diff --git a/.gitignore b/.gitignore deleted file mode 100644 index ebb0850..0000000 --- a/.gitignore +++ /dev/null @@ -1,151 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# static files generated from Django application using `collectstatic` -media -static - -# pycharm -.idea/ - -# vscode -.vscode/ - -debug/ \ No newline at end of file diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index dff55b5..0000000 --- a/LICENSE.md +++ /dev/null @@ -1,16 +0,0 @@ -Copyright 2020 Scale AI, Inc. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. - - -You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. diff --git a/README.md b/README.md deleted file mode 100644 index 548bea9..0000000 --- a/README.md +++ /dev/null @@ -1,319 +0,0 @@ -# pandaset-devkit - -![Header Animation](../assets/animations/semseg-photo-labels.gif) - - -## Overview - -Welcome to the repository of the [PandaSet](https://pandaset.org/ "Pandaset Official Website") Devkit. - -## Dataset -### Download - -To download the dataset, please visit the official [PandaSet](https://pandaset.org/ "Pandaset Official Website") webpage and sign up through the form. -You will then be forwarded to a page with download links to the raw data and annotations. - -### Unpack - -Unpack the archive into any directory on your hard disk. The path will be referenced in usage of `pandaset-devkit` later, and does not have to be in the same directory as your scripts. - -### Structure - -#### Files & Folders - -```text -. -├── LICENSE.txt -├── annotations -│   ├── cuboids -│   │   ├── 00.pkl.gz -│   │   . -│   │   . -│   │   . -│   │   └── 79.pkl.gz -│  └── semseg // Semantic Segmentation is available for specific scenes -│   ├── 00.pkl.gz -│   . -│   . -│   . -│   ├── 79.pkl.gz -│   └── classes.json -├── camera -│   ├── back_camera -│   │   ├── 00.jpg -│   │   . -│   │   . -│   │   . -│   │   ├── 79.jpg -│   │   ├── intrinsics.json -│   │   ├── poses.json -│   │   └── timestamps.json -│   ├── front_camera -│   │   └── ... -│   ├── front_left_camera -│   │   └── ... -│   ├── front_right_camera -│   │   └── ... -│   ├── left_camera -│   │   └── ... -│   └── right_camera -│   └── ... -├── lidar -│   ├── 00.pkl.gz -│   . -│   . -│   . -│   ├── 79.pkl.gz -│   ├── poses.json -│   └── timestamps.json -└── meta - ├── gps.json - └── timestamps.json -``` - -## Instructions - -### Setup - -1. Create a Python>=3.6 environment with `pip` installed. -2. Clone the repository `git clone git@github.com:scaleapi/pandaset-devkit.git` -3. `cd` into `pandaset-devkit/python` -4. Execute `pip install .` - -The `pandaset-devkit` is now installed in your Python>=3.6 environment and can be used. - -### Usage - -To get familiar with the API you can point directly to the downloaded dataset. - -#### Initialization -First, we need to create a `DataSet` object that searches for sequences. -``` ->>> from pandaset import DataSet ->>> dataset = DataSet('/data/pandaset') -``` -Afterwards we can list all the sequence IDs that have been found in the data folder. -``` ->>> print(dataset.sequences()) -['002',...] -``` - -Since semantic segmentation annotations are not always available for scenes, we can filter to get only scenes that have both semantic segmentation as well as cuboid annotations. -``` ->>> print(dataset.sequences(with_semseg=True)) -['002',...] -``` - -Now, we access a specific sequence by choosing its key from the previously returned list, in this case sequence ID `'002'` -``` ->>> seq002 = dataset['002'] -``` - - - -API Reference: [DataSet class](https://scaleapi.github.io/pandaset-devkit/dataset.html#pandaset.dataset.DataSet) - -#### Loading -The devkit will automatically search the sequence directory for available sensor data, metadata and annotations and prepare the directory to be loaded explicitly. At this point no point clouds or images have been loaded into memory. -To execute the loading of sensor data and metadata into memory, we simply call the `load()` method on the sequence object. This will load all available sensor data and metadata. -``` ->>> seq002.load() -``` - -If only certain data is required for analysis, there are more specific methods available, which can also be chained to each other. -``` ->>> seq002.load_lidar().load_cuboids() -``` - -API Reference: [Sequence class](https://scaleapi.github.io/pandaset-devkit/sequence.html#pandaset.sequence.Sequence) - -#### Data Access - -##### LiDAR -The LiDAR point clouds are stored as [pandas.DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) and therefore you are able to leverage their extensive API for data manipulation. This includes the simple return as a [numpy.ndarray](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html). -``` ->>> pc0 = seq002.lidar[0] ->>> print(pc0) - x y z i t d -index -0 -75.131138 -79.331690 3.511804 7.0 1.557540e+09 0 -1 -112.588306 -118.666002 1.423499 31.0 1.557540e+09 0 -2 -42.085902 -44.384891 0.593491 7.0 1.557540e+09 0 -3 -27.329435 -28.795053 -0.403781 0.0 1.557540e+09 0 -4 -6.196208 -6.621082 1.130009 3.0 1.557540e+09 0 - ... ... ... ... ... .. -166763 27.670526 17.159726 3.778677 25.0 1.557540e+09 1 -166764 27.703935 17.114063 3.780626 27.0 1.557540e+09 1 -166765 27.560664 16.955518 3.767948 18.0 1.557540e+09 1 -166766 27.384433 16.783824 3.752670 22.0 1.557540e+09 1 -166767 27.228821 16.626038 3.739154 20.0 1.557540e+09 1 -[166768 rows x 6 columns] -``` -``` ->>> pc0_np = seq002.lidar[0].values # Returns the first LiDAR frame in the sequence as an numpy ndarray ->>> print(pc0_np) -[[-7.51311379e+01 -7.93316897e+01 3.51180427e+00 7.00000000e+00 - 1.55753996e+09 0.00000000e+00] - [-1.12588306e+02 -1.18666002e+02 1.42349938e+00 3.10000000e+01 - 1.55753996e+09 0.00000000e+00] - [-4.20859017e+01 -4.43848908e+01 5.93490847e-01 7.00000000e+00 - 1.55753996e+09 0.00000000e+00] - ... - [ 2.75606640e+01 1.69555183e+01 3.76794770e+00 1.80000000e+01 - 1.55753996e+09 1.00000000e+00] - [ 2.73844334e+01 1.67838237e+01 3.75266969e+00 2.20000000e+01 - 1.55753996e+09 1.00000000e+00] - [ 2.72288210e+01 1.66260378e+01 3.73915448e+00 2.00000000e+01 - 1.55753996e+09 1.00000000e+00]] -``` - -The LiDAR points are stored in a world coordinate system; therefore it is not required to transform them using the vehicle's pose graph. This allows you to query all LiDAR frames in the sequence or a certain sampling rate and simply visualize them using your preferred library. - -Instead of using always all of the point clouds available, it is also possible to simply slice the `lidar` property as one is used from python lists. -``` ->>> pc_all = seq002.lidar[:] # Returns all LiDAR frames from the sequence -``` -``` ->>> pc_sampled = seq002.lidar[::2] # Returns every second LiDAR frame from the sequence -``` - -In addition to the LiDAR points, the `lidar` property also holds the sensor pose (`lidar.poses`) in world coordinate system and timestamp (`lidar.timestamps`) for every LiDAR frame recorded. Both objects can be sliced in the same way as the `lidar` property holding the point clouds. -``` ->>> sl = slice(None, None, 5) # Equivalent to [::5] # Extract every fifth frame including sensor pose and timestamps ->>> lidar_obj = seq002.lidar ->>> pcs = lidar_obj[sl] ->>> poses = lidar_obj.poses[sl] ->>> timestamps = lidar_obj.timestamps[sl] ->>> print( len(pcs) == len(poses) == len(timestamps) ) -True -``` - -The LiDAR point clouds include by default the points from both the mechanical 360° LiDAR and the front-facing LiDAR. To select only one of the sensors, the `set_sensor` method is available. -``` ->>> pc0 = s002.lidar[0] ->>> print(pc0.shape) -(166768, 6) ->>> s002.lidar.set_sensor(0) # set to include only mechanical 360° LiDAR ->>> pc0_sensor0 = s002.lidar[0] ->>> print(pc0_sensor0.shape) -(106169, 6) ->>> s002.lidar.set_sensor(1) # set to include only front-facing LiDAR ->>> pc0_sensor1 = s002.lidar[0] ->>> print(pc0_sensor1.shape) -(60599, 6) -``` -Since the applied filter operation leaves the original row index intact for each point (relevant for joining with `SemanticSegmentation`), one can easily test that no point was left out in filtering: -``` ->>> import pandas as pd ->>> pc0_concat = pd.concat([pc0_sensor0, pc0_sensor1]) ->>> print(pc0_concat.shape) -(166768, 6) ->>> print(pc0 == pc0_concat) - x y z i t d -index -0 True True True True True True -1 True True True True True True -2 True True True True True True -3 True True True True True True -4 True True True True True True - ... ... ... ... ... ... -166763 True True True True True True -166764 True True True True True True -166765 True True True True True True -166766 True True True True True True -166767 True True True True True True -[166768 rows x 6 columns] ->>> print((~(pc0 == pc0_concat)).sum()) # Counts the number of cells with `False` value, i.e., the ones where original point cloud and concatenated filtered point cloud differentiate -x 0 -y 0 -z 0 -i 0 -t 0 -d 0 -dtype: int64 -``` - -API Reference: [Lidar class](https://scaleapi.github.io/pandaset-devkit/sensors.html#pandaset.sensors.Lidar) - -##### Cameras -Since the recording vehicle was equipped with multiple cameras, first we need to list which cameras have been used to record the sequence. -``` ->>> print(seq002.camera.keys()) -['front_camera', 'left_camera', 'back_camera', 'right_camera', 'front_left_camera', 'front_right_camera'] -``` -The camera count and names should be equal for all sequences. - -Each camera name has its recordings loaded as [Pillow Image](https://pillow.readthedocs.io/en/stable/reference/Image.html) object, and can be accessed via normal list slicing. In the following example, we select the first image from the front camera and display it using the Pillow library in Python. -``` ->>> front_camera = seq002.camera['front_camera'] ->>> img0 = front_camera[0] ->>> img0.show() -``` -Afterwards the extensive Pillow Image API can be used for image manipulation, conversion or export. - -Similar to the `Lidar` object, each `Camera` object has properties that hold the camera pose (`camera.poses`) and timestamp (`camera.timestamps`) for every recorded frame, as well as the camera intrinsics (`camera.intrinsics`). -Again, the objects can be sliced the same way as the `Camera` object: - -``` ->>> sl = slice(None, None, 5) # Equivalent to [::5] ->>> camera_obj = seq002.camera['front_camera'] ->>> pcs = camera_obj[sl] ->>> poses = camera_obj.poses[sl] ->>> timestamps = camera_obj.timestamps[sl] ->>> intrinsics = camera_obj.intrinsics -``` - -API Reference: [Camera class](https://scaleapi.github.io/pandaset-devkit/sensors.html#pandaset.sensors.Camera) - -#### Meta -In addition to the sensor data, the loaded dataset also contains the following meta information: -* GPS Positions -* Timestamps - -These can be directly accessed through the known list slicing operations, and read in their dict format. The following example shows how to get the GPS coordinates of the vehicle on the first frame. -``` ->>> pose0 = seq002.gps[0] ->>> print(pose0['lat']) -37.776089291519924 ->>> print(pose0['long']) --122.39931707791749 -``` - -API Reference: [GPS class](https://scaleapi.github.io/pandaset-devkit/meta.html#pandaset.meta.GPS) - -API Reference: [Timestamps class](https://scaleapi.github.io/pandaset-devkit/meta.html#pandaset.meta.Timestamps) - -#### Annotations - -##### Cuboids -The LiDAR Cuboid annotations are also stored inside the sequence object as a [pandas.DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) for each timestamp. -The position coordinates (`position.x`,`position.y`,`position.z`) are located at the center of a cuboid. `dimensions.x` is the width of the cuboid from left to right, `dimensions.y` is the length of the cuboid from front to back and `dimensions.z` is the height of the cuboid from top to bottom. - -``` ->>> cuboids0 = seq002.cuboids[0] # Returns the cuboid annotations for the first LiDAR frame in the sequence ->>> print(cuboids0.columns) -Index(['uuid', 'label', 'yaw', 'stationary', 'camera_used', 'position.x', - 'position.y', 'position.z', 'dimensions.x', 'dimensions.y', - 'dimensions.z', 'attributes.object_motion', 'cuboids.sibling_id', - 'cuboids.sensor_id', 'attributes.rider_status', - 'attributes.pedestrian_behavior', 'attributes.pedestrian_age'], - dtype='object') -``` - -API Reference: [Cuboids class](https://scaleapi.github.io/pandaset-devkit/annotations.html#pandaset.annotations.Cuboids) - -##### Semantic Segmentation -Analogous to the cuboid annotations, the Semantic Segmentation can be accessed using the `semseg` property on the sequence object. The index of each Semantic Segmentation data frame corresponds to the index of each LiDAR point cloud data frame, and can be joined using the index. -``` ->>> semseg0 = seq002.semseg[0] # Returns the semantic segmentation for the first LiDAR frame in the sequence ->>> print(semseg0.columns) -Index(['class'], dtype='object') ->>> print(seq002.semseg.classes) -{'1': 'Smoke', '2': 'Exhaust', '3': 'Spray or rain', '4': 'Reflection', '5': 'Vegetation', '6': 'Ground', '7': 'Road', '8': 'Lane Line Marking', '9': 'Stop Line Marking', '10': 'Other Road Marking', '11': 'Sidewalk', '12': 'Driveway', '13': 'Car', '14': 'Pickup Truck', '15': 'Medium-sized Truck', '16': 'Semi-truck', '17': 'Towed Object', '18': 'Motorcycle', '19': 'Other Vehicle - Construction Vehicle', '20': 'Other Vehicle - Uncommon', '21': 'Other Vehicle - Pedicab', '22': 'Emergency Vehicle', '23': 'Bus', '24': 'Personal Mobility Device', '25': 'Motorized Scooter', '26': 'Bicycle', '27': 'Train', '28': 'Trolley', '29': 'Tram / Subway', '30': 'Pedestrian', '31': 'Pedestrian with Object', '32': 'Animals - Bird', '33': 'Animals - Other', '34': 'Pylons', '35': 'Road Barriers', '36': 'Signs', '37': 'Cones', '38': 'Construction Signs', '39': 'Temporary Construction Barriers', '40': 'Rolling Containers', '41': 'Building', '42': 'Other Static Object'} -``` - -API Reference: [SemanticSegmentation class](https://scaleapi.github.io/pandaset-devkit/annotations.html#pandaset.annotations.SemanticSegmentation) - - - -![Header Animation](../assets/static/montage-semseg-projection.jpg) \ No newline at end of file diff --git a/animations/semseg-photo-labels.gif b/animations/semseg-photo-labels.gif new file mode 100644 index 0000000..333bcf4 Binary files /dev/null and b/animations/semseg-photo-labels.gif differ diff --git a/docs/annotation_instructions_cuboids.pdf b/docs/annotation_instructions_cuboids.pdf deleted file mode 100644 index b2d0bc0..0000000 Binary files a/docs/annotation_instructions_cuboids.pdf and /dev/null differ diff --git a/docs/annotation_instructions_semseg.pdf b/docs/annotation_instructions_semseg.pdf deleted file mode 100644 index b89ecf2..0000000 Binary files a/docs/annotation_instructions_semseg.pdf and /dev/null differ diff --git a/docs/static_extrinsic_calibration.yaml b/docs/static_extrinsic_calibration.yaml deleted file mode 100644 index 9b32bf9..0000000 --- a/docs/static_extrinsic_calibration.yaml +++ /dev/null @@ -1,65 +0,0 @@ -back_camera: - extrinsic: - transform: - rotation: {w: 0.713789231075861, x: 0.7003585531940812, y: -0.001595758695393934, - z: -0.0005330311533742299} - translation: {x: -0.0004217634029916384, y: -0.21683144949675118, z: -1.0553445472201475} - intrinsic: - D: [-0.1619, 0.0113, -0.00028815, -7.9827e-05, 0.0067] - K: [933.4667, 0, 896.4692, 0, 934.6754, 507.3557, 0, 0, 1] -front_camera: - extrinsic: - transform: - rotation: {w: 0.016213200031258722, x: 0.0030578899383849464, y: 0.7114721800418571, - z: -0.7025205466606356} - translation: {x: 0.0002585796504896516, y: -0.03907777167811011, z: -0.0440125762408362} - intrinsic: - D: [-0.5894, 0.66, 0.0011, -0.001, -1.0088] - K: [1970.0131, 0, 970.0002, 0, 1970.0091, 483.2988, 0, 0, 1] -front_gt: - extrinsic: - transform: - rotation: {w: 0.021475754959146356, x: -0.002060907279494794, y: 0.01134678181520767, - z: 0.9997028534282365} - translation: {x: -0.000451117754, y: -0.605646431446, z: -0.301525235176} -front_left_camera: - extrinsic: - transform: - rotation: {w: 0.33540022607039827, x: 0.3277491469609924, y: -0.6283486651480494, - z: 0.6206973014480826} - translation: {x: -0.25842240863267835, y: -0.3070654284505582, z: -0.9244245686318884} - intrinsic: - D: [-0.165, 0.0099, -0.00075376, 5.3699e-05, 0.01] - K: [929.8429, 0, 972.1794, 0, 930.0592, 508.0057, 0, 0, 1] -front_right_camera: - extrinsic: - transform: - rotation: {w: 0.3537633879725252, x: 0.34931795852655334, y: 0.6120314641083645, - z: -0.6150170047424814} - translation: {x: 0.2546935700219631, y: -0.24929449717803095, z: -0.8686597280810242} - intrinsic: - D: [-0.1614, -0.0027, -0.00029662, -0.00028927, 0.0181] - K: [930.0407, 0, 965.0525, 0, 930.0324, 463.4161, 0, 0, 1] -left_camera: - extrinsic: - transform: - rotation: {w: 0.5050391917998245, x: 0.49253073152800625, y: -0.4989265501075421, - z: 0.503409565706149} - translation: {x: 0.23864835336611942, y: -0.2801448284013492, z: -0.5376795959387791} - intrinsic: - D: [-0.1582, -0.0266, -0.00015221, 0.00059011, 0.0449] - K: [930.4514, 0, 991.6883, 0, 930.0891, 541.6057, 0, 0, 1] -main_pandar64: - extrinsic: - transform: - rotation: {w: 1.0, x: 0.0, y: 0.0, z: 0.0} - translation: {x: 0.0, y: 0.0, z: 0.0} -right_camera: - extrinsic: - transform: - rotation: {w: 0.5087448402081216, x: 0.4947520981649951, y: 0.4977829953071897, - z: -0.49860920419297333} - translation: {x: -0.23097163411257893, y: -0.30843497058841024, z: -0.6850441215571058} - intrinsic: - D: [-0.1648, 0.0191, 0.0027, -8.5282e-07, -9.6983e-05] - K: [922.5465, 0, 945.057, 0, 922.4229, 517.575, 0, 0, 1] diff --git a/python/pandaset/__init__.py b/python/pandaset/__init__.py deleted file mode 100644 index 92c5130..0000000 --- a/python/pandaset/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env python3 -from .dataset import DataSet -from .geometry import projection diff --git a/python/pandaset/annotations.py b/python/pandaset/annotations.py deleted file mode 100644 index 181ba17..0000000 --- a/python/pandaset/annotations.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -import glob -import json -import os -from abc import ABCMeta, abstractmethod -from typing import overload, List, TypeVar, Dict - -import pandas as pd - -T = TypeVar('T') - - -class Annotation: - """Meta class inherited by subclasses for more specific annotation types. - - ``Annotation`` provides generic preparation and loading methods for PandaSet folder structures. Subclasses - for specific annotation styles must implement certain methods, as well as can override existing ones for extension. - - Args: - directory: Absolute or relative path where annotation files are stored - - Attributes: - data: List of annotation data objects. The type of list elements depends on the subclass implementation of protected method ``_load_data_file`` - """ - __metaclass__ = ABCMeta - - @property - @abstractmethod - def _data_file_extension(self) -> str: - ... - - @property - def data(self) -> List[T]: - """Returns annotation data array. - - Subclasses can use any type inside array. - """ - return self._data - - def __init__(self, directory: str) -> None: - self._directory: str = directory - self._data_structure: List[str] = None - self._data: List[T] = None - self._load_structure() - - @overload - def __getitem__(self, item: int) -> T: - ... - - @overload - def __getitem__(self, item: slice) -> List[T]: - ... - - def __getitem__(self, item): - return self.data[item] - - def _load_structure(self) -> None: - self._load_data_structure() - - def _load_data_structure(self) -> None: - self._data_structure = sorted( - glob.glob(f'{self._directory}/*.{self._data_file_extension}')) - - def load(self) -> None: - """Loads all annotation files from disk into memory. - - All annotation files are loaded into memory in filename order. - """ - self._load_data() - - def _load_data(self) -> None: - self._data = [] - for fp in self._data_structure: - self._data.append(self._load_data_file(fp)) - - @abstractmethod - def _load_data_file(self, fp: str) -> None: - ... - - -class Cuboids(Annotation): - """Loads and provides Cuboid annotations. Subclass of ``Annotation``. - - ``Cuboids`` loads files in `{sequence_id}/annotations/annotations/cuboids/` containing cuboid annotations. - - Args: - directory: Absolute or relative path where annotation files are stored - - Attributes: - data: List of cuboids for each frame of scene. - """ - - @property - def _data_file_extension(self) -> str: - return 'pkl.gz' - - @property - def data(self) -> List[pd.DataFrame]: - """Returns annotation data array. - - Returns: - List of cuboid data frames. Each data frame has columns as follows: - - index: `int` - - Each row corresponds to one cuboid. The index order is arbitrary. - - `uuid`: `str - - Unique identifier for an object. If object is tracked within the sequence, the `uuid` stays the same on every frame. - - `label`: `str` - - Contains name of object class associated with drawn cuboid. - - `yaw`: `str` - - Rotation of cuboid around the z-axis. Given in _radians_ from which the cuboid is rotated along the z-axis. 0 radians is equivalent to the direction of the vector `(0, 1, 0)`. The vector points at the length-side. Rotation happens counter-clockwise, i.e., PI/2 is pointing in the same direction as the vector `(-1, 0, 0)`. - - `stationary`: `bool` - - `True` if object is stationary in the whole scene, e.g., a parked car or traffic light. Otherwise `False`. - - `camera_used`: `int` - - Reference to the camera which was used to validate cuboid position in projection. If no camera was explicitly used, value is set to `-1`. - - `position.x`: `float` - - Position of the cuboid expressed as the center of the cuboid. Value is in world-coordinate system. - - `position.y`: `float` - - Position of the cuboid expressed as the center of the cuboid. Value is in world-coordinate system. - - `position.z`: `float` - - Position of the cuboid expressed as the center of the cuboid. Value is in world-coordinate system. - - `dimensions.x`: `float` - - The dimensions of the cuboid based on the world dimensions. Width of the cuboid from left to right. - - `dimensions.y`: `float` - - The dimensions of the cuboid based on the world dimensions. Length of the cuboid from front to back. - - `dimensions.z`: `float` - - The dimensions of the cuboid based on the world dimensions. Height of the cuboid from top to bottom. - - `attributes.object_motion`: `str` - - Values are `Parked`, `Stopped` or `Moving`. - - Set for cuboids with `label` values in - - _Car_ - - _Pickup Truck_ - - _Medium-sized Truck_ - - _Semi-truck_ - - _Towed Object_ - - _Motorcycle_ - - _Other Vehicle - Construction Vehicle_ - - _Other Vehicle - Uncommon_ - - _Other Vehicle - Pedicab_ - - _Emergency Vehicle_ - - _Bus_ - - _Personal Mobility Device_ - - _Motorized Scooter_ - - _Bicycle_ - - _Train_ - - _Trolley_ - - _Tram / Subway_ - - `attributes.rider_status`: `str` - - Values are `With Rider` or `Without Rider`. - - Set for cuboids with `label` values in - - _Motorcycle_ - - _Personal Mobility Device_ - - _Motorized Scooter_ - - _Bicycle_ - - _Animals - Other_ - - `attributes.pedestrian_behavior`: `str` - - Value are `Sitting`, `Lying`, `Walking` or `Standing` - - Set for cuboids with `label` values in - - _Pedestrian_ - - _Pedestrian with Object_ - - `attributes.pedestrian_age`: `str` - - Value are `Adult` or `Child` (less than ~18 years old) - - Set for cuboids with `label` values in - - _Pedestrian_ - - _Pedestrian with Object_ - - `cuboids.sensor_id`: `int` - - For the overlap area between mechanical 360° LiDAR and front-facing LiDAR, moving objects received two cuboids to compensate for synchronization differences of both sensors. If cuboid is in this overlapping area and moving, this value is either `0` (mechanical 360° LiDAR) or `1` (front-facing LiDAR). All other cuboids have value `-1`. - - `cuboids.sibling_id`: `str` - - For cuboids which have `cuboids.sensor_id` set to `0` or `1`: this field stores the `uuid` of the sibling cuboid, i.e., measuring the same object in the overlap region, but with the other respective sensor. - - """ - return self._data - - def __init__(self, directory: str) -> None: - Annotation.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> pd.DataFrame: - ... - - @overload - def __getitem__(self, item: slice) -> List[pd.DataFrame]: - ... - - def __getitem__(self, item): - return super().__getitem__(item) - - def _load_data_file(self, fp: str) -> None: - return pd.read_pickle(fp) - - -class SemanticSegmentation(Annotation): - """Loads and provides Semantic Segmentation annotations. Subclass of ``Annotation``. - - ``SemanticSegmentation`` loads files in `{sequence_id}/annotations/annotations/semseg/` containing semantic segmentation annotations for point clouds and class name mapping. - - Args: - directory: Absolute or relative path where annotation files are stored - - Attributes: - data: List of points and their class ID for each frame. - classes: Dict containing class ID to class name mapping. - """ - - @property - def _data_file_extension(self) -> str: - return 'pkl.gz' - - @property - def data(self) -> List[pd.DataFrame]: - """Returns annotation data array. - - Returns: - List of semantic segmentation data frames. Each data frame has columns as follows: - - index: `int` - - Index order corresponds to the order of point cloud in ``lidar`` property. - - `class`: `str` - - Class ID as a number in string format. Can be used to find class name from ``classes`` property. - """ - return self._data - - @property - def classes(self) -> Dict[str, str]: - """Returns class id to class name mapping. - - Returns: - Dictionary with class ID as key and class name as value. Valid for the complete scene. - """ - return self._classes - - def __init__(self, directory: str) -> None: - self._classes_structure: str = None - self._classes: Dict[str, str] = None - Annotation.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> pd.DataFrame: - ... - - @overload - def __getitem__(self, item: slice) -> List[pd.DataFrame]: - ... - - def __getitem__(self, item): - return super().__getitem__(item) - - def load(self) -> None: - super().load() - self._load_classes() - - def _load_structure(self) -> None: - super()._load_structure() - self._load_classes_structure() - - def _load_classes_structure(self) -> None: - classes_file = f'{self._directory}/classes.json' - if os.path.isfile(classes_file): - self._classes_structure = classes_file - - def _load_data_file(self, fp: str) -> None: - return pd.read_pickle(fp) - - def _load_classes(self) -> None: - with open(self._classes_structure, 'r') as f: - file_data = json.load(f) - self._classes = file_data - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/dataset.py b/python/pandaset/dataset.py deleted file mode 100644 index ce2a1be..0000000 --- a/python/pandaset/dataset.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 -from typing import overload, List, Dict - -from .sequence import Sequence -from .utils import subdirectories - - -class DataSet: - """Top-level class to load PandaSet - - ``DataSet`` prepares and loads ``Sequence`` objects for every sequence found in provided directory. - Access to a specific sequence is provided by using the sequence name as a key on the ``DataSet`` object. - - Args: - directory: Absolute or relative path where PandaSet has been extracted to. - - Examples: - >>> pandaset = DataSet('/data/pandaset') - >>> s = pandaset['002'] - """ - - def __init__(self, directory: str) -> None: - self._directory: str = directory - self._sequences: Dict[str, Sequence] = None - self._load_sequences() - - def __getitem__(self, item) -> Sequence: - return self._sequences[item] - - def _load_sequences(self) -> None: - self._sequences = {} - sequence_directories = subdirectories(self._directory) - for sd in sequence_directories: - seq_id = sd.split('/')[-1].split('\\')[-1] - self._sequences[seq_id] = Sequence(sd) - - def sequences(self, with_semseg: bool = False) -> List[str]: - """ Lists all available sequence names - - Args: - with_semseg: Set `True` if only sequences with semantic segmentation annotations should be returned. Set `False` to return all sequences (with or without semantic segmentation). - - Returns: - List of sequence names. - - Examples: - >>> pandaset = DataSet('/data/pandaset') - >>> print(pandaset.sequences()) - ['002','004','080'] - - - """ - if with_semseg: - return [s for s in list(self._sequences.keys()) if self._sequences[s].semseg] - else: - return list(self._sequences.keys()) - - def unload(self, sequence: str): - """ Removes all sequence file data from memory if previously loaded from disk. - - This is useful if you intend to iterate over all sequences and perform some - operation. If you do not unload the sequences, it quickly leads to sigkill. - - Args: - sequence: The sequence name - - Returns: - None - - Examples: - >>> pandaset = DataSet('...') - >>> for sequence in pandaset.sequences(): - >>> seq = pandaset[sequence] - >>> seq.load() - >>> # do operations on sequence here... - >>> # when finished, unload the sequence from memory - >>> pandaset.unload(sequence) - - """ - if sequence in self._sequences: - del self._sequences[sequence] - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/geometry.py b/python/pandaset/geometry.py deleted file mode 100644 index 7cac324..0000000 --- a/python/pandaset/geometry.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np -import transforms3d as t3d - -from .sensors import Lidar -from .sensors import Camera - - -def _heading_position_to_mat(heading, position): - quat = np.array([heading["w"], heading["x"], heading["y"], heading["z"]]) - pos = np.array([position["x"], position["y"], position["z"]]) - transform_matrix = t3d.affines.compose(np.array(pos), - t3d.quaternions.quat2mat(quat), - [1.0, 1.0, 1.0]) - return transform_matrix - - -def projection(lidar_points, camera_data, camera_pose, camera_intrinsics, filter_outliers=True): - camera_heading = camera_pose['heading'] - camera_position = camera_pose['position'] - camera_pose_mat = _heading_position_to_mat(camera_heading, camera_position) - - trans_lidar_to_camera = np.linalg.inv(camera_pose_mat) - points3d_lidar = lidar_points - points3d_camera = trans_lidar_to_camera[:3, :3] @ (points3d_lidar.T) + \ - trans_lidar_to_camera[:3, 3].reshape(3, 1) - - K = np.eye(3, dtype=np.float64) - K[0, 0] = camera_intrinsics.fx - K[1, 1] = camera_intrinsics.fy - K[0, 2] = camera_intrinsics.cx - K[1, 2] = camera_intrinsics.cy - - inliner_indices_arr = np.arange(points3d_camera.shape[1]) - if filter_outliers: - condition = points3d_camera[2, :] > 0.0 - points3d_camera = points3d_camera[:, condition] - inliner_indices_arr = inliner_indices_arr[condition] - - points2d_camera = K @ points3d_camera - points2d_camera = (points2d_camera[:2, :] / points2d_camera[2, :]).T - - if filter_outliers: - image_w, image_h = camera_data.size - condition = np.logical_and( - (points2d_camera[:, 1] < image_h) & (points2d_camera[:, 1] > 0), - (points2d_camera[:, 0] < image_w) & (points2d_camera[:, 0] > 0)) - points2d_camera = points2d_camera[condition] - points3d_camera = (points3d_camera.T)[condition] - inliner_indices_arr = inliner_indices_arr[condition] - return points2d_camera, points3d_camera, inliner_indices_arr - - -def lidar_points_to_ego(points, lidar_pose): - lidar_pose_mat = _heading_position_to_mat( - lidar_pose['heading'], lidar_pose['position']) - transform_matrix = np.linalg.inv(lidar_pose_mat) - return (transform_matrix[:3, :3] @ points.T + transform_matrix[:3, [3]]).T - - -def center_box_to_corners(box): - pos_x, pos_y, pos_z, dim_x, dim_y, dim_z, yaw = box - half_dim_x, half_dim_y, half_dim_z = dim_x/2.0, dim_y/2.0, dim_z/2.0 - corners = np.array([[half_dim_x, half_dim_y, -half_dim_z], - [half_dim_x, -half_dim_y, -half_dim_z], - [-half_dim_x, -half_dim_y, -half_dim_z], - [-half_dim_x, half_dim_y, -half_dim_z], - [half_dim_x, half_dim_y, half_dim_z], - [half_dim_x, -half_dim_y, half_dim_z], - [-half_dim_x, -half_dim_y, half_dim_z], - [-half_dim_x, half_dim_y, half_dim_z]]) - transform_matrix = np.array([ - [np.cos(yaw), -np.sin(yaw), 0, pos_x], - [np.sin(yaw), np.cos(yaw), 0, pos_y], - [0, 0, 1.0, pos_z], - [0, 0, 0, 1.0], - ]) - corners = (transform_matrix[:3, :3] @ corners.T + transform_matrix[:3, [3]]).T - return corners - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/meta.py b/python/pandaset/meta.py deleted file mode 100644 index fbac25b..0000000 --- a/python/pandaset/meta.py +++ /dev/null @@ -1,165 +0,0 @@ -#!/usr/bin/env python3 -import json -import os.path -from abc import ABCMeta, abstractmethod -from typing import TypeVar, List, overload, Dict - -T = TypeVar('T') - - -class Meta: - """Meta class inherited by subclasses for more specific meta data types. - - ``Meta`` provides generic preparation and loading methods for PandaSet folder structures. Subclasses - for specific meta data types must implement certain methods, as well as can override existing ones for extension. - - Args: - directory: Absolute or relative path where annotation files are stored - - Attributes: - data: List of meta data objects. The type of list elements depends on the subclass specific meta data type. - """ - __metaclass__ = ABCMeta - - @property - @abstractmethod - def _filename(self) -> str: - ... - - @property - def data(self) -> List[T]: - """Returns meta data array. - - Subclasses can use any type inside array. - """ - return self._data - - def __init__(self, directory: str) -> None: - self._directory: str = directory - self._data_structure: str = None - self._data: List[T] = None - self._load_data_structure() - - @overload - def __getitem__(self, item: int) -> T: - ... - - @overload - def __getitem__(self, item: slice) -> List[T]: - ... - - def __getitem__(self, item): - return self._data[item] - - def load(self) -> None: - """Loads all meta data files from disk into memory. - - All meta data files are loaded into memory in filename order. - """ - self._load_data() - - def _load_data_structure(self) -> None: - meta_file = f'{self._directory}/{self._filename}' - if os.path.isfile(meta_file): - self._data_structure = meta_file - - def _load_data(self) -> None: - self._data = [] - with open(self._data_structure, 'r') as f: - file_data = json.load(f) - for entry in file_data: - self._data.append(entry) - - -class GPS(Meta): - """GPS data for each timestamp in this sequence. - - ``GPS`` provides GPS data for each timestamp. GPS data can be retrieved by slicing an instanced ``GPS`` class. (see example) - - Args: - directory: Absolute or relative path where annotation files are stored - - Attributes: - data: List of meta data objects. The type of list elements depends on the subclass specific meta data type. - - Examples: - Assuming an instance `s` of class ``Sequence``, you can get GPS data for the first 5 frames in the sequence as follows: - >>> s.load_gps() - >>> gps_data_0_5 = s.gps[:5] - >>> print(gps_data_0_5) - [{'lat': 37.776089291519924, 'long': -122.39931707791749, 'height': 2.950900131607181, 'xvel': 0.0014639192106827986, 'yvel': 0.15895995994754034}, ...] - """ - @property - def _filename(self) -> str: - return 'gps.json' - - @property - def data(self) -> List[Dict[str, float]]: - """Returns GPS data array. - - For every timestamp in the sequence, the GPS data contains vehicle latitude, longitude, height and velocity. - - Returns: - List of dictionaries. Each dictionary has `str` keys and return types as follows: - - `lat`: `float` - - Latitude in decimal degree format. Positive value corresponds to North, negative value to South. - - `long`: `float` - - Longitude in decimal degree format. Positive value indicates East, negative value to West. - - `height`: `float` - - Measured height in meters. - - `xvel`: `float` - - Velocity in m/s - - `yvel`: `float` - - Velocity in m/s - - """ - return self._data - - def __init__(self, directory: str) -> None: - Meta.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> Dict[str, T]: - ... - - @overload - def __getitem__(self, item: slice) -> List[Dict[str, T]]: - ... - - def __getitem__(self, item): - return self._data[item] - - -class Timestamps(Meta): - @property - def _filename(self) -> str: - return 'timestamps.json' - - @property - def data(self) -> List[float]: - """Returns timestamp array. - - For every frame in this sequence, this property stores the recorded timestamp. - - Returns: - List of timestamps as `float` - """ - return self._data - - def __init__(self, directory: str) -> None: - Meta.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> float: - ... - - @overload - def __getitem__(self, item: slice) -> List[float]: - ... - - def __getitem__(self, item): - return self._data[item] - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/sensors.py b/python/pandaset/sensors.py deleted file mode 100644 index a3dec3f..0000000 --- a/python/pandaset/sensors.py +++ /dev/null @@ -1,382 +0,0 @@ -#!/usr/bin/env python3 -import glob -import json -import os.path -from typing import List, overload, TypeVar, Dict -from abc import ABCMeta, abstractmethod - -import pandas as pd -from PIL import Image -from PIL.JpegImagePlugin import JpegImageFile -from pandas.core.frame import DataFrame - -T = TypeVar('T') - - -class Sensor: - """Meta class inherited by subclasses for more specific sensor types. - - ``Sensor`` provides generic preparation and loading methods for PandaSet folder structures. Subclasses - for specific sensor types must implement certain methods, as well as can override existing ones for extension. - - Args: - directory: Absolute or relative path where sensor files are stored - - Attributes: - data: List of sensor data objects. The type of list elements depends on the subclass implementation of protected method ``_load_data_file`` - poses: List of sensor poses in world-coordinates - timestamps: List of recording timestamps for sensor - """ - __metaclass__ = ABCMeta - - @property - @abstractmethod - def _data_file_extension(self) -> str: - ... - - @property - def data(self) -> List[T]: - """Returns sensor data array. - - Subclasses can use any type inside array. - """ - return self._data - - @property - def poses(self) -> List[T]: - """Returns sensor pose array. - - Subclasses can use any type inside array. - """ - return self._poses - - @property - def timestamps(self) -> List[T]: - """Returns sensor timestamp array. - - Subclasses can use any type inside array. - """ - return self._timestamps - - def __init__(self, directory: str) -> None: - self._directory: str = directory - self._data_structure: List[str] = None - self._data: List[T] = None - self._poses_structure: str = None - self._poses: List[Dict[str, T]] = None - self._timestamps_structure: str = None - self._timestamps: List[float] = None - self._load_structure() - - @overload - def __getitem__(self, item: int) -> T: - ... - - @overload - def __getitem__(self, item: slice) -> List[T]: - ... - - def __getitem__(self, item): - return self.data[item] - - def _load_structure(self) -> None: - self._load_data_structure() - self._load_poses_structure() - self._load_timestamps_structure() - - def _load_data_structure(self) -> None: - self._data_structure = sorted( - glob.glob(f'{self._directory}/*.{self._data_file_extension}')) - - def _load_poses_structure(self) -> None: - poses_file = f'{self._directory}/poses.json' - if os.path.isfile(poses_file): - self._poses_structure = poses_file - - def _load_timestamps_structure(self) -> None: - timestamps_file = f'{self._directory}/timestamps.json' - if os.path.isfile(timestamps_file): - self._timestamps_structure = timestamps_file - - def load(self) -> None: - """Loads all sensor files from disk into memory. - - All sensor and associated meta data files are loaded into memory in filename order. - """ - self._load_data() - self._load_poses() - self._load_timestamps() - - def _load_data(self) -> None: - self._data = [] - for fp in self._data_structure: - self._data.append(self._load_data_file(fp)) - - def _load_poses(self) -> None: - self._poses = [] - with open(self._poses_structure, 'r') as f: - file_data = json.load(f) - for entry in file_data: - self._poses.append(entry) - - def _load_timestamps(self) -> None: - self._timestamps = [] - with open(self._timestamps_structure, 'r') as f: - file_data = json.load(f) - for entry in file_data: - self._timestamps.append(entry) - - @abstractmethod - def _load_data_file(self, fp: str) -> None: - ... - - -class Lidar(Sensor): - @property - def _data_file_extension(self) -> str: - return 'pkl.gz' - - @property - def data(self) -> List[pd.DataFrame]: - """Returns (filtered) LiDAR point cloud array. - - Point cloud data is in a world-coordinate system, i.e., a static object which is a position `(10,10,0)` in frame 1, will be at position `(10,10,0)` in all other frames, too. - - Returns: - List of point cloud data frames for each timestamp. Each data frame has columns as follows: - - index: `int` - - Ordered point cloud. When joining the raw point cloud with data from ``SemanticSegmentation``, it is important to keep the index order. - - `x`: `float` - - Position of point in world-coordinate system (x-axis) in meter - - `y`: `float` - - Position of point in world-coordinate system (y-axis) in meter - - `z`: `float` - - Position of point in world-coordinate system (z-axis) in meter - - `i`: `float` - - Reflection intensity in a range `[0,255]` - - `t`: `float` - - Recorded timestamp for specific point - - `d`: `int` - - Sensor ID. `0` -> mechnical 360° LiDAR, `1` -> forward-facing LiDAR - """ - if self._sensor_id in [0, 1]: - return [df.loc[df['d'] == self._sensor_id] for df in self._data] - else: - return self._data - - @property - def poses(self) -> List[Dict[str, Dict[str, float]]]: - """Returns LiDAR sensor pose array. - - Returns: - A pose dictionary of the LiDAR sensor in world-coordinates for each frame. The dictionary keys return the following types: - - `position`: `dict` - - `x`: `float` - - Position of LiDAR sensor in world-coordinate system (x-axis) in meter - - `y`: `float` - - Position of LiDAR sensor in world-coordinate system (y-axis) in meter - - `z`: `float` - - Position of LiDAR sensor in world-coordinate system (z-axis) in meter - - `heading`: `dict` - - `w`: `float` - - Real part of _Quaternion_ - - `x`: `float` - - First imaginary part of _Quaternion_ - - `y`: `float` - - Second imaginary part of _Quaternion_ - - `z`: `float` - - Third imaginary part of _Quaternion_ - """ - return self._poses - - @property - def timestamps(self) -> List[float]: - """Returns LiDAR sensor recording timestamps array. - - Returns: - A list of timestamps in `float` format for each point cloud recorded in this sequence. To get point-wise timestamps, please refer to column `t` in `data` property return values. - """ - return self._timestamps - - def __init__(self, directory: str) -> None: - self._sensor_id = -1 - Sensor.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> DataFrame: - ... - - @overload - def __getitem__(self, item: slice) -> List[DataFrame]: - ... - - def __getitem__(self, item): - return super().__getitem__(item) - - def set_sensor(self, sensor_id: int) -> None: - """Specifies a sensor which should be returned exclusively in the data objects - - Args: - sensor_id: Set `-1` for both LiDAR sensors, set `0` for mechanical 360° LiDAR, set `1` for front-facing LiDAR. - - """ - self._sensor_id = sensor_id - - def _load_data_file(self, fp: str) -> DataFrame: - return pd.read_pickle(fp) - - -class Camera(Sensor): - @property - def _data_file_extension(self) -> str: - return 'jpg' - - @property - def data(self) -> List[JpegImageFile]: - """Returns Camera image array. - - Returns: - List of camera images for each timestamp. Camera images are loaded as [``JpegImageFile``](https://pillow.readthedocs.io/en/stable/reference/plugins.html#PIL.JpegImagePlugin.JpegImageFile). - """ - return self._data - - @property - def poses(self) -> List[Dict[str, Dict[str, float]]]: - """Returns Camera sensor pose array. - - Returns: - A pose dictionary of the Camera sensor in world-coordinates for each frame. The dictionary keys return the following types: - - `position`: `dict` - - `x`: `float` - - Position of LiDAR sensor in world-coordinate system (x-axis) in meter - - `y`: `float` - - Position of LiDAR sensor in world-coordinate system (y-axis) in meter - - `z`: `float` - - Position of LiDAR sensor in world-coordinate system (z-axis) in meter - - `heading`: `dict` - - `w`: `float` - - Real part of _Quaternion_ - - `x`: `float` - - First imaginary part of _Quaternion_ - - `y`: `float` - - Second imaginary part of _Quaternion_ - - `z`: `float` - - Third imaginary part of _Quaternion_ - """ - return self._poses - - @property - def timestamps(self) -> List[float]: - """Returns Camera sensor recording timestamps array. - - Returns: - A list of timestamps in `float` format for each camera image recorded in this sequence. To get point-wise timestamps, please refer to column `t` in `data` property return values. - """ - return self._timestamps - - @property - def intrinsics(self) -> 'Intrinsics': - """Camera specific intrinsic data. - - Returns: - Instance of class ``Intrinsics`` - """ - return self._intrinsics - - def __init__(self, directory: str) -> None: - self._intrinsics_structure: str = None - self._intrinsics: Intrinsics = None - Sensor.__init__(self, directory) - - @overload - def __getitem__(self, item: int) -> JpegImageFile: - ... - - @overload - def __getitem__(self, item: slice) -> List[JpegImageFile]: - ... - - def __getitem__(self, item): - return super().__getitem__(item) - - def load(self) -> None: - super().load() - self._load_intrinsics() - - def _load_structure(self) -> None: - super()._load_structure() - self._load_intrinsics_structure() - - def _load_intrinsics_structure(self) -> None: - intrinsics_file = f'{self._directory}/intrinsics.json' - if os.path.isfile(intrinsics_file): - self._intrinsics_structure = intrinsics_file - - def _load_data_file(self, fp: str) -> JpegImageFile: - # solve this bug: https://github.com/python-pillow/Pillow/issues/1237 - img = Image.open(fp) - image = img.copy() - img.close() - return image - - def _load_intrinsics(self) -> None: - with open(self._intrinsics_structure, 'r') as f: - file_data = json.load(f) - self._intrinsics = Intrinsics(fx=file_data['fx'], - fy=file_data['fy'], - cx=file_data['cx'], - cy=file_data['cy']) - - -class Intrinsics: - """Camera intrinsics - - Contains camera intrinsics with properties `fx`, `fy`, `cx`, `cy`, for easy usage with [OpenCV framework](https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html). - There is no `skew` factor in the camera recordings. - """ - - @property - def fx(self) -> float: - """Focal length x-axis - - Returns: - Focal length x-axis component - """ - return self._fx - - @property - def fy(self) -> float: - """Focal length y-axis - - Returns: - Focal length y-axis component - """ - return self._fy - - @property - def cx(self) -> float: - """Principal point x-axis - - Returns: - Principal point x-axis component - """ - return self._cx - - @property - def cy(self) -> float: - """Principal point y-axis - - Returns: - Principal point y-axis component - """ - return self._cy - - def __init__(self, fx: float, fy: float, cx: float, cy: float): - self._fx: float = fx - self._fy: float = fy - self._cx: float = cx - self._cy: float = cy - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/sequence.py b/python/pandaset/sequence.py deleted file mode 100644 index c29cc80..0000000 --- a/python/pandaset/sequence.py +++ /dev/null @@ -1,202 +0,0 @@ -#!/usr/bin/env python3 -from typing import Dict - -from .annotations import Cuboids -from .annotations import SemanticSegmentation -from .meta import GPS -from .meta import Timestamps -from .sensors import Camera -from .sensors import Lidar -from .utils import subdirectories - - -class Sequence: - """Provides all sensor and annotations for a single sequence. - - ``Sequence`` provides generic preparation and loading methods for a single PandaSet sequence folder structure. - - Args: - directory: Absolute or relative path where annotation files are stored - """ - - @property - def lidar(self) -> Lidar: - """ Stores ``Lidar`` object for sequence - - Returns: - Instance of ``Lidar`` class. - """ - return self._lidar - - @property - def camera(self) -> Dict[str, Camera]: - """ Stores all ``Camera`` objects for sequence. - - Access data by entering the key of a specific camera (see example). - - Returns: - Dictionary of all cameras available for sequence. - - Examples: - >>> print(s.camera.keys()) - dict_keys(['front_camera', 'left_camera', 'back_camera', 'right_camera', 'front_left_camera', 'front_right_camera']) - >>> cam_front = s.camera['front_camera'] - """ - return self._camera - - @property - def gps(self) -> GPS: - """ Stores ``GPS`` object for sequence - - Returns: - Instance of ``GPS`` class. - """ - return self._gps - - @property - def timestamps(self) -> Timestamps: - """ Stores ``Timestamps`` object for sequence - - Returns: - Instance of ``Timestamps`` class. - """ - return self._timestamps - - @property - def cuboids(self) -> Cuboids: - """ Stores ``Cuboids`` object for sequence - - Returns: - Instance of ``Cuboids`` class. - """ - return self._cuboids - - @property - def semseg(self) -> SemanticSegmentation: - """ Stores ``SemanticSegmentation`` object for sequence - - Returns: - Instance of ``SemanticSegmentation`` class. - """ - return self._semseg - - def __init__(self, directory: str) -> None: - self._directory: str = directory - self._lidar: Lidar = None - self._camera: Dict[str, Camera] = None - self._gps: GPS = None - self._timestamps: Timestamps = None - self._cuboids: Cuboids = None - self._semseg: SemanticSegmentation = None - self._load_data_structure() - - def _load_data_structure(self) -> None: - data_directories = subdirectories(self._directory) - - for dd in data_directories: - if dd.endswith('lidar'): - self._lidar = Lidar(dd) - elif dd.endswith('camera'): - self._camera = {} - camera_directories = subdirectories(dd) - for cd in camera_directories: - camera_name = cd.split('/')[-1].split('\\')[-1] - self._camera[camera_name] = Camera(cd) - elif dd.endswith('meta'): - self._gps = GPS(dd) - self._timestamps = Timestamps(dd) - elif dd.endswith('annotations'): - annotation_directories = subdirectories(dd) - for ad in annotation_directories: - if ad.endswith('cuboids'): - self._cuboids = Cuboids(ad) - elif ad.endswith('semseg'): - self._semseg = SemanticSegmentation(ad) - - def load(self) -> 'Sequence': - """Loads all sequence files from disk into memory. - - All sequence files are loaded into memory, including sensor, meta and annotation data. - - Returns: - Current instance of ``Sequence`` - """ - self.load_lidar() - self.load_camera() - self.load_gps() - self.load_timestamps() - self.load_cuboids() - self.load_semseg() - return self - - def load_lidar(self) -> 'Sequence': - """Loads all LiDAR files from disk into memory. - - All LiDAR point cloud files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - self._lidar.load() - return self - - def load_camera(self) -> 'Sequence': - """Loads all camera files from disk into memory. - - All camera image files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - for cam in self._camera.values(): - cam.load() - return self - - def load_gps(self) -> 'Sequence': - """Loads all gps files from disk into memory. - - All gps data files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - self._gps.load() - return self - - def load_timestamps(self) -> 'Sequence': - """Loads all timestamp files from disk into memory. - - All timestamp files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - self._timestamps.load() - return self - - def load_cuboids(self) -> 'Sequence': - """Loads all cuboid annotation files from disk into memory. - - All cuboid annotation files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - self._cuboids.load() - return self - - def load_semseg(self) -> 'Sequence': - """Loads all semantic segmentation files from disk into memory. - - All semantic segmentation files are loaded into memory. - - Returns: - Current instance of ``Sequence`` - """ - if self.semseg: - self.semseg.load() - return self - - -if __name__ == '__main__': - pass diff --git a/python/pandaset/utils.py b/python/pandaset/utils.py deleted file mode 100644 index 8a8458d..0000000 --- a/python/pandaset/utils.py +++ /dev/null @@ -1,19 +0,0 @@ -#!/usr/bin/env python3 -import os -from typing import List - - -def subdirectories(directory: str) -> List[str]: - """List all subdirectories of a directory. - - Args: - directory: Relative or absolute path - - Returns: - List of path strings for every subdirectory in `directory`. - """ - return [d.path for d in os.scandir(directory) if d.is_dir()] - - -if __name__ == '__main__': - pass diff --git a/python/requirements.txt b/python/requirements.txt deleted file mode 100644 index b8b8f69..0000000 --- a/python/requirements.txt +++ /dev/null @@ -1,58 +0,0 @@ -appnope>=0.1.0 -attrs>=19.3.0 -backcall>=0.1.0 -bleach>=3.1.4 -certifi>=2019.11.28 -chardet>=3.0.4 -decorator>=4.4.2 -defusedxml>=0.6.0 -entrypoints>=0.3 -gmplot>=1.2.0 -idna>=2.9 -importlib-metadata>=1.5.2 -ipykernel>=5.2.0 -ipython>=7.13.0 -ipython-genutils>=0.2.0 -ipywidgets>=7.5.1 -jedi>=0.16.0 -Jinja2>=2.11.1 -jsonschema>=3.2.0 -jupyter>=1.0.0 -jupyter-client>=6.1.2 -jupyter-console>=6.1.0 -jupyter-core>=4.6.3 -MarkupSafe>=1.1.1 -mistune>=0.8.4 -nbconvert>=5.6.1 -nbformat>=5.0.4 -notebook>=6.0.3 -numpy>=1.18.2 -pandas>=1.0.3 -pandocfilters>=1.4.2 -parso>=0.6.2 -pexpect>=4.8.0 -pickleshare>=0.7.5 -Pillow>=7.0.0 -prometheus-client>=0.7.1 -prompt-toolkit>=3.0.5 -ptyprocess>=0.6.0 -Pygments>=2.6.1 -pyrsistent>=0.16.0 -python-dateutil>=2.8.1 -pytz>=2019.3 -pyzmq>=19.0.0 -qtconsole>=4.7.2 -QtPy>=1.9.0 -requests>=2.23.0 -Send2Trash>=1.5.0 -six>=1.14.0 -terminado>=0.8.3 -testpath>=0.4.4 -tornado>=6.0.4 -traitlets>=4.3.3 -urllib3>=1.25.8 -wcwidth>=0.1.9 -webencodings>=0.5.1 -widgetsnbextension>=3.5.1 -zipp>=3.1.0 -transforms3d>=0.3.1 diff --git a/python/setup.py b/python/setup.py deleted file mode 100644 index faad075..0000000 --- a/python/setup.py +++ /dev/null @@ -1,15 +0,0 @@ -from distutils.core import setup - -with open('requirements.txt', 'r') as f: - requirements = f.read().splitlines() - -setup( - name='pandaset', - version='0.3dev', - author='Nisse Knudsen, Pengchuan Xiao', - author_email='nisse@scale.com, xiaopengchuan_intern@hesaitech.com', - packages=['pandaset'], - python_requires='>=3.6', - long_description='Pandaset Devkit for Python3', - install_requires=requirements -) diff --git a/static/montage-semseg-projection.jpg b/static/montage-semseg-projection.jpg new file mode 100644 index 0000000..02bf666 Binary files /dev/null and b/static/montage-semseg-projection.jpg differ diff --git a/static/social-preview.png b/static/social-preview.png new file mode 100644 index 0000000..33fde2b Binary files /dev/null and b/static/social-preview.png differ diff --git a/tutorials/README.md b/tutorials/README.md deleted file mode 100644 index 2fb7492..0000000 --- a/tutorials/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Tutorial Creation Guidelines - -We are super excited to see new tutorials and playground coming in from our community! - - -Here are the guidelines for tutorial creation, which we kindly ask to adhere to: -- For every tutorial, please create a subfolder -- Add all necessary dependencies to the `requirements.txt` in this folder, which are not in the `pandaset` python package. We would like to keep the Devkit dependencies _lean_, but can add anything we need to the tutorial requirements.txt -- If you think your code contains methods that should rather be part of the data set objects from the Devkit, feel free to add a PR for it, too, so we can review and decide where to best place it. -- Add markdown cells between code cells, or at least use code comments so that everyone in the community can follow your steps. - - -If you have other tutorials with other dependencies or build systems (for ex: Docker), feel free to include them in your tutorial's subfolder including a README.md for build instructions. - - diff --git a/tutorials/map_route/map_route.ipynb b/tutorials/map_route/map_route.ipynb deleted file mode 100644 index e35b60e..0000000 --- a/tutorials/map_route/map_route.ipynb +++ /dev/null @@ -1,229 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:24.364782Z", - "start_time": "2020-05-14T14:57:23.979245Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "import os\n", - "import webbrowser\n", - "from gmplot import gmplot\n", - "from pandaset import DataSet" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:25.404339Z", - "start_time": "2020-05-14T14:57:24.375197Z" - }, - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset = DataSet('/data/PandaSet')\n", - "seq_id = dataset.sequences()[0]\n", - "seq = dataset[seq_id]\n", - "seq.load_gps()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:43:47.811451Z", - "start_time": "2020-05-14T14:43:47.806607Z" - }, - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "lats = [x['lat'] for x in seq.gps]\n", - "longs = [x['long'] for x in seq.gps]\n", - "\n", - "mean_lat = lats[len(lats)//2]\n", - "mean_long = longs[len(longs)//2]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:43:48.330265Z", - "start_time": "2020-05-14T14:43:48.327206Z" - }, - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "gmap = gmplot.GoogleMapPlotter(mean_lat, mean_long, 18)\n", - "gmap.plot(lats, longs, 'cornflowerblue', edge_width=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:43:49.002129Z", - "start_time": "2020-05-14T14:43:48.998907Z" - }, - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "gmap.draw(f'{seq_id}_map.html')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:43:49.750109Z", - "start_time": "2020-05-14T14:43:49.571779Z" - }, - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "webbrowser.open(f'file://{os.getcwd()}/{seq_id}_map.html')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/tutorials/pointcloud_concat_world_frames/pointcloud_concat_world_frames.ipynb b/tutorials/pointcloud_concat_world_frames/pointcloud_concat_world_frames.ipynb deleted file mode 100644 index 2e49162..0000000 --- a/tutorials/pointcloud_concat_world_frames/pointcloud_concat_world_frames.ipynb +++ /dev/null @@ -1,200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Concatenation of Pointclouds into one World Frame Tutorial\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Instead of visualizing only one point cloud at a time, we can simply aggregate all point cloud data frames into a single large one for visualization." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Load a sequence and its LiDAR point clouds" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "name": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ], - "output_type": "stream" - }, - { - "data": { - "text/plain": "" - }, - "metadata": {}, - "output_type": "execute_result", - "execution_count": 28 - } - ], - "source": [ - "import pandaset\n", - "\n", - "dataset = pandaset.DataSet('/data/PandaSet')\n", - "seq002 = dataset['002']\n", - "seq002.load_lidar()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Use pandas concat method to concatenate all frames in selected slice" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "selected_data = seq002.lidar[::5] # Take every 5th frame from sequence\n", - "_ = list(map(lambda xy: xy[1].insert(3,'f', xy[0]), enumerate(selected_data)))# Add column 'f' to each data frame in order\n", - "\n", - "selected_data = pd.concat(selected_data) # Concatenate in order" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### For better visualization we can scale the values in column `f` to `[0,1]` so it can be used for better point cloud colors." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "selected_data['f'] = (selected_data['f'] - selected_data['f'].min()) + 0.1*(selected_data['f'].max() - selected_data['f'].min()) # Add 10% of color range as base color (otherwise frame0 has white points)\n", - "selected_data['f'] /= selected_data['f'].max()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Now we can use the concatenated point clouds with open3d visualizer" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n", - "is_executing": false - } - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [], - "source": [ - "import open3d as o3d\n", - "import numpy as np\n", - "\n", - "o3d_pc = o3d.geometry.PointCloud()\n", - "o3d_pc.points = o3d.utility.Vector3dVector(selected_data.to_numpy()[:, :3])\n", - "blue_colors = np.zeros((selected_data['f'].size,3))\n", - "blue_colors[:,2] = selected_data['f'].transpose()\n", - "o3d_pc.colors = o3d.utility.Vector3dVector(blue_colors)\n", - "o3d.visualization.draw_geometries([o3d_pc], window_name=\"concat frame\")\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/tutorials/pointcloud_world_to_ego/pointcloud_world_to_ego.ipynb b/tutorials/pointcloud_world_to_ego/pointcloud_world_to_ego.ipynb deleted file mode 100644 index 8468cfe..0000000 --- a/tutorials/pointcloud_world_to_ego/pointcloud_world_to_ego.ipynb +++ /dev/null @@ -1,210 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pointcloud World to Ego Coordinates Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### This tutorials show how to plot pointcloud in the world coordinate and ego coordinate.\n", - "#### 1.Import required python modules and load sequence data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:52:19.627445Z", - "start_time": "2020-05-14T14:52:14.839155Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "import pandaset\n", - "import os\n", - "\n", - "# load dataset\n", - "dataset = pandaset.DataSet(\"/data/PandaSet\")\n", - "seq002 = dataset[\"002\"]\n", - "seq002.load_lidar().load_semseg()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.Plot LIDAR points for Pandar64 and PandarGT in the world coordinate.\n", - "- Plot Pandar64 pointcloud by points' labels ```d=0``` colorized as blue\n", - "- Plot PandarGT pointcloud by points' labels ```d=1``` colorized as red" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2020-05-14T14:52:38.946Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "import open3d as o3d\n", - "\n", - "seq_idx = 40\n", - "\n", - "# get Pandar64 points\n", - "seq002.lidar.set_sensor(0)\n", - "pandar64_points = seq002.lidar[seq_idx].to_numpy()\n", - "print(\"Pandar64 has points: \", pandar64_points.shape)\n", - "\n", - "# get PandarGT points\n", - "seq002.lidar.set_sensor(1)\n", - "pandarGT_points = seq002.lidar[seq_idx].to_numpy()\n", - "print(\"PandarGT has points: \", pandarGT_points.shape)\n", - "\n", - "axis_pcd = o3d.geometry.TriangleMesh.create_coordinate_frame(size=2.0, origin=[0, 0, 0])\n", - "\n", - "p64_pc = o3d.geometry.PointCloud()\n", - "p64_pc.points = o3d.utility.Vector3dVector(pandar64_points[:, :3])\n", - "p64_pc.colors = o3d.utility.Vector3dVector([[0, 0, 1] for _ in range(pandar64_points.shape[0])])\n", - "\n", - "gt_pc = o3d.geometry.PointCloud()\n", - "gt_pc.points = o3d.utility.Vector3dVector(pandarGT_points[:, :3])\n", - "gt_pc.colors = o3d.utility.Vector3dVector([[1, 0, 0] for _ in range(pandarGT_points.shape[0])])\n", - "\n", - "o3d.visualization.draw_geometries([axis_pcd, p64_pc, gt_pc], window_name=\"world frame\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.Plot LIDAR points for Pandar64 and PandarGT in the ego coordinate.\n", - "- Use geometry.lidar_points_to_ego to transform points in the world coordinate to the ego coordinate.\n", - "- ***geometry.lidar_points_to_ego***\n", - " - input\n", - " - ***lidar_points***(np.array(\\[N, 3\\])): lidar points in the world coordinates.\n", - " - ***lidar_pose***: pose in the world coordinates for one camera in one frame.\n", - " - output\n", - " - ***lidar_points_in_ego***(np.array(\\[N, 2\\])): lidar points in the ego coordinates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "import open3d as o3d\n", - "from pandaset import geometry\n", - "\n", - "ego_pandar64_points = geometry.lidar_points_to_ego(pandar64_points[:, :3], seq002.lidar.poses[seq_idx])\n", - "p64_pc = o3d.geometry.PointCloud()\n", - "p64_pc.points = o3d.utility.Vector3dVector(ego_pandar64_points)\n", - "p64_pc.colors = o3d.utility.Vector3dVector([[0, 0, 1] for _ in range(pandar64_points.shape[0])])\n", - "\n", - "ego_pandarGT_points = geometry.lidar_points_to_ego(pandarGT_points[:, :3], seq002.lidar.poses[seq_idx])\n", - "gt_pc = o3d.geometry.PointCloud()\n", - "gt_pc.points = o3d.utility.Vector3dVector(ego_pandarGT_points)\n", - "gt_pc.colors = o3d.utility.Vector3dVector([[1, 0, 0] for _ in range(pandarGT_points.shape[0])])\n", - "\n", - "o3d.visualization.draw_geometries([axis_pcd, p64_pc, gt_pc], window_name=\"ego frame\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3.7", - "language": "python", - "name": "python3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "metadata": { - "collapsed": false - }, - "source": [] - } - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/projection/projection.ipynb b/tutorials/projection/projection.ipynb deleted file mode 100644 index 534c5c8..0000000 --- a/tutorials/projection/projection.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Projection tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### This tutorial shows how to project points and semantic segmentation on a image.\n", - "#### 1.Import required python modules and load sequence data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:38.302888Z", - "start_time": "2020-05-14T14:57:34.153304Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "avaliable cameras: dict_keys(['back_camera', 'front_camera', 'front_left_camera', 'front_right_camera', 'left_camera', 'right_camera'])\n" - ] - } - ], - "source": [ - "import pandaset\n", - "import os\n", - "\n", - "# load dataset\n", - "dataset = pandaset.DataSet(\"/data/PandaSet\")\n", - "seq002 = dataset[\"002\"]\n", - "seq002.load()\n", - "\n", - "print(\"avaliable cameras: \", seq002.camera.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.Use projection function in pandaset.geometry to get projection 2d-points on image.\n", - "- ***geometry.projection***\n", - " - input\n", - " - ***lidar_points***(np.array(\\[N, 3\\])): lidar points in the world coordinates.\n", - " - ***camera_data***(PIL.Image): image for one camera in one frame.\n", - " - ***camera_pose***: pose in the world coordinates for one camera in one frame.\n", - " - ***camera_intrinsics***: intrinsics for one camera in one frame.\n", - " - ***filter_outliers***(bool): filtering projected 2d-points out of image.\n", - " - output\n", - " - ***projection_points2d***(np.array(\\[K, 2\\])): projected 2d-points in pixels.\n", - " - ***camera_points_3d***(np.array(\\[K, 3\\])): 3d-points in pixels in the camera frame.\n", - " - ***inliner_idx***(np.array(\\[K, 2\\])): the indices for *lidar_points* whose projected 2d-points are inside image." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:38.349167Z", - "start_time": "2020-05-14T14:57:38.315122Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "projection 2d-points inside image count: (68659, 2)\n" - ] - } - ], - "source": [ - "from pandaset import geometry\n", - "\n", - "# generate projected points\n", - "seq_idx = 1\n", - "camera_name = \"front_camera\"\n", - "lidar = seq002.lidar\n", - "points3d_lidar_xyz = lidar.data[seq_idx].to_numpy()[:, :3]\n", - "choosen_camera = seq002.camera[camera_name]\n", - "projected_points2d, camera_points_3d, inner_indices = geometry.projection(lidar_points=points3d_lidar_xyz, \n", - " camera_data=choosen_camera[seq_idx],\n", - " camera_pose=choosen_camera.poses[seq_idx],\n", - " camera_intrinsics=choosen_camera.intrinsics,\n", - " filter_outliers=True)\n", - "print(\"projection 2d-points inside image count:\", projected_points2d.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.Show original image." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:40.188209Z", - "start_time": "2020-05-14T14:57:39.492066Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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h89Qms+kciRUaxIK1uNDXQFa1IdfMjesHVFWgqStOnlxjNKqZHiZEotkYmhaxK/8JAu18RjdPHOzvv5cIHNqHLpB7ZxsE4JKQLzq1/Cw5WXf8r5ynLpeK5V1EZHbBv3wsLDCxBZti+AmL60tRAgOgVH53gY5gAdECYWCLBltUigsOMeiCAm8UZeAC3xZg5dBR8I2aB8tT1WApEw62cGKoHNs07L2oIQku/LIpBXMFHaJYVkB0dwS/ixXj4xc9gKkRUyrB4+MF8sGxbJ+X4iXQ+5wWDDz5Mwa/VkCL9YnHFGIJ0FH0GiXwLWSCCoFgEZlglqZmNwJSRLU2CM4ZPjljQtkAZwaDQUElLeCtYHNRqcUucs4ep1EyAcmVI3+KVIlcVGLI9pyaHfaz0RPJJvODOkHFoDVEyESiZFQTk27CzuqWKe6gKHOm08zqanRL1uYz0NMeHdL2kb02ExpBamgv32QUa6Z5QpWEJGL3iq2PeTZPc4hDhCWoJVkQPkQX9DpADwvGU5lXNxTEFTk2D3tMmR7MOL99mkBvUJmvm0qULImexB9++Xe4/uwzhLZjtRFknqj7OUkauqM5mY5z65G+n8GBELP1VFq7zloQpFMkZyQpmhRqIUum0czjjz5KqoXLb71DO51yZvsk99xzjrfeeptp23H23D1cunyJC/ee4bFHHkc0knP09V7WrI/PsH2X9q/HM0CJQWk75Vt/8SzffvUKjzz6GP/kH32Ozc0VDg7mVHVD6BboQAgRDUIQ24OpU27e2Gd/b87pnW1WViO7u4dI5Uwxsb0nauQBUWPxzGZHqGZm08zu7TndLDGuGt6vfbiF/oC8LAnzY2b0EstG7zyZgoYwsHGKsC/sBFmoCEdZl26chm8GSbN8Ey2sh3446ljH1QT0Ingqw1elDwHD0Q2tsE049A13p4uwy0vsHi3BtOJyMyifgtUbfNM5Dm4KRYqlF+cmHMn+XUDV8f1itZfuFnw3V24NF8u/9NEeavCyJLvVL+hAj7TRVXBopCLnsARrLYS0TdOSe1qgNQ9xDS42Ni6ByqxPNbZRdihlgRtHwgAP9RAyMWSymldmHkWB3AoEtzRRi474fSuHM3qb5lxZDC5BIJgOEPfaBu/MvCkdxtZgk1DUgUAgkasZ/dGU7dURdc701FTSk3p75tVVY+hIAkJinifs3ZyxvrrCze46o7XzpJhpTq7Qv9GhbU9Q6MsThA4T1M460xKIX3rWwVvOi3Xg2jaIWGBeq4EhU9Z6UQQHqeXWdMaFM1sm8LWieMyqaspHZ/zhl3+HK99+Fmlb9tsJVajN66wjmUQ3S9RROewT7cy8oiBQSSJqImmGGNAAdVWxUo88UK+EkNk8s8VRq1y7+g4kePShB5AIL736CpubJwkpc/XqVT725FOMRyOLwYiNx4K9Z7h6fg+0ZBD+mCe2u7fHc3/7Cpdeu8bP//JPcfLUSWQ0RqrGgto+rEHCoDyDx0Ayifl8yskTp8mpoRmNCKGwhBj2hURFCMwnSs4zxjVUsUJRQoiMRjWnNre5ef2dd3d4qX24hb78kI+XONL2/8K2KE6XewBi1ukC1y8XLAJnwe9VZYlhw3BdVfcSjgl865wO0ElY6m85LhT5SVEpZgHGIbIv7h6iEGJGSUaX0wBa+U9yYZUo7BJxfrVZfC5cxNxsE3bGyIAlOqemoX9CQIJT5dQ5PCEO8JMJ9TK+rsCketczmhWe0FwNG0VlEVAVkSWB6jZvdj659NZl8bEiuBek7skoaGVKyd1qcZ42WmG4vTGSlEiOtkGiFIFmc2eKzCmdCuIuu4RkbnI2ZS4EixGVIDlO+5MSe2kNqskjIFrf65lRadKKHS0Mwl2KQTFQUW2+Q+wXYxWyUSFDj4Q5OfdMVdkUg4GiKCELt64dce6enYFKmWOL5ETXdexcHNMdJlYytFXF9aNbjM5t03WJtB/opaZg7qbchSGmIm40FO9PC30SLEZTCA2Ffx8Z4iSIz2k06Ewjt3ePSCuJiye2bI0VY4EAYQ5kuv1d/u2X/09uvP0KX/j8wxxOdlk7tU0cNXzjm9+BOvIzP/kko7hKTtDmTFWNTCGnRGEZtV1HiJFQNTDczT1Vycz6nldefZ2VpuGRhx/i2rVr7M+nbJ3c4crbl7lw7jyPP/wwOYk9c/R15jvWjItCuVwWCov1H0SYzaYcHuyzMmrYWFth/dSY7TPrSFgj+zxLQQvEvNhADdTs3T5i99YBqspotWFltWE+nVo+glREItG9+a7LTA6mrIwbmlFEZczBZMbetSmqgXpFWNuMSGrJHzZM/x/a3k2RKjOwEPyqS5iiHVjOsIV3DLsZrgY4+4ZiGRZcHoogXw7gDBQtHErBjhvc9+Vry0JRyCD9TTkNmhsPrqoMimAIHA8mfrlNoW4WJRRdIGW3bo4/Ry5Bu9DZMSEMOG4RogzWpuO7AwUTo+0Jx55ZPJYweDm4Be64vT1T8TyK15SJjlkj2dlW5f6FsipoX4LoFtxUNcESxPB5xSEswWCHzOBVFEeM0FsAMkcT+E4pXCwhZzSpLEFnjq1qdIvJr1uC1epYvjg/K3pQloBQYbsyk4NZmAXrL/fMfTSRJP3SdJrwFIvcGeSWIvO9GSsr66gYZFbFxNGsY3PnBHGUDT3PSkXgYPeQamWMxEyulNXV01zfPeTU+hZ1VzPNgf0OUpWQTiCP3IMqgX2DDVQrNLkSLAaS5CG+s0hEs5iLLRtXBu6F5dBzsLdPrGu21lcJGXKu0dRgyqNHJZnA/5//BfvNLf6Dn32M62/d4qjruPnW2/zgB1c4d+Y8OztbXH7jJn13A82BpjHabi6Bc8nG/XdvMmgwjxncW/VAuiirzQnO37PDi6+9xOapHQJzbty8xlNPfJTReExSUA9WDaitlFjWohkZYqEMQgi0bcvB/j5VVbG2skYQJdWRXRI6rtE+EzQi1ARtfS/ZvtnfP2Jv75DtnZOcOnVq2LcUGSMQpCInZTaZ04wSsYbVjTVSVm4d7JG6nlPbq+ycrTHItIaBDcj7tg+10C/GyKLp8HmZHJv6AqUsn+tWv4Yib49dq1ANy9F2TAmO3NkRd2TzwuUCEK04xtUfDg9OWwRjOCxx/4fTF/dWzBqwz71f0i0xY6wPIQR3K8XZJwZbSMhuHbgFkqOBIYIFthSnBRZvwBdvScBSE+YpVw4XRWPDBI9HiIB2WMJTBBobi7CYoMGb0iKBF7QxUTHlloHKgqIhCIgJ2qK/B/64BkKIpoQFDPZaUk6SIQgSDZcfIB8pyWju4eD9cOt0uZ8BV1SFQ69m6WnuLYEreOJS6IspgAhEepu7XCFpbDPpAe1sDwuehGWBbFtXhoI1tlLluEGhwHTak7rAxlZDDkYK6lNHn+dsba2bZ4R5QtODxGjckMeC9kqfJxxOp5w7dYaVoMSqJlTCbD45ts7QxmM63dJnZrEKva0XiYPxo8UzWDa8grPIUkXQRCuJ3ekMgO2NVUhKLw6phR6VDmXOZP9t/tWv/i9Q3eITP/MUb16+waMPPsy3n3uBH1y6yqd+/DOcO7dJ3/dICMxmU5qmJM5hij9DCTh3fW9QCQzw03IsKAOahXfeucHJU6d5+/pbnN/e4fGnPknfBVJWj885LuCGy0C9FBmE/TJDp8+JyWRCDIG18diWQk5IFOoQ0XlHTSBJgbw8vyHAfNYzmcw4ubXG+voqq6sV874n+poIYjh927akLpFzYmVllaquCfTcuH6TECxLeHN9nfW1yORwSqwcouxBNCDpPWTYUvtQC314TxFsGhHfwM6mWebmiy4JoWNQULHil+iWg8UKBfrQAs0cu64OMk0oE+qaZCk2gHsJxlT0QC2eHUpAdGEFGrabi08x9GPxvYubUKiZ/gzROT6qLtwDMWS7gtMAQzSgOYTlZ0i+eMuGTh4ANiGlWjlU0xMlICTItQETDnOZUM8LKGBQTHadnAMMgsLum8Gza0vw1b6yzWwYeZSegTelAc1KrPNCYILDWSOHTzJ5mHKbLMsnMM673T3g9B5UMzngrBfIBc8O3kNJhNwAlZc8MEEeJDhxypRHjnNEzIoLRJBEVghq2P4iuAeSasvHkI6Ua0KOFlLXihx6w9hF6bXlsNvl3NmzqMzt/hq49NY17ru4Q0Vv3ksOXP7Bm6xurbG+uULKoEl5/sXnuefsBcajFtVAs+7Pox30ELK4QrJYjCJoCkBCokOKDsuJBseaHbMPRo2GPCS6ocFgPAI3r95gbWeDtbUV35OWHWtD1hK15fre2/ybf/Gr1EH56Gc+zsEbN3nwwXv5+je+w3Te8fnPfYKKwN7tXfMkqoa27ejmadhXBpNQ9BQQyRIx2u+Sl68QYhzkw+bWDnv7uzxw38Nsb1lwfIB/tVjVvorcXbX1abBPUehd13J4eIgAq6urvh7zoBgCkZgD0gni8aoQTBjP2p7Z9dugFVsn11hda5jP3biLGZVE382ZTeekLNSVMB6PmE3nzNqWya0jjg4O2T61xer6iKOjGdCjUkHKaK+otBAyfd9arsX7tA+/0L9D6qsuM3QW/x6z4pfPXWbODJCQs0EGC3+h4RefLV9Ql67KHV6DJVEtLF3DpgsuLx48LMwIRAeBOJR4KFKQoqyO/3vMuxg8G4ZrGaRTaqp4wkyBQjSwbKkp6sLSPQEX0lp2TEmcGpLAirDHN4Lz5J0tNCREsdxvzyJ0poG6FT3EXopQBINqyrnYRslSlBMDzILGgd0TYr/EKvE58FyFgV1EQJMYfFeCwc6IYqAuLil9SRAmiNaWwBYcjtI8KGoh+5wKEh0CEWcrldIRecGESWFprhUXopbFa4o+EqTluW9/j7jecOFMhLxO0MS1ty/xyrVbPPLQlnkGmuhy4BvPPMsv/kefR7QjaM900jF/c8bswpyWCSll2nYfzUdkZhwcTNhcGw9lI8xbU3TIUXDKbIHsNJBT8ax8zRSPWvCYQM88zrn19nXObO8QV6qFZ53LvuuIkrh57Qpf/rX/ndM58GNfvMik22Vj8xx/8Eff4yNP3MeF0zvEqA5puQ+sVu4ghIXhsOiH2/FakpDGlFpH6ol1x/azKGdXRoybEWmw4oe/jhmVy1te1WAkzYmjyRFd2zEajahCGDz+4qWpKl1KUEWSGwgSIpPDjt3bh6Q+sLO9RUqKaouEzsuCKH2XnE4LK6sj5vNDQohMjmbsHxwwSmM2TqxRxcR4ZWQKRgJ9Tg5NW3xLUiRpRILSccT7tQ+50C+sm+OWvH/jvyyseO74dbGh33XZJddaBqu+nLJIthuknd9P/M9CcVgXlmAb8RhDKowSHCsuqflupbuAXgjCJU9l6B9Dp8r9zarwBRs8+CjmFYhDJZZUU/puUE0ZwwLxDM256aGUOVDrp9H1otetcWuXBLF4B9avIWnHdpoHTovAV/9ZwosJVstFsGxdYUlgZJ8Pz3VQdVjK+jbkNDjOP2RTe5auukC2hzX8XgcISpDcmLeQy5gPU+7zYglbSnQB6fi3CqU0heQaSQGNLUlM8QRHCkQCCROAFhDvPOPWV2PKaDQ4LeRA6CuyTDncn9JNj/jLP7+Ntok+weHBhP1Jyx+88VX6Wc982tGmxK3DOb/+4u8yP+pJmlGdEboJ3/yrhk6Fvm+JepsTp7Y4uj3hG197ml/8x/8xiYzkBVtHWOwB61xExOE9kmfa+hwUhSlKzEI7S+wx5+T5HRoZkYY9Ua6ciaHj+osv8ZV/9ZucG9c8+JMX2G1vMH2n5pkfPMvPfvGLnL93nTcuX6GWhhCssJqtkEBKyTKyHSbNDq8UCER9DrOmoS5ezotkv5yKlwxBlPFoNGz+Y6rkmGwpEE9y3H7GdHZIkMjqyorvg+V9KkOgVwt7WwKHB0cctQeM6w3Onttmf/+QqrL9nDN0rTKf9R5/MM+oGY0RIrOpMjnYYzxuGI0aNk+u0VSB2aQi5UxwTr84/NTnTJY55A6dH9HPMgfXb/N+7UMu9GHBqXehNWAfsCy8huDnsc+LB1Cs8IVWMANVYAluWVyr3CMPAkmKBTlkozpjp1ijfpshwzOaB2DW51JlQ09eCiJEo5MMiWJ4dp2KuXySSzbgouTZIuCDW68mUNXZLUgLWg/PnEumZAjDsBa6vSAAACAASURBVIgusgpj9GxL325mhahlCKp4ElPhyRucYTh6IKBUQdBO0BCMhUJJ2vKgqStdY8K4QsVcZ2hsNgTQbHlnkgkeNCxiRD0beUHPlcVYYUlewpLAsoMQsaAnpahXNu9LS5E8kvUrV+XxfV10/uPaPzqPPtrx2b24QELzCCSSZYZBgxUqLUESEY8zaABPlkNMaYpbz7FV/uaPvs3BXKiqiqqqSBH2925x/tQWt1fHVFXDaqzoQsXJzTXG4zH1GeH1y9dowzrrJ2vCgbC1M0KbMftvvMFbb72KjIQvfemT5NwPrCN1Raa+bowRZKtL1am+wfMHBvZNROnN2zhqmdyYceb+DepQgu22xiyekgn0XPrOd/h3v/7bXPz0GvdsbbO7u8frr+1BGvNPf/nnWG0yN/avcdjvEWYNpepMFQwC7LqeGMNgSZeAqyT3C4PDk2WMnaE2ZLBnQGtLFqtbQkygDUMymR5P5rRAsQXy55Oev/r293j40YtsrTe0cx+nQdjafst5AROLKqGGzBzVGad3zpBTTahsX3Zdz9FRh0hmY31MM4qsrY+ZakIlcHh4SNsJfQ/bpzYYNQ27u4moBkPlbPwk1UxOGQ0Z1Q7a1uMFMy698jIHNybsbG7yfu1DL/QHK3DJmgCOTdjis8VZy1dYYP8FxpBj5xy/VuERHncFjaMtx/uwMO/f1Q+lQ6qeUtitBJuD491RSkVHHfogUpJ5HPuuFkkhxbrWpcWqRdi7gM1pieueBagW1qpXnTSUxjNtcSWlVg3TZLwlJAV/XgsC20KPsSRjFUEdyCk4dOEWVjZ6oAyB5cJM8mQoUUIVvdiZP7dv3GJ9ltGUHCEHC2APQToodM3l/AfjwJfgt19TwdgqbjGGNJhkwZ89ZAjJa/1Ij4pt5jDMi2HaJaErx2xsGI2eXdwbZVFcyQ9Cs2QFlziRWH2ikCBVpoTDlJQSv/Jf/mO279liNG7ce4t88xt/S2wSn/nMx8g5kBEm0456pUYV0iShVcN0ltncqLhy+YCL96+QQuS5r/01//bXLlOtbbKyukrIimRIoTdl51a7lOqUumTxi1GHRSsTkmplIgKZCXNut0dcvHgKlUTO1bCGQY36mTte+Ju/4c9+47d48MnTXPixHd565QbfffUqTzz8FF/4iUdQmfPm9SvUKbLSRVpjMpvl2pkRomoMdh1yOGyb2TiapW95BpbollWJYsLb4k8Lj9r+VBSu2vECaSX3pCOnmm//7Wt899lXuHDxImtrJ0DnxFg8cjl2bggLwy+lnkor6CMn1jcRqZi3ytFhx2yijMcVq6sNOXeEWghVJOXAwcFtINM0DafPbLK/2xGCQbJWk8dgTvOCrbia5hbVnn4e2L11k3GdmN6+wdkfO88jT11AmPN+7UMt9KUYmuDJNL7Bl2iWi5oXsoB25E4hXM7DBQiDZCmsDMr5/mlZYMstvMsluEO9uJdRrlnKGxQ2BEQT+p7FqTEt9VVMICKOTVZDJYFCSS1FyI5h9I4rBheeC1jKmSwR9wYMSx4yK0tfdVEwKzlVTyjCmMHaDcFd39RYP6OXQ9boVEC36EMeoJCFAi2Zxz1DCeoAJRlNPKnJBHkZ9UCQghVnNJcsQ8u6DZVh7VlBPVchBA9UepDd+lwmunhTGZJnAZcyylVPLhz9HCydSzskW5ayRk8uApREyHnw2ozCKcbmiYkQuuJAojhH3q3LEDwHYYC3lCwV9cYIVlfp6xoJkOaBB5/4OFf2XufpF17i048/hvSBKo0IqaHTQKyFJAfUIZnnIauuSDP1akTrHsZKW6i+YQliLJRMtXwGKzLonnTQwTjSbEHxEHsOb+4zbZTzZzYge3G8lD2DNZBlTpCeb3/zr/iL3/8yX/zJxwn3BL79zEvsvjXjS5/6GI8/+TC9znnz6E3SyTn9vnDP/fdwe/cICZHcJaIEcu7oupa+a6lqyCgaKlRrYp/J9FD3pH5iy05sT+e+eNQZzXOSe4g6qwnhpBkopbquuGIIQowVP3j9Kl//xousbG/zj37pP2R1ZUTXHRLj8T2flzK1yr7LvjJijoQUmM5aqmaESMXq6pj5vKeqMzlbUb8uCfsHhxxNJ4ybGshUVTRvxA0lqyVlN9GcyJrJyaz6vp1x+9Y1dm/dYK0a80u/9DlOrEVS25Fay194v/ahFvoAAx577CM5JviLFB+O0gIpeBus5cUlXT8M7oH9XS2MfPVrl2NFGKzG4YyFWghFmA7fueB3C7gcX4rEiSoiNV571o51WGaRBu4b0MvTWuErpzkKlBowDJZ09ip7tT2As0OswNXCExrqinhgkihI1RJxgamOS7ugNCGaEAIxLCwnFUUrryLpOQfDcxfijE+AqiA6ouD1EjISyyxkouBWI1aUDIOmqAxSIUckBaJ09nm0VPTQe39jh4oQ0hJ0Z70yJVWeWTF2Q1Fq0SxUTYb5akjuOi+wbFsL2fqgYv1Wn5Nszy0IeJ38wg7JMSM5EdSydwOV504Ybz2LcrS/xxuvXeNTO+cg16TQsjebI3XDUw8+wPcvvcpfv/o9nrrnMTTW5BigH5Gk5eXvvsyF+y4wOrGCyITMOooSRg1tXVGPDf+mWlT4pNAIS4BfcG+wQHPWdw0QtCeJcmNywEgCO5s1ZDea1GizqNXTbPrMN//0q3zjL/6En/3ER9mXCX/9x9/nnu2TfOGnP87OveeYtVNuXHmbtZEgGxuMR5Gv/smf8c61XSoqQsqsjVdIXU9KlqVaV9CTyaEiJRgTKTGeXBhZguH8fVgYXCFZpREJrK6e5b77HqBPybKnfV81MXNzd8qf//uXaXvlsz/1ObbPnSKlzN7uEZtNJASlT0bbDVLguoXXDVbXiCBIpeQoyGrDuKqZzIWSJIkEcqcc7E2oRhWjqubkqTXSUW9lPrIpYXLnRpQx2Czm0JFTy/Sg5eY7V9h75zZnt7d46uH7qatA6hPTaSKE6Gyrd6Mgy+3DL/SXBOgPfZ+vMGTTGlSwgEDk2ClucQ8Kwf92QT+8XAQGq/i4sV+iNVDKFxcPY0i4WhrwRYXMpfrlwYNVKohYtqIET8leqlVjf7s1IoLRLXtKWQSz8F0w+bGWiFZZQS7BmSKLxJVF3xx+yJW5yGJ1aVLJIwjZ42fBsM4SD5AMYiUgsrOoSu6AeNzA4IMlhTlkOQtWJ18ojCCVbjFG4n3VxQsxzOrsPEoKSEPJi7DniYToeKwYZIRGQkiOWWNZrWKfZ4dbgii5QGuusAjZgrxVhjj3OQgGk5VCYxQl69CN88St3o8rMM+SNgu/M0ipMsWmoV/MjTOYrr19m/FozYqFORvojRdf5PSpbbbXzvP4vY/y7KVn+euX/5aPP/YJSwmrFCTx4vOXeeRjHwFRmqZFxApxjVYjoj11TlRpRi1jckxIrk3J0yC5WDfZ+u8CPHhwEjKShd2be6yfXGFtdQSpJ2mFSm8K2ksskA/42m9/hRefe5qf+Zkn+cHzb3Ll1g0+8tT93PvofWyNz3Obm+wdXodtYavZ4uatQ77xJ09T58hWF0E7RuMxVRQOdifEGFmJFZVYTSWD3Gw+RSOZSC9KFCF66ZEuBmJlAc9eKyoB0UiXe5J2nglra7Pver7+l9/nlUsTPvb5j3PPffcg0eAYCyU37rUKMUZ7gxXH9/dip0KfExqNyproyJIIEqkC5L7j5jst/VxZW204sbXK7VsTSlxQi4HpF8vZlC30ZJ1ycHOPt157DelnnD+3yUMfexBFjQ00lC9ZyMi/673nH3qhbwJ5wG38U71j8GUhxN/FglmcAxzDxMvGLr+rylABePheFldfusyxvr0rU7hALejgRtrxArk2azE49i09Su/1PYo/5331zzRbkapl6iG6wMBtPPLQqRLoRGuU3i2fwKKXJuxKFqulh9eIGq+4lHnABb+6sBtsZ0/Dt2Nc4IsgA+ZoJaMp7JtyXGgt4QsoqfwDHOX3MmvTX/BSMm7JaEgknaNYhVBN7j0NrB6DT8IwP0W5e7BPA7HEdYIaxq8wOOeSkCphNHN7lmFtaRmz6Fm/y6PocEmZjFIHKBkbyPqdiFHIJIciAto3iJpCu/joOXKw5PnQVdx+9SqfevxBNFiM52P3P8n3XniaFy49y8ce/CQRZXe3ZfvCBvWoIWimStFyGwRGMZOnh4zSGlVqqSXTR0Vjb15U9relFdjvGFwIiBULuH1zj1PjE1RVbYWQpaYkR2SHKOfdIX/6lX/JpRef53M//ym++/0XWG/W+fRHHuf8g2epVk5ydOM6k3afpofNC6eZH7W8+cI1mlyBtCTtObmxhYpya/c2o8aUZM4ZzZ17koFWrcRFkEwlSiUCfaZTyBLQoHTd3Mohq8UxRHtUO1PAWkEQ9vcP+NpX/4q1zR1+8Z99iZVmk739GUjlilyIwbxxxCiSwxuzhr2vS1teiSrUKoQ+UWelCpGjqXLjaI/ZtGX7zCasCTnN3St3qqwrLFUlaw/MLZ7Rddy69javP3+VtVHDoxfPsbbeoKlHk6BilWBFbNekVKql3sHOe4/2oRf65QHcTj7mut953DK6cgyF8ctYOnz5fDlGYGfLUNq38MzffZ/hhVsFhhn0jAu+4L+r9deqV+KWn+P7IoOFbm8GKwqp80nzVPDyYgVR1FkNJWnGKnwGL9hmr50bRiLogK0Yq8UYNwyKwZ9ZnQst5XkKDOJCXpQYM8kTy8ziTUsq1lk24tY3liWpAz5m1Edxi2vI0nUMuSRO2WsGBKsP5MpGI5prg6pkToiJUPdm2aQSgBQk2uJXzRa+8NMt+OxZpmrjHMgekhRCigYFxYBGXWJVlQGqXRep1RdSU1A5u0dTjHuthlSGNCRmJXublYOyOTvz0DOQC++/T4F7Lt7P2lpNJwFCz3yaOXv+LCk2kONAOX3kwkd57cqzvPzGszzyyEdpVoQnP/0p42ZrQmXEvA00AQ6vH5HSiLlGJrPMyomK8kalkCOSKlJondGlRLJ7Xj3kQJoot6YTtk5vIaG8i7WmCj0aS25GZC5H/P5X/lf2nnmDxx97gr9++jk+8omLyHri3OpZxuN1rh29yVwOqdfHbDU7PPfdl/nEJx9jpao4FEg5Qe45PDqg7zoqIPctPYrGYC8iCULXJ9AeUqJZGRkomDOpsnIFZrSZjZ6TlW3IYHBk31Jp5KibMpl2xNiwsbrC2R87BVIzmbTkHIiNoMkUpw65IrpECS3PblBqYaKJQKjs1aNk4Wiv5eBgl6ANZ86cRPYmNHVN13VkDf42OUgkOmnJ0hv1VBPTySGX3rjO4a0bnN08wVOP3k/dRFLq6VPyuKQZUsvoQs6ZGAuF+f/n8M6d3V/ygu74vOz2xUO/q27P+yhAM9IKxmzUvxI7kDs0SCnGNMA7LNyqhU6xQKLIIgCsqkguHO7eLD4tgjN4YFEHLHx4jmWGikIpvxxFTJhm9arLRQPlpYEzJRK9prv1I0OpTulCTtVe4Sdu/UksCV6ZmAM5j8hZLOAp2ahkpf4M2LtepbHzpTMWTIlllDHUMkWmvkvw2WIOzsRR4+pbgKygDrVlOcbOLXLnaqu59vbe2uisV3tZCiGZgtXGGEcaSJJcaGXoR27F+RulRBherysCfTTYOpiFjkNKoWTwGkgHmKVNUXWhVODsBuW6yGMwqMjedaDHbJciQDLw6CeeQhqb59Kntm/46IOf5fsvPsdLz7zCxvY2O1tnqaVHyexNDulHkc3VMc1oFZGajkgXalIU9/LE10Z7zChaCDJIvXJ4MOHMzhYZC9haCenKBc0cpWc2O+Irv/cb7P7gNdY2am5MLvHzn/so08kRGzsPME4Nb19+k7TWs57WqVZqvv6v/z0p1dSf+ihBO3JKxMrgwXnbMnjW5b2WqrbmMuwfzpBYcWKlpm9bECFWkT5nNGdiBiSTg3gVECPGz/sereDajZvEakRVN1QxQJOYh54mViiBtitkh0zXZ2aTKeO1YXaGvZ/zUsnzpb1fXhaUvfz5qe0t+rnDe96yujLKmZQsSU6Seae3r93g0isv0s3mnD93mkc++jBRkvMO0mBcmsduTLgQ3TPG6L4hLFhG79c+9EIfGFx8W7iFDcEgsJakSfnLPsOrAbqmtuxDKG8Hwj8r2bSGFVfIsKH9u8LXF9u0xWIVh1XE4QgTBhmRyrwGLZts6V7+GkXzWJyG5e+bNf5gb2nvHqAdFlYpySCBkNS74UFGVXs/qPoz+pDY0HQsCoCJM0fCYrgGWAlzO+29hqUsydCiJ2nlLOA1+A1SsvRvE64WiFbH/y2RDEoNHas4XxgUBd9vF3Om0euA2Wv1YnSJl8ztNk62Vwu1ql7DAg+eQJalHzouwQOZ/vYnC+hmFw7m/cSqHxJuRKJTXW2uDeHSJY8vmteF14+X4O9lVRZvCnMXIPRAIOQGScFLGPiol9dJSk+mJ0SrAR+AXkGrQjC0eddeqKKSovDkE0/y3He+wxsvvMGFnzhLCHM6FWazAzbjNtU4srG5TqUdMZY3qM1dIbk1j0JfU2JNUZQkwmS3R6rAxrlVoCfkMpf20ppi1U6O9vjK7/xfvP3qy0RNPPbpi5x5ZJvdG0dcuP8Bks65tP8m1TqsnlqjvzTh61//S+iUnOfQdUwPD2x5CvTiVOGUEIW+V9+wzsqSSKxG3J5MObEyonaoMc97CpwZo8VfLL7ja1qgipE2d6yMV8nZoEOAXnuDiCK02Xju0kVSF6hjYG11jRBmqJYyIOrrrKxVM7zwPSdEUsikSqlXA7X0dNnKM+A5ACCUkj9V6EiTxGvPvcTGaiId7vLIxfOsjBq6vrUwkGegD3t0oQsH5CIs4dEO2g5FJH9Y+9AL/RAKRlXgijKAQClj4A97vJVzFuyWwuJY0BLvGESJS2GD6JagwtKLNYpppDBY8lrW6KCQrPKfFVFrKOVgAcPwcwV5xFA+AKdo5coVgdeQ0d5jmC6oizQPLjSlM2GSLZGqwEzLbfH6OluYDsU6j967UJSF4nV+DM9U53FbBcZkVnXvn0nxjkrlzOy1SHyhkgmqhOywlvP/DWbB4JKAj6uPmwaE3j0Dx+CzIiRSxFg7vvLLc6nbx1nNIlVt7KGG0giyeD7s4a3qp02aodsl1wGPXagHZ035B1l6YYxna5KDB20tmJlzIERjISnBAsNAyGEIIJc1WVVqfO3UUoUEMRJyDTlTBWHUWDAaTLfNZx31qCJIjebIR5/4JN/57nd4+Znn+MinHiPFivPnzzBuVogCEmv6HMgp0eae7ASEIOVtZYLGDiHSZ1sP06SECtbXa3t1g8qCihszqDFYbk/e5vd/8ze5+sYltk5EfvxjT3Fip+FWO+O+++9jfpTYu/oOYRw4PT7Fqy9c5vmnv8c4NBYbyHNS1zGdTYw1r1BLJPeZUEXmqWdUVSRVegzTj2RGVWBz3NCrv3ku21oIwTLO5zkR3NBS1DOfTX7koMYP8MJ/QqChQqY9fddTVSuMmjGxqsg1VCL03Zxl2fkuTJ8hg8ignhypK1872lMVj12NGZXVGFSaptx45wavvPAqIWUuntvkiUc/YeSCjO+B8l5eJYaS9atu5Yvz+KtjAr+IpoWZ+MPbh1roLzxQhwkoQKoL4CVmyCJhaensJfhlAEgUSiGz5Z9SN92+6wdIBZ+AoXyBWIp68O/se9yAC1ZONSyXI/D6+bpQEBZdd6qmB04Nl4ygnlAVen8Pq7spmOUIkZwStXQ0oSXW2TI/cyalTOphngJIJMaGRbDRxyGUyuMON7HE3S8eE4LmxjyEkAcBaWUe3LImekxZyKFfyDQ1Dj3SW2Dca9kISqzUEXXnT4feE8Wc7+9liYd0NsHgGOkgLI4rZYFFoifgGhZvmLRZsOVRQg7kXpAoJH81YJBMkA4cNgpesyWVjGtVVOwFIJqDU0vVFVB2YVUC5vjbsHwN2s4lRMNslR4J1UD1CxpsKnNmrgGtIau94LsXQdueOI6U1xiGHJAsNHVFqTU0mylPffJJXn/1OZ559SUeefwxRlEJvc9PbSWH+6M5QiammopICh05e3E9Gx1CFvau7rF6fp2VzbIOTRlbgp6Na0ot09khX/6932L3xhs88fn72NlYYRUlTTseuOchDm/f5h29yUojbO1s8Fd/+i1uXN6lampIBm3mNnHp8ltIJeTUEUaVjV+MaM40CLlvqerK69zYGPfaY5UUlJTdwBL3sgbje2GwVMWyzqWsSE8MtRlOAk0fib1QSUWXTKBXVaDrW8zPKrJj4fIuvxM3ODEiqw5c8JCUKmUkdc4eC3TdjLY7QiaZW9ff5uqlyzRxxGMXdzi5MUZ7gxcRJ5FoOMYUKkycqqqomsZlih77dyHu/i5xb+1DLfTNEivNuOKD6w5mYQ6meoE3ipD0F4yU4wukABQJteDeezo+lVcclIVlruXeUDIZtVzPg2NS3l41JCPlwc2XaMGxBURQ3IqFlT+8WD1HK3YVYXhvLSXtG6/sWNHpEQ88sMFK3bB1UkhHR8wOMu/sHXF7PuXGjSMuXz/g/kefZG284rBVbYnuakozumUPbnEXbLzAUVazebCOxX1xC3C7yyuymI3yUpS8eO9rzJU9V+zRKEPMJOfFXApW+z5iUMaCK27G9MBwoKbUG9ESACYAtQXCSsDYqYY2PW6FlekRyzXIavNsyViOxwfvnxrvvsq2+FK0905ZEpLDXmSPa6t7Ew5N5GCliV3wBMkkjwMYPFhRYe+LlVrQNlHL2KpgZixJrDI4SVPjiicZFCVjG7uQSQ1UTeShJ5/kuRdf4rvPPccTFx9l3sEIoakb4igQgyKz1mruy4wsYlnEDnvqtOUgBdbOrDKqEzFYdnEjNta5T3RVS68ds9t7fPl3v0x3sM9Pfu4J4ihD3SPNFtsndrh2+yrp4ICVMyus9cLTX/5/OJpMCRKoQwTpaPsJkLl0+RK3D25yoh7TpUTOapTKZF5YjkqXO1SFuUJUGUwvyc4nGzLUDWAPAZK7LXUMSIBe1byu1BNSj9RqgWO1arfTLkEVaKqaoyOn+LpxRshOS86+5nSAKzMcq8FjsFIPobLaOkcdk5UJ82nP9DDx1ltvs7+3y+nNMU8+cp5RUyMlZwOvlKug2cuEq0mSEI0FF2M0q15kgG6W8Q7biq6k/h5y/8Mt9Fk4K1YGwd3N8t2A0bD0xh8W3NVQpqgc7+afmGW/SIJycGbp3Z/lnuU7+8CEX1i88skWnGN7upQHUAJzxaVf5sgveyWqxXMRjPXhhRhEhwWALI4QVfpuRoyrVLElxooQe1I+ZI0pk3xEpXOEnqe/9Qw//aUvIbFHckSpDC/Ag0iYoEVrchq5sE7mfZQXrqiY9+HJWVaMDM929bnQsozEKaBCqYVj0E4LlZrwSg0pB2JcYjChQLvE2S8zVjlcVGrdlxICgtXTCQ5DWQKZpMpKHEsga+8vgilvzXLPIfsdvXS0bZVFzSPBoTLvRPBzw/I7EgYH0ei0Kp31NTV+XnYhoMgADfrLcmJv0amYyQmqRkja2zOmTF0BoYW85gHfjliJw2gZbSG1mbBeQ+j4yKOP8MxLr/Ds977PEw89SGANqRUdKTJWep2yN7vBtRu7rDSrTCaJpC2p7zi6doPVzTMmzEKmJ9Mq0CZC7lmJq/TTKa9ducTzX/8W921v89Tj9xKrlqOVzNbKKbbDKa4cXGYmLTtrmxzcnPLVv/wW9RwIkWZU07cddWPGwihWzG5N2Gg20G5OFWuzmquKvu0saJltXwQRxm4UEIRZ31HVFlA2lk4y5RoyMUIkIbEiiZLwPA4JpFRBVxkBQI1em2rIjaDR4lhlm2c1Jk7qM7WIB1F9E6oO5UJKlnyRE6WEuUR48/WrtC9fYf/6ASc2au69eIrHLlxkFCtycsM1uP0VFnBMLiiACMRArOtBdi0L+PJ7KQNxnGzyd7cPvdAvTR04Fyn4dkHx9V0arjDKbZJswkrFR/sqDNdYMHCWo97iPHo5NtoF8llOhDDOuk+kW/sF1xb3QgqN0yz2pdjAAK2YQjM6X7A67ASCzCmQVKkwLGqY46gKNCP3RkIiRrNy0YSIYcW5HUE/IhaPwd9iZN1aVqYApapkGpTW8oiSLDFqUbJ2MS8lk9YsLoOpxOvkKwaZaO7tdzrrayi8YhsHVeOZD2UrBErJ6pwL9LZweXMSxOvCHEuIiz6OeNE6j4EEB5YUp7yFDrqGnMJSEp2thcIqyoUIgE9tya8IruA9KKgijih1TrO1YK9mZ0EFPMkrW/0b3+gpB2JjCiqIonNlta5QifYOWGyeqiaa1xA7uqPOLEVRVMeIwlOPfITnX3qBly9f4tNnHkNCpkOYZ6Gn5fUXn0GnKxyeEG5dnXP6xIidizvcc/oCiGUR975fRBPd3h6pV/aPjvjWN77O7huXefT+U1x88ATj1UxH5vT6JuOwypXLbyAIZy6c4dWX3+LSN58zCzUldGRCNrUdaKaWQOo7YlVZPaPKFlqMka5t7aXvKiSJFIKFomSHbVbqxgyO6v+l7s1+Pcuu+77P2nuf85vuvXVr6qrq7upu9sCZoiU5liPZkBMFQSwYMIIADvIQxEYAPyR5CJCHGPkL/BTATwEM5CFGAsRJZCB+CBDJdpzQEimRoklKZDfZA3uorvHWnX/DOWfvvfKw9j6/XzUHyVYCkIdoVtW9v3GfvdfwXd/1XWIG35kiUxaLtAU4vrhknQRCMn375JHGsSEzrfITKLkVokvgwOYnQEo2uD6XHoGktbGuOvttGLcbDGqBnVOOhJBZnh1x5/YNvvzGp2zYTTKnnxEIVTfKvtuuHaqwjqI478hoGf+5GyBuz93WFu0YPx3/7ydePzdGH+pCV4YM9ifVqO8a0zrP1Q5/vT9bAaZcqIAKBJz6mmSZ0SrwkJROzRqdjwwY3UaOUKv7lRJZojtN26Wvzyt86O2VnzG+EnpDfqjwUjH4ZfqTPT8ypEjsmQ0FPwAAIABJREFUOh6tzlh9eInLA8tuxcnxJauzJWddIuUFq82FiZs5BUwUTAt90DZa0bZRozhWuGqse0geu1xNZrk3yGd3Y0lpwCoqlvb6BUf1WLSqYg1BGVRcwfENUy4IU0nTBVeym0pfRX3huKcSVVMS/VA6mIvTdgl10Sh7yRc8vEhb18/lHc73I7pWoTslIb78QAMu22euXOjoLRJXnVhUmBO189hpLHUFc+pZq0OLaBWu2xG606EpPUI9TqMNbtFQNHcCuZmQoxA14kIqEaeDXCZnuUyYtsURCjk5cMqnX3+Z3/uD7/Ct732fz790AGmJ9FO+9v/839y6ep0c57x/cp/lyQbp1qSJCXpFzcScIBmzqmmEa/uHnJ71TFRplqe8fnefwy9cpZm3rM5X3H7uLtFnPlp+hO4NvDK9y9nROcPDE8QFg8FaD0MkM9DOHI14vDMOPa6BxqN9b4V0ta5pTaWI73Ymujmh8XbWcjaZkZhs74izLuYggqdBRbnoVlxkR0gWBPnCBotNAg1oMrbURB3SpSrqYFU7VWJKDNoXKNACzFz7R2BHosOVqWt27zebxLrv+bf/vb/E1Wv7drpjncEcyOQdUordOyvIGoZfi7W56jrplupdTchoOlRKf04Jb8eMuRSxf7rN/9k3+j/CtefZhbBrl/OsbOV1n32NXYe4pWlKybYq+4UihVwoWRKpTubZWbk7765buKP+veSA4yfS6pxGNhGjo3qmQ7jQ66xI5cefG6ykdOsl3/jmV0jnN/no9B7LbsDJAetN5vz4jP6iI+fGGnialRlLKAa+BqdbyCunYGqXThCaIhSWcX5TMO7S8OH6LTxWnWx1pGqIa6loAomUFKdVqEzI9XWkdvyCkEanYjCM1WWSKlXlkgLHSCneoRRNFXNKlD4HzTvvm7VorITC+LG1VrEpSznbhDDDdhmjI481YUVtyh0r3ZKFxVK/oxkDb9mZC+abqqN0dVCOjgwTpTiCtF27OowmZ6N+olo048241WwrZ8u8kgr0DeVmMXYMlTGYTia8/tprHG/u8Y1vv4fPiYmu2Dw45s2PnvLa579MOtqQu0tyyNDZXm6wzNGJJ3hHHiKnDx5y94UXOb7/NndffQ55aZ9wZeA4bnjjlTfoNhue3juive5ZXNnnt3/n/+KNV17CFYhO+4SfNUVaxPpJUC3cdMipI7hAaxsLaTxD3xnjPNu01+yUKJkUMzFicgsFE6kzcUFpCs6dnIBTFvtzJn5uk+Ow5qwkRluuxAVVJQ9Cs85l+oHZDCeO0ELOjuVywMmGRZPZzqNmpIVKsEh8vVmTc2YyaXju9hWQageKY0DKPSxWoMSUoWl+1J5JlTXZgYDLa1FrjcWOiXPjc3av6jx+2vUzb/TrVeD30jJeIJ0xui542vjAzBY2qNX2T8A3I1Q0UI2xFF6wPaoWNWsmIc/i2NihHLH7UuzcSQDH99pCUZixUCh8rh+DxW1Fz0xNyvD1rZJlxjUtpAkp7tO0SlJD3PvB08eAaEMcDCPMGm1jVwciybDmMjTcXt8gDnHDuJamoW6ftypn1mahmnajeXQi1lxl90OcklUYdDuMxNbD7odB49nosOX+6Dh/V0oB1ox3edYo5qaqRiF0RsnU5KwRyxd7iEVjIFsaZ6Y0m6V698vn3E4yy6pIMkdgbKTSLOcLzu9MT96lpnxOb7DCOLdXLTqs0gaYITU1y5pFlEOuiibwLhRc1w53aBqSVr0XIaWypxwojrhUVAIEo/JaOcgCFHVCypnPvfxF3l7m4rgS6/Wa1AY8EXWZKBHHFHRVtxveW21keXEOLvDS7Vs8fvIOX/jSHbg2s1pAHHj9+kucDqdcLE84nOyTH2e+/pWvGgzxspJacPMJceiNWjl0TNqizyP2ffqhZ+/wgLjpCCJ0KZI2PSpCEnCtJ2pGUzQphBDGTtiM7b0kQsxppF87BJ/Ap0wrdnuctyzAecuQzfXWBqpM9g3dkPnhvY9ZDpnVWc+w6dis1og0eGn4zKeuARHBsH3nTIsqxsxms8IHz2wxLyYnUQU4nd8JUKt9KGttseDWhnzy/O9CrzW+333MLnrwzM934J4/6fo5MPpbvLim8pYql/+r8Ijbtkvves/65GcxMKjLpxpMb2bMEMyjFiLVMxnEjzpQO9hotiKjezZqNxyuZBSYt9bClRYBL3V4xZgUUIvLWjnmpXgtajBEFotEcwcxZaaLOcRM59eIN3PjXKGXjVFs7TAu6eVYZzBYQ3Mp8PptlELFGxkKtm3icOpMIyRUGU1VVEwACxGcxLJzMYPkjHu/4yotknE2xkTraMcsJfXHDG0uPGdJRTDN9oK4CHXwi7OoufL3R3lzraufCtQyIafCqZeEYDBVLufS4XDJG53RufL+RX65BBcjoiWK+L4QN4uGjStQlTYQJ4jflMjdjxkiKki2HgWip+tyuQ/2fsMqoc5mHTdlqLumbHRPZ1h7t16xd3UORb6ZbLIdUnDnKRto1rz44g2aVuiTMyE3BPyM880K8ZmUV+P9DSmz2axRhXY6g+A4evABr/7CS7RhxqEesr7seP3F11k+fcqT6WOu3rjOkyfH/PFXv8temhKc8uj+BUkcEk3sLmhGvEd0wIuny8LHj86YTj37h8psPoVNKnsLJGVElZQz3ms9hoaHlwxIjdaGT4lGgFyKsmSydyWgUpPVEDUCmhcCiiSraTnn8V5wTea8G7jb7HH12j7xSmRvvmC1XhKaQL/pYViZvIGjTNLqUVXatmWxmBe4KY97ZJzBoNUI79idQgZQVZvhu4PF71Iw7efPOoVdu1P7Sqp+z5gS/itc/9pGX0TuAv8AuIUdh7+vqn9PRK4B/xB4BXgf+BuqeiL2Df8e8JvACvibqvrNP/mNCsa14/FGGmcxYiOfdgdO2UI6P21BDAZwBd7QekDLopb82dJ6tp1uBpOUGDRvLfYzOv/jOjHq8ADjJKvy23FzbD9RoRACo0bN6PgyvnHouqeZeSaXkTQs7bFug/iB1CgMqUSdpoejSInWq3aIwQPjpqtZUS5OSsQi++L1XIETaiRdklYq15to3H/B4WUrCSsulX6Fwn0fYQtbs0pHdaXpxDIa+6o5Z6oip6NG6aXOUDj94EzNsH72MV0uHUcVUnMD5KbmguVz12KxIxY4SZ2Ng8wuE0bOfhgnmNkz68StWGCGouWeLZtyiL1XBgkDI4OpUFqDoa7EHGkaUwnFZ5xmfJNwrrHsUzNDNOVJJONVOXn4Qx5/nOi1Ny2WOBA7sRpPTnRHZ8S5MKwvEV0yZEdignYDaeiIG0ii+CaguafvN4SU8W1rchNRaTYXfO4XP8PeZML+1SkpKdfvvMCD4wfki47nb7/IR197n3fee5tp2yCtBS8Xj8+5dv0G5+sVoQRR2Qk5eM7P17x3/4xln3j9hRsGvWUl5mRZqpR7V2CLXNhllYUH4J3JWpNBndBLthpNVusBSGpFV9meu/pnTp4UBQmMQ1bUDciBcPP5ObnzIwE4JtNrSinhsiDe0/UbEGEymYzGOqXabFnOcH42qKzGeiSdVNvg3Pgao7bPzu/NjrCD5lTkwP7Q4li2IxN3IsbR5vz0aP/PEulH4L9S1W+KyD7whyLyO8DfBP6pqv5dEfk7wN8B/mvgrwJvlP9+Bfjvyp8/8RK2Rtsw7e3Pa+RXw3tLm2rotpsS6e6rFRjHogBEcblqY7gC0VSYpjy3Ro06vvOorrDF7qo5qcZTDHna+QjjJqz0Rs0FhqjPolpUqwto9fxsv58YrVCHgWbuWUyNarjRHo0rhE0ZOiMGeWgsjUjGW0e09BS48f2EvDP9KI8wmOJLQxElQikYooJh6buF81KaGg2lNXWJ68r7mLxCLjrhlIaj6kxFIWulY+5kPQWysIi+wCQF1K9NO1bHNJaDqis6Orbxcy5ca2ecb5upavvEMhfbVDZLVwFrFjPIyfNMIyBGE3RZcHlCRq3ZC1futRZqbKEJW5pn0FBx9M6ZwcopQeqZ7M8sCIiBNPRMZt7YI+X+OG/7VJNy9OgDfuf//Eese0/MA04aFrMrXJyf4oJCE5lPJiw3GxgGQpuIDpi0xM2GIURevHuduLnk6NExk1nDwe1biAiDZObJsXpyxKf/3BvodU+7H+h85Mb+83x4fo91e8lLL9zhj771HU7ffcReaMeoOpNJG+Xo4wd4l3C5xKAqvPvwKY9O1mgjNLNA04SSxdhjYrbBKThnw0Ky4rNFw1lg0FIH0FwMptXMGhwuQdXgpGjwVBR0bIbMSiaYgJvdDXJWGgm4mGkVNtkGoMeUjZqribOzR0jcsLi+z2TSlgCzBpLPRuhSDJTtN6sjGCvQFeYOO3i9PWfsshX5hMHf1ie1pJcxWq+I9ztDYOpj6+eoyAe76MSPv/61jb6qPgAelL9fiMibwAvAXwf+SnnY/wD8c8zo/3XgH6h9w6+JyKGI3Cmv8xMv92O+gJTF2wpkwdg1sUXQPvE6dhM0ixkNKVS6glJI6QY1g6+Gp1MX0HDWGtvaAkg51FBjx906gqOO+CuQxeiV09bA1+fsOAVjxxQVPYpjKRGPOkW1Z94KU6dcubZPdkKTNsRJIIcJky6TNh7fZpyHIUV805TXtM5iGWUBCr2xKn3mWswuUFR1XLrd2FZbqFO4ANLovLR2FbsMUtvii7SEi+XrqUXPKZQYpjQLlcYtoURzGOvF7kXlSNUPYwM/pKyV1oggV4dTlr2oYVpyE0cjXgmZUjaOMY6K7EQeClbsDfoqNR+rcZghsnviTEfJ6QjZ14+n6mz2sNh6atbxsZlCDKB0S2drKgreBPDs9QzqcDUckMxXf//3CLOGhp4hr/nClz/DRx99zGuv3+Lt777D1JvcANLjgjL0HT3CIu3RtFMeP3jIw/sPbP/sLUgp8+jkGBWlkUi33vDFP/8G6Xk4XAt7bsH+5Ar3373PxA8c7t3gW//462zyhmY6Iw8D/dDTyIQhDzShofEOHaxZ8eRizbtPjljlRJg0LKYzruztc+2526T+goBpOTlN0FsTGsVZI9YxPqQEvqxCVlwqndzZBtBUPSbbq5ngPLlb00UlekcIAe+EjSaSLwKHCEPMSHS4oWTvYvsyxg1HRx8j/YqbV6+wWNwoWaYfA8pqn63mkqjZejXqzpd9gQE+KW4zgt1rlxQyEhvKY7aSD7tQa3UOZvTGf4sFmGMy8Ank4Mdd/59g+iLyCvCLwO8Dt3YM+UMM/gFzCB/tPO1e+dkzRl9E/jbwtwFu3rrzY99v9Iyya+LTyP745Jd2JUIoVUgqT3/LnqlNDrVjokoMf+I9xzCCT7jT3awilU1S+e7P4nUVox+zgur5q3XNgDpSsoYZpUIhoBohdPRuyaMnj1mmzEUfWaXM+XrF8uycfp3RoeHa7BBpHV/75nf49V/7N/ClS3iLMZd0YgdtHymtbJ2NCqYno5XNotuMip10tli+XGfEYk1OqqYJb81hdZVqim3OQWVbYHMjfm6jIasGkj03GqZfXydDTamS1CKxt6iP3YPjC9RlUFKtBZmeiWH0mj1OTS5CVCoLs5yp4hxKAJ8ExCteMpkdWWsFXCInhw8F643BkKSmQ8llprASglC7vhHwE49KMn8pQuojjWsQ3/Pw43s8eechzXTCEFe8+vpdJlPhudtXt+P7FPKQIPeICE0T8KFFVOlWZ7zx6l10fcjZySV53dGoZyKgPjNrO37hVz+P7gmLzYTrkxt0h8J7x++yf22Ou1zwla/8Lk6FA21Bbb6DawJpiGVfOlLO9JvIR0+ecrxcMUxa5tM95vsHHOxf4db1m6YM269JuSMPalOyirGvkEkmEtTRoqQUTehEixoqkErWFHM0jcIKucQImPhabVyKMZLEVC01VOMKrZsQ2NBI4uziiOMnRzgRnr92yJQ5Ac8wJLQpUfrIp699GFvjW9NT55xlJGWk6a5ez2j0f0wY7pwbHciuPam2YZzFW15Hd563fVyN9iuE+5OvP7PRF5E94LeA/1JVzz9RUVb56aD6j1yq+veBvw/wxme/MOY6n1yq0aNJwblGkKSmXNsFLObVHu5ro9b4a6AWEwtbBsairHX82yuIlkKs09KiXY3kzk1BoEg1GB2xsheKZPGYDewUhi2k395MC7qpuqL2FqW7lESSDa2f8rQ/RjTSpBaiIybrKs4ObLIUnF+sUbFJStT0ssyXHYviNUpwWwbU7qjDrBX2UXICchi7U3OqxXVBvBV9ba22kFfOgqh1q2bttq+ba5ompR+iwjz2fUUzo5xGKhEdHqE16eXkTJuHiMvbgdp1pqhzrlBxPZqDmQxnLCGbHJVHrM5lxWGD7DW70mylkLb9ALtsLiuL2KQqryVjFBNek8L2tfb9AUItv5VMMiq4HmitezglpA0FEggFqvD4CWSXcMvIPBexMVE+fO99PA2+mZlwmzjq8BrvAt2mY9NnNusz5gfCL3zxFa4fwp1r15HhJjgrZF48veCP3n+Pz37pC0wlsN5P7O1fRy6Vxx8+YP/KHqePL3j7n/2AqRPCTEgp0seBpvUMMdGqFMUOE4tbp0SXMyymXN1fMG8W3HjhJUKzx2Q6QYYN8VLxOdP3Ay4I3kEe1NaxBBpaotum0lnF6mEOe5xTwYu3WoxqyVidFUnxBRLEstzc41LGO7Ugohzpfn3B29/6DtP5guevX7N1LHWt5IQYI20TthCKPdN2b94xurLNykWKncifxNmf/cezdnI0RM88dovtb39nTl5KzKjbBxqetg3qfsr1ZzL6YsNYfwv4n1T1H5UfP6qwjYjcAR6Xn38M3N15+ovlZ3/a9xr/bmtUPSdbo0NdpGcXueKAdrkdgytlsap0ct5ZP2/6GgU/ltrn6xjhly3csOX710LRaMizpY7Cs166fpNnu+nUNNdqdyxKShkyhCyI9LTiacThJplVf0njBDdMbVJTMghCQ+1USGR6csikISNDT78e6F1m78qemWd1WwhtlzVQoB2DW/y4qbw3aIRCq7TJei1bEy9mAXFb+QNV6w0TB6kpVMhoypLYiEata589iInYmay6AhHRhowjqZSiX2mYc1a0rutd/YgbZTakQC4eivid8f3r8BZAbWqYlgNtELNa5pLtvUzQ0KR/vfqCDZszSCJItnmtWRXverJGYsw0jSN1HReXK2vaaQbu3X9A6w/5xj/9Hv/OX/t1lISXQK0zaAZHMFhIAxogTixT8d6R+oaHH58QJhvuvnqDlAcaZ4YfNTqjkknrDbevHXDn1j6SlITy6Ogx03bC5WXH0ZMnfOEvfJGZNrSh4bnr+yyXHe9tTrh1eIN73/4h7733HrNFSx8HsnM4j6lJ5oTPPV6NEpsZYDHBucyNuWfiHNI2ZDKLK3OECc63uH6DjxliNHaSmAnK5fCqgC/d7KpamCrgxTHN9h1GJK2eM2fZ4aAG66h3BW6xM+cFXBmmW1/39hu3eXV6l0lwdMngxAw4H7bQiXOFTbTLoClMop+Ax1emWU4l6KhQtNYGz2dtwDbUs2+1GwzWLLpmc7uMn/qU7Wvq9lX+BFD/z8LeEeC/B95U1f9251f/GPhPgL9b/vzfd37+X4jI/4wVcM/+JDx/572AXQ8mz/4x/rPia8qYYEjtUyuHnQry2lXFjgx3tlZqMFW77nIFSUECq1XPbD6hmYXidFqkOBB0G+mPn7Hg5iodxlWv2LF97k9ybOvNc9njw8DNa8L7H33EydmG0+MlZ6cnnJ6ecvT4gsPnFvS6wW8SkzDhfBjIeYWuVyS3MB52r+gwsD5d8ua3v0dAmQXP4wdHXKwSk7nw8mde4e4Ld6mG0Qqx7GQWQAavrUH+lKJqKWCKE4JLaBqwSVOlgQZj15hb2GE5oFv9mlJQp3Y9SwbM8NnSlHRHCke+qj+KoKHOwC2dv1LhmnIfar9FjYacmmJpLjz6ipdLxhX5ZrMJpU+gfE4tDWM4K7oLaoXaUkyq0JsTU1f97vs/JCwyr9+6xvH7P+T8yZpbr7/Mw3vHvPvBI159/XnSasV7b73JTGe8+eYH/MZv/nlcG/CikL05P7BmuWB7etMPDN7hnDIMAzeuLbj1/A0kOM6fPjV6q4DmyKbbMJ819DPPamWO1mkP2eH8hJ4p9D1Hq1M+/cufQ/cGwqXn1q3neHhxzOn6Kdeu7POdP/g6/XtrWglkHZBBbWxC6YdIm4h3jssucu/+Yz73uZdx4ogiLCaBXj1DUrJkpDF56k2O9Men7A0RnOBdoI9KrFpHOZf1BE1W+7IGecPsB9lGuame/2JEs5RRmalkY7VbtYSDmoNlnAV7v7K/T4wDm8HusWVZsgOlCCEUdtXOGX32jG+hW60wW93dn9DFyRVZLo7KoMWyi2T7s2ftQTVhz1LOtwSSZynWzxSWf8r1Z4n0fw34j4E/EpFvlZ/9N5ix/19E5D8FPgD+Rvnd/4HRNd/BKJt/60/zJlKNcvkX7Br5SuerAI5uu2ErLDRyAMFeKJuRrlQqoTQVKaKRhx+9yXe+/sccXrnCG59+hcW0MZ2QJKwuHXOd2Q1qFzTNPiKekbVZnMfuZcJpVjEe8T8+mYYV+EjMqLgoTELPZv2UGIWIFbR823CxiexPFkymU65PrjCbLgiLTL/MND6zPnF064H1+Rkyn7E3W/BLn/8CWQfUJV7/3GfNQGNsmrEdQNIIdVRjrCIlAotGO9VchoaUgm5FR5xp3JiMrYmz2YjGAgFl4+qLUwh24DWDpGYnWtmZF0Ad1j2SK5GmHAaUOqMXr4zVeIn4MGAjFj0VL7fIJ6P0NlmoOphsxTktWkQ2qcuAQmNwWBYkzgSSDX6TouW0FaOzaDKi0hAGx5v/4h0ezO7j+g0z57n/4I8hR165eZO5a5ErCz7z5QULN/DVf/kN7n3wPi8+/zxu4sm0iBdiv6FtTZphSF0ZDyiIZnLK7F/d597pQ1pp8dJS5x5AwoWMd45ZKLN4G7F2idzgfebs7JjJfssbn3mV+TQQW+Hm6y/ywZMH5Jy4nvf5xj/+NtpHJsERtSNEkGyfJ2vGk8htw72jE548viisMo/ETGoE6c0YRe1xYs1am/4czfsctA10VgUZiGRfxfRq9F4w/mBzabVk0GKlGrwTvPNoshqVZmOnmW/WMnymqtHXyNgTE9aolS3qzoMFf8E1xfhu4YFn4BF1JXs35lmFWwzGKe9fzUvZb7tGGS3nosCCz2T6hR49Pp5no/ltxL9TqS0/3/2M236BMi/k/69IX1X/BT/ZqfzGj3m8Av/5v8p72CJs05+6KGYgKylGnlmoEkbbEmmxwVJhmOIRtw+z1vyiPLk6echBI/ziF15nHSNeEgHrzNQcmbYNoVDIVqdLvvm9b3Pn5de4+dwtZvOWLYXjR74FozHDxrhZEGob2jlXnE9G6CF13L/3iJPjFY+fnvDW9z7i9q07rNeXtG5AhjOizukmHY+WGxxzVo9POX9yxumlZ7E4ZHJwCNPAMkb6MmBbcxgjKAr2KLWDSwuOWmEdsXRXxvpA9Wpu+5V26hNbCKQWWl2JLrF1kVg6dXVLkZcylL1IIUtlBmHPsaYkgWg8d+dr7Ja38E0qAyekQE5ltil1r5S1dqLl/QyCo1BX1clOSaawrcSVesI2a1Pd7rPiygoEZMYfF/nMp2/xxmvP0RSlVCQTSNz78B6xnXDr+UPCOrI838fJY9zkkv/tH/4z/tZ/9h9yGS85vGpCaoQB8dlqO7FnGnpcXuIaazC7eu0O3/v6x5ADf+HXvsyHH3yMSOZ8dcbB3hztE8PQIVnolhu6PtPmyA8/fJe9vQWHt6ZM9oSJzri9d4P3H39ghvQ88tVv/iFtag1GGnpCP+DahiEmHEojLUMU3n7/I842kXbScrA3M0Mrpq+UxeODI+hA65XHDz7gziuvQJgyl0x3Cqmx2hOFqRTF9p9rnNGVs82JsOYsIEPjnGV8ycTsDA51xFKfGpwyqNDnSJt8GV2oSOPRorBZMwUw7v+zxVC2++YTtmU38i5PePaU7xhvpZ6NH70q1DOKq9XsukJVZYdvP4dWb2i7LpfRjDssHZM6rwD3j362T14/4x25WgyhPPuz6m2RsXL/TIAtJSWsS6FieaKz5xsFssahdtOdKPcf3mdYbkj9Gm0Ss05g1RDaltPlCZebnm4YmE6nDOr58N59js+WLOY3+Y3f+MtkGUak59lNtP3sNvRBLUrMW4W9iqdnZ4Z3dbnkBz+4T9u0uGFK1wfCdI/bL1xlk5as1o71sfL13/0uz7/8aS6PL8iDY+/ggCRKv4FpC0WLoby9K+uZRv2SOuyBcbBGMf7PrLd9Zqlrq6WGUn1ZhasUXNplQgnOxQK7mG6MM/6lpdQ+WtZQRd6cDa9xBLKzeaeizqZBUeQUKBLNlAjPZVDr7DSpm1hgPilsIDFHJ6A+4bBRllqUO1WcwVaq1Hmj9fho7TsoSp5CSdOpZ65mlaWrVLxptqs3tQsSPZ7rL7/M6uycd777Ic+98hJumpFmH5le59bhgt/9J7/NG7/wF7l6bUHqlnz8/pvQbYjLS87OTsiGd6AaUCInx8f8uc+/QXKJ44tjMlY/iNoR/B6ZDYc3Go6enLA6P+LBAyF1a9Z6zpW9CR/88B2+8Eu/zOLGHh/cf5s8mXB6/5gHf/geTfB4jeTUMWlb+l5IXqGBtgk8PL7gvQfHxCDM9vY4PLzKi3eew3cXSI64LtMlJXmHuDmroee12y8w0Tl9yuCCaeQPGZeFIM7mxGYd1TkFw/CTKtI0I3ZfJSosOraB6eZbzUY476FPpGFgRc9ib48mNJwPK3oGzFFbluuLZHNtDty9fhxWD1sqpSsNVprSM48ZqZsVyrTjM2b1I9Oqnped59WzVmfojrUAbECRBSoFTh77RyrkUUglO2Ngf9r1M270rYAKlTZXg8zKoS8GSRXJDWM7J5Gqs0JNfdWDmtKkx6ILKAzOwTNgAAAgAElEQVSZDN571htYruH63gHzxSk5Lcns2wBnSZwfd9y8veB4eYabTTm+PGZ12bN/CC4NxMmO5ylYJBSHg91oRy4yu0VkrOitiwoaCwNholy5MmWzWpIXc2gC2TemOSPQR+W4hzgEJLaElJnPDljJipwiQkNK2RQtYzKOs1CkA2yDmBRCcYhFWhZXI3tF81i63uHBCy4VWppowcet8DresZ3mkTr6cZvaVoqbLYu6UOoEVfWUgk2DZiukixSKZJFZru+hxZep0xINuvGA5UrHFMpUoqKiGFuEYGqgSMkkXAmsiiYSCi6TNGzhLkq/SC4FwbIjVY2xREioWsOZV1+mfVWhugGXAwd7hzSveN75+CPuvvQiU4Hm0vPKp2/x4Ufv8tm/9iII/OC73+XBD/+I24dzhi4yDMreIlitKXq8d3z/rbeNSpgjKTrmExj6M25dEdrJBWFP2Ww63vh8y8HBmpPL9zk/3+C9px96Uo587atf4dW7r3Dz6nXefest1mdLJk2DRItCdQDcQJg7hqTghE1Wzs4uaCae/atXmLYNL770IgezBen+mtRYp7IPAUmJqEpMGU9gUIf0A2nIBBFa78girGMEjThnTChrujUdHhsuADla1D9m86VHhhwNsisd5jkJUz8lTyYMcc1msyHqgJ9b45/LJgNuFqDYjZ2o/pP20ogclnnWDtjtZzABuS3CYJmhKxnBLq1cC8Gg7nE3OpX63N2GLJsRkIvzs9ajyqarn7cM3aHUDjQXttBuhvCTr59xo19lv2ohdpveWzTHtlAixtFVFFcBdjFerbEyaus+VjQrN8WVQeCiShs8K4xZMHWBKBlcRH0wfvW85bXXbvPgG/doZo0pyjjHsusZshKybCN9jFaGbDMxi5RdgSKsSYe6SYpxEYQ4OJoQaGYmw6tB0ZRwwb5vIw2yybgeOucI+/sMTy/x2pAHe6MYDXc2CKI2LFk0YHBLoZ5pbUiTsc3dBrnUtS3Rg+SiMBiK4Y5Q5IUpkY0KDC4XBkPG1+KqGDZuYfL2oKUdWltWLYJ2jP/VzKymFGMziu6m0zvMhbwdN0nJBra7BrQMPjdHZOtpeETpPRABEtZjkQ02Ul+izPLrMkkLxXoCxr8LGu04ORFiLvRSFdBAch2z2ZzXbr7IRyf3eW56wEzg5HLFL/27v45feNLpU85++ANuH85xrfLxww+YNofs7R8iOFKCOGSSCtdv3OBgPmPSZN5995sc3FBOzo+Y64TV0xXLdeT6tQUPT9ckJ0U/RuiHjpSVQQceP/qAk/sfsIk9Korf24eN4tYDs8YGfTu/4OLpBftXp3QxsTebGquqNSrjYn+Bp0WjY/CQG8/lekNuWnIsQVDODMOG1cUZVzdhNIrqBA0ObRwuZpyYuJorDK1cmiVc1hKkFQPta4tdQx8V9QH1DTEJnfZ0aUlOjtl0jyCey3SM9NGmcuUM4nHekWIcI+pPGko7q1u2z26EX8+3uG1fj37CgQCjU9D6M6kMw13cvkJCdSpg+Z5qwZZmHfd3ffzue6hqOevgC1vo59roW6NMpuLvYzbDLgRRF2j7OFNkrAtV1PFKGFlxsrowRekc0cQkeDyZtmmYTwPrwSONqRWGScuGDhcEsiM0DVkTSUzhchChyTpK+RY1GVQpjV5FL1t1y7l3lLZ9sSJnSducy0hIpH6FpiktBl+lIkolImjTElkTvGc2meJmHY2LJHWk1uFDxLkBreP6XDLp4Fy6cmseUidYqXHDa2OJFoG2XFXJfCqYu/HhLXW1TZldtE5EGKEzO5ZVvkHADdY8lymwmzU1Sfnuqia2Zfd5GAtSWuh71QmV8sP2/qpgmkJWUzDKZ90r5R6IZQeZAS1ytw52lEsZD2P9dDIWax34MqJRdOz2LTzF8TIoyFuanaOtsBsMtsrmPJMLEAKvvnCHDx+ccByU26Hl8194FZHEO29/Bzfp8O2UrltxbeG4Om9opsp0Fth0Jno3pA2H166xNxXuvft1PGcMK6W7hKGBzdBx5coVLvtEfym4pkF9x7VpQ98NON+QusjR+pyYeq7d2WNzfknf9+QsHO4d4KOSNPD+hw84PVnzpf07NFHwogQnxGSwnTrh9OKCScqEaHe0JKRUuGLYDKwvLgmHC3QwKC1jWv4Jq6QMzmbU2noNiDfOvcYytCaX/VYZeCIkCeQAmpQ8DOQQmM1moEonHVk7ogScgk+MEJ0ldPrMLNrtfdTRxtQCqYJBRwVmqlLYYyCyY09qIGovs4PzjxOyLOjIKZGyZcKadQzOVBVXzHJONk/Ajc2juwVf292pyDmkGOlTouu6n2+jD2xhEjuPY0rmxIb3WZZWbmQxDs/0xFccHS3yujUV2npfykaYthObr9m0PH5yzsH+dWZ7CiRapng5xc1avASChyCRmCOTnGgCuKKEiZRGEzUHJAgEa1vyWdEGkqcY2Crjq8UIGR1xSA7xzrTTgmegw7dGNUx9T1wtrQjddGx0zUVaErsOkYAyweVYGoTKd1WMfVMid+PPS4nSS2OUqA0JLzi6uTNvhlIbVEzaweaxbCUWRIYRyrL5szoWrERr70PlHEOdYKJleI0NKccKuiLbdoExqtpaVy3H3iZjZXQcnl1wVklldF3eeYZBW7Uxx146GRtG1SSQi7BbTcfJZRA5FIey48hkOywFMHy5SGDjCo1UK/QI6gbLPAaFCD7OmWzu0YaBvatXEO9ZP3nK0aOP2Js0tBJwzZT54R0a3yI0HATHatnT58TNwxusz46YZEdKG/zcM2wik8mE9RC5evWAEDybyx4mcHLacf3mnE3flabCDXET2XSKauDiUeKiM7kJp4npzTnLVcfDD1cs+8DelX2QBpWBJngGjWxKMfLp8ROem12la42+S1JzxGYf8a7l5HTFzf0bTKZXCPGcVVKaqSOmHp8g4MhRC7SkpFIPsqOUR2OZEMQ3RBFSTuRhGFltMUGfhWHoUWesKlc09r022EjMGmjUPSAjQ+iTDU1a4Js6AL2259nnKftbyn8oFF6+zUTY0j5HlCdXuwRJhxK8KC64IguuhRnH2AukBS4ancGoypvRlNh0HV3XF+VQT9N42nbxIzWKT14/+0Yf2AnvPpEabaddGfa1a+x3LokIecSvoUS3QqHpGaZmTJPEZr3kn//27/Pv/wf/EdPpI3pNhGHCXqfM/NR4ypJQIl4TaVjxztvfY4hTPve5V5n6OGrLg7VIKbDqz3jrO+9xeOMahzcCbWqZzueENmCsHiBH/uAbf8gwXCJ+j8kskHPHD773Fn2fWMWGeRCOeIfnnrvG5WXP733tj2HVITh89pBbfuFLnwesmCgJxIcSTbvCfCnRjAKFUy8OHB6StwHaEku0GqrXsMPhS8LpzLDlAm9YGJS2Ble38023BauaAtvKkF2BXDKV+TACMnn377VTWkqmpOPhE6mvUZhWYwQGMsJYglBxdmPyWOm2OgIpJ9ODNlRtIakjtooIW9F5w+lOzQOsVkOdlqb2eXcDFTIpD7RNg6jj7q0XuHVtH512ZJf44AffZh5g0jaAY+gji9nEMjA1XfgQGpyLDN0T1utjdAgcnx0znwVOziLJKZMWLi6W3Lh+yNWDwNFRT79eGeWyUZyfcLm6MOMkgayBk5MN0/k+fe5Z98L9x6c4cexdmzHp5zx/8xZ5OAOXGWJEnVoTk2toZnP8dEL2jjQU+KxExE0IpEG4efs5vGvQ2HNxcYZXSDERQlvGQm7I3qaNOSekmK2z3NqlTKLaT8gipKGnyQbFbtpA0sy674nO40XxoZzpcqMsjhKSWg1hVx9ghAt3/r3Lwtltrto1pM9w5kWtLWBk45QznwzO3D52961KnXJHgE2zksaC8VB+L9QRrSkmYozEGMk50bYNk8mEEBoLLgua8HMP7wA7q1WKh0BV0KswTu2e1GrwRzigvARQ+flSKYcjOiCYtUumAimZlJU+z0jNnEnIbDYbpq2juVwykwipw5Nt3J0Tcu7pNyu+8e23efW1V1jMLL2LGg1K8ZnsIxs54Z2n75I+fou//Kuv85WvvEmXPE3bEsJVvvylz/LK3as8eXxMwhH8jExC8sD11vPw6UCImcXCcdgE5mGKrIThpOeXvvRZ1nlFMzS88959YkwksWJaVkqjU1XXNMx7xCx31kiLDjqlSUhrQqwmI5BLX4SWWoR1qcpIJqjr/knGVWl5rYtepkglw8xVrBacZSuRLFZbyAWBcSbTaQ4pZ+PoF7nokVJJgXpquq12cPx4kAsn31dIrzCM1IaeaOmIFalwVpGnzlKyya1me92HDmcZV8mCYsk6M9lGPyZXBnmoSRd7wCeSOA72rxJ8x+b4PqvTJzQT46Fnl9GgpLykW0c2XWbZrUjaMm8G9g7OWQ5LTpY9rhH69QWhEdQLiysNqxNP7pTLuKEblL3Dlv15w+Z8w9HpMe3elItV5Mp8ihBZi2O1WjOfe1abzKYXFvsw248ssuBCV86P1W2c80hWhn7ANRNS660+I77g3BXSMh5733V0qzNCmHLj2gH9+oTasJTL45yWJr6UCUU62wi8jl5Ny957oW08y5TpY7LoOwRowKkVgnevsWkJoU/JtPFlRP52ApFnDXpFEsT70YHVPbVl20lJWGVE4kflzAJDxmSDflyRYWcMjLbduXbmGA1S1QnL2Yy8RtMNEqAJDSFMbe+6Ck/LDnJh8xt+viP9AqmKeJza7E0ZsXzYYreMvBTjXhdaZ664uu4s+NbEUVM1EbJkIsn+p45Ag+YN0yGwHhyhzcTs8A6UnrYoUuZsEbIPkUXboXlNn2Wkn7lQdOC5RLSHuGZCy3CRuVwmFlcPEZRNt+FytQJ3C+/bCkyZZnjjyRrJyxV39q8Qppmu6zlermiahps3puxdb2jbKd1S8UeBwXmDKHpLB625tQyM8ZXhsoOPiyDZlwxooBbLpRw91DZYLrCFFiE0isyCjNmYNQVlKbh5qcpqiUI0lrfDRNCMAeQsMq4NbDVXkIS4nXuNN7mJkjab/ondS2sHyDbrtYCeqopEv90DeMtSsrWWjtS6EgSoliI7A3U6llF+c2lPSOV0t+AHy2qylETUhpnXbCZ7JSab5quYn42D4qc25Dwnj6aWNmeO3n2b0ASaidWevvb7X0eI3D5omE6v0DZzk16ew6o/IemG1A1sVh3L3LNwLTkm+qRcXjYcn2YSkeUysYnK3iRwcbqmX/XEIdBdZtwGXFhyfJnps4OcQAcmi4azy8hUWyRATBdsSKTYM2+m5JBR15RgIRMqLg0MmhlyZNK2xHrEUFxQbr50wBBbVk/PaFzFpoW2bcjrNePIGucYQmOzaoeBSQj4EPA4+pxY94M58uDMqVaqbt0jWo26jEY/k0h5KOqcaUczp4J/FSbeRvBjj0dNiUtj2mhOtcDG5Xl5p6tWNJc9lum6NaERnAsE3+JcEWMExsFLKEO2ech93wOOpmlo29Y+a9hG8lZj2Na6nLOzZZ95l/75k6+fbaMPMB5wVyiF2QyR2O00KrXiywButTCr3HnD3CrOP44hq4yPMj+WwkbJbiBJ5mLZ09DgYiQ0DWElTBYQ5z3TaWbieuKQ0dyQpScQmE08v/znXuT7b32TH7z9Aa+89GleuXObFz91C5We99/9Nh/88Iju6Yrp3oKvfeUtJrMFm27NxE/BDXz3B2/TpQGHw+lAFthsNog4LmPPa88fsJjOOO4vODp+wot3XqBpI8EHhsuO6QSGizWeDKnHM8GXqUsOSvRumuNFyXirvJkzUsTCoFI6i5SCZoxn7wozCotwsxYYy4q0qKKxHCRXNGl8kT/OBafNVhCr8uRjYSqaMJ34ZNCSqzQ1VyhuYrMBfGHRO8hVc6ek7S6bw9k6+AqjVipdJqtNuBLF4CuJQLDXD7FEbBTpZIvkfCnMiezssZrWQJGQVsiUw5/xLhr/vEwNC0Mi9BeEuCB1GemVhVPiOhKTR8OAn0z44Xsf8fj+E37j13+F567towjOeT58csyTixPW8Yzo4Ph8IGminStNI6Ts6U8T8SQxaVoulgmY4ljT+oaz5YbFfEIjSho6plOPE896lQmzQNsGnAfX9lw5yExape8zfb+h73uGfqDXnsliYlO+sM7rbn3J0CQ2uTOOPMLleombTRikIZIZ8sDlibI+X9MsVxAjTSiT4HLGSYNgnc9JhOVlxyy0TNycXhNDNCll7x1KlcG2e+JQ64lwbJVqdwy47d3IpluWpiarU9Uxa2Nhl22UD0XvZtfow+gIwNhxu/Z1WwB+dsDKarVmb3+K95YJ1rxaxATdLOOx504mE5qmIWe1bGqHDWTRv8FMioyOTsQcZfkUfwp7+rNu9OtNpaTaYN6sSgIXbBqASknMtQ15q34vtapajL1o0ZQXrKouxuv3rsflnn7ouHqwoO963HyKXPbEIKTFJb4djNlQisPeCzk6pPc4d0nOPWdHl7zXf4hG4e5rz5NVyHHg4rIjZ48K7N08wKGcXWxwrUCOdHHGew+eMBVHHHrSoAQHPgTayZSbd67z4N5jcq+8cPdF5ldbHndnOIRHT58ybC45O9lw0M5ptENln+zNUdYB0VZhgDqD10kGV7HOWIL+ZPBPqgZ8juYSoahxX0zCoABqArnILihNXXWLRjIGq9XzooVCO3b4Vg9d6gZkXGli0Vj41E2G3JKyR7wNXKem0W574ChjBoXC1nJqrCEMhrHGqhayt6JtGYilSfHqTC7Cl0Kw9Ca6l93Y1V0hK+c7KIqaVsyOeKvskjUiyQq33//OH/Ct3/86g8CL1w7Imx4/a4ldT9s2PD1+yMLd5nQ2o22Uod/w5pvf5403XufG1WslbjEK8rA+JeZzwjwQ+4HlpeLbPbrzns4L3dCTs2cySSz2pzx6sMK1QugbWAh9DGyOexBh/6Yndcr5CsLcMWkijQ+cX/Rcnwo3DuccPdogAa5dWdBFWC572qkSYod3C9R5XBICLW3T0vuGIXVb4TQniPPMgufo7CGH164xu3mN/UPP8PEpOYPzAVKPpmT0yQB9DjCdsyzRm3eVtFF58LVpKRfiQc3UzODvqlvmZLCibyClSJc7XFZmk6ZksDXy2EI9W2HGisGXDJWK4RvkWTOJPLJpFO8Mh4jZ8fG9I959911OTo549dU7fPGLn7YuaRrA6jNtOxkz25gsghcn5BzHbKNmAojgSl0OKBBSgSl3mEN/mutn2+gDUjov6zR7LTRBw9120qvasCG53JSqRV/br6lh3+jBjQtTqu1xwt3bL3P8/iN8Ug72pgQcbXCkrsfnS9p0iQ+ZwWemyeRhW2lYY1GINHAwE0Q2DDqw7JSYHc4F2smE1XDJk+ML1svI3rU9plNH7DN5Gk0rpCg1gjf5BxmYTALD0HNlb5/vfe8tXn/5U5yeXSKThtnEod2Gs+WK6f6ENiZutI5f/Quv8t5yY3BFUjRlq1I7GGLk0YOPefH2S7gQELEIy4lF2cZQCXz87vu88/2P8PvX+Dd/9S+WFNL6CszhWYyUNUO2gSZINoZFMY7KsIP9u1Hnx1FlDig4vKIuokQeffyA//V//C2cT/TrOf/Wb/5VfvlXPkWu0b+kHa0jpYzRsCS9RgCFWWHaPGLFYnUGyWiPYUyerJUbbbUX62iM1K7H2ruQaRAiIkOJMD2atqm8ZQ8DWTo+eOctus3Ay6/d5f4HH3Bxck6XlTtXp6zzGc/Nb9BJop0I8wPQdEHOmclkj29+64/xYcqnP/Np4035SOzWnK3WnFweM0hkvenp1hk/y8yuDpAz5yeRGAPOZTarFUNKrFbg+96YK+ue1XJgtudoCHTrRF4p9NBdRmTWMojiPcxmEzZdJMwdzcITpePsKJKY8vQS9hvh2gHsh47zk2ja9V22KVYxMuRoVEuMSQOOF+7cpgkeTYIkITRCHoQhJryYsFmvSmagEgZqV7WKlolr/IhccQait+zTqzVdCZapZVXme8rh1Sm4lpwuOL98SpA9Ju2k8P4dpvK5DeCQgiaU/SUuW7DgfIn+CzRUDLSM2j8WnT94eMm//PabnJ6eIERIQr9ckUsR34lHJOwY6nIOSm3RunH9WDeov3dl1nGuxn6n6DyCtM/87CdfP9tGX9hS5QokUAt0aMFwkXHSEQWyMddb8XpXDr01QFTOd10os2OZJ0cPuLInTH3PybDC7x8QdSD4GclBECFerskRonPgAz017RpoZg15OoEeps7MUIpWpMnJWsQvl+fk7Dk77ckyMLk9QXMm9tEiGjJNzrQ4+uBo/QzUhjk/efyE82XP8WbJ/UdHPHfzFjezgkZcv2Lftwx9IAFvffSQbnqNg6x4tYhMfMluIjTS8P3vv8WnPv8aExGQREwrTj7+AGHgnbePOLhxh9OnT4jrEtXT2WEUG9umuj0sFvjYYXVuGNc3E1mnSBvsM3SdMgttqYvUzM02d3aGTw5pycMn97l5a8HDJ8ecnR+hvIBzYTTGuuV07mS0pZ6jxTh4ShBQ/tO8k2UYO9ya+uyxWdMzKXOuOKHPZvTVJJUTVuwfyQQSQYUYFGJkeRZZD44/+N2v8/Deh4TWoT3cu3/EQEQml6hLTAZH1zv81NHMAo+fHvPBR4/5tb/0qzQhkJ2w4oJvf+urpAjJJY6OVgy94jzcemHGJnZol5k45cqNGacnp8zCAe0EuknP5UVkUGXatuighHbG5nzD6mTD3Rev8fTBGT40LNcZHwZu3Z5werZGB4dGR7wYaGVgeeGQdkU7n6LBcbpZsrq45GD/BlkHjh+f08QeRU0OYohkDzmCquPBR/c5WCzQfIy73LA3rAsXvyFrGQc4gPfQ+EwvVVKj4u52L7clvBLglbOVJdG0Qu5SmV4FTSPcvDUl6wVNaDk9echqfY5jQb4bOTw8ZDKdlwBFt2NCs+BDJsVoekSabXRjrjQRLcVnM67ee5sx0FkU/84PPkJo7XlByykYCKH04rDVyRkHsuhIQRn338hfGff31pDruA7bx+5CUz/XRl/AMGTVcbBIUVkrvy/RQNWNEVN7lIK7jnp7o20KY2RZ2+spDUK3r91i0SbedRPysCLFE4IcQ/spBpkxmc3wfkH2wib3TNaJQMCpEMUTnKeXQOwTQ8rIEEmaCmacbHPm8juCTfRJC/q4ISQlJEFaZZaE6cQwO02B6bTlweMnvDCZ8KXbLxBSx7Wph8ePGJZn7E8n3HnuKovbz/Hdd465SAJLYT4xfLkPxiLKEsghExrh1ks3uPLcjPv33+O5m8/T5zX7baZNPd6tSeenNLdfIswnfPmXX0PDBRkI2ZrXIJDdGs1tYU4lsu+MBa0CaYPrzkkZus0KN2lgM/DwaMnNg6v0GXJc4VJk2AxoXJPdNVZhyuMH99k/vIKbOpLrGZJHc8JTRj7iLeov/Q/qa1BAgZJkhHy0dHSKWBMQqQiaScKGb7uiullqE4kyrMUa53LpOHaaIPtxdq1lNZTGtUJRLWyUT3/2FcKVhj/8Jx8Su/+XujeLtSxL87t+a9rDme+9cSMiY8iMnCqzhq6unqobl7EFxoYWRggJ1AgEDyAM4gWJB5558QNP5gEJXkBqnloYiRY2YIzddk/VTVdld1Vn5RiZGRE3xjvfM+6918jD2ufeqLJdjcSg9JEy742Ikyfy3LP3t9b6vv//9++QStGkhrfu3eGT+0+4//CMO68NqKSnqgxtlMwXkQ/eu8/rr73Bjb1p5k7IxKPnL3hw0bBZt8gyIIPGd4lyEFitLDIalqscHXl+dA5FwcpZJqKkKqHbQKEF0eZ2WLPcoOUAgWMxt4iqILWJGFtu7u6Cb1i1ILyjChJdFsQkca6jHpYMKkGhAp2PmHqAbz2LoyM2XaQKDpE8IiWk1vmeTB4hA27dsPYQvUc0LePa9P6NQJJZc29jpFKQXESY7f16ZcGkL4uCLIUsy5J121AYgS4l9aiiWa+RAsq6RBUCUwXaNtC5BiEF8/mc8bBCV7m1onsshVRZcuk6y8MHTxBlYFAacKDrgmpQg5TsjSa5PRsj3kZ8zMleUgqquoBkCHLBN3/2VR5+0fLk+QkuCGxM/dwp50ckudX4b+GC+XrdniGiSFeFvZc7+y30SYgeCXLVhrqaX/wEKuKf8PhSF30SfYDJ1Y5uKyHMKy2X6pC+h3C1BG5bALw0ZBHb2DPPduKdLyuJUpp6lFA6IQPsTvbYtGtcEMSuRGEQKqJLjyzbnBgVNdGDjI5SJkxwuNpDFdGFzLwU6fOWJ2YeTsZ9R7yzrFYbnPd01mIDTE2iPlkgRgMefPIBtqv4F/6Vv8DixRfUD0+YpkBJx54xiKSIi5aNGTLa3UWOd7h2q2Tx8Bm+iZgk+t54/juzryBBCgTb0Z4ccP7kY8LFU3ZHQ6yM6NhHPOL53h99n+ASiGN+8N4P+cPff0Q396SQeP3db/Bv/NpfAmXxKhLbhuai4+K84+LsiOXJY2xzyKaNeCfQA43zlleKyIWS6HKCKkuMLDG6oK4lH3zwPm9/+88zHU+Zn52zbgMplsRgkWQ+y3ZXlrd4vZRS9CH3l2as3uhGDjyJQhD63b7C5+HfZdB7NhPl2UR46dq5yj6lV6lsjaAEddU+7PvJ/RpA13V8+N57yIHh93/3T5jNctJUjIo/+ZOHaFNQjSTf+dmvE5Zzjoo59587Prj/gpUac2dnmjcsUkJyTKsxlR5x7tfsTWvCMLDeNHl3bj3RBRSadWtxyaC9xsSEvci5tK/cMdnx2ikQFl1VNJ3L71fm+Y0uJD4Y5s0S0oZiOGQ6HHL8+RLbSLSRlAODqQyb5ZpkDOuNp6xrhCpobKBtHUpHCI4AdD639UJIKClxnSPFnII1qGu6aLNLVuSBqVIaZSKqAO/725mXvl6WwPwhVLWmHACVZzCWmNLQdBtm10UfuN6iDGza0LdUBNZailKwXJ/y4NEnhNfusDe9wScfPwCVP2ujJOvVnIvDOXGcmJ0AACAASURBVIXWDE2FVIbJ7jWULnhy8JS98YjZbA9T1ig0KWmu8jsS777zFnvjIdU9Q7P2PH764tKolpHf/cXy8iF1W7Auv14eOS9nDVeF/Gqfv51B/N9R7Lz8+FIX/Xysk5c7++2RbmvkyQqM/iZEAIoYtxLJ7Sv0Bb+3Qcftikm/GPRXVyT1MsMSv4qMrk+hyFzz2IEWGROQVxKPFBElIlrBxkk2naemZTT0hMISbKBZS1IoIVlC0P3JJPcAfQiE0EuwUiQliVpH/MELJl+/x2yokJ3g+eePGI922Iyvceed6xy89x5FyhjZIvRH98GIk8YTlOTOq3e5OFnhUv7RpeCJIQ8YZeh7lkREWDOrNQ8ffcSdn/lm35vu+9PKU8rIqgMZA59/8hGPHzxnMhozP2up6ilfPLjPo0cPefjgmBiHzCYle7vXuLY/5d23v0ZRvcrF/IyAozGKLhZ8Z3BKFJLT45bJrdsoU+WBoEm8WLbcfX2H8zoX+EJJ2hR6pEGWdm6DWeBqkRc9LUFcFuFEjFnlIRDIeMXVR3pIPXK3d0GLlE+GUSWyb2CrLwe2PVRB9gSkgEi6N/70N19/DE8pMRgM+PY/9xdJbsl7v/UPiRIKZUjrBXfu3eTF4QnFqEPLDh8cxkgIgXbV8PVvvI1zpySZWwkqRm5f22M6Kjk4FMxPHG3sGE0TpXa0ItAERXCJqpDMyoKTU4cPAaEUwpIll9azWmWVmw8bzEDQeU9KFcsLhzYKukQ9K9BlTVKCxUWLj4FqovDBMt6tabqWuqhxPhGFwYVAosEljesyglymiPeOkDIpMkQohGFUDdApoxKSDwjVE2Zl34YjoYTEO0dKPXbhpTqwbbuE/jNpWo+sJKOdguRakm8odGTdtJAK2sYzHBYMRwXOWSQSJRXeR1bLFdf3b/Di5IInj4+x65aV29Bn0jMeVJR1xLkFQcJkOGKzWTIcDnl6+pRTW/POuOC6Lgk9IHErEgkxMtsZE6OnnlS8/ZU7vPrqDXb2NEqpPtay51TF7MYlpcsh8eVAmav20Y8V/LRdALmcm8XeyPIyIvrPWgO+1EUf8oAPMmcja8cvt/x5EJvyDZ+ph7mHmFU926FkDu348SPPS4tB8v3vCELIyNW2SzRhQKEMWpdsVgGhE9aXCG/wPtJgc3CETJRSo5KgWS3x5El8KSUieEQK2byvNb7zfVZJIimJB5yPVFHgcFg5IKVEFeDNr7/Lp3/wEY8fPOStb74KAaxRTN99hxAVmoBcdXgHCyHwTegNMYHZ7gBtUlYVCUFIHhl8RhYoh1AtQkRuTCa42ZoYAzKJHhORuegpREY7FU3rEBJ2r1+nMorhdILtTvn4+99nUlSI0zX/7F/+JW69qhBJEYSnwlFJyWQww+rERw9OkFrzrK2ISBqlGBmFktmFHBwM6sjpwWfMl3OUzDtJpSPC5xNKFJn5A9k2r0XK2aqCvoWXA8cRKaN2hclD5ZCLuZA96ZCAiIrsBgbSlWpIbC31kNtYUROjysEvbI1EfcALkJLuKUaggsihMDLRNBucc+AiptSkACeHp/jWY5wmc4DyEDqIhkIE9scFT48kQip0DMTQ8fH9+zx69gznASkRSVFMA7qIuMMOmcY0ocMMDaaGdBoY7xbYZoNdCISpULJE9W1PaxvKuqAcGs4PNwRrSMFhSoHWiVIVzE8t1kbGk4rRbokTEtdaFIKT0xWFKJHDDJIT0hJjhxSKEEWmuiaZqa4JJpMhBQ7RNEyuT7n72uvUZcWHf/Q+w8KA9NmwZiqiLhAJdIi0KfRu1Zihazk3k9GkptCSolKoyuLTaTYfuuxUbVtHkolhOaRrW8pS0KwiukgEAt5HdvfGrBcXdKcn+eQvFZvWUw3yCet8uYJoMYVhFec0TcvFectwXOFS4s7Nm7im5f7xfcajIYPpBOs8e6Pd3muSo0WlTsymE9LEU1YaUkkS+RrbklrFS7OBGK+Kf4p9OnZKGe/Qu31D37beyqy3M7Htc+OlUeyf4p4+kHfBiFy8Ra/L79+x3HJdEsAWhUu/CcuDkq3K4/Iofln8t8ei7EzNplKfX0YrnARNm4uzFEStcKpCqQkmipyoFCLOJQgKrUtu3L3Hk7AgyBJrHZ3vsN4iVZZgGVWSYptZG2RIUoxZriZExElojeTho6fcvf1Nqls3+MVvfY31+oTTIlHuzdhUhrBoCfMFabVBrg03xyPk9YKiFDx79oLXbu8gZGAwdtzbHZEuPmS1cFk6KiLnF+c8fviEm/s3sLYAVZLocjZ5yq5F71uSh9//u38AdkFaRc584Oxsye2bO/zyt3+R5y9OuXj/DLs6pjluMUEjVCRUBjkZXQaTjxJctI7PVgGpNWWq2Y9DlOhdnjFw7+aMTXtM5RsKGXr4WmB5csT6+DHVZB9hRtl4F3tJJnlHnnfmOUA9f/6RJF3uwW+xzb3Mj35wna+Al4aEgj4habuL0vm0IETvIYAtTro/Y2SxaRKkeIVhJgXKQU05mqDCBSUKHSKzumQZAq/fehVcXoxcXKFSpKwLtIHgyqyd0vDJJx9z//lntCngQ4LoMTqysVBHzaAs2TQB4RNxGdh0HhMV65OWroVyLHHJE5q80FEr0tJAa0gbz2BgmHcBicKUCSk1TdsilMI5hfeJloZyIlmdW4Sw7FybURjN2cmcEA2TyiC1x9t8IorB5yyH4NEkxhW8+vqrXL/xCoOqAiHoFg0qADqfsowxVNMx0+u3iElju8jRp58hJZS1ZjAYU49GyFIyHBVIb9ks56xWC1rboguJi4HC1KwuEl60jIxi79qA5aLFOUEkoxqUKGiTY+5WJMAUBSppgpXMNx3KBLTODBzbrFGFxHpHs4HOXVAOC46PnvLkIC9Wu7tDuiea2bXrjKYjVFDIqC77DFoXCGHQSl4O/rfb8J+EvP0k6fOS/qn6BUJkv8qWyb9tPcr+tXLRz8KWGP+pLvrZOp9vVPUSSTNdUTN7FEO+WdVLfdmtzKtvurL9YW3lm1dfYr94dN4ilMDGjslI0W3W6FSRyB9g8pBCSUIwGg8oygIhJVE1fPjxx7x6e8ByIIhBgvIIs2bj5pwdnuC6lvWmy64+sXXz5Z5nCAElJI5AO9McPz8iPX7K8eGc48NzkgSrDYOiZHF0yPPvfcBUa7TRzMZ7fOcX7mDFEqEEZ08v+Oqb75CS7Y20ApHmyKAzA0hKsI5PHxxx//EGozWfPl2wu2O4c/M63XrDpweHHByccf3aPr/y7Xf4+a9e56//jf8BH0Brkel/MbC/P+LeKzXaW7QATCL0JpkkMhBPBSisJi4a3r63jzSa9aajWa+oR/Xlok4qUCkgoyDYQDzdoF1CxwWb02dcHD5HFEPK8ZDh+BrKlHQhm+dKWV4N/VK2qOeZTVZ0xd55vd3JbzG2eai/dWz35r2Yh6hRpH7BEEif9dxpm1jUa/bVVg7cp4NJ6XsWiwStCBHa0GGjpRxqlptANRjhhaYJlrKs8E3Let0Qo8dbTULQ2oYvnj3CSgE+sjNSoAXOBoKVrDeenZFkuWqIsUA4yco6YjJIrSgmBam0dEuPSRXOtahk6FxEbCx2E9FaYwpJWRX4LrJadIxnBZvNhqRgUtc0tqFbGGKA8WjM/KxhvFszGQ9YXFhWC89s11CYOgeKZMMGpiq5fe8ur969wbDOnmSffDZh6RxPaPrb1HUdq41jvLdHNIJN6Lj1+m2GA01VSOzK0s4vCBcdi8ctAyGw6yWqDBRVdqqHEGn8hmv7knK4Q7veYG2+NsAhFbmNKAJSJ7q2YzocYbuOwVDTxpxDUdZQDwxd45CqP1FYTygc49mIi8WGowYiDm0Sm2dLSqXRruXQwO1XXutzKBQxBLQybMms2UC1BUNeFf0f4/ikK0dtNq31m5h+k5u2PKeXWzuSK9f3VR/kpz6+1EU/kbBtQ1nVkBxIx+MnT7g2qrl55zqfffSQi4ViML7Ozs6EcSWIqu/h9u/+EmqUtjv/XHSlEBwcHPDxB59yenHGctXx7V+4y83ZCNtcsLPjefz8FKUBFeiswKhIdIIyyZy8IwJJOZJUODRfvFjRpA6/sVRjxbNnD/iffnPJs+Nj9nem+KBJ0eF9gwue4SDLHlOKBJ8DrK9PBrQXA46fnmEKw+GzU+7c2UPgmJ8uaDrL7N03cAdPKJREKU9MERMcCYMQJtM+Y0LIxPfe/wHvfuVrTEyeh0QREcICOdxCSo2NmmfHlo8//VNGpacYVrz9lTeoR0Om12coKbCQtf4KICD7o7jzFp8Csp+LSC+ROnKxPGE22UGi2J8MmIwSpcyp2mmg2BmPkcETkFxKb1JkOqr5D//dXwUEUpENLDFRyAh+STidc3L8DEzNMng+P3zOq9e+yv7+La7v7+Qde0pZwUPIX0NCb8NiVI9KjqJ3JvdN+ZC11kJFIJBkL3EN2x6puBq+JZlnH8QeiZ0ZK4vVko5zCmuIqw5RGpJJhCTwHlRUVFKyXl0QUqBZdKCyZFMkSYiBhOCLLx5ga0N75vCNpPMWd6kdb6iGNdYnOmtQCIZjzXzlWTcW5RVyILJLuIWVS1TFgG4dwOVBafQFrRMUA52xBg6qQc5S874P3xG98TAFkoamzRyYk6M5RVUiXB+C0li0k3mH6zwhBlSRFVY+dpCGaJWR0Hmn65HRI8noAaUNaW3ZzBekskFuHLppaF+0LDcbRMgziyQCRurczhooVAFmoHrcuULKSFKB9aals9m3mznzHuEj3oKQikLBZFRRFQKFIcYWXQiQiqbpGI0LqoHKsL9+dz4aG1yylLpkdd7gZDZimQLEIHHRLohnzyknA65VN/LlrNJV54E8YLvKgOZqc8pVwd9+L17ekYqXFgp5tSikl1tDL68N6cdPEf+4x5e66J+fnvA3f+Nvs7dzk29/5ysMZ3D/gz9lvXeL73/2O3z+x8esN7u4tMuf/5Vf4Zd/8e4loQ7ym4/yCqVMzOEYp6dHfPD+B5wcXzAZTxmXJUUwrE6W6ElJXDfo1qHbhBaK2EWWJ5bgJcZUmKRJ0WcQkhQoBT7aPskIDCXWJhbLlp1ZIjlJbGGYauZdh64Eqg/6ds4hCBQoZnXNHhXHSfP0xQui0bz99ptMZzWPS890Z0JXWOg6jg4cggLVA9I6IXL4eQoEJFEmYur4+//gQ/7u33vOf/Qf/BVGdZFllEkRtcGm7Hg+PXlOuwncfmWf7/zi2xweP+eLg0Uunkoh6wKpJGUqaFMDPtCsNli3xuiXe+FXO5CnR08YD0YoJZhcH5CCYNk4QhLsDIeIlPk+GUbGpV5fJMFwWOZ+bkogsvolv3TexRcEUlgzE4Jv7O/w3/763+LP/eW/yL/4V77dt4tyitp2qNVDuNlSOaGnZPaGvu3NHXUAmQiBfkbw0s1I/vsvh22CXkDQO86i4MXzY1rfMC0NugicnC0IsaNtAg8fvMCvN3z3t/80UySJoDxptEcQmoDAJ8d8dcxnn3/AQrT4DjbO4m1iOjVQe5SvCN2aZVvQbgS6cKyakE9hBkgWJUt8lzc+qoh0XUJqQ6AjBU2ImV9jrciegZnGlJqL85bQQkySefSUReDu7TGPny8QaIY7BVU9oesEy7BAlBJTJ6pasrnwICXJu5xx6wMxhCwOiBGxlRhqAbXCdzYjjSWA5PDBAWOjkd6DBKkVdZnR3cVA0iSPFxYMbFKT23xKEX3CWU9ZS9Yrm1VUzhPDMg/+fZZVW5tNd0NKtAlsuhatCrw3+OQoBhVhkxdOrRXeQgxdpl9GTSojSgiGo5LFfEOQGqlze9dowfJoznN1TNwN7O9eRyTdk2GzwOSqJl0NXP+R4JafMFZtWzkv//72mr68jmUOdwkh/Nj1+tMeX+qin1JgPZ+jbSSe7hJ84Ju373GySJx1gk0acuvuXZIvSGlDkvkG3J6K5OUMgD7UQhCDJ5H4/MEBKRZcf+Umy/U5KEXTZMuQi4LVEazniU8/fcqm6xBigI3w6NkJUY6IKTKbDFFFTde2LFcrJFAOaq7vTNCDARW77IwNg8EeA6NhJBlNa1wMaGMoqiECwWBgGBaG2agmfnHMV+++xdvfnvC7f/wnCJkIqWU4SHz0e7/N4uSCPWMYocDAar3mu9/7iJPNgi8OXpBSzep3P6SqPaaIeKUpqjHf//gFWgWapsPFiDCD3iym+epX3+b9H37O0xcX/M3f/C63br2CLEp0YXCtZ7VoSdFyvmgQncdMCqrxhOZiQZs6Ti5O2Lk+ZaDNpRwtw8g6lDB4GYlSc7TKqiIz1NTEXqWRLucu0A+htuEmCKSW+XONgI8Qe0Ru77NQeFK75uCTj3jw9j5aSG7dfR3VKz0uE7EyYy0XeSCnZWXctki57ZXdxXlXJnqYVqYq50FvZlr0+7Dk89uMiSQCCM+bb94FETg/XZBEboMkDLtv3ubpwwPKkWL3+oSvffUednOGLir+6P3Ps7kwBlq3RogB052K48MF1ieG+wXzszXVoKaVDu8FRVFwepaLrB5KaiVJwaOGNbLqmOyVPHl6gZCGGzdqTl8saKzDVArnHONpTbuyeJeY7FWMporN2iEwVEKwbAOdilSV5vxkjZ8HogBDomnmBC8YDEvakF3Uw2mF3XQ0y6ZfFDMK2Lk89woh5FBzEfDeIuuCTewolEKFgNERZwMqRtRAkzFaCisiPliCkIQKgnCElP9JApJXOJuJrbIF2wIqIqRCkXEaqzYghSYFQdt2KC0pykiSgrLMpsioPI0N+ATrps0buTggBcVyYRE6okOkMIlKacpa0viEs+BcR9dahPKcLj/Fvf0aolAM04DhcETCkPEcfY7vS9GiLz8u2f6pb9T0mJh8FedvQp+RmBeDXOBDyGavGMJlKMyf9fhSF/2yHPDGW28gteCzF08JDy7AViRVUYkBX7k7QySPd56TF5/yD3/rUc7ADHkarrTn6PyEs7M1oYXrN/YpVEDryGu3bxGDol0vGI+HhBDZtJb/84f3aZTiR48PGF2b8nf+3nfZrD3vf/wB48mU3/yffwdtZviN5+b1PUyR+MGPvuDFs1Oshbe+8gazYc1wWDBWY4rC0HSe0bjCe80kZH9A27YE7/phZw6EOD1puV0MaBUU4wpTFgiVh5ZeQKw0rjCoskD5rF5JQlAOp0wqQ/PZCUEIVk3H/s0ZUglaIsdHLxgdjtjfmXJ2OCcSuXPnJl/7+js8PXhKOShJMiCFYe00iyBwi4YPHxzwgz/8mF/8xj3u3LrD3VdH7KSSf+bP3SERaDeeZ08X/OiTP+TJ8g7/0nd+CRn60HKZIOQ2EkmTZMxMoRRzBGbK3kTgUkx1ydtP2U6vJPydv/8HWGPAr/nVv/CdXGi3/1lKGC+JbGjaFf/r//bbTE3NO187QQkI4wE/97Wv9/GPAqKE2EdHIi4DMPKhOyeHpZB6C99L0LlL+mZEppiLkMztM9GnmSWZA+YTgenODBUFyRQkGbh24xpHT08oq45inBjuCIwyKN0vLqogRoUNjk8+e8TTowsIJSlaihAoDcw3DT4FQrNhb3fCeGwgaNquY77qUJXBhgblJfa0IYYMsHPWIhQMhgW+Pzl1rSORAXax9fmUGEEVkmaTcv8bT0yCdiNQlLgA61WimlU0G0u3jpRGsmwdlezpoyHio0BHSQySrnXEYS5IG9uCTtiuo7EN1dDQtpZBWREWHaRAXVc0KeLliE4r5v6C0RS8bIkuz2aSSn10pcB2FiETxiQ6J1BaEZNmtbKklFA6IJShay3dSiJVATExHFbYANFHYrBUSrNqVqQIqixp1i2liqzmLQHBcBRyFCEFTkVcZUk24VudI0oRhLhmNpzx/PEzukVHZSq+/u471NWE+fyE8WRCxkX3MgBxVdRfFpnEmIN6Lu8L+o0RPQhwe3oV25PCFZhwq+rZAt/+SY8vddFPCXSd+7NBlcjhdVZxQ10VDJJEmx6bW25/fvk4JoXCp0hVZPTBem2ZH7fszm4wmGjGoyIfb1OWZgEgPPWooixqHEeM9vcp60igZDjJJozVes16tUR2komqqKoSobq+96ywzpFSQmuNkhKtNWVZEvshsjEGZcTlcyAHsislEFEiMcSnC2a3btKVmTO/Wm4oSgPKoDZrRjL7irOjr/85FYrCFDiWyDjFeo+uSmIMTKfXuHvrOm3X8fDBM95+8x439nZwrsU2LcF7mk1DCAEXwUUQRlGXY1YHz3Gx5v6pJYSCxeFTyruvE6XAyERR1CAi03JG3ZSETYMsi3zxSYWPgqAyZzJHzkQ618fspYSQ4arQsz3G5vcUY279rBcJOZrQNR0xaKRw/Wymb7VIwaxrmHzvR1y8/jr2rTf4/MljUueIuxN+5uvfoI6BqGLe/SeZiZ8qstU9O5WHYcr391lv6BJKkKWdFhslAUehe9hbrBAEJA4ZPV57hEusTk5ojzf4bo0vsudA2wXjCuaNx2M4OT5jd5Rtp1JqRCrzfCU5nhyfMO+y+aAoNdZ72hUEGxlOK7QWNBub3ZzR5yEpkkKATxJrM88GkSW7z140VGVJIWMetsuSZu0pKsHe9ZKqLDk7X2X9fJ2Y7QuGreT0DNoNVMOSLmyQOlENBEURqYSgjYJNjz24uFhigrrCbuPxvsO5bZsuowhOjo+xvsMVHUIpZA2xjBSxwJ8HfCWodMXxRUMHmGHExhZBRlE75wgyIFOgMAIlPdWgJBBYdZ5uE6i0hqioakOILaaItI1HmxLvO2KULOZ5yBsdFKqkrhXlzhBrHednlkFdU9cli0VAl4Kq8pSFYb3qsG3CWsFgWFCNFe3SUw8USpeIGOk2loOzp0x2xrzx1qscPT3l5OSEt958g2E9QqpE8PrSOXs1c6RXkf14y2dL4syO75f6/i89Lcb4jyh/ftrjS130hRAUemugkSQpKQcFSia2bN7tkGMbgp37wwmtEhGLkg4hclapI8cUonMv15g8SBVCUhgDKWCUxHmLMQYpG0QPcUrJUg8No8k+CFier1nM57mFYnvqfMpKIQcEmQhkja2WGtWz4rcfWD7m9SiB3i8mY2K1XiLaMctlg/UW6yLWO4LSDGXBxEAMEh89KuTjYty0xMJRRknXNnRNzfPDOSeHh8hgWJwd8Y1vvsNgMuDg4ID93RqhPcpIhJZUgxKtJCGovOPdxr4RcFiKUYl1ksPzL1DDU05/aw5tS7QO60oa32AKRZcakovUqgKle4mouFQrSA2DmBeBqpeb+QhNa/nwR/cZzmrKQlGVQ6Q0SBPQOkBoEEFmuJXa9un7ro1KOdAmesLqAudWdH6EQSJP13zx3o/660IQEoR+MLs1yZASuoBhUVMJjRoY9GzAsKqoyhwCfnjwjF//7/4WrQz82//xv86be1NILTElwuqc+dEXnC0bTl8c0J13nB1uCG2LV5HUdSR7gZEXrC2cLgLvvj3m+YtHjIcz1qsWXVSsT89YHD4gTARKJdp1g5qMWG46kJlRY21AmkS7gd1rNSvXYa2jqMqMmnYKqVrqgUb7hO1CDiSJnqZxOXDEwWAiGI4lUklctFgX0Ahk0qSgaDeWoijxyXG2XDDeqbk2UXjrqEpFMdMcnTrGe0NMIdFKkprQY6Idg2HNdKrxbs18KVhs4Mb1a4ToWK0XeOHpbENdlqAStowU+wXORKL0TGceCk1rBGeLjmjpTV8RpGVnx1AXhhhKNo2js54US1ob2awaCplPKEVZ0jZdRiRox3Awoulagg9UU42Jgeg9PuTWrw2O3d0hMTmEbDC1xVpBt8kmqrISCBeITuFbi1CaeqDzpjAWSFHmKNNg2XQdn37+lGcPj9ib1vzpH/+An/nWtxiNp7nPmAB51dLJ2oO8c8/cp8Q2Se4ytIdtQe+PxvQD3ZR6OWfi0iL+Ux5f6qIP9FLNvCMTKaKV6ntcV5KnjDzd8rHzv/PimNUixHwEVymihMlSvd6h+2OrYko5wUm6rK2XefAi+gGjSAoXPF23YTgr2SkHpOQ5ePKCxodenuXRuiCSGI5rlApUI421lrIsUbwsMRUvfUgSGRO7P/8WvpAsnz/nxo1dykrR2Y7nrmNTlgShuHbrGsNRRZcS9z99wi/XA4xcoY1iNMlW/o9+9Bk3r+1x786Aazd2M3HSOSpVcPT8jNF4RJQttk2cnTQ4K3MUXkgQsq1eEvoLsJ+PpIQwimK4w6ePPsIoSakVISk+fXDMrTvX2b3mScOMF269o9KBLR3VGAk+QRfodB6Uns+XIAVOKKY7M4S1RO9pbcdqE/ApYpsOGR2r9Zqy0AjhL+VticRqWPEkBIZFYBQsrmlJukBZj7crXnvzLWIyeNW7u1PqPQk95yR5fJczEhabjuZswWppaTYd3nesV885P3qcd68nHXaaEEWH9nC+mbNInvXzY776rXscH7wAKVk+3uA6y8AYVDQkX1CXubc7GQ9R3T7LzRKbHFJq5vNNZsYDUnmGszHLtccwQMYWWQSM6b0DVcCGwLoNbDaS4C1lkQF/VaGQPcRvMKzYdCtGwwlnJ1AiGYwF9Qha13C+CFjvkUrjPYTG55OvzJp7XQhiLOi8oN1EmsZDhKKosiGq8xA0SIuRCllHZGtYLi/Yv2HQJnJ4eoIuKmQpWLsm48ZSRBmBTxHXOJyPDAYlTiba0CErSUotjU9YF3GdxFpPVcFsZ0BZCJzrWx0xMBmMOV10GKFoOklQgqIE20baNiJ3HLNZSWsbhFJUhaBSiU1rIVWkYNGlyO2lVYPRFatVR11pqkLTthYXHKawDCclIWg2m0ApA9OdMSE2CCFZLFtCSpRjg29bnh4cYDvBeHwDYwQk2W/2XjKLXkqGuSzwWw3+pdu3f97VXn57Ir6yYW3HYpdAyp/y+NIX/ZeDBK5+OOnHfn/LpL4KE+gHJ2IbOpB3/lp6NFUfcbbtf20xCCBSxDmP1ob5Yo4xgiQUPm2zKXNfNyXP08fPkTLr1ttlPZT1VQAAIABJREFUDumuCsnF6Rmrs1M2rWU4HELsMsu763JLJ+XA5ZgSEUnaYhj6OLc8YMzyx8ywT0ymIyY713nUHJOk5ODZESFYbr52i2Vd8r//H7/N3n5FUQ05OT/LBpjC8HD+CB8crm87CSJSGgptiEhQguvXdvnghyeYeoA2kmADH/7gh1TVEC1yas9ms8lO1AjO5qP7Nn0qa+LhuEn8L//gA6qJguh4953bvHnLMbgjkCof5xWeED2SzNFZzhuGdYUqZO7HSsF8sWJ3OqMeK0iSf+1Xv4NAEYOn8XlBuFQoCBAmQAG2KhlLQV2WmOkAoxXGlzw/f8HPzL5Fh0KoeBn2suX2JUDEnJQbhM1D//4PlMhBF599NOF7f/QeRgm++7d/j/df3eON67uMb+4R1Jqjp4e0qzPs5xsuXjzGugmpKkBm2J0aVgzKEuMtIThKoVj7yM5oQnSWzqxQgz1iEUm2o6o0FyvHZtEwHFaMr2mcC2zWuVWhCsH56YYQJEIbZiNJXXVEWeBbgITrHOW4YDIt80mJQLMGXRtEK3CdQZsCGxN1ZXBNoN3knXSMCWUy0rtrEzF2tNGAKVC1pnUtUTuCF8SQ2L+1w+mLJb7TCNHx6r0Z1i3xoWcPOceLFxuUhhAcIUSGdUXXepwL1APDcr1GKYNzOYO3KjqS0AxHJUsfKE3FzkwjxQbvBW1r8CGipaJZJ6Q0eG+xzlKYEqUFXWdJvoTgidKiikihK9q2I1xYQtQMTZaxttaTkLhW4VTEeklwCR88LmbIWQgCbRIubNAGyrrAhyZnA8hAUWWT2HrV4UJg/8aUV27dQcmCa9Pd3EKMClRPD3ipzqW+6r/sG3m57m0v+Ze/zy7eiFSSkDKa/f8X4JrIDpjvA09TSn9VCPE68BvAHvAe8O+klKwQogT+e+AXgFPg11JKD3/aayfSZcECLqfVW3falrsN9G60q0UipZBDsl1AeI/Wkk8//ZDoXc98VTnbNiViNL3ULzG5NmBcjPj+779H5zqUKqA02QqeAsqDNjm0WerscC31AFMJnh4+Y7Me0y7WXNvfxzYtWieSSznZJ0Sads16A/s3rqFk1qifHZ9z68Ye0Ti+ePCIuNE5bk5qJJJbt+6gCo3WmtOzc/auXWcyKbg2GzPQhocPNjx/csFsd5/BaJd2ecjtm3vcfmWXRlo++eQ+xpdIJRgORzl3ViTuvX6XxWLFm2/c5sOPHtH6SAwdpSmZn52TeTJD7v/oA+q6YjQesUXCKpkXwxATMXqiMgxme2w2C1YnHZ9WLX/wO7/Hz/3Cm/zs69eY7O+wV1U8OD7gh58d8J2fe5fhpMa1iZPzI07bJWohCN4zMxIjIcWMMhYEhEoYUyDjlbIhRlDKY0QgugQFdOsF8/UFjw9OeeON17l16zpd6HI7zJl87cj8uaWYejGnwqIgZkxv0JH5YkWdNIOyoqo067Gm8wX/3l/9OV599RUuTtYcHp3x5NHH+OaCRRMZ3xzyjZ/7eZ4drmnuP+P8eM6zs3MWF3N2xgNUAboEJ7NZ8OLpOdEm9E7He+9/Dz1yFJXEBpG9CVREV3G8WVMK8I1Ao9gbSzamoE0W30RCsJiqYNV4Nm2iLitC2pB8YnHhCVGCl5RlxmtEXdM5S7dZo5TCrR3NOmcMNKsseRYyUNSSvVcGrJYruiBIFuYBWhcYTMdoERkWinIQGO1IQmNwbo3QHUqB7QJbQKK1DukFCIWPHmsjSoLQAi00gshm0WJ9YHZ9AMKjks8oBd0R0TibUFHRdh3OBWKQ2C5S1hKfBMImdicFr9weoErLalPy4oknpIiPAaMDzTrgvMLKjAhfrjOeASSuldgOlPFE4akqxbAAUSjWrcMFTdvmLoKp8lyo2XREB9WownnH4rghpJqiNqyXcxYXNa+98Saz0QCZVC8f75Eifd7ytm4lrhy323qHyIKyq+5GP9Due8IhbMUGfQjMP2Yu8JOP/zd2+v8J8BEw6X/9XwB/I6X0G0KI/wb494H/uv96nlJ6Swjxb/bP+7Wf9sICgerDia9S3vuJdcqF9GW963aVy30yjzJgCoVSsFiveOWVW+xOB0wnNVJApOX77/2QyXiv390ZtEmkdcd3vv1Nnjx7zM61W9R709zW0IKLZ6dMd8Z4sqY7ycTHH3yKCwmhat586x0On37Bz3/rG5l5o6AsJE2zwRjN8mLFg4dnvPX2GzRuiTGKJBxf+errONVwtDjj5r17KKmQOmKdY/f6iHXnSDEXvr29PfZ2KwaV5vTomBQ7yiKz+aXwxBDReshoNqFUjsGoJq4lSuUFq7OO4XhAFx1JaapBATKfdkxRUFYl1llCSIQUGQ4HLBZL2s0ZzluePX/EoKopqxIpBa51KBOJKVDWAy5YYlRuM5w+X/B7T894fnjIu197CzUucDFhySA7kSK7+zPCRw95ZbrPMsxxnaWoDBDRIn/OT549YTTdYVJO8OlKnSD6nfnN23e488qE4+NjhpM92k0LwTG/OOP504dIAikGnPe9rtkjfTZiWR+IsSAlhdGSwkiC61gnzVd+9mfQ45rZW9+mGyg6VVIOam6+prn2+pS7e5bnH/2QLw5WzI+W/PCLZyxxLFaWohjQLo54fDhHfuV1dCWQNpuFILFqW1ShGAwHLNaCqDtsMCyOPY0DVUK3WSFUpKyG+QQYHF0nmV90CK3wXUs9GORi1kRCp7B4JtMxvsu8FlEpVBEJhWV3NqI9XVMPS+wF2CbgRWA01RlWmDRdGy8H2a5tockY4BACXmZ2kV1GgghMbyhKFdmfSsKOR+kxSQh8FLjYYq1EOHDOUtcVLjR5BpAivs9ubtcNPgSataCcCIS2EBNSaLouMtsZ4n1gs+5y8HzKUY1Sd9Ta4IMjBMlsp8a6JRHPpje+DXc9RSHQJPBF5g+VJeW4JkWLSproJM45ikLByrNp84zu5u0hw2Fg1ViczEwnpSMpFGxWoETEFAmtYl6kVJklyhHadkXwazbtEutXhFde59U7b/XIij4+VMQrckivXosvbXC3Cp8rp27+sxjzvfaTrt4UEz4FtP7/sOgLIe4A/zLw14H/VOT/i38e+Lf6p/w68J+Ti/6/2n8P8D8C/5UQQqSf5iYQL7VvthKn/tnZonyFNN2acbIM8IrJk1TGCgulGY53GM8qxuOsOpHaoAclt9+8x/zigroeoEXi6LPnDGcjymVJSGCdRSSJEgZjCrTuzT8yS/WUcXQ+p/DIAnyKJBT1oKCsNMYINm2HkBXGWOpqwM5shz01YDAsOTs8R8gCIQNCaxKJi9NTlIrU0xFRxn7xi5c/EykVUQiOlxeM9oacHa1YHi756rtf4eEnj1iuHF0SGJXzBKTICUVaG5xz3Ll7i08ePmI8vAZKkfqiL8gXT+cdu7vXODs9ZzAZI7TG2SOGdYVfOVaLDdZ5VD1gOJgyvTEjtIERJZvRBC0NN4XCfHaASJE9abh/vOLi0xWV0dzYm/JL33iTUkm8h11Z4ZctLjhEEzFaoMuSgyfPSbHk8KTlWtwwvj1BpsT5xTmT6YycfZyvh7rK/W0XQBlJ9A1+teb5x39Kch1CS8qqhBRRSkKRUQnRBUqjiTYSpSDUhpQSrYXIu4xmU/7SjR2CMtzZnQCCoBIyJuzYMP2Ftyk39xnUhq6Ba6Ma23gWqyWilAihOFsumOobRGc4PGtp5x5ZDikHa5Q0yAiDasDBwTldVxKjYlBAOZLYaLOKpdKExnCxdIwmhuFQs1olXBeQIstNZdS4LhJiJARHoQVFmYezO1WF8AGXJN0iQCoYjhSTiSAmwfy8wVsoSkVRKlxoweUBcRARNDgnid4yHRsm04LZ7gAvIlG0CBFQSmCdyMonLQidwLWRQhmkEmhyOlewFi0N3kZsY0FLRlPPaKfGBkd0WWK7alrKSpGSQKiKprF5PiclbZPLgg+CKDrM2GCEZNOtEAiCAyMFKTi8zpKMsjLEkDg/aRmNTL9JDExnQ4wZ4J+fE6Ritba8OJozHZfYxrOzN0MWEJ1jtQgsly1aS5wLVAONaB1FITBVpNv0yAkTEULz4JMXLJ43OJu499prJJfL7pYUkHfvmSO13eBmCNuWwitekjTDdsi7nQO8rN75s3b58P98p/9fAv8ZMO5/vQdcpNSjK+EJcLv//jbwuH+zXggx759/8vILCiH+GvDXAKazKdsw4myI2b6hXOjFpYJ1uyL2PzRBjhxLARljz+eJPbxII3qOjxAGXYyxATYrh/drdqY9PEtqhCwIPhGTQkOeCeh02Vbq3fsoDTJmUJkQEEPi+OSMcpAHSoKIEiVCBLQQNMuOi9NzIgvq2hDWq4wPTrA/nPDGqzco37oLKbJxHbGwzN02vzYPsnN/PodbD2qJHRSUozFlXXLr9i7DscL0yAnbWabVGEnCNmuCd5ACr93c46P3n3B3f4QB2uTpLJSlolBVnjeQ82W1ysiGIBRlVfHavTsILdnMF3RNS0p9olaINO0K100x9QizM6WKjvXZGXQWXKQRiePnS17sH2ZHpoTjizW7+/8XdW/yK2mWnvf9zvhNEXHnzKrMKnY32YNEsimTNAgZEmAbhr0QDK1swIAX/icMGNr7H/BWgFba2DAMmNJGtmRbTViyBQ4Sxe5ms6uruisrK8c7RnzTmb04kdktQ2rCtA2Q3+bmzRs3bmbcL855z/s+z+8JjLeRxmamcSULwauHW+zmMXe3gZwOfHh5Rd8qHqYHNqenrC5zcXXB233Bqray2Sl885sfkFzm7GLDMEgOh0wWijFmeuOIk0bIFXGAcXHIx4+QptQwjQRGGoRJ5JLom4Z/9z/8dWRRdbaRAgKJyIE0er68+YSiHdMbz+//y0/ZfniFdwdCVIwy47cN58aS5hk3Jv7Hf/C/c366Y9i0zFlSguOwOsockbTEGdpOEnx1tDabBi88y5RIa0G3CttppmUiBolzghAS63KMf0wZ2wg2Q8s6euYpsbp638ayUFKDmx1KNcx+RIqObtsw9A1TLrg4VT9MkUhRaPoGtwakSESfMS2cfiApEYzVHA6H6mJdEyFKlNTkWLlYEk/fGURIkFKVMOdMkZqQKreoNIq+MUf4YCEuHm0VEbCNZnUT85Tp24Hiq+ooFzBaE3yCEjFWs/qaTezXjKJhnFba1iINWCFrkl0yaFMpqjE5jG4QQrEsiXnaU4qjGzrGCcKiuVsCSsJsI7bJKN1ws3+LNQ2CzNAbYomIrMghk61noAbDaG1IHlLU3NzPvLq75clHX6nrD8e16ihfzulne/g/s8q9W/OOM7VynAlyPOGmnFBa1xOvVMfwpZ8/yf0zL/pCiP8YeFNK+X0hxL/3Z32e//tVSvnbwN8G+Oijj8q79o56z4vO7/OMpVD/ioEBjuqd42OlUEipSLm+uO/wwTUbt77Yv/LNb/Ltv/LLmBJQWvP2zXP+wSevycogSuLDD875hW9+VN/sJfHF+mUdmByNUVLW/nb92T89jomSOd2dcna2I+fEzc1d1aXrhGwjT37hQ4y5wJqGZz98gxKVGnp2suFrX39KUZXPnVG8fvsl0zjBu56fyvgQefnyDWSFaiSq02yHHVmD6S0nuwHTWpIMCKVA6WNAIaRYDSBDP9A0BStAaEnWguQLGIXoGlwKSATLsmCtracuKar1WwDypy02gJRr3u5u6IhuJaVAXyQ3L9+SUaQkam9ea3T7mIvHH4JUFKk5pM/43/7hH7JrNkx3rxH3iW/81d/k3/8b/xF/9INP+cH9j1mj4L/7e/+Uv/kf/DVYEjrDl69ec/PqnqgvWbwni8rekUKAkriYQWiuzh4hmqpjb4dz1u3Mg1/p+0LDkZWvFM5HslA16jHKSu+M1MhNjjC5YxEiiyBdC+xksM3C58sr7pjozQXj7cpKZvutr3P6G7/GT/7u/8CTocGoUlOxPr8n68JgTzi9fIxuBkJemd0DIUn8IdL3lr5vUECYAjJZYo44p1imkdOTjmkcSaFBa4m1deFURtHtNIgErUYWxYkCpSX7g8K7RNNbYszYrmdeI8EfUEqSheLsauDhOqBQSEUtVkyh6RQ+O548OuVsZ7i7X4lpwjaJGANCKoxu8T6QYs2WbYykacEvksU5etNTcq4BK1pgrUJqybwsbLaWaVxoG4sLkcPkiTkhgkRmw7Qv5CAwWrH6lZIzw6Yyg7Q05BxZJ0lOlhQyKVnmCMOJwrlC8RohqnnTNKIGvJSqTPOu9vVPLnaEkGiMILvKQ+pPLP1geP3yhpiWSoptJDlHmsaS1kwI9X1lug4XJwSaEAVdZzi72GB0y+OPPiALiUg/bUm/q+bfXdVYJd7LNWv/vha2VYj408fnd/bGlI4qRPH+pPDzrv83lf5fA/6mEOJvAC21p//fAKdCCH2s9j8Cvjw+/kvgY+C5qGnEJ9SB7r/xCiHw/ItXwNFMeVTt5FRIIdQ3qqgD0pwFKSVKyaRS+2Y5JZZpROkOUaaq4lfHPUFIlIDD7TXPnj8ny4RWBXd9A1JgJEjV8MXLa9ay1p201Dff+eVZzfEUVQ2ipKr9OeoHEeHN81fcvH6NQjGtE7/2y7+CtYYQHOubO/7Rb/9PZOGRKJrSUpYVKTP3z/f89vf+fm31ZSBbLh+fs/3qVVWb54yeA88//4LmZIehxdxI2tvI9PKG3Te36NtEWR8Iq8DFlbO7hlYUOL5eycP8By9RWvAL8oS7P/gCO9WWSkbA7GldrvgEclW55Hf9xMI4Tbx+BZNbORsGSq7tEuccbVas80Sz6ck5Y/qG0lvS7JA642KpUkDZYttzMgGhMyl6DrOg6Vr0+WP6VhD1AGZAGkvKmaXA9Rj5+//zP+W3fvWCaVm5O6wUY8mq4pitbfBupbcWHwM4w/3NAV8OhJRRBH7lN34VEzy/+51n/Na3/xK3t/eMq2M3DJAhpMTZ08c8/+y6UlpTOp4rBVkdN7koQEaSDPyf3/ku1+M1uWvJppDizMWw49ntC8Yf/An7H37KWZvJyuEzTIeVcQRzdoJqBm7vr1mnlRhGtn2HIOFdRmjJ4lZkFljZsp9nlNFIlWpq1VQoUaN1oesTSMm4LxRhuL93NK2p8Z050A6GkjJKCrouYbqevDjaTpFEwjSG3FaqZVgDRmlyysdkpoTWEgh8/ZceMa8jUWc++PCEednTtgPOJeYx8uBGrK0pcY1VGGNYlokQBS4kyuJrIFEskBTz7GrEphJ4X/CxuoZ1I7E5YZwiHc2WfjxmaxRJyhljFElHbNcwTYnsq/Epp1TvSalY1sDuosU0K+OaKCkThGLbSIRMzGsizIqUI12rUQdPWgM2C4KISFWIweGcQusN07xijSSXAEWwLonoJTknmq5BpMJ223HYB0IUNCLw+OmOb331L7PEmnlRcsXDCI7BMO8Lc/Fenfh+IzhCIimltiJTPs4E6pelqObVEN5lTssqR/o515950S+l/C3gbwEcK/3/spTynwsh/nvgP6EqeP4L4LeP3/L3jp//H8ev/68/t58PLMvK97/7CVqZaqs3Bd00rPNK3xiUqDugd4m2banbQqouR/FuN6wvjMyJ9XZk4wNxPOBMoN0Xls+u+eyzmUIml8j2pEHNgVe/9ylynAnTwstPoWSJCWClJW3eEkKkMjUSi1mRfUM+LLz93R9x5Q1nHwnG0bFzDZfZsv6TT7nvIv6p5uOrM9JOo6SnyRlXBH/yxSd0bzJX14XHenN0rIJIhumze/SHFwgiZXW8/ZefowWInSSmyOZBc5lawprQvnCSLbtbx/zmS4QUfEX3lCJIpW5UMmuEW1ljJD/a4W4nTAeilWglYFzYJsH1w4pqI60ULIcRYib7KjmUZC7OLjjbKeb7BSkSIURevHyN04pH7cDd/CU/+d5zLo2lKINUilhC1Sa7avOHSrtMwfNwWLm8VMQkiMtKOlyjRKZrJf0gadaVb7yd2faWl5/ecvnBFSFmpJYE5xBRcrm54PnLVywy4l1hCho9PObDJ6fE5YF3JvfWdvzmNz/EDiO//OtXjGPg/OKrqJRIIhKz5PYPH0iiYETF3OZMDWop9WNIjj/4yb/gR3c32MZSnOP8tKWUlTs3YmzhTCZUlsxrIAbNYT5Q0NhtT3cyHBdhi902tCky3ydCyGgpKCGQY010U1bQbW2lWtLhV4/IYI1BNIWhbzlMM7ZRCCLb3cDiV2xjGUeHmwJBJKQGIxWHu5m+15SYWNZKaPXTzLZTDH3PVCKH+yrzHbbVTZ6KR7YJnSREwRj3lJJZnScFyKou1sjqyp1dolMCHwvrkkhCsvpEo6qrfHGRVmukrByhdfX4EFkmhTAe20HTaNzs6HtLYwvWtswLFCdou9roXOY9fjEoNMZEQgHdCDat5Tpk7m4WLh91KD0hjWENmf0hsTnV5CRZl1Dl1xvB4eBJTlAwRBzDpmNdPC9ejiQRaTaKoW/QQiFVoR8sD7czqxcUoSkq4B2sDmQL3o18+fkXqLBl++gD+q085n6/Ux9SF35R8yOqNvSIANEKKY4hT0VQSqSoKjeuCrwqia0tn/qcNTku/2tW059e/3/o9P8r4L8VQvzXwD8H/s7x7/8O8HeFED8CboH/7E97omHT8au//nVOT06hZKSGh4eRz3/ynN/4t75NTgGpNF88+5Krq0dYqyikasoiobTgD7/73fpLLZG7H3zBxgw8PNG4p5L5M8cTzikxoIuhlIY1Uxkih4icExcY8ApQZBkBTZ7e5clW5sjAgKegckGPYDTIx5Lxx/c84jEiZ7KEIhLqPqOyIMaEzBFbNEklcIkm9WiZCFZiF8haIo1AThIdqueYAkYotCyUmh9HkpB0YUqBTW9Y+8SycyiliXtHGy1JCKJZSMagtYV9gmQoxxQnVPUDJhGhL7AmVKlDJCUSJnl0KuAdGyQPt3vsZmAtta0TQiZlxeXHj/nizR0xV+VUFy1KCIoUzIcDMgZEiqzr0WBFrWJ8LOzOTxCy8ObZl5yGzMmTUzKZs6Hl6bZn4wtPfuOXePvZF/zzV3u+8/vPUDkRk8L5teYSSEUpiRgl0xgZTCaO13QIrp70fPn8jt//vT+mUVuePO54enFKazP3n39ObA5QAj95s0ebLdP9yO3DysbIWufLen8BFAk5OuJhJuXIqgo7nWhE5DB61tRW41MuzOOKbesiIoWioIkhs9tsUEJUDwkZFxKHqW4wRgu0EkSlWIInRIdEI6VkmT2kY+i5jJi+ZfKOlANad7gp8ub5CFqij6cR2yha3ZAzLOPKdmgxRnF/H/DRkkNGWUOwmnvnKUHStJl1DWRaVJs4Pz0nrQsW8FMhqxpQrpUmJ0831KhHv9bg9s425FRDZmpbBYwy3N0utKZFZE/agUuZFAwuetqtRqbIPGpSVNidJhtfT+daMM8HcrE1GS6viCzwq2J7pmm0YDysXJ1vgMKyOoQJhFny9kvHZtNREITR0w4b5rvENM5VzacK65RYvUCV6sqXpp6USykYA11naAzoI0p7u+sRMtJ0AudW/ByxZsPq09FYVVEqh/3KXXvH9uSE1FcVnxSmRk8ePULeFbyLhBBw3uO8Z/GO1VUDpXOFec6sSyClQswOV1ZOHxn++r/zm8eNhNre+1PAa/+fLPqllH8M/OPjnz8Dfutf85gV+E//Hz4vw6ZDKBBopKjDGyk0Stb+pVCqygo3W3ycq5X8SC2URxZ1ypIkCkqBMgoZMvhAha1HghpJVz2pJNatYTqAO5fM7oEPvrLh7npk6HZkl5BzoV2qkUgUSBS8DPhCdTKWiA4Ny12CUaIVvNlN2I8GgoTDfuHjx1c8PH+LObOMCe7CAdMo+rOOP/n+K3La8I3mku2HF+QSuH/2BpWOvPrqKCMJiTr+/DV74lnkvl1pdoHlcWAsC+3Qs8YDRp0zLom0S8RWI4ygDYVukkdHIIh0PELKSNZVMRVlIkpN0CA6SX4AVKnxb2OmDR6zSJI4OpeVRm4Ny5eeGCNjKjBsWOLK3TgyldonXuYJpY+oh1yHeW3X4zeAFbSbgb4YbLchJ0GKCe8iybQM5xte/N6egxV8+r1P2DRbGttBUewfxmptL4l22PL6+i0lD1zfz3z3H/0uX/3oF2hEz+uxYb5feLW/5cnHA6kMqPNHbLeG/bNn/PiH9wR7jiqW/du3yLNLSslImRDyWFkJwe985zv8yx/+iKQEV0qx6RVTOCBNA6FUKJfMzLm2I/vzjoM7oBLEIrgfHf3GIN2eeVo43Bf6AUTRLJMjS0mmwsuUbghrLWgk5cjCkVVskBICgRl6gsvkFOltpWBqXegvLBjBel+Y7wON7pGm4IsnHWWxtq/ehegLJEGeVs6fNJhVIkzk8tE5KXqgxVhY1xVjLWSJCyshZpZDIkZHPzRoWb0tISZyrHOv5WHEq6rKSclzMrQUmUgRDvczw8aQ1wVjTjAm0VhYXUCZamJc50IOlsUlsky0m+paDbNnuFIIGzlRLSVFCgJlYTi1PLyOlKR5uPeQaz7vnVtQAvqmxS0R5zyIFmktqaSqDowRJRVtq0kuEEeQpz2SRNdqlkNFtVuruXy0QWTD/Z1nXCJGGWQouATWCF6/ecHD3UgIEkOLXwqLq0q/6CMlSRrbIFVtNbUbQ5aQSqBpq1owl8jJ4wEhLYuHk80WrXLNHk7v0rnEX2zgmveJty8nbBMQuqJS53lhf1h4dX1f7fgUHg4L17f3SJuwRR2NVJGUQBRJjgkhNVFmvAGdFdJJgpKoRjPGgLr0dZdsa1jKImfutWP7qOF2PRB3Dc2DpIkZMR95XaIwZo+X+rgBSLIUFBSWhl41ICQxaIgFPwXimri/i9VFKBIig0oS4QXX4wFhO24fRiY70D7sIYOOdT5RB4kQRSZqiRUCKQ2mJKbndxjTkB9Av9A0h4FsoRwkfhdRcybeOpq+phU1pqeo6n6MZKKojmYpCnEfISeyFMgM0R+zP4+X0AKVFr6QoQaCAAAgAElEQVS9f4WYPX908QExRIpILFni3IrQmbWRdLsNWm7ZL19ycXLGmzdvgYZ5WSlSHknjhQ8fnfPX/+rX6+9z/w1+6Rc/IkvJFD3t9oQleE5Ky2e/8y/Y5EIjE4NSKDxudqyr59B2qKHF3gcKC8viyJzy3e8/4xACn376PTZdw1eefhW04c3ryH7q2Ow6vvf9P6T7tV/E7CLf+vYln312w73wfPrlZ/zqbvdeaUERlFz4wR//kM8++QnLuPLo3DAMESMVbbIsQSBNAiPpW01KEVUk053H2AYjMkobGiVpgkEuDWAxpgLUxsNSndOdxq2O3ug6a1G58t+yQJqKjV7XqvpKMbLZNSAdZ097ZCnoGbZnlik+EBdFWGuQidQr014gtcFaTVw91hiKLqTi2e06xhCZpsql2jU9h7uFYdcSfWK8X5ASpA/0G8s478kJYsho3VTqaE6sa6itiKiQStI1HQ+3jt1Jg5Se2Ul0FqQkQIM0iScfXnKYA9udIQRPCAKtFM4nstAsq6f2viU5KUQyNZA9CGKCEh3y6Lh3XrPfZ1KWRJeq4krWLFylBI2VFEIN1KEh+IKIDttIvA8oXXEdKWeUUAhRZxBDZ3h4WLCmod1ZdB9QWJIDbRMfnm5Zp5l5jKB6AoKUC8JG7KbBKrj84JwoLCenA88+fUl0gW/9parsubkdOTnvcSFxc3/PZtfhXeHhcKDvG3KUBFdd/alAKhFRasawfCfn/DnXn+tFXwhwydPovlIac+AweSiKL5+/rBrdmLl+e8/F5YeEZULFehrwyZGJvH71FqNPEFoQu8xbOdIhWd4sGN9hiiavEfXJhMfg7YgoDfObe7TYsP6B52S/RfWR4ALWtiA1oUiyKQQpECmw8ZIpVM5PVNBrxVQCQQlOphb1UrHeJK5US7wtbC+eoFwiTyvaDMz7BVU0u0vLi1efEYsn3h8AiYipOoZLASHrIEtklnWPxGCE5cI+5mGNlFVhk+Tk9ASzsfD4MckLsl4Q8YroZmYNB5HYIDFZEbRiTgvjPqJMw8a2uHEh4xAU9CrIUw2aL6ngDwFEw3df7ZEh4nbU/nPxCFFVPofDASkkz58/Z9N3UCTWdGhl2G4HPn/2Q/6Xf/gCisPlnus3M8ZsWeeRgw/c5YnPnz8njYGz3QkPE/SbQshQtprQSEwI1XWtBEpVw5UWgsuLCw73txQhuHl9h06Fq76D64lP5mtc9jw9ecwGw3f+ySfYTc/rFweev/wdTs8TuoHxLnJYOv7Z7/4hp+dfp+8GKAEhA0pKDpOrLs0suHmorB8jJNN9ot0a5sOMUB0+g+0UxRV8qG0hhoJtDB9+/AjWRLiesaYnN4G4BpSSBB/IWb6v3PJRNosohGNln0shFwkxkzw8XC/ooaCVYz64yty5XyjOsrpEd2rJXaI1uSJHYmJeIlCFENF5+s0GFyNeRsRYh7j7u1uGreKpNZQc8bFUhUtnGK/3dJ2l5MDZxZZ1icyTq8jwnMlZE0uiUxptAl2vKUS6vmFZFrRt8M6z2TVcXFhSihgDqx/RSqJKZf2va8K0hbPLlmlyxCiYbxfOziP6Q4nqFHkuhAQuJUqWhFkggkRLyNJjtWZxAtFU5UyMAaOrPt5YSS61iEy5dgFSfMfpAK8yVoNMhfvDhDSSUGZsbgiLYM6BodMMJ5qSHCenDYWFXCKxSKbD8WQkCqe7K2yzYRoDywo+1m7F3bxSMtwte5gyyQvmsWZbe5+Z/UJiJrrEOiXmJdB0P2XsyyJIfxp4hz/ni76Uilefv+J253n8C1fc3Lzm5s2eRlqeffFF7V2JGm7c6AapM9IlVCkkpWltSxIaLWWt/rXES9CikKUl6MoVUW1D8gWtIGdFUYmc60Dx493XiHHGhRFUh4iQhOQmr8SUEVbw+OqcQmK+eSDeRuRWMC6FIBS3wiOaiIuOvKn5ubIR2K2mZE3bdYzrxL5TnDlFEZKu66E1TBJKSPiUoUhEUZw/PkOkGZMTj65OeHlzx63MlBQRrcGKzJIzRWtkkiQf6mln8WQEiywEkdG5VL66FIQY8CKyRs9Gd/h4RBQIiDmhhSQeT4xCSjrVMMo9P9lsSTqzS4GrBFJopKixcCUdFQpSE2KuKgQJIWeyCYR8QBZYs2IugiWtfO/ZjzDSMh48D4eFTKAxHXf7Bw7zymoUXyqF1Q3nJz3d/oE5BIw1BB9JIpNj4v52j5VwqgyNVKTRU3pNt+nZ7h3Be+7Ht0jRso1nzC+uMdbw9lXg5U1iGBIlRcYDiEby/OUzHn/0AY3S2CXzgz/6LoeycHP3GiEybo2cCImJYBlQ5NoXdoUkM1lENsdgcC8ySmqWaeLzZ68YVMNWt/i9ohRNjBHbVOPhskQEVRkCiSLqAmZaQQngfSCmBFkgTMEoxbCxrJMH1xBkoLWaaXI0ClRMzAfPjDxWxxVpokyi3ShyVDzcTsQi6BqFkorVZWga/JKY7hybK8FgNCFYKIV1v5DUQNcZQliZx1grd1nDS6bDSrfTRJFwKYAtFFHDzFGGw1xQrcWqQnCeEC1RRfq+J4aVGCPBa7TSdE0hhqWy7aWh2Wg2fWHvAnd3ka6pMmo3V0CcyPmoQFKcPbZICvm2oAzHtuExS0EofEicnhiir8DEZqOIIiIx3L6dKGSarkepwv1hoTU9Qng0guk+0V91lLIgiqaUStxtekuICZEKloHdcEY4JB7eHhil5zBFHozi7u4e0xu+//3XtY2D4M0bgVsywUcQdVNGvIuDLchU8Ra787qRlJxIucqp/0LHJRbAkzg7PamGBSFZ3Ey7bSoywBiKUOAlpEyWNbuVd/C1BEpYcio0jSFlV6f0nWHxC0NjCSmStQRdUEIRRSGpQiwKlzO3aUSq5ah3lpSYWVWu/T8D3aMdU4lsdhua6UAyC8kE2tMdTbRECSkKpGlJs2PtFSl5lsMD19NIJwzdpuGhzHSnW16He3ZFIUvmJmeUrdAzzht4WXj0+JS4NQx6gz61lOsDWRl0kWxXhSqqLjT7kWf+QGgsKsOJNngyXgt0FgijuVlX+jaz9rpGEFtNkoVVRLKpcH0NjGFFaki+AqhmFF5IYnQIVecnYV4oWiO0omhJObqoSzm6h0t6T0stShCT4OawBSG5f9izPkwMQ497dsuVs5xcCcLqWP1K+GgglapoycqQpCII8OLdfZJBCmbvkKapqU+9Yt2vlKSQEt68ec1Zf0LQUOaZj7/1IUIW5vyW+1cvORl6/OpJRdZgazLOZxoZuH75nN3pCXOItBROPzjn4c2PkcYRQyZ7wYtnjqtdZPaJk1IDsbtOkbOkDqUEKWaatiWsC2H1jD/6hHJ2xsU3vsZpvIC84Y17jmgSGw1u8UzrynY7ELwnhMAaBNLUsBalBQWJS5lhqGYhURTz6rEWkioY1SC0ws+hBuUoiUQickI2VT1le0NIgvE2YOwGrT27jUGWhvWmut61rlwilUVVjomMaArGdkz7hHN1PqSUJORITIXGCi4vB5LKTGNCCEvTFFKAcR/Ybra4g+fuZqQ71VxcdZi2oLEcxpG2kdWhrQRSZlKucs1SSk1zK4mgBO3QoMpKoeVwt7DsFQKNUtDuoNnWU4bMgt2Zxa2ltsSkJMZCVJluC7ZNGKsZxwOD3WKbKg0VOlZE+X5kO7Sc9h1KZVKBNUSkFcwPCb8IbBtp24aMQFuNNBKjd0xTHbDevL7lo688oj/tWaYbmpSR3mE6Q3agpSbKQmMM4gh3pAiSyMcNShxTuKoxNaZ4xGfAz8Yy/rzrz/WiD4Kv/uVvIKlGKUmibTRSFIxp6bsN47IigP3DA7ELaJ/RWuJIjA8HQOFcpNFt1fLnzDwHktRVqSNAxYzIhVlmslLkDC5mshHcZM9OmJoC1EZ6LZCx0GWIxuBbTZjr/CAJxewyShT6qFmXyNDbGsotK6TNikIWoJVCa43Q8NE3P2L9yedcfeUDnn/yQy6fXpKSOOaoakJS3IQVpVqu72a0iTi/Yh6qkcqXTEgJJzLnNuE7wTMR8dKQjj/LhUQUhVAyqoAnshi4bAWvyoJuFadNxxwTo18QJaH7CijzZESr6NUGqTXeSPSmY5MtKXvyuqKHRCqF5EPVoB2Dm1NO5Fwdg/AzkuQCKEMhom0BWbHBXZScJsFjYUi95GZ1LFIyLysmD+AjQhZ8ksQQ/xWr+hoSLYHSChZVOKwLk5DEaSIbwfUyknLi0cmO+3lm8Z7DHLFby5u7a7Q0qCSZ11Kr7lLIMfPDH33GB1/7Grt+wCXPIXvcdODytOH2ZsEVyWAadqZFisPx/rS4xVcOihLMJRPI+IcF2SsQkV4pNkbz+PyU6eFLSpG0RuFLJJVIv1UIo1Ea5ikiUFXC6CJ2UwuDUhRtY0gpsNlsuL2+o9tKtn2L94n9Q20TdVtFd9aQI4TFs+1afCk8PAhyyiCraGKaFzabAecrHdb2EucLJ1cbdlcZoq++FGN4GBeWqUCRJJNpB2i2kBZBdIVCIMRAig1u8nh3NFJpVRfb5UDM9XNWgZgVi1toNx1SW+bgsFYgxEqKAr82CGEhR2IIRypnZiNatFEUPH1rmG4DNagnctIaui4SY6ZpOkSJBF8jVVMpxJAwQ+TssiGOAhdmhlOFd55GtQxnDby+ISwSbTRriFwNGzSRIgaWNVHwyFhdwU2raxwpBSUNTdNg1A7awtn2hPBB4PLjR5RY2PZdRa2XgiyZTWtJoSrahtaS88pCbTmVnCAXkgCpAKEQRR7zIY6EzVIR83+he/olJfYvb4hFcvn4DFUkzgVaU8ih9rBiCMgMXd8RbS2qKqggsX3ymLevP6ExbXUWGomxNeEGlwg6I5QglxpgEKWoiFIKSUFKkYfGM7cgRCESuc8GrzIpOU77U/zbe4zIXC8LKQEXDTf+gExbxpIJ60QqmU235aE4HmLFNPd2YE0RESPFCKxpyJ1ANZY/+vzHpCXUWMCiGLoNp1+7QpjC9e0dbp2JAb76ix+zJs/deODh5gaS5tc+2jKNe8Ylcvn0AqUTL378HB9jvX0EkOErH33I5W5Dv9HszjuUKCxBgC/oxnC4fcA0HdMUuLw443C4J4RE323J0wq5sG0temM53O2Zbh7oz05xb+5QR36+pMo5I7G6B2vSDLpIXIr1GIyg2+2YZ0+OGXO24TCt7EpGTQWsIJFZUmBHom0LW6s4jCPe+Zo4pRUhObSC1UW8W9GyBp8LmfFEVNGIrOibnpuHhbt5JReB1RprMjlpXMlIF9jaoVb1fU97cgZR8Lvf+Wd0uqlYi+Lp+oCVgkYGmvMtj8+2bJJkuRt5uM+0SmFIfNwZ3swTXvb0bTWZuVjYdDvivHB6uuXFjz9HG0FYfW0vFk0sGWJGGMu6etrGIEQtYJbFkY6FEEC30cyzYJ0WTq96ilCMi8dPFaKhdPVuxpQY946ub5jCSso1yMcvCeFqBxQhcIuj71pSiJRQsLLBNAW3BHqjWWPE+0JJAiET2a9sNicolUglQlaYLGmMIhEpwuDxgCaHhIuxurqtoTEL/UmDnzK3dzNFFYqeMZ1lOUBW1cegVcHNAj9lvKsniSIKbTHcfrknZYnoEiLpqkgypVJwYwInUNLifSKGSAi6Bo4UccQWJCQZaTJD25ByJrpEWVfSIdBIQyiKcCQD+Ji5Xz2dKaRUuw1SJZRMZKFourqpIQTd5oxOnqJyQBvN2eNHnF0+5sXnb0BYlnWGIikh452nZFGjOQVYpZEYYvZ1picqjEAWASWTyBRsbf2Umk4nS34fJ/pvuv5cL/pxdaTre3CRu598yaIiqq0MGHG0rIr3MWLH/+lxUZMV1nM0aUmEkBQyPnmUFAgNWSV8CERfcQOhZE5OTogpYaxAFc39/p51mirYTRR0MnzzV75JsgFywSSIMXF/NxGDoN/1TGHGeY9fVjA1F7ZxDZfn52xPOn74J59hQkuKiZICFa9QkAXKHMHVvmJKCZElZ2enXFxseXFzzTLPkDJhzVxdXPCw3/P4yRPm5QGdbA0N1w1n51t++de+jlCe+e0dbonkdEziyZKvP/1F+g8sd/sD5voNdr9nOrsC2bHZ7Fjnic3pCQ/zDcPJCaFEivMUrWjbFqUEplWktJJLYs4OER0yS7SQlFxvUqP1e6dgjJF3lKjgPTlEApnDoXB9IziMgfsUmVLmi7bQiArFcmGtgS4xMO0nPAvNdkuhIMyAbht2fWXpL9MtAnAuoKTCh/WI4lZHrLJCaUPOgcuzMw7jiCgK7xyN1JyeXeGSY7s7A6lZ945GNDzenaNTpteShzThE9jmI/p2wMcHNjpzuJuZo8QdMR1JWu7nwgfiArF67HbDnXtgu+nQskUtVRUyBYEuBZFBJ3j61af88Z98ijIgTKExChLMLjAtgda2kAvWWkbvOTUtroxVQbNUk5PRgr7PaKtwAdY5M4+BVlvWJSFp8S7WeU+Kxwxa0FaTk2CeHEY1PLq8otsYQvJ07Q0CeHgTCcEQiLSDpG8H7qaFVmpEpjqFk0GqwPbU4Lzj4rJjususY6DvDbaBpotIK3GhOoKnaUVKRdASnxfclLGdhqZF6oLtEuvkKkde1EGyiwtKakIqhLtMcisSQ8qR3U4zbC1tI2t2j/P1o4/kIKuPJQhaepSURDkTokJJi24EKXnu384M2xMKBSsl3cbg54BkQ4gBSsRHj2lU9VbYhURBiA2HvUdqR38mkLpUbIsRSC0IKVJklb4qWRP0hFQ1/Jw6mDXa1IF+SfzMGfk9g6ccnfI513ZVEeV9Ct/Pu/5cL/qExNOzc65fvWB8+xa6DmlP3x/dYgx10f8Zlv67kIx65YoMhvczgGGzoWk1b15fc3Z6wt3DQ+1vIjGo9zjTrmsQQEgBRXfELAh0Vjz9+BGrcsz3E522HNaR67s9PgkuNgNLWKAUGq1pjCbkiFGavm0ZBoOU5b18TKKOx7cMPuLuJz7YXLBtN5SSkEXRqubozKuyM796iJIc3vFRDI+fXvLo5And9pTDuOfp+aMqySOzUxtOzndoXVHVcYkon1DK0LQdHFbO18g+OELXkV2kBNAFNIXsM8UXrNTkmI5VfMuLFzc8utgRvOfkgzN8qdGOLkeavsONM8nXf6OQmpTqjZxzZl4WfvjZc+zuhGKfwGbHWjznVz2H25kvDyONdny17VF4rASRS9Whdx3f/iv/NpuTc7TRNUGdyPXLZ3z2x3cEX00qOdZhYskBY1rWZSWoWP0EXiCC4uTkksPdHZ0Z2A4b0E0NO0dQkuNy15OSwceR2Tv2DkyjyT7gtGUzXLI4y+nJCdfXz9m7wsX5CX7OyJTQtqGcn9NNI2vKRAS2MXSNYlkF7SBZrjNFGaJJKCN4dLnjs08btt3AEmas0kQZITqUqTMebSTKSEzM+LQynGsOd5FBSewgcDnSnlhScuhS8RkiQdNJpsVDVoRQoARIAqUNfWsRMmKP6WSPri55dPWUh8NMcXeAJpaAEIZNJxCNJIuCThltBUqXetoVK91pwTQRpQXGV4dpt8nsTlvmw8TmpAZbH8bC/X7BtqEO/ZfEIgO7RxKVFNuNZk2Jwz7R95puiEcMhSSmRDNYRKKiMpRGtRklMpcfNOzONNkrlGi4vrlBt5IiBcJKSBEEaAPGKKIvBK8JHgQF5x3brWXbCJRKnJ4plnlFycJ20Ly9nVBG0w4SZohRcHvtKBhsY9k/LLTNDms61gVcgHkRNG3P7f3MvCZiKORUC9caBVlNjCXXwmgcx/dLYYH3/DGO2GX1jsaZMiJzjCaVf8EXfeDm5o6Hly/olCEKzfnJljFHdNdiWo2InpJBpkzMuQKNckFKRZE1wrAOdgsFWTHCWlYcgVAIGREl1gFRlpQkEdkQYqTvLAVwa0AL8R7yKYTCophlPUFoUfNFM4mYatspp0zIAZUKJSRSqEt28AmZJSIUFIqcI6RMyoHgHaSEiAWlEllEdC6EUohrhqzQtufi8imX5ye4NPLmxWuGriGuklmMtE2HFpq7+z1XT06P2amJ+XCPaCvh0K6Z/nRAKcV4eMB84xfZq4i/XZFoYqw3EkWhhCCUWI/JrYFcK4+iMmSqK1go7LZjXQMlxhor6dY6GPceIy1oTYpVWaWKqjK4sOLfgt62hCKxShCyIcU7dAzIGDhc31GEJ8aMLposwHYtd4cDP/jsFX4ZKSGSS2R3aljnFa1akhQc0og1GmMH7OaM7aCxXYvcDLjDA+fbhmktnD3eYKSmGXqiirjXCdQJZ43hWx8/4dmLT8naMIlEUop2o8lLYQyRw2HFmC3f/9GBx+cf4+I1Jgvs2cA03nL24RZnM25O5BRAZAZj0D7hYkAKQ3KHmnObC1EUjJYYbdDG0DOAtGhbaAc43I3YTpGjxC0TTduCSuhm4PxU49Y9w/nAtpEs00heJKI9uq1nwcGDKi3adJxZzWE8EGSk3W2QRvF0O0CKzHHhoycfMy8glaZRGhElwjQseabbgJQFl8EXz3Zn2U+BXArNVjN0kkYY1rlw2CfCHBm2knZoCNEQ8xFuh8V2NanLWIm2NVwlusqTub5daPoeJRsSnu2ZwqwSvSpyFJTiaTrL5CKmUzTGUIJju2kQFMKSiDqyGXoCjpwjbasJxSKQWClZpj277hThjw7knBDG4GNi6CS6dtpqpkYreftiImeDD6CjPOLXA8ZYxntQ1pOKRiiN1QZlNSdtw3a7qaFQSdB3DW/uR6a1rlttEXUumCuVt0SBbTq0dqR0HJBHz7tULQGQCui6oRYq1bRGx/4FVu8A5MXXOLBcCDmxHg60Z6eE4IkhEGOkFEWKpfbDlCTLhKqIuqriyYWca1sj50rY1LpmVVamhYBjdq04Qo+Cz0Rb0LWW/r+oe5Nmza7rTO/Z3em+9nbZJzoCJABCBNVQJbk8cEU4PLc9cFSEJ/4hHvlPeOJweOiRIzxQDapcZVdYomxRKkoEQIBEk8ju3pu3+9rT7NaDfW6CExU9hL4IBBIIIJvznbPOXmu97/NmKBR5aeWTISmPCBFtZM4uHbuNfFLIBL7bqDN5G/4iAMRrXjZA8CHPvVOkd5G2dywajZiUKJvhbU6RtehlQVKa6fEJ9988pOueMdgdUmZ2iBAC7x3ODzSlAmKmYBqPSDX1tMpReLisApCCfes4enSPIe4Q20tEuI1ny7I4+C6VR0k5tq75BXjbFQkB2mikzSdkyNjXFBNH9+7SNAUvzy9zYpkUOTIxWYpOoOPAYl5z1ua5s5x6hFtTi5JlNcckwUDB/N6c6rDhB6Wmj4bNpiV0PZtXF6QokFViVs8oUYhyQjU/4dgUSA2zquDs1QqixVrHYWmYqDnBpaxYCj6HhscISnL06AG7zUAv4PPnz+iGHV4qhgh3Dg4IuzUETwE4o7HJ44WgRaIXhzx4dJfV1Q4tG9atw3cDw86hiZRSMimnDG5AiAZsom4kDkvfOYRQaFWxmEwpjGQQERd6ZtMJSmasQQySohQsZgW+H3j24pyjNw85uluxuoF2EGg1pbvqaHvPyeM5vumxY7zlwwePqHXJ9uySIFu2KaC0QmmNkSqPaBBIIREiq9psp/AtqKNIWZBHFFHS7iMEhSpAiYAbPHU9IfSCnc/L0t6qPB6Vkra3SKXpuoDrJCEE6mlJOwz5fBYiMUmMMXgfiULQULDf9lRKkqqsPkreYoeMmV7tesqJIXqPJv8/tu+pdU0xl9ys1pTFHLsLxKDo94EYIkqnHGIzrbIkUxQoa6kWAd1ISCEbK0eVzzBIntxsmE+WVAJs5wi9pTSaZpoJwEOfUEZSGENTz1CqRBtNomBnI2XZsG0HgipJpqQ5OKRttyTlqScVAonzPaaqmFUmj0BHNIMf64ke79UY88k/xtu6lb5DMf9HPt/7oj9/eBc7Gei/eoWeFiwWU3b9QG0KqsYQpGY97HFR4J2i0AXCOJL1GCGRSmQ4UQIhVSZ0jtrdGMa0KF1iXQYb+eBzeyUUSSiCc5wcLTE6p/U4L7i+uSIWnrAfaDd9NkSkirKsCU5SmAJSREuJGVEQEoG1ljStkEohlSKGgDYG57OlYrBkb4F1bNZrQrQYL9FlBS6/yEoh8H0LYZlxv1FC1EipKOuCtmtJITKbTUgpIEhIk/Au0na7HMYiJVFJNAIpihz/Rsoa+ywMQytDjJksKUeEdF6TCMSYfAQJ7z0hxJH3kcZs4jyTDCmStEKWmtmsYT5peOvtd/Ch4/Cw4uDoiNNvXmLqhpkdePHsKSfzCSkEXCn5ZnWTX7YiZxVvVhvuHC94+eIc4Q3Lgyk/eP9dympKMZW8+Oo31PMZWp9QzCZsd6+4utwgllNOZiecLA/o7A0X7RV2aymLBmlgajTRW6zTdEEyXwr6V2cUxZStiyzvPuTo/gJPott5JsdLButziHaz4+tn3zJsW+gt9x485vzihqacZTRwMLT7NQyBQkeQivWupR9A11NkYejYU5oZm01L0+R8hg8/eAuZYg6+KRXbdsezl2uMKbDOgZRoUVE2FYKbPOZEUTcLrk7XDLtrDmeC2EXulAsOT2a87DZEH1kUNbLQbGREyAQ+e2K0zAccISKSrHDRWhNiTykltk1M71RMaw/ComrBQWmIzpKUQ1mNUg3XN5boxxn4TOEjJCtZbyLNYQHOkbzCB5BlJCpH02QzVdcLgpfYPuaAklKyl5ZiolgclAQ75HAUrZDJZMSD09i+JwZBOdUoA4qGs+db6lmDtgWu93nuHQNFkTKkzwa61tEIzRaHGyKq0aAspsy5AN020O0NgkSpa7yNdL5FHyjqhUQqnd3CUdG1FpEMQmqa6YzCNEhZUNc13gmUUWgps8M9Si5Pr/DeITWUE8l2fUNZ1gCofqCuDc18xn6/Z9d3r4NTYkxopVBKEUUkxoAcwVy/T7kD/wSK/urikrBZIwMslwsO3rjDLz9/wh9/8BMuuyvErmS92/PN6TU2Jh7cW7I8UR2OKLoAACAASURBVESVMEDS4GwgYRAyoSSUMhsoiCDJS55JU+cilQAhMmbYOwYbaZqKbLYVOO9Zff4NCcHB0RHnl5cUWqHkhGpyQhjWKC0pmwq/3lD6QNKGwlTElN2W+IDDgQBnx5mqzIs0KUFGwWE9JclIJUs678HnU7tRBhkTbj9k5VKSyACHsyXLowVffXuO7ySHy2NCcvmlgCEqKMoSGRNeJrxzJJHnhCJpRFlQNQ22DwxDR5JxdHyC6zqUlogE0cHs3hLrB5A5XzSgOD9fIYTg+P49dpuvc3ZwPWVazxExUB0fEkLketNjisR7777LkydfcXLvhHUrmJ00fHDyA7b7yLSZ4q2lMSVKwEwpppWikQK/b/loVhI7yexwwcvzl5Qnx2jVcLRY5Gas8xTBQQzUVclu33K9bXl+9QztE1EmphPNy1dPcUlRacHJYsLhm/c4RnN1/pK5UShixqtpyT/84gmuswTrUVKiS0OpYVlpHixPGOYtp6cXHNWRI2b0z2+wuw07kdhurmlwqEkJUjA7qbCuZegHVustSmkQOVQkWsHQwXa3R0D+TvY9Q+tw1iCcZ1YqQqWZ1xPKQvE4BDCGJDVKG5yD6CPJWtoOlKkptMQUHdb1uOApKJAi86NEEigURhYoZUg+g+ukTCgl8gGgyIvw3VmgV5HySGCSR8SSzT4SAbcTtNZhpCbJSNdFXBxYHlXYvaPtBbveovuIihKEzIILD4MNWOuJIlHWmt22R+mE0AaUoDKCoXWYQqNKj+sTuoHoPZXwDPtINZtQNZHNxjLEht1as78auH+/Zj8EZCGzIUt6ilJkyacN7NvI4eEhZaOxwzVFZRgCpF6z20ZMjGgFsgg084q+GyjNBK1y9m7wnrKqWdQlbohs+8R7P3zMfHqA94YYIkOXWEwrfOtI3iK0ppnWBFnghg4dEsuyApfoEQx94vK6Yzmr6W9VOyiQgZSB5+SBcU4HEVks9E+/6CchOHzwkLN+Nw4NBGVVZKSC8AgNSSWiyFIpWWi6wbFE58UoOsuhkkTrAu9dPoHGiFKZu61HPbaLEINHCZ1dp3XNerVDFzWrdocNfry4Y4hGFLjeM+wHVFWRhKc+qVn1l9iux0tJ1BVOBiZHBzlA3GWs8aSqkUVF7wKizCEJAbAhYINH6IrroQVtUKlDlgqXJMJD00wyZsJ5jKx4eHKPyaSk7fY8+6aji4p2veezL57y8R++SVKSxf172N5nD4KPaKFQywlCaHSSyCBYHJ5w709+yKtXVzx79oLlyQF9axFEuq7jzskJ690OKRUnJydcri8JZJlYBK7PV0yWB0ynUwRgrUcXFWcvL0G4LIMNASIcHh/S+Yrl0Xv4NlLNoI8DppK88XCCSD1XT56TtpbKR8TNBr8LrLxn0AGn8rVYv7pCtpHNq5a4bHjwzgO8dPz21TP8SlNVc7rdLjuPYyDJlCdMAYwwLA4rzGxOv9ozdJ75dEmUgjvhmOfPn1PNG4q65GDxkIMfv41UO3wy7LY7mnnNpNSY3nFzfcHO3lDVNc+fvuDu3fucrk+x1uOVBnLWcUOFF7But8TowICYHzI3M9bbHT/7k7eQKhCSxEjBbrclyIKj4/t88u1nXG82EAUBR1CJ83RNIlNSUxo4Z4tIibpZoMTAftvjRcSGxLTUqEoxU1Oi7dn2Fhk1UpSUZQE25aD7SqCTRqJRSiMVaG2IOr8E3DAwJMfhwxrrIqsLi3c5ZSypxHQuKRvYrSPJK2SZ8QkhBJoyd4hBa7qtR1XZbWytx9mMKi6KgqqS6EISU2R+pBBjkMiwdaRJQVWVJO9BDMja4ATUZYX1HVIrjNFcXmzxXmKUpo+BgTxeK8oimzy3A+2uR4oG2ShO7jwkpkS3L7m5OmV+XHO12hE6BXqgmdVgBnQqEYNkdbOjqjPIcTpfMKvnNLoiOc1vn74iWOj3jhAidQ22G0jOEJ0DAsXO8uBmIJWwKgt2yjJbTPO+a2/RhQEC+zG8PqaUC7343eCifPLPXfeIjPgnz95JiVdffkPaD6QxoxZ9+9qTDN4SyLbudugoqibPDMU0mziSQFPikyT6SHCRpMD1jmgjg8zIVkk20yRNBhn5wNAPmSSoasqipJYCUg4qkFIiksAUgsVyihIlkpoYAj4FJlXFxdU1s7Kink253u0olGRo2ywHnU3IBATFYnGAKubcvfsepSr54I9O8OS59xAg+azH/fZsS3SC2miii9hQo4Ti4z/7UyaLDH769a8+59vPv2F9fc27737EnbuP8XHgD352N4dhyJi110hiFAgRKPQ5wZP1wB7AoFSF1GAHh5AJby0XF5fIQhNFzNKzpAgJmsWEzfaaUhqK0oCCxfGSGCVdt0cbkLogpbyYUwp6G/nF3/4aHR2V3VDfOWQoKnaXEREF7z0+4dBMWHvHZQpsphpRK3ACnySy1hhV0u12uCqQwo54uWG7vaFelqzXa/7w7ffpLlZIWtbKoLTA+oBRgcMAB2rOmkDfeUKC7bDlb//m7wkx0e+3FNIglOWHH77Lq82Kbt/n9l952rbDbEo0krosmc+n3Hz7nKbW+L7l+dffEAaHSwIfB7zPGbD74Dk8PGTf7xDJYYzh/hsnfPHpS+7df8yf/POf8Ztff8rhoubTz76iMEsuTl8gAjx9ekYyCmKW/iUyVDCJ28VePvMB7PoVP3zviDsHM85eBGQwoKdMF9DdbBj2e+bVlDSfI5tsuEsiB3yUUiKagpqSrnW4MT1ImgJVzvE+EFtDvxck7XFkt3aMAYSh7y1qmmfyVJLJdE7XdrjBIERGFAtl6HzPfDLBJY8pK4R0lEXJfhVxo2FvaCN+K5hMFNVRge0tikCKUOiGrt+TkkNrRbdzmUvkFM5K+t6jlcg/TwgI4ylnsN93+fTdlxRVJnkKrbg8f8F8fowbDL4r6DcRJSWqclRNxpFbP1AVEVOM65+yQCuB0ooheY6PD+huPKSSSTUluKynj0GRokcn2O1aVJKcDju2tePuwRLZDcgkKaqKQpXIInciVVWwudpycXGRefsyEoLI+7ZbUUkWRJMR/Xmn9k8aw4AQ3HnjDt/+9npMSFTs+g43OFbrPTfXa7yXiEFQVA4Td/jO8fVnO0KUKBMJPZnBkfKMWWpFVRR00iF04sGje5w9PWd2uERJwenLcwKSqqowZo8dOoRSTOcLnOuI0ZNiRCZNfrVGtEoIHMPuFcp7ogxMakPwjpQiOuUiaVQJGLqQcnuraq52A8/+7nP84AnWEeJI04wRn3JyESIiYpZKkiRJlHz0swUTk7jarVj7CYdxikgD02bCqnSUdUlMCecSUt7G7EhSyh0OMhKjJ6aE70ewThJ5Ce0TSmpAYkoDMXH88D43m2vcYElEdII/+7M/4vTsWWZSiUzk3F9KDidvoIuaQXq6rsf7nDVQNor7bx9z9vycMipq1/OfNTVPLVwuZrjNlsn0gPl0SdvuOXy8ZHX6gsrmoCpKM7JNIs726NLgY1YRCZEj+3SSVCriri9ZKsEewY1tuXPnmNVqy247sFn1bC4vKB6fEKuS1eUKKSWLacUuBWb6HtO6pFnWzE4mvLy4xKhEMRUURUlRCpyF/U3Hddri9CFSldi+46037+Vg+cHiQiJGh/c1+HFflCLzyRJve07u3McgaZRiOVH86pe/4vzrF9z/T3/MV98+56hZEC10nSckkROBRCRDGDQi3eK18h7m1qwVpeNyu0OkObY4oDfHyFRio2M/OB7dX8Dg2HY71lcbDo/uYUxCyYhWHisV/XbLwfIR3jmG0LPeOW66Hcv7jzhMR/S2JfQVMZ1TVIHW+rzvqAuMSpiSHEafAkpFXBA4K1DTgv1gwWS0tjY5pEQESXSCciqQCZTWTKcNgoALnlJo7JDot5aikshYM+w0i2OT1WRJ0u8cGM3QWpQSpADaGNqto1kUeG+RSeJ3jqpsaA6zIsl2EZzl6vkLpFIcHdV0dkuhEw6IAdr1lvqgRhXQGMN+nxhsxh47O7A8uovQmrIwSBQuOoq6IWdzZ1TEdDpht+koVEm4uCGVBZeuxfpEDIr1dUfXrbG2p24ExydHnF9dsestKEnymWsVQkAXAqMNpjLENOr8+V01/z/++X4XfRK73R4lCibLijb1VEmRUuTy8oau9QiReTOFAqMiPnkGr4lJcLCY4oZVpnHGSJIST8SOahlnB65XGzb7noEVpc6jGx8jN+sdvQtZ7RI8fdfhfA5adiEgosX7TORzTiBkixFZ+RBd4mhSs1rnNj6pDHvzQyRFR+cSWPChf52B6QmgcpGPMYyJShUxeZTUeOuwNhBDoCgU6/U17334AafPT3nzzft88/kXLJpDVPuCIyN5+LhkfXOWjWsiEwV1ka9d8BACxOSy+SNkzbYuI1LmP5NOKpMSpSbgiCJwfHTMtGppt2uis0xqg20zLz2KSAiOYVPy3/23/5K//Ku/5NPffM79d+YcPDjhYHHIq8sborT86R9/iE8CHSSvgmeSJI1J3H8jkVJOJxN3T5gYx+nqjBQsMuZ9S4yB1DtIGXlbxogjuxj1dMpmt6aqC367v+Z6s0XriqqZ8urmMiu3tGI1U8igadxApRRN0+SRXap59427PHzrbfrO0rYtz5+eZZxtjs1CGsm8mVMog3oQQGSTz8YPPPnqgn3vqeuCo+MlFYkMSoskOZ7GU8JbT6GXXK0Hvv35pyyKCZubLVUy7Nstf/03v0S2oGuPnk15dblCSI2MAxPp0UayC4I4dpzOZ6VYHBVVSmZ2/Ns/uoNLmuM7Jww7y9mqo64aTq0mxTqfxB8dY1ViGEcE0oEZdz2/+fQUr/MOqtt1RCa8ePEMWea848PlIdc3W1os5YHgzr0C5TuqwuTrUGmCFGgMpIDUiXY7EK0kOEm3tygjcEPA9ZFmKlBlDnmh08RoqY0kBE3RFbgWhC/xvR8R1xpr8/cSg0TSMNhAiommNPghoCUopXOwuzTEIWCkRojAMDgKmoyGEJLJbAIiMNg9sXeEBMIUGGOom8iQPDYU2CEQkyYMPqOqhcZ3Dj3NweQaybNvniKpUMJQFjVCBry17HYDhamyqCMCLhGdRcqcYra6vCYEx/o64IeBw5MTDo7uoAvNZr0nRM9kWlHVhtl0Rtu1BE8WPMj/f3P973XRz2ClLckl1vsVphTYp1dUuma72eW2NEaklDgfcCGPD4QShGHg6lU3crcTsYtZHjkEtiIwDBZlJDc3mzFLFVJUWB9ISKx34whEYHuLCwGjJTfrFboqEMJTiIR1FjumaBmjCV4wjYmHB0ds/ZZ9P4CI2L7NuaAqklSJUBDjgA8xOwxHhoxRkhQCMea5+GCHjH6VCqkz2tbHRAwaoQxSw7Mvf83LJ99wvHzA249PuCpXSHp667P81Dl8iBhbZ04NkSQSWitM0eC9Zjqdjm1worWb3AkQODo+4MXTb+g2N1iZA6ev9nuCc9y9fwwuURSaojYI6RniKf/L//o/c3x/wfLNiush8fjgPkF4ttZgbcvNiycjRVNiQp6LSsFrQqAxhsNlyUV3homBdxzU6yErFwI4lU9tKeTubRBwzgCxZn/Rcv+dB0g/EOoZN9sW53bZaZYS0rucbZw0ovUslwsO78xIAlxyFKLgl3/z6yyh3bYcH8xIKnF1vUc3krsPThjaFev9wGRSMtzskcHTX11x8uCY2SjPDQkkeUQglMkjRhKIAEqwudkxJMH85IgfvfkW519+y0038OjdBzgvmM565ocz6sMp53/9KVpHTEqYJJk0S44WS9zvBAW5EElC5gLoAmZa88cff8xsMUcaxb/5Vz8nrvd0qWVzuRoNQKPTU+SQE4FAxLy4rKWgCoKVjPTlrTIre1rwlhQk5+vnSA0+SNLeUM4kKE27gfU6UlLk/AfrUSXUJfS7Atd6Yoho0yA0yNBTLiVaKuy2J7QmRwiqxD4KRJ9QZaRSNTvXk4gUDaTgc5SqksiJYT0MyCDwMdNKUwxUZop1A30r8FuLUokgEpNqghYlSmUprp42bF5ts2FLBeqqIggIqOzWTQPBK7ptZv4oHWmqEqM1RkrazY5h2nFy54h/8Z//BCk9Imr8EBmGbBDtXY8h0duBu0cN0UXc4AmFwhPY7Xq8tQgdEClxfXmNkgKtFiBKfEqUVY3rE223o209hMThYkkSERlARDFKxP/xz/e66CtjYLng7luP+Ppv/x4xDPSr3WuteHSRnCqRiEEgVda9Jw/EbFi4TRXK+vhsrRajBJEI/X7I5EGhEFKO7Lr8grhV+AQfuXv3LsVSc/n8BaHfY6oKLWXmYxiDUooQE9IY6knNy26H1TkNBz/gZUSZkm0HxUzklC9SXtDENOpuA+iMi4gJ8IkUE0IKLANC+OzIxFMIz8uvfstvfvsFYTfw4GDJen3KT//5z7hOn/Dk9AIdoW33DNZyeHCIEMN4YmVMh4ftes/QJ8rnmna/ZbAWEaDb72HU/R8fH6Okoq4qZK1H/0Hg+npN09S8d/QeQgj6vmezWTNpKk4vW4Q0OQhddZTaszp7gq4km5tT3vnhY549OWcICW8jddkQbMQ7x2awFGmBkh1WOJ7vVrzrNfZ6R0QidIEWEUXC7jsgMZsobsSGiRd0my0P332LeHnJrhuyFDWIUeJa5x8LQRQSN2SMtE+Ru4/f5uzVFcoUJOcpTUNRFBRHJfWdI4xJ7Dc7ri837DaO8r7ATCRu0Jy8+SgrOYIjl2GBR0IQ2TGaxnhIKbm62KETdKnlR/ff5+HjR/zgzUd88/UzPvjDD/l3f/HvIXlubtbcbDZs91uikQSh2XrHw7eOODi6y3/4h89xQ4JIdkr7iJSRg6bmxz/7Mee7M+RE0F0nnj15xb7bo/RtZGNA6cyUyvvAmA88SbBPiVXIkYAxSuiyDDfFhCB89/wAMuRgFj8onn0zMFuWJFkytIJdq3CDJwHzY0kSHSFG6iYSTMl26AhbQVUICqXodoHQFSQkSfqMGW8ChfZsrj3pwHD3jQnbbU6dSrHAW0EygZgCstAk65Hj/k+Wkm4IqCSYFjXeGaIYmC5guTRcXfQUZUMMPWU9QR1O2G32VDMoSoFIE0pzxNPnL16DzEytcicusjdICmh9pJqDLDUCgRICQYE2GqMTVZMQSgKT1x6YPI7J2G3ncje+euV49a9/PiZfWcrG8OOPfghkWquPuZ654LDeZOqqzYbIpADx++f58D0v+jEm5lVFHHpSdOgYiFpAEqPzNVEYRR8HRJCo0ZoN5D5nnLkztr1xNBlJKcYw4XFmHhIxWKwNOWAYidYSKQSBHG0XksA5R/QehcAOQy6KwYNPSJlPckYldn2iXhZoX5BCpK4m9L7nZm+Z311w/GjCl59ck3qdTWMjJ0KInLWZHy6FDykbpAQ8fvdtZGl4/uU3aBfYXJ5x9mzN+asbRDDU+h4//Wd/SDGZ8/FHH/L//N+/QKXqdbrYi/Yinz6lzPGICHwIiBhJQfLtk6d512EHtDKkEUWgiwpRVEC2roURGZFIuN6BVAyDy4tpmbk81jr63tLUFdE7fvWLf6DUNoe5RJg0c4IMnDw+wjvo9j1n37xAMao7Avi44OnTM5YHczZKsNcdhwuDjop2s6VqSnx00GRy6KAAN+RT96srqvd+QKUMbz58xGRygFIlb773BovlkqIsCUSWyyWzySQb27TCC5AioUVOjMpGF8eri5bmzhJle1QV8H3i7OkVs0WD6zu6/YBIPZ/8h9/gRSZU5nvPs1l3pCBQMuRISQnz6YSua9GpJPY9VrScnq7ygnNvGVxg37ckAfN+QKmBlBpCTBwczEE0nF/07PYaU0lwHq0VwXgEkenRjOvVmuv1ij947yP+1f/5/0JwKBNpZg0kQT84TF3k9W/MhaKQEqnHkpASUUQKIt47dvsdzrrXbJf8ZGXxoJL5PqnNMX/w8U9ZnhySZDYWvtbch562u+bqYsXqZk1QG5Te04cBGQJFylykfYTBJYppwvkEw5TSKBwdKMWApTCaznqquiSMkwxZaPzgKBqDsAqlJkSZzYOmymyrmRI465nMBJQD06VCoahSQ12UvByucNFSS007ws+a6TH37t1lv9sT0x4zi6B8Nm7FQFFEKnOHg4MT6mLCrW0/3Yafp2z6TOM1zuZICCKNy/iCOGLh54c1B8uKq8uWpAKHd4+Zziq8i2RssiTnK2tirEhkSqjCI5PJGBoh+H11/3td9AORb5++ICWLmc/oZEGwHqREhJidrCKTpX2IGCFzW0gci3t2/N3Km1JKxCQIo3RT3er1ydv4JDO5T5AZ+JDfHVLBenWF3iTwCqUrorQoY5A+z2qVyqcmKWTGKAdJjBBcz9m3F+imwBXw1jFcvHiK7SLCJgjjrzeuYCSKIDN8C5lzZwlwc7FCIbm7fMRiOuPTzz4DF/noxx9h6oKT+/eJwvL5J5/x/g/eZug9CI8St0ER+SEVcUza8ZG6rlksZjhn2e22tF2XjWNSopX67uYZ54SjZoSsFpCvv6dsAsu8+LqecHPdUdQa6we8s5x/8zX3Jpr15TVDOaOeT7neXqID9J0dX2wyo259zGM25xgsDAHMZIrfXlP4jGFYFBWzvR+/n4Igwbn8/TqjeRUc+6s9zkZ+9icfw+KAd95+k4PlkkggivywSGGQQo8TjhxYn8ZiJ1J++ceoWC4NMgSGwaNEQa0Nx8eC2XKGSIkYBQrLZHaMbgq6vifEwKyqOPv6W5rJhJvVJddXG65XO5SCl5sNk0nFbrdns75gu92hY4VOEGyP0GAHC8EhU6CzHSIGDmePsa3j9PKS2UyRvCfKOMZVSooCrLCcfXvGgYLzry85fXqKpmewe/xGM5nOMnOJ/L3G1+vg0Y0dQ7b0A723xGHA93mBn2LKjJdMtEJGzeAlRw+W3Dl+g7//uxck8Zzju4ZZoWkmC8rJFKMLjhZv8sEHP2Gz2zHse85entLv9yxmhhR7rA2QFEEYpPGEEeCXYsSUiqATIVpQCSP3aJ0y6tt6+sGj9Z6m1DT1IZVaUhQaqbPSJaWENgpkGvlclrBIeN+z2+1o9xFrNYdHNf2wJmFo5jXlwRRZe6LucTbS+jWL6YS+bVGmYDI9RKYKmRTr6y31yQEpRYyoRrl47vqQmbEzciLz9ZZ52aw0JJ+4eLXh/oO7CJE4vz7j6OAwi09kmbEoAoi38MiQaxs5YyA7cm8f2H/KM30l6aclRVXRxwA+XyglBMpoQu/yAiRGhEpImYcz2T4uXv8FjOiAXPDy8mVEJEiVb3QY38ajBCqOF278V1pJilIRizzjM1KhlSGoiFEFiJCXoCkHiETlcSKPae698Q4//PB9vnz+FesrS6Xu887bBcIJXNcjkDlNyDmU0SQBIeRAh5dPX5BixO9nzA4fMnCDM/lN9Kd/9sdMjyuePnvGl1/8kpmGwlT8atOihcIGO7Z88vWfJRFp6oZ62dC2LWdn58AoQ5UaQc6B9T6TMfNiKC/5hBQIkcb2/rsTjZCaFAWlKbj/6BGmusD7Adu3mNpQzEp61zM9OaRGY4Mj+ID3jMhjOTLCBUVdUpUTiiKbh6wd6LuOJBJPtCU0BRrH+6oEZ5Fi1CWHgE4Cnw9WXF7f8MZP3ueNj97jN1+fM60PGLpAkoIgJF0PWpmcK5oSiEQ3tDnSjoQkn8yH3qMLw9C2tN2e2XJKpSVGT7F9dnkjAiFpejHjzqymmY7dpU6IqyvK2QHv3L3Du0rwq88+Zb3ZMtlO2NuMXnjy2RM6F6hKw/mrGeevrtHTwDD0XL9YIWvD4cMT7L5l13uWswP2+3OUSiQ/LrhTzEAxIieLJRWCuw9nPD0/h+Dz6BFJtAHb9swWs5HUKIhC5F2Pd/TrPT4MRB9HDXhiVld55zQC/+J4cEhIkIaf/OQj3vnoDQpT8MEHDdY6nHPYzpJSzpW9Wl3x1W9fsjyYMl+W6LqEpHn+7Jr90mCUwxhNURQInYgWbJ+Irh/d3hoRDZIiM5zQyJSoTMmkKnn58pJp0XAwLUEZJpVBJI8NPXbIh7z9PpKER6sCJaesVx2zWcV8MR3xFg2eLV60mFKy2q6J4lvmzRyh8ss1hop27ygLiVION6yxwxZv9zTNlG0H5eIOadTQ337kiCzJz+CtxPb2kGn4P/71X3N+vs5uaBURwvD3f/s5v/rkKR/8wfu8+dYx9IBQI1hS5pFwSmiRw15y8l9C/Z4sle910ReQSYVB5xtWSsbzRjZfyXyzQjYsxJTD09MY4CFELmbfBW3k4p+LbAByyMfrL2fk68jb70dkK3qMGaFwuFwiRKbfVWWJFJpoIlWVOT7WD4BCGYOzEmmnHMwUpmr4zZdfMLgBFSWt9SSxYbsfsN1A8Dn9JqWE1ArpI0kKHj26w+MH95gdnKDLj/mv/uuf8Rd/8b/z608+48OPf0SXLvnkr75iUs7Yb9e0hHFxrblz/x6HR9PxFJcde1LIPIsV4LxjMq2YzsrXJ3ohswkkxUTX2ZzNGjOsTry+Y+XIwc+eQCEFPkEMieXhEp163ng44cnX19RGZJxztAQkQiVM9IQQCAGULlkeHOSOKAxIrfLJJUo22w2T2ZQ7D47ZrtZchUsmzZS7R8dMjEZt9gy//gaJf70Et1rRERC6ZDKf87P/5E+5Xvcspkec31wR+iz/89GPM1U96ppBqQRKwb6HJJEiRxWmJKGzaCEoiwn7ztEGB1G9HhsmJZEhIkPB6tqOcX55T6TKBYvDQxIRJQvKZoFcW5bzI+zFGV99/hVFKZkfzHj88B7ffvuKnd8zlyUew3pSMUjJkVKoZkLnEwulKcuGUjOmwUkmzYTN6oZZVWJ3G6aLCqeWnJ9tAZCm5OjwiMvLC/p2j9Ywn8+IMbFZ7Qgxy2BT9EgFSuTnJaHy38eWOI37tJQkiQl//i/+nB9/8AY2igy6C5GkPEkVNMsDSl0RhogPnjfee5Prqxt8bwnOc3l1/baG0AAAIABJREFUBVGw27Y8eLDEDgND7wjBZfe6EaimIsbI4C2xa7HOIQQURUEIguvrFQlF13ZEARMpQAe2Nyu0Al0YClUgxmV1jIJC15jC4Ceeze4a2SnKoqauSrwDVUZ29gpdaLSyBLel7zs6Z3E+x1OGISfJTWYebQx931MUZVbTLAJCBDTjCfz2uo3153W5STmR7OsvTzk7XyO0JyZHEgElQEXwfcdXX37Omz/4c6TQOWBdfNdli/GFLV93X2I0pv7jn+910ZcyB0DU0wbXWZSQNEVB2/booiAogZKRXjgKoyBBU5X5LUg+lV7f3KBVjkyUclQpSIF1o7ytMESft+VSk9vLEDBmxJZmBhdt2xLCAu/sqGHPBbKu82wtRiirOSEpvI+sTh/x3/zL/4Ji/pzryxU3l+f84uefYkOOQEuxoBQN5XyK0CrzbVJOhZIS0BXHB4es2htefPoC7X7M/L+UnH76CaK/4vzJlna/ozQlu5tLusHnxXfypOB5cXbJw9JQGoUxZizQkZTysk6pscMZpY9h/H1Z614/4PllkMY9Rx535OVefL2DEEKghsj9SsOTpyzefZPLEGlbS1OVQKYHihCIKY8h7NATY2K1XSFij/eKd996zNnVBQwO3w7Mlgs+/MlPuOm3xM5RP7zPycmS7aalODqiPDgmnG4J2zXrfkNMERkdvTH42vDeT35Es5whS4t3kWHYoXSFVIIiZQhfSm7sYm6XuUCQvHpyTti3OcRkHGtFKUArTF1RVQatFWU1wzQlolHYBDDg2zR2k6OUEs3LZyuESOiyJKYKbSqULpgfTLg4O8d5z3q14nB2wMadEewGv5/grKN6OOfe0T2MKjk6OsC1A8uTuxTljNPn31IpRRIJ5y2u65nducf2esWDu/dYryQ/+OA9JospL1+e0u2uswKMwNB2MM1yxcV0Qtu2EAQ2RW5rRpbIghcJDxmpnUQ+dqkp73/0MQd3D+jdwObKE2ipa03XBprFDC0FwYvM3okKqWsOT2qi89xcXjFbOCpTcrO5wOsKiSB2WeIaE0TnGdwuy4sDv3NfJoZhnwUQEoRMlDUIAvvOfQc5DJLgPT0dMWQTVUyRfdtCzDr3aV1T1xX7do/Mkjr2m4GinLPdvuLG7SiLiEyG48O73KxWSHKWRtKS/RC4c3CMYsq0mjExNTIohLqFJeRuWaXba5p+Z0gKSsCXn5/mQwFhZOKH7wLOhaTfdfguYKQhhtEId/tzxeyLl8YgRe7Eft8q93td9BMJl7IcDZnhRmHkT78OEVCCKPN/q6V+XZDkWPjrusYOPreI4yL39sSfE+THE8wYucd40o3j4iXFNMq/svomIrHW0vXjDSgzp0TpkhTAxYD3mrp8zBtvPODf/NW/ZbuPHB1WvPvxD7i6WtH3ieAljaiZzCWX2yvCLpJQBJFPxHboWd+8Yt3dALDp/pL/4b//t6xWn9FLS1lV7PcOX0HwCaXNeM0gEWi7LdPpOxADQoC1FmfzHND73OXkHcc42krx9bIpz3Tk6x8KkcZOKbxWbQhuH8I86f/RD97ijdWaZ2c9w7vH+N9daDFeb59xBM45UkwUQXF4eIc0DLiXL7krFZfnl5wsl6R2z/NffsKF8lRlybSZst729B6+/OYpMirUwZJ0MCfEFuUs1y9esleC93/6IXfffsxnv/g7fvPJV+y2PSEkbPT4MHoUiGNXcwuSyC+wAmiEGHctAhECKsbX901KoyAgRFAFctFw9NbJaMypKauSqipz0ExVUpgKrYsMLrOROwdTTo4nfP2Fp3214b0P32F3teLJs3OePTslukBIke1uS4yC/tTCvuT+g4eIKOhjZPfqhot/+ILJYsHQaESMnL88RWvN4GG1c+z6yPLekjuPH/Lw0QkHXx7y4sm3lKbi4uIcYs9qtUEpiRAalKCeTKipXn9nSmbjDzLQTOo88gw5VPzBO+/y7kc/5A//6EPa1YbffvG3/PSnH4PI3+9+24POwvEQhzySVeBDJCdDJJJSiCKia0NVzfB9CZOA0RZTTElEfMiM/sLUWWcfQs7gjaMaykW86wnRkUL+mWP0WY0q8hAF4ZEqkWJOo4sxQoDBCYY2sdbjHktrlNQslg3ODRzXRzTzCm8DQlZs9zs2O8edOwrXedqdRxqJoODhvfuUFNSFuX0IX9cwyIvbrOYV4+4kP0N957m8vEJIR0pZ+g35ebt91lIcR5ivT/O83lWmNNat8Vm7pe3+xz7f66I/DnhISGLyxOAQCPquxZRVJsxBdp56/91FGpe2QmaeTgjZvJI1zRGiGDXfMac5jWwW13tiFLkojie227m/1prtrsUUhqJq8vJyPOnmZVh8jRxOoifoL/mf/scrbP8MNbHcPNcspzPuHxywuvGkKDEJShK11oQi4qPAEnFux+rqgpXv6D0QFUJe8PzG0w8dRaGJXUvX9xTVQT7ljmjnvEguCBFWN3tsP4w3yOiaFfK72eJY8MU4psiKlfHKy/FlOKoQxIjZBcbRGq9bSjUEfv6//V985jsOjk7Yr19CaBlkGs1mKc94rR1R2DnQOUm4fP6UN6JAf33B5P4Jq23Pqr/OiI2J4cZ4Ht57SKE1vWsJQnC6Oid2kSGCkhERI855QqGpmxlvvv8j/t2//yt++8nX+DDkHkWobK6SaRzLjN0gfHdtlCBoSUfMy76YcM7mzgiJFoxdWkYFpyQIqyte/vJZJpKm71bdcmzBhVRoU1BVhkIa6qKkWFQcH89ZVg2r/prFnQU/mk5YX295dX5JkpmuGRJ477m+OeXo/jGrmzUJySC2zIzjcn/FboBGG6amRtQZ3LY4OMTUijv3DojeEaLl3oMltVE8kXmU9vL5U7SWKJNA3aJFstvXj3GG6HGME/IJFLIm/+Gj+3z0z97iRz9+GztYPvviW+49foAQHh9+R++vKp49P0WbjDZxIdENgRjNiGYYUMHhu5Zuu2W7bdkPW4IfEMMA1uKjRAj1WuGGyC/s1/evyIbD/Ns1yOQx6ruqK4TMDaoeF9Cj3FTi0Ublzn8cD/bO5ntEZZDbvJkyb5Y4JeisJ7IHoei7yOqqZ9o0qFKhbqM5x5p1e29l8QivnyORF2SIJEkxoLTk5mrL0LscmSYimcL1XeFOgFD5n7OUU76uZT5GvA8jp+e7X+f3fb7XRT+lLG+KdiAGO7Zt+YIRIiF4kpMInwgpYJMbFyX5VCHJGntvPVIoosjgpxTzad5aS+ryG1grhVRZtVJNMv7YGIPWmSl/W9xtb0G4caF5++WMc04SSSZkSHj/BdeDxEgJHUhhaNcW++Vz2j7i/ECwAy5aXMomC8gjmNKM82IpOVjMqOv/j7k3+7Uty9K7fmPOubrdnPb2ETf6jMysrMqqtIsqC4MNGIEANxJCfsUGyS9gIfGCxV/gVz8hWUgIJCRAvNgPGCEhY8ouV+PKyqzMrKjKjMyIuPdG3O70u1ndbHgYc+19slyVNpQK5ZZunHtPnN2cteYcc4zv+8Y3aqzxXJxfMY4jYnXhOjEYErPGMI5e7ZWrkuCFkDxdN+bXNDviOmGUupCYtfp5shg6XzORFPvEICbhjOywSGsVozTGZLWS3qeu6Ln/M79EGDyfnr2kKiOXn6xYmsh8UROCZH5g4lZS/j0MV8OG5vCY+dcfcxEt7WFNwDN2W7bdluOTY2xZ8OlnTzAmUZSW0HZUVU2RIjEKMQrGloSh5Gd/5st8+tkzXr2+YXm8YBzL3PWsnZuTlGkHe97KysRI/qWUW0iScE2x20g+JVJ0GjgkiwrQEt2IQmbtdsgbPVcR0eN9z+omkYJWCukLz7OZ5d0P36ZplgxjS1kLx3cO6IaBs7PXuMIQQyCNSWWi/TVDWmBNjast88ePSK7Gri+onaFa1my7gRLh7cdvUpcGP6yRyvH66iXL4phNlzj74pLUdRwtD+l9TyAo75K8zp9IQsIiMTAOfbYZ1+sTgYN7D3n/vQ/4uS//HGEULi8veX15xb/yja+TBoVgn37xiuOTJdt2jYhweueEYRD1w3EDQxc4X71ms77Ej1u2NysuXp7T9ltCxrNNCmq0lmW8Agq1icKReaAvOgxV4RMb9c8QExJD3pNCEMFbGEQbs/S+x7x+p2ANmESy+dgWQ1mec//+hnfee5eRgKwT86Yitj3zpqawwmKxpDAlcYzaSxHVwFDyiFbR8lCFCkqS6JAWVJDSbrTqxzqSMrW3FmaOZeoRR8pD0LUbOSMa+UAU2SsU/0WPP1bQF5Ej4L8FfjZ/yv8E+H3gfwbeAT4F/mpK6VI0Ov4d4N8HtsBfSyl98ye/g5Y5wzAQw0CMUbNRa9lkf2mBXTDxOduPaKaaUiCMieAT47iF7A/urKWsHPP5XMmaXN5pms5Oc79bFDmjN4ZcDivB2/c94zjQNHOM0bmX3o9YhODXhDFy994jIsL52TkpJLq2BXEk27M4rimaQ5IIFy9fYyV3Aied7xpi4vz8VW6MGZVgNI40JqQucGXKpd+kv0+s12uEAufqTFrLfvkYHU4uedF4r23kYsBOh55Tb34xUDijlsrYndKDPP9WJohDhBAsOLULbkPi5fNLQh9IC62SQs5eYoyZxFWYaBxGjLM8395wvDignM/xUa1vgwt02zWH1nF+8ZqqKjE2EWMg+IHN0O0wZ4CUDKWruP/oAd/+6GNimGSlJhNbVqu8HJDz+s3PTbvPN2tm+Xv5oIBsuZ0nr2UH12kss8ICQgg+6/Nv7Q9rkCx9FRSX1vsg1IuCl2dn3L9TYQGcJir37h+TGCEJbXdNjBHnKmL03NxcQirY4Jg9us/cFhy/+YjnL15ydOeA4fUl627Ni7ME54E3JXLvnTnjakNYznj+6TOKNBBToBLBFW5HwjtjMIXRQeFRq5hJCBGSQmFlUfJgNuf09JDgAn7j+fSHT3nn8T1S7BErtG1LIFDUlqc/fMHdu/f0XkQhBoUix0F7MfaqMqGal5giMAxbxKrXFCkSs1pMSDtpsGTnXL1/6qs1CRbGfO9M7seJGf6wIvq9KTnLY0pzyaBfIqpey98b+sA4Bl6/fs3h6RGzWaOzjouCfhypZhVVUdMUDdbaXTbuJsQhTUOFcoaf+4M04wfBYozngy8/4JMnnxE1xdXnSVYRJh28osFeYcjbiet0RKigZa+q+0mPP26m/3eA/z2l9B+JSAnMgP8a+D9TSn9bRP4W8LeA/wr494Av5T+/DPw3+esf+Ygh4seBcUyEYcQHjxhLPV8wBp/7rvJgYCO4TNQYa3OQ0OzUmgLrnGbnesbuyLaYG40kl2XG5tm1eT2q1EoZ8+h71usbJT5jwI8j282WWbPMnXI6FMUZi6Hi1evnXJ1f56awSMyGW6SG49NTPvyZr2ArwTUFv/6rl6xfvNYgLYmiLKmbBWLG3FVsby1IKIqKvg8Eb1QtYiEEnbKzWCyIWufkxaAWB8aq94z+rg5jSv2+CIYETvBBsxFrZDeTM+ZtQoKgVpzq/pn7HW5WA2HY8OH7d7hzp+BXf+OSnshyuUTQzMyHsDuUy7LU+ycJ8WqANdqe0+OGru3ZtBtCp+3uF2eXdEPHg/v3tOvZDxgn3H/0FjEEQlJpJeK4c3zK+abHj5oV6a8uWZarm2IHXzFBV+ygAhKEMWgmZqYSQHR2KROkKhjncHkTihFcUTAMIzFkhQYa5aexdTsZsZXshSSkaLDRcHl+wRvvvMnFywvqSrtR796/y83NluHVJYSRsj7i/Pyco8NT2u2GZy8vedc1rNPAw3vHnD07Y3WWMFXB0dER617lkuaLK/7JP/p7zJqGu+/eIO05lYx0yVBbIQhEMYR+wAevhoBWEAuI20FfY/S0Q8/spOL4fsNn3/4m4fIJZn7A6vWG9z94SIqRz5+/5uL8FW+9+T4Ew+pmy+O3Srz3xKhcQIyJvtdZEDF4Yib4owiurDIB3uqeNCYLDsj3z2QVjsnCg0QKQkwGnyLJR1WpGcEUBQltnowh4Y2OEbTGaDNZ9qnRhDHDfCaB2XOCzawm4bm5vmG+WHB6dMJ6Y6lsgY8BKQRGde8dR4+VasobdwfJdMjrwcUUadSPCVhtL3n8zh0+ffZJPsz2mbrkG3B6cpKtINhVpSGFrPjbc5y35ek/6fH/OeiLyCHw54C/pr9jGoBBRP4K8G/kH/vvgf8LDfp/Bfgfkv5WvyYiRyLyMKX0/I98D6NBO8ZEUS92gz/mzZymVEimKNwuS5Xb2vEsjUqy34Sy2/D55lunZFIuzY3oJrfWZEVOZBh6+r6jH1rGTnHIEAIhZw9NMUeSY17PNYg5dFMHOD45JYSAdYmLiwuwiv2RPDdX5/zgo+8TSWz7DTfX52SpOiKG4ANt2xEnlUxSV0IIWGsZB1XHbLdb+n5kDGPGPQ3GdVR1Q9NUO0JogqH09weFpBRHnBpGYjAIDjElIY74PuRrODVfeZLfY47GCsZBXVkaW3F5tiUCv/yLv8DTpy/wnZJ6MSQdWKMevPpZRbBFxb37DzhYNrjