{
"cells": [
{
"cell_type": "markdown",
"id": "f914ef1f4e9f7e72",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"# Semivariogram exploration\n",
"\n",
"Rushing into Kriging is a bad idea. You can interpolate values automatically but never know if a process is spatially dependent. The kriging model will return something even when there is no spatial dependency, and it is a dangerous situation because the output might look correct, being wildly wrong.\n",
"\n",
"The easiest way to deal with this problem is to check the variogram before modeling. Feel obligated to do this manual step because it could save you time and money! Variograms will tell you if there is a spatial relation between point pairs and how distance defines it.\n",
"\n",
"In this exercise, we compute the experimental semivariogram, check it, and apply different theoretical models to find the best fit."
]
},
{
"cell_type": "markdown",
"id": "d2a9ee6c5cedaa4c",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Prerequisites\n",
"\n",
"- **Domain**:\n",
" - semivariance and covariance functions\n",
"- **Package**:\n",
" - installation\n",
"- **Programming**:\n",
" - Python basics"
]
},
{
"cell_type": "markdown",
"id": "9fa87d432fd91c46",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Table of contents\n",
"\n",
"1. Data preparation\n",
"2. Create the experimental variogram\n",
"3. Set manually different semivariogram models\n",
"4. Fit the theoretical model automatically\n",
"5. Export model\n",
"6. Import model"
]
},
{
"cell_type": "markdown",
"id": "88bca1ba3dbd9ec5",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Dataset\n",
"\n",
"Dataset in this tutorial is the Digital Elevation Model around Gorzów Wielkopolski city in Poland."
]
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-21T14:55:30.426177Z",
"start_time": "2025-12-21T14:55:30.422208Z"
}
},
"source": [
"import geopandas as gpd\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from pyinterpolate import reproject_flat, ExperimentalVariogram, build_theoretical_variogram, TheoreticalVariogram"
],
"outputs": [],
"execution_count": 42
},
{
"cell_type": "markdown",
"id": "55538d7e43faebe4",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 1: data preparation"
]
},
{
"cell_type": "code",
"id": "ac39949d6265b27f",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:30.544635Z",
"start_time": "2025-12-21T14:55:30.541497Z"
}
},
"source": [
"DEM_FILE = '../data/dem.csv'"
],
"outputs": [],
"execution_count": 43
},
{
"cell_type": "code",
"id": "c731e3a33a69296",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:30.670373Z",
"start_time": "2025-12-21T14:55:30.651236Z"
}
},
"source": [
"df = pd.read_csv(DEM_FILE)\n",
"df.head()"
],
"outputs": [
{
"data": {
"text/plain": [
" longitude latitude dem\n",
"0 15.115241 52.765146 91.275597\n",
"1 15.115241 52.742790 96.548294\n",
"2 15.115241 52.710706 51.254551\n",
"3 15.115241 52.708844 48.958282\n",
"4 15.115241 52.671378 16.817863"
],
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
longitude
\n",
"
latitude
\n",
"
dem
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
15.115241
\n",
"
52.765146
\n",
"
91.275597
\n",
"
\n",
"
\n",
"
1
\n",
"
15.115241
\n",
"
52.742790
\n",
"
96.548294
\n",
"
\n",
"
\n",
"
2
\n",
"
15.115241
\n",
"
52.710706
\n",
"
51.254551
\n",
"
\n",
"
\n",
"
3
\n",
"
15.115241
\n",
"
52.708844
\n",
"
48.958282
\n",
"
\n",
"
\n",
"
4
\n",
"
15.115241
\n",
"
52.671378
\n",
"
16.817863
\n",
"
\n",
" \n",
"
\n",
"
"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 44
},
{
"cell_type": "markdown",
"id": "3a7d7a164d317c5e",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"Data is stored in EPSG:4326 and has geographic coordinates, it must be reprojected to projected coordinate reference system. Polish area can be projected into EPSG:2180 (metric).\n",
"\n",
"We cannot reproject points easily, first we need to transform `longitude` and `latitude` columns into shapely `Point()` objects, and set **CRS**, and in the next step we can transform those points into EPSG:2180. `Pyinterpolate` has function `reproject_flat()` which takes care of all those steps and returns the same object type as input to it (numpy array or pandas DataFrame)."
]
},
{
"cell_type": "code",
"id": "9afcb119508d446c",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:30.974252Z",
"start_time": "2025-12-21T14:55:30.826064Z"
}
},
"source": [
"r_df = reproject_flat(\n",
" ds=df,\n",
" in_crs=4326,\n",
" out_crs=2180,\n",
" lon_col='longitude',\n",
" lat_col='latitude'\n",
")"
],
"outputs": [],
"execution_count": 45
},
{
"cell_type": "code",
"id": "dc7b40e11395a256",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:31.172320Z",
"start_time": "2025-12-21T14:55:31.163548Z"
}
},
"source": [
"r_df.head()"
],
"outputs": [
{
"data": {
"text/plain": [
" dem longitude latitude\n",
"0 91.275597 238012.301750 551466.805222\n",
"1 96.548294 237878.001294 548982.351111\n",
"2 51.254551 237685.325393 545416.708315\n",
"3 48.958282 237674.140301 545209.671315\n",
"4 16.817863 237449.254870 541045.934750"
],
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
dem
\n",
"
longitude
\n",
"
latitude
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
91.275597
\n",
"
238012.301750
\n",
"
551466.805222
\n",
"
\n",
"
\n",
"
1
\n",
"
96.548294
\n",
"
237878.001294
\n",
"
548982.351111
\n",
"
\n",
"
\n",
"
2
\n",
"
51.254551
\n",
"
237685.325393
\n",
"
545416.708315
\n",
"
\n",
"
\n",
"
3
\n",
"
48.958282
\n",
"
237674.140301
\n",
"
545209.671315
\n",
"
\n",
"
\n",
"
4
\n",
"
16.817863
\n",
"
237449.254870
\n",
"
541045.934750
\n",
"
\n",
" \n",
"
\n",
"
"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 46
},
{
"cell_type": "markdown",
"id": "75ceb506ea170cf4",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"We might transform this `DataFrame` into `GeoDataFrame` - mostly for plotting purposes, to see what lies behind this dataset."
]
},
{
"cell_type": "code",
"id": "923322b6ddaa6b5",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:32.147209Z",
"start_time": "2025-12-21T14:55:31.396151Z"
}
},
"source": [
"dem_geometry = gpd.points_from_xy(x=r_df['longitude'], y=r_df['latitude'], crs=2180)\n",
"dem = gpd.GeoDataFrame(r_df, geometry=dem_geometry)\n",
"\n",
"dem.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 47
},
{
"cell_type": "markdown",
"id": "3ae9643b3df24cc1",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"Data covers almost whole area, and this scenario is not realistic, so we will let only 1% of points in a dataset and then proceed to the variogram analysis and modeling."
]
},
{
"cell_type": "code",
"id": "57a0088ab80c62eb",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:32.404771Z",
"start_time": "2025-12-21T14:55:32.233775Z"
}
},
"source": [
"sample = dem.sample(int(0.01 * len(dem)))\n",
"sample.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 48
},
{
"cell_type": "markdown",
"id": "53bc2b295ab985a",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 2: Experimental Variogram\n",
"\n",
"To be sure that we can interpolate missing values with Kriging we must check if there is any relation between observation pairs, and how strong is this relation. We use variograms - plots of the dissimilarity between point pairs along the increasing distance. There are two \"types\" of variograms that we must be aware of:\n",
"\n",
"- The experimental (semi)variogram: directly showing differences between point pairs at specific distances - lags. It could be very messy. We use it to visually check if there is a spatial correlation.\n",
"- Theoretical (semi)variogram: is a function applied to the experimental variogram. It cannot be any function, there are few of those in literature (and implemented in `pyinterpolate`). We can apply those mappings manually or automatically minimizing OLS error between experimental POINTS and theoretical FUNCTION.\n",
"\n",
"We start our analysis by setting up the experimental model. We use class `ExperimentalVariogram` to set up the object (the class has an alias function `build_experimental_variogram()` but we won't use it). Class takes multiple parameters:\n",
"\n",
"- `values` : ArrayLike object with data values.\n",
"- `geometries` : ArrayLike object with geometries (could be GeoSeries or array with two coordinates in each row).\n",
"- `step_size` : float - The fixed distance between lags grouping point neighbors.\n",
"- `max_range` : float - The maximum distance at which the semivariance is calculated.\n",
"- `direction` : float, optional - Direction of semivariogram, values from 0 to 360 degrees.\n",
"- `tolerance` : float, optional - Controlling the cone within points are treated as neighbors in directional variogram.\n",
"- `custom_bins` : numpy array, optional - Custom lags (new in Pyinterpolate 1.x).\n",
"- `custom_weights` : numpy array, optional - Custom weights assigned to points. Only semivariance values are weighted.\n",
"- `is_semivariance` : bool, default=True - Calculate experimental semivariance.\n",
"- `is_covariance` : bool, default=True - Calculate experimental coviariance.\n",
"- `as_cloud` : bool, default=False - Calculate semivariance point-pairs cloud.\n",
"\n",
"We won't set `direction`, `tolerance`, `custom_bins`, `custom_weights`, and `as_cloud` parameters. Other parameters are set as in the cell below."
]
},
{
"cell_type": "code",
"id": "5dc7e1a2ac5b93db",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:32.510625Z",
"start_time": "2025-12-21T14:55:32.504509Z"
}
},
"source": [
"step_size = 1000 # meters - remember, that this parameter is related to projection!\n",
"max_range = 20000 # meters - remember, that this parameter is related to projection!\n",
"\n",
"is_semivariance = True\n",
"is_covariance = True\n",
"\n",
"ds = sample[['longitude', 'latitude', 'dem']]"
],
"outputs": [],
"execution_count": 49
},
{
"cell_type": "code",
"id": "ca9dd4f2e0da51c5",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:32.626141Z",
"start_time": "2025-12-21T14:55:32.605552Z"
}
},
"source": [
"experimental_variogram = ExperimentalVariogram(\n",
" values=ds['dem'],\n",
" geometries=ds[['longitude', 'latitude']],\n",
" step_size=step_size,\n",
" max_range=max_range,\n",
" is_semivariance=is_semivariance,\n",
" is_covariance=is_covariance\n",
") "
],
"outputs": [],
"execution_count": 50
},
{
"cell_type": "markdown",
"id": "3a4d8addcb4e9b07",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"The `ExperimentalVariogram` object might represent:\n",
"\n",
"- **semivariance**: dissimilarity over a distance, and point cloud semivariances - non-averaged dissimilarities between point pairs,\n",
"- **covariance**: similarity over a distance,\n",
"- **variance**: non-spatial variance of all points.\n",
"\n",
"The class has two useful methods:\n",
"\n",
"1. We can print object statistics with the `print()` function,\n",
"2. We can plot semivariance, covariance, and variance and check how they behave with the `.plot()` method.\n",
"\n",
"Let's check both!"
]
},
{
"cell_type": "code",
"id": "bf846dea7a63a62a",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:32.733098Z",
"start_time": "2025-12-21T14:55:32.726737Z"
}
},
"source": [
"print(experimental_variogram)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------+--------------------+---------------------+\n",
"| lag | semivariance | covariance |\n",
"+---------+--------------------+---------------------+\n",
"| 1000.0 | 66.71388540855338 | 378.36063443979623 |\n",
"| 2000.0 | 130.66628344322854 | 321.7000533191133 |\n",
"| 3000.0 | 377.5405401178886 | 150.76532645491605 |\n",
"| 4000.0 | 250.66718626708865 | 252.71512355654235 |\n",
"| 5000.0 | 325.5561456427749 | 133.18403989988315 |\n",
"| 6000.0 | 387.4814906048632 | 30.731695978081564 |\n",
"| 7000.0 | 302.07493544556206 | 49.092832725093004 |\n",
"| 8000.0 | 469.53885776562294 | -62.21223171644848 |\n",
"| 9000.0 | 486.79931370125183 | -83.13560908274104 |\n",
"| 10000.0 | 512.0646265352149 | -119.11920214846046 |\n",
"| 11000.0 | 529.991767276145 | -76.66066356407669 |\n",
"| 12000.0 | 582.4717932210419 | -80.88872027056644 |\n",
"| 13000.0 | 692.506923505966 | -185.99762744031003 |\n",
"| 14000.0 | 649.5775483966735 | -129.2268505373204 |\n",
"| 15000.0 | 624.8696046337229 | -151.08544277438932 |\n",
"| 16000.0 | 643.74627487684 | -135.27647734328391 |\n",
"| 17000.0 | 571.3688648923311 | -127.91473349841077 |\n",
"| 18000.0 | 540.0470107890337 | -123.14348266241309 |\n",
"| 19000.0 | 600.2575887426275 | -153.57635203377805 |\n",
"+---------+--------------------+---------------------+\n"
]
}
],
"execution_count": 51
},
{
"cell_type": "markdown",
"id": "d5b2c724b4d8452a",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"- **Lag** is a column with the lag center,\n",
"- **semivariance** is a column with a dissimilarity metric,\n",
"- **covariance** is a column with a similarity metric,\n",
"\n",
"We can plot semivariance and covariance to understand their relation."
]
},
{
"cell_type": "code",
"id": "dc6705ca30fcf75b",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.058115Z",
"start_time": "2025-12-21T14:55:32.897852Z"
}
},
"source": [
"experimental_variogram.plot(\n",
" semivariance=True,\n",
" covariance=True,\n",
" variance=True\n",
")"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 52
},
{
"cell_type": "markdown",
"id": "11d2e707e0832fce",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"Our plot shows three objects:\n",
"\n",
"- **Circles** represent semivariance,\n",
"- **Plus signs** represent covariance,\n",
"- **The dashed line** is a variance.\n",
"\n",
"We see here that semivariance and covariance are mirrored. What does it mean? It is normal behavior, and we should expect it - semivariance and covariance have symmetrical trends (they differ in a sign). We can read it as:\n",
"\n",
"- **semivariances** show that the dissimilarity between point pairs over a distance increases,\n",
"- **covariances** show that the similarity between point pairs over a distance decreases.\n",
"\n",
"In the best-case scenario (data is stationary, e.g., mean and variance don't change with a distance), variance and covariance should be equal to semivariance."
]
},
{
"cell_type": "markdown",
"id": "fe10113f06939b7c",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 3: Theoretical Variogram\n",
"\n",
"### Manual setting\n",
"\n",
"We can start modeling theoretical function that optimally describes observed data. Our role is to choose the function type and set three hyperparameters: *nugget*, *sill*, and *range*. You can read more about semivariogram models here: [Geostatistics: Theoretical Variogram Models](https://ml-gis-service.com/index.php/2022/04/01/geostatistics-theoretical-variogram-models/).\n",
"\n",
"Semivariogram has three basic properties:\n",
"\n",
"- **nugget**: the initial value at a zero distance. In most cases, it is zero, but sometimes it represents a bias.\n",
"- **sill**: a distance where the semivariogram flattens and reaches approximately 95% of dissimilarity. Sometimes we cannot find a sill; for example, if differences grow exponentially, but in pyinterpolate it is usually set close to the variance of data. In `pyinterpolate` we set **partial_sill** which is a difference between **total sill** and **nugget**.\n",
"- **range**: is a distance where a variogram reaches its sill. Larger distances are negligible for interpolation.\n",
"\n",
"We are not forced to know all of them at the beginning. The package may easily derive them all with the class `TheoriticalVariogram.autofit()` method.\n",
"\n",
"We can create a theoretical model in two ways:\n",
"\n",
"1. Manually, setting model type, nugget, sill, and range.\n",
"2. Semi-automatically, leaving some parameters (from the above) not filled. We will do it in the next chapter.\n",
"\n",
"### Models\n",
"\n",
"We can choose from a set of predefined models:\n",
"\n",
"- circular\n",
"- cubic\n",
"- exponential\n",
"- gaussian\n",
"- linear\n",
"- power\n",
"- spherical\n",
"\n",
"We will set the following:\n",
"\n",
"- **sill** (partial sill) to the experimental variogram variance, (but it might be set to the the last five lags average value of semivariances),\n",
"- **nugget** to zero,\n",
"- **range** to 8000 (we see in the experimental variogram plot that the semivariance *flattens* around this range, and it becomes close to the variance)."
]
},
{
"cell_type": "code",
"id": "37cdb8aa62c51d8c",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.216238Z",
"start_time": "2025-12-21T14:55:33.211992Z"
}
},
"source": [
"sill = experimental_variogram.variance\n",
"nugget = 0\n",
"variogram_range = 8000"
],
"outputs": [],
"execution_count": 53
},
{
"cell_type": "code",
"id": "6ae710075dc528e7",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.312653Z",
"start_time": "2025-12-21T14:55:33.308305Z"
}
},
"source": [
"# circular\n",
"\n",
"circular_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='circular',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)"
],
"outputs": [],
"execution_count": 54
},
{
"cell_type": "code",
"id": "bdc24ca1fbaf6071",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.392081Z",
"start_time": "2025-12-21T14:55:33.385051Z"
}
},
"source": [
"print(circular_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Circular model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 106.4369154595279\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 76.02124683696297 | 66.71388540855338 | -9.307361428409592 |\n",
"| 2000.0 | 150.8372591039797 | 130.66628344322854 | -20.170975660751168 |\n",
"| 3000.0 | 223.18223486037513 | 377.5405401178886 | 154.35830525751348 |\n",
"| 4000.0 | 291.65249083970144 | 250.66718626708865 | -40.98530457261279 |\n",
"| 5000.0 | 354.58310049417867 | 325.5561456427749 | -29.026954851403787 |\n",
"| 6000.0 | 409.8026413531391 | 387.4814906048632 | -22.321150748275898 |\n",
"| 7000.0 | 453.9807623558251 | 302.07493544556206 | -151.90582691026304 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 55
},
{
"cell_type": "code",
"id": "f741dffc75b4919f",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.633059Z",
"start_time": "2025-12-21T14:55:33.472823Z"
}
},
"source": [
"circular_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 56
},
{
"cell_type": "code",
"id": "3a68b2f62068d2e9",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.743775Z",
"start_time": "2025-12-21T14:55:33.738473Z"
}
},
"source": [
"# cubic\n",
"\n",
"cubic_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='cubic',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)"
],
"outputs": [],
"execution_count": 57
},
{
"cell_type": "code",
"id": "45dbf2b21beb22e9",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:33.879918Z",
"start_time": "2025-12-21T14:55:33.872679Z"
}
},
"source": [
"print(cubic_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Cubic model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 112.51542324377124\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 44.24686603199425 | 66.71388540855338 | 22.46701937655913 |\n",
"| 2000.0 | 145.66080834697556 | 130.66628344322854 | -14.99452490374702 |\n",
"| 3000.0 | 262.4988714324439 | 377.5405401178886 | 115.04166868544473 |\n",
"| 4000.0 | 363.8560662826773 | 250.66718626708865 | -113.18888001558867 |\n",
"| 5000.0 | 432.92635147156795 | 325.5561456427749 | -107.37020582879308 |\n",
"| 6000.0 | 467.67401160879393 | 387.4814906048632 | -80.19252100393072 |\n",
"| 7000.0 | 478.0522323071066 | 302.07493544556206 | -175.97729686154452 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 58
},
{
"cell_type": "code",
"id": "195bc2ba89ecadb5",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.162619Z",
"start_time": "2025-12-21T14:55:34.000302Z"
}
},
"source": [
"cubic_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 59
},
{
"cell_type": "code",
"id": "80cc1841542d882",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.237973Z",
"start_time": "2025-12-21T14:55:34.230449Z"
}
},
"source": [
"# Exponential model\n",
"\n",
"exponential_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='exponential',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)\n",
"print(exponential_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Exponential model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 172.56017194999464\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| 1000.0 | 56.27289968745207 | 66.71388540855338 | 10.440985721101313 |\n",
"| 2000.0 | 105.93355936108222 | 130.66628344322854 | 24.73272408214632 |\n",
"| 3000.0 | 149.7589377033685 | 377.5405401178886 | 227.7816024145201 |\n",
"| 4000.0 | 188.4346983450342 | 250.66718626708865 | 62.232487922054446 |\n",
"| 5000.0 | 222.56593731640734 | 325.5561456427749 | 102.99020832636754 |\n",
"| 6000.0 | 252.68664999001882 | 387.4814906048632 | 134.7948406148444 |\n",
"| 7000.0 | 279.26808562812147 | 302.07493544556206 | 22.806849817440593 |\n",
"| 8000.0 | 302.72612024499887 | 469.53885776562294 | 166.81273752062407 |\n",
"| 9000.0 | 323.42776313511536 | 486.79931370125183 | 163.37155056613648 |\n",
"| 10000.0 | 341.6968988640556 | 512.0646265352149 | 170.3677276711593 |\n",
"| 11000.0 | 357.81935455774294 | 529.991767276145 | 172.17241271840203 |\n",
"| 12000.0 | 372.04737176947935 | 582.4717932210419 | 210.42442145156258 |\n",
"| 13000.0 | 384.60355288875706 | 692.506923505966 | 307.90337061720896 |\n",
"| 14000.0 | 395.6843438348108 | 649.5775483966735 | 253.89320456186266 |\n",
"| 15000.0 | 405.4631075228907 | 624.8696046337229 | 219.40649711083222 |\n",
"| 16000.0 | 414.0928361887279 | 643.74627487684 | 229.65343868811203 |\n",
"| 17000.0 | 421.7085450064747 | 571.3688648923311 | 149.66031988585638 |\n",
"| 18000.0 | 428.4293844491225 | 540.0470107890337 | 111.61762633991123 |\n",
"| 19000.0 | 434.3605044400275 | 600.2575887426275 | 165.89708430259998 |\n",
"+---------+--------------------+--------------------+--------------------+\n"
]
}
],
"execution_count": 60
},
{
"cell_type": "code",
"id": "766c790982515393",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.493978Z",
"start_time": "2025-12-21T14:55:34.333238Z"
}
},
"source": [
"exponential_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 61
},
{
"cell_type": "code",
"id": "48c65e35433f62ce",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.559847Z",
"start_time": "2025-12-21T14:55:34.553788Z"
}
},
"source": [
"# Gaussian model\n",
"\n",
"gaussian_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='gaussian',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)\n",
"print(gaussian_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Gaussian model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 159.98897643128805\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| 1000.0 | 7.424744235536801 | 66.71388540855338 | 59.28914117301658 |\n",
"| 2000.0 | 29.015427794333753 | 130.66628344322854 | 101.65085564889478 |\n",
"| 3000.0 | 62.825213477796176 | 377.5405401178886 | 314.7153266400924 |\n",
"| 4000.0 | 105.93355936108222 | 250.66718626708865 | 144.73362690600644 |\n",
"| 5000.0 | 154.86188481421806 | 325.5561456427749 | 170.69426082855682 |\n",
"| 6000.0 | 206.03344490697575 | 387.4814906048632 | 181.44804569788747 |\n",
"| 7000.0 | 256.19385082954307 | 302.07493544556206 | 45.881084616018995 |\n",
"| 8000.0 | 302.72612024499887 | 469.53885776562294 | 166.81273752062407 |\n",
"| 9000.0 | 343.8241237029969 | 486.79931370125183 | 142.9751899982549 |\n",
"| 10000.0 | 378.52158882000424 | 512.0646265352149 | 133.54303771521063 |\n",
"| 11000.0 | 406.6017289289635 | 529.991767276145 | 123.39003834718147 |\n",
"| 12000.0 | 428.4293844491225 | 582.4717932210419 | 154.04240877191944 |\n",
"| 13000.0 | 444.7517070227298 | 692.506923505966 | 247.7552164832362 |\n",
"| 14000.0 | 456.506954502525 | 649.5775483966735 | 193.0705938941485 |\n",
"| 15000.0 | 464.6681804730809 | 624.8696046337229 | 160.20142416064203 |\n",
"| 16000.0 | 470.13420746058165 | 643.74627487684 | 173.6120674162583 |\n",
"| 17000.0 | 473.66799080758676 | 571.3688648923311 | 97.70087408474433 |\n",
"| 18000.0 | 475.8743341758106 | 540.0470107890337 | 64.17267661322313 |\n",
"| 19000.0 | 477.20524505947054 | 600.2575887426275 | 123.05234368315695 |\n",
"+---------+--------------------+--------------------+--------------------+\n"
]
}
],
"execution_count": 62
},
{
"cell_type": "code",
"id": "2518fc035c5992cd",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.827178Z",
"start_time": "2025-12-21T14:55:34.642752Z"
}
},
"source": [
"gaussian_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 63
},
{
"cell_type": "code",
"id": "71485073080d8247",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:34.895896Z",
"start_time": "2025-12-21T14:55:34.889607Z"
}
},
"source": [
"# Linear model\n",
"\n",
"linear_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='linear',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)\n",
"print(linear_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Linear model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 107.47814162764915\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 59.86320884856388 | 66.71388540855338 | 6.850676559989502 |\n",
"| 2000.0 | 119.72641769712776 | 130.66628344322854 | 10.939865746100779 |\n",
"| 3000.0 | 179.58962654569163 | 377.5405401178886 | 197.95091357219698 |\n",
"| 4000.0 | 239.45283539425552 | 250.66718626708865 | 11.214350872833137 |\n",
"| 5000.0 | 299.3160442428194 | 325.5561456427749 | 26.240101399955506 |\n",
"| 6000.0 | 359.17925309138326 | 387.4814906048632 | 28.30223751347995 |\n",
"| 7000.0 | 419.04246193994715 | 302.07493544556206 | -116.96752649438508 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 64
},
{
"cell_type": "code",
"id": "f6f466091c7697f5",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.135733Z",
"start_time": "2025-12-21T14:55:34.981617Z"
}
},
"source": [
"linear_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 65
},
{
"cell_type": "code",
"id": "df0d52ab7af65d8",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.201511Z",
"start_time": "2025-12-21T14:55:35.194494Z"
}
},
"source": [
"# Power model\n",
"\n",
"power_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='power',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)\n",
"print(power_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Power model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 131.6193788004333\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+--------------------+\n",
"| 1000.0 | 7.482901106070485 | 66.71388540855338 | 59.230984302482895 |\n",
"| 2000.0 | 29.93160442428194 | 130.66628344322854 | 100.7346790189466 |\n",
"| 3000.0 | 67.34610995463436 | 377.5405401178886 | 310.19443016325425 |\n",
"| 4000.0 | 119.72641769712776 | 250.66718626708865 | 130.94076856996088 |\n",
"| 5000.0 | 187.07252765176213 | 325.5561456427749 | 138.48361799101275 |\n",
"| 6000.0 | 269.38443981853743 | 387.4814906048632 | 118.09705078632578 |\n",
"| 7000.0 | 366.66215419745373 | 302.07493544556206 | -64.58721875189167 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+--------------------+\n"
]
}
],
"execution_count": 66
},
{
"cell_type": "code",
"id": "a09ce4a481ebade7",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.506762Z",
"start_time": "2025-12-21T14:55:35.291434Z"
}
},
"source": [
"power_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 67
},
{
"cell_type": "code",
"id": "6d92684930a0e367",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.579391Z",
"start_time": "2025-12-21T14:55:35.572937Z"
}
},
"source": [
"# Spherical model\n",
"\n",
"spherical_model = build_theoretical_variogram(experimental_variogram=experimental_variogram,\n",
" models_group='spherical',\n",
" sill=sill,\n",
" rang=variogram_range,\n",
" nugget=nugget)\n",
"\n",
"print(spherical_model)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Spherical model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 108.209074255922\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 89.32713195371642 | 66.71388540855338 | -22.613246545163037 |\n",
"| 2000.0 | 175.8481759926564 | 130.66628344322854 | -45.18189254942786 |\n",
"| 3000.0 | 256.7570442020435 | 377.5405401178886 | 120.7834959158451 |\n",
"| 4000.0 | 329.2476486671013 | 250.66718626708865 | -78.58046240001266 |\n",
"| 5000.0 | 390.5139014730534 | 325.5561456427749 | -64.95775583027853 |\n",
"| 6000.0 | 437.7497147051234 | 387.4814906048632 | -50.26822410026017 |\n",
"| 7000.0 | 468.1490004485347 | 302.07493544556206 | -166.07406500297265 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 68
},
{
"cell_type": "code",
"id": "b6784bb105646144",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.845172Z",
"start_time": "2025-12-21T14:55:35.665600Z"
}
},
"source": [
"spherical_model.plot()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 69
},
{
"cell_type": "markdown",
"id": "2764af77c903765",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"A quick look into Root Mean Squared Errors (RMSE), and we can bet that the best model is **linear**. We can check each table printed with the `print()` manually, but instead, we will automatically select model with the lowest RMSE to be sure that we have chosen the best function."
