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"# Block-bootstrap uncertainty for footprint climatologies\n",
"\n",
"A footprint climatology is usually reported as a single raster with a contour\n",
"drawn at 80 % of the source weight, and that contour is then used to decide\n",
"something consequential: which land-cover pixels the tower \"sees\", whether a\n",
"satellite product may be compared against the flux, whether a treatment plot\n",
"lies inside the source area. Drawn that way the contour is a **point estimate\n",
"with no error bar** — it is one realisation of a season of turbulence, and a\n",
"different season, or the same season resampled, would put the line somewhere\n",
"else.\n",
"\n",
"[`fluxfootprints.bootstrap_footprint_uncertainty`](../fluxfootprints.rst) puts an\n",
"error bar on it. It runs a **moving-block bootstrap** over the half-hourly\n",
"record and reports, on the same grid as the climatology:\n",
"\n",
"| band | meaning |\n",
"|---|---|\n",
"| `p_include_{R}` | probability that a pixel falls inside the *R* % source area |\n",
"| `w_mean`, `w_p05`, `w_p50`, `w_p95` | the per-pixel weight distribution across members |\n",
"| `w_cv` | coefficient of variation, masked where the mean weight is negligible |\n",
"\n",
"`p_include_80` is the band that replaces the hard contour. A pixel with\n",
"`p_include_80 = 0.98` is inside the 80 % source area under essentially any\n",
"resampling of the season; a pixel at 0.45 is a coin flip, and any land-cover\n",
"statistic that depends on it is not defensible.\n",
"\n",
"## What this notebook does\n",
"\n",
"It works through the module end to end on a real site — **US-UTE**, a 2 m\n",
"cropland tower near Escalante, Utah — in the three phases the module is built\n",
"around:\n",
"\n",
"1. **Accumulate.** Run the footprint model once per block and cache `B` rasters\n",
" (`accumulate_blocks` → `BlockStack`).\n",
"2. **Resample.** Form bootstrap members as integer combinations of those cached\n",
" blocks (`block_bootstrap` → `BootstrapResult`).\n",
"3. **Write.** Export every band to one multiband GeoTIFF (`write_geotiff`).\n",
"\n",
"Along the way it covers the two choices that actually change the answer — the\n",
"**block length**, and the distinction between **random** spread and a\n",
"**systematic** sonic-azimuth offset (`rotate_climatology`) — and the diagnostic\n",
"that tells you whether the result can be trusted at all, the **in-domain\n",
"captured fraction**.\n",
"\n",
"> **Runtime.** Phase 1 runs the Kljun et al. (2015) model over 3 549 half-hours and takes\n",
"> roughly two to three minutes. Phases 2 and 3 take under a second, which is the\n",
"> whole point of caching blocks rather than periods."
]
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"source": [
"## 1. Setup\n",
"\n",
"Beyond the package itself this needs only NumPy, pandas, matplotlib and\n",
"rasterio. The `logging.disable` call silences the per-period chatter the\n",
"footprint model emits — over 3 000 periods it would otherwise bury the output."
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"scratch directory: C:\\Users\\PAULIN~1\\AppData\\Local\\Temp\\fp-bootstrap-s6ffx36l\n"
]
}
],
"source": [
"import logging\n",
"import tempfile\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import rasterio\n",
"from matplotlib.colors import LinearSegmentedColormap\n",
"\n",
"from fluxfootprints import FFPModel\n",
"from fluxfootprints.bootstrap_footprint_uncertainty import (\n",
" BlockStack,\n",
" GridSpec,\n",
" accumulate_blocks,\n",
" block_bootstrap,\n",
" block_labels,\n",
" rotate_climatology,\n",
" source_area_mask,\n",
" write_geotiff,\n",
")\n",
"\n",
"# The footprint model logs once per period; 3000+ lines is not useful here.\n",
"logging.disable(logging.CRITICAL)\n",
"warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
"\n",
"DATA = Path(\"..\") / \"..\" / \"data\" # repository data/ directory\n",
"WORK = Path(tempfile.mkdtemp(prefix=\"fp-bootstrap-\")) # scratch GeoTIFF\n",
"\n",
"SITE = \"US-UTE\"\n",
"TOWER_LAT, TOWER_LON = 37.7353, -111.5708 # UFLUX Escalante, UT\n",
"Z_M, Z_0 = 2.0, 0.05 # (z - d) [m], roughness length [m]\n",
"CRS = \"EPSG:32612\" # UTM 12N\n",
"\n",
"print(f\"scratch directory: {WORK}\")"
]
},
{
"cell_type": "markdown",
"id": "cbce128a",
"metadata": {},
"source": [
"### Plot styling\n",
"\n",
"One palette for the whole notebook, so the same quantity is always the same\n",
"colour. Three sequential ramps, each a single hue light→dark, and one diverging\n",
"blue↔red with a neutral grey midpoint for the signed difference map in §10:\n",
"\n",
"- **blue** — footprint weight (a density)\n",
"- **aqua** — inclusion probability (the bootstrap deliverable)\n",
"- **orange** — coefficient of variation (relative spread)\n",
"\n",
"The categorical hues used for line charts are slots 1–3 of the reference\n",
"palette, which is validated colour-vision-safe across all pairs."
]
},
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"%matplotlib inline\n",
"\n",
"# Categorical slots 1-3 (validated CVD-safe across all pairs).\n",
"C_BLUE, C_ORANGE, C_AQUA = \"#2a78d6\", \"#eb6834\", \"#1baf7a\"\n",
"INK, MUTED, GRID = \"#0b0b0b\", \"#898781\", \"#e1e0d9\"\n",
"SURFACE = \"#fcfcfb\"\n",
"\n",
"def ramp(name, stops):\n",
" \"\"\"One-hue sequential colormap, near-surface at zero to dark at the maximum.\"\"\"\n",
" return LinearSegmentedColormap.from_list(name, stops)\n",
"\n",
"CM_WEIGHT = ramp(\"weight\", [SURFACE, \"#cde2fb\", \"#86b6ef\", \"#3987e5\",\n",
" \"#256abf\", \"#184f95\", \"#0d366b\"])\n",
"CM_PROB = ramp(\"prob\", [SURFACE, \"#cdeee1\", \"#8ad9bd\", \"#3fc196\",\n",
" \"#1baf7a\", \"#128257\", \"#0a5539\"])\n",
"CM_CV = ramp(\"cv\", [SURFACE, \"#fbe0d3\", \"#f6b898\", \"#f08d5e\",\n",
" \"#eb6834\", \"#b74a20\", \"#7d3014\"])\n",
"# Diverging: warm/cool poles, neutral grey midpoint - never a hue at the middle.\n",
"CM_DIFF = ramp(\"diff\", [\"#0d366b\", \"#256abf\", \"#86b6ef\", \"#f0efec\",\n",
" \"#f0a3a2\", \"#d03b3b\", \"#8a1f1f\"])\n",
"\n",
"plt.rcParams.update({\n",
" \"figure.facecolor\": SURFACE,\n",
" \"axes.facecolor\": SURFACE,\n",
" \"axes.edgecolor\": \"#c3c2b7\",\n",
" \"axes.labelcolor\": INK,\n",
" \"axes.titlesize\": 10,\n",
" \"axes.titleweight\": \"semibold\",\n",
" \"axes.titlelocation\": \"left\",\n",
" \"axes.labelsize\": 9,\n",
" \"axes.linewidth\": 0.8,\n",
" \"axes.grid\": True,\n",
" \"grid.color\": GRID,\n",
" \"grid.linewidth\": 0.6,\n",
" \"grid.linestyle\": \"-\", # solid hairlines; dashed grids read as thresholds\n",
" \"axes.axisbelow\": True,\n",
" \"xtick.color\": MUTED,\n",
" \"ytick.color\": MUTED,\n",
" \"xtick.labelsize\": 8,\n",
" \"ytick.labelsize\": 8,\n",
" \"legend.frameon\": False,\n",
" \"legend.fontsize\": 8,\n",
" \"lines.linewidth\": 1.8,\n",
" \"font.size\": 9,\n",
" \"font.family\": \"sans-serif\",\n",
" \"figure.dpi\": 120,\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "8394e0a1",
"metadata": {},
"source": [
"## 2. The tower record\n",
"\n",
"US-UTE is an AmeriFlux cropland site on flat terrain at 1 729 m. The receptor\n",
"sits 2 m above the displacement height over a roughness of about 5 cm, which\n",
"makes for a compact footprint — a few hundred metres at most — and that in turn\n",
"makes a modest domain adequate.\n",
"\n",
"The module's period adapter needs six half-hourly drivers:\n",
"\n",
"| column | symbol | role |\n",
"|---|---|---|\n",
"| `WS` | $\\overline{u}$ | mean wind speed at $z_m$ |\n",
"| `WD` | — | wind direction, meteorological convention |\n",
"| `USTAR` | $u_*$ | friction velocity |\n",
"| `MO_LENGTH` | $L$ | Obukhov length |\n",
"| `V_SIGMA` | $\\sigma_v$ | lateral velocity standard deviation |\n",
"| `PBLH_F` | $h$ | boundary-layer height |\n",
"\n",
"We take one summer season. Rows missing any driver are dropped up front so the\n",
"period count below is the count the model will actually attempt."
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
"US-UTE: 3549 complete half-hours\n",
" 2024-06-18 16:30 to 2024-08-31 23:30\n",
" spanning 75 calendar days\n"
]
},
{
"data": {
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"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
count
\n",
"
mean
\n",
"
min
\n",
"
max
\n",
"
\n",
" \n",
" \n",
"
\n",
"
WS
\n",
"
3549.0
\n",
"
2.64
\n",
"
0.35
\n",
"
7.94
\n",
"
\n",
"
\n",
"
WD
\n",
"
3549.0
\n",
"
249.08
\n",
"
0.01
\n",
"
359.99
\n",
"
\n",
"
\n",
"
USTAR
\n",
"
3549.0
\n",
"
0.26
\n",
"
0.02
\n",
"
0.87
\n",
"
\n",
"
\n",
"
MO_LENGTH
\n",
"
3549.0
\n",
"
22.08
\n",
"
-3682.34
\n",
"
4255.85
\n",
"
\n",
"
\n",
"
V_SIGMA
\n",
"
3549.0
\n",
"
0.82
\n",
"
0.15
\n",
"
3.50
\n",
"
\n",
"
\n",
"
PBLH_F
\n",
"
3549.0
\n",
"
584.48
\n",
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108.92
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"
2095.41
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" count mean min max\n",
"WS 3549.0 2.64 0.35 7.94\n",
"WD 3549.0 249.08 0.01 359.99\n",
"USTAR 3549.0 0.26 0.02 0.87\n",
"MO_LENGTH 3549.0 22.08 -3682.34 4255.85\n",
"V_SIGMA 3549.0 0.82 0.15 3.50\n",
"PBLH_F 3549.0 584.48 108.92 2095.41"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"REQUIRED = [\"WS\", \"WD\", \"USTAR\", \"MO_LENGTH\", \"V_SIGMA\", \"PBLH_F\"]\n",
"\n",
"raw = pd.read_csv(\n",
" DATA / \"station_data\" / \"US-UTE_HH_202401220330_202409021900.csv\",\n",
" na_values=[-9999.0],\n",
")\n",
"raw.index = pd.to_datetime(\n",
" raw[\"TIMESTAMP_START\"].astype(\"int64\").astype(str), format=\"%Y%m%d%H%M\"\n",
")\n",
"\n",
"season = raw.loc[\"2024-06-01\":\"2024-08-31\", REQUIRED].dropna()\n",
"\n",
"print(f\"{SITE}: {len(season)} complete half-hours\")\n",
"print(f\" {season.index.min():%Y-%m-%d %H:%M} to {season.index.max():%Y-%m-%d %H:%M}\")\n",
"print(f\" spanning {(season.index.max() - season.index.min()).days + 1} calendar days\")\n",
"season.describe().T[[\"count\", \"mean\", \"min\", \"max\"]].round(2)"
]
},
{
"cell_type": "markdown",
"id": "1656e9e9",
"metadata": {},
"source": [
"### Why blocks, and how long?\n",
"\n",
"A bootstrap that resampled individual half-hours would treat consecutive periods\n",
"as independent draws. They are not. Footprint geometry is set almost entirely by\n",
"wind direction and by the stability that controls the along-wind extent, and at\n",
"a site like this both are **diurnally periodic** — daytime up-valley flow,\n",
"nighttime drainage. Resampling half-hours independently would shuffle 3 a.m.\n",
"drainage flow into midday convection, destroy the diurnal pairing, and report an\n",
"interval far narrower than the season actually supports.\n",
"\n",
"The figure below makes that concrete. The left panel is the wind rose; the right\n",
"is the lag autocorrelation of the wind-direction unit vector\n",
"$(\\cos\\theta, \\sin\\theta)$, which is the circular-safe way to autocorrelate an\n",
"angle."
]
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SnUESgyBXbr/9thYj/ccdd1yb5Zx66inOFHsGxF3Np1hhpFFzxNeAzvIbN27AW2+9hdTUNEyYMF7Vev/662/O93fVcrE3y+np9ru28WN3B3ZOYKo5a9LpUTFs2FC1fNbcu0KhZuHCr1X2BH0rGOxFR0er1p6ux4Gp9OxmwWwFjoIbvgiGqNDRdvHxDz+sVDXyDHIprDCg5XtpXNk6Rb+n56ekpNTlPRM6PQc8NsZoN48Psw8cnRWGqJaOzCox4H5t2LBe7S/bG06ePEm1vVyyZKlzO4y2meT000/DlCmT22w3R98NLwOu34Bii2GW6epp0F/L6el10NV11RHnnnsusrOzOn0PrydX7r33zzj//PPwxRdfqGPL6+GQQw5W4lVrz5HeLL81l1xyibPTROvrTRAEQRAEQRCErvGrr6/rndOdIPgAc+Ycg2XLlqngesWK5U4zQEEQBEEQBEEQBKF/EGFBGLAwNT46OkZlTzCbglkhgiAIgiAIgiAIwiAvhRCE7sKyissuu1QJCiIqCIIgCIIgCIIg6EEyFgRBEARBEARBEARB6DVScC4IgiAIgiAIgiAIQq8RYUEQBEEQBEEQBEEQhF4jwoIgCIIgCIIgCIIgCL1GhAVBEARBEARBEARBEHqNCAuCIAiCIAiCIAiCIPQaERYEQRAEQRAEQRAEQeg1IiwIgiAIgiAIgiAIgtBrRFgQBEEQBEEQBEEQBKHXiLAgCIIgCIIgCIIgCEKvEWFBEARBEARBEARBEIReI8KCIAiCIAiCIAiCIAi9RoQFQRAEQRAEQRAEQRB6jQgLgiAIgiAIgiAIgiD0GhEWBEEQBEEQBEEQBEHoNSIsCIIgCIIgCIIgCILQa0RYEARBEARBEARBEASh14iwIAiCIAiCIAiCIAhCrxFhQRAEQRAEQRAEQRCEXiPCgiAIgiAIgiAIgiAIvUaEBUEQBEEQBEEQBEEQeo0IC8KAo7AgH0889k+89cYraGpqcs7PSE9T8wVBEARBEARBEIT+Q4QFYcBBMaGhoQHFxUXYsnljm/mCIAiCIAiCIAhC/yHCgjBgmTZ9JlasWIq6ujpPb4ogCIIgCIIgCMKARYQFYcCy3/4TYQm2YPVvv3h6UwRBEARBEARBEAYsIiwIAxY/Pz/MPmoOfv3lJ1RWVnh6cwRBEARBEARBEAYkJk9vgCDoZNiwEUhOGYIff1iB8eP3k4MtCMKgor6+HgUFBepxYGAgoqOj4c342vYKUOeL543ExsYiICCgX875QL8WKisrUV5erh6HhISoyZ3Lpu8Ujy+9pywWC8LDwztcXlFREWpra9VjngeeD0Fwx7Vht9tRVlamHttsNoSGhg7oA2/38f2VjAVhwDN79tHYtHE9CgryPb0pgiD0kYqKCvWjeTCSk5PjnPLy8lBSUuIM6Dpi69atSEkZoqbjjjsO3kB+fr5zP1w793jr9gqdM3v2kc5ztmfPnh4fro7O+UC/Fp5++hnn/v3zn/9y27LT0tJw8smnICQkFImJSeo9V199TafLO/XU05zLW716db9uq+Ad8HvVuC/35DtW97XxwgsvOJf/17/+DQOdFzy0v709/60RYUEY8ETHxOKAAybh559WenpTBEHoI48++hief/6/g+44VldXO39scEpKSkZMTCyCgy0YMmQozjjjTHzxxZfwBQ4++BDnfhQWFnp6cwRhUPGHP1yBr776CjU1NTCbzYiPj0dERMfZCsLg4Ikn/u28L/OxMLh4op/Ov5RCCIOCw2ccgS1bNnl6MwRB6CPR0VHIzs4e9McxJiZGjfYXFxer4/Hpp5+q6YwzzsBrr72qAgbCtFQGDo5jF+P1x83XtlfoO3LO3Qe7ZC1fvlw9tlqtSE3di8jISDdugTDQYPmDcc8OCgry9OYIHkaEBWFAZijcePOfYDI1X95Wmw3XXHdzm7RbQRB8i7lz5+H000/Htddei6SkJAxWli5dgvHjx6vSkPfffx+33XY7SktL8eGHH8Jms+Kll15S7xs1ahRWr/5NPXatfe2ofp0pkKyXbR1sVFVVqYl12N2poedoKN8fERHRYhncxsbGRuc8lnS41ud3tL3t1aEyi4PL9/dvm3zZ0f7x7/jj1/X7oTM8cZwIl83aZUKRyHU9LCVhXTzhD3oaFfdkOzmPAWbrdXYXHneeQwamHcHv2tzcXOdzbmNwcHC7dfzdPefGulkCRLj+sLCwFq8zA8ZoMU3xrbvnuTVcB6+TzvbR2E/WQ3P/Wm9Lb6E3Qnt11a77Tnjd0zuhq210Xe7evXudn7eoqCh1/TH1mdve3eUY8Brg56krbwheq/zcc/m8Bnri0+HqFcFjwprzjur6uT1cDp8bf89lct281nvi/cG/4efSgNcRr932rs/ubovrsaCnBaee0NF6+uMc8LPK7xIDPuZ10Z06///978UW29XX7eV7+Jnqyf2pO8e1o+3h9dWZv0hfl2/3sv1tb3v6cv5bI6UQwoCDX/Dt/Zjgzb23PzIEQfAO1q1bh8bGBpVOf/rpZ+CKK65sMS1cuBCDCf5AuPTSS/HGG284573++hvYuHFjj+rXd+3ahVmzjkB4eATOOedc9R7+sP7LXx7AmDFjERoahri4ePX6SSed3G4tLYOUhx76Jw44YKKq32apBmu477zzLhUQvffee2p9rhknkyZNblGf31ldPX8Q3XvvfRg9egzCwsLV9vDfE06Yi+XLV7R4b+vlcJ0nnjhfbT/3Zf78k5CZmdnl8fXEcSLcH2O9CxZc2GUpSVfbyR+Yd911N4YOHabmcZ2xsXGYN+9EvPPOOy3Eno7gMnmsecw5HXfc8R0eQwaErqU7yckpKgMlMjJKle1s2rSpV14KGRkZah/43iOPPLLNOkeMGKle43VliC/dJTU1FWeddbbz+PDf4cNH4PLLL8cvv7RsW71jxw51XngMuV9RUdFISEjEFVdc0a3rqj2+/34JJkzYTx0jLu+OO+5s4aHCjKTW5VA8D8OGDcctt9zqNHzriPvvvx8HHnhQi2NpLOvll1/u0bY+/PAjat8jIiIxcuQofPTRR23e8+WXX+Goo452fiZ4rXP/6PlgiD8GM2bMdG4Lt6ur9GzXuv5ff/1VXdvcFh4T3kcWLfoG06cfDpstBPHxCWrdU6ZMwX333d+tjLfrr7++xbHmueUyDjroIFUD70pX20IWLlyEOXOOcR4L/svP/1NPPdWtz17r9fC+0p/ngH//8MMPO5/zcXfr/DvyWOjp9vI48D7K7eTnjyV+7777bqfr7slxdd2e3377DQ888Ff1OePnl/eNt956q9+Wv9rL9rer7enL+W9DfX1dk0xyDOQakGtArgG5BnzhGnjllZebTj755A6n9957z+PbqGOqqChnupVz2rhxQ5v3jBkzxvn6gw8+qOatW7fWOW/y5EnO97rOHz16dNOoUaOaAgICmuLj45vOPPPMprKy0qZDDjnE+Z6goKCmiIiIFs+//fZb5/LKy8uapk2b1mIbrVZrk7+/v3r85ptvNL311ptq+cY8TnFxcWoep507d3S4vaWlJU1Tpx7kfM3Pz08t33jOZb7xxuvt7h+Py/77798UHBys/s6Yf+yxx3Z53D1xnPi+hQsXOl8/4YQTWmxTcnKy87WcnOxubef999/f4lhFRka22IaMjPROj0NeXm5TSkpKi2VYLBZ1XIcNG+acv3XrFvV+HhfjvHKKiYlpCgwMdL4vKiqqKTs7q9vXqOv8+fPnO+cvX77MOf/1119zzr/uumt7/Bk7+OCDnX9vNpubQkNDnc957Rnv+/XXX1q8xnPsum+8prdv39bl+vgZdT3HXKfrNc3piSced77/o48+bHFMeQ5dr2fX68R12XfddZead8899zTFxsa2OIfGsl588YVOt/Xwww93/t2pp56q1svPkzGP+7958ybn+x999JEW+2Gz2Vo8nzNnTlNNTbXz/bxejdd2797lnP/AAw845/Nxe9tz3HHHqX/5ueO+LF++XJ0T4/WwsDB1bI3n9957b5fn5pprrm5xrF3PN6ennnqyW9vCe9q///1Ei/sWz5vJZHLOu+CC87t1feo8B/wMh4SEOF/jY2Pfee/o7natXLmi19t70003tdle/s3cuXOd826++Wbn+3t6XF23Z968ec57mOs6+R3VH8s/1cv2t6vt6cv5bz1JxoIgCILgMyxYsAAfffRhh9Ppp5+GwcrYsWOdj9PSUrv9dzt37sScOUejuLgImZkZeOedt5WrPEffyBVX/AFbtmzGxo0b8PHHH6msMI6A33zzTc5l8P0///yzepyYmKhKNUpLS9QyX3/9NTUqdPbZZ6vl83WD9et/V/M4DR8+vMNtfOqpp7F69Rr1eMqUyUhPT1PLf+65Z9U8jtZcf/0NKquhNRxdnj9/vnr/2rVrnKnJ3377rTPN2puOU2vaK/Xo6XYy04cwDZrXRn4+u4oUqwwf+nJ0lSb+zDPPOkeSJ0+ehIyMdLXdPKcc6W8NU66N87pnz251THbs2I5zzz3Xmabb3ghrd7jhhhucj59//nnn43feaR7tYxZPT/n999/Vv8yE4LHh8cvKysSLL76AqVOnuqz/Rud188c/XqWOQ1FRIc4880xneQ+zT3rCd999h88++wxlZaX45z//6Zz/4YfNx+jkk092HtPt27dh06aN6vMzYsQI9TrPZXp6eofreOCBv6jr32DIkCHO5Z1//vktus4YnWfaY9u2bdi7d486Psccc4yax9Hvzz77XD1mRsDdd9+jHvMz8OGHH6hjxPNvbCv31zXLqq/X+7p1a1XnL+4Lz42RAv7ggw+qc1NeXqY+m/feey8SExO6XOaTTz6plsXrnMd569Yt+OSTT5yvv/jii93aFqapM/OE8P62cuUKbNiwXk2HHHKImv/mm2/hhx9+6NE+9/c52LVrJ26//Xbn8vnYuDbuvffPPdq23mxvVlYWnn76aef7+V3O7f3993XO+2vr1P2+HFde2/xscx28JgyMY9bX5W/zsv3tanv68/yLsCAIgiD4HAwk9+zZq75IGdiIf4rjh4JBT3qJs5zi8ccfb1Fj/eWXzR0mXnjhRYwaNVqlRZ522unOY71x4yZn6v4XX3zhfP8999yNmTNnqh+0rM8877zzcMwxc/p0vulib3DzzTcjISFBLZ+p5yNHjlTzaWT500+OoN0V1ofef/99Kng+4IADnO8n7QXFvnycOtrOgw8+2Fmnzx+NDJyWLl2GCRPG491331F17Z2xdOlS5+Mbb7wRcXFxKgC8444/OeveXeG+P/bY45g4cZJKv2YqOcsKWHZhkJqa1qv9O/roozBp0kT1+IMPPlTlIBQqvvnmGzVv6tSDMHny5B4v1xAPKMIwnZ3B4+bNW5Tw8uyzz7pcYz85/+Zvf/ub+qwxgOQ1ZvD111/36J500kknKTHI8Xh+i9aQBqzZv/rqq9X1xTINptrz+Lq2+HR9f09wLb0xJpb2tMdtt92K5ORktd/HH39cGzHz+++/V+U+ZPr06TjllFPUYwa0V111lfP9X331NfqDu+66U32uDSZOPMDpI8D09r/97e/quqPgwM/clVde2eUyGdwZpVNMU2cpz6mnntrltdt6Wxi88zNH6G9hlHywHME1gFy5smfCgredg75u78qVK51lP4cddpgS0ch+++2H888/r83y+npcb7nlZuXBwnvY3Xff5fQp4LJ2797d5+Xf5mX729X29CdScC4IgiD4FKtWrVKj01ThDSZNmoSnnnpS1dEORlhPboy4kjFjxnT7b4cPH+bsImFgGAcaxk8d+dMYxoOu72e9Zn/DgM6AQaorFBn4Y9Cx3W3bVw4dOrSFW7nF0mxe5lrD7ivHyQgYerKd/GHJdTPAevttx2QwY8YMNaLJH9odwdE0A9asG3B/KUq07nv+j388hPvuu6+F2EEBghklxmh/6zr7nsAaeHqq8Fi88sqrCA8Pcy6vN9kK5I03Xsett96mMlmeeOIJ53xeO1dddaUSa1yvQwavrqaYrpk4holndw0RaWBp4GrG5np9nnLKqereRyhGcd0MFHhejX2vq+v+9eyK0XbSlfYEo+5sq+s17npMHM+br53utJrt6lpvnalFaOpLQ9sHHngAa9asUf8a8Bp/5pmnlVjUERRnWMduZD/xGqfRJTGyODq6dltvi+s+8lh1ZPDJ89kT3HkO+oOuttfVlJT3c1da3+/747i63sMMs1sjI4vb0tflj/Ky/e3O/aW/kIwFQRAEwWfgDzuav40cOQLvv/8eVqxYrlK9+cOAZo50Sh6MPPfcc04XfgZCxghId2gvDd6148a///2EMy3SdeJoh/GjlaMhBjTGao3r6G1Pf0S33h6mgbsKKq4jtq7bYWAytdy/3qzf3ccpOLhZGHAt12AKrdH9oSfbyXkPP/wvVULCFOAff/wBd9xxh3qNWT//93//6HSZrj/Ed+7c0cI9vD0zPJaCGLCUgKUFPBYcce8PmLpvZFnQTO+tt952/mg2yi14bRhp/d0JoChAUWBhujD3kQID04aZVs9SnO+++14F34Z4xBFE1wyB7du3Ox8z6O9JlwVXQaq965Pn3RAVGFQwrT8vL1cd03Hj2s8s6AmzZs1sc926fs56sq2u13jrZWzf3nztpKQ0v8+1U4Hr9b5+/fout7319c5strlzT8DPP/+kBDGmi/O7gp9BfnYuu+zyTo09Fy1a5BQVWBZTWFigPjM//fRjj7fF9Vgwa6i9+wPLLW6//Tb0BB3noLf3xf7YXtfgmuUkrrB8ozV9Pa6u9zCanrp2sOG29HX5Ji/b3662p7P5PUWEBUEQBMFn+PTTz1SN99tvv60c5JmpMHfuXHzwwfvqy5fBwGCAP5DpPs+0bNac33zzLc7X7rnnHlU/3RfOPvss5+Pbb/+TamPJAH7t2rVqNPCCCxbgsssuc77nvPMcwRx56KGHVOo4Ay266bMVpms9fWxs88j44sWLVWDKFord3R52VPjmm2/V8m+99VZnYMva00MPPRTuRNdxMuqgCVNdv/56ITZs2IDLL/9Dr7bzT3+6AxdeeKHqLMAf0QyQjVHY7qTQz5s31/n4X/96WAXZ7OZwzTXXtDuqbLU2j3ZnZ+eokpMPPvighSdCX+AIu5HSzh/my5cvV4/psWK0baO3hpHWf/TRXZeYML34nnv+jCVLlqrAkyO7rqP2FIj4fN68ec55V131RxX80nX9pptuds4/6yyH30J/4bodHC1PS0tXXTrYJYWlNt4ExRgjk2PDho3KfZ/niB4SRkkJOfPM5s+Oq78KHe557p555hlVUtJTNm/erMpauC5+DnnsKCoYwRWzazoTmlyPdUVFufpssDyG57ovx2LFihXqM86OPTweixd/5+wm09+ZA705B673Zd5z+JmlKNedrJG+Mnv2bOdxZ8cYlq9we/k97+qd0l/HlRlV7MLCe9jVV1/jzEA58MApSizWfd5mu3l/u0O/nf+euubKJMdArgG5BuQakGvAU9fAX/5yf9N9993X7ms33nhjCxf1gdwVor2JXQBcndN747hvTLW1NU3nnHNOp+s7++yzne+vq6ttuuSSizt87zvvvN2hGzYndjrobLvoXs7uBh0tn87vy5Yt7db+8bnx2m+//drpcffkcXJ1Bzem888/r8uuEO1t5/XXX9fpNrKjQmfHobKyoungg6e2+Tt