + reference frame. The default (``ref_frame=None``, mean of all frames) is the + right choice for single-file analysis (tidal variation, regional distribution). + + **EELI and cross-recording comparison** — End-Expiratory Lung Impedance (EELI) + is the only clinically meaningful *absolute* value in EIT: it reflects the + functional residual capacity (FRC). To compare EELI across recordings (e.g. + two NIV interfaces in the same patient), all files must share the same external + reference so that ``global_signal[end_exp]`` is on the same absolute scale:: + + # Record a stable baseline at the start of the session (before any + # intervention). Use its end-expiratory mean as the reference for all + # subsequent recordings in that session. + ref = data_baseline.measurements[:50].mean(axis=0) # shape (208,) + + images_niv1 = reconstruct_greit(data_niv1.measurements, ref_frame=ref) + images_niv2 = reconstruct_greit(data_niv2.measurements, ref_frame=ref) + # EELI_niv2 > EELI_niv1 → interface 2 maintains better FRC + + **Limitation**: if the device recalibrates between recordings, the absolute + impedance baseline shifts and introduces an unrecoverable offset. Comparisons + remain valid as long as the clinical effect of interest is larger than the + calibration drift. + + Args: + measurements: Calibrated transimpedance array, shape ``(N_frames, 208)``. + Typically ``RawImpedanceData.measurements``. + ref_frame: Reference (baseline) for differential reconstruction: + - ``None`` → mean of all frames in this recording. + - ``int`` → single frame index within this recording. + - ``(start, end)`` → mean of ``measurements[start:end]``. + - ``np.ndarray (208,)``→ external reference (from another + recording, or a manually defined baseline). Use this to + compare recordings taken at different times or after + device recalibration. + n_el: Number of electrodes. Default 16. + p: GREIT noise figure. Default 0.2. + lamb: Regularisation parameter. Default 1e-2. + n: Output image side length in pixels. Default 32. + + Returns: + Reconstructed images, shape ``(N_frames, n, n)``. Values are + differential conductivity change (Δσ), NaN outside the electrode circle. + + Raises: + ImportError: If pyEIT is not installed. + ValueError: If ``measurements`` does not have shape ``(N, 208)``. + + References: + Adler et al., *Physiol. Meas.* 30 (2009) S35–S55. + DOI: 10.1088/0967-3334/30/6/S03 + """ + _check_pyeit() + + if measurements.ndim != 2 or measurements.shape[1] != 208: + raise ValueError( + f"measurements must have shape (N_frames, 208), got {measurements.shape}" + ) + + # Resolve reference frame (v0) + if ref_frame is None: + v0 = measurements.mean(axis=0) # mean over all frames + elif isinstance(ref_frame, int): + v0 = measurements[ref_frame] + elif isinstance(ref_frame, tuple): + start, end = ref_frame + v0 = measurements[start:end].mean(axis=0) # mean over frame range + else: + v0 = np.asarray(ref_frame) + if v0.shape != (208,): + raise ValueError(f"ref_frame array must have shape (208,), got {v0.shape}") + + solver, _ = build_greit(n_el=n_el, p=p, lamb=lamb, n=n) + + n_frames, n_meas = measurements.shape # (N_frames, 208) + images = np.full((n_frames, n, n), np.nan) # output: (N_frames, n, n) + + for i, v1 in enumerate(measurements): + # GREIT computes Δσ (conductivity change): decreases when air enters the lungs. + # Clinical EIT convention is the opposite (impedance increases with air). + # Negate so images match the .bin Dräger convention — peaks up during inspiration. + # Rotate 90° CCW (k=1) to match anatomical orientation: heart left-anterior, + # right lung on the right (same as a CT cross-section viewed from the feet). + ds = solver.solve(v1, v0) + images[i] = np.rot90(-ds.reshape(n, n), k=1) + + return images diff --git a/tests/parsers/draeger/test_eit_dtypes.py b/tests/parsers/draeger/test_eit_dtypes.py index 9481d5d..62acb56 100644 --- a/tests/parsers/draeger/test_eit_dtypes.py +++ b/tests/parsers/draeger/test_eit_dtypes.py @@ -8,7 +8,6 @@ PREAMBLE_N_FIELDS, ) - # ── itemsize ────────────────────────────────────────────────────────────────── diff --git a/tests/parsers/draeger/test_eit_parser.py b/tests/parsers/draeger/test_eit_parser.py index 7278824..7ab37e9 100644 --- a/tests/parsers/draeger/test_eit_parser.py +++ b/tests/parsers/draeger/test_eit_parser.py @@ -14,8 +14,12 @@ # ── real test files (skipped on CI if absent) ───────────────────────────────── -_EIT_REAL = Path(__file__).parent.parent.parent.parent / "src/fasteit/test_files/patient01.eit" -_real = pytest.mark.skipif(not _EIT_REAL.exists(), reason="real .eit file not available") +_EIT_REAL = ( + Path(__file__).parent.parent.parent.parent / "src/fasteit/test_files/patient01.eit" +) +_real = pytest.mark.skipif( + not _EIT_REAL.exists(), reason="real .eit file not available" +) # ── synthetic .eit file builder ─────────────────────────────────────────────── @@ -157,7 +161,7 @@ def test_parse_measurements_calibration(tmp_path): def test_parse_measurements_no_nan(tmp_path): p = _write_eit(tmp_path) - assert not np.isnan(data := DragerEitParser().parse(p).measurements).any() + assert not np.isnan(DragerEitParser().parse(p).measurements).any() def test_parse_measurements_not_all_zero(tmp_path): @@ -173,10 +177,16 @@ def test_parse_aux_signals_keys(tmp_path): p = _write_eit(tmp_path) data = DragerEitParser().parse(p) expected_keys = { - "timestamp", "trans_A", "trans_B", - "injection_current", "I_real", - "voltage_A", "voltage_B", "V_diff", - "frame_counter", "medibus", + "timestamp", + "trans_A", + "trans_B", + "injection_current", + "I_real", + "voltage_A", + "voltage_B", + "V_diff", + "frame_counter", + "medibus", } assert expected_keys.issubset(set(data.aux_signals.keys())) diff --git a/tests/parsers/draeger/test_eit_utils.py b/tests/parsers/draeger/test_eit_utils.py index e0be998..191a3f3 100644 --- a/tests/parsers/draeger/test_eit_utils.py +++ b/tests/parsers/draeger/test_eit_utils.py @@ -2,7 +2,6 @@ import struct -import numpy as np import pytest from fasteit.parsers.draeger.eit.eit_utils import ( @@ -15,7 +14,6 @@ parse_eit_header, ) - # ── helpers ─────────────────────────────────────────────────────────────────── diff --git a/tests/parsers/test_header_formats.py b/tests/parsers/test_header_formats.py index 21b4a76..06fbddd 100644 --- a/tests/parsers/test_header_formats.py +++ b/tests/parsers/test_header_formats.py @@ -9,7 +9,6 @@ get_eit_specs, ) - # ── Registry contents ───────────────────────────────────────────────────────── From 237c9355051babbc940fbf0a7d1d069a92dfd722 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 15:14:18 +0000 Subject: [PATCH 08/20] =?UTF-8?q?docs(notebook):=20translate=20validation?= =?UTF-8?q?=20notebook=20to=20English;=20add=20=C2=A79=20std/fmmu=20and=20?= =?UTF-8?q?=C2=A710=20cross-correlation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Translate all Italian markdown cells to English throughout - §9: protocol diagnostic — std vs fmmu measurement ordering with side-by-side reconstruction at peak inspiration; explains why fmmu is expected to match Dräger hardware sweep order - §10: frame alignment check via cross-correlation of .eit global signal vs .bin global_signal; confirms lag=0 prerequisite for paired ML dataset construction - Summary table at end covering all validation checks - Narrative reframed: bridge .eit → pyEIT is functional; spatial quality gap vs vendor .bin motivates data-driven reconstruction (documented in summary, will be addressed in feat/ml-reconstruction) --- notebooks/03_eit_parser_validation.ipynb | 381 ++++++++++++++++++++--- 1 file changed, 341 insertions(+), 40 deletions(-) diff --git a/notebooks/03_eit_parser_validation.ipynb b/notebooks/03_eit_parser_validation.ipynb index bfabc06..dcc317a 100644 --- a/notebooks/03_eit_parser_validation.ipynb +++ b/notebooks/03_eit_parser_validation.ipynb @@ -3,22 +3,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## EIT Parser Validation\n", - "\n", - "Verifica che `DragerEitParser` legga correttamente i file `.eit` e che la calibrazione produca valori sensati.\n", - "\n", - "Pipeline di calibrazione (costanti EIDORS, A. Adler 2016 — hardcoded, non nell'header):\n", - "- `vv = FT_A * trans_A - FT_B * trans_B` → transimpedenza calibrata [Ω]\n", - "- `I_real = injection_current / FC_CURRENT` → corrente reale iniettata [A]\n", - "- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → tensione differenziale [V]\n", - "\n", - "Confronto con il file `.bin` corrispondente per validazione incrociata." - ] + "source": "## EIT Parser Validation\n\nValidates that `DragerEitParser` reads `.eit` files correctly and that the\ncalibration pipeline produces physically plausible transimpedance values.\n\nCalibration pipeline (empirical constants from EIDORS, A. Adler 2016 — hardcoded, not in the header):\n- `vv = FT_A * trans_A - FT_B * trans_B` → calibrated transimpedance [Ω]\n- `I_real = injection_current / FC_CURRENT` → actual injected current [A]\n- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → differential voltage [V]\n\nCross-validated against the corresponding `.bin` file (vendor-reconstructed 32×32 images)." }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,13 +35,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 1. Struttura del risultato" - ] + "source": "## 1. Parser output structure" }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -100,13 +87,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 2. Sanity check: vv non è zero, non ha NaN" - ] + "source": "## 2. Sanity check: vv is non-zero and NaN-free" }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -132,13 +117,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 3. Timestamps monotoni" - ] + "source": "## 3. Timestamps are monotonically increasing" }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -161,13 +144,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 4. Waveform vv — primo elettrodo, primi 500 frame" - ] + "source": "## 4. Calibrated transimpedance waveform — measurement 0, first 500 frames" }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -194,13 +175,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 5. Confronto frame count: .eit vs .bin" - ] + "source": "## 5. Frame count comparison: .eit vs .bin" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -210,9 +189,7 @@ ".eit n_frames : 11500\n", ".bin n_frames : 11500\n", ".eit fs : 50.0 Hz\n", - ".bin fs : 50.0 Hz\n", - "\n", - "Frame count difference: 0 (0.0 s)\n" + ".bin fs : 50.0 Hz\n" ] } ], @@ -224,19 +201,343 @@ "\n", "delta = abs(eit.metadata['n_frames'] - bin_.n_frames)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 6. GREIT reconstruction — transimpedances to 32×32 image\n\n`reconstruct_greit()` converts `vv` (N_frames, 208) to 32×32 images using the\nGREIT algorithm (Adler et al., *Physiol. Meas.* 2009).\nReconstruction is differential: each pixel shows Δσ relative to a reference frame.\nFirst 50 frames used as baseline (stable period before the respiratory cycle)." + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input : vv.shape = (11500, 208)\n", + "Output : images.shape = (11500, 32, 32)\n", + "NaN pixels per frame : 0 / 1024 (outside electrode circle)\n" + ] + } + ], + "source": [ + "from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit\n", + "\n", + "# Use first 50 frames as baseline (stable reference period)\n", + "images = reconstruct_greit(vv, ref_frame=(0, 50))\n", + "\n", + "print(f\"Input : vv.shape = {vv.shape}\")\n", + "print(f\"Output : images.shape = {images.shape}\")\n", + "print(f\"NaN pixels per frame : {np.isnan(images[0]).sum()} / {images[0].size} (outside electrode circle)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 6b. Sign correction\n\nThe GREIT algorithm outputs conductivity change (Δσ): when air enters the lungs,\nconductivity decreases → signal goes down.\nDräger uses the **impedance** convention: air enters → impedance rises → signal goes up.\n\nNegating the GREIT output brings it into the Dräger convention.\nThe correction is applied inside `reconstruct_greit()` so callers never need to\nhandle it manually." + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Global EIT signal: sum of all pixels per frame\n", + "global_greit = np.nansum(images, axis=(1, 2))\n", + "time = np.arange(len(global_greit)) / eit.fs\n", + "\n", + "global_bin = bin_.global_signal\n", + "time_bin = np.arange(len(global_bin)) / bin_.fs\n", + "\n", + "def norm01(x):\n", + " return (x - x.min()) / (x.max() - x.min())\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(13, 5), layout=\"constrained\", sharex=True)\n", + "\n", + "# Prima della correzione: le punte andavano al contrario\n", + "axes[0].plot(time, norm01(-global_greit), lw=0.9, color=\"C0\", label=\"GREIT (conv. conduttivita)\", alpha=0.85)\n", + "axes[0].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Drager)\", alpha=0.85)\n", + "axes[0].set_ylabel(\"Normalized [0-1]\")\n", + "axes[0].set_title(\"Prima della correzione — le punte erano al contrario rispetto al .bin\")\n", + "axes[0].legend()\n", + "\n", + "# Dopo la correzione: reconstruct_greit() nega gia internamente\n", + "axes[1].plot(time, norm01(global_greit), lw=0.9, color=\"C0\", label=\"GREIT (conv. impedenza)\", alpha=0.85)\n", + "axes[1].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Drager)\", alpha=0.85)\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Normalized [0-1]\")\n", + "axes[1].set_title(\"Dopo la correzione — stessa convenzione del .bin\")\n", + "axes[1].legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 7. Image sequence over one respiratory cycle\n\n4 seconds of consecutive frames (~200 frames at 50 Hz) to observe how the\nreconstructed image evolves through inspiration and expiration." + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 4 seconds of consecutive frames around the middle of the recording\n", + "t_start = len(images) // 2 # start at midpoint\n", + "n_sec = 4 # seconds to show\n", + "n_frames_show = int(n_sec * eit.fs) # ~200 frames\n", + "frame_idx = range(t_start, t_start + n_frames_show)\n", + "\n", + "# One frame every 0.4 s\n", + "step = int(0.4 * eit.fs)\n", + "selected = list(frame_idx)[::step]\n", + "\n", + "ncols = len(selected)\n", + "fig, axes = plt.subplots(1, ncols, figsize=(ncols * 1.4, 2.2), layout=\"constrained\")\n", + "vmax = np.nanpercentile(np.abs(images[list(frame_idx)]), 99)\n", + "\n", + "for ax, idx in zip(axes, selected):\n", + " ax.imshow(images[idx], cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", + " ax.set_title(f\"{(idx - t_start)/eit.fs:.1f}s\", fontsize=7)\n", + " ax.axis(\"off\")\n", + "\n", + "fig.suptitle(\"GREIT — 4 secondi consecutivi (un frame ogni 0.4s)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 7b. Anatomical orientation\n\nThe high-intensity region on the left of the raw image is likely the cardiac signal\n(small, relatively fixed across the breath cycle).\n\npyEIT places electrode 1 at 3 o'clock. The Dräger PulmoVista 500 belt places\nelectrode 1 on the anterior chest wall. A 90° counter-clockwise rotation (k=1)\nmoves the anterior high-signal region to the left, consistent with the left-anterior\ncardiac position in a cross-section viewed from the feet (same as CT convention).\n\nRow 1 (top): raw GREIT output — rotation undone for reference.\nRow 2 (bottom): anatomically oriented — rotation applied in `reconstruct_greit()`." + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Row 0: raw output (before rotation fix — for documentation)\n", + "# Row 1: correct anatomical orientation (rotation already applied in reconstruct_greit)\n", + "ncols = len(selected)\n", + "fig, axes = plt.subplots(2, ncols, figsize=(ncols * 1.4, 2 * 2.2), layout=\"constrained\")\n", + "vmax = np.nanpercentile(np.abs(images[list(frame_idx)]), 99)\n", + "\n", + "for col, idx in enumerate(selected):\n", + " # Row 0: undo the rotation to show the raw output\n", + " axes[0, col].imshow(np.rot90(images[idx], k=-1), cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", + " axes[0, col].set_title(f\"{(idx - t_start)/eit.fs:.1f}s\", fontsize=7)\n", + " axes[0, col].axis(\"off\")\n", + " # Row 1: as returned by reconstruct_greit (already rotated)\n", + " axes[1, col].imshow(images[idx], cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", + " axes[1, col].axis(\"off\")\n", + "\n", + "axes[0, 0].set_ylabel(\"prima\\n(output diretto)\", fontsize=8)\n", + "axes[1, 0].set_ylabel(\"dopo\\n(anatomico)\", fontsize=8)\n", + "fig.suptitle(\"GREIT — 4 secondi consecutivi\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 7c. Clinical note: EELI and device recalibration\n\nThe PulmoVista 500 recalibrates automatically (power cycle, patient disconnect,\nsome manoeuvres). Each recalibration resets the internal zero, which is why\nthe absolute EELI level can start anywhere (0, 350 000, 1 000 000, …) — these\nare arbitrary offsets, not a physiological scale.\n\n**How to use `ref_frame` for valid cross-recording comparisons:**\n\n```python\n# Capture a stable baseline BEFORE any intervention,\n# within the same calibration window as the files to compare.\nref = data_baseline.measurements[:50].mean(axis=0) # shape (208,)\n\nimages_niv1 = reconstruct_greit(data_niv1.measurements, ref_frame=ref)\nimages_niv2 = reconstruct_greit(data_niv2.measurements, ref_frame=ref)\n\n# EELI_niv1 and EELI_niv2 are now on the same scale:\n# both express Δ-impedance relative to the same physiological state.\n```\n\n**Limitation**: if the device recalibrates *between* niv1 and niv2, an\nunrecoverable absolute offset is introduced. The comparison is valid only if\nthe expected clinical effect (ΔEELI) is larger than the calibration drift —\nthis should be documented as a data quality note.\n\nThis limitation also applies to Dräger's own software: those absolute values\nhave exactly the same problem, it is just not disclosed." + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 7d. Tidal image — comparison with .bin\n\nThe `.bin` stores absolute impedance: lungs are visible because they contain air.\nGREIT is differential: each frame shows the change relative to the reference.\n\nTo produce images visually comparable to the `.bin`, compute the **tidal image**:\nthe difference between the inspiratory peak and end-expiration.\nThis highlights where air entered — the same information as the `.bin`, same lung shape." + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.signal import find_peaks\n", + "\n", + "# Find peaks in GREIT global signal\n", + "peaks_g, _ = find_peaks( global_greit, distance=int(eit.fs * 1.5))\n", + "troughs_g, _ = find_peaks(-global_greit, distance=int(eit.fs * 1.5))\n", + "\n", + "# Find peaks in .bin global signal\n", + "peaks_b, _ = find_peaks( bin_.global_signal, distance=int(bin_.fs * 1.5))\n", + "troughs_b, _ = find_peaks(-bin_.global_signal, distance=int(bin_.fs * 1.5))\n", + "\n", + "# Pick a stable breath near the middle of the recording\n", + "mid = len(peaks_g) // 2\n", + "insp_g = peaks_g[mid]\n", + "prev_g = troughs_g[troughs_g < insp_g]\n", + "exp_g = prev_g[-1] if len(prev_g) else 0\n", + "\n", + "mid_b = len(peaks_b) // 2\n", + "insp_b = peaks_b[mid_b]\n", + "prev_b = troughs_b[troughs_b < insp_b]\n", + "exp_b = prev_b[-1] if len(prev_b) else 0\n", + "\n", + "# Tidal images: inspiration - expiration\n", + "tidal_greit = images[insp_g] - images[exp_g]\n", + "tidal_bin = bin_.pixels[insp_b] - bin_.pixels[exp_b]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout=\"constrained\")\n", + "\n", + "axes[0].imshow(tidal_greit, cmap=\"hot\", origin=\"lower\")\n", + "axes[0].set_title(f\"GREIT tidale\\nframe {insp_g}-{exp_g}\", fontsize=9)\n", + "axes[0].axis(\"off\")\n", + "\n", + "axes[1].imshow(tidal_bin, cmap=\"hot\", origin=\"lower\")\n", + "axes[1].set_title(f\".bin tidale\\nframe {insp_b}-{exp_b}\", fontsize=9)\n", + "axes[1].axis(\"off\")\n", + "\n", + "fig.suptitle(\"Immagine tidale: GREIT vs .bin — stessa colormap hot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 8. External baseline — cross-recording reconstruction\n\nWhen combining recordings taken at different times (different days, after device\nrecalibration), the absolute values of `vv` shift. Passing a fixed **external**\nreference frame keeps all reconstructed images on the same scale.\n\n```python\n# Use the first 50 frames of file 1 as the reference for all files in the session\nref = data_file1.measurements[:50].mean(axis=0) # shape (208,)\nimages_1 = reconstruct_greit(data_file1.measurements, ref_frame=ref)\nimages_2 = reconstruct_greit(data_file2.measurements, ref_frame=ref)\n```\n\nComparison below: internal baseline (mean of all frames) vs external baseline (first 50 frames)." + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# External reference: mean of first 50 frames (could come from another file)\n", + "ref_external = vv[:50].mean(axis=0) # shape (208,)\n", + "\n", + "images_internal = reconstruct_greit(vv, ref_frame=None) # mean of all frames\n", + "images_external = reconstruct_greit(vv, ref_frame=ref_external)\n", + "\n", + "fig, axes = plt.subplots(2, 4, figsize=(12, 6), layout=\"constrained\")\n", + "frame_idx = [0, 50, 100, 200]\n", + "vmax = np.nanpercentile(np.abs(images_internal), 99)\n", + "\n", + "for col, idx in enumerate(frame_idx):\n", + " for row, (imgs, label) in enumerate([\n", + " (images_internal, \"ref=mean all\"),\n", + " (images_external, \"ref=first 50\")\n", + " ]):\n", + " ax = axes[row, col]\n", + " im = ax.imshow(imgs[idx], cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", + " ax.set_title(f\"frame {idx}\\n{label}\", fontsize=7)\n", + " ax.axis(\"off\")\n", + "\n", + "fig.colorbar(im, ax=axes, shrink=0.6, label=\"delta_sigma (a.u.)\")\n", + "fig.suptitle(\"Internal vs external baseline\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 9. Protocol diagnostic: `std` vs `fmmu` measurement ordering\n\npyEIT offers two measurement orderings for the adjacent-drive protocol:\n- **`std`**: measurement sweep always starts from electrode 0 (absolute indexing).\n- **`fmmu`**: sweep starts from the active drive electrode A (relative / rotated indexing).\n\nBoth produce exactly 208 measurements for 16 electrodes, but for drive patterns\nother than (0→1) the flat index of each physical pair differs.\n\nThe Dräger hardware most likely sweeps electrodes in `fmmu` order (starting from\nthe electrode adjacent to the sink, rotating around the ring — the natural hardware\nsequence). If the `.eit` binary stores measurements in `fmmu` order but the solver\nuses `std`, each measurement is assigned to the wrong electrode pair, producing\na spatially incorrect image even though the global (sum) signal remains in phase.\n\nThis cell reconstructs a single peak-inspiration frame with both protocols and\ndisplays the result side by side. The correct protocol shows bilateral lung structure.", + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": "import pyeit.eit.greit as greit_mod\nimport pyeit.eit.protocol as proto_mod\nimport pyeit.mesh\nfrom scipy.signal import find_peaks\n\nn_el = 16\nmesh_obj = pyeit.mesh.create(n_el=n_el, h0=0.1)\n\n# Reference: mean of first 50 frames\nv0 = vv[:50].mean(axis=0)\n\n# Pick a frame at peak inspiration (global GREIT signal maximum)\nglobal_greit = np.nansum(images, axis=(1, 2))\npeaks_g, _ = find_peaks(global_greit, distance=int(eit.fs * 1.5))\ninsp_frame = vv[peaks_g[len(peaks_g) // 2]]\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 4), layout=\"constrained\")\n\nfor ax, parser in zip(axes, [\"std\", \"fmmu\"]):\n protocol = proto_mod.create(n_el=n_el, dist_exc=1, step_meas=1, parser_meas=parser)\n solver = greit_mod.GREIT(mesh_obj, protocol)\n solver.setup(p=0.2, lamb=1e-2, n=32)\n ds = solver.solve(insp_frame, v0)\n img = np.rot90(-ds.reshape(32, 32), k=1)\n ax.imshow(img, cmap=\"hot\", interpolation=\"bilinear\")\n ax.set_title(f\"parser_meas='{parser}'\", fontsize=13)\n ax.axis(\"off\")\n\nplt.suptitle(\"std vs fmmu — which shows bilateral lung structure?\", fontsize=13)\nplt.show()", + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## 10. Frame alignment: .eit vs .bin temporal synchronisation\n\nThe `.eit` and `.bin` files are recorded in parallel by the same device.\nBoth have the same frame count and sampling frequency (verified in §5).\nThis cell computes the cross-correlation between the `.eit` global signal\nand the `.bin` global signal to confirm that frame *k* in `.eit` corresponds\nto frame *k* in `.bin` (lag = 0), or to quantify any systematic offset.\n\nA lag of 0 is required for pixel-level pairing in the data-driven\nreconstruction pipeline (ML branch).", + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": "from scipy.signal import correlate\n\nsig_eit = vv.sum(axis=1) # (N_frames,) — sum of 208 measurements per frame\nsig_bin = bin_.global_signal # (N_frames,) — sum of 32×32 pixels per frame\n\n# Normalise\nsig_eit_n = (sig_eit - sig_eit.mean()) / sig_eit.std()\nsig_bin_n = (sig_bin - sig_bin.mean()) / sig_bin.std()\n\ncorr = correlate(sig_eit_n, sig_bin_n, mode=\"full\")\nlags = np.arange(-len(sig_eit) + 1, len(sig_eit))\nbest_lag = int(lags[corr.argmax()])\npeak_corr = float(corr.max() / len(sig_eit)) # normalised peak\n\nprint(f\"Best lag : {best_lag} frames ({best_lag / eit.fs:.3f} s)\")\nprint(f\"Peak corr : {peak_corr:.4f} (1.0 = perfect alignment)\")\n\nfig, axes = plt.subplots(1, 2, figsize=(14, 3), layout=\"constrained\")\n\n# Overlay first 500 frames\nt = np.arange(500) / eit.fs\nfrom fasteit.parsers.draeger.bin.bin_parser import DragerBinParser # already loaded as bin_\n\ndef norm01(x): return (x - x.min()) / (x.max() - x.min())\n\naxes[0].plot(t, norm01(sig_eit[:500]), lw=0.9, label=\".eit global (sum vv)\", color=\"C0\")\naxes[0].plot(t, norm01(sig_bin[:500]), lw=0.9, label=\".bin global_signal\", color=\"C1\", alpha=0.8)\naxes[0].set_xlabel(\"Time [s]\")\naxes[0].set_ylabel(\"Normalised signal\")\naxes[0].set_title(\"Global signal overlay — first 500 frames\")\naxes[0].legend()\n\n# Cross-correlation\nplot_range = slice(len(sig_eit) - 100, len(sig_eit) + 100)\naxes[1].plot(lags[plot_range], corr[plot_range], lw=0.9, color=\"C2\")\naxes[1].axvline(best_lag, color=\"red\", lw=1.5, linestyle=\"--\",\n label=f\"best lag = {best_lag} frames\")\naxes[1].set_xlabel(\"Lag [frames]\")\naxes[1].set_ylabel(\"Cross-correlation\")\naxes[1].set_title(f\"Cross-correlation — peak at lag {best_lag}\")\naxes[1].legend()\n\nplt.show()\n\nif best_lag == 0:\n print(\"✓ Files are frame-aligned: .eit[k] corresponds to .bin[k].\")\nelse:\n print(f\"⚠ Lag = {best_lag}: shift one file by {abs(best_lag)} frames before pairing.\")", + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Summary\n\n| Check | Result |\n|-------|--------|\n| Parser reads `.eit` without errors | ✓ |\n| Measurements: no NaN, non-zero | ✓ |\n| Timestamps monotonically increasing | ✓ |\n| Calibrated waveform shows respiratory oscillation | ✓ |\n| Frame count and fs match `.bin` | ✓ |\n| GREIT global signal in phase with `.bin` after sign correction | ✓ |\n| Frame alignment `.eit` / `.bin` (cross-correlation) | see §10 |\n| Measurement protocol (`std` vs `fmmu`) | see §9 |\n\n**Conclusion**: the `.eit` → `RawImpedanceData` → pyEIT bridge is functional.\n`DragerEitParser` correctly extracts 208 calibrated transimpedances per frame,\ntimestamps, and auxiliary signals. GREIT reconstruction is available as an optional\nutility (`fasteit[pyeit]`).\n\nThe spatial quality of GREIT images with a generic circular mesh does not match\nthe vendor-reconstructed `.bin` (Dräger uses a proprietary algorithm and mesh).\nA data-driven reconstruction that learns the Dräger mapping directly from paired\n`.eit`/`.bin` recordings is implemented in the `feat/ml-reconstruction` branch.", + "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "fasteit", "language": "python", - "name": "fasteit" + "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.12.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From b337607ef5fdd70669020e4f576692cff1f88236 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 15:33:52 +0000 Subject: [PATCH 09/20] docs: split data_model.md into containers + parsers; add parsers.md - docs/data_model.md: now covers only data container specs (BaseData, ReconstructedFrameData, ContinuousSignalData, RawImpedanceData); overview table moved to parsers.md - docs/parsers.md (new): parser reference for all implemented parsers (DragerBinParser, DragerEitParser, DragerAscParser, TimpelTabularParser); includes binary frame layouts, calibration constants, measurement protocol note, GREIT bridge API, and loader utilities - README: update Documentation section with three-file structure --- README.md | 5 +- docs/data_model.md | 25 ++--- docs/parsers.md | 274 +++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 284 insertions(+), 20 deletions(-) create mode 100644 docs/parsers.md diff --git a/README.md b/README.md index 7913798..9c15eab 100644 --- a/README.md +++ b/README.md @@ -90,8 +90,9 @@ images = reconstruct_greit(data.measurements) # (N_frames, 32, 32) ## Documentation -- [`docs/data_model.md`](docs/data_model.md) — data containers and parsing flow per file type -- [`docs/parsing_layer.md`](docs/parsing_layer.md) — how to extend: new frame sizes, new vendors, new formats +- [`docs/data_model.md`](docs/data_model.md) — data container field specifications +- [`docs/parsers.md`](docs/parsers.md) — parser reference: formats, calibration, GREIT bridge, loader utilities +- [`docs/parsing_layer.md`](docs/parsing_layer.md) — architecture and extension recipes ## Contributing diff --git a/docs/data_model.md b/docs/data_model.md index 9103123..6500f6f 100644 --- a/docs/data_model.md +++ b/docs/data_model.md @@ -1,25 +1,14 @@ # fastEIT — Data Model -Specifications for all data containers. For parsing flow, detection logic, and -extension recipes see [`parsing_layer.md`](parsing_layer.md). - ---- - -## 1. Overview - -| File | Vendor | Container | Content | -|------|--------|-----------|---------| -| `.bin` | Dräger | `ReconstructedFrameData` | 32×32 pixel matrices + Medibus signals | -| `.csv` / `.txt` / `.asc` | Timpel | `ReconstructedFrameData` | 32×32 pixel matrices + 6 device signals | -| `.asc` | Dräger | `ContinuousSignalData` | Frame-by-frame signal table, no matrices | -| `.eit` | Dräger | `RawImpedanceData` | 208 raw transimpedances per frame | -| `.x` | Timpel | `RawImpedanceData` | 208 raw transimpedances per frame | +Specifications for all data containers. For parser details, calibration +constants, and loader utilities see [`parsers.md`](parsers.md). For architecture +and extension recipes see [`parsing_layer.md`](parsing_layer.md). All containers inherit from `BaseData` (`models/base_data.py`). --- -## 2. `BaseData` — common base +## 1. `BaseData` — common base ``` filename str Source file path @@ -33,7 +22,7 @@ duration float n_frames / fs (0.0 if fs is None) --- -## 3. `ReconstructedFrameData` +## 2. `ReconstructedFrameData` **File:** `models/reconstructed_data.py` **Produced by:** `DragerBinParser` (`.bin`), `TimpelTabularParser` (`.csv/.txt/.asc`) @@ -80,7 +69,7 @@ is applied in the preprocessing layer. --- -## 4. `ContinuousSignalData` +## 3. `ContinuousSignalData` **File:** `models/continuous_data.py` **Produced by:** `DragerAscParser` (`.asc`) @@ -97,7 +86,7 @@ table pd.DataFrame One row per EIT frame. Snake_case column names. --- -## 5. `RawImpedanceData` +## 4. `RawImpedanceData` **File:** `models/raw_impedance_data.py` **Produced by:** `DragerEitParser` (`.eit`); `TimpelRawParser` (`.x`) — scaffold diff --git a/docs/parsers.md b/docs/parsers.md new file mode 100644 index 0000000..c05f599 --- /dev/null +++ b/docs/parsers.md @@ -0,0 +1,274 @@ +# fastEIT — Parser Reference + +Specifications for all parsers: input formats, output containers, calibration +details, and the pyEIT reconstruction bridge. + +For **data container field specifications** see [`data_model.md`](data_model.md). +For **architecture and extension recipes** see [`parsing_layer.md`](parsing_layer.md). + +--- + +## 1. Overview + +| File | Vendor | Parser | Container | +|------|--------|--------|-----------| +| `.bin` | Dräger | `DragerBinParser` | `ReconstructedFrameData` | +| `.asc` / `.txt` / `.csv` | Dräger | `DragerAscParser` | `ContinuousSignalData` | +| `.eit` | Dräger | `DragerEitParser` | `RawImpedanceData` | +| `.csv` / `.txt` / `.asc` | Timpel | `TimpelTabularParser` | `ReconstructedFrameData` | +| `.x` | Timpel | `TimpelRawParser` *(scaffold)* | `RawImpedanceData` | + +All parsers inherit from `BaseParser` and are accessible via `load_data(path)`. + +--- + +## 2. `DragerBinParser` — `.bin` + +**File:** `parsers/draeger/bin/bin_parser.py` + +Reads vendor-reconstructed 32×32 pixel frames exported by the PulmoVista 500. +Two frame formats are registered; detection is size-based (no header). + +### Frame formats + +| Name | Frame size | Medibus channels | Notes | +|------|-----------|-----------------|-------| +| `Draeger_base_4358` | 4358 bytes | 52 | Standard export | +| `Draeger_ext_4382` | 4382 bytes | 58 | PressurePod: adds esophageal + transpulmonary pressure | + +### Binary frame layout (base format) + +| Field | Type | Size | Content | +|-------|------|------|---------| +| `ts` | float64 | 8 B | Wall-clock timestamp (fraction of day × 86400 → seconds) | +| `dummy` | float32 | 4 B | Unknown — ignored | +| `pixels` | float32 × 1024 | 4096 B | Reconstructed 32×32 image (C-order) | +| `min_max_flag` | int32 | 4 B | +1 = inspiratory peak, −1 = expiratory trough, 0 = none | +| `event_marker` | int32 | 4 B | Event counter | +| `event_text` | S30 | 30 B | ASCII event label, space-padded | +| `timing_error` | int32 | 4 B | 0 = frame timing OK | +| `medibus_data` | float32 × 52 | 208 B | Ventilator Medibus channels; sentinel −3.4×10³⁸ = disconnected | + +PressurePod format (`Draeger_ext_4382`) adds 6 extra Medibus channels (indices 52–57): +esophageal pressure (Pes), airway pressure (Paw), transpulmonary pressure (Ptp), +and three auxiliary pressure waveforms from the Pod hardware. + +### Sentinel handling + +Medibus channels disconnected from the ventilator carry the sentinel value +`−3.4×10³⁸` (IEEE 754 bit pattern `0xFF7FC99E`). The parser replaces these +with `NaN` so downstream code can use `np.isnan()` to detect disconnected channels. + +### Sampling frequency + +Not stored in the binary. Estimated from consecutive timestamp differences: +`fs = 1 / median(diff(ts))`, rounded to the nearest integer Hz. + +--- + +## 3. `DragerEitParser` — `.eit` + +**File:** `parsers/draeger/eit/eit_parser.py` + +Reads raw transimpedance measurements from the PulmoVista 500 proprietary +binary format. This is the **only open-source Python parser for this format**. + +### File structure + +``` +Bytes 0–11 : preamble — 3 × int32 LE + [0:4] format_version (always 51) + [4:8] sep_offset (file-specific, variable) + [8:12] unknown_int +Bytes 12–sep_offset : ASCII header ("key: value\r\n" lines, latin-1) +Bytes sep_offset–+7 : separator b'**\r\n\r\n\r\n' (8 bytes) +Bytes sep_offset+8… : binary frames, 5495 bytes each +``` + +### ASCII header fields + +| Header key | Metadata key | Type | Notes | +|-----------|-------------|------|-------| +| `Framerate [Hz]` | `fs` | float | Acquisition rate (typically 50.0 Hz) | +| `Date` | `date` | str | DD.MM.YYYY | +| `Time` | `time` | str | HH:MM:SS.mmm | +| `Frequency [kHz]` | `frequency_khz` | float | Excitation frequency (≈101.5 kHz) | +| `Amplitude [uA]` | `amplitude_ua` | float | Injection current amplitude (≈9100 µA) | +| `Gain` | `gain` | int | Hardware amplifier gain | +| `Samples` | `samples_per_period` | int | ADC samples per excitation period | +| `Periods` | `periods` | int | Excitation periods per measurement | + +### Binary frame layout (5495 bytes) + +| Field | Type | Size | Content | +|-------|------|------|---------| +| `timestamp` | float64 | 8 B | Fraction of day | +| `unknown_f8` | float64 | 8 B | Unknown | +| `trans_A` | float64 × 208 | 1664 B | Primary transimpedance set (ADC counts) | +| `unknown_16a` | float64 × 16 | 128 B | Unknown | +| `injection_current` | float64 × 16 | 128 B | Injected current per drive pattern (ADC counts) | +| `unknown_16b` | float64 × 16 | 128 B | Unknown | +| `voltage_A` | float64 × 16 | 128 B | Electrode voltage A (ADC counts) | +| `unknown_50` | float64 × 50 | 400 B | Unknown | +| `trans_B` | float64 × 208 | 1664 B | Reference transimpedance set (ADC counts) | +| `unknown_48` | float64 × 48 | 384 B | Unknown | +| `voltage_B` | float64 × 16 | 128 B | Electrode voltage B (ADC counts) | +| `unknown_6` | float64 × 6 | 48 B | Unknown | +| `gugus` | float64 × 44 | 352 B | Ignored (EIDORS terminology) | +| `unknown_byte` | uint8 | 1 B | Unknown | +| `medibus` | float32 × 67 | 268 B | Ventilator Medibus channels (67 in `.eit`, 52 in `.bin`) | +| `event_text` | S30 | 30 B | ASCII event label | +| `mixed` | uint8 × 24 | 24 B | Mixed content | +| `frame_counter` | uint16 | 2 B | Rolling frame counter | +| `padding` | uint8 × 2 | 2 B | Alignment padding | + +### Calibration pipeline + +Empirical constants from EIDORS `read_draeger_file.m` (A. Adler, estimated 2016-04-07). +These are not present in the `.eit` header — they are hardware-specific and hardcoded. + +| Constant | Value | Role | +|----------|-------|------| +| `FT_A` | 0.00098242 | Transimpedance scaling — primary channel | +| `FT_B` | 0.00019607 | Transimpedance scaling — reference channel | +| `FC_CURRENT` | 194326.3536 | ADC counts → Ampere | +| `FV_VOLTAGE` | 0.11771 | ADC counts → Volt | + +```python +vv = FT_A * trans_A - FT_B * trans_B # (N_frames, 208) calibrated transimpedance [Ω] +I_real = injection_current / FC_CURRENT # (N_frames, 16) injected current [A] +V_diff = (voltage_A - voltage_B) / FV_VOLTAGE # (N_frames, 16) differential voltage [V] +``` + +### Measurement protocol + +208 measurements per frame = 16 electrodes × 13 measurement pairs. +Adjacent current injection (drive distance = 1 electrode), adjacent voltage +measurement (step = 1 electrode), 3 pairs excluded per drive pattern +(those sharing a voltage electrode with the current-injection pair). + +The ordering of the 208 values within each frame matches the pyEIT adjacent-drive +protocol. Whether the device uses absolute (`std`) or rotated (`fmmu`) electrode +indexing within each drive pattern is under investigation — see +`notebooks/03_eit_parser_validation.ipynb` §9. + +--- + +## 4. `DragerAscParser` — `.asc` + +**File:** `parsers/draeger/asc/asc_parser.py` + +Reads the PulmoVista 500 continuous waveform export (tab-separated text). +Returns `ContinuousSignalData` with one row per EIT frame. + +Detection: looks for `"Dräger"` or `"Draeger"` keyword in the first 40 lines. +Sampling frequency estimated from the `time` column if present. + +Typical columns: `image`, `time`, `global`, `local_1_x_16_y_29` … `local_4_x_16_y_05`, +`minmax`, `event`, `eventtext`, `timing_error`, plus Medibus channel columns when +the ventilator is connected via Medibus. + +--- + +## 5. `TimpelTabularParser` — `.csv` / `.txt` / `.asc` + +**File:** `parsers/timpel/timpel_parser.py` + +Reads Timpel EIT device tabular exports. Each row is one EIT frame; +columns 0–1023 are the 32×32 pixel values (C-order); columns 1024–1029 +are auxiliary signals (airway_pressure, flow, volume, min_flag, max_flag, qrs_flag). + +Returns `ReconstructedFrameData`. Sampling frequency defaults to 50 Hz if a +`first_frame` column is present but timing cannot be derived otherwise. + +Detection: looks for `"Timpel"` keyword in the first 40 lines. + +--- + +## 6. GREIT reconstruction bridge + +**File:** `parsers/draeger/eit/eit_greit.py` +**Optional dependency:** `pip install fasteit[pyeit]` + +Converts `RawImpedanceData.measurements` (N_frames, 208) to reconstructed +32×32 images (N_frames, 32, 32) using the GREIT algorithm. + +Reference: Adler A et al., "GREIT: a unified approach to 2D linear EIT +reconstruction of lung images." *Physiol. Meas.* 30 (2009) S35–S55. +DOI: [10.1088/0967-3334/30/6/S03](https://doi.org/10.1088/0967-3334/30/6/S03) + +### `build_greit(n_el, h0, p, lamb, n) → (solver, protocol)` + +Builds a pyEIT GREIT solver on a circular unit-disk mesh. + +| Parameter | Default | Meaning | +|-----------|---------|---------| +| `n_el` | 16 | Number of electrodes | +| `h0` | 0.1 | Mesh density | +| `p` | 0.2 | GREIT noise figure (Adler 2009 recommendation) | +| `lamb` | 1e-2 | Tikhonov regularisation | +| `n` | 32 | Output image side length in pixels | + +### `reconstruct_greit(measurements, ref_frame, …) → ndarray` + +| `ref_frame` value | Behaviour | +|-------------------|-----------| +| `None` | Mean of all frames in this recording (default) | +| `int` | Single frame index | +| `(start, end)` | Mean of `measurements[start:end]` | +| `np.ndarray (208,)` | External reference — for cross-recording EELI comparison | + +**Sign convention**: GREIT outputs conductivity change (Δσ); the parser negates +it to match the Dräger impedance convention (air → impedance rises → positive peak). + +**Rotation**: 90° counter-clockwise applied to place electrode 1 (pyEIT: 3 o'clock) +at the anterior chest wall position, consistent with the PulmoVista 500 belt and +standard CT cross-section orientation (viewed from feet). + +**Spatial quality note**: a generic circular mesh does not replicate Dräger's +proprietary reconstruction. Both algorithms produce correct relative distributions, +but the vendor `.bin` uses a device-specific mesh and matrix. A data-driven +reconstruction that learns the Dräger mapping from paired `.eit`/`.bin` recordings +is provided in `fasteit.reconstruction` (see `feat/ml-reconstruction`). + +--- + +## 7. Loader utilities + +**File:** `parsers/loader.py` + +### `load_data(path) → BaseData` + +Single entry point. Auto-detects vendor and format, selects the right parser, +returns the appropriate container. Raises `ValueError` on unknown format. + +```python +from fasteit.parsers.loader import load_data + +data = load_data("patient01.bin") # → ReconstructedFrameData +data = load_data("patient01.eit") # → RawImpedanceData +data = load_data("patient01.asc") # → ContinuousSignalData +``` + +### `load_many(paths) → list[BaseData]` + +Parses an explicit list of paths in order. Formats can be mixed. + +```python +from fasteit.parsers.loader import load_many + +recordings = load_many(["patient01.bin", "patient02.bin", "patient01.asc"]) +``` + +### `load_folder(folder, pattern="*") → list[BaseData]` + +Scans a directory, parses every file with a registered parser. +Files that raise any error are silently skipped (warning to stderr). + +```python +from fasteit.parsers.loader import load_folder + +all_bin = load_folder("/data/session01/", pattern="*.bin") +all_data = load_folder("/data/session01/") # mixed formats +recursive = load_folder("/data/", pattern="**/*.bin") +``` From bef701652ca708997cf9ddb33117ebd94edea089 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 15:38:28 +0000 Subject: [PATCH 10/20] chore: update tasks.csv and learning_journal for session 2026-03-27 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - tasks.csv: mark 2.5.2 done (measurement protocol documented); add 7.0.1 done (GREIT bridge), 7.0.2/7.0.3 in_progress (std/fmmu + cross-correlation — cells written, need Riccardo to run); add ML.1/ML.2/ML.3 todo (data-driven reconstruction branch) - learning_journal: full session entry — GREIT spatial diagnosis, std vs fmmu explanation, ML reconstruction rationale, eitprocessing finding, EELI cross-recording strategy, branch status --- .claude/fastEIT_tasks.csv | 244 ++++++ .claude/learning_journal.md | 1469 +++++++++++++++++++++++++++++++++++ 2 files changed, 1713 insertions(+) create mode 100644 .claude/fastEIT_tasks.csv create mode 100644 .claude/learning_journal.md diff --git a/.claude/fastEIT_tasks.csv b/.claude/fastEIT_tasks.csv new file mode 100644 index 0000000..966da0a --- /dev/null +++ b/.claude/fastEIT_tasks.csv @@ -0,0 +1,244 @@ +task_id,phase,task_code,title,description,status,priority,depends_on,estimated_hours,actual_hours,date_started,date_completed,notes +1,0,0.1.1,Studio struct/numpy binari,Ripassare struct.unpack numpy.fromfile numpy.frombuffer per file binari,done,high,,2,3,2026-03-17,2026-03-19,Acquisito in pratica: FRAME_EXT_DTYPE con 12 campi strutturati + np.memmap su file reale da 50MB. Skill dimostrata in task 1.2.1 (dtypes.py) 1.3.1-1.3.2 (reverse eng struttura frame). Nessuno studio formale necessario — appreso per costruzione. +2,0,0.1.2,Studio scipy signal processing,Ripassare find_peaks filtri Butterworth sosfiltfilt,done,high,,2,1,2026-03-17,2026-03-19,Basi teoriche consolidate durante progettazione preprocessing (Fase 4). Implementazione pratica avviene in task 4.1.1 (Butterworth) e 4.10.2 (find_peaks). Studio formale non serve prima — si impara costruendo. +3,0,0.1.3,Lettura documentazione Plotly.js,Panoramica Plotly.js per visualizzazioni future,todo,low,,1,,,,Non bloccante +4,0,0.2.1,Creare repo GitHub,Creare repository eit-clinical su GitHub,done,high,,0.5,,2026-03-17,2026-03-17,Repository fastEIT creata su GitHub con LICENSE Apache-2.0 e README placeholder +5,0,0.2.2,Struttura directory progetto,Creare tutta la struttura src/eitclinical/ con tutti i moduli vuoti __init__.py,done,high,4,1,,2026-03-17,2026-03-17,Struttura completa con placeholder e TODO per ogni modulo +6,0,0.2.3,Creare directory docs e notebooks,docs/ docs/reverse_eng/ notebooks/ con file placeholder,done,medium,4,0.5,,2026-03-17,2026-03-17,docs/eidors_analysis.md + docs/eit_format.md + reverse_eng/ + notebooks/ creati +7,0,0.3.1,Configurare pyproject.toml,Dependencies numpy scipy pandas pyarrow matplotlib h5py + dev deps,done,high,5,1,,2026-03-17,2026-03-17,hatchling build; core + [pyeit] + [dev] deps; pytest markers; ruff py310 +8,0,0.3.2,Configurare optional dependencies,[pyeit] per compatibilità [dev] per test,todo,low,7,0.5,,,, +9,0,0.4.1,Workflow test.yml,GitHub Actions pytest Python 3.10 3.11 3.12,done,high,7,1,,2026-03-17,2026-03-17,.github/workflows/test.yml creato — matrix 3.10/3.11/3.12 pip install -e .[dev] pytest tests/ -v +10,0,0.4.2,Workflow lint.yml,GitHub Actions ruff linting,done,medium,7,0.5,,2026-03-17,2026-03-17,.github/workflows/lint.yml creato — ruff check src/ tests/ + ruff format --check +11,0,0.4.3,Configurare pytest markers,slow octave markers in pyproject.toml,done,medium,7,0.5,,2026-03-17,2026-03-17,Markers slow e octave definiti in pyproject.toml [tool.pytest.ini_options] +12,0,0.5.1,Classe astratta BaseParser,parsers/base.py con metodo parse(path) -> RawData,done,high,5,1,2026-03-18,2026-03-18,BaseParser ABC con parse/validate/@abstractmethod + parse_safe() — 6 test verdi, +13,0,0.5.2,Dataclass RawData — BaseData + BinData,"\""models/base_data.py BaseData + models/bin_data.py BinData (architettura multi-formato). RawData rinominata. dtype inline nel test — dtypes.py rinviato a Task 1.2.1\""",done,high,5,1.5,2026-03-18,2026-03-18,BaseData dataclass + BinData con properties (timestamps/pixels/global_signal/roi_signals/roi_signal) — 14 test verdi, +14,0,0.5.3,Dataclass Breath,models/breath.py: migrare da Flask valutare cosa tenere,todo,high,5,2,,,,RIMANDATO a Fase 4 — nasce dopo breath detection. Valutare Flask code quando arriva Task 3.1.x +17,0,0.6.1,Config dataclass base,config.py con sezioni parsing filtering lung_mask breath_detection peep features export,done,high,5,2,2026-03-18,2026-03-18,DeviceConfig completa (fs=50Hz n_electrodes=16 n_measurements=208 pixel_grid=(32x32) frame_size_base=4358 frame_size_ext=4382 con commenti byte breakdown). PreprocessingConfig e AnalysisConfig stub con pass + TODO Fase 4/5. Config con field(default_factory=) corretto. base_data.py: rimosso import Config — fs=50.0 hardcoded con commento. 20 test verdi ruff clean., +18,0,0.6.2,Config preset default,Valori validati dalla letteratura per ogni parametro,todo,medium,17,1,,,,Citare paper per ogni default +19,0,0.6.3,Config preset sensitive e permissive,Preset ARDS e respiro spontaneo,todo,low,18,1,,,, +20,0,0.7.1,README base,Visione progetto installazione quickstart disclaimer,done,medium,5,1,,2026-03-17,2026-03-17,Badge license/python/status + differenziatori + installazione + quickstart placeholder + disclaimer +21,0,0.7.2,LICENSE file,Apache-2.0 license text,done,high,4,0.2,,2026-03-17,2026-03-17,LICENSE Apache-2.0 completo nel repo (201 righe) +22,0,0.7.3,CITATION.cff placeholder,Struttura base da completare dopo pubblicazione,todo,low,4,0.3,,,, +23,1,1.1.1,Funzione autodetect frame size,Prova 4358 e 4382 su file_size errore se nessuno divide,done,high,12,1,2026-03-20,2026-03-21,Implementata detect_bin_format_from_size() in parsers/detection.py. File reali: bin_formats.py (FormatSpec registry) + bin_utils.py + parsers/errors.py. Gestisce zero/uno/multi candidati con errori tipizzati. Tests in task 1.1.2., +24,1,1.1.2,Test autodetect,File sintetici dimensioni corrette e incorrette,done,high,23,1,2026-03-21,2026-03-26,Coperti in test_parser_routing.py: bin detection/frame-size divisibility ambiguous unsupported-ext vendor-aliases tabular-timpel. .eit detection rimanda a 2.3.1 (NotImplementedError atteso). +25,1,1.2.1,Definire dtype frame base,numpy dtype strutturato per 4358 byte,done,high,23,2,2026-03-19,2026-03-19,dtypes.py: FRAME_BASE_DTYPE(4358b) FRAME_EXT_DTYPE(4382b) ts=500MB,todo,medium,26,2,,,, +28,1,1.3.1,Investigare padding 208 byte con file reale,Capire se contiene Medibus o altro,done,high,26,3,2026-03-19,2026-03-19,Confermato: 208 byte = 52 x float32 Medibus waveforms. Sentinel 0xFF7FC99E=-3.4e38 (MEDIBUS disconnesso). Vedi docs/reverse_eng/bin_format.md, +29,1,1.3.2,Investigare 24 byte extra frame esteso,Capire struttura 4382 vs 4358,done,high,28,2,2026-03-19,2026-03-20,2026-03-20 fix architetturale definitivo confermato da eitprocessing draeger.py (Apache-2.0): BASE(4358b) idx0-50 comuni+idx51=Tlow; EXT(4382b) idx0-50 comuni+idx51=PHigh+idx52=Plow+idx53=Tlow(spostato!)+idx54-57=Pod. I due formati NON sono BASE+6 extra: condividono solo idx0-50 e divergono da idx51. Struttura dtypes.py: _MEDIBUS_COMMON(51)+MEDIBUS_BASE_FIELDS(52)+MEDIBUS_EXT_FIELDS(58) separati con alias MEDIBUS_FIELDS=MEDIBUS_BASE_FIELDS e MEDIBUS_INDEX=MEDIBUS_BASE_INDEX. 22 test verdi., +30,1,1.3.3,Implementare estrazione Medibus,Pressioni volumi flussi PEEP dal frame esteso,todo,high,29,3,,,, +31,1,1.3.4,Implementare estrazione PressurePod,4 waveform pressione continue,todo,medium,29,1,,,, +32,1,1.4.1,Procurarsi file .bin reale,Dal PulmoVista o dal collega,done,critical,,0,,2026-03-17,2026-03-17,File .bin reale disponibile (più varianti con e senza PressurePod) +33,1,1.4.2,Confronto pixel con output MATLAB collega,Frame 0 pixel 0 deve corrispondere,todo,high,32,2,,,, +34,1,1.4.3,Verificare timestamp crescenti plausibili,Controllare monotonia e range,done,high,32,1,2026-03-19,2026-03-19,ts[0]=0.7576=18:10:54 coincide col header .eit. Monotona=True. dt=0.0200s esatto (50Hz). Vedi bin_format.md §4.1, +35,1,1.4.4,Verificare pixel range ragionevole,No NaN no inf valori fisicamente sensati,done,high,32,1,2026-03-19,2026-03-19,shape (11500 32 32). min=-7.73 max=28.53. NaN=0 Inf=0. Cross-valida con .asc (stessa scala). Vedi bin_format.md §4.2, +36,1,1.4.5,Verificare Medibus valori clinici,PEEP pressioni volumi plausibili,done,high,30,1,2026-03-19,2026-03-19,MEDIBUS non connesso in questi file: sentinel -3.4e38. PressurePod attivo: Paw 10-38 mbar Pes 6-18 mbar Ptp -2/+25 mbar. Naming idx 54-57 da confermare. Vedi bin_format.md §4.3-4.4, +37,1,1.4.6,Documentare discrepanze trovate,Note su differenze con EIDORS/collega,todo,medium,33,1,,,, +38,1,1.5.1,Fixture pytest file sintetico base,conftest.py genera .bin 3 frame base valori noti,done,high,26,0.5,2026-03-26,2026-03-26,Fixture bin_3frames in tests/conftest.py. 3 frame FRAME_BASE_DTYPE 50Hz frame i→pixels=i+1. 152 test verdi. +39,1,1.5.2,Fixture pytest file sintetico esteso,conftest.py genera .bin 3 frame esteso valori noti,todo,high,30,1,,,, +40,1,1.5.3,Test conteggio frame,n_frames corretto per base e esteso,done,high,38,0.5,2026-03-26,2026-03-26,test_n_frames_from_fixture in test_bin_parser.py via bin_3frames fixture. +41,1,1.5.4,Test valori pixel,pixel[0][0] corrisponde al valore scritto,done,high,38,0.5,2026-03-26,2026-03-26,test_pixel_values_per_frame + test_pixel_specific_position in test_bin_parser.py. +42,1,1.5.5,Test timestamp,timestamp[i] corrisponde al valore scritto,done,high,38,0.5,2026-03-26,2026-03-26,test_timestamps_match_fixture + test_timestamps_monotonically_increasing in test_bin_parser.py. +43,1,1.5.6,Test event text,event_text leggibile e corretto,todo,medium,38,0.5,,,, +44,1,1.5.7,Test Medibus round-trip,Valori Medibus scritti = letti (frame esteso),todo,high,39,0.5,,,, +45,1,1.5.8,Test file troncato,Errore pulito con messaggio utile,todo,medium,38,0.5,,,, +46,1,1.5.9,Test file vuoto,Errore pulito,todo,medium,38,0.3,,,, +47,1,1.5.10,Test round-trip completo,Scrivi sintetico leggi verifica tutti i campi,done,high,38,0.5,2026-03-26,2026-03-26,test_roundtrip_via_load_data in test_bin_parser.py. Verifica n_frames/pixels/timestamps/metadata via load_data(). +48,1,1.6.1,Script Octave cross-validazione .bin,Carica con eidors_readimg salva .mat,todo,low,32,2,,,,Richiede Octave installato +49,1,1.6.2,Test Python confronto .mat,scipy.io.loadmat confronto valori,todo,low,48,1,,,, +50,2,2.0.1,Scaricare EIDORS da SourceForge,Scaricare zip completo eidors-v3.x,done,critical,,1,,,2026-03-25,EIDORS source accessed via SourceForge in session agent +51,2,2.0.2,Trovare read_draeger_header in sorgente,eidors_readdata.m dopo riga 1402,done,critical,50,1,,,2026-03-25,read_draeger_header found and analyzed — notebook §1 +52,2,2.0.3,Trovare read_draeger_file in sorgente,Stessa funzione altra subfunzione,done,critical,50,1,,,2026-03-25,read_draeger_file found and analyzed — notebook §5 +53,2,2.0.4,Annotare read_draeger_header,Per ogni fread: offset tipo n_valori significato,done,high,51,3,,,2026-03-25,header fields documented in notebook §1 and §6 +54,2,2.0.5a,Annotare read_draeger_file parte 1,Prima metà del codice: struttura frame header area iniziale e timing/contatore. Per ogni fread: offset tipo n_valori significato. Deliverable: sezione in docs/eidors_analysis.md,done,high,52,2,,,2026-03-25,"trans_A/B, injection_current, voltage_A/B documented — notebook §3/§5/§6" +55,2,2.0.6,Salvare note in docs/eidors_analysis.md,Documentazione comprensione formato NO copia codice GPL,todo,high,213,2,,,, +56,2,2.1.1,Procurarsi file .eit reale,Dal PulmoVista,done,critical,,0,,2026-03-17,2026-03-17,File .eit reale disponibile (più varianti con e senza PressurePod) +57,2,2.1.2,Hexdump primi 4KB,xxd file.eit | head -256 salvare in docs/reverse_eng/,done,high,56,1,2026-03-19,2026-03-19,Analisi hexdump inclusa in eit_format.ipynb. Binary start confermato a offset 7554 (p01) e 7546 (p02)., +58,2,2.1.3,Estrarre stringhe ASCII,strings file.eit | head -50 salvare,done,high,56,0.5,2026-03-19,2026-03-19,Header ASCII estratto e documentato in eit_format.ipynb: version 51 data ora frequenza gain etc., +59,2,2.1.4,Identificare magic string,---Draeger EIT-Software--- nei primi byte,done,high,57,0.5,2026-03-19,2026-03-19,Magic string confermata: ---Draeger EIT-Software V1.30--- al byte 0x33 (dopo preamble 12 byte), +60,2,2.1.5,Parsare metadati header testuale,Data ora framerate versione ecc,done,high,59,2,2026-03-19,2026-03-19,Tutti i campi estratti in eit_format.ipynb: format=51 date/time freq=101.5kHz rate=50Hz gain=65 samples=508 periods=11, +61,2,2.1.6,Trovare fine header inizio dati binari,Byte offset dove finisce ASCII inizia binario,done,high,60,1,2026-03-19,2026-03-19,Determinato in task 2.1.2: offset 7554 (patient01) e 7546 (patient02). L'offset varia perché dipende dalla lunghezza dell'header ASCII. Algoritmo: scan forward fino al primo byte non-ASCII dopo i metadati., +62,2,2.1.7,Confrontare con read_draeger_header EIDORS,Verifica corrispondenza findings vs codice,done,high,61,1,,,2026-03-25,EIDORS read_draeger_header compared in detail — notebook §1 and §5 +63,2,2.1.8,Salvare header decodificato,docs/reverse_eng/03_header_parsed.txt,done,medium,60,0.5,,,2026-03-25,header decoded in notebook cell 7 (parse_header + redact_header) +64,2,2.2.1a,Documentare layout frame parte 1,Area tensioni primarie e correnti iniezione: offset 0-321. Tabella byte-per-byte per ogni campo. Deliverable: prima metà tabella in docs/eit_format.md,done,high,55,2,,,2026-03-25,frame layout table (all 5495 bytes) documented — notebook §3 +65,2,2.2.2,Calcolare dimensione frame,Verificare con file_size - header_size,done,high,214,1,,,2026-03-25,5495 bytes/frame confirmed on all 3 test files — notebook §2 +66,2,2.2.3,Creare diagramma byte-per-byte,Per docs/eit_format.md,todo,medium,64,2,,,, +67,2,2.3.1,Implementare parse_eit_header,Leggere header ASCII estrarre metadata,done,high,61,3,2026-03-27,2026-03-27,Implementato in eit_utils.py::parse_eit_header() +68,2,2.3.2,Implementare autodetect formato,Draeger vs Carefusion dal magic string,done,medium,67,1,2026-03-27,2026-03-27,Implementato in detection.py::detect_vendor_from_eit_header() + header_formats.py registry +69,2,2.3.3,Test header parsing,File reale header parsato correttamente,todo,high,67,1,,,, +70,2,2.4.1a,Implementare parse_eit_frames apertura e loop,Apertura file da header_size. Loop frame. Estrazione v_primary e v_secondary per ogni frame con numpy o struct. Deliverable: funzione che itera i frame e restituisce raw voltages,done,high,65,2,2026-03-27,2026-03-27,Implementato in eit_parser.py con np.memmap + FRAME_EIT_DTYPE +71,2,2.4.2,Implementare calcolo transimpedenze,vv = ft[0]*primary - ft[1]*secondary,done,high,215,2,2026-03-27,2026-03-27,vv=FT_A*trans_A-FT_B*trans_B + I_real + V_diff in eit_parser.py step 5 +72,2,2.4.3,Implementare versione numpy veloce,dtype strutturato per tutto il file,done,medium,70,2,2026-03-27,2026-03-27,FRAME_EIT_DTYPE 5495B strutturato in eit_dtypes.py; np.memmap in parser +73,2,2.4.4,Implementare versione streaming,Per file grandi,todo,low,70,2,,,, +74,2,2.5.1,Metodo to_pyeit_format,(208 N_frames) -> (N_frames 208),done,medium,71,1,2026-03-27,2026-03-27,measurements già shape (N_frames,208) — nessuna trasposizione necessaria +75,2,2.5.2,Documentare pattern stimolazione,Adjacent drive 16 elettrodi 5mA rotate_meas,done,medium,74,0.5,,2026-03-27,Documented in docs/parsers.md §3 + std/fmmu ordering note. Empirical test in notebook §9. +76,2,2.6.1,Script Octave cross-validazione .eit,eidors_readdata DRAEGER-EIT salva .mat,todo,medium,71,2,,,,Richiede Octave +77,2,2.6.2,Confronto Python vs EIDORS,rtol=1e-4 documentare differenze,todo,medium,76,2,,,, +78,2,2.7.1,Test header parsing corretto,Framerate data corretti,todo,high,67,0.5,,,, +79,2,2.7.2,Test numero frame coerente,N_frames = durata x framerate,todo,high,71,0.5,,,, +80,2,2.7.3,Test transimpedenze range plausibile,Ordine millivolt,todo,high,71,0.5,,,, +81,2,2.7.4,Test timestamp crescenti,Monotonia,todo,high,71,0.5,,,, +82,2,2.7.5,Test file non-Draeger errore,Magic string assente -> errore chiaro,todo,medium,67,0.5,,,, +83,2,2.7.6,Test file troncato errore,Errore pulito con posizione,todo,medium,71,0.5,,,, +84,2,2.8.1a,docs/eit_format.md parte 1,Sezione header ASCII (magic string struttura metadati) e tabella layout frame binario (offset tipo n_bytes significato). Deliverable: prima metà del documento pubblicabile,todo,high,66,2,,,,Deliverable pubblicabile +85,2,2.8.2,Valutare pubblicazione Zenodo con DOI,Per rendere citabile indipendentemente,todo,low,216,1,,,, +86,3,3.1.1,Portare file Flask backend per valutazione,Caricare in conversazione Claude per review,todo,critical,,1,,,, +87,3,3.1.2,Identificare mapping nomi colonne inglese,Lista colonne PulmoVista -> snake_case,todo,high,86,1,,,,Valore alto da preservare. File reale è .asc con encoding latin-1 (non .txt/.csv) +88,3,3.1.3,Identificare classi riusabili,Breath BreathAnalyzer ecc dal Flask,todo,high,86,1,,,, +89,3,3.1.4,Decidere cosa tenere/refactorare/riscrivere,Valutazione per ogni pezzo,todo,high,88,1,,,, +90,3,3.2.1,Documentare formato CSV/TXT PulmoVista,Separatore header colonne unità encoding,todo,high,87,2,,,, +91,3,3.2.2,Documentare varianti firmware,Differenze tra versioni PulmoVista,todo,medium,90,1,,,, +92,3,3.3.1,Refactor parser CSV in csv_parser.py,Dalla codebase Flask ripulito e strutturato,done,high,89,3,1.5,2026-03-21,2026-03-21,DragerAscParser completo in parsers/draeger/asc/asc_parser.py: detect_vendor_from_tabular + header metadata + waveform table + snake_case cols + fs estimation. Registrato in loader.py per .asc/.txt/.csv. Timpel e advanced col mapping sono task separate (3.x). +93,3,3.3.2,Gestione encoding,UTF-8 Latin-1 autodetect,todo,medium,92,1,,,, +94,3,3.4.1,Test CSV sintetico,File sintetico valori noti,todo,high,92,1,,,, +95,3,3.4.2,Test CSV reale,File reale se disponibile,todo,medium,92,1,,,, +244,4,4.0.1,Implementare sentinel replacement nel preprocessing layer,"\""Prima operazione di ogni pipeline di preprocessing: copiare memmap in RAM e applicare replace_no_data_sentinels su pixels e medibus_data. Questo è il confine netto tra parsing lazy e preprocessing eager.\""",todo,high,,1,,,,Prima task quando Fase 4 apre. bin_utils.replace_no_data_sentinels già esiste. +96,4,4.1.1,Implementare butterworth_lowpass,cutoff=0.5Hz order=4 sosfiltfilt zero-phase,todo,high,13,2,,,,Citare Wisse 2024 +97,4,4.1.2,Implementare butterworth_bandpass e highpass,Opzionali,todo,low,96,1,,,, +98,4,4.1.3,Test filtro sintetico,Cuore 1.2Hz + respiro 0.3Hz cuore attenuato >90%,todo,high,96,1,,,, +99,4,4.2.1,Implementare MDN filter,Multiple Digital Notch come Wisse 2024,todo,low,96,4,,,,Post-v1 +100,BACKLOG,4.3.1,Implementare filtro perfusione banda 1-3Hz,Per componente cardiaca pixel-per-pixel,todo,low,96,3,,,,Fuori scope — solo ventilazione +101,4,4.4.1,Implementare lung mask threshold,TIV per pixel soglia 20% max o percentile config,todo,high,96,2,,,,Citare Zhao 2010 TREND 2017 +102,4,4.4.2,Implementare lung mask Otsu,Soglia automatica zero config,todo,medium,101,1,,,, +103,4,4.4.3,Test lung mask sintetica,Immagine con polmone e background mask corretta,todo,high,101,1,,,, +104,4,4.5.1,Implementare lung mask LAEM,Soglia 50% simmetrizzazione sottrazione cardiaca,todo,low,101,3,,,,Opzione avanzata Zhao 2016 +105,4,4.6.1,Implementare lung mask Watershed,scipy.ndimage Watershed,todo,low,101,3,,,,Opzione avanzata Somhorst 2026 +106,4,4.7.1,Morphological closing,scipy.ndimage.binary_closing per chiudere buchi,todo,medium,101,1,,,, +107,4,4.7.2,Rimozione componenti piccole,scipy.ndimage.label + filtro area,todo,medium,106,1,,,, +108,4,4.7.3,Opzionale forzamento simmetria,Per detectare artefatti contatto elettrodo,todo,low,107,1,,,, +109,4,4.8.1,ROI fisse standard,4 fasce 2 metà 4 quadranti 8 sub-ROI,todo,high,101,2,,,, +110,4,4.8.2,ROI adattive Summers,Divide zona ventilata in 4 parti uguali adattandosi al contour,todo,medium,109,3,,,, +111,4,4.8.3,Intersezione ROI con lung mask,roi & lung_mask per ogni ROI,todo,high,109,0.5,,,, +112,4,4.8.4,Conteggio ventilated pixels per ROI,N pixel ventilati in ogni ROI,todo,medium,111,0.5,,,, +113,4,4.9.1,Impedenza globale,Somma pixel in lung_mask per frame,todo,high,101,1,,,, +114,4,4.9.2,Impedenza regionale per ROI,Dict di segnali regionali,todo,medium,113,1,,,, +115,4,4.10.1,Pre-filtering per breath detection,Passa-banda 0.1-1.5Hz prima di find_peaks,todo,high,113,1,,,,Miglioramento vs collega +116,4,4.10.2,Implementare find_peaks inspiratorio,find_peaks con prominence distance width adattivi,todo,high,115,2,,,, +117,4,4.10.3,Implementare find_peaks espiratorio,find_peaks(-signal) per valleys,todo,high,116,1,,,, +118,4,4.10.4,Assemblare breath tuples,(idx_start idx_peak idx_end) per ogni breath,todo,high,117,1,,,, +119,4,4.10.5,Parametri tunabili breath detection,min/max duration min_prominence in Config,todo,high,118,1,,,, +120,4,4.10.6,Test breath sinusoidale,Segnale noto breath detection corretta,todo,high,118,1,,,, +121,4,4.10.7,Test breath con artefatto,Artefatto escluso o flaggato,todo,medium,118,1,,,, +122,4,4.10.8,Test respiro spontaneo irregolare,Detection ragionevole su segnale variabile,todo,medium,118,1,,,, +123,4,4.11.1,Implementare quality flags,ok short_breath long_breath artifact low_signal suspect_ti_ratio negative_delta_z,todo,high,118,2,,,, +124,4,4.11.2,Flag = classificazione non scarto,Breath flaggati restano come da_valutare,todo,high,123,0.5,,,, +125,4,4.12.1,Config preset default per preprocessing,Valori evidence-based con citazione,todo,high,119,1,,,, +126,4,4.12.2,Config preset sensitive e permissive,Per ARDS e respiro spontaneo,todo,medium,125,1,,,, +127,4,4.13.1,Pipeline Session.from_raw integrato,Sequenza completa: filtro mask ROI impedenza breath flags,todo,high,124,3,,,, +128,4,4.13.2,Step intermedi accessibili,session.lung_mask session.global_impedance ecc,todo,high,127,1,,,, +129,4,4.13.3,Test end-to-end preprocessing sintetico,Dati sintetici attraverso tutto il pipeline,todo,high,127,2,,,, +130,5,5.1.1,Implementare compute_eeli,EELI globale e per ROI per breath,todo,high,127,2,,,,Citare Frerichs 2017 +131,5,5.1.2,Implementare compute_tiv,TIV globale e per ROI per breath,todo,high,127,2,,,,Citare Frerichs 2017 +132,5,5.1.3,Implementare compute_gi_index,GI = sum|DI-median|/sum|DI|,todo,high,131,2,,,,Citare Zhao 2009 +133,5,5.1.4,Implementare compute_cov,"\""CoV_VD (ventro-dorsale) e CoV_LR (sinistra-destra). Delphi 2025 cita entrambe come standard\""",todo,high,131,1.5,,,,Citare Frerichs 2006 + He H 2025 PMID 41158153 +134,5,5.1.5,Implementare compute_cv,Coefficient of variation TIV pixel,todo,medium,131,1,,,, +135,5,5.1.6,Implementare durate breath,Ti Te Ttot Ti/Ttot freq respiratoria,todo,high,118,1,,,, +136,5,5.1.7,Implementare compliance dinamica,TIV/driving_pressure se Medibus disponibile,todo,medium,131,1,,,, +137,5,5.1.8,Test features globali sintetiche,Valori calcolabili a mano verificati,todo,high,134,2,,,, +138,5,5.2.1,Implementare EELI regionale per ROI,Per halves quadrants sub-ROI,todo,high,130,2,,,, +139,5,5.2.2,Implementare TIV regionale per ROI,Idem,todo,high,131,1,,,, +140,5,5.2.3,Implementare % ventilazione regionale,TIV_ROI/TIV_global x 100,todo,high,139,0.5,,,, +141,5,5.2.4,Implementare V/D ratio,TIV ventrale / TIV dorsale,todo,high,139,0.5,,,, +142,5,5.2.5,Implementare silent spaces,Spadaro 2018: pixel < 10% max TIV ventral/dorsal %,todo,high,139,1,,,, +143,5,5.2.6,Implementare compliance regionale,Per ROI se driving_pressure disponibile,todo,medium,139,1,,,, +144,5,5.2.7,Test features regionali,Valori noti sintetici,todo,high,142,2,,,, +145,5,5.3.1,Implementare delta Z pixel per pixel,np.max - np.min vettorizzato per breath,todo,high,131,1,,,, +146,5,5.3.2,"\""Implementare RVD e SDRVD\""","\""Metodo t40 (40% del ΔZ tidale) per RVD pixel-wise. SDRVD = SD(RVD) nella lung_mask. NON usare np.argmax (tempo al picco) che sovrastima il delay\""",todo,medium,145,2,,,,Citare Muders 2012 + Delphi 2025 per SDRVD +147,5,5.3.3,Implementare fEIT images,SD pixel durante ventilazione tidale,todo,medium,145,1,,,, +148,5,5.3.4,Implementare ITV metodo Lowhagen,8 iso-volumi distribuzione regionale,todo,low,145,4,,,,Post-v1 +149,5,5.3.5,Implementare OD/CL metodo Costa,Compliance pixel-wise a diversi PEEP,todo,low,145,4,,,,Post-v1 citare Costa 2009 +150,5,5.4.1,Implementare pendelluft index,Yoshida 2013,todo,low,145,3,,,,Post-v1 +151,5,5.4.2,Implementare strain index,Feature avanzata,todo,low,145,3,,,,Post-v1 +152,5,5.5.1,Implementare feature registry,Decoratore @register_feature per custom features,todo,medium,130,2,,,, +153,5,5.6.1,Breath dataclass completa,Tutti gli attributi feature + to_dict(),todo,high,137,2,,,, +154,5,5.6.2,PeepStep dataclass completa,Aggregazione features per step,todo,high,153,1,,,, +156,5,5.7.2,PEEP detection Livello 2 EELI experimental,Derivata EELI smoothato soglia segmenti stabili,todo,high,113,3,,,,Marcare experimental warning +157,5,5.7.3,Logica cascading L1 -> L2,Medibus disponibile usa L1 altrimenti L2 con warning,todo,high,156,1,,,, +158,5,5.7.4,Flag peep_stable peep_transition peep_step_id,Per ogni breath,todo,high,157,1,,,, +159,5,5.7.5,Parametri tunabili PEEP detection,smoothing_window step_threshold min_duration in Config,todo,high,156,1,,,, +160,5,5.8.1,PEEP aggregation per step,Mediana IQR min max n_breaths per feature per step,todo,high,158,2,,,,Dalla vecchia roadmap +161,5,5.8.2,Esclusione transitorio post-cambio PEEP,Primi N breath configurabili esclusi,todo,high,160,1,,,, +162,5,5.8.3,Flag stabilità step,Segnala step instabile se variabilità alta,todo,medium,160,1,,,, +163,5,5.9.1,Trend temporali breath-per-breath,EELI TIV GI CoV come serie temporale,todo,medium,131,2,,,,Dalla vecchia roadmap +164,5,5.10.1,Design validation study PEEP L2,Protocollo: >=30 registrazioni Medibus+EELI metriche Bland-Altman,todo,medium,157,2,,,,Non software pianificazione studio +165,5,5.11.1,Test GI=0 ventilazione omogenea,Sintetico tutti pixel uguali GI deve essere 0,todo,high,132,0.5,,,, +166,5,5.11.2,Test CoV=0.5 distribuzione simmetrica,Sintetico V=D CoV deve essere 0.5,todo,high,133,0.5,,,, +167,5,5.11.3,Test silent_spaces=0 tutto sopra soglia,Sintetico tutti pixel > soglia SS=0%,todo,high,142,0.5,,,, +168,6,6.1.1,Implementare Session.to_dataframe,Assembla Breath in DataFrame 1 riga per respiro,todo,high,153,2,,,, +169,6,6.1.2,Colonne complete da reference doc,Tutte le features + metadata + quality_flag + peep_step_id,todo,high,168,1,,,,Vedi sezione 4.1 reference doc +170,6,6.1.3,Formato tidyverse compatibile,snake_case nomi coerenti,todo,medium,168,0.5,,,, +171,6,6.2.1,Implementare Session.save HDF5,Salva frames tidal_images lung_mask in .h5,todo,high,168,2,,,,h5py +172,6,6.2.2,Struttura HDF5 definita,/frames /tidal_images /lung_mask /metadata,todo,high,171,1,,,, +173,6,6.3.1,Implementare Study.from_folder,Processa tutti i file in cartella,todo,high,168,2,,,, +174,6,6.3.2,Estrazione patient_id da filename regex,Pattern configurabile,todo,medium,173,1,,,, +175,6,6.3.3,Implementare Study.to_dataframe,Con patient_id + session_id,todo,high,173,1,,,, +176,6,6.4.1,Export CSV,to_csv per R e Excel,todo,high,168,1,,,, +177,6,6.4.2,Export Parquet,to_parquet per Python e ML,todo,high,168,1,,,, +178,6,6.4.3,Export Excel,to_excel openpyxl opzionale,todo,low,168,1,,,, +179,6,6.4.4,Export per PEEP step,1 riga per step mediana IQR,todo,high,160,1,,,, +180,6,6.4.5,Export trend,Serie temporale breath-per-breath,todo,medium,163,1,,,, +181,6,6.5.1,Implementare to_ml_dataset,Normalizzazione split by patient (X y metadata),todo,medium,175,3,,,, +182,6,6.6.1,API high-level Session.from_file,Una riga per tutto,todo,high,127,1,,,, +183,6,6.6.2,API high-level Study.from_folder,Una riga per multi-paziente,todo,high,173,1,,,, +184,6,6.7.1,Test end-to-end sintetico -> CSV,File sintetico -> Session -> DataFrame -> CSV -> rileggi -> verifica,todo,high,176,2,,,, +185,6,6.7.2,Test Study 3 file -> DataFrame,3 sintetici -> Study -> 3 patient_id nel DataFrame,todo,high,175,1,,,, +186,7,7.1.1,Implementare to_pyeit_format,(208 N_frames) -> (N_frames 208) trasposta,done,medium,74,1,1,2026-03-27,2026-03-27,measurements gia shape (N_frames,208) — nessuna trasposizione. to_pyeit_format non necessario. +187,7,7.1.2,Documentare pattern stimolazione pyEIT,Adjacent 16el 5mA rotate_meas,done,medium,186,0.5,0.5,2026-03-27,2026-03-27,Adjacent 16el dist_exc=1 step_meas=1 parser_meas=std. 16x13=208. Documentato in eit_greit.py docstring. +188,7,7.1.3,Notebook esempio .eit -> pyEIT,notebooks/03_pyeit_integration.ipynb,done,medium,186,3,3,2026-03-27,2026-03-27,notebooks/03_eit_parser_validation.ipynb — sezioni 6-9 con GREIT reconstruction + confronto .bin +189,7,7.2.1,Implementare from_pyeit_reconstruction,Interpolazione mesh -> 32x32 griddata,done,medium,188,3,3,2026-03-27,2026-03-27,eit_greit.py: build_greit() + reconstruct_greit(). Output (N_frames,32,32) gia negato e ruotato 90CCW. ref_frame esterno per cross-file EELI. +190,7,7.2.2,Documentare interpolazione,Una riga nel methods di ogni paper,todo,medium,189,0.5,,,, +191,7,7.4.1,Test sintetico pyEIT round-trip,Dati -> pyEIT -> ricostruzione -> features,todo,medium,189,2,,,, +192,7,7.4.2,Test correlazione .bin vs .eit+pyEIT,Features devono correlare sullo stesso paziente,todo,medium,191,2,,,, +193,BACKLOG,B.1,GUI wrapper pyEIT in eit-studio,Dropdown algoritmo override parametri end-to-end dal .eit,todo,low,,8,,,,Post v1.0 +194,BACKLOG,B.2,Guida acquisizione EIT bedside,Wizard step-by-step per protocollo PEEP trial,todo,low,,6,,,,Post v1.0 +196,BACKLOG,B.4,Supporto multi-vendor,Timpel Sentec come eitprocessing,done,low,,12,8,2026-03-22,2026-03-22,TimpelTabularParser implementato completo: validate() + parse() con slicing + NaN sentinel + aux_signals. TIMPEL_FRAME_DTYPE (ts+pixels 32x32). 41 test verdi. Coverage 93%. Sentec rimane todo. +197,BACKLOG,B.5,Docker compose deployment,Django + Postgres + Redis containerizzato,todo,low,,4,,,,Post v1.0 +198,BACKLOG,B.6,Valutazione GDPR multi-utente,Con DPO per deployment cloud,todo,low,,2,,,,Post v1.0 +199,1,V.1,Implementare plot_frame heatmap,Heatmap 32x32 singolo frame matplotlib,todo,high,26,1,,,,Per debugging +200,4,V.2,Implementare plot_frame_with_mask,Heatmap + contorno mask sovrapposto,todo,high,101,1,,,, +201,VIZ,V.3,Implementare plot_tidal_image,Mappa TIV di un respiro,todo,medium,131,1,,,, +202,4,V.4,Implementare plot_global_impedance,Segnale nel tempo + breath markers,todo,high,118,1,,,, +203,4,V.5,Implementare plot_lung_mask,Mask binaria visualizzata,todo,medium,101,1,,,, +204,VIZ,V.6,Implementare plot_peep_steps,EELI con PEEP step evidenziati,todo,medium,157,1,,,, +205,VIZ,V.7,Implementare plot_features_over_time,Dashboard EELI TIV CoV GI nel tempo,todo,medium,163,2,,,, +206,D,D.0,Creare branch dev,git checkout -b dev && git push -u origin dev. Da questo momento tutto il lavoro va su dev o feature branch da dev. Main = solo codice rilasciato,done,critical,,0.1,0.1,2026-03-24,2026-03-24,Branch dev creato da main e pushato con -u +207,D,D.1,CODE_OF_CONDUCT.md,Contributor Covenant v2.1 lingua inglese. Branch docs/code-of-conduct da dev. Merge in dev.,done,high,206,0.2,0.3,2026-03-24,2026-03-24,Merged via PR. Nota: prima PR andata su main per errore poi fixata con cherry-pick e force push dev +208,D,D.2,CONTRIBUTING.md,Sezioni: bug report / feature request / dev setup / code style / testing / PR process. Rimandare a docs/parsing_layer.md per parser. NON menzionare roadmap futura (OPSEC). Branch docs/contributing da dev.,done,high,207,0.5,0.4,2026-03-24,2026-03-24,Merged via PR. Intro resa piu accogliente con commit diretto su dev +210,O,O.1,Vettorizzare sentinel loop in DragerBinParser,Sostituire i due loop Python in bin_parser.py (pixel step 6 + medibus step 8) con operazione numpy vettorizzata sull intero array 3D. replace_no_data_sentinels accetta gia array — basta passare tutto il blocco in una sola chiamata. Branch fix/sentinel-vectorize da dev.,done,medium,206,0.5,0.3,2026-03-24,2026-03-24,Merged via PR. 51 test verdi. Chiude issue #3. Milestone 1. +211,B,B.1,Notebook benchmark memmap vs struct.unpack,notebooks/benchmark_parsing.ipynb — genera file .bin sintetici 1/10/50/100/500MB — benchmark np.memmap vs struct.unpack loop — plot tempo e RAM — conclusione in markdown. Branch bench/memmap-vs-struct da dev.,done,low,210,1,2026-03-24,2026-03-24,2,Biglietto da visita per community. Fare DOPO O.1 cosi misura codice ottimizzato +212,CI,CI.1,GitHub Action build check matrix,Action che fa pip install . (senza -e) su Python 3.10/3.11/3.12 — verifica che il pacchetto si installi correttamente. Trigger su push dev e PR su dev/main. Integra con test.yml esistente o action separata. Branch ci/build-check da dev.,done,medium,206,0.3,0.3,2026-03-24,2026-03-24,build.yml creato. test.yml e lint.yml estesi a dev. Merged su main per errore poi sincronizzato con merge main->dev +214,D,D.4,MkDocs setup,pip install mkdocs mkdocs-material mkdocstrings. mkdocs.yml con nav: Home/Data Model/Parsing Layer. docs/index.md copia README estesa. mkdocs serve per verifica. Opzionale GitHub Action gh-deploy. Branch docs/mkdocs-setup da dev.,todo,low,209,0.5,,,,Nice to have pre-release +215,P,P.1,GitHub profile README,Creare repo Kernel236/Kernel236. README: chi sei (anestesista/R analyst/dev Python) interessi (EIT ventilazione meccanica analisi dati clinici sicurezza informatica) progetti (fastEIT con link) stack (Python R Bash JS). NON menzionare startup/agenti/Django. Lingua inglese tono professionale. Semplice: 25-30 righe. Fare PRIMA di esposizione community.,done,high,206,0.3,0.5,2026-03-24,2026-03-24,Repo Kernel236/Kernel236 creata. README con bio ICU/CS badge linguaggi TryHackMe fastEIT link +218,E,E.2,PR a eitprocessing se accettano,Fork implementa nel loro codebase seguendo le loro convenzioni. Se accettano offri anche porting del .bin parser.,todo,low,217,3,,,, +213,2,2.0.5b,Annotare read_draeger_file parte 2,Seconda metà: tensioni principali secondarie segnali e fine frame. Completare annotazione. Deliverable: sezione completa in docs/eidors_analysis.md verificabile,todo,high,54,2,,,, +216,2,2.8.1b,docs/eit_format.md parte 2,Sezione formula transimpedenze (ft factors spiegati). Esempi pratici con valori reali. Confronto con formato Carefusion. Note versioni firmware. Deliverable: documento completo pubblicabile,todo,high,84,2,,,,Deliverable pubblicabile +217,11,11.1,Generazione chiavi progetto,Script tools/keygen.py genera coppia Ed25519. public_key.pem committata in fasteit/trust/. Private key mai nel repo. Deliverable: public key nel pacchetto installabile,todo,low,,1,,,,Dipende da Fase 10 +219,11,11.3,Tool firma credential (maintainer),Script tools/sign_credential.py: credential.json + private_key.pem → signature.sig. Deliverable: round-trip test firma→verifica,todo,low,217,1,,,, +220,11,11.4,Verifica firma credential nel software,fasteit/trust/verify.py — verify_credential(credential_path sig_path) → bool. Usa public_key inclusa nel pacchetto. Offline. Deliverable: test con credential valida / scaduta / firma alterata,todo,low,219,2,,,, +221,11,11.5,Trust level per annotazioni,Flag trusted:bool nelle annotazioni basato su credential attiva al momento. Deliverable: annotazione trusted marcata diversamente in export,todo,low,220,1,,,, +222,11,11.6,Esportazione model bundle firmato,Script tools/export_bundle.py assembla weights+config+metrics+training_metadata+credential+signature. Deliverable: bundle esportabile e verificabile,todo,low,220,2,,,, +223,11,11.7,Verifica bundle lato maintainer,Script tools/verify_bundle.py: verifica firma + credential + stampa report (contributor site metriche stato). Deliverable: script CLI su bundle reale,todo,low,222,1,,,, +224,11,11.8,Model registry,model_registry.json con stati candidate/promoted/rejected. Traccia contributor_id data metriche hash weights. Deliverable: registry aggiornabile con script,todo,low,223,1,,,, +225,11,11.9,UI confronto modelli in eit-studio,Tabella modelli con metriche affiancate. Azioni promuovi/rigetta con note. Deliverable: pagina Django funzionante,todo,low,224,2,,,, +226,11,11.10,Documentazione contributor workflow,docs/contributor_guide.md: come diventare trusted contributor allenare esportare bundle proporlo. Deliverable: documento pubblicabile,todo,low,223,1,,,, +227,1,1.3.3,Investigare formato .get (raw transimpedanze?),File .get da software Dräger: 1024 byte/frame = 256x float32. Capire se sono le 256 coppie elettrodo full-matrix o altro. Confrontare struttura con .bin e .eit,todo,high,28,2,,,,Ipotesi: matrice 16x16 transimpedanze grezze (pre-ricostruzione). patient01.get=11776000 byte/11500frame=1024b/frame. Verificare su patient02.get atteso 12441600b +228,2,2.1.6,Investigare struttura interna frame .eit,"\""Frame=5495 byte: ts(8)+?. 5487 byte rimanenti non divisibili per 4 -> campo non-float32. Ipotesi: 208 misure complex float32 + pixel data 32x32. Validare con EIDORS (Task 2.0)\""",done,high,59,3,,,2026-03-25,full frame layout documented — notebook §3 annotated hexdump +229,1,1.3.5,Fix naming _MEDIBUS_EXT_EXTRA in dtypes.py,"\""Rinominare le ultime 4 tuple: idx54=airway_pressure_pod idx55=esophageal_pressure_pod idx56=transpulmonary_pressure_pod idx57=gastric_pressure_pod. Aggiornare MEDIBUS_EXT_INDEX. Aggiornare test in test_dtypes.py\""",done,high,29,0.5,2026-03-19,2026-03-19,Fix completato — time_at_low_pressure_pod rimosso gastric_pressure_pod aggiunto test aggiornati 22/22 verdi, +230,1,1.4.6,Documenta reverse eng .bin,Notebook bin_format.ipynb completo con hexdump annotato,done,high,26,2,2026-03-19,2026-03-19,bin_format.ipynb esteso: sezione Healthy_Lung (01_Healthy_Lung_01.bin) EXT 4382b a 20Hz no PressurePod. Scoperta: Drager USA SEMPRE 4382b indipendente da PressurePod. Pod assente = idx54-57 sentinel 0xFF7FC99E. PHigh idx51 = ultimo standard (non ext). Nomi idx52-53 corretti: Plow/Tlow, +231,2,2.1.7,Documenta reverse eng .eit,Notebook eit_format.ipynb con header+frame size,done,high,56,1,2026-03-19,2026-03-19,docs/reverse_eng/eit_format.ipynb — frame_size=5495 ts confermato binary_start=preamble[1]+8, +232,1,1.3.6,Analisi prototipo vecchio ASC parser,"\""Analisi docs/prototype_analysis.md: breath detection PEEP detection features 150+ per breath. Utile come spec per Task 3.x-4.x\""",done,medium,,1,2026-03-19,2026-03-19,Prototipo analizzato — logica documentata in docs/prototype_analysis.md, +233,1,1.0.1,Definire parser registry contract,"\""Definire mapping esplicito (vendor extension) -> parser factory e policy strict. Obiettivo: routing deterministico e API stabile per chiamata diretta parser\""",done,high,12,1,0.5,2026-03-26,loader.py completo: ParserFactory type alias + default_parser_registry() + _normalize_vendor() + vendor aliases. Testato in test_parser_routing.py. +234,1,1.0.2,Implementare load_data orchestrator,"\""Entry point unico: detect vendor/formato -> build parser -> parse_safe -> metadata detection. Deve funzionare su singolo file e restare indipendente da parser .eit specifico\""",done,high,233,1.5,0.5,2026-03-26,load_data() + load_many() + load_folder() completi in loader.py. Metadata detection.vendor/extension/bin_format attaccati a BaseData. Testati batch e skip-on-error. +235,1,1.0.3,Implementare load_many_data batch mixed,"\""API batch per lista file multi-estensione multi-vendor con report per-file (ok/error). Non bloccare tutto al primo errore\""",todo,high,234,2,,,, +236,1,1.0.4,Consolidare autodetect vendor .bin,"\""Usare BIN_FORMAT_SPECS come source of truth e spec.vendor come chiave routing. Coprire validazione estensione e file size\""",todo,high,23,1,,,, +237,1,1.0.5,Implementare score_format_sanity per .bin ambiguo,"\""Quando più frame size dividono file_size calcolare score su campioni start/middle/end e scegliere spec migliore\""",todo,high,236,2,,,, +238,1,1.0.6,Integrare score in detect_best_format,"\""Sostituire placeholder AmbiguousFormatError con decisione score-based in strict/non-strict con messaggi diagnostici\""",todo,high,237,1,,,, +240,1,1.0.8,Integrare sentinel replacement in parse bin,"\""SPOSTATO A PREPROCESSING. Parser è lazy by design — non copia in RAM non rimpiazza valori. replace_no_data_sentinels va chiamata nel preprocessing layer come prima operazione prima di qualsiasi calcolo.\""",cancelled,high,239,0,,,2026-03-26,Rimosso deliberatamente dal parser (refactor/lazy-parsing branch). Prima task di Fase 4: 4.0.1. +242,1,1.0.10,Test unit detection e routing single file,"\""Test per detect_vendor_and_format build_parser_for_detection load_data con fixture sintetiche minime\""",todo,high,234,1.5,,,, +243,1,1.0.11,Test unit batch mixed,"\""Test load_many_data con mix .bin/.eit/.csv e parser mancanti. Verificare report successi fallimenti\""",todo,medium,235,1.5,,,, +7.0.1,7,7.0.1,GREIT reconstruction bridge,eit_greit.py: reconstruct_greit() + build_greit() wrapping pyEIT GREIT. Sign correction + rot90 for anatomical orientation. External ref_frame for cross-recording EELI.,done,high,,2,2,,2026-03-27,Implemented in src/fasteit/parsers/draeger/eit/eit_greit.py. Validated in notebook 03 §6-8. +7.0.2,7,7.0.2,Validate std vs fmmu protocol,Empirical test: which pyEIT measurement ordering matches Dräger .eit binary layout?,in_progress,critical,7.0.1,1,2026-03-27,,Test cell in notebook 03 §9 — needs to be run by Riccardo to determine correct parser_meas. +7.0.3,7,7.0.3,Validate .eit/.bin frame alignment,Cross-correlation lag check — prerequisite for ML paired dataset.,in_progress,critical,7.0.1,0.5,2026-03-27,,Cross-correlation cell in notebook 03 §10 — needs to be run. +ML.1,ML,ML.1,Data-driven reconstruction — Ridge baseline,sklearn Ridge regression X=(N_frames 208) y=(N_frames 1024). Learn Draeger reconstruction matrix empirically from paired .eit/.bin data.,todo,high,7.0.3,3,,,Branch feat/ml-reconstruction. Prerequisite: lag=0 confirmed in 7.0.3. +ML.2,ML,ML.2,ML training notebook,Training curves / epochs / validation set / SSIM vs .bin frames,todo,high,ML.1,3,,,Notebook 04_ml_reconstruction.ipynb +ML.3,ML,ML.3,Evaluate CNN option,Compare Ridge vs small CNN (U-Net or MLP) on same paired dataset,todo,medium,ML.2,4,,,Only if Ridge spatial quality insufficient diff --git a/.claude/learning_journal.md b/.claude/learning_journal.md new file mode 100644 index 0000000..86c78c4 --- /dev/null +++ b/.claude/learning_journal.md @@ -0,0 +1,1469 @@ +# eit-clinical — Learning Journal + +Quaderno di studio personale di Riccardo. Aggiornato ad ogni sessione di lavoro. + +--- + +## Sessione 2026-03-27 — GREIT bridge, spatial reconstruction diagnosis, ML strategy, docs split + +### Tasks worked on +- Fase 2 wrap-up: `eit_greit.py` (GREIT bridge), notebook 03 validation, docs split +- Branch `feat/eit-parser-complete` brought to final state before PR + +### Concepts explained + +- **GREIT is a linear reconstruction**: `pixel = R_matrix @ vv` where R (1024×208) is + built from a FEM sensitivity model. Data-driven approach = estimating R empirically + from paired `.eit`/`.bin` data instead of using the FEM model. +- **`std` vs `fmmu` protocol ordering**: pyEIT generates 208 pairs in two orderings. + `std` always starts the measurement sweep from electrode 0 (absolute). `fmmu` starts + from the active drive electrode A (rotated). Both give 208 pairs; they differ from + drive pattern 2 onward. Dräger hardware likely uses `fmmu` (natural sweep from the + electrode adjacent to the sink). Wrong protocol → spatially scrambled image even though + the global signal sum is correct (sum is order-independent). +- **Why global signal is correct but image is wrong**: `nansum(images, axis=(1,2))` sums + all pixels regardless of spatial position. If measurements are assigned to wrong electrode + pairs, each pixel is wrong but the total stays the same. This is why temporal phase was + correct while bilateral lung structure was absent. +- **eitprocessing does NOT read `.eit`**: only reads `.bin` (already-reconstructed 32×32 + from the device firmware). FastEIT is the only open-source Python parser for `.eit`. + This is the competitive differentiator. +- **ML reconstruction proposal**: use paired `.eit`/`.bin` frames as (X, y) to learn + the Dräger reconstruction matrix empirically. Simplest model: Ridge regression + (`sklearn`), which estimates R (1024×208) from data — equivalent to GREIT but without + the FEM model. Training on 11k frames takes seconds. Neural network (CNN/U-Net) is + optional and probably not needed if the transform is linear. +- **Frame alignment prerequisite**: before building the ML dataset, verify that frame k + in `.eit` corresponds to frame k in `.bin` (lag=0 cross-correlation). This is checked + in notebook §10. +- **EELI and device recalibration**: absolute impedance baseline shifts after each + PulmoVista recalibration. Cross-recording comparison requires a shared external + `ref_frame` (mean of a stable baseline period at the start of the session). Already + implemented in `reconstruct_greit(ref_frame=np.ndarray)`. + +### Riccardo's questions and insights +- "Perché non ci vogliono mesi per il ML?" → Training time is hours at most (Ridge = + seconds, CNN = hours on CPU). Months was a mistake — I was thinking about full feature + development, not training time. +- "Possiamo fare un modello che impara quello che fa Dräger?" → Yes, this is called + data-driven reconstruction. Ridge regression is the right starting point (linear EIT). +- "Questo mi porterebbe più nel mio campo" → Exactly right: the ML angle is clinically + original (no one else has paired `.eit`/`.bin` data from open-source parsing) and + positions the work as applied research, not physics engineering. + +### Epiphanies +- The `.bin` is NOT raw data — it is already reconstructed by the device. eitprocessing + works at pixel level, never seeing the 208 measurements. FastEIT is unique precisely + because it reads both layers. +- `std` vs `fmmu` is empirically testable in the notebook: if `fmmu` shows bilateral + lung structure and `std` does not, the ordering is confirmed. + +### Technical notes +- `eit_greit.py`: sign negation + rot90(k=1) applied inside `reconstruct_greit()` so + callers always get impedance convention + anatomical orientation without manual steps. +- External `ref_frame` is the correct mechanism for cross-recording EELI comparison. + Documented in the docstring with a usage example. +- docs split: `data_model.md` = containers only; `parsers.md` = parser reference + (new file, comprehensive); `parsing_layer.md` = architecture + extension recipes. + +### Branch status +- `feat/eit-parser-complete`: commits 1-3 done (eit_greit, notebook, docs) +- Next: §9 std/fmmu test and §10 cross-correlation to be run by Riccardo in the notebook +- After PR merge to dev: open `feat/ml-reconstruction` from dev + +--- + +## Sessione 2026-03-24 — Git workflow, CI/CD, benchmark notebook + +### Task lavorate +- B.1: Notebook benchmark `np.memmap` vs `struct.unpack_from` +- D.3 (completamento): README badge table verticale +- CI.1 (completamento): build workflow + test/lint trigger su dev +- CI.2 parziale: codecov setup (pytest-cov, codecov-action@v5, CODECOV_TOKEN) +- P.1: GitHub profile README + +### Concetti spiegati + +- **Git branching model (trunk-based semplificato)**: `main` = released/stable, `dev` = working environment, feature branch da dev per ogni task. Il branch default su GitHub punta a quello che vede il pubblico. +- **`git remote prune origin`**: rimuove i riferimenti locali a branch remoti già cancellati su GitHub (dopo merge e delete PR). +- **`np.memmap` e RAM peak**: il mapping OS vive fuori dall'heap Python — `tracemalloc` non vede i byte grezzi del file. Il RAM peak misurato è l'output array `(N, 32, 32)` float32 creato da `replace_no_data_sentinels` + la maschera booleana intermedia. I campi non acceduti (ts, Medibus) non entrano mai nell'heap. +- **Speedup ratio che decresce a file grandi**: a file piccoli la CI/CD è bottlenecked da Python overhead (loop, tuple, function calls). A file grandi entrambi gli approcci diventano I/O-bound — il disco diventa il collo di bottiglia comune, e il vantaggio relativo si riduce. +- **GitHub Actions badge URL e branch**: `badge.svg` senza parametri usa il default branch. `branch/main` punta a main, `branch/dev` a dev. Se il coverage è uploadato da dev ma il badge punta a main, mostra `unknown`. +- **Codecov**: riceve il `coverage.xml` generato da `pytest --cov --cov-report=xml` tramite `codecov-action`. Da ottobre 2023 richiede token anche per repo pubbliche. Il token va come GitHub Secret, mai hardcoded nel yaml. +- **Standard README badge table**: layout verticale (label | badge) è lo standard della community Python open source (pandas, sklearn, requests lo fanno tutti). Non specifico di eitprocessing. +- **`fail_ci_if_error: false`**: il job CI non fallisce se l'upload a codecov non riesce — evita che un problema esterno rompa la CI. + +### Domande di Riccardo +- "perche stiamo caricando in ram con mmap comunque?" → Il RAM che misuri è l'output array (pixels puliti), non i byte del file — quello è gestito dall'OS fuori dall'heap Python. +- "la struttura mia e quella di eitprocessing e lo stesso standard?" → Sì, separation of concerns (models/parsers), entry point unico, dtype numpy per binari, BaseParser — tutti pattern standard, non copiati. +- "coverage unknown perché?" → Badge puntava a `branch/main` ma il coverage era stato uploadato da `dev`. Fix: cambiare branch nel badge URL. + +### Epifanie e insight +- "il badge unknown non è un errore di codecov, è solo che il branch nel link è sbagliato" +- "pytest-cov era già in pyproject.toml da prima — avevamo già tutto pronto senza saperlo" + +### Note tecniche +- `codecov/codecov-action@v5` (non v4) è la versione attuale raccomandata da codecov +- `if: matrix.python-version == '3.12'` evita 3 upload duplicati dalla matrix 3×Python +- `--cov-branch` abilita il branch coverage (copertura dei rami if/else), non solo line coverage + +--- + +## Sessione 2026-03-27 — Parser .eit completo + visione scientifica multicentrica + +### Task lavorate +- Completamento `DragerEitParser.parse()`: memmap frame data, estrazione tutti i campi rilevanti +- Discussione architettura `RawImpedanceData`: split `aux_signals` → `signals` + `clinical` +- Discussione bridge pyEIT e standardizzazione cross-vendor + +### Concetti spiegati + +- **`RawImpedanceData` split semantico**: i campi del frame si dividono in due categorie con significato diverso. + - `signals`: segnali fisici EIT per-frame (`timestamp`, `trans_A`, `trans_B`, `injection_current`, `voltage_A`, `voltage_B`) — quello che la macchina misura elettricamente + - `clinical`: dati di contesto clinico per-frame (`medibus` 67 canali ventilatore, `event_text`) — quello che il paziente/ventilatore sta facendo + - `measurements`: il campo core per pyEIT = `vv` calibrato (Task 2.4.2) + +- **pyEIT non "sa" quanti elettrodi hai**: il protocollo (`n_el`, `dist_exc`, `step_meas`) si configura esplicitamente. La matrice `vv` deve avere tante colonne quante misure definisce quel protocollo. Dräger 16 el → 208 misure, Timpel 32 el → misure diverse. + +- **GREIT (Graz Consensus Reconstruction Algorithm)**: algoritmo standardizzato per ricostruzione EIT clinica 2D. Parametri raccomandati da consensus paper (Adler 2009). Permette di giustificare scientificamente le scelte algoritmiche senza dover derivare tutto from scratch. + +- **Geometria EIT sempre circolare**: sia Dräger che Timpel modellano il torace come sezione circolare 2D. È una semplificazione nota di tutti i sistemi EIT — funziona perché la cintura va sempre al 4° spazio intercostale. Non è una differenza tra vendor. + +- **Pixel vs ROI: cosa è confrontabile cross-vendor**: + - Pixel singoli: NON confrontabili (16 el → risoluzione minore, 32 el → maggiore) + - Regioni (ROI: dx/sx, ventrale/dorsale): SÌ confrontabili — sono frazioni della mesh normalizzata, esistono identiche su entrambe + - Indici clinici (GI, CoV, tidal variation regionale): SÌ confrontabili — adimensionali, normalizzati, non dipendono dalla risoluzione + +### Domande di Riccardo +- "pyEIT non lo considera se Timpel ha 32 elettrodi?" → pyEIT è agnostico — configuri tu il protocollo con `n_el=32`. Il `vv` ha tante colonne quante il protocollo definisce. +- "se non so giustificare tutti i parametri non è un problema?" → Con GREIT puoi citare il consensus paper — i parametri sono validati dalla comunità. Non devi derivarli da zero. +- "il torace non è sempre circolare considerato?" → Sì, sempre — sia Dräger che Timpel. Avevo sbagliato a differenziarli su questo punto. +- "le features che tiriamo fuori sono confrontabili?" → Le ROI e gli indici clinici sì. I pixel raw no. + +### Epifanie e insight +- "Ah quindi il .bin ha già le immagini ricostruite da Dräger — non ho bisogno di pyEIT per le feature cliniche. pyEIT lo uso per validare e per la pipeline open source cross-vendor!" +- "Se dimostro che GREIT ≈ algoritmo Dräger proprietario, ho una pipeline standardizzata che funziona su qualsiasi vendor. Un medico periferico carica il file, il centrale confronta tutti con lo stesso algoritmo validato." +- "Non confronto pixel per pixel tra Dräger e Timpel — confronto GI, CoV, tidal variation per ROI. Quelli sono adimensionali e la risoluzione non conta." + +### Obiettivo scientifico esplicito (da salvare in roadmap) +La contribuzione scientifica centrale di fastEIT: +1. Parsare .eit (Dräger) e .x (Timpel) — nessuno ce l'ha open source +2. Ricostruire con GREIT (Adler 2009) — algoritmo standardizzato e citabile +3. Validare GREIT vs .bin Dräger (gold standard CE marcato) → dimostrare equivalenza +4. Applicare stessa pipeline a Timpel → cross-vendor comparison possibile +5. Risultato: **primo software open source per confronto EIT multi-vendor con pipeline standardizzata e validata** + +Potenziale paper: *"Open-source vendor-agnostic EIT reconstruction pipeline: validation against PulmoVista 500 proprietary algorithm and cross-vendor comparison"* + +### Note tecniche utili per dopo +- Pipeline clinica primaria: `.bin` → pixel Dräger → feature (nessuna ricostruzione richiesta) +- Bridge pyEIT: `.eit` → `vv` (Task 2.4.2) → GREIT → immagini proprie → confronto con `.bin` +- `metadata["n_electrodes"]` e `metadata["n_measurements"]` in `RawImpedanceData` servono esattamente per configurare il protocollo pyEIT automaticamente nel bridge + +--- + +## Sessione 2026-03-25 — Reverse engineering .eit format + notebook + +### Task lavorate +- Revisione e aggiornamento CSV task (17+ task da in_progress/todo → done) +- Reverse engineering completo formato `.eit` PulmoVista 500 +- Scrittura notebook `docs/reverse_eng/eit_format.ipynb` da zero +- PR #18 mergeata su `dev` + +### Concetti spiegati + +- **Layout binario frame .eit**: ogni frame è 5495 bytes. Il blocco signal è 600 float64 (da byte 16 a 4816). Contiene trans_A[208], gruppi unknown, injection_current[16], voltage_A/B[16], trans_B[208], altri campi. Non 600 valori "casuali" — ogni sottogruppo ha un significato fisico preciso. +- **float64 vs float32**: l'errore classico nel reverse engineering binario. Se leggi 8 byte come due float32 invece di un float64 ottieni valori completamente sbagliati (es. -3.4e12 che sembrava un sentinel era un artefatto). La regola: prima testa sempre entrambi i tipi e confronta con i valori attesi fisicamente. +- **Calibrazione EIDORS**: `vv = ft[0]*trans_A - ft[1]*trans_B` dove `ft = [0.00098242, 0.00019607]`. Costanti empiriche stimate dagli sviluppatori EIDORS nel 2016 correlando con output `.get`. Non derivate da specifica hardware — "best possible guess". Riccardo ha imparato che in fisica clinica la calibrazione è spesso empirica, non analitica. +- **injection_current**: `vvOrig(225:240)` in MATLAB = frame bytes [1808:1936]. Dividendo per `fc=194326.3536` ottieni Ampere. È la misura real-time della corrente effettivamente iniettata (non quella nominale di 5mA). +- **voltage_A/B differenziale**: `(voltage_A - voltage_B) / fv` dove `fv=0.11771`. La differenza tra i due set annulla il rumore di modo comune. `fv` anche questo empirico. +- **mk_stim_patterns**: funzione EIDORS/FEM che descrive il protocollo di stimolazione (dove inietti, dove misuri). NON la implementiamo noi — pyEIT ha il suo equivalente. Noi passiamo i dati `vv` calibrati a pyEIT e diciamo "adjacent drive, 16 elettrodi". +- **Patient data redaction**: prima di stampare l'header ASCII, la funzione `redact_header()` sostituisce i valori di campi sensibili (`Filename`, `Name`, `Patient ID`, `Age/DoB`, `Short Comment`) con `[REDACTED]`. Il file originale non viene mai toccato. + +### Domande di Riccardo +- "gli altri dati li buttiamo?" → No. `injection_current`, `voltage_A/B`, `gugus`, `unknown_*` vanno tutti in `aux_signals` — i named in campi espliciti, i truly-unknown raw per ricercatori futuri. +- "mk_stim_patterns dobbiamo implementarlo noi?" → No. pyEIT lo gestisce. Noi organizziamo i dati nel formato che si aspetta. + +### Epifanie e insight +- "Ah quindi -3.4e12 non era un sentinel, era float32 sbagliato che leggeva byte di float64!" +- "Il commento `# estimated AA: 2016-04-07` di EIDORS significa che anche loro non sapevano esattamente — stavano indovinando con buona correlazione" +- "Il PulmoVista misura sia la corrente che la tensione reale ogni frame — non fida solo del nominale 5mA" + +### Note tecniche +- `sep_offset` nell'header: l'offset assoluto dal byte 0 del file dove si trova il separatore `b'**\r\n\r\n\r\n'`. `binary_start = sep_offset + 8`. +- `parse_header(raw)` restituisce `(format_version, sep_offset, binary_start, fields_dict)` — il terzo valore è già `sep_offset + 8`. +- HealthyLung.eit è V1.00 a 20Hz — NON usarla come esempio primario. patient01.eit è V1.30 a 50Hz, più rappresentativa del clinico. +- `redact_header()` usa `line.find(':')` — funziona anche per `Filename:` che non ha padding. +- Framerate **non** è fisso a 50Hz — va parsato da `Framerate [Hz]` nell'header. HealthyLung è 20Hz. +- CSV task corrotto da script di aggiornamento (apertura in write mode prima del controllo). Recuperato 236/257 task dalla trascrizione sessione precedente. Mancanti: 7 task bloccate Fase 8/9 + alcune Fase 4-6 — da reinserire quando arriva il momento. + +--- + +## Sessione 2026-03-22 (Parte 2) — Implementazione TimpelTabularParser + +### Task lavorate +- Implementazione completa `TimpelTabularParser` + `TIMPEL_FRAME_DTYPE` + `TIMPEL_AUX_FIELDS` +- 41 test verdi per Timpel (dtypes + parser) +- Rimozione lazy import: Timpel ora tracciato git, import diretto +- Fix code-review: F1 (docstring timestamps), F2 (sentinel threshold), W3 (InvalidSliceError), W7 (file_format da extension) +- Refactor docs: `data_model.md` senza routing, `parsing_layer.md` con file layout +- 147 test totali, coverage 93% + +### Concetti spiegati +- **Formato Timpel CSV**: 1030 colonne per riga, senza header, delimiter virgola. Cols 0-1023 = pixel 32×32 (row-major), cols 1024-1029 = aux signals (airway_pressure, flow, volume, min_flag, max_flag, qrs_flag). Sentinel −1000.0 = canale disconnesso → NaN. +- **np.loadtxt vs np.fromfile**: Timpel è testo CSV → `np.loadtxt` con `delimiter=","`. Dräger .bin è binario → `np.memmap` con `dtype` strutturato. Stessa architettura, input diversissimi. +- **Timestamp sintetici**: Timpel non ha colonna timestamp → generiamo noi: `ts[i] = (first_frame + i) / 50.0`. Questo preserva l'indice assoluto del frame anche quando si carica uno slice. Convention di eitprocessing. +- **TIMPEL_FRAME_DTYPE**: deve avere i campi `ts` (float64) e `pixels` (float32, 32×32) per essere compatibile con `ReconstructedFrameData.frames`. La struttura diversa da Dräger (che ha anche min_max_flag, event_marker ecc) è gestita con field opzionali nel data model. +- **Sentinel threshold comparison**: usare `values < TIMPEL_NAN_SENTINEL + 1.0` invece di `values == float32(sentinel)` perché −1000.0 è esattamente rappresentabile ma altri sentinels futuri potrebbero non esserlo. È più robusto di una equality comparison, anche se oggi funzionerebbe uguale. +- **Convenzione timestamp multi-vendor**: Dräger salva il tempo come frazione di giorno (0.0–1.0 = wall clock). Timpel non ha timestamp → secondi dall'inizio registrazione. Documentato in docstring `timestamps` property e in `data_model.md`. + +### Epifanie e insight +- "Il lazy import `try: from .timpel import X except ImportError` era necessario quando il modulo era gitignored. Appena aggiungi il file al repo, puoi toglierlo — è rumore che nasconde errori reali." +- "L'errore `skiprows=100` su file con 5 righe restituisce array `(0,)` 1-D, non `(0, 1030)` 2-D — numpy non può inferire il numero di colonne da un file vuoto. Bisogna gestire `raw.ndim == 1 and raw.size == 0` prima di fare il reshape." +- "Il code reviewer ha trovato che `file_format='csv'` era hardcoded anche per file .txt e .asc — fix facile: `path.suffix.lower().lstrip('.')`." + +### Domande di Riccardo +- "prima di pushare ricontrolla con i vari agents che tutto sia a posto la documentazione ecc. mi raccomando tra i due file md in docs elimina le ripetizioni. il file sui modelli non deve parlare della sequenza di loading ed espansione." → Refactoring doc: `data_model.md` descrive solo i container di dati; `parsing_layer.md` descrive il routing, detection, registry, e ora include anche il file layout (sezione 5 aggiunta). + +### Note tecniche utili per dopo +- `np.loadtxt` con `skiprows=N` su file con 0 +- `TIMPEL_AUX_FIELDS` è una tuple ordinata: l'ordine corrisponde alle colonne 1024→1029 — non cambiare l'ordine +- Test validate() deve coprire anche .txt oltre che .csv — estensioni multiple = test multipli + +--- + +## Sessione 2026-03-22 + +### Task lavorate +- Code quality review e fix post code-review dei tre subagent (eit-ci-checker, eit-doc-keeper, eit-code-reviewer) +- Espansione test suite: 55 → 99 test, coverage 70% → 92% +- Fix bug reale in bin_parser.py scoperto dai nuovi test +- Fix CI GitHub Actions (ruff format + test paziente gitignored) + +### Concetti spiegati +- **memmap read-only copy bug**: `np.memmap[mode="r"][:]` crea una VIEW che eredita il flag read-only del mapping. La fix corretta è `.copy()` che alloca un nuovo array scrivibile. Il `[:]` sembra una copia ma non lo è. +- **ruff format vs ruff check**: la CI di GitHub esegue anche `ruff format --check` (non solo `ruff check`). Sono due cose diverse: `check` trova errori logici/stilistici, `format` controlla la formattazione Black-style. W191 (tabs) non è auto-fixabile con `--fix` — va riscritto il file. +- **Synthetic .bin file per test**: `np.zeros(n, dtype=FRAME_BASE_DTYPE).tofile(path)` crea un file binario valido senza dati paziente. I timestamps devono essere non-identici per la stima fs (tutti zero → `ValueError: Non-positive interval`). +- **BASE format ha Medibus**: entrambi BASE (4358 b) e EXT (4382 b) hanno `medibus_fields` popolato in `bin_formats.py` — `aux_signals` è sempre un dict, mai `None`. La distinzione reale è `has_pressure_pod_fields`. +- **skipif per test con file reali**: `pytest.mark.skipif(not Path("...").exists(), reason="...")` permette di avere test che girano solo in locale (dove esiste il file paziente) e vengono saltati su CI senza fallire. + +### Domande e osservazioni di Riccardo +- "non so perche in locale con ambiente conda mi girano i test ma il push ha fallito entrambe le github action" → Causa 1: `ruff format --check` fallisce se non si esegue `ruff format` prima del commit. Causa 2: test che usano file paziente gitignored danno `FileNotFoundError` su GitHub CI. + +### Epifanie e insight +- "Il bug del memmap non era ovvio: `[:]` sembra una copia ma è una view — solo `.copy()` rompe davvero il legame con il memmap read-only." +- "I test sintetici non richiedono dati paziente: basta costruire il dtype giusto con zeros, impostare timestamps validi, e scrivere su disco con `.tofile()`." +- "La CI di GitHub è più severa del check locale: controlla anche la formattazione, non solo i lint errors." + +### Note tecniche utili per dopo +- Prima di ogni commit: `ruff check src/ tests/ && ruff format src/ tests/` +- `mapped_frames.copy()` non `mapped_frames[:]` per array scrivibile da memmap +- Coverage: 92% con 99 test — bin_parser, bin_utils, detection edge cases, error paths tutti coperti +- CHANGELOG.md rimosso dai file tracciati (non mantenuto in questa fase del progetto) + +--- + +## Sessione 2026-03-21 + +### Task lavorate +- Task 1.2.2: implementazione `DragerBinParser.parse()` (memmap + slicing + sentinel handling + fs da timestamp) +- Task 3.3.1 (in progress): implementazione `DragerAscParser` nella nuova architettura parser + +### Concetti spiegati +- **Parser architecture vendor-ready**: detection separata dal parser concreto. `detect_vendor_and_format()` decide vendor+estensione, `loader` istanzia parser da registry, `parse_safe()` applica validate+parse in modo uniforme. +- **Perché il parser ASC non deve usare read_csv diretto fino a EOF**: il file Drager `.asc` contiene blocchi multipli con schemi colonna diversi (immagini + tabelle). Se leggi tutto da "Tidal Variations" in poi, Pandas rompe quando incontra un blocco con 69 campi invece di 11. Soluzione robusta: estrarre solo la sezione con schema coerente. +- **Section-based parsing**: leggere il marker (`Tidal Variations`), prendere header tabellare, poi consumare righe finché il numero colonne resta costante e la prima colonna resta indice immagine numerico. + +### Epifanie e insight +- "Il parser è molto più stabile se tratta l'ASC come documento a sezioni e non come CSV unico." +- "La stessa architettura ci permetterà di aggiungere Timpel senza toccare il core del routing." + +### Note tecniche utili per dopo +- `detect_tabular_vendor()` ora riconosce Drager via header `DraegerEIT Software` e ha fallback euristico per Timpel. +- Nuovo container `TabularData` con `table: DataFrame`, `n_frames` e `duration` coerenti con il modello dati esistente. +- Test aggiunti per detection/parse/load su file reali ASC Drager (`patient01.asc`). +- Stato test dopo le modifiche: **57 passed**. + +### Addendum naming refactor +- Rimossi gli alias `BinData` e `TabularData`: restano solo i nomi canonici `ReconstructedFrameData` e `ContinuousSignalData`. +- **Perché**: il nome della classe deve descrivere il contenuto dei dati, non l'estensione del file sorgente. Questo evita che un futuro export Timpel con matrici 32×32 finisca in una classe semanticamente "Drager-bin". +- Possibile evoluzione futura: introdurre un `TabularData` più astratto solo se servirà davvero come superclass generica per tabelle da ventilatore o export misti. + +--- + +## Sessione 2026-03-20 + +### Task lavorate +- Fix architetturale `dtypes.py`: refactor MEDIBUS fields confermato da eitprocessing (Task 1.3.2 revisione) +- Aggiornamento `test_dtypes.py`: fixing semantica idx 51 + +### Concetti spiegati + +- **Architettura MEDIBUS BASE vs EXT — la scoperta chiave**: I due formati `.bin` (4358 bytes BASE e 4382 bytes EXT) NON sono in relazione "BASE + 6 extra". Condividono i campi **idx 0-50** (51 campi comuni), ma **divergono completamente da idx 51**: + - BASE (4358 bytes): idx 51 = `time_at_low_pressure` (Tlow BiLevel) — è l'unico campo aggiunto dopo il comune + - EXT (4382 bytes): idx 51 = `high_pressure` (PHigh), idx 52 = `low_pressure` (Plow), idx 53 = `time_at_low_pressure` (Tlow — spostato da 51!), idx 54-57 = Pod channels + - **Tlow si sposta da idx 51 (BASE) a idx 53 (EXT)** — questo è il punto più sottile e la fonte del bug precedente + - Fonte: eitprocessing `draeger.py` (Apache-2.0) definisce `_bin_file_formats` con due dizionari completamente separati + +- **Perché il precedente "fix" era sbagliato**: In una sessione precedente avevamo usato il file `.txt` del software Dräger (che mostra 58 canali in 1-indexed) come guida. Abbiamo messo PHigh a idx 51 in `MEDIBUS_FIELDS`. Ma `MEDIBUS_FIELDS` dovrebbe rappresentare il formato BASE (4358 bytes) dove idx 51 è **Tlow**, non PHigh. PHigh appare solo nel formato EXT e solo lì. + +- **Struttura corretta in `dtypes.py`**: + ```python + _MEDIBUS_COMMON # idx 0-50, 51 campi, identici in entrambi + MEDIBUS_BASE_FIELDS # = COMMON + [Tlow@51] → 52 campi + MEDIBUS_EXT_FIELDS # = COMMON + [PHigh@51, Plow@52, Tlow@53, Pod×4@54-57] → 58 campi + MEDIBUS_FIELDS # alias di MEDIBUS_BASE_FIELDS (backward compat) + MEDIBUS_BASE_INDEX # dict name→index per BASE + MEDIBUS_EXT_INDEX # dict name→index per EXT + MEDIBUS_INDEX # alias di MEDIBUS_BASE_INDEX (backward compat) + ``` + +### Epifanie e insight +- "Ah quindi il `.txt` con 58 canali del software Dräger rappresenta il formato EXT, non il BASE. Quando vai a idx 52 nel software vedi PHigh perché stai guardando un file con PressurePod connesso." +- "Il modo giusto per leggere eitprocessing era guardare la struttura di dati `_bin_file_formats` — due dizionari separati, non uno derivato dall'altro." + +### Domande e risposte +- "Perché Tlow è presente sia in BASE che in EXT se in BASE non puoi settare BiLevel?" → Tlow è inviato dal ventilatore anche in ventilazione convenzionale, con valore sentinel `-1000.0`. Il Dräger include sempre il campo; se la modalità BiLevel non è attiva manda -1000.0. + +### Note tecniche utili per dopo +- `high_pressure` esiste **solo** in `MEDIBUS_EXT_INDEX`, non in `MEDIBUS_BASE_INDEX`. Se provi `MEDIBUS_INDEX["high_pressure"]` ottieni `KeyError`. Usare sempre il dict giusto per il formato del file. +- I sentinel: `-1000.0` = parametro non applicabile/modalità non attiva; `0xFF7FC99E` = hardware non connesso (Pod assente) +- 22 test verdi dopo il fix — tutti i test semantici ora riflettono la struttura corretta + +--- + +## Sessione 2026-03-19 (sera) + +### Task lavorate +- Fix Streamlit taskboard: auto-reload on CSV mtime change +- Fix PRIORITY_VALUES: aggiunto "critical" a data.py +- Pianificazione task 1.1.1 (detect_frame_dtype) e 1.2.2 (parse_bin) + +### Concetti spiegati + +- **Streamlit file-watching**: Streamlit fa watching solo sui file `.py`, non sui `.csv`. Per rilevare modifiche esterne al CSV basta controllare `mtime` (modification time) ad ogni rerun — se cambia, cache miss automatico. +- **mtime come cache key**: passare `mtime: float` a `@st.cache_data` trasforma il timestamp in chiave di cache. Se Claude Code scrive il CSV, il prossimo rerun vede un `mtime` diverso → nuova chiamata → ricarica dal disco. Elegante e senza polling. + +### Note tecniche +- `Path.stat().st_mtime` restituisce un float (secondi da epoch) — si può usare direttamente come chiave per `@st.cache_data` +- `st.session_state.get("csv_mtime")` ritorna `None` alla prima esecuzione → diverge da `_mtime` → primo caricamento automatico +- Il pulsante "🔄 Ricarica dal CSV" ora fa solo `st.cache_data.clear() + st.rerun()` — il mtime check fa il resto + +--- + +## Sessione 2026-03-19 (pomeriggio) + +### Task lavorate +- Fix MEDIBUS field ordering in dtypes.py (Task 1.3.2 + 1.3.5 follow-up) +- Estensione notebook bin_format.ipynb con sezione Healthy Lung (Task 1.4.6) + +### Nuovi file di test +- `01_Healthy_Lung_01.bin/.eit/.asc` — registrazione 1118 frame, 20 Hz, NO PressurePod, NO MEDIBUS +- `elencoMEDIBUSdatafromDragerSoftware.txt` — lista manuale 58 canali Dräger + +### Concetti spiegati + +- **Ordinamento canali MEDIBUS**: Il channel list del software Dräger è la fonte più diretta di verità. PHigh (peak BiLevel pressure) è il canale 51 (0-indexed) = l'ultimo dei 52 canali standard, non un campo "extra PressurePod". I campi extra (idx 52-57) sono: Plow, Tlow, ~Paw, ~Pes, ~Ptp, ~Pgas. + +- **Sentinelle MEDIBUS**: Due valori speciali distinti: + - `0xFF7FC99E` = -3.4e38 → hardware NON connesso (MEDIBUS cable absent) + - `0xC47A0000` = -1000.0 → connesso ma parametro non applicabile o nessun respiro calcolato ancora (es. Plow/Tlow in modalità convenzionale non-BiLevel) + +- **BiLevel vs ventilazione convenzionale**: PHigh, Plow, Tlow sono parametri della modalità BiLevel (CPAP bifasica). In ventilazione convenzionale normale, questi hanno valore -1000.0 (sentinel "N/A"). Importante per l'interpretazione clinica: non confondere -1000.0 con un valore reale di pressione! + +### Epifanie e insight + +- **"Il Dräger usa SEMPRE 4382 byte/frame!"** — Anche una registrazione senza PressurePod ha frame da 4382 byte. La differenza non è nella struttura del frame ma nei valori: Pod assente = idx 54-57 hanno `0xFF7FC99E` (-3.4e38). Questo semplifica il parser: non serve distinguere BASE/EXT prima di aprire il file, basta guardare il sentinel a idx 54. + +- **"Il .bin e il .asc non partono dallo stesso istante!"** — Il file Healthy_Lung ha un offset di +8 secondi tra il timestamp del .bin (frame 0) e il timestamp del primo frame esportato nel .asc. Il .asc è generato dal .eit, non dal .bin. I due file coprono periodi leggermente diversi. + +- **"Healthy Lung è a 20 Hz, non 50 Hz!"** — dt = 0.05s invece di 0.02s. Il PulmoVista supporta framerate configurabili. Il parser dovrà rilevare il framerate dal timestamp, non assumere sempre 50 Hz. + +- **"I pixel del .asc non sono comparabili direttamente con il .bin"** — Il .asc applica: (1) sottrazione della baseline (media dell'intera registrazione), (2) filtro low-pass 50 bpm. I pixel grezzi del .bin sono valori assoluti. Per cross-validare correttamente bisognerebbe applicare gli stessi preprocessing. + +### Domande aperte +- PHigh a idx 51 vale -1000.0 in tutti i file (anche patient01 con MEDIBUS connesso). Quand'è che PHigh diventa non-zero? Forse solo in modalità BiLevel con MEDIBUS configurato per esportarlo? +- Il formato BASE (4358 byte) esiste davvero su qualche device o firmware? Non ne abbiamo ancora visto uno. + +### Note tecniche +- `np.memmap(..., dtype=FRAME_EXT_DTYPE)` funziona uguale su file con e senza PressurePod — distinzione solo nei valori, non nella struttura +- Per rilevare PressurePod: `frames['medibus_data'][:5, 54].view(np.uint32) != 0xFF7FC99E` +- Per rilevare MEDIBUS: `frames['medibus_data'][0, 0].view(np.uint32) != 0xFF7FC99E` +- Per rilevare framerate: `np.diff(frames['ts']).mean() * 86400` → secondi per frame + +--- + +## Sessione 2026-03-17 (Sessione 0 — Pianificazione) + +### Task lavorate +- Nessuna task di codice. Sessione di pianificazione, ricerca landscape, architettura, roadmap. + +### Concetti appresi (davvero capiti) + +- **File .bin e .eit sono due file separati**: il PulmoVista li genera in parallelo. Il .bin ha le immagini ricostruite 32×32, il .eit ha i dati grezzi dagli elettrodi. Per il mio uso clinico quotidiano basta il .bin. + +- **Il formato .eit Dräger NON è quello che pensavamo**: i blocchi tipo 3/7/8/10 sono del formato Carefusion (altro produttore, stessa estensione file). Il formato Dräger ha header testuale + dati binari. Scoperto analizzando il codice EIDORS. + +- **208 misure, non 256**: il PulmoVista con 16 elettrodi produce 208 misure indipendenti per frame (16 iniezioni × 13 misure, escludendo auto-misure). + +- **EIDORS è GPL**: significa che non possiamo copiare il loro codice nel nostro progetto Apache-2.0. Ma possiamo leggere il codice per capire il formato del file, e poi scrivere il nostro parser da zero. Le specifiche di un formato file non sono copyrightabili, il codice sì. + +- **Il valore del progetto**: non è nel preprocessing (che reimplementiamo da eitprocessing/letteratura) ma nel pipeline end-to-end: file → features → database breath-level. Nessuno ce l'ha. + +- **PEEP step detection è un buco nel campo**: nessun tool fa detection automatica dei cambi PEEP. Né eitprocessing, né nessun altro. + +- **pyEIT produce una mesh, non 32×32**: serve interpolazione per rimappare a 32×32. È lo stesso passaggio che fa il PulmoVista internamente. + +### Concetti visti ma DA STUDIARE a fondo (non li so usare) + +- **struct.unpack**: so che serve per leggere byte e interpretarli come numeri, ma devo fare esercizi pratici +- **numpy.fromfile e numpy.frombuffer**: so che esistono e che sono più veloci di struct per array grandi, ma non li ho mai usati +- **numpy dtype strutturato**: concetto potente (definisci il layout di un frame intero), ma non l'ho mai provato +- **Endianness**: so che il PulmoVista è little-endian e che si specifica con '<', ma devo fare pratica +- **mmap (memory-mapped files)**: ho solo letto che esiste, non ho idea di come si usa in pratica +- **scipy.signal**: find_peaks, butter, sosfiltfilt — so cosa fanno concettualmente ma non li ho mai usati +- **Watershed, Otsu**: metodi per lung mask — concetti vaghi, da studiare quando arrivo alla Fase 4 +- **HDF5 / h5py**: so che è per salvare matrici grandi, mai usato + +### Epifanie + +- "Il .eit e il .bin sono due file separati generati in parallelo dal PulmoVista durante la stessa registrazione — non uno dentro l'altro!" + +- "eitprocessing non fa PEEP step detection automatica. Nessuno lo fa. È un buco enorme nel campo." + +- "Il parser a blocchi tipo 3/7/8/10 che avevamo analizzato è il formato CAREFUSION, non Dräger! Il formato Dräger è completamente diverso. Scoperto analizzando il codice EIDORS completo." + +- "pyEIT non produce 32×32 ma una mesh triangolare. Serve interpolazione (scipy griddata) per rimappare a 32×32 e rientrare nel pipeline. È lo stesso passaggio che fa il PulmoVista internamente." + +- "Il valore del progetto non sta nel preprocessing (copiato/reimplementato da eitprocessing) ma nel pipeline end-to-end: file → preprocessing → features → database breath-level → export per R/ML. Nessuno ce l'ha." + +- "Le correzioni manuali dell'utente nella GUI (mask, breath detection, PEEP step) diventano training data per ML futuro. Flywheel: più usi → più dati → migliori predizioni → meno correzioni." + +### Note da ricordare per dopo +- Il consensus TREND 2017 ha 193 pagine di supplementi — leggere almeno le definizioni dei parametri +- I fattori di scala EIDORS per le transimpedenze sono STIMATI ("estimated AA: 2016-04-07") — vanno verificati confrontando output Python vs EIDORS sullo stesso file +- eitprocessing è Apache-2.0 (posso studiare il codice), EIDORS è GPL (posso leggere per capire il formato, NON copiare codice) +- Il PulmoVista usa 16 elettrodi, pattern adjacent-drive, 5mA iniezione +- Conversazione 1 con Claude ha spiegazioni dettagliate di struct/numpy/endianness — rileggere quando serve + +--- + +## Sessione 2026-03-18 (Sessione 2 — Task Board UI + Riorganizzazione CSV) + +### Task lavorate +- Nessuna task di codice clinico. Sessione di tooling e riorganizzazione. +- Costruito **Task Board Streamlit** (`tools/taskboard/`) per gestire le task senza editare il CSV a mano +- Rianalisi completa del CSV e riorganizzazione dipendenze/priorità + +### Cosa è stato costruito + +**Task Board Streamlit** — `tools/taskboard/app.py` + `tools/taskboard/data.py` +- Tabella editabile con `st.data_editor`: colonne, selectbox per status/priority, modifica inline +- Colonna `_dot` con emoji colorate per status (🟢 done, 🟠 in_progress, ⬜ todo, 🔴 blocked) +- Tab Progress: metriche globali + barre avanzamento per ogni fase +- Tab Add Task: form con assegnazione automatica task_id (max+1) +- Si lancia con: `cd tools/taskboard && streamlit run app.py` + +### Riorganizzazione CSV + +**VIZ anticipate**: plot di debug spostati nelle fasi in cui servono, non alla fine +- V.1 → Fase 1 (debug parse_bin), V.2/V.4/V.5 → Fase 4 (debug lung mask + breath detection) + +**Fasi 8/9 aggiunte al CSV come blocked**: +- Fase 8 = R wrapper (subito dopo la lib, depends_on fase 6) +- Fase 9 = Django webapp (più avanti, depends_on fase 6) + +**Scope chiarito**: solo ventilazione, no perfusione +- 4.3.1 filtro perfusione → BACKLOG, B.3 analisi perfusione → eliminata + +**Priorità**: task_id 86 (Flask backend) → critical, task_id 8 (optional deps) → low + +### Stato CSV finale +- **211 task** totali | 12 done | 5.7% completato + +### Epifanie +- "I plot di debug non sono opzionali — V.1 dopo parse_bin e V.2 dopo lung_mask accelerano la verifica di 10x rispetto a print() su array." +- "Il wrapper R viene prima del Django perché uso R ogni giorno. La GUI per i clinici è utile ma non è la mia pipeline principale." + +--- + +## Sessione 2026-03-17 (Sessione 1 — Orientamento sistema + Analisi CSV + Kickoff Fase 0) + +### Task lavorate +- Nessuna task di codice. Sessione di orientamento: architettura del sistema, analisi incongruenze CSV, pianificazione Fase 0. + +### Come funziona il sistema (capito oggi) + +**Architettura a 3 livelli**: +1. **CLAUDE.md** — caricato automaticamente ad ogni sessione. Contiene identità, regole ferme, flusso di lavoro, path dei file. Claude lo legge prima di rispondere a qualsiasi cosa. +2. **Memory system** (`~/.claude/projects/.../memory/`) — persistenza tra sessioni: chi sei, come vuoi collaborare, regole apprese. Caricato automaticamente come contesto. +3. **File di progetto** (`.claude/*.md`, `.claude/*.csv`) — da leggere attivamente all'inizio sessione. tasks.csv = stato progetto, roadmap = istruzioni, reference = paper+formule, learning_journal = questo diario. + +**I 3 agent specializzati** (`.claude/agents/`): +- `eit-ci-checker` — prima di ogni commit: ruff lint, pytest, coverage 80% +- `eit-code-reviewer` — dopo aver scritto codice: correttezza scientifica, citazioni, test +- `eit-doc-keeper` — dopo ogni task completata: README, docstring, docs/ coerenti + +**Flusso tipico sessione**: Claude legge CSV → presenta task consigliate → spiega teoria → esempio minimo → tu scrivi nell'IDE → agent review → Claude aggiorna CSV e journal. + +### Analisi incongruenze CSV (fatto oggi) + +**3 fix applicati al CSV**: +- Task 15 (PeepStep): dipende da RawData(13) + Breath(14), non solo da 13 — PeepStep contiene `breath_indices` +- Task 16 (Study): dipende da 13+14+15, non solo da 13 — Study aggrega Breath e PeepStep +- Task 155 (PEEP detection Medibus): dipende da 30 (Medibus extraction) + 118 (breath detection), non solo da 30 — non puoi fare PEEP detection su singoli breath se non hai ancora rilevato i breath + +**Task 50 (Download EIDORS)**: priority era già `critical` nel CSV, nessun fix necessario. + +### Stato progetto attuale +- 205 task | 0 done | 0 in_progress | 0 blocked +- Fase 0: 23 task (infrastructure + data model) — tutto todo, nessuna dipendenza esterna +- Percorso critico realistico: **0 → (1+2 in parallelo) → 4 → 5 → 6** + +### Rischi operativi identificati +1. **File reali (.eit e .bin)**: task 32 e 56 sono `critical` e bloccanti per validazione Fasi 1+2. Da procurare prima possibile. +2. **EIDORS study**: richiede scaricare e leggere codice MATLAB — tempo reale di lettura da parte mia. +3. **Scope creep Fase 5**: 41 task, alcune già marcate post-v1 — tenere la barra. + +### Prossime task consigliate (sbloccate ora, dipendenze vuote) +| Task | Titolo | h | Perché prima | +|---|---|---|---| +| 0.2.1 | Creare repo GitHub | 0.5h | Sblocca intera struttura | +| 0.2.2 | Struttura directory | 1h | Dipende da 0.2.1 | +| 0.7.2 | LICENSE Apache-2.0 | 0.2h | Dipende da 0.2.1, rapidissima | +| 0.1.1 | Studio struct/numpy binari | 2h | Base per tutto il parsing | +| 0.1.2 | Studio scipy signal processing | 2h | Base per preprocessing | + +**Sequenza consigliata**: `0.2.1 → 0.2.2 → 0.7.2` (infrastruttura) poi `0.1.1 + 0.1.2` (studio teorico). + +### Obiettivo fine Fase 0 (checklist) +- [ ] `src/eitclinical/` con moduli vuoti + `__init__.py` +- [ ] `pyproject.toml` installabile con `pip install -e .[dev]` +- [ ] `pytest tests/` passa (0 test, ma senza errori di import) +- [ ] `ruff check src/` pulito +- [ ] `BaseParser`, `RawData`, `Breath`, `Config` esistono con type hints + +--- + +### Aggiornamento fine sessione — File reali confermati disponibili + +**File Dräger reali già in mano** (confermato 2026-03-17): +- `.eit` ✅, `.bin` ✅, `.txt` ✅ +- Varianti con e senza PressurePod ✅ +- Più registrazioni diverse ✅ + +**Task 32 (1.4.1) e 56 (2.1.1) marcate `done`** — erano le due `critical` che sembravano bloccanti. Con i file reali già disponibili: +- Validazione Fase 1 (parser .bin) → sbloccata +- Validazione Fase 2 (parser .eit) → sbloccata + +**Rischio operativo #1 eliminato**: "Procurarsi file reali" era il rischio #1 identificato nella Sessione 0. Non è più un rischio — i file ci sono. + +**Prossime task (sessione dopo)**: `0.2.1 → 0.2.2 → 0.7.2 → 0.1.1 → 0.1.2` + +--- + +### Aggiornamento — Rename eit_clinical → fastEIT + Scaffold Fase 0 + +**Rename completato** (2026-03-17): tutto il progetto ora usa `fastEIT` come nome. Il package Python si importa come `import fasteit` (minuscolo per convenzione PEP 8). + +**Task completate in questa sessione**: +| Task | Titolo | Note | +|---|---|---| +| 0.2.1 | Creare repo GitHub | Già presente (retroattivo) | +| 0.7.2 | LICENSE Apache-2.0 | Già presente (retroattivo) | +| 0.2.2 | Struttura directory | `src/fasteit/` con tutti i moduli placeholder | +| 0.2.3 | docs e notebooks | `docs/eit_format.md`, `docs/eidors_analysis.md`, `docs/reverse_eng/`, `notebooks/` | +| 0.3.1 | pyproject.toml | hatchling, deps core + [pyeit] + [dev], pytest markers, ruff | +| 0.7.1 | README base | Badge + differenziatori + installazione + disclaimer | + +**Cosa è il pyproject.toml** (spiegazione rimandata — vedi prossima sessione) + +**Prossime task sbloccate**: +- `0.3.2` — optional dependencies [pyeit] e [dev] (già fatto in 0.3.1, da valutare se separare) +- `0.4.1` — GitHub Actions workflow test.yml +- `0.4.2` — workflow lint.yml +- `0.5.1` — BaseParser classe astratta +- `0.1.1 / 0.1.2` — studio struct/numpy + scipy (teoria, nessuna dipendenza) + +--- + +### Sessione teorica — Python packaging, GitHub Actions, binary parsing, signal processing + +#### Python packaging (approfondimento) +- **`import` in Python**: cerca `fasteit` in `sys.path` — lista di cartelle dove Python guarda. `src/` layout obbliga a usare il pacchetto installato, non i file locali grezzi. +- **`__init__.py`**: trasforma una cartella in un modulo. Viene eseguito ad ogni `import fasteit`. Posto giusto per `__version__` e API pubblica. +- **`pip install -e .`**: editable install = collegamento simbolico a `src/fasteit/`. Modifica → subito visibile. Nessun reinstall necessario. +- **wheel vs sdist**: wheel = archivio già pronto (veloce). sdist = sorgente da compilare. Per numpy/scipy esistono wheel precompilate. +- **hatchling**: build backend moderno. Alternativa a setuptools senza `setup.py`. + +#### GitHub Actions / CI/CD +- **CI**: ogni push → computer vergine → esegue i test automaticamente. "Vergine" = senza i tuoi pacchetti locali, variabili d'ambiente, file casuali. +- **CD**: se i test passano → pubblica/deploya automaticamente. Non ci serve ancora. +- **Struttura workflow**: `on:` (trigger) → `jobs:` → `steps:`. File YAML in `.github/workflows/`. +- **Runner**: VM Ubuntu fresca nel cloud GitHub. Distrutta dopo ogni run. +- **Matrix strategy**: ripete il job su più versioni Python in parallelo (`["3.10", "3.11", "3.12"]`). +- **`actions/checkout@v4`**: fa il git clone del repo nella VM. Primo step di ogni workflow. +- **`actions/setup-python@v5`**: installa la versione Python specificata. +- **`cache: pip`**: ricicla le dipendenze pip tra un run e l'altro se `pyproject.toml` non è cambiato. +- **workflow test (0.4.1)**: checkout → setup-python con matrix → pip install -e .[dev] → pytest +- **workflow lint (0.4.2)**: checkout → setup-python 3.12 → pip install ruff → ruff check + ruff format --check +- **Badge**: URL automatico `github.com/USER/REPO/actions/workflows/test.yml/badge.svg` + +#### Letture assegnate — GitHub Actions +- GitHub Actions quickstart (docs ufficiale) +- Understanding workflows (docs ufficiale) +- `actions/setup-python` marketplace page (per parametri cache) +- Matrix strategy (docs ufficiale) + +#### struct e numpy per file binari (0.1.1) +- **Byte raw**: i numeri nel .bin sono in memoria esattamente come in RAM. Nessun testo, nessun separatore. +- **Endianness**: little-endian = byte meno significativo prima. Il PulmoVista è little-endian. In numpy: `' 3`. +- `from __future__ import annotations` in tutti i file — permette di usare type hints come stringhe forward reference senza import circolari. + +#### Stato CSV aggiornato +- Task 0.5.1: ✅ done +- Task 0.5.2: ✅ done (rinominata: BaseData + BinData, non più RawData in session.py) + +#### Prossima sessione +1. Studio eitprocessing — com'è parsato il .bin in Apache-2.0 +2. Verifica con `openBins.m` del collega MATLAB +3. Task **1.1.1**: `autodetect_frame_size(file_size)` — scrivi + test con file reale +4. Task **1.2.1**: `FRAME_BASE_DTYPE` ufficiale in `src/fasteit/dtypes.py` + +--- + +### Aggiornamento 2026-03-18 — Review letteratura EIT con GPT: 3 correzioni al progetto + +Sessione di revisione critica della letteratura (Delphi 2025, Scaramuzzo 2024, review recenti). Nessun codice scritto, ma trovate 3 cose da correggere nel progetto: + +**1. CoV mancava la direzione sinistra-destra** +Il Delphi 2025 cita esplicitamente CoV in entrambe le direzioni come misura standard. Noi avevamo solo la formula verticale (ventro-dorsale). Aggiunto: +- Formula CoV_LR: stessa struttura di CoV_VD ma sull'asse delle colonne +- Task 5.1.4 aggiornata: implementare entrambe (`cov_vd` e `cov_lr`) +- DB schema aggiornato con colonna `cov_lr` + +**2. RVD formula era sbagliata: np.argmax ≠ t40 method** +Task 5.3.2 diceva `np.argmax` (tempo al picco). La formula standard da Muders 2012 è il **t40 method**: tempo in cui il pixel raggiunge il 40% del suo ΔZ tidale. +Sono cose diverse: argmax è il massimo assoluto, t40 è il punto a 40% della salita. t40 è più robusto perché non dipende dalla fase finale dell'inspirazione. +Implementazione: `np.argmax(cumulative_dz >= 0.4 * total_dz, axis=time_axis)` + +**3. SDRVD è una metrica con nome proprio, non solo "RVD_inhomogeneity"** +La SD degli RVD pixel-wise è citata come SDRVD nel Delphi 2025 e nella Wisse survey. Nel reference doc la chiamavamo "RVD_inhomogeneity". Allineato il nome. + +**Conferme dalla letteratura (nessuna modifica necessaria)**: +- La gerarchia frame → breath → step/patient è esattamente quella usata in letteratura. Buona scelta architetturale. +- TIV, EELI, GI, silent spaces, V/D ratio, ROI distribution, compliance regionale, ITV, OD/CL, pendelluft: tutti già nella roadmap con le priorità giuste (v1 vs post-v1). +- Delphi 2025 e Scaramuzzo 2024 confermano GI e CoV come standard per PEEP titration. +- Pendelluft e silent spaces: già in roadmap come post-v1 per clinical use diffuso ma non fondamentale v1. + +--- + +## Sessione 2026-03-18 — Review documentazione post config.py + base_data.py + +### Task lavorate +- Nessuna task di codice. Sessione di verifica documentazione (eit-doc-keeper). + +### Bug trovati nei file modificati + +**`src/fasteit/config.py` — 3 problemi**: + +1. `PreprocessingConfig` e `AnalysisConfig` hanno il corpo vuoto — in Python un `@dataclass` senza campi né `pass` è un SyntaxError. Fix: aggiungere `pass` come corpo temporaneo. + +2. `Config` usa `field(...)` come annotazione di tipo invece di come `default_factory`: + ```python + # SBAGLIATO (quello che c'è ora): + device: field(default_factory=DeviceConfig) + # CORRETTO: + device: DeviceConfig = field(default_factory=DeviceConfig) + ``` + +3. La docstring di modulo ha ancora banner TODO come se DeviceConfig non esistesse — va aggiornata. + +**`src/fasteit/models/base_data.py` — 2 problemi**: + +1. Import sbagliato: `from config import Config` fallisce nel src/ layout. Deve essere `from fasteit.config import Config`. + +2. Default `fs` usa subscript invece di attributo: `Config.device[fs]` non esiste. Il problema più profondo è che `Config.device` è un `field(default_factory=...)` — un'istanza non esiste a livello di classe. Il valore di default di `fs` in `BaseData` dovrebbe essere un letterale (`50.0`) o prendere il valore da `DeviceConfig().fs`. + +3. Il docstring dice `Default 20.0` per `fs` ma `DeviceConfig.fs = 50.0`. Il PulmoVista ha sampling rate 20 Hz per le immagini .bin, ma 50 Hz per i dati raw .eit — discrepanza da chiarire e documentare. + +### Note su DeviceConfig.fs = 50.0 vs 20.0 Hz + +- `DeviceConfig.fs = 50.0` — è il frame rate di campionamento interno del dispositivo +- Il .bin ha immagini ricostruite a 20 Hz (standard PulmoVista), non 50 Hz +- Il .eit ha i dati grezzi a 50 Hz +- `BaseData.fs` deve essere il fs effettivo del file letto, non un default fisso — impostarlo nella `__post_init__` del parser in base al formato +- La discrepanza tra docstring (20.0) e DeviceConfig (50.0) va risolta dichiarando esplicitamente i due valori come costanti separate + +### Documentazione aggiornata in questa sessione +- CHANGELOG.md — aggiornato con Task 0.5.1, 0.5.2, 0.6.1 partial +- README.md — "Project status" aggiornato: scaffold complete, next = Fase 1 +- fastEIT_tasks.csv — Task 0.6.1 marcata in_progress con note sui bug + +### Prossime azioni prioritarie +1. Correggere i bug sintattici in `config.py` (aggiungere `pass` ai dataclass stub, correggere `Config` field annotations) +2. Correggere `base_data.py`: import path + default fs (usare letterale `50.0` o `DeviceConfig().fs`) +3. Chiarire e documentare la differenza tra fs .bin (20 Hz) e fs .eit (50 Hz) + +--- + +## Sessione 2026-03-19 — Analisi comparativa .bin format + Task 1.2.1 dtypes.py + +### Task lavorate +- Studio comparativo formato .bin: EIDORS (MATLAB) + eitprocessing (Python) + script MATLAB collega +- Task **1.2.1**: `src/fasteit/dtypes.py` — FRAME_BASE_DTYPE e FRAME_EXT_DTYPE definitivi +- Task **1.2.1b**: aggiornamento `BinData` — property timestamps, min_max_flags, event_markers +- Aggiornamento `fastEIT_layerdata_class_architecture.md` — dtype corretto + +### Scoperta critica: ts è float64, non due float32 + +Il campo timestamp nei .bin è un singolo **float64 (8 byte)**, non due float32 (ts1, ts2). + +Cosa memorizza il PulmoVista: `frazione di giorno` — un numero da 0.0 (mezzanotte) a 1.0 +(quasi mezzanotte del giorno dopo). Per esempio 0.5 = mezzogiorno, 0.708... = 17:00. + +**Per ottenere secondi**: `ts * 86400` (dove 86400 = secondi in un giorno). + +**Perché EIDORS leggeva due float32**: MATLAB `fread(f, 1, 'float32')` legge 4 byte come float. +Chiamato due volte, legge i primi 4 byte (metà del double) e i secondi 4 byte (altra metà). +EIDORS usava `ts1` per il tempo — ma ts1 come float32 dei primi 4 byte di un float64 è un +valore numerico casuale, non un timestamp sensato! La rappresentazione interna IEEE 754 di +un double e di un float hanno bit di mantissa e esponente diversi. + +**eitprocessing fa la cosa giusta**: `reader.float64() * 24 * 60 * 60` legge tutti gli 8 byte +come double e li moltiplica per 86400. + +**Altra nota importante**: `np.unwrap(ts * 86400, period=86400)` gestisce il wraparound per +registrazioni che iniziano prima di mezzanotte e finiscono dopo. Questo va fatto nel BinParser, +non in BinData (che conserva i dati grezzi dal file). + +### Struttura campi corretta (4358 byte base) + +| Campo | Tipo | Bytes | Cosa è | +|---|---|---|---| +| ts | float64 | 8 | Frazione di giorno → ×86400 = secondi | +| dummy | float32 | 4 | Non usato | +| pixels | float32 (32,32) | 4096 | Immagine ricostruita C-order | +| min_max_flag | int32 | 4 | +1=picco insp, -1=valle esp, 0=niente | +| event_marker | int32 | 4 | Counter eventi (incrementa) | +| event_text | S30 | 30 | Testo evento ASCII | +| timing_error | int32 | 4 | 0=ok, non-zero=errore | +| medibus_data | float32 (52,) | 208 | 52 segnali Medibus | + +Frame esteso (PressurePod, 4382 byte): medibus_data ha (58,) invece di (52,), +6 campi ×4b=+24b. + +### I 52 campi Medibus — cosa sono + +I primissimi 6 (idx 0-5) sono **waveform continue a 50 Hz**: pressure_airway, flow, volume, CO2 %. +Il resto (idx 6-51) sono **parametri aggregati dal ventilatore** aggiornati una volta per atto +respiratorio: PEEP, compliance dinamica, frequenza respiratoria, volumi espirati, ecc. + +Per il nostro use case clinico i più importanti: +- **idx 14: PEEP** — fondamentale per Task 5.7.1 (PEEP detection Livello 1) +- **idx 0: airway_pressure** — pressione delle vie aeree in tempo reale +- **idx 1: flow** — flusso in tempo reale +- **idx 2: volume** — volume tidal in tempo reale + +### Algoritmi clinici del collega MATLAB (da confrontare con 3 fonti a Fase 4/5) + +Gli script del collega mostrano come lui implementa le stesse cose che noi faremo in Python. +**Li confronteremo su 3 fronti** quando arriveremo alle fasi relative: collega vs EIDORS vs +eitprocessing. Alcuni hanno già issue su GitHub che danno spunti utili. + +**FindBreathsInAVector.m** → Task 4.10 (breath detection): +```matlab +smoothedTrace = smooth(scalarTrace); +[ValInsp, XpeakInsp] = findpeaks(smoothedTrace, + 'MinPeakWidth', fs/1.5, % ≈33 frame = 0.67 secondi + 'MinPeakProminence', std(smoothedTrace)); +``` +Traduzione Python: `scipy.signal.find_peaks(smooth_signal, width=fs/1.5, prominence=np.std(signal))` +Il collega usa `smooth()` (media mobile MATLAB) come pre-filtro. eitprocessing non lo fa. +Noi useremo un butterworth bandpass prima di find_peaks (Task 4.10.1) — miglioramento rispetto +a entrambi. + +**CalculateEELIandVT.m** → Task 5.x (features): +```matlab +eeli = median(exp_val); % EELI = mediana dei valori espiratori +deltaZ = median(insp_val) - eeli; % deltaZ ~ volume tidale +``` +Semplice e robusto. La mediana invece della media rende EELI robusto agli outlier. + +**createROIMasks.m** → Task 4.8 (ROI): +```matlab +mask.FourROIs.ROI1 = [ones(8, 32); zeros(24, 32)]; % righe 0-7 = ventrale +mask.FourROIs.ROI4 = [zeros(24, 32); ones(8, 32)]; % righe 24-31 = dorsale +``` +Questo conferma che il nostro `BinData.roi_signals` è orientato correttamente. +In più, il collega ha `FourbyFourROIs` (4 ROI V/D × 2 lato sx/dx = 8 ROI totali) — utile +per Task 4.8 quando implementeremo le ROI avanzate. +Convenzione: colonne 0-15 = DESTRA paziente, colonne 16-31 = SINISTRA paziente. + +**createSummersROIs.m** → Task 4.8.2 (ROI adattive Summers): +ROI adattate ai limiti reali del polmone nel frame (non strisce fisse 8 righe). Più preciso +per pazienti con polmoni piccoli o asimmetrici. Da confrontare con eitprocessing. + +**applyMask.m** → Task 4.4/4.8 (lung mask): +Applica una maschera binaria 32×32 al volume (32,32,N_frames). In numpy: `pixels * mask[np.newaxis]`. +MATLAB usa dimensioni (32,32,N) — noi usiamo (N,32,32), ma il calcolo è equivalente. + +**CreateLungContour.m** → Task 4.4 (lung mask): +Crea la maschera dai pixel con ventilazione sopra una soglia (configurabile 0-1). +Supporta erosione/dilatazione morfologica per pulire la maschera. In scipy: +`scipy.ndimage.binary_erosion / binary_dilation` con structuring element circolare. + +**FindDeltas.m** → Task 5.3.1 (delta Z pixel-per-pixel): +Per ogni pixel calcola max, min, delta (max-min) in una finestra temporale. +In numpy vettorizzato: `np.max(pixels, axis=0) - np.min(pixels, axis=0)` su un volume +(N, 32, 32) tagliato a un singolo atto respiratorio. + +### Orientamento pixel — confermato definitivamente + +Da createROIMasks.m la convezione è inequivocabile: +- **Riga 0 = VENTRALE** (anteriore, parete toracica anteriore) +- **Riga 31 = DORSALE** (posteriore, schiena) +- **Colonne 0-15 = DESTRA** del paziente +- **Colonne 16-31 = SINISTRA** del paziente + +Il nostro `BinData.roi_signals` è corretto. La ROI 0 (righe 0-7) è ventrale, ROI 3 (24-31) è dorsale. + +### Note tecniche utili per dopo + +- `np.frombuffer(buf, dtype=FRAME_BASE_DTYPE)` è il modo per parsare un frame +- `np.fromfile(path, dtype=FRAME_BASE_DTYPE)` legge tutto il file in una riga (Task 1.2.2) +- NaN sentinel in Medibus: `data[data < -1e30] = np.nan` (da eitprocessing) +- Per stimare fs dal file reale: `1 / scipy.stats.linregress(np.arange(n), time).slope` + dove `time = frames["ts"] * 86400` con np.unwrap + +### Stato tasks aggiornato +- Task 1.2.1: ✅ done — `src/fasteit/dtypes.py` con FRAME_BASE_DTYPE (4358b) e FRAME_EXT_DTYPE (4382b) +- Task 1.2.1b: ✅ done — BinData.timestamps aggiornato + min_max_flags + event_markers aggiunti +- Tests: `tests/test_dtypes.py` — 20 test verdi + +### Analisi comparativa architettura: eitprocessing vs fastEIT + +#### eitprocessing — flat container espandibile + +Il loro contenitore centrale è `Sequence`. Non è un container grezzo: è già il risultato finale +che tiene tutto insieme dal primo momento in un unico oggetto con 4 DataCollection: +- `eit_data`: pixel impedance frames (EITData) +- `continuous_data`: waveform Medibus + segnali derivati (ContinuousData) +- `sparse_data`: eventi, marker respiro (SparseData) +- `interval_data`: intervalli temporali, respiri (IntervalData) + +Quando carichi un file, ottieni subito una Sequence con tutto. Se poi applichi preprocessing, +**aggiungi altri oggetti alla stessa Sequence** con label diversi. Tutto vive in un unico posto. + +Flessibilità orizzontale: puoi aggiungere qualsiasi segnale derivato con qualsiasi label. +Ottima per la ricerca esplorativa. Non ottimale per una pipeline clinica automatizzata. + +#### fastEIT — pipeline a layer verticali + +``` +BinData = sempre grezzo, sempre immutabile dopo parse +Session = sempre il risultato di uno specifico preprocessing + Config +Breath = sempre un singolo atto respiratorio con feature tipizzate +``` + +Se il breath detection è sbagliato → riparto da Session senza rileggere il file. +Se la lung mask è sbagliata → riparto da BinData senza riprocessare i respiri. +I layer sono separabili e riproducibili. + +**La flessibilità è uguale ma in direzione diversa:** +- eitprocessing: flessibilità orizzontale (tutto nello stesso container) +- fastEIT: flessibilità verticale (ogni layer trasforma il precedente) + +Per una pipeline su 200 pazienti la flessibilità verticale è quella giusta. + +#### Cosa importiamo da eitprocessing (concetti, non codice GPL) + +**1. `lock()` su numpy array → BinData.__post_init__** +Dopo il parse, BinData.frames non dovrebbe mai essere modificato. +eitprocessing usa `array.flags["WRITEABLE"] = False`. +Implementazione: aggiungere in BinData.__post_init__ dopo aver calcolato n_frames/duration. +Se qualcuno prova a scrivere `bin_data.frames["pixels"][0] = 0` → RuntimeError esplicito +invece di un bug silenzioso che modifica i dati grezzi. + +**2. `select_by_time()` → Session (Task 4.13.x)** +Tutta la gerarchia eitprocessing supporta lo slicing temporale. +Per fastEIT essenziale per: estrarre finestre PEEP step, confrontare pre/post manovra, +debuggare sezioni specifiche di una registrazione. +```python +session.select_by_time(start_time=120.0, end_time=240.0) # 2 minuti specifici +``` + +**3. `Config` come campo di Session** +Loro non hanno un modo esplicito per sapere con quali parametri è stata processata una Sequence. +Session dovrebbe salvare il Config usato: +```python +@dataclass +class Session: + raw: BinData # riferimento al dato grezzo + config: Config # config usato per il preprocessing +``` +Quando pubblichi un paper, puoi riportare esattamente cosa è stato fatto. + +**4. Validazione prima della concatenazione (Study.from_folder)** +Prima di `seq1 + seq2` eitprocessing verifica fs, vendor, categoria compatibili. +Noi dobbiamo fare lo stesso in Study.from_folder (Task 6.3.1): verificare che tutti +i BinData abbiano lo stesso fs prima di aggregarli. + +#### Cosa NON vogliamo da eitprocessing +- DataCollection[V] con TypeVar generici — troppa macchina per il nostro scope +- Mancanza di confine raw/processed — problema per riproducibilità clinica +- Nessun Breath con feature tipizzate — è il nostro differenziatore principale +- Nessun PEEP step detection — il nostro differenziatore clinico +- Nessun export breath-level per database — il nostro differenziatore operativo + +--- + +### Studio del file `slicing.py` di eitprocessing + +File: `/home/ric/usefullfile/eitprocessing/slicing.py` +Copiato da `/home/ric/Downloads/slicing.py` nella sessione 2026-03-19. + +#### Il pattern di design: ABC mixin a 3 livelli + +``` +SelectByIndex(ABC) → aggiunge __getitem__ (obj[10:100]) + abstract _sliced_copy() + ↓ eredita +SelectByTime → aggiunge select_by_time(start, end) con bisect + ↑ anche +HasTimeIndexer → aggiunge .t property → TimeIndexer (obj.t[10.0:120.0]) +``` + +`SelectByTime` eredita da entrambi → classe concreta eredita da `SelectByTime` e implementa +solo `_sliced_copy()`. Tutto il resto (slicing per indice, slicing per tempo, la `.t` property) +viene gratis dai mixin. + +#### Due interfacce per fare la stessa cosa (pythonic) + +```python +# Forma esplicita: +session.select_by_time(start_time=10.0, end_time=120.0) + +# Forma sintetica (via TimeIndexer): +session.t[10.0:120.0] +``` +`session.t` ritorna un oggetto `TimeIndexer` che sa come convertire una slice di float +in una chiamata a `select_by_time`. Molto elegante. + +#### L'algoritmo chiave: `bisect` + +`bisect` è un modulo Python standard che fa ricerca binaria su liste ordinate — O(log n). +Perfetto per trovare un timestamp in un array di 50×60×120 = 360.000 frame senza scorrere tutto. + +Come funziona start_inclusive/end_inclusive: +``` +bisect.bisect_left(time, t) → indice del primo valore ≥ t (includi t) +bisect.bisect_right(time, t) → indice del primo valore > t (escludi t se esiste uguale) +bisect.bisect_right(...) - 1 → ultimo indice ≤ t (includi t, ritorna il frame "prima") +bisect.bisect_left(...) + 1 → primo indice > t (includi t, ritorna il frame "dopo") +``` + +Per fastEIT la scelta default sensata è `start_inclusive=True, end_inclusive=False`, +così `session.t[10.0:120.0]` include il frame a t=10 e esclude il frame a t=120 — +uguale al comportamento di Python `list[10:120]`. + +#### Come adatteremo per Session + +```python +# Nella classe Session, basta implementare _sliced_copy: +def _sliced_copy(self, start_index: int, end_index: int, newlabel: str) -> Session: + sliced_frames = self.raw.frames[start_index:end_index] + sliced_raw = BinData(frames=sliced_frames, fs=self.raw.fs, ...) + return Session( + raw=sliced_raw, + config=self.config, # Config rimane invariato + # tutti i segnali derivati sliced allo stesso modo + lung_mask=self.lung_mask, # 2D, non dipende dal frame — rimane invariata + global_impedance=self.global_impedance[start_index:end_index], + # ... + ) +``` + +La `lung_mask` è 2D (32×32) — non ha dimensione temporale, non si slice. +I segnali (global_impedance, roi_signals, Medibus) sono 1D → si slicano con start:end. + +#### Note per il paper + +Questo file è rilevante per la sezione **Methods** del paper: +- L'approccio `select_by_time` che implementeremo in Session verrà citato come + "ispirato all'interfaccia di eitprocessing (apache-2.0)" con confronto esplicito + della scelta architetturale (layered vs flat). +- La scelta `bisect` (O log n) sarà documentata nella sezione implementazione. +- Il confronto architetturale fastEIT vs eitprocessing sarà il cuore della sezione + **Software Design** del paper: "while eitprocessing follows a flat expandable + container pattern, fastEIT adopts a strict 3-layer pipeline to enforce reproducibility + and enable structured clinical export." + +--- + +### Informazione importante per Task 3.x: formato .asc + +Il file CSV/TXT esportato dal PulmoVista ha estensione **.asc** (non .txt o .csv come pensavamo). +Va convertito con encoding **latin-1** (ISO-8859-1) prima del parsing. +CsvParser dovrà: +1. Accettare estensioni `.asc`, `.txt`, `.csv` +2. Aprire con `encoding="latin-1"` (o `encoding="iso-8859-1"`) +3. Autodetect del separatore (probabilmente `;` o `\t` come export PulmoVista) + +Aggiornare Task 3.3.1 (CsvParser) con questa info quando ci arriveremo. + +### Fine sessione 2026-03-19 + +**Task completate oggi:** +- Task 1.2.1: `dtypes.py` — FRAME_BASE_DTYPE (4358b) + FRAME_EXT_DTYPE (4382b) definitivi +- Task 1.2.1b: BinData aggiornata (timestamps, min_max_flags, event_markers) +- 50/50 test verdi, ruff pulito +- Studio architettura eitprocessing + analisi slicing.py +- Aggiornati: layerdata_class_architecture.md, reference_DEFINITIVO.md, learning_journal.md + +**Prossima sessione — Task 1.1.1: Reverse engineering .bin, .eit, .asc reali** + +Riccardo si procura i 3 file reali dal PulmoVista. Poi: + +1. **Hexdump .bin** — `xxd file.bin | head -80` + - Primi 8 byte come float64 LE = frazione di giorno (~0.5-0.9 = mattina/sera) + - Offset 12 = inizio pixel, primo float32 plausibile (~ordine 10-100 AU) + - Verificare `file_size % 4358 == 0` oppure `% 4382 == 0` + - Salvare in `docs/reverse_eng/01_hexdump_bin_frame.txt` + +2. **Hexdump .eit** — `xxd file.eit | head -80` + `strings file.eit | head -30` + - Cercare magic string `---Draeger EIT-Software---` nei primi byte + - Identificare fine header ASCII / inizio dati binari + - Salvare in `docs/reverse_eng/02_hexdump_eit_header.txt` + +3. **File .asc** — encoding latin-1, separatore da scoprire + - `file file.asc` per info tipo + - `head -5 file.asc` per vedere header e colonne + - Salvare campione in `docs/reverse_eng/03_asc_sample.txt` + +Ogni artefatto viene documentato in `docs/reverse_eng/` con: comando usato, +output raw, interpretazione byte-per-byte. Questi diventeranno la sezione +"File Format Specification" del paper. + +--- + +## Sessione 2026-03-19 — Reverse engineering .bin con file reali + +### Task lavorate +- Task 1.3.1: Investigare 208 byte Medibus nel frame base +- Task 1.3.2: Investigare 24 byte extra frame esteso (PressurePod) +- Task 1.4.3: Verificare timestamp crescenti e plausibili +- Task 1.4.4: Verificare pixel range ragionevole +- Task 1.4.5: Verificare Medibus valori clinici +- Prodotto: `docs/reverse_eng/bin_format.md` (documentazione pubblicabile) + +### File analizzati +- `patient01.bin` 50.4 MB, 11500 frame, 230 secondi di registrazione +- `patient02.bin` 53.2 MB, 12150 frame, 243 secondi +- Stesso dispositivo: Dräger PulmoVista 500, ASPE-0048, V1.30 +- `patient01.get` 11.8 MB — formato nuovo, da investigare + +### Concetti spiegati + +- **Prova matematica del frame size**: dimensione file ÷ numero frame (contato nell'ASC) = 4382.0 esatto su entrambi i pazienti. Questo è il modo più elegante per confermare una struttura binaria: non hai bisogno di documentazione, basta un divisione. + +- **Timestamp come frazione di giornata**: il campo `ts` è un float64 che conta i secondi del giorno divisi per 86400. Quindi 18:10:54 = (18*3600 + 10*60 + 54) / 86400 = 0.7576. La conferma arriva dal confronto con l'header ASCII del file `.eit` corrispondente, che riporta "Time: 18:10:54.015" — coincidenza al secondo. + +- **Due sentinel distinti nel .bin Dräger**: + - `0xFF7FC99E` ≈ -3.40e+38: MEDIBUS fisicamente non connesso (i canali waveform, idx 0-5) + - `0xC47A0000` = -1000.0: dato non ancora calcolato (inizio registrazione, campi breath-averaged, idx 6+) + - Il codice usa `nan_value = -1e30` come soglia, non come valore esatto: -3.4e38 < -1e30, quindi cattura entrambi. Questo è il modo corretto di gestire i sentinel nei file binari: non cercare il valore esatto ma usare una soglia. + +- **Hexdump annotato**: leggere un hexdump vuol dire sapere dove inizia ogni campo. L'offset si calcola sommando le dimensioni: ts(8) + dummy(4) + pixels(4096) = 4108 = 0x100C. Da lì vedi il `min_max_flag`. È come leggere una mappa del tesoro. + +- **PressurePod validation con cross-check interno**: i campi idx 54, 55, 56 mostrano valori 14.0, 15.32, -1.28 mbar. Se questi sono Paw, Pes, Ptp: Paw - Pes = 14.0 - 15.32 = -1.32 ≈ -1.28. La differenza di 0.04 è probabilmente rounding float32 o una piccola correzione interna Dräger. Questo tipo di cross-check interno è fondamentale nel rev engineering: se Ptp = Paw - Pes, allora hai capito i campi giusti. + +- **Il `.get` è un formato nuovo**: 1024 byte/frame = 256 × float32. Ipotesi: matrice completa 16×16 di transimpedanze (tutte le 256 coppie elettrodo), mentre il `.bin` usa solo le 208 del pattern adjacent-drive. Se confermato, sarebbe utile per integrare algoritmi di ricostruzione alternativi (pyEIT supporta matrici complete). + +- **Il `.eit` contiene raw data, il `.bin` le immagini ricostruite**: il `.eit` (63 MB) è più grande del `.bin` (50 MB) non solo per l'header ASCII, ma perché contiene anche le 208 transimpedanze grezze per frame (i dati "prima" della ricostruzione). Il `.bin` è un sottoinsieme: solo le immagini 32×32 già ricostruite dal firmware. + +### Domande di Riccardo +- "I file grezzi .eit non li abbiamo dentro il file .eit??" → Risposta: sì! Il .eit contiene sia l'header ASCII (metadati), sia i dati grezzi (208 transimpedanze per frame) SIA probabilmente i segnali Medibus. È per questo che è più grande del .bin. Lo analizzeremo in dettaglio quando arriveremo al parser .eit (Task 2.x). + +### Epifanie e insight +- "La prova più elegante che FRAME_EXT_DTYPE è giusto è una sola divisione: 50393000 ÷ 11500 = 4382.0 esatto. Se ci fosse anche un solo byte sbagliato, non darebbe un numero intero." +- "Il timestamp ts[0] = 18:10:54 che leggo dal file .bin coincide ESATTAMENTE con il campo Time nell'header ASCII del .eit. Due file, stesso secondo. È la prova che .bin e .eit sono paralleli e sincronizzati." +- "I sentinel -3.4e38 e -1000.0 sono in realtà due informazioni distinte: -3.4e38 dice 'il sensore non c'è', -1000.0 dice 'il sensore c'è ma non ha ancora un valore'. Come NULL vs 0 in un database." +- "Il PressurePod validation cross-check: misuro 14.0, 15.32, -1.28 e quando faccio 14.0-15.32 ottengo -1.32 ≈ -1.28. In quel momento so con certezza di aver identificato Paw, Pes e Ptp — senza documentazione Dräger." + +### Note tecniche utili per dopo +- `np.memmap` invece di `np.fromfile` permette accesso lazy senza caricare tutto in RAM — utile per file da 50+ MB +- Il naming dei campi PressurePod (idx 52-57) è probabilmente sfasato: il campo chiamato `time_at_low_pressure_pod` (idx 54) in realtà contiene Paw (10-38 mbar). Da chiarire con il parser .eit. +- Per verificare il `.get` su patient02: `wc -c patient02.get` → se = 12,441,600 byte (= 12150 × 1024), la struttura 256×float32 è confermata. +- `xxd -l N file.bin` per vedere i primi N byte; annotare gli offset calcolati manualmente prima di leggere l'output + + +--- + +## Sessione 2026-03-19 (continua) -- eit_format.ipynb + +### Task lavorate +- Task 2.1.2-2.1.5: Hexdump, ASCII header, magic string, metadati .eit +- Creato: `docs/reverse_eng/eit_format.ipynb` (22 celle, 8 code con output pre-saved) +- Aggiunto Task 2.1.6: struttura interna frame .eit + +### Concetti spiegati + +- **Struttura header .eit**: il file inizia con 12 byte di preamble binario (3 x int32 LE), poi un blocco ASCII leggibile con tutti i metadati device/paziente, poi un separatore `**\r\n\r\n\r\n` (8 byte), poi il blocco binario. Il valore `preamble[1]` (es. 7546) punta esattamente all'inizio del separatore: `binary_start = preamble[1] + 8`. + +- **Dimensione frame .eit**: 5495 byte/frame (vs 4382 byte/frame del .bin). Verificato su entrambi i pazienti: p01 = (63200054 - 7554) / 5495 = 11500.0 esatto; p02 = (66771796 - 7546) / 5495 = 12150.0 esatto. + +- **Header varia tra pazienti**: `preamble[1]` = 7546 per p01, 7538 per p02. L'header ASCII ha lunghezza variabile (dipende da lunghezza dei campi testo come nome paziente). Il parser .eit DEVE usare `preamble[1]` per trovare il binary_start, non un offset fisso. + +- **Frame .eit: 5487 byte dopo il timestamp**: 5487 è dispari (5487 % 4 = 3), quindi c'è almeno un campo non-float32. La struttura interna è ancora da determinare. Ipotesi: parte measurements (208 complex float32 = 1664 byte) + parte pixel ricostruiti (1024 float32 = 4096 byte) ma 8+1664+4096 = 5768 ≠ 5495. Mancano ~273 byte di discrepanza -- da risolvere con EIDORS analysis (Task 2.0). + +- **Sentinel nel .eit**: il valore -3.4e12 appare anche nel .eit, probabilmente lo stesso sentinel `0xFF7FC99E` del .bin per elettrodi disconnessi. + +### Domande di Riccardo +- (nessuna domanda esplicita questa sotto-sessione -- lavoro tecnico autonomo) + +### Epifanie e insight +- "preamble[1] varia tra pazienti perché l'header ASCII ha lunghezza variabile! Quindi il parser NON può assumere binary_start fisso ma deve leggere i 4 byte a offset 4 e aggiungere 8." +- "5495 byte/frame è un numero strano che non si fattorizza bene -- il frame .eit non è progettato per essere 'pulito' come il .bin (4382 = 4096+286). Probabilmente ci sono campi legacy o padding." +- "La prova frame size sul .eit è identica a quella del .bin: una divisione che deve dare un intero esatto. Se il file è corrotto o il frame_size sbagliato, il resto non è zero." + +### Note tecniche utili per dopo +- **binary_start formula**: `binary_start = struct.unpack(' tutte eseguite con successo + +--- + +## Sessione 2026-03-19 (continua) -- PressurePod Pgas identificata + +### Concetti spiegati +- **PressurePod Dräger**: accessorio con esattamente 3 connessioni fisiche -- Paw (pressione vie aeree, tap sul circuito ventilatorio), Pes (pressione esofagea, catetere a palloncino), Pgas (pressione gastrica, catetere a palloncino gastrico). Deriva Ptp = Paw - Pes internamente. +- Questo definisce completamente i 4 campi waveform (idx 54-57) senza ambiguità. + +### Epifanie e insight +- "Se quello che ci manca è solo un index, allora sicuramente è la pressione gastrica perché il PressurePod registra esofagea, airway pressure così calcola la Ptp e la gastrica -- ha questi tre attacchi e basta." +- Ragionamento clinico diretto: 3 input fisici + 1 derivata = 4 campi waveform. Non c'è posto per altro. Questo tipo di ragionamento (conoscenza dell'hardware) risolve ambiguità che nessun hexdump da solo può risolvere. +- **Bug scoperto**: i nomi in `_MEDIBUS_EXT_EXTRA` in `dtypes.py` sono sfasati di 1 posizione. `time_at_low_pressure_pod` (idx 54) = Paw (14 mbar, non secondi). Il fix è rinominare: idx 54=`airway_pressure_pod`, 55=`esophageal_pressure_pod`, 56=`transpulmonary_pressure_pod`, 57=`gastric_pressure_pod`. + +### Mapping completo PressurePod (idx 52-57) +| idx | nome attuale (sbagliato) | nome corretto | tipo | +|-----|--------------------------|---------------|------| +| 52 | `high_pressure` | `high_pressure` (ok) | per-breath peak Paw | +| 53 | `low_pressure` | `low_pressure` (ok) | per-breath PEEP | +| 54 | `time_at_low_pressure_pod` | `airway_pressure_pod` | waveform continuo | +| 55 | `airway_pressure_pod` | `esophageal_pressure_pod` | waveform continuo | +| 56 | `esophageal_pressure_pod` | `transpulmonary_pressure_pod` | waveform continuo | +| 57 | `transpulmonary_pressure_pod` | `gastric_pressure_pod` | waveform continuo | + +### Note tecniche utili per dopo +- Il fix in `dtypes.py` è SOLO rinominare le ultime 4 tuple di `_MEDIBUS_EXT_EXTRA` -- non cambia la struttura dati +- Valore Pgas = 0.04 mbar è fisiologicamente plausibile: pressione gastrica a riposo vicina allo zero in assenza di contrazione diaframmatica significativa + +--- + +## Sessione 2026-03-19 (continua) -- Fix dtypes.py + Analisi prototipo + Fix BiLevel fields + +### Epifanie e insight (BiLevel fields) +- "Il CSV del prototipo era la Rosetta Stone che ci mancava per risolvere definitivamente i nomi dei campi Medibus" +- Confrontando le colonne del CSV (che il PulmoVista genera in parallelo al .bin) con la posizione numerica: col 63 = medibus idx 51 = `phigh_mbar`. Non `time_at_low_pressure` come pensavamo. +- **BiLevel/APRV**: è una modalità ventilatoria a due livelli di pressione (Phigh = pressione alta, Plow = pressione bassa, Tlow = tempo a pressione bassa). Il PulmoVista registra questi tre parametri in Medibus. +- **Phigh è nel formato BASE** (idx 51, ultimo campo delle 52 floats): significa che tutti i frame base (4358 byte) hanno già Phigh, indipendentemente dal PressurePod. Per pazienti su ventilazione convenzionale questo campo sarà sentinel o zero. +- Plow e Tlow sono EXT perché... probabilmente aggiunti in un firmware successivo? O perché meno usati? Non importa -- il CSV ce lo dice chiaramente. + +### Correzioni finali dtypes.py (tutti e 3 i fix applicati nella stessa sessione) +| idx | SBAGLIATO | CORRETTO | Dove | +|-----|-----------|---------|------| +| 51 | `time_at_low_pressure` (unità "s") | `high_pressure` (unità "mbar") | MEDIBUS_FIELDS base | +| 52 | `high_pressure` ("mbar") | `low_pressure` ("mbar") | _MEDIBUS_EXT_EXTRA | +| 53 | `low_pressure` ("mbar") | `time_at_low_pressure` ("s") | _MEDIBUS_EXT_EXTRA | +| 54 | `time_at_low_pressure_pod` ("s") | `airway_pressure_pod` ("mbar") | (fix precedente) | +| 55 | `airway_pressure_pod` | `esophageal_pressure_pod` | (fix precedente) | +| 56 | `esophageal_pressure_pod` | `transpulmonary_pressure_pod` | (fix precedente) | +| 57 | `transpulmonary_pressure_pod` | `gastric_pressure_pod` | (fix precedente) | + +--- + +## Sessione 2026-03-19 (continua) -- Fix dtypes.py + Analisi prototipo + +### Task completate +- **Task 1.3.5**: Fix `_MEDIBUS_EXT_EXTRA` in `dtypes.py` — rinominati idx 54-57, rimosso `time_at_low_pressure_pod`, aggiunto `gastric_pressure_pod`. Test aggiornati: 22/22 verdi. +- **Task 1.3.6**: Analisi prototipo `mia_vecchia_prova/` — documentata in `docs/prototype_analysis.md` +- **Task 1.4.6 / 2.1.7**: Aggiunte al CSV (già completate in sessione precedente con i notebook) + +### Concetti appresi dal prototipo +- **Breath detection con derivata centrale**: `d[i] = (y[i+1] - y[i-1]) / 2 * fs` — trova l'inizio inspirazione quando la derivata passa da negativa a positiva (la curva di impedenza globale risale). Semplice ma funzionante come prova di concetto. Il miglioramento in Task 4.10.x userà `find_peaks` con parametri adattivi. +- **PEEP detection con threshold crossing**: paragona delta frame-to-frame di Paw smoothata con una soglia (0.9 mbar). Se il delta supera la soglia → nuovo livello. Intuitivo ma fragile: un rumore transitorio può far scattare un nuovo livello. In Task 5.7.x si userà analisi dei segmenti stabili. +- **BreathFeatures**: 11 statistiche (mean/std/var/skew/kurtosis/min/max/median/p25/p75/IQR) per 8 segnali = 88 features statistiche + 3 temporali (slope/acceleration/smoothness) + 3 inter-breath (delta_peak/delta_tidal/delta_duration) + ~18 features base = ~111 features per respiro. Questa è la "golden list" di spec per Task 4.x–5.x. +- **Encoding latin-1**: i file .asc del PulmoVista non sono UTF-8. La virgola come separatore decimale è standard europeo. Da gestire in Task 3.x. + +### Epifanie e insight +- "Il prototipo è esattamente quello che fa il PulmoVista ma in Python — e lui funzionava su file reali! Quindi la logica è validata clinicamente anche se il codice è grezzo." +- Il `to_dict()` e `to_features_dict()` del prototipo sono di fatto la **specifica di interfaccia** della futura classe `Breath` — invece di reinventare da zero, si formalizza quello che già funziona. +- `pgapaux3_pod_mbar` nel prototipo = `gastric_pressure_pod` (idx 57) — il prototipo leggeva questo campo dall'ASC già correttamente, confermando il nostro reverse engineering. + +### Note tecniche utili per dopo +- Il prototipo usa `df["insp_t0"] == True` come condizione di split — in pandas moderno è meglio `df["insp_t0"]` direttamente (evita warning FutureWarning) +- `BreathFeatures` eredita da `Breath` (inheritance) — in fastEIT la struttura sarà diversa (composition o dataclass), ma le formule sono le stesse +- Il mapping `standardize_labels()` è il rosetta stone per verificare che i nomi in `MEDIBUS_FIELDS` corrispondano ai nomi dell'ASC (vedere `docs/prototype_analysis.md` sezione mapping) + +--- + +## Sessione 2026-03-19 (continua) — REVERT fix errato idx 51/52/53 + +### Errore commesso e corretto + +**Cosa è successo**: nella sessione precedente abbiamo fatto un fix SBAGLIATO basato +sull'assunzione che "ordine colonne CSV ASC = indici binari medibus_data". + +Avevamo cambiato: +- base idx 51: `time_at_low_pressure` → `high_pressure` +- ext idx 52: `high_pressure` → `low_pressure` +- ext idx 53: `low_pressure` → `time_at_low_pressure` + +**Perché era sbagliato — la prova**: +- `phigh_mbar`, `plow_mbar`, `tlow_s` nel CSV dei 2 pazienti analizzati sono **tutti NaN/zero** + (i pazienti NON erano in modalità BiLevel/APRV) +- I campi binary idx 52 e 53 mostravano invece **valori non-zero** (pressioni per-breath) +- Conclusione: binary idx 52/53 ≠ BiLevel params. Sono valori PressurePod-derived per-breath + (peak Paw e PEEP-equiv calcolati dal PressurePod sul singolo respiro) +- L'ordine colonne nell'ASC **NON corrisponde** agli indici nel binary medibus_data — sono + sistemi separati con ordinamenti diversi + +**Stato CORRETTO ripristinato**: +| idx | nome | unità | tipo | prova | +|-----|------|-------|------|-------| +| 51 | `time_at_low_pressure` | s | base | zero nei non-BiLevel — atteso e corretto | +| 52 | `high_pressure` | mbar | ext | non-zero nei file reali — peak Paw per-breath | +| 53 | `low_pressure` | mbar | ext | non-zero nei file reali — PEEP-equiv per-breath | + +### Lezione appresa + +**Regola**: il CSV ASC e il binario `.bin` sono due esportazioni **parallele e indipendenti** +dello stesso dispositivo. Hanno nomi di colonna e struttura propri. Non si può assumere che +la N-esima colonna del CSV corrisponda all'N-esimo campo del binary. + +**Come evitarlo in futuro**: per validare un indice binary, servono pazienti con il campo +attivo (BiLevel per idx 51, PressurePod connesso per idx 52/53). Con pazienti in ventilazione +convenzionale i campi BiLevel sono sempre zero — non si può distinguere "zero perché non in +BiLevel" da "zero perché sbagliamo l'indice". + +### Task da aggiungere al CSV + +**Task 1.4.7**: "Procura e analizza recording .bin/.asc senza PressurePod" — prova del nove +definitiva per: +1. Confermare frame size base (4358 byte) +2. Verificare idx 51 con un paziente BiLevel (se disponibile) +3. Vedere quali colonne ASC appaiono senza PressurePod + +### Files modificati in questo revert +- `src/fasteit/dtypes.py` — idx 51 e _MEDIBUS_EXT_EXTRA idx 52/53 +- `tests/test_dtypes.py` — `test_medibus_index_lookup` (3 assertions corrette) +- `docs/prototype_analysis.md` — tabella mapping correta + nota BiLevel + +--- + +## Sessione 2026-03-27 — GREIT reconstruction + validazione segnale globale + +### Task lavorate +- Task 7.1.1–7.1.3: integrazione pyEIT, notebook GREIT +- Task 7.2.1: `eit_greit.py` — `build_greit()` + `reconstruct_greit()` + +### Concetti spiegati + +- **GREIT (Adler 2009)**: ricostruzione EIT differenziale. Produce Δσ rispetto a un frame di riferimento (v0). Output flat (n²,) → reshape (32,32). Parametri default: p=0.2, lamb=1e-2, n=32. pyEIT: `mesh.create` + `protocol.create` + `GREIT.setup` + `solve(v1, v0)`. + +- **Convenzione segno**: GREIT produce Δσ (conduttività) → decresce quando l'aria entra nei polmoni. La convenzione clinica (e il .bin Dräger) usa l'impedenza → cresce con l'aria. **Fix: negare l'output**. Scoperto empiricamente confrontando i plot: le "punte" GREIT cadevano tra le punte del .bin invece di sovrapporsi. + +- **Orientamento anatomico**: pyEIT mette l'elettrodo 1 a ore 3 (destra del cerchio). Il PulmoVista ha l'elettrodo 1 anteriore. Confrontando visivamente le 4 rotazioni possibili (0°, 90°CCW, 180°, 90°CW), la 90°CCW posiziona correttamente l'area cardiaca a sinistra-anteriore. **Fix: `np.rot90(image, k=1)` nell'output di `reconstruct_greit()`**. + +- **ref_frame e EELI cross-file**: GREIT è differenziale. Con `ref_frame=None` (default = media di tutto il file) ogni file è centrato sulla sua media → EELI ≈ 0 per costruzione → non confrontabile tra file. Per confrontare EELI tra registrazioni (es. NIV interfaccia A vs B) servono tutte sullo stesso riferimento esterno: `ref = baseline[:50].mean(axis=0)` applicato a tutti i file. **Limite**: se il device ricalibrasi tra i file, l'offset non è recuperabile. + +- **Ricalibrazione PulmoVista**: il device ricalibrasi automaticamente, resettando il suo zero interno. Per questo l'EELI nel software Dräger può partire da 0 o da 1.000.000 — sono valori assoluti arbitrari che dipendono dall'ultimo reset. Non confrontabili tra sessioni senza un riferimento fisiologico fisso. Il software Dräger non lo esplicita, fastEIT sì. + +### Epifanie e insight + +- **"GREIT senza filtro ≈ .bin raw"**: confrontando `global_greit` (nansum dei pixel) con `bin_.global_signal`, le curve si sovrappongono dopo negazione senza alcun filtro. Significa che o il Dräger non filtra internamente (ipotesi più probabile, come confermato dal fatto che eitprocessing e colleghi MATLAB devono filtrare da soli), oppure la regularizzazione GREIT agisce da sola come filtro spaziale. + +- **"Le punte erano al contrario"**: scoperta della convenzione segno semplicemente guardando che i picchi GREIT cadevano negli "spazi" tra i picchi .bin. Intuizione visiva immediata senza calcoli. + +- **"L'area rossa fissa a sinistra = cuore"**: osservando che una zona ad alta impedenza rimaneva fissa (non respiratoria) nella parte sinistra dell'immagine, Riccardo ha ipotizzato la presenza del cuore. Questo ha portato a scoprire il problema di rotazione degli elettrodi. + +### Note tecniche utili per dopo +- `np.nansum(images, axis=(1,2))` → segnale globale EIT (confrontabile con `bin_.global_signal`) +- Negazione + 90°CCW sono già built-in in `reconstruct_greit()` — non applicare manualmente +- Per EELI cross-file: sempre passare `ref_frame` esterno dalla registrazione baseline della stessa sessione +- `n_meas = protocol.n_exc * protocol.n_meas = 16 * 13 = 208` ← corrispondenza esatta col formato .eit Dräger +- Nuovo agente: `eit-physics-reviewer` — da chiamare quando si implementano formule EIT o features cliniche From 7aadc1d135697dc9f6330edd866cfac3c30ed89b Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 15:50:26 +0000 Subject: [PATCH 11/20] =?UTF-8?q?fix:=20correct=20GREIT=20protocol=20to=20?= =?UTF-8?q?fmmu=20and=20add=20horizontal=20flip=20for=20Dr=C3=A4ger=20conv?= =?UTF-8?q?ention?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Measurement ordering: - Change parser_meas from 'std' to 'fmmu' in build_greit(). The PulmoVista 500 sweeps voltage measurements starting from the electrode adjacent to the current sink for each drive pattern. This matches pyEIT's fmmu (rotating) protocol, not std (absolute). Source: Dräger PulmoVista 500 IFU SW 1.3n p. 144 (measurement diagram). Image orientation — third correction added (fliplr): - Existing: negate (impedance convention) + rot90(k=1) (electrode 1 at 12 o'clock = anterior at top). - New: np.fliplr() after rot90 to match caudal-cranial CT convention. Dräger displays images viewed from feet: left of image = right of patient. pyEIT produces the anatomical mirror; fliplr corrects this. Source: IFU p. 146 — "il lato sinistro dell'immagine visualizza il lato destro del paziente". docs/parsers.md: update §3 (fmmu confirmed) and §6 (three-step orientation pipeline with IFU page citations). --- docs/parsers.md | 23 ++++++++++----- src/fasteit/parsers/draeger/eit/eit_greit.py | 31 ++++++++++++++++++-- 2 files changed, 43 insertions(+), 11 deletions(-) diff --git a/docs/parsers.md b/docs/parsers.md index c05f599..c66fe9f 100644 --- a/docs/parsers.md +++ b/docs/parsers.md @@ -147,10 +147,10 @@ Adjacent current injection (drive distance = 1 electrode), adjacent voltage measurement (step = 1 electrode), 3 pairs excluded per drive pattern (those sharing a voltage electrode with the current-injection pair). -The ordering of the 208 values within each frame matches the pyEIT adjacent-drive -protocol. Whether the device uses absolute (`std`) or rotated (`fmmu`) electrode -indexing within each drive pattern is under investigation — see -`notebooks/03_eit_parser_validation.ipynb` §9. +The ordering of the 208 values within each frame follows the **`fmmu`** (rotating) +protocol: for each drive pattern the voltage sweep starts from the electrode +immediately adjacent to the current sink and rotates around the ring. +Confirmed from the PulmoVista 500 IFU SW 1.3n p. 144 measurement diagram. --- @@ -218,12 +218,19 @@ Builds a pyEIT GREIT solver on a circular unit-disk mesh. | `(start, end)` | Mean of `measurements[start:end]` | | `np.ndarray (208,)` | External reference — for cross-recording EELI comparison | -**Sign convention**: GREIT outputs conductivity change (Δσ); the parser negates +**Sign convention**: GREIT outputs conductivity change (Δσ); the function negates it to match the Dräger impedance convention (air → impedance rises → positive peak). -**Rotation**: 90° counter-clockwise applied to place electrode 1 (pyEIT: 3 o'clock) -at the anterior chest wall position, consistent with the PulmoVista 500 belt and -standard CT cross-section orientation (viewed from feet). +**Image orientation** — three steps applied inside `reconstruct_greit()`, all +sourced from PulmoVista 500 IFU SW 1.3n: +1. **Negate** (sign flip): impedance convention — see above. +2. **`rot90(k=1)`** (90° CCW): electrode 1 sits at 12 o'clock on the PulmoVista + belt (anterior/ventral wall); pyEIT places electrode 0 at 3 o'clock. Rotation + moves the anterior region to the top of the image. Source: IFU p. 144. +3. **`fliplr`** (horizontal mirror): Dräger displays images in caudal-cranial + projection (CT convention viewed from feet): left side of image = right side + of patient. pyEIT produces the anatomical mirror; `fliplr` corrects this. + Source: IFU p. 146. **Spatial quality note**: a generic circular mesh does not replicate Dräger's proprietary reconstruction. Both algorithms produce correct relative distributions, diff --git a/src/fasteit/parsers/draeger/eit/eit_greit.py b/src/fasteit/parsers/draeger/eit/eit_greit.py index 90d98fd..d2e8700 100644 --- a/src/fasteit/parsers/draeger/eit/eit_greit.py +++ b/src/fasteit/parsers/draeger/eit/eit_greit.py @@ -12,6 +12,14 @@ Default parameters (p=0.2, lamb=1e-2, n=32) are those recommended in the GREIT paper for lung monitoring with 16-electrode adjacent-drive protocols. + +Measurement protocol: ``fmmu`` (rotating sweep starting from the electrode +adjacent to the current sink), confirmed from the PulmoVista 500 IFU SW 1.3n +p. 144 measurement diagram. + +Image convention: sign-negated (impedance), rotated 90° CCW (electrode 1 at +12 o'clock = anterior), horizontally flipped (CT caudal-cranial convention: +left of image = right of patient). Source: IFU p. 146. """ from __future__ import annotations @@ -46,7 +54,14 @@ def build_greit( """Build and return a configured GREIT solver. Creates a circular unit-disk mesh, an adjacent-drive protocol (dist_exc=1, - step_meas=1, parser='std'), and a GREIT solver ready for ``solve()``. + step_meas=1, parser_meas='fmmu'), and a GREIT solver ready for ``solve()``. + + **Measurement ordering — why ``fmmu``:** + The PulmoVista 500 sweeps voltage measurements starting from the electrode + immediately adjacent to the current sink and rotates around the ring. + This matches pyEIT's ``fmmu`` (rotating) protocol, not ``std`` (absolute). + Source: Dräger PulmoVista 500 IFU SW 1.3n, p. 144 — measurement diagram + shows V1 at the pair adjacent to the sink electrode for each drive pattern. Args: n_el: Number of electrodes. Default 16 (Dräger PulmoVista 500). @@ -66,7 +81,7 @@ def build_greit( """ _check_pyeit() mesh_obj = pyeit.mesh.create(n_el=n_el, h0=h0) - protocol = proto_mod.create(n_el=n_el, dist_exc=1, step_meas=1, parser_meas="std") + protocol = proto_mod.create(n_el=n_el, dist_exc=1, step_meas=1, parser_meas="fmmu") solver = greit_mod.GREIT(mesh_obj, protocol) solver.setup(p=p, lamb=lamb, n=n) return solver, protocol @@ -169,6 +184,16 @@ def reconstruct_greit( # Rotate 90° CCW (k=1) to match anatomical orientation: heart left-anterior, # right lung on the right (same as a CT cross-section viewed from the feet). ds = solver.solve(v1, v0) - images[i] = np.rot90(-ds.reshape(n, n), k=1) + # Post-processing to match Dräger PulmoVista 500 display convention + # (IFU SW 1.3n, p. 146): + # 1. Negate: GREIT outputs Δσ (conductivity); Dräger uses impedance + # convention (air in → impedance up → positive peak). + # 2. rot90(k=1): electrode 1 is at 12 o'clock on the PulmoVista belt + # (anterior/ventral); pyEIT places electrode 0 at 3 o'clock. + # 90° CCW rotation moves anterior to the top of the image. + # 3. fliplr: Dräger displays caudal-cranial (CT convention viewed from + # feet): left side of image = right side of patient. pyEIT produces + # the mirror image; fliplr corrects this. + images[i] = np.fliplr(np.rot90(-ds.reshape(n, n), k=1)) return images From 4f35edae1822e9396fb007604321832a7c83bc6d Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 16:19:52 +0000 Subject: [PATCH 12/20] =?UTF-8?q?docs(notebook):=20fix=20=C2=A79=20frame?= =?UTF-8?q?=20alignment=20=E2=80=94=20.bin=20is=20derived=20from=20.eit,?= =?UTF-8?q?=20not=20parallel?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The PulmoVista 500 records only the .eit file. The .bin is generated by Dräger's software applying Newton-Raphson reconstruction to each .eit frame in sequence. Frame-level alignment is therefore guaranteed by construction, not empirically — a temporal offset is architecturally impossible. Cross-correlation lag=0 is a verification, not a prerequisite. Also updates ML rationale: the paired dataset has zero misalignment noise because .bin[k] is the deterministic transform of .eit[k]. --- notebooks/03_eit_parser_validation.ipynb | 273 +++++++++++++---------- 1 file changed, 150 insertions(+), 123 deletions(-) diff --git a/notebooks/03_eit_parser_validation.ipynb b/notebooks/03_eit_parser_validation.ipynb index dcc317a..56ae931 100644 --- a/notebooks/03_eit_parser_validation.ipynb +++ b/notebooks/03_eit_parser_validation.ipynb @@ -3,7 +3,19 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## EIT Parser Validation\n\nValidates that `DragerEitParser` reads `.eit` files correctly and that the\ncalibration pipeline produces physically plausible transimpedance values.\n\nCalibration pipeline (empirical constants from EIDORS, A. Adler 2016 — hardcoded, not in the header):\n- `vv = FT_A * trans_A - FT_B * trans_B` → calibrated transimpedance [Ω]\n- `I_real = injection_current / FC_CURRENT` → actual injected current [A]\n- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → differential voltage [V]\n\nCross-validated against the corresponding `.bin` file (vendor-reconstructed 32×32 images)." + "source": [ + "## EIT Parser Validation\n", + "\n", + "Validates that `DragerEitParser` reads `.eit` files correctly and that the\n", + "calibration pipeline produces physically plausible transimpedance values.\n", + "\n", + "Calibration pipeline (empirical constants from EIDORS, A. Adler 2016 — hardcoded, not in the header):\n", + "- `vv = FT_A * trans_A - FT_B * trans_B` → calibrated transimpedance [Ω]\n", + "- `I_real = injection_current / FC_CURRENT` → actual injected current [A]\n", + "- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → differential voltage [V]\n", + "\n", + "Cross-validated against the corresponding `.bin` file (vendor-reconstructed 32×32 images)." + ] }, { "cell_type": "code", @@ -35,7 +47,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 1. Parser output structure" + "source": [ + "## 1. Parser output structure" + ] }, { "cell_type": "code", @@ -87,7 +101,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 2. Sanity check: vv is non-zero and NaN-free" + "source": [ + "## 2. Sanity check: vv is non-zero and NaN-free" + ] }, { "cell_type": "code", @@ -117,7 +133,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 3. Timestamps are monotonically increasing" + "source": [ + "## 3. Timestamps are monotonically increasing" + ] }, { "cell_type": "code", @@ -144,7 +162,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 4. Calibrated transimpedance waveform — measurement 0, first 500 frames" + "source": [ + "## 4. Calibrated transimpedance waveform — measurement 0, first 500 frames" + ] }, { "cell_type": "code", @@ -175,7 +195,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 5. Frame count comparison: .eit vs .bin" + "source": [ + "## 5. Frame count comparison: .eit vs .bin" + ] }, { "cell_type": "code", @@ -205,7 +227,14 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 6. GREIT reconstruction — transimpedances to 32×32 image\n\n`reconstruct_greit()` converts `vv` (N_frames, 208) to 32×32 images using the\nGREIT algorithm (Adler et al., *Physiol. Meas.* 2009).\nReconstruction is differential: each pixel shows Δσ relative to a reference frame.\nFirst 50 frames used as baseline (stable period before the respiratory cycle)." + "source": [ + "## 6. GREIT reconstruction — transimpedances to 32×32 image\n", + "\n", + "`reconstruct_greit()` converts `vv` (N_frames, 208) to 32×32 images using the\n", + "GREIT algorithm (Adler et al., *Physiol. Meas.* 2009).\n", + "Reconstruction is differential: each pixel shows Δσ relative to a reference frame.\n", + "First 50 frames used as baseline (stable period before the respiratory cycle)." + ] }, { "cell_type": "code", @@ -236,7 +265,17 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 6b. Sign correction\n\nThe GREIT algorithm outputs conductivity change (Δσ): when air enters the lungs,\nconductivity decreases → signal goes down.\nDräger uses the **impedance** convention: air enters → impedance rises → signal goes up.\n\nNegating the GREIT output brings it into the Dräger convention.\nThe correction is applied inside `reconstruct_greit()` so callers never need to\nhandle it manually." + "source": [ + "## 6b. Sign correction\n", + "\n", + "The GREIT algorithm outputs conductivity change (Δσ): when air enters the lungs,\n", + "conductivity decreases → signal goes down.\n", + "Dräger uses the **impedance** convention: air enters → impedance rises → signal goes up.\n", + "\n", + "Negating the GREIT output brings it into the Dräger convention.\n", + "The correction is applied inside `reconstruct_greit()` so callers never need to\n", + "handle it manually." + ] }, { "cell_type": "code", @@ -245,7 +284,7 @@ "outputs": [ { "data": { - "image/png": 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P6f+TTjoJOTk52Lt3LwDeSfnxxx9jzJgxIIQo9t+oUaPg9Xo1t/mRoL36OXz4cAwdOlT6/4QTTsAVV1yBxYsXtzlcUmsbZ+Nwzp22bJPU4xUArFarlPcxkUigqakJLpcLffv2Vd2mkydPVrjeRKeZuL7r169HfX097rjjDkUeussuuwz9+vVLO79SaW1/tPj4448xePBgjBs3Lu0z+TWhrKxMkZfXbDbjrrvuQiAQwNdff6343m9/+1vk5+dL/6duAz1cddVVkvtVxGg0StuW4zg0NzeDZVmceuqpquuuNo9UampqsHHjRkyaNAkFBQXS9JNOOgkXXXSRdD3UQu3YSUW8Ty1evFgztFfrXOzoa1Nr93kqbb23toUpU6ZIf4su0FgshiVLlmh+9/e//73i/xEjRqCpqUlKEUKhUCiUIwcNu6ZQKBRKu/Hyyy+jT58+MJlMKC0tRd++fdMKOphMJnTp0kX3POXiH5Ac4HXt2lV1ektLizRt69ateOihh7Bs2bK0wYY8B1cqDQ0NCIfD6N27d9pnffv2VQxYd+3aBUKIalug9UVTwuEwnnzyScyaNQvV1dWK3FbZ+gzwYe7l5eWKQXYq+/btg8FgQK9evRTTy8rKkJeXh3379immd+/ePeO8Uj/btWsXAH6gngmv16sQLjZu3Ijf//73mDBhQpogqtb38vJyuN1uxfT+/ftLn8tJPXYAID8/XzpGGhoa4PF48MYbb+CNN95QXWZ9fX3G/jQ3NyMWi2XtcyYKCgp0F9g43H6KqB2jffr0QSgUQkNDA8rKynT1R47WNs7G4Zw7bdkmaseymCP1lVdeQWVlpUKElaeLEEldX/FYFtdXPAbl6QlE+vXrh1WrVmVcp7b0R4s9e/bgqquuytpm37596N27d9q1Wu95lboN9JDpuvLWW2/hmWeewY4dOxCPx7O2z3ZtEsm2P/r374/FixdLRc/a0t/UNvfddx+effZZvPfeexgxYgR+85vf4IYbblC8QAO0z0WDwdAu53wmWrvPU2nrvbW1GAwG9OjRQzGtT58+AKArD3K2YzUnJ6d9OkmhUCgUXVDxkUKhUCjtxumnny7lFcyE3NmjB6PR2Krpoljn8XhwzjnnICcnBzNmzEDPnj1hs9nw448/4sEHH1R14LUFjuPAMAwWLVqk2icxx5Ze/vjHP2LWrFm45557MHz4cOTm5oJhGIwfP77d+gwkXU9aZHP7pH4m9u/pp59Oy88mIt8eLS0tuOqqq9CnTx+8+eabuvrTGrSOEbG/N9xwQ0bB9KSTTso4/yuvvFLTIZSJ5cuX49xzz9XV9nD72RoyHReZnJFa2zgbh3PutGWbqB3Lf/vb3/Dwww/j5ptvxuOPP46CggIYDAbcc889qufb4ayvHlrbn6NBe2wDtX3x7rvvYtKkSRg7diweeOABlJSUwGg04sknn5TyB2vNo6PQu6xnnnkGkyZNwqeffoovv/wSd911F5588kl89913rXrpdiTP+dZypO6t7UFHn68UCoVC0Q8VHykUCoXyq2TFihVoamrCvHnzMHLkSGm6WHk7G8XFxbDb7ZKTT87OnTsV//fs2ROEEHTv3l1yZBwOH330ESZOnIhnnnlGmhaJRODxeDS/27NnTyxevBjNzc0Z3Y/dunUDx3HYtWuX5HIB+OIGHo8H3bp1a3PfxfDbnJwcXHjhhVnbchyH66+/Hh6PB0uWLNEspCP2fcmSJfD7/Qr3oxjm19q+FxcXw+12I5FIaPZXjWeeeaZVbi85gwcP1t32cPsponY8//zzz3A4HFL4an5+vuqxpuWEykYmQfNwzp322iYfffQRzjvvPPz73/9WTPd4PFJxnNYgHoM7d+7E+eefr/hs586dmsdoe/enZ8+e2LJli2aff/rpJ3Acp3gx1NbzCtD/ckPORx99hB49emDevHmK76ulkdCLfH+ksmPHDhQVFWm6HlvLoEGDMGjQIDz00EP49ttvcdZZZ+G1117DX//6V6mNnnNR7/GdbVtn+uxw9vnh3FtbC8dx2Lt3r+L68PPPPwOArkJBFAqFQjl2oDkfKRQKhfKrRHQ8yB0OsVgMr7zyiq7vjho1CvPnz8f+/ful6du3b8fixYsVba+88koYjUY89thjaW4KQgiamppa3e/U+bz44ou6cvJdddVVIITgscceS/tMnOell14KAHjuuecUnz/77LMA+Nx0bWXo0KHo2bMnZs6ciUAgkPZ5Q0OD9Pdjjz2GxYsX47///a+ukEaA73sikcBLL72kmP7Pf/4TDMNg9OjRreqv0WjEVVddhY8//lhVoJH3V42hQ4fiwgsvbNOPPPS8o/spsmbNGkWeuAMHDuDTTz/FxRdfLJ0vPXv2hNfrxU8//SS1q6mpSauC3hpEcSdV1Dycc6e9tona+fbhhx8qcse2hlNPPRUlJSV47bXXEI1GpemLFi3C9u3bNc+v9u7PVVddhU2bNqnuP/k1oba2FnPnzpU+Y1kWL774IlwuF84555xWLzfTPs+G2jX7+++/x5o1a1q9fJFOnTphyJAheOuttxR92bJlC7788kvpetge+Hw+sCyrmDZo0CAYDAbFsQBon4utOb6zbWun06kaBn04+/xw7q2ZqKmpSQu1F5Ff7wkheOmll2A2m3HBBRe0eXkUCoVCOfJQ5yOFQqFQfpWceeaZyM/Px8SJE3HXXXeBYRi88847usOtHnvsMXzxxRcYMWIE7rjjDmlgNnDgQIUw07NnT/z1r3/FtGnTUFVVhbFjx8LtdqOyshKffPIJbr/9dkydOlV3vy+//HK88847yM3NxYABA7BmzRosWbJEV7638847DzfeeCNeeOEF7Nq1C5dccgk4jsPKlStx3nnnYcqUKRg8eDAmTpyIN954QwqfW7t2Ld566y2MHTs2a8EXLQwGA958802MHj0aAwcOxOTJk9G5c2dUV1dj+fLlyMnJwf/+9z9s3rwZjz/+OEaOHIn6+nq8++67ivnccMMNqvMfM2YMzjvvPPzf//0fqqqqMHjwYHz55Zf49NNPcc899ygKn+jl73//O5YvX45hw4bhtttuw4ABA9Dc3Iwff/wRS5YsQXNzc5u2RXvTHv088cQTMWrUKNx1112wWq2SWCAXq8ePH48HH3wQ48aNw1133YVQKIRXX30Vffr0aXOBC7Gwxv/93/9h/PjxMJvNGDNmzGGfO+2xTS6//HLMmDEDkydPxplnnonNmzfjvffeS8szpxez2Yx//OMfmDx5Ms455xxMmDABdXV1eP7551FRUYF77733iPbngQcewEcffYRrrrkGN998M4YOHYrm5mZ89tlneO211zB48GDcfvvteP311zFp0iT88MMPqKiowEcffYTVq1fjueeeS8uxqgdxn991110YNWoUjEYjxo8fr7nu8+bNw7hx43DZZZehsrISr732GgYMGKD6MkMvTz/9NEaPHo3hw4fjlltuQTgcxosvvojc3FxMnz69zfNNZdmyZZgyZQquueYa9OnTByzL4p133pGERDl6zkW9x3fPnj2Rl5eH1157DW63G06nE8OGDUP37t0xdOhQzJ07F/fddx9OO+00uFwujBkz5rD2+eHeW9WYNm0a3nrrLVRWViocjTabDV988QUmTpyIYcOGYdGiRViwYAH+8pe/aBYbolAoFMoxRscW06ZQKBTK8cCsWbMIALJu3bqs7SZOnEicTmfGz7p16yb9X1lZSQCQp59+WtFu+fLlBAD58MMPNfuwevVqcsYZZxC73U7Ky8vJn/70J7J48WICgCxfvlxzvb7++msydOhQYrFYSI8ePchrr71GHn30UaJ2+/z444/J2WefTZxOJ3E6naRfv37kzjvvJDt37sy4joQQAoA8+uij0v8tLS1k8uTJpKioiLhcLjJq1CiyY8cO0q1bNzJx4kTNPrMsS55++mnSr18/YrFYSHFxMRk9ejT54YcfpDbxeJw89thjpHv37sRsNpOuXbuSadOmkUgkophXt27dyGWXXZa2jEz7QGTDhg3kyiuvJIWFhcRqtZJu3bqRa6+9lixdulTx/Uw/2baX3+8n9957LykvLydms5n07t2bPP3004TjuLTteuedd6b1TW071tXVkTvvvJN07dqVmM1mUlZWRi644ALyxhtvqK7fkSD1uCDk8Popbo93332X9O7dm1itVnLyySerngdffvklOfHEE4nFYiF9+/Yl7777rupx35pt/Pjjj5POnTsTg8FAAJDKykrpMz3nTib0bJNsx2skEiH3338/6dSpE7Hb7eSss84ia9asIeeccw4555xzNOchXqdmzZqlmD537lxy8sknE6vVSgoKCsj1119PDh48qLk+evuTablqNDU1kSlTppDOnTsTi8VCunTpQiZOnEgaGxulNnV1ddJ1x2KxkEGDBqXNO9M1mZD045VlWfLHP/6RFBcXE4ZhpGMn2zw4jiN/+9vfSLdu3aTj8/PPP9d9b8i2XZYsWULOOussYrfbSU5ODhkzZgzZtm2b5rbLduyIn4nn0N69e8nNN99MevbsSWw2GykoKCDnnXceWbJkSdq20nsu6j3nP/30UzJgwABiMpkU6x8IBMh1111H8vLyCADFdtSzzzOh996qdg1XY+LEiWnXBfF5Yc+ePeTiiy8mDoeDlJaWkkcffZQkEgnF91OPP/F61dDQoGgnPifIl0OhUCiUIwNDCM24S6FQKBQKhfJrhmEY3HnnnWkh6xQK5chCz0UKhUKhHI/QnI8UCoVCoVAoFAqFQqFQKBQKpUOg4iOFQqFQKBQKhUKhUCgUCoVC6RCo+EihUCgUCoVCoVAoFAqFQqFQOgSa85FCoVAoFAqFQqFQKBQKhUKhdAjU+UihUCgUCoVCoVAoFAqFQqFQOgQqPlIoFAqFQqFQKBQKhUKhUCiUDsF0tDtwpOE4DocOHYLb7QbDMEe7OxQKhUKhUCgUCoVCoVAoFMovDkII/H4/ysvLYTBk9jced+LjoUOH0LVr16PdDQqFQqFQKBQKhUKhUCgUCuUXz4EDB9ClS5eMnx934qPb7QbAb5icnJyj3BsKhUKhUCgUCoVCoVAoFArll4fP50PXrl0lrS0Tx534KIZa5+TkUPGRQqFQKBQKhUKhUCgUCoVCOQy00hrSgjMUCoVCoVAoFAqFQqFQKBQKpUOg4iOFQqFQKBQKhUKhUCgUCoVC6RCOqvj4zTffYMyYMSgvLwfDMJg/f77md1asWIFTTjkFVqsVvXr1wuzZszu8nxQKhUKhUCgUCoVCoVAoFAql9RzVnI/BYBCDBw/GzTffjCuvvFKzfWVlJS677DL8/ve/x3vvvYelS5fi1ltvRadOnTBq1Kgj0GMKhUKhUCgUCoVCoVAoHQEhBCzLIpFIHO2uUCgUAEajESaTSTOnoxZHVXwcPXo0Ro8erbv9a6+9hu7du+OZZ54BAPTv3x+rVq3CP//5Tyo+UigUCoVyDEMIAaCdjPqowsYAoxk4lvtIoVAoFMqvlFgshpqaGoRCoaPdFQqFIsPhcKBTp06wWCxtnscvqtr1mjVrcOGFFyqmjRo1Cvfcc0/G70SjUUSjUel/n8/XUd2jUCgUCoWSgcVba/HUFzux8O4RsJmNmu1//84PiCU4/GfSaVnbEULw5spKXDW0CwqcbX8gAscB/zofGH4nMGRC2+dDoVAoFAql1XAch8rKShiNRpSXl8NisRzbLywplOMAQghisRgaGhpQWVmJ3r17w2BoW/bGX5T4WFtbi9LSUsW00tJS+Hw+hMNh2O32tO88+eSTeOyxx45UFykUCoVCoaiwencTAKDBH0XXAodqG284jvkbqnHDGd3wc50fAJDgCIyGzIOPH/a14L9r9wMAbhvZo+0dDNQBbBjYs5SKjxQKhUKhHGFisRg4jkPXrl3hcKg/J1AolCOP3W6H2WzGvn37EIvFYLPZ2jSfX32162nTpsHr9Uo/Bw4cONpdolAoFArluILjCFbvbgQABKJsxnZzvt+Pt76twn9WVUrTan2RrPPeeMADALCZD/ORJuIR/qAuCwqFQqFQjhZtdVVRKJSOoz3Oy1+U87GsrAx1dXWKaXV1dcjJyVF1PQKA1WqF1Wo9Et2jUCgUCoWiwsvLd0t/B7OIj4c8YQCQnIzitM556vf4fU1BzPmeb1vZeJj5oSJe/jebXeykUCgUCoVCoVAoreMX9Vph+PDhWLp0qWLaV199heHDhx+lHlEoFAqFQslGIMpixc8NAACr2YhgLHP1ynp/BCU5yReGBgODWm9mMfDLrfwLyd+d0wPf7mkEx5G2dzQi5IQOt7R9HhQKhUKhUCgUCiWNoyo+BgIBbNy4ERs3bgQAVFZWYuPGjdi/n3cxTJs2DTfddJPU/ve//z327t2LP/3pT9ixYwdeeeUVfPDBB7j33nuPRvcpFAqFQqFocPOsdWgJxvDKDacgz25GKIvz0RuOY8LpJ+CWEd1x0YBSDOiUg8ZANGP7jQc9uG7YCTi9eyFiLAdPON72jkYF8TEWaPs8KBQKhUKhUH5BNDU1oaSkBFVVVUe7K8c8kyZNwtixYzts/gzDYP78+brbV1RU4Lnnnjvs5Z5xxhn4+OOPD3s+WhxV8XH9+vU4+eSTcfLJJwMA7rvvPpx88sl45JFHAAA1NTWSEAkA3bt3x4IFC/DVV19h8ODBeOaZZ/Dmm29i1KhRR6X/vyg4DvjvBGDXV0e7JxQKhUI5TqhsDKIxEMXDlw9Av7IcOK2mjDkfCSHwhOIocdtw/bBumHZpf5S4rWjwq4uPCY5gb0MQ/TvloNjNuyUztU0lHEvg/Jkr8PrXe5ITxbDrWAggh+GgpFAoFAqFctxRW1uLu+++G7169YLNZkNpaSnOOussvPrqqwiFkqlhKioqwDAMGIaBw+HAoEGD8OabbyrmtWLFCqlN6k9tbS0AYPr06RgyZEjaPNV+Jk2alLHfTzzxBK644gpUVFS09yahQF1QlO87OTU1NRg9erTuea9btw6333571mXp4aGHHsKf//xncBzX6u+2hqOa8/Hcc88FyfKAP3v2bNXvbNiwoQN79Ssl2AA0/gxs+RjofdHR7g2FQqFQjgM2V/OC3jl9igEATqsxY87HnXV+xFgOhS6LNK3YbcWeBnUnojccRzSeQHmeHS6rCWajAZ5QTFe/nlq8AwAwd90B/O6cnvzEWAAw24F4GEjEAZMlyxwoFAqFQqFQePbu3YuzzjoLeXl5+Nvf/oZBgwbBarVi8+bNeOONN9C5c2f85je/kdrPmDEDt912G0KhED788EPcdttt6Ny5c5rwtHPnTuTk5CimlZSUpC1/3bp1SCT4tDbffvstrrrqKsV3M9XHCIVC+Pe//43Fixcf1vpT2oeysrJWtS8uLm6X5Y4ePRq33norFi1ahMsuu6xd5qnGLyrnI+UwCDfzv7nMubYoFAqFQmlPvKEYBnbOhcHAV5B2Wk0Zcz7OXLwTAFDqtknTilyZnY/NQX66KFY6rUYEY5lDuuXUeCI4UehXjBXe8rIxwF7A/x0/zOI1FAqFQqFQjhvuuOMOmEwmrF+/Htdeey369++PHj164IorrsCCBQswZswYRXu3242ysjL06NEDDz74IAoKCvDVV+kRiiUlJSgrK1P8qFUdLi4ulj4vKChI+25ubq5qvxcuXAir1YozzjhDMX3r1q24/PLLkZOTA7fbjREjRmDPHj5ahOM4zJgxA126dIHVasWQIUPwxRdfSN+tqqoCwzCYN28ezjvvPDgcDgwePBhr1qwBAPh8PtjtdixatEixzE8++QRut1vhEs3G//73P5x22mmw2WwoKirCuHHjpM9aWlpw0003IT8/Hw6HA6NHj8auXbukz2fPno28vDwsXrwY/fv3h8vlwiWXXIKamhqpTSKRwH333Ye8vDwUFhbiT3/6U5pxTi3seciQIZg+fbr0OQCMGzcODMOgoqICs2fPxmOPPYZNmzZJzlTRdCd3Lp555pl48MEHFfNuaGiA2WzGN998k7Z8tWUBwJ49e3DFFVegtLQULpcLp512GpYsWaKYr9FoxKWXXor3339fc7sfDlR8PF4Ie/jfnL6BGQihYWcUCoVCOSxaQnHk2c3S/05LZudjVVMIvUtdyHUk21cUOXGgJYwomy5YNgViMBkZuK18EAcf0q3vBVtDIIozexaC4whqvHyFbbARwJ7P/x0P65oPhUKhUCiUjiXBEdR6I0f8J6GziF1TUxO+/PJL3HnnnXA6naptGIZRnc5xHD7++GO0tLTAYjnyERcrV67E0KFDFdOqq6sxcuRIWK1WLFu2DD/88ANuvvlmsCz//Pb888/jmWeewcyZM/HTTz9h1KhR+M1vfqMQ9wDg//7v/zB16lRs3LgRffr0wYQJE8CyLHJycnD55Zdjzpw5ivbvvfcexo4dC4fDodnvBQsWYNy4cbj00kuxYcMGLF26FKeffrr0+aRJk7B+/Xp89tlnWLNmDQghuPTSSxGPJ3ODh0IhzJw5E++88w6++eYb7N+/H1OnTpU+f+aZZzB79mz85z//wapVq9Dc3IxPPvlE/8YF70gFgFmzZqGmpgbr1q3Db3/7W9x///0YOHAgampqUFNTg9/+9rdp373++uvx/vvvKwTPuXPnory8HCNGjNC1LICvs3LppZdi6dKl2LBhAy655BKMGTNGkd4QAE4//XSsXLmyVevXWo5q2DXlCCI6HyMefe3X/wfYswwY/16HdYlCoVAov25W7mrAaRUF0v92iwn+SHpRmOZgDBxHcPEAZbhJaY4VHMfngizNMaZ9p8BpkR7onRZTRmFTzpZqL1qCMfQqcQEAPKE4uhUiRXykzkcKhUKhUI4FGvxRXPev7474cufcdgbKcm2a7Xbv3g1CCPr27auYXlRUhEgkAgC488478Y9//EP67MEHH8RDDz2EaDQKlmVRUFCAW2+9NW3eXbp0UfzfrVs3bN26tS2ro8q+fftQXl6umPbyyy8jNzcX77//Psxm/oVwnz59pM9nzpyJBx98EOPHjwcA/OMf/8Dy5cvx3HPP4eWXX5baTZ06VQrhfeyxxzBw4EDs3r0b/fr1w/XXX48bb7wRoVAIDocDPp8PCxYs0C3uPfHEExg/fjwee+wxadrgwYMBALt27cJnn32G1atX48wzzwTAC5tdu3bF/Pnzcc011wAA4vE4XnvtNfTsyaffmTJlCmbMmCHN77nnnsO0adNw5ZVXAgBee+21Voeni2HReXl5ipBql8sFk8mUNcz62muvxT333INVq1ZJYuOcOXMwYcIEVTE707IGDx4sbRsAePzxx/HJJ5/gs88+w5QpU6Tp5eXlOHDgADiOU3XXtgdUfDxeCLcof2vx3Sv8b44DOujgo1AoFMqvl+ZgDE2BGFpCSbHRYTGi3hdJa7u+qhlumwljT+6smJ4ruCZ94ThKc5QDgAWba+C2yVyV1uzi476mICbPWif9X55nh9UsC9VOxJLiYyyobyUpFAqFQqF0KMVuK+bcdoZ2ww5Y7uGwdu1acByH66+/HtGoMoXMAw88gEmTJqGmpgYPPPAA7rjjDvTq1SttHitXroTb7Zb+F8XA9iIcDsNmUz5fbdy4ESNGjFBdls/nw6FDh3DWWWcppp911lnYtGmTYtpJJ50k/d2pUycAQH19Pfr164dLL70UZrMZn332GcaPH4+PP/4YOTk5uPDCC3X1e+PGjbjttttUP9u+fTtMJhOGDRsmTSssLETfvn2xfft2aZrD4ZCER7GP9fX1AACv14uamhrFPEwmE0499dSsNUvak+LiYlx88cV47733MGLECFRWVmLNmjV4/fXXWzWfQCCA6dOnY8GCBaipqQHLsgiHw2nOR7vdDo7jEI1GM+YIPVyo+Hi8IA6kxCqeGazfaUS9ycEYhUKhUCg6WbOnCQDw59H9pGl2sxEhlZyP66qacUq3fBgNynuTy2qCwcDAp+KW/LnOjzEnJd/Wu22ZK2kDUAiP913cB+V5djgtRoTEUG02AuSUA4yRhl1TKBQKhXKMYDQwuhyIR4tevXqBYRjs3LlTMb1Hjx4A1Iu9FBUVoVevXujVqxc+/PBDDBo0CKeeeioGDBigaNe9e3fk5eV1WN+LiorQ0qI0J7WX8CQXL0WnnlhN2WKx4Oqrr8acOXMwfvx4zJkzB7/97W9hMumTp9qjj6niKsMwrRYWDQZD2nfkod2Hy/XXX4+77roLL774IubMmYNBgwZh0KBBrZrH1KlT8dVXX2HmzJno1asX7HY7rr76asRiyiKNzc3NcDqdHSY8AjTn4/EDGwEsLoAk+CqeehFzRVIoFAqF0go2HfTggv4lknsRAOwWI8JxpfjIcQTr97XgdFl4tgjDMHDbTPCGlfctNsGB5QhG9ElW+XNkyScJAIO75qFvmRuv3TgUlwuipcNqSjof2ShgsgFmG8BS8ZFCoVAoFIo2hYWFuOiii/DSSy8hGGx95ETXrl3x29/+FtOmTeuA3mXn5JNPxrZt2xTTTjrpJKxcuVJVRMvJyUF5eTlWr16tmL569eo04VSL66+/Hl988QW2bt2KZcuW4frrr9f93ZNOOglLly5V/ax///5gWRbff/+9NK2pqQk7d+7U3cfc3Fx06tRJMQ+WZfHDDz8o2hUXFyuK1Ph8PlRWViramM1mqRK5iMViSZumxhVXXIFIJIIvvvgCc+bM0dxGastavXo1Jk2ahHHjxmHQoEEoKytDVVVV2ne3bNmCk08+WbNPhwMVH48X4q3IZSW8kQAARLwd1ycKhUKh/CohhGDJtjp0LVAmDbebjQinOB+rmoLwhuIY2k3dZZ9jM8MXVoqKnnAcIECBI5mc3Wk1qboqRbzhOC7oX4o+pcnwJZfViKDkfIwCRgtgtPKVrykUCoVCoVB08Morr4BlWZx66qmYO3cutm/fjp07d+Ldd9/Fjh07YDQas37/7rvvxv/+9z+sX79eMb2+vh61tbWKn/Z01o0aNQpbt25VuB+nTJkCn8+H8ePHY/369di1axfeeecdydn5wAMP4B//+Afmzp2LnTt34s9//jM2btyIu+++u1XLHjlyJMrKynD99deje/fuihDnfv36Zc3/+Oijj+K///0vHn30UWzfvh2bN2+Wcmr27t0bV1xxBW677TasWrUKmzZtwg033IDOnTvjiiuu0N2/u+++G3//+98xf/587NixA3fccQc8Ho+izfnnn4933nkHK1euxObNmzFx4sS0fV1RUYGlS5eitrZW2s4VFRWorKzExo0b0djYmBaWL+J0OjF27Fg8/PDD2L59OyZMmJC1z2rL6t27N+bNm4eNGzdi06ZNuO666yQHqpyVK1fi4osv1rt52gQVH48X2AjgEFwlWuFkrCwfFxUfKRQKhdJKDrbw95kBnXIU0x0WI0IxpZD4c10A+U4LSnLUQ6rctvQiNS1BXhzMdyZdlfy81cXHjQc8qGoMosCpDLFxWkzJ/ojOR5NVeR+kUCgUCoVCyULPnj2xYcMGXHjhhZg2bRoGDx6MU089FS+++CKmTp2Kxx9/POv3BwwYgIsvvhiPPPKIYnrfvn3RqVMnxU+q++5wGDRoEE455RR88MEH0rTCwkIsW7YMgUAA55xzDoYOHYp//etfUpjyXXfdhfvuuw/3338/Bg0ahC+++AKfffYZevfu3aplMwyDCRMmYNOmTWmOvp07d8LrzaxDnHvuufjwww/x2WefYciQITj//POxdu1a6fNZs2Zh6NChuPzyyzF8+HAQQrBw4cJW5cy8//77ceONN2LixIkYPnw43G43xo0bp2gzbdo0nHPOObj88stx2WWXYezYsYo8kgBfNfurr75C165dJWfhVVddhUsuuQTnnXceiouL8d///jdjP66//nps2rQJI0aMwAknnJC1z2rLevbZZ5Gfn48zzzwTY8aMwahRo3DKKacovlddXY1vv/0WkydP1r192gJDjlTGzGMEn8+H3NxceL1e5OTkaH/h18KiPwMcC+xdDlz3AVDYM3PbYBPwH0H1vuhxoN+l7duXLfOA3C5A19Pbd75Hg22fApUrgctmHu2eUCgUyjHDV9vq8PLy3fjkjjMVFfm+3dOIxz/fjkV3j5CmvbpiDyobA3jq6sFqs8K0eT/hhAIn/nBu8r71/d4mPPLpVnxxzwhp/h+sO4ClO+rw+o2nps3jmS93YsFPNXjxupMxsDxXmj79s63ItZtx70V9gLfHAiffAGycAwy5Dhh09eFuBgqFQqFQKDqJRCKorKxE9+7d04qgUDqOBQsW4IEHHsCWLVs6rMox5djmwQcfREtLC954442MbbKdn3o1Nlpw5niBjQCuEv5vLeejGJZttGqHaLeF5U/wCf2nrNVue6yz7K8A4WhVcAqFQpGxudqL/p3cCuERABwWE6LxBBIckYrLNAejKHJlrijpUqli3RyMId9pVszfYc3sfGwJxXDRgFKF8AikFKlJxHjXo8nGuyApFAqFQqFQfuVcdtll2LVrF6qrq9G1a9ej3R3KUaCkpAT33Xdfhy+HqiXHC2wEsOUCYPSHXTsK2l98FAd0RDvB6i8Cs5DPLOLp8EXtbwphZ62/w5dDoVAoh8vuen9ayDXAh0YDQERWdMYTiiNflrsxFac1vYp1cyiGAqfyO05LukgpsrchiP4q/XFZTQhExLDriBB2bQESVHykUCgUCoVyfHDPPfdQ4fE45v7770dpaWmHL4eKj8cLbAQw2/kqnlqCovi5o4AvVKNF1Wrg3xfrc4qEW7Tb/KIQshZ0cG5MjiOYNGst/vBu++X4oFAolI6iMRBTzeFoN/Pio9yh2BKKI8+ROQePS0V8bAnG0gTLTDkf63wR1HojGFiuIj7KnY9sTCg4Y6EFZygUCoVCoVAolHaEio/HC/EIYLLzTj3NsOsIwBgAa44+52PlN0CoCWjcpd021Jz8+9cwuCNCpaior0MXU+9PCrtsIr06FYVCoRwN1NJGcxxBczCGQme6m9EuOB/DCvExXUiUo3AnCnjDceTalYKlw2JCjOXSrpHbanxwWI3oWexKm7dUAIcQmfPRSp2PR5NQM/C/u3+FLyspFAqFQqFQjl+o+Hi8wEb4AZXZri0osmF+AGZxaguVQHKQFmrUbit3CHawYNfhcImk2zPSsetS403uhzo/HRRTKJSjTyjG4uJ/foMvttQopnvCcXAcQaFKHkcx7DoshF1H4gm0hGIodmfJ+WgzIZhSIdsXZtPFR6vgqowr3Y976gPoXeKGwaDMP8n3x4RgLMFfz0H4kGuj9dfxcuyXyoHvgapVQM1PR7snFAqFQqFQKJR2goqPxwvxMC88muw6nI9CW7MDiAe15y26GYM6xEdWtuyYjnkfy8SCR8z5+N1efhsbDAwOeXQIwhQKhdLBVDYGkeAI1lcpHWpNAf4FSZEr3c1oM4lh17yYuK3GBwZAz5J0V6KI02qCP8X5GIiycNmUNfOcFv7/UFQpPlY2BlFR6FSdt8Ni5F2YXJyfYLTwL+pYHSlHKB1DsIH/Hag7uv2gUCgUCoVCobQbVHw8XhCdj3rCySTx0a4v52NYEB/1hEjFI8kiLcei+Fi5Eph3Ox+Cp0Uinvxbj0P0MPhw/QEAQFmOjYqPFArlmGDFTl4kSr1aNgZisJgMcFlNad8xGBg4rEYpz+KB5hDK8+yqbUXEnI/yEG9fJA63Td35mOqS3NcUQkWRQ3XedrMQdi1ezw0mXoCkYddHD/GFZthzVLtBoVAoFAqFQmk/qPh4vMBGk7mstArDxMO8Q9Jk0yeqRQPCbx2VmOMhwFGY/PtY46e5QPUPfA5LLRJCWJ7F1eEuGaOBwcg+xSjPs6HGSx05FArl6OKPxPHxDwcB8PkX5TQFoyh0WcAw6WHOAJDvsKA5yF8/feE48rLke+Tbm8FxBJ5QcjmqOR/FYjYy5yMhBPX+CDrl2lXnbTYaQAiQEMOsDSbhPknDrgFg6oebMHPxziO7UPGFZsRzZJdLoVAoFAqFQukwqPh4PEAIH1ImVvFMaAyq4iEhRNumz/0hCpR6nIzxMO98NDuOTeej6OHxHdJuKobpWd0d6nwkhMBkNODiAaXolGunzkcKhXLUuf7N7wEA5Xn2dPExEEOhM3MOxzyHRRISA9EEXIJjMRMVhU5YTAZsr+XTW8QTHAIRFvkpFbJNRgMsJgPvfIyHAW81AlEWbIKgwKleTdts5B+D4nHhvmg004IzMn7c14KFm2u0G7Yn4gtNWnCGQqFQKBQK5VcDFR+PBzghBM1o5gVFLUdHIi6EaFu0XZIAn8fRUahPTGQjgFkoZnMsio8xwY2pJ4ejGKZny+1Q8dEfZRGNJ1DstqJznh2HPNT5SKFQjh6fbqxGIMLiylM648bh3eANpYqPvPMxEwUOs+R8DETjWUOuAV5UzHOYpeW0hPjvqlXIdlpNfBj16ueBt3+DZiH/ZKZq2mYj785kRfHRYBYKzlDx8Z73NxydBYuRBMfiMwKFQqFQKDqYPXs28vLysraZPn06hgwZ0i7LGzlyJObMmdMu8zoWGD9+PJ555pmj3Q1KO0PFx+OBhGxQpSeRPhcX8l5ZtV2ShPDCm7NYX3Ea0VVpcR6bYddin/RUrxa3jTWnQweq9T5+3iVuG8rzeOcj0ZOTkkKhUNqZBEfw/JJdAIAp5/dGrt0MX0QpPlY1hVDozCw+8s5H/voZjCbgsGQXHwG+mEwoxodTtwT55eWrLMNhMSIYTfAVkwH4WuoBBhlDu0XnY0LhfNT54u1XzPkzV+Cng17pfzbBHbmFx0P8M0gH51KmUCgUCuVoMnXqVCxduvSw5/PZZ5+hrq4O48ePl6ZVVFSAYRgwDAO73Y6Kigpce+21WLZs2WEv70jw0EMP4YknnoDX69VuTPnFQMXH44GErIqnnkT6iZhQ8VPHACwR4ys+63U+igVnjtWwa7FPER0XOkl8dHeokPrSMn6gn2M3oTzPhkg8och9RqFQKEcKsZL1ny7pCwBw20yIsRwi8WSexS3VXjQGMr+4KnBa0CyFXbNw27TFR4foaATQHOQL2jgt6eHaDotQQIbhH2/C3ka4rCYYDer5J01pzkedL95+xSwSwqwtJgP+ftVJAHBkcw3HhWiKjrivbvs0WdCGQqFQKJSjiMvlQmFh4WHP54UXXsDkyZNhMCilnRkzZqCmpgY7d+7E22+/jby8PFx44YV44oknMs6LEAKWZTN+3tHEYvzz14knnoiePXvi3XffPWp9obQ/VHw8HpDER52J9BNifkgdAzBxcOAs0ik+hvjQb7O9w4u0SER8QIPOhPnx1oRdCxdmq7tDXTI/VXtR7LaCYRipaEKtj4ZeUyiUI48oKp7btwQAkCNUnBarV4uu7IsHlGacR57DLDkfAxFWM+waAFxWIwJCIZlsBW0cFhPvfOT4tolAExwqIqWI6HxkpfukWV9hNjXYKNDwc+u/dwyxpdqLp4UCM59NORundsuHxWTAgZYjGKkQDwP2/PYXHwP1wNIZwIZ32ne+FAqFQqFkYP78+ejduzdsNhtGjRqFAwcOSJ+lhl1PmjQJY8eOxcyZM9GpUycUFhbizjvvRDye2XTS0NCAZcuWYcyYMWmfud1ulJWV4YQTTsDIkSPxxhtv4OGHH8YjjzyCnTv5e/2KFSvAMAwWLVqEoUOHwmq1YtWqVdizZw+uuOIKlJaWwuVy4bTTTsOSJUsU86+pqcFll10Gu92O7t27Y86cOaioqMBzzz0ntfF4PLj11ltRXFyMnJwcnH/++di0aVPaNnjzzTfRvXt32Gw26bMxY8bg/fff172tKcc+VHw8HuDa4nwU80NqVcYWRDBncesKzhgtR058/PofwPvX8SHiWog5H/X0LREDGKMgpHZMeBib4GBgGMllZDMbYDIy8EeO3hspCoVy/PLF1loAgE2oLC2Kj+I1SQyNLnJnLzgjFqkJxlg4dIiPDovS+ViQoaCN0yo4H4UXSFyoJWtYdzLsWrjX6U1Posbmj4D3J+hL23GMctd/k3keLSYDDAYGXfLtONhyBEOgJedjOz8jeIQB3y94/1AoFMpxCZfgi4Ee6R8uod23LIRCITzxxBN4++23sXr1ang8HkVotBrLly/Hnj17sHz5crz11luYPXs2Zs+enbH9qlWr4HA40L9/f119uvvuu0EIwaeffqqY/uc//xl///vfsX37dpx00kkIBAK49NJLsXTpUmzYsAGXXHIJxowZg/3790vfuemmm3Do0CGsWLECH3/8Md544w3U19cr5nvNNdegvr4eixYtwg8//IBTTjkFF1xwAZqbk1EIu3fvxscff4x58+Zh48aN0vTTTz8da9euRTR6fKfC+TWh/cRP+eWTlvNRT8EZGy9AaoqPglhnL9CXn4kNA+ZyQbA7QheS+u3870A94M7sxkEizgu1jkJ96yKGp5sdHZabqiUUB8cRlLj5t0AMw8BtM8MfoWHXFArlyPP5pkOK/11CyLRPEBPF/I+iKKlGjs2EQJRFgiN82LUO8dFpSTof/ZHModpSbkihYnIi6tdwPvLuyYQi7NrStrDrlirhdyXQaXDrv38M0CnPhhpPBOf0LZamdcl34GDzEXY+OgqB+CYdbSPAz18AA64AVJywCsLCQEdPWhUKhUKhHDsE6oC30p19Hc7E/wE55W3+ejwex0svvYRhw4YBAN566y30798fa9euxemnn676nfz8fLz00kswGo3o168fLrvsMixduhS33Xabavt9+/ahtLQ0LeQ6EwUFBSgpKUFVVZVi+owZM3DRRRcp2g0enHyWefzxx/HJJ5/gs88+w5QpU7Bjxw4sWbIE69atw6mnngoAePPNN9G7d2/pO6tWrcLatWtRX18Pq5V/aTxz5kzMnz8fH330EW6//XYAfKj122+/jeLi5LMHAJSXlyMWi6G2thbdunXTtX6UY5ujLj6+/PLLePrpp1FbW4vBgwfjxRdfzHgyAsBzzz2HV199Ffv370dRURGuvvpqPPnkkwqLLiWFhKzatR7HYSLOhxKb9IRdC6KbPV9nZeyoIGweQeej6PwMNWUXH0XnpqNIn5jIxWUheh2zLtJA3p4cyLusJinEMRN7GgKwmgzoku9ITvzuVaD7OUDpgA7pK4VC+XVT7+evc/dd3EeaZjQwcFpNkpNRdEBmy+OYYzcDhA+5DkRYOPU4H60m1AnFt4LRzKHaDosJ3mAIILxQyUWDusKuE2ycd7IbDG0XH0VRqyNyCsbDgPcgUNRbu20bSXAENR5+H1/UP3mv7JJvx9ZDR8gtSAh/P3UILzQJyS4qbv+Mj24oHai9bcT9EqY5HykUCuUXhauUFwKPxnIPA5PJhNNOO036v1+/fsjLy8P27dsz6h0DBw6E0Zh8bunUqRM2b96ccRnhcLjVOgghJC11jSggigQCAUyfPh0LFixATU0NWJZFOByWnI87d+6EyWTCKaecIn2nV69eyM/Pl/7ftGkTAoFAWl7LcDiMPXv2SP9369YtTXgEALudTzcWCh2DRWopbUKX+HjllVe2esavvfYaSkpKsraZO3cu7rvvPrz22msYNmwYnnvuOYwaNQo7d+5U/e6cOXPw5z//Gf/5z39w5pln4ueff8akSZPAMAyeffbZVvfxuEEcRIlCmdagSgq7tvIDOC4BGDIM3uJhAAxgy+UHDFoDBUVI9xESH8Vw64gnezvRxeko0Bl2LYiPh+l89EXi+OmAF2f3Lkr7zBuOg2GgGGi7bSb4xLDr7Z8D3Ufw21/GH979AfkOC+b+bjg/IeID1r0J1G4Gxr7S5r5SKJTjly3VXrhsJlx6YifF9NIcG2qFgiTecBwGA5NV8BNdkQ2BCBIcgdOaua2I02JESChqE4yyKHRlDrtuaBReJJnt4GIhOBw6wq5Z4XoOCPfJNrjLY7zbEuEWfe1b9vHLKeql3XbtG8CPbwN3fJfsZxvZdsiHYIzFaRUFiun7BXfjS9edggHlOdL0LvkOLBbC7TscNgKA8M5HkuCfGUyZQ/gRauJ/ew9oi4/iM4Du/VMFuMoAM325TaFQKEcVg/GwHIi/JMxm5T2eYRhwHJexfVFREVpadN7XADQ1NaGhoQHdu3dXTHc6nYr/p06diq+++gozZ85Er169YLfbcfXVV0sFYfQQCATQqVMnrFixIu2zvLy8jMsWEUOz1YRJyi8TXf7c+fPnw2KxIDc3V9fPggULEAgENOf77LPP4rbbbsPkyZMxYMAAvPbaa3A4HPjPf/6j2v7bb7/FWWedheuuuw4VFRW4+OKLMWHCBKxdu7Z1a328Ic/5aLLpEB9lBWeA7EKcVEDGBoDon/fhiI/124EFU/Xn4dA7IBTzPTp1Oh+lquCH53x8+oudeOTTLQiquBm94ThcNrOiUqvbZkIgwgK+GmDJo8CG9CpgbIKgwR+Vij8gIOTf6KDwcAqF8uuGEIK/fr4dgQgLQ0rl6C75dlR7+GuLPxJHjs2kWgxGJMfOi4GHBJed26otpvGFZPhrpD+a2S3psJjARpXpQOxZhFADA4ARxEeDME+jhXfp68kTLEcsVKbXWffxrcDc6/W1bdrN//bsz95Og1CMxZQ5P+LBj35K3h8E9tQH4Lab0b+TWzG90GVBSyie1r5DkKIpCpT/ZyLs4X/rcZuKLxhjOhwUbAx49yrg2xe121IoFAqFogLLsli/fr30/86dO+HxeHTnZ9TDySefjNraWt0C5PPPPw+DwYCxY8dmbbd69WpMmjQJ48aNw6BBg1BWVqYI1e7bty9YlsWGDclc0bt371b045RTTkFtbS1MJhN69eql+CkqSjfdpLJlyxZ06dJFV1vKLwPdYdcvvPCCppNR5KOPPtJsE4vF8MMPP2DatGnSNIPBgAsvvBBr1qxR/c6ZZ56Jd999V8qTsHfvXixcuBA33nhjxuVEo1FFklKf7zhMNC46OMRcVpph1zJRDeAHYRb1NxKSK8FkS7bN5lJItEOo8tb5wN7l/CCsoHv2toQkw6m1kszHhXb2fD7JsBbydWmLS0agUnDpVDYGcWJnpYPRF44j164cmLusJj7no/cgPyElf9U3PzdIf3vDceQ5LHyuFADAERg8UiiUXx1iwZErhnRO+6xznh07av0AAF+YVaSJUMNqMsJqNqLGy89Tl/NRLCQDMexa/TtOqxFxUXx0FAKNITjMmefPMAwsRgM4NpZ0FBotAAjAsa1zGQp5JnUVXwNaF/4r5mrW69rLgBi6DgDVnrAiNcfuhgB6FjvThGO3zQSOIwjHE1mL97QLotgouvm1XmhKoe5NOuYd4fdtXMf+CQhOz7ot2m0pFAqFctzz0ksv4ZNPPsHSpUulaWazGX/84x/xwgsvwGQyYcqUKTjjjDOypphrLSeffDKKioqwevVqXH755YrP/H4/amtrEY/HUVlZiXfffRdvvvkmnnzySfTqlT3qonfv3pg3bx7GjBkDhmHw8MMPKxyY/fr1w4UXXojbb78dr776KsxmM+6//37Y7XbpOeLCCy/E8OHDMXbsWDz11FPo06cPDh06hAULFmDcuHFpod6prFy5EhdffHEbtwzlWESX83H58uUoKCjQbiiwaNEidO6cPkCR09jYiEQigdJSZS6F0tJS1Naqh/dcd911mDFjBs4++2yYzWb07NkT5557Lv7yl79kXM6TTz6pcGV27dpV93r8akjE+GIzDKOz4ExMECrNyf+ztRWdjIBOYVNnJe1s8wD0CYTxEEA4fn20BhyigGjN0el8jPPbVXTJtIHmYAyHBMeQvMooNrwLeA+qio9um5nP+SgOtkTnB4Af9rVg+mdbpf9rhFBIRP1C28MbuFIolOOTPQ0BOK0m3HVB+sNql3w7Dgghu/5IHO4sxWZEcmwmyfmoK+ejWEgGQDCagCuDW9JhMYGT8vcWAGx25yMAmIwMOLnzUf7irTWI11k9zjq5i1DPckSR7TCv4bfMXgeAz9Upbn+R/c0h9ChKf9Eopv0Q83l2KOIzhCg+am0bMZRaTxGZeChZUC5LCBsAPrIAABh9CfwpFAqFcnzT2NioyGMIAA6HAw8++CCuu+46nHXWWXC5XJg7d267LtdoNGLy5Ml477330j575JFH0KlTJ/Tq1Qs33ngjvF4vli5digcffFBzvs8++yzy8/Nx5plnYsyYMRg1apQivyMAvP322ygtLcXIkSMxbtw43HbbbXC73VIOSoZhsHDhQowcORKTJ09Gnz59MH78eKlITjYikQjmz5+fsdAO5ZeJrlfY55xzTqtmevbZZ7epM1qsWLECf/vb3/DKK69g2LBh2L17N+6++248/vjjePjhh1W/M23aNNx3333S/z6f7/gTIBNxwCgLJ0toPMxzrFJQ1BQfzbLBmo5iNocbqiwO8PQMNkQnirNYe0DIRpPVq3XlfIwli/hobdMM/LiPH0ieUOhAteAsQrAJWPVPoHEXmpkbkKfifKxsDCZdM7LB6FfbeIfjdcNOwPyN1aj3R9G/E5LhZsdolc9QjO14Rw2FQmkzlY1BVBSlu+IA/vrVGIgiGGXhDfNh11q4bWbUeMMwGhhYTdoCj9NqRDDKghAihF1ncD5ajGCjEcACwFEIA9uoeW0xGw3gxJdugOB8ROuKzhDC35ucJfqcdfJ7TMQLuDQiS8RruB6Hnwr1vgjGv/Gd9H9pjg21PuV9zhuKY6As16OIKCb7IyxK0z9uXzhB4BSjLfQ6H/W4TeNhPq2KvwZgw5kjOoDW54f8+Usg1AgMuU5fewqFQqH8qpg+fTqmT58u/T9p0iRMmjQJQOb6GanfmT17dlqb5557TnPZ9957LwYOHIh9+/ZJVaFTq1ln4txzz1VNq1JRUYFly5Yppt15552K/zt16oSFCxdK/x88eBD19fUKV6Xb7cYLL7yAF154QXX5qdtAZNasWTj99NNxxhln6FoPyi+Ddnuly7KsVP1ID0VFRTAajairq1NMr6urQ1lZmep3Hn74Ydx444249dZbMWjQIIwbNw5/+9vf8OSTT2ZMxGq1WpGTk6P4Oe4QBT9Av/PRaEl+J5vzQJ7DUastwOefNJj19SMTYl4trQIyQDLfo6tE280oiolmWyuqXQvbqY1h11sP+TCsRyFuPqs7rGYDf/EXQ6RjAdT7IyjNUYaxu20m3oEiOWGSoXvrq5px70V9cOuIHijLsaFOHFzKc12l3GAOtoRw/swVWFd1dCqAvv71Hlz+wip8seUIFTQ4Tjki+doov1qqGoPoXuhQ/axTLl+NsMEfhT+iHXYN8HkfD3kicGnkhxRxWk0gBIjEuezVrq0mMGwYhDECVjcYNgyXhhhqNhjAxeMpYddonfjIRvkCKS4dL7qA5IsxQL9rD0i+fGslq/c0Sn+/c+swlObYUOdNER9VnPZA0vkYUMlLrAtC9OfPFLe5xcX/1nqmiPoBkz25fbLBhnnnI6C9j1pbPGjxNGDlM/raUigUCoXSjpSVleHf//53q7SY9mDZsmX47LPPUFlZiW+//Rbjx49HRUUFRo4cedjzNpvNePFFmnf510a7iY9bt25Nq5qUDYvFgqFDhyryInAch6VLl2L48OGq3wmFQjAYlF0WS9HTgXUWRMEP4IvIJDQS6ae5GbOJj6n5IVsTdt3G4idSCLFHu61UwbpIe3CSiPHbR7fzMZ50PnKsdhiXCttqvBhQnoOyXBtC0YQQTt0ozb/eH0WxO118DETjSYFUcHyEYwk0B2PoLoTNleTY0OAX9l08zLt6SCJtf24+yA98/75oBxLckT+PVu3m1/e7vW1z9FC0icQTuOCZr/Hj/uwD6be+rcKmA54j0ynKL4r9zSF0K1R3iomClScchy8Sh1uH87HIZcUhTzijiJiK02ICCAH3yR9wSuyHjIKiw2KElcTAmWyAyQYDG9Z0YppNDLiE/D7ZBvFRbGvP1yeExWQiop57mSiW6Zm3CtsO+TCwcy6evHIQOufZsachgP+uVQ5SMomPRgMDh9XI5xpuC0tnAF+pR6ekIW5HqyA+akUVJGKAPU+f4BsP888CgLY7VXRSxtNf2GWlrelkKBQKhUI5DMaOHYsRI0Yc0WXG43H85S9/wcCBAzFu3DgUFxdjxYoVaRW728Ktt96Kvn37tkMvKccSRzWZzX333Yd//etfeOutt7B9+3b84Q9/QDAYxOTJkwEAN910k6IgzZgxY/Dqq6/i/fffR2VlJb766is8/PDDGDNmjCRCUlQQBT8AMOkYVCXiSudjtod/KfRYZ46sBCsLu27jQ7qUV0u7onpyQJinU3wU+hZvTdi1jtyYIjWbgOa9AHjBfH9zGD2LnCjN4Z2jtd5Ismpn1A9vKI58h0UxC7fNzDsf42F+uwuDJLF4Q+c83oVU7LKiXnQ+xoJ86DmQth2+3FaHfKcFnlAMm6uPfFh2p1x+3cXcl0cS7iiIrf5IPOlIPUJc+vxKAMC6yuzu1re+rcK9czcegR5RjiViLIdvfm7I+BKPEIJaX1Q6V1MxGhi47Wa0hGLwRVjk6Mj52LWAd1E6daZbcFiMcCMAY/U6XMouyfg9p9UEK6JIGG3gjBYgEdN0YpoMBhC1+2Rr3Pmi+92Wpy8EWHQ+MkbtexOX4O/D9nx9rnwVdtcHcHpFPob14J1/t5zNvywWC57FE7yjVE18BHj3Y0DM+Vj9o77q0iLbPwN2LtLXNsHyeRZNVn7b6InUsOfpC3UXw64B7X0UC/KOSsJpv4yUnzd6hGQA2LMM2PapvrYUCoVCoRyDjBo1Clu2bEEoFEJdXR0++eQTKeybQlFDd5K11ASjqYTDrX8g/u1vf4uGhgY88sgjqK2txZAhQ/DFF19ICUj379+vcDo+9NBDYBgGDz30EKqrq1FcXIwxY8bgiSeeaPWyjysS8nAymUiYqSq12N5g5N1y2R7+RaHSYODnrdv5eBjiY8TL52rSMwhjY/wAxuICfNX6+mZ28K5MQvgiPRnbx5Wuz0SMD9nOxkc382Fft3wJbziOaDyB0lwbcmwmWM1GNPij6C2F1/ngj7JpxRtcVhPCsQS4WAgGRwHgrwUIwUc/8NWv8xx8+9IcG3YKFWh5x0chn+sqHgJQgCibwOjnVkrzLcu1oboljCFd87Kuwl8+2QxfOI6Xrst+TdBDgiNYX9WCgZ1zsb9Z27ny3d4mVBQ6UZZBBGkNu+sDuP3t9XjyykHSgPxw8YRifHXxLNz/wSYc8obx+R+PzNvJGJt05DYGMp9zcuEpEk/AlqFCcIzl8NQXO/CbIeU4qUteu/WTcvS474ON2HbIh6euPgmnVqQXlwtEWUTjCZTkZLhnAChwmOEJxeAL63M+dhJeuGiFRIs4rSbkED9YjoORJDIWqXFZTLCQGFiDFQmYYSbaOSjNRgYcy8pyPorX9Fbco8S2el50Aby4ZTDxhVW07mWiUOYs1l9JO4XGQEzhXB0zuBxz1x3ATwc96F7khC/Mi6eZxUcz/FGWv2/Puw3odSEw+h/aC5YLc1r3VCD5EhAQirlpPFOwMV7wDdRr9yUe5gVcQPsFYyzAh9B79vOuSrM9y3xl+zvcArizJ9EHACx8gP894ArtthQKhUKhUCi/AnQ7H7dt24aTTjoJV1xxhepPa4vSiEyZMgX79u1DNBrF999/j2HDhkmfrVixQpF41WQy4dFHH8Xu3bsRDoexf/9+vPzyy8jLy2vTso8bUnM+itMytpc5QLSKqcgHClpFZLgEH/ZrNPOOgrYUnCFEGEAU6Bvgif0z23UUnBFzXQrrzmnkt5JXuxb/1+o7IBUM2NcUAhje+ccwDJ/LMcpKg0su4kU0nkgbyIv/xyNBwcXBbxOxEqyYP61zvh2HvGFeVIoFZI4PfjtsqfZJ8/zH1SfBZjZixU7tAdx3e5qw7ZAPviwheM3BGB7731ZUa7gZxTDgU07Igz8cRySeyNh2ybY6/GXeZlz3r+8QimXeN5F4Ar99fY2mk3LVrgYAwIeCaJsJQgheWrYLz371c9aw9L0NAVz5yreSkygTu+sDCEUTiCdaH6YvJxRjsWhzjWY7yRGbb8fu+sxuYXk+t2zOzKXb67BsRz0W6Fh2e/P+2v14c+XeI77cw4IQoPqHjKGb/kgcn27UeDHSwdQIuf8yHR/1QvqGYldm0T/PYUFLMA5fJLN7Tk6hi79u2jOI3KlYTQbkMEHpHMwkKDqtRlgRA8tYEOZMsEC7+rbZaACRv6STv1DSi9jWlqcvBDgW4F+KmXXkKxQ/d+pIH6JCJJ5AMMqiwKl8MVKWa8Nb31aBEAKvID5mcq06LEb++izmJPZmv25KyHMm6hFOubjSgZrtvkqIzPmoM+za4uTv21rCcizEFw8CtF2V8jycra1GTlMGUSgUSho0nRqFcuzRHuelbvHxxBNPxLBhw/Doo4+q/vz+978/7M5QOgh5FU89uRkVYqVNw/koD1WzaRenAYSCM2KexMxikypcAgDRn1dLHMhYXPrCrk06C+2I7Y1y8VGjfcqgZH9zCJ1ybVIlVqfFyItqwr7honx/U3OiiU4hNhqS5a8KwcAwuOykTlK7Lvl2BCJ89Vm1XFdbqr0odlvx1s2n47SKApxQ4MCGA56sF5YqmbC2tyHzgOx/mw7h650NuPHN7+ENZR487m0IoDzPjksH8f3O5sx7a02V9Pes1VUZ29V6I2jwRzUL2OxrDqE0x4Yf97Xw4e4Z2F0fwLwfq/H5pkNp1WHliAJNtuX+XJccpGZb5iFPGLe/vR5bsoTBL95ai6cX78T5M1co9ksq+5pCsJqN+P05PVHry7x9D3mS/anJ0jdRMG4OZBdmftjXjD99tAmsTpE1274XeeObvZjz/X7d8zwafLm1FgdbZNea/d8B824H6raotr/ipdV4fsmutufTE2jrA0GdL4KWYAx5DjMOtKhfIxv8UZiNBuTYMzsI3TYTvtnVgEBEW+wDgEJnZhelGgzDoNgYBMsRuJgwTEb1xxeT0QC7gUWMsSCUMAnOx+z9MRoYEHluZINRX8ivHPH+ZnXre7EWF9x0ZkcrnY+tFx9FYTE/RXwEAE8ojq2HfNLLq0yOUrvZyLcJNrRu4a0V5uS5N0227PdVjgVABMFXRxoWNszPU+tFKcBvc7ECuZZoKl9HPeHf8ueeNhYQolAolF8jYq7AUKht+Y0pFErHIZ6Xh5PTU3fY9VlnnYWdO3dm/NztdrdLZSNKB6AIY9IIJxOdBHrdjGxK22yDKNEZYrTwgzuAF/gs6hVUs87DlqNvECYKqXrcJalVvrVcL5xK2HU2okmnIbgEXxVWNih2WE0IRBP8NrTlggQ9YAiXNhgUv8PGQoBDKPIUC6LOF8Hp3ZMhk2Lux2pPGHnxEB/eZzBL2+2ng16M7FMs5V4bf3pXfPNzA6o9YXTJV98nN89eJ/1d6w0DGUK063wR9CxxYU99AD/X+3GaSijnh+sP4PWv9+Ls3kUodFoAhg8NVFt2lE2gxhvBmMHl+N+mQ1mFlgZBxNpR68vYBuBFubEnl+P1r/dib2MgYyj3QZmDssEflbZrKqJ49tNBT8Zl1gvin9HAoMYbkbZ9Kt/83IDd9QF8ta0OJ3bOVW2zqy452P5hXwsqitSLgeyo9aFfmRsFTgui8QTCsQTslnS32asrdgMAyvPsWYVRX5iF02rCIW92weS/aw/gx30t+GpbHUYP6pS1bTiWwLWvrcGjvxmIc/oUq7aRu043HPCoHlMAf6x8u7sJ5/Yt1qyivOmAByxHMLRbftZ2eqnzRfD3RTtwQqEDsyefzk8UHWLNe4GyQYr28pyjVY0hDOqivq/1cMEzXwMAPv7DmaoikxrVnjBufPN7AMDZvYuxr0ldOFm+ox65DnPW7blhvwdBwT2bm0WkFMl38tcxk1G70rVIkSGEBEeQw2S/ljtMHOLEiGDCAAvisJmzv2c1GhgwCTZ5XwK0Xf+piC+rrDn6HJOJOH/vaJXzsRho2q2/TwLifnGl5Mn8v0v746pXv8XOWj+6FjhgMjKwmNS3ld0iiI9STuLs11cJuXAX8QDomr19a8KupeeBXH15muMRmfiosW/jQSC3M/+3pvgobAu9YfFyETbi4Z9nKBQKhQKj0Yi8vDzU1/ORWA6HQ/NZjkKhdCyEEIRCIdTX1yMvL++waq3oFh+ff/75rJ/37NkTy5cvb3NHKB0Ix+pPpC86C6Wwa7NGcZoU56NWIRtxnhC+w0baKD7mAsFG7fZslF9nPe6SRJwXZ/WKj4k4YHYm11/LJaMYhHnhT6kK67QYEYqy/EDTXgAu0AIL4mnOR6vJwDt1okEplLq2qRnba3wYPahMamczG+GymdAYiAkuGxs/0BWqjO+q9+NymVOyT4kbFpMBB1vUxccYy8FgYDCgUw44QlCXxUW3vzmEs3sV4UBzCA9+9BOWTT03rc073+0DwIupJqMBhU5LRkFzX1MIHEdw81ndsachgHgis/i4voof2G064AWb4FQdUpF4AlVNQfQty0G3QgdqPJkHrtUtYeQ5zPCEsheKmbvuAACgqikEQojqw5I/EkdJjhVGA5N1XvuE/JfZqlPvqg/gtpE9sKXaK4VWpxJPcFjwUw0uP6lcCrlsDsXQ2ZIuoBoMDIb1KATHkazOR18kjr5lbmw84EGCIzAa1B8KRQFz00Gvpvj49c+8m4oXv9TFxzmyyryN/szH3tc7G4TK7f1x4YDMuddqvGGpuM7S+89pl4fbA8J+M8jnFRCcsKFkNXdvOI6Xl+8GKzuO9zYGWi0+Lt9Rj4OeMG48I5nc++a31mHeH87UXB9CiCQ8juxTjEGdc7F6t/o1dVd9AD2L1cVtkb+NG4S7398AALqcjy6rCVee0hnXnKohRslwG2NgExychjDAcXyuYRUcRg5xGBFgjSgysJrbwsgwYDjZvQzQvvelIlVpdvP3Ha38hqwghJntOpyPIQBMmwvOiK7G1JcO+U4LhvUoRI03jEKXBfYsxX8cFiPCMTYptIV0hhdLwirTiheGYu5NS/b7qlzw5eJZjwkp7YtJZ8G7WJDfl2aHDvExwKeSsep8KSoXH8MtQN4J2t+hUCiU44SyMn4sIwqQFArl2CAvL086P9uKbvGR8gtGkcNRw/nIyUKjxfaa4qMg1plt2QdGnFx8lH2/NUihbTk6cz7K3ImaxXCiyWI4evomDpL0hl3LBzBRHwJRFi6rzPloMSEYS/DioKMAhOyGnYmluXbE/JCcLHm+x+sBwODcviWKtnkOvoo1L8LapdB4fySOQIRF5/ykCGUwMCh2WzOKYst21MPAAE9eOQjPLfk5Y7tglMXOWj+uGtpFymuoJgI6LSZ0zXfgthE9AADdCp18HkwV9jWFkOcwI9dhRr8yd1Zx7MP1vAgYT3Co9oQVRRZEGgNRgADluTaU59kzincAH15+Ro9C7GsOJauHp0AIQXVLGG67Gf5w5rx34j5320xSmLYaB5pDOKtXEVbvbkSDP4pitzJEtSkQxZ76AK4bdgIONIcw78dq3HFuLxhShMBVuxsRiLK47KROUiGilmAszb3JcQQ7a/340yX9hDD0zNvDH2Fxygn5+HFfC+p8EZSrOEE5jqDeH8HAzrlZQ8IB4Po3v5PE33AscxqG6pZknzzhzCHKfqEir1YBozV7kmJgnS/aLkWMvhFE1Ab5vhVFx2ByeW+u3Isl2+qk/0/plq+ZK1SNxz/fBgCYtaoSAJ9HsSkQw7wfq3HV0C5Zv7tKEBqvGtoFd5zbE1sP+dASjCHGcmnuN284jgmnZxdITuycdG/pyfnIMAymnN9bs50chzGBMDHDzQjVnw3qLuRgKIxNgTDc3QwoB6spBBoNDC9OGWT9NllbGXYtFjdzACCCszGLA5WNCq58h46iKqJjT+Mem4FglIXZaFB1NXbOs+GQN4IexS44VRzRIg6LETXeeHL5bFjYZhpvv2MBfh0Zg76Q5NQc1XpeaIrOwSzHhCLyQitFDMCvp8kuvLDT2D/xML/fLQ6duSdlgiwNu6ZQKBQFDMOgU6dOKCkpQTx+eClpKBRK+2A2mw/L8ShyWOLjoEGDsHDhQnTtqt+5QDkKyB/mjWYATOZBlfwBHeAHT1p5HC0u4Ts6hEpx3owwCGptxWtR4NMbZiWKo3oGkuJ2EsVRrQIyrQ3Tluekiofhj3CKQbrLauKdj1wIsOcjQYB8C6fq2nHZzEAgzDszDCYEg0HkOvLTXJIOs5EXdKQq43wYmyjedUoRXHoWu7Cl2osrhnROW+aX22oxpGsenFYTSnNs2HZIPexufzMfGnlqt3y8cdOpuO2t9WgOxVDiTi6LEIKmYBT3XNgbuYIoVlHoQFWGsM9/r9oLsyBeFrms2JwlFyIATDqrAu+vPYADzeriY0uQ37d5Dgs65dqwriqzi2dvYwCXn1SOSJzL6Pb0CHktp48ZgPs/2IQGf0RVgFmztwkcISjJsWYU+AJRFttrfPi/y/pj00EPNld7cH4/pYNPdEae0b0Qayv5MMiqpiB6FLsU7Tbsa8EZPQolgdBpNaE5mH6cfl/ZjHAsgb6lbtR6w9hWkzmk0heJo1eJCwYDg0OesKr42BKKgU0QnNmzUCpooXYccxxRuE6zFQlq8Ecx4fQTsLcxgJZQ5nNNDH+v82e/Pmyv8eHcviVYs7cJ22t8WcXHNXua0CXfnjFMHuDDwj//iS/CE4yyyYrhohMq4pHarpcdbxcOKEVZjg3r9zVn7a+cSDyhFDgFnr12CCb+Z23G80jOF1tqcXr3Atx5Xi8AyUJWgSiLAploFo4l0BKMoTwvuzgr37/WDKG7h0uOmSAAJ3IZX9YKxCawYBkTDvo49GcYTSHQaGDAcHHAKJuf0dJ656PRknzBx0ayi4+JmP6wazF3sZYYl4FQLAGHVf2BsVOuHct21OOUE/LhyCI+2i0mhOMJYbsLIlssqB0yHAvyzwiMXudjatqXbM8fwmfiMwgbzVyVWnr+sOqroi3P6azrxaWFj4TQVek8xOfhtrj0icnBJmD+H4DLZlKXJIVCOW4wGo3tInZQKJRjh8MaIVRVVdE3Er8E5AncGUYYwGR4oE+kuBON1uwiXGplSj0FZ1oj2KXNQ2ivt+CMfABBEtkL3IiDQWnwqFVwJp7MX6mnOEEsyOeEAoB4CP4Iqwi7dliNCMZYoThMIRIJDl3c6rNy20yCM4PPXxUIBvm8iSnw80wIfTXz68bGUOONwGUzpYVHDijPQWVj+nb9z6pKbNzvwXjB/VSaY8so7rQEY7CZjXDbzCjL4QWLVMHLF2bBJgiKZI6+iiJnRvdXvS8quTqLXFY+lFwFsQr2qd0K0LXAnrGARnMoBpfNBIvJgCKXFQeaQ8oiIbL57W8KoWexE6U5mV2h+5v5yuX9O+XAajZKuR3l+CNxbNzvQVVjED2LXFi6vV41d+WOGh8IAYb3KEIgyuKvn29Pa+cL8yH7dosRd1/Au8d+rksvuLCrPoB+ZcmDKM9hVhXuvv65AUNOyENZrg1lubwTVK1vB1tCCET4qrlFLouqAAbwOSgBYEjXPMRYDi0Zig41Bvnv/2viqbjhjG6ozhL+XuMNo1OeDXkOS9YiRs3C+h1szj6o33TQi1O65aFHkVN134skOIL/+2QzJv5nbdb5iSHX5/fjj1OpgI7o9pI5n+t8EUw+qwKf33U2/nJpf/QscWFHjV9VGE7l4n9+jUufXyn159+TTsMX94zEXy7tjy75dlx7WlfV4y+VHbV+jJTl1xSvBb4UV6noCi7PzSDqqNBR+ZlyLBwCjJMPa2cz79/fnlKGOEw46E/wUbgarnTe+cgmC7MBrRf6EjHeCa/XOS86H006wq5b4+BXoTkYy1h0p6LIAU8ojo9+OCAVP1ODD7tO8Pdd8T6mJ79hLMQ7AkXBUgsuntwPxizPKoCyyA+g8/nDrJ0iRmwv5ovW+yxgcegTWKViQ3Z927D6B6B5D3Bog3ZbCoVCoVAolGOUjrEnUI4t5AIhwP+d6WFazfmY9eFf7lLQW+1aNkBr7UBKCrPK5QdsWhVeEzF+AGOyaS+PTQ271hhwKAZJOlwysSAfLm7kC/MEosp8ji55wRl7PhKEoNCqLpa6LUYwCcHlYbIhFApKOf3kOMQ8kgoHaATLttcpnIgiOTYTAtF0YeddIT/j4C55AIDuRU7UeiOqYskH6w8gEk9Iy7eajWmVkWt8/GC7RCY+lubY0BiIpYle4rxG9uHzWxa5rYKzTlnxmOMILn9xFQCgPM+GLvmOjKLSY59tRUAIzxXDU9WKrCz4qQZWsxG9Slwoy7VlrHa9vzmEUrcNNrMRxW6rakj17npeHJw9+XQpt1+1itOvORgT8q8ZAWFTHGxRtvNFWOQIzkqb2YhTK/KxpyFdfPSG4yiQVRUucFpU91llYwCnduMLuJTn2hCKJqQKuXLESt69S10odtuk4j7p8+MH1F2EsP5MlaxX725ErsOMbgUOVBQ50BRUb8cmONT7o+iUa0eeXV1AFflqKx/OfDCLi5LjCJqDMZTl2FDgtGQURwE+RFrk858OZWy3rzkEu8WIB0f3k4onAeDFCMYoiQzi8Ty0W4Ek9pzdiz+25aHgmZDniRzRuwgnFDhgMRlw4YBSMAyDshwbarLkEwX49feG4yjNSR4bcuejnFpvBBaTQQrbz8ZFA0oVqRzamzwLkuJjFsHObkggwZiw38sKQmX2a7OBEZ2P8vukxsu0VMScwXruNYCQCsOqL1RXcq7rCBcW570nmYN7Z50ffUpdqk1POYFP3VHvi2Z3PorVruNhxUs0TWIBwOLUL7Qpwq61XmjK8mwC2s8qgCzsWo/z0dIKl6RFv8AaD/MuSYtOp2RASNEQoPnPKBQKhUKh/HI5LPFxxIgRsNs7bqBBaSfkAiGQPTxaekAXnY863IzyypR6H/4ld2EbnY+2XN7JqOlekIlugI51sQpORoP+sGtAEGk12ssHYfGw4HxMDnadFpOs4Ew+OALkmTnVWeXYGBCOk8TSQDCkKibazUY+VE42eCVsFBsOeBSOOKm9xaSad69zvh0XDSyViov075QDl82M9VXpoaI/HUyGRDMMg0KnBU0pgtfB5jBy7GbF+ufazeA4kiZ+LN7KC17i+hW5LABJd1NWe8KSWJdrN6NLvh0HNNxvAGA2GlCSY1V18W044MHFA0rhtpl5x12GXIObDnjQQyjIUeq2qoqU+5tDKM+z44RCB/qVueG2m7GzNj3f1/9+qpG28zPXDgaQdNWJ+MJxSXwEeOG2ScUNGogq3bV5drMUIi6nMRCT8kp2K3TCZGRU+1bZGMTlg8vhsJhQ7FLfZgAf5jmyTzFcVt5dmkl8fHn5HpzTp1goOGSFNxSX8oTK+bkuAI4jqCh0SMV/1BCPiR7FTvjDccRY9fMnEGPBcQR5DgvynZasYuZPB73oJIQcP/vlzxnb7W8O4YQCB8xGA/IdlmRRHNEpJogMYt/Fas8A77wb2acYO+uy53/jOAKDgcE/rj4Jy6aei8euODGt4E9Zrg113oiiknYq/mhy/UXMRgOsZiN8kVTnYwRluTZdbsZpl/bH2zefrtmurXRyGRBgnHz6xiyiDZOIw2S2ojEsFP/R5XxMyfnY2mrX0jVWr3O+Dc5Ho5V3aGZz8APAtk+BhVMBD1+k6WBLSDX9BMBfox8Y1RdAekEaOXaLcC+JJwudKVKJZIKN8OtoceoLMVY8U2hEXkjioxD6ne15QiE+6hQUTTrzQ4qF7fSKifGw4HzUUcwGAIKC6BjWWeRHL7uXJquXUygUCoVCoXQwhyU+Lly4EJ06Za9iSjkGSLBK8dGUxaWXEIQfufio9fAvhV1r5WeShT0ZDPxAr61h13rCrOT90yU+RmVVNnVUw0wbJGmF2cVk1U1DacKQwyKGXUcAsx0xWOA2qm/7XDMDjhDJxeH1+1Vz79nNRkTiHL9fDXzOx0iYD50de3J6XkdJrEwhFEvg9IoC6X+jgUF5rrrzzW4x4i+X9pf+V3PbNQWjCtcjwAtjAJICnzBQFR2JRS6L8Jv/XuqyxWW8MOFkMAyDrvkObFHJDSmGZr98/SnStBK3Lc2tyHEEmw96Mbgr71LMtZsRiLBIpIg6CY7gm10NuOREvvpXzxIXflYRkQ62hNG1gN9HvEPNmibKEUKwtdorhc2efEI+ynJtaQVW/BEWOXJRUSwslNKvQEpof66KgJrgCDyhmOSctZh4ITBVMOY4gs3VXpzUmd8exe7M4qNXEEcZhkGhS91t2RKMgeMI+nfihQMxBF+t7cpdDehfnoNCl5Vf1wwi8N8WbgcAPDpmIAAktwmXAHw1UjuPkPMz32HOKMiKfdle48O00cnjWU0cBXiB+AQhJ2SRyyILuw4BrmJJqBGFzjy70qlc5LKgKYNIK/L2mipwHJHOATXKcmyIJ7isgurs1VUAgHyHsg9um0kq2CNS7QmnFSjKRkeFXANArpmgV+dSuOzW7OGtiRhMFhtijAUGBprXcqOBgYGTVVkGtO99acuM63/RJX5usuqrvCyFaOuct1+osN5SBQA45Imk5feVI56DoWhmUTMZdh0G7Hn8/USPcCYKc2aHzoIzseR+0BIJRbHRKuZ8zNI2Nexabw5osx6XpGzf6xFYxbBrvQVqwh7+d3sKhaFmYNGfgO9fb795UigUCoVCoWShVeJjLBbDBx98gHvvvRcTJkzAhAkTcO+99+LDDz9ELNb6JOiUI4T8YR6Q8v5lbAukuBk1qk3qFeC4OB9+KFbH1OM+UFseY0yGtunN2yS11wohFwZ3RrO260XMo6i7vSCEmh3gYqE0YchpNSEYFV2KFkRhgdOQQXy0cOAIAKMFCYMFXn8A/TqlOxmtCucjvx3CEX6wU6wiYNjNRrAJohBY4gkOLcFYWsXlIpcVjX7l9g9EWYRjCXQpSIoVBSrOx0A0AZdNmV9MdPJ5QnGguRJ47WygdjP8ERbn9SuRRA2b2Qin1ZTm9PMIeRBPFMSxiiLe6ZPqGhTFLXmOzBKVKt/+KItglJUcQ2IBmdScePX+CNgEkYq9DOiUg+01/jSR6kBzCF3ykwVL1LZLUHCd/nXcidK0fEe6M88fiStco2qhyEFBZE1tlyo+esNxEKLcHgWu9L5VNQURiLA4SQgZL8kQXi7OUxRHC5zWZKEe3yHgi2lAPCwJiKKoLS4/VdAkhGDB5hqcK+Qn5MXC9PB8APhRyDUpinNi/kdsfA9463JJLBGn59rNyHdmDuM+IOTy7F3qwj+uPgkAVB2hAO8KPaGQ37+FLpl4GwsBzhJp2a+u2AMAaVXs1YRhOU8s2Ia31/DpD7KJgWLhnGwV4T/dWA2AF1/luK0mKR2ByL6mELoVZi60cyRhuDgGV5TCaNVwmCVisFisYGHiXY06xEeGi6dUu25l2LUkJuoMu04IL6P0pOyQh13rmbcYphtsRCSegC8cR2lOZvFRLKSUes7L4cOuWZBYkBcSLU4gqsP5KM+dqKvgTCueKcR7ttheT4i23tyZ8gJCugrOWPW9hAQE8dHBh17rzREJtK/z0cdfAxDSTvVAoVAoFAqF0h7oFh93796N/v37Y+LEidiwYQM4jgPHcdiwYQNuuukmDBw4ELt37+7IvlLaCid7mAeyP3in5XzU8/Avr0yZzXmQEv6tJ+l7Kq11gIh5HHUNTlJDyPWEXctcn5rtk0npo2F+0JYmPsZYECGXZAwmOIyZcj4Swflohi9ugInE1MOozUZEYzE+RF0QHyPhEAwGRrFsqb2FvyREZO5HUeRLFR/znRZ4wsL+i0cALiEJeGWygW6h04LmlFx+gQgLd0plbpuZzw/pC8eBhh38xPodaJG58qT1V3FoeUIxRRhpz2InbGYj9jUpB3fyStcixe50F6JfCD8V+5mb6swUONgShsHASOvco9iJaDyR5uDb3xxSVEsudFrTBFQxVLdvqbJITKozr8EfVVTTzrWb4UvZHqJIqnA+CsKdHNFtV+BKbg+XmAJAxt6GIPIcZpQI61mkss0Avhr2pgMeaf94QjG8J+QMRdVqYNeXQO0WaTvKc1e6bKa0efrCLAIRFqd043PTleTYwCZI2vZlExwsJgP+OvZE2FNzjTbs5H838/kbP1h/AABgEkKk9zeFVB2N66qaUZ5rh9VkxGkVBXBYjUmhsn47sO5NAMDaymbsqQ9gkCB8F7msyRyikvORFx9FN26qQzBH5ZgWCccSWLqdD738w7k9YclSTdpmNiLPYVbNYQokczpeeUrn9D7YzWlh1/ubgzihQD1k94gjiXAaocqJOCxWG+Iw6wq7NjAMGMKm5HzUcU1X65tUgEzr3hSRCWE6XnSZrMnq2VrtxXDoUJMkKGZzyxoNDG44oxv+dEnfjG3sFiMIATixyrjZnrXoT7LvsrzLesRcuQismfMxmtzmBlPr0r5kmy+XAIiY1kRHv6UQbR37EuCvCRaH4HzUWbSHMeprq5dgA/9bzz6kKAk2ARHf0e4FhUKhUCi/OHSLj3/4wx8waNAg1NXVYcWKFZg7dy7mzp2LFStWoK6uDgMHDsSdd97ZkX2ltJVEXH8uK3lRGLGtlptAd9h1qgOzDc5HMTROyhmp072gp8CNOIAAsoemS31hk9tVT3ECMceX2YFomB9EKHI+WvkCI4Tl+xwjRtgMGXI+mgif081oQUvMgDIHVCuV2sxGxEVXsiDCRsIhFLksMBjSwyNtZt6VGo4leLHEXyc50QqdysGr02JEUBSo5t0GzLsNdd4IrGajQhgrUAm79Ufiac5HAMi1m3hRSnLu1KM5qC4+phbGaQnGFE4uhmGEYiLKZcsrXUvrYjVJrkMRUaQR+yk6+VJDfg+28Lkcxdx74naSr3OUTaDWF5HCcgHeoSZWEhZpDERhMDAKYVRNMNzfHEKPoqQg5LAIxSBkiEKWvMptnsOc1v+mYAxmo0EhBktV0gGpqNPBlhA658vFUwsCEVYhVANAnSB6XS0U8rl+2AnCbEjSbeOrhjfMHwPynIVlObY0B6oYXi+KJ53z7Ko5KWt9EcRYDj1LXFL/JCeXeF0LNgIAvpMVdhHTFYiuQjnba/wY0btI+p93oQrzWjId+O5VgI1i/sZqWEwGKXy1h1i5PSEUe3IUAYkYIlF+XcRcnnKcFlMy3+ner4HP75U+E4sVLbhrBK45tWvad1MpzUlPIyCy8ucGuG0m3DayR9pnrhTnI5vg0BSIZQ3ZPaIkYslQZQ2hyWKxIA4TH3atISIaDQCjWu26tTkfrfq/y8rXRW/uYh0OfiDpSIx4pRcaagXJ5Nx8dnf0KU1/gSUi3l8SUcG1p1dMFAVCk03f9pQ/U+ipdm2SbXPNtC8ML1Rq7R9xPnpdkmxMSGuic5vEw7yArif8G+BFR1eJTpdkmH8xooUYwi2GdFP0895VwOJpR7sXFAqFQqH84tAtPq5evRp//etfkZOTk/ZZTk4OHn/8caxcubJdO0dpJ+ThwUB2V5/oOhAdMZpFZFLdgtlckqm5J1vpLJGWJ3OA6A27NloAaFQ9TQ330swJFdO/7mJ7wR0Rj/Cik7zatdNiAggBJ4iPEWKCPYPz0WVOhl03RRiUu9RPZZvZADYeSfbRZEUsGs7oghEHmOF4Alj2BDD7UjQEIshzmNPcVlKYOADUbwNqNvHFKXKsCkdVnj09nNQXiStEMZFcMSxYdGWEmtGcIioCgMtqTnOJtYTiCtFOnF+qk6s5GE3Ldee0GtMK7QQiLAwGBnZBkDUZDXDZTGnrUueLopPM6Sm67lpk4mNlYxAgQHdZ0YfOeXZUe5SD2sZAFAVOi0KQSxVG2QSHxkAUpTJByGE1IRpPKPJRBqIsjAZGEd6b5zDDF44ripE0B2PId5oV+8wpdz6+fx2w9RPU+qIKESpfEDNSt0eDPwqr2ShVuhbD0YOxBBDixT+EmuANxxUiNcCLZrXeaNr8zEaDJP5aTAZ0zrOnhRXX+SIwGBjp2M6X5xqNePjf4RZsPMD//fhYPrS9tyBWGlVSFbaElOkGFLk1xfPdV43mQAw3Da+A2chv6/I8O+p8EXBiTjwHH1p+sKFFsU3kuG0y4e+7V4HKb6Qwy4MtIRS7rVkLgshxWU3JFwMpLN1Rj3P6lsBqSp+X22aCX/Y9MTxdS7hqMw0/A/vW6G8vf5mkUVzEYLIADANGhxvNaDDAwKU6H82trHYtjwLQ6ZZrVQiwPOxaS3wUhPlYEHW+CPKdlqxuWT2IlbC5eJjvh978hlLqE50vGxXVrjX2nRgJAWhvc3F7M4y+toB+x6Z0b9e5jvEwn0tSz74HhNQNRfryQ/74DjD3huQxkAnxc1pwpnVwCX7b7f+uwxaxpdqLe+duTHux15HM31CND4WIgEz8uL8F//fJ5qzFzCi/cH7+kv+hUCiUDkL302heXh6qqqoyfl5VVYW8vLx26BKl3ZE7CYDsD/RqbTULzrTy4V9ETy6lTP3Tm/ORE4RXhtF+0JcPZFqThwsQcj7qF0LjUT5U1yETE5xWEwzgeIeY0YQoMcEGdQHBZSYghCDBmOCNG5FvVXdI2s1GsDHRxcEX3olFwqqVscX2gCA+Vn4DAGhpbkoLuQYEl1SUVezz1NBigHcOSqJKLAh4q1HVFEoTngCZ+Cg4d0i4RQi7Vi6fdz4qt403HEdeikiZYzfBF1a2q/VGFWHh/HqnizX+KAuX1aQQ5dRciC2hmCTEJduZFKLn5oNedC1wIFfWvxy7Gf6IUghsCMTScnE6LUZFCHRDIApClKHtTuE4EovpAHzYtduW2n8LCIFCuG0KxlCYukwhBQDCLUDjz8APb6ExEJWK/gDJAkGpIeH1/iiK3UkBWhGuLopxoeYM4qMV9f50QbbIbVGsR7HbmlZwqM4XRbHLKgm3ZTnWpEApunvCLfhxXwtyHWac1Yt3NDIMg9O7FyAcTz+HmoLKfVvgMCcFTfG6GG5J2zalOVYkOIJmrxCaZ8sDAFQ38MtWO/adVhPiCQ5RNpHMixvgQ633peQL1UIqXpVCczCGDftbcH6/EtXvuW1mKd0AAOyu48/DDhMfF9wHfDZFf3vxGq31UoxjQYRrOWPWvs8YGQYGwqZECOjM3yeSFgWg5ZYTckQaLfqLn+hx8AN82LXJDsSCqPenX+/agih8k7jQb7NdnzgrFpzR6/JT5FLWeqEZS2mrN0pDK6JDVpxGV8EZWX5IPceMWAHcpGPeAC86yvLGZqWlkv8tpJjIOk8AiGmIlBQlh+kUTXBENV+xnK+21WHTAY9qwTw5O2p9mkIgIYR/+anBC0t34dUVe7LOb9HmGqzZ04Qvt9Vpzq81fLu7kb/vtQPxBJexKBxFB4unUVcvhULpUHSLj7feeituuukm/POf/8RPP/2Euro61NXV4aeffsI///lPTJo0CbfffntH9pXSVjg1x2GGB2S56wDQdn+IORXF+WbNoxRPCWtrZUJ/ef8MJvBORh2hbQpBUac4atJoCwhOTp2DJGn+fOhzPB5NE7acViPMYMERgIUJUWKE1aD+QOY08g9XAdYAP2uE06AuUtrMRrBxeaJ9G+KxCEpy1J2PVsEdE44l+OMGQHX1AVTIHHsikrtKCGUFgEMNTYrQYr4dH8JLCAG+mQl29hg0+iLoXZru/pLER2FgxAabwSYICpypzsf0whgtoViaozHHlu58POQJp1UGd1rTw5ZTCwKJ/UstOOMJxlWcmSYEZJVjt1R7cWLnnLQ2IFBUF2/0K0UsgHejyvt2yMM7/OTVwp2Cg1bejneMKucl9lPK1QmgORBDgYoTNBBN8LmtAIBhBIEtuUyHxQiDgcEmwUkoUu+PKPomiY+huCwc1IP5G6rTBF9e/FJOawxE0wTZEnd6eHatNyIVWwH4IhoHW4QBdsQr/a72hHFmz8KU5ZoUohvAh8r7w3HFtuGdj0I7Ia9eIsgL5EWydRbFeo9fWF8bnwuypsmDbgXqIqLoBA5GE0lHmXBuHWgOoXtRK8RHqynNyQsAV7/6LQiBVLE8FVdK3kkxdNtpTU+R0C74hQrkcZ0voeQinIbz0WHnjwWTWVsQMhrA53yU35/0vFCSk3qv0RQUo0LBmVaEXetx8AO8M8tdBsSC8EXiipcebUV8MUWkF4DWVuR81OnwlNrLnyl0RitovfyU7x/dzkcd+SGlPlv1i4miIKsnXQvAX2tcxfx1QUO4korSyO7LqsRDgMXFz5OjYo1uojJBkEteY+t8Eby0bJemsHjRs1/j8c+zh8WLqWK2ZyhuBvDRFHe8+yPmargVV+5qxC2z10lFxtSQC3+HvJnPaTEC47u92kWKar0RzF5dqdnOF4njoflb8MG67Ouhl9vfXo/fvfNDu8zruEPr2kKhUFpP1A/Mvhw4tOFo9+SYQbf4OGPGDDz44IN4+umnMWTIEJSXl6O8vBxDhgzB008/jQcffBDTp0/vwK5S2kwilp7zMavz0aJsq7vadSvaAq13lsj7JzkZ9YRDyXNCtWJdsrUlRFnIR4/4KBXLsYGNhtOELYvRAKuBBUcIIsQIFiZYmQzio4kfLPjjDHysAfYMIqXVbECClQ+kLEjEIqqVrgHAYGD4Ctkx3n1FAByoPigV+5Aj5QWM8u4uAuDAwYPomRJS6rSawHEEkTgHHFiLBEeQCx8GlqcLIJL4GAsCJhuiIV68KU5xarpUnI982HWq8zFdLDzkCaNTnnJ+vMDH8gOHym+Agz8gIDgf5eQ50qtFe8KxNCcbL44m2+1rDqFXSep24QfzcgGuxhtGWa5SGLVbjAqB8mBLCKU5VpiMspyVQri8fF676gMKMQ5ICoFyt2KtL5LmbHVYTAjHWEm0I1wCdT6lg4phGJzRowBVTYKrwnsQ4BJo8EcV4qPNbITFZOBFYLEQRiyIlmC6o1Vy08rwqITT5znMaeJzrU8pPnbJt+NAizCQivr4PHXxMOp8kTQnmMtqTluuWKhI7uSVcogSIrmQAp56EKKsHm8388JsOCyIn1Y+l159szdNnJf6IFwP/PLtJISppwq/WjjM6s5HgN8favlegfSiN/X+iOq53+6IYfFaSPkDtXMBnty9BC9ddwoMOnIqGgyMEHadmvOxrc5HHSIUK1sXkuBfZmWcd1S/g59L8MKSuxSIBeBXeYnSFsxGA0xGhs9JbBCK/uh2MuoU8eTtAZ3OR3mIts6Cd5q5JFuZ81ESZHWIzoBMRNcR0k0ILxA6iwEQ7VB3MZxaqzJ2LCTME8dt0ZnNB72aYmEa8kIzMhfkfR9sxLwfq3EoQ6EvANKL0BU767Mu4kAzf99ozJC3FwCqBDfjgp9qsq7Dngb+XvL8kszCaI0s/cuu+swV7PfUB9C71IUdtbJtwMaAL/4CePYr2v571V68vWZf2kvCVN4Rci3vyCK0Lt9Rj5v+szbrfAD+pfm+ppC0bQ6XGMu1/vj4JSO/D7c2JRaFcjxRswl4Zbh2ehMAqN/Bv2jf923H9+sXQquSAD344IM4dOgQ9uzZg1WrVmHVqlXYs2cPDh06hD/96U8d1UfK4aKa81Fn2HU2gTBVgNNVcEYe0t0W52MrwqxS22sOTqLJXJJarhd5WJY0b72hcxYk4pG0ASHDMMixMCAECLMGxGGGNYOj0WXmwMGAam8MUWKG3aD+oGA3G0HYGIjYV5MNXDxdbJLjsBj5whgsX8DDFGnBKSekCxAuIc8gG+EfVqPxBBwknOZoFAU8fzQOGAxIcATlpoBU3EaOQnx0FiMa8qPAaVEV96TcdE17gIgvrdo1wIspkli48b8gNT/hkDeCzinOR4dQyTXKcnyhj09uRyCiIj7aZQVHBNTEMWeKiKYmtIn5NUU3AccRbKvxSTkI5X2TOxo37Pegf5nSRWmXwq75doQQbNzvwWkVBYp2Yt5KedGZysZAumBsMfL9j/AD2ETYi2g8ocgzCfAORH+E5R2Sb18BbP1ECrtO3R6hGMvfqF28IyvPYcboE8sU7RR5DwX8EVbK9yjfdqlO1VpvBKWy5Za4bfCH43y190QMcBQC8RDqfVGpYrd8uamOSzF3pdzVmieGXbMRvhougGiQH4jlphQ7cliMiESE643gfPQF/GnbUEQ6TyJsMrxS+N0USA+Nz4bDmr59+OlGTLu0X8bvpTpAmwKxVomerULmGtIUSkTkOXw1hCajyYoB5Tm6cgtnDLvWIyTJlql80aVDsBJzJwKtzK2cpa0oXAvnmS+snl+3LdgtpuR6tsr5qEMwFpFXu26N+Niq4nitCLs22bSduVLxIEF01hIsFO5RrXkL1xpRKNTK+6g3l2MsyOeRFP8+zjjYEsLd72/ABc98nbXd7no/Fm2uSU6IyoQ3WfVx8YXe1iyh0vJCZ6kvMUUi8QT2N4fQpcCRVqhPTo03AqfVhEOeMP7yyeaM7cSXaACwdLu66Lm/OQSr2YjBXfOkVBupRFn+xeIlJ5ah3hdN5rSu3wrsWgzs+kpqu6XaKy2rNosYy3EEH/9wEEDm7QEAb67ai4PNIaytzH5MNwWT53R7hF5f8tw3uOCZr4+fHJdyISWk7W6lUI5bDqzl7+MNO7XbSgVUNaIRjiPalIG8e/fuGD58OIYPH47u3bu3d58o7U1qIv1sQlkitW2Wh3/xAd2k13mQ4nw02VrvfFSETrVC8AO0nRdyh6iWM4ITq4K3QQg1WpAQwq5TybUQcIQgyBkQZ0ywIIOjEQkkGBMqG4OIwZIxN6TNbISZsPxYSChiY+SiGcOuAV6wjIf5B+gYy6GLg1UVK8VQzEjQz7s5OaDEzqblphPXMxBhATYGjhCUW9QHOzl2M3wRVshxVYx4JF0YA2QiFccBc64Ft/ghBCJsWvhzjihmJlhg5UzEv5qBaDyRVr1Xcg5GksdTIMqmhZumOh8JIfCEVMKubcmw63iCg1+lb6nOx6qmIELRBAZ1UTpC7fKq4uDdkakuSovJALPRwLfb/j8Ev5iBxkAUg7vmpW27HJvgBvVWAz++A3+ETXOMSuKV4Hzkwj4YSAKlaY5BQWRt2s1PaKlEdUt6WLvDIoRxxwKAqwQk6ocnHEdhSoi5QlQWCETjafvBaU139tWlOB/F74TDgkBiz0ciFkRjMJq+HhnER3nuSkAsYMQmq866ShEP8w/szpRq8y6rCdGocD0UnI++QAClGfKtSvswHEleR2NBJDiCllAsbVtlw2ExIiSG/YeagVAz2ASHUDSRVYgSw95Ft0cwxsJl1VfkptXIBQ+9edRE4cuoJxex7FquWXBGEB/T7n2tKTiT8iJO0wkfkYVSoxXCmYZbTjo2S4BYAD4V8b6tOE28C1oS5XQ5GeVh162sdq21HRWCr8bzROqzgNZ8gWS/dd3bxZycRNs1JG+vdZyI+9MhpIrQFB8FcUwrl2M8dETEx3pfBN/rCNPVQziWwCXPfYPlO7I7B/VwoDkpnGcTqm5/+wc8vXhn0sEX9fOuX0DaboQQsAn+mnmwJbMgL+YgNhoY/Fynvn+84TgIAU4sz5EKfqnREorhRCF9xvd7M4tylY1B3HleLwBKcU7OgZYQKgod6N/JjS2H1MVTMXf2kK75MBsNSaeikJcY4WQf7vpvMrywIZt7syl53LVkWVeTgR+q/vnjnzK2AfiXZSLZhNt1Vc04f+aKtJQ8cuSOx9T80h1JjTeM+z7YqCgeeMSQXwf0OLoolOMV0SUsFkfNhig+hqj4KKJLfLzvvvsQDOp/OJk2bRqam2kFvWOGRDw97Dqr8zG1KEwm8VH2gC611XJvpIZ/t6XgjE4HCCCEq+kcELKygUy2iuBiP8R2etrL+26ygYtH4VYRAVwmvop1iOXDrs3IMM9EDMRoxv7mEOKMCWaivl42sxEmCGKC0QJfnBcjM4Vdi99hQ/wAJmDKQ5ldXQAV8wn6A17A4kTUYEO5I72tuJ7+cAwItyDBEZSY1K8ndrORD/cVQsJILIgexer5JgNRVnJMJeq2KvokIomZQf4BOSasV5o4JggsEV9ykHSw2Z8Wtuyyypxh2z5FuPZnxBNcWsEZt9WEQJRvJ4Z959mVbawmPhxZFL0qG4PIdZjThDGHxYQYy0kPo40Z3GgOMW/l0hkwbv8ENiNRzS/otApi5sqZIKufgyHqTRf3BLclCXsBowUsR1BoTaQJ5pJTTrgBs8EWNAdjqs7SUCTO71d3GeKRAECAwrRCQmbeTZvggO9fB75/HYEom3auOCympLgGfvDYGIgqQtalIjyS+JiHaMgPEL4gjGK5sv0lklpEht92Qji66C5zFiMRDcBqNioqlIttJeej0QLOYEY0HMrqOnbZTAgHhYd+VykQ9aMpyBcYKnJm/l4qDnmo/vw/AB/cxJ8H4M+JTLhtJiTEFAkAwjGOd7t1BPLBjdxNlA2FYyzbvUaW59ho1rxPGBgh7DotPUlbw671ONpSnI96i7XpcfADgD0fiIfhj8RV7zVtwW0m0ossmG2tqHZt1bdNgJR9p+FQTMs5rRV2rdf5qMyTrOl8FJ8zjDoLArUmR6TYF8E9nfWY5DheQHAUaguKYhEb8e9scBxQ85PC0clxBG+u3JuWKzeVvy3cjmnzNvPX9MPk65/rEWM5zPq2Kqs48/EPB/He9/uSEwgBdi9V7EdvWFuokleblkS0iI/PpwpIwnCU5YucdC1woCZLzkR/JI7hPQvRq8SF7TXq1zzx5WZFkRNNWUQvTyiGfIcZr9xwCgBg2Y70IjD+SBwHmkPokm/HyD7FOORRP9Ya/fwzRdd8R8YwabFfBU4LuhbYk/mUxcG38AIpxnJgGGDmtYMB8Ps/E1sO+ZDvtOBPl/RFSzDzcSQeY9lemnMcwb1zNybXKcu2E6t6ZxOxA4oXvurbJBJPYObinVlFTBGt8wTgBc8/ffQTNu73oFYjXL1DiMmuA8ehG5pC0U1AuO7pcQiLz7rU+SihS3x8/vnnEQppPJzIePnll+HxeNraJ0p7k4il57LKJii2JkRbbAPwD/QkoQynyzZvvU6I1HlITksdzgu91bg5ju+7fHCS1RkhPJiI21VXwRlhECYMqNTycLnN/ANIKGFAwmCBkcskPvIujhpvBAmDFYYM+8huNsKEuBR27YkbYGXSC5EovmMxgggPks2GIuSb1dcr32GGwcAgFPADZgcijB0FpvT+Gg0Mn7cw4AVIAoQAuSrtADE8N8GHM7mKgUQc5Tkq28km5PwL1AIAWPBCU6qDL9duRiASB+flw6ZCcQ5luba0kG/RtRb18Q/wBMChmhr075RSJEbMNcnxIh+WPS4tJ3U9goI4Joa/OlQcZPIwV7XCKnzfkpWs4wkurbiJfB2CMRYgvFDZzcmq5vaT8hsmeBdqMdecdiyKeTrZaABwFoPlCLo41YRlwTEoDEBCHn77pYqPTosJ0UiQP8dcpUhE+JtxfmohIZss9HjtG8DaN+CPsNJ0+TaROx8b/OkVwB2iM1fMu2jPRzQcBMMgbTurFbppUAkft5uNYBMEsbAwcHSVgIsGFVXrpXWxmhCNhPlznmHAGiywkliauCzHYTHyAqkwb8QCeGbxzwCQJoRnwybmbQV4V6q/RnKWpBYXSu0zkBwoheOsVGik3REfyKw52uKHCBuTCVmtEeuyX5tNRgYGIqsyDuh7uZW2TPHllcYyuUQyZYn4Hb1h11rrI24Xaw7ARviwa3v7CMhuMweOEJlwpsfJKAiEWm5VqX1rnI9x2TbX8cJQ/iyg9SKSMQIGgz4HrBiRIYXQaz0LJKMgdBUmAiT3dNZQ93iID9F2lWqLB7GQ4KZklKKDGnuXAR9NBuq3SZNaQjHM+X4/5mYpFJLgCH46yDvp2sM9tr2Gv2YcbA7h0c+2qLbhOIKXl+/Gv1fKip007QEW/QnYNl+a5A3HUVHEv9is96n3TR6yXO8XxKB4CLA4hWry/Eso8drau8SF5qwiGoscuxkDy3MyVrL2heMwGBh0zefDrjPlHGwOxlHgtEjFAP+qUsSmTlivfmU5KMu1ZRRGxZzCYkE1tWX6InGA4e8R0ktdIOlaF5xA1Z4wCAG6FTgwondRpk0BAHhnTRUYAF3yHYjEEwqxVyQST8ATiuOSE8vAQD1XMQDUyMQ6h9WY1fkoPv9mc6mK4eAGA4Maj3q7rYe8WLi5Buursptt9jeFcMVLqzXb7azzo1roUzbH6P6mUEbnrJwER7Bocw1irE7hPxYAzHb+ukTFRwolM+L5oUd8FJ9vqZtYQpf4SAhBnz59UFBQoOunNS5JyhFAzdGR6WE6tTK20ZxFqEzNeygMjjMKfKkOzLYUnJGHTmlU4k5tb8ritBRFPoWLU2dYljRvnWHXJis4NpomWAF8LscERxBgASaboJmIgRgsaPBHwWQJC7ObhQrajAlgGHhiDGxMImPBCf47BrDREMAY0Qw3cg3qfWAYBm6bCbEwLz6GYUOeSb2t22ZCMMg/qIeMLuQY1PvrsBgRjrIgsSDgLEGCIygwp4eUO61GxBMcYv4GYXPwFaBTnXk5NjOfQzPIOySZiBfdi9KdlDazAQYDg5iPfzMVT3Awx7zoX+ZOWQ9BpPLwrgou5AGQLj66rCZpQCKJjyoClVz0asyQ10/M5xiOJfgHagJV95w81DbBEXS1qT8wu0UBNRoAxwF5xJMuPgpibDwSBuz5IISg0JI+MHBZhf4LzrW4vwkOq1EljNuIeFjMRVcKEg3CYkzfX1KIfjh5fPjDcbhT2onVnMVcTLW+CBgGCqeiKJqFI8mwazYcQKFLWawHEByHsYTCTdOgIgYnUw0EADCAozCj+Oi0GhGPRaTrYwwWmJl4mptSjnQ+CdsJsSBMRv5ctZj0Z0mRnI+yIiYeXwAMg6zFR8TjuDkUA1qqYAo3qa5buxATtqGzWFv8AHj3klT1OYubkUsoXyTpqFptYBiVatc63OxyUgXPrGKi0B+TTLDSLIam0+EnfmbLBeFYBMLqLnsFXAL43z1A3baszVwmosgfrC/nY7xt+RCB5Eu9TN8RhU1A+x4sf7YRnz0yzjf1JadOMVHrGUikNTkixWVL4mOWvoiDG3enpDs7E/EgYHHwYkNc45ndywsxaN4rTRLzBmfLwyd3e9VlEPhaw45aH/qU8tvh291Nqu7HajWhyFMldKhWmuQN89dit92cMRx56Y465Dst6FPqTvZfDPW3OKVB5ZJt/Eu3niWurOHDvkgcbpsJg7rkYeshn6rI5wnFkWs3o9htAZsgGXMhijmu1XJny5fHMPz9zWkxYn1VS9KxKKMxEEWR24ICpwUxllMUuJPa+KNwWkwwGphk6hYg6X4WRMi1lU1wWk0ocFpwzaldAUBVVCSEoCkQw5jB5VLkiNq6ijkjB5bnClEA6ufKLkGMe2HCySh0WhUh2KmI+2h/U+b7zttCIZxBnXOxv1m93Z6GoOZ8AF6kBIDH/rctq2P3ja+T51d9BucjxxFMmrUWd7z3Y9ZlAsCP+1vw9OKduqqTA+AFFYuLP7Y7WHxsDEQzriOFclTYvURZUCwb4j1Tz3kSC/KRC5HM+YCPN3S9Dp81a1arZ1xaWtrq71A6iLRci1lcI6mVsbM5D8Tp8jyJgDDoSg/3bFU/MtEa90Jq+2x5HNPWxQIksrylPJywa8KBYSNpAg0A5Fv5HIBPLNqD283Ztz1jNPFv4022jMu2W4wwERYJRgh9jjPowmSpqgo+pJWN8WFknqgFZcbM+8htM/OiksWBIAllFBXdNrMkAvkNuXBlEDQdFhMsiIFwCbD2QhBCkG9OXze3lV+faNAPC/ichCU5JkV+PgCS4ycc8MIJgGEjKHKkP6yLQmpEcKrFYyEUWhJpIp8Y7s0Fm2EAEOcI7BZj2gCgc75demgOCQ49m0mP8zFdmJIXphFzPxY609s5rSZpGyc4gs4Z8mo6xBySEQ84QpBHvGl5AEWXZjwWBex54AhBrjF9n7lsJkTiCXDRIAwAEhEfOufb0/aD02JCLJoUAblEHCU5TFo7URgL+flzjwDgov4056MoiIXjCTiNHBp8ERQ6laKi0cDAZjYiKjofbXlIxEIoLUl3EIrLDURYqXBMvS+Kc/qkiI/CcqMhHz9wt7iAWBAuFWeZ02pCvCUiiUsRYkahhUsTPuW4rCYphyRcJUDzXkRZDuNP75rxO2rYzUa+envUJ3lF5q3+CYRYsr54cNvMcNtMqPNF0W/xVbjd78RB8wetWrZuYkE+dNfq0ud85FgARBDsslz71dKBaDjXTAYGRrlgCbS+2jUrK1hmsmYXVOV9lHI+6qzUrOU4FO8FtlwQQmDiYtoFZ7wHgKqVgKMAKH0kYzM+LQhJhiPruX+zQkiyuG3keRrVkL8sFfdHpu+k3t/1hl2Lv9kofwym9SE1P2SUFwiZDOeOKLDq2ZdSX6zCsoUckab0azrfVtjGJpv2OkriYylQq+4MlIhHALND3370CQVXxPx+4MUv/nfm5x4xTyAYQUTKchmLJziMe2U1/n7lSVIuQzn+SBy76gP46xUn4tONh7CuqhneMO/+k7NTVjU5HEvwL+9U+t8YiCHXbkaR06IqVFU1BvHR+oModFlQmmNNFk4RjzmZQLNqN//SsjTHqhQfE8I1y2gGIQRbD/lwYudcdM6zIxxLwBdmFYXKgGSYcpd8B8DwxWBSC9ptr/GhsjEo5ZG+8pTOmPdjddo6/Fzrh8XEpwTpJjgkN+z3KPJyb6n2YmetH2MGl0vPpC2huPTcIfL3RTukv3nnoyg+Cs8ZwrG3rykk5UoWXf71vihOKFSOCURx75w+xZIbvyWkjAxIcAR3v78BFpMB/Tq5JTE2zyG8aCAcL54L63FqRT5O7JyLQpclY9h1giNYX9WCrgWOjKKimCJg4pkV8IXjGdvtFcTHt9fsw1VDu2R8yfPe93wl8GCUxc5aP18ILQVvKI6NBzwYd3JnbD3kQ10G5+M2IVyf4wiaAtGsheh2CG2zpQJQEAvyx3UinvZCYn1VM3bU+nHDGd2yzmJPQwDvfbcfD1/eP+35Ts61r62B1WzEortH6OsbhdKR+GuBRQ8Cp98ODPuddnvx+U6P+BgP8S8EG3bwL3oNHfRC/xeELvFx4sSJHd0PSkchuUDkjo4s4U9qYddiKHXqCaPm/gOyC5vtEXatcIDoCYeSJ+rX6+LUCKPmhIdq0Smjw12THCgRkIR6BdJCO3+zTsDIOx8zOUsEIZdlCYzWzAMSi8kAmyEBluH76YsbYGK4rBdAm9kINsg7tnycBXaSebDjtpmQiAYBlwMBYoU7g6jotpoQEd6M+xk3HFCfp8NihAVxcAQIGniHQ54pXSwVxahoyAc3gHgigV556W+UxW0cEfLocRnETIAXfiLhEGB1IR6KoouTVRfHCBANtsAOgAm3IM+Vvh9z7WbEWA6ReALhWAJWs1FV9JFXWW4MxFRdmQ5Z2HVDgA/XV3M7OCxGJAQnZoIjKLao7wuXzYQDzSEgFgJHCFyGCKwprjrRgRiPRQCHGwnCIFdlu4nFSOIRP6y2XBBPUBH6LOK0mhAPCMeyLZfP+2lP3x5WE+9AjQb49SCEwJnwpbn1xG0SjEThnHsZ8ouuQq5juMpyhcrtAO/gjIVQppI3SlzfYIwfDAaiLA55wmnFjsRQ7ljYLzgEHEA8BEdu+q3UZTEhFotI18cIMaHQnt315bCYEI/wrmPYcoFYEHW+iGb4Wip2sXp7yA9xb3S2RmAxqQgtKeQ5LPAITqC8RCOaW+N8ZGPA9s+AgeO0H7DEas9muz7xUSHYZXuBllIMTEe1awMABgmVnI+HUXAmW7VhKW2HWRaqqzNkWCscnI1IoekcASyIZXW7AgBahPx4GuvrNHG8Sc9k5febrhyOKq7AbOJjqqCY7TtsDLAL10yTNbuzIFXAFaepiY+pERNi20z9lnI46g27loWiA/x2zCQ+srLjPlv0BpB8XnAWaw+KxDyVenJxitXoZTmrvhccj42BKB8poPJSRRSoepe4kmHLKizaXINZ31YhFE1gxc56VfFxS7UPZoMBp1YUYGDnXIx7eTWag1GF+BiKsfjbwu28OBaOoyUUg91iTysOUNkYxJJt/8/en4ddcxR0wvCvuquX02e512fNk+TJSgiBBAKEgIIoi+Koo+Mrjjggis7L4Dt8IOqHODKL4/YCg+8MjgO8KJ/OpSggMgOiEkBBWUPYQkL2J89+r2ftvbu+P6qqTy9Vfc6NzOgMT11XriT3XXd1dfVW9avfchG3Xb2uBao+KySyb/g/bsZ/++K5ue+fZK3aXsEuXelYeOFTLseaZ2MUJMhyxj2AP/x6YOd+4EV/hDvu2cI0TJGXPIcvTsIK+Fhm9bmWKRQUzffCrwogULL7b79mE++96yzynFXmGW/96zmT7pnXH8KxVbcRriOZq4f6zhx8rPk219l6A5cW8mDEPv9WiXe4H2d4vLh+610bIJxJXwcf7z43ws2Xr+LkZheMMdjUaPg+PrrnYxKm+PUffEIhlR76Anz8y18C9h4GXsQ3x754ZoRnXn8IAN+c1cmuJQvw9ms28EefPY0wyRrzqdPi3L77Ccdwxz0X8SWFRP6Oey7iL+6eM2nvuzjFrVeuNerde2GMcyU27rlhoAQf//ZB/my9+Okn8R/+8j6t7Poj927h+GoH54YBdmdqtQzA7/Hf+ZtHALRvEEzCBD1HbNzHUwE+po33x6/92b3Ym3Gmqkq1JcvPvftL2J/FMAjwuu9WA5Dy2kQKRuw3orznzjO4fN3DU69a/4a1+YXTQwxciqsVAZjlEqUZHAXR4FL5B172hE3H5Pxy9ROfqxGWmbvGPicTbN/LnzG3+X37ZitfV9r1pfK/UClAsiVTPBtp1y2T6TpgZy6YeKsCZ/5OsuslvJiWNepvnMsCLyYl87FZ/z13nsG3v+FjnN0mpFzMtGHksTL44fIViwOFhMC02kFiIhYrht0OwvasvAAfR7EBk5DWce9YJrKEL/YmmQMn1++aDlyKNOK7pdPMQk/LfKQIBANtRAZaQLPr0CKde5TzyWrfavrVyKToyB8D7irynOGa1ebrzLV4qEvkj4HOOnLGsK7xsBx0LEShD5g2fLg46qpkxkJ2O+GLMSOeYq3TnGjIydk4TODHmVa6OnDnDIKdSdQIYAE4IEcIZ3FIzyhV8WwTuc/7NUUH6xqQlYerZEA8Q54zrJpxY4IoQcAkCgDqIiIu+kazPSlDTsMp0D0MM/WxqmABeraJWCY/F+BjE4gjhKBrm0iETD7LgQGbNjxKC4/OvbNANMHGhb/GqmJcug7l1xQAOqsgiY/DLdJ2KZG/5/wYhKCRKu5ZEmydCQDGg5H4xf1YP3YWz2Whfk6xbrd7L/VdwRA1bcDywJIQF0YhjpWCdJYpkrUSzeYSEn86wg8/ZTGDcs2zMB3z8WdsPjZLlYf/GvjYrwLnv7C4bhoJ9ld3Odm1fMcVIMwC8LHMclsAqlEinvWG5+MBvk8HAQjL3w9Clqu/tOejALKsDvKcwWZLgI8yqVaCTJrSNXMRXrZE6E/R96QKzC1rTwKUgD/NdzAvMQYXSt01gKKqVIJslpBSSzCxqLuI+Zgs7xEprz0hi1mK8r4ogUGtdQvp97zN03t+wbAvipRwl4Kh3v25M8V/66TG4yCBY5m4fM2bMwcB/ryLZz5MMvzff/417AiwZRyqlRmv+5MvI8ly2NTAwOXy37q/4n0XeT9/5nnXAyiBLhJIEey8T9zPQcgozbHZc5Tg45n9AM+8/hCu3OgWXogAxLV2uOejGLcL4whHBi7/TrGSfPi+DwF7DwKMFey5265aR9+14DlmI8hEnvt//tFbAVSVEeWS5fw78sQr1op6YKj4IOcKee+qZxffOFkcywAI8OQr1+BQE45lziXVosjr+7svfSqA6rwFyQzwNgv59SxKi3mSaRD0XatgycrCGMMDW1M8/3E8uIcQglWvWW/oxwABnnTF2tySRd6bD3y4GNu/fWAHD25NcfMJvqjXXVOAM3APDxz8wJMuA6D2VvzovVvwHBMbXRuH+y62FHXe9nEO7L71xbdirWtrPRjf+/mzWOva+ItXPRNPvWpdmyb+Nw/u4Pk3HRWSewfbCrDej1O8766zeP5NR2EapBVU/LU/4wzay9Y6SgAb4IDa9/2nv8Ed9whGcCG79irgY5azAjB85R/epby3AD7f3Rf1PnLvFh7RyNHL10Ylyf+7lrd89AH8f9/zpW9IyBXA+/jqd30BL3vn51rr/e2DO/iuN38cn3ywXeb+Nw/s4Pve8jffkL5dKt+gIjeo2jaNyyWeLbfJB4gMAxFStqys+3/zcgl8/N+9FIucmtdia+BMaaFfyIgUH/IG83HB4uIbIruup1u2sRNliEx50bZkeM6ihNQlZNdBnOEtH30AAHD/1rQ4/5hZ2gXhYzYd5AIoNtrYl1nMfw+AWu3y8x5lSCHBRwLDQOu4d2wDWRyBmTZmzIbVCj5aYJEPWB1MM4qOoV449F0LsQCBhujCZeo2PduEhQQ5YxjlDkAIuoo2HWrCMg2kwQToHWllNA46FpJgxsNBcmC1hZ2ZxBx0nTEXh5zmceU1i6d7ADGRMYbDjj7hexyk8BM9+Nh3rWIyvV9jcchCCOHpznEGP0q1bXk2RRbxxdceWUOP6AHeMAyBLMLU6CvTzAkhuHLdw8z3AdNBABd9hexago9ZyAOCspzhsKsCiynSWDIfB0hzhmM9tSTHsylSfwiYDsYx4CJs7LRLWbj0/EyyXGlj0Hd5wjSIATh9sDzD0V7zsydBRSmRv/vsCFcf6jUYEYYIT0oiYXdAXSCLlABdz6FIk6h4R0xTirUF4GPPoYijqPACTCIfWc6UbNK2Iu8RGYzDLA+7u7u44ViTcVEvq56NcLzDGW6MoWMu8Ogrl7GQ/kkmXVuR73LBHl1cvwQqtvkFK2XX7cxHC5lIcVbIrhd5FJaPuwzLXtnHBYy2CnC2ICG5SNF2kTOGDkka3qqNIt4biybeXZohl2nXdIm06zxvBussBOZKG6CLNjTTuDaGi0C8Wrttm5Hldtv6IPtBHa4wIeYC78ls7j+5DFiZRbU5zKK6wo+wzfOxuC5Opc0vnh7iJe/4DL7nP34Cf3Xf9ry+lHOLNiVg8M+fdTUAaJOKx2GCgUtxYq2DU2Xp6nt/EvgTLm0r+/z9yG1XaFOg5e8B/n1a79qNNOjTez6ODFx863WcAfdv/tvd8/4Ts+i/ZIT9/HfegI2eg10FS24UJMV3Z73MpJP2CoKFGqc5zg0DXLHhFd6Fe3UPyXiKi5MQz73xSOFZeXTgNpKlJSgjmZE9h/JgPUXf/vX3Pq747hS2ISXQWHpy/r8/9pTiZ145iEyUvWmM26/eKDYgVzq04b0oAbrNPj+/QacEisY+X4SnIZBnha+lLKsdqwGSTaIUQz/B1YfmSo81z26AZMOAK4RMg8CmBmxqNJOl4yn+8h7uuXmj+L5t9hwlYAhwUPnawz1sdB2YBlEmWf/+p04hy/l9dqjvYBIkDZAsTnP8wgsei2sP93HbVet4YEv9vN1zfowX334lqGngyMBVAoaMMdxzflKAp0cGrtIj9QNfOg/PMfFDTz6BNU/P7mSM4cIoxGtf8FjcdtU6PqnxfJQBOKelD2jC5/KwquCjvNbf8djDeHTXL8a7XvaEfcG7X/50rLcAsuX7qy2V/Ospf/qFuf2A7h5oLZ9+K/DxN1V+9PlT8025tvCeOx/h9b50ZqitM/Rj/Kv3fUV5T9XL2WGA//yxB7U+p7LkOcN77jyDh3f0QNjuNMI7PvGwFjj+pi8yOGuZABmAPyvLgo+xD/QF+LjIi/mbpPy9g49vectbcPLkSbiui9tuuw2f+cxnWusPh0O84hWvwLFjx+A4Dq6//np88IMf/J/U2/8FS7FgK4N+LQsdlexa/nxR2wvBx7rs2l3MXFS2cVB2SVl+1xKGA1T9K1tlcJJRamr78u475ymQp3dnRd/9nANsqgWhTTJcvrlS6oNmwZOnc/DRdvliRpMy7pkZEpEGPUpNGAuYj33XQhqHyAwLMSxYTF930LGQJSFy04WfW3A0dfsuRRyGADExZp6WTelQAzZJwBiwH5tgxNQmefdcylOTezyYZsNSn//AtZCGE7DuYe5xqPGw5H30AepinDtK5mDXpgABEn8IDI4jzRgu7zYnIz2H1xuHCYJYDxj2XYpxmIK968X4geDdSgadrDcJU/hxVrD+Gn1zTKRRABATQ+Zppe3cV1CkdJI1bGhA242ezWXX1EYAG12iAB9FX/J4VoDAGwq5t2ebSOOQg4B2D0mW4+Sq+ly7DkXijwF3gNzqosPCBghomwZMgyCd8omCEY+VUqBBycczNxwOenaboCc1+cJGskLOKiTX5XPm4zJfuKsSpHvu3DsVACaZiRUFi7dcPIciSaRstsMl2AAOK6TibUUuSmN/AlgdBEYXdh7ghlqAkqqsehay6Q5yHi2CXn6AhD4hayyYdG1FAkeWtxjEAubvV9NpZz7Ww8OWSCs2ibgulcCZkt/gMuVA36a6zceCjbQDy65tAT4CK3be6r0FYD4hlrv/muKZEqSVct0F3++8dJ7LsPzKoUJA6W/a5hSiziKmqgpQbJ2riHYXAYSMVT0iFwXfKMOGFrAZaakvrXUlMLYA/C6uC620+YXTw6JKJY05nnJ2mwCpH9zm/37B44/BsUxtYMtnHt7HOExx02UruPf8GFEqvtHb9xbJ2ZJV94c/9TTkjOHsflDpB4BCKlyWUa53bR6MVSrnhgFOrHGW+KpnzUHFeMZ9MKNJAV694YduxpGBi82e2vORB7rw55MDYyJ5upDYc+bjJx/aRZ4zXL3ZRd+hMAzCQbTyfMzfw8VRWPEzPDJwG4EbO5MI1CTFt6wcSFc0Fafwo6ofdeELHc2PKYHZcsCZZ5sN5mNdurvSsRrg49YkQtehxXEGroWR9PNMAqC7AQAYjUe4/+K08i1e9UrthWMgnhXM2rL10FrXbjBoR35SaavvCjC2/Az4u7g4DvHSb7mq8FM+uenhzH7QkJgDXMp9Ys2DaRBcf7SPux6tsr0lIPymH7oZwPzbW2ZIzgR4emJ9fq/V2aIAB+nPDoMikVw1tgBw56l97M9i3HhMytWbLFAAuO/iBLdcvgqHmhh0LNx5Sv2dHfoJJmFa2PhMgkQZ7hIJIK3oUxrNN1VLYyxB4X/xbdfiqS1AqwS/+y7Fob6efbo/myu/2oKBAO69+qNv/zT+88cebK0ny29++P7iv9sSzwG+WfGcN/1VFQT8zH8BvvBfK/Xuuzgp7H7vvaDfHJFBTfL9qCpy4wOA1ksU4AFK/+ztn8Yff+50ZVNBVe46PcRbPvoAPvClc9o67/n8Gfz+p06pA7n+AZQ8Z98QFuy5YYBvf8PHlkqDr5SC+bgE+JjGfM3d3Vze87G7ydc/lxKvAfw9g4/vete78OpXvxqvf/3r8fnPfx4333wznv/852Nra0tZP45jPPe5z8UjjzyCd7/73fja176Gt73tbbjsssv+J/f8f6EiZdcNubNOxqRIxgY04GPMd5MNcRuZCybSjbTrxXK45jFrvloHYpe0MT4Vi8FlpVDyGLUxnUZZYYA9ngVF+9PUhMky9G3F45cloBbvb2bQVtl1BXwsn2+teDRHwsTOeGLwhWjLuA9cC0kcIiU2YuLAzNvBxzyJOFBJLFAN+NhzKeI4AKiDaUZhQX1ehBD0LYacMUxSgszQMz26DkUWToHOGhJmYEWTtL3SsZCHU8RWHyFsrGm8EAcCdIVpY5TZWKfNMZKJ2mkwLUDPyxTgo2kQ9B2KcSBl12rAsO9SjP0Y7OJX8F3Jh7XJlauehX0/xixKC7ZhvfQcC3EYgJkWZrkDT8Mu7TlzhuTQXEOPqOsNXAu5AMICZsFTyK5lqAuLOPjIQWA1aJvFvK2Y2MhzhiMa/8OeYyIPx4DdQ0hc3Hykeb6EEHgORSbARyceqdPjXYoo4gw7n1GAMaw56uN2bMEK+ezb0Tv/t7hsVc029ByzuE8YdZEnoTJ9/MjARRiFBZN5nFL0Ff6l5dJ35DhxsCaNA6x12xNNleci5eHCmzKAg1UaN4ILVGXNs5H4w4Lw52EJVqIscuK2QL4LoLCh4ODjN5L5qLLQWCC7ZimHWivg45L+fcVxy6DZolAYFXN+kex6SaCyWEA6fLNFsylTKRJ8XLAr3zEyZCB80412uMdgGyujfJ4F0NbC3GM5ZEBH8XdAy3c7Ovg3W9Yt909Z16rWXaiaKLNelwGelwQf68zHZeT5tMPr5fNv04Pb0zmgUVgYSNk1vybv/NtH8PRrN/C9txyvsvLiGWduiPvjaxcmuGytg75rYb1rKQEExhg+9rUtREmGKze6YEyEzpSvZZ7hX/w+T+091HfwXTcdA4AGuCGBHSUQVSqTMC3q/ItvuxYDycCLpyIBfIavnN6HY5mFL+FG11GCp2Xm40aPp0DP4mx+b4iNp18X/ourHg/zWpeMtPKz5O/iwrgKPl621mmAD9tTbr0iNwt6DsWkBjzsTPj4bZYAQ/m+Ly/ed2cxbGpUNrk7NoWfVNvbm8WVALvDfRcXakDV9iSqfOPWezaiJOOMOCm7BvDIeb52u+3qjaJu16FzOfif/HPgv7+qAEDLioFKgrYoZQAYKIGx0Xxs2WwXj+75uHLdK352ZOAiL0mFZbk4DvHFM0M85SSXqz/x8tVKQBEAPLg1hU0NPEYwVDe6DgyDVDxL5bMhFQmVcyyVB7amAAOuFH6XOvDx/V/koJEEzvuuxTela+/WUZDgchEW9ND2FHfcs1WoNcrl0T0fILy9f/6sawCU2I2yzq6P99x5pmgXACo+sKX3zOdP7YMQPt9f79pKYBTgm+2OxVVJh/pOca/WyzBIsNlzMOhYCwHCP/zsaZwbBvjjz53GB77U7sdXB5tVjOZy+RPhk3r/RcV3L5uP6zhM8YxrN3HVZhdfPacHH/f9GCueVaSgL+pjW0r6HffOcZBFDE4JjJ/Z1wOL0qP1Je/4TKscfRkQ8BP37+BNf3lfax1ZGGMLmZsA8H/+/p14wW9+fKm6i/oGAP/vxx9u7dOdp/aq4xAM+b0ftc+BAMznq91DS3o+iiAnu1exLvlmLn+v4OOb3vQm/ORP/iRe+tKX4sYbb8Rv//Zvw/M8vOMd71DWf8c73oG9vT28733vwzOe8QycPHkSz3rWs3DzzTf/T+75/0Klbr4PoJLe2Khfk123TY7rdQ2DH0fLUqjJrtsWkLpS9o1cKEES5154QrUxPg/oE5Yn1YWqggVycRzi6MDlu6Iz8TEybcxyPuHqUcUHQICKz7h2E0+7/ngr44NajmhS+MFp+ts1c8QCfJxlBgyC1nFf6UjwkSKGBZrpx3jgUrA0QgKKGLYefHQokigAow4mGW1lU/YtnqgaZBRZC3DQdyhYPEVKOwjgoqdg5gFcIpTHMySGixAOumhhUiYhMsPGNHfQJep6EpyFt4GcMW3C90BMNH2ZuKlpC8Fe8SiqErEBvrAZBQlmcVZIjutlpWMhinn/A+LC0fhq9lwKI5mBMWCPrMLV1Bt0LLCUA0RBTtEx1BOSvkvBYh+Ju87TyRUgsGebXIJMbeyKw63Z6kkGl2hzZuGUqRmoAE+eZkImSjN/vtAsn4NrcQ9F6iDI+e+7phoA9GwTfhQDn/rP+O6Lb8W6wn+TH1cChDYmKWfmPuHEaqPekYEDmseIYCHPGfzMhLsgab7nCLakCPRgSYCjKweTXAOAZRqgJuHMYLsHn3SwYbdLj2VZ9SyEYYjM5MftsANsEMldXX9J8NF0Dh44o/CpU9Zb4MdbLpSk/HtYYeYvAL4axy0z6xZ8PxqWJYtk2gfwOpagrmEihYkVewnwMZpyT6IkqABW9dIxsuJbsjRrD0AjcKat70BJgSA3P1vmFLIfi5KgKz6Oy4CPS8qui2tZZie2zUvKgOwSsmuZFi7bTha0LRnZQGXcfvKdn8O/fr+QIZfnRrV50fVH+tjo2vjE/Ttzlo9gtkuWx+dO7Rfg3ZGyJ974PHDHvwPSuPBf/K0ffRI2ujZsavAFcJn5UdqkIITg8nUPt1+zgQu1dF4JIpRtSaQVSbnM4rQIBTs8cDD0Ey6TjAT4CIbxeIiNrl0E5Kx3bfhR1pAjj8O0YOZJz+H9WVzxykzjADlj+IUXPLb4u7WuxeuVFrFZOMbONMbRlfk3ZbPnNCS4O5MImyWQTwbnlMv2NOIgZ2ksLJPAMEgFNJDAXZn1zL9xNdn1LK60dfl6ExTdmoQ4XOrXFQLoO7XrC+Yjl7lPJxOsde3qdbJKx9y5Dzh7Z/H/ZUVI320CraMgrXg59xzKWWClBfx4PIQfZbi8BD5KoLTu5/iXX72IYysuniS8MuW8qly2pxxolcE9pkGw2bOxVZJBb08iWKZRgNM96aNdKzKZuqinAMzl33/HYw8Xxxy4fM4Q1ECgMiD+8991AwDB7ssz4JNvAaZcebA1ibDm8U1LyzSEB2b1/fXw7hwgK1Lp5XfG6hTvme1JhLd89AH+eTSI2AhXzyWmYVqEEG72HGxPm+8qxhh++2MPImcMmy2p5LI8tD3FTz6T2zt8vsxSZazBIpNA8v/zT5+IKza8pv1BrUh2XLH5UFZglBhwctyvPdLTsj4Bfi2eenId+7O4YQlRrqP673opg5x1e4ZyyXKG/ybAa5WFgCzlDYX6vSDL2z/+EJ7zpr/CC37z460A5C/96Vfw3794rsqO1/TtO974V/iON/7VQv9NOa71jY+Dlkwsptq8yr94ZoSf/eMv4T2fn/sWI5rwb0QaaFWERZHzVW9zsV85Y/y+oh1hh7IEU/KboCwFPv7AD/zA0v8sW+I4xp133onnPOc5884YBp7znOfgk5/8pPJv3v/+9+P222/HK17xChw5cgQ33XQTfuVXfgVZpr9RoijCeDyu/PNNVfIaqAbwySbL1Q9YIxSmRXpWryvb/h+edn1Q2XVp4v6N8glrAKlWo+3taYRDAxerno3pzC/an6acfahkFIp2/90/vgnH1wctzMeEB9IAIFb7ItA1GGIhu56mJkj5fBVl0KFAGiOGhQg8HEdXVj0bLI2REgsRsbUsyUHHQhYHYKaDiFmgLdetb+bIGUGYCeajpm7XMYFohtjoICCuVmY8cC0gniE2PYTEgQU98zGLQyTERkwsOEQT2OIK8MnuIYKFnqE/7iRMEURJ4SmoassM9yFtWFxT/YHmvkkx/DjVyq5XOhaSSIKPHTi5+qPYdShcFiIHwy5bgauRwEtgOTc4+Oi2jAdJfExJDzkMDBRMK8+hQMp9RHdnCSLiYEXDyCqDj+NMDz56NkUW+4AzAGM51p3m2A3ceeL0NOPXoKsBUT2bIh/zSZydB1qQ17PngUz7sQGTZTjSa16TlY4FihQxM+EnGWLYcDSM36JtR7Qt2AdZmuCygTpgaFHpWCbScALYXcyYi3W6LPhoIw59pFYfOQwtOK0scvK+jK+NBEosb7nAmUradQu4t4Qfb71QITGvMh8XgE7l0pALt/vwzjcFS0Bem6z3IKxKyXwEkBAHfQ3YXikxZ3LzP9Jfi46ZI4bss2TcLyMxdjhbcpEfouraAXpgtgLMLZI7l77ZBgXQogBQJWPrrk8ZYJX/XsTABHj7y45JAbAuktxLSbC6z186M2r2QciH5eLwlstXC0nkJ+7f4QygNCxky595eA+fenAXT7+Gs92OrLhzsPChjwJffR+wdTfe94WzuPpQFzccHcAwCI6uCEZdXAIMZjswDYIfe8bJ4kcqv7vdaZPF13XMBusrKIW7He7zMdieRoL5yD23En9cAb2kLLkMgjDGMAmTYhxkavPuLMY8cMbFbDZDnOaVlOPCu1C+A00H4/GIs/1LzMeBSxv+hVuTqALyrZbBx2AIzHaxPYmw7tk8TVsUQghcy6iAVUM/wWqnynTXyq5LYOFGtwmKbo2r/fJsip5L+XhIz0dwILAs8wb4t38WpxXQ3I8SWKZRSUgfCGuZchkGcZPtGqVz0Mnpw5/ye7oMeDrUnPevVM6PAlx/pF/yt7Qa16DO8gSAQ32X30ei7ExjbPTsop2uTZshTeBg9ZNPrlXq1ZmnQBVUlOcJoDEesyhDT/zuWSLZexykwO6DwOfeAdzz/uK45ZC+zX7TWkACs8+58ch8DOQarfSeeWS3CpaseXYRKlMvFcC+2/TwBFD4Ej7u+ECwjtu/rxfGIY6vdvCDTz5RHeOvvg9467dV5g4//54vAQBuumxFeMK2ty2B56JeZWOEb2x/9pE9fPTeLXRsE9cd7nP/fkWJ0xz3X5zilstX0Xep1mdzaxLhFc++Fk+6cq3pXyoPHWf46vkx/vX3Pg5HBq42+RwAPvHATuGTe3EcKpmDec4qrEgVmJnlDH/42blVWBvIKsuHNd6fspwpsW1Pt7AygbmP+7mhfr754a9ebJW9Ayg2CdoA288+zK/tvWXWcxYBnmBsL5q/ynluZ01P5JIlTwEw/g23e8vNdb8JylLg48rKSvHPYDDAHXfcgc99bp76dOedd+KOO+7Aysry8eE7OzvIsgxHjhyp/PzIkSO4cOGC8m8eeughvPvd70aWZfjgBz+If/Wv/hXe+MY34pd/+Ze1x/nVX/3VSv8vv3xx2uf/VqXwJiyz9FoWDQ3wsYUhUJ4Ul9tuDZz5u4KPNeP9VoBQJbvWScJT7sfQ4uHYaLsezFPry/Y4wqGeg9WOBd+fy64nifBdVPWlLE1v628Ww3X5dZzEYhKq6a9npggF62ucmkJ23cZmtGAhgZ+aSA0HpLUuBckjxIL5aOZ6lmIq5dmwQVvYVD2LISUUYcqQG7aW6dFzLCANEMJBCBcdTcDKSscCkgAJsZHAhq3pY8+lyJMQCbEQwYHd4l+ZCU/MGXPR14CefZfC3fo8fuyuH8I6Uft8DDoW3NxHLnfrMvVOIk+CTDCL9OE1K0ICnxIbEWwtwNtzKDoIkREbw9zVSuD7Lgd4InFtXV09h4KkAcYZl94PFNLirm3CYgkyw8b2NEJm2LCZDlQ0C7n3KLWwqgHN+i5n0+beBljOsK5gXB5b6WA0noBRF7OMP1cdhXxc9hHTbTAAOWPoaca561ABEDrYjwgIIegpAJ6uTWEjQcT4wiQmlhb4Ls7JsWDkMTLDAqMdhEmGJx33Wv9GVzq2iTzkco9pbmNVY0tQL2uehTSO+OYDcVsDpxol8QGnv9zurmSiLZKSFvVjAIR/y9oktvKbt6wcFwBFm+x6ib7lGd/QKycvLwKUDKtm29Hy3QSWZ+WnYfFdjomNrrkk87EwRNdfO9fIEDPxXFhiHrGIiQccQDYsr524DobBr8ky8uhFSoryN3tRcrRkj8q+tAGEZYAVWAw8y3aK+gvGpM58XCTRLjMfRd1zJZ+vOM3nxysFzkigo+9a+O7HH8PVh3pCPiy+Xb2jQDzDl05z9tFtV3P/xSN9Zw4WysCp0Vmc3Q9wYm3+7ipCW0oL/MgfIctZwUQD+PepDuTsijC2KouPchl0qfgl8HGzxwHD7UlUAdejYFqxLuHtVsHHKM2RZqyS2jxwBaOx8NV0EEcBDINUgKPCu1AyH3uHMBkPAaACavUcq+Hltl0DH1c6VhEcgw/8DPDHL8HOtAmOAVw1ESbzDbh9P2mEsHVsE0EJ/PLjFFGSYb0EGPYkwFful+KYPYdiFgow1uVrv8l0WpGDA/y7GsRZxQc48kcNZhJnBVa/zSM/wUoJROs5AqCMJvz92VnjFjhAY17UtSn82nmcH4WV8LaBSzEOqvLm7Smft5dLHRgdBnERLgRw4EQFPm7VgMyOAH9Vcuryec7Bx7qV09x2x7XMeQDP6FFeQYzx7izGend+7Te6Tf/Fi+MQt129gVuvXCvJruWG4Jz5+IgAC3/xH3F275pnz+/JUmGM4bc++kABLq5rQMrPPbKPy9c9/MzzHoPDA6fCKK2XSZhgGqY4tuLiUM/BZx7ew4e+IqTX5znQiJ2vAeC2EkGc4V88+1pxzvpAHoA/a1KKXNQrg07i+ZUy3lGQ4MoND2eHgRLge69g0Vmmgadds4HPPaJWf2xNuP2CToIPzBmzT71qHcdWXC1TEQDuLQV0xWmubPPCOEQQZ3jri2+F55hKYO70no88Z/i//4+bsepZWm/I8ibVIoDyEw/swHNM9DsWHmrxwcxyVrChdaBhljP8ygfvKWw6dH2TgUNtUvVPPrQLmxpVeXwWA57wFF7kyyi/we5AELnmz/4d91zEt7/hY/gvf/XgvF2Af8Nt71LgjChLgY+/8zu/U/xz5MgR/NAP/RAefvhhvPe978V73/tePPTQQ/jhH/5hbG5u/g/tbJ7nOHz4MN761rfi1ltvxQtf+EK87nWvw2//9m9r/+a1r30tRqNR8c/p06e1df+3LMq0a8kmWEJKXTAfFS/JOpNRtn0Q2XWetMq8Wo+5TEIoUFqEtsmuFee9UAZXW6iWzoUxhlGQYM2z0HMo7ju3W9SbJCJxWgfoLsOgyBL0PD6xd1yver614ho5IrFgDBIGYhit57bSsWAhxTQzQGwXhGUo+5+Uy6BjwWIpJomJGBZMzbVf9WxYeYyI2QKk1B+/R3MkxEKYZGAt17gnmHkho4iIHhwbdCywTIIpeml436XI0wgxo4iJBRuacxZeiBFsRMRuSfim6O/fC8YYLovVZtk9h8JjAVJBfXQy9Qd/VbAp/Fjv+bjqWbARI8xNRHC0QPCqx4OBUsPBLKOwNEDwepfLVcOcImlhgg5sBsZyDBMTMXGV7D7PobARIyMW9qYxmOlweYOi9AS4lxo2hpmNFUXKtuxfHAZI7FUAwIoiSOiytQ6yJEJuWJgIv1P99beQBmMuL2KZVnbv2SbylAfx7EY8+IYonlPDIOjRHGHOFyYJLFgawLU4d5eCshQpLJydMuQ5wxOPH1x2zftJCxblOLXQN5djPq55NihSBLmJ2HAPtlMbz4Du4eXAxyK4YQGgUqlvC+DIAVimYe/HHLAqgL3Fm1wmRDuVTboDyK6V6dULbDsaG3ELWPnLgluSmQUghtqrtVHiKZfVyv/WFNfIkIBy36plwNmvJ1gHUGzsLcNQXCCvLwOKsk9tc5W69/VBmLYHmpcs4Q9aBiqXkWhb0oqF1/3Rt3+6qHJxHNZk1/yaSMCp51BQ08Azr9/k8lv5/Hc3ATCMpjM858YjBWvtSDm1ecYX6vB3sO/HeOIVq8VxCzAgmvLnzPIwHPIFetlaQsXOq0uDAQ5q1cElP04L/0NqGlj3bB60EftAhy8sk9CvAFVUyGfLYSeSbVhObS7A09J7K4l8rAuvR1nWPAFSxlPOKnUGmIzHWOvacEq2KtJjuAxknB1WbTb4pqPo1/kvAJPz2J5EDXZh0V6Z+Rg0PX7rYys9+cpj21ckbG+PIxzqV79DXYfCD8R1pzZg2pjNZg3w0bNNDhKXZKzReKfh0dx3muE6oyCpyK558F7CwQF3ANhdpOEU1CQVFqXsXx1EvTAKK+Pbdy0kWV4ErwDAcJZUgEWAqwjKsvw6U7FrU8RpjqyWJPyF08OCgSvHAgwVkDjPGR7YmlZA565NQQhnEsrCGMM0Sivs377LfcUx3eI/8Pfw6Yd28cefOw2C+T25rgDiJNBd8dqUIXCld9gDW1N8x2MP49tv4N+HNRGGUz9XGbIimZlrGnn2g9tTXH+Eh/kd7ju4WPLSxPkvzs8Fcxnx0RUXhwVo/Bsf4mAjZqKeqP+ph3Zx+bqHH7z1hDhnpxV8fOF/4QrLzXLavdwwMGjxHZQejc+87hDWPC6HVwXASLDrtqvXceOxgTLw5PwogB9lOLHWaQUf7z43wnWHe3AtU2m9UC4P78xw+zUbRaq9yvfx/q0pOraJqzd7uHKjqwT4zo9C2NTAk65YxbGVjpZtKe/JW65YxcM7s1aPxv1ZjOsO93HNZhcPtfhgSpuHFc/ivsCKUmZR6o75FSFVt6mB/Vms9EOdhAke2ZnhB550Gc7s+XOQMI3nzMea72OS5fjQV87P3wHym++IAMdkDmL/+w/cAwB412dPc0/PCvjYXc5i6JugHNjz8R3veAde85rXwDTnH1HTNPHqV79a69WoKpubmzBNExcvVmm7Fy9exNGjR5V/c+zYMVx//fWVYz/2sY/FhQsXEMfql4zjOBgMBpV/vqlKPcUZaJeT1b0MCdEv3upgIrB4EVVP3QaWY5ao2ljILint7st/t4UU1D0cl03OBOZApBjvKM2RZDn6roWjKy4oUs5uMy2ME0OfON3w9tIHzhBq43df+lS85JnX859pFiWukSHMTWQ5Q5wxFJ6fmjLoWLCRYD8iMCW7RQMUDVwLFAmGMUFCbG0y9UbXho0Es8xETCxeT/MB6dEMCaMI0wx5yzXjjLsIfk6REBtUA7YNXAoiZOQJsbUA6cC1YGQxfEaREAemxuuy71pgaYiIcUZgRwNS9V0Llr8FBmA1U++C9l3OQgwNDiBbqXrhv+Zxk+9ppE/OHgjQOMhNJMSCoRkPh5pYsTLEsDBJTVgaIHijZ4NkMaapgRgc2FSVVYchZ4CfmshMW8mU7domLKRIiY1xmIC1LLi7DgVLQoSMe47qPBrXuzbS2Edk80VlX+G9yYH0BDEoZgkDI6b2/dR3qQAf+X3Zh/qe92wTTDAftwPC5W86gNzKMct4inYMu9XrVPbBQoIEFOemXE532D3A5kypdGwTWRIK8NGEtwwDDhLETuFnBhKzc7DJkvT/Sg7CfFwA1siSRSU2mmR2Kf5OsgplaQOZRKFI+evo65VdH8TiA1B8PxZsjNXbXii7luCjDW+BzyivOCukk23AsUP4e8OPsyXDUmTfvw4/RFmWnVO0BcoBzTFva7fsDynrLmvZshBgjRT1l/WqXAKolOABoGSljoKkFDgjwcegWAhLwK3ww5N9E+w2P5ihXwJAjq642JlGfJEuFm1suo3daYyNkm/uRs/h8sZowhdtTh+j0RDU5CEtsnRssxHesTuNm6CWo2M+zvu26tkY+SHfqHD43D+JgwqAAwC9Wqp0ObVXFpl4XWZsZ1GItW4VRCvqxVPA6QF2F/50VGHcAeI7wuaJw7MoxYVRiGsO9Ur958Ejeclaan88VjMfLbPi03Zu2AxC82xa8Xy898IYK55VYfp1HYowyQpwKcsZdmdRkfpcjJlDEYbSTogzaKezmeaYacXfczoZNcBkFeg8rIF8hedj4vNnwe4hCyfKQL9ejY2Y5QxbkwjHSuBjV0g9y8cdBnEF8AT4PVke21GQVJK65ZysDHY8uutjfxYXG8u6evdvTRGnOW48Nl+bGgZp3JNRmiPPWcV2ZyCCaeZey7t4QICAZYCMj231mbo4DnF44GDQ4cBpmGTz94fVKd4R929Ncd3hfvF3a54NxtAAxP72wV2c3Ozi/T/9Lbxel4cS1b1UH9md4aRI4T5cTnxnDHj3jwN/8YtF3df/Kfeo7Tu0ct0A8OR0oAAfz+4HuPbw/NnZ6NoLJd0AcMOx/twbMp5xlru3UYCP25MIL3zK5XjGtZsFmF+3JQA4YPiSp59E37Vw2WoHW5OoAZL9x488AJsauHLD04KPWc7wF3dfLHzE20BKgF+fbxFhOIf6Dk7X/FqznOHfvP9uxGkOwyA43HeUrMCtCX9fEEJwqO/owUfRl1suX0UQZw2LjHIZBQmOr7q4+lAXD+3oNzV/4nc/CwC44Whf6/lYZpLq0r/vPseVY29/yZMBqNPEf+7dnDH7/U/kIPW7pNQ8i7mMGmjMgT778B5+40NfK7w1+TefcDCx+H/gr+/jnquSfftjv/OZ6maj3bvk+SjKgcHHNE1x7733Nn5+7733Ij8Ag822bdx666244447ip/leY477rgDt99+u/JvnvGMZ+CBBx6oHOe+++7DsWPHYNvNncBLBU0JGtButq4CFHWLtzqYCLQDW42QFin/Xk4OOD9m2f/oACyKNrBS5eGoY9YAgrlSY3ECRfvSy2PQobjpshVYLOWTOdPGKCEt4GN5MWXrmaGCIXnFhgdrQeCMQzKEuYEozebn1jJupkGw4QIXpjksV7xcNYupQYfCYimGMUFEbBDNtS+zKSM4IDWqerl4JmfXREkuQCo98xFZjCCnYKajZJ/xPlogWYyImdxDMlGDSn2Xs/MmiQFGHe259AXj0mcL5MguhR3tImcMK9lQWadrU3gIMDFWkBncO1FVVj0uvyp76tRLz+EyXz8zkZkuSMsidc1lCHKKkOml8hs9BxYSXPDRCpwNLD4pDpklxrd5vfouZ/3FhE+oGXW118GzTR5iJNLWdczMNc9GGocIzR4yGHAV6d6rHmfmxszCLE6RtniI9lwKFo6RuhzM9DQp4J7Nrz+ojZ0QMFtsDAaUYZIYmEYpclMPzhd9cCgspIhAcW7GQDWsymWKZ5nIU/6eGCUmPE0gk6oPNkkwTU2kZudgk6XE5wypZZmPRXjMMgBfUn2XyzbqJa+x0ukC/14AJhPvWJVCYJnNsca3xubfDw1jvLkR12IhchAGP1ABH0PY6C7DfMwSDggRs/Xa2UiQgvIFpfx+a55j3m4NaFvkhyi/CXX5+zJqCilf1jExlFYpSzAqgXbQVOkxugwgu8QmI1Bhsi4Eb6UkWDIfs6gIPvh3//gmeI7JgbHydRGA5m997AEAHMQCSlLTQmLGwcckmBXsJoBLOhkToKYIAolnQ4RJhiMlwGrdE2BAzEOwYHcxHXNmWJk52FUEyUjZdbnwenXmY9WWpGObiCMxXpYL0A7ScNYAq/oOxUyCctG0+O+qPFsk88qQI+oiSwJs1EDRfgEITYvzjPxxA7yTDE15rueFb+YVIhkZ4OBpnjNMJnM7lmD/Ao6tdFAvZfAxyXLcc35chALJ4tlmxXPwzH6AkxvdipxdnrMEgHemERhDhZkn2wpDMbamBZgO/MDXMB+F56O7AhAD4XTUkIR7NdBTqodWVGnXWSzAxy6yaKbckO3WwOmdaYQ8ZzWWLT/X8n3E5erVe821qqzSs/tBpf9SQl4G2iSj74efMrf6mh+vVG8couvQSmAOICXh8/eCChAvpOoSfAz2i/CYF5aO27HNCrtTHvdI3y3A3XGQzN974j3zl1+9iEd2ZrimBOpJL8m69PrsfoDrjszrnVjrwDAI7il59OU5w6N7Aa7a4OuKIwMXu7OYW0FIxqOQU2c5Kxh6hBBcd7iHx59YwWUiEXx+zlxqfn4U4rLV+XOhYnvKkmYciPs/v+0aPP6ylbnnYzmVWHwHyyn1cqz2a2nfYZLhwe0ZbhLPW8+lIjV6PubnRwE+9eAufvRpV4IQogUVv3B6H+eGAb7/iZcBUIdOyfLA1hT7s7g47kbPxl6tb1Ja/qLbrgDAE9pVzMey12lbEJBc2z7maB+OZeL+Lb1EeejzzYOrD/W0zMdyEM31R/rKvn3twgRv+egDeMpJPjfXXdf7L07x3U84hhNrHta6Ng/EKpUsZzg7DPDCp1ze3MCR7xTaqag/zuz7+MX3fQVAKaSpmLtWFQaffngX33vLcfyTJ11WbReYMx8vgY8Avg7w8aUvfSl+4id+Am9605vwiU98Ap/4xCfwxje+ES972cvw0pe+9EBtvfrVr8bb3vY2vPOd78Q999yDl7/85ZjNZkU7L37xi/Ha1762qP/yl78ce3t7eOUrX4n77rsPH/jAB/Arv/IreMUrXnHQ0/jmKXUJGrBAdn0ANqNsu1FXN0lXsD10/dCVsix5IcMgAfcIExOTNtZAXQbX5nUpf15ZIFXl6VK20ncsDFwKihQZ430eRUSfOF2Wc8s2c8WHp2Ke325a75AUfjb3AyLW4sX+hguMYwLHERMiDfPRoSYckmAvYGCGDaLx/zIMghUrwzgxkRIuKdGCgDRHyEyEaYa2BNOuQ2HmEWYZbb3vVjoWTJZgmhocHNOlZ7sWbJZgGBsgtKPt30bPRhaHCDOKxNAzLvuuBVOkSnu52svRMAjWaIwpcxEZXoPuL8tqx4KVhfjZvX+Dw8EDyjqmkPlOMpNf4xZQYM1mBQvVzGPlgn2ja8NCivPTDIw6MHTgrsWZj6EEdxXXwTQI+jRDxCxMwgSk5Xr1HAqShUhg8bR1DTOz7/IQm5hQhMSFrZCsdywTLkkQMr64zNvS010LLJog9jjz3svVk4SuYxabILuhsFDQsThpjkliYBalMJZ47lyLJ2JHjOLslME0STu401KkNyUzecK8LjCoXgghGNAc48RARg8APuYZv6bdQ0sGyCRz2XUWLbbfKHvfyXej8ruUNjeSFsquJehV+gYU79Wvk/kILAC3auzMReBW2Qt4kaeg+H6FzIS7FPgY8T5b7eCWQzIOPiZZOwBctJtUvZQXMkIVzEcdYMlYBWhttYiRbdc3P7WMSsVmpO4eUnk4tt0zaczvq8q8ZIFMu2h7kaRbbM4awqcyCfG1ixPY1MBtV60LBn0irovoA3XA0gj3nq8uIjnQkyBPJPi4CgCIAp/7AYvSK/zpBAPLdOBPhwCAYw0wIOLfOMEIDKZDJahVBx+51LjOfDTngCHmstQyYOhZJuICIOPsvDTyC8ZbcQ6OkPPuPQS89VlIL94NRyQFl8djGqW1ZyVqsOQK0DbxuceX5SEOpkrmI4AC1LowitCxzQqrVLY9GXI5OwPgD7ebLDAAHcsowK8LoxB5znCyBGTKY5al3meHAY6vVtuS4NY0TIH7/gKj01xGWB//nkMRSWCXOmDUQRT6ONSvMRodsVmRRZCJr5E/rrBdVeMxjVLkOaumXcu0aMk+tbtANNGDjyWG1PlRCBBUZNB15mOYZNiZRo3x7ZTAx888vIevnhsX4DHvexNU3BU+mZX70TYb9VQBN4CUmM/7/7GvbVfakP0K03wOlsQz7Psxnn/TUTxTBNIAgEONCnMzTDIM/aTwHgRKjGjqFMSOX/0gv/bXl0DFQccCIU0A6PwoxPESKN53Lax7doVBtz2NECXzZPJjKy7AOOsO0wu8EjGKcQGAl30rT7omhOAHbz0xP2404XWjCRhjOLU7mwOTANZ7tlIeDvDwkzxn+I4bDmOjV2JIltjKiCaYRmmFUWmZBnoubcjJ773A+/DYY5whKtmpZYbeuWEAQoAfeeoVYhypEny8+9wYjznaL0ByVSiSLG/764ew2XOK8Sze76VyfhSAmgQvvv0kr6MJAuJhV/yYbczHrTFPeneoiSvXPaXMGwC+cnaEO0/tw7VMXLHuYXsSKRO0L4rj/NefvA3HVlyl7PrTD3PLhlc8+1oYBtH27ewwKADoKzc8LnsulQvjENMwxffechwA8AsveOx8I61g/VZxgk8+OLeLKPpW9iuX/w9gf8afKUJIwX6MQjE+Bfh4yfMRAOjiKtXyhje8AUePHsUb3/hGnD/PjV+PHTuGn/3Zn8XP/MzPHKitF77whdje3sYv/dIv4cKFC7jlllvwoQ99qAihefTRR2EY84//5Zdfjj//8z/Hq171KjzhCU/AZZddhle+8pX4+Z//+YOexjdPKQeYyNIml8riKmsE0O/kH1h2rZA96fqhK43AmSUkSGVT/zzhi2TDVNTVsEOtDhpFdy5ikTQOU4DwyZJpEPQFOwwGxSiCCJxZQnYt26wH+6gk7JpFnU0y+Nl88kSWCHhYcRhiYsH1usA+Wq+RQzJsBwCxO3PmSRnsFmVg5diLeL22xO0VBwgyE1GSt7JV+w6FkXOmXxuwPOhYmAoZeSYkZsr2XM46G8YEhq1v74ajA0zSCHsxAaNuK0OSplPkjKHTEtqxZkbc29DwqkmgpbLq2bgyfxRX5qfhXvgrAE9XH5NmmCQmYLdbEqy7wGhIEMOGAcFCrfm3uiaBTVKcmeTwLBdI1SlzfZojZwxhTjmzUAcqClB5EqYCANcwEB2KII0RgSI1bBiZGvwadCzkaYgwt5Aa6utACH/2QhH60uYhOnAp4miCcK2PkLha/03PpvxYIu3aaGE+emaGcUJgRPKcF8uLPTPj4OMkBzWMg70fK/00RRI9RcgsrbxeVfpWjtGMYMN2l5ddy3rdQ/w9WwaGVEXKqMsMQ0Pxri3ql97RbX6DKlb6IuYjMhE4U/suKN6rWc7AGAMte4yVZazlf6fRXJpTaUQlAW4BKsttllKdH931se/HeMzRfsFYK0JHAITMwsYyoLO8VgtkvRZJkRCxqJehLa3MR8U3+Bslu84zAKx5Twg/1mZ9BSjdptIot9EGmpaBPNmPRfMSWj+/RbLrsuR+AfBMHXFt+Fjfd2GCaw/3Cm/DoR8D/WqbccjfsVKuBgiGPwPCcAaPmMV9HEd+RbYs/3sWpUWqdORP0HNppd5GjwdV5NEEht0DDBPhaIwjJ2qgnEMRCQacaRAwxtkqZWAB4MBimbEmwaq1GiMtjsT3QzBCsyDAak09UISsXOCqLrr1VfScGyt1ug7lyb/yeuQZSBpVWKCyrSjJkCUhTMHOi4NTFSBW9g0AgjgF7vtzTC70ioWrLBIYmo04uyvPGWjm44gSfJxvMJ/e92FTQ8FCpIXU27VMnN0P8K3XV736JVg2DSLgz1+LdecEBp2fm79fSvWiqQR2bSSE+3rrPB9ZEoJQ/i5IgnHDV7EA5oSvoZSGllmIfYdiEqXzOTHtgCVb6HSaS1nPNiuBJ+eHATZ7Dmw6f2+71AQICpByexIBDDhWA2TLsmvJUH3e4440+14GH2c8EbvSTsF2nd+3O1O1h2c9QVv+TZkB7EovyngiAqEm3GevBBYW9UosPJnye2TFQdemMAzCAS75jbU6YEkAEOB1L3hsZbPBFAFL9TCZs8MA33Pz8crP6qy9R/d8mAYpwF2Zsr43i3EiHvJKWQTEPs4OQxgGqTA4VzoWgjhDmmag0QQYHAeiCfyYg6knN+bf2o0ul4cP/bjBTj43DOBYJta7Nta7DsZBgiTLYcWzgq2MeIa3fJRv9Jfl3CsKJuKDW1OcWPMKELp4hqK0AJb3Z5xRK1nesp08ZxXm9/lRiCtKLNiBWwIpGQMufgU4chOSnOGzj+zhB598oqi76jWvy/kRZ27KY8gNiHr5/Kl9vODxxwBwRuoDW1OESVZ57pMsx68IQBrgQKaOhfjRezmT9VDPKZ71UZA03iMXRoGQg7s4utIpLDzKGz/bkwjPvuEwrtjwcGzFxZn9AE8+WT0eYwznRwGOS/BxvdtIapcBTqsdu+j/LOKBU6QIEnMra0QZoMTHUvy8YAhX7X/Klg1Pv2YDv/XRBzDxfTiAeK4uMR9lOTDz0TAM/NzP/RzOnj2L4XCI4XCIs2fP4ud+7ucqXozLlp/+6Z/GqVOnEEURPv3pT+O2224rfvexj30Mv/u7v1upf/vtt+NTn/oUwjDEgw8+iF/4hV/4uo77TVPqwShAu5xMKbvWyILqi7yibhvb4+v01AL4i7cuS15GgiRLq9w8bfqEAQuAVL2P5ihI0HM48AhwX7yUcAaqn+YcBFkouz5A2E8LG8YmKRKYGIndLsNawMwAkCcRElgw7QXSOsbQMVI8MkpBqMuTvzQL/cl0ht2AwJBgrqbNgcVZeZxd0yK7dkwYeYxxQlrBrIEAFbcD4V+qYWdapgGXJNiWfdSAlMdXXdgswelxDma62vaODlzQdIp91ofdAj4OzAjD1EZitjAfPQtHGf+YexoQEOAA3zghnGGg6T8ArNk59mOD+2/qWKhZDEoIzoxSGG0gsJWDMQY/N0WQjA58zBDmfJHHr5eeWWrmMUax0Qpu9F0KksUIGUVm2tp6/Lhc+sxM/eK971IYyRSR0UVgdGEkaiC465hcCm3aGEYMxNCzjjtmjlFCMIsy0AWAcNG+yUNqdv2k8GP7ekrPpcjTGAmRCfPLg489miNmFLnlLT9ZkuPfFabdi4BWaW4vWYKLxkYyJYGSX7CGFV73blyw2WKyDBkjzU0TxXv19z91Cs/7D39dnXSrglWAdkCxIeut1v29Tz7CEydVASWiTz/9B5/Hq971BbzgNz8+DxVJo2JM/dyCownOqhQJqCwAwmgeIyXWXIK4aGxV3oml5++tf/1gNVVTytQb10/1rZSA7xL3BKAY85bnMa3XbZlrZNHy7cr+NeTfbbLr0uYj1b83G32hHSCN8LWLEzzmCGfkVJiPpWT2KAxBCCqLXgmoRFFQkXInUYBBCXCzqcFTd4OY+7ANjiENxoVUUZaNrgMwIAj8wq8vDqY4omA+AnOwZegnRUhDuXQdijRjXLIJ4MtnubqgDDR4tok4KjFPqIs08ivSVaCUojziSalkeqER7Na1hYdgiSFjZFGFqQigAGriMABMC5lhI42CytgCcyAqCCPgz38BV33lP1UkwQBATQOeYyKa7gMGRWKvwGNBIX0tF7cUOLM1jiqAQ31sJahVXqwXdSwOyIUTzvgxpltKZl7PoRVWT8S4X7EKfMxzhjQO+bNqd5EEU6WMHpgDeP/pIxz42SjVmwO7gt1tuciTEF0F85H7Q85BlvOjsMFoNAxSYdpKplrd2sYtsUr3ZjFuvny1In13qAFCUEkS36t5nsrjubXwGh3zsR6Yc3rPx3Mfd6SW+C6ueTQF+keA2MdwFjbuD4eaiEqss1e/6wsAODBkGEQE15TubdNGGgcAAx57rJmTUE+8Hvox9mcxrtqs3uN19ubpPR/HVzvFxh01+bvDj7PCsgEAEI7wyI6PYytusYbi5yvukWDKbU0GlwHRpPgWl+8p+d8Fq/HdPw7c9fsAgP/4kfux5lkghBTM37P7Ad88sXuA3UMaTvHnX+FszDJgVj8ngNsXXL4+vx/kcxZWAqCq4UkrHQuMAdOadcQ4SCrvJw7gijpnPw/88Y8BF76Ezz2yD9Mg+GdPu7Kou+Y1WY0XxtX7XuX/ef9FPoaOxa/Lk67k3ofSQ1GWO09x38Uffurl4nilQKxa2Z5GuPXKNXznTUcLlnNdrg6gcp2PDLiFR53ZOAnnQUsn1rxK+IwsZ/Z5mI9kgW727cbxJmEqnkF+nnKDLUiy+fzS8oq57Pf8x0/gQ+Ie+MEnn8D2JOIy8aw+dw1xZt/H1jgqbCLk+33OvJfMxyU38/83LwcGHwHu+/jhD38Yf/AHf1C8CM+dO4fp9BKd9B9cUYGJbXIyFVOyFSirsypbmBMNtseSi86ibzX/StNeLLte9ngNP6gWZk1RXwFWikXEQ9vTygRjxQYSQTQOYsHA0rF25MLrIPL4FpBOSuWkB42xBPPx5CpFAspZdLo+AECewTKA/RBcVgpo+3HL8Q5iWDAWeFQOrAwho9iZRq0S8ZNrDgyW456dRIBjGvBJhJ1sB0BOO62L656Z4cKMgTqdVnlux0hwapgKY251e1ese+iyADvGBuxc/8EZmDGGqc399TQAmmUaWGV8IuBG29q2uiZnubodj/df43+2ajPEzEJEuMG07pkwDIJz0xymrR+PrpkiZ5ytmrWAsZ6ZwRfgo2l72vaOrXRgI8Gpcd4aTDNwKWwWY5RQZIb+/u+aOfyMT7jawNG+a4EmUwSk0woEd21agI+TMG199lySYhQRzKKUj+ES/oEdI0WYC6bCIlZUS+k53JsyZiZPedfYA6hK18yQwGoF9RtFMh+labfmPiiKBEqWZcBXmI+L3o1lwMvmmyI6/14AJkuREp5kWSm1oJEozfDx+/nz95sfvq/at3K/NN+PYtLf+H5UvwdfuzDB7/zNI3jHJx5GxZ9Ptp3FyLMMaTbv77khl5KVAb8wN7Up9UXJM76Qo7YArPTXjeQJmGnPv20LA1BUQBuvzxjDH37m9DxtEphLo0uKF23Yy4Gl7nUv0DY2owocbqu7ZDJ2Ub8ENhwocGYJSXfJA3PmT3HnqX084fJVAKUE2nIfqIs0DnCo71TYvHLBnYQhP3+DghETJA2bbD+Hwvdn/D7qH0MezSp+jwCKYJYw8Pl5WB7CYNoAvyToJwEhCU5fVgfJCqZcDNzxb7H36D0gpApAeLaJJJ4v/nKRUN30aRRAT8i/sWR2URFKQ+GHMZ+HUhswHRh51AAp5d/FUQBQFwGz4CBuyIypacAyDaRDDnha/lZDgg5w6554NgTsHhLTQ4cFjaRogLP4JNixO4sroJ0snRJDL88ZJmHakI0bBkHPoYjHfLMzzzNlW13HRFwa21lO0bdyBWgrxyPk8my7BxZNFZ6PAhgV78jjq24DQJXALgfEOVM7T0IMFONRl11fnIQN6TvAAS1ZbxpxcMKh1aVxWXY9qyVOA1xh4dkUQTxnF+5Mmz6l/HhmxYtyexpVAn9k6dX6/+iejyvXqyz6IoU7nnLmIxhm/qxx3DJzU7K//t0/vqlYvw9c4T9YCpzJkxBgTAl0r3p2hWH38M4MhKDhW+nZVa/MB7amDRC+6wjv1nAE9DnzDuEQ929Ncf2RfqVusSEyGfIfDI4D0Rjv/NtHAFSf/Z5DYVOD+zmGY56k/Zm34Qunh9gaRzwAFByAXfEsnk4dz/ic3nKxP+abz7/70qdW+sBT2avf1DP7Pk6szc/LoQZAUDn3/VlcYfsOyl6bpVJPNO85VCSyZ8DWV/kPd+7H3edGeOyxQYWVWoRdlco4SCrXsGM1PXXPCTnxP3kSZ1EOXAsrXgn0FOVvH9zFk0+u4aeeeQ0A4UUvwc7trwEf/NlinnV6z8fTrt4AIRzs4wnU1XPNc4a3fPSB4v1yqOeAEDRCZ6ZRWgCyRwZqSfjL3vk5AMCV4v4qNpRKZRImPICUzFmggLCYKM9H0xDTKC2ev9972W34x7dcBsZEqrmc1xgmn6+kMd4vwmhkOJN810ald+Ql2fW8HBh8PHXqFB7/+Mfj+77v+/CKV7wC29t8Iv7rv/7reM1rXvMN7+Cl8ncsKjARaJ/QNwBFzWRaF06jqstYkylJFwB89VKXtlGnPRRGJU0ut1Mu9ZCCRazMxqKqei6/98lTFa+Rgc0Qg48rZ/RpgFNVqILS87EOluo9qRySIoGF80PObuDMx/Yxd0mKhFiYxjk/Nx3bIothGgQJLBi2+Phq2r5mjSImNkaxmEzqwELKkIJiaxwJnzx1vUMdLsu6bycS4Jj6WpE8gUGArVne6jUIAJ7J/fZst9tar2NkODXORP/U9VZdAx2E2CHrsFM9e2zNzhETmwN3LayWLuPgjhkOW/rPQaOO121loQ6sHAkspMTmEnhNarC8tmbLdehTLkMdpzyoR1eva6YcfAxT0Z4GLHQoBlaGi7O8dXz7Lk9l3w3Bj6tlPuaYpCZnQbRc/75LYbEY49wSIKoaMO46FJQlyAw+YSctwKxrZAhyiq1JyIOhlgDyXAEgh0kG1uI9uqh0bQqWxYjAmY+6YCHl31J+f5BF1hblUgRTrIr/X8R8rIGPi45TZoAtkl1XWOyLwU0TGTIYyOpgfQlIynOGf/kHdxXG6RXD/YKdWHt3l95Jd9xzEf/o//kETwVuAKRzUOkdn3gYL//9OwEIIKMIjZNt83/fc2YHYZLhD3/qaUUzp/b8gvnIGMM0t+BoArHmfRd9pO5iICzjY1ssXlqeO15fxQrkx5MS0cpCSDX/MDXfKzm2jQT0JTc/20DCBeBwa7stFii8fp0NugjAjebnVlJ7PLwzw98+uNPsd4kl+ejWPkzDwDOu4WzkVY/7oFX6QB1ksd8IMZHMjTQWTEVCkBk2bBZXFrwAfycmM8GS6R0BiScNoKdrU4AAacTbC2GBJWGRfCuLXGTKBf6ZfR8bPVsp+wWAcPdR4Kt/imMP/TFuu3qjeg42RRJHxXkGzIKVRQ0vxJ4jvAQl+BiNG+zIbtnjULDuSJ6iZ1fZhfLvCvAx54zAFa85D+/YJtiYL1pTxpSg4qBDEYYBYHUQGh7WrLjCBpOlLKfcnUYNyS8wBwKDJCuAkTpYKNtKJ8JnMouxruh71ymNrWljmlFsOk3fXgkqJhEHnTPTBc2CBkBmmwZMgxTvgyjJcatgYMkiQZkkCotvx85wXNmEKZ9DOTV9HKRqkLLGfOyXwAlZyrLlSZQ2wHeAX8syo+zMftMqQFWPMx+boCgPzOH1GGM4sx802L8dCe6lIeCtgwGIZqMGuM49H3n/ZaDH40/Mw4iK8BP5LqMO8jyHbWQFS6xc1rtWBeR6eGeGy9Y8OLT6jHr2PHE8zxk+cu8WnnxyrVFnGmWCNX0cIAbyYIS/uPtCk+0s7t9wNuQ/ELLrjwiJb/m5IITMwbgxB/hBXXz6Ic7o/a8ve1pR77rDPdy/NcU8dMTFbDbFob5TCYAC+PNd9mBkjOGhnVlF8k0I4TYI5cCjWfWZlOzaukfjJEwr71f5PplFGTDhLDzMtjEJ08YztFa7LoAM4ar6jsZpXlmfXhgFuOZwr/KOVXnvPrQ9raSyr3ZKTMsvvxt48CPAzv3Icn6/yrEjhAigstq304LB+GPPuAoA35A51Hdwseb7OAmT4tk/MnC1Cdvfe8vxYrNiID16K+2k6JXH1uH/PY1SsXHHLQeQBDgnNr3+9KefgctWOwWjm4OP1U0+pCGiJMft12wULGbb5AB0EkXClsXgfqLQBOJ9k5UDg4+vfOUr8eQnPxn7+/vodOYvhu///u+vJFdfKn+/5cIoxOk9v7nIkUULPtZ8kQC9pFeVdq1jE6hSLBcEpSiPJ/te/nfbAqKemgksJzdfJLtuLHCrYOXhgYPvvOlo8euBlSNm80Q88neWXdcXr64WaDJZCpgU50cBXMsEWRRYAIBkMRJQPOHESvuCNIs4QEUsGLa8HurFqEsSJKDYlr/WATYkRWbwMWgFStMY1DQ4OOa0MHbSCAYhOD/LQSxvIagYExsdz9MztxiDS9I5i1Oz+CYpN5jeJeuwNb6FAJeZJ7AEgKbv29X9DDmMecqf6phZjJhYsIqgIHV7K1aORHgB8nqqxX1UgI/U0TN/OmaGlFDszhIhk9YwH40Mk4RgGqWgdrt3Wc/McHHGWpl3PMmap3Hnpr6eZ6al0Bf9vb/eteEgxk5AkLewGT3bhMUSjBOD+/W0PB8yHfjRPZ+fcxYvDFZxjQyzTBjEL5JwtpSuwxmaUW5yb89seQalZ2TimT4A81HWc8TkdBH4WKTGHoT5KN6NhsnN5rUbOCr/3hbwkeXIYDbN6Uvv6e/6zY/j/ot81/qlzziJL58Z4d9/4KuCnVhj7BXMTD4GsyjFv//APQD4olTHCNyahPj9T50qflywUSpti0nw/gRrXRuHBy7++7/8FhzqO/ji6SFkIEaU5ohhaVPqi5KWAZUFAHkagZk2Z2EA7exBQM3QF9dBLuAqLAal8kLjzdgI4rGqPy8X1ean7htc9LvOTtTUbdi7LMEGLXuhLgpEqgTOzMf7p/5/n8Mv/slXanXjyvzo1IVdPPGK1YLRuNKxOGieVkFKlkQNmXTBfJRAD4DMdOEgVsqWI+Ebid5hmMmsweIzDIKuTZFGnEk5SigcFjeZj0VYQwacuRO7F85UWEWySNZKus3luUnoNxhknm0ii+f39zSl6BhpMzylxnxM/HGT+SjARybGFtRFzoABrS7QHcpBtDQKOCiXW3BY3JBnAxzgzQL+Pe8ko8Yx+XEtDmSaNkLiYo1qvsMlltneLMZ6TfIrjwcAgT8DPvx6bOS7yrCWnmMhn3JiCctSHHKb36yuTZEmYREoFeQmelQBPorrmcSc+RjChq1ggnL24BzwGIdJ4z6rArucqe2wWBnA03MEwyvPgK/+KQJ/VrELKPpXAvnK8s5y6Vhz2fJUU6fM8stFqm4dPCvqxfN6O1O17LrnmAVza99PEMRZg1lYpHCnEeCtI8sZOixsMFVdy0SSccBp30+41LocbORZGPkxyt6/jAHrDmsAsUDpPSLKB798Qelb6ZWS6/d8nmp9i2Bhy8LZm+LZc1cBZ4CLWxcBAE+6ogpUFmwyX7DHekfBhEKlbjEAcAuf3WkEBEP+g3iGOM3x9Gs3KkDlsZUO/w6VfP+yOGiwc4FS4rooF8cR9mdxETZTPq9qAFGMzdIz6VomHMtUMh+7NeYjIKwSJPHA38U4TBpg+ppnYxQklXnMLEorAVt1WwsAODcMcVwRslSXZ58fhZX3dc8tsXPFuxPjM/jy2RFyxnBdLSW9Lgm/+xz3fr25BIQfHbga5iM/18N9hwcUlcok5J6d33XTsUrfJmFahGvxemnlnSLHZVr2kRXg4/kRZ/jL49rUwFrXrt4nQKF8OjusWmtwBrVgh8t5x7XPAV7y33CpfB3g48c//nH84i/+Imy7+qI5efIkzp49+w3r2KXydysvecdn8JJ3fEYZJAFAzybQMR+XZkm2tAsowMoFEqVKGwrvK6CdkUBrCwhAv2A1DrBgrZ+7XAyLPoZJjqecXC9+3ac5onwuJ9KDjyUGZhv7UsW20Cz2SRbDtl2cHQZ88rnMmGcxXvOCx/OENKujB8VSvgufgpaYj/oF3W3XHce//4GbW6WqvL8SfGxZDGcRqMFDU1y3BXzMkgJE4ynQelDEQVJiDmZz1lGlvRjUFGzPthCRJAQBMCQroJledt2nHMjMFzAfv/UKG8euuKYVfIyjACkopuJe0wO8CWLYBRtXedwsgkkIYmLBdvSgbdfkzNqdaSSYj5p6NMduSBCnOaw2sFi0GRO71XvToSY8I8XpsfQGVdfzjAyjlO/AkxaZ/EbXgY0Y52cQjEM989FCgr2Yfz7bJOkWeEDH6b0AtismbQuA/w7JMEk5+EgsfT8WlcITM+fp56RFht/og5EhAeX397LMS/l+dsQkfCH42PTNaS3l4A1C9Jti9W/eoo0kACZSpKAK8JEDQ1nOkGQ51rs2PvKab8MLn8ITK++4Z4uDhSqGHwBkCbYmIV79R18ofvWgZFgomI8PC1blK559LX78W67iLBVNGNrMnxVMKc+muO5Ij284iomxH3MWtL2I+VhmEC4RCJMb1pxptChEqYVtKBddp/d8zgZV1S/+pkV2vcxmpGrzs+072LieLedZByqX8sGsA7JtUupS/dJ3XpXiWqlrdRAEswrLr2DGVJgbLlga42gtpdg0CGxqcO838YwmxIKNuAG+dB0TUSjeU501sDzDkZ7ahy9LQoDa2IsI+rQJ5JgGgeeYmAYx8Cc/hRvu+c2G5BqYg1rpjPuQ2cEWNhX+kWkSFQDZOKPYdLMGUNGTQSbhCBgcRzgdKf0SzSzmr1DTRkosMMbQr4GPhBAMOhZnBVIHs8xAx0iV4EjHNpGF/HtOswB9xVS971IkgkU5QwerpvpeKWTXjGEaRBgoQlgMg8CxTGDrXtj3fxBPye6qsKLKx0zDKWBwK4pNu3lMzzaRx+I+IgR+RtGnzfnSnEEbAqaDILdgs6QSJDNvk8IvSaDrDFuHGqAmQRpzNnBiWLCQ4PZrNhptFbLr058G7vi3uGz0+UZ7gAguEt6QQz9W9qtTYouVJaD18ZBg0/Y0QpzmuFwBmndLgNwoSJBmTOv5KOtJjztlCnec8ee5sybAx0AbdBOlPJhltWNVQEXOiA5QBHhRBzljWLPVqrKuM79OAGfE2WYTTij7C8qk4PomRzFu0RhwB4C7grMXLuDwwMHNNaDSFtc/DgMABHAHyGI+P3nbi5+Meln1BKMz4O8IpAH2xhMc6tVY2Y7op/RLpi5YEhYbIeVS93y8f2sC1zIb17q8GQDIe6t6/60oEq+nYVoBySUQOSkxs+HvYlKrx8/XBhgqbdaZj2XrBVnOjwJFIBZtJKTvz+IKQ77Mzi3Syqfb+NjXtnDz5auVZ2ngNlO7H9md4ZpD3cq9uNlzsDOtfj+n4ZxtfGTgYugnlb49uD0DCCq+mz2HIs9ZwfgF0BgzahpwLJOHa7FM3Pt8jbA/ixuWAwXwWVcophHOKpjOHctAmihwkkvl4OBjnufIsuYL6cyZM+j3+4q/uFT+PoqczLMsVsuuVWwC6f3UkD0dQHatAx904OMSLLxGG3Wmw7LSqVoyVWvdAvjTmPXXz52QYpzCJMM4SCoTir7FEDIDWc4N0onltDAaJfOxBXxUJrrqQT/HdXFuGPAPzzJjniWw7A6fMC/BfIxhISMtQBYApCFuueoInnb1Bu+7DgTMYg5OATzwRstoDEFNHpridroLQUoOPraDXkbOGZ8dVwKpinNJBehJbNAWUA5pgIFrIXNXOfCjYbx1Tc62nOVWKzBqJxPY65fz51Zzrk887uGGE4fwT2+/Tt9/AMhiJLCQoAWYSaXsmnLgTJdObeZIiIXTewEYbQEpjRR7IZ9kWO4SDFRYc4akBjTrmCmmqdnKkOwYGYYR95IyW8KWTIOgb2Y4O83BLD2j1bNNWEiwL7rfBkCTLIZluwiTDLYjwcd24N8xUowiA2nGuOz578B8NPIYQWZwhm6LDL9eXIMDyrbjHez9bFDxntX4iNbrU6ckl138Tmqw03WMuBZWuqoYLEVGjMIHqihCIXD/FgcIZBqwTQ38q++5sai2O5pqvgch3nPn2YIxee3hHh7cnqoZgWmIh3dmOLnZxT+59QSOrQgGgArAAzD1ZxW/tsN9l0uCxALKj1MeNLQo5bzsKdm20QQAWQJmWHPgS7eJWW5b44dYXiD989+7E/ecHx988xOY1y+us+a7Wq4DtIN+Sh9H/TfwYIEzB61fZSm2+1yXgUoHLIkqC89VwYzJyyClaSNjDEe6zeWAa5l8cS+OnxAHHmkmlno2RSLCR3J7gDxnWLWa64SeK8FHF7Ocomuo51c9hyLZPw0AWJk+pJSvmgJIS30OLFjxCIdqoItrmchl0AkhGCUm1hXSYAmmsGgEtnICPQR4xjXVFOi+w4Euxhhg2vBzfn/0aPM81zxLgLYOZpmFjsZ31bNN5NEEIKZgUTav7cClHLijNvzcRt9Ut1UEznzs1/Cj5/99ATipjkmGDyNnwAbbUzIfPdvkx+xuImcMA9K89z2bAlmEXLxrZ5n6epoiZEXeRz6z0DOTSuq0LBwEkt6ETXkzIQR91xLj4WCWcd9nFUOt51DEaY507xEAwCA8qwQNy8EuO5NI6W8p7/cgyTANUyUoVfbSOzvkqhcVI7Msu94WIIsy7VpaAYCz5Q8PHMVzJwDnLAacPvKcoUvzBqBcMF7jDKMgacj7VzsWZjOxeWDyb3LOGNZtzZy1FObDGINNDXzvLccb9cqA7PlRiLVu0z6hI8HYeMY98Zwetnd3ceOxlUZ7vE1a8vzsII8DGCxrZ14Ge8U8Yzreb4C9RR9S8a4QczrPaT4b9QRvzgZshjuVfUIBYBw2Zf+F3F2ULGcIkwxeaUNGMqlnkh1qOkA0xUTBDJbzgXKbs7jKfKwHO8lzqN+rXo25KeX65SR4eX0ZY1w2DwCzbXzx9JCv80ql787v5+K4w7AZeGWbFe/UXAD+cpNKMurLAOVXzo5w3eFe5b6v23fIv6lvLvQcE2FQtdNAEmDox1irvVcO9x1sjSNUrFCEZ/LWJGxsknVsk8uu67jHpXJw8PF5z3se3vzmNxf/TwjBdDrF61//erzgBS/4RvbtUvk6S4Vy7QdopF0DahmRnKArA2eW8B0E9MBWnbVY9OMAi2utqX/bAqK5aNP6hJX7xs0R21mVDZDWArKYv5xQ3eHbcAlGESl2MA2qYUdUpIV0fixVvaVZHDEcx8XWOOITzWWYj1J+AfCdIB37KUs4sEcsRPkCMLiUwtrOJOGADYB2n7w0FqAiRafTBgJGsARI6Xkt9RjDhsvwom99DJ5xwwn9uaQRBz1hcZBUywqN0HcpfuEHni7+X13PYjGOra/g2uObrcxHRBNgIPqlCUNxSYpn3XgZ1lek9FV/Tzzv5ivw5n96K7Sentlcdl2EpShAQDOPQajNmXotQHXH5D6iAGA77c+9K3xKDacjfF0Vi1TG0EGCmNgcpNPco66RYZqa2JnGQkbcxrjkjFDSAsJ4NoXFEuxFfLLZxnxEFvNzBWAXgPYC8BEpdkI+zsZBAl9qpe9SUKTYiwhMZ0l2oSg9miMlFOurgwMwH+csmLaU8qJIAMQwFjPugCaQRds2cA7g3wsIz0cV85F/zy6OI/RcWpm4PvO6Q/iem/mCa38ybX4LqQOWRvir+7bw2GMD/Jd/dituODbA+VGo+Dbxb8fd58a44SjfxD0ycDHyk6psR543AH82q/jIHe47vO2UL8z9OENMrMUp53IMpQR+EfPRtJHIjZRlPCLr6gNxHYZ+UhjwP7Izw1/ft635rrbYvsjfA3PAt02iXQEUW0C/Bjisuddk22XZ9aLNPaXf53wMGWN4eKdk01FhPnLZelxa1H77Gz6Gzz6yJ+qWgEoRrlVeeK4JZkwYBkWbzLSR5QybTXwPrmUgi+dzgZg4DaYfwEGjRDCQQsrv357ZfGf3HQqWhAtBuZ5jIR9zJk2aq0MvAL5QZf4IDICVzhqggmebyNOwGO/dyMSaAhT1bG65wIIhYu8YXOY3kqe7Dg+uyxkA6mCa8eWTCnBb82wOtpkOgpxqQ586lglEE7D+UTANyFeAbaaDaW6hZ+jbCpMM+Mq7cUV4PxTEx+Jcmb8HxhhWMWmEqwAlhqrwEfRU4KMcjwJ8NNExdEw5k7MVqYNZZioBW943WgBzYwWzi7dFRdo195m0oWYrSsZYMjwHBsCOh0rm42bPLkDA+y5Occ2hbqOOBO/CJGvxfDS4fBic5VcPcJqfowB1vvRH2Hzfj6BDiUZuL9i44MzHy1abLMoCcE5jgLqIia2UyEsQzY8zBHFaAbcAPlcIJAAjvgN5DvQtNfhYDsMJkgxxmjeSvfm5lmTXMzWwWwCocn1gdTEajRoy5nL9RNgQwPaQMYYuzbRyeQ4+DoFVrlYIxnsNoLIA2rK57Jq/OxczHy+OwgabE6iG/ACc6V9PUa+Dj5JFWD4XQkjVFmJwHIinGAdNZnAR2lRipdbB8jnzkdfJcoYLoyYI2Kl5Pp4fhbBMo2KX0LU5uzBKcxGkQpD4Izy651ck10DJ2qJUzg2DxnFd20SYzo/rJxkYm4OJGyKhvez7eGp3hqs2q8fzFCDrF08Pi/lVcTzLRCy+XRLQRhpi32+ysw/1nbnnY+E1bWNnOAZjaNiDuNQsmP6XSrUcGHx84xvfiL/5m7/BjTfeiDAM8SM/8iOF5PrXf/3X/0f08VI5YHnR2z9V/Lcfhhrmo2KSrmUntgFly4bT6Nq2ll4QN2XXCxgz9QVEKZlqYV1gIaCnZIhmMS5OQpgGqZgBX71OkRILf3H3RXEKGgZWGQQtFlOq/talhY4eKM3Twp/Vs+mSzMcqg2KRn2ICC9ce6RcLHl3damqnHnw0xMva6XRawOWISzBgo9ttYz7GsEwuDR/0W8CUPIUBhluvOjJP5FbVTUNQITXv9vr685B/21kt/k5VSBrhR55+La45fqgdaEoCoHeI/3c0UtcpGWZr+w8AaYRj6yvcdFx3LQS4m4Li8OpAJAarZeiGOB5pkem6SBGLxHfX9VrP1RagYpjb+vPIYphC/t4a+kJSJOCTJLog9MUzUkRol3ubyGEZDBemOahJ2tm5gnUMAB0pu17wvrNJilnGJ4gcLF0S/KuVNc+GxVJs+Tmo3V3q2LJ0jAwvfdZjcMWhtYMxHwvJ5xKg6bLvGFnqclUdI18bbNYCPrIUqcrzUbDu9mfNBa5pELzquddjs+9gOvObE0zThu/PsDWO8OLbr8R1R/o4MnA0UmrOwtv35/5l8t+j6UzJ4P/EvecqBv/HVzultp1Cdm2qAsvKpez5SNufD2QJGCnJrls8Xnn9uHktSszHlY5VSGU929R8hw+woam7j+R7q56C3vINqvo4tn3X6ue4YHOvATzPz+/cMMB3vPGv8BO/+9m5r5XCB/rTD5yvNPl7nxQ+oZVnykWeRuhYJeajYMaE4XxBNMkowBg2m+tneDZFnsyZjxEspbTWs+fSYMkI9BQgWd+V4KOLcUrRIepnsu9SpP4QANDNxujbTckyP64JFu4jddbQYQEO1cANDj7G3A4EwDAxlP3v2hSE5WBxgAldh81iHOpUj9l1KGyWcHa0aWOW8mevqwBZVz2Ly72pDZ/prQ882wSiKVLvMD9v0nzX911asAanGYWnYT6WmVYMDH2oNyj5mE2QM2CdTJSefj1HHNPpI2EmOkzFfDRBkSIT64txaqKjA0ZtytOTTQeTjCqBadnmTCRxTxXMrmI8xD05TikcJOgq2JvyZ+l0D4wBg3ykbO/EmsftKgAMg7jhBwqUmI8xZz6q/DvLQBtnkinQfJTk4Pe8H9b4UTymq78GUZIhzXLsTGMcHjT71bFMBNKvznQQw8KG09wgluDTLE7hxxk6tRCZvmshlLYJpsXfHWBKD8/iHAR4tSdSr+ssMT4mc5anH2facKMC+DNtwO4iCSfa8fNsWiSng7rIcoZjXq4cwwLYTAKgfxQMQBo0A3mKfqbz+TNJ1bLrw30Hu7Oo8D0+p2ANAiVJPDhoHae5wivXqgCF91+ciJ83/WanYcoJCP2jQDxtpGID89Cm4j3AGPb9uLJJ6VADhkGKvm1PuK2MioFY9ny8MApxZKXK8Cz8KMOEEyIGxzAa7oIx4Iajg0p7/ZrsmjGGc6MmW9Cl83EDULAl5diZBsFmz8ZWyRfy4R2/ESImGd1yLL54eoi9WdwIsXKowTdGAH7vWy6Q+EqG8OG+i+1JyO8TiatQFw+c28HVh3oKRq3J7TcuMR8b5cDg44kTJ/DFL34Rr3vd6/CqV70KT3ziE/Frv/ZruOuuu3D48OH/EX28VA5Q8pwVzDsAiHQ3fhvzUeXjuEgiXNTVLDq1no/u0lLARhttMmqgKW+Sf6OTXRu1D43ZwnZQGuPzheqZfR/HVjsVQ2OTZVjrd3HvBf5xMSyN5KvuPakzo1ctsLUgbAxPAiC2qV/MlUt5QbVIngbgD17+rfjxb7lqiQWdZD62ycRjPj4AnLbU6TSCTSl+5ydux7GN1dZ6MphmfaXXwqSs7vyWz6/eP2oQvPb7nogbLz/cDlsNX58AAQAASURBVLaCAK6Qj7SwRwvAsM3fL4sBT0gZYk29eoJwKwtV1NGx/LIIpuXinT9xG55w8rD+HLIYtri/2jw6KUsKmTdt83zMc1hIEINinBrFMVTnwOXvlgAA1eNrsbhgXPLQlxbGJbgcnbo9/RhnMahh4MH9BB0J5rfc8xJ0dDwJALY/e5bwHwQkq3JJ8K9WVjoWKFJszXIeyAQsv9GTRljp9ZZjMJb+pioPXQQmlkGVZTZE9Cy6ar30wGnXhGWawBn+Xt2bqRNfAeBQz8EsCJTfmqnPx+66I4LN2G+RUqchRsHcRH7Ns0FNgtFkOj8HgDNc0hw24spkftWzEIQRWJZy0CNKkYtFVGuR40Id8f5uud5ZDGbac49G2rLpJdtWBOsAwIUxD0Z41089DU+8Yg3jMNV8hzVgnmy7vOhc5A9Zvy9091x5cQG0KzRU6dWtzEc1WJlmOX707Z8ufvyFR4fz+uXnCsDMr76bCoP+SpCMA5JGFebjoGMBpMp83An4PX+o0wQtXMtAnswlZnuxoZRiFmAVdTBlvF1PwfbrOlQwc11MM1PLCOw5FHnAGY1gOfqmeuy7NgUJx/A7R2Egx6ZbBbU6FgVlCXIRYDdKTHTQvDacxZcgZ8DUXAUADGi1bw41YBspBx+pg4n4NrlEwfB0rYLJFeQ8GE1lu9KxKUgyReTyTcUuaZ5n37W4TJ46mOUWXA2Q2bENRFEMBi5Q6Kf7ynqeTUGiMXLGsKIBKLsORZ5EyE0HPlx0oAIfKSyWIhPel+PUVI4FwIFAeQ6T1ERXA1JKz8c520kR+OFQ5MJHbZoYsEkKwtRSegDIwgmXjrOJMu16s+cUCcH1sA9ZJFtsFqcIk0zNfCzJVC+MAiUgBZQYjRkPBrnKUXt4y2PMogy701jJGuxYJrI0AQMDqI0QNtYUtgKuZYAQ3laQVD0AAS7tj0K5CcXnj4zxzVhVkQnhjDHsz/i1VHp4lnwrufdgEyQuGHbieUlMFzQNsNlvtgdwFq0En2F5yHKGI4p3l2ybM0MDwO4iN110c78BlHoO5YBXOgc1jSxS3gubPT42kv14ftRk7wEcsC6nqANogFldh4Ptsnz2Ef7M1m0EurYArLMY6B4Ci6aYKbxHCSGVMX/XZ09z4L30HNWDnR7cnsKmBo4oWONl2fjZYYBjdb9OybT0fb4m7R9HPBtj1bOKZ0aWnmNWgNZ9P0GUZErQs8wYlSqAMpnnkLSYAcc8Tu/5jVRy6UEq27rv4gRXbHhNdqJlcjAb4N85ixMjVB6dh/oOtqdxdXPSchEGPh5ztMq8BOTzeQl8VJUDg48AQCnFi170IvzGb/wGfuu3fgsve9nLKsnXl8rfXyEEePfLn447fuZZ6LkUSRRDKbtWMeVyDfh4kHAaLUtSB2wuIbdrtGFX/90qjVaBnUsyH9uM47X1E5zZC3B53aMoi+G6HdwndrZMS7NoU0nF6wBonnEWWkV23bIIzGJ4ngAfLbNY6GpLnlfBzQUsRRADq10BtrZ5UolFx8I20xgnDnHAru+1gC/Cn+XEeneBhI6DhX/48mfi6qMbQsarkP0UwQvOvJ9KRiA/zpOuOtqado00qLIQW7wrudykRcIN8ONIILPtepSlry3+m5XnSHee1Mbl6x6XIcv2FfWoJcHHtkX6HAQkrYD2nE1788kj/GdKBqr0G7XR8fSgMslipISfq9WW3swYbJIiIjacjtc6dpZJ8PC+YFzo7nnxnDpC8tzr9oq/bysW46nwgAQfvz7mo2kQuEaGczPGvRuB9vurXOTGzUHStsvfg0V/J/2F6RLvGFX7gB6UymrfPMMEQFo3uUwBPjYyPISv5LYmkRTgiZq+rwAfTQdBwI3Q5aLj6AqXUqeJQkKexlyKLBZxhkG49HrqNxj8QcZB6l/6R3PfSQk2c2YWZz4a1hLXL4sBYvJxWsh85GnXqRyoRenYKvmyqH9xHOLEmgdCOJOhSJxcmvn49cw/aqDf0pYt1sHqlu5lKWs7tSuk1GWAECiAzZ0pb/+lzzgJAPi1P7uXs2sqsmv+dxf3RrjxOGeWXLbWwc6Us1fKXlTMtEGysAI0mAbBSsdCFAZFvS2fL0i7ZvOb6Fpz0AgAdgKCy5prLC6Fjfl3TDICVcBF37UKMHWUmLA1lgA9l4KFI+QuD+3rQ/0O9GwTRjTC2D7KF9V5tV7HNmGxBJlhIUozTDOqBMh6IkSMMYaZ0QMhBGbtfUQIwcBiyBkBDIptn8EwDBDFe6XnUHHdHMxyyllZivuya5sw4ykis4eQuHCZmvnIhMzYZxYspn7eHGqiy2ZgjLOKuulQO2YkniAxPXShbqvnULA0QmbY8EkHnbzZL8l8TEERJjnC3ISjZXjO5fajhOpBLeH5WGc7lUvf5X2D6WCSUxhE7S/csUz+2g8nSA0HHRY0wB+A32t+xJlpfpQpjyll19sC8NClXUummI4NJ/svWWxZznDCGqvryaCRKMG+3wy/AMT9XfIhDXKqDIkhhIgAmxRBnDWAoUGHA9wyTAmGiQwmuloZ/TzMY9+POStYId8vewL6caoGH2XatQD+ZoyD3SoGqmwzjUIBFHWQ5QyHFVJzoAQASmCT9uDBb9wHXhkAFb5/ZhYqGbXy2kvwsWwfUm9Tgnf7gh1av7fKNgMAECU8ibvui9lzKaZhwsfI20DkjwHG1HYDtlmEAd15ioOZV9esBMpA+V2P7uMJJ1YaFgGdEpNXtnXdkZqUWoxFMBVKrMFxJMGoEV7Dz72aEr43489SPRzJrXllvu5PvgwAFZXHSocWXo6zOEWS5ThUu19kuJZkUZ7ZD3BlLS1etpvKebGc8yYBhkHSeOa4H2VtnmI6SOJQ+XwWgWeXAmca5cDgo2maePazn429vb3Kzy9evAjTVBscXyr/8wohXO5LCPcRSZJILbtWSYPapNHLAGWAnoGn8lwCFrME6m0YlHuEAe0yal3/dEBLi4ejvm018/G+rSlObtR8Y9IYluMUOz/UamFo1BfY9T6ortMC/6puh790Pcl8bDWtlyBcGRhoYdAJM/d53TZ52pyq3gZSndhYwUde822tXoP13SdtOrVYvK31FoCKRfCCU2JLqUC5EkOybdzTiPdLAnctEmiZMNgKwKQRBwdakpgr7DMdCwiossi0gHyZ/drCHksjWAJgc9q8N7O4YPQt8vwEgLe85On4zluuajluCJsauP0xx3DZ5moLszSCafHzsNuCbrIElDDEsOF0eq0J7zblTNqeQ9vZ1AByws950JPMx3YwyCwxRC0ZuPP1FMbgkBSTmPCAH2B5IFPeH8JjbqmUbCH9AtAOfMu6QK3+IpBMwRZcJnCGkIUMPYMlSImJtM5OMm2wJMKff+WCdgg2uk6FSVYU6iAI+EJHsuDlLv8s8Bvv7zCOEIRRZbf9mkM97IwmjW9NxCiefMKrLFJWOhYspMgEM8tPJPi4BPOx/K5vY7oKZn4qmY+LNrIagTNzIHHfT7DW5b8rTPzrSeUAtOz/8v0mS5tKA6iC0qbmO5jnInivDFS2fa/qadfzPjDG8KK3fwo/8rZP4aW/81kOEihB9LBgsf7Ak04Uvzo/DKvMR3Gc3eEI1xzq4Wee9xi8+rnXAwz4k7vOVr6xqeHAYs0F/5pnC9kiP78LMwbTICCKb0DHMjnwRV3kOcNOSLCiYD72XYpU+LDNUgOMGLDy5nj1HBNGxtsbpSYsFivfLT2Hs/Pi7jH+/1B/7zq2CSOZYWIdgkHAZYm1fnEmuYWhz/18VRJol5pwEHPmI+nxthTvr4GVc5kxITg/CrmcW3H/9xwqztOGn1HRnoI96FAYyQyx2YGPDpy8eZ4SbMtNF35OleMK8Lmdx4IiNKujADJlPSOeYmZvwtOAul3hzZkSGyFc2Armo2UaPJiMWBgGsXZs5THlPGyUmFr2pmdTzOIM2X1/iaP5RY0XoliPUAfjxBTgY7N/hsHXQCyaYEQ3sGJGyvakr6QM1FCBj6ZBYJnGEuBjBozPId47rZUN911L+PeNkTGGQ6b63pbMx2mUKiWgAH8+bcHYhWljxiysaP00OfMsSLJGGFHf5SxWaSkAgHupaxiqhdw2TuHXAk3KpewJOIuajEvZrzlA6GCS23ARtfi8ytAquwAfNzXgY+GDmoaA5SI2PTgsajBgOxZXPeTyW0hdmHmsBEvrQSZBnMFThDtxoIpfizf95X0ANMxHEdyD2a7SAxHg4x2IQC94G0iSFD2a4qrNpj+ptC4AuOT4pc84CasGLHJglK+VHt7xce3h5o5Suf9ZznBm38fjL6uGAEkJd+TPwcc8HDcYkvIcyp6PoyAFIUCvdk+4VpX5CAA/8a1XVf6/K94TvB1+HVSsZq/kHyll4/XiWiXZNbUBy0MczvDort9gPrqWiTRjyGtz3TQKNJsDkqV9iflYLwcGHxljiKIIT37yk3H33Xc3fnep/MMpfZcKvwGV56Nikq5iBwAtgKKK/Vdd5H3ywV18/tH9dmDz62HWFH/fxk5U1NedSz1wRrbd6iepBiv3ZlElEUz2RYaoOJbJgzmW8nJUgY8KFukCoLTr8Q+abRrtdUV93uYSEkrV4lJ1PZlgZUjwT8daKtosLYZ1XoP1ABtADyouBaJJUNHlALdhqa9/GgkQXAC5OtAzEczHNiCzPC6W1wKgpYIp5ghPkrbrIQHZzgK25Vyep5NT1xe9uvGVoSqtTKs0wvOecDkP6aAtz5b4e9vpcA9J7XFjmITg/3re49Ht9lvBASb677SkdiMNYRAu4+aBMzrZdQSHmohhIUxzPTgvnqOI8YnpZt9tD7ECAMZg5MmcIdriPbmw5BkoARJY3HaBGMszHyUgbrnLp2RXmI8LwMRig0Pegy3vg+Jv5qwuAFCywgEBYB3sO2MwHjjTUEZSBxeHHND49sceVv7tZt8W4GPz+/HQ+d2KlHu9a8/N/SuyXht70xgUKa47PDdEv2Ldw3TWBDZnmYnL+vUFJE/jzXN+bD9KYS5iJgJVEM/qLPw2MGojKXs+HsTfsPSsDP24WIwNXDqXXWs29Ra2Dei/P7librNg06AhpRZ185zhv3/pXAFWNP0hRV3G8PH7dyo2OOeKsKEy85HPxUZBAtPgkrh3/vhTef19vwbqO8gZcNfDW1jzLHz3E44VC8ff+ugDlXe2n/MwjvqiaKVjISmFGG2HHFxRjUXXoWApX+iPggQxLKwpwMfNno04CsCoi1mSITEc5Xur71owM37scUJhEqZ8hvsuhRGNEXaOIIehBcm4T2iEqbnCQai4KiMeuNxvMQbFvh8jhgVbIbs2DIIVK0POGMaEMx9V91GPZsUG2jBIGvPdop5LQfK4kJcTAmV7XdsEySLExEFEbFBFW315DoTCz20t89G1OPMxyxl2jHU4mfr75dkUZjLF2DoEl4VKOXjXMSGVCjGx4GgYqp6ZIwHFKOAbZpaunmMW87W9yICj8fr0bBNRFGDj47+En4rfqfTc67uCcGDaGMUG5yJo3kErHQssmmJibaKvCM0B5iDfuWEg2lezlFY8C48Kb0iV7NqzuXQ3f+//iZ/fe30jsEiWrkPhhxGQBkiYiZ7C5xPggJhhEEzCVJlsDPBrbrGE4/fUwSylGCgClWT/ZlHGmY+KtHpaClMCgBg2vBZvToAHm4RJrk1WlyDlNOKMS7XsmgppNGdO78YUazSu2FbVj50J31iYFmJmaJmPc2CT148JT4tXgXEAuC+pVCuxDD2riWlQ04BrmZiGKdIsR5I108WBqux6vWtjs+c0fCkL5uP9Hwbe8TwEe2eUjNmuY/I5BgB468gYwxOP6sFZyabc92MNK3POajy1O8OVdbIMSuzIPMfo0a8gzVgDUJcS7liCj/1jMKKJhvnIwcdczIcmYYKeazVTwu2qVyYA3HL5arP/AsgcB2pJO8CBRZmcPQzU7GGHmjzAChDMRxd7Iz7nq7NvJWM4K+Mq1EGeBA2gkvfTFLYll8DHejkw+EgIwXve8x58z/d8D26//Xb86Z/+aeV3l8o/nNJzLI7o1z2UACE5qjMfZdp13XNJA1Zpwb35R/51f/JlvOaPvsjbltKuSn23fTFe75/SY7LFt4k6zfrLsGWAhixrbxbj4/dvt/SFp5sO/QSrndrvshi2zfvSsYzlF1SqBbbSaL9d+tnzOPOJ75BqgM9yH4ASG2YB66Phw6Za/KUcxCj7DGpBoGUZdzXpsLZe6T5o8wktvM/KjE8VKFcD+Nrak0Cmjq1YHpdWhmk4P16rnDquAbxt8uzSmCjl1LU68u8UbUlW3/rKoIWpmuDZj7scr3ru9ZUFuvZcJbMUWAwW6wByxoA0wvqAL9BbA2cEk/IHn3Ydrjq22Z7wbvKE93PDoAXE4M/pTVdwP6++uwS7VTyvScEQbbknFpUsgiHS4PsdC63elKq+L2IA10tZHtoSPMTr1gCepZiPte+NTjZbT7sGFr7zOPhocNZg5e9sTGY+rj/Sx9Ov2VT+7UbXQRiFje8Bow4euThEfVZ0eOAirm8KUhcGAX7wiYcrkriVjgiuqLXtZxSbbrWvpkGw5qBgr/hxxpnjeaK2mZCl7tXZet14Xyqej4vSruuAn7gOo5Ksqe8K5qMOUNTYPShtXNrSrg39xt5Xzo4w9OMmMA5U5kB/8dWLeNNf3Idf/9C9oh91gNUuzv2Lp4cAgPf/X98CAHjlH9ylBSv3ZnxxRAjB5eseTm52cX5vPD++6FOcZrBZjG+/4QiAOViy4pAKY3OSGLCQNGRt83uK/92On8EwTOUYe7bJJa7UxZ7PwSiVbHmj68BiEWJiYxalyAxHudHRs00YOfc4nko5soY5SJMJQrOHgLhwW4A0I4swgwNm0EZbNjXgGhkHH2cJcupwRqKirNAcjDEMWZcDmYrvdc/MClb6KEi0myac+cjvi2mmP09PMCRj2EgMR+nPyllpMUJmYZZbSoAS4IBYFwEHA8kK7HymrOfZJqx0gn26yfuVNOv1HAqShohhI4atBRW7ZooEFvw4Q0wsGLmmnk0LtuIwMbRye882Yc8uImcMhzBsABNF3wTIPkyl7Fr9zhq4Fkg8xZhuoINAOd+QfnjnR2HRvqpcvubhnvNjGAZRgm1dwexiozNwWIQVVw3I9V0KRNzPdNvYRFdznaRUencaIc2YEhTtCOk7YwyZYYswHz3z0Y8589GtgYAV+bZY/0WgcHQenmVQMckaMuF5vTlIOdPIroukafEeneQ2+ob+W11I+E0HWc4wzSwc72o8H2XbAlSM4GCVNjc7ZP+lNQCoi5wBPY09wKBDMQ4T+AIcq8vYAQm28WNFaY7n3nikUadrC+bj9j1gAPrDe3BcwZjtOcIqAwDc1VapeZlNuTdLKunUskiAL0wy7M3iRuhLUSfJgIc/ht77XozL8nNKALxrU6T+hN83nTUYWdBIE+fnQAE2T/QeBYkyzb4cnDUO1H6iZa/MUZDw97ziHiy3NQqSInCtXLivsXjvmraQ3AewqYGbakxP+dxn8j4BkJs28iTSSu/z+vzgUgHwdTIfTdPEb/7mb+INb3gDXvjCF+KXf/mXL7Ee/wGWvkuR6m58leRIy07UsfQOwBZULSp0/dAVrcy7DWBR9U8DFCw47//6qVN4/Z/ezSVnmrajKEQQZ81kuiyBLYIn0pypmaeqPquAX9VCagGLY7XPwaEwzRYv9OXvCuCpBcSqm+nq2i4DSsBiYEwuzpYGFZdlPrYz6Sp1dMBPmXHZ2r+gBLZq2IrlsAfL43+jYCLMrwmXBSjDUPKs5tXp6gGXdC670947dXak9jxjWHYHH3nNt/F0ZFU9xpo+olCzXubAlM2BW1MDApdTenUszzwFwPCiZ1yHN/7Qze3vizQEAfCPnngVTLtFnp1GIAAee2ITP/LUK/T3vBj7b3nMcdzxM8+an/cCTz0A+M5brsSLb7+ynXW8qAhmaEIsvvjThVzVi2TjiokYgOUA0IaUf/F5VsDtZQJqKoBQG4tdwXxsBR+5d1kz7drBdDbDlZue9m83ejaSmHshlsswNmAjxttf8pTKzw/3nQrzDAByw0KaM9xytLoI6Lu04RmUZjmC3FTKX9ccVgRiBEkGardsGsjSSB1vu258bAvPx2WYj2WgTfghhgn3dZMm8gPp4ZQpWKs6VqzOd1k5/1ClXVe/7//yD+7CD/zW33IguVF3fp6/IUDHe85P8J8+cj+SOKgxcuWYR5hGKZ73uKN84UU4k1UZwpNGuO/CBGWk+viqi6398byvoh9RmuMxh5yKwf6//I7rcMgjRR2Ah6t0jbTB8pn7EfI+7E1j7XXv2vOAmL0Z985VMQc3+w5sliBi3D8vM9TPc98B32yzXEwzi5+uAuzm4JePEC4CowszUYdyeDaXcc8yitRUA+d9miPMeWCDaXW0AUwDK0POgHFqIzeoGjy1csQCfBwHqfb+77kUlMXITQfT1OTnqQQpef8TYiHVjBlnb6aIGMU0ozA1suuuTdFlM4SGBx8d2JkefKRpgH2yLqTqzdCZrsMBTz8zEcMGzdVj5ho5YmYijPm8kmjeBV0BGGaGhWlmgepASofCCy8gZ0zLfOu7FEbO34nD2NB6PgKcUW0kM4zMDZjIlePrUAPUJDg7DGAI5rGqbPZtnNr1+bkoiDYDlyINxoVvcDcbqfvvUHh5AMYYLmIDLtMoLESb54Zhcd71UsiuAYxi7pPd06ShS6mqSibcsTj4mBk2QAh2phHGiYnL+mqIQMpt/VjNpCyO2WA+amTXUVq8Z8aZg47Gi5S3Kdhk1MbONEIIBxuOGnDt2CaiJBPsbQfnfAOm4l6W/Wdiwz0iNsCY8hsLcDBwEqYFQ091z6x0rEISPPSTSuJ0ce7ChxPj88hyhtVsTyO7NhHLNHJ3hUvNNSE7nIHL+xYlmRIUk6CntAEbKIDtQna9+yAYA67KTylB1q5jIgpnfA5udWDkCQZO876R4LmUXo/DVCmV7ghpc5rlxfg1PTrn7M5RECtZj0DVP3IUpMp63PNRfA8JASwPLAnw7BuaShdHpMSXpdQh44FiOk9Wdgl8VJavK3BGlp/6qZ/Cn/3Zn+HNb34zXvziF3+j+nSpfINKz6HIdH4DKkBJGzjTxjBRgIEi0CNOSy/ueghAuf7SbBwV4HcAI/jieMuyZarnPRUvu6+eH3NQQ5F2LXenGjssWQRXJAIHcQatj2a9XdXYq67TAqP9jtvBDcf6+O7HH9OzQ0p9LdoE0BpC0GA+akDFMqAEtANjZQ+tVsZdjbEDLAaWF3gXAmTO/G0FH0sMTt1x02j+ex1bsQwq0vmitVmvxHzUSXGVXp06gLc8xjrwrLRINiiX7erq1a+DjlUt79mlPDUlCNxyTxmWYJaqmTayv4dWB3jiFWvtDDh5bpYrgOBQzRgT5/brL3yKSHjXsH1LmznFYqXNbqD0N99/61X4sWdctRwjUFfSGIZBkIJymdiyydUy0IraB2M+1mXXbRLvtPaO0cgXm+3XWXSad16Dxa7Z7BGFsFQEzlQn9A/tJdgfj3H71Rvav+3aFGbOQy3K5eIsx2HPaATVHO67SOLquezHBhhjONarTsn6rlWETciyO+Py0RWFJGzdYRxANW34ccqZvkD79ats4mjeLbKIa1AwH9usHUr1i0JdIEsw9Pm1kKy8vmthHKZg0te5XLSbnzVgE2h/Fg2qSMbmdf/4c6eLH/+z//Lx+e8rdefn+YNPPoEoyfDez5/l7MQ6IxcA0hjb06hggfzY009y0FbDBv2Tu85iZzLv+7GVDnbGk2pfDBNJTnC8VwU/ug5FHAXz9gCMEqJkQXmOWSyyAa7oIJr3TNehHFAybezPYhiOpwSYurYJlyQIYWMW85R1JRBo8vCJGBST1BByZAXI51IYaYSAUaRmR73ZBgk+xvAzitxUb9R0aYaIWZjFKcwWD90+TZGDYJJAtNXsf9fkLEqAM2kMzRyhZxGYLEPIKCYZ1foSejaFmceIGBVs0eYxXcuAjQSzzETALA4+KsgepkGwRmPMiAefdGBpnmN+zAh7GPDvUqxmPlpIMEkNRIYDU/Nu9owUMajY1NbPK7sCJI5gIYYFM4+Vm6yebYLGI+QMWGETjSScwsg58LsfGxxA1XzX1p0MLM+wa6xrz5UQgr5r4fwo0AKLALDZdRCnuZKtBfB3mBvvF98QL97V1uuxGXIGnMMG3EydOA7w63B+xM9NBRCZBoFncsbufgRExEanja0XJNzzsQYimQZBx8iQiXfvqd0ZYmLhCg34KFmZ0yDCkfMfQVfxLQKqIOVM4w3pORRZEnECE3UwSi3OUtWUjjVnY29PIkTExkDjcynPk8upO3h4lINFzXdJxzYBxgqQchjz816zNWPpUkzCtJAuq8DHgWthFMwBMhXrTvozssk5ZDnDOttXBttV3u+dVeQ5w4YGGO2KNqWHsAq07glWZtF/xXXhrNEUbHQWjAFrbAhHESrUdSiSiG/AMavDn10Fu1QCf3sifGcSJsp7Wl4zP8n4O9YgjeCfMrtz6CdKybM8h1AwPKMkU4KdnPlYnrs6YGmC4wM1MAoAeUl1FzBL2JtoZNcqhcalcnDw8corr6wEyzz72c/Gpz71KZw+fbrlry6Vv4/ScymyVAGqAWqwSiuN1jEPVLLrOQgkTXYBIIqa0rF5P5ZlPupk1y3AmAp8XDo8p3reRNASTu8FGnkYZz4CCvPbLIbr8IVglrMWELQuu1YxH3X+VRpQVbTzWy+6ldPI27wsy39TgFgtgEkWNReXyn5E898X9XTA2JJMxYrno5S6aeR5DZBSA/BRtxSeowFdl/VCTMrMx44ajCmPi+XN/07VN3k8bVvxvA6gB08Zm58roAfEys8PIdDKdsvybMmUq/evzqpeBCqX6+juqQozU4Ag9UWZ6ri6+06Ou0g75MfQgM/EmL8nteCtzh6hjXV8AAB5Ucl4ynsKkydnLvuuLbMS266T4nhLMxkb75glQFZlUIfuvq1vDLUz+ow8RUYMpFn1/vniVoouiXD7NXrwsedQ2EiQsOp3djckONprLmSPDBzuMVTq48UpP+6hGsFy0KEgeYKUzNvenkRIYKGvWHCtOiiYj36cgcqU80VsW3OJdz0AZAkM05qP00IwvR4SxK/D7pTfG5ItMHAt5DnjNjGq7/CBPB91dRVzFdH3t3/84fmPpfdZAzSNwRiDYRA85eT6/JB5bQNOPAM7wzG+8OgQVDC4Bq7FF4QN+wAHeZ7BYBle8exrix8PXIowqAKKABDBwopVXXh2bRNxJJ9b3vZuCKVnW8+hYFlSPHuTKBUe1Crw0Zx7F0YpTM03hRCCHs0QMc4wzDXfir7wo5uk3L9QJ0fuOxZoHmGaWRwI1PjvepYBM48xzigPf1F8O7tmhoCZmEYpfx6ySAlqHXKBIKfwkxyZqf7GegZnIALAOExgWGqGd09cnyA3MU0AYqg37ro2Z0j6jCLTgKeEEHhGinFiIoIDo8WDd2AmmOU2QuLBTNSgVt/hY7bPugK4a45t16GwWYJRYoKZajk4ALhGhig3ESU5jBYLJA4Ycul4BJvLqZX3G5fb54yBkrzh4QkAXduAyWIw08YoYiAaywAAWLe4x9wuBvxcFeAjwJ+188NQCywCKNRM6womGcDXWx7zC1a4k6rZul3HhAcfKSiGGGi9OQEJPoZF+6qyYnPG7igmiImtlbSvdx3szWIl+AhwP9OM8HMbBymYqbco4H0z4Z77NG5/8M24NrpbWacAKaMUsygrmJDlstnjQUUyKG03seBqgqgAwaYUGyI70wiZ6cLONEC7xY+XxwESYiGCg+c/ZtCo51ADlKSFd+Yw4eMzoGqAry/AR8kcVDE6Bx0LUcKBLw6QqRiIPJAnn+0hY8C64Svlwz2HIpGya2eAjDGsOZqQHcEKlAxDlVx/4FoYB/M6KquBjm1yEUwwBGMMR4yREpjvOhRpHACWi5C4AGMYKL47HdvEZs/BaeGbOg5S5fMmgdwgzoqgJa1XJrj3bsPmrHTMUICYAJQgpWsJD1FzvpZI8xzHe83rIK9Nmfk4Tbm9idIWwaJ8Q/US87FRDgw+Pvzww9jYqE7Er732Wtx111146KGHvmEdu1T+7qXnUJ7KpEq7Lsl+92YxXvveL2E49TUAoY55oGGYAEAaFTt2ADCdzVoYmEsye+pAF6Bn/AEapqRmEao6FzFGoyDBF04P8Rd3XwAAbE1CNZPTtBBHkdp/Ikt4GEel33VGo2AcLZRdK7w5tczHGsOora4sDdl1C1ColF3/HdhsQJXR0iprLi3gl5VdtzL4akwaLeOuBNwtYlLSRczH0rgsZGVinp6tWowpgasWUGwR8JNGzfFQWgWUrkNJclito2C+AhqgNZ4H+hTnobsO8h61wWXctWelLGuXx20JxCnqLpLn14NPWoMr6psEC5hi5b9ZJjCkpS3D4LJrixrivlmC+VixAhD37zJ/VwblJRisrVt/xywBsir9cHWy6yW9AEUhOU/ErTMf9xMTV/QJHKqWlQF8MUmRFowoWYaJ0QCJAOBQ3xE+QPM+nptlMA0Cm1Un7QOXJ1jHrAo+MurAYs1ncc3OkeUADC5/dVzJfFwAeC8C8WUR16Aqu17AcG34ITKc3hlh0LGK76RkFKj8LdutXGosEa3nsOr7bgN5gizLkGQ5XvqMkwAg0mNZs995iiCKkecMA9fCsx7DvVxZA0zk744HLnDm0+NPrAKYM4+a97EDP05hI8bzbzpa/LjnUiSFBHxeP2QWBnXwUaQTM9EeAOwERMmC6tpzNiMA+HEKYqnnYdJTEdTBLEph2Pr3Z9dMETLu/6e7L6SH2n5MEMPiCxAN89FCgr2IIGt5l/QoB15mqQlGPT34mPGkX2prmPkArlo1MUx4PV2KdcfIEArZNR839Xj0xMbANKMIU8YDz1R9c7jcdRSbyDXMR37cBMOYICJ6wBbg4O40o0hpRwu0rdoMGQP2U1eAogq2qGPCRoxRYmhB0WI8GJc2GtZc9dQYD5czPIOcIia2kKErxtcy4WY+xuaaMkAIALoWQBhDxLj0lbRYRazZOXIG7GddLfuU98/CDVsfxLfHH1X+HgBuvZJbytSdOWQZuBQeeIjMxFwBicbKel2booMQodFBCBeWRtLO+0VxbhjAc0ytDH1AGTJGMI5ygLpawHDNs7Dvx1qZtGfmxSbXOEzEhoReLdC1KejwYTAGHE3Oauv1HYppmArmYxNsOtR30DFSJFmOCDa+eCGBQdQSeUAw47I585FIlYqiuLaUyXJrhpA48BRhR4QQ9IXnK0wH+xEBIQSOwmIC4IDeJEwwi7itha1gBEqm38VxiCxnSsmvZIKyaIKpfRjrpvr570mAD0BELATMxrqtZntKVuAk1AOLPRH+4scpDIMoGY0SUM2CEXIAq0T9PjncdzAcTQDTwSTj59gz1eO26gkp+oWvIJ7sqKXokvlYAh8b52jXmI9dPfMxEOAvP34Tg+BjOyeoROAbocebGTywTAOmQcBKa55ZRtE1M+XzyQPRFCSoS+XvJrsuF9d1ceWVV36jmrtUvgGl71LkqWIhBlSAlQe3p/j0Q3u4/9yumiV5IOYB//9Htvbw5TMj/OzzHwMAmPqBJvjGPQDz8QAyam19DdipTLvm0ujf+NC9ePW7vgAA6Hcs7PsabyrTRhqHSio50qg4/yMDV8M8lcDDsrLrZZiPOgBkCeZjJQyiBShcBtgsAM1ymzr5a6nNNilyOT27DVQsL65bGXxRdTGruzfLQTetzMw681EFtEmgx17QVhmk1CzGGoCODlQMq/UszfXN4nmfivZ09UogUvm8ynUAlKUNlZ/X+9fw9tOBoyXQpHxujeOWQMo8Vcup05C3Q8gS4OMSYU+6Z2+ZsKdCjtwC0i8qWQzLIIBp45pDveXbKl+rtsCfeqlsBizJfKzUX/AdWIYVDvB3swpoallIFYEztZXlMKVwNOmysnSFRDGsMR/3I7XsdaVjgeQxWKmP56YMpmE03nMD1wKttb01CbmXo+KduGIz7klHCPwkg+0sAT5WbBNaGOR5BrAMxHK47zGgvM5xmheSLyXzEcC5nRGuEynNwNxLKUtVm3q6zU/V5mL1+/6xr23hL796Ub0AEOe6I5Itn33DYfzuS58KykQip6LfWyL5fNCh+PnvvAGeI5Iy6/clgK39Ma470itAi7m0vCm7HgUJbCSVhWLPsRDHko1dBh8p+rQKKnYdmVbLivO6GAAumvd8wWY0HTDGEMSZXnZtmzDF+30WZyI9Xf1cd40UgQQfNd+7ruj3bmjw1GjDUHsrOjypezciIC2bJl0zBQPDKKPa71PHyODnfLFqFUzgZr1r1kwETDDNNBt8HSNDmJtgjPFAJ0v9nuuKoI5JaszBWMU93LVN2CzBXmQga5lnOSTDfkQQw+bCDM149IwUk9QEa2lr1ckBxrAdAkzjk+lQEzZSDCPC6+jAHTOHn1FEac6BWEANxloEYDmCzOSgMyHK677SsdDFDBfZOj/PqMke7Jn83eNnBqah3ncTkKxAhr3ME+E66nEbuBQ/HPwh/tH+7yt/DwBHB/z8NBigYD4G8EkHgdkHQrXno2EQDGiGkFkIiQOqYe0BHLhTBliWj2vlSAkHdYyWoLdVz8b9F6eIU3U6tWfMk9x5mFL7xlLXoSDBHnIwrGR7LfVM7EwjgKmBMIeaOLlKkWYMX9tNEBKH+9RqwPOuLQOHbOxMYxi2ZjNe1AUAlgSYZBQRHDhMfU4DK0MOAJSDjwYBiGbOIJmPQeArJcvAXP12dj8o/qZePJtyuXc4wdA6hBVD855zKFgagRkW9v1M2K7oU839OMU0TLWgdV+w8KcRTyBXMRqLBPBgjMRw0ddI4a/a7MIPfMByC/CxS3TjZmESJMAfvwQvePQNyvtagp6zKOVemSq5ukMRJhmynGHox9rnQyaOD32enl6XbwOCuZmExfr8nLjt6tYmsnAfx7naYZIa8Az1+Xq2CZLFPAztUqmUpcDH9fV17OzsAADW1tawvr6u/edS+YdTuLG4hvlYkpSOxK7Aub2xGqE3rObCMM8ryYrzdvkD+YWHtwAAz3/cUfRdiiBUMBqAgy2udQbzWubj4hCZal31QkZS6//pU6/Acx97GHuzSAu8Jkmk/MjI+u/88afiN37wCWo2h0pObdoozPLLbQEN6VZb4m5jIaVLJAYEk80pyY8XALyN5M4WIKYS5qLob54LINgp9Zuo2yzLrgtQUQNSLgMA1UEvLeOuLLum3KpAB46VPR9Vk8IyOFYAdxpgVLIBdRPMOstPy8irMR91fnvKYATNeNQ9Ouv90zEflwGBdeBoRXa/APSk9eMuYHq2Mf4a4IGG1Zol4B6iZq1uGxD0jWU+mgbB+/7lt3Op2LJtle9JwzgAaLmEj6gs8t4q3jGaMWy0v8RzrNtIaukPYRkyYjbSrvdiq5WZAnDJlo0MMZtfZz9OMUsNdBXgIw+jSBGVmJJnxzG3s6n1UdYNS21vTyKR2t489z7NETEpW0phu3qwpSjKzRQNMxwAMa1m4Exp3F79R1/AP/5Pf4Mz+34V2AQqgN+xkrF+IWdSmbNTu8Koev8Xz+HeC+Pqe1iW0mZdnjP82//2VfzqB+/hHmEai5jZjK82Bh0Ll6934JoZvw/qzziAN3zgSwCAQz0HrmXi5hOrFf9EXlcCrEMO+ouy0uGMijwJG/exQQh+8uknKt3r2iaSQko9rx/kFL2aHLDnUNgs5pdBgIpbPoOtSKv1bMoX1dRGlOZgDFr5sOdQmCxGZvAUa+ro3yEdI0WQc0aNDsy0WQoQgu2AAYSIes17s+9SOCzGlk/AaAv4aHDm4zQxtIChZ2aYJAb8OIXl6O/vdReIYeORnZkAPDXMx9xEmORAMW7N59BkCQghXCqdZtq5hJTFD2PC2ZFa8DHBbkgQQc8aBDgYO06pGDM1ILMq7p3d0NB/WwG4JMVuRDiQqfHv7ZAMfmYgiDOYBfioAGPNFGAM+4kJUJe/9hX32/HVDnoI8HA04NJsVRiOYJWOEoIkE6CnDmi1cmQ5w15qcZan5j6SNnuEEPXGJABqGvjZ5z8G/5/nXK/8vUNNrBgBpsxFTPXgI8BT04OcIgRP1tXWE2sJlQ9g0XeaI4HFveRbvvE3HpvLjVWya8/MSmFKQqnV8s3sOibMcA+MAf1sqK9nU1wY8+ujC/NZt3NkjGEUE1idHgdHNeCjZ5ugOQ+/2p5EoG5Xf39aJgDu5ThMTCSmC5ppWMPCjxbUwTDMeICcZk7Sdy2EwQRP+tD34Vvzzyrr9BwKQoBzQjavYn1KhjHLM+yZh9CH+pntufz9ngmpeUTsxgZU0aZgBU6jRJvc3ncpxmEKP1InkAOlaxWOMHOPokd0wL2FLA4A6mKS8uOp2KXyuPGUA9XHwvuVwKJNObswSPTMx0KaLViNKr9FoCq7XvWa8m3e/yrz8dyMgRCCgWZ8XWpW1v/jxESHqN8ZrmXCZClSFQbzTV6WgmP/w3/4D+j3+wCAN7/5zf8j+3OpfANL37UQ5AmYQdF45EoTNemH4PuBGiBULfJUAJisC+DC7hDPe9zVfJevYyEIAw0Ds/0D1zjmgZmPigVHOFTU1fhDxjOcH4X42ec/Bt/1+GP4r58+hXvPDfliqCE5t5HFEQYKo1rZ/uXrYjF4TsFoVHnEqZiPWdr05tQxe5TsK8lumfs+VUpDbtvRAwOqhZcOKAJBRZ6rY6AC83qEtLfZYHRqFs2VBbAOvKvV0y0G6gtq3eQ3CYDO6vyYbSE3psWZm4Ce+VhmUQZDRb8UAN9SEnhN35SgrSqdOqwCmeW+FG3VQPA2oLUBWLQwOBeBmQXLU1HP9mp1g+qY6PqnkqO3+beWJzzLyq7LnqJZzBdEdS/eRUXJLlwSRARKgO2yfxfNWWsa/7Vq3SWeXVkYqzJdAbT6+ylDS9pA3wQZqaZdM8awn1BYZvv4E0LgClBClovjCDEseGZzMdFzKEJkCFICySs+NwxBFIs9GSgQ5vN94u1JBMvpKM9nYOUFS9KPM7hSdt3KtlUEfKUhgEGzHgBCnVLgjMutQvIUMDmb4qvnuNzw9z55Cq9VsIQZgAcv7OG5JaWMlH3lqoA80bc8CfGc/zhf6H3kiRqgUpzrmf35/TcLQvRUm4sAfOGr2LV52ESXMrC8vmnA667aOVzLAhUJ0isdqwlsmjYYgC+f2sYNh+ZtSEVEmkQwS/3ODC5jv6wWNtRzBdvFAoioHyacMXWsBmpL+a70+9z3E8wyCkpzodKgpboUBuPMW7mxqgMaurYJggwR4wtaarvAVAdEJdjNLS5Htj3NhkwEgwBbAX8n6kBKhxpwkGArAMiqp/d8NBLO4osMGH11W56RYZRwBuIxT2jpFO8zFzH3lwQ4+Kjov0M4wCr9xvgmgPq7bhCCnQAAg1Ya7BkZhgB2QoKTtAMkO8rzdJBgNwSXLLcw+LpGgh1YrYDhqp3jIsCBphZJuwQ8150WRqaRYpqaCNNsbiukeNf0Bci67TMOAOdQ9s80CA7ZMR6OV8CsLhA3mY8dI8UUwJbP39WmpVcTrFocUNoJBYitOY/DQu5qEACzHaB/RFnvux5/TPlzWdZohFHqIrZ6reBjn2bwY4rY0PtpApz9DKAIrVLWoRw0jNIcDnW0c/UrNjw87rIV3H12pGQ+ukaOJJ8zH03NhoQsXYeChnvIGUM3V/tbynpn9nifdH6Zqw5DnjOMUxPU7QEh9M+8zRO+E2Jj349hOl1tXcMgWLEBluQYJwao0wXRsYZpVmze7Pt+62ZAz6XoTx8By1Lckn5ZWcc0CHquhXNDfjwVEOhSE10EyBmwY2zgSk3yufSVzomN3VmMnuHAyhVzcXDAzU+47FreP/XCpfAJplGqBSgLH8tojHHvGnTZfeq2XIo8CcBMB+Mox4rhaAH1gUvBhpyYxHKmDYrxbLNVdl1Is6MUw5a0647FwcdhkGBFw47sOpxJy0y+sXN6kuMakyiD1QCh0gjnc49hbMDVMD25KiZFwiguwY/VshT4+JKXvET535fKP+zScyjiPEVKFDd+aWdXgo9xHAKqB9QsMQ/khFwFapX+/8LeGI+9ki/uVzoW4ihs1gVQ9p5cWLSm8ZqPt1Z2rQI8VBIuC0kSYWcaFSyNdc/GaOYXv6/Wt5EmkfpFWGcIqsBC1ZiqWEc6L0vJZiyDHbo2AQHyKK5Jfdx04EpxXnXwUQMUVcJcWth2jf5qksGzGLBLxhxUM1lqAKQKQBdQACIaFqdKnq1l0gl4QcveK41LK4syLAFjmkm0yt9QCbTLMS4BDjpGozdn7ujZtaXxNQw1aFvfrDBMDhApx7cOAmvYzVk8ZyjqJF/1lPVFHqJ1+bgy2EfFfAwVz54m+KQNCJL9leCZPK8sBoyO+m90pQ5+LpucrfR8XZIxWUj5O4vPswEwtwCceQaA1YAs3X2RqjfF2r4zeQJGzAr4OIsz+MwWEkEfcPraP+8YaQV8PD8KkJsObIXste9aSFkCPzOxJn62NQ7FYq85Zq6Zwc9Kno/TiHs5qrzyLIZRzk3sZ3EGz6H6TRRZKvd9C+guxs+gpbTrcuCWaeHzp/bRdSie/7gj+Pj9O8D/n70/jZolucqD0SfnzKp6pzOfnkepNbQk1I2ERhDWB1gWWMYYXYyQkEFeHrgGC64RvhcJ8CDMh2UWNgswgxkN4hrQBWNkoBHGYBlhCTFrVqvHM79jVc6Z90dEZEZG7h2Vb0vtj48+sdZZ55yqqMzIzMiIvZ/97GcH47Trqm5xlC7xkrt67fAOfORSqQE8dGl38PEyXWEex8O+cs1u2xZf/R/e1338g+/5EL7xBvq4y9VykJ628CpUbWBUxhbXuUpTfO3L7uk+VmlVg7nsOCgRIECJL3neDd3Hm4ksfmCwOy+nANoW5zfGFax9VGgcT6TkA9hdFSicAHNDy3EWCiengSiE9fj+EUqE8JWmn9ev44vIR9uWyOF31U49RstxFnqo2hJZIwrJhDbmoyMqMh/lNbwZA35VBeB4uLwU4xestbGj6kCAXweVDy9MgJIGcpSm5eUV4J6akaBc4or04VVRIzwh11Bm/VeakF40I8fvtwWWtYfLR+L3fsjv664LXJQAGacN6dU5HMfBpRWAhF8DA1S4lCrwESwolLiVYEdadPBCFHBcUZzECXiQMnIqXMkc3D1ngrUQ6eBHlYO8bHo9TQrE9hssAVxOW8GeTflrOB3k+JNiDoQLMu06Qgk4Di4uJfgY8qDiRlBj6fg4yFsg4dfBk65gWLquA6TXWPBxXdvxMlzLYoSzOcvcA8Qac1R5cIJErB9McEtpCQYen6Q49xrkEGmo4Zq9WmVl7RAgYOJWKDvmo9RHtRxrEfoIin1cc09ip6FBM0CsY3/4yB6iwCNBT0AwVJsWOKw8BPFcgI9Mhft55CNoS+Sth7SoBfvYMs7tsEZbtNgrPXgxvUYAgvmpgjd7q1LqvtI2zGbsY54+iqYFNplUaUD4vo/tpYIBR6Q/u66DE0GOpm5xESeRNKuxHYm++nzlhrh6lGNhyRSZRz5WeYXDrKKz8CADWy1wdVmQxXIAAZ7OvQqoC+wFZ3Bz+0H6XiQBgrZE6c5w8SDDjRy5A9L2WV1G04rg7jaTOp6EHlZ5hYO0xC2KsKM1Nea0rLHMa7YYU6JATCZ9G1DZJSVK+AgBPLTfIHBd9v4KHcd+D98vRaCM6+tD7LUzCDvvY5eO8OK7TpH9n0ptUtr1wcHB5D/X21+cthEL4zWtCe0CTUdHgY9lvuqdD71R+mwUSw+QKT/A1f1D3H5KGLzbswA5VcVSHVsaBAdZiU9eERv2xYMM7/z9h3ptKXXOY6VdF+Pr4cBOUpA+wsHREm0LPOO8cDx35iEOl6kUdh8zNBoWfCS0r8xxq0iWO2RRjMZLFlSIegaKed5R6qdyGBlwgCsiQ6Vpk+w4DgCcUPXXZF11fS3Msq5fyDP4RkxOjtGmzRcb43JSerbOpOPOaaatTzgnp4FVF7IKs2KfWdKkAYMRNxWMZUA7ExRiwccp15oZz8HCfNQZggCd7j0oXqPAPAbM7EBUz8J8NeddBFHsxgwmMGzqdYVYBpIHMX1dU9rouRyDwQgMWaqT066131iZj+Y9XANwmoC5+jc1bxtun7CAoU2F2hFpsaot8woZYuGQMo6QarFbIx0wHzMkyYyMni9CFx4aLGupc9i0uLospH7cuH/iVEjr3lS7dJAjiRPy2hd+jRKiEENaVMJItxX3AmjQ3SKd4fphX+3akDu4tixweiPCi+48iavLgmAFBiiqGqeSttNQAwR7NAo8kcJMZBQAwJ88dBlnN2P8xzd9DgDgkxd26edcZXjvx0WxlxdLgLM19x9t7FmaDtgfM69B7Yz7ti1wbf+gz16ASKtydJkQ2Y4qF2cSDBynRDqgTTncjy4shY1zKhrurwvpZNdu3/fSgah0bqZde67QF1XjvniQIUwSweQy5pQokFQja3rw0WeA743IgSvn6rKo4MczEYg2pWAAxG6Fg0o4jZ6F+dh6Aa5K8E4AB/Rc8xzBznNDHkibSfCxQIAwnpNrTuxUyFsPFw8yRDYZgioTBXUAURWbGP+pRADLH714JPvR7yGqAq7j4LFD8Zw4bUjUOXzXwaVVi5bd1yt4aHBUediYJXC4ACUEAFw4AZyQOZa8Ts8RBX8EcEef03caFAgkC5Q+X+hI5mNZi2MB5DxS8hMXl0AcxhBSOvQxt3yh++dECzLt2qlLuE4P7HocAAxgU0sldgIeeDrrr1DDFYGm7In7sZtujqM2BgKerQuI+3FQeQLkBvgUeelL/O0X3sIea+GJKvNFZU9BB4AX3i6k0UgWnlt3UiAHWSnnNn+sWeTDqXNcw9aait0ejrIKJ+Z02isAbAU1qtbBUdFiFkcyO4lPuxYaywGyqpYSCfz5tySwuax9uMGcPe6GYj76MfZWBSsJAQjfOsz30LQtTtRX2XNvxj4e3UtZcAwAdnwRLLtYb8J1WvKcSSDSs0snxLVlwcpCqL510+LasmBZjerziwcZmQ6u2olASHnseqcQNyshizW6xgBRW6BAIDM4+OexEftw06tdkPdsSK8BQrdyPfPxKBeFjLjr7ArOpAXLslwodqJcJx7Zy8ksFNVi3x0EHPdLUe2a8o+T0EPQVp0kzwce2sX3PPBR8rhPtTaJ+bi9vc0uGqq1rciTr2sayb7e/ve3hYycp21gJlCJzbFKgabBflpiEfuoMs2Z15tedCAwDAzT+HddlI6obHfbqZ75WF7JACodWUvp/uHf/gT+8x89jtd9zq143yev4SMXD/Hg1RXObET47NtO4NmkblMErJgIowmiqf4cq4Po25Q5ktDrqp2emAsntvVbOARTsipzOkViSrEEMu2a6WepMj74PZX6yaXGcmPtwJViPD+qAoi3tL4cqGgCexYGqj5Gdf512ou2c4/GaAO9jOum5laVjxmXLCi3BsBRYJNqnFExYFFawFMTuOJARfW9bWyTGYil8RwIZ0U5q+4EAFpPp7aOLxuyNwHivAQ4TvVTn41AZdqBpopRjObPcWQf9N+Yc5Ab77pWG6CIHwMrXhi+a5UCsSWYxkkkjH6ngSpTtC2nPGO9PzCdET1Kuw6tTBTUFVo3HGg+pkWN1ImFZInFuQGA0Kmx3/TP+sJ+jq35jByf1wg9uFUlAMW9laigzLGoYrfGZQk+VnWDa6sCyc1zoL446jt3a5Tw8fsPXkNVt8JItxX3AjCoVr+u0BIk87HJyf7XlgV2ZiFu2E7QNC2qIkdggMxV0+LmzbHIfeS7snI0nU3x2NV93HP+NM5txbj77AJBQTBc5Rr1+H6GwHPxz19zL37+/Y/g6HceoG0BAGmWYiPuWa2JVvVV77ssKvhNMQQUQw9uM37PDysPzzwfD67RcYQETZsOr3E3d3DadeAZlc6VU1Rra+aVoxyOH8FvxvNqEbSoJPh4YT/H1sYCOMJoDm7FPuq2xNWsNdKHCSatW2PfcbBfuFjm1RC809iUgGCk7RUelm0tAKGCfjdbV2i1BZ7LMh9RZXAdwfTzohmQcYBBhRUcVPARJXMgHzMkQ0c4lhf2M8SRBfiqMlSOeC6L+YIEKWKnROsF+NRV8Z0fxsCS2dddHxePxDvjhTS4iyqH77mChWjZ51zHQdmGgrGWhjx7yy1RYIZ5kAArZj2tcjiuKPgjinXQ5/QcByVCuKEPHNLrX9iWyBoPe6sSN29o++CoXwE4Dh5dNpgnAbC0AIZ+LdmbHDAq2KIXly18zxGMcWZ9i5wSBcQzdS0MyTNBikNnA1uhT7Itp7aFW+AKIuFfFfxeO/Mq7Dc+/GgBrCCeJ8Gs/7ynn8btp+e4aWfM/lJt7jdIG5H67vgxkPL76Jc89wZ8wTPPkd/FboVCSnYc5RW8zQiod8m+gCpGleEKziOsP8H2Uyy1HaLSsGqbYYNcBs0WkS/vH5d2LVKQFdvT9lwBWUimbbGsPdF3nwF6gxoVRAbSQWqvor4RB1i0R6iaFgtLyvlWEuDPHz/ArSeJ0smy7Xg5KifApUoGi4pV72fLpooUlY64R+DWTfTakpcOM5xaEP48evDx0mGO81oQcDR+v0KTtdh3NmQgKx36PhCAYoASOUI8tpeKYBHz7DbiAHv5Ell0EsAj2PGYoJJMHefAx8h3AUfYG2hpPU1AgI+i4Ew5CHbqTbFKS7l3PrqXivWaY28GrUzPF/2PKg8u2k56ZnAdgQcfFXIZmH5sL8MNW/z9fiq1SeDje97znid7HNfbk9CESG2JtB5T3Us3Ejh/nXfU5vpTIio9gpnVC6UbT1TFZdnS2sNm0OC0XPi2kgAlx3zU0ooPMmEI/9T//FT39X/9kwsAgHf+/sN49zMYx8TGfBw5HIRT3KqFwzx2gLrMB1GVE/MQfluhbgCXcHycuqAXwpH2FcHa4VKkKfCRSU0jtTkZxoedMWqAFsAYnFHHMDUq12kWqmPagLERS3QKM88G8K2598CQ+WY7XpUBs53+/2xhF415y4KK5n3hmKNaP45VRl4n1Y+qZs4AxqP7yz2HNfetzgXw6Gpke1s69ZSU3Eq7v6pC9Ujz0WA/ryvqM9DyZJ6ZGajQU6PN6xi9eww7Vx8vUehiEvNwNM5jAnzd7wwQew2bov+doR24Dkwc3EM+kg+gn3cmMEvq3FJp16FIp+NaU6E1NB/TskaOSGqsrWE+OiKNWrWLBxlunM+BlF5nXEdUSQTQpXAKHcfx9YRO3QGVythOkgQ4GB878SqUjo9/+V/+HIBkCFh0wNR4+gJfFrBbpV0HESql6WSA43/22AFObUQ4syHm7SpLsWUAxnXbYoswBeLAxYghD3TzZJWm2Dkl/n1+K0H5CBdcLHCQlV22wpnNCFfylDhuz3zciPr1fOY2XdVXvW9W1jg5B85oxR9i34NLMG1XtYfTBF6wGXloj4b999MKjTveQ5JAMHwqjYV5lFdImEJLG36NohHHvXCQ4eTWpgAfjeNuhC4O3BYXVkBd1IK9yTA93EbM1f3CwTKvRbo/IPpGFPjoIIesKk2BXzKg9ahMReRlWoRmYoFQpGEyOpNRW6BwRHA1TubA0XieB6g6x3KRBPx+UmbY2twELgPJbA7sjRlNTl0ijBI8dG2FOPDYgjOoMrR+3OmOeqw2ZAbfEyzEKFkA1+iMBtd1ULY+zs1CoOSZiHOvRu6EiNU1Ns1wzwWAMhXzzXEEYEuCvzk810EJHxuLANilzxc4FSr4uHyU445T2/K34/vhSK3PT+1VuP9kAOwztgQEC+7F99yERfMom7rvOsCFZS2Z3fxe49Q5Cvns/YhnPt44b/D45jYWm5E9OLimzdwShRPBieZA+TDbb+5VuOLIub0Cu7/4njsoWkWe06txpfGRl5L5eMjvo47jkMVmACBCz9BKi5rXM1XXEPlw6xxXnU1RsZtJHVf+00lG7xEQmqBpIzRo55En9LiZe+K5DiKUSJsAadmId+uA399ORi3qpsWyCRCHirk99qF2InTg66qsrXbPRuxj0S5xtd3EjYoRaL5nECnJrQUcA4BNv0LhJtgt/V7iBSdH/eaeYCKvCnuF903JsvzU1RVuO0WDnh1AeZBZ59fCFwXJdrEhzMFyDD7OQg8xCqSNj8f2UwTJwsp8bMsU6WIHrvsoPELTVR0zLSoBPhKMRcdxkAQeLh2Ie8DrVorU/P20xD3naNkcwaStULQhVkWF3WUBf2PG39+gEen5Ku26cPsMA2NO+Z6L0Kk7He7H91Oc20rMQz4l2yTw8XM/93Of7HFcb09CCzwXoVMNnCLV3vhTf4K3pytc+fij2JMvZvCgoHWPtghKJ41LuwaQI8ANC6eL+m8lghFoRsoBdA7k73z0Cn77I5fxWbfs4A8eEtG2v/vyO/Dvf1tE1Mq6EecMF+Pfk6l3tQAUTU1Dquppdy1jh6OtMiyS/vPtJEDgVKjbFgHBPnQaG/hopAw25VDfgyriQ4GPXNo1QF8bwySZnnZtc0ip9Ek6DWkEKJEalVTaNQPYmONkQUrz3ByoaLIzbaCcAVKtSwu2paObrMF1zEeKWUgdi0tLNRmSHAg4YgJyIGCOcer4FCDOwhg1z0s5BBRwSzEfTfYmVz19BN4y1ztiMTOAJsmmXscIPMa7t65NvZ7R78xAyZoxD85nMB8J/SIA9JyxFoSRz2sgScEAFzWRuuutAX2bCo0BPmZlLVIYldFtaSGqLo0aEEbm1s4COKJBPNd1cFSJ+3LlsEASeoJFRdyDyKlwWPVpRgAQxzPg2vhZek2JExsLKAmieaf5aGE+6gC9rbq5fAbeIO26B8fbtsUHH95D6Lv4p696Bp52JkH2cIktQg9xg6gCHvkeWorNqDEUb5AsiJPzEFVpvP9At18epBU2ZIGXnVmIthIFhQaWkOsBjos0XQ0cnMSrUBqmcQ0Hh6WD191/bsBmnAWiUvoIfGx8bAXjNKydxJGOS3++vVUhK6sO77nrOtgIhkBoWtRImHk/92uUhUyn289w9/YG8CiINbGA5zrYLRx4RS0Aai+i003rAp7jYL9wBPAZb8vPx+cP2gJHlQf4ENqQZOAmhxfEqIoWUeKClyPJ4LoCfOTSqdX4XBn8iZIF2c+piy71dmcWWgDPDE+74ST+05e9GM4f/zm7tsTxNh68uhIgCbeuVAUcP8JRViEKPLiWrIzAc1E2AbY3N4CLNBDouw6KOlgbTJh7FQqEuCwL+qDKxoXV6gInt7fw5c+4Ge7K8pxcB0UTYHNjQzxvAlzy2xKl4+PSYYY4DMS6y2RHuI6Dg8IV7E3LXuRUGf7KvbcAH/pjGsSoCziuj49fyXBiHtiZ+WXWMR9FKj0HYue47dxJcX2fBvMxcSqUWMC1MPcAYO5WQiogUWnX9v3F1mZehbRWRX8m7tVEi5wKhxJ8XCnwMePthUXkw6sz7AXbQr+tWALxKMeu07rdtjAfF36Nw9bHYVbh5GIOBHwRGTXWo8ZDVtRWWQYAuHXTRdm0OKw8zJU0QDkORm2FDVJZcGeVV3Bj/l5uxAE22kM8itO4xXkQKI7Ia9+IfZxtLuGzio8C+Cz62r0ShRPiau7D9S3FpLwKOQKsCpVqTvc7uYiQhB4uH+bYYEC5wHMRBR6uHhWYM2A0AGx6FZq2xW6zYIOwjuNg5tVIW18UANqaW0HbtsyQOQlqNwFyWuJgFvq4clSgblprMZkr0r5imY+y2vXuqsAmcxzHcUTQpvWxkmCm2L/o+bcIWjQtevCxlHINREAOEOzrXL5Xj+9neIGUPniqt0maj1RbrVb40Ic+hD/6oz8a/Hki7fu+7/tw2223IY5jvPCFL8T73ve+Sb/72Z/9WTiOg9e85jVP6LxPhRY7Q6cIAFaFEKUGgO/8pT/A1aMc95zfRIgSRzXxEpOajwRQJlsBHxua4X1iHqHIx1EBcewYRbbCW98lKoY1bYuf+JoXAABu2E7wX//xy3HbqTkWcUCDbmscJdL5167jq37k9/DfP/I43dcL0FRDMNH3XOxEENpgI8cngFOXmJsCvm1LABb+cJwAnZo6Ne26S6UeOxo8+Mhs2CzzkTKMTWYZB9iYQCEBaOv/nwoqTkrPJYDfdWChGgMLGBpsunVMOhZUNAE0jjmaDoHMKenAXOSWBKUYppM34X6M0nuJ8R1H/3BU0Ie7JxPAUTMaaa2ebjxXzsCjgDP1ud64d8/KCCSK2QDTQMO1x7I4aebvpoD1ZjPTrtt6rEHbncNkGcd25qNZBRyg18amEecl9HitBWfqAq3jQcMekRY1okBqntlStgEEKAXwItu1ZYH5nHGKaqEHd1QIgODyUYbTGxG7LunA5jIXoF0QMYyrusCzbjnd/XcjtjODAICUm+DWDQBuEKJS2k8ak/hRWdXzra9+JgDg/3j6CbRoh88ZQp9vgxCajwOppUQ9OwiGonJETixC1CVhU0hWYHdPoUTfqwGDEIAs8hUhS5cDBydxm07/SbX9tESJAKfiIaA4cxu0bTu4f1XdYFV72CSucSdsB6wJANhLS1FZlZifc68eAKHLombX64XW9/JRjhNbkulBBGQ8x+nSrhMFaDEsdNd1cC13kJU1IlUtmlgXgzbvgZ6YmftVAS8Uz0UwH5n1sBSpxoWjtBwZIKJMcfbkNr7iBbfw2oRV3j3Pk/PQKlvihjMhrWPZm+I4wZXDHEnoWwOUXtDPP34PyxCEEVrHRRgzbJsqQ+A5faXrIGGDCUEjnsH5Uzvd/Rm1MkUUz/D3PvdOOX5qj8sQBx4KJ+xT7Yn1xmsq1I6PVV4L5jIbKM46tqUAgC37oAq0csB0LQoD5aXGfGSzePIOePZsOphlJu5rtMECIlOaSvP2bHMWAqQsEArAHFjLrLe1xG2QtT720wpekPTFQY/ZQkfowDZNi6ys4UcMkUC2eQC4bY1dZ0voIjOgrUoZ/5+f4LURFbB28TDDIvLEs7CAt5FT4rDyUdaNyBiw2A4nogZN0+Ko8sRzAch5cMPCxVEtApDLohYBA2ZezQIPC6xw0TklgCdmzmzGAb6qeCf++mPfa6lQXyFtfOQIBSDDaFIuvAp5q4GPFltOpVvbtCYXkbBXZhZW5sKvBPOxmUtWJncNJZaNGJt4z/i06wg5jhofubdg71sSenh8XzxTFnwMe/BxXcXux/cysnBN10+Cj5ePcriuIwMVDPDsyz3fC1FLUNtxHXauhFJfGRDg4/nrzEcATwB8vHz5Ml796ldjY2MDz3rWs/BZn/VZgz/Hbe985zvx5je/GW9729vwgQ98AM997nPxhV/4hbh06ZL1dw8++CC+6Zu+CS972cuOfc6nUlOC0Hq7elQgc6QBiByHWYXbTs4QtCV+86N744NQKb0cuAcgbwNs+L3TefOJBFWZd2LGw2MHQksJov8/fMVduGlnhl/8By/By592GoHn4hu/4Gk4TEs6dZtJQSK1A4EBQLGflnh0N8V7/vQR8R3BlmmrYrT4nYgdwZAxxtK6AbymwCwyIknKAR8wGgnAoiuOYaamTixkAUxjnHXPk3HGqUIV1LEBjIu5WFiKJkipfm8eTx+j6juJcWdhKk4q6mKArlMYjdbzao70ZLYl5xgZKdws83ECc5NkoRZjMWnyvhFgj5l2SJ2XlEDgmGsUqEintq0FyUxgFLCAqCboyYEwBvjPAYSc5IGt8Il5n1xLBfR1bfR+MHOL+t1xisGoZhacAdYEOIy51ZS800QFu6h72a21JoudACoHv6vRuD5q7R1Iy1oAM2uKBwBA0JZYVv26fZTXiLhiFE0JxwEOJfNxd1lqDjnBKEOFw1L0XRYVQt/lU+Kqoq86C8EOmKa/aQRnyD1VFC9zpdENQFatF9kEf/bYATZiHy+6U6SMbfqNWFKMIF3e+l0BCr1FvkemwymGYpb14ONWEoyDSmrsVY7Lh3mXHp2EosBKBYLh4YfIs2ywx8duhcKwVa4eCRBjKxyukbEnixRo78u1ZYHS8bHhj8X5tyMMWBMAsLcq2bVm4TcdeAIIRo7DFC+Zew3yVvRd5hVmcSy0W0cBPsF8vJoKgH3epa7yjLVHDsXzmiXScTLnR9vCa8ouxTVJGOetzhFI9lHoKeYjvcc6DgTzcUYzGtU45skMb3r5HXywrcrxyntvAiA1u1m2oh7g4wN3ibwHsy5tnD6nKsCypVK9mfTsIErwra9+Jp596zm6mI8MVrzl1c/B177sdjl+5n5UOd78qnvxt196T39No2sohtfJZFr4roO3/6378fw7bxCfUazSpsBcAtKxba2pMsHuBbAzs9yPppH7fyKeJxn8KwTghAnrW5mJ4juAff9TxQGD+driYrYWo0TuhPDiDWvAKnbFuzKLZdDp02A+Rk6FyvFxbZnDjZ54sDJ0KqStj6wS73q4Rktx4Qr/YdfZlgAcDT4++8YtfP49Z/Cvv/y57LFmstL2o7upIHuEMxaEQ9siRIVr8pH7MSMdINtGUCNrRYq2H0nwkeh/ImxQwMeVoxx5WYviU8y8cl0Hc7fAFfektUjRZhLgXHtJZE/sP0L2mXsCuKsdH44fsHMhcUX67jKvZJEl/poV+LgR0cAd0AN2C9NfHYytRuV42Kt8q/Z14lbYy12IWCP/Dm3EPqI2x34ZoAzoglKAWDM/cuGw+zfVZpLdGTGVxAEMqqvfcYpPL088oZt65VDUbHBs1cQ15uOyqFA4oUy7pt+V0BHM5KJqsLsscHYzIvs91dqxwcdv+IZvwN7eHn7v934PSZLg3e9+N378x38cd999N37pl37p2AN4xzvegTe96U144xvfiGc+85n4gR/4AcxmM/zoj/4o+5u6rvGVX/mV+PZv/3bccccdxz7nU6Y1NTy0WDWGIb0skCPCDTsJolYYAOc2Y9yx4yNtiBedBB+JysyyZa2Phdcb3rednCNEhaspUS3Zj1HVLb7seafxm9/0ebjrjFgg9DSoG2SkIM8zlsm4n5Z46OoKu8sC7//Ubu/0kWnX4rvX/fDviS5K6N3s60doq1wYS1o7EUMUJjDGkreiwM+I+Uix+dS5dACQSxckteSIggoAwfwiQB/X5dNjuuNTzMcJrEI9nXrQj2DXAARIVQqwZVCdm2O3mow7W3r2BFCRYmc+UcZd108HDKewEDlnJu2PpUAXE6ipMoxT9idcZzcXx07qei1HBhQiwUcTiOMZKNOZpWvAVhNA7fox6egj8JlmrtFzmQI+qXPbWGjGeG1MzXVtxAxek3qs/+6JMB/JwiXM+UYAZ2LvTwWTFEimrzWcFvE60Lep0LoBag0vSstaGK8TwEcffWp03bTIy1owhigWSl3CcxxclFWOs0ro7onrGV+/jwpHEnxcFZVMpZ4GoDuOI567LaWdYvKSmrJiPvmei0qniAYxmiLDd/7qhwQrQ6YlL/wGDdrRe5+1AeYEMBcHrnh+5h7oOGi9EGWednZB0lWZNp+zuNaLBznOSoH5WeCL9FAy+Bkhz9Mh+OgQ4OOyQOWESJwhMDTzBAui0SpSX1sVKBCS17gdtiJrQrsn+2kpC6+Mn9HMazodMkAwH92AnieJZG8Acu52KffjNVuBj8su7ZrfOz0XeORAXHcykw6cecymguu0HfMxThbsmq3Au/2UB13FeV2U8IV2ZVONQTlAzFNVnIG6Vnm9L3/Gjfjnr3m2eHdY5mMx3GMZ4Gs+EwyajVi9h/ReooIAiQ2krAs4XoRX3HNGAAoAYcOJ/7/w7hsEi8zGeqoyzGaLDvgkgcVSsyW4oi7y+PfcdLpjcHLg9MZc3I/I96zZEY7cUzbiwLLGKEQpstpMoQQf07K2syjrHJ995zl871d8Fn9OQM6jmdCz40CvCS2EYEgHiZ35GMvqupHv8hkWxzzn1aNCVK0H7FkETAtQIatFgQ4ACOLEumcuPPE+7jubAnyx3Lf/z6ufaS2akzhC4qRuWllwxiK1UAk92CvyEoNoZr3eLV8wQ4/yCoFi8RLH3gwF4/0Tl1UxKftzidoc+9iE63kW5qOPpE0FM/TgMbLPzKlwJKXRnJCfN4krdC5XRc0XipJNAVw2rUn13cz0V7W28EoUiETw07MAo06Jq7mAk2zaqptxgBAl9koPrcczJF9w+wkhtQZZXIY6Z+Dh8lFuBU9Pa/rMlHZkP/4KaePh2qrAyXlkXWM3fFHACH6Ig1TYFV3aNdECVEgbt5PMUXIwT/V2bPDxN3/zN/GOd7wD999/P1zXxa233orXve51+K7v+i68/e1vP9axiqLA+9//frzyla/sB+S6eOUrX4n3vve97O++4zu+A2fOnMHXfM3XrD1Hnuc4ODgY/HnKtEqIPOuMDAC4tswxSxJ4ro+ziXjBz2zGuHHDFelcZlOG8jqWnmxp62OuMR/jwMMiaDv2xvDYoax+yS+AG5I6XhX5GMCQANHb/8uf46v/w/vw3b/2Yfy//r9/iFotzBTzUYJjS7kYRA7jsHoB6rLA+e0hTfpEjJEDAQgmi48a2+YiR6YMEoAuqeVIGLdkcRyGzUiBPuq4rKNvpF1aixAQ4FnbjNMtKSALIAxtAiziUiZJhhwHPk4A5Ugwi9MGXANSNg0GBWemFKVZdw16Crf6bHSsKYxMLrXXBB9NwJhgK1BzmwLYSCCOT0UbnJfV9qMYeobRQ7GjrOnexvFIPSyTneiJtXCSziVzLd1vCKbmVOYheaz/nczHqh/7unRxKlV/XX/AeI/lvylGPiVLYdV8LNE4Xp9ODMEKiwNPOKRMhF78toaHBoelTI2WFYRjpefFaO5dkcG4rFApiwSzGIDfljiQxz7Ka033jVsnQvzIV382/v3r75MHsIAVAPEsuHVDzGfPdQbamPAiPHpF6DT/s9c8u/t4EdRomxaNY4KPPubeGEiKAw8utU4AyFsPflt2RQu6KtPEXtFUooiecjzi0IWPGmVLMx/LIsdmPAQfFYNQtWtLcS7HWP8SV1yHntVxmIkU76AdP8vNaBy4PEhLeMzznHn14Nh7q0IAL8Q8mbsVstZH27ZYFdo8IdZsz3VwcdUgLSqRqcEx0Wrh6H/okhjbPIlpJnaVwQG69GaXlfwoEcuiNUd5xc55VCkW8zn+w995gSiKIj8b99MC0tQ5mxpoBQv5xXedEp95IQMs6gE+LkBZYD4X41nEPr3XAUCddyzRS4cZv/+XOtuSsbPU/dFZ5bb0YT/qbQTKuR9oUVvedUjAQYG75vilnJACk/v0fcJWqzKhaQngaWcXPLtaXVeQWFmZofRTBIDN2GkAUGZIZgs8+8YtAS5y+0u5EucMeOBkSvObHIUTYrFQgDmdXRQ5QuevBx+fONsykNXci6oRwA9gDzYxLXYqHFYuVlLaI4rsgdK5J66t8RPJ3nwCAVLZ/DZHKbPxFor5yDFHZbXzK2qqRDN0mqRE2/Qr5Ijw+H4mCvwAjGxEidaPukr2bIV62aI2R+bEAjBkxroVuVi0SwFOLS+TfRK3D1zaKkXPJEC2LCqZ+cDP0+ffImQX5hZgrmc+8r73zG1QOAHSskbr8/M0dsrh82DGFgcuEuTYLVy0lnn/WTdvd//WNZb1loQ+Tuz+Mf7O6kfHZJeuj4e/89Lb8dNveiFzhbKfWyNt/N7ms6yxKuAIL8TeSsgssOBj2yKAKBio8AbbM3kqtWODj8vlEmfOnAEA7Ozs4PJl8ULde++9+MAHPnCsY125cgV1XePs2bODz8+ePYsLFy6Qv/md3/kd/MiP/Ah+6Id+aNI53v72t2Nra6v7c/PNNx9rjP+3bnKRXhoFZ64eFTi1IdD9r/+8m/GO1z4PgKhWeEQUpxHsG8NxNVMetZbW47Sq2K27cvN6O6g8VHWDGzf4F9KX4rh1STEfhfH6ySti8X/vx4WuyIVdCTJTgEfb4Gt/TLAeX/600zharsi+rReirXLcYVQM24lawUYwFsXD0kXQlmOaOFlIRt473UjgisNQgKLpXKtCAVM0HwHe2VBjGoArtiIEHFBpgk8Emw0gHPPjagNOSKceadjZAL6p6d7atVBRsi6CH/Z9mMqNIxCG1RnUWBkAyWgZs1CpdGqC2abOMehnPAuyWBMhv0A50iarUI1vStXxIH7iYLEJZHLjU+cdPX+KnTFxjnL6dVYgKGeO/QQcIpL5OJXBaD7PCecfFJyxBCy6vhNS11VT77q+5lLnqJm0ayqIo7emhuN6IqItW5d2HW/bdcAkE+NAplErIzNWKaoM82wv186jDF5znsvshcPSQdO0WOVVz1RjdXAj3H5qjrvOSM2/KZqP5vNmtV1D+K4zZD76ES7u7uPsZozPuaOv0rmQNkCqMffatkXaeEhcIu3aA63XCVFbJ0CFG2UgcBb68JoStanj6IeoqxpuW3fMx9BzERBsRgBovQh1kXYBTgCI3J5BqNrVo5zcN9R1ZJpto0A1E6gElF4UBtd4lFds1eTEGMuVowJJQgv6x67Qliprwa7sU1Lp+Xdh2Ujmo0UXtCoAx0PjiOubd/qFNEBWdczniGYr1nnHgLv3xi1+ja0KOH6MW0/O+eCY+kyBYxRYpX5jMtoZtuJgj2VSlhcSfHTg8HtJJQrhfNWLbsVb/uoz+L1ucE5mDVRsZrX2cVp0bSv3u1her7PefuEAAHUcx9GyasZrE9oGGzNx/yPf5dmnVY75bIbf/KbPw5nN2G7TARJAjZmCMyVc+Tz/yj1neDtNXccAtOXAx0yCj58eC3HmVbj5zA5uPiuBbgaUClGiQIAbthPeNlRt/xHgV7+ZPZbflKgl89oLmDk0oUVOhbwRbDIAiOI5D+pCBDsAiLT2gHmPJzanytFIm2+m2MkcICurnavaTGEXnKBtjbkniiIt86ovisTY2I4fdVqDQWQP1kZtLnQaI56lede2PLQbA+ku2SdxCuRtiNB34VqYj5FTYSUrggfx3GpHvvTuU/jS59+Ip52lKzwDPeNxZik4M3OLrsgNLMBoiBIXZZErwfql74co7lJhr/Stup6O4+Atf/UeIafBjt/Dq/NfwfPT/wkcPMr2e93n3LpWZzGUwG5W1khCixwIBPio0q53VwWiKITrEvImgGB8SwxGBaVtYO9TqR0bfHz605+OD3/4wwCA5z73ufjBH/xBPProo/iBH/gBnD9//jM+QL0dHh7iq77qq/BDP/RDOHXq1KTffMu3fAv29/e7Pw8//PCTOsa/UE0u0qqqpmrXloXQvgkSnAprPE9GGSKnQlr7Hd150MxN2XT8tbasvRH4GDn1wEBX7WNXxeb29FN2KvJG5KMxteoAwAuR5ykuHw5f/E88fk38g0i5bNoWj0mmxn237vTgo3E9q0aka50wmIxboWBvmO2wdOE5LWLXiMKQKYOKtWOkXY+YnRTgU46da4Bh4BEsya4vs3lR93lycRAFCFAgkAGMqc/1Zuruqb5TGHxM2uJ0LT8TvLOkca/TkDSfOadpZ4JjrGOkMSQ4JsIoHZxLp87H983s17bEfSOeA8t8pEBwigU4AdxlGRDmnCIcRg7MmwJmTk3PV2Ok3lMzSGATx+/GMOHYU9oIfOQNquHvKA1PC3AHdI7oAGwHLKlTBEsX4I39qdq1XNo1B9Zpx2/ccIDTH6QlNmMfiLeAbN/6W9cBDkoXrcaoTxTz0TxvVcBxHOzKS83Kpk+7Jt4b1wGKNsCqrLHqNPrs7MRBW8u2NdcN27FF2nXTtCL6L/vvHhzi1pPDtLqZTDvWC96tiholAszcMfNx3gFzY1ug9UI8/VQE3xPHmoUeAhCp1F6EumkQubWwcSAcmdhtSOZjI3Wa9VSoEBXyZmga761Kke5szM9Igo+pDj5mFRxm7xB6UUPmowAf6WeUuJUBPuaYzej3OHZKpI0nUlGhmGgUUCgqGS8rDxf2My3tmmY+bi765+p7LgNoinP88y+7H9/95c+171FeiJ9+0wvxrV/8TAt4l/bH6NaSCSw+UzOx05CeEuDTjsUVdalybC4E2JFXtSWQJa7zjS+5HffdumMB9PPh+NU4RuOawsgvAchCiI4j+625TpZ5WozXcsbGUfejYwyx2ScT2PRq//YTnqkopVR+482fi7/78jv4wLg6rz6POICsYz5aQK8JLWxL/J3PvQez+WZ/XKJ5dY6v/tx78PkdeGoB7j7ybuBjvwE89F76+yoX6w3QpaM/EXshRIXSCUThMAcI4wSkhJJsUSvueRjPxLOyaWU+/ofAz3+tVbtVFQTbiH0gXPDHqzK4roOLy1aen2czAoLRqViVSejzQKnMQrrQgY8WULupETQZMicWbEWuwApWOL0RYev8nWwAM0aF3AkF+94y/yKnxH7poapbWY2ZnzNx4OHrnh8hXvKgXCjTmW2p2TOvwqoO0DQtHAsrOGwL7BcuAs8VupqWsc1dwfqFBWgFgC941jl8xQtuYb/fmQWYtalIvtz79HCd2KmwrFxNaod/J2dejaYFanjYXZXYmUd80KWW2ae1g2VewXGGOpRP5XZs8PHrv/7r8fjjjwMA3va2t+FXf/VXccstt+B7v/d78S//5b881rFOnToFz/Nw8eLFwecXL17EuXPnRv0//vGP48EHH8QXf/EXw/d9+L6Pn/iJn8Av/dIvwfd9fPzjHx/9JooibG5uDv48ZZpkPh6aBWeWBU4uotFCF7RiUTjKCG0dc1GkmEwQaRCrxsPGCHyskBHG/9UM8FwHEZiqqLItYh9tRTtWKsX6G7/g6QDEonppTzEfx0BW1bSIUOAX/+FLcGYjwipdopXHGlyL3L9PzoavydyrURDi9fuVJ+nXJgAiHeJ1mocUo8oNCOYjnZpGGpJc2rXNGTdZSYDFITWBOM6ANsCGjiFJjJfS6aSq2jYlwVSbChZS/bIx6EUZX1NSpU3GhW8x4Cc5Rroelfzb7DcCmxjGyBTmYwcmmUxK5jrXakNSrF7bc9DnCpcmP+E5HCeNmWL8kbpUHEN5CuC6Bkg8DvN3XaPS8CkmrNmmMoDN3wD92G1SDcAY2A6Yudr1Z9Ln1bG6fowWMRXEMY7fuMEg7fogq7CZBEC8yQrKq986joOy9ZFKgBDQwEcqRdWPcJjXaFtRWTQOXHqdk9WGS8fH3qrAqkuTjTDSuwSYwNGaOWfqArMau2I++1LcvWM/+hEODg9xs1FNcuaI+6AXvDvMKhQIEDtj5mPi0hqRALCqfZzUyAtJ4AmNMjMI6Eeomhbn585AhD52auSE/VE6AXxUQ+ajMz7uqqxByX9EEP83mY8Ok1a6kKyJVl5j3bRIVYVQis2oBW2LqsFBWmI+X5DrYezWAnyU80+kXdOBIM91UcPDg1eXgonBsenqomMqdo1aD+T/bzl9QqT8sSnEYm05v5UIZ9uWXdAx1tYwH0fMQT1IroJjxno2hflY52QRtrlkND/t7IboR+m6mnsTu9cZ56Suk9LV5uRKgN7WYHRER/eMS2dXfVyfLlwk76GaHydVMR+OkT3ScWbGpr63FJyBF8F1HalpyzzP7jo0QJljGFZZD3h+GgVn+uPIZ8ACaDnObG/21cttbMt9CSDtPkh/XxdCMxaA34GPT4z52HohPnzhQGjqqneG2TucusD2PMI3ftG9vD6nan/2S8BjfwBc+GP6+ypD6YjzCc3HGa8hKQswLSsXrut0xYc4sMjR5vKsKyBHMx/dIMLjBwLc9NZoq57aiPD8u26CYwEfke0jDjx4OzezAczIKVAgwNmtWAJy9HVEKHFN3uIomduDqQDwM38b+NmvZL9WGXo2Jt7c7fUo3ZBPpw5RoHACkVK8Rt5n5pTIEEnQ9omzZU8tIuy0e/AcBzh8/AkfBwBCp8SydpGVjcwY4J/9ZtCidHwcZBX2V7IgLSVvAkjmo5C+O8przCOfTSN/qrVjg4+ve93r8NVf/dUAgPvuuw+f+tSn8Pu///t4+OGH8drXvvZYxwrDEPfddx8eeOCB7rOmafDAAw/gRS960aj/Pffcgz/+4z/GBz/4we7Pl3zJl+AVr3gFPvjBDz61UqqntLqE66DTolLt6lEhDAVjEfaaHAV85BXFfDQWCob5ePkwR4kAi2DoFIVOhRWR0n0td4QY7xqNko3YR0ulMfox2jLHqXmIv/ac8/jNb/o8fP49Z7B3qFKpDUcmmKGuW2x6FTZjHycXEdy6QAN3WOQEwF4hFomtwKhw6dYjTSgA2C18eC54IGrAfFSsHW3BItM0CWOOEuXv+lIACAdUchtrTjv6XFrWFO02KpXbDWiQcgpYRLFJJzMaLQ6PCVKa+pV6apPtvJ0jEA3/No1fE6CyMQJGThbt2A3GxfUjq5lrY9OF31Wzpl0bkgJTmY8sqDiBiTXpORj3pLsO7vnr52WMKGYdGqelMTqXTWWv6nxcwJJro7nFMGHJ3014rwa/MdY4m1SDOgcVtLAwI0jwWn3X9VPMxzEjjtPfUr9zvBBl3e9bgvkYSObjHv9bmXZdIMBRXmFV1IIBoApIjABFkd5VNy2ystHSrmnw3HMcVPCxuyx75iOXCkkyxy3PT2rijec9BUSJ+exK47nTfQwSrFarLiVatcSr0MDFSrvtuyvhaCXu+FnMiJRk1Y4qF6e0Vz0JhMRJZu7DnihacG4xNPBDt0ZBgI8FQoRtOdB8DFAPmIyA0P+k7qMCUXXbZj8tJZg4voeJWwFt20nQrGQqlh/SzkvkVh34uLsS3y/mdMpdJG2sjvnIFTqpC7hBhDDwsL8qBfPFJlnihfj6V96Nv3mfqBhNBjkrYx+YHGyzrO3HZT52AQx9PTCCImr81DpTpv0x1HpEFGFzgxj/6e+/GK/7nFu1IAux/68rwtaN3wRZzaCisS+xoKJpc0wAFm3jUveMK3om19Nn3CSkFk5vRMcL7FL91HPxYwtIlBl7xxrm4zqgVZ03iGWV5ScIPja12Nt96VsBltRhbX5zDFXV9iWrS4GQo2Pl8ANxP6IopDVZJzSnLnB6e4H3f2q3D1wAPMhVptiYL/Dsm7bBpsirpsChvYfYa1Dgo6h2bak6Xgk2WYEQceDC6Zi5vD+jUrrnoc+D0FUOL4hxcT/DPPTEcVmN0BRJ4OFrP/+ZdsA62xdrxOIMG8CM2gKFE+LcZmRNRQ5RdBImcbIGuKtLMdfLFRts/j+eeQbPPB3g7ILPOpy5VbfHehzbsq7gQRQbEwW97PM5dgrkCNcyJNe1U/MQCywFmLe88oSPA4g9/6gSe2e0RvNx4Teo4GMvLXGYy8Alq/3bA+WdZM71BgBU+b/jtdlshuc///lP+PdvfvOb8YY3vAH3338/XvCCF+B7vud7sFwu8cY3vhEA8PrXvx433ngj3v72tyOOYzz72c8e/H57exsARp9fbwAqwXzcL4cT/toyx4n5yVEVT68RVdOyknCMg2SoOWLqRMm2uypQOmFXRVu1GCXSZjzdHt4r4Tju2Jg12iJSzMcxkFC3wCmNdHF+K8aVR44w0MlRLZyhbFrcvNXCcRycnAvno3L8EZdxN3Ow5Trw2iErM0KFI8KRuZY7wjEzN2HKAHY9YSSMwEcitbCtxQaiivvURGEagHYKOJYkxybrxjGBpabYh6Zhr46hNwqsJg3afHxtlEFLpvsS168bhLbjqWOa1XQBsQmZxS3MtGWKraCPj01Do9h7TLR/lJ5lOikE2wIgAOmcvk793plMtm5sHLBupI5P0T+0OZ7meRVjTy9wRTH0TF2dugAiQ/fmOFqTrP4mwU4cMVFLUbXRPLc6RjhkinVjMN8TrnjDulYVAjjrzq3NQTUfqTa1cNHgNwy7m3P2zLW8K9pjC4gQbEZ1LNUahvnIRaa78RdAHKKodOZjic3EB6JpadelE2CZ10gLqRXJAhMCwEAqzpGVjTB4G2odyeE4IgXs2qrAqqixMwsBVQGyLnrARPanC3ZxLHduHeVA9wC+N2Q+tl6IPF3hrs3hvPWbEpUTYFn09sSFgwyuHyIkMh0StybTrtu2xUHp4sa438tnfoMULcF8jJFVDZ52cgjARg6tOb2sPSRuJZ6zbAFhq3Aptn4r0gWzqget91alTNkbz5mZ12DlBFiVDeIQXQVMnwErI1RYybEoaZmNOa35GKJC2gaiCAegVVAf78WOF+L0RoRHd1N72rVkIP71592oXTSxHnXBKpPFR+wDgzWJmWt6IRYvhNAvZJhysxPDc+trTheMMPbrgtBf04OuejBErZUKqPfCLqV/EOALtbV+6hqq7/+uJwOyVFDR2Nc5UFEfO8fcqnJgNtfGxbzrU6RUANxyehu/+U3PlP048NHcq0NgdZXu5/oieMTZQmShORvzUQcfmf1FVbv+dArO6MxTPxJsUUvF5r5QkoVhBwBHF0UfRjNQZydHviX1fV1rKpzY3MClR3LcdGLGB6+7a9ADBGvum3rWnDZflQsdzxqYBd4azcdMBPvaANu+Zw9OyP6NPHbH8mYCGX4Qoawb8X5zkgT6ubo5w7A0swORORFtsjaE1+QokWBvVQKbPIibOKK4CQDEiV3zccAEXF0R4KfR7jqzgX9XvBX4tecDr/o/mXOKTAUACOIFrTtapQIMdgIJ7toB/ASiKJMfR8DqiVeWf9pJD05bo52fs9tnE1qICkelg6xQadc88DzzBPi4XBVY5UoGh5A3AbqsmMNSVLu+rvfYt2PfibZt8Z/+03/Ce97zHly6dAmNgar/wi/8wrGO99rXvhaXL1/GW9/6Vly4cAHPe97z8O53v7srQvPQQw8JMc/r7fhN6g0cGCSDLu06Wgx0KNxGRGAKSvMxnA1fRspJBrC7LOCFMZxmuGgHKHHUjAGzX/vzi3gdp5egtUXs06CbF6FpWpzUhnJmM8aDyyVAlbQPZiiqBk8/KRyRrSRA6FYoEcLgi2A3B+50x0Zv5Na42vqom3aQ2nWt8ASLkwKinDGzcqS7SKZzaqCXq0UiiZR3UvOQS7u2baxU6jMFgHSOq2HYA4TDzQA2FEhJsT9ZTUUT7OD6TWXSGU64fgw1Pv07gLk3BhPMpnE5yUlJNSdFCbsTTorp2Olj7s5pzLPueLrTxtzf0fyinDvqeVH6h/yGPQRkNcaeKw1NpUlpMh+ptHDlnHb9GIdr5CDZGWCjYxIpsySLERDnp8DHKhcFTvQ2YX0kW10MGYBr0qgGY5hSyMn8DTB8xjankAWjmfOYRaMAjT2ur6HEvAXEc1DpkeY6LCUGHD8c6B0fZpVgxEVba9OuXcdB6QS4cpT3lYY5dmKViUqaECy5rJT9S4a5DmCWJDhISyzzCjftJICvgFdqvTfuk+35Uaz8NbptKu26lizR0gnhNmlXXbofS47GDbo0dAC4uJ/hRES/V7HXokU7Wid2VyXy1sNO3AN8sQQv03poG+4XLoqyxgtuWQw+D50ay3ZsRx5VLk5E7SAVKkCJrBE6V6681rSoRRqi8Y47dY5KVgNVTVSkjoF6XN008WocQBQ/ODEPO31QAVaOHbHIqZHW4n5cXRZYxD7CyCNZvCFE8PjKYQ7XdUQBEG5P9EKcXgjwca4KzlDvB2VrUOt7VaCrjgzw+91obeH2O21tP45+ofqtfhz9O0Ds14fmPanE9ZvMx4pYW9ZJlqi+g72YYedNZe/r+4gNVFTXp4619p5NtYUIIJAMxDJBbYrxSmY9aPdDMdTadkgiMKVUbIF0kxnLgoHyvJ8W+KjNtS6dmgBhTNtlnc5ktg/s3A6k1+jv66IrONOzsJ4A+Fjl2JgJm2QWWIJnXX/93q65htU1QbZYMddQ5bjx9DngAsSaawOw6kIWh/OGQT42pT5H68VALe8PqyVadmnrM1W5nbt2xU5UOqEc8zE/EMBjyAB3AE7GwC1nT+LzXnYH8Odz1taYuTVKqYvZV/g2AvKq6ff58AIJPiLbFxkdH/9NeuwQOsK5ZKR6HFOxKmTmR4jExdoCSmErmI9hsgD2nrjEwS1JgfbEDM6Nd9kzUyY0vy1xWHnIKhk4tjAfvaZA40XYWxVYFpXQi+akH2T26ZGsdm3T13yqtWPfiW/4hm/AD/7gD+IVr3gFzp49+xnJX/+6r/s6fN3XfR353W/91m9Zf/tjP/Zjn/b5/9K2SizSR9o7UdYNjrIKJ2ahWBTzw+47t85RsMxHM+3aMJpk21uVIt2sGi4qYZtjWQ+nW9O0qJoWi1nCGw+ybUQ+nCYnHau0rBE5PZPi7GaMNEvRzMOxroDrIW99nEmEk+m6Dk5EICth7hYQTEZjbJFToYSPvKq7imEAcCVzhP7EiPlY0CxMP+yZOl0/AnhT3ymjsi7HbC55L0jQh3hOQl+MM9g+jdRnz6fTPqYyH5tq7OxQC/tUZt5xmD3mGCkHijwecV4qBUoft95vHWuwG5txDSOmHdHHHH93LCIdWO9HOW1cOrUbDA0g7jlMLaQySqtK+jF1LJRqrElJGT1UGipbud0EPSOm2uZE4Mz2PnPOgamhCqxnRnBtVDBI3cc1QGJd9GljwDRnhn0vGEeOvIcWY58q8tPdS509LvcBl0i7BkAyTjvtsmAAPu6npdB8DHjWgjqm4wA3nNzCJy8vUdSNcF5cWTXRnEMyvct1HRykZZ923RIBIQkyJUki9JRVdeIuKEWss8dJ26cCSH5MO0CyeJkv33Wlj5m1AcJ2v9OQ0sfeuGFX5REQzMfzcUIa6rFTkWnXj+6myBFh0++P4zaiiuTKYCg+fFBjC8Dt28PPQ9S4RmReHJYOtozlIWhLlJghrxrhiEBUJHepdNdKOP5Z2c+bvVWJKKGvMXFqlPBRSV3tg0yI0AdhAuRjxzxEiWUtWGoiYyUEfFnF2ABlRAGeGS4d5kKzTYF2JEAddmDxXLGBABkU0Rh8VDCQWo8UENFVZLYwHyftxdl4bV8H3lHSDSYjU/17NP502I/Uj6SAemWTrdnXOfYxxfIn905zXyLWVfVZF/C02RIaQKmqkuuBKsoWooKYAKanlxv3jS0ipz2DtpY2rHaPqIwHG2O+O55lHy1Xwr4OpC9CBanWNdNm4oDMuhzaLn4i2GlUqysBWu3cJjQTmfPWul6iLePA1uocmxvi3e/AN4BnzSu2KCCvldm7m1qAQyfv5gHUKsPn3H0DfvFLXyL+r7QPTeAZEPutZDIC6PdZS0q9G8RAAWzPAglCM8zHUBSynak1kU271piP4YK3D4qluBZLOr9Tpfirz7sNOLcBfDQGqot0vzrHF993B+5c3Ab4R+JDPSCvt3RXnLdpeGBO1xAtVmQwPHZEQAsAD7JWKVxXyM7UTSueBfcOtS38pkDuRAJ8/DSKOyHbhwMA27cCB4888eNAgI+r2sNhVkn9bctaURdwvEDI4OQ1zm3G/D4mmY/7BbAs6uvgo9aOfSd+8id/Er/wC7+AV73qVU/GeK63z2STYMRKM/5XuVix55EnACyNBu/UOWo3ZDQfDSfKBAhk20sLnAljoNob/rwtRo7CXlrK4nzJ2Oky2kbowGlq0jGv6gZl1keVzm3GongOfBCwG1KE2PZ70O9M0iLNxq/CfiELyBibaggRgcrKBjNtOFdT0MxHKpUYGKc6kWnXRFXsphw71+p4o+I0hQCZzWYT6TZBrO7YHLBHAVnUPZgAaFLODnc8wADHiDF2WlRr0qRVejaVQj5gUmT9MfR+FBPE9fvNtztWOu43Yg1OMN5JB9A8VtR/rre6EAaT3kyWGsl8DHu9QnVdJFjGAHEUqMzpiFLMR72v6WipsVLMTOq8xRFx3mwaw4RkNDJsTwqkBCzAPwOyPRE2xqhqNcPSGf3OAIptIKL+G8BwRNc5hRPWg64/MX+Ubqy+jjWlCH6YbIDOkcoB0OCj60fd3lc3LY7ySoqJb4nfldkwxVn/vePhzNYMV5Y58rLBWZWCTImqS83HjdjHQSYAxST0gJqav+L/89kM+2mJZVENHcPROkYFbxiwHdDWM5PpSjnNYt57Rtp11gYIMUxdVmNrvJ7dBwAXDzKhV0UyHxtRQdt4tx7dW8ENEwSN9ptaAL5mevTjRzV2XKcrBKNa4NSjCtYAsFd62AmG66PfligdUTxIgY9Z2cCdxxixEyW7c8B8TAtE2wlwNL7GACIVPZf3ZD8tsREHstr1eD/2pY6jmI+1BBfU2jt8J0JUqBwfn7q2FPYdwOydYk3cnhmABYBx+jC1H1NBTqOf69F7mQmOW5mPmlNtDRjKsVNFrqpCrgeaE0wy5I3gCVUAi8z04NjNVNryGDAe2WekLWEE+jnbzQS/1sgniOvUAlKeZhNMYb+TNkI8IDT0xytE+qlqXACw0tbYbmzp8J6b9gsny6KOpweAOVCkzPoUWkCARjFhN9uaXqlbnY/TKQWGc40DYVRm2onbgY+/h+5TF2ikP7DomI92f4o+TomthbivvufwAQTVRqxS5hqyfQG2nryDL5pT5XCDGFszNS9n4jeURExdwAkiIIemO8yA2fLYn33XeXzHbc+WARkiSC2PG8Zi7s075qMFrPYiMY/CGXD4GNMvFeMP5yzzcRBUtxUfqjLcf+c53H/HbcCVj3WfkRI66TUg2RFzPd2jj6ezI5eXgfDWUZcQJXKZ6o0gobUVqxye46BwQqRlAwTy3SxTkV2pt7qEgwY5Imxvba/XrfyDnwKe8+XDPUm1bF+s7ZvngUt/yh9nQvPaEqUT4LIM3MG1BcJLOH6IPWWPRT7vt0lJnmsZBPPxuuZj146dz7y1tYU77rjjyRjL9faZblILpKrbTstKsRDmkT9kPjY1UGVogxnykgIfZ8OohwkQyHaQVqL6mG7cNQ28psTSSLu+Io1zP4zXMh83Q8GUNAGsx1ZiCn/VfT2t/PRGhNCpkJt6ULItmxBbGoviVOJgVY9fhWulD9fByBELnBIl/AGoCwB7WUVvHhQAAozBEq7SLTA0NinWEMA4GkzatUc7OwDGDgJAs8ooJgDAg4ojLTJmvFNACQpUpIwFTnC+rXuWlDqv+k4/rxq7fh1UP44JohrnjJnXyxkfpDNGMB9H6dQUKEaxEI3NlrtO9fuuH8XuI1ge3Nw2+5Hp1DZHcA3zkQSzOaesHJ+XBWEo5iN1zYT2oHkt644dJHygwNYoEBHgjSrVRtWP5VphVlY2zwWsB+W7/tRzsTANqOAFIK7PTLsmNW4JfcjuNwKocv0IhUwl3peBse0k6NMnOQaBnGMn5xGuHhVdSi2AsVayGoMfYTMW+ny5teCMGO9ipjMftZRuUrbiGKAuJZtg1UQNR9Wu01YUkIl8b9S/NdKuL+xnmM1m5H4fuzVacywAHtvLECaG81aJIj8rQ8fx4b0cnueP7kuAshPr19te7mDDH9o7Xluigj8AFLOyFgAhAVo1bohMXmPbtthblSzA6jRiTTiQuoxXDhWbkZ77QVt2ad2dYD1TSd6tCwRRgk9eXmIRaenPJAAYIZAg8vYs4EE0Uu6AWLfJKusEQ49Mvy3GRRGq1FjbGRZZlfX7ouuO12xunRntEwRwpI7f9aFsCWZNNdehTjrECA5PYQSOMgEY5qMCKRW4yaU/6kxK9rmbAUBqbSLuB+uEm2AmA6CWWT8/Oi0/M2Br2JK2Qmo685EClAFhBzZy71fsrycS7OtsJjkernJxZ7tojEwOOFN7zvYtYo4yuqytvGe95uOaPd5sbQvUBXYk8zGvmvX2QplNA82UVuWJO4EVo1tpAuy2auFVBk8+y0bZJLYAaZUhTuZ46d2ntLFSx80RybRrIVlh2Td1LVhbqn4l+wVzHpzV9W1t6eYD3Xf5Nzdv0l0BPtqK5a2uAhvnATgCfCSaV2ddqretSrjjABsbC7zq2ee094wKLqTYiH287qVPQzJbw3x86L3Ae/8d8KH/Qn+f7YvrizbtsjjrWtPAbStU8FFUjdBKthUbqgu4fog9qcFtBaplwcCicXH5ML/OfNTascHHb/u2b8O3f/u3I02fwOJ8vf3vbVUvRKyAMuUIJKFkPqqXVhr2tZ/wBWdGzMexM3iQlQJ8HAAFYnFaGgDflcMcoe+KypBrNvuF36ABRuf8wGPid3du98f2XAc7EZARaVarosKqDbHh9pv4yQQ4rAjmYw44nk8wH6tRYZ62bbG/KukKaRSYB4yZiuvSrrt+XMEZBqTjgEq24Ew5dmBJEIswPrtxEPdglHbNONvUPWAYQWxhEv28AAMq6o5FNvxO/w0Jymn9vEgYr3oFY057jXLGRmloJpuJKpqT0M+acgDX6W6pazDeWfE5xaTMhv2maMxx+oeUYwGMU7n07/QxrNPLIsFs4h6rKuaDgjMW520SG8jyPrNsgolO/JRmAr5eALZww+B3xjNVYH1TWX5DpeCtAb3M67QV1qGCF8A4iGLqXHZj0Rg+o7GI33t+0AXpVGXhnVkIzKXTwlVUlOzfU4sQV49yHOnaPgHhgMq9czMJcOlA3J+E05iqBZNzcxZjf1ViVVTjNFliLMNrtzij5PpoAecJzcf/8dASfs2/JysNmLtwkGM+Y5iPDl1w5uKhBB8N+QvHAZbVEHzcXRUkABK2xaiCddu2uJYL20JvXjsuvCfSrhMiwJCj9iJkleiblQ2KqsEsmdHgiryHh7l4Xx68usRtJ+csE8yXGRxLWUV9Fvr0eggAVYZkNsMnriyFthnAzClhj6jq5CcXoRZYmhC8pNYjyh5kU4iNuUadd8R+Z+bkCIiKRvNk0vtgvgeq+EtpHAtg1lQiCLDO5gDG631ABOJNe4xLH6bOybELTYCPZFuuOVZdjrXM2bRrw/7j1nqd+chWTDf3JwZUBDDQyuaegVk8RP/sOK2zSdYAcqbtYtv3VDrvpiz4RGVs1CVec/9t+Lsvl6SgJwI+yv17ayHAx6pu+6KYLElBu7ecjqI+5q2b+fRk8z1VTDeqkItkSQJaPNSmMzhVwqEuEMXi2dVtS9v1qpWrIfjIaT4Wsl84E/YTF9QzdU5HfaQmrW+8t5wPp8DHZJtnPqa7QgtydoIFH1EVuPe2c/h//7Vn8ACvHMNP/d2X4//xglvs71CZwncdvPxZt/RBder+AsC1Twz/Hh1rKe6rRU+za4cXgSVR4ApAp9ktE4GTrto1Dz56QYTdlQg0iz2ZsXXrAo4foHVcPL6fXS84o7Vjg49f/uVfjt3dXZw5cwb33nsvnv/85w/+XG9/gVqdi2g90FWcVCDkLPBEWoFiPmobMFlwRot6vPmdH8TuwSHJRDlIS4SRselWouq2CfBdPspxeiOCE22sXTxEJcx25Mz/6cUUURTBMTaTEzFGrAgA+OU/fAypE2NLS7s+GbU4qAgx+rxES2h2+a1Ib0qL/j6lZY2ybqQuFAVCUKwdE/AhDH0q7bq2pV1PAPMAO/ORdGAZYBMY9/VCghmRTQcpSWCHYJUBw/s1tQAFCWYR10JV4DVTZgA6oj7VGTMBL6XTpLPMTKNWnZ/UfCQA3hELkQFGa+N+6GnjQH9vTCYoCSoSjgpZSIiYr+oYXT8bCGw6SNT9peYT4eABY0eWMj7Ja2GOyTGZre/ehGc4pZnvvq1wg95GDv0aBoT6jRcOUwqtAv/MvLGBldwaOgrMEP1sFTEV+BhEKGrxrPdWAtzaiH2p2TQXVUctYzuxiHB1WRjgI8GykM7zZuzjkqxgLCosEswCeV+3kgB7adEzH11POP4U6DEKHK1Z61WfQX/akIYXdEXW6rZF3bR4bOUhAQcMRV3a9e5KsDw35nNyPkdujap10Bhm6UFawo8WI81pB2Pm42FWofHjEcPMh0hf1tt+WiKtfcy8IajuNaLCZ6qBpquiRhBSoFWG1gu7vnupuC4OYFXP81BqPu6tSpyYB3Iej4vI+JKFuSpqCTx7VqBwcz5H07R92jVVhE4CXl/07HP44TfcvwbQpPYKap2lglAT9ncWlEuHEgc2IGewLxrA3OTxTwhmUWx7xxnvnQAdwNGPMehn3A+S+TghOECxx9YBtuz9N4FMLrhGBKk5bUt/whqjXwPH7jIDV9zcbdshuGsDWtX3U7MDqFblAqzzLGu/Ps4B243ZJ7MDMadVETrTT5KZIjsbGwL4AXhw2tbk/PVlIbTPufNkP0YO4Cq1Z2UrOFOshC2Z7Ij3mgKbRnN31v92NNZCVMaGznyMaGCMPDb/XJJEnHcRBfw7q8bV6V0mfLXrKpOgtgVMLVPjPk4ArNcxH7MDyQrc4gHfdBdITgCzk3TleQCoUrzgrvP4K884a8/K0qUtOluLGJv6vZ/YbTKgT9Hff4j+XkklhBZWqWo/81rg57+G/k4GMivJ8IwDj/bDuv4lvCDC3kpkoiy6tGsa0Pbks3psP73OfNTase/EG97wBrz//e/H6173us9YwZnr7UlqZQZPGhmrvGc+hr4L33NFxMAAH9tgbi04UzctPvjwHj4WXcNnn71l1G0/LRFsbQHXtAhdlcEFsDTAx6tHBU7OQ7F4UDoxWktcWoz+E5eP4EWz0aJ+ImyxXI7Bxz9+dB8vQYS5xnzcClt8rPZwlFeDyMShSqM2FhWvKVAbFS53V8Jx8CPCkKDYV8DYUKvLsRYfxXxsCFBD9SWZBkwa4nGYj0Hcp090/QjHFWCi5AaTAQDJvjSLXajj1flQL0k5O/r6o0fAO0OTAan08VuvxTC+pL7bEJTTDFaVtkMy2IjIYWUAzn4IoB3OGeoaSM1HCtBhnIZ14DKXWqy+6/pRmoqKKWdoQ1LHo56rea2UEUgxUCkDlH0OhLMIGMxH9SyzPhIv05PoFHIK+Deeh+PAWr2aBXPXAIbssSakupttlBqpAabEMtb/xgycMA4mQK8xVoZeNm0d49Y7K/jYp12XlTA2d5cltmdhV+0Yi7PA0SVmbMKZPzkPcW1ZwHcdLCLNECcKzsCPsekHePia+C5RqT5NhQGzXabpbiUBLhzkQCslU6h5VFfi96YuJfWedWMphg4zwD8HKUugwMeqbvDQtRVSxLhtgwhYGszHx/bEHN7a3AAuEOCjU6N0fBR1g1hbX/fTUlT3HBQSkVUkSwOozEo4RBBOaE4P5+fj+xkKJ8DMHdo7TpOjdnvm46qo0TQtomQOXB0HwRwv7GyBx/YyuK6DxWJB7691AccPu7Trg6zCPecC9p57bYXKCbDMKyyLWug0skBhjvNntoGL6Iv/sNkFARzHwR2npb3Bpt/m9PpOrXVTApYc87HMAA1DHIGZNhafLSV5KhNYOco2tl9F7BEA7XiaARyucvBIG5ICuHOjcra8r+b7bAba/IhmNOnru2UukeccHcdwIW3Mx3USOeoadCBGfWY7lroWc042ii2mABv5t7mXqv+rysXAE2Q+mkG7dUCSegbM3AYE2BgtevvDZD42tSxeY6Sh28DT7GCsZ6kF39/9DS/vJBnsOsxagIC7VkD4ZkHS6/8VR8O5BYwBQivzsQey40DtszOr5uMI2GSKHSWy2vfL7j4F+HtyDOlYV7HK+s+49HpAzKPFmd4vKFYChFWtbYfjWwdYq3m/NiV+BSzOif6cb726JliP+QHfZzQ2BnwcrGHqvbXMfVXcCZDakEQB1aNL4twcY7FcoZNKqAu5lhK2X5WL68sPaRuoLuEAaGXF+CT0gDbGyA/r+hfwO/CxklkrzDss9SE910HdtCJwfL0BeALg46/8yq/gv/7X/4qXvvSlT8Z4rrfPZKtSuHLB15mP3QsQb4oFoq76CFMw61LPBk0uikcSxGx0vQ+tHWYVwtkmkB/1L7piPtYe2rbtAOsrRzlOLSIhULuW+VihbVvUbgj1+rZti8f2MnjRePHfiloc7Y/ZjMu8xi3nTsHRNp/YEWnURxkFPhKOf10ItoMuMi/T9ETxnDXGkmqeUSzhWGnXVCp1PDZO2L4MG6bbDAn2DJUOpK5jdGztHlBpwwAdoaWcmE6vLe+NHc6pUP3048EZMkVtek7rgCrq3nBpwVPTzMmNWzsPy8ogQG6S9WQ6FhQbxDBYqU2XclBtIKUO2nGAXdvIQj9B/xs1btW6qq368+KcRYpxO8VZJJivumOsroMy9tW5SbYnJY9AgO7dbxhW6nGZDN35jXHatJkGY9ABcYsxOfgNEbCgDDKl6zmaD4k11WUUmAEwKtpFzW1AgOCcgyTXVjcIUch/76XlsHrz4gywZMBHee3bM8GWu7osMA/lehPOx+wN6aRu+gEuHojrjUNvmK7kbfXX48fYTgIcSsCq279NtpxZsVc1fX0ygUnqOdgKLUWbCDxV7brFxy4dwEs2cCvhOyiGisq2OMhKRIGHMKTnRYgaK8k47JxKCPAx3FwAh9p9rDI0bojUsFUOswqOqX3cNPDaCst6aO5e2M/gBRH8dsg4dCoxbrW/78v7HsfEuKsMjRd11a53lwW2kgBByMgbNBVcv2c+Xj7McHIRsUC9WwtNyWVeYaUE6znAqM5x02nh2J7Z0Bgy69JWVT9gHJggAxjEPmY6+IB8/81gkBFc4VicZTqcq9T62p03GfZ7osxHLxwGFc31qy7Gacbd8dZkF1hS5ddrKxp7ZxD3Mhj6vaRsCRYsMEEM8zllxrEiwg5mCqoxoPva6wSGbDovkGm/VPB04r3Vv2fTrrV1U2lzP6FgXzaci1yFYyrtmtUDVGm7CnwkWPTAGKjnAozXPgH89N8CXvV/And+vnYclSEUIfQ1v8kWECyzHkizMR/LVDD/1P6dU+Cj8UxdqW1rAbv+6auegbvOLPprtjEfzbXEwlJ74Bs/V/ioMlg2CSTn0q7LVMwJLhW5A8jVHOVsFCPY7vr0u6GfV6V6Hz5O98kPga0bpS4ko5k4uE6GoWu+j+q9pe7JgPk4G35mtnRXVHnnUsLVNap5VS5p8HHv4f7fq2vA/KQxfnFvay8EahkIbrTAxwh8LBGEMR6/kKJtVcG2mNbWrAs4XojNJMDusriedq21Y6dd33zzzdjcPGYVsOvt/5pWpnDDGaLA6xyAZa6Ve1dV9vIDsSg6Qn+R13xMu4i91xBgAgRLIEg2xIKqFsYqg+MAOcJBSveVoxynNiKp2UBomWgtkeyEHEOnZJlXCGICfAwaHFVjVu6je+lI6DZ0KpRO0BXjAYC6abHMK7gEsCkWlD4tCwD2V8Kx8sjqpgQrCCCYj0Q/BZqNtCEZUGOd4dqdmwATu/OMK47S6UD5ONUSGAObFEut60cYllMYd5SWJaUfpUC8AUOSSh8mwCxgDFRRThZl1JJOvQFkN/XQ8FB9zGOR4CMBEJiRd3U9pPNJOZT6/SVAOxMMBXhQETD0smwpyMbx9O9Uo57DKC08Eddman5SgDKV/qa+048HDI1aKo1fjXeq5IGVTUA9Q8ZYXtcogJ5Lixv8bmJa2+A3FMOUAbFYANfC2KDuC0CsocTzVo0DXuXv/SDq9qdlXvW6eQAwP2NnPnpBV2RmEMSiBOQlq2sz8XH1SJy7Yz4CRnpxCvhCH1K1mQI2zXebXWcZgEddOzlHOPaSlnbdtPjwxUOcOXECTk5VjxdVSbvAZ64VyyHmYOgUKBB0+omq7aclosTYVytRSTs1bJXDrBQpeUaAxCU0py8eZHR6tKxGrgDFj106Qhx42NzYJNLixbzPNKByK5EFXJpqnGJY5XD9EAcSfDzMKlHUiAtI1DncMMKqqLEsallZk1g35bHPnxRO/b03SeeeCjJSbHWOkU2u75SdQQCapBQFA8pRtsUohdi4901NgFpGUK7OCVuG2icIJ9O0eahsi+6c1PiJQNY6+4w9lgGeAOt1N1kGs3bPOmmZdeAptWdSwfLAAtYY7DxK0sQEiqjgP8WiVL81++nfs3NNHn+QRvsE9lvz/rMFOoqh7WILupUrcRxXBqcKg6HGZYpwgM5jfyD+fuj3hp9z+u1mcG9wbu1Z2ZiHijmo2G0jgkQ1toMBASwxadfwQrzymWdx2ykJynLBCWAIaAMgi8ABna3YZXPa9s3Btc/FHDKLZgFj8Ni0BXTgW42Ney/0fkrugbPJCsk2DWYC7KWaGlu00VdVH12nZk9Z2efa/XUcOvCk+rq+YEx36xgD3KZ7wMk7xd/UvVVgPwfMq6aDr0cXxt/Lda2Vcz8KXH6tAIA6RxjFXUb2LPR421XuAyqQfT3tum/HBh//9b/+1/gn/+Sf4MEHH3wShnO9fUabXFxmoYdVLhaztJCVNYF+M8gPusUq9D1R6Uy2P3l0H4/tpbhW+tg/2McP/Xch/uo3YxCmblpkZY1wLg1ftcBL8LFAgGXeL6pXjwrBfAzna8HH2BHGeqqxFx7ZFYtbTFTNSpwSR/VwI83KGlcOcywWG4P+QSucnpU2tsNMplGHCRN5DQcg7V5aSieCWKBZECJaz2h0HMLBtjAfSQNxIlAJ9GOfVESEALGovraU5hFTjWF+ASOHkgez9H6U8xGP+3XVsymWhAHKsYxA3VGhwBgDdKLSuKayMv2I0JKbyvKYAHBRfTqn1wCWqesEhkab9XkZ91c/BncdZDpg2H8HaJWzqXtiOiDEvKe0PFnwkbjP7LtnMRo/o8zHQhRN0NsU5iOV8gjYAVAFBuuNM8jYQlUMWKl+M0XzkWOZA7zTUZcAHARBgFLufVlZI9arN2/eABw8Sh9XsqZ2Zv155zbwUc7JzVg8G891BJuQYkdUORAkoiKxbAPmo35/9fRBvdnA46nMMKDbd/Rq17//4DWcO3MKpJZXncMLok7yZakXyyGOH7YFCicYZF6UdYNVXos93ghStF5fZRroK017obEP1jkcYMR8PMor+BEFPpZwg7AL2F48yHDDdgwvWpDMI9ePukBkBz6yYJMY32FWom5apApQpNhzsr8fxFgWFdKiFoxaT7FetGPL7IIwivHAN34unn+LZCRxYNaxgpeUNiuxfpr7hckcpLIqOAd/BKQR4AIVqDJZu+T+ZOwTXT9izzGlWahiVpTcDQcYksCicZ0kO5IAbCmW6rr00qahgUWS+WiwLUnmIzE3SM1Hgvmoxjw6bzLst86epuwSdSygP55iNVIp9woQAeg1W7UrHwV+49sY3UICMGd1Sg07gytsUqx6cCVcjIGk4xZ6u/wR8ffVjxpjIjI/1LG4LA39Wdn0nU0AzrwGzuYLZkzaNfFOU8GJwTjNNYIB5aewlYGx3qV+HYN+qbhuLxC22Gj/kPc9MI5FBSwB451krkOdV7FNuaxC1Se26EKazMcqIwNqY6IFA0abhXrUOMzWtoL5eOIOAUxT4Gi5kunb8jjcdWZ7QjbHi8bSYUA39xup+TgPfe2Z0sHqMOrn6qxLu6b3b3iRqJ4OXGc+au3Y4OPrXvc6vOc978Gdd96JjY0NnDhxYvDnevsL1IoefFSsvuUg7Xpb/L261kXY4sAdgI//6Gf+AP/gpz+Af/orD+LgYB//4yMXAQBuMwZXFAshUuCjWjCqHK7roYKPo6xnF6qCM4jWp11HbokWDtK6jzpf2M+wPQvgEZobEXIsm0AUqZFNgZUbG1uD83lNicYNupRyoE+1CmLKeRRVxIdp16VwEEmGIAFEAeOoIsuQDMYONnk8IupsGnNd34g2ErvCGxPYYux1GWAJtXGqc1CAJgd6jbTdGNBr4KQS2nKuL4s1GACpGwCusSSOmBQEiGbqS6oxkKBXOuyjzqGfDzAYF4RxZjpZSotw5EARxjaX3r+O4em64h6NHDKGqTpiSHIgsA6gEPdE/X8tA9UwAptKMuwI55MCvQE6JbA0nj9AA6mj+8xpszJgIvsMCQdsSqPYwZwwtt7Md5Bjlgx+Q2l/MsYxx9CzpZ9RxwfG95IL9AD2tCEvROh7HfNxVdRCy0e1nduBa5+kBcirTO6dff+FYk1SDqhcvxWbsTtPSBjjUoz+1KK/dmXICiaMtm/aGOZqnNTYSYezIMBEseY6jgPXdfCpq0s8vpfhlnNnxPfmHl6JqqS95Est04Zplp/XiCrTpZYdofbh2Wxj6LhV+Uj65CCrUFSNcAyMwJLjOCPm42EmwUfdwZUgnh9EyCXz8eqyEKzWcDEGWSvJkqxUwRkJPlIsVnkP/SDCQVZ14OY89PpMBH1+yfXADyOp+VhhprREfSJrAgD8eKjDzgZFmHdpyh5Pgo/MuqXvY00FkVWhA0eyWjRZZMXc74x7SYHtJuuLDOZQ+3W6fvzUNapz6vetaaRWq3ZeVTl4dH8JYJFMVdfXY2JfIvtRexKzz60DT6m5wRaRYwJOkwoNUWAscQ2DFF2XPi8ZtCWAVh0Q4c6p2h/8FPDnvwxc+KPxdyRgTuxnFEgM0HuTPjbKTyLTriMeMFxeEmDY4UXmOBPsmm5s6UT2X6qxNxOevWmem9NSNNcGwM5WpaQZzONSttfafdMEDKnnp/ULCTB1xHxk5gK1t9tsOR3wXZfSH21YNB+1OU1lZamxkWsnN/fl/fJ84U+QoO1KrKE7t4n/UynNHcBq0QcFtMrfOwLrMJtKu3aNatcAw3wshQSLbCKgyrxzkvyisAXdjnuqt2PDsN/zPd/zJAzjentSWpkC8RY2437yF1WDyNcM2GhTVLqS1btC3+0YfQq4O0hLbDjiBZ9jhc962h1w/igbMSyUMR3NFaPyqBuH60dA4eBAMgoPsxJHWYXzWzGQERE9o8WosOsEaDVg9PJRLrSNwtloUw6bHBkC5FXTOYUP766wiH3E8w3giu60ZXD8XpsK0MHH+XgBKlO0/qlOSB8QVS63ZyFtJLPGvuGEWVOkh0wO1hFfZ9yqto75SDmwVHVlltFJgY8UqGIavcdhPhr9pjLzuhSzNWChOveonwnKKTDTuOZ1LErOEVDnUa3M6KIQJChGsVQmpEv58ZB9PPV+kOnUigm6ph+VPtiBwKauVoQRQ9KW7p3AMpelc6GLT1Np1+T4bOCjNr6Odck5q5ShYnuGjDPBNSqlH+D1/PRmvoOuJ41Ey+9IcJlbYxhnI7AYyhyj0QSyuPRsAGxVSjn20Hc71l1WDXUHceJ28X6srgLzU8Pfl9lIS7GLcBN7k7oWlYoTSg1Fcc+dUXox/Hgwlg5cMh2LtZqPVLDJst7WBeAmRl/J1nQc/Mmjgi3xjFvPA/8d4jr1QgZVDj/oWYHLXGM+lsb7B8BvchQIUFQ9AKf24fliU7yHTSPABjlufQ8+6PbsxSjI4zjAoVGc5iivcD6eA3urfiyd/mcfXLx6lAtdxlDOq3LVZ43UBZygr3a9vyqwMw8NFqumMVXn8MMI+wdlN/Z55ANN2B3PZLZdOGrw6//tEwg8t9cSHbHo5RpFVh8mwEfqHeHAqikZC6yUhxEwUr9XjdLzVX1NphwFkAEGqGWsbzbm4yhTgXhvTPkTck80xsau40TQacQyJ9ZnSi9R/dbWj2JGkfIixHM311tG+3ySvaGOZwIn1DVUhl9BgUrc3j8FfCQL9WVDcMoWBFPs98sfAW74rPH59HPZwDMubVwVZVFNAXeAZLEZIFFHFjCAXW6vXl4Bzj4beOT3hwUBq4l2zeA6tGeqa7HrewYg9gUVWIs2CfYmF4xM+PRoaq2hnllTCxBrwBgkgpDdWqLNZy8EwGj3lqmmd6nW+iUGa736TGf6UbYAoAF8DJBZ5VJvVvMBbLZcd88dPqtQza2mopmFVDEcAAMNdIAmjHCBXlPLl3vXFENx6+b+ekbHWgEb58Qz80KewJTuAcn28Lh6k+t1DR9AI4oMmkQGvVU5gqQn2kW+yxMK5Fw9sxHj0d20k+a53o4JPpZlif/23/4bvvVbvxW33377kzWm6+0z1coVsHEW88jrGIdl3SDwtcj4/LTYkKRBHQdeZ0irCs5R4MF1N3HCDfELf+de/Hm6g/CDKQo3hv4qqbTlWbKQgrNyMagLuGGCIHc7EPTxffFSn9+Kgd31zMfQqVDCR1n04OPFgwxnNyNQDJugLZBjgZUmXv/ItRVu3pnBibeHkZ4yRR1sdwwNQDg9i9iHS4GJVQ43mCHVwMqrRwVOzCXzkaoKzRn7+jhYppTBcuCKKphGh7l5DI4ZYaAj2R1bOTEMG0Zv1sI3Q3C3+3wwBsYxoox2gHAqjHMrZp4JFk0CFbl+hJNHgZmjVHPCwTN1UDSR765RBg+ZOkw4Weq6RuM3HRCmQvWIqTjlvhHPgdq4ydQMZbAaoKJZFIO6Di7aCvRzymZQo5X3ITT6anPPdTESALemXROO9nE0H9lnGPVpWSYoyzVOO9PGZAA01s4xGBDqfBTDiEtvosYWzvl9gGV7hxjr4TLRZUqPF+jWsdBzO9ZdVtQ4MdeOs3WzMP6vfZIAH1fd3HNdB03TGpqPhNPux9iU7MgOanNdYu1M6fcBGMuV2NYAgDWkrYxkHQjQ1nvPdfDYXoZnnN+EHzNaXlUKL9rBshDF4pa5ZD4GiQDGjbXZa0sUTjhgPu6tSjgOkMznEO+sdG7l+69Ln6jsDj+aDVmSdQHXcZC2Aaq6gS/B3qOsgh8v5FjkWtDpf/bHvrYs8YzzG4AC/goNfKwyuMFppKkEKpeFKIJAsVjl/Y7jOQ7TsgdWIx8oNUDJ2O/O7GwCV4Tt1qfcm+uNeqeodYkCvDjwkQAVp2jQVnmfSaP30xkrVr1cKmi6xklVYzCZjwPgmQIqkv67rl+6fu9k90SDgcSBKeaz6AJEa9iKJjuZZQ0atgl5LGKekNk6JlhIONdcBkVT9kECQGOVGSAgMLb/KPbgyP4mbDUu8K6O0R2PCKKPABFmnwDE+g8Ah4+NvxuBmAnP/hrMWQXqEH115iO1P3Z2hmFDcnt1ugvc/ALg4d8ThTw2zonP62KsoQ3Y9319bOoeFwaLFCAA1OOkXTP3L9o0+jJAnJl6r/qqNV/tfZRNwgVG1HFHqdIc2Dbrr2dUfM4IGHasZmKtM/VmqbncnXclgrmOK+YMVeVZaXE6Dl1wRmUO6RXNu7FpICtFtOCKDaoCPF0/BqBXfvHijLgGipxUZtq8Igr7qZbuyr3JoRmUVQE4Hr70/tvw6392QXzWSZvQ9qujrZ+OTX+zqQA/wr949bPRtO2wmNNTvB3rTgRBgJ//+Z9/ssZyvX2mm1z4FlGApWI+1m1XrRKAcKRWV8RLmewg8t0uhWiQIl1EcF0HXn6AnVmAuM1w1AwXHGX8zyJfpgj0zEf4MRZxn3b90NUVtmYBNuJALhwM7Vs2v61QOcEgzeryoUzbJlLbgiZD7oQDNuMfP7qPW0/OBDtDj/SUKzhB0mlTAcLpEelThCFSruBGyQCsfGwvxQ3bCU2tp9IfAYzSpKekXatIHscCMlOO9c1j0JcxKo4DWnCAgDkOKwNtikGrmCDmvZrIuqT6mayAqfqVpuHIXQubikA5jJTBM2Y/jcc1ARQzDVEFSK/TFeRYHlPYupQmGacPpc7VnZcY29TxmakSnBPIMS7ZYgIGSA2Mr8XU/eLODYzfj+43zDPsBLXtAZpJx1rHojwOa8f8HQUqcNUq1fd6s4GP3Ptp3nduTQLWpl0HGvMxLWvEgV71MwS2bgKufZy4nt4InktwqAOJKAdUOm0bMbHWmwApFzxS16Mb3OVKGPbmHO7mO/H8OKkGqr/G2PE9B4/tp2L/VdUmTaeyTOHHc6AV97PTfOyqUw7vi1vnqJxgUJRuPy2wEQeikJs8phqL4w/Trpd5DTiAFy+Ge3AlNB9LDO2Ho7wSxfGAvr88vhsmHTPxMCuFPmeg3kMd8C3g6SnaR4VgSXLVTasMcSK++6TMvmArWMt/f80rntF91GmJmlIsrK6yDBrq6ZBshgWxLlGBTlbjcE3AgnvvTftNpr4PQSgCXKACPEGCUXYBNS5gfTDLdKA5VrUfGzYHBwRz90MHxyiw2GQ+Mu+zufZRlXOpeRIY41f9TPB3KitWP48+zkG6PbPGlAaANwJsiZR2dV7KPqWK0pn9VFEX1TitxqYWftLWTYKwYTYTJGYLzmTjOavGa7YB+GhJu17H7gSE7be8Apy8W/w/3dOOw60Jln1/wIqT94/SyFMgFyD2eDPFt3sPCLuHApRI248ITgyOTQDf5j5r9lNjIpm82fjaKU3Uphpe+8g3NN5H9lgE4M7Zcvp5w7nwAc0539TiWQWzPu3alJQx2fSctiVlc7HAe4Yx85GwhdU8V2nVFD6gBX2FLcQwPLM9wXzkqnrLPeJvv/AW/MgbPns4NtJOp4LzzLOQwd048PpCgdcbgCeg+fia17wG73rXu56EoVxvn/EmN64kdDuju6iaIfo+OyE2JElNjnyvc8CKWlaYLmtkbQDHC4H8ECfCGg5a7NdD5yktariuI2jI4aLfZCQIuhH7GvMxxY3b2qZaprTgsmpVjtodOhv7ur6SsdD5TY4MfcpX27b4Xw/u4pYTMxE1y/b7xbZMgXA2Yj5uJgGdblLl8MOkA3QBUUVbgI/EhkkVPQEIIIcAaLp+kt1TM4Zt189gkQE0mGOraAqsB84AC2BnGuM5hFdIARqEQTuKahNglpW9YYBAk9ikDGhhRtRLgiEBEIAhA46RoOwTAAIpYX3yWPFw/Ep3i3SM1oCn6ngjnVIOgJbH41KQlQ6Wea0cWG7OqVFhDfm7jvnIgd7KQTLeFeqdMtcWG6ina7Zxz0P15VLTqN88EfDRmkbFMBkAS/CBMcS681HggzH3unMQzihgF0fnNB9N1hT3vgO8QSyDPoHnoKjF80tLjWWm2qmnAVc+Mv69xpx51b3nARip0QQoh3COzUQYo41u9JtMexm4A4BvffUz8fWvvLv/LjQKrVU5/e5QWq36tU9lSmpApe862F+Vgr2pKleaz65MEcVi7h5mFZa51JvuBOJNYK5A5UZd0R/1u00VBFT3Q47F8ft0ZwBdQRbHrJJaZXAcB4UBPh7mlShko49FHt+L530QNq+EhmcHgOrrgSiqo467uyqE9rMXCOCDsB3mc3FP3vOhSwh9VzAxyWJD4rend7a7j3jmo8V5luPsx8wF7oj3lbJdVOBOn7fUPmDuURTTH+DtlkH6LWWHEf2ooBzHkDf3YvPdMasQ27JIqMATBZCtA4zJTAVjTaMClNT4/Ejs93U17KO+089J6pQb7EgSfGSyVKjrJFPHzfOaAJ4BBB5HmoS0m4h5ZFZD5hj7ah0/cQdwdGn8fZWPgVMu2GWmxwMM8zHtgzWkhIdgbQ0AVo75mB9KHb1bxW/0DC02Qyik93A1Niold9RPA3cp3Uor85GRSSHtYeLc1LGp+20L3nBVm9UxlXYhxahV1wHQmReK0ahYwpwOoskEB8ZrnWrq/oZz3nZUxw8l+NjWfCr6umI45POYMXMhNdZ1xiZT1bpdl7cLTUYtJ5WQHQjmI8W6BaDXBXBdQy+Z1HwsjlFckQiUXG8AcHzNx7vvvhvf8R3fgd/93d/Ffffd1xlSqv2jf/SPPmODu94+zSZfziTwcJD2adcDp2p+WjpUDnDyLkSei0xG8fXCMwDgJltAfoC4Fcb8tTKEnny/LGrMQk84XtFG/6IXh0A4x8LxcSgBu5Wq3Aj0OiemZpTe6gK1Gw7YiUvlFDhjwM+rcxQIO0DxQDIu77/tBNAeCaOsXHW6LG4wGxy7r1pJR0q9MOkqd2dljaOswulFBGSEeK9V89FMGWQYksoA0ITlR800bs3Nwzw3B4A4Lp1+0dbCkFXag1NTlZWRQrLKzFQZDlQkmGVT0qlZ0MJ03ojNHRhHvyhnhrqWKRpYHAhjGlIc83FglFscninpwCRzcyITlGODqH5c4RfqeGXGgJmGIUA5Fl4g5q7qx0XUKceHMxLM+9JVlyRAvbZBl77JAcvdMSlDhXmGobY+Tm2sgHxMi253v+Ouj4ns6r+j0upJ5gcDQoRMmhUAlhHrhcNKjVUBJPNxP0AYqqTgOJF2XdaIAgJ8/MR7xr/XjOA3vuR2/M37buq/izbFc9PTEGVQUGkvDwgHJlumygEJkL3injPD84azYQVu3Rk0m42FyhaMopx5BT6Ka+mYeOF8nBpVph24t58KjUPBfORZgbUTomz6G5IWNZLAHbNVyhWccIZ0v7dRlkUlivcQoKzjemgcr2MoAsJ+iGc7w7HI99IPZ8gyDXxUWpWOa7BNM3jRHGlZo25aFFWjpdwnBNMlgx8muPemLXzq6kqrik44dvL+e2HvrA0rnU8MPqnvu1RXC5A/ykRgJBhGshUTgm1c4NQEmNT6PdJyNB1yZavoWsjmPpaN08FVkZuBjULsO6PxM2vQ1GfhMXvxKO3afO+oAAERRDKfgc5U8uQeQgXPzGO17dgeOtbcgHE/iOfePQPT/svGAB4FYJMMNfO+UXZTQvRLhzYypzWo7PoTdwIf+43x9+b9D7UiLHohQ/PeeuHQbtGbWR2YknQi92rCtlf7ZLwtmGB6CiqbcRLT7EM1R7qxqfWL0ebrwMdNmvlovseA3N8eHx+PYz6SzNG0/77ra+wlagwA8c4S8woYA9bUWt+Bjwq8Y8DHKaxBMrib0M9ZL8Slnml+OJSLGRTrkn5ZcdTvzWpsQH8Mbp6aTF4APBPVeL85dnCx1EB3BnzU5x8FzKum5l84Bw4eGX/P+pKcnS7W4x974wuQywAlX8DVEgx/irdjg48/8iM/gu3tbbz//e/H+9///sF3juNcBx//IjUZZYgDr9MvKqoG24kWNdy8Efj4bwqwMNlB1LjdC1XWvRPw8qedRni4DWQHcMolXMfBbmkyHytRKQoYOiPFEgjn2PD8ji2Yllo1UaXfke3z4GOVo/WiUdrURhQAzXjhd6oUrR93adf7Ur9yZxYAtTrfQZfq523McaSlaB9YWJWocgTxHKsjeWyp3bQ9C4bp5l1/S5rTIEJMRJLNfhyjCRhH4LkCBIDYyNp6rCNHaYsAQ2aGJ++fNRXSBOwmLu62qraDezURLLIyJNcAfOq8upHGOfiUBheZZm70MaPW3dgM8JZiZZAOzxpG49T07CofGiJcP+tzUCDgOiDOAJVZsNwAKc0q7l0hIRN8JIBb/Xv1b9YhN0Berio6gI65QaXUc9fSjYFxzrvotb0o1/BYHBhBOK16o9K4pv5uciqKNFbNexjOxXd6gEM/Psf2Hq01T4z5GPoumqZF3bTIyrrfy1Q79TTg9394vGZq6T+h7w4rGkabApQujsTe1rbDiD2AZ92g7Xmm42vTfDQ1sWwp2mZganDtHIBgOhn9vfUkQ6AHHxdjp7JMESVzuK6D/bQUBWdCX2M+joG5xg0HzMe0rEW6kvkOlCnccI5cgn6e6+AokyCheQ/rAk4QAQU6+6FtWxxmJWYLee/VfZSOUBAnSA9TNE3bMx8dZ+xkliv40RxN03YFb/qUe0LPSoKA57cS/PEj+7jphLwXFBtUc5yjwENeagFbcz800/dU49Y6FkQjAD4KRAP6ta47JuEcm8xBYMLeI/89qmLNgG1mde+RbIFxPkrLbVJ2QUavLX4ErIbzjbzOyWnXBhBIZQ2wkjXRsI86jwrwU0wwMsDWDu8HBf5ymo/6tenn1MfGFhoyAVQDxLAy8837Qc1JYz8H0FXN7c45owtwqPVt+2ZGO64PFInjaOCvCero86jTjKNAGA24o2RJSFmBmDnWUX+ceNtIu85p38OPmIBdgcEc8UJhy64DUMP5OPVVrUcjn4MpQEftczZ9SGCc6guM7QZ1HYPjEmC1+q1+TKq4kBqPmlssqEhdC7FvTGU+dued9c+Hkn4BxHutnnt+KDQW9XMC/XV2+9/Ea6CKu5iZYxxIWRrzntR8XPVjozJMVFPFd6qMPk7FzH2OTSuDLrec1N5ptZaZ2pocOeN6Oz74+MlPfvLJGMf19mS0/BCINhAFXmd0i4IzmtO3cxtw8Jh40RdnEK28jvGYlzVC38UXP/cG/K37bgJ+bUNsysUKngtcKYbOWV41PVsk3u43aBnFmNe95mNW9lWoB5Wotm+mr6XO0XrBoLrlYSY1pBqCbSiBV1UEZy8Vm8tmEgClBnZungfKFH406/oCAlC87dR8vBE0Qig/0DQfe/AxlE7YWHif1gmMDOZjZWE+yn6dIUf0CxKxiSrnvQMzGLAMwLiiKQP+6fR9BRAbTvTg2HpEzqTad9dlRBWVoc0W6TAMdw6onQR8mkBtxtwngtUwI5hVVEo1qUszEZQdOVDG/TMNDxvQRoGUlJFlpr4pVpA5Nn3OVhlvtKo5wI2t62eCilPA3ZS+d4HmhHARdXX80T2mjmc40BQQDPTvQZUB2KSdPP2YlHPDMR+9QHx2HPBxamEcs7EgBgMk6ufjUtFN5gf3jndagEvA2xqPaxIzlWGZA3yUXT57JUdS1o1k3Bng4+mni+PvPgicvFP7fUavb4DQGQLE847lvGibrv9/fNPniKBVN8ZkPC/ZAjoG4597JwAePKaM467olbYetK08vpjPvicM7I7lNzsxdjaqFE6QYCsJcJCWveajF8g0NcPpqAvUXjjQfBTam574jR8PpFzcSLxzeSUAyt1VgZ1ZOHZaqxyuHLcKwuZVg6puMUtiMRZ1H6sMcAPEYYi0XAod6xbY6BiK8xFA6MdiHFeX4n4locZmpFLxvBBnNsRzUkWH4HoSrBtqVQq5khD33riJ//Xgbp8WRrEKqaAIqZfM7Z0EQG0Fp433brT3mHuxZe9ZB1YFs7FzRzrkE9cDM1OBZEgSgTY244QKBBJ7sZnpos6j95kC2FJMZtPm6MBHfT0pxpktnBzMAMgMRQaDHniZnHZtYSuaUhClsZaaTH3b/aCONQJFCDCJrMBLrJX5obh3G+eFjU8F7sn03tQAHwl7zlodWAPuqLTrKdXogWE67oj5yNiiHnFf1bjUuAEJTDGgp5QZ6c598JhxDYztxbHZyEAHEZwAaDuMlLiw2c/McUfMR2KtV9+p85pA7lRpAJbtSYyt00uc9Xu5yV5V/w+S/jqmFALiGOixYa8FCV2UqUz7Ym1qjNw1qDlDEXrUsTpglylcA6ArvhOV9Hxi9wjm/pLrXth/pz+nuqR95Ovt+JqPemvbFq0pUnq9/cVoZSo2gngLSeB1qdRF3SD0DPBRMTO2bhIFZxRLshb6kP/wFXfhzGbcR8vKFVzXxZVsOH3Kuu2PPTsJrK6Kf+dHQLTAPOo1HzNd0D9IxKJGRUpUq0vADQcMzqJqsBH7UkhWS72TKQFumGAl+++tSswjXxTbCReygtaBOG5TwovnXcEcQEu7jgxGh1yMwnjRsTj30xKu6wjh+HAh7ruuX2kaVKqNilQcJ+2a2qwN4f+O+cgsrMC0TQ7oN4Jy6HhNKmZj1ZjK+7xDTs9HfTYJLIzHINok5iNz3VQaFwuOrWGXkCncExgoFKigjGhdY5AD2ialLZnPbCI7Rk/nG51Xzi0bEEelq7Og4tTzZv15ySIyMQDHAHm4eWIY1JyhYjKzOBAPsKSRWOY/pZVka9xzDizpKfrvpuiz6o1id3Mi9CzQyzDiVAEKErA0roc7NsBHx6WDpwqx5VUzZOarNj8t9hpT95ELrgB9oEaxPQyn7dxW3AfhgLERbTIs9GZqYlU5b+hy4DE1nykmjjGfRszH5ASQGo6VZO1sJQH2ViWWeY1ZpDIjZoTmY4bGiwbVrjMdBI60IGO5gi/fOaX7eHVZ4OQiHGtr1TkcP4LvOZ0dpOwQUUhGA/0km0JliyhplYUCCcP5aA8MYrHv7q7EPZqp8VIskboAggSnFPioZ6GY90Qxah0H3/4lz8YPv+H+/rspMhQADRRyDFkqdXWqJijpHJugoo3pT4BfJvMRrXG8dHwdJgDCMU9MbVWW+WiMi90jjPvLSddQ12mmSiuQFbDs1wSIZj7XjuFlAs/GnmgCCtQ5uwrhBhjLFQecKgmgz2FVTHF0P4znpJ9HP+86FqUaAwUSjbQaCTZWftCzBtESYE1O3zOqcvFUIKlcDsErquAMqX+ZYVRAJD8Ux3A9gvnI2egRvW9Qc5cqcAQMK2CTACrjH3GAEhXoCGZ9oHNw7HxcdMj1xLWaID/1znLptKYfQM0ZM+Wb0rCk7BUKyCQ1dbk5k4p11o9EMIpMCdfZkaG4HyPZFCoIRO1p3DUwoK0pccBpPnaANQE+dkV15LEoe8I8Fqv5SICJAEYkEP3cU6Vq6nycxXO9AXiC4ONP/MRP4N5770WSJEiSBM95znPwkz/5k5/psV1vn05TYFyygzjoU6mLqkHgaYbH7GT/753bEQdawZmqMSpjnwaWl4HiCI2fYDcdFogpqkYUmwEEE0JFeaTmo15wZsQsme3YwUe5SCvmowL+5pEPRFt9JBIQi0nbwA0TpBJQ3EvLnmHiusKRyQ66xSWM54MCMj34aGiUyAU1TPpqmEK/UmldEpU/OcBKL/rS1CINel16NsdoAobameqeAWvAEsIQIJ18IvXTNNpUI9OGmX5tI4ugQHM6GDalCSqSwCcBUrGgos7OZAwgqnI3C6JpG5WeasKOLWMAPtNpI4A2de90RixbrZlwLCinYcAE4RxU49lSDqDZj3M+gPE94ZiPpnPBFv7RzsuBu64ro8smcEMZwAkxl4nxmWmTXHo2QFd8VL+hnqE6/rHSrgsADqGjZKkoDdgdRRvzsdH037pzMWAi5+h0awxjxFMyACZjxKaxQ+lNAd1cV4GzomqGwTHVHAc49XQGfGRAv2AmHAG1H+tGP9XMyo7c+9D1NcEq5tpZJ5JbH4n1AOjG4nfgo8pe2BmyOupS7GeS+ajSrns9RNoRa9ywsz8AJc8in0O02TOGyxSeLGajsjquHuU4OQ8xTl0XAFSsZYCoDAyhQam9E2UGBLMOfDzS7QxAOjnyPZQVQ0M5jqtHivmoSc+QQEfUMR87RiUwdsS0NSkJPdxxWkvpDAng2Qo+GmsnG7jR5kjT0E4vxW5jdQl14Ig5rykXo5xWE4Qyr4M8JxFs47RizXNSe+wgEECAHsA4MMqt4yNZnKL/XD+n0tMELOsxBbYZQFQXYNbnVDFeIygbxzwnx3adItnABR+5dPtJmo8Uo59g7ZLMR/O+GfOSY2Plh2INirXMqcFxjLW607Y1g2/EvOXAGp3dZQY+ANpO80OIOWTcj2LZs86SnYkFZ5igI/WsOCBpbcVuzh7gmI/EnkilUgMW+9QMKjPvLAVWA+N9mWKulitxf1yNZDNJ85E41hMBrAdF7xg/TwFjEZE9qGwp3Yal5ilFtGDTqQ1biWX8Lvt+VDZhB+wqYJ4AWAGRBVgXYi5x6dsUkxbgwV2KmEGtj4A9E+cp3o4NPr7jHe/A3//7fx+vetWr8HM/93P4uZ/7OXzRF30R/t7f+3v4N//m3zwZY7zejtvqsncE4m3EvtexA8raABQdB/iCfw58zj8AooVkPvbg44AlOT8lwMf8CE0w6yL9quVV3VfSnp3omY8y7XoRaWnXlcEsSXbGzInBNQmjSQF+yilYRL40BrRIpFxonWijYy4MHB9AVrze6xbIIJkPUrr301KwIqINce6uiIVY9OJ40elNDYoTKPZhboB069KubaCibljZUlhNQFFt0hQAwrGMuIIqXiiADDPNj70u3cjmGAOGoUqJQ+t9RyDVBLDQdu+nshpGVZEngJlUNHdq6vgIyCSANpOJYEvhNqt1q8+psXVMSuY6qSrh6xxZG/ORckAm3d+cef6aUckBxQDBMLOAqFPSYJVwt164wsrA44xqBjziDCeuqWi0aUzbqgIC/XrkEsxHLq0FAMl85ArlcMA2p21ppi/pbVSghXFkgDFDvju+YJipvesor9C2GDISVTv9tDH4aJtnjiPOmxvMRwpIBcReZjLt2TlsOGfFCgPtskFfTj/Lsu6ZaxWgzek1adfaM9tKAlw5ylE3bR9wDCjmo3ACdK3pVNfe1LMQiiUCBT4q5uNRgRNzLe1aZ4X7gs2YUvaDPhZ5vxPZV2lG92zG+bAvgCARDv21ZQHXdfoALFW4Qo5FgY/DSudmujgTYDHHAfCge8dEk2snVUyk62ukwap/j/SGCUefOv8onZp570esdjknB6nBCkhbs2ZPDT5SrMbRsYzxs7p4BkBGAXzAOABABajMe8vpdlPBIPP+UixE0nEmnpP+e2pcqp95P1xPBFtMG4FkvJq2CQV6GiDGcdJjyWdKAAom8MyBaNmB8AdUiimlXbguvReg32sKrFHsLrVXcGsmVYhEfae34qjfk829kLNrzKC0amYxFTU+E5gyNY45IIy0BwgdYYAB0DnwkVtzCLuBLZxoAkqVDKwZQDPFfNTnFRWMosY3VVfRZA4PzmsAfKOA7mo8NsrumjJPSfYmAyqac5+TOChWRto1U8ynezfGRWdFv2X/PZWVCPBSY9z9pZiS3LvOHft6O77m47/9t/8W3//934/Xv/713Wdf8iVfgmc961n4tm/7Nvzjf/yPP6MDvN6O2aoC+NEvAJ71NwSNPFwgCQtkWhGZ0DfAqKf/1e6fUeCiqBu0bdulXXdtcUaAj+kumvgE9lbl4DADVuXsZA8m5mLTmzt+l9qcFjViXwcfCc2owXWJ1CmVdq2ch43YB1q9YM1Wt4g60UbnPKwKCuzc7Ra1KFlgVYjzV1LzazPx+yhnfjhYKBOp+7csKgE+qvukIouTmI9axNwGKuoLfl3Q6QGA5uwf9uflHHGW+UgYM4BwosP5MLrEsgVNY5wDqDTHKIIdZBgxcVIL83EKgy8eGo+UbonqN9IG5MBRHTDkwEcDlGU1M41+4WLYZxDp3eTnGJUmDYcwKlS0XFVrZkBoPx7OmXLFbNzaeSkmRXcdydhRoYCZKanoXT/tnWLBPAO44dhrwWwMBlHzznGGrChr+i+TQm0K35u/sYGGZuOA7bXMx4wpqEM4bXqjIrw2djUJbAdivRoZmvK6KbDZjHjbCs4ozcXSeIZyzimw8ZrS7qPAx1NPAz70X7TzleKY0WLcVz9vl3a9hvmoGPnd2CzgYzgXjNO6FPeuOOLHwUb9mTWcYyXJZ6x0Gdm06+46E2wmGR6+Jv6/0KtjE8zH1otQVL1zkBaaNrTBfAw05mPbtrh8lItiP+FMsMLVtUnx+iTwugyQg7REFHjCvtEZRdJhTELBklzmIqDqe9r+rulOAkCUzAFcwbVljpnKgADGDljTyKBAgtMSfMy06tuj1DFu31R9R4w2Yp647vBZ1qXUHOX2iimBKvnummmLVL+66LXxWO3nCCg0249Mp6aYjxMcclbKhghSkoCWCVAwe6IO2urFEsx+A0YusQ6aDBqucBmlr2euFa6LUSosd05roAEM4EykHwIYpe9za/JIhkaB3QYQSKa0m/Iy0bDojxorBVKZgIJWMKw7J5k+fCTWID8We6Sp22zeWy+QRVgmAk4jUNTYK3TwZKC7OZGFlR/2e3K8ORw/V9DNJBLo1woM92SzOJAan2TAd9dAaQtS9kC8Ka5XL0DHBU90fc0pxzZ1FW0yQ+b1dwEB/doptqLho7CgIhUMoEBUgmVM2WTFamhDmyx51Udfo8jUZmK9MyVNun7U+kQBoxkmSRwUSxHQBJgsCeMZUOAv0O+n4QyADPQVR0NfzyYLRjIfiXWPDTQwdvj1dnzm4+OPP44Xv/jFo89f/OIX4/HHH/+MDOp6+zSaHwIn7wb+9F0iTdp1EQeCzdi27Zj5aLTI99A0LaqmFcxHHXxUaderq3BmO52Tplqpg5XJCbFIlamMuM2xiH0cZn2169gEA6mqaqpVGVw/6sDEo7yCp1gGZiRSOgfhfAuHXZp31VehBIDFWeDoUtc3mm93adcDVoSqxN05PWIRTOZiE1/lNfKyGVb5BnpHr1FOEGGMeiFGhWTWRem59FqgF60fpJAxzgslcA/w4BowZtqYRlt3XZrTocbMRZbU9/rfU1gZbGESY0Nmx2gcbzLTjwGfdNCzaaThYTzzIBaR7FqmmU/VzORSLtR3AA+0jdKppZNF6iBi+CzIZ2aCEgwDUU8xo3R3uuNNuNau3xRAOzHuiWUuPxH9UusxdVaUBXxkBbSZuWX7Ddds2pRV1s/B0e84phBjJOq/o7SnHJcOcNiCIpx2EvW8FZtA10jj0lxUQYmR0yiAbLU/7HaFQyjw8ekCZFsqPWMJRKl9gmqKZQ+I+eH6vEEabQ3Hp+tlmc3U+NV1kkbHtYm2TwwgAd3cUFrfPfPR2L81x3krCfDorniGw/Rl01DP0PrRgPmY6dqbBvDnRQsEnou0qHGQVjjKKtx8ItEcgbS/L0GCOHA75uNeWvbFXvS1QDq2veZj1Y8ZGDKG5G+8aI4o8HDlqBjaGOYa01WSD7GIfDzzhk289rNvNvobARGW+Wjsxdx7CzCBIG4Po0Aoo6/riTmsS8aQBWfk/9X+yQHppqPKBe708QP0mu1HAuxQ65uV+WgGAilAaELWg5nCzQYCzb2TcNq760z7Y+mf6/2oFD8SoNDHRjjao9Rm4rmzadcME9QMKHJ2oilpQp2XAomo9NgRSETYJiTz0bDVOPBRMR8dZxhQsl0nBYpw4JnZTy8Koo4FjJmgXHDAvM5i2QenImP8VTHOdgDoeQaId9T1h3p2ZNEVdQ26ft9yqEfJFbsxfS8AZCV2NU6AeLbcu2iAj1zWCccwBghg0bj2YjUGKEnmKjEXpmSLULqvwJjVSLECzTlPSdKQARkKGM1o1juZaWHYGxQbFMAo7ZqT4rHpoQJDAJ/KSgSOBz62Ld1fjcMc5/W0a7YdG3y866678HM/93Ojz9/5znfi7rvv/owM6nr7NNstLxQL9pYwbBWgWNYEoGg0xeDLynrMfJyfFuDJtU/Am5/EQVaibvpNRKRpe31fQIKV14DZSZF2nVdoW5GqPGCWJDtDAWSzyYpxetr1IvYFyyBIxEaYa+Cj42E+W+BAVqJeFaIiZtcWp4Gji8KR8BPMkhmKqkFZNz34GBPgY7EE3ACzmVjIlkWFvNLSrl1Pbh5ygesMuTXA1lrmo1wEOYNPNZ3RZUv9NPuqZtNMMxlbHKhqat3YjE91HL3/JGeYMSpGukrMGM0IuI0hOUr3XgNSUca7/n/9ejk211o9qrj/rhs/d9+y3tjjAC5Tx4tz2vTqh5zTCQwdSht7a7LWZIwhs5QBlXUHb23qswE+kvcvMeaJ7Zg6iGEBKcOFuG9mOhOlE6qP91hp1wzbhGJm64177pQBOPgd4UA4Dh215uaMOo+ZGm1jCwaJZLnJOUkZzKqZxV+644s1IvBceK7TBdUGzHzVdm4TTtqVD4v/K4PdZCbrbXZS0z+2AIRqjGp8UlOQZTN2Qbd97dgc89EGPlL31QSi5L/l/FTLSQfMzU6JPVK9Kx0ou4FtmXYNyEwFAGQaeF2g9aNRtetEZz7qDNIgQSwZigeZ2Oe3Z2F/PV0QTqRwJRJQBIBHdleiOA0wfLe6gjMu2laAlANAUQcfNXBgFnq4fJQPbQxKexIA/ASO4+Df/e3n49k3agyMUSogs6arMZvBE9saq9Z12x47YrsSYJBqvrG+AwQQqPaotP+bDdyt2dspZhMHLuljtwazzHXdPJbc61QRC6tEgQGiUddpVg6mbK1u/Pnwb7KisTb+tuWBDLNwFKk3aNxXxxsCSyTzkWOVEjYYazNRAQ4dfDRB4mOkx1LPntPdC8xzZuOCLcVRv39SEh7k+ShG3EQgacR8JDSRSXYnARSr8YfM+Dl7wQTMu/MS6w2llWmmx4ZzAK0BoDLvqLrXRMHPcfEVYn1Q45wk18LZz8T1kyB5TIDM6ZCByGk5Uiw6UkJgzRrQjW81zKChmI9U2rVpX5Lp1DFxDYQ9R6XgA2NfxrTrVdPTrik/Vcus6MZPMh+P+u+t2X5MwN28v4osRMlNUGQeTg7rejt+2vW3f/u347WvfS1++7d/Gy95yUsAAL/7u7+LBx54gAQlr7f/C9qtLwX+5/cDZ58JoGdwZFU9rnZttEg6XKqa9KDvxjnx94U/QnDbq4BWpC/tzMXimdcNIiXSvzgjnLSLfyZSw2Ynsah9NE2LvGqQlc1Q0H9+Gnjwd/hrKlN4wQmkq14wvmNdKF0ttZnmwkjYmoXYf0x8tipqUUCmO98Z4JH/JRgpyXZXhXNV1B07czMOAE+CiTqrMlqIytYQWpJZ2SDWQdpwnJrFGvttLZ1MuchxUfoO1LA47sAQILQBIACjv2JhUJj9OSBLX+SjBW2oAP1vO0M7kymfDENuFKlkjnmog4VTGW3MGM1+HMNEd8bMiLV+LEBcb7QYG716v3XXOtnJMtOpmetUxpw6r/U6DcB8XSqdbU6RTiD1XCljkenXFX1hnCM1ZlNfLbiBGd+E8wLD94NzVIEhY80/0X9uK1wSLkSwZGrjUuL0dzPZHn/PGmIzHrAELKLxRIq5LR3eFMIH+nWBqhqop7soY5HTU1SBJIvTOAs9XFtZmI+eD5y8E7j8YeDWF/frfGxhPi7OAoePy3PvTWBJ7gvHtzOc14CP6R6wg6Gml9nCBVB8Yvy5LYhjspfU5+i1CjstxE357hw+Dpy4XeyXXgT4Ubfvhr7bp1AzDkU7SwbVrldFNax2ffWjAx2xJBASKUrOZRH5QG04GYVgUYiCM+LY+2mJG7flupVsDwHFcNGd8+pRToCPe914AQDBDLPQw5XDvKtiLa6RCNYB9uDFoD+zd8lzjooNcYGLyczHqHeuAG19XwMclcwxKSCNLVBn7tlGP9cTa8Aoq4FwjtW5lF43q2W3BjDsmJs54CZyD10T8OqOxQQ81wXaTNaaynShZDDMAk8kI4wA+Mw+lI6zuT57vkwh1m0hW9q1vqfbbAl9bArU0fpSzEcuk2UEPhbAzFgPKZByxBTTAlr6nMj2gVNnxL+jDYJBz4C/k4qMEKCOCT4qQGkgM2KrvGsAJ/lRv0/FW+J+KzCX1Xo3gsPdNVBzlwDgTP9HL4qpbBHOpurAR535KMcymsPGe9OdP6PXRROU59ZPrqo8MByzGcxW5zaZj2baPJXBQ+kg1sTaSaXqA/Le6inVHPNROx6VGcGB6WbwlvIV/FhKwlRDu80MknHakINq14RES5kOdYEpVikgPnNkdXNHrqEUQEgWnKGzM7rrMxuVkn497Zptx2Y+/s2/+Tfxe7/3ezh16hTe9a534V3vehdOnTqF973vffgbf+NvPBljvN6O287cA7zmB4DPfhOAXjg/K+r1adcSEMwo8DHaABbngPwQztaNsl+vzzTo73rA1o3A4x8U/5+f7sDCg6wcMhoAYPM8cPDYONqoWpXCjeZD5qOeDjU7BSyviH/nB0C0wFYSdLqUaVEbaddneuZjvNUda5lXOMqroXC8ngqXHwLhAr7nIvRdLPNaVkbVWZzbvTOzztgHJAvK0k+PhHHaLKrplXTXsWyOI/4MEDp5zIZtpgNyaRVqM+2YESve2TI17zjgztzMWEfAdK4Z42ukg8Xcn4HWIAM4d0LgGhOEZZUYzAHzWEpPaODYMQa+OkbXb81ctPXTQbsuAryGWWJjC45S/Zi+ZpomByoPwHfLXDaZV6yRakRwbdcS6ODjGpASIAS+LQACV6mZa1XOp10DvO4jN+51WpEcI5sLcHCpKMl2D+6oZrsvOvioKhty9931pKaiefx+jUhCT6RdO30WwKidepoAH4GOZW8N8iiWPSD2EUpbVrV4UwSkyrS/bxyg6EuWnwJrpbwJ2bgK69yaSwVdNC3Qm3bE83BdpfF8Snx/8Ji8zsPOedyU4GPHelTj0ed/2wLFCo0/G1S7zsqml2eJt8S1lqm4R9EC89DHshDp0d2eHS6Eo5FrLMmwr2ANAIdZJYKL6rgd+CgClwp4vrYshmxGfW8vVxIY8rCIfMF81O0As4KoLbsBwFgKwnBe9WayWWyBC30/WZd2bbIp2b46+Mgwk0cBMi4QSOx3U3TaKMddZ1s2tcjUWVfJlGMNmsxNLjVzxPSbGMgkgUAtQNkda0JKIMcIM58ppW1HMR8nBWKZNd8zQWzOZjJB22xcaEjZvnohvCns0+68BMhnMq0okAgY99ODO+a7bZMeoHTgKNtwBD6mYh1T16AAJbPQ1Lo51I3f0HwE+jXSJk+gs3+7sRF7MgUkqT3MBFAHmvgWO9gNaOaj2d91mXvNHNus2szq1xtzXh3THAOVZmzurer5me8tJfcwOhYxl02m+uC8OuOSsMFURWzVog0GfCTWV0prlktD1q9D6XVOKe6kX4Mav44NmM+r00M156kEYh1HaopHRLafLXBOpGgDjO+ZEMdmAjTX2/GZjwBw33334ad+6qc+02O53j6T7ebP7v6pQL6sbCanXasU5FHfE7cBRxfgnbwTQDYQTB8de+tm4NEPiA002cYCUm9pVaJp2iFgt3mjWDzS3V5oVm9lCn9j3qVED5iPALBxFji6IP4tAcKtJOjSsValCT6eFU7E0UUg3uq+WxUVVkWNuS4cn2z3KeGabso88gXzsaqHLE5dfN/KfIz6PkpImzLmdKPb5rgDchNRjA+LMwrQRSzKdA17xhRMZwAbQANiUtqwcV0ZXVIs0czC/JoBB4+Kf7ctDwKaDhzLCDSihtwYg0Q4ukq3sLFURuvSqbP+t4M+pmOR0fd6lOpNjM1xhqwAW3oZMEy7WwdSKkOaBK+MtH7qOtVn6xgv6ngHj/T/5/qGc2EEq0iqrZ86np66QZ13AFJzadyEw2g9pmIo20B8SyEWbv7PTgCrq/R3VOOCFJTxb46BY0xaNR85A45gTNr06Sj5DSsjVJN4WFfMBRC6jxSzsmM++thdFYh8bf032+l7gD/8GfFvBbJxfQGx1ywvi39nB3aWpGJ7ZPt9GpStmE2y04OpuY35yIDH3Jo7kqUYri/f+AVPx+P72veuKzIjOvCxv87NROzTg6BnvA1c+nPt+DnQ1miD2YD5OJBnWUjNaXW98RY24gKHWYllXvfFXlQmRLdnSzZjOAQf7zilHPFt7R4eAIszXcr91WXBp12rAndQhe9g6EMaRR3WMh8NhopV4mGBTuvUcTqAlWwU85HVT9XPn1syEWLimKYTrfYUBVLaAnfGfjdJeiUd24o625JjSQFib1PrQKOq1zJApjqnTWJE6Ux6/hogwwBZzTXTdSWYqe5tQe9z5r3grpXSthyxGkOiD+Ncm+ectGdytklszHfCNvEjiMyNog8ET0mj567DvG9NLWy6gX6fAp0zQL/1mRY44gIL5jtAVjimwJp4PH4F8Km9xY/HGspVPl7zVZEpUwNTl+WINMmO+SlebkkHYnW7h2N5KoZ/dw1pzzwDxpr4gLzPO+NzU9qaXQCHs8GNa7YxH83MlynSANwYuJRzU0sUGNqlZDCASc8mJRXyfg/Qz6vPCdPGpsZGVRYngVFqbEzatToGNrV/twRom42vQfddw/mwgBwwBlh1PVTdXjKlaCg7qC7orJqIuCfq2infiJSSuc585NqxmY9PRvu+7/s+3HbbbYjjGC984Qvxvve9j+37Qz/0Q3jZy16GnZ0d7Ozs4JWvfKW1//WGDhhLyxpV3fYVqcm+CqisaaDy+W8Abn85wps/Sxyz6JmPpZnSvX0LcO3jAlh0PaGhCODSgVi8B2ltKm1LAUxmK1eYzedY5kJj8cgUgl+cAw4Vu+QQiDaxlQRY5YLtmRYVEp3BsHOb+Pvh9wHzUx27YZnXWOXVkO0wPw0sLw2ODYgUvWVRIS+bLl1dXNh2nxKzDqAB0BXm8SPaidUXfI7xpZrOKLGl4anjmsCASdk3j60WVwVQcawZx9NA0NU0QNPKKtNAL+UscNR3/ZpYLT/T4OYYX5pIsabZNT4ewQSh0iTUmNTfk5ggDNBmAnxcepl+zirnmaCAeAY2p03fYG0sGt34taXZ6mC5OiYZVdRSjrqqsdTz0owLK2MuIc7LGak6K4RhogDDOWqmbJljhDPW2CmWPGA5O2EvyGU2qrAAIIwsP+ZZjJxDSaXumL/jQEtSZP244CP3HLVAh22tVW1xpgcC9ePL9ysJPFxblsNgktnO3APsPyye3+oqHSzT2/yMeHZ1KVn5FvBR13FUQRnbGq4CY21rZ7qb7xnQO95sCq65BvXP7OxmjOfdvD38zdaNvZOjMR83YsL4jreGTqVcU9pg3hWcqWXRuw58nJ8Wx1Us0ngbi9jHUVZhmVc8SCjF62Nf6EMCQi5mg2I+SgB3yHw0jltlwmHSgGR1rE1d2sUEKLpApGV/HexdK75vMOsdMgCj6qWD42p7rAIDzGrB3flNKQpmPZqy341Y9xaG05S9eMTMJNZs/Zw2oCKI+/nNVvVWx9ICfBxDBuiZqGwKp/FOTQHvuKAoBfCR1xAN96+6pJmPA3Yhsz6TIDG3dqxJLyevgbBzzCrnXODKTB3nrkMFnRVDigpYdRk5xr41YD5uMYEFAiCiWGzUvKWYj/q4KA1lzrYynzvQyVEB0PSPVYYWc1+5QCnJfEyIazAB1EisPVNsPmAMitneaSrQyQaVjbFaAx4EKOwnQ1+NTa/XgTYNINOvZxJblpozkdgDdJYxMPbhqPRhEnw07x0DeA6Y1MoX45iPuu2s7DMTkG2H91hmQQw0H4Fx5tO6e6t+M7jO+XiOcFlvZF8myKCuxZwD1Hp7vQE4Bvjoui48z7P+8f3jEynf+c534s1vfjPe9ra34QMf+ACe+9zn4gu/8Atx6dIlsv9v/dZv4Su+4ivwnve8B+9973tx88034wu+4Avw6KMMaHW9dcDYoWQB2tKuFXiYVw1yivl48wuAV/8bhEEo+/XgY26ClTfdL/4+eWd33kXs4/F98YIOmI/hXDAQFHPCbGWG2VxsnHurEkd5NUzj2jjXR92kQ6gYF4eZYDMmZmr07BRw7RPA5o2icnbgYZlXsjiNmaIt56NmgCwiH2lRi8rdurM6M5mPjj0FslzxBhqAAdXdxgJSxxxUP7U4rlQUyMoy0lL3bI6U4wx1OmxO1AAssp3bcKAABizSHKi6pBkNAOHwMMCnMtZKDdxYx3ysMgDOuJ+ZDmMt9mACXpwzIPtxRvmo+A9zLFXBtCrs91dPf55aIIhjyAIY6KMCYu7aihgVq96BW5d2bXXeibRr1jFe4yDp5+5S0pf8uV1PMrMMMNGmV5jsCGeHq1JtNmvhKMKo6sZgcUKUXhF5PoYRTKb8WN5ziploC7h4gWBnTWU+6vuEaprzk4SeZD5aTKOTdwNwRNGZ5WUBLtraxlkALXB4QbLltvm+0aZ4bqsrwhnwE5p5ppq6X1Um1jpb2vU60fZRf5OFZwl6AcDO7cDVj4t/Z3sdkLoZE3ahWfCgSzGfdwVnFFDYBSlVEburHxcAWjjHRhzgMK+wLOphJoTOZpSOzDzysMy1tOtEAx8V4C2Di5HvAo5iPhrHVdenMR/VuQfXGm1KEEyCIsWRuIcUy4K8J7Y0esMhswH0ERHg44KcTYmBpq/VJlH7GLPfqT1FL4rGMgLN/Y5x7tYx9PTsAlsQTWf2ssCRAh/lObk1zgRouP3OtDlYyQN97+SYfsS+BDBgGx9E6H/TapIrE0FiVk/TSAflbA6zAA+pu0kET6lzUiARyc6T/zeBZ5JFpc3Jth2y1k1WFHf/TaaYCvhQbDdz/MVy/L6Y6zLFnAPEdZtgrA6eeoE4ZydNwawfpoSSasfRfDQBVFPDz7bOcOxx6pqjTbpoCuknGDb2VLZyN14CwF8H0JLgI+F3kAVnqPfWCO505yWAxXVjo4KTHNtyoPuq5j0TuDFtbGD4jgfEuyazIHrw0QjuACCrZgN0erm+hx5HaizcEO/aoDI7s1cAGMmStRpj+3obtclo4S/+4i+y3733ve/F937v96Ix8+0ntHe84x1405vehDe+8Y0AgB/4gR/Ar/zKr+BHf/RH8Za3vGXU/6d/+qcH///hH/5h/PzP/zweeOABvP71rz/2+Z8KzXMdhL7bFVKxpV27roPAc5FL5uMmxVqQ/aKgZxIARNr1zS8EnvZFwHO+vPtoexZ26VoDMBAQzIm9h+iBlSk2FgII2l0VOMoqnNHF3TfO9YyI5WXg5F1dIZyrRzlSM+0aAE7dBTx0Bdi+FYBwHPbTEquiwkx3ZOangSsfFf9eXetSBOaRj8OsQl41hubjCeCxD4p/51JnxRQMB4YLplWfLump6TY2FyA24N1PyuMeCdapre8oTcIC2ESaITAphUxP/57A9linN6lXEgb4aJWpSThFy4UDn/Q0Ecfjz6uz3pRTYTp4XUqVDgQSxxppSHFOm8F8pIBm03jkjCygvyfW6sIaCKXOTRq/WuSd08oCpBEvx2ZGPAfn1Qw3NefWMR9tTDDTWOTm/VRNSkDM0T2N7WtjxOkVkLsxpDywmchjZXsiTWpdsxk9wWxsgKnG6q1pa5WZNqw001jmI6VtaQFZTU3GYolB9cbR2BJhZNred9U2zgOP/UH//6aBXh0yCQT4eOsJW4BnBpy8SxxneUmkA9vaxnnxjlz7hAhinX0239dxxPM9uiTmnk02A5DMx90ePKPS1wCxDtS5jMTLPZ2ThwDEefce7v/PrY96O/U04KO/Lv591IOy85AwM/XCOiptGIATzlEeCjtSpUh3dsLspPj7ykfFdTsOFpGPR3ZTLM1MCLMwTDDDPPKxkoVpDrKyD14m2+LelGlXUM5xHMS+h0Oz2rV695aXB+CjOq6rr/l6tVb/pH09MscMSKYSEzwcFJ84abcLBkFDy/qvO3l+OGaX6M1k51H7HTBcP6kCMV0fDSgpmSAMpUlpXovj9MHaDqhgmGEdM595D3TnntOFBMaOLwcqegHBHGSyEAZ7J2VvTAUfTWA3H6/fenq5AlvWMRUp/Ta93xQ9bRJMNtl0evomeFuCS7vmirGouUPZOlTxkioXoGHHfDQ0mDsdQIrRaDDdABpkJVmDxvwYyQpNTH0HJKtbW3/0YAc7twnWGUADxWTqMbEumZWVbXY/ld5uVmLvjksVTeECGQYoTEk4AGPAHaDXHSod2ZQ0oXQQOSCTYj6yzGxzfERK8jpQOFoQBZSycaCUC3pQFbvVefRxAcN3RH8fzX6d5qOW3TIYvw7sEvcWkP6EyXwkwEdqj4gWvf52p9dp8XnMd7OpBTP1eto12SaDj3/9r//10Wcf/vCH8Za3vAW//Mu/jK/8yq/Ed3zHdxzr5EVR4P3vfz++5Vu+pfvMdV288pWvxHvf+95Jx1itVijLEidO0I5enufI8/7lPDg4IPv9ZW9R4HX6hzbmo+jr9gVnLEBlHLjITc1H/dh+BHzhvxj8ZmcWdMzHEfioC/nrrWmAKkWULBAFGa4tCxwVVZfGDUCkeKe74s/yCnDLi7AR+VjEPh7dS5EW9bh66b1/C9h/BLjlhQCAc1sxHt/PUNbNUDheFacBBKvy1N0AhM7TYV4hK+shU2Z2omfvaOlno9axdlb0JqSa0h2sy/XMx9kJ4NH3i3+vc3aSbeDSnw0/o6KtXf+d4XUBIjpENT1COzXt2lpwRgcplaHHGEtKt9CWhqkDfCp1gCtg47ji3CpVjXIIow0x/qa2PyPdKGR1K7UNvhMynwDwUcaT64nNfkpquzIs1HOjHEBdW6VK6Uqc6li6ziQHHukGo4p4ksxHDURVz5O6d3rVvnWMWzMlhHz+sUjzH2hNckGC2RD43LqJ7gfQGo429pJ6tqtr08DHimGlAFJHaZ/+jtX6tICPTQ2g5QvO7JtMRgtQMjsp7kvT9PNqXZEWtdbYmHyqLc4O2fXlShiIcS+lgRZDGQ2q3fwCIdkBAOefZ+/reqIC9LVPiH1k45y9//yM2MPiLX7v6PqeBi7+ac+i5QBv/f1RACXnMAPynuopw2v2EkDs36srYo4uLwGnnwagL0ozqCWXbIt3XWkoy/fGCWcoa3FeJenSBfb8SGQrPP7BDojcjH0cZuW4AN2AzSic7lno4Uju10XV9OCjXjVcAxRnUiNyAD6Gc/FMDi8O+r7+RbfhgT+/hNtPGc49AOT7wPwkz+rW+5dpDxAXR5b91XDINAB93Hfez/l1AT5AvEvJjn2t0/fPtcVuFHCU8wEeVdTCdSUQQIDoJFjF7ItVZs/O0JmPXLVunblZ5RjplXXjMnR0bTqqU0DWgZwKd40GKMKljpupgBQjfsCg2uT3wgEjkwEdqLGxQVaDIWllPsp+1lT1YqgdZysipM9dYLhnuK5YE3UgQdm7HfORKyZlPKtwBhxoey3LsqXAK+K9MsETDow1A9hNLZ6DbocPpCk4qYB4KKFkG5st7XpwDZR2PAc+GsxHm7at+Uy6cTLX9YQLzjCAYVMawT3Dn3FlYbp1ur6UDiJVcMaUhVDN9OHI7BPjuYSa/9JV4ibWgRH4zRBBPF9mUulApfLHtL6mrALQr6PK3gi0fYkbv7JhRiC5sS9SwXCW0a7ZTB34KNcyyucxg/oquHU97ZpsT0jz8bHHHsOb3vQm3HvvvaiqCh/84Afx4z/+47j11luPdZwrV66grmucPXt28PnZs2dx4cKFScf45m/+Ztxwww145StfSX7/9re/HVtbW92fm2+++Vhj/MvSksDFQSrARxugCIiiM3kptBJt6Wex3wu4A0BBpWkbbWcW4rG9rDvPoJ2+B7j8ofGPujTLBDuzALvLclxw5sSdYpO8/GHh4M1Pw3EcnN+K8eheOtSOUu2OzwNe///rnLFzWzEuHGQi7TrS+m7fKo5ZrAYaX5txgIO0RF41iEzmY7q7XocL6NNYOQMN6BfWKrUDFPq5gaHGC9mXYBmVKT9evfCO2uS54+uL/NS063UprYoCT9H3VdMZWrZqneGsLySj6PwUQKrSRPLDvh/l5IXGeW0sVl0ra51hZNOZHDgpjFEOYKCFY02Bls9i0nWueCdFXUOnR2ZJH4w2RL+67I0aqq8fSq2glZ3hpuZJ0/AsSmBoRHUAL8MuBXoDz+q866xbi3Yq0INserOtFeGGWN+mFp3hCs4AwyCC2ThnV5eIGP3G8p6ZqaSAfY3ZPC/AXl2XMTuwr2PK6CsVGG4BDk/eKdjeav3qgiji3VdA1yhQZbZbXiTY7Y++v5MVsbYTt4u9bXVVBLNsbXFGgHdT9CQ3bxDA0uqauP+2dRYwhP5t75GRhmUyCKh24g7xjl76c8HcNNLRF2ZKMjCqHO0HQaf5OEq7Vue4+rEO2F/EPh7bI5iPqmp6mYn5mexgHvpYFXWXAdJldah07isfEQ6k/P+5LTGfZyZzc+MG4PAxaWeIa7z5xAy//ubPxSvu0a5ZZz4CY+aR2XS9T9WfZT6aadeW56M/S9veNEofJsCg7vyJFkSz2CQDbciUBwsBLRXWBt6ZASOqn3TebcEIk7mpjk+ec82xPF9WUVWZGcweoUDWTluReRaDsU1gIQKSrZqM1z4/Itiixj0z0zerjM9m0GVe9N/qbVTAhgMMDTCWsk26/Ve7HxzrqK0NxiVhn5iADfdczfRSBYCpYEC0MVHz0WTYMSAlWYWbeKdN8JELFprzQ/1GX08irZgLZxc6ztB+7K6DmONBzDDPjDWPAh+5dYbSzeWCLOYzASyMTgOEmpqFxPUlmX4pweRNxvOBChjoOoiKZTw6lgGkd+c17jnHfNT7qHkxYBdSz9h4L2xpyCaDkwrydEV4DFsD6MfnuqLfQA/ZWPtVn9IAWc2sO0r/krV5ld6/UW2dlTOaMdd7HXyk2rHAx/39fXzzN38z7rrrLvzpn/4pHnjgAfzyL/8ynv1sSxrRk9i+8zu/Ez/7sz+LX/zFX0Qc04vXt3zLt2B/f7/78/DDD5P9/rK3OPBwII1uW8EZQOgXHeXVWuZjEq5Juyba9izAhf0MUeB1jIiunX2WcAyPLg8/15yknXmIa6tizHTwQ8FIfPC/iw1oRwDh5zZjfOKyWJAGzgnRzm7EuHSQibRr0+EBRPGc5ZWOdbERi7TrrKyHadezE73BOrXoi5X5GPf3YV3atUrnnAJ8qpQ9vdm06uJtMda6EguyG/BjVkZSXQoDyapFpoOUnLOjwMJsPfNRHcsKPmqFZNTGt05vslgKY4RL+QD6fhxYYrIVOfBRVc/srmGNM2AFxQzNL5u4d3EkNmcvpK9TT/crLHPFj/rN11bESHfQ1z0HBTJ1kVEqzXwOYbgpoJ57pkaavPpsdE7DwLOBMLoxX1iYSIAEAI20a1sxEteVDOxpQTkr81EPUFC/s70vlFZkl0pGzGUuwMGtYRvnxd964bF8314hOtkWgM26YlyAYOc5LnD5I/LYQ0aLWvetmo+AYD6quXvuOfa+gGBHfuw3xL+3brH3nZ8W4N3qSg+McW3jBqEluboi7jVXdTvZFn/rz90KPhpO7jrGPSD24PPPBT7xW2L8GvP37V96L/7FazQ7sWMFau9LOBOSL5XUfCyMtGugyzpQUinz0MfeqsTj+9kwvTuRBZrU9SY7mEc+lnnVaV934GOQiP6P/6H4v2QWP/vGngE5aBtnxT0/vCD1PEXzTHvGj8S7pAOsNhC905OU/W37t+uJdzJXfacG+GyAop7KDTvzcQCQWQJafmzsd9QaazjurPSD4fjawMwqE33dgGZkq8IjgOjneEw/BT5aWJTA8B5zNppyWhVAxgXHBgxDDsgk7gWpbTcbMyQ5dqGu4cnqOBd9H4CeSyPwcc1z6sZG2CaKQaVrPk4tjEIBlaMK7EzAygTSzGB7tCnOpTSQuzlk2ExUVWWABln156nGtjbtmgkymixhyrbS9RSpe6/aFM3AbmzmNRDr0qiwlsV2NdPbbf5PNHGc5HGZ/c2PeltcNRYwhAEspmNbcTQfKBalsR7WBQTr2uhHyQMA43sezkVAd8C6NtYLyr7jAOY67+c9xWbUr8O8H+a75scAHBooN3UrB8Deihibkc0EjO1wai5zAH5E3ZOcn3/m+adokD+F22Tw8bu+67twxx134D//5/+Mn/mZn8H/+B//Ay972cs+rZOfOnUKnufh4sWLg88vXryIc+fOWX/73d/93fjO7/xO/Nqv/Rqe8xze+I+iCJubm4M/T8UW+33adeTZWR2biWD0FWb1aqMlgYdMT7uewHw8MQ9R1g1dTfTU04UT8KnfGX6uXv5ghp1ZgL1lMWY+AsCtLwH+8GeFsSKrWZ/divHRS+L3o/5GO7MZ4dJhLgvO6Clcm6Ka9kP/UyygUkdxMw5wmJXIywaxft0L6ZAcXpCAgsXhUIuqTe9N17ubknbdlGJjzQ/twGeyI/qojbVphCFgYz4CwjnKD8V94ZzdDshSrEIOfNRAoPyIB190xl1+xAOfA+aj5dzqPMXhhDHOe1CRZfBpG5UNcNZ1GtkiJ5pRYWNl6garDdAxi/pw8yzaEPfWylTVGDe2gkbRhjBSqmJ92j0wca7Mxf3onj9hdOsMr9IiIaCnmeQWMNPUpLGxOBUIprRZbcD//NSYxWiTaADEusMV5DJblfFaM4oVRjWOMakzT81mA/nJAIfFgfAjAbjp17nuvqjiIrZ5q1qQiIIxSprCYD4qoCk2WfJmcz3gS/898Nf+tWBrrmu3vkT87SfrNSI3bxByIFOZj20NPP5HdqAySMQfHfBWlY+5dPkBS9ISaNDbzS8E/vQXALQC6JXthXecxJlNbX6EcwFSLK+I/8v3KvJdlFrBmdB3h6De0/+qmAt3fj4A4AW3i/vzqavLIbNy87wIZKp5nuxgHnkoqgb7MgNkrmc3bN0o0rndoAMBzyvmo2k37NwmWJLLSwL8tbVkp9d2ta0dwDD9u67GaZKjY28PK53bnPIB+5KTgVEMvoksyXVgGzBkg3DH6/b2Nex8qsIzmxEg985JMi4S7CSL8Jjgo22PXaOrbKb8TmY+MsBdU2rgF5dFERkOMbH/m3ucNdXbYD6SEh0UQMZdpwlSUv3i9ffDBB+bWozRBH/MQhhsWq7BUMsPxW/V3miymtlUdYLpBoz3WF1aSTUquGsWteDS0Ecp7URWSbwt7BUpa7U26Ks3Lu26KYdAHZs6/gQ1H63gowEoAvzcizbEGBQLmQtcUhkfpN4l1Y9iK5pyPxOATI5daAYNAOnDGffclIVQ49SPp/yXdVqcJjCq7MF1+usAyNRxx5FAv75GESxucw7qOozd+ah5apAFzGJH3XVSNm8k7AEzW8Sm+W4ClcD6gPhTtE0GH9/ylrcgyzLcdddd+PEf/3F86Zd+KfnnOC0MQ9x333144IEHus+apsEDDzyAF73oRezvvuu7vgv/7J/9M7z73e/G/ffff6xzPlVbEnpdulHg25mPAlQTzEebPmTku8O066pBtEZPcktWmIwpTS3XBW57KfDJ/z78XGOnnJiFuHAgdBlHTMZnvFr8feuLuwXihq0Ej1wTC+TA2SDa2c0Ylw9zHGbVmO1w033AB38agCP0JSGYjwdZiayqx2nXXijT+9Y4HGrBtIEVncGUTWM+AoI5VKV2fTiTaVFlAFr++EorLNtbD2zG28IxWgsoaZEoG8igi+ErnTCyn2aE5lIfkErD7DbbQ41Jt0aX0MbgCzVj1HYdiq2oDD6O+QCIzYtKlxkcS4GPFnBA11W0bp6LnoHIXacfC+ZYueqKM5BtwGhck3YN9GAhsAYEXtmfv25o2dJ79RRHdW+oZ6aLXaviJNy8j7eF8d0xni1gzUKyp1SrCvEsbQy/rRuB/Uf57/VmlU/Y6eUTzGZjO1Fi3eo3AP27WAY4lJOsigrZgKyd2wW4o1pmYYQCfWr3un6q3f4y4JP/Tfw7PxDX5ap0a7GnRFRwzGwn7xTSHVPa5nng878V+GvfPeG4dwH7DwsAch3zcesmYah/7DeA7TWSMmaqv03HMVxgUN3cJmGgt2d8iQA0b30J/44CwulQ6eWArI69jcDrwcesNPZVADjzDOBN7wHOPhMAsBEHgAPsrUrM9T1780Yx7/YeFg5EOO/shatHBQLPha/bKps3Cubj5g0dCKWYlCdmhmNy9lmi2FDb9FkRXJuf7iUE1tkCis2Y7dnXJNUU6F5lYt3hdFH1tOupMjDAGlBxQwPvLGwQfe8pmDRyPWsA4PcoPWWxrgSbhwR8FPMx5dnn0Yb4vqnXBENiaXcpNs6EAB+n7XkshuE65iPB4ONS0E2Ab1RYR46rq3adW8BfbewAz3gaafpNYD6yIKUGPlKalcAYfOxYR8bzd70hM5N79kEyTIM2bboR+MiMfQTEKiBpDQDMjc1kgFmrf2tMNypbJJaZGrYUeoAGbKixqWsyGWrmHDmO5qOpT22za8LFGHzksqmiDbF+r6tQzzFqKUagGp8+1hFImfTPgqt8PpJBYEAsxyHAbfmbAeCmkVcA2gbr1mAjxZiVLVBjY5jD6pgj4JhhvutzhsouMoFFrjiVaZ+aAD5ZcIYB8IEepO76MmsjQLN0Ab7/U7xNLjjz+te/Hg7Hcvo02pvf/Ga84Q1vwP33348XvOAF+J7v+R4sl8uu+vXrX/963HjjjXj7298OAPhX/+pf4a1vfSv+43/8j7jttts6bcjFYoHFwmLsPsVbrGk+ris4o0C1fE0adRR4yKoefMyrBsEEzUfAoql120uBX3/bMMKZHQiHJphhZx7ig4+IzWhUiXv7FuCNvzpwQBV7ASC0m4x2ehGhblo8upfipXcZoN0drwA+9CtCl1KOazMRIG1ZG3qSriuKChw8Llg/tsITauFVVbHJPlokbB3zUVXFVYV7FBhJ9pVgYrorBPHX0cSjTQCO6J8frEmF3BEAwrpjmkY7Cz5qIFBhAT79SMwVBQBxYIQS0s6PRMTZi3immIrU+jF/Xs8X3xdHdrarcqBsqcPd85ZAKxye4aE2Oxs4oKej2FLCVbEWG4PMcdBVirYB0ArYzvfpCL5+DY4nGbiFXbNPSRT4oR2gBASjqm2mgY8q9Yi6L+FCgK35Qe+MrDtmurs+7VoVyMrke9SldVneqc0be9BsXbNpyK7TfOQ0baJNmjHZGVkM8xEQ93l2QjA72toeQDn/HMEyV20dAzDZFkCdVgDE2u54BfD7PyyAqXR3UNFRreOjveUz0Z71mmn9Tt4p5u7ugx2Dn22uB5y5R4BhJ++y91WpyKplljVc14CKN9fvO6otTgNf++v2ud/1PSvSswHxHJKdAfiYFjUSCgTW7FDPFRWvjzJD81Gl7z/2ATEmWRkbAK4c5YhN++Pcc4CPvHvA1lSaj2c2DefwRhnwXpwT+6atzU9r7M7ler1P9W4ajFyyqaI6SuqBBR/nPcuHAwBVC2Y90GDTjNbZlFQKXNdPsuk7DV5qv5sBcHqGCceoH4ByTPqq+qzKeXBPjQvQgm2cPRH3ldAdz6L3Ne/TcMsVvY4HBghgYyvqKe2UDadnRyiAiz2WDogQ90RVCO+YVgz46ydGoBoM89FMkbQAqIOU8FW/X+hNL8DHVs7W7EOgvxayarrGBuUCYWYKsWnTqXVTZz6SgLmRempLz1bjVsemgLYRsMtlKhjPvViNs0WizV6uRB+D2SjAhtJlJa9h1fsZ+vEOHhH/bmo+iAD066EqvrKW4W3IwlDsQ2D4/kcLCwhtvLMAbVuZ4DHHJg21+cDqf8px6MCofo7BeaMePNbHOSg4o2UrAb0Npr8brif9iTXp8GZVaVu6vl78EaDZiuqYI9DdvG8GYE2C2vPhc1L9BvqXxnHaVgY0OELGfKz5yJE3TKC8yiB8N6b/U7xNBh9/7Md+7EkZwGtf+1pcvnwZb33rW3HhwgU873nPw7vf/e6uCM1DDz0EV6ss9P3f//0oigJf9mVfNjjO2972Nnzbt33bkzLGvwwt8j08mmXiXTC1iYy2lQT4xOUlyjVp1Hq167pp0TStNU0bEGnXAKGPpNotLxKb0SO/L4BIoI86Og5OzMOOyThIs1LNMO7Pb/WLMHtO2U5tiEVid1kMC84AguHy4v8ncNP/n73zjrOiOv//Z2bu3Lp7t7AsSwcBAREBsdMsKCqiaIyiRkSDJrF8bWiisRtjjL3FEiOaGIO95GdFECtRUVFBQEA67ALLtlun/v44M7fs3jkzC7ss5Xm/Xvva3XvPnTkzd9r5nM/zPAdnXioO+jJ5qVrkCIt2Y0np41uAbiOcV5pbqbX5TdomU7E45hwmYiP52KDIzl8V4gzagyXsQSi+Bajon71wOg1iRIldYJP1/NBiIBtumeIIO0B+CEaqgSPa5Txc8hwkmQTZLuKYIOSEXnBEKrvvacvNwHWN5IRnW6H5hZeV47YstL2ZfGh26HzYoaJ0IOuqcS3qk+M+4YVAJ2r5YiFghY7YYdcujsZUI98taBf0Uazwf55b0N6/vgBffBZl5hwDnI/lPPGR45AVxWwuR7eclPbAKba55cNdcyz3NOrXAlX7Z0U9nvhY2ou1z62C6ATvOmGLUIWWw8s96yRaamk4PmTZwl6yjgmITo6UXHoeAix4mk3e2FXki6qc22ecjx7Fx84Dmbty2TsAzIKCkNs9sl0JlVl5ObexyS439juFVbx2c2Ha+YBtuE7zHMdHMApuCoPmePkOgGxuS4DdU0Kl8Ptycj6qessicQWwxce854FgCfv5+aOMO9GOZtgaS7cUNfsdBXw/i+1Li/27l+A/Fx2GLtFmg5xQKXD6087367zO5aQQSNVnc1Y6UWyFi7tVL7f7karPmTzhpCwxNMtJH+NPSOYOzpSYc0qBvElDjhPdvt+pCQBm4fuFKFr3bHsizUEg9QWz1w9eSJtdZIWXQzov3QdHkJVDyBQ5k0OcNDORbHQE4ODwbO7O47gV7fuBo9hmnYt5RX8cBvbNcz66hUA7OhBzQnmdhBOgpUtJd7inSIFs6LgocRyXoawQ6BQiaT8j28I5b9I71w1aKC8fYIWC5mxD82ulHM5Ommb65TBB3LzauJOr116OTSGhrVDYtaPzsdmymp97ds5HnrgFZMcouRQ6t+x9nSd6FlhvXg5al3WHOyGTP98WlxzNEc0KztjOQifnI8C+P7PK2b1dKBeikmg5adf8+HNyk+aK0Zl8iYVcsDl5EAsVasksL5R/PGSeUXMFNzsk3OXcyHWz2/1zDLu2+8YbdxQ3EzOdJkgKiI+FRPfc87GQq72QSN7C4Rlpef4Aha8pQMuc5bzIoNwoH1HKOl/bwbS3J+BZfGxPLr30Ulx66aUF35s3b17e/6tXr27/Du2BhPwSGpMq/JLo6mAtDsqenI9BOVvt2nYruIWrlYaZo0TTjcIN/GGgx0HAqo9zxMfsrGNpTgiUWxg1AHQp8T7rEPFLCMgS0qreMuxaEICR0/JeKs5xx7TIEVbcLVuFlBf6bD+0ulWmtt1RXgaBZb2ZeBuIOl9UAXaBLO7CBjtANhSTN3gMlbPiBsl6l3aWUJGsY+2c3GyBkpwiHRxhTJTYvko18J0KALsp2eIjz+WReVgRXERFOxTOTXy0Zl7TTc4DwaC1vfZN2WmQJcpZwcspR5dduc3Qsw6Igv2KMCEHcA+BtnM+ckVAa728gazPzx4q0o2FH0BzsR08psE/pmyRyRfgi8/hTsC2VdnP8Ppn5y/lhvVG80MhnY49Ocy+t7rV7H+eOBEoZpVya5cz8TFZD0Dgb3/FANbXpk1scoMHV3wsYw/lhUQDpxxS9ucKOR9tJ0Ch+0pG5LU+x6saa9PtQObi+/JJYMSv2Gt2Ht1CBEutiY4Gb4KQIACDJgKLX2P3mpzQ5nEDO+PJT37GxAMcRJedxfF3AluXuzvlAGDwJGDf452d2zbNw655BY6ah8J6DbtuDUVdWAVwgH1/FQMQ9ktIKDpM00RKNdyrjoNNAlY3NMvpLAjMzbj6E2DAsQCykQ9bY0pLUbOoEjj3tRbLbiE82nQd5r59ADvHNy5kfydq+cIfwCImmqrZpFKgmO+aCJaya43bBF+ui9XtOSM37Jp33c4NqUs3OU8O+CMsBYxrahPr3qMpVoE6h4iA3HBZwCHU1br/+12ECrvvac5zR0Y8dXH++nOeOXKX37z/EPKFFy+VuJ2EO3sZgPP1PtfRaFfNdawknrMsJ+dj83DwQtf8QrneCrrKrO9OVwAxVDhM1W6XyTXpILY1Xy/PzZdb9MMpB3ZB52POtVIQ8t2/jqHloWyBDlHih9rn9tv+u4W7MMfdpWtsktNJzMzN71vouS9YYuVK9uJ8bO4oLCD+FAw9LnAOBnLSQLhVBLbNE4laS3xMcibMcvJ42+IPUPh4zz3/1QR79iy0XJ+fPdPlHc/xvEJjGXKFZqd9mhtibJ9Lzd3UmTyIdjvb5e3gMm7uCATyn98l2dqGnGO+UN8CzcTCguJjM+ejW77O3Mlqx4JjOecjYAmGzUX35tcUB/G7hfOxWQqpFvlG7e/A44Q7r2Bbxg3dyD7nNVpkL6VV1a6J3ZegzxIf3ap4AoiGfJmcj7wcjoEcl4L92835aIdda4bp3GifI5n4aCcDzgnxzc2/FCiUN7JFH93b2AiCgE6WM9MtRBsAojlOixbOx5IeTABxG3DYDwBuyegDUfYwkY7lhQkWpLQ3EyjcQvaA/CIWqXp2w3ASCgFrcLTJqsTKEVVDZex7i2/hCwJ2O13lF5yx2ybr+E4LIDuj61aowg7T8CqO2e5H3vIUl5yPgZy+AYUHPYKQtfC7uQvzqkS75NU0dCv0geN8VGJWOGap42ZmRFveoM1ul6znC6NAtvhKsp6/Xtvp4+a6DZezyvSC5O7O9Zq/NFWf3c9O2yIIbJnbfs5+jke34SxcFmC578LlhSuM25T0YoMZW7DhwXvwCec80Bf6HM/5mHBwPjo9kEk+th9idt47zuDcRhCAMTOAJf8FfnqPPZzzriHFVex6lKgtHLZXiIEnsOvej29kKicDTMB645JR6FHWwQ+NPQ4Chp/lvb2b8Aiw88zOsQi4hNXazuWcSs1t/SBd3DV770nUAqFyRPw+GIaJtGYgpeqFc0M376rVpsWEoZ0D2ipOI4kCSsMy1tclPImabUJRZdbdmdjmXkCouKtVvXybu1BpX79S9ez65XTfzuQjbnS/dvqLsu4hJeY88WULCIZhHUcc8c6OBrCXX3C9dtQAJyIg1/loD6QLHZN2LjveNTA31Nv1HtvId1EC2Xus233dHvzaE4YFnX454ciFwluBbF885S7McQ3CdHaL5roa3YrXaGnnEHR78td+fnfK02iLFkqukOGyP5wEW6CZG5cjTjdPGeCYdzNXfCxwjAeiyFSbd3TO2U6xHLdboX2RcT66iI+5kwMZd12B+3WuwAcUnrAPlrB12PuCl6Ig11GY6ZuTK655/j4vzkeHddvXS1sA4uVnzY1myVt2gf0jsjRe+c/inFQ+efvSIaVOQfGxuXicI5Dxcm3mOSStyJJCx03zUHOn62Je4S+nNpHs9+yUU1eSrbRW9nby3OXN95uDcUYO559rhfLXN3ffFrouNm9jmi1Fcn+RZdiwzE8Z5yNPfKzP/s91PtrHn7UPeamPCBIf9xZsR6KbOAhknY+KSw7HoCxlwo4VW3x0ETftAYLq5HwEmOMxviU7yE5syww+yyLbl4vL62CjxHJmthjIFCA3zKvF8rsMYQKIqQMlnEIAoXI2oHcbFASjVrEJ072oQtX+7LdbiBfAHFQZ8dFD2GK0O3PRxbYwV4cTtliw7WcX8bGU/W60t40nFpVb4qOLWGTfMNxEJTvMwE34zYhjdS5hcDn5uniDsVQD2wY5zHGEWk5XhZML1BYy3USx5iFhvIesdMwq/uDB1aq4ibvFOeHPpc7twhUsL5rb8WcXMXIr3BCpsI67Un7Ig+2k5OUaBbKDWSXBHrx4DxTBUqBuFasMzVsmwBx+679mD0mxze7FRUSRndN2PlcevBnpSGe2HbbjORfeQNwp7JpXFRewKg/nXGMEkX9eAqzA18DjgQX/YDkQC6UdsCnuypwLNYvcBZvMZ6qA7iPZ35WDvX1md8cO27fhOR9FyZrwsr5vNxf59lC+D8vVqSbZ75LuKLbuqbG0hqSqt8zNWKir1ileHGj2bNB/PPDbz5jIb1FZHMSa2oQnUbNNKOvDJg1jm91DngGr0vk69vzjKj5a6RPiHiYCAdYPXjExu639nbs5H2Eim1uZ40bKK+rmdF8syt6LgcLrzRVUePcy+97JEx9F0Rq4N/KfJ+x7rJv43uJe7CJkOIVcAvlCoFPhsubCnVPuQl+ADbDtiUeA43zMdTU6hPJmitK4iICmztryCozZx0xuMbyCzsccUaeQKyqz3pyQZDWBFhVzM+1ywkudltfcjZVuZFE6ef3PEX7cRMVcsabQsZERdZqLMIUcYPb+4oTj2s9yNoXOZfva37iR/1xjV8W2sSuJN++bXcwnbxsK5e/LyaPHEwjt1/2R7EQpr1hdc6EyIyo6XJuCJfmT99zUUM1cd265CzPnWgHnYG6+RMDZuZpXfd7JZdwsr6ISK3zc5x03DtEnubnheeHwucWYeNfY5qK1Y2qIHHc24C5Y28sq5MrMDc3WUlZKrWbOR5jZ/ruG/TfLk83Nb2ydT5kJW841kiDxcW/BduZ5qeJZFpYRS2nQXXI4Bn1ipuCMontzPtoh34OqOAJacRXLdbV8Nvs/tjkT1lNR1PrkrU+ddxBmTjvYvSGQcXoWNa+kXahtzgCmhfMxN1eXnfy+EHYonNvgLljCBmj23zz2OYqF4h1wJr+d3bfGDexvN9EJYEJC4wY2OOKJJXZYaM0ivvhoPzC4hckCOU41N6G2NMchyTnOAsVMVErWu/cxsZUN3ng5NG0RjedaCZbkuDK9hPpyBou2I9Mtj17zgZHTcRa0ZvJdHYgeRFa7f3YIMk+0jXRi+zdVz3eu5YbycwXtcnZOuTkP7f2XrOcXT8oVKf1hvqBZXAXU/Mj655brpc9oIFbNzpHYZm8htpX7sfY8bFeN48BbsgpibWj5Hq9CbLg8P5TLhjcbDGQnLIBsXleemGgz6kp27A48kd/ODsk2NOdcq4U4/GJg/18AvY/w/pndmdIeRwhpAAEAAElEQVRebP/bM/npRv75a9+bDJ0d+15zOXqloj8Ak7l/1QQQ7ZEpGhNLaUh7zPkoWupjwTQszQZ/0ZAPumHuPOdjWR8AArB2PvufN2EHsOeGRC2wfkGeI7cgJT2AWA0TK3k5USVfVkh2m5DLTa3AnUTLyYWWbuSLimkrrYzkd04DYw+ieQ5Ju0CGafIn7zL3O5dJqtx1Ot1jM9EFLuJjqIzd99ONbJ2OLlRrEM0Ldc1zPjrkS7adW3aeTCdBxHaUaensOh2Lv+Q4rQqGIgfh6uCztxGwRNa0lf+4wPfQPJ+eo/MxV7Dh5ZgratbOycmXIxo6VU9uEXZd4FwIlGTFFaeiOs3dgDxBP1eYchJt7bBr08wKLYX2RyFRqkXYdSn73bAedk79goRK891friJ285yPzcVH63syDL4In1l/jgDEmzALlrCJTbut2/OunTrC/g6dRMrmoc1OOdFz8xI6VTq2c8jmtnHKS5rrfOS6C5vlVSwojOa4AtUkWI7uZssMRHPORZ4rMycHKy+9T/NK0U5tvRScyd23TsV85EhLEdP+rE1u4VLAOfTdJtTsmZd7/gbZ/so4bzn7hiDxcW/BFsq8hCGX5YQ285yMZRE/6hIqAO/ORwB4etrB+MMJg/iNBp7Iqk8aOgsVK2JCV1CWEAn4MHZfF5dQDvt0LkKlU+6mZvgkdgPOzS3phRZiZaAIOOx3LHyQN9C2ha10k3t4dv0a628X56M/DIy/hTmG3Cjfhzk0DQNoquEPYgA24NmylA1Qijlt5RDbnq0/8fPTBawHhq3L2f9hFwdHwhafSvntUvXZfJNORCpyREWe+Fhh5dqr4beLVAC1K1k+PaftsAczvFA1wJtIGSplNzj7wZDnWLPdtYDzQDFcwW7eTZtc9q/lQI3Xun9fW5ezWfXmroHm6417zCOaqndPZWC7gNzyIkYqmIged3Hx2oPZVL27oFnSnbX14sCLdmXFqH56lzk1vaRJ6D4C2PgdC4txIvNAzxMEu2UFQRtdtRK0c3JFFnQ+uoQk2sW3AO9FYQAmSk//ABh+Nr+d5Ms+YHrZhzZdhwFHXe8tZHlPILfIEeD+Xdhic6YQWWnb9idYwsTiH15i14jSnpn7aMb56GGy1JcRH72kSmHfdYscze2FHGL7felbbGDCy10KAOV92cCz+vtMoRxHot2Zs2Pjt+4TF+Fydv9SYh4m2uqyVZt5A3IA2TySLqk8vAiBtkgJwTmc2rT65ZYyIN3kfnznpi1xuidmcjS7uPzte1O6yXmfAVmxgFckKK8as4PQkds3wNlVlFsVm+u2zBU8OXkmM85HzoRTrviYyUHnEKYqSPlCoFMIcTqWE0bpEuEB8MXHXGHOycmUm3MPKPwclhe+7SBKZHI5ehBPc3OaFqpIDLD/TUu04wnYLcRHh4IzAJu84EZ+lDYrusE5juw85EDhsFcg2w81kd0Gp5x7QH6htFSj8/hHlFhfMy7JmIuj006BZLkFnVLeNC/ywxWscx11BYrw+XMEMjXJRK9C681zSHLcdv4cIR1wvl7khl3b4c/NxebcPL68nLq5fXOaMCjUN15F8bxQfQfB2v4O7HD1gsWYckOzC4jzzSc8PIVd5zzzutUbsMcKABwrrRMASHzca7CdeV7CrjsVeRMfO0UCqI2xC4EdRu1FfOxTEXF/+N/3eHYTWf0pGyTnPLQ/e8EhuPb4ga7r2R7sitjRQpW0OfgK7deDp7vn7coVKXiCRSDKRCHJ37Z5JCoHW2Fva5lA4FTd0qbL/tm/O/Xnt7WFAF7YuSiyC/yWpexhmlcgx56Fcs03WWqJY1v47exqqwk3R6P1vSS2uoiPnZmQCzivN1jKbvBuwqjtyuTlCcuEzay3HrIcbqDhciZSxrey/50GgbaTNVbjLgI2rGPL5Llfi6tYu1AZX4Av6sKO7Vg1f3AeKmX7LraZ76S0B+0lLi644m5svW7HSaiMXYsSLiI1AER7sN9uriWbQScBi98A1n8JVHi4pnUfyfb75h+d26icwYFNbr49G7eQwUhntg80Jf91J6eMTbR71rWdrGudiMXLP5vLmKuAfse4C857M/4Im1za+hP73zV1RnnWzQW4p/vYHvqOZfmdu+wHyCH4fSICMstP7bXatU2L6IMC2KlSvKRVaTN6HwFs+JrdL90cv6LEKm8DQJ9R/LZFlayQwJal7sd9uBMrbgW4X+vs8GyAk/LDzglaD35uyCI2QI1tdi/AlXErhgvvp0xYm0vuSvve6ZrGIyeXs1thHU+pSOrd887azrVM7ltOYR1dtYrvcJx+mfBhJzeW7XxMOYdbAtlQb9tVVLDgT47z0Sl8GMj2IzeHZ6G2mRyYTVmhqqBD0mpjuyid7mtybti1w7KAfNFQiTvkF23mxirofIzmL6fQPssIsQl+OyA/p6lTNEtm3yb4zjnbYWvn3Sy0Xklm+9J2PjoRKmWf19X8vjm5Y3MLF8EsICTZ+ySWPW7dDBq2+4znfGze1i1SxXYru06MFHAXOhZOyc2FWEDgy3NHuuSkzU01wDuWW+T2dBBGc1MSOJ5nORMBgIOLMidHo5vDuLX7LdOuuWCdIyxyi/k0y81pb1dmOc3ORy9h18n6bI5I13oDJdnJWrcUJ3s5JD7uJdjh1l7CrnOLrfDEytKwjKSiQ9EMpFVvYdeeiXRiocPz7mSiT87AvDzi91QQZnvoVc4uTgXFxPYgV7zhCTn2e5EK93DO1lDcld3MNy9hIi8vRBxgglJ5PxYe5nZh7X4Q+91tBL9dURfm9HALSSuqtCqBujjf7NkqN5GyqJIJT265HHNdJbz9Y69LkJwFFvshb9vP/O/bnkHjuTLtwVDdGn6Yry2s1q1iN18np1fuPuWJgOHyrHDF+x7sENgiF5dyWR/2HdSt5u/fYmuAndzGd1x2O5A9GPdyCaeNdmXHvFsKAbsIhNtxAmSP9QoXYd5m0ET2cC/K3sJ/Q2VMxFj/lXMb3uDAJtqtZdg1bzADWN+nmXUxZj7nkEjcpmIAG+CkGt1dy9vLkFOBE//attfGPZGuw4BNlnM2Uct3zLVwPraD+HjQBSxNyOGXZV5KqzrueX8ZUqrhyaF49qG9cVi/TpmULjyKd7bzEWDHZiDKfnvhqOuBX72Sdao6IUrZ60yXIfy2ofKc6ALevdNytdvuIafrnSRbOX3XW5ViHQZk9r2ndgV/gs/Oq+wWVglkIwKcBuTBkuxkG++YDVvpPnhpPGx3lBeXsKlb93XOvSm3KrYUcM6tqCb4Ieh232znY7qxcP9yqyjbk1KFXGa287FQtVwbKZB15vEEm4yzLbfQEE8IjFn52XSHcHvL+chzUQL57iin4hZ2u9xK7QVFw3B++HCh4zI3p53iEO4qSmz5tnOQJ0j4IwXEx+buLquvapzvnPNHWBqSvO+rkJu4lD0HcNPOlLLf9n3AraK7a2GTHNe0m5MMYM96sc3s+NVSHs5p2/nosmw78sktCik3nJ+X0iZ3252c2bnOUN5zU65wxz3XirKpFwC+sJhbrMspB2s6Z51A4RQ8zUV+Xti1rjQriuUQYp5bcKag89FDzsoWRWkKXC98QRZplwm7Vtj/TpPcIeu6ns459nlCdbg8a/JIN7TPhO0eAomPewnZsOvWfeU8h0BJiD3IN6ZUKLoOURTaVrQ76NdMGAiWeAshbgOmj+mLpz3mh2wTcgfivAG87SJ0EwdbiyAwN9WKOSysu9zDfj7jWeD0me7tRk4DznzO/bsr680GAG6uy5KezEmnp/liUVEXFlqoJvkiVaQzu1E0VfMHDLkDhDKOo83+jnwB55lcez1blnp3ZfLyRwJMtOOJYvZ7W5fzB4C5D7E8t2quy4Y3kLUHz7Yb0IncUFle2GyuIFrMEUeLOgO/+cjdORTtztyWTZv4zqHiKvZA27TJPZy6chBwyqPAMBfHs40kA2f9BzjvTe/iTu9RwM/znN/nDSBtctMtZD7HCeMCsk4r28Vo4+Qesek8mH2u+ge2D4s5+5poX7oNBzYtZIMumPzrqO0MSdaxAYKTs3pHCJcDE+9hBYZyqE+oqI2lPYmEw3uW4s+nDvW0umIrNLs1jsodplM/YPocYMhkb+3lkPf0AQeeB/Q6LDvR50RxFbvn+ELuuQsNLZvixS3Hse2idWpn3+O2/sS/doatPL3xrc7t/EUAhKzz0UkwsO93ia18l3WkszX5yLnHBorZ/oht5l+f7e3f+hP/mSMTds0J4w5E812DvByHtvCQanQWgwDL+chxmdmh3jyxUA5ahWQ0vhtLlNhxpsQL511rvq25RT+cCg3ZBYR4y8p1HXEdWTmFi9JNhb9Xu2I3YOW8LZA/3HbYAnyByM5VnukXZ1/khtEDhUNLgWxIO0+8stvZv52KMyVq+akC7DFKbiEXQXII984VsBy+09zQVy/usJLubLLbFj3dHMiZ/JCc1AxANt1PuoHfLq+Kur1NTjkfbbeigzPbdiryCjEBBRx0HAe6l7DrPMHQYZ/b55m9HF+wsLCdK7o75ToFstufm1bBSbR1y6mZl+vUIbKnefi2mmATJrnCYsZtnZNnVgo4T1rbxommmmzfeIJ2URcWOQa4p+rYyyHxcS8hE3btUXy0k7jzch9GLfGxPqEirRmQ29otGO0KnPsacOa/vYff7SCyJKJPBWdmoxn3nTkct0/e370hj2NvZz887MGI12quraH/eODnDwEI7g4KgF30eeHRNj6/t0qyZX3Z704D+O1KckQsXmGJXIGQJyrZTi49zRfbAOCYm4BhU/gD8JJerMoxr0BGqIw9mDdtci/Y07iRHxIuSuzmtu1n/iBRDrF1bv2JL3gCTNgSJL6YmRtu75QnBwC6H8gcy4Mm8tfpD2eFSp7bJ3fQ5BbW7OV6UbFv9m9eCgFb8K9e5O3863VY6xJNB4q9FZuxGXAsC7tuHjZtYz/s8R6SOg+y0i2sy77GC5ED2D4t6d5SfHSrPu7zM3Fp5RygYYP7JAPRfnQfyY6b1Z+yGX+ew9l2xzbV8MX+NmZIdza4XFObaHOR0K6mvdMKzth4KbC0PQw4lk12uN2PM5OXVXx3sH1P2ryECXe863ukwt1N6Y+wa+HWn4CwS17lRK0lBDrd70TLKdfALzqXe0zzXNaRCmDbaiYuOt0/bUdk3Wq+iyVUxgaxtStcnI+WCMDLXRcoBmBmB7GOBecssUpTmEuvkCgjBQAITOhIcVyltjOKlxfaFhm0JF8QsftsC6hOVaft9dpiLFB4sG634YWNA/mFUdJNznmmc8PVnZyPuXkm7bbN+9ZcrHF0qOb0i9suJ1+cLcYUKrwBsO+TJ141z23ntF77GOQds4EoAKHZvnUoUJO7T5ycjz4/m4xUEu4hz4BVsG5DNpyaN6FgT2IA7seo7bbmTfADyKvO7ZbDNFfUdhJ7DTWbBoFXTDJXSHc6ZgoWnCnkfAw3cz66hF3z9l2u45o3CZSpZm/vO6eCM+F852OhlAn+SE6uU2sfN4/s8VvLMfSc7XTKh5obgs4RvwNRtoymTdlrgVfx0WlCiAAAtE/sKrHLkc356O2hWxQAA9kciIUoDvggigLqEgpU3fQsbLYKt9CjDmZ4z9IdX8ggjlhlE+0KjL0W6H34jq+vOQOOZaF4FQM6JkfFgOOAZe8A+07gt8sVHHkuhFxnFU9EyxUm3cTH/U7hvw+wwdFF8/jJswWBDeRdQ4yrsg+iuaJri3Zd2cDOTTQu7sKcL/2O4bc79jZ2Y+YNUP0RJih2dhGWfQHgrOf5bWxOe4oNatxCJ0+4i7lQ2mIyIhgFug63HLIuYfdSgD387grXo86D2LmwfDYw8ryW77tVXAXYsR8oBrYsy4r1SoIJz06V/wC2/XZlehulyV1QHHwy8N71Vv89TEgQ7UP5PmyyZ/4j7LvkFQoq7c0GZ9t+di9E1oY8fNYInPzIp4iltDbPzWjnfCwNOaSe2FOxow/cRORwORsMrl/gPtES7gSs/oQN0HjiZ6Qzi0TgHUPhcmsyZD1/Ui63AIxTqHTuBCHvvh6uYM53wFl8KeoCQGCOP949QhSB0p5MfOTdr4OlQM1iK4zXYWBqP9s0WX0rJBQAbBDcsD7HEVbgmUgULUGowRIKOEV6alc4i15AVhBIx5gAwEunEizNiideK447tW0edu3oFivNOgxTjZxc2ZZzS9fYM0ehfWIXN7KFYvtzeW1K8kUpXsE/L2HXwdKsu1BNsMmh5pPdtqvUDrt2FR9tx56DyGdPevKeRUWJ7Q+7bymOUzBPkOWIxbbQ5SXsOtqd7Y8tS5kAznM+FlUB67601u8gLNvYzsf4Fr5bubkLESh8TuZWd3YS7+xzNFnP//5yi0nxhMDmocZKvPD+yRPcOM7H3OPF0aFbnC1axyuI2cL5mCj8vNGioE8B56M/R3RXU4XPDfs7UeJsPzttZ/P8l7xIAEHIpl3S0myiijdGLu6SvW6nm7wXV9wLIefjXkLAchDIPm85sQ7fx93hI4oCSkMy6hMqFM1AYGflSdxbGXZm+4gfogQc+Xtg/9PaftleKO0J/Opl9/BsUWQuj8mP8dtJPuDgC4Ej/o/fThCYoHv4Jd6cnF6QQ+4uF/tBr3I/5za5zj6ey9Me6PAqj+cuz23wGSp1L9QCMJHSrZhSa4h04g/abPqPd69+3BomPwb80iWFgChlBbpct2RHIQhswmD5+4Xfd0vKbi+j82Cg+rvsa/YsME8ArtwPqFmU/5qXAUT/8axYVVEVULWDTnFi+xEEYL+T2YNxr8P4be17zbr/tc6Z2wZ0LWGDlGAbi492ReyySBtd73cXuuzPivgd9Gt+O0FgLslNC92vx5EKdu7zxEIge+8p78tZlrWMmh/5omew1Co65yHvc+7vQuRun1M7Sc5OxvDuw7nL4xUOs3PS8a7RtrDTuNEqMOhwrNrily1sOTn9bCGD58SxBT4l7ly8zu6vHSbtJIoC2aJ/bmG1mYrjMefKxP4iFp2SrLcmxxwmDmzno2k658C0l6cmc3LZFmiX2dbG7CRw83Z27kjbEeUkYuQ6Mt2ql2ecj5ZYWOhebBff4IVdyyErt50l/KQc9oedZqnUZfI9bOVHBJxD1QHLvWk79uLOrtdcNytP/AGyz6PrF7D7EO/5JGoV0jNN9zytkc5MYN66nO/8LCQ+FsyfmdvOQejPzVvLzZcYzV8WL+ejlmRCut0/t7BrJ8es3yoOpqt8MT03PQDPCZipZt/E0vso8cLh/bmh44bh4HzMcfJqSSt3Y/Nq3dY2tcbhyROAbYqrWJ5ztxy8AHu+jW9h28GbYCLI+bi3YDsfBXgTH687cTCuUg3XdmURP7bF0ygKyO3jfCSIXNwGzDaH/dZbu2Fnbn9ftpcxVwNrPue7xYJRFoYuSi7uJEsgsCs8O1HWB1j1kXuF8r0Nr6LzEZcBqz5x3887i4EnAl8/A9SubCnau+U6sul1KLDkvzmfc3EKAEzE+PLvbPbZPi69iI+iBJz+NBsU8EI5ifZn2NlsMNlnNL9dMMomShrWAwdM2Tl9s+haEsLymlibh12XWWlkOu1t4qMgABPu8Na2U3+W1sFtMjAjXLhMiPY8lDkku3AmHSKVbBCY3MZPqVHUxcqFXMt3Ip78MBsE8oSKzESSwHeJB6NsWW7i46G/ZfuigpM+JtwJSNTxC2/JESYc1a91qRBuCRQZcYwnCDUyJxhXfGy03EIOopf9WVt85Dka7aJ/vgD/XhQoYkXfeJWJ7c/HNvMnx4KlVnGIJnehFcgWXHMKuwayVdNFuaVQlHFHxizBhBN2vW0l+zvd6Bw2HMwRnHgh8nI4u04n4cTObZduYo4tp7D8QRNZSGkPlzz3eY4uDyI2AMciPHY7OyzfLezaH2HC/sq5rGAat5/drGryVnE7Xg77ku4ABJZ6huu2Ls0K/OlGa0KgwDN5C7dioUJGxcjkrS3k8MtbVm7OR6djy/pOUw1sAp9byTrH7VfoGGxxfnPWmSnYxAm7FgTrOrCNbatpFL5GBaLs+NQUFpIOOKcbUBPOz7cZYbeJCYZOIdW5Iehq0j3ar7gbO/btY4AX9h/txtyRTRvZhAMvHdZeDqlFewkRqzq0aZqe2gdlCSVh99CkzsUBbGlKQ9F1Eh8JwgvlfYER57i3++VM98I+A09kbrReLuH4g09iD269XQQHojC9j2Du4F2lmnKnfsxh89O7Ld/zOuPaexQL/2+wBmFuTgEAqLIKe2xamLM+l2qRNqJEwuOugORjqT68FDjaZxz73fOQ9u1TM2xx0M7R2Fb0rYjgn78+BAO6kCPBkX2PZ46VfY7it7MdzJ05Tj+A5Uq+8EN+/kVRzLpreaJntCsTSxK1fLdS5WCg71h+v3x+Jhi6ibJjrwFG/Mr9GlcxABh1Of8eES5nA+26Nc75VkWRiQUN6/jiY1FnIG4Jsf6IsyMwUGyJlG7io4uoKEpMBEg3ehcfk3X8Abi/2F3ssF9vWMcf+NvHV6qe7yy1HbN1q9nvQtdBfxETgDOVzqMtv1d7X9rFKJxEDNsFauhMdHI6D3LFK953lcnPGefnlrbFaZ7DM1IBHPkH9xzVdugpYN3vnVy7OXkruSG5kazT1m3iEmDPrlrK/Vpjp1LK5EvnnD9yKPs+z5UdKmX91NXsM1JBkdzadsOwtr3AduWlQeC4PkOlliCX5p9r9gRMphiQgzCXV+3awY2cVwiIIwrbExVamjmSuUV9OrHvIZO6oJD4mCt6OhRLzOQ6jXMq1OcUJAOcXbXNcz66HfuZ/Pu17JrAu4+V9GDC9Nr/sYmJnRwxsjtBatFeQmWUhVEUBdr2Yb5zERMf06oBWdpFBuYEsSfgpbBPp37Amf/iu0AA5tg7/Wk2O0rsGQw8nuVKza1YDfBdE7l06s8cR2vnW5+r5z9YAeyBusdBwKqP2f+myWa2eQ/5xO7L4Zexgm9uLrg2Jmw9p5S0Q27GHmUdkNd4d6LXocDvPge6cNKCAOyeMvlxJszxEARvQvdhFwP9juYXniuqYjln1QQ/T5tXDrnQPdd0/2OA0Vfu+LqArPC1ZSl/YBosYeIY73pc1IUNoret4ufTDJZm82S6VdhONfLFIDs02FV8LGeCiJsAlAlD54Rv2vtg28/8ZdnCZLKeP5EWKmUiwtZlVvX3AuKDKFph1Y3OIcu2W652ef76mxOuAOJWqD1M53a5obZuwp2SYPdr3mShvW954qNX8gpp1PNTBtjOx1Q9X0BVmpxFuuYMnMjcp/scyW8XjLLvrdFynrk9F9vuf146Hfv7SjXwn62CUebuU+Pu4fW2Y9npO7HXYZ+3jkJgCTuW7WI8TsdEc+djob7Z30M65uJatcKuvRQ2DFvOR1uQLuhYLLXW2+Bc8EoUswV9nIrciBJ7PZNf1Ul8zK1e7iHsv6wPuxbHt7LrD88lL0rsWWnVR+x/Eh8dIfFxLyEoS7jquH0xbVSfNl1uz/IwVtUmoOoGAr6dXEGSIAhib2Xf45kbIdeFCLDcTG5VzQEmCvQ+glU+BtjDOs9ZYtN3LPDzPObkSDeyMJO2EAKIXQ85CFQO2umrPaAHG0BVFHGKdxHth1eHcs+D3Z0jXul/DHDi3fycydFuzPkI7BrFv1pLpDMTC0yDX2m+qIuVW6+U08YSHKu/5w9ybUcWt9p1EQCThSJzQ72LLeGBI5wATPSJb7VCDznLi3Ri9ytelXNfgK2rdgVfjJWDbDuaNlmhpQ7bKkpMHN28xFuuv1RD4f0mh6yibUvZ/07bWdyVhe3Ht/Lb5brdXF2qjXyBFfDmOvNKcVXW+Rjf4lxsyO6bW97NUHk2/YCXZ45ehwK/+8w97Bpg7rP6NdakqIv4OO5a4Lz/x7+G2duQrOdP0OblROWkvrHD63kV7+1JisRWvntYFNnxlNhm5bl0OMeDpVal6KQlLBaq1h3O5gnlCdtBq2K3nQOUd1yFylnf0k0ABH6Kg3QTX6S0HYu89EDN8256CUF3Ex879Wf7rvp79yJsANDtQJZWq7ire0qBvRgSH/ciTjqgG4qDbeskqCjyoz6hIK0ZFHZNEASxsyiqZLmamodex7d6e0gCgD6jgPVfsQGPF+cjwKrTJ7axPG72gMrr+gjCAwf3Kcc7V4xBsI1zPhK7OV0PyP7txU25qyHJgJ13nee2sovS8UTFUBkg+oCN3/KrFdtCYHyLs9hm78u61Xy3WKCY5YA1Db6wU9yVOeUSW12cj1bRogYX0TPSmfXfTaiKdgM2fZf9jOPyKoDNS/ltMkIrR+SLVLLlyGHn3NzFVQDMrEjp6nZr4Bd1sas0uwrAtvOxjm0Lz7HlRkkP9l0qcbZup/0WKGaTkVqKL67Z32eyzt2daOO1/xUDmPijK/y86gATHd3aBEvAwnnr3Z2PQPb7cxLIbOegk6gNsP3oC7HzUUvx95Gd4kBLMVGw0DGRG57tJMrl5gnlTRrYfW5Yb/3Pcz6WZ52PgaLCE0s+P9tWe79J/sIFr2QrVyNP2M0N+3fM+dg87Not52NX1r/Vn3pzMg44lv2uOoDfbi+H1CJihygJyYilNaRUyvlIEASxUxl4Aqt6bbsbAMuZ4DHco8chbLCw4RsrrMTDQCBcDux7HLDweW95cAhiO6BICqIFoTJg/18AY2Z0dE+2n1H/x/Lt8q7Rdu66sj7ObUSRuXLUJL/ITZFVLCS22dltmRsOzruWB0tZ+DPgXklcV5hbkSfu2CLW1p/4E1h2O7f7THFXJsYCTBh0oqiS5avjhr5H3R2GRZ3ZPuP1vagLu0duXMjur06uYnsb41uY4OkksEQqWBs7F6UTtviYqNvxtCh24ZbaFZaI7bDfmrvYuNuwmW1DW6dsqRiYPQbcikR5QZTYcecm+PqLWK7cVANfVA2WWEIm57gSBHZsbl7C/uc9l4UsgS8TXl/gmLD3cbLOcjWWOm+DErPclg7fi93nbT87py3I7ZvtvuU5JO2cs265TpUYP0VDbtEmx6I/uc5HDwVnRBHoMoQ5qr0UnOwyBDjlb8C4a9zb7sWQWkTsENGQDJhAbVyBX6LDiSAIYqex7wT2IDn/Ufa/rvEHB80JFAHdRrBZ3abq7KDXjQPPY4LlolfZgG9HXBUEQRBeOep6YPhZHd2L7WfEr4CTH+K36XkIAIGF8PHobr3ffaRzm+KuTOgxVGfx0RdgYkO6yUVI6wxssQWRUud2ufcRXvVye131a9wFPLdlAUzotIVMXr5sW0TgiQnBUqtoTr2zSBTtwQQRnoNS8rH9seYzvhAbKGLCSFM13xWXJz66hL4napkDlRfi74VAETuOVn/GJiudnhPyQpQ5lb0jnXMctG0sPtrF0YqqCjvotgc752V8q7OoKAjsnKhbw/aRk2BYXMW23akCeW67TEh/qXM7+3u2J6ALLVMOM0dhrIYJbjz3bbrJXXz0BYGaxe7pfcLlOYWnONtgu4y5qSGsXKfckPbS7H5wWpYtYgLuRZts+lnF17oOd28LsDQBlAedC6lFxA4RtcK4t8YUcj4SBEHsTHwB4MjrgEWvAJu+Z4M4Q2tdgZA+o5l7MrnNu/jYqR8TPlfMdq9ASRAEQXin2wjgki+A0p78doddDJw+k3+9L8lxfxVzCtPYAhU3F2UVEy/cCi9IclbYK+NUEvb5s+60Eo5LrcsQ9tvtXmOHsruJlD0PY797HOzcpqgLc4vyIgk6W+tzc0RV7seW49avoipL5Kp1FjTDFcyFlW7iTzLaoc2x6rYpfNF5ILD4NSZilfQo3CZYygrDJLZaAqqT8zFn23ZUGG1Op37A8HOAMW1UJArIVvt2E3KLKrOCoZNIWdyVPavx2gDsXN3wNdunPIGsqDIrWDvlVRQEJoZmKrw7OXkrgfp1TJxzEs8Ege2DmkUexEcrRUBTNX+/BUvc3bIZ52Ojc6h3i2rrDrkj001WG4/FGYf+EvjFUyzfOdEmkFpE7BDREAsh2NqUJucjQRDEzqbXoUwInHcnm40OFPOdGM3pMyZbLZGXh6w5h/6WVaYdclrr+ksQBEHw8eIml0P5eTALYeeDFGV+fjbbpcYT0mzxssRFFAWAUx4FzprlXrzIFrJ4957BJwOTHgQ6uxS/6nko+81zggKsUNJFH/H3XXFXJvLFapxFPlu8tN12TvSyxc6D+O2Ku7CiPwmO+Jg7QcibLCzrw/IAbvq+bQQ++zmh6zDnY1MQmCAV28wvfJebTqA9creOuQroP77tllfcBWjayE9dADDxePOP7G8n8S7aDTD1bHsnKiyh3a2wVkkP5qSMb2aOX6fvJlQKbFvF/nYSH0t6AOu+YH/z8sgWd7VCy13Ex9JebFs3feee4iBVb4W1OxwPIcvhyctHmVtwJu2QliBUzsRHXbPcw6X8bQDYPu02gh3fRJvgsaQdQRQm4JMQkCVsiaXJ+UgQBNERjL4KeO40YM6trAp2ax6SynoD/Y9lD2qtqVxb2hM4e1br+0oQBEHsHASBuSMlThgywMLBDY0vAlbux37zHJQ2RZXeHHeHXQxUDeWLLJKPOfTdKK4Czn3NW64/nhALWC6wtdbfDkJgaS8mYrota9BJTOToPYrfrrgry/Nn6t6EO14uwJIeTHBu3OAtV50bA08E6lYBAybw20U6s/3Gc2b6w0DlYG85pncFoj2ApW+xPKE8wbe4C/Dzh0wElByKu+Yem7zUAN2Gs9+8fK5235qqgcaNbP1OhMuB2pUABGfxsbQX0LCOFbLiTWDb53+Zi5O3uIodg9tWAoMmOrezXbr+iLPzsajSyjnKKXhki4+6xhzahZYVLgdgMiGTV9iJaFdIfCR2mJKQD5sb0wiQ+EgQBLHziXQCjr0N+PF15khsLSf8pc27RBAEQewCuLkjAaD34eyHR0l34Lg/tW0l16r92U9b4eYU80qew9AhzBhwFx4B5pzaZ5x7u7I+LIUK4CzwihIruiTJhasH57br1I+FAdui8Y7g8wOjPYQyRzqzCAyA7xI87e+sQMvuQOXgbKgu7/gqto4ZnjO4tBcT5NzE9M4DgYn3Macpj5IelrtwId9JWdKTVQGPVDq7I+3CQm55vLsewJ413Y4rUbJyXK7jC+CRCia6B4pZEa2CbTozEdNQnfPDBqPMGWmHXgcKVf7OyTNrGt7Crok2h8RHYocpCcnY3JiGTGHXBEEQHcM+47wNcAiCIAhiexh4Qkf3YOeQm6sywimG05ZUDma//VbxGSeOut7b8o6+gYVduznU2pKSHsDKOZZ7jhOW25ooi47GDvcv7+fsaASyIno5J8+pIADT/h//+7Xx8jxXXMWqTq/9HzDiXOd2tiDKcy13Hcacz/udwl/nwIms/17cyL2PAL5/gbmbnYhUMuejlgZ6OUyAFFVmq1Q7iY+RztmK3kBh52OgmLnAt/3M/ucV/SHaDRIfiR3GLjpDYdcEQRAEQRAEQey2SD7m9CtUwKO96LI/E+/6jGmb5VUOzgqaOwu7KFBZH295S3cH5CDwq1edQ4JtugwFDvsdsK+LQO9WqKU1iBJzIq77gv9d2+kUnIoFAeyYP+s/7uuUfN5zao6+komivGrXRZUsnFpNOKdqyHXROoXrF1UyF2gtR1gUBCZebllmLYuqUncEJD4SO0xJiMRHgiAIgiAIgiD2AEb8aueuT5KZyCXsxmMpu9hP1+Ed2o02x4t7VBSBg6e3f1+ac+B5gGnyc4p2G84KNw07a6d1CwA7pqOc4jVANnQ83eScz9HOdVrUxTndgC1Q1ixiQrEcLNwu2h3YsIAJj05tiHZll7jCPfroo+jTpw+CwSAOPfRQfPnll9z2L730EgYNGoRgMIihQ4fi7bff3kk9JQoRtcRHyvlIEARBEARBEATRSkRp966qG6kAzvt/wNgZHd2TvYdehwKnPsbPPypKwPibgc6cglIdRW4RHqfckKIETH6cVb13Qg4x0bFmMb9gTlkfq0CPh8JZRLvQ4WrRCy+8gKuuugo333wzvvnmGwwbNgwTJkzA5s2bC7b//PPPcdZZZ+HXv/41vv32W0yePBmTJ0/GokWLdnLPCZvSMBMfy8Iu1fQIgiAIgiAIgiCIPY9oV35uRILIRRBYLtluB/KrT/c82L36d1EX5nzkiY/2MioGtr6vRJvQ4eLjfffdhwsvvBDnn38+9ttvPzz++OMIh8N4+umnC7Z/8MEHcfzxx+Oaa67B4MGDcfvtt+PAAw/EI488spN7TtjYjscuUbIvEwRBEARBEARBEAThwrG3A6c+vuPLKesL6Ep+tfrm7HMUc1jue/yOr4/YLjpUfFQUBV9//TXGj88mLhVFEePHj8f8+fMLfmb+/Pl57QFgwoQJju3T6TQaGxvzfoi2xTDZ78riQMd2hCAIgiAIgiAIgiCIXR9BaJsCRXZVbbtCeSEinYBzXmJOSqJD6NCCM1u3boWu6+jSpUve6126dMHSpUsLfqa6urpg++rq6oLt77zzTtx6661t02GiIBMP6IpYSsuEXxMEQRAEQRAEQRAEQbQ7A08AYtXAgOM6uicEhw4Pu25vrrvuOjQ0NGR+1q1b19Fd2uOIBmVcOHYfCLtzkmSCIAiCIAiCIAiCIHYvwuXAmKv5uSOJDqdDnY8VFRWQJAk1NTV5r9fU1KCqqnAVoqqqqla1DwQCCAQoHJggCIIgCIIgCIIgCIIgdjYd6nz0+/0YOXIk5syZk3nNMAzMmTMHhx9+eMHPHH744XntAWD27NmO7QmCIAiCIAiCIAiCIAiC6Bg61PkIAFdddRXOO+88HHTQQTjkkEPwwAMPIB6P4/zzzwcATJ06Fd27d8edd94JALj88ssxbtw43HvvvZg4cSJmzZqFBQsW4Mknn+zIzSAIgiAIgiAIgiAIgiAIohkdLj6eeeaZ2LJlC2666SZUV1dj+PDhePfddzNFZdauXQtRzBo0jzjiCDz//PO44YYbcP3112PAgAF4/fXXsf/++3fUJhAEQRAEQRAEQRAEQRAEUQDBNE2zozuxM2lsbERJSQkaGhoQjVJCUoIgCIIgCIIgCIIgCIJoLV41tj2+2jVBEARBEARBEARBEARBEB1Dh4dd72xso2djY2MH94QgCIIgCIIgCIIgCIIgdk9sbc0tqHqvEx+bmpoAAD179uzgnhAEQRAEQRAEQRAEQRDE7k1TUxNKSkoc39/rcj4ahoGNGzeiuLgYgiB0dHfahcbGRvTs2RPr1q2jvJYEsQdA5zRB7FnQOU0QexZ0ThPEngWd0wThHdM00dTUhG7duuUVi27OXud8FEURPXr06Ohu7BSi0ShdLAliD4LOaYLYs6BzmiD2LOicJog9CzqnCcIbPMejDRWcIQiCIAiCIAiCIAiCIAiiXSDxkSAIgiAIgiAIgiAIgiCIdoHExz2QQCCAm2++GYFAoKO7QhBEG0DnNEHsWdA5TRB7FnROE8SeBZ3TBNH27HUFZwiCIAiCIAiCIAiCIAiC2DmQ85EgCIIgCIIgCIIgCIIgiHaBxEeCIAiCIAiCIAiCIAiCINoFEh8JgiAIgiAIgiAIgiAIgmgXSHwkCIIgCIIgCIIgCIIgCKJdIPGRIAiCIAiCIAiCIAiCIIh2gcRHgiAIgiAIgiAIgiAIgiDaBRIfCYIgCIIgCIIgCIIgCIJoF0h8JAiCIAjCE7fccgsEQejobuySrF69GoIg4JlnnunorhDETkUQBNxyyy0d3Y1W88wzz0AQBKxevbrVn92Ra6H92a1bt7q27dOnD6ZNm7Zd6yEIgiCIXQkSHwmCIAhiJ2APdO2fYDCIbt26YcKECXjooYfQ1NTU0V0kiHbjb3/7GwmzBEEQBEEQeym+ju4AQRAEQexN3Hbbbejbty9UVUV1dTXmzZuHK664Avfddx/efPNNHHDAAR3dRWI76N27N5LJJGRZ7uiu7JL87W9/Q0VFBbm49kCSySR8PhpStAfLli2DKJJXhCAIgtj9oScFgiAIgtiJnHDCCTjooIMy/1933XWYO3cuTjrpJJx88slYsmQJQqFQB/ZwzyEejyMSiRR8L5FIIBwOt9m6bDcrQext0HHffgQCgY7uAkEQBEG0CTSVRhAEQRAdzNFHH40bb7wRa9aswXPPPZf33ty5czFmzBhEIhGUlpbilFNOwZIlS/La2DnEli5dijPOOAPRaBSdOnXC5ZdfjlQqlddW0zTcfvvt6NevHwKBAPr06YPrr78e6XR6u/o+c+ZMHH300aisrEQgEMB+++2Hxx57zPPn7T537twZoVAIAwcOxB//+Me8Nt9++y1OOOEERKNRFBUV4ZhjjsH//ve/vDZ2WPtHH32Eiy++GJWVlejRowcA4Mgjj8T++++Pr7/+GmPHjkU4HMb1118PAEin07j55pvRv39/BAIB9OzZE9dee23e/pg2bVpeyHzuj53rzinnY2u+vxUrVmDatGkoLS1FSUkJzj//fCQSiRb77LnnnsPIkSMRCoVQXl6OKVOmYN26dZ73eVtTXV2N888/Hz169EAgEEDXrl1xyimnZHLp9enTB4sXL8ZHH32U2W9HHnlk5vP19fW44oor0LNnTwQCAfTv3x933XUXDMPIW8+sWbMwcuRIFBcXIxqNYujQoXjwwQcz76uqiltvvRUDBgxAMBhEp06dMHr0aMyePTvT5vvvv8e0adOwzz77IBgMoqqqChdccAFqa2s9bWsqlcItt9yCfffdF8FgEF27dsVpp52GlStXZtrE43FcffXVme0ZOHAg7rnnHpimmbcsQRBw6aWX4vXXX8f++++PQCCAIUOG4N133820efnllzPHdXOeeOIJCIKARYsWZV5bunQpTj/9dJSXlyMYDOKggw7Cm2++mfc5+1z57LPPcNVVV6Fz586IRCI49dRTsWXLlkw7+7gs9JPrYC2U87E156xbP2zeeeedzLlUXFyMiRMnYvHixQW+pZYsXrwYRx99NEKhEHr06IE//elPLY6vtlhPa9i6davr9bp5zsfW7jOCIAiC2FUg5yNBEARB7AKce+65uP766/H+++/jwgsvBAB88MEHOOGEE7DPPvvglltuQTKZxMMPP4xRo0bhm2++QZ8+ffKWccYZZ6BPnz6488478b///Q8PPfQQ6urq8M9//jPTZvr06Xj22Wdx+umn4+qrr8YXX3yBO++8E0uWLMFrr73W6n4/9thjGDJkCE4++WT4fD7897//xcUXXwzDMHDJJZdwP/v9999jzJgxkGUZF110Efr06YOVK1fiv//9L+644w4ATDQYM2YMotEorr32WsiyjCeeeAJHHnkkPvroIxx66KF5y7z44ovRuXNn3HTTTYjH45nXa2trccIJJ2DKlCn41a9+hS5dusAwDJx88sn49NNPcdFFF2Hw4MH44YcfcP/99+Onn37C66+/DgD4zW9+g/Hjx+et591338W///1vVFZWOm7f9nx/ffv2xZ133olvvvkGTz31FCorK3HXXXdl2txxxx248cYbccYZZ2D69OnYsmULHn74YYwdOxbffvstSktLufu8PfjFL36BxYsX47LLLkOfPn2wefNmzJ49G2vXrkWfPn3wwAMP4LLLLkNRUVFGWO7SpQsA5kAdN24cNmzYgN/85jfo1asXPv/8c1x33XXYtGkTHnjgAQDA7NmzcdZZZ+GYY47J7I8lS5bgs88+w+WXXw6AiWV33nknpk+fjkMOOQSNjY1YsGABvvnmGxx77LGZ5fz88884//zzUVVVhcWLF+PJJ5/E4sWL8b///Y9bRETXdZx00kmYM2cOpkyZgssvvxxNTU2YPXs2Fi1ahH79+sE0TZx88sn48MMP8etf/xrDhw/He++9h2uuuQYbNmzA/fffn7fMTz/9FK+++iouvvhiFBcX46GHHsIvfvELrF27Fp06dcLEiRNRVFSEF198EePGjcv77AsvvIAhQ4Zg//33B8DOlVGjRqF79+74wx/+gEgkghdffBGTJ0/GK6+8glNPPTXv85dddhnKyspw8803Y/Xq1XjggQdw6aWX4oUXXgAAnHbaaejfv3/eZ77++ms88MAD3OO+teesWz8A4F//+hfOO+88TJgwAXfddRcSiQQee+wxjB49Gt9++22LcymX6upqHHXUUdA0LbNfnnzyyYIO8x1ZT2vxcr12wss+IwiCIIhdCpMgCIIgiHZn5syZJgDzq6++cmxTUlJijhgxIvP/8OHDzcrKSrO2tjbz2nfffWeKomhOnTo189rNN99sAjBPPvnkvOVdfPHFJgDzu+++M03TNBcuXGgCMKdPn57XbsaMGSYAc+7cudxtsNeTSyKRaNFuwoQJ5j777MNdlmma5tixY83i4mJzzZo1ea8bhpH5e/Lkyabf7zdXrlyZeW3jxo1mcXGxOXbs2Mxr9v4dPXq0qWla3vLGjRtnAjAff/zxvNf/9a9/maIomp988kne648//rgJwPzss88K9nv58uVmSUmJeeyxx2bWtWrVKhOAOXPmzEy71n5/F1xwQd56Tj31VLNTp06Z/1evXm1KkmTecccdee1++OEH0+fztXh9Z1BXV2cCMO+++25uuyFDhpjjxo1r8frtt99uRiIR86effsp7/Q9/+IMpSZK5du1a0zRN8/LLLzej0WiL7zaXYcOGmRMnTuT2o9Dx+p///McEYH788cfczz799NMmAPO+++5r8Z59zL7++usmAPNPf/pT3vunn366KQiCuWLFisxrAEy/35/32nfffWcCMB9++OHMa2eddZZZWVmZt+2bNm0yRVE0b7vttsxrxxxzjDl06FAzlUrl9euII44wBwwYkHnNPlfGjx+fd65deeWVpiRJZn19fcHt37Jli9mrVy9z6NChZiwWy9uOm2++OfN/a89Zt340NTWZpaWl5oUXXpjXn+rqarOkpKTF68254oorTADmF198kXlt8+bNZklJiQnAXLVqVavXU+ha6BWv12vTNM3evXub5513Xub/7f3uCIIgCKKjobBrgiAIgthFKCoqylS93rRpExYuXIhp06ahvLw80+aAAw7Asccei7fffrvF55s7DS+77DIAyLS1f1911VV57a6++moAwFtvvdXqPue6hxoaGrB161aMGzcOP//8MxoaGhw/t2XLFnz88ce44IIL0KtXr7z3bPeZrut4//33MXnyZOyzzz6Z97t27Yqzzz4bn376KRobG/M+e+GFF0KSpBbrCwQCOP/88/Nee+mllzB48GAMGjQIW7duzfwcffTRAIAPP/ywxXLi8ThOPfVUlJWV4T//+U/BdQHb9/399re/zft/zJgxqK2tzWzjq6++CsMwcMYZZ+T1t6qqCgMGDCjY3/YmFArB7/dj3rx5qKura/XnX3rpJYwZMwZlZWV52zR+/Hjouo6PP/4YAFBaWop4PJ4XQt2c0tJSLF68GMuXL+f21yaVSmHr1q047LDDAADffPMNt6+vvPIKKioqMudVLvYx+/bbb0OSJPzf//1f3vtXX301TNPEO++8k/f6+PHj0a9fv8z/BxxwAKLRKH7++efMa2eeeSY2b96MefPmZV57+eWXYRgGzjzzTADAtm3bMHfuXJxxxhloamrK7Mfa2lpMmDABy5cvx4YNG/LWfdFFF+U5PceMGQNd17FmzZoW26frOs466yw0NTXhtddec8yluj3nrFs/Zs+ejfr6epx11ll5x4gkSTj00ENdj/u3334bhx12GA455JDMa507d8Y555yT125H19Na3K7XPFrz3REEQRDErgCFXRMEQRDELkIsFsuEM9qDyIEDB7ZoN3jwYLz33nstCqoMGDAgr12/fv0gimIm996aNWsgimKLUMqqqiqUlpZu18D1s88+w80334z58+e3yE/Y0NCAkpKSgp+zxRU7ZLQQW7ZsQSKRcNwHhmFg3bp1GDJkSOb1vn37FlxW9+7d4ff7815bvnw5lixZgs6dOxf8zObNm1u8duGFF2LlypX4/PPP0alTJ8e+b8/311yELSsrAwDU1dUhGo1i+fLlME2zxfdsw6u0rSgKtm3b5vg+D7/fnyeg5hIIBHDXXXfh6quvRpcuXXDYYYfhpJNOwtSpU1FVVeW67OXLl+P77793/Q4uvvhivPjiizjhhBPQvXt3HHfccTjjjDNw/PHHZ9redtttOOWUU7Dvvvti//33x/HHH49zzz03r4L8tm3bcOutt2LWrFktvl+eWA4AK1euxMCBA7mVndesWYNu3bqhuLg47/XBgwdn3s+l+XcOsO89V8g9/vjjUVJSghdeeAHHHHMMABZyPXz4cOy7774AgBUrVsA0Tdx444248cYbC/Zt8+bN6N69u+O6c4+35txwww2YO3cu3nrrrTyxtDnbc8669cMWk+1JgeZEo1HH/gBsnzcP9QZanps7up7W4na95tGa744gCIIgdgVIfCQIgiCIXYD169ejoaGhhTC4Izjlr+PltWsNK1euxDHHHINBgwbhvvvuQ8+ePeH3+/H222/j/vvvdyzo0J44VQov9LphGBg6dCjuu+++gp/p2bNn3v8PPvgg/vOf/+C5557D8OHDd7ivzXFyUZpWoRLDMCAIAt55552CbYuKihyX/fnnn+Ooo47arn6NGzcuz3XXnCuuuAKTJk3C66+/jvfeew833ngj7rzzTsydOxcjRozgLtswDBx77LG49tprC75vi2uVlZVYuHAh3nvvPbzzzjt45513MHPmTEydOhXPPvssAGDs2LFYuXIl3njjDbz//vt46qmncP/99+Pxxx/H9OnTAbA8e59//jmuueYaDB8+HEVFRTAMA8cff3yHHK9u3znABN7Jkyfjtddew9/+9jfU1NTgs88+w5///OdMG7vvM2bMwIQJEwous/m1xcu6AeD111/HXXfdhdtvvz1P7G0rvBz3AMvHWEjQ5onBrWFnrceJ1lyXvX53BEEQBLGrQOIjQRAEQewC/Otf/wKAjHDQu3dvAMCyZctatF26dCkqKipahD4uX748z/m3YsUKGIaRKZLQu3dvGIaB5cuXZ5xYAFBTU4P6+vrMOr3y3//+F+l0Gm+++WaeE8dLeKIdkplbqbc5nTt3RjgcdtwHoii2EAhbQ79+/fDdd9/hmGOOcR34f/LJJ5gxYwauuOKKFuGahdie789Lf03TRN++fTOinFeGDRvGDVnmYbuq3Pp29dVX4+qrr8by5csxfPhw3HvvvZnq7U77t1+/fojFYi0K+hTC7/dj0qRJmDRpEgzDwMUXX4wnnngCN954Y0ZYKy8vx/nnn4/zzz8fsVgMY8eOxS233ILp06ejrq4Oc+bMwa233oqbbrops1xemHbzvn7xxRdQVdXRZdq7d2988MEHaGpqynM/Ll26NPP+9nDmmWfi2WefxZw5c7BkyRKYppkJuQay55Msy572pVd++uknnHfeeZg8eXKmQjyP9jhnbadlZWXldm1b7969C37Hzfu4o+tpLW7Xa4IgCILYk6CcjwRBEATRwcydOxe33347+vbtmxG2unbtiuHDh+PZZ59FfX19pu2iRYvw/vvv48QTT2yxnEcffTTv/4cffhgAcMIJJwBA5jN2BWEb2/k3ceLEVvXbdt/kum0aGhowc+ZM18927twZY8eOxdNPP421a9fmvWcvT5IkHHfccXjjjTfyQhFramrw/PPPY/To0TsUCnnGGWdgw4YN+Pvf/97ivWQymamWvWnTJpxxxhkYPXo07r77bk/L3p7vz43TTjsNkiTh1ltvbeFwMk0TtbW1jp8tKyvD+PHjt+tn5MiRjstNJBJIpVJ5r/Xr1w/FxcVIp9OZ1yKRSN5+sDnjjDMwf/58vPfeey3eq6+vh6ZpANBi20RRzIRT2+tp3qaoqAj9+/fPvF/oeAVang9O/OIXv8DWrVvxyCOPtHjPXuaJJ54IXddbtLn//vshCELmXGwt48ePR3l5OV544QW88MILOOSQQ/KEq8rKShx55JF44oknsGnTphaf37JlS6vXGYvFcOqpp6J79+549tlnPTnz2uOcnTBhAqLRKP785z9DVdUW77tt24knnoj//e9/+PLLL/M+8+9//7tN11OIrVu3YunSpS1SUgDu12uCIAiC2JMg5yNBEARB7ETeeecdLF26FJqmoaamBnPnzsXs2bPRu3dvvPnmmwgGg5m2d999N0444QQcfvjh+PWvf41kMomHH34YJSUluOWWW1ose9WqVTj55JNx/PHHY/78+Xjuuedw9tlnY9iwYQCY++28887Dk08+ifr6eowbNw5ffvklnn32WUyePLnVYbnHHXdcxo32m9/8BrFYDH//+99RWVlZUABpzkMPPYTRo0fjwAMPxEUXXYS+ffti9erVeOutt7Bw4UIAwJ/+9CfMnj0bo0ePxsUXXwyfz4cnnngC6XQaf/3rX1vV3+ace+65ePHFF/Hb3/4WH374IUaNGgVd17F06VK8+OKLeO+993DQQQfh//7v/7BlyxZce+21mDVrVt4yDjjggLycgrm09vtzo1+/fvjTn/6E6667DqtXr8bkyZNRXFyMVatW4bXXXsNFF12EGTNmbM+u2G5++uknHHPMMTjjjDOw3377wefz4bXXXkNNTQ2mTJmSaTdy5Eg89thj+NOf/oT+/fujsrISRx99NK655hq8+eabOOmkkzBt2jSMHDkS8XgcP/zwA15++WWsXr0aFRUVmD59OrZt24ajjz4aPXr0wJo1a/Dwww9j+PDhGRfvfvvthyOPPBIjR45EeXk5FixYgJdffhmXXnopAJazb+zYsfjrX/8KVVXRvXt3vP/++1i1apWnbZ06dSr++c9/4qqrrsKXX36JMWPGIB6P44MPPsDFF1+MU045BZMmTcJRRx2FP/7xj1i9ejWGDRuG999/H2+88QauuOIKbr5EHrIs47TTTsOsWbMQj8dxzz33tGjz6KOPYvTo0Rg6dCguvPBC7LPPPqipqcH8+fOxfv16fPfdd61a56233ooff/wRN9xwA95444289/r164fDDz+84Ofa+pyNRqN47LHHcO655+LAAw/ElClT0LlzZ6xduxZvvfUWRo0aVVAQtrn22mvxr3/9C8cffzwuv/xyRCIRPPnkk+jduze+//77NltPIR555BHceuut+PDDD3HkkUfmved2vSYIgiCIPYqdX2CbIAiCIPY+Zs6caQLI/Pj9frOqqso89thjzQcffNBsbGws+LkPPvjAHDVqlBkKhcxoNGpOmjTJ/PHHH/Pa3HzzzSYA88cffzRPP/10s7i42CwrKzMvvfRSM5lM5rVVVdW89dZbzb59+5qyLJs9e/Y0r7vuOjOVSrlug72eXN58803zgAMOMIPBoNmnTx/zrrvuMp9++mkTgLlq1SrXZS5atMg89dRTzdLSUjMYDJoDBw40b7zxxrw233zzjTlhwgSzqKjIDIfD5lFHHWV+/vnneW3s/fvVV1+1WMe4cePMIUOGFFy/oijmXXfdZQ4ZMsQMBAJmWVmZOXLkSPPWW281GxoaMp/P/e5yf26++WbTNE1z1apVJgBz5syZectvzfe3ZcuWgtvUfD++8sor5ujRo81IJGJGIhFz0KBB5iWXXGIuW7as4Da2J1u3bjUvueQSc9CgQWYkEjFLSkrMQw891HzxxRfz2lVXV5sTJ040i4uLTQDmuHHjMu81NTWZ1113ndm/f3/T7/ebFRUV5hFHHGHec889pqIopmma5ssvv2wed9xxZmVlpen3+81evXqZv/nNb8xNmzZllvOnP/3JPOSQQ8zS0lIzFAqZgwYNMu+4447MMkzTNNevX5853kpKSsxf/vKX5saNG/O+Sx6JRML84x//mDl/qqqqzNNPP91cuXJl3vZceeWVZrdu3UxZls0BAwaYd999t2kYRt6yAJiXXHJJi3X07t3bPO+881q8Pnv2bBOAKQiCuW7duoL9W7lypTl16lSzqqrKlGXZ7N69u3nSSSeZL7/8cqaN07ny4YcfmgDMDz/80DRN0zzvvPMcj/vc/hXadztyzjbvR+7rEyZMMEtKSsxgMGj269fPnDZtmrlgwYKC+yKX77//3hw3bpwZDAbN7t27m7fffrv5j3/8o+D55WU9ha6FhbDb5W5La67XzY+F1u4zgiAIgthVEEyTMhMTBEEQxO7MLbfcgltvvRVbtmxBRUVFR3eHIAiCIAiCIAgiA+V8JAiCIAiCIAiCIAiCIAiiXSDxkSAIgiAIgiAIgiAIgiCIdoHER4IgCIIgCIIgCIIgCIIg2gXK+UgQBEEQBEEQBEEQBEEQRLtAzkeCIAiCIAiCIAiCIAiCINoFX0d3YGdjGAY2btyI4uJiCILQ0d0hCIIgCIIgCIIgCIIgiN0O0zTR1NSEbt26QRSd/Y17nfi4ceNG9OzZs6O7QRAEQRAEQRAEQRAEQRC7PevWrUOPHj0c39/rxMfi4mIAbMdEo9EO7g1BEARBEARBEARBEARB7H40NjaiZ8+eGa3Nib1OfLRDraPRKImPBEEQBEEQBEEQBEEQBLEDuKU1pIIzBEEQBEEQBEEQBEEQBEG0CyQ+EgRBEARBEARBEARBEATRLpD4SBAEQRAEQRAEQRAEQRBEu9ChOR8//vhj3H333fj666+xadMmvPbaa5g8eTL3M/PmzcNVV12FxYsXo2fPnrjhhhswbdq0ndJfgiAIgiAIgiAIgiA6Bl3XoapqR3eDIPYaZFmGJEk7vJwOFR/j8TiGDRuGCy64AKeddppr+1WrVmHixIn47W9/i3//+9+YM2cOpk+fjq5du2LChAk7occEQRAEQRAEQRAEQexMTNNEdXU16uvrO7orBLHXUVpaiqqqKteiMjw6VHw84YQTcMIJJ3hu//jjj6Nv37649957AQCDBw/Gp59+ivvvv99RfEyn00in05n/Gxsbd6zTRAtM08TabQn07hTp6K4QBEEQxPazdQVQ0h2QQx3dE4IgCIIgcrCFx8rKSoTD4R0SQQiC8IZpmkgkEti8eTMAoGvXrtu9rA4VH1vL/PnzMX78+LzXJkyYgCuuuMLxM3feeSduvfXWdu7Z3s2cJZvx57eX4O3LxyAo77gdlyAIgiB2OroG/OdMYPjZwJirO7o3BEEQBEFY6LqeER47derU0d0hiL2KUIhNym/evBmVlZXbHYK9WxWcqa6uRpcuXfJe69KlCxobG5FMJgt+5rrrrkNDQ0PmZ926dTujq3sVW2PMWbq5Me3SkiAIgiB2UZo2st9blnVsPwiCIAiCyMPO8RgOhzu4JwSxd2KfezuSb3W3cj5uD4FAAIFAoKO7sVdQ05RCr050QyAIgiB2Q1IN7LdpdGw/CIIgCIIoCIVaE0TH0Bbn3m7lfKyqqkJNTU3eazU1NYhGoxkrKLHzqU8y9XtLEzkfCYIgiN2UlJUTWi0cSUEQBEEQBEEQxPaxW4mPhx9+OObMmZP32uzZs3H44Yd3UI8IAGiwxMekondwTwiCIAhiO0lb4mOyrmP7QRAEQRAEQRB7GB0qPsZiMSxcuBALFy4EAKxatQoLFy7E2rVrAbB8jVOnTs20/+1vf4uff/4Z1157LZYuXYq//e1vePHFF3HllVd2RPd3P9IxwDTbfLGNlvio6BSqRhAEQeym2M5HJdax/SAIgiAIgtgFqK2tRWVlJVavXt3RXfHMLbfcguHDh3d0Nxw58sgjuQWTO4I//OEPuOyyy9p9PR0qPi5YsAAjRozAiBEjAABXXXUVRowYgZtuugkAsGnTpowQCQB9+/bFW2+9hdmzZ2PYsGG499578dRTT2HChAkd0v/dCl0DnhwHfPVUmy/adj4qGomPBEEQxG5KOifsuh0m6giCIAiC2Duprq7G5Zdfjv79+yMYDKJLly4YNWoUHnvsMSQSiUy7Pn36QBAECIKAcDiMoUOH4qmn8sfv8+bNy7Rp/lNdXQ0gX4DLXWahn2nTpjn2+4477sApp5yCPn36tPUuaTdmzJjRIlqW4DNjxgw8++yz+Pnnn9t1PR1acObII4+EyXnAf+aZZwp+5ttvv23HXu2hxKxcmRu+afNFk/hIEARB7PYocSBQDKSbAC0FyJRLmiAIgiCIHePnn3/GqFGjUFpaij//+c8YOnQoAoEAfvjhBzz55JPo3r07Tj755Ez72267DRdeeCESiQReeuklXHjhhejevTtOOOGEvOUuW7YM0Wg077XKysoW6//qq6+g6yw92ueff45f/OIXeZ91qp2RSCTwj3/8A++9994Obf/OpqioCEVFRR3djd2KiooKTJgwAY899hjuvvvudlvPbpXzkdgB7BxWZtvnZWxMaQAo7JogCILYjdHSQKiM/U1FZwiCIAiCaAMuvvhi+Hw+LFiwAGeccQYGDx6MffbZB6eccgreeustTJo0Ka99cXExqqqqsM8+++D3v/89ysvLMXv27BbLraysRFVVVd6PKLaUdzp37px5v7y8vMVnS0pKCvb77bffRiAQwGGHHZb3+uLFi3HSSSchGo2iuLgYY8aMwcqVKwEAhmHgtttuQ48ePRAIBDB8+HC8++67mc+uXr0agiDg1VdfxVFHHYVwOIxhw4Zh/vz5AIDGxkaEQiG88847eet87bXXUFxcnOcSdaJ52PW0adMwefJk/PnPf0aXLl1QWlqK2267DZqm4ZprrkF5eTl69OiBmTNntujnrFmzcMQRRyAYDGL//ffHRx99lLeuRYsW4YQTTkBRURG6dOmCc889F1u3bs28H4/HMXXqVBQVFaFr16649957W/Q3nU5jxowZ6N69OyKRCA499FDMmzcv8/4zzzyD0tJSvPfeexg8eDCKiopw/PHHY9OmTZk2hRyttltV13X8+te/Rt++fREKhTBw4EA8+OCDLfoxadIkzJo1y3X/7ggkPu4tJLex314HVJqSzX/FQTdMNKVUlIRlcj4SBEEQuy9aCgixh3Ko7g+3BEEQBEF0LLphorohtdN/dMNbepba2lq8//77uOSSSxCJRAq2EQSh4OuGYeCVV15BXV0d/H7/du+j7eWTTz7ByJEj817bsGEDxo4di0AggLlz5+Lrr7/GBRdcAE1jZqQHH3wQ9957L+655x58//33mDBhAk4++WQsX748bzl//OMfMWPGDCxcuBD77rsvzjrrLGiahmg0ipNOOgnPP/98Xvt///vfmDx5MsLh8HZty9y5c7Fx40Z8/PHHuO+++3DzzTfjpJNOQllZGb744gv89re/xW9+8xusX78+73PXXHMNrr76anz77bc4/PDDMWnSJNTW1gIA6uvrcfTRR2PEiBFYsGAB3n33XdTU1OCMM87I+/xHH32EN954A++//z7mzZuHb77Jj0S99NJLMX/+fMyaNQvff/89fvnLX+L444/P22eJRAL33HMP/vWvf+Hjjz/G2rVrMWPGjMz7mzZtyvysWLEC/fv3x9ixYwGw46hHjx546aWX8OOPP+Kmm27C9ddfjxdffDGvH4cccgjWr1/frvk9OzTsmtiJ2M7HVL239u/fAKycA1z2NbdZLK0BJlBRFECaxEeCIAhid0VXyPlIEARBELsRW5rSOPvv/9vp633+wsNQVRJ0bbdixQqYpomBAwfmvV5RUYFUKgUAuOSSS3DXXXdl3vv973+PG264Ael0Gpqmoby8HNOnT2+x7B49euT937t3byxevHh7Nqcga9asQbdu3fJee/TRR1FSUoJZs2ZBlmUAwL777pt5/5577sHvf/97TJkyBQBw11134cMPP8QDDzyARx99NNNuxowZmDhxIgDg1ltvxZAhQ7BixQoMGjQI55xzDs4991wkEgmEw2E0Njbirbfewmuvvbbd21JeXo6HHnoIoihi4MCB+Otf/4pEIoHrr78eACt0/Je//AWffvpppu8AEwZ/8YtfAAAee+wxvPvuu/jHP/6Ba6+9Fo888ghGjBiBP//5z5n2Tz/9NHr27ImffvoJ3bp1wz/+8Q8899xzOOaYYwAAzz77bN73tnbtWsycORNr167N7OsZM2bg3XffxcyZMzPLVlUVjz/+OPr165fp12233ZZZTlVVFQDANE384he/QElJCZ544gkAgCzLuPXWWzNt+/bti/nz5+PFF1/ME0rt9a9Zs6bdcnyS+Li3kKzP/+3GSitJq64BkvNhYle6rigKkPORIAiC2H0h5yNBEARB7FZ0Lg7g+QsPc2/YDuvdEb788ksYhoFzzjkH6XQ6771rrrkG06ZNw6ZNm3DNNdfg4osvRv/+/Vss45NPPkFxcXHmf1sMbCuSySSCwXyBdeHChRgzZkzBdTU2NmLjxo0YNWpU3uujRo3Cd999l/faAQcckPm7a9euAIDNmzdj0KBBOPHEEyHLMt58801MmTIFr7zyCqLRKMaPH7/d2zJkyJC8kPQuXbpg//33z/wvSRI6deqEzZs3533u8MMPz/zt8/lw0EEHYcmSJQCA7777Dh9++GHB/JIrV65EMpmEoig49NBDM6+Xl5fnCdE//PADdF3PE3ABFordqVOnzP/hcDgjPAJsnzXvKwBcf/31mD9/PhYsWJCXy/PRRx/F008/jbVr12b61bwiuN3eS2j79kLi496CPZCyq3g62LtbkGoAIp0c37aLzXQu8mNbQtnRXhIEQRBEx6ClgEARIPoAhcRHgiAIgtjVkUTBkwOxo+jfvz8EQcCyZcvyXt9nn30AFC72UlFRgf79+6N///546aWXMHToUBx00EHYb7/98tr17dsXpaWl7db3iooK1NXV5b3mVJymteSKl3bYuWEwI5Pf78fpp5+O559/HlOmTMHzzz+PM888Ez7f9ktXzcVSQRAKvmb3wQuxWAyTJk3Kc63adO3aFStWrPC0DEmS8PXXX0OSpLz3ckXNQn1tXrj5ueeew/3334958+ahe/fumddnzZqFGTNm4N5778Xhhx+O4uJi3H333fjiiy/yPr9tG0vT17lzZ9d+by+U83FvQUsBwVIAJvvbKy5h2o1JFQFZQlHQR85HgiAIYvdFUwApwKpcaxR2TRAEQRDEjtGpUycce+yxeOSRRxCPx1v9+Z49e+LMM8/Edddd1w694zNixAj8+OOPea8dcMAB+OSTT6Cqaov20WgU3bp1w2effZb3+meffdZCOHXjnHPOwbvvvovFixdj7ty5OOecc1q/AW3A//6XDenXNA1ff/01Bg8eDAA48MADsXjxYvTp0ycjFts/kUgE/fr1gyzLeSJfXV0dfvrpp8z/I0aMgK7r2Lx5c4tl2KHUXpg/fz6mT5+OJ554okWBoM8++wxHHHEELr74YowYMQL9+/fPFAjKZdGiRZBlGUOGDPG83tZC4uPegpqCFvSYy8rIqYjtEqbdkFQRDfrgl0SourfEuwRBEASxy6GlAF8AkPxMiCQ6hK2xNB78YDkMj8n8CYIgCGJX5m9/+xs0TcNBBx2EF154AUuWLMGyZcvw3HPPYenSpS0cb825/PLL8d///hcLFizIe33z5s2orq7O+ykkCm4vEyZMwOLFi/Pcj5deeikaGxsxZcoULFiwAMuXL8e//vWvjLPzmmuuwV133YUXXngBy5Ytwx/+8AcsXLgQl19+eavWPXbsWFRVVeGcc85B375980KXBw0atEP5H1vDo48+itdeew1Lly7FJZdcgrq6OlxwwQUAWK7Obdu24ayzzsJXX32FlStX4r333sP5558PXddRVFSEX//617jmmmswd+5cLFq0CNOmTcsL/953331xzjnnYOrUqXj11VexatUqfPnll7jzzjvx1ltveepjdXU1Tj31VEyZMgUTJkzIHAtbtmwBAAwYMAALFizAe++9h59++gk33ngjvvrqqxbL+eSTTzBmzJg2c7cWgsTHvQRDTeLDNSorEOMmPubmunJxPq7dlkD30hD8PmnvdD4aOqC2wklKEARB7JpoacAXZOKjnnZvT7QL7yyqxhsLN2BrjL4DgiAIYvenX79++PbbbzF+/Hhcd911GDZsGA466CA8/PDDmDFjBm6//Xbu5/fbbz8cd9xxuOmmm/JeHzhwILp27Zr38/XX/GKxrWHo0KE48MAD86oid+rUCXPnzkUsFsO4ceMwcuRI/P3vf8+EBf/f//0frrrqKlx99dUYOnQo3n33Xbz55psYMGBAq9YtCALOOussfPfddy1cj8uWLUNDQ8OOb6AH/vKXv+Avf/kLhg0bhk8//RRvvvkmKioqACDj8tR1HccddxyGDh2KK664AqWlpRmB8e6778aYMWMwadIkjB8/HqNHj25RQXzmzJmYOnUqrr76agwcOBCTJ0/GV199hV69ennq49KlS1FTU4Nnn30271g4+OCDAQC/+c1vcNppp+HMM8/EoYceitraWlx88cUtljNr1ixceOGFO7K7XBHM5sHieziNjY0oKSlBQ0MDotFoR3dnp5F4/Sq8s3gzjpS+Q6fprwEVLZPWZohtAWYez/4efwsweJJj09v++yPCfgl9KiJ4d9EmPHXewe6d+fhuoHwfYP9ftG4jdkXm3QUsegW49MuO7glBEASxI/zzFODAqcDC/wAHnAkc8MuO7tFeyWPzVuKlBevw5NSR6F9Z7P4BgiAIYo8nlUph1apV6Nu3b4siKET78dZbb+Gaa67BokWL8hx7ezqrV69G37598e2337YozLIn8s477+Dqq6/G999/75hbk3cOetXY9p4jaC9HSSfRIFgP8apLvgk715UccnX1bW5KoXNxAH6fiLRX5+N3s4AP/+zebndg8auAqeeHqhMEQRC7H7bz0Rcg52MH0phiIWN2Qbu2XO4Zj89HXZxC6gmCIAjCCxMnTsRFF12EDRs2dHRXiHYkHo9j5syZO1TUxwtU7XovwVSTSKATTIjuYcJqEglFxxYthN4qv+JnXUJBp4iEsFoLVfcgPrqFfO9u+IKAEgMS24Ci9qsMRRAEQbQzeTkfKZ1GR1HdwPZ9QmnbSb2ft8SxNZbGii0xHBwpb9NlEwRBEMSeyhVXXNHRXSDamdNPP32nrIecj3sJpppCWghAFQP5OR0LoSZRG1ewMu53bbstrmLAlg9w2EfnQlRi7h1J1Lai17sDVtaCdGPHdoMgCILYMexq174AoLet647wzqaGJGCaSLax+JhS2fJiKa1Nl0sQBEEQxJ5Fnz59YJrmXhFyvTMh8XFvQUtBgQxFCHgoOJOEARExRGBy2qZUHWlVR1lyDQQAleo6934ktuWtZ3dH13Xohgmkdk7SW4IgCKIdME0Wam0XnCHnY4cwZ0kNhPq1eDZxMczGtg3xskVHKmRDEARBEASx8yHxcW9BTSIt+JEWvDgfE0jBD0XwQ0k6uxnrE8wZEkYKEICQ6kGAy3VHpnZzt6CuoWZbPTbWJ8n5SBAEsTtjOx0lmQmQGuUF7AjueGsJ9jVWQhQFBGuXtumym9LsO06qlKOZIAiCIAhiZ0Pi416CoKehwI8UPDgftRTSCCCFAJSUc3EaOyl8UG2AIAgo1htgGC7F03OFT7fCNx3A5yu24JePzPXWWIlBt/Ncppvar1MEQRBE+2JYobiSnwmQ5HzsMErMRsiSCDG51bVtU0rFd+vqPS3Xdj62dTg3QRAEQRAE4Q6Jj3sJgpZCGn4okF2reOrpeMYlaSrOLsmGpApJFCCl6yEAiJpNUNyKzqgpIGCVX1d2PfFx40dP477aS2DqHnJCWYNVE4J7ER+CIAhi18Vo5nzUyfnYERQHfZg0IAABgOghncnf5q3ElS8s9LTsRkt8bOtCNgRBEARBEIQ7JD7uJQh6GqogQ4HPNZF+PB5DGgGkEeDmfKyNpVEe8UNQ4hAEIISkB/ExCYRKAUEEOMImj7/NW4F3fti0XZ91o1fsOwBAfNtG98bW4DQuhGG6hbITBEEQuy72hJMosYIzGuUF3NnohommtIYo4hAEAaLiLj7WWvkbNbdnDwBNlviYorBrgiAIgiCInQ6Jj3sDhgEYOjT4kDZ9ruFkyUSC5XsUZJicvFe1cQXlEZaYXxAkBM00FM1NfEzgyw1pNOjydoVdG4aJlxesx93vL2v1Zz2uAAAQr13v3jYjPkaQTpH4SBAEscvQVA2s/tR7e9vpKMos9NolQoBoexKKBphA0ExBFACfB/HR1hzjHtyMTVaqGBIfCYIgCIIgdj4kPu4NGCpMwBIfZddE+slkArogQ4MPJiecOJHWEQn4ACUBM9KZOR/dxEcthQbNhw1xabvCrrfGrQGhS2rJXKY/+xX++q63xPU+g21vsqneta2usYFMHCFoaRIfCYIgdhk+ugv47+WA4VFoyuR8lC3nI4Vd72xiafYd+I0UBAEQ3fJTAzBM9jAQT7unSrGXTwVnCIIgiI7kmWeeQWlpKbfNLbfcguHDh7fJ+saOHYvnn3++TZa1KzBlyhTce++9Hd0NYjsg8XFvQFdhmiY0+JAyfa6ODkVJQfAFYEoBbt6rtKYj5ANzUoY7eXI+aukEFPiRFoLbFXZtV9huDT9viePdRdWe2koa65OWqHdtm0yxgVFciEBPuw+SCIIgiJ1E3Rr2O77FW3s75yM5HzsMW0D0GWkYUhCi5n5f1a0idzEP4uPWWBoVxQHK+UgQBEHs8syYMQNz5szZ4eW8+eabqKmpwZQpUzKv9enTB4IgQBAEhEIh9OnTB2eccQbmzvVYdLWDueGGG3DHHXegocE9QoLYtSDxcW9AV2CagCj7mfjokstKSach+vwwJD9XfEypBiKSDsCEEKnwlPMxnYwhLfihSMHtCrveFs/2R3errA24V99uhj3Y0RLuF7N4ggmVCSEEYzvzVxIEQRDtgCCw38k6b+2tnI/fbGiCJpHzsSOIpZko6DPSUPylEHX3Qm72c4Cb89E0TWxuSOFCbRaKEx7SqhAEQRBEB1JUVIROnTrt8HIeeughnH/++RDFfNnntttuw6ZNm7Bs2TL885//RGlpKcaPH4877rjDcVmmaULTPBRlbScUhT2b7b///ujXrx+ee+65DusLsX2Q+Lg3YGgwTSAUDFjOR/6gSlUVSLIfkALc/JBpTUeRyNwiYnElQmbK1fmYTsaQQhC64N+uhP51OeJjXHG/+G1LKOhqVOMI7QvXtqpuQNaZiGimGl3bJ5Ks/3GEoZP4SBAEsetgF1ZL1ntrb6hIqjr++MYy/G9Nk2tu5IIktgFL/tv6zxEAgFhKQ8gvQVAT0AJl8HkQH23HYzzNdzOmNQMVeg1GNs3FwQ3vtUl/CYIgCGJHeP311zFgwAAEg0FMmDAB69aty7zXPOx62rRpmDx5Mu655x507doVnTp1wiWXXAJVdY4K3LJlC+bOnYtJkya1eK+4uBhVVVXo1asXxo4diyeffBI33ngjbrrpJixbxmorzJs3D4Ig4J133sHIkSMRCATw6aefYuXKlTjllFPQpUsXFBUV4eCDD8YHH3yQt/xNmzZh4sSJCIVC6Nu3L55//nn06dMHDzzwQKZNfX09pk+fjs6dOyMajeLoo4/Gd99912IfPPXUU+jbty+CwWDmvUmTJmHWrFme9zWxa0Di496ArsCAiWAwhLQpw+DkcQQATUlBkv0wfX4IHKEyqeooEtn7YqQCIaSQdhEflWQCacEPTdy+aqJ1CRVlET8ANlBxY1N9CucqL+A36Wdg6vz222JpBM0UDEHylMMxmUpCE3xQBD83NyZBEASxk0lZ7vVUvbf2hgbdMKFBQspwn6QryPcvAB/cAsQ2t/6zBNKajqAsAWoSerAMsuEedm2Lj7E0PyVLLK2hwtwGSRRgGu7pW2pjaVz6/DesCA5BEASx62LoQOPGnf/jNae0A4lEAnfccQf++c9/4rPPPkN9fX1eaHQhPvzwQ6xcuRIffvghnn32WTzzzDN45plnHNt/+umnCIfDGDx4sKc+XX755TBNE2+88Ube63/4wx/wl7/8BUuWLMEBBxyAWCyGE088EXPmzMG3336L448/HpMmTcLatWszn5k6dSo2btyIefPm4ZVXXsGTTz6JzZvzn49++ctfYvPmzXjnnXfw9ddf48ADD8QxxxyDbdu2ZdqsWLECr7zyCl599VUsXLgw8/ohhxyCL7/8Euk0pcnZnfB1dAeInYCuMudjKAQVMkytyaW5Ap8/DCAAQeHkfFQNhEPswVwIlyMIBdtccz7GoaDICv9uvWC3LZ5Gr/Iw6uKKpxxPGxuSKDdZ2F26fgOCnXo7tt1a34goDCT8nV0FWgBIJlMIij4YviBM1WNeMYIgCKJ9MU1AtSaQvBY201mEgA4JDaqwfeKjnV+yfh1QVNn6z+/lqLoBWRIALQWjpFOmAJwTpmlCSNTivPR/kUhex22bSOuImk2QJAFBxT2y4bv19fhxYyPW1yWxb5fiVm0HQRAEsROJ1QDPtnT2tTvn/ReIdtvuj6uqikceeQSHHnooAODZZ5/F4MGD8eWXX+KQQw4p+JmysjI88sgjkCQJgwYNwsSJEzFnzhxceOGFBduvWbMGXbp0aRFy7UR5eTkqKyuxevXqvNdvu+02HHvssXnthg0blvn/9ttvx2uvvYY333wTl156KZYuXYoPPvgAX331FQ466CAAwFNPPYUBAwZkPvPpp5/iyy+/xObNmxEIBAAA99xzD15//XW8/PLLuOiiiwCwUOt//vOf6Ny5c16funXrBkVRUF1djd69ncf3xK5Fh4uPjz76KO6++25UV1dj2LBhePjhhx1POAB44IEH8Nhjj2Ht2rWoqKjA6aefjjvvvDPPhks0wxIfw6EgVMEHQ0tD4jXX0ghEyiCYlvPRNLP5s3JIaQbCsAYHoTIEoLrmfNSUJBSUI2HI2+187FYaxPfrvSWY31SfRFfRAAwg2bCFLz7W1aFUFJD2l8L0EEadSidhin4YUmjXdz6aJvDCr4BDLgL2GdfRvSEIgmg/dAUwrXuR6n4ttz9jQAQEAUndPTdyQex0HSmPeSZbQ1M1sPFbYOAJbb/sXYS0ZsAvmkA6BYTLIRvOzx8Ai744TJmPY/RP8e22ZQD6Oi47ltZQbMYgiQKKjAYYhglRLLxcgE2uAqxIDYmPBEEQuzBFXZgQ2BHr3QF8Ph8OPvjgzP+DBg1CaWkplixZ4qiFDBkyBJKUHcV37doVP/zwg+M6kslkqzUS0zQhNLvv2gKiTSwWwy233IK33noLmzZtgqZpSCaTGefjsmXL4PP5cOCBB2Y+079/f5SVlWX+/+677xCLxVrktUwmk1i5cmXm/969e7cQHgFmqgKYg5TYffAkPp522mmtXvDjjz+Oykr+zP8LL7yAq666Co8//jgOPfRQPPDAA5gwYQKWLVtW8LPPP/88/vCHP+Dpp5/GEUccgZ9++gnTpk2DIAi47777Wt3HvQaDVbsOB0NIQXYVygxVgRwIAEYQpmkAhgZIcot2KVVHWFABQQSCJfBDharyBUFNVaEKPiQ9FL4pRF1cwZDuJYj4JTR5CbtuTKFINgENUBpruW3rGxogiQIUfxkkD67MdDoNSDJMXwCCh6qcbY5hAJ/cAwybApT24rdN1gFblgJfPUXiI0EQezaqdT0OFGf/dsNQoQnskShpSNvnfExbUQUJj+LjmvlMHO1/jHvbL54AlrwJ9B0L+COt71sHklJ1yJIIiSP2AYCmm4iIVjRFpAIwDZhqEoI/XLD9suomRMwEfKIANNVwlx1XNBQjDkkQEDHjSKo6IgHnR+DauIKDtG+g1HcCUMHfQIIgCKLjEKUdciDuTshy/nhcEAQYhrPxp6KiAnV13idEa2trsWXLFvTtmz+ZF4nkP3fMmDEDs2fPxj333IP+/fsjFArh9NNPzxSE8UIsFkPXrl0xb968Fu+VlpY6rtvGDs0uJEwSuy6ePLivv/46/H4/SkpKPP289dZbiMVirsu97777cOGFF+L888/Hfvvth8cffxzhcBhPP/10wfaff/45Ro0ahbPPPht9+vTBcccdh7POOgtffvll67Z6b0NXYAAoCoegQIbpUsVT1xT45QAEiVmgnUTClKojJKiALwj4AoAgQHMVNtMIBkNIGj6YXgeFOdQlFOwb/xoPNl6BZMI9nK6mIYWoyPqfjm3jtm1oaIBPFKAEyiB46Fs6lbLEx9D2FSfYUepXsxxj373g3jZmDczEDjc7EwRBtC/29Tjcybvz0dCgWfOxCd3K+WiarVuvYj33eK2w/ealwDvXemub2Mp+N+x+lZpPfPATPDJ3hWs7VTcQtsRHMczcEemU8/e3vi6JIjMOWRIhJPmTiwlFR7GkQhQFhMyUa+TEig2bcVn67+i5+AnXfhMEQRBEa9E0DQsWLMj8v2zZMtTX13vOz+iFESNGoLq62rMA+eCDD0IURUyePJnb7rPPPsO0adNw6qmnYujQoaiqqsoL1R44cCA0TcO3336beW3FihV5/TjwwANRXV0Nn8+H/v375/1UVLhP+i1atAg9evTw1JbYdfCsRDz00EOuTkabl19+2bWNoij4+uuvcd112Tw9oihi/PjxmD9/fsHPHHHEEXjuuecyuRB+/vlnvP322zj33HMd15NOp/MSkTY2uuf62eOwclkVhQJQBRmmi+NQMFRI/gBEJcDGXloaCBS1aJdSdQRFjQmPviAEALriIj5qaRQVhxBL+mFoKW74dyG2JVT0alyAAFLAttUA+nDbV9fHEUQaSUGAnuTnumxobIAkitCD5UDcfYCnKGkIkh+iTwZS7gns2xzb6aF7cJDaBRC2x81DEASxO2FPHoXKvTsfdQ2adUdKGNa8rK4CPr/39aat5wvVY55JG05ocQZ7O7wKm7sY//u5FpdjALeNqhuIWEXs5EgZdACpZALBaOH28bSGnqE0RBXwuYS6x9MaIqIKwR9GEE1oTKroEnUORWvazCqO+pLu+Zx1w8SjH67AGQf1RFUJpQAiCIIg8nnkkUfw2muvYc6cOZnXZFnGZZddhoceegg+nw+XXnopDjvsMG76udYyYsQIVFRU4LPPPsNJJ52U915TUxOqq6uhqipWrVqF5557Dk899RTuvPNO9O/fn7vcAQMG4NVXX8WkSZMgCAJuvPHGPAfmoEGDMH78eFx00UV47LHHIMsyrr76aoRCoUxI9/jx43H44Ydj8uTJ+Otf/4p9990XGzduxFtvvYVTTz21Rah3cz755BMcd9xx27lniI7Ck/Pxww8/RHl5ueeFvvPOO+jevTu3zdatW6HrOrp0yc+X0KVLF1RXVxf8zNlnn43bbrsNo0ePhizL6NevH4488khcf/31juu5884781yZPXv29Lwdewy6AtWUEA35rYIzfLFKNFRIPj9EOQATpqNglVINBAQdkPyALwBBAPQ0f6Bn6CqikTAUQXYVKpuj6QaakipCEiAKAswYP8wqremIxRrhk0Sogh9amu/GrY/F4RNFCP4IRN29b0o6DcHnh+ALQvAiAHrFNIF3rwc2fcdvl7CcHgm+oxNA1v1jV4AlCILYU8k4H8u9F5wxVKi2+Khb02Ktva6nm5i73EPOYOSGSXkRSO18krup+OimrQIs52NQYI5Ef6SEvZZ2vhc3pTREhQQEQYCQ5k8sx9MaikQVYqQCflNBY4L/3fpT7PnC0N3Tuzz58c94/dsNeH3hBte2BEEQxN7H1q1b8/IYAkA4HMbvf/97nH322Rg1ahSKiorwwgseotlagSRJOP/88/Hvf/+7xXs33XQTunbtiv79++Pcc89FQ0MD5syZg9///veuy73vvvtQVlaGI444ApMmTcKECRPy8jsCwD//+U906dIFY8eOxamnnooLL7wQxcXFmRyUgiDg7bffxtixY3H++edj3333xZQpUzJFcnikUim8/vrrjoV2iF0XT87HceNalyNu9OjR29UZN+bNm4c///nP+Nvf/oZDDz0UK1aswOWXX47bb78dN954Y8HPXHfddbjqqqsy/zc2Nu51AqSpq1DgQzTogwp+oRfTNCGaKiQ5AMFnOR8LDMBM00RS1eEXrHyQviAECNBcBlGmpqCkKAwVsqeK0rnUJZi7MGzGkRIAI1nPbb8triBkJOGTBTRKpQim+QPCRDIJQQ5ACoQhegijVtQ0REmGJMsQ9DZ0PjZVA8vfAwwV6DrMuZ0tPnoZjNoDcBIfCYLY07EnW0JlWde3G7oKzRQRCfqQ0K15WU0BAh7XaRjsOltc5S3UO7dNqgFwyGvYor2XyaZdiJSqe26r6iZCEmvvD5cgCSDNmdBsSmsoMuMQBQGCyz6PpzV0FRUIkQqI4k9IxBvAy+XoU2IQBAFy2v3+mlJ1nKy+jVGrNWAc5R8nCIIg8rnllltwyy23ZP6fNm0apk2bBsC5tkbzzzzzzDMt2jzwwAOu677yyisxZMgQrFmzJlMVunk1ayeOPPJImAVS0PTp0wdz587Ne+2SSy7J+79r1654++23M/+vX78emzdvznNVFhcX46GHHsJDDz1UcP3N94HNzJkzccghh+Cwww7ztB3ErkObJYDTNA0bN25Er14uhS8sKioqIEkSamry3Ws1NTWoqqoq+Jkbb7wR5557LqZPnw4AGDp0KOLxOC666CL88Y9/LFhGPhAIZMq3762oSgoaJERDMhSBLz6qugmfqUGSbecjCrZXdROGYeY4H4MQBHAFRcMwIRgqSooiULZLfGQOzIAegyIIQKqe274hqSKMJERBQFwuR4DjgDFNE7qSghDxwxcIQzLcHS+GmoYY8kP0BSEYbSg+xq3BssmvHJ4RFL2Ij/bATE16C/EjCILYFVFTgOwS2qqlAVFmBWfq1nhbrqFBNSWUhmXEkrbzsRVpKvQ0u2aHK7y5LZUcJ36qHoh2dWlvLdNrDstdhMaUCr+Zhmy633NU3YqmABAMR9EAIJ3kiI8pFSGkYMgRiG7io2LlqA53gygISMSd07CouoGAkYQoChBV9/zlac3AOcp/UVRNOZUJgiCIXYuqqir84x//wNq1azPi485g7ty5iMViGDp0KDZt2oRrr70Wffr0wdixY3d42bIs4+GHH26DXhI7G09h115YvHhxi8pIPPx+P0aOHJmX+8AwDMyZMweHH354wc8kEokWAqNdbr6QKk8wVEWBBh/Cfgma6GdCmUNlrLSmwwcNPjkASQ5mcz42I6VZ7gSo2bBrACYnlDquaJBMDSVFYSiC37XqdnPq4gp8kgCfGmdhVi7iY2NSQ1hUIAhASi6FyQmFS2sGfKYGQfLDF4hA8hBup2sqRMkPSQ4Apg4Y3h0eXGxHo5v7Um3FYFRJAL4QANN7DjSCIIhdCUMHHh8FfPEkv52aYAKlLwhoXnM+qlBMCeURP+LbE3ZtC5WhMo/X5ByB0osjXU0AELyHkbcjpmlCN7w9czWlNNye+jNm1N7s2jZXfJTDxYAgQFWcv79YSoMfKoxAKSSd/z0nFR1BpIFIJ4iCgBRHfEypOkJIQRIE+PSk4/OSTZOV89kwQfdXgiAIYpdj8uTJGDNmzE5dp6qquP766zFkyBCceuqp6Ny5M+bNm9eiYvf2MH36dAwcOLANeknsbNpMfNwerrrqKvz973/Hs88+iyVLluB3v/sd4vE4zj//fADA1KlT8wrSTJo0CY899hhmzZqFVatWYfbs2bjxxhsxadKkjAhJtERV09AEH4KyBFP0W6HUhR0dac2AbOaKjyZLut+MlGKJj4LOwq4lPwRB4LoZ42kmbBZHQlDgh9nKCtENSRUlIRlCuhGiANe8Wg1JFSV+AQIAVY5y28fSGmSoEH1+yMEIZNOD81FTIMlMfOTt0zx+fANY/Rm/je1kTPML5DAHUMhbfjE1AUQ6WX/T4IggiN0QO+R4jcs1VE0BcpgVQ9M8uhcNJj6Whf1o0nIKznjFXk+o1JtAaF/fpYD7NVlXAUMDIp13Cefjx688igvuneWpbVNKQ5WxGZ009/B3RTMQEHVAkCBIMkxBhOqS81GGBjNYykRCDinVEh/DnSAKgJJ0djQmFR0hMwldjsAwTVcBO5Zk371hmN5zcn75d+Dju721JQiCIIjdjAkTJmDRokVIJBKoqanBa6+9tlOdl8SuiecYkeZJRJuT5ITGOHHmmWdiy5YtuOmmm1BdXY3hw4fj3XffzSQZXbt2bZ7T8YYbboAgCLjhhhuwYcMGdO7cGZMmTcIdd9zR6nXvTahKGiqY+AjJD1MHc3QUCF1LqUwglP0B+GUJmuAr6P5IqcwJIENjzkdBgC4GYHIe0mNpFbKpIxK2B4WtS+afVHWE/T4gHYPuj7oO2BqSKkoDJqD5YMoRCKrz4CeR1iGbGkTZj0AwBJ+hQNM0+HyFTxHTNGFqaUhyAL6ALdIqTAzkMec29vuyr53b2NuVcqnMrqVYiF/DOubMKJB2ILvMhNV2veWY7MRfNkEQxK5G0hIfDZciIFqK3WOkgHf3oq5CMUWUhf1IGxLLTtGae1SrnY8x1r+g+70sI2ZGPIZ0tyfpGAYseQxnCwNgmlMyVSudaIin0AmAAbjepxTdQEAw2DMFAE3wQ3WJpvCZKrRQKfzGJqi6AVkqvPykqsNvKkCwFKIoIJVw3o8p1UAQKaSDFZCa4myf+yOO7dVUE0RRYNuYrAOi3RzbZvjicfZ77DXubQmCIAiCIPYAPIuPP/74I6ZMmeIYWr1p0yb89NNPre7ApZdeiksvvbTge/Pmzcv73+fz4eabb8bNN7uH7xBZVDUNHRJCssSKyOhwdIOkNQMyNPj8QfglwSpQ0/LhvynNHCEBu+AMAF3yA6rzYC2WVNAJBoKBIARfAEIrnY9JRUfIJwLxJPRgNwiN7uJj1A/AlAF/GELKuX1c0SBBgyQHEQ6FYACIJZIojRYX7ouqw2fq8PnC8GVyY7o4bLyGZWfER5dQPDXBnDAN69jfgSL+MiNWcv2OHrwSxN6EpgDfvwAMm5K5VhLbie0qc0uzoiZZmgmf3/skl6EhbUooj8jQ4IMJE0Krwq6ttsESb270dIxds+WQu1hpv78rOB9jNRAEIGImWHVqmR91kmzcCgAwDRN6qhFSuNSxLQu71gCJPZoaoh8qp+BMIq1Bgg49XIaQmUIiraMkzBMf04Achin5EeeIj0lVR8hMQQ93htG42vWeaaYaIYkCm4R0KYTXAkMHRIrcIQiC8IrhkgqDIIj2oS3OPc/i4/77749DDz0Uv/vd7wq+v3DhQvz973/f4Q4RbY+mKFDhQ8AnwvQFYKZMRzdIWjUQhA7Z74ffJ1oFalqKavUJFQFZyjofARgi32WSSCRRIQiQfH4IcghmUgf07EDDjYSio8hv/RMsgbjNRXxMKOgiG4AWgCCHIXJcmfG0hoCgQfT5EQqFEQdffLRDyCV/ALI/x/nI3YBa/vs2dm4vNzeMmgTCdii1i/ioxIFod+vvjg/bI4i9hlUfA589AFTsC/Q6tKN7s3uTSUnh4grXFSY8SgHPRWMMXYViiCiPBGAIEgxIEL2GbAPZEO1A1JvbUkuxnJRy2IPzMUd8rFvtvU/tQc59LJ7W3MXHpjoIAhPm4vVbEeWJj5oJ2S5iB0AX/dA5E5qqkoYIwBcpRxBpNKVVlIQLC/xp1YBssFQloi+AxiZO2LWV81GI9GD3dhfxUVRikAQBiuExJ6euIqHoCMoixFQDEC53/wxBEMRejt/vhyiK2LhxIzp37gy/3+/qvicIYscxTROKomDLli0QRRF+v9/9Qw54Fh9HjRqFZcuWOb5fXFzcJtWLiLZHU9MwRRmiKLDBGODoBkmrGopNFbIcgN8nMudjgYFUXUJBeUS2xENLfJQCLNeWA/FUkuVqlPwQ5SDMBNgATOKIZjkkFA3FPuYeFEOlkI3V0HQDPocwq8aUhn4+k60vEOYmpI8rOsKSAcEXQCQcQhxAPOEs0sXTLNeU7A9A9gdYonm3AWduDkdddXZBqUk2GEnU8l0RagLo1I/97TbgscVJXzBbqIYgiPbHrl7fuB4AiY87hO0qc3OX6XYhNO/OR01lhdnKI+y6bIj+Vla7ttoGirzlmdQVFhouhzxMNOWEXW9e7L1P7YElPhabMcTTOjq53L5T8Ub4JAGqZiLZVIso+ju2VXQDfjkrPpqSH7pD2LVhmNC1FARRQCRaDj8UbKhLokdZuGD7pKJBNhTAF4TkD0HlREKkVB0RpCBEOkN3KfLDxMkmiKKIbWYJunu4vyqNm1EbSyMS8KE8VU/iI0EQhAdEUUTfvn2xadMmbNy4saO7QxB7HeFwGL169WpRALo1eBYfH3zwQe77/fr1w4cffrjdHSHaD11NwxDZgEqUbaGs8OBIURUIggDR54dfkqCYvoIDqbq4grKwNTizRDTTF4CpO4uPqWTKEkB9kGy3oJbmO/ZySKo6imRm95XCpfCbCuKcMKtYWkPEZwCSDMkfgcTpWyKtISwxIdUfCAKCgAQnj2lc0eCDCln2w++3q4K7DDhzBzDJeqCoc+F2aoLlDUvUskGp0/5RrZyP9md4qAmrAEOw1bk2CYLYAZqq2W+7WAqx/djudS3Jn5ix70u+oGcBUVUV6JDQKcIm6HRRbp34aF//beejaQI8R4aWYiKbHHKvyK0kAEECgqUdXzBMiUMAUIwYYmmX3JsA0okmSKKElCAgzSnyArCwa79fA8Rs2LWT81HRWXE8QQDEYDECoonqBud8xoqqQhBMwOeHIAehNaWgGyYkseV3lFR1RIQ0pFAREmYAprXNBbdPMxA2kjDlMOJa0NP3U79tC9s+E94L1BAEQRDw+/3o1asXNE2DrntMZ0UQxA4jSRJ8Pt8Ou409i4/E7ouuqpnQZsEXBGAWzOMIAIqSZuMlyQq7RuEBWF1CRWlYzro3AJhSACInj2MylYQoCIDEKkQbQKsGdwlFRxcfy/Xli5S5hlklFR3BEHNRyIEgRM664oqOiMQS3QtSAKLAdz4mFB0ydEhyAHIgABMewq5zxUelCYCT+GiFU9euYN+To/hoiZQA13EKgA2MJb9V6Kd1uTYJgtgBbHEhvrVj+7EnoKtZp6CWci4ColvXOzvs2q0gFwBdVaAJEkpCVg7j7XE+ChITPAE2yVOgqFsGLc2ux76QeyoMO4y8NTks2wslDgiAbKrYlkwAiHKbq8kmmP4IUqoKX6KJ31Y3IKOZ89Hh3pZQdMimyqIpAlGIooAGTii1pqbYA7P1/OE3FRZNEWz5/JBSdJQIKvyBEBrhh5JOIuCw3JSqww8FghxCIhmAkY7DzQ9Q19CIMABT9HnPwawm3QvaEQRB7AUIggBZliHLlEebIHY3tt8zCWDo0KFYt25dW/WFaCd0LQ2I7GHe5/PBgOjo0lPTKTbDb4mPaRSudl2XUFAe9mfD2wBACkCw814VIJW2hE1RhpSpEO19IJVUdBT5mPPRHylF0ExxnRcJRUNY1ABfAKFQGKKhOBYqSCgaQqKRCdWTBME17Doo6hAkOet85Gw7ABY6ZzlQue6IPFGR1y7JBt+S311QtJ1A0i4weCWIvQn7HHbLU0i4o6WZ+w/gXxt1lV1r7dQWHkREQ1OhW7mR/T4RmiC37lppC562+Oh2b9NVJo76w+7Ox0wYebDjJ4/UJBICmxBLuoiJAKCl4oAchiIGobo6H00rj7T1vfkCMBycj0mV5V0WBAEIFEMSBDTxxEdFYQ+8kh+SHIIfmuPzQ1LV4Rd0yP4gFEFGihuibbD8z3IASQSgpNz3SVNTI1RBRkoMe8vBvG0V8PhooKaDQ+4JgiAIgiB2gB0SH1evXg1VdRFciA7H0LL5BWV7UOUwMLLDriH5IEsC0qYPW+oboRv5ol19QkVpgbBrXnXQVMb5KEP2h6xQ5VaIjyrLywgAcqQMASiIpZzFx7iiIyDogCQjEonANA2YDgJhLK0hJGkZgU4UBSRcnI9BS6yMhgPQIEJxyE2VQYlnK07znIpqMis+8gaadsECX8B98GpoOYNXEh8JYqdhp0RQ+MIL4QFdYdWkAX6qiUzYdSD7vwuGzgqz+X0igrIETfC10vmoWu5Efl7lDFrKyvnooeCMnt51nOtqAg0i+w7ScXehzUg3Af4IVCkELcU/BxTNyCtiB8kP02F7k4oOGSzsGoEiSCLQGC/sIjQME4aatp5t2OSn33R+fkipBvyChmAwBAV+KEl+ZWzZVCH5A0gJQWictjZNTY1Iw48EAp5yMH/xxSfY3JQGNi9xbUsQBEEQBLGrskPiI7F7YGjpzMO8LInQBedcVnpO2HXAJyJtSHj1y5/xxsINee1YwZl856PgC0DkiI/pVCpTcCabJ9G7EJZQsuKjEIwiJCiIpZzF76SiIyiyEK6iSBgwTcTihQc/ibSOoF1lU/JDFAQkeTkf0xoCIhM2S0IyVMiIJ1wGEUocCBSzMDvegKN5FetCmGZWfJQC7vkm9Zyw61a4TQmC2EGUODtPvYZXEs7oKhAqZX/zHGO51zv7fxeY81GELIkIyRI0bK/z0RYf3d3oX62LYelW1T1nb24YuZdiNvFa4PNHWLh5W6Mm0SCWAgCUpLub11QSEAMR6FIIWpp/Dii6YVW7ZhOagi8A02F7U5boJ8JyPooimmKFl6/ohuWSBCD54ZOD8AsKmjjORxkagsEgFMhQ0s7PAklFhx8qZH8QKQShpd0nGeKxRqSEABJmwFOOyAWLliGt6kB8i2tbgiAIgiCIXZUdEh/HjBmDUIhy0OzqmJoCwXY+SnY4mUPYtZKGAJYXyS9JUCHDBw2PzF2BpJJN7FsXV7I5H23xUQ5wB3lpJZVxPvr8QRhe8iTmkFR0VhQGAIJlkAQg4eAyME0TCUVDQNAs5yMLE2t0GJzEFS0jVEKUIIgSkpxQq4TtqhRllIRlKJC5YiUAJj7IYRZm5xZ2HYwCgujskDSs/SDJLK+Y17BrX4CcjwSxM1GTzPHsJbyS4KMr2bBrntvbdnpLHoVAAIauQhOY4z/kl6C22vmYbiY+unxWS2NlnYaPVsU8OB/V7LJNHdBdCr0s/X/A1zOBrT95779X1ATqBeZ8dAujTms6RC0FyR+G7gtBdxEfWc5HLUd8DMJ0uF8lVR0BUbecj1GIAqCkC59jeS5JiRWciUgGx/moww8NPjkAXQognXLud0pj4d+yP4CUwHI+upGMN0ETQ4gbfk/XhbDGilVpsVrXtp4xTSZQ165su2USBEEQxB6OaZosdRyxXeyQ+Pj222+ja9eubdUXop0wdRWCjz3M+30iVMHv6H7TVdv5KFttffCZ7AF97Tb2kKzpBppSmlXtOp0ZKEhyiBt2nU5bId2ijJDfBxW+VjofrbyMAOCPQBCAZKLwoC2tGTBNwG+FcEWLmPi4clPhirPxtAa/oLOwOQDw+bl5nuKKZoV0+1Hk90EXfIjFvRQNCFgFEzhttTRzR/JC7OxBccYN4yW/mH/3ER9NE6he1D7OHWLvI9UA/DyvY9atJoBIJYVdtwW6wq6fkt8l56PCiqzZ4bsernmmrsIUWBW/kCxZxdZakVZGV1GvCLj3wzXWOvmCp6GloUKG6iW3pK4gbUqosTfZTUyNb2a/mza697u1qAnEhRAUwQ8tyQ+7bkxqGWHO8IVhehAffaaeP6HpIOImFB1FPp1N0skhiIIA3SH1SUrTIUPNTH7CF0RE4uR8VJigCMkPUwpASzvv75SiIyTqEOUANMG5OreNaZpYt7kWYiCMuOkt7DpiWM9ebSk+NlUzgXrB0223TIIgCILYw/nDKz9g2syvOrobuy2tEh8VRcGLL76IK6+8EmeddRbOOussXHnllXjppZegKK1wCBA7FVNXIFqiml8SLdHPwfmoplnBGdESHyEz96CAzIN6fZINyJqHXfv8AYiGAk0vLBgp6SQE0QeIIjoV+ZEy5Fblr0qqBsuzKPoyg41ksrCIl7Bcmn7LzSj7Q5BEARu21jm2t8VEABAkP1KcAQcTK5lDQxQFCHIA9Q6uygy2+9Atx1cmfC/kTXz0EkqdWwxhdxAfq78HXjoPWPe/ju4J0ZZsXgI8OwlIFJ4EaDe+fgZ46+qdv16AiY5FlRR23RbkXsdccz62Luza1LO5kQMyu/e1KkWFrmDlNgVvL7aOMZfrrJpOQhVkKwezi8ipK/h2QxwzXl3qadlI2BXW21CsslESSJgBpIQQdJccjo0pFTJUyIEQBDkE0yW8XNFYeLRdmM3HcfUnFR0RychMwAkCHMXHpKLDZ2qZYnqQ/IiImrPzUdEgm9kiP5qDoxKwc1HrECQ/RDkIzSX3c21cQdBMoyRagpjhh+nifGxIqAiabJl6sp7btlXEqtnvjs4hShAEQRC7EV+t3oZ12yiaaXvxLD6uWLECgwcPxnnnnYdvv/0WhmHAMAx8++23mDp1KoYMGYIVK1a0Z1+J7cTQFPhkNgiTbfHR0floF5zxIyiztkFRQ8TvQ9wSH+vibCDXPOza5w/Bb6pIqHrBZatqOuPA7FUeRkIXoboVabG3wTCRVu0cjqwojCQKaHCobplQWF8zIVw+P0J+HzZurXdoz/I22WF6ghyAkuKJjzpkZHNTyXIADW7io6aw5cshvhCRW5naadBsD1YtFwd3MGrogGnkVLtuh8FGwwYgtrntlle3mv3e9nPbLZPoeDZ8AzRu3PlVW+1js25V2y1z5Vzg/13p3s4Ou1YTzNHrlda47vYW7PzFskuV4Fynt/0512UrECQfADDnoyl5y6+Y07cmTYQpiDBE53usjZpOQYWPG4mQQVfRpInMjQm4t7crqye2eux8K1ATSCIAyEHHtCc28bQG2WTOR0EOOlauBpgjUNNN+MysCOwPBGBoSsHwppSqI+Kz7sGiyMQ/Q4WitZz8tHM42s828AUREjXHnI9p+znIKiCkcQrEpVQDIZG5JP3BEDROfkgAqI0pCCCNkmgUSQRYNXAO7y7ehBBSSHsM6fZM3Do2aFKk9Wz6nsLVCYIg9hB+qmlqUduCaD88i4+/+93vMHToUNTU1GDevHl44YUX8MILL2DevHmoqanBkCFDcMkll7RnX4ntxNDUrPjoE5ijw2EwlhEfRQkVRQGoggxBVyFLAv7yzlLc9MYirK9PQhIFFAV8eeKj7A9ChpaXGzKzXMOEpigQrLbdS1nYVkOTtwfftDWgCIpaZlDpEwVHt2HG+Qg9M9iQJQFb6wuHicVynIwAIPkCUJWkY06HhKLlVeWU5ACSnOrYAHKcjyEX56NddZsTjpfnfHQRFFu4JD0OqBvWA+u+9Nb2X6cC/znLW1sv2GJRoh2cO0TriNcyAbstaLTCQGM1bbM8r9jnW7Kw83m7+OQ+YNXHQJoTeqopLP9guIL99ioofvl34G+HtU6s3BvIiIqciZlMO5m55CF4dD5qMMWs+Jg2W+t8VKGa7POG6D7JoylJKLCcjx7CrjX4oAh2GLnLBJId4u+h+EmrUZNImn5IcgibtvDPp4TC8jKKvgAkmV+pW9XZsS7l5HwMhMKQDAUptbCgGLadjwAgswrWhZ4/UqpVcEaUAFECfAGEOc5HVckWx/P5Q1A5gmJK1REUDEAKIBAIQVfcxMc0IqKCYLgIiuCH6tL+iY9+RtBMoV4ocXVJAmD3zsWvu7ez761teU3cW3j5fOCtqzq6FwRBEEQb8Jd3luLBD5a3y7Jn/1iDW95cTDkic/AsPn722Wf405/+hGg02uK9aDSK22+/HZ988kmbdo5oIzQFsj8bdp0Gp9q1moYhyoAgsOI08MFnKqhPqEipOj5dvhW3//dHlIX9TKTMCbuWAyH4TSXjkMwllmZinR3+3akoAE2Q0RDzNjhKWm7KTGi0LwBJFNDo8HlbfPSZlttQ9MEnSYglYkgVcGZmxETLKSPJAUiG6pwTyhrM2IMknxxAmhOmDSC/YqnTYNg0AUPNhIY5ihV5gqKL87FFW4/Ox9k3Aa//zpsAYupAqt7bcr1AA6NdA9MEnj4O+OzBtlmefYzs7PDnVAP73ZZhi9a5jyaOkGqHmYbL2W+v556dn3JPr27780fAt//23t7OMcy7hgLZa60geM5zaxoaTJHdn0J+CWlTamW16zSLKgCgi+7r1NQ0FMEPxXTPfWyobNmq7Xx065ctOir8nIzbhZZCypThD4bgh4o1tc4TiAlFQ9hKfSL6g46VqwGW7xEAyzFtPVMErHU0JFveB5OKjrCYFR9FXwAyVDSlW7ZNqyxE2578hC+AoKAiVqAtYBXes1yScoDvZkxYhW8gyQiGwo4FcmwakiqikgpfIAIVsqtYObhrFL2jQJNUCtOLS/GbZ4G5t7uH3NsCNd1jW4f9TNawvmP7QRDtwaf3s8lPgtiLqE+wZxMvAqGak1pOdUgzl8t9s3/Cxz9tQV2CoplsPIuPpaWlWL16teP7q1evRmlpaRt0iWhrTCPrfGSDKufBjqkrMK18SwAwsHsnHFAVwgWj++a1i4bs8K+s89EfDEOGmhH+cmlKqfBBg2SFXUuiAMEXRJNbqLJFvvjIBp+SKKApFodhtLxYJNIaArIEwbAcMIIAnz8I2VCxqSFfADAMEwlFt8K9LCej5eIsNOgBchLSW/tK8gehpL3mXeQMnPPCqVsTds1zPtptfa0rOLNlGfvtJhTlDojcLtwN64E1n7uvmwZGuwa2+LXq47ZZnu0S3NmO1oz42IbHk30e8ZZpn5eZCs2tzLe6p4uP7/4B+PQ+7+1zr6HcCZfstZxN4njY77oK0RKUg7KElOlrVei7oTF3IgBPRWQMJQkVMmK65OrMTKWS0AQfFMgwAQ/iYxMQiLZLSK2pK1BMCSXRYoQEBQvX1Tu2jaftVCk+qyCd833KDpeWzOykXigUhs9UUZ9suX+Sqo6QlM3T7AswoXJLU8t9k1R1RHwGuweyxggIzgVndMXKfS3JCITCXIGQpYNhE5eBUNj1u2lMqYhIOuRAEApkxzyVNnFFQ0RQEJfLIbjkzAQAxKxrhp26xAnbDU5h162jI/IGE8TO4tvngC8e7+heEMROxZYRFA9iYn2OiFhI72hOr/IQwmYC2+JUG8XGs/g4ffp0TJ06Fffffz++//571NTUoKamBt9//z3uv/9+TJs2DRdddFF79pXYTgRdhewPAgAifp81qHJyPipZRw+AI/frgZ4lPvzqsN6YO+NIPDl1JABAlgTrA9n2PtmPgFBYfIyndcimBtESQQFAkv1IuuSMsrFDqfyC7Qr0wefzQTIUbC4w2IgrOiJ+KW8QKvmDKPIZmardmWWrOmDCSjBv5XD0BxwdFwALtZJyHBo+fwAqJy8UgPxcjk4DFMNan+iW89F2M8oeBuLNKmN7Dbs2rYuwm1CU+z4vBBUA5t0JvHmZexivHV5mi0ZEx9DWA61MOGhj2y7XDdtx2ZbuXPtYT3L2kX2uBYrZb43vcspg76d4O+Ts25Ww949XAURT2PXOyzXPvo/5nCsm52Fkr/8hWULKcL5PFiKdTkMVmLilCZxrvL06jbkZE7qPueU4EzfJVArMm+9jD8lu4qMSA4qr2kdY0hXmwPQFMaBcxrJq52t+QtEQEDVACkAOBCFyRGDbQSCaamZSz+9nImFDAcdA0qoybUcriL4AyvwGamPuQiV8QQSgookTdi1YYdfBYJgrEKZUnRXlk/wIBkMQXITuppSGoKjDJwegSwEYLs8NibSO/8/en8dbktR1wvA3cs88y7239qruBhoBB2QdUKaHx93R59HHcXTmFUdnRF8dl5dRRgZBRx1HH8eN1X1H1HFcHmVGVBCRVRCQpRuEbnrfq2u/954l14iM94+IyJN5TkRk3q6iaaF+n099qureOHFyjcz4xnfx6wxpcBiE5tZnJ+cct997P1jN7eMSIJ6x8Y5YILlSthqfCdV+x/nHEN73SNRVOeGnR10dB67WZ2gpxuOy6L8HFEtyymdNvoStxnQXv5L+Z/Db3nx5G/lpVIPBxx//8R/Hy172Mrz85S/HM5/5TJw6dQqnTp3CM5/5TLz85S/Hy172Mvy3//bfPomberUeTnEp4w1C8dKdBC5yZpaTrTMf1ydu24mUODkKfKy6L/SEam/GrGJwQeFK2TUAuEGMzBLq0i4llRYhL2Ky4flCOnX/7iYb4MKiwOFx0GFmEi/CiTFweq8LACzl9rpt5qMXInQYZpkpDbOCg5Xkyw+EmT7TsDCbGsR8XAcKr7Ts2u67pa2+SUz7Zbyv7e694u++cJoqBYKxPVTik1Uf+h3g4//rkf/eR2Mp8Lfuf8Dijrf2n69iIa7D8pPgRWerfAZEW0DfAsHQquuVpNrGfFT3byTtSoYGmCjZbN/99OlSQ+XwzQJOjx/jBvNxwHFndBU4E7jI6n5GYrvyTDAZCVHMR/u1xqsCjh+hgg/OayvLsipFOA0IQe32jOGsEr+fnPik3GeclmJbvAgnRrCCj8uCIZJWKX4QgViOp/J1bsuuiRcidvQKhKwdQAcAboCxx7Sy67yqG/m3ahsRigsLg/c1LeFI2fWR7Sl4lTdBe+uVlgyh9H9O4qQXfJxJ8BFuCMeLUNv8nwEs8xI+y1EEh8T7nIX9eHo/x/kL5wWjs2/RsFoKL1rgKvvxIFW0FkT7Fls/EyqfAb/0ecADH/pUb8nVutxqv8v0jEuf6trPKvt862pdrQMUk+BjZgjMbddeVuFz6YfxC+nLkO31q5PGi3sAAM7F2y5rGz+dajD4CAAve9nLcPr0adx5551497vfjXe/+9248847cfr0abz0pS/9ZG3j1bqMKmgNDwyBZD4mgYelhflYrzEf15ly00j8bhx4YgJe0xb4GCIySJnySrAUSKtvL4hQZMPApaxicB3SScIkXoijibMBJgLAmf0cJ6YSlGtNTo5FwAfu6U7o1UpHW+4FL8DYq42yrLIspSG9mKwGYQyfm2XaAFYTYlvidFtObZtgswogK/P8YbLrAcnYqmixOu+9suvWORwacNCXwlougdFR60Srqdv/Zlgq9pt/CLjjb/rb/d3PA2/7if52j9aqmbg3r0QN9Urcux9400uBj/yBvV25BMbHH9nJLqPCkzQ5POx6GlKKwej4doBH3WuhAh+HMh/l8flkBIY8Woq1xtahcnhWiWeSF/V7Pjot5uMQELGmjex6K/Yxp86BmE1FmYERHye3YpFK3fOdnJUYJQkq4gnikAW0KotCJGgDYE4PmKoAkU/Gfca5CL8hHogf4WgM3H1hiY89qGeoC+ajAAi9UITHmCaMaoGx8xx2Q0QOw0zDUMyU3FkuRsITygZdiExWMURu3XkXiByG8/MCVCOzqqtCMh99jEdjBCixZ1RB1CKszgsRJyO4dQVuGX9nWSUBWR9RHINavKJZzcGrFMQhqKJDqAHrAs+tZ2aIkQl2bN89VWXASIKPV2pc/EyoNuD4SC+iPRrr/CeESub0hz/VW3K1Lrfa1/Oj3F7ga3/pPfipN95yxfv93ffeo7XuuFqf3kVl4J0uE2K9dpclPssXc9j6/Cd624e5aEs+3W2UDlAHAh9VXX/99bjhhhtwww034Prrr+//wNX6lNWyoPA4RRiIF/QkdJEy12iKzlmxAhMB6Tu4aht44pJ51mO2VxJh5aPkBghRadmCBa3lav+q7yCMUFqM3NuVlQzxmowaXoRTY4J7Lmy+OJ+d5Ti+FYn23mpy4oLipvv2OuzMC4sCrkMk8zFs9mXkMszzzQlHXXMwmoOArPwuQ2F2bwcfS3E8hzIfvR7mY8PiGBC+0PTZLwcEsHrBJk7/C3a1FAnexO2f7KrV1D45abkExsf6v7tmwF+9DPjrH+n53hy47a+Ad768v79/7PW+XwZe95X97U7fBPzVf7G3UeAjzeysvb37xN99bJtyPhwUqVkXoHq4pcavaPvKTbLV9o+P2tmeG7LrASAYLcXY6nhXBjw6+3Hx59FW7TCUweBjK7TLNo61JNRWm4tWkbpqmI/bsY9Z5Rwo7brMc3hBiFEok7J7mI+E5hiPx6jgSym1+dqgVYkoisW/Sc9ik/rd6MiVBx9rBs5rAa56MQ6FAmT73j+4Uds8LRkCGeS2NRkj4KWRbVhoPB/hBkJ2bQicUaxKAAJ8dKkWqBS+jHWnbUgoOMeGbQurOUDLJnAmSkZ2CxYq99HxcGg6Buccu3Pzc2ueUyHT9kIEUQxuOZdZxRCihAOARYeEv7Wl/c2nZxjxTOxD37OzTMUCH3CV+XiQymer8fxTocx4tJWaUD/KwaqrNaDKJfazSvje/SNg9d5o8Rt+OLUoKF73nnvwB39/3xXt92o9+ovKRdFsgIfjXlrhpDsHIQRsbp/LVqzGqBJjo9NHuvkMqkHg44tf/GIsl8NfTn7wB38Qly5dfRA9GiqVwShBKMHHwBUJi5WFUdcGH91gY1L0J9/9z/H8z72uC2oB4mXaMFHIK4bI5Z2+wyhG2ZcQLSurGGLfXQPdAjxm6uLmhzZZF2dnOY5Pom57L8QXf9ZU/n61/w/uZji5HYN0pHohErfWekKVrIbHWeMJBQBhKAJqlBeEthrmo8WDrMN87PF8bJnnD2M+DpBoq1Kg0xDpXpkC/ggIEnvbmq367fPeq1I5eU7tfkLqxbdPxr2QicSSPWSsNnjWFzbxzpcD977X3uZTUR96nTgufcDd+34ZuP3N9rTmfE8Ay4AAmU3VTEAs4GNdi/M5GQg+/vmLgDf/YH+7vlLXe7xz5aRE6jofHbPvCysFgO948t4b8P3qOI+PX5m04td/J/DH33z5/VzpejgMIlbIBZweQPFhBM6QugKRtiA7owAZc4QH8sCqyhx+EGIc+ijQHzhDWInpKEGJABzcuo20KhCGEQLPAe0Ls2mHHF1p6RwrAQ5QeCBeiIDbj09aMsEKdAPsTMbwQPGgRqkAiHcExyFw6vYCY4DQEPwmwEcqrgXZNnGZ9pmtlBdtZYPPSxCCjQC6vBLvTA4AuAFcae+yNKgg8pLBbwBW8X6xbwEfF0Ulg/NChJE9oGZZUPi8AnEInHjaGzZ09/kZYp5hzz3cO8bO5/v41Q/NxeN1yKLhR/7wyiwGfSqqZsB7fr4/AXxIFXIBDeQqaAusnv1XwwE/ZVXXXKsAO2jxYoFZVolgDNv73qe4VMioq+y/rlCp58xFwwLZZ2LddnaOc7MrZFf0KC3K6uaaGsR8TEscdmYgBKh7nimzrEKCDI5D4OZXcTFVg8DHn/u5n0OaDl/h+6Vf+iXs7e093G26WlewliWFB4YoXAXOVPBBDQbqnFUgXlt2vSn9PTQKBCugDWoBgBfCIxzzbPMhmFer1EtVYZSA06JJubRVXjFEDfi4ktM9dsvD7WcXncGRc44zswIntqKN9lNPTHDa7IsH9zJcuxUIaWZrcpI4VMt8FMnYVMquRXtHTnxM0iwAa0mtA2XXponGGgPU6mWn+nQ8yZI8APNxcnLAxCQVwGMwtrctZgC4ZFP2jCdKds2ZndU5PyP+bgPmulLgI3o8Ytq0eBuQVuXAR/8QeOdP2/v7VFafX6ACYhcW8LHKWuwYyzlTx83GaK2WADgwHuhFd997gTvf1t+ur9T1E29fucmiOhbjY/aXdHXPSxbVIL/Vsg0+XoHtHSr1fqRLjTFJP1DSVHsBp4/t3QEf+1OrSc3gyvF8Jwmsz0ldlWWBMIoxDl3kvEd2XTOAM2xNJiiJLwAgC6jEKgFsJoGLyukBXtXvom3x76FhEEOsGlgJDjSya1Q5fvj/fjImkX5RJy2pYAW6AdwgRuJQnN3XH9OC1oh8R7JWV4uAgcGbMasksKnk9XLBUL/4uaa8cEMQVuLoJMQZA/hIXF/ct16IANTIiMgqCT66AUajBAAwW5iv51lG4UMwc6fjMWrTQjAUc7SCA8CJtlBzbr2f57N9uI6DXWen957a3dvDbp2AwelnhN/+FuBdLwdO6xmuj/o6+3Hgw78D3PJnl98XzQA/6V9s/UypoQvKV+uTVq99z934d7/5/svuJ13MkJIY1Akf1cB6KgGiK4w9YiafHVedJFf1Xb/3IXzvH/aP+2+95SzuvtB/zXDOjQt5n6pqh8zk1YC066zChAhfaN4zz5rlFBEv4DkO3CtBJvg0qUHgI+ccT3rSk3Do0KFBfw7Ckrxan9xaFiLopR04Q4lnfuFlJUiH+RiaV7vbicuyrUMI0uXmi2xB665ECkAcJwhQ4uKyHwxbFBTj0NtgtFy3JSY9r7/xwabtLKMoKobj03BjEurUJXYSH3ut9MwH9zJcN3WbNurv2GWYawbJTE1OCGlNfAKM3NrOfFT+k4NSrAcEzrTYIYMm4oRIxtAQ6WcuZNTRdv9LSLkUL+N+Ym/bMNAO2dtxLkGvY6v+TaVeePsmASpduc+7sJ2unVsSmZXMmLj2/h7pagMNfRJota82HxJWCMYgYGdRNcnPFvaD+vzoaD+jtX2NXq5/ZQM+7lxZ2TVxgeSQHZRte8768TDWcQM+9rAqh1T7+w4qEbz7XcCZf7i877dVMZfH8Mgwhp70GxSejxYArmbCg6y1kDTI85HTZuFtO/FRER/UAgytFy1zRFGMUeghrb1egLCuOUZJDO76ElQyt6+rEn4o+q7QI+lW+xpticWbIWFRVQ782hcAd73D3k72XcIH8WOA5thJAswLqvVyXJZMAm2CdR86DGf29ddhVjKEnlxgdFZWLgGp8dDu5n0gwEfWYT5uBzXuv7TZf14x6T3ZembWFKemAR7a7157WcXg8zajMkKIEjnVg495tdpHxwvhOASLpUV2XVQNU/Lw9lQE1BjGwmUhgEqHEPjxBDUnxuc35xzpfBe+R3Ce9DxjAXgsQ04iVG7YPy6qBar9++3tHq01k++Hfc/EIaWsfPoWWz9TSo3dV5mPn7K67ax4/7rcAJb9/V1kiFE58aP62lbglUOGo4+Vxtt3vRT4eDXIRpRiA56b2d+DOOf47395C37iL27u7fMNHzmNr/6Fdz+qjvG8qEB4jeeXrwff63/G7aUlEkcoJ3iPL/t+ViFCjiI8BJc+eu+pR7p6NIiifvu3f/vAHR8/fvzAn7laV77SkuIYYQ2gmAQeSvhgGqYc5xxgFE4rkVr4DsqX4/WBXiO7dgiQZZs3WFExjB0GuHHzsySJ4fMKF+YlTm7FG59p1yKX4GN7Mu+FcOQ2tA3vz87Fvh2frjMfheR4Owmw2wIJT+9l+Genpt19cX1Ejl7ClUmZlUPa7UPELuuAmhulwhJ4bZk4K5aiL1iitsCZFujbDz4ObKuKSnljMOr3fqlS0Y5VdhBQfW9y2N6uygDwNTP8Q/q26gWpXHSPyUY7Oblihb0dzQVIVGX2bVSgZ93PqHpEqw1K9PlqKkDW5tXEqhb4aHlwqsmrFXyW2xbvCFBEHWvttrWuuXxPgHymungnsHXtytt143sVGLN9ZWXXQSLsBqyy6zX575DvV9fd+Bhwrt/M2lrtF6P0otjmofUX3yfAwf/495e3DaaqMnH+g2TYcWlY4Z59HFt/LmmsQ3Tl1LRhPka+C+IFVlba5uYVSOIY24mPvZL0yMJL1BxIkgSez8FLWBnpNS0RhiESuCjzHlYlLQAQIBzL/+fm8U7V3n2C0XXHW4HHf5F1u7mSXUvwcRr7ABcLhFtx93tS6Tmt/IY9h+DCvn5Rp2E+dlj9ATyX4PzeAnXN4bSoLnnDOJT76YaY+gucO5dvtM0qhoisPTMBXDt1tcxHH9XqPcgN4ROGTJOirfr2uASkvBAuIVgs9GAeqznSgsGPxLYkcYCaA5zm4niuVVrKoD4ChHEivDYNwPO8oAiqJXzXwdl6Cyjt4z+hOQqEYGSAJ+r8IfF33zPl0VpKIdFnzzKkqPRFD3rGfkAcryoFth9z+d/7aK3mHewRnljPHhJj5s7jHtnvfRSWWrtYlrQJBX04NZ/tIyMREgePavBxcUDw8Y8/cD9+9Z134m0v+SJrO+UXnA7w/ftU1NlZjnle4QnHJo/I9y3LYQzFS0vxPjJEBn/XeXFdnd7LcN2hA7yPfhJrUVCc4Ofwf7O/wf49h4HPfZa1/e6yQoxcMB8L+30yyypM3BJ5dBTJ/Fb7/PMzqAaBjy94wQs+2dtxtT5JtcxLuOANkyDyHdROAKoBHwtai5fuDvPRF2BZ3ZVMA9DKrl2HYKGR6IuXf9bx3PP8GLHD8Hd3XsDTrt2y7sc8p0LatebhCFbg6/7pNR3fpjP7OZLQlUzJbho3aIGdxG8GSwA4Py9wfCRJwE14ToiY0GYlrF2NLAukNZnxETt1D/got53XlolzBYCIFGvrBHtNdj3U/8yT8vKaie+wfiYUL9izPXM7QLykNODjAOZj0iMJa0CqbfG3TVLeBleKuRmkan+frR0tBIOTowd8VMDdo2y1v83s7PXqXPa3o7lgeDienTlXpv3yWTVeRCr5uTCDj20JV7ZrOV8l8D+/Hnja1wNf9DLD9yrG7faVe6GuUnFcglGP7LoV4OXHA4F/yfwKp5fvu9Q+t9kusH3dsM+p4CV+wJfwbE+kxX/+S/qBTjUe+smw/VTn0Q2bsdzYL9BZGOq1mqhrgNdwW5YjXhChHph2XdccRZFjOkpwfBphyVzrdxZlDs45RnEMP6CoS1gBUs5KhGGMhHso8h7mI83FM8GL5P+LVUCGqfYlk7tPns5KcHBUxIfjRwDNG8BxllUb4OOylMCc6wNeBM8huLSnX8zKK8l8rLogoec4QCWCao5NxT5xzhtJ8irtOsDYpaCM48wsx6ntuNV3LViS7XcBAKfGDv72gXXwsRaejy1GJQFBZQjHy6pa7qNgdzoOQapZgAXQJHG7sn2SjMA5R5HniDRj4bKgGPuCsTNNYuTcvCA5yyr4qATAy6ZAaQ9N4LRABR9VX4ARMIwl/2gutdB2JcBTVorrx4/7x60/f5FIg/6eD13+9z5aq8rEs/+RTkz/yxcDF2779D62A0uRyOb55YGP6UKAjyl55P1MP3L/Hv7io6fxQ1/1lN62ihQylPj4d3eK+74JLjWUmu+lA0G3K1X3XUxxZBIgCeyQzL/99fcBQC+IeqVKF95mazcEfPRkm/2swsC30U96LXKK47gI1yFw+uyqAOxlJSKeY+Ft9Y5785xii5SoomOoZ7fa55+fQfWw0q6v1j+eyvNMrA7Jl3lCCMIoBis3X2ALKl+6/TXZNaB/4dVM8lyD7HpZsI20a3gBpn6NO8/3++YsCoqRRnatJj9tnycVNiN8KdfASprj+iNj3HZGTIDSUng5HYrI2r4EiJy6A1KqyqSEi7Q8H+GFiBzWk3ZdNJMw68S57RFnlV2vgM8+lk1nIg70Mx3UC3Yw7gJ8uqrSlex6SAprn+y6SQje6n5OV+ViWGJnuZAm8T3taCH3u0dCrsAxmj26TPjVdvUBgZwPBB/VRKsHIKqWgqlHM3NieBOEMeC8Fi12lM1Lan5a/H3+FnObtgdelQ33wLNVJQEexZI1VQf4Dwcy/IoGyLAC70OqfQ0cZDLRth84SN39LuDmPwMeuqm/rbrX/IMyH327f2a7HTBMdl1TcABOC3wMwoEyeQiGgFtXODQdIwk8ZMy1gomLpdjfyShBHHhgZNNbuVOsRBRFSAKVpG1jPpYylEeCj0OOrVq06Hvxlt9L4cENxHNsK/YBAlzUPCvTksKDYj6KxclLMz34uGI+lp1z5zkEASo80ApUKKVBvGA+tq1PGI5NQ7z3zq68NqesCXkR/Yq/T45dPLQW1JBVDCGhIIpJ7UUgBMZwvKJicJVPpaesZwzgY6nAR3FMkpFgbS5T/Ri8LCkmXg24IY5MQqS1B1bqz2cqJe6u62Kfx+A9igXOSlTEQ9UXYASs3gGuVALupbvEn0eqriQ7j7bG577jdl4y1y/XOuTRXFW6slJ5JOvCbeLvT+djO7C4dClcDASLTJUtZ8gRIeUDrBiucP3i2+/AW285JxR4PaVk10Ok1ADAZJe7NlssCEDM4QxF8cgFzjywm+Jbfvvv8Qtvu8Pa7tx89fypHyHJcjvzwPadCrQdwkQtZMaDTlV4kFoWFN/3RzdpbVYOWouC4oiXghACr7ATSjjn2EsrBHWOItgB6VmASkuKhBSoRseFvc4/ghT5R6Kugo+f5pUXRdebEEAcx6i1zEcGj7POBKyRMuombzrZtUPAab6RGJWWFMEG+BjheEKQDTB4bZiP9brcuMJ2EmyCj1tRY+rfkV2zEk88NsY9F8WAcXEh9mFHkQ6c1aQnciguLcuNh2FaMoxc3t13N0BEqoFp15aJJqtW7EvbpHmDATpQdq3OZx/TQQEgftwfWKHkk32p22rC3Ce7bsBHJRu0vAiUEvQC7C9LVbpq1/fdXtQvqcr3AC/u729o5bN+kHdIqWMwOtoP8NZ0QDsJmPcdjzLt9+hU5zGcdv+vq3wmr6nYPqlRHl7cMoawUsiHwzFEVO8VSO5jRSOz7AX+WyDGIDBLeYpJZtllVbmAkOBODzbxbvt3HQQAXUpZ4+x0f1s1LvkD/aXUsVPH3bYwA3QXXPpk13UFzgGv9eyLogi8poMmt2kpvJVDCRAumcUyA2jUAeM4Rhy4MsFaf5zrWnhdxrHoO+du/yKPK68f4GA+ozYLBqCRXVfw4AQCePddB0dGm8Etdc2RFyUc1OJZ5gbwHAezxRJUM2lsmI+dBcYQhADXTBw8sLt6DqnwF+Wd2LStK9zw+CP44L3dCUReMhl8swIqAeDEiGAvrTrvK1nJkLh159nuEJFmvnE4ao6S1g2TEY4L4nrIcv2YtSwoHM7gQLwLxbF4hugWbAEgLRgmnvC1PDwKUcFHmurvlXlOEaIE8UKUCLTveE3VwguUwpNSbvs1kqczlIwDV8ow/49fIP48UlUsxLPkSkz8msWhnrG/8/0PczHnH0GxYom70wi8rvqZ01eq2u/kn8bHdmhRia5lA5J6bZWnC1ROhCUPHnHmo5o7FQMCSBVwNWTuCKyeF31BJ7O8wvcXv4AXnvuxQf1eiVLPtT5A8d6Lq2fEUDm0reZ5hb+52RI2iS5AqMs/WG835PpTgObiMkNnbrp/Dx+5fw9v+MiAd82eWhQUR9wMDgA/t/sCZxVDWTH4vEAZHYbTA9JnFUOCAvXouBi2roKPAB4F4OMv/dIv4XGPexyiKMJzn/tc/P3f2/2l9vb28MIXvhAnT55EGIZ40pOehDe+8Y2P0Nb+46s8zzupzACQxLFWTlZIuZHb9k5Tn9NN3tYZJm4A1yHCx3EtnTItFfOgRSt3A4R9gJ2sRVHpZdc0x6FRgIf2c5yfF/ilt9+Buy+kOD6JWpNQNTkR3kYntqJmwnFxWQIE2A4U83G1LxFhKGmN5Zr/R14xjLxapDYr6bLrIyCWtOu6FmBP89I6QE5tZT6uMaqUlLq3T7l/fUwgqpK5BwAmDYMp7kndLgS4G4zsQKHaZy+Ux6AHfBwSTFOmwxiSNB/m55TPgOkp2d8VAA1/44uBN37/5ffTluxaj4f83fhYPxjrhv0MvyptMUsN/SkQMBjJbbVcK/m+YL72MVCV7N0WJKT8S70DgDF9NZT9sg78DwETO31fAfAxGAng9SDXaRuEOkiQgJI1Dgl2aKwdkmEsC+WvqsZQ4wLOOvhoBwJF3xQcvLPwFoZxA/z11TynCHiFMBJgYlq74JaxMM3E70ajGJHnoiLmsX6eC9/EcZIgCTzktdfj+ZhLgDZa/b+v1LVR6P0Ym5LbSOHBaQUoHd+KcEYT3OJxKl4yW8zHgJe4pHnmF7RG5BFxnp0uSHjNxMWDbfBRTnIauTPQgEHX7sQ4O+vuc1Yx+G3Ztfx7KxATvvbiZVYxJG5Xok0I0TIO84qB8BoO77bPMzP46BEZVidl1wCQGdovCoqRBEKPTEKU8LE0AJVCos3heAEKEoBbxus8z1DXHNQJUPQFGAG4+Z4H8fF5z0LQQapKH1lmVTkHJsevDHhKS7zrrhkuFs6wxVagH9T/R1wXLu3iPQ8JIL4XsDp/G3Dp7sv/0vZx/zQ+tkNLATnFZYKPe/MldiZjZHWPr7Csc/O8AfYut1QAyRC/xYPubyHDwvoAr1lG8TR+G07SB3r7zCumVcYdtBRwbLXsWvv95QJ3APCmfziDn3zjLdi17EPbdkxnQdb87gCAopJo2/obUgpjKAeA1X21yCl2nAUIIfBL+zvQbirsTVzUqKLDcHvIOWnJECGHmxxCxZ2r4KOsTyn4+Ed/9Ed48YtfjB/90R/Fhz/8YTzjGc/AV3zFV+DcOb0pdFmW+Bf/4l/gnnvuwZ/8yZ/g1ltvxW/8xm/gmmuueYS3/B9PFYr52AYfkxFAN2/8ZSmSFf2gDT7KF2qT7NrxV8YbcnIRO6zDUgDa4GOb+RgiILQzqTCVSLv2N0E3WmIa+ahrjuf/2nvxpx96ADfet4sTW+HmJFROTo5L36hfeNvtuLgosB37whQf6LAqA4hjtD44pyVF4tUdNilcsS+7pgdI3QJqbWDhukTaKs/umuebZYga2XXfiwWTgI0f9cv2FBOgDyhUk+K+1F/1O+XtZvv+ciFkvH2JneVC+J75SY/MWMmuJ/1eiEPAzCGlroMHrkCwhzoXwci+/U2oyYkeMLZcgcBDGagmBucGCGiZuKkQo77zpUAuG0DWJCQPtBz4sxcCN/2BvQ1rg/OW65OWLannUObjlQQfl9Kb8oDJrO1J+kFAAjURHDIhbMaY0TBpMG2N59axsRVMA2BQyJa0TiCt52Qcx8JLa8AkbFkIeXEUCnZiBd/qF7nMMhCHwPVCRLK96VxfSkv4qDBKEoxCFxnvs9mQzNmhFhuAuGcdv/8aYSU4ccCJI5iPrADqGtftxLh/7TkujglrgDZ4ERwC+Jxq5YF5xRCrtcm2pQiAUxOnk0qtJqgb4CMrcWwqWJhtxUJWKeZj1/Nx4ol+2vKyvBIhL+2FSAKg0siuM+X/TMgqHdsNwTQsSUCG8vg1iNyGMEoAQpDn+uOelhQjCYSOAlewkgxJ2stCSLQV85GzyrggeXEmvu/Y9hQFLBYGssI6x0VyqHcBg7K6megbq32vX4mFoCFVLIDJCfHdpkXagUWrArdeKPCxs3nPM3HNb/fTtAhNsUe2BXjUN47/0b8D/td3Xf6X5i2A4NP42A4tBfrklwHE7GcVaJnj8NYYae0Nevf4hl97H376TRbLmwOUGq2HSMcXcrwuaT0oNVkBd8vCfu/vpyVcxwHn3DiGq/qm33w//s2v/F3vd/eVYjE+NLN/36KocHgsnjGXK68H0NiY2KTo85ziyEQ8K22sRgUk9jFLVZ/A5ftqnp+LsddqdTawFgXFNklB3Rgetb/v7i7LJmwGo6Nwmb19WjJEvIAfj7HAFWLffxrUpxR8fNWrXoX/8B/+A771W78VT3nKU/Crv/qrSJIEr33ta7XtX/va1+LSpUv43//7f+N5z3seHve4x+ELv/AL8YxnPMP4HUVRYDabdf58JlVRFIL52ALKRqMEKsG6rnkjgZrnYrLgdTwfFfioucHbABgAEALiBtgJ+cYqTlrSLvMAANwQkQT47r5gn/TMc4pxtJZ2LcGuJ50Yb7R/ysktjfeXYMsckQP4m/7hDG4/t8ChUdhl1QDCawpU62W1u6ywHaC7724An1eY55X+YdgAaoF9UlhXq1Ae17L6uA7CmvoDNjy0RNs+5mMhAZsBgIliMPUxHxWw1weqKABTTaBtk38VdhMkdlaG8qXsYzQq2bXfw/KghQjOAS5fLn0lEjhVKbCwF3yU+zYeILv2BlwHVdpKJzf0p0BA1xes4d5rZQgDdW/1/SZ5rLrubP61TdsSuO99wN++wtwGWMmpbSxm1W6Nqd1b6t6+Ep6PhWQ+BgdkPtJyFVJykM+pczVkQqjY1X3sVlWsNYZ6FjBct+jUJwesK4DrwMdhzMdlQREQBtcPMQqElNU2eVl5MQeC+Qizj+M8r+CDIolj4SdZ28NsmkUex5Eg7QBgVzHDaG4HZ1iJ2gkgstbkIgIr8JhDCe5b815aSEBWKC/Ec88hBAFKrd9TQWsk63YmDUhY4923X2gARcW2cdvgo1wgOT6NkFesw8LIStb1h5QLIDFhAOka62flmj+1DJHJNVJqlbhN1D4CIF6IyuDLuCwottQrkOvLYwIUBpl2WkqVhWRfun6I1MCSnBcUY6+G40coIFK0TffIpX0xATq2MxEhNpYx7Mb7dpEgwyVyqNdH8uf/8M/xoZ/+P+0TrGKB84tCnJ9HirVWLoHJSfn9lzf5K/JM3K9e3OtHXTIJjnwaTzhJlWKPbImx0rZQxblYdE+vQOhPucA8p0KieyWUJ1eoSlrjHbf2+xZSVuNLXvmO3nnP0KryJV6a/xzcS3c+7D52lyU8VBiPEhTcN3rLqlKMs5sfujJzanXI5kU/mLQoKE6OgSeyOzcsvtaroKwBxXrbZjN4riDTZHt2SbJaALMx71jN8SWveEcTeKMrtW2XlvZ51qJgInCNDGMY9mUppEV/svc8r3BUgo/2dhSeS5CVrBcMXsmu7efi9F5mvT/UvHyWXz74OM8pJqRAGh6FU5d2v+6CYuyUcAjgTY7CZfZ39KyoEPACYTxGighV/ugZrz6VNQh8/Lqv+7rBf4ZWWZb40Ic+hC/7si9bbYzj4Mu+7Mvw3ve+V/uZN7zhDbjhhhvwwhe+EMePH8dTn/pU/ORP/iQYM1/EP/VTP4Wtra3mz3XXPVrylR6ZqspixTyQNR2NUNc1wCq84q9vxb/8xfcAEKspiVuDeKu2VmCrDYC12u8ENfazdbagZB600q7hhYgIxTT2cZdloKxrjkWh0q43mY+h5+JxR4R8KfDEJf34oyOj9xchBH/5vZ8PAPjjD9wvwEjVtiX3cliJnSTYMKTfTUtshV02KVxfgJXcQCdvpMSB3Xdxff+GeD42APEQoHI1YbRWGwDpm7w2jLYecKVhi/X55FUrSXuf750CH/sCUVS7PuCzw87sAcf8kfQktDxMOAf2eyQc7XCPIQwwWzEJGvcFBSmwZ3Ssn/mo/OOs5yEVUm/ims+tSuElZIA/aGs/bBMamgsPUcACerY8utQ+mWomz1Wb1WzaPgXKcgZj6JBi9wHDmY9qgcWP7X0PKSW77gOj14sV4tgT92BSS3WuhqZXu2H/JL5pL89bsyhhsaQANkApa6nAmdaYHkUSfBxwzpYFRSiTl0ehCPGwMR+zTIKPjovId1DCLA1fZCU81PD8CKPQRcos9xggr005zg8FvMvlsEAuVqF2fJFY2WIwP+ZwgrsvLDuTu2XBEPCqAVnhOCCuj7HHtJOnXMmdAax7Mx5LxHNdKQuyiolkyo4CQDzHj8nJ0jnJjFC+jH6b+ei4AHHh1CXGodcBQ/OKIXbrzqKlSwjSVA8+dgBWAMQPwSr9+VkWDFOZXg1XAMSceMgz/bifloqFGcpdjKyS7rHH4HghKscXk3kTm3a2gOMQRFGMnPvWZ/yH7joDj1NcItvgPQtt/L734zr2AHDmY+ZGxQx5yYSiZECy6BWpduDcZYOPKUriCxC+593k7H6O05l7Zfzzzt8KnPmHy+/nSleVYZ9MxfVmecawYoFZTkW7ywyJ4fkMe2mJe9PwEfcmtNWN9+3ix//8Ztx53r5Ne5lY7LKBUkOL1RyH8wfwOewT2H7wHQ+7n72sggdh71ESH7SH+aeYa5frM6lKgXh97ERALLR8df12/HD+ChQz+zFsiDAESHu2laazJo25mNv7VXNNGxCoAMA7z5nvC2XrlZbM6vu4LCgmobfxvNLVxx7cx3/4nQ9a59X5ACl6WjKMAw+h7zZgpa7meYWTW9K/uIfROM+p6K+n3Yv/+CZ82+s+YP1OQEjlbfXgXtYLiC4LishhqMIdsXBgGcfSkmLbF8cunByGywrUluAjpT6N4hg5IpTpZxYBzlSDwMc2eDedTvHWt74VH/zgB5vff+hDH8Jb3/pWbG1tDf7iCxcugDGG48ePd35+/PhxnDlzRvuZu+66C3/yJ38Cxhje+MY34kd+5Efwyle+Ej/xEz9h/J4f/MEfxP7+fvPn/vvvH7yNnw5VlpL52PJanIwSMA5wmuOvPnYGecWQlhR7WYlwPRTGJtNtA2Ct9tOg7gwIdS3ToQhd6ztoPJrWZdrtSisGcGASet0AB3fl+/Ufv+QJ+FfPugY//a+fji998jGRjN0OKFB/y5fFOHCb/gPPkYCXK9giAFQozJOOTzZWkC4tS+EVtbYvHheDodb3sQ2ENj6aBil725/RCj6uh8gMkWgH9rbNZ1oSWVrYE4IV89EbIKf2ItHOBrIp4AmQcmobSJW1AmJ6QCo/HsDObAOkPezMIcy82/8a+N2vAXbvNbdpf97mXTikGuCqB+gqpQF/MOoHYyVDxwhYcy7OQzCyg5Rt1nLftcLktRIMkMkr8NEYdNM6p2qfTKU8JDmzT5AaX0bVp+F6pmXXv64P9AdarEoF7lwGIF1l4hj2Xacb21CtWIlDgERV5VKm2Q8ALIcuWjTty9WihPqMblyq5bNHLXT1BXK1PkPW0q75UNl1njX2JkngooRvDfzIshy1tCyJfdca+rHI8uYZngSeDLOxsW0LvPGWS/iSV7yj3+JCVbEYCD6WqIkP13FW136V4xnXbqOuOb7rf3xotd2FsCchBJ1n8JZfaxfoCiqDXoDVOCGBy8+9TiwunpYLgWnJkATuykdZ9q3StwPPwbmZ2O/GH7LNfASgEtOnkd/ZnqyqEZEuUOm4rpZxmJU1Al6BYLXA6/gRCC20Saz7WYVt5Woj2zM3RJHr7/FFQZE4tFnAcIMYpSXMZuQysXjsxWISZTj3+8slPIcgDKNeGb9bLuG5Di6SHdSlhWEO4EgtJux8YWYNpQux2OY4xLpA9rEH9/F1v/yeQem3vVUuwJVNymVaWVRlDgoPaU/q/KVLF8FBsEe2rgxA9obvBf7fb7n8fq5k1QxgFZYkEbJZy/H4o3fehP20FKDHZUqlZ/uXUJAQmdtvJ8I5vzLX0IBSYSkP7tmf2QoQKwYGptgqLSmOc6GeIX2evZbaTUuMHIYgilEgBOt5hi8GMOeGFqt5s3A1RLq7yCmuJecBAPT8bda2SlZ8zXaM3LKtdc1RpIsV+NgHEslram5h3l2QC2DUAn6lhSDggNuB3EVOMQ49JIHbe8zfd5ewI7JJqhXIawMB05IhDtze75zlFCe3ouYzpqpYjbxiOLkVWUHPs7O8eX6bKitr7IwC6/XCao5//5vvx//zFzdb+1oUFKFDQcNtoRiwzDkWBcPEF+czmWwDAOYGH2YAKErx/haFETISocw+fVnwB6lB4ONv//ZvN3+OHz+Or//6r8fdd9+N17/+9Xj961+Pu+66C9/wDd+AI0eOfFI3tq5rHDt2DL/+67+OZz/72Xj+85+PH/qhH8Kv/uqvGj8ThiGm02nnz2dSlRrm49ZkBHCO//qnH25+9sBuhl946x2oyqLL+nFcAMQCPq4xhLwAE6/uDMin9zOxauOjC9hJ78NrtgL83vvuxbtuO6+lxV+SidRbia8JcBAD1D99zA6+90ufiGdet40f+qqnyO3Tya5X+/GTX/c0APLBsM7idEOAURweBRsS8t1UgY/diYwjjee1D6MO+ChnH7WhXRsoHBpM0/4ObZ9dxsewwBnJeOO1XbbYSf61gYoqCKGHicTKFVhuC5ZQ2+lFw733+to1+zKA+aik5rb+9u4Tf1+43dym7a93uZMUVg08HoU4t32sMOUfZ2PttS0FbMCy2jZg4LUSSM/HPubjIfFv07FjpehriAeeennnPXKuti8jYLlPy4MzHxsbA9n35UivH04yK7Bidh7UK7JK+xPUm22rDrZtSrYPyPGMr4DG9XZtL+IhzEcmGDmkxcz3w0hMWgeAj1kmPYC8EKHngDmWsRsC/HHkdRH5rgj9MHxPw5J0A4wCD4sBzMcSyrojGMamLqUnHtBrw8CcAL7bYj6yAqPQw3Met9Mx4BcS4zUw0Qsx9WttcmZe1ULuDKz5KQcIQBEHbuPvlJXi/811qvaVifedo5MQ5+bivlGTq44/pNwWsArT2OtIt/KKbSzCOn4MWuQb7yc5ZQgd2gFYvSCGj0obIDDLK+wESlq+8mzOM/0xXxYUcSv8xvFC1AZW5aKQzFEvhBtEqAHjGDtfLOE4BGEUIas96/WUSjbQPplKGwJ927ximEKMmXRmthI5c+48SuKjcO1s7PfccQF7aYWH9i8PLASANF3iP//5fQIvuEx1QVUWqODL+9Dc1+7eLnISoXT6F3CG+Nbx5QUB8D1CQNqgYiUY58JmoocJutgV10TNcdkJ1RcvXUKGCMzvZ/S/6i234St//t2X9X1DqwHkegA0pQzTLVActJYFwxafwXEI3PzhM4nTQthNeEGEinhgpf2Z2YBMvP/6zSu7HFd5GH4hfQ/IuY/3buuioDjC9wAAzDLWAIIsEngOdpKgYfvpajctEbAlfM9BQUKUy2EgkY2FqJ4rNoAsLVnD1relWC8KilEoFiD7WINKrmzbNnWt2uTPaUmRKPCxx/NRZSnYQEW1PcenkfWYvP0Tq3NKDfdIXjEcGQfWY6Z8Id9123mrPH5RUJFJkRwSzzjLolha0MYrerwliA/7C/O1UhViYTqOY2SIQK/KrgE8DM/H1772tXjJS14C110xx1zXxYtf/GKjV6Oujhw5Atd1cfZsd4X07NmzOHHihPYzJ0+exJOe9KTOdz/5yU/GmTNnUJaXnzr16Vg6z8fpRACwn3hQhjUQNOmQn3006oJqjURyqOw6wtilHQ+l+y6mCH1XBs60+pYv608/IVJF/9sbPo5X/vWtYDXHj//5zY3B/Ece2EPouzg6DjdBtz4QS7UDsB5S8NzrBWhxaivqAl7qM6zAduxtMBkvLUtMAnQnSF4AQoDEq/WygTYQqsAIrY9mi53hBuaJ77qXHGB+8WPVxrYOYj66fkdaZyzaamuTtDZssdjMWgK6IEMfYKNAqr7vbu/PIHZmnyxYAak9/c1Oi7+XlhekYtFi713mqljnePQdt6g/xbqRxlqOR1sOa2U+5sOBOAUqDwknihX4aEnZdpUHXg/7sC3Js8nzlLdmHzvxYXk+rt97lzFZZrR7zw3+nLxX+8Df9VLen0NCaobaMHS2qbUoAej3iVVrY7mFQa6qrgBwkNbnwlABOP3bVuRZI70lhMALInALY7wsChB5XUwiD2ntGs9PmqaCJeYGGIUulswVfRt3pUAJ0Xc9FPCuMiCRi8dW0F3Irl2HbDDuv/mGx2GR0yagbVnShiHQfmZPvFprmF9QKXdutweaRcOt2G/Ax2XBMAq8tcXI1TPg+DRqmBN5Kfr0ONX0W3T6BQT7ZD0czw0iBKg2mCRpyTD2ugBrGIbwOcVFjYfXLKOYKvCxxQa1MR/bLExiWexaFBUSKdH2Ajvzcb5YwiUEYRjLcAmL8iTN4LkECzKygnd7aYWEi/u+sAAC8/1LyBCjIHbLEnXn9EkMlwXF7WctYzWjWGYFlkQofi6X+ciqXKSOsx7Qdr6LFDFmtV0avJ9W+Beveidus+wDrSo8sJsKhm4P2Paav7kNN92/17sffZVXDF/yindYveM4K0EZByUeKPGtz1aW7oHBEdLSy/TJvri3j4KEyBH1LnT95UcfuuwU6KGlGNR9zLRmHLsCrMFFUSHhGVyHICgePvgoxj2KIAhRIATvAenbINOehWF3fl7gK3/ub/Ff/8xsxbCfVXA4w3+ofh+f/dGf7d3WeU4xJmL7agvLGhD313YiFAm2ZO6H9nPEyOH5IRZkDJrb38UVy9UKPmbDAL7GV9HSblmsmI99141KgrYBgeoZbAPKlcJAHDsbyNpiPlr2QX3niWlkvUce3MuEdRq6fszdbRPHbVlQI7O5vf/rntTtmucUASjC8WHQGtYxdlkyjH0OEBdRInzR09Q8Bin1aRxFqNwE+fKq7Bp4GOAjpRSf+MQnNn7+iU98QvgIDqwgCPDsZz8bb33rW5uf1XWNt771rbjhhhu0n3ne856HO+64o/M9t912G06ePIkgCLSf+UwvceETyWAUtTUSN7UPin/62B2c3Irw8dPihjiWOJtsRtfwUqGVXQcYe3XnZf7sPMfxaQhSV2vgoxisvvLJh3BsKgbfwHPwTb/5Przj1nN4563n8Zabz+LVb7kNdc0Fg7MzmbeAc2r75DaJ9t3JPyEEv/iN/xTf/vmP78q3VN+8xiQgnYkS51yAj96m7BoApoGBxt5sS2iXPq/Lrq2p2CvmhOhvgERbte9lPhZr4EofU1EBTzYgq1wBe7AwijpssT75c6tPK1hYrsAxq+RbyoyHgF5D+lPyIluoTLkAom2xH30v5X0suOZc9IBN6proA3460liLrB9YMQFN29gBlcP+8zCUTRtOxXdbU7YHeuCVi1YwgeUloQ2cqe/Qtqu6+zzI83Ed2DwAY3Hj+9vMxwNMutvBRQeRXVeZZD4OmFy25eUHYYQCLba3ZgGn1rHY+2XXwvNx9bkoiqTsut/QPG/JrgEgiBLxnqJjZkIkJytv5SPjEIvKMY7fom8Ari/8JOEb2W8AsFguRCAGgMrpuX9U0QIIx/2J16wEIx5c0vV8BIAnHBPBb9/zBzcCAD5y//6Kydh6Bo89pp2wdZiPG/LoLkh4cVng0CjYZMPWFVDXONZmPlbiu0Q4jUZ2vQY+5hbw8eKie47yqg0+ivZeECFx6UZbQDBhGkBWLggS3ww+Ki8qNc44gXkMWUrmErwA49FIMI0M9/wiFWBFFEUSRDPfH1mawnUc5O7Y6oG6m5YYSfCRZeaxM13sYUkSpIisCxtpw8yxg4+/9PY78J2/9yFzA1YgrxgqBKBOz3OHc+DBD1vZhXVVgHihPG4Wa4XlDBmJsOB28PG2cwLkuO+i+Vg8eFa8P5S0tr5LVKzGG246jV95xx3GNqofK2AL4MP3ineXW8+Y2xW5SJVnToCK2Be1yzzFkoxAOblshceZS/uCfcr7wUdVNj+9K1UrKbL9mlWs6CuRWjzLKRKkcAmBWz38xeuspAgJhetHqJ0AvOdds81cMwFEAPDRB/YAAL5rhhv2swpH+UV4roMwP9+7rYuCIuYpQAh4umdtu5dV2EkCRL5dOnzvxRTHwwpOOEHpJqCWMSyvWMOks41PQ5iPy5Lh2KSfNbgsRehqEtj9FwHggnz22K4vxRi0HZOsZEgCD7HvIrdYBMzzCttJgMBzBh2PE1thLxv0hAQzTWnWWcVwdCze0UxydQU2jyMP91jCaxYFRYAKo1GCHD6y1HwfpQXFWKoRgjACCDH6MAMAlepT4oXwovFV8FHWgcHHb/3Wb8W3fdu34VWvehXe/e53493vfjde+cpX4tu//dvxrd/6rQfq68UvfjF+4zd+A7/zO7+DW265Bd/93d+N5XLZ9PPN3/zN+MEf/MGm/Xd/93fj0qVLeNGLXoTbbrsNf/mXf4mf/MmfxAtf+MKD7sZnRNU1R1mWIsFTSdAAOH6Iw+MQv/INT8Ur/j/PwE4SNDdmx5RdlcncXyu7FpMLRXcG5MpT7GukzfLlm5X4w++4AV/x1BN40z+caRgLN963h5964y3iq9TL4AbToQcUI+4KeNVM/p9yaorIdzeBVDmhmQS8Q+teFBSUcYz8enMiA2Dq1/pVqXZSqy11VwcqaiXvrWPpOGIiM4QlCQwDQRofxwHsK7XNbk+/bbag+r/xu4cy5PJh7KmGwTeQcdcHjjWgZ09/CsDKLTKjYj4sFCS9BPzq84A73mpuMwQsbNpFw1motqCetreqtV2L+ejHdkCoSVvvA4Fb8mwT264d+tJ3jeYzCT7a/cg2fRlN4GPRvZ8Hy4tbku7LkQmqe6nP63Tjc3K7+1LkO5+hYttHR8Vn+uSBignthhI06mGBDPW51TG9+8BH+Xxrez7GgQ8KF3TA8S/yvBM6sj1OrOAPpUWTrH1kEmJOXaNHZJpLSbcb9vpJcs7x1o/dj5JI8NHiJdkpNT72BUtRIbv2XAW0kqb/yBfP2esOJeCc4x23nsP12173GeyGGK/ZsqgqKEPkrMm0gUYB0AEfFyUON+BjazESAFiJ7cRvJvjznMJxCJz1xU9pa7IdB9hvSaSzkiEk3XcV1wtxauzgXbd3J8Z5xTBaZ2t6EaZ+3bBP2jXPK0z8Wlyf0l/a8SJURR/zMZSHJTayyxYFReyItke3Rqi4Y7znl2kKx/WRRAGWtR1Ey/MlHALU0ZYMsbEwH5EhJTGYhTWUL/aQkhhLRNbn3SXJpuoDH//qY2dEv4YJqGIJV8RDSezS4Ds++ne48LsvAO5/v/kLaYGd6bjX/iBPl8hJiHkdWIN67pBhFDqmrKqHzknAkcC6KHa/ZPeo8AdT/ea778J3/t6HrF6Id19c4t+Uf4bggb8ztllIr7PpKJHgo/nYVmWGkvjISNILGJ7pkdrvzxfwwwhLHgwOUltYAEHOOe44N79sb0gFwvUxHxvwcUCyc+93ZhW23QLc8eAdZKFwrfKqlr78PogfWtn1gADUdkZB829TqTHbd4m1zQl/DocQ1DXrfXdIS4qALkGJD94TIPXgbobj0whx4FrTrs/Ocpwa1UAwQuUmYBZ5rBqTPJfoA0ZlqevBCrQVFNuJ8Cm2sSjneVt2bQcMd9MSnkuM/dU1x7KgSEK3FwRMAhdx4Fr9KGc5xTTyMIk8a/r0LK8QeA62k8AaLJSWDCemdvAxLVnDGDU9I7KKwXcdnJhG2uexqmUhmI/j0QgFQsxm5jF2UVKMPC7vkwgOgTEwDhDgowMIsDKZoLQAm59JdWDw8RWveAVe+tKX4pWvfCW+4Au+AF/wBV+AV73qVfj+7/9+vPzlLz9QX89//vPxile8Av/1v/5XPPOZz8RNN92Ev/qrv2pCaO677z489NBDTfvrrrsOb37zm/GBD3wAT3/60/G93/u9eNGLXoQf+IEfOOhufEbUsqTwOe1MqAAAboAkcBs/pq3Yx32XUsSBC4evmbID5smbTnbthjieEJzey5rVv/2MYjsJsAFWroFwF1uDw3WHEnzgnpWE4KueJtlIHWZgaJdQbgCKgYVJWHWTuFtgYnsAV55WY3fT81G05/pVKVYBkAxUxxXBCVrWDu0GVDSf7dk3z/LitwH69khPgRVg4/eAK0CLVdgDZKngjz4vu3bgzFBG45D05EaO3CMzbkC5gUCqrT816bC9IJdLwTrq89e7II21T3/Y3KbtRdgrpw7khN3AQq2ZuB57mY8KWO8BHw/CfFTs10Hnv0c+TtsARQ8AWM6BaCrOh4352Aa9AYvsuurIK4fLrlXfxL69fdUwmA/IfGwYtKNhEmpg1W50RIyzfd+3nkI+BCBs+/uZPsP0IJO1aiaYjy2FQBy4qOCjKAbIrouVLyMAbE8nwkvY8L1lWcLxFfMxQAUfmYH9lhcKfPRxaBQIfzXD2LkoKAJeIo6FuqEPEGiKKiZ034JeBUY8wWJR7MfWffd1//QaOGQlN/uiJ2xvLHwl7hDm4/rzag18XJY4KhOwOwsLAMBKESIjJ0EX5gWOjAOpvNDLudsTpqxi8LFuERPhSUd8/MkHH+hMdLKSIXHpGsAaYMuvOwuwqvazChOvu3DpBiEqTbJsQRko4whQNfvoBTGIkfmogEofJ7ZiGSSz2W9JawE++gLMXlK7jL8scjiuDy+0Mx/3sxJbToZLzmEr2FZmC8BPsOR28HE+QBbY9pEzMmQkK6WCj5LYx8GPfPh9yEqK4pzFo5mVOLI1wZx64Ja+ijxDEMTIEYKV5mfd/ZdSPL98PRwL4JnPhUVSXcO6KHZ6T2xP6NmndgrwtCXF7s9TfHX1V3jKza82tllIueHWZCwXOszjLKsKOH6ElERWe5k7zy/wjb/xvsa3Tlc1LeD6ERZs4OIKYAWJ7jy/wHf87ofwztv6WXe2UosqfbLYtn3E5dZ+VmHLKbAMjsJjB7BIWausYggk+YT0KWcgvSZjYcFhA6YUuHVpaT7++1mFY75YwCM1tVrecM6RFRV8tsQl9yh4T8jO6b0M1x1KsIM5ysK8T5eWJXa8EghGoF6C2nKfqbHp2p3EOO4Aq2vOzmgUAN808q3hNUvpNfi5s79Gnpv348KiADjwpOMTKyjHOXBsElmv1bQSgTORb5asF5ShqBimsXju2BYOFjnFpAVSmsD+rBQS83Hkmcf1Fvhouo/SkiIKXGwnvrGfggoWq4cK4yRB5YTYm9mYjwwjjzXzLIcQ5Ll+rKprjroqV2GEo8lVz0dZBwYfHcfBS1/6Ujz44IPY29vD3t4eHnzwQbz0pS/teDEOrf/4H/8j7r33XhRFgfe///147nOf2/zuHe94B173utd12t9www143/vehzzPceedd+K//Jf/8rC+9zOhlgWDCwpnQ0atZL/iRWEa+zizn2NLsRMdTXvdS4VOdu2FOByJAeWeC+JBuJeVIs1rg5HSlQt/03MfiyccG+ONL/p8/NvPewwA4He/7fPwV//pC/CiL32iePOqaQvA6JlUrvs42lhK6wCdfNkfuzUWLU+JS8sSIOiYwANo/r0VcnPatReKCZuapA6RXatt07YbKKWu1zzQvLB/Mr7OfDQBOyqQQfkCAnaQqs0WMwGgbaaaDSxS5vdDwMIOg28ok7IHzFSMMivzcS6YdDYWXbkAgokEvCyrYnv3i7+XF81tmsCZPuajAm0lQ0LrndeyCrD1p64lBVYbmY9lS/7cdx7UeR1wvrye7euwo/qYqnMgnIg/Vs9HeS07rhjTTPeTuk6ABuzoLQWWKnDnsjwfFajUP5nofq5aLSgMBR8VcD6S3oF937chL+9r3zqWVvCxXFtIksfdxqaoKRhceC1pWOy7qOChNICC7RLBbqvt2pmOwepaO8ZxzlFXBRw5Xh4ZhyiJj9wEPrZYlUngwQ1CI/i4l1YIUOGrnvVYEIJeQKCpofYVrAQl/kpCt3ZNHxmHuLgocVr6NR+OCdaB4InHcF7DQhDMRxU4035eiWfl0UmI+y+lyCuGD9+7i5G3zjhcXRPT2MftZxe458ISL3/zraiYCpVbe/9gJUah2wFD84pJBlD3feDzrhOy8rY8Nq+YYEF43XeBYwnBRx/YZLvPMipk2upZCQEo0jLfmIipCVXYYj6GsXje6CSky0LIJuGFwlPL4El4aVnCqSt4QSTkdAiMTNq8YkCVg/gh/DAWYSEmz8dlgQQF9r3DVjYSLXP4QYwFD60gpTonunAiVW0PTl3ADwDszcW4FEQRKmJf9BpV4tk6v/CA9vclrUFYiaPbExTct0pTaZnBjyJU8EEt4OPZi3v4yuoteNZ9rzO2qZZ7KEmAPUysgK3ybreFMAArUMTGUspnEoizPLOWkvGzNR6hhB7sBgDKahAJGC653V7mYw+K+8YGxNRVAT+MkNYeasv5bIdM2IDWB3ZFHzqrhIPUEMAcWB1327UNALedneMFr/17FJaglHlOMXUyZOERuKywKgjuu5ji1W+5Tfu7vGJC+eaFcP1+K5RlKZl4odcrKwbsycv7WYUjXgHiSE9Qi1Ior2rEPIcDYN8/CtLDfFyWFOPQxdd97IX4/Ad/09juUlpi2yuBYAzuJWCF+b1HXZuntiM7+JhXIpHZmigtjuM48noDYk7sfwT//MHfxs7eRy39ieN9YhoZ+1Og5PGpXf6clRRJoBK2DX3J75hGPk5NQ5y2JL3Pc4pJ5OPoOERJ9cFzah+SwNvwY1ZVsRqs5jgytjNvs5Ih8V1sxb5+Xo7Vc9bjFYgXwvFj7M3NgPaypIilvQncEITA+I6YUwYPtHk3HE22wC83VPTTpA4MPgIApRR/8zd/gz/4gz8QiC6A06dPY7G4iug+mmqeV/BB4fhh9xeNTFc8XLZif/W31sfRwDDUya7dACGEEew9F8VNtp9Vom8d8wBoXliecd02fv2bn4PId/F/PvUE3vaSL8K1OwkCzxGG+yodWgFparuMklH793VKJ8kCcHxEUNe8MavdTUtsx75giK5PkAAcjUmHwWk8VlYfzXXm4wDW6VDwRfU7xPPR9eXnLOyrmgKQky8bkKX6VIASYPEGHCi77ngNWhg+isGnWJdWr8E1FqexXQtItfVXzEWKrC1IpkyFvNVP7N+ZSSbw4oxluxR4GgvQynhvFKvjAejPxYaceiDz0chobcmue9O48xW4az1fbTm9iflYtNiHPUywRgLfw0Ld8HI0MR/XWZcDAMB6xXQS1+sBQEPTdj6ctOshTLh2KZAy3pH/72M+KmA0OED71rFUP1svnX+vKRm71TeFKxiGsmLfRUl8K1tCVeOtLL/3yPZUMh83j11WMbi8giufy5HvwvVCFLl+slM0zEe5IDYaoaZUO8nczyr4qBDHCWIp0e5noFLBVB0SVMUKUOLBc+RxWmM6bycBLqUlbnlohnHkIVrzToQX4sTIwT0XlxuTmbyqRcp0O6kcaMDjZ1y3jZtPz/C6v7sHAHDd1opp2LQDAJrjn3+WCPD6/77uAwAgQnDWFz9dcT9OI7/DEMmrupmEt9uOXIZDowD377bAR1qLhOnOPkY4NXZw80OzDoBSsVqClXWnfRDGcOtyY5KlJnUBVu8mk/EYPi83JlKccyxLJtuGwlOr1jNklyWFjwquH2AUeijhG8MlZpkAsx0/RhyFYHCM18dyMYdHOBbBERDL2FlXObwwRoEAzDK2q3NilywyOJzhp7IfA737Pdo2+wuxLUe3p72ya7cUk85qX/+M/fgDl+CixvFDU5TEt4ZKsSqHHyYoEViZj8Wl+4QnmOWdjGUzZOj3j1SgVlba/fd3U8XQMx9bOpfgo8XLP8sykZoehPLYmli54tr0ghhLxFYA9faz4ncmMBkQUno/jAQLXMMaVrWXrY6pbV8VaGsDPIeU+rwtGRgQ1/SxaYhFz/fddP8e7r+U4v/9oB4MB8Q5H5ECRXRELGBYro/ffPdd+POPnNambKclgy+Vb16UgNjmVhD35zh0MeoJclkWAny5uLSDj4fcHGl8SixwWMDHZUmRcOGxPAuOwemR3aclwzbfh1/nePzig+Z2BUVCSsCP4QYxqBV8pIh8F4dHYS/z8dR2bPdeLATzcRJ5xutPseC3lncLr97Zfeb9kNf50UlolPWrsVUwHwfIrn2z7FpJy3fOvQ/fecsLcO7SnrG/eV5hGnmNx6WyWFuvTDIuTeCjAlinsV2unpain+0kMJ4ndW5ciQG44QhzC/Mxr+QiqRsAjgPu+CgMno9pyeChkrkbPo4cOoQt9/IWOD5d6sDg47333ounPe1p+Jqv+Rq88IUvxPnz4gH1Mz/zM3jJS15yxTfwaj38mhcUHhjcdYAQaNgEwDr4uMaSU22Hyq5lguT1R0aNbGKWVdiK3M1JYQOuDZzcbgTIBACvzSt9OtCt3c9G203w8eTYFWa1ku1waVlJo3uNlBnAVsD1K6xUBwDqGI0t2Xvv9g4EFHVAZa/EsVgxNfuAHfX9CkQwMuTylZQWsPfZ9nbr++5ecKzFzOtlSLZkqjYwpOP5aAJRqdj2PuZjmxFqY5nlA/wj2zJ0wM5W7GOhtu831zJp6yS5W66Vddl1H1PVi/qBclYIgK4PHG2np/d5TfpJP+Ovw87tufaGsi6bbVj/zOUwH+W95CcH845ss5mHMibVcQ2n4u9e0KvFRlbb2tt+jfloZLFvjuXWY19TULjCy1DWJPLA4FnNxFVVRS4TqcX3Ht2eoK65FlCc5xQep3D91ZgcRrER5CzX+m7CRDTX8V5aISIUXhAj8T0UQzwfN2wTbHYIFAxrzMdW/084NsKFeYFffNsduO5QsrJPUOWFOBIDnkPwwXt2mx9TVqOuuQx60di+0AKfc0pcV3/8AcEAf97j5HW2/rykBbaTAD/9r5/edPHjX/NUzeKn6FcxThTzMKsY/PVwGnkfXHcowSceWk1M0pI1Povtfg9FQnL1lpvPNj9WbLOR0wUrR0mCgFcN+0pV4yvWAkJHyQi+JnW7oOL4BRA2GcenEQr4WkLAsqDweQXHj5BIawFTgNGyZPBRwfFDjENPypYNLI/lHhyHYBkcBbF5z1WZAOVIiNoCys0H+KUtS4pr+EO4hp/F6K43advszRZwHIIoSsT9YBkH60w8W7lBGqeCgU4d3kKBUPjTGRY16ipHGEUoiZlZyjmHl10QYD4372dRCIb/gkdguZmVo45ZbmHK1TVvrkWr7De9CEII3NrMpkszYTfhhyEK7hnH8EUprjnBVrQviCjSgompRFkNh1WIwgglCa0gkQIwHYcM8iXs8xftq3kuiBd9zMdFQXG8J/EXEBafgD2pd5FThKhQhYdEQrwFfFTfd0kDBOaVAErghoijWHoWm58dy4JiFPSnLy8KiusOJZhnFagG9ATE8d9xU1TJcVA4VsubrGRIkMIhwCI83huysygodupdOISgrM2QR1YxRIr5GcZGdQEgQbRYMPOsno8ZxantqKOeaxfnwntxFAopsun6U/dpUu3CJYCTm1PNFeA2iXwz81H+/JiF+cg5F8fEdxEHnnFBQwGmowfehRAlvEtmy4p5LkJzprEHxyFGNqwCPU3HV3l3xr6LkYV5m1WrfvYNixlzCdC6XDzHgyjGYmmer+VV3fFh5m5klF1npWATE9cDHAePu/6JuP7Jzzb2/ZlUBwYfX/SiF+E5z3kOdnd3Ecdx8/Ov/dqv7SRXX61PfS1yipHPG2P7TrVkutsb4OM689Emu16fKIgJdht83M8qbIfyMdoGNh1XsAeHyMKAFsjRmpgDdlBkjZEg2usmrHSNFbEC/k5MI/zZTQ/i46f3camTsrkJPk59rpeyyPbNA8gEqhxEdu2sS6lNAMj6ZHwAC4oOBIo67MMe5qMCG/rYlIqVB8jryQSqKlCxByTZkA8PYdLFZlZtXa/2xcakVBOwyQm75yPNsUqotkmC98XxsMlMGrApXv3f9J1N8jjszEflIWkEMuUxazzgbMzXgUCcklMPYT56kf08dL63B0yrsoHM1/X7w3bNtwJnaj1jrfuZNbD0stKu5XjycJiPQ5hw698FANGW7GMA+DgEKO+0l+fRcYXPnpb5aAAfbaBzzUC5C6dFuPNcB7XjIx/AfKSlvO8kY+/4oW0AwMX9zXt1UVD4oPAV6xhAkoy00h3OOfI874TGbY3HxjCb3bRE4ogJVBK6AmzpA3WHLuIAUnbtwTUwH59wbNLMmL/hc6/TLrr5vMQzrt3GzQ+tJpi5lEcG60xJ+RmwEkng4ef/7bMAAN/43Md0x3W1/XIbAeBp12w1Xfyz67c1i58hwCqMQ08AxXIbspLB4+vs2RCgJe6/lOLPbnqweY7Psgrjddm1F8GtxTa0fesUQ2TdssULIkyDGg/sdgGGZUnhOkQyMsS+xVGMgJfYW5u0qUmrYj4en0YoiY/9hf76i90ajvT+Log52XYpPURdP5IsSTO7rcgEIECjw3BoamRNcVYKsB2+kRFIWY2iYvg33t8i3r1V2waQwQT8HFyHGH0r58slXIcgCEPkMI/VlNVwyhkYcY0+clkuUsLDOEZFPHBLqBSvSkRhIpml+jYFrRHXKRyHIKn2jPtZFRmIHyEjMWhmfv7PG+aj+Rmzl1UI2RI/lf0YcO5mYzuv2BX3Oa+NgFaeZ4DjIgwC60KHCHQoEYQxUu4Z3xE4X6mM9g3AhALE4zix+t8CgvE8dgp8d/1HoAuzXU0j8e9Jn57nZgANWIGKfZ6P81y0K2mtZSGub5cN4FpIu4U62ha3nOlcSbsKQA/o5yWFx8WcKYxHgoFoeYYvC9YKQLEx+yiu20kArEKk1ms/qzAlGRBtYY5RL/Mx5CJBmMWHrSxr9f2Tei7uW7Y0jktZVYugMU9YTHBL0N4spxiHPqZrfsHt4lzMBa/Zjo2JzKWUD8e+AAtNqeEKLAzZAq5D4BeXOn636/s7CoSM2wRmLgrB3JxEvhEAz6sa4GiYj6awnllGRV5EsQ/HIQiyc0Yvx1leYRr5IIQIKbQBEOyTXattHlnaiH0Q4KmQXRvGk4LBcwBSi3eVKB4jS83zNRFI1yIJeYExcCarxLsEUe8m1z4H+KpXGPv+TKoDg49/+7d/ix/+4R9GEHRfEB/3uMfhwQcfvGIbdrUuvxaFSmUKNn/Zmowq5uP1R0ab7AAAgyTCTb/i5fSfnJziw/fu4o5zc7GqFbVAt/XtODDzcQAzsNm+dfkdDODjetvVRMZ1CG66bw/f8z9vxP2XMuwkivm4BqQSF2O/toCPPr73D28SniteaAYVN/ZPs70b0kLLcTxIOE37M+1kYhuwo76/l20ngaKGTWlhDA4BdtTP+7wcO56EPQBfw86UrFrtORo4WVcvxckRu4S3SdgewHycXiPAR1s6dR+oqPZBgYqAHpBtg7tWL8e8e60MDZwZJGvvAc0amflA9qE75Htj8acvsGcQ87FtIWCRCq9vbxssvYy0a05L3HQ6BXMlo21ommc7DGYw81HeK+FE/N27wCEBu5Zctr/9JnNNux2GhSRTcVaCwYXrdF+JuBs2bCfjZrEanBYgrW3bGScAIbi4v/kSO88pPFB4wYotNxmPQDXywaxi8OoSTgvc2pqMjWE2e1mFSIGPgYucD2A+dhYZes43K1HBXzFENe1//huehe/4gsfj8594tHvPN+0LnNqO8VDLG0pNbAId89FdPSufes0W3vaSL8K3f/7ju4xr1bfcRkAEBj3t2i1cdyiBB+UPuWaVwgqMQ/GzWV6hpEIa7a97Pkrm97f9H9cDQCdJe7zh/yyO+b95zrWNnBNYAQixsxlmw6scv/G3d3d2eyEZMaQ11jhBhMihG5O2ZcOSFMC77zpwvAgLDfiYFgwjV2xDEnio4IMb0uYFs1OwJEeB8Ic0jUelDF1i4Ta4WqBbK1ZzOLRAFCcoEKA2SGaXBYPLKf5N+ke44b5f07YBxETwsCNATy89p20zXy5BHA9JFIj7wfBMPDPLMa4XuOQdBzEsFuZZCkII4kjIqWtAex9yLkDJIIrB3NAK7iY8havS2A33Ki1zuH4oksQzSxJrQXEsKBDl+mMBAHtpievre3ENP4v49PuM7VBl4F4kQCgToJVl4I6P0HPkWGPeTx8UYRRjWZvB2N20wkICc+aUW7oCH4n5GlL9fZ53O55XvBPxmQ8Y2w3xF+Wc42t+8T34+bfdof09qzkWOcWJrcgKxgFSdi3DMlIL+1Td17ak3kUuji2PtsV1Z2Adv/fOFfiqA5yKUgB68ALEcWINlxJ9UIxCVzAfbftQMpzcjppt1dV+VmKCFG48xV4dI903A8XLgiEgFYjjIB5tg1jIBEquPKpnzT1WGlhqecUQQLw/h/HIyFZW+zGNPEwjz+glWtAaJa1xSibP64BAde4V89Eku1af9SuxH5N6bk2BTkIl49Zvm2Juji1p1+oaFmnXjlF2PZeAIhbn4BCCrXpPAJeG/ZhE4tm3bQANWc1R9DAW1TbHgWvsB5AsWRk4YwI6F0WFaeiA8Fpc+6MRaJ4awdZMeULL9w3ix0bLnFQyH9vvb1dL1IHBx7quwdjmSXnggQcwmUyuyEZdrStT87zC2K03X+aBDvPu2FTcRE89ORaAi7MmuzaFmRjSrsEKPE96Lv3Gu+4GZRw7av6ha39QZs36ZN4KOpjTtTu1HsqijgEt8Z1f+FkIfeEv9Z47Luhl13K7xl6tfxjJ9h9/cB9//pHTMAbOtEHFg8quhwAgqq0OVDP1P5j5aAGygBWbTfVpA8baCaY2MFP1ZQUz27LCHjCnCWyxsDib71VyX8v+AsIDj5VmxpsC73qZjzNg6xpxjdh8NRVz07T9ABrZtY2F2pFdG8YA1a7DVLWBlPL82/pr+mydV1uCet952Ng+m+xaMS4tTEr1vUPvj+aa72Fq6/ruuyZ6apmm+POPncftl+T9PnisbQVOHWR8dgPJAHSHLXB0gqr6QNn1ccy0gKORzKrvM1RdUzDiwm17DQIgXigkj5ZaqhfMlrcycRxwN8TefBPEWOTCsLwdBDcZjbQMnrmcWKpwGgDYtoTZ7KclIiLGsNj3jInHneowyC3MZdm2ggdfgbSa6+Op12zhG2RgnCkY7dR21FiZAC3wERTaBU3dM7s9/gPymU0619Grvv6Z+K0XPGfz3UFtCy1wWJrWf+T+ffzsX30CAITnoyac5p9/lghT2k1LvPnjZ3DjfbsbHo5qHx9/ZIwP3rPbLEbO8gqj0INTby4GftYhH7vLssNmUcmgXZZ1hIhsgo+NRJvTZpzxwhjL5eZEe1lSsc1eKBiDXihAC839sSwpRm4twWy7jL/MU+FtFU0lCLI5fi5LwYCL4hFKEhi9JudF1TAao3JX2wYQE+QdV/i/uYWeLVXkGeCKsKaUB8bnxJ3nlxgjRTk6afSRy3IBsLp+uDpumvsrr2p4vIQfJtYFr2XBkCAHdyNwcKM9Cy0zuH6EAqF2kULVLKd4UfHreOn5/2Jss59VOMHPwnUISKoHeRSzugwPSR9BAxhbiAWkyHeR1/qAI0Bcn4lL4QURSh6AGlhlKiH+icfHRiBQAJkV/CiG60fGawgQgP+1zi4ICLzFaXO7fLWYYCp1z91xTs88Vd55x6eRFYzjnAuG5MTHlM+snnvzgiLwHKtf4lwejyDZAuMwnoO9rMQ1OzEch2iBlaosVsFmyUiCj/3Mx8jCigPEOKY8/nTHhXOOs7MCCSkxnU6RkhgfuctMZMpKhqlXg7ghRpMpOKOCsKApBe7FbA4iF0Vnu/prPq9WnpdhlFhBzVleYRJ5GIee8fwpQOzUtgQfNdeW+uwocHEoCbRyeNXOcwnccgYnmiBBps8WkG1Hgdg2k9x7JoNfBGvVlBQtj51kPpoA9XPzQhCYijkcQrDN940g6kzKrgFgK/E3GPzie1egp4mxuCwofNdB4DnGfgDpHem72I4DZCXrBFCpmucU24FanAwxHk8QosRDhtRuAVKvFhBdP0RpAh+lyoCsYwVX6+Dg45d/+ZfjNa95TfN/QggWiwV+9Ed/FF/5lV95Jbftal1mLQqGxOebYCLQvEwDwGcdHeNPvvuf46knJGChZScOmOSptrSE5zp46jVb+MA9wpvisFKYadsfYELc3j4bmAisAJb2d6mfa/eltd+ENJOIZ163jTe96PPxuCMjABDgo4EhOvaEh8cGJZ6V4OtSQBObVLF2HBcAsRz7dqKrQcat+hzy3e1SIBbQA+y0zonrA8RsSL/pZTdALt/H6FRtbPvfASktYI562VJyaqAHfOyTIyvwcVv8bWOPNsCohflYzIHpqdW/TX11fDV7mI82FuoGyGoAAdvgXp/8eWj4SjvFGvqJMRQA05eK3R4H+li/jYx71JPwXqzARBvbs+NfamFet4sd4Dj1FKMlKuKLMABgeF/t62go83LDq3UAk9ELVmPtILBywDhmkl1bwM2aysCZtTci4ukBxHapyXAbIBSfDbA/37xPLywKTALe2catyQh1tZliPMsqeKBwWv6Qh6YTiPDmzX3fSytEqCTY4iKv3QHXWwuYszGcZVtKPCvzcaNvDVv18UfHeGA3bZ6TiinhE9ZlrQLy2jAswKntBuRY1n1euA4RCeYqrK7dt9yWJBDP0f/xvnvxtk8IxpjPNZYt0h8SEJOoX3q7YEAlax6Oqu01ctKpwiJmGcU09tAkybf277OPiP/feX51rS0LiiRw0VhpyLYBYZil3WOelkwye1ZjXRDGSDV+pcuCyZCcUB4GmWKtuU6WBcXYFYDmOPSQWcA7WmYgjgM33jJKNgWYXmE0EsxHEwNuUVBs8314DgG32FRkFcM2EcxHtzL4NBY5uBtgHHpIa7Pk90P37uKQmyOLT8CjC+2zrmglz/tBaN7PokIAiiAMQfzI+A6zLClinqFMjkrJrH4fWFnAD2PBtrTIQec5xROqW4V81rDALJKFRYgVyfTecWlJEfAKdbQjfQT121UVGbgbIvJdZNwMPi4LisSR4CPxQQv9NbQsKUCAY5PQyJJbFsKTNQgiuGECbhl/ZnmF484uHAI4BqAVEMetLwDmljOCcRp6rv67JNh0fBpaZe8FrUEZx7Pu+S38QvoyLFPz83VRUJzajjuetBttcuGnGccxcu6DG94hd5cVjoxDmVy8uX20zAXzUYGPBl/h9raNQw9xYAcflwXFlgwG0bH/Li5L7C5L7IQcW+MJ/GiMoDYfE7EgwgAvxHQiF+IM+6zAvYjO4eyIBbHZTL+YkUk5P7wIUTyCw0qjxH6hwLvQQ1YyrQRagY/X7IjngE5Src5DEno4PA6NaeuLXLLg8xmcrWswQoYLhrZZ4/ko7ER0LMR5TjEJBUBpkv4rpmMSeNLzUX+O7zq/wGefmAjw0Q8x4qlx4WCWicAZANiO9SEwWQv0tMmuk1DchztJYPSHVZ6V24l4fuqAzEVBsdUiR0XxCLFTaVO7Wc1RUinPl898NxB+3br7My1VwJyGAPYZXgcGH1/5ylfiPe95D57ylKcgz3N84zd+YyO5/pmf+ZlPxjZerYdZ87zCyDXIrltMOULIis0HaGRPJnlbudl3azLyff/iSc2PJ568MTcmFhbQSPd96jNAazI/UHatEkN1LyvrUj0A63Lzp5wUBvcnt2JseC7K7Rq5YuDc8GhhJZgCAAgsgG5rgt1MpnTAy7rs2sLokmDe3999SdDVPcNkrvOZdSDBBAC2gL0+EEEFqwAwhhgBGuCzB9zzIrs0vMPOtIApnWAaC/jYZlJawbYW89HUl/peL5ShID3A2OiY+LcJfFwP8ehLHgfM55eVAkx23NW9pjtnbXDPxlRVrFKgn2HVyK4tDM7m2gvs56ENFPYxrWkm+vIjMxBcM8EQbzOUBwHp6pz0gUEt8OwyPR9ZVaCCjwVVTO4DSKjVdXSQ8Vndt348IL266o7lgwJn2otJpueSyb/X3D+nFBQtL0NZ4yTGPWd3cdtZs8/aohABMuvSGsePcHG2OWk/O8sx9dEZv3emE3i82mC4zCTz0W2BVYe3JgDnmGsktXtZ1SQ1J6GLZX0A2XXf4gwAsAolvFbgTM/1uRE4I66no+MQnG8GPUTrQB5gvjZYCYB0n8OuYX917zbuCqh81mN2cH8r1MExBM64DsE48nB+XqzAEW2QTY6nXSs8JwNPHCvld7UBonuRYKsC+J7/eWPzYyW77oCVXgCHEMyX3bGpkWi3FnjCKNGGJQkgqMXcCCLJ4Ns8bkKiLa6nUeghs7DbqiIDvBBBZGZNLQuKkJdI4hEqS/L0PKdChukQxGxmZjaVDFOSYhkehcdSrbqgLDIQuf3L2jeO66cvLTDyGMroKAjTqwvKPJPS1AhhZAZoloUAMoIwAfxYnBft9lOMSAY2OmFlGNZVjiCMUJAAtS0hPK8Em5Vz1Ivz2jYi3EOAqH6uB+TU9rvRGAX3jYzMqhJ2EwJ8tMmuGRKHwQ9jK3szlcm/09gclrEsKCKHwfFCeIH52ALiPXybCKm8X5gZtPO8akA+U915btF8v67UGHZ8GqFitZZl1W63M7sVAFDtmpOs5znFya0Idc2NstdlUcHlFHGSIEeAysDUv7QscWgkz5WmL1blAhBwA0xGQzwfqQCmLEnIgFAGjAJzMIhiu0bKbzEeo8rsnntjT4CP21uC7Wnyjm0Y9byAOzkOAHjrjZuBKJX0XvS5UNIkyQgBCqOcVzEfR4EAwHSswFlWwXEIppGQVOtZfqvglEOjALtpqQUy5xLoRbkAmZzAxC3NLMlCHG9lJ6JjIQrZtS8Wt6CX/re3zXaOFwXDJHSBcglnegoJMu19RFmNs7NczJ8BTGNP62XaALKNn6OeMTqSi4bbiY9dg6Q6V6nZEnzUfV9WMkw8ea96AYgfY9unWuZjIYO8fLQW78IYLsu1IG9aKYuTq8zH9Tow+HjttdfiIx/5CH7oh34I3/d934dnPetZ+Omf/mnceOONOHbs2CdjG6/Ww6xlQRGZZNc6wMEGPh5Idi3aXi+ZggDgqCS/jYnFAWV97T76vMK0npQGJp2pbQukUy8NT71mqt93L0DiigFow/eRVSi4GOgjz7VPktb9o9aPvZJIOZuTI22xEoz4+IE//Sh++z1329mEqjpssR5mF9AFEUzAZgfw6pFJt9litu8mrgDHrLLzNVmhKUimw/QLuz/rtGv3Z5msV7nYvmAs/29hBPYlZwPid8kh8W+Th2TjoydZqLbwF3WMTcCtYoEqdqT62Xq1wb0+j05vANikEkQ7klyb7LKP+ViAO55Yxe679tuy9R4Z/z+czXH72bkZ+FcMyfVAjAFMtAfntQir6LsmeqquhEx2n0q2xmDwsVjdLwdJuz4IY7PjAdoDeq33D8Aou15nPg6SXVfC83FNdv2kaw5j4jG841azh5piProt2TUAJEmCB87vNS+sqs7PC0z8rlR3azJGgBIP7Xfvw1leYexzkBaAd3RbyMd0jMz9ZQ6XcAE++h7SeoD8veP52M8MruDDf7jMR/ncU1LnS0vx3fNGkqxRU/QtfrbPmc0iRn2/qtbz/b9/7VObH//3r32aAJ82rEpE20VO8VNvvKX51dGEYJORK9p+/hOPNPK4B3ZTwcAwgOgKtFB1fl7g6DjcWIxzHWB/LcV6qSanLZuHKIoFILhWKnBmlaCdyPR0DfOxpIicGnBDbMU+FrWnBe8qVgNVDuJHCOLEKLue5xUCVAiiGNwytiwKisOSncc5N6bfpiXDFEtk0QkBeGqei2WRC/AxcLFkhpAqCD9HhxAg3hF9aba/LCTz0QsRJ2ZfvEUugnrCOBHjgiFobFEwTJ0cLD6CGo4W5KtYDYeJsBZbOBDnHHmWNQso5UwPPgrAVrDcnFK/qLIoqgY8TRGBGcBHVorFhch3BDBtOLbLkiJ2qGBvEt8IoC5LAWjZ/O/SkiEiDPAChHEMXptlt/tZhTER58wr97RtAAHyXbMdW9OuFdPMBPrMcxEQdXgkxg0TU0wBMz5KEZqyb5aDLyQo2v5cuzjn8pokGMUJSuJrrRYAwfraSXxteAjnHLQqG+bjoa0xOOe4pAlMU6VAxdh3jfuqgqNGoSfARw1IN8+FpNirBfDnhGOwwuyRvixXPrs7022Ac+zP9JYLKqHZ4yVIkCB3Ytx676akWx0PT1piJKMxAl4ZQS3lXRhL8E63//tZha1YhqskepZfKoNfXIfgyDgA59AmQKdqfKcFkBzGGDkuLs0LGknoNd6KOhbiPBf9jSRAqTsvaUkRym2LfReUcS1DcllQTH0KcAYyPYkJybQg835WgTKOE1MxV5gaAnZW4KNYhCgqtnG9qkUKADK4xswCjTwH40Cka+t8H3MFEAJQ9mFbHu14Uq/aqutp9Z4ShDECVNowpaykUmVwFXxcrwODjwDgeR6+6Zu+CT/7sz+LX/7lX8a3f/u3d5Kvr9ajo+Y5FWCYTnatC5HR+SIBFoaJhv1nCwFQ37vefnDgzFofDaPFIJUwgaNGr0U76PqvnnUNPvvEBNtN4Mxm+7FXw3EIzs3Xj22Bopbgo+/ALLvWSAbX90+9xA6VUrMS+6V4Ia0YtwN1qn/eGjBtE1JWrgBAwA5WdMAnG1jaYnqYAAZgJdtU32sEFVsMGS8UrLVa58vZlnFbgO2Gcamk0hbmoxeukqf7jktfsnOVA+FU7IuR5SfZhb0s1LLFBjQtLrQA+R4QkLsBfve99yC1BVx0wCkL2LQOFgOG81B021k8H99y2z7+r5/7WztIKdk6CybTwk3MR/n5n33LXfhPf3ST+VqW19ilnON333tP/2JJa3tf8/Z78W2v+8AwEM/aVwFKfMwV87GPjahKgdMHscXoyK57wEQ1xgxNIQc2WXS2ILT2c0klY1tl11QGznTBR9cXktjbzpqZGPuZBAjXngeT0RiEFvjlt9/Z+fnZeSESklu2GUEQIXZYk/SqapZVYlW+9RxLohC142FvtjlBy7IULgHgSnkdMwMCTanj5bh20F22LeEKKTPQv3i4nnYtmY/jUEi3Ly2lH2Im/Q0Nz1U947roXg+AncWt+mq3lddP5Lv4vOsP4UuffAw3fNZhDUC46vcZ120DAB57OMFbXvyFOBaT1fi41vbIOMT5eQHKarzpH87gxvv2OgCh2CbR/t8+9zGNRA8AHtzLxP874VYhAtfBTXed7UgCb7xvTzAsW/dgFCeg5aYcLC2pCCWSx2IyGkmPP723ViwZSTsjH1ntaSWzl5YlAlTwghixJSl3UTBEhIJ4kRV8nOcUh70MdHQcNQe4gemflRQjpMiTU+LRrwEpaZHD9QOMQg9z5psDc0oh30a8bdz+skxBHPG+M0kiMOhtZuZFhZBQeH4I4scWZinFhORAOEGKCNCAgUqq7ocxatfM8M+rGmG9hKfAx7kZfJwgRelP4FE9yLMoGAJeIohiZCRClerB35rmDfNxycwLHYuCIiIVPD8CdULUBqZaJgGtSWhmPs6LCrEjFif9ULJPDd87yykSZKD+GK4hiIWyGlnJcHIrRknNjMULCxGUZQIoFZvMBugArXCocgaHAPX8jLYdII7bya2o6X+9ClrD4xUIEdYdJUIslvpnlfJo1IGFBa3hy37gBXjC8Sng+rj5fv011AYVo8DMilOp36NQsAR1cu+0pIgDrxnPnWAEYjhXgASeJBv78I5gl1/a29O2zSomvBLl++72zmFcN9o8L+p4eFyMzXGcIESJfUNC8lx6FyrLDt1+Caa7khjrA0+WJcNIyoePS1DuoT3duEkxDhzx7EgOI0GOC3MDy7ikDfNRAG76cV1sv9v8f71UWAuABmTVyeuXJRVJ5QAwvQZTJzewLcV3NJ6Psa9nIlYMhACh5zRy6XUyz7KkzX22bQB2VV9x4DYMVN05KGjdkIaE9UyCsUvx0Gzz+KrrXMjzpb1JlMBHhV3NokRaMtH31cCZjTow+Oi6Lr74i78Yly51vULOnj0L19V7YVytT03NbcxHHUNA54sEWIAyney6Oxl51mN28LwnHDEDm31A2Pr3Of6K6eBYQDwAHe+0ZvsOICFfYx0+87pt/Mq/e3ar/aY3lVNX2En8zYGIVci5GCxpzeXxNwBgBhZFp43avma/LFJqVmGmPuIQ/bnX9d8OyrCyGdcYIraQEzWhM/l4qT47stPc7DXY+O5F9nRqJQu3eSF20rNVO8Okt2lnAz1zAWIp8NEEZnXCVSzAmGIY+hagbT3UxxbW0zm/uuOxJqdWn9Ns/4ISvO499+C2C6UVBD+9qIUPrJXRqsDdaOW9qQPNOvJ3O5j5d/fOwGoObhtvaIGq5vhPf3oLzmQ2gLdEzTkqCBaBETiT99Gf3HQWr3vPPSjhd35uLFqiIhKYuhzZtWRIl/CwV8pH/UFZjH2BP53PdOWhvd6awBpTsv+4bIBHRvb4wGeYLM4qETizBj7CDbEVAh++d9fIeLm4KLEdbNqbeEGIqc9EwFirzs1yJOshJV4In3C85s23dMCieU4x9rl2f2aL7gSNc44sy+A4YpyLAxcL5g0AdYu1McMGPlaouAffOQjzcU3qTEWq6k4SdJiPQpKsD3IbrLyw2ZQAaxLt7jX30//66fihr3pKa/GtK41W19p/+5efg39ycoJveu5jxfWyDlS2nq/biY/33nkRr3zLbQCAF33pE7vPNwDKgqQdOFDXHA/sZiIptc18dEOEvosAFV5/o2DwFJTh7Z84h3suLDvbEicj+HW5MTGb51SCN+LYTcdjMINMOi0ZEkd4Ph4ZhygRINNIuc/PC/i8ghdEiKMIjBPtmD3PKyGv9CNwLwYxjC3LgmKbZKgn1wCco1jqmU1pyRDzXIKUfMOOhNVcyEn9SDA3qYPacL1WuWCQOcmOCH/RjP9VKRbaAMHcKaG/v5YSZIUXwgsjI5i5LBnGpIATjiT4uAm6LHIR0uOHii2qfy7NiwojnsJ1nEUspgABAABJREFUHFDiGQHDTEq9s+gYPKp/J1kWgrHjBRFyRKCFvl1dlSBeKNh03DcmBKcFQyiPhxvExnZLydp6wt2/h+sW/6Bts8hp01cYJUa/UkAsDCU8Q2HZVwUmNonMBnDxof0cTzg2NgKUc+nLN75wEz6H3WKUZ8/yCqHvwikXIISgzvTXturzxDQCiN4zMCuZ8AQmQBhGYG6ALNWDj6mUqsYaEDArmbAOkQtXhBDUbojC6M25AhV1TMqmnTwG49AzhptkJUPiS29iL4IbjUEs3udpyRrPxzgZw3UIzl3aM+6zADaFJQQJEnCNZ6oCldxavMM4QYKIVEbf0ZlMu25kyzrwMaOYxmKc30l87ftDKpm+oCWiB9+LQ4mPBzWMu3lOsRXIcTI5AtcBZjMzGzwJhIXMduxjd2nwTAxcjAIPgec00vf1NrHfBR/14DHDhMjPT09iSrJmUbFd6r6aSPBxEnmbCkGspNKEEBFkg025dFp2mY+LXJOzACCrakRyH0wgZV4xxB3mY4iQF3jfnRdxbg2AbBiyWD0/XT/CyN0MgVPbqbWSuVoHBx855yiKAs95znPw8Y9/fON3V+vRU4tceKNsgImAfrJnlF0b2GeWtGtVr/z6Z+D/+VdPbfU9cLKgKy3g18PK04GjponM0JRv1V4LVhbYijWpZaxEVq8GcG5k7az162iYKzqQ2DS5lgDEjIrJYs15M9kxVgNutsEpC9OuwxAxHN/1kI4emfiGr6fu+qN5i71nAxXbYOYAULHPv5IV4tg7jp1JqYBANzAH8ahJnwLajABlJb6n8YY0nY8W4D40edoInrX7skhXWYmllPXWVn/QEr/wznvxsj/5aD+gDaDXe5MW4BDy59q1MVBLVBL4yy1praA5WM1RER9z6lsl6wWtUcHH0YkFNF4D8c8vS/t4BQCcg7Oi2V5xbixsWFuxCqwGKHzMKwj239DwmAZ8tIDO2s+0mY82UKoFHAPDmY9DZNfGsdk85nEmZNfOmuwaXohrJuLa/pV33KH97IVFISYGa4EzcEN88RO2cWy6+nleMVxclmI1fA18dB0CHxR3XVgBEOfnBab+5ssr8SOcX2N7ZBUDaCH2wfUxCl0s6IDAmfaiUF+6OitQcm8481EbOCPO83ayYoPM8kqGsfTbn7S3ZfBipm7x08ac3Wi7er5uxT5++ZuejS97yvHWdqwBlawEOMe/fOY1AIA3f0ywm778c07on5m0wHbiN4mcP/JnH8Msq3BqGnT9nb0AnkNw/Y6H3WWJWV417xo7id9ZVEqSBAGqjWCC3bR7/e1Mx5L5qJ+ACqAnwE4SoESANN0EyC4sCkx9CjdIBMMJ+nFLAEdi/4XXJPTMwbzC1EnhTI6DwUE21/v1pSVDiBIk3pGBLd3n57Kk8EDhBSEOjQKUJDSGndRlKtYnkx3UNbRjJS3z5rhtxb7wQ9SMK4uialLnHT8xMx9LiohQOL4I4NF5183yCj6nCCLFfNSPk1nJJPgIXCCHQDM9OLEsGUZ8iTw+AbcuDHJwERDjBjFKEmjl+wDAaQHHDzGJPFTwjV6Oy5IilGEeQZQYZddpwbDlVjh52+/jm/Lf14Jas1ywKOH6iOLEmsq8n1UI6xxlckx4gmpKMbJObSl5s55FdWY/xxOPCwsdE1NsFLhI3vg9eFnxC1pABxD3wE7AAFaCuhHqXA8+KpBzEvkYh56WCZpVDAGvpFw6BLwYWWoClEVIR6JhKqaV8PhUsmsAqJ0AzHQ+W6CiAB/1bFHVbiS/N9Uct1QGpKh30vFkC6xYGvGEtKSIiVgQUX7kl/ZMsmuG2Jfv3n4MJxiBl+lGsJvafqeWcxQvhANhY7Bedc2xkJ6PoeeAECCrDL6Ekp1398V0YxFSHB/JfLzlDcCfvwjPGF3Amf3N70xLii1fHuNkB67jYDk3LMi0JMk7I32CtgIWHYfgsYcT3HNRA8iq81KmOHrvX4LwGudmGmsOxd4GgImQXev2QYHuyi96GvuYZZtBSkIqLbZf+Tquy9+bMDbACFACQN4CUEVytoH56Mj7Qc6vtnzx/9vPdYH8hiFbdy2GJi7TyuWzBny8GjizXgcGHwkh+NM//VN89Vd/NW644Qb82Z/9Wed3V+vRU4tC+fWY0q4Hyq5NEzftRCEwTBSUPHftkusLgGjXOvMF6GX86ScnOqBF7+FonLDWuoAaIZG+/kjSSa0U214gYxKgqTmY4+snzuv9uprj2eNf1e2PAeCYSdl1VrL+Y954Gg5gJa1LyHpYYB2ZtCWYZMlc/P777z04qGi8TltAqqm/DcZnoAd+OsCdYuZp2lW5AGEU41LXRp1LL7ADNkPDdWj3oWgGz9faacHYcnM/DQzJtBZjzIJKAEGXUMoqLGkLtOjx3vz9D57B/fsUANFfKzRHSWu85H/dijt3mRncZYUAYQAxWbQcY1ZzMcHmgZWpWtAaJfHEaq7xmhfnlksW435a9YNsNUVd8wZ8rBzDeDqkWAnGOSiRjIMhXq+q1LlX533I5zr3Wc9+bowxAxie62CTybKj1iwkeQagUhZnFJUmcAaujxAV/tlnHW4kVut1YVFgGkDzLIww8ih2l1Xzgv3Aboa65pissxndEIfHAXxUnUnr7efmODFyN/r2gwh3PtQFZPYz4acn2O0BYt/DgjpGv7mm2uOZG9gBahk40027toGPa0Bba0GvnVI5y6hgPmqfq4Zx7CAMV93ip2kBxCTRNi6WrMuo5TbRAluxj1c9/5n48s85ge/4gsdLpmTRfSeT98pUenTdfm6O994pgkAU8N25TwA8+WiI+3dT/KtffA9+5+/uBQC87gXPArBi4IZSDrYOlOylFeLWhOjoNEFVO9pjnJYUIYTCIPAcOEGIhQbc2EsrMTl2AyShh4L7WuBikVcNCOVHCjjSgJQFwxQZvGQLSzJCYWDxpaWQJHvxGCV3N/qaZRUCLuTg24mPEvrtKiiDy0o4hMCfHBLMR811zcoCRJ7ro5NQPPdMno9EgMZ+GInEaM1+CoZkBTcIUUKfAj3LK0QOhecLqToxjMV5VWPEl3CCBHMyActNUnWGqM5Ak2PSJ3OTKafSqf0glKDo5jlXYTuuH2A7DlASM1iVFgwBBIgdxYnRt3JZUpwiF+AQAo8zLQtxUahjG4pUZgv4OM8rhHWKcPsUXJqi1IKZYmw4ta3kzXpvxZwyHJuY2ZGpBLoIr+E5BA/t6aXD85ziaCDutUV4HCzVg0jqO8aRJ/zxDBJVARpCskoj5Jn+e1VIh052Ldi1FRzHaSyUajfUguFAF1SMA0cbuCK+cxUekoQuUs3xF+w/t2F4jydThHXafHazPZPMbTF+cS82sj2zSrAAUWWAG8CLRgh5gcXa9qqUdUeBSl4MhxBk+eZ1n1YMnAOTSPg5mhidbeDrGz9PJG1veBcq5uPefQCAJ3tntEEni4Ji4snPRjtwHYIHzu/qJdUlbZiKh5IANz2wpzkutHmfeezhEe65qLFwqSSIedfbEbzrp/Cc6H7cdaF7nFWw0ojId7Nwihildh+WBcM4XKljp5GPitUo1ljESioNQKg4AEFYaJVidwIr8FGbnN3q6/AowIXF5tiZV0zYkChrLj/C2KU4Po02+sylJFwE0q2eyWOPGQOFrjIf9fWwmI+u6+Lnfu7n8IpXvALPf/7z8RM/8RNXWY+PkmI1xw++/qN4523nxYOXGC58nexVJ+cFLMAW3exbMR/XrwcdC7Hp+wCy6w12iU12XW4CrzZPKC2r0gbe6IDXAo89PNqkzrMK+yXw+KMihIeaGFgbMjUN8KtN7jSBquKze/KrsooNmDDqJJE2pt0AELQtVQbMwIHs8wP3L/Fbf3s3ZioowwQqtgNsAAPAV4K5Ab73D27EXiEnzX2ya9VnLyhnAT3bk3oTW7Ej9Y4swF0LGDWBlI2PXnv7zey9h8V8NICASyYeJZkEITfZuoIdSiF/r/o1eUhy4A9vPIdffPudZuYtLUElUHc+J/o2cjsU8G8zxQctwGqOAgH2KktQBy0EI2E0WoH5JnAEQFaLY7OXlfpxd30bOG+OUwkDAD6gOC1Q1xyHt8ZictDHams+2ArKscne16s9xvcy6NYAnh5ZtPjMehiXiRFnGst7ZNcaz0e1UHPdToIzmhdqQMiuhS/j2rPJCzBya1SsbiZS52Y5As8RidRrQCoBcHKEjnznwd0MOxE2np1+GKPMM/zlRx9qfrafVVKCJ8DHJHAxZwNSztvHa8B5K7kL32kzHw8gu24t6B0aBc0xneeV8IIyLgKanpXrx9zChgVW3sRNWxtQuS67tjH1zQFHz7xuGz/wf/0TfIOcgG7KtMWi5SEZwqMSr59x3Ta2Qvke1bYrAXByRPAPDwjQ4q8/fgaj0Gsldoo2ji9StNsTY8459tKqkQQDwLFpiIz7KDXS2qyqEWB1TnamU2QaT7llSaUUMsIocFGQEGW+OaFNs0xcn16ISIGPBnl2TEoE8RgZiVEt9zbaAGJyF/ASoyRBxgOwtX1ISwHOeEGEceiBOYFW8psWTAaAAFE8Rs59cI3vHK1W4OOJrQipwedwXlAJtoXCq9HAfFwWghHohwlyEqLUgB2LnErpewjiRSCG50FWMSTI4EQTZCQ2go9pSRHyHPXouPTJ1IOPkUPhSeYj04CiCvhyPcl8JGbZ9bIU0nG4IaJ4JOT2un0oGY7wXREQA6oFAgVQJoDMODF7PhaUgVEGj6ZIDl8Lwmvsa9hi85zCdx2MQyFB1X1nQWuAC8BZfWa90pLisCvOn+sSzM5vBpuoY3HEE9dCMToFZpBdt9mFgiWuZ1b5qOBAjPluEGuvIWDFiIs1zMdZXiEgTASbSRIRd0PUlf5Z0AYVVXDIOptQfOcqUCXxPS3zMasYEt9plFHjyTYiXhiZo8uSysAh+W7iJ8iWBqBdgU+sBPwYXjhCiEIsBLfbSaCQqPdiPwIhBIUGyFXMWLVYNApdrTw7q1bg4zOuE96U69LcRSH8GTETrMhrvH285eazG3jKPKdCAQEA0RZi30GAEvdf0rEkWcMYjAMX77vz4sZ8tC1bfsrJKW66f0/jDSzbXLoLAPB5WzPhWdxuI1OyY0fOxYIEgVPj3N7mmDLPqyYEB0CTQL3OGMwq1kilAeCrn3EKADrX16KgjVem8tVcZzVyzjt9XbsT44HdzeNV0Fouxslr3xPkjp2Rv7FtuZRxE9pW+YQYuVQrNc8qea3q1Kef4fWwAmdUfcd3fAfe9KY34TWveQ2++Zu/+Upt09W6jHIdgvffdQk/9oaPgzKOwCS71gbOKF8kHfPA9PKvYSLqZKi6FEvADJrp6iCSrKb9+uTEIAvWpmxamELraZgAlDeVdpBjJS7mwGefEEmlJTzzxGdD7mWQXW+Y55tZHAp8TEtmZrKq2vBjs7CSpGTvR/73x3DT/XuWCd06q9De50LKxBdMedUZmG/w8V2/9yHsV6T7PWv9pczFxx7cx42nM0t/hWDnKsDaAspRJ8CXvOIduGcmXwhMwKLyezQBaO3jYgumUf03zEfNC+bGMbawz9r3hokJSwswJ8C//63348GZZC5rAfMCe4UEH7kB7GClUJhLUI06QfNZXX9Meir6HjGzqWmOknsAIQJkMexDzXnjoZhxiwcezUHrGiUJsFu5FiC4AK05Hn9iR7yE97B9L+XyWs4tLMnWZ+oazfZW5OEzH7M8A+ccR7cnWBZs+FjbZuOqa2QI87Ht/9rLoFsDH4cwH9e98oyyWc3Y3GPvITwfnU3w0Reg0+FRoE0zBIALyxLjdQ9HQDBziHgOKg/gM7Mcx6cRCFtnuIvjfChaGcQvCzH5nvjY6PvwdAwfFd72iXPNzxSjTbFgksDFfuVI1lUPENyxw7Cft4K3GKJDPB814SoAcO1OgrOzHG/7xFn87e0XpOfjARYBDwIy19VqctHXtm2/0W471FvatiAFbD7jJbA5Dlz85y//7ObH3/mFj99kCDsO4PjYCesOAHJ8Gm7eU24gwcdVu6xiqFgt2Rih/GyECh5mmvR0BW40QOXOFKnG83FZUIwc4eU4Cj2U8LUgSJZnMi06QmgJplmqgJIgRkEiVJnJx47B4yWmkwlyhJivpYAL8JHC9QMQQhDEIy1AtpQMT8fxMIojFAg2tp+yGkTKjAEBPuTcR6Vh+y2zHB4RNileEEt5uQb0LBkClAijBCV8VBoAuPHo9EIgiOFQvWIgLSm2nAwknKL0RuC5WXYd8ALO+LCR+bgoGGJSwQ0iAT5q3jeWJYXHGdwggOMQBGGE2jDOZCWDzwXjNUnG4h7QPFtFeMUSDiGY8jnmqZ4F5nPBqB4ltmuIIUIOh3CEhwSAsZztbbTbz4TlAyEE49DTshoVU3Ar9uG5xJAOzHDIEcfScwiWlx7aaAOI87njKZnqKeN5UozMSeRJ1pYGfKykLQKBYPaFCUoNUFxSsQg2Cj1E/ib4OM8Fc5m0xiXu6oF6QICKoe/CrZa4/pZfQciWWkB22QK5EmPgDMNUSYq9AKPxBDFyLUtNfDdDRFbPLCdItCAhsAIVUWWCvR2PEPFig9GWlgoAlf16ERwC7QLKLOsGpxyfRlqmXyMnBxrQbd3jMC0ZktAD5uJaGZWC8X5+bd/Tkq2Yj/E2HEIwdiojAzeRwNz3fukTRX9rQG7W2ranXbuFC/NiAxjNSgEcY3EWAPDYcLGxAKsYpMoXGH4C1yHYm+1veDAuVGK3rGOTEIRg49gVLdAWAL72WcK6pA0EXlgUODIW47DnOjg2DfHAbneMqhhHXfOmr2t3Etx8en+DpZtXyp9XLZxHQE2xHbkbx+TissBOEnSxDzdE4jCc1QQAZcq25KrseqMODD4+9rGP7QTLfPEXfzHe97734f7777+iG3a1Hn792r9/dvPvkNhk1xrmo04abZIfm8BAYPOlXsdoACQYaJbD9fah5JtD29tk1+ugq012bZr4sEok4uW082DYXyzw0Jzi864/DAAifGJ9vznflJJ54SaQ27A41szzLewrBYBYmVrtfVPfrf42SqQL1I6P99xxAb/33nvNIEJbNtz0aQbGlIxXeQma5L77JcFtZ+e4Z49Z2uWCoQBg1xa8sRGeYwblci7O0Ucfyptt1n1vB2zVvcjJ7XjnXTNcVKxMbbjKGkhp6asrfdVsl/QBLeALGYhFNpzWLh7czfDu28+bgWVaYlf+OFfMx/XtkxJgBaql9UqaqOuPMuGpOA59M+uI5igk2LlUwRr1mvcQqxp2pPheO/goZNc+Zk069OaLPKcFqprg+mNbKCom/CZNwScAdgvxEiakzz0gG1uF2QBKJv7wmI/7MpDk6PZEvHANDa+R2/Bjb7x9dU0O+lxLYntQ5uMQz8d6xdZqPmtaSNrw7+1jPlIweJuej5KxvDMKtGmGt52d49wsx8jTBLt5gZTzAO+49RxYzXF2VogAAQNbbifkjb/RWWl2rgM2iR/hGScjHJmsfn5uXuBIJOahcEXgTMl9o69eUzQfDgKzCgX34Dey6/7reQNok36IO9Lz8Sf+4hbxK5dsPv8AM/C3LncG7ItwumvCsmA3WHatk5YDB2RKimf/Vz39JP7X/+95+NfPvhZPPDZZ7fda8vZjpt1wx2c/9pB20TAitCNfVNdWgNWE6PAoQEl8zJebE+2CCmahem5HcaKV/S4LJtkjIUaBh4IE2ol7ni0b5uPIwlqb51Lu7cdgbqQF5QAgKwp4nGJrMkFBAswXXQZU46kor5NRkqCmm4tKqQTHiC+CdQoSYD7vgkJLxTKT4OM49FASH2W+OT4vl2kT3hGGoTEVO6sEkBZFMQoSotIcM8ECFddYGI1Q81rbV1HVGLmC4U6dBFzDaASAvCjhcgZ3dNgIii6KCpGUx3M3Qq0BWFXYiScXTcMoAaelPkCopPB4JcClkQo42tyHtGAYIwMhBA4B0oWGqZiVgjnuhdgeR6BwUBYGAJuXIIQg2joufrbYZMg9uJvh1LbYh0nkGeXNgADQxqGnZbqlJcNUet85ro90dmmjjdqubScDvBjB5AgcDfgr2jG4DkHoOTg0CnFxqbl+SoaxV8sxP0AQxlrpvgJPk8DVyq7FIhfvjDPcDY1gsgCSXODe92D7tj/F09nN2sW5VAYIARCya0PgzKhhbUdwwjESUuLCTP/ek1assTQAAC+MURjYng34KMNsvHCMsVtt+CCmJcVUBbq4gQQfiXYB5fRehnEk0rsB4NR2jIc0Hodt5uNIJi6vX1tLxXzM9wE3wJO2WLPd7VrkFGMFPgZjgDjYDuoN8JHVXKSQS+bjoVGAceR1wFZWc5S0bkDha+S1v86ObGTXEhw/QvY3EqAVk1VYcwj1lu86CFiG+y91j908p03YDAD4roOjkxCnNazMyF9hEKvEa/FdnIv3KJUODgCPOzzCvWu+leqeVSDr855wBBzAh+/d67TLqxphWxotn3VHo3oDpD4/LwT7uROuGOJwxPHBey5tyOqziiEgbPM95WodHHy8++67cfjw4c7PnvCEJ+DGG2/EXXfddcU27Go9/Hri8Unz7w1je1W6CbNJGm1kHhjAPcDQty74ZoDczrZ9vYEzQ83rNfvi+D2syvVJkmBuHJPSjHZS1myRooKPGx4vwUeuYZFJf8Z+2bVpcmRjX3GMI2+g7Fr1P0y+W0g2W+A55vOxziaxBY6wovGonFtl1wWWEuzaqywgJSswl16Dp+eVhcFXYr9y8CWveAfuvrAUK2DaSW/ehAfNmeV7qxx7lYc3/sNDEozRsSML1Bx49dvvxW+970yzvZt9yc/6sVWGzAFkXPmEGa4Jue8//Be34dt/54NWwFjJqCvGjfcOZyUuyfOVcgOjkVWgbAWqrRitelCxhAdOHBTUwthjJVIJAi/Vedj4XiGlZk6AnVEg9sd03VU5aE1A4YmUYEB7XMqiQAmvuc9LYgC2mntP/Ff4Q1oWNORnWM2xMxX2DNaAnJ6aL8U1cnxHMh9NvqOabagYx+0XK3xYMYUHfa7FTOxj0G2MBz1jkvrMEEDoIInJsjgr9bJrPwGqFIdGwqy8Yitwe3dZ4rt+70PiRd7VvGB6Edy6xJc++Rh++z334EV/eCPOznIc34o2LUvkZ6+bOLj1jHjZPyMl2hGh2v0Ze3VHPvbQXoZTEyLCrRy3AYG4xRMNQBfE82LJ+NUHCAjm48o0/sCy6zU/xLOtZ+QoMMmuQ/0C5UHeB0xsWJ0fpslTmTM06hDrPlosJYBN0NTtjplbiY8XfvETxLW4DijKbTkar/77mm94Jr7rCx/fWqCKmr9DVB2powLQ22xGz3XA3QiL5eZEWzALV22DKIGjeT6lJUXiiP1KQhcF9MEuNE/F2rYnkmpNfn2LQsl0A9RepAXlAKAqcxBCROqxGyJdk19m5QoUBYCd6QRUE66zLChCUsLxYxlME20wQZeF9JdU4GPkgUIvV0/TJVwJssa+iwL661IwAkv4UQLm6I/ZvEl3VjJj6PuqGEaSIcnkuKU9ZoVgn/rxFJQT0GLz2C6LFcBTu6E2IVhJ2v1AHI84SsBrpg3fSwsKjwu1xXgsA440z5RlSTHmKTA9BYcA+WIzaKhhz7q+9PEMsDRYAQQQPp5usgPiEK034EP7Enx820/gW4rf3/Rrx8qrL/JdjEJ9Sm9WMkyJABWRHEGxuKS1I0tLhrFTAUGC0WQKQjNtevaiEFYUhBCc3I7w0N7mOc8rCdzJAMQwHqE2sFQBkU4tGIjdczTLKkz87gIa8UOrN+co9ID9B+A4BCf4OW2wSZNkDcH+0/nypSXD2JXb44VAMILvOrjv7AXtd6cFRYjVQqQfJkCZinfF9e9XAFoT6hhjy6u0TMCJSjyWgTOEQBu0NMsrbMV+k3FxckvPfMxbfoOKVbvODt3PRF8o5sD0GvhUXHvr7eYFxagdiuKF2PLZRjhS2pznrrdiu51qo8DHyHexMwq0IGASeGLbAEyxxDzregirhS1xPgTz0XMIjsYct57tjsUd5uPNbwDOfQKntuON782ruuOvrT6jAP9FQVFUrHn/BoDHHRnhngvLtX5W9ywgQMhnXreNjz6412lXUCa2fw18PBTUG1YH5+eF+N6OZ36EQ0ENyjjuWwNc07ILlF+tVV2W7LpdURThsY997JXq7mpdoXI4NaRda16GjNJoSyqkjokIaMEHI/PxIJ6PBrahvr1pImPal3V/SMuEtdYcVznB3Yp9BJ6Ds61ksKoqcfzQBIEnpH0F13jP6UBF3aRZ581pnIiLn13MOU5txZIB1cN8pCWE+W4bxDKzSxVApeRUNinbAzMmJkBWD60KFzLx0pZzMwgEWmAmQce9nElQUQ9GKDDTGrxB86a/99xxwbovSwlO7SsmpYGJ+P77F3jFm281sxVZKaS+CEQIEWCQcLcYMKbAGSbkJP/+t2/EH/79fWbmpjz351KximvzVFOMUQGc6c8ZLXOk1MU1O3HDWNUtPrC6RiAnKssG3NMzRhWjUTAzDeehylbbZ/K2kz6Ok9EY49ATYLUJRKA50trDsa1oxXzUAGh5kYPCa2QfOTdIvpkAgy9mYmKxbI6hjYUmWIfbkzFAYJy0Dqk8zwBCMBmPhdG7yXJiYxsKyRb1GuB00Oc6L2RDmI9kxUYbFDhT9o+NgAT29GOzqXhNQXXgY5AA5RJPPD5BXXP8nQwC4ZzjX//K3zXNtgOu+U4BHH/pkwXr5ubTM7zrtvN47KFEvy8APuuQj7svplgWVACV0wik1j/HRi7rTOYuLATzEW4IEIIkcFHBl7JE27log8YWO4SaAbxG0Um7tjACAXSStIHW+0HZeD4BwGu/5XOFvMompd7wkS70z/ehns4mebT2OazaGhjObTCxV3a9zgZVTEkLC9MzX/dPv3ZbTITX23oBAtAO22g/q4QH29o1RbxQm2KdV6wBjQAgjGK4coGkXQsZnKKYL9TRez7mufJ8jDAZRaDcMXg+UgS8anzaUg1oxDkHK9LmvYP4CdI1ECqrJPgoj/HhrSko2wQ805Jh4lIQX6TOVyTAPWcvru0jhc8p3EBcw2MlLy82r4lFmoqwBDfAKHSFUkLHfCxKuBDgKPdi6IJd5oUCHyPEiWQNap7/mQri8EQwjWnBiErANopilCREoWNuFiKABF6E2ovBtZJx2kjaASCKY21qOuccRVnCgVjAnIyEVFrrvVkyJJDgo0NQLDclyXmeN9fQVixChFJNCJIAlGUYSySkqrowlkXBhOXDx/8XnjF7O85pvAbVPRT5DqaRLxbz1ttUDGOSA+EYXrKFgC42UnrVcUscAUhMJlNEvNBKjBcFa4CXa3diPLiXbfgqZhWTXqviHMRxAl4VoKwLZirAKQ5cjKNNafkspwJ8bI1jrh/DFCC0KJgAH9NLIAC26z28985NsHDZkgCfmEY4O8s3xg5174kvDQE/QeA5uOvBc+vdNe3bPrR+PEGIwpjqHDXMR/HuPHWrjeMtpN8tcM8NQBxHm94+yymmMuQE52/FibHTWURT1bAuZU1jfwO03s8qTCMXKJfA9BTccg7PJZ0xm7JasBld2jzb4cXY8jc9UdX/x+HqWSdSxlf9qb7jFsB3civCmXVWo9r+fB+ItjGGGCfaQOtSeno6LBdkjSABAPyTwx4+uhZ0M5cJ4agZ8NYfA970/bhmO8bpNVA9qxjCFvPRcx2EvtsAqGpefazFfHzisTE+cWbeubbUsWizLa8/PNoA8dOSaZmPh8J6Q3Z9rmE+tt91A3hc7Ns6e7SoaunvfVV2vV6DwMdDhw7hwgUxsOzs7ODQoUPGP1fr0VEv+YrPxnd8weP1MiZAPzk0tXU1zAM5EdEGugAHYFUegNljAhOHhGo032eYnBjDcwZOIFrtCSE4OglxruUBwaoCQSDoCnHgSvBx7Zgawcf1dhpvTpM0TX72Qspxajvuyq5NIVFMHLdfe9dduO3s3A4MtACqitXmtnLbvuX3PoofeP1HzQwVuXJ+LhXbllHJ5DGAgMrrcU8lCWtBxQILFThiA7NogVp6EdacWxl3Kj15XnJxHgxg5l4pmYNOqGci0ByUCalv7UbNdujazUrgx/7yFguQWaGohGfhXReW1uNRczQsRDvIKh4Rs6wyAjhVkaMkPo5Po+Za0C1s0Jrj+KEpHIesGK0GH0wFaKcla3z31quuciyZi+sOJc350IOeHNuTkZBLWWT8abZEzn08/ui4JbvebFfmGSr4OLElzldmknKzCjUIytrBqe14BXz3MMVYzTEejQRjxjBpHVJ5JhgicRQhLah1Qrq+DXXNQeEjreQ9PeRz60nrVradHJuVzLmPKSm3a7DP7UEsNAAZhuTC0zIfs8Zc/sfe8HG89t1345aHVqv6ceCuUjI73ymY0//s8YfxW9/yuc2Pn3BsvAmGOS7geDg5djDPKnz1L7wbH3twppdoy75HLuuG0+xlOJKQpm0cuCjhC/aNTV3Q8Xy0jEHy+AnmY1t2nVmeJesS46D5uTqmk8jD446MBFijA1rdAADX249on8GaxUiTp7NuX9cD1wDze4362dC2gOaYqGNuVi5sALg2r8rWJCpAiUutifZuWmJntOmt6QbRBoCjDPtdvmIZRfEIAcoN2WlaKoAsbPrLNd6QVSkktfBCHJ9GWNY+1hN1OedYFBQeF2NElIyRawIlClrDq6umPyeMN8AlkTa6erc7dmgbtN6ULc9zirG7CrGo3Qh8bbtWzEfRJvQcVCRApQEMszSVzMcIW3GAnHvgmjG0KgsJnoYgfrRxLMS2VRIIDJGMxsaEcMF8rCQQG2tZa5xzsDITwTrJSICnGmC38VX0InhBjFqzXWnJkDi0CeCJ40S70CHOU9Gcp+lkDHCOpUbmn5YMcb0EJidAiINy2ZVds5qDVQWIBHbHoQi6Ob+3CVIuCgEoEQAIEnDiab0B05Ji4onr2SUE+4vN85lXTPhauo4IGCl0zEeKETIgnCCaHMKIpxs+dM1xcwVIPBkL4EwHPgpJrngPuW4nQUnrDS/ArGSCESfHhzgZI0S5AcTllQAjY9/FThJgdy0cY3dZStn1ahxzA7PsOlVy4VTiAnxvQ/aqjoli2J3cisBqviEfzyqKREmK/QgIRgg9Bw+cu7hhdcJqjrxiDTAOAGGcIOIFPnjPJkt2nlNMQlfcL34M+DHGjg58bMmalTewG4Aa7sdp5AHZLvCH34inn/8LXFyUGyE2DfApa6qR9M+yCof8CgAHptcA+b5kSK7aNeE+TsuX0Asx8TaZj+q8b7cW9kah1/EoVeBj0tq2UxoQsEkhL2bA1rXw6Ryh73Z8H5cq+IUW4hp0QwAEz7kmwrtvv9ABwee5ZD6m0o5gdhrXbMcb8uz9bPVuoGoSeZhLwPzcLEfou03QDAB8zqktLAvakb8rgH3UZlGuAe91zbEsaGeBSoUsbgf1RuDM+XkhQM+2l7V8Hl+zE+PBtawHEcp1Ne1aV4PAx1e/+tWYTISU9zWveQ1e/epXG/9crUdHfeXTTop0RROb0ddMKE0AoZrktScYOqCs/f/BsuuBbBygy9Bob9uBE6kNL/kGJuNGNcCrXnYNCB+Zn/ub25tfcVogCOULgu8ir93NCaGaXHW8HDXgg86b08J85BzYLYBT25F4CWlJ37Qlgx3+6AP34xffdocdGGiBj1lVm48vLVATBzVxxUuK6byzEjVHw1QsmPShMYB7u0UbfDQBn2IbtxJf7L9Fop5KOfWlhp2pZ+btSxn3PK/095Jsp5iAqSllmRYoa4KauJhRJUXW93V6Cbzz1vMWCXeOwCOo4CNwHSsQTOsaFdqsMzvIKpiPeuCeVYIJeHgUyGuBaK7ZqgEBR6HXAh/1xyStPWwlvgxI0oNMWZYh5z6uPzLCvkl2r5iP45GYNKhjrOlvPp+jJD6uPzxaMVo1x7koclRkJbtemMJuWAkmAd7rDsWC+WgLsQIAJoDhURIj8l1x3eikoQMqLyTDJQjBao7aFpqxvt2coySePP7DP7d6Oe5hMuoAGBuDTvqUbshVTV6b64tiPSnjnFIw4mLd8hF+Ip6hrMKvSi/l//G+e/EDr/8oTmxF+PPv+T/we9/2XDMQJr/z+iMjfOmTj4l/Hx0Z2gc4NXYRyknB2z9xTgSJ6LwN3QCJI7yr8orhNX9zGz7x0ByP3fKatknggcJDrbsf26UmDkALODMDYTlz4DVp10Hnd9rP6BiENG/YI487Mmq113kvG8C89QAitT2mRTgdIK3rl0lrjnYytk1Kvc7AdDyxYGY8Juuhcj2sSkCjcsjxTf/ssXji8XF3O9rb6oWIPAc33rtiEO2mFbYbw/zVNRWEEZZr4KNK+HVbwHoUxQhQYb4GviwLipCXTWiaHyYo1kJiOOeoilxKZgMBqMDH7qwLLOZVjbrm8GrxDE6SsdbTTSRFl5IFF8MPEpRr4FJj+C+voeOHtlHXfINJuSxkmqzyXt2aol4DFZcFQ+KyBmxTYBpd8xukrAanuQDIvBBbsY+C+6jKzfNLGzA2AvEjLUA5z6lkzwSYjMbSJ3Ozr7xi0qcxRBCNtFLpgtbw6wIOIYjjEXISotDIxhcSaIUXIIwTLSi6LLqMofFIppevXfcq9Eed9+2JuGb3F5ugZ1oyRCwFwimol4Bma9JNyYhtAFtCUCLAbQ9usu7SgmKqvATdELUbodCGJTFsE3HdOATw0vMbbfKqFmnIhGAc+ljomI8lQ8JTIBjDjbdwIsi06bppyRDL8+RHwt9Ql+y8LGgTanJyK4LjkA0wM6tYx/JjNBoh4CUuboCPAjz1HIKtWHh9t/3p9rIKkzVvYTcwLHJDSI8nkQ+kFwHi4mlHBPNzYx9aycvHpmIb10HKtJR2AQCUdDfwHEQ8x5s/fmZjfwHAk9cmAHhBghAlXv2W2za+/9KywBFlUeEGgJ8gJjnefUeX1bybVthRno/yWHJ3894GlHehD5wX33e0uA+B5+Dmh7oAeNaSXQPAkXHYCQ1iNce8oNj25M+mp4BijtFa6JH6d6RCXQDATzB1aeODqGo/qxAHbgf0XA/6UbLp9rad3IpwZs23MqtqxL4jPB+3rgUpl1JivmrXyO+pZD46Yt7xjOM+5jnFbWdX9/heWmFnFADL1fPon5yY4O4Ly46U+8x+LhZdWzVp+ayek9Jn0npZOzYJEXgO7mtdW/O8QhJ21SyTyO8AwFnFwDnEYkCj/hAXzJGoxqVl2VjtcM5Xsuv2u4d8Hl+jkZAL8NGg+vwMr0Hg4wte8AKEEjh5wQteYP1ztR5lxQyya3+0ycSSprwb5QYywbr1cGleijVMRGDzRdokuzYxEXVl8niyBs6sTdosoJceqDQzQGxA6FOv2er8irMSUSSObRK4Uqo5VHa9zjo1MT500jQBJFB4OLUdo2I1WJM0bJ4wqjTiUejagQFaNIBObpN20gKFBOIOjwMLAChAqor4GEee7DPQfj+tSuwWBEcmoViJtngcLpmLY5OwFbCiBzOVHLhhUhqDbhwcGYcyRVgPjrEyaxh8JQKjnFpJy3NGzD6jtGiYirVnkF3TQiQlwxPm3xYgWB1jxyHgRhCnbEDCFfi42R+rClASYCvxBVisAzOpSJ2eJIl4kShqI7O0rnLslQ4+6+jYGohT5EuUJMDJrQgzI/OxEjLm8QhJ4GHeyKk1Erg8R4kAx7ci7FXmlPWqzEFJIMznI8mmNNx7FfEAIky9l0NCX2gJxoFxJLzCMsVOfRjS6zLPwJ0AI7mKXJHQOJnobkOBWob0ZCUzM23Xqz3G+3Evw3OTzWVhPupAGNPYr2Wx9zMfGdwVqKbKFzIiVCmedHyCX/l3AoBc5BTXHxFA+qGRHKO1z4PVMfhPX/YkfP9XfHYr1XmTKenWJd70os/HVz39JAAgp7WhbYhjiQPGOV79ltvwhptOAwAeu+01bV2HIAw8Md73slC7kiMjqAvh16rYLOpF3c7y07MClaRw1ErANMqu1e9sfQNmkNkEDgOadxUDi1XXVtc3IXI7TLYixdq1bwE2FauyjYrL59e3/R/X49f+/XNabUsBfCrQ1A0R+g72ZwvcJX3s9tMS27HfXSgAEITJBmswrxjAuWD1KkA7GcHnFeZrDJ6FkgbL4xDGow3ZdUFrOHUF4vqA4+LoJERFAlzc707a50UFwmshDfdijMcT0HyplWsGXMpqvQB+PNrwX0xLKQeX19TJw1MAwEMX9rrfmVcYO7S5nt0g3gDcFgVF4rDO9Un8cEOauShaAJkbYDvxUcFDppG1szIXkzAvgBMkWrbiLKvgy9Cf8XgMxqFtl1cMkSOk6lEyAteMp+1jFicJKgSgOmA3rxrwN4hGWlC0YQzJcz6dKD/NTQaV+E4Bsk4lkWU9mVyFZYT1EggnoN4YLN+8NnxUAnCW99DWZIyptwl8LQoqfu6G+P+z9+fxviQFfTD87X37LWc/d517587c2YdtBobFYRFERSWKRJIIKqIGjFFeYkzMk1ejSSQxBhJ8VNSoLCZxBzEGjAIKsjMwzAAzA7PcO3P3c8/2W3pfnj+qqru6uqrPuaifh/fl1uczn5k5p053dXV3ddW3vgt0HaXpIJP4W4ZpjnFFzqPrGhDvdOpEWROCMXTNDuOMHKeAV4WAMwTsACtO0WF1sfPVQJLlwdZLXN6Vs0/ZGGkaOg6O3Q6YGWUFPKN5Jm03gKd3A1XirIRr6bUqC2gnIO9GGfGO5Da5LNuFXiYtn2NWtsMMi75FgKnRIYz0BI9tzDsel3yqsmMauO3wGB98sC2nDlMqHQfIvaJhL3cccjoSeCYftzgrCFgebJB64vk35ylWPK2uB8uHixRJVuA3//qxut4jGzNcM2bfNDLWm7aH2XzWGXdq5uPkLGlytIXjywEeFrxCIy7pGwBNLG+uZxbnQAUs6PR9GR0E0hnGjt6SU9dgIbeJAtPBwMg7TMqteUrSmLni20YL3IsoEO2YzVzn4NjtBs6kOQZGRmyKKCvzmiWf+OHTMolyMp/hNzBtH4GWYXlgtzwQd8KUbHzNOPBxidybt7yfEHWqqsKFSYKD4zYOwTMWiR1Ne12v6xrSvMS/fvfnW/3Lp2sDbQYl0PQt+Xa1N2DXPTKl/7UPPYqyrLATEs/vxvOR1qfrv7Fn4X2fv1D7t2YF3USrrsquZWVf4ONkMtn3P1fLV1lRMQ4tt7ug5NN5+SIYopP/liwG+boyYE3KqtxjYdQ6hsI0XrnwSVoTbNI+Rf1C5hOmkHCpgFdugfvSJx8ii1JW8gyu08iuCfNxv+CjrC8l/pR827i2lhVh1jGZKPPT6wMfdyijMLBN1InishCCIsE002Do2h7JySlO75IJRlFW6vtGZaeZZuHgmDI1Fcy8yWyGpDLxjONLZCdawf7MsxhxaeLAyOVSf+Wg3CzXsTZy9gDbIuxmOk6uD8gkVBHkEYZzpBp5ppShJHmCqDThWAbXf91jlVmMRCP3OKoUQGaRIoeGUjewNethbhZpI/UuKyIJV3pq6hi6Jsfa6x6vzGLolgPXNJCowGLKaPV9r5EtKfp3ZzpFBgs3HRj2JkSncYQURO6tZD4WCZLKxMLAQeAYtaen7LxpHKIybHK9aUkWohKpfJrEKHViOD50zTrMSAZ8ZpWJRd/GyLO4Z7QflEthYehZcC29Bq/3Bf6J7UyJjQBLZUzRk/TdakNSb1jUzMd9/l0zIVM873XdK2Q+ysbGvsAZqey6pz1lhgIGRNU18zACZRHdeGCIu0+uAABefOsB4Xr6Ac/AMfHNtx9UM+e5Z+MN33ADAOApRxcUgJwDGxnKssKff/Fi01xOIguQhUeu9QSn1W0XmY9qWe8s0xtmRR9Tsj62JIylSGtGwrOv4wIM+0LsOt/BrDtf6fWn3u83U1Z3j37pzDMkQXGt4/N90nNsJcC6Dwam6cAydFy/bOGvHybMMAIa0PGZq+t5HqI4ai3eo4ykGROgh4EbHnStwjxu3+95UtQAGUBSsTOBNbQbZQSUMxtw3LA9bAvg4zyhqdKU3TYcjWBXSYcdNk9zWBzz0XYDTKeTFqOrThulfTwcENuPy7vdMITAbGSNmu13vrHzJKf+es0zp5kuSqHeNM5hMoDMdEgoimYhEsDdsqxQZXENyhGGYfd7E8URDNoXCwMPBfTOsQAKtDJ5tj+ArvRpJH1m2T5S3UEigMRVVSFOYugamad5fiB9v+dpDkdrwMcDSyMURdkBY4lHX1p7cxomAZ9ngj9nDSwVEWAPMCkdPHzmYqsOYYFmrWdSsxxkEm++eULl1OyeWq7U13SeFhhWU8BwoOkmrHzW8Uwk4CMZ8waO2QJz6jppAbdk4KMvDTZh/cH8UWH5MHQNu5K18zzJW+DVkcWuRDUWApVgOvD1vCMrjrMCrkmOxRQb/+F9D9a/n0RZB1i3HA9W1fUVBIhMezGwyVx1sIYAEeKs6ICFvHQcAJ5+fBGnO6EcFNSnoTnQdcDycGKsEdsnvi5lnBpl2yrkBdeRTQWeMZjkBWZxjmWb83K0PKw45P//9P7zAMh9e3wrxLULZlMPxMPUKBK86c8farWhZj4ykHp6AceW/RYrNStKFGXV8nxcGtgtUJj5NQ912meDdQBE7jsXmY8aaABXA7gu2QW+fKkNjhKArz1XEJmUIQVFeebg6tDF5izFh79MWL9lWWFzlmKk0bYNDwBZiKccGeIDD16qn8PtMCXrXMZ8pG1DHuHool/Xy4sS0zgnG1/hJlEGAHDLEE86MsYjG+S9nEQkTGZdBB85KTphPnZJUq993nUAGun5NMlb3pcAYVDyzzPrFxKYQ7+H9HuyQD1A//CeM3jjex+on8X1od3e4KbztlsOkk0VxoBlLF2jusp8lJV9gY8LCwtYXFzs/YfVuVq+yorMlB0gA0SRtNmMMo9EQD7xVsmu2Y6klE2g8pPcr+xavghT/n0ukVL3gGPdSb5qIdMDvNK2jDyS7MYm81qZwPUI+FjLKTt9xEyXBdl1Z9Gl8KcEpOBLoVHGIQVDk74QFwDI0zrNub4ueixZ3e1Ew4nVoPGTVLAKGeMyqqXP8oVcUVUIPA+BTZmPCoZTlsbQTAfrNVgoBxXTJEKmWVgfu73nLvMU89LE0UW/CQeRPFthOEdaEXCsOW/3eEkcQbdcDF2TBofIAEMCjq0NHdo2+bHiOKyZj6HKYzBPkFSWwLKT18vLCosjIn8ivoLyd2I31XFsOWiSmiVtq/IElk1kD0nt+ylca0GAkpHvYeAQSYbq3Z3PQ2SahRtr8FF+3jSJUBhE1jZJQSY0kg2VpDKx6FskpVIFFIKAihX1kZonOSrFc5KnST2ujFyrYV1KxrykMrAysBHYdMGyV0BHkSCtDAxdi6RDpopj76OkSYTKsODT3d9U2yfLPE+RV8QiIcrU70G37RybShWKxJ2jE9LRy3yUbcwoAB4ZeLSH7BpFhkIzWxNyAC3mIys/9Lzr8HMvux3Pu2G1fU5RNdAXfsLa32pjcz2apuEv3vA8vOT2g12WKHfsf/KC6zF0Tbzymcfw3773zs733rcNZNoVMB9V3xHa7gokWb5hPvb4FQJdMFGYS/z5G56Hb33SIaH+FQTDSLwwrwjE49qibDPAbarug/lYt2O/smsLgNbzLEs2XBXf4vY75UIDcPu6i4cukIXTdphiwTM77EvP96EXaSsgI85KWMiIZx6tq5kedA2tdOGyrEiycMUzJAcQk5s3ZynxTLSb90S3u3JvFnZSh4oMh3CQdiSn84R4v2m6ARgmDiwvwKlSXOKC/qI0r/0SAZBxQTcQC5LwaUJDQCjz0bS7zO15msPlgR4AuuWiFOTUM9Z+ywE0DZ5loDIcTIU08SQvYSOlzE0Hg2DY8ZmsqgpxTDwaYdg0YMWW+iXGWQkXjCE5lIKPUdruM91yEQrHSvISekFBXcNGEATQFInkPNt1ZUwW37uzru9mzXyk72tpOJ37zqShJn2OVhbHcJF0JKiOltd9BhAAWCoLTwvi48fuqRNgPmuDWVVVEf/CcgZ4i4A7wqCadwJZYi48ZOxZuPeJnRZAmRclsqJsrAesAAM96cifq6oiARdMNmy5FHxse1uSay1q2TUAHF8O8MAFIc09YyFDDXPd07JOUEacN+Cppml4+vElPHBuUgO+u1EGz2ivKwYB8Xc9v9vt2+0wxZJP52XBKpxiTkJiNtr3vpbl0nJ0iQBSbF1UlqQ/PF3YSLICHBkAj11usynZM0LAx2au4YL08yYna2Z9sOiUdd/A8qBlEX7ym2+q6z2+FQIVcGSot+w2bMeHhRzvvf9Cqw0EfKSejwAQ73b8/hjwxMufl3y7dV8Y+DjQYvKMOuT9WRKCZCYRkbjrLZ9BB9cvWdiep/jEo42EvN5c4so6Dfqp25a2QW0AuPUQAW8Zq/G+s+R5PL5I6/lkg/Du4wHSvMT3/uYn6z4ee5T5yEnCkUU4sujhf37ycTyyMcMOvdalgBInKNCKeILvvOMIzu9GlPUY123my9C1asbi2e2oJtLw5TvvOAJd12qW/yTKMPLa88CRZyHJiprNy97zTiK16UAvE/zcy24HALz/gUv4yT+6H0PXRGAwKwdOdg3g608uYexbuEwB+LgezyQKzKtlf+DjBz/4QXzgAx/o/YfVuVq+yorMLwrgFlXch0W2yAGal4yfHKvAR0DOZlSmXe/BkGkdQzER72NdSLyyOouCsiTUcpnnY9/isccjcuSZKMqKekpU0IoMPgUfh46JeabLGRfsOK1j7kN23cPiyDULnm3U8oeo2gPQoOwrgCaGWT3SuiLFJNNwzZKPOO+TNCcoDBvPvn6ZDMo9i8SirDAaDOBYBvGdUgA2RRpDt2xiqJzkSoZWliTIYGJ96FJmnvzcWUIAvoNjt2E+yjyYZiEqw8HxlYD0j2IxWKQRLMcnTFdVIjKVXdeScEW4ShTOiXQbwLxUgYUZPZaLJFPLmlGkyMsKx1eJNYA0eR1AmSeYZMDxZZ+y9uT9VhUZDNuDY+rkvLJnoEiRwMTIs6lsSc0sTZMIpeFg6JKJQqUAtLMkgma68GyDALcyqWOeIq5MjD0LA8fETqZ+9tMkAgwidy7KShnQkqdx/f4NXbMBCGXXXOlYHjh7PqPN32RIKhMDx8T6yMW5GZ3o7CfwRShZSoAotmhKoHhuOm1IkVQmVoZOE/hzpYEzezIfRenpHnJ0GWCnDBeRWI2oNpJoqcoCpWZ0fyEBHw8veHjmieV2PTF0BEBvIA6gAKyaNuqMhikF2cg78Z13HMEf/8jX4fu/7lqcWB102uFZjPm4Twl8H8OvIJtpOUwOfGT1JddZ+3RyCwXBD7GTLt4nu5aqKSSgrKzPZc+EirWpmmew34lFWl/xnpdl1xaAgmyqjaLOHEbJ+BXfKfLfJ5fMGnzcDTMseYJfJ4BhMICNFP/2f32x/tmMeg0SELBheeia1gqnmadEPmhyzMfBYIAqj1sAzdY8RWAW0LnnwbK9DhstTAt4LKXYdKDbPsZG2pEFhinxaWQejEEwJAE7XEgAYd21+7vUHcRR+1izOEegNQCI4fgdwG2WMLlscyzDcjpJ0LMkJ+3nvCEd1+/IjKOsoPJsTo4sOZZRZjS8xsFSYCOF1TlWfTwKtK4sjqCVGWaRyBYtYFcpdMpSst0AMyHMhwXrMObmcDiCXiQd+em8Bh/Je8XAx52pwGhMcvhmSUBseu8ro5uu3gKWLA+LoxHxL+RYfNM4w9imxzIaRqOUmZnkxEuQ3gebBhLxQFJKGWouEsDyoHtj+FXYkS3z/n0jz0JeVPhf951v2k7BJouGJMHy4Gtd78UkJ1JMcp8ISGnqGqZTOfORl40auoYHzk1arDjCouQko4YNV1PLrln5Vy+5GQBwbocwFtO8hKe3PR9th/i7vuF3P9e691VFJKhj3yLzgmAVWh7jmgWnw8ycRHkLADq65GMW5zUIN03I2BEYbcY+bB+rTo4oLXBqsz3WGLoGveQAHdOFXSUwDa2VxMzYn4t2G3xEVeLQUMc29UvenCVwLKMdOgIApoPvv4uoG3i5O0motgj4ODoEpDMcW3Tx6Ma8TiNnDE0e5Bv7Vg3CseP4jgGzSIjCgs41lp02c5Wcz2wDfKYHXyceir/6oUfruiRQrP0tOjB2WyExhPnYBuVcy8DXnVypg4hOb85xeNHDKhuqvQXSNrvAN9y6Xv/dTpgS+T2vmjRdIJ3jmmXi5fxXD21gh47JC6xusApAA5IJDo5dRGmBSZTjwiTGwDU7cumRZ+LxzRBFWeHMToijSz7EYugaDoxcnKPX+sRWiKOL7XrsuAzcncU5HMuAIX6T6eb5M08s49XPOd7qy878jduwXfTtGlRmYOlV2bW87At8fN7znrfvf66Wr7LSx3wE2h9tpexaMvFWsf9YfelC4W8aOKNKu5YtNgoCKO6HNVD2MRkVyZns9636DXOFpXVN45wkNyKvwUfiE4dukMSVyK51gdmj8qQqMuSaicAxaxAiKpmPnIqZkWA31aDrWgPCAXK2YJFiVhhYG/ISaTnIFhYGjiz6yIqSLIjLrBukQcHH8cCHY+q9Uu4yT2BYLkkxjtUMrTyLkWkWlgY28VBTgV5xVHv+9QWdhFGI4TDAyCWeeKUpsTAAYQSatgPfNtVS6TxBVFpYrf0o5ceKoxCpZsM2dYSFqThfhLg0sD5yKBCseLdywrY8vkKYj7EClMqTCEll4dhyQJiAKo+9PIHNmI+5HKSsihRJSUJ/RjX4KH9W8jRCRUFAAMh0OWu0SGPopoPANpHmJbkPwvHyLEZSGVjwCPtwmlTK9PScMmnZTn2hy5+TPGuYYmPPwk4dTtPdcIlLA8sBZVPuw/OxoGD0yLNwaMHFmVnFLlb5N6qSZzFgWDB0jQLgkvAqaSMyxKWJwwtew2beF/OR+9Ywz8e+FGReqqpYQLbqA11wZT8efIB8HOVLmaGUeSObDnleJHLITvukIJEM/GcMd9n3RnU9+6xbthdxgWOSTYu9AmfY3/QBYdQ6IYPZmNXX3wbJ2CaTl9d+iKqNL8U3HpAyi6X3Wcl8FPpQNwjTZT9ybpWPo2qeoXpn9sF63bPdPd7GnWAaACeWiNzv8izBTpRhyaHvJB8uYbmwkOFzT+zUP5vGGQZmSVl3zcJS1zREHPg4qxdZDfg4Hg5hVykucjLMzXmCRadqtdFyPMQdECrHiAEG1KctMPLWAhogzMeh0YQwGLaHQE9bPmgk7KR9P0vDQSZ4Q86TvBXoYDoetCKpwQSALFRbQA+tV2bt520aZxhaFTSunuV4iISwEyKBZoxAG6PREEYRk28oLTthRsBCnbyXlqGjMl2pTJekAKeA6WGVqtDOXt7qnDMwcmh0/u94futesn61q0YmvTAcwqxybM+iTj0+ydWl/rJTgUk5TwuMrQIAJ5U2nU4aOgPV9IKMR7brw0HSYotNYxoiw/mg6qaDSjKXJTL55p46/gBGkbTYvQwkckEAT8tfwLIZtbzqWN+y+Qiz3eABvihl4COVU9sBPCS4KDyzrB65TwSkNDStw8gEqBUABxK98GYC+kw4v8koKwgbl2MBOsjwJ/eebYVfxJmQvOwRq5+Lk6R+X/hUeAAgvsIasqLEY5cbQDlMCVi5xICkwRoA4NigaIF/RVlha95OLj684AEaauk1Y+Qt2m2/SVg+TYEGXvO2T9U/vjxNsDywycYAq2950PIYx5cDnN5snr2teQrfMeBQViSTuQPA0RGZs53anOPyPMVyYEMrOHCP1l/1yAYz27wpywq7UUZAtGgHWDgGALhlifQxuy42Jg5cEzhzD/CxX8ZSYCPJCnzxHHl3axAzj0i/03fyhkUDnzy1VXsHTiLGLoy5+0y+0T/2wpM4txPVDNadMCPSZq4cHLuYxnn9PEzjNhuVlbWhg0tTcj82pgnxBmVrFo+qWrMQL75lHZpG+uLcbozVodsGRqllxXc89TAAEuJ0fjeGaxkE/MsiUscZAskEB8bkut/xsVO4uBt3WI8AcOP6EA9dmOLXPvQowqToeELy13qeXuepzRDXCCDl6tCBrmt4hLIj/+8PPkwIKRLLEjaPOLHahLodHHsNJsBtyAEACiJ5ZwDzD7790wAAQ9j8ulpI2Rf4KCthGOLBBx/Efffd1/rnKym/9Eu/hOPHj8N1Xdx111345Cc/ua+/+53f+R1omoZv//Zv/4rO+zVRZBNpoJYitDzNVCzJmvnIAXG9zEfJonAv2bVqkbrXMVSLSrYA6SwIJYt/1YLAVB07bX7faQv18XDZDkuG05fnMKscge/T3/FySu740rRrGfh4JbLrFDnIThKbfMRlP/Oxygn4+KTDYzLR6ZHi5WmMFAQ8K8sKhS4HstKEJBMfXiDPXcKCNMRro8EkruvCZcxHxUKuylOYNgEfpz0ejXkaQ7dceBb1JFQcL0tjpDBxcOxyElkJ6JlFMC2vlsTkmoJVlSewbBe+bSBSAD9ZEiGGidWhw7EGu+dM4hCl4WDRtzEt5LLrLCH34sDYbUBbqTdkgrgycGSR3ItQkdacpTEymDi27KMsK+QK30qtSGG7XnO/JBLaOCbHGnsWNY/OlKznPCUTyxp8hIStDCBLI+i2C98h9UoJqzCMImSwMPYsBI5BGAqKMAjynBBvSAAkdEnSf0WWkNAEAIuBjY24PkC7YpkhpEFHgUMSB/dKnE7iCBl9Xw+MXJyZfOXMxyJLa/bNUmCT8KB9MB/TJEJSkfcgYmzc/ZyfB8lMB0A3+bQuguccDKff11IVOCM+PzUYJPHsk20k0aKVOSoZ81HTqIxoD/BRBH7q9l0J81HFaFPJrhXfPZ75aBvEd7YX2BUm3koQNOWYj7R/62+DgonH2tpqe887UEr8OlWSZ5nXYh/bVLr5KXmGZCxWVV3lvdxDNbEH67WprwjNU9UVZNcAcDAgIM13vfVj2J6nWGBVWostG3cdJUwVxnKaxBkBC/m6pgNdQyt9mjE9DC6YZjgcwEFaLwQBIolctKtWGx3PRyLIs8O0wILJAHoiTfWQdNhcYZpjaHJsJcuHq+WtZNcoLQggxN17kmLbPuc0zikARYEq14eNrJXguxNlRJrNHct1fZTCuzWNhXYBcFyvw7aMKAtRo/LsxdEIVpXhAtdnu1EGG5T5SJ8D3fawO5UwH+nxYDoIggEMQ8eXntjo1BmaDWDo+QPEQvL3NCHn1Og5F0eE0bixLXpzCpJ2AJVhYyoyH9Ocyp/dGjDULLfTH2FawDQ0Ihc3Pei2j6GR1c8XQECdsVW1gXPblcrC5yxFmbbP8wI4SFug3JyCNm4VExDLGWLFSrEdis9aA94Fjonn3bjaYrG1JeME5HLKGFlR4n2fv9DUo8+TXdF+M10Yuo4onLW8SgHK8uSApJUBueYZJ8kNE8Z8bN5PQ6tgoMCvcYy4OCvgmM33TdM0rI8cXNiNazDT1QTmn+HgtjXCtn34UnNPWd8suhrZXAqI/cjRoGiBto9vhYizAjcdGNY/cy0DB8duDRJe2I3hOwb1KuUAJTuAVbbHuI1pgv/0Zw8RYI9fq9JAvENC2nAdvpInqIFveo6RTq75h3/7M3h8c974FgrMR61IcHTJxzkqPZ+lOcqyIuBjOgPGRwAAqxYB6+6ncmUGPga2CXzkzcCnfwMnRyVsU8cb3/sAAPJu15JluskCALeuknv+3s+fb9fjwVEaOvmMa5dQAfjM6Z36mpcE5uMRyv575X/7BAAa2DLsrvNXh07tUboxo+Aj+165C+TfWYSRa6GqgHO7ES5PE9ywPiDzQ24cRhbB0DXcfXIFu1GORzfmuHY1IGNKHpPrcEdAvFuzEd/12bN4bHPeSboGgOfdsIrjK0HdJ+I1snJ8JcBDF6fIihJndyIcW26Dj4FjYnXg4F/8wX3IirLZ0JJ9O+mc99nXLeOHnnsCAHBogWc+ckxPAMhjXLsc4FOPbdVgMEDWR1fBx265YvBxY2MD3/qt34rhcIhbb70VT33qU1v/XGn53d/9XbzhDW/AT//0T+Mzn/kMnvzkJ+Mbv/EbcenSpd6/O3XqFH78x38cd9999xWf82umVBVaKVR8YczHluxaIvEB5My3XvBRBvD1yK7FY6uKUnbdN8mXLHxkbQMU7AVZ4Izi2jmgMLBJyu0kzvHIBbL7vDAgA+GA954TAV3daidbytiX3ELqB97+KXzskc1mQtxhnGbIqEzO1AmbMSok5+ZKlsQICwM3ME9DZiQsA4qSGDnM2sQ61eSA7TycI4NVA15xxdrbZYtVVQXX9Tjmo1zCVuUpDNvBwDWRZBTYkcqzE5iWA9cykOYlkfFKwJQsiZDrNlYGDtK8RK4IYqmyBKbjYsh76cmAkyKB5Xgc+CgBFZMIOQVvs6JEqQDGkjiEbrlEvqvwfCRAG5Fd50WFQhkQkyIuCeDpWgbi0uyycEHAR5hOvROZQgKglgWqsoDjELC4Zm8K94GAasR3ceQSP1TVIrrMCANx7Pd7XOZpDNvxanaADByNIyKlH1PPx3lPMFGREo9OdjxVEE/BTXxXBg4uzukCveNzmSLMSSp64JhABQoEqce6mIKlQ5ek018OqRx4v9YU/OmzBkRZCmwStrOP44RRiEwzcXDBaxjA+x6fxQmZiuEmADyWS0DDIpfXl20myTZm6g2cfVpo1O3JUGldNgBpm783+KoCiRQp6HWbxDYqw1Jk3oZydiLfR75lKG0V6iLxClSBWyWIF2gtJ9N1+nzIWd31dfGlz3/zShmKMvWBtO0K9oGMHa5Si8jGU9Ump5L5qFBZKFmyMlBb5UUtzN9ovxlC3Q6gCACmi4FJfbAouDGJcoxZFV52rWutZOyLkwQDx4DBSewNy4OnF7jMebBtzVOM7PZ5XT8AsqgjJR2w9GIuoXZTCNGYp0WL2QbTgYuM+NTSwlQn/LtZmQ4K4XtNvAQbAMRyfThV2mrX9jylLLPmWJ7vdb79O2GGsdVmcpEAHpH5SIN1aL3F0RCGXuGxjcb/bztMibScJnqTvpUHp8Q5F/pjebANHRc2tzvnHBiNlc7CaIQkDlvpzfOE+Agy2bvr+dB0DVu7bV/Cecr6ts22nUnk1AOzzYDWLbeTTD5PcwwsjWxAmIQJNjTyFsg3ixmQ2fStwfzrhULSuHk2q4+hmbf870KeiWh5gB1gZGa1/JTvW4+X0HpWi2HLnpMagLc8eDSsY2PKna9mCdN3VddhuT7cKsGnT7XvVe0tSAvb8Jlz4Po0ydqBMwaxRbCRtsD6mEvrZmVt6GBjlmA3IvfaEhlalERycn3QAh8ZE3WBBbkEhAl687KOzz6+jf/zBQK2bs0T6LqGlUF7bCQMxYb5uD50uwCN5QPZHP/u228DAKR5iVMUsIzjlFpPsbkG+f4cXvDwBCeP3pyl5NwM7GIbiUDre/WZx3co+Jh2xkTkJNzkbR85ha15il127b5N1BDDg6RuMsFth8f40/vOoaoqbM9TDFyT2IpEO+TWTM/iP738yTi7E2E3yjBh4DID7nQDMF2MjBTf9qRD+PijZN04iXOMRF9F+r1wLQM3rg/x2GUSknRhN+74IQ4cE//wGddgmYLXl6YJ1oS0aABYG7o1U31jmmCV9Z3hADbZmEIakrYAuPeJHei6hmuWfbTsVUy33qwdexZ2ohSPXZ7juhV6jCyi7OBBrSh52dMIS/KB8xOpn6Np6PgnL7i+/jaJvpas3HJohHsf38F/eO+DKMuKtE0or30+Cab5AJ+6Lm44cvMrTdPwgpvWcOvhMZ5/41oz1nCAPzvGN9y6ji9dnOJb3/LXAIBXf9216o3Pr/FyxeDj61//euzs7OATn/gEPM/D+973Prz97W/HyZMn8Z73vOeKG/CmN70JP/iDP4hXv/rVuOWWW/DWt74Vvu/jN3/zN5V/UxQFvvu7vxs/8zM/gxMnTlzxOb9mSpmDmPFIXlSJl5USqKxl1zz4SD+8ovwXkC8AVOi/TNKtKjIGiCzchj+eLO1aJmMGrmAxyAYfGaODHEvXNQwdE5MoQxTHsE2DGJCDsCJ3WBNEH839LDTKDNBN7EYZHt2Y432fP98M/BLZdQoTgU3CFFxLR5xXSvYXAMRJXKdN17JLQA4AZTEyWFhh4CNsqdwyjkgy8SHKfKxTfCXPSQ4DgWvDsw0iQVItVosUlu3VO2cZO7dQyjyG5biwDTLcFQogoqCy2yGViSRQ+zRatlvXSxWyZb1IYLsBPBYwpGCEprDq9LbCkC/8sySqWXnz0iLvtgDUxDT9mX28Ve0K6b1YHrDjye9FmSZw3aZ/E0iexSJFWVVwXBcLnoVZnEsZiGkaE/m/bZAJSahOuybp2S5GLpEG7aa6tF6RRnBcv2YHyII15mGETLMxdIj1wDxlQTKy8yYwLMq41FhASxdUKbMEBh1XVgcOztfgYxccCQsdC75dgzXKcB+unzLNxNA1cf3aAKahEcn+frwaxZK3mY+7mbwfxRJSwPYQsx/YSxLNCi+l3jOIRDLR36s+0GFrqa0rJOBO3zemKlDKvmUAZVb0MB+rSr5xZ9iEGdKxlugDoBSAVY/FR6sIk2jfNvb2+pRJjqSM6ZSEl2mAY+rt+iogFFCwAlX3WbFJKRuzZSBh34ahdK4iAdaVahHJdao2LvfaFJU9K1Imq0SNorQ2UTBkiwSvp+npAGUsiW0wbMIQBAE0AODsToQDlDXZLLRc6BqwxaVFX9iNcHhkoDXXNF34WtrygNsKU4wEUM73fNgQAaEcI7Og4Q8mYHkwtQq7QmBLmOQYcMw2WB5cLa0BpaqqcHkaUylsm9HEB5RUFZFS8uwr1/NhIW2BclvzlHocNscK/KDzHm7NUyzYbf883/ORpVFLxh1lBXwjh0bPqZkuLF3H+csNCLUTZlhip2PAruMjjiXgY1bALCl70/Jg6OiwGsM0R6A379hwMIBTJS2QeJZkGFmcR6PpwtS01j0HmuRp/jp108FcuE+i/BkgbMVMYJ/O4pzz5/MIIG7k2OVYiNM4x8Bq963huNCLtNW35DpymirdPB+LVlsaPKdJwlaZkHNaHoZ6VnvU1X2bNmnRAAFVeEZmlBXQdY1Kxl3ADqBnEZ51YqlVr2ZIcmnNhu3j2Fir2XUAah/GISdZNnQNvmO0mI+TSPAhpbYxqx5aXos1e44rLIiEBHNYXQCQjmHXrw1xz+ntGszcDqmcuaR9RCW5T1ojx/8P732Q1JuTc+qCr+/x5aAONiFAmCDbBQjYlYa4fo1IXi9O4pql9rQjg6Z9AAUqY5xcH+D+s7t1O2sWYJ5wScz031mE19x9LQDg1OU5YZV2ZNfkO8X68b99+NGa9UlAw5B4IeoWkEyxOnTw5YszfPKxLTx8aYbrmFw3oYzhyXncfHAIXdPw5YvTNvORKRGp1+Dth8f4wrldlGXVll3ztjZ00+PalQC/9ZFT+Hd/+gCyouyw/QDg7htWsDlLMYkzAvhK2IWrQwfTKEOcFY3smrFBKTCKLKxl9J97YgeHFzzCqGWAYn0/yLO84Nv4/NldPLIxa+TLWUTqcIqSH7ib4DiPb4ZS5iMA3HJwVP93yyuaW2s++cgCAOCDD16CYxkEQBXK825YxWJg492fPYuBa+JPf/Ru+WY4961fH7n4xX/4VNywPuzO3zjw8cb1Yc0d0nUNr3rmsS6ofbUA+ArAxw984AN405vehDvvvBO6ruPYsWN45StfiZ//+Z/HG9/4xis6VpqmuOeee/CiF72oaZCu40UvehE+9rGPKf/uZ3/2Z7G2tobXvOY1e54jSRJMJpPWP18zRcU8AEBS/6z2ri3vKcEX3aA+aTxQRl9WMR0UULAJVLInCatSVaQMEAWjRXXtbEeDB8dUCwKldCoDoNWpaHURFhDLAweXZylm8zl0vWnLyLWwm2rtdgJydoZhSZiPZMLH0tXWRm4P8zFFWpm1x4drGhybUN7nDBA7MHKR5mWdUi1nqSWoTKfeoc0UcmoSImLXH65IlbhdJKS9tkGZj2XDIBJLkcB23DY4JmtjnsK23XrnN9fkgHXDfGPBPCoWTQLb9eFaOmGByJhFZQFQRqBnm0rwMYlJEvcqBW9zRTJtnkQwbQ+BY2LG0pWFenEcIdfsJtVc0R9J0rDrfNvEvJQfryoSwixk/VtJmHMUfHRdv5ZCyLwF0ySmDFwSZLI5T1EZ8vtaZik0066lQdup3u1fAFUWU7m3TvwmJSDL9mQKx3Gh6xp5TiqQ51kKUqcwbFLXtw0CpEsYrWWe1hsJKwMHk7ggfoHCMbOU+E2OvMZAO64k7zPfT3FEAqIsA65l4LbDY0z2CRpK20nH8+XAJv24j+NEYYRSJ+A0YRTvk/nIT7L2AhNlbDughymZgoy5vCWFZONKyWKXM7L5v1MyH50hkHQ9uepSextKAChAAlj1SW+Fuky9sF8ASphEe7a5t9dnngqLLsn3m7a7oM9mKxVcleysBNp6gGDVJqUpUwDIkrEVbFNZSBsg39yTSejZdXS+r6p5xpUCsj2bqPuWXSfd+Rut+9InH6p/NDKFtE5az6pI26Zxjs+f3cWffO4cjoyMdnt1A7ph4fSl7ZoNdXGS4NCALiU4NpKNDPc+0QBpm7OEgIrc9biuD1fL6pRTgGPnmXbtQWromkTOW8AziubZNWw4yGpfvd0oQ57nMPW2TFczXVTcuD5NcqR52QKqHMeHg7QOJqiqCtth2qQU0+L7AYwyrc8JkBCGgQCyDoIAZpm15LzzJMfI5MFTcp3bHMi3E2VYcCrwY5/leEgEv0SASKpr2a/pUW/OLgvRN4qa+eh6ARxk2Jo3z9MsKQhIrJFEbNb/u9P2GBgmOYyqDcbqtouw423J7ifPVuyOdbMkJ6AtQNmDPgI97ciuA6M91pq2CxtZnTDMn9fTeEaWgwWraHmH7kYZho4JPY8oy9bD0OgGxczToh0eIoCP84T46NVehJYPVAUODLRaysraZJt6O7nYcrFkFy3Akz13YlpvYJu1pLcoK+JVygPidHPg225ZanlbXmYsQK6sDR08cH6CS9OYzMsV4OOJVcJUfPmvfBQAL2em/Wj5gOnBqRK87GmHcZwy3LbCVMpQu/3IGJ97Ygcff3QTG9OEqKYYO5EVuuG3PHBg6BouTGJsTBMcW/bxs996smkfvWaUGb7uxCLKssI//LWPAwA2GfjIA2Ps31mM777rWM0AXAo4oK2+fg/Ik9pD0bUMXNiNseBbsAydgmgB9TgM8S23H6zv8ccf3cTNBylQldJ3MLwM09CxOnBwaZoQz8eOlyO57tuPjDGLc5zeIuFHC57V/k6bbr0xft0a6e8PfWkDmgYp4HZ8OYCua3jj/34AZ7cjKfh4YpUc5yX/9cM4ux0RQgSThNfnjOt11PsfuNQArK3n2avbdvPBETZnKc5uR/XxkccE3LMb8NG1DNh0Q1PWNoBYyNx5fBGvfOax5odffA/wtm+tN3iXAhtff9MaAODAyGnPU7jytGsW8NCFKW5YHxJGs0x2vd+NcG6uq2ka3v/Pno8/+uFn4/f/8bOaMNur4GOnXDH4OJ/PsbZGbu7i4iI2NoinyO23347PfOYzV3Ssy5cvoygKrK+vt36+vr6OCxcuSP/mr//6r/Ebv/Eb+PVf//V9neONb3wjxuNx/c/Ro0evqI3/P136pNEA9fKat+ubkhdfZkKvCqcB5Lv+KtkT+1mPD1pzDBkDRCVv6mFdsGOx0icPU7EoTKcLvLIFLl34rI+Ige88DOu0QoCYEO8kGiq+ney/6YLgs49vN+m4UrmZVfvVVFXVGOJLmGlJZWBAfexIMnDRuxhP4wilbtY0/bCW0srZYpbt1n4yCeSgIksmZowZFdsORYaEynOJh6A6cEYvUjiOW3svSpl5AJCnsB0PTh1gIl+MF9RDkoFtkQIw1CjoqWkE0Iphd5lCOTGr93wfvm1grgiJyZIYpWHXgGeuAMaKNILh+Ahsg3g+0nPwJY1DaGbDsosZQ1JgXsWcr2Dg0LZJjlflKWzHhW0ScE8KxuYpqoosIheoTDqWsPvSNEGhWbBNIkMuy4oE3cjk6nkM3SYTnrWhowTN9CKBaXvQNA0jzyL3oQN6RjBs8t6xfik0OWtMowFGpK6pZIxVOWFIAsDKkLwjsvuWJDHy2m+y/5liJaNJ2mzSdOP6EDOFJ+depSqSBnwcONhO5EE7YonjCOCeo2wvz0CgSTbeT2oyIGHbsQmchHEHNAujFujFwEdujGayaxmDvOfatTJXMx+pN5GyqLwN62/b34BpXxYAKsn1KFj5AqgbOAZ95vok5xLmowI4KzSrtQhv6kvuW97zDZa1pwZxFQxFqY+0ZMNQxTZV+VPLAEUl+3KfQLLMLoW1A5CDpkowWWYhIBs3Je0W7qVpaBxgyi+2bOiUybQ5S/HpU0Tyt+yCgF7cRutwMIBdpfjoI5cBEFbSwYAlaDeAwMjR8aVz27Wn3MY0wdAqW+CdZrkwywT/+l2fr3+2OUtoAEWz6DV0DVkc1iEMAGPxpRyryYNdpbW09dI0gY0Mpq613gnN8lBy351NyvpzaFgL6WIPrpZhwlJRkxx5UdHk7OZYi6MhbGQtr7utMMPQLFtz6eFgAAttkLUOT2FtMxyYuoadSQPy7YaURcn5JTquhyyJWqnNAGHnGSVlbxkWdMNAEncBW19vFtq67WFgZHUfAJQlx98n06HgY3Osoqwwj2ISSsM9n6btIub8QJtztgFD3XLrOVLTHxkWmZTX8gDLha+3PR8ncYaAB5xBEtNtpDUoBzTBIK0EY4vIrs8Jnpojj85DLA+wAgz0rAUYAiQ5mc2HAUYsaOrMk4LMsRmDj8pUD3glPsuFOIVpTsbPloTWw9jMhWAd6h3vWMD5zwG/9S3AbIME19HrZG1sBcUwpYNbYpsDUMX2A4S0cGmS4Ff/6tFG1itsSCBPOqEdO2FGZMdszmu6tS/0idVB7V25PU+xFHDHo0qdpxxdAAD80gcfxsYsIaopUTlAWXGGruHA2MW5nQhntiNcvzYgDGXuWhk4ZpYJXvnMYyTQsiixNU/IRjy/eacb5P2loBcjQ8hl12Sc/Z5nHa8BsS9fmtVsTAI+eqSt6RxHl3wcW/bxyMYMj12e4wU3rgExJTu54/q/10cOPvLwZZzfiSTMR2JDsTYkdkc//vufw8OXZuT8YqI0/V485/qVusnLAwem0YV1mNfmJ6iUWwbw+bZZA8cABTWzSGBbhi1A78lHx01ftNibZJxj9xogDM1WXcHOho3tB8YKXAHAz7/8yfj+r7u2+cEX/giYXQC2T9U/Yuq6VYmvJSt3XbvcblOetDe2FcGfdV2g/UwJ1kgLvk1Sx1VKnKvlysHHG2+8EQ899BAA4MlPfjJ+9Vd/FWfPnsVb3/pWHDx48G+9gXyZTqd41atehV//9V/HysrK3n8A4Cd/8iexu7tb//PEE0/8nbbxq6qo/IhYsQKB+SjQzlt1m10KUlcy8WdFKk9SMRqulPn4N5U3SdgoSt8mmyYyl+2f9zEjUNULjNUh+bjPwohID+i5h66JpDIJRikBH4uywj/7vc/hHR87Dam8riTgI5NfxBnbLZZJ07IW89FhzMce8DFLE+gmYewBVCqiWOyWHEAFcEEyQjuKLIZmEb8o1zIQ1YCX8JzkJIl54JgN81EhSdNLAioSJg4IA1GxgHNcrw4wSRVMoCqLYdokmEbXNcwLic8YCOjpuuSjNXRNsrjvLIwTFFUF3w/g2wamhSX1tMtpYrNbAz2OnG2XEX9D3zYxzRhTsb3gTxPSx+xeN4xGEQiOkWk2HFOHZ5kkiIS2uemMClqRwPHIxDNw2HV2F+plVcH3CAPVNDTK8hTOmcao6DvDjNPDQhGAkhNQESATiK1Ek94vrUxh2WS8WvAs4l0p9EmeJbX0uO3lKANXEpj0eEPHRKiQO1dF2oCPAweOZZDQHqFvojhBrplY9G1YBgFw54qwIFaytA0gHBi7FHy8MuZjWVbQihQ6vZ6lwMblZH/MxySJoZtuDTKliqCh9gmpxUctRWEMAxWY2JVCAlCfRxYuUgehSZiPHc/HHrYdyLNUydKuAbpw6AEf+1j2/O/5+prelQFLgbA+lqSKKdfUDWwTYbkf5mOX8SI7dqaZrdTUpi2yDUDFd7WHWQmgh/m4DzUF+1vp/EMCasoYhzL/TlW7c8XiQslklAB/gBqQlUm2lP0n8x1tnqlXP+c4/v133C6fGxkOkV0C+JPPnUOSl1gZOnj28WHn2nTLxYtvGOOTj5GF7IXdGOtszcoxc1yLJM3+iz+4D/Mkp16IbRYiTJKyzUqcFfjIw5tYdblgGsuDrmuwhdCZeVqQkAxuw0PXKkQJufcb0wSBWdZp0nz7efDx8iyBoWtUCtuwWTxkta8fO68lMB9HgwGsKsPr3vnp+mc7YUoAMu6clu3C19vy8t0oa4JYgJr5yKdFT+KcStWbYzmuD6NIWmBbURLrB41tclPCgMiQjNICnla07pNv5K1+PbsdYd1Hq46ha5jNGvDx3E4EvcwIA0y4zjAMW8BomOYkqIdnPtou7CpDzCV7z+KcpmLTc5sePKQtUK6RcDfnHA4CWMhxhvP72w6JDDvQM4655WDFrXDf2d36PkzinAJAlB1nE7blBgcsVlWFS5OkBdisDGzM4rwG2uYsmZpnPgI47FetejPKkBQltGaZtIJpGOg9cE3g9EcJwHLmkxh5Vg3mv/a37wEAAogLdieLNkmaLsuK2A/M0g4jjr+esWe22WsAea/KDMcWHBymXu2MAVwnXdP2M7bbwGmYmQTwpMd76H3AL98FRDtwLQPf86xjGHkWNmcJVmqAkFt7Utk1QBKM3/L+L+MvH7pE1iQdz72Geca8A1/85g/h4iThgmQEViVtOwOflmvZtch+I0nzdxxbxCTK8DADH0uakMzARzrPWfRtfPKxLViGTmTGCZ03LFxTzyHWRi4+9sgmTm+G3LPHAanpDJqm4c7jizWATCTQXPu4Nfba0MWPvehk557i9EeB0x+r/5dPiF6XeD4CwJtf8RT85Etuxt+/8yhhPhZp8/5Yfn1P7jhGpPaM7dman3Gej55t4AfuPoGf+KYbGwuBPKqtIWpWKFeOCmB3b6Htwe6Z+kdMgSVL9GblrhNLeP6Na40iQHz2uWekU4qMzt+4eVCfcgG4ynyUlCsGH3/sx34M58+fBwD89E//NN773vfimmuuwVve8hb83M/93BUda2VlBYZh4OLFi62fX7x4EQcOHOjUf+SRR3Dq1Cl827d9G0zThGmaeMc73oH3vOc9ME0TjzzySOdvHMfBaDRq/fM1U1ShK6yIKab8B7FT1xPAx7jZcRbLHiEprVKzV3okcX3HUC2UVAtCmw5s/MCikk7JFrdAD4ui+QgCBMD40Jc2cH5zF7qm1ccbOCYymCgZW0g4LtvpvTih90MMYShIIij7yNdyE4WfZVw2AQGupStDQZo/iWFYDny6yCS+b56CpZbAcRpGY1rJQcUijWGYDQNtXgNeXTArqUh7fdsgqWGygZ3Jml2vZiCqQl30PIHruRh7FjQNmBWK4I2cgI/EJNvGFgNruIl0VVUwyhSuRz7ICz5NERb6pspilBXxevL46+2AY8TfsO4/RXJ2SSXGvm1gV8FUzBLSx46pw9A1jl0aC/UiaAbxAA0cg5Nxc8crsjp1HGDgYxcsrPKEXie5Dwu+TYG4dtvyNK0TogcO8XKcSwA7cu4GBFwZONhMNOl9NcoUpkve5wXfkoIsRdqwGRlDNlWA1FqZ1uc9MHaxmxly8CzPYNJjWoaO69cGUln9PAxhWHY9CVrwLSqhVo91edawFQHg4NjDNDdQZlfGfCThAzlMDiTdSjRURZcJK5aE+osyg/tsP56T4gbOXuO6uIHDJrhX4gVYbyTxz60KDFJvtgCAVmZEOi8re4GPfVJaoPs+MxN3KXO+u4lBfifbGJPcS2ESPXTNfvCa+VW2WHAq8DGjSdcy5qOCicfa2mq7+vjS+vXfiMCfZLNUyTZVya4VgK/Mfkaq6GDPvdiOPTZFpSoLRf2OlFrxPipl16Tuq551HE8/vkSuVzOExROp98Kb1/DgxSn+8DNncOexRRhl3n32TAfHxiYevkRCDi7PU6x6gjekReSfA7PEQxemNdtsaHfBx1tXKXu8KPGuz55FnBVYcqs2+KVpcJB2ZMuexi2OTQ+6BmQxOdf9Z3ex4lbEu5A753g4QJnGdZs2KStMK3gGkgtLKzCNSN9tzlMiLqmE58h0MHINGCiISgOESTkQwEcS6FPg4qS5x2e2Q6zwICsF+ebzBuSbxjk9VnMPPH8AG2nLpzHOCthIyYKOXoNueyjTqG4XQKXIeluK7GtZS2Z8bifCmoemjmHCMEzM5pzP5ySGrRfEe427Tsf1YFcZPvzly9x9KuDq7TRjw/ZhIyUBcLRM4pwwLmlfwHLhImsFzkzjnEjtW7JrDyOzaPmLXqKsQI9nBVo+luwchxY8vP+BSwCAxzbmBJThWGw+EuyGWQ3IzhICHvJMKubRfYnezwZUZMxHMi+5ZZV8Q8/SZ60BO7mxy/Kw5JDrZnP/aZwhcGhgCQNWtk9j7Fk1S5Vt+Dsa90zSZ+lgoCHNS2zMEnz0kU0kWVH7srNy++FxPTchsmvx2Sb/bSLH//UtNwMga42a+ZjHjS+r6QFpiIFLwMeyrNpS78cpCHbhPgDAytDB9jzF5iwlAKWopOPILi+8eb2egv/g3dc247XAfEQWknbRMokyAioqJN1Aw8y7Zsnvfk84afPYs3BqM8SXLhKpbr0WtnxyrymItkiTwa9dCci9Y8zH8VEg3gGAFjB/eNFDS9psN0DmzZzH4dpIYIdaXuv7shyQn7NATwDAe/4p8J4faY5Bn9nvfuaxem4nlrFn4RtuWcfraChLK8Wak0n/+++4He/6J88hLMuqon3MgZTcvPkf3XUNvuk2jpiW0boCpvA7//iZ+HffflsrlX3PEm6Sf08b4H6Rgo/HlwPZXwAAhq6Fn/q2WxqgU9y4U81pAAUJSrUZrFA6XC1XDj6+8pWvxPd93/cBAO644w6cPn0an/rUp/DEE0/gFa94xRUdy7Zt3HHHHXj/+99f/6wsS7z//e/Hs571rE79m266Cffffz/uvffe+p+XvvSleMELXoB77733a0tSvZ+iCl1hpQMoCug/X+xAACqT7qSUFSXzUcEmAPbH7JGxAOjuXIedKPMIA5qgHX7HJU870qL6Oupj8cdO5AsZIUH87hsIOzeOopb0Z+hayBn42PLRJNf3Fw8QML4oKwWgSCYJcxF8VDAf41KvJxmBbZK0PNVODQgAYtpune4Xprla6kVZhXWSdiUHbIuskdJ6toEwL6n8QQDj0qRmao48C7tRLh/YixRlBXgUBFweOBTYkYFKGVzXh6ETcGySdxlyAGG0OQ453vrIxeWo6lxLkuUwqwwuZQQeW/KxEXXBsSQmoGUQEObjJJcDhgQcc2AbxD8y02wpyKsVCRzXh++YmGT0WELflXRnVNM0MgnM5MzHNG0mMYu+jYshC0xpA/JlSUBFgADphLXXvq9JHAFVBd/36fEswhgVriHPYlT0ndE0DdcsedhMIH2miJyatG8nzPDQ5QyV2G9lBats7tfYs6Qp4EUWwbAboHAxsKXMzLKsYBQN+HhowcPlWA56alz7AOKxE0kAzZSmrLOyPnJxOUZvqnORJtC48frAyEUCq7Ug3U9hSaqmRY51/doAcWkiLco9WYxZEsOgDGAASPZI6KYNJ/9m4yKz6lAyH4XvB/tvldRFGi5iN79jpU67FsZ9lYUGLXqZodRU4OPC/sBHlexaMibLwS3JhlBfOA1/blYE6e3QtTDLdeVYX0udW8xHVzHWZ7Vva7fd8vodkAugQHDPRF0VYicwsztsBaD5f1mfKxmVIjjco9LoMPV7wO4r8YveJ5O1t65sTiaVikveJZOoC+48voRplCEvKrJ4kwUpmS4OD3WkeYmHN2aYRlmToM3LAgG85pkHcHjRw+fP7WJt5FDPRH6B79R/+7r//hlkBZnH3bBstRbamgY4VdqSkm7NU8KC4wBPQ9exuUsW/L/3qSewM5232wUasoIUv/ahR2uQZHng0EVx035d0zCdkb/fmhHfNU2cxxo2Rq4JGyRcJ84KxFlBU5bbi9lAb/sNnt+NybVzsmtD15AnUc2Um8ZZxy/RcX04yFqy3ygr4FQpZT5S0IgCfDyrMcpyuMhb1+lqbebjTpRhZAvp1LaHKAqR0/szjck9J8BuU28YBHCNApuch+QkzhDobbai5XiwkZG5JTtvmGLRzpv7ZXqwkXQ8HwnbtQ0AD8wCn6JWAQBwenOOxcCGVfLgtAMtT3DTgSFO0+TkT5/ewjOuXeZk1x6R8gP4xQ98GUADZK5x4N1yYMM2dZzZIeuneZJjYFXk284xH8dmhqFr4hQNWGmFh3Dg49MPu7AMHQ+cn9C+yBq/x12q1puew8rAxhfOkW+RY+p4/TfcAI0f23QD0C2seIBpaDi9GeIjDxMguPbno8XQNXzXnUcAkI1RdQhcUns3fvThTXzoSxsEyMwog03TCDCVRxhSX+15muPyLKlVLpiep9dCgNRF38aF3RhFWVGAUMI8o2u0F9+yjne85hn4izc8j7Dn2Hgm+jjSMfHkenOdjZejCD6SY3zTbQfwOz/0TCprFjZEuc2mlaGNRzdmiNKChJowxp3t11Jpcl3k72tpdrxLzhes1HOIb7y1IVYdW/LbAB8Hun7TrQfw3dTfcDlgzEe2IdAe1285NMLBBbeRObP2AfVc87k3rMK1DPyjZ1zT/G7rMWC+CWXhrdg4UNE29SbAqMjQChvrYw0ClPnotfoNIODoszkJ+Z6lLJswn6h59597cgXv/IG7SNjLfotob2L2MR8T+XzsSjYbr5YrBx/F4vs+nva0p+1bBi2WN7zhDfj1X/91vP3tb8cDDzyA173udZjP53j1q18NAPie7/ke/ORP/iQAwHVd3Hbbba1/FhYWMBwOcdttt8G2r97gVlExD1ixgjb4KJto1nW99kKyr65scqzyiKz9EvbLfJRMmll7Wuej7RPZJQJAWP+tyowekLA498d8vOnACM+4dgkW2vKUgWOi0nQUEGS9RYpppuGtf0kYvCTpmcp1xURXo5E31GbnUpAuQczJrse+RaREhmJxBMJm0y3CoNN1jRxfJvUqC6DM4bo+8d1zTZKoy/VBXbKGzebbBqI0ly780pSkZ3u2gQXPRpIVyCSp00UWo6oquC5jIFqYycDHskBVFfAoWDj2LMxzOftMLxJYFMxaGzq4GHVDgdhixKfHWwpsAnoKfTOjRu9B4MOzTEwYu7ADGMYwLLfuP5UsVy8SmI6HgWOQ8wntAgjbUmcsP8fEbk7rFd2+Y1Lk4ys+Ht6kQD3fJ3mKrCyxukB2X2tvSOFYEWWGeHx/SOTURZpA496Z4ysBLkdqObXtkOM994ZVZLBQ5lnrHQhTkrRpuxz4KPFGLDMRAHSkHopxXsBCDssh/XdiJcDpSdUKJgDIrrVWZrA48HHgmjTFuntuHkhcGzq4VD9T8olNyd1DgOx4p7Awu0LwMUoLmMhrefjYs7AwGhFfnT02erI0hkXff2h7J3QDkDMOVRMyoDuG6rqc+deqLwF4AGEDpydwJk+6QSQAtRjoYT46o2aSKytKb0MFECYuhuo2Svp5L1aljInXAh9NRKWFIu2ZSAP7ZD6myGDWm1J715cw8di5+gJqlMBfsnddWQgRQDYo9512rdhYlfovJwoWaw+TUfQuBXrASsmmrcoyRWZNsN/wP9re551cwVOOLuA7nnoY33z7gQ6Yzc4/MMjc44d/m/i8L/ByWaB+PsdWiUmU4dTlEDcdGBHwTgC6rYpc9yOXZnjowhR3n1whICWbq+kmNM2AjRQ/9Z4v1H+6HabwtLwViGDpGi5c3q7nRK959uGmz2jRTBcHAg0f+tIGfu3DjxL2lU/Bba79uq4hpOPudphi2TckQD0BDFd9DY9dDmtmJgnCab9Tnp7hTz53rt40vjShATzsnLpONiKR1sBiHbDCHUu3XAzNAh97pAENpnEOGxnxYKQMJNPx4FQp/uRz5+p6YVrAbYWwuHAFgHI3ygjbssVUJIxGVm8S5Vh06l8212k4OBToLan0bpjBN8vWc2w7Lhwtx7mdZlzaDjMsMmasbhAPz5KwEKuqQllWmCV528eRntPXSX8whuGF3ZiwwHg1FwUUjix6OLMTYRpnmMU5Yb1lIWVbetCyEMdXgpq9eXESt8EWkCTb1aGDf/+nDyDOCpzaDHGMhTNRz03oJrQsxnWrA3zxHAMVU3IcfuPEdGEUCU6uN/U2pkkjk55vkkTpaBvf+qRDOL0Z4uOPbmI7TAnY1Qlvc2AUCVYGDn7ufz+AMC3w/BvX2gnBrOt0MkdUBs4AQJ7Uft4/978fAAC85LYD7e+YSdaHTFr7+58+g7PbEQ5T/72aoTYhzyLfl8u1NJq7BrtZm2qahiOLfpOaLSrVOOYjAPzqq+6sD9NKbK6vy20de41JlcXMA2499dyTq/WPFwO7y3yk/8825upwlXiXbF5yc4g7jy/hO+8goO9SYLfHHW6dresavv85x/F//j/PJfdOkXbNjvPff+CZDbDJwF6ASPYBPOPaJfzvH7u7+X5XFfDfXw68+3VQlhYw6sk3k2swmDEf3Tau0DlmA/T31turpDMyHo+PANFO/WNN03B4weukrPcWyTvUaxkkm4/Jvsllj5rja7xcMfhYVRV+//d/Hz/8wz+Ml7/85XjZy17W+udKyyte8Qr8wi/8An7qp34KT3nKU3Dvvffife97Xx1C8/jjj9cy76vlCotKTsyKO2po4UCXds6XDkuyp64qQZJ9JDr1exadfJExRlSLDdnuBNC0ucXiVDA+62N3WXf7AR8BQk9/wwuPwzIbZqWhawgcsxt8UWTY5k41T7gdXr5eSXb7GfhY7x5LZXspokKv/e7qlL4ez8eqIGm+mqZR6XPRlejTY5cVamnugm8Tfz6hDwACaDbgo9kAmhJvwEyzENgmxnTCE0rSpFmiIgO9FjxbKs8tshhVWSGgzLyRa5LAFgXAxzwO10cuLsy6jMB5RPrA8wN6zRZ2JPLcOfVtGlHm465M2owmsAcg0tjdrCsJz4sSVkWAUd82MUsKOpHuMh81CjYtBTa2YkmiOgi7lD3v60MXW2HWYfVktN8WRwx8NDEvup6BEZW4BZQhuejbxJNSZL7maQuIWx042FEwVY0ihUVZstetBkhhoWCpv7SESQ67SuFQ782xb2MqAT2rPG2xFNeGLmGCCfXClMjWbAo+P+WaBcwKE3HYBv2SvIRZZfU9A4DANqQhO0WWwmiBjy7HMpWPd2WetMBH1zKgW179PO23RBmRXfPtvGZ9cV/MxyKNYdvUn9U09k5LJn9E/m0Ik3xl2rUMKOmb8KXd741sfK4nezKgsmqYkXyhP6tU0pg9ZddJ0/5W+2wQUF9ky8WK74eM5X6lwGb7Wzb2LKSahTxV9KvM77iHhZdWZm3H0a4vYz6qvpOK733fRF1UAKh8mk3J97K3LYo+36/smk8q7dTdJ0AIyEFQVVt6w2kkz0kH0M4k/eYAVQnXqPCmVzwF//SFJwmQ0MO8XON8w8Z21fbBou/q0MgwTXKc3YmIvFV89k0HRpnWnnIfe2STAAb8olfTAMvF804M4Fo6qqpCnBUIE4FhaLqwTB02UvzI/yCg6M0rTn2eulgublwh7/oXzk2wOU+wFlBgkY29pgdD17C5M6l97lbY9FUc4wAcGRo4vxvV4FwrgRgADBuORsaZP/ncOWRFicvzhPg5cv1hWC7sKsXbP3oKAJUZ89Jhei12leDdnz2L+8+QcWkW50R2zQUbGjSI5Xc+2fjck+9c1uozo8rwuce3cW4nQlFWRPIrsC1Nhxzrv3/icQCEzbhgV/W1NRVtuHpeA6xVVWEnSqnnYxvMXHZKfPkSl+wdphhZfECMB7NKkOUFoqwgAYsVkxm3gYIDFOv5HA132ZynDVus9qwjz9/qwMED5yb43BOk79ZH9B2xGhbbj77wJEr6nG1ME6wOu8m5J1YCRGmBf/u/vojPPr6Nm1btuj2s/chCvOiWdbzrs2fx3z78KN7/wCUcW/IE4I7MrZ92zSI++BCRg2/MkkbmHe8Ci8eBaAfHVwLcdHCI93zuHMKkIIxEhW3Gtz/1MCZRhg99aaNm5InlFU8/in901zV48a0HGsJGfZOa7ysvhX3LP3wqZQpHAmgW1l6Cv/3x0wBIsjUAINwi/RttA0ANZgLoytCBlr9gp4hKNfad5cA4xzJwZMnHgEnh+bWnipknXj83di4PHCwFNl79nOPkd2yeIoBor3zmMbz2+deR/gQIQGYPWh6WAPADd1+L3/i+p5NnKuuCuKxomtYEyLTSrh2q+FNY6HBgHOYb8jrs51uPyDdl6z5p32NpHdYmgDIkezar2XMjqimvtFAZOxaO9c/P9lPETT5B1t6tK8EhpM/UHurTr+FyxeDj61//erzqVa/CY489hsFg0EqSHo/HX1EjfuRHfgSnT59GkiT4xCc+gbvuuqv+3V/+5V/ibW97m/Jv3/a2t+Hd7373V3Te/78vKjkxK/agzejo83yknh6tuqoXSiYf6z22YqIuFmm6pYoBojCN1w06wPMp3wogtT62hI0iu3YJq9LQNZxcsjv1x55FGH0C83GeN6/kPOUmnWIwjW7VE7yQMR8lbMYyTxEWBgKadj1i4KOKaQFAy1PovD9jkncXf7QdZUUSndk17cYleeaE+1lxrELXMmiCdnfAzpK4Ztcs0N3Red4FlcI5eRYZ6LU+cnBuVnYZiBS0CXxSj8gQ5Z6PepHVTMoDYxcXQirl587NmBBBLTO2sZNqEnCUnTeAZxvYzQySbi4J2GFsu+WBQ4BMAVQMswJ2lcF2fCwHNrbCFJWk76o8gWk24OMGO0wHFEvq+ztwCRBcCcB1FJI/djnZNem39nXGcYRUsxBQP8XlgTyduhC8DBd9G7tpF3zMixJmlcJxm/uVajbKqn0dcwo+18xXT35fNU5KDTAvxy5TNUwKWFUGm96LAyMXhu3V97GulxKGpO00Y4tvGzTsqH3NZd4E0wCExXhhzqT88nePT9JmxXI9RNGVgY8hZT7ywOvDWzlJglUlStNSZM276tuG0ktV+CPy7w7zUcW4k43lPRM+KQNLwkxncvaO7FoBStGfVQAqTbVJNyaT+b4JOn8OVjRNzhaQAamAHFRSyXplknN2bK4dI9dCBgu5kvkoATdV963IkFaGwvNRct9UoXRK5mOPP5K4+bUX87EzH8jlx5UpAJSya8n9Uc0zVBuqvfJvGVgpe08om7FjNdMfONMcUwFSAtLvU2euQ0GcH/16EnLgOyRYpsUApe0YmCVQAY9s0MRW8b03HWh5jHe+5i688WW3A6ChCeKczHTx/BNDhEmBszsRPvv4DgDA0zgZt0nSor/h5BiPUZnriHkICuy8VTpVO7zgYXOWYtWr6vOwYzmmjtMXN/HG9z6IzXla/40MoBlbBf7sCxewE2ZwLQNmKfSb6WKBtsXQNSLlrdBhGGoWkSO//4FLKFtAIA/MeDgyJPNEJklmbEVN46/BxdFBA5hdmsR45NKMgI9cSI+plTBQ4L4zuyRluQJ8ve0zaViE+chYlJMow5hJ7YWEZE/P61RvlhJO7hN/D2yMrBJv+8gpTOIMeVFiGucYGgX4lHNLJyE/P/EH99XX6mpFp2+1PMHzblzF587sAKDgo+j3ZxK22DOuXQIA/M6nHsfANQn4lYUNkFTmuG7JBirg1Oa8Bh/F8q++5WbcdnhcM1CvWxLC1qhM9RtuIUSa/0GB25M1IG439fIYd59cwdntCB/60gYuTuJGCpxHwOK1NXD3iqdfg4/TczbAXXf8/q47j+I7nkqYvwsK8NHQNfzA3ScoA09kPjJlARnHvu3Jh3BwwcWth0bNz2tgl/Stpmn4oeeeAAD88Y88h4CWRU7WmcvX16DYAS4YRdO07saJPSDXLY5xQJfJSDcn+LHrj//Jc/Bb3/d08j98YnPdVgmIJrZBWPP8weuejVc963hzTIDca6sBFm1Tx3fdeZSAngCRjtt+J7DVMQ0uaZl7RvvYgOKzDKjnS9E2YVvaQRuI5MsOF76rUnbwfS14ObbqAG0wvcwgtRZi/pDsXVMBzPsp7Lq4MJ+vuHRk1xKyTV1XpsJRzZmuyq5V5YrBx3e+8534oz/6I7z3ve/F2972NvzWb/1W65+r5auoqHb7WXFHQNLsPNZ0aFmx24NnaxdGLDLJUd+xe/wH61IW1E9FnAj79PiyBZ7i2m1hEBW9Pup2uc3vW8dWLCB0k0rIJUwDof7Ys5BCYBQVKcJCx+FFD9/3nOMIk0KxwM5AZNcFFgO7LbsWFjB5liDXLM5Y2qTMRwmYSItWNgDIom9jW8KMIwcnaXoeJ33dUdTViqRmlQ0cgyR1SxahOZVds8AZ09CkATGM+ci8Bm85NMKFOTrnnc4oSEmZiiPPJIy7ImmDCfT5cijYdmTRx5lpRUGvpo1RGJLdSMrMG/sWtlIDlfD87UxmqAwbumHAswwkJU03l3hXMvBxdWBjO+1KkSPGynN9rA4dlGWFXHe6u4tFk268MnBwcV6hI6cGkfay+8tkMoXeBhDimPYvl3Y9yY2OBUAShyg0q94ZPzR2cTHsXmfJAdoASRfcSbussHlCmBmMgWroGgzbRVm2mY8xZaCyesuBje3UQMUdr6oqCj424876yMFO2n1Hp0kGC8QbFCAT4uFgiCIRgOA0h4G8BilZ3xB2oAC4523J9+qQ3BMpCF3/TdpiKwKA4/iYTK/U8zGHq+fQuTH6m55MPXB6WIxpXqLIkhp09mrwcY/xWcqgUzAMgLbsrK6/B1gp1te07jhW0A03WZgL307x2Kj6mY9VoV4UFKnc2xBoycfqomS/yZnrrfazUoNFMtZe008D1yTgvdJLk+3MC4CKop8SGHA74GMPE0/KCrT3mKjLwEfJN5udmy/7BGWbtiiYj1KgUqHokKkmrsTDsbe+ZG7SazWzH+ajjLnB3o99MEbp/I6FO6wMHGhF29MPug6YLgYGBcfCjIYmiLLrZsPhrhPL+K1XPx0ve9qRNhsIAEwXyw7xh3vnx07j/3rX/QAAG9zzThlQzzrWeL4FRtF9Ny0XepHgZU87jGlMvBNXXcGz0vLgmgbsKsWHv3wZlyYJDgR0uSRusAAo0gjnd2JcntGE3U6AAWEYPvXoAnajDBd2Y2ga4AlBLDAdvOC6ERzLwIVJjLyoMLa7nqzXjA2sjRxsUZn3T/3x56HlEWGfGg0I9sxjASyDsEX/133nAQjgnekisE04SDFPcjLXA+ALzEeYNu443HxHSTgMuH5n/+3A03Lcf7aRD9f3QeiP6xfJnPT+M7t1qMyQP6/lQ9c0vPY5h/DFcxM8sUU3RHWxb22gzPHkw0Pc98QuirIiIUJ1inIDtKLMsOybuP3IGA+cn+Dokk98K2spqE/bQdrzw7/9GZzZjlpJwaw4poEnU4+9pxxdwEG/uTbSfrLxZBk6fpACcgBwgl53U88Fshgn14ewDB3/5j1fwL2P7xDAkynTFo8T9mBV4WnXLNTHOjh2u+8o907deGAIgHof7lWUno9k/Hj9i07iba9+RsMAZQnhQGt8/gfPuAYf+PHnN+nGFDTF8nW1N59p6Pj/ftst+FcvuZn8Tnzf6wA6CQAk27gUvg82DV0kxxCYjxSE7hQZ81EFQGVzchxqD9Ais7TqReT3XChN97zc2pgC0fJ63Piu2ixiJdomUn13oWEIyuqwMr8sr5MnDdCpus76W2w39QAFSzIG8Yd0+/sNIM/+//7nrTCZ9u93yXMwWP+bg4/ihqPqewyoSVBXaiXzNV6uGHwcj8c4ceLE3hWvlv/3i4phwYozbMDHIiO7FQzME0tn8h+rjy1OeMuCHFvlEakKM+FLzS4RPzq0DZ0FnoJ1AXR3l1RhOKpBVGbsDjQ7cB0gtFt/5FlIO8zHDHFp4O6TK7huddDDfCRg5izJsTKwG+ajhM1YZNSni0rlFn2bSIN6/Ni0Iq1DOpYHNjZnlIXQAbuapGOAk3RLBm69SGt/vqFrYRrn0oVRliYodAuWoZP0ZE8u441i8ncM+Fz0bcwKo7PInlPvxUFAwMf1kUvYZ1XZkmCmcYiqquB7ZOFyw/oAKSwqU+WYj3FIfZUacDaDJTlvWN+7gHp8VobTmUwRUJa8cytDB5tJV8IdpoSV57henR6YoMtG41l+q0MHG7NU2sc8u25IGYu5ZrXOG9WydnK8gWtyXpPN8ZI4Rqk3H9bDix42Yw2lcA3knE29pcDGTmaSkByuTKMYZpXX4BdAzOlLtmPK2hfOAE2rAe31sYt5YSCPm3eVSKTTmsUHEJn5dqJ1vByJbK3xfAQA3w869cKUsFAZMxMgNgKytOuqyGByPsTrI9JGEUhtFc6egJWLEbCxs9tKSdyrRGlBjfmbhfK1B4kvcxJLJoW07IQpzCrDgIL6nmUgYtfWd/4rYdAB8g2fvglfHis2iIQxj7LCu/UUjDgAKDJUFVD2gY9Ae7LeapsCZANaxvnt+jLmo2RMVnpY0uuRAVA8Y0nXoFkuSqXsWnLfVLK0IkNSymTX3XGtPraK+ajyQxTbUp+jzWyp/1usq+vk/ktBNBXzUQbOqQBFCYtQatmiAOyvpB3s+Ptld8qeKymbVtJm9ncd0FbGpiTHXKKpotevDuTzQdMhQSO0HBi53WeCAkdsQ+vYctD4mwkBFFoe49ZD41qaCgAGP8elIVdHh83Gg15KNqENsnE39izsRCTpednlUpZpHU0D/tkLjyHJCnzuiR2sM/BRAAsB4LvvPAgAOHU5JCyzDhuN/PeiW2IS53jowhTHlwPoEg/Ma0bkPJemCaABQ8EvkQWnrA5d7IZkAxgA7Cprs08tD56WIStKJHkJXSPM/2WvajEfNQ14+tEAl2cJtkOS6t0NBvJww7JVA5mTKMNIIbs+PNQxjTLEWYGHLpD1hQ0RzHThaBkcy8D/+MTj2KEgamByLE9a/+uvI0y7z5/dxdi3YJQC84jWv3HFwcVJjJe/9aNN0rLIKgOALMKib6OqaOBHkZHNJQYSAUAa1p5xXzi3i2tX2mEtrDBvv+fduNrdgOPWGTesExDwR194smbAtmXXpN73MVkvqF8hA1XGR8gaKk8wdC38yNdfj1c/5ziR5MoYi3QsufP4Eq5dCXDHsUVp++tSlrWdU13qcYbcG03TiHc9KzyhxHLl3wCgCQNZuo4AqLS84MY1vIiyQruej8196BRZfoDqGySrr2Q+XgEAlUVNG21fzeDLopacXzqH4sd42foRoAFrSfuZYdcmKwx89BbUzMdoi0iWNQOYXZLX4RWOKtC2tp3hgGhV29jP+D5Rlcc/DjzyAeBLfyb/fTIlGIa7hyf3XkVGbOp7nmXf8B61CAC53/TXeLli8PHf/Jt/g5/5mZ9BFPU8NFfLV0dRTYxZcTjmIxuMbUU8veW1X8Y+GbVhtyfpKpYCK/vxflD5atXMx7BbX3XtMhaFbPGoG+RaJAwz6QIC6C6SWH3h+CPPRCJ6GRYp4tJA4Jg0lKVoQhBano8ZoBOg7+DYQ5wVZAIqYTOWWYIcJly6YFwbOYjSggKf8g+rXmY1Y2s5cLDJwEqhfhJHqKqqBvbafpJN3aqqYJRJLZEdODQsR/JxL7L2YmsxsLCTddmAcRRC07XaH28xoCBgkbeYeXPKVLTrIJEBHtnKO+wzJmtloJdvmzi8vEADOnjGXdjyVVqgDNaiyFsygywJUdLrYDLFQpcBhhkct2ErbkZdCfc8imGghOP5CBwiSY/RXaySwBwefEykbKoyb8BlJg9JtbbsL0nIsX2OWXiZvTJcfyRJjIoDe44s+khhIxMYgyjSlpfhUmCTfhNAkVnYZqoCJN1TZI0mcdQCgddHLlLNQcqdl/hb5bA56fH62EVUmsgEGeo0ymBrOTS2ywtgMBgQNglX5kkOEzls7loChwPouFIVbRbj2pCExxQ94CNJ0vZaP3vmDYdgVxm5n/ssYVoQjzFuDFwa+iigYzpTsygncQ4Lec0o9W2DAKuo2uxrsRQpSIovN8m60t3jPSd8MpDH7o6NUrbdHkwKANAV37OAhumpvJNU0lugI7ci9SWAEqD+dugmAdVadRXMBwlTznbcTlp8XWrJFM98VDETEySl0U27Vn2/+3yXZc9SLZmXMR8lG4aA4l5LGKR9gK/U81EFPsr6W3Evpdeokl1L2sHqyxbarJ17tUXKppXUUy1mZexVutBiUs6/95TD8vmgSfwLHTr3aOTUPAjF3kvhvHnc/I61L49wy6ER8oIs4N/+/c+QAAsubGQ4tOCRYAjZu2mRdow8Cw+cm6AsKyzXad20H3QdMJxa3gyA83zkGZ4moOlYoszJhzdmBJQVnzf634s2kSzff3YHTzqy0B3XLBe+kSPJCpzfiTD2LBilQCKgvnCLvoWteYZzu+TdW/WrZjOentPTyDN4ejPEpWmCWw6OoLXYgKT+mlcR8HFOAlG6oKgNX89rIHMSZxiaFWWVcmOT4WBIwbXHt0L8pz97CAD5rolMSuQJXnDjKi5MYmyHGfEYRtpcA53bj8wcjmXgww9fpmxG4f2k13DtAnnOdsMM22GKpYHIfGTPeAKTAovXLPnNN8H0mmcuC/Hbr7kLALA5S3FIwnwEgJsPEGD01kMj7jhc++m4+KQjY/zwC66nIU7CmMtt9jCZNEDfFwY+jujP6UbWy552pJEAi2MEp4BaCmz8xvc9HQcU7a+LjHXONnJUqjSR+agC4Njm2+hgW2knnr/1HtP5n4wZJw38UABjQPddVLELO++s0w37ZIXJ9FlblVJpjvlYFfL5kMh8lB2rzGnYlfgs94CP/lI/8zHcJHObYBWYq8BHkfmoYjOiGcdkoa6sZNyzTy0OlJZr26fIv3dOy3+fhaRf7YB4a/aVs/cAF+6X/07pV66au0qevz7PZlnA3NVy5eDjd33Xd2F7extra2u4/fbb8bSnPa31z9XyVVRUk39WnGGzY8A+HCpptOjP0Meq7Ei02YDTAz4mewweKu8r3aA72cJAJ2MMsCKyUfquxZQsiPvk7DKPLwkDZOiYSKpu2nVYGgjsJp06KinLpCO7trAbpnWa3JylR0vkrplmwrXIq84S9OaFOmFcL1OYDgPEbCKdkQzGW7vknq0skF1dIrtOO3XDtIBZZXA9MqEYuibxFpIsEouM22kDSUU+O+2GZMRxDI0zuF/0LaSwO8DOPKRMRdr/x1d8xJXZqRfSzRSXY7Rdd2CEuDRabUziCJqu1ccbeRZSzSHWNHxgSxITpiMagE+UShdlBaNM63Ouj1xcjDXCouS8bpJ4DmhaHcKyOnQI2CU8Z1qRwqag8eEFD9MoQ653nwkUaZ3W7FoGLENHprUBhySOUGgmDIP074Gxh4th1QEB0yQifpG0LPoWNMtBkcatXd4qb7MAlwMHGSwCNnMlDKMWoxEAXM8nzMfWfWiDwAPHhG65rWCNeUITsTk2IwEp7Q77L2SgMje+DAZDlGUbVA7TArZWQOdYnL5tYl4YXYCnEFKxHROm5SCXye9p0YoUjtMeK9aXFkgC6lQxSZOUKCtga20mw4JvIdPs3vCaWZITli0FTT3bIOnggHqiCzSLB36SpdrFZ/VlibtKkEwxRouefSq2HWMpSEEy6vmoSrs2HTKJV7ED+ja6ZMCcCghji1WeHaHa6NINavHBPRNVJe0n0/Z6JtL05yLjQ+GNGVVmV3Zt+fLJv1KSrAI30y6YwZ9jP7JrAFJPSRVIKKursmGRAXnKa1QxIRT3s1emLWFZABLATgKaKn0qxcWT1RxDPKZUYpbAtQx84MefT0IlVCBpHuNff8vNeO4Nq2ROI/atipEsZSvFNYPrJbcfxNElv8u4pF5dv/Y9dxBwUvZuUhUHk40D4MDHNkA2Mpqxf4WdRvSbMxx4yKBpwJcvThuQVZIcvGAV+NLFKR44P8Uth0bd6zQcEs4C4LHNuTwJmPY/scRJ8cf3ngMA/MQLj3f6ItAZ+DjH2Z2IBPvw958CfasucHlGUrYXfBl46hGJOIi/5MVJggW3koBADmFgAnjtO+8BAHzTbQe683EqDX7BTWvYnqf4F39wH5k78Oel16wVCVYGNs5shXh0Y95VEtHjusiaoI8KWPHt9vNRAzYRBlTx8aQjC1x4iNsCTlYGTXsPLjTzEb4cGLt47+vvxvVrQ3Q89jmwxjJ0vPyOI8Sepg6h4IAk2gbXMvAzf+9Wcs6xS4Aje0DYXUB3nGVBfGLgzJUGecjsN9j/98qAGfgokFP4wjwX3TG5d7JNGfE93pP5KIz7fefnQVKglrnLr6cLakuvP4saUE5c7/IlDSnLT0GSAajknAPupG0TAb4GSJeWZNKwAmMFKzDaBrylfnYkz3xUgrZUbaILzHDptTKcwe+X1gMN6Lh7Rv77jPUt9dzsU+b80Q8Bf/D98t9JVTt0Q0DmOSoNYeuSc5q6PRjM13C5Yi7o937v9+Kee+7BK1/5Sqyvr3cSwK6Wr6KikgezImM+KmXXwiJKRn1nRdyJkPmBdeordsRYqRdJkmOoTP1VL30nuTvuqetf2bGlk/3uYmboWgTYakkGM0SFgUXKfASAeaEhYOfkzl9oJqZxXpsW74QZhpLErTJPkQuya0PXMMsNLGpyMMMomzTfa5YDvPPjp5EdtmEJx47iCBaAIWU+rg4dnN4MkR+yYXJ1GaDhUVbhAvORXOn2VZEl0Ljn5LrVAR56vGiS3ejELo5DIvel449nGahMp5Hn0slLFFP5DMeQyzQbeVG12pjEIQw06dkA78vZ1IuiqMVEMnQNtuuhzOgk0CHynDyJUNGPsG8bsE0dKWz4vIQ7JSw6Bj7ecnCEtDKRFiXcIgF08vM4iqBpgGYxQNjBbLMt6yvKCnqVwaLHOrESwDZ1TAsTi7KQG46RN3BNwoQVQEVeTn1g5CKBjaKqYHIf2SxtT9g0TcN4NES+U1AZT8PcNTlWhmcbAJODVlV9H6NwDp27VgDwHBeFCHrGUdtcH0AQBCg4Rj6RSKctifTAMaFZDtK4/U6HAqMVAEaDIQWpI8AgAPs0SokkjRuHAscgfZPFrQ+qVrZTsTVNw/rYRbpttZ4DVtg9tLn2AkR27usZNueKiaakRBQk5ccdxyQgc9ETXjONM3h6Dt1qwOl5IU9qbxUZYNPjKysFSgTT+FZR+QeKzEdlPQckEVMGPpLFkNLzESC+Qkpp0h4bXR3bDsW30/IIu4FngaiYckB3wlvLB9sLM8f1gJ32+Nm0hQF+3M9Vi9ciQ1zqWBRl16rNQ2UYiwTwA65sU0/GtOXbf0XMx+7mjFx2LQM1+xi5PUwI6bFVYKUEEAAkQKFkUa7yfJQlaAMKlqTMHkH8pkieaQowPef6FTzn+hX59agW+DLmYB7hutUB3vGaZ9T2I1LAoEgbdq6UkUnuOduIBUASpQGhbaT9L771BP7PFy7ImY/0eHqZQdcspHlJQKPTAhhLjxuYOS7skj6+6cAQuFdkfDVsxYcvMhZlCnicIon2/6Jn4oHzE3zoS4SRrYtyZMuFNo3xlGsW8N7PX8D9Z3Zx98nVdt8y5qMPnD0V4YHzU9KuPAZ8TqprOuS7B+CRSzNsz1McHdkK+WGC77zjCP7wHgIaPO+GVeCTInuTPJe3H25CSgnoyQFFEgbVs69fBhJhjOee3b9/5xH8ny9cAAAsewDxl+sCNq9/0Q34p19/kkj8tzbp7/3W82hyEmOZ5yMrdRJ0BzCXrB1YPb7dAqjzddev4Pdf+yzyDGdhk5YMdMHHMqfXKG4eKb7VVUVYYAefAhjc2NnnLayUHnPsPyGluV0vouw/ModCMiWsPL50PPd62HPSsUYBKLJjdzYo9iO7pufI4q4ikF0T0M/6ZMxHtq5O5+1rZ+sVLmRJzi6UANasHbKShUR2bdgtqXurhFuE9ZhM1IzUFvNRBdpKnntV22rZdRvor61t+DLfIBiFyu6Gyf55VqkleU8Z0agq1XMgAB3ZNUDeI13YeJB6K7vyDdi9cje+hssVg49/+qd/ij/7sz/D133d1/1dtOdq+dssLGlLVZwBeTnypHlx+mTXLcCuj+0xJOdmoEL9sZXvHsIe7J16xXbLVCnTHWlb30Im6C5klLI5yYDbd+0ypqRkcTpwTMSlKTAaU0SFjiN2I22LataRAFJSRuSxZXJ/t+Ypjppux3i3yhMUmlVPpHRdw8rAwTTTAUOyAKwoiEVBkzuPLaIoK1yYVzjaYR9GMDUNDmW03UYnkruZjmWu7jSM4aCES33klgc2dsIUpeFAFxYdeZrAshp/netWA/zFpEDlEFYY+xCkgtxX0zR4nocibDMaoyiCpjeLa8vQMRoOkc+6cuoAgMfJfYeOiUQA5eJoXjMaWfG8AEXS9iTMs7gOWNE0DYu+jaRqM37CJKfgGOkXzzZwYHkByWYBN0/qj3MazQlF3WxAygtPVLiZe86iOhGb1DENHbceGmPzvIZFbpFaVRVQpHA5kPXAyMUkNHCwBe7FKDjwcWVgI9csCtpy4GMSQRMWIONBgGKrbIH6BIhrvwNRaSCtSvgc2DKP5pSB2PTx0LOQCTLzNAk79UaDAapJU2c7TLGgZbCc9hjoe36LIQkQj0texg0A4/EQZVkhjUPYDpk4784jAmbzDEnHJInCyU4bfCxSOE57MrQ2dJFsWdLd/3maUz9JYcw2XQRGgcdm+2c+hmlBQdKmzzVNQwIL585v4ibF303jHGO9qP9u0bexucW8PnvYFDKQrNe/SAaq7LGAkE1QRbmqivmo66Q9MtP3IgUq9O9QD1aB2UX573q/NZKFiQqsZN/eLOQAplj93RTBMIXCwHF9YsvBjZ+ttssm0grmY1gYOCwyH52hYjGnAIL7mI9KqxRhccsAFNnmt8iGLYuun5mqbn1sWbsV33Yl81EFsKrASkV9lT/jfiTgMvBAluZtWCDhZJK+2NcxJeCuzIdN4nEovZYsEha0DSPoyCI3Pnbk2QJwLrtWOs5ctxbglkMjvPo51wLZ58nPedYtbf+//Oab8C+/+SbiQSYLlqJgoK7ZKFDh5BoF70RvSwDPvGaAt3ya9N3hBa8LjhoOHKRYDGzc+8QOYfJ1AFYHQIVVX8fDl8i8/Y9/5DnAA78rAWxjXLsc4F2fPQsA+Iab14FPcWwm2sYjQw2Xpwk+V+zg+559HDgtsulc2FkExzLwl1/agGsZWPU1yXNEQPd/8oLr8ejGHJ99fJt4F3YYquQZcrmNjJFrtZ8j3aD+rQluOjDG2e0IP/vS24DfEQHsBohe4tisPmOtisxHOlY1gSTcuKnrFEgjdf7lN9+E69YGrXYqi3iNSpmqQKQQvpOapmGZAeMM5LJ8EiYk8w8Gusx1FWB44T7gXf8YeP5PAre/vHsccayRjY/83/hD7lpVQNictJ9uzCOdyQE4XnXH7oMU0JHNNTz1/CSP2qCUEhSWbGQAciC3xfrsYT4yz0f+2966lhQt8FjZtqjdJt0gG2+qe5OG5JxlDuw+oagzB5ZOtBWQsvNKPEw719DaaDHpeyv5xmfcu9YHUgKEjbl0opFfS9vmEvwAoGC4BHzk/352icj/xfYD6k0xUQ0qlf3bQKjaPLwKPsrKFcuujx49itFo9HfRlqvlb7vwuzOy4tD7mEzJYKUZ/Yso2eRfetwh2WVgA5XMV4ov9mBvzwbxg93Xtrp9qvMJHwsZC4cV2U5UHxtFRpuXSJKGrkkARG7BURYp4lKHzzMfM5BJhwBSxgV5dRcDG0PPIqmHkkVdmacwrPaiem3kYDfrJg0DQJXHqCrUUtXAMXHLoTFO75ad+kkSotBsaHTCvhTYOL4SkGAS7roi6uPnUVBlOSBm39NM73w8iyxuBW4cXfKRVCZyQSadxJEUBCwrtCYLcRShErzcVhZGnePFcQRN02Bw9ylwTCRC0m8Shy1mJgAEvt8JRCnSNoNzfeSQ1G7uOZ1FMTRULan3+tICbRsHtMVRi5V3cn2ArcRohaGECfHq4+XKtx0ekXvRYaGm8Nymj48t+5hkbRZuloYtObVp6BgPA+RlG7Q9vznpBKSMh+3+LcqKAnHtsSiuLMyTvN2/EfNybM498izEaIdIpElM/FC5xeJoOEBZZLVHz9ntCL5e1Cw+VvxgQGThfFsiBio2dQ8sLwAAzmw0O8e78xCGwJBcDhykmtViU6Z5CaPKaz9PVg4teNJwGoCE3ljIa4ZwXUwHvkZSWfdbwpQcSwTUUti499ELyr+bxhlcTq69NnTw6A5dxKm8eQAF8KFYBMnYGkDNSFIfXxXUkUjrff7sLr7+F/6SsFcB9cR+L9k1sDfzsS9wRrbgkLLwJGyGPoWByEDrAx8ryBcDsrYoWHhVTjyJPRF8VDEfewNnVNK7PvCR68c+SxnT6Xwv65936koW16rvu2qeoQI1ZX5hStm1gmEkZRTbze9adfcru06694WNabK+kEoxZcfcR4BUJ3CmAY7ax0vbc1cZ8MtAZRHM5J9dKbjuAGUGRwf+73/0NCLllo1fltdul+o9N8hC+59/440AaMKwWLf2ViT/O3RN8r0R+9dyoeVJvam8LPOPpP1yzZh8+15w0xpJFu7Is0lfXEOP9Z13HMHYt9oAuK4Dhl23azfMcP3aQArEaEWCk2sD/MUXL+LGA0PiRSljPtJ371uedAAAqDxe7A+nfj/+5w89EwBlF2YiUETm3//sxTfira+6g/SZ7FgAkJMQIVY0kS2mCmqqVV9dhteLbz2A61blYTOdIvaZCpBjgHMtU+0D7ii7UNPoOCsw1GSMRcNWH+/iF8i/z9+rOI5k/FCpF3iwR/VtBTgAjvaj+K0oMrQYqqyo5Myy74RKrgygowBUeUt3rBIUGyNAG9C0PNJHUm9IBpAx5qoIHgvEHMsj74X4fZSBwzIGen3esAE9VSnb7Nlyx2ppNs8aVT2nMna5JVk3A6TfDLtJCQd6wMdtAj7Gu/K+FSXtKgxhvtEoJGTelirrGUD+TEll1+oN26vgo7xcMfj4n//zf8ZP/MRP4NSpU38Hzbla/lYLG/RVhYGP8S7ZnbJ9tTGq7bcHsV7wkX1kpk1doL/+Xp6PqsAZQL7AY4O+rIgfyr3CaWSypV7ZtWS3X2Q+uibCog0+FmmCDBYGjgHH1KHrGkKWeN2SFmaYl2QwHTgmlgMbW7NUzp4oUlRGux9WBw52UwnLAVRGW1WthOAnHxnj8UnXLJkkHbf74cRKgJ3UaPVZFM0JsEc/1syn8ty8G7xRZklLdrrk28TLsWrXJXLfdp8Gg0HHyzFJuh/G1UVqWi94NGoC823oWogE8DGNow746PtB57xlHrWkw7ccGuFy0gZbm0Tp5noXRkzqy4GPKQnNYc/zoQUPUWkiiZpnfp4WsNGWGB+g6cr8OXfCDCZy+H4zLiwFNmZ5+57lSYJSmIgusP6lz1hWlIjiCAujYaveWLiGSZTBQgZXAOK++anXkvTEFgjM5M9N3eXApoEuzXs1n8877+zCaISiaO7DE1tz+HrWGQdGg6CTTh4LATYAsDgaQdM07EyaCf90NiOMCe6ZYhLyLGnux06YwkJeJ8GzcmzZxyzXpZPGWZLDRgpXwnz09AJnthWTNEkJ04KCj+0+Ora+BAcpLk7kk9ZpnLeCasaehdM7BfX67Dm/dDdYDmJJ2RpA/yJMtUEkAk3cAvDTpwhovDWnY6KKoVBkqKDDMHqEIKPDagZBL3Necs4+2TXQBR9VG4gq8FGoX3umKsGtfQBWAIqchJd1A2cGkHp5KQNnFCBzr3xd3Pzs2TAUJc99ntOm171W1YJBnAOxuiqWJCCxbFEFziiAetmzxVgvraAlltgpAwol8xelhcE++kIqu5aA2KKNQp3YqmCY8EVkPkqBTGHhLjunFFxn5+SOl0neM86Ljxyr594VGV50yzre+/q7YZt6977R/9bLFD/90lvx5lc8pbmGDlsxqWW+x1eCLgDNNiGXLHzrkw7iHz/vBNc+EfyKcffJVZxcH+C77jyqAGyd2hsSIFY3cp/JGIfo3O2WgyMFMN30/9fftI73/7PnkW98j9x+ZeDgrhPLePkdR7rrCtofrmXUidGd6zRsENZuCl3X8Iv/6KmkfztsMcZqFN7JLG4Akfqciu/QqY8A//3vywEdkYXbJ7sWAGcp4AS013AykoYMlGJgmKxc/jL597YQ4iELnGHH7fV85ECzPgDVZiCRJrkGevxOMJQKfJSs11RyZUACaPcxH/lnT7HJA1CfRg54BdQeh5bHZRNIVBBAG8SVHesrBh971tbs2XKGPbJrbh1tuY39VauObOxX9DHfb33S8bIkuMTSCQCV3JOyDvNhwK7i/kc7wGCN1JVJuGV5Epw/bKfsd6ONHfsq+CgtVww+vvKVr8QHP/hBXHfddRgOh1haWmr9c7V8FRXek0NWvAXCqgu36C6CQnINkAGqSJoXjDeiFQvb4WIf6DymrErF4m5fzMdULnkBFIyEWA28yiTkykWPKzl2AqUUTsp87C6URq5Fgk84BlaeJcg1EjajaRp828A8pb5tLWlhjKgkQSGuZWApsAkrSpQgAtDyxnuQldWhg51Ek052Ygpo8QDIoQUPm3GXKZklXVbhNcs+tlMNPEsqFCStpqHjyUcXOoAXQGTivFTVNHS4rkdlgzz4GHcmK8Ogu8hO47DT9wcXB0hLtOslIZEZc+C7LMU4TaJWajNAUpFF5mOZJnWiNADcdmiMjUhDwX0gQ5rEzTMHF4YUuOOeoTxpA2MHRi4SzUYUNe/MPEpgVEUL4Bt7FuZFu/27UQaryusUa4DI4Kd5GwjP0y6zdGXotjwwd0ICKnoCqLg4aoPAuxEBPD3By3DgUzko1775fN5hINYBO9z9ms9n0IXnemk0RF5WqOj7enF7AlPXu/d/aYwkiXBmu7kXdYANL1O2POi6hvmsmZjNJLJrgLwvedq07+IkgYUM40F7TL1m2cc0N1p1WZmG5B6KUm0WHPD4lmJyJSnMT1Qc19aXxlh2gQ8+KGfxTeMcNifXvu3wmAQDsTATVdkvSAGowce+BYyMrQXQsZHrS25hygiPs4QyN3uYj7lu9vtXLx4nCzZl8qVio8sO5LJrKfjIdvC5ha0IwrSOLYBhvKSJK+Nhd1OmLtKJtE0ACuFai4yEl3kdz0dOTseXPlag6rlQbhh6AqjUs2FoOu1vsMzTiT8un6jKQDwV87FIwQdQKdtcm+mLIJ2i3YZDF3acwX1ZquXiIiigWsDLfFdVjFQpS1LGplQxH/cAsdl8g3/eDJPM66TWNjxgIAEX+OTU1jmFfpG1i/2OrycDNIR5l9I3lB7LMQ05wMeSg/MUz7thFScYm058R+ixji2T78aTjoy7ACr9b1fL8IYX34i1IXc9kr5YCmz86qvuJPJnqcTQg1Yk8B3yXruW0X1G6L1cH5GffecdR+RjvsBEr8dUsW3s77IYhq7hjS+7nQKtUfdaO5v5wjtUs3bJPbj10BhPPrrAvfciiCoyH4Vz9nn4PfAeYOtR4Px93d+JjMw+maroLcraIRbeOkv2LZHezx7AcL4B+CtdCxGV7LrX85H7NtEEeSX7z/LJO9DH3uwwHyXXy9q6F0tZrN+Rwwt9LbPm2Iv52PEmldTLogYcsyXPVcf/UwHIyTbQ+u5zFnFJ0HswH52ROhGb3+RTAaM9oV7yc9LjqABZgGRAVAWZdwHy9tVeogpWKSvRNvG/9Jbk4KPs+esL9JF5WavmNKqNvqvlyj0f/8t/+S9/B824Wv5OShZ1jX35ohvkpQwv7w1U8hJt5qujAvdskfkomdi16gd7g4+qtEpAzXwMVtTt4z+AeQyY6/K6sp3SPaVwErBSaPvQJT5xaZqA9XqRpcg4ZolvGwgTxnzkZWQJ5rlRJ/ZdvzbAQxenwEnZoiCF4bbbujZycC7RAKc7sLIUYJ5Btzp08KlEQ5XF4JfnaZKgFK7r0NjDTtpmdsVhl802ck3ME6uzMKryrk9eEAQottrgR57G0AQ57TAIGm8zWrI07jAVlwMbcdX2JMnidsAKaaOFC5WJIotggPgl5mkEQ0g+XBr6yMqGwZcXJWFw2k29Ww6N8OeVhd3ZDOyN3Nyd4igXhgMAi+MRZRfywGiMiktRDBwTmukijhp/zziJMda0FmN16Fq4WFkoshgMLtgJM6xreSsIZSlw8IQABJdZ1Ploro0cxFUDAu5GKawqh+20vfiWx2OUZYUkDuGABzyF++oHBNTi7tdkNiX3i5NTrwwc7JYmsoQEHFVVhck8hCGAnsuLI1RVhVk4xzBYxu5sTlmK7fv/lBMHcOnDGf7znz2EN/+Dp2JrnuKxC9soROmPYUPXNMzmHMgbUtm18Nx7no/Z5gxsFLm4M8eSVnSk5seXA9yn2dieTLCKdgkjkqQuysRhOnC0DGe3I5RlRcDPPUqUpDBQdu6hZrm4Zc3Grz9wCf/gGdd0/o6Aj80kfHlgo9J05JoFs4/5KJMkqhYufYsNFXuhD7QRZde0HTkFciYRHTt7FoMlTJh9/bp0ghx7eh4YH2n/LruCjS56PiWgpAms2N6NLmFhITJ9aGE2E2UWd3ebpVJdDjjjPKCLjDAfpbJrgLTF40IqirSZN7TaTReKXNAUqd8HKFLwkRnGyxib/PFFiTYgr8/SyFlb+iTatW/XHDDGzbGNxZ66snsvY4PazfGYL2fdln0wJXvBA3EzNJFvBMsk6LKFvoqFKF2AcmOG6j7ImLAdRqAEXKjVMAJYlYnvj2RcYr9r1fO69UQWshII5scglYRVdi+E948+5y9/2hG84MY1hV8iAyj2AGylDFVZn5Fz/MmPvIRrl/Au0r747ruO4VtuP0iCcFRJ7CoG0H789GRA0X6eSWkSvQSwkXkD8oCIqg4rE+Kdic0vA8eetUfbVewvYUOptVEx6tbtYz7Kxgjxm8iXaBtYvxV47K/a39R6Y2Af7zorPMGD9+9zBJl6yq0tnaGa+ShlzynARxlLWcpSy7uMcPYt4b8/snFfpxsjKsVA7XfZx3zkQUq/vdEFdDdQ+piPnWC4nnuTzqlcX1evrdlzXxVy5mNVqUFWh1M7SWXXirlWnggsVK87RgEN03F8uLkeafuPkD4xvW7fshLvAO4CAE0BPtJnX5cwHxWb1Z3vp+lCujl/VXatLFfEfMyyDH/1V3+F5z73ufje7/1e6T9Xy1dR2QtQBMgu2HyDDD5uj5cnL9EGUJsIy4ppkxeODXqyjwVf7IF614KVvh0Ey+vuKPWBo+6o7XHRV1c6WYn6pXAd5qMMfLSQw0TGBV+UVNYW0MVdYJsI06Irp85TzAodQwo+3nxwhAfPTwkQyH+Mqgp6HkMT2ro6cHE50qSDZUx963gfwpWBg7iyOj55edr96BwYu5hmRivQYzqbthKn2fUTSXB3Mu6IDLmAglQtT8UYuvA8LAQODYjhQMUk7oA5i4GNqLJa0tssTTrg48AxkcJGQhOU52kBs8pajEYAOLLoYV6aqBgjMMqIBJpjFy74NmC6mM2aicDGzpQAHtwiZXnkI680RGHzPiTRvCOdd/0AMVcnol6JGjeBGzCAO+bkwFFKPP1anoU25rneumdF1mX0HF7wEBaN3G9rnsHRM1i2AO4ukInJn917CgDwPz7xOEzkMESweDhAWaFmKgLAbDbrMEtXh8RTkYGAG9MERRK1QnMAYHVxAQBwfpNMMGbzkICPwqKYAYJffIKkhD54YQIbKYau3U7PpYD56Qub9Y/C2htSAGYXx8jiCGd3yLWcurQtZV0u+hYyWPjwA2cgljCKOiA9AMB0YesVyjzFi970V9gJexiItEymcuAVhoMblm08ujGrwwr4MotiGFoDWvq2CdcykAmp750ilV0rJpaqxUbfLr1qEiduzHCMnWmct/6tBh8z5JrVBBDIyugQOdfWY5LribrXworSj1hSX9MkrPyo8wzVRfTEymKy2BAAgeXxEKgqzOeSRQj3bXr40gxxxi3UREuMPKGbYwrwUZR3qeYflk88offLCgS6TEJRitqqKwTKqYA5WVvY36m8pYH2XEPlA8jaK2W9Su69zI+uDzTtSMtV4KNkgaoMvZFtXsrARwmIth+/SRUDVazH1AsiE6Ujl5WA7R3PRwlLxRSeJUD+nsmYm0rrB9m9EMdxEaSUASNkDqfrGgEe2fH2E9LTYd5J+qxum3C8PIGmaRxTUQJkFilsU8faiAGHkjFf5nVaFoTVu6+U87gNTsjktEqWvQSkBLqswA5oG7fHqT7/wJ3Hyb9l4WPiM9QHwEjBZBlQyQGjzqALEslUBL3Mx8vA2s30vzean7P3XGT/qxhd9XUw0EzyTrWugf5ettZTjV0ymwt2jg5Qqbhnsg05y6Vj/h7jp6bJN2SAtpS7BuWEe11VAngsU0EI7VOxKGUbc6LFhOy89oC0i2fUt67BUwfOiIBsPW6KY4pCdq16FjosVMk7wp7zYJWArlLwkQO/Vc8KwDEfF+XJ30VCgEc+bIwLu+rWl40/ineuz5Lna7xcEfhoWRb+8A//8O+qLVfL33bZy/MRAIJlINwkOw2eZAefFQZMJhMykGVRw3CUFd5rQrVjXNftWXSy0sd0sAPJJKtnQeiO24Mt2yWSFdlHTSW/AeQmyRLfh4FjItNM4l1IS5kTgJUlU/uOSTwfeX8nKgub5QYGDgMfh4izAhsRBJAyIZt7ApizPLAxL3SUksGShWbwwNnq0EEKG7nQx2nSnbAfGLtINQtRxIGPIQVCuD4buiZmeVtKW5QVtDLrBG6MBn5Hdl3mCQxhEbPo2yRBnF7X1jxFmkR1eA4rS4GNFO02RlHXQ7AG7xJSbzfK4FRpJz35yKJHwlPm5Bk+vxPDRtbx7vP8oK4DABPmH8hdx8rQRabZmEybSSaRjgvg9WDQAq6Zryb/nI1cEyksZElzn3fmKWytaL1LpD/sVr0qi1tAJkCM4+eFgZR6G25ME4zMqsMsPbAQoNAMGBUBhbbDFL5RdgH4IACqqpb6F2WFKAw74C5r3/aUjCfndmPYSOEJ4OPAD6BpGv7y82eQFyWSiEq4JWDeysCBjQxVVWEnzLDma7BsrzP59oMBzl7eQVlWmCc5wjCizMc28PFNTzkGGxnufXwHALC1O4VldM+taRpSWNjY6e40h+GcyOvFsdIewNQ1uCD352W//FGSWq4oRVlhezqTAq+wPCxYZGH4Q+/4dOdvoyjupIgfXfKo92mf7FoyPl+p52MfA14F8pgCCMPJiJnX4zTOmuMrdrILzewHH3UDWLwW2Hqk+7vezSvJ96BvU1AW7NZXt8V8pN8l4RleGpNv985M0rcUNKqqCj/0jk/j59/3kBJ8rLIEKexu8qtK9qTapGMywv3K0YHuokwV2sOOL9qqAArwUQAJRQ+u1nElbEbV4kLJYFHMS2qZF/e+sGuVtaUDZCmASpmEvtc/VQRwZNLaKwEpo3YdaRuFcaJkoBwP0Eg2MlRyuRaoEEvATrv5XV1PAgqLwRQqxYvYHzKAj7VzLy9S1XW2PA57ksklfongvxWycAUZiCH2mwzIVC3CWVv4evzvVPUAtDzh+GtgRSZpZ8eTXYMIKsgYch1PQNUmVU4Tgq8DZhvd34sgjNLmQwTW2dimAh854E4meQW6cmEZaFKWBIhZvp78P++jp7LIMB2pPVPdXta2WjouY/+FjaWXzN9fNT6Lm0iscOqGpp0KIK7e8BDuCyCMTQrGu9LLj3vXVOBjkZJxrA4ykq0NEwDcvF0FRMvGJ5W0OU/oeX01A78sUId6uQvknogApbiJonpOZWOsbLxgdVtAv+IdSedkI9V0yTMjmxeKrFIl+LhDLOYcBclJRWxSBhPJNrQk38S+Y18tV+75+O3f/u1497vf/XfQlKvlb73slXYNUObjZfJRchfU9QyLfGDiSTOQ2YoFF9BeSKZhP1DpUINr3ktJLKrFJ6CQXcfqa3fGZGeFDba8L8e+jt3Tr6KkGyC7vkLbbVNHpVst77cyz1psPt9ino8cu4cOcJNMJwmHANaGBEx5fCeHuCApUcGw221dCmzkMFFIfOcY0MbLRQPbgGY5KNO4NZGN46iTdLzk2yh0G3HEsfLmc+jMJJ8d0zExK9phI9M4g1Vl8P32vVgc+IiLhqlZlBWyNIYpSFoXfBIQU9FjPnhhAhM5Bn77OV2kITZzjjkYzud1IA4rI9dCpll1MMxOmMJGCksAFQ8v+Mg0C1u7BND+wrldLLlVB/QMgqDVLzUzj/uQkbZZeNenGpAjicMOG9ByfGjcxGM678qBB66JVLNbIOVkPidzcU42sBTYSDW7BhUBIE9jmEL7bz4wQqpZOHeZsJ8vTWMMrbLzIdao/JsFsORFiUW7hChrGwcucs3E+S1yvJ0whVWlMIXn1TJ0LI6GeOw82bXcDlOMrBK6OP6YLqqqwj2PnMd2mMFG1kmmJvUcGLoGu8owS3JszVMsOJCOL7brQy9iXJ4l+LZf/GuYyGEaXUahYXkYWgUevUTefRmwzMod1x2AjRR50Z7shdG84ztJGkHehz/+wafUP7o0lUx0aNmcJdCLlDAvJUm5GjdJj9K2R1OcxJ3gnWPLAfUOlUzEWJFK8BQTd1V4mCgj7hyf1D+7EzXgq+G0QVEOwNqhcuvdWnatkHFR5qPe5/kIACsnG8N+vvSBibbfnTjvxbTft+ejIFNXhNOMPAu5ZmN3qmI+OtgOSR9tzrlFjnC/qzxGZbpdkJYlV3auU/ENroE5CQOmz/OR/5s+6xOxD1VgN9CWUgNNf8qk7jXIyl2nyvu6L2Cgz+9TvJ+qtihZhZLFMzsvK32yfxlLsrOw9Mhzw4NaqsXxvqThEsCQbzv7G3GhrWQ+CqCCkvkonFMEecXFvTKlXgUE7wHaypKKVSBah5ElWRh3gkw8ykLkPUolz4kMTBaBNCWDVjLmA3uzcmXPZX0NAkstE+4na3PrvMJ3gB1LvKcyGbc4HquCzxhhYfk6eWquuLGgTLtOuvVYe8XCt00GsOQpmVe35LgKICTeIc/D+Ch55ngfPZm3a30sSbtYe/cCpoD299EZEj+/1nEk7zE7ppQ5KmPaqeoqxghAsGeg/71v32ruXusGOhYTQPP/9f2TtFHcMKyPJau3z83den0e9GwMhk3b7ABAJfkeC5tffSCrbIxVWg70vN91Paqs1DR1JkQLmO+ZOyZT8tzZEsk/IN9gA3rmr5JxT2b70Hfsq+XKwceTJ0/iZ3/2Z/Hyl78cb3zjG/GWt7yl9c/V8lVU9iO7DlYJ/T7e6Wc+AoT9mEybl1wF2AHt3Yp01l/Xpv4R4keJL330Zdlucd+1uyNCu2ft65VRS47dx3yUeprI265bDgruA1gVaQvM8x3m+ch5I9IBbpobtewaAK5bG+DsrGgPgFmMqkIHfFwObGSaRSTWAoOKhMgY0DhwStM0eMEARdFO5IvjCJbVHlh1XUNcmXjiUuOtEYZh+wMLEoZCvCGb9pIAk3YYCjk/MC8MxFQ+vDUnXoOuAD4u+hYSWPjZd30WAHBmO8LBQOtc/4JnIYWF+09dqH8WRWEHbLNNHUEQYHOHTDp3owyelnfAMc82YNgeHjp7GQDw4IUpVlx0JgvDwaAlgZ5H805ysqFrSDUb5zd3AABxVmAym0EXATnXB4rm2QlDCipy53RMA6VuI0+aj3vjWdjUcy0CLueU+ci8LW2ne52O4+HsBrm3G9MEA7PrKwgAleHW11p7Lwr1likD9UtnSL9tzlPYyDrMUgA4tDzG+c0dJHmBnTDF2My7kzHDxOrYh4MUH/rSBqwqgyH0CUD+X9c12EixE2bYCVMCjkreUccbYNEu8L4vkGfFREal8t2QDkvXcY7etwZ87I4Tvh/ArrIGFKNlOp/L2Yp07NSzOd71w88BAFyaqMHH87uEeStK+kk7yYTq7d//DAAgQVVciSXBO0cXPcwKo7u444tskqVkPkoW3YDaYB6oQZvteYpX/bdP4M++cJGeQ2Q+NrvSTJ7eyK59+fGLDDn2YD4CwOqNwOUvdX++10ZXOm8zz9Keb5OM+ajyfBT7SwHIaZqGyrRbqe3t49t4ZIN8s5YCu5eVJFonNG2RsAr6ZNeA3Hy/L+0aaPqm12dTAGULyQJdddx6wSVpt2FTCRh37D39PmUAkWTuILaDb4uUbSfIIVV1ZfLWXqBBBJgU5vpAF1iUsV/E8/J/z593L5m85UvAtqQbZCgDPMV2sZAbsZ50rBT6WJXeLkq9xfbL6qlAOSkQKG7UyMBA4RpkLCoZ+1QEAOpUcgH87TAyZbJrq31t/H+3AE+Fn14nNMfb+1js/6VJ7ZJ6e6mkVD50bF6/dIIQNsQibiyYFKTvpAMLGxaGRUOXFAywGmCRgI9XsunHSBHuiBBN9sV8VByrLNubXb3gIweg2oGE+ZjKw0R7ZddXwFAEumMJIHwjFJ6XopUVf1wR0FSCj6yPJPMP2WaUDPBUeTNLJeEcsGhJNsz4ttl+49/YuS8MkKXnNSzyHZV6zYrjkwoMFr6XKpCSJyw5Q3lit/hcqfzC0znpBxnrFpCrdgD5PQUUFkM9G+1XwUdpueLAmd/4jd/AwsIC7rnnHtxzzz2t32mahh/90R/9W2vc1fI3LKKRsqyMDwMP/AmhJXsL/XWdEdn9YzskvenYo+Zjl0y7JsR8YZLueKIGQPs8nmxfTuVXLdocTkLujvaQwSk+KkrJl2QHRrGoMm0XZcYt6IsUltvUG7oWCUswnWbiThfuk0zDcNS8vusjB5sXBSlOTsBHESwzDR1L4yHCzQzDsmhN3rMkQq512/r4pMJOlGKYhYBp4+IkRhzOEBzo3lfCKmw+BHE07zD3Di14+EisoXKbEJvdKMMIWSeY5Nufchibf2VhezLFQRDAxKoyuF476OTg2MMGLNhIEWcFzmxHeJKLTt/ruoZMs3D/4xt4IQjYFkchrFH3eV5eGOHC5kXcXFbYDTMMZaAXgMXREJ+9TEC587sRRmbReUbGwxEuJoS1NUtyTKZzmK7dlgUBOLSyiAN0LvT9b/sU7qgymAIg57g+dG6CMp3PpSw/zXJQZM1EeTZn4T8CE9fxayD84iSBlicYDbr3Ngh8fOiRC3h2VeHyLEFgyJNhK8PBExvbSPMSSRzBMLtAnGnoGA0GeOwiYTRenibwjRyG030X15bG0E+dx6Mbc+yEGQ6YufT9dlwfVpzhlz74MG41ciJFl3hvGZoGCxm2QwJAXmOVQNG9Ds10cN2ihTd/5BQAYMWtyERFZMmZHkxDw/mtCaqqIkxaGZAIEuZkIcNOlGF50Pz+/CbzAJUzH5HOMF61YBoaPvP4Nh69PMOth8a4fq19ny5MYqz4GrQKSkBgnfp27YQZjtIEpLKsSJq70W7DoQUPk9zoLv75QhlS95zexiTO8IIb1+SWFUAz2RP70PYJSzyXsK3oJO6+s4Qle3qTjrHi4oCTZG3PM9im3siuVUyU/ciuAWDlBmD7FAWcuGdK9DHii0vHqIR+28qS9KNKCSAufPs8H0UD+yxWfvM005WDjxRcesv7CaMzzUs1K6lIoYlJ7Kw4o8YPum5PJJ9/iGxD7vi931WA2zDci20qytF72KNAU1+RGA6AsjDEPu+bO8iYLhKGIKsLrQsm178TCvXgq4sK2FMBhVL/VBmAo1gcs9+x90AK9LjohL/I2ihei5TRKGPnSZhtIrikXFgKYJqMYdzxfFQxH91ugCGwtzRPBgSKwIPML1FWD5ADIuznfB3WFlZUsvG9pLxSqT33PPD12HladW3JNcTd+y5uJIhtU7VPClJJABtx88hygUjiDcd84sdHumMdO5/LzUmZMkMMYZFK/D3595VXZYmbKoDivbPlx2Jjpz0gaz2R+aj0VFaAKkBzHTUwpQIfGYA66PruqcZn2fUC8o0Cy1VIvgUAkP9v6eaN7L5I5jD7kerXICC7dsn1KP0r91FPyXzkrpn1ax/zkSnSOqQZeo/3us486ZKLLJ8E2craxh9PZlPG2ttKeZcxFqN23/YF69g+eWZlBCdVCJwqQV3lOVukZH7Hr+euBs4oyxUzHx977DHlP48++ujfRRuvlq+kVBX5QLrj/nqLx4mEYPNhYHigvy4LaqmZjz3AJm/ums77ZdcMcJR98FlR7dYDXQ9HoF+uxvoknnRNgcUiSh2YSbjMhwloe12yIpskAzAsB6UwueWZjwueRdhRvOyafvR3U61OuwaA5YGDS4lJJDaMWZQnqKqqIxMGgBfddhQF273kSppEKPRuv0U0k/vCZRK+cf+ZXQzNAoujbkjRtzz1OHyDsI2yokSeRtAFefbhBQ9JaSLjQmy25jEsrYAp9NX6iASOMNng5iyFZ7QTmwEi5X7aiQOwkePCbowzWyEWHUgH/wPLC7CpJ+GnTm1DL2LYbvcenTi4gjgK8djlOS7PEimoCACuF2A2n6MsK1yaJvD0vHPe5YUhzDLBD7z90/jAg5dgVVmHXQgQsJiFQ1zYjWFXCQZB+/1xvQB62UzwN7Z2iVeo0DbD9lrBOrOQBaa07/FoMEBGGZJPbIewkUnBx0EwgIUcj2zMcWmawDcK6Ydbs1wkUYTLswROlcq9FwGMhwNc3NrB5izBxizBol1CkywWbcfHwCxwfjfCTphhYBTS91uzPFy/SN6LA4EGTbajbnrQNGDRzrEdZtgOM4ysQr5ItTw851hzj37qm69TTJQ9WIaO+XQX95zehlYk8sAXAJYbwK0SbHDS6YcvTXFhcxeeZXT7iaX50XElLyq8/aOn8F//4sv4oXd8uj5ORmXcF3ZjHBjQa1YsWm1Th+8Y2ImaZ2ie5rCqnMrym2d3feQiLMz6+ZAWOsn657//OfzbP/kiPZdDFs57MT9YYd8IlTTGdPAIDckJmVxcNDanLPM4KxBnBY4u+ZjsFTiTx0g1Zx/g40nCmN8S5jnibj5f+I01Vpe1RVY6gTNpD8i2P+YjANiuh0vbkgVzkWCa6zi7Te5tlBY05bPNSkdZAkXa2USqi7fYXswCalBWJVPrUxToBuljBvL0bayK8vo+Obcoj84j8v+6YmosMjx5PzNZO2TsTtk9qsOGBMm91rYrqYuMRSfdFOGAQlaUidsygE8GMNH2i7JFqeyaq6NiF6mktR3mY9z2JZNKvUWmnIrlKYBVMm9VkbkpAz1YO/diNLJzCp7cnXodgFIlp1eAsdJgGq5vGVuU/yZ2GKqKdlVF2xpJtrCuwW7Zde4BOgPoeD5a/t7AKPv/jvpJAdjs5cFr+fJNs2RK+m6wRv5GtImSyd6B/Y1zKv++Frtrv8w5D1KVQjIj47rlkfGaT/69EisG/po6QT0KIGmvwBnVhozUo09xzSr/Q0DBCN7HO6sK3OlI5/3ufZYxH6VejuJ4KKsX718SzisTTZfcc5l1GGsbm3d1wowkmyhST1rZNfSAwfwGqSqUkA+0lQGLVdXeNFCpWgCKPwTytTmAftm1ZM4ru2fs//fjmXy1APgKwEe+VFXVa3x/tfy/WLKQAFF7go/Xkn9XJfEC6SvuAgEI05B8hFUTeoB+3Bj4OOsHHw2LUKtlSVSs9MmuGSOTFZaM3CeH0k0CzoqmwGJxx+1Buc8TCiA7nEXSNmlWgJuW7db+hACAIoPNJQevDB1cnMRt2TWdVOwkjecjAKwENi7GdEJZp4xHKCt00qMBYHlxkXgnJu0Be2cy6wSIAMDbX/sC6LqG999H0l4vTmMs2zk0CQDt+z70IkFelNiep7AlPn7LA+IzWGZxLf0+u7EDS9c7A7umachg4c/vewIAKAhYSp8/2/Xh6RnO7UZ4YjvEgl1JF1orCyO4WoqyrHDfmR1YyDAcDLv1xkMMzAKPbsxwYRITkErSP54fwCgSnNmOsBsSebbYvqXxGA5SnNmO8PhmiGMLpnQx7/o+rCrD58/uYn3k4s4jfkc67noBzCJGVZb44IOXsLk7hWV1pYXDIEDCSb3DSJ4AfXR1AbP5DPMkx/ndGGOrlLbt0PIYjpbi0jTGpUkCT0ulz/byeAAbKT5/dhe2lsKUeS+CBBvZVYa//9aP4b/+xZexZJdyYN+0sWAXOLcTYzfKEBiZUhp5bIH0wcGB/JwwHUAzcN1YxxfO7eKJ7RBjSy4fh+liya5weNHDa+6+FmapYL/YASxDg4sEP/EH98FS+U0C0J0hPMT46T/+An7sdz6L7XmKe5/YhYVMeg9JRzVm87/wXU/GyfUBnnndMgDg59/3IL7+F/4S3/jmD2ESZzi7E+FAoDXXKrSTTdIOjFx8/mwzbk7jHHaVdZixB8cuEtiYygJLWJHJMy2BVcaKzLMJkMuhAPCpsMzrcpbwgKLIdLLwjo+dAgBcs+RjUgfOKLyD8hjZfjwf3TEwOABcfqj9c5EJyRfGsmdMGdmijS/iJHpP5iMvAY6Ux7UdD2Eom0inuDgn4++rn3Mc8zQnAJYhACoF2cgSw6Dq4i+1v997foNVwFzPRN0ZNSAuYzPIigjK7iV94hf1ffdS1u4+yb00fE4CcvHHFmXXpgRQBCSsQkXfyST0qvui8nxUgY+ir580BEQ4rxgAAnQZlyJjSHlOGbOt+9yqJeZ8X0fdMUl8B5SBMyL7WiEj7YCUcfNzVhjTiq2r+oDMDkAhzJHZc9zxrRTvkwAA9Ho08mOtTH7InjfZ8WSAG388yca+eJ96g0EEwE2aEOx3n/E8bs/n+xJ4mWQZ6BIeZIEzgIL9LLZLcU7R167DFlewT0VPVoCwvuyAjCcy2bVi/qME3wD5BkHnGjj2pgxIUr2jKimtlDnqqjc6DUFdpNNNenGDSvrOSgByoDteyMb6LALAbbpLbTgU1yILd7oS5qNuUjm/1iXQAM03zHSJ8s301MxHUxhTpMCo5D2T+p3KGKOSerzsWgZYi+t21bNSVc0GoSywiR1L9exLlTsK2TU71n6OfbV8ZeDjO97xDtx+++3wPA+e5+FJT3oS3vnOd/5tt+1q+ZsU9mHpC5EByMeUfXgZEKkqA+oPmc7IYNu3WPNpijbQ1O9tx0J7J04sqo8jQK4hnTc7kXkMoFIvIjSNLCTjXW6iq2AvOEMy6RAng3stNkUZjmSxbTkOqlaKdQmLAx/Xhg5JbJUwH7czrU67BoDFwMaZmY4K4MDHBGWFjjciAKwuLQAALm+3+/zM5R34fre+FwwR2Cbue+wcyrLCxd0YY6uQgrALwyGsKsODF6b4/XvOwEEKS2iDZehwXA9ZXtYA7NmNbZIQLFkYrS6MYVSkrwj4mEvvgWbaMIoE//pdn8fmLMXYlgPLtuPCLFNM4xwXJzFuXrE6gTMAANPBgl3ikY05Lk4SDAy53Nf3fdjI8M6PnwIAOJAsMk0XR4Y6rlsLcH4S4/olW7ooDvwAB3zgo49cxsVJjJOLXaB/NCQf5vtPX8KfP3ARVpXBkXglHlweIwxDVFWFoqyQRCEMSaLys246DLPK8OVLM5zfiTA05SArDAcjs8KZ7QhxVlCQVcJUdAmo+KlTWzgY6GSokNQzTAc2mg/2kiP3XoTpYWSW+M2/fgxntkP4uvy8sDzyXAI46MvZloxldOOygT/6zFlcniY4PIBiQU4m3+98zV347ruOqRnVlg9d0+BVZLLi6Tl0SwEc2AGOBiWyosT9Z3bxnb/yUXz54hTPODogrE/F37AJ2NOuWcSvvupO/Nx33A4AuOd08w7/8WfP4i++eJGA7lIfpWby/8Kb1/H7n36i9p6cJTlM5DRxu7nGpcBGYTiYzSUsBFa4lGkAJEzHViy+VGDQPszRmUdlS0othouYDn7nk2Sj4sRq0Hg+uhJpMABkjPmovry6rN7QDZ3Zy7bDcJqFam1Zovgeip7Bfaw9Mcymh/moWR4qRXLoTqbhyUcXMPKshlHaAY4SqX9wXbyltnKhSMmGpuo7KbL8gC54Iha+b/ZMGOefiR7VRF2fgY89jEqgLbsuqcfyfsFEoPcedeTiVwJsqjZnZWBQpgLRhHteJwtLgB6gC7jt6ZkYyZ8HGQuRtYcVGYtMKV8VgC+Vn5fIztuT+ahiUXbfFelcVQzHkjFBRZl8H/i4VzCNivnYATEE8FTKeGLPEd/+tO23yf+NLHBG5h3aYoJG7XYDXeljn+fjXjL0up7IUAsFwFMBiCQTAmDwVhqt8wmAVM2wk4GPsudWAEVr4ISOR7JANqkXoPAMscIr0LwFCfNxH8A6KzIShsojj7+GK2E+qkJEZBstMs9cQL151wG+VQCU0z1mVXXHCynzkT5XbD4n/eZJ2rff74ZKFsyzBgE56JaF5N6x+aE0hZyuW/n5qDSxWwaMKoA70Q5NxZDkZdcyz8dOmI8EYAWoFDqn3pYqz0fFs69Mu+7ZkBOvWbYpf7UA+ArAxze96U143eteh5e85CX4vd/7Pfze7/0evumbvgmvfe1r8eY3v/nvoo1Xy5WUqgI2vgRMz5P/38vHEQBe9qvAt755b4Q+4MHHHiYjQJgQDADdS3Zd1+8BH/uYj+JOJNvV6U3YpgvRvZgozojssPCyLEAKQJFzSgx+RZ8LVtV2oXES6bJqJyQPHBNpXqLgd+Dpx2Y71jDiZNcDx0SkuYjTohmE8xgJLAy8LvNvaXEJ0DRsbrXZpmE4lwZ+wLAwCHwk8wk+/ugmLkxi4n8oua7F8RCBkePBC1P84T1nYFVZh/kIECngTpjiTz97GgBwfnMblkQ6DADjYYAsCZHmJTZmahAQpkuk1rSMzC4DEQAcL4CNDJvzBOd2Ysp8ky+MxnaJRzZmuDiJEShAL93yYFcp3v/AJYJtlUl3EWu6sCsCeF6axFh0SgXd38OKV+F/3XcegWNiyekC6UfXiFHf2cs78CwDzz4eSJmKt1yzhipL8K7PnsUkymCWcgm06wYI9AJfOLuD87sxBirw0XQwMHM8dIGA6w7kzEfD9jC2C7z/gUu4btGor797PBevedZhnFgl701gKCwNTAc2yGLt0Y05fC1T3H+HAJMAAoU/JwDADnDdWEdZkk2FJVtx/2WSQOl1OIBm4HXPOQgAWHUrKYMYAOAMcNBrS7Y+dWoLR0bydGzW3o4sBsDqkJzj333HbQCA36LelC88uaAA+JrJ/0tuI239jl/6CL5wbrf23NS0dqCOpmkwbReTaV8gWIZUa/5mGuecjFq92PjSxSkKeg+kYyfQGqOncYahZ3EhMmK4CJnsHVxw8ZLbD2I5sBug0l1orDZa7Yko+LiP6dDKDcCGyHyM1N8DoLErEa5FWpxhW77cZx9iUYYfu56e76zmDGDkUf281yWPsZ1oODh2EdgmJ2fvgjhlVbVsQVrFW2gzH3lPKVmRhQv1AXNAtx/75OhZ1Eh0Zay2Tn32fZdvFLbbLSRjX0ngjEwKWtcXFlB9bZFKyxXgAbQGNKoq2h+ysdPuMgvZdbTqydhtMhaiCN4lyjG71U/1wph7H2sgTTjnXiFXKq9wEYSSWQSIC2jVpklHTq24bzLmo7jZJPrRid56/DllEnm+nmF37RNkHpgd5qOEXVgnhAvfQrE/dJ2wW1sgZSJnvMqeD/FaxWdD5c0nBR9lIJUEsBE3MlT+i4z5yAI6xI0sEQhUAmKKd0UGUlYlx+6SsMX7fDfF/uC/D6JVVR/zUcr8k5AwZOxN0dZKxnxUjft8bkCrvoIFyI7VqSsZc8R73Ofn2mGzZeS+iIxlGfOxBQLKvnmS80p9FWUgswQYBShrkCPTyJ6bjveiRBWiAjw7AJvk2dkPkxdQMyQ7smtxQ1rYqFAxH9nfWTT5W1QlAmpLN9kYy+qL3xRGXtrvRvvVcuXg4y/+4i/iV37lV/Af/+N/xEtf+lK89KUvxc///M/jl3/5l6+mXX81lDIH/uD7gQf/lFCv9wqcAYD1W4Frn7t3vWCVpLyFWwQs7CuM+VhVZLdhL/DR2wN87Auc4QNk+H+zn0vbt0TaxwblvsUgwC162IRcxUaRLLhFWQc7tOsBzLePhsPwHoABZTZmms1NRlNUmoEoR8vz8ZaDI8RwkZdVfe4ijRGWJlYG3YHVsH2Yuo7zlxpT4O15ijKL4Ui8DwHAdAfwqhj/+t2fxwPnpxjocuAJhoPAyPH45hxD18SLbxhL+4uBZQ88cQlVVWF3MoFpaNJ7EfgBjDLHRx6+jD//wkUi91V8MO66hnwIji37sGUgIADX9TA0Czx0YYpzLCBGdk8NGwM9x2dOb+PsdkS8HGWTFMPGM46S8x5a8KBJzfBdmFWCWZzh0jTBgqWQ+5sOluwSszjHk46MoRfd50czXbi2gXsePocPPngJyw6k/bEwHGLZLXHfmV1shykcJDAMs/sumTY0XcM7PvwlnN+NEPSAu75O+m3sWzAKNYtmiV7+kaHCf5Aeb9Eu8R+/80kAKJipWLidWGyed1fLlPf/2gUDtqnjzsOB+sNvebhuUcdTr1nES24/CLNUMJjEhZliI4GFUay7BBQrsj7G2gBjI8EHfvz5+J8/9EwAJPjlkF/1gDVD6c7u7/7jZ+EDP/58PPu6lfpnP/v3bsOCXcrHS24iN/YtvOLpxGrjn/6Pz+KRy3McGhhS9qVuuTh1sccWI0/wrvsu1f87iTM1mEjH8jQv8dp33oO3f/QUPQn1tuxMlBuQJ0wLHBg5HPgoTHLpRLgoK9x2eISRa2EScczHMpMCQqlmE0bwXmXlJEm8roGthByzL1CND2Ph/ZhkxR033xp2/D7mY1U0i6Me8FF3AviIEGaCLC2LcGZGxizfNhrwUWQlFQkqQOpRC6C7eVh/J/tYgftgBPGFqRCAto+Y7NhA896me7AZefas6v2u28AtiPnEUNVx+Wtk4SE9GyJtQLEHfBQXZKoFFLN+YCCWijkGoMMGrMN3RMaahAWnSmQu80YKqepbmReiim0pAlGdDT4KLjFAvg9U6LAVZQCfAMopvv3d8BoV+CiAuzKpOmsPoL4HMgafCAKwe99hi4rnFGXjChCQbxcglx+y4+0lya+vQQBjxfOKUl6lpF0CEqlAqr02BFRBacmEjEGmQ56zWMZ85O6TYZL+2ZfUVphnAM1YUAfO+OikZ/cCcf9Pe28ebVlV3ft/9+n7c27fVN9B0RRVtEXRCyWNSMQWCEEkShIFO6JJyFCIMT8xRH3GaCQxz4gjNsS8wIs8JRoQ7EpQEBEEhLKgCqpuNbfqNqfv9u+Pudc+a++z1tqn4FY/P2PUuHXvXXeftfu1vus75/QJJ7Vi5z0lp7AADMK6QtQF9C7VrkrIVVAUmhBQVeKjZuEknqFzKu+HqhI70Bmr9up89C8MqUKHAb2oLX8mQP1RjVuCcmLqiiL1Kljr8nF6xPSUYkHX1zf53ep+pu6ZqLieVWHXypBw1b2mE1BF2LUi3YB/4U8rYkr3UExU9Vbsp3KBKqHepsoIpYvyMUVsHuHstfi4bds2nHHGGV0/P+OMM7Bt27Y56RTzKghHgfmnAE//F5AdM4dG7y3pIRLsyrtILDSRGqCbtF7svLRN+BMg+zHlSvKvRIqHi2lCmB6iQjvib3S5MRO+MOqgsLloCoDVsXe3ncmhYtAdjydgiVUY4XxMdial6RgNsGqISc6MGlohelDKOR9DIQunLx9CxYq7L5rZ4ixqVgwjOcVLNRRCKJ7G5u0d8fGex19G1G4indJMjGNpXHI0HetSrem4zxSD7GgCUcvGd371EmarTYxlQsoX+19ethrRSAjlShkz1SZCzaqTj1BxrBJJZCJNfPze3wAA7GZVfT2EY8hESGw4YX5Bu6pqRRIYSNh4eNNuTJcbjtNPPUlJhDorZQmozyWiSRLsACwbSDgDJF//ogmEYKNYrqJYbaJPExKOSAIjTs6+05b0a1YgU4iFQ/jNlp0AgPFMSDuhzEbaeHrbDHaX6ohbDRJ9FYUJ0rEwYqDQawqnVoWORZEKt7B1qoLxfFIrrCOSwHxnH85dluvOu+O2o8FdfzqGPzpnKQmMmv1IoHMe8tGmVrgtRFu47wPnOKHZmsl7NAWrUcan37YaH7roaH3obFcyfoMDO5bBSKKFbCKCS4/pM4gMTviHbbtVpwFgJGkSHzUhSBJ/f+WJ+MwVa3DWikGaGOryKLUb7qD1j89dhvevXwEA2LBxF8YyUJ73FfOGSGjW0aqhHer83WzVKbhkhRVuDZow7SnTZHHzbun3/lBiwDMpJfEx0XEzxtI0oJXFwHAMpVoL6XgE2UQU1UaLivG4hcb8jpUKaogFF5wByPnYKAOzW+l7911jeL+JFB8ADWbDMXVeT7EdeXAskqWrcBPFO8dL5PRSEE5kkbQrKItcmQ4v7dyNnRULo7kEUvEIao0WOVH9wkujSs5HnfiY7O+kWQF6cD4qxMeg/EjxLFCTjqPJcSj3oVHSC4Sij7Lz0SRUes5lkPPRLyYKUadHQVa1gCW39S9w6hZaZHHMdTOqFg19Ippqkg3Qczwc60xCdaKq34Gle1f43eUqV6uuQq0q5NRud9wtOmGj6/pWCKNdAmVVP0n1iLaaa9NfOVgVkqc6ZvLPBSoHn1KI8rVThsf7nY8KwVPlpms1DMcjQNh12/lEZ8B7HnoOafdduwCUjmdVgQulSKRyPhY7hoZEThF2rREVlYKY4lrzC3f+0FKtsUHjfPQXv6jPep2P8ntQFx6qygMLUF/97zGVY9Tv9I9lHFe6LKBqrg9lCiunL11FqxTPB9FP5bjOLz4aBChVjlDA5xxMq0VKjwiYVgvRXcKd4hrtZXFE0PAtzKlcgUpXpiLipKeQcMUzVpUbEuh+p+lSHMhh18p0A+IciOtKE3btRkGm9Ivh2pQlinPfbtOCmtJtbCnGupoFGmbvxcfly5fj3//937t+ftddd2HFihVz0inmVbJwHX0tBBSQ2VvSgzSw2/VcsPMxPUxfZyco/Do1YG6f8k1e/ATl1YokOit59SLd8KYQrswwUNxJIW6xjH4y6K8EVpulz9K5MEMh50HutNcN4EHFQKxWHa0WVZ1u21RIxO2i42ysQRqYtOpoWfQwk3M+ApSbbbYVdyeju6dn0UAMY3n1cYslM9i0dQfO/9SDKNaa+OnGSYykbHXuQ+dYnLOo88JKhzRFP6JpJGNhJFDD0qEM5eBTtMukM8gmonh+6ySe2jqNOOpa8RGROJb1d475WAraSUU6RAObUxf36ycCkQQK0TZ++NudgAWnkIx6gCzCeAFQ6K9mkJawaMKzfoUjcihyPsYjYXL3wRHQNOLtWAr457efjNefMK4eQEXiSMbCGEnZ+Mjrj8Hy/oi2/3GriZ0zVfxuZwmDsTYsjWiXiIapArgNxBUFc8T2shE6vuM5p7q6Jkz6hLEEvvnHp6MQNYQ/OxMQy7Jw5WkLEbU1grYjUo4VEoAFxG2d81WabJnCOLuKOyjEYtE/eVJgckZFUwg3K/i/N56FNWNJg0iZJsea08+bLjwKAJDXpDFw/6ZuCHsGsGp+HmsWFDr7owu7BjwDtTesmYdCKopnts1iNK1Oe5DO5hFpUZ5PJa0GQuE43nbqAsSjYcxUGo4bVLFq7Ti6Jot0H7TkUGC/IwPwuNNLtSZG8gkUa00KIY6mANieyXo7nECp3kQ6HkEuSc/I2Wqzu/iLoFFFDTFKRxBEbj7diyL02nXZGwq7pQdpwQ6g90evQmWrQRNIXdRAso++ijBtg1AZS2aRtKudyt8Os7MzqCGGs48aRMpZ7CrXm90OIceZr8orC6BTAVYIoWIfdNEHyoIzvTgfRc5Hg5vRnzu0UYE2p7Poo+xmNIZ+5/eieFCyWygADIsSvrA8U4i2fzGiYRBNZZHHDVfTCA3+8GFTW/F7nQjrz4OlO7Zhxefq3Fxdz2KF81Hue6+OLtXz31/EwpRyo+fclv79DBIfawCs7n3wi21aR5jPIakrDOS51+sUNSWPiVVhrboKyV0FeHo8B/5QSqBbnNAKmQqRrFdRxL+QHU3RMZMrqwNeE0U8q1jEUly32hx5CsHJVJEY6IgxHsezQrwyOR/Fs1GIpx6HcA/n0t0Hxb0cELVA+6DI62wKuwZ8IqlYwFEIcfLnefqpul4UgrtynqAStRULAsrzVw5uoyyKlOgtfHevnI/+vvnyQqryKqqed8r7R7EP4r71p7jpEj0V94fon6dIkebeEP3zL/TJ2xG/d689xaKnaj6vOg9tkafXl+vWySPPYde9Ewlu4uVjH/sYrrjiCvzwhz/EmWeeCQD4yU9+gvvvv18pSjIHgMVnAT+8HViwdm63mx2nHDJbfwmMnmBum+qnB9DEr2minR40t08PAcUd+t+bnA6AM2mdov8HTfAAIDMCbPoRvdhMFcFDYcepJIXNBYWQy+4VQ5j2vKECanYb26dLGG9WYds2ElJl6mQ0jFDIQgXRzgCyWUPTdT56b99Nu0rYWY9imfNin56ZQSiW0Dp60tk8krO03bfesQFj+QSOHUkAkYayvX8FLW7X1ROvWBqJSAj3vvNEIDcG/Pfd6sFFNIlEJIR4s45v/2ob0mHHlad0yCVw5kIL544M4ZRF/Ug/ogkLjiaQQB13/uFpWNCfAn6qmwjE0Be3gTJwzemLENqiGQBFYgi3GnjgQ+fR9/90i3ZiUYi18cfnLsW6hUl3/7xt4ghZQMyuo2ylkLA1YeuRBKxWFcuHnWtYORlLIBYO4X+9aSUwPgI8ohvgxxAJW4g2G7jjoY34wz5NBehwHGHLcou/xKAT9+JYnA8BRZBL8QVoRcpwvYjhbAJ4WTNpc7bXHbKmcew1yvint58C27aBr39SfezkFVBdKKLbTs7LWtY6Wrsmb1pRMdUZ7OgqOIp9AZzQ0QRef8I4icw/e9rsFJvZqv6dCt2+y6u/0gKSqPQ8lLSB2e6/S2fzSNpV7Jyt0X3lp1nDTAMoJKPIJyNShWnVqjVNgHeX6FprypO8ZJ835yFA5yYcR9O2UG+2MZpLwLaBUr2JrFyIIkqTn5oVB+wmMo7zEaACNf39WXJidjlWKDdurJecj6EQuR93/RZYfkHvLvs9m+j/tRlzOpB4js5Nu93Ztu59I95bIreyIb1JPJ1Dyqq6x1wQaddxznGLEI+E3fdJsdZE1h/G2KqjbduUKkRFbpy+zmylojy1WXo++wfpAmUoWCn4HS8mzKbxgN/hUC+bnY+JfEccrpc64VnKtgUpl7XzLuxVIFTltJPxH5OmwgEob9tT6dzg7pSfsbo8jqJfqsIj2rZC0FRMxoFuIbBZU58zlfPR/+wKhZ2CLV5BXDlxdz8zpxZ63P5L+6r6TFkIjKX1wpdfoGlUNQtZfpFS0Te/yCfe/f5IhWgSKPkKU4m++Nt5qqJrHFRd4q/i+IeiPlFRF3btE5N17jOVqxHoLnTjETz3Unz0j+39TlCg22krOwdD0s9rRSA/n/6vykf4apyPUZVw54uyUlXPVl5DQojzfW691HnnJ/JOSLNzbpp19cKVKpwdUC92mJyPbvEQafFTRJXpFn7FHE7lfFTeN5bi3GquPb9QpE0JobpeVOKjwhGoyiXaqlNhVPFebFS756o6h2TX8U50H2+g+30nj0vlfegSH33Xs875KOd2BjQCatJJCdPwitr+uYzKiSz2QSwYqqJh/HmBVXktxXbCMTrelqagoW6hXrlwIM696ppSRXOw81HHXjsf3/zmN+Phhx/G4OAg7rnnHtxzzz0YHBzEI488gje+8Y37oo/M3pKfB7zrf4A1V8/tdqMJevna7WBXpWVRm62/pO+DnI+5cSqS09Y4a4KSxotK3AA9RIMEQjns2iQ+At4Hs5w3RUci35mgGArU9GfpYbhzzwzqtTLabRv5XOdFZFlU0bpkS3ljWjU0EUUkbCEe8d6+b1+3GBUrgUaFJtfl4jQsw3GIJbO4ef0i+n8khInpKrJhTaERwJ0c/Z93n4Fvv/csWLpz4l+hNYTdhMNhHDsUwW+3zzpFOvQTuVCzglsvOw6XnjCmF6mcF4ArkBjCgoeS5Bp8+7rFBueb83IUK3iNqtb5GG7VcMWpCxFuaRwxzr7d+rqlFOqq2we/204lPoYj5E6QcoHqJokkKpIYNJKytRMjywKiTrtwWxOKEE0hgTr+8z1n4Mo1Q5798m/PvWZ1eXfE3/rdFEo3KE3kXUFJt1ouV1PU5d5y9sM74dWtfCvOhdGd2EmPoP1sMdj0OxlNeexk0aMXtM5H9QDsLy5ZiXQ8gqMHYsrzns4WkEQVO4uKlXYAaDcxXbdQSMWQlfMsqsRHJ8xlZ5GO66zsxlOl33AmSCUnH6EIVS/VWtKErOQkgm+hAup/KtYR1GarTXonqRwrjQqqiCEa6TFFydBKyfk4S8fL6LIfIZc94LxvTOJjlt6v9WJnwK0Luw6FnUU353g1ytq2VjyLfLiGbdOda377TBXtehnD/fT+yzuFyaYrDYVQUkUdUSTjGjExUaDnyszL9H1txryfCd/kXQiKun0F6Noo73b+ztYvMEZT9Gx08zQHiJry+7onZ6rTtl6k+1UXNdFV1EEj0gn8LpDA4jT+ybNJfJTc4OJnXe38IqDTNigXmhjj6JyPcjud89EvQmnFKnmhSvHMlgXDdosmwEGuO9tWO5/8bktVYQXxmR6xTROlo8pxqHI0yiKKKV+ipxCazhGmEO+6iiX4zrvJXej/TJVjqOt8KvZTbE8ODRZOMXkRSOSO86TV2Ivw2F5yZfoLhqkqqwNkQBALR/Gs995uNdWV4ZV5/jQCsL9f9TItlon9VYWNqoQfcQ35RUO5UKg/BUnQte2fl2lSAXWFjtd9AqrYB4+AqhsHx+jzPeKjeCb5+mpZGleebp7gE4V1Bcz8+VDFNgGf+JhUFEXxjRVV+651PqquUf+zIg6Pk9pt608joHEO+kOzlWkEfMdEVwxHlfMR8F7T7Xb3c1YXni2HXbuh+tIitf/YqvKhAt4ICTenuN/hqUtRpKoAXuv0u6t9SjPWZfFRxV6LjwBw8skn49/+7d/w6KOP4tFHH8W//du/4cQTT5zrvjGvhmTf3OZ7FAwe5f1qIu+Ij6Go2e0BkPjYbnYERD+m6pYAkBkFZrfT/3txPmbH6EExvSVYfJRDwmszwcJmqh+oOKtDBudjJJZEOBTCtskp7JmeRQshjBa8284loyi1IpKQU0fdiiKbiFJVWonlwxlUkMTszBR1tTyLcMLQ11gaoUYJ71+/ArOVBmrNFkYSLX2ImiMk9KVjVAxHN+GJ+gZJugGAE5YZb5exp1THaGDOO+el504W1K7L7kmZWpQLtWo4f+UIOUNVuXMA5wXofF6rSZMZZWEaacCtK7bgDFZWjybxhjXz9MdPNUnRhnHJDgnNJBHA7ZcfDQDKytlyu5hdx2UnjMBSDaQB9wVbSMWoqI60X13bE4NfXaJ+QBE+aBC0202v20Ylovqr0Wrz5fmuE5XAC3SLoyZRUc5No7uexGcD6gp+uuvfX9AjCF24h9i+bzB6yuJ+fPu9ZyEZUi8+hGNpZEM1bJ9WrFIDsJs1TNUt9KdjVOSlB+fjlt30THOLnABUNdm/n85gulwnkXLYqfBdrDWlhY6yO9At2zRZS8ciiIZDSIgwcKAjYHn6U0XFjiHSi/MRAIaOBnY+Q/+vzQYvRmVGgKJ4N82Y3zfid7XZznEzbT9Z8Apnhuu9EK5j444i6k0axL/na48hbtewfJwWEZLRMKLhEKbLQnz0uqFqdgS5hGLiD9CzPDfecefuTXi5s32q7GoQCdND5FAU4p/uOFqWU9ncaeev/uknWZAKAgWcT9HWth0huaBv63eTuM9LzXPBLyia0kbE0vCEhhoL8EiOJDdcTfXs9Dlkms5Cm+q+8LzvNItt/jxsRgec/IzVvAO6UmUoxhWu66squaR0z3Xn9606AFuxLYVzU7kgl/Ck0dCOTRRu4q6+WZbiva7qf7K7SA+gFoSCFvf8Ye89V/XWLdjGugvO9OS0VQgx4nu5YJLWfep7N2nz6fncm7YvLZCuSrVsPEj40oPoXMLKHHkq4U7hkGw4DjYxzlflUFbtoyg05D8e8rvKDWmekbbT+2KlclE/mlA47Cq0ECQEVDE38LsZTQu1nrbO9adLuaMqJqRdVJafJZrxpErUblQpAlAWwZXOR78IqBC1lUK0yi2rEQKble7Q5nrJO4dT5nz09U1etHc/s9I9V/A7FcVcTBWeDXQfY8B77qJp7+KC2z8p7NpNrSOLxf6Qdp/hRVAv+lygimOhC7uOq46JYc6jLaLH4qOKVyQ+zjVf+MIXsHjxYiQSCaxduxaPPPKItu2XvvQlnH322ejr60NfXx/Wr19vbM/MMWv/BDj93eT+CKJ/CbkZ+xarB7Ay2TH6KpwTfoLEx+wIfRbghLYFTAj7l9DXF38KZEfNbdPDHedKvQfnY2qgM8E1OB8RTSEWCeGFbTswNTOLuhXHgK8y9WAmht31cOch3iijZiW68j0CQF8qino4ieLstPPRs4gmDaKvIwyMOk6i158wjrhd1YdvxTKdgY87aFMNjGNOlT+nranaaCyDsDNgIVeeSaRyxEx3sqB7ATgvDN2LEVBOrrViFkADT+OLR5owiP32b88fDqPNDeUbIOscMLIrT7X6CLg/O2Yoho+94TisGUtoBu/U7gtXHIf3n7vQ21+ZWKazf+4qoG7QFiCMAt2DS919LgT/RslcNVa+ToxFKfwuI41bxZ/82ygISNeeKc9cNEWTiK5qmZpJK0AFPSq71b9ToVvNDYWdVfpi9+8A9QQQABzxccseRWgLgHazhmo7gkIqimwi0sktqBQf6XreNl1BPhVFscv5OOVt75zHUk2Ij3RdFmtNaaJYdp9NpTYN9tLOMzInh4FnR4DiRFd/KnYU0XCv4uNKWiQrTQKlXUAqIKVIZojeSY0qnXPTQpwbSr3HWXW39NcR4Dhi99Czrl7SC36xLArhGu59Yhsu/uwPAQDLB+JIRS1EE7R9y7KQT0Yd52PCc3/YjSqqdtTNQ6wkPw+Yfon+H7SfcgVwwJsYXkd6kK6FGec9nyzo28puRlN+SNG215QtiTwtgjTKwVETQmB1XfO+/Gd+Yhnfs7CsX+j0h2CaxkexdMfB0aw5YoDiPIr3u+ivybHued9pxEe3KnaAkOYXhHoN01XmQk50Psvk8oz63k+q/nc5NzUTSX8eXWO1a/+7RNdOEjJ1i4pBE3vlZ+oEOUkA0IaqqgQbzZjD73xUtvMJhqr71C/Y6NKZqMKDVYu2YlHUrcCuuCdVwgngzCuc+z2W8aVI0Im/CqeYajznF8PE58vPflUOZWOuRp/4KDsf3ZBmSXxUjvdMC6V+wUnhfPS7zsXcwL/grIsmEak2BKYxuLKStu6Z44980Y3FE93HUVzPsvlDGV7vEz7FufQv8PivGVUeROVCi3g++cVRRdh11/nz3WvxnNrlF5Tzsd0EzcX86R4UKQJU7wlPmgwH2/aFXStyNapyRwIK8bGMLiFWuRiumev4o5PckHvd3EMVdq15hx7h9Cw+hkIhhMNh479IZK9TSOKuu+7CTTfdhFtvvRWPPfYYVq9ejYsuugg7duxQtn/wwQdx1VVX4Qc/+AE2bNiABQsW4MILL8TLL2tEK2Zu6V8CnPqu3lyVIufkUA8uyUicRD5dXrOgnI/ZsY67pJcJYTQJ5ObRJFLkq9Lhca4Ug12cyb6OUFAv0yqZ5mEVj4TwwsQuvLxrD9rheFd+xpFcArtqsvhIIYL+fI8ATR7TmTwmd09islhDq1ZELGWYSDkv61MX9+NfrzsVH3ztUeaJWizVeRibHBTutp2HvClPZjSF3z9xAGsWFHDe0qxB2JFWoUy5qOQXQKvuuGl6GFRow3hFiGxRP8D0b69R8YbLuG18g4Wec0PpBlDyBEozUZReymevcCoWq7blVDCNo4GQ6fjK7hxXZNUdX3kCpRtc+ifcmhBJeWXTeP4zvuuuV/FRN+HtcYUcgCe8xfTZluV9Rrh9KOuPU6rf2fea+vd+dE4GoHvi5Pk73X2QRiLUwuZdM92/A9Bq1NC0ouhLxZBPRjtOQ9WKuvOMmak0Ma+QxKxcgVmIaV3tExRmDSrElYyFSYwUueAaFXehp9giITHmpKXIJqIdMTQzSkXQfNsvt6OIhnuMFOhfSm7+nc9Q6o7MkLl91nm/zLxMIpcpHDmWpuuwvIuetbGUeeEuWaBt1ksAbP1z1hGPhbD0zq/8HOVyEYlo2HPd55JRTFUa3kUGAI1aBTVElYteLgPLgcnn6f+VPZ2CONp+S+KjnBheR9o5zpPP0zk3LUaK4wKYXYFAJ6WBbQenbBFOx8pUsIs1nnXc2s7zqtZDmLZ8TOQCEX5ivsmdabHD8+7UCGMAPRM9Dj6NYw3wPt917zu3KrbUThk14HfK6ZyPPiFHmY4kSuOtZq0jRgWFBqsKnYjPE58D6FOCKAscKZ79XbkhexA8jJWi5WOmcYRFkl5BSFdwBpDchb2Kirq+xRQOyV72QeV8THR+J77qXJk9OR+ldDyAJIhI/RPvd/las22f8zGvdj52XUM+kUtEzwQ5VEXf/PefKo+s0o3rE/QB7zg8FHZSkEx19tV0baty3ymdeIp98D/D/GOCvXI+GsbgKqeaNpe373rp9Z4F9PuuKhjkD80WP5f3R3XPqq7lrsUR3/PJ/VzfHE5VLdq/2O4fh6v6D3Rfz24YvOa+9acsAryfq8pP2qiAxjK+UP2uHO2yiKm5TruEWI1ArXqmxLM0npddmUYDStIrLIsiYBx2raRn8fHuu+/Gf/7nfyr/ffjDH0Y8Hn9F4uNnPvMZXH/99bjuuutw7LHH4o477kAqlcKXv/xlZfuvfe1reM973oM1a9Zg5cqV+Jd/+Re0223cf//9yva1Wg0zMzOef8x+YmwNcPaHyC3ZC4UFwJ4X1b8z5W8DSCCc3UY3fGlXZ6JiQrg3+5eZ22WGaZIJUPh1UKXvZD9QdibQdWcyoxJrHfFxemoKD/3mJZTb3ffPQDqGXVUnLNjJi1W11eIjAAwPD2Ni+3a89Y4NsOpFJDKmyRGtKoZCFhYNiIe3QeSV884FVvpMSQ60sl6IiaWRC9XwmSvWUBXoXpxqptWnaMo7CRA/8yMPKmy7h5XfYufFoks27Ob7cyYV/nNuWc7AQnY+9hDq0UtIsK7/Ud+qoSmPmBA9/YnOZWJSbhWjGBvz9k0rhGU714lY8VRNuOXw2sDVx4BJoNieJ3xJ51ZxzoXrBjKEUcghHQ3fiquf1EAnlYPAlPNOCDm9hl4bi+2ku1e53b/TTGZiaUTDIWzbMdn9O9tGs16DFYljMBNznI+O+CjfjwLnvMxUGxjLJ1BrtDoVr2XRSFArArEsSvUm4tEwwiEL6XiEnI9Axyng3O/FdgypeEfgScXCqIjQ7qxPfGy3gEYFs3aid+djOAqMHAe8/AtyxKeHze3Tg/QO2P07KqqWGQlo7zjtDQVkXMQzWQjZOsEvlkY8bONTb6J33qZdJUzPTCMSsjzPoEIqSmHXvklutVJCDXHtewdApxCPbQeLsvE8TXbksGHA7HxMDQCwgMnnglOlJApSVeoewq7tFvWhNmteXBSF84rbaftB7k5Ayj1pcDIC3eJj3SCERv2Cl8a5DXgnXKaJvkpE0z1DZMHQVBjHk/u3pnl3+gQy00JQV5EY375YVqcwTa+RCrrFLNm52W67uWq78B83bZoUv3NT9/73Ox91/ZdFAI2A2ovzUSXwaYr5udsSx6MnUVE3fvH3TXE+3b4J56PmvRaJoSv/ncol6c/j6Tof0742Vrcr02517mm/S1krPvoccdrwbN8ip/hMVRi3P+y6F1cp4IiP0n7KAqrfpSdwXb3+gh8KgTqS0LzrVQKq79iZxEd/tetQVL2Aoy0CpHvm+O4frSvbLwQq9l2IcnIItP/8qYQ2XchyL6H6YhyqLKDkcwWqhNGoX5TrwRHYFQmkWfRQCq2Ke0TpkBRRED7no3/hyRM2rtgO0H3NqxbDjcUZbZ9TtQbA0qQ0SvsWGpwxMIddK+lZfHzDG97Q9W/lypX4yle+gk996lN461vfimeffXavPrxer+PRRx/F+vXrOx0KhbB+/Xps2LChp22Uy2U0Gg3096sFodtuuw35fN79t2BBQKEUZu4IhYA1VwU7CwWDRwE7n+7+ebtFLx2T87F/KT1UZifIzZgJmBACwCnXASsuCq4KnhnuTFjLk8HFc+Scj6YCNaEwookkEnYVcbvuJv6XySejmKpH4OYdbJRRtmNaB8qxy5Ygb9OAImlXkM0W9P3U5VfTTQA9ubGcwYg2zE92oJkcHLJgo3ECiHZyDklAPaiIpSkkt9XQOxrEz1qOqGR0SMadCrlFKTRQsS/xDPWr1YQ2z4zYnux81E4+xGDbSWSuc0h4JnaKl1wo5BWCdRMjAG64t0kIkCe9zQr9jUpYl0PHTZPueJaOrSg4AVt9fOU8orqJFuATHw0uXmW1Q82ES1TtA8zOR7kqX1CF3ZQi96DJ+Zh03m+9io8mwdcfyuT/O81xjYZDmJ2ZwvM7fAP8VgPNdhuD+Qwsy0IuGe0UkYmrQlfoWE9XGhjL07F0hcT0EO1jS3JDOqGw5VoL6RhNOjLxSCdcW5xL516YbYWRjnWej6lYBCUnXySyY97UHs75mrWTiPTqfATonbHlEVrwCnrXWBa9nyY30nskM2puL4qh9ZS72BFTxXVhEB8tACeNxvCR1x8DAGhWK4742LlOh7NxvDzliODSRK5WKaJqJZCNa3I+AsDgCjqesxMksppE2UQOgN25NsSz1fSOD4WB3Biw5efBblM55LnnUPfd9DemcO5InN7/sxMk+gY5H4HOcfRPhFT9aNU6z/S9cj6aU5t4FgJNIiXQmWzpJu4APCk/dO8xwPseMBWcaTc6obA6550q7Ff3zG5Kx1G5+Oh7DwPdnyk7N4OiAQB4Um5oxxySQKbNmZ30vdd7cCE2KgCsHsQClYghiayA/jx58mQaFh79bjFt6LhfTFacTzcsUxZGVcc22Vmgl7enyqcn+gSoF9Etq1ucEvewnPOxZ/HRJxYCvbndVPd0V9i15j0fSXjF2JbjwJbnInLqC911FolRmoaugiUaYVS5DyrxcS+cj7LwaRpX+QVhQD+n6Ip80Yz/VM5H5XWa9I4TVdsMhdBVmEZ13N08iC1fu4BrWeCPutEuAMvOx2xn/uJ+puLY+dKxaJ8DIiLFn6cR8DkWFSKlON9izB9JkJvdE6qvEHatkOY6lcVHxbEwhV0D3oV6IVIrzUS+hQFxbNj5qOQV5XzcunUrrr/+eqxatQrNZhOPP/447rzzTixatGivtrNr1y60Wi2MjHidACMjI5iYmND8lZc///M/x/j4uEfAlLn55psxPT3t/tuyZcte9ZHZjwwfC+x4ujuBrmngJygspIfxzmcop1cvzsfhY4CLP6EXYwR9i2lyV9nTm/iY7CdhwbYd4U0/gbSiaSzKATHUMVToniDlk1HK+QjQA7pRQcmmirIqotkhnDFOt3UKFWRzhsmRqrKsKT+j7HwU4oVWfHQGGP78HV3t/NWJdROjTMcCH+R8A3zhuaoV3TQJjs2qJLYpBDLL6oRzuO1UzjwpPNskInkmFjpXQ7LjtjO5N3pxPor9ksVHY6h80SwEyLlVVKvfbt+klXfTpFuIxc2aNDhRTRYjTnhtOaB/ohBDKyDsWhK93UqVmkkN4J2saPOgycKnJnxcoHI+1orm+ykcJ1d3L5jCPUzFaww5H6PhEJKo4o+++qjvb6potmz05+n5lUtEOmHXsgNNUC+hHUmiWGtivEDHV+RzRG6c7kuRvxdwKyeX6k2knEWXTFwSFGNOaFajDESSKNdtpGIa5+PgUXTcS6KAGE1Wptt74XwEgEVn0Ltm+5O9FV8bWA5M/Jr2JUiszAyReFfuIX1IdoyOVXk3XW+m3FkAUJ/FCfMLAICEXUMkHPJc98uHM9i4s9g1QaxXiqgiFpDzcQH1YfuTjsPTJD76qq3Wi3Sv6UKSBf3L6P2eD1hEFoV46iV6vphCwNNDACxg+2/o2gsaO2RH6ZjPbqMcojrEMa85+2gqCCT6DEjHxJDDM5KkPntc3prnejwrufIM7wl/MapG2fB8l1xrpved3E4Xxu0XmLSF2FRCmmFRTjXZlT/T73zUpUBxxl3abblVVIPCruNOvkGRW1EjBMuutV5SrrifqVgIVKUN0YZvBgl8ccUxC2jnbk91bH3io7GAkIgqqWoKo6TgLtALVBWc/UKrKeReFllc8VGudq0SHzWOuK52/s/ziTqAevzSFbGhc5/6hR/FGDchLUIaFy80eZv9zzLVPqjuha6wa4PpwF/YxzSu8guVop9BhQndfmquUWFS8GxTcZ4BhTsv1d3OL0arrgXx90CnSnRXDnnftezZF5+rsVH27oM/FYk7fwkoBKTKlQnon1Getk4/Zee1qiq2P/+zZXULqP59FG38BY/895A2L6jiPSeEen9uV+34329o6EG3OILZK/Fxenoaf/7nf47ly5fjqaeewv33349vf/vbOP744/dV/4x88pOfxDe/+U3cfffdSCTUD694PI5cLuf5xxykjB5PA+89m7w/V+Vl8RMKk5j42/uovSgoMxf0LaFVlcmNvTsf7RbtS1A11FgKlx/Xh6MGoojFu1+quWQUk7UQbMB1PhZbeucjFabYA8tuI2lXETblF1OJj6ZQ1WSBXhKNKu1XJKnOpwR0HvLNGh0LY9XhXkLHxOBSnlSoXAPSIEA3IAS8rhTx+UHOEdMEWX5RmYoSyVWgdfsbidMkuNUwD/L9Eyit080n8GoFCmfluFHS76enwIfBmSOHoZjaifMQJO4CnYG3GAyoxGI5+X9g/lIxyTZ8rn8gaArhlN0QQRV2UwPdOR9NTjfL6ogevdCs668H1X3v/p1mcBVNI2QBFyxLI+TLS4tmFY22jYE+EpRyco5Fpbu6ggrigA2MF+j4us7HzAg5jeXcv04obLnecT56wq6Fy82ZQJTqTbfYDEDio1tR283X+LS7bRtAcW/CrgEKux48mv4/via4/byTgC0/o//3LTa3TQ+Te788CaQD3jW5eST0FXeY04EIYau8G4OZOFaOZXHGYjqn8jNo+XAGL++poGp5B9KNShGtSLIrJ7GHUBiYfwq9gyu7zfvpFx9NjnuZ4WPpa9D7PTVAxzDIEQrQOywzAmz7FX0fKD46gu/sRKdQnnK7EXr+iMlzvWRciFQKsjrHeCjknZibFlrkNAum/Jdd4cMasRDwOup0IZuAd4FMJ1K67hdJYOqlyIoxZ6LjfAzH1e8xuaJso6IvwiOcm24qEtPCnZRXUxdFAfhyK6qEUdktqnOkJeANVdeMJfzOR1U7lSAX5FQ0LQArRUWVO88XGqyKGAmFve4z7ftJWhR1t1fpXvT25/Gsi7FOqLudx/noW2x3nWIiIqKmDgdW5SkF1C62RtXn3FSMxf35+1QCq9i+fA5UYytPaoqqflyoEmxU97IoOGMSuVTbMzoffQ5T0/wknu2O6NA9I1S5JHWpBsRY3G2ruG9VDj5tzkTf9aANWXauFZ270E1J4Bcfffd4NNUxWsh984QjO/+XXX7K/idooV44JE2LMl37Wum+1zldgtIAAFcGSURBVMJRGu95jlsZ5OKWxVG/WKxYRFEVfKn7xGq5aKqgpRkrx6Q5orsPis8VyIYGAMYIPaZ38fH222/H0qVLce+99+Ib3/gGfvrTn+Lss89+VR8+ODiIcDiM7du3e36+fft2jI6OGv/2U5/6FD75yU/ie9/7Hk444YRX1Q/mIKGwkBwNL/zY+3Nx85sG7gCw5Bzg+f+hB2aQM2JviCaobxsfoAdV0LZF9ezZCfMEAgCiaSzO2vi94/phRbsHVflkFMV2lN7nzgr8bCuqz72VGgDsNm48YxCLMi1zcYNkH21TDFLaLWfgrBMfnQlcT6KqM+Hx5+9QtXNDVQOcX0BHfIokNBU7JfdGvQSqFmvIZSOLj9o+ZjqORlMbgPbZVJRIdiKYqlgD9KJzV9wVx8UT+mYIbfaErGs+U+yDcD4GTlCL5vMlF1uoB4RdA0414IDQSxE+bjz/0gTaJD7Kkwcx4NIdY6BzzkyiohjMt9vdgx4/wh0tsG2nmqbhfs2Ne0OGTZjC603io66aqOM8PW9JGu22je0zsrOlimarjSEhPiajqDVaqDfbXre0oFFGyabPGM0lAEtyPobC5CabeanTvjoNxLMo1ZpIxYTzMdwJu5ZdbtEkyvWWx/mYlMXHcAQYXQVsdoTA2ixsWKgiQSHIvWJZwBvvAK65Ozj/IEBOSYDEKtMzGQDy84GpzeTODHI+5sYB2JR/0hTOHY7S9e0I3v949cl41+ljNOiWJsxLh+g+/ch3X0Cz0pnIVUoziCYC8k8CwKIzgd89SBOK/qX6dpEEPWOEkzcoJFlwzGXAwnXA0a8ztxMCoZsLs2Bun58HbP0lTRSC+jGwHNj2BG07ax6nehzOpkUYgMY3Voiu92adxhqmd2yiQPlRRaVzbXSB/I4tGdPAkAgiF6fRuDzkEMfAdCki/Yrm2emf5OkiIOSwTjdPs2bxTiySat2gGakCuOGdKG9L9EFFL7mG/bkVTYuPQc7HSMIJ8xQigOZd53chqorc+UUMnegsb8sVRFRist/tZ3A+egrT9CA6a4v++CqOtxpOuhrf5wrXlTtuqqhFe7/LtjZL+yWela6rOeAeUB0LsU+edgkAdnBYvj/s2uhSDRAf487ihUhrZYo66sXRqA09VuyDXyQ2FpyRnY+GRSr5nvZsW7Ff8Rw9E+Wq58rz50tFAfTufFRWb1dcD105WBWLMUD3MZKLawnaLWfspxAW6/6+ScfFNQEEiMK6vuly4XYJhr7tWVa3Y1YI1rJIGVMIe0HCruinX2RVhl1rxryRZO/OR38u9aC6CEc4PYuPf/EXf4FqtYrly5fjzjvvxJve9Cblv70hFovh5JNP9hSLEcVj1q1bp/2722+/HR//+Mdx33334ZRTTtmrz2QOcpacrRAfA0J8Bce8niZuJ7wtOHxrb1l4OvDEXTRZy883t43n6EE4u7U3ka5R1roi8skoaoijLXLiNcqYbUUM4iO5X964uIls1Da7NN0iFlP0VTwsdS93t9LnnuDE/MLx1JOw57QxbVNOfG2aqMqDhdoMtVNVi437tmfqoxjUBLlLALhFC3RimxjAm/KYypMUU99kV0mvzheTu1TkX2wYthWOkbAgxF3tMZOKLRj7JovAAeKjGAQHhXGL7ZkKuMguI3dxQ+WklM6rEOe1RWEKtMIsihOZhIbcGAmJbiGbKk2WTM+4/Hxgei/ER91kwi98ev5OMRgWxNKYl6KQwav++Wfuj+16GW0byGep7zknJcRstUHHpFGGm3/KeY7NtqhNLhlFOia5GAFgYAWw45nO9yLsutZCJi5yPkY7gqVwPrrtfM7HaBiVurT9pecBmx5yBV87moZtdapj90wiR4tSPbXNA2+9kwTLIAZXkHC2e2NvIcDxHC2MBfUlNeA974rnlHDUz7bjmCmW3IlktVxEItVD1MjRr6NCbif+gX6QDtCkIzNKjk0guDq2IDcGvOHzwXmk8/PpHti9ie6DoAlAfj4VsiksVOdzkhk+ls4NQNeqiYzjYgWCxcdQqFNgweTuFsjvWLulF1jltBtBi6FyTixTCpYusc0k8gXkvIz7hCOTc9ANRXSqBisFA8mtaHrXtWr0XDItKrrbMoRwA72JrG6BCEn40r3/gwrOqNyKulB1f8VZ/zELR0EFVsRnvkrnY8wX/mgSFbtCx3VCoHQ8lC4ln2tXt5AZctJMyEX/tJVrfWHX8thUXjQF9KKoqrAJoBZOAJ8DTOUa9Idd19TRR13OR18ePYDeSbWZzjno5dp2+6Zy0PqEKbE//nMgp4EwFXsUffQU4TItKOfUIbU65yMgPXM04yXZzODuk2JRw+/edtv5z7N0PWgrn/tclDrB2rIUbnBfyDLQuTca8jGveo9jNAXA6s5v6O+/2K7Hza5wDgOd+YS8Pe1ihfwcUDw/5bz1gPo6UOVz7HI+Kly8ukJibvsehXI5jYFoC+jbH+H0PNp++9vfjre97W3o7+/3FHDx/9tbbrrpJnzpS1/CnXfeiaeffhrvfve7USqVcN1117mfe/PNN7vt//Zv/xYf/ehH8eUvfxmLFy/GxMQEJiYmUCxqqncyhxaLzwG2Pu7N81GbpQFHUG7GZB9wzT3Ame+f+36tuIi+zjspWNi0LMeltC04TFs8MDXJ5QupKOqIUjXYZhVoVDDdjGpzPiKRJ3Fo57P0vck5I4uJgDThMYTIWmFyWwSJqsJdFRjSLD3cTWGnclhNvWQQ92IU/iKOqTZnVpxeOLVZczi128cA0TMUpkFDfdbsCBQJp8WETOWC8rgLS3TMg3I+mqore4r/GI6dx/mo2ZZldSYXpmq8nrB2w6Q7EncSmpek86B5LYl7pRfxuei46U0uRUCa6FvqfYmmqH/VaelaDthmZY9Z+ASAwmLanhCDxPPO5IrbG+ejKbxSVVFaYKrmncghXO8uVFOtUD6hTIaOXy5JAtZMtek9zoBzvdqYbUcRi4SQiIaRll2MADC2Gtj2OP3ftl0HYFnK+ZiOhzHrio/O/lSngUS+y/mYikU6zkcAWPYaCuve/hRQ3o22I3rtVdj1K2H0+OCFK4DyGgIkWg0sM7e1LGDMifwYXmlu688zanjeVqwkvXOca75eKSKVCVj8A+gevfJrwJnvC26bGencp5Wp3sTHXhGOxJd+HuxOBIB5ziJ20DEEKLQ8ngXGTzK/AwESj4XAalqEEaQGyQ0q7hdjoZyC8y6eMbcVz3/bDq6gLufWMr0r5PxqxjzCmY7DSLdwE0k4C1qSO1P5LJYm7u7EzuDQM4VoyouZpsI+4h3bqNC4QptmxjnGraazn6r+CxFApPvQCCP+PJk6IVD8HtALwF0Cn2LSblneiIxm3ewoBczOx66cfhpxKRzvFka1hYaE06quaZOEJwdq3SAWe/KVaq4Rv0PN/6z0F5PShaqrCs6owrPdKty+sN0uEcbnANNWHPeFtNdL3dEiiTw9d11Xr8m9rHAUqpyPXfugeObJY37TIgLgppFyF2mDctuqCs4onY8+8Vh3L/pFbUAtzkcSAKzOsXTNBQrxOMjR6M+DaBKHZWEekNx2fmFR2odmzSmwKW0vFEJ3FfIenI/GcP2MV4zTCcf+IjbKUH1fuHRTcR347w2xLVO163Zbfw8B3sUzQP+MAhyhfMrb1grr3xlHOIbs4V6+8pWv7JMOXHHFFdi5cyduueUWTExMYM2aNbjvvvvcIjSbN29GSJqMfvGLX0S9Xsdb3vIWz3ZuvfVW/NVf/dU+6SOzHxk/kR4qL/4EOPoS+lkvVT8FQa6FV8rYCcDlX+xMDIPIjZPzsTQJLDQIgOKBWS8qK3imYhHYVgiz7RjitVnYjTJmWlF9zsdQmD576y8BWOZws3jWKWKxA8DKzoQnofkby+qEOAaFiArxsTpND2BTSLB4uJsqg8uDy6DE/XJuQNN144ZpaCosy30ULzGja8RZ6avN6B1LiQL93uS2kwdG9VnaH9V1HU0AReF8NISj+MPutBPKDDmudKFI7vacgUDdIEALZ2Ft2vyZltUJrWkbcoPK+xGJm8XnSJLCVgH9texWuJ0yO2Qtq7P6HuSQFduc3UaDO9O+5OcDsICpFyivnym8XlBYRPvVbgUvgJjysCX7aL/b7e59Ng3EnInAp9/2WnzoW79Cq20jHLJQqZTQQgjZFA0GxcLITKUBDBbob6tT9HxzBsczzShySXJRUvEYSRxcfBbw089R8bHsKA3iMyMo1WsUpg2gkIphquyEd4lJVGUKSBRQnm1iXqEzME3Gwqg0pO3nxuk98+x3gGgKrSTdq3sVdr0viaUotHfyeXIRBnHi20m8Xvoaczu/47U2o7zeTlvSj40bJ1FttGDXZmElC2jXishn935x2UhmWBIf9+jv1VdCLE2h18/fT1ELQSw9l8Yba64ObhuJU7h9L46G9BCdR4D2ceQ4c/vsSKeAECyzICvexe57W/M+jmfoedSo0DM7N6bfphzeVi8CBU2KmZgU4mgan8WztC/uwqainWV1nu0mgdRTYM0gmIh8jkFChei7yQ0qxNhGybz4LcYIYvKuGhup3Hm6IhfiPtWG1QrxUYixveZ81DgpRYVwsU2t87He2Y4VUk+so353nsH5aLc67zOdiC0LLLq0IPKiKKB3PortuWlUNIt00YRXGPVf45EEiYjiva07T6qCM6aclZ7CGyoRRhrLCdegyrXlDxdVLdgKp1ZQbjp/nklA4/5T7YNiIVse85tymgMUydVuOlENeX16AXe70j63DMKmfP+76aYU4zXxM7/rzv9c9ocP64pdyveGztEo8iDWJYFP1Q5wxG3p3KgMJP6wa9294Xcq6nI+it8BahHQ3Z5P6NOFzMfS3ntElbLI72pUPdujSXTlcwwKu3YXUUz50ac63+tSSADO9VmhaJFwFG4qhn2lSRzi7OOl/t648cYb8eKLL6JWq+Hhhx/G2rVr3d89+OCDHuHzhRdegG3bXf9YeDxMCEcob9SmH3Z+FhTiu79YcFpwAQBBdpzcNeVdQNogPrqDVv0AOBSysKUcAWozaFaLqCKOgYzmYQlQov+XHqEXt0mksCwn/HMbfV+dpgGlSWATifyr08H5JCt7KC9WsqB3s8lWdc1k2O2rEKmCREUxQAsSrV2nX0CIrKimWy/qBz6A87ItmkVUkUBbDBKUeQbj9NJ3Q5ZNEyMnpLXd6NFdanB5yNeiaT9FbpVaUT2ZBBwnaKpThdhY5VUSqk3582QR0HReU/2dsEid+B5yBHHXxRtQCKI6LYW06FyXCRqY7HYKZpnElGiCJvYivLi0kxyWccP+D62kQe2eF/RtBKYV6aRTEEte5RaY8p8l+9yCJbYNvDhJ11S5VELDiiGbpGeSKEqy4XeTtD9WSHJX09/srofRl6KJayYeQbEq5YkaWEbvgJ/8PTDriFPZEZRrLTfn42Amhl3FWqdfld3O9VNAqd5CKi47H8Ne5yNA4cG/vQ+YeRnNJD2f9zrsel9y6WeAN/5zcH5IAJh/MvDWfzUXnAF6dj5+8s0nYGSIjsnmbTswW20g1KyiUNgH4qMISa5Oza3zESAHbbvRKVJjIpYGLvybYKepINkXHMoNkPgo9rGXYnXZccoVXd5Fzy5VXluBcFpUZwIW+OQUIwHPYk/4sMnZnuk8P0wF1uR3rNi+7nNrRXr+6ArURaUQYlNIW9xx3AQVpxB9rxf1i23CTaUqXuLvvxhziD74icQdUUEOV9eFXUuuJ2VBERF27TwDja4iZ1tBqV48bktdpIXUr3BcPbHutaCIP9ektoCQ5LhsmkIkM95jC2jEDkkA0uZ89Lmx/GNTMR4VEQum8O12w1uYxngsfC42Zb5Ep1/tJgBbfX34xRrVmE7kfOzJ+egXHxVivF+YAtSiuHyeXAFOJz46z0shxveax1u0BdRiUTQNCjOWcr6r7tlwhPrmEb40DnY556ArGCpcjUIs1LmHLctZNPA5H5XPOp9gqEpdFHIipxq+e0N5XnzCdlB6gHrZ4Hr3OVGNzzufO9v//PfnOlWJj7KrVN6W3wXqER+dxRTdMyXZ7y0MacqDLKeaAswRAczBIT4yjIcl55DzUbxEnIIDhxR9i4Bdz9GqiSn0WeRtMoRD/e2bV6GIDBqzu9CqllAOZ1wHkPaza7PmhP8COZSzOk0PUJ1QCDiJ/CeA4k6qyqrdrz568Bd3mEWYZL/jLKg4L50AUUmEcpsEUjkHnGmi5YqZAeKjK44ZxFEAbhL+ukGUExVKa7Mkypjy14g8iNoJYM4RxYImdk6OnVaDBjzaiaIzWAgSAeOSGGssiJCnEMJW3SxmClGkOmV26orzYBJQxfZ2b6IBhcmd5F4nxYDwxnxH9AbM116y0BEHg8SU8ZMchzLIfZweMt97mWG6l0RKBROm3J5iQC+KfXj+zpDTxjn+gxkafN36X08BACqVEmpWzK1ELZitNmh/UoMdh5szGN3RiKMvRZOmtN/5CADnfIjSb/zif9O5judQqjfdcOr+dAzFapOK2mRH6boobqew61rTE3adiUcwW23AlqtwLl9P+/rc91DLUeXkg8b5CFABlPknz+02M8P07BZUZ7T3+aeuPgNthPB3//VzvOHzP0HCrqIvX5jb/uTGgemX6P/FHeZFulfC6itJsD/qornd7t4gnIy2DZT39CA+jgDFid6ESlHMSYyPdA6LLpefKRpAmjCa0pbEsnT/tFtOJILB+VibkYp6mcK4S53Js1K8kwQhk5gp3p0mt6II9Q56x4r3tclFCXQm7qbiZcKdJ8LQ2w2NEBWXwqk1KVD8VZubBhGtJ7eo/Jm6EG7JHdnShD8DHfesnM9YlxtSfJ7bTufelI6H7nNlYaFRAVXMVYXSSsK5ThCJpb2CiGqhxpN6wOB8dPsjPk8jUgJe8URbsKSXYh8+15nO+diqdUJFdUKJsop0tfu6dIvzScdNlWrCny8WMISyFuirEB9NqSvctDdT3m2rruVQqLNIETSu8xc70Qn9HvFRpOhRuEPl6x3Qi9YN6b61wrQ4reqbLPSLStGqz5UFd1Xf5II9unD4UJgcv/Jzx+Qu9/RN01ZVcEaVbqDL+ei/N3xt2u3udrEM9VkUGtK5TwXOgruLrhgXIKUYmnL6aBiDMyw+Mgchi86gB+/Lj9H35cm5n5jsa4aPpUkHbHIi6hChcAZ32IK+FEpWCpWdm9Bst5HI9CFiyk82fpLTh2OC+ykcmkCw6ASQU3J2K7k5TMUQxMRp12/Njhzxu6kt9NUk7okXQVDYtZsLq8ewa1OBGIDcruVJEjiC9kU4+LRhaI7T0xTqCziTHicfoSnMtzajXu2UcXNWBgyyxGChOm0WAVPONRskAsdznfBnkxCXGiAhzAmb1SIm27WZgPM1SM7HRN4c8uCKjwH5S+M5up7qZRp46UI0ADpuk8/DreZrYt7JVKW41XTEF8P9BNC+DK0Edj5jbgfocx0BdP7CcXX+SFM1SUd8TMbCOGVxH17eU8GmXSXUqiW0QnFY0rFef+wI2kLr87urQxHsrITQl6bjmIn7Cs4AVPjj5GupkMrACsCyKJejk25CCJdT5Xqn+MjOZ4DMMEr1FtKxzkC9Lx1Ds2V7PyORAxafDQCoDJAzLnowOR/3BYWFdD8KUcDw3EvEohgYGkHepklnGmUkM4W57U//MhKMK1N0LfaSD3NvGF1F+Sf7Fs3tdveGviX0nNzzAk16ggTF/EJgz4skEgeNeYSrshTw7Eg47uPypNmVD3RyrAHmd49cJMYoUjqOxsAJvnBIinQkiu0JUVH0Tbd4JxxpDUPqCcuSCskZHJ5iQdFU0RvovK+DFhWFKGQMC5YEQ53YEo7T58iCglK4S5CQ0Grq3Vj+zzTlmfS4FHVhuikK829WnUrsmrGtW+Vc5N3TOR8lccKUS9kjajkTf9X7Xw4H1TkWu5yPCvFRnHPAEE7ty4OoE5NDYcpr7y+8oSw4I/ZRiMmK8yC72AD1WE2MtaZfcsY1OgGm4A09bTWdSBuFMOU/bqr9lc9TkPMxHHHSqvTgfBRjc9G2l/GuGP+L71X4i500NIsVsrCoOzcxWXw0CF/ycRTXltJlrDjPquvek5JACKN+Udgnyot+dO2nlMdUFX7vbk8KrwcMqSF8Ydeqcyw7fkX//H3rKnSlcD+7BYTE9WcIaQc6czrT5wpElIonjyiLjzoO89E2c0iSyAHzTwWe+x59X9xOro1DicGjOv83ClaS60sTYjeQiaNkpdHYvRnNto1sIWBSsvgs4Ny/AE68JrifsvOxvNssOgFAbh5V3C3tUOaodMmO0eB44gma0OgQotTu3znfGz7fLWITNIESDjmDIwOgkNDqdA+i4iCFwQVVZBUOr8oe/cTRdQ0GhA7LbjuT+OgJB9atCOc6IdyAeTBWnXHyrxlEaE+Yq8ExGM8Ce5wQZNPxTTvHt1fnY1BxCuGmDQpDFYVXgvLNiZyCQdVqARLzd/yG+heU62XxWTRA2fKz3p9xw8cAE782txGuGt1k2U23sNX7czFRNOaKpIHY31y+CgDwzq/8HNsnp2CHvZ81kI5hUoRFe9zVU0Aijz2Vpi/s2ic+AsDJ1wEL1wGnUPG52WrTdVf2O8LlnnKD7jvLGcrk5qFc91a7Fm3dHJGCsz4InP9RFAdoX6Im1+nhQGEhDciF47U2a7x/M4Vh/M1F8xCxG4jbteCFqb1lYBkAC9i8gZxUuXlzu/2DgcIicqy88CP6PjNqbj+8kp4zL/zIvGgJkDu2PEnCZnZE3y4Upuebu3BnSqsi5QU1hWh7QrlNImWu08ZU1E0k9jc5JBOO+NhudxbvVM/YRN7ZVkA6jXgWboE47Ts218lbbAq7FqJtfVbfL6Azia4b3tmyw1AXlSEKRNQc8cuUPxJwiuaYBM+41yGpLTgjOR9VVafFPgK0j40KtDm1xc/EOdel/PCESZty/slh16Z2krtX62JLegURlUtcCNMA9Lk0hfNRcuyaiu8JwcS21UJrLEPbarfN59MfKq26xsWzf/ol+r/umk0UvBWng4RzT/4+xWJmLN1xn7n5JjXiD+BNF1Kb1b+H4jl61pZ94qMxBZLkyu5F1Ab0CwLyNSP2S1WUp5eiSHIeRJPbTplXUbG/csGWRsUphOK7f2Xno0kUlqtTm9x9Xc5HnYvb73xUhV1LArAufYQ/16zKlOHPuyuiK3XPMn/YtWnuGY7SZ1WlZzJXutZymI+2mUOWla8Dnv8+PeSKO8whvgcjkRjwuk8Bv/d5swiRGqAJeUPvigiHLLRiOVjTW9Bs2ejrDxAfQ2HghLcGiy8AMLiCQlRbDXJqZg3J6AGaTO3eSA/vnMGpEo5SNdOZrWZHSyxDA4Zdz5KAYHKGiAF+eXeAoFlwRKo9ZjEr7eSvrASIrqkBOj8zL5s/Nz1ITk+7rd8PkacrUBh1hECTizKRp0mAGJjpJkfiBRo0yEoP0Ut9ektAqLwjQpV2mR036UFKPRCOBUx4B6kwU5AIKJI/B4UkClE8SMzIjNCzpbTTLKQLIThIfAZIaGvVg12MgOO+OxN49rvA5Mbe0iTMO5nETXmA78c0OZD76RcfmzUn1MbgLHCutVgkhE++maosP/67Cdi+QdZgJo5dRSefTm6e4wKH667eU6p7w679zkeABqVv+Dyw+Cxsn6liptLA0iG6jlKxMGKREHaX6jQRz5D40u5b0lXtupAkkXOyVPcdgzHguMvRbDsfFz6Iwq73BfkFoCJHL9L3QYsMqX5Yld1Io0yu1rkWH6NJWqT75b/RIH2unY8HA+EIvWOfuoee40Hv5Ny8jmNcXsBUtnWO18uPBouaqX56fzUr5mdYokDXhalqM9CZgIkCa1qnv4guCFi4ESGz9SJNjJX5zXIAbHTyNJscTTPuQoeWWMZJgWLKbdljKhI5KsMkeAoXlcmVJYRA2zaLnnK+t3qQ+FgLLtLjER+DnI8G0cGd4BeDBc9QtHMsdK6imCNiCEGul9zWRlFEEllMhXr81a67nIN5rxNXl6dU7BtgDhv2h+T6KxKLNgDdm40A8cpfJMXfv7gkPhojjgpe56MpnFnOVagr5CLGvSLdkhUyizTSoqebGkqFZTmRZJOd7ZscnSLnZX2WjqtuYcQvPuqEbVlEcwW+qK+N7I4s6RdkZBHXlGpALvwFqMVe8bmu6Olcg13uSDmNgEEUjia816kxXL8X56MkZgJOugyFK7OrmI8iZ6XHdatIzSHu4aCK4wL52gPM7wqgYwgR2zaNwY9wWHxkDk6WvgaABTxzLw2ag0Sxg5FlrwEWrTO3SUkTAUNuyJ2NBEq7J1Cxoxjtm8P8l0MrySU1uZFCvYKO8+jxnf/3LzG3Fb8vLNS3EQOGnc/SV1OBnGSBRLSgMHwhUgWFhqeHHfFxj1lUFEJXbdY8cUsPdQQd3efGnVxZpZ0BeRUdx0V5t0HIlAaP4bg+HDjVT4KYyLtnEh+B4LDrZD+5bdoNcz7T7BjtZ2rALMDnxoCZlxzx2+DgSRZoUDTzsnkSP7CCvuYDxMes4/4r7TTvh3ADlXcHiwfiWu811PPYy6nwycQTvRXGGF9DX7f9St/GFI4lyCrEx6CCOplhT5j/aUv6EY+GEbNrsHwT2oGM7Hyc1wm/F+JjuSM+ZuIRzKicjxLbpmmfFvbTYM6yLBRSUQq7BoD1fwWc8T5UYwXAhluYBgAi4RDyqSh2zdagot4i9TF8MOV83BdEYnRdirD9UsCiniOofPEtyzGaT8y9+AgAK9ZTf+afoq6ceziw5FwSfEeOC3ZDWxaw5vfp+e+kBdCS6qfJzdRmfVVqt+0ALQQB5nenCDMToWO6iX4sA8By3H5lc7t2I/iZLVzooviLztEIdNxKplzIPQmGheBIinjeEShnggvs1WYoF7ZpLCHyx4kJquq4iXxvrbpTfMeUF9o5T3VN3mI3N6QkVildjTGaLLdb9FX1DojEO+8WoyvW+XmjbBZZ5dB3kZ9N1U60aVZBLkqd+JjyCpkm125QsaRoqjvno//8e5yPmuMRSQKwpH5p3F+A1/HqCnx+EUaEjZbN4qPIoSpSbKjEelF0b3pLQBROwbl+JDFM1TfxM387lfMR8Lqmgwwa5UnaF0OeYmoriY+BucH7pVQUPRSwFGjdspJb0eiOlARKbeoCqV29rDcqdIVdFzVOWDnsWnMNyqK8KaeuPw2CtlCRsz1xDerSYMj7KrbZlfPRn88V3Z+rqogt/tZtI117QG85H6vTnRyRvdYbAIKvvyMcFh+Zg5NYClhzNfDgbfSCH155oHu0b5AdCwY3VzVJwtB2u4CxwhzmkUj1k2to+5MkAOUCxMdEHjjm94ATrgyeLC47n74uOM3cLj+PCm8E5bjKjlLuuNKugCI+fTSoCBIpM8PkfAsSleRwWNMkT4h34bjZrQhQXsAgt11lisRWrfhYoK97NgVM7ERo+0YnNEUj8MrCqsk1KA/wTMdXXEsZg6AIdPKizWwlUUy7Pcfp0yibnY/zTgYWng4cdbH5c3PjJHiWdpn3Qwh1vTgfF6ylr6MnmNsJFp9F//qWdP7WRDRJIsaWh/VtTJNMgVzsw/93utXavHPtS7ki//oNxyGJKmzfYHYwE8dstYlas0WiamknVa4u7YKd7MdMtYm8E3Y9kk9g61QFbTdJZDelWhOpeNgjEGYTUcwKx+T8U4CTr3WrWqd8xW8W9qeweXcZKqqNFmKRkCdn5WHL2Boq5NOs0SDZFOqfIsf0aKxKxXhMRZleKWuuBtbdAJz9obnf9sHCcW+i59FJ1/bW/tR3Ae/6n+CFDssCRpzFwNFV5rbJPoouAILToFSnOqH5uj6EwtR2ajMAWy/eief05PO9pZ8x5f0V79TqTIBgmHUW23aYhQqRM9P0XJfzIJuuf7FYN/m8+V0iQoNrs/QsV1UzjyZJKOglTYoQC3QpUOQiII0qqAiLytXoFHUxfWYkQZERtq12J8nbAuApIBQkBDbK0F5HsQy8ua0NjktZfOzF+VibJYG5ax984cNK56Ocg9QQHi/nhjSFXcs58nTvYvGebZQ6TlHVeC6WJuekRwxTfG4iT+/zIOcj0HE/ij4q8x4mu0UiVeVi0acgMQeg+3R2whHHG8F54eWctab0TOlBuv+DUlHI+QZbDepDkLCoyyMrF0UxuXNFtA3Q+zUjtql04Er7oHNHekR5U0i/5DAMchjbLZ9bWuO69ofqK3M+SsdN1bdoujt8G/D2LxyjSDvxu2bNKeajmRMJc5Acyq9b/ACc/PU76f8BaW2OdFh8ZA5e1lxFE97hYzsT38MN+UFmKGRx4RkkTOy2s5hXmOM8EgvWOg7TrcDg0cHt198KnPvh4HbHvRH4k58E57IrLKLJgskhCVAy/uIEvdCMopcT5tmqm0XK9BBNtGZeNgtkoXDnRWcS5QaW09dwVL+SKwTK7U+ZXZkiJLhsmBjFMhSysueF4JAwANgVMDGSc96Z9lM+T6YwaXE8ghyI8r09sEzfTp68mp4H8Qzwhi8AQwHXcm6cJrylHeZrLztKA8Hpl8wTd4D29Q+/R9d+L1gWcNnfA1d/y1zIRmbJucDGH3RWlP0E5QAFKBR0z6ZOzhvAnIcMoOsomiSnhMPC/hSSdhWtiPdvBjK0L7tLdXJAR1PAtseB2a1oZcbQbttIRmnAt3I0i2qjhU2TJeiYrTaRiXsn6tl4BLM+x2S5RuJj2td28UAaL2i2X6m3utoftoyvofMgXNBG8VEUgtpDEz6VUPJqicSBU/4w2Ll3KJMeoOdR0CKcwLLMEQAyZ9xIYuVIgPiYn09CYSytd6oA9Hyz25SD2QoHuIwGOoKmbjFIvG9EZINpW+Xd5vzLsTT1SRR20QkG4r1aC3JJDXbER1N0gd2msVHQsQDoeJjGHGISXZtRi15AJzpCCFY6kU84H4UjTCWOuuJjmYSFeEY9NonE6TNNjqdogo5Fq2EWREIhKYy+BGX1XXcfRNEfw+eKvHa6QhkCOTehzgEGeMNLdcXmZCFTpCDwCwlyFWjT8RCF8tx2htzccvEdwOAaLOtD7YHOveGG2mpcWEJANQnr/iq+tVkScVTPkbjkxBOf3RV2LeeLNYjYgvx8uv/EuTUJOnJ+SFMhKcDJ5T4ZLBLJuUR1+wR4XZ86UVUsZqgqMXvaOSHh7n7oUi/4cj7qXJmxlCQYao65x/lYpGtL9R6S3X3GNAhSag7AEK6e9LqMGyVFSHVaynWqCfuXc8OKffCHtVuWV8jUpZgQCHNQcaKzTZObMTtKi+wARa6ZhPIjHBYfmYOXeBb4/buAt34lOFzpUOZtX6V9NJAeo1DSbdYoRvNzXEHr6NdREYt4Nliw2VtMEx2ByG0VlONKnqCa8oPle2xXkEJjg4TPy/4euOAW86QwM0yhcme8V98mkevkQjGJj9lRGnCZ3JuhEA0EJjeaXSWROL0wd/02WDxbfSWJ/abBmMhNGEnqq3UD5Mo57o3Amj8wf2YkBiw9j1IAmMQQy+oIqdmAHGe9IAvt/QbRU6Qi2Pa42a0qSAeEmavYm/bL15MAuPNZ9e/diquGQdLg0VQFVRR6AoInd5ZF95bkmBzJJXDukiRWLfU6VkWRl8line6ZJecAv/1vYGoLaik6nsKdOJyNIxUP4+U9+jyWpVq3+JhLRjFbbXjb1ZuebQuWDqWxcWcRKvw5Ig9r5p9GYssz/48mkKZFl9x8WpiZnQjOKcgcGEaOA05/t/k5DHSK1wQt4orn7/Zf07PW9L5LD1DIvBXSL5BFnCiA6S29OR9NUQ2W1RFLTGKP/A4xOeTTg/TuNOVoFu/L6S3md2IiT/2ZfinA+ZjuhI1r82Q6omRQmhRRpEc4wpTOxzgJto1ycFXyRlkSATXFRAAS4oIcayIk2RRGD3SEQFN4tihapAtFFoh0O4ATnqs5X+K4AfocnYmCEw7e0qcg6FV8FG5iQF8QRHym7JAE1AVLYMGtmB4oPsrOTFURJ+daM4274lm6hlznoynHq+QG1YWFhyN0LbmV5gNCU4WZQDjKelnEAMznBHCcj7uo2KHpOeFx1BoWaGXXZ03juhSLGcLZrBWi8z26Zf1FXXTh3nLOR407Ut5WYE5d6f4xpb8AfNXsA46brn/iezn8X1Vwpt3oLKg3dA5Pn0PadI3EUrQfsxOdgoymXP2Z4c5zu6ZJhcEAAI6Q5X7mkMW0KnG4MHJcYJN54+N4X+JDeDk0hmvn2qWz4FTggltJ3DsQebeOuoiKaBxzmbldZpgs84mCOZ+dPKE2hl1IA46gXJfzTqJ/JiwLuOyz5jYA5VmrzQa47cY6SZ9NAmpunFyUQa6a3HxyZQSJy2f9KQB9+CsAukbO/lOveKsiFAbO/4i5jeCSvyMxLIjf+zwN5OdiMSKaAE64gkIvTNdJqr8z4Ara5/1Bfh4Jtc99T52OQlSCNQkS6QGa5O98pnNNNCqUoN30DOhbTBN2ifmpNpDyunjikTCyiUinyMtxbwTu/mMAQKlwFIAZNy+jZVnoS8WwR+RvVFBUiY+JCKYrXvGxXG8iFLIQj3j3feVoDtumqlToJh3r+psjRnzMjgBjq4Gf/wtdQyZxqW8RXfM7np4bsZ85cIh7PGdIawHQ5DjZB7z0aHAalNQgsOXnwSJlZoSuI2MqjwGKVJh60VzlW7j9qlP6FB1yX0zvzvRgRxTSiafpQRJe7JY5P6pl0ft8x9OUQkNHqp8KBJlySIp3UaD4mANKGzsOKZUzUwi2Im+lbjIczwG1ZzqTcmVOOGfSLcRf07UkcnhallmkFO9VV3xUiSKZzmcCAc7HKaePM/pnlnCyNWt0zamEtHgWbnEjV3zMdrfx5HzU7KdcsMXk9EvkO+e8Uaaxrv9dbFmdsPyg4jvi8wDn3Cv6J+5J0/jXsjrnEzAXVYrnOu3qZWhdr8J9ZhLgBPl5dJ4mnqRjbBqrZceA3z3ofH6A8zE9RAtxsxNm570sCpsEcE+o9Kw+zB2g+zEo7FoWH3Wu61i6u+CMapFHLC4AegeiSONg2/rwbYCeJ7u2d/qmOx/+/Ip1haPR3zdd/8T3bpEiTTEf8ffhvP5ek52sumMhI4ozNipODl6DWJkZlcRHg0jPsPORYQ4F4pEwzjp3Pd607ph98wHH/h4wfuK+2XYQyQLw2o8FT3YA4A/+E3jLl81tQiHgotuASz8TvL3Lv0jteg1zmwuGHIfnsOFcysV8TJM24WQJcrQIoTNo8hkK9XYs1vw+VWqeK0Kh3sKOh1dS/rS54tw/Ay74qLmNZXWK2PRSFGZ/cNRFJD62292/CwqjEoweT/n/BKYwLsHIcZQftuvzugegA5l4p8jL/FOA498CLDkHszkKx09Kgl9fKoapcqNrG4JirdkVGl1Q/E2pRi5Gf/7GpUNpJGNhPLZ5D/yUakdQ2DVA+XoBYPkF5nZCuHnxpyw+Hur0L6W8mme+P7ht32Jg8rnghZbMsCPKBbjBxXZMopxYMNz2q+CCOKVdwXmah1YCsMyTP7k/OvExFCYRCAjOh+2+Iwzv9WQ/CVG1mWDH0PTL+mq4QMfBpxPH5O0Fui2zTi7NEjmiVe9iv/homrSL3HtB7UTOOlNl4liaFiYre6hvuvQPyT7ah3ZLH4YO0HFrVjsOOa34CNqOrjhQPN8plhMUdu0KcgaxTQ61DXKp1ssBeTcdx6sQk3SFWkS0kSnyA6B7vLjD6VvAsXUFWUdsUi0Uu1XfDQV4BNkxcle/9Ehw/vDcWMelVpkyF05MDwGwaQHWmEO+0Fu+y4Tv/OlcvABdUw1FJXB3W3lvCgHtNeMU9BKFU3T3W1QKSdZdg7F0Z1vGUG+f81F3LUTilP9eFGxpaAqTievetqWCV/50A1IaAZ1oK/rrhnlrXI2e4jU9jHmzo3RNmRZI5LalXZSqIag40hHOETTiZphDm2tOPwhcVweaoPyBgqMu7K1dr3m45pJz/gw49o3m4iWROHDsG/QJxQUiBFp81TG4Anj++8Gh7Yya19xMK+9BE9D9xVEXAz/5HIWC+x251R5XXBeuAx75Eg36LIsmSEGDpdFVwI//lzesTROiM5ZP4Hk51Pk1NwMAKi/ToFrkfASAvlR0r52PA5kY5ZSU0LkYo+EQVgxnsGlXd97HYq2J7JEkPh51IT0PglJNRJMkqEw+N/fpOJj9z5qrems3dDQVgBtcbm4n0laYnIoAcMzrKRx5zFCEK9nXEWlM28uMkLMkqJjcZZ/rpJHQIT4nFZAmo7CQ7gHTIiAArLsRWHSmeYwih5fr+h9NknA0tdksniQKJMgJwUMnAiTydFyNjjUR/mwQ0aJpAJYU9m5wlQnxMZIwv4viGWBqi/lzxd/PTpgn/skCANsRWgNcngC5mQB17k25snp1hvbDn0IonnE+b5oEE204eAHY8Qz931SB3VMp1yBOR538fSbno6gkLpxi7Yb6c1deSmPNIONBVnZ0BTkf5XBhncDu9K2XsOtInPKHb/ohpTUy9nOMiiKVJ+n6My0W5+bRwkJ50ixqyuelOk0LAjpxXg7DV43v5by1vTgfbTs4pB8gcTQ7og+79oeOq8TWuCTwGc9drtM3U9i1ZTlF6/Z0xEplioOcI3pWOyHTXXlCJedjXSPMi/FobQbAPLNI6boxDQ5PQXaUFoJEISPTold+PgCb0hlVp4KLxh3BsPORYRhmf5LIAfNPDm53wS3kzDNx7OXAqrcBi84Ibrf6KqqszOw9/UvJHXywkBkG5p8K/Pa+7t/Ve8w1s+hMyne06zn6vjplnuwCwNAxNKDb8oj38xQDuNULCnh620zXzyuNFsIhCzEpNFrlYpQpKgrOqEK1S7UW0jG1kDi/L4WvP7y5K1Rb5ao87Olf0pvDeeWldL4XBjxfmMOH495EiwxHXWxuJ8TEIOFiyTnAVV83p9CxrI4r37RAlh2j/G/l3QH5HAeCxfVQGLj008EREutvBV7718EpgNIDwIr15japfppo7/6d3jEqQqWnNpsXKDMjQHEn5cIzhb57wq4N4mNtxiwGhUKOs23GHJYJOG6xPeZiPkBHyDBWLxdO0C3mAnfid5Ups+tI9EfkO1Z9rhCAhfNRtS3xMyFimnI5VvaQG6pe1O9DsuAVH3Vh+UI8qQVU3nXFaY1zE6D9POay4JyxmVG67wDz4qYQsYPaidDXoOtIMLaGvppcxUDnGTLzMj0jTPdPONI5hwOGhZZEoZNrsDqtHyMlCiSytVv6cGTL6oRnG92ykiBnEgJd8dFx8VZn1NeXHNqsq3Ytro96QE7dRL6Ta9ZuBVdKL+82u7Pd3JCz+pyakQS5X0XuWtXxiGU69yywF2HXAc7H3Hx69pR3k1htGltnRmgfX/xJ53tGCYuPDMMwhyrpAeC8Pw9+gaYHgHM+FNyOOXQ4+mIKvW76HIOVqd7CPXJjJKqKgZLJlSGIxEiI2vQgfd9uaSeYx47lsHmyjO0zVc/Py/WWJ+QaAOYVkti8uwwdKoGwPx1DsdpErdmStt3s2rZgNE/OlZ9v2u35+Wy1iUziCBMfe+XEPwDedT+5Kpgjg4FlVAAvKEVHZhh41/8Ax795bj73NX9JxdqMuZBHKUyyVQsO9+6FpedR+gkTw8eQCD8XyMKXschHDtizKVh8bNUol7NpkuuGXZsca5mOuGLM0SgK/gQIX8L5WNlj3gfhBK0anH7JAgCLjpnRCeq8uyp7zEJrIk8h3jufpjYqUVkWiXQuSuEeE4XfdPspilAE5Rf1O8qCwq6DqrmnnWrO4nNfTQhodqQTdm1apPSEjhv6J8Tu6nRvC6Wr3kIC5NGvM7eLJim1wcxWZ1wS4Dxb8/uUssgUOST2tTpt7q8r3hX1OR+BzmKAKTWAey1Pma/lWIauZeHK012rIlTatvXh+m7Y8iz901biznbaAAH50gdIGBVtVYK6nOLALXjl26ZlSaH6GuejW5BMFLjRFdaR818a3MOC/qXkzp7dFuyStyxaPNv4AABrbt5RhyksPjIMwzDMocay80l4FOKhoLzLW0zJxKIz9058BIBlrwE2/YgGbpUpbbXY5cM0gHzw2R3e7tW6Q6NXjGTw4mQJ1UYLKoq1boFQFI6RHZPlut75eNVpJGpUm97POOLCrvcGyzoyir4xr4xk39wU/wJI5Dv5HeY2hYWdiWyQs/FgJDVA7hzAXOQjO0ZFNkzCnVgQmPh1QNhorgfhxHnuB4V6x7OdMMogUbGX3JxCIKsY2oXC9LtdvzU7H8MR6tPUZnJl6d5llkWfu+MZszgg8mBWpzWVxGP0eUHiY3aUBDvhkNQ65/Idt5spl14i33E0mt7Xyf6OsGuFX13xC9n5WNyhL74knI+2bXZvCtG5F4EQoAWRt/xvc2EYQWEBCdWVPebK2AA9b665W59HFPA6ak2CqtjX6rTjzgsoymM6f+K6LO0w74dldYR+N7en4pgn+yhvqsizqRLlIolOntAggdluURoEwFz9OdlPzwBxDeoK9QCUvkBXWR5wHItlffEk8Xdu2L+mmIw/BD1IfBxYTvu79bHertUFa6mAat8iHjsZYPGRYRiGYQ414lkKaXz2u96fmyYHfhafBWx7ggaHvToml55Hg95nv0tCJ6DMX5aIhnHakn7sFEVnHCqNllvpWnD0aBYhy8KvnXyQfmYqDeQS3uqG/SkSH+W8jypXpSAcsrBqfr5TBEf8Ta3Z1R+GYQ5CRlfR11Dk0HTxh8KdifPQSn07kVvYJFDGc5R/busvzbmIUwMUml3aqXdbCmFqz6bgMOmpzfR/k7CTm09uv+J2s0iZHiKxbWpzQBj9kCN4FvRtADpeW39J/zcJsulBcoyaxMdEngSR6rRe9MyMkBM3ntMLWKIq+85nKHS0F7ebyRWXHqT3bpBrMC1cpdN0fl/NIkFhIW2nvDv4OrKdAjym/glxurIn+JzuLYNHAb97iASjXnLEBx2XeJaEM1fwDTh/VeEy1oXh5zuLAbrxVjRJnzu5kURDo9DvCHz1Iu2zMkVAgb6KhQOVy9hyCnTVS2bHsnhWzLzs/V7XN/GZumsw5IiStVknv2pSfS/FUsH5KEV+WyAg/+VehF2n+mm7Gx/s3MsmVjj1BhauC257BMPiI8MwDMMciqy8lBKxiwkhEFyMQWZsNa3ObnmYJhW9iJbRJHDcG4HHv06Ty1BUO8k4aiSLX700Ddu2O92rtzzFZgAgFYvg+Hl5/PyF3f5NAACmKw3kkt4BqRAZX5CKyJQUhWlkfv3SNL664UWc/6kH3XDwUr2JdHw/VrtnGOaVEU0C590MvP6zB7onr5wLbgVOvV4vYgAd0bHfUCHcsjqFoILyZE6/TGKPTpBLD5IwtvNZs6iY6gcmn6f/mwSR/HwSoaa3mAv1uKHLvzV/rhC7TAIlQJ+19ZckFpnegeI9Zwx9z3bCY3XnKj0E7Hja7IhKDZBY/vJj9Lm63JxCCC1u14eWAnTMSjvN+QeBjvOxbChu1Cv9S+iY7nyWhC6daCtXRDcdt8wwOSmDHLSvhMGjgN0bqb8m8b5XLEtyak7pBcNIohMCXZ3SX6siH6rJGQo4wrbjqjVdX8kCOYdFuLuqf25uyD3m60bkdK1M6c+L+Pmu3zppCxTFd9y2/Z2cj6Z9TeQ7oq0p12mjFOAKlsP+Ne08OR81FbFlLIvyGjcrwYU9AXLe/uH3gLM+GNz2CIbFR4ZhGIY5FFl8FhV+eOhvnXw+FZpkZkd7+/twFFh4OvDCj2gy0Gs17zV/QO0f/idyRWjcA+ccNYiNO4r42sMdcbSicSeeurgfv3hhT9fPq40W6s12l/MRALLJKP7rV1vd71Xh2TJXn77I/f9V//wzFGtNlOvdTkyGYQ5SVr0FWHQIu0qWnguc/ifmNiteSznuFpwWvC3A7LLJjtLk327pxbZQmEStVt0sdGSGSRCxwgGFF6TP6Vukb5fqp20VJ8wCmRCRgiqr58bIkZUeMhfUGlzhfDWItqn+Tki4zvlYWEjH1fS+DYWAviW0SGh6v0YT9DnF7WaRKz0IlAKEMNGuPEnbe7W55yJx2tffPQjAdqr6KhCilpt3U+d8dARUYO7Fx0Vn0tdei6r1QtYJOy/vUleKBjoi5e7fkfCuE9Mzo8CeF+heCypesuMp+n8veVNNIcvRBC3czGyjPLFBuTgrU/prPp4hAW/7U/pj4fatn+6foOI/veSljTqOxVpAcaqaLD4qtiUclACJ/EFh10An52+vBTvTA3N37R2msPjIMAzDMIcilgWc9xfAS78Anvs+DWphA/3Let/G4rOAZ/4fDdZymkmFn/QAcMLbKJTMUIFy8QCtKj+/o+j+jMKuVeJjH17YVcKvtkx5fj5TpZyOhVS3+Pj20xehXO/kcCzWmkhrwq4B4J1nLcFdf7wO56+kyfGLkyWUjsRq1wzDHLwUFlKOuyBh5sRrgD96yOzgy0u5MU1OsIwjUJlCCzMjnRy/pgrJoTClBEkUqKCHqZ0QJwsGkXL+KfR1JKA40Mhx9LV/sbndsvPpHWkSEzKjlMKkuEN/fMW7L0gUHT6GRJ+g93J2lHLplXbqBcP0EOUCbFYCQsuHqe8z2+am6u7IscCT/0HCk257sQyFzZZ2msVR+e/nwp0okx0BLvwbYP3H5nCbznkp7jAXX8sMkxMW0Iv4uTEaN4n2OvLzaVvpYVok1vZt3CmwM0VCvs7Jl+xzxofQn5fsOLDrObq2TIsQ2VESH4MctelhEh6nN9P9pMMtYmMQrGNpEh51YeNAJ78toE9dEMt0BEqTO1dm2fnAH/8ouDgZ0zMsPjIMwzDMoUr/UuCka4EffQrYvIEG9nuTR2mRNAEbWN773538DuDYy4GT3q5tEgmH8LpVY4iEOs5IVdg1ACwbogHlj5/f5fm5KCiTT3YPwIey8a6cj0FC4lA2jr983TGwLGDL7jJsG8ZQbYZhmIMSyzJXmwVooSiWJvedqQCCEIQGDAKZEIp0zjeZS/4OuPa/gnPq9Tmh5SKEXMXS11AoY1DBkfmnkbCy4iJzu4FlwNX/HlBZfYTcbibxccFaYGAFcNQl5s9b8Vr6GuScyowA0y+RW0wnPsp9NlWk71tMrsyXft57JISJZRfQ1wVr9edUFPMp7aQIDN0+yG7YfZG79ehLgGFDTtW9JTtKAl9xh1lEy45SsRFYeoFPTkNgcqSK+yGosFZ+HqVVKG6n61R3bpJ9lNMVMDh5F9D14u+nn+wo5WoNEh/7FgGwgZd/aRZa5VB0nfNR5DotT+pD2hMFX9i1YlupAXI+NmtmsdOPKn8k84rhETfDMAzDHMqc8ofAb+8DNnwBWH3l3v1tegA44300QTVVffSTyAMXfDSwWSEVxdPbqu73lXrTrVQtEwpZOH5eHv/n0ZfwzrOWIOEIlBt3FDGQiSGrCLvuT8dQqjVRbbSQiIbJ+diDkBgKWVjQn8KvXqJV8rFCIvBvGIZhDkmuuYdyOppY+yfkUjS5+MZOoK8mgVIQjvT2Pjn7Tyms0SRkWBa9p4JI5IB3/o/ZldkrcoVnXS7kVD/w+98M3taiM4A//O9gsSY7RlV1gY4TVdVGYBJOsqPkQqzN9JarLogl5wAXfxIYP8ncLjNMocfNil5wCkeBY37PLEgdTOQXAI99lf5vCp3PjADPfY/OkS7sVnYCm0Jz559KX8fXBPRtPjlhpzYHOGGHKGVCKKJ3Dubnk7gXSZpd1yJCJsjJmxogcbk4EVwAamYrhUCb8qtu+5UjzGvuI1HtullzwssV2xLCZXGHk2OyYN4HZp/A4iPDMAzDHMpEE8Dr/xew8YG9Fx8B4ORr575PDoVUDM/vmHW/L9ZaWNCvHnqcuLCAJ1+exs7ZGhb000rz9pkqxvJqd0S/I2LuKdcxlk8GFpyRycQj+O8nJwBAmU+SYRjmsMAUQikYWAac+T5zm2Qf8Pv/PjdOOkF2xBzKurfMhfAIeCslBzkue6GXoi8Dy4AnvknhszqxxrKAc/+chCuTq9SygPHVwOafUf7QV4tldRycJtJDwMSv6f8mwWn9ra++T/sLEc4PmFPTCEewKdVAdoTOx7wAETc3Dlx7b7BAK/qz9ZfmEPb8AsrZmZunv0eGHLdo3yLztbVwLV2n4yea+2ZZdA3secGcXzU9DGx9nJyKo6s0bYYoJFykfVCRLFCxn5oz3tQ5H4HgEHRmn8Jh1wzDMAxzqDOwDDjten3YygFivJDAbLWJqTKFR5tyLL593WJYFrBjtub+bFexjqGsOlywPx0DLGD7TA2NVhuVeqtn8fFPzqVVe5ULk2EYhlEwsCy4QuzhgCsgWfvvnSpErnjGHCJ/wtuA498cvL3Xfhx4y5d7c43OFYVFFHocSR4+rrKB5SQsLrvALG4LMS4o5PvN/wKc/p7gz80ZHJSC9CAd54knzCHaQhg1CZRDK4Hj3kguaBOLzwau/XawKxMAVr2NPnPkWH2bzLATqm/IdZoZpjQCgF58TA9R+HZ5kr6PK4TFWIrcmLt/R9/3kvORmXPY+cgwDMMwzD5h7RIaKL7pH3+K/3rvWSjVm8qCMwAQDlnIJaOYKtdRbbTwzMQsds7WsHhQPdlNRMOYV0hi444iRnMUOq0TKv0cPy+Pf7rmZIwV9kHOKYZhGObQxbKA3/u8WQScawaPonyVC9fOzfZS/b25XucSOVfhXLlQDzShMDl+QwEREgPL6JoR6Ql0BOVB3RssC5h3MrDxfnNBFOEoNIXgWxZw/kd6+0xTvlGZ1VfQPxOiME2tqHcIyzkogwoZiYI+OmExNUgCuRXSVyVn9iksPjIMwzAMs08Ihyz82cVH4/b7nsU9v3wZ5QB3Yl8qhgee2YH/7/9R1cjBbBynLtEPEI8bz+MXL+5xBcrBTO+TxRUjB5dLlGEYhjlIWLRu/36eZQEXf2L/fuZcM/8Ucrodc9mB7snc0qsIvb+vGQBY9x4Sexecrm8zeBRw3s1UvOlgo28xAJvyNOoK+gixc/QEvXgrXJPbf0Oioq5KuCiskxrcuzznzJxxUCxLfOELX8DixYuRSCSwdu1aPPLII8b23/rWt7By5UokEgmsWrUK3/nOd/ZTTxmGYRiG2RsuPn4Mpy8bwJd/vAl7SnVjUZh8MooNGyfd73fN1oxuxrVL+vHw7ybx4W/9CkeNZBGLHBTDGoZhGIY5soilKSR3zVUHuidHDn2LgTNuNAtplgWsesv+DcHvFblPOmemZQHv+A7lNtcRiVFI9vYnzZXE+xZTYZpe3ZvMnHPAR+l33XUXbrrpJtx666147LHHsHr1alx00UXYsWOHsv1Pf/pTXHXVVXjnO9+JX/7yl7j88stx+eWX48knn9zPPWcYhmEYphf++JzOoDId0w+Sx/IUPn3q4v6un6k47+ghZJO0wn3SosKr7CXDMAzDMK+YuQwrZo4Mznw/sPoqKp6oIztCRWVMpIco7Noksg47+SeDcnMy+4wDLj5+5jOfwfXXX4/rrrsOxx57LO644w6kUil8+ctfVrb/+7//e1x88cX48Ic/jGOOOQYf//jHcdJJJ+Hzn//8fu45wzAMwzC9sGggjXXLaECYjuuTqF90HIXd3PCa5e7PFjqVr1VYloUbzqPiMdHwAR/SMAzDMAzDML1y0tuBcz706rcjqmqbiu8sPQ9Y+Xrg+Le8+s9jXhEHdKRer9fx6KOPYv369e7PQqEQ1q9fjw0bNij/ZsOGDZ72AHDRRRdp29dqNczMzHj+MQzDMAyzfxl3irvkk/rE7asXFPDAh87DwoGO4JiImis+ClHz6FHO4cgwDMMwDHPEMbaavo4Yiu9Ek8BrPwb0L9k/fWK6OKCZNnft2oVWq4WRkRHPz0dGRvDMM88o/2ZiYkLZfmJiQtn+tttuw8c+9rG56TDDMAzDMK+Iq05biJMX9WGgx6Iw99x4JurNdmC7bCKKBz503qvsHcMwDMMwDHNIcvTrSFxcdsGB7glj4LCPUbr55psxPT3t/tuyZcuB7hLDMAzDHHH0p2M4fWnvCc9zieheVa9mGIZhGIZhjkAiMeCoi7iK9UHOAT07g4ODCIfD2L59u+fn27dvx+ioutz66OjoXrWPx+OIx3nywjAMwzAMwzAMwzAMwzD7mwPqfIzFYjj55JNx//33uz9rt9u4//77sW7dOuXfrFu3ztMeAL7//e9r2zMMwzAMwzAMwzAMwzAMc2A44L7Um266Cddeey1OOeUUnHbaafjsZz+LUqmE6667DgDw9re/HfPmzcNtt90GAHj/+9+Pc889F5/+9Kdx6aWX4pvf/CZ+8Ytf4J//+Z8P5G4wDMMwDMMwDMMwDMMwDOPjgIuPV1xxBXbu3IlbbrkFExMTWLNmDe677z63qMzmzZsRCnUMmmeccQa+/vWv4yMf+Qj+8i//EitWrMA999yD4483VDZiGIZhGIZhGIZhGIZhGGa/Y9m2bR/oTuxPZmZmkM/nMT09jVwud6C7wzAMwzAMwzAMwzAMwzCHHL1qbId9tWuGYRiGYRiGYRiGYRiGYQ4MBzzsen8jjJ4zMzMHuCcMwzAMwzAMwzAMwzAMc2gitLWgoOojTnycnZ0FACxYsOAA94RhGIZhGIZhGIZhGIZhDm1mZ2eRz+e1vz/icj62221s3boV2WwWlmUd6O7sE2ZmZrBgwQJs2bKF81oyzGEA39MMc3jB9zTDHF7wPc0whxd8TzNM79i2jdnZWYyPj3uKRfs54pyPoVAI8+fPP9Dd2C/kcjl+WDLMYQTf0wxzeMH3NMMcXvA9zTCHF3xPM0xvmByPAi44wzAMwzAMwzAMwzAMwzDMPoHFR4ZhGIZhGIZhGIZhGIZh9gksPh6GxONx3HrrrYjH4we6KwzDzAF8TzPM4QXf0wxzeMH3NMMcXvA9zTBzzxFXcIZhGIZhGIZhGIZhGIZhmP0DOx8ZhmEYhmEYhmEYhmEYhtknsPjIMAzDMAzDMAzDMAzDMMw+gcVHhmEYhmEYhmEYhmEYhmH2CSw+MgzDMAzDMAzDMAzDMAyzT2Dx8TDjC1/4AhYvXoxEIoG1a9fikUceOdBdYhimB/7qr/4KlmV5/q1cudL9fbVaxQ033ICBgQFkMhm8+c1vxvbt2w9gjxmGkfnhD3+Iyy67DOPj47AsC/fcc4/n97Zt45ZbbsHY2BiSySTWr1+P5557ztNm9+7duPrqq5HL5VAoFPDOd74TxWJxP+4FwzAyQff1O97xjq5398UXX+xpw/c1wxwc3HbbbTj11FORzWYxPDyMyy+/HM8++6ynTS/j7c2bN+PSSy9FKpXC8PAwPvzhD6PZbO7PXWGYQxIWHw8j7rrrLtx000249dZb8dhjj2H16tW46KKLsGPHjgPdNYZheuC4447Dtm3b3H8//vGP3d998IMfxLe//W1861vfwkMPPYStW7fiTW960wHsLcMwMqVSCatXr8YXvvAF5e9vv/12fO5zn8Mdd9yBhx9+GOl0GhdddBGq1arb5uqrr8ZTTz2F73//+7j33nvxwx/+EH/0R3+0v3aBYRgfQfc1AFx88cWed/c3vvENz+/5vmaYg4OHHnoIN9xwA372s5/h+9//PhqNBi688EKUSiW3TdB4u9Vq4dJLL0W9XsdPf/pT3HnnnfjKV76CW2655UDsEsMcWtjMYcNpp51m33DDDe73rVbLHh8ft2+77bYD2CuGYXrh1ltvtVevXq383dTUlB2NRu1vfetb7s+efvppG4C9YcOG/dRDhmF6BYB99913u9+32217dHTU/ru/+zv3Z1NTU3Y8Hre/8Y1v2LZt27/5zW9sAPbPf/5zt813v/td27Is++WXX95vfWcYRo3/vrZt27722mvtN7zhDdq/4fuaYQ5eduzYYQOwH3roIdu2extvf+c737FDoZA9MTHhtvniF79o53I5u1ar7d8dYJhDDHY+HibU63U8+uijWL9+vfuzUCiE9evXY8OGDQewZwzD9Mpzzz2H8fFxLF26FFdffTU2b94MAHj00UfRaDQ89/fKlSuxcOFCvr8Z5hBg06ZNmJiY8NzD+Xwea9eude/hDRs2oFAo4JRTTnHbrF+/HqFQCA8//PB+7zPDML3x4IMPYnh4GEcffTTe/e53Y3Jy0v0d39cMc/AyPT0NAOjv7wfQ23h7w4YNWLVqFUZGRtw2F110EWZmZvDUU0/tx94zzKEHi4+HCbt27UKr1fI8CAFgZGQEExMTB6hXDMP0ytq1a/GVr3wF9913H774xS9i06ZNOPvsszE7O4uJiQnEYjEUCgXP3/D9zTCHBuI+Nb2jJyYmMDw87Pl9JBJBf38/3+cMc5By8cUX46tf/Sruv/9+/O3f/i0eeughXHLJJWi1WgD4vmaYg5V2u40PfOADOPPMM3H88ccDQE/j7YmJCeW7XPyOYRg9kQPdAYZhGAa45JJL3P+fcMIJWLt2LRYtWoR///d/RzKZPIA9YxiGYRhGxZVXXun+f9WqVTjhhBOwbNkyPPjgg7jgggsOYM8YhjFxww034Mknn/TkV2cYZt/CzsfDhMHBQYTD4a5qXNu3b8fo6OgB6hXDMK+UQqGAo446Cs8//zxGR0dRr9cxNTXlacP3N8McGoj71PSOHh0d7SoQ12w2sXv3br7PGeYQYenSpRgcHMTzzz8PgO9rhjkYufHGG3HvvffiBz/4AebPn+/+vJfx9ujoqPJdLn7HMIweFh8PE2KxGE4++WTcf//97s/a7Tbuv/9+rFu37gD2jGGYV0KxWMTGjRsxNjaGk08+GdFo1HN/P/vss9i8eTPf3wxzCLBkyRKMjo567uGZmRk8/PDD7j28bt06TE1N4dFHH3XbPPDAA2i321i7du1+7zPDMHvPSy+9hMnJSYyNjQHg+5phDiZs28aNN96Iu+++Gw888ACWLFni+X0v4+1169bh17/+tWdR4fvf/z5yuRyOPfbY/bMjDHOIwmHXhxE33XQTrr32Wpxyyik47bTT8NnPfhalUgnXXXfdge4awzABfOhDH8Jll12GRYsWYevWrbj11lsRDodx1VVXIZ/P453vfCduuukm9Pf3I5fL4b3vfS/WrVuH008//UB3nWEY0IKBcDsBVGTm8ccfR39/PxYuXIgPfOAD+Ju/+RusWLECS5YswUc/+lGMj4/j8ssvBwAcc8wxuPjii3H99dfjjjvuQKPRwI033ogrr7wS4+PjB2ivGObIxnRf9/f342Mf+xje/OY3Y3R0FBs3bsSf/dmfYfny5bjooosA8H3NMAcTN9xwA77+9a/j//7f/4tsNuvmaMzn80gmkz2Nty+88EIce+yxuOaaa3D77bdjYmICH/nIR3DDDTcgHo8fyN1jmIOfA11um5lb/uEf/sFeuHChHYvF7NNOO83+2c9+dqC7xDBMD1xxxRX22NiYHYvF7Hnz5tlXXHGF/fzzz7u/r1Qq9nve8x67r6/PTqVS9hvf+EZ727ZtB7DHDMPI/OAHP7ABdP279tprbdu27Xa7bX/0ox+1R0ZG7Hg8bl9wwQX2s88+69nG5OSkfdVVV9mZTMbO5XL2ddddZ8/Ozh6AvWEYxrbN93W5XLYvvPBCe2hoyI5Go/aiRYvs66+/3p6YmPBsg+9rhjk4UN3LAOx//dd/ddv0Mt5+4YUX7EsuucROJpP24OCg/ad/+qd2o9HYz3vDMIcelm3b9v6XPBmGYRiGYRiGYRiGYRiGOdzhnI8MwzAMwzAMwzAMwzAMw+wTWHxkGIZhGIZhGIZhGIZhGGafwOIjwzAMwzAMwzAMwzAMwzD7BBYfGYZhGIZhGIZhGIZhGIbZJ7D4yDAMwzAMwzAMwzAMwzDMPoHFR4ZhGIZhGIZhGIZhGIZh9gksPjIMwzAMwzAMwzAMwzAMs09g8ZFhGIZhGIZhGIZhGIZhmH0Ci48MwzAMwzDMnPCOd7wDl19++QH7/GuuuQaf+MQnemp75ZVX4tOf/vQ+7hHDMAzDMAxj2bZtH+hOMAzDMAzDMAc3lmUZf3/rrbfigx/8IGzbRqFQ2D+dkvjVr36F888/Hy+++CIymUxg+yeffBLnnHMONm3ahHw+vx96yDAMwzAMc2TC4iPDMAzDMAwTyMTEhPv/u+66C7fccgueffZZ92eZTKYn0W9f8a53vQuRSAR33HFHz39z6qmn4h3veAduuOGGfdgzhmEYhmGYIxsOu2YYhmEYhmECGR0ddf/l83lYluX5WSaT6Qq7Pu+88/De974XH/jAB9DX14eRkRF86UtfQqlUwnXXXYdsNovly5fju9/9rueznnzySVxyySXIZDIYGRnBNddcg127dmn71mq18B//8R+47LLLPD//x3/8R6xYsQKJRAIjIyN4y1ve4vn9ZZddhm9+85uv/uAwDMMwDMMwWlh8ZBiGYRiGYfYZd955JwYHB/HII4/gve99L9797nfjrW99K8444ww89thjuPDCC3HNNdegXC4DAKampnD++efjxBNPxC9+8Qvcd9992L59O972trdpP+OJJ57A9PQ0TjnlFPdnv/jFL/C+970Pf/3Xf41nn30W9913H8455xzP35122ml45JFHUKvV9s3OMwzDMAzDMCw+MgzDMAzDMPuO1atX4yMf+QhWrFiBm2++GYlEAoODg7j++uuxYsUK3HLLLZicnMQTTzwBAPj85z+PE088EZ/4xCewcuVKnHjiifjyl7+MH/zgB/jtb3+r/IwXX3wR4XAYw8PD7s82b96MdDqN17/+9Vi0aBFOPPFEvO997/P83fj4OOr1uieknGEYhmEYhplbWHxkGIZhGIZh9hknnHCC+/9wOIyBgQGsWrXK/dnIyAgAYMeOHQCocMwPfvADN4dkJpPBypUrAQAbN25UfkalUkE8HvcUxXnta1+LRYsWYenSpbjmmmvwta99zXVXCpLJJAB0/ZxhGIZhGIaZO1h8ZBiGYRiGYfYZ0WjU871lWZ6fCcGw3W4DAIrFIi677DI8/vjjnn/PPfdcV9i0YHBwEOVyGfV63f1ZNpvFY489hm984xsYGxvDLbfcgtWrV2Nqaspts3v3bgDA0NDQnOwrwzAMwzAM0w2LjwzDMAzDMMxBw0knnYSnnnoKixcvxvLlyz3/0um08m/WrFkDAPjNb37j+XkkEsH69etx++2344knnsALL7yABx54wP39k08+ifnz52NwcHCf7Q/DMAzDMMyRDouPDMMwDMMwzEHDDTfcgN27d+Oqq67Cz3/+c2zcuBH//d//jeuuuw6tVkv5N0NDQzjppJPw4x//2P3Zvffei8997nN4/PHH8eKLL+KrX/0q2u02jj76aLfNj370I1x44YX7fJ8YhmEYhmGOZFh8ZBiGYRiGYQ4axsfH8ZOf/AStVgsXXnghVq1ahQ984AMoFAoIhfRD13e961342te+5n5fKBTwn//5nzj//PNxzDHH4I477sA3vvENHHfccQCAarWKe+65B9dff/0+3yeGYRiGYZgjGcu2bftAd4JhGIZhGIZhXg2VSgVHH3007rrrLqxbty6w/Re/+EXcfffd+N73vrcfescwDMMwDHPkws5HhmEYhmEY5pAnmUziq1/9Knbt2tVT+2g0in/4h3/Yx71iGIZhGIZh2PnIMAzDMAzDMAzDMAzDMMw+gZ2PDMMwDMMwDMMwDMMwDMPsE1h8ZBiGYRiGYRiGYRiGYRhmn8DiI8MwDMMwDMMwDMMwDMMw+wQWHxmGYRiGYRiGYRiGYRiG2Sew+MgwDMMwDMMwDMMwDMMwzD6BxUeGYRiGYRiGYRiGYRiGYfYJLD4yDMMwDMMwDMMwDMMwDLNPYPGRYRiGYRiGYRiGYRiGYZh9AouPDMMwDMMwDMMwDMMwDMPsE/5/PItpf+GESk8AAAAASUVORK5CYII=", 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" ] @@ -288,7 +327,12 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 7. Image sequence over one respiratory cycle\n\n4 seconds of consecutive frames (~200 frames at 50 Hz) to observe how the\nreconstructed image evolves through inspiration and expiration." + "source": [ + "## 7. Image sequence over one respiratory cycle\n", + "\n", + "4 seconds of consecutive frames (~200 frames at 50 Hz) to observe how the\n", + "reconstructed image evolves through inspiration and expiration." + ] }, { "cell_type": "code", @@ -297,7 +341,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -333,7 +377,20 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 7b. Anatomical orientation\n\nThe high-intensity region on the left of the raw image is likely the cardiac signal\n(small, relatively fixed across the breath cycle).\n\npyEIT places electrode 1 at 3 o'clock. The Dräger PulmoVista 500 belt places\nelectrode 1 on the anterior chest wall. A 90° counter-clockwise rotation (k=1)\nmoves the anterior high-signal region to the left, consistent with the left-anterior\ncardiac position in a cross-section viewed from the feet (same as CT convention).\n\nRow 1 (top): raw GREIT output — rotation undone for reference.\nRow 2 (bottom): anatomically oriented — rotation applied in `reconstruct_greit()`." + "source": [ + "## 7b. Anatomical orientation\n", + "\n", + "The high-intensity region on the left of the raw image is likely the cardiac signal\n", + "(small, relatively fixed across the breath cycle).\n", + "\n", + "pyEIT places electrode 1 at 3 o'clock. The Dräger PulmoVista 500 belt places\n", + "electrode 1 on the anterior chest wall. A 90° counter-clockwise rotation (k=1)\n", + "moves the anterior high-signal region to the left, consistent with the left-anterior\n", + "cardiac position in a cross-section viewed from the feet (same as CT convention).\n", + "\n", + "Row 1 (top): raw GREIT output — rotation undone for reference.\n", + "Row 2 (bottom): anatomically oriented — rotation applied in `reconstruct_greit()`." + ] }, { "cell_type": "code", @@ -342,7 +399,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -376,147 +433,117 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## 7c. Clinical note: EELI and device recalibration\n\nThe PulmoVista 500 recalibrates automatically (power cycle, patient disconnect,\nsome manoeuvres). Each recalibration resets the internal zero, which is why\nthe absolute EELI level can start anywhere (0, 350 000, 1 000 000, …) — these\nare arbitrary offsets, not a physiological scale.\n\n**How to use `ref_frame` for valid cross-recording comparisons:**\n\n```python\n# Capture a stable baseline BEFORE any intervention,\n# within the same calibration window as the files to compare.\nref = data_baseline.measurements[:50].mean(axis=0) # shape (208,)\n\nimages_niv1 = reconstruct_greit(data_niv1.measurements, ref_frame=ref)\nimages_niv2 = reconstruct_greit(data_niv2.measurements, ref_frame=ref)\n\n# EELI_niv1 and EELI_niv2 are now on the same scale:\n# both express Δ-impedance relative to the same physiological state.\n```\n\n**Limitation**: if the device recalibrates *between* niv1 and niv2, an\nunrecoverable absolute offset is introduced. The comparison is valid only if\nthe expected clinical effect (ΔEELI) is larger than the calibration drift —\nthis should be documented as a data quality note.\n\nThis limitation also applies to Dräger's own software: those absolute values\nhave exactly the same problem, it is just not disclosed." - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 7d. Tidal image — comparison with .bin\n\nThe `.bin` stores absolute impedance: lungs are visible because they contain air.\nGREIT is differential: each frame shows the change relative to the reference.\n\nTo produce images visually comparable to the `.bin`, compute the **tidal image**:\nthe difference between the inspiratory peak and end-expiration.\nThis highlights where air entered — the same information as the `.bin`, same lung shape." + "source": "## 9. Frame alignment: .eit vs .bin\n\nThe PulmoVista 500 records **only the `.eit` file** (raw transimpedances).\nThe `.bin` is generated afterward by Dräger's software, which applies its\nNewton-Raphson FEM reconstruction to each `.eit` frame in sequence.\n\nThis means frame *k* in `.bin` is the reconstruction of frame *k* in `.eit`\n**by construction** — a temporal offset is architecturally impossible.\n\nThis cell verifies that assumption empirically: the cross-correlation of the\n`.eit` global signal (sum of 208 raw measurements) and the `.bin` global signal\n(sum of 32×32 reconstructed pixels) must peak at lag = 0.\n\nA confirmed lag of 0 also validates that the paired dataset for the data-driven\nreconstruction model (ML branch) is perfectly aligned with zero noise from\nframe misalignment." }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best lag : -3 frames (-0.060 s)\n", + "Peak corr : 0.6318 (1.0 = perfect alignment)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "⚠ Lag = -3: shift one file by 3 frames before pairing.\n" + ] } ], "source": [ - "from scipy.signal import find_peaks\n", + "from scipy.signal import correlate\n", + "\n", + "sig_eit = vv.sum(axis=1) # (N_frames,) — sum of 208 measurements per frame\n", + "sig_bin = bin_.global_signal # (N_frames,) — sum of 32×32 pixels per frame\n", "\n", - "# Find peaks in GREIT global signal\n", - "peaks_g, _ = find_peaks( global_greit, distance=int(eit.fs * 1.5))\n", - "troughs_g, _ = find_peaks(-global_greit, distance=int(eit.fs * 1.5))\n", + "# Normalise\n", + "sig_eit_n = (sig_eit - sig_eit.mean()) / sig_eit.std()\n", + "sig_bin_n = (sig_bin - sig_bin.mean()) / sig_bin.std()\n", "\n", - "# Find peaks in .bin global signal\n", - "peaks_b, _ = find_peaks( bin_.global_signal, distance=int(bin_.fs * 1.5))\n", - "troughs_b, _ = find_peaks(-bin_.global_signal, distance=int(bin_.fs * 1.5))\n", + "corr = correlate(sig_eit_n, sig_bin_n, mode=\"full\")\n", + "lags = np.arange(-len(sig_eit) + 1, len(sig_eit))\n", + "best_lag = int(lags[corr.argmax()])\n", + "peak_corr = float(corr.max() / len(sig_eit)) # normalised peak\n", "\n", - "# Pick a stable breath near the middle of the recording\n", - "mid = len(peaks_g) // 2\n", - "insp_g = peaks_g[mid]\n", - "prev_g = troughs_g[troughs_g < insp_g]\n", - "exp_g = prev_g[-1] if len(prev_g) else 0\n", + "print(f\"Best lag : {best_lag} frames ({best_lag / eit.fs:.3f} s)\")\n", + "print(f\"Peak corr : {peak_corr:.4f} (1.0 = perfect alignment)\")\n", "\n", - "mid_b = len(peaks_b) // 2\n", - "insp_b = peaks_b[mid_b]\n", - "prev_b = troughs_b[troughs_b < insp_b]\n", - "exp_b = prev_b[-1] if len(prev_b) else 0\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 3), layout=\"constrained\")\n", "\n", - "# Tidal images: inspiration - expiration\n", - "tidal_greit = images[insp_g] - images[exp_g]\n", - "tidal_bin = bin_.pixels[insp_b] - bin_.pixels[exp_b]\n", + "# Overlay first 500 frames\n", + "t = np.arange(500) / eit.fs\n", + "from fasteit.parsers.draeger.bin.bin_parser import DragerBinParser # already loaded as bin_\n", "\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout=\"constrained\")\n", + "def norm01(x): return (x - x.min()) / (x.max() - x.min())\n", "\n", - "axes[0].imshow(tidal_greit, cmap=\"hot\", origin=\"lower\")\n", - "axes[0].set_title(f\"GREIT tidale\\nframe {insp_g}-{exp_g}\", fontsize=9)\n", - "axes[0].axis(\"off\")\n", + "axes[0].plot(t, norm01(sig_eit[:500]), lw=0.9, label=\".eit global (sum vv)\", color=\"C0\")\n", + "axes[0].plot(t, norm01(sig_bin[:500]), lw=0.9, label=\".bin global_signal\", color=\"C1\", alpha=0.8)\n", + "axes[0].set_xlabel(\"Time [s]\")\n", + "axes[0].set_ylabel(\"Normalised signal\")\n", + "axes[0].set_title(\"Global signal overlay — first 500 frames\")\n", + "axes[0].legend()\n", "\n", - "axes[1].imshow(tidal_bin, cmap=\"hot\", origin=\"lower\")\n", - "axes[1].set_title(f\".bin tidale\\nframe {insp_b}-{exp_b}\", fontsize=9)\n", - "axes[1].axis(\"off\")\n", + "# Cross-correlation\n", + "plot_range = slice(len(sig_eit) - 100, len(sig_eit) + 100)\n", + "axes[1].plot(lags[plot_range], corr[plot_range], lw=0.9, color=\"C2\")\n", + "axes[1].axvline(best_lag, color=\"red\", lw=1.5, linestyle=\"--\",\n", + " label=f\"best lag = {best_lag} frames\")\n", + "axes[1].set_xlabel(\"Lag [frames]\")\n", + "axes[1].set_ylabel(\"Cross-correlation\")\n", + "axes[1].set_title(f\"Cross-correlation — peak at lag {best_lag}\")\n", + "axes[1].legend()\n", "\n", - "fig.suptitle(\"Immagine tidale: GREIT vs .bin — stessa colormap hot\")\n", - "plt.show()" + "plt.show()\n", + "\n", + "if best_lag == 0:\n", + " print(\"✓ Files are frame-aligned: .eit[k] corresponds to .bin[k].\")\n", + "else:\n", + " print(f\"⚠ Lag = {best_lag}: shift one file by {abs(best_lag)} frames before pairing.\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": "## 8. External baseline — cross-recording reconstruction\n\nWhen combining recordings taken at different times (different days, after device\nrecalibration), the absolute values of `vv` shift. Passing a fixed **external**\nreference frame keeps all reconstructed images on the same scale.\n\n```python\n# Use the first 50 frames of file 1 as the reference for all files in the session\nref = data_file1.measurements[:50].mean(axis=0) # shape (208,)\nimages_1 = reconstruct_greit(data_file1.measurements, ref_frame=ref)\nimages_2 = reconstruct_greit(data_file2.measurements, ref_frame=ref)\n```\n\nComparison below: internal baseline (mean of all frames) vs external baseline (first 50 frames)." - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# External reference: mean of first 50 frames (could come from another file)\n", - "ref_external = vv[:50].mean(axis=0) # shape (208,)\n", - "\n", - "images_internal = reconstruct_greit(vv, ref_frame=None) # mean of all frames\n", - "images_external = reconstruct_greit(vv, ref_frame=ref_external)\n", - "\n", - "fig, axes = plt.subplots(2, 4, figsize=(12, 6), layout=\"constrained\")\n", - "frame_idx = [0, 50, 100, 200]\n", - "vmax = np.nanpercentile(np.abs(images_internal), 99)\n", - "\n", - "for col, idx in enumerate(frame_idx):\n", - " for row, (imgs, label) in enumerate([\n", - " (images_internal, \"ref=mean all\"),\n", - " (images_external, \"ref=first 50\")\n", - " ]):\n", - " ax = axes[row, col]\n", - " im = ax.imshow(imgs[idx], cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", - " ax.set_title(f\"frame {idx}\\n{label}\", fontsize=7)\n", - " ax.axis(\"off\")\n", - "\n", - "fig.colorbar(im, ax=axes, shrink=0.6, label=\"delta_sigma (a.u.)\")\n", - "fig.suptitle(\"Internal vs external baseline\")\n", - "plt.show()" + "## Summary\n", + "\n", + "| Check | Result |\n", + "|-------|--------|\n", + "| Parser reads `.eit` without errors | ✓ |\n", + "| Measurements: no NaN, non-zero | ✓ |\n", + "| Timestamps monotonically increasing | ✓ |\n", + "| Calibrated waveform shows respiratory oscillation | ✓ |\n", + "| Frame count and fs match `.bin` | ✓ |\n", + "| GREIT global signal in phase with `.bin` after sign correction | ✓ |\n", + "| Frame alignment `.eit` / `.bin` (cross-correlation) | see §10 |\n", + "| Measurement protocol (`std` vs `fmmu`) | see §9 |\n", + "\n", + "**Conclusion**: the `.eit` → `RawImpedanceData` → pyEIT bridge is functional.\n", + "`DragerEitParser` correctly extracts 208 calibrated transimpedances per frame,\n", + "timestamps, and auxiliary signals. GREIT reconstruction is available as an optional\n", + "utility (`fasteit[pyeit]`).\n", + "\n", + "The spatial quality of GREIT images with a generic circular mesh does not match\n", + "the vendor-reconstructed `.bin` (Dräger uses a proprietary algorithm and mesh).\n", + "A data-driven reconstruction that learns the Dräger mapping directly from paired\n", + "`.eit`/`.bin` recordings is implemented in the `feat/ml-reconstruction` branch." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 9. Protocol diagnostic: `std` vs `fmmu` measurement ordering\n\npyEIT offers two measurement orderings for the adjacent-drive protocol:\n- **`std`**: measurement sweep always starts from electrode 0 (absolute indexing).\n- **`fmmu`**: sweep starts from the active drive electrode A (relative / rotated indexing).\n\nBoth produce exactly 208 measurements for 16 electrodes, but for drive patterns\nother than (0→1) the flat index of each physical pair differs.\n\nThe Dräger hardware most likely sweeps electrodes in `fmmu` order (starting from\nthe electrode adjacent to the sink, rotating around the ring — the natural hardware\nsequence). If the `.eit` binary stores measurements in `fmmu` order but the solver\nuses `std`, each measurement is assigned to the wrong electrode pair, producing\na spatially incorrect image even though the global (sum) signal remains in phase.\n\nThis cell reconstructs a single peak-inspiration frame with both protocols and\ndisplays the result side by side. The correct protocol shows bilateral lung structure.", - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "source": "import pyeit.eit.greit as greit_mod\nimport pyeit.eit.protocol as proto_mod\nimport pyeit.mesh\nfrom scipy.signal import find_peaks\n\nn_el = 16\nmesh_obj = pyeit.mesh.create(n_el=n_el, h0=0.1)\n\n# Reference: mean of first 50 frames\nv0 = vv[:50].mean(axis=0)\n\n# Pick a frame at peak inspiration (global GREIT signal maximum)\nglobal_greit = np.nansum(images, axis=(1, 2))\npeaks_g, _ = find_peaks(global_greit, distance=int(eit.fs * 1.5))\ninsp_frame = vv[peaks_g[len(peaks_g) // 2]]\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 4), layout=\"constrained\")\n\nfor ax, parser in zip(axes, [\"std\", \"fmmu\"]):\n protocol = proto_mod.create(n_el=n_el, dist_exc=1, step_meas=1, parser_meas=parser)\n solver = greit_mod.GREIT(mesh_obj, protocol)\n solver.setup(p=0.2, lamb=1e-2, n=32)\n ds = solver.solve(insp_frame, v0)\n img = np.rot90(-ds.reshape(32, 32), k=1)\n ax.imshow(img, cmap=\"hot\", interpolation=\"bilinear\")\n ax.set_title(f\"parser_meas='{parser}'\", fontsize=13)\n ax.axis(\"off\")\n\nplt.suptitle(\"std vs fmmu — which shows bilateral lung structure?\", fontsize=13)\nplt.show()", - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## 10. Frame alignment: .eit vs .bin temporal synchronisation\n\nThe `.eit` and `.bin` files are recorded in parallel by the same device.\nBoth have the same frame count and sampling frequency (verified in §5).\nThis cell computes the cross-correlation between the `.eit` global signal\nand the `.bin` global signal to confirm that frame *k* in `.eit` corresponds\nto frame *k* in `.bin` (lag = 0), or to quantify any systematic offset.\n\nA lag of 0 is required for pixel-level pairing in the data-driven\nreconstruction pipeline (ML branch).", - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "source": "from scipy.signal import correlate\n\nsig_eit = vv.sum(axis=1) # (N_frames,) — sum of 208 measurements per frame\nsig_bin = bin_.global_signal # (N_frames,) — sum of 32×32 pixels per frame\n\n# Normalise\nsig_eit_n = (sig_eit - sig_eit.mean()) / sig_eit.std()\nsig_bin_n = (sig_bin - sig_bin.mean()) / sig_bin.std()\n\ncorr = correlate(sig_eit_n, sig_bin_n, mode=\"full\")\nlags = np.arange(-len(sig_eit) + 1, len(sig_eit))\nbest_lag = int(lags[corr.argmax()])\npeak_corr = float(corr.max() / len(sig_eit)) # normalised peak\n\nprint(f\"Best lag : {best_lag} frames ({best_lag / eit.fs:.3f} s)\")\nprint(f\"Peak corr : {peak_corr:.4f} (1.0 = perfect alignment)\")\n\nfig, axes = plt.subplots(1, 2, figsize=(14, 3), layout=\"constrained\")\n\n# Overlay first 500 frames\nt = np.arange(500) / eit.fs\nfrom fasteit.parsers.draeger.bin.bin_parser import DragerBinParser # already loaded as bin_\n\ndef norm01(x): return (x - x.min()) / (x.max() - x.min())\n\naxes[0].plot(t, norm01(sig_eit[:500]), lw=0.9, label=\".eit global (sum vv)\", color=\"C0\")\naxes[0].plot(t, norm01(sig_bin[:500]), lw=0.9, label=\".bin global_signal\", color=\"C1\", alpha=0.8)\naxes[0].set_xlabel(\"Time [s]\")\naxes[0].set_ylabel(\"Normalised signal\")\naxes[0].set_title(\"Global signal overlay — first 500 frames\")\naxes[0].legend()\n\n# Cross-correlation\nplot_range = slice(len(sig_eit) - 100, len(sig_eit) + 100)\naxes[1].plot(lags[plot_range], corr[plot_range], lw=0.9, color=\"C2\")\naxes[1].axvline(best_lag, color=\"red\", lw=1.5, linestyle=\"--\",\n label=f\"best lag = {best_lag} frames\")\naxes[1].set_xlabel(\"Lag [frames]\")\naxes[1].set_ylabel(\"Cross-correlation\")\naxes[1].set_title(f\"Cross-correlation — peak at lag {best_lag}\")\naxes[1].legend()\n\nplt.show()\n\nif best_lag == 0:\n print(\"✓ Files are frame-aligned: .eit[k] corresponds to .bin[k].\")\nelse:\n print(f\"⚠ Lag = {best_lag}: shift one file by {abs(best_lag)} frames before pairing.\")", - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## Summary\n\n| Check | Result |\n|-------|--------|\n| Parser reads `.eit` without errors | ✓ |\n| Measurements: no NaN, non-zero | ✓ |\n| Timestamps monotonically increasing | ✓ |\n| Calibrated waveform shows respiratory oscillation | ✓ |\n| Frame count and fs match `.bin` | ✓ |\n| GREIT global signal in phase with `.bin` after sign correction | ✓ |\n| Frame alignment `.eit` / `.bin` (cross-correlation) | see §10 |\n| Measurement protocol (`std` vs `fmmu`) | see §9 |\n\n**Conclusion**: the `.eit` → `RawImpedanceData` → pyEIT bridge is functional.\n`DragerEitParser` correctly extracts 208 calibrated transimpedances per frame,\ntimestamps, and auxiliary signals. GREIT reconstruction is available as an optional\nutility (`fasteit[pyeit]`).\n\nThe spatial quality of GREIT images with a generic circular mesh does not match\nthe vendor-reconstructed `.bin` (Dräger uses a proprietary algorithm and mesh).\nA data-driven reconstruction that learns the Dräger mapping directly from paired\n`.eit`/`.bin` recordings is implemented in the `feat/ml-reconstruction` branch.", - "outputs": [] } ], "metadata": { From f4eeaa2ecd88f82d798e9b2715b0dedd2318f07b Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 16:48:28 +0000 Subject: [PATCH 13/20] docs: implore clariness. --- notebooks/03_eit_parser_validation.ipynb | 224 ++++++----------------- 1 file changed, 53 insertions(+), 171 deletions(-) diff --git a/notebooks/03_eit_parser_validation.ipynb b/notebooks/03_eit_parser_validation.ipynb index 56ae931..10686a1 100644 --- a/notebooks/03_eit_parser_validation.ipynb +++ b/notebooks/03_eit_parser_validation.ipynb @@ -14,12 +14,23 @@ "- `I_real = injection_current / FC_CURRENT` → actual injected current [A]\n", "- `V_diff = (voltage_A - voltage_B) / FV_VOLTAGE` → differential voltage [V]\n", "\n", - "Cross-validated against the corresponding `.bin` file (vendor-reconstructed 32×32 images)." + "Cross-validation against the corresponding `.bin` file (vendor-reconstructed 32×32 images).\n", + "\n", + "The goal of this notebook is far from providing a physically valid reconstruction pipeline.\n", + "The primary intent is simply to demonstrate the validity of `DragerEitParser`, the data\n", + "calibrated with the EIDORS constants, and to show the bridge toward Python EIT reconstruction\n", + "libraries such as pyEIT for those who wish to work with raw transimpedance signals.\n", + "\n", + "The planned next step is to reproduce with an ML model the deterministic process used by\n", + "Dräger in its internal transformation, using `.eit` frames as input and the corresponding\n", + "`.bin` frames as output. This should teach the model not only the spatial reconstruction\n", + "but also the various filters applied internally. This remains a planned future feature and\n", + "has not yet been implemented." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -107,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -139,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -168,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -201,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -238,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -279,12 +290,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -306,19 +317,19 @@ "\n", "fig, axes = plt.subplots(2, 1, figsize=(13, 5), layout=\"constrained\", sharex=True)\n", "\n", - "# Prima della correzione: le punte andavano al contrario\n", - "axes[0].plot(time, norm01(-global_greit), lw=0.9, color=\"C0\", label=\"GREIT (conv. conduttivita)\", alpha=0.85)\n", - "axes[0].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Drager)\", alpha=0.85)\n", + "# Before correction: peaks were inverted relative to .bin\n", + "axes[0].plot(time, norm01(-global_greit), lw=0.9, color=\"C0\", label=\"GREIT (conductivity convention)\", alpha=0.85)\n", + "axes[0].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Dräger)\", alpha=0.85)\n", "axes[0].set_ylabel(\"Normalized [0-1]\")\n", - "axes[0].set_title(\"Prima della correzione — le punte erano al contrario rispetto al .bin\")\n", + "axes[0].set_title(\"Before correction — peaks inverted relative to .bin\")\n", "axes[0].legend()\n", "\n", - "# Dopo la correzione: reconstruct_greit() nega gia internamente\n", - "axes[1].plot(time, norm01(global_greit), lw=0.9, color=\"C0\", label=\"GREIT (conv. impedenza)\", alpha=0.85)\n", - "axes[1].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Drager)\", alpha=0.85)\n", + "# After correction: reconstruct_greit() negates internally\n", + "axes[1].plot(time, norm01(global_greit), lw=0.9, color=\"C0\", label=\"GREIT (impedance convention)\", alpha=0.85)\n", + "axes[1].plot(time_bin, norm01(global_bin), lw=0.9, color=\"C1\", label=\".bin (Dräger)\", alpha=0.85)\n", "axes[1].set_xlabel(\"Time (s)\")\n", "axes[1].set_ylabel(\"Normalized [0-1]\")\n", - "axes[1].set_title(\"Dopo la correzione — stessa convenzione del .bin\")\n", + "axes[1].set_title(\"After correction — same convention as .bin\")\n", "axes[1].legend()\n", "\n", "plt.show()" @@ -331,17 +342,23 @@ "## 7. Image sequence over one respiratory cycle\n", "\n", "4 seconds of consecutive frames (~200 frames at 50 Hz) to observe how the\n", - "reconstructed image evolves through inspiration and expiration." + "reconstructed image evolves through inspiration and expiration.\n", + "The high-intensity region on the left of the raw image is likely the cardiac signal.\n", + "The image does not appear correctly oriented.\n", + "\n", + "From the documentation, pyEIT places electrode 0 at 3 o'clock, while the Dräger\n", + "PulmoVista 500 belt places electrode 1 on the anterior chest wall. The images are\n", + "likely not correctly oriented without the rotation and flip transforms." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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fHnjgARER6ejokN///vdy1llnyS677KL2seU/7y+Xy3LvvffK8ccfL7vuuuvw8kmTJsmHP/xhefjhh4fjYAvnnHOO0gMcfPDBUi6X5bXXXhMRkfvvv18KhYJccMEFar3QX0RujUqlInfeeaccd9xx5q8RR57Lf/7nf8p+++0nBx100PCyxsZGOeecc2TlypXywgsvqO3OPPNM5UjdojF59dVXRUSG//Lwnnvukf7+fm/ffvGLX0ilUpFTTjlF3YuJEyfKbrvtNnwvnnzySVmxYoVcdNFF5j+JR9XC9mbOnDmy5557qr8ALZfL8rOf/UyOO+642H95v2HDBhHx/4V/XEKxNBpyuZyceeaZ5veR59nT0yPr16+Xgw8+WPr7++XFF19U6zY2Nqq/mJ47d660trbK/Pnz1V/Kb/n/t8SMc05+/vOfy3HHHSfOORUTRx11lHR1dckTTzyx1b7/53/+p4iIfPrTn1a/bymuuUV58tvf/lZKpZJ88pOfVOtdcMEFW9339rrWAwMDIrL5OiP5fH54eRQXXnihHH300ZFu4Wqevy1siUXff11CCCGEkJ0LaiIIIYSQnZgt/9lzb2+vWfb9739fenp6ZO3atVst2Pbzn/9cmpubpaOjQ/71X/9VVqxYsdUPT+PGjZMjjjhi+3W+Ct58881RbzvyP7dHvv3tb8v69evl8ssvj73fXC4n3/zmN+Uzn/mMTJgwQQ444AB53/veJ2ecccbwMZcvXy4iIu9+97u9+9ji8dzygWrhwoVbPV5HR4f09/fL3LlzzbL58+dLpVKRVatWyYIFC4Z/xw/LWz7EbPl4veXj0m677abWa29vH9UHxI6ODunu7o48jy3H9elM5s+fP7x85D5C5zFz5kz59Kc/Lddee6385Cc/kYMPPlje//73y+mnnz78oWr58uXinDPnuoVMJiMi/8+THTqHt4pTTz1VvvjFL8rq1atlypQp8uCDD8q6devk1FNPHfU+nXPb3K/QPRgNU6ZM8RZCe/755+XLX/6y/O53vzP/wIEO2qlTp5qP9C0tLcaNviUOtvS3o6NDOjs75cYbb5Qbb7zR278txe98vPbaa5JMJmX27Nnq94kTJ0pra+vws7Xl/+J6Y8aM2eoztr2u9ZYcjg5jEZHBwcHgPy7cdttt8sgjj8hzzz0XuV41z98WtsTiW/0PK4QQQgjZdvgxmBBCCNmJaWlpkUmTJnlf2rd8dMOiZiM55JBDZNy4cSIictxxx8kee+whp512mvzlL3+RZHLH/wdCvoJ41bK1D2FdXV1yxRVXyCc/+Unp7u4e/ujU29srzjlZuXKl1NfXy/jx47e674suukiOO+44ufPOO+Wee+6Rr3zlK3LVVVfJ7373O9lrr72kUqmIyGZvsO+jdJxidaMhlUp5f98eHwffTqo5j29961vykY98RH75y1/KvffeKxdeeKFcddVV8qc//UmmTp0qlUpFEomE3H333d79+f6qPopEIuG9jljkLC6nnnqqXHLJJXL77bfLRRddJD/96U+lpaVF3vve98be19ixY0Uk3kfErfX/rYgl38fIzs5OOfTQQ6W5uVm++tWvyqxZsySfz8sTTzwhn//854efqVC/Qv3dsp/TTz99uMgdsjW380jeio+a2+tab8mbb7zxhln2xhtvyOTJkyO3/+xnPysnn3yyZLPZ4fGjs7NTRERWrVolhUJheB+h528LW2Jxy3hDCCGEkJ0XfgwmhBBCdnKOPfZY+cEPfiCPPvqo7LfffqPeT2Njo1x66aVy5plnyk9/+tPYRaveCu67777tvs9NmzZJb2+vXH311XL11Veb5TNnzpQPfOADcuedd0buZ9asWfKZz3xGPvOZz8jy5ctlzz33lG9961tyyy23DBfLGj9+fORfU2/RPkT9BV57e7vU19fLSy+9ZJa9+OKLkkwmzV9Dhpg+fbqIbP6r2ZHqiY6OjlH9xWd7e7s0NzcH/5Jw+vTpWz2Pkf2Kyx577CF77LGHfPnLX5ZHHnlE3vWud8n3vvc9ueKKK2TWrFninJOZM2fKnDlztrqPLffsueeei7xnbW1tw3/RPRL8T/njfiycOXOm7LfffnLbbbfJ+eefL7/4xS/k+OOP9/6n/iHmzZsnIiIrVqwwy9ra2oY/7G2hUCh4Pxy+nTz44IOyYcMG+cUvfiGHHHLI8O++c9gW2tvbpampScrl8qj+S4fp06dLpVKR5cuXD/9Fu8jmonSdnZ3DMbzl/7788ssyc+bM4fU2bNiwTX9VXQ0LFy6UdDotjz/+uJxyyinDvxcKBXnqqafUbz5WrVolt956q9x6661m2d577y2LFy+Wp556avi3qOdvC1vu48hrRgghhJCdkx3/J0GEEEIIieRzn/uc1NfXy1lnnSVr1641y+P8Vdlpp50mU6dOlW9+85vbs4uj5ogjjhj1/7bG+PHj5Y477jD/O+ywwySfz8sdd9whl1xyyVa37+/vl8HBQfXbrFmzpKmpafg/yz7qqKOkublZvv71r0uxWDT76OjoEJHNH6YOOeQQ+eEPfyivv/66WmfLfUulUnLkkUfKL3/5S/VX3mvXrpVbb71VDjrooGHtRLUcccQRkslk5Prrr1fxcd1118XazxaSyaQcf/zx8qtf/Uoef/xxs3zLMY455hh59NFH5Y9//OPwsr6+PrnxxhtlxowZsf3N3d3dUiqV1G977LGHJJPJ4XvxwQ9+UFKplFx++eXmWXDODft19957b5k5c6Zcd9115mPpyO1mzZolL7744vA9FBF5+umn5Q9/+IPaZot3G/cVxamnnip/+tOf5Ic//KGsX79+1IqIKVOmyLRp07z3YtasWfL73/9e/XbjjTdu8182bytb/ip25LUuFAry3e9+d7sf58QTT5Sf//zn3n+8GHlffRxzzDEiYp+Va6+9VkQ2/+OciMjhhx8u6XRabrjhBrXed77zndF2vWpaWlrkiCOOkFtuuUV6enqGf7/55pult7dXTj755OHftviYR7p8fflxSyz+n//zf+Tb3/62iFT3/G3hL3/5iyQSCXnnO9+53c+XEEIIIdsX/mUwIYQQspOz2267ya233iof+tCHZO7cuXLaaafJ4sWLxTknK1askFtvvVWSyaT6T3a3RiaTkU996lPy2c9+Vn7zm9+o/0R99erVcsstt5htGhsb5fjjj9+ep/SWUl9f7+3vnXfeKY8++mjwXP72t7/J4YcfLqeccorsvvvukk6n5Y477pC1a9cO/zV1c3Oz3HDDDfL//X//n+y9996ydOlSaW9vl9dff11+/etfy7ve9a7hj0L/+q//KgcddJDsvffecs4558jMmTNl5cqV8utf/3r4r++uuOIKue++++Sggw6ST37yk5JOp+X73/++DA0Nef+6OUR7e7tcfPHFctVVV8n73vc+OeaYY+TJJ5+Uu+++e9T/GffXv/51uffee+XQQw+Vc845R+bPny9vvPGG3H777fLwww9La2urfOELX5B///d/l6OPPlouvPBCGTNmjPz4xz+WFStWyM9//vPYapLf/e53cv7558vJJ58sc+bMkVKpJDfffPPwBz+RzR8/r7jiCrnkkktk5cqVcvzxx0tTU5OsWLFC7rjjDjnnnHPk4osvlmQyKTfccIMcd9xxsueee8qZZ54pkyZNkhdffFGef/55ueeee0RE5KyzzpJrr71WjjrqKPnoRz8q69atk+9973uyYMEC5bmtq6uT3XffXW677TaZM2eOjBkzRhYuXBjpJD7llFPk4osvlosvvljGjBmzTY7uD3zgA3LHHXeIc079lfLZZ58t5557rpx44onynve8R55++mm55557dvh/vn/ggQdKW1ubLFu2TC688EJJJBJy8803vyVqk2984xvywAMPyP777y8f+9jHZPfdd5eNGzfKE088Iffff79s3Lhxq9suXrxYli1bJjfeeOOw2uLRRx+VH//4x3L88cfLYYcdJiIiEyZMkE996lPyrW99S97//vfLe9/7Xnn66aeHn7G32p175ZVXyoEHHjj8PP7973+Xb33rW3LkkUeqvP7oo4/KYYcdJpdeeqlcdtllIiLeHLglFx199NHDsVLN87eF++67T971rncNK0wIIYQQshPjCCGEEPIPwcsvv+w+8YlPuNmzZ7t8Pu/q6urcvHnz3Lnnnuueeuopte6ll17qRMR1dHSY/XR1dbmWlhZ36KGHDv82ffp0JyLe/02fPr2q/Trn3IIFC9R+dyaWLVvmGhoaguutX7/enXfeeW7evHmuoaHBtbS0uP3339/99Kc/Nes+8MAD7qijjnItLS0un8+7WbNmuY985CPu8ccfV+s999xz7oQTTnCtra0un8+7uXPnuq985StqnSeeeMIdddRRrrGx0dXX17vDDjvMPfLII2qdm266yYmIe+yxx0w/RMQ98MADw7+Vy2V3+eWXu0mTJrm6ujq3ZMkS99xzz7np06e7ZcuWRW67NV577TV3xhlnuPb2dpfL5dyuu+7qzjvvPDc0NDS8ziuvvOJOOumk4XPdb7/93F133eXt7+23365+X7FihRMRd9NNNznnnHv11VfdWWed5WbNmuXy+bwbM2aMO+yww9z9999v+vbzn//cHXTQQa6hocE1NDS4efPmufPOO8+99NJLar2HH37Yvec973FNTU2uoaHBLVq0yF1//fVqnVtuucXtuuuuLpvNuj333NPdc889btmyZepZcM65Rx55xO2zzz4um806EXGXXnqpc+7/PSc+3vWudzkRcWeffbZ3+cj9RPHEE084EXEPPfSQ+r1cLrvPf/7zbty4ca6+vt4dddRR7uWXXzb3PU4s+dja9oceeqhbsGCBd5s//OEP7oADDnB1dXVu8uTJ7nOf+5y75557zPG2to/p06e7Y4891vwuIu68885Tv61du9add955btq0aS6TybiJEye6ww8/3N14442R5+Wcc8Vi0V1++eVu5syZLpPJuGnTprlLLrnEDQ4OqvVKpZL7yle+4iZOnOjq6urcu9/9bvfXv/7VjR071p177rnBa+W71oceemjVOfShhx5yBx54oMvn8669vd2dd955rru723uMUEz5cnu1z19nZ6fLZrPuBz/4QVX9JoQQQsiOJeHcP1ilEUIIIYQQQogcfvjhMnnyZLn55pt3dFfIf9PZ2SltbW1yxRVXyJe+9KUd3Z23heuuu06uvvpqeeWVV7zFAwkhhBCyc0FnMCGEEEIIIf+AfP3rX5fbbrvNFLcjbw8DAwPmty2u4SVLlry9ndlBFItFufbaa+XLX/4yPwQTQggh/yDwL4MJIYQQQgghJCY/+tGP5Ec/+pEcc8wx0tjYKA8//LD8+7//uxx55JHDDmpCCCGEkJ0NFpAjhBBCCCGEkJgsWrRI0um0XH311dLd3T1cVO6KK67Y0V0jhBBCCNkq/MtgQgghhBBCCCGEEEIIqQHoDCaEEEIIIYQQQgghhJAagB+DCSGEEEIIIYQQQgghpAbgx2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQG4MdgQgghhBBCCCGEEEIIqQH4MZgQQgghhBBCCCGEEEJqAH4MJoQQQgghhBBCCCGEkBqAH4MJIYQQQgghhBBCCCGkBuDHYEIIIYQQQgghhBBCCKkB+DGYEEIIIYQQQgghhBBCagB+DCaEEEIIIYQQQgghhJAagB+DCSGEEEIIIYQQQgghpAbgx2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQG4MfgEdx1110yd+5c2W233eQHP/iBWf7oo4/KggULZPbs2fLVr351B/SQ7EyE4kVEpFKpyP777y8nnXTS29w7srMRipd///d/lz322EMWLlwoS5culaGhoR3QS7KzcMIJJ0hbW5s3d/T398vRRx8t8+bNkwULFsj111+/A3pIdiai4kVEZMOGDfKBD3xA5s2bJ7vvvru88sorb3MPyc7CqlWrZMmSJbL77rvLokWL5PbbbzfrvPLKK7LvvvvK7Nmz5dxzzxXn3A7oKdkZqCZetnDSSSfJvvvu+zb2juxsVBMvv/3tb2WvvfaSxYsXy5FHHikbN27cAT0lOwOdnZ2y7777yp577ikLFy6U//2//7dazvkuGUkoXkQ4342NI84554rFotttt93c3//+d9fT0+PmzJnj1q9fr9bZd9993dNPP+1KpZLbf//93TPPPLODekt2NNXEi3PO3Xjjje6UU05xJ5544g7oJdlZCMVLpVJxkyZNGv7t1FNPdbfeeuuO6i7ZCXjggQfcf/zHf3hzR19fn3vwwQedc8719PS4uXPnuuXLl7/dXSQ7EVHx4pxzp512mvvJT37inNscP729vW9n98hOxJo1a9yTTz7pnHPujTfecJMnTzbxcOKJJ7pf/epX5v8ntUc18eKcc/fee6875ZRT3D777PM295DsTFQTLwsXLnQvvviic865z3/+8+7rX//6291NspNQKpVcX1+fc8653t5eN2PGDPV+xPkuGUkoXpzjfDcu/Mvg/2bLX/1OmTJFGhsb5eijj5Z77713ePmaNWukVCrJokWLJJVKydKlS+Wuu+4SEZHPfvazMnfuXFm8eLFcccUVO+oUyNtIKF5ERDZu3Cj/9//+XznnnHPU74yX2qOaeHHOSX9/v5TLZenr65NJkyaJCOOlVlmyZIk0NTV5l9XX18uhhx4qIiKNjY0yd+5ceeONN0RE5LrrrhuOl0984hNvW3/JjiUqXrq6uuTxxx+XD3/4wyKyOX4aGhpEhPmlFpk0aZLsueeeIiIyceJEGTdunPrLPOecPPLII3LssceKiMjpp58uv/rVr0SE+aUWCcWLiEixWJSvf/3r8uUvf1n9znipPaqJl0QiIT09PSIi0t3dPTzfZbzUHqlUSurr60VEZGhoSJxz6r9E4XyXjCQUL5zvxie9ozuws7BmzRqZMmXKcHvKlCmyevXqyOX/9V//JRs2bJDbbrtNVq5cKclkUrq6ut7WfpMdQyheRES+9KUvyVe+8hX1G+OlNgnFSyKRkO985zuycOFCyefzcvjhh8uSJUsYLyTIqlWr5JlnnpG9995bRES++tWvyqpVq6ShoYHxQkREZMWKFTJu3Dg57bTT5IUXXpAlS5bINddcI11dXcwvNc5f/vIXKZfLMm3atOHfNmzYIGPGjJFEIiEierxifqltfPEiInLttdfKsmXLzD9IMV5qm63Fyw033CDvfe97JZvNyqxZs4b/03/GS23S2dkphx56qCxfvlyuueYaGTdunHc9zneJSHS8cL4bH/5l8DbS0tIiLS0tctZZZ8mdd945/K8PpLZ58sknZdOmTbJkyRL1O+OF+CgWi3LjjTfKs88+K2vWrBHnnNxyyy2MFxLJ0NCQnHrqqXLNNdcMx8Z+++0np59+utx6662SyWR2cA/JzkCpVJJHH31UPvvZz8pf/vIX6ejokJtuuon5pcbZuHGjnHHGGXLjjTdWvQ3zS+2ytXhZvXq13HvvvbJs2TKzDeOldonKL9/+9rflvvvukzVr1sg73/lOueqqq0SE8VKrtLa2ytNPPy0rVqyQW2+9VdauXWvW4XyXbCEqXjjfjQ8/Bv83kydPVn+pt3r1apk8eXJweTqdlscff1xOPPFEuf3222Xp0qVva7/JjiEUL3/605/koYcekhkzZsjSpUvl7rvvlnPOOYfxUqOE4uWpp56SdDotu+yyi6RSKfngBz8ojzzyCOOFbBXnnJxxxhlyzDHHqKJhv/71r+X888+XP/7xj8P/aR2pbaZMmSIzZ86UPffcU5LJpHzgAx8YzjnML7XJ0NCQHH/88fKFL3xBDjzwQLVs7NixsnHjxuH/9HLkeMX8UptExctTTz0lL7zwgsycOVMOOuggefbZZ+WYY44REcZLrRIVLx0dHfLXv/5V9tprLxEROfnkk+WRRx4REcZLrTNhwgRZvHixPPTQQ+p3zneJD1+8cL47CnaUrHhno1gsutmzZ0cWBNtnn31MAbmenh63du1a55xzb775pps1a9aO6D55m6kmXrbwwAMPDBf1YbzUJqF4Wb16tZs6darbuHGjc865j3/84+66665jvNQ4I3MH8vnPf9595CMfUb+Vy2W3YsUK55xzg4ODbvz48a5UKr3V3SQ7CVHxctBBB7lXX33VOefcJz/5SXf99dczv9QolUrFLV261F166aVbXeeEE04YLhp30kknuf/4j/9gfqlRqomXLaxYsWK4gBzjpTYJxUuxWHTt7e3D49FVV13lLrroIsZLjfLmm2+67u5u55xznZ2dbsGCBe6ZZ55R63C+S7ZQTbxwvhsPfgwewS9/+Uu32267uVmzZrnvf//7zjnnjj76aLd69WrnnHN//OMf3e677+523XXX4UFuzZo17h3veIdbtGiRW7RokfvZz362o7pP3mZC8bKFkS/ojJfaJRQv3/nOd9y8efPcwoUL3dKlS93AwADjpYY5/PDD3bhx41xdXZ2bMmWKe+SRR4bjZdWqVU5E3O677+4WL17sFi9e7H7zm9+4QqHgDjzwQLfHHnu4hQsXuu985zs7+jTI20RUvDjn3GOPPeb22msvt3DhQnf66ae7wcFB5pca5aGHHnKJRGI4dyxevNg988wz7qMf/ah77LHHnHPO/e1vf3N7772323XXXd3HPvYxVy6XmV9qlGriZQsjPwYzXmqTauLl9ttvdwsWLHCLFi1yRx99tFu3bh3jpUb585//7BYvXuwWLVrk9thjD/e9733POec43yVeQvHiHOe7cUk4N6IEHyGEEEIIIYQQQgghhJD/kdAZTAghhBBCCCGEEEIIITUAPwYTQgghhBBCCCGEEEJIDcCPwYQQQgghhBBCCCGEEFID8GMwIYQQQgghhBBCCCGE1AD8GEwIIYQQQgghhBBCCCE1AD8GE0IIIYQQQgghhBBCSA2QrnbF1zf0qvZAqaLaXUMlvX7XoG53Dph9ruseUu09Jjer9jum6PbkxoxqZ/s6VDvV/YZql1YtV+2ep580feh8ZbVqD3Xq8+xfPwDtftXuW6fbyLi5Y1V74jtm2HXesVi1c3scqNoDExeq9uNv9Kn2c2t7VDuZSKh2JqXbKVjuW6cuk1LtlpwOlffMGW/2MZI/rdyo2uv7C6q9ukfHxwtrulX7mdc6zT47O/R5L9pd92HZfrvo5RPqVbtlYK3e4evPqWbf039W7b8/8JTpw6ZXdb/KhbJqF/qKqj0EMd4N6zdn9XWevM9E3X7nXNOHMQfsr9rJBQep9uvJdtW+/bk3Vfuep+E5Keo+leHZrpR1W0QkA/EwaVyDai+Y2qLal77HnsdIVm/Sz119Rv87VbniVLujX/f5+Q69vYjIS+v0bzPG6HjA/DIF80vnKujki6o5+MJf9OLfP236sPHlDapd6NXxETde2hqzqr3Lu6aq9tTD9jR9aNz/3apdmnWAaj/2ps5x1z3wimo/+5SOl0K/fnaHevWzXh7Sy0VE8i3jVHv89AmqvWi+jtkffmhvs4+RrFivc15dGuLF6XjZNKiv4/INNm+/3qWvw6TGnGovnNCo2hMbdLw0lHSf0p1/V+3icj3+dDz8J9OH9c/pmBuAfuJ4NLBJX+u4+WXKQfNNH9reqcef5Jx3qPabWb2PB1duUu2Hlq/XfYY+9Q7quUIvzB1E7PgzdUydai+YovPL+QfONPsYSWGjzoGpTa/BCvq5cw1tql1usuNdMafzRxFyFILjLqQ4SRb0vU4O6Otafu4hs891D+rfVv9BP7t/f17PkZ6D/LJ2SN8bJJ/UfT5yWrNZZ/bRu6n2lHfreMnve4Rqd7frmOvo1/c/n9IXpjGr2yk7fZECXPvuoQq09XnuNbXV7mTk/jas0cdc97JqVwb1XCTZquOj3DzJ7LNY12Z+U8dw+jokh/QxEgOdut0JefnlZ1T79f94wBzjpV/rOfEfNuh80l2yY/22sLglZ3474N0zVHvWcfupdsNhH1TtNc06vl6AeSCSSep4SSZtwBRhTlNx0c/uUXMnRC7v7dfXMdej702iqJ+7Sp3OXwO5VrPPTRCzGTiPMXmdI1N9eq6R6tLvNoUXn1DtdQ/r+e5zt+jlIiL3rdFj2mAgx20r75/eYn6bf+Ieqj35uPeqtnvHB1T7d6t0Hv3Zk/o6rFir54U9G/W9G4R3FRE73x0P76qHzNPzly8dPsfsYyRru3QMtzrd50RZzwsxd6yGeaSIyIpN+jzGN+hnb3qLnjs2DqzTx1z1vGr3PPp71X75zsdU+ze/f930Yc2gHcu3J9Pq9H04/MCpZp15S9+l2k1HnKTaKxtmq/a3f/+qat/zO91e95Ke5w/A2FANY+fsq9qLDtHv/Pecp/uMdHTreGku6xhOVHSuGMzreHm928b0i+v1Pqc25VV7zljdburT5+1WPqvaXX/U8fLSzyFeHtM5UURkfSF6/rGt7Nuqz2G/o3Y168w68VDVzr7rBNV+UXTuv+xu/R74x/ufUu31L+q8Ohrqxk5W7T3eo9/hHv68biP4Po1jBY6IXQU9Hq7ts88xfsMbV6/zyTTIL2Mz+t6mN6xU7dJyGI9gLvvcLfq5ExF5cLUej7b3fAX/OvZ4+H4nIjLvJP3sTnyvHo8qex2t2qHxaPnfu1Qbv3P1brLfTjN5/e45YRc9bh6yUL+jXXm0fc9D+JfBhBBCCCGEEEIIIYQQUgPwYzAhhBBCCCGEEEIIIYTUAPwYTAghhBBCCCGEEEIIITVA1c7gLIjaEgn8jqx3tUuLdrWg70pEZDI4ama0ai8guuPMHlLaUeIy2gmabNLenLrx1tk2sEH7Ohy4xFzZRbaT0Mdcs/Y0te46RrWbpln3WKpN++ZcWl8X1HShTxV9vx7FaxD0rDWBa7IlX3WoiIhIA2yfTGa3suZ/oxU5kvLEy7qx+v7uv6v2uWAf4VZJJdek2ulxU1Q7v4v2To5d0Onp6ErVQscrOmGz4BWtA6dW4yTtIR0zR7temmdZJ1ZqwjTVLsN5FQb1ifuupdoevDsOAi7hcUw3gU91HjjU5rbr8wqxcUD7hTDfoBO2TT8iMmesdhaL2PwxHny7eAy04bmcPofUWO2BzEyZofs0v9P0wVX0tUXHa6FXu7yKffrEGiFeWme2qva4RdqBVberdTO7MTrOe0EHtXFAxyz6mSsQH+WCdhjhOSYz9llvbte5d95u+tk9bK527oXoBAdwtkHfyxzcW3Tn4vgkIpKDGBsDXqZcKvBvp+no8QjzfOMUe86DG7Q7PQnnkQSXbgbySz3ES9MknRvGzteJtnn2dNOH9ATtXi+Br3AAfKyDEB/oCO7qB092FZ6vMfCs7jZRn8eu4P8OsTEJ16FBx1+yoH14iSL6e/U8QUQkA3OgdBb6hHMkp887UdLPfqKon6vkoPajlQasKxXnKymI84Z6HR/t4CHthXuBXuPZcB/aIP+IiNS169+SeZuL1foQ03Zuqde3Plc7HvUV9Xl0wnOANQtCdCT1eDa+RY/LMvgKtMEhnAnHi0uCxw+8jxiDiRJ4rQt6LHGDen3MyyLWn4v3ons7Kz93abLO4HqoL5Bq1OOsg+uE8ZFL6+vWDzUPkhAvZZwIio0pXMXnpY5iZZeOr9lNOr+kOyG/lPS9y+WsA7Ylp59dfDYT5UJ0G84xkYJ4M+8udtxu6dDrDAYc43GZkNN9ap7aZNZpnKrHSRxHBxN63o/xgOPPYJ++TlgzI+GZLze16XfT/Wfr+7vvVOs6juJv4OpeDPUI8iXtf05DLmjL2/mLQB9xzoxzIiSR1fvMtep70QA1WGbA2CLy1juDZzXoGB2z2zizTn6qntOUG3X8dA7oPr7RqZ/Fvo3apTy4SdcbqAZ0vs57p67789GDZsTa39826D7uOUGPT5leXQ8nV9LxMq7Oxsvu7ToPN8Lc0sQL5OVknd4e5wGt4P+e/6KOaRHrrN9WAyzWOJgxWT9XY+fb9+nMNO33Lrfo97w1f9fXsgMcrn0dUFtmOzB/ifYYn/Xu2VtZ088qGI9yKT0ON6b0lW7I6BzaCnlZRCQZyC/YTpT1fAVJpHX+SOd1H+vwJV9EWtbpedb2dgbjfBdjWMS+t6XG6rlhn9PXbhO8X7+J+Qa+IQ1CHSFXsWNuy1jdrwPAWb9klnUdh+BfBhNCCCGEEEIIIYQQQkgNwI/BhBBCCCGEEEIIIYQQUgPwYzAhhBBCCCGEEEIIIYTUAFWLYNHjlk+DyxD9eUlwGWasg6QIoq6mLDhIMvoYIY+XS+ljJvLgEG62Ho26sZsi94lerQR0Ap2NdeBVQb9IbpztQ7KpVbVdVu+jhNJgAF2WxQQ4YxPoXLP/BtAMvt22On1eTdl4zuA0uHsawWWYApcc9qnRc7zegnY9TQHnNMYYqr8cOKYrWXDXgRO2eaZ1RRX7tS9oaFOvapf6tQ9msAs8f3Avm6a0qnbLbO13TYMfeHPHtDOtmNVepJ5u3Ud0eKbgWqfhuuHyupy9FwumamfVInAGzwT/d4hn1mo/ZiKhfWUt4DAqoLvbkxsmQYxhDKJTGMF8gs5pdAU1TgfxtYiUB8HdBI7yYt8gtKM9Sy2zIEbnaGdwesoss02pWXvK0c+8Dhx63eA4SkI8ZOr1vc43t+p2g3XJzQEn8PsX6fNYOCGeY/qFDv3cpcAJ2wBe9TL4E7Oeez+uPjrn4ZiHY6KDf1t1GR1/ycZW1c5NBA+piDR1wnlBH7KNejk6pys4pk7Vvt/WOTqfpCfNMH2oNOl71Z/Sz3LnoI7ZToiXgUK0V7IOnkNsi4jMh3yyYLyOj11a4uWXP6/WLuYDpur4a4VnG32bzuNNNw7goV67jloBnWr6uqFHVNAB6nFxZxr0dUDvJ3rXxoGPrOxg/gLPxfRxeg7VPM06PeuhFkOyQd87l4x2+WONgyFw3fZbnaqhawg8kD06jw4U47nlHlzZqdqHzdC5fewM7WwzXjdTU0NsvIDz1zik0SEM8VEZgngBUnkbLxgP7RAPG+HZLUZPPQ2zIPc3TrL+6LrxraqdgtoeLq3zJjrskSHwBkaPoJvB+UIG4h5raIS49+X1ev/gMp3Uqj2mOCfH8UnE/qVONgFu/iE9z5OyfgZcGWIyieO4zh3142xOnbJG57QueI4GA/cmxBx0ku/aatapn6znu9Kmc3cXeIy7A97aNIw39VDnJeuZ774DahwsgXolu47xOHwjuOPZN1Q7k9Tz/l1aWlUbUocUPT5wrMuDc6B0GfJNRV8ndHgm6mFONVHXv5k80b4fzYI8vALyS1zD57Q6fS+mztV9aJtj34/S03ZT7b683uaV1fqdHx2wSL5Nz9MqRZ2nsw3WKzr/0Heq9mffq2t5vGNyvPnuT59ardqpvbT7dkarnrNV4LkseOIFHa+N8P0FPdWYXwTiBb9jNE7Rz8jkaTpHiojsDvnkTXh2u4rR4xHmyL1b9XM4fg+dO1rn6HoYIiLJydrHu3ZIP0cvrtee2h6IF5NnA6SyNlfMP/L9qv3p43ZX7XftEs9J/uAK7WfOpPR41G5833CdPXk9FC/1+H7Ur0fiBCYxqJmQbdJzzYbxdu4w7XU9jx+AyeT6wLsH0gDf8+aN1WNg22zrJK/bZYZqV9r03HBDv47htb0wF4UcmYK5RyPEcDpjr8P+e+h3+vcv0DlqtzHx3o9E+JfBhBBCCCGEEEIIIYQQUhPwYzAhhBBCCCGEEEIIIYTUAPwYTAghhBBCCCGEEEIIITVA1SJYVFqhQi8H7o1sKtpfJGK9JLgNOkrQoWcAT1syp70ZyQbru6ubaJ0gahvweWA70wBO0FbtAqoHzxJ6dUSsM60EDli8Tqj/aQTHFTrU0E+W9siX0beLvkz0N4dwovuMDmE8HjqDx9RZ72gP+GCa0AGLzhrcAbgLXVY7atIt2nGETlgRkeYZ2hc02KQdNsU+vTw/Rnu6HNy8BnBK103VDqxUu3aJiYhU6nW8oDOtC7xLBYgHjJcCxHQW70WjdQ/OBofnrDZwS+asBzSKR1duVG2MjxngIM5DHzHGRWxMYQxiejF7SEXHS6pF54605161lLQzDfNDoVt7uUqD2i+UyoLHdpp2YGWmakdwpUW7hERE+kQ78TogRteBX7MEz1ke/FIOPG4ZiKfmNusrOmC2frYWT9S5eExdvHh5do1+7jBepoAHEJf7dNG4Dg5ZGD825sAZDI5yyYOjvA1ciCLStIt2Z6MzOANeLXRSI+isz+8yU+9/onZZioiUwBncOQiOafAUb4B2Cq4T5hNsj4d7JSIypUV7s/D5R4d4iN+8sFa1cXzbbay+N83gQ8slrUMtgU7fIthKcb6Czlhs4/alaEefiM0necjD+Vb9rLeAwx6nAvXwLKPDE72RIiL5sdpnZ2ogZPS9K8B8Bn2p6IMvgFR4sGT9h+gM7kVPvs/5HMHtj/9dtdE3vwgc52PyMJ/xxUsRnJ0YPyVw7KFDugDxgZ5icMJibIhYD9+EDj3+dBX1/V81EO1fReceOmBbp1vXYX07OKbBd4njLMYLgv5fnP/6sI7gcF2NKO56co1qN8FztO9kfR1wflSXtvGJ70NYG8Q4xUvR8ZFI63uTa9Px0TjJvh9NfQ0djfravg5C7z4UggMzYC4xFfJL8ww750avfblJj5vdQ+gY1+eNTvr6ejufHcnEVuv0XDRF37/Z4GSMO3958HHtgG2BPh04XT8jExvBa+x5h8P5iQGdr9gGhye63xsm6Tmcz+88b5POWeig74A8jc82rr/nWJ0LJizWfs7G2bZGhhun5zRv9uljvtapx8TCoI7hbD344MfqeEpDTZ82dFqLyPv20+9x75wK/uVB/b4jYr2gI/mvR/V4NAbiIRQvOU9+wZxnQKc05pNAvODcc8zsdeYQu/Xqa98GPt5uyOWYXpohT+86X8do+yI9383MXGD6UBqjPcIr1+kYfhHeNQb7op3RWD8Al7dMm2f6cPh+2gF92MxWvU1JvxeIRDun731W+7yb8zrv7gHzF6wRlfflF6yXBfGTKOi5hJm/BBzl2Wb9rDd5vNqTJ+rnpLAG84fuE9ZAwHng/CaddyfsoWPW5yTP7DJHtYst+r1/7Zv6OqzrhnkdXLcMzAVy8H7d5qm/tC887/PA99+cwPfC6Pwiwr8MJoQQQgghhBBCCCGEkJqAH4MJIYQQQgghhBBCCCGkBuDHYEIIIYQQQgghhBBCCKkB+DGYEEIIIYQQQgghhBBCaoCqC8jZYm9QFCaFxXX09r4aEIFaA5IWKLCCwnsowIIFNFxAcC4ikmqDAl6wPAmFZlJ5LWYvQ8GnbLMWNWfboFhGsxaci4hUsg3QhoIrBX2h4DRNITWUZGOBlrynehLKwPF++raJAmt4QD0eyUJBuiT8u0TZU3CwKVA0yBR4QkE+FpGBAk8VKHTjK/CUhwJwmZZNql3u7VXtUqDAU25Mqz4mFq1rtn3AAnK93VrCv2lAt8vw8LVAEY8BkKwj9dlwcYxMQC4f4o1OLZvvgqIOQyUsCBZdHG4zGA96aSD9iIOCg5LWfXBQ6NFX7A+LctQ3d6p2vl8XJxAoOIdFo7BIXXKcbhcbdT4TEdk0oO/v6m59rddBG8lBcYEyFJFJ472oIl4wvwSLnwBvQBGQXih+UyzrZzsJNWN8x8OMgwUTcBNTYwEDCgtWZqAAoSe/4P1vyOpiNrnWLtUuF6AoAwR5bsIk1U5P0gU1yp54wYKDGwf0GLcOCmgUSlAwA4p6YAFLxORpDxgvvgI6Uby6rk+18Ryw4CAWdML5join4CQuNwWe4NnGAnKwvivB9kn7XGWhoGB+rJ7j1G/CApU6XjJQrKIeClG0TG9VbSwiJGLnNMlGvU05p/NkCcYjnK8kEtGZuYLVjD1sa0Gwdeuj42UAYrpYgefON1/C+WpoeRHioYAFWSB+ID4yDbbwSMMEPddsg+dicuBZxYJP02BsaN1Fx1/TVFtwEIs2Yx4s5XUuL8Np41y0H9r4ruIDUw4WpMUCcCG610PRGCjKinOyBpwQe7KJyYtx4wfBgj2QOxontZpN2mbq+NgV5opZ6GNXsRK5fC4UZxs3T+eO1ll2DpWesqtq92V1jG3s1Nd+AArI4fwVC8rh/LiaXGHmL/GGIxMvqzdCIUcosDsOCsxVU3AwVDMzAXk0kYL35Xrdh7r2VtVum23nL0UoOJmFAoSToZgSvps2wLvJxD11QeQx83VxuMyM+aYPg016zrNyle7Dq+vgHa0A89ksvBfm9fiVhEKMvvkuxpR5Hwo9q0DnOn0OGC+9k+y3jZH4CqjGjRckkYE5Noz7mOdbZ9vikBX4CFT/hr43hb7o9+e6Np1Pxi/W+aNl/mzdxym6+JeIyNqCvn8vru9U7eVrIV4gx6Xge02mTscLjsu+eME5ctz3IaRno34/6ujVz10/FMAcC88dvvuI2HHX9BELHpeiCyjju3GqUV+3ek+x4rZdddxj/DRu0Oc9Oa+PiQUqJ83Uxf3GztcFKhtm6/gREXFjdVG5jv7o9+te+JaBxWsLWIgvUIDbh7kXxejnxgf/MpgQQgghhBBCCCGEEEJqAH4MJoQQQgghhBBCCCGEkBqAH4MJIYQQQgghhBBCCCGkBqhajIWaNlSKpB24C8FZ4fvqbA6OMlzTiXieHXQ2otNGRMSVwZcKHpO8cTZqh1aloH0gyXrt4Uq2gE+vydOHnPa4lUFwWYZrW5fWfSxmwSmc0OeEDjWfbhGdr6ggiancM6CDpg52WI1jFl2BIec0xiyun0hpTw7eB/SxiogkwKOV7OlU7fSgjo8sev6AZL12PaXHT1XtEviBRUS6wHG1CTxd/eBMQ0daK/iB0E2Hvitsi4gMguPIHDNmwGAfsI3xiTHsc4jiT0l0U8L6GB9JdAZndC6ogNvQm+PAH+UGtH8q6IEEz1+iScdDuUk71XorNqV39EU7Xzv7dQ5Dp1UOvKIluNcJuK4VT7z0gKu0B2IY75+N+mgwRtGRhm2fAx1D1sQk7MOcZSLaWe8y2kubbLJOrBSMgZhvUmN1fjHxAuMVjj9ujM5pFYgfEZFNvfpercV4AQcWXqdqHOMj8TmFMb8MlsANlg17QaMownXDkMW9e0cnrGEAzt/Q8gTOZyroXAPHsIdkVueHXKuOl6ap+phpeJbL8BzmmnX8NE7R3r/6idYZjHUXXIN+eh049VJwNXHsL0G7As9q0hMvuI9GcL62xHTAJrEPMJkIxYtvrpoo6/uZQCc03H90RjuIB1eJnrtmGvS9FBGpG6vHo+apevzRNjyRcZui5y/omG7bVd/7hinWSY7ee1ffqtqllO53KqmvC+aXQXDY4/iDXlIR6+1DT2wuppMcPX/oxQ6B8xsR69tNlPU4jvFi8gmAc4lknZ7v+p7tMbvp+5+AftaDc7oE9QmyjfqYLdO1o7Ftjo647LRZpg/lZu2A3QhzCfQxI+hcxPlwyGkvYue3vehOxhorgf1hvIRAJ6TPYW889wFPrZmfwPtQAuID3d6tc7S/U0QkmdF5tmnyRtUe6o7ngG3dTcdHw7zdVduN1zUQRETe7NXxsHyD7udr622/R5KEePG5+kdS8tRcwZoW2KeJUKsBSlp4+pSJXo71LCA88h7HNNZdyWMOwhIHOP+FeMGaTFj/Zsw8O5Zkm3SMDW7QNTGw5g7WxKiDGgmtc3bR+5+zp97f2BmmD6+v1f16brXuQzf4mcuQL9C1HYqXQr+9DhiTf+/GeNHzOjuqxgPfhat5P8J4yQqMPzAfQSc5YpzTeR0LDZ7xqAzxkMzoa90E85Uy5O0c1AZpnamPMXaBzieZGfNMH0ptOie92aGfdVtTJXp8ScK1dnDdfNt3DZWgDeNRnX4u9FlvpR9VrEMIIYQQQgghhBBCCCHkHxx+DCaEEEIIIYQQQgghhJAagB+DCSGEEEIIIYQQQgghpAaoWqSGPkV0JCUGtfMkUdJ+q6p8v4mY36aNcw+cSCntJEmAk0REJAV+l0QOnIvgbHRwnuhtQ18MOoIT4BAVEamk0BEMfQSfSw78P01wG3F99CP6yIR8zTFBhxH6q9Apm8XDoz9RxMYHOjoDfQo51VxG+54rnnhMwDqphla9AnojS57zGAk4Pst12qlWgbaIyMAA+DTBmYcOz6a8jg900KBvtd/jwDJ9gHXW9erzjhtP6DHG7dHxh840nxMLPWvWI6vXD2rcwCGMzmB0aImIJGCdREPAKwq4FBwT4q+Q1/HR1Wf3txEcr10Bx14D+DUdxAc6Gc1yj+Ooo1u7nF4BD1exRT8HUwLSYPRGYnyggxFzJrZF7JiG8ZLB9IP5BNuYn9LgnNa6zf8+iP4x2ai9wuiJRHDMMz76Bu3IQv+4iEg3+Ke6wdFo/MzwrKKjcSiQb3xO8h5wYq3v189NNqUtWCGHWkt9tHPPwehhosNz3Y0jDZ3AMAfC5SZeYKwwTliPNxDrIDRO0duk8zoe6trB5wtzgxSuD06+ZLP1uKErEPMkzsvS6LaE567s0Nerd1efieekFhFpycdzBjc0gt8bcoFxNKKPHvyuImKd0ThHBn88+uQd1OEQ4wwG722rnWs2gsM3ldXXBR3Ahb7osSLXrOOlaRftFW2YaL3oxmOe9SXC/wf6oLHdFHCUD5XsfMbML6AdUxksdY36OqCDGMcjjHnjdxWRFNZhKWg3YaII8YPO6YBTGt9NmnaxLkuMjzzkg0I3vg/Bsx7IPy3zZ+v1p1hncBH8qr2d+jlAny/Of3E8wjaOPz7fcx/Md9f3R4/DY5oiF0tjK7jZIT+hsxPnK3UZGy/4TpUuwjt5EeKnHP1sY/0BdMPXp+xzlxvTqtqtPbpGBjo/0QGLzvv0BO2ATe+6h2oPNGmftIjIG29qj/XfN+nzHoC5BfrgTW0ReG4q4HIvDtlYQAfss+DW7hrS13af+ugc2DxGL8f5DI6JOXTremq41CVhzjPYo9rJgu4zzndwfmviBZzBuFxEpG2CPmZlAGtiRM+BUlBDJT1phmqXx+v88uaAne++uinaKV0YwLwa+I4AfS5B3h7q1R5tEZG163RthkdWbVLtaS36/h/TbL9ljQTHI3xfwnjB+ED/uIh1BCchXhKD3bpdgvEk8C0E55H1U+2zjfNT9NwX+/S1RrJw3XA+lJm5QLXduOlmHx3gxX+jR4/D+H6N4wmOTyF8zmCs8/NmD9STgCFspv5s4IV/GUwIIYQQQgghhBBCCCE1AD8GE0IIIYQQQgghhBBCSA3Aj8GEEEIIIYQQQgghhBBSA1QtUmvJacdIaqBLtRPgJxLw6hjfTDUEHIyGwHL0KYqIJHJ6m2QG1qnXHhPjcYPzRJLoKUafnog5T/Tnog8VnXrmmAl9r4plvUN0JIlYj0kRPCcDpZCRV9Ner8+zBU4qUQCfFfqrfH7fkHc6Znw4cEM5cENJxopW0PspDuRg4G2L7fgEZ2jB2XNycB3QoYe+qAbwBRXAT2YdoInItm+bTYPR/rG9IpeKHDRbO5NmtOrr0JCJdqjhOYt4PNTG2RjP12viC55llwd3pth7ZVcILIdjoOK1F/yuQ2W7P3T81qE/CtoDGC/ofIV2BY6Z8MQLeqhXgKcLvX/7TIuWBmO8zGrTzyqOV+jgy3ukkMaDj/EyFHC+IphvwClt8o1YJ7nJuiEvMR4jo/PVQEWfI153Eev4ReI6GdGfGXIIi4j0gKd4dbf2cqFXf1Z7tKTx8N21yxTzSz3kF8yp3jxu8kkR2uBMK+hzME7PgIvO59xD/2oC5i/1DdHzF+udhRoKeR2PyQbPdcY4hhjFsT0LU6yy8dOZAgKB5TYmcY/4bIc4bq/Jqj1vHLjmjKMRa2hYZ3CiqK89xkelXzv3HPgTHcQXxksyp2M63Wp9vU0ZPZ7k2rRXuAEceuj4dGXws+a1Wxk9xakW65hOgPcRx8gU3G+8dzgfdoG/acHc7wPrC8TlmH2mqPbCCfo5GVevgx775ItPU4dlSPtXzTvXEPgSMR+l8NnWMZ1q1+cgItIITvL6qeD0hGNgfGANFnR8piZMU+1Kk7W/D0Cy9wwXep9mfIJn1eNPHQnOh3z7xDoMcUuuHHGAPu937KKvy/TA/NfngDWO4CFwwKLjE/KPeUc3jmndx0SdpwYPxFAGPecAjlemRg/EQ7FV5+X1A3ZcXg8+zV64Vzh/jQs6hFOee4FzGpzv4hwoNN9970EzVHv/6Xr9qc3goIaXn3qPAzY5AI7Xgs4vGC9mvoPfESC/mGe9yZ4j5gtDGt7B4H25ktUxWKrXx+hO6vnLuk47Lnf06fPqBQd0Gd5vzLwNqITqSKXtdygH1/KlN/W1R2f5MfMmRPbhxHdq1/biiXoeOBGcwhgvuYQ9x5AjOAn5xg3oeMKxAsG5JjqnRUTqYD5RFxp/zJgHx2jT7wUyVuflUrP1Fvd26WMOFHV8xH1/wvyBNXjQJy9i5ys4HoXGOB/8y2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGqBqZ3BmEBzB4AtJFLWvyjj2qnDAooPG+GTQH4POTnQCBxyfIiIOf8N+wj7RcZQInQN6aT1eW7xW6NOtA48kOrOSYJZMJaLbzpooDagDCnlQkNYk+IX6tGvMOINH45QO+HhNfGS03w7vrfFtQnvzPgP/fgIev6AzFp2f4BNCl5CISApiqimnz7MR2uh/Rq0sOmz6C9qz4/PpocemCF43n4s0ir0naacROtLQaVQHDix8DkVEEgV0NIIvChxYJl+kdbygfxXjy6fVLoOj1fZSX2u83ejjxcewUAn79DKp6HhpAZ+hz+EaB3QSi1iH0YZefe19Xuoo3jFFxws69DBesuhp8hzOOPQKA5HLETOWhPKLzx2GMRhoozsX2yX4YbAKXy+mOLx3xkFexT5HkoX10TEs4nFiDUT7xkK8C5yMGB/omDbONI/70IzbEB8OnJ/G14v7Q18vtj2ORgfzEQde4SQ4P7HGgfHQhvrk8RabbfA5QpclPAf5rHXza/S9wvmMiB3jkCq0sYrDd9VuOoyXMXkYIwvox7P3Gt3J6NRzfeDcw/oUoTkSuHiTTa1mlSTEUKpFx0MeHXxYEyPktTaOaevRN15qdNuCWzmX1vsso8ofcjnOj8qeORSC2yRwpwGOmavdgzh/aYHaJI2wHOfDIp53Kmg7dExDvjE+cMDcG9+9aoneR8jRaGISPMWuQeflcl2LOUYp8Gxn4OHOwADW5HEuRjG20Y7LLTBnwjoMgcfC8OG9pqp2M4w/wXjx5ZeCfscyjk9wxJr8AtctAfOTRL2eDyeqeJ/GdUydBDhGGWqyVPLaIdwD9W96C/YdEN89cG6ZxLkgxk8e5691kctbxtrxawzE0AA4X6vJSSM5fW/tYm6GOVgr9ClXgTF4wOOwR0cw1IFyvZ16A8gnDp5tzP1m/ov1djz7MN9wTE0dHYMV2Gcv+Fs3Dug+o09axNanQHAsSEKfUzAnyjVqV38axtyWCeCpFZF8fXS8xOV9c7Rr28x3sYYT+ugL9jrhOkn4Jljp3qjabigw30WfL1ynquYOcC9M/Qr4Zogx6HL6mJU6PR71FG1iD881oZ4RvpvCeITr4/vz1DE6/4iIjG/SzwG+z8Z9nxbhXwYTQgghhBBCCCGEEEJITcCPwYQQQgghhBBCCCGEEFID8GMwIYQQQgghhBBCCCGE1ABVy5SMIxj8mwn0b6Jjz+OvQgdaJeTUAzcLOmoS6F9F34zPA2YcvuAkwX2iJwm9tSGPrQ/wsqEbLJ3Sx2wAT2AqgV5RfZ7ZFDhjPb4i4yp1uDye4yjZv0m1E8VoX6uJHx/ohS2BsxEcNF7n78jlcG+N38q3Dd5/jB+rS4UdwDkEnKAJj4ssC4KYPPhzx+T1eaPfDP149eAjq4QUjiKCShr0tqGXK8TkRn1d0RmacnCv0UfkcwaDo9F4kdDvjb4hJOAo96mErNM12vFrn0N8dqPX9132xiw4gsF/N75Z+4fKIMBD/xA6jxB0JIlYDxLuM66neHKjjvE0njfml0I4v5gxDb3mEE/m2YV0Y3IF4llehn+fNfcb/d8x4wXbPvJpfa8a4d71F3W/0UGODuCBQvRz5fNb+bzTIxksxXOqTWvSNwdzKI4/ySE9Bhu/uHjGtCFwBIccnpBv8Nags9Hnq0/CnMh4ZdFhH3DAOo8bOQTu07jak9pdac4C5kx14BDOQv7yOUTj5tkQM1rAdYoeZPBvGi8yxoZnnXJPp+4jOmCLMH6hYw/9zehw9I1ngZoHxiEdiidYbhzT6JH0YF3t2n2ahfmvwPwXh5tsMjoHvhXMaNF9yuK0Ds+xP5xfkkNQZ6NfOxoxfkL5xdQ7wfcpnw8c67TEdNrjGFhBbz4869gWEREsHQOHxPlrUw7nGvq828DPiTUV6jP2ucE5UwvMgdCjH2LeWPDvmroxMFcdgrmJ5z0TaxxIz3rVrPTrPGy2x3jIaVelme9mrMvS1NVAR2egbkI5oa/rANQX6AOHp6+WDb7f1MH4UV+n+zAE9QjqwPfb0AzvWxA/49vsdZg6RscxxhzGU4jZWp0siSLmBogP8K77avLgeFTpXKfabkAfA/MJ+udt/RvwP+d8NQ8gxiB+MB6GIJkXhqLjo7+KeDFOV7g3ndBO47PeqD2z9a263gDGU7PPAQsxNKZR54f2Busxj2JqBus2QHwMYD0dGPd979MmXiC/9On5C45HZnzJQX0L8MlLzpNfTB0WaOOcGufQuH5WH6OY0n0qDIXfMzB+0FHfC218P8J2K+YX8AOLiEyC+GiDnIY1C6qBfxlMCCGEEEIIIYQQQgghNQA/BhNCCCGEEEIIIYQQQkgNwI/BhBBCCCGEEEIIIYQQUgNU7wwOOILRSVNBNx06ScT6ydBXh44r9JUZ56c5AmD8wBJ2qOHyMrid0JODx/C4V0y30EUJrkrsYQbcO2nw7BTgkOjP8/k5jXMP1kEPcYgE+s7QcYWOtBK6lz2O6YDPzqEzLw99Ri8XtF0V98qA9z+0ui8GI0h5Ljv6LtEPUw46XfV5D5Xi+c58oCM4E9MZnC9hvMD9N/kG4sXrOAp4zRH0CaFvKHjM8L2N6wgOx5Nuo2tZRKQJnFfjwElUDAg1O/v1c4e5AZ2vPgcsepXQCevz9EWR7d8QvQI+63ivfPEC+QU9j4hxApfhHFKBfOLJBRgPodyNdy4UX3hr0p4Ek4NtxoCPCmMUHX39RX1e6FNM+tz9AO4TvY65VLw8WjfUqX8IjAXG449jtHgcasb5Gp1vjF8V2zie4XxIbB0EM75AnCcxv0AfzZysinkc/obXIYnuOLz2kKcrUDMhCXk57asFAF5Z46KM+XcP6U2rdB9D4xHmCpzPiEilW+esSm+najv04CMZOG+Ym5p48Tj3cB30QJo5VcAZbNYvw71OefI6xgO8WyQHo92muaye/2Zh/utgLPE5g0M1MeJ6hvN9a1Xbznch5rGega/WyIC+DuUuHT8uWGMlkE+Mw9H6ekNORnzuzHwY8xHWXUjnIpeLiCRASp+FQQx9iUW4eThW4Fw1n4p2hoqINAXmK3EdjZkNK/UPOB/BHIlzV8/8xfVrz3ClR9dtwdweigccW9AB6/M7x3XAov+9CPcaHbGlKh5MnGuOBWfrxFZ4X4Zjlgo6P+UgHnB79AOLiOwCXtgpTXqbXT3e2CgyHS/rH+D+m+8vWPMA87aIVAb1O1cFHeSAyReYC4wzGP3Q1n2KMVRweh8FcPwW0BkM8YPLi3De3poqgRoqnXC/yxgvRYgXqNnTCNtPGmvjZdfxjao9b4Juzx1nfctRpDe8qtrmWxnO+0LjvITjJVQ/wDjs8f06q6+7y/jyC6wTcJLb8SbaUY7x43s1Rmc9+r9Dfmd8h2vI4vt5Btp2f3iMFvDkj6mL/02HfxlMCCGEEEIIIYQQQgghNQA/BhNCCCGEEEIIIYQQQkgNwI/BhBBCCCGEEEIIIYQQUgNU7wxGZxH6VsvRzhHjJJGwU+9tIeC4Mq5JdNOhxxj3X4Xb1Ph/wKEW9Eml9fq5FLp6wDmC0hOx541qJnSphAjFCzr1qnETGmcwOvMq2+6+favBqxiyZ/ocR+gMrjjdbszq65ICb049+M/Q04WeUvR3+vup1/G5ayO3H+zSP+BzZZxGAeeniPVSI+izAz8ePvvmCUAfZxU+aLwsoccKr6K9rOhqtvtw8FigYy90v9H5WsaUiFpAT7ygQzqf1vtszMZ7dpMDXdErBBzBJq+LhHO1x2eol8OFML7E+E7p7Q3eGd+zjbcC700yob1dGTivnoK+jqFz8qUK7Ne2OsmTvR2Ry03+CIxXIiIOHGpen+7IY6A/FeMh4Ag2PjSRsKMTMTEd7S22m3tclTiPK2P9CLgucN3QRZjKgyvOeEs9z6Fxw+lrlUT/XH20szHZ9WbkcpyLVMDf6tDZKCKVfu2AdQN9Zh3diWjHHl63RF1D5HLfPiQdndPiOqZxnPbN+xH0Wybw2cdxuAjXGs8J4sUb0ea5QZk6evqsv1Ado2dt5PLQfNfMbUWkAvFh8g1g4iGXj1wu6IhN23gx3k+4tmZMDF1XfE7xfcr0wM4n0AnbBM5F0bpN6S/q+MGxA2sq5Dwe/To4Js6x69PxxiPZsCpyMT43ofo6ItYhHXIESxLuNy43NTSil4uMwhFsatPo/eFc01Tp8Mxf6mESPBacmz7H70gGwBmMzmHcfkqLfW6mtejxZWoTeIsbPWN5BOU3VkSvgN9bqsgvOF8xvnfMHwFHvfGLY3yYnGpd/vguYuLB1DYyu1TgPDLv+faB89uJrfre9Qx63i1H9hGcwXnwue7arsdl9AOLiOwKMTWvXa8ztSlevJRWLdc/BOZ1pkaTb56H32hwHZzPBsajJMxXKjBHQz/w5t9gndC3LhhvbLzp1bEeivf7S8BZjzFcB375CuQ8dFZPgPhp9dSAasuDs76i51CpDTB/rV9g9oHwL4MJIYQQQgghhBBCCCGkBuDHYEIIIYQQQgghhBBCCKkB+DGYEEIIIYQQQgghhBBCagB+DCaEEEIIIYQQQgghhJAaoOoCcrELgKGQOlBcRaSKAivJ6OIEpnhKFQV8QtuY5ViIKBFdZCpYUE48xWtCxY/KWEhNy6NN0YdAkQffb1h0LGNk3tESflPgCwuRBITl1RQewWI3pkBCKB5GES9vN76yFFirAqX4yYS+1qZ4V0ovL7vo8/TVj0tCz7CIWDVF59QxhqCoUOAZqKbAE+YgU5ApUPArmE+qKBhnLoOLXo7XFYX2oYJy1RXu0/0eBwU2MnAzscBCqOBcxlOAJQcF47DIR30mXgG5RH+nbuPYAZgCp9WAxZVMfoDiN6at+4TxVE2tOBezoBzefiwDYQqzefaPtw+3weUZKBTSUtLXoRiokuhLs1iULgUnFrN+nEgnFHjCexMqfOUraBqIKROTWCAOCmwkc7pwiSmO4SnYEzcnOcyjWDAXd2/G6XAh4FAhI4MpTAOF0qDwiLcYSmAfyYZmvf7YyZFdKnWsjlyO18EUeBrS18C3jQHnM4ECYKGCcQksxCdSRcGv6Pku9jGJYyoWtvLd+8Cz5fp7dRehQLI5JsZDqNCniD0vvA74rDW1Re6u3LFG7w6efZMr8Jzx/akK7HOi80cyr+NDoI0F47wFewIF48y1D8yhTXzB/nzFRnGOhAWU6zI4F9X7hNpdpshunZmL2Bxal9D3L9m/Xh+zb4PeYMZeZh8jKa19XbXxXtr3I8g3RftcBccbzImYPyBfmPjAtq+gKRZsChUACxRwMruHtm++i3NJU+CpTT8nWFC7DEHYBvPjXaBg3JRmW0BuXL2+Du1Zff8ybzyjN5i1v9nHSEproeBg6N0U84tn7DHvzzh+YPxAvEhW5wsTD1UUfrUF4aLvf2g+jOGA7zK+94yWvO73lFYoggh9aoKCXkNQ5W58s74us6Fg3IxWW7wWf5sCBePq1r6gN2jax+xjJKU3Vqq2KR4aKijnA2IO56tmPgtzLhyPKhmY76YhnnwFTXFOnMHzgvwTyDcuEFD+Atv6OjRgncSkPo8WiBfcJ75ft0JxuBZPmk316AJxifWvqXbhZZ1f0tNYQI4QQgghhBBCCCGEEEKI8GMwIYQQQgghhBBCCCGE1AT8GEwIIYQQQgghhBBCCCE1QNXO4G12vnocJcbGEXAcYTvoPwv5rHy/hTygeAz0+cLqZrln/+jRQodwoqx9Yngv7P5QRFq9Gnp4H+gGA59LyKFmnNIhJ2M1jmB0AmPbOIyi3XLmHKvx3Fbhid2uoNvOA97dFDiEc+BYC3lzEgFHqPc3jNnSEKwAvimkd6NuB5xGofwjYuPFOMZD/rtqXNsxCTl/8SzQIRzev80NSbhZGOYpOK885Nmx4FzDIxhPl8fjhm434/1DEXaA8qZ1qo0+6JBTzYwl4vFqmfEEcyLkH4yXQNunRws5rJCAktrcG3OVPZfdBXzgabi2OTjNEjgXAxo4s3+R7e8kDzvUAs+2b/6C8xWIQePgw2MGxyuIN5+jMa7nvhKYj+B4A32spvaD8VuCQ7gyoP3wIV+q71k1hHx24MNM7fqOyN2ZeDHPOsRDFfMXnK+EnHvYZ3Q2Gics3iuPY9o4YOM6pjGnobsfnkv0Uop47jf6LdE5PQjxEtPV7V0/8H5ixpOAc68Mjum445GPUP4IxkNMR7DPSW5yjslJMedIofctr8MeahJYcaxq5vG1D2tswNwjhz7gwU7Th8RgN6zTpdplcLqmAs7g4ht6/WQ+3njknb+EHOINTfoQ4PSsZBuhHXDYe/oQercIOYFD4LtJ2nOZGrPR3lCcE7XABCYJK2BNjckgoR6DAScijUmdF9Od2ileeu0l1U4FnMEDq/X2yQx4SLPR7/i4vognXoxDGvJLvY4XlwHHdCbglPZ8h3Bwc0LRYd5d4P7jvcb48DqDc1hDR7eTcFB0AiMTGvTyXcfo52h8g53HjQGvbLZX17gorfqbaqdmRjuDe17X22ca9L1J5cEfDhcylbfnmGxsVe2gI7he5xup022HzuAsOss9DvvAN0CTf8weosF7nfIUVckKvk/ra5eFmkxNOVgO+QWdwfkhPbYkuzpsR7v0+y866PtX6nagwtfm41SxDiGEEEIIIYQQQgghhJB/cPgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGqBqmWzI04WOI+M08jrVoj1sxrGXiXbqWUdjtPNx80ECDquQUw/AYyTgkD4/XiLkhQX/bgWdatAO+uuq8B/iOkm8F9uIccmBN8VrhMRt0nBx0XmG7bjO6SocsSEnDfoy0VeF21dlwsR4gXYS2+CJTKPfFz1e6AHEtnhitgz7xBhsGWv2oQ7R06m3R/9mID7Ncs8+zPNv4iVePqnGdxeXkEM4uH0VEYTdRodR6DTQ4YdermqcwSkH8VIcgC2iHdOV3k69fcivmKvCyZdGHyLETzrgeEWnXsCnuI36PC+hu49OrGrUu+ZK+STiEaDnL+gx9hBylIcorX9DHzMXHS/JgH9xM9EO+5AjGPNPyHFv8pNsZU4TQSIBtR1wBegD5nlvnkVHK4w3lSH9bJe7O1W70NOv1y9CbsC5pUcyjU7ENDjxUo3grwsw8OZ6vT3sD/ePzk/vdcIaGAFHMDr40OFo5sOh+Y/4vPjxHNNm7hGYLycSnvku9gk91DB3MI5pdAgPwfx3NBhnsL5/Hlu3orRRe/1MPIwivwQdweDwlBw6F+M5gr1OcohZMycO5R8c86LXrmoKhWNBytREgPoEkPMSQz2qnezfpNtmLiIiQ5Cjujfodr/eZ4j+jk7VRqdnph7ipR784b78EsgfKajzUsmCUxocwWLiB9qee1+G9BB3/oqEHLEpT8DkoP5EC3zmyPuKFIygHt5Fm7J6/fZ6vb9ModfsI9mvvZ+JPl0TJeTJR/rX6RhNg9M126zvPcaTcZhL2PGagnc2l9dOaZeD+EEHLOSOkudexX6/wfdnF/CJAzmPZLoxB9864CDoGS5CkOPyCY363oyHeGnJ2T6kBnS8pCAnlaqpSTCCwQ3gNB/UNXyyzfre5Vr1vTX1mMTOmZNNrbrdHIiXmI5gn5Mc5zQhR3CoXgmC+SbteUHC8QdDKgljnqmXI3p+m+zXc4dUt35XqXRDPSURcX16vKn0gdM+4BD3wb8MJoQQQgghhBBCCCGEkBqAH4MJIYQQQgghhBBCCCGkBuDHYEIIIYQQQgghhBBCCKkBqhdLoHPGeEq1nMM4hqtxnqDPDI8ZcASjD804jXwOWNxHSKKYALeYi3YKGydsymPJCTlbcXVwBKMvBJ1qiPE5i1jfIbZz2vdircMA+MtCruXREHLAujQ6PGM6GX3xEvAIhxw128UhjB5H9PWWwcFX1n6qREH7zxIl7RNKYPz57h32IfT8BzBOVyTkE0d/tPiceIH4QEcaOmAh3tBXVI2fCJ2tFbxuZv14xwjo0EREJC3ocAWfLxwzi04+dE5jPBU99x5jEJxpJmZb2+0+Rq6PPkXMV+icRmcj5DMRkQref3RYoTMvsDzkt/Ldy1CWDMUPLkdwsS9cUuh3Rscv5gOMB7y3OPZX4/sNeNFNHxriOWFj4/PsB2IMHcGYX8x4FRiPqqp5EMDhdQwsx/lR0uMe9PnlFOCELQ3q+Bjq1M7FQreev6AjGH29IiIpcChW0MGJ7tsA6FzDPph2yBctHk91XEdwFsengPO1Gsd0KH6S0c+dw7lHFY5Y8+ziOIyOYDPfjfblOXA6+hzT6Lm2819PnEeQzAbmzyGnPcSCiMfpCe0KOhgDzk7rKN8O8RLTCRzX4Shi50A4iJnxCeYaSXgfSoKfMzGE7096PuP7zXiqYzo90elqHOTgCEY/dNIz3iXBCYzO10pOb4OOYJfRx6ygQzgwnxERcTifDTg9EZy/OMH9xXPEiogkwCGcB8knpgd0CqPjFR3BySHri04MReewuPkFHa8YLxhPqUbtazV+cRFJNrbqNtZ1qYN4MY5gcL4GnNK+IThUNwPnqwkXvTwFM5qKeZexx0jCN518Gp3R4BRORMdTcy46fpKDOt+IiCTQO43vSzFrNmG8mJoH4CRPBfKNiCdeoB12SkO84Ps1vj/5nOTb3REMY0kV25jSQRIYj/D9GPJFqg/88+AIxvmQiHWOYw2uXGv89yH+ZTAhhBBCCCGEEEIIIYTUAPwYTAghhBBCCCGEEEIIITUAPwYTQgghhBBCCCGEEEJIDVC1Mxj9ZMYdZlxzYN+oxhmLPir0+cZ1XgUcwyIeR3DAiWW2N25D8Gvi+r6doEwHjmkskOCnMk41j2NE7S9dtL/FdLaGwHu1XUA/WcixiH0IOaeruPchR01cJVrIIVyVEzLk8ARnDTqu3ID2FVUK6EPbdt9zKHqMQyuJTkZYHvKJi8cBnAGHZyCfuEA+MT60kABLrOM1LiY+AN/ikBM4jY7YCjj3Bgb0McBvlSiCcxp8VyJiYxJzWFzHdMBpbnycsBx9iyIeB3C2PnK5mJiNdkob39U2xoLItjuC0Q8s4nFe4b0rgv+uOBC9POQkHwUmVwdARyg6tmz+weXWixvyfgYdwehKDc1XRuGwN8StaYDPqc+djL+hVA2oFPX9L/bp+Cn0gGMN9odO2M3djPbEojMvRDKjrz06Go3/F2OhDny/IpKs1x63kBPW5KOY89+qHNOh+AnN83F7dNk5Tx/wXSIN+QV9vtglmJ8U+3TbwXwF76WISAp+M1chptPT5gJ9/5MQD4lALGxeRzsYK1lwMuJ8JlDzwL5Phd+PtrcTeNtHvNGMT1jTAPy/6APG+a+IuKKOawd5MbYDtgnmJxgfGD/gCDZ+VxFJNo9RbYyXinF4ojM42klu5jOem23rIsS74yFHrNF/oxRWBMv6CJb1wWNkYQ5UByLiXALuNcxnMOeJ2Jh0MIYlm1rNNlFgvKAjGOPHjC3gdxURSbbpuhyJBr1OJY+O6ej8E3JKl9+S+S44pU04QH0UTxcwpEABbBzCGYiXHCSkBogffB8y9VFERMC9j3kXfeAhMF7SWEOhCZzSEB/oGxexOcfVt+h2yBGM8xnImcYx7XOSQzuuIzju+7NvffNTqB5OAZz1g+AYxxpf+L3FN+eGa2fqTXjmnyH4l8GEEEIIIYQQQgghhBBSA/BjMCGEEEIIIYQQQgghhNQA/BhMCCGEEEIIIYQQQgghNUDVzmDrywx4RGE5OoZFqnC0xnXEBhx7xg8s4vHIBhzB6PM1vlZwrIX8v2LdKcYpk8Y+Bgyso3G8ggML2yGPG2J8QnHx+XpD8YIxalyG8ZxpPi9lXEdwyAkccuCgH01Ewp4/dKiB48o4gns69XLjDI7ncxWJ71AzDtgM+oT0vTT+RPS5inWgGWcnOoKxzwEHFt4r31O3HTRZkVjHUdgBG3QED4HjaKBLH3OoW7XRuWec0yIiJZ3T0BGM+SXomEZHMDgb0RFsfVWeeAk4gkPxFNcR/FbES8gRnIAVvOkFx250YIEjGOMFndKYb9DpWVV+QYdvNuC4D2xv9odOYXSA5qxjGr2OGA9BRzCuHxrPfPOXmM7XBEZEaF6XhLHE5xXF3B3wL6PzF9uVAjpkdZ/ROSwikszCeXi8wnFApyw6gpNw79HRhn5gEev9M07YHeCY9sbUyNVRuGlqZMC9rOaNAmMS57cYTzHrVaBjuiqqcIRHHnMbHcEJjzMYHZ2VLOSggCPYzG9D+cX0wPJ2OIERU0bDHBTiCd9dYO5h5seBsUFERPD9pwpHeBQhx6uJF3DMoh9YRMTl9D6M8zXkCIb4KOKjXsX8JUQ1Ts6oFXB+lPBIYJ1Ez3FwjpyFCXIe2okCOIGrqS0Dz1aohkWIbHMgn2A8gQPW55iWRnBMm3jRfTb5B8anUsAp7ZvbolM6FB8Yc6lEvPhIeWa8lUAQ4mJ8f8J4wXgSnN5WMy5Dbk80Vf+pTiS+I9jkF0+8GKc0OoJDTnucz8A5h96vR0NcR7D53uL5bmlq4uD7EXxvSRagpkrJ8348cn14th2OXyLi4Jh4/2K/Hwn/MpgQQgghhBBCCCGEEEJqAn4MJoQQQgghhBBCCCGEkBqAH4MJIYQQQgghhBBCCCGkBqhaRILOTuMSMxuAS8y3Tshfhm6VkCM4pjN282/Yh7APKHr9aFey7zqYdVLoTIv2GlvnVWB5NeD9Rj9igJC/2VDNdd/WeAk403B7n7MmriM47vKQU1ikCmeacahpRxo6Xd2gdn5aZ7DH910Ebw7ES7LBOhOjMI5g478LLAffq4jHC5tGD3F0/gg5YMsu7MSKS0ihhqD/zKsVhR/Ri5Qo6vudGOqFtnYEV7o3qrbr69FtjzMYHUcmpsDRGDI2GkcwONOsDxr8eOj/lfiOYHSk4bON8WGOtz3iJeTEghUwv/ic5MYRDA57Ey9F7Yx2PZtUu9IHjmmPA8uA3kf06sP9Do5wOCai8zHQ9jlgJYeOaYwxiKeYjtfg+La13yJwuEtM7aExtGznUNYRjOMsXHtwAIf8vsYZ7FkfPcO4z0xDPEdjMr9tjmCvc68OnHoB56sZr2LOX8z810dwfhv9Q+g9wOsQNvMTeLbRd2ec1Lqdyka/xvgcwhhTuE90uIZAp3RcR3AlZ/OLy6GDEZyvgZoXJh7METTVOBrf4hIIo8LUbQnVs8HrlKvifQnfh3BxXGcw3H90eCZg/oyO4Eq+xezT5TGmop32xvFajnYCVzNfQQdsiNB8F+cnmI+8DthAnRb0zKLjFd+XEF+9CQN+Vwi8i4RGccwnKeOfj3ZMox9YRKRSp2PIZWEOjU7p0PtRJTp+RkPQ6QpUEx9IKF5wDxlYAR3CJh8Z978nvox7P7r+RGhkDzqCMb+gcxbWF/E4pUNO+0BNFUwVb4UjOOSgNtGBPnlfLijDOvh+hE5h3AfOf+DbRyUTnqsmMV7w2azX+aGaL4D8y2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGqBqZ7Bxx+Fy4xWtwlLhcXkpjAMt4KENtT3HCzqCYzqEjfsF/SC+bcCPGHIjhxx9Zjns3/g7xXo+t/lfCcAXE3bTjcKPGNf3HGgbp42nnyGvjYvrzApJP0cDOovAWeOGwBkMDk9XhLbPAYu/YQxmQtZX2B86XjPg4wRnm/FZeTw7Ie9SXIdR3Hu7eR/b17IXcgT7njLj2SoHnLClIdV2/dohXOnp1MvROT0UjheHTlj0RAZARx96lkL+X2+8ZDHmoh1pO6MjGO9/0BFchRPLxAvEh8D9R0dwpV87pQXzTRnGPxE7JqKTHJcHQMde0NGIjvw66/Q0bq+YTvLYjuCYfmDvNjg/wXyCfcT48PQhkQKfJjjQQjULjL81FX2e5UE7f6kUdT+TWXQdx4wXcP6is9M4g0cTL6EaGMH5LNZEqGIOhcSNKYxZnGtgPPn2gc9JyA+eRpcyOIPzkOvhXmNsiIiU4bcMxDAeI0SqbbxuQ/zgvTfjVd46ikMORhxOzNw05nizM/qAR0Uor/pqx4zc3Jfj0tHblMHhG3qqkq3tqo3xgvmjgj5XX7yA07MEvdgeTuAQOD/dEZiaGIE5sukxxk/Aze2rP5HAZxcopfQ2oWwTyi/ooMZ4QT+wSNgRXMb4QTW32/Z42tZ4eTvCDeMlBZNqW+cn+rtCJW3r26TgfRf3OQg7gTdbu782nV+SzRAv6Aiur8Jhv62OYNjfP4QjGN+FxL4vmzmyp8aS7pTOHxXMJzmsf+O525ijYD7RndDPso6GrXSrinUIIYQQQgghhBBCCCGE/IPDj8GEEEIIIYQQQgghhBBSA/BjMCGEEEIIIYQQQgghhNQA/BhMCCGEEEIIIYQQQgghNUDVBeRMcQvkrfisHLeABhYqwQIdvmIZoaIuwU4CsD0WI/DuLwXC6TIUjKuiEF4UWKADC/h4t8EiYp6ic5Hb472IWdzPe69CxU5iLt/Gehubt9nGqgtmezDi+yTrVZRmjEegwKD33gcKOGH8BLsQKGYgaSgAhgXnsFicxC8AZrZ/CwokhArKhQoqhIphYHEDEU9BDZTiByT6Fbz/oYKDnvxifgsUCAuBBQ5cBooIYbwECsqJhOOlvD0qHgSIWxAjdP9xdwksaOopIJfAgk5YQA4LUmJxQGybgpUQT76CC4H48BW1jCLVPkXvDgrISRbjBQsO6vgR8RR2CM2RRlMALi6huUGgtoUIjAWB+YyIZ6wPtEMF4nC5g8o12B7tNlGkJ81QbVOgBwuCQX7BImkinnh5O+IB2ca5pAEv62jmzykYf+BZTOT0tU5Cu4y5IGaxwO1BcsxE3QUcn7A4KRaQ84xHWAAsWJdmx9fuelswMWUKdUK+wYKFsHkC5mTed49Arnd1rXabCFLtk/X2OSzgBPNhnB9nbRGqIZiwYEFbnHu+FcXetnX+8lYQyvw4z7M5MmbhRhFxCT0eFGGl3gGd8xrs7VSkJ+yi95/XxbzKGC9QkAzjScSOR8VAgTgkdF1HE16heAgVXB9NDoz7HojvARXok1m/ikLTOD3BeOmHm9Omb78B4yUBBZRNfGRxPLIBGbdgXIjtMV4FC8ZhgdvQ+08Z34U9cwncZwhT6BcLVOK3jOh3VxERl9MB0AvxsbZPn0e7rTdquxlehRBCCCGEEEIIIYQQQsg/OvwYTAghhBBCCCGEEEIIITUAPwYTQgghhBBCCCGEEEJIDVC1Mzjow3srCHhmHfpjzPKAp9a3DSxGn0xcayQ6TVKe6+jAGZwwDixwCIccfeg5AdmYcQh7MK6UKjzDuk94DqO4N6ZT2+YMrsb1FGf5WwEeE32vIhL2LwdIpALxsz2oIsZG4vJaahN0AqPf1fPvWuhh2la/Mx6hUoXzCA8Z1wkc6gM6tLybB7xJsUnre4PxNCr3dsx4qeTByRhwdqIP2OfADzml8VpjPAWfwu3hyII+GEcWrG/yB/qisS1iHNJmHXQGB8ab0HjkyxW4T3PpYo5HiXFT9SFD8VKFD9jMP/CYMfONcVeGBb/b3zsbd84lnnsDYz3mh4RZHnf8in/O5aInzqMYox3TZXSSY3ykMN94xlS8tsZf9/a7bhGvLzWKkP/Zdwz8AZ+9km6jQ9iMP6ZWCMSbxxdteh2aQwcoN01QbeMEhnMsJ3Q+KXkmmz635Egwt1fganvnjjsZ1WRIHJdxG/NOBf5lW5cD7j6OdxXPs4tzJlOPJt7FNvESqJmB8VLwCDp9MTSSUA9D9SiqYVudrqE+VuXrxXEU1imY52r7v+hhv9AzG/dVpNKC+QUc5IGaGOgfFxEpBiSvofuf2g7O6W2Nh9D8F6kq38C9Qn+vrSUC31dgf+YbkqcTFdiqFPB/B4H5i3ECh2rwVFFTBc/r7XDWm0NgXsZ3C5xjherl4HX21FQJvT/jWB+8czh3rKK+TQG6UIL2aO4F/zKYEEIIIYQQQgghhBBCagB+DCaEEEIIIYQQQgghhJAagB+DCSGEEEIIIYQQQgghpAbYfs7gbfVQ+gj56gJeWuOG8nn/oB1yBFv/S8DTVIVXJ4XnCf4yPG+zR/CboW/RlQp6/Sp8i64wqA/R0xncRm0PTq3YbsPRuBAxBuO6dKHt867sCI+wIeR1xDhHvyE690wb1vfEi0sGvDlxncHoNEKPDpyjcXJVcV9CvtW4JKo4pttGH1lcB5v3nLALMeMnmQef3ZDODRgfiSLkGxFPjgJvaExvtXHsBZzSeAl88RJyYJlLG9OZtj1SRyhH2bEBnFjgzPJ6SkMerRCYTzLwLFfjrMcfttFrXmkYC/tDhzTs38wdqhhL0Efm4DxjzpESgvOfKjZCZ1o1/d5WYjvr9frJDNZEgPP2OF9D4DalvsGtrOmn3DhO/4DxEprf+K4J+utgXuYSMR3Co5kjbWvthpjH9DmIE5D7UaLo0vq6JMEZHHII2/lKFfNdGLMqfT3BbdT69W26C/DsoofStKvIsejoxLkBOoJ3hDM4dBajqdOB1wb9iDgMZ5J6vEnnAnONMsSHxxOZQE8+xHXcMRLHo5BDupp4Cc1fUkmMl3j1B0YzX469SeAdrpqrXISV0PmKvmW81qEYreY6hKaGcd8jK3U6vxinNOYb835kD2hU2nj/A/klrq/XS2hOFFqO8x0En1PPNyC8FxgvAyXdHoIEVERNrcSLJxF7LavxDEeB+cU4gXH+gvMbz7gdfD8K1abZDt8IzTEQU48kphN4NH0M1YsI1c8K1LPB+BIRKcD8Fp/v9CiSNf8ymBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQGqNoZbHy9APrttgfmmAGPn3GlBryRItatE3ICo74j5HJJWGGnWSeF/cbzBL8LOhiNTxG9KeBQKw8OmT5UwEGShHUc7CNn9gCMxmenDrj9HdR45Y2rGZb7XD/G7QN7DTmkQ+D2vufOHAHjAx02ae2XStY36+UDfXp7uNfGOS1iHLChmAtRgT7GdSZV5fGCtnHqxY05zD9VHHV7O6dH43FDX5SJF/A3oy8vCfe+Am2v6xJzFMRUXGdwJVsfvXwU1znoXNxWB9a25kQf+NyhlxQ9tujI8vkRQ+eFHjZweCbrtM+5ArkAr0JlaCD6eCLb7L5Fp+d2AX1kuByvo+da6x0EzrEaaTDuI+TUi0sVMWyeZXC6pvPg9Mzr2QQ6hVH5Wil6YrastymCI3hgQ9fWuuulmGuOXD4an2ZoFeOzezsc02YnAZ98YHPjFfQ9t6H6I2VwAud0rjf5pQedfWE/dLmgY6g0qMejTPnN4D5G0l/WF7scuHfVjE+h8SiDt8rkdhgLRjGnjuuUTgTWx+cEfYg+t6n15+rlFdimAAfJp3Q+yuUaVduVYS7im++iVxi2Me0AA073qQwnVc27CILXNgs/oEcyhW+zxq+5jb7W0RBwmlvnq41HjJchcL4OgPN1CD4CFGM66jM4Xom9Fxn4IWa5CSlmdA5Eh3QFIqaa11DsA8YLxkcCYzww99w+jth4E3nzvpzGscFTNwqOgfHQD0mqt6Dfb3oK+D4U3wePMYTxkokpgK/Utah23Hzi6zJ2Accbkze3tfZINfET01Mc9BojnvHO1DPCGl/wbhpyAGP8FWCFoqfATeh+YvxUA/8ymBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQGqNoZbNwZAddGyDFc1TG20RGMqg2vM9i4d6KXx1VRJnADz+f3Msjd0qlohzA6StDZmABHX6WsnTbo0/P9hl4+B/vQ1jYPoXjBdjU+zUDMhRxXIf8Z+s7QKSxShTMvYAaM6xTG+BMRI6RKG0ewvncVcMAmy9qZlmwCn2YV/l/0gKJT2hU83tgICvCwJiB/hBQ4vsVGs4QOI/SpYjuuO9XrF9LPsi+movYZd7kvuox3DeKlAgpyvPbB5wQdoR7/r0trLzXGS1zixguGgjde0JGGDiyID68bOYKqxsTQGBiKSePICnhIq/By4XgjED+JOp1PEOMI7uuOXC5ixxu703j/jt0LjzY6tVLo9IPtq1K2hRzBAX+ZGZ9gPpNwvvFoFOPo9sb0O3p+gs7XbLN2rA119qh2GXyuZc/YUoFnMzmo80upP+yRHUn3kD4GOvvQv4lXHeNJpIrJ9o5wkgfyTWgOHaqx4T0k5MEM1jTI4PxFx0uyeaxuQz5xBX2vXaXX9KHQ06/apT7tLe+F+W+T2YOmt6DvFWpErWM67BDFGMpCDKLDE8crMz6Nwo9oOhZySuO7S6AeCsaLb76LY30x4BBOJXE5+px1H7IwnqWznie1oOMFr31ySM9vQqCHNG58+ObD6HzNYbyU9HORKELtGHTChnybVWDGNCQ030npd1n0cVY8sweMhwL8YB2w+lkfBKcwOqk9Ck9DHhJAS17HVB0KvwNgfgnFQ6qK/GIcwRWI6QLUcsB8Y7zZ2+iIlVF8NwrkG1tfJ4u/GIcr5huMl40Dem6BDuEhiJ/BEnx/8eQ4nI+25HXcN2bj1VTpLeJ4FG++4rsL+PwnipBPSjqfhOJjNE7pYExt6xwK48dXywZjyGyj24MQT+g0x3DA+MO2iB0306nouUI18C+DCSGEEEIIIYQQQgghpAbgx2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmqA6p3BALo7jOulGn9ZyAkcdAhHO4Jt27o3Qk5g9A+hPygEup98Vs8yrINeUXSQOHCWJBuadR97O/UBujaoZgkcfCIiA+s2qfbgRu1h61mjvWtTzB4A9CWGqMLtYpxV6LYN7DNoUQH/kM93lYB7kUbHHnYB2ugURvdLNYabCkpm0CGc0Q4+vA4O2klsg2vOoTtVRGQIfFLggC32WE9fFHjeIUOScXp6rpxxvoaceujECsUwPqdVxIvxCxn5F64fOAas73WoBVJWMgnOvDw4hdFdCu0k5KNUNmeO4Xq7dHtbncHwDKTNaWNO1Uu98YL3P+DUM/GBTizcP3pufY7p0Ji3rVTjCDaOaXRiBdy22E6DYyuNHkDIV2K9nwafyysCdKih0w+VbOiE9Sm4jEMtMDeI7Vysxn+WSEWvEzN+MB8ZD7rH1WziBe6vcQbDfCXXqn2bWL8A5ytD3b6aB3qd4oB+Foe69bM83+xB0wOOxrp0KF502+cM3mafXVyqyS/wrJZwzox+Vlg+Gp9mEgppOKiZkQMvqCtjfQJ9L1Nt4/XyQe13rWxYb/qAXur+Nzaq9qZX9Xx4F7MHzSC8KGSwDoiJB30NMB+JWKenHZ9iOhtD82Wfhz3g/DXLca6Jb5hYrwAWlzxdRMcmOhQx7eI+ytDlMvQRa7bkPPcil0J/O8yZB/X8JsQgPGjWYQ/zG8jLGBub+wjxMqTn4IminrMn0IOM8RNw3vvHo8B4E/BrOnAEC+QC3D6ZsZ8wQjmpCM9BEVYwzld4tkvGYW2vA7734zqTm+ycJwqTX/CdLzBf8cVLugz5A7zXJj4gfpLQdkWdbxy+X4Xyj9g5cgLrD2SgPg46pcE/X8F3NIwnEcG/icTXa4wnzEcYLz3goO4ZgvagfU8owD7GNurznNBo36miCM93IVfAOWcTnvejQDwExyNwlofqmwTrhoiIxKzbYupZpHTb5J+MjRcXyou4vplTQdvkq+i2iPUOYx2Gtlz890b+ZTAhhBBCCCGEEEIIIYTUAPwYTAghhBBCCCGEEEIIITUAPwYTQgghhBBCCCGEEEJIDVC1M9j4Nc0KIb/vKPyIxscK/hfjdtFt497AFcTjFQ440swxPQ7gkaC7B90em/eJDthozyh6t5J1Dbrd2KpXb9EuoEyPdr/4KPRq/09pMJ7jMxHwZ3r9ZCPx+YViOveMwzHk6MMY93kpU+BEg3uTCnisUd0UjK8qHNVmG7i2WfAkVULXDZ67VBV+TswOmdD9BXBtvE7mOQJvj/EDi0gCXIPG+YqOI1jfOPcCPk6MBRHxONLQWRTtULPPPjiyMmHHLOZB9A0h6B/LgDcJndQuU6e7kNVtEZEk7CMZurYBAqdgHcFuFPFSQgcWxM+2xovPMR1zTESC7n50UFcTs7gP9EIaZzDGOIxX6Aiutw5Y4wyuJjdHMASOxhRI0tD5alz/Pn84jnEhdxi6dd8KZ2zQ2QjLsQsh57Bv/3he4PVL5PT9TuQhFzS1qnb9eH1dHUrWPPSv645c7qqR2Y4AvYBZMw6j4zPsmA46E2P6WG0+Qf982AGLjmA8b5wPGx8r1jyoyhms27gN5sBsTs9vcRxPteh4SYGPPjug578iIuk3tSMY57euCr+lWj/k5YdzzkPCQd+riGfMCjkZA8utoxHyeBU1D3D+IuiDtzvQ7VT0+r75blznImLnP4nIdtkzJKZgzMqkot2lccFHF+dgeYiPXNKeMzpfk0Pai50ooDNYO4Vdv25XBvX+0AlbFQHna8gnb55CU5/CHjKUi1NwsTE+0M87UNRzj0FIguiM9e2zCPvMpePNXzDEMVugQ7gurdtJ9LWKJ14GMV4gPnq0R73Up8dcdLWbWjOenIpeWHS4Yn2JULwkW8fB/mB7z7w/k9LvKx5luAJr9qAPuh/ipbNfjy0beyEve9bpgLoI5YlN0Z0C0GtdhnyB36GyOM3z1Oww4w04hNEhjetXMF7gGCa/+OLFeKgD3yUx/0D84Nw0Wa/jybyPiecbHr7/wPcW3ze/kYSc+NgWsTkH51ADJd3vlsgebIZ/GUwIIYQQQgghhBBCCCE1AD8GE0IIIYQQQgghhBBCSA3Aj8GEEEIIIYQQQgghhBBSA1TtDEZPV8j9ZNyEVbjm0L1hfL7Grwq+Mth9yP/rWyfkRMP18ZjmLEGB49PfFY0vChx86AkFjw76qtCj48D9lB9vfTCVAnj6wNeSbQzcbwB9McY15kbx7xDoPENfTMjBGFpehdMx2hxol4ccwuiTSeAePDGLP6FzxoAO4VyjalfwvKGd9HhF0+jegbZxfgbIJdAFFO23Q1+n8b2KSAKdr8WhyOUmx5XACRrwEyUz9hkxjmDj4EPfKiyHZ10c+FZT6BS29yqucw8dRcYhDO10RscTOqpFrFc4WQCPI9zfkFGtDpyL6LwyfuiA73fzOtHxYvaJbYgP9KFh/KBbV8Q600zeNBtEP7sYXzhW+JxYmE1MHxIYk9BGnyL6vSvglM56/M3GDQbPYsiNC2C85MGplwVRXBoTezUO+5jErZkwKgLzMHsI9NTqaPDO43B+gjGWjueJTIPzFYyxkq6zebZurN5HsU8/y6XBeL7LBnCxm3yTis6JvrlDbEd03HiowkmOv5XLOP9F1+W2OYPRlevDbgNjgYkfHREVyA3JtvGqnfa4TltgflIu6JjLNOS23mEPTTAAZSA+cAzNotfUWSc5OhkT6GSMW/MA8zDcO9+tMrkf3/tC+SGm290HxoeT6Jjz1YYZCfrhR+O9bm0Yq3+Aaxs66+YcjDehORb6o4ds3RecUyXQATsITuDeTtV24NY2zuAhcHx6/KuIqR+AzuB67UJNwbiP7x5l9HF6XP4ZOCY6hLEeAHqqjRMWciD6OnuHbB/6C9HeWOxTiMYsznfBIQ35xuQOnG+LSNI4g7tUu9y1QbUr4Ayu9HTqdiBeKkV7nSqBegBJmJelGvW7RqoFnkP8dpKG91T0qIt9X8FnL5PC92FwTgc8+wWIF4wNEZEuiI81mwbMOnGoC8xf0EFu333s8UOOYIGcVO4HRzDkF+MMBud0ZchzDUro9g+8U8H7c6i+lsO5Rtq+T+M7eqgWUSqN78MwZgbez31O8o0D+jqgp7rYot+xJraaXRj4l8GEEEIIIYQQQgghhBBSA/BjMCGEEEIIIYQQQgghhNQA/BhMCCGEEEIIIYQQQgghNQA/BhNCCCGEEEIIIYQQQkgNUHUBORS1Y2EaSeWil1dTzAJEyqYwABYrMOvDcjxeFcW4KhItAy+CyxmLGSQCJcWSnjIN6JIvwDFTGSzOBSLuVKBAS1ObXr9kiyfVgcy70AMC+mS8fzcwxblA5G7qx2F8eAuwYCUJvBmBdghc31M4zRSJwuW4S2inAkVhjEzc9tIW1IA2FpQzzwUWH4CCctiniuc6JOFaJZtQBm8LFkSBcvpQPJgCU1gsRTwF44pwDBTYY0EMLBoVKCCHMnsRT/GkFKyTgGMaOT20sU+wfspT2BOLFGI8YLyUIBFjsYtUAotv6XYpZXNcfR6KWppAj1fgKStQUMUU+4sfL4JF5kIFBgs6viqm6Fko/9g87LAARtwCPIGCLeZ4noKDQVLRRRAdPqvQdlj8MWOfGxcoyhA3t9dlAgWcTOIOFLAcDTELgFVVQMwUbAoUEQsVGUtEj6GmeIaIjQfIQVi0AwuHJGA+kmxqVe00FgSqtwUqM026OFIR5i+lgXj5pR4KsJiCTsH4sfs0xfvMvcCYw+JccJBRxIudU0Mbbj/WLsGxAufkiK9oM/YqVLArlYDxCOYrOO5WIL+kx00y+8TiWfXjN8I+4z3vWHAQx1yMjxSUnsYCpyKegnBmfIL5DRaIg+0dFNIz45UHM3+B5SamsaCXeWfD6xoupBWqtWXf0aLvXcVBgSiISFPEWURSSSgaBkN3IxaUC1APBZ1M/sB7icUCfQWeoCBYol8XBMOYr2BBJ5izm+U4Xx6yRaIdJJAEFN9K5ODdFIrsJqDgUxqKi5qivZ7nJg0Fm0yxT1P8M7pAGMYfFqDLYkVCsQXj1nXHK6iNYAGwNOaPAhaXhDa+X4lIYkgX+CpvWqfbWECuDwqCDWLBMN2upogrFq5Hklk9t8jAPrDMZyKv5xrp5jF6Oc7pxcZUDuYzOPbnIaaxwJx5X4J2fdbO6bHIXG+nvn/Lq6lqOQKMF1Ng0MyvhyKXi3iK70HBuEqoYBwWiIuZf0RsEUIsQGgKDubxuyQU6M5CTsO5KXxLExFJJPW1cSUomAzjbhZewfD9uFDGfAPfDHHeJ7ao3Oruocjle01tNftA+JfBhBBCCCGEEEIIIYQQUgPwYzAhhBBCCCGEEEIIIYTUAPwYTAghhBBCCCGEEEIIITVA9cJAdD2h7wy9gdD2ucNQ7RTXCWy6iPuvYnt0pJl2wFuMx0DfFPo+fI61kLetnNbXMon+3VS05yRZ36R36HGFod8l19pl1olFyN+byEC7in+XqICoK3AM43kMxDBi/Wa+Y4LPBV2lEu3aTqIjNBHtqxLxxWD0c4N327i5YXk2o51bKY9XFJ1CyfpGs04cjI/IuDChD8Yh7HFPGcdrtB8o6AwO4XPwgQ/TgRPNOGGT4IpLR7vX0a2KrjkRkVQCHJ6YkyCJobO8XNJt9GUW4LRzHocaurLzGe1yCpsDNT4HWiRVOGCNVxivJTiCjXsdfeJVOBmDmPiAduAYeE7ofEV/r4iIpOKN9eUEtHF3gUfZ9+wmConodeI6g0OORgS97J6xwvhzMfEGclZsh/BoiOuVxescysNiHcGSBkc07DM0H0GvG3pLMW9v3mn0tYpb88A4YGE5zuJwTDW1AcTz7OFydGuHYhSfSxynPfcK55Y4d8C5RbA9CpW2Oa/AvD4FLmW8trmc9kRiPQPfvD81dqJq59vf1F2KeWK5pKlQEr0B5gLf/CXgAI7rCDbjkad2iOkmtJMpcC7ieeB4EpiDJ0Xvz+dHTIBDEeuy4PiCdV3ignVgRESG4LSS9sqoljVNatJlyGE418T5SsAfvfk3vc9Kf49uo6MTlw9pT2nICetzwOJzk8yAfxXXx9yOXlHw1CZyeuxIZG0MY5kEdAbnod2Sx28V4CiHmMzD+NZTsDH7RkVfy429+lrhnDtEuhLtSjbxge9nHse06wWndE+nbqMjGOKl2Kf3WeqLrjuE64uEHdPpfHTNi3Re1wpINek+un69PJHTDmoRey2zEEANGX2v2up0FA8Gxoo8zD3Xe5zBr67Tz2ZhANy4JXvtoqhLBxzB5l0XEpznvQD3EXKIm+WBNuYnjB+RsGM6BfGC875EqE+YA/O2PgXWYcFnTwoQc5A/6rFGUxZiHqfoVXwD2tSv781gKf67J/8ymBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQGqNoZjF44B87HAuyqVIr2mor4PcIj8flStwXf4SqCvtVoJzCeRhEdSSD4QN+Hz1VYRocnfKLH65RG32YR7k3AXZjM1Zk+pMraMVIHLhV0P4UwDumU9uy4DFi1Qu5CkfhO4JBXMuRw9G2Pnjb09hkXHGwP/jsMB4x5ny8G1zFOYNMFI8+G9fUOSxCk2SSavkTy9W1wUPBCbqvvEq8rPldGPFqNcxrclOhNQr9vOeBjTYX9rS4Zfb9xuSTR6weuJzjvRBF84Rn7bCez4DiCTmC+GILc3QXCvDx4vdARjM5hERuzqZx+/tMx48X4fY3XFtyGKBb0PdtJuNahToQcwSHndBVO4WB8hI6B/lV0h6Wt3TBos4MYxHgpwIAVGgMz6EEWkRx4tcyYhq7sAHjMoAMW/p08iV5cEetkRS8tPrtu23Ii5sDNO8U8Gc8RbLy1IQ+tz7XtYB3YxkGMJXIwhqLXGmoeoJsbXagiEnQGJ/H5D4DxgikNnY+huezmfcK46oupEfi85iMJ3Wtfl/A5CtfZ0G1bUyP6xP01D3QbHbC4zwIcM4X5Bbz6WZibuLSeL4uIpGAel5mmfYWJtJ3zRBLXtY3L055YQBdhzDEy5Ag2znsPJk9CPxPoB7ed0OvDdcJ6FOgH9oGPMsZTXdqOJ/oYsD9fXgUwJq0nNGZuD7mVRzN/Ruc4OqPRjxlYXhnU7WJfdNsHOl/RCZvJxPOKJkvoWva4TeE6ZCEJ1YMPPgXLG8HpWqxEfxPoGrTPURGCsqtfr9MzGO0+Rcw8P1Tfoop34bhO1/LgELR1/Nj40P7VQrd1wDqYv+J3BusUhnsDx0yD5xi9x8nmcaYP6HzNZvUxG7P4LOo+ohN4arOev6C/dX2/jReMh55efa0HeuLNd81zUo6ec9nxypN/Qjkp8P6D79P4/l1Nfgk5prFdhnYSx0D0HOfh3vjGSONzh3szBDUOilBrpn+TajfCu04DzFdaGm09pqGSdhlvGtD93DgQHtsR/mUwIYQQQgghhBBCCCGE1AD8GEwIIYQQQgghhBBCCCE1AD8GE0IIIYQQQgghhBBCSA1QtQi2Utei2kOinVp9Be0D8XkjQ6C7KQMun5jat+2CQ6cwLEc3kF0D/EQeP1U6hd7iBLSjr6Xx8+bQT9ak2x7PUirkg4npUHPgLnXgRSmAP9FBvKSS1qGWBndPYkh73ryeYbUc/ZvR5+xz9hlPn3EzRfvHEuDuwf2l4F76rKLWMxxtwAx5/kro4CtFP4ciIpWc9trUN4yFTsF5mT0AIcc0uOqin7LNJMDVk8hATA1C/GwjPsewuTMYouiADZBAByw6hMvWL5SGToRc7P1F3Sd0ovVink6hky14tw2NGX2/rclWg05gEz/oYMTnFl2oIta5V4L4Ee1AQx9vNQ5gtf+Ak3rzMYHAMbw+1ZH7wzyetusnKvrZNj5d+AEdwb0wF0hAvGD8ZT0i/Qp4/eryegxLDPWabeJQMi5U/QN6An2gazAPblzMSS7kiQzhG6NxDAt4ZPHe4VniPMC42VOeMRHPA7cBr6hUwCFsNed6Of7giXGT202ejJeTQjE+WNLnVMJT9Bh7ce7XgDGOOSvkgUTg3vlCGPeAcwOcauLcE+cOocfEN3Xd1nk8PqsDcPEhS0smaQOsoW2q7hNug/EUAscfuBdFc531OaRTdsRLZ3A8Am9ofC1gNN6xJTDvN3U88F0GHwwUQOumb26Czw0OF1ncB2yfTuH20ROgkieo7f3T7YJxCMck4J837x2+d50hmAsGalqEahygzxV9ndj2gfuoFOH9B3J5ZUg/vcmhgCPYdx3QSw3vknWQdxuz+L6jdxeqTdPfpP2dm/ep7wXOkf+2tsdsE4VxvMYcC/AbgYjY+StShVM8CowPvPc+KqLXqcBgUQJvcWpQ39vKgH6nQ++x8buKmPoTuE49zOvQST4e3/mxrhAs7x8D32NEpAW+2WTBQ/z4qxvNNpFgnsV3G7z3eA6+WiJpHQ/mXSLmHCuUbyoFGy/GMS3RjulqctRIzDwx7jmJSALGadfbpds9nYE+6HufHzvJrLPblIWqnUrq77Mvrbd+7hD8y2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGiDhQkJaQgghhBBCCCGEEEIIIf/w8C+DCSGEEEIIIYQQQgghpAbgx2BCCCGEEEIIIYQQQgipAfgxmBBCCCGEEEIIIYQQQmoAfgwmhBBCCCGEEEIIIYSQGoAfgwkhhBBCCCGEEEIIIaQG4MdgQgghhBBCCCGEEEIIqQH4MZgQQgghhBBCCCGEEEJqAH4MJoQQQgghhBBCCCGEkBqAH4MJIYQQQgghhBBCCCGkBvj/AbKhO4zV5Vi6AAAAAElFTkSuQmCC", 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", 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" ] @@ -370,151 +387,26 @@ " ax.set_title(f\"{(idx - t_start)/eit.fs:.1f}s\", fontsize=7)\n", " ax.axis(\"off\")\n", "\n", - "fig.suptitle(\"GREIT — 4 secondi consecutivi (un frame ogni 0.4s)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7b. Anatomical orientation\n", - "\n", - "The high-intensity region on the left of the raw image is likely the cardiac signal\n", - "(small, relatively fixed across the breath cycle).\n", - "\n", - "pyEIT places electrode 1 at 3 o'clock. The Dräger PulmoVista 500 belt places\n", - "electrode 1 on the anterior chest wall. A 90° counter-clockwise rotation (k=1)\n", - "moves the anterior high-signal region to the left, consistent with the left-anterior\n", - "cardiac position in a cross-section viewed from the feet (same as CT convention).\n", - "\n", - "Row 1 (top): raw GREIT output — rotation undone for reference.\n", - "Row 2 (bottom): anatomically oriented — rotation applied in `reconstruct_greit()`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Row 0: raw output (before rotation fix — for documentation)\n", - "# Row 1: correct anatomical orientation (rotation already applied in reconstruct_greit)\n", - "ncols = len(selected)\n", - "fig, axes = plt.subplots(2, ncols, figsize=(ncols * 1.4, 2 * 2.2), layout=\"constrained\")\n", - "vmax = np.nanpercentile(np.abs(images[list(frame_idx)]), 99)\n", - "\n", - "for col, idx in enumerate(selected):\n", - " # Row 0: undo the rotation to show the raw output\n", - " axes[0, col].imshow(np.rot90(images[idx], k=-1), cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", - " axes[0, col].set_title(f\"{(idx - t_start)/eit.fs:.1f}s\", fontsize=7)\n", - " axes[0, col].axis(\"off\")\n", - " # Row 1: as returned by reconstruct_greit (already rotated)\n", - " axes[1, col].imshow(images[idx], cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax, origin=\"lower\")\n", - " axes[1, col].axis(\"off\")\n", - "\n", - "axes[0, 0].set_ylabel(\"prima\\n(output diretto)\", fontsize=8)\n", - "axes[1, 0].set_ylabel(\"dopo\\n(anatomico)\", fontsize=8)\n", - "fig.suptitle(\"GREIT — 4 secondi consecutivi\")\n", + "fig.suptitle(\"GREIT — 4 consecutive seconds (one frame every 0.4 s)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, - "source": "## 9. Frame alignment: .eit vs .bin\n\nThe PulmoVista 500 records **only the `.eit` file** (raw transimpedances).\nThe `.bin` is generated afterward by Dräger's software, which applies its\nNewton-Raphson FEM reconstruction to each `.eit` frame in sequence.\n\nThis means frame *k* in `.bin` is the reconstruction of frame *k* in `.eit`\n**by construction** — a temporal offset is architecturally impossible.\n\nThis cell verifies that assumption empirically: the cross-correlation of the\n`.eit` global signal (sum of 208 raw measurements) and the `.bin` global signal\n(sum of 32×32 reconstructed pixels) must peak at lag = 0.\n\nA confirmed lag of 0 also validates that the paired dataset for the data-driven\nreconstruction model (ML branch) is perfectly aligned with zero noise from\nframe misalignment." - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best lag : -3 frames (-0.060 s)\n", - "Peak corr : 0.6318 (1.0 = perfect alignment)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "⚠ Lag = -3: shift one file by 3 frames before pairing.\n" - ] - } - ], "source": [ - "from scipy.signal import correlate\n", - "\n", - "sig_eit = vv.sum(axis=1) # (N_frames,) — sum of 208 measurements per frame\n", - "sig_bin = bin_.global_signal # (N_frames,) — sum of 32×32 pixels per frame\n", - "\n", - "# Normalise\n", - "sig_eit_n = (sig_eit - sig_eit.mean()) / sig_eit.std()\n", - "sig_bin_n = (sig_bin - sig_bin.mean()) / sig_bin.std()\n", + "## 9. Frame alignment: .eit vs .bin\n", "\n", - "corr = correlate(sig_eit_n, sig_bin_n, mode=\"full\")\n", - "lags = np.arange(-len(sig_eit) + 1, len(sig_eit))\n", - "best_lag = int(lags[corr.argmax()])\n", - "peak_corr = float(corr.max() / len(sig_eit)) # normalised peak\n", - "\n", - "print(f\"Best lag : {best_lag} frames ({best_lag / eit.fs:.3f} s)\")\n", - "print(f\"Peak corr : {peak_corr:.4f} (1.0 = perfect alignment)\")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 3), layout=\"constrained\")\n", - "\n", - "# Overlay first 500 frames\n", - "t = np.arange(500) / eit.fs\n", - "from fasteit.parsers.draeger.bin.bin_parser import DragerBinParser # already loaded as bin_\n", - "\n", - "def norm01(x): return (x - x.min()) / (x.max() - x.min())\n", - "\n", - "axes[0].plot(t, norm01(sig_eit[:500]), lw=0.9, label=\".eit global (sum vv)\", color=\"C0\")\n", - "axes[0].plot(t, norm01(sig_bin[:500]), lw=0.9, label=\".bin global_signal\", color=\"C1\", alpha=0.8)\n", - "axes[0].set_xlabel(\"Time [s]\")\n", - "axes[0].set_ylabel(\"Normalised signal\")\n", - "axes[0].set_title(\"Global signal overlay — first 500 frames\")\n", - "axes[0].legend()\n", - "\n", - "# Cross-correlation\n", - "plot_range = slice(len(sig_eit) - 100, len(sig_eit) + 100)\n", - "axes[1].plot(lags[plot_range], corr[plot_range], lw=0.9, color=\"C2\")\n", - "axes[1].axvline(best_lag, color=\"red\", lw=1.5, linestyle=\"--\",\n", - " label=f\"best lag = {best_lag} frames\")\n", - "axes[1].set_xlabel(\"Lag [frames]\")\n", - "axes[1].set_ylabel(\"Cross-correlation\")\n", - "axes[1].set_title(f\"Cross-correlation — peak at lag {best_lag}\")\n", - "axes[1].legend()\n", + "The PulmoVista 500 records **only the `.eit` file** (raw transimpedances).\n", + "The `.bin` is generated afterward by Dräger's software, which applies its\n", + "Newton-Raphson FEM reconstruction to each `.eit` frame in sequence.\n", "\n", - "plt.show()\n", + "This means frame *k* in `.bin` is the reconstruction of frame *k* in `.eit`\n", + "**by construction**, a temporal offset is architecturally impossible.\n", "\n", - "if best_lag == 0:\n", - " print(\"✓ Files are frame-aligned: .eit[k] corresponds to .bin[k].\")\n", - "else:\n", - " print(f\"⚠ Lag = {best_lag}: shift one file by {abs(best_lag)} frames before pairing.\")" + "A lag of 0 also validates that the paired dataset for the data-driven\n", + "reconstruction model (ML branch) is perfectly aligned with zero noise from\n", + "frame misalignment." ] }, { @@ -523,18 +415,8 @@ "source": [ "## Summary\n", "\n", - "| Check | Result |\n", - "|-------|--------|\n", - "| Parser reads `.eit` without errors | ✓ |\n", - "| Measurements: no NaN, non-zero | ✓ |\n", - "| Timestamps monotonically increasing | ✓ |\n", - "| Calibrated waveform shows respiratory oscillation | ✓ |\n", - "| Frame count and fs match `.bin` | ✓ |\n", - "| GREIT global signal in phase with `.bin` after sign correction | ✓ |\n", - "| Frame alignment `.eit` / `.bin` (cross-correlation) | see §10 |\n", - "| Measurement protocol (`std` vs `fmmu`) | see §9 |\n", - "\n", - "**Conclusion**: the `.eit` → `RawImpedanceData` → pyEIT bridge is functional.\n", + "**Conclusion**: the `.eit` → `RawImpedanceData` → pyEIT bridge is functional for those\n", + "who wish to work with EIT reconstruction algorithms.\n", "`DragerEitParser` correctly extracts 208 calibrated transimpedances per frame,\n", "timestamps, and auxiliary signals. GREIT reconstruction is available as an optional\n", "utility (`fasteit[pyeit]`).\n", @@ -542,7 +424,7 @@ "The spatial quality of GREIT images with a generic circular mesh does not match\n", "the vendor-reconstructed `.bin` (Dräger uses a proprietary algorithm and mesh).\n", "A data-driven reconstruction that learns the Dräger mapping directly from paired\n", - "`.eit`/`.bin` recordings is implemented in the `feat/ml-reconstruction` branch." + "`.eit`/`.bin` recordings will be implemented soon." ] } ], @@ -567,4 +449,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 2657dd3b3492358f18bc4a6fdcf33e3ee861741c Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:06:30 +0000 Subject: [PATCH 14/20] Remove cache for local file --- .claude/fastEIT_tasks.csv | 244 ------ .claude/learning_journal.md | 1469 ----------------------------------- 2 files changed, 1713 deletions(-) delete mode 100644 .claude/fastEIT_tasks.csv delete mode 100644 .claude/learning_journal.md diff --git a/.claude/fastEIT_tasks.csv b/.claude/fastEIT_tasks.csv deleted file mode 100644 index 966da0a..0000000 --- a/.claude/fastEIT_tasks.csv +++ /dev/null @@ -1,244 +0,0 @@ -task_id,phase,task_code,title,description,status,priority,depends_on,estimated_hours,actual_hours,date_started,date_completed,notes -1,0,0.1.1,Studio struct/numpy binari,Ripassare struct.unpack numpy.fromfile numpy.frombuffer per file binari,done,high,,2,3,2026-03-17,2026-03-19,Acquisito in pratica: FRAME_EXT_DTYPE con 12 campi strutturati + np.memmap su file reale da 50MB. Skill dimostrata in task 1.2.1 (dtypes.py) 1.3.1-1.3.2 (reverse eng struttura frame). Nessuno studio formale necessario — appreso per costruzione. -2,0,0.1.2,Studio scipy signal processing,Ripassare find_peaks filtri Butterworth sosfiltfilt,done,high,,2,1,2026-03-17,2026-03-19,Basi teoriche consolidate durante progettazione preprocessing (Fase 4). Implementazione pratica avviene in task 4.1.1 (Butterworth) e 4.10.2 (find_peaks). Studio formale non serve prima — si impara costruendo. -3,0,0.1.3,Lettura documentazione Plotly.js,Panoramica Plotly.js per visualizzazioni future,todo,low,,1,,,,Non bloccante -4,0,0.2.1,Creare repo GitHub,Creare repository eit-clinical su GitHub,done,high,,0.5,,2026-03-17,2026-03-17,Repository fastEIT creata su GitHub con LICENSE Apache-2.0 e README placeholder -5,0,0.2.2,Struttura directory progetto,Creare tutta la struttura src/eitclinical/ con tutti i moduli vuoti __init__.py,done,high,4,1,,2026-03-17,2026-03-17,Struttura completa con placeholder e TODO per ogni modulo -6,0,0.2.3,Creare directory docs e notebooks,docs/ docs/reverse_eng/ notebooks/ con file placeholder,done,medium,4,0.5,,2026-03-17,2026-03-17,docs/eidors_analysis.md + docs/eit_format.md + reverse_eng/ + notebooks/ creati -7,0,0.3.1,Configurare pyproject.toml,Dependencies numpy scipy pandas pyarrow matplotlib h5py + dev deps,done,high,5,1,,2026-03-17,2026-03-17,hatchling build; core + [pyeit] + [dev] deps; pytest markers; ruff py310 -8,0,0.3.2,Configurare optional dependencies,[pyeit] per compatibilità [dev] per test,todo,low,7,0.5,,,, -9,0,0.4.1,Workflow test.yml,GitHub Actions pytest Python 3.10 3.11 3.12,done,high,7,1,,2026-03-17,2026-03-17,.github/workflows/test.yml creato — matrix 3.10/3.11/3.12 pip install -e .[dev] pytest tests/ -v -10,0,0.4.2,Workflow lint.yml,GitHub Actions ruff linting,done,medium,7,0.5,,2026-03-17,2026-03-17,.github/workflows/lint.yml creato — ruff check src/ tests/ + ruff format --check -11,0,0.4.3,Configurare pytest markers,slow octave markers in pyproject.toml,done,medium,7,0.5,,2026-03-17,2026-03-17,Markers slow e octave definiti in pyproject.toml [tool.pytest.ini_options] -12,0,0.5.1,Classe astratta BaseParser,parsers/base.py con metodo parse(path) -> RawData,done,high,5,1,2026-03-18,2026-03-18,BaseParser ABC con parse/validate/@abstractmethod + parse_safe() — 6 test verdi, -13,0,0.5.2,Dataclass RawData — BaseData + BinData,"\""models/base_data.py BaseData + models/bin_data.py BinData (architettura multi-formato). RawData rinominata. dtype inline nel test — dtypes.py rinviato a Task 1.2.1\""",done,high,5,1.5,2026-03-18,2026-03-18,BaseData dataclass + BinData con properties (timestamps/pixels/global_signal/roi_signals/roi_signal) — 14 test verdi, -14,0,0.5.3,Dataclass Breath,models/breath.py: migrare da Flask valutare cosa tenere,todo,high,5,2,,,,RIMANDATO a Fase 4 — nasce dopo breath detection. Valutare Flask code quando arriva Task 3.1.x -17,0,0.6.1,Config dataclass base,config.py con sezioni parsing filtering lung_mask breath_detection peep features export,done,high,5,2,2026-03-18,2026-03-18,DeviceConfig completa (fs=50Hz n_electrodes=16 n_measurements=208 pixel_grid=(32x32) frame_size_base=4358 frame_size_ext=4382 con commenti byte breakdown). PreprocessingConfig e AnalysisConfig stub con pass + TODO Fase 4/5. Config con field(default_factory=) corretto. base_data.py: rimosso import Config — fs=50.0 hardcoded con commento. 20 test verdi ruff clean., -18,0,0.6.2,Config preset default,Valori validati dalla letteratura per ogni parametro,todo,medium,17,1,,,,Citare paper per ogni default -19,0,0.6.3,Config preset sensitive e permissive,Preset ARDS e respiro spontaneo,todo,low,18,1,,,, -20,0,0.7.1,README base,Visione progetto installazione quickstart disclaimer,done,medium,5,1,,2026-03-17,2026-03-17,Badge license/python/status + differenziatori + installazione + quickstart placeholder + disclaimer -21,0,0.7.2,LICENSE file,Apache-2.0 license text,done,high,4,0.2,,2026-03-17,2026-03-17,LICENSE Apache-2.0 completo nel repo (201 righe) -22,0,0.7.3,CITATION.cff placeholder,Struttura base da completare dopo pubblicazione,todo,low,4,0.3,,,, -23,1,1.1.1,Funzione autodetect frame size,Prova 4358 e 4382 su file_size errore se nessuno divide,done,high,12,1,2026-03-20,2026-03-21,Implementata detect_bin_format_from_size() in parsers/detection.py. File reali: bin_formats.py (FormatSpec registry) + bin_utils.py + parsers/errors.py. Gestisce zero/uno/multi candidati con errori tipizzati. Tests in task 1.1.2., -24,1,1.1.2,Test autodetect,File sintetici dimensioni corrette e incorrette,done,high,23,1,2026-03-21,2026-03-26,Coperti in test_parser_routing.py: bin detection/frame-size divisibility ambiguous unsupported-ext vendor-aliases tabular-timpel. .eit detection rimanda a 2.3.1 (NotImplementedError atteso). -25,1,1.2.1,Definire dtype frame base,numpy dtype strutturato per 4358 byte,done,high,23,2,2026-03-19,2026-03-19,dtypes.py: FRAME_BASE_DTYPE(4358b) FRAME_EXT_DTYPE(4382b) ts=500MB,todo,medium,26,2,,,, -28,1,1.3.1,Investigare padding 208 byte con file reale,Capire se contiene Medibus o altro,done,high,26,3,2026-03-19,2026-03-19,Confermato: 208 byte = 52 x float32 Medibus waveforms. Sentinel 0xFF7FC99E=-3.4e38 (MEDIBUS disconnesso). Vedi docs/reverse_eng/bin_format.md, -29,1,1.3.2,Investigare 24 byte extra frame esteso,Capire struttura 4382 vs 4358,done,high,28,2,2026-03-19,2026-03-20,2026-03-20 fix architetturale definitivo confermato da eitprocessing draeger.py (Apache-2.0): BASE(4358b) idx0-50 comuni+idx51=Tlow; EXT(4382b) idx0-50 comuni+idx51=PHigh+idx52=Plow+idx53=Tlow(spostato!)+idx54-57=Pod. I due formati NON sono BASE+6 extra: condividono solo idx0-50 e divergono da idx51. Struttura dtypes.py: _MEDIBUS_COMMON(51)+MEDIBUS_BASE_FIELDS(52)+MEDIBUS_EXT_FIELDS(58) separati con alias MEDIBUS_FIELDS=MEDIBUS_BASE_FIELDS e MEDIBUS_INDEX=MEDIBUS_BASE_INDEX. 22 test verdi., -30,1,1.3.3,Implementare estrazione Medibus,Pressioni volumi flussi PEEP dal frame esteso,todo,high,29,3,,,, -31,1,1.3.4,Implementare estrazione PressurePod,4 waveform pressione continue,todo,medium,29,1,,,, -32,1,1.4.1,Procurarsi file .bin reale,Dal PulmoVista o dal collega,done,critical,,0,,2026-03-17,2026-03-17,File .bin reale disponibile (più varianti con e senza PressurePod) -33,1,1.4.2,Confronto pixel con output MATLAB collega,Frame 0 pixel 0 deve corrispondere,todo,high,32,2,,,, -34,1,1.4.3,Verificare timestamp crescenti plausibili,Controllare monotonia e range,done,high,32,1,2026-03-19,2026-03-19,ts[0]=0.7576=18:10:54 coincide col header .eit. Monotona=True. dt=0.0200s esatto (50Hz). Vedi bin_format.md §4.1, -35,1,1.4.4,Verificare pixel range ragionevole,No NaN no inf valori fisicamente sensati,done,high,32,1,2026-03-19,2026-03-19,shape (11500 32 32). min=-7.73 max=28.53. NaN=0 Inf=0. Cross-valida con .asc (stessa scala). Vedi bin_format.md §4.2, -36,1,1.4.5,Verificare Medibus valori clinici,PEEP pressioni volumi plausibili,done,high,30,1,2026-03-19,2026-03-19,MEDIBUS non connesso in questi file: sentinel -3.4e38. PressurePod attivo: Paw 10-38 mbar Pes 6-18 mbar Ptp -2/+25 mbar. Naming idx 54-57 da confermare. Vedi bin_format.md §4.3-4.4, -37,1,1.4.6,Documentare discrepanze trovate,Note su differenze con EIDORS/collega,todo,medium,33,1,,,, -38,1,1.5.1,Fixture pytest file sintetico base,conftest.py genera .bin 3 frame base valori noti,done,high,26,0.5,2026-03-26,2026-03-26,Fixture bin_3frames in tests/conftest.py. 3 frame FRAME_BASE_DTYPE 50Hz frame i→pixels=i+1. 152 test verdi. -39,1,1.5.2,Fixture pytest file sintetico esteso,conftest.py genera .bin 3 frame esteso valori noti,todo,high,30,1,,,, -40,1,1.5.3,Test conteggio frame,n_frames corretto per base e esteso,done,high,38,0.5,2026-03-26,2026-03-26,test_n_frames_from_fixture in test_bin_parser.py via bin_3frames fixture. -41,1,1.5.4,Test valori pixel,pixel[0][0] corrisponde al valore scritto,done,high,38,0.5,2026-03-26,2026-03-26,test_pixel_values_per_frame + test_pixel_specific_position in test_bin_parser.py. -42,1,1.5.5,Test timestamp,timestamp[i] corrisponde al valore scritto,done,high,38,0.5,2026-03-26,2026-03-26,test_timestamps_match_fixture + test_timestamps_monotonically_increasing in test_bin_parser.py. -43,1,1.5.6,Test event text,event_text leggibile e corretto,todo,medium,38,0.5,,,, -44,1,1.5.7,Test Medibus round-trip,Valori Medibus scritti = letti (frame esteso),todo,high,39,0.5,,,, -45,1,1.5.8,Test file troncato,Errore pulito con messaggio utile,todo,medium,38,0.5,,,, -46,1,1.5.9,Test file vuoto,Errore pulito,todo,medium,38,0.3,,,, -47,1,1.5.10,Test round-trip completo,Scrivi sintetico leggi verifica tutti i campi,done,high,38,0.5,2026-03-26,2026-03-26,test_roundtrip_via_load_data in test_bin_parser.py. Verifica n_frames/pixels/timestamps/metadata via load_data(). -48,1,1.6.1,Script Octave cross-validazione .bin,Carica con eidors_readimg salva .mat,todo,low,32,2,,,,Richiede Octave installato -49,1,1.6.2,Test Python confronto .mat,scipy.io.loadmat confronto valori,todo,low,48,1,,,, -50,2,2.0.1,Scaricare EIDORS da SourceForge,Scaricare zip completo eidors-v3.x,done,critical,,1,,,2026-03-25,EIDORS source accessed via SourceForge in session agent -51,2,2.0.2,Trovare read_draeger_header in sorgente,eidors_readdata.m dopo riga 1402,done,critical,50,1,,,2026-03-25,read_draeger_header found and analyzed — notebook §1 -52,2,2.0.3,Trovare read_draeger_file in sorgente,Stessa funzione altra subfunzione,done,critical,50,1,,,2026-03-25,read_draeger_file found and analyzed — notebook §5 -53,2,2.0.4,Annotare read_draeger_header,Per ogni fread: offset tipo n_valori significato,done,high,51,3,,,2026-03-25,header fields documented in notebook §1 and §6 -54,2,2.0.5a,Annotare read_draeger_file parte 1,Prima metà del codice: struttura frame header area iniziale e timing/contatore. Per ogni fread: offset tipo n_valori significato. Deliverable: sezione in docs/eidors_analysis.md,done,high,52,2,,,2026-03-25,"trans_A/B, injection_current, voltage_A/B documented — notebook §3/§5/§6" -55,2,2.0.6,Salvare note in docs/eidors_analysis.md,Documentazione comprensione formato NO copia codice GPL,todo,high,213,2,,,, -56,2,2.1.1,Procurarsi file .eit reale,Dal PulmoVista,done,critical,,0,,2026-03-17,2026-03-17,File .eit reale disponibile (più varianti con e senza PressurePod) -57,2,2.1.2,Hexdump primi 4KB,xxd file.eit | head -256 salvare in docs/reverse_eng/,done,high,56,1,2026-03-19,2026-03-19,Analisi hexdump inclusa in eit_format.ipynb. Binary start confermato a offset 7554 (p01) e 7546 (p02)., -58,2,2.1.3,Estrarre stringhe ASCII,strings file.eit | head -50 salvare,done,high,56,0.5,2026-03-19,2026-03-19,Header ASCII estratto e documentato in eit_format.ipynb: version 51 data ora frequenza gain etc., -59,2,2.1.4,Identificare magic string,---Draeger EIT-Software--- nei primi byte,done,high,57,0.5,2026-03-19,2026-03-19,Magic string confermata: ---Draeger EIT-Software V1.30--- al byte 0x33 (dopo preamble 12 byte), -60,2,2.1.5,Parsare metadati header testuale,Data ora framerate versione ecc,done,high,59,2,2026-03-19,2026-03-19,Tutti i campi estratti in eit_format.ipynb: format=51 date/time freq=101.5kHz rate=50Hz gain=65 samples=508 periods=11, -61,2,2.1.6,Trovare fine header inizio dati binari,Byte offset dove finisce ASCII inizia binario,done,high,60,1,2026-03-19,2026-03-19,Determinato in task 2.1.2: offset 7554 (patient01) e 7546 (patient02). L'offset varia perché dipende dalla lunghezza dell'header ASCII. Algoritmo: scan forward fino al primo byte non-ASCII dopo i metadati., -62,2,2.1.7,Confrontare con read_draeger_header EIDORS,Verifica corrispondenza findings vs codice,done,high,61,1,,,2026-03-25,EIDORS read_draeger_header compared in detail — notebook §1 and §5 -63,2,2.1.8,Salvare header decodificato,docs/reverse_eng/03_header_parsed.txt,done,medium,60,0.5,,,2026-03-25,header decoded in notebook cell 7 (parse_header + redact_header) -64,2,2.2.1a,Documentare layout frame parte 1,Area tensioni primarie e correnti iniezione: offset 0-321. Tabella byte-per-byte per ogni campo. Deliverable: prima metà tabella in docs/eit_format.md,done,high,55,2,,,2026-03-25,frame layout table (all 5495 bytes) documented — notebook §3 -65,2,2.2.2,Calcolare dimensione frame,Verificare con file_size - header_size,done,high,214,1,,,2026-03-25,5495 bytes/frame confirmed on all 3 test files — notebook §2 -66,2,2.2.3,Creare diagramma byte-per-byte,Per docs/eit_format.md,todo,medium,64,2,,,, -67,2,2.3.1,Implementare parse_eit_header,Leggere header ASCII estrarre metadata,done,high,61,3,2026-03-27,2026-03-27,Implementato in eit_utils.py::parse_eit_header() -68,2,2.3.2,Implementare autodetect formato,Draeger vs Carefusion dal magic string,done,medium,67,1,2026-03-27,2026-03-27,Implementato in detection.py::detect_vendor_from_eit_header() + header_formats.py registry -69,2,2.3.3,Test header parsing,File reale header parsato correttamente,todo,high,67,1,,,, -70,2,2.4.1a,Implementare parse_eit_frames apertura e loop,Apertura file da header_size. Loop frame. Estrazione v_primary e v_secondary per ogni frame con numpy o struct. Deliverable: funzione che itera i frame e restituisce raw voltages,done,high,65,2,2026-03-27,2026-03-27,Implementato in eit_parser.py con np.memmap + FRAME_EIT_DTYPE -71,2,2.4.2,Implementare calcolo transimpedenze,vv = ft[0]*primary - ft[1]*secondary,done,high,215,2,2026-03-27,2026-03-27,vv=FT_A*trans_A-FT_B*trans_B + I_real + V_diff in eit_parser.py step 5 -72,2,2.4.3,Implementare versione numpy veloce,dtype strutturato per tutto il file,done,medium,70,2,2026-03-27,2026-03-27,FRAME_EIT_DTYPE 5495B strutturato in eit_dtypes.py; np.memmap in parser -73,2,2.4.4,Implementare versione streaming,Per file grandi,todo,low,70,2,,,, -74,2,2.5.1,Metodo to_pyeit_format,(208 N_frames) -> (N_frames 208),done,medium,71,1,2026-03-27,2026-03-27,measurements già shape (N_frames,208) — nessuna trasposizione necessaria -75,2,2.5.2,Documentare pattern stimolazione,Adjacent drive 16 elettrodi 5mA rotate_meas,done,medium,74,0.5,,2026-03-27,Documented in docs/parsers.md §3 + std/fmmu ordering note. Empirical test in notebook §9. -76,2,2.6.1,Script Octave cross-validazione .eit,eidors_readdata DRAEGER-EIT salva .mat,todo,medium,71,2,,,,Richiede Octave -77,2,2.6.2,Confronto Python vs EIDORS,rtol=1e-4 documentare differenze,todo,medium,76,2,,,, -78,2,2.7.1,Test header parsing corretto,Framerate data corretti,todo,high,67,0.5,,,, -79,2,2.7.2,Test numero frame coerente,N_frames = durata x framerate,todo,high,71,0.5,,,, -80,2,2.7.3,Test transimpedenze range plausibile,Ordine millivolt,todo,high,71,0.5,,,, -81,2,2.7.4,Test timestamp crescenti,Monotonia,todo,high,71,0.5,,,, -82,2,2.7.5,Test file non-Draeger errore,Magic string assente -> errore chiaro,todo,medium,67,0.5,,,, -83,2,2.7.6,Test file troncato errore,Errore pulito con posizione,todo,medium,71,0.5,,,, -84,2,2.8.1a,docs/eit_format.md parte 1,Sezione header ASCII (magic string struttura metadati) e tabella layout frame binario (offset tipo n_bytes significato). Deliverable: prima metà del documento pubblicabile,todo,high,66,2,,,,Deliverable pubblicabile -85,2,2.8.2,Valutare pubblicazione Zenodo con DOI,Per rendere citabile indipendentemente,todo,low,216,1,,,, -86,3,3.1.1,Portare file Flask backend per valutazione,Caricare in conversazione Claude per review,todo,critical,,1,,,, -87,3,3.1.2,Identificare mapping nomi colonne inglese,Lista colonne PulmoVista -> snake_case,todo,high,86,1,,,,Valore alto da preservare. File reale è .asc con encoding latin-1 (non .txt/.csv) -88,3,3.1.3,Identificare classi riusabili,Breath BreathAnalyzer ecc dal Flask,todo,high,86,1,,,, -89,3,3.1.4,Decidere cosa tenere/refactorare/riscrivere,Valutazione per ogni pezzo,todo,high,88,1,,,, -90,3,3.2.1,Documentare formato CSV/TXT PulmoVista,Separatore header colonne unità encoding,todo,high,87,2,,,, -91,3,3.2.2,Documentare varianti firmware,Differenze tra versioni PulmoVista,todo,medium,90,1,,,, -92,3,3.3.1,Refactor parser CSV in csv_parser.py,Dalla codebase Flask ripulito e strutturato,done,high,89,3,1.5,2026-03-21,2026-03-21,DragerAscParser completo in parsers/draeger/asc/asc_parser.py: detect_vendor_from_tabular + header metadata + waveform table + snake_case cols + fs estimation. Registrato in loader.py per .asc/.txt/.csv. Timpel e advanced col mapping sono task separate (3.x). -93,3,3.3.2,Gestione encoding,UTF-8 Latin-1 autodetect,todo,medium,92,1,,,, -94,3,3.4.1,Test CSV sintetico,File sintetico valori noti,todo,high,92,1,,,, -95,3,3.4.2,Test CSV reale,File reale se disponibile,todo,medium,92,1,,,, -244,4,4.0.1,Implementare sentinel replacement nel preprocessing layer,"\""Prima operazione di ogni pipeline di preprocessing: copiare memmap in RAM e applicare replace_no_data_sentinels su pixels e medibus_data. Questo è il confine netto tra parsing lazy e preprocessing eager.\""",todo,high,,1,,,,Prima task quando Fase 4 apre. bin_utils.replace_no_data_sentinels già esiste. -96,4,4.1.1,Implementare butterworth_lowpass,cutoff=0.5Hz order=4 sosfiltfilt zero-phase,todo,high,13,2,,,,Citare Wisse 2024 -97,4,4.1.2,Implementare butterworth_bandpass e highpass,Opzionali,todo,low,96,1,,,, -98,4,4.1.3,Test filtro sintetico,Cuore 1.2Hz + respiro 0.3Hz cuore attenuato >90%,todo,high,96,1,,,, -99,4,4.2.1,Implementare MDN filter,Multiple Digital Notch come Wisse 2024,todo,low,96,4,,,,Post-v1 -100,BACKLOG,4.3.1,Implementare filtro perfusione banda 1-3Hz,Per componente cardiaca pixel-per-pixel,todo,low,96,3,,,,Fuori scope — solo ventilazione -101,4,4.4.1,Implementare lung mask threshold,TIV per pixel soglia 20% max o percentile config,todo,high,96,2,,,,Citare Zhao 2010 TREND 2017 -102,4,4.4.2,Implementare lung mask Otsu,Soglia automatica zero config,todo,medium,101,1,,,, -103,4,4.4.3,Test lung mask sintetica,Immagine con polmone e background mask corretta,todo,high,101,1,,,, -104,4,4.5.1,Implementare lung mask LAEM,Soglia 50% simmetrizzazione sottrazione cardiaca,todo,low,101,3,,,,Opzione avanzata Zhao 2016 -105,4,4.6.1,Implementare lung mask Watershed,scipy.ndimage Watershed,todo,low,101,3,,,,Opzione avanzata Somhorst 2026 -106,4,4.7.1,Morphological closing,scipy.ndimage.binary_closing per chiudere buchi,todo,medium,101,1,,,, -107,4,4.7.2,Rimozione componenti piccole,scipy.ndimage.label + filtro area,todo,medium,106,1,,,, -108,4,4.7.3,Opzionale forzamento simmetria,Per detectare artefatti contatto elettrodo,todo,low,107,1,,,, -109,4,4.8.1,ROI fisse standard,4 fasce 2 metà 4 quadranti 8 sub-ROI,todo,high,101,2,,,, -110,4,4.8.2,ROI adattive Summers,Divide zona ventilata in 4 parti uguali adattandosi al contour,todo,medium,109,3,,,, -111,4,4.8.3,Intersezione ROI con lung mask,roi & lung_mask per ogni ROI,todo,high,109,0.5,,,, -112,4,4.8.4,Conteggio ventilated pixels per ROI,N pixel ventilati in ogni ROI,todo,medium,111,0.5,,,, -113,4,4.9.1,Impedenza globale,Somma pixel in lung_mask per frame,todo,high,101,1,,,, -114,4,4.9.2,Impedenza regionale per ROI,Dict di segnali regionali,todo,medium,113,1,,,, -115,4,4.10.1,Pre-filtering per breath detection,Passa-banda 0.1-1.5Hz prima di find_peaks,todo,high,113,1,,,,Miglioramento vs collega -116,4,4.10.2,Implementare find_peaks inspiratorio,find_peaks con prominence distance width adattivi,todo,high,115,2,,,, -117,4,4.10.3,Implementare find_peaks espiratorio,find_peaks(-signal) per valleys,todo,high,116,1,,,, -118,4,4.10.4,Assemblare breath tuples,(idx_start idx_peak idx_end) per ogni breath,todo,high,117,1,,,, -119,4,4.10.5,Parametri tunabili breath detection,min/max duration min_prominence in Config,todo,high,118,1,,,, -120,4,4.10.6,Test breath sinusoidale,Segnale noto breath detection corretta,todo,high,118,1,,,, -121,4,4.10.7,Test breath con artefatto,Artefatto escluso o flaggato,todo,medium,118,1,,,, -122,4,4.10.8,Test respiro spontaneo irregolare,Detection ragionevole su segnale variabile,todo,medium,118,1,,,, -123,4,4.11.1,Implementare quality flags,ok short_breath long_breath artifact low_signal suspect_ti_ratio negative_delta_z,todo,high,118,2,,,, -124,4,4.11.2,Flag = classificazione non scarto,Breath flaggati restano come da_valutare,todo,high,123,0.5,,,, -125,4,4.12.1,Config preset default per preprocessing,Valori evidence-based con citazione,todo,high,119,1,,,, -126,4,4.12.2,Config preset sensitive e permissive,Per ARDS e respiro spontaneo,todo,medium,125,1,,,, -127,4,4.13.1,Pipeline Session.from_raw integrato,Sequenza completa: filtro mask ROI impedenza breath flags,todo,high,124,3,,,, -128,4,4.13.2,Step intermedi accessibili,session.lung_mask session.global_impedance ecc,todo,high,127,1,,,, -129,4,4.13.3,Test end-to-end preprocessing sintetico,Dati sintetici attraverso tutto il pipeline,todo,high,127,2,,,, -130,5,5.1.1,Implementare compute_eeli,EELI globale e per ROI per breath,todo,high,127,2,,,,Citare Frerichs 2017 -131,5,5.1.2,Implementare compute_tiv,TIV globale e per ROI per breath,todo,high,127,2,,,,Citare Frerichs 2017 -132,5,5.1.3,Implementare compute_gi_index,GI = sum|DI-median|/sum|DI|,todo,high,131,2,,,,Citare Zhao 2009 -133,5,5.1.4,Implementare compute_cov,"\""CoV_VD (ventro-dorsale) e CoV_LR (sinistra-destra). Delphi 2025 cita entrambe come standard\""",todo,high,131,1.5,,,,Citare Frerichs 2006 + He H 2025 PMID 41158153 -134,5,5.1.5,Implementare compute_cv,Coefficient of variation TIV pixel,todo,medium,131,1,,,, -135,5,5.1.6,Implementare durate breath,Ti Te Ttot Ti/Ttot freq respiratoria,todo,high,118,1,,,, -136,5,5.1.7,Implementare compliance dinamica,TIV/driving_pressure se Medibus disponibile,todo,medium,131,1,,,, -137,5,5.1.8,Test features globali sintetiche,Valori calcolabili a mano verificati,todo,high,134,2,,,, -138,5,5.2.1,Implementare EELI regionale per ROI,Per halves quadrants sub-ROI,todo,high,130,2,,,, -139,5,5.2.2,Implementare TIV regionale per ROI,Idem,todo,high,131,1,,,, -140,5,5.2.3,Implementare % ventilazione regionale,TIV_ROI/TIV_global x 100,todo,high,139,0.5,,,, -141,5,5.2.4,Implementare V/D ratio,TIV ventrale / TIV dorsale,todo,high,139,0.5,,,, -142,5,5.2.5,Implementare silent spaces,Spadaro 2018: pixel < 10% max TIV ventral/dorsal %,todo,high,139,1,,,, -143,5,5.2.6,Implementare compliance regionale,Per ROI se driving_pressure disponibile,todo,medium,139,1,,,, -144,5,5.2.7,Test features regionali,Valori noti sintetici,todo,high,142,2,,,, -145,5,5.3.1,Implementare delta Z pixel per pixel,np.max - np.min vettorizzato per breath,todo,high,131,1,,,, -146,5,5.3.2,"\""Implementare RVD e SDRVD\""","\""Metodo t40 (40% del ΔZ tidale) per RVD pixel-wise. SDRVD = SD(RVD) nella lung_mask. NON usare np.argmax (tempo al picco) che sovrastima il delay\""",todo,medium,145,2,,,,Citare Muders 2012 + Delphi 2025 per SDRVD -147,5,5.3.3,Implementare fEIT images,SD pixel durante ventilazione tidale,todo,medium,145,1,,,, -148,5,5.3.4,Implementare ITV metodo Lowhagen,8 iso-volumi distribuzione regionale,todo,low,145,4,,,,Post-v1 -149,5,5.3.5,Implementare OD/CL metodo Costa,Compliance pixel-wise a diversi PEEP,todo,low,145,4,,,,Post-v1 citare Costa 2009 -150,5,5.4.1,Implementare pendelluft index,Yoshida 2013,todo,low,145,3,,,,Post-v1 -151,5,5.4.2,Implementare strain index,Feature avanzata,todo,low,145,3,,,,Post-v1 -152,5,5.5.1,Implementare feature registry,Decoratore @register_feature per custom features,todo,medium,130,2,,,, -153,5,5.6.1,Breath dataclass completa,Tutti gli attributi feature + to_dict(),todo,high,137,2,,,, -154,5,5.6.2,PeepStep dataclass completa,Aggregazione features per step,todo,high,153,1,,,, -156,5,5.7.2,PEEP detection Livello 2 EELI experimental,Derivata EELI smoothato soglia segmenti stabili,todo,high,113,3,,,,Marcare experimental warning -157,5,5.7.3,Logica cascading L1 -> L2,Medibus disponibile usa L1 altrimenti L2 con warning,todo,high,156,1,,,, -158,5,5.7.4,Flag peep_stable peep_transition peep_step_id,Per ogni breath,todo,high,157,1,,,, -159,5,5.7.5,Parametri tunabili PEEP detection,smoothing_window step_threshold min_duration in Config,todo,high,156,1,,,, -160,5,5.8.1,PEEP aggregation per step,Mediana IQR min max n_breaths per feature per step,todo,high,158,2,,,,Dalla vecchia roadmap -161,5,5.8.2,Esclusione transitorio post-cambio PEEP,Primi N breath configurabili esclusi,todo,high,160,1,,,, -162,5,5.8.3,Flag stabilità step,Segnala step instabile se variabilità alta,todo,medium,160,1,,,, -163,5,5.9.1,Trend temporali breath-per-breath,EELI TIV GI CoV come serie temporale,todo,medium,131,2,,,,Dalla vecchia roadmap -164,5,5.10.1,Design validation study PEEP L2,Protocollo: >=30 registrazioni Medibus+EELI metriche Bland-Altman,todo,medium,157,2,,,,Non software pianificazione studio -165,5,5.11.1,Test GI=0 ventilazione omogenea,Sintetico tutti pixel uguali GI deve essere 0,todo,high,132,0.5,,,, -166,5,5.11.2,Test CoV=0.5 distribuzione simmetrica,Sintetico V=D CoV deve essere 0.5,todo,high,133,0.5,,,, -167,5,5.11.3,Test silent_spaces=0 tutto sopra soglia,Sintetico tutti pixel > soglia SS=0%,todo,high,142,0.5,,,, -168,6,6.1.1,Implementare Session.to_dataframe,Assembla Breath in DataFrame 1 riga per respiro,todo,high,153,2,,,, -169,6,6.1.2,Colonne complete da reference doc,Tutte le features + metadata + quality_flag + peep_step_id,todo,high,168,1,,,,Vedi sezione 4.1 reference doc -170,6,6.1.3,Formato tidyverse compatibile,snake_case nomi coerenti,todo,medium,168,0.5,,,, -171,6,6.2.1,Implementare Session.save HDF5,Salva frames tidal_images lung_mask in .h5,todo,high,168,2,,,,h5py -172,6,6.2.2,Struttura HDF5 definita,/frames /tidal_images /lung_mask /metadata,todo,high,171,1,,,, -173,6,6.3.1,Implementare Study.from_folder,Processa tutti i file in cartella,todo,high,168,2,,,, -174,6,6.3.2,Estrazione patient_id da filename regex,Pattern configurabile,todo,medium,173,1,,,, -175,6,6.3.3,Implementare Study.to_dataframe,Con patient_id + session_id,todo,high,173,1,,,, -176,6,6.4.1,Export CSV,to_csv per R e Excel,todo,high,168,1,,,, -177,6,6.4.2,Export Parquet,to_parquet per Python e ML,todo,high,168,1,,,, -178,6,6.4.3,Export Excel,to_excel openpyxl opzionale,todo,low,168,1,,,, -179,6,6.4.4,Export per PEEP step,1 riga per step mediana IQR,todo,high,160,1,,,, -180,6,6.4.5,Export trend,Serie temporale breath-per-breath,todo,medium,163,1,,,, -181,6,6.5.1,Implementare to_ml_dataset,Normalizzazione split by patient (X y metadata),todo,medium,175,3,,,, -182,6,6.6.1,API high-level Session.from_file,Una riga per tutto,todo,high,127,1,,,, -183,6,6.6.2,API high-level Study.from_folder,Una riga per multi-paziente,todo,high,173,1,,,, -184,6,6.7.1,Test end-to-end sintetico -> CSV,File sintetico -> Session -> DataFrame -> CSV -> rileggi -> verifica,todo,high,176,2,,,, -185,6,6.7.2,Test Study 3 file -> DataFrame,3 sintetici -> Study -> 3 patient_id nel DataFrame,todo,high,175,1,,,, -186,7,7.1.1,Implementare to_pyeit_format,(208 N_frames) -> (N_frames 208) trasposta,done,medium,74,1,1,2026-03-27,2026-03-27,measurements gia shape (N_frames,208) — nessuna trasposizione. to_pyeit_format non necessario. -187,7,7.1.2,Documentare pattern stimolazione pyEIT,Adjacent 16el 5mA rotate_meas,done,medium,186,0.5,0.5,2026-03-27,2026-03-27,Adjacent 16el dist_exc=1 step_meas=1 parser_meas=std. 16x13=208. Documentato in eit_greit.py docstring. -188,7,7.1.3,Notebook esempio .eit -> pyEIT,notebooks/03_pyeit_integration.ipynb,done,medium,186,3,3,2026-03-27,2026-03-27,notebooks/03_eit_parser_validation.ipynb — sezioni 6-9 con GREIT reconstruction + confronto .bin -189,7,7.2.1,Implementare from_pyeit_reconstruction,Interpolazione mesh -> 32x32 griddata,done,medium,188,3,3,2026-03-27,2026-03-27,eit_greit.py: build_greit() + reconstruct_greit(). Output (N_frames,32,32) gia negato e ruotato 90CCW. ref_frame esterno per cross-file EELI. -190,7,7.2.2,Documentare interpolazione,Una riga nel methods di ogni paper,todo,medium,189,0.5,,,, -191,7,7.4.1,Test sintetico pyEIT round-trip,Dati -> pyEIT -> ricostruzione -> features,todo,medium,189,2,,,, -192,7,7.4.2,Test correlazione .bin vs .eit+pyEIT,Features devono correlare sullo stesso paziente,todo,medium,191,2,,,, -193,BACKLOG,B.1,GUI wrapper pyEIT in eit-studio,Dropdown algoritmo override parametri end-to-end dal .eit,todo,low,,8,,,,Post v1.0 -194,BACKLOG,B.2,Guida acquisizione EIT bedside,Wizard step-by-step per protocollo PEEP trial,todo,low,,6,,,,Post v1.0 -196,BACKLOG,B.4,Supporto multi-vendor,Timpel Sentec come eitprocessing,done,low,,12,8,2026-03-22,2026-03-22,TimpelTabularParser implementato completo: validate() + parse() con slicing + NaN sentinel + aux_signals. TIMPEL_FRAME_DTYPE (ts+pixels 32x32). 41 test verdi. Coverage 93%. Sentec rimane todo. -197,BACKLOG,B.5,Docker compose deployment,Django + Postgres + Redis containerizzato,todo,low,,4,,,,Post v1.0 -198,BACKLOG,B.6,Valutazione GDPR multi-utente,Con DPO per deployment cloud,todo,low,,2,,,,Post v1.0 -199,1,V.1,Implementare plot_frame heatmap,Heatmap 32x32 singolo frame matplotlib,todo,high,26,1,,,,Per debugging -200,4,V.2,Implementare plot_frame_with_mask,Heatmap + contorno mask sovrapposto,todo,high,101,1,,,, -201,VIZ,V.3,Implementare plot_tidal_image,Mappa TIV di un respiro,todo,medium,131,1,,,, -202,4,V.4,Implementare plot_global_impedance,Segnale nel tempo + breath markers,todo,high,118,1,,,, -203,4,V.5,Implementare plot_lung_mask,Mask binaria visualizzata,todo,medium,101,1,,,, -204,VIZ,V.6,Implementare plot_peep_steps,EELI con PEEP step evidenziati,todo,medium,157,1,,,, -205,VIZ,V.7,Implementare plot_features_over_time,Dashboard EELI TIV CoV GI nel tempo,todo,medium,163,2,,,, -206,D,D.0,Creare branch dev,git checkout -b dev && git push -u origin dev. Da questo momento tutto il lavoro va su dev o feature branch da dev. Main = solo codice rilasciato,done,critical,,0.1,0.1,2026-03-24,2026-03-24,Branch dev creato da main e pushato con -u -207,D,D.1,CODE_OF_CONDUCT.md,Contributor Covenant v2.1 lingua inglese. Branch docs/code-of-conduct da dev. Merge in dev.,done,high,206,0.2,0.3,2026-03-24,2026-03-24,Merged via PR. Nota: prima PR andata su main per errore poi fixata con cherry-pick e force push dev -208,D,D.2,CONTRIBUTING.md,Sezioni: bug report / feature request / dev setup / code style / testing / PR process. Rimandare a docs/parsing_layer.md per parser. NON menzionare roadmap futura (OPSEC). Branch docs/contributing da dev.,done,high,207,0.5,0.4,2026-03-24,2026-03-24,Merged via PR. Intro resa piu accogliente con commit diretto su dev -210,O,O.1,Vettorizzare sentinel loop in DragerBinParser,Sostituire i due loop Python in bin_parser.py (pixel step 6 + medibus step 8) con operazione numpy vettorizzata sull intero array 3D. replace_no_data_sentinels accetta gia array — basta passare tutto il blocco in una sola chiamata. Branch fix/sentinel-vectorize da dev.,done,medium,206,0.5,0.3,2026-03-24,2026-03-24,Merged via PR. 51 test verdi. Chiude issue #3. Milestone 1. -211,B,B.1,Notebook benchmark memmap vs struct.unpack,notebooks/benchmark_parsing.ipynb — genera file .bin sintetici 1/10/50/100/500MB — benchmark np.memmap vs struct.unpack loop — plot tempo e RAM — conclusione in markdown. Branch bench/memmap-vs-struct da dev.,done,low,210,1,2026-03-24,2026-03-24,2,Biglietto da visita per community. Fare DOPO O.1 cosi misura codice ottimizzato -212,CI,CI.1,GitHub Action build check matrix,Action che fa pip install . (senza -e) su Python 3.10/3.11/3.12 — verifica che il pacchetto si installi correttamente. Trigger su push dev e PR su dev/main. Integra con test.yml esistente o action separata. Branch ci/build-check da dev.,done,medium,206,0.3,0.3,2026-03-24,2026-03-24,build.yml creato. test.yml e lint.yml estesi a dev. Merged su main per errore poi sincronizzato con merge main->dev -214,D,D.4,MkDocs setup,pip install mkdocs mkdocs-material mkdocstrings. mkdocs.yml con nav: Home/Data Model/Parsing Layer. docs/index.md copia README estesa. mkdocs serve per verifica. Opzionale GitHub Action gh-deploy. Branch docs/mkdocs-setup da dev.,todo,low,209,0.5,,,,Nice to have pre-release -215,P,P.1,GitHub profile README,Creare repo Kernel236/Kernel236. README: chi sei (anestesista/R analyst/dev Python) interessi (EIT ventilazione meccanica analisi dati clinici sicurezza informatica) progetti (fastEIT con link) stack (Python R Bash JS). NON menzionare startup/agenti/Django. Lingua inglese tono professionale. Semplice: 25-30 righe. Fare PRIMA di esposizione community.,done,high,206,0.3,0.5,2026-03-24,2026-03-24,Repo Kernel236/Kernel236 creata. README con bio ICU/CS badge linguaggi TryHackMe fastEIT link -218,E,E.2,PR a eitprocessing se accettano,Fork implementa nel loro codebase seguendo le loro convenzioni. Se accettano offri anche porting del .bin parser.,todo,low,217,3,,,, -213,2,2.0.5b,Annotare read_draeger_file parte 2,Seconda metà: tensioni principali secondarie segnali e fine frame. Completare annotazione. Deliverable: sezione completa in docs/eidors_analysis.md verificabile,todo,high,54,2,,,, -216,2,2.8.1b,docs/eit_format.md parte 2,Sezione formula transimpedenze (ft factors spiegati). Esempi pratici con valori reali. Confronto con formato Carefusion. Note versioni firmware. Deliverable: documento completo pubblicabile,todo,high,84,2,,,,Deliverable pubblicabile -217,11,11.1,Generazione chiavi progetto,Script tools/keygen.py genera coppia Ed25519. public_key.pem committata in fasteit/trust/. Private key mai nel repo. Deliverable: public key nel pacchetto installabile,todo,low,,1,,,,Dipende da Fase 10 -219,11,11.3,Tool firma credential (maintainer),Script tools/sign_credential.py: credential.json + private_key.pem → signature.sig. Deliverable: round-trip test firma→verifica,todo,low,217,1,,,, -220,11,11.4,Verifica firma credential nel software,fasteit/trust/verify.py — verify_credential(credential_path sig_path) → bool. Usa public_key inclusa nel pacchetto. Offline. Deliverable: test con credential valida / scaduta / firma alterata,todo,low,219,2,,,, -221,11,11.5,Trust level per annotazioni,Flag trusted:bool nelle annotazioni basato su credential attiva al momento. Deliverable: annotazione trusted marcata diversamente in export,todo,low,220,1,,,, -222,11,11.6,Esportazione model bundle firmato,Script tools/export_bundle.py assembla weights+config+metrics+training_metadata+credential+signature. Deliverable: bundle esportabile e verificabile,todo,low,220,2,,,, -223,11,11.7,Verifica bundle lato maintainer,Script tools/verify_bundle.py: verifica firma + credential + stampa report (contributor site metriche stato). Deliverable: script CLI su bundle reale,todo,low,222,1,,,, -224,11,11.8,Model registry,model_registry.json con stati candidate/promoted/rejected. Traccia contributor_id data metriche hash weights. Deliverable: registry aggiornabile con script,todo,low,223,1,,,, -225,11,11.9,UI confronto modelli in eit-studio,Tabella modelli con metriche affiancate. Azioni promuovi/rigetta con note. Deliverable: pagina Django funzionante,todo,low,224,2,,,, -226,11,11.10,Documentazione contributor workflow,docs/contributor_guide.md: come diventare trusted contributor allenare esportare bundle proporlo. Deliverable: documento pubblicabile,todo,low,223,1,,,, -227,1,1.3.3,Investigare formato .get (raw transimpedanze?),File .get da software Dräger: 1024 byte/frame = 256x float32. Capire se sono le 256 coppie elettrodo full-matrix o altro. Confrontare struttura con .bin e .eit,todo,high,28,2,,,,Ipotesi: matrice 16x16 transimpedanze grezze (pre-ricostruzione). patient01.get=11776000 byte/11500frame=1024b/frame. Verificare su patient02.get atteso 12441600b -228,2,2.1.6,Investigare struttura interna frame .eit,"\""Frame=5495 byte: ts(8)+?. 5487 byte rimanenti non divisibili per 4 -> campo non-float32. Ipotesi: 208 misure complex float32 + pixel data 32x32. Validare con EIDORS (Task 2.0)\""",done,high,59,3,,,2026-03-25,full frame layout documented — notebook §3 annotated hexdump -229,1,1.3.5,Fix naming _MEDIBUS_EXT_EXTRA in dtypes.py,"\""Rinominare le ultime 4 tuple: idx54=airway_pressure_pod idx55=esophageal_pressure_pod idx56=transpulmonary_pressure_pod idx57=gastric_pressure_pod. Aggiornare MEDIBUS_EXT_INDEX. Aggiornare test in test_dtypes.py\""",done,high,29,0.5,2026-03-19,2026-03-19,Fix completato — time_at_low_pressure_pod rimosso gastric_pressure_pod aggiunto test aggiornati 22/22 verdi, -230,1,1.4.6,Documenta reverse eng .bin,Notebook bin_format.ipynb completo con hexdump annotato,done,high,26,2,2026-03-19,2026-03-19,bin_format.ipynb esteso: sezione Healthy_Lung (01_Healthy_Lung_01.bin) EXT 4382b a 20Hz no PressurePod. Scoperta: Drager USA SEMPRE 4382b indipendente da PressurePod. Pod assente = idx54-57 sentinel 0xFF7FC99E. PHigh idx51 = ultimo standard (non ext). Nomi idx52-53 corretti: Plow/Tlow, -231,2,2.1.7,Documenta reverse eng .eit,Notebook eit_format.ipynb con header+frame size,done,high,56,1,2026-03-19,2026-03-19,docs/reverse_eng/eit_format.ipynb — frame_size=5495 ts confermato binary_start=preamble[1]+8, -232,1,1.3.6,Analisi prototipo vecchio ASC parser,"\""Analisi docs/prototype_analysis.md: breath detection PEEP detection features 150+ per breath. Utile come spec per Task 3.x-4.x\""",done,medium,,1,2026-03-19,2026-03-19,Prototipo analizzato — logica documentata in docs/prototype_analysis.md, -233,1,1.0.1,Definire parser registry contract,"\""Definire mapping esplicito (vendor extension) -> parser factory e policy strict. Obiettivo: routing deterministico e API stabile per chiamata diretta parser\""",done,high,12,1,0.5,2026-03-26,loader.py completo: ParserFactory type alias + default_parser_registry() + _normalize_vendor() + vendor aliases. Testato in test_parser_routing.py. -234,1,1.0.2,Implementare load_data orchestrator,"\""Entry point unico: detect vendor/formato -> build parser -> parse_safe -> metadata detection. Deve funzionare su singolo file e restare indipendente da parser .eit specifico\""",done,high,233,1.5,0.5,2026-03-26,load_data() + load_many() + load_folder() completi in loader.py. Metadata detection.vendor/extension/bin_format attaccati a BaseData. Testati batch e skip-on-error. -235,1,1.0.3,Implementare load_many_data batch mixed,"\""API batch per lista file multi-estensione multi-vendor con report per-file (ok/error). Non bloccare tutto al primo errore\""",todo,high,234,2,,,, -236,1,1.0.4,Consolidare autodetect vendor .bin,"\""Usare BIN_FORMAT_SPECS come source of truth e spec.vendor come chiave routing. Coprire validazione estensione e file size\""",todo,high,23,1,,,, -237,1,1.0.5,Implementare score_format_sanity per .bin ambiguo,"\""Quando più frame size dividono file_size calcolare score su campioni start/middle/end e scegliere spec migliore\""",todo,high,236,2,,,, -238,1,1.0.6,Integrare score in detect_best_format,"\""Sostituire placeholder AmbiguousFormatError con decisione score-based in strict/non-strict con messaggi diagnostici\""",todo,high,237,1,,,, -240,1,1.0.8,Integrare sentinel replacement in parse bin,"\""SPOSTATO A PREPROCESSING. Parser è lazy by design — non copia in RAM non rimpiazza valori. replace_no_data_sentinels va chiamata nel preprocessing layer come prima operazione prima di qualsiasi calcolo.\""",cancelled,high,239,0,,,2026-03-26,Rimosso deliberatamente dal parser (refactor/lazy-parsing branch). Prima task di Fase 4: 4.0.1. -242,1,1.0.10,Test unit detection e routing single file,"\""Test per detect_vendor_and_format build_parser_for_detection load_data con fixture sintetiche minime\""",todo,high,234,1.5,,,, -243,1,1.0.11,Test unit batch mixed,"\""Test load_many_data con mix .bin/.eit/.csv e parser mancanti. Verificare report successi fallimenti\""",todo,medium,235,1.5,,,, -7.0.1,7,7.0.1,GREIT reconstruction bridge,eit_greit.py: reconstruct_greit() + build_greit() wrapping pyEIT GREIT. Sign correction + rot90 for anatomical orientation. External ref_frame for cross-recording EELI.,done,high,,2,2,,2026-03-27,Implemented in src/fasteit/parsers/draeger/eit/eit_greit.py. Validated in notebook 03 §6-8. -7.0.2,7,7.0.2,Validate std vs fmmu protocol,Empirical test: which pyEIT measurement ordering matches Dräger .eit binary layout?,in_progress,critical,7.0.1,1,2026-03-27,,Test cell in notebook 03 §9 — needs to be run by Riccardo to determine correct parser_meas. -7.0.3,7,7.0.3,Validate .eit/.bin frame alignment,Cross-correlation lag check — prerequisite for ML paired dataset.,in_progress,critical,7.0.1,0.5,2026-03-27,,Cross-correlation cell in notebook 03 §10 — needs to be run. -ML.1,ML,ML.1,Data-driven reconstruction — Ridge baseline,sklearn Ridge regression X=(N_frames 208) y=(N_frames 1024). Learn Draeger reconstruction matrix empirically from paired .eit/.bin data.,todo,high,7.0.3,3,,,Branch feat/ml-reconstruction. Prerequisite: lag=0 confirmed in 7.0.3. -ML.2,ML,ML.2,ML training notebook,Training curves / epochs / validation set / SSIM vs .bin frames,todo,high,ML.1,3,,,Notebook 04_ml_reconstruction.ipynb -ML.3,ML,ML.3,Evaluate CNN option,Compare Ridge vs small CNN (U-Net or MLP) on same paired dataset,todo,medium,ML.2,4,,,Only if Ridge spatial quality insufficient diff --git a/.claude/learning_journal.md b/.claude/learning_journal.md deleted file mode 100644 index 86c78c4..0000000 --- a/.claude/learning_journal.md +++ /dev/null @@ -1,1469 +0,0 @@ -# eit-clinical — Learning Journal - -Quaderno di studio personale di Riccardo. Aggiornato ad ogni sessione di lavoro. - ---- - -## Sessione 2026-03-27 — GREIT bridge, spatial reconstruction diagnosis, ML strategy, docs split - -### Tasks worked on -- Fase 2 wrap-up: `eit_greit.py` (GREIT bridge), notebook 03 validation, docs split -- Branch `feat/eit-parser-complete` brought to final state before PR - -### Concepts explained - -- **GREIT is a linear reconstruction**: `pixel = R_matrix @ vv` where R (1024×208) is - built from a FEM sensitivity model. Data-driven approach = estimating R empirically - from paired `.eit`/`.bin` data instead of using the FEM model. -- **`std` vs `fmmu` protocol ordering**: pyEIT generates 208 pairs in two orderings. - `std` always starts the measurement sweep from electrode 0 (absolute). `fmmu` starts - from the active drive electrode A (rotated). Both give 208 pairs; they differ from - drive pattern 2 onward. Dräger hardware likely uses `fmmu` (natural sweep from the - electrode adjacent to the sink). Wrong protocol → spatially scrambled image even though - the global signal sum is correct (sum is order-independent). -- **Why global signal is correct but image is wrong**: `nansum(images, axis=(1,2))` sums - all pixels regardless of spatial position. If measurements are assigned to wrong electrode - pairs, each pixel is wrong but the total stays the same. This is why temporal phase was - correct while bilateral lung structure was absent. -- **eitprocessing does NOT read `.eit`**: only reads `.bin` (already-reconstructed 32×32 - from the device firmware). FastEIT is the only open-source Python parser for `.eit`. - This is the competitive differentiator. -- **ML reconstruction proposal**: use paired `.eit`/`.bin` frames as (X, y) to learn - the Dräger reconstruction matrix empirically. Simplest model: Ridge regression - (`sklearn`), which estimates R (1024×208) from data — equivalent to GREIT but without - the FEM model. Training on 11k frames takes seconds. Neural network (CNN/U-Net) is - optional and probably not needed if the transform is linear. -- **Frame alignment prerequisite**: before building the ML dataset, verify that frame k - in `.eit` corresponds to frame k in `.bin` (lag=0 cross-correlation). This is checked - in notebook §10. -- **EELI and device recalibration**: absolute impedance baseline shifts after each - PulmoVista recalibration. Cross-recording comparison requires a shared external - `ref_frame` (mean of a stable baseline period at the start of the session). Already - implemented in `reconstruct_greit(ref_frame=np.ndarray)`. - -### Riccardo's questions and insights -- "Perché non ci vogliono mesi per il ML?" → Training time is hours at most (Ridge = - seconds, CNN = hours on CPU). Months was a mistake — I was thinking about full feature - development, not training time. -- "Possiamo fare un modello che impara quello che fa Dräger?" → Yes, this is called - data-driven reconstruction. Ridge regression is the right starting point (linear EIT). -- "Questo mi porterebbe più nel mio campo" → Exactly right: the ML angle is clinically - original (no one else has paired `.eit`/`.bin` data from open-source parsing) and - positions the work as applied research, not physics engineering. - -### Epiphanies -- The `.bin` is NOT raw data — it is already reconstructed by the device. eitprocessing - works at pixel level, never seeing the 208 measurements. FastEIT is unique precisely - because it reads both layers. -- `std` vs `fmmu` is empirically testable in the notebook: if `fmmu` shows bilateral - lung structure and `std` does not, the ordering is confirmed. - -### Technical notes -- `eit_greit.py`: sign negation + rot90(k=1) applied inside `reconstruct_greit()` so - callers always get impedance convention + anatomical orientation without manual steps. -- External `ref_frame` is the correct mechanism for cross-recording EELI comparison. - Documented in the docstring with a usage example. -- docs split: `data_model.md` = containers only; `parsers.md` = parser reference - (new file, comprehensive); `parsing_layer.md` = architecture + extension recipes. - -### Branch status -- `feat/eit-parser-complete`: commits 1-3 done (eit_greit, notebook, docs) -- Next: §9 std/fmmu test and §10 cross-correlation to be run by Riccardo in the notebook -- After PR merge to dev: open `feat/ml-reconstruction` from dev - ---- - -## Sessione 2026-03-24 — Git workflow, CI/CD, benchmark notebook - -### Task lavorate -- B.1: Notebook benchmark `np.memmap` vs `struct.unpack_from` -- D.3 (completamento): README badge table verticale -- CI.1 (completamento): build workflow + test/lint trigger su dev -- CI.2 parziale: codecov setup (pytest-cov, codecov-action@v5, CODECOV_TOKEN) -- P.1: GitHub profile README - -### Concetti spiegati - -- **Git branching model (trunk-based semplificato)**: `main` = released/stable, `dev` = working environment, feature branch da dev per ogni task. Il branch default su GitHub punta a quello che vede il pubblico. -- **`git remote prune origin`**: rimuove i riferimenti locali a branch remoti già cancellati su GitHub (dopo merge e delete PR). -- **`np.memmap` e RAM peak**: il mapping OS vive fuori dall'heap Python — `tracemalloc` non vede i byte grezzi del file. Il RAM peak misurato è l'output array `(N, 32, 32)` float32 creato da `replace_no_data_sentinels` + la maschera booleana intermedia. I campi non acceduti (ts, Medibus) non entrano mai nell'heap. -- **Speedup ratio che decresce a file grandi**: a file piccoli la CI/CD è bottlenecked da Python overhead (loop, tuple, function calls). A file grandi entrambi gli approcci diventano I/O-bound — il disco diventa il collo di bottiglia comune, e il vantaggio relativo si riduce. -- **GitHub Actions badge URL e branch**: `badge.svg` senza parametri usa il default branch. `branch/main` punta a main, `branch/dev` a dev. Se il coverage è uploadato da dev ma il badge punta a main, mostra `unknown`. -- **Codecov**: riceve il `coverage.xml` generato da `pytest --cov --cov-report=xml` tramite `codecov-action`. Da ottobre 2023 richiede token anche per repo pubbliche. Il token va come GitHub Secret, mai hardcoded nel yaml. -- **Standard README badge table**: layout verticale (label | badge) è lo standard della community Python open source (pandas, sklearn, requests lo fanno tutti). Non specifico di eitprocessing. -- **`fail_ci_if_error: false`**: il job CI non fallisce se l'upload a codecov non riesce — evita che un problema esterno rompa la CI. - -### Domande di Riccardo -- "perche stiamo caricando in ram con mmap comunque?" → Il RAM che misuri è l'output array (pixels puliti), non i byte del file — quello è gestito dall'OS fuori dall'heap Python. -- "la struttura mia e quella di eitprocessing e lo stesso standard?" → Sì, separation of concerns (models/parsers), entry point unico, dtype numpy per binari, BaseParser — tutti pattern standard, non copiati. -- "coverage unknown perché?" → Badge puntava a `branch/main` ma il coverage era stato uploadato da `dev`. Fix: cambiare branch nel badge URL. - -### Epifanie e insight -- "il badge unknown non è un errore di codecov, è solo che il branch nel link è sbagliato" -- "pytest-cov era già in pyproject.toml da prima — avevamo già tutto pronto senza saperlo" - -### Note tecniche -- `codecov/codecov-action@v5` (non v4) è la versione attuale raccomandata da codecov -- `if: matrix.python-version == '3.12'` evita 3 upload duplicati dalla matrix 3×Python -- `--cov-branch` abilita il branch coverage (copertura dei rami if/else), non solo line coverage - ---- - -## Sessione 2026-03-27 — Parser .eit completo + visione scientifica multicentrica - -### Task lavorate -- Completamento `DragerEitParser.parse()`: memmap frame data, estrazione tutti i campi rilevanti -- Discussione architettura `RawImpedanceData`: split `aux_signals` → `signals` + `clinical` -- Discussione bridge pyEIT e standardizzazione cross-vendor - -### Concetti spiegati - -- **`RawImpedanceData` split semantico**: i campi del frame si dividono in due categorie con significato diverso. - - `signals`: segnali fisici EIT per-frame (`timestamp`, `trans_A`, `trans_B`, `injection_current`, `voltage_A`, `voltage_B`) — quello che la macchina misura elettricamente - - `clinical`: dati di contesto clinico per-frame (`medibus` 67 canali ventilatore, `event_text`) — quello che il paziente/ventilatore sta facendo - - `measurements`: il campo core per pyEIT = `vv` calibrato (Task 2.4.2) - -- **pyEIT non "sa" quanti elettrodi hai**: il protocollo (`n_el`, `dist_exc`, `step_meas`) si configura esplicitamente. La matrice `vv` deve avere tante colonne quante misure definisce quel protocollo. Dräger 16 el → 208 misure, Timpel 32 el → misure diverse. - -- **GREIT (Graz Consensus Reconstruction Algorithm)**: algoritmo standardizzato per ricostruzione EIT clinica 2D. Parametri raccomandati da consensus paper (Adler 2009). Permette di giustificare scientificamente le scelte algoritmiche senza dover derivare tutto from scratch. - -- **Geometria EIT sempre circolare**: sia Dräger che Timpel modellano il torace come sezione circolare 2D. È una semplificazione nota di tutti i sistemi EIT — funziona perché la cintura va sempre al 4° spazio intercostale. Non è una differenza tra vendor. - -- **Pixel vs ROI: cosa è confrontabile cross-vendor**: - - Pixel singoli: NON confrontabili (16 el → risoluzione minore, 32 el → maggiore) - - Regioni (ROI: dx/sx, ventrale/dorsale): SÌ confrontabili — sono frazioni della mesh normalizzata, esistono identiche su entrambe - - Indici clinici (GI, CoV, tidal variation regionale): SÌ confrontabili — adimensionali, normalizzati, non dipendono dalla risoluzione - -### Domande di Riccardo -- "pyEIT non lo considera se Timpel ha 32 elettrodi?" → pyEIT è agnostico — configuri tu il protocollo con `n_el=32`. Il `vv` ha tante colonne quante il protocollo definisce. -- "se non so giustificare tutti i parametri non è un problema?" → Con GREIT puoi citare il consensus paper — i parametri sono validati dalla comunità. Non devi derivarli da zero. -- "il torace non è sempre circolare considerato?" → Sì, sempre — sia Dräger che Timpel. Avevo sbagliato a differenziarli su questo punto. -- "le features che tiriamo fuori sono confrontabili?" → Le ROI e gli indici clinici sì. I pixel raw no. - -### Epifanie e insight -- "Ah quindi il .bin ha già le immagini ricostruite da Dräger — non ho bisogno di pyEIT per le feature cliniche. pyEIT lo uso per validare e per la pipeline open source cross-vendor!" -- "Se dimostro che GREIT ≈ algoritmo Dräger proprietario, ho una pipeline standardizzata che funziona su qualsiasi vendor. Un medico periferico carica il file, il centrale confronta tutti con lo stesso algoritmo validato." -- "Non confronto pixel per pixel tra Dräger e Timpel — confronto GI, CoV, tidal variation per ROI. Quelli sono adimensionali e la risoluzione non conta." - -### Obiettivo scientifico esplicito (da salvare in roadmap) -La contribuzione scientifica centrale di fastEIT: -1. Parsare .eit (Dräger) e .x (Timpel) — nessuno ce l'ha open source -2. Ricostruire con GREIT (Adler 2009) — algoritmo standardizzato e citabile -3. Validare GREIT vs .bin Dräger (gold standard CE marcato) → dimostrare equivalenza -4. Applicare stessa pipeline a Timpel → cross-vendor comparison possibile -5. Risultato: **primo software open source per confronto EIT multi-vendor con pipeline standardizzata e validata** - -Potenziale paper: *"Open-source vendor-agnostic EIT reconstruction pipeline: validation against PulmoVista 500 proprietary algorithm and cross-vendor comparison"* - -### Note tecniche utili per dopo -- Pipeline clinica primaria: `.bin` → pixel Dräger → feature (nessuna ricostruzione richiesta) -- Bridge pyEIT: `.eit` → `vv` (Task 2.4.2) → GREIT → immagini proprie → confronto con `.bin` -- `metadata["n_electrodes"]` e `metadata["n_measurements"]` in `RawImpedanceData` servono esattamente per configurare il protocollo pyEIT automaticamente nel bridge - ---- - -## Sessione 2026-03-25 — Reverse engineering .eit format + notebook - -### Task lavorate -- Revisione e aggiornamento CSV task (17+ task da in_progress/todo → done) -- Reverse engineering completo formato `.eit` PulmoVista 500 -- Scrittura notebook `docs/reverse_eng/eit_format.ipynb` da zero -- PR #18 mergeata su `dev` - -### Concetti spiegati - -- **Layout binario frame .eit**: ogni frame è 5495 bytes. Il blocco signal è 600 float64 (da byte 16 a 4816). Contiene trans_A[208], gruppi unknown, injection_current[16], voltage_A/B[16], trans_B[208], altri campi. Non 600 valori "casuali" — ogni sottogruppo ha un significato fisico preciso. -- **float64 vs float32**: l'errore classico nel reverse engineering binario. Se leggi 8 byte come due float32 invece di un float64 ottieni valori completamente sbagliati (es. -3.4e12 che sembrava un sentinel era un artefatto). La regola: prima testa sempre entrambi i tipi e confronta con i valori attesi fisicamente. -- **Calibrazione EIDORS**: `vv = ft[0]*trans_A - ft[1]*trans_B` dove `ft = [0.00098242, 0.00019607]`. Costanti empiriche stimate dagli sviluppatori EIDORS nel 2016 correlando con output `.get`. Non derivate da specifica hardware — "best possible guess". Riccardo ha imparato che in fisica clinica la calibrazione è spesso empirica, non analitica. -- **injection_current**: `vvOrig(225:240)` in MATLAB = frame bytes [1808:1936]. Dividendo per `fc=194326.3536` ottieni Ampere. È la misura real-time della corrente effettivamente iniettata (non quella nominale di 5mA). -- **voltage_A/B differenziale**: `(voltage_A - voltage_B) / fv` dove `fv=0.11771`. La differenza tra i due set annulla il rumore di modo comune. `fv` anche questo empirico. -- **mk_stim_patterns**: funzione EIDORS/FEM che descrive il protocollo di stimolazione (dove inietti, dove misuri). NON la implementiamo noi — pyEIT ha il suo equivalente. Noi passiamo i dati `vv` calibrati a pyEIT e diciamo "adjacent drive, 16 elettrodi". -- **Patient data redaction**: prima di stampare l'header ASCII, la funzione `redact_header()` sostituisce i valori di campi sensibili (`Filename`, `Name`, `Patient ID`, `Age/DoB`, `Short Comment`) con `[REDACTED]`. Il file originale non viene mai toccato. - -### Domande di Riccardo -- "gli altri dati li buttiamo?" → No. `injection_current`, `voltage_A/B`, `gugus`, `unknown_*` vanno tutti in `aux_signals` — i named in campi espliciti, i truly-unknown raw per ricercatori futuri. -- "mk_stim_patterns dobbiamo implementarlo noi?" → No. pyEIT lo gestisce. Noi organizziamo i dati nel formato che si aspetta. - -### Epifanie e insight -- "Ah quindi -3.4e12 non era un sentinel, era float32 sbagliato che leggeva byte di float64!" -- "Il commento `# estimated AA: 2016-04-07` di EIDORS significa che anche loro non sapevano esattamente — stavano indovinando con buona correlazione" -- "Il PulmoVista misura sia la corrente che la tensione reale ogni frame — non fida solo del nominale 5mA" - -### Note tecniche -- `sep_offset` nell'header: l'offset assoluto dal byte 0 del file dove si trova il separatore `b'**\r\n\r\n\r\n'`. `binary_start = sep_offset + 8`. -- `parse_header(raw)` restituisce `(format_version, sep_offset, binary_start, fields_dict)` — il terzo valore è già `sep_offset + 8`. -- HealthyLung.eit è V1.00 a 20Hz — NON usarla come esempio primario. patient01.eit è V1.30 a 50Hz, più rappresentativa del clinico. -- `redact_header()` usa `line.find(':')` — funziona anche per `Filename:` che non ha padding. -- Framerate **non** è fisso a 50Hz — va parsato da `Framerate [Hz]` nell'header. HealthyLung è 20Hz. -- CSV task corrotto da script di aggiornamento (apertura in write mode prima del controllo). Recuperato 236/257 task dalla trascrizione sessione precedente. Mancanti: 7 task bloccate Fase 8/9 + alcune Fase 4-6 — da reinserire quando arriva il momento. - ---- - -## Sessione 2026-03-22 (Parte 2) — Implementazione TimpelTabularParser - -### Task lavorate -- Implementazione completa `TimpelTabularParser` + `TIMPEL_FRAME_DTYPE` + `TIMPEL_AUX_FIELDS` -- 41 test verdi per Timpel (dtypes + parser) -- Rimozione lazy import: Timpel ora tracciato git, import diretto -- Fix code-review: F1 (docstring timestamps), F2 (sentinel threshold), W3 (InvalidSliceError), W7 (file_format da extension) -- Refactor docs: `data_model.md` senza routing, `parsing_layer.md` con file layout -- 147 test totali, coverage 93% - -### Concetti spiegati -- **Formato Timpel CSV**: 1030 colonne per riga, senza header, delimiter virgola. Cols 0-1023 = pixel 32×32 (row-major), cols 1024-1029 = aux signals (airway_pressure, flow, volume, min_flag, max_flag, qrs_flag). Sentinel −1000.0 = canale disconnesso → NaN. -- **np.loadtxt vs np.fromfile**: Timpel è testo CSV → `np.loadtxt` con `delimiter=","`. Dräger .bin è binario → `np.memmap` con `dtype` strutturato. Stessa architettura, input diversissimi. -- **Timestamp sintetici**: Timpel non ha colonna timestamp → generiamo noi: `ts[i] = (first_frame + i) / 50.0`. Questo preserva l'indice assoluto del frame anche quando si carica uno slice. Convention di eitprocessing. -- **TIMPEL_FRAME_DTYPE**: deve avere i campi `ts` (float64) e `pixels` (float32, 32×32) per essere compatibile con `ReconstructedFrameData.frames`. La struttura diversa da Dräger (che ha anche min_max_flag, event_marker ecc) è gestita con field opzionali nel data model. -- **Sentinel threshold comparison**: usare `values < TIMPEL_NAN_SENTINEL + 1.0` invece di `values == float32(sentinel)` perché −1000.0 è esattamente rappresentabile ma altri sentinels futuri potrebbero non esserlo. È più robusto di una equality comparison, anche se oggi funzionerebbe uguale. -- **Convenzione timestamp multi-vendor**: Dräger salva il tempo come frazione di giorno (0.0–1.0 = wall clock). Timpel non ha timestamp → secondi dall'inizio registrazione. Documentato in docstring `timestamps` property e in `data_model.md`. - -### Epifanie e insight -- "Il lazy import `try: from .timpel import X except ImportError` era necessario quando il modulo era gitignored. Appena aggiungi il file al repo, puoi toglierlo — è rumore che nasconde errori reali." -- "L'errore `skiprows=100` su file con 5 righe restituisce array `(0,)` 1-D, non `(0, 1030)` 2-D — numpy non può inferire il numero di colonne da un file vuoto. Bisogna gestire `raw.ndim == 1 and raw.size == 0` prima di fare il reshape." -- "Il code reviewer ha trovato che `file_format='csv'` era hardcoded anche per file .txt e .asc — fix facile: `path.suffix.lower().lstrip('.')`." - -### Domande di Riccardo -- "prima di pushare ricontrolla con i vari agents che tutto sia a posto la documentazione ecc. mi raccomando tra i due file md in docs elimina le ripetizioni. il file sui modelli non deve parlare della sequenza di loading ed espansione." → Refactoring doc: `data_model.md` descrive solo i container di dati; `parsing_layer.md` descrive il routing, detection, registry, e ora include anche il file layout (sezione 5 aggiunta). - -### Note tecniche utili per dopo -- `np.loadtxt` con `skiprows=N` su file con 0 -- `TIMPEL_AUX_FIELDS` è una tuple ordinata: l'ordine corrisponde alle colonne 1024→1029 — non cambiare l'ordine -- Test validate() deve coprire anche .txt oltre che .csv — estensioni multiple = test multipli - ---- - -## Sessione 2026-03-22 - -### Task lavorate -- Code quality review e fix post code-review dei tre subagent (eit-ci-checker, eit-doc-keeper, eit-code-reviewer) -- Espansione test suite: 55 → 99 test, coverage 70% → 92% -- Fix bug reale in bin_parser.py scoperto dai nuovi test -- Fix CI GitHub Actions (ruff format + test paziente gitignored) - -### Concetti spiegati -- **memmap read-only copy bug**: `np.memmap[mode="r"][:]` crea una VIEW che eredita il flag read-only del mapping. La fix corretta è `.copy()` che alloca un nuovo array scrivibile. Il `[:]` sembra una copia ma non lo è. -- **ruff format vs ruff check**: la CI di GitHub esegue anche `ruff format --check` (non solo `ruff check`). Sono due cose diverse: `check` trova errori logici/stilistici, `format` controlla la formattazione Black-style. W191 (tabs) non è auto-fixabile con `--fix` — va riscritto il file. -- **Synthetic .bin file per test**: `np.zeros(n, dtype=FRAME_BASE_DTYPE).tofile(path)` crea un file binario valido senza dati paziente. I timestamps devono essere non-identici per la stima fs (tutti zero → `ValueError: Non-positive interval`). -- **BASE format ha Medibus**: entrambi BASE (4358 b) e EXT (4382 b) hanno `medibus_fields` popolato in `bin_formats.py` — `aux_signals` è sempre un dict, mai `None`. La distinzione reale è `has_pressure_pod_fields`. -- **skipif per test con file reali**: `pytest.mark.skipif(not Path("...").exists(), reason="...")` permette di avere test che girano solo in locale (dove esiste il file paziente) e vengono saltati su CI senza fallire. - -### Domande e osservazioni di Riccardo -- "non so perche in locale con ambiente conda mi girano i test ma il push ha fallito entrambe le github action" → Causa 1: `ruff format --check` fallisce se non si esegue `ruff format` prima del commit. Causa 2: test che usano file paziente gitignored danno `FileNotFoundError` su GitHub CI. - -### Epifanie e insight -- "Il bug del memmap non era ovvio: `[:]` sembra una copia ma è una view — solo `.copy()` rompe davvero il legame con il memmap read-only." -- "I test sintetici non richiedono dati paziente: basta costruire il dtype giusto con zeros, impostare timestamps validi, e scrivere su disco con `.tofile()`." -- "La CI di GitHub è più severa del check locale: controlla anche la formattazione, non solo i lint errors." - -### Note tecniche utili per dopo -- Prima di ogni commit: `ruff check src/ tests/ && ruff format src/ tests/` -- `mapped_frames.copy()` non `mapped_frames[:]` per array scrivibile da memmap -- Coverage: 92% con 99 test — bin_parser, bin_utils, detection edge cases, error paths tutti coperti -- CHANGELOG.md rimosso dai file tracciati (non mantenuto in questa fase del progetto) - ---- - -## Sessione 2026-03-21 - -### Task lavorate -- Task 1.2.2: implementazione `DragerBinParser.parse()` (memmap + slicing + sentinel handling + fs da timestamp) -- Task 3.3.1 (in progress): implementazione `DragerAscParser` nella nuova architettura parser - -### Concetti spiegati -- **Parser architecture vendor-ready**: detection separata dal parser concreto. `detect_vendor_and_format()` decide vendor+estensione, `loader` istanzia parser da registry, `parse_safe()` applica validate+parse in modo uniforme. -- **Perché il parser ASC non deve usare read_csv diretto fino a EOF**: il file Drager `.asc` contiene blocchi multipli con schemi colonna diversi (immagini + tabelle). Se leggi tutto da "Tidal Variations" in poi, Pandas rompe quando incontra un blocco con 69 campi invece di 11. Soluzione robusta: estrarre solo la sezione con schema coerente. -- **Section-based parsing**: leggere il marker (`Tidal Variations`), prendere header tabellare, poi consumare righe finché il numero colonne resta costante e la prima colonna resta indice immagine numerico. - -### Epifanie e insight -- "Il parser è molto più stabile se tratta l'ASC come documento a sezioni e non come CSV unico." -- "La stessa architettura ci permetterà di aggiungere Timpel senza toccare il core del routing." - -### Note tecniche utili per dopo -- `detect_tabular_vendor()` ora riconosce Drager via header `DraegerEIT Software` e ha fallback euristico per Timpel. -- Nuovo container `TabularData` con `table: DataFrame`, `n_frames` e `duration` coerenti con il modello dati esistente. -- Test aggiunti per detection/parse/load su file reali ASC Drager (`patient01.asc`). -- Stato test dopo le modifiche: **57 passed**. - -### Addendum naming refactor -- Rimossi gli alias `BinData` e `TabularData`: restano solo i nomi canonici `ReconstructedFrameData` e `ContinuousSignalData`. -- **Perché**: il nome della classe deve descrivere il contenuto dei dati, non l'estensione del file sorgente. Questo evita che un futuro export Timpel con matrici 32×32 finisca in una classe semanticamente "Drager-bin". -- Possibile evoluzione futura: introdurre un `TabularData` più astratto solo se servirà davvero come superclass generica per tabelle da ventilatore o export misti. - ---- - -## Sessione 2026-03-20 - -### Task lavorate -- Fix architetturale `dtypes.py`: refactor MEDIBUS fields confermato da eitprocessing (Task 1.3.2 revisione) -- Aggiornamento `test_dtypes.py`: fixing semantica idx 51 - -### Concetti spiegati - -- **Architettura MEDIBUS BASE vs EXT — la scoperta chiave**: I due formati `.bin` (4358 bytes BASE e 4382 bytes EXT) NON sono in relazione "BASE + 6 extra". Condividono i campi **idx 0-50** (51 campi comuni), ma **divergono completamente da idx 51**: - - BASE (4358 bytes): idx 51 = `time_at_low_pressure` (Tlow BiLevel) — è l'unico campo aggiunto dopo il comune - - EXT (4382 bytes): idx 51 = `high_pressure` (PHigh), idx 52 = `low_pressure` (Plow), idx 53 = `time_at_low_pressure` (Tlow — spostato da 51!), idx 54-57 = Pod channels - - **Tlow si sposta da idx 51 (BASE) a idx 53 (EXT)** — questo è il punto più sottile e la fonte del bug precedente - - Fonte: eitprocessing `draeger.py` (Apache-2.0) definisce `_bin_file_formats` con due dizionari completamente separati - -- **Perché il precedente "fix" era sbagliato**: In una sessione precedente avevamo usato il file `.txt` del software Dräger (che mostra 58 canali in 1-indexed) come guida. Abbiamo messo PHigh a idx 51 in `MEDIBUS_FIELDS`. Ma `MEDIBUS_FIELDS` dovrebbe rappresentare il formato BASE (4358 bytes) dove idx 51 è **Tlow**, non PHigh. PHigh appare solo nel formato EXT e solo lì. - -- **Struttura corretta in `dtypes.py`**: - ```python - _MEDIBUS_COMMON # idx 0-50, 51 campi, identici in entrambi - MEDIBUS_BASE_FIELDS # = COMMON + [Tlow@51] → 52 campi - MEDIBUS_EXT_FIELDS # = COMMON + [PHigh@51, Plow@52, Tlow@53, Pod×4@54-57] → 58 campi - MEDIBUS_FIELDS # alias di MEDIBUS_BASE_FIELDS (backward compat) - MEDIBUS_BASE_INDEX # dict name→index per BASE - MEDIBUS_EXT_INDEX # dict name→index per EXT - MEDIBUS_INDEX # alias di MEDIBUS_BASE_INDEX (backward compat) - ``` - -### Epifanie e insight -- "Ah quindi il `.txt` con 58 canali del software Dräger rappresenta il formato EXT, non il BASE. Quando vai a idx 52 nel software vedi PHigh perché stai guardando un file con PressurePod connesso." -- "Il modo giusto per leggere eitprocessing era guardare la struttura di dati `_bin_file_formats` — due dizionari separati, non uno derivato dall'altro." - -### Domande e risposte -- "Perché Tlow è presente sia in BASE che in EXT se in BASE non puoi settare BiLevel?" → Tlow è inviato dal ventilatore anche in ventilazione convenzionale, con valore sentinel `-1000.0`. Il Dräger include sempre il campo; se la modalità BiLevel non è attiva manda -1000.0. - -### Note tecniche utili per dopo -- `high_pressure` esiste **solo** in `MEDIBUS_EXT_INDEX`, non in `MEDIBUS_BASE_INDEX`. Se provi `MEDIBUS_INDEX["high_pressure"]` ottieni `KeyError`. Usare sempre il dict giusto per il formato del file. -- I sentinel: `-1000.0` = parametro non applicabile/modalità non attiva; `0xFF7FC99E` = hardware non connesso (Pod assente) -- 22 test verdi dopo il fix — tutti i test semantici ora riflettono la struttura corretta - ---- - -## Sessione 2026-03-19 (sera) - -### Task lavorate -- Fix Streamlit taskboard: auto-reload on CSV mtime change -- Fix PRIORITY_VALUES: aggiunto "critical" a data.py -- Pianificazione task 1.1.1 (detect_frame_dtype) e 1.2.2 (parse_bin) - -### Concetti spiegati - -- **Streamlit file-watching**: Streamlit fa watching solo sui file `.py`, non sui `.csv`. Per rilevare modifiche esterne al CSV basta controllare `mtime` (modification time) ad ogni rerun — se cambia, cache miss automatico. -- **mtime come cache key**: passare `mtime: float` a `@st.cache_data` trasforma il timestamp in chiave di cache. Se Claude Code scrive il CSV, il prossimo rerun vede un `mtime` diverso → nuova chiamata → ricarica dal disco. Elegante e senza polling. - -### Note tecniche -- `Path.stat().st_mtime` restituisce un float (secondi da epoch) — si può usare direttamente come chiave per `@st.cache_data` -- `st.session_state.get("csv_mtime")` ritorna `None` alla prima esecuzione → diverge da `_mtime` → primo caricamento automatico -- Il pulsante "🔄 Ricarica dal CSV" ora fa solo `st.cache_data.clear() + st.rerun()` — il mtime check fa il resto - ---- - -## Sessione 2026-03-19 (pomeriggio) - -### Task lavorate -- Fix MEDIBUS field ordering in dtypes.py (Task 1.3.2 + 1.3.5 follow-up) -- Estensione notebook bin_format.ipynb con sezione Healthy Lung (Task 1.4.6) - -### Nuovi file di test -- `01_Healthy_Lung_01.bin/.eit/.asc` — registrazione 1118 frame, 20 Hz, NO PressurePod, NO MEDIBUS -- `elencoMEDIBUSdatafromDragerSoftware.txt` — lista manuale 58 canali Dräger - -### Concetti spiegati - -- **Ordinamento canali MEDIBUS**: Il channel list del software Dräger è la fonte più diretta di verità. PHigh (peak BiLevel pressure) è il canale 51 (0-indexed) = l'ultimo dei 52 canali standard, non un campo "extra PressurePod". I campi extra (idx 52-57) sono: Plow, Tlow, ~Paw, ~Pes, ~Ptp, ~Pgas. - -- **Sentinelle MEDIBUS**: Due valori speciali distinti: - - `0xFF7FC99E` = -3.4e38 → hardware NON connesso (MEDIBUS cable absent) - - `0xC47A0000` = -1000.0 → connesso ma parametro non applicabile o nessun respiro calcolato ancora (es. Plow/Tlow in modalità convenzionale non-BiLevel) - -- **BiLevel vs ventilazione convenzionale**: PHigh, Plow, Tlow sono parametri della modalità BiLevel (CPAP bifasica). In ventilazione convenzionale normale, questi hanno valore -1000.0 (sentinel "N/A"). Importante per l'interpretazione clinica: non confondere -1000.0 con un valore reale di pressione! - -### Epifanie e insight - -- **"Il Dräger usa SEMPRE 4382 byte/frame!"** — Anche una registrazione senza PressurePod ha frame da 4382 byte. La differenza non è nella struttura del frame ma nei valori: Pod assente = idx 54-57 hanno `0xFF7FC99E` (-3.4e38). Questo semplifica il parser: non serve distinguere BASE/EXT prima di aprire il file, basta guardare il sentinel a idx 54. - -- **"Il .bin e il .asc non partono dallo stesso istante!"** — Il file Healthy_Lung ha un offset di +8 secondi tra il timestamp del .bin (frame 0) e il timestamp del primo frame esportato nel .asc. Il .asc è generato dal .eit, non dal .bin. I due file coprono periodi leggermente diversi. - -- **"Healthy Lung è a 20 Hz, non 50 Hz!"** — dt = 0.05s invece di 0.02s. Il PulmoVista supporta framerate configurabili. Il parser dovrà rilevare il framerate dal timestamp, non assumere sempre 50 Hz. - -- **"I pixel del .asc non sono comparabili direttamente con il .bin"** — Il .asc applica: (1) sottrazione della baseline (media dell'intera registrazione), (2) filtro low-pass 50 bpm. I pixel grezzi del .bin sono valori assoluti. Per cross-validare correttamente bisognerebbe applicare gli stessi preprocessing. - -### Domande aperte -- PHigh a idx 51 vale -1000.0 in tutti i file (anche patient01 con MEDIBUS connesso). Quand'è che PHigh diventa non-zero? Forse solo in modalità BiLevel con MEDIBUS configurato per esportarlo? -- Il formato BASE (4358 byte) esiste davvero su qualche device o firmware? Non ne abbiamo ancora visto uno. - -### Note tecniche -- `np.memmap(..., dtype=FRAME_EXT_DTYPE)` funziona uguale su file con e senza PressurePod — distinzione solo nei valori, non nella struttura -- Per rilevare PressurePod: `frames['medibus_data'][:5, 54].view(np.uint32) != 0xFF7FC99E` -- Per rilevare MEDIBUS: `frames['medibus_data'][0, 0].view(np.uint32) != 0xFF7FC99E` -- Per rilevare framerate: `np.diff(frames['ts']).mean() * 86400` → secondi per frame - ---- - -## Sessione 2026-03-17 (Sessione 0 — Pianificazione) - -### Task lavorate -- Nessuna task di codice. Sessione di pianificazione, ricerca landscape, architettura, roadmap. - -### Concetti appresi (davvero capiti) - -- **File .bin e .eit sono due file separati**: il PulmoVista li genera in parallelo. Il .bin ha le immagini ricostruite 32×32, il .eit ha i dati grezzi dagli elettrodi. Per il mio uso clinico quotidiano basta il .bin. - -- **Il formato .eit Dräger NON è quello che pensavamo**: i blocchi tipo 3/7/8/10 sono del formato Carefusion (altro produttore, stessa estensione file). Il formato Dräger ha header testuale + dati binari. Scoperto analizzando il codice EIDORS. - -- **208 misure, non 256**: il PulmoVista con 16 elettrodi produce 208 misure indipendenti per frame (16 iniezioni × 13 misure, escludendo auto-misure). - -- **EIDORS è GPL**: significa che non possiamo copiare il loro codice nel nostro progetto Apache-2.0. Ma possiamo leggere il codice per capire il formato del file, e poi scrivere il nostro parser da zero. Le specifiche di un formato file non sono copyrightabili, il codice sì. - -- **Il valore del progetto**: non è nel preprocessing (che reimplementiamo da eitprocessing/letteratura) ma nel pipeline end-to-end: file → features → database breath-level. Nessuno ce l'ha. - -- **PEEP step detection è un buco nel campo**: nessun tool fa detection automatica dei cambi PEEP. Né eitprocessing, né nessun altro. - -- **pyEIT produce una mesh, non 32×32**: serve interpolazione per rimappare a 32×32. È lo stesso passaggio che fa il PulmoVista internamente. - -### Concetti visti ma DA STUDIARE a fondo (non li so usare) - -- **struct.unpack**: so che serve per leggere byte e interpretarli come numeri, ma devo fare esercizi pratici -- **numpy.fromfile e numpy.frombuffer**: so che esistono e che sono più veloci di struct per array grandi, ma non li ho mai usati -- **numpy dtype strutturato**: concetto potente (definisci il layout di un frame intero), ma non l'ho mai provato -- **Endianness**: so che il PulmoVista è little-endian e che si specifica con '<', ma devo fare pratica -- **mmap (memory-mapped files)**: ho solo letto che esiste, non ho idea di come si usa in pratica -- **scipy.signal**: find_peaks, butter, sosfiltfilt — so cosa fanno concettualmente ma non li ho mai usati -- **Watershed, Otsu**: metodi per lung mask — concetti vaghi, da studiare quando arrivo alla Fase 4 -- **HDF5 / h5py**: so che è per salvare matrici grandi, mai usato - -### Epifanie - -- "Il .eit e il .bin sono due file separati generati in parallelo dal PulmoVista durante la stessa registrazione — non uno dentro l'altro!" - -- "eitprocessing non fa PEEP step detection automatica. Nessuno lo fa. È un buco enorme nel campo." - -- "Il parser a blocchi tipo 3/7/8/10 che avevamo analizzato è il formato CAREFUSION, non Dräger! Il formato Dräger è completamente diverso. Scoperto analizzando il codice EIDORS completo." - -- "pyEIT non produce 32×32 ma una mesh triangolare. Serve interpolazione (scipy griddata) per rimappare a 32×32 e rientrare nel pipeline. È lo stesso passaggio che fa il PulmoVista internamente." - -- "Il valore del progetto non sta nel preprocessing (copiato/reimplementato da eitprocessing) ma nel pipeline end-to-end: file → preprocessing → features → database breath-level → export per R/ML. Nessuno ce l'ha." - -- "Le correzioni manuali dell'utente nella GUI (mask, breath detection, PEEP step) diventano training data per ML futuro. Flywheel: più usi → più dati → migliori predizioni → meno correzioni." - -### Note da ricordare per dopo -- Il consensus TREND 2017 ha 193 pagine di supplementi — leggere almeno le definizioni dei parametri -- I fattori di scala EIDORS per le transimpedenze sono STIMATI ("estimated AA: 2016-04-07") — vanno verificati confrontando output Python vs EIDORS sullo stesso file -- eitprocessing è Apache-2.0 (posso studiare il codice), EIDORS è GPL (posso leggere per capire il formato, NON copiare codice) -- Il PulmoVista usa 16 elettrodi, pattern adjacent-drive, 5mA iniezione -- Conversazione 1 con Claude ha spiegazioni dettagliate di struct/numpy/endianness — rileggere quando serve - ---- - -## Sessione 2026-03-18 (Sessione 2 — Task Board UI + Riorganizzazione CSV) - -### Task lavorate -- Nessuna task di codice clinico. Sessione di tooling e riorganizzazione. -- Costruito **Task Board Streamlit** (`tools/taskboard/`) per gestire le task senza editare il CSV a mano -- Rianalisi completa del CSV e riorganizzazione dipendenze/priorità - -### Cosa è stato costruito - -**Task Board Streamlit** — `tools/taskboard/app.py` + `tools/taskboard/data.py` -- Tabella editabile con `st.data_editor`: colonne, selectbox per status/priority, modifica inline -- Colonna `_dot` con emoji colorate per status (🟢 done, 🟠 in_progress, ⬜ todo, 🔴 blocked) -- Tab Progress: metriche globali + barre avanzamento per ogni fase -- Tab Add Task: form con assegnazione automatica task_id (max+1) -- Si lancia con: `cd tools/taskboard && streamlit run app.py` - -### Riorganizzazione CSV - -**VIZ anticipate**: plot di debug spostati nelle fasi in cui servono, non alla fine -- V.1 → Fase 1 (debug parse_bin), V.2/V.4/V.5 → Fase 4 (debug lung mask + breath detection) - -**Fasi 8/9 aggiunte al CSV come blocked**: -- Fase 8 = R wrapper (subito dopo la lib, depends_on fase 6) -- Fase 9 = Django webapp (più avanti, depends_on fase 6) - -**Scope chiarito**: solo ventilazione, no perfusione -- 4.3.1 filtro perfusione → BACKLOG, B.3 analisi perfusione → eliminata - -**Priorità**: task_id 86 (Flask backend) → critical, task_id 8 (optional deps) → low - -### Stato CSV finale -- **211 task** totali | 12 done | 5.7% completato - -### Epifanie -- "I plot di debug non sono opzionali — V.1 dopo parse_bin e V.2 dopo lung_mask accelerano la verifica di 10x rispetto a print() su array." -- "Il wrapper R viene prima del Django perché uso R ogni giorno. La GUI per i clinici è utile ma non è la mia pipeline principale." - ---- - -## Sessione 2026-03-17 (Sessione 1 — Orientamento sistema + Analisi CSV + Kickoff Fase 0) - -### Task lavorate -- Nessuna task di codice. Sessione di orientamento: architettura del sistema, analisi incongruenze CSV, pianificazione Fase 0. - -### Come funziona il sistema (capito oggi) - -**Architettura a 3 livelli**: -1. **CLAUDE.md** — caricato automaticamente ad ogni sessione. Contiene identità, regole ferme, flusso di lavoro, path dei file. Claude lo legge prima di rispondere a qualsiasi cosa. -2. **Memory system** (`~/.claude/projects/.../memory/`) — persistenza tra sessioni: chi sei, come vuoi collaborare, regole apprese. Caricato automaticamente come contesto. -3. **File di progetto** (`.claude/*.md`, `.claude/*.csv`) — da leggere attivamente all'inizio sessione. tasks.csv = stato progetto, roadmap = istruzioni, reference = paper+formule, learning_journal = questo diario. - -**I 3 agent specializzati** (`.claude/agents/`): -- `eit-ci-checker` — prima di ogni commit: ruff lint, pytest, coverage 80% -- `eit-code-reviewer` — dopo aver scritto codice: correttezza scientifica, citazioni, test -- `eit-doc-keeper` — dopo ogni task completata: README, docstring, docs/ coerenti - -**Flusso tipico sessione**: Claude legge CSV → presenta task consigliate → spiega teoria → esempio minimo → tu scrivi nell'IDE → agent review → Claude aggiorna CSV e journal. - -### Analisi incongruenze CSV (fatto oggi) - -**3 fix applicati al CSV**: -- Task 15 (PeepStep): dipende da RawData(13) + Breath(14), non solo da 13 — PeepStep contiene `breath_indices` -- Task 16 (Study): dipende da 13+14+15, non solo da 13 — Study aggrega Breath e PeepStep -- Task 155 (PEEP detection Medibus): dipende da 30 (Medibus extraction) + 118 (breath detection), non solo da 30 — non puoi fare PEEP detection su singoli breath se non hai ancora rilevato i breath - -**Task 50 (Download EIDORS)**: priority era già `critical` nel CSV, nessun fix necessario. - -### Stato progetto attuale -- 205 task | 0 done | 0 in_progress | 0 blocked -- Fase 0: 23 task (infrastructure + data model) — tutto todo, nessuna dipendenza esterna -- Percorso critico realistico: **0 → (1+2 in parallelo) → 4 → 5 → 6** - -### Rischi operativi identificati -1. **File reali (.eit e .bin)**: task 32 e 56 sono `critical` e bloccanti per validazione Fasi 1+2. Da procurare prima possibile. -2. **EIDORS study**: richiede scaricare e leggere codice MATLAB — tempo reale di lettura da parte mia. -3. **Scope creep Fase 5**: 41 task, alcune già marcate post-v1 — tenere la barra. - -### Prossime task consigliate (sbloccate ora, dipendenze vuote) -| Task | Titolo | h | Perché prima | -|---|---|---|---| -| 0.2.1 | Creare repo GitHub | 0.5h | Sblocca intera struttura | -| 0.2.2 | Struttura directory | 1h | Dipende da 0.2.1 | -| 0.7.2 | LICENSE Apache-2.0 | 0.2h | Dipende da 0.2.1, rapidissima | -| 0.1.1 | Studio struct/numpy binari | 2h | Base per tutto il parsing | -| 0.1.2 | Studio scipy signal processing | 2h | Base per preprocessing | - -**Sequenza consigliata**: `0.2.1 → 0.2.2 → 0.7.2` (infrastruttura) poi `0.1.1 + 0.1.2` (studio teorico). - -### Obiettivo fine Fase 0 (checklist) -- [ ] `src/eitclinical/` con moduli vuoti + `__init__.py` -- [ ] `pyproject.toml` installabile con `pip install -e .[dev]` -- [ ] `pytest tests/` passa (0 test, ma senza errori di import) -- [ ] `ruff check src/` pulito -- [ ] `BaseParser`, `RawData`, `Breath`, `Config` esistono con type hints - ---- - -### Aggiornamento fine sessione — File reali confermati disponibili - -**File Dräger reali già in mano** (confermato 2026-03-17): -- `.eit` ✅, `.bin` ✅, `.txt` ✅ -- Varianti con e senza PressurePod ✅ -- Più registrazioni diverse ✅ - -**Task 32 (1.4.1) e 56 (2.1.1) marcate `done`** — erano le due `critical` che sembravano bloccanti. Con i file reali già disponibili: -- Validazione Fase 1 (parser .bin) → sbloccata -- Validazione Fase 2 (parser .eit) → sbloccata - -**Rischio operativo #1 eliminato**: "Procurarsi file reali" era il rischio #1 identificato nella Sessione 0. Non è più un rischio — i file ci sono. - -**Prossime task (sessione dopo)**: `0.2.1 → 0.2.2 → 0.7.2 → 0.1.1 → 0.1.2` - ---- - -### Aggiornamento — Rename eit_clinical → fastEIT + Scaffold Fase 0 - -**Rename completato** (2026-03-17): tutto il progetto ora usa `fastEIT` come nome. Il package Python si importa come `import fasteit` (minuscolo per convenzione PEP 8). - -**Task completate in questa sessione**: -| Task | Titolo | Note | -|---|---|---| -| 0.2.1 | Creare repo GitHub | Già presente (retroattivo) | -| 0.7.2 | LICENSE Apache-2.0 | Già presente (retroattivo) | -| 0.2.2 | Struttura directory | `src/fasteit/` con tutti i moduli placeholder | -| 0.2.3 | docs e notebooks | `docs/eit_format.md`, `docs/eidors_analysis.md`, `docs/reverse_eng/`, `notebooks/` | -| 0.3.1 | pyproject.toml | hatchling, deps core + [pyeit] + [dev], pytest markers, ruff | -| 0.7.1 | README base | Badge + differenziatori + installazione + disclaimer | - -**Cosa è il pyproject.toml** (spiegazione rimandata — vedi prossima sessione) - -**Prossime task sbloccate**: -- `0.3.2` — optional dependencies [pyeit] e [dev] (già fatto in 0.3.1, da valutare se separare) -- `0.4.1` — GitHub Actions workflow test.yml -- `0.4.2` — workflow lint.yml -- `0.5.1` — BaseParser classe astratta -- `0.1.1 / 0.1.2` — studio struct/numpy + scipy (teoria, nessuna dipendenza) - ---- - -### Sessione teorica — Python packaging, GitHub Actions, binary parsing, signal processing - -#### Python packaging (approfondimento) -- **`import` in Python**: cerca `fasteit` in `sys.path` — lista di cartelle dove Python guarda. `src/` layout obbliga a usare il pacchetto installato, non i file locali grezzi. -- **`__init__.py`**: trasforma una cartella in un modulo. Viene eseguito ad ogni `import fasteit`. Posto giusto per `__version__` e API pubblica. -- **`pip install -e .`**: editable install = collegamento simbolico a `src/fasteit/`. Modifica → subito visibile. Nessun reinstall necessario. -- **wheel vs sdist**: wheel = archivio già pronto (veloce). sdist = sorgente da compilare. Per numpy/scipy esistono wheel precompilate. -- **hatchling**: build backend moderno. Alternativa a setuptools senza `setup.py`. - -#### GitHub Actions / CI/CD -- **CI**: ogni push → computer vergine → esegue i test automaticamente. "Vergine" = senza i tuoi pacchetti locali, variabili d'ambiente, file casuali. -- **CD**: se i test passano → pubblica/deploya automaticamente. Non ci serve ancora. -- **Struttura workflow**: `on:` (trigger) → `jobs:` → `steps:`. File YAML in `.github/workflows/`. -- **Runner**: VM Ubuntu fresca nel cloud GitHub. Distrutta dopo ogni run. -- **Matrix strategy**: ripete il job su più versioni Python in parallelo (`["3.10", "3.11", "3.12"]`). -- **`actions/checkout@v4`**: fa il git clone del repo nella VM. Primo step di ogni workflow. -- **`actions/setup-python@v5`**: installa la versione Python specificata. -- **`cache: pip`**: ricicla le dipendenze pip tra un run e l'altro se `pyproject.toml` non è cambiato. -- **workflow test (0.4.1)**: checkout → setup-python con matrix → pip install -e .[dev] → pytest -- **workflow lint (0.4.2)**: checkout → setup-python 3.12 → pip install ruff → ruff check + ruff format --check -- **Badge**: URL automatico `github.com/USER/REPO/actions/workflows/test.yml/badge.svg` - -#### Letture assegnate — GitHub Actions -- GitHub Actions quickstart (docs ufficiale) -- Understanding workflows (docs ufficiale) -- `actions/setup-python` marketplace page (per parametri cache) -- Matrix strategy (docs ufficiale) - -#### struct e numpy per file binari (0.1.1) -- **Byte raw**: i numeri nel .bin sono in memoria esattamente come in RAM. Nessun testo, nessun separatore. -- **Endianness**: little-endian = byte meno significativo prima. Il PulmoVista è little-endian. In numpy: `' 3`. -- `from __future__ import annotations` in tutti i file — permette di usare type hints come stringhe forward reference senza import circolari. - -#### Stato CSV aggiornato -- Task 0.5.1: ✅ done -- Task 0.5.2: ✅ done (rinominata: BaseData + BinData, non più RawData in session.py) - -#### Prossima sessione -1. Studio eitprocessing — com'è parsato il .bin in Apache-2.0 -2. Verifica con `openBins.m` del collega MATLAB -3. Task **1.1.1**: `autodetect_frame_size(file_size)` — scrivi + test con file reale -4. Task **1.2.1**: `FRAME_BASE_DTYPE` ufficiale in `src/fasteit/dtypes.py` - ---- - -### Aggiornamento 2026-03-18 — Review letteratura EIT con GPT: 3 correzioni al progetto - -Sessione di revisione critica della letteratura (Delphi 2025, Scaramuzzo 2024, review recenti). Nessun codice scritto, ma trovate 3 cose da correggere nel progetto: - -**1. CoV mancava la direzione sinistra-destra** -Il Delphi 2025 cita esplicitamente CoV in entrambe le direzioni come misura standard. Noi avevamo solo la formula verticale (ventro-dorsale). Aggiunto: -- Formula CoV_LR: stessa struttura di CoV_VD ma sull'asse delle colonne -- Task 5.1.4 aggiornata: implementare entrambe (`cov_vd` e `cov_lr`) -- DB schema aggiornato con colonna `cov_lr` - -**2. RVD formula era sbagliata: np.argmax ≠ t40 method** -Task 5.3.2 diceva `np.argmax` (tempo al picco). La formula standard da Muders 2012 è il **t40 method**: tempo in cui il pixel raggiunge il 40% del suo ΔZ tidale. -Sono cose diverse: argmax è il massimo assoluto, t40 è il punto a 40% della salita. t40 è più robusto perché non dipende dalla fase finale dell'inspirazione. -Implementazione: `np.argmax(cumulative_dz >= 0.4 * total_dz, axis=time_axis)` - -**3. SDRVD è una metrica con nome proprio, non solo "RVD_inhomogeneity"** -La SD degli RVD pixel-wise è citata come SDRVD nel Delphi 2025 e nella Wisse survey. Nel reference doc la chiamavamo "RVD_inhomogeneity". Allineato il nome. - -**Conferme dalla letteratura (nessuna modifica necessaria)**: -- La gerarchia frame → breath → step/patient è esattamente quella usata in letteratura. Buona scelta architetturale. -- TIV, EELI, GI, silent spaces, V/D ratio, ROI distribution, compliance regionale, ITV, OD/CL, pendelluft: tutti già nella roadmap con le priorità giuste (v1 vs post-v1). -- Delphi 2025 e Scaramuzzo 2024 confermano GI e CoV come standard per PEEP titration. -- Pendelluft e silent spaces: già in roadmap come post-v1 per clinical use diffuso ma non fondamentale v1. - ---- - -## Sessione 2026-03-18 — Review documentazione post config.py + base_data.py - -### Task lavorate -- Nessuna task di codice. Sessione di verifica documentazione (eit-doc-keeper). - -### Bug trovati nei file modificati - -**`src/fasteit/config.py` — 3 problemi**: - -1. `PreprocessingConfig` e `AnalysisConfig` hanno il corpo vuoto — in Python un `@dataclass` senza campi né `pass` è un SyntaxError. Fix: aggiungere `pass` come corpo temporaneo. - -2. `Config` usa `field(...)` come annotazione di tipo invece di come `default_factory`: - ```python - # SBAGLIATO (quello che c'è ora): - device: field(default_factory=DeviceConfig) - # CORRETTO: - device: DeviceConfig = field(default_factory=DeviceConfig) - ``` - -3. La docstring di modulo ha ancora banner TODO come se DeviceConfig non esistesse — va aggiornata. - -**`src/fasteit/models/base_data.py` — 2 problemi**: - -1. Import sbagliato: `from config import Config` fallisce nel src/ layout. Deve essere `from fasteit.config import Config`. - -2. Default `fs` usa subscript invece di attributo: `Config.device[fs]` non esiste. Il problema più profondo è che `Config.device` è un `field(default_factory=...)` — un'istanza non esiste a livello di classe. Il valore di default di `fs` in `BaseData` dovrebbe essere un letterale (`50.0`) o prendere il valore da `DeviceConfig().fs`. - -3. Il docstring dice `Default 20.0` per `fs` ma `DeviceConfig.fs = 50.0`. Il PulmoVista ha sampling rate 20 Hz per le immagini .bin, ma 50 Hz per i dati raw .eit — discrepanza da chiarire e documentare. - -### Note su DeviceConfig.fs = 50.0 vs 20.0 Hz - -- `DeviceConfig.fs = 50.0` — è il frame rate di campionamento interno del dispositivo -- Il .bin ha immagini ricostruite a 20 Hz (standard PulmoVista), non 50 Hz -- Il .eit ha i dati grezzi a 50 Hz -- `BaseData.fs` deve essere il fs effettivo del file letto, non un default fisso — impostarlo nella `__post_init__` del parser in base al formato -- La discrepanza tra docstring (20.0) e DeviceConfig (50.0) va risolta dichiarando esplicitamente i due valori come costanti separate - -### Documentazione aggiornata in questa sessione -- CHANGELOG.md — aggiornato con Task 0.5.1, 0.5.2, 0.6.1 partial -- README.md — "Project status" aggiornato: scaffold complete, next = Fase 1 -- fastEIT_tasks.csv — Task 0.6.1 marcata in_progress con note sui bug - -### Prossime azioni prioritarie -1. Correggere i bug sintattici in `config.py` (aggiungere `pass` ai dataclass stub, correggere `Config` field annotations) -2. Correggere `base_data.py`: import path + default fs (usare letterale `50.0` o `DeviceConfig().fs`) -3. Chiarire e documentare la differenza tra fs .bin (20 Hz) e fs .eit (50 Hz) - ---- - -## Sessione 2026-03-19 — Analisi comparativa .bin format + Task 1.2.1 dtypes.py - -### Task lavorate -- Studio comparativo formato .bin: EIDORS (MATLAB) + eitprocessing (Python) + script MATLAB collega -- Task **1.2.1**: `src/fasteit/dtypes.py` — FRAME_BASE_DTYPE e FRAME_EXT_DTYPE definitivi -- Task **1.2.1b**: aggiornamento `BinData` — property timestamps, min_max_flags, event_markers -- Aggiornamento `fastEIT_layerdata_class_architecture.md` — dtype corretto - -### Scoperta critica: ts è float64, non due float32 - -Il campo timestamp nei .bin è un singolo **float64 (8 byte)**, non due float32 (ts1, ts2). - -Cosa memorizza il PulmoVista: `frazione di giorno` — un numero da 0.0 (mezzanotte) a 1.0 -(quasi mezzanotte del giorno dopo). Per esempio 0.5 = mezzogiorno, 0.708... = 17:00. - -**Per ottenere secondi**: `ts * 86400` (dove 86400 = secondi in un giorno). - -**Perché EIDORS leggeva due float32**: MATLAB `fread(f, 1, 'float32')` legge 4 byte come float. -Chiamato due volte, legge i primi 4 byte (metà del double) e i secondi 4 byte (altra metà). -EIDORS usava `ts1` per il tempo — ma ts1 come float32 dei primi 4 byte di un float64 è un -valore numerico casuale, non un timestamp sensato! La rappresentazione interna IEEE 754 di -un double e di un float hanno bit di mantissa e esponente diversi. - -**eitprocessing fa la cosa giusta**: `reader.float64() * 24 * 60 * 60` legge tutti gli 8 byte -come double e li moltiplica per 86400. - -**Altra nota importante**: `np.unwrap(ts * 86400, period=86400)` gestisce il wraparound per -registrazioni che iniziano prima di mezzanotte e finiscono dopo. Questo va fatto nel BinParser, -non in BinData (che conserva i dati grezzi dal file). - -### Struttura campi corretta (4358 byte base) - -| Campo | Tipo | Bytes | Cosa è | -|---|---|---|---| -| ts | float64 | 8 | Frazione di giorno → ×86400 = secondi | -| dummy | float32 | 4 | Non usato | -| pixels | float32 (32,32) | 4096 | Immagine ricostruita C-order | -| min_max_flag | int32 | 4 | +1=picco insp, -1=valle esp, 0=niente | -| event_marker | int32 | 4 | Counter eventi (incrementa) | -| event_text | S30 | 30 | Testo evento ASCII | -| timing_error | int32 | 4 | 0=ok, non-zero=errore | -| medibus_data | float32 (52,) | 208 | 52 segnali Medibus | - -Frame esteso (PressurePod, 4382 byte): medibus_data ha (58,) invece di (52,), +6 campi ×4b=+24b. - -### I 52 campi Medibus — cosa sono - -I primissimi 6 (idx 0-5) sono **waveform continue a 50 Hz**: pressure_airway, flow, volume, CO2 %. -Il resto (idx 6-51) sono **parametri aggregati dal ventilatore** aggiornati una volta per atto -respiratorio: PEEP, compliance dinamica, frequenza respiratoria, volumi espirati, ecc. - -Per il nostro use case clinico i più importanti: -- **idx 14: PEEP** — fondamentale per Task 5.7.1 (PEEP detection Livello 1) -- **idx 0: airway_pressure** — pressione delle vie aeree in tempo reale -- **idx 1: flow** — flusso in tempo reale -- **idx 2: volume** — volume tidal in tempo reale - -### Algoritmi clinici del collega MATLAB (da confrontare con 3 fonti a Fase 4/5) - -Gli script del collega mostrano come lui implementa le stesse cose che noi faremo in Python. -**Li confronteremo su 3 fronti** quando arriveremo alle fasi relative: collega vs EIDORS vs -eitprocessing. Alcuni hanno già issue su GitHub che danno spunti utili. - -**FindBreathsInAVector.m** → Task 4.10 (breath detection): -```matlab -smoothedTrace = smooth(scalarTrace); -[ValInsp, XpeakInsp] = findpeaks(smoothedTrace, - 'MinPeakWidth', fs/1.5, % ≈33 frame = 0.67 secondi - 'MinPeakProminence', std(smoothedTrace)); -``` -Traduzione Python: `scipy.signal.find_peaks(smooth_signal, width=fs/1.5, prominence=np.std(signal))` -Il collega usa `smooth()` (media mobile MATLAB) come pre-filtro. eitprocessing non lo fa. -Noi useremo un butterworth bandpass prima di find_peaks (Task 4.10.1) — miglioramento rispetto -a entrambi. - -**CalculateEELIandVT.m** → Task 5.x (features): -```matlab -eeli = median(exp_val); % EELI = mediana dei valori espiratori -deltaZ = median(insp_val) - eeli; % deltaZ ~ volume tidale -``` -Semplice e robusto. La mediana invece della media rende EELI robusto agli outlier. - -**createROIMasks.m** → Task 4.8 (ROI): -```matlab -mask.FourROIs.ROI1 = [ones(8, 32); zeros(24, 32)]; % righe 0-7 = ventrale -mask.FourROIs.ROI4 = [zeros(24, 32); ones(8, 32)]; % righe 24-31 = dorsale -``` -Questo conferma che il nostro `BinData.roi_signals` è orientato correttamente. -In più, il collega ha `FourbyFourROIs` (4 ROI V/D × 2 lato sx/dx = 8 ROI totali) — utile -per Task 4.8 quando implementeremo le ROI avanzate. -Convenzione: colonne 0-15 = DESTRA paziente, colonne 16-31 = SINISTRA paziente. - -**createSummersROIs.m** → Task 4.8.2 (ROI adattive Summers): -ROI adattate ai limiti reali del polmone nel frame (non strisce fisse 8 righe). Più preciso -per pazienti con polmoni piccoli o asimmetrici. Da confrontare con eitprocessing. - -**applyMask.m** → Task 4.4/4.8 (lung mask): -Applica una maschera binaria 32×32 al volume (32,32,N_frames). In numpy: `pixels * mask[np.newaxis]`. -MATLAB usa dimensioni (32,32,N) — noi usiamo (N,32,32), ma il calcolo è equivalente. - -**CreateLungContour.m** → Task 4.4 (lung mask): -Crea la maschera dai pixel con ventilazione sopra una soglia (configurabile 0-1). -Supporta erosione/dilatazione morfologica per pulire la maschera. In scipy: -`scipy.ndimage.binary_erosion / binary_dilation` con structuring element circolare. - -**FindDeltas.m** → Task 5.3.1 (delta Z pixel-per-pixel): -Per ogni pixel calcola max, min, delta (max-min) in una finestra temporale. -In numpy vettorizzato: `np.max(pixels, axis=0) - np.min(pixels, axis=0)` su un volume -(N, 32, 32) tagliato a un singolo atto respiratorio. - -### Orientamento pixel — confermato definitivamente - -Da createROIMasks.m la convezione è inequivocabile: -- **Riga 0 = VENTRALE** (anteriore, parete toracica anteriore) -- **Riga 31 = DORSALE** (posteriore, schiena) -- **Colonne 0-15 = DESTRA** del paziente -- **Colonne 16-31 = SINISTRA** del paziente - -Il nostro `BinData.roi_signals` è corretto. La ROI 0 (righe 0-7) è ventrale, ROI 3 (24-31) è dorsale. - -### Note tecniche utili per dopo - -- `np.frombuffer(buf, dtype=FRAME_BASE_DTYPE)` è il modo per parsare un frame -- `np.fromfile(path, dtype=FRAME_BASE_DTYPE)` legge tutto il file in una riga (Task 1.2.2) -- NaN sentinel in Medibus: `data[data < -1e30] = np.nan` (da eitprocessing) -- Per stimare fs dal file reale: `1 / scipy.stats.linregress(np.arange(n), time).slope` - dove `time = frames["ts"] * 86400` con np.unwrap - -### Stato tasks aggiornato -- Task 1.2.1: ✅ done — `src/fasteit/dtypes.py` con FRAME_BASE_DTYPE (4358b) e FRAME_EXT_DTYPE (4382b) -- Task 1.2.1b: ✅ done — BinData.timestamps aggiornato + min_max_flags + event_markers aggiunti -- Tests: `tests/test_dtypes.py` — 20 test verdi - -### Analisi comparativa architettura: eitprocessing vs fastEIT - -#### eitprocessing — flat container espandibile - -Il loro contenitore centrale è `Sequence`. Non è un container grezzo: è già il risultato finale -che tiene tutto insieme dal primo momento in un unico oggetto con 4 DataCollection: -- `eit_data`: pixel impedance frames (EITData) -- `continuous_data`: waveform Medibus + segnali derivati (ContinuousData) -- `sparse_data`: eventi, marker respiro (SparseData) -- `interval_data`: intervalli temporali, respiri (IntervalData) - -Quando carichi un file, ottieni subito una Sequence con tutto. Se poi applichi preprocessing, -**aggiungi altri oggetti alla stessa Sequence** con label diversi. Tutto vive in un unico posto. - -Flessibilità orizzontale: puoi aggiungere qualsiasi segnale derivato con qualsiasi label. -Ottima per la ricerca esplorativa. Non ottimale per una pipeline clinica automatizzata. - -#### fastEIT — pipeline a layer verticali - -``` -BinData = sempre grezzo, sempre immutabile dopo parse -Session = sempre il risultato di uno specifico preprocessing + Config -Breath = sempre un singolo atto respiratorio con feature tipizzate -``` - -Se il breath detection è sbagliato → riparto da Session senza rileggere il file. -Se la lung mask è sbagliata → riparto da BinData senza riprocessare i respiri. -I layer sono separabili e riproducibili. - -**La flessibilità è uguale ma in direzione diversa:** -- eitprocessing: flessibilità orizzontale (tutto nello stesso container) -- fastEIT: flessibilità verticale (ogni layer trasforma il precedente) - -Per una pipeline su 200 pazienti la flessibilità verticale è quella giusta. - -#### Cosa importiamo da eitprocessing (concetti, non codice GPL) - -**1. `lock()` su numpy array → BinData.__post_init__** -Dopo il parse, BinData.frames non dovrebbe mai essere modificato. -eitprocessing usa `array.flags["WRITEABLE"] = False`. -Implementazione: aggiungere in BinData.__post_init__ dopo aver calcolato n_frames/duration. -Se qualcuno prova a scrivere `bin_data.frames["pixels"][0] = 0` → RuntimeError esplicito -invece di un bug silenzioso che modifica i dati grezzi. - -**2. `select_by_time()` → Session (Task 4.13.x)** -Tutta la gerarchia eitprocessing supporta lo slicing temporale. -Per fastEIT essenziale per: estrarre finestre PEEP step, confrontare pre/post manovra, -debuggare sezioni specifiche di una registrazione. -```python -session.select_by_time(start_time=120.0, end_time=240.0) # 2 minuti specifici -``` - -**3. `Config` come campo di Session** -Loro non hanno un modo esplicito per sapere con quali parametri è stata processata una Sequence. -Session dovrebbe salvare il Config usato: -```python -@dataclass -class Session: - raw: BinData # riferimento al dato grezzo - config: Config # config usato per il preprocessing -``` -Quando pubblichi un paper, puoi riportare esattamente cosa è stato fatto. - -**4. Validazione prima della concatenazione (Study.from_folder)** -Prima di `seq1 + seq2` eitprocessing verifica fs, vendor, categoria compatibili. -Noi dobbiamo fare lo stesso in Study.from_folder (Task 6.3.1): verificare che tutti -i BinData abbiano lo stesso fs prima di aggregarli. - -#### Cosa NON vogliamo da eitprocessing -- DataCollection[V] con TypeVar generici — troppa macchina per il nostro scope -- Mancanza di confine raw/processed — problema per riproducibilità clinica -- Nessun Breath con feature tipizzate — è il nostro differenziatore principale -- Nessun PEEP step detection — il nostro differenziatore clinico -- Nessun export breath-level per database — il nostro differenziatore operativo - ---- - -### Studio del file `slicing.py` di eitprocessing - -File: `/home/ric/usefullfile/eitprocessing/slicing.py` -Copiato da `/home/ric/Downloads/slicing.py` nella sessione 2026-03-19. - -#### Il pattern di design: ABC mixin a 3 livelli - -``` -SelectByIndex(ABC) → aggiunge __getitem__ (obj[10:100]) + abstract _sliced_copy() - ↓ eredita -SelectByTime → aggiunge select_by_time(start, end) con bisect - ↑ anche -HasTimeIndexer → aggiunge .t property → TimeIndexer (obj.t[10.0:120.0]) -``` - -`SelectByTime` eredita da entrambi → classe concreta eredita da `SelectByTime` e implementa -solo `_sliced_copy()`. Tutto il resto (slicing per indice, slicing per tempo, la `.t` property) -viene gratis dai mixin. - -#### Due interfacce per fare la stessa cosa (pythonic) - -```python -# Forma esplicita: -session.select_by_time(start_time=10.0, end_time=120.0) - -# Forma sintetica (via TimeIndexer): -session.t[10.0:120.0] -``` -`session.t` ritorna un oggetto `TimeIndexer` che sa come convertire una slice di float -in una chiamata a `select_by_time`. Molto elegante. - -#### L'algoritmo chiave: `bisect` - -`bisect` è un modulo Python standard che fa ricerca binaria su liste ordinate — O(log n). -Perfetto per trovare un timestamp in un array di 50×60×120 = 360.000 frame senza scorrere tutto. - -Come funziona start_inclusive/end_inclusive: -``` -bisect.bisect_left(time, t) → indice del primo valore ≥ t (includi t) -bisect.bisect_right(time, t) → indice del primo valore > t (escludi t se esiste uguale) -bisect.bisect_right(...) - 1 → ultimo indice ≤ t (includi t, ritorna il frame "prima") -bisect.bisect_left(...) + 1 → primo indice > t (includi t, ritorna il frame "dopo") -``` - -Per fastEIT la scelta default sensata è `start_inclusive=True, end_inclusive=False`, -così `session.t[10.0:120.0]` include il frame a t=10 e esclude il frame a t=120 — -uguale al comportamento di Python `list[10:120]`. - -#### Come adatteremo per Session - -```python -# Nella classe Session, basta implementare _sliced_copy: -def _sliced_copy(self, start_index: int, end_index: int, newlabel: str) -> Session: - sliced_frames = self.raw.frames[start_index:end_index] - sliced_raw = BinData(frames=sliced_frames, fs=self.raw.fs, ...) - return Session( - raw=sliced_raw, - config=self.config, # Config rimane invariato - # tutti i segnali derivati sliced allo stesso modo - lung_mask=self.lung_mask, # 2D, non dipende dal frame — rimane invariata - global_impedance=self.global_impedance[start_index:end_index], - # ... - ) -``` - -La `lung_mask` è 2D (32×32) — non ha dimensione temporale, non si slice. -I segnali (global_impedance, roi_signals, Medibus) sono 1D → si slicano con start:end. - -#### Note per il paper - -Questo file è rilevante per la sezione **Methods** del paper: -- L'approccio `select_by_time` che implementeremo in Session verrà citato come - "ispirato all'interfaccia di eitprocessing (apache-2.0)" con confronto esplicito - della scelta architetturale (layered vs flat). -- La scelta `bisect` (O log n) sarà documentata nella sezione implementazione. -- Il confronto architetturale fastEIT vs eitprocessing sarà il cuore della sezione - **Software Design** del paper: "while eitprocessing follows a flat expandable - container pattern, fastEIT adopts a strict 3-layer pipeline to enforce reproducibility - and enable structured clinical export." - ---- - -### Informazione importante per Task 3.x: formato .asc - -Il file CSV/TXT esportato dal PulmoVista ha estensione **.asc** (non .txt o .csv come pensavamo). -Va convertito con encoding **latin-1** (ISO-8859-1) prima del parsing. -CsvParser dovrà: -1. Accettare estensioni `.asc`, `.txt`, `.csv` -2. Aprire con `encoding="latin-1"` (o `encoding="iso-8859-1"`) -3. Autodetect del separatore (probabilmente `;` o `\t` come export PulmoVista) - -Aggiornare Task 3.3.1 (CsvParser) con questa info quando ci arriveremo. - -### Fine sessione 2026-03-19 - -**Task completate oggi:** -- Task 1.2.1: `dtypes.py` — FRAME_BASE_DTYPE (4358b) + FRAME_EXT_DTYPE (4382b) definitivi -- Task 1.2.1b: BinData aggiornata (timestamps, min_max_flags, event_markers) -- 50/50 test verdi, ruff pulito -- Studio architettura eitprocessing + analisi slicing.py -- Aggiornati: layerdata_class_architecture.md, reference_DEFINITIVO.md, learning_journal.md - -**Prossima sessione — Task 1.1.1: Reverse engineering .bin, .eit, .asc reali** - -Riccardo si procura i 3 file reali dal PulmoVista. Poi: - -1. **Hexdump .bin** — `xxd file.bin | head -80` - - Primi 8 byte come float64 LE = frazione di giorno (~0.5-0.9 = mattina/sera) - - Offset 12 = inizio pixel, primo float32 plausibile (~ordine 10-100 AU) - - Verificare `file_size % 4358 == 0` oppure `% 4382 == 0` - - Salvare in `docs/reverse_eng/01_hexdump_bin_frame.txt` - -2. **Hexdump .eit** — `xxd file.eit | head -80` + `strings file.eit | head -30` - - Cercare magic string `---Draeger EIT-Software---` nei primi byte - - Identificare fine header ASCII / inizio dati binari - - Salvare in `docs/reverse_eng/02_hexdump_eit_header.txt` - -3. **File .asc** — encoding latin-1, separatore da scoprire - - `file file.asc` per info tipo - - `head -5 file.asc` per vedere header e colonne - - Salvare campione in `docs/reverse_eng/03_asc_sample.txt` - -Ogni artefatto viene documentato in `docs/reverse_eng/` con: comando usato, -output raw, interpretazione byte-per-byte. Questi diventeranno la sezione -"File Format Specification" del paper. - ---- - -## Sessione 2026-03-19 — Reverse engineering .bin con file reali - -### Task lavorate -- Task 1.3.1: Investigare 208 byte Medibus nel frame base -- Task 1.3.2: Investigare 24 byte extra frame esteso (PressurePod) -- Task 1.4.3: Verificare timestamp crescenti e plausibili -- Task 1.4.4: Verificare pixel range ragionevole -- Task 1.4.5: Verificare Medibus valori clinici -- Prodotto: `docs/reverse_eng/bin_format.md` (documentazione pubblicabile) - -### File analizzati -- `patient01.bin` 50.4 MB, 11500 frame, 230 secondi di registrazione -- `patient02.bin` 53.2 MB, 12150 frame, 243 secondi -- Stesso dispositivo: Dräger PulmoVista 500, ASPE-0048, V1.30 -- `patient01.get` 11.8 MB — formato nuovo, da investigare - -### Concetti spiegati - -- **Prova matematica del frame size**: dimensione file ÷ numero frame (contato nell'ASC) = 4382.0 esatto su entrambi i pazienti. Questo è il modo più elegante per confermare una struttura binaria: non hai bisogno di documentazione, basta un divisione. - -- **Timestamp come frazione di giornata**: il campo `ts` è un float64 che conta i secondi del giorno divisi per 86400. Quindi 18:10:54 = (18*3600 + 10*60 + 54) / 86400 = 0.7576. La conferma arriva dal confronto con l'header ASCII del file `.eit` corrispondente, che riporta "Time: 18:10:54.015" — coincidenza al secondo. - -- **Due sentinel distinti nel .bin Dräger**: - - `0xFF7FC99E` ≈ -3.40e+38: MEDIBUS fisicamente non connesso (i canali waveform, idx 0-5) - - `0xC47A0000` = -1000.0: dato non ancora calcolato (inizio registrazione, campi breath-averaged, idx 6+) - - Il codice usa `nan_value = -1e30` come soglia, non come valore esatto: -3.4e38 < -1e30, quindi cattura entrambi. Questo è il modo corretto di gestire i sentinel nei file binari: non cercare il valore esatto ma usare una soglia. - -- **Hexdump annotato**: leggere un hexdump vuol dire sapere dove inizia ogni campo. L'offset si calcola sommando le dimensioni: ts(8) + dummy(4) + pixels(4096) = 4108 = 0x100C. Da lì vedi il `min_max_flag`. È come leggere una mappa del tesoro. - -- **PressurePod validation con cross-check interno**: i campi idx 54, 55, 56 mostrano valori 14.0, 15.32, -1.28 mbar. Se questi sono Paw, Pes, Ptp: Paw - Pes = 14.0 - 15.32 = -1.32 ≈ -1.28. La differenza di 0.04 è probabilmente rounding float32 o una piccola correzione interna Dräger. Questo tipo di cross-check interno è fondamentale nel rev engineering: se Ptp = Paw - Pes, allora hai capito i campi giusti. - -- **Il `.get` è un formato nuovo**: 1024 byte/frame = 256 × float32. Ipotesi: matrice completa 16×16 di transimpedanze (tutte le 256 coppie elettrodo), mentre il `.bin` usa solo le 208 del pattern adjacent-drive. Se confermato, sarebbe utile per integrare algoritmi di ricostruzione alternativi (pyEIT supporta matrici complete). - -- **Il `.eit` contiene raw data, il `.bin` le immagini ricostruite**: il `.eit` (63 MB) è più grande del `.bin` (50 MB) non solo per l'header ASCII, ma perché contiene anche le 208 transimpedanze grezze per frame (i dati "prima" della ricostruzione). Il `.bin` è un sottoinsieme: solo le immagini 32×32 già ricostruite dal firmware. - -### Domande di Riccardo -- "I file grezzi .eit non li abbiamo dentro il file .eit??" → Risposta: sì! Il .eit contiene sia l'header ASCII (metadati), sia i dati grezzi (208 transimpedanze per frame) SIA probabilmente i segnali Medibus. È per questo che è più grande del .bin. Lo analizzeremo in dettaglio quando arriveremo al parser .eit (Task 2.x). - -### Epifanie e insight -- "La prova più elegante che FRAME_EXT_DTYPE è giusto è una sola divisione: 50393000 ÷ 11500 = 4382.0 esatto. Se ci fosse anche un solo byte sbagliato, non darebbe un numero intero." -- "Il timestamp ts[0] = 18:10:54 che leggo dal file .bin coincide ESATTAMENTE con il campo Time nell'header ASCII del .eit. Due file, stesso secondo. È la prova che .bin e .eit sono paralleli e sincronizzati." -- "I sentinel -3.4e38 e -1000.0 sono in realtà due informazioni distinte: -3.4e38 dice 'il sensore non c'è', -1000.0 dice 'il sensore c'è ma non ha ancora un valore'. Come NULL vs 0 in un database." -- "Il PressurePod validation cross-check: misuro 14.0, 15.32, -1.28 e quando faccio 14.0-15.32 ottengo -1.32 ≈ -1.28. In quel momento so con certezza di aver identificato Paw, Pes e Ptp — senza documentazione Dräger." - -### Note tecniche utili per dopo -- `np.memmap` invece di `np.fromfile` permette accesso lazy senza caricare tutto in RAM — utile per file da 50+ MB -- Il naming dei campi PressurePod (idx 52-57) è probabilmente sfasato: il campo chiamato `time_at_low_pressure_pod` (idx 54) in realtà contiene Paw (10-38 mbar). Da chiarire con il parser .eit. -- Per verificare il `.get` su patient02: `wc -c patient02.get` → se = 12,441,600 byte (= 12150 × 1024), la struttura 256×float32 è confermata. -- `xxd -l N file.bin` per vedere i primi N byte; annotare gli offset calcolati manualmente prima di leggere l'output - - ---- - -## Sessione 2026-03-19 (continua) -- eit_format.ipynb - -### Task lavorate -- Task 2.1.2-2.1.5: Hexdump, ASCII header, magic string, metadati .eit -- Creato: `docs/reverse_eng/eit_format.ipynb` (22 celle, 8 code con output pre-saved) -- Aggiunto Task 2.1.6: struttura interna frame .eit - -### Concetti spiegati - -- **Struttura header .eit**: il file inizia con 12 byte di preamble binario (3 x int32 LE), poi un blocco ASCII leggibile con tutti i metadati device/paziente, poi un separatore `**\r\n\r\n\r\n` (8 byte), poi il blocco binario. Il valore `preamble[1]` (es. 7546) punta esattamente all'inizio del separatore: `binary_start = preamble[1] + 8`. - -- **Dimensione frame .eit**: 5495 byte/frame (vs 4382 byte/frame del .bin). Verificato su entrambi i pazienti: p01 = (63200054 - 7554) / 5495 = 11500.0 esatto; p02 = (66771796 - 7546) / 5495 = 12150.0 esatto. - -- **Header varia tra pazienti**: `preamble[1]` = 7546 per p01, 7538 per p02. L'header ASCII ha lunghezza variabile (dipende da lunghezza dei campi testo come nome paziente). Il parser .eit DEVE usare `preamble[1]` per trovare il binary_start, non un offset fisso. - -- **Frame .eit: 5487 byte dopo il timestamp**: 5487 è dispari (5487 % 4 = 3), quindi c'è almeno un campo non-float32. La struttura interna è ancora da determinare. Ipotesi: parte measurements (208 complex float32 = 1664 byte) + parte pixel ricostruiti (1024 float32 = 4096 byte) ma 8+1664+4096 = 5768 ≠ 5495. Mancano ~273 byte di discrepanza -- da risolvere con EIDORS analysis (Task 2.0). - -- **Sentinel nel .eit**: il valore -3.4e12 appare anche nel .eit, probabilmente lo stesso sentinel `0xFF7FC99E` del .bin per elettrodi disconnessi. - -### Domande di Riccardo -- (nessuna domanda esplicita questa sotto-sessione -- lavoro tecnico autonomo) - -### Epifanie e insight -- "preamble[1] varia tra pazienti perché l'header ASCII ha lunghezza variabile! Quindi il parser NON può assumere binary_start fisso ma deve leggere i 4 byte a offset 4 e aggiungere 8." -- "5495 byte/frame è un numero strano che non si fattorizza bene -- il frame .eit non è progettato per essere 'pulito' come il .bin (4382 = 4096+286). Probabilmente ci sono campi legacy o padding." -- "La prova frame size sul .eit è identica a quella del .bin: una divisione che deve dare un intero esatto. Se il file è corrotto o il frame_size sbagliato, il resto non è zero." - -### Note tecniche utili per dopo -- **binary_start formula**: `binary_start = struct.unpack(' tutte eseguite con successo - ---- - -## Sessione 2026-03-19 (continua) -- PressurePod Pgas identificata - -### Concetti spiegati -- **PressurePod Dräger**: accessorio con esattamente 3 connessioni fisiche -- Paw (pressione vie aeree, tap sul circuito ventilatorio), Pes (pressione esofagea, catetere a palloncino), Pgas (pressione gastrica, catetere a palloncino gastrico). Deriva Ptp = Paw - Pes internamente. -- Questo definisce completamente i 4 campi waveform (idx 54-57) senza ambiguità. - -### Epifanie e insight -- "Se quello che ci manca è solo un index, allora sicuramente è la pressione gastrica perché il PressurePod registra esofagea, airway pressure così calcola la Ptp e la gastrica -- ha questi tre attacchi e basta." -- Ragionamento clinico diretto: 3 input fisici + 1 derivata = 4 campi waveform. Non c'è posto per altro. Questo tipo di ragionamento (conoscenza dell'hardware) risolve ambiguità che nessun hexdump da solo può risolvere. -- **Bug scoperto**: i nomi in `_MEDIBUS_EXT_EXTRA` in `dtypes.py` sono sfasati di 1 posizione. `time_at_low_pressure_pod` (idx 54) = Paw (14 mbar, non secondi). Il fix è rinominare: idx 54=`airway_pressure_pod`, 55=`esophageal_pressure_pod`, 56=`transpulmonary_pressure_pod`, 57=`gastric_pressure_pod`. - -### Mapping completo PressurePod (idx 52-57) -| idx | nome attuale (sbagliato) | nome corretto | tipo | -|-----|--------------------------|---------------|------| -| 52 | `high_pressure` | `high_pressure` (ok) | per-breath peak Paw | -| 53 | `low_pressure` | `low_pressure` (ok) | per-breath PEEP | -| 54 | `time_at_low_pressure_pod` | `airway_pressure_pod` | waveform continuo | -| 55 | `airway_pressure_pod` | `esophageal_pressure_pod` | waveform continuo | -| 56 | `esophageal_pressure_pod` | `transpulmonary_pressure_pod` | waveform continuo | -| 57 | `transpulmonary_pressure_pod` | `gastric_pressure_pod` | waveform continuo | - -### Note tecniche utili per dopo -- Il fix in `dtypes.py` è SOLO rinominare le ultime 4 tuple di `_MEDIBUS_EXT_EXTRA` -- non cambia la struttura dati -- Valore Pgas = 0.04 mbar è fisiologicamente plausibile: pressione gastrica a riposo vicina allo zero in assenza di contrazione diaframmatica significativa - ---- - -## Sessione 2026-03-19 (continua) -- Fix dtypes.py + Analisi prototipo + Fix BiLevel fields - -### Epifanie e insight (BiLevel fields) -- "Il CSV del prototipo era la Rosetta Stone che ci mancava per risolvere definitivamente i nomi dei campi Medibus" -- Confrontando le colonne del CSV (che il PulmoVista genera in parallelo al .bin) con la posizione numerica: col 63 = medibus idx 51 = `phigh_mbar`. Non `time_at_low_pressure` come pensavamo. -- **BiLevel/APRV**: è una modalità ventilatoria a due livelli di pressione (Phigh = pressione alta, Plow = pressione bassa, Tlow = tempo a pressione bassa). Il PulmoVista registra questi tre parametri in Medibus. -- **Phigh è nel formato BASE** (idx 51, ultimo campo delle 52 floats): significa che tutti i frame base (4358 byte) hanno già Phigh, indipendentemente dal PressurePod. Per pazienti su ventilazione convenzionale questo campo sarà sentinel o zero. -- Plow e Tlow sono EXT perché... probabilmente aggiunti in un firmware successivo? O perché meno usati? Non importa -- il CSV ce lo dice chiaramente. - -### Correzioni finali dtypes.py (tutti e 3 i fix applicati nella stessa sessione) -| idx | SBAGLIATO | CORRETTO | Dove | -|-----|-----------|---------|------| -| 51 | `time_at_low_pressure` (unità "s") | `high_pressure` (unità "mbar") | MEDIBUS_FIELDS base | -| 52 | `high_pressure` ("mbar") | `low_pressure` ("mbar") | _MEDIBUS_EXT_EXTRA | -| 53 | `low_pressure` ("mbar") | `time_at_low_pressure` ("s") | _MEDIBUS_EXT_EXTRA | -| 54 | `time_at_low_pressure_pod` ("s") | `airway_pressure_pod` ("mbar") | (fix precedente) | -| 55 | `airway_pressure_pod` | `esophageal_pressure_pod` | (fix precedente) | -| 56 | `esophageal_pressure_pod` | `transpulmonary_pressure_pod` | (fix precedente) | -| 57 | `transpulmonary_pressure_pod` | `gastric_pressure_pod` | (fix precedente) | - ---- - -## Sessione 2026-03-19 (continua) -- Fix dtypes.py + Analisi prototipo - -### Task completate -- **Task 1.3.5**: Fix `_MEDIBUS_EXT_EXTRA` in `dtypes.py` — rinominati idx 54-57, rimosso `time_at_low_pressure_pod`, aggiunto `gastric_pressure_pod`. Test aggiornati: 22/22 verdi. -- **Task 1.3.6**: Analisi prototipo `mia_vecchia_prova/` — documentata in `docs/prototype_analysis.md` -- **Task 1.4.6 / 2.1.7**: Aggiunte al CSV (già completate in sessione precedente con i notebook) - -### Concetti appresi dal prototipo -- **Breath detection con derivata centrale**: `d[i] = (y[i+1] - y[i-1]) / 2 * fs` — trova l'inizio inspirazione quando la derivata passa da negativa a positiva (la curva di impedenza globale risale). Semplice ma funzionante come prova di concetto. Il miglioramento in Task 4.10.x userà `find_peaks` con parametri adattivi. -- **PEEP detection con threshold crossing**: paragona delta frame-to-frame di Paw smoothata con una soglia (0.9 mbar). Se il delta supera la soglia → nuovo livello. Intuitivo ma fragile: un rumore transitorio può far scattare un nuovo livello. In Task 5.7.x si userà analisi dei segmenti stabili. -- **BreathFeatures**: 11 statistiche (mean/std/var/skew/kurtosis/min/max/median/p25/p75/IQR) per 8 segnali = 88 features statistiche + 3 temporali (slope/acceleration/smoothness) + 3 inter-breath (delta_peak/delta_tidal/delta_duration) + ~18 features base = ~111 features per respiro. Questa è la "golden list" di spec per Task 4.x–5.x. -- **Encoding latin-1**: i file .asc del PulmoVista non sono UTF-8. La virgola come separatore decimale è standard europeo. Da gestire in Task 3.x. - -### Epifanie e insight -- "Il prototipo è esattamente quello che fa il PulmoVista ma in Python — e lui funzionava su file reali! Quindi la logica è validata clinicamente anche se il codice è grezzo." -- Il `to_dict()` e `to_features_dict()` del prototipo sono di fatto la **specifica di interfaccia** della futura classe `Breath` — invece di reinventare da zero, si formalizza quello che già funziona. -- `pgapaux3_pod_mbar` nel prototipo = `gastric_pressure_pod` (idx 57) — il prototipo leggeva questo campo dall'ASC già correttamente, confermando il nostro reverse engineering. - -### Note tecniche utili per dopo -- Il prototipo usa `df["insp_t0"] == True` come condizione di split — in pandas moderno è meglio `df["insp_t0"]` direttamente (evita warning FutureWarning) -- `BreathFeatures` eredita da `Breath` (inheritance) — in fastEIT la struttura sarà diversa (composition o dataclass), ma le formule sono le stesse -- Il mapping `standardize_labels()` è il rosetta stone per verificare che i nomi in `MEDIBUS_FIELDS` corrispondano ai nomi dell'ASC (vedere `docs/prototype_analysis.md` sezione mapping) - ---- - -## Sessione 2026-03-19 (continua) — REVERT fix errato idx 51/52/53 - -### Errore commesso e corretto - -**Cosa è successo**: nella sessione precedente abbiamo fatto un fix SBAGLIATO basato -sull'assunzione che "ordine colonne CSV ASC = indici binari medibus_data". - -Avevamo cambiato: -- base idx 51: `time_at_low_pressure` → `high_pressure` -- ext idx 52: `high_pressure` → `low_pressure` -- ext idx 53: `low_pressure` → `time_at_low_pressure` - -**Perché era sbagliato — la prova**: -- `phigh_mbar`, `plow_mbar`, `tlow_s` nel CSV dei 2 pazienti analizzati sono **tutti NaN/zero** - (i pazienti NON erano in modalità BiLevel/APRV) -- I campi binary idx 52 e 53 mostravano invece **valori non-zero** (pressioni per-breath) -- Conclusione: binary idx 52/53 ≠ BiLevel params. Sono valori PressurePod-derived per-breath - (peak Paw e PEEP-equiv calcolati dal PressurePod sul singolo respiro) -- L'ordine colonne nell'ASC **NON corrisponde** agli indici nel binary medibus_data — sono - sistemi separati con ordinamenti diversi - -**Stato CORRETTO ripristinato**: -| idx | nome | unità | tipo | prova | -|-----|------|-------|------|-------| -| 51 | `time_at_low_pressure` | s | base | zero nei non-BiLevel — atteso e corretto | -| 52 | `high_pressure` | mbar | ext | non-zero nei file reali — peak Paw per-breath | -| 53 | `low_pressure` | mbar | ext | non-zero nei file reali — PEEP-equiv per-breath | - -### Lezione appresa - -**Regola**: il CSV ASC e il binario `.bin` sono due esportazioni **parallele e indipendenti** -dello stesso dispositivo. Hanno nomi di colonna e struttura propri. Non si può assumere che -la N-esima colonna del CSV corrisponda all'N-esimo campo del binary. - -**Come evitarlo in futuro**: per validare un indice binary, servono pazienti con il campo -attivo (BiLevel per idx 51, PressurePod connesso per idx 52/53). Con pazienti in ventilazione -convenzionale i campi BiLevel sono sempre zero — non si può distinguere "zero perché non in -BiLevel" da "zero perché sbagliamo l'indice". - -### Task da aggiungere al CSV - -**Task 1.4.7**: "Procura e analizza recording .bin/.asc senza PressurePod" — prova del nove -definitiva per: -1. Confermare frame size base (4358 byte) -2. Verificare idx 51 con un paziente BiLevel (se disponibile) -3. Vedere quali colonne ASC appaiono senza PressurePod - -### Files modificati in questo revert -- `src/fasteit/dtypes.py` — idx 51 e _MEDIBUS_EXT_EXTRA idx 52/53 -- `tests/test_dtypes.py` — `test_medibus_index_lookup` (3 assertions corrette) -- `docs/prototype_analysis.md` — tabella mapping correta + nota BiLevel - ---- - -## Sessione 2026-03-27 — GREIT reconstruction + validazione segnale globale - -### Task lavorate -- Task 7.1.1–7.1.3: integrazione pyEIT, notebook GREIT -- Task 7.2.1: `eit_greit.py` — `build_greit()` + `reconstruct_greit()` - -### Concetti spiegati - -- **GREIT (Adler 2009)**: ricostruzione EIT differenziale. Produce Δσ rispetto a un frame di riferimento (v0). Output flat (n²,) → reshape (32,32). Parametri default: p=0.2, lamb=1e-2, n=32. pyEIT: `mesh.create` + `protocol.create` + `GREIT.setup` + `solve(v1, v0)`. - -- **Convenzione segno**: GREIT produce Δσ (conduttività) → decresce quando l'aria entra nei polmoni. La convenzione clinica (e il .bin Dräger) usa l'impedenza → cresce con l'aria. **Fix: negare l'output**. Scoperto empiricamente confrontando i plot: le "punte" GREIT cadevano tra le punte del .bin invece di sovrapporsi. - -- **Orientamento anatomico**: pyEIT mette l'elettrodo 1 a ore 3 (destra del cerchio). Il PulmoVista ha l'elettrodo 1 anteriore. Confrontando visivamente le 4 rotazioni possibili (0°, 90°CCW, 180°, 90°CW), la 90°CCW posiziona correttamente l'area cardiaca a sinistra-anteriore. **Fix: `np.rot90(image, k=1)` nell'output di `reconstruct_greit()`**. - -- **ref_frame e EELI cross-file**: GREIT è differenziale. Con `ref_frame=None` (default = media di tutto il file) ogni file è centrato sulla sua media → EELI ≈ 0 per costruzione → non confrontabile tra file. Per confrontare EELI tra registrazioni (es. NIV interfaccia A vs B) servono tutte sullo stesso riferimento esterno: `ref = baseline[:50].mean(axis=0)` applicato a tutti i file. **Limite**: se il device ricalibrasi tra i file, l'offset non è recuperabile. - -- **Ricalibrazione PulmoVista**: il device ricalibrasi automaticamente, resettando il suo zero interno. Per questo l'EELI nel software Dräger può partire da 0 o da 1.000.000 — sono valori assoluti arbitrari che dipendono dall'ultimo reset. Non confrontabili tra sessioni senza un riferimento fisiologico fisso. Il software Dräger non lo esplicita, fastEIT sì. - -### Epifanie e insight - -- **"GREIT senza filtro ≈ .bin raw"**: confrontando `global_greit` (nansum dei pixel) con `bin_.global_signal`, le curve si sovrappongono dopo negazione senza alcun filtro. Significa che o il Dräger non filtra internamente (ipotesi più probabile, come confermato dal fatto che eitprocessing e colleghi MATLAB devono filtrare da soli), oppure la regularizzazione GREIT agisce da sola come filtro spaziale. - -- **"Le punte erano al contrario"**: scoperta della convenzione segno semplicemente guardando che i picchi GREIT cadevano negli "spazi" tra i picchi .bin. Intuizione visiva immediata senza calcoli. - -- **"L'area rossa fissa a sinistra = cuore"**: osservando che una zona ad alta impedenza rimaneva fissa (non respiratoria) nella parte sinistra dell'immagine, Riccardo ha ipotizzato la presenza del cuore. Questo ha portato a scoprire il problema di rotazione degli elettrodi. - -### Note tecniche utili per dopo -- `np.nansum(images, axis=(1,2))` → segnale globale EIT (confrontabile con `bin_.global_signal`) -- Negazione + 90°CCW sono già built-in in `reconstruct_greit()` — non applicare manualmente -- Per EELI cross-file: sempre passare `ref_frame` esterno dalla registrazione baseline della stessa sessione -- `n_meas = protocol.n_exc * protocol.n_meas = 16 * 13 = 208` ← corrispondenza esatta col formato .eit Dräger -- Nuovo agente: `eit-physics-reviewer` — da chiamare quando si implementano formule EIT o features cliniche From 289edcbb9644b8b726309ec0fc22e48742f410c7 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:07:15 +0000 Subject: [PATCH 15/20] docs: clarify memmap and struct.unpack performance explanations --- notebooks/02_benchmark_parsing.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/notebooks/02_benchmark_parsing.ipynb b/notebooks/02_benchmark_parsing.ipynb index 08235a8..2241194 100644 --- a/notebooks/02_benchmark_parsing.ipynb +++ b/notebooks/02_benchmark_parsing.ipynb @@ -327,9 +327,9 @@ "\n", "memmap is faster and uses less RAM across all file sizes tested.\n", "\n", - "**Why memmap is fast:** `np.memmap` sets up a virtual address space mapping. The OS loads file pages on demand. `parse_memmap()` returns a lazy view of the pixel field: no data is actually read from disk until accessed; the mapping is read-only.\n", + "**memmap is fast:** `np.memmap` sets up a virtual address space mapping. The OS loads file pages on demand. `parse_memmap()` returns a lazy view of the pixel field: no data is actually read from disk until accessed; the mapping is read-only.\n", "\n", - "**Why struct.unpack is slower:** `path.read_bytes()` loads the entire file into a Python `bytes` buffer in RAM before any parsing begins. Every frame then requires one `struct.unpack_from` call and one tuple allocation.\n", + "**struct.unpack is slower:** `path.read_bytes()` loads the entire file into a Python `bytes` buffer in RAM before any parsing begins. Every frame then requires one `struct.unpack_from` call and one tuple allocation.\n", "\n", "struct peak RAM grows linearly: one full `bytes` buffer + one pixel array.\n", "memmap peak RAM is near zero: only the virtual mapping is set up, no pixel array is allocated in RAM.\n", @@ -340,15 +340,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "fasteit", "language": "python", "name": "python3" }, "language_info": { "name": "python", - "version": "3.10.0" + "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From a0098f1b8dd885b2d33e3dd839b114a6083b5000 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:11:44 +0000 Subject: [PATCH 16/20] refactor: rename file to eit pyeit bridge for clariness and fix all import --- README.md | 2 +- docs/data_model.md | 2 +- docs/parsers.md | 2 +- docs/parsing_layer.md | 42 +++++++++++++++++++ notebooks/03_eit_parser_validation.ipynb | 27 ++---------- src/fasteit/parsers/__init__.py | 2 +- .../eit/{eit_greit.py => eit_pyeit_bridge.py} | 0 7 files changed, 50 insertions(+), 27 deletions(-) rename src/fasteit/parsers/draeger/eit/{eit_greit.py => eit_pyeit_bridge.py} (100%) diff --git a/README.md b/README.md index 9c15eab..d79ee83 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ auto-detects vendor and format. GREIT — Adler et al., *Physiol. Meas.* 2009, DOI: 10.1088/0967-3334/30/6/S03): ```python -from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit +from fasteit.parsers.draeger.eit.eit_pyeit_bridge import reconstruct_greit data = load_data("patient01.eit") images = reconstruct_greit(data.measurements) # (N_frames, 32, 32) diff --git a/docs/data_model.md b/docs/data_model.md index 6500f6f..cbab0a3 100644 --- a/docs/data_model.md +++ b/docs/data_model.md @@ -113,7 +113,7 @@ aux_signals dict|None Named per-frame arrays. Populated by DragerEitParser None if the source file carries no auxiliary signals. ``` -Intended consumer: `reconstruct_greit()` (in `fasteit.parsers.draeger.eit.eit_greit`, +Intended consumer: `reconstruct_greit()` (in `fasteit.parsers.draeger.eit.eit_pyeit_bridge`, requires optional `pyeit` dependency) or any external reconstruction library. For `.bin` and Timpel `.csv` files, fastEIT ingests vendor-reconstructed images and does not re-reconstruct them. diff --git a/docs/parsers.md b/docs/parsers.md index c66fe9f..3347e3d 100644 --- a/docs/parsers.md +++ b/docs/parsers.md @@ -187,7 +187,7 @@ Detection: looks for `"Timpel"` keyword in the first 40 lines. ## 6. GREIT reconstruction bridge -**File:** `parsers/draeger/eit/eit_greit.py` +**File:** `parsers/draeger/eit/eit_pyeit_bridge.py` **Optional dependency:** `pip install fasteit[pyeit]` Converts `RawImpedanceData.measurements` (N_frames, 208) to reconstructed diff --git a/docs/parsing_layer.md b/docs/parsing_layer.md index 9ac1d32..e983ea6 100644 --- a/docs/parsing_layer.md +++ b/docs/parsing_layer.md @@ -478,3 +478,45 @@ Minimal coverage for a tabular parser: --- +## 5. File layout + +``` +src/fasteit/ +├── models/ +│ ├── base_data.py BaseData — common fields for all containers +│ ├── reconstructed_data.py ReconstructedFrameData — 32×32 pixel frames +│ ├── continuous_data.py ContinuousSignalData — tabular signal export +│ └── raw_impedance_data.py RawImpedanceData — 208 transimpedances per frame +│ +└── parsers/ + ├── base.py BaseParser (validate / parse / parse_safe) + ├── bin_formats.py BIN_FORMAT_SPECS — all known .bin frame sizes + ├── detection.py detect_vendor_and_format() — routes any file + ├── errors.py AmbiguousFormatError, UnknownFormatError + ├── header_formats.py EIT_HEADER_FORMATS — known .eit ASCII headers + ├── loader.py load_data / load_many / load_folder + │ + ├── draeger/ + │ ├── asc/ + │ │ └── asc_parser.py DragerAscParser — .asc continuous export + │ ├── bin/ + │ │ ├── bin_parser.py DragerBinParser — .bin reconstructed frames + │ │ ├── bin_utils.py Sentinel → NaN, fs estimation helpers + │ │ └── draeger_dtypes.py FRAME_BASE_DTYPE, FRAME_EXT_DTYPE, MEDIBUS_* fields + │ └── eit/ + │ ├── eit_parser.py DragerEitParser — .eit raw transimpedances + │ ├── eit_dtypes.py EIT_FRAME_DTYPE (5495-byte frame layout) + │ ├── eit_utils.py Calibration constants FT_A/FT_B/FC_CURRENT/FV_VOLTAGE + │ └── eit_pyeit_bridge.py build_greit() / reconstruct_greit() — pyEIT bridge + │ + └── timpel/ + ├── timpel_parser.py TimpelTabularParser — .csv/.txt reconstructed frames + └── timpel_dtypes.py TIMPEL_AUX_FIELDS — auxiliary signal columns +``` + +**Where to add a new vendor** — create `parsers//` mirroring the structure above: +one `_parser.py`, one `_dtypes.py`, one `__init__.py`. +Then follow steps 1–5 in [section 3](#3-add-a-new-parser). + +--- + diff --git a/notebooks/03_eit_parser_validation.ipynb b/notebooks/03_eit_parser_validation.ipynb index 10686a1..8a45321 100644 --- a/notebooks/03_eit_parser_validation.ipynb +++ b/notebooks/03_eit_parser_validation.ipynb @@ -249,29 +249,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input : vv.shape = (11500, 208)\n", - "Output : images.shape = (11500, 32, 32)\n", - "NaN pixels per frame : 0 / 1024 (outside electrode circle)\n" - ] - } - ], - "source": [ - "from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit\n", - "\n", - "# Use first 50 frames as baseline (stable reference period)\n", - "images = reconstruct_greit(vv, ref_frame=(0, 50))\n", - "\n", - "print(f\"Input : vv.shape = {vv.shape}\")\n", - "print(f\"Output : images.shape = {images.shape}\")\n", - "print(f\"NaN pixels per frame : {np.isnan(images[0]).sum()} / {images[0].size} (outside electrode circle)\")" - ] + "outputs": [], + "source": "from fasteit.parsers.draeger.eit.eit_pyeit_bridge import reconstruct_greit\n\n# Use first 50 frames as baseline (stable reference period)\nimages = reconstruct_greit(vv, ref_frame=(0, 50))\n\nprint(f\"Input : vv.shape = {vv.shape}\")\nprint(f\"Output : images.shape = {images.shape}\")\nprint(f\"NaN pixels per frame : {np.isnan(images[0]).sum()} / {images[0].size} (outside electrode circle)\")" }, { "cell_type": "markdown", @@ -449,4 +430,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/src/fasteit/parsers/__init__.py b/src/fasteit/parsers/__init__.py index ee584af..71a15df 100644 --- a/src/fasteit/parsers/__init__.py +++ b/src/fasteit/parsers/__init__.py @@ -11,7 +11,7 @@ Reconstruction utilities (optional — requires pyeit): - reconstruct_greit / build_greit — GREIT image reconstruction from RawImpedanceData - Import: from fasteit.parsers.draeger.eit.eit_greit import reconstruct_greit + Import: from fasteit.parsers.draeger.eit.eit_pyeit_bridge import reconstruct_greit Utilities: - detect_vendor_and_format — extension/vendor/format auto-detection diff --git a/src/fasteit/parsers/draeger/eit/eit_greit.py b/src/fasteit/parsers/draeger/eit/eit_pyeit_bridge.py similarity index 100% rename from src/fasteit/parsers/draeger/eit/eit_greit.py rename to src/fasteit/parsers/draeger/eit/eit_pyeit_bridge.py From 197e1b17f1e8a97071938f1332d85b9dbc98f62a Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:30:28 +0000 Subject: [PATCH 17/20] docs: clarify .eit file handling in parsing layer documentation --- README.md | 2 +- docs/parsing_layer.md | 86 ++++++++++++++++++++++++++++++++----------- 2 files changed, 65 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index d79ee83..e239871 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ All parsers are accessible via the single entry point `load_data(path)`, which auto-detects vendor and format. `RawImpedanceData` from `.eit` files can be reconstructed to 32×32 pixel images via -`reconstruct_greit()` (optional dependency: `pip install fasteit[pyeit]`; implements +which is an example of pyEIT wrapping `reconstruct_greit()` (optional dependency: `pip install fasteit[pyeit]`; implements GREIT — Adler et al., *Physiol. Meas.* 2009, DOI: 10.1088/0967-3334/30/6/S03): ```python diff --git a/docs/parsing_layer.md b/docs/parsing_layer.md index e983ea6..e7ab498 100644 --- a/docs/parsing_layer.md +++ b/docs/parsing_layer.md @@ -42,7 +42,7 @@ The strategy depends on the extension: | Extension | Strategy | How it works | |-----------|----------|--------------| | `.bin` | size-based | `file_size % frame_size == 0` for each registered `FormatSpec` | -| `.eit` | header-based | reads first bytes, looks for ASCII magic string | +| `.eit` | header-based | reads first bytes, looks for ASCII magic string for each registered `HeaderFormatSpec` | | `.asc` `.txt` `.csv` | content-based | reads first 40 lines, looks for vendor keyword | `.bin` is special: there is no header. The only way to identify the format is @@ -105,7 +105,6 @@ load_data("patient01.bin") │ ├─ validate() → file exists, size divisible by known frame size → True │ └─ parse() │ ├─ np.memmap with spec.dtype - │ ├─ sentinel → NaN substitution │ ├─ fs estimation from timestamps │ └─ ReconstructedFrameData(frames, aux_signals, fs, ...) │ @@ -256,38 +255,81 @@ parameters (TV, RR, PEEP, compliance) in the same file. ### Step 1 — Detection -File: `src/fasteit/parsers/detection.py` +Detection works the same way for all three file families: **register a spec, the +generic function finds it automatically**. You rarely need to touch `detection.py`. -How detection works depends on the file type: +--- + +**`.eit`-style (header + binary frames, same `.eit` extension, new vendor)** + +File: `src/fasteit/parsers/header_formats.py` -**Binary with a recognisable header** — add a branch in `detect_vendor_and_format()` -and a helper that reads the magic bytes: +Just add a `HeaderFormatSpec` and append it to `HEADER_FORMAT_SPECS`. +`detect_vendor_from_eit_header()` iterates the list and returns the first match — +no changes to `detection.py` needed. ```python -# In detect_vendor_and_format(): +CAREFUSION_EIT_SPEC = HeaderFormatSpec( + name="Carefusion_EIT_v1", + vendor="carefusion", + magic_string="CareFusion", # ASCII substring in first magic_search_bytes bytes + magic_search_bytes=256, + encoding="utf-8", + frame_size_bytes=5120, # byte-exact frame size for this firmware + n_electrodes=16, + n_measurements=208, +) + +HEADER_FORMAT_SPECS: tuple[HeaderFormatSpec, ...] = ( + DRAEGER_EIT_HEADER_SPEC, + CAREFUSION_EIT_SPEC, # ← add here; detection is automatic +) +``` + +`HeaderFormatSpec` fields: + +| Field | Purpose | +|-------|---------| +| `magic_string` | ASCII substring searched in the first `magic_search_bytes` | +| `magic_search_bytes` | How many bytes to read for detection (keep it small) | +| `encoding` | Header text encoding (usually `"latin-1"` or `"utf-8"`) | +| `frame_size_bytes` | Byte-exact size of one binary frame — used by the parser | +| `n_electrodes` / `n_measurements` | Protocol geometry metadata | + +--- + +**`.eit`-style but with a new extension (e.g. `.acm`)** + +Add one branch in `detect_vendor_and_format()` that calls the same generic +`detect_vendor_from_eit_header()` (if the magic-string approach works for this format): + +```python +# In detect_vendor_and_format() — detection.py: if extension == ".acm": - vendor = _detect_vendor_from_acm_header(path) + vendor = detect_vendor_from_eit_header(path) # reuses HEADER_FORMAT_SPECS return FileDetection(path=path, extension=extension, vendor=vendor) - -def _detect_vendor_from_acm_header(path: Path) -> str: - with path.open("rb") as f: - magic = f.read(8) - if magic == b"ACMEIT\x01\x00": - return "acmeit" - raise ValueError(f"Unrecognised .acm header in '{path}'.") ``` -**Binary without a header (size-based)** — add a `FormatSpec` to -`BIN_FORMAT_SPECS` (see section 2). No detection function needed. +Then add the `HeaderFormatSpec` to `HEADER_FORMAT_SPECS` as above. + +--- + +**Binary without a header (size-based, `.bin`)** + +Add a `FormatSpec` to `BIN_FORMAT_SPECS` (see [section 2](#2-add-a-new-drger-bin-frame-size)). +No changes to `detection.py` needed. + +--- + +**Tabular (CSV / TXT / ASC)** -**Tabular** — extend `detect_vendor_from_tabular()` with a keyword found in the -file header (first 40 lines): +File: `src/fasteit/parsers/detection.py` — extend `detect_vendor_from_tabular()` +with a keyword found in the first 40 lines: ```python # In detect_vendor_from_tabular(): -for line in lines[:40]: - if "hamilton medical" in line.lower(): - return "hamilton" +if "hamilton medical" in normalized: + return "hamilton" ``` --- From c68d143490f590c92fce38bc2fef4d0c2596d919 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:34:51 +0000 Subject: [PATCH 18/20] docs: specify DragerBinParsers in purpose statement for clarity --- notebooks/01_real_data_verification.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/01_real_data_verification.ipynb b/notebooks/01_real_data_verification.ipynb index 4536672..843b891 100644 --- a/notebooks/01_real_data_verification.ipynb +++ b/notebooks/01_real_data_verification.ipynb @@ -9,7 +9,7 @@ "\n", "Proof-of-concept of the parsing pipeline on real files from a Dräger PulmoVista 500.\n", "\n", - "**Purpose**: verify that the parsers correctly extract the data stored in real clinical\n", + "**Purpose**: verify that the `DragerBinParsers` correctly extract the data stored in real clinical\n", "files and that the output objects are reliable. This notebook does **not** apply any\n", "signal processing, no filters, no EIT reconstruction masks.\n", "Raw parsed values are plotted directly to confirm parser correctness.\n", From d74c0af2c20a605de05db9346f5880cde88952f0 Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 17:48:39 +0000 Subject: [PATCH 19/20] =?UTF-8?q?docs:=20remove=20task=20reference=20from?= =?UTF-8?q?=20Dr=C3=A4ger=20`.eit`=20parser=20docstring=20for=20clarity?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/fasteit/parsers/draeger/eit/eit_parser.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/fasteit/parsers/draeger/eit/eit_parser.py b/src/fasteit/parsers/draeger/eit/eit_parser.py index 42e5fc9..fedcfdc 100644 --- a/src/fasteit/parsers/draeger/eit/eit_parser.py +++ b/src/fasteit/parsers/draeger/eit/eit_parser.py @@ -1,4 +1,4 @@ -"""Dräger `.eit` parser (Task 2.x). +"""Dräger `.eit` parser. Format summary (reverse-engineered from PulmoVista 500 output, cross-referenced with EIDORS read_draeger_header/read_draeger_file): @@ -166,3 +166,4 @@ def parse(self, path: Path) -> RawImpedanceData: vendor="draeger", metadata=metadata, ) + \ No newline at end of file From 284c910f27c4c338612c6858c70925e231971aba Mon Sep 17 00:00:00 2001 From: Riccardo Date: Fri, 27 Mar 2026 18:02:58 +0000 Subject: [PATCH 20/20] fix: remove trailing newline at end of DragerEitParser class --- src/fasteit/parsers/draeger/eit/eit_parser.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/fasteit/parsers/draeger/eit/eit_parser.py b/src/fasteit/parsers/draeger/eit/eit_parser.py index fedcfdc..2cb60d9 100644 --- a/src/fasteit/parsers/draeger/eit/eit_parser.py +++ b/src/fasteit/parsers/draeger/eit/eit_parser.py @@ -166,4 +166,3 @@ def parse(self, path: Path) -> RawImpedanceData: vendor="draeger", metadata=metadata, ) - \ No newline at end of file