]
},
{
"cell_type": "code",
"id": "9d7f191940c5ecb4",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:35.920991Z",
"start_time": "2025-12-21T14:55:35.915911Z"
}
},
"source": [
"models = [\n",
" circular_model, cubic_model, exponential_model, gaussian_model, linear_model, power_model, spherical_model\n",
"]\n",
"\n",
"lowest_rmse = np.inf\n",
"chosen_model = ''\n",
"\n",
"for _model in models:\n",
" \n",
" # Get attrs\n",
" model_name = _model.name\n",
" model_rmse = _model.rmse\n",
" \n",
" # Check error\n",
" if model_rmse < lowest_rmse:\n",
" lowest_rmse = model_rmse\n",
" chosen_model = model_name\n",
" \n",
" # Print status\n",
" msg = f'Model: {model_name}, RMSE: {model_rmse}'\n",
" print(msg)\n",
"\n",
"msg = f'\\nThe best model is {chosen_model} with RMSE {lowest_rmse}'\n",
"print(msg)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: circular, RMSE: 106.4369154595279\n",
"Model: cubic, RMSE: 112.51542324377124\n",
"Model: exponential, RMSE: 172.56017194999464\n",
"Model: gaussian, RMSE: 159.98897643128805\n",
"Model: linear, RMSE: 107.47814162764915\n",
"Model: power, RMSE: 131.6193788004333\n",
"Model: spherical, RMSE: 108.209074255922\n",
"\n",
"The best model is circular with RMSE 106.4369154595279\n"
]
}
],
"execution_count": 70
},
{
"cell_type": "markdown",
"id": "cd022c8488e65b67",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 4: Fit semivariogram model automatically\n",
"\n",
"We may find the optimal semivariogram model automatically, leaving parameters empty in `build_variogram_model()` function. This function creates the instance of `TheoreticalVariogram` object, and calls `.autofit()` method.\n",
"\n",
"The `build_variogram_model()` function takes multiple parameters. The most important for us are:\n",
"\n",
"- `experimental_variogram` - the experimental variogram object (`ExperimentalVariogram` type)\n",
"- `models_group` - it could be a single model name, list with multiple model types, or special text aliases to test group of models. By default, this parameter is set to `safe` - it compares a list of *safe* variogram functions that are rather resistant to overfitting. The full list of available options:\n",
" - `'all'` - the same as list with all models,\n",
" - `'safe'` - ['linear', 'power', 'spherical']\n",
" - as a list: multiple model types to test\n",
" - as a single model type from:\n",
" - `'circular'`\n",
" - `'cubic'`\n",
" - `'exponential'`\n",
" - `'gaussian'`\n",
" - `'linear'`\n",
" - `'power'`\n",
" - `'spherical'`\n",
"- `nugget` - nugget (bias) of a variogram. If given, then the nugget is fixed to this value\n",
"- `min_nugget` - default = 0.0 - the minimal fraction of the nugget at a distance 0 to search for the optimal nugget\n",
"- `max_nugget` - default = 0.5 - the maximum fraction of the nugget at a distance 0 to search for the optimal nugget\n",
"- `number_of_nuggets` - default = 16 - how many equally spaced steps between `min_nugget` and `max_nugget` to check\n",
"- `rang` - if given, then the range is fixed to this value\n",
"- `min_range` - default = 0.1 - the minimal fraction of a variogram range, `0 < min_range <= max_range`\n",
"- `max_range` - default = 0.5 - the maximum fraction of a variogram range, `min_range <= max_range <= 1`. Parameter `max_range` greater than **0.5** raises warning\n",
"- `number_of_ranges` - default = 16 - how many equally spaced ranges are tested between `min_range` and `max_range`.\n",
"- `sill` - if given, it is fixed to this value\n",
"- `min_sill` - default = 0 - the minimal fraction of the variogram variance at lag 0 to find a sill, `0 <= min_sill <= max_sill`\n",
"- `max_sill` - default = 1 - the maximum fraction of the variogram variance at lag 0 to find a sill. It *should be* lower or equal to 1. It is possible to set it above 1, but then warning is printed\n",
"- `number_of_sills` - default = 16 - how many equally spaced sill values are tested between `min_sill` and `max_sill`\n",
"- `error_estimator` - default = `'rmse'` - Error estimation to choose the best model. Available options are:\n",
" - `rmse`: Root Mean Squared Error\n",
" - `mae`: Mean Absolute Error\n",
" - `bias`: Forecast Bias\n",
" - `smape`: Symmetric Mean Absolute Percentage Error\n",
"\n",
"In the first run, we will set nugget, sill, and range as fixed values, and we will see which model type (name) algorithm chooses from the `all` possible models.\n",
"\n"
]
},
{
"cell_type": "code",
"id": "c618126951b3b893",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:36.030968Z",
"start_time": "2025-12-21T14:55:36.021Z"
}
},
"source": [
"fitted = build_theoretical_variogram(\n",
" experimental_variogram=experimental_variogram,\n",
" models_group='all',\n",
" nugget=0,\n",
" rang=variogram_range,\n",
" sill=sill\n",
")"
],
"outputs": [],
"execution_count": 71
},
{
"cell_type": "code",
"id": "c75d9a9757db3de0",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:36.148593Z",
"start_time": "2025-12-21T14:55:36.141816Z"
}
},
"source": [
"print(fitted)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Circular model\n",
"* Nugget: 0\n",
"* Sill: 478.90567078851103\n",
"* Range: 8000\n",
"* Spatial Dependency Strength is Undefined: nugget equal to 0, cannot estimate\n",
"* Mean Bias: None\n",
"* Mean RMSE: 106.4369154595279\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 76.02124683696297 | 66.71388540855338 | -9.307361428409592 |\n",
"| 2000.0 | 150.8372591039797 | 130.66628344322854 | -20.170975660751168 |\n",
"| 3000.0 | 223.18223486037513 | 377.5405401178886 | 154.35830525751348 |\n",
"| 4000.0 | 291.65249083970144 | 250.66718626708865 | -40.98530457261279 |\n",
"| 5000.0 | 354.58310049417867 | 325.5561456427749 | -29.026954851403787 |\n",
"| 6000.0 | 409.8026413531391 | 387.4814906048632 | -22.321150748275898 |\n",
"| 7000.0 | 453.9807623558251 | 302.07493544556206 | -151.90582691026304 |\n",
"| 8000.0 | 478.90567078851103 | 469.53885776562294 | -9.366813022888095 |\n",
"| 9000.0 | 478.90567078851103 | 486.79931370125183 | 7.893642912740802 |\n",
"| 10000.0 | 478.90567078851103 | 512.0646265352149 | 33.15895574670384 |\n",
"| 11000.0 | 478.90567078851103 | 529.991767276145 | 51.08609648763394 |\n",
"| 12000.0 | 478.90567078851103 | 582.4717932210419 | 103.5661224325309 |\n",
"| 13000.0 | 478.90567078851103 | 692.506923505966 | 213.601252717455 |\n",
"| 14000.0 | 478.90567078851103 | 649.5775483966735 | 170.67187760816245 |\n",
"| 15000.0 | 478.90567078851103 | 624.8696046337229 | 145.9639338452119 |\n",
"| 16000.0 | 478.90567078851103 | 643.74627487684 | 164.84060408832892 |\n",
"| 17000.0 | 478.90567078851103 | 571.3688648923311 | 92.46319410382006 |\n",
"| 18000.0 | 478.90567078851103 | 540.0470107890337 | 61.14134000052269 |\n",
"| 19000.0 | 478.90567078851103 | 600.2575887426275 | 121.35191795411646 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 72
},
{
"cell_type": "markdown",
"id": "800cd0eced009e67",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"The chosen model is (most likely) **linear**, automatically set as the class parameter. The algorithm performs the same steps as we did before. It has selected a model based on the RMSE. All other parameters were untouched.\n",
"\n",
"Now, we let the algorithm find the optimal parameters (model type, nugget, sill, range).\n",
"\n"
]
},
{
"cell_type": "code",
"id": "10d72fbeeba00adb",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:36.799206Z",
"start_time": "2025-12-21T14:55:36.306402Z"
}
},
"source": [
"fitted = build_theoretical_variogram(experimental_variogram=experimental_variogram)"
],
"outputs": [],
"execution_count": 73
},
{
"cell_type": "code",
"id": "967f7f9acf0e6459",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:36.862187Z",
"start_time": "2025-12-21T14:55:36.856149Z"
}
},
"source": [
"print(fitted)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Spherical model\n",
"* Nugget: 24.46175798313624\n",
"* Sill: 596.0578687869111\n",
"* Range: 14980.760125261822\n",
"* Spatial Dependency Strength is strong\n",
"* Mean Bias: None\n",
"* Mean RMSE: 58.37586447036475\n",
"* Error-lag weighting method: equal\n",
"\n",
"\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| lag | theoretical | experimental | bias (real-yhat) |\n",
"+---------+--------------------+--------------------+---------------------+\n",
"| 1000.0 | 84.05545137223926 | 66.71388540855338 | -17.34156596368588 |\n",
"| 2000.0 | 143.11727153379871 | 130.66628344322854 | -12.450988090570178 |\n",
"| 3000.0 | 201.1153452402711 | 377.5405401178886 | 176.4251948776175 |\n",
"| 4000.0 | 257.5177992641128 | 250.66718626708865 | -6.850612997024143 |\n",
"| 5000.0 | 311.7927603777803 | 325.5561456427749 | 13.763385264994554 |\n",
"| 6000.0 | 363.40835535373014 | 387.4814906048632 | 24.073135251133067 |\n",
"| 7000.0 | 411.83271096441854 | 302.07493544556206 | -109.75777551885648 |\n",
"| 8000.0 | 456.53395398230225 | 469.53885776562294 | 13.004903783320685 |\n",
"| 9000.0 | 496.98021117983745 | 486.79931370125183 | -10.18089747858562 |\n",
"| 10000.0 | 532.6396093294809 | 512.0646265352149 | -20.57498279426602 |\n",
"| 11000.0 | 562.9802752036886 | 529.991767276145 | -32.98850792754365 |\n",
"| 12000.0 | 587.4703355749175 | 582.4717932210419 | -4.998542353875564 |\n",
"| 13000.0 | 605.5779172156238 | 692.506923505966 | 86.92900629034227 |\n",
"| 14000.0 | 616.7711468982636 | 649.5775483966735 | 32.806401498409855 |\n",
"| 15000.0 | 620.5196267700474 | 624.8696046337229 | 4.349977863675576 |\n",
"| 16000.0 | 620.5196267700474 | 643.74627487684 | 23.226648106792595 |\n",
"| 17000.0 | 620.5196267700474 | 571.3688648923311 | -49.15076187771626 |\n",
"| 18000.0 | 620.5196267700474 | 540.0470107890337 | -80.47261598101363 |\n",
"| 19000.0 | 620.5196267700474 | 600.2575887426275 | -20.262038027419862 |\n",
"+---------+--------------------+--------------------+---------------------+\n"
]
}
],
"execution_count": 74
},
{
"cell_type": "markdown",
"id": "f6263f63765278df",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"As you see, the model is better, but parameters are different than we have set. Still, the best model is **linear**. Let's compare both those models on a single plot."
]
},
{
"cell_type": "code",
"id": "9695dca84c3a1a6f",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.217584Z",
"start_time": "2025-12-21T14:55:36.978151Z"
}
},
"source": [
"_lags = experimental_variogram.lags\n",
"_experimental = experimental_variogram.semivariances\n",
"_linear_manual = linear_model.yhat\n",
"_automatic = fitted.yhat\n",
"\n",
"plt.figure(figsize=(20, 8))\n",
"plt.scatter(_lags, _experimental, color='#762a83') # Experimental\n",
"plt.plot(_lags, _linear_manual, ':p', color='#e7d4e8', mec='black')\n",
"plt.plot(_lags, _automatic, ':o', color='#1b7837', mec='black')\n",
"\n",
"plt.title('Comparison of theoretical semivariance models')\n",
"plt.legend(['Experimental Semivariances',\n",
" 'Manual - ' + linear_model.name,\n",
" 'Automatic - ' + fitted.name])\n",
"plt.xlabel('Distance')\n",
"plt.ylabel('Semivariance')\n",
"plt.show()"
],
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAABlkAAAK9CAYAAACn9ApRAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA8npJREFUeJzs3Qd8U9UXwPHTvQejLWWUjWzZAoLspQj+AReKiHsLbtwbxQGKW1GGAxVREWWJqCAgGwUE2bNs6KQz+X/OrXkkbYESWtLx+/4//ae57+blvvuSWN7JucfLbrfbBQAAAAAAAAAAAGfE+8y6AwAAAAAAAAAAQBFkAQAAAAAAAAAAcANBFgAAAAAAAAAAADcQZAEAAAAAAAAAAHADQRYAAAAAAAAAAAA3EGQBAAAAAAAAAABwA0EWAAAAAAAAAAAANxBkAQAAAAAAAAAAcANBFgAAAAAAAAAAADcQZAEAAECZ5+XlJU8//bSUdJMnT5b69euLn5+fREZGnvHjt2/fbubi1VdfldKmRo0acv311xfZ/kvia6io5+RUJkyYYOZMX3Nw36+//mrmUW/PFOcAAACgcBBkAQAAgGzZskVuvfVWqVWrlgQGBkp4eLhceOGF8sYbb8jx48c9PTwUwIYNG8wF89q1a8uHH34oH3zwwUn7/vTTTyUuIFAQixYtMsd17NgxTw8FAAAAQBnh6+kBAAAAwLN+/PFHufzyyyUgIECuu+46ady4sWRkZMjChQvlwQcflHXr1p3ygn1poIEkX9+S/aexfpPdZrOZwFidOnVO2VeDLG+//XapC7RokOWZZ54xwabcmTwbN24Ub2++Y1Zc5mTIkCFy1VVXmc8dAAAAoCQr2f+SBAAAwFnZtm2budBZvXp1+eWXXyQ2Ntbaduedd8rmzZtNEKY00oCEBpM0c0d/SroDBw6YW3eWCSuu0tLSxN/fv1ACAVzMLx5zkpKSIiEhIeLj42N+AAAAgJKOr3IBAACUYaNHj5bk5GQZP368S4DFQTMi7r33Xut+VlaWPPfcc2ZJKr1AqzUdHn30UUlPT3d5nLb37dvXZFe0atVKgoKCpEmTJlbdgGnTppn7Gtxo2bKlrFq1yuXxmokQGhoqW7dulV69epmLspUrV5Znn31W7Ha7S1+tH9K+fXupUKGCeR7d39SpU/Mci9YeuOuuu+Szzz6TRo0amfHPmjXL2uac1ZGUlCTDhw83x6H9oqOjpUePHrJy5UqXfX799dfm+fR5K1asKNdee63s2bMn32PR9ssuu8z8HhUVJQ888IBkZ2cX6Dy988471ph1HjQA5rwklo7zqaeeMr/rvk9VH0THo1ksjuN2/OSm2UuO89y6dWtZtmxZvkuUDRo0SMqXL2/OpZ7r6dOn5+mn51GzpbRfcHCwtG3bNk/wzlFbYsqUKfL4449LlSpVTN/ExESz/c8//5TevXtLRESEae/UqZP88ccf1uP1eDXzStWsWdM6Lke9ifzqj+gcjhgxwjrPVatWNdlchw4dMts1CPfkk0+ac6zPq6/Djh07yvz588Vd48aNM+dSj6FcuXJmzj7//HOXPvpaueGGGyQmJsaMS/t//PHH+c7XV199ZbJ3dL7CwsLM+UhISDDvSX0N62tXX3PDhg3L933qmJPly5eb/U2cODHPmGfPnm22zZgxw9zfsWOH3HHHHXLeeeeZ176+9/T85q7t4aj58dtvv5n+OhadY+dtzo/5/vvv5ZJLLjGvcT1uff3p503u90nnzp1Nxt369eulS5cuZi71+PXzLL9Anb426tWrZ16j+jk3YMAAs0Sic8B17NixZp61j867Lp949OjR055Px/t7586d5jNPf9exON5jf//9t3Tt2tW8djSYnftcF/T9oXbv3m0+Q3RfOpf62s19Th1O9345GX0d6Geufp7pudX3kr4WAQAAcHJksgAAAJRhP/zwg6nDokGKgrjpppvMRVi9kHv//febC3mjRo2Sf/75R7799luXvpoFM3jwYHOxUoMPGgy59NJL5b333jOBGb3oqvTxV1xxRZ6li/TCql4k1AuOevFUAyIaSNBAjwZbHHR5rH79+sk111xjLorrRXq9YKkXhPWCrTPN1tGL0hps0YuIepE5P7fddpsJ1Gi/hg0byuHDh83yaXqcLVq0sC4S64VrDUDoMezfv9+MRS9katDIOaNEj0UvXF5wwQVmHn7++Wd57bXXzEXk22+//ZRzrheI9SJ69+7dTV+dp3fffdcEPfS5tMi9XiCeNGmSOQe6TS/0Nm3aNN/96fnYu3evzJ07VyZPnpxvH70QrIEm7asXwnX+9cK0XgzW51O6jJzW7dELyo888oi58KtzqxeBv/nmG/nf//5n+um86OsrNTVV7rnnHnNBXl9Des50jh39HPSiumavaBBKLyDr73re+vTpY4Id+hrQ18knn3xiLl4vWLBA2rRpY8b377//yhdffCFjxowx59cRdMqPBhc1YKLnVC8i63nV4IoGifRitj5eAzwfffSRXH311XLzzTebOdGApJ7LpUuXSrNmzeRMaK0cnQN9/2jwUgMAf/31l3kf6XvFMV/6mncEBXX8M2fOlBtvvNGMRwMnzvS1pxfD9Rzoe06DOHqOdI40SKCvnyVLlpjXq14w16BRfjTYo58Feg6HDh3qsu3LL780ASE9bqWvPV2aTbPgNGiigRJ93WnwQwMfelHfmb7X9Tj0uTWT5WR0jPrave+++8ytnnd9jB73K6+84tJXj00/H/S86+eHvpYefvhhE7zV14rjfaeBj3nz5pmx6pzrOdTX/tq1a837T+nr3PF+1vOjGX5vvfWWeR873mOnos+jz3nRRReZ94oGcvXc6XviscceM59NOk797NMgXrt27cy5OJP3hy5p2K1bNxPM0X4aiNL3r85RbgV5v5wsG65nz57mXOnrST/D9NxqUBwAAACnYAcAAECZlJCQoCkh9v79+xeo/+rVq03/m266yaX9gQceMO2//PKL1Va9enXTtmjRIqtt9uzZpi0oKMi+Y8cOq/3999837fPnz7fahg4datruvvtuq81ms9kvueQSu7+/v/3gwYNWe2pqqst4MjIy7I0bN7Z37drVpV335+3tbV+3bl2eY9NtTz31lHU/IiLCfuedd550LvQ5oqOjzfMcP37cap8xY4bZ15NPPpnnWJ599lmXfTRv3tzesmVL+6kcOHDAHG/Pnj3t2dnZVvtbb71l9vnxxx9bbTp+bXOem5PRY8vvnwLbtm0z7RUqVLAfOXLEav/+++9N+w8//GC1devWzd6kSRN7Wlqayzlq3769vW7dulbb8OHDzWMXLFhgtSUlJdlr1qxpr1GjhnVcev61X61atVzOqe5T99erVy/zu4P20X306NHDanvllVfMPvQ4ctPXpJ4LBz1H2nfatGl5+jqeJysry56enu6y7ejRo/aYmBj7DTfccMrXUH70vdaoUaNT9rnxxhvtsbGx9kOHDrm0X3XVVeZ16Zgbx3zpa1Bfjw5XX3213cvLy96nTx+Xx7dr187MwanmZOTIkXY/Pz+Xc6/HHxkZ6XK8ud9zavHixWY8kyZNsto++eQT09ahQwczl84c25zPVX77vfXWW+3BwcEur7NOnTrleS4dZ6VKlewDBw602vT9of1ef/31k55jfV1qn88++8xl+6xZs/Jtz83x/n7xxRddXiP6OafnYcqUKVb7hg0b8rxOCvr+GDt2rOn31VdfWf1SUlLsderUcfn8PJP3S+5z8O2335r7y5YtO+UxAwAAwBXLhQEAAJRRjmWYdImhgtBi6Uq/Ze5MM1pU7uVtNANEv7HtoFkcSr9NHRcXl6ddsyRy02+DOzi+2a/ZKpoJ4qDf4nf+drsulaQZCrmX9lK6ZI6O63T0G9yaXaAZHydbUke/9a3f0Heu56KZM/Xr1893qR/NjnGmY8zvmJ3pcerxavaCc5aPZlWEh4cXWb2cK6+80mQuOI9VOcZ75MgR8215zSDQzADNANEfzfjRbIdNmzZZy6bp60a/Od+hQwdrf5qlcMstt5hvyWvmgzPNonA+p6tXrzb700wP3b/juTQjQr/Z//vvv5vlns6UZtucf/75eTJplGP5NK0Zopk0Sp9Dj1szqTTrI7/XV0FeV5olk9/Sa0pjNTouzfjS3x3Hqj86r/razv28mhnhnGmh7yd9bO4lnrR9165dZvynOu+ZmZkumQtz5swxy6rpNgfn86P99bzo0oJ6fPnNi75eC1J/xXm/jteVvvY0y0OXpnOmryHNkHPQ86SvM+f3lM6lZiTdfffdJz3HuuSfLqmlywE6z7dmgehzFHRpOM3yc9B50KXUNJNF3yMO2qbbnMdY0PeH9tOlzjQLykEzhrSfs7N5vziy7zQLUM8rAAAACoYgCwAAQBmlF+kdFzMLQusw6IV+vZjqrFKlSubinG535hxIUXohU1WrVi3f9tz1D/S5dPkiZ1pXQTnXcdALgrq8kgY7tKaBLnWjSxfpBencHEv0nI4u+aPLCelY9QKoLrnkfGHUcax60TQ3DbLkngsdW+5lqzSIcbqaDyd7Hr2grHOT+3kKS+5z5wi4OMary1LphfwnnnjCHJfzj6M2jAahHMeQ3zw1aNDA5RhPdo70grEj+JL7uXQpL11SLL9zfTpak0PrepyOLt2kS6/pOdSlnPR5NbjlznPqclZ6AV1fU3Xr1jW1dZzrZBw8eNAENLQeTu5j1aWsnOfVnfeZXlw/1bg16KSvX10ezEF/10CFBkcddOkqXcZLn0Nrp+h2HaOO/Wzed7oEnQa9dKz6+aT7dARScu9XlynLXUso93tKz7G+9nx9T75Ktr6+dN9a4yT3nOuScrnnOz/5vb/1GPIbo7Y7j7Gg7w+91c/e3PvL/dizeb9oEHrgwIFmeUI9p/379zfLjJ2s7gsAAAByUJMFAACgjNKLmLquvwYTzkR+RdLzc7Jvrp+sPXdB+4LQ+gJau0BrIWhxeP2mt36rXy8M5ldg2vmb8qei3z7Xb9BrjRP9Jr/Wg3j55ZfNN/wd9R7OREG+xV+cnO4cOb4Jr3VTHHU6cssdjCuo3OfI8Vx6Dk5WA0UDF0Xh008/NYXNtc7Mgw8+aC7E69xoHRTnwukFpRfOtaaOBga1xpBmWujrVgMWemHbcawaWMhdF8Uhd62dwn6facbKCy+8YLIfNMtNa9RoTRrnQIVmhuh7TDOsNFtNAwf6uaB1T/LLkijI+04DNHqRXz+XtOaS1kvR4IVmxmhwKvd+C+tzRPer51XrqOTnZDV9zvVnXUGdzftFz6HWgdEaPlqva/bs2SYjSutHaVtRvc8AAABKOoIsAAAAZZgWhdZvzS9evNhlaa/8VK9e3VzA029KO75l7SjcrBdIdXth0ufS7BFH9orSwubKUbBeL1LrhVi9GKjfqHfQC8BnSwM2uhyY/ui32bUwul581iCL41j1grnzN/wdbYU1F87P45zVo0uIaXHu7t27u7XfggbKTsYxFg1onW4Megw6/twcyz+dbq4cxcn14vvpnutMjkv3e7oAo15w1mPV4Jrzvh3ZOu7QJaQ0kKE/eh61ILq+rkaOHGku6GtgQwupu3tuz5aOSwM++t6KiYkxywpq8CT3vGgQSC++O6SlpZnPAXf9+uuvZnkrnWsNmjro69xdeo512T9d+upkxeu1jy7Ld+GFFxY4CFuYCvr+0Ft9vWqAxvm1mPuxZ/J+ORnNDNQffV1qsPqaa66RKVOmuCyJBgAAgBNYLgwAAKAMe+ihh8xFX714psGS3PTb+m+88Yb5/eKLLza3Y8eOdenz+uuvW/VICttbb71l/a4XF/W+XizV2gKOb4rrBUe9KO2gS4l99913bj+n7iv3cjr6TXfN+nEsm6M1ObTtvffec1lKZ+bMmfLPP/8U2lzoRVJdGuzNN990+fb7+PHjzRjdfR4958rdi+J67J07d5b3339f4uPj82zXZa8c9HWzdOlSE8hz0PoQGtzTYNnpauRobQy9cPzqq6+a5ZtO9Vxncly6LNKaNWtMtlJujrl2ZCI4z71etHc+ljOhQQRnem71+HX/GgjQ59NxaYAjvwCQ87EWFQ2gNmnSxCwTpj8abHQOeigdZ+5sjHHjxrm8D89UfnOtQSjN9HGXzqVm5Dh/jjg4nkez1nTczz33XJ4+Wr/mbAJHBVHQ94f20xpRGuBy0Fo12s/d90tuuoxZ7vPqyIZhyTAAAICTI5MFAACgDNOLcfpNZf32ul5c1SLaWqdCL24uWrTIFIXW5ZIc9Rr02+t6Uc+xtI9eHNSaFbqcUpcuXQp1bJqhoksq6XNq0W4NYGgtjEcffdRawkeDDBrk6d27tyn0rBknb7/9tlmq6q+//nLrebVGjdZS0ALTesy6RI5+012LlTu+ua+BHl0+TOtk6DzockoapNKAlF4YHTFiRKHMgR6nZjhoZoEeoy6Npt9c1wvPrVu3din8fSb0Qqy65557zHJfeoE7d7bC6eg8a7FuvSCvhc0140PnQC8Wa3F3DWCoRx55RL744guTAaTPp3Vz9DWjGQoaTNDaO6ei27WWhD6+UaNGZs6rVKkie/bsMUXJ9Rv7urSR83E99thj5nj0PGkReUfwxZku/6UXrC+//HKzJJI+Vgvb6/JYGjzTc6+ZXppZoXVC9LWmY9ZteuE7vwvYp9OzZ09Tw0izJjRLRANyGgDQfWsGi3rppZfMcelrXudVn0vHpctm6etQfy9q+nmgS5jpe/DGG2/Mc450XiZPnmyWCdPx6TnXsWnNGne1b9/e1FTR97u+TjR4qs9xNktr6efZpEmT5L777jOfVboEoAYwdKyaoaY1R/T9e+utt5ol4LRovJ4jfd1oxp5+/ul72rnYfGEr6PtDXwv6WtFjWrFihQl+6fwEBwe7/X7JTZ9XP1v09a7/bdDPwg8//NA8xhFkBwAAQD7sAAAAKPP+/fdf+80332yvUaOG3d/f3x4WFma/8MIL7ePGjbOnpaVZ/TIzM+3PPPOMvWbNmnY/Pz97tWrV7CNHjnTpo6pXr26/5JJL8jyP/vl55513urRt27bNtL/yyitW29ChQ+0hISH2LVu22Hv27GkPDg62x8TE2J966il7dna2y+PHjx9vr1u3rj0gIMBev359+yeffGL65f5TN7/ndt6mj1Hp6en2Bx980H7++eebedBx6O/vvPNOnsd9+eWX9ubNm5vnLl++vP2aa66x796926WP41hyy2+MJ/PWW2+ZY9M513m4/fbb7UePHs13fwcPHjzt/rKysux33323PSoqyu7l5WWNI79zkd8cOej5ue666+yVKlUyY6tSpYq9b9++9qlTp+bpN2jQIHtkZKQ9MDDQ3qZNG/uMGTNc+syfP988x9dff53vmFetWmUfMGCAvUKFCma+9TV2xRVX2OfNm+fS77nnnjPj8Pb2NvvTY1LaX8+Fs8OHD9vvuusu019f91WrVjV9Dh06ZLbbbDb7iy++aB6rz6nnWsetfbTtdPOT2/vvv2+/6KKLrGOoXbu2ea0lJCS49Nu/f795rer7S+dV57dbt272Dz744LTzpa9/bV+2bNlpXx/5zYnatGmT6as/CxcuzLNdX3vDhg2zV6xY0R4aGmrv1auXfcOGDXn2d7KxOG9znB/1xx9/2Nu2bWsPCgqyV65c2f7QQw/ZZ8+ebfrp8Tp06tTJ3qhRozz7zO+8pKam2h977DHrM0vnUl+L+pp0pnPbsmVL89z6vm/SpIl5/r179+Z5noK8v082xvw+Gwvy/lA7duyw9+vXz3we6tzfe++99lmzZuWZn4K+X3Kfg5UrV9qvvvpqe1xcnHlMdHS0eT8vX778lHMAAABQ1nnp/+UXfAEAAAA8RbNnNMvAnWwBAAAAAADOFWqyAAAAAAAAAAAAuIEgCwAAAAAAAAAAgBsIsgAAAAAAAAAAALiBmiwAAAAAAAAAAABuIJMFAAAAAAAAAADADQRZAAAAAAAAAAAA3ODrzoNKG5vNJnv37pWwsDDx8vLy9HAAAAAAAAAAAIAHaaWVpKQkqVy5snh7nzxfhSCLiAmwVKtWzdPDAAAAAAAAAAAAxciuXbukatWqJ91OkEXEZLA4Jis8PNzTwwEAAAAAAAAAAB6UmJhokjMc8YOTIcgiYi0RpgEWgiwAAAAAAAAAAECdrsQIhe8BAAAAAAAAAADcQJAFAAAAAAAAAADADQRZAAAAAAAAAAAA3EBNFgAAAAAAAAA4BbvdLllZWZKdne3poQAoJD4+PuLr63vamiunQ5AFAAAAAAAAAE4iIyND4uPjJTU11dNDAVDIgoODJTY2Vvz9/d3eB0EWAAAAAAAAAMiHzWaTbdu2mW+8V65c2VyIPdtvvQMoHtlpGkA9ePCgeY/XrVtXvL3dq65CkAUAAAAAAAAA8qEXYTXQUq1aNfONdwClR1BQkPj5+cmOHTvMez0wMNCt/VD4HgAAAAAAAABOwd1vuAMo/e9tPh0AAAAAAAAAAADcQJAFAAAAAAAAAADADQRZAAAAAAAAAADnzPXXXy+XXXaZlGQ1atSQsWPHSlke24QJEyQyMlLKOoIsAAAAAAAAAFDKghheXl55fnr37i3FwRtvvGEu0BcHOi/fffddoe83NTVVRo4cKbVr1zYF1aOioqRTp07y/fffS1FbtmyZ3HLLLUX+PFdeeaX8+++/Utb5enoAAAAAAAAAAFCa2bJtsm3FTkk6mCxhUaFSs2WcePsU7fffNaDyySefuLQFBASIJ2VnZ5ugRkREhJR2t912m/z5558ybtw4adiwoRw+fFgWLVpkbouaBnSKWmZmpgQFBZmfso5MFgAAAAAAAAAoImvnbpCXeoyTD4Z9Kl889J251fvaXpQ0oFKpUiWXn3Llypltv/76q/j7+8uCBQus/qNHj5bo6GjZv3+/ud+5c2e56667zI8GRSpWrChPPPGE2O126zHp6enywAMPSJUqVSQkJEQuuOACs+/cy0lNnz7dBBp0TDt37syzXJg+19133y3Dhw83Y4yJiZEPP/xQUlJSZNiwYRIWFiZ16tSRmTNnus7t2rXSp08fCQ0NNY8ZMmSIHDp0yGW/99xzjzz00ENSvnx5MwdPP/20y7Ja6n//+58J/jjub9myRfr372/2qftu3bq1/Pzzz2c0/3rMjz76qFx88cVmvy1btjTHeMMNN5zx/M2YMUPOO+88CQ4OlkGDBpksmYkTJ5r96nzpMWoAK7/lwgYPHmwyTnIHSPR8Tpo0ydyfNWuWdOjQwTxXhQoVpG/fvmYOHLZv327m58svvzTZOJqZ89lnn+VZLmxLAeZNx/biiy+aedDzGhcXJx988IFLn927d8vVV19tzpnOS6tWrUzAykGzgVq0aGHGUatWLXnmmWckKyvLbNPXp55j3a++3ipXrmzmp9QGWXRC80tbu/POO832tLQ087ueWD0pAwcOtN7kDvqmvOSSS8wLTD8EHnzwQWtCAQAAAAAAAMBTNJAyecRUSdif5NKecCDJtBd1oOVkNPigAQ0NSiQkJMiqVatMAOWjjz4yF8gd9EK+r6+vLF261Czx9frrr5s+DhqAWbx4sUyZMkX++usvufzyy00GzaZNm6w+GhB4+eWXzePWrVtnruHmR59LL/zrc2kw4vbbbzf7a9++vaxcuVJ69uxpxqv7U8eOHZOuXbtK8+bNZfny5SZQoNeOr7jiijz71Qv1epFeA0nPPvuszJ0711pWS2nGT3x8vHU/OTnZBEfmzZtn5kaP6dJLLzXXogtKAzo//fSTJCW5nntnBZ2/N9980/TRY9QgjAaFdN/6M3nyZHn//fdl6tSp+T7HNddcIz/88IM5JofZs2eb/ep+lAaz7rvvPjOPesze3t5mm81mc9nXI488Ivfee6/8888/0qtXrzzPlVzAeXvttddM4ET73HHHHeZcb9y40dqHBnL27NljAlVr1qwxQTLHWDQweN1115lxrF+/3hy7BnteeOEFs/2bb76RMWPGmHadR10KrkmTJlKk7B504MABe3x8vPUzd+5cDYPa58+fb7bfdttt9mrVqtnnzZtnX758ub1t27b29u3bW4/PysqyN27c2N69e3f7qlWr7D/99JO9YsWK9pEjR57ROBISEszz6i0AAAAAAAAAqOPHj9vXr19vbs9Udla2/YUuY+0PNXwu/59Gz9lf6PqG6VfYhg4davfx8bGHhIS4/LzwwgtWn/T0dHuzZs3sV1xxhb1hw4b2m2++2WUfnTp1sjdo0MBus9mstocffti0qR07dpjn2LNnj8vjunXrZl2f/eSTT8x119WrV+cZX//+/V2eq0OHDi7XfXW8Q4YMsdr0+rHua/Hixeb+c889Z+/Zs6fLfnft2mX6bNy4Md/9qtatW5vjcND+33777WnntFGjRvZx48ZZ96tXr24fM2bMSfv/9ttv9qpVq9r9/PzsrVq1sg8fPty+cOFCa/uZzN/mzZut7bfeeqs9ODjYnpSUZLX16tXLtOc3tszMTHPNfNKkSdb2q6++2n7llVeedOwHDx40z/v333+b+9u2bTP3x44d69JPxxcREWE/03m79tprrfv6+oqOjra/++675v77779vDwsLsx8+fDjf/en8vPjiiy5tkydPtsfGxprfX3vtNXu9evXsGRkZ9rN9jxc0buDRTBZdG845XU3TnrQQkEaqNII6fvx4Ex3ViKSmU2lEUdetW7JkiXn8nDlzTLTq008/lWbNmpnUsOeee07efvttycjI8OShAQAAAAAAACjDtAZL7gwWF3aRhH2Jpl9R6NKli6xevdrlR+uEOOhyYbrkk37zX1cU0m//59a2bVuz8pBDu3btTHaALk31999/m9t69eqZVYgcP7/99pvLUlP6PE2bNj3teJ37+Pj4mNWNnDMQHBk2Bw4cMLea4TB//nyX565fv77Z5vz8uZ87NjbW2sfJaDaFLuPVoEEDsxyW7luzN84kk+Wiiy6SrVu3mqwOXeJLs3g6duxorl+rgs6fruCk18yd50FXiNK+zm0nOybNRNLsHj3XjqwVXW5LM1wc9Jzq8ly69FZ4eLi1bFru49Xsk8KYt6ZO50RfXxobcIxfX6eanaRLheVHz7tmIznP2c0332wykTQ7R7OBjh8/bo5F27/99tsiX/mq2BS+16CIBks0LUkndsWKFWZtuO7du1t99E2ia6lpCpW+wfVW32jOKWyapqTpRfqi1ZORH13rTn8cEhMTi/joAAAAAAAAAJQlWuS+MPudKV0iS+uYnIp+oV0dOXLE/OhjCkovqGswRK/j6q0z5wCAFkZ3DtScjJ+fn8t9fYxzm2MfjmWj9Pl1KSpdiiw3DaScar+5l8HKTQMFuqTYq6++auZQj0EDJWf6xX59bg2s6M/DDz8szz//vAkQ6O8Fnb/TzUtBjkkDKprYoIEMPS49Hl3Ky0HnsXr16qYOjtYw0X01btw4z/Ge7vXxQAHn7VTj18ecis6b1mAZMGBAnm1ao6VatWpm6TGtBaNj0eXIXnnlFRO8yv28pS7Iomuj6Tp6WvRI7du3z0Q5nQvnKA2o6DZHH+cAi2O7Y9vJjBo1ypwIAAAAAAAAACgKYVGhhdqvsGm2xIgRI8yFdS1oPnToUHNhWutxODgXG1e6wlDdunVNUEC/4K6ZGHrhXoMI55oWPtcsHM260GwNd+mFd+ei8eqPP/4w16kdNUv0wr4Wfz9bDRs2NFkVmjl0LudP69po8EHP88yZM022hyPgcPjwYROU0NeBYxwLFy5063n+KIR50ywXrd+jQb/8sln0vOt4TxVA1ECNBo70R2u+a/KGZg7pY4uCR5cLc6ZLg+lyXxopK2ojR440y5E5fnbt2lXkzwkAAAAAAACg7KjZMk4iYsJETpbE4SUSUSnc9CsKupKPfhHd+efQoUNmm17cv/baa82qQMOGDTNlGrTwuhYkd6bLPOnKQ3pR+4svvpBx48aZguNKl7nSDAktQj5t2jTZtm2bKVqvX3D/8ccfpajpxXO9EK/LXGnBeg0aaUF3PZ7cQZNT0SCNLuml83P06FHTpoEkPSZdukqXpxo8ePBps19y69y5sym+rpkqGmjQIvWPPvqoWcZNl+Q61/Onx/Dee++Z7A7npcLKlStnlmb74IMPZPPmzfLLL7+Yc+6OuoUwb3o+dfmwyy67zARtdMk1DabpqlbqySeflEmTJpkkCl3NSpcjmzJlijz++ONm+4QJE0ysYe3ateaxunqWBl00U6eoFIsgy44dO0yU9KabbrLadCI1jUizW5zt37/fbHP00fu5tzu2nUxAQIB5ITv/AAAAAACA4s+WbZMtS7fL6h/Xmlu9DwDFkbePt/Qb2SvnTu5Ay3/3+z3S0/QrCrNmzTLLZjn/dOjQwWx74YUXzDVZDQIo3aYX2fVCtV4cd9AAgNa3aNOmjQlqaIDllltusbZrcEb73H///XLeeeeZC+Ma8NCSD0VNv6yvF+E1oNKzZ09TVmL48OFmZSTnbJzT0cCSBh4008NRfkLrhGvwQTNANBtCg1FnmgWhj5k4caIZm9Youfvuu03bV1995ZH508CK1jevUqWKXHjhhVa7zpUGKTQYpEuEaXaTLq/ljtcLYd50dSutxR4dHS0XX3yxOa8vvfSStaSa7lNru2uf1q1bm7IiWk/IEUTR869ZOXqMmhWjcYcffvjBBJKKipfdbreLhz399NPmDa0ZJY7ULs0wiYqKMhHSgQMHmjaNmGpqj6Mmi6Y29e3b1xS10UlX+mHw4IMPmjQrDaYUhNZkiYiIMM9JwAUAAAAAgOJp7dwNMn3UbJdC0votcb2I2bhHTrFjAChMuqyTZhjUrFnT1HsotM+uSuEmwFKcP7s0E6NZs2YyduxYTw8F8Mh7vKBxA4/XZNF0IY3Y6Zp/zmvn6eBvvPFGk5qka6/pQWi0r127dibAojQKqOvYDRkyREaPHm1SujTaqlHVggZYAAAAAABA8acXKSePmCqS66uiCQeSTPuQMYOK9cVKAGWXfjY17FpPtq3YaYrcaw0WXSKsqDJYAJxbHg+yaLqOru13ww035NmmaT6arqSZLLqGoKYCvfPOO9Z2TRHS1KDbb7/dBF9CQkJMsObZZ589x0cBAAAAAACKii4Jpt8Czx1gMbTNS2T6S3PMRUwuWgIojvSzqXabGp4eBoDSulyYp7FcGAAAAAAAxZfWXvlg2Ken7XfLJ9dyERNAsVsuDEDpXi6Mr3cAAAAAAIBiTZfXKcx+AAAAhYUgCwAAAAAAKNa0fkFh9gMAACgsBFkAAAAAAECxpgWiI2LCTO2VfHmJRFQKN/0AAADOJYIsAAAAAACg2BeM7jeyV86d3IGW/+73e6QnRe8BAMA5x18fAAAAAACg2Gvco74MGTNIIqLDXNojYsJNu24HAAA413zP+TMCAAAAAAC4QQMpDbvWk20rdpoi91qDRZcII4MFAAB4Cn+FAAAAAACAEkMDKrXb1JBmlzQ2twRYAJQka9as8fQQij0vLy/57rvvzmofTz/9tDRr1sy6f/3118tll11WCKMD8uIvEQAAAAAAAAAoQikpKTJs2DBz4V9vU1NTi/T5NKigwYrbbrstz7Y777zTbNM+ZcUbb7whEyZM8PQwUEoRZAEAAAAAAACAIrJ+/Xpp07qNfPXlV3LLdbeaW72v7UWpWrVqMmXKFDl+/LjVlpaWJp9//rnExcVJWRIRESGRkZGeHoZkZGR4eggoAgRZAAAAAAAAAOAM2Gx282O32602u6PNdqJt4sSJ0rp1a8nOtMnMKbPkmYeekZ+mzJSs9CzTPnHCxNPv1+n3M9GiRQsTaJk2bZrVpr9rgKV58+YufWfNmiUdOnQwgYgKFSpI3759ZcuWLdb27du3m+wXfXyXLl0kODhYzj//fFm8ePFJl+hSY8eOlRo1alj3ly1bJj169JCKFSuawEenTp1k5cqVUtRyLxfWuXNnueeee+Shhx6S8uXLS6VKlcz4nR07dkxuuukmiYqKkvDwcOnatavLcm86P/3795eYmBgJDQ015/Pnn3922Yce+3PPPSfXXXed2cctt9xS5MeKc48gCwAAAAAAAACcgQP/HDA/9uwTAZCUwymmLTE+ydxfsWKFubjfoU0H+emLn6Re7Xqm/bza55n7F7a+UK4fdr3p53Dw34NmH1np2Vbb8aMnMlHO1A033CCffPKJdf/jjz82y5Xlt5zZfffdJ8uXL5d58+aJt7e3/O9//xObzebS77HHHpMHHnhAVq9eLfXq1ZOrr75asrKyCjyepKQkGTp0qCxcuFCWLFkidevWlYsvvti0n2saAAsJCZE///xTRo8eLc8++6zMnTvX2n755ZfLgQMHZObMmeYcadCqW7ducuTIEbM9OTnZjF3na9WqVdK7d2+59NJLZefOnS7P8+qrr5qAlPZ54oknzvlxouj5noPnAAAAAAAAAIAyRS/Kt72grRw5ekSCAoNctgUFBcuRY0ekdcvWpl9Rufbaa2XkyJGyY8cOc/+PP/4wS4j9+uuvLv0GDhzocl+DMZrBoUuaNW7c2GrXAMsll1xifn/mmWekUaNGsnnzZqlfv36BxqPZIM4++OADkz3z22+/meyZc6lp06by1FNPmd812PPWW2+ZgIlm2mgQaOnSpSbIEhAQYAVLvvvuO5k6darJSNHAif44aMbKt99+K9OnT5e77rrL5Zjvv//+c3psOLfIZAEAAAAAAACAMxDdINr8ePl4WW0hFUJMW3hsmLmvy2s9/czTsnzNcvlt0W8uj/910a+yYs0Kee7550w/h6h6UWYfvgE+VltQOdcAzZnQQIkGRbTou2a06O+6VFdumzZtMlkptWrVMstaOZb4yp2VoYEJh9jYWHOrgYiC2r9/v9x8880mqKHLhelzaUZI7uc5mc8++8wszeX4WbBggbjL+Vgcx+M4Fl0WTMelS6c5P9+2bdusZdR0uwadGjRoYAJFuv2ff/7JcyytWrVye4woGchkAQAAAAAAAIAz4O19IjDi4OXtJblbe/bsabJZXnl7tJQvV95qf+Wt0dKubTvp2avn6ffrFIRxd8kwR2bF22+/nW8fXeaqevXq8uGHH0rlypXNMmGawZK7ULufn1+ecTmWFNMlxnLXj8nMzHS5r0uFHT58WN544w3zfJol0q5duwIXhO/Xr59ccMEF1v0qVaqIu5yPxXE8jmPRAIoGXXJn/CgNqCgNsOjyYprhUqdOHQkKCpJBgwblORZdkgylG0EWAAAAAAAAACgCjmwWrdfR6wrXgMrs2bPPOoBSEPrceuFfn6tXr155tmvQY+PGjSbA0rFjR9Omy2W5kzWzb98+E2hxHJfWbnGmy5W98847ppaJ2rVrlxw6dKjAzxEWFmZ+ipou4abH4uvra2X15KbHojV3tHaNIzCzffv2Ih8bih+CLAAAAAAAAABQRDSbZeXKlS5ZHhqEaNas2Tl5fh8fH7OMleP33MqVK2eWxdL6KJq9octdPfLII2f8PJ07d5aDBw+aIvKa0TFr1ixTNF6XBHPQZcImT55sltBKTEyUBx980GSAFDfdu3c3GTaXXXaZOZ569erJ3r175ccffzRBFR2/Hsu0adNMFpCeTy1q78iEQdlCTRYAAAAAAAAAKCJ6Ab558+YmO8Lxo/fPRRaLgwY6nIMdznSZrylTpsiKFSvMEmEjRoyQV1555YyfQ2uTaJaKLkmmBeG1cLwuqeVs/PjxcvToUTMHQ4YMkXvuuUeio6OluNFz89NPP8lFF10kw4YNM0GWq666Snbs2CExMTGmz+uvv24CVO3btzeBFs0S0uNC2eNlz71QXhmkUVMttJSQkHDSDxsAAAAAAAAAZUtaWpopdl6zZk0JDAz09HAAnMP3eEHjBmSyAAAAAAAAAAAAuIEgCwAAAAAAAAAAgBsIsgAAAAAAAAAAALiBIAsAAAAAAAAAAIAbCLIAAAAAAAAAAAC4gSALAAAAAAAAAACAGwiyAAAAAAAAAAAAuIEgCwAAAAAAAAAAgBsIsgAAAAAAAAAAALiBIAsAAAAAAAAAFKHs7Gz59ddf5YsvvjC3eh+Fp0aNGjJ27Fgpzjp37izDhw8vEfu+/vrr5bLLLiu0/ZV2BFkAAAAAAAAAoIhMmzZNatSqKV26dJHBgwebW72v7UVt8eLF4uPjI5dccolbj3/66aelWbNmUlxMmDBBIiMj87QvW7ZMbrnlFimr9LX03HPPeXoYZRZBFgAAAAAAAAAooovfgwYNksMByRI1pKHE3tfK3Op9bS/qQMv48ePl7rvvlt9//1327t0rpVVUVJQEBwdLWZORkWFuy5cvL2FhYZ4eTplFkAUAAAAAAAAAzkBqxnHzY7fbrbaMrEzTlp6Vc+FblwS7d8RwCawTKeUG1BX/KmHi7e9jbvW+tut256XDHPu12WxWW2Z2lltjTE5Oli+//FJuv/12k8miWSCnywr57rvvxMvLy9r+zDPPyJo1a0yb/jj2sXPnTunfv7+EhoZKeHi4XHHFFbJ///48GTAff/yxxMXFmX533HGHOdbRo0dLpUqVJDo6Wl544QWX53/99delSZMmEhISItWqVTOP0eNQuszasGHDJCEhwRqPPk9+y4UdO3ZMbr31VomJiZHAwEBp3LixzJgxQ87G1KlTzdiCgoKkQoUK0r17d0lJSXFZXkvnSwM+Oie33XabFQRx0PP60EMPmaCIzoFj/M7jvummm6x9dO3a1cx/7nn96KOPpGbNmubY8lsuLD09XR5++GEzhwEBAVKnTh0TcFN6Dm688UbzeD2W8847T954442zmpuyjiALAAAAAAAAAJyBek91MT9HUo5Zbe8t+NS0PT79VXN/wYIFsnvnLgltG2sFLhz0fkjbWLNd+zm0Hf0/s49NB7dbbV+tcC848NVXX0n9+vXNRfRrr73WBDycg0Knc+WVV8r9998vjRo1kvj4ePOjbRoo0ADLkSNH5LfffpO5c+fK1q1bzTZnW7ZskZkzZ8qsWbNMLRq9yK/Bnt27d5vHvfzyy/L444/Ln3/+aT3G29tb3nzzTVm3bp1MnDhRfvnlFxOUUO3btzeBFA0+OMbzwAMP5Bm3jq9Pnz7yxx9/yKeffirr16+Xl156ySyb5i59rquvvlpuuOEG+eeff0zAZ8CAAS7zOW/ePGubHq9mKWnQxZkekwaQ9Jg12PTss8+a+XO4/PLL5cCBA2beVqxYIS1atJBu3bqZuXbYvHmzfPPNN2b/q1evzne81113nRmDzqWO6f333zeBLsf8VK1aVb7++mszN08++aQ8+uij5vUC9/i6+TgAAAAAAAAAwCkuzCvfqPyXsfKrGOzSr7BpUEODK6p3794mA0SDG5r1UBCa5aAX5n19fU3WhYMGBf7++2/Ztm2byZRQkyZNMsEYrY3SunVr62K+BnZ0GauGDRuaWjQbN26Un376yQRTNPijgZb58+fLBRdcYB7jnI2h2SnPP/+8yQh55513xN/fXyIiIkyAynk8uf3888+ydOlSE1yoV6+eaatVq5acDT1HWVlZJrBSvXp106ZZLc50fHq8umyZzoUGUB588EFTK0WPVzVt2lSeeuop83vdunXlrbfeMsGZHj16yMKFC824Ncii2Sfq1VdfNdlFmkXjqDmj2TE635rtkp9///3XBEz0PGm2Te7j9/Pzcwn+aEaL1u7Rx2hGEs4cQRYAAAAAAIBSzpZtk20rdkrSwWQJiwqVmi3jxNuHBU4Ad/37zHxzG+SXs1yTuq3jtXLThVeJj3dOxkRsbKy5zTqYapYIyy3zUKpLP7XkoW/NbaBvzkV2dUXLvmc8Pg1m6AX7b7/N2Z8GSjTTRAMvBQ2ynIwGLzS44giwKA2i6NJjus0RZNEgiXOdEF26S7NJHAEHR5sGFZwDJKNGjZINGzZIYmKiCWykpaVJampqgWuuaHaHZmo4Aiyno0EczXhxcCxP5uz88883GSUaWOnVq5f07NnT1NQpV66cSx/nMbZr187sa9euXVZgRoMszvTcO45flwXT/roUmbPjx4+brCAH3dfJAiyO49d57tSp00n7vP322yYgpMu+6f41cKPLkME9BFkAAAAAAABKsbVzN8j0UbMlYX+S1RYREyb9RvaSxj3qe3RsQEkV7B+Up83f10/8xc+637FjR6kaV00OL4mXcgNCXZYM02WmUpbES7Xqcabfqfbr53Pml3A1mKIBisqVK7s8p2ZIaPaEZoRosCP38mGZmZlSWDRjwpkef35tjvoz27dvl759+5oaMlqrReuWaHaH1g/RIEBBgyyagXMmNOMkv2XHnGnQQjNDFi1aJHPmzJFx48bJY489Zpb90kyQgjrV8WuARYMuutxYbs61c3S5sbM5/ilTppjjfe2110wgSANhr7zyisuybTgzfGUBAAAAAACgFAdYJo+Y6hJgUQkHkky7bgdQNPTC/Btjxkra5mNydNomSd+TJLb0bHOr97V97OtjzqpWSH40uKLLSelFdM1qcPxopoQGXbRWh9JsiKSkJKt4u8pd40OXwNJC6c4aNGhgsjP0x0Fre2jRds1ocZfWINGAg467bdu2JhNl7969px1PbpotonVfdNmsgoiOjjaF4R0/J6MBkQsvvNAstbVq1SozFkemkNL51awQhyVLlpjl1pwzfk5F66/s27fPZB05j0d/KlasKAWl2TY6j7o0XH60Vo3Wt7njjjukefPmZv/OmTI4cwRZAAAAAAAASukSYZrBIvnVuf6vbfpLc0w/AEVDa3hoPY0K6aFyaPJ6iR+z3NxWzAgz7bq9sM2YMUOOHj1qMkAaN27s8jNw4ECT5aK0Dopmh2jRc73I/vnnn8uECRNc9qVLfmntFQ2+HDp0SNLT002dD72Qf80118jKlSvNsmRaaF2Xp2rVqpXb49aL/ZpJo1kiW7dulcmTJ8t7772XZzya8aF1THQ8uoxYbjqOiy66yByrZp/o+LWQ/KxZs9wem2Z5vPjii7J8+XKzxJYWnT948KAJODloto3OuQactO6M1l656667XJZHOxWdV80sueyyy0y2jGb2aOaMZszo8xaUztHQoUPlhhtuMPVc9Pg1O8ZR2F5rwej+Zs+ebQJRTzzxhKmlA/cRZAEAAAAAACiFtAZL7gwWF3aRhH2Jph+AoqOBlO1bt5kC7xrI0NttW7YWSYBFaRBFL9jrkmC5aeBBL7D/9ddfZjkurUWiAQENmmiGy9NPP52nf+/evU3Res180T6a0fH999+beiQazNDn0sLqX3755VmNW2uavP766/Lyyy+bgNBnn31m6rM40wwMraGi9WV0PKNHj853X998842pDXP11Veb7JqHHnrotBkwpxIeHi6///67XHzxxSbD5vHHHzcZN3369LH6aM0WDWDonOj4+vXrl2c+T0XnVc+FPn7YsGHmea666irZsWOHqV1zJt59911TM0azVerXry8333yzlbF06623mteejlEDbYcPHzb94D4ve+6F98ogLaKkHzoJCQnmDQMAAAAAAFDSrf5xrXzx0Hen7Xf16Muk2SWNz8mYUDZodpQG75IOJktYVKjUbBkn3j4l87veWnRdMwG07kZg4Iki94Cz66+/3iyXppkjKD3v8YLGDSh8DwAAAAAAUArpxe3C7AcUhNb50WXqnLOoImLCpN/IXtK4R32Pjg0AikLJDCEDAAAAAADglDR7QC9ui9dJOniJRFQKN/2AwgqwTB4xNc8ydQkHkky7bgeA0oYgCwAAAAAAQCmkyzNp9oCRO9Dy3/1+j/Qsscs4ofgtEaYZLFrrJ4//2qa/NMf0A0qbCRMmsFRYGcZ/RQEAAAAAAEopXZ5pyJhBEhEd5tIeERNu2lm+CYVFa7DkzmBxYRdJ2Jdo+gFAaUJNFgAAAAAAgFJMAykNu9YrNYXIUTzpa6sw+xU3dnt+KToASrrCeG8TZAEAAAAAACjlNKBSu00NTw8DpZgG7wqzX3Hh5+dnblNTUyUoKMjTwwFQyPS97fxedwdBFgAAAAAAAABnRbOjImLCTJH7fOuyeOUsU6f9ShIfHx+JjIyUAwcOmPvBwcHi5ZW7yBGAkpjBogEWfW/re1zf6+4iyAIAAAAAAADgrLOl+o3sJZNHTDUBFZdAy38xiX6P9CyRy9RVqlTJ3DoCLQBKj8jISOs97i4vOwsKSmJiokREREhCQoKEh4d7ejgAAAAAAABAibR27gaZPmq2JOxPstoiKoWbAIvWByrJsrOzJTMz09PDAFBIdImwU2WwFDRuQCYLAAAAAAAAgEKhgZSGXevJthU7TZF7rcGiS4SVxAyW3PRi7NksKQSgdCLIAgAAAAAAAKDQaECldpsanh4GAJwTJT+EDAAAAAAAAAAA4AEEWQAAAAAAAAAAANxAkAUAAAAAAAAAAMANBFkAAAAAAAAAAADcQJAFAAAAAAAAAADADb7uPAgAAAAAAAAo7WzZNtm2YqckHUyWsKhQqdkyTrx9+M4yAOAEgiwAAAAAAABALmvnbpDpo2ZLwv4kqy0iJkz6jewljXvU9+jYAADFB6F3AAAAAAAAIFeAZfKIqS4BFpVwIMm063YAABSZLAAAAAAAAIDTEmGawSJ2EZvdJrtTd0hyVpKE+oZJ1eDq4u3tLdNfmiMNu9Zj6bCTyM7OlgULFkh8fLzExsZKx44dxcfHx9PDKhGYO/cwb+5h3goHQRYAAAAAAADgP1qDRTNY/k1cJ/MOzJSkjARrW5h/hHSL7iP17I1Mv9ptanh0rMXRtGnT5N4Rw2X3zl1WW9W4avLGmLEyYMAAj46tuGPu3MO8uYd5KzyE2wEAAAAAAID/aJF7DbB8t3uKZFbzlqghDSX2vlbmVu9ru27Xfsh70XbQoEFyOCDZZd70vrbrduSPuXMP8+Ye5q1wedntdruUcYmJiRIRESEJCQkSHh7u6eEAAAAAAADAQ/5dvEVadW5pAirlB9UVLy8va5teRjsydZP477LLsl+XS712tT061uK27FCNWjXNRdpyA/LO29Fpm6RiRphs27KV5YhyYe7cw7y5h3kr/LgBy4UBAAAAAACUcqy7nyMjK1NSMlIlwNdfgv2DTNvxzDRZsnWVZGRnSq+GF5kaLLpEWFT7hi4XH5XeD20fK4cmr5ftSVuknuQEWWau+1V+/XeJdKzTWvo26WbabDabjPx+tPn9yYvvkZCAYPP7vA1/yJx/FkibGufLwOZ9rH0/9v2rkmXLkkd63S7lgiNM2++b/pQf186XZlUbytWt+1l9n5kxVlIz0+S+bjdJTHhF07Zk2yr5dvVsaVy5ngy54MRSPy/OelsSjifJXZ2vk2rlKpu2lTvXypcrZkjd6Jpy04VXWn1fnfuhHEw+LLd2GCy1ouJM2997NsinS7+T6uWryB2dhlh9x82fILuP7ZNh7S6X+pVqm9eXLjuk34bPb95C2sbKrsnrTb/OnTvLtkO75L0Fn0nF0PLyYI9brL7j//hS/j2wTa5ocYm0rN7EtO05tk/enD9BwgND5bE+d1l9P/3zW/l770a57Pye0q5WC9N2IOmwvPbzhxLkFyhP9x1u9f1y+QxZuWutXNyoi3Sqd4FpO3Y8UUbNekd8vX3khf4PWn2nrZolf25fLT3qd5DuDTqYttSM4/LMj2/kzGn/B8XHO+f9M+PvX2TB5qXSqe4FcnHjLqYtMztLHp/+qvldx6BjUXPW/y7zNi6SdjVbyGXNelrPN2TU7QWeu1+SVkp6VoaZM5079ceWFTL9r7nSpEp9ubbNZdZjn/tpnCSnp8i9XW+QyhHRpm3Z9jUyddVMqR9TW4a1v9zqO3rOe3I45ZjcftG1UqNCVdO2Zvc/8vmy76V2xTi5peNgq++YeeNlX+JB89rR15BaH79JJi75RqqVi5W7Og+1+r792yTZeWSvDG07UBrG1jVtmw9slw//mCIxYRXlvu43WX0/XPiFbD64Q65qdak0r9bItOljdR/lgyPk4V63W30nLJ4qs3+eU6B5e+rDUZIZ4yv9mnaXC2u3MtsPJx+V0XPfF38fP3mu3/3W46au/EmW7fjLfBZ0Pa+9aUtKS5HnZ44TL/GSl/73sNX3+zVzZdHWFaaf9ldpmeny1IwxOfN/6f3i7+tX7D4jfOIzzui9itNjuTAAAAAAAIBSTJd90W8td+nSRQYPHmxu9X5xXg7maGqCbD+82wQHHPR3vUCoP8704vlD00aZC90OelG+xxvXSu9x17n0ffjbUdLkuV7yyeKvXfY7ZMIIufnTR8y3uPcf2G/afaNyLnjm5lcxpz1+f7zVtnrXevls6XeyYsffLn21TX/0orjD2r0bTdvS7Wtc+k5Z/oNp14v5Dhv2bTFteiHXmV4k1/bEtBPzoxeute23TX+69P1u9RzTfiTlRG2ZbYd3mbb5Gxe59J3x98+m/UDyYatt19F40zb3nwUuffWisbbvTciZLw3gFWje/uunwRx9/A9//ezSb/6/i027jtHhSMox05b73P++ealp33Rgm9WWlJZs2r5e+ZNLX51Dbf9n32arLTUjzbR9sWy6S99lO9aYdg3gOOg5dJxP54WBVu1aa9pW7VpntdnsNqtvVna21b5mzwbTphfxnc1Z/muB5+6rFTPMPvTCv8O/+7eatj82L3N53LerZ5n2Y6knzv2WQztNm17wd/b9mpxzr8EHhx1Hdpu2nzf84dL3x7W/mHYNtDjsPbbftM1a95tL39nrfjftu4+eeL/sTzpk2nQ/zjS4oO07Du+x2g4lHzFtGtBw9tumJTJ7+S8Fmrel63LOvb6fHJIzUk2bBhudaSBC2zVo5JCWmfM6+WzZdy59l+/4y7Sv2b3easu2ZVvnPtueXSw/IxavW3ZG71WcHpksAAAAAAAApXzd/cA6keZby3pRLetgqhxeEm/ap06dekYFjvXisvM3n/Vb5hoQiStXWcqF5Hyzen/iIXMBXr+9f2Wrvlbfd36bLKt3r5fr2w2S9rVamja94D10wv0SGRwuc+6ZbPV94JsXZPb63+Wlyx6Way/4n2k7kHRI7v7yKYkICpf/Netl9dWLi9+smik1K1aTjnXamDYvL2+zb81QcObIXtFvmzuEBgSb7A8db5Yt22T6KJ0n/ypheeYg81Cqua1SOefb/uqium0k2D/Q+vZ9zhi8rAwNRyaDaluzhWlvXPk8l/2O6HajZNuyJCww1GprWb2p6XtejOuyZJqVosdQIaSc1XZ+1Qamb+2o6i59b+042GQzOL7NrjSjQPvGla/i0veG9leY81k1spLVVi+6pulbOSLGpa9my+g5qVmhmrlf0Hlz9KsSWcnsNzLIdQmeQc0vltbVm1pZDyo6rKLpGxIQ4tK3X9Me0ii2rjRzmvfywZGmb4BvgEtfzTKpVbGa2bdDWECI6evISnHoXr+jVAqPkgtqNrfaAv0CrPPp7XXie+ud67U1GTYt43KybpSPl7fV15HJoDrUbiX+Pr7StEoDl+e7utP/5K3vRxdo7u6pPEwyszPNe8ZBX3f6fI6sEoc7LhpiMrei/st4UU0qn2f61vjvvDnc3OEqSTieKLGRJ86zvu60b5XInHPmcH27y+Vw8hGpXv7Ee0Azn7RvTHiUS99r2lwm3eq3lzpRNay2uPKVTV/n16+6vOUlJiNJM6McYiOiTd/wQNd50c+AkEPe8vb0V047b91adJJLqodLi7jG1raIoDCzX18f18vjvRt2Mtk4F9Ro5vK54Zxt5dCt/oVSMbSctK5xvtXm5+Nn9fX19i2WnxGp24/KrzKjwO9VnB41WajJAgAAAAAAyui6+wGJXjJ22nsy1GnZoGd/fEPWxW+SB7rfbF081EDG9RMfMBfUZ98zyep7+Yd3yOKtK+Xtq56T/uf3MG0rdq6V/u/eZAIvix46kS0zbNIDMvefhTJ6wEgZ3Lq/adu4f6t0GzvYLH3z9xMnshTu/eoZE6h5vM9dcl3bgdYyUDlBljD54JpRVl/95vzG/VukQ+3W1vJSuiyYjksvaraq3tQ6dr3oqBfU/XJdWD3TeaNeQV7Mm/uYO/cwb+5h3gqOmiwAAAAAAABlzNaDO+X7v+aabzrXyYw57br7WlvkrS8+dAmy6JJGf25bJUP+yyBRft6+Zoma5PScbzg7ZxhohoNzJkB0WHmTNaD1FpxpYKVT3bbSKq6pyzfaZ9zxsckmcfbGFU/JG/JUrueqIF/e9FaeY+7dqJP5cabZA466G840G+F09KLiG2PGmkwfvdio86TL5+i3u1OWxEva5mMydupHZf7iY27Mm/uYO/cwb+5h3gofmSxksgAAAAAAgGJMiyNrDQNd8sl5OaD7v3lBlmxdKa8OfMwq+q11Fq79ZLhZOuammL6mBkvsfa3E2z/vxTJberbEj1kuQx+7VSY8/57VPn/jYlMQXIsu65JOjuLwugyYBkMcxbbLwlJr944YbgJVDtWqx8nY18ec0RJrZQ3z5j7mzj3Mm3uYt8KLGxBkIcgCAAAAAACKiV82LpL18ZulV8OOVkBFgx5amF3rFPx872dWXw2maFDllQGPytWt+1kF38f+8rHUjaoh9bIrmyL3msmS37r76XuSTCbL/PnzpXPnzufwKEvWsjoLFiwwBaC1PkHHjh35dncBMG/uY+7cw7y5h3k7NYIsZ4AgCwAAAAAAKCpmjfvUBLOEVeh/hbv/3b9NnvvpTdM2fshoq+81Hw+X3zYtMdkpV7W61LRtObhDuoy52gRd5g0/EWRZuXOtZGZnSb2YmqamSW6suw8AgPuoyQIAAAAAAHAOJaYly8LNyyQlPVUub3mJ1X7D5AdNwffXBj4uV7bqa9q8vbxk/r+LzfJbGvBwBEA61W0jFUPLSbVysdbjtdj8lud+z1OsvUVc41OOh3X3AQAoegRZAAAAAAAATuN4RpoE+Qda9z/981uZ/+8SuapVX+nRoKNp2594UG75bKSEBYTIoBYXW4ETLQ6vDqUcsR5ftVysjB4w0gRTnIMst3QcnOe5vb29Rf/nDl1Xf+rUqTnr7k9e77ru/tSPWHcfAICzRJAFAAAAAACUGEW5fvzOI3tl04FtEle+slUPRdv6vHW92O02Wf/Uz1bfv/dulNnrf5MGlepYQZaqkbHSvFojiStXWTKyMyXA19+0P9r7Tnmm7wgJ9AuwHq+/D27dX84FDaT079+fdfcBACgCBFkAAAAAAECJMG3atJyMjJ27rLaqcdXMkliny8hwzhZJOJ4kb/06UQ4mHZGxVzxp9dG2z5d9LyO63Sj3d7/ZtEWFlpeE44nW4yKCcgrI92vaXRpWqiOtqje1Hq+ZLj/cMT7Pczse40kaUKG4PQAAhY8gCwAAAAAAKBEBFq0tElgnUqKGNBTfqGDJOpgqh5fEm/avvvpKWnRuI3uP7ZcOdVq7BE4+XvSVDLlggAmeKK1t8u7vn5rfn7l0hBUEqRdTSxrF1pPIoHCXwMkvw7+QypHRVtF6dWHtVuYHAACUbQRZAAAAAABAsV8iTDNYNMBSbkBdKyPFv0qYlBsQaoq6j7j/PrFdFSte3l5mWa/wwFDTx2a3yYGkw7LzyB5rf8H+QXJnp+tMloq314laJzddeKX5ya1eTM7SYQAAALkRZAEAAAAAAMVSVnaWfLt6tkyfNcMsEaYZLI4Ai4PeD2kba4q6NzgaJ5UaVJNjqYlWkGVg8z7SuV47U2fF2cjed5zTYwEAoLixZdtk24qdknQwWcKiQqVmyzjx9jnx5QMUDEEWAAAAAADgcZptsmjLClMQvnejTqbNx9tHnvnxDdm7Yqu5r0uE5cevYk77453vkMGDB7tsqxJZyfwAAIAT1s7dINNHzZaE/UlWW0RMmPQb2Usa96jv0bGVNB4PS+3Zs0euvfZaqVChggQFBUmTJk1k+fLlLoXpnnzySYmNjTXbu3fvLps2bXLZx5EjR+Saa66R8PBwiYyMlBtvvFGSk5M9cDQAAAAAAOB09hzbJz+u/UUOJR+x2hZuXiZ3ffmkvLcgp1aKI0vlylaXSv+2vc19rcGSn8xDOe2VK7tmqwAAgPwDLJNHTHUJsKiEA0mmXbejhARZjh49KhdeeKH4+fnJzJkzZf369fLaa69JuXLlrD6jR4+WN998U9577z35888/JSQkRHr16iVpaWlWHw2wrFu3TubOnSszZsyQ33//XW655RYPHRUAAAAAAHDYn3hIVu5c69J2y2ePyq2fPSp/bFlhtTWr1lCaV2skLeOauvR94uK75aOH3pCqcdUkeUm8+TKmM72fsiReqlWPk44dOxbx0QAAUPKXCNMMFnH9z2mO/9qmvzTH9EMJWC7s5ZdflmrVqsknn3xitdWsWdPlD6WxY8fK448/Lv379zdtkyZNkpiYGPnuu+/kqquukn/++UdmzZoly5Ytk1atWpk+48aNk4svvlheffVVvsUCAAAAAMA5cjQlwdyWC4kwt2t2/yOXvD1MKoaWk1WP/mTVU2kV10Rstmzx8T7x3c9aFePkhzvG57tfHx8feWPMWBk0aJApcq81WHSJMM1g0QBL2uZjMnbqR6YfAAA4Oa3BkjuDxYVdJGFfoulXu02Nczm0EsujmSzTp083gZHLL79coqOjpXnz5vLhhx9a27dt2yb79u0zS4Q5REREyAUXXCCLFy829/VWlwhzBFiU9vf29jaZL/lJT0+XxMRElx8AAAAAAFBwKemuS3c9Mf01afJ8L/l06bdW23kxtSTA118qhJSTxLQTy3o/3Xe4zLx7ovRt0q3AzzdgwACZOnWqVEgPlUOT10v8mOXmtmJGmGnX7QAA4NS0yH1h9oOHM1m2bt0q7777rtx3333y6KOPmmyUe+65R/z9/WXo0KEmwKI0c8WZ3nds01sN0Djz9fWV8uXLW31yGzVqlDzzzDNFdlwAAAAAAJQmutKEIwvleEaa9H5rqGw7tEvWPjlHwgNDTXtc+ZyVJPYm7Lcep0XstU+QX6DL/hz7OlMaSNGVLhYsWCDx8fGmfqsuEUYGCwAABRMWFVqo/eDhIIvNZjMZKC+++KK5r5ksa9euNfVXNMhSVEaOHGkCOw6ayaLLlgEAAAAAgBNmrftNXv35A1Mr5ZUBj5q2IP9ASc9MF5vdJv/Eb5YLajYz7Ve07Gt+IoLCXPaRO8BytjSg0rlz50LdJwAAZUXNlnESERNmitznW5fFSyQiJtz0QwlYLky/cdKwYUOXtgYNGsjOnTvN75UqVTK3+/ef+BaM475jm94eOHDAZXtWVpYcOXLE6pNbQECAhIeHu/wAAAAAAFCWvTDzLbn4retlffwmq83by0s27NsiK3euc+n74bUvyerHfrICLEqDK7kDLAAAoHjx9vGWfiN75dzJnVj63/1+j/Q0/VAwHp2pCy+8UDZu3OjS9u+//0r16tXN7zVr1jSBknnz5rlknWitlXbt2pn7envs2DFZsWKF1eeXX34xWTJauwUAAAAAAJyw48geefKH1+Wx719xaf97zwb5a88GWbXrREClTc1mMn7IaPl02BiXvk2q1JeKoeXP2ZgBAEDhadyjvgwZM0giol2/HKEZLNqu21FwXnZdWNVDtAZL+/btTX2UK664QpYuXSo333yzfPDBB3LNNdeYPi+//LK89NJLMnHiRBN0eeKJJ+Svv/6S9evXS2BgTspxnz59THaLLjOWmZkpw4YNM8uQff755wUahwZuIiIiJCEhgawWAAAAAECp8fumP+W3TX9KjwYdpW3N5qbt3/3bpOvYqyXYP0j+eepn8fHOqWfyy8ZFkppxXNrUaCbRYRU8PHIAAFDUbNk22bZipylyrzVYdIkwMljOPG7g0ZosrVu3lm+//dbUSHn22WdNEGXs2LFWgEU99NBDkpKSIrfccovJWOnQoYPMmjXLCrCozz77TO666y7p1q2beHt7y8CBA+XNN9/00FEBAAAAAHBupWWmy6//LpF/D2yVe7oMs9p/XDtfPlv6nQmkOIIstaPi5NaOg6VRbD3JttmsIEvX89p7bPwAAODc04BK7TY1PD2MEs+jmSzFBZksAAAAAIBzKTs7WxYsWCDx8fGmXmnHjh1NQfeCOJqSICt3rZWwwBCTdaI0A6X+091MMfrlI3+QSuFRpn3O+t9N8KVnw4ukc722RXpMAAAApUmJyGQBAAAAAKCsmTZtmtw7Yrjs3rnLaqsaV03eGDNWBgwY4NL32PFEUyulVVxTCfLPWdFhyoofTJH6Sxp3tYIsuvTXxY27mMBLVnaW9XgNrugPAAAAigZBFgAAAAAAzmGAZdCgQRJYJ1KihjQU36hgyTqYKoeXxJv2tz9+V26//larf483rpX4hAMy9ZZ3reW+mlVtKHWiqku18rEu+35v8Avn/HgAAADKOpYLY7kwAAAAAMA5WiKsRq2acjggWcoNqCteXl7WNv2n+ZFv/hU5kinJBxKspcNunPywbNi3RZ7pO1y6N+jgwdEDAACULYksFwYAAAAAQPGhNVh0iTDNYHEOsCi9H9qushyavF5+/fVX6datm2l/f/AL4uvDP90BAACKK29PDwAAAAAAgNJIa6Ms2rpCxswbbzJVtMi90iXC8uNXMaf9wIEDVhsBFgAAgOKNv9YAAAAAACgEGkhJzTguIQE5wZIsW7ZcN+E+SctMl96NOklsbE4NFa3B4l8lLM/jMw+lmltHPwAAABR/ZLIAAAAAAHCW/tiyXDq8Okhu/vQRqy3QL0D6Nu4m/2vWS7y9vOXC9hdKRFCkJC2KNwEZZ3o/eVG8RASVM/0AAABQMpDJAgAAAADAGVgfv0nmbfhDOtRpLc2rNTJtUaEVZMeRPXI45ahkZGWKv6+faR97xZPW47Ys3S5dKvSW77ZMkSNTN0lo+1izRJhmsGiAJW3LUeld9SrZuXqP1G5Tw2PHBwAAgIIjyAIAAAAAwCkcTj4qFULLWffHL/pKvlz+gxxNTbCCLHWja8ik61+XC2o0swIsuSUdTJZ64Y3ksqpXybxdM02Re4dw/0gTYNHt2g8AAAAlA0EWAAAAAADyoRkpl75zo6yL/1eWPTJdYiOiTXuvBh3lWGqitIhrYvX18vKSrue1P+X+wqJCza0GUuqENZDdqTskOStJQn3DpGpwdbOkmHM/AAAAFH8EWQAAAAAAZd7+xEPyw9/zJDM7U26/6FrTphkpvj6+JoCyZvc/VpClZ8OLzM+ZqtkyTiJiwiThQJJ4i7fEhdR07eAlEhETbvoBAACgZKDwPQAAAACgzDl2PNEs9+Ww/fBueXrGGHn390/FZrNZ7a8OfFTWPDZTejfqdNbP6e3jLf1G9sq545Vr43/3+z3S0/QDAABAycBfbgAAAACAMuWFmW9J0+d6y2dLv7PaWsQ1lh4NOsgdFw2RjOxMq71BpTpSPiSy0J67cY/6MmTMIImIDnNp1wwWbdftAAAAKDlYLgwAAAAAUCpl27LlqxU/yoLNS+WVAY9KSECwaa8aWUlsdptsPrDD6uvn4yufXPfqORmXBlIadq0n21bsNEXutQaLLhFGBgsAwJZt478PQAlDkAUAAAAAUCocz0yTvcf2S+2o6ua+FpIfN3+C7Dy6VwY06y3dG3Qw7f2b9TS/V4ms5LGx6gWz2m1qeOz5AQDFz9q5G2T6qNmSsD/JatNaXrrUJJmOQPFFGBQAAAAAUOL9uW21NH62p9ww+SGrTQvWD203UEZ0u9EKvKjIoHCPBlgAAMgvwDJ5xFSXAItKOJBk2nU7gOKJTBYAAAAAQImycPMy+Xb1bOlcr61c2rS7aWsQW0eybNmSmnFcEo4nSURQTs2TWzte4+HRAgBw+iXCNINF7Pls1DYvkekvzTFLTbJ0GFD88K4EAAAAABRbmdlZsmz7GlNfxWHZjr/kyxUz5Ie/51lt4YGhsvCBr2Xpw99bARYAAEoCrcGSO4PFhV0kYV+i6Qeg+CGTBQAAAABQLNntdrnotctl19F4+f72j6RlXGPT3qvhRZKcniLd6+fUWHGoVq6yh0YKAID7tMh9YfYDcG4RZAEAAAAAeNzuo/Hy3oLPJPF4srx55dNWTZWmVRpIUlqKxCfsF5GcIEvD2LrmBwCA0iAsKrRQ+wE4twiyAAAAAADclp2dLQsWLJD4+HiJjY2Vjh07io+Pz2kzVNbHb5JAvwCXgvQTFk8VH28feb7/A2b5L/XS/x42v2s7AAClUc2WcRIRE2aK3Odbl8VLJCIm3PQDUPxQkwUAAAAA4JZp06ZJjVo1pUuXLjJ48GBzq/e1/VRenPW29Bp3nXy4cIrVVrVcrNzb9QZ5f/CL4u/jZ7WXC44gwAIAKNW0mH2/kb1y7njl2vjf/X6P9KToPVBM8c4EAAAAAJwxDaQMGjRIDgckS9SQhhJ7Xytzq/e13RFoee3nD6X3uOtk26Fd1mMvqNFMgv2DxCvXhaQHe9wivRt1MhkuAACUJY171JchYwZJRHSYS7tmsGi7bgdQPHnZNU+7jEtMTJSIiAhJSEiQ8PBwTw8HAAAAAIr9EmGasaIBlXID6praKQ76T8yj0zZJxYww2bZlq1z18d2yeOtKeb7fA3J9u0GmT2Z2ltjsNgnw9ffgUQAAUPzYsm2ybcVOU+Rea7DoEmFksADFO25AkIUgCwAAAACckV9//dUsDaaZK/5VXL9xq9L3JMmhyetl/vz5khXjKwlpSdK5blupEFrOI+MFAAAAiipuQOF7AAAAAMAZWbVhjbn1jQrOd7tfxZz2+Ph4ubrz1ed0bAAAAMC5RK4ZAAAAAOCUdAEEm81m3T/qlWJusw6m5ts/81BOe2xs7DkaIQAAAOAZBFkAAAAAACc1ack30nXs1TJz/a9W273X3C6hFcMleXG8CcA40/spS+KlWvU46dixowdGDAAAAJw7BFkAAAAAAJa9CQdcAie7jsbLpgPbZcZf86w2ra0y8f1PJG3LMVPkXmuw2NKzza3eT9t8TMa+PkZ8fHw8dBQAAADAuUFNFgAAAACAWQ7syvF3yeKtK2XuvZ9Kg0p1TPtVrS6VetE1pVejTi79BwwYIFOnTpV7RwyX3ZPXW+2awTJ26kdmOwAAAFDaEWQBAAAAgDLocPJRWbPnH+l6Xntz39vbW8IDw8TLy0tW7lxrBVlqR1U3P/nRQEr//v1lwYIFpsi91mDRJcLIYAEAAEBZ4WXPvYBuGZSYmCgRERGSkJAg4eHhnh4OAAAAABSpnUf2SsfXLhcfL29Z/fhMCQ8MNe1bD+6UkIBgiQmv6OkhAgAAACUibkAmCwAAAACUYqkZx2XuPwslMztLBrXoY9qqlYuVWhWrSaBvgMQnHLCCLLWi4jw8WgAAAKBkIcgCAAAAAKXYLxsXyZ1TnpCqkZVkYPPeZjkw/Zl++3gJCwzx9PAAAACAEs3b0wMAAAAAABSOpdtXy/CvnpXv18y12rqdd6E0jK0rA5r3lrSsdKudAAsAAABw9shkAQAAAIASKtuWLV7iZYrWqz+3rZapq36Sg8mHpf/5PUxbkH+gzLlnsodHCgAAAJROZLIAAAAAQAn08ux3pdWoS2XFzr+ttn7n95Dr2w2SEd1u9OjYAAAAgLKCIAsAAAAAFHN2u122Htzp0hafcFAOJh+Rmet+tdqql68iz/d7QFpVb+qBUQIAAABlD8uFAQAAAEAxlpKeKr3HDZUdR/bI8pE/SHRYBdN+U4cr5dKm3aVjndaeHiIAAABQZpHJAgAAAADFyLZDu2T+xsXW/ZCAYIkMDhd/Xz9Zu3ej1d648nnSrX570w4AAADAM8hkAQAAAIBiYtn2NfK/92+VCiHlZMXIH8TXJ+efbG9c/pTEhFc0ARcAAAAAxQdBFgAAAADwgANJh2XG3/MkOqyi9G3S1bQ1r9bILAfWoFJdOZxyzARWVK2oOA+PFgAAoPiyZdtk24qdknQwWcKiQqVmyzjx9mERJ5wbBFkAAAAAwAM0wPLkD6+bwIojyKKZK4senCaBfgGeHh4AAECJsHbuBpk+arYk7E+y2iJiwqTfyF7SuEd9j44NZQPhPAAAAAAoYjPX/SrXTbhPfvv3T6utb5Nu0qJaY+nftIfY7XarnQALAABAwQMsk0dMdQmwqIQDSaZdtwNFjUwWAAAAAChkaZnpLsGShZuXyy8bF0n54EjpVO8C06bLgk2/4yMPjhIAAKBkLxGmGSxy4rsqJ2ibl8j0l+ZIw671WDoMRYpXFwAAAAAUEpvNJvd8+bQ0fb63bD+822q/slVfub/7zXJPl+s9Oj4AAIDSQmuw5M5gcWEXSdiXaPoBRYkgCwAAAAC4KTM7S9bHb7Lue3t7y6HkI5KacVx+3vCH1d60Sn0Z0e1GCtgDAAAUEi1yX5j9AHexXBgAAAAAiEh2drYsWLBA4uPjJTY2Vjp27Cg+Pj4n7b/7aLz0eet6ycjKlNWP/yRBfoGm/YEet8hDPW+T86s2OIejBwAAKFvCokILtR/gLjJZAAAAAJR506ZNkxq1akqXLl1k8ODB5lbva7vSwvQrdq6VX/9dYj2mSmQlCfEPMrVXthzcYbW3iGsszao1FC8vL48cCwAAQFlQs2WcRMSEmdor+fISiagUbvoBRcnLrv9aKOMSExMlIiJCEhISJDw83NPDAQAAAHAOaSBl0KBBElgnUkLbxopvVLBkHUyV5CXxkrb5mEydOlV8aofJnVOekLrRNeSX4V9YARStu1I1spL4+rBIAAAAwLm2du4GmTxias4d56vc/wVehowZJI171PfI2FB24gYEWQiyAAAAAGV6iTDNWDkckCzlBtR1yT7RfyodnbZJKmaEyZp1f0mnMVdKp7oXyOj/jZQg/5ylwSBiy7aZgrK63rkux6HfFvX2YdEEAABw7gIt00fNloT9SVabZrD0e6QnARack7gBX7cCAAAAUGZpDZbdO3dJ1JC8y3vp/ZC2sbJr8npZs2yVrBw5g4yVglzUiAmTfiN7cVEDAACcE/o3R8Ou9fjSBzyGfyEAAAAAKLMW/b3U3OoSYfnxq5jTHh8fT4DlZMtz5FobIeFAkmlneQ4AAHCuaECldpsanh4GyijCeQAAAADKrAV7V5hbrcGSn8xDOe2xsbHndFwlYYkwzWDJHWAx/mub/tIc0w8AAAAozQiyAAAAACgT1uz+Rx759mVJSkux2h4aeq+EVowwRe5zl6vU+ylL4qVa9Tjp2LGjB0ZcfOlyHM5LhOVhF0nYl2j6AQAAAKUZQRYAAAAApZ4GTIZ//Yx8uvRb+Xb1LKu9S/32MvH9jyVt8zFT5D59T5LY0rPNrd7X9rGvjxEfHx+Pjr+40fXOC7MfAAAAUFIRZAEAAABQ6qzb+6+8PPtdsdlsVhH7G9pfIZed31POr9rQpe+AAQNk6tSpUiE9VA5NXi/xY5ab24oZYaZdt8OVFpQtzH4AAABASeVlz50TXwYlJiZKRESEJCQkSHh4uKeHAwAAAOAsHM9MkxYvXCJJ6SnyxY1vSsc6bQr0uOzsbFmwYIEpcq81WHSJMDJY8qe1Vl7qMc4Uuc+3LouXSERMuDwy5y5TiBYAAAAorXED33M6KgAAAAAoZBv2bZE/t62Soe0GmftBfoFyZatLZX/iQakQUq7A+9GASufOnYtwpKWHBk76jewlk0dMNQEVl0CL3heRfo/0JMACAACAUo9MFjJZAAAAgBIrPuGAtHm5v/n9jwe+kbjylc3v+s8cXSIMRWvt3A0yfdRsSdifZLVFVAo3AZbGPep7dGwAAADA2SCTBQAAAECps/nAdtl0cLv0aZSTcRIbES1d6rWTAF9/yczOtPoRYDk3NJDSsGs92bZipylyrzVYaraMI4MFAAAAZQZBFgAAAAAlwoqda6X/uzdJeGCodK7bVoL8A037J9e9Ij7e1E7xFA2o1G5Tw9PDAAAAADyCrxcBAAAAKJa2Htopy7avse43r9pQalaoJm1rNpejxxOtdgIsAAAAADyFTBYAAAAAxc5Pa+fLLZ+NlPNiasnP935mlv/y9vaWufd+KoF+AZ4eHgAAAAAYZLIAAAAA8LgdR/aYeisOF9ZuJSH+wVIlspIkpadY7QRYAAAAABQnZLIAAAAA8KiJi6fKY9Nfld6NOslH175s2iKCwmTpI9+b23PFlm2jgDsAAACAM0KQBQAAAMA5tefYPvHz8ZPosArmfttaLcxyYJlZWZJty7ZqrJzLAMvauRtk+qjZkrA/yWqLiAmTfiN7SeMe9c/ZOAAAAACULHwtCwAAAMA5M2beeGk3eoB8uPALq03rrix9+HuZeP1rHilirwGWySOmugRYVMKBJNOu2wEAAAAgPwRZAAAAABSZ+IQDcjwjzbrfoFIdsdltJpvFWWxEtAdGl7NEmGawiD2fjf+1TX9pjukHAAAAALkRZAEAAABQJJ76YYy0Hf0/+W7NHKute/0L5ff7vpJ3rn5eigOtwZI7g8WFXSRhX6LpBwAAAAC5EWQBAAAAUCgOJh0Wu/1ESkiliChTY+XvPSeW2/L18ZVaUXFSXGiR+8LsBwAAAKBsofA9AAAAgLN239TnZNqqWTLlprekbc3mpu2qVpeazJW60TWluAqLCi3UfgAAAADKFjJZAAAAAJyx5PQUl/tasD7Lli1/bFlutZULjijWARZVs2WcRMSEiXidpIOXSESlcNMPAAAAAHIjyAIAAACgwDKzs+TOL56QZi9cLHsTDljtd3UeKnPv/VTu736zlCTePt7Sb2SvnDu5Ay3/3e/3SE/TDwAAAABy418KAAAAAE4pIyvT+t3Px1f2Jx2StMx0mbdhodVevXwVaVCpjpREjXvUlyFjBklEdJhLe0RMuGnX7QAAAACQHy+7c2XKMioxMVEiIiIkISFBwsPDPT0cAAAAoFg4mpogT/7wuizeulIWPjBVAv0CTPuqXetMsKVx5fOkNLFl22Tbip2myL3WYNElwshgAQAAAMqmxALGDSh8DwAAAMBis9nE2zsnsBAWECJLtq2SfYkH5fdNf0rPhheZ9ubVGklppAGV2m1qeHoYAAAAAEoQj34t6+mnnxYvLy+Xn/r1T6Tip6WlyZ133ikVKlSQ0NBQGThwoOzfv99lHzt37pRLLrlEgoODJTo6Wh588EHJysrywNEAAAAAJZfWVxnx9bMy8IPbxJHs7uvjKy/2f1Bm3PGx9GjQ0dNDBAAAAIBix+OZLI0aNZKff/7Zuu/re2JII0aMkB9//FG+/vprk5Zz1113yYABA+SPP/4w27Ozs02ApVKlSrJo0SKJj4+X6667Tvz8/OTFF1/0yPEAAAAAJVGQb4BM/+tnSc/KkPXxm6RR5XqmneAKAAAAABTjIIsGVTRIkpuuczZ+/Hj5/PPPpWvXrqbtk08+kQYNGsiSJUukbdu2MmfOHFm/fr0J0sTExEizZs3kueeek4cffthkyfj7+3vgiAAAAADP0S8iLViwwHwBKTY2Vjp27Cg+Pj4ufbYf3i1v/zbJZKy8OvAx01YuJEKeufQ+aRBTWxrG1vXQ6AEAAACgZPF4FcdNmzZJ5cqVpVatWnLNNdeY5b/UihUrJDMzU7p372711aXE4uLiZPHixea+3jZp0sQEWBx69eplCtKsW7fupM+Znp5u+jj/AAAAACXdtGnTpEatmtKlSxcZPHiwudX72u4sOT1Fvlg2Xb5ZNVMOJx+12q9tc5m0rN7ELOMLAAAAACjmQZYLLrhAJkyYILNmzZJ3331Xtm3bZr5pl5SUJPv27TOZKJGRkS6P0YCKblN66xxgcWx3bDuZUaNGmeXHHD/VqlUrkuMDAAAAzhUNpAwaNEgOByRL1JCGEntfK3Or9wcOGig3P3eP1bdx5fPk3i7DZMqNb0n5ENe/twEAAAAAJWS5sD59+li/N23a1ARdqlevLl999ZUEBQUV2fOOHDlS7rvvPuu+ZrIQaAEAAEBJXiLs3hHDJbBOpJQbUNfKRPGvEiblBoSK/Zt/ZdKYj2Tcw69IoH+A2fZgz1s9PGoAAAAAKPk8vlyYM81aqVevnmzevNnUacnIyJBjx4659Nm/f79Vw0Vv9X7u7Y5tJxMQECDh4eEuPwAAAEBJpTVYdu/cJaFtY/Ms9aX3Q9tVloyjx2XxH4s8NkYAAAAAKI2KVZAlOTlZtmzZYgp0tmzZUvz8/GTevHnW9o0bN5qaLe3atTP39fbvv/+WAwcOWH3mzp1rgiYNGzb0yDEAAAAA55oWuVe+UcH5bverGHzaJXUBAAAAACVsubAHHnhALr30UrNE2N69e+Wpp54SHx8fufrqq02tlBtvvNEs61W+fHkTOLn77rtNYKVt27bm8T179jTBlCFDhsjo0aPNPxoff/xxufPOO022CgAAAFCardq1TiKDws2XlFTWwVSzRFhumYdSza2jHwAAAACgFARZdu/ebQIqhw8flqioKOnQoYMsWbLE/K7GjBkj3t7eMnDgQElPT5devXrJO++8Yz1eAzIzZsyQ22+/3QRfQkJCZOjQofLss8968KgAAACAovf6zx/J6/M+kkHNL5bXBj4mVeOqyeEl8aYGi/OSYXa7XVKWxEu16nHSsWNHj44ZAAAAAEobL7v+q6uM08L3mjmTkJBAfRYAAAAUS+lZGWKz2STIP9DcX71rvfzv/VtkYPM+8sqAR+Xbb7+VQYMGSWCdSAlpG2uWCNMMFg2wpG0+JlOnTpUBAwZ4+jAAAAAAoFTFDQiyEGQBAABAMTd15UwZNettub7dILm7y/VW+6HkI1IxtLx1f9q0aXLviOGye+cuq61aXJyMHTOGAAsAAAAAFEHcwKPLhQEAAAAomP1Jh2Tmul/lrs5DreXAnAMsql5YQ7m11ghZ57VWkrOSJNQ3TBrVbGzaAQAAAACFj0wWMlkAAABQjKyP3yTvL/hcLm7cRXo1vMi0ZWZnyQ9//Sx9m3QTf1+/fB+3du4GmTxiqkjuv+7/K88yZMwgadyjflEPHwAAAADKVNzA+5yOCgAAAMApzfh7nnyzaqa8/eskq83Px1cGNO990gCLLdsm00fNzhtgUf+1TX9pjukHAAAAACg8BFkAAAAAD9EMlW9Xz5bNB7ZbbcPaXS6Xnd9Tnut3f4H3s23FTknYn3TyDnaRhH2Jph8AAAAAoPAQZAEAAAA85PHpr8rdXz4l7/w+2WqLCqsgb131rJxftUGB95N0MLlQ+wEAAAAACoYgCwAAAHCOHEg6LElpKdb9K1r2lYqh5aROVI2z2m9YVGih9gMAAAAAFAxBFgAAABRra9askdJg3PwJ0vbly2TSn99YbS3jGsufD38vd3Qaclb7rtkyTiJiwqwi9+pAWvyJO14iEZXCTT+Undfcuca8uYd5cx9z5x7mzT3Mm3uYN/cxd+5h3tzDvJ0dgiwAAAAollJSUmTYsGHSrFkzc5uamiolid1uNz8OlcKjJCM7U1bvWu/SL8DX/6yfy9vHW/qN7GV+z7BnyE97p8mEre/IzL3fSqY9w7T3e6Sn6YfS+5rzFObNPcyb+5g79zBv7mHe3MO8uY+5cw/z5h7mrZDYYU9ISNB//ZpbAAAAeN66devsDRs0tAcHBdtvue5Wc9uoYSPTXhL8/M8f9kvfvtE+a91vVlt6ZoZ92fY1Rfq83334gz0mLNYeGBBo5k1vK4VVNu0o3a85T2He3MO8uY+5cw/z5h7mzT3Mm/uYO/cwb+5h3govbsBX2QAAAFCsTJw4UVq3bi3ZWTaZOWWWPPPQM/LTlJmSmZ5l2idNmmT6ZaVnSUZKhmRnZFuPtdvspk1/nDn6Zjn3tZ/o65xxon1M3/Ss0/bNzqevWrxphazctVbG//Gl1eZj95amFevn6ZuRmrNfm81pv5k5+81MO0nfbNuJvlk5fcd/NF4G33ulRMaEy+yv5ph5m/XVbAmPDjPtOm+2LFvOfo9nuuxX75v9Zp3Y7xn1zc7pm5Gaq29aTl89nrx9Xc+RHmvuvtb5zNXXOveZhXPurddcZrbLay4rI+c19/GHHxfodWLOkWO/zufzTPr+d+6zCnLurdeJ67zrecj7OjnN+cw+83NfkHnT1+fpXyd5z6etEM99cfuMONm8OT7jPhn/SZF8RuR+nZzNuffUZ8T4D8af2X8fCukzwuqb4eh7+nNfnD4j9L8P+c1bltO8FcVnhPP5zNPXce6L8WfEyf4ecXzG6fai+jvilOe+iP6OKKzPiNP+t+Gjj0vd3xGF9Rlxxv99KOF/RxTWZ8T4jz4+5bw5/ttQWv6OKKzPiJP9N9XxXnWeN5weQRYAAAAUGytWrJDrr79eOrTpID99/pPUq13PtJ9X+zyZ+cVPcmHrC2Xo0KGmX8qhFDmy/agcT0izHq//eNA2/XGWeiQ1p+/R41ab3bnviX9zyPGjOX1Tj5zoqxx97dknOh9POC5bN+6QV2d+IJsPbLfaL4vtLsMaD5I3BjxltaUlpJnHJx88UfheHd15zLTbnP5Rlp6UntP3QLJL32O7E0y78z/2MpIzZP6Pv8pNN9+UM29fzDzpvC35Y4l5fOK+JJf9JuxNNO3O/8DVf9xqm25zpo/Vdud/XOo/5E3fPQkufZP2J+fpq2PXtmO7XPsmH8zpq8du9c3M6Xt0x7FcfXPOvc6p8z+8zTna4XruUw7nnM+0Y/mfe5fXXK6509egzt2Nt9xo5tj5H7LHjx3/73XiuqSCY7/OFxuOJ+ace33N5nfunf+hn5b437k/mOvc70rI0zc9OSOn737XvnoetN35H9l6DrQtKfe535P33Gcczzn3ifG5zn38f+c+NaPA87Zk4RLr8VnpOeczYXeu83ngv3OfnJ73dbLzJOc+0alvVs651/l0Vlw+I8y5P5zzOjnVvDneqzfcdIPpV5ifETnnPldfx+vE6UKcntvi+hmxcN5CuenWmwr034fC/IxwpufcvE6cz73d6dwXw88Il/8+5Jq3n/L570NhfUY4X/jO99zv++/cJ2cUy88InbeT/T3i+IzT7dqvMD8jrL47/nudOF2MLsq/IwrrM+LPRX+e/r8NN+f8N7W0/B1RWJ8RZ/Pfh5L4d0RhfUbofNx0842n/RvYMW8l/e+IwvqMWLJgyUn/m+p4rzrPG07PtwB9AAAAgHOiRYsWckGbC+TIsSMSFBjksi0oKNi0t2vbzvRLPpAiPv4+4u1zotq7/qZtuXn7+uT09XX6jpGXV/59fbzz9nXer5dr35eXvS9zty+UQ9lH5dWBj5n2SuWi5Z4210v58HJOY8jZr0+u/fr6++ZcbHPar9d/Y/Dxy9s32yvbjN3q6+0lzZs1l9YtWhdo3vQfrT5+rsetz6X/ENR9Oe83Zwy5+vr5iM3fJl4++fT1z9tX21z2+9+855nf/+bHua/jHDmf45z9/tc3V40Z0+Y0Ny77zaevatGghbS9oO0p565V81ZmjvN9neTar2+Aj5gvIDqNQ8d/steUmfd8++Y9Rzo3+fbN5xzlTJ9TX++T9NUx2At67r3Flp0zDn0tFWTeWrRo6bRfyX+//50jb+8T86NDz29+He8jl/f9f31dXjvO73sPf0Y497Xm7ejJ561NyzamX2F+RuTbV8+DPWd73r7F7zOiRfPm0rJZywL+9yG50D4j8n39+RagbzH5jCjofx9aNm9hLmgX1mdE7vOZX9/c816cPiN03k73Gde2bVvTL/dr6mw+Ixx8A3xzLsg79y3CvyMK6zOiIP9taN2ydc68lZK/IwrrM8Kd/z6U5L8jCuszQufDfMYdPf1/G0rD3xGF9RnRvEWLAv83FQVUgKXHSj1qsgAAABQPGccz7F98MMX8bfbF+1Ps8Wv3WT+fv/+FaZ89e7ZHx7h613p74vFk6/7Sbavtvd4cYv/hr589Oq5Zs2YV63krzpg79zBv7mHe3MfcuYd5cw/z5h7mzX3MnXuYN/cwb4UbN/DS/5MyLjExUSIiIiQhIUHCw8M9PRwAAIAyS/80Pbz1sPQZ1EdsNpuMevwla9sjzz0sgSGB8seiP/J8y/BceWjaKPl82ffyeJ+75baLrnEZt6fG5DyG9u3aS8bxjGI3b8Udc+ce5s09zJv7mDv3MG/uYd7cw7y5j7lzD/PmHuatcOMGBFkIsgAAAHhUekqG+Af7WX/E61rRc+fNlT59+uTpO3v2bOnZs+c5G1tGVqb4ep9YomTK8h/kkW9fkps6XGUCLcWNzk/v3r09Pm8lEXPnHubNPcyb+5g79zBv7mHe3MO8uY+5cw/z5h7m7fQIspwBgiwAAACeoUUujx9Lk/DYMAkuH2y1/z3nH3n/sY8l6fCJwo1hFULk1hdukCY9G5yTsU1cPFXe/HWivHzZw9K9QQfTlp6VIYeTj0rlyBgpjvRP+9WrV5tbBw1eNWvWjG+inQZz5x7mzT3Mm/uYO/cwb+5h3tzDvLmPuXMP8+Ye5u30CLKcAYIsAAAAnpFyKMUU2g2NDpXQqBDTtnbuBpk8Yqopturiv7/zh4wZJI171C/ysT0/c5y89/tn0qdRZ/nw2hMp9AAAAACA0i+xgHED33M6KgAAAJRptmyb2G128fHzMfeDKwSLf6i/+AX6Wdunj5qdN8CitM1LZPpLc6Rh13ri7ZOzhFdh+GffZnn/98/l7i5DpXZUddN2Y/srpW5UTbmsGanyAAAAAID8Fd6/TAEAAIBTyEjNkEObD8ux3QlWSrqmoTsCLGrbip2SsD/p5DuxiyTsSzT9CtPoOe/J1FU/yQcLv7DaYiOi5cpWfSXA179QnwsAAAAAUHqQyQIAAIBzwtvXx2Sx2LJs5seRzeIs6WBygfZV0H75ycrOkp/WzZeu57WX0ICcJcpuv+haCfANkMGt+7u9XwAAAABA2UOQBQAAAEUmOzPbCqb4+vtIubhI8Q3yE2/v/AsphkWFFmi/Be2Xn6ETH5DfNi2Rpy65V27ucLVpa1OjmfkBAAAAAOBMsFwYAAAACp0uB5YYnyQHNx2SrPQsq90/xP+kARZVs2WcRMSEWUXu8/ASiagUbvoV1OHko9byZOrixp2lfEik+PucWKYMAAAAAAB3EGQBAABAkcjKyDI1VNKT0gv8GC1m329kr5w7uQMt/93v90jPAhe9f+6nN6XNy/1l/r+LrbbLW1wifz78nQxtN6jA4wIAAAAAID8EWQAAAFAotN6Kc0H7iMrhZnmwkIo5dU8KqnGP+jJkzCCJiA5zaY+ICTftur3AY7LbJT0rQ37ZuMhq8/f1kyC/wDMaEwAAAAAA+fGyO6+dUEYlJiZKRESEJCQkSHh4uKeHAwAAUOJkHs+UhD2JEhgeIKHR7tdLcWbLtsm2FTtNkXutwaJLhJ0qg2XO+t/l/QWfyysDH5VaFXOWE9uXeFB2HN4jbWqcbwI/AAAAAAAUZtyAwvcAAAA4a1p3RX9Sj9pM5orXKequFJQGVGq3qVHg/p8u/U7+3L5aPlo4RV687CHTVik8yvwAAAAAAFAUCLIAAADALZoQ7cgOCYwINJknQRFBhRJgUdnZ2bJgwQKJj4+X2NhY6dixo/j4+JhtR1MT5LOl38uwdoMkJCDYtN3V+TppUKmO3ND+ikJ5fgAAAAAATocgCwAAAM44uJJyKFXSEtOkQs3yJqiiwZaQCmdWe+VUpk2bJveOGC67d+6y2qrGVZM3xoyVAQMGyFUf3S3r4v+VIL8AufHCK832NjWamR8AAAAAAM4VgiwAAAA4I/Zsu6QeTjWZK8cT0iS4XFCh7l8DLIMGDZLAOpESNaSh+EYFS9bBVDm8JN60T506Va694DL59M/vJK585UJ9bgAAAAAAzgSF7yl8DwAAcEZLg6m0pHSxZ9vMMmGFWVBelwirUaumHA5IlnID6rrsW8dwdNomqZgRJps3bRI/Xz+K2QMAAAAAPBo38C6apwcAAEBpkZWRJUe3H5WMlAyrLTAsQIIigwo9yKE1WHSJsNC2sXn2bZYkaxsru3bslEV/LCLAAgAAAADwOIIsAAAAOCWtv5KRmimJ+5JMNklR+m31H+ZWlwjLj1/FnPb4+PgiHQcAAAAAAAVBkAUAAACnFBYTKoHhARJZLaLIs0d+27Pc3GoNlvxkHsppj42NLdJxAAAAAABQEARZAAAAYNFMldSjxyVpf5LV5u3jLZHVIsXX37fQn2/5jr8kLTPduj/q9iclPCpSkpfE58ma0fspS+KlWvU46dixY6GPBQAAAACAM0WQBQAAAJastCxJ3Jv43xJhJ2qwFIX7pz4vl713i3y+7Hur7YJazeWT98ZL2uZjpsh9+p4ksaVnm1u9r+1jXx8jPj4+RTo2AAAAAAAKgiALAAAALH5BfhJcIVhCY0LN70WpWbWG4uvtI4eSj7i0DxgwQKZOnSoV0kPl0OT1Ej9mubmtmBFm2nU7AAAAAADFgZe9qKuXlgCJiYkSEREhCQkJEh4e7unhAAAAnDO2LJskHUg2dVd0WbCi8teeDfLa3A/llo6D5cLaLU1bRlam7Es8KHHlK+f7mOzsbFmwYIEpcq81WHSJMDJYAAAAAADFKW5Q+AtrAwAAoMQ4uuuYZKZmasETiagSUWTP8+XyGTJv4x+SmnncCrL4+/qdNMCiNKDSuXPnIhsTAAAAAABniyALAABAGaYZLInxSRJcPrhQ97s+fpOUD4mUSuFR5v5dna+T45nH5a7OQwv1eQAAAAAA8CRqsgAAAJQh6Unp5sfBP9hfKtQqX6j1V8bO+1h6vjlE3vzlE6stNiJaXh/0hNSqGFdozwMAAAAAgKcRZAEAACgj0hLT5OjOY5KwJ9HUYnHw8vIq1OdpU/N8s8/UjDSh/B8AAAAAoDRjuTAAAIAyIiA0QHwDfMU/xF+8vAsnsLLl4A55fd54aVerhVzb5jLT1r5WS1lw/9dSo0LVQnkOAAAAAACKKzJZAAAASimbzS6pR1OtbBINrOjSYOGxYYUWZPl901L5fs0cszRYVnaW1U6ABQAAAABQFpDJAgAAUArZbXY5vPWwZKdnm6W7giKDTPvZBle2HdolmdlZUi+mprl/det+sjb+X7mh3eXi68OflgAAAACAsoVMFgAAgFJIgylB4YHi7ettfgrD1JU/SecxV8kTP7xmtQX6BchrAx+TRpXrFcpzAAAAAABQkvB1QwAAgFIi83imCaj4+PmY+yFRIRJcIVi8fQonyKJ1V3y8vMXfx09SM45LsH9OdgwAAAAAAGUVQRYAAIBSQGuvJO5NEv9QfykXF2mWCDM/Pu4tD7Y34YCpsxISECxPXHy3aasSWUl+v/8rqVoutpBHDwAAAABAycRyYQAAAKWAf7C/iJfkZK3k1Lk/K1sObpdPl34rExZ/LUdSjlntBFgAAAAAADiBTBYAAIASyG63S1ZalvgF+Zn7vgG+UrFOBfH1d+/Pu32JB2X30XhpVb2pud+hdmu5+cKrpHejzlI+JLJQxw4AAAAAQGlBkAUAAKCEsWXZ5OjOo5KVni0Vape3AivuBlgWbV0hQz4ZIVGh5eX3+78Wf18/s9TYU32HF/LIAQAAAAAoXVguDAAAoIQxdVa8cmqtZGdku50J49C8aiOJCAqT2IhoOZRytNDGCQAAAABAaUcmCwAAQAmQlZEtPn7eVkH7yCoRpgaLj5/PGe3naEqCvP37JNlxeI98eO1Lpi3IP1B+vPMTqRQeZfYNAAAAAAAKhiALAABAMZd6JFUS9yVJWHSohFQMMW0+/mcWXHFITE+WjxZOkSxbtvy9Z4M0qVLftGsWCwAAAAAAODMEWQAAAEoCu0hGSoYEVwg+o2yTo6kJsmrXOul6Xntzv3r5KvJQz9vkvJja0rjyeUU4YAAAAAAASj+CLAAAAMWM1kuxZ9vF2zenfF5QuSDze0BYwBkFWLYf3i29x11nslYWPThNosMqmPY7Og0psrEDAAAAAFCWEGQBAAAoRrIzsyVhb6LYsmxSoVZ5qwZLYHhggQM0jkCMZq3Uja4p6VkZcij5iBVkAQAAAAAAhYMgCwAAQDGTeTxT7Da7ufUP9i/QY1IzjsuHC7+QOesXyPe3fyi+Pr4m2PLJda9I+eBI8fbOyYoBAAAAAACFh39tAwAAeJgGVBx8/HwkskqEVKxdocABFrMPu10+XvS1rNnzj/y0br7VXjG0PAEWAAAAAACKiNv/4p48ebJceOGFUrlyZdmxY4dpGzt2rHz//feFOT4AAIBSLS0hTQ5uOmSyVhy09opvgO9pM1dm/D3Puh8SECxPXHyPvHXls3JJ465FOmYAAAAAAHAWQZZ3331X7rvvPrn44ovl2LFjkp2dbdojIyNNoAUAAAB5rVmzJk/b8cQ0U38l5XBqgfejAZaOr14ut33+mKzetd5qH9Sij1zWrKf4ePsU2pgBAAAAAEAhB1nGjRsnH374oTz22GPi43PiH/GtWrWSv//+251dAgAAlFopKSkybNgwadasmbnV+w4RseESGh0iEZXDT7kPm81m/R7sHyQd67SR6uWrSnL6iX0BAAAAAIASEGTZtm2bNG/ePE97QECAy0UDAACAsm79+vXSpnUb+erLr+SW626VL7/8Slq3bG3albevt4RGhYqXt1e+j8/KzpLxf3wpnV6/Uo6kHLPan7l0hPx23xTpUKf1OTsWAAAAAABQCEGWmjVryurVq/O0z5o1Sxo0aODOLgEAAEqdiRMnSuvWrSU7yyYzp8ySZx56RmZOmSnZWdmmfdKkSafdhy79NXXlT7Lt8C6ZtGSa1R4RFCa+Pqeu2wIAAAAAAIqWW/8y13osd955p6SlpYndbpelS5fKF198IaNGjZKPPvqo8EcJAABQwqxYsUKuv/566dm5p7z7ynsSHBRs2s+rfZ7M/GKm3PbgbTJ06FBp1KiRtGzZ0npcelaG/PDXz/K/Zr1MgMXLy0se63OXbD+8W65o2deDRwQAAAAAAAolyHLTTTdJUFCQPP7445KamiqDBw+WypUryxtvvCFXXXWVO7sEAAAoVVq0aGGWCTty9IgEBQa5bAsKCpYjx45Iu7btTD8H/fJK/3dvkrV7/zXBlYHN+5h2XRKMZcEAAAAAACh+3F5j4pprrjE/GmRJTk6W6Ojowh0ZAABASWYXue/W++Sqm66S+QvnS2BAgOw/dEBiKkbL8fQ0WbFmhcyePdsEVjSgovS2b5PucjDpiPh4+Xj6CAAAAAAAwGl42fVf9m4Uvs/KypK6deu6tG/atEn8/PykRo0aUpIkJiZKRESEJCQkSHh4uKeHAwAASom0pDRpfH5j2bV3l2SkZ1jtfv7+UrNGDXn+89dl7C+fyPvXvCCNK59nth3PTDO3QX6BHhs3AAAAAABlXWIB4wZuFb7X9cUXLVqUp/3PP/802wAAAMoau80uSfuTJD053Wr7ae5PsmX7VvGqGixRQxpK7H2tzK13tWD5d9MmGf/ZBNlxZLd8uHCK9RgNrhBgAQAAAACgFGeyaNRm5cqVUqdOHZf2zZs3S6tWreTYsWNSkpDJAgAAzlbywWRJPpAi3n7eElWnotjsNqlRq6YcDkiWcgPqWkuCKf3z6+i0TRJ5PEgen/SSXH/hIAn2d63bAgAAAAAASmkmi14kSEpKytOuT5adne3OLgEAAEq04Aoh4hfkJ+GVwsTL20sWLFggu3fuktC2sS4BFqX3Q9rGSvzuvdLQqxoBFgAAAAAASii3giwXXXSRjBo1yiWgor9rW4cOHQpzfAAAAMVSZlqmyV5x8Pb2kvI1y0lgeM5SX/Hx8ebWNyo438f7VQx26QcAAAAAAEoeX3ce9PLLL5tAy3nnnScdO3Y0bfptTU2f+eWXXwp7jAAAAMVKdla2HN56RMQu4hvgawVWHBkr2w7tki/W/Wh+zzqYKv5VwvLsI/NQqrmNjY09p2MHAAAAAAAezmRp2LCh/PXXX3LFFVfIgQMHzNJh1113nWzYsEEaN25ciMMDAAAofnx8fSSkfLAEhPmbJcJy+2bVLFmRuUkCygVL8pJ4U4PFmd5PWRIv1arHWV9YAQAAAAAAZaTwfWlD4XsAAHAq+udS6pHjEhQRKN6+Od9RcfwJpdkrxzPSJCUjVSqGljdtyekp8si3L0u9lBgZfvPdElgn0tRg0SXCNINFAyxpm4/J1KlTZcCAAR49NgAAAAAA4H7cwK3lwtSxY8dk6dKlJpPFZrO5bNOsFgAAgNIiMT5Jjh89LhmpGRJZNcIEVhxLg/2+6U+5b+rz0jKuibx/zYumLTQgRN666lnze9VysXLviOGye/J6a3+awTJ26kcEWAAAAAAAKOHcCrL88MMPcs0110hycrKJ4DguMij9nSALAAAoTYLLBUlaYpoEhPjn2RYdVlEOJB2Wv/b8I4lpyRIeGOqyXQMp/fv3N/XrtMi91mDRJcJ8fHzO4REAAAAAAIBis1xYvXr15OKLL5YXX3xRgoODpaRjuTAAAOAsOzNbstKzJCA0wGqzZdvE28db/t6zQbYd3iX9mvawtv367xJpW7O5BPqd6A8AAAAAAEp/3MCtwvd79uyRe+65p1ADLC+99JLJghk+fLjVlpaWJnfeeadUqFBBQkNDZeDAgbJ//36Xx+3cuVMuueQSM5bo6Gh58MEHJSsrq9DGBQAAypbM45lyaPNhObYrwQRbHDTAsnT7aunz1vXy0LRRcjj5qLWtc722BFgAAAAAACiD3Aqy9OrVS5YvX15og1i2bJm8//770rRpU5f2ESNGmKXJvv76a/ntt99k7969LmuXZ2dnmwBLRkaGLFq0SCZOnCgTJkyQJ598stDGBgAAyhbfQF/x8fcR3wBfU9zeOem3VVxTaVqlvvRo0FGy7a416QAAAAAAQNnj1nJh48ePl2effVaGDRsmTZo0ET8/P5ft/fr1K/C+tK5LixYt5J133pHnn39emjVrJmPHjjUpOFFRUfL555/LoEGDTN8NGzZIgwYNZPHixdK2bVuZOXOm9O3b1wRfYmJiTJ/33ntPHn74YTl48KD4++ddNz0/LBcGAEDZpX8KZaRkiH+Iv1VnLjsrWzJsmTJxyTcyb+MfMuXGceLjnVNDJS0znawVAAAAAABKucQCxg3cKnx/8803m1sNtOSmFyc0w6SgdDkwzUbp3r27CbI4rFixQjIzM027Q/369SUuLs4KsuitBnkcARZHls3tt98u69atk+bNm+f7nOnp6ebHebIAAEDZlLAnUdIS0iQ8NkyCy+csherj6yNpKcny5vxPTDH7met+k75NupptBFgAAAAAAMBZBVlstsJZHmPKlCmycuVKs1xYbvv27TOZKJGRkS7tGlDRbY4+zgEWx3bHtpMZNWqUPPPMM4VyDAAAoGTzC/SVtAQRu80umw5sk7rRNU17uZAIefziu8XX20f6NOrk6WECAAAAAIDSUpOlMOzatUvuvfde+eyzzyQwMPCcPvfIkSNNio/jR8cCAADKBluWzaWgfXCFYAmvES63/fC4dB07WNbHb7K2DW7dX65o2ddaKgwAAAAAAOCsM1lUSkqKKUa/c+dOU3je2T333HPax+tyYAcOHDD1WBx0mbHff/9d3nrrLZk9e7bZ77Fjx1yyWfbv3y+VKlUyv+vt0qVLXfar2x3bTiYgIMD8AACAsiUjNUOO7Uowhe3L1yhnljnVn+CQIAkNCDZZK6t2rZOGsXU9PVQAAAAAAFBaC9+vWrVKLr74YklNTTXBlvLly8uhQ4ckODhYoqOjZevWrafdR1JSkuzYscOlbdiwYabuihaur1atmil8/8UXX8jAgQPN9o0bN5rtuQvfx8fHm+dVH3zwgTz44IMmgFPQQAqF7wEAKBuyMrLk8JYjkmI7LtN2z5ZbO10jYYEhZtueY/skMztLalSo6ulhAgAAAACA0lz4fsSIEXLppZfKe++9Z55kyZIl4ufnJ9dee61ZAqwgwsLCpHHjxi5tISEhUqFCBav9xhtvlPvuu88EcfQg7r77bmnXrp0JsKiePXtKw4YNZciQITJ69GhTh+Xxxx+XO++8k0wVAABg6NJgPn45y335+vtKueqRctukJ2TZjjWSLdnycK/bzbYqkSfPggUAAAAAACi0miyrV6+W+++/X7y9vcXHx0fS09NN5okGOh599FEpLGPGjDGZKprJctFFF5klwKZNm2Zt1+eeMWOGudXgiwZ5rrvuOnn22WcLbQwAAKBk0kL2ifGJcnDTIclMyxRH8q5/sL/cdtE1UiequrSq3tTTwwQAAAAAAGVtuTBdxmvRokVSt25dqVevnowbN0569eolGzZskJYtW5olxEoSlgsDAKBgbNk22bZipyQdTJawqFCp2TJOvH3c+s5GkdM/cY7uPCZLt66WN/+aKHd3v176NOpsbcu2ZYuvj9vl6QAAAAAAQClWpMuFNW/eXJYtW2aCLJ06dZInn3zS1GSZPHlyniXAAABA6bB27gaZPmq2JOxPstoiYsKk38he0rhHfSku2SviJVZB+4jK4bLq73/kr30b5M1fPpHeDTtZ2wiwAAAAAAAAj2SyLF++3BSu79Kliykwr0t0OTJbPv74Yzn//POlJCGTBQCA0wdYJo+YKpL7rwavnJshYwZ5PNCSeTxTju1OkGTfVAkoHyiVI6JNe1Jairz+84dye6chEh1WwaNjBAAAAAAApStu4FaQpbQhyAIAwKmXCHupxziXDBYXXprREi6PzLnLo0uHHT92XL5cOENe+PNt6Va/vbx3zYseGwsAAAAAACgbcYPiuYg6AAAoNrQGy0kDLMoukrAv0fQ715y/KxIYEShN6zSQtOx0iU88KMcz0875eAAAAAAAQNlS4MXIW7RoIfPmzZNy5cqZmiy6lvnJrFy5srDGBwAAPEyL3Bdmv8IKriQfSJaZa38TW7hdrmp9qfnbpEX9xjLjjo+laZX6p/xbBQAAAAAA4JwGWfr37y8BAQHm98suu6xQnhwAABR/YVGhhdqvMNiz7TLn7wVy37znJdQ/RHo27CjlQyLNtvOrNjhn4wAAAAAAAGVbgYMsTz31lLnNzs42Be+bNm0qkZE5FzMAAEDpVbNlnETEhEnCgaS8he+darJov6LOXsm2ZYuvj694+3pLnzZdZNKGadK+bivx9/Ur0ucGAAAAAAAolJosPj4+0rNnTzl69OiZPhQAAJRAWsy+38heOXdyr8D13/1+j/Qs0qL3h44ekQc/e1H6vXOT2Gw20xYcHiTf3vGBPNzrNgkNCCmy5wYAAAAAADgZt66GNG7cWLZu3erOQwEgX7Zsm2xZul1W/7jW3Op9AMVH4x71ZciYQRIRHebSrhks2q7bi1LKkVSZ8e8v8tfeDfL75qVWu7d30QV2AAAAAAAATsfLrmtvnKFZs2bJyJEj5bnnnpOWLVtKSIjrt0fDw8OlJElMTJSIiAhJSEgocWMHSoO1czfI9FGzJWF/ktWmSxPpN+eL+sItgDOjAdBtK3aaIvdag0WXCCuKDBZdFmzZjr+kbc3m1vN+9ut3Ur1KVbmofptCfz4AAAAAAAB34gZuBVmcvzXq5XVi3RDdld7Xui0lCUEWwLMBlskjpuat8/DfR8u5+IY8gOIlJT1V+r11k/x7aJvMuXeyNKhUx9NDAgAAAAAAZUxiAeMGBS5872z+/PlnMzYAsL6Zrhks+RbS1jYvkekvzZGGXesVaa0HAMWLv81PqgXHSrz/AdmydwdBFgAAAAAAUGy5FWTp1KlT4Y8EQJmjSw45LxGWh10kYV+i6Ve7TY1zOTQA59Cuo3vlrV8nyeN97pawwBDxC/KTJ3reLUEBgRJbJcbTwwMAAAAAACjcIItDamqq7Ny5UzIyMlzamzZteja7BVBGaE2HwuwHoOTRpUZvmPiQ/LN/s5QPipCHe99u2mvXJrAKAAAAAABKaZDl4MGDMmzYMJk5c2a+20taTRYAnqFFswuzH4CSIT0rQ/x9/Ky6bjc3uUqmeE2XTpUoaA8AAAAAAEoWt4ocDB8+XI4dOyZ//vmnBAUFyaxZs2TixIlSt25dmT59euGPEkCpVLNlnETEhFlF7vPwEomoFG76ASgdfvjrZ7notStk9vrfzX0NtPRv20Pev+RFaVmfTFgAAAAAAFAGgiy//PKLvP7669KqVSvx9vaW6tWry7XXXiujR4+WUaNGFf4oAZRKWsy+38heOXdyB1r+u9/vkZ4UvQdKkX/2bZY9x/bJxwu/stoCQgKkYu0K4hfo59GxAQAAAAAAnCm3rlympKRIdHS0+b1cuXJm+TDVpEkTWblypTu7BFBGNe5RX4aMGSQR0WEu7REx4aZdtwMoudbHbzKF7R2ub3a5DG8xTF69cKTYsmxWu2PpMAAAAAAAgFJfk+W8886TjRs3So0aNeT888+X999/3/z+3nvvSWxsbOGPEkCppoGUhl3rybYVO02Re63BokuEkcEClGwf/fGlPPPjWOnbuKu8O/gF0xZVsbxc33yQBIT6i5c3gRUAAAAAAFAGgyz33nuvxMfHm9+feuop6d27t3z22Wfi7+8vEyZMKOwxAigDNKBSu00NTw8DQCFqX6uFeImXZGfaJDMrU/x8/UxgpUKt8gRYAAAAAABAqeBlt9vtZ7uT1NRU2bBhg8TFxUnFihWlpElMTJSIiAhJSEiQ8PBwTw8HAIBiKzs7WxYsWGC+bKHZqx07dhQfHx/JyMqUSX9OE38fX7mu7UDT126zy1+r10tMQEWJqBIuQZFBnh4+AAAAAABAocYN3MpkWbhwoXTo0MG6HxwcLC1atHBnVwAAoISYNm2a3DtiuOzeuctqqxpXTd4YM1Z864TJ0zPGSHhgqFzapLuUC4kw2Sq1q1aX1GPHxcfPx6NjBwAAAAAAKApuBVm6du0qVapUkauvvlquvfZaadiwYeGPDAAAFKsAy6BBgySwTqREDWkovlHBknUwVQ7/v737AI+qyv8//pmWnpk0SuhFwS5SFETsFBsiouK6Cqtrr7gq4m/trvjHgrpr31VYXWURsaGIWEAUdAXEhgURCCW0hEx6mfJ/zsEMCU0ImdT363lmZ+65Z+65cecyyXzmnO/n2bZ96tSpOqFbP528f38luOIiz0tskaiE9ARqLAEAAAAAgCapRsuFbd68WVOmTNErr7yiBQsW6LDDDtMFF1xgQ5d27dqpsWG5MAAAdr9EWKcunZUTW6jU4fvL4dhWT8X8GrFl+jJllCfr+y+/U9HGYsUkxSi1Q0q1fgAAAAAAAE0xN6jR10pN3ZVrrrlGn332mZYvX65zzjlHkydPVqdOnewsFwAA0HSYGixmibCkvpk7BCdmO7FvplavytL/vvqf5NDWWSv7XPENAAAAAACg4dvntTs6d+6sW2+9VQ888IAOPfRQzZ07t3bODAAANAhr1q6x92aJsJ3xZGxt35izURn7ZSil3dZ6LAAAAAAAAE3dPoUsZibLVVddpczMTP3hD3/QIYcconfeeaf2zg4AANSrorJiTVww2T42NVh2pmLz1nbz+4A7hgL3AAAAAACg+ahRyDJu3Dg7g8UsDZaVlaXHHntM69ev14svvqghQ4bU/lkCAIB6kRiboJ5H9ZLbF6v8+etsDZaqzHbB/HVKy0jXgAED6u08AQAAAAAAGk3I8sknn+jmm2/W2rVrNWPGDFvwPiFh50uIAACAxqOkvFRPzP23ncFS6bwugxQsrlDZ8jxteW2ZytYWKFQWtPd2e3mecjfnaMmSJfV67gAAAAAAAHXNXdNlwgAAQNMz+t836bPlC1VQWqRbB19p2046+gT1Oby3cjblqKSoROteXBrp36ZNW2V2TFWrzFbq2bNnPZ45AAAAAABAAw5Z3nrrLZ1yyinyeDz28e4MHTq0Ns4NAADUsYuPPlcrNq/WoW27R9ocDoduG3ubhp0zTP956mXFxcZqw+aNapXRUiVlpfrjlRfoqWefsv0AAAAAAACaE0d4+8XVd8HpdNq6Ky1btrSPd3lAh0PBYFCNSX5+vnw+n/x+v7xeb32fDgAAdWJLsV8PzX5OJ3Trq5MPPMa2mV8LygLlinXHKBwMy+ne+p4fCoXUr28/BcoCGv/XByLHuPXesYpLjNNn8z8jZAEAAAAAAE3GnuYGezyTxXy4srPHAACgcfrnp1M0+fNpmrvscx3fra/cLrcNSjxya8uqPIWCIaV3SbNt5gsW99x7j4YMGaLB5w6qdpxZs2YRsAAAAAAAgGapRjVZAABA41QRDMjj2vr2f+Vxf9RXq7/X1cePsgFLtX4lFXZWS0VJQDEJHts2aNAgLV682LZXMuFKjx496vinAAAAAAAAaGTLhW3vyy+/1Mcff6yNGzfuMLPlkUceUWPCcmEAgKZunX+j7n3nMRuKPHn+fTvsD4fCcji3zUYpKyiTK8YldyzfxwAAAAAAAM1Pfm0vF1bV/fffr7/+9a/q3r27WrVqVW2JEJYLAQCg4dlSlKd3vvvYPr5l0Bp1Sm9nH5vvWpTml6pgfaFSO6TIE7911kpscmy9ni8AAAAAAEBjUKOQ5bHHHtPzzz+v0aNH1/4ZAQCAfWbCk6wt69Qxra3dPrhNN9152vXq2/mISMBSqTS/TKFASEU5xUpp56unMwYAAAAAAGgmIYspftu/f//aPxsAALDPcovydOUrf9W3a3/UvL+8qvSkVNt+Sf/zqoUwZvapufkyvSqOK1FiRkI9njUAAAAAAEDj46zJk8aMGaMnnnii9s8GAADsM198svKK81UWqNDi1d9V2xcKhpS3xq/CjYWRNqfbqaQWiSz5CQAAAAAAUBeF702h+9NOO00///yzDjroIHk8W9dvrzR9+nQ1JhS+BwA0ZsFQUO9895FOP+QkO9vUWJq9TMlxiWqf2qZa39KCMuVl5UkOqcX+GXJ5XPV01gAAAAAAAM208P11112njz/+WCeccILS09P55isAAPXEfFfi3Oeu1hcrl6hsRLnO6XWabT8oc/+d9o9LjlViRqItbE/AAgAAAAAAsG9qFLJMnjxZr732mp3NAgAA6o/5osNJB/TXj+uX73R/WWGZijYXK6VDipzOrV+KSG6VVMdnCQAAAAAA0DTVKGRJS0tT165da/9sAADAbpVUlOqZT17WqYecoG6tOkcK2o/sfYbSElOq9Q2HwvKvy1eoIqSizUVKbkm4AgAAAAAAUO+F7++66y7deeedKi4urtWTAQAAu3f3jMf00AfP6s4Zj9ilwoxYd8wOAYvhcDrka+NVfGq8EtMT6uFsAQAAAAAAmrYazWR5/PHHtXz5crVq1UqdOnXaofD94sWLa+v8AABAFVce90d9uvxLnd9n6A77zMyVgo2Fik2KUWxSrG0z95WPAQAAAAAA0ABClmHDhtXyaQAAgO3lleTr4dnP2VkqY066xLZ1TGurT26cKqdzx8moZkmw4pxileaXqsV+GXYmCwAAAAAAABpYyGKWCgMAANE1f/kivbDgVcW6Y3XhUWcpIynNtu8sYDESMhJVVlSuxIxEAhYAAAAAAICGWpPFyMvL0z//+U+NGzdOubm5kWXC1q5dW5vnBwBAs1JYVhR5fMrBx9twZfKohyIBS1UVJRUq3FgY2XY6HUrrlKq4ZJYHAwAAAAAAaLAzWb755hudfPLJ8vl8WrlypS699FKlpaVp+vTpysrK0r///e/aP1MAAJqwnMItuuPtR/Rd9s+afd1LinF75HA4NH7Y2J32D1YElbMiVwpL7nhPJFgxzwEAAAAAAEADnsly4403avTo0Vq2bJni4uIi7aeeeqo++eST2jw/AACaBY/bo89+XahfN2fp8xWLf7e/y+NSYlqCYpNj5Yn31Mk5AgAAAAAAoBZmsnz55Zd65plndmhv27at1q9fX5NDAgDQrITDYX295gf1aH+Q3fbGJenhs29XK2+6DmnTfaf9i3NLFO+Lk9O99TsSSa2S7D2zVwAAAAAAABrRTJbY2Fjl5+fv0P7zzz+rRYsWtXFeAAA0WYFgQH984Qad/uTF+nLl15H2kw44eqcBi5G/Ll8F6wvkz863gUtluELAAgAAAAAAUH9qFLIMHTpU99xzjyoqKuy2+YDH1GIZO3aszj777No+RwAAmhS3y602Ka0U647RzxtX7NFzEtIS5HA5FJtEUXsAAAAAAICGwhGu/DrsXvD7/RoxYoQWLlyogoICtWnTRtnZ2erXr59mzpypxMRENSZmVo7P57M/l9frre/TAQA0McFQUFMWvq1TDz5BqYm+SKH7ovISdUhrs9PnBMqDCpYHqoUqoVBYTiczVwAAAAAAABpKblCjmizmwLNnz9ann36qb775RoWFherVq5dOOumkfTlnAADqRCgY0opFWSrYVKjkFknq3KuDnK4aTe7cIze8eo9eXzJLS7OX6W9n3mzb0pNSla7UnfavKKlQ7sot9nHGfum2yL1BwAIAAAAAANCw7FXIsmDBAuXk5Oj000+328ccc4yWL1+uCRMmqLi4WMOGDdPf//53W7MFAICG6LvZP+qt8bPk31AQafO1StbQcYN1yMADojLm+X2G6qMf52u/Fp32qL87zi1XjEsOE6rs9XxTAAAAAAAA1JW9+tquqcPy/fffR7a//fZbXXrppRo4cKBuvfVWvf322xo/fnw0zhMAgFoJWF4cM61awGL4NxbYdrN/X5VWlOnxj1/Q2998EGk7uksvfT72Df3p6HN2+hyzcmdZYVm1gvapHVOV1inVhi0AAAAAAABoAiHLkiVLqi0JNmXKFB155JF67rnndOONN+rxxx/X1KlTo3GeAADs8xJhZgbLTmeG/Nb21gPv23774j//e0MT3n9Gd814VMXlJZH25Lid1yszwYp/jV9bVuWpZMu2/i6304YtAAAAAAAAaCIhy5YtW9SqVavI9ty5c3XKKadEtvv06aPVq1fX7hkCAFALTA2W7WewVBOW/Ovzbb+9FQptC2YuOHKY+nY+Qrefep3iPXG/+1wTpHjiPZJZGSzE2mAAAAAAAABNNmQxAcuKFSvs4/Lyci1evFh9+/aN7C8oKJDH46n9swQAYB+ZIve12c/wlxTozrcn6uIXb44s9RXnidW0y57SsB6DdjkTJRQIKVgRjGwnpCcoo2u6EjN2PtsFAAAAAAAATSBkOfXUU23tlXnz5mncuHFKSEjQgAEDIvu/+eYbde3aNRrnCQDAPklukVSr/Yzcojy9+MV0ffDjZ1qyZukePae8qFybl+cob42/Wg0Wd6x7j8cFAAAAAABAw7BXn+jce++9Gj58uI477jglJSVp8uTJiomJiex//vnnNWjQoGicJwAA+6Rzrw7ytUq2Re53WpfFIflaeW2/3Vmfv0mtvS22HjOjvV0WrGuLDjqi/cF7dB5Oj9MuCxYOhuyMFpeHwvYAAAAAAACNlSNc+TXaveD3+23I4nJV/2AoNzfXtlcNXhqD/Px8+Xw++3N5vd76Ph0AQJR8N/tHvThm2taNqu9+v63qdeHEETpk4AE7fW5Jealumn6/3l/6iebcOEVtU1rv8bhmabCqYUp5cbk8cR45nBS2BwAAAAAAaMy5wV4tF1bJHHj7gMVIS0trdAELAKD5MAGKCVJ8LZOrtZsZLLsLWCprrZhZLKWBMs1d9sUejWdmrPjX5WvTss0KlAUi7TEJMQQsAAAAAAAAzXUmS1PDTBYAaF5CwZBWLMqyRe5NDRazRJjTVf17B+btcc7Pn+uY/frI49q6uubPG1bYkOWwtrsOY7Y/xpasPJUXliu5dbIS0xOi8vMAAAAAAACgfnIDquwCAJodE6h0PbLTbvtc+9879cbX7+uu08foz/3Ps23dWnXeo9krZvkxU8ze3HxtvAqUBxWbyExPAAAAAACApqZGy4UBANCYBYNBzZkzR6+88oq9N9vb69elp2JcHpVWlO3xcU2tlc3Lc1S0qSjSZmqxELAAAAAAAAA0TcxkAQA0K9OnT9f1Y27QmqzVkbZ2Hdrr7KvO16g/XKQj2h9s20b2PkPH7n+k2qe22eNjB8uD9laSV6rEjETqrgAAAAAAADRx1GShJgsANKuAZcSIEYrbL0VJfTPlbpGgwKZiFSzIVukvW9Tzzyfoy2c+kNO55xM9zduoWRas8nFxboniU+J2qPECAAAAAACAppcbELIQsgBAs2CWBOvUpbNyYguVOnz/SDBimLfC3Nd+VkKhW+tWrVFsTOwe1V4p3FyksoIypXdOY9YKAAAAAABAM8wN+JotAKBZmDdvnl0izMxgqRqwGGY7qV8b5a7frAXzF+zR8UzIUpJbrEBpQKX5pVE6awAAAAAAADRk1GQBADQL2dnZ9t4sEbYznoyEav1+b2kwp9spb1ufDVvifXFROWcAAAAAAAA0bMxkAQA0eSYcyczMtI9NDZadqdi8tb2y3/YCZQHlrtii8uLySFtcciwBCwAAAAAAQDNGyAIAaNLhyquL3tHpT1ysXkf1VrsO7VX4ebZt375f0efZat+xgwYMGLDTYxVtLlJFSYXyswt2eD4AAAAAAACaJ5YLAwA0WaUVZXr4g+e0Jm+9XvryDT028VGNGDFCW6YvU2LfTLtEmJnBYgKW0l/y9Oi0f8rlcu30WMmtk2WyleRWSTvUdAF+TygY0opFWSrYVKjkFknq3KuDnC6+6wIAAAAAQGNHyAIAaFLKAxWKcXvs4/iYOP3tzJv144bluuTo82z7tGnTdP2YG7TmxaWR55gZLCZgGT58uN02M1VKtpQoWB604YphPhBPaeerp58Kjdl3s3/UW+Nnyb+hINLma5WsoeMG65CBB9TruQEAAAAAgH3jCLPmifLz8+Xz+eT3++X1euv7dAAANfTBD5/qr289pPHDxuqE7v122S8YDGrevHm2yL2pwWKWCKs6g8UsC5bza659nNY5TTEJW0MboCYBy4tjpknb/7b122SoCyeOIGgBAAAAAKAR5wb1uk7FU089pcMOO8yeoLn169dPM2fOjOwvLS3V1VdfrfT0dCUlJenss8/Whg0bqh0jKytLp512mhISEtSyZUvdfPPNCgQC9fDTAADq2/xfF9mlwZ765KXd9jOByvHHH6+DDjrI3m+/RJgn3qOE9AQ7i8UTz6RP1HyJMDODZYeAxfit7a0H3rf9AAAAAABA41SvIUu7du30wAMPaNGiRVq4cKFOPPFEnXnmmfr+++/t/jFjxujtt9/Wq6++qrlz52rdunWRpVwqv4lsApby8nLNnz9fkydP1qRJk3THHXfU408FAKgrFcGA8ksLI9s3nvxn3XTyZZo06qHdPq+oqEh/+tOf1KNHD3tf4C+Qf62/2ofd3tbJSkxPoP4KaszUYKm6RNgOwpJ/fb7tBwAAAAAAGqcGt1xYWlqaHnzwQVuYuEWLFnr55ZftY+PHH3/UgQceqAULFqhv37521svpp59uw5dWrVrZPk8//bTGjh2rTZs2KSYmZo/GZLkwAGh8vl7zg26cdp8ObN1V/xh5zx4/b+nSpTpnxDlauXKl/njOhXrp1RfVoV0HPf3QM+rRq4d8bXkfQO1Y8s53euWWN3633/kThqnHaYfUyTkBAAAAAIAmtFxYVWZWypQpU+y3i82yYWZ2S0VFhU4++eRInwMOOEAdOnSwIYth7g899NBIwGIMHjzY/vCVs2F2pqyszPapegMAND4/b/xVn/zyP+UUbtmj/mbGY58+fRQMhDRzynu6+5a79e6UmQqFwjp15Cl67b3Xon7OaD6SWyTVaj8AAAAAANDw1HvI8u2339p6K7Gxsbriiiv0+uuv2zXy169fb2eipKSkVOtvAhWzzzD3VQOWyv2V+3Zl/PjxNoGqvLVv3z4qPxsAoPaEQiEt37Qqsn14uwP1j/Pu0dwb/6v0pNTffb4J70ePHq1jjjxG7778rrp17Wbbu3ftrpmvvKv+R/bXJX++2PYDakPnXh3ka5UcKXK/A4fka+21/QAAAAAAQONU7yFL9+7dtWTJEn3xxRe68sorNWrUKLuUSzSNGzfOTvGpvK1evTqq4wEA9s3Gghyd9czlGvrUpdVmrZx5+EClJvj26Bg9e/ZU36P6KjcvV/Fx8dX2xccn2PZ+ffvZfkBtcLqcGjpu8NaN7YOW37aH3jrI9gMAAAAAAI1Tvf9Vb2ar7LfffurVq5edYXL44YfrscceU+vWrW1B+7y8vGr9N2zYYPcZ5t5sb7+/ct+umFkzZg21qjcAQMOVluBTSUWpAsGAvl33U42OYQrY33X3XVq4ZKHmzp9bbd+c+XO06OtFdj+F7lGbDhl4gC6cOEK+lsnV2n2tvLbd7AcAAAAAAI2XWw1wORhTM8WELh6PRx9++KHOPvtsu++nn35SVlaWrdlimPu//e1v2rhxo1q2bGnbZs+ebUMTs+QYAKBxCofD+mz5QvXv2tuGHm6XW38/724lxyWpjW/rv/d7KhQMqdRfqvjUeA0aNMjOZnnwiQlKS02L9HnwHxPsLJaBAwdG4adBc2eClINO7KYVi7JUsKnQ1mAxS4QxgwUAAAAAgMavXkMWs2zXKaecYovZFxQU6OWXX9acOXM0a9YsWyvlkksu0Y033qi0tDQbnFx77bU2WOnbt699vvmwzIQpF154oSZMmGDrsPz1r3/V1VdfbWerAAAaZ8By5St/1YxvP9TEEbfrnF6n2fburbrs/bFCYeX8mqtgeVAOl0Pxvng7W2XIkCEafO6gan3New+zWBAtJlDpemSn+j4NAAAAAADQlEIWMwPloosuUnZ2tg1VDjvsMPshV+U3iSdOnCin02lnspjZLYMHD9aTTz4Zeb7L5dKMGTNsLRcTviQmJtqaLvfcc089/lQAgH1hgo5D23bXrKVzlVuct2/HcppgJU4leaVyuV2RgH7x4sU2zKk6Zo8ePfb53AEAAAAAANC8OMJVP2VqpvLz823I4/f7qc8CAPXgs+WLlOlroS4ZHex2RTCg1bnr1KXF1u29UV5ULleMSy7P1lDFvM2ZGS0szQQAAAAAAIDazg34xAkAUK+e+/QVnffPq3XbGxMis0s8LneNApainGLlrtwi/7r8yLHMLBUCFgAAAAAAAEQDnzoBAOrVwAMHKN4Tp64tOqk8WLFPx4pNjJEcksvtlJr9PE0AAAAAAABEG8uFsVwYANSpb9b+qJ83/KoRPU+NtOUUblF6UupeH8ssA1ZRGlBMgifSZorcm+XCAAAAAAAAgGjnBvVa+B4A0Lx8u/ZHnf7ExfK4POrT6XB1TGtr22sSsAQrgtqyKs/ep3dNl/u3YIWABQAAAAAAAHWFkAUAUGcOadNd/bv2UlpiqhI8cft0LKfbKYfLIQW2Bi6VIQsAAAAAAABQVwhZAABR8+umLD376Su6d+hfbDF7U4T+hYseUpwntkbHC5QF7EwVcxxzS2nrk5yOrTVYAAAAAAAAgDpGyAIAiIqKYEDn/esaZfs3qkNaG1113IW2vaYBS1FOsQo2FCi5ZZISMxJtG0uDAQAAAAAAoD7x1V8AQFSYmSs3D7xMx+3fV6ccfPw+H8/hMJXupfKSCoXD4Vo5RwAAAAAAAGBfOMJ8UqX8/Hz5fD75/X55vd76Ph0AaJTMjJV73nlMfz7mfPXqcIhtq3yLMUt77S3z3HAwbGuvVG6XFZYrNimmRscDAAAAAAAAajs3YCYLAKBWPPbRC3r72w91+1sPVQtXahKImEL2W1Zu0ZasLdWOFZccS8ACAAAAAACABoOaLACAGjMBSGXoYZYGW5u3XrcOvrJWgpCK0oAZQIHSgDzxnlo4WwAAAAAAAKB2sVwYy4UBwF7LK8nXA+89pRi3R/eccWOtHDMcCsvh3BbOlBWW2cL27hi+DwAAAAAAAIC6xXJhAICo+X7dz3rpf69r8uevac2W7H06lsn6S/JKtOnnTaooqYi0xybFErAAAAAAAACgQePTKwDAHimtKFOcJ9Y+7t+1t647YbSO3f8otUvN3Pdj55cpFAyrOLdYvra+WjhbAAAAAAAAIPqYyQIA2K2SilKNf+9JnTjxfBWWFUXabxl0hfp2PqLGx61a0N7bxqvkVkn2HgAAAAAAAGgsCFkAAL9rxrcfKmvLOr31zQf7fKxQIKS81Xkq3FgYaXO5nUrMSLSBCwAAAAAAANBYsFwYAGAHOYVblJ6Uah/He+I0Yfg4FZYVa/BBx+7zscuLy+3yYHJICWkJcnlctXDGAAAAAAAAQN1jJgsAoJp/fjpFfSecpdk/zIu0mRostRGwGHHeOCW2SFR65zQCFgAAAAAAADRqhCwAgGo2FubYOiy1sTSYUVZYptwVuQqFttZgMZJbJskT76