2Ihg7dmybrhD//e/zbd7Ljgeu+244nvf2GmUnC1f3f06LFy92vs7OKcZ8dq/o6jM2cuTITq+vPXt2q/ft3bun0/dOmTK5KT8/r8v1tde5wVh+688Fp1NOOaXNug466MAWXUaM/e9o2Txmxnw6wXf3/tOR8/8zzzztnH/llVc453/88UctHOhbT1df/ccWy//444/bvIedRC677NIuu0K4bg+nDRvWd3qtz5w5s9N9zc3NaUpISGjzd9dee00LB//ubAunTz75pE33AdeJbv3suuLpc8DPC79DWr+P29+b7erp9j755L/brJvH+fLLL2tzz+jNcXXdnvbuh+wc4vod0pflr/Sy/e3O9vT2/LeexGNBEARB8BkSE5NUujJTxl1T91gruGrVzzj88MMxEOG+utZA8zlrQ1lbzhFZjtRfdNGFbTor8D3G37l2HOhovgFNrd588w018sp6fI6OMD2YKZxjxoxWRmYnnnhii+2h2R3LVOg0zlFcvp9eDxdffFGLmmaa3NXUVKsRI5au8FzGxcV2ul1MMWbJy/vvn67S3rds2aJS0ePj43DEEbNx/fXXqVT27uwfnxuvdWVy6cnjxJr/v/zlL8o4sb6+Aeecc44yh5s2bZqzNtZw0O9qO//1r3/hqKOOUlkD9CbJyclVppY0C6MnAVPHu8oQoDkiR9Y4iswRfXba+Nvf/orjjz/BWYJkjAozS4PbRENLpvGPGzdOdSVg+QezL4hRJ9zba5THmBkjb7zxpnpOU87Zs49wvu5aB0+Ph65g6jw9KBYt+kZ5djAln6N4NFejYaWRBZSSkoLVq3/Ds88+pzoxcESb55WfvZNPPkllUrim9ndESIjNuX88F67XujHf+FyQV199RZWsLFz4Nez2KrWv//jHP3DllVc53x8UFNjtZbuOUHYFS82a19HsV8JyD2N+WFh4CzPKNWtWq6yDH3/8Efn5Beq9NDa8+OKLceKJzVkfjvfPx1tvvamOKTOxaL75j3/8nzJtNZbPfepqe8iECRNUF5LXX39DlY9w5JXlETx/zHK74Ybru9zX7793jALTo8Fms6rMoyuvvEKZhbbOauhsW8j8+SeqThHPPfcfZdrHjCGeE96vZs2apbKYXLOHPHUOxo8fr64xdq5hdxHjHtPVtdzRdvV0e5n9xPlPPvmU8jvg9vD+wnPwxRcOk1xXb4G+HFfeS4844gg88sijalSex+TPf77H2WGhN8v3tv2N7uH29Pb8t8aP6kKP/kIQBEEQPFgCcNBBBymn60suuUTVfrO+8Pnn/6sCJv6Qcv0hLQjCwOXdd99VQR/561//qhzeDV5++WVl8EgYoB533LEe205BEDwLBWgKLITtGinQCv2PZCwIgiAIPgNdvT/++BPceOMNOPfc81oYGn3yycciKgjCIICeHBQZn3nmWeeo2uWXN3tZkGXLljlHJ0VUEARB0I8IC4IgCIJPwT71TAVkijWN+2ja2LplkyAIA5eDDz5Epcwb3HLLLW3aJW7duk2ZYD766CMe2EJBEITBhwgLgiAIgs/w888/IyjIjIMOOlC5N7u2+3N9TRCEgQu9B+jzwH/POuts3HHHn9r1TBAEQeiOF4bQP4iwIAiCIPgMNH4LDw9vVzzga2w1J8KCIAxs2A5NEAShu3z8cXPLY0EfDjthQRAEQfBxWHPt6qIsCIIgCIIguAfJWBAEQRC8nvfeew+ff/6FajPINnRr165t8XpZWRl++OEH1RJMEARBEARBcC8iLAiCIAheD/uQNzTUq3+NxwbsIZ+SkoyXX35J9T8XBEEQBEEQ3ItffX1dk5vXKQiCIAi9zlywWq2YP3++HEFBEARBEAQvQYQFwWdoampSo5S1tbWoq6tBba1jqlPPa9HU1Kje4zrx9aqqSgQGBsFqDVEjmy0nf5gCAxEUaFYusYFB/NeMwEAzTCaTeo8gCN7Frl27ERoagri4OOc8tp6sq6vDsGHDMJCor69FWWkJgszB8PcXWyRBEARBEHoOsz1ra6oRFh4Bk0lPZwwphRC8BooAdnuFUyxQwkEdH1NAqFWPmxobXf7CT9VaUwDwDzCBEoBTCKBoACAgIEA95b8UGhqbGqlQqHl8DjShobIR9fV1anLMMxbh3yw2BDaLDg4RwgyL1ab+FfFBENxr0Hjeeedh4cKvW8znZ/ecc87Ft99+g9DQ0AFzSigqbNu23tObIQiCIAjCAGDcuEmIim4emOlPRFgQPAJFg8qKclRUlqGyogyVleVqHmGgzgyDgACTEgQ4Wa02hPiHqHl8jQG/aV9WQYA/hQV/JQQoWWGfuKCkBT/HusrLihEdk9AsHDCjYV8w0tjYoDIhGurrVeaDQ8yoddRz8/WGRrWM6mo76tV7KEA4BA5ui80WCltIGEL2/StigyDo47PPPseMGTMQFRXVYn5ycjImTJiA7777HqeeesqAOQXMVDB+CARbrBjs5OZkID4hBYMdOQ5yLFyprqrCO2+/gXPPW4BgiwWDHfl8yHGQa6ItpSXF2Lt3m/N3hQ5EWBC0w6C8oqJZQOBjBvBEZQAEmVVwHhERpcoV+DzAFOgUFRzCgeOxEg96WJ4QHGxVwgL/7ZVZXEMDGvaJD3zsEBcc2RXcDwoN+XlZyNwnjJhMgQgJCYMtJBQ2W5h6LGKDIPQPubk5CAkJafc1zs/KyhxQh9oof6CowPvjYIeishwHOQ5yTbTGD1VVVfvuEzYMduQ+IcdBron2BUiis6xShAWhX2EGAMWD4uICp5jgKiKYzcEq0GYJA4NuKussLWCZAQNybyor4AfP3z8ICOzE86G+3lmuUVVth72y3Ck2FOTnIDNjr3ov941CA/c9IiIGoaHhXrWvguArjBkzBg8//Ajuu+9edR8xqKiowJIl3+Okk8TUURAEQRAEwd2IsCD0mYaGBpSVFqGouADFRfkqsOYPfmYItBQRrA7PgkDvExF6A7efxo+cYA1BOKJdDCYdYgPLJyorK5T4UK8yG7KV2MD9j4yMQWRUrMrUYImHIAhdM2/ePNx33/045ZRTcPXVVyMpKRlpaWl48sknYbFYcfTRR8thFARBEARBcDMSzQi9goEzsxI4lZYUqrIBh5AQiuBgm3Ic5XN6EAwEEaFHYoOJhpJtxQYlNNRUobysBHZ7pcroyM/PVn8THh6lRAaKDczqEAShfdhq8oMP3scVV1yBBQsudM4/5JBDVCtK+q4IgiAIgiAI7kV+gQndgsFxlb0SRcX5KC5imUOpCohZ6xoZGa0yE0JCwhFssSkxwZ1CggrcG4FG5ctodHXwg78fYArw8yqxwWINUaUQ7EBRXVWJ8ooyVJSXoqamGql7t2PP7q3Kb8LIZuDjwSLKCEJ32W+//fDDDz9g165dyM/PR0JCAoYPHy4HUBAEQRAEwUOIsCB0GrAz6C0ozFViQk1NlRoNpJgQG5uAsLBIWG0hKkNBxyhhQyMFgyYlDvi7BNecV17diFJ7A4oqG1BQXo/iygZU1jaitr4JtQ1NOHi4FVOHc+Tf+4Jyo+sFp9CwSJXNwJIJe2UFysqKUF1dg5ycDGRk7FG+FBQZomPi1fEWkUEQHDBLqrq6GoWFhQgPD1emqvx8GC1mBUEQBEEQBPchwoLQBnok0HgwNzcTVVWVKjWftcuRkVEIC4+C1WKDOdjab66iFANMAXCKByX2BqQW1GJvQR0yiuuQWVyHrOI6FFQ0KAGBokKXzMY+YcH7ob8CPSg4xcQmoLq6Sh33stJiVFXZUVpapM6FOdiC+LhkxMYlKsFBEAYrK1euxO23/wmbN29Wz++//37k5eXh8cefwCeffOzpzRMEQRAEQRh0iLAgOLMTysqKkZebhcLCPDDGd3QwSEF4RIyzDWRfR8zrGppUiUKAvx9q6hqxLacW69OrsCWrBrvza5FWWNc94aAL6vu+CI/AdpoWi01NkZGxqq0lxYWS4gLVbSMzcw/S03epLIa4+GRERERLFoMwqEhPT8dZZ52Nyy67DG+++SZeeuklNX/27NnK1HH9+vWYNGmSlnW//tpLKCzIV4+HDh2O0888p9P387P7/XffYO+ePTAFmrD//hMxY+Zs+cwKgiAIgjDgEGFhkMP04fz8LORkZ6h0fBouRkVFq4A1JDRCBbi9FRMoVtQ3AIEmPyUo7Mitwe9p1diUWY3NmTXYU1CrvBF0UN9geC34LjzuQUHBamIZBM8PS1OKiwtRVVWBrVvWqdcSElIQF5+kSisEYaDz6aef4vjjj8eDD/5dPXctfZg8eTJWrVqlTVg4/4KL1X1txfIlKMh3CAyd8dWXn6muOQsuvBT2Kjs++/RD5bUybfoMLdsnCIIgCILgKURYGKQwSM3OTkd+XhYaG5sQGhq+zzAwRhkGqq4GfWRtajV+3GnHr3vs2JBRrUQGd0EfhoHkR8B9MTIZoqLjVPZCSXGhMtGkF0NGxm7ExCYiMWGI8r0QhIFKWVkZhg0b1uHnhIG8LgwRg5lFXVFSUoy9e3bjwosvR3hEhJoOPXQ6fln1Y6+Ehbd/LsHlR8tnWxAEQRAE70SEhcFW7lBajOzsNNUmkiPcHAmPiHB4J/QlO6E97ngvB7ll9fAEvloK0V1PBp630NAIJRCVlxWrLAaWS+TlZqrWlQmJQ5RQNJDEFUEgo0ePxsMPP4I777wDFoulxUFhtsIZZ5zuFQcqLzdXCRFxcfHOeQmJSaioqFDtZq1WW5u/sVdWqswGV9gxhizdUonLj3bDhguCIAiCIPQCERYGCSUlhUhL3alGuikgxMQkICo6VgWnOowAmQVx2CgLPltbDk+gMhYwsHHNYoiMikN5eQmKi/LVOd629Xc1f8jQUYiKihWBQRgwzJ8/H3/5ywM46qijcdVVVyEzM1NlMVx//Q0wmQIwc+ZMeAO1tTUwm1veW4ODHYayNTU17QoL69atxk8/rmwxj+LJgQdNRFFlPTLSd8lnGUBmxm69J89HkOMgx8KAHXJIdlaq8z4z2JHPhxwHuSZaUlGhPyYTYWGAU1FRpgQFdhZgiUNi4hAVaNpCwtTIty4am4Dpo60eExbo6TCYBuuZfRIVFae8MSgsFBflobS0BNu3rUdISDiGDhuN8PBIT2+mIPQZq9WKjz/+CFdeeRVuuukm5/xDDz0Ub7/9dr91q+krgUFBqK2tbTGPggIJCmrfD2XKlKkYO25Cq7+pRlrqNpWFZY0ahijb4P7aZrCQnDISgx05DnIsXGEWFElMGtauaDnYkM+HHAe5JtpSVJiPoqKu/aH6wuD+hTKAoRt5etpO1eGBhozx8UmqBp9Bpjt+eJsC/DBjjOe+3GgKOZiEBQN//wCVhcLzHFVRhoKCHJSWFmPzptVKdKDAQIFJEHyZcePGYdmypdizZy9yc3MQH5+AESOGw5uIjY1T5rjsIhEdE6vm5eXmKGHE1oEPitVmU5MrdnsF0lIdj/PKGga9sCAIgiAIgnfiHUM7Qr+m3+7etQXr1v6E8ooyxMYmYPiIsRg2fJyqy3fnaF6ENQBj4j3TqYBdIQahrtCiTCIklJkKYzB8+BjVNYJ+DOt/X4Ud2zeiurrK05soCL2CYoIx8k8xYdq0aV4jKrzy0n+xatWP6jG766SkDMHyZUtQVVWFoqJC/LLqJ0ycNKXXy8/zkGeNIAiCIAhCV0jGwgChvr4OWZmpypiRhmHR0bGIiYlHWHg0TCaTx4L7aaOs2JHbMh3YfRkLg1lacEAhKVy1Dg1Xxp0FhTnq33Vrf0R8fApSUkaolG1B8BVeeeUVNZ199llYsGCBajHpLhZ+/QW2btmExkaHO+wTj/1TZSNceNFl6jk7UjTte42ceNJp+Gbhl/jPs/9W9+X99p+I6YfP6vX6RVgQBEEQBMFbEWHBx2lsbEBOdoZqOUjYESA6Og4RkTGq7t6T+PsBM8ZY8fqPJW5fdz1NHgQn9NOIjIpFaFgESkuKVIkEp7y8LCQlDUVS8jCtnhuC0F9cc83VCAsLw5tvvon//vcFTJo0CRdeuABnnXU2IiMjtB7oY4+bi2OOPaHFPFcB85LLrmzxPCQkBKefeY7qyNMfQqenuuwIgiAIgiB0hZRC+Cj8ocqgcO2aH5GWtguhoWEYOnQURowcj9i4JI+LCsTf3w9Th1sQGOD+zAERFtrHZApEdEy8uk6GKUPHCGRlpWHN6h9UtktT0wDu0ykMCOLj43HrrbdgzZrVWLjwa0yaNBEPPPBXjB8/Hpdddjk2b96sbd3MOmAGmOvEea6vt1du1l/ZU5KxIAiCIAiCtyLCgg/C+vjNm9Zg187NCA62YMiQERg+YhziE1JgNntXmyFzoD8mDXH/NtU3uH2VPgVbjMbFJ2PEiPEYOmykMnRM3bsDGzb8psziBMEXmD59Op555hls27ZViQ2ffPIJFi5chIGKCAuCIAiCIHgrkvvsY1kKubmZKgAMDAxUrSOZnWC1hnitn0BdfZNqO7l6r3vNAhukFKJbmIMtSEgYioiIGOTnZaKwsEAZPA4ZMgpJyUPh5yfao+C9sJ3jl19+hTfeeAPff/89oqOjMX78OAxURFgQBEEQBMFbEWHBh7IUdu3arIz36KPAbg9R0XFeXxcfaPLDrLFWPL240K3rFWGh+1CUslhsSBkySrWpzMvLRnr6LhQV5WHU6P2UcCUI3sT69euVmPDee++jvLwcxxwzB6+99ipOOOEEJboOVERYEARBEATBW/HuqFRoN0uBKewMBL01S6E14xLNCA32R3m1++r3pRSi5/j7ByAqOh5Wayjy8jIke0HwSh599DH89a9/xZgxY3DjjTfi/PPPU74Lg4HSqkbU1DWqEjNBEARBEARvQoQFL6ZGZSlsQWlpkU9lKbSG8sehIy34bnOl280b83IzUFfnaHeZnZWqNsZPbZFjpF6ZrQWYEOAf0Pw4IAAB/ib4d2DENtAJtlhdsheyXLIX9ofVavP05gmDHLaXXLRoIaZNm4bBSH55A1KiBt99SRAEQRAE78a3ItTB1PEhNxN792UpJCSkKGNGX8pScKW+EZg2yuoRYSE6JlF1OsjNSVeZHmji/9V/1PyGxgY0NjSo/vP1DfVorK1Rjxv4uNHhAGkID+yoEBgUhKBAs/p3IPsPuGYv5OZloKgw3+G9MHQkkpKG+eR1KAwMWPYwmGHLyZSogVvuIQiCIAiCbyLCgpdRU1Otuj04shQiERub6JNZCq6w3eSscTbg83y3rbNhX9WFoxWcox1ce8cwsAuBh5MSGRoaUFdfh7raGtgry1FfX4cAUyCCAoMQGGRWYoMpMGjAZTgwe4FGjqFG9kLaLhQV5mH06P1hkewFQXA74rMgCIIgCII34rvR6gCEgdvePdtUwOrIUqCXgvd2fOgJSRGBSIowIauk3i3rq29wZCz0BR53Tv7+QUqBMMPifI3ZDiyxqKutRW2di9gQYFKtHCk2BAdbVZbDQMlesFhDYMnNRFFRAX7/fRWGDhuFxMShA+L6FARfQYQFQRAEQRC8EREWvIDGxkak7t2OnJyMAZOlQDjaz+C7utoOW0i4Kof4aHWZW0shdMEyiKCgYDXZWu0vsxpqa6tRXlaiMhiCLTYlMlBw8OUgnKU4Q4aOQkhoOPJys5ShaGVFOUaOmrAvM0QQBN2IsCAIgiAIgjfi25HrAICB6PbtG1BeXorY2HgkJg6D1RbqswEoR/JrqqtRXV2pBAWwlWGwDY2NTTh8tDuFBbgdnjOKB5xsCHMIDbU1qKq2o6Q4XwlIwcEWJTSYzRafLJtg9kI0vRcsNmRnm1FQkItNG+0YN34SzOZgT2+eIAx4RFgQBEEQBMEbEWHBg9jtFdi65XdlEpgQn4zEpKEq4PQ1aHZIEaG6qhI1NVUIDDSr2nwaJ7IUwBBJpo+xUmdAU5M7tskNK+mO0GAOVhO7erBUorrKjsqKMhQX5an5FF14rHwtO4VlEewcERgYhLz8HGxY/wvGjZ+M0NBwT2+aMMD5+eeflXh30EEH9ui1gYIIC4IgCIIgeCO+Fc0MIIqK8rFzx0aVSk8vhfj4ZJ+qx+doPMUEegvQcNIcbFGp8hGRsR2mxYcGB2B8ghlbsmt8vhSiN/D8soyAE8Wk6uoqJTTQqJPBEDNVfKnzB7c5KXmEEpLy8jKxaeNqjBo1HrFxSZ7eNGEA8/XXCxEeHt6ueMDXIiIiBrSwwK4QgiAIgiAI3oYICx4IyLMy9yItbZca3WUbSaaW+0paPEfdKSZUVpbDZDLBagtDZFRct7a/rqFJ+Sy4Q1gwukJ4c0mB1RqiJl4TVVWVsFeWobSkQLV4pMjAbABvhyISr+Egsxk52enYuXMzKu2VGDZstM8IJMLAoaCgAEOHDsVAJr+8Qd0z5PMlCIIgCII3IcKCm0sGdu3ajMKCXERFxSAhcSjCwiK9/gcif8SyxKGiolR5BjANPiY2sceBb4AfMGOsFS+vLIYvdIVwFzz/hshA4aaysgwF+dnODAcaP3rzNcJti4yMVRkM2VlpyMlOQ5W9AmPGHuBTWTiCd/Pee+/h88+/wJYtWxAYGIi1a9e2eL2srAw//PADrrzyCgxErEH+ToG2xN6ISJsYpgqCIAiC4D2IsOAmWC6wbevvqnwgPj4JiUnDVNq7N0OzQfpAVFaUKhPGkJAwlV3Bjgi9wd/fDwcOs8Bs8kNNfdOAM2/sDxiIh4dHIywsSmUxlJeXoLSkELaQMNhsoSrTwVvFhZCQcAwdOloJTvnKd+FXjJ8w2euvc8E34P2ooaFe/Ws8dr3+UlKS8fLLL2Hy5MkYiETbArCrqNlnQYQFQRAEQRC8CREW3ACDw21b16tygcTEIUhIGILAIDO8ObOiorwEdnu58oCIiIhRRoP9MWoeGOCHKcOCsWpXFQabx0JvsxjYurKivAw5ZenqeWhYhNeaPdJrI2XISDWinJfnEBfGjp2IiMhoT2+a4OOce+65amLmgtVqxfz58zGYiAqhqNjoFBbGJXrvd4ggCIIgCIMP74xOBhD5+dnYtXOzCgjj4pKUsV1H5oaehqOALHdghoLFEoLYuP43lKyrb8L0UVbtwgI7TzQ2NcHfi0sIugvFnajoYDVCW1FeitzcDJW9EBoa4ZUZDLxmEpOGq+3mtm7ZshbDR4xFYuLArn0X3MPZZ589KA+1q7AgBo6CIAiCIHgbIixoJDcnA7t3b0V4eCQSEoeoOnRvrJWnhwLr+svLSmA2B2sRFAwCTX6YOdaGJ74phG4aGwEvjLt7DbMUwiOile9CWVkxcnPSERISocokvM38k9ujfDiCHKaOe/dsV5kwKSkjPL1pgg/yzjvv4LPPPlMZC3a7XT3uCL7n5JNPxkAjSnkq1KnH0nJSEARBEARvQ4QFTWRnp6lgKiIiGsnJwxDqhSaNRjeCstIiJSRExyQoAz7djI4PQoTVXxmQ6aShsQkmOkYOMCgwUKSqC3EIDBW56QgLjVSdJLzpGuO28PoPNAXCz98P6Wm7VFbMkCEjvWo7Be+HQhWve/5rPO7svQORaFvzPouwIAiCIAiCtyHCggYy2U4ydSciI2OQnDJcpax7Y5cHCgqAw9GftfHugiHlYSOtWLSxQut6fNxmoUtokkgzTXowlJYWqTIWdhkJtti8KnBnRsWQlJHwgx8yM/YocUHaUQo9LX9wLYEYjOUQjlIIB3nlzcaVgiAIgiAI3oAIC/0csGdk7EFG+m7VTjI5ZYRyyvcmamuqUVpWhMaGBo8FoezYMG20fmHB1w0cuwu9DGJiEp1iUXl5KcLDo9wqFnUFsylo6shrLTsrFU2NDRg+YpxXCSCC4P2lEA4kY0EQBEEQBG9DhIV+FBXS03chM2MvoqJikTJkBGy2MHgLHCUuLS1U7S49nTbPzhCzxupvQdjgoy0newPPZXCwFWazRZW3FBfnq7KW8IgYrzELpYGpIS7k5mYqc82RI8eLuCD0ipqaGmRkZLZoO0liYmIRFRU54I5qdIiUQgiCIAiC4L2IsNBPMFOBokJ0dJwyqGPg7i1QTCgpLlAj2PHxQ7yiBjkuzIQhUYFIL3KYkenyWBhsGG0qLRYrykqLkZeboXwOLNYQeAMWiw0pLIvwA/LzstT2jpDMBaGH3HXXXXj++f8qQ9DW3H///bjllpsH3DENt/ojwN8hmNKfpqauEeZAz9/LBUEQBEEQiAgL/eSp4Ch/cGQqWK2hXpWlUFNtR0RkrBrR9hYY9E8fbUX6L6Ua14FBi5+fv+ogwVKXkuJ8lcXgLdkLwRYrklNGqpag7Jzi7+ePYcPHSOaC0C0WLlyI119/A48//jgmTpwIk6nlNZ2YmDggj2SAv5/KWjDKIPLLG5ASJcKCIAiCIAjegQgL/dD9gUaN9FTwJlHBmaVgtiDOS7IUWnP4aCve0ygsDBaPhc5g+9C4+OTm7IXIGJU14GkoctGDhCVE/Az5B/hj6NDRnt4swQfYsGEDLrroQlx88UUYbMSFBTiFBf6bEqWnLbAgCIIgCEJPEWGhD3C0lS0lHd0fvMNTgVkKZfu8FLwtS6H16Nu0UVb4++nr3jAYSyG6yl6g90KVvcIrshcocBjiAsuImLlADwZB6Izo6BiUlpYNyoMUH2bCRtSox9IZQhAEQRAEb8L7hrF9hPz8bOzevRUREVFISRnuFd0faqqr1Kg0U8yZpeCtooKB1eyP/ZPN2pZf37b8elCjshfikuEfYFLXCcsjvMLQMWUEoqJjkZ6+G1lZqZ7eJMHLmTPnaFUOUVhYiMEGvWkMpDOEIAiCIAjehGQs9IKyshLs2rkZ4eGRSE4ejpDQCHgSjviWljBLoRIREbGqht0XqGtoUlkLGzIcI3A6SiGYwVFX51i+wz3eTxkHchR/MLY6ZEmMMnNU2Qt5SlyIiIjxaKmMakXJzIXGJqTu3aEEMfqVCEJ7/PbbatURYurUg3H44dNhs7U0Jj3jjNNxwgknDMiDFxsqwoIgCIIgCN6JCAs9pKamGtu3/a5GWhMThyIsPAqehMFyUWEu/P0DEBefov71FUz+wMyxNrywrFjL8uvqG2G3V6C0pEA9z8vNpAyjhBhOFBdYDhAQYII///V3ebxvPqeBKEA4shdSlCCVn5eJ6JgEmEyeq9dmGVFi0lDU19dhx/aNmDjpEPUZE4TWVFXZMWbMGPW4urpGTa7Y7VUD9qBJxoIgCIIgCN6KCAs9gK3Ntm39XQXv8fFJygjPk9TW1qCoMEcZRoaGRfpcAMztnZgSDEugH6rq+t8PoaHJDyEhYWrKzNiNxKRhztcoLDCbobGhHg2NDerc8nF9Qx0aaqsd8xvq1XsCA80IDDIjKChIPWYA7mvHusPshcgYVFaWqdaPUVFxqiWppwgLi0R8QgoyM/Zg65bflbgQGBjkse0RvJMFCxaoaTDiKizk7jNxFARBEARB8AZEWOgmDERZ/lBdXYXExBTExCZ5NLg0RuKZxm7x4ZFdU4Afpg63YOUOe78vu76hY7GC586RlRCAzsbpKTiwlKKutlaNlLK7QmNjwz6xIQhBgWYVjDOzwRfhcaA/CMWSoqI8JVBRiPHUtkRHx6OutgZZWWnYvn0DJkw40Cs7mgiCp8wbDcRjQRAEQRAEb8I3oyEPkJm5F4WFuSpTISFxqMcc9SlwlJcVK2EhOiYRQUH6zA91UldXqzpXBAbZMG201e3CQndxiA/WFkaYFBaYLcJ9qGJbz5JCFZjT28ISbIUpMMjnMhq4f7FxSSgsyEF9XY3qGuGJfaCIwPaYPL45uZnKc2HEyHFu3w7Be6murlZTR1gsFpjNZm33382bNmLv3t0IDAzEhP32x5AhzZlQraEguX79OuTm5sBkMmHosOEYN25Crz9brhkL+eUN+0q6fOteIwiCIAjCwESGArsBR3LT03YpQzmm03sqPZtp+fRToM9DbFyyT4kK/AFcU1OlavpzctJRkJ+Nhvo6mAKAWWNtWtZZp6ndJEthGIiHhkaoEXZeE/TaoOBA8Sk3Jx0lxQVKOOF++woUR3hd1Tc0oKAgW2VreGo7eExjouPUtZKrvDEEwcE//vEQhg0b3uH0zDPPajtUK1csxYrlS5CcnILQ0FB8+P472LVrR4fv/+ij97B580YMGzYCMTGx+P67b/DjD8t7vX6b2V9NpLa+CaVVjb1eliAIgiAIQn8iGQtdUFlZgR3bNyEsLAKJiUOUm74noKkdR5ODzMGIio73iVEqh5hQDbu9HDXVdlUu4HD8j1PijLEPI2KB6JAAFFY0+GS7Se5HcLBFTU3hTepcVVdVoqysGPV1eWo+y1W4795+3pgxQLGkrKzIaeroCSGN5pI0c2RWyJ7dW2GxWJUHgyBceOECzJo1s8WBKC0tw3vvvYv8/AKcfvppWg4Sy+B++3UVTjntTIwcOVrNq6+vxw8rl2PUKIeZpCv2ykpkpKdhwUWXIT4+Yd/cJqxbuwYzZs7u9XbEhQVgT36jsxwiwuo7hr2CIAiCIAxcRFjoBAY127auU0FOQkKyqj/3BDXVVfvq3yOUe763B6c0PbRXVihTQD9/f9isoQgLi1KpwO3R2NSEw0ZZ8dXv5f3eztLd8NwwEOfE64XHorrKjvLyEpSUFKhjwfaKnuzA0J19CA+PVvtQkJ+FiMhYjwhqvNYTEoegrq4O27aux6RJh3rUXFLwDkaPHq2m1px22qmYPftIlJWVaVlvdlaWEkuHDx/pnDdixCj8suonVbrTOoPMHBysyjJKSoqdwkJxcTEiozruJEQxwl7VsiyM4qwrcaEm7Mmvcxo4jk3wncw1QRAEQRAGLiIsdFJ2sH3bBvVvQkIKIqM8kyVQVVWJkuJ8REbFtajz9zb4g5s/risrStXInsVqQ1Q0MxPMXR63xkbgcE3CgqdrkJmlYQsJUxOFKkcHhkwEBgUrk0Sz2eK1QhG7jVAAKSzIRWNDg9oHd0NzUpo5ZmTsxdatv+OAiYd4zN9EgNdn20ybdhjWrFmLSZMm9fvy7fZKBFssLcxErTaH4Ga329sIC7xOTz/zXHz1xaf4fd0a1NZQfAjC/JM6zqhYt241fvpxZYt5FCcOPGgicnMy1N+HmJrLH7anZmOkdfBVNLLLjyDHQa6JZgzfmeysVAQHB8vHQz4fcr+U7442VFT0b5zVHiIsdMDevdtRUVGK+PgUxMWneMSZ3tH5odCrTRoZuFfZK9SxamxqRIgtXI1w9+R4sTPEzLH9L5o0NFJY4Ag8vAJmADBQDg+Pcp5bwq4MLJXwxu4HQUHBytSRnhhNaFLb6vZOETGJqKmpQXZ2Onbu2ISx4yZ6rRgjeJbNmzdjypQpWpbNz2dDq/oq43l7n116lKxY9j1Cw8Kw//6TlLC4+rdfsGbNr5g568h21zFlylSMHTehTcZCWuo21YrVag3B8IQCYHuxeq02IBLJKdEYbKJCckpz1shgRY6DHIvWwiehP5HV6pmSXW9CPh9yHOSaaEtRYT6KivKhExEW2oFmcRwdio1NUHXeHaXw66SyshxlpUWIiU30mFlkV4ICvRPYocJkcqT998VDICrEhBGxgc4U3/6Av/m90TrRz89fpfkzI6C2plqJMvRjoBkkswK8LWhm1gKvQyUuNDWp7XQnHPl1lETUIC8vG5kZe5AyRAKLwco333yDZctaGiDS12T16jXYunUrXnjhBS3rjYiIVFlZ/AFv/HBnmQO/H0JCQtu8f8/uXcjMzMB1N9zq/A6JjonBu2+/gUMOnaZK7FrDDAgjC8KAImRaavudIaTlpCAIgiAI3oIIC62gk//ePdvUqDJFhfZ+/OmmsqIM5eXFXikqMLDkMaLowe4IUVHxylCyP1pDTh9lxZ78UvR3xoK3QgGBngGcmO5PcYEiA00KmcHgTQKD6hgRm6S6RfAacLeRIj8HHImpratFevoeRETGqFISYfCxd+9erFzZslyAJQIjR47AY489iqSkJC3rjU9IRGhoGNauWY0ZM49QZXIscRg1eowzY2HxN19jyL6WksGWYJW1UFpSjOiYWPV6YUGBEhn64rEiwoIgCIIgCN6ICAsuMGDatXOzCmJi4xLVqLJnRIUSxMQmeZ3BH00kS8uKeKCUuR8D4v4Kfv39gMPH2PDWz/0nLFCs8GJdoQWBQWbVgYEtOSnalO8TGLypk0SAyeTMXCDuFhdoIBkfl6yuQ35OJ0461CvLRwS9XHnllWpyN7zWTpg3H5998hH27t2NGtY0+wHHz53vfE9q6l6EhDqyF1JShmLipCl4841XkJiYjLr6OhTk5+HoOcf1ySdEhAVBEARBELwRERZcyMnJQFlZiWoryZaInih/cGQqeJeowPRfBrtMNw4Lj1IBXn8Hu/7+fjhkhAUmf6C+n1qzq+X4irKwD5o58vwbWSEV5aXqmHsic6YjM0pDXOA14O6yiPCIaERVlCIrKw0ZGXswdOgot65fGNwMHTocV1x1LbKzMmEKNCnBwFUkOObYE5SngsFxx8/DtOkzUFxUpN4XExur2s/2hZbCgpt66gqCIAiCIHSBCAv7YCCXlroDERFRiI9v+WPRHbCO1vBU8BZRQaXxlhaqEWJ3tLq0BPlj4pBgrE1t2V6tt9SzFEIpC94x4t9deIwp3jBbgddFcVGeyqJhUO0N14YhLuTnZcEPfggJDXfrqHFcXLKqc8/M2IuoqFgpiRDcitlsxvAR7Xt8DBs+os28sLBwNfUX0bYAleHV2AQU2xtQW9+IICqygiAIgiAIHkR+jewrgdhplEDEJqr6dne3lGSHAG/yVGBAm5ebgQD/AMQnDFHdAHSn5NfVN2HaKGu/eiz4MjzeNluocoNnVxC2qWSpDK9XbxEX6AnBbXInLMGJi0uCxWrFzp2bVK27IAwW2EUnJqRZ+M4vl6wFQRAEQRA8jwgLqgQiHeVlJWr0M9LNJRDMBigpLlD19d4gKjBLobAwV3V7iI6OV6Pk7qpjNwUAM8f2X5ukVp3hfBZ2kWDXDZZIsFymsCAH9fX1XtMtguU7FKLcCa/L6KhY9fnJkJ72wiBDfBYEQRAEQfA2Br2wUFXFEoidiIiIdnsJBD0LiorylJ8DR6Q9TdW+LAVTgAlx8cn90u2hpyP0+yWZYTP3z2Xp6xkLrXGYiiYpv4X8vAyvyF6guBAVnYDSkgLlxeHWkoj4ZPW5zcxIRYWbsyYEwZOIsCAIgiAIgrcxqIUF1QVi12blyB8Xl+DWEgimb3PkmSPRTO32dJZCUWGuandoZClwlNwTBOwzcewP6gaYsECUYaIze6HMK7IXKIpFRMSoa6ihod6tRpdx8UmwsiRih5RECINTWMgu9Xz2kiAIgiAIwqA2b8zJTleu+wkJKYiIjHOroEFDPo48h4S4v6Vla38HlmJYrSGIjIr1mKBgUNfg8FlYurWyz8tqGMCl947shWRUlJeo7IWw8Gjlx+ApKMrV1dUqcYGih7taZLLtaVRUGbKyUpGRvhtDh412y3oFz1NTU4OMjMw2YlZMTCyiotzbCtXdDI9pLpvbkeO+TCFBEARBEISO8B/UJRBpOxEeHqXM8dxZAsHMAGYshEfEwFNQ3GAXCppGRnk4S8GVwAA/zBrbPwaODQ2+1xGit9kLFBgoEHmyNILb4h8QgJLifLdth6MkIslREpG5VwmFwsDnrrvuQmJiEg466CAccsihLaZXXnkFA51xic2lc9tEWBAEQRAEwQswDdoSiJ2bVQq3KoGw9J9hYFfQ5I5eBhxtdteobmsoajBjorGxQdXs0+HfmxgSHYT4MBNyy/qW4lvfOJBlhbbZC0VFuSgoyEZUVLzb26USXs+RkXHIz89S/g/uakNplERUV1ep7i6TJh8Kf3/377/gHhYuXIjXX38Djz/+OCZOnAgTXV9dSExMHPCnYmxCc8bC7vxa1VEn0DQY7naCIAiCIHgr3hVRuonc3EzVJi8hYYhbSyBqa6uVyV1MTKJHAj/DMJJdH4ICzSpTwVPiRmc0NjbhsFEWfLa2vE/LqafHgvftnraR++joBJWFwraUnuoy4tiOeOTnZcEUGIjg4P5rH9plSUR0GbIyU5GZsRdDho5yy3oF97NhwwZcdNGFuPjiiwbt4Q8NDkBypAmZxfWq+82u/FqMd8liEARBEARBcDeez313M6zHzUjfhbCwCDXK6a4An+tl/XlEZKwyi/QEbM3HgI+1+BGRMV4pKhDqAYeP7ntAWt/QNFh0BQXPJ0tawsIiUZCfpfwzPNcpIk5lxdB3wW0lEbFJqrQpKyvNrR0qBPcSHR0jGSlACyFhW7Zc74IgCIIgeJZBJyxkZ6WhoaERkZExbiuBaGpqVFkCVluYW8suXGFqOltb0qAxJCTca0UFYgrww4wxNvR1Ew3zxuKifPVvdZUd1dV21NZUq8wNT7dq1IXVFqoyFkpKClBeVuyR/WR5QlhYlBLTWHrjlnUGWxAVHatEhoyMPW5Zp+B+5sw5WpVDFBYWDurDPy6hWVjYKsKCIAiCIAgeZlCVQnD0lO7xzFaIjIpzW3BdUlIIU4AJoaERcDcMKmnQWFNThdjYJJWe7guEWwMwOi4IO3Jr+1QKwVNstztKKlj+QhjoNjbWqzabfv7+CPAPUD4TNB7kaDvLRAKDgrzOe6InBAUFIy4uGYUFueq6d+f1bmALCVPrZuaCu8puIiNiUBJeiLzcTCQlDXVbKYbgPn77bbXqCDF16sE4/PDpsNlatgk+44zTccIJJwwqA8ftYuAoCIIgCIKH8d3IqRew9pqdD6KjYxEcbHHLOjlCzhKEuPgUtwd2RltLh0ljshrJ9RVYxjB9tLVPwkJDYxP8/YDklJHIzNiNmNjENsdHiQwN9WhobFBCQ319rRIgmErP80WDT5au0K+A7UF9yRSQwkhsXCKKivJRWJCD6BgG9+69BliaQc8HGpYyk0I3PFfcz/LyUqSn7caYsQdoX6fg/o4+Y8aMUY+rq2vU5IrdXjUoMxZ4P/PmTDRBEARBEAY2g0ZYYHCfk5OuSiDoc+AOGNCz9R4NIt0d1PNHJrsENDU2qbR4b2gl2R3q6+tRXV0JkylI+Sy89kNJ75fV4PAd6Ai+Ro8NToHtHD/6YtTV1qC2rlaVklCkYeBqCbYi2GJT2Q3eDs97VBT9DvJRQHEhOsGt1yKPMT9vhQXZqlTBHVkg9FkIC6fPRA6SkocpTxFh4LBgwQI1DXYSI0wIDfZHeXWjmrJL6pEU6f33JEEQBEEQBia+EW32A+npu2EKDEJMTJwahXYHLEFgKra7siNcPR1Y244meL2owACe2QHsZpCXm4H8vAz13N/PDwePsCAwwK9vXSH6EBBTOLBYQ1SgymyHhMRhyp+irr5OmWDm5qSjtJRlJtVe7deg2kBGxcIUEKgCfHd5Hhjw82azhaG4ON8tx4nnLSYmXn320lJ3al+f4Bl4He/Zsxc//PADUlNTvfozqOtz3cLAUcohBEEQBEHwIN4bcfYj9soK5OdnIzwsAuHhMW5ZJ40CGXCGhUfDnfDHdWFhHn91em07SSObo6K8VIkJFEG43UybZ/DOEfYgczCCTP6YPDS4T+UU/QlH+mm+GRkZi4TEoepfP/iprJTc3HSUl5WoLAdvxJE5EKNKOjwhLoSGRaKxoQF2e4Vb1kfjyLDwCOVvUlpa5JZ1Cu5j1apVmDZtOqZMmYJ5807EpEmTccQRs7Fu3bpBdRpcfRakM4QgCIIgCJ5kUAgLaWk7VdYARzHdYV6oSiBK8lXg6c60c6P8gVICg3NvFBWYjcCSgpzsdNTWViMiIgbxCUOUqMBOAq7bXFvfhOmjrH3uCqED5b9gDkZYeJTyz4iKjFP+DLk5DqHEG7MYHO0oY1SZSWFhjlvFBaMkoqy00C3iC8tbYmMSYLWFqKwFbzsXQu/Jy8vDGWeciZEjR+D999/DihXL8c47byM6Ohqnn34GSksdJq2DzWdBMhYEQRAEQfAkA15YKCsrQXFxgeoEwSDQfSUQNlVT7m6jRv7rbZkK3CZmcLB8gAEtU9UpJnA7eYw62tYgkx9mjbV6pBSiNyIDOy8wk4EdGXguuL8cofemoNbIXKDXgZEp4taSiJBwt5VEhIRGIDwsEhWq1aqj5ajg+3z66WeYPHkS3n77bRx33HGYNGkS5s6diw8+eB/Jycn49ttvMRgzFqTlpCAIgiAInmRACwsMXtJSd8BqtSmvAXcYxzWXQLhHxHBtKclMiWgvExV4LArys5UXAdsPJiQMVWnxHFHuDmMTzcqgrLddIdwNM1RCQsOVcBIaFoGK8hLVFYHdQbzKc2FfNo0y+HSjuMCWq+4qieD+xcQkICQkTGUt0XtE8H1yc3Nw2GHT2tznTCYTDj30UOTk5GCwMCo2CKZ9t9LM4nqUVTV4epMEQRAEQRikDGhhgZkKbDsXFhapJt0wsC/2QAlEZWUZamqqEBXtPUaNdXW1qsVhcVGuSkdnuYDVGtJj0YPvPmxk77IW6jz4G5v7ST8GtvlkMM1afwosLAXxHkPHOBXkl5cVu3e9RklEvf6SCIpZERFRSvDLy8vWvj5BP4mJSfj555/aCGLsKLNq1c/q9cFCoMlPiQsG23N6355XEARBEAShL3hHFKorWyFtpwosYmLd02KPWQMWN5dAsI0mTQOZkeHulpbtwR/3LAMoyM9S5QEcuWdHgN5mUdQ3AtNGWXwmY6FdgcEagvj4FCU0UGxhCQKFF2/YNpajMHugyk2miiTQjSUR3Edm8bAUKiN9NxoaZETX1znllJOxceNGnHvuufj666+xZs1afP755zj11NOQm5uH4447FoMJ13KI7dIZQhAEQRAED+H5SFRjtkKVvRIR4REqFVo3zBhwdwlEfX0diorylFEjfQs8CQNE1rKzXaR/gMlRChAa0ecMCrabnDXO5nXmjb0JcCly8biwMwOFF2YKeNp/geVBFBdKSgrcmk2hSiIaG1BdVal9XRR2IiKjlZhTUDB40uQHKjExMfj440+QmZmJc889D0cddRQWLLgQVVVV+OSTjxEaGorBRAsDx2zvyIgSBEEQBGHwod90wENkZ6fBZgtR6d66ywOUx0FpkSq3cFfWAB39OfrNOn53Zkh0JHCw5WJjYxNiYpNU4NyfJEYEIjnSpGqIPdlusj/g9UGPCQa7JRS/qrIQGRXb78esp6aK7M7BTIrYuCS3eJFQaKEIxxKRYItNqy+IMqyMiEFxUQGys9IQF5fkVT4kQs+ZOvUgrFy5EllZWcjOzlamjQkJCYPyULYwcJSMBUEQBEEQPMSAzFiorKxAWWkxbLZQNemGxnwUFxgsurMDBEsNWGbgKbgdlSpLIRNms1UFpToCZJY0TOtF20l3dYXoDcwwYfkKr09H9kKJR7MXeO3SA8OdnSKCg60wBZhgryzXvi7uGzOXqqoq1b1BGBgkJSVh6tSpg1ZUaJ2xsDO3FnVeKKgKgiAIgjDwGZAZCznZaarlH0eC/f27132gtzAIKystQnh4tNtGQcvKilXGgifbStJLwZGl0KglS6E100db8eFvZT32Z/BmjPIIZpzwWDLo9WT2AjMp6gprVSYFW1K649oKC49EYUGuEjZ0ZvsoP4moWJSUFKlspvAI95UsCX3nnXfewWeffaZ8Fex2u3rcEXzPySefPGgOe7g1AInhJmSX1itRYW9BLcbEN4sNgiAIgiAI7mDAZSywjjo/PwchIaGqjls3NL6jeOGucgSa7HHypKhAPwlHlkKwtiwFVwL8/XD4aCt6urveYN7Y/eyFRNU9g9kLFBg82Smitq5GdRpxBxQAmXnDzBfdUMRhhgj9V7yp/afQNRSdWKLDf43HHU3eYGLryXII8VkQBEEQBMETDLiMhbzczH0t7aK1B7xNTY0oLytCZJR7gvyGhnpVkx4dE4+AAL2ZGB3BAJAZExxZZyq7uwgJDsD4RDO2ZNX4tMdCR/D6CQkJR1CgGYWFuaivq0NIaLjbxSMGZdFR8cjPp3BkcUv2BL1JKKjYQkK1Zhg5jCpjlR9KdnY6RowYp21dQv9y9tlnq8n1udCyHGLpVocguTW7BvOnyNERBEEQBMG9DKihHQb6OTkZCA0NQ1iY/lRndkEIDDSrkXvdsOSCKepWW6ga5XU3xvorKkoRE5voVlGBMMWX5RC+2hWiu3D0Pi4+WWUt0EeD17S7MQUGIjQ0UpVnuMNvgeIFDRzpM6EbZjHRayE/L0sJdYLv8fPPP6sWkz19bSAjGQuCIAiCIHiaASUslBQXqpZ5oaHh2ksT6C1QUV7itvaSVVUVqvsCR3fdTUNDAwoKslHfUIfYuGSPeAAE+AMzeigs+KqJGUfWY+MSmcfgCIDr3R8As2yA66eQ5A54XVfay7XvKztghEdEqs9vQUGu1nUJevj664VYunRph68tW7Zs0B16ZnMZbMup8XgbW0EQBEEQBh8DSljIzc2E1WpDRIR+I8Xy8hI1au+OIJsjq6UlRftaZ7o3NZ5iBv0UmKIfHZ3gsfplfz8/TBlmgdnkN+A8FtqDLVJZbmKxhCAvL1N5h7jfbyFWiWfuWDfFFHY4YZmNO0QMll2wbEoYWBQUFCAszHOdcjxFUoQJIWbHvbnE3oi88gZPb5IgCIIgCIOMAeOxwEyF4uJCREfHqXIB3YE+ze3i4lLgrhIEms5xtNWdsM6fmQqs9Wf9v6cJDPDDgcMs+HmXfUB0hehOcB8aFoEAkwkF+dmqPaU7rwGaSrJTBEsi2PlDt6jFTKPcnHTU1YVrFewo1liVYJOlWtPabO5pEyv0jffeew+ff/4FtmzZgsDAQKxd27LkoaysDD/88AOuvPKKQXeo/f39MDYhCGtSq50GjvFhA+brXRAEQRAEH2DAZCwwSAgI8FemjRz91AlrwW3WUJhM+n+4sQMEswYY4Lm9u0ZBlgpsvUFUILX19FmwDIqMBVes1hDV/rGwIAe1NY7AwV0wi8BdJRE0bgwJjUC55qwFZt1ERsUokUayFnwHlq9Q1OW/xmNjamxsQEpKMl5++SVMnjwZgxFXnwUaOAqCIAiCILiTATGkwVH9vNwsZcqmu8Ukf8CyxWR8Qop7SiBK2QUi0a0lEEamQnhYlPbsj54QZPLDrLE2PL6ocMCaN3aExWKDH/xUxwh3Zi4YJREsh3FH6Q+FjJzyNCWmMWNCFxTLmAWUn5+NocNGe6zLitB9zj33XDUxc8FqtWL+/PkeOXzMjMhIT1XX57DhI2A2m7v8fsrISENZaRmSkpMRGRmlrTOEgbScFARBEATB3QyIjAW2j6upqUJISKh208bKynIEW6zasyIIW0sy0HJn+jsDOooKrEP3FlGBP8xpxMgMhNHxZkRaAwZd1gLhdWdkLrjTc6G5JKJAuykcswmYDcS2pjrhZyosLEKJd0VFeVrXJfQvbDU5YcJ+yMtred6ysrKQmpqq9XDv2LENr7z0PLZv34rffluFl//3PIqLizp8f2VFBd58/WV8u+hrpKbuwReffYxNGzdoN3DcniMZC4IgCIIguJcBISwUFOTAwqArIkbryD6DKgY8zIzQDVPe62qrVSmCW7s/5Ds8FRwp8J6lvqFJHfPNWTV45rtCVNc1obGpCYeN6lk5BNs2EqbYs0UoWzny+NbX1/ucezozF8IjotV5ogjkLng9MFunutquf10hYbDby1W6u07CwiNV1kJBfo7W9Qj9b9B43nnntSlF42f5nHPORXl5ubb74+JvvsbhM47AqaedhfMvuBiJSUlYuuS7Dv9m4cIvEBYejksuuxLzTjwZF1x4KSKj9GQsjIoLgmnfN3pqYR2KKqWdqiAIgiAI7sPnhQX+mCwuKkBwsEUFXTphUMWU6aCgYO37xCwMjhKzO4A74DqLCnNhsYZ41FPByDAorKjHi8uKcPwje3Hec+l4aXkxfthRiYaGJkwb1f22k45yCIfY1NjUiLq6Gtgry1U2CNP7szL3KMNAjlpXlJeipqZae0DbH54LLPlhWYS7tpWCHVurlpUWaRdjmCHBzxhLjnTC+4XZbEFpabEKGgXf4LPPPseMGTMQ1SpAT05OxoQJE/Ddd99rWW92dhbsdjsOmNjs4cDHe/fsavf6KS0pwd49u3HYtBnYvXsntm7ZhPLyMiQlJWvZPnOgPw5Iaf5u+m1PlZb1CIIgkLvuuguLF3csrAqCMPjweY8Fmspx5JbBlklz/TcDT5sbgu6a6io1Omy1uq8UobSkUKWhswTCU9kJpgA/bM6sxssrSrBka0Ubj4Tl2ypxzP4hmDm2+8ICdQp6BHAEPDw8uv0yi7pa1NXWoJaigzLLrEWQ2QJLsNVtZS+9GdWnSFJcnI8oN7UhpccC20/yGHGkXycUt0pKHN1QdO0bzys7UVBU4vUfFR2nZT1C/5Kbm4OQkPY7eXB+VpaeNqJlpaUwm4MRHNwcvIeHhStxjyUPzExwpaAgX127S77/FuYgM/wD/LFo0VeYM+e4FuKEK/bKStirWmYFUezsLoeNtGJdmuP9q3bZcdwB3lHOJgjCwOOXX37F+PETPL0ZgiB4Ed4XMfWQoqICBAYFqRFcncEVg08KGLqzIhjolpUVqdFhdxk2sjyAHhWxccluNYk0MhQC/P3w6x47nllchPUZHf+I/mGHHf5+fogLC8TQ6ECkFXZdClDfhccC95f19pyMM+tI+a9CdVWlyhzhCLrVFqLEK3Yu8Aa43RGRsSjIz0J5eYlbBCFH1kK0Ki2xWm1as2mCzI7gjSUrOn1TQkLDVBlVEQUaERZ8gjFjxuDhhx/Bfffdq9pOGlRUVGDJku9x0knztd2b/fxb3h/9/P2d2VCt4X2EfzNi5Cgcdtjhat769evw3XffYPyE/dvtKrRu3Wr89OPKFvMsFgsOPGgicnMyEBTUuXg+Mqz5fvfDtjJkZlRiIJKZsdvTm+AVyHGQY2FQXe347ZSdldpC/HSlqqoKH370KdauXafec+TsWTjmmKPb/d330suv4eefVuHOO2/DyJEj2l1ebW21akftrdeht26Xu5HjIMfCoKJCT6nogBIWiovyYbXYYLHqDfgrK8uUmaHuwJv1//DzU6PD7oDZEfQeiI1LUhkL7oaH85rXMrFye9e1+4UVDcqUjLXE00dZkVZYqsW8keIBRQRODAwourB8oqy0WAlL9KDQ3R2hO/BajIqOR35eFgJNgaqMRTccseW+K68RjR1YuG/0deDnTqewoLJSgi2qnEoFjm4W1oSeM2/ePNx33/045ZRTcPXVV6vSgrS0NDz55JNKJDr66KO1HFabzYaaaoc3iyEKMFPB8Vrbz54xb9jQ4c55Q4cOU113SktLEB0d0+ZvpkyZirHjJrTJWEhL3aY6EfGe1BmxCY0I/nq38qPJKgP8Q4YgMUJfdxVP/UhOThmJwY4cBzkWrtjtDhExMWmYEv7bY9q06TjyyNk466xzkZOTjfvu/zty84pwxx13tHjfqlWr8PHHn2PXrl0ItoR1+HljySIHOLzx8yifDzkOck20pagwH0VF+dCJTwsL9DxgIM50ZtZK66KpqRFV9go1oq89W6G0CJGRsW4JcJiBwTTwyKg4ra39OqUJSI7s/rqXbq3EyNggHD7Gind/6Yaw0MfSeb99Ig8n1lEz0KVxYmCQGeFhkepfT8J0fooLhQXZ6hy6Y3uYTcNjYLWFaRWjmCVSVubwP9DVDpLlUwzWiosLVVmV7na1Qt9hq8kPPngfV1xxBRYsuNA5/5BDDlGtKNvLBOgP2CqS1+Gundsxbvx+zi4RiUnJzsyJLVs2ISoqGvHxCYiLT1CjgiyJSEhMUq8XFhSoe0poaPvmuFabTU2usPQorZvNLoJM/jhwqAU/7bI7yyFOneo5zxxBELyHr776so03zXPPPddCWKCPzHXXXY9HH30Up556apfLrKurw1NPPYXffluNuLhY3HjjjUhJ0d+OXRAE78SnzRs5ysjR5fCISK2BeFWVHYGBZu3BN4NWrkN3y8xm08t8NfrOEVtP4e/vhxuPjUa4pXuX4srtlcqLgbXEAd34k4Z+NBpkUMGSg4TEoQg2W1Q3Egoz7uzO0B4s46B/BLfFHV0umLHAa5R+CzrhZ5seFxT1dOEI8iJUORXLqgTfYL/99sMPP/yANWtWY9Gihfj993VYvPhbjBjRnB2g43PGjhDffvM1fli5HIu/XYgN69fhiCOOcr7nx5XLsWf3TvWYAses2Udj6ZLFWLliKX76cQUWLfoS06bP7LKkoS+4ds35ZbcYOAqC4KC1qJCfX6CEUFeYDXbiiSfiwAOndOuwPfjgg6oTzxlnnI7MzEycddbZPtdtSxCE/sOnMxZYF810U6slRHvAr7v9Ig3AystKEB2TAHfAfWK6gCc7QPDLh8Gp2RKGa46Oxj++7Do9Z0NGNSprGmEz+2P/pOBOPRlIfYOeYJSCDEtjuP15eZnKZDDMjV08WsMyCGbvsKyFGQW64b6q/Q4J02puyeNaUlyg1qNLPGQZFcupKLQNGzZayzoEPfdM1hUXFhYiPDxclSjwGtGV3UIOOXQaYmPjVLcHZiMsuPBSxMQ2m36On7Af4uMTnc8nTZqC6Kho7Nq1Qz0/cf4pGDas/Xrl/uIw1TWnUD1etdsuJT6CILRh/fr1eOGFF/D88/9xzlu2bBmWL1+OFSuWKz+G7nDOOWfj7rvvVo8PP/xwjBo1GtnZ2UhKcmRpCYIwuPDZjAWOEpeVlqhRJLNGPwKup76uVrtpI0dlDRNB3XCfGICyNs5TNeU0w6Q3AOuHA/ybcPZh4Rgd1/UoHjtFMGuhrr4Jh4229Nm8sS+oLhrhUYiPT0FDfT3ycjOV2aDnzBxjUFlZjtraGu3rY2YNPxNcn05Yw8kzyA4YumAZFScKMyyvEryflStXYsaMmTj88Bk4//wL8PXXC1UGwxlnnKl93cNHjMSRRx+DmbOObCEqkBkzZyuzRleSU4bgiNlHq/frFhXI+EQzQoMdX+355Q3Yk+/ZjCpBELyLjRs34vTTz8Cf/3wP5s93mN2WlZXh+utvwNNPPwWzufu/Q127QsTEOHxjSkr0ZjMKguC9+KywUFLMEZkmNXKqc4SKgQbr63UG4GrkvqLULdkDRgmESv/2gAEh109Rg90MOBrNDA2OeDNz7s75sd1aBttOsgxi1hhbt9pYusvnIDQsEoWFuap1IX053A23IzzC0bXBHamIvF5p4qhzXfzcUcCobtWCr7/XwXIqll6wvErwbtLT01W6LU0a165di+uvv17Nnz17NoqLi9VI3GCGXXYOGdEsujJrQRAEgfz222846aSTcddddynzW4MlS5ao++dDD/1TiQ4XXXSxmn/XXXfjhRde7PDgieGxIAgDohSCZRA0XbOF6O3TzYCGadg6YdcBtkcwWuzphIEgBRmbB0ogaMLHoJfrj41LaWGyRt+EQ0daceR4mzJo7AzVdtLfDwekBMMS5Ieq2qZOMxzcBa9Hjnyz/VJ+XjaiY+K1lgm0B4NwZr/Q9DBcc0kEhSlOXB/LQnRBYa+kpEBriQfLqVhWxftKYtJQbesR+s6nn36K448/Hg8++Hf13FVYnjx5snI0nzRp0qA+1CyH+H5LpdPA8bxpYkoqCIMdZnoxw+tf//onzj333BavTZ8+HS+//JLzeWVlpSqNOP3003DEEbM8sLWCIPgiJl+trWXGQlhYhNa2jFwP+/RGmeOhE0e2gr4a8hYlEOXFiI1NdrvKzNKHwoIcFfgyQGxv/Y2NTSprgcJBXSeZBkWVDdiZW43R8cGYOtzSaavKzpajAwY5zF5Q3gu5mYiOjneLYNS6JILr5rHWXVpD0Y0ZKPR40HVNcR8aG+rV9avLQJXlVFwP2/DoXI/Qd5iyO2zYsHZf4zVIAXOwQ3Nbg1/3VKm2u8xkEARhcMLfs2eeeRZCQ0Px3nvvq8ngo48+RFxcHI455hjnPKOcgd12xo0b55FtFgTB9/BJYaG8vAQNDfUICQnVms7PMggGhTpb6jGIqautUQGobtjKkpkKpn2t0dwFa9c5ih8WHq3KHzqCWQjxYSYsmB6Bl1cWd7rMxZsrMTQ6CNNHWTsVFnR6LHTaaSAsUrUyLCzMUR0bdI7ot4ZZEiwRKi0tRExMolYRicIeSz/ogUA/BB0YLT/5edRVLmR0/CgszENJCY+be0xUhZ4zevRoPPzwI7jzzjtgsbT0WWG2At3JBzsjYgMRGxqgPBbKqxuxNbsG+ye7T+AUBMG74PfoG2+83u33h4SE4MMPP8DYsWM7fM9DD/2jjUkj/2boUMn6E4TBik96LBQXF8BsDtbec56BjCVYr2mjvbJ832iv3lNBQz8aJbq7CwTN/ejqHx2d0KmoYMBRtavnRCE6pHPfDIoJ7Nk+a6zN4x4LHcGMgZjYJFWWQDHMnVDIaGxocJTZaP6xwvOq28QxWLPPAmFZFctZxGfBu6HZGNN0jzrqaLz88iuqxRl9F2g8ZjIFYObMmRjs8HPJ0jIDlkMIgjC47wnMSGhvag+WqvI1dtzpCGYzJCcnt5jHv6EoIQjC4MQnhYWK8jKnk7suaEhnGDfqXAcDMt2tLI1shdCwCK3ZF+21tCwrK0JMbGKPygEoLtx4XMveyq3ZmFGNqtoGDI8NQkwnIoRRCuEpgyFm1FBc4HlmyYC74P5yBJ7nXbeRI0WMKnulSrXUBT/rLEvSvY4gsxkVyodE8FasVis+/vgj5Vx+00034f3338dLL72ErVu34u2333brPc6bOWyki4HjLr0CoyAIgiAI7oMtYX/99Vd4Gz73C8wIxqmmBgbpK4NgEGMKMCHAxWCwv6FwofZDc3eGmuoqVXLhDgHDwAikY2OSerx/gQF+OOXAMOyX1LE/ACscVmy3K1+GaaM7Fn/qGxzeDUnJI5wZDLX1jWqeu+A5jo1NhN1e4dbMBY7yA36qFEV36QUziGjiqAsGixSndLaDVGaUpkC1jvr6em3rEfrGL7/8or5Qly1binXr1mHRooXq32+//QZDhgyRw+ti4GiwNq1K3fcEQRAEQfB99uzZg2OOORbehs8JC1VVDCYbVCCj03GfadeOwExvGYTu2nsKMaWlRR0aJuqAgSxHyqNjEnvt58BuDvec1Hn7ySVbKsGEBPosdJaxQO+GR77Ox61vZ+OfX+XjtR9K8O2mCuzJr1UCA43NdMNrlX4H7MrBTA53oNoohke5JWuBopXdrrccgmVJOoUFHi+r1fGZd9c5EnrON998g8WLv1OPR4wYjmnTpql/hZYkRgRiaLTj/ltd14T16dVyiARBEATBC1i/fj02btzYZn5FRQU2bdqE1NRUt7SOx2AXFviD36jr1gVPZFV1peYyiEZV/846fJ04ArEm7etx7f5Ao0a2WuxLJgbbT04cYsHcSR3X6v24w44Af2Dm2E4yFvaJBmtTq5SY8O6qUjz5bSFufzcHp/w7FX//nO0v3QOzX6JjElBWWqz8LtyBOdiiOhzoDpS5nrq6OmWqqgvDwFHnjZZiIjNMKiv0iiRC72FHiB07tssh7AZSDiEIgiAI3sX33y/B7NlH4g9/uKLF/Keffhrjxo1X8+fMOQazZs1SAoMv4XvCQsU+f4Vgff4KLBtg8KKzRKGmuhqBgWb4+3duUtgXuA/lZSWqQ4E7shXY5o0tJcMjovulQ0BjUxNunxsLS2D7215sb0BaQS0ibSaMjA3quBSik0D0wGEWuFMQ5DUVGRWLosJcdZ25A3otVJSXag3IHZ0bLFozCijMsDyJZUq6MAcFq/tLhWQseC1nnHEG1qxZi1dffQ3l5SIAdUYLA8fdYuAoCIIgCJ6ktLQUt956Ky644IIW82lE/ec/34uXXvoffvrpR2zZshnR0TH461//1uUyi4tLsGXLFtTU1MDT+JywUFFZjqCgIO1tJjk6qjMYVxkRFn0ZEUYnCJaN6My8MGDQWlSUC4vVBqu1f7JJ/P38EGkLwKWzIjt8z8KNFco3YXoHPgssc+gsnp42yqqyI9wJz0doaLgSF3SaERqolqkBAVqDfmdGQZVvryNw371FMha8l8ceewy7du3CDTfcgJSUIYiLi28xPfnkU57eRK/hEBcDRxreVtaIz4IgCPpYsWIFrrrqKlxwwQK88MILasCpO/C+ffrpZ2D79u39sjxB8FbuuOMOXHTRRW1auRYWFqpY6pBDDlXPAwMDcdBBB6GgoKDT5d1440044ogjcM4552LSpMnKyNqT6DMp0GXcWFGOiIgoBAV1bOzXV+pqa7R3g2BwFBvbsv9vf1NZUaraS7ojW4FGjX5gJ4Kofl0uO0RcdkQUPlpdhpzStmn2K7ZV4srZUZgxxoo3f2prjFjXibCQHGlCbKhnPgK2kHDU1tWirLQQEZGde0n0B7wOKipKtZbEUCgrKSlQZT662qdSJGEWjlYDx0ATSkocBo4sixC8i7lz52HIkI77pE+depBbt8ebibKZMC4hCNtyakHvxjV7qzBrnHvK4gRBGFzQ++b888/H7bffjqSkRPzrXw9j06bNeOKJxzv9u1WrVuGVV15RgjFHc/u6PEHwVr766its3boNzzzzDJ555tkWr02cOBFz556AW265BZdeeglycnLw3nvv4amnnuqyPHTDhvUqtrz00svw73//G8899xw8hU/9aq52k3EjR/pZPqDTh4Au9701NuwOrHWvrq5yS9DK48Ua/rj4lH4VMZhtQGGBi7z1hBjli9CaTZk1qKlvwsEjLDD501Oh9TI6Xv7BI6zOdbgbHqeIiGjk5Wao88QyAp1QUCgtKVTXnq5sH5b1sLyHZT66snG4/Lq6GnUD1SGYURCxWOjrkas6m4SH67sPCL2DwoGIBz3rDkFhgSzfXinCgiAMUu666y5Vs71jx06sWbMGF198MY4++qh+W/4///lPXH75Zbj99tvU81GjRuGEE+aq58nJye3+jd1ux3XXXY9HH30Up556ap+XJwjeSmFhIW6//U947713ERDQtgyev2nPPPNM3Hvvfdi+fRuKiooxZcpkjB8/rtPlXnnlFc6/5+ebLbg9iU+VQjjqnvUaN1K44ETDO11UV+k1hiT2ygpVlqC7pzsDvOLifISFR/er2MPyhtyyelz3ehaqahtx/MRQHDSsrW8DvRl/3mVHcKA/Jg0Jbnc5HVVCHDrCvf4K7QXiERGxyuxSd0mEYXiq28SRggLLfHTBmzGPm05/Coow9HOQzhDeAz8fzCDh/cZ43NHkiy7KOpntkqHwzcYK1SlHEITBxy+//Io//vFqlJSU4Lzzzm03YHn++edVSUJHE71t2qO2tlaJFXPmzHHOO+yww2C1WtV6O+K+++7HiSeeiAMPnNIvyxMEb+Uvf3kAhxxyiBLTfv31V2RkpKvW2XzMeUuWLMW1116HTz75GD/++CM2b96E5OQUnHfe+Z0uNySk2eSeWbY0UvckPpWxwDKI4GCaq/XdGLAjeDPjqKjO8gHWutPgUGvJSGUZoqLj4I4SCAoKVmvH3Rt6Cs0Wv99Sgfs/zlM1wQ98kod/nZOAe06Kw1nPpCkxwZVFGyowc4xN+SWsSa3utsfCtNHu91doLxA3V1lQWlqISM3ZJWxtmp+XqVpQ6ipVoGBWkJ+Npgg9GQUkMMisNfOC5RbBZov4LHgRDzzwVzzxxBO4//77VaosH3cE33PLLTe7dfu8mYOGWxAXZkJeWT2KKxuwapcdM8dKOYQgDEZOOeUU3H//fR2+Pnv2bJUZ0FnadXsUFxcrYTcmJsY5j78BoqOjUVCQ3+7fLFu2DMuXL8eKFctVgNXX5QmCNxMSYsPmzZtx5513qee5uTkoKChUz//73+dVlkJcXCzGjXMIfhwYZgbCW2+9BV/C5GsZC/RWYGChC6ZZ08BNF7xRskyhP7omdARd8/38/ZVAohOOGvOcxPdTCQQFEWoAjy8swKs/NNfRs03kpsxqHJASjFMPClN+C678uLMSzCrij+Vnvy9quY3K56fJq/wVWkORKTcnHXU2Xnv6zhmzcHhNVFXZ+1UIcoXBPm+GDPx1+aAEBQYpHxRo2gfDHFY6Q3gPZ511pqo/nDjxAKXG83FH8D1CMyz1OmFiCF7bd0/96vdyERYEYZAyefLkTl8fP368mnqKIfS3dqXn8/YGAcrKynD99TfgxRdfgNlsbiMs9HR5guDt/OMf/2hjWErR4LvvFqvnNCa9//6/KHNHZvFQdHjwwQcxb948+BLeEVl1M+i0q5pn3zZurKm2w6y740SVXaVz6zZtZLZCiC2sX0ogGplZAOC2d7Lx3ea2qfR//jAXH98wDDcfH6PSeStc3M1L7I3ILK7DhCQzQsz+LV6rb53esI+pwy1oaGxESVEeLNYQWCw8J56pDGIgHhoWgdKyIsTEJGpdF6+Lao3CgrNzQ7Vd2+eU4otuA0emk1WX2JUIqNPPRegeBxxwgJpcnwvdZ97kUKew8N2WClVeZgnyqUpIQRD6ATrNdwZLIRYt+qbTjIeLL76ozfyoqEhERkYqA0aWLBBml9HRfuTIEW3ev2TJEpWV8NBD/3QOupG77rob55xzDq644g89Wp4g+BqJiQktfsuwS8SiRQvxwgsvKqNSm82KSy65GJdffnm7f8+45eCDD24xLzY2BhMmTIAn8ZlfzAxUqOawDII11r5q3FhbV6NVGHF0nKhEVHQ8dMIRaZ6T+ISO3dl7gr+/n9r2kOD2z+3egjq89VMJzpsWgSuPjMJji1q2X6HYcPkRUcrEcenWyi6FhUNHWEFbA4oKFKzYzcBmDVXdGjzRCcBmC0NFeSlqaqpgNlu0ll6UlhZpMz8kvL7t9growi0GjlZHqjgNHMM03g8EwR1MSDRjRGwg9uTXoaq2Ccu2VeKEifq8igRB8E16WwphiA4vvfQyzjjjDJWF8N//voDY2FhMnz5dvc6Aae/evXjwwb+reS+//JLzbysrK1VpxOmnn4YjjpjVreUJgi9z1llnqal1RtHTT3evZfaIEcOd2Q4GzG7wdIaDzwgLNCMkVluItoDIHcaNzIiwWvX9oHOY2jVpTxUrKy1CSGhEv5pD8rz+9TSHL8Sna9qaDD69uBCnTg3DgsMj8OFvpUgtbDYoWbypAhcdHonpo60thIWOukLQXyHQ5I9AU4gavadazvac7NJAk8Ow8Ei3ZjBw3xnAMuhnG1Jd1zhH3ymc1NZUw6ypEwUzCmpLC+EOA0dd17mVBo4BAaisrBBhwQv47bff8Pvv67v13kMOORiTJk3Svk2+BO8n8yaF4pnvipzlECIsCILQX6UQhN4Np512Og488CBERUUhPT1dtZE0siS2bduGDRs2qMdxcXE45phjnH9LQ0lCczujxryr5QmC4H34jLDATAIG/EEafQPqlHFjkLagjiOsOoMhwjT34GC9ZRAUR3g+dJhDdiYuVNU14YGP8/CPsxPwp3mxuPb1LOdrm7NqlOnjrLE2/AP5LbpCtCYpwqTMzFxhsE2vA4olpSUFyMvNVGaKNPJzF8yeKC8vUVkLOstxgi02VKmSHIs28aKpsUlrGQF9UHQaOJoCHT4LvM4Fz7N48WI8+2xzz+fq6hpn7S1HsozHNPflj1ERFtoy10VYWLmjEqX2BoRb9WX/CYLgXTz00D+QlJSkbfkM/pcs+V6JwBTlp0yZ0sKxnuUN5eXl7f4t3/fhhx+odPDuLk8QBO/D39eEBf92en/22zqUcaNOY8haFWjpbAFZXV2p0t11UlFRBltImLYRfUNcOOWgsDavfbOpAjtya1Qv9sNHN+8nOz+sSa1CSlQg4sNNLbpCsEWpKyyXcMxvC0epWUbC7IHCwlyUlhS6rX0d9zskJFwdX50oD4SqSm37xf2gASKFOl1QYFQGjpoI8A9Qn1Wd6xC6z5133om0tDQ1bdq0CYmJibjvvvtUWm1eXi52796F2267DSNHjuywHnGwMzQ6CAekmJ2mtjTFFQRh8MBsgOTkZK3r4O9bto6cOXNmGxGAmQita8JdB3eYwRAeHt7t5QmC4H34jrBQV6OCPk66YBChNyNCr78CPSgoXuis0WepSFVVpfIE0Eln4sId7+aoTIS75sfC5HIFf/F7mRIM2HbSgD+gW+duHDLC2mELStfsgbj4FNTV16KwMAeNNGRwAyz1qaut3lfSogfHKH+T5nWY1WdW2/KD9C7ff9+9RjIWvI/33/9A/UC+9dZbEBkZoeaxBdm99/5Z/Wj+8suvPL2JXsuJk5vvpyyHEARBEARBGHTCAkc/lbDgr696g4EWU6B9NSPCMP7TWQZBr4vgYItWgceVB9oRF+itQI8FZiecfWizur18q10JBq6ZDMq8sdXhoA+DKaDrY8R9jI5OUPX8hQXuERccxoEhyjRQ3zr8VGcSXi+64HWuc7Q/0BSE+jp9wogjsyhAq3gh9I6srMwOR904n68L7XP8ASHw33frW51ahZwSfZ8hQRAEQRAGFz4jLHDkkKOIOkshOOKvNyOCNeF6MyJ0ChfEbi/Xnq1AmKZfXJSvUvbby1x4bGEBquuacP2xMYiwOi7j0qpGFFTUY8YYto5sNm/068JfoasgnF4LTNMrKsx1S1kEjy87Vehcl+5SAmbmsHODLngfYPaMrmPkMIj011rOIfSO4cOH45NPPkFxccuWo7m5uVi4cKF6XWifmFATDhvpEF750Vm4UcohBEEQBMFd1NTUKBNTmpF2h+rqalUCylarnbF+/Xps3LgRnsZ3hIW6Gvj7+WsL/BmgMFDR1crSYdyoz2yOsAxCZykHMzpoyOcOQ0OaGHJd7NPaXlkEjRz/+kkuzCY/XDcn2jl/6dYKhFkCMCY+qN2MBforNHbgr9ARXH9EZKz6l8aOuuE1ooJa3aUEOj0KAkzqOFOs0wHPBT+r/MzqgMtmC1Re8+4qgxG6B1uPWa1WTJ06FTfffAseeughXH/9Dao8gq3Q5s6dK4eyE+ZNbu5KJOUQgiAIguAelixZigkT9sOCBRdi2rTpOPfcc1FV1X72MLvV3Xff/er9V1xxBSZPnoLzzjtPtWZtzfffL8Hs2UfiD3+4Ap7GJ4QFBg8N9fUq2NIV+Buigq4ygnrNxo0ULhgo0i1fF9XV7DjhCPR1wv1g60d2nTAMItsTF77eUIG9BbU489Bwp5Dwyepy5b8wfZ/PAj0X/Fv5K/RQV3CuPzIqTh0DTrpxGCzatYoXFG50Bc08Xo7ODTrFiwCtwoXJFOQU7ATvwWKxYPHib3HVVVcqhf7119/A1q1bceutt+Hzzz9zW5mWrzJnPxuCTI57+NbsGuzOk+tbEARBGNwwwP/111+d7U/Xrl3Xr8u32+3KXPqGG27A77+vU9kF27fvwBNPPNHu+ykgDBmSgu3bt+HHH39U79+8eQuefvrpFu8rLS3FrbfeigsuuADegE+0m6zdl46ssxWk7jKIerbeM+k73AwS/fz9tLX3Iwx02Q1CewlEcT7Cw6Pb7Et7rShvfisbH10/FHfPj8Ol/8vAluwaJSYcPsaKV38oUeaNrhkL07rpr9AeFIWYuVBSXKCMHXV292BLSGZHhIVHaQycA/eZferJQDEFmNR1qQuVsaBJWCDsbGEIXbqOkdA7bDYb7rjjDjUJPSMkOACzx9mcXSG+Wl+O645pzvoSBEEQhMHGnj17cMwxx+LCCy/E0qVLkJycgkWLFrZ532+//dZpGe7EiRNV6+vWLF26VIkLHBQhNJ++5JJL8Oqrr+Kuu+5q8352SGGmggHff8AB+yMrK7vF+/g76KKLLlIxLLfN0/iEsGDUgussI2AA5K8xKGcApNN4UndHC45s19ZWI8ocD52UlxWrgJQGhu3RWlygkeNX6ytUJsPRE2z4fksltmTVYOpwixqVYymE/z4xKjHChPge+Ct0lElAg8zS0kLlvaDTo4DXpDIUNQXqM1is0xc08/OkK6OABGgWLgy/EukM4b2wDCIsLAzXXHONpzfF58ohnMLC7+W4dk6U9kw0QRAEQfB24uLiOvUquOeeP6syhY54+eWXMHTo0Dbzd+zYiSFDhqisS4OxY8dg9+7dKsbqaLBy27ZtKCoqwtq1a/HLL7/iww8/cL721VdfYevWbXjmmWfwzDPPwhvwCWHBcGY3mzUGzpozFhoa9S5fd8cJigo0ntQ5Ss8PakVlGeLjU9r9kdvY1ISXlhfjrEPCW4gLD36eh2P3D8Gd82OxcoddjcBNGWbBlKHBqiyCBPgDBw93+Cuwdr4vhEdEIzcnHbU2fe1DVecGswW1NdXahAUKUTyvuuD1rrdzQ4D6XOnsPMF16DS5FPpGVVW11ha+A5WZY6wIDfZHeXUjMorrsDa1GgcN19emWBAEQRB8geuuu7bT19vLYugOtbU1bTIZKDJQVKirq+swxv3f//6Hn39ehb179+Ckk07CkCEO0aKwsBC33/4nvPfeu15VAurvK6UQHJ0M0BRgEY58BmjybzCWr7OjBQO4QI3HR2VEaP4BT18FmzW03XIOCgRLtlTiyW8Lccq/U1Fib3B6LrA7xD+/ykNsqAkXHh6BL38vR219E6aNsqquECTA3w+H0LixH5oIUFwJCQ1HRUUpdKIMFjV6FJgCA1FXrzHw9w/QWwrBzhAal88bNUWdWvFYEAYY5kB/1XrSwCgtEwRBEITBTFRU5yXILDegF0NHU3V1+wN2UVHRKvPAlYKCQoSEhHQ6cP6vf/0Ly5cvU5kL2dk5yqOB/OUvDyjTapZXcL0ZGelOnwjO8xQ+kbGg2igGBuoN/BsbtLaCVBkRwTo9Fhq0lnIwuGKHBl1QsausLEdcfPv96emLYDiYF1U24KQnUvHJDcOcmQsfry7D5UdE4aqjovDZ2jKUVzdg5lgrVu91uK3SVmF6H/wVWmOzhSInO90hSGk67iz90WngqDo3aCxVUKUQGjMKuP011e276fbX9rPNqGQsCAORU6eG44PfHILCoo3luOPEWFjNPjHWIAiCIAgeobelEIcccjAyMjKwa9cujBo1Ss1bvnw5Dj74YOd7Vq9erbpbxcTEKJHAtWyCj6dPn4bPPvtcPQ8JsWHz5s24806HP0Nubo4SKvj8v/993rkOd+MTwgJHbVXGgm4PhGCdGQv0WNC3/MZGBrj6ls/gKiwsUtvyq6sqEWQ2d5j2T0PGR89LxJItFfjXV/nILK7HyU+k4uMbhjrFhZvezMa71w7FjcfF4OdddsydFArbvh/KiZGBiA8P7FfjQAotdnsFQkMjoIOgfR4INInRUf/MfeB1qWv5vB61Chf7tl8X3H5/f5N4LHgx999/n0e8Aez2SiU69SSLy15ZqUx8w8LC4Q1MTDFjZGwQdufXwl7bpDwXXLvuCIIgCILQP6UQkyZNwvHHH4/LL/8D7rzzDuzYsQNvv/023n//Ped75s6dh8cff0x1eFi4cBE+/fRTnHLKKYiOjsKGDRvw1FNP4c4771Tv/cc//tFi+U8++RTeeustfPfdYo+eMp8QFupqax195XV6IKiRZ72Bv67tZ2Cos6sFW3Fy0lXrTxigW23N/dVbw1IGMnOsDbPG2vDKymK8uKwIJ/+7OXPhvo/zsHRLBU4+MAz/+ioP8yaF4qBhDrWP//aHv4Ir3N7SkkJtwoK65v0DlIGjDuNSlnQwJmtqaoSfX4CmdpD1eoWLRo2lEOr4+0u7SS9Gp+dLexQXF+GLzz5W/zLLauTI0Zh74skqo64zSoqL8dqrL6rtve6GW+EN8DNJIeHxRQXq+SdrykRYEARBEAYlHCx0zR7Qwcsvv4THHntcCQQRERFKWJg9e7bz9YMPnqqyFchpp52qshLeeedd5ObmIiUlGS+88IISJ9ojMTEBBxxwADyNTwgLRmCic2SKKdscndQb+OtZPn/g+vkxSNTzI9vYdl3Hn8eHJoKRUY7Mg84I3FfKcMnMSJxxcDgeXZiPU/+dig+vd2Qu/N/n+Up8mD85DDX1TcpXgdDIkf4K/XmEgoKCVdDfmZtrX2GLUjXqH6izswKv/f4XFoxrksdHh+hlbLsu4YKqC4WoxqZ9Rh2Cx2BN4++/r+/We5luyJEBHXzx+SeIjo7BBRdeipqaarz3zptYsXwJjp5zXId/w+tz4defY/Tosdi9eye8iZOmhOLJbwuUFw3LxtILazEkWl/3JUEQBEHwRkaMGK59tN9ms+Hee//c4evs8uDKscceq6bucNZZZ6nJ0/Q40v193Rps3LgeFyy4xDlvw4bfsXzZ9zjl1DOQkuKoK+EP/hf/+yyOP+FEDB8xsk8b6RhRZeCgT1hoatQUnBiBvxod1rV8zR0tNGdzMDhnYNuTddArIcLqj7+dHo8Fh0eobIX7T4nD3SfF4tc9VUpQqG8AxiQ4UpVHxPa/fwbPp8kUtK9lo0WfAaLGUXlm0fD86mrl6sgq0HN96k6Bdy6/k37FvaGsrAyvv/a/NvMjwiNUwCq0ZfHixXj22eZWStXVNaipae4WZDym4zLLI3QICwX5ecjLzcFJJ5+mhEQ1unHIYVi6ZDGOOvrYDq/H335dhbDwCIwcOcrrhIWYUBNmjLFh+bZK9fzTteW47phoT2+WIAg9oKq2EUu3OD7DuaX1GKHPDksQBKF/hYXExGQs/nYhysvLERrqSF3fu3sXgs3BSN27xyksZGVloLKyAolJSegrjhFJR9q2PvQJCxRG/DVlExCdBoKkUbMxZF1dLQKDehbYsqxhW06Nqgu+YnYUXro8Bd9sLEdwoKP7A2PBQJP+2mvlg1Bbq09Y2Dcqr4sAf70GjhTUKKzpg+eYgb+uc+2n7j/9fT+orqrCeRdcjIiIZt+S/izTGWiwptCoK+R3z8yZs3DRRRfhsssuQ2RkhGq79Oyzzym1//LLL9eyDQWFBaqTius5i4mNUw7QlZWVytm5NYUF+Vi3djUWXHQZUvfu7pYPg72VYSszI3Ry6kFhTmGBxrdXHx3lLD0TBME7Yacsekmxvfb3myvQUFeNc8OAy1/KwN/OdgiGgiAI7qbH0WJsXJxypkxP24v99p+ofnSnpafiqKOOwbp1azBjpqNWJC11LxISEmE2t+zZ2XthgY9888eOCkw0qiJGRoSvGk+qVpY97MjBsoYJScGqBeUNb2bh9KnhmLOfzVkyw8PNL17+yx/JdQ1NzjKK/oSCiM4f/o6MAp0GiHoDfz9n4K9p+cojQs/HyxAa+1tYMOBIu9UqQ0s95f33P1Atlm699RbnvOjoaJVe+Pvvv+PLL7/C6aefpqWlb1ArL4WgfYIoxdHW8HP19ddfYPaRc1o4O3fGunWr8dOPK1vM498eeNBE5OZkONfXn4wOaUJYMFBWDeSU1uOrVbtx0BDv/q7NzOhapBkMyHEYfMeivLoJb65uwtIdTSh1+elh9mt+/ZpXs3DxoX44+0C9JcTezmC5JrpCjoMcC4OKCkd3Pa8SFniTGjJ0uBIOKCzk5eWqbIWx4ybgu8WLlIM6R3HTUlMxdNjwftlIxw97R7CoC2112vvQe2tv0rp8prLrqMF3LYXozLixo1IInrMrjoxSggFTAbfn1GJ8YpCqF6aYwPewmwTRISqo7TAFoVLjB5XHvb2gpd9gYK4x8NesK2hfAe8JuoQFoXdkZWUiObn9trScz9d1YA4OVtkJrt8VbAdllGC0ZvVvvyghID4hAaWlJSoTgX/Lx1arrV3DxylTpqrvUlcoXKalbkN8Qgqs1rZZEf3BSQfm482fStTjlelWnDQ9Ed78Izk5pW/llQMBOQ6D71gUVzbgxpcz1G+d1gyNMgH7qib5jfXKL03IqLSpclGjO9ZgYrBcE10hx0GOhStFhfkoKsqHTnqV3z5s2HD8/NMP6nF6WiqGDB2mWm8lJCQhPS1NPc/JycKMWUf0y0Y2/7DXZx6oFc0ZCw7dRePyVZmI94k6jr9pQnFlPfbk12FcQpA6DhQVOuoA0djUpI5Xf6X6+jPw1BzY9neNf4vla5a8uHy9x0ff4dGdsfD2m6/Bz+U6HDVqDE6YO1/LugYSw4cPx8MPP4KbbrpJlUEY0DV54cKFOPbYR7Sslxl4zELIzs5CUpJD2MjMSEd4eITyWzDKNIKCAlWmHkWH0pISZfBIKBDW1taq5/Qeak94t9psamrdMSctFdrLIQxh4bvNlSirakCYRZ+YLAhCzyixN+CqVzJbiApxoQE4YVIo5k0OxbDwBvznWWBSihm/pjteX7ypAnvya/H4+YkYHiOmrIIg6KdXwgJ/EH37zdeq5ZYjc8HR3oKCQlraXvVjmSnWSUkp/VhKoL8QQpvHgo9nLDjiQj/vzBZpAiJtJjW5QlGBpRCM2+rq2bXBIThQCOjXXfH1EW3NwoXChzMWiK7ze/qZ57So1zeZJJDrDmeccQaeeeZZTJ06VfV3jo+PQ2ZmFj799BOMHz8Bc+fO1XK+QkPDMG68IzNvzpzjVAbCL6t+wuEzZjnf8947b2D/AyZi2vSZOGL2UWoy2Lplk/InuuKqa+FtjEs0Y3yiGVuza1Bb34Sv15fjnMP0tNEVBKFnlNobcOXLmerzaXxt33dKnBIEjUESu93hk/LY+Ul4brkdb+0TCnfl1eL859Lx5IJEHCyujoIgeKOwwB/DHKXZs2cXMjPTcfzcE9X8oUOHKcGBQWJyyhCVxdAfcHk0mMvL05Pi6q46JN3L58iWTkpLC7UtuyA/u1d/Fx4ehcDQCGU+NjYhCAnhbdOL+cXL0JBlEUY2AyskKmsbsT6tCrPGhaCoMBdVVY4v5sF47ZSVFWtbdk2NI11cFznZadqWTaNFXYjHQu+g58Dixd/i6aefxjfffIucnBwkJSXh1ltvwx//eJXWDjbHH38ifli5HIsWfQmTKRDTZ8zEgQc1970OCQ3t0FeInVcoTngrDFIe+tKRIvnpmjIRFgTBC2D2EDMVXEWFv54Wj1MOav9eEhgA3HliLPZPMuOvn+apttsVNY24+tUsPHxuAo4cr6ecShAEgfQ68mfWwupff0FoWBhsNseNKiExCWVlpdi+fauqFe0vKCyw1jw2LglBQX03g+wocEtKHqEla4EpsAxc4xOGQAf2ynJUV1chKjpOy/JLSwqViWBoqJ4RrIKCbNhsYbBYeu9ifMQ4m8pQqG9ohCnAX5U8BPg7xrINb0I+Jw2OBBg1UVTg30VExiEquufnvra2GiXFBYiL75/snHbPbU0VoqL0nNuSkkIViOk6t/n5WQgNjURwsJ6uGVmZe5GQOESLB0h9fT0qKjZqNUYVet8L+o477lCTO6FZ65FHH4MjcUy7r59z7oIO/3bU6DFq8laYTv3owgJldLsxswY7c2swOr7/2/QKgtB9UeGPr2Ric5ZDVCB/OTWuQ1HBlZMODMOo+CBc/3oW8ssblMBw81vZynNh/hTvFTgFQRjEwsKG9etajNaw/CE5eYjKZBg2fET/Gqjxf25JqfZBB919XgPaFu/vjyaNnQNUS8X6fa5D3UxNr290GDLSzIg/gKnm80fw9NHW5swEXjBNDqNHZik4TB2bu0Mwg/DVFcXILa/HzDFWTB1ugTnQH7X1jQgy+Xc7+NTa6lNzq1JHGY1OAw2t9h+aP7OOe44qn3GDx4IpwISrrr5ey7oEoSsirAE4crxNtfAlH68uw+3zYuXACYIH4O+Qa17LUiKfwf2nxuG0qeHdXsZ+ScF49Yohqowio7hO/Qa6+4NclFU14vzpUuokCEL/0+uIaOzY8Rh67U1t2l+ddPJpqKuv73Z7re4b2MGn29bprMNXpSIaU7Y5ol2rsaUiW03W1HZ/+cw4+M/3hVix3Y7UwlpcOisSl82KUtfH1qwajE0MamPSSI8Fhuc8D5zSi+rUyNyFMyOQV1qPuz7IwXVvVOO9a4ZiWA9MjtgqMzDIrLfVp8bUbgpGfoE6I3+9Yp1OsdH5me3nm0JYWDiuvvamNvMHcVewHkNjxK+++hqpqaloaGgpSh5xxBE47LDDPLZtvsxpU8OcwsIna8pw7THRsAZJxo4guJtXVpRgfXrz7yJ6KpxxcPdFBYOUqEC8emWKKqfYmeswfmTJUymzIY6KGtTtKAVB8CJhgdkJ7fVgZ6ooJy0j8j5qAqdbWOCIOT0otC3f36QCXF0wMK+oKO3WeykG0FjshWXFmDcpFM9dnIRwawC2Z9fgo9WluPPEOJRXNSAkOEBdNixzYMYCYdYCTzPfS7OyVbuq8NZP+bjpuGi8cGkKduX1PPW3rq4GISH6lP/GhnqYOqjZ7jfhwt/ks21cVcaFjy2fy2vv3il0D3ZemD37SKSlpSExMaFNxlB0dIwIC73k8NFWDI0ORFphHcqrG/HlunKcdWjPgxlBEHpPWmEt/rusyPmcv1HOPKT3n8PYUBNe+UMKrn0tC7/vEyue+75IZXz+aV6s8zeSIAhCX9EXUfR7YO5oe6h3HXqWz/rvxsYGbUEWR7R1Bv70V+D264KmZhx1ZCs3CladvjfADycfGIbDRloQF2ZSNYi3vJWlhKHHzktEsb0Ba/dW4aj9QpCWX4ukyED1pVlYUY8Qsz+ySuodCv7KElw8M0L5Lsx9bC+uOioK50+LUMKFUSrRFTyfbB+nP2NB38e0obFBnV9fzLhwR5tYR76F/OjyJj788CNlfLl16xbExMR4enMGFMzsOuewcDz8VYF6/tbPJTjzkDAZ1RQEN8Hvtf/7PF91ZyH7JZlx8czm7kG9he1jn780Gbe8lY0fd9rVvHdWlSKrpA7/PDsRNrNkJgmC0Hd84k7i5+e/z2NBXyDhrzE4N8wndQXnrsKFDhgY1jfUa1s+j4/JFKTKCroDv3CjQkzq7458aI9TVGBq393v5+C4iaFYsa0SQ6KDsD3HsczNmTXKP+GbTeUqmyHc4o8XlxXj4BEWPLUgCY9+XYDTnkzFr3scX7j0aeiK+vo6JYToLFWg4BKgwZjQNSNCZ+DP61KHsSIxlq2vTSzVTN0ZEUJPycvLxYknzhNRQWN3CEuQn7NV3a979HZ1EQShmYUbKpyBPys57z0lzlnS2VdY1sTfOydMbO4MsXybHZe8kI6c0jo5DYIgDA5hIdAUiMaGRjWirQsGb1rLCXxcuODXms7jY7HaYLeXd+u9QSY/Z1bBE+cnKlGhvLoBpzyRihuPi0F1XaMyHuP7vt/iqBfenlutxIKRsUH4fnMFTpwSio9+K1XiwpShwXj9qhRkFNXhj69k4ZrXMpFdUteluMDt7Usni+60OqQ5pCkwUGPg3+izgb9u/wnjnsOMGsF7GDVqNLKymKUk6CA0OAAnubjGv/1ziRxoQXADzMB8+CtHy1dy7mHh2D+5f0shA01+eOisBFx+RHMWxLacWiz4Tzq2ZOnz0hIEwfNozvP1IWEhyOxIlW9l0tXvgX+jvuX7+7hwERhoRm1d9zIKeoPVGoKqKnuPxaPZ40NQWdOoRIWZY23YP9mMl1cUY/LQYOWqvGyrQ/mvrQc2Zdbg4OEWPPx1vtqnxy9IxFOLC5W4cEByMN784xAlWKzcbsfJ/07FmtQqVRrRUVBur6yA1RYKXdSxzCIwUGXs6IDXi97AX182hLF8rWUcjfXqGPW7Z4zQJ5itsHbtOnz00ceoqdF3TxrMnDetuZ57yZZKJbQKgqAX/h4pqHD8josNDcB1x0RrK3niIMwDp8XBaICVV96AS17MwNKtjsEYQRAGDgXl9XhpRRHuej9H+7p8Qlhg5wmmnev1EdBrUKhduFDCQr1Wcae7pQq9gT4CwcEWVFaWdftvaMyYWVSLU/+dqno03z4vRrVRen5JEWaNsynzsao6h1BBL4WV2ysRYg6AvaYJr6woxug4M+bsZ3OKC+MTzXjn6iEq02HKUIsSITryW6iqqlCZBDpHsynkUNDRRWOj7sBfr39Do2bjSW4/P1PsWiJ4D4888gi2bduGSy+9FAkJiYiLi28xPfnkU57eRJ9nVJxZ+dgQJm6990v3zHUFQegdGzKqW3zO7jgxVpVt6oStK5+9OBmhwY5QoKq2CTe9ma1KSQVB8G3qGpqwZEsFrn8jC8c+vAdPLCpEdqm+ONGnzBv5w57CQn2DvlET5SNQp3P5eoULU0CgOka6CAoyo7Kye6UKvSUkNAJFhTkICaFZmH+XokJ+eT0ufiEDhZUNuGZOlOrDfuf7OepLckx8EL7b1zatWViw45o50Tj5wFC8uLwIZxwShgdOi8eybbuVuED+MDsSH143VHWa4A/q9nQFZitUlJciLIwtLqE3Y0GjMSSvlwCTnjILZ+Cv2XhSbylEgypF4bUveA9z587DkCFDO3x96tSD3Lo9A5XzpkVg1W6Hv8KHv5Uqg9vgQJ8YixAEn4K/Z/72aZ6zffLMsVYcu3+zD4JOpo2y4rUrHR0jaG7N3z33fpSLD68fiugQnwgRBEFoBQUF3lOMDCh34hN3DSMVmYGWTo+FmgZ9JlUcua2v07f9DEBrqvVtP4Or4qJ8re0DuY6goGCUlRYjPCK60y/hokpH2h7T95IiTLhsVpTySFi0oUJ9IXMLv/y9WQjx9/PDxoxqVNU2KuOiV38owSNfF+DBM+Jxy3Ex+NfXBXjmu0L1hc6Wk/z7jgyT2BqTwoc52DGipwMe55raKthCmmud+xt2tNA5Gu8O40mTxowRJWbW10kphJdB4UDEA/3MHm9T91YGGyX2RixcX45Tp0rrSUHob95eVYKt2Y6MULPJD3fPj3OraTAzlN64agjOfiZNBSL8ffWXT/Lw5AWJYl4sCD5EU1MT3vixBI8sLHAKlQbMwj51/yCgyTGQqgufGH4wRgxrNabiq1IIjS0VdWcsBAUGoU6jBwK332QyobZGr7lPeEQM7PaKDtdDUYHmjJe+mIHsEkdKz61zY8Dv4FdWFqvn00Zb1ft+2uesTEz+fqBdQk5pPcYkmJXbMoWHLVk1OPuwcFUG8dzFSRiXYFblDx31da6rq0VFeQkio2K1fuGq7JOmJq2lFixt0ekf4GhlqbdUQWdGhOEpIqUQnofeK8weMQxH+bijSXsb0kEChVXeGw3e+rlUjq0g9DPMvHz2uyLnc2YGsSW2u4kJNeGvp8c7ny/bWokPf+t+aaogCJ6FhvMPfZmPh79uFhViQgJUJvYXNw/DS39IUYOnuvEpYYFBnc6WilrNFf31eiBw5FYZXGrsnBFssaG6ujlY13UeIiKiUVzM7Ii2+8KAn3WHZxwcpsobpg4PxrH7h6oL+f5T4/H+tUNx1iHhKLbTX6H5WrlwRiR+vX80RsQGKUGAZo28lF7/sQRBJn+8d+1QHDzCqkyNOoLXXklxPkLDImHSWEJAeJyDg236Wik2NSkhSqeHg37zRnos6Fu+4SmisxxF6B4PPPBXREfH4PHHn3A+7mjie4T+4fSp4WoElXBE9fd0cY0XhP7k8YUFyoCajIgNxMUzmrs1uBsaYLsat9LoOq1QX6atIAj9g722ETe9lY23f272aTlwaDA+vH4Ybjg2BkOj3WdC7hOlEByVpHs9AwkGm35+AZq6KjhGu3QEcwz8OQqta/lcJtfBYNFs1pOiHxxsRVFhLsLCo7SO1ltUh4hKlJYUqZKI9tZ18cxIHDrSCqvZX6l0AfsyDMYkBKGmvhFxYSb8/Yx4JT4Y5QshIc1fmJceEQlLoB8OH2NTBifMUujIqNGgvLyEBxo2m77yBIPqqkolYOiC1yI/UzoDf+URERGk1XxSlzkkP6csFSHiseB5zjrrTEycOBETJx6Auro69bgj+B6hf6BvzbzJoap9L3nrpxJlbCsIQt9ZvbcKX7iUbN41P061g/QkNx0Xg5932bEnv06ZOd71fi5evSKlwyxOQRA8n/V0/etZ2JzVnLXOku+/nR4Pswd8kXxCWDA6Q1BUMFrk9TesmefEEX8dwZa/v78SSOizoGsElCnbHGXVJSw40vKbHHXnGlP0SURkDArys1UwH9ZOgM00XZYvMMHANcuAXgrGCNuJk0PR0MhWk43Kt8EQFiggHDXBBiO5oytBgVRUlMFeWY7YuCTtNYcUuJidYzb3b/9qV7h8nSPxhkinq1SBy9ZZCsH7gHEv0Cm+CN3jgAMOUJPrc8E9nD8twiksLN5UgaziOiRFuj9VWxAGEizX/L/P85zPjzsgRBkpehpLkD/+cVYCFvwnHfWNjm4VLy4rwh+P1tP6UhCE3sPv48v+l6G8kAwuPyIS1x8T3WkGNgZ7KQRhENTQ2Ki1nECVQ2j0WWDaea1WA8cgrctnQG2xhmrvDkEoHkXHJKDKXoGysuJ2S2CooHf2waH4wNaRLHVoXVZBAaK7CnxlRRkqyosRE5uotabfwF5ZAYslpMvOGH2BAhR9OfQtv1aN9OsSYRj0U6zTt3xHdpTOUhGhdzz33HO49trr8MEHH6KgoEAOo2bGJZqV6RNhoPHc93qNnwRhMMDWkjtyHb/XggP9cNsJMfAW9ksKVh20DJ5fWqQEBkEQvCtT4YqXM52iAjO07zslDjceF+MxUcGnhAWOxtMDQacPAuvmdbZsZOBv1G3rwBxkUZ0hdJqX2WyhqLKXt+t/0N8wiGcwr8oiSgvdbhzG9VHUYNZETGySdl8FY52V9jJ1nHVSXVOFIE2ZLYbxoc6sFtUqM0Df+aCA2dDQqDKlBO9izJixyMjIwLXXXovRo8dg5sxZ+POf78V3332Pqip9nXEGM2zna/D5unLsytP3PSYIA53CinrVhcrgyiOjkBDhXVlAl86KxIHDHFmTzPy8+4Mc1NTp/90nCELXlNgbcOXLmUgvqnNmXj95QRLOPMTznZt8R1gIojlhg9bOCgyEdHae4Aiu4TSvA1NgoBrF1dkdgsE1R3Ht9kq4A4oLsbGJKpDMz8vUKsy4wvWxFKOm2q7KH9whKhAKQ/5+/lrLFLhvbNWo0zvA0XHC7LsdLShiNjaIcaMXcswxc/Dpp58gNXUvPvzwQxx55JFYunQJzjjjDAwbNhxvvPGGpzdxwEFj28PHONK02ef+6cWStSAIveWJRQUor3YE6cOiA3HRjAivO5jM+HzwjATYzI4wIbWgztl5SxAEz1FR3YCrX83ErrxaZ6bCI+cmYNY4m1ecFp8RFhikMCBqaKzXug6dgSsDcnos6Bx5p8FidZXezg0hoeGorHBf6zFVFhGdAFtIGAoKsjssjegPuFwaPVLEsFisKlPBHeUPBly3LSRcq4+Do+OEVes6KG7pFC4o0OlsA0nhhVkL0mrSewkODsbMmTNw7LHH4JhjjsWwYcNQU1MDu13v/W+wcsOxzanR322uxEZJjRaEHvN7WhU+XdtcTnrn/FhVrumNsO3ljS6f+xeWFSNdukQIgseoqm3Eda9nYVOmI1blz3ga1R81IcRrzop33s3aIWifsMA+5TrXwYBIV9DqNHDUWG7hjpaQhjmk7vW44revG0NsXApqa6qRl5sBuyrJ6J9zxeWw5CI/P0v5OlBQCAmN0G7U6EpNTbW6NqxWvTcICk8UFvSO9uszbnRHRgT3oa6+TjpCeCGpqal4+umnccYZZ2L48BH4wx+uUKURf/rT7diyZTOuvPJKT2/igIR11zSYM3jyW/G3EISewA5WD36e73w+Zz8bZozxjlHGjjjr0HBMSHJ819bWN+GhL/PdXpYqCAKUEf3Nb2djTWqz38m9J8fhxMn6O9UNSGHBYrGpmxmDSV04uk34+Xy5BUdadYoXDLbDwqJQVlrk9i8Yk8mkTB3Dw6OViWROTprajvq63u0vharysmLk5qSjvKwEoSHhSlTQ3fWiXT+H0kLVYlKnmEHTw9raapg1CgvKGDIoSKtxI69xneeourpSXVMWq3f/6BuMvPTSy7jnnj/DYrHg66+/wvbt2/Diiy/gggsuQFJSkqc3b0Bz7ZxoZwvfn3dVYdUuyQ4RhO7CEqKt2Y7ff+xeddvcWK8/eCyJYPBifJ2v2G5XGUuCILg3Rnjgkzz8uKP5O/fWE2K8wlPBZ4UFjuIyUKmuqlKBhQ64fGWwqNGjQHe5BfeBQaP2rIVgixJidAo9ne1jsMWK2NgkxEQnqA8cMw2ys1JRWJCjhAZmHzAwdJjwOYQiii08LhQS+L6c7DTk5WWo16Oi4pSXgmXfdeZuuF28rnVnK9AzIigoWGXP6DVu1OivUFerPC90nSdloFlZoR7rNtEUes6cOUdj/vwTsXz5Mpx88slYsGABXnjhRezatVsOp2ZGxAbh5AObR0f+/W2BjF4KQjf4fnMF/re82aPgqqOikOwjbVsPSAnGWS4BzD+/zIe9RowcBcFdfLKmTBknG/zxqChcPDPSK0+AzwgLDIQYdDE41BmYs6Zaa+cGs0UFkTpH+i1u8FlgUBceHqWCdHd0iOhMqAmPiEZC4lAlDPAaadrXIpJ+DHm5mcjLTVfvpRkjOzwwgKeAEBOTiMTEYYiIjEGQOdgjgkJztkIRwsKjtG9DFcsgLFbt4gWFJ11Q+NNtbllX5yiD0OkTIfSOI444Am+++Sb27NmD999/HxMnTsQHH3yAQw89FJMmTcYXX3whh1YjVx8Vpdr4ko0ZNViyRUYvBaEz9hbU4p4Pc53PZ4yxqq4LvuaxEmljVi+QW1aP/ywp8vQmCcKgYEduDf7xRXMJ1akHheHqo5s7NXkbPiMsEJr31dbVau2swIBFZ6mCI327SbPPglUFXzrXQRiMc/S7rNTzTsEMyDmKTcGAggfbVFJsSEwahsSk4eo9SnyITVJCBAUIdtHwlJjQ2rDRPyBAq+8BYZcDiloWS4heb4K6OqcPh7ZSC50ZEbU1KitCshW8m4CAAIwfP14JC5ySkhKV/8KOHTs9vWkDGrbFO+fQ5tHLpxYXqtpxQRDawpH9m9/KRuW+Ef6kCBMeOitBlRj4EmGWAJV6bfDGj8Uq4BEEQe/947Z3slFd5/iOHRUXhLvmx3pF7DIghIUQW6hqx0fzPq0ZCxoNHFUaf7BNa0aBn5+/CrDtlfrLFMIjYmC3VyjjQaHnMICtKC9BZKT+GwXPU3CwRQVkuqjel62gc18o/OnMWKBwWVNbo4RMwfvYtm0bHnzwQRx77HEYMWIkLrroYqxfvx7nn38+Fi1aiOuvv87TmzjguXx2pLMNHVteffm7+0viBMHb4e/Iv3yS62wLR1+Fx89PRLhV33ewTk6aEoqDhgWrx/WNwIOf5UkplCBovH/8/fM87Ml3DBIHB/qptpKWIO8O3b1761rBH/pMY6+0O+qfdcCRY90GjswooDmcTthBgeaGus0VGaRGRESjpJhOwVJz1xN4boqL85VhI7MtdOLwDSiH1aY3WK6uqlSlOLpwi3FjlV1lLYRoPlZC73j77Xfw0UcfY/LkyXj99dewZ89ufPPNItx1112YNm2aMngV9BJlM+GiGRHO588sLkRNndz/BcGVN34qwcINzb9X/3xyHCYkOQJzX4QDBtwHozsm3ek/c2mdKQhC//HpmjJ84eKrcM9JcRgV5/3luT4lLDQbODqM7nzVwJFp4kwX1919gsEqAz3dMDuC6yv1gpIIX6KivNTZRlM37ASBpiaYzfp+1FBYqqmp0ttxwi3GjY4buS1EjBu9kbvuuhOrV/+GRx55GCeeeCLCwkQA8gQXzYh01lxnl9bjvV9KPbIdguCNrN5bhccWFrRo23jKQb5/rxodb8aFM5r9IR5bVIBSu77fsoIwGNmZW4P/c/FVOOXAUJ+5f/iUsNBs4Fiv3cCxVnvnBoeJo05CQsJQUVEGd8CSCIoYTLcXuobnnt4K7iiBILwOmPGjc1011dWqG4TOUgt+7t1h3Mh1iHGjd2I2e79iPxhgKcRVRzYbSP13aRHKqyXAEISyqgbc+V4OGvaNfx2QYsYd85r9CXwdfu4Twh2ZYcWVDXjy22YBRRCEvlFR3YDb381p6atwUhx8BZ8SFggN1ZhNoNPA0R3tGh2dG/RmEwRbbCptXKdIYsBgMjo6HqUlBW5Zny/D4LW4KF+1uNRdAmGsr7amClar3hF4lvfo7jjBz6VOk0vDuJF+LoIgdA5b0CVHOgKM0qpGvLxCstYE4eGv8lXnBBJh9cdj5yYiyKgfGABYzf6448RY5/MPfivD+nTx2RKEvlJV24jrXs9y+rLQV+HhcxJg9XJfBVd8Z0v3wVHXat0GjkFmNDbUa+2qQPGCaeM6fQk4Os2sBbaEdAcc5Y2IiEFRYY4SNIS2sISnsCAHYWGRWlsyusIWm1ZbqMr40QVLCKo0B/08dizp0NlxgoIlhTExbhSErgk0+eG6Y6Kdz9/4sQR5+wIqQRiMLN1agU9dfAfoScBOKgONoyfYcMQ4x/c9rbz+/lmedIcRhD5QW+/oIEPvEoN7T45T5Ue+hM8JCxxJZIChM+Xe0blBb9YCR/iZNk6RRCcMkDgC666uDfRboEFgYUGuNh8MX4XBd1FhrgqM3RW48twzMyY0NELzempUNxKtporVdtXiVKdAUl1VpYQFyVgQhO4xd2Ioxic6fvgwdfM/3xfKoRMGJSX2BjzwSZ7z+QkTQ3DcAQMz+42/k+88MU51uiBbs2vw7irxWRGE3lDX0KTKH37c2Rx33j43Bicd6Bu+Cj4tLFhtDgPHKo0GjkYZgc6WkMTqhpaQDPbYdaCstMhtbYEYxAYFBamReREXWooKfv7+CI9oHuHTDbNVQkIi4O+vt70Vr2Nez/rLIGzalu8wbnR4kkjGgnfxn//8B2+88YZ6/OOPP2L16tWe3iRhH/7+frjpuOZ72sdryrAn35HGKQiDif/7PA+FFQ6fkZiQANztQ3XRvSElKhBXuPisPL24EPnlkrEkCD2hobEJf/4wB0u2NJfHXzsnqoVJqi/hc8ICAySL1eYwcNTcuYFp1zoDY47uM5NAd9kAA77GxgbUaM6OMKDwQzNHtn0TcaFZVCD0VXCHWSPhyDuvL91BsiODqBI2jR4OPIYUFnS2snQaNwYGiXGjl1FQUIDs7Bz1eNGib7Bs2XJPb5LgwvTRVhw20lGiRMM6MXMTBhvfbCxv0VryvlPjEGHVK+h7A5fMjMDwGEepR0VNIx79WowcBaG7NDY24a+f5uHr9c33jktnReJKF8HO1/DJht/sL19eXqp8FnTVWzPdmmnXDGZ0jcRyHRaLDfbKCoSG6UtVZyAbFh6F0rIiVdfvjsCW64iIjEVJSQEKCrIRHZ2gtVuAt8Kg28hUcKeoQJilQi8HnaUDpKqqUrWxDDDpu51Q5DMFmLSvQxk3uqlMReg+w4cPxwsvvIipU6ciKysLZWVl+Omnn9p97/+3dx7wTdfpH/90pW26kqZNutl7DwFFlgKC4kBBEScqbk9P77zzznOdp/7Pc887zz04EHEBMlwoKrIEZCNQupM2q0mbtknT/+t50nQwFEqzn/frcm3SSJJffvnl93y+n+fzFBQUIDc31yeblwTtH75fi8LCA4iLjUX/AYMweMiwY96/rLQEW37aBKPJiOTkFAwZMgzde/REuEHHtTvOysClLxXz9S921mBrsQND8v2TIyMIgcRod+Efn7QfDTexr28dfMEChVKSM+P610v5+vJtNlwwIhVjevg2yFkQwoHnPjfiw02t0/suGZ3GDkB/1gqIdMcCoVJpuJjx2pZ9BdmufT0dgqZc0OvwdZsCZUZEUwuJH8dBsrigymDxp9JQ6tMRocEIrYDT66Zi2N+iAu23rkYXhzb6Gtp/ff041Jbky2BIggQ+ym6h44sQXFx44YVITk7CzJkzsWjRIrz22muYNm36US8LFy7y2fNYtXI5DuzfhwkTz8Cw4SPxzZovsW3rT0e9b3l5Gdas+RLduvfE1KnTUVDQBR9/tBj79+9DODIgN4F7yr08vdLot/Y7QQgUtI///WMDzLWeFghdaizubjMxIRIgEWH64NbP/j8+MfDIPEEQjs3WIgdeazNJ6bxhKbjnHP+MoPclIelYUKk9ak51tRWZWhdiYnzzMsh2XV3tySbw1RtNkxRoRZkmRPiycPK4FjQwm/Q8EtDXPfdtHzctLZ3t5eRcIBcDuTTCHWo7MZkM7ERJSkr164GC9lerxYi0VLXPH5faLWiCiq+Lfpo4QeKMr6DpLNXVZnaYqNMj66QwFFAqlVi2bBlsNhseeOBBpKam4rrrrj3qfelvvsBut2HXzu2YM/dK5Obm8W1WqxUb1q87qmtBq9Xh0rlXtlzXZWWjqOgQftm3Fz169EI4QhMiPt9hh8sNbCp04K3vLLjq9NDsExWE4+GDjdX4sk1v9IMztUhJiDx35h+mZ+LbPbXcDnHI6MTdiyrw3OU5iIkO7SJJEHw1AeL+Dw08UYU4tYcSD1yg48yiUCckHQskJKSmqbl/3KeTG2Jj2X5NFmlfQYVfUnIa7Dbfp+mSXZ0KQCo6/Q21k2gysmG1VPk1SNLf0Oui8Y4kKlAhnJyc5nf1kQrk2Ng4DiD1NXa7FUlJvn2N1J5Ahb8vJ07U19GxpI73U/qcCMFJSkoKLr10DmbOvIDbHY52ofv4gorychaBc3Ja2yzy8gtgsZjhcByZX3N46xcdG8wmI9LS0o75GLU1Naiqqmx3MZv9My64MyjQKHDpmNa2vqdWVuG7fa1FlyCEEz+X1OHRpa0tELNPScVpvcJ/4eRoZKbE4q/ntYrya/fW4skVkrcgCEfjlTVmHGgOOU5UROH+C7SIjQl9USFkHQtEujoThw7tY/syrQj7Ch476aj1WZYDoVQmcbFNBZQviyeCXAsGfYnHWp7o3x44hSIemdpcmM2V3CKgVmeyYyOcWh/MJjrJaEKmNoeLe39DIhhNaNDqcn0uaFDoKO1HaVm+bR3whjb68vXUOuzcXkXbTQhuRo4cyT9//vlnfPHFlygrK0VeXj7OOmsq+vTp47PHrauvQ0JCQrv9MDHR871AY4O9vx+Lb7/5Co3uRgwdNuKY99myZRNnOLSF/t1hwwdBX1HC03aCnVkDmrBpP7BTD7ibgD8sKMPTF0YjT9V5n9/SkgOd9m+FMrIdArctLI4m3L7YDWez47+7Bpg72I7SksAIaSSME+Vlh/g4FQiGaoBLhkVh4U+ehaO3v7dAHWvF9P6BWcOUz4dsh2DcJwpNTXh1TetggKtOAZpqilHqh0MHOS99TcgKC2RXPnhwD6/OZmRm8VhFX0CrvhS+R+GHvips6Llz1oK9Gip1BnwJrbhRQW82G6CNz/NbS0RbtwkFOdbW2rg1gtwaNJ4ylHuKeEyhvRo2m5lfC72mQLweWtUnYYPGWfqqPagtNTU2ntDi61DOOkcNUlLUPn3/qq1mFvbSpQ0iJHjkkUfw+OP/4haJzMxM6PV6PPjgg3jggftx2223+eQxacpNg9PZ7raGBs+KQ1zcr4uI361dg927dmL2JXORkHBsAWLo0BHo3adfu9vImVd0aA90WXk+H+naWbwwz4U5LxZDX+1CTQPwj89j8O6N+Z1iEacTw9y87oh0ZDsEblu4Gptw/5ulqKrxOJVSEqLx/FUFPH4xUNBkJiI7pwsvVgWKe3KaUFVfzgGuxItrmzC4ZxZGdffvQpZ8PmQ7BOM+0ehuwt1LS+Bye4TAIfkJuOGsPL+1DJmMlTDxAqjvCMlWCILsynSSRSot2Zh9BTkIqEj0ZTsEQSMBKTjO16MnCZoMEaiWiJb2j6RUZGrzeLIHOShotTgU2yNoNZ3cFw6HHRmZOUgOoEjCLRBxcX7JsKAsAhJTqNXDl1CxT0n8tM/68jHofaTPOgl8QnDz009b8MQTT+L5559DcXERtmz5iX8+9tijnL+wf/9+nzyuJj0DLqcTVqul5TZjVRXi4+ORlHTsgv/rLz/Hrp07MOfSK6BW//oIKWVSEjIyMttd1OrQyyjQJMfimcuyER/rORYWVjnxp0UVfFIlCKHO858bsf6AR1Sgr/vHZmcFVFQIJqhH/B+zstA32+NGpbyVOxeU41CVR4QVhEjmvXUWbqEi4mKi8MBMbdjlkISssOB1LVBBSjZmX0FFIiXe0+qsL6EVZipqbNWtJ62+bongjAqHb6de/NYKoCYjC2lpGi6KKyvLOMQyFKDQwqrKch6nSWICiQq+bmP51edTTy0QNNEgwy/CBmUrULHv69dMbR0kIPryNZEjgnrk6XgSys6ZSOGrr77CnDmX4LLLLmsZpUrHkvnz52PatGlYs+YbnzxuplaLzEwttyqQCFpfX49NG39E334DWvabxe8vwNYtm/l3us/qlctxsHA/5sy9Aqm/kq0QjvTPTcCDM3Xteq6fXR0YMVsQOgsKJ22b5H7jpHSM6xOZuQrHQqmIxrOXZyMj2eNQqna4cds7Zah2yKQIIXIpMTnxXJvvwPkT1OihDZ928LAQFtLVGbzaSDZmX652K5UpXIC73b49KFKBSivf1Kvva7glIp1aIir98njHgk7IKetBq81FclIqW/mpYKftHWwOBk8x4YCxqoIv5AzQ6fJ9Xvj+Fo2NjRwWqfJTCwQ9HgkLqalqn29vapnxZYYKQa+FHAt0PBGCHxJEj7Xyn56eznkHvuKccy9AeXkpXnjuKbz84jNISk7G+AmTWv5utVjgaBZrCwsPYNu2LWior8eihe/itVdf5guNrIwUzh6SgmvHtx4nXv/WjBU/+77HUxB8wcHKBtz7QUXL9XG9lbhh4q+7kCKVrLQ4PHt5TjvX0mPLfGvBFoRgH0tb5/TUNT20Clw7PjyPHSGbseBtH6AVUyoKSGCgcEBfQD3k1BdLrQq+tH7T49C/T0GO6ZrWlR5fQYGUNA6RimQKVfSu/gUCrzMkUZnM25nGfLqtTeziIGHH1338vwYJSvSc2LXS1MT7nTpdG9Dt1fZgRRkgJG7QtvMHlCVBj+frcEpyI8XGKbi9w1dQm4XdXs3vJeWoCMHPwIED8ec/34Pf/e42aLWtI0gLCwt5JOXs2bN89tgaTQbmXXMDjzoml8ThvcwXzZ7DrRFEXm4+rr7m+iP+jVAIYOzsEZT79PX4Zo9HcPnnskqM75PEq5qCECrU1Lvx+/fKUdvgKQzy1HF4dHZWWIyH8xUD8xLw94t0uHuhR4xZusWGi0elYWiB71obBSEYeWOtBT/s93wH0joktUDENYtu4UZIf7NTMepph3CwndmX0Kop9ZT7ehWdgv9oRY6s9v6AXpciPgFmkyEoHAKe/IUUFjooZNLlbIC+oojbJOw2i9/cFZ6C08ruiYryIm41oJYNrS6PxZ9gERWoFYOeS4qP3QNeaPuTyOLLMMW2bRC+divU1dVwTosn8DLyZo+HItOnT4dOp8OIESNx44034v77H8B1183HqaeexqLDuHHjfP4cUlPTjhqQplKpkdg8bSdOoWAh4vBLSopv9+lgg/pHqQddm+pZx6iyN+Ld7/3T8icInfVde98Sfct4OFqFf2puNlIT5Tvjt5g2KAWTB7Quejy2tFKyVoSIYuXPNh697GXuGBWG5IevuBb46ugkIfsyORaoCPQlVHxT2e3rEEdvkUiuBX8V8tSXT2F8turgmZdOz4sCOskZkJXdhYt5p9OJSkMZ9BXFsJir2EFATpWTFUTov6d/hwpZKtQpTLLSUMLiDokc9PjkICHXSjD14NfUVLPgQdvIX8+LsjCoZcXXRTi9H05nfUuR5isol4KEErW0QYQM5BRYvnwZbrvtVvzyy34sXrwYRUVFuOeee/DBB4uD6jMqeEhOiMFNZ6S3a4mw1Eq/tRAavPmdBat3tGZ53Xe+Fn2awwmF3+auaRktLRE7y+rx4aZq2WxCRLCp0IG/LNa3XB/WJQF3TPXtiPZAE9KtEATZl6kYJzszrTLTSacvoJPV5ORUfhxqIfAlVMza7RbuFf610WSd+dqocKbpBmQ9D7aRZvT+Up4BXVgEaKhHfQNNA6llFwNN0oiNVSAmNpYL3pjoWETHxPB/17bI8OZk0Cx5+m/cjZ6fVMTS2E1qpYmLi+fVa4Wi/bz6YKO+zsFBn5naHL+5J3i71zmgysr3+WORO4jcCr4aI0uQmEZCCe1TIiyEFjSn/e677+aLEBqcPywVb6w141CVE/Z6N177xow7p0muiRDcrD9Qi6fbrDbOGZ2Gc4dFluvoZMlVx2HeODVe/sqzYEYBdlMHJovjQwj7TJbb3y2Ds9Gz+Nk1Iw7PXpaD+LiQX9P/VUL+1VEhmZZGgV11bGv2JdTrT4WVr+34VNCmpqaza8Ff7Qm0HUlcsFqquBUjWKFtQ+6RlBQVP19dVj47CkgMIOEhNiaOxQMKWaQVfbvNyuIDYa+hkD4HCwoUcpiQmMTCVFZ2AV/o36PMCRKOgllUICGEwhrT07U+zzloi7XaxNvd10KGN9OCsix8CQlTtK+TYOirfBZBEDzExkThtsmtKzUL1llQYQ1ccLAg/Ba0f/5xYQW8U1KHFiTgj9MzZcN1gGvGq5Gj8iz8mWsb8eKX/nHlCkIgMNpduPmtUp6IQqQnxeDFK3ORpgz/9qmQFxYITYbOE/hn9a2VnwoqHj1p972Ni4pkoAmOWt+N0jwcKq7IVm8yVvi85aOz3xdqmyCnRXJKGosMVHRnZGQjI5MuOXw/up6u0fLfqUCm+5OIQG6FUIFEBcp9oNdA4x79BbUbuZxOnxf7BLW4JCQofT7hwma3wm63QeOHoFRBEIApA5IxINcj4tW7mvDv5hVMQQg2Glxu3LWgAuYaT8uOJjkG/5qTHbaBa74mIS4ad01vdSgt/NHCoa6CEG7UNrhx29tlKDW7+HpCXBReuDIHeen+WwgMJGEhLFARScVhdbXF56GHtLpJI/DIRu3z7AN1BqxWE9v1/QUVdCp1JoxVer8FSArHBzlljFXl7LLwZ7sK7euUaUFZHL52cpBDh4S7ZB8H3FHblMVsRFOTG5nabJ8+liAIHuj4cfvU1uLio83VbBcVhGDjn8ur8HOJZ4ElJhp4/JLWAFKhY0zun4xR3T0LIo1uT5BjMISGC0Jn4XQ14e6F5dhe6qmfaGjMPy/JwoDchIjZyGEhLJCNPzMzi1c6a3wc4kjWc+q/p6A/X0OPQw4JKur8efAltwSJGjSGMpScC+GMx6lQxtMYKIPDn1itRnZ2JPg4SNE7YpLyMWjf9yW1NdWora1Benomj6wVBME/jOmhxJgercXF858bZdMLQcXb35mxaH3rueSdZ2VgZDfff/9FgrD4p3MyWaghNhx0tAvFFIRQhrIU7l5U3jJamfjzjExM7BtcuXW+JiyEBUKry+UVdpOpyuduApraYLNZfP44RGqqmleqHQ7/HnzbigvBnLkQCXjbH1JS0/3SinB4CwRlEVDrha8h8YzCFGmf9/Xj0PQPEjHouCEIgn/53ZRW1wIVFtubV4YFIdC894MFj3/WGtY4bVAyLj9NFdDnFE700sXjktGt2/OJz6pQU+/7c2lB8CWN7ib8dXEFvtjZmvV33QQ15rTZ1yOFsBEWaBWX2hSoWPB1EU5ZBNTT7+sRl16Fl3IPrBb/tkR4xQVv5gJtV8H/UFioN1PB306FlhYIdaZfJk+QCyg2JsbnU1dom1K2Aj0OBb8KguBfBuYlcN6Cl2dXtxZyghAoFq234LFllS3Xh3dJwAMzdUEd5hyK3HxGOtTNIXblVhf+8akh0E9JEDqM292E+5boseLn1trzyrGqdmHFkURYNYzR6uPBA7t51ZNG1fkS6nOn8Yz0ONSK4Wshg4pKKvJocoE/v+Qoc0GTkQWjUQ+XswHJKSr5kvUTlDVA+7I6PZPfB3/DLRAJiX55bO/oRwpi9fX+TW4jGhubk9tV9uUQxeFwYPnyz3Do0KEjBNfx48dj9OjRAXtuwvFx62QNvtxl53aIdfsd+G5fDcb2otBiQfA/SzZa8fAnraICTYB44cpcKBVhs/4WNKQmxnBLxJ/fr+DrS7fYcFpPJWYMlTGeQuiJCg99bMCnW1rb4y8dk4a7pvk+kyxYCSthISNDh8KDe3k6hEaT5dMRcpS1kJiYDJvNzKF2vobaLwz6Ep4SQbkL/oT63bXaXA50JFu+v1awIxWy6lstRh6ZmanN8etIyfYtEA5odXl+E1FojKivsxUotNFsqUJjoxtarWdaiBBa2Gw2TJgwEUVFRcjOzjpieohGkyHCQgjQLVOBC4an4oONnilL//qsCqO7K3kspSD4k483V+PBj1tXzQfmxePFK3OQFC/nOb7i7CEpLCZ6C7KHPzFgSH4C8jWSeSSEzrn6I0srsWRT66TAi0am4k9nZ0asqBB2wgKdYGq12TAaDbDbLLy670tSUlXQ60uQnJzm8+LP2xJBUwFoFdnXo/gOhx6P0vPN5ioOEUzXZCE2Nqx2n6CgsbERJqMeUdHRyNTmBkTA8bRAVPpNQHK7G2G3W1rGgvoSepwaux0aTaZPhUfBd3zwwRLEx8dj9+5dyMjwvagr+I5bztTgs2021DY0Yb+hAYs3WiOyJ1UIHMu2VuO+D/Xw5mP3z4nHy1flIjkhdMZQhyp/OVeLrcV1KDI6+Rjwp/cr8Ob8fMSJuCiEgKjwxIqqdiGv5w1Lwd/OoymFkSsqEGEnx2Zl5/OquslUyUWar4vt5KRUtnD7A09LRCpMJkNARvRERUVDrc5EojKZ20BoRV3oPCh8lLYrrdxrNLqAiAq0X5nNlYhPUPqt/cJWbUFCYpLPpzOQYGI2VbIbIyu7wKePJfgOg0GPc845W0SFMCAjJRbzJ7TmnLz4hRHVDt9+bwuCl2VbqvHXxa2iQt/sePz76ly26gu+hxwh/3dxFmKbN/f2kno+BghCsPPvr0x46ztLy/Xpg5Px4Ew6b49sUSEshQWeZqDSeEZP1rTaU3wFZQ6QZZzEDH9ALRHkXiCrfCCgx05JUXHfv8lo4HR9f0zHCGd4GoLVxG4Uyu6gQMFA2agof4B61ukz5A8aXS7U1Np8PgmCoDYie42N80pSUtJ8/niCb+jRoyfKyspk84YJlLifq/a43yy1brz8lSnQT0mIAJaSqPCBHu5mUaGnTsGiQlpzqKDgHwbkJrSbEvPat2as2986rk8QgnEc7Ytftn5Pndk/Cf+4KAsxIiqEp7BAZOcU8Ix6k7HS5yv7tKpMhTYVhv6ACs70dB27BagvPVDQarYuK49FBYOhRNwLJ+lSIGGK8gyUysDNu6XJH7U11eyWIHeKP6i2eYJWfd3a4x0xabdVIzu7IKL730Idciv89NMWLFnyIerr6wP9dISTJD4uGndOay0s/rfOgoOV/hHqhcgVFe49TFT47zW5UCeJqBAIrjxNxeGNBJ2y09g+U41/p6AJwvHwwUZru3G0tN96XDdyThnWwgKt+JJzgRwLvh49SSQlp3JhWF/vn1ncJGZQOCW1YASyHSE6Ogbp6VqkpWnEvXASLgXK6KA8EH/nZrTF2VDPuQqUneGv50GfmTpHjV/cA/Q5qa62cLsFTZ4QQpd//etf2LNnD+bNm4esrGxotbp2l2effS7QT1E4QSb3T8aIrp4xsy438MSK1nR+QfClqNCrWVRIT5LMqEBB9vGHL9K1CDuVtkYe3xeIll9BOBYrfrbxBAgvw7ok4Mm52VDEhmUp3WHC8khKq5GUtVB4cA8sZiNPb/DlCiX922TlrrYaOYDOH6uhsXFxLe0IgZoc4IVEHErzp/YMg6EUqjQNB0zKqvDRIQHKaqlCNIWN6vICKigQlEVC40TTVBl+DTQkYYVaiUig8vljVZthI7dCToFMNAlxpk8/G/n5x87IGDFiuF+fj3Dy0HfF3WdnYM5Lxbxi+c2eWhk/KXQ6n/5UjXu5YG0VFV4RUSFo8lZIXLjlLU+bGx0DFqyzYu6pEuYqBJ41u+34y/sVLceOfjnxeP6KHBlHGynCApGZmY3iov2wWIxcgFPx60so0JAcEnShFWh/tSNQGwYVhZmZOQEtmGJiYpCu0bKd3mo1ItoWw84RCiIUWlfoqZimn6lpap8LXscDrQjQFAraf/3ZhkF5By6Xk/cZfwg5nrYoN3S6XJ8/nuBbSDgQ8SD86JeTwOMnP2we3SXjJ4XO5Nu9Ne1Ehd5ZCrwyL0/aH4KIcb2TOHPlne89oXhPrqxiJ1OfbJngJASOtXtrcOeCCnbTET20Cp4ckyKTY45K2Po3qNDNy+/G9mcqnHxtqaICkcbz2arNXDD5C2rDUMTFwxygSRGHQwIOZwUkJcNo0sNYVeG3YMtghfYHmuRRVVmO+PhE6LLyoVSmBIWoQLkDJEj5IzyxrUOCBT81zfqN9v2UC5OBHQuUrSAjJsOHn3/+GU8//Qzuvvtubn+g9gghtLltsoaT4gkaP/n+htZRXoLQUWob3Hj4YzpH8lwXUSF4uWOqhqdzEA2uJty9qByOBgkIFwLDd/tqcMd75XA2eg4eeeo4DnmVPJYIFBYInS6PCwmz2YTaWpvPH4/6t8naTSPt/FXkewSNDLjdjbwaHgziAj0nCuTLyspnx0JVZRlvE38KLsEATVegwp3aQ2JjYllQSE5JC7ig4MVut6Khvg7qdK1fnxNtE3JH+MPNQqMlTaYqfn05uV19/niCf3jkkUcwfvwEPP7441i1ajUeffRRnHbaWDz3nOQrhP74yVaR86mVVdhZ5p/sIiF8eeVrE8qtnjBAtTJGCoMghvrVKQwvIc5zTnKw0onHl0vmiuB/fvilBre/W84CF5GjiuU8Fm1q2Jr9O4WwFhZoJTa/oAeP0Kuqot4Y36uenjaIJr+Mumw3KUKThbp6BzsmggVajaZWDSqoo2NiuMCmtg0K0gsGAcSXkx7IoaCvKGkRuGiMZCBbVY4mKtBUkYyMbL8+r1pqgWhuBfFXmwe5lnJzuyE2Vr4MwgGaCPHEE0/i+eefQ3FxEbZs+Yl/PvbYo3jggQexf//+QD9F4SS47FQVumV6MoPqnE24491yGO2SEC90jP2Gery5tvW86PdnaaBJlu+CYKZbpgJ/Piez5frijdVYtd33i4OC4IVGnv7unVZRITstFq9em4ccdeDy7EKF4Kl0fERGRhavjlZbzbD7YTxjoFoiqPWDikTKOAgmcYGgcD7KW8jKKkB8fALM5ioesUjFLY2rDAfoddTU2Ph1UTFL7hUKEFWpMgIezng4JHrZbVZkZGYjxo/FNrVAWP3UAuEVMSwWE+Li4pCVnefzxxP8w1dffYU5cy7BZZdd1iKKkWg0f/58TJs2DWvWfCNvRYiPn3x6bg6Sm1siKqwu3LWgHM7mEzxBOBFx+ZFPK1t6o4cVJOC8YamyAUOAmSNScdbA1tynBz8yoNwSWa5XITD8yKJCGeqbv3OymkWFXBEVjouwFxao0C/o0hN2u4173KllwNdQUZni55aIFnEhMxs1tTZ2aQQbVASQo8O7gk82/IryIl7dJ0Ek1EQGem/Jam82V6KioohHJyY3OzRS/DTt4ESprbFx3gCJUP6cJOLNc6DsDX+0QJA7yVjlcSvk5/cIyvdC6HgYp1qdftS/paeno64ucCN4hc5bsSQ7tLdDa/OhOjwmdmjhBFm+1YYNBz3Hg5ho4K/naXm0oRAa5+5/O1/L9nPCVufGn9+vQL0ztM4ThdBiU6EDt71Txm45Qpcai1evyUVeujgVjpewFxYIlUqDlFQVrFYLFxr+IMnbEuEHl0RbaHU8IyOHV6VtfnqtHfnCoIkW6RodF+EUPlljt6Ki/BCHPdI2o3yCYIRW3ak4p5aO8rJD7A6hAl2nzYMmI4vDK4MlQ+FwaJ+wWk0eUSHOvwdJEo64BcJPIZE1dhu/Vno/MrXZfnlMwT8MHDgQH374IQyG1nnSRGFhIZYtW8Z/F0KfcX2ScPsUTcv199dbsWh9cH6nCcFHtaMR/1pR1a7FpneWTBcIJVITY/DYxVksChE/HarDLW+XoaZexAWh89lVVofb3m4VFShL4dVrc5GvUcjmPgEiQligQq9Ll15c3FDWgj+KVnpMtVoLm82/LRFeWzCNn6TAymAJdPw1lwUFGmZk5iAru4DHHlIGA+UTGPSl/PzpfWt0uQLyOmhfqXPU8ip/paEM+ooiOOpqWBghUSRTm8vuBH+2FHQEajshoYn2C3LU+HsbcgtEun9aIMj5Qp9zan0q6NIjaIUeoWNMnz4dOp0OI0aMxI033oj7738A1103H6eeehqLCuPGjZNNGybMG6fGtEGtdujHllZie3nwfp8JwcMLXxhhtDe2FAg3ndEqUgmhw9CCRJ4W42X9AQfmv1YCc43v3cdC5HCoqgE3vVkGe7NolZEcw06FAhEVTpjgroY6kZSUNC5sKGuBVjLT07U+f0xaFU5JUXNLBLUo+LPAYedCZg6qqsrRhCakpqYHfYFFdnXKw6ALiQjUKkEiAzkYLM56kmu4KKZJH3GKeHYK0Ouk13Wyr42KUXdjI5yuBh6P6Wyo5xBGeh6ex/O0t8QnJPilOO5MqC2Ggxoz/dv+0L4FIgUKhe9bIAhykVitZhasKM9BCC9IOF2+fBmeffZZngjx7bdrkZubi3vuuQc33XRj0B/nhOOH3ssHZ+pQWOXE7vJ67pV/eKUbQ3o5kaUSa6pwdHaW1mHhj62jSu8+O6NljKkQmgIjrSs9s9rI17eX1mPef0vw8tU5yEqT44BwcuirXbjhjVKYmsWqlIRovHx1LrpkiFOhI0SMsEAUFPTA1i3r2G5PRaI/Vm6TklN5xd1usyDFTzbwwwMd6fWaXQYOlQymyQS/dUIZn5DIF2+BSoV/g7Oei36y9ZMThG7zihIkMtBrpgkU0VHRzQVGFP/PW3A2tYgILm5roMwNr4OF/g0qvEm0IHEjTaVpES5CEdpm5BQgcYZEpkBMRSDXjMvpRHq6zi+PR+9llVHPwY0DBowI2fdO+HUSEhJw991380UIbxIV0XjmsmzMeamYVymtdcAfF1bgtevyEBcjn2+hPbUNbjz4sQHuZmPLab2UmDKg1fUihB70PX7thHRujXj4UwOLDAcqG3DVf0rw73m56CoFoNBBLLWNuPGNUpRZPHUAjTl9/oocaZs6CSJKWKBikfqtLWYjLBYjMjOz/TQKUsujFmPjFNzz7U+o0KbXTJMYqirLoNFkBb1t/1jbkZ53Il0O24YkFFBBSSJDY7NQ0ETiAX37tBkxStejmreJQqFATHQsixCd5XoIJkg0MZn0/JqoXSMQghKF7FErC4ka/tq25EiiC+Wq+GOkpeAfWAx0u/mzyyLjrwS90n3C6bMsANmqODwxJxvzXy9BoxvYWlyHZ1ZV4Q/TxZEktELBfne8W4ZdZeRwBBSxUfjLDGrBk+NBODB7VBpSEqPxl8UVcDUC5VYXrnqlBC9dmYP+uf5xRArhQ229G7e8VYb9hga+HhsNPHFpNoZ18SxoCh0jNJavOxFKiHe5XDwSsKGhzi+PSYWrJl0Hi7mSbfb+hqz7ZAmn/AISOKjgCyeoaCb3CbkbSDwiNwpNnaARl+Q6oAtB1+l2+rtSmcL3p/+O/vtwOvGgfYzGXlIoJglJgRAVvJ8xyhnxV6YDOVgoW4GmAtAkGCF8ePDBh6DRZOCpp55u+f1YF7qPEH6M7JaI29qEOb71nQVf7bIH9DkJwYOzsQl/WFiBdftbp8LcNS1DeqTDjGmDUvDc5Tm8skyQi+nq/5Zg1XZboJ+aEELQ+OI7F5Tj5xJPPUQlwMMXZWFcb/8u/oYjobd0fZLExyfwTHuDvgxVlRXIzunil6KSRuylpWm4LYFWkGlVzZ/Qa+T2j1gFTMYKpKZpkJSU4tfnIPgearshAYv2Nco1CAS0mkz7GGUcJCQq/fKYtIpNQgZlK2RkZsm+HWbMnj0LgwYNwqBBA+F0Ovn3Y0H3EcKTq8eq8f1uI9Yf8ly/9wM9Ft4cL6PAIpxGdxP+8n4F1uyuabnt1jM1uHSMKqDPS/ANY3sl4T/zcnm1mcZQUor/H/5XgZvPaMANk4I/T0wILHS+eP9Henz/S23LbffMyMTZQ6Qm6gwiTlgg8vK6w1hlgNFo4AwEWsn2B1To0Wqy2aSHJsO/YY5eqNDLiM2B0VjhGf+XJgfhcDlQUo6Hvaaax176KyjxaM+DhA1q+0nmkav+gUaAVlZWoKnJzRNghPCCpj20HSMpIyUjk+joKPxhUjRu/zCabdBUVPxxYTnenJ8HBflYhYjD7W7CM2uasHpPq3vl2vFqzJ8orXDhPi3inRvycds7ZSgyeiavvfiliW3tD13on0wnITR57nMjlm5pdbjcdEY65owWEbKziMhvYgqx69GzH4+jo5GGFAboL6iQpzRBq9WTbhsIyJqu1eayyEEOCurHF0IXcgiYTQY4HLX8vgZKVCBI3KA2CLU6w2/CGbVA6PUlqK62oGu3vjzFQwhfXn31NSxatOiE/yaEBykJUXh8TjZim01/O0rr8cSKqkA/LSFAQvZjyyqxek/rCNK5Y9LwuykaWbWOALplKvDuDfkY3b21J37ldju3RlTaZSytcCSL1lvw3zXmlusXjUzFjZP8s7gcKUSksEBQuJtOlwuz2YjKynL+gvIH3jDH+jrPGMVAQRMQaGWbRAaDvoQt9ELoQfsRvX+Uo0EhnZTnEShoH6JpHZoMnd9GcnpbIEymKh4nm5EhKxXhTlFREUpKSo/6t8LCQpSXV/j08UuKi7D226+x7ofvYDabOv3+wm8zOD8Bd56V0XJ9wTorVv4sPdaRxjs/WPC/NmMlZw5Pxd1nS1hjJJGmjMGLV+Xi0jGtDkkK77xjiRtFRv9nmgnBy7rCJjzyaWXL9XG9lfjruVoRITuZiBUWiC5de/GkAWqJqK5uVbD8UdSna3T8mDQKMFCQyEHBhiR00FhCk8nA4xeF0HApWMxVMJkNUKkyuKj2VzF/NMj9Qi0QtF/7U9xo2wLRvXtf+YKIYKxWK7Zs2YLMzNaCs7PZvGkDPlyyyPP5s5jx1hv/RVlpSafdXzh+LjtVhTP7twZt/W2JHluKAvd9KviXSpsLL3ze6vycPjgZ912g5XYZIbKgsbP3zNDi3vMyOdmfMNWCMxgo3FEQthXX4bHPaVqcZ1sMyI3H45eQ802OF51NRAsLVAD17NG/pSWiwY8tEeQUoGLQZDSwlTuQxMcnQqvL4+kB+gpxLwQ7JEaRS8Hd5IZOl+e3gMRjQa001FJDIpU/2zDatkB0kxaIsOf++x9AWpoKTz9NkyEe5N/bXgoKumDHjh2YOnWqTx7f2dDAzoPJU6Zj/IQzMG36DAwYMAjfrPmyU+4vnLgwTr3U+elxfJ0C3G59uwy/6P33PS4EDho3WtvgqRLy1Z5E9xgRFSKai0ep2L0QH+spFg8Znbj93TLUOY89nlgIf8i5ctvbZah3ea7nqePw/BU5UMZHdAnsMyIyvLEtaap0bomoqtIjKancb1MiiIQEJVJS0rgoy8jM8fukiLaQqEAr34mJSTCbKtnWTu0i5K4QggNa9ay2muCoq2l5rwKNdwIEPRca4ekvPC0QFdwCkZ6eye0XQnhz0UUXon//fliyZAmUyiRMm3ZWy9/omK1SqXDKKaOgVvsmhKmsrJQnUvTs1bvltl59+mLbti18e1xc3Endn6itqUGtozWpmvCOB6477PZIpaGhAbW1nqA++nZ64mIVbnjLCHOtG9UON254owSvXJmBrLSYiNkOkcb20gZ88lNr68t1oxrhrK9BYJdoAgtlLHl+Ultr5OYLDM4GHjgvDX9ZYkYTorClqA5/XliKh2eqEB2B0yIi+ThBVNkbcT1/P3icK2mJUXjyYhUSo+tQG4FfqXV+cMlHvLDgbYmwWIyoMpK4kMIrr/4iKTmtecW3HBmZ2QEv5L3uhepqE68GB0sBG+mQS4EEHxpbSi6FQO8nBLUfUHEfGxvXHErqPyjLobJSzwJDN2mBiAgGDx7MlwEDBnBR3qdPH78+vt1uR0JiYjtBIDk5hfdBEgTSVKqTuj+xZcsm/PD92na3JSYmYtjwQdizZ5tPXlcoUlHRvp3kpmEKPLFOh/rGaFTaSFwoxd2nViBZ4Y6o7RAJkJX5/77PorMVvj5EV4usmEps3RJ526ItJFYSO7ZvOqpoGUnQ2cisfil4f5fnvOTL3XW4/397MKufBZFIJB4niFpnFP61LgtlNgVfj4t244ahepiLC2EuRkTiPMaiRmciwkJzS0SPnv2xc8dmGAxlSFQm+y1ZnlbaqCijKRFV5FzIIHEhsPacdu4FcyWHTNJzlLT9wGQXUBZHQ0NdUIk8VBwZjXoWOFRq/4ZlUQsEtS5RC0Sv3gNlv4ww2o6apNUYm619aJ9SqeRivLOhXbzJ26DZTJO7uXCNOvn7E0OHjkDvPv2OcCwUHdqDPn0GB7ztKRigdj1dVl6724YAyC6ox52LTHA2AvqaOLy6szuen5sOpSI6YrZDJLBsWy0KrZ7AxrgY4G8XdkFcfXxEbovDHQsb1v+EAQNHIFGOE8jKKkF0shILN3iWpVcfTMOgnvm4+JTgOIfyF5F6nKDWuNsXGFFq8whuFKXw5zObcM4pwxHJWCwmHCrc69PHEGGhmbS0dP7wVVVWQJlUjhw/tkRwiGKaBhZLFbdF0LSGQIsLXveCTpfPq8P0vOLjE1hgoBVqwbc0NrpYUKhz1CA5WQW1OjMo9om2kxhov1Wn+zdRlwWNKm8LhBYajbRARCKrV6/Gvff+DXv37uV2nLbcf//9uPPO33f6Y6ampnGRX19fj/h4j/BcbavmzyU5EU72/oQyKYkvbSEba9EhsKigVCYj0lEoFEfdDhMGJOPR2Qr8cSGFuQI7y5y492MbnrssB3HNPdeRsB3CGXtdI1782tBy/cqxavTOVaG0xBRx2+JIPPu4py0xsornY30+/nxuDqpqyvHFTs/Us6c+r0ZeRhImD4icfSUSjxOuxib8aUk5tpa0Nkc9eKEOIzKrIm5bHE6dw/etEMFRqQQJXbr0RCxPidBzL7s/8fQIZ/DjU9F2+MlyoKDnlZycBl1WPmLjFKg0lPI0Aip8hc6HpnLQhA5qQ4mOiubtnpKqCipRwWwycAdnejqNlfTvCTuJXJSHQs9DpkBEJsXFxbj66nm44ILzcf755+Pyyy/HCy88jyFDhqBfv36YNesinzxudk4utzbs3PFzy207tv+MLl26teTj0EjJwsKDx31/oXOZOjAF98zIbLn+/b5aPLWqSjZzmPCfr80w2j290tqUGMyfIPPnhWNDYZ6PzMrCoDxPqDQJjn9cWI5PfwrcqHfBt9C54YMf6bFmt0dMIv4wPQPnDUuVTe8ngqNaCbKWiBq7jVsiGppDs/wqLtDKdEwMr8oGi7hAUGGbmqqGVpfPwjin8VtNQfUcQxnajrZqC/QVxZ5pD9o8zvoIhiyFw50K7qYmaDT+n/1LbSHeFgjKVYhTePrmhMji88+/wOTJk3HPPfegS5cu6N69O4sLK1euQF1dHQyG1hXNzoTEgMmTz8I333yFTz/5EO8vfBfFRYWYMOnMlvvs2L4NFeWlx31/ofOZM1qFGya1FpzvfG/BV7siN7wsXCisasA7P7SOBf/9WRmS6i78JomKaDx3RTYKNB6nbaMb+OsHerz9vf9GzAv+O0d9cmUVPm4T7HrNODU7mwT/Ia0QR2mJyMrOh0FfBkV8PPLyurPg4C/YXq7ObG6LKIcmCDIX2kIny+SsIBcDWfX1FUUcQEmhl/7cTuHkUKipscFut/KoRpoOQqNIgw0KaiQnTxSiuP3A36ICbSfqFTQaDfz4GTIFImIpKSnBgAH9+ffExAQOSfT8nogzzjgDmzdvxsiRI33y2H369odOl42iokJ2l3Xr3rNdnsPoU8dCo8k47vsLvuHmM9Kxu7y+ZdXq3g/0WHRLPHLV0sYXirjdTfjn8kq4PGYFDC1IwNlD/DeFSAht0pNi8fp1ebjxjVLs0zfwbY8vr4KlphG3Ttb4/XxG8E37w6NLDXh/Q6sbZebwVNw+1X9h/IKH4KlYg4guXXohKTkFVZV6XiGloioQbRFxcfGoqiznoirYoJwF6nGnQpjaIqjoI4t8Q4PMED/e1XdqKakoL+bfNZosLpiDUVQgN4WxSs+tGekBEBVIhabsk8rKchZfevRsH24nRBYul6sl5yUnJwfbt29v+ZteX+FzgVOlVmPwkGHoP2DQESLBwIGDkZ2dc9z3F3wDHaMevlCH7DTPvmCrc7MF2umK3DF8oYqzsQl/WazH2r2eED76+vnzOf4NDBZCn8wUj7gwrMDTFkG8ssaMhz8xoPGwkF0htHA0uHHngvJ2osKkfkn42/n+d9YKIiwcFXIIUAI3fYNRS4TJVMnFjT/hQEeVhgMTqaAK1kwDKoTJYcEZDLEKHj9IOQy0Ci9tEu3hMXO1dhaL6EL7GQWGkkATrBM3PKNQqViL8XtQoxdP5kQpF5R9+g4RZ4zQwrRp07BmzRrMn389brjhBqxYsRLjxp0uW0hAmjIG/5yThdjm5ZPtJfV4ZrXkLYRawXD7u2VYvq3V2jxndBr657YWh4JwvKQmxuDlq3MxrnfrdB0qRu9eWIEGl7T1hiLmmkbMf70UX7fJVDhnSAr+dUk2YmkUhOB3xLHwKwVz375DUFfn4FVlsqr7G+8oSmViMgyGUr9nPpwIVHhSyKAuqwDJKSo4HHZUVBTxqjy5GPwtzAQT5Eig4rii/BCHDyqTUrjdht7bYG4foedNIhGJHv4eKemFBKryimL+2bvPICQkyIpvpHP11Vdj9uxZ/LtWq8XChQs5V+HQoSK8+uqr6N27d6CfohAkDMlPxO1TW1tT3vpO8hZCBWttI254o7TFqUDMPiUVd5/dGs4pCB3JXHj6shzMaNNKs3qHHTe/VcZTR4TQocTkxJWvFGNbcWttNG+cGv+4SBeWk4BCheCtaoIAyg3o2WsA9u75GWWlh9Cli8Lvc8SpmKOCPTYuDkZjBY+lpMI0WKHnS+OO6EIrzLW1Ng78I2jbJSQoeYxlONuTSERpaKhDnaMWdXW17NxQJiUjMzOX38dQwOGogcVcGdD9jUb1lZcdgsVs5LBGyj8RhG7durbbCJMmTeSLIByNK8eqsLHQ0S5v4f1b4pEjeQtBi6HahRvfLMUvzf3wxPUT03HLmelhfe4g+Ie4mCg8fJGOXU3v/mDh29YfcOCaV0vx0lU50CRLaRTs7Cmvx01vlqKqeUoMHRb+dHYm5p6qCvRTi3jEsfAbUN97Xn53mM1VKCsr5FXcQECFOuUZUGAirX6HggOAwspokgS1SWgyshATHcPPn4pFEhtIdAjG/IiOQOKBo9YOk8nAzgR6jziIM12LrOwCLtBDQVSg/YqmU5DThHIfAiUqNLpcqCgv4rBGnS4PWVl5AXkeQvB8vkioPJ5LKBwbBf/nLWS1yVv4w8Jy1DvF+hyMFBkbcOV/ituJCnefnSEhe0KnEh0dxfvV76a0hvtR4OuV/ylBsTEw5/nC8XHA0IDrX28VFRSxUXj8kiwRFYIEERaOg7y8bpwjUFVlQAWNAwxQMUztGZnaXDid9c3jKEOjKKcTO3ruKTSuUpvLuQLxCYlw1NZw8VhpKIPNZkF9vSNkchnoedKKut1m5bwET5uDjdsGMrV50OryuNWBrofKCguFlFIAJ7kVtLpcKOITArZtaZxpZWUFTx/p2k2s7ZHOgw8+xNMWjufy1FNPB/rpCkEGrUzSiWfbvIUHPzKICBVk7CytwxX/KUGZxZMpRe/XI7N0uPw0GRcndD50bnbdhHQ8cIEW0c2nacUmJ++Du8qCt/U40tsfrn+9BOZaT/2TkhCNl6/KxdSBwevkjjTE73OcBx9qidj+8wZUGsoRz8VjbkAKRsoyoBGUVquRC/L0IJ0k8GtQrkBSUipfqJilHIv6Ogesjhq4nA38d3pNcYp4KOLi+WcgR25SoUtOFWdDPRqc9fyTwjQpmZ6eG7XM0PsQTGNBO+IQoFYbCuDM1GYjKiowr4VWm80mPQwGT7gl5SqE8nYVOgfKVBg0aNBx3XfQoIGy2YUjGFKQiLumZ+L/llXy9aVbbeiZpcA146TFKhhY90st7nivDLUNHsdRQlwUnpiTjXF9kgL91IQw58KRaVAnxXCIY72rCaaaRm6LeHpuNkb38G/7s3Bs9NUuzH+9BAabR1RIVEThxatyOEtHCB5EWDiBgp7CHLdtW4+KilIuKMnFEAi84yhr7NW8Wq5Oz+TsglCEClhvJoO3sHS5nBz4SAV8taOGi/romBgWGUh0oN/pJ70n0dGe30+m+CThgNwfJBbQFAR3o/d3Fz+2R0QgoUOBeEUCr6KT8BEqToTfgkJBjUY9v67klLSAvi5btZk/X+QGGTjolJATzQTfMHDgQL4Iwskwd0wa9lXUY8kmz1iyZ1YZ0T1TgYl9k2XDBpCVP9twz+IKuJpNmKmJ0Xj+ihwMLZCCQfAPk/ol4z/zcnHr22XcLlVT7+ZAx0dn62Q1PAgw1bjYqVBq9riZ4mOj8NzlIioEIyIsnABk3+/TdzB27tiMivIStrnTqnugSEpO5b59k9GAlJQ0JCUHtijszLYJLiib+/u9YgO1gDS6PEW/xzXg+d3bEhJNYkN0DKKio8FbISrK87P5/6l4RlMTaD2EnBJeAYFaskmkYKGi+d+g32PjlDzhIpxEhMOh9o1qq5GnPnjFnUBBLRjUalRdbeFxr0lJcrIvHJudO3fio48+QmFhIWbMmIG+ffuipqYWw4YNlc0mHBU6jv/1XC0Kqxqw+VAdH/v/vKgCb9+Qj1664Bz5G+78b50Fjy6jkd6e69rUWLx8VQ56yvsh+JlhXRLx5vw83PhmGQeIOhub8MeFFexgmDNaQgEDRbWjETe+UYaDlc6WFqknLs3GqO6huaAa7oiwcIJQGCEl1B/Yv4ut8PkF3QPqFqAJC5naHA5DrKt3sIsimEcYnrTYcBRIeKALiwzkOGhqzmloFhHoJ42/VFLh3CwQ0L/pdT2QayJchYNjQaKM1VLFjgwKBQ20M4CmaFCop9FYifz87kjXaAP6fITg5r777sdzzz2HAQMGwG63o2/ffhg+fDjmzr0MP/zwPeJCIChVCAw0huzJudmY+1Ix9/KT9f5375ThvRsL2A4t+Af6zn7xSxP+/ZWp5bZumXHcL52tks+vEBhI0Hr7+jwedVpY5WTB65FPK2G0N+LmM2Qqib+pbXDjlrfKOFiToCyMxy7OwnhpkQpapHm5A+h0ucjKzucV8NKSg5wPEEhI4KDMB4UiAQZ9Ca9CR1IyOokC1ApBxTG5SrytFYnKZCjp0ux8oOvev5EYRPenVopIExXIGUD7CQkrFNIYeFGhHqUlhRzWqMnQITevW0CfjxDcfPfdd3jjjTewatVKrF37Lc4//3y+PS8vD126dMEXX3wR6KcoBDnpSbF49vIcKBWeYz/Za+9aUA6nK3K+NwPNW99Z2okKg/IS8Ob8fBEVhIBDwhbti7RPeqF99eFPDGh0yzHCX5Bj5A//K8fW4tYgzQdnSmtKsCPCQgfp2rU3MjKzuBgqLT3IPeGBhIpjclNkZGSjxm7lID5awReEti4FcrZYrSYOm0xTaQIW0uiFHBNlpYUc1piWlo6ePQdEnNAjnBhr167F5ZdfhlNOOeWIv/Xo0R379++XTSr8Jr2z4vHo7CyviQ0bCx24d0mFFA5+YNV2G55YUdVyfWwvJV65JhcqpThGhOCA3Eu0T9K+6eX9DdVc6Da4QmN6WSjjdjfh/iV6rN1b23Lbn8/JxPnDA9d+LhwfIix0ECp+evToD41GB0NlBcpKD7KdO9BQqCS7F+LiI9K9IPy6S4EyJGjkZ3yARkkeS1RISVWhd5/BMgFC+E3IZVRX57FFHo7FYpFsDuGEAtt+N7l1jv1n2+x4ZCn1+8t3pq/46ZADf1msb7k+rEsCp+8rFXI6KgQXtE+Ss2nGkNZRhl/srMFjSz2TZQTfQMffJ1dW8eQeLzdMTMfcUyXnIhSQI/lJj6HsD7UqAwZDBdu5ydYdaNi9kJbOYynt7F7Qi3shol0KBh5PSi4FlUoTFMU7hXGWlxVCbyjjcZ0U1hgMz0sIfsaMGY2FCxdi8+af2t1uMpmwatVqjB17WsCemxB6XDNejUtGp7Vcf3+9FU+vMoq44AMoNJPyLBqaW066ZsTh2ctyEB8nx34hOImLicLDF+lw5djWonbxxmp88pNnsozQ+by+1sytUl5mjUzFzWfKWOBQIbxS/gIAWcl79R6IvXu28corFfW5uV3ZORBoaGoFrU7TCD9arU5N03DmgFjNI8elYDFXIVGZBK06L2gKd4+ocAh6fTkHavbtN4RDNAXheBg3bhzGjTsdZ555JiZMmMCCQmJiIt566y2cd9556NWrl2xI4bih78N7zslETZ27ZYXs9W/NSI6PxvyJcjLbWRjtLtz8ZhmsDneL1fzFK3ORJu0PQpATHR2Fu6ZloNLmYlcTQXkLfbPjuaVK6Dw+3lyNp1caW66f2T8Jfz1PK3VLCBEclUaIQwVb7z6DkJKaBoOhDCUlB4PCuXB090IFW9CF8IUKd85SsJBLQQuVKiNoRAVP+8NBVFSUctBm337Dwm6KieB73n77bdx7770wGo2oqKhAbW0trrvuWjz99FOy+YUOFQ4PXajDpH6tI3ef+9yIBetaV82EjuNocON375SjxOwZF5cQF4Xnr8hBXrpMfxBCAzqXvv98HbpnesKu65xNHPhqq/OMOxdOnjW77Xjgo9Y2qZFdE/HY7CzE0CgIIWQIjmojTPp++/YdgpQUFTsXSksOBDzQ8WjuBRpPWVVZBrPJwAWoED5QWCc5FAyGUsTGKaDV5fH7HSx4MxUq9GVITFSif//hiI0VUUE4Md5440189NFHuOuuO/Htt99g7949/PPWW28NGgFNCD1iY6Lwz4uzMLp76zHz0aWVvIImdJw6pxt3LijHzyWe8yGqEf7v4qx2ifuCEAoo46Px5KXZSGyeJnPI6MR9S/TSNtUJ7Citwx8XUniu53qfLAWeuTxb2qRCEDkL68yNGR2DPn2HQKVKh75FXAjsKMrDFVcSPnRZBbxKTAWoxWLkPnwhdHG73ai2mqDXlwBRNA41nyeEBFOR5RkpeRB6fRmSlCno13+YiApChzAY9Ni3b59sPaHToV7/Zy7LweD81qL3/g/1WLpFxIWOzqC/7e0yfLevfbI7hWYKQijSXavAAxfo2oU5vv29OJtOhhKTE7e8VcYuECJHFYsXr8pFSoK0yIYiwVN5hAlUzPXqPQjp6ZnsXCgpPoC6utYv1WB5jtQeodPlUfwqDPpiVFebuUAVQis5126zQl9RDFejix0p1PYQbHkFNC3FKyokp6Sib7+h0v4gdJgRI0bim2++lVUiwWerki9emYPeWR7LM42tv/cDERdOlJp6NxcLPx5oXVy56Yx0zBkjye5CaDN9cAouHdMa+PrUyipsLgyeRcRQotrRiFveLoWpxrPAmZoYjZeuykVmirhZQxV553wlLvQaiP1RO1FZWcEnwDk5XaBMah1ZEwyQa0GlzkBySppnxbuimMf+JSWlSlBKEEP7k6PWzmJQXJwCGRlZQREWejTqHLUoKyvkz0FaWrqMlBROmvz8PM5WmD37YsyceQE0mtZxgUTv3n3QvXs32dJCh0lNjMF/5uXiutdK8Yu+gcWFv35Almfg3GEyR/23oL5zCmrcWtzaDnrbZI2EYQphA4U5UnvP9pJ6tu//fkE5/ntNLnrpgvNcLBhpcLlxx7vlOFjpbJnAQY6xbs05FkJoIsKCD9sOevTsj+iYGOgrSuB0OpGdnY80lSboivbY2DgeRUh2dRIYaBWcHA2JiUlB91wjXVAg9wu9RyReqdMzgypD4WhuirKyQzCbq5CerkXPXgOCqj1DCE3effc97N27ly+rV68+4u/3338/7rzz9wF5bkL4kJ4Uy4XC/NdKsU/fwKLCvdRPDeA8ERd+dQXyxjdKsb20vl0RdtXpav+8cYLgBxSx0XhiTjYuebEIllo3zDWNuPbVErwyLw99skVc+C3c7ib8bYkeG9s4Pf4xS4cRXYPznFY4fkRY8CFUlHfr1oeD6goP7uOwxAZnPTIysoOywKKAx4zMbNTXOVBdbeICNik5FUlJKZwfIQQGalEhhwJN9UDzlI+EBGXQij5NTW6YjJWoqKAWGwvy8rsjL69b0D5fIbT405/uxh133EEfhaNCoycFobPEhVcOExfoZJh+nj9cnAuHY61txPzXS7G7vL5dpsLcU6X9QQg/slVxeO7yHNz4Zhm3/pDAcO1rJfjP1bnonyvhpL+28ERTd7yjO4k7ztJg2qDgcnULHSP4qtswg4qp7OwC9Os/FA5HLUpLClFeVgiXM3gnMtAYwExtLq8yOxvqUVFezNMGZEylfyEhikZG6iuK2KlA+QmUoxDMThKaTEH7S3HxAdjt1TyGNT+/e9A+XyH0+Oc/H8frr78OlUp11Et8vKwWCZ3tXMhryVwgUeG+D/X4aJNVNvNviAp/O08rooIQ1gwpSOS2qZQETzlV7XDz58A7BUVoT4XVidveKcer35hbbrt4VBrmiaMpbBBhwU+oVBoMGjwKUVHRKCujwmt/0IU6Ho4iPoFbJHRZeeywqKos5wutnpPiKPio3cFRC2NVBU/toK1MIo8mI4sFn2Au0Bvq61BScgClpYd40sjAQadAo2lNTxaEzkCtVsNsbj0pEQRfo06K8Vic24kLBizeIOKCV1S4/o1WUYG+ph6cqcXsUa0Bd4IQrtDo1Ffm5XLwIGGrc+OG10uxtUgCHdu2Pixab8HMZ4vwzZ6altvH91GyqymYz22FE0OEBT9CLRFUbFFYol5fiqJDv3AferAX6RTySPb7rOx8DqCkleiK8iJeTRcXQ+fgcrmaAzSLOJQxIVGJrKwCFqQoAyPYqa2xoajoF1SUl/BzHzx4FLfQCEJnM3XqFCxfvhwWi4z4EvwsLlyTh75t+qcf+tiABesiez+kTIUb3ijFrrI2osIFWswcIaKCEDlQ68Or1+RBpfSUVfZ6N38uNhwI7gVEf1BY1YBrXyvFw59UcstIW6fCv+ZkIzZGRIVwQjIW/L3BY2PRt+8QFBXtR1lpIdvds7LyOYgv2BU7clsolcl8IUGBiklyMFDhS/Z8KihDoQgOFhpdLjjqalHnqOHsDWViMjSa4J3wcDRIFCOBqZzaZSxGaHW5nCsSjBkiQnhw6JDHETN8+AiceeaZ0GjS2/192rTpmDhxQsCenxC+qJTkXMjFjW+WYkdzOOGjSyvhbGzClWPVESsq7GwWFYgHLtDiAhEVhAiEQhupbYpaISjMsbahCTe9VYb/uzgLZ/ZPRiRCLoXHl1eh3tW6gNolIw4PXCBBjeGKCAsBgASELl16coG+/5edPDGioaEOmdocdgeEAjTmkCZckJOBWjrIvm+zWbigTCCRIUHJYZDBLpb4uwgnQYaEBNpmrkYXbydygaQn6EKuGKdQyaqqcugrSmG321hQoLYZec8FX1JUVIS0tDS+0GSIwxk8eLC8AYLPSFN6RlHe9GYZtjWPU/zXZ1Vwuppw7YT2IlckTH/wCizE/eJUECKc3lnxeO3aXFz/eikqbY1ocDXhrgXl+Nv5Wlw0Mi2iznefXW1sl6UQEw1cfboaN05KR3xcaJ3vCsdPaFSxYUpmZhYSExKxe/c27kuncY9Z2QVckIcKVESSW4EuXDg31PMqvMVcyYUnFc7kZKCxiKFWOHfWhIT6ujrU1dXwduHtlZDEooxCkRCyRTgJJAZ9CQyGcnbd9B8wDGlpkXNSLQSO66+/ni+CEChSEmLw76tzcfNbpfjpkEdceGa1EQ2NTXzSHKrH9eNla7EDf12sR5GxNYT6vggrnAThWPTQxuOt6/PZzUOfEXcT8OBHBs9IyvHqsD8+kIPrwY/0+OQnW8ttlE/z0IU69MuRaRnhjggLAYbyFgYPGYU9u7eivLyExQVa9U1OTgu5gw89Xwp8pAsVmVRwkpOhxl4Ns8nAt1NRTcJJbJwi5F7f8UDiCo8Vbajj115f70BcXDyLKzRmlFpFQvl10+ujFhi9vgRGYyU7VyiUlAQkQfA3VqsV5eXlyMujY2ZkWk2FwJAUH42XrsrFbW+XYcNBT0jbS1+auIf4rmkZIX2cPxZUMPznKxNeWWPiYqnt9IdZp4ioIAhectVxeHN+Hm5+q6wlf4RW8E01jfjDtAxER4ff8YGopePf/8rx3b7WbInxfZLwz0uyoFRE3uJiJCLCQhBAhfaAgSNw8MAeGAxlqKtzICNDx5MAqHALVaiIJuGELm53I78uagGw2608lpDEBUVcPOIUCi6+6bWG0smYV0QglwZlJNBPWsmPjo7h95RcHCp1JmJiYhAuAZMmYwUqK/UcMJmenokePQdwbogg+JMdO3bgzjvvwrp161pumzBhAp566kn06NFD3gzBL9CJ8vNX5OCOd8vxw37PifRb31lgc7hx3wVaxIRR8XDA0IC/LK5ol6dA4sq952XinCGpAX1ughCMaJJj8eo1ubj93fIW8fGd7y0wVLvY4ZOaGB7nhl6MdhdufbusXXvUzBGpLDxKQGPkIBVBkEDFaI+e/TnE8cD+XSguPoiaGhuH4YWie+For88b/EhQm4SzuRivr6/j6RgsNsQqoCChQUFCA11ohT86iJwIJB60ERFiYprFkXgkpiaxSEKvNZxgl0KtDfqKEpjMRrgbG9Gz1wBkZGSF/H4phB4mkxnnnnseevfujZdffglZWdkoLS3Bf//7Kt++ceMGKJXioBH8Q6IiGs9eno0/LarAl7s8Y9Q+3FwNW70bj83WQREb+O+vkz3+L1hnxVMr2wewjeyaiIcv0iFHLYHNgnAskhNi8OKVObhnsR6f77Dzbau227HlkAP3z9RhXO+ksNh4W4o87VHFptb2qBsmpePmM8K/NUxojwgLQQatAqekqFBYSO6FcjgctWHhXjgcylug3AW6eGkVGxpaxAYq5klYoFV/CrakQj4mus3v3tujYzqU4UAZCJQwTxd3o8vzu7vN740udlvQc4uJjYMiThHWIsLRXQp6VFZWsEtBrc5A9x79QioHRAgvPvnkE3Tt2hVLl37azi0za9YsTJgwEStXrsLMmRcE9DkKkQUFkdHYtAfa9BVTEfG7ejeenJsdshZgan14+BMDPtxU3XJbXEwUfjdFgytOU4WtnVsQOvv48PglWTxBZtF6K99msDXilrfKeEX/D9MzOLclVENcKV9m8QYrmpp1Rzos/PVcLWaPkvaoSESEhSCEVul79RoIjUYXlu6FExEbaLWEinoq7qnI9wgAjXA1OtHYUNdGAGjk+dnsbqCf9H/NuQ9eKsqLPP8mmuj/+N+mi0eciGkWLWL599j4RM/tJFiwcBEdttv911wKBn0pTAP9K44AACk/SURBVKYq3r49e/ZHRmZ2RG0HIfgoKSnGxIkTj2jBSUhIwOmnn87uBV9CU3z0+nLExcZBq/tt105tTQ07fZKTU6BSRd5IwkiBrL4PzdRxgfDuDxa+7ftfanHD66V4+rJstkWHEpwV8b9yfN+mV5oC2P4xK4uT7wVBOH6oLere87QY00OJv3/iCXIkSLT74ZdaPDhTi1N7JoXUOeLqHXY8trQSVXbPayESFVF4dFYWzojQ8ZqCCAuh4V44GN7uhV+DTtq9hf+vvWavSEAOBO/1tj9pggEVxfxvepQH/rdJiJBCuT2NLheMRj2qqipgtZqhUmegh7gUhCBBq9VixYqVR9xOn/UtW7Zg9OhRPnvs0pJifPzRYhYJ6urrEK+Ix6zZlyLpKMGRtbW1+PKLlSgpLmJBwWisgk6XjfMuuIjbvYTwg1bw7z47AyplNF74wsS3bS2uw4ynDnEa/OWnqZAQAmPWqAeceqV3l7f2Sp83LIX7wkO9tUMQAsnkAckY3jUBj35aiZXbPa0RFVYXbnjD416g4Ndgz16osDjx8KcGfLOnVXT0hjT+ZUamtEdFOKEloUeqe6E3uRe0OHBgN4pLPO4FnS4XSWHsXjhRPCIBbYvoXw2TFH7LpWBnEcbrUqDcj0xxKQhBxIwZM3Dffffjpptuwrx586DT6VBaWoqXXnoZ+/btw5QpU332+fhs+acYNHgoxo2fxE6qxYvew5qvv8DZM84/4v4UVturd1+cM+MCPjbR9bffeg0bN/yI08aO88lzFAIPvdc3TNKwc+GxZZUtq/+UCL/wRytum6LBjCEpQdtG8Iu+ni3a5VZXy23SKy0InUd6Uiwen5ONyT/b8I9PDbDUulvcC2v31uAv52pxZpCu+O831OO610phbONSyEyJwZ/OycSUAclSkwgiLIQK6RotUlJVONjOvZAFTYYuYtwLgo+zFEx6VFU2uxRUGs5SiI+XmcNCcJGTk4MFCxbg9ttvx3vvLWi5ncIc339/EdRqlU8et6KiHFarBcNHnMLXqT1q6LAR+OyzpSwyHJ7xkp6u4YuXhIREZGRkwm5vne0thC9zT1WhS0YcHl9ehQOVDXybvtqFez/QczI89VWP6h5cIaM/7q/FnQvKYavzFDox0Z5RkheOlF5pQehszhqUgpHdEvHIp5XcVkBU2hrx+/fKuUi/Z0YmMlKCZ/13T3k9rn+9FOZaj6hAa3mzT0nD7VM9QqogEMGzxwq/CQkIvXsPglGjw8EDu1BccgA2m4XFhbS0dA4xFIQTgVwJtmozqowVsFrM4lIQQoJJkyby9Ic9e/agvLwcubm56NOnT4dGnxYXH2Jh7VjQ1InERCUsZjO3MCQlta4kqdTpcDmdsNvtSE399ZF7FosZRUWHcM5R3A1t8xhqHe3tpRRkK4QmY3slYfStSl6JfOELzwx7gloMaNWPigcSGLJVgXXTud1NeGOtmV0V7uYANqUiigMpTw+T1HpBCEYoe+WJS7M57PWRTw0teQUkNPx4oBZ/PicTM4YGfpzrjtI63PBGKaod7pbjwzOX5WB0j+ASR4XAI5VoCEJtEampKhw6tA+VhnLY7NVIS1Vx9gLdHu6TCoSTh1ZYa+zW5hwFCxyOGnbFdO3aW1wKQtBjMBi4yB80aBBfCJPJxO0KGk2rS+B42P7zNi7ojwW1PZCwQNNiYg4TLrxCBgXK/ho1djuWfLAQgwYNQc+evY95vy1bNuGH79e2uy0xMRHDhg/ica+SzeChtOQAQonTcoChlzRh8ZYoLNnWhPpmHYuKh2/22HHxsCjMGhIFRWyU37eDvb4JT3zlxrrC1tvUSuChs6PQTamHj7NQI3af6Gzq6jwCZHnZIQ6yFUJrn+iXBrw4uwmv/hCFlbs96h4V8X9ZrMeW/QZcNSoK0R1sfT7Z7bBL34R7l7lR6zFeIUkB/P2cKOTFV4TM8SEU9wlf4A/HpAgLIexe6NlzAHJyuqDo0C/cHmG3V7OwQCGFycmpngkJgnB4jkKNrSWYkfI6UtPUPPEhOUXsrkLwY7Vace655+Gjjz6EStXa9mA2W3DllVfgiy++OKET6+lnn3tc91MqlahzONq1PdTWegSJROWxV22qq6s5i6Frt+6YdMaUX32MoUNHoHeffkc4FooO7YEuKw9KZXD23fr7xDA3rztCkXu6A/POdOLZVUYs3eo5wSOR4e0NTfjylxj8cXomJvVLOq4+5c7YDrvK6nDXRxUoMXtWIYmhBQnc/61LDZ3Tw1DeJzoL77EoO6cLlEpxmYTqPvF4D+Ci/bV46CMDSsxOvm3RT02ocSfz1Jm4DoiPJ7MdNhU6cO+y0hZRIS0xGv++Ohf9c0NPvArVfaIzMRkrYTJ5sn98Reh8cwhHhU40+/YbiupqCwsMFRWlrEilpXkcDEplioSpCCwokCuBpj1YLSbYbFYkJaWgX/9h3EYjIaBCqPDxxx9j1KhRyM72THnx0qNHd3Tv3gOff/45Bzx2NtnZOfw5KTpUyCIBcajwIOcmxMd7xu+VlBS1GytpNpuweNEC9O3Xn50Pv4UyKYkvbaFA1aJDnf5yhACRlRaHR2Zn8Yx3mmvvnbxQanbhjvfKOQjt9F5JGNtbiVN7Kn3Su9zobuL2DAqXbHA19z4AuOI0Fe44KwNxMcEZLCkIkQCNpFx8awHuXlSBb/Z4BKNlW208reWpudl+mRphrW3EOz9Y8OZaM+qcnmOEOikGr8zLlXGzwq8iwkKYQE6FAQNHwGI2oqjoF5SWFsFmq4ZKlY50jQ4JCUopHiOUurpamIwGWCxGFqAU8QnNk0Z0sk8IIUdJSckx2x3o9qKiIp88LrVDDBs+EqtWLuepDjROcsP6dTz1wcvKz5ZhwMBBGHPq6dxitHDBO9BkZCAvvwAHD+7n+ygTldBltRdFhMhjWJdELLgpH0s2VuPZ1VWwNvcuU3jbh5ur+ULhiUPyEzCiayK6ZSrQNYMucUjuoNhQYXWyoECPSUGSXpLio/HgTC2mDkzptNcnCELHUcZH4+m52Xh0qQHvb6jm2zYcdOCqV0rw0pU5yPJRLou5phFvf2/GgnVWnmbjJSM5Bq9ck4seWo+ILgjHQoSFMIJW09TpGVCpNZzuX1S8H8XFB/kEV80J5VrExycG+mkKfqKhoR4WcyWMxkpUV5s53LNrtz7QanOOSLAXhFChW7duPFryvvv+1m4/rq+vxzfffIMpUyb77LEnTDyTJz3s37+Px9deMHN2i3uByM3LR1qzW4GEh8xMLf++eeOGlvvosrJEWBCYmOgodi5MHZiMl74yYdmW6haBgWh0A5sP1fGlLXSSn5vaiNP6GnFaTyUG5CYg9hgugzqnG+sPOPD+Biu+3VPTEs7opadOgScvzWbRQhCE4IE+0/eep+VwVwpWJfYbGnD5v4t5UsvAvAQMyI3nAMjDIVGgzOLEL6VNqHI7OL8lPjba8zMuCo2NTXA4m+BooOwE+t2NjQcd+N+PFjga2h8kemgV7JSQY4RwPES5XM0eFyHsoF5gCvwqKTnIv1N7BIkLqWnpHNAXSfb3SOmtopYHEhRISCCXAv2k23JyuyI7uwAxMRLsKYQ2lFkwdOgwjBkzGrfeeiuPnySXwhNPPIkDB/Zjw4YNYRVeRq0QW7esw5ChYyRjIcyP5dSisL2kDmv31mLtvhrsKPW0SfwWKQnRGNU9kVsnyH1AxYf3UmJyHiEmeG3NF41MxXUT0qFUhLbQHM77xIlkLLz0wjO46ZbbJWMhDPeJpVuqcd+HeriOkhOclRbLAgNRZnGhzOxsJ1B2lO6ZClw/MR1nDUpmETTUCbd9oqMZC3v2bPXp+YQ4FsIYWs3LzimAVpeDstJDKCsrQnW1FSkpqdw6oVJlIFGZLKvXYZOhYIfFbILVauIgz8ZGF7Ky8pGb15XDPgUhHKCxjgsX/g/z5s3D9Olnt9zeq1cvLFy4MKxEBSGyoJP3IQWJfLllsgZGuwvr9tfiF30DCqvo4kSR0QlnY3ulwFbnxhc7a/jyW5AAQbPnz+iXfMJBcIIgBAYaOZmZEos7F5Tz570tFVYXXzqLXjoFbpiUjsn9qT6QY4RwYoiwEAGQBT6/oAcXmRX6EnYxWCwmmM1GnsmuTs9EcrKqQzPghcDS2NiImppqmE2VLCZQKCONG9XpcpGVnS+jI4Ww5JRTTsGWLVuwefNmVFZWIisrG8OGDRWRVAgryOJ8zpD2M+xdjU0otzjxzc9F2GtKwfe/1LbLSzgcMiYWpMdhYr8kzBqZhi7S8iAIIcnoHkos/X1XrN1bgx1l9dhRUsfhr/VtAli9UABrjioWKQonomMTOKSV2qI8P5sQGw0kKqKbL1H8kyY+UM7KpL5JIigIHUYqyQgiTqFAfn535OZ2hbGqAuXlxTxFgsYOKpNSoFZpkJKiQnxCYkS1SYRku0N9HQsJZnMlj4wkG2RiYhK6devD40al5UEId0gI7dOnDzsY8vLyRFQQIqbvOl+jwMSe0bgsT8ffBwcrnfhhfy02HqzltgeyMPfUKtBdq+DQx4S40G51EAShtYXp3GGpOHeY5zq5lw4YGlhgoPwEEhNy1HHQJMWwOOCx/+fL5hP8hggLEdoikanN4QKUVrjLy4tgrNLzRInk5FTPuDR1Bo+qFBdDcLkTamttPN3BbrPyWFGXy8nvVZeuvWVspBAx7NixA3feeRfWrVvXctuECRPw1FNPokePHgF9boLgT2gRgAQEulx2qko2viBEEORM6JMdzxdBCAZEWIjwExLKWqBLfX0dKg1lMBjKuEffZKriACCaMMEuhvhEWREMALQaVV/ngL3GCrPJyDkKNTV2zkygdgfKz6BRooIQKZhMZpx77nno3bs3Xn75JW6DKC0twX//+yrfvnHjBiiV8pkQBEEQBEHwJyIsCAxNicijNom8brBaTNDrS2EyVfLqeFJSCgeipaamc4poQqKScxsE3zkT6utqUeuo4feivt7B7Q4ulwsqlQZ9+nRnl4KMjBQikU8++QRdu3bF0qWftnNUzZo1CxMmTMTKlaswc+YFAX2OgiAIgiAIkYZUh8IRLgZyKdCFxhZWVpZzm4TBUIGqKgMLCyRCJCenITklFYkJSsTGKSST4SRxOhtQV1eLGrsNNpsZ9fX1PGaORAbKTsjKLoBWmyNhjELEU1JSjIkTJx7RpkXi5+mnn87uBUEQBEEQBMG/iLAgHBOFIp6DHulCrRJmcxVPH6A2CaPRwAVvQkIit0xQu0SiMolbJiT48TgDGBvqUOeoRbXNwoICbWMSEzwtKmrk5/eAOj1DWh0EoQ1arRYrVqw86meKJkWMHj1KtpcgCIIgCIKfEWFBOC7IpZCVlceXxkaXZ1ylqdIjNpiroFBUIDFRycKCSpXOooMiPpGnE4jQ4IG2G4kHDmpxsJo4O8HhqOXbqLVErc5Abl5XbneIjY2TPVMQjsKMGTNw333346abbsK8efOg0+lQWlqKl156Gfv27cOUKVNluwmCIAiCIPgZERaEE4aKYI1GyxdaJaQJBaZmNwONrjQa9dwyQQGDCoUCSUmpPMKSHBB0G/334Sw20DZxuxu5lcTZUI86ciLUeBwJNMWhhlocXC7eJunpWnYlkONDMhME4bfJycnBggULcPvtt+O99xa03E5hju+/vwhqtSTjC4IgCIIg+BsRFoSTggSClFQVX7p06ck5AWaTx8VAIgOt0gOl7GSIj4/nlfh2YkOcAnGK+JAVG1hEaGxEg9MrIjhQW2NvERFIXKirq6N7Ijo6hkd55uZ0ZTGBXB2h+JoFIdBMmjSRpz/s2bMH5eXlyM3NRZ8+fWQ8riAIgiAIQoAQYUHoVGj0YXZOAV94VGK9A3a7DTX2ap5sYK0282q9R2xIgEKRwMWAIj4eyUkpLEDExMay0ECFOLVSBFp0oNdBIYput4t/ei7U1uDgbATKSqCJDR4RwcH/DT1vmqahVmciKTmVXxtN0xAhQRBOjm3btiEuLg79+vXDoEGD+HK0vwmCIAiCIAj+Q4QFwWdQEU1CA10yMnR8m1dsoILcXuMRHGw2K1xmJ/TNgZHkavAICjHcHhAVFY3YuDh2N/DfuZ0iBjHRsYhuFh5iomPoAel/9MhH/CTcbjc7B5qa0O4niwaNLjS6G1t+dzqdLS4Ech643Z72Bvo36D50G7kSWkWEVKjTM5GclIqkZBrPKSKCIPiCDz5YgrS0tKOKB7/2N0EQBEEQBMF3iLAgBExs0LQTG+pQU1PNbQS08t/gbOCinnMKnA3t/g1yMpD4QE4HrwBB4kNbMaGpySsqtPLLvu3Nv7UXF9xNbm5n8DoRSDSg39tC2RCezIh4xCcouZ2DciRIUKDJGOJEEITAY7VakZeXh3DCI4iCJ8gIQENDA0/PiXRkO8i2aEudw4HExMTm4wSf4EQ08vmQ7SD7xJF4F0S95xW+IMrlcsoRSAhqmprcHgdBcxgiiQ4tvzdfdzkb+INCIkXLhb5cm38nosj9wMJD+4tXMKCsB8Vhv9NP+rsEKwpCYFm6dCk+//wLbNy4kfNa2rZAENXV1Vi+fDk++eRjjBoVPiMny8uKUVi4J9BPQxAEQRCEMKBr1z7Izsn3yb8tjgUh6CE3AhX7dBEEITLR6w3YtWsXTCYTYmKisWtX69cXCYQqVRoee+yxsBIViKjoWOzauRdTzzoH6RoNIhmz2YyPP1yM82fOglqtRqQi20G2hewT8vmQ44QcM08Uk9GIVSuXoVv3AfAVIiwIgiAIQc+1117Dl9dffwPJyUmYPXs2IgFq+TKbLRz+Su1XkUxtrQMOh4ODfyN5W8h2kG0h+4R8PuQ4IcfMjnx30PkEnVf4ChEWBEEQhJBh3ryrA/0UBEEQBEEQhMOIPvwGQRAEQRAEQRAEQRCE40WEBUEQBCFkeOihvyM9XXPMy9NPPxPopygIgiAIghBxSCuEIAiCEDJccMH56NOn9xETIRYv/gD19fU466ypCCeUiUqcetrp/DPSkW0h20H2CflsyHFCjpfy3RG836EyblIQBEEIeWgk7fjxE/D666+hb9++gX46giAIgiAIEYW0QgiCIAghT1xcHMaOHYsNGzYE+qkIgiAIgiBEHCIsCIIgCGHBgQMH0NTUFOinIQiCIAiCEHFIK4QgCIIQMqxduxbr17d3JbhcTmzatAlff70GP/64Dl27dg3Y8xMEQRAEQYhEJLxREARBCBk2bNjIOQptiYtToFu3bli8+H0RFQRBEARBEAKAOBYEQRAEQRAEQRAEQegw4lgQBEEQhCCkstKAtd+ugdlkRFqaCqedPh7Z2TmIRPT6Cvy47jsYq6qQkanFhIlnIDU1DeGMsaoSSz5Y1HKdXnPvPv1arrtcLmzbuhn79u7hUatarQ6jTx0LtTod4cbKFctQdKiQf09NS8Mlcy5v9/fa2hr88P1alJaW8PWcnFycNnYclMokhNv0m61bNuOXX/aiob4euqxsjBkzFmkq1RH33bdvD77+8nMMHjIUo8eMRbhBY4bX//g9ykpLEBsXh549e2PEyFGIiYnhvzc2NmLdD2vxy769iIqK4s/O6DGn8e/hBOUK7d61Azu2/wx7jQ0aTQZGjx4LrU7Xbr/ZsP4HHNj/C6KiozF06AgMGDgI4Qq994vfX4BqqxXXzr8J0dGeSMHyslJs3ryBv1vp2DBgwOCw3g7Erp3b+Txi+IhT+PPR9vaft21BTU0Nf5cOH3kKunXrgZNFhAVBEAQhpKirq8Obb76J7dt3wGKxtPvbJZdcjBkzZiDUqa2txaKF76J//4EYO3Y89u3bjfcXvoer583nwiqSKC0p5gJ71OgxGHPq6bBVV3MReda0cxDOqNTpuHjOZfz7u2+/jgans93fv1u7hosKEpziFQn48cfvseh/7+Dqa25AfHw8wolx4ybCeaoTO3f8jJ07th/x92VLP4a7sRFTp07n62u+/gLLPv0Isy/xbL9w4dtvvkJMTCxOHzeBW8CocF648B3Mu+YGnozTVmj55usvERsbC4ejDuHI56s+Q6/efTB06HDYbDZ8/vkKOBwOFuCIb9Z8yYX01Glno7HRjVUrlnEez+njJiKc2L17J0pKijFqzKlQJirx889bsWih5ziQnJwMt9uNDz9YyMeK8RPOQGKiEps2rWfhITNTi3Dku7XfwOV0orra2nKb2WzCV199zgU2CW0GfQVWr16BRncjBg8einDEZrPxtiBhpb6+9ThAnwsSa6eedQ50WVkoPHgAHy15H3Mvvxo6XdZJPaYIC4IgCELIQKu0U6ZMZUGBTqQVCgWUSiU2b96M/Px8FhbCASqg6LVNOmMKX6eTwAMH9mPr1p8wbnx4nRj/Fl9/9TmGDB3WsupKK/Pde/REuEMrr+RUIaKijhziNW78pJaVOOKsaWfjuWee4BPm/IIuCCeUSR7nQUJC4jHFp/POvwhZzY6eUaNPw0cfvs/FVDitUE+cNLndez5l6nS89MIzvAJLLg0vn69awauT5GYJVy64cHbLtiAX0+DBw7Bnzy6+7mxowLatP+GcGRcgP9/zWSAx5ssvVuHU08a1uBrCgT59+qFfvwHt9hFytVSUl6Fnr97Ys3sXqqoqce38m1sEx2nTZ4TtBKWyslIc2L8P4yZM4mLZCx1L5152Vcv1jIxMFBcXcZEdrsLCqhXL2Lm15adN7W4nISo3Lx/9Bwzk6+RyoX2G3D8nKyzIuElBEAQhZFi+fLnH1rlhPc4991xcfPHF+PLLL/Dpp5/AarVi1KhWq18oU1FRhtzc/Ha35eXlQ19RjkiCnBsVFeVQqdTs2Hjz9Vfw2bJP2LUQ6bQtMAmDwcA/vWJEJJGXV4ADB37hYoku9HtefkFYiQpHe88rDQZ+jWlt2oLI4uyoc2DI0OEIZ9pui4aGBhQdOtgirlRWVbIInZuX13IfKqSoZchiNiGc94mDB/fzPuFthaDr9Fn4cd33eP21f7OradfOHWH32SDo3GDlZ0tZcIuNif3V7USQIKc6ShtROLB1y2ZERUeh/4AjWz3y8vNRVWlocXQYDHrY7fYjzjk6gjgWBEEQhJBhz569mDZtGhISEnjVqaGhnm8fN24cJk+ejC+++AKXXnopwqHdIz1d0+42Wq2tq3MgkqitsfPP9T/+gDMmT0VKcirWrVuL9xe9hyuvvq6d/TuScThqsWrlMowYMSriWmWIs2ech8Xv/w/PP/skX09LS8Os2aF/HPg1amrs+Hz1Cpwy6lQkJSfzbXa7Dd+s+YozKMKxcDwcOi5s2bIJtTU1XDzTaj1RX1d3hMMlMdHzu6P5b+EEiSivv/pvOF1OuJwuzDh3ZksGTY3djtLSYm4BOPe8C2HQ6/lYER0TzW6HcGsV6tK1G4tIhwoP/up9qY3IbrPhlFPGINywWixY98N3mHt5q0OjLd2798SIkaPx2n9fRnx8Ap9HTZ4yvV0uR0cRYUEIWw4e2M/2H6vVwl+6dAAdOGjIUVVLQRBCA1LVqWggMjI02L271eqrVqthMoXHalRcbByvvrSFVuUirZCOjfW83mHDR/LJEEF9oc8/+wRbfcPN8t8RqJ9+8aIFyMnJw4RJZyLSoKA2yuBIT09vyd3YsH4dlixZhEvnXhlWtve2x8HFi95DQZeubPH3smrFcow8ZTRUajUiAQqm7N2nL4f0UUAduZnOPf9CxMZ5yhunswEKRXzL8ZMIx2Mo7eOUx0LfGXTuu3zZx5h98VxuDaJtQefAlK/Q2gJwiF0L4SQs0Gui137lVdcenyD10ybeRl5RLlxoamrCihVLcepppyMlJfWo96Ecjs2b1uOccy/gBQxyQn715WokJSWha7fuJ/X4IiwIYUlRUSE+/mgxH0i7dOmKmtoa7N2zGxs3/IhRo08N9NMTBKETGD58OP71rydw6aVzWDD8+OOP8cILz4fFttVkZByx4lJVZeBeyEiCVt8ppI7yJrxQYUDvN63ORToUzkUFJgksZ04+KyJWqQ+HLL10YnzBzNkcVkecccYUvPTiM9xbfrI9w8EGFdHk2KGcEW8GixeyvdNr3rxpQ4voRK1EJSVFuPyKeQg3yJFAF2qVGtvUxJMAaPVek+45TlZWViI319MOUVVZyceNcJyawu0wzS1QJBzQ1JB9+/aysEDXaZ9pCx1Pw62d7OCBA9w698brr/B12g+IV195qd1EHQq9pQkal1x6RVjuC06nEyXFRfyeU/uL191kMhk5U4LcTDt+3sotEr169eG/03kFHTuojUqEBUE4xgGGvkzI+sUfGmSioKArp+MKghC6DBo0kMMaiTFjxuCMM87AtGmeJPgpU6bwJRzo138gr7pS+Boln9OoPbpcfNiYvXCHCoH+/QfwWKxevftyC8ymjev5xDhSR2+2tbtSgUn7x4SJkedUaCs+0Yrt3j07MXyEJ2Nl956dfFtq6tFX7EIVSranrBEKXTvadIPrrr+53fXlyz7hooGmqYTbduDQvSHDWGik7AQKvE3XaHgSBl1odN6679fi/Jmz0OR287janr36tBMpwwE6HtICGgVYEiQikdg2fPjIlu8SWlSj3BFyfVHBuXfPLowMsxYAmhpEIb9eKIiQ9n9yJXjDX2lVnvabOZdeEbYtY3FxcUccBz7+6APk5xe0vOfqdA0OHSrkFjqaEkJZC2WlpezqPlmiXC5neMaCChHNju3bOEn8nHNn8ocpHK2QgiB4OHDgII9S6tu3b1it2NKJMp0IUXh3U5ObCwlqCYg0yMK84rNP2eZKBQOtUE45azqLxeHOK/9+gX/abNWIT0iAIk6BU0aNwdBhI3jEIs2v9/ZSe5l4xuSWlahwgQqjnzZv5H2B+oGTk1P4BPrqa67nv1Oh9PVXX7TY3al4nDjpzJZVynDhk48+4NXowy3Ok6dMQ7fuR86gJxEiU6vjbRFO0Go09chTQB2d31EmDQV4Ug6LN5uG2kWWfvohu1nIHk7BdDPOu4ALqXCivKwUX335OcxmI79OOkaSwNbWnUttD199uYp/d7pcGDRoyBETRsINcvyRg+X3d/2ZXye1zr37zhtISEzk46gXEmRmXjgb4cx777zBToTTxo7n6+TsoJDLQ4cOslhPnx86Vk4962zef04GERaEsIQOrqTibtv2E6uzdODo0aMXRo4chbgwU6sFQQhfyGVFdmY6GY50gZQKSppH7w1hiwQoI+hw6ESQArdovzg8h4OgWfbh9j1HJ75t57ATJCIeLqo4HA6QtnissZShDoUUHq0FSKlMOmp2AFmg6bgRrtuDzvW8x8djFcm0Kkv7SrhuAy/k2gCa+Nhw7O+SWnb8hbOg0FZ8ov3f2yLivX44MTGxLS1U4Yrdbm8W5ROOyKepcziQ2In7hAgLQthDKeo01/bbNV8hKSkZsy4O76RoQRAEQRAEQRAEfyLhjULYQyo19ZU1ulz45OMlrFqerNVHEARBEARBEARB8BD+XhghIqExMtRT5rWJkoV29+5dbIkSUUEQBEEQBEEQBKHzkFYIISyhxOAfvl+L/b/s5Tno1Jup02VzsE+4jZ4SBEEQBEEQBEEIJCIsCGGNN9iH2iEiPfhMEARBEARBEATBF4iwIAiCIAiCIAiCIAhCh5GMBUEQBEEQBEEQBEEQOowIC4IgCIIgCIIgCIIgdBgRFgRBEARBEARBEARB6DCxHf9PBUEQBEEQBCH8ef7556FSqXD55Zf75fEefvhhVFTo+ffbb/8devXqxb8/8cSTKCjIx+zZsxHsvPLKK9i6dRv/fvHFszF+/PhAPyVBEHyIOBYEQRAEQRAE4VdYtWo1vvvuO79to48++hhVVZU45ZRTkJKS0nL78uXL8eOP6xEK9OzZi5//4sWLsXv37kA/HUEQfIwIC4IgCIIgCIIQZAwZMgRXXXUlsrKyEIpMmjSRn79CERfopyIIgh+QVghBEARBEARBOAHeeustrF+/gX9PSUlGr169MWfOJVAqle3ut3HjRnYfNDTUY9SoUejevQdee+01PPvsM4iO7vj63ubNP+GLLz6H2WzGiBEjceGFMxEVFdXuPnv37sWSJUtgMFQiNzcXs2fPQkFBQcvfH3zwIYwZMxpnnXVWy22bNm3G66+/jueff67lNm/7RZcuXbBs2XJ+zCeffAIOhwPvv78Ye/fu4TaRKVOmYMSIER1+TYIghDbiWBAEQRAEQRCEE6Bbt25s86dLTk4OFi5ciDFjTkV1dXXLfRYv/gBTpkyF2Wzigv6NN97kjIa3334bbre7w9t72bJluP322xEXp+A2id///g7ce+/f2t1nxYoVOO20sdi9ew969OiOH374gZ/fjz/+2HKfDz/8ENu2eTIQvBw8eJCfX1uo/eJvf7sPd9xxB9LS0lg8qK+vx5lnTsZ7772H/Px8xMTE4p57/oJ33nmnw69LEITQRhwLgiAIgiAIgnACjBs3ji9ebrrpJpx66ml49dXXuNCnwvuvf/0rbrvtNjz00IN8n5tvvhmTJ09BaWnpyZ28x8Zi1aqVSExM5Ovp6el44IEH8fDDf2fXQkNDA+688y5cdtlleOaZp/k+t9xyC2bPvhh33nlnh7IiGhsbsXLlypa8h02bNmHPnj3sVtDpdHzbH/5wF0pKSk7qtQmCELqIsCAIgiAIgiAIJwA5DlavXo0NGzagsrKKC28SE3bv3sV/p6K7oqKC2w+8UOvDrFkXcVF+MowfP65FVCD69OmL2tpaGI1GZGRkcAsEiReXXTa33X936aWXYt68eaiqquL7ndhjjm8XIkkOjKSkJPz97w/j+uvnY8CAAYiJiWH3giAIkYm0QgiCIAiCIAjCCUAtDXfc8Xs0NDjRv38/bolQq9Ww2Wz8dxIbCI1G0+6/O/x6R0hMbJ/jEBPjOZ0ncYPQ6w3Nj9VePMjM9FzX6z1jLE8Eem3t/61MrFjxGRobXZg7dy66du2Ga665FocOHTrhf1sQhPBAHAuCIAiCIAiCcJwcPFjIIYarV6/iQEYvb7zxRsvv+fl5/LO4uJgzGLwUFRX7fDt36eIJaKQin/IVvHiLfm+AY3x8PLdNtIWCGY+XwYMH46WXXuLf9+8/wO0W8+dfz20agiBEHuJYEARBEARBEITjpKmpiX9WV3vcCd6wxM2bN7dc79mzJwYOHIAXXnixJajRbLbwNAlf06NHDwwbNgzPPvssnE4n32a32/Hyy//G1KlTW1oa6Dl+//33La+HpjxQGOPxsHPnznbBjyRgjBgxnNsxBEGITMSxIAiCIAiCIAjHSffu3XDppXNw1VVXYfLkybBarVxk9+7du12eAhX2F100C6effjp69+7DeQzkJigqKjqpUZO/BQU4vvDC8xzWSJMgyFmwceMGJCQk4okn/tVyvz/+8Q84//wLMHHiJBYjfvrpJx5LeTzExcXhpptu5t979eqFyspKzo6gxxUEITIRYUEQBEEQBEEQfoXbbrsVSmVrtsHLL7+M+fM3Yd++fTyCcezYsdiyZQsaG1vHSNJYxi1bfsLXX69BQ0M9/va3e9nZsGvX7g4LC3fddecRwYtU2JOIkZqa2nIbhSlu3rwJa9d+h8pKA66++ip+jjRRwsvQoUP5PjSKksSIv//9Ic6M+Oabb47rMdes+Zpfs3cbjB49mn8KghCZRLlcTo//SRAEQRAEQRCEToGmM6hUKmi1Wr5OzoYpU6Zi0KBBePXV//7qfzty5Ck8+WHIkCG4/fbfcSEfarzyyivYunUbFi5ciH/8g6ZHXB/opyQIgg8Rx4IgCIIgCIIgdDI0pWHatOno3r07iwTkDOjSpQs7A36Le++9l4UIou2Yx1CiZ89eUCjieWLG6NGtIZeCIIQn4lgQBEEQBEEQBB9QW1uL9es3oKqqCt26dcPw4cO47UAQBCHcEGFBEARBEARBEARBEIQOI+MmBUEQBEEQBEEQBEHoMCIsCIIgCIIgCIIgCILQYURYEARBEARBEARBEAShw4iwIAiCIAiCIAiCIAhChxFhQRAEQRAEQRAEQRCEDiPCgiAIgiAIgiAIgiAIHUaEBUEQBEEQBEEQBEEQOowIC4IgCIIgCIIgCIIgdBgRFgRBEARBEARBEARB6DAiLAiCIAiCIAiCIAiC0GFEWBAEQRAEQRAEQRAEAR3l/wHwdb10rR5udQAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"r at 6 h: +0.05 (within-day memory is gone)\n",
"r at 12 h: -0.23 (anti-phase: day flow vs night drainage)\n",
"r at 24 h: +0.40 (the diurnal cycle repeats)\n",
"r at 48 h: +0.34 (and still persists across days)\n",
"\n",
"vector-mean wind direction 335 deg - flow is from the NNW, so the\n",
"source area will sit NNW of the tower.\n"
]
}
],
"source": [
"regular = season.reindex(\n",
" pd.date_range(season.index.min(), season.index.max(), freq=\"30min\")\n",
")\n",
"theta = np.deg2rad(regular[\"WD\"])\n",
"lags = np.arange(0, 97) # 0 to 48 h in half-hour steps\n",
"acf = 0.5 * np.array([\n",
" theta.pipe(np.cos).autocorr(l) + theta.pipe(np.sin).autocorr(l) for l in lags\n",
"])\n",
"hours = lags / 2.0\n",
"\n",
"fig, (ax_rose, ax_acf) = plt.subplots(\n",
" 1, 2, figsize=(9.2, 3.6),\n",
" subplot_kw=None, gridspec_kw={\"width_ratios\": [1, 1.35]},\n",
")\n",
"ax_rose.remove()\n",
"ax_rose = fig.add_subplot(1, 2, 1, projection=\"polar\")\n",
"\n",
"edges = np.deg2rad(np.arange(0, 361, 15))\n",
"counts, _ = np.histogram(np.deg2rad(season[\"WD\"]), bins=edges)\n",
"ax_rose.bar(edges[:-1], counts / counts.sum() * 100, width=np.deg2rad(14),\n",
" align=\"edge\", color=C_BLUE, edgecolor=SURFACE, linewidth=0.8)\n",
"ax_rose.set_theta_zero_location(\"N\")\n",
"ax_rose.set_theta_direction(-1)\n",
"ax_rose.set_xticks(np.deg2rad([0, 90, 180, 270]))\n",
"ax_rose.set_xticklabels([\"N\", \"E\", \"S\", \"W\"], color=MUTED, fontsize=8)\n",
"ax_rose.set_yticklabels([])\n",
"ax_rose.grid(color=GRID, linewidth=0.6)\n",
"ax_rose.set_title(\"Wind direction, summer 2024\", pad=14)\n",
"\n",
"ax_acf.axhline(0.0, color=\"#c3c2b7\", linewidth=0.8)\n",
"ax_acf.plot(hours, acf, color=C_BLUE)\n",
"for h, dx_lbl, ha in ((24, 4, \"left\"), (48, -4, \"right\")):\n",
" ax_acf.axvline(h, color=MUTED, linewidth=0.7)\n",
" ax_acf.annotate(f\"{h} h\\nr = {acf[h * 2]:.2f}\", xy=(h, acf[h * 2]),\n",
" xytext=(dx_lbl, 10), textcoords=\"offset points\",\n",
" fontsize=8, color=INK, ha=ha)\n",
"ax_acf.set_xlabel(\"lag [hours]\")\n",
"ax_acf.set_ylabel(\"autocorrelation of wind-direction unit vector\")\n",
"ax_acf.set_xlim(0, 48)\n",
"ax_acf.set_xticks([0, 6, 12, 18, 24, 30, 36, 42, 48])\n",
"ax_acf.set_title(\"Direction recurs daily, so half-hours are not independent\")\n",
"fig.tight_layout()\n",
"plt.show()\n",
"\n",
"print(f\"r at 6 h: {acf[12]:+.2f} (within-day memory is gone)\")\n",
"print(f\"r at 12 h: {acf[24]:+.2f} (anti-phase: day flow vs night drainage)\")\n",
"print(f\"r at 24 h: {acf[48]:+.2f} (the diurnal cycle repeats)\")\n",
"print(f\"r at 48 h: {acf[96]:+.2f} (and still persists across days)\")\n",
"\n",
"# The vector mean is the only meaningful \"average\" of an angle: the arithmetic\n",
"# mean of WD is nonsense when the distribution straddles 0/360.\n",
"rad = np.deg2rad(season[\"WD\"])\n",
"vec = np.rad2deg(np.arctan2(np.sin(rad).mean(), np.cos(rad).mean())) % 360\n",
"print(f\"\\nvector-mean wind direction {vec:.0f} deg - flow is from the NNW, so the\")\n",
"print(f\"source area will sit NNW of the tower.\")"
]
},
{
"cell_type": "markdown",
"id": "f4c9c94a",
"metadata": {},
"source": [
"The rose is strongly bimodal-to-unimodal about the north: nearly 30 % of\n",
"half-hours arrive from 345–360°, with a secondary spread through the south.\n",
"That concentration is what makes the source area compact, and it is also why a\n",
"few degrees of azimuth error matters so much here (§10).\n",
"\n",
"The autocorrelation falls through zero within about six hours, swings **negative\n",
"at 12 h** — midday flow is close to the reverse of the small-hours flow — and\n",
"returns to $r \\approx 0.4$ at 24 h and $0.33$ at 48 h. Two things follow:\n",
"\n",
"- **A block must contain a whole diurnal cycle.** Anything shorter splits the\n",
" day/night pair, so the block length floor here is one day. This is the\n",
" `block_days=1` default.\n",
"- **Day-to-day memory has not vanished either.** The residual correlation at\n",
" 48 h argues that 2–3 day blocks would be defensible, and we test that\n",
" in §9 rather than asserting it."
