diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..369ceb2
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,75 @@
+# Generated caches and downloads (regenerate via pipeline steps)
+data/
+
+# Environment and secrets
+.env
+.env.*
+!.env.example
+.venv/
+venv/
+env/
+ENV/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+*.pyd
+.Python
+
+# uv
+.uv/
+
+# Testing & coverage
+.pytest_cache/
+.coverage
+.coverage.*
+coverage.xml
+nosetests.xml
+htmlcov/
+.tox/
+
+# Linting & type checking
+.ruff_cache/
+.mypy_cache/
+.dmypy.json
+dmypy.json
+.pytype/
+
+# Distribution / packaging
+build/
+dist/
+*.egg-info/
+*.egg
+MANIFEST
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Logs
+*.log
+nohup.out
+
+# Jupyter
+.ipynb_checkpoints/
+profile_default/
+ipython_config.py
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+desktop.ini
+
+# Merge artifacts
+*.orig
+*.rej
+
+# Agents
+AGENTS.md
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..6ba9060
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,8 @@
+repos:
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: v0.8.4
+ hooks:
+ - id: ruff
+ args: [--fix]
+ - id: ruff-format
+
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..3e72aa6
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11.10
diff --git a/1-phenocam.py b/1-phenocam.py
new file mode 100644
index 0000000..89ca797
--- /dev/null
+++ b/1-phenocam.py
@@ -0,0 +1,284 @@
+"""Step 1: download worldwide PhenoCam sites for a calendar year.
+
+Inputs (``data/``): none — queries the PhenoCam API.
+
+Outputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``phenocam/{year}.json`` — site list manifest
+- ``phenocam/{year}/{sitename}.json`` — camera + ROI metadata
+- ``phenocam/{year}/{sitename}_1day.csv`` — ``one_day_summary`` GCC CSV
+
+CLI: ``--evaluation-year`` (default 2025), ``--sites`` (optional comma-separated filter).
+
+Next step: :mod:`2-phenocam-screening`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from datetime import date
+from pathlib import Path
+from typing import Any
+
+import requests
+
+PROCESSING_DIR = Path(__file__).resolve().parents[1] / "processing"
+if str(PROCESSING_DIR) not in sys.path:
+ sys.path.insert(0, str(PROCESSING_DIR))
+
+from acquisition_phenocam import PHENOCAM_API # noqa: E402
+from acquisition_phenocam_all_europe import _paginate_cameras, _parse_iso_date # noqa: E402
+
+EVALUATION_YEAR = 2025
+HOST_PROBE = "https://phenocam.nau.edu/api/cameras/?limit=1"
+ONE_DAY_CSV_SUFFIX = "_1day.csv"
+
+
+def check_phenocam_host() -> None:
+ try:
+ response = requests.get(HOST_PROBE, timeout=30)
+ response.raise_for_status()
+ except requests.RequestException as exc:
+ raise RuntimeError(
+ f"PhenoCam API unreachable (phenocam.nau.edu): "
+ f"{exc.__class__.__name__}: {exc}"
+ ) from exc
+
+
+def _overlaps_year(first: str | None, last: str | None, season: int) -> bool:
+ start = _parse_iso_date(first)
+ end = _parse_iso_date(last)
+ if start is None or end is None:
+ return False
+ return start <= date(season, 12, 31) and end >= date(season, 1, 1)
+
+
+def sites_dir(cache_dir: Path, evaluation_year: int) -> Path:
+ return cache_dir / "phenocam" / str(evaluation_year)
+
+
+def site_json_path(cache_dir: Path, evaluation_year: int, sitename: str) -> Path:
+ return sites_dir(cache_dir, evaluation_year) / f"{sitename}.json"
+
+
+def site_csv_path(cache_dir: Path, evaluation_year: int, sitename: str) -> Path:
+ return sites_dir(cache_dir, evaluation_year) / f"{sitename}{ONE_DAY_CSV_SUFFIX}"
+
+
+def load_candidate_cameras(
+ evaluation_year: int,
+ *,
+ site_filter: set[str] | None = None,
+ active_only: bool = False,
+ limit: int | None = None,
+) -> list[dict[str, Any]]:
+ cameras: list[dict[str, Any]] = []
+ for camera in _paginate_cameras():
+ if active_only and not camera.get("active"):
+ continue
+ sitename = str(camera["Sitename"])
+ if site_filter is not None and sitename not in site_filter:
+ continue
+ if not _overlaps_year(
+ camera.get("date_first"), camera.get("date_last"), evaluation_year
+ ):
+ continue
+ cameras.append(dict(camera))
+ cameras.sort(key=lambda item: str(item["Sitename"]))
+ if limit is not None:
+ cameras = cameras[:limit]
+ return cameras
+
+
+def fetch_roi_record(site_name: str) -> dict[str, Any] | None:
+ rois: list[dict[str, Any]] = []
+ url = f"{PHENOCAM_API}/roilists/"
+ params: dict[str, Any] | None = {"site": site_name}
+ while url:
+ response = requests.get(url, params=params, timeout=60)
+ response.raise_for_status()
+ payload = response.json()
+ rois.extend(
+ item for item in payload.get("results", []) if item.get("site") == site_name
+ )
+ url = payload.get("next")
+ params = None
+ if rois:
+ break
+ return dict(rois[0]) if rois else None
+
+
+def download_one_day_csv(csv_url: str, output_path: Path) -> None:
+ response = requests.get(csv_url, timeout=60)
+ response.raise_for_status()
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ output_path.write_text(response.text, encoding="utf-8")
+
+
+def download_site(
+ camera: dict[str, Any],
+ evaluation_year: int,
+ cache_dir: Path,
+) -> str:
+ sitename = str(camera["Sitename"])
+ roi = fetch_roi_record(sitename)
+ payload = {"response": {"camera": camera, "roi": roi}}
+ json_path = site_json_path(cache_dir, evaluation_year, sitename)
+ json_path.parent.mkdir(parents=True, exist_ok=True)
+ json_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+
+ csv_url = roi.get("one_day_summary") if roi else None
+ if csv_url:
+ download_one_day_csv(
+ csv_url, site_csv_path(cache_dir, evaluation_year, sitename)
+ )
+ return sitename
+
+
+def load_or_download_site(
+ camera: dict[str, Any],
+ evaluation_year: int,
+ cache_dir: Path,
+ *,
+ refresh: bool,
+) -> str:
+ sitename = str(camera["Sitename"])
+ json_path = site_json_path(cache_dir, evaluation_year, sitename)
+ csv_path = site_csv_path(cache_dir, evaluation_year, sitename)
+ if not refresh and json_path.is_file():
+ if not csv_path.is_file():
+ payload = json.loads(json_path.read_text(encoding="utf-8"))
+ roi = payload.get("response", {}).get("roi") or {}
+ csv_url = roi.get("one_day_summary")
+ if csv_url:
+ download_one_day_csv(csv_url, csv_path)
+ return sitename
+ return download_site(camera, evaluation_year, cache_dir)
+
+
+def run_download(
+ *,
+ cache_dir: Path,
+ evaluation_year: int,
+ active_only: bool = False,
+ site_filter: set[str] | None = None,
+ limit: int | None = None,
+ refresh: bool = False,
+) -> list[str]:
+ check_phenocam_host()
+ candidates = load_candidate_cameras(
+ evaluation_year,
+ site_filter=site_filter,
+ active_only=active_only,
+ limit=limit,
+ )
+ print(
+ f"[PhenoCam-1] {len(candidates)} candidate(s) with archive overlap for "
+ f"{evaluation_year}"
+ )
+
+ sitenames: list[str] = []
+ for index, camera in enumerate(candidates, start=1):
+ sitename = str(camera["Sitename"])
+ print(
+ f"[PhenoCam-1] ({index}/{len(candidates)}) {sitename} "
+ f"({float(camera['Lat']):.4f}, {float(camera['Lon']):.4f})"
+ )
+ sitenames.append(
+ load_or_download_site(
+ camera,
+ evaluation_year,
+ cache_dir,
+ refresh=refresh,
+ )
+ )
+ return sorted(sitenames)
+
+
+def write_manifest(
+ sitenames: list[str],
+ output_path: Path,
+ cache_dir: Path,
+ evaluation_year: int,
+) -> None:
+ rel_sites_dir = sites_dir(cache_dir, evaluation_year).relative_to(
+ output_path.parent
+ )
+ payload = {
+ "evaluation_year": evaluation_year,
+ "count": len(sitenames),
+ "sites_dir": rel_sites_dir.as_posix(),
+ "sites": sitenames,
+ }
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ output_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+ print(f"[PhenoCam-1] Wrote {output_path}")
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument(
+ "--cache-dir",
+ type=Path,
+ default=Path("data"),
+ help="Base directory for per-site files and manifest",
+ )
+ parser.add_argument(
+ "--evaluation-year",
+ type=int,
+ default=EVALUATION_YEAR,
+ help=f"Calendar year to download (default: {EVALUATION_YEAR})",
+ )
+ parser.add_argument(
+ "--active-only",
+ action="store_true",
+ help="Restrict candidates to cameras marked active in the API",
+ )
+ parser.add_argument(
+ "--limit",
+ type=int,
+ default=None,
+ help="Process only the first N candidate sites (testing)",
+ )
+ parser.add_argument(
+ "--sites",
+ type=str,
+ default=None,
+ help="Comma-separated sitenames to download (testing)",
+ )
+ parser.add_argument(
+ "--refresh",
+ action="store_true",
+ help="Re-download sites even when cache files exist",
+ )
+ parser.add_argument(
+ "--output-json",
+ type=Path,
+ default=None,
+ help="Manifest output path (default: data/phenocam/{year}.json)",
+ )
+ args = parser.parse_args(argv)
+
+ site_filter = None
+ if args.sites:
+ site_filter = {name.strip() for name in args.sites.split(",") if name.strip()}
+
+ sitenames = run_download(
+ cache_dir=args.cache_dir,
+ evaluation_year=args.evaluation_year,
+ active_only=args.active_only,
+ site_filter=site_filter,
+ limit=args.limit,
+ refresh=args.refresh,
+ )
+ manifest_path = args.output_json or (
+ args.cache_dir / "phenocam" / f"{args.evaluation_year}.json"
+ )
+ write_manifest(sitenames, manifest_path, args.cache_dir, args.evaluation_year)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/2-phenocam-screening.py b/2-phenocam-screening.py
new file mode 100644
index 0000000..5848ae9
--- /dev/null
+++ b/2-phenocam-screening.py
@@ -0,0 +1,494 @@
+"""Step 2: PhenoCam GCC + SNR screening on step-1 cache.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``phenocam/{year}.json`` — step-1 manifest
+- ``phenocam/{year}/{sitename}.json`` — per-site metadata
+- ``phenocam/{year}/{sitename}_1day.csv`` — GCC timeseries
+
+Outputs (``data/phenocam_screening/``):
+
+- ``{year}.json`` — full per-site results
+- ``{year}.csv`` — flat summary table
+
+CLI: ``--evaluation-year`` (default 2025), ``--sites`` (optional; default: all manifest sites).
+
+Next step: :mod:`3-sentinel-clouds`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import sys
+from datetime import date, datetime
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from scipy.interpolate import UnivariateSpline
+
+PROCESSING_DIR = Path(__file__).resolve().parents[1] / "processing"
+if str(PROCESSING_DIR) not in sys.path:
+ sys.path.insert(0, str(PROCESSING_DIR))
+
+from acquisition_phenocam import _phenocam_summary_gcc_value # noqa: E402
+
+MIN_GCC_POINTS = 30
+SNR_THRESHOLD = 2.0
+CLUSTER_RADIUS_M = 500.0
+GATE_ORDER = ("phenocam", "snr", "cluster")
+ONE_DAY_CSV_SUFFIX = "_1day.csv"
+_EARTH_RADIUS_M = 6371000.0
+
+
+def load_manifest(path: Path) -> dict[str, Any]:
+ payload = json.loads(path.read_text(encoding="utf-8"))
+ for key in ("evaluation_year", "sites_dir", "sites"):
+ if key not in payload:
+ raise ValueError(f"Expected '{key}' in manifest {path}")
+ return payload
+
+
+def resolve_sites_dir(manifest_path: Path, manifest: dict[str, Any]) -> Path:
+ return (manifest_path.parent / manifest["sites_dir"]).resolve()
+
+
+def load_site_entry(sites_dir: Path, sitename: str) -> dict[str, Any]:
+ json_path = sites_dir / f"{sitename}.json"
+ payload = json.loads(json_path.read_text(encoding="utf-8"))
+ csv_path = sites_dir / f"{sitename}{ONE_DAY_CSV_SUFFIX}"
+ payload["_one_day_csv"] = csv_path if csv_path.is_file() else None
+ return payload
+
+
+def parse_gcc90_series(csv_path: Path, evaluation_year: int) -> list[tuple[str, float]]:
+ lines = [
+ line
+ for line in csv_path.read_text(encoding="utf-8").split("\n")
+ if line and not line.startswith("#")
+ ]
+ reader = csv.DictReader(lines)
+ fieldnames = reader.fieldnames or ()
+ use_mean_fallback = "gcc_90" not in fieldnames
+
+ year_start = date(evaluation_year, 1, 1)
+ year_end = date(evaluation_year, 12, 31)
+ series: list[tuple[str, float]] = []
+ for row in reader:
+ date_str = row.get("date")
+ if not date_str:
+ continue
+ try:
+ row_date = datetime.strptime(date_str, "%Y-%m-%d").date()
+ except ValueError:
+ continue
+ if not (year_start <= row_date <= year_end):
+ continue
+ gcc = _phenocam_summary_gcc_value(row, use_mean_fallback)
+ if gcc is None:
+ continue
+ series.append((row_date.isoformat(), float(gcc)))
+ series.sort(key=lambda item: item[0])
+ return series
+
+
+def _months_covered(day_strings: list[str]) -> int:
+ months: set[int] = set()
+ for day in day_strings:
+ months.add(datetime.strptime(day, "%Y-%m-%d").month)
+ return len(months)
+
+
+def _aic_for_spline(x: np.ndarray, y: np.ndarray, spline: UnivariateSpline) -> float:
+ residuals = y - spline(x)
+ rss = float(np.sum(residuals**2))
+ n = len(y)
+ if rss <= 0 or n < 4:
+ return math.inf
+ edf = float(spline.get_knots().shape[0] + spline.get_coeffs().shape[0])
+ return n * math.log(rss / n) + 2.0 * edf
+
+
+def compute_snr_aic_spline(series: list[tuple[str, float]]) -> float | None:
+ if len(series) < MIN_GCC_POINTS:
+ return None
+
+ dates = [datetime.strptime(day, "%Y-%m-%d").date() for day, _ in series]
+ x = np.array([(d - dates[0]).days for d in dates], dtype=float)
+ y = np.array([value for _, value in series], dtype=float)
+ if len(np.unique(x)) < 5:
+ return None
+
+ y_var = float(np.var(y))
+ if y_var <= 0:
+ return None
+
+ candidates = np.logspace(-4, 2, 40) * y_var * len(y)
+ best_spline: UnivariateSpline | None = None
+ best_aic = math.inf
+ for smoothing in candidates:
+ try:
+ spline = UnivariateSpline(x, y, k=3, s=float(smoothing))
+ except Exception:
+ continue
+ aic = _aic_for_spline(x, y, spline)
+ if aic < best_aic:
+ best_aic = aic
+ best_spline = spline
+
+ if best_spline is None:
+ return None
+
+ residuals = y - best_spline(x)
+ rmse = float(np.sqrt(np.mean(residuals**2)))
+ amplitude = float(np.max(y) - np.min(y))
+ if rmse <= 0:
+ return None
+ return amplitude / rmse
+
+
+def screen_site(
+ site_entry: dict[str, Any],
+ *,
+ evaluation_year: int,
+ min_gcc_points: int,
+ snr_threshold: float,
+) -> dict[str, Any]:
+ response = site_entry["response"]
+ roi = response.get("roi")
+ csv_path = site_entry.get("_one_day_csv")
+ calculations: dict[str, Any] = {
+ "evaluation_year": evaluation_year,
+ "n_gcc_points": 0,
+ "first_gcc_date": None,
+ "last_gcc_date": None,
+ "months_with_gcc": 0,
+ "snr": None,
+ "min_gcc_points": min_gcc_points,
+ "snr_threshold": snr_threshold,
+ "status": "FAIL",
+ "failing_gate": None,
+ "passed_gates": [],
+ "reason": None,
+ }
+
+ if roi is None or not roi.get("one_day_summary") or csv_path is None:
+ calculations["failing_gate"] = "phenocam"
+ calculations["reason"] = "no_roi"
+ return {"response": response, "calculations": calculations}
+
+ series = parse_gcc90_series(csv_path, evaluation_year)
+ calculations["n_gcc_points"] = len(series)
+ if calculations["n_gcc_points"] == 0:
+ calculations["failing_gate"] = "phenocam"
+ calculations["reason"] = "no_gcc_in_year"
+ return {"response": response, "calculations": calculations}
+
+ day_strings = [day for day, _ in series]
+ calculations["first_gcc_date"] = day_strings[0]
+ calculations["last_gcc_date"] = day_strings[-1]
+ calculations["months_with_gcc"] = _months_covered(day_strings)
+
+ if calculations["n_gcc_points"] < min_gcc_points:
+ calculations["failing_gate"] = "phenocam"
+ calculations["reason"] = "insufficient_gcc_points"
+ return {"response": response, "calculations": calculations}
+
+ calculations["passed_gates"].append("phenocam")
+
+ snr = compute_snr_aic_spline(series)
+ calculations["snr"] = snr
+ if snr is None or snr < snr_threshold:
+ calculations["failing_gate"] = "snr"
+ calculations["reason"] = (
+ "insufficient_snr" if snr is not None else "snr_undefined"
+ )
+ return {"response": response, "calculations": calculations}
+
+ calculations["passed_gates"].append("snr")
+ calculations["status"] = "PASS"
+ calculations["failing_gate"] = None
+ calculations["reason"] = None
+ return {"response": response, "calculations": calculations}
+
+
+def _haversine_m(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+ p1, p2 = math.radians(lat1), math.radians(lat2)
+ dlat = math.radians(lat2 - lat1)
+ dlon = math.radians(lon2 - lon1)
+ a = math.sin(dlat / 2) ** 2 + math.cos(p1) * math.cos(p2) * math.sin(dlon / 2) ** 2
+ return 2 * _EARTH_RADIUS_M * math.asin(math.sqrt(a))
+
+
+def _site_coords(row: dict[str, Any]) -> tuple[float, float] | None:
+ camera = row["response"]["camera"]
+ lat, lon = camera.get("Lat"), camera.get("Lon")
+ if lat is None or lon is None:
+ return None
+ return float(lat), float(lon)
+
+
+def _cluster_rank(row: dict[str, Any]) -> tuple[int, float]:
+ calc = row["calculations"]
+ return calc["n_gcc_points"], float(calc.get("snr") or 0.0)
+
+
+def apply_cluster_gate(results: list[dict[str, Any]], *, radius_m: float) -> int:
+ pool: list[tuple[int, float, float]] = []
+ for idx, row in enumerate(results):
+ if "snr" not in row["calculations"]["passed_gates"]:
+ continue
+ coords = _site_coords(row)
+ if coords is None:
+ row["calculations"]["passed_gates"].append("cluster")
+ continue
+ pool.append((idx, coords[0], coords[1]))
+
+ n = len(pool)
+ parent = list(range(n))
+
+ def find(x: int) -> int:
+ while parent[x] != x:
+ parent[x] = parent[parent[x]]
+ x = parent[x]
+ return x
+
+ def union(a: int, b: int) -> None:
+ ra, rb = find(a), find(b)
+ if ra != rb:
+ parent[rb] = ra
+
+ for i in range(n):
+ _, lat1, lon1 = pool[i]
+ for j in range(i + 1, n):
+ _, lat2, lon2 = pool[j]
+ if _haversine_m(lat1, lon1, lat2, lon2) <= radius_m:
+ union(i, j)
+
+ clusters: dict[int, list[int]] = {}
+ for i in range(n):
+ clusters.setdefault(find(i), []).append(i)
+
+ demoted = 0
+ for members in clusters.values():
+ result_indices = [pool[i][0] for i in members]
+ cluster_size = len(result_indices)
+ winner_idx = max(result_indices, key=lambda idx: _cluster_rank(results[idx]))
+ winner_name = str(results[winner_idx]["response"]["camera"]["Sitename"])
+ for idx in result_indices:
+ calc = results[idx]["calculations"]
+ calc["cluster_size"] = cluster_size
+ if idx == winner_idx:
+ calc["passed_gates"].append("cluster")
+ else:
+ calc["status"] = "FAIL"
+ calc["failing_gate"] = "cluster"
+ calc["reason"] = "nearby_duplicate"
+ calc["cluster_winner"] = winner_name
+ demoted += 1
+ return demoted
+
+
+def run_screening(
+ manifest: dict[str, Any],
+ sites_dir: Path,
+ *,
+ evaluation_year: int,
+ min_gcc_points: int,
+ snr_threshold: float,
+ site_filter: set[str] | None = None,
+) -> list[dict[str, Any]]:
+ results: list[dict[str, Any]] = []
+ sitenames = manifest["sites"]
+ if site_filter is not None:
+ sitenames = [name for name in sitenames if name in site_filter]
+ for index, sitename in enumerate(sitenames, start=1):
+ print(f"[PhenoCam-2] ({index}/{len(sitenames)}) {sitename}")
+ site_entry = load_site_entry(sites_dir, sitename)
+ results.append(
+ screen_site(
+ site_entry,
+ evaluation_year=evaluation_year,
+ min_gcc_points=min_gcc_points,
+ snr_threshold=snr_threshold,
+ )
+ )
+ return results
+
+
+def print_summary(results: list[dict[str, Any]], evaluation_year: int) -> None:
+ passing = [row for row in results if row["calculations"]["status"] == "PASS"]
+ gates_label = " + ".join(GATE_ORDER)
+ print(
+ f"\n[PhenoCam-2] Screening for {evaluation_year}: "
+ f"{len(passing)}/{len(results)} pass ({gates_label})"
+ )
+
+ for gate in GATE_ORDER:
+ fails = sum(1 for row in results if row["calculations"]["failing_gate"] == gate)
+ after = sum(1 for row in results if gate in row["calculations"]["passed_gates"])
+ print(f" after_{gate}: {after}, fail_at_{gate}: {fails}")
+
+ print("\nPer-site table")
+ print(f"{'site':<24} {'n':>4} {'mon':>3} {'snr':>6} {'status':>6} gate reason")
+ print("-" * 72)
+ for row in sorted(
+ results,
+ key=lambda item: str(item["response"]["camera"]["Sitename"]),
+ ):
+ camera = row["response"]["camera"]
+ calc = row["calculations"]
+ snr_text = f"{calc['snr']:.2f}" if calc["snr"] is not None else ""
+ print(
+ f"{camera['Sitename']:<24} {calc['n_gcc_points']:4d} "
+ f"{calc['months_with_gcc']:3d} {snr_text:>6} "
+ f"{calc['status']:>6} {(calc['failing_gate'] or '-'):<8} "
+ f"{calc['reason'] or '-'}"
+ )
+
+
+def write_screening_json(
+ results: list[dict[str, Any]],
+ output_path: Path,
+ evaluation_year: int,
+) -> None:
+ passing = [row for row in results if row["calculations"]["status"] == "PASS"]
+ payload = {
+ "evaluation_year": evaluation_year,
+ "count": len(results),
+ "qualifying_count": len(passing),
+ "sites": sorted(
+ results,
+ key=lambda item: str(item["response"]["camera"]["Sitename"]),
+ ),
+ }
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ output_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+ print(f"[PhenoCam-2] Wrote {output_path}")
+
+
+def write_screening_csv(results: list[dict[str, Any]], output_path: Path) -> None:
+ rows: list[dict[str, Any]] = []
+ for row in results:
+ camera = row["response"]["camera"]
+ metadata = camera.get("sitemetadata") or {}
+ roi = row["response"].get("roi") or {}
+ calc = row["calculations"]
+ rows.append(
+ {
+ "Sitename": camera.get("Sitename"),
+ "Lat": camera.get("Lat"),
+ "Lon": camera.get("Lon"),
+ "site_description": metadata.get("site_description"),
+ "primary_veg_type": metadata.get("primary_veg_type"),
+ "site_type": metadata.get("site_type"),
+ "one_day_summary": roi.get("one_day_summary"),
+ **calc,
+ }
+ )
+ fieldnames = list(rows[0].keys()) if rows else ["Sitename", "status"]
+ if rows:
+ extra = [k for row in rows for k in row if k not in fieldnames]
+ fieldnames.extend(dict.fromkeys(extra))
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ with output_path.open("w", encoding="utf-8", newline="") as handle:
+ writer = csv.DictWriter(handle, fieldnames=fieldnames)
+ writer.writeheader()
+ writer.writerows(rows)
+ print(f"[PhenoCam-2] Wrote {output_path}")
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument(
+ "--evaluation-year",
+ type=int,
+ default=2025,
+ help="Evaluation year (default: 2025)",
+ )
+ parser.add_argument(
+ "--sites",
+ type=str,
+ default=None,
+ help="Comma-separated sitenames (default: all sites in step-1 manifest)",
+ )
+ parser.add_argument(
+ "--min-gcc-points",
+ type=int,
+ default=MIN_GCC_POINTS,
+ help=f"Minimum valid gcc_90 observations in-year (default: {MIN_GCC_POINTS})",
+ )
+ parser.add_argument(
+ "--snr-threshold",
+ type=float,
+ default=SNR_THRESHOLD,
+ help=f"Minimum AIC-spline SNR (default: {SNR_THRESHOLD})",
+ )
+ parser.add_argument(
+ "--output-json",
+ type=Path,
+ default=None,
+ help="Screening output (default: data/phenocam_screening/{year}.json)",
+ )
+ parser.add_argument(
+ "--output-csv",
+ type=Path,
+ default=None,
+ help="Flat CSV summary path",
+ )
+ parser.add_argument(
+ "--cluster-radius-m",
+ type=float,
+ default=CLUSTER_RADIUS_M,
+ help=f"Deduplicate SNR-passed sites within this radius (default: {CLUSTER_RADIUS_M})",
+ )
+ parser.add_argument(
+ "--no-cluster",
+ action="store_true",
+ help="Skip nearby-site deduplication gate",
+ )
+ args = parser.parse_args(argv)
+
+ evaluation_year = args.evaluation_year
+ manifest_path = Path("data") / "phenocam" / f"{evaluation_year}.json"
+ if not manifest_path.is_file():
+ raise SystemExit(f"Step-1 manifest not found: {manifest_path}")
+
+ site_filter = None
+ if args.sites:
+ site_filter = {name.strip() for name in args.sites.split(",") if name.strip()}
+
+ manifest = load_manifest(manifest_path)
+ sites_dir_path = resolve_sites_dir(manifest_path, manifest)
+
+ results = run_screening(
+ manifest,
+ sites_dir_path,
+ evaluation_year=evaluation_year,
+ min_gcc_points=args.min_gcc_points,
+ snr_threshold=args.snr_threshold,
+ site_filter=site_filter,
+ )
+ if not args.no_cluster:
+ demoted = apply_cluster_gate(results, radius_m=args.cluster_radius_m)
+ if demoted:
+ print(f"[PhenoCam-2] Cluster dedup: demoted {demoted} nearby duplicate(s)")
+ print_summary(results, evaluation_year)
+
+ default_dir = Path("data") / "phenocam_screening"
+ json_name = f"{evaluation_year}.json"
+ csv_name = f"{evaluation_year}.csv"
+ write_screening_json(
+ results,
+ args.output_json or (default_dir / json_name),
+ evaluation_year,
+ )
+ write_screening_csv(results, args.output_csv or (default_dir / csv_name))
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/3-sentinel-data.py b/3-sentinel-data.py
new file mode 100644
index 0000000..75877bf
--- /dev/null
+++ b/3-sentinel-data.py
@@ -0,0 +1,931 @@
+"""Step 3: Download S2 and S3 rasters and prepare EFAST inputs.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``phenocam_screening/{year}.json`` — step-2 PASS sites (coordinates included)
+
+Outputs (``data/``):
+
+- ``sentinel_data/{year}/{sitename}/raw/s3/*.tif`` — S3 SYN L2 per-date GeoTIFFs
+- ``sentinel_data/{year}/{sitename}/prepared/s2/`` — S2 REFL + DIST_CLOUD GeoTIFFs
+- ``sentinel_data/{year}/{sitename}/prepared/s3/`` — S3 composite GeoTIFFs
+- ``sentinel_data/{year}/{sitename}/data.json`` — run summary
+
+Requires ``CDSE_USER`` / ``CDSE_PASSWORD`` in ``../.env`` (workspace root; ``uv sync`` installs efast).
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--site`` (optional; default: all step-2 PASS sites)
+
+Prior step: :mod:`2-phenocam-screening`.
+Next step: :mod:`4-fusion`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import netCDF4
+import numpy as np
+import openeo
+import rasterio
+import requests
+from dotenv import load_dotenv
+from pystac_client import Client
+from rasterio import shutil as rio_shutil
+from rasterio.enums import Resampling
+from rasterio.errors import WindowError
+from rasterio.transform import from_bounds
+from rasterio.vrt import WarpedVRT
+from rasterio.warp import transform_geom
+from rasterio.windows import Window
+from rasterio.windows import from_bounds as window_from_bounds
+from rasterio.windows import transform as window_transform
+from shapely import wkt as shapely_wkt
+from tqdm import tqdm
+
+# ---------------------------------------------------------------------------
+# Public constants — edit here to change pipeline behaviour
+# ---------------------------------------------------------------------------
+
+S2_BANDS = ["B02", "B03", "B04"]
+
+S3_BANDS = [
+ "Syn_Oa04_reflectance",
+ "Syn_Oa06_reflectance",
+ "Syn_Oa08_reflectance",
+ "Syn_Oa17_reflectance",
+]
+S3_BAND_NAMES = ["SDR_Oa04", "SDR_Oa06", "SDR_Oa08", "SDR_Oa17"]
+
+RESOLUTION_RATIO = 30
+S3_MOSAIC_DAYS = 100
+S3_COMPOSITE_STEP = 2
+S3_COMPOSITE_SIGMA_DOY = 10
+S3_COMPOSITE_D = 20
+S3_SMOOTHING_STD = 1
+S3_REFLECTANCE_SCALE = 10_000 # OpenEO SYN L2 SDR → 0–1 (EFAST expects < 5)
+
+# ---------------------------------------------------------------------------
+# Internal S2 constants
+# ---------------------------------------------------------------------------
+
+EARTH_SEARCH_URL = "https://earth-search.aws.element84.com/v1"
+
+_BAND_ASSETS: dict[str, str] = {
+ "B02": "blue",
+ "B03": "green",
+ "B04": "red",
+ "B05": "rededge1",
+ "B06": "rededge2",
+ "B07": "rededge3",
+ "B08": "nir",
+ "B8A": "nir08",
+ "B11": "swir16",
+ "B12": "swir22",
+}
+_SCL_ASSET = "scl"
+_MIN_BBOX_HALF_DEG = 0.008
+
+_GDAL_COG_ENV = {
+ # HTTP/1.1 avoids HTTP/2 multiplexing connection-reset cascades on S3.
+ "GDAL_HTTP_VERSION": "1.1",
+ "GDAL_HTTP_MERGE_CONSECUTIVE_RANGES": "YES",
+ "GDAL_HTTP_TCP_KEEPALIVE": "YES",
+ "GDAL_DISABLE_READDIR_ON_OPEN": "EMPTY_DIR",
+ "CPL_VSIL_CURL_CACHE_SIZE": "200000000",
+ # Built-in GDAL retries for 429/502/503/504 and transient resets.
+ "GDAL_HTTP_MAX_RETRY": "3",
+ "GDAL_HTTP_RETRY_DELAY": "0.5",
+ "AWS_NO_SIGN_REQUEST": "YES",
+}
+
+# ---------------------------------------------------------------------------
+# Internal S3 constants
+# ---------------------------------------------------------------------------
+
+CDSE_TOKEN_URL = (
+ "https://identity.dataspace.copernicus.eu/auth/realms/CDSE/"
+ "protocol/openid-connect/token"
+)
+OPENEO_URL = "openeo.dataspace.copernicus.eu"
+S3_COLLECTION = "SENTINEL3_SYN_L2_SYN"
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+WORKSPACE_ROOT = Path(__file__).resolve().parent.parent
+
+
+# ---------------------------------------------------------------------------
+# Credentials
+# ---------------------------------------------------------------------------
+
+
+def _cdse_credentials() -> dict[str, str | None]:
+ load_dotenv(WORKSPACE_ROOT / ".env")
+ return {
+ "username": os.getenv("CDSE_USER"),
+ "password": os.getenv("CDSE_PASSWORD"),
+ }
+
+
+# ---------------------------------------------------------------------------
+# Screening manifest helpers
+# ---------------------------------------------------------------------------
+
+
+def _load_screening_pass_sites(year: int) -> list[dict[str, Any]]:
+ """Return list of PASS-site dicts from step-2 screening JSON.
+
+ Each entry has ``sitename``, ``lat``, ``lon`` keys.
+ """
+ path = DATA_DIR / "phenocam_screening" / f"{year}.json"
+ if not path.is_file():
+ raise FileNotFoundError(f"Step-2 screening manifest not found: {path}")
+ payload = json.loads(path.read_text(encoding="utf-8"))
+ sites = []
+ for row in payload.get("sites", []):
+ calc = row.get("calculations", {})
+ if calc.get("status") != "PASS":
+ continue
+ camera = row.get("response", {}).get("camera", {})
+ name = camera.get("Sitename")
+ lat = camera.get("Lat")
+ lon = camera.get("Lon")
+ if name and lat is not None and lon is not None:
+ sites.append({"sitename": str(name), "lat": float(lat), "lon": float(lon)})
+ return sites
+
+
+# ---------------------------------------------------------------------------
+# S2: geometry helpers (from s2_cloud_native.py)
+# ---------------------------------------------------------------------------
+
+
+def wkt_to_bbox(geometry_wkt: str) -> list[float]:
+ """Convert a WKT geometry to a ``[west, south, east, north]`` bbox."""
+ geom = shapely_wkt.loads(geometry_wkt)
+ minx, miny, maxx, maxy = geom.bounds
+ if minx == maxx and miny == maxy:
+ minx -= _MIN_BBOX_HALF_DEG
+ maxx += _MIN_BBOX_HALF_DEG
+ miny -= _MIN_BBOX_HALF_DEG
+ maxy += _MIN_BBOX_HALF_DEG
+ return [minx, miny, maxx, maxy]
+
+
+def _boa_offset(item: Any) -> int:
+ """Return the BOA additive offset for a STAC item.
+
+ Processing baseline >= 04.00 applies a -1000 offset; earlier baselines use 0.
+ """
+ if item.properties.get("earthsearch:boa_offset_applied"):
+ return 0
+ baseline_str = str(
+ item.properties.get("processing:baseline")
+ or item.properties.get("s2:processing_baseline")
+ or "0"
+ )
+ try:
+ baseline = float(baseline_str)
+ except ValueError:
+ baseline = 0.0
+ return -1000 if baseline >= 4.0 else 0
+
+
+def _window_for_bbox(
+ src: rasterio.io.DatasetReader,
+ bbox_4326: list[float],
+) -> Window | None:
+ """Return the rasterio Window for a EPSG:4326 bbox clipped to src bounds."""
+ bbox_geom = {
+ "type": "Polygon",
+ "coordinates": [
+ [
+ [bbox_4326[0], bbox_4326[1]],
+ [bbox_4326[2], bbox_4326[1]],
+ [bbox_4326[2], bbox_4326[3]],
+ [bbox_4326[0], bbox_4326[3]],
+ [bbox_4326[0], bbox_4326[1]],
+ ]
+ ],
+ }
+ src_geom = transform_geom("EPSG:4326", src.crs.to_wkt(), bbox_geom)
+ xs = [c[0] for c in src_geom["coordinates"][0][:4]]
+ ys = [c[1] for c in src_geom["coordinates"][0][:4]]
+ win = window_from_bounds(min(xs), min(ys), max(xs), max(ys), src.transform)
+ try:
+ return win.intersection(Window(0, 0, src.width, src.height))
+ except WindowError:
+ return None
+
+
+def _read_window(
+ href: str,
+ bbox_4326: list[float],
+ out_shape: tuple[int, int] | None = None,
+ resampling: Resampling = Resampling.bilinear,
+) -> tuple[np.ndarray, dict[str, Any]] | None:
+ """Range-read a single-band array for the bbox window from a COG URL."""
+ with rasterio.open(href) as src:
+ win = _window_for_bbox(src, bbox_4326)
+ if win is None:
+ return None
+ data = src.read(1, window=win, out_shape=out_shape, resampling=resampling)
+ profile: dict[str, Any] = {
+ "crs": src.crs,
+ "transform": window_transform(win, src.transform),
+ "height": data.shape[0],
+ "width": data.shape[1],
+ "dtype": src.dtypes[0],
+ }
+ return data, profile
+
+
+def _read_bands(
+ item: Any,
+ bbox: list[float],
+ bands: list[str],
+) -> tuple[list[np.ndarray], dict[str, Any]] | None:
+ """Range-read all requested bands for one STAC item."""
+ band_arrays: list[np.ndarray] = []
+ ref_profile: dict[str, Any] | None = None
+
+ for band_name in bands:
+ asset_key = _BAND_ASSETS.get(band_name)
+ if asset_key is None or asset_key not in item.assets:
+ return None
+ ref_shape = (
+ (ref_profile["height"], ref_profile["width"]) if ref_profile else None
+ )
+ result = _read_window(item.assets[asset_key].href, bbox, out_shape=ref_shape)
+ if result is None:
+ return None
+ data, profile = result
+ if ref_profile is None:
+ ref_profile = profile
+ band_arrays.append(data.astype("float32"))
+
+ return (band_arrays, ref_profile) if ref_profile is not None else None
+
+
+def _cloud_mask(item: Any, bbox: list[float], shape: tuple[int, int]) -> np.ndarray:
+ """Return a boolean cloud/shadow mask from the item's SCL band.
+
+ Masks SCL classes 0 (no data), 3 (cloud shadow), and >7 (clouds, cirrus, snow).
+ """
+ scl = item.assets.get(_SCL_ASSET)
+ result = (
+ _read_window(scl.href, bbox, out_shape=shape, resampling=Resampling.nearest)
+ if scl
+ else None
+ )
+ if result is None:
+ return np.zeros(shape, dtype=bool)
+ scl_data, _ = result
+ return (scl_data == 0) | (scl_data == 3) | (scl_data > 7)
+
+
+def _pad_to_multiple(arr: np.ndarray, ratio: int) -> np.ndarray:
+ """Zero-pad (bands, H, W) so H and W are multiples of ``ratio``."""
+ pad_h = (ratio - arr.shape[1] % ratio) % ratio
+ pad_w = (ratio - arr.shape[2] % ratio) % ratio
+ if pad_h or pad_w:
+ arr = np.pad(arr, ((0, 0), (0, pad_h), (0, pad_w)), constant_values=0)
+ return arr
+
+
+# ---------------------------------------------------------------------------
+# S2: STAC search + download (from s2_cloud_native.py)
+# ---------------------------------------------------------------------------
+
+
+def stac_search_s2(
+ bbox: list[float],
+ start_date: datetime,
+ end_date: datetime,
+) -> list[Any]:
+ """Search Earth Search for S2 L2A items intersecting a bbox."""
+ client = Client.open(EARTH_SEARCH_URL)
+ search = client.search(
+ collections=["sentinel-2-l2a"],
+ bbox=bbox,
+ datetime=(
+ f"{start_date.strftime('%Y-%m-%dT%H:%M:%SZ')}/"
+ f"{end_date.strftime('%Y-%m-%dT23:59:59Z')}"
+ ),
+ max_items=10_000,
+ )
+ return list({item.id: item for item in search.items()}.values())
+
+
+def _process_item(
+ item: Any,
+ bbox: list[float],
+ bands: list[str],
+ output_dir: Path,
+ ratio: int,
+) -> str | None:
+ """Range-read one S2 item and write a masked REFL GeoTIFF.
+
+ Returns a skip-message string when the item cannot be processed, else None.
+ """
+ out_path = output_dir / f"{item.id}_REFL.tif"
+ if out_path.is_file():
+ return None
+ bands_result = _read_bands(item, bbox, bands)
+ if bands_result is None:
+ return f"[S2] Skipping {item.id}: missing asset or no bbox overlap"
+ band_arrays, ref_profile = bands_result
+ mask = _cloud_mask(item, bbox, (ref_profile["height"], ref_profile["width"]))
+ stacked = (np.stack(band_arrays) + _boa_offset(item)) / 10_000.0
+ np.clip(stacked, 0, None, out=stacked)
+ stacked[:, mask] = 0.0
+ stacked = _pad_to_multiple(stacked, ratio)
+ out_profile = {
+ "driver": "GTiff",
+ "count": len(bands),
+ "dtype": "float32",
+ "nodata": 0,
+ "crs": ref_profile["crs"],
+ "transform": ref_profile["transform"],
+ "height": stacked.shape[1],
+ "width": stacked.shape[2],
+ "compress": "lzw",
+ }
+ with rasterio.open(out_path, "w", **out_profile) as dst:
+ dst.write(stacked)
+ for i, band_name in enumerate(bands, 1):
+ dst.set_band_description(i, band_name)
+ return None
+
+
+def download_s2_window(
+ items: list[Any],
+ bbox: list[float],
+ output_dir: Path,
+ bands: list[str],
+ ratio: int = RESOLUTION_RATIO,
+ max_workers: int = 12,
+) -> None:
+ """Range-read S2 L2A COG windows and write masked REFL GeoTIFFs.
+
+ Writes ``{item.id}_REFL.tif`` directly — no intermediate raw download.
+ Cloud/shadow pixels (SCL 0, 3, >7) are zeroed. BOA offset is inferred from
+ ``processing:baseline``. Output is zero-padded to multiples of ``ratio``.
+ Items are fetched in parallel using ``max_workers`` threads.
+ """
+ output_dir.mkdir(parents=True, exist_ok=True)
+ with rasterio.Env(**_GDAL_COG_ENV):
+ with ThreadPoolExecutor(max_workers=max_workers) as pool:
+ futures = {
+ pool.submit(
+ _process_item, item, bbox, bands, output_dir, ratio
+ ): item.id
+ for item in items
+ }
+ with tqdm(
+ total=len(futures), unit="granule", desc="S2 COG window read"
+ ) as pbar:
+ for fut in as_completed(futures):
+ msg = fut.result()
+ if msg:
+ tqdm.write(msg)
+ pbar.update(1)
+
+
+# ---------------------------------------------------------------------------
+# S3: download (from s3_openeo.py)
+# ---------------------------------------------------------------------------
+
+
+def _utm_epsg(bbox: list[float]) -> int:
+ """Return the UTM EPSG code for the centre of a ``[W, S, E, N]`` bbox."""
+ lon = (bbox[0] + bbox[2]) / 2
+ lat = (bbox[1] + bbox[3]) / 2
+ zone = int((lon + 180) / 6) + 1
+ return 32600 + zone if lat >= 0 else 32700 + zone
+
+
+def _cdse_token(username: str, password: str) -> str:
+ """Obtain a CDSE bearer token via password grant."""
+ resp = requests.post(
+ CDSE_TOKEN_URL,
+ data={
+ "grant_type": "password",
+ "username": username,
+ "password": password,
+ "client_id": "cdse-public",
+ },
+ timeout=30,
+ )
+ resp.raise_for_status()
+ return resp.json()["access_token"]
+
+
+def _netcdf_to_geotiffs(nc_path: Path, output_dir: Path, epsg: int) -> int:
+ """Split an OpenEO NetCDF into per-date GeoTIFFs.
+
+ Output filenames match the ``S3*__YYYYMMDDTHHMMSS.tif`` pattern that
+ ``s3_processing.produce_median_composite`` expects.
+
+ Handles half-pixel cell-centre coordinates, ascending y-axis (flip_y),
+ and fills NetCDF masked values with NaN.
+ """
+ written = 0
+ with netCDF4.Dataset(str(nc_path), "r") as nc:
+ times = netCDF4.num2date(nc.variables["t"][:], nc.variables["t"].units)
+ x_coords = np.asarray(nc.variables["x"][:], dtype=float)
+ y_coords = np.asarray(nc.variables["y"][:], dtype=float)
+
+ half_x = abs(x_coords[1] - x_coords[0]) / 2 if len(x_coords) > 1 else 0.0
+ half_y = abs(y_coords[1] - y_coords[0]) / 2 if len(y_coords) > 1 else 0.0
+ transform = from_bounds(
+ x_coords.min() - half_x,
+ y_coords.min() - half_y,
+ x_coords.max() + half_x,
+ y_coords.max() + half_y,
+ len(x_coords),
+ len(y_coords),
+ )
+ flip_y = len(y_coords) > 1 and y_coords[0] < y_coords[-1]
+
+ date_counts: dict[str, int] = {}
+ for t_idx, time_val in enumerate(times):
+ date_str = time_val.strftime("%Y%m%d")
+ n = date_counts.get(date_str, 0)
+ date_counts[date_str] = n + 1
+
+ raw = np.stack([nc.variables[b][t_idx, :, :] for b in S3_BANDS], axis=0)
+ stacked = (
+ np.ma.filled(raw, fill_value=np.nan).astype("float32")
+ / S3_REFLECTANCE_SCALE
+ )
+ if flip_y:
+ stacked = stacked[:, ::-1, :]
+
+ filename = f"S3_{date_str}_{n}__{date_str}T120000.tif"
+ with rasterio.open(
+ output_dir / filename,
+ "w",
+ driver="GTiff",
+ height=len(y_coords),
+ width=len(x_coords),
+ count=len(S3_BANDS),
+ dtype="float32",
+ nodata=float("nan"),
+ crs=f"EPSG:{epsg}",
+ transform=transform,
+ compress="lzw",
+ ) as dst:
+ dst.write(stacked)
+ for i, band_name in enumerate(S3_BAND_NAMES, 1):
+ dst.set_band_description(i, band_name)
+ written += 1
+
+ return written
+
+
+_S3_DOWNLOAD_RETRIES = 4
+_S3_DOWNLOAD_BACKOFF = 30 # seconds; doubled on each retry
+
+
+def _download_with_retry(datacube: Any, nc_path: Path) -> None:
+ """Download an OpenEO datacube to *nc_path*, retrying on transient errors.
+
+ Retries up to ``_S3_DOWNLOAD_RETRIES`` times with exponential backoff
+ starting at ``_S3_DOWNLOAD_BACKOFF`` seconds. Re-authenticates on each
+ attempt so an expired token never blocks a retry.
+ """
+ delay = _S3_DOWNLOAD_BACKOFF
+ last_exc: Exception | None = None
+ for attempt in range(1, _S3_DOWNLOAD_RETRIES + 1):
+ try:
+ if nc_path.exists():
+ nc_path.unlink()
+ datacube.download(str(nc_path), format="NetCDF")
+ return
+ except Exception as exc:
+ last_exc = exc
+ if attempt < _S3_DOWNLOAD_RETRIES:
+ print(
+ f"[S3-OEO] Download attempt {attempt} failed ({exc}); "
+ f"retrying in {delay}s..."
+ )
+ time.sleep(delay)
+ delay *= 2
+ else:
+ print(f"[S3-OEO] All {_S3_DOWNLOAD_RETRIES} download attempts failed")
+ raise RuntimeError(
+ f"S3 download failed after {_S3_DOWNLOAD_RETRIES} attempts"
+ ) from last_exc
+
+
+def download_s3_openeo(
+ start_date: datetime,
+ end_date: datetime,
+ aoi_geometry: str,
+ output_dir: Path,
+ credentials: dict[str, str | None],
+) -> None:
+ """Download S3 SYN L2 SDR for an AOI via CDSE OpenEO, server-side clipped.
+
+ Writes per-date ``S3_{YYYYMMDD}_{n}__{YYYYMMDD}T120000.tif`` files to
+ ``output_dir``, ready for ``s3_processing.produce_median_composite``.
+ Skips if any ``S3*.tif`` files already exist.
+ """
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ if any(output_dir.glob("S3*.tif")):
+ print("[S3-OEO] Skipping — output_dir already contains S3 GeoTIFFs")
+ return
+
+ bbox = wkt_to_bbox(aoi_geometry)
+ epsg = _utm_epsg(bbox)
+ spatial_extent = {
+ "west": bbox[0],
+ "east": bbox[2],
+ "south": bbox[1],
+ "north": bbox[3],
+ }
+
+ print("[S3-OEO] Authenticating with CDSE...")
+ token = _cdse_token(credentials["username"], credentials["password"]) # type: ignore[arg-type]
+ conn = openeo.connect(OPENEO_URL)
+ conn.authenticate_oidc_access_token(token)
+
+ start_str = start_date.strftime("%Y-%m-%d")
+ end_str = end_date.strftime("%Y-%m-%d")
+ print(f"[S3-OEO] Loading {S3_COLLECTION} ({start_str} → {end_str})...")
+ datacube = conn.load_collection(
+ S3_COLLECTION,
+ spatial_extent=spatial_extent,
+ temporal_extent=[start_str, end_str],
+ bands=S3_BANDS,
+ ).resample_spatial(projection=epsg)
+
+ nc_path = output_dir / "_s3_syn_l2.nc"
+ print(f"[S3-OEO] Downloading NetCDF to {nc_path}...")
+ t0 = time.time()
+ _download_with_retry(datacube, nc_path)
+ print(f"[S3-OEO] Download completed in {time.time() - t0:.1f}s")
+
+ print("[S3-OEO] Splitting into per-date GeoTIFFs...")
+ written = _netcdf_to_geotiffs(nc_path, output_dir, epsg)
+ nc_path.unlink(missing_ok=True)
+ print(f"[S3-OEO] {written} GeoTIFFs written to {output_dir}")
+
+
+# ---------------------------------------------------------------------------
+# S2: distance_to_clouds helper
+# ---------------------------------------------------------------------------
+
+
+def _import_distance_to_clouds():
+ try:
+ from efast.s2_processing import distance_to_clouds
+
+ return distance_to_clouds
+ except ImportError as exc:
+ raise ImportError("efast not found. Install with: uv sync") from exc
+
+
+def _normalize_s2_grid(s2_dir: Path) -> None:
+ """Remove REFL (and companion DIST_CLOUD/GCC) files that don't match the dominant shape.
+
+ Sites at MGRS tile boundaries can have S2 downloads from two tiles with different
+ spatial extents. For example, a site at the top edge of tile 16SDA will produce
+ narrow (e.g. 30×180) REFL files from 16SDA and full-extent (180×180) files from
+ 16SDB. EFAST requires all S2 files to share the same grid, so we keep only the
+ shape with the most pixels (largest tile coverage) and discard the rest together
+ with any already-computed companions.
+ """
+ refl_files = sorted(s2_dir.glob("*_REFL.tif"))
+ if not refl_files:
+ return
+
+ shape_to_files: dict[tuple[int, int], list[Path]] = {}
+ for f in refl_files:
+ with rasterio.open(f) as src:
+ shape: tuple[int, int] = src.shape # type: ignore[assignment]
+ shape_to_files.setdefault(shape, []).append(f)
+
+ if len(shape_to_files) <= 1:
+ return
+
+ ref_shape = max(shape_to_files.keys(), key=lambda s: s[0] * s[1])
+ shapes_summary = ", ".join(
+ f"{s[0]}×{s[1]}({len(fs)})" for s, fs in shape_to_files.items()
+ )
+
+ n_removed = 0
+ for shape, files in shape_to_files.items():
+ if shape == ref_shape:
+ continue
+ for refl_path in files:
+ stem = refl_path.stem[: -len("_REFL")] # e.g. "S2A_16SDA_20250106_0_L2A"
+ for companion in s2_dir.glob(f"{stem}_*.tif"):
+ companion.unlink()
+ refl_path.unlink(missing_ok=True)
+ n_removed += 1
+
+ print(
+ f"[S2-NORM] Tile boundary — shapes: {shapes_summary}; "
+ f"removed {n_removed} file-sets, kept {ref_shape[0]}×{ref_shape[1]}"
+ )
+
+
+def _rescale_dist_cloud(s2_dir: Path) -> None:
+ """Ensure DIST_CLOUD values are in pixel units (not normalised to [0,1])."""
+ for dc_path in s2_dir.glob("*DIST_CLOUD.tif"):
+ with rasterio.open(dc_path) as src:
+ d = src.read(1)
+ if float(np.nanmax(d)) <= 1:
+ with rasterio.open(dc_path, "r+") as dst:
+ dst.write(np.where(d > 0, 2.0, d).astype(np.float32), 1)
+
+
+# ---------------------------------------------------------------------------
+# S3: compositing + reprojection helpers (from 4-sentinel-data.py)
+# ---------------------------------------------------------------------------
+
+
+def _import_s3_processing():
+ try:
+ from efast import s3_processing
+
+ return s3_processing
+ except ImportError as exc:
+ raise ImportError("efast not found. Install with: uv sync") from exc
+
+
+def _reproject_s3_composites_to_s2_grid(
+ composite_dir: Path,
+ s2_refl_path: Path,
+ s3_out_dir: Path,
+ *,
+ resolution_ratio: int = RESOLUTION_RATIO,
+) -> None:
+ """Reproject S3 composites to the S2 spatial grid at LR resolution."""
+ s3_out_dir.mkdir(parents=True, exist_ok=True)
+ with rasterio.open(s2_refl_path) as s2_ref:
+ target_bounds = s2_ref.bounds
+ target_crs = s2_ref.crs
+ width = s2_ref.width // resolution_ratio
+ height = s2_ref.height // resolution_ratio
+ s3_transform = rasterio.transform.from_bounds(
+ target_bounds.left,
+ target_bounds.bottom,
+ target_bounds.right,
+ target_bounds.top,
+ width,
+ height,
+ )
+
+ for sen3_path in sorted(composite_dir.glob("composite_*.tif")):
+ date_part = sen3_path.stem.split("_", 1)[1].replace("-", "")
+ outfile = s3_out_dir / f"composite_{date_part}.tif"
+ vrt_options = {
+ "transform": s3_transform,
+ "height": height,
+ "width": width,
+ "crs": target_crs,
+ "resampling": Resampling.cubic,
+ }
+ with rasterio.open(sen3_path) as s3_src:
+ with WarpedVRT(s3_src, **vrt_options) as vrt:
+ profile = vrt.profile.copy()
+ profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
+ rio_shutil.copy(vrt, outfile, **profile)
+
+
+def _s3_reflectance_scale(raw_s3_dir: Path) -> float:
+ """Return multiplier that maps raw SYN L2 SDR values to 0–1 reflectance."""
+ for path in raw_s3_dir.glob("S3*.tif"):
+ with rasterio.open(path) as src:
+ data = src.read()
+ if not np.any(np.isfinite(data)):
+ continue
+ mx = float(np.nanmax(data))
+ if np.isfinite(mx) and mx > 5:
+ return 1.0 / S3_REFLECTANCE_SCALE
+ return 1.0
+
+
+def _stage_s3_for_efast(raw_s3_dir: Path, staging_dir: Path) -> int:
+ """Copy ``S3_*.tif`` inputs, scaling reflectance when still in DN form."""
+ scale = _s3_reflectance_scale(raw_s3_dir)
+ if staging_dir.exists():
+ shutil.rmtree(staging_dir)
+ staging_dir.mkdir(parents=True)
+
+ count = 0
+ for src_path in sorted(raw_s3_dir.glob("S3*.tif")):
+ dst_path = staging_dir / src_path.name
+ with rasterio.open(src_path) as src:
+ data = src.read().astype("float32") * scale
+ profile = src.profile.copy()
+ profile.update(dtype="float32")
+ descriptions = src.descriptions
+ with rasterio.open(dst_path, "w", **profile) as dst:
+ dst.write(data)
+ for i, desc in enumerate(descriptions, 1):
+ if desc:
+ dst.set_band_description(i, desc)
+ count += 1
+
+ if scale != 1.0:
+ print(f"[S3-PREP] Scaled raw SDR by {scale:g} for EFAST compositing")
+ return count
+
+
+def _prepare_s3(
+ raw_s3_dir: Path,
+ s2_refl_path: Path,
+ s3_out_dir: Path,
+ *,
+ work_dir: Path | None = None,
+) -> None:
+ """Run EFAST S3 compositing pipeline and reproject to S2 grid."""
+ s3 = _import_s3_processing()
+ base = work_dir or (s3_out_dir / "_efast_work")
+ staging = base / "scaled"
+ composites = base / "composites"
+ blurred = base / "blurred"
+ calibrated = base / "calibrated"
+
+ for directory in (staging, composites, blurred, calibrated):
+ if directory.exists():
+ shutil.rmtree(directory)
+ directory.mkdir(parents=True, exist_ok=True)
+
+ staged = _stage_s3_for_efast(raw_s3_dir, staging)
+ if staged == 0:
+ raise ValueError(f"No S3*.tif files found in {raw_s3_dir}")
+
+ print(
+ f"[S3-PREP] produce_median_composite: mosaic_days={S3_MOSAIC_DAYS}, "
+ f"step={S3_COMPOSITE_STEP}, sigma_doy={S3_COMPOSITE_SIGMA_DOY}, "
+ f"D={S3_COMPOSITE_D}"
+ )
+ s3.produce_median_composite(
+ staging,
+ composites,
+ step=S3_COMPOSITE_STEP,
+ mosaic_days=S3_MOSAIC_DAYS,
+ s3_bands=[1, 2, 3, 4],
+ D=S3_COMPOSITE_D,
+ sigma_doy=S3_COMPOSITE_SIGMA_DOY,
+ )
+ s3.smoothing(
+ composites,
+ blurred,
+ product="composite",
+ std=S3_SMOOTHING_STD,
+ preserve_nan=False,
+ )
+ s3.reformat_s3(blurred, calibrated, product="composite", scaling_factor=1)
+
+ for old in s3_out_dir.glob("composite_*.tif"):
+ old.unlink()
+ _reproject_s3_composites_to_s2_grid(calibrated, s2_refl_path, s3_out_dir)
+
+ if work_dir is None and base.exists():
+ shutil.rmtree(base)
+
+ n_out = len(list(s3_out_dir.glob("composite_*.tif")))
+ print(f"[S3-PREP] Wrote {n_out} composites")
+
+
+# ---------------------------------------------------------------------------
+# Per-site pipeline
+# ---------------------------------------------------------------------------
+
+
+def process_site(
+ sitename: str,
+ lat: float,
+ lon: float,
+ year: int,
+) -> dict[str, Any]:
+ """Download S2 + S3 and run EFAST preparation for one site."""
+ site_dir = DATA_DIR / "sentinel_data" / str(year) / sitename
+ s2_out = site_dir / "prepared" / "s2"
+ s3_raw = site_dir / "raw" / "s3"
+ s3_out = site_dir / "prepared" / "s3"
+ aoi_wkt = f"POINT ({lon} {lat})"
+ bbox = wkt_to_bbox(aoi_wkt)
+ creds = _cdse_credentials()
+
+ # S3 download
+ print(f"[{sitename}] Downloading S3...")
+ download_s3_openeo(
+ start_date=datetime(year, 1, 1),
+ end_date=datetime(year, 12, 31),
+ aoi_geometry=aoi_wkt,
+ output_dir=s3_raw,
+ credentials=creds,
+ )
+
+ # S2 download
+ print(f"[{sitename}] Searching S2 on Earth Search...")
+ items = stac_search_s2(bbox, datetime(year, 1, 1), datetime(year, 12, 31))
+ print(f"[{sitename}] {len(items)} S2 items found — downloading windows...")
+ download_s2_window(items, bbox, s2_out, S2_BANDS, RESOLUTION_RATIO)
+ _normalize_s2_grid(s2_out)
+
+ # S2 distance-to-clouds
+ print(f"[{sitename}] Computing distance-to-clouds...")
+ distance_to_clouds = _import_distance_to_clouds()
+ distance_to_clouds(s2_out, ratio=RESOLUTION_RATIO)
+ _rescale_dist_cloud(s2_out)
+
+ # S3 compositing
+ s2_refl_path = next(iter(s2_out.glob("*_REFL.tif")), None)
+ if s2_refl_path is None:
+ raise ValueError(f"No REFL files in {s2_out} — S2 download may have failed")
+ s3_out.mkdir(parents=True, exist_ok=True)
+ print(f"[{sitename}] Running S3 compositing pipeline...")
+ _prepare_s3(s3_raw, s2_refl_path, s3_out)
+
+ summary = {
+ "sitename": sitename,
+ "evaluation_year": year,
+ "lat": lat,
+ "lon": lon,
+ "s2_refl_count": len(list(s2_out.glob("*_REFL.tif"))),
+ "s2_dist_cloud_count": len(list(s2_out.glob("*_DIST_CLOUD.tif"))),
+ "s3_raw_count": len(list(s3_raw.glob("S3*.tif"))),
+ "s3_composite_count": len(list(s3_out.glob("composite_*.tif"))),
+ }
+ site_dir.mkdir(parents=True, exist_ok=True)
+ (site_dir / "data.json").write_text(
+ json.dumps(summary, indent=2) + "\n", encoding="utf-8"
+ )
+ return summary
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+ parser.add_argument(
+ "--site",
+ type=str,
+ default=None,
+ help="Single sitename to process (default: all step-2 PASS sites)",
+ )
+ parser.add_argument(
+ "--skip-downloaded",
+ action="store_true",
+ help="Skip sites whose directory already exists under data/sentinel_data/{year}/",
+ )
+ args = parser.parse_args(argv)
+ year = args.evaluation_year
+
+ pass_sites = _load_screening_pass_sites(year)
+ if not pass_sites:
+ print("[Sentinel data] No PASS sites found in step-2 screening output")
+ return 1
+
+ if args.site:
+ pass_sites = [s for s in pass_sites if s["sitename"] == args.site]
+ if not pass_sites:
+ print(f"[Sentinel data] Site '{args.site}' not found in step-2 PASS sites")
+ return 1
+
+ print(f"[Sentinel data] Processing {len(pass_sites)} site(s)")
+ for i, site in enumerate(pass_sites, 1):
+ sitename = site["sitename"]
+ site_dir = DATA_DIR / "sentinel_data" / str(year) / sitename
+ if args.skip_downloaded and site_dir.exists():
+ print(
+ f"[Sentinel data] ({i}/{len(pass_sites)}) {sitename} — skipping (directory exists)"
+ )
+ continue
+ print(f"[Sentinel data] ({i}/{len(pass_sites)}) {sitename}")
+ summary = process_site(sitename, site["lat"], site["lon"], year)
+ print(
+ f"[Sentinel data] {sitename} done — "
+ f"{summary['s2_refl_count']} REFL, "
+ f"{summary['s3_composite_count']} composites"
+ )
+
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/4-fusion.py b/4-fusion.py
new file mode 100644
index 0000000..5950c44
--- /dev/null
+++ b/4-fusion.py
@@ -0,0 +1,349 @@
+"""Step 4: Compute GCC and run EFAST BtI + ItB fusion for prepared sites.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``sentinel_data/{year}/{sitename}/prepared/s2/`` — ``*_REFL.tif`` + ``*_DIST_CLOUD.tif``
+- ``sentinel_data/{year}/{sitename}/prepared/s3/`` — ``composite_*.tif`` (4-band)
+
+Outputs (``data/``):
+
+- ``sentinel_data/{year}/{sitename}/prepared/s2/*_GCC.tif`` — S2 GCC (in-place)
+- ``sentinel_data/{year}/{sitename}/prepared/gcc_s3/*.tif`` — S3 GCC composites
+- ``fusion/{year}/{sitename}/bti/fusion/REFL_*.tif`` — BtI fused 4-band reflectance
+- ``fusion/{year}/{sitename}/bti/gcc/GCC_*.tif`` — GCC derived from BtI fusion
+- ``fusion/{year}/{sitename}/itb/s2/GCC_*.tif`` — per-acquisition S2 GCC (simplified names)
+- ``fusion/{year}/{sitename}/itb/s3/GCC_*.tif`` — per-composite S3 GCC (simplified names)
+- ``fusion/{year}/{sitename}/itb/fusion/GCC_*.tif`` — ItB fused GCC
+
+Requires ``uv sync`` (efast).
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--site`` (optional; default: all prepared sites under ``sentinel_data/{year}/``)
+
+Prior step: :mod:`3-sentinel-data`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import shutil
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import rasterio
+from dateutil import rrule
+
+# ---------------------------------------------------------------------------
+# Public constants
+# ---------------------------------------------------------------------------
+
+RESOLUTION_RATIO = 30
+MOSAIC_STEP = 2
+MAX_DAYS = 100
+MINIMUM_ACQUISITION_IMPORTANCE = 0
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+
+
+# ---------------------------------------------------------------------------
+# efast import helper
+# ---------------------------------------------------------------------------
+
+
+def _import_efast():
+ try:
+ import efast.efast as efast_module
+
+ return efast_module
+ except ImportError as exc:
+ raise ImportError("efast not found. Install with: uv sync") from exc
+
+
+# ---------------------------------------------------------------------------
+# GCC computation (from s2_cloud_native.py and s3_openeo.py)
+# ---------------------------------------------------------------------------
+
+
+def compute_gcc_s2(s2_dir: Path, output_dir: Path) -> None:
+ """Compute GCC from S2 REFL files and write ``*_GCC.tif`` to ``output_dir``.
+
+ Reads every ``*_REFL.tif`` (band order B02/B03/B04) and writes a co-located
+ single-band GCC file. Cloud-masked pixels (zero in all bands) remain zero.
+ """
+ output_dir.mkdir(parents=True, exist_ok=True)
+ for src_path in sorted(s2_dir.glob("*_REFL.tif")):
+ out_path = output_dir / src_path.name.replace("_REFL.tif", "_GCC.tif")
+ if out_path.is_file():
+ continue
+ with rasterio.open(src_path) as src:
+ b, g, r = src.read(1), src.read(2), src.read(3)
+ profile = src.profile
+ total = b + g + r
+ gcc = g / (total + 1e-10)
+ gcc[total == 0] = 0
+ profile.update(count=1)
+ with rasterio.open(out_path, "w", **profile) as dst:
+ dst.write(gcc[np.newaxis].astype("float32"))
+
+
+def compute_gcc_s3(s3_dir: Path, output_dir: Path) -> None:
+ """Compute GCC from S3 composite files and write single-band GeoTIFFs.
+
+ Reads every ``composite_*.tif`` (band order Oa04/Oa06/Oa08/Oa17) and writes
+ a single-band GCC file. NaN pixels in the input remain NaN.
+ """
+ output_dir.mkdir(parents=True, exist_ok=True)
+ for src_path in sorted(s3_dir.glob("composite_*.tif")):
+ out_path = output_dir / src_path.name
+ if out_path.is_file():
+ continue
+ with rasterio.open(src_path) as src:
+ b, g, r = src.read(1), src.read(2), src.read(3)
+ profile = src.profile
+ total = b + g + r
+ gcc = g / (total + 1e-10)
+ gcc[np.isnan(total)] = np.nan
+ profile.update(count=1, dtype="float32")
+ with rasterio.open(out_path, "w", **profile) as dst:
+ dst.write(gcc[np.newaxis].astype("float32"))
+
+
+def compute_gcc_from_refl(refl_dir: Path, gcc_dir: Path) -> None:
+ """Derive GCC from ``REFL_YYYYMMDD.tif`` files (BtI fusion output).
+
+ Reads every ``REFL_*.tif`` and writes a co-located single-band
+ ``GCC_YYYYMMDD.tif``. Pixels where any reflectance band is negative (which
+ can arise from EFAST's temporal correction) are written as NaN; the GCC
+ ratio is undefined there because the denominator B+G+R is near-zero or
+ negative due to band cancellation. All-zero pixels (cloud mask) remain
+ zero.
+ """
+ gcc_dir.mkdir(parents=True, exist_ok=True)
+ for src_path in sorted(refl_dir.glob("REFL_*.tif")):
+ out_path = gcc_dir / src_path.name.replace("REFL_", "GCC_")
+ if out_path.is_file():
+ continue
+ with rasterio.open(src_path) as src:
+ b, g, r = src.read(1), src.read(2), src.read(3)
+ profile = src.profile
+ total = b + g + r
+ invalid = (b < 0) | (g < 0) | (r < 0)
+ gcc = np.where(invalid, np.nan, g / (total + 1e-10))
+ gcc[total == 0] = np.nan
+ profile.update(count=1, dtype="float32")
+ with rasterio.open(out_path, "w", **profile) as dst:
+ dst.write(gcc[np.newaxis].astype("float32"))
+
+
+# ---------------------------------------------------------------------------
+# Date-range detection
+# ---------------------------------------------------------------------------
+
+
+def _refl_date_range(s2_dir: Path) -> tuple[datetime, datetime] | None:
+ """Return (start, end) datetime from REFL filenames in ``s2_dir``.
+
+ Filenames are expected to follow the S2 product naming convention, where
+ the acquisition date ``YYYYMMDD`` appears at position index 2 when the
+ stem is split by ``_``, e.g.
+ ``S2A_MSIL2A_20230911T114111_N0509_R025_T29PKT_20230911T153131_REFL.tif``.
+ """
+ dates: list[datetime] = []
+ for p in s2_dir.glob("*_REFL.tif"):
+ parts = p.stem.split("_")
+ if len(parts) >= 3:
+ try:
+ dates.append(datetime.strptime(parts[2][:8], "%Y%m%d"))
+ except ValueError:
+ pass
+ if not dates:
+ return None
+ return min(dates), max(dates)
+
+
+# ---------------------------------------------------------------------------
+# Per-site fusion
+# ---------------------------------------------------------------------------
+
+
+def fuse_site(sitename: str, year: int) -> dict[str, Any]:
+ """Run GCC computation and EFAST BtI + ItB fusion for one prepared site."""
+ efast = _import_efast()
+
+ s2_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s2"
+ s3_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s3"
+ gcc_s3_dir = (
+ DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "gcc_s3"
+ )
+ base = DATA_DIR / "fusion" / str(year) / sitename
+
+ if not s2_dir.is_dir() or not any(s2_dir.glob("*_REFL.tif")):
+ raise FileNotFoundError(f"No REFL files in {s2_dir}")
+ if not s3_dir.is_dir() or not any(s3_dir.glob("composite_*.tif")):
+ raise FileNotFoundError(f"No composite files in {s3_dir}")
+
+ print(f"[{sitename}] Computing S2 GCC (in-place)...")
+ compute_gcc_s2(s2_dir, s2_dir)
+
+ print(f"[{sitename}] Computing S3 GCC...")
+ compute_gcc_s3(s3_dir, gcc_s3_dir)
+
+ date_range = _refl_date_range(s2_dir)
+ if date_range is None:
+ raise ValueError(f"Could not detect date range from REFL filenames in {s2_dir}")
+ start, end = date_range
+ print(f"[{sitename}] Date range: {start.date()} → {end.date()}")
+
+ fusion_dates = list(
+ rrule.rrule(
+ rrule.DAILY,
+ dtstart=start + timedelta(MOSAIC_STEP),
+ until=end - timedelta(MOSAIC_STEP),
+ interval=MOSAIC_STEP,
+ )
+ )
+
+ _fusion_kwargs = dict(
+ ratio=RESOLUTION_RATIO,
+ max_days=MAX_DAYS,
+ minimum_acquisition_importance=MINIMUM_ACQUISITION_IMPORTANCE,
+ )
+
+ # --- ItB: GCC first, then fuse GCC ---
+ itb_s2 = base / "itb" / "s2"
+ itb_s3 = base / "itb" / "s3"
+ itb_fusion = base / "itb" / "fusion"
+ itb_s2.mkdir(parents=True, exist_ok=True)
+ itb_s3.mkdir(parents=True, exist_ok=True)
+ itb_fusion.mkdir(parents=True, exist_ok=True)
+
+ for p in sorted(s2_dir.glob("*_GCC.tif")):
+ dst = itb_s2 / f"GCC_{p.stem.split('_')[2][:8]}.tif"
+ if not dst.exists():
+ shutil.copy2(p, dst)
+ for p in sorted(gcc_s3_dir.glob("composite_*.tif")):
+ dst = itb_s3 / f"GCC_{p.stem.split('_')[1]}.tif"
+ if not dst.exists():
+ shutil.copy2(p, dst)
+
+ print(f"[{sitename}] ItB: fusing GCC over {len(fusion_dates)} dates...")
+ for date in fusion_dates:
+ efast.fusion(
+ date, gcc_s3_dir, s2_dir, itb_fusion, product="GCC", **_fusion_kwargs
+ )
+
+ # --- BtI: fuse reflectance (3-band, matching S2 B02/B03/B04), then derive GCC ---
+ # S3 composites have 4 bands; strip band 4 (Oa17/NIR) so shapes match S2 REFL.
+ s3_rgb_dir = (
+ DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s3_rgb"
+ )
+ s3_rgb_dir.mkdir(parents=True, exist_ok=True)
+ for p in sorted(s3_dir.glob("composite_*.tif")):
+ out = s3_rgb_dir / p.name
+ if not out.exists():
+ with rasterio.open(p) as src:
+ data = src.read([1, 2, 3])
+ profile = src.profile.copy()
+ profile.update(count=3)
+ with rasterio.open(out, "w", **profile) as dst:
+ dst.write(data)
+
+ bti_fusion = base / "bti" / "fusion"
+ bti_gcc = base / "bti" / "gcc"
+ bti_fusion.mkdir(parents=True, exist_ok=True)
+
+ print(f"[{sitename}] BtI: fusing REFL over {len(fusion_dates)} dates...")
+ for date in fusion_dates:
+ efast.fusion(
+ date, s3_rgb_dir, s2_dir, bti_fusion, product="REFL", **_fusion_kwargs
+ )
+
+ print(f"[{sitename}] BtI: deriving GCC from fused REFL...")
+ compute_gcc_from_refl(bti_fusion, bti_gcc)
+
+ return {
+ "sitename": sitename,
+ "evaluation_year": year,
+ "start": start.date().isoformat(),
+ "end": end.date().isoformat(),
+ "fusion_dates": len(fusion_dates),
+ "itb_fusion_files": len(list(itb_fusion.glob("*.tif"))),
+ "bti_fusion_files": len(list(bti_fusion.glob("*.tif"))),
+ "bti_gcc_files": len(list(bti_gcc.glob("*.tif"))),
+ }
+
+
+# ---------------------------------------------------------------------------
+# Site discovery
+# ---------------------------------------------------------------------------
+
+
+def _discover_sites(year: int) -> list[str]:
+ """Return sitenames that have prepared S2 REFL files under sentinel_data."""
+ base = DATA_DIR / "sentinel_data" / str(year)
+ if not base.is_dir():
+ return []
+ return sorted(
+ d.name
+ for d in base.iterdir()
+ if d.is_dir() and any((d / "prepared" / "s2").glob("*_REFL.tif"))
+ )
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+ parser.add_argument(
+ "--site",
+ type=str,
+ default=None,
+ help="Single sitename to fuse (default: all prepared sites)",
+ )
+ parser.add_argument(
+ "--skip-blended",
+ action="store_true",
+ help="Skip sites whose fusion directory already exists under data/fusion/{year}/",
+ )
+ args = parser.parse_args(argv)
+ year = args.evaluation_year
+
+ if args.site:
+ sites = [args.site]
+ else:
+ sites = _discover_sites(year)
+ if not sites:
+ print(f"[Fusion] No prepared sites found under data/sentinel_data/{year}/")
+ return 1
+
+ print(f"[Fusion] Processing {len(sites)} site(s)")
+ for i, sitename in enumerate(sites, 1):
+ fusion_dir = DATA_DIR / "fusion" / str(year) / sitename
+ if args.skip_blended and fusion_dir.exists():
+ print(
+ f"[Fusion] ({i}/{len(sites)}) {sitename} — skipping (fusion directory exists)"
+ )
+ continue
+ print(f"[Fusion] ({i}/{len(sites)}) {sitename}")
+ summary = fuse_site(sitename, year)
+ print(
+ f"[Fusion] {sitename} done — "
+ f"{summary['fusion_dates']} dates, "
+ f"itb={summary['itb_fusion_files']} bti={summary['bti_fusion_files']} "
+ f"bti_gcc={summary['bti_gcc_files']}"
+ )
+
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/5-metrics.py b/5-metrics.py
new file mode 100644
index 0000000..9658797
--- /dev/null
+++ b/5-metrics.py
@@ -0,0 +1,788 @@
+"""Step 5: Pre-compute per-site GCC timeseries + raster index for the webapp.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``phenocam_screening/{year}.json`` — qualifying sites + metadata
+- ``phenocam/{year}/{site}_1day.csv`` — daily GCC timeseries
+- ``sentinel_data/{year}/{site}/prepared/s2/*_GCC.tif`` — S2 GCC rasters
+- ``sentinel_data/{year}/{site}/prepared/gcc_s3/composite_*.tif`` — S3 GCC rasters
+- ``fusion/{year}/{site}/bti/gcc/GCC_*.tif`` — BtI GCC rasters
+- ``fusion/{year}/{site}/itb/fusion/GCC_*.tif`` — ItB GCC rasters
+
+Outputs (``data/metrics/``):
+
+- ``manifest.json`` — years + per-site metadata
+- ``{year}/{site}/gcc_phenocam.json`` — PhenoCam ``gcc_90`` at matched dates
+- ``{year}/{site}/gcc_s2.json`` — S2 GCC (center pixel, cloud-free scenes)
+- ``{year}/{site}/gcc_s2_whittaker.json`` — Whittaker-smoothed S2 GCC
+- ``{year}/{site}/gcc_s3.json`` — S3 composite GCC
+- ``{year}/{site}/gcc_s3_smooth.json`` — S3 5-day moving average
+- ``{year}/{site}/gcc_fusion_bti.json`` — BtI fused GCC
+- ``{year}/{site}/gcc_fusion_itb.json`` — ItB fused GCC
+- ``{year}/{site}/phenocam_images.json`` — midday photo URLs for the viewer
+- ``{year}/{site}/rasters_s2_refl.json`` — S2 REFL paths (BtI view)
+- ``{year}/{site}/rasters_s3_composite.json`` — S3 composite paths (BtI view)
+- ``{year}/{site}/rasters_s2_gcc.json`` — S2 GCC paths (ItB view)
+- ``{year}/{site}/rasters_s3_gcc.json`` — S3 GCC paths (ItB view)
+- ``{year}/{site}/rasters_fusion_bti_refl.json`` — BtI fused REFL paths
+- ``{year}/{site}/rasters_fusion_itb_gcc.json`` — ItB fused GCC paths
+- ``{year}/{site}/metrics.json`` — NSE, RMSE, nRMSE, Pearson r vs PhenoCam per series
+- ``{year}/{site}/bands_s2.json`` — S2 center-pixel reflectance (B02, B03, B04) per scene
+- ``{year}/{site}/bands_s3.json`` — S3 center-pixel reflectance (Oa04, Oa06, Oa08, Oa17) per composite
+- ``{year}/{site}/covariates.json`` — spatial CV/std, S2/S3 counts, gap stats
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--site`` (optional; default: all qualifying sites with sentinel data)
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+import datetime
+import numpy as np
+import rasterio
+from rasterio.crs import CRS
+from rasterio.transform import rowcol
+from pyproj import Transformer
+from scipy.stats import pearsonr
+from tqdm import tqdm
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+
+# GCC smoothing window for S3 moving average (days)
+S3_SMOOTH_WINDOW = 5
+
+# Whittaker lambda (penalised smoothing strength for S2)
+WHITTAKER_LAMBDA = 400.0
+
+# Half-width in metres for the spatial heterogeneity footprint (~300 m = 1 S3 pixel)
+SPATIAL_CV_HALF_M = 150
+
+# PhenoCam archive image URL pattern
+PHENOCAM_IMAGE_URL = (
+ "https://phenocam.nau.edu/data/archive/{site}/{year}/{month}/{filename}"
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers: raster pixel extraction
+# ---------------------------------------------------------------------------
+
+
+def _window_mean(data: np.ndarray) -> float | None:
+ """Mean of a pixel window; ``None`` when every value is NaN."""
+ valid = data[~np.isnan(data)]
+ if valid.size == 0:
+ return None
+ return float(np.mean(valid))
+
+
+def _read_center_pixel(path: Path, lat: float, lon: float) -> float | None:
+ """Return the 3×3 mean GCC value at (lat, lon) from a single-band raster.
+
+ Returns ``None`` when the pixel is masked/zero/NaN.
+ """
+ try:
+ with rasterio.open(path) as src:
+ transformer = Transformer.from_crs(
+ CRS.from_epsg(4326), src.crs, always_xy=True
+ )
+ x, y = transformer.transform(lon, lat)
+ row, col = rowcol(src.transform, x, y)
+ h, w = src.height, src.width
+ r0, r1 = max(0, row - 1), min(h, row + 2)
+ c0, c1 = max(0, col - 1), min(w, col + 2)
+ window = rasterio.windows.Window(c0, r0, c1 - c0, r1 - r0)
+ data = src.read(1, window=window).astype(float)
+ nodata = src.nodata
+ if nodata is not None:
+ data = np.where(data == nodata, np.nan, data)
+ data[data == 0] = np.nan
+ return _window_mean(data)
+ except Exception:
+ return None
+
+
+# ---------------------------------------------------------------------------
+# Helpers: date extraction from filenames
+# ---------------------------------------------------------------------------
+
+
+def _date_from_gcc_tif(path: Path) -> str | None:
+ """Extract YYYYMMDD from ``GCC_YYYYMMDD.tif`` or ``composite_YYYYMMDD.tif``."""
+ m = re.search(r"(\d{8})", path.stem)
+ return m.group(1) if m else None
+
+
+def _date_from_s2_tif(path: Path) -> str | None:
+ """Extract YYYYMMDD from S2 product name ``S2X_TTTT_YYYYMMDD_…``."""
+ parts = path.stem.split("_")
+ if len(parts) >= 3:
+ m = re.match(r"(\d{8})", parts[2])
+ return m.group(1) if m else None
+ return None
+
+
+# ---------------------------------------------------------------------------
+# Helpers: Whittaker smoother (2nd-order differences, tridiagonal solver)
+# ---------------------------------------------------------------------------
+
+
+def _whittaker_smooth(
+ values: list[float | None], lam: float = WHITTAKER_LAMBDA
+) -> list[float | None]:
+ """Penalised least-squares smoother (Whittaker, 2nd-order differences).
+
+ Masked (None) values are filled via the smooth and then re-set to None in
+ the output so the caller can distinguish observed from gap-filled points.
+ """
+ n = len(values)
+ if n < 4:
+ return values[:]
+
+ obs_mask = [v is not None for v in values]
+ y = np.array([v if v is not None else 0.0 for v in values], dtype=float)
+ w = np.array([1.0 if m else 0.0 for m in obs_mask], dtype=float)
+
+ W = np.diag(w)
+ D = np.diff(np.eye(n), n=2, axis=0) # (n-2) x n second-difference matrix
+ A = W + lam * D.T @ D
+ try:
+ z = np.linalg.solve(A, w * y)
+ except np.linalg.LinAlgError:
+ return values[:]
+
+ result: list[float | None] = []
+ for i, m in enumerate(obs_mask):
+ result.append(float(z[i]) if m else None)
+ return result
+
+
+# ---------------------------------------------------------------------------
+# Helpers: PhenoCam CSV parsing
+# ---------------------------------------------------------------------------
+
+
+def _parse_phenocam_csv(
+ csv_path: Path, year: int, site: str
+) -> tuple[list[dict], list[dict]]:
+ """Return (gcc_series, image_list) filtered to ``year``.
+
+ ``gcc_series`` entries: ``{"date": "YYYY-MM-DD", "gcc_90": float}``
+ ``image_list`` entries: ``{"date": "YYYY-MM-DD", "url": str}``
+ """
+ gcc_series: list[dict] = []
+ image_list: list[dict] = []
+ year_str = str(year)
+
+ if not csv_path.is_file():
+ return gcc_series, image_list
+
+ with csv_path.open() as f:
+ lines = [line for line in f if not line.startswith("#")]
+
+ reader = csv.DictReader(lines)
+ for row in reader:
+ if row.get("year") != year_str:
+ continue
+ date = row.get("date", "")
+ gcc_raw = row.get("gcc_90")
+ if gcc_raw and gcc_raw not in ("NA", ""):
+ try:
+ gcc_series.append({"date": date, "gcc_90": float(gcc_raw)})
+ except ValueError:
+ pass
+ fn = row.get("midday_filename", "").strip()
+ if fn and fn != "NA" and date:
+ month = date[5:7]
+ url = PHENOCAM_IMAGE_URL.format(
+ site=site, year=year_str, month=month, filename=fn
+ )
+ image_list.append({"date": date, "url": url})
+
+ return gcc_series, image_list
+
+
+# ---------------------------------------------------------------------------
+# Helpers: moving average
+# ---------------------------------------------------------------------------
+
+
+def _moving_average(series: list[dict], value_key: str, window: int) -> list[dict]:
+ """Compute centred moving average; returns new list with ``_smooth`` suffix key."""
+ if not series:
+ return []
+ vals = [p[value_key] for p in series]
+ half = window // 2
+ smoothed = []
+ for i, pt in enumerate(series):
+ chunk = [v for v in vals[max(0, i - half) : i + half + 1] if v is not None]
+ smoothed.append(
+ {
+ "date": pt["date"],
+ value_key + "_smooth": (sum(chunk) / len(chunk)) if chunk else None,
+ }
+ )
+ return smoothed
+
+
+# ---------------------------------------------------------------------------
+# Helpers: validation metrics
+# ---------------------------------------------------------------------------
+
+MATCH_TOLERANCE_DAYS = 5
+
+
+def compute_metrics(
+ ref: list[dict],
+ ref_key: str,
+ pred: list[dict],
+ pred_key: str,
+) -> dict | None:
+ """Compute NSE, RMSE, nRMSE, Pearson r between pred and ref.
+
+ Each pred point is matched to the nearest ref date within
+ ``MATCH_TOLERANCE_DAYS``. Returns a dict or ``None`` if fewer than
+ 2 matched pairs exist.
+ """
+ ref_lookup: dict[str, float] = {
+ p["date"]: p[ref_key] for p in ref if p.get(ref_key) is not None
+ }
+ if not ref_lookup:
+ return None
+
+ ref_dates = sorted(ref_lookup)
+
+ obs, sim = [], []
+ for pt in pred:
+ v = pt.get(pred_key)
+ if v is None:
+ continue
+ nearest = min(
+ ref_dates,
+ key=lambda d: abs(
+ (np.datetime64(pt["date"]) - np.datetime64(d)) / np.timedelta64(1, "D")
+ ),
+ )
+ gap = abs(
+ (np.datetime64(pt["date"]) - np.datetime64(nearest))
+ / np.timedelta64(1, "D")
+ )
+ if gap <= MATCH_TOLERANCE_DAYS and nearest in ref_lookup:
+ obs.append(ref_lookup[nearest])
+ sim.append(v)
+
+ if len(obs) < 2:
+ return None
+
+ obs_arr = np.array(obs)
+ sim_arr = np.array(sim)
+ obs_mean = obs_arr.mean()
+
+ rmse = float(np.sqrt(np.mean((sim_arr - obs_arr) ** 2)))
+ nrmse = rmse / obs_mean if obs_mean else None
+ ss_res = float(np.sum((obs_arr - sim_arr) ** 2))
+ ss_tot = float(np.sum((obs_arr - obs_mean) ** 2))
+ nse = (1.0 - ss_res / ss_tot) if ss_tot else None
+ r, _ = pearsonr(obs_arr, sim_arr)
+
+ def _r4(v: float | None) -> float | None:
+ return round(v, 4) if v is not None else None
+
+ return {
+ "n": len(obs),
+ "rmse": _r4(rmse),
+ "nrmse": _r4(nrmse),
+ "nse": _r4(nse),
+ "r": _r4(float(r)),
+ }
+
+
+S2_BAND_NAMES = ["B02", "B03", "B04"]
+S3_BAND_NAMES = ["Oa04", "Oa06", "Oa08", "Oa17"]
+
+
+def _read_multiband_center(
+ path: Path, lat: float, lon: float, band_names: list[str]
+) -> dict[str, float | None]:
+ """Return 3×3 mean per band at (lat, lon). Keys are ``band_names``, values float or None."""
+ try:
+ with rasterio.open(path) as src:
+ transformer = Transformer.from_crs(
+ CRS.from_epsg(4326), src.crs, always_xy=True
+ )
+ x, y = transformer.transform(lon, lat)
+ row, col = rowcol(src.transform, x, y)
+ h, w = src.height, src.width
+ r0, r1 = max(0, row - 1), min(h, row + 2)
+ c0, c1 = max(0, col - 1), min(w, col + 2)
+ window = rasterio.windows.Window(c0, r0, c1 - c0, r1 - r0)
+ nodata = src.nodata
+ result = {}
+ for i, name in enumerate(band_names, 1):
+ if i > src.count:
+ result[name] = None
+ continue
+ data = src.read(i, window=window).astype(float)
+ if nodata is not None:
+ data = np.where(data == nodata, np.nan, data)
+ data[data == 0] = np.nan
+ val = _window_mean(data)
+ result[name] = None if val is None else round(val, 6)
+ return result
+ except Exception:
+ return {name: None for name in band_names}
+
+
+def _multiband_series(
+ tif_paths: list[Path],
+ date_fn,
+ lat: float,
+ lon: float,
+ band_names: list[str],
+ desc: str,
+) -> list[dict]:
+ """Extract center-pixel values for all bands; return ``[{date, band1, band2, …}]``."""
+ result = []
+ for p in tqdm(tif_paths, desc=desc, leave=False):
+ date = date_fn(p)
+ if date is None:
+ continue
+ vals = _read_multiband_center(p, lat, lon, band_names)
+ if any(v is not None for v in vals.values()):
+ result.append({"date": f"{date[:4]}-{date[4:6]}-{date[6:]}", **vals})
+ return sorted(result, key=lambda x: x["date"])
+
+
+# ---------------------------------------------------------------------------
+# Helpers: spatial heterogeneity + observation density
+# ---------------------------------------------------------------------------
+
+
+def _read_footprint_stats(
+ path: Path, lat: float, lon: float, half_m: float = SPATIAL_CV_HALF_M
+) -> tuple[float, float] | tuple[None, None]:
+ """Return (mean, std) of valid GCC pixels within a ±half_m metre square window.
+
+ Returns ``(None, None)`` on any error or when fewer than 4 valid pixels exist.
+ """
+ try:
+ with rasterio.open(path) as src:
+ transformer = Transformer.from_crs(
+ CRS.from_epsg(4326), src.crs, always_xy=True
+ )
+ x, y = transformer.transform(lon, lat)
+ res = abs(src.transform.a) # pixel size in CRS units (metres for UTM)
+ half_px = max(1, int(round(half_m / res)))
+ row, col = rowcol(src.transform, x, y)
+ h, w = src.height, src.width
+ r0, r1 = max(0, row - half_px), min(h, row + half_px + 1)
+ c0, c1 = max(0, col - half_px), min(w, col + half_px + 1)
+ window = rasterio.windows.Window(c0, r0, c1 - c0, r1 - r0)
+ data = src.read(1, window=window).astype(float)
+ nodata = src.nodata
+ if nodata is not None:
+ data = np.where(data == nodata, np.nan, data)
+ data[data <= 0] = np.nan
+ valid = data[~np.isnan(data)]
+ if len(valid) < 4:
+ return None, None
+ return float(np.mean(valid)), float(np.std(valid))
+ except Exception:
+ return None, None
+
+
+def compute_covariates(
+ s2_gcc_paths: list[Path],
+ s2_series: list[dict],
+ s3_series: list[dict],
+ n_gcc_points: int | None,
+ lat: float,
+ lon: float,
+) -> dict:
+ """Compute spatial heterogeneity and temporal observation density covariates."""
+ # Spatial GCC statistics over ~300 m footprint
+ means, stds = [], []
+ for p in s2_gcc_paths:
+ m, s = _read_footprint_stats(p, lat, lon)
+ if m is not None and m > 0:
+ means.append(m)
+ stds.append(s)
+
+ spatial_gcc_cv = (
+ round(float(np.mean([s / m for s, m in zip(stds, means)])), 4)
+ if means
+ else None
+ )
+ spatial_gcc_std = round(float(np.mean(stds)), 4) if stds else None
+
+ # S2 temporal gap statistics
+ s2_dates = [datetime.date.fromisoformat(p["date"]) for p in s2_series]
+ if len(s2_dates) >= 2:
+ gaps = [(s2_dates[i + 1] - s2_dates[i]).days for i in range(len(s2_dates) - 1)]
+ s2_mean_gap = round(float(np.mean(gaps)), 1)
+ s2_max_gap = int(max(gaps))
+ else:
+ s2_mean_gap = None
+ s2_max_gap = None
+
+ return {
+ "spatial_gcc_cv": spatial_gcc_cv,
+ "spatial_gcc_std": spatial_gcc_std,
+ "s2_scene_count": len(s2_series),
+ "s2_mean_gap_days": s2_mean_gap,
+ "s2_max_gap_days": s2_max_gap,
+ "s3_composite_count": len(s3_series),
+ "n_gcc_points": n_gcc_points,
+ }
+
+
+# ---------------------------------------------------------------------------
+# Per-site export
+# ---------------------------------------------------------------------------
+
+
+def _write_json(path: Path, data: Any) -> None:
+ path.write_text(json.dumps(data, separators=(",", ":")))
+
+
+def _raster_series(
+ tif_paths: list[Path],
+ date_fn,
+ lat: float,
+ lon: float,
+ desc: str,
+) -> list[dict]:
+ """Extract center-pixel GCC from each tif, return ``[{date, gcc}]`` sorted."""
+ result = []
+ for p in tqdm(tif_paths, desc=desc, leave=False):
+ date = date_fn(p)
+ if date is None:
+ continue
+ val = _read_center_pixel(p, lat, lon)
+ if val is not None:
+ result.append({"date": f"{date[:4]}-{date[4:6]}-{date[6:]}", "gcc": val})
+ return sorted(result, key=lambda x: x["date"])
+
+
+def _raster_index(tif_paths: list[Path], date_fn, rel_root: Path) -> list[dict]:
+ """Build raster index: ``[{date, path}]`` sorted by date."""
+ result = []
+ for p in tif_paths:
+ date = date_fn(p)
+ if date is None:
+ continue
+ try:
+ rel = str(p.relative_to(rel_root))
+ except ValueError:
+ rel = str(p)
+ result.append({"date": date, "path": rel})
+ return sorted(result, key=lambda x: x["date"])
+
+
+def export_site(
+ site: str,
+ year: int,
+ lat: float,
+ lon: float,
+ out_dir: Path,
+ n_gcc_points: int | None = None,
+) -> bool:
+ """Export timeseries.json and rasters.json for one site. Returns True on success."""
+ sentinel_base = DATA_DIR / "sentinel_data" / str(year) / site / "prepared"
+ fusion_base = DATA_DIR / "fusion" / str(year) / site
+
+ s2_gcc_dir = sentinel_base / "s2"
+ s3_gcc_dir = sentinel_base / "gcc_s3"
+ bti_gcc_dir = fusion_base / "bti" / "gcc"
+ itb_gcc_dir = fusion_base / "itb" / "fusion"
+
+ # Raster slider sources
+ s2_refl_dir = sentinel_base / "s2"
+ s3_comp_dir = sentinel_base / "s3"
+ bti_refl_dir = fusion_base / "bti" / "fusion"
+
+ has_fusion = bti_gcc_dir.is_dir() and any(bti_gcc_dir.glob("GCC_*.tif"))
+ if not has_fusion:
+ return False
+
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ # --- GCC timeseries from rasters ---
+ s2_gcc_paths = sorted(s2_gcc_dir.glob("*_GCC.tif"))
+ s3_gcc_paths = sorted(s3_gcc_dir.glob("composite_*.tif"))
+ bti_paths = sorted(bti_gcc_dir.glob("GCC_*.tif"))
+ itb_paths = sorted(itb_gcc_dir.glob("GCC_*.tif"))
+
+ s2_series = _raster_series(s2_gcc_paths, _date_from_s2_tif, lat, lon, f"{site} S2")
+ s3_series = _raster_series(s3_gcc_paths, _date_from_gcc_tif, lat, lon, f"{site} S3")
+ bti_series = _raster_series(bti_paths, _date_from_gcc_tif, lat, lon, f"{site} BtI")
+ itb_series = _raster_series(itb_paths, _date_from_gcc_tif, lat, lon, f"{site} ItB")
+
+ # Whittaker on S2
+ s2_vals = [p["gcc"] for p in s2_series]
+ s2_smooth_vals = _whittaker_smooth(s2_vals)
+ s2_whittaker = [
+ {"date": p["date"], "gcc": v}
+ for p, v in zip(s2_series, s2_smooth_vals)
+ if v is not None
+ ]
+
+ # S3 5-day moving average
+ s3_smooth = _moving_average(s3_series, "gcc", S3_SMOOTH_WINDOW)
+
+ # PhenoCam CSV
+ csv_path = DATA_DIR / "phenocam" / str(year) / f"{site}_1day.csv"
+ phenocam_series, image_list = _parse_phenocam_csv(csv_path, year, site)
+
+ s3_smooth_series = [
+ {"date": p["date"], "gcc": p["gcc_smooth"]}
+ for p in s3_smooth
+ if p.get("gcc_smooth") is not None
+ ]
+
+ # Band reflectance timeseries (multi-band center-pixel)
+ bands_s2 = _multiband_series(
+ sorted(s2_refl_dir.glob("*_REFL.tif")),
+ _date_from_s2_tif,
+ lat,
+ lon,
+ S2_BAND_NAMES,
+ f"{site} S2 bands",
+ )
+ bands_s3 = _multiband_series(
+ sorted(s3_comp_dir.glob("composite_*.tif")),
+ _date_from_gcc_tif,
+ lat,
+ lon,
+ S3_BAND_NAMES,
+ f"{site} S3 bands",
+ )
+
+ # --- Per-metric JSON outputs ---
+ _write_json(out_dir / "gcc_phenocam.json", phenocam_series)
+ _write_json(out_dir / "gcc_s2.json", s2_series)
+ _write_json(out_dir / "gcc_s2_whittaker.json", s2_whittaker)
+ _write_json(out_dir / "gcc_s3.json", s3_series)
+ _write_json(out_dir / "gcc_s3_smooth.json", s3_smooth_series)
+ _write_json(out_dir / "gcc_fusion_bti.json", bti_series)
+ _write_json(out_dir / "gcc_fusion_itb.json", itb_series)
+ _write_json(out_dir / "phenocam_images.json", image_list)
+ _write_json(out_dir / "bands_s2.json", bands_s2)
+ _write_json(out_dir / "bands_s3.json", bands_s3)
+
+ # --- Raster index for slider ---
+ rel_root = DATA_DIR.parent # paths relative to project root
+
+ # Valid-pixel sets: only show S2/S3 rasters where the center pixel had
+ # usable data (non-zero GCC). This excludes cloud-masked / snow-covered
+ # scenes that would render as black or visually nonsensical.
+ s2_valid_dates = {p["date"].replace("-", "") for p in s2_series}
+ s3_valid_dates = {p["date"].replace("-", "") for p in s3_series}
+
+ s2_refl = [
+ r
+ for r in _raster_index(
+ sorted(s2_refl_dir.glob("*_REFL.tif")), _date_from_s2_tif, rel_root
+ )
+ if r["date"] in s2_valid_dates
+ ]
+ s3_comp = [
+ r
+ for r in _raster_index(
+ sorted(s3_comp_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root
+ )
+ if r["date"] in s3_valid_dates
+ ]
+ s2_gcc = [
+ r
+ for r in _raster_index(
+ sorted(s2_gcc_dir.glob("*_GCC.tif")), _date_from_s2_tif, rel_root
+ )
+ if r["date"] in s2_valid_dates
+ ]
+ s3_gcc = [
+ r
+ for r in _raster_index(
+ sorted(s3_gcc_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root
+ )
+ if r["date"] in s3_valid_dates
+ ]
+ bti_refl = _raster_index(
+ sorted(bti_refl_dir.glob("REFL_*.tif")), _date_from_gcc_tif, rel_root
+ )
+ itb_gcc = _raster_index(
+ sorted(itb_gcc_dir.glob("GCC_*.tif")), _date_from_gcc_tif, rel_root
+ )
+
+ _write_json(out_dir / "rasters_s2_refl.json", s2_refl)
+ _write_json(out_dir / "rasters_s3_composite.json", s3_comp)
+ _write_json(out_dir / "rasters_s2_gcc.json", s2_gcc)
+ _write_json(out_dir / "rasters_s3_gcc.json", s3_gcc)
+ _write_json(out_dir / "rasters_fusion_bti_refl.json", bti_refl)
+ _write_json(out_dir / "rasters_fusion_itb_gcc.json", itb_gcc)
+
+ # --- Site covariates (heterogeneity + observation density) ---
+ _write_json(
+ out_dir / "covariates.json",
+ compute_covariates(s2_gcc_paths, s2_series, s3_series, n_gcc_points, lat, lon),
+ )
+
+ # --- Validation metrics vs PhenoCam gcc_90 ---
+ _write_json(
+ out_dir / "metrics.json",
+ {
+ "bti": compute_metrics(phenocam_series, "gcc_90", bti_series, "gcc"),
+ "itb": compute_metrics(phenocam_series, "gcc_90", itb_series, "gcc"),
+ "s2_whittaker": compute_metrics(
+ phenocam_series, "gcc_90", s2_whittaker, "gcc"
+ ),
+ "s3_smooth": compute_metrics(
+ phenocam_series, "gcc_90", s3_smooth_series, "gcc"
+ ),
+ "s2": compute_metrics(phenocam_series, "gcc_90", s2_series, "gcc"),
+ "s3": compute_metrics(phenocam_series, "gcc_90", s3_series, "gcc"),
+ },
+ )
+
+ # Remove legacy bundled outputs if present
+ for legacy in ("timeseries.json", "rasters.json"):
+ (out_dir / legacy).unlink(missing_ok=True)
+ return True
+
+
+# ---------------------------------------------------------------------------
+# Manifest
+# ---------------------------------------------------------------------------
+
+VEG_TYPE_LABELS = {
+ "AG": "Agriculture",
+ "DB": "Deciduous broadleaf",
+ "DN": "Deciduous needleleaf",
+ "EB": "Evergreen broadleaf",
+ "EN": "Evergreen needleleaf",
+ "GR": "Grassland",
+ "MX": "Mixed",
+ "SH": "Shrubland",
+ "TN": "Tundra",
+ "UN": "Unknown",
+ "WL": "Wetland",
+ "RF": "Reference",
+}
+
+
+def build_manifest(years: list[int], filter_site: str | None = None) -> dict:
+ manifest: dict[str, Any] = {"years": years, "sites": {}}
+
+ for year in years:
+ screening_path = DATA_DIR / "phenocam_screening" / f"{year}.json"
+ if not screening_path.is_file():
+ continue
+ data = json.loads(screening_path.read_text())
+ sites_meta: dict[str, Any] = {}
+ for entry in data.get("sites", []):
+ if entry.get("calculations", {}).get("status") != "PASS":
+ continue
+ cam = entry.get("response", {}).get("camera", {})
+ roi = entry.get("response", {}).get("roi", {})
+ calc = entry.get("calculations", {})
+ site = cam.get("Sitename", "")
+ if not site:
+ continue
+ if filter_site and site != filter_site:
+ continue
+ sm = cam.get("sitemetadata", {})
+ veg_raw = sm.get("primary_veg_type") or roi.get("roitype") or "UN"
+ fusion_dir = DATA_DIR / "fusion" / str(year) / site / "bti" / "gcc"
+ has_fusion = fusion_dir.is_dir() and any(fusion_dir.glob("GCC_*.tif"))
+ sites_meta[site] = {
+ "lat": cam.get("Lat"),
+ "lon": cam.get("Lon"),
+ "veg_type": veg_raw,
+ "veg_label": VEG_TYPE_LABELS.get(veg_raw, veg_raw),
+ "description": sm.get("site_description", ""),
+ "dominant_species": sm.get("dominant_species", ""),
+ "group": sm.get("group", ""),
+ "snr": calc.get("snr"),
+ "n_gcc_points": calc.get("n_gcc_points"),
+ "has_fusion": has_fusion,
+ }
+ manifest["sites"][str(year)] = sites_meta
+
+ return manifest
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+ parser.add_argument("--site", type=str, default=None)
+ args = parser.parse_args()
+
+ year = args.evaluation_year
+ filter_site = args.site
+
+ out_base = DATA_DIR / "metrics"
+ out_base.mkdir(parents=True, exist_ok=True)
+
+ # Determine years with screening data
+ screening_dir = DATA_DIR / "phenocam_screening"
+ years = sorted(
+ int(p.stem) for p in screening_dir.glob("*.json") if p.stem.isdigit()
+ )
+ if not years:
+ years = [year]
+
+ print(f"Building manifest for years: {years}")
+ manifest = build_manifest(years, filter_site)
+
+ # Export per-site data for the requested year
+ year_sites = manifest["sites"].get(str(year), {})
+ fusion_sites = {s: m for s, m in year_sites.items() if m["has_fusion"]}
+
+ print(f"Exporting {len(fusion_sites)} site(s) with fusion data for {year}")
+ for site, meta in tqdm(fusion_sites.items(), desc="Sites"):
+ out_dir = out_base / str(year) / site
+ ok = export_site(
+ site, year, meta["lat"], meta["lon"], out_dir, meta.get("n_gcc_points")
+ )
+ if ok:
+ print(f" ✓ {site}")
+ else:
+ print(f" ✗ {site} — no fusion data found")
+
+ manifest_path = out_base / "manifest.json"
+ if filter_site and manifest_path.is_file():
+ # Merge: update only the filtered site(s); preserve all other entries.
+ existing: dict = json.loads(manifest_path.read_text())
+ for year_key, year_sites_new in manifest["sites"].items():
+ existing.setdefault("sites", {}).setdefault(year_key, {}).update(
+ year_sites_new
+ )
+ all_years = sorted(set(existing.get("years", [])) | set(manifest["years"]))
+ existing["years"] = all_years
+ manifest_path.write_text(json.dumps(existing, separators=(",", ":")))
+ else:
+ manifest_path.write_text(json.dumps(manifest, separators=(",", ":")))
+ print(f"Manifest written → {manifest_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/6-statistics-fusion-order.py b/6-statistics-fusion-order.py
new file mode 100644
index 0000000..0ec0541
--- /dev/null
+++ b/6-statistics-fusion-order.py
@@ -0,0 +1,257 @@
+"""Step 6: Paired ItB-vs-BtI significance test across the full sample.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``metrics/{year}/{site}/metrics.json`` — per-site validation metrics (Step 5)
+
+Outputs (``data/statistics_fusion_order/``):
+
+- ``{year}.json`` — paired Wilcoxon + t-test summary for NSE, RMSE, nRMSE, r;
+ includes ``dropped_sites`` (union) and per-metric ``dropped_sites`` lists
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--alpha`` (default 0.05) — significance threshold for ``better_order``
+
+This step aggregates across all sites with Step 5 output; it does not accept
+``--site`` (a single-site filter would not support a sample-level test).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from scipy.stats import ttest_rel, wilcoxon
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+DEFAULT_ALPHA = 0.05
+
+METRICS = ["nse", "rmse", "nrmse", "r"]
+LOWER_IS_BETTER = {"rmse", "nrmse"}
+
+MIN_PAIRS_WARNING = 6
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _r4(v: float | None) -> float | None:
+ return round(v, 4) if v is not None else None
+
+
+def _load_site_metrics(year: int) -> list[tuple[str, dict[str, Any]]]:
+ """Return ``(sitename, metrics.json payload)`` for every site under ``{year}``."""
+ metrics_dir = DATA_DIR / "metrics" / str(year)
+ if not metrics_dir.is_dir():
+ return []
+
+ payloads: list[tuple[str, dict[str, Any]]] = []
+ for site_dir in sorted(metrics_dir.iterdir()):
+ if not site_dir.is_dir():
+ continue
+ path = site_dir / "metrics.json"
+ if not path.is_file():
+ continue
+ payloads.append((site_dir.name, json.loads(path.read_text())))
+ return payloads
+
+
+def collect_pairs(
+ site_metrics: list[tuple[str, dict[str, Any]]], metric: str
+) -> tuple[list[float], list[float], list[str]]:
+ """Return paired BtI / ItB values for ``metric`` and dropped site names."""
+ bti_vals: list[float] = []
+ itb_vals: list[float] = []
+ dropped_sites: list[str] = []
+
+ for site, payload in site_metrics:
+ bti = payload.get("bti")
+ itb = payload.get("itb")
+ if not isinstance(bti, dict) or not isinstance(itb, dict):
+ dropped_sites.append(site)
+ continue
+
+ bti_v = bti.get(metric)
+ itb_v = itb.get(metric)
+ if bti_v is None or itb_v is None:
+ dropped_sites.append(site)
+ continue
+
+ bti_vals.append(float(bti_v))
+ itb_vals.append(float(itb_v))
+
+ return bti_vals, itb_vals, dropped_sites
+
+
+def _better_order(
+ bti_vals: list[float],
+ itb_vals: list[float],
+ metric: str,
+ p_value: float | None,
+ alpha: float,
+) -> str:
+ """Name the better fusion order when Wilcoxon p < alpha, else no difference."""
+ if p_value is None or p_value >= alpha:
+ return "no significant difference"
+
+ mean_diff = float(np.mean(itb_vals) - np.mean(bti_vals))
+ if metric in LOWER_IS_BETTER:
+ return "itb" if mean_diff < 0 else "bti"
+ return "itb" if mean_diff > 0 else "bti"
+
+
+def paired_test(
+ bti_vals: list[float],
+ itb_vals: list[float],
+ metric: str,
+ alpha: float,
+) -> dict[str, Any]:
+ """Run paired Wilcoxon (primary) and t-test; return summary dict."""
+ n_pairs = len(bti_vals)
+ bti_arr = np.array(bti_vals, dtype=float)
+ itb_arr = np.array(itb_vals, dtype=float)
+ diffs = itb_arr - bti_arr
+
+ result: dict[str, Any] = {
+ "n_pairs": n_pairs,
+ "bti_mean": _r4(float(bti_arr.mean())) if n_pairs else None,
+ "bti_median": _r4(float(np.median(bti_arr))) if n_pairs else None,
+ "itb_mean": _r4(float(itb_arr.mean())) if n_pairs else None,
+ "itb_median": _r4(float(np.median(itb_arr))) if n_pairs else None,
+ "mean_diff": _r4(float(diffs.mean())) if n_pairs else None,
+ "median_diff": _r4(float(np.median(diffs))) if n_pairs else None,
+ "wilcoxon": {"statistic": None, "p_value": None},
+ "ttest": {"statistic": None, "p_value": None},
+ "better_order": "insufficient data",
+ }
+
+ if n_pairs < 2:
+ return result
+
+ wilcoxon_stat: float | None = None
+ wilcoxon_p: float | None = None
+ if np.any(diffs != 0):
+ try:
+ w_stat, w_p = wilcoxon(itb_arr, bti_arr)
+ wilcoxon_stat = float(w_stat)
+ wilcoxon_p = float(w_p)
+ except ValueError:
+ pass
+
+ t_stat, t_p = ttest_rel(itb_arr, bti_arr)
+
+ result["wilcoxon"] = {
+ "statistic": _r4(wilcoxon_stat),
+ "p_value": _r4(wilcoxon_p),
+ }
+ result["ttest"] = {
+ "statistic": _r4(float(t_stat)),
+ "p_value": _r4(float(t_p)),
+ }
+ result["better_order"] = _better_order(
+ bti_vals, itb_vals, metric, wilcoxon_p, alpha
+ )
+ return result
+
+
+def _print_summary(
+ year: int, alpha: float, n_sites_total: int, metrics_out: dict
+) -> None:
+ print(f"\nPaired ItB vs BtI test — {year} (alpha={alpha}, sites={n_sites_total})")
+ print(
+ f"{'metric':<8} {'n':>4} {'BtI mean':>10} {'ItB mean':>10} "
+ f"{'diff':>10} {'W p':>8} {'t p':>8} better"
+ )
+ print("-" * 78)
+ for metric in METRICS:
+ m = metrics_out[metric]
+ bti_mean = m["bti_mean"]
+ itb_mean = m["itb_mean"]
+ mean_diff = m["mean_diff"]
+ w_p = m["wilcoxon"]["p_value"]
+ t_p = m["ttest"]["p_value"]
+ better = m["better_order"]
+
+ def _fmt(v: float | None) -> str:
+ return f"{v:10.4f}" if v is not None else f"{'—':>10}"
+
+ print(
+ f"{metric:<8} {m['n_pairs']:>4} {_fmt(bti_mean)} {_fmt(itb_mean)} "
+ f"{_fmt(mean_diff)} "
+ f"{w_p if w_p is not None else '—':>8} "
+ f"{t_p if t_p is not None else '—':>8} {better}"
+ )
+ if 0 < m["n_pairs"] < MIN_PAIRS_WARNING:
+ print(
+ f" warning: only {m['n_pairs']} pair(s) for {metric}; "
+ "interpret p-values cautiously"
+ )
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+ parser.add_argument(
+ "--alpha",
+ type=float,
+ default=DEFAULT_ALPHA,
+ help="Significance threshold for better_order (default 0.05)",
+ )
+ args = parser.parse_args()
+
+ year = args.evaluation_year
+ alpha = args.alpha
+
+ site_metrics = _load_site_metrics(year)
+ n_sites_total = len(site_metrics)
+ if n_sites_total == 0:
+ raise SystemExit(
+ f"No Step 5 metrics found under {DATA_DIR / 'metrics' / str(year)}"
+ )
+
+ metrics_out: dict[str, Any] = {}
+ all_dropped: set[str] = set()
+ for metric in METRICS:
+ bti_vals, itb_vals, dropped_sites = collect_pairs(site_metrics, metric)
+ summary = paired_test(bti_vals, itb_vals, metric, alpha)
+ summary["n_dropped"] = len(dropped_sites)
+ summary["dropped_sites"] = dropped_sites
+ all_dropped.update(dropped_sites)
+ metrics_out[metric] = summary
+
+ payload = {
+ "year": year,
+ "alpha": alpha,
+ "n_sites_total": n_sites_total,
+ "dropped_sites": sorted(all_dropped),
+ "metrics": metrics_out,
+ }
+
+ out_dir = DATA_DIR / "statistics_fusion_order"
+ out_dir.mkdir(parents=True, exist_ok=True)
+ out_path = out_dir / f"{year}.json"
+ out_path.write_text(json.dumps(payload, separators=(",", ":")))
+
+ _print_summary(year, alpha, n_sites_total, metrics_out)
+ print(f"\nWritten → {out_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/7-gcc-suitability.py b/7-gcc-suitability.py
new file mode 100644
index 0000000..32cc4e5
--- /dev/null
+++ b/7-gcc-suitability.py
@@ -0,0 +1,742 @@
+"""Step 7: PhenoCam GCC suitability as a fusion-accuracy reference.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``metrics/{year}/{site}/gcc_s2.json``, ``gcc_phenocam.json`` — Step 5 timeseries
+- ``metrics/manifest.json`` — site lat/lon
+- ``sentinel_data/{year}/{site}/prepared/s2/`` — S2 REFL/GCC/DIST_CLOUD (Steps 3–4)
+- ``sentinel_data/{year}/{site}/prepared/gcc_s3/``, ``prepared/s3_rgb/`` — Step 4
+
+Outputs (``data/gcc_suitability/``):
+
+- ``{year}.json`` — representativeness (Line A), LOOCV concordance (Line B),
+ per-site and aggregate suitability verdict
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--min-cloudfree-s2`` (default 10) — minimum cloud-free S2 dates for LOOCV
+- ``--alpha`` (default 0.05) — reserved for future significance tests
+
+Full-sample aggregate; does not accept ``--site``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import shutil
+import tempfile
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import rasterio
+from pyproj import Transformer
+from rasterio.crs import CRS
+from rasterio.transform import rowcol
+from scipy.stats import linregress, pearsonr, spearmanr
+from tqdm import tqdm
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+DEFAULT_ALPHA = 0.05
+MIN_CLOUDFREE_S2 = 10
+REPR_R_THRESHOLD = 0.7
+MATCH_TOLERANCE_DAYS = 5
+
+RESOLUTION_RATIO = 30
+MAX_DAYS = 100
+MINIMUM_ACQUISITION_IMPORTANCE = 0
+
+SMALL_SAMPLE_SITES = 6
+
+
+# ---------------------------------------------------------------------------
+# efast import
+# ---------------------------------------------------------------------------
+
+
+def _import_efast():
+ try:
+ import efast.efast as efast_module
+
+ return efast_module
+ except ImportError as exc:
+ raise ImportError("efast not found. Install with: uv sync") from exc
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _r4(v: float | None) -> float | None:
+ return round(v, 4) if v is not None else None
+
+
+def _window_mean(data: np.ndarray) -> float | None:
+ valid = data[~np.isnan(data)]
+ if valid.size == 0:
+ return None
+ return float(np.mean(valid))
+
+
+def _read_center_pixel(path: Path, lat: float, lon: float) -> float | None:
+ try:
+ with rasterio.open(path) as src:
+ transformer = Transformer.from_crs(
+ CRS.from_epsg(4326), src.crs, always_xy=True
+ )
+ x, y = transformer.transform(lon, lat)
+ row, col = rowcol(src.transform, x, y)
+ h, w = src.height, src.width
+ r0, r1 = max(0, row - 1), min(h, row + 2)
+ c0, c1 = max(0, col - 1), min(w, col + 2)
+ window = rasterio.windows.Window(c0, r0, c1 - c0, r1 - r0)
+ data = src.read(1, window=window).astype(float)
+ nodata = src.nodata
+ if nodata is not None:
+ data = np.where(data == nodata, np.nan, data)
+ data[data == 0] = np.nan
+ return _window_mean(data)
+ except Exception:
+ return None
+
+
+def _date_from_s2_tif(path: Path) -> str | None:
+ parts = path.stem.split("_")
+ if len(parts) >= 3:
+ m = re.match(r"(\d{8})", parts[2])
+ return m.group(1) if m else None
+ return None
+
+
+def _iso_to_yyyymmdd(iso: str) -> str:
+ return iso.replace("-", "")
+
+
+def _yyyymmdd_to_iso(d: str) -> str:
+ return f"{d[:4]}-{d[4:6]}-{d[6:]}"
+
+
+def _day_gap(a: str, b: str) -> float:
+ return abs((np.datetime64(a) - np.datetime64(b)) / np.timedelta64(1, "D"))
+
+
+def _match_series(
+ ref: list[dict],
+ ref_key: str,
+ pred: list[dict],
+ pred_key: str,
+ tolerance_days: int = MATCH_TOLERANCE_DAYS,
+) -> tuple[list[float], list[float], list[str]]:
+ """Return paired (ref_vals, pred_vals, ref_dates) within tolerance."""
+ ref_lookup: dict[str, float] = {
+ p["date"]: p[ref_key] for p in ref if p.get(ref_key) is not None
+ }
+ if not ref_lookup:
+ return [], [], []
+
+ ref_dates = sorted(ref_lookup)
+ obs, sim, matched_dates = [], [], []
+ for pt in pred:
+ v = pt.get(pred_key)
+ if v is None:
+ continue
+ nearest = min(ref_dates, key=lambda d: _day_gap(pt["date"], d))
+ if _day_gap(pt["date"], nearest) <= tolerance_days:
+ obs.append(ref_lookup[nearest])
+ sim.append(v)
+ matched_dates.append(nearest)
+ return obs, sim, matched_dates
+
+
+def _accuracy_metrics(obs: list[float], sim: list[float]) -> dict[str, Any] | None:
+ if len(obs) < 2:
+ return None
+ obs_arr = np.array(obs, dtype=float)
+ sim_arr = np.array(sim, dtype=float)
+ diff = sim_arr - obs_arr
+ rmse = float(np.sqrt(np.mean(diff**2)))
+ mae = float(np.mean(np.abs(diff)))
+ bias = float(np.mean(diff))
+ r, _ = pearsonr(obs_arr, sim_arr)
+ return {
+ "n": len(obs),
+ "rmse": _r4(rmse),
+ "mae": _r4(mae),
+ "bias": _r4(bias),
+ "r": _r4(float(r)),
+ }
+
+
+def _gcc_from_refl_file(refl_path: Path, gcc_path: Path) -> None:
+ with rasterio.open(refl_path) as src:
+ b, g, r = src.read(1), src.read(2), src.read(3)
+ profile = src.profile
+ total = b + g + r
+ invalid = (b < 0) | (g < 0) | (r < 0)
+ gcc = np.where(invalid, np.nan, g / (total + 1e-10))
+ gcc[total == 0] = np.nan
+ profile.update(count=1, dtype="float32")
+ with rasterio.open(gcc_path, "w", **profile) as dst:
+ dst.write(gcc[np.newaxis].astype("float32"))
+
+
+def _load_json_series(path: Path) -> list[dict]:
+ if not path.is_file():
+ return []
+ return json.loads(path.read_text())
+
+
+def _load_site_coords(year: int) -> dict[str, tuple[float, float]]:
+ manifest_path = DATA_DIR / "metrics" / "manifest.json"
+ if not manifest_path.is_file():
+ return {}
+ manifest = json.loads(manifest_path.read_text())
+ sites = manifest.get("sites", {}).get(str(year), {})
+ coords: dict[str, tuple[float, float]] = {}
+ for site, meta in sites.items():
+ lat, lon = meta.get("lat"), meta.get("lon")
+ if lat is not None and lon is not None:
+ coords[site] = (float(lat), float(lon))
+ return coords
+
+
+def _discover_sites(year: int) -> list[str]:
+ metrics_dir = DATA_DIR / "metrics" / str(year)
+ if not metrics_dir.is_dir():
+ return []
+ return sorted(
+ d.name
+ for d in metrics_dir.iterdir()
+ if d.is_dir() and (d / "gcc_s2.json").is_file()
+ )
+
+
+def _build_hr_symlink_dir(s2_dir: Path, holdout_yyyymmdd: str, dest: Path) -> None:
+ """Symlink all S2 hr inputs except the held-out acquisition date."""
+ dest.mkdir(parents=True, exist_ok=True)
+ for pattern in ("*_REFL.tif", "*_GCC.tif", "*_DIST_CLOUD.tif"):
+ for src in sorted(s2_dir.glob(pattern)):
+ date_token = (
+ src.stem.split("_")[2][:8] if len(src.stem.split("_")) >= 3 else ""
+ )
+ if date_token == holdout_yyyymmdd:
+ continue
+ link = dest / src.name
+ if link.exists() or link.is_symlink():
+ link.unlink()
+ link.symlink_to(src.resolve())
+
+
+# ---------------------------------------------------------------------------
+# Line A — representativeness
+# ---------------------------------------------------------------------------
+
+
+def compute_representativeness(phenocam: list[dict], s2: list[dict]) -> dict[str, Any]:
+ """PhenoCam gcc_90 vs co-located observed S2 GCC."""
+ obs, sim, _ = _match_series(phenocam, "gcc_90", s2, "gcc")
+ result: dict[str, Any] = {
+ "n": len(obs),
+ "r": None,
+ "spearman": None,
+ "slope": None,
+ "intercept": None,
+ "rmse": None,
+ "bias": None,
+ "peak_offset_days": None,
+ "representative": False,
+ }
+ if len(obs) < 2:
+ return result
+
+ obs_arr = np.array(obs, dtype=float)
+ sim_arr = np.array(sim, dtype=float)
+ r, _ = pearsonr(obs_arr, sim_arr)
+ sp, _ = spearmanr(obs_arr, sim_arr)
+ reg = linregress(sim_arr, obs_arr)
+ diff = sim_arr - obs_arr
+ result.update(
+ {
+ "r": _r4(float(r)),
+ "spearman": _r4(float(sp)),
+ "slope": _r4(float(reg.slope)),
+ "intercept": _r4(float(reg.intercept)),
+ "rmse": _r4(float(np.sqrt(np.mean(diff**2)))),
+ "bias": _r4(float(np.mean(diff))),
+ "representative": float(r) >= REPR_R_THRESHOLD,
+ }
+ )
+
+ pc_dates = [p["date"] for p in phenocam if p.get("gcc_90") is not None]
+ s2_dates = [p["date"] for p in s2 if p.get("gcc") is not None]
+ if pc_dates and s2_dates:
+ pc_peak = max(
+ phenocam,
+ key=lambda p: p["gcc_90"] if p.get("gcc_90") is not None else -1,
+ )["date"]
+ s2_peak = max(s2, key=lambda p: p["gcc"] if p.get("gcc") is not None else -1)[
+ "date"
+ ]
+ result["peak_offset_days"] = int(_day_gap(pc_peak, s2_peak))
+
+ return result
+
+
+# ---------------------------------------------------------------------------
+# Line B — LOOCV
+# ---------------------------------------------------------------------------
+
+
+def _phenocam_lookup(phenocam: list[dict]) -> dict[str, float]:
+ return {p["date"]: p["gcc_90"] for p in phenocam if p.get("gcc_90") is not None}
+
+
+def _nearest_phenocam(iso_date: str, lookup: dict[str, float]) -> float | None:
+ if not lookup:
+ return None
+ dates = sorted(lookup)
+ nearest = min(dates, key=lambda d: _day_gap(iso_date, d))
+ if _day_gap(iso_date, nearest) <= MATCH_TOLERANCE_DAYS:
+ return lookup[nearest]
+ return None
+
+
+def run_loocv_site(
+ site: str,
+ year: int,
+ lat: float,
+ lon: float,
+ s2_series: list[dict],
+ phenocam: list[dict],
+ efast,
+) -> list[dict[str, Any]]:
+ """Leave-one-out EFAST for each cloud-free S2 date; return per-date records."""
+ s2_dir = DATA_DIR / "sentinel_data" / str(year) / site / "prepared" / "s2"
+ gcc_s3_dir = DATA_DIR / "sentinel_data" / str(year) / site / "prepared" / "gcc_s3"
+ s3_rgb_dir = DATA_DIR / "sentinel_data" / str(year) / site / "prepared" / "s3_rgb"
+
+ pc_lookup = _phenocam_lookup(phenocam)
+ s2_truth = {p["date"]: p["gcc"] for p in s2_series}
+ fusion_kwargs = dict(
+ ratio=RESOLUTION_RATIO,
+ max_days=MAX_DAYS,
+ minimum_acquisition_importance=MINIMUM_ACQUISITION_IMPORTANCE,
+ )
+
+ records: list[dict[str, Any]] = []
+ dates = [p["date"] for p in s2_series]
+
+ with tempfile.TemporaryDirectory(prefix=f"loocv_{site}_") as tmp_root:
+ tmp = Path(tmp_root)
+ hr_dir = tmp / "hr"
+ itb_out = tmp / "itb"
+ bti_out = tmp / "bti"
+ bti_gcc = tmp / "bti_gcc"
+ itb_out.mkdir()
+ bti_out.mkdir()
+ bti_gcc.mkdir()
+
+ for iso_date in tqdm(dates, desc=f"{site} LOOCV", leave=False):
+ yyyymmdd = _iso_to_yyyymmdd(iso_date)
+ truth = s2_truth.get(iso_date)
+ if truth is None:
+ continue
+
+ if hr_dir.exists():
+ shutil.rmtree(hr_dir)
+ _build_hr_symlink_dir(s2_dir, yyyymmdd, hr_dir)
+
+ pred_date = datetime.strptime(yyyymmdd, "%Y%m%d")
+
+ for f in itb_out.glob("*.tif"):
+ f.unlink()
+ for f in bti_out.glob("*.tif"):
+ f.unlink()
+ for f in bti_gcc.glob("*.tif"):
+ f.unlink()
+
+ efast.fusion(
+ pred_date,
+ gcc_s3_dir,
+ hr_dir,
+ itb_out,
+ product="GCC",
+ **fusion_kwargs,
+ )
+ efast.fusion(
+ pred_date,
+ s3_rgb_dir,
+ hr_dir,
+ bti_out,
+ product="REFL",
+ **fusion_kwargs,
+ )
+
+ itb_path = itb_out / f"GCC_{yyyymmdd}.tif"
+ refl_path = bti_out / f"REFL_{yyyymmdd}.tif"
+ bti_path = bti_gcc / f"GCC_{yyyymmdd}.tif"
+
+ pred_itb = (
+ _read_center_pixel(itb_path, lat, lon) if itb_path.is_file() else None
+ )
+ pred_bti = None
+ if refl_path.is_file():
+ _gcc_from_refl_file(refl_path, bti_path)
+ if bti_path.is_file():
+ pred_bti = _read_center_pixel(bti_path, lat, lon)
+
+ pc_val = _nearest_phenocam(iso_date, pc_lookup)
+
+ records.append(
+ {
+ "date": iso_date,
+ "s2_truth": truth,
+ "pred_bti": pred_bti,
+ "pred_itb": pred_itb,
+ "phenocam": pc_val,
+ }
+ )
+
+ return records
+
+
+def _method_accuracy(records: list[dict], pred_key: str, ref_key: str) -> dict | None:
+ obs, sim = [], []
+ for rec in records:
+ pred = rec.get(pred_key)
+ ref = rec.get(ref_key)
+ if pred is None or ref is None:
+ continue
+ obs.append(ref)
+ sim.append(pred)
+ return _accuracy_metrics(obs, sim)
+
+
+def _winner(rmse_bti: float | None, rmse_itb: float | None) -> str | None:
+ if rmse_bti is None or rmse_itb is None:
+ return None
+ if rmse_bti < rmse_itb:
+ return "bti"
+ if rmse_itb < rmse_bti:
+ return "itb"
+ return "tie"
+
+
+def summarize_loocv(records: list[dict]) -> dict[str, Any]:
+ bti_vs_s2 = _method_accuracy(records, "pred_bti", "s2_truth")
+ itb_vs_s2 = _method_accuracy(records, "pred_itb", "s2_truth")
+ bti_vs_pc = _method_accuracy(records, "pred_bti", "phenocam")
+ itb_vs_pc = _method_accuracy(records, "pred_itb", "phenocam")
+
+ winner_s2 = _winner(
+ bti_vs_s2["rmse"] if bti_vs_s2 else None,
+ itb_vs_s2["rmse"] if itb_vs_s2 else None,
+ )
+ winner_pc = _winner(
+ bti_vs_pc["rmse"] if bti_vs_pc else None,
+ itb_vs_pc["rmse"] if itb_vs_pc else None,
+ )
+ agreement = (
+ winner_s2 == winner_pc
+ if winner_s2 and winner_pc and winner_s2 != "tie" and winner_pc != "tie"
+ else None
+ )
+
+ return {
+ "n_dates": len(records),
+ "bti": {"vs_s2": bti_vs_s2, "vs_phenocam": bti_vs_pc},
+ "itb": {"vs_s2": itb_vs_s2, "vs_phenocam": itb_vs_pc},
+ "winner_s2": winner_s2,
+ "winner_phenocam": winner_pc,
+ "winner_agreement": agreement,
+ }
+
+
+# ---------------------------------------------------------------------------
+# Aggregate concordance
+# ---------------------------------------------------------------------------
+
+
+def _pooled_concordance(
+ all_records: list[dict[str, Any]],
+) -> dict[str, Any]:
+ """Pooled metrics across all held-out dates."""
+ residual_pairs: list[tuple[float, float]] = []
+ vec_s2: list[float] = []
+ vec_pc: list[float] = []
+
+ for site_data in all_records:
+ for rec in site_data.get("records", []):
+ truth = rec.get("s2_truth")
+ pc = rec.get("phenocam")
+ for key in ("pred_bti", "pred_itb"):
+ pred = rec.get(key)
+ if pred is None or truth is None:
+ continue
+ err_s2 = abs(pred - truth)
+ if pc is not None:
+ err_pc = abs(pred - pc)
+ vec_s2.append(err_s2)
+ vec_pc.append(err_pc)
+ residual_pairs.append((err_s2, err_pc))
+
+ pooled_spearman = None
+ if len(vec_s2) >= 3:
+ sp, _ = spearmanr(vec_s2, vec_pc)
+ if not np.isnan(sp):
+ pooled_spearman = _r4(float(sp))
+
+ residual_corr = None
+ if len(residual_pairs) >= 3:
+ xs = np.array([p[0] for p in residual_pairs])
+ ys = np.array([p[1] for p in residual_pairs])
+ rc, _ = pearsonr(xs, ys)
+ residual_corr = _r4(float(rc))
+
+ agreements = [
+ s.get("winner_agreement")
+ for s in all_records
+ if s.get("eligible") and s.get("winner_agreement") is not None
+ ]
+ winner_agreement_rate = (
+ _r4(sum(1 for a in agreements if a) / len(agreements)) if agreements else None
+ )
+
+ n_loocv_dates = sum(len(s.get("records", [])) for s in all_records)
+
+ return {
+ "pooled_spearman": pooled_spearman,
+ "residual_corr": residual_corr,
+ "winner_agreement_rate": winner_agreement_rate,
+ "n_loocv_dates": n_loocv_dates,
+ }
+
+
+def _suitability_verdict(
+ n_repr_pass: int,
+ n_eligible: int,
+ n_total: int,
+ pooled: dict[str, Any],
+) -> str:
+ if n_eligible == 0:
+ return "insufficient data"
+ repr_rate = n_repr_pass / n_total if n_total else 0
+ agree = pooled.get("winner_agreement_rate")
+ sp = pooled.get("pooled_spearman")
+ rc = pooled.get("residual_corr")
+
+ strong = 0
+ if repr_rate >= 0.6:
+ strong += 1
+ if agree is not None and agree >= 0.7:
+ strong += 1
+ if sp is not None and sp >= 0.8:
+ strong += 1
+ if rc is not None and rc >= 0.5:
+ strong += 1
+
+ if strong >= 3:
+ return "suitable"
+ if strong >= 1 or repr_rate >= 0.4:
+ return "partially suitable"
+ return "not suitable"
+
+
+# ---------------------------------------------------------------------------
+# Per-site processing
+# ---------------------------------------------------------------------------
+
+
+def process_site(
+ site: str,
+ year: int,
+ lat: float,
+ lon: float,
+ min_cloudfree: int,
+ efast,
+) -> dict[str, Any]:
+ metrics_dir = DATA_DIR / "metrics" / str(year) / site
+ phenocam = _load_json_series(metrics_dir / "gcc_phenocam.json")
+ s2_series = _load_json_series(metrics_dir / "gcc_s2.json")
+
+ repr_metrics = compute_representativeness(phenocam, s2_series)
+ n_cloudfree = len(s2_series)
+ eligible = n_cloudfree >= min_cloudfree
+
+ result: dict[str, Any] = {
+ "eligible": eligible,
+ "n_cloudfree_s2": n_cloudfree,
+ "representativeness": repr_metrics,
+ "loocv": None,
+ "winner_s2": None,
+ "winner_phenocam": None,
+ "winner_agreement": None,
+ "records": [],
+ }
+
+ if not eligible:
+ return result
+
+ records = run_loocv_site(site, year, lat, lon, s2_series, phenocam, efast)
+ loocv = summarize_loocv(records)
+ result["loocv"] = loocv
+ result["winner_s2"] = loocv["winner_s2"]
+ result["winner_phenocam"] = loocv["winner_phenocam"]
+ result["winner_agreement"] = loocv["winner_agreement"]
+ result["records"] = records
+ return result
+
+
+# ---------------------------------------------------------------------------
+# Output / summary
+# ---------------------------------------------------------------------------
+
+
+def _compact_site_payload(site_result: dict[str, Any]) -> dict[str, Any]:
+ """Drop raw LOOCV records from JSON output (keep summaries only)."""
+ out = {
+ "eligible": site_result["eligible"],
+ "n_cloudfree_s2": site_result["n_cloudfree_s2"],
+ "representativeness": site_result["representativeness"],
+ "winner_s2": site_result.get("winner_s2"),
+ "winner_phenocam": site_result.get("winner_phenocam"),
+ "winner_agreement": site_result.get("winner_agreement"),
+ }
+ if site_result.get("loocv"):
+ out["loocv"] = site_result["loocv"]
+ return out
+
+
+def _print_summary(payload: dict[str, Any]) -> None:
+ year = payload["year"]
+ agg = payload["aggregate"]
+ print(
+ f"\nPhenoCam GCC suitability — {year} "
+ f"({payload['n_sites_total']} site(s), "
+ f"{payload['n_sites_eligible']} LOOCV-eligible, "
+ f"{payload['n_sites_repr_pass']} representative)"
+ )
+ print(f"Verdict: {agg['suitability_verdict']}")
+ print(
+ f" pooled Spearman (method errors): {agg.get('pooled_spearman', '—')} "
+ f"residual corr: {agg.get('residual_corr', '—')} "
+ f"winner agreement: {agg.get('winner_agreement_rate', '—')} "
+ f"LOOCV dates: {agg.get('n_loocv_dates', '—')}"
+ )
+ print(f"\n{'site':<28} {'repr r':>8} {'pass':>5} {'LOOCV n':>8} {'win agree':>10}")
+ print("-" * 65)
+ for site, data in sorted(payload["sites"].items()):
+ rep = data["representativeness"]
+ loocv_n = data.get("loocv", {}).get("n_dates") if data.get("loocv") else "—"
+ agree = data.get("winner_agreement")
+ agree_s = "yes" if agree else ("no" if agree is False else "—")
+ pass_s = "yes" if rep.get("representative") else "no"
+ print(
+ f"{site:<28} {rep.get('r') or '—':>8} {pass_s:>5} "
+ f"{loocv_n!s:>8} {agree_s:>10}"
+ )
+ if payload["n_sites_total"] < SMALL_SAMPLE_SITES:
+ print(
+ f"\nNote: only {payload['n_sites_total']} site(s); "
+ "interpret cross-site aggregates cautiously."
+ )
+ if payload.get("dropped_sites"):
+ print(f"Dropped/ineligible: {', '.join(payload['dropped_sites'])}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+ parser.add_argument(
+ "--min-cloudfree-s2",
+ type=int,
+ default=MIN_CLOUDFREE_S2,
+ help="Minimum cloud-free S2 dates for LOOCV (default 10)",
+ )
+ parser.add_argument(
+ "--alpha",
+ type=float,
+ default=DEFAULT_ALPHA,
+ help="Significance threshold (reserved; default 0.05)",
+ )
+ args = parser.parse_args()
+
+ year = args.evaluation_year
+ min_cloudfree = args.min_cloudfree_s2
+
+ sites = _discover_sites(year)
+ if not sites:
+ raise SystemExit(
+ f"No Step 5 metrics found under {DATA_DIR / 'metrics' / str(year)}"
+ )
+
+ coords = _load_site_coords(year)
+ efast = _import_efast()
+
+ site_results: dict[str, dict[str, Any]] = {}
+ dropped: list[str] = []
+
+ for site in tqdm(sites, desc="Sites"):
+ if site not in coords:
+ dropped.append(site)
+ continue
+ lat, lon = coords[site]
+ site_results[site] = process_site(site, year, lat, lon, min_cloudfree, efast)
+ if not site_results[site]["eligible"]:
+ dropped.append(site)
+
+ n_eligible = sum(1 for s in site_results.values() if s["eligible"])
+ n_repr_pass = sum(
+ 1
+ for s in site_results.values()
+ if s["representativeness"].get("representative")
+ )
+
+ pooled = _pooled_concordance(list(site_results.values()))
+ verdict = _suitability_verdict(n_repr_pass, n_eligible, len(sites), pooled)
+
+ payload = {
+ "year": year,
+ "alpha": args.alpha,
+ "repr_r_threshold": REPR_R_THRESHOLD,
+ "min_cloudfree_s2": min_cloudfree,
+ "n_sites_total": len(sites),
+ "n_sites_eligible": n_eligible,
+ "n_sites_repr_pass": n_repr_pass,
+ "aggregate": {
+ "suitability_verdict": verdict,
+ **pooled,
+ },
+ "sites": {
+ site: _compact_site_payload(data)
+ for site, data in sorted(site_results.items())
+ },
+ "dropped_sites": sorted(set(dropped)),
+ }
+
+ out_dir = DATA_DIR / "gcc_suitability"
+ out_dir.mkdir(parents=True, exist_ok=True)
+ out_path = out_dir / f"{year}.json"
+ out_path.write_text(json.dumps(payload, separators=(",", ":")))
+
+ _print_summary(payload)
+ print(f"\nWritten → {out_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..8f8092c
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Felix Delattre
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..baf3ba7
--- /dev/null
+++ b/README.md
@@ -0,0 +1,92 @@
+# EFAST fusion with phenocam validation.
+
+End-to-end pipeline from selecting sites from the global [PhenoCam Network](https://phenocam.nau.edu/) to run [EFAST](https://github.com/DHI-GRAS/efast) spatio-temporal fusion with Sentinel-2 / Sentinel-3 and validate GCCs accross sensors. The numbered steps cover site selection, Sentinel data acquisition, different fusion orders, accuracy metrics, and sample-level statistics, all feeding a static web QA viewer.
+
+---
+
+## Pipeline overview
+
+| Step | Script | What it does |
+|------|--------|--------------|
+| 1 | `1-phenocam.py` | Download PhenoCam metadata and `one_day_summary` GCC CSVs |
+| 2 | `2-phenocam-screening.py` | Apply PhenoCam count, SNR, and proximity gates to select feasible sites |
+| 3 | `3-sentinel-data.py` | Acquire S2 (Earth Search COG) and S3 OLCI SYN L2 (CDSE OpenEO); prepare REFL, DIST_CLOUD, and composite GeoTIFFs |
+| 4 | `4-fusion.py` | Run EFAST BtI (fuse reflectance → GCC) and ItB (fuse GCC directly) for each screened site |
+| 5 | `5-metrics.py` | Extract PhenoCam-matched timeseries, compute NSE/RMSE/r baselines and fusion metrics, emit per-site JSON and webapp manifest |
+| 6 | `6-statistics-fusion-order.py` | Paired ItB-vs-BtI significance test (Wilcoxon + t-test) across all sites |
+| 7 | `7-gcc-suitability.py` | PhenoCam GCC suitability as a fusion-accuracy reference (representativeness + LOOCV concordance) |
+
+---
+
+## Quick start
+
+### Run pipeline wrapper (recommended)
+
+```bash
+uv sync
+uv run python run-pipeline.py --evaluation-year 2025
+```
+
+Runs all five steps in order. Steps 1 and 2 are skipped when their output already exists. Each site in steps 3–5 is skipped when `data/metrics/{year}/{site}/metrics.json` is present. Any failure stops the run immediately, so one can fix the issue and re-run; completed work is never repeated.
+
+```bash
+# single site (steps 1 and 2 still skip if already done)
+uv run python run-pipeline.py --evaluation-year 2025 --site ICOSFR-Fon1
+```
+
+### Step by step
+
+```bash
+uv sync
+uv run python 1-phenocam.py --evaluation-year 2025
+uv run python 2-phenocam-screening.py --evaluation-year 2025
+uv run python 3-sentinel-data.py --evaluation-year 2025
+uv run python 4-fusion.py --evaluation-year 2025
+uv run python 5-metrics.py --evaluation-year 2025
+uv run python 6-statistics-fusion-order.py --evaluation-year 2025
+uv run python 7-gcc-suitability.py --evaluation-year 2025
+```
+
+Steps 1–5 accept `--evaluation-year` (default `2025`) and `--site` (optional, for single-site runs). Steps 6–7 are full-sample aggregates and only accept `--evaluation-year` (Step 6 and 7 also accept `--alpha`; Step 7 adds `--min-cloudfree-s2`, default `10`). Steps 3–5 are resumable — existing output files are skipped.
+
+```bash
+# single site
+uv run python 3-sentinel-data.py --evaluation-year 2025 --site ICOSFR-Fon1
+uv run python 4-fusion.py --evaluation-year 2025 --site ICOSFR-Fon1
+uv run python 5-metrics.py --evaluation-year 2025 --site ICOSFR-Fon1
+```
+
+### Credentials
+
+Step 3 S3 download uses CDSE OpenEO (`SENTINEL3_SYN_L2_SYN`). Set `CDSE_USER` and `CDSE_PASSWORD` in `../.env` at the workspace root. S2 uses AWS Earth Search COG range reads (no auth required).
+
+---
+
+## Outputs (under `data/`)
+
+| Artifact | Step | Role |
+|----------|------|------|
+| `phenocam/{year}.json` | 1 | Site list + `sites_dir` pointer |
+| `phenocam/{year}/{site}.json`, `{site}_1day.csv` | 1 | Raw API payload and GCC CSV |
+| `phenocam_screening/{year}.json` / `.csv` | 2 | Gate results (pass/fail per site) |
+| `sentinel_data/{year}/{site}/prepared/s2/` | 3 | S2 REFL + DIST_CLOUD GeoTIFFs |
+| `sentinel_data/{year}/{site}/prepared/s3/` | 3 | S3 composite GeoTIFFs |
+| `fusion/{year}/{site}/bti/`, `.../itb/` | 4 | BtI fused reflectance + GCC; ItB fused GCC |
+| `metrics/{year}/{site}/` | 5 | Per-site timeseries, metrics, covariates JSON |
+| `metrics/manifest.json` | 5 | Webapp manifest (years + site metadata) |
+| `statistics_fusion_order/{year}.json` | 6 | Paired ItB-vs-BtI test summary (NSE, RMSE, nRMSE, r) |
+| `gcc_suitability/{year}.json` | 7 | PhenoCam GCC suitability summary (representativeness + LOOCV concordance) |
+
+---
+
+## Web viewer
+
+
+
+`python3 -m http.server 8080` runs the webapp on [http://localhost:8000/index.html](http://localhost:8000/index.html). Requires step 5 output (`data/metrics/manifest.json`). The Statistics overlay GCC suitability tab uses step 7 output (`data/gcc_suitability/{year}.json`).
+
+---
+
+## License
+
+[MIT](LICENSE)
diff --git a/amoros2011multitemporal.pdf b/amoros2011multitemporal.pdf
deleted file mode 100644
index 5a49dff..0000000
--- a/amoros2011multitemporal.pdf
+++ /dev/null
@@ -1,9635 +0,0 @@
-%PDF-1.5
-%
-1 0 obj
-<<
-/OCProperties <<
-/D <<
-/RBGroups []
-/Order []
->>
-/OCGs [2 0 R]
->>
-/Type /Catalog
-/FICL#3AEnfocus 3 0 R
-/Metadata 4 0 R
-/Pages 5 0 R
->>
-endobj
-6 0 obj
-<<
-/Producer <7064665465582D312E34302E31303B206D6F646966696564207573696E67206954657874AE20372E312E3120A9323030302D323031382069546578742047726F7570204E5620284147504C2D76657273696F6E29>
-/IEEE#20Publication#20ID (5983499)
-/Meeting#20Ending#20Date (14 July 2011)
-/Trapped (False)
-/ModDate (D:20181126164003-05'00')
-/Subject (2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images \(Multi-Temp\);2011; ; ;10.1109/Multi-Temp.2011.6005053)
-/Application ('Certified by IEEE PDFeXpress at 06/17/2011 3:24:51 AM')
-/PTEX.Fullbanner (This is pdfTeX, Version 3.1415926-1.40.10-2.2 \(TeX Live 2009/Debian\) kpathsea version 5.0.0)
-/Creator ('Certified by IEEE PDFeXpress at 06/17/2011 3:24:51 AM')
-/IEEE#20Issue#20ID (6005026)
-/IEEE#20Article#20ID (6005053)
-/Title (Multitemporal fusion of Landsat and MERIS images)
-/Meeting#20Starting#20Date (12 July 2011)
-/CreationDate (D:20110617121830+02'00')
->>
-endobj
-2 0 obj
-<<
-/Usage <<
-/PageElement <<
-/Subtype /HF
->>
->>
-/Type /OCG
-/Name (Headers/Footers)
->>
-endobj
-3 0 obj
-<<
-/CertifiedWF 7 0 R
->>
-endobj
-4 0 obj
-<<
-/Length 3528
-/Type /Metadata
-/Subtype /XML
->>
-stream
-
-
-
-application/pdfIEEE2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp);2011; ; ;10.1109/Multi-Temp.2011.6005053Multi-resolutiondata fusionspatial unmixingsub-pixelLandsat TMMERISMultitemporal fusion of Landsat and MERIS imagesJ. Amoros-LopezL. Gomez-ChovaL. GuanterL. AlonsoJ. MorenoG. Camps-Valls
-2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)81 July 201110.1109/Multi-Temp.2011.600505384
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-endstream
-endobj
-5 0 obj
-<<
-/Type /Pages
-/Count 4
-/Kids [8 0 R 9 0 R 10 0 R 11 0 R]
->>
-endobj
-7 0 obj
-<<
-/Stamp [61 158 234 37 46 215 123 102 121 190
-16 110 0 160 172 209 121 174 11 44]
-/Version 1
-/Sessions [12 0 R 13 0 R 14 0 R 15 0 R]
->>
-endobj
-8 0 obj
-<<
-/Parent 5 0 R
-/Type /Page
-/Contents [16 0 R 17 0 R 18 0 R]
-/Resources <<
-/ExtGState <<
-/GS0 19 0 R
->>
-/XObject <<
-/Fm0 20 0 R
-/Fm1 21 0 R
-/Fm2 22 0 R
->>
-/ProcSet [/PDF /Text]
-/Font <<
-/T1_3 23 0 R
-/T1_1 24 0 R
-/T1_4 25 0 R
-/T1_2 26 0 R
-/T1_5 27 0 R
-/T1_6 28 0 R
-/T1_0 29 0 R
-/T1_7 30 0 R
-/F9 31 0 R
->>
->>
-/MediaBox [0 0 612 792]
->>
-endobj
-9 0 obj
-<<
-/Group 32 0 R
-/Parent 5 0 R
-/Type /Page
-/Contents [33 0 R 34 0 R 35 0 R]
-/Resources <<
-/ExtGState <<
-/GS0 36 0 R
->>
-/XObject <<
-/Fm0 37 0 R
-/Fm1 38 0 R
-/Fm2 39 0 R
-/Im3 40 0 R
-/Fm3 41 0 R
-/Im1 42 0 R
-/Im2 43 0 R
-/Im0 44 0 R
->>
-/ProcSet [/PDF /Text /ImageC]
-/Font <<
-/T1_1 23 0 R
-/T1_2 30 0 R
-/T1_0 29 0 R
-/F4 31 0 R
->>
->>
-/MediaBox [0 0 612 792]
->>
-endobj
-10 0 obj
-<<
-/Group 45 0 R
-/Parent 5 0 R
-/Type /Page
-/Contents [46 0 R 47 0 R 48 0 R]
-/Resources <<
-/ExtGState <<
-/GS0 49 0 R
->>
-/XObject <<
-/Fm0 50 0 R
-/Fm1 51 0 R
-/Fm2 52 0 R
-/Fm3 53 0 R
-/Fm4 54 0 R
-/Fm5 55 0 R
-/Fm6 56 0 R
->>
-/ProcSet [/PDF /Text]
-/Font <<
-/T1_3 28 0 R
-/T1_1 23 0 R
-/T1_4 30 0 R
-/T1_2 27 0 R
-/T1_0 29 0 R
-/F6 31 0 R
->>
->>
-/MediaBox [0 0 612 792]
->>
-endobj
-11 0 obj
-<<
-/Group 57 0 R
-/Parent 5 0 R
-/Type /Page
-/Contents [58 0 R 59 0 R 60 0 R]
-/Resources <<
-/ExtGState <<
-/GS0 61 0 R
->>
-/XObject <<
-/Fm0 62 0 R
-/Fm1 63 0 R
-/Fm2 64 0 R
-/Fm3 65 0 R
->>
-/ProcSet [/PDF /Text]
-/Font <<
-/T1_1 23 0 R
-/T1_2 24 0 R
-/T1_0 29 0 R
-/F4 31 0 R
->>
->>
-/MediaBox [0 0 612 792]
->>
-endobj
-12 0 obj
-<<
-/Length 25686
-/Stamp <03C9E20B79A667795E349A0D00A115EE>
-/Version 1
-/Filter /FlateDecode
->>
-stream
-x اg] zZ
-@@@Nc$@ $d@23~Ģ`ۨRE 7\Ń
-EqU+߳ ʜSk{}߾\r|q./_e}?WO߿o{ǟwxި雞~ z#sYnCJЍO5GN=k7={g1ġG#KxQZd֚?Ъ>r!ӛ4^?ҽO{ltvo7<{W]!z gwo}O=ȭx[Nt䖛wo9sl30ǟ;zӷcs郸2'Nߓ̙
|ٳ\KN߱xpGn%[eţ+v ۿ:?~gzQ>욫x ]Mӷ9rj ԇˎ çn.UCM~hVC]ܩ#GW$==%)>O?$Ї8+`b_yꙛW:~mgo5'?}{ ~ 9vbo9yTV;ʎc.\y>FntVtV {3 ?rYo>#kNrYTzC.
æ~هݏܽi#8F Fƫ=?&w\~g`g7(jMh~rfy3G7nG͵_xI+.:3{reOtdž vm`o?9R? / {7 9u'^eyZe ":nnl| gO Ź/K
-$NN!~m. h>7I{'e/5u'$LiS;ԏa<
;q柶 f+Y-Ux+_qqLX">5ӎn ͥ>LDyJ_
i?dZՃgmM?4v}{ WKY~|L}]5_%/ 懾J:m6zv Gns5L\mtT̛q U*gڨg+9} ,Q33O;~vc_
-cvudG3&1Hgy}n/"~G ,ۭ}K}~lO`;Nc˳ Wy\B ϺJ&=2ʸeeOnk7yC w__9B:`
-Tgl9C k?n> _>!]*F30H觨_4ZCШiyfЭOױb̠zSfl/6 @h`: /vPA^|߄_.lWv8ufgV'݀ dC7?D3h&@qI9qeO~۱|UM;*'#4] &vG# ŝ3P6
-1ac ڪ1N
- bXv2YkݎYu;19Vs
-ouX v_m Hh +/HO\iaɇ(zj\3}1v d9
x ~,|@ Z@Z7`-ʵ8yǦgtX 5t +W8J`6yS8 uHbW Eٻ
-/bB 7Fd娐% :DK^W| h.дJTM r%LVN!b*H"[4X9>cp
-H :aL$.j0UHQZ<-3<+^]:7@s)k@ _YX]!ԷC&+xD!rQ WYǙAu"a*RF8f
-.
-5)vLs@Q?yCSBda L
- @qxApd(,\xpZY+`-&ApB)hIڐtl H,31Aɔ!4B2dr0Ʋ)WncK,dm.%-6 kE % YbA[sK9wuw7,Ƶc~KlHWF p1K,q鎃8 Zeȧ؟sXE=) B7Oc\*{ +.`x4hI΄quF. 4 :DւCٰؒĂEC_ *ո ?Obz~^XKd5LׁB^K 5c\ED-RIW vUKָF=qMHr\(c\U+ ɋ'_b=LK "YGƐ%ثj@WŻˋ
-Ă+dX>
-~A
-y4?,dԈ٘ilriIsn{q \ƬAƃ9.v<(3
.|P\Ŵ/GF_U1@Ur9(W? eTM dn^ 5 Ԝ
-*/. qRkF & UA /r K@f"VWRH?
O k}<1b"ɤm /b֦Ņ+Ad 9>FZaYφe*~/I]l 3w"9zO1~1 88
-2:E JxZs[+53O!1af$d+ ɤH+(]zJj'qzUv |^׃A[ si *4vJ%B} !βt99*7>v5Ƨ ;EƦ@sS(k- DG , > _XJN)A]FAS /]Ĩ{P-@
-Bve)<dA^qh
,#XI
-x#-ā .0 B+V)` x1n
-tok M`gB4 4U$NB~{
->55kx4OBĝ
-e _ 끻b;)ըTr/6D-A̰h-ٽ\Fg%Q}/jii|QK߉iiϲ4?Gr[4j {<2TҴ[zi= -jy=Q ˍ{2܋a|}\DCKs454P *faU0:U%'7)
-I y]uGzV6۸glR6AJY߀FlH8T PWul -\(dTp@S)kzOAAAQ"j!uE!6S!GFMRt{3
)h1F
-;~g#ӱev$:rxNo1;^燿Ƌ1n<]9&> Ǩ" Yu ݖXly X[0>*bT9aal=,픖.0lιY6L!6}\L:G$fmq,1ҍ
-͌˅8n^ưm6!_,ȋ_}Il\Y7 F3Q^ٙ"=(Y,ГC>lK2U&b!-M~pŸ%lVZHjNBVv1\j7meiYhùТe3_j<,-ՒfFk3xY[vۂe eZjIXh
4 @ H ӑrxTbHϰ}.EFѫx
-"9D3
-6Zp茿 ;CJ,[J 1G6WA株k|H>rP,Vcʫ `L]j'zCh'db~QB!OltjTpG%ΐfR0T12O5 S_G [Ctz}b! ~K~
*&@g`l=б)0 1ţ5ui
-PXOc)tj
-9n _Bbʱ544]MCuA3
|?}[ңY@:tuvA Hl&E -ц2"Iő=OC-p&FjI\PI&דCW\#gdz *FU<霘Lz7xNwD/t
-3H힉V^v>J:< dro;3ZEdM[x=vI]]CvKe4:Ϛˮޡ hd(EhTa :=,> ?o@(jrqd(hqHw~f^$+3,BC]?Ky*HRbT ҵ@}+XmhP1Bѻe,X/-dh
-sԊ T֢aj FW 2b AXܴ4
-%?$L%PDE ͭF1QwJ L1%a븑DvHO`m̡DJ & ܃e
vLTLcŤovb X'9
-ZeHr^ s* di. ,&Yoǁ!O0a 4ƁS*fDR 5P&(0B<1Y.'꧓O 8{)yfւzT 'al91IB $kybOFb,{sB*'y`Xgaj9/Z\;/EC3ZAJyqbC ̞ 8zb 1WĤYƆ{5N $ ꆒR
- D1F&I?M-?2 2GG2ò $&2ᕄRg
-7j'u FV Bzٸℶ`}"\Cd\
^Ot1C\,X9UαK 3qM I-mFIb.#e;>711Ex)ϥ
iB CC XyeV<| W>@{P`'} l쐫 V 5! ,?;
-+@SpSlJK `4Y iG# ifa8 Vr$< VdϑxmA1nOX K1I HiU+^'3E n'0 Xc[8j]w!5Ion(@f.0À.#;G)a!t}!;PTfgɀDm <$H!ļf$wp X-ڍ
-3e,,,Ӭ%K΅./bǸ;:5i'R)$F.'E TJ61L
- T]@
1i *312T52R|dIG`&$ͶyiajyŮ~0v;Ri3 ~=i ĝ5wOs%T> ;q3vSfWRe`]ClmA14'W|ج0 NXf43_[YNϴTK*OEyVR`
͚ÖaPzXZfz KwJby
78|7 4}QdF $· H I9X[# &J^OХT]Jva>;Ɲ̙oqʷ Fχb,}u\mHNvf?&U[gAē2i,h߅N CK.0sp!)HAh*ŤSnӿ ,3XrNv,CgDA(T603u6ǢbK.
7Ih1-@|ANSXš >S;o4*F5Ma@tu6x)H.6ZM_:COSW0:t5T>B'N5t4\M'*8MzI8̶vK پ?JaMOrwicv7T.Փ9@б X d $o\Ỏm 6BNSX`)0ћ6 >-zKä{@ v 영${d{cz
-e N[i
- ;Map. %V~I wa :vk$Z1Y x:O 9lpܪE+EP'!> 7t91 0n?IQ.1_{r5hH>r q [LpصϘQw 12
+~ɥJ&h ge[S,C`,[\}8!-Nb\ 8ZXFfU=UqЗ@Kv}ݓkyb1~gFdB uK7aKa-iK+m~ veZ!^Bd5mXBٶ&)"q,8(%GAjIyuq95e#jV`ESCVN
mIJE.%~(otRY(z67\-Q< C Y)`cI̡x`.YHRtn8r`ӡai\a0.L<0QL6d+-'(Kvbq`o, om]?Jz!3x`R} x gĂu WV,5iiF6Z$煽y1Zߋ-!N)ֶRgƊl_\T_ŹgңUkv
-
-'(ƚDdpN/Bm0MA*X^w#!6_Da*ӱ _`}d EU6*VF[iyKG8͡V"riQ.X61 RZ[;G((5t-Q P_E?QQvl(xA[=je)> Bk%C2a$=/&@BUTXEW68)
&piHe[Ga$A\%A)+U+5 Y386a H:Y]kN% ܌| jJFIDF@ 9HqX.Xj hY`u@l'/Xf|F,$<\J>ZW`H CYH
6 A?Ѕ SZ+Bmv>$ ̤]@>d)8zUe'͖^$`dfL}"/q B4 SRѢNiPX,
FEh\|{)39(Xr`z Ih*d vmO1 [Hu'A 3
-#7! *l2['-cF9"f7y"b*T4[;Fl :'ʂ:1 yz! c2%ȚAݔ_td)ׄ[JQ(gbN6~' ,X?5(63*Ϲ7{㠵ÈLt w`ov~pS(sF-8z]ۂIȷBҗ =$e ,Sm@p8C1fȮ#K_j%\.0fӘ
-l0cUbaAcŦ)0ypPlCCv8峄 , "Œ YWq$T
/EKqa0m u tc٩GZ6X0_A[~IYk,scVM,/<&4Dr#6]>ǂ0sCA;
-eEb*
-E*zK;oi؎mlbgM(ȟN5$JtFI˅f\@NCvNaKpp,[Kk' ,Ħ͜<=aǸː>C!$0IK9nR ~QRLiԡ|x3CL v%_oJ>g8PMh&H"HS>:F Pa]{N[;u M#fb .3h`w`ahJA/ l:,6-CXM-1i2?7dd ױnC.au⡦z vcٞ+,CrbU V
hP;La;1Wd"C0L]L1mTKd[5*3y X Z|hg,6ZWݲĞ;۱4d_YBPesm; j:]d
Nv(mFXtˋm#+12Qv8l48-'Y*aǸ;iNZḆ e0X Om\ EaSNvlUAa3*H vc^:(nL
<12s8&UQА> UlgoVuN4笿FӸ {j+幎L~mԺ+&i>;PH=JY섈`}zLz9@4nRsS4 AJ"Ƃg\ACw4HQ!ZCA>+UFlá o`RT3I!>L&>| FsyӸ K;Cg6u" v9e[TbJj P2b0bDfIgeCCrk`|wN4nJngb^`N3&.vX6*Ǝa640<Ռ0Dp!M\ĭ W~t,nwDb bd
-lrXΙW2] Fv5emMv&@>'-G9 bG *}>w`5cy6xİi6H C!D!&Nvls?ldF+t!T :7Aw5DAO\
ީ`Ǹ]I)]( fCyoВ/qN4nǶ rMvq16c3.f4ybl، d k3>x rނ$+-3۰: $D
IJLENKԟ v5eL3zP-\H%gǥKQ05ysq$ELi [ VEa R V*c jzT)C2(TNxA&{3ZjuqJBDcՃRlL ֊Ԉ`;u݅@壃dY/57#wYD}5J̿,MV(ح^ f{bV)$3sIL
L*>2Mc1 LK ghh d$?N=kSpFE9 0 ɺM1<5˹W>F'EVHI^w&
Rg|!fz5ga ^Zߪmմ^[z Q[R G3mKbڳc{FOS`LO",С:v58vSv@A@yG_NC)tj
->I!j]OS`G'k"yBR |1б)0ؚ__XA"[CG |x,DY plX:tAǮfn u
-" |څs RMZ1Kj]]GúAnyɇ/ROZ|h4f a0t
-En<4Vs
-^;7E`Y"j)t:M9Z|}-29QZ&[F}C vt@ 'R
`&d9&Bz ;&N,BCl
!CRqw')mܦt$2;Ծ 9OG>ZQ DQYZpfZdMsMK?GI˻X͙R}k~B8_q%YKV)QRX x)L 4m)%hxwtbcm?1.qSU4jr,G%AoʧbJ3RȎG%xO,.X\)S8{҂w6l ,AҖ$Nv luKypo[8?b0IAu`7
-iNs>yPד\Uױ>1YXL Z[w].4$-%{ Ш@\9,j/ ek5
-kե1 Xd[
-b
-Vņ )F)%*[#K\sc;QB^KG*|% J4禎.Dž5.?&Y4Dgqc,yjܸ}5<[ˢ\:`UKaHdbwB2.7ߴb|z-r2+T:[@Hm `}/ Uh5?\oI 4VJOwi!3 os>%t'B5ISИΕd[Td(pm1hV:!!t'j% tMkF~UJW*N MF&F WV(5{ 7SCmE~g#tl`PWЕTtIZw5cU:VV34˺U+$
9l٤}eR wՒD դaƥoו遍#vx =LJ
-h,tjTRCVqZI^seԼ;̤ ^aƆXįcem,H j~qۃӰ]&QMZܛűٌDUX4; X]!#-RDkmd_}[@
MsQK0YäP
W exYs
U R`t@8d;
-g)vJGQ_B
;J zti;;۱:ek~Epi{GԂ/ؖoG?#aǸ'cKOQQKrUdsnE7(6um5|J pKyY};ntnw`2 DU*b]fv]"fw Ci:? )~q33LB6f/k\8sBQ;T 4)kqxvTu!q-Q![yc
-f4YUCLY˘U_bU
i孈}IEN"$[ZL縭:Vf Xɡl5]]]]]8ޅZæ3a\VMe#d<x C٬,Etɕ =*ՊkbXskB vaOnt\"r]{#CH[GPXRB#9q', u> y4[g@Eejث)se\aFc܁%ϊså6i˃8ZZó^jĠ-NVw A1zqa| {әYeb|[.IDX2HAiA$yshN?:ҸRq4 ݙPmN{{0t:oɡS]C풎Y_16.EYih`4jNJڗZ >gr+Z;5s,{V aqvu )ii~|5tGð:+};ZVq',k k)e,Q5!j ng6A]\{ (V gQټ uԵ Bfef'Wsm>$dI[-.}W/Zv(]iF m&Q5:6scǸk$XF
-0 "|Pt)]wl?:b? v;$ך{
ݹ[c⏈ ͂]P+Q;3h7nZ*ϙ:nXqm'^]1yuYKN6S.&pP"w4CǠʰf);[/H"C
-;t&7sc
-\aݐ|9C{#})ha%)BdW,^aq;VGM/ʂ"oY$&,oW-Kw 4MDDE :N"иnX$ Z]' >,xY-(JA1 x4RR昤v W#j5n+^ɚۆ5RLlyʌ
-2 0~M X-/$mD֫fsnxҩJ P?aY3RK6VJb\IU`',+y\/ Xjqq_3V:%f3¡Cw *~9#f8%.`qvXSnÝ0E
-X41.,|!*X sZ3#eK;3 2CY!&Z~5.o]kXJX-n3,Ф|fZb%6qa@_bk
`LB|f5. l7j4ݸu0j\#f7~y7 d甅hSH$ġ 1у,He OƋPbԊ|Q*PӤ 12%H &@
--ޏb3p0 )j, 9%"[;Ќ>APjI)@TiDvcZ\h T ۇN7 z#= 9 ?{ULd$ҽ"pj9E: 7迬cB#\BtE^_)%:phP,@[xi'z 3
-[A9} ШIZ@X" _u@s %
-L"JK)t, 1'C]4 c 1]W8dSJ\ρ #GW4|=oSi
-
4.;ay=,<2;б:v5X3ի9WKڃ(s va`Wpg3́O%k1/_s$b9б9кrXXl6r5pew2)8ׁU]\wRauTsW4Atbam [BHh6^/j8x= :~e|,n C2ƣU[+G)mK]o : PNPhoa?$-{l (:x o$qEgEv?0K2خ_ߡz)LIέ_r7wI;tz;^L VO۴j wlE+x= X=rz:N2"a=K sV ű̲)Nhi7Ye\
-ȤO$5O+;CN[.KBG.u5>97n3Ɉd\kDVit\Dc+ R=s 3&fft$pL~ ,~c`IJdӔ!mVPQ WXn+1ALk,4۱Qyk\ńK+JB[|Z7%*>k':ocIb v-,/1MX-]//80ư~<U/DD@ CR8˾q A_ 73.+ڷ6]g(|H&s>s~AY;y B4 V% 6fzj X1dMSD XIDs \dJXtT@_
4@,F cYe3j@@A >*6FѢ)kI`
-8 gs=Y\Zj2utbL
,!RsU/v%+4EXiOpgð rG_A[;X o*.RHQ7m%t9,82\+g9<
\Q;,8aL#LRtm0g2GsTDDBe]'K #3&:YSh"H`3X&hHT˰c ݁ O%O56 vf+-LI>4q(Ԛ#vmh ,O r XrD7bbkV%~ ?F~JLg;"$v&N܌&jv
%)4k bSLmIb0>=o{v;)P7=esP},<$piu+_Ⴝ>ioP 辷81 Ad2**Sb FYD"JRBo$y2`M 6mYpJ湀rQ/娒'KFrk HTwሎ H cPGe j$LVjj \ig\kLc2LsضX0Ǯ߲ҋ"va2^UZ%H-ނlAiMF6bӵdgWFĐ}a+qV2;.cWl)oZ/*9 aS|E S2"ϬFzQUʋHAڰFqbL,=ZX Fʘļ=*- aySt[$N!eː6%B2 "VDY4yD
Ȭ8
Z Fe돒< )!cE`LP?G`QM ` ١J# ᛟ{1&p@@%!9ROh
-4 ,%=$)֔Haj5:9".aaX`'e7u1&FA٠ұGe%aE{E} mX|
-i*%Ok:RaU*Z)RA N-yiZF) Qً9ht h8
-YG:$ ZnJߊ K.N=P)f%=Hܩd),
˳`ƹ.%9GP"+mإnp}/)&E1ui5cti8UYENKdoJHH3\CSc]PRpq?J7T1$0LuF'l k%C8 IVcN .!p>OD,8M(%
,|Jzc7ՠ})Y->}U3iyRٕLH1: ai_z,mkQ8O `f"cΡTc Ya 2]d q,ǜ %z6)=MI<%ǙxZ*6rN")?` Msjj9hk6[k,mۙ%0 ,hef<Й]ޔ8%>|
-2lXbv^OK$#Y7Qf,1&PfEBJ*} dv왈Rq
-/V_(9-Ҁ.+ȸr'4I]6{v[w61_ {˿䭷~IZ~r'ź̙ VwO ;}aՆ?8o;ћOn >9eo; y}|Grdݣ {
- [?Y/~͓ vW~_S/|#W|K7G}╿?r'Ư לKoKW^8Goo:{zk{g{z~sOO'}s?<G?s~
oɟ۽>Y;zLJ7eG~/~-_!?uz!O~+Nߕ%v/y7}ŧ%?~]:rӋ__t}ݛrv߿~O?^}h{VWn_W[Gio|pW~K>x|^y/ ;n_W~řOyC]o|_3?ŷ}_Wz 3_wg^rw?|7}+ o9s?o:; !x^/;~7A~ o<_a z=W=9}ћ#O~_G}kgGvi/ |OYo O_pɡw_?x7{ݽ ySӾ/_}<{/yO}M/:rWo~Ӯy÷_yW_? Ϟw}_9|O| :|kq sӏ?_qW|mto̿|[_to7}ƫ^>i++U?/g^{w~oK5?qs>~)}>˿ y{;n+77H_/ w7~-{MO}^?ϯ'|k_^ҟ_| n?~/ ~Ux3~\m toO=跮}'>>m;ym[gW+x#37p>^?oo{ w^o7~9
Ƿڳφqw'~ÿⅿ?O/~ WUoC 3- ~/1/{ ԋ [ y /yw,>W?~7$u&.D! vI qGn\Ozȩ;?fwH+BvvsO\ݽ 7
-/Version 1
-/Filter /FlateDecode
->>
-stream
-xR!O1$Ymd`B ^I @ @ @ @ AA%x
->LF(*l _\^]?<>=|~!D l-J)|Lnb`8?h5N/f*4E*U[FUI)rM c\ F]L..aDf 4dwYsBF 8lLru5:`qWR'`MVJQ,:duӂCJ̑_*l(! >? |<"Ԙ_ x^` ZxE(oJv[|p&cZFR3Z,@WHr %ٯ3DR͕G@
-z "
-endstream
-endobj
-14 0 obj
-<<
-/Length 22856
-/Stamp
-/Version 1
-/Filter /FlateDecode
->>
-stream
-xgۙ畏tWBq MuZ-5[j{ :(S3:fLD@@@@@@@@@@@@@@@@@@@@@@@@@ |?ZkT][{uy?W???3~_=S=tO>7_??}~.ď>7oo4ggW_2b#o?_O|ͧcM l/N?xOC?_ 7?x_Ұ۟?y_>߾_o|/|MϿ'",_=
k}7sy^O{~~y_{+}|?|~o}Ƈ/'~o_}Ϗ@?wԧ9'? O/+_}mM9^p,?yOOk,A~#/a41>}ï~_/7O;! ȗe@/|a+ wcǵ~.7u _O}ޜӜӘ s~7_hƿ {̘m'o߽ / Ǎk 5Mͮ7~7?O6麍/0~vlo|(볟?p6{ɇ*SiU뽕?-}~*=<Sͯ\oA\_9ߛ jp< דA`w=\Q}Ujwޠ &Tk֞ۓU~/ll˗ac{cv>lLB6lzU[
-II2S簹*Ys*{CN|Wyjry <5*=hj1W߫ݫޮsUq Z{kyR}%?M΅ !5\Cj|koF^}{kJ40hFAKΗ^o3yai?݇}ʫB w <~~ v ^Mu @c3ZeW5 REôKҒ[E7jئRﻠٌ3mcL]L_5QGMTgDr]DתSUMȟZrWů뗹ԧ{![^ģ* -&?s }~@EBJPW_|,יu{ZCSיͷQ([F(u,tkcFq: t U}>ƥJYZmP^>+(d\cQ V@Gz ƞPQ
:i5Ԃ.>WݖE$@]?Z Z}Aft dEmW-bŪ|;QUwk1/V](_L+ jmX0Me]cпԔXgS:*\2lwKY D fU6-:/ Eub7)T-
.r El>4HAO5j
*bB4Ia۰w Fa.sFP!oy
-~~G:I T Ul(W~X U{o5 ` *iGtwk]%\YH6jm% uD.Q5Ob"p$#EFy+Q'!fz]3}5O$t !=%QW9qҫ߇H Z@{t.@N:Ul+v[X)5tMv8 3j@lMۜ*8 z+dob6c\ JH&+y XpZb*s[mK F]_ Ls2!Jt\ NDIl1h]xM0^hr qzD9"b*"
-! YꅫX"hI4iݛ"J, %̷K&+8aTu ь`mP_I`ϙJzfW2P!QKAҙ &1lN¤Au{EiTWT@Gak%JVlնت '9ajH <&!z"N/a#!KhdjcR?*8D\S9^yk ,!<]
M \9HEν`S-qcAN:i 7h7uR <6nElGu.!\h'N8]
-̍?ZuɧڟsXUs\*=Usܪ͡9" Y`Ê?7`FBZ+B|(Cš[{ !w\b ե6n>.S!W^ĈLr7!u@qQuH%H%a\]Kø=qC7HuL>0jr #*b3~%Ʌha
-WR"5] 8xhba=NcԻ+..+
-/or#}By@,EtXA.v&I4HɈ5#c=Ɨu:Z{eb]Ú'rbpy҅/bUl;^uTְ橪-AYC\28sbH -Ԝ *W \NjiBǀ\PMR(.a7~mo6. c'(2Gh(#p&e=ۚ 7 ]V0
y~`> ~7,p:]-MW')l+O 0KYŮA#3J
-* a"Xao);R0 RNJ>I3:U$ۤ'YPQjq= $L+v4LNSB^M*J[ҪB sPcrM|B$ J46"AkmӢ[R 7(/X?#x'itX8&_XL7 Ԥ.JEەO.ۺћt5dw,#گT9bqx/``gvZ'IoB\XeAPk
-N)h 8 "[ڥ{B׆iX 5+0 z94<&hUЎXQ'a߳~PRS9 Uw\ -UO+N{@$FU;롻2OIevrh`lh% XN$fVl],7ȃiLEfۙr=en]IFVD+e7"/bVӔ' :]A Ef(C%c\Mwgf^]7:ث+-9z=}s縒qǸl["z?WL4K uE1KV_֍qbQ̹{}EQrǰoiKOtUQ&f ).+w.DCUӯ204FFMY55؆uoL{rZ4n-K,沊k! :cQs%{ŐERfSĴpWSo{<TЄFtC
SdbhUm3fӞZqsB?B$kD+p'tr`Kn.b< fz a&펴j^ce⮾;1?G&-auX
MKǨ 4#4 G\#cuܺ7ý4z5GAA:)h-,+}`YFE=k~ ^vq]̭y5rvV70ܢQm Prx0ner+ ī լ Jw3VB ㊟Yc)jBLnH/z»v2 Znup(e 0tۨ )lJ!/*UFv"i=xQPpDS=SQg82%7b1WB̟BV Gxq#z'Rz3fh1pM
-͎c- rdw W ӯM k|wKʘR9.yjQ̷QOؘAAඔƚEDhg @kb`sG/[ >:Mj:lA\_.VX|rSF\`ݑ%OgnK4Dl-3;N\#l:ERڍ*Q,3Ffr He,fh,jD"/azn)2!Lb* v5DmDlK\NsTkf,a; 7y.df)eQJ} $au;S$6tOK7-q%͝^#X">NdtsvQZ shd3x^Tu)R`$љǷ jsfb &b2np4)̨@T: /1,_HQڰHKa2,:#MtiLA~N to
nܰDI9`*! J!> }\ .]"~a
Kd X,r!Bwl $;c͐0=ޠDwZ ,ѡΦN2HVOC҉.!t Nze:es;!9TD\#j|;>70Nd}Xnt-Nc!&\WӫE$e% /d)T4)C$}a Y=2 & II>V«y|% 7(}fFnU7|{둺RG/4bZ3 r,\
l04Mǐ~@Ϸ BH:oQr , #>Mަ@*8L>7e
-{tm
-x|,S8m
-9m7.1WS2sDۚ tMa:E3x}?I׃mA5B rDnBE} b$Ⱦt@U(%__qOFv ;Ŕ X%/3 n.߰#0_~@ϗ_pz]ޞIʦXГL^qgP;3&J=H-uM}=~o8(k8 m߿=x־ Wx=~VGue,J*Iz$=I
yOϦ@)jv]̾qw~' d#ˊ˄N(?typ Pd%ė>I ŵL$2~4A-[n 0e}dI@dYZ]LVsTHD!"$B+m:k|Y2o 7tE6H&+ulo+w9
-KkFٰ%p&vpsbt$@b0{G #,cBCnUCCu0\Y=G⎊ԅ
" 50<}J˯ThAb ;V@`Ezą1f{Q$`IXJ
EFDyX-s># rG*r[#q)|PnTb#g\ҁ1I[ ?ΘH[BZ-3 䓢8 Nx`&K $C{pEmrubM-"+N_F9N:ya$Ezb83A6a6[UDt`q`+Q'4Yl"#+22N؟^ 2Y*#c5] % Pzl,_$B l6la>(7 QP'*wR7F m _|)W-ÕEdQR(E⥄ V h&)X{\݃A+E0IvM)'ϮՅfp&7զ$S l X
pv_q8F$6xҀVAX٠3@ I YUMV$<`1y G Iؠc;9~
oߐ,wH7 $S F5 2rJHx)$x y -ϭhvJ4mBu3<,ISK.yf, ɻ$/aqxE b
-C٢z8MՌr.H|{{b}$7"6aju (
-#E9쉥RLN q N-T+`"RN4@^ĞXrA"Eɖ@&Zፄ8.Kc
K
-Ӱ\S Jd,wZjٲ ^P69 %8M QwJ
F JK穈U"A9%K)/ 3sLãCQ)qY Ӧ}yc1 XX K$dxW8%sVc 5^n XX;RgCm'*m=$&N8-!i%9}zȊ@ jy:~THip3B o^nʰ/&d9/NL Ӹոܩ7TUo~{`b+[sߡQۤW>< _x_qt+Wf`VUGr<<+PLF،x\7iwJl-n9! QZ2v̚"$aDaГ,G9Mq1m|u7D°9N7-UNΊS<Ջ6mTf;`>MD fF8mn %d`Uv[ s
-2OzMf $PP8xrc+k(Kh釱 ˳"-
K$f]I#: C䆭-\m/aa( eL5jjsX3dfS)k8m,p"b1)W\fvf
Byf:6L Gŋm |
-iXjϺȧS}7b!5̼fW{{brg$~=5i)]3wi>>-K11 m؍$2H`!?d6ex,)
_6]/aq7,Y& t+ YR9tnY@= Îa/P<,x3 |IǝXF @>ǧQ_D51ǰ' GExjk4CoB>!RKws+ŀ wǒ3 ȷ QY'Kl#tO[җۨS/,)8)Nư.:Ÿ ]b %6B"HIX*Ŧ#ntֱ ߄,0mYm+} 8u
-w9_x&9
:^˔>=<Yob,('n,億neeyZ f|g崤^b<v> s/#0;{b*UAh=3^ƞX*ԣa0:Ob01^ w!_ +" Z}~aOJdP$Ҷ/ÅbPE ==Hk:|NI9SBH+
tB^WKLn R q6-6B,>XlB,>X|
-bO!B,EJv#
Ɛr@ʘ!tʼnD+$RStlGbQsm
-6NHOSb.%ܧP-)]I6`!odN/Hg3dw
-cn<FvTqI kt os8 .XR"jxWb9)MN>ru}
-vMDoZm3 -ɄoK؍^ ,ٛ*,[SiM6 v\%vY wȰ̀Dni>
jF$;o1iGZť:<TDDz qO}֓RV
SI 7+4*a%"jgCD HB:|ǣh4]kdƗlX N"2bp隭R Yik=.iW֎nq3F2G6 [tS^V/{G>lk-(q&0VZ贬GdꆇդU
-V+[\~ :crVTѢ:D)yZ) ~c
-mGlV
-<)8Y#GjYyu85#jw`[)۩S#b;}V˙ Z/3isruN9 XǡRT l 9( C`"$q`Zea1P;i֑Cq`zh^'0![Py` V8
[@y:PL($ 6DIqNƁIpb!Dl4 Ӭ4B-q^m>B/q^uf&$T<hG umTb~
-gӥ_չg֣zUkv
blhAs*hk#&Ll}*Z^w#U, , nOb&{7zЉ\%P ج=7lLXRvK,Dpv;tÓSf K,PlFcLFJ ,)~հ l<90 @v¨cLءu?aTc(v+u7A{ߜ5 8 ^Ei)&k5BM+$bJK7"Uy_X)%jx"J& P@kuqyz% jYAGx {_|d+ŷ[df)ƙFL8旰۰?
-eG<+S`N0G's'799nƍŌ{1-+d})襹9%5;.'+;A)<,#B>mK}K/OjeR 0eròil 0
3v#%9쉅" ~Q\Z+M!ɋ[{95F,ֻ bX+\H<!=cp3lŅal tҩÃGnm
-̷gҖ{i~Wc
yҽ#*M,g9 ɦޗ(O7~
z@ߜPW] [vش)RخNh\:^n &V{ ihZȢ"͐T) Z^ cs}
y /vX.ղ)hXZ[k'e)DdyoKsK1ơ0fRFmM穿kX
-
-JɦQ F=oJ/3Է> ? ? ? SN]`[? j2IH ,2 Bd\ū/{
.-śA~a
-ƈX:A/cqO5=,Xi7aFayٰ 8Mo 1Ӆ%/f$ [͐KC=zAyQ4pA_n Xxb
FU ֵv6A,v3=="{5x3wXm^-qurw m9e'z/}Uoúɟnз%mїVڒY ZS\;Fu3 bW^ۧHa6f>N:|63*)yh9j[t >==srBTjcX)Sz 4-U$ژb|{`=l2(JO땞bͺ's1G'ពTa>*6YF]A.9vkl>Zxk S 9ߍ bI/ca(o1%/g9*.R'>f;1Q/bq,a zR =; +rڐJ=
/aaOR6\ȑ HA^$Hra6,=7{`Ӕ:<ـitxa+b CsV36ŐQW]7#26 ON eʭ
-~7mkMLk?{y02y6B/K{Zm*_ƞÞb" 3f\gk.m], Bnc.$&f^5XI -ÌI lqY n# BSR!Pv7{ l3x ÞXO42LIvC5@ Qx*fg
-42v.V0F/VHҕPan̒JZUE zj}{{bP`Z!!'P~̷x[W v )Fb3)6Slƒ?f|X،Ob3b3Xg3 xy?U?+۠dRMr@m{nŬ6*_YDB Ej6h@+F uI
-nP,s~f^eIS(@B>*Ra]-eGg d%Y1֯@uIR3|,e.xmgG LXvj
:riE5?>.lnHkypfE4~; *䡰kBqY?%:p Պ}FMV2W(Fud}PK 4#SjuU3/=c4߅`;wGoWaY-ݡ6E6SٰBK$nI[Ӧr[u0#1kua|tLcX`昨)im}.
ِ' whj'BD3
4t`[3O
8RGoE$Sd5+l*qH6kCUw1z]sg_VV }BIVj5x qЬTuLnS8)SD$Wyu2Id}8N|sB1 y fw
kx|fp`o3?IievC7ez0?Ru
s& ^F
- [볶6Ө- 霁Eifp`)\Rt0H 3)dGgEwO7E,}
-M VmjE&7JKx$ }Ğ=g`] $6 =, PQB}O@ DJ`X@ ܜVqs1}?K:T6} 1T ^oFe r^ϮO@7Vg)$牁 ,, mx9l4 fְMYaΔqbp&;1e>hnAǑI0hA1iƑyUNeEjV9)h{d7L:sdvRF+lD(V>ebgqd 9EG
-љ75,4ǑѨ6, ֖S"
Ư5*2 ]ƑIVt&ݵufDqf
Sjլqf\ y DvVugR.PPwL&Y (`XoWW9V 8ՂW9$J 7fKHRܨJKßz@a`VuFˊ"TwhhDՁ"0 pJ, Yû4fQذb'Y9n@ 欬bE+Q[$Ao.aJ VȎR
-;VW* ΞÇ,sqJ dre Z+Wbu2IK
B?ʃQ`Qtv[o+% Ғ} yRכ]XUn C:, sf˕ICVך@X@FL v²ٴdPg5JuYL|Ɵ |U=:Z*B궢ش#p(
-7XlQ֠ q͋T
zo ZlXד4IJw >.F>.1-nWkm2.E}tѪá[%.k7 Vb^L%uFg mA ,-V%!p -XX+=}H %) \"[Y|,, \da8S8
-tVnc(m1vC`m.GnNۜcXȤ5iR,!6>".٘Ssܯ)D
-5YޚаRrB->XOmQ(!iUt
,=;z@*uW AixuRȦnN9l٬dR>ƫ-c 5I&[^,ね@:&q =Z x4F !m%7Q< PqUvVs^{ RCFoL`xi-dD4ZGf_oGM}
{T<] 296M'3/f3Ad.Sɢi
-Z M}k6N.a^b4!=F=Ns^ $Bc3Kxr=
92 d;52gEvʄo}o%=k`]^n Xl 18yl̯(8uXZ> zɛ,]E3myqSѨ#Ң|i }^]t
RW3a$Ǘ'4Rf TܦZju,BL#!5.
ũ(kY V ^Fn XSڪcsӾg
άh\χdY:,f~ @z1KM4"1i9
6E۱C wÒ?XG0D]ք::t=oʵO@fžJ8{JL`Q/д
-,~:FB ^Uyh٥QѺ
-k;3ؔCCaw'6$9D
-0 !| nwl?ٟE?==T[ͽNjs&;/⏉ ˂=2+^ e+ϢݺQ{ptMT* &L
-7('e)>wZ/X"K
-8V롾/st'ͥ%2CϽ5>/uY9H@FfYfx {`==R_7TlXm&P[ѷ2z=C2ÌAtqq1ZiO}U&S/}z{bQ&z_}Zav? yYbD*vzA9B3Smha4D S4 ~хcP746==SδO S!)baʪ"~)bS§O mØ/F/ jZ?[JY!Xw,ju5N,F>&ZXl! ͥ<`VC}M'trݾ5bK '֤<;hoX$ke~K- ft w~#k(Mx A:!L:iYHd3ʍwsh^,zA:;i km/H2,z{P責\J@0T:2뱷KRcg :˭7YE,~W9 {!AUr=}W,SN;'cf* T[ aT["|fE-"Fd'3"n{*Q@/FL]hq&j3@ i$1 @
N)IxE8frS~E%m\nC7*Nz:ʪՇ9H&S+혀5] |κ3
-hLJUFzX_OT^+Fj|5t/T S
Jk9~$;nǧ]9fLP8 $5fр A0^-3KwTG-|h{+1j\<ב*8MPx }̫Gxqp}$ oe9KǭH-
-6S<ƊFF{_>@xS(HlR..
-9tN% 끠 >tC 7ٟ}^)>}/Omna$֑QFC;G %7JX >9&Zڬ+H jbxrC3GJ1ಠ Ђ}{4,ہNG<
~QC(qE _}['1dȾ}?IAHK/i-| .1qj|C !y&uO9{Wtr8`:z^yC"/6@%5+3nϠНg6MPGØ}#|'X9GC WQWX0܌f yÁjH<ցJK^;#zs3U!6`7u֥6-A fk75 }mn`yu$BAx$c=z ^Z︽?/t_
- %u& r+z=_B ,]FgMvXtl?/V;o_Hi)_O =_Ori]=_Ov
Ucov+|?
|N/~oG1Do։'x~q,~X{+FF-ؖ)1VH&]F{${O7d6S%C[X%۠fw
0۷N$}׃u{"bi!V}Ry5*
*Ky";vm_K> TD\ke ,!ø:a
X
qF W]oL8X; j0.o5p"xu_XKNOkxF_cܰJv62"q/3mXoC//Zc_ƑR[q p fRo,`>.3r /F'YhDDy
d-4c J$(̜4O-
~ 8Z N-ۈ*>hkӇFGzl#T@:(Y-@$XfsH%kl5uBtakGTGEo2ZMXRR,5fRĈ .Eou` Y w+@~_Kfv51 "4K:s z9ծW'!F"BZF('>9sH|CʬV{ءȝV[!ׅc 䭐dl%@`iˣ ] .AMrMxɰct ɪu}?oevr.X.,%X]Hb*@kU, k8ggWtb`fŧ <+e-6,kxVN+FR0y6*bqE W_"ag:∙@-
{uFQJLb GkbJniR&J{
i#2@e5랶Q֯7"6茧*J:~=!Ҋؠy8G
+ 6u`3 קo~ m
-4_c5@Z.ROq.M\G uxbW|{ "+C| 9:(
Q=i]1>j[ fQְ2kQMUjkh^VMyTj2^ 9VBҶ
-D-UH <"< #V7lKݡ宦m R$/G:,=_L-ye #4]'A" TY1E18+Z aloˢ%$yćÚ,h\1J.sc^Vaڞ
{i,+XulY#>Tb^s-5,(8lyY +m; J t jx88sˇQ[gw ſQF2k, }
- ThB&Xd AHq(LBV aǙDLX%W`F0rZ\]ظ)p] gMK3,Űɽ-/8-/p!
XI(B[~ժGQT]f-?09,W4aviv4Ӈq6V +2f8r>b7O/_}S9~W_o5?~ &.OV WN o~f7WO_>?J~o/懷_ͯ=_ƛs__ կZ_\?W_??'Ѿ_Oo~ <}|
/<}Իϟ!oyξ o~??>ӧ<}Z/~ӏ_?>c99]xb~gg=P<g?<ܟac=D<
-/Version 1
-/Filter /FlateDecode
->>
-stream
-xgٝ畯OB#_Oo=u!~O{_>>? O
g!n/~͏x qpw)xG.y ߷Wm g }/~d=75/5/y7_?_[}wOޏ^|; 'o{WW^wG?zf?7oE?_Om?|_|Y/Ͼ> rwO۞ %'ovw[}ׯ}C J wO_WK{VՇF /m
nVX NS3} _O?zyC}Km ֧wct?e{Ḟ?BLS{ݺ?|~/^?SMnc7o) [߶F$g?TZ~zo)O!疟JO"D ~T+[4Wu«\)OGdP5X]+WbzT_ڝ7Ň "ưc|k |+e^]ӫл
^ՖBxOt)mas\^_kzŽ!'j55ʷ\wJs>5% }z}y4jc@jK4V1 >h
-U H;MQ[R ??q ri; YXMTy^{Fm-2Hbm m!xƨ뒴$g͡.j6c zmSSpw-k uJAd)uNtJ>?^%a]U|~K}ʺ" H|V@yB,V:Cu[uFsbE*FFE,t>]5koAMIu6E%vĚAnYeӢ\T'vӞB"WR_C5?9vFPUAE7]uV"<,ZxV(UEׂ~!-OA1OBӏH:j
:8jC q |z@%zğ+p?+ )Fڂ$!(6*;I_.qרGDTLEDA":[Ypu+|_-"{@$_EVvDzGT"7J.#ߞt
7 LSX~ ]9SqQI5L*T&1$ 4yi0H:#$#-_I4nz(>-y
- ,\y:sDɊ[PàD:1l@
)'D56=XO߉" 1t$d Lm,[RE;粚+ b1+V6o%簴+V77ִa:+Vt 0ɸȹl꺻2n :I^b#Njƭ"w%+}IK'Q.TSbnVp q=tv+Qbu[9='[$# lXZHHKqŠ[{ %{H`8t+a#.+V,Z㸺TMeJ2!_|؋In&d]6.:jϸ] <b b7:n[ Wɻ{Y]TpvD!Wl$M8LJJ MW,8zzqӅQbE
0Yna2_H H2Ond9IF1Q1Ʊz$}t?R0 wXQpa2v ^7^kXTX .O]W̳q'tK綾 ގZ$)bX , 0v{MARЉu09P㶂Ct}^tO]0
Ftf!AZ/-
-ڑ+$,P{
-r[j7p<'N #bi/yg=tWB)LbSSM ]$ kթ̊Fy0Hl;SVgb"Ls#Э+Ȋbż FSXuutr$!_GKc>h,"es̫FB{uu%G|:\WObN W2~WWMcKD犉f N:H>y 41N,9bϺO(*WCWVnJb\S1 x5eVMO7)
EZTxW^C+mܻVvl^cNvt#
T6E%;N$Yx Bv/
-
-h*{*,GPDF,B6^QȪO ?nX$6X_o,4.Iaןّqe w"].*մc~u/cn <]S
-1 QM<63(0ܖXSy-^ lNbk=GiVM-˗
-^vʈ ,;,ۭsisMefgk[YHja WQ%
-e]ܨX. irp8l_hQ%> [o
"bX&4d_A%îf' >Tūizد3Z/^p7Jt&vFt92-ei1ӹ_i \ w &JFLB&Jp54%0%*&~"%c/#LA2 G9vxܔ0 4gھ*_x58/$ 7\h3/6~oq=RwC腔F,[k~@ŘA
5ZGImL{;Po N?z8 ¸!5W <9d.Ql
>s oD] 8Cҥ
-0sAB{yԤ٧z}=AZ_-?*WnţDv$gp`) HѸLyM}
-vMA26%*4sJ^f`h[sn8 p:,_|hf7)z-(O9zX]mA[(/xuYc]`d ٗ ~JX +Ɉq .]y}GVcA t&$-z$& |;. VϷv3Vv 5W z x; j|{dSCɷG\ixo?/e
ڷ㊑o?/JUiT}0j2"iKIa݈(o eg$C(X]Exkqc$.J qC@{
JluQ:"&i`K3 Iwx]H ef|RT l0{=dV6Bz`уՑpSdrh@MN@ 1 X\?)0Ed~`˲Pb3I'/HOx&&f l2l%|&-YydeV\F&ˢcS;K%tDbYK;b[y\q
:Dr -\ۆG#x ÞІzL*ST Cfo[ҜkxwÒe`NrܱQx%SQz?Kh)}j`l!ò7t)
lpx|ETh|{{b/~"}$^4砶F39&$/ R*ut='"^ /aqw,90|\U;< /jq=BWuK }z@=²Lm؍ia S{ ~%^nC.$$Rl:OWm ;Mr fpދ=ΈC[p}/GmNlCLycIa/Ƃ"}k]QNHZf P;^M@}yz ZriV
-{
-Y\NK~)c lX0 =G=x*;rn |g:-&R4* pK."PH'uĔbqO,W b)S)SŧO!KHb)_ d:r` w, $؈"IGzQHi"M(P8N{zDiw,5ŔBU KU oQ pfKjzuQk_ܰVm Ɔ5igY{`rc p~{*C΄Ro6१Z;(!WQ3iɲX8Őc%6Q@ `VHdQ:Ezm=due7SR0!D΅Ҏ;̽". T
, .҇D *R}
->n38 6NH*M =$t8SKF^ 0LƓ\]{L`dwK{ wO6Ⴅ %fw!CI^yDS w[-ާp`)\H9nbL8XYݨ{}N^>k jMnS`)΅K^b q H38 .`֎`D8v( yU\ó^M~mJEz,+0 ot]Їa=)e) 0p@~B ]"߭{v=D`aX$)w]*UX+{f=Wk'Ph(' ƆL$8(6bbJ)fߧe^\u:JnC7 eԅ+U6WJ2~7-4 uTGky2L֢0+%j`-JsK =fb-@ֲV Xx!MGa@[wՑ+(ptIՅ.x?ЏE?&XI6aCL8CEɝ;T\$a$ ^$+/i[85ʇÕȆui2b9(Ve)YqL]K `EŁ9L3,c䤹
-R;`j;ڧQJ([#p2捊ұaw⾋zx }ձg HԡsX&0+ |Y꽅1]R6jLLaA4M+ՌQgu448$lZRؤQÑu t 9);:łK<6}y. FF$ X
H]"7#l$idc KMM4X `
- `:YxǍ}:)rElhя VCX(EX1*SB)z/irL!w5[ {v˄5.a"Hyi0ONM7<9e JaD EjDqs} {{b7|rS)sVoX̸yڂMȏB֗^ XR{|3 2y/=P *CN=^gdV,
-S&7,Ʀ P0c7b^ÞXh!GťҘua SCNa8)a+ f':'
-(^ }slpl) Wx4FF Z}{{baYJ_ 4ˆH( 1lz˸4.,!}6c%^Gj\CvҞKp8o0(7J\ði=0gA9%f? ٫ěĚ@o3}Ro!m5'箓V_n Xρ.+>0}3zNuև ,n XԖe
- 4L!J>ECQ(6#-töѝWI9X]D` CDQۢ[!9ܘkR{ J:눧9lB&maAQzxZ <k=ݘK'w؍?==d*
+PJߜ6ꪝ<
twϩW/F۸Xke3Ѳs_nw 0MҴ~{{@1} .y={eΉ@V)uz81i^aNtz{`\
Kԓ88Xv+0tІUj4i~ { {B`2F4X@
-f&DӴ>`mL?C[S!/@A(<:}_wb(Sn-Twn XkbrX)3 䄑ͳzY*nVy-2 YN4#:\sieb rs!10GuG,FOhfLJNeCCrNVt}/p ڕ$H>|[6 9 f#P zby7dJbĶRJXS!@u0;S ۸v ҄1Z}B
-suOgT>8\
ժ*J3WCNs·
-)1 f|=HX b0|Qf;Tj nKu;6V;ںNۨ|bg MGRXIؠׯ){ݶ')A
z%M] K%un.C [tdX2a$I$rK hLE.NJG՟1 av2a۩^#72?9 ;OaG#MEҬ,SPR> eU0}T+w5Yi*\| ?d ~ !hVG25"Z f_.zh Q#w ɧGoWaY-ݡ6E?WKdulX%k}}$YiSmgiͺgZH>:1,0sLY>fxll`l
-N4[!ql:{[)#cu7"y)wCZC$5Hݡ٪REc7^ל|@)3ǩiPRwU`RZ]x́jbY^2ct uɇѧmM4j @:g`am0 s
- cf
-Y S Me(Kf|=pnSg3)b38'@X#1I( m$} sbP_8.m/"dh YmRm38 .de
y};djE qFnN{+&zRc}ᗐrӜi
-[yiq!`doZ8ZRRYN$ͰC:"dEh"ɩ[@!(&/ĉmhI~^_>!P®Gn@i8DQ!y+Anm,-(
LXv݊'+"iQf&`;VNB!^ȯsEX~LۘXFÙeR[9 El%'7Uz\6aLfy Oĵ"kG6כC=M UY
-yb .#ˡj( cGF^#/
rY5lSV3z NL&g%[qd. ;2ZiPLڄqdr^SYUFJ0Z ٍ# ъ [-o:OXr G`ё~4qtM
qd4
K %ikyd#Cׁqd IDwm+9Qv}ÔZ5xWGއ%jjƙ IhJ!X:Ul¥7s@U:cRͅ?!7 PdFdba9D칽Ѳ"H2]:,(Z".!`uH2L20m9`Ko.Y<6I$v[-,9+iJ#IAЛm҂㨔Օ
-g-hFy>*>dY_lrE֊ߕXtRƏ9q?"dT:ݰJ:d9k @ f?Vkcձǐln$Kf:(HܺYreҐ&PQ],`}6Y%q7B!R]1 jgnqk.AUtNP(6`% y#
V9[T5cG\b;B^[G*}1$cgt븒%>nQKy۸q=KQaC tpV<"~b;e`˚
U&'SIݷB Ej[Ȇ-}`U j??H 4JO B'?rc
-w. 23&YX871e[L X`K۪6g2iMwZT;K
oK6 kJ6ѺBM9pch֮14lTP4{ S!}[chgt6ʁ-dHu]i`ώ
-gke:迧Ǫungxny |EidSd6f?ƫOjX"yfҰIֿx`k=Ia q0naB# %CHgF;|"To\ǰT#Q0&(^aZcK,먬2͠8Q~n_n r0D=/OG? xM̋L|T?==B-ru{ml_Z@a/AQOhӜIF^ xOkC7Cν ==4=+θvFY2![[eaO:7X۸[nr>B Nm.(8+J2Nc;V,!|;==z;KpuWwL[^ Ts4H(9nZj#q )W6]Chu#L; 7U}z? Ų8+94Fo/4pt-M>švj4 R #_V)xp&2_n Psesf91{%q(Ąa~j yY?fHvwƐBy켎A[jދ=܊皛tP | O,G}7Q);vZJEEEEY>ղ-6t"<,{o+ a P0HSy6VqF̺Wh&҆WY-E9
9Lc()[esʲ={#KhGl(T@1c
K_[ܺhvV]af=q\/`qO,®2ѷFNR,"j}9IKmܓ,ZIЦN?d k\E{mfMN[i$u L[i9 =G=7䠢6}Rcb* !qi(N
DYkZ2rV l=CnpfEvz>,&^a5K}܅ЋY:-m9'OCh8)Zߎ <:
-|x ꒵&ԡQ-6iV+>bc>*6*ͻ3
:G@:&d.Pz4'+ hK
-=&0zUeNGE* vp
-`S
-kfLmǞ)4$t K2, ޱ:f RIn5:uê}Ι S?&r, ~xzz@M|HUڵߋ==s^N4:6Sz3+ܠD^P¶Pi`ܛ.1B*즓LZQ ilP7 =#ag)B"Ee=]%6hK}ZXP:c@gnFʰvY3MCih1e?MɺV,Na9E}Yi0eaڪLHp&>.Caޟa{ iZN1&bL$ EjC~ Ni#"B)bSU?E,S§O ">nØ/F/ jZ?[JY!Xw,ju5N,F>&ZXl! ͥ<`VC}M'trݾ5bK '֤<;hoX$ke~ݥ3:};?̑q5k<ozfUPaB&^m۬g$SLF]4X/ Vc{ۇ6$=`a(~tYwd%b
*Zj o%M߱3~q֛,q"n| ~JʠVj Ѿ+Bl1NxH3 [Wlߎl-0l-wU(߮Yb{~vȺ,,(:Ɍ:sJ6vTl#?!3SZZnn<; PB"*O!ҘmrI6[ ".nk@Y݈=\@vh=m@c 8ƴ} ]7"{ 8L(=( 'X;vѕr7nHEkז:<|-$Tc*S Գ#ϵ?gT9ʣW@NS~$\ҼRIBSِS1b<<<֠ ]ܣHRv7KC]`+J`?]=nfӾm^0 R*$CZOt t)dvsRQW `n^ɷ
)B
Xx caTO\L._i瀆v /O=E]]'+%mTϽws wk`YNm}G5Pj5 eP uLg@ xZۉx76[E<ւ+øͱY=:V;or;Hyɞ ˝w+vG(72,2ĵMDrB됰F5Fw僀Ic`c W:*vR/RfJoX}lI1I6Ncɗq+;yb?cS0E_Q? : KFZ?-)Bm\ R6 h@¶ŪZijb)|%X >Y6>NxjƍԶFQ|/ꍊS^jasJ;&aͼ5rWlc$_Lzw?R nJ8_s
9&ԶqAonz!έisN@/$ɡDMY4Br2W UQK86|70H(#\ #%, -m5vMaAo3{I͊ r13hd&t;t5MnS890f V鲖 >{8q uC)s\'˨ns89`1lϢ|T8 7ٶ9|Bp`9py.RfEuR⯗9NFĈ^nLu8&XMuiKo^/X*m WV;nﷆ9 ||": a%iIݣ\&pboKWYqF/d> |yZJS0w t6Y*]o-I`^HXZHU T^8J
-7*bN qW'2O!UeA7sهK}0gaC;CmܰpD6Ab5N,68)25?K[M8\^ݗ;֒ vW(*7lݾ?b
H )+ˌw֛ˋ'X.q߰Tt q K/غKŌ|æIE )Q^|DYe ,A0~1 J#3'SK_G`CSK36
-O$Z%х x4
-da & ٜ%8k1)`JacǢ5Q8X
-ݺ8FIb2!n`j@e~ ?XCkGihBa1VSk/ s
Rj Jɐh| vl"h$ZV '[tu<(
-;wB oHMH Pa>Q/PDC4O," %2 I@4:̘_!lsTj#dR* P3(^Ss5]<:cʈ9j0 $=Q'ߺVѷH6bZ[T paԊ 5*I 5 lW ڳ+Qj:1~?uNwS2
-AX}Qi5B+o#L)Y5ꆌZtTSZů+4US )4ժ-៌Bbmn.b,2@GU )
ےvtwbti[1t!+7 ζqKtK vBAMI%#UVLQ ;VGpX&hI68:I aŰ8 >W X|Us^um
-V !hr9\f(#gu
;0K2J-f)Lt(,&:ﷇRTrEL`+Tk_*k -,$k1SY O]}jY4 d)[DLHAJV6.-q Y#I.9_P.R6܇˸!K1v,o
-}?dF_!!H1_tˡC6niY|K QMqkE5nt#;\uFnB_S3Z-1oArnKVۯ O/寈K<~u?ͻ_<՟.?G?_w_}z=~ր_}wۋo}Ox?՛{m__g~?z7~֟h?|o=>>? >[gOۿcc~߽{k_x}7_7~x S~c-ї'ݛo~}?{Ϝg?ó㳶V+o~6͛wO
zgg-#~
~Q*WN)3n;[~W>.x4
-endstream
-endobj
-16 0 obj
-<<
-/Length 10
-/Filter /FlateDecode
->>
-stream
-x+ |
-endstream
-endobj
-17 0 obj
-<<
-/Length 4614
-/Filter /FlateDecode
->>
-stream
-HWYs~Sj2 n)pW v%J DmO
-Ryϯ{^TYKi'ANVҍm8|}$Hۍ x{pI2Lno 7A ~y#D dm$q?m@ʪiہ-v"UF IЕ{E4Ul#>pdAbAA}^z }?Yaƒa cߟtu=/s։ Y, l'\[^b;x=At:-Đ`pKFcZs/čj#ȟkUxm*cև~h E[Q}A|+hHkNEY z ``ޛթi{US O+ ;Fȑ ]y %ڛiWԜ犬FAmk o"KQ!ilt(L}VFmr^Wi
-hv
-s[чH MTǐ U2 # v=;:ĩmP:/ G M͖QY[0\R
-hP/%],"E n9Ul>QgU QE^L
-y֜- BNΈEz#;T- Uj@d (i]
-32.!c>iiͰr5t&nݬ !70DEAL vT$|N,/>Q N1m5`,Sb B/O QgOCɮ%tt08/ |1^-5"+!7^ q6)LCXz/I 6<Cs> juze
_(|qOV ΅u/0& aV*"Pλz6 ;45XZ h`K:SpIƐ\_z-EIFA9Y&BKRT8ԡSA Wn}Cf;07a ^e!-)d:o_uk.q唽XxULyt 4v$ ̦nWbULLBsV s#;%Y w"" muq xkǴ t ;BW7 b& |2Ԧ-h_A)I膽u_D5&/h /͗HB#w
$o/tk{ WMeB85 xnG沋"x8Q5C
-(C9=҄X0de̎6FKX~iaU z0Q'֓|w d2C;tM iG5撉A% UH 6~0B6lRt=q1Lρr-ˎD'qYzfKBv!Kfg ÔczuހqǵC6m0Y(pM3:lc'|2}?Yn>0NQS:YɄ@ҤMZݱ6QQL,+leD"m :3nYܑQdJIG0.rPNc ᣁQ6EzH0^]裁軄hˬ_?7qNxxwD8m
ۨt.(l7c;^dxe+?B~~~84-/aYpxÆKĎ_vتNb߃R"c3l١cwġĐߟMm%ACg-ׁoPL Ov0Zgʱ$^Yp_J";3aԘ-
-e '1abTJO :(|i7 Ǹ{fO#4}kܚoVBfm<ޚg5 ƥC}#!7Exm~ %5|Ďc }DD$eQ
- R:hřʗ+R!HA+
?<l~: 9̍&8 {hL !X+UiMPX@-`>_j}R+J]$ =֥*[Kˈs(+?<ȥ\ { g7[%q| g-LLQ`O S~ (]|9u܍l 9VaxCgYbUf\nܩYO!Aͽ5w% Ŋ C
,?؆s%,Q9#h1S{ju*#n-j9:[*3^*;Fu'I8M݊h^:cU8c
/h?Ka-p%o+{kfeO aş졠o/j)(Y5@PܝOS WmwvVTmݦk30l~mI + u! $,0E_. j
-+yxGS8B-"B=H݆l;JZ#6UOc" ilvL=h6 Vyvr="> pyܱH5Uo
- X" g#"JT)V;cs"X
-вOQ ƋڡL y f|#ʆjZ2Az٭խáM݂T_P
`(w_j .=1S> 02^<\5-mFeOo% s3 )+Z#[7ѡ1^(ԫ$6͂d?|s=Z"=\q5ՔA@{Skh݃
#zeUڮFE(bx<+Swy*7u"rk*E8+ej, b"`rCjDIa l$@"
("RQJ +0'hڊ> S
3'7[M6#:U)WIa0'+O r!Q)c uz
8O&ovmuba67:x<%UCAƲ5,^Gٱ*L]MjO_?w9VQNi :\ZƙK})No"MCg%*](Hysz7M!{BQk_kaYI先վnS2:RFT[J.{h FNv"߿{5v8
F$XxlzԳן/O]"5Rl3S0j!S*VNHElj9N(%eLeDrt rC6g 3\` iH:*uv/^XGu3|D=Um
D"Y^} {r'pq
- ~ܽ|?6ɢ(v,19U*U. HW= LD
-endstream
-endobj
-18 0 obj
-<<
-/Length 191
-/Filter /FlateDecode
->>
-stream
-xn0Ds,Re K&7iU #q
F__Wsy4CKM
-%+'Zّv
zML
-m1 kz|1M.w2' } aB6ţ=:V>ocӽPJ9BjV+np|w۶8 )Y0JHAR
-endstream
-endobj
-19 0 obj
-<<
-/ca 1.0
-/Type /ExtGState
-/CA 1.0
->>
-endobj
-20 0 obj
-<<
-/Length 130
-/Resources 66 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 54.442 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 68 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H
-P E| u&mB:H6q,Xכ8{ckDcәZ! Ɗ^]^m|=]/HCtPAo*Y SQee)n9O #
-endstream
-endobj
-21 0 obj
-<<
-/Length 157
-/Resources 69 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 140.504 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 70 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H,L0 `Hz>ҮH -U+$:T }2a#;F & Ą[h弣GzE:$c; J"t]RF-+WjYfK.da #+AUaqTK̐+{> `*|
-endstream
-endobj
-22 0 obj
-<<
-/Length 107
-/Resources 71 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 8.0 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 72 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H2PHW0PpL R5PH
|d*Db..b< 4.t 5327VIҰ0Tr
0 1 _
-endstream
-endobj
-23 0 obj
-<<
-/LastChar 180
-/BaseFont /NPSLDN+NimbusRomNo9L-Regu
-/Type /Font
-/Subtype /Type1
-/Encoding 73 0 R
-/Widths [556 556 167 333 611 278 333 333 0 333
-564 0 611 444 333 278 0 0 0 0
-0 0 0 0 0 0 0 0 333 180
-250 333 408 500 500 833 778 333 333 333
-500 564 250 333 250 278 500 500 500 500
-500 500 500 500 500 500 278 278 564 564
-564 444 921 722 667 667 722 611 556 722
-722 333 389 722 611 889 722 722 556 722
-667 556 611 722 722 944 722 722 611 333
-278 333 469 500 333 444 500 444 500 444
-333 500 500 278 278 500 278 778 500 500
-500 500 333 389 278 500 500 722 500 500
-444 480 200 480 541 0 0 0 333 500
-444 1000 500 500 333 1000 556 333 889 0
-0 0 0 0 0 444 444 350 500 1000
-333 980 389 333 722 0 0 722 0 333
-500 500 500 500 200 500 333 760 276 500
-564 333 760 333 400 564 300 300 333]
-/FontDescriptor 74 0 R
-/FirstChar 2
->>
-endobj
-24 0 obj
-<<
-/LastChar 180
-/BaseFont /BTYMOO+NimbusRomNo9L-ReguItal
-/Type /Font
-/Subtype /Type1
-/Encoding 73 0 R
-/Widths [250 333 250 278 500 500 500 500 500 500
-500 500 500 500 333 333 675 675 675 500
-920 611 611 667 722 611 611 722 722 333
-444 667 556 833 667 722 611 722 611 500
-556 722 611 833 611 556 556 389 278 389
-422 500 333 500 500 444 500 444 278 500
-500 278 278 444 278 722 500 500 500 500
-389 389 278 500 444 667 444 444 389 400
-275 400 541 0 0 0 333 500 556 889
-500 500 333 1000 500 333 944 0 0 0
-0 0 0 556 556 350 500 889 333 980
-389 333 667 0 0 556 0 389 500 500
-500 500 275 500 333 760 276 500 675 333
-760 333 400 675 300 300 333]
-/FontDescriptor 75 0 R
-/FirstChar 44
->>
-endobj
-25 0 obj
-<<
-/LastChar 120
-/BaseFont /SJISBO+NimbusRomNo9L-MediItal
-/Type /Font
-/Subtype /Type1
-/Encoding 73 0 R
-/Widths [389 500 667 611 889 722 722 611 722 667
-556 611 722 667 889 667 611 611 333 278
-333 570 500 333 500 500 444 500 444 333
-500 556 278 278 500 278 778 556 500 500
-500 389 389 278 556 444 667 500]
-/FontDescriptor 76 0 R
-/FirstChar 73
->>
-endobj
-26 0 obj
-<<
-/LastChar 50
-/BaseFont /PXOHER+CMR8
-/Type /Font
-/Subtype /Type1
-/Widths [531.3 531.3]
-/FontDescriptor 77 0 R
-/FirstChar 49
->>
-endobj
-27 0 obj
-<<
-/LastChar 61
-/BaseFont /MFHPJJ+CMR10
-/Type /Font
-/Subtype /Type1
-/Widths [500 500 500 500 500 500 500 500 500 500
-277.8 277.8 277.8 777.8]
-/FontDescriptor 78 0 R
-/FirstChar 48
->>
-endobj
-28 0 obj
-<<
-/LastChar 2
-/BaseFont /SOSCBV+CMSY10
-/Type /Font
-/Subtype /Type1
-/Widths [777.8 277.8 777.8]
-/FontDescriptor 79 0 R
-/FirstChar 0
->>
-endobj
-29 0 obj
-<<
-/LastChar 151
-/BaseFont /BVATLI+NimbusRomNo9L-Medi
-/Type /Font
-/Subtype /Type1
-/Encoding 73 0 R
-/Widths [333 250 278 500 500 500 500 500 500 500
-500 500 500 333 333 570 570 570 500 930
-722 667 722 722 667 611 778 778 389 500
-778 667 944 722 778 611 778 722 556 667
-722 722 1000 722 722 667 333 278 333 581
-500 333 500 556 444 556 444 333 500 556
-278 333 556 278 833 556 500 556 556 444
-389 333 556 500 722 500 500 444 394 220
-394 520 0 0 0 333 500 500 1000 500
-500 333 1000 556 333 1000 0 0 0 0
-0 0 500 500 350 500 1000]
-/FontDescriptor 80 0 R
-/FirstChar 45
->>
-endobj
-30 0 obj
-<<
-/LastChar 119
-/BaseFont /XTJNWJ+CMMI10
-/Type /Font
-/Subtype /Type1
-/Widths [639.7 565.6 517.7 444.4 405.9 437.5 496.5 469.4 353.9 576.2
-583.3 602.6 494 437.5 570 517 571.4 437.2 540.3 595.8
-625.7 651.4 622.5 466.3 591.4 828.1 517 362.8 654.2 1000
-1000 1000 1000 277.8 277.8 500 500 500 500 500
-500 500 500 500 500 500 500 277.8 277.8 777.8
-500 777.8 500 530.9 750 758.5 714.7 827.9 738.2 643.1
-786.3 831.3 439.6 554.5 849.3 680.6 970.1 803.5 762.8 642
-790.6 759.3 613.2 584.4 682.8 583.3 944.4 828.5 580.6 682.6
-388.9 388.9 388.9 1000 1000 416.7 528.6 429.2 432.8 520.5
-465.6 489.6 477 576.2 344.5 411.8 520.6 298.4 878 600.2
-484.7 503.1 446.4 451.2 468.8 361.1 572.5 484.7 715.9]
-/FontDescriptor 81 0 R
-/FirstChar 11
->>
-endobj
-31 0 obj
-<<
-/Type /Font
-/Subtype /Type1
-/BaseFont /Helvetica
-/Encoding /WinAnsiEncoding
->>
-endobj
-32 0 obj
-<<
-/Type /Group
-/S /Transparency
-/CS /DeviceRGB
->>
-endobj
-33 0 obj
-<<
-/Length 10
-/Filter /FlateDecode
->>
-stream
-x+ |
-endstream
-endobj
-34 0 obj
-<<
-/Length 2943
-/Filter /FlateDecode
->>
-stream
-HWێ}W
-<4룍NlovGxP$%qMz<ԭIJKo$ VWߪOU 4I V b)'N-k,0 X6 #Bg3wqN626D@q
-H_E;vkH'_ ժ?e>Th_D{pu)9hoN/*䋺iM niF߮/Љ4A&^0;$f8WJ;@D:ZS Ǿ9NqAaRm^#%%\JXZG`t -qd:Gظ+v p*4@S['2 ~;4yS _Dkq$i$t dƜ4Ե: Xҽy{Xon`ˇd
-x%<1.S^q)
8+ cvdG 6'` A$ RVm+=:N4%:`Sw 9 sSS5+n}i/^KfP bn s1RQZS'*~߆W˼39@:WH1i]3ʏ eP-0(8gE1WM)I@8O T)FiP
!j_)" ջ w$z6 %HH*&Cj!;T5WB vMXґ&j}ʨj ,pM
ne,c[q(q} Pq=Rs9b˲;,Ax(a,^4C~JĹAud֭[}( 'dS&%b;H
zDZ;+y %T{4R^z Qa!
Xq9gպ'X,p
-T;a[F!yލtX!
-PD!&bk¦gIhϵp҈" yQbu<(kId d>"Z崧!!آ@ھc?;/}7 |w4~eCxZiq˶CW 6LBSw7o1 h0ƾvY[Ph px=[7``@cGk)My5 ZܟhjcV =hʷ:AF9 BXu4qq#Zj-,uI#M#9\|Pw<ܺVA`rb(9I(d>a0:a0lXBJH"O(IOy}/sG"80JV n;So :EWRm/9#HPiٚyD)`>?Y;6HRBt/,2AşZÿ %
-endstream
-endobj
-35 0 obj
-<<
-/Length 192
-/Filter /FlateDecode
->>
-stream
-xn0DsRe ȭbT~}ifG+Mӏ7͞s@ j*$ʙKB,9?囏hrڔ9qۛ 1O,'+wy |UR%RJI%_M88F {SdGt /?hAM
-endstream
-endobj
-36 0 obj
-<<
-/ca 1.0
-/Type /ExtGState
-/CA 1.0
->>
-endobj
-37 0 obj
-<<
-/Length 109
-/Group 32 0 R
-/PTEX.PageNumber 1
-/Type /XObject
-/PTEX.FileName (./Images/esquema.pdf)
-/Resources <<
-/ExtGState <<
-/a0 <<
-/ca 1
-/CA 1
->>
->>
-/Pattern <<
-/p5 82 0 R
->>
->>
-/Subtype /Form
-/BBox [0 0 440.269 409.878]
-/FormType 1
-/PTEX.InfoDict 83 0 R
-/Filter /FlateDecode
->>
-stream
-xe10D~O/[*5B$
0B"J9_zIiZ{h|CFe95+O8 DzgzꜱW
>t}%D ;
-
-endstream
-endobj
-38 0 obj
-<<
-/Length 130
-/Resources 84 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 54.442 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 85 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H
-P E| u&mB:H6q,Xכ8{ckDcәZ! Ɗ^]^m|=]/HCtPAo*Y SQee)n9O #
-endstream
-endobj
-39 0 obj
-<<
-/Length 157
-/Resources 86 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 140.504 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 87 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H,L0 `Hz>ҮH -U+$:T }2a#;F & Ą[h弣GzE:$c; J"t]RF-+WjYfK.da #+AUaqTK̐+{> `*|
-endstream
-endobj
-40 0 obj
-<<
-/Length 83685
-/Type /XObject
-/ColorSpace /DeviceRGB
-/Subtype /Image
-/BitsPerComponent 8
-/Width 498
-/Height 498
-/Filter /JPXDecode
->>
-stream
- jP
-
- ftypjp2 jp2 jpxbjpx rreq @ - -jp2h ihdr colr jp2cOQ / R \ #B@ H H P H H P H H P H H P H H P
- ϴ1P҇
-RST?/Q ІiaՇal].wϵ АI_9U9! ym[zo`FɆ*}L
- pB{m]# t}F} j0.^9V @YwӞ'ϴ fHIʪ[/,%ݗt h rc0TmCL> 텨u8' IBxϴ
+ 88U\~b` uT Ud+10t4yЯS, l"H#w@
- p\Oď:CO.8qAs :O6 `ll)Njϴ|Ul\Cժsn7ή4haI\6
C;ZUM TA3(Oyϴ ЅȯC]@. F5L>1y !:SiO_]T˫oOLLt
- i}f 6S1Hi\&M)gKW8e8XEf/Pc /|YNT/uf3+/C8'M mKS橗4g]
-o]ɜ$=X5>KL2 m.i Y}f`_a_H0$ƜT=xGʽ
:ucCm%j59Lҁ4"ˑų;Fw{0J>ŗNL0u^66M:pH*Ͱ!C%B LZ?OK&i Q}f6+:O9ȼcDŮ_ rd P:K3
N C 3q'Q2 %sX"Y3/+'6@O_8mP*}FԻ{gR'Q+ @!F9+5wp>
- k Y> ?]Z\ ݰc[8UzG ;{+H&U$2 لZ _58JR}[);jd fo%C ⋩;viv43p}9z`^ix_PJ`b'Wx"@$b*Ƚ?ztm Q}F@6Eʠī1,ڋɹ&n=˙Tj(ߑ9ӌVZ Y3 ȫ{Ʊ^g7 M5^ ٩hLwS
-ԋC[yS䷕)NOW[gQ#cz 'K6PŲƤo Q>` s\}'뢭ډ<.a2w%D#
&CU#t)XUpP9a.
ՏI#Ū̧宵v#/}2bB 2H G~uV_ Uy!./(KenoJM;
- _ Q "$ ?lR_mښɞgTu OZa^D~
emG$ r:Ó0ts? wFq
'zQB'+DN0ge9Q3#awp
-fQ D+~B|z 9i>`6`ۉ&EBM9eW `o/P~('JI$0:`xQ +zN ɈxG:gۿM}fʍZLXVͩQϬR#Ͻ\2+i__n.0 /mE\.) П\
u.xc Q> &GaP1AvrEV^ V<d_~9ߚǀ*xtw2)2fq,$,nL<B~%-+A Q1XA&$[abupdV[~6A
- _> $ 3~C+De>e` O$[ e%iX]+]aWa|w ~b)DlcUKV>+> TaV>>}TLYiSON`=;b1g[d] Q>?.TZ*bU-Q1Fu;s2l2P|&SS
bŊDխ.2Ee wB9Ce.l|M2)~(z{FвO55hq~d" Ufι$ Yt>u`CIXm"
=-S^Cm ;U,5ȲåП#CƄ6 ]#7u[~; 6;(
-$G`sr9vVl wI
- Ѫ#QiEudbf]& KG&( CL"}-GZ(PRKhϗ>?F0$ (ՇqV-S^P*×L%ߕط /@qK1gd hxTe _
og'FSO5#沦[uRU(CSpzhnKu;ff$CKְϑ-ю ҋ#JZS %귧Wע^p<̿ec+]f*S\GRj3ԮVK^1aht1ȸ*AS"P-A*q`2s鹊Xf&*D~lXij&ld0|
-~Ff(ˎQoyIi:/% 0 ^O+^{bO]dG Jysb;*.%W9*s!ȪJ2ٕ ~
m>*?"DfՏ=ŁʧU2}ϮVѧF%hk%JNק d L7窌v x-fU3y!mrwR~vfDj9D%O2Gu_/h@J!<:X6ޭ=NӚnY,Ð ćw 毪8 *5T?%M {A!LW6PP@Ir:R0ot+4GO4H#$L,%RCz||
-)^
-%[9A5mG-$ <ѯ^O*d]D a] *?FokǺ:61pŚjoѫVѧR,"[ xZ ?e6P+'S?A)6 pZg?է I!~f2 XN X@94Y/'vdF*c&ѭ1?B`>$8]"Z2}Ó3ٟ &V9*-X9 V8ᝇ+͚ۖN\3lBsᚎG߆g(I5:C0G utyFng@:Z(N1arSyI7#z #L :lTcgx`ja?_ڇI?GFIAMǓ WQrk^^ 1xʞSoz$_ 9%(*fZ4+2< =FbXUIʑ ԟ@
&0;֠iA -j- H7BLIm_BuMú* lD O}Yp
- ϮTC *1\-`9a{X쎏k/dplA094?h4 vaғ51
-́D( Þ vt *}9DCh 2) >iFsIrLvvާ93dgiСO/ގt^mʐO% L,649OѨct 9iUJ%ѱG
-ծ긏1P?Jqu_}@YDT6fd9j=xp$d=
--Yh
evenw[IS$ݞewQb4g%
-3gk`=t` H,9$ J˞+Y{Aq ]wO.ڄ+$oŷ=.@7/[E FP*|'3NnoNX#Ҭ e4:WEr.* x6E?5lAO.heFN)R TBoL7}45 a}%aOcv qNJ]/A!: |M=/+ *$.2IjyM_
P諢plpΩ SתHnM3[ek/2̑*!~v!r|PuԤ^TY?0G =p0@3.lI~/>^q:;0&z-Ϯ]c F6LjX1:mFpG]A*'ؙgվbb Nj,$ 8̜vAV) 1 [vuϚdۥPW%3Bً@Mh'U]"/B՟j~ {:pƵ s T2[̗iBjhJl{h qu"afݜ-`SMn01IHgM6L_PΒ$ή[%3'a,4%3W 2 7o211BɫW 41Ŏ
9GTdfj5q^ε^ $eJf(Uu9?C^1 LG8?cnR'}كŠRR
-to oƞ? C9bUv1m z.@B zT@ ˭`V 9$Edl^(ȶӔY?Y$ r~UNʨ7*=뺰
-p|$ E?/X (gj hǩa9`Ga{|f
ѩiMŢo JQ$
-]ŖBc QM
-֯gx} #kط:0\ Q}R3ƌU'=dYo)]K !EuƔJ
h8yrb6i&
lio cRi4:C36.ft|Q
-O+qMQۙT^6jjwO|1pǷm u~ԍ|Ԟ ߴ5~4me,u< I= Z%w+,hcb7GZaDNߝ]2 6,ZS$߫P0$M:M5RKBc3 ۴% BdK jI՝QBM*n@ss`BĥI]I-uR-i3$.5Nɯ'r}V ƶ*Ȗ&>$qlꪾH]
GPB?G 1c9YѨOϢF(аRA/O\Lk G M|:sUlJᝒaoa.["%l &LVN%+(WeCn}&OR"QT.-{,sq` Tz}ʹzxB E}ft+]0qb̰A4E]_kq$ߛ d[WF5K,G
- R
-_k څ|{R*lI
-1@;w֊9t&gؑs2
-
- z]g\.zXs} Fot-Vj uU` lAvd'vK#G72+" R0et%uɹ ]6' ['` -Xon(Wȓ&!ۃD^ ZZs^?Y=AL,&js͂aZ"!xF
- ѣQh vrC
-q[ 7rOy bwFΚ[N x7@ǁh+_(œg'XS Jg8 :)as%ukC\oH8p~S`z*4N_, u
wǒX;%cn9V9vI
00W ) fj|;χOE!ۅ,8jc
&pvϞiNaf|=m]X2OTe+h
-0s ̐BYcFr"(~M1ew?s
|c?Wq+7CA51s-ˤdrL,#YC
x%ױѢ_O=";8mP\x$q@B >gc! 9:;R:(bά]wok"
t]j'
KT$uWq+#)ug}hػUҁ&5\OQ*eY)YD-a 53Y5+$֣h|4>x+r:gW]r۬(Vs?Su{f
NA3/NUXO$+ZFtUoӰ0mo 2nQNdܴxN
-*KGt#1R
- ߈{`ic8e99:!w8,I)U ^m~L_WiY/Aݕ0 c בq 3^ (YA{تIkqSm0[n\xL=_ǽn7 2!M{JTP_v-XIRa#xT|ÌSKiɁc XD<*e^{jMŖ!_ZE.qh,"7b#(IBJIFʹґ6jXӌ39ӦdKՇGZ{Bu('.^rHc/{&'8`%~2HYU +.L{ hfk2 $\}Q wZcwЏrJ[R^~]r G8UlI
-WXMOExZ7b
-G"q{5eB}`f N.KzvX9mI?_*&n D "]/ e(FE .guq;ؾԂ!h=A̔xY2_\U2 ]TqƊ
4{ {4kQ2
N ٳ7 _\į]Dg9K(]!o [RCgv$H@H@ؑ--D|nڙ_>qG)?6>KuuXCN|2.`}8;Lm>YDŽR,dMQ--=RL y20yk~\}?$Mn8oxÉ;bd C7 80`{K=0I:18zygM/-{:֔kk8%b3ocE f,:&ګ}S7F
-Jg %9az]z"OKOo|F/GK(8q$ OR+w;ɓM(x/)[Xڬ{3KY@
-9܉
-]2U
-h0c~$1sFdF)o dK+EM73%r[מZ3`SVwUӃeBzv]}pϣD=bk5D%뱴\s*8OdtB]\øiKn c<Uu4 {k"0D[E 7d tNa`PI sUc
-߁HWaϷ6p9u'6:F
-ltʷ4ڸM{$זkYفNFvNv`w:=$糽+wp~k^cT
?<5:ܼU$ N!-U~[؆ >%^I 6`^*]. Y?GG)Y@A'iǤ{*E"dN 8RZ [4 k@ #fTy@=aHV&q#VJ
-@X;0 ?V5Y`T{]`n FG}w6ESo4W,TDg4
-;8q)A25O?O q*e8eHkr qK6Ǵsf@R'ݹĂz@4y&MG3 4a*-APq [=A &nJiR cMB8LsbrdAhjJ>6ݩ (`odƮ$|MƉE\X8EmH<2 OS<燍 #pm_JSEˋ=c}ݾs1
wa8'htց=mf[
í/wDA`#Fg@n<ɖ3-eh.$8[x#p_.tUӑeqL
{ .տW=Yz72zV\jk.yv{ҋzO =|%P#qPi=~Ui9>l/"_풲5ոL3E@b LLPs)?EO"^,UFE8g]x $_7zr8 bVPSB/Z żG5 tǦč̂Q&g T2ڈprLSy h*a9WzsLQ ݩcv4̓ v& ph
aa $ʦ~BAH 2+8g/, u+I
-zl90|2D ;c{Eg73H/
-rThBS eOG4YEQ$4Ja78g ,+ܸRsbi&n҂N$Ã<4b8'WFy b_qThh[Jo9T&< U4o}<=tCċ0ه]JhmƫOg T
O C<0L Txj2%ݑ.($wYK/ޅ:1ܠ3cq]~v Є NT+ā C%^#Æ4t"?o J{E`XAzR' ;߈z7G{R /If8#tG ~R7U""VT24w~f!@Rf{xO;2CA$Ku= .[a~vǫѤm2,|}' KB *} ;F'z1P+kϵIpWu`vEZ%JALCʈQ p*5ĸ]`uf7uIC>NuReϐy6zHpjQb`J\S3HeAN}d{!g%Dvu6F7* mHYu M)9a!E}Bֿ lV O&.x۴F, ( ;<* BH X
-Pn|N[)? Hkn(9^
-*#Ӓ{CrigN05]V
$HgyUo/>?wT k*=niu T-~=sa-z 3]PX) 4_ DI ٷR>`h~` ɂ%j 7_zX4OPV Ynۇ7̌o6lȭ6٫,R^8*!1[N+To w) k &4%u`X_ήE*JlboJr?^Of H2S Wq nCtߝ?XlۜPH[57UVa3M"{{>B *-vP29s?6=]vk `$2$#B3gˎx%
\6q*x-ʩd *"Mu
cnuf^#gXʻ?kOubq|Ql͢9 $Ӓf|ǘݚ|bn
-BaA{͙%3\Mt/]"MAt#SyA+^#=m 0>ɾn\{VT A4+,jowrf>k3[a 3:/! 2G3/ժqppZK*#2 ]^ Z)0nH{2Cphfl݂& x*o\?t&ćE m :۰Q*@ly9(/t TgY'-J8@NXw4Kra{{jP "h; Hjč(ZٹaBB3ӓjݪQsbf}L@\Paü<|swܼ0HNaw,BDв3#MLY5 4jDT~Ջ&+s5xF +Bt{n ~$'ٶm&3
-_uIȒiIDHGH0td
-]UG Ii|¸> ' ^ Ӣ0d֩-MBjY^+y r~~6,Lӎ*t,.ićPD| +?~>ٸG8,`H/@nUϣ3|z- KS.{ҴmD:>Vu%AsY(KN Fs}.C|~HG=5vnn[WM❀N> 6LƠtN@ӐfR IZ t.\L1H yp:!_כ~L͑9I'~IHۘ'=?$+5Wm#aMn!`F3 :{Wrn_W{ Us~#g bݬR2
-0_XJƅm3ӆl/>)c. J% a {᷐5kL>PLPh-l ?do GJ!BQlCTx J ]ľ+&1mP jpnFh73`}Ӗ/q~ʴei/6:Vߒ~XCc"=0 X_f̽2a+Mb B nz'x * s -qe D bOI.;T9)('+H"W~!+̗2<{J
Sy)K_yP`+ж}kt_)l2ӗQ
WO"N
-[!n6 E^zD8rʍ Z}bg +͵##D(i ZNǔ ?'G,@:߶K4cfEhJ"vm4-
-,_u@7\F-{GWgAčI') ) o3g:cPo W"h7) tW5d%VV-C֧1bFh&q`PjNXϑXړ~Lg\j
r
Wtp%I- ~Mm9}eSrs)!
-i*m7CmCMJφOAfvk^Xvu`݈/xjZN (7O4>l [ )jR#Z Joy
G@LBI2>_nC{EO?gE?-Xc{[^SnSq\ C֙b{X[ph
ֈmdUFZo0nD ,~4/"[2͎
-0
-#`yQ*n% < ;[;
-w/qDti A9T`Yˢl>9c#vg +^]~羖I 54v%RlD@Dw1Vw7c (} H{1Xޥmʂ[AFQCbjCbIǃk8*Ml&0}
-αbggdoe@=I ĀKрJ7=sKm!w#{'~ft- QoN?ڻ''7=OΓ9˲!,- $;a"ߔYQ?$Ql3N3~A3>ލ_W:L
-+o-40dz|?.U O)x/>JlEy'xZ"73-Y\g-}^G< F bQUbnE(g4;AU~KH\-fP)5`$xk 5#Ɩ46\wR~0@iEb$h*!1JS9-ĝwӝD(}Vb.ҟ3Uۧ4#D!
?q1&Qjfo\k\lj ^k$!ϙ٠.Gcq xzMK{5XGGYCGVƁC7n!"9&ʙī Kr77 Q? m,j a; hEEQF>q7:t1iT: gMjRZ{c _VsL܁' BaF[P+4 ɒRWG)x2esFr3h5l O k&6-Mvb J^A;P2|aR߾
- ވ{7D Q8՟ẀcuoՑbl9A3l$[An5u`lg,%I*"
m2]l4#Tvc2;( kgv OW#s, ߙ㦁1QlSd
W Au0!bn;RP1cg4ƝD/Qnj6ȱ2rj>Axyc27X;'n+\`^"BW b9y ;-뾂/Cvp ]|
-̨&|Yw(Sp&'S~{7*Ga bfX0@
>S2V]F+e9ݵ Qnv٨LUWh! Qյ3UH~f;w2{;73JI9sZ_֏w6W4;9mkbz:UW~U \0ĥ YT}{pw42x+msB&v 9|W[~ŝT@69L忕n\幻
-%Z 1B{S }|6֓ɩ5G]B|_Q
]m
5Ɲ!6 ։~1أ'kSBC<'&# >?>礋7kwCQ劀0=ɖ]Ay
K|x}Uk@zr$xPpv;˃ {Dfk 1nIKrBb]Y)B mOQ?0X^+Ni,YIzr"RYJQՅ(~`^m3^ ò>1\EbGڄ-zl%\_-ϣ>^ Gcv: G8Y3}|{|0D2mS
86 ^)ESD H:Z[sῢ9BK9ޠڀ ËiGIZ<n.IG%`xit
.Ib,ax9*k OL Cuqb8dK!"WqVS@aQ r@qRP\y`f MZR]K[ᖊ44-<"x4P# La2˒*/ng?CbXWat ybk
x@*a(L.}Nt 6Đ
-6{kMc(B`fSxjAHgr-JFKFNDF]F 5/oMT!L
Z|xE[l >HְF ȔC=O'w4U 57/ Y&Ӫ
-p!NsxMoj2eZQmC=5]Ek@62#
vzQF%:tsp ~3acUʛ Sd>,{iFWV #u!slU RuI"^ܔӘ*YIHh@Dq6UY&u_Ĭ 4OL2DAK%cBsf.VX#+WIss >Xż2NA=rщ2B+$O]Kz&a"[:`q0'*+J'7 Do aePL0rYm ?(~6O8,`}jAs?bL
U
H
-VTdYkDivϵ̿ԫ~
e݈-}1zhʋqV:5 F] $6S7^kkXzu"t=y4WFw
-$&8]2n[VVC▝paX$PM #EX;abzsRfBp!KM>E8 &(5dT1Z̧Rzf*LXw ;[Myj+Q]^ ͻ_'p6;<rK.LJ N
miھ@!{EkhFNSe 9cXP<..VD4VwDuJc~?/ŸCNL ް+gϩr|-/jkR_qX彟2eE9Y3r PZڝW[ dYi1%M69_B?&%yYK QGS@ֻ4ϣs\;l{y#i§$x,%'3Um__F\9y0-]̆CX :n*;YYNA%9m]fE ٱ\_m;8gWDc0@- i}gPr;.5!pۊ-,?dg} 䧑En[P}~3 F5'ާcS7š .h^`(T b$6R`..`ݭp^Ə$p+4J! @2H㊞J}2W%.2:8ӽ_5C@wMKDխV_v~V)0&~XJF'KT۷h(.4@s8>> wHY ʖmNjbFH8qzk4lu an~ZD|.Q>z7 xs6x
Ib(~>x-+R?:_,D _ezh۞<{_4fsMo4"?VKgntHe,1UB .-wCoσA }:9yy<fl?-
-9} S4C"üK?a}9Qn;EʅdJ},cf TPRƿҠ+ObdaWx~ҷzS\=!"x2jZ{gYpɊOvI q֏SxXN,iJ8d.VLٺ Q _[dᙎ-kV[#@`΅'ت<Y:ڧHPAp`įDӫLP}i
-
{k /]IFL Cb,g{Pw?ķR&dt,G#) = tNO`q}̵ L>o[$8y GUǝSlZKMVvF*#g73)K21} R^L~(l*C_jv(%>f k:SڕIjl"W*7p}?%fa+wBsh0P ߙHĠl@1cQGw'so գI $G!A7s >LL}2j{ilXUv L_K{) ~?-]=*Lwhթ-Td\-_N.iֲ-$ȟ{#\{[P䞡`͈ eJ5a.\ N:pYKN~ć} (DRAG/voJ;MMwEC --s*&`+XjH2 ڽ17躨ƐtY;<;<ғ6>=[ԩ.Qҿ0@o,Q5Uk=rr.`I $yC}}@ 0q`2X, eq m|?]8=; d1Y4N
-* 1 cIUEĀ&Q4\8kۍ\{(??CdPѥ~$\xX]YS r4q' M3 ]=Ը52&.퀼
-Xaxc
Ņ^ ]dCKqZi<_Gۈs`@7W9^5
3+, qdK(79XH\1'ry(Ԟ|Sl4Lӫ#l)/ ΎuY+S} ҝn\b s%/O=TG
JPfq:Ps@i&N
Gfq*# e>[\)XQ(~nILR=%)K=@hV[<hNrRYؔ
-M =JeLADHU]:TN%d- $AH6 c6o=uQM!YЌ}xԜٴ;w, Fbhӣy 1 6\6KC+;}6". +đY2'^4<=̗g85 Վi}鐰K65u:m\(C+c6JJi*GY豾u>@K /z l1EaGlEJ 6K=B l6a ܠ v# `L)g'M1.Q\н ' w=o &,;J}-;3&X
-; !lqdBGSLxz:ۖ rJtS2q-F@;Xɫl k9hCɥ A5@'&v2?W9! d sr ( *]aG wE61$ 9Y , v ! uf3K^8 Ѣ-8MM:I )RSʐ}
Ȧq3>csQCɔ>ahmhkzvzZ :)y/ aJtv,Gf¹>>X
->k5ʗcsC,nʂX@BT*I#l4Puu`7ȭAZiڨ@oU!:y)ڟy_6^YF|6}luED8g1 X{A!DiwG]_*
-g@kbh/]MM@gi8wEl,T#`륮0lfw(ffKm)f33 ܷȷowlު.d٪+ ŵ[fb_%XZ7C)q7#a $س jF8(:S)na>ƅV\>Hܑ2%d#Cg
-/PZ& vƇ? qY˫^-BoKq FWIImx1| E?.6 %%D.=Ex mt&0̿e4jR* nSuo/[NIT겷<?`)N~?EGVZ;Emh)R0}rMx;CF==*| @AFלb=GYEcP'Lc(揜\G(n\l_+ 0D -xbbҺIpcJC:Ix>S~e6tx?~RNu
-Nj|~utэ22gwNV ;0MxT4qa (o/Ӡ7k7=^om8K2` :,_⨍Xf q.n4W˟n{pQٓb]hLz3Ss>PȒ+,C
- İO(*\#j,?~aLE9ͤ h%3l
-37;m^!ֲg$m-N
- " u{rO߿ߋػџ.Pt]Lw$D{G\N!L<`Fx}؎Ψ˻J[o.$mAB?Ԯ@k3 Yh~]RUN֙KMbcFVy
sn0N&v7fee!I
Feg.6D5GhbВqgNہ<27Tx; τ;*kSѬ,e i<@UNuz^K6łWeeQ+p YЊDRYo aC[* %Ӑn>; l-6vYgc]/?q[ ǯ%(ܘÜ)MzNPu?W(3f8R A{",.58H72<*"#~S,lz
FdG`nXuغ]E3Fn8iom-~%^ q3
BT(MdsW[;lH%_G Ud^dOAu|̞VFCc$ 4:
-$r-&~$Oi.bdm]av.|
v o-Q,|!Bg#1UĶJ`ãվ7vԖ :-TDXYd7+ JN) (exp.}w%eg334oR;"hY:kzn/aBH2 ig;1[b8'DuZ< z nª >9 65]c`WA
-eQlJRpy5wkP'Su~E"|- cveH84fO{0WKF@6 t-pGZdx?zZ(Jt3_ 4V4Z@ w!q1&u]
-2ǦtS ,~ϙA
-/IAL⺩D;uU+ $hR\h{(kx'YBF5^ߧEȑRlʄC>:S P0ޘ4 uUp3=\Q\[^0H-\6iB ڎa䑑:=d@
WUQ P 8MŃRQ[O̍5ޥF}sagV=7 )t+? [ Gx`B-*zc@s5s9"
-4_F֖Zl<] U
^@+!;`[1UO
-X?$\L͏-19Qz)B2ML0f?YZX/ZLq 2]ɻdl;dVm]
-WZgP5;0iw6O^Ҭ9 ( {,G.:[Ejubvأ*Y )j&=s}7d\e $'&3*S jTY3Y79!
-HfzLCQc bŊ\ gy u*z aNT\
- FMV TLǂ}<.+ʆYXEJEcC5it "چ0_R-+h'yLYz>sҼqy7<nv^9V!k!ҐB Z+" cZ$R&F$/StY/Rȩb{7Ȃ"{j Ӆl>]G,Ls M\DuXɔ!iCw W$}^BuKuKo1ȸ+r}D@:uXO ˫v'y0GؖAnFJ8HDH!TU "vZp 1>3Cu;#pX31?uD Zjֈ\Osd.;o%_f_}M|= ?zبm)3 2bpg<98',xS I/m_kVfK)o#6W UՆnkH?-a9ظוSkUe
Mc^GB̍0d-!dL ?WȚ4lE2l)$zR,d VEGTCï6"9w~ ޝo($o$}{ubI1v(.l)F J̙`urᭊ$l 0NȖY;.TkRp⥶bj Qah
-i!AiްvzNk{s^sb^y 7lSMlfq +@u-swVH{J]: kEGD]m&v/A5[DN 6l"{31b8;FJ+`D.[2iH""{.G呐wd=kd&:' 胃5?@%夸OJbA"~4s.LO@R`A@ΙxlB *<@dܐ +'1ď+>y
Defuf)K0` {
-o˛&Ԛ!Z;f3ƬџBu;gd;wgɦuJŧ'F!e"rhK'X#
kQapBEυ7!aAr%LNoXTiԓ2\L1%pߟ(Oue ,8A1ɩW2 QB慗NÉ`RTQ龳c
kԺAA)iMH ;Jd6ZYN/PP֧H۷> ҽ~%9n
-CmQ7Eo̽ѩs~yv|a 4#-F8ZtG$K-qaXj|9Kp[I]bmEg%*as jWwV
-hWU6 5:E6E"pe {Fo}qJrU\]"jp |Kr>qC
2]TZ>gx6UVɺ"l6Ԛ 1U08 ٻf VPi1 ɥ*- P0?A12O;T9&U01Ǧel]!)4\Cѧ]ҙB 6me :4W>qK. ۬gij F L3MMUW1}V70'ȑhsx]-UěarlȔȗZd
)
-̃z5`MG_W5IZ߹NG2 .l?$, E(Y> hqhh}O]a?1KF=Rf=YGd=)gN q&u}\4,{e/jqUi-jܫ=zkH,E*R!Ke|>+
-H;2))Btƙ [AisHb +ʨ7C;L6Ϟu
-6R v,,u
Ggcs `j4)"DՇodt9X vBaLW^vq6R*g AD a,+]&F`@xis>3i -NC߫J
-#oi grܲ۲
-p[(҉?tmZ"ץg /.?ז$={¿+܄봚LI[4uωΈ Qм\%o%e2'(}j-܇Z \EF@ydMiOPғP"m9;S1= VjRf3%$6Z^'3-@F( "|bF9Q]d^:4S#C):H'^Vɜdj|c|Fe +i#;4lqyɍ}E#=
G|}R8ED`
xPΆbA`Kݚ>cm-Vw+D8 ri#dg~ P{< WFz$ :s=m@PqjeF<] #DŽjf^z`:[]Z%~n?J(ƄiJVr8"}&(3Df,6Uo TG8_~B+ ۋ"(+qL~@VA@C.V>:f9`Rg E( /Vqg/'*
-( U
Շ؍V%w+xU/_~,oK -E,F1\K hdk+/ J ݞ@N;?Xg7- `3Q
ŤPt%p TJ ;:ʆ ϝvinȄ/0PL}q|Y{ć|&~g7k+3Γ _̜S)c۾:`V,5 H*wгQ睈!G
0)Fg%&DS iG qD A8;q]&6htԀp> 2_\3:=\j F
-r']\Wشt_lbYmDĖLi0K:'9+3O)my}.`')kDwry _ 4~^;BL
^"̢ oq
-|SnC _W x4LfL"1'0N?hfNo/szI (6B^Ce|aϙ`q2ڄ"sk7ĝע`\TR!=op4M)kg_`ɹgc,1ord*L3
bvIqo]q
-e9VM5&#BOaZ=j,ϸ1 `5w4(n?I=ح Z˴נ{(j
-|\đg8ߢ
X:te"VqH! 3څO":@v/t=՞No39kx26Y +,
- Mh {y= C @.r1"
$W4 R-$/GU9Dӧ/b)B\ki-W@M:
-TMUӆ Ĕ;wʮ_Hd68Q2y[R-eqᘫ.j2^|)BM
-;%gm-D-q~^I֬,AW >4 I>gq ^UZ-9*Xfݪߎ o ]oD3~77t
-y <)XH]hw,7mt)jAQ\%7I
-֛.ԅU.uW;
cپ i1s{. #F2cнoq߾8Yz l672Oivʹ6`OZ ?@qaW
- C\p{VaA>a[/uEa|\ȵ XX7iz锱:HkFE?C \`+$'%x{FVSJ&="F1ȃVr>6#8ײo: _XH4 dx14;qQ5# 9:`[SiHTGx %kQ/-JVֳq !2)` 4tۗ)(k o рZJ ɝ% o*ҫ[,j p9P)K:R!797[c"
- !z?'BoW=AzS Ex6p >=JXOgd/=Q) chiK DJ$o{3G IBiv]K/)[L`3:[3k&*W-rOU>)#\vIQ&*jJo8 VgDlZo VfJX ȶbo( /h[@y*Zֱצs Ԟ] g=~C$nHB#Qk(2~;ixP559_I0ߦj
a
w vK>>UtKkrSG= CƏ?;F a֢TO:B"%կ r -P)[Bx[+[5%b?ﳁl
$?vblb;jF_c{^ͬ>s㐴Nn?06u(mḿ`؎@.S'q0 0 R. !SR9)b)b Br`. c-9gJ+C+`9&Xô?9#i ,` p][yjSeF``t\gK U
-9ց0ArZ -dx2&ݗ>Vx ʛ)*KrK[e7i!\߽ozAa\+zmuV6q6Ѱ$qZc1Fu(@s]Bmi"2M߇ЗX,l T8d$T3,1phaJlgU|;ƢP볞 / ڈxnGM'Dj[8|Zନx I Z qz)e 2yAvGxfm"L(O! ~ոaXVsDBm0%v4B\Qlܻ.>;
{g4?2r/",gk2`Vr
iG/hsr LzP>"^M, wbW>zk42D\<"b=aT! ylSe $)& X^HGsSk5j2r0Ki"zF$.FjWŦ:,Rd ޮVj]TIoG"9}F@vs =ai'<p|rÄI62X]iZ v7h GeMW>=Rh0p]#QU9 ET9tB+Yi=Iso K DGv2Sgj<}Ǡ,]ŨJbu.o ܂bA _Ky9 }Y:Iw1V"E?h Sdq$cX~{6՝%qp#/wթqv/~b0W+6dY[dڶ-DjR?{U `x~ MVFlU=c')ЛO0ٌc a[M3l=Pع
-P4|0TiuB:Vv!Vr
-0+6
GwFɽO 7qI͆#_T>+#*Ud\XaNn߶U"mz!0wriaX ĥQ9*
h:K rZzNql+
x)ŻRqk]d /:kpW%_i B ߕ],6ZZlL 9>-4woT$ DųV 2)l`!DVpd r8 sfգU3y٥Lz/(a W&O3{Ҹ&jNSC;A* qc72~.Y}(iy7Vg5Pf`ͺD p!f)k?b
-8XC F n KH`3({4/O&HeD`:]d "tCb #
-7BY4/F\xW4DT(.%I~>- tD3h &{YJQkWaZ0 {b܅ε%~lP o4]}rh)ʹACp:Klp1tT'_ҨiXoьg /(0֑ -N0P& x,}vtoPb$6N
YPvC1P.uu+u#~^_W/1zGg(w)Zp( ~omEkGjاREB> bwȞr3Ϝ $Z{X&֝ԕ5x!}0(U RhH&Kc'Nt {C&d"ل A ![kl=t?Z{].[s2.A6u%͕Mt2hgvBˋ !gB)Z(Rk'OwO+X\nZ钉&gu"E<S?Z
90l=">hVᎲ!Ps[ -Eu"* Ɔ_g&ECosS1PRـB$o4*ycÞW| բԸ>J ?1/;8i 9lp|# q&5X n])6@ PT$
-Se^ -*\_ f Ibl3dqQ!ѰKLpl:4pUSr;Hd, y CcQ ~t `U?SD8֠5 'ZLq.x%VsslB)'] '0B LpIjJqDCduZ+Xt;ÒӀcH37bh:H yI^)/ktp*PG[&Kq.#렣?l|r+.S}i)M LbFQAh~IHQ))oxMV^ #ryϢiH( ;
-XgKqk$W "FIKp,kE FV-e %^7Tr*"h>Vt *4D )w_] &eDP\ 4hKt@JM
:`m:~w{tmgFF*z&"ʋV
-JVW3$B;A1[z^h$6[!rAϬs' c`ÕiU AZ QӰ?ˤ?zDx?:n0 @5\օd 3Q4f"4k?M@.R ?~n +JtZ|`(cB!ST>X#J)`'];dJ ّ
a5-'~P5۬.niS ӞʏBE"s"6Р(oT O`^vҤ ȇw mzO{ Z {S)U~2@;:_Z)ĩFu/-ć;y9o^_&*ǾjΰR(73T!WD@ ־xvOiov[S )^LdgڍגyrRӅbJ,x8eMvLh]lyjAr77v
1 eK$d.Z_
-@zL)H@OW_ƎlS^f-ZXt<] lF2DhWD7 4 ʐO?K]nʳxr3öZUT, - |z[ ˴? dp.FNݣdLS Ăx,MtYzY0cS#C(D%xMt]T@d2/E@4^"Jb;KfvCdaNRbM0
P3, s9sL0dw1¾2!9ձQixBe>9 eu[,< pg5%rA&P(
-xXOߵ 遊Z,}DoƟLƭ(M\C羧TT l6(Nl8zjaa{67
-G9J胉vOlX4.W>6}ye E< tGLwu7O}ɻ,6
g~ 4Xu6'%. 5^Rډ -T /tQe.i+$tzy(ę>Lօ@o:_N:23ɠn٨ OْaBVD[RckxJ\֕ OI{3KJu_DqUw0i 4 m!"<L9ᳮMPb:<}蠬Jʥ|ɾ euS阶a=/z ҳ U6]iקp=$ |OTso2)W~QN-:ݬ ,̚m
@3{z8ƭPGյ6
hhzUA糄 ӈE/UF= F F+F֔T P7%'1r(E)@2ԃ~>1K3K$Ti/o1zA.8{%Yt@[/|"F"@ IOs|qu*Τ;&͓I"v}#k*M! ,B2>H IsJc[jwamJ{)_zf|m҆ZgA->ffzfKlnuS(, a^<`ׯY 5Z . NԮ3̱6Ґ >Xc~'+j;_2eG҃L~`~-h)WV [b( t|ԻcƃY[G֔,5mԕ4Q%VWn8~ LAYҐiC=kNe]# K2?CIdTHΔavFŧ #J)HO,˯ЍdԱu3,Dg )7w7i|y*#Tf6b'Nڐ$3k]d na
-K`Φt_it&k0Xum0qHgy~I
R/R"ŦdM \!
-V-8aޒr$y~\YRaƙ`ك c6l-X{ 0zQ ;eXۂ Q 2+7rU1g/bB55"ΨB8B
-&K̆Z={ݴ,JO6Rk=u^UvTJj{{HŵN$Й\]`3ͲE?zۗlU&1m̐cccOVu%*G`>Sp i}RkdqK_0#,FC#M pfеM-]u\
-^a
m4
s6( yͦ
-?mȐ \mEpÏN+d ]#
zס_3Ϥ`-^I4PP x9eL 궾+s1B
GO5)|#t 1[qPgN\nHe몢"wy[}G8F<߽u|1%H8qU'{dWQLO(gQO>/[\˨
#b#Sf0- ]B7E8v8[s-?4F$
U ½^9ei.K *Jp$P\>: 0-"
Fٵ^% @ &|X㋵ozwq~=.֖7 nm%b܆BoB_H0iEᛥq5jJ[tX̕k v'7XgM &1Ҿ#HQؼij6 5];=q]b
%`2.E
Os*gq[P=l|8\}BNV:0gO@WcSp/@'
E훼V\o|.X: fb?N9/d78HX*= KB2jdq#$^`=P\2;p ))FhQ!Y?觚xџoACpȎz[#q^id|gIo+ Iaٟ^& [O!HrX|zؿ
-~{8_ ,AD*hx$y+BʈO@30FH? S
+ӏ`t9m`
-oμ S.N&EX<Q0%+4
- %r'?R]UWwi=]wR 76(f-aN,JZew0 CNT`fsد@j凂͵/vD+9nn1'COJ`nSx}w*>_1 t/7í~͍'-|c6źCw)"ȋ [^kc&m !\>JkwAhY˙4C[kkw,嫼 BJ('# _ܩiqjHuOVp}thdj͕.êE[ǔ -9+rnS']
u}r e'K'>+EϯcJF4gGGdZ$a']X-㝉/g+bra+S` :̸HPQGX!1pI.Olޔ_hG .m-d /R OS;$aL :}\_]spzl)%~Ɵ] 5wNS N$prwCY > ,[
-dV8@3Q /Q+*Qf6^ƜW2t_ ?JRJdvD DbZP}FnRM$ !TFxZ% ղ*[:ަ-uYD`2K?~s.C v24$yW
-
* ?C> ~a/+P;R+B2ñ8g^)r̀?*AgS^vIpok(u@y8pw4# 8!a~ȳ%U|Zb! M <rbfZs# R#: @Zc}[era}W,Lq~Ewc1i$&ݙu/
-Pp U1` Xd' >(LP1.V#E v |N&z'Օ%#Qf-oB!_:ʿ#8o}
%\ƪ '1 <Ai#²h|̰~3Cv/=-i> 2fܮ'?8WQE
-i:t!ř#A:k<?5 2L89 X1 [4&;1 ̥߲1@ V]l
-jpn2lŋ a !ڰʖ=y3͂>B_QK0y?W6fN9/>o`~bv빳/ڒ, E"!V$A}$u41glm?fz$@x/5?3!u"+jg
-m 傎ۚ}yHv h_ u 8'XKm{lzlzÒR&%/Q皖5>
-܇1滴<+ $,Xƣ!RtI(O4ۄbɳMQXKTD}5-19~6(ԅl OR̈́aA x!ܝl=儇?NTyzsPZμ.,{1![I^?]S/"hC:j>r=ϟbe q37l9MW ߄h.j ^`$5crA)\;f: >X8KEThRvђ3晴 Yѝ>?DI ( D˨d݃$lxF˶&=4)2o۰SLs12VEdf P"X3HP#Hr|͗[5v*Az]65Cεz͜`[
*:]G2 #=\F38 NL()e cGf܆#! kk
-&D5ԽO2qE?2\ *E0CdH9"MWT v}"jr}E햿(ΨtU4Xϛd. EiR˰;P֓b 4d.ui t Qab,PLZ+ dSob 5Ah |kzEh;'υo
"O;0
M3՜?m,%8OQ
- NT{9XX 'W7怱Zro$a ,eU[o$z˫Q #0*amdcPe ڿC] {bvJrgd`fیǨh'E9dx*#ض,OZz=Lڱ)ڤ4-)9g|9bK rJ ^:F)=+aW: FoclXnk8P6s:'mٴJ[c6,gRle67mL ԫ#jIH_'%,l LM4 l:4f}þ3` 7mn /ğG !Bd3zQz83Xյ,px˽++\i_G n~,f1iE^+Id@gC os$TR uOT+ 8b1$Ri"oE
- ՙU>a
-a?VyJϡ?@ᵔQL_ MX ??qy$PAGZ (۞^!ޝ
{bo ЅaY5i^)O Y(8(G08F0,')%Ѭ/y ?]orD{ ycvWL& ]7bs |ؿyL -w=tb_*zWCD/%!!$N 5Ue_
Ҵ7n:kzi|~Ժ&@^{/ꗞ1`vJ~weôi ])1ؤpt7=:{FX~ Onؽ Kr< wy$-s6n>ϮkMFaZg lfkT(K֤^ T5,ǻ $ F
-![p;
~k;k }¤x]cds^CM>gf+E/c=B Ճ~E# ~y餣(\};Gp@7l|g^L!HI'm{J22 S;c̨F}exJ&MuH@H2JZR{2dN3tDK mA:a7%IFy앗I}|bBWOو
-Qwv v'Iɾ]"Rm71Er]]ÿD3w G'5vghx]0uKn<ZЦx6ozyD g~+#clO6
})i6X_SoQ*z=Pe3% 4'_S'[=Is&}JQ
-@m]aCc}F6a:o$?$%·\vRl7 4TGՇo@Y )#ڴRd1oNe!MB^61'ζ:G b nNlknJ Ӿq-
+,% )ѓT*#Ɇ0"Hޕٕr,ydj^O}_ Y8t upOc9PԔAE> 5=ʻ_T3'W0` `fXLs܁J|Un3~
1\fl)(
-ZfRi:6~ in4I9u#-!\421l02<5 |bw_Ы~׃\% P:fB ƟV#/G APr".s.Z}Θ;gρ ؋SnX.c7'˗pD ~e6PF+ (9WGߟFJ|nE3MStXqۊ^<
(yXHLyn> B4Q[u|:Q](% 1a+K fDT
l냡ZX%,wM7p{%Wu^xqe `ǂ'k%`
-sbK^1hg}&\:Sϕsv+jRA G6 #X ;7ϟ:ՔY3fr=!GaY{@NpK/>G?;:4Ě{xz?:j~q$E;,eS'I ( Yb)tGm"4U >5& 89eG3.{
"N,ݭe锖.zOל .~^~^|=!6 XHV gv[J],e"SEt9Pg@'0to S'֝BЭDBCp4OΜzU
ŀ,ܭVpYkWz0Us1^nB[ o]6ƁM%0 tLA9k`F9]FB>N5+;"
-!ihP+ xn:ITy}<҇l*/xv=)<(@W,> gDC Yp
Ðy'Oɻ=A}?ۥvA.]}#{N f}~6cBg)Cm@V(ثյ ~? {ITl}6CNBc `|y.P#аg[HLSMSX)u"^* <;9wZVϏsMnjˏ(kWw蘒b]6iW . kT1i o*O3 jT`o+bbgx s!7j&iixok3ھh?BGsA-̢֤Qܴ-['XUO-YE SѕO4g]U'ۣn( ]eNH=|H6Kp
CCxk 4&>Yh%ҟ lLb|^ۓ F ?4S#p9bȲ HfB>M iO
-7K_x9O l!pJO'iB~Ft?/H讪
-ЩEx+uK o S=rY!BS Gc
z אҿQ"%UP:gB
~,RE.KG7m6J>d Uț?\["d,eD-"N24@by >x k8\3 IqfFBUH (sJbA_F´k۞ϣ@c 2(WR; d(#{G30x-~R\oK6J%͓HsBfAbzyдID4 Y`
-ѷFI90yB;~e!ʌ@oj
-yk_. PZ ?1fZZ0]t`KJ C(/{xxĄۄ3 4ɑd
-~vXh2#?"nO oM:NQ5?
-\;/
-_ 1AYGDlLDw7/9 D4[Ef<4|kcצ2nNwQQdhyݺH/}_0Ef>8}4D֕Q7FUQ^Ɲ 7=E)C /> rjk| ݿvMX_r ?ZJ ԓHT#`k0,ڈC[o TL )KvM %ka+ l Xgu Nc
VL"1mur|:yel-ZUC 8xO>Χ%¬o^ K9Y67~_*O> %)^c\C9 >Wd6Y_zmÍ;IBdΠ vB\]]U7dji.[wyzm)NeEZZ>KlL!A]>Xk2h\L
-2 ӯc=kM$1}%9nso%*-un U|
-DܒoxH %g&hlՓ_[8 EbH`Ǡ= &b@M 2kYV nJANF
Iѝ:N1/(j{I(VmZh22~a
-ō5N3heyo;o* .ԫ8
F7N3+bȿb b*X~w ubB}Fdjlپ?Mc`sb}3b̠LBŘ15~R UIhn~5q /v+wJ;!T¸YZ_˳ăa_%m{µBF }_FeE"*9ڧB2ar'@GBR({>ƃH*G[bQh5\-EVݧ^1
YX&uQ^0t9iN"
YQ
Gtݯ$S O#]IKb7 dɟyƠͮޭ52v9ːĀhKfra㉆Mm~rbGZvXW[:ޤe9{ia ρ?$ Y18GW;o?kwm/d
-#?oo>h8Hz)xX xDqLPRB˼7E D+V%*ETNb T w$@iz'c+ a;|^)oU X ֹ }1>ՓE,q9O}1qޢ]A@@6Rެ F&1~'U;3ax 1*7]JD?҆0xtr9&_EvA3ٕ< )sf\VXԬ92ۙFP,%A7& RXsWVa7sޭ&x \>k/
-sĝ&TFş9Bc} 6(oHyqI#JZln^+KKA]3IwW^ g! c_:Vm|A? x͔1&0'' VKY \jBx'"0zXC#i;Y4ԩ{J{i:5$s˄5sz#QSkziL'ɧunyoٻDxrʲPb9 ö`4; sxI|C~~q` 1uIAp d|
-dc ;dqO~XFRv-q 8!;oУy R<}{O ! zD^k߆r 1ܹ$R:>pl:@O<&
ڈ˙+ϑ JAL ?QS1|z"\IQMJ"!)g^9#Ù5X /ibYe E+
-eT_E9VN Å>u }cFWO3W fĤvQ/o;} G_`NBA@BҨ[r&SYHJwlUnBz6ɂ˫]!-20x0U DwD 4 ,O&s=EΌ`,hD>n_4~i.HF7b%:#89JR7E?zF25DM$qTɓK
ӟ"N.,9D.f-pe]"m
![$!AQ=#>B;rA?lp ePTFSɁ6xA&) wǚͱj펣5@P\`#0"x@%T$c@ slo4sQag.7U*
`~M?z 8jMd1,5 4WhKmCUs* Xdh[z;.qz!mA-E?WþetR/Wÿ5ڟþ (İe+h7> UL@kJqH% p RWS%. `f-Hy
Y.fջr 6leFuB3 Q/pCDw5) \y-)揯6z ςqʂ
-![c
-XMI;Byv6B%:bX"_3%7%
qwot1w H5 #yQG E-_AbN L(I<}4G}ҁY?o-.p^@n,4tpAmґWdVOI\[YXTN՜&<eƾ1& CV08Ԝ]d6ʏе,Pڍt `SP'agZ?/"ޭﷵ"2r,Y
U_ IO
-y* 3.7kfm@~4dk% {♑&Էjc!FV_ՌSp!1j4ϙp gdd%0JuԫGI 5mBDM f_f.QWɮ'TL _'M`hMKlȨn.ugJ 喸g@k>!wxq]k9 -saC"R%%r巕|\~E-Q9柍] ~ShǔMA{R9=7D|nsjJh[zm4TmK}vn>Oc Y q P!)' cG i%/r n*&ٖ60ؒQ$6+E,?HDѬy/^V&Ib ^h
!%9*nEܬ,tɧTكr]7 A
@%F qDgF oI/#N ktvB X'6Q!r֠]o\HM nJ 98\.R ni~ 2IKuXY|M}m,'ɜ 1)PvM) CKݯA979Td%` bD{
-E=JH VM( ?P[}3#U3 ߟ`N?ÿc%Oø].:ۍw_,LE"F4&VQdmy5qwʼ`
A;K.F8f4feHKgj-.iLJ,Yb Q.1R#Kߘv Okkua EpXE 9@vF:R[O'BM Ogq1ĶZrTU7蜆Ǒd'6*5aW+-$ôjO $r90FbYϰbv El{e+TYpraOޝf4`ѳX D-
->BiXu.giݩ-ѓ&cn[B0[29 Qӳwld9BiH ,]:[83 4eXt C+?zL4Ll1 r8lU;TެajR7ZѪ @}5ng!{4v *㬇0m$*5% ɐO:GWnF[D QJ Ei!ng.TPqq?OCIG[ |o&n[鐛M#pfZ5\/lCl[Rhm[njgd;5 "ͰP
-T)ZF]` X2]O 2"s۱J#Y
\yN7ڭD
- bzv]uk?{Ztl{UEwz@{{Eu7!ף"A:z K tFeRo|7.g0fA!M>?--(H̾ I Ʈ搀e_xbE/ Mf=C^w=c55?IAC/ !%][ ߚ5LQ aA~e/хͷ0 F (AO7.jI( Qs
-6Ĭr䐇;)W.
d-#%5W/_q0p ̂2l P]aݤPG8YnRAKQ=cv?FbM>.5,u'ݞA:qn/I˯5rTe8нҷOݪ-)+a_^?ڋhP@w]MWZ7;UD,\$SX
pmE+|CBb9zSlۮ
c.02kzjɲ.iuY @ QԈ%! >xIuO"S0 4
✰Y/7 b F<)!_$ظ)X\Ag>6O09a/iF0 ڊp,Ze7܄Wؿ 6lRZt&Cڸa Vkh(0ЮQzg/x^f縣8L!47 2x< Ϊt )ɗB Ng 4e,iLƺdUZg v ;BD{ȧܸ[/Y^puAG-Q̂$N1 [_|!uzW! Mͱ?Z!m4{-yM fH&)|>ȦfZ<> l噑
-\dhotT4CǕT\ʱA@x
-\JAK#a6\sꁣwc)t ~TeLJ {BpJA* tk&Td =Pq
gy%7Q]XT'lVYped
-߱+4j.FPF8Cth hfQԃ?etۤ/ `QT+Eř]
-H7wҍ`},Wc}ڟ' .1d ¬1M% U e?Wp$ٍV:^<;:- lp%C _ Z'b̻/\ϻ5b.2"NMj3{#F{)Y
4y4(Ｚ;Oݲw^*iÍZm^W:1 spWgmIs
}:Խr͉* :i5ԅ,FYZN C5 ʚdַb&wLeߺ iXT[7g!~/_ϡrKK\y $ ;'V?1w܍4sz~&#woW f.ŰCoFQ1 6s< O :@w *fpgٵ5$ojq ?< 2 \};3d#t͚}gY?`% wR̮^}brXUlƆW¤|pm R! IB
6`Ewm> ߖu+oFP7hmʛGСF{eby 5ZC, ; & EY\@҉Fx5Ʃ$bzM 3b+Xv谭{ &FdQ WYES oϛR׳GaDLG J筜9Vؘa48p;w ] Y$ AT1*PXBɹUEa8t""@|!s>(F@ /9|= ̚ZsE5( ec 'Z L1+gXJJJ/PEPd [4;Ev%q.?<mMC X{h7\?Q5;q!Yq"zŒ֣C>UKm촐| atLwꐝMYA;yٷCH2)}xNdC0Nv bC@tPFA3
"I <jL9̓ lObL4,0rFVA ȴ 4MUOO8ް M(4'NNڃf ޜTRSK# UVglg
$,vML\fف3YL> 40`@dNx *ʁ&#٫"
/^ƠGD);<&|8q
rV ] Yکo5ܮOl&tC pIy4މ~'v9^V }Dw,uMMp֗t*N% rkr-H/h2Xy| {r -e5flۋ)K7Q!!g5ÄގCZpp+ (.9>oȳ73e߅^3AVi QSS< ևԠۦŚ!{
Cmp˕Y 1 Y 1J]AqgsP "MCZQtmZ@=Gԅ R1٧}@: 2d8A4J:<~e
-^N >! 9Ok*&NABL3oAɁ dT& G)HLhu`pP^T/*sbn
3X W-sQ{0eK;3d8ط 2uVp0o~HDkVx
KeBBR%6N߲
[˭axoE\.o(ZuK˺ᮻ]ѿohg{a. ê~ wEooaM{tYܻ6mmͰk ]M2M<: Ofx=OHQ=mmfkj>6m8ŵ1@Q(s9Ġk/)YHxԘ*!*KG2CIo7rЏjhTe$F&m\\IˣG~Vwk Mri ]`fIM7F;mj0'-7mdU3GqdG.@r^cʨyeHdyd{)%y166Xc:pp LDb3iZb#{]])Q%j.$Do 2nֆb]T`複@o =Xd")~B;gh,@mG/;ҷ 48 #FgA;w,^7 ŦHmqnM1]DKǑ$0cgino bvҡJdž\RN֘V B%K1zm ܞ rUAj2@ ?}65f{s"Vߓ:W|O'
0^X5FcC Ptm^Q.%3צV{w dK6T YR{'fG% +@ԥCkc݊@+5р:T#ada?a ,1O@o\_# `9~_Iu/K].zPk^/unڳI ő=cMƝPP7=9 ab6%0QQ[Z3ϗhĊҜs\%=!O9^N1JzP^T2p>Ėm +%]nN;j9imhwXB`xD( 3_!_u`"5ZpV=`ʝ><.[d8S:(~e-.J
-Vi%u6u8&1Z9' R1 477mI` { W} BD|L Vnw Z܍$=`y( kCjb<9R@'eo4A6>.j
qyQU,c >Pk$r.^(QNïo..G(=u< 4{DEH&%)Z=a|:PLvHhL7 &"VD-vO
ħ['z 9i "jpw G
2BKinD$[ۨtXo!%N"7jZy:%P,50wiiLV21ǻ+?d)F*s5uZ>ҧ^ D ]eũX EdשyJ3h0(n?$5zN
e< C5HN{ŤF *o4vb$Vj q!BzG~0sd z3~ дJh4D
-0BHu26 yQ ;}:\&{ Òܷ%L 4y,z#Lsɘ@ vђ r
-4élT5e -n+_+ת@Ȩ@]9΄Aت̿VQRGYc,Xxi}$0J
-hFbbۻ, !C k#M8gr$.eD2u̯HLJ@/:^S _5 LeU uؿ o f}$jYKAR#;SRPKƷY./\t*a40UZ4V㕄LbWІeց':!oZ Y
-̀Jmj"
- îq{)촁 A1G3cf]ygZ;VQ R"6V`H$\A_Z)5g~WvܤPe2vu]\{ 3xXwk ?(o Ϟ}LEɇu@l*x?w9!Mi2xLE[IiY?k ktݱ۷PGԁi
- :c ۟_h}Ѿ_t_lӫOm Ҁ)],|-~_V
uR{
a I]ǃkF?/ 1/B]8ZcE?!`ge;C;_ԯh]0G?n$$.p5#( Ů1D
-$} *$a/}]AE Jx~.% NOd;/1C2S[ 0ǖ\%+Z8o Ԍ4`]L$:_@-e?00ox?^M-d q&Ur4
~JWi65+4 i|HC@p=)@JCyꥼTsPVQaPp<㲊^I DPփA>OY1R8X,h_^\c4B; X' Fk;LYS oE&_ rz$@$' nHv vy 2ŊVisNֹɚl'q9m'r˽Gm%^p YZ,d3^N,;qŭa\ y}q2 TY>ԑE
-\z~ZZ'1?7r@zI]~_2;)q9 Tܷ ѧQ 3_҆s4}?^7ܤ:C..ԇ LjyQhPF`\Ɋأ\K Y QPR{Oy* ˀ"9}DYb{ fq^̚J;S,y x}9aZ
- :qm#p[5P`&}E/xWvDF}F徱>3&Wn6 nѯ,_o c+NӋ"lխ ܞk]k6QJp:$3NMʻ"bRYAx< -0zߔl`Ĉ~O8~<+}_^TF4I;/:\a=7^zSTR/[FTsPH(+I_N.MS$ 4Y-S4qV .Ы%مQ'\R1ZjceZcGP( ++ңOowtBNHg66gPLAuJA [aNMH.x9^`d
-5S oD=PKkiˡQ
BDklJ9Ts֟3,I z hvwLYN?}] |aN >D}'Oϵ8/ |b7p7!\[Jq)}=8M uSdF7/?q@_UӔ FG=^9Z(vUοR a-vM -j
-KakBNNr ?aa*rc,ku >C^"*!,'Mlള* Tπ#: e~G&0Uy1l=LQ.IO18E2 D֧FRCyhȯ#_m'>¹L>3~&XֻGmVԱH
>\AQ W(X7UlKC
-%Ku]^%RI\*fn.^rsE/H$aȯRW1
Qʈ3v#Vd.2K͖C\LwgX|?_F,ڭE(x[n6YD+5EoL@-6!nޠ&6W=ӏ:%PVaq
- ?o<ذỤ@1H TX߰2:v\SWq =9
T#K;?۷@¦҉/,co! S4 5+r?<0,Sݐ'_<|M;y"mA$aF(Ƽ npsl5ߴBGA+N X= ju}ځ mYpaй:Jw~CEآEۯZ2a#Cy
-JZnQ+Mk5:~V0 2 @2TFxj Wt e4".>~<1#NȪM<@t6^{y]?ڟiDõT蛺 ;Wkޭ?lhv FFP=+6xddA :kQex־[wOB+ixgq!W;zKZdb3z[TAޕ@FH. z{Y$Êj#[ XTs%$&!cXj.isTBEyIFm1}
x˗YIY XG _=S?bz ?`æ ]"SSϼۖSJ?Q >%*,C}b4Ģo|tQպ cR\S[>e` O`ƽɍ a.q,JhVT `#;^dg$TIiڏ--"_9ٺmdl%= 6֩?:m^"cXC9;8+T!{,ީ:Mڹ yj"FuA0G?2 a;@KsH~
fJ?3
|
w6K@-f~WVEB_ .Xsɝg|_l/Mѐ;EylvM#Yj2u
-a 7Gܯn3>VM 9qᅺ &39 ԅQZRRp{Sܩhm=E:(7# ,Hً1% tyn-?s% t" JVxjC;YzmMEmKT;nUug0A(5xe:
ٌuO|qǗJ5`[@ɸU7 Y\e["˴ 8$?!\RѦhy$QbJr0=hK))~|{Ӗn|Mɍ\5ab#Bm
59U)49/i,w:[0 źӋ 3?K9 &
tTO҃P QuQz'6 d/"T7.1u\jDQUACAXXGtϳJziþh! 3^vءo8pS&"
-7vvNA* Iy g,kٌ2 zMC .rr0E4K=QHXڌ
-
--i(`@HKLఫOUIi\\LR 'b8
-ǏP\֤W 5^Z_K;'+`ܸH՟@h/z"ֳ) E]CX<{ |rb[ڵ[Vy+ . |(ֻMSf5 ݺ*CYSF\i u_RL Ba{ eثO9>AgI) źϣiL4u(adtBaQ0|i"Nz̀eӰs@3661 [p:=DcL 6pV%Yd(]tt$C\b_nQqX4 `zX(Iߪ!l m.f3B}(%F,N(z S[v}ޤ_-!Pa1%-o }A7}4k:BFP^{ g@o2ԩW5jMxcy܂(ȡIz " x~~5SɇOv`I5?H+
Moum:0uځd15YC1I#بA6>WPq I]8>LtA$m$i\{ otP'aL!|@}'W/T[f_bz$G=xALKJ"2uC))կk)Eo UTmp!h-@ǯƠs5
-@,;kph#i%GՄKAJИ{UwS&za 8H @/ ˱ 6 Uzη?'c죨].3@O2ҝgpW?Qи g%Bp'r ] Ú'EF ?8/>ߤ =IYܴ m8"0& 3Ғew &lmwr3/&M(Y|s/PU/F
χz'cnϳ 䨓_oZеHM * ܨDs\^-!waԔQ
-o$P =da~[#syc!i #_~0C8(( r[8We,tJ01YFx'B}1^?B`e' b֙hxwyDt^E;Nud:٦ʶ@ '䔽[șb!F@ZF47-Jw`Wb EϾLbUk&!ӈ>z VJ IMX3e;K/x5::cKW&?C7;B^GU2*[2Aqυy
IEvX [c>wE.u}0T..&$Z$jR'j64}[Nms
-T'x6O5R$rg UݗJv1ëݔͭJd!-~W[p&v76+I*ĝA 4O E 7
[p=xI#zzOxxknh[ s Y6r7ĺ{^/s !-w ^FbTi]`S 3yZkuhlV`8)'?nPF46j` >KZƣ
a6w]sHBDDBuTeXIcn{62QS;lkxT$1Q!kxBˉ)tFE-}ϵ5$!uQ(s_ % bFRmaCluNM jpCF\ r˰KPx^
I%'MoDa߈t I+1@0bɫB\_aVHCH
ss:7&~jVn\T]Řrǂ Vk+"\}Q_ƉHs{j Ecea|g5ò?_.{Pve$o&(k}a[A%66̧$ rHKfYѵ=yISD'Ւ Qz=`(SrM(!, P͘_i{
- Sx9sNO4fj$@ "*Ss[; "tVɀRΤ*Le7I"i#_4qCeA>X w)O TqM(QlI6:s|҇wl"z*)I-
-{ϰ: O k6d،Kka Mk,tmvlPg ܦRb3elH(!-vfT4$?1e%tT N>O# WLo>#%6 U
- !h>qiQߺm3n5~>Vﶕ~ߴu7>_twP֑v 9_@XLu"N˓_F_'hoVi0ޙ#q~#)>з5;FsFڹ?
-0PL'Tx"J ѓ5xJѨʴ;J[4I`W*R5)ޅOoTgXk.7+k+:r=~FXk
]ihGxT*1烎gl,Wz)9 J䪚V@6@oNb y snR㒋ĝswiw^S[[T}/E
-k3R3ؽjݔ0WA3-]s|;dYTx{[@ k"Rݑaӡ^>* fnC[ 0V$
S!DEΜLy]|N;/čP L83P6̊C8nږJ@2ƸM JxlfBX!K x68 (MM(+ Kkm8Xo.0;"S (iKG`!}U$ K3fE_/
-\
-ԫp^I94&80z378_ Ws[ώdÞ1Ԡ0-! ZlYH6Rg@d' 'h~ Pr+du
-N7nkQw!vuq
3H <۔W Y C Ky,
tZ*6UB1<~UPIx?DLks _g4
kmx?_."pJyٽலg: v+;Ah8߸TD0} iU"8f{* L:r3d~|繵W gk5f rQz_lc BBDZPjlPSCW ҝ&̽i:!sbʡOU` dWkXǕ(u䫔&+p6oZٿqv_=#?aح+5z oau vV@P-לI YJDJe_g(i}j5Ily1^ bP?}C[ʢYaբ
- aqAlHtrk< ^'j4Z;Rai~)%f/wNDɺ| kJ+Gjߐ'|B8208 [x-c!nMOō
-Q߂u5 = D Gz*{IЋI^{#,p]0>ؖofXk/G=ځS@
-od9JHaγI3
-кFZX __7 8Lto0"dQp ӘirQl'aq=_zBLKK41"Q]{ ]3tEB{U
-Z
--
`J}yZi =2`,`j0{z U2ء)FǍ&Jb@ uKjj*5mri
k+!:&'AҜGh[X{ `VKw7b hB gXO$f+BωƆag(k^%e⼖^\3 b$k?b3]z\%$ejm]
-S|C IҙG#A=Uk;K8J+єd8 UVPK$ϫR n-ч[9Cys }Yܖ], 2B녆W:8vw͵tmk(1w5,յa58>IkIOUm}StR{w^^;?oS;W?Y Hokl ևVdx)rxPv@!߹)ʆݹ w4xs櫪zP:iaqJ0>Q}t;ڏ!nҧ̐|@TX !> -$#oD\˟`a{ey k=NWh'VOp9[ P=*ũ~ E{8*+ۂ+.ۤ (> \
3I!`|*>SN,,Ihtq6cd_Iyx\&TxY ߀7 {K {$2d,I= 8bjrq&|z;-5mqW b lcB*l XOjNp]:2XTFu u5s?7V ^5@LE-e+@F ?!zDR a$UG| N|zw=m'T `s5%_V+|"- fDx"@4vP2s\_%Nz)^j)1u\Mipp0ɡ $"=+./P%F)!Dk9¬Xd4p1N[5)6f53A(YXUEy9'J; KŎi.$j CY#y-Q;;x5Ys ~՛A% GVc}Ψl)]ejG1r|0Į$HE &0R+C 2Jqjlmo6.dYp~Cȏ;3MM3gnn<WFk7
-YyF .h0` kէ?}%܃=u_Rܐ31*
)P) <}E]7 #d|$D gG>/\C.=F\J'yyZwB 0mw9nU<7u fc6 =y&u G?<
M[D7c1}*D9 T72F{=HLP#FI$eSf<#3 ܒF(1q*h; zg:J:y}!V|}[x}4: 8ۊk .*ULyg<
-x۬a/I\̇WTh\+
-\iyNvqf%!X4PdT<5&- KM^R .RIN+VM0a$"ܿudwJ 8W(%1sln'N_eHd#D϶7JF<8a` 5!aL6O15mŮHg_* \ yoP$'(5ӇU\z/Ҝ-=A`fZAjl }cqCݵ<;vgðQB74[;A\Ro',w9P}y:_c$012MRQm`A#P}Yr>z5v{\$.,_ гbSJI̠b
-B t]Uߖc Y2W"jV?LOqSiG@2v ,s ʳെyI8|G{mUARn
W/I@,ёqy$_M(a) ¯Z̈H:ڢ7 =|#nEUFDQԛpK&Muro% 7,)W']# t'( j
-s+e2zxe<96C$Z+ښy1!k3O5Q*d#ΝF63 (X Ƶ&?F.g<꽢p tVY9 =1 qӝlo?>v e}3Cw
)J1s8S
-FMH^BRsJ
@m]0tn]x ny`} D^ZΚ1ʘc5LBF*R_TZ%$ezowA:']5ݮ ź-u?
ŸMFj Y dWQ9Ѯ%1ojdR [X"2JxRW[38< 4p^bwՓ˩P| LE;
>˖zw
-lY[Vo4(2IGf*n UM e;tR([f|A2R,N. $ٞ=ZW~ s:H
-x1+Aճ$,، Jm'P>JR۪g笲$ >W/eT*v{`dX'vB:(,b
-Hݘw-IB{QXIB$IioX\S" n~h!elVJs wE6QSp BM*z?nwΖiw=_;sq/bI)] =V 7VAz|tcSTfܘNbݾD[⽄ "^XnNE+9-TnltVnX 5o|k{¦`߃ʕeoE0`yx;.=W'ac(gɞ\,Q֠B *2㤰RAӎ C5Цu zm[kR."Yg IxЭk%B0iF;g*E{C=oN BK}@M2VsCNżS'LlHCyBJgҝDFjt `߬2{r8
-˷6?tЖhNZA_oKmNj%V)~I\-ūOr S/l K.ÚΞ|sڰY٨IfwL2M2Yƺ ;n[e$:=9 I$Ha練A Y0إMQd`q70x-L& ]_x n ʑ_A3RQ7!^ՠw0\;#̴@5ռCiӂ?_ۊ>$0Od DVHõRL#C
--C5 # '3K,spg}N~ In <Ղ5Y |L S
-g;`JDkצ6ΡH @8`% " t gwk s Мhm
-A| >œ>l=Ō߯X@oNg 9YK8g4%F%T(9?SLmG+zjꗻ
9xe$ I:MEf =@V}
->B ;FZ iz6+> (Ĩ£GfrQNp+#E-d
*3#0(("7>=M6kݫ<8xa,N;
-ARC[noN/I:F cP,
-QUK"1,;I} x@U
-x-g1Ko6l+j ˺_@mhܼa!*L >e_ ѧp\z.tDY˪=ǯO($9sB hDXk|>ٗveoon6
M M7>wtHH&_]cCfL 2ڽpK}ҭ c*7<sbk+3/r0Ԕ^/$Xwk%XSo?HVA }hG%RXI'I t%\ПȌ !-6BZǞS4Ckf7zid_I(tٮYEx0iY`X1%iLW lxϯAJ= \fA#Hoiퟭ-W]^=Cn{_=}w}\ .ԺF4I0E Kľ
- =F
5q#@eЃq*R!c Kcy7c>{ȝsHݦ+v]gY M6@p>;; `;G6eL$H{?tR
-ZʲХz'3A
-uWy9lj^ؒ/eٹe)žRv[`VN'h
H yg8(/$' ?fdp3kp>IQ*Hɣgy Q4g N|{?5K M7' 2mQ5
Eh+'dl,^zyy33Vx !sV< q|BLI"TNP x[m e {PpHƶLuz'AA~}~;MD$Jfg
)ȿ2M?2-y iX
m[*X{%=wh/m>r#2~mODzs
-@ܲC崇W) bpd`
-sRC
-DSB B*vQNkwX,ӥWepsVgu߹ |6 n [5c}A9u;?t'I/_THI.s g <@CcJ 겧.k81/ȟJ
-4&|8ryt<8/*˨ip ,s7ӜݠV%V* AQ6.Ng9ke, PY(gM6` K8p0L˿Tތ,Iz?lrYƂV:.:h'nĻ&ѫ8)N9[o@E
i6*o?dI C牔*`. w|uygɡ&4$n2u /#y[i[g Vf l-}o S|y¨-џZi e-q쓫>/U_ܞ>NB h Af<9j
-#j7Ċ' !!N4*+tDʪt &P_L @DAbC}\X?oL-O1L *vvt ]q|iD[\0G1 "=BG|`h7aȖ3Pg+Q>O}i7JLT v#U;)3: xӞΒl@
-5:Cez7;~ds*b5W;fg:u Y^n=Y|2MO7^^mhtqi
-_گ\%yCm=
g)VP!U LSͭM*G@\;X8n0C^+a
-KyDsDNK܄ʋA96QbrvC=y[ߡ̩I$öO(
-endstream
-endobj
-41 0 obj
-<<
-/Length 107
-/Resources 88 0 R
-/LastModified (D:20110805110440+02'00')
-/Subtype /Form
-/BBox [0.0 -8.0 8.0 0.0]
-/Matrix [1.0 0.0 0.0 1.0 0.0 0.0]
-/OC 67 0 R
-/PieceInfo <<
-/ADBE_CompoundType <<
-/Private /Header
-/DocSettings 89 0 R
-/LastModified (D:20110805110440+02'00')
->>
->>
-/Filter /FlateDecode
->>
-stream
-H2PHW0PpL R5PH
|d*Db..b< 4.t 5327VIҰ0Tr
0 1 `
-endstream
-endobj
-42 0 obj
-<<
-/Length 60705
-/Type /XObject
-/ColorSpace /DeviceRGB
-/Subtype /Image
-/BitsPerComponent 8
-/Width 498
-/Height 498
-/Filter /JPXDecode
->>
-stream
- jP
-
- ftypjp2 jp2 jpxbjpx rreq @ - -jp2h ihdr colr jp2cOQ / R \ #B@ H H P H H P H H P H H P H H P
- ϴ+ju\KksvAӡ·ށ
-7b@_F$ӇϏP_$U NϊN"ح_ջw ۬ݒޜrdJxT=J *aVvYSL*1 ـD#d(&
- ϴ|lh/خ!0זR x메N(o w 46!HEv)@PY϶wkg"@ _!;C$BԦFyc4g Ofҽ
- ϴ)@ oJ?# 9mcbe; 5иHzo+`sW8*LaeQ0=|:NJĿZgsV[?o#SA2gc 5=g3mI/qy
- ϴG4W nJAot'!a]`6Takj^($gRPӍO;IOv8=F&-IǍvnw!p?zpKVkKV]-I
- { i}ǀ\8G +Z_ff2Z_ Nfɗ~lecdr
-aH =V$QWXW ٿPq[6F!}3U&Z߈֣dz}-$1t67po"zE{61J) GfϢbhUM
-,md=w@ }apt
9yq!%gugvT`{<%:* Br5Ḧ+P*%l6uo/VXV0_ \=2$@ _ :EiW ŋB8B}M/HQ_NH!f^0*hJz@Hմ4Ԅ 9a0:0.i,炟`LJIh
- } Y>@-*TW]Jy MwD)!h Vz/"2[c ̏oͪM$,4t_\Cxd@Ts>N* gnvEܐb/ ,J_PXgkEpeO ~C*KNbjGP|4>E>DoJMt :)L}UEGhA# vxE.J@łŅ#u!ÝSm13hOۊ$BOҳϓc"y/ɷk.~|-|/@|+ ?P9R,{~q3yl6c<g4 6] E>,>4RxLn&٠MvN"O
- hGh_ F%BJ`|E\gFf v_d u
-2_{UekHK8=R1dfaeڳ2Isj[7bREr/yՃO +f~
-*-P2<$i鸯O ꄼo>nr#'_Ӱ'- kSѐΘ"G%" w4!πlQTSU7`=b,iV[懡
-q #M4rkQ
VWts<ɟ4P/%Z4pITxf91 . 찰(:6Ja.N0 0A+#97ʇ
?F2-,m /kq@1L=ӷ-Z}b.ܶБk7Zs0i:0F!Hv 0y)U+RǪeQ8ze`@w#;c+c $W\ |;\P\y%q|24 f\ Qe = ]يtq!pH8IڎċC!)Pwt2n5ݐou]0Nj~ggؠ
-Y3~+IGqΜJsn9>}:NZD|ČOM"0JD=V [0> 8l?!3(vtk BqD0Aӑn4ORX|IixO0[MrJz%$zmSpN bKFvu F"X(K !Oae@~Td=` U/H/yVa:z"Q.En SlaUdV 1
-k8¢3wG/2be;ZŔfXRɀ"D ~$nYX)LH7N-T ~F%? OrtRuq6 HRX(|HZA:s(FYs"2
-_qBTHߜi⎵
- ϶ȏ֏Yu@=jKW B&^
%LRDm1-?wMqkF
X Ve"*j|[ّDj,;b̻9^<&E~Kw j'DUd#vT"5ݼa Z̦/7
-eYΕ@-nK[8d4F$)IpD!%pan*i?|#-BsRqjƆY?nh} U ^D G'|/Zl%qp8U[aq&Kc.~`c^7}ʖ tܺK{
9 Vx/|C%ˢp kG:b T̜2Pݏ(u4z'F@ٞWX["@\ ! -&ڥؕ( K&4;ŷ'~zP sKd e> !A_Pn(e%(VY QL1!T]b( H;0_*6DD{n-PjC E#Ǵ -Uyc&wׯ7( D)͋Qg KȤp ' U+Pֵs0S1I܋h!@ ׇ5>J\(|b#)f<70#?ӞF<Ԟ&bS5թ,GK >RgЧg$ -]ģĴkU
~
-05dU!+5֘?% {zu՟a啽f1[z{gYt_6 ^ڜTG(q5i+pz R`
-7Oҍ=c
- VdGbG\ Mʻ# T H:&:PC mhUOnW< d~1mZi "Ie}J#w eW) 큇ۿ]p֏LA9, ]W}HxإfiB_%z^ GyD;Qxni>=Owo͌qE &ڔ
-,_9Y)L)J6f!V2~\k&+g6blAU~_'iy+b5o7 *)G:2?Erc+ӜA3x!̴kXk G
-WzJx]83uZy\h ,Eo$&;}^LRt"%)g= +BBG ';P;Qt ߵ*|^Ic5 @)KΘ'цT>41/8
6[NnyM@BvTsF q
|zS}eКsn~MR Ie͊T +5ޕ6hxP=RX`|; I^=%bIkX{KEWq-U jw1/Q 3& b[L/Y c `|]i`C
_ p+uÒ22 :L-nɋ] Ò
X4_uBc_/U
-hAsۺꗖ)܊Cۗ1 V|*?)-M w K;>;#E̶7 OX.@Jl;w2˭Gm#N^abo u( `E\&/0CE)AX;h繀V/ 359aZ ft%;i]
-RmMP(Ŧ}E42$OJ?r}]l
wK? QsZI32ӧR<ۍ?[@ Z( t6֑@|,0
-=%
SO@ Ugn6jp:Bk\$.+$cp
- Ѧcꮣ Q 5$^`Vuj?9:Vdh"Dϴukjsʨ,t>n Jbl|!;)(}vl:l\D9_$3-MxNe
-\%#6?nϭ A EH,Vd?y
-,'+YmZ!ZT0
ŏUd_=sS~[2v2@!n8d8Rich_`k|SA!sGa.m K[8leA,2Z5z |@I ZgHcIV_.{!F\=*[P%r_ 7 o+22 /Oz@pܙhmzvje0d˫4I|``ڼԏ|ʣsJ!G`: dR5lJe ޓY ,p> ,fLI+kexT㋯*n%M-X"n|=6֬_h/x4^⺒(˄:GϯUWByeY _XyBc|RL#p4X35^tJQ$Iṅ#mG W1|1csC@>XJo0EG0rEX@;|8] o5,eȟ )IfT)Wګ6 [Sr_|}mk.enHEQRFmSU 9)L H 5D[ [)z"~"cL/'.M 'w
(
-+0(Y,9jPR
Z
- Xϫ u ? |pZICRezh8eiT\B cl(g ɜ"ۨ"MBGdNhk:HqЍG.`J,hr(ɼPgApϮPxUyIA1`ņ7=2xL ǷxVz;J3A@5 +DՃ813SʩB5`\͐~#,~<}+_L1ߪ zؐAULWbe {XRlN"vlK|,SqYBp}j >Ȳ KMIIFt~U"V0,7)Nk5K!?=_.֯`l%g[t AYz ,t3:E}^.l46QDR_h3Ι"tәWL9(#N ~_^ӧ ={eWfͺAjxQ'̣!^?%.71E2L &:ER-1kp`}i!-:lg_8 W.bv#;xdP ;[Ҩ.;cĘx ay xfҮ%tÿeOᆾ'T߲ ' 5D4@
ÛxgE#cڈL
-
F&.c^9rƕ4g@Kl9$hPB<ڑ#R Dm} L !\eɧ@'*c@py4: }ia0wILֳԛ%|;@Aֿ(Ͼƨ
`P$0a0 ЄjKIhjL+wOzM4sW`["$$Dݫa6ь[E
-1bcv@_a#gX 7Hi>%$2$ée5\3&sѬw~QEV l/DXw,o
CS{Z($~/
-l-ͣɂFd O@$-5:aY!Qbn蟚C^ƿܥöpB!B~/8j6-a%d˽LE5FՏi@vvΪ^=L*h\So;jM-Amq.-f|7%y%*x՝ۭk@ 1WA} p(
- ̢%Y6İE`2-<} xHaMW):DSAH'~&1
6v߸ԛDֹH@~ʼn:@QI QCw$'fb@.ڽ
̔{j$4NI #gth
- vXMLhi\-CtR^Ti/1D xm j5kq6r۞^Nx MLsy Hs$1v@_k70ʟ# >{.:GEj9J0Fb+Yh1tq5֏xc}
-NQ=v@ոN k p=AY݉ /|ZM ,⅓vvϗ3*:1ᢳo{i`=L>>>VŎˋm0(nˏyrʠ~IV~'I~T?M+x hM<=W=0, ]Jڷ4)^~t
@ҫj z 0 4 R V
-B%XݟR/S)ȞH+ G|,8Lz8ͼ|$3?Qk:K]g H ^a.@bg$ЗCjB4 oAxrs"%sf3
x_v;qJ8C"q3aU4#
-'@l4 Q/˪P`H+]T5U1q?ę/&#~fdх ȯPQGy
- Dk+rM~ "#npCaB^Ea{VNDwG{hZF}^>Z,U
'!Sgc]W XbO['F< +G3䄹a
-V9퍔U\35yBlxgQ *rKVTqL҅I毖6[(~?꧸Jq=ۖ*l% .( _5B
4'XY
Sp]}uSv܅\!'?=8nV]yOoL
-o+VZ Y
-b-} k377mmm4z)h.J`74^I0W
NM ZrQpGL[!h%KgnQ[/,'a}El9Kb-.>kKm >d#c` | FR>h MfD~Bد
r&:B%4U9p9\8tE܄tt 5 &^)0gC;w-dμ# mhdXоdrɇ]ɜ#T@Ѫ^k pw~a_uՍ`U9?hR&Q^eHp OjRҺĥT{ҽgo![ kkzElSUxJW #Ƕ5M4t/e^.m
-a)EEqyj)
`5K{͈rW+$9=64
-Z[drf9,,\6ɴZJږ2?n=k !U5S5
oR
-yw< K-! \.c78C$G@W~f4}tO=*V5' ߕeEuYj*?6n y__pt2
TB,Kpiv~ASHo;!Yfld&7cm߭'
-3w G7 gC^$,\eqp~2dt;M]G|9KPqQRx ߾أV\n(s>4xGAkE56"B#+~Py-1=yÕfj,Q3s|:H LJF )
-|d l9P\mZs:Wz[r k%@T~P +H
-$K8f[.+W
?Lva 9AhIc\i~Y,u;Kyk a]DUdgc"@j2PեPvilws
%6M찢5~vv[[ۿVOD (F zCASHY_ 39sxݸ$a%2
e6aJAdԳ:0蘊W Oz3xiʆ <#̐2"p-)׳WZU"F
4q=GnbU~ ['kNyx4\ c`[
J§CFp<ۗ38.f@a/{,S@?{\{qT&@gW͜MҔ8>Hܡ! }T>W5rOeu^PRCƽm/[K >/6 d: ;S*_/+ [n {]c! !w\+XA_ŸV3@Eʤ 9w9IJo3UEGjɒ fLh!yo91
N0%Ҿa^k eIΏ#M>-4v 쵦Sc F>= 7 t
7T<)S osq->KD.y6(r!fl\b"#玪V3o8cx1]:ê4roWdw%+F7s`ZX(
Hdd ayڰ*q 1K||_H: kg XxƻG=#
-RM̢ ueX?k.P?)g+)<ޅ lE0 h^v ٷF9^ = '̣FɌ0*b'Q ^W?JI{z=YU)b}Oo^ P5cu$gD=&o&OCpL|ͦvc
-ypb\Эwa m ycZAG~a"
ٺRU"6M
-L e\{It|V^ (]H/3\\
-~~wD5OgG뎴a~eUL4]
-lc nC=mg=ٺJr-][ N lI۹x!,orD&YD ]E0n &G 3Y!yI|*тLt9qpQ߽͍7h-A\̨6 z*ejT)02xZm^S[}LJ3@LY^]ŅzzoW:.@4 j: it-վBCMa]uE){x> jU0X 읤y0Dr3>.`yy % Ibf%ԯ J(Qq]YmɱzmaZDCsSCvicӑeq*tU2.b|њ-L%rO^۟Nꬋp
%`މOҕ[*0]¨.n ƪ)%fYqC +HNrI*7WC}~/&n9/.UU=+ 9@d 7i>l:'H۪0x{הXDɯ #E\{oc!GZCh~{Ur=UU8ɖL Dk}3gUIHHdN#"p[.a ^DonЫ5\yUU2ƫ B8=8ܙ-6%
- >GQ4孫9G~&pE87u1;Ϩa=HLɧסIO@,tqQAÑlSKn_4dvewh XĥYcTJ9{r LWC0c2+FЕwOs MSMV0Kbw 6 䩣RHBd}B\tRsAz^z[Y80hX$[rw?*`)eWx'0=ܼI@+k$%<ѶD,oxt> ӀA H z{bS;~xFQ'ңٽJtYlo !>[
[`)'5D\0{w SXarm{څT(w
-+:U
z|Q37>R4(ߍmbZl\߶Z|= 24 8w | O#dn(囎Fg6\
-/l4֚ǦU{'Xޖ#J(yKC>
-qWtlU*2M-9ro3wVܸtDx'8MbcEjWnQئQω A$aE(6+~ ٬+xnյ.SDp$G
n P1&X2` nEiBl{jsmeRx'&JAvsv4l`W ziYU,qo q0
ݯ)^@]f"sܸ.C]m ~ [4\~A'#^-$u:63~d + } 1ԾŪ u tthv(#X`RER^AsAB{h5DT{ ka˱Fqv',rի@5lcTQl@D+ q7Zg֔[/,&❆Sf/Gh[P
=AEFl@8B_^SNX4 n@v?>%$0 Oa='8lANϏzT(+_lNQRf_ վ [V%f
\*ff4!Bk^ *8:xY +Diϗz HaP/mL=2@UZ`JZʿ øw?dQK)|3DZyMCSCjK.uS`7u~8wӽ|ٴ磶Oo+80Y!t%#$ey?+b[WNBag{_H?Sf
-v {>"1ԯ@GFB捩]TRFp7 YbHN2>8{x<$:Cg,>G-Hg\=! _"5 L%=nNZ mvXJZz ]ȕP"أ@E87i?U+sV _ zG8ݭʮ%q$W3\Y{;"رۧF`٬9d .o1 ĔM &mD p,6})x>vzXo
- Cv;|ܛG?'S'M'S?'__ʾО1z=KGx_\I*`o9G &j*QX=d
-~-ZLJMV.MMeϪ9X k=Q7u*;$~}ߖbL^X@jS2ojɁB,
-I%iHf}vG _ zsX0r:1 v^]~Ui
c{w,g-Ut
-!*o,a^
f0a WovEE8DG1=P]_ϓ*_㺩m%E,Ĭ12<S[p.i*'[:JI-B<|qU!_ 5,|{+_Xjr?6'%{c-9el䬸rä%b+kpc4 :"cBnW|o|͏iʌhsZm)Y\NBW:f-8Кdf^ҵumрw͆@@iOѼѣQHx :e^GRߌ>T)p7EԊ"RoND\,ܱJT Yb$/Dy KJFS;;vsi&crI;StQzf ,qivtѮ qZ) uC VWgfs_^Q db-ňym*a*@^.
v0Yݺ7oRS9߱خA*~.1x vAމPcst^#c\ґ]Z c.xRh0G4 d1%v`e.mDJ#4camG
B4
-f@yTKO% VTmqxƜ&!*`az_0WfCz^ZLw#tE@
[ZpcpO :Gۺ,q#]ᦂ]&ej<Ğ]*
-f傦af9x_iZVST=U,AަϜ|8""U0DJlϛg حWc"C $ vBu8BP
-d xJ\),Vɳ:| E=FJRډO tdӯ~6|w] O810zR_nҘOE +x2d8FU 2gۘTz ^V !%v @#S` ۴SI\cM{ë . wd4uw%3anBjlE7:AEUxOו L K_"aMՍhkq$RD|\/?Ό! -X#mPs/[þ ^ӿ@mO-{D /hL\u B^1da?5혪dS,npw=D|Rʐa9ŏ֑lYpe U4q o*m
- K[ko_#~ Z^O:: Rj9{+DHV+(,65n tEz40Y-Bty KQD=e nL1] S"** Pg,b)ҙNbJd*Yon>Cзs32q%![Ž\o$3 hp 4AndUJV. P6uBqIq'V3VyWB\r%=.TF[<}j.,8;QAgd?ļE[4YkhFb-K)= #r]}~&( J3L|gWtFIAZ;
i+*4Rn
-Q׳ b(w q%=LcH`(Fwɗo;b }{pe9¾,%eX\Vi f|G7Z1m<.D1nx[uhy
-2nAWS1V=ةNR5kvHiw = {sd
BG]g%
ˋ5a{& }
-ʆ;5c_
-0̝m< 8z:T-3\C7]Ng_`NrЛ?Y4ь +_VDu# FuL4<h˝6R7{A
-욑K~Ǒ/b^<%+iٕ:.SA. dF ֞/m\rO;1l[&6W'
LI8*?g0} VCH'ѯ ;GT1
2 8Љ~-
-ά<50EA `(eST'SzuF* vpZ+_/_t`(d gXt(Gܙ0/Ё^ v>{ݍ=t(IrǸio?G#LC}9{ Y GFwy_˚C˂"ͰJ 5dz:s>ϯpϋ֎Oqz\W o\h,Jlz Ib4构殿aTvVʾloq-=h-_
ooDK$1@96K'< WuouNwp| lLjt- :S9SBz@Co(*O U A4E 8|f
-!#BVWhQ VMڪ~2<1X ԙuwd.U4(%_G ITfN5 aTB jOڰܖ =@8u2$ަ %aFZ0&n jSch[_ξz'Dhs5 AW*AgVVc'Z(|")UޝwGg EiW%Af:P
-BLl5;÷,ꐴ,˨\|ؑ:6\~O0H>`hso}y>ʍzi)Ѩ_x afI_
-o#GppwYW`GAYܲFN>z+uX3pX^Twe6_
-E$^ױsX˧ Gx ۮ2G9VYTDlXZ+7^ ( ^.33H+v8dr [y'aʂ 2uu5_ۨCuK/ *벉LѰ*+8 HL_+Jޙ',g!a go ޖP prL[
-Ekԥ/-§֥bCCPaTs FrKBu{46E.(P{Gp _Nzs}w u"D)Qco)
-.a9.m#^NJTeЅ}($3D6+jepX p )+ ӷDA qS qRn Q¾ {刷\s
- cGv X M\ QD8|}T ,ͨOTmU< w[e0}j 2 i){, jp@_<@}Y&_:M9ꖮ8aL#P; D x y;塪[涢6Sxh kɕ
fGki+vuވ Rl;2rո&' ǚͭ /0+f"tw5\ :uv '͘z:(U,oKYϡI
- Tc@<Z4f(#'`DOa2M(5=](;7cQ<}"A& oi<tFS+,%@F]d@̧اV ++-QO-Hf:gvet`+0 :,xg[ ; qEje\OoVe vEAMGw ҆E _';*3 iJP9UiU:%E4C,s S=ro[
-]a549%$>*{֣{^pՖv&V+P!\RFX mdp 9ajBj\ͩ;\EmN_uT41.>4jI֨GU^~i:*Fs]Iߜz"i)hvS[R&xe[98?8W
Պh 29e)&\ua%XLˊ [L| |jB9M_ n\gXZTl[qbXi2e|3kuL2]@vD %5dͲr%Fjg$֣c
-b 1& z;ȃYR`Τ_U;jR!5R["8O8], _F}|Q:(]"'%8=}LH; avJ Ht_/@ aO2ם%mGNE`D
ba
-ߔMf9&M~mҳb.k'h56ĩYbN樍~__sأPywj-FPOܭ܇J2d۫$z^Ϫ̘+ '9d/uXNs.
-:I:?(KE`^'QҡOSr8 tq34mu9XovXvdsȘɏhTZa3 A!fIVi-P8%cgZgQieOD58xpH 9@ǟ|jY
-/JFװɳ{QrQ》:ad|8@1$U T%κ㻄mo͝~pJ<|`[e?~D"u4g5[i9`_p R L=]T2DΫ
- r,Rf͈luB[IU(k1wb>J_**1_
-X:.z+UF*xM_" bX7 f $ؚ>/|im?tihH5eb{aJgUN! f<6C v@#> %ͣcu[;ezh]]}wpV5s{0S!CnkJ#GH*f}EM I齃=k=ܦ, U@* NBGGkV7 eG'u5/4i;\0R{vdbR :
-!FԨzy(O[gcS)E
}7 0'䘥|FNz=JW[O
aoYzDrPB}FFPgӵ!3fIyaX禬}g%g隘"UĨCFnt
-5xOh˦B;'><9`K++ u7`0"\ |Q:in Sq ?KpTYۻ[+ʳDN9Z#
-MZw!WN0
63nҺcn䵜3ȉt ˘n-̜tԂ=
-8v O10 tIBhaUI*Yd{Յ!1V/foMG %O{1U\ǶE03(J2mX3=O9 vyBa,j%W?e0 Q^hdSL%t0.Hri@қ&\ DFƚM`Lb ';vevfVװ$i`UT1,>Al
#\aia%7VB7I[
-{ {=Hgss9lF(Z+n3MƖʒnJ*|t+Ѷqoks+, ?J頬3bG# g9e%Bݻt)?a eƸ0=lME *QOjAvp9$uȖàD=X ~
-
-(WtCkp!/i]0ϫ`o'>*Iy C7◞ƌrMF:VD9]'*Q3G ?5|R=]NG\t bVt)&Ycff >ZXfca`I+B`x^1? Pݘ8DzKq%?< GeB*jS`9E05>ecRTQ9Hz""fu hxY!^}C
A`8DD ZhXpdcpԾ z!^a"#%~]8%WhFF/֩)G5V=MMW({6\RIgbe: O\tyx;P, }C
h/V4uXTfs%+~ 1w ~Fa@Koކ/wCƙdž 7x<); ,/S&H~BnD>JŐ裒nfRe%1cvgEMFm_2wj2fnoBzocΘ U\ l #Ch#wD=18IbezTxF*q4(|n?D`%dHz:_C+RS{Ju]t&~ݯ4'ʹ;Vdvnj~Sq[GBt\W`lj
->E l pګhHڌZvl@!$܅l\};s}QGY?Di" 5UĶa8BA*zTf^:|7QF LF#gzLL@K~`N-_`A#Ki `(\G;peRwBJbs{_qLvEtД9G C[e$y?'A()a8 LBDdǶM }vh(F/cTL =C}i*ke
-8vOvbc2Q~rJ2N2TS b(%i;,=7]fC%{ea.v:D#&51D_߆r~
F2jȈ: ~niXYlA- ɇfw AB\Z]G0/ kvZvsMm ]sLOyM3e4|#W];pf8ʕ`E[P'6>Yx70[uImܣٲSFI@
-D 'Q!s2{w\ a"J9 0DOʎ&==*̏/+"~dK#{pۺf:
&W'oN 9v:7~F cdaQ}C4h=6ʼnEi4a.ڙR Ѩo-nLSxhM P" [Daԭ:f}2=[V"ΎSA%hM v&0oaG@N>B&8ry]*T̀
- v;|//OؿԿynk-_N\
zM#!#xj]0Jd2pi9Ƕ|[ZK nL> %Gol*2=Coa\ ~ ) +ojplXHGL@: T98fo -+3cQPyy`?? DXuݯIRZ{$Wv [:Pϝ _9W ]x l4Pic쪈OTD*Ի^~}3[u;'&D (
'D XdUh>+=|*<4vN\pml?L = `8?犵&?-U_+dweHS`^(qa:Z2k{ Dr:]3 mi"RR淀V L vI&S/Gl4elM(3fwۥKp _]0$ɚO]͖O=w,,;Es8\־7wQ!y'| >c*u Ipab3ݿ1)^ d mrV <,(wĦ⭌ vgL/
n;nu@ Z]{<{W)OtǴ,SԻu+ @OeZ dÑj vĩsEW%HƜCPP腕TzU O6 8-E *nT3+bIy4 J{"3qE?J.ozaxWZBO! MĺŊGn2?yiEq-&j7-k 4V1TSY'.w`|,1p$`^Eo9@\_HH Io?X<tEy9lxL K5HO^#/<鵅dȡ&2RFAC{+ Z27 ib0Ò0)2Ǽj}G:YVU
u6MJi룮 Mdd@)190C4n L0{=EIc;^NQI@{ۂl4?CuL Cͭ\8UcMY̤Ϻi#HR-ݾ,; vq?ܯBt]j @8+tHP6(Y99.iA^M|3,1
-HRS jQ"b2xgوaMB5 LD UY*aegR1#Y[srY^4'|0 {-Ӷ F
-fJj:\ ,X./Fk/G>p:m#ڨ6vQڏ_E_^wVtX"HHMݢ <ކCY: 9\Ν+$W| j%$dꦄ$azݏH6}KP
-vb|&Z%T\Vث ss%`bHr|7,mBSo9s֪e9|`K#FIAO"[m|Bo-d\&*E[lfHV G d#="ێ|
-sTԎ Mi+z\o<:"e]&Sd<+Q*ؕ|TmK24Om,St DyNZ}i|W+y1w썥ZZC7(s=8*>!3a6Aebg-[5pYE5tVs
-*jO^Vhk-Aoz.=AN<ɥ wT\#O## r5A?#iT'@T"`
-G_E!n ],(@A2/u=;UY ?hC֟J zVϪ{"6y,ێUn(8 0& ~TШH#.eAVa)U\!v[u
-zn_-uVdC'
-Abf:~Wý{qd)ްo%UǴ4)%g,[4
=ƿ+3 3q)U(aSo"WR4I z!sMRNDbۄ
-pe-r CV>P z|| 5C1 RD
-y#@KVdO&;cd챻M.
-Q~+
p떏V] $"%ҹC!B槻-
-`Фnc<~#-
XtЈabZs=8 -2:#C9GoV_gz߾~\(1Wn|v9's>: 0Jql2=xA=T]ޝ?6Ug=he{{.sr̤/2gn汤 y0OC0;ԲSD$GaOd=$3dgT%Bh 6D|9Ibik/A13+.pq[)+@TFXɜN:̰?r au 0!,;TnU2~D8rʏtq+/*@7C~8JdA7-#qOeD mL*ɖݞ>rP1m K9P`Jq2
-dl7DY4`7;gfv弘~V"ؔ M fUb8 Yy,tqլ(ݪaiϠϺ۲A va o`&T;t]ъ
-q]d킢B73 2VcU3]럋Pe9$xG) f?!9 jˤ d/n)b&/Ɂ9I&ڲ`u 6?Jg\<[
-i# Xl4=?``_<#}MӞ}K=gQ-uKvic6^{ȗ\=^~Sl͂{jRQ{S ư^VƦo
-;&xyˈTrA8JOR#GQ@M,I|nwu49ߨbj.B]R~Ri WlEo6 "m3SjO˓;5j!kLoT1i'SIflphc
2e/q| ĵ`>PEoW֭xG"Ѣ (%d!r 4
-1/LL_MWldP˸.~R>WL 8 a\Rd9nߟNo2{'Dy 9ye$u6=;Zx(6C7 1NȦ6¿\
-2\v!7 tPHz ^ζ٘ݖcu6ߔT8Vy 9Hz]chh/5 Cmyx}wwzntud-a_w,{_˚n+? iy&>
fˑ} j7XKhtr~PdVv\ aT? SrMt&AP;̸қ!>h{7'|eLL XFz_▍[Bǽ i;zF[ %5uf
ͥB'L
7afX 8H%%+}{!2 鷅&$ZM~G7t`AV;mԑ|3u ȍn0AdƗTg n-_v*$#֒B)OV;>cW;h{jDH0 B/xJ> S*噽C }TL|MǮg *6nPS"'FF
CM2ǣLKf ct zʧMaݹ⳿cMb}S_ny!$vVhUJ+1e |VȈy? l-\BkXO?:.> ~3#^L H4::AOVȄ$PˬSnH*r@)mYo -TAv#x3ԟ4}ʗ[ IS٠"RV^;FFj
-{>*#?bk 5b6p'Jz;C9҂;msJ /٦k|ze1viQ%{k> 4L=npFwӍǡZ"6 :pQKt;&[ވ\fawL"9ӈr2c
Ve%,mw<^`:/
A K9.T _F<5RRq# .ϘU@a ,+? 't<][>fq*>ƧL9F-U[wT!#hW ņ, \((XgIYǶ~aNۆ&%,yԿsNqKj.-^8ȯsbVň `_O ~M1ݾF%^,
- wu#uu?7x??\?[.0ml#szTSGUR!٨hwIdU"fS)ZۋJپ-&>ptdEFNi }l^\frfƚN IL3@i}WF\G- _GThf27 19 ts10FXVtlFp̚? /OCϩ u0Oɠ $+תY+]T G*'cm]F!SxVs״~v:u #?fͫ_ |
-_FIk-HfMvy9f.xFcOѕH\p:(.bXkDF-\ .ܩdd`h>)d@% o~^d{#ҒpcaJ/s%O sG(w0b<>z?b>ꕬkp8s.bBAFN$wjgWD~=p 'ՀZ?wUeSM]g
I
- YECNGQ]'|MK j PֱWR^TZd?(( iJ8MT㽪{ :Oo |"ӓ@,Ra&Cܡl *R?F)d5NG/EΚ J}e8z' 8%YZ k1|,>dMlȸDEm4-dk¯B9RLOyΦ] fV_}n* UVC;xo#~wBmCp"<kËm/Sq>AhKRq Co
-οMٵ&d|eU d2Sn|y.I
51Q+l>/-%>aDH>Q' OY1Q
:2TXr}S!D (]#-DDrJsk@9WlmC8tWjfwK|oU_GNReD嗗baRhD^3)D<6e:,j 5V?(_5r"-;JzD}~j,$kS5 &V|r-^g ֆ.4!DZ`ʾ1)s̎ۋ$5 Gǀ9|:XqyAV狷v ڹkVkHkbdo~p&ko"AsfiC
D0pt
-g8ԅ %f¦T.êP\(MH؏wC~fåY/D j"$9hU.$֩RN2nz
-\Jlk_y JLf]O%lOӇY%2?%?d=9}!S# \1T1
-Qr-̮s&-7i?nJuH2֩q@9Ӣ@a+R !$ZIs !v܂bdW'O!Sn0?Q7.«;Q!ZT: z [aZҞ bU駱
h>vhtg" 0 }H#izbטl57 2{:.GD5FTy lMZv:ctgO{8מ 8qmIg"o?L0,}9؏I)#2ڪ P6XΓI=z [TDۺh팡9 ^륈stQX5N /ZxWG`夲 4#&|+lfcR{{9 ar1`Bo車
yp/e
z
-|@5c
-@1N<FE'@DL2`^2M
-: vޚ
4o[YEBqʮ(/0kkgo3
- /bc6NIkQ25pV/]WT $kVdƵ[r,ʐAUjH?pY,(ݥ( w5ZzYvI)![kE zC0"m (2Ë #]J7#"WLռn= "|x;< 7
+hX 9 cg`ߠ
-@$X&ˡ^4 ҟ
-1g KiREP
a0,%"G.(frX7ODn>c+
=B"yGE)^ g{I:Z%H x[+-X*{
Ian'%meV|RNWGBr hʦ{d)&EH{amZ r!ᗭgǚ~gdsp'UYX,b0Y" Q6W|LU Dd[hn+ #WDj2ll
.,c߫}UatD@37" <MVrkg."#aC h>g#W!03_'>>4St 6#KdowU t ؏o3: XCT{&[U}j }q)"VOY iN\Yz~CB-Y6Itly*b } :+b{[.z:]wl7}3C)0#^%4erD[:)/䑾t&qt`?ՋQ1GBM g;G/H4NxGZdaQ"#KР]ɛq 9+o}
֒oüaG*hsNH u%LKj4 ,B.j$}19C}9!yBw|?D pt͈ZT;SΏ ""qFM3{ y|A{[`Gݎ1_dПnq_ p r# {:ػ9PW ޜ ґu Ra%}ljɣZkapUDA
-0E $c,pT:/A>qx,B{=! ϖ@ S7UՋoq)/?1rW4oaS|-Yg2`R(V%0)EE.!I!UW##+!-5
o<1/hN=\vc59ň oȹ ; #hW()J C 7 Rs7+e;le6[aNCn_@. }j.3Ed eXI/n$բPK 룾bA4_Q|_ Tk# pt峐!%`:d={DVA8Nױ &df.C4R/e/'XeL)
-p:-&M4ƃV+ skD EKh<#O.~4'RCQ UBvlMp0sjQ)A_Z٤V0}MOL։5Y'1mYO]( 㦽V=qeϦ>3aF܆ rK[&^eֲ<|f]VX!ftѱV*O&VO
-@AnPĎBX~a4cg[G^qJNK:!]:^(!)_ȻB[~6Ҿ w 1ihʞJv RD $/ e ,q*y@cD5]wv3F2x *
Rw*[8f}w Vveq%qaZf
- wO _?ӟI z[z[7z ?_ôC ӿO =;{?{IV.o,I@V"6K:d;ᠾ)xEMaOW OxҘ!Yn >Ldmݍ-9fyE#e{9N6.zŅ7nd!: 6O$]CssGe m'Scˀq: 莳T5۱ŗ,@w J Rw)[ѱpKBWQՆW^UǮVMX>?Խ+&h5C
-S%&
-iT* D $k皅)7Oo2\ *Oz
-< yPG6
>hRe22VS%-Cp:NNii :7; "U9l\ ْl{ ~;F&sЎY)U\ ;zfG#J4Tq"oI7B_- f",-12Y"*,q3OPz@7`c îd*;lտ Ƨ;@|]y%Ϊc;X;A_cĮm締a|ۭxkrĩ DZFZџ٥D ObϧH47[q%
MG^ Έ2x uZ]ހU u"=7\.}
T vaSuM<6+X! Q;\/TIDOC U+? % xkX5vfEI
-O@q8X[)gK7-$M\Bg HUPl5ҴL%/ٲ@AѼWy~.0D[.!r/Б j/ZmdTշJQm# -/tiyq T#Ӯx`W1UDkKw%xt3>sv k}aaCڐ>ڠŢr)criEDZ]V-MHa3^,a!
+xU 0G=K rݥʢwW#dٿՁPg8Ӱev'q߱S[ q,"-d^k3M jAwPcXȎm+r
- %E*R+r므Q_{09_Sq,& ZmES ֠RDoo$$_4Ǵt%D&Q
b@f>>E#%tizlW`yf@BTn6vm8`DJ»K.Mwڧ ]՝?tu`r 2Lp^`9JFU
-|D-j&Uz*l)Ae>qg^o`!Ot{".p
- 97ts9KǫF`+\Z_WIV{!r)Ӫ 7Vd%Öhhx-'Qb,f4]21dWQ9⠥nZQgK8+L$E
;\ΐӯA'r)ִio.{Ydߞd65i-D/( \ -j˒B
G%es";E뵂GG1,W! (\g¢tg ЛS?LJ|%Q^Ɖ3}A^K^6j|yvU~A{ÿgEĭ:e M6o*FXL|]:} gr .˧L:j ^R É XpݚksmIPqTx`
-;l*TNNaFO0yty>S399F\g;3TQA5[N<d nTwL=
Ljz; b,ܭ_42@0 115 ;n>bmX&Bjn+3H݉2(py6Oݳr6*
7֚I|
pZ^';ӿX:9qŠ5+BhX(R(A* \.$"K7e RFna0 \Q"w4jՋH"clQ= >:ʕ7TwV~ߌQƨ?pv%ޙp'(i
ZCNs`⢜>ꑄH s!^UknsZZdk:/~5S_6SN9&U=ǜLҟf^&|ݪKhkLnwEfҡ rMs5²dc]E_D=@Q±,Tb"j㶤snQ0`6 *1ƘJJ@\ܵ c_
-1)Djzض1
mq6;^X #I2oTNQs" 8] K*٨wn
=(^x~PT 4>BZICnۏ}}/(oN-t$hpr9HuF67|꺵 NNy NG Vg+Ot}Mmdqh?8G_9LnY`avCg ,Pp#p>G-&p-<+-8N/>Q DfC𡶡:I0B"(q`~QDw*HCMCaahnD& 3c/!Q"+GG33m
EӁ g~db⌡P AE~g?n'ȹvA&MwsrسDϰ-`1
KG=I}x~>J6 Fe 0>_ e;َ}L}6F@|T+)v6X9]hHm?s9nO^r]ѥ %P` ? R.JJ85瓁wa(af (mRbc1ann3>e
-Rb ATج% f*Z @H`9iWgu1$K \QuΔ wnt&ʆl
- .f0Qmb5>x":~oJS<νKW'|wҬOQE|{ =
LN<6?CC=hpD @LJnv.yWM t2!-Ay RJ\yXGj%7o%>]Yzf-ÝFjƫ pj6-^"R-z̡txgö> ?ónZl8r Tv۳Cpt/Â/9]+&,x"% C3խ
wf: pG=6Н6PZn4n̈ӹx[Q[_1B2ltaa C`6zґf}N/!=!IHo" h1gL0Q
-XF
-9ճbk\̱xN$ت{XO͂~٦+0j za.ntC 2ך#Z J2x?/2toK t夐YTUҜXf5I?5Ce)lQ:3S|Ƿ
-6Ī4"vl
K W X/P֩IHYUy }])d&<3j./$l
- <2 |f+l7r#
B#o )R.h`3Elt2>2ueh<'vlץW9} 5|6"t]'mj}7Q(6S(R$)/NG3xo e݅ ZI`-ghC`cʴtTo8t;cNM 2s"}QL>g{"o GYyÂP%OES,wcHk/0 Ն!
@lsx<-{&}ja߉oLLYM>G G$ѝE 9s,
- b;/-{K =7L L_N~ =|=;r;{k õ bzw~ =?T ={#t>~>O({u?㽵7 խ b+ Nsc1썼]y kꊒU4|rсRUSjfZl,D5q܊%%;4j|j69V0}
guR PjHZ0EtE }TDX5TV.y T0o[{Gꎋ`
-X yk_5뗉qT5xN)J\UeF(a_P><*+:-XM6AǭbG xGH`fgxҢ
#WQ A `-~F2#v0*y|#ec¬Ɉ-',#٤ސ2N#ǡXk+g8b6˛,m r'JWKPɽ.;r;(VOa s韗6Ѕ<} Jh0MR}z_o9Z^ly=u+Z =MMH\x[+OnxDkwi\͒k`-sNpNab 1z,dg>\v-IZm fL;Carm҈k^rǬ\K 5E8s\010M g'q"tk IE":S$1 F3˭DfU 1Uإ
7ٿЕt+U^=I:=5ZgB}auVD
O{< ctxI"Ӝ`[ؐ!Gu@طLJgH!F^}»j/:onݧqBFH<9;\Kal7GxBG19.gKDj ~~t ?fuJ v.b C K- wCDS*XװO̧ my CHTN P Ij !E5*Dv5 T5L3~ 4` P CQot#X4k #W7t'>&W}ˌ)OwI)ɢn~ڮXޅYrk
-Q q,.}Dξs2(h˔xd` r2>q> nѻD9X*HŎ~7[р
`u$ KIH˝UjZ:lh/ [;v܉yU1N%%؇`wĚ ;E6f fz`wD$$9Sd} 9^-pG5`G*73M^1 ws qMr<9qSEK~a/2v[ bVGh09!@Vֲ((FΪ&_/Cju
QU!toz~<ēÔFP ([fZH ~tz[W ᬅO镓D|)LGAT 6keR=9*Ns%/Hu}RG۩mǡRs9|+O !LBihb xDre-?y
U$aڿk*ز\ 䕱MX/. uLAh A߀#51P!+#&MB5 (m( c9=^ ȏ3&ƾİKG4l =(˞ At%<3]~F,ˉJקTJu\%۳3laep؊__"ϴEPvN{?PGMw(ʱQ=%TD4rmj;&K$GL"@pkoSVlWf
4s0 _κ2ᴛ M#qh!k{
-5ZS$yNJIƝ
ܑUJH
"%H x-YyQ՞}x> z> ^7ĢUJ Zmܰlpf@V,c#L[i{^$
k]U,_ޣ [Q1*ԝRnaΏ,Yvp;?qq=݆' T}WiiJPΟa
-5
2$M$fu!K&`|, #d,x 䘛nPb&y+\kb }W8XHr/ ѐ^^_F
9!Gi T5<.;8od׆;W,[kcd2 ,i>>so:D=dG:rcZy
~l{#w*%'ŗv Tf]03Gm>s|K{xL .>ގ1K~ U91 Gj B1 ٍ:V{in3^G/WgƷNN&f H01}S04X'ƠC=updX/˰Gpq5~ͪVC쫊-f/â~ tY }̶^0 (-LZ?B%a!l3,)CUl*yw&(Em<_X73>=1KJA'_'~)dE0k 119_ '5Ă"܆3c]n鑆-G1c #d:*K@ q6%6y|"N&3y!"hH?7t<,a 7 jT5)
wA!bmT_i;Gy~wܘ(kVA^m!)A0s`@gQ(&uM@,H%#
2:i b
-iY{}
-!$ÏpUYMݣ)BE$ό \owְ{ɢC%~5Y,'4 3L#H% ڿv#XnȳD
8GKL.Q|v#"rp*C>V9\MRI;ꌈN@w pGn2zk bL+o듴 KK1y3GJ4j) zuaZ*zoCzcAn-CocF[p# ?a,,ji r]A*,t\18&8oskQ*u\-ڷh(_3F 7p yή R9À
>9~^A
- :rF%ܨF ZmQ{| v˵PobTV= 2 @m}A:tA:fdcEFgL﹂ƚk/vc}b Qp>4FЃ2`9MGԎ>3i"Jt֣T(uN-_h8SܱCEס9 Or~#YFOj LPހiEE !p<,GONTTC$>}B+hdh= xgi00%mZ RsB~\zQF4CayF s ,.톬TlFr~4P>qR.XtuCjA?0;ѯk>{:2w Si#?J*&ʿ}Yں_Ñ#01y1JM _Þ *$C([΄W,-?~~pN+
H XyK-EF<,Ob9uGl}Uk\"!Ms?
-t5aKX
hU >aގ۰P kO%<ԱOZo ;U&3b :J,vӒVY]K %nNʌ1vckz|l7d>$|}r_ 2 Y,>Zf9
-ipQY[a.g@wa~#
l|VST6EvPhRͶ!uK8m6):t?ҙ- Aqua 骀E?XS(J6%Wɶ u|!ԁ Biہ'qg;nBу`\>!0ȟ0Ao.9 VC.k B`D)pdY#Vl!tP6;A^_ ]F:A"i8aȮ*o#D~m/2K8ޗ`H9|d~U}9 )'fLx uȋW#ϸq(
-:C1" :6Ml!*,Ȅc+f͂F`R&o'oxnT]3k p A6S .mDNI_@D챛ЩG&V]<=C"t ?2HK *ÒØn2KJy] jJuOW;]@ėhC
- |;;W?io؟{ɿzrz^{?ݟIm_moֿ;þ~ lGxto7~{==Eև!V?k2լO2Uѣi ·Z?T/ddD}ԧ "ͥ]i3K
-7՜ڡFG
Ivt}u687+>
uy? 6[6s q.纪AcIA xח_B#gUB6[^(iP
cE3uDgQAzS)8bӈ[WS5L?3Q\u`U__
_} +fX{=5=bm1 ^|9@d{ۦ ̂=ZI.gU$ښ v1XG&&H,mI^v193'ܖt5DK*t: 7ӈ/$D[(ASUWի7&2Ro
8#jtK9KBS X t-Hd^Ԝ 0UYN);-0ӷ=4(@k`ʧ^l4lS|zx
-sg'bJz>|LG3eJ 'smg$5z' g#&!v(uP@/, yw0ԠKO:"A-Zd== /h
I6L$ dF&gZ`Fqs hd,ڎ\n2 j^|_XhE5.ba]
-VYU(qC%@H@G/00һT-_? UѪTLFd
epX
-.S$[B[ ZK'ދL[`}pNV6~ Z4 j_{_d
GgLC X߿峛n젲15]]+/``A&1>(M
'&[%فpųݹeBAEBXَkcnSij+W.Ee* ő>Jϴv o?O|N,sK⥴gkLh`
6{!H{)Ix7pƲMIS%բq [ WLfBXec ꂤB y [hcJTxƹcd
+qXE Y< B-¶{ F~; uW -W \ca| NSp9Ӎ;4鞪I@snҿb:7oP33EsԼ"J(a\ƾ*t+Xּ,
\zecD635yaYlufy< *b1eHPt11ܱŊi[܇mA ZS|)j2bPTzoe݁36Jq1_^ Vwi̅Fl0@ NX-TbyY kAѪ_`:5 ;40Cn~ ;\+R06u[4 @~
-j!vxܱ@%db#-"4#P^; >s7'y ᨟[hF`Ųt:B 4Qxts5Ox_콈C7/
-*DyVB} 2P!0vtm xWpDM7y=#k2=D[/ ^s.ط_*+}'Č}))1;rY\1N1@pzy|P]] uAy<~dᡅS۫];_ zq}ளƐ#&
-|YF`gVQp
#uig#4l1۪F4!XrcѿI$RS\|L* MaNޑF vV&'g\v\^m6 tK?-vB
-.KN`Q2Ev=BZVSS*?ϒ r*)(TzO -@Vbp ̨WL
ȷxaOdF?v|
-jm_>CE>k*Ϗ:y"tUrK@9G:fo:gͳnd['`yYRQct&]B+$f]gK
I!OKט5cu7 )N2^Q FJ?%KeK\g VPDdzP! !̈́;|g/+ 55,Pg5e rT_4sQr**๔^+D:,~YE{^u]\
ʢ4XQC~3JUWJ+' -V*0خ hWۢ`j7s7T'tf,3`QWi0xT
2--kԺ9#
vI>T-AN(]-UCUurWӫ OʺZoU~E+ErIYAՌM /k
-if*6!:$`Ak Ha l{> Rd5=%cxS +%3`=̰6\j=& $(.@o2'(G5g51r((wA/I rx/{ТFy?3 #, F {50e
maKs
.1E%)P= Pf-ydaCY G7m
ةUy@| b|11Ko@I-Yi\7f ^ ;~^tkӮvRA'`y!7-F5k+*'1~B0tY"Ur( ıHUC(b2zi{{j8 0owd 㶨^4MVː~Qq '+SPxJ2 !ۚv.lO*D'KyU0Qgؔ˦Nn̂E[,XyK
pFgYVVHzFZ3jE4iy=d*55/4)|Gq7 7>
-劥U "Jtp*5 꿞
B{zq&
-_\읔u=BitŰ%R1G]lA\?V`I/SR?Ԃm
-aԭBӌh 'Q~kutzF3pΛGa7 _+AatcCJ<3?}@
eώ!;֪Vz IoUSgic$+] Ipq5YRT0*yQ)2!!Ќj-Cv`2=J
-\v.& 7YBB"☡pcnwd CR?Q$Ut<gv ronR" >o`j!X1GG-6N F9UR#.V\ R~/A])Ak
cOtl#]U^ $y!
-%џov 8JY"=l$#?ڴBĹo3SkUiM3 wB}IHk^_\VVptmk :;-)|(
- ={c=Ewa={ZwRO== =au pzCCܟd??[=9z'ߺ/ݏݗoU n[H6\hC1wNRx) WӃD@A,]B 3 qN ӎ0e6UiN7D m[ݺIМmn=`6Z :U dʅcqA7v
-a7M}9[eڍFr) 4 ^DCja[GC?eֱsz$jٔVG.~3/YeedF"CΏ-)ENG LyG!e ;7 (X ?$p)ji_X j &CCCiQ}O7-._ D*.>b6Y!))ڏ'L;Шˀ`Y&L] r "
-ֽl
y:5 !ӿ"= ׅg/0йǂ{Y_N otX.;B#'n\ӚoXơf?7#3d)f G ?&eQVwj@Xe6/ 7`(~;Qe'ƭ'!Fa\~m4j eO \,/YRq1,%&-oM*qɕ_:, (аFSZK_07 е}a'?B8w4D-,E w;عA\KjO&ωl"u&aU
-)91rq[ ˧̄#|}oM9Ys6؍+ Y] 8 9; /@@,tv4h鶳nX7vW{,l6% z_J3,1I:ߎ=DZ=17lB jEg !vEuQWFE. z χ;lv1|C
-K qe7$Z0qh3hK?u?B1 a/7
+'WS!5q< f]Vʟu&-y,iщ݊)R^CV % LҜMF8.3ONzS~uΒDFw[d1kW(]D;*c Wd^tʓ>
-5ͮ<3:v![ZXc 6Dj.mg%!'?i9z Z~ iSzM;JylK3T뾙i|X^jp2VZ֔Z
-,"p2u !BDMx ד HpǞ(; cX[
}&z%ǟ>] ( BPhRۡѠ&hJ
-VK-G? Bc0\cn
-C& y*iv)P{ :\'+&[VNoZ\!kkaq5 ĆpRT26P W QՑ?MR(0W=l]1
@h[f'P> 2ļ&FBȔji?͋jwYjwb)5661 pZHV I12TgwHb5 3T
-v;:UD/Kٖ*f%s|0$)%MB4|{|&8y|̞`1_kl%՛6v, +GF?( h0+xў $T#OTt
-.Ps;twX8?2h3i;B8w{{{\ptm"wO|hm5NT1MT ?M8e;ފǗ.y )bdfIUZ?J70trZ" kHۮz bxW^$ uklJ Xs>D $Q`a&FYrrr#*jWD+ mԽ ><5khz;&5|$"#Z7Vnk%Cu>ӛ {Fna&Oj/s_ݳMʮnM R ꬥ}f1ZD3RSθS<,+Pݸ 9G5U彺vln#gɟM
-ʔwKPK먽R(nBөL K}E
(n%]}ꀌ(b-qG
{HndZM}.)NfI^1G;Gl ]6?W n,#C=,gÁ {zw-)=ޘა{8##@ T$j=KS`XSF!T@\{ɼקJ
-
*~+r~]XJA
kf0!m5yCK?)W
T([WJ9I
-$ ׀
-h|p!FFB퍈(1UVRt+7W⪕L}2ǃ)R!EIsJIG @| y2:xFC ~R61g a h>tzN3Ru ߬|= 0cJX, LuDi5%5TqQ {[v͵DXZh:DWIo!ʓro՜ Ҟ FB϶ZƟFi<Ä9&ُ۶̵uԳr(,g{ 9S[w$-BmNv8<^Y-Mu= J}S 3}Ɲ0-[8:q#sƭp+% m?q̵QGWFytv`DӦ Z,?:N ^zw
-Ԣo&22jک<&{b}P{Qw^C۞q\>m >nu" o99g
{_j#p<5ў63px?МNVP
?ib*Z,3Neѫ˂ x`D E>
<{GE:2c:mdpx!yTAuAUetuM}h0 ^g hܧB&#(>N o˴ >RFM*%~rI`⏨.i|u7ZHjz.8m гT'4r?#Ń&/
?&!4Goc +(c73Lit3D.qG*WDyWG .U=bH{tp&uBQ
7PB @i3О[A>bfb &|Q-Xrgc"iӲC#Xu-0::n]n Ϳ '4Xu^E3 0G$Ms0ى[;zkmJ (3> cMnTC řc4hdZ-KW]=:H'hݞ [Q7}m-ƾ_,d05#/k<;K}Vo8Y1FwxQ -
-:R b3reMf[QjgmCJ;}I,_ԈC0' _ܝb?0]NhéD3*Ki%7ug\ ]nԓt`worJqdr#pJi{au E1ED8e¶-u[GO0֠ڏBbnD<
-XRDPr3U1 ADz lBSA.EZ(Arl#i^d.^GhHMj!&GJ,QV 6 j6 @fGieN'iHH_j}0Ϙ h >a`C*0wnȀ
-endstream
-endobj
-43 0 obj
-<<
-/Length 87232
-/Type /XObject
-/ColorSpace /DeviceRGB
-/Subtype /Image
-/BitsPerComponent 8
-/Width 498
-/Height 498
-/Filter /JPXDecode
->>
-stream
- jP
-
- ftypjp2 jp2 jpxbjpx rreq @ - -jp2h ihdr colr jp2cOQ / R \ #B@ H H P H H P H H P H H P H H P
- p"Oc1 =W ,tI߾Tԋ(_imV$0)Гϵ АI_iY{ӣ 62%08ݹϴEVIR]}6^x1Q
8vbH,u_QD)uٴI#xv(eHd
- rx]٩Z̼vWɜ\dא s! Ř'Q ϴ,BBM;-w>cR6qa:ڲB=M+JBϴ+kٓ
--%W
-4
-3%GDOU7 *xsnIK)[~0
- tq9 曄OoMMp&)".7A'eQ&% A٭ 4AFy}ϴá]yd/(V1Ha(֯e* tF jBFq4Bϴ r
Wqa} `ym;tek}`q
-R7XMZ+SDJlx2
- ϴ.6% VivG/QlS9;H0$#DpHLPL^f[* U@ϴ|K,zLACq UXc5tn [ Nm! %p>u`ϴ {fv 6# :=(V^%~
T ż?JJ);x*KM:*n4Pƌ,@ `m`ʝ,]5$#A
-J[xV|4Ȧ+c݈<a g6L{tWf wB)Yɷk Q}f`'dmX1e K8> mt(OZ[FJ y=h\q X5VrpξYO3 +~ح`uC|d%Avϸ C8_ضGEu\,/KΠWpCxҤJs};: bAx}uI7,
-AkE8r{p;ETO84sw}?%B*nvA*fTv A5AxYYU*)ϫ 1bCs C"
-i# x
-3lOu:Wcas4'.o7BTuvOLI䡘fF.J=QCvg0Ieؽl i]h Z_!L=yo Y}F`6"l? +Ik j^Bp ~ 42M*XǢbA IXTQ]e#
-6' F\ M
Ai
-H 5@7: [Җ!2'<l֠ڠ*"@sUUF֚! } k Y>v` srbM Ԟ wldp ~4!LPw'`y %c2\yG*PQXs3>SxH0![DQpZ[
-OOG䖬1WSa6 wOca_ڰ_c2N
- e Q *(W/L
_0Ww
-SP0[TܷpKJ⎠ pΦJtnbeg3
P $,²c'wǺD7,:vW
-Z|۽, S@
IWK^i>v@6`'y#*r+DSo"A
g3QQl>z$HuE;y,S hhL?*u}
\\^5VFDzM/+q^ъ Z5T kߍrc'3Uc Q> Ķ.l\#5j~JwV ;+ڜlyK5QX30h9ߘZY^2iMC <9T1IG 5 9^lw?A0@ҞcphM$d{l@8!]q180oNtuս
- _>u$ nAw\ s|*.Yw{%C@ B@y,JZMHn, JpӲ>k@}W>{gkk9q RK )g+3_ Q|>{ݧ=!(
45nD(COױE0۴;4C] }['e{9ja66^8dZOS%4 [ 2ɑc^~O4\짎I4C!_^U Qt>u@CImwn} [7Sՠd4H
-mL4*)](;%U?_ikVeK`fV`~bb7Sn SğA;B1ʜ#ܭ "_ U=# {6
- ѩiE g!ȓ͛gwϓT2bbz3ab:PQ h |A~?k ,('Qx
-!
"U +W
#%Up^3ƝG['7?2ry5BxLS~ TX E.uIeiNY t%2iM|~_QSIZ( uH^s~`3(3PV 'G@c&tWjA%X8oDiO[HL i\É.zS brӕreLkܝ*7x@F$ኡ\cz 2(쵈-[ 5QE>_Qv-+ DV[k+p'nh1_V\1x$!B2|oC2(7
] )|Bwf
-]Ѭ^S*͡93v&FdN וϮ]U FF4+gBso]'t (=UAbVwg'u
- ; a}h!b`m+q>DR5bE&F$}rCQfHtB8Rwn~Ѹ#R?Eች vx_g Cx5Cg.^yo K`jdK.kNД}>
-?4
AnV oz+welAN{uTs1#$d }"of]5Æe0qᶇ3#J@ߨfl{Z &`Q Lxxj?HRXh+3]5& 孆&J>f/t4"LN4ܫ]89$dߔ~Rͤ b2}jT#k(.n\SSSgQ3+Տd" XWس|=6'# z.։o !h/ %[ٖ`c2xJcY&%zwj5+ |Y?
uZ|,:kvIc`bspwRuBk mQrt|ÖWÐ7 n9]s`=2ʻ @11g|T@>[h-+ af+_,(9_*$o[On]4H.WW SkT).dr=(H1N
-9ڧ-e)̋kяEj% loN)0}~5u[ҨSz..y9 ckT
-_^ >`
-K<`g:SE" Wa
-2JNu''5
- ϪQC)1X
-\\j -aCPwY icb+P"M+"y:ZhaIAO.@ṵ Zڇ))Hbh\
-|읈B""]ahm z#A{g "̿nYgQ7W]yVA@-82N?9N\$J:]rT~{w[1k$i-* Q< Od2DlcBi&qSpT;6E#/nUS7\m
, ͭ9<\g
ѻ|a@ cp3J)KEqt3fY@af5o[ukϳ$Ĉ|ۑᲭT(7!Hx1%ʅaX8Ѩձi
Ol:={ n?5"F[;N-Qx_\aI]4!CRvcn$#xKkb֩T`tlJ0UrY{HF˥hγfn 4$)zW5 ЬNw)S;` =) ҟp^d/H ֪/(ªS(+
-iו .)ϛk٢>κ#?`ƹ BiM2v) `)"idw:xG-zml\WMJ p/[f7^! BMor9&n8Ai5E
-G.Va]VUaڣdXH7h
-H]UJ+x5SҽV=d`߿! +ʦۊ|V{tnà&'CuFk NVG
-"oJ`'{g/oD * nϮUa F6:4J)c`zTn1pY|a &HXT~}̧>a rNTD,\'/FZ *2AfdѩcQi0jjRf OUgDa w=C/RّcBA+rސp.iQ9`*jQ@"Ou *d#)Ʉ\o)LI? E`%iFde,JDf! Ѻ3|8@Axa߫*ˁ ? &eb @&{Mh*0/FiK!]5\ ZųJ ޥE(!\U W\s&|=+sO ^rcP*ZXX३J ]Bcqg8ꥳ {U̦ImĴVڨ$aOB2cʼ%.|vK^dBg *Yt'R/J!, rȎ 7egN!\0o0tH F}0@0uᶷhѩ'MѣF$bA~L߹$4& ȷ?䓖ІL*?5{MS8.^ZpZve|Z&<Xzo4ygO4~ .YMjL㐧]:dR5 Kq fZVD/ )gNGix Vt+g[{kq>d4ti/(!.5W+ Krwxa;O$sZ Ebj1HAYVL&GS#$cZ9{zӓ
8 DҊwcL?o{w"6c`ǰ+t;Q6 ' & #`cR8 4j5X>Ü>T
-բd heNFJ6&ډ&ƔZ6!i#&F<̒AQs+AyP\dg=i]Dp?Z5-s+m5.QWYvT( o<%
- Ѣ|1~o
.O=_h. i )v' +R&=$ezYAɗ3gn'5
e;}[ }oMz䐖ʙNF:0LjbR79iң/ T6c+?Teqpb : EոI Ex\XM YF!WQDF+l_h N 将{x0j`ig@XJ<ٹGKzoh))
ӡ Rθ>5h ݑm<|2R! W1"v?[,RoU#w a'R$_1' F$X 5 B34v>1 Lh,qªWѡc1\1Xչ˞ gWKb
-G '(U@'-o#MaC
-c ܞyX`B'rдMTn_3C =S@B['kP ⋫S4N膘Ѽ"RjXg+>? f)2 S_/ ݴ,xl3[09xh)Ji7Dkj"+$!%8vz,`i扟hq"~2C!)X@ ۅh<7f~ HYOd,MPTI~w?/uF㥤=ll&Ka ͭWNәIӡA%d
|U3S= q%К
\9PK^qM=/Ѡ帀* 4 avTWY()xT
- mѢ) c*,-lT:؆emB,&]teG f`QWUhPH>IheYj?Sf
E_| |ϟхSz`US\whmw 0#XN [-!o8\.N8=LҁyGmO PaOβ&s~{Lbd^ni>+j /M7 O@ rma'0yˍB/(qS >c$NV*֦PJAc W k@
-"aе }3_{0pj֣& |wl-<
- X{ a=gPu0
-2Ҩ%~:T4֦`;?0ͷ` />m&} ZMspbh^!,I_ 3P׆p[A)hBZ=ԝn擇
#i!|Ӻ$ . ȭCPnm)"@Ηtqtm`#dirL ! (?g
-~e=L(rqx c fS=:f67SJ\z5c|7F;< h׃$guJ|IDa|jI^cjPl ~-84@Ɋ#ĝ AM (yhzM0[n!
+8&
be( UzhYSgWW t+CO2(2P)v F9UvFbfǓ6 <$`|h6r{ Q ꙣxvuZ#qh9}= V~4dzyDHٛq?Qpp MzoAr;rAeEIukQۗ/,}`UG
-[[-B_ n73=ĝ$.%Y 4UG 8!Ci+C jD
-DFfERI4&YY#5e5nz$jٕuKB2נf(+ W~tPcqRT# y 9:AI
-{5˜?Ԯ^<]f/a( E1̌LtcV\
S Tn hUV3FӚ 92iuy|y}>
1xs_xIN
-1jE>RV`eHL9`TK.@vC2mfy%јieK|+}^Vu 5`C nnGpr+~/r'
-Ơ$$1*YTz5<gTH}LCK_2AtX8r`39q# e/(CUE|{ބ b L(֠Ez|
-CH d,\Xsa
9j.[f,ٶtsR.!9n!0
- Y'*^fE
lwX$f_u:=bDtQiV[1.L)# @8M'7
-t@!@*ɧ2lj
-PCiˑo֎^^>,3҅WAE+o}H%cH&s(ǃOa#8m[H
-0T1M Edh& k研%ᐨ-;
-O{vy%qV[- xcBԊ"J_smꥑ-dF%ђhIxϣ4=G܀b=n\BDZScK}Wp=qZ$ j>q tci OE0HWJ˵v aʎم!h í28۽>e:`r|/E 8FЖDi_A%ŜXJ?q+QTuT4<ʊ33c$Rqў*-NJn}0aeh=kJ7 66*=Yg: C5)y٥?u!Nım4
-W1G2^G6 Pd`-c?q Fn;r$´ ?ZW_|gB0lryR˂Qgl-z ^E&،٩[~ fuc~=#0ras.뜉Rus`y?U
-Hp}
|EV`h%&IaaZۣX8#G9|?HN86eKUyR0Hf^SI