mV4wMAAAAAAAD1heXCAKCZyy3KUzAUVIvkdLt9w4kX64BWXXVWj8H7fOxwKCz/unyFKkIq3lykpJZJtXDGAAAAAAAAQMPATBYAaMY++OFTHfvwufrrWw9H2hJi4jX8iCG1UoTe4XTI18arhLR4W9geAAAAAAAAaEoIWQCgGcv0tVR+aaF+3ZylgtKiWpm5kp+db5cIqxSbFCtvptcGLgAAAAAAAEBTwnJhANCMFJUVa2n2MvXpdLjdPrhNN0358991ZMfD5Xbt+1tC4eYiFeeWqLSgTC32yyBYAQAAAAAAQJNGyAIAzcTKnDU657mrbNAy58b/quVvNViO7tKr1sZITE9QeVG5klokErAAAAAAAACgyWO5MABoJtqnZqplUrpS4r1an7+pVo5ZUVKhwo2FkW2ny6n0zml2iTAAAAAAAACgqWMmCwA0UeWBCr321Uyd1+t0OZ1OuZwuPXPB/UpPSlW8J26fjx+sCCpnRa4UltzxHsUlE6wAAAAAAACgeSFkAYBGLBgMat68ecrOzlZmZqYGDBggl8ulUCiks565XF+vWapQOKQLjhxm+7dLzay1sV0elxLTEmzYEhPvqbXjAgAAAAAAAI0FIQsANFLTp0/X9WNu0Jqs1ZG2dh3a67GJj2r48OEadvhArctbL1+8t1bGC4fDKs4pVnxKvJzuratNJrVKksNB7RUAAAAAAAA0T46w+dSsmcvPz5fP55Pf75fXWzsfRgJAtAOWESNGKG6/FCX1zZS7RYICm4pV+Hm2Sn/J07Rp0zT0zKEqKi+RLz65VsbMW+tXaV6p4ryxSmmfUivHBAAAAAAAABpzbkDIQsgCoBEuEdapS2flxBYqdfj+1WaSmH/St0xfpozyZK1Y/qtdOqy2mCL3uau2yNs6WXG+OGawAAAAAAAAQM09N9i63gsAoNEwNVjMEmFmBsv2QYfZTuybqdWrsmy/fREoD6issCyy7Yn3qGW3Fna5MAIWAAAAAAAAgJAFABodU+TeMEuE7YwnI6Fav5rOWsn5JUd5q/22sH0lh5NwBQAAAAAAAKhEyAIAjUy+o8TemxosO1OxeWt7ZmZmjcdwx7nlinXLE+eu8TEAAAAAAACApo6aLNRkAdDIBAIBpbROUzDVrbSzd1KT5bVlyqjYu5os5nnlheWKSYqJHC8UCMnhcrA0GAAAAAAAAJqdfGqyAEDTsHrLOo174/+pLFBut91ut+656l6V/rJFudOWqWxtgUJlQXtvtkt+2aIbR/1lrwKWvDV+bcnKU0ne1lkyhtPtJGABAAAAAAAAdoN1YACgAQuGgjrvuWuUtWWdWiZnaMxJlygUDKn0U2lYu5H6cPVMbX5xaaS/NyZFQ9qPVPGnsv2crt/P0k2QEhPvUVlBmcKhKP9AAAAAAAAAQBNCyAIADZjL6dLYwVfqxS+m68zDBtq2FYuy5N9QoG7eg7Vf8oFaU7xKhYECJbmT1S6ho5wOp/zr822/rkd22ulxg4GQmcIil2frbJeE9ATFJsfKHcvbAgAAAAAAALCn+DQNABqQkvJSTfzoXxp4wDHq0+lw2zb0sJN1xqEnyencOiulYFNhpL8JVDokdt7psar2q6qsqFz+1Xm2sH1ap1Q7k8XcCFgAAAAAAACAvUNNFgBoQB758J96cu6LuuX18aoIBmybCUAqAxYjuUXSHh1rV/1cHqeZxKJwMGyL2wMAAAAAAACoGUIWAGhArjruQh3aprvGDb5KHtfOZ5Z07tVBvlbJ0q5q0jskX2uv7VcpWB6MPHbHuJXaMVXpXdIiy4UBAAAAAAAA2HuELABQT8LhsN78erYmvP90pC01wad3r5mkQQcdu8vnmWL2Q8cN3rqxfdDy2/bQWwfZfuFQWP61fm36ZbMCZVtnxhgxCR45nLtKaQAAAAAAAADsCUIWAKgnS7OX6eopt+vxjydpUdZ3kXazPNjvOWTgAbpw4gj5WiZXa/e18tp2s3/rwSqL3EvlReW1/0MAAAAAAAAAzRhVjgGgnhzcpptG9R2hjKRUHdKm214/3wQpB53YTSsWZWnhF4vU+6hekSXCzCyZyoL2vjZeu1xYTGJMFH4KAAAAAAAAoPliJgsA1JEf1v+iS168RfmlhZG2v515k8acdIli3TULQEpKS3TfU3dr5HUj7H1+br5ylueqaHNRpI+pu0LAAgAAAAAAANQ+QhYAqAOhUEjXTLlDs5Z+oodmP1srx1y6dKmO7HOkpv53qi676HJ7f/SxR2vpj0tVkldq67EAAAAAAAAAiB5CFgCoA06nU/cO/YuGHHycrjz2j/t8vMmTJ6tPnz623srMKe/p7lvu1rtTZipUEdKpI0/RO5/NoLA9AAAAAAAAEGWOsFm4v5nLz8+Xz+eT3++X1+ut79MB0ATkleTrbzP/oRO7H61TDj6+Vo+9aNEi9e7dW4OOH6SnHnxaCfEJkX3FxUW64uYrNHvubC1cuFC9evWq1bEBAAAAAACA5iB/D3MDZrIAQBRMXjBNr3z5lm5/62GVBcpr9dg9e/ZU36P6KjcvV/Fx8dX2xccn2PZ+ffvZfgAAAAAAAACixx3FYwNAs3X5gAu0ZM1Se1/TovY7YyYfOhwO3XX3XRoyZIjmzp+r4/tvmykzZ/4cLfp6kWbNmmX7AQAAAAAAAIgelgtjuTAAtVDU/sUvpuur1Uv16Ll3RG2citKA8tf5ldw6WZ54j47ud7TKS8o1/q8PRPrceu9YxSXG6bP5nxGyAAAAAAAAAFHODZjJAgD7aGXOGt05Y6ICoaCGHzFYx+5/VFTGKc4pUkVJQAXrC5XWOTUym2XwuYOq9WMWCwAAAAAAAFA3CFkAYB+W7TK6tOigmwZepsSYBPXv2jtqY5oZLEZSyyQ79qBBg7R48WJ7LpVMe48ePaJ2DgAAAAAAAAC2YbkwlgsDsJe+XPm17nn3cT17wXhl+lpGZQzzT3NxbolCFcFIuAIAAAAAAACgYeUGzjo6HwBoEkz4cd/Mf+ir1d/rodnPRm2cipIKFawvUFFOsX0MAAAAAAAAoOFhuTAA2Ivlwczt/511q56fP1W3nXJ11MaLSYhRQnqC3DEuueP4pxoAAAAAAABoiJjJAgC7sT5/ky596Va9+MX0SNsBrbtqwvBxSomvveUFgxVB5a3xKxQMRdq8rZOVkJZAEXsAAAAAAACggSJkAYDdmPX9XM38fo4mvP+MistLojZLZktWnkr9pSrYUBiVMQAAAAAAAADUPtagAYDthEIhOZ1bM+g/HnWWftywXBceNVwJMfFRGc/MVPFmJqtgfaES0xOiMgYAAAAAAACA2ucIm69QN3P5+fny+Xzy+/3yemtv+R8AjUt5oEJPzn1R839dpCmX/D0StERDib9UTqdDscmxO9R9AQAAAAAAANA4cgOWCwOA32wu2qKnPnnJhizv/zAvauOU5JXIv8Yv/7p8hQLbarAQsAAAAAAAAACNC8uFAWjWgqGgXE6XfdzG11L3Dr1RHpdHgw86NmpjxnnjVJRTbGexOFwEKwAAAAAAAEBjxUwWAM3WBz98qmMfPk8/bfg10nZur9N1Vo/BtTqrJBQMqTi32C4HZjicDqV3SVNyyyRmrwAAAAAAAACNGCELgGbr5S/f1KrcNXrso+ejNkY4FFbO8hzlZxeoNL8s0k64AgAAAAAAADR+LBcGoNkwM0kCoaA8rq3/9N135s3q1qqLrj/hT1Eb08xaiU+Jt4XuXR5ybQAAAAAAAKAp4RM/AM3Cis2rNfJf1+rRD/8VaTM1WG4dfKXiY+JqdayyonIFK4KR7cQWicromq6YhJhaHQcAAAAAAABA/SJkAdAsfJ+9TJ8tX6gXFryq/NLCqI1jCtpvWblF+evyt9VgcTjsjBYAAAAAAAAATQvLhQFoskoryhTnibWPTzvkBN088DIN6zFY3rikqI0ZkxgjOSSnxyWZjIVsBQAAAAAAAGiymMkCoMkpLi/R3TMe1an/GG2DlsrZJNefeLE6prWt9cL25cXlkW1PnFst9suQr42X2SsAAAAAAABAE0fIAqDJqQgG9OY3s/XzxhX64MdPozaOqbuS82uOtqzKU6B8Ww0WV4wramMCAAAAAAAAaDhYLgxAk5m9khATbx/74pP10Nn/J6fDqeO79Y3amE63Uw6XU45gWCFT6J5wBQAAAAAAAGhWmMkCoNF77auZ6jvhLC34dXGk7cTuR0clYAmUBaoVtE9p51NG1/SttVgAAAAAAAAANCuELAAavf+t/Fq5RXmatGBaVMcp2lykzctzVJxbEmlzeVx2RgsAAAAAAACA5oflwgA0OoFgQOXBisjyYLcNuVr7t+ikUf1GRHVcW8g+LFUUlyucFm9nsgAAAAAAAABovvj6NYBGZWn2Mp3x5CW6c8bESJupwfLnY0bK46rd3NgsCxYKhCLb8anxSu2YIl87HwELAAAAAAAAAEIWAI1LQWmRvl33k2Z+N8cuERYtwfKgcldu0ZasLdVqsMQmxRKwAAAAAAAAALBYLgxAg7elyK/URJ99fFTnHnro7P/TSd2PVlpiSvQGdUiB0oB9aO498Z7ojQUAAAAAAACgUSJkAdAgBINBzZs3T9nZ2crMzNSAAQNUXFGq2996WJ/88oU+GvOKUuK9tu/I3mdE5RxCobCcpu7KbwXtU9r75Ipxyx3jisp4AAAAAAAAABo3QhYA9W769Om6fswNWpO1OtLWrkN7PfTQQ1qy5nttKszV3J+/0JmHD4zK+GY5sJK8UhVsKFBax9TIrBWzNBgAAAAAAAAANMiaLOPHj1efPn2UnJysli1batiwYfrpp5+q9SktLdXVV1+t9PR0JSUl6eyzz9aGDRuq9cnKytJpp52mhIQEe5ybb75ZgcDWZX4ANPyAZcSIEcqJLVSLCw9S5o297b3ZPv+8kTo9qZ/euOK5qAUslcoKyhQOhlWcWxzVcQAAAAAAAAA0HY5wZUXnejBkyBCNHDnSBi0mFLntttv03XffaenSpUpMTLR9rrzySr3zzjuaNGmSfD6frrnmGjmdTn322WeRJYZ69Oih1q1b68EHH7RLDV100UW69NJLdf/99+/ReeTn59tj+/1+eb1blyMCEH3m+u3UpbMNVFKH71+toLz5p2nL9GXKKE/WiuW/yuWq3SW7qhazt+cSCKnUX6KEtAQK2wMAAAAAAADNXP4e5gb1GrJsb9OmTXYmyty5c3Xsscfak2/RooVefvll+01348cff9SBBx6oBQsWqG/fvpo5c6ZOP/10rVu3Tq1atbJ9nn76aY0dO9YeLyYm5nfHJWQB6secOXN0wgkn2JkrMW2Td9hftrZAm19cqo8//ljHH398rY1rApX8dflyx7qU3GrHcQEAAAAAAAA0b/l7mBvU63Jh2zMna6Slpdn7RYsWqaKiQieffHKkzwEHHKAOHTrYkMUw94ceemgkYDEGDx5s/wN8//33Ox2nrKzM7q96A1D3Vq9dY+/dLRJ2ut+TsbXdzFCrTRXF5XZ5sKKcYgUrgrV6bAAAAAAAAADNR4MJWUKhkG644Qb1799fhxxyiG1bv369nYmSkpJSra8JVMy+yj5VA5bK/ZX7dlULxiRQlbf27dtH6acCsCs/bfhVj3/+b/s4sGnndVAqNm9tz8zMrNWx47xxSmqRqPTOaXJ5ancZMgAAAAAAAADNR4MJWUxxe1OPZcqUKVEfa9y4cXbWTOVt9erVUR8TwI42JxfLkxKnws+zIzVSKpntos+z1b5jBw0YMGCfxiktKFPOilyFQtvGSGqZJE+8Z5+OCwAAAAAAAKB5axAhiylmP2PGDFt3oV27dpF2U8y+vLxceXl51fpv2LDB7qvsY7a331+5b2diY2PtGmpVbwCiL69k29J83Vt10ZMX3Kdn/vG0Sn/Js0XuTQ2WUFnQ3ptt0/7oIxP3qeh9OBS29VcqiitUvLmoln4SAAAAAAAAAKjnkMV8U90ELK+//ro++ugjde7cudr+Xr16yePx6MMPP4y0/fTTT8rKylK/fv3strn/9ttvtXHjxkif2bNn2+DkoIMOqsOfBsCulAcqdO+7f9fRE4Zrbd62ZfxOOfh4/emCUZo2bZrSy5JskfvsiQvtfUZ5sm0fPnz4Po3tcDrka+tVQnqCEjMSa+GnAQAAAAAAAICtHOHt1+ipQ1dddZVefvllvfnmm+revXuk3dRJiY+Pt4+vvPJKvfvuu5o0aZINTq699lrbPn/+fHsfDAbVo0cPtWnTRhMmTLB1WC688EL9+c9/1v33379H52EK35sxzdJhzGoBolNz6exnr9CXq77RHadep8sG/GGHPuZanjdvni1yb2qwmCXCajKDJRQMqWBDoeK8sYpNiq2lnwAAAAAAAABAc5K/h7lBvYYsDodjp+0vvPCCRo8ebR+XlpbqL3/5i1555RWVlZVp8ODBevLJJ6stBbZq1SobxsyZM0eJiYkaNWqUHnjgAbnd7j06D0IWoPYVlRUr3hMnp3PrhLnlm1ZpxebVOvnAY6I6bsHGQhVtKpLT41SL/TLsTBYAAAAAAAAAaHIhS0NByALUrk9/+VI3vfY3O2Pl4qPPrdOxzUyWvNV5dmkwZrIAAAAAAAAAiGZu0CAK3wNoWn7dnKU1eev1n/+9oWAoGNWxyosr7OyVSk6XU2md0ghYAAAAAAAAAETdnq2nBQC/o7i8RAkxW2sp/fHIs2yx+/P7DJXLufd1VfZUsCKo3JW5UljyxHsUl0ywAgAAAAAAAKDuMJMFwD7ZUuTXtf+9U+c+d3Vk1oqpw/LnY0YqMTYhqmO7PC4lpCUozhenmHhPVMcCAAAAAAAAgO0RsgDYJ6WBMn3ww6f6Zu2P+t/Kr6M6likhVbipSKFAKNKW3CpJKe18crr55wwAAAAAAABA3WK5MAB7rbSiTHGerUtzZfpa6pFzbldrbwsd0f7gqI7rX5uvUn+pAqUVSmmfYtscDkdUxwQAAAAAAACAXeGr3wD2aibJa1/NVL8JZ2lp9rJI+ykHHx/1gMVITE+whe1jvbH2XAAAAAAAAACgPhGyANgrM7+fo02FuXru01eicvyvv9625FigLKCywrLItilu36JbhuJ98cxgAQAAAAAAAFDvCFkA7JaZMVIRDNjHJti4/8xbdNuQqzVh+G21Ok5RUZH+9Kc/qUePHvY+b7Nfm5fnKG+NX8GKYKSfw0m4AgAAAAAAAKBhIGQBsEtZues08l/X6uEPnou0tUxO11XHXSiPq/ZKOi1dulRH9jlSU/87VZdddLm9P+a4/lqetVyeOE+tjQMAAAAAAAAAtYmQBcAumborny1fqEkLXtWWYn9Uxpg8ebL69OmjYEVIM6e8p7tvuVvvTpmpQHlAg84eqLfnviWXxxWVsQEAAAAAAABgXzjCVI9Wfn6+fD6f/H6/vF5vfZ8OUK8CwYDcVWap/P3jSTr90JPUOaN9rY+1aNEi9e7dW4OOH6SnHnxaCfEJkX3FxUW64uYrNHvubC1cuFC9evWq9fEBAAAAAAAAYF9yA2ayAIiEK0/OfVGD/z5KJeWlkfZrTxgdlYDF6Nmzp/oe1Ve5ebmKj4uvti8+PsG29+vbz/YDAAAAAAAAgIaGkAWAVVxRqufnT9VPG5brta9mRn08U8w+FAjprrvv0sIlCzV3/txq++fMn6NFXy+y+x0Oit0DAAAAAAAAaHhYLozlwtCMhUIhOZ3bstY5P3+uDfmbdW6v06IabJQVlilvjV/uWLdSO6ao/9H9VV5SrvF/fSDS59Z7xyouMU6fzf+MkAUAAAAAAABAg8wNthVeANCsfLP2R9302t90x6nX6Zj9+ti247v1rZOxXTEuKRRW2N5kZ6sMGTJEg88dVK3frFmzCFgAAAAAAAAANFiELEAz9d+FM7Q0e5nGz3pKM7r2jnqYESgPym3CFfMPT4xbqZ3S5Ilzy+F0aNCgQVq8eLGqTqwz59OjR4+onhMAAAAAAAAA7AtCFqAZMSFGZZgybsiVMg/HnHhJVAMWM1vFvy5fpfmlyuiabpcIM2ISPJE+ZvwjjjgiaucAAAAAAAAAANFAyAI0A8XlJZrw/jMqLCvSQ2f/n21Lik3UfUNviv7gDtkC9wpL5UXlkZAFAAAAAAAAABo7Pu0EmoEf1i/Xv+b/185kuaT/eTqw9X5RHS8UDNllwMwMFXPztfUqWBFUTEJMVMcFAAAAAAAAgLpEyAI0g6XBenU4RGMHXaGDMvePesBiZqvkrfUrITVeSS2SbJvL47I3AAAAAAAAAGhKnPV9AgBq38c/LdDpT1ys3KK8SNs1x4/Sid2PjvrYZsZKqCKkkrxSW48FAAAAAAAAAJoqQhagiQmGgrpv5j/09dof9PjHk+pkzKphSpwvTt7MZKV3SbNLhgEAAAAAAABAU8VyYUAT43K69NDZt+nNr2frlkGXRz1cKdxYqLKicqV33hqqmCXKEtISojouAAAAAAAAADQEhCxAI5dTuEW3v/2wBh44QGf1GGzbjmh/sL1FmwlZSvJKFAqGVVZQZmexAAAAAAAAAEBzQcgCNHL/XTRDb33zgRb8ulinHHy84jyxUR0vHA7b2SqG0+2Ut63PPo5Lju64AAAAAAAAANDQELIAjdylx5yvH9b/Yu+jHbBUlFbIvzbf1lyJSYixbYQrAAAAAAAAAJorCt8DjYiZRTJ10QxdPeUO+9jwuNz6+3l367C2B0R9/OKcYgVKAypYXxj1sQAAAAAAAACgoWMmC9CIrM1br3FvTFBZoFynH3qiXR6sLiW3TpbkUFKrxDodFwAAAAAAAAAaIkIWoBFpl5qpWwdfqYpgQAMPOCaqY5mZMmbmSjAQkteGK5LT5ZSvrTeq4wIAAAAAAABAY8FyYUADtmLzal006UZl5a6LtJnaK1cdd6HcruhmpBUlFSrYUGiDFvMYAAAAAAAAAFAdIQvQgN3x9iP66Kf5unPGI3U+tilsn5CeYIvcu+OY9AYAAAAAAAAA2yNkARqwe864UScf0F93nT4m6mMFy4PKW+NXKBiKtJllwhLSEuRwOKI+PgAAAAAAAAA0Nnw9HWggTJ2Vpz95SYmxCbr46HNtW+eM9po06uGoj23qr2xZnadAaUAOp0O+NtRdAQAAAAAAAIDfQ8gCNBCzls7V/3v/acW6YzXkoOPUJqVVnY1tZqqYWSsFGwuVmJ5QZ+MCAAAAAAAAQGNGyAI0EKcdcqKGHnayTuzeX5m+llGfuVLqL5XT5VRscqxti0mMUVqnVJYGAwAAAAAAAIA9RE0WoJ58tfp7XfvfOxUIBuy2CTeePP8+jeh5StSDDhOw+Nfmy78uX6HAthosBCwAAAAAAAAAsOeYyQLUg5LyUo2a/BflFuXpkDbddPmAC+p0/DhvnIpyihXnjZXDRbACAAAAAAAAADVByALUg/iYON1x6nWau+wLndPztKiPZ2arlPhLlZAWb2ermOL26V3SmLkCAAAAAAAAAPvAETbFGZq5/Px8+Xw++f1+eb3e+j4dNGLBYFDz5s1Tdna2MjMzNWDAALlcLhWVFdui9uf0PFWHtj2gTs8pHApr0y+bFaoIydfOp3hfXJ2ODwAAAAAAAABNNTdgJgtQS6ZPn67rx9ygNVmrI23tOrTXYxMf1WL3r5q0YJo+X/GVZl4zSS6nq87Oy8xaiU+Jt3VYXJ66GxcAAAAAAAAAmjpCFqCWApYRI0Yobr8UtbjwILlbJCiwqVg5n2fb9udffEGHtz1QYwdfUScBS1lhmdyx7kioktQiUUkZiTZwAQAAAAAAAADUDpYLY7kw1MISYZ26dFZObKFSh+9frc6Juby2TF+mjPJk/frLcrnd0c81izYXqWBDoWKTYpTSIYW6KwAAAAAAAAAQpdzAubcHBlCdqcFilghL6pu5Q6BhthP7Zmr1qix9+umndXI+MUmxkkNyxbikZh+hAgAAAAAAAED0sFwYsI9MkXvDLBG2M56MhGr9olHYvqK0QjEJMVvHi3Orxf4Z1F8BAAAAAAAAgChjJguwj5LTtk4VMzVYdqZi89b2zMzMWh87WBHU5uU52rIqT4HyYKSdgAUAAAAAAAAAoo+QBdgHpubKM8tek8sXq4IF2XZ7+/1Fn2erfccOGjBgQK2P73Q77c0UtA8FtoUsAAAAAAAAAIDoY7kwYB+Ymis3Dbpcvy78ST/8e4Etcm9qsJglwswMFhOwlP6Sp0en/VMuV+3MLgmUBWy9FTO2uaW089mQxekiMwUAAAAAAACAusSnssBe+vDHz/TZ8kWR7eO6HaWv/zVX06ZNU3pZkja/uFTZExfa+4zyZNs+fPjwWhm7aHORNv+So+LckmpLgxGwAAAAAAAAAEDdYyYLsBemf/Werpt6l9r4WunDG15WclyibXe73DZIOfPMMzVv3jxb5N7UYDFLhNXWDBbDzFgxKkoqau2YAAAAAAAAAICaIWQB9sKQg49T5/T2GnTQALl3Ep445FD7hE5K8WYoOSHJbu8LU9MlFAzL5d46UyU+Nd4uFRaTGLNPxwUAAAAAAAAA7DtCFmA3/CUFeve7j3V+n6F2OyEmXu9f96LiY+J26Pvd7B/11vhZ8m8oiLT5WiVr6LjBOmTgAXs9dqA8KP8av4lalNY5LVKDJTYpdh9/KgAAAAAAAABAbaCQA7ALxeUlGvT4hbp5+v2a/cO8SPuuApYXx0yrFrAY/o0Ftt3s31sOx9Yi94GyoL0HAAAAAAAAADQshCzALphZK6cfepJdHiwtIWWX/ULBkJ3BovBOdv7W9tYD79t+v6dqH1PQPqW9Txld0+WJ89TshwAAAAAAAAAARA0hC1DFnJ8/V07hlsj2zQMvs8uD9ep46C6fs2JR1g4zWKoJS/71+bbfLruEwyrOLdamZZurFbU3S4OZGiwAAAAAAAAAgIaHkAX4zeMfv6A/vnCD/u+thyJtcZ7YnS4PVlXBpsI9Ov7v9SsrLFc4GFbxlpI9PGMAAAAAAAAAQH0iZAF+c0K3fopxedTa20LBUHCPn5fcIqlG/czsFXMzTEF7bxuvklsny5uZvJdnDgAAAAAAAACoD+56GRVoAPJLC/Xzhl/Vu+NhdvvQtgfo05tfUxtfy706TudeHeRrlWyL3O+0LotD8rXy2n6VghVB5Wfnyx3rVnKrraGKy+1UYnrCPv5UAAAAAAAAAIC6wkwWNEsrNq/WSY/+QaMm36QN+Zsj7XsbsBhOl1NDxw3euuHYbudv20NvHWT7VTJ1V8oKylWUU6xgYFuxewAAAAAAAABA40HIgmapfWqmWiSlKSXeq82Fuft8vEMGHqALJ46Qr2X1pb7MDBbTbvZXLg1mxHnjlNQiUeld0uwMFgAAAAAAAABA4+MIV/3kt5nKz8+Xz+eT3++X1+ut79NBlHy1+nv1aHeQrX9irM1br9QEnxJi4mttjFAwpBWLsrTwi0XqfVQvu0SYmcFSml9qZ62kdUyVw7n9dBcAAAAAAAAAQGPMDfgKPZqFO95+RGc8eYle/vLNSFvblNa1GrAYJaUluu+puzXyuhH2vrSsVOFQWPnZBaoorlBRTlGtjgcAAAAAAAAAqD+ELGgW2qa0svert2RHbYylS5fqyD5Haup/p+qyiy6392b7hx9/kK+tV4kZCUpMT4za+AAAAAAAAACAukXIgiapoLRI2f6Nke0/9x+pGVc9r1sHXxmV8SZPnqw+ffrYIvYzp7ynu2+5W+9OmalAecC2/3f6f5XcKpmlwgAAAAAAAACgCSFkQZOzOOs7nfzYBbrmv3cqFArZNpfTpR7tD4rKeIsWLdLo0aN1zJHH6N2X31W3rt1se/eu3e12/z79NWrUKNsPAAAAAAAAANB0ELKgyclIStOWYr+y8zZoff6mqI/Xs2dP9T2qr3LzchUfV73GS3x8gm3v17ef7QcAAAAAAAAAaDrc9X0CQG0wS4Nl+lraxx3S2ujfox/RoW26KzE2IepjV5RU6JYxYzV85FmaO3+uju9/fGTfnPlztOjrRZo1a5YcDpYKAwAAAAAAAICmhJAFjVpZoFx3zZioqYve1axrJ2u/lp1se9/OR9TJ+MHyoHJXbFHfg/vaIvcPPjFBaalpkf0P/mOCncUycODAOjkfAAAAAAAAAEDdIWRBoxbj8mj1lmyVBco0Z9kXkZClrrhiXEpIT1AoGNJdd92lU087VYPPHVStD7NYAAAAAAAAAKBpcoTD4bCaufz8fPl8Pvn9fnm93vo+HfyOwrIixbpj5XFtzQjX+TdqxeYs9e/aO+pjh0NhFeUUKSE1QU731pJG5hIyIYq5X7Jkib2vZNp79OhByAIAAAAAAAAATTA3YCYLGpX5vy7Sja/eq5G9h+qGky62bW18Le2tLvjX+lWaX6ZAaUAp7VNsW2WAYu6POKJulikDAAAAAAAAANS/rV/FBxqJ9f5NWpO3XtOXvGfrsdS1xIxEO4MlzhtX52MDAAAAAAAAABoWZrKgwSupKFW8Z2uocVaPwSouL9GwHoMU646J+tiBsoCCFUHFJsXabU+8Ry32z5DDyfJfAAAAAAAAANDcMZMFDVZRWbH+780HdcaTl0RmrZgluf541FlKik2M+vjlxeXavDxHeWv8NmipRMACAAAAAAAAADAIWdBgmWDlne8+0o/rl2vOz5/X+fhm1oo71m3vAQAAAAAAAADYHsuFoUEJBANyu7a+LNMSU/TIiNsV4/LomP36RH3scDissoIyxSbH2hkz5pbWMVUO19bHAAAAAAAAAABUxUwWNBjzf12k4x4ZqS9Xfh1pO7H70XUWsORl5SlvtV8leaWRdlPknoAFAAAAAAAAALAzhCxoMKYtflerctfokQ//WedjmyAlJjFGMnlKOFzn4wMAAAAAAAAAGp96DVk++eQTnXHGGWrTpo39kPuNN97YYXbBHXfcoczMTMXHx+vkk0/WsmXLqvXJzc3VBRdcIK/Xq5SUFF1yySUqLCys458ENWX+P65052k36IpjL9CzFzxQJ2ObYvZVC9onpCcoY78MJaQl1Mn4AAAAAAAAAIDGrV5DlqKiIh1++OF64okndrp/woQJevzxx/X000/riy++UGJiogYPHqzS0m3LOZmA5fvvv9fs2bM1Y8YMG9xcdtlldfhToCaKy0t0+1sP669vPRxp88Un66+nXKvkuMSoj19WWKbNv+Qob40/EvSYoM8d44r62AAAAAAAAACApsERrjqVoB6ZD7hff/11DRs2zG6b0zIzXP7yl7/opptusm1+v1+tWrXSpEmTNHLkSP3www866KCD9OWXX6p37962z3vvvadTTz1Va9assc/fE/n5+fL5fPb4ZkYM6qb+yrnPXW3/f58zZoq6tuhYp+MHygPK+SVHrli3LW5vaq8AAAAAAAAAALA3uUGD/WR5xYoVWr9+vV0irJL5gY466igtWLDAbpt7s0RYZcBimP5Op9POfNmVsrIy+x+o6g116+guvTTmpEv00uhH6yxgMcFKJXeMW2md05TeJY2ABQAAAAAAAABQIw3202UTsBhm5kpVZrtyn7lv2bJltf1ut1tpaWmRPjszfvx4G9hU3tq3bx+VnwHbfL7iK53z3FXKL91WL+cvJ1+q47odFfWxw6Gw8lbn2eXBAmXbghZPvMfOpAEAAAAAAAAAoEmFLNE0btw4O8Wn8rZ69er6PqUmLRAM6Jbp47Xg18V67KPn6/4EHFIoGJbCUnlxRd2PDwAAAAAAAABoktxqoFq3bm3vN2zYoMzMzEi72e7Ro0ekz8aNG6s9LxAIKDc3N/L8nYmNjbU31A23y62Hzr5NUxe9o+tPvLhOxgwFQ3I4HXamirn52noVCoTs7BUAAAAAAAAAAJr0TJbOnTvboOTDDz+MtJnaKabWSr9+/ey2uc/Ly9OiRYsifT766COFQiFbuwX1o6S8VHfNeFTvfT830nZkpx566Oz/kzcuKerjlxWWafPyHBVtLoq0uTwuAhYAAAAAAAAAQNOZyVJYWKhffvmlWrH7JUuW2JoqHTp00A033KD77rtP+++/vw1dbr/9drVp00bDhg2z/Q888EANGTJEl156qZ5++mlVVFTommuu0ciRI20/1I/nF0zVPz+boje/fl/H7n+kEmLi63R8M2MlVBFSqb9UiemJdkYLAAAAAAAAAABNKmRZuHChTjjhhMj2jTfeaO9HjRqlSZMm6ZZbblFRUZEuu+wyO2PlmGOO0Xvvvae4uLjIc/7zn//YYOWkk06S0+nU2Wefrccff7xefh5sdcnR52n+8kW6pP95dRawmOL2lWFKnC/ObselxBOwAAAAAAAAAACixhEOh8Nq5swyZD6fT36/X16vt75Pp9H5cuXXeve7j3XHadfb+id1yYQpBRsKVV5crvTOaYQqAAAAAAAAAIA6yw0abOF7NA4bC3I08l/XqixQrl4dD9Xph55U5yFLqb9EoWDY1mKJ826b5QQAAAAAAAAAQDQRsmCftExO19XHXaQ1edkasN+RdTKmmXxVOWPG6XbK29YnsxWbHFsn4wMAAAAAAAAAYBCyYKeCwaDmzZun7OxsZWZmasCAAXK5XCqpKNWjHz6v0f1GKNPX0vYdc9IldbZMWEVJhfzr8uXNTFZMQoxtiyNcAQAAAAAAAADUA0IW7GD69Om6fswNWpO1OtLWrkN7PTbxUX1YvkRvfv2+flj/iyaPetiGK3VZh6Uot1iB0oCtw2JqsAAAAAAAAAAAUF+c9TYyGmzAMmLECOXEFqrFhQcp88be9t5sm/YDizPVMa2tRvUdUedF7g1v62TFp8YrtX1KnY8NAAAAAAAAAEBVjrApcNHM5efny+fzye/3y+v1qjkvEdapS2cbqKQO379aiGJeJlumL1NGebKW/fyzYmOiv0SXGbNoc7FCwZANVwAAAAAAAAAAaEi5ATNZEGFqsJglwpL6Zu4wS8VsJ/bN1OpVWVowf0Gd1V8p3Fio4pxi+xgAAAAAAAAAgIaEmiyIMEXuDXeLhJ3u92QkVOsXbaawfWJ6glyxbrnjeKkCAAAAAAAAABoWZrIgIjMz094HNhXvdH/F5uJq/WpboDygvDV+uzxYpeTWyUpIja+X+i8AAAAAAAAAAOwOIQsiBgwYoHYd2qvw82xbD2WH+iifZ6t9xw62X20zx8/L8qvUX6qCDYW1fnwAAAAAAAAAAGobIQsiXC6XHpv4qEp/ybNF7svWFihUFrT3Ztu0P/rIRNuvtpmZKt7MZMUkepT427JkAAAAAAAAAAA0ZIQsqGb48OGaNm2a0suStPnFpcqeuNDeZ5Qn23azv7ZmrhRvKVFZQVmkLSYxRqkdU+WOof4KAAAAAAAAAKDhc4S3XxeqGcrPz5fP55Pf75fX663v02kQgsGg5s2bZ4vcmxosZomw2pzBYgKW/HX5crqdyuiabu8BAAAAAAAAAGhMuQFTBrBTJlA5/vjjo3b8eF+cinOLFeeNk8NFUXsAAAAAAAAAQONDyII6EQqEVOIvVWL61norDqdD6V3SbC0WAAAAAAAAAAAaI0IWRF04FNbm5Tk2aDHLgplZLAYBCwAAAAAAAACgMaMQBqLOzFqJT42XO9Yld0zt1XUBAAAAAAAAAKA+MZMFUVFWUCZ3nFsuz9ZQJalFopIyEm3gAgAAAAAAAABAU0DIgp0KBUNasShLBZsKldwiSZ17dZDTtWcTnwo3FalwY6Fik2KU0iHFLgtmlwYjXwEAAAAAAAAANCGELNjBd7N/1FvjZ8m/oSDS5muVrKHjBuuQgQf87vNjk2NVuKlQrhheXgAAAAAAAACApouaLNghYHlxzLRqAYvh31hg283+7YVCYZUXl0e2PXFutdg/Q97MZIrbAwAAAAAAAACaLEIWVFsizMxgUXgnO39re+uB922/SsGKoHKW52jLqjwFy4OR9spaLAAAAAAAAAAANFWELIgwNVi2n8FSTVjyr8+3/So53U57MwXtg4Ft4QsAAAAAAAAAAE0dRTMQYYrc74mSvBKFw+FIQfuUdj4bsjhdZHYAAAAAAAAAgOaDT8URkdwiaYe2jaXZ1bb7nNVD6W1SVZxbUm1pMAIWAAAAAAAAAEBzwyfjiOjcq4N8rZIlh1QeKte766Zr0q9Paua611URKrftTtfW2SuB0or6Pl0AAAAAAAAAAOoVIQsizGyUoeMGa3PpRv1n1bP6peQHXXbR5VpWslQvrXrWtnc7pqtSO6XK19ZX36cLAAAAAAAAAEC9ImRBNYvWfaFX1v5TKa19mjX1fd19y916b+ospbT22vbF2f9TbGJMfZ8mAAAAAAAAAAD1jpAFEYsWLdLo0aM1oO8AvTf1PXXr2s22d+/aXe/99z0dc9QxGjVqlO0HAAAAAAAAAEBzR8iCiJ49e6rvUX2Vm5er+Lj4avvi4xNse7++/Ww/AAAAAAAAAACaO0IWRJiC9nfdfZcWLlmoufPnVts3Z/4cLfp6kd1v+gEAAAAAAAAA0Nw5wuFwWM1cfn6+fD6f/H6/vF6vmjPzcji639EqLynX+L8+EGm/9d6xikuM02fzPyNkAQAAAAAAAAA0aXuaG7jr9KzQaGazDBkyRIPPHVRt36xZswhYAAAAAAAAAAD4DSELdjBo0CAtXrzYzmqpZMKVHj161Ot5AQAAAAAAAADQkBCyYAcmUDniiCPq+zQAAAAAAAAAAGjQKHwPAAAAAAAAAABQA4QsAAAAAAAAAAAANUDIAgAAAAAAAAAAUAOELAAAAAAAAAAAADVAyAIAAAAAAAAAAFADhCwAAAAAAAAAAAA1QMgCAAAAAAAAAABQA4QsAAAAAAAAAAAANUDIAgAAAAAAAAAAUAOELAAAAAAAAAAAADVAyAIAAAAAAAAAAFADhCwAAAAAAAAAAAA1QMgCAAAAAAAAAABQA4QsAAAAAAAAAAAANUDIAgAAAAAAAAAAUAOELAAAAAAAAAAAADVAyAIAAAAAAAAAAFADhCwAAAAAAAAAAAA1QMgCAAAAAAAAAABQA4QsAAAAAAAAAAAANUDIAgAAAAAAAAAAUAPumjypqQmHw/Y+Pz+/vk8FAAAAAAAAAADUs8q8oDI/2BVCFkkFBQX2vn379vV9KgAAAAAAAAAAoAHlBz6fb5f7HeHfi2GagVAopHXr1ik5OVkOh6O+TweIegJrAsXVq1fL6/XW9+kA9YZrAdiG6wHYhusB2IprAdiG6wHYhusBzUk4HLYBS5s2beR07rryCjNZTGEap1Pt2rWr79MA6pR5I+TNEOBaAKriegC24XoAtuJaALbhegC24XpAc+HbzQyWShS+BwAAAAAAAAAAqAFCFgAAAAAAAAAAgBogZAGamdjYWN155532HmjOuBaAbbgegG24HoCtuBaAbbgegG24HoAdUfgeAAAAAAAAAACgBpjJAgAAAAAAAAAAUAOELAAAAAAAAAAAADVAyAIAAAAAAAAAAFADhCwAAAAAAAAAAAA1QMgCNDLjx49Xnz59lJycrJYtW2rYsGH66aefqvU5/vjj5XA4qt2uuOKKan2ysrJ02mmnKSEhwR7n5ptvViAQqNZnzpw56tmzp2JjY7Xffvtp0qRJdfIzAnvqrrvu2uG1fsABB0T2l5aW6uqrr1Z6erqSkpJ09tlna8OGDdWOwbWApqJTp047XA/mZq4Bg/cGNFWffPKJzjjjDLVp08a+rt94441q+8PhsO644w5lZmYqPj5eJ598spYtW1atT25uri644AJ5vV6lpKTokksuUWFhYbU+33zzjQYMGKC4uDi1b99eEyZM2OFcXn31Vfs+ZPoceuihevfdd6P0UwN7fz1UVFRo7Nix9rWZmJho+1x00UVat27d776fPPDAA9X6cD2gKbw/jB49eofX+pAhQ6r14f0BzeFa2NnfEOb24IMPRvrw3gDsHiEL0MjMnTvXfmD2+eefa/bs2faPpUGDBqmoqKhav0svvVTZ2dmRW9U3t2AwaD9EKy8v1/z58zV58mT7IZn5AKLSihUrbJ8TTjhBS5Ys0Q033KA///nPmjVrVp3+vMDvOfjgg6u91j/99NPIvjFjxujtt9+2v8iZa8d8iDB8+PDIfq4FNCVffvlltWvBvEcY55xzTqQP7w1oiszvQIcffrieeOKJne43r/PHH39cTz/9tL744gv74fLgwYNtEF/JfID2/fff2+tmxowZ9sOIyy67LLI/Pz/f/r7VsWNHLVq0yH7oYIL+Z599NtLHXDfnn3++/QDuq6++sl+EMbfvvvsuyv8FgD27HoqLi7V48WLdfvvt9n769On2y1pDhw7doe8999xT7f3i2muvjezjekBTeX8wTKhS9bX+yiuvVNvP+wOaw7VQ9Rowt+eff96GKOZLilXx3gDsRhhAo7Zx48awuZTnzp0baTvuuOPC119//S6f8+6774adTmd4/fr1kbannnoq7PV6w2VlZXb7lltuCR988MHVnnfeeeeFBw8eHJWfA6iJO++8M3z44YfvdF9eXl7Y4/GEX3311UjbDz/8YK+XBQsW2G2uBTRl5n2ga9eu4VAoZLd5b0BzYP6Nf/311yPb5vXfunXr8IMPPljt/SE2Njb8yiuv2O2lS5fa53355ZeRPjNnzgw7HI7w2rVr7faTTz4ZTk1NjVwLxtixY8Pdu3ePbJ977rnh0047rdr5HHXUUeHLL788Sj8tsHfXw87873//s/1WrVoVaevYsWN44sSJu3wO1wOayvUwatSo8JlnnrnL5/D+gOb63mCuixNPPLFaG+8NwO4xkwVo5Px+v71PS0ur1v6f//xHGRkZOuSQQzRu3Dj7zbVKCxYssNMyW7VqFWkz3+g03zww39Kp7GOW06jK9DHtQENilnwx0567dOliv2lmljsyzLdnzEyvqq9jMy25Q4cOkdcx1wKaKjMb5aWXXtLFF19sv4VWifcGNDdm9tX69eurvW59Pp+OOuqoau8FZgmY3r17R/qY/k6n0858qexz7LHHKiYmptpr38wC2LJlS6QP1wca498S5n3CXANVmSVgzHKrRxxxhP02ctWlI7ke0JSYZVDNEqndu3fXlVdeqZycnMg+3h/QHJnltd955x0722R7vDcAu+bezT4ADVwoFLJLtfTv399+YFbpD3/4g52iaT54NmtimrWXzRubWRLAMB82VP0QzajcNvt218d82FZSUmLXNAfqm/mQzCxnZP4oMtOV7777brsGrJlubF7D5he87T80MK/j33udV+7bXR+uBTRkZp3lvLw8u9Z4Jd4b0BxVvnZ39rqt+ro2H7BV5Xa77RdYqvbp3LnzDseo3JeamrrL66PyGEBDY5bMM+8FZukWU2+i0nXXXWdrb5lrwCztYkJ583vWI488YvdzPaCpMEuFmaWEzet5+fLluu2223TKKafYD3xdLhfvD2iWzJLBpgZw1WW2Dd4bgN0jZAEaMVObxXyYXLUGhVF1jVjzrWRT6PWkk06yvzh27dq1Hs4UiA7zR1Clww47zIYu5kPkqVOn8mEvmrV//etf9vowgUol3hsAAJXMbN9zzz3XLB+up556qtq+G2+8sdrvV+ZLK5dffrnGjx+v2NjYejhbIDpGjhxZ7Xcj83o3vxOZ2S3mdySgOTL1WMwKEaYwfVW8NwC7x3JhQCN1zTXX2MJ7H3/8sdq1a7fbvuaDZ+OXX36x961bt7ZTQKuq3Db7dtfHfMuND6/RUJlZK926dbOvdfMaNksmmW/zb/86/r3XeeW+3fXhWkBDtWrVKn3wwQe2IP3u8N6A5qDytbuz123V1/XGjRur7TfLX+Tm5tbK+0XlfqChBSzm/cIU8646i2VX7xfmmli5cqXd5npAU2WWHzbLqlb93Yj3BzQn8+bNszPdf+/vCIP3BqA6QhagkTHfNjMBy+uvv66PPvpoh+mYO7NkyRJ7b761bPTr10/ffvtttV8YK//AOuiggyJ9Pvzww2rHMX1MO9BQFRYW2m/lm9d6r1695PF4qr2OzS+MpmZL5euYawFN0QsvvGCXtjjttNN224/3BjQH5vck84d71detWd7OrKVf9b3ABPKmllcl8zuWWZa1Mow0fT755BP74XTV175ZrtIsf1HZh+sDjSVgMTXtTCBv1tb/Peb9wtSgqFw2iesBTdWaNWtsTZaqvxvx/oDmNhve/B19+OGH/25f3huA7YQBNCpXXnll2OfzhefMmRPOzs6O3IqLi+3+X375JXzPPfeEFy5cGF6xYkX4zTffDHfp0iV87LHHRo4RCATChxxySHjQoEHhJUuWhN97771wixYtwuPGjYv0+fXXX8MJCQnhm2++OfzDDz+En3jiibDL5bJ9gYbiL3/5i70WzGv9s88+C5988snhjIyM8MaNG+3+K664ItyhQ4fwRx99ZK+Jfv362VslrgU0NcFg0L7mx44dW62d9wY0ZQUFBeGvvvrK3syfN4888oh9vGrVKrv/gQceCKekpNjX/TfffBM+88wzw507dw6XlJREjjFkyJDwEUccEf7iiy/Cn376aXj//fcPn3/++ZH9eXl54VatWoUvvPDC8HfffReeMmWKvRaeeeaZSB/zPuR2u8MPPfSQvT7uvPPOsMfjCX/77bd1/F8Ezdnurofy8vLw0KFDw+3atbP/zlf9W6KsrMw+f/78+eGJEyfa/cuXLw+/9NJL9r3goosuiozB9YCmcD2YfTfddFN4wYIF9nejDz74INyzZ0/7739paWnkGLw/oDn8rmT4/X772n3qqad2eD7vDcDvI2QBGhnzhriz2wsvvGD3Z2Vl2Q/N0tLSwrGxseH99tvPfhhm3jCrWrlyZfiUU04Jx8fH2w+lzYfVFRUV1fp8/PHH4R49eoRjYmLsh3GVYwANxXnnnRfOzMy0r9G2bdvabfNhciXzAdpVV10VTk1Ntb/gnXXWWfaDhKq4FtCUzJo1y74n/PTTT9XaeW9AU2Zekzv73WjUqFF2fygUCt9+++32D3/z+j/ppJN2uEZycnLsh2ZJSUlhr9cb/tOf/mQ/kKjq66+/Dh9zzDH2GOY9x4Q325s6dWq4W7du9vo4+OCDw++8806Uf3pgz68H80Hyrv6WMM8zFi1aFD7qqKPsl7ri4uLCBx54YPj++++v9qGzwfWAxn49mC8pmi+WmA+KzYe8HTt2DF966aXh9evXVzsG7w9oDr8rGSYMMX8DmLBke7w3AL/PYf5n+9ktAAAAAAAAAAAA2D1qsgAAAAAAAAAAANQAIQsAAAAAAAAAAEANELIAAAAAAAAAAADUACELAAAAAAAAAABADRCyAAAAAAAAAAAA1AAhCwAAAAAAAAAAQA0QsgAAAAAAAAAAANQAIQsAAAAAAAAAAEANELIAAAAAaPQcDofeeOON+j4NAAAAAM0MIQsAAACABmv06NE2QDE3j8ejVq1aaeDAgXr++ecVCoUi/bKzs3XKKafs0TEJZAAAAADUFkIWAAAAAA3akCFDbIiycuVKzZw5UyeccIKuv/56nX766QoEArZP69atFRsbW9+nCgAAAKCZIWQBAAAA0KCZ8MSEKG3btlXPnj1122236c0337SBy6RJk3aYnVJeXq5rrrlGmZmZiouLU8eOHTV+/Hi7r1OnTvb+rLPOss+p3F6+fLnOPPNMO1MmKSlJffr00QcffFDtPEzf+++/XxdffLGSk5PVoUMHPfvss9X6rFmzRueff77S0tKUmJio3r1764svvojsN+dtfgZzXl26dNHdd98dCYoAAAAAND6ELAAAAAAanRNPPFGHH364pk+fvsO+xx9/XG+99ZamTp2qn376Sf/5z38iYcqXX35p71944QU7O6Zyu7CwUKeeeqo+/PBDffXVV3b2zBlnnKGsrKxqx3744YdtcGL6XHXVVbryyivtGJXHOO6447R27Vo7/tdff61bbrklsqzZvHnzdNFFF9lZOEuXLtUzzzxjQ6K//e1vUf/vBQAAACA63FE6LgAAAABE1QEHHKBvvvlmh3YTjOy///465phj7GwVM5OlUosWLex9SkqKnR1TyQQ25lbp3nvv1euvv27DEjMrppIJYky4YowdO1YTJ07Uxx9/rO7du+vll1/Wpk2bbHBjZrIY++23X+S5ZtbKrbfeqlGjRtltM5PFjGOCmDvvvLOW/+sAAAAAqAuELAAAAAAapXA4bEOU7Y0ePVoDBw60wYeZkWJqtwwaNGi3xzKzUO666y698847doaLWcKrpKRkh5kshx12WOSxGdsENRs3brTbS5Ys0RFHHBEJWLZnZrZ89tln1WauBINBlZaWqri4WAkJCXv93wAAAABA/SJkAQAAANAo/fDDD+rcufMO7abmyYoVK2zNFlNX5dxzz9XJJ5+sadOm7fJYN910k2bPnq2HHnrIzj6Jj4/XiBEjbH2XqjweT7VtE7RULgdmnvN7QY6ZzTJ8+PAd9pkaLQAAAAAaH0IWAAAAAI3ORx99pG+//VZjxozZ6X6v16vzzjvP3kxYYma05Obm2lkmJigxM0iqMjNMzAyYs846KxKIrFy5cq/Oycxy+ec//xkZZ2fhj6nfUnUJMQAAAACNGyELAAAAgAatrKxM69evt8HIhg0b9N5772n8+PF2GTBTSH57jzzyiDIzM+3SXU6nU6+++qpd1svUYTE6depkC9z3799fsbGxSk1NtTVcpk+fbovdm9kpt99+e2SGyp46//zzdf/992vYsGH2/Mw5fPXVV2rTpo369eunO+64w55zhw4dbPBjzs0sIfbdd9/pvvvuq7X/XgAAAADqjrMOxwIAAACAvWZCFRNYmHDEzEgxheYff/xxvfnmm3K5XDv0T05O1oQJE9S7d2/16dPHzkh59913bahhPPzww3ZpsPbt29sgpjKYMWHL0UcfbYOWwYMH25kneyMmJkbvv/++WrZsqVNPPVWHHnqoHnjggcg5mmPOmDHD9jHn1bdvX02cOFEdO3aslf9OAAAAAOqeI2yqRQIAAAAAAAAAAGCvMJMFAAAAAAAAAACgBghZAAAAAAAAAAAAaoCQBQAAAAAAAAAAoAYIWQAAAAAAAAAAAGqAkAUAAAAAAAAAAKAGCFkAAAAAAAAAAABqgJAFAAAAAAAAAACgBghZAAAAAAAAAAAAaoCQBQAAAAAAAAAAoAYIWQAAAAAAAAAAAGqAkAUAAAAAAAAAAEB77/8DjIyckDej8kgAAAAASUVORK5CYII="
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 75
},
{
"cell_type": "markdown",
"id": "c0940c3f7cb262ba",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 5: Exporting model\n",
"\n",
"Models could be exported and used for other purposes. It is vital for the semivariogram regularization. Those calculations are computationally intensive, and in production, it is not a good idea to regularize semivariogram every time when you run the Poisson Kriging pipeline. Moreover, theoretical model parameters should be traced and stored for reproducibility and explainability purposes.\n",
"\n",
"Model can be exported to dict with `.to_dict()` method or to json with `.to_json()` methods."
]
},
{
"cell_type": "code",
"id": "4454453fe5c0a42f",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.273212Z",
"start_time": "2025-12-21T14:55:37.270601Z"
}
},
"source": [
"dict_representation = fitted.to_dict()"
],
"outputs": [],
"execution_count": 76
},
{
"cell_type": "code",
"id": "fb31a058a0e8ab92",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.364416Z",
"start_time": "2025-12-21T14:55:37.357111Z"
}
},
"source": [
"dict_representation"
],
"outputs": [
{
"data": {
"text/plain": [
"{'experimental_variogram': None,\n",
" 'nugget': 24.46175798313624,\n",
" 'sill': 596.0578687869111,\n",
" 'rang': 14980.760125261822,\n",
" 'variogram_model_type': 'spherical',\n",
" 'direction': None,\n",
" 'spatial_dependence': None,\n",
" 'spatial_index': None,\n",
" 'yhat': None,\n",
" 'errors': None}"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 77
},
{
"cell_type": "code",
"id": "edfbc47e92931e84",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.475448Z",
"start_time": "2025-12-21T14:55:37.471249Z"
}
},
"source": [
"# Save to json\n",
"\n",
"fitted.to_json('fitted.json')"
],
"outputs": [],
"execution_count": 78
},
{
"cell_type": "markdown",
"id": "b23dfb8388b13453",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Chapter 6: Importing fitted model\n",
"\n",
"We can import a semivariogram model into a `TheoreticalVariogram` class instance without passing the experimental semivariogram or actual data points. It is helpful for some applications focused on kriging, where we are sure that our semivariogram model fits data well.\n",
"\n",
"We can import a model with two methods `.from_dict()` if we have our model parameters in the Python dictionary or `.from_json()` if model parameters are stored in a flat file:"
]
},
{
"cell_type": "code",
"id": "c1ddee5c22a5c4ec",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.574490Z",
"start_time": "2025-12-21T14:55:37.570833Z"
}
},
"source": [
"model_from_dict = TheoreticalVariogram()\n",
"model_from_dict.from_dict(dict_representation)"
],
"outputs": [],
"execution_count": 79
},
{
"cell_type": "code",
"id": "fe273279bbaf018b",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.710668Z",
"start_time": "2025-12-21T14:55:37.706947Z"
}
},
"source": [
"print(model_from_dict)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Spherical model\n",
"* Nugget: 24.46175798313624\n",
"* Sill: 596.0578687869111\n",
"* Range: 14980.760125261822\n",
"* Spatial Dependency Strength is strong\n",
"* Mean Bias: 0.0\n",
"* Mean RMSE: 0.0\n",
"* Error-lag weighting method: None\n",
"\n",
"\n"
]
}
],
"execution_count": 80
},
{
"cell_type": "code",
"id": "1298349556d65748",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.833154Z",
"start_time": "2025-12-21T14:55:37.827834Z"
}
},
"source": [
"model_from_json = TheoreticalVariogram()\n",
"model_from_json.from_json('fitted.json')"
],
"outputs": [],
"execution_count": 81
},
{
"cell_type": "code",
"id": "7866ab5a91073505",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"ExecuteTime": {
"end_time": "2025-12-21T14:55:37.950580Z",
"start_time": "2025-12-21T14:55:37.944810Z"
}
},
"source": [
"print(model_from_json)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Selected model: Spherical model\n",
"* Nugget: 24.46175798313624\n",
"* Sill: 596.0578687869111\n",
"* Range: 14980.760125261822\n",
"* Spatial Dependency Strength is strong\n",
"* Mean Bias: 0.0\n",
"* Mean RMSE: 0.0\n",
"* Error-lag weighting method: None\n",
"\n",
"\n"
]
}
],
"execution_count": 82
},
{
"cell_type": "markdown",
"id": "d96c4117fb67ac03",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"That's all, well done!"
]
},
{
"cell_type": "markdown",
"id": "83c0cf0ec2281f7d",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"-----"
]
},
{
"cell_type": "markdown",
"id": "9c2e7314cc497794",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Changelog\n",
"\n",
"| Date | Changes | Author |\n",
"|------------|--------------------------------------------------------------------------------------------------------------|----------------------------------|\n",
"| 2025-11-07 | Used **values** and **geometries** parameters instead of **ds** in the experimental variogram initialization | @SimonMolinsky (Szymon Moliński) |\n",
"| 2025-10-11 | Updated **sill** descriptions | @SimonMolinsky (Szymon Moliński) |\n",
"| 2025-09-16 | Removed `dir_neighbors_selection_method` parameter | @SimonMolinsky (Szymon Moliński) |\n",
"| 2025-02-15 | Tutorial has been adapted to the 1.0 release | @SimonMolinsky (Szymon Moliński) |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}