]
},
{
"cell_type": "markdown",
"id": "156942f8",
"metadata": {},
"source": [
"## 3. The grid\n",
"\n",
"`GridSpec` is a regular metric grid centred on the tower: `x` positive east, `y`\n",
"positive north, both in metres from the receptor. `GridSpec.square` is the\n",
"convenient constructor — a half-width and a cell size.\n",
"\n",
"Domain size is a real decision, not a formality. Too small and the footprint is\n",
"truncated at the edge, the captured fraction drops, and an \"80 % source area\"\n",
"silently becomes 80 % of whatever stayed inside the box. Too large and you pay\n",
"for empty cells in every one of the 3 271 model runs. For a 2 m receptor a\n",
"±300 m box at 5 m resolution captures about 95 % of the source weight while\n",
"keeping the grid to 120 × 120."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a1f89e71",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:06:50.726433Z",
"iopub.status.busy": "2026-09-18T20:06:50.726269Z",
"iopub.status.idle": "2026-09-18T20:06:50.731277Z",
"shell.execute_reply": "2026-09-18T20:06:50.730477Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shape (120, 120) (ny, nx)\n",
"cell 5 x 5 m = 25 m^2\n",
"x extent -297.5 to +297.5 m (east positive)\n",
"y extent -297.5 to +297.5 m (north positive)\n",
"crs EPSG:32612\n"
]
}
],
"source": [
"grid = GridSpec.square(\n",
" half_width=300.0, dx=5.0,\n",
" tower_lat=TOWER_LAT, tower_lon=TOWER_LON, crs=CRS,\n",
")\n",
"\n",
"print(f\"shape {grid.shape} (ny, nx)\")\n",
"print(f\"cell {grid.dx:g} x {grid.dy:g} m = {grid.cell_area:g} m^2\")\n",
"print(f\"x extent {grid.x.min():+.1f} to {grid.x.max():+.1f} m (east positive)\")\n",
"print(f\"y extent {grid.y.min():+.1f} to {grid.y.max():+.1f} m (north positive)\")\n",
"print(f\"crs {grid.crs}\")\n",
"\n",
"# Plot extent in metres from the tower, for imshow. Row 0 is the north edge,\n",
"# hence origin=\"upper\".\n",
"EXTENT = [grid.x.min() - grid.dx / 2, grid.x.max() + grid.dx / 2,\n",
" grid.y.min() - grid.dy / 2, grid.y.max() + grid.dy / 2]"
]
},
{
"cell_type": "markdown",
"id": "d0994823",
"metadata": {},
"source": [
"### Array order: the one thing that silently corrupts the output\n",
"\n",
"The footprint models in this package return arrays indexed `(x, y)` with `y`\n",
"ascending north. GeoTIFFs are `(row, col)` with row 0 at the **north** edge.\n",
"Getting this wrong produces a plausible-looking raster that is transposed or\n",
"flipped, and no downstream check will catch it.\n",
"\n",
"`GridSpec.to_raster_order` does the conversion and validates the shape while it\n",
"is at it. Pass `model_order=\"xy\"` for this package's models, `\"yx\"` for a model\n",
"that already returns row-major arrays. The demonstration below uses a marker\n",
"placed at a known corner so the transformation is visible rather than asserted."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "38e20011",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:06:50.733284Z",
"iopub.status.busy": "2026-09-18T20:06:50.733155Z",
"iopub.status.idle": "2026-09-18T20:06:50.737711Z",
"shell.execute_reply": "2026-09-18T20:06:50.737067Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model order (x, y), y ascending north:\n",
"[[0. 0. 0.]\n",
" [0. 0. 0.]\n",
" [0. 0. 1.]]\n",
"\n",
"raster order (row, col), row 0 = north:\n",
"[[0. 0. 1.]\n",
" [0. 0. 0.]\n",
" [0. 0. 0.]]\n",
"\n",
"-> the spike moves to the top-right cell, i.e. north-east. Correct.\n",
"\n",
"mismatched input raises: footprint shape (10, 10) does not match grid (120, 120); check model_order and the model's domain settings\n"
]
}
],
"source": [
"# A 3x3 toy grid with a single spike at the far north-east corner.\n",
"toy = GridSpec.square(half_width=1.5, dx=1.0, tower_lat=TOWER_LAT, tower_lon=TOWER_LON)\n",
"model_out = np.zeros(toy.shape) # (x, y) order as the models emit it\n",
"model_out[-1, -1] = 1.0 # max x (east), max y (north)\n",
"\n",
"print(\"model order (x, y), y ascending north:\")\n",
"print(model_out)\n",
"print(\"\\nraster order (row, col), row 0 = north:\")\n",
"print(toy.to_raster_order(model_out, model_order=\"xy\"))\n",
"print(\"\\n-> the spike moves to the top-right cell, i.e. north-east. Correct.\")\n",
"\n",
"# The shape check catches a wrong model_order or a mismatched domain.\n",
"try:\n",
" grid.to_raster_order(np.zeros((10, 10)), model_order=\"xy\")\n",
"except ValueError as exc:\n",
" print(f\"\\nmismatched input raises: {exc}\")"
]
},
{
"cell_type": "markdown",
"id": "422036b6",
"metadata": {},
"source": [
"## 4. Wiring in the footprint model\n",
"\n",
"`accumulate_blocks` does not know anything about a particular footprint model.\n",
"It takes a **`period_footprint` callable**: given one row of the driver frame,\n",
"return a 2-D footprint density on the grid (units $\\mathrm{m^{-2}}$), or `None`\n",
"if the period is unusable. Everything model-specific lives in that one function,\n",
"which is what makes the module reusable across the Kljun, Kormann–Meixner and\n",
"Lagrangian models in this package.\n",
"\n",
"The filters below are not optional garnish — they are the validity envelope of\n",
"Kljun et al. (2015), and periods outside it produce numbers rather than\n",
"footprints:\n",
"\n",
"| filter | reason |\n",
"|---|---|\n",
"| $h > 10\\ \\mathrm{m}$, $u_* > 0.1\\ \\mathrm{m\\,s^{-1}}$, $\\sigma_v > 0$ | physically meaningless otherwise |\n",
"| $20 z_0 < z_m < 0.8 h$ | Kljun et al. (2015) eq. 27 — receptor above the roughness sublayer and inside the boundary layer |\n",
"| $z_m / L \\geq -15.5$ | the parameterisation's convective limit |\n",
"\n",
"
\n",
"\n",
"**Note on `ffp_period_adapter`.** The module ships a convenience adapter of the\n",
"same shape, but its call signature predates the current `FFPModel`: it passes\n",
"scalars (`zm=`, `ol=`, …) where `FFPModel` now takes a one-row DataFrame, and it\n",
"reads a `fclim_2d` key where `run()` now returns `footprint_climatology`. Calling\n",
"it raises `TypeError: FFPModel.__init__() missing 1 required positional\n",
"argument: 'df'`. The adapter below is the same logic against the current API;\n",
"use it until the shipped one is updated.\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d4baa453",
"metadata": {
"execution": {
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"shell.execute_reply": "2026-09-18T20:06:50.789655Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-06-20 18:30:00: WD=166 deg, u*=0.34, L=+184 m\n",
" footprint (120, 120) in (x, y) order, integrates to 0.950 of the source\n"
]
}
],
"source": [
"def ffp_adapter(grid: GridSpec, z_m: float, z_0: float):\n",
" \"\"\"Return a ``period_footprint`` callable backed by the Kljun et al. (2015) model.\n",
"\n",
" Returns None for any period outside the parameterisation's validity envelope,\n",
" which tells ``accumulate_blocks`` to skip it rather than fail.\n",
" \"\"\"\n",
" domain = [float(grid.x.min()), float(grid.x.max()),\n",
" float(grid.y.min()), float(grid.y.max())]\n",
"\n",
" def period_footprint(row: pd.Series) -> np.ndarray | None:\n",
" L, h, ustar = float(row.MO_LENGTH), float(row.PBLH_F), float(row.USTAR)\n",
" sigmav = float(row.V_SIGMA)\n",
"\n",
" if h <= 10.0 or ustar <= 0.1 or sigmav <= 0.0:\n",
" return None\n",
" if not (20.0 * z_0 < z_m < 0.8 * h): # Kljun et al. (2015) eq. 27\n",
" return None\n",
" if z_m / L < -15.5: # convective limit\n",
" return None\n",
"\n",
" one = pd.DataFrame(\n",
" {\"zm\": [z_m], \"z0\": [z_0], \"h\": [h], \"ol\": [L], \"sigmav\": [sigmav],\n",
" \"ustar\": [ustar], \"wind_dir\": [float(row.WD)], \"umean\": [float(row.WS)]},\n",
" index=[row.name],\n",
" )\n",
" result = FFPModel(one, domain=domain, dx=grid.dx, dy=grid.dy,\n",
" verbosity=0).run()\n",
" return np.asarray(result[\"footprint_climatology\"]) # dims (x, y)\n",
"\n",
" return period_footprint\n",
"\n",
"\n",
"period_footprint = ffp_adapter(grid, Z_M, Z_0)\n",
"\n",
"# Smoke-test on one period before committing to 3271 of them.\n",
"probe = season.iloc[100]\n",
"fp = period_footprint(probe)\n",
"print(f\"{probe.name}: WD={probe.WD:.0f} deg, u*={probe.USTAR:.2f}, L={probe.MO_LENGTH:+.0f} m\")\n",
"print(f\" footprint {fp.shape} in (x, y) order, \"\n",
" f\"integrates to {fp.sum() * grid.cell_area:.3f} of the source\")"
]
},
{
"cell_type": "markdown",
"id": "678edb69",
"metadata": {},
"source": [
"## 5. Phase 1 — accumulate the blocks\n",
"\n",
"This is the step the module's design is built around. A moving-block bootstrap\n",
"resamples *blocks*, so every bootstrap member is a non-negative integer\n",
"combination of the per-block footprint sums. Running the model once per block\n",
"and caching `B ≈ 75` rasters instead of `N = 3 271` means the resampling in §7\n",
"is a single matrix multiply, and 500 members cost essentially nothing.\n",
"\n",
"`block_labels` assigns the contiguous calendar blocks; `accumulate_blocks` calls\n",
"it internally, but it is worth looking at directly."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a7c7c36d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:06:50.791915Z",
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"shell.execute_reply": "2026-09-18T20:06:50.795393Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"75 daily blocks, labels 0..74\n",
"first five rows -> blocks [0 0 0 0 0]\n",
"periods in the busiest block: 48\n"
]
}
],
"source": [
"labels = block_labels(season.index, block_days=1)\n",
"print(f\"{np.unique(labels).size} daily blocks, labels {labels.min()}..{labels.max()}\")\n",
"print(f\"first five rows -> blocks {labels[:5]}\")\n",
"print(f\"periods in the busiest block: {np.bincount(labels).max()}\")"
]
},
{
"cell_type": "markdown",
"id": "5726745d",
"metadata": {},
"source": [
"Now the expensive part. `accumulate_blocks` iterates the frame, calls the\n",
"adapter, converts each footprint to raster order, and adds it into its block's\n",
"slot. Periods that return `None`, raise, or produce non-finite values are\n",
"counted as skipped rather than aborting the run.\n",
"\n",
"Two details worth knowing before reading the output:\n",
"\n",
"- **Footprints are accumulated raw, not individually renormalised.** Rescaling\n",
" each period to unit mass first would give a long, heavily truncated nocturnal\n",
" footprint the same total weight as a short, fully captured daytime one, which\n",
" manufactures variance at the domain edge. The member climatology is normalised\n",
" once, at the end.\n",
"- **The in-domain captured mass is tracked per block**, so truncation is\n",
" measurable rather than assumed."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "80643f04",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:06:50.797626Z",
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"shell.execute_reply": "2026-09-18T20:08:57.569991Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 500/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 750/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1000/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1250/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1500/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1750/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2000/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2250/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2500/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2750/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3000/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3250/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3500/3549 periods\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" done: 3271 usable, 278 skipped, 75 blocks\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 2min 4s\n",
"Wall time: 2min 6s\n"
]
}
],
"source": [
"%%time\n",
"blocks = accumulate_blocks(\n",
" season,\n",
" grid,\n",
" period_footprint,\n",
" block_days=1,\n",
" model_order=\"xy\",\n",
" progress=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c1a121aa",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:57.572402Z",
"iopub.status.busy": "2026-09-18T20:08:57.572279Z",
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"shell.execute_reply": "2026-09-18T20:08:57.575969Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"blocks kept 75\n",
"usable periods 3271 of 3549\n",
"periods per block 9-48 (median 44)\n",
"mean capture 0.955\n",
"cached stack (75, 120, 120) float32 = 4.3 MB\n"
]
}
],
"source": [
"print(f\"blocks kept {blocks.n_blocks}\")\n",
"print(f\"usable periods {int(blocks.n_periods.sum())} of {len(season)}\")\n",
"print(f\"periods per block {blocks.n_periods.min()}-{blocks.n_periods.max()} \"\n",
" f\"(median {int(np.median(blocks.n_periods))})\")\n",
"print(f\"mean capture {blocks.mean_capture:.3f}\")\n",
"print(f\"cached stack {blocks.stack.shape} float32 = \"\n",
" f\"{blocks.stack.nbytes / 1e6:.1f} MB\")"
]
},
{
"cell_type": "markdown",
"id": "7b710411",
"metadata": {},
"source": [
"### Reading the block diagnostics\n",
"\n",
"`BlockStack` drops empty blocks in `__post_init__`, so `n_blocks` is the number\n",
"of blocks that actually carry data. Both diagnostics below matter for what\n",
"follows.\n",
"\n",
"**Periods per block** is the bootstrap's effective weighting. A block with 9\n",
"usable half-hours contributes as much to a member as one with 48 whenever it is\n",
"drawn, and blocks are drawn with equal probability. Heavily gap-filled seasons\n",
"therefore give more erratic members, which is a property of the record, not a\n",
"bug — the bootstrap is telling you the truth about your data coverage.\n",
"\n",
"**Captured fraction per block** is the truncation diagnostic. Values well below\n",
"1 mean the domain is clipping the footprint and the R % contours are R % of the\n",
"captured mass, not of the real footprint."
]
},
{
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"execution_count": 11,
"id": "322175e8",
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{
"data": {
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czzsQ/yAKDAQq6elptH7davr8048pI8PTW0dL5y5dyWq1iFpKi8FoIINery7zCROFEVCSiQScjsGJYXxRVhA4kSYqOoq2bv5FiiqrVy2n1JQUGSxzgQ+FERBsvmqnTp4QxV9ucOGjZ68+0i584/o19X6D0SgDYm27DRdPUBgBJcXdu3fo5MnjkjpTEHjf5Bnts2dOyXidzVi5tVKZHceYHQQTPE7iYygpMTHPx7EvT5269WjVqmVS/NZ2UOj1bh1FzVq1xXOkLBdGgk45whd2x44doZYtW4sbe059UvcSEshkNqtSUoYH0M2at6CflyymiIgI6tNvgI/XHICSiTrl3lyWlvLyvXsJsq9nf9ytm/GSaf7g0JH00+IfZOalectWdOb0SbJkWiTyC4CShOX7PIvN7TG8v3LaDBdKsqtH+H5Oo9EarvKXeK/e/WjJ4oVS5G7broOc4wEItmOILxJPHD9GUdHRcl4fOmykqGO1cAGRadmqjVxcLlr0vRhws0qQFbSsHizrA2NQ+rCp6to1q+jSxfPS1ptw9y6NHf8w1a1b3+NxfL5nlVNYWJh6X40aNalJ02a05KdFohbs229gKbwDAEoXboFZ/vNPlJySLMEKfOw8OGyEHC9aWBnCx8yQB4dLStmPC+dTj159xTaCUypHjZlAwUbQRPnyl/zKFUtpyrTHJe88pxMxXygePnxQBgz16zdUB9vcU3vkyEFpo2FZHs+WABDI6imefefjoF//gVSpchWa9fF7MoDQRnPxjAvH8rI58SiXPPXypYtSZLyXeI9q1axNffoOgK8IKFHYE4R9n6pUriqFab5Ie+8/79L4CY949Luytwg7qLMMlC/mFE6fOiES62rValCfvv3l/wEINtatXUV379yWc7nZHCqeO+wRNfWxJ9SxDhcg9+/fI8dPs2YtpDi+a+c2OnL4kMzIt2rdRozqURwBJQ1PxPBYmydlWN7P3wFsFDx23MPydzaE3LmDx+aHaNjw0ZIiqezH27dtoaNHD1GnTl2pfYdOGLODoIOvab/4bBZ179FLFN3Jycm0YP4cKRY+NPFROYezooQL3vz7kcnTRBTAysKNG9bRlSuXqHxMBerRo7eELwQbQaMcOXPmlPRJ8QUhx9QdOniA7A4HxcZ2kAvC1SuXi2SUe2e1fenKIGDaY0/kWFQBINBgH4Yevfp4mJGx0/s9jWyVK8ncbtO7b3+Px/HF6KRHH/P5OoPg3l8HDx4mkk8FLnCw0kkpjrDsmg0keeYju0EY+4o8OHQEUjRAUHPq5HExjufCCMPqKm0UOxfCuc3y0SnTqVy58ur4h5MJ+AcAX6qcuHA38ZEpqscZ7693NK1gq1b8LIlKj2Xzu+EiHnd98f3hERH40EBQcvrUKTl22FSViY6OljSnNatX0IH9e6VgsuiH+dIiw2lPyqQ/j61Gjh5HwU6ZLI6w3Jr7q9hxnfulGO6hctjttH/fbpkF4WpyQkICrVi+VHqyhg4flWN1WempBSBQ4X2fJdR8kcnpMXyS5B8tXCHmi00Fltg9NuNJzLgAn8Mzf7du3ZR9lJOSxOw3W8QupxBo91cu4LFZZE7+T/zlD0AwkZaaSgn3EiSOkRM8GB4PHTl0UDyh7iXcFZVVaFiYtJvxoLn/gAdwvgelAs9qczsXF0D4go5/OB2SzX8rVqosvjdc/I6pUIGW/bxYkvKGjxyT4/7K/9u7T/9SeR8AlBa3b90kR1aWnPN5HMR+gKy00iaO8f3corZ3z065Bp7xxNM45wdDcYQloDzbzfJqPmlyv9CDD46g+g0aipcCtwNcvnJJZkb4QpGx2220bevm+/puASgLXLp0gVYu/5l0eh0lJyVRixataOADD94Xt1i+fHlpo9GC9jHga1gBsn7tKjKaTLK/cnwct8JkL3qI0inBXRyBzB8Ap/keFz0OHzpABoORbDarnO+5RYbVV+w5suD7OTJ5xBeYXCjhNpofF31PM596Dud84POJGx6zs6pJx+lHFCIqPzaHHDFyDG3e/It4IGRkpFPPXn3F+4xbZrigN/3xp/BpgaCHi4o/L11MSYn3xEuTi4ojR42jBg0ayeTS8p8XU5++A6Vdho8dVtcuXrRAWmkqVqwU9NsvN8qUq9ae3TulwjzzqWfp6V+9SG3atKMlPy2U+7h1JkSno8yMDFVWytSr31DiRwEoa7BEmgcRPCM488lnxW/n4sXztHb1ivse62yrcV9sAuBr2FiPpdKjRo+X/fWhhyfTkcNxtHXLphz3V54ZBwC4OXwoTlI6+MLxqWeep67detKKZUvowoXzFF2uHI0YNVYGzDxA7tipi5jSd+vWk9LT08V8DwBfsnvXDrp5M14Kc08/84J42vCYPf7GdfFCGzP2ISnYjR77kLQBVK1ajTp17iIXdpmZuafrARAs8Pmdj4unnnmBnnz6OTKbzbTwh+8oK8tBo8dOIKvNRvPmfiXeUYMeeFCKJjyZ5LA7SnvV/ZoyVRy5eOEcNW3WQoofPNPIleaGjZpINBHPlA8Y8ICYOLHfiDLLwioTfgwAZY3r167JwKJxE6dnCMvtRowaR8ePH6WzZ0/fd7HJ3gzsaA1AaXDp4gWKqVBRouKYWrXr0OAhw2jP7h0e3gjK/pqI4ggA9xnPc3oY+6PxGIgLIHxRuWbVcmlXY3UtP4al1QpsNs+tazzjCICvx+ysauILOiVVjMMQOG6dx+esHuTI6XCX0pvvO3rksCietJOcAASrN8+VK5clQYzP95xCw34hfD93RLAyZNLkafTrl1+jyVOmSxcFF8rZq6diJahGgqY4YjKH3jf7zQkcLL8+deqEFE54+ZcNa+n7776lr2d/JgNsnlkHoKzBcaU8I5iRkeHhJcItZJzAlN3DgdGasgLgS3iAzHJ/voBTYBPt2rXr0M4c9leWkLK3AgDAfc7XtpsxvXr3lUmhI4cPygUoX3xyqyW333w/fw6dO3tGEmzQmgZ8DRc42P9GS9/+AyWC9PSpkxI5zZM6i39cIG3xc7+dLQqnYcNH4cMCQQ9PfnKQiPacz8dUz159xK9HGR9x6w0Xxdlzk6N9Bw4cgvN9MBVHGjRoKEoQ7cVgVFSUDLBPnzwhyxzFO+Pxp6lV63Y0cNAQiTRSzGoAKEtUr16DwsLCVKWUAhsxsWyVL0QVuOLMbWiRUUhkAqUDx8WxRwKfw7Ww78i5c2dkNkSB0zR4f1UMtwEAPAZqJMdKUlKSx2C5ZavWEmnNDBsxSpYzM9IlyYBnFFmJBYCv4ZnsEyeOSfFOgdMyGjVuKhOaPBs+dvxEiWfnx3Tv2ZsmPDQJKTQAuAxW69drQAcP7vfYHmwjwed9RSHO4/tLly6K8fG4CROpSdNm2H5lpTjCxk0pKXl7g/CMuMlkpm1bPXvUuR8rOSVZXebEDh4csGwbgEBFW9zIrarcoWMX8eLRHjvsCM+zhNpjghk0eKhqVAyAr/dX3vdatW4rclBWhShUrVZN1CTsvK7AbZK8v7JxKwDBgLQZJLuLHjnBLTWsqtq8aYPH/VWrVle/A3jQ3KNnHzl+uKUBihFQWmP2lq3ayLl8+7bN2fbXapI6yURGRlKfvgNkMpOLfzklkgFQFmE/TO2kUE506txVWpJZAajA53RWXCnHEE8isYKQjyMOJwFlpDjCrQFfffmpuO7mBe8AAwYNFjnR8WNH1AHFtatX5GQLQFlh545t9PlnH9P1a1fzfBwrpXhwwceOxeKcneHjgU+eFSuixxz4hg3r19CXX8wS09W8YDmo3WGnVSt/lsE1wz214eERYiQJQDDCx8K3X39Bi36Y79F2lh0+r7PpHqd/7N+3R73/6tXLYr4KgC/gQjaP2VcuX5L/mH3gYDqwfy+dOH7UPWa/hjE7CG44POHLz2fRvr278nxc9Ro1RQHIPj3cjsZwutOdO7eoCq57i0yIzWblxFu/5mZ8PC1f/pP0VQ0dPoqaNm3u8XfeIXimhP1EYmIqyKCAI+tq1aotUjx+g+PGT0T7DCgz/PD9PBmA8ODikcnTPGZTeCDNxwC7ubMpMc/YL1o4nyxWC1WpUk0GHv36DaQWLVuX6nsAwcNXsz+VmEZW7Y0eM8Hjb6wS2b1ruyhHuJh369ZN+nHh9zKjGFOhgqSNDR02SuIdAQhGUlNS6Lt538g5v2fvvtS+fSePvyfeu0ebNq2nzl26y8zgsaOHac3qFXI7K4soPSONxk+YRNHR0aX2HkDwcOPGdUke41SZESPHqqbwCmyyumXTL+L3x98J+/buljF8rVp15MKOkyV5zM5twQAEI7t2bacTx45SYmIizXjiaZnk1HLmzCk6cvgQjRo9Tgrmy5f9RBfOn6faderSzZs3RGXFaisorYqGgQIAPllWq1ZDHKo3b9xADRs2loEzV5g3/rJOVCI8KOBeRcVTgQfSLDWKioqWvkZIR0FZgo+JgQ88SD98P1f2f6XQceXyJakgc/JAn34D5D6OcOQYX4545GIhz9RgkAx8Ce93I0aMkQs8dkuvV6++3H/m9Clav261mK62bddB7mM56PTHn6Qzp0+LioRjR1k5AkAwn++5XYbbznZs20LNm7dUWyC5DS0ubp8UTDh1huHvA77QPH/+rDyO2224zRIAn+yv6ekyo80TlDxRyWNwZczOgQjsM9K1a3cxXGW4KM6PkTF7dLSYBmPMDoKZjPQM6tipK+3evV3MiB8cOkLuZ98QVtZyy0y/foOk+MHn9pGjxtGlSxfozu3b1K1bD6pWvUZpv4WAJiCKI+kZ6WQymahHz95yUt27Zyd17dZTdoqKlSrTYzOeuk8VwheHiKYDZRU+JrhVLLZ9RzlxNm7cVPwX+IeLH9ln2XlgwmlNAJQGDruDatSsJb5QG39ZS1OnPSGDX06oGTFqrKQoaWHvqBYtW+HDAsB1vjebzBLNy6kz27dtkfM8ExMTI8cTm89r4aI4y60BKK0xe5euPejkyeO0b99u6tKlu4zZuYA3fcaT9/mbYcwOgGdBnJVTXABh5XdsbAcpeISazTLe54j27AVEFhDwDyhDxZHr16/JDLglM1MG0JxcoK1C8yCaHXe7d+8lF4PlYyrQ3Tt3qHuPXqW63gCUBNwSw5K5+PgbMvjt2LGLh/mk1WKVggcXCY8dPUJbXSbEHTp0hr8O8DkcGXf48EGRS7PhL88EKl/c3DajNzhnrXv36Sd9tDt3bKW0tDTxGOHzOgDBzq2bN+nokUOUnp4mCXuNGjfx8F3jmF4+5/fu05+W/byYqlevSbduxYvJHgC+hv3Ojh07QlaLRZRKderW8xizc4GbJy27de9JW7dsloSxhLt3ZRmAYIdbYU6fPknnz52V9ngeu7M60POcbxIFIKc1bdiwVhSD3F7TLtapsgVl3JCVzSWX/rSIDAYj2ew2+nHR9x5Gk7KTmMxym2fEWZq3dvUKGYQDUNa4dy+Bvpr9GV27dlXaX9hgePXqFerf2b06ROf0GOGTat269cRjhC82+WQKgC/hAt5XX30mZqtsmrpr5zaRUnsUt13nby6E1KxZm3Zs3+qSg/pNfR6AUiPuwD5a+MM8zmYUv4UlPy2UlhjPi03nub1mzVoUGhpKq1ctU9toAPAlrFxauvRH2SetNqvMbLPPiHbMzhOaTL36DcnhsNPa1SsxZgfA5QvIx8zePbtkjM/Xuwu+n+uRTJORnqZe9zZs1EQec/hQHFWoWAnb0AeU+siUe6TYWf3RqdMpIsJpOMN96adOnZSeRWVgEBEZSVu3bKRDBw9Qixat6NChOBisgjIHF/6WL1tCPXv2lbhppmq16rRs6WKpNPNsPMvt+GKT+3O5f5f9GNinwW63YRYe+BT+Ml+xfIn4grABGFO+fHkpfvTrP8gtsTabZZaEPaP4go5nEZXiHgDBXlzcv38PTXr0MfW4yMjIoJMnjov3gnKxyaqR3bt2SFtxkybNZAzExxUAvoQ9o9i/bMqUGRQe4fSCmjfnKzp96qQaEypjFLNZVN58QcfKEv4d4Xo8AMEM+0SxH+YDg4fKmL59h8708Yf/kSRJRYHF4yZWEXKh/PatW1Igib9xjaKjYKodFMURTtLgBBqlMMKwsape7xa16PR6KYw0a96Spj42U2RFPFi4fPmi9LEDUJYuNtmsUimMKMcDn0AV12lO/UhOTqK1a1ZS7779pf/w6pXLtGrlMhlU86wiAL6A2yBbtmyjFkbU/VVz/tbrdDLrsX3rZokZ5S//06dO0I4d22QGBUaRIJjhMdADg4ephRGGZxPtmllEPkb27N4pZvRcRClfPkZUJpcvXZTzPwA+21+TnGN2pTDCRLnGKAo6nV7SZ7hFftpjM2Vyk5Wtly5dhFEkCHp4KM9jIeWY4TG72Ryabdykp5+XLKbOXbrRsOGjRX317ddfSjG9Vu06Qb8Ny1SUL3+oB1zyf+6nbd2mXY4xQ1/P/ox69OpDjRo5e2758UmJ9zxOqsosOgCBXAhho7Irly6JeR4blvHv7HAfOvs5THxkikcGOrcn8GyiAo4JUNI+OCwDvX7tGlWqXFm+tHNKkeHZ7fj46xLhqHDh/DkpimjP2dhfQbBx585tiS1NTkqSFmE21M5pHLNg/hxq3rI1tW7dVpatVqtEWnNMowKOH1DScKsMt+yyEoR9cFq1apPjmH32F59IOp5SJGcPKp7AYdUr9lcQzJw4flSCRLiFmM/3nOCUHU6g+fzTj+jZ519W2yf5ejkyIlIKiwo45/sOn1UXeHC8YP5c6ZfiL3iW261cvlTaCLSkpqZI77rWcZdNnbLHEqEwAgIZnjHnATArPpo1byE+I99++yXdvnXzvsdy+0z9bOkzdevW9yiMMDgmQEkWRuZ++xUl3rsn++vVq1dozrezZQCc0/7KfeZa+EIw+/6J/RUEE6yemjfna0mV4djSPbt30E+Lf5ABrxYuhLBBfb167nM+t59pCyMMjh9Qkpw9c5oWLpgnPiFsCrlxwzpao/E+017YsZqkdm33/smqEm1hBPsrCEY2/rJOWoxZ8cfJMz98P1cCFHIaM9WsVVstjDCcRqktjDA455fB4gjvIOxSzbONnTp3pQkPTabTZ07RgQN7PR7Hzr08I847Cc+s79q5neI1Rk8AlAXOnT0jg4rRYyZQy1ZtaPyER6hWzdq0dMmPHqZMXDw8f+Ec1a/fSB1gc9IHAL7k6NHD4m/AcupWrdvSwxMfpajIaFr+808eBW5Oprl69bLqlXDxwnmZKQcg2Nm1azu1i21P3br3ovYdOtHER6bK+ZzHRtkHyjExFaSIwoUT9iPh+wDwJdu3b6EePftIfDSP2yc89IgkSrLvn5bz585I8YQLeDx24YCFm/Hx+LBAUMMdD6y6GjVmvHRJDBw0RJLG1qxeLgpCLefOnVHHTCkpyeIlyEVyEATFkbS0VDJrvBCqVK0qsbzch84+CR47SYOGYt731ZefSmEkex46AIEOHw+cLKNUglmq+sCQoeKlc2C/u2DIM4js2RAeEU4rV/xMPy9d7NGbDoBP9tfUVAo1u8/frFp6cOgIkV2zZFTb7lWhQkWy2ay0ZPFCWr9utUc8HQDBfAxxX0YRXKQAABLFSURBVLkCHxd9+g0UBYlWgeUcAzUS48tvvvpcJowio6JKaa1BUJ/zNTHrrN7u2q0HbdmyUYrgCufOnZUx+6mTJ2TMzvHSoWGIZwfBDZup8sSRdtzUoWNnCRrRpvlxAZwnkbjtmJP++JyvVZCAMmLIevfuHTp86CClpiRTg0aNqVmzFnJ/5SpV6MK5s2oPLcNZzXv27JRqNPdicavBxQsXxJmXB9+Dhwy7T0oKQCDBMynHjx+lK5cviT9Dx06dxXy4cpWqlHD3rrTTiLkekRQBY9t3oLi4faKuUmZleGL+m6+/oLZtY2n6408h4QOUGBZLJh09clj8Ddj/hmcN+YKOpdX79+31MPzlizs23Dt4YB81b97Stb+eFZf1eXO/pk6dutLwkWNguAqCzmD18OE4aUFj47w2bWPlfj7nXzh/VmbhFTh5b+f2rTJm6t6jl3oM8fiHTYvZx4El2QCUFDyLfeTwQUpNTaWGjRpT06bN5X4+53OcdPMWznM7w4qnvXt208kTx2Q2nMfsrGq6e+e2jEs4tQxmkSDYYNsIPl+zhpbH6dxSxuN6LnLwMcTqcAUWBXBsb2LiPZno5IQaq9VCPy/5kapWq0aPTpmRo/cgCGDlCBc55s/7Rlx1jSYTrVi2RJ1VZCk2q0Fu376lPp5Ppg3qNxRpqXIhyVFfXF2bMu1xFEZAQMMmZhxxd/L4MUnw4Pi7xT/+IH+rXr2GzLBz25gWjmjkwXVqSoos2+0OqlmrFj366HSRuCL6FJQUPHv9zVdfSLx6VHQ0HTlyiFYsXyp/45lso9FA+/d5tsg0adpM1E1Kaw3PgrAfzrTHnpACH5JoQLANktk7ihWAYeHhtGH9GvWY4THQ5cuX6MqVy+rjWTnYqHFTdQzEF5usKOSCyrTpM1EYASUKF8K//+5bOW9zQY7bJFkB4txf20gRhD0AFUwmM9WrX5+uufZXlv5zeiQX0R+dOgOFERB0cErkhg1rxGeHCx5c+EhKSpKxD08esUE9n9cVeMKf/Ue05/xKlSpLYZFN7FEYKWPKET6Bbt+2hR5+ZApVrFhJ7uMoOj65snqEXay5orx65TKaOGmqOmg2GI3qjsO56DwznpMbNgCBxrq1qySbvEfP3rLctGkz+vqrz1W1CPcfLv5xATVt1kLie5XjQYmvZnr17ovjAfgENsjmQS4r+hg22Fu0cL5IqHkGpHuP3tILyxdzPKvIGA1GjwIIq/1w/gbB2obA5/xx4x5WDeQ5oeDEiePUvkNnKYhz8XvNquU0ZeoMmUBiuOioNziPIT6WOPoUxxDwhWKE/ct4PM4TNYzNaqWTJ49J0ZvP8zwDzhd/7DGltAAbNOd8VhFizA6ClUOH4iglOZmmTHGez7nI+NknH4pCkAvc3IZ2/NhR2rF9C/Xs1Vf9P+cx5Lz8rluvPj1adwbO+WVVOcLRdP36D1ILI+7sc/fAmStjbEK5eNECqZqx0uTE8WMerTYYFICyQmRktFoYYZSKsHJMNGjYSGSqS39aJLJWbmVgj4amzZpLZZnB8QB8AReouRdWKYx47q/Orwn+smeJ/6IfvpPZRZaDcu9sm7bt1f0U+ysIZh+prt17eiTrRUdHi2eUwoBBQ0Rl9cOC7ySp7PSpk3Qwbr/aesPgGAK+gMfsAwYOVgsjsr+Wc4/ZeT8cOmykTHxyqhIrBI8eOURnTp/CmB0A9hVJSxOTeqXQzWOlyKhodcwUGRklE0asHtmyeaMYFW/auEH+zgl+OOf7LyE2m9UzS9eLsCEfxxPxbKQCy444EowTDWIqVJSLR21sLwBlFR4Ms6Hq0796weN+TvOIO7BPLlC5v5fTDLLH9ALga7gAwgZh3OKowDMjPAvCcmz+gueLOj6/I2IOgPtZt2YlGU1m6tO3v3oft0xu3LiOLl68IO2W3br1FK8HAEobVrJyWyRP2ihwcWTjL+vp+rUrVKFiJerZq4+k0wAAPOHx0Yfv/5smPzqNKlaq7NFuuXXLRmldrl2nnnwfREVFY/P5MYaS3EmuXLkkJ1ItbEDD0UYABBvs5aCtFiuwxw7/AOB/+6szXk6BiyDsfcM/AID8jqGLEuGoJSIykoYNH41NB/wKnpy5cvky9ek7wON+jpUeM3ZCqa0XAIECq6u41UxbGGF43J/T2B8EYZTvtWtXxLyJdxKWkXJv1tkzp0vq5QDwezjyrkGDhmqqE7ckKEaWAPgb510RjUx8/A3atnVTaa8SAAEDz7inpqaIepZh/7VjRw+X9moBkCOs5g4PD5c2GxmzHzwgsdIAgILB6ZLKmCkzM0NaaFJc4QogsCgx5ci5s86BNTuzs4kfG/r17/9ASb0cAH5v1nfrZrz0o7NE9fixw9S5S3cZhKDHHPgbt2/dlFhfNg5es3qFfOl3h1oEgAJz7uwZqlO3Ht25fZt+2bCGLFYr9R+AMRD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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"per-block capture ranges 0.946 to 0.978\n",
"highest R level that is safely below it: 0.90\n"
]
}
],
"source": [
"day = pd.to_datetime(season.index.min().normalize()) + pd.to_timedelta(\n",
" np.asarray(blocks.labels), unit=\"D\"\n",
")\n",
"per_block_capture = blocks.captured / blocks.n_periods\n",
"\n",
"fig, (ax_n, ax_c) = plt.subplots(2, 1, figsize=(9.2, 4.6), sharex=True)\n",
"\n",
"ax_n.bar(day, blocks.n_periods, width=0.8, color=C_BLUE, linewidth=0)\n",
"ax_n.axhline(48, color=MUTED, linewidth=0.8)\n",
"ax_n.annotate(\"hairline = 48, a complete day\", xy=(0.995, 0.96),\n",
" xycoords=\"axes fraction\", ha=\"right\", va=\"top\",\n",
" fontsize=8, color=MUTED)\n",
"thin = int(np.argmin(blocks.n_periods))\n",
"ax_n.annotate(f\"{blocks.n_periods[thin]} periods —\\nweighted the same\",\n",
" xy=(day[thin], blocks.n_periods[thin]), xytext=(6, 6),\n",
" textcoords=\"offset points\", fontsize=8, color=INK)\n",
"ax_n.set_ylabel(\"usable half-hours\")\n",
"ax_n.set_ylim(0, 56)\n",
"ax_n.set_title(\"Each block counts once when drawn, however many periods it holds\")\n",
"\n",
"ax_c.plot(day, per_block_capture, color=C_ORANGE)\n",
"ax_c.axhline(blocks.mean_capture, color=MUTED, linewidth=0.8)\n",
"ax_c.annotate(f\"season mean {blocks.mean_capture:.3f}\", xy=(day[-1], blocks.mean_capture),\n",
" xytext=(-2, 6), textcoords=\"offset points\", fontsize=8,\n",
" color=MUTED, ha=\"right\")\n",
"ax_c.set_ylabel(\"captured fraction\")\n",
"ax_c.set_xlabel(\"block (one calendar day)\")\n",
"ax_c.set_title(\"In-domain captured mass per block\")\n",
"fig.autofmt_xdate()\n",
"fig.tight_layout()\n",
"plt.show()\n",
"\n",
"lo, hi = per_block_capture.min(), per_block_capture.max()\n",
"print(f\"per-block capture ranges {lo:.3f} to {hi:.3f}\")\n",
"print(f\"highest R level that is safely below it: {np.floor(lo * 20) / 20:.2f}\")"
]
},
{
"cell_type": "markdown",
"id": "bfea220d",
"metadata": {},
"source": [
"Capture sits in a tight band, 0.946 to 0.978 — **and that number sets the\n",
"ceiling on the R levels you may legitimately request.** About 4 % of the source\n",
"weight falls outside the ±300 m box, so a \"90 % source area\" computed here is\n",
"really 90 % of the captured 96 %. That is acceptable for R = 0.5 and R = 0.8,\n",
"and it is why this notebook does not push to R = 0.95 on this domain. If you\n",
"need higher levels, widen the domain — no amount of resampling will recover mass\n",
"that was never on the grid.\n",
"\n",
"Two cautions on reading this diagnostic:\n",
"\n",
"- `mean_capture` inherits the underlying `FFPModel` climatology normalisation,\n",
" which on larger domains can push the per-period integral a little **above**\n",
" 1.0. Treat it as a truncation *index* rather than a strict fraction: near 1 is\n",
" good, drifting well below 1 is a warning, and the shape of the curve across\n",
" blocks is the thing to watch.\n",
"- A flat capture curve, as here, means truncation is a property of the *domain*,\n",
" not of particular weather. A curve with excursions would point at specific\n",
" days — typically stable nights with long footprints — and those are the days\n",
" worth inspecting individually."
]
},
{
"cell_type": "markdown",
"id": "fb2269de",
"metadata": {},
"source": [
"## 6. The point estimate, for reference\n",
"\n",
"Before the bootstrap, the conventional answer: sum every block, normalise once,\n",
"and cut contours with `source_area_mask`. This is the raster most footprint\n",
"workflows stop at, and it is the thing we are about to put an error bar on.\n",
"\n",
"`source_area_mask(w, R)` returns the **smallest set of pixels holding fraction\n",
"R** of the in-domain weight — it sorts pixels by weight descending and takes the\n",
"cumulative sum, which is the standard definition of an R % source area and makes\n",
"no assumption that the region is connected or convex."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ce9cb17c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:57.828034Z",
"iopub.status.busy": "2026-09-18T20:08:57.827831Z",
"iopub.status.idle": "2026-09-18T20:08:57.837928Z",
"shell.execute_reply": "2026-09-18T20:08:57.837376Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"normalised climatology integrates to 1.000000\n"
]
},
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(5.4, 5.0))\n",
"im = ax.imshow(point, origin=\"upper\", extent=EXTENT, cmap=CM_WEIGHT,\n",
" interpolation=\"nearest\")\n",
"\n",
"# One contour per level, all in the same ink - the labels carry identity,\n",
"# not the colour, so the weight ramp keeps the colour channel to itself.\n",
"cs = ax.contour(masks[0.5].astype(float), levels=[0.5], origin=\"upper\",\n",
" extent=EXTENT, colors=[INK], linewidths=1.4)\n",
"for R, ls in ((0.8, \"--\"), (0.9, \":\")):\n",
" ax.contour(masks[R].astype(float), levels=[0.5], origin=\"upper\",\n",
" extent=EXTENT, colors=[INK], linewidths=1.2, linestyles=ls)\n",
"\n",
"ax.plot(0, 0, marker=\"^\", markersize=9, color=INK,\n",
" markeredgecolor=SURFACE, markeredgewidth=1.2, zorder=5)\n",
"ax.annotate(\"tower\", xy=(0, 0), xytext=(8, -14), textcoords=\"offset points\",\n",
" fontsize=8, color=INK)\n",
"\n",
"handles = [plt.Line2D([], [], color=INK, lw=1.4, label=\"50 % source area\"),\n",
" plt.Line2D([], [], color=INK, lw=1.2, ls=\"--\", label=\"80 %\"),\n",
" plt.Line2D([], [], color=INK, lw=1.2, ls=\":\", label=\"90 %\")]\n",
"ax.legend(handles=handles, loc=\"lower left\")\n",
"\n",
"ax.set_xlabel(\"east of tower [m]\")\n",
"ax.set_ylabel(\"north of tower [m]\")\n",
"ax.set_title(\"Season climatology, point estimate\")\n",
"ax.grid(False)\n",
"cb = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.03)\n",
"cb.set_label(\"footprint weight [m$^{-2}$]\", fontsize=8)\n",
"cb.ax.tick_params(labelsize=7)\n",
"cb.formatter.set_powerlimits((0, 0))\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6377dc7d",
"metadata": {},
"source": [
"The source area sits just north-north-west of the tower, matching the\n",
"vector-mean wind direction of 335°, and the 80 % area is 0.70 ha — a patch under\n",
"100 m across inside a 600 m box. That headroom is deliberate: it is what\n",
"keeps the captured fraction near 1, and it is why the maps in §8 are worth\n",
"zooming into.\n",
"\n",
"Every line in this figure is drawn with full confidence, and none of that\n",
"confidence is warranted. Nothing here says whether the dashed line would land in\n",
"the same place given a different sample of the same season. That is the question\n",
"§7 answers."
]
},
{
"cell_type": "markdown",
"id": "4ac80e2e",
"metadata": {},
"source": [
"## 7. Phase 2 — the bootstrap\n",
"\n",
"`block_bootstrap` draws `B` blocks with replacement, `n_members` times. Because\n",
"each member is just `counts @ block_stack`, all 500 members are formed in one\n",
"chunked matmul and the whole phase runs in well under a second.\n",
"\n",
"The arguments that matter:\n",
"\n",
"- **`levels`** — the R values to evaluate. Keep `max(levels)` comfortably below\n",
" the captured fraction from §5.\n",
"- **`min_capture`** — members whose capture falls below this are dropped. It\n",
" defaults to `max(levels) + 0.05`, so `(0.5, 0.8, 0.9)` implies 0.95.\n",
"- **`cv_floor_quantile`** — `w_cv` is masked to NaN where the mean weight falls\n",
" below this quantile of the weight distribution. The CV of a near-zero weight\n",
" is arbitrarily large and would swamp the map. The default of 0.5 keeps the\n",
" upper half of *pixels*, which is the right instinct but the wrong number here:\n",
" this footprint occupies about 6 % of the grid, so a 0.5 floor would paint CV\n",
" across a vast low-weight halo that nobody cares about. Set the floor so the\n",
" retained pixels roughly match the source area you are reporting — for a\n",
" 90 % source area of ~800 of 14 400 pixels, that is **0.94**.\n",
"- **`seed`** — set it. A bootstrap that cannot be reproduced cannot be reviewed."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e3d1f923",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:58.101043Z",
"iopub.status.busy": "2026-09-18T20:08:58.100916Z",
"iopub.status.idle": "2026-09-18T20:08:58.105125Z",
"shell.execute_reply": "2026-09-18T20:08:58.104567Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"801 of 14400 pixels lie inside the 90% source area\n",
"-> cv_floor_quantile = 0.94 (the default 0.50 would keep 7200 pixels)\n"
]
}
],
"source": [
"# Choose the CV floor from the geometry rather than accepting the default:\n",
"# retain roughly the pixels inside the largest source area we report.\n",
"cv_floor_q = 1.0 - masks[max(LEVELS)].sum() / masks[max(LEVELS)].size\n",
"print(f\"{int(masks[max(LEVELS)].sum())} of {masks[max(LEVELS)].size} pixels lie \"\n",
" f\"inside the {max(LEVELS):.0%} source area\")\n",
"print(f\"-> cv_floor_quantile = {cv_floor_q:.2f} (the default 0.50 would keep \"\n",
" f\"{masks[max(LEVELS)].size // 2} pixels)\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "761cc2d1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:58.106755Z",
"iopub.status.busy": "2026-09-18T20:08:58.106588Z",
"iopub.status.idle": "2026-09-18T20:08:58.790850Z",
"shell.execute_reply": "2026-09-18T20:08:58.789647Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 1.06 s\n",
"Wall time: 679 ms\n"
]
}
],
"source": [
"%%time\n",
"result = block_bootstrap(\n",
" blocks,\n",
" n_members=500,\n",
" levels=LEVELS,\n",
" min_capture=None, # -> max(levels) + 0.05 = 0.95\n",
" cv_floor_quantile=cv_floor_q, # keep CV over the 90 % source area only\n",
" seed=0,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "41811980",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:58.793419Z",
"iopub.status.busy": "2026-09-18T20:08:58.793163Z",
"iopub.status.idle": "2026-09-18T20:08:58.798158Z",
"shell.execute_reply": "2026-09-18T20:08:58.797505Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"members kept 500\n",
"members dropped 0\n",
"member capture 0.9538 - 0.9570\n",
"bands ['p_include_50', 'p_include_80', 'p_include_90', 'w_mean', 'w_p05', 'w_p50', 'w_p95', 'w_cv']\n",
"\n",
" n_blocks 75\n",
" n_periods 3271\n",
" mean_capture 0.9553053634692791\n",
" min_capture 0.9500000000000001\n",
" cv_floor 3.5848896877155283e-06\n",
" seed 0\n"
]
}
],
"source": [
"print(f\"members kept {result.n_members}\")\n",
"print(f\"members dropped {result.n_dropped}\")\n",
"print(f\"member capture {result.member_capture.min():.4f} - \"\n",
" f\"{result.member_capture.max():.4f}\")\n",
"print(f\"bands {[name for name, _ in result.bands()]}\")\n",
"print()\n",
"for k, v in result.meta.items():\n",
" print(f\" {k:<14} {v}\")"
]
},
{
"cell_type": "markdown",
"id": "55581ada",
"metadata": {},
"source": [
"Nothing was dropped, but look at how close the call was: `min_capture` is 0.95\n",
"and the *minimum* member capture is 0.9538. Shift the domain in a little, or ask\n",
"for `levels=(0.5, 0.8, 0.95)` — which would raise the threshold to 0.99 — and\n",
"every member fails, at which point `block_bootstrap` raises rather than quietly\n",
"returning an ensemble of one. That guard is doing real work here.\n",
"\n",
"Note also how narrow the member-capture range is: 0.9538 to 0.9570. Each member\n",
"averages 75 blocks, so block-to-block truncation differences wash out almost\n",
"entirely. **Capture is a property of the domain, not of the resampling** — the\n",
"bootstrap cannot rescue a domain that is too small, and a tight capture range is\n",
"not evidence that the domain is adequate."
]
},
{
"cell_type": "markdown",
"id": "eae075ff",
"metadata": {},
"source": [
"## 8. Reading the result\n",
"\n",
"Four bands, four questions:\n",
"\n",
"- **`w_mean`** — where is the source area? Essentially reproduces §6.\n",
"- **`w_cv`** — how variable is the weight in each pixel, relative to its own\n",
" mean? Masked below the median weight.\n",
"- **`p_include_80` / `p_include_90`** — how often does this pixel make the cut?\n",
" This is the band that replaces the hard contour."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c47fb663",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:58.800714Z",
"iopub.status.busy": "2026-09-18T20:08:58.800550Z",
"iopub.status.idle": "2026-09-18T20:08:59.334853Z",
"shell.execute_reply": "2026-09-18T20:08:59.333892Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"VIEW = 160.0 # the footprint occupies a small part of the domain; zoom in\n",
"\n",
"panels = [\n",
" (\"w_mean\", result.w_mean, CM_WEIGHT, \"footprint weight [m$^{-2}$]\", None),\n",
" (\"w_cv\", result.w_cv, CM_CV, \"coefficient of variation\", None),\n",
" (\"p_include_80\", result.p_include[0.8], CM_PROB, \"P(inside 80 % source area)\", (0, 1)),\n",
" (\"p_include_90\", result.p_include[0.9], CM_PROB, \"P(inside 90 % source area)\", (0, 1)),\n",
"]\n",
"\n",
"fig, axes = plt.subplots(2, 2, figsize=(9.4, 8.4))\n",
"for ax, (name, arr, cmap, label, lim) in zip(axes.ravel(), panels):\n",
" kw = {\"vmin\": lim[0], \"vmax\": lim[1]} if lim else {}\n",
" im = ax.imshow(arr, origin=\"upper\", extent=EXTENT, cmap=cmap,\n",
" interpolation=\"nearest\", **kw)\n",
" ax.plot(0, 0, marker=\"^\", markersize=6, color=INK,\n",
" markeredgecolor=SURFACE, markeredgewidth=1.0, zorder=5)\n",
" ax.set_xlim(-VIEW, VIEW)\n",
" ax.set_ylim(-VIEW, VIEW)\n",
" ax.set_aspect(\"equal\")\n",
" ax.set_title(name)\n",
" ax.set_xlabel(\"east [m]\")\n",
" ax.set_ylabel(\"north [m]\")\n",
" ax.grid(False)\n",
" cb = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.03)\n",
" cb.set_label(label, fontsize=8)\n",
" cb.ax.tick_params(labelsize=7)\n",
" if lim is None:\n",
" cb.formatter.set_powerlimits((0, 0))\n",
"fig.suptitle(f\"{SITE} summer 2024 — {result.n_members} block-bootstrap members, \"\n",
" f\"zoomed to ±{VIEW:.0f} m of the ±300 m domain\",\n",
" x=0.02, ha=\"left\", fontsize=11, fontweight=\"semibold\")\n",
"fig.tight_layout(rect=(0, 0, 1, 0.97))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "839b0ae7",
"metadata": {},
"source": [
"Two features of `w_cv` are worth reading rather than skipping past. The pale\n",
"wedge running NNW from the tower — **low relative spread straight down the\n",
"prevailing wind** — is there because almost every block contributes weight along\n",
"that axis, so resampling barely changes it. The saturated flanks on either side\n",
"are directions that only some blocks visit, so which blocks get drawn matters a\n",
"great deal. **Relative uncertainty is lowest where the footprint is most\n",
"certain of its direction**, which is the same reason the margin in the next\n",
"table hugs the sides of the source area rather than its nose.\n",
"\n",
"### Core, margin, and what you may actually claim\n",
"\n",
"The useful way to read `p_include` is to split it into three zones:\n",
"\n",
"- **core** ($p \\geq 0.95$) — inside the source area under essentially any\n",
" resampling. Claim these pixels.\n",
"- **margin** ($0.05 < p < 0.95$) — the pixels the point-estimate contour decides\n",
" arbitrarily. Any land-cover fraction or flux attribution that turns on these\n",
" is not defensible without stating the uncertainty.\n",
"- **outside** ($p \\leq 0.05$).\n",
"\n",
"The margin-to-core ratio is the one-number summary of how sharp the contour is."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "01fd6ae1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:59.336937Z",
"iopub.status.busy": "2026-09-18T20:08:59.336673Z",
"iopub.status.idle": "2026-09-18T20:08:59.348059Z",
"shell.execute_reply": "2026-09-18T20:08:59.347223Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
core [ha]
\n",
"
margin [ha]
\n",
"
point est. [ha]
\n",
"
margin / core
\n",
"
\n",
"
\n",
"
R
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
50%
\n",
"
0.120
\n",
"
0.022
\n",
"
0.132
\n",
"
0.188
\n",
"
\n",
"
\n",
"
80%
\n",
"
0.650
\n",
"
0.102
\n",
"
0.700
\n",
"
0.158
\n",
"
\n",
"
\n",
"
90%
\n",
"
1.828
\n",
"
0.338
\n",
"
2.002
\n",
"
0.185
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" core [ha] margin [ha] point est. [ha] margin / core\n",
"R \n",
"50% 0.120 0.022 0.132 0.188\n",
"80% 0.650 0.102 0.700 0.158\n",
"90% 1.828 0.338 2.002 0.185"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zones = []\n",
"for R in result.levels:\n",
" p = result.p_include[R]\n",
" core = int((p >= 0.95).sum())\n",
" margin = int(((p > 0.05) & (p < 0.95)).sum())\n",
" zones.append({\n",
" \"R\": f\"{R:.0%}\",\n",
" \"core [ha]\": core * grid.cell_area / 1e4,\n",
" \"margin [ha]\": margin * grid.cell_area / 1e4,\n",
" \"point est. [ha]\": int(masks[R].sum()) * grid.cell_area / 1e4,\n",
" \"margin / core\": margin / max(core, 1),\n",
" })\n",
"zone_table = pd.DataFrame(zones).set_index(\"R\").round(3)\n",
"zone_table"
]
},
{
"cell_type": "markdown",
"id": "d005dc8b",
"metadata": {},
"source": [
"The point estimate lands between core and core + margin at every level, as it\n",
"should. The margin runs 15–20 % of the core area throughout — so the 80 %\n",
"source area at this site is **0.65 ha of confident core plus about 0.10 ha of\n",
"genuinely ambiguous edge**.\n",
"\n",
"It is worth being precise about what that margin looks like on the ground,\n",
"because \"16 % uncertain\" sounds worse than it is. The figure below overlays the\n",
"point-estimate 80 % boundary on `p_include_80`. The ambiguous rim is one to two\n",
"cells wide — **roughly ±5 to 10 m of positional uncertainty on the contour** —\n",
"which is reassuring for a homogeneous field and disqualifying if a field edge,\n",
"road or canopy break runs within 10 m of that line."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "4d888c35",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:59.350110Z",
"iopub.status.busy": "2026-09-18T20:08:59.349972Z",
"iopub.status.idle": "2026-09-18T20:08:59.533773Z",
"shell.execute_reply": "2026-09-18T20:08:59.533046Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Zoom to the 80 % source area plus a small margin, in metres from the tower.\n",
"rows_i, cols_i = np.where(result.p_include[0.8] > 0.05)\n",
"pad = 8\n",
"r0, r1 = max(rows_i.min() - pad, 0), min(rows_i.max() + pad, grid.shape[0] - 1)\n",
"c0, c1 = max(cols_i.min() - pad, 0), min(cols_i.max() + pad, grid.shape[1] - 1)\n",
"# Row 0 is north, so the top of the window is the larger y.\n",
"zoom_extent = [grid.x[c0] - grid.dx / 2, grid.x[c1] + grid.dx / 2,\n",
" grid.y[grid.shape[0] - 1 - r1] - grid.dy / 2,\n",
" grid.y[grid.shape[0] - 1 - r0] + grid.dy / 2]\n",
"sub = np.s_[r0:r1 + 1, c0:c1 + 1]\n",
"\n",
"fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
"im = ax.imshow(result.p_include[0.8][sub], origin=\"upper\", extent=zoom_extent,\n",
" cmap=CM_PROB, vmin=0, vmax=1, interpolation=\"nearest\")\n",
"ax.contour(masks[0.8][sub].astype(float), levels=[0.5], origin=\"upper\",\n",
" extent=zoom_extent, colors=[INK], linewidths=1.6)\n",
"ax.plot(0, 0, marker=\"^\", markersize=9, color=INK,\n",
" markeredgecolor=SURFACE, markeredgewidth=1.2, zorder=5)\n",
"ax.annotate(\"black line = point-estimate 80 % contour\\n\"\n",
" \"pale rim = pixels the resampling disagrees about\",\n",
" xy=(0.03, 0.97), xycoords=\"axes fraction\", va=\"top\",\n",
" fontsize=8, color=INK)\n",
"ax.set_xlabel(\"east of tower [m]\")\n",
"ax.set_ylabel(\"north of tower [m]\")\n",
"ax.set_aspect(\"equal\")\n",
"ax.set_title(\"The contour has a rim, not an edge\")\n",
"ax.grid(False)\n",
"cb = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.03)\n",
"cb.set_label(\"P(inside 80 % source area)\", fontsize=8)\n",
"cb.ax.tick_params(labelsize=7)\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "32372ea3",
"metadata": {},
"source": [
"## 9. Block length is the choice that moves the answer\n",
"\n",
"Section 2 argued from the autocorrelation that blocks must be at least a day.\n",
"Here we measure what block length actually does to the interval.\n",
"\n",
"Re-running `accumulate_blocks` for each candidate length would cost several\n",
"minutes per setting. It is also unnecessary: a `k`-day block is exactly the sum\n",
"of `k` consecutive daily blocks, so the daily `BlockStack` can be coarsened\n",
"arithmetically. The helper below is worth keeping — it turns a block-length\n",
"sensitivity test into a couple of seconds."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "76f7751b",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:08:59.535407Z",
"iopub.status.busy": "2026-09-18T20:08:59.535291Z",
"iopub.status.idle": "2026-09-18T20:09:00.912262Z",
"shell.execute_reply": "2026-09-18T20:09:00.911683Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
" B core [ha] margin [ha] core + margin [ha] median CV B < 20\n",
"block_days \n",
"1 75 0.648 0.102 0.750 0.072 False\n",
"2 38 0.642 0.110 0.752 0.070 False\n",
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},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def coarsen(stack: BlockStack, factor: int) -> BlockStack:\n",
" \"\"\"Merge groups of ``factor`` consecutive daily blocks into longer blocks.\n",
"\n",
" Exactly equivalent to re-running ``accumulate_blocks`` with\n",
" ``block_days=factor``, because a block sum is additive.\n",
" \"\"\"\n",
" if factor == 1:\n",
" return stack\n",
" group = np.asarray(stack.labels) // factor\n",
" uniq = np.unique(group)\n",
" ny, nx = stack.grid.shape\n",
" merged = np.zeros((uniq.size, ny, nx), dtype=np.float32)\n",
" n_per = np.zeros(uniq.size, dtype=np.int64)\n",
" cap = np.zeros(uniq.size, dtype=np.float64)\n",
" for i, g in enumerate(uniq):\n",
" sel = group == g\n",
" merged[i] = stack.stack[sel].sum(axis=0)\n",
" n_per[i] = stack.n_periods[sel].sum()\n",
" cap[i] = stack.captured[sel].sum()\n",
" return BlockStack(stack=merged, n_periods=n_per, captured=cap,\n",
" labels=list(uniq), grid=stack.grid)\n",
"\n",
"\n",
"sweep = []\n",
"for days in (1, 2, 3, 5, 7, 14):\n",
" bs = coarsen(blocks, days)\n",
" with warnings.catch_warnings(record=True) as caught:\n",
" warnings.simplefilter(\"always\")\n",
" r = block_bootstrap(bs, n_members=300, levels=(0.8,), min_capture=0.90,\n",
" cv_floor_quantile=cv_floor_q, seed=1)\n",
" warned = any(\"blocks\" in str(w.message) for w in caught)\n",
" p = r.p_include[0.8]\n",
" core = int((p >= 0.95).sum()) * grid.cell_area / 1e4\n",
" margin = int(((p > 0.05) & (p < 0.95)).sum()) * grid.cell_area / 1e4\n",
" sweep.append({\n",
" \"block_days\": days,\n",
" \"B\": bs.n_blocks,\n",
" \"core [ha]\": core,\n",
" \"margin [ha]\": margin,\n",
" \"core + margin [ha]\": core + margin,\n",
" \"median CV\": float(np.nanmedian(r.w_cv)),\n",
" \"B < 20\": warned,\n",
" })\n",
"sweep = pd.DataFrame(sweep).set_index(\"block_days\")\n",
"sweep.round(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bbc72298",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:09:00.913756Z",
"iopub.status.busy": "2026-09-18T20:09:00.913641Z",
"iopub.status.idle": "2026-09-18T20:09:01.071381Z",
"shell.execute_reply": "2026-09-18T20:09:01.070559Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(len(sweep))\n",
"fig, ax = plt.subplots(figsize=(7.4, 4.0))\n",
"\n",
"# Shade the settings the module itself flags as unreliable.\n",
"first_warn = int(np.argmax(sweep[\"B < 20\"].to_numpy()))\n",
"ax.axvspan(first_warn - 0.5, len(sweep) - 0.5, color=GRID, zorder=0)\n",
"ax.annotate(\"B < 20 — the module warns here,\\nand the trend stops behaving\",\n",
" xy=((first_warn + len(sweep) - 1) / 2, 0.985),\n",
" xycoords=(\"data\", \"axes fraction\"), ha=\"center\", va=\"top\",\n",
" fontsize=8, color=MUTED)\n",
"\n",
"# Stacked: the core recedes so the margin - the quantity that actually moves -\n",
"# carries the saturated hue. A 2px surface gap separates the segments.\n",
"ax.bar(x, sweep[\"core [ha]\"], width=0.62, color=\"#b7d3f6\", linewidth=0,\n",
" label=\"core\")\n",
"ax.bar(x, sweep[\"margin [ha]\"], width=0.62, bottom=sweep[\"core [ha]\"] + 0.004,\n",
" color=C_AQUA, linewidth=0, label=\"ambiguous margin\")\n",
"\n",
"for xi, (_, row) in zip(x, sweep.iterrows()):\n",
" ax.annotate(f\"{row['margin [ha]']:.3f} ha\",\n",
" xy=(xi, row[\"core [ha]\"] + row[\"margin [ha]\"] + 0.014),\n",
" ha=\"center\", fontsize=8, color=INK)\n",
" ax.annotate(f\"B = {int(row['B'])}\", xy=(xi, 0.02), ha=\"center\",\n",
" fontsize=8, color=\"#52514e\")\n",
"\n",
"ax.set_xticks(x, [str(d) for d in sweep.index])\n",
"ax.set_xlabel(\"block length [days]\")\n",
"ax.set_ylabel(\"80 % source area [ha]\")\n",
"ax.set_ylim(0, 0.92)\n",
"ax.set_title(\"The source area holds still; confidence in its edge erodes\")\n",
"ax.legend(loc=\"upper left\", ncols=2)\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "154b9de6",
"metadata": {},
"source": [
"Two things happen, and only one of them is the intended lesson.\n",
"\n",
"**Over the usable range (1 → 3 days, `B` = 75 → 25) the margin grows\n",
"monotonically, 0.102 → 0.122 ha, while the core shrinks slightly, 0.648 →\n",
"0.638 ha.** The source area itself barely moves; what changes is how much of it\n",
"the method is willing to vouch for. That is exactly right: longer blocks\n",
"acknowledge more serial dependence, which means fewer effectively independent\n",
"samples and a wider interval. The apparent precision of a short-block bootstrap\n",
"is not precision — it is dependence being ignored.\n",
"\n",
"**Past `B` = 20 the trend stops being a trend.** The margin goes 0.132, then\n",
"*down* to 0.128, then 0.140; the median CV wanders in the same non-monotone way.\n",
"With 15 or fewer blocks the multinomial resampling has too few distinct\n",
"configurations for the ensemble to be a distribution, so these numbers are noise\n",
"about noise — which is precisely what `block_bootstrap`'s `B < 20` warning is\n",
"for. **The breakdown of the trend is the evidence, not a nuisance**: if you are\n",
"unsure whether your block length is too long, extend the sweep until it goes\n",
"non-monotone and stop short of that.\n",
"\n",
"**For this record, 1–3 days is the defensible range.** One day is the floor set\n",
"by the diurnal cycle (§2); three days is the ceiling set by keeping `B ≥ 25`.\n",
"The spread across that range — 0.10 to 0.12 ha of margin — is a fair statement\n",
"of the method uncertainty, and quoting one block length without it overstates\n",
"what the bootstrap knows."
]
},
{
"cell_type": "markdown",
"id": "d0b6da5a",
"metadata": {},
"source": [
"## 10. Systematic error the bootstrap cannot see\n",
"\n",
"The bootstrap quantifies **sampling** uncertainty — what a different draw of the\n",
"same season would give. It is blind to anything that biases every period in the\n",
"same direction, and the most common such bias at an eddy-covariance site is a\n",
"**sonic azimuth offset**: a few degrees of error in the instrument's declared\n",
"orientation rotates every half-hourly footprint by the same angle, so it is\n",
"coherent across all 500 members and the ensemble spread says nothing about it.\n",
"\n",
"`rotate_climatology` is the tool for probing it. Rotate the mean climatology by\n",
"the plausible offset, recompute the source area, and compare. The metric below\n",
"is the intersection-over-union of the 80 % mask against its unrotated self —\n",
"1.0 means the rotation changed nothing, 0.0 means no overlap."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f26f0109",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:09:01.073630Z",
"iopub.status.busy": "2026-09-18T20:09:01.073464Z",
"iopub.status.idle": "2026-09-18T20:09:01.090488Z",
"shell.execute_reply": "2026-09-18T20:09:01.089726Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mass is conserved by the rotation: 1.0000\n",
" -10 deg -> 80 % source area IoU 0.860 (14.0% of the combined area does not overlap)\n",
" -6 deg -> 80 % source area IoU 0.915 (8.5% of the combined area does not overlap)\n",
" +6 deg -> 80 % source area IoU 0.908 (9.2% of the combined area does not overlap)\n",
" +10 deg -> 80 % source area IoU 0.854 (14.6% of the combined area does not overlap)\n"
]
}
],
"source": [
"offsets = np.arange(-12, 13, 2)\n",
"base_mask = source_area_mask(result.w_mean, 0.8)\n",
"\n",
"iou = {}\n",
"for deg in offsets:\n",
" rotated = rotate_climatology(result.w_mean, float(deg))\n",
" m = source_area_mask(rotated, 0.8)\n",
" iou[int(deg)] = (np.logical_and(base_mask, m).sum()\n",
" / np.logical_or(base_mask, m).sum())\n",
"iou_curve = np.array([iou[int(deg)] for deg in offsets])\n",
"\n",
"print(f\"mass is conserved by the rotation: \"\n",
" f\"{rotate_climatology(result.w_mean, 10.0).sum() * grid.cell_area:.4f}\")\n",
"for deg in (-10, -6, 6, 10):\n",
" print(f\" {deg:+3d} deg -> 80 % source area IoU {iou[deg]:.3f} \"\n",
" f\"({1 - iou[deg]:.1%} of the combined area does not overlap)\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "2988a630",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:09:01.092642Z",
"iopub.status.busy": "2026-09-18T20:09:01.092486Z",
"iopub.status.idle": "2026-09-18T20:09:01.460079Z",
"shell.execute_reply": "2026-09-18T20:09:01.459030Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rot10 = rotate_climatology(result.w_mean, 10.0)\n",
"diff = rot10 - result.w_mean\n",
"lim = float(np.abs(diff).max())\n",
"\n",
"fig, (ax_i, ax_d) = plt.subplots(1, 2, figsize=(9.8, 4.4),\n",
" gridspec_kw={\"width_ratios\": [1, 1.05]})\n",
"\n",
"ax_i.plot(offsets, iou_curve, color=C_BLUE, marker=\"o\", markersize=5,\n",
" markeredgecolor=SURFACE, markeredgewidth=0.8)\n",
"ax_i.axvline(0, color=MUTED, linewidth=0.8)\n",
"ax_i.annotate(f\"{1 - iou[10]:.0%} of the combined\\narea does not overlap\",\n",
" xy=(10, iou[10]), xytext=(0.5, iou_curve.min() + 0.004),\n",
" fontsize=8, color=INK, ha=\"center\", va=\"bottom\",\n",
" arrowprops={\"arrowstyle\": \"-\", \"color\": MUTED, \"linewidth\": 0.8,\n",
" \"shrinkA\": 2, \"shrinkB\": 4})\n",
"ax_i.set_xlabel(\"sonic azimuth offset [deg, positive clockwise]\")\n",
"ax_i.set_ylabel(\"IoU of the 80 % source area vs 0 deg\")\n",
"ax_i.set_xticks(offsets[::2])\n",
"ax_i.set_ylim(iou_curve.min() - 0.04, 1.02)\n",
"ax_i.set_title(\"A systematic rotation moves the source area\")\n",
"\n",
"im = ax_d.imshow(diff, origin=\"upper\", extent=EXTENT, cmap=CM_DIFF,\n",
" vmin=-lim, vmax=lim, interpolation=\"nearest\")\n",
"ax_d.plot(0, 0, marker=\"^\", markersize=6, color=INK,\n",
" markeredgecolor=SURFACE, markeredgewidth=1.0, zorder=5)\n",
"ax_d.set_xlabel(\"east [m]\")\n",
"ax_d.set_ylabel(\"north [m]\")\n",
"ax_d.set_xlim(-110, 110)\n",
"ax_d.set_ylim(-110, 110)\n",
"ax_d.set_aspect(\"equal\")\n",
"ax_d.set_title(\"Rotated +10 deg, minus the original\")\n",
"ax_d.grid(False)\n",
"cb = fig.colorbar(im, ax=ax_d, fraction=0.046, pad=0.03)\n",
"cb.set_label(\"weight difference [m$^{-2}$]\", fontsize=8)\n",
"cb.ax.tick_params(labelsize=7)\n",
"cb.formatter.set_powerlimits((0, 0))\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e42cdd42",
"metadata": {},
"source": [
"A ±10° azimuth offset — not an unusual magnitude for a compass-set sonic —\n",
"leaves about 15 % of the combined 80 % source area non-overlapping. Set that\n",
"against the bootstrap margin from §8, which was 15–20 % of the core. **The two\n",
"error sources are comparable in size at this site**, and only one of them\n",
"appears in `p_include_80`. A footprint uncertainty statement that reports the\n",
"bootstrap alone is reporting about half the story.\n",
"\n",
"The right way to use this is as a scenario, not a correction: rotate by the\n",
"instrument's stated azimuth tolerance, recompute whatever downstream quantity\n",
"the footprint feeds, and report the range. The module docstring's caveat applies\n",
"here too — a coherent rotation error in the model itself would be invisible to\n",
"the bootstrap for exactly the same reason."
]
},
{
"cell_type": "markdown",
"id": "9ca0e782",
"metadata": {},
"source": [
"## 11. Phase 3 — write the GeoTIFF\n",
"\n",
"`write_geotiff` projects the tower's latitude and longitude into the grid's CRS,\n",
"builds the affine transform from the grid geometry, and writes every band into\n",
"one tiled, deflate-compressed float32 GeoTIFF with the band names set as\n",
"descriptions and the run metadata as tags.\n",
"\n",
"Band descriptions and tags are what make the file self-documenting six months\n",
"later, so the read-back below checks them rather than assuming."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "bf4bc98e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:09:01.461865Z",
"iopub.status.busy": "2026-09-18T20:09:01.461647Z",
"iopub.status.idle": "2026-09-18T20:09:01.525996Z",
"shell.execute_reply": "2026-09-18T20:09:01.525231Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"US-UTE_footprint_uncertainty.tif (232 KB)\n",
" crs EPSG:32612\n",
" size 120 x 120 at 5 m\n",
" bounds 449405.5, 4176300.0 to 450005.5, 4176900.0\n",
" dtype float32, nodata nan\n",
" bands\n",
" 1. p_include_50 0 .. 1\n",
" 2. p_include_80 0 .. 1\n",
" 3. p_include_90 0 .. 1\n",
" 4. w_mean 1.88e-09 .. 0.000871\n",
" 5. w_p05 1.12e-09 .. 0.000825\n",
" 6. w_p50 1.85e-09 .. 0.000872\n",
" 7. w_p95 2.7e-09 .. 0.000917\n",
" 8. w_cv 0.0226 .. 0.142\n",
" tags\n",
" n_blocks 75\n",
" n_periods 3271\n",
" n_members 500\n",
" n_dropped 0\n",
" levels 0.5,0.8,0.9\n",
" mean_capture 0.9553053634692791\n",
" min_capture 0.9500000000000001\n",
" seed 0\n",
" note weights are m^-2 densities; p_include is a probability in [0,1]\n"
]
}
],
"source": [
"tif_path = WORK / f\"{SITE}_footprint_uncertainty.tif\"\n",
"write_geotiff(result, str(tif_path), compress=\"deflate\")\n",
"\n",
"with rasterio.open(tif_path) as src:\n",
" print(f\"{tif_path.name} ({tif_path.stat().st_size / 1024:.0f} KB)\")\n",
" print(f\" crs {src.crs}\")\n",
" print(f\" size {src.width} x {src.height} at {src.res[0]:g} m\")\n",
" print(f\" bounds {src.bounds.left:.1f}, {src.bounds.bottom:.1f} to \"\n",
" f\"{src.bounds.right:.1f}, {src.bounds.top:.1f}\")\n",
" print(f\" dtype {src.dtypes[0]}, nodata {src.nodatavals[0]}\")\n",
" print(\" bands\")\n",
" for i, name in enumerate(src.descriptions, start=1):\n",
" band = src.read(i, masked=True)\n",
" print(f\" {i}. {name:<14} {band.min():.3g} .. {band.max():.3g}\")\n",
" print(\" tags\")\n",
" for key in (\"n_blocks\", \"n_periods\", \"n_members\", \"n_dropped\",\n",
" \"levels\", \"mean_capture\", \"min_capture\", \"seed\", \"note\"):\n",
" print(f\" {key:<14} {src.tags().get(key)}\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "960cf1ea",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T20:09:01.527849Z",
"iopub.status.busy": "2026-09-18T20:09:01.527651Z",
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{
"name": "stdout",
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"text": [
"tower projected to 449705.5, 4176600.0\n",
"raster centre at 449708.0, 4176597.5\n",
"offset 2.5, 2.5 m (expected <= half a cell, 2.5 m)\n"
]
}
],
"source": [
"# Confirm the georeferencing puts the tower where it belongs: the centre of the\n",
"# raster should land on the tower's projected coordinates.\n",
"from pyproj import Transformer\n",
"\n",
"with rasterio.open(tif_path) as src:\n",
" cx, cy = src.xy(src.height // 2, src.width // 2)\n",
"\n",
"east, north = Transformer.from_crs(\"EPSG:4326\", CRS, always_xy=True).transform(\n",
" TOWER_LON, TOWER_LAT\n",
")\n",
"print(f\"tower projected to {east:.1f}, {north:.1f}\")\n",
"print(f\"raster centre at {cx:.1f}, {cy:.1f}\")\n",
"print(f\"offset {abs(cx - east):.1f}, {abs(cy - north):.1f} m \"\n",
" f\"(expected <= half a cell, {grid.dx / 2:g} m)\")"
]
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"source": [
"## 12. What to carry away\n",
"\n",
"**The workflow, in four lines.**\n",
"\n",
"```python\n",
"grid = GridSpec.square(half_width=300.0, dx=5.0, tower_lat=..., tower_lon=...)\n",
"blocks = accumulate_blocks(season, grid, ffp_adapter(grid, z_m, z_0), block_days=1)\n",
"result = block_bootstrap(blocks, n_members=500, levels=(0.5, 0.8, 0.9), seed=0)\n",
"write_geotiff(result, \"site_footprint_uncertainty.tif\")\n",
"```\n",
"\n",
"**The checks that decide whether the output means anything.**\n",
"\n",
"| check | where | what it should say |\n",
"|---|---|---|\n",
"| captured fraction | `blocks.mean_capture` | near 1; and `max(levels)` well below it |\n",
"| block count | `blocks.n_blocks` | ≥ 20–30, else the module warns |\n",
"| block length | your choice | ≥ one diurnal cycle; justify it from the autocorrelation |\n",
"| members dropped | `result.n_dropped` | 0, or a stated small fraction |\n",
"| seed | `result.meta[\"seed\"]` | set, and recorded |\n",
"\n",
"**The three claims this notebook supports.**\n",
"\n",
"1. `p_include_{R}` replaces the hard contour with a core and a margin. At\n",
" US-UTE the 80 % source area is 0.65 ha of core with 0.10 ha of genuinely\n",
" ambiguous edge — and any statistic that turns on that edge needs the\n",
" uncertainty attached.\n",
"2. **Block length is a modelling decision with a defensible range, not a\n",
" default.** One day is the floor set by the diurnal cycle; three days is the\n",
" ceiling set by keeping enough blocks. Report the range, not one setting.\n",
"3. **The bootstrap is half the error budget.** A ±10° sonic azimuth offset\n",
" displaces as much of the source area as the entire sampling interval, and no\n",
" amount of resampling will reveal it. Probe it separately with\n",
" `rotate_climatology`.\n",
"\n",
"**Where to go next.** `p_include_80` multiplies naturally against a land-cover\n",
"raster to give a *probability-weighted* composition rather than a hard-masked\n",
"one — see [`openet_masking`](../fluxfootprints.rst) for the masking utilities and\n",
"the [representativeness worked example](representativeness_example.ipynb) for the\n",
"Chu et al. (2021) target-area comparison this feeds into."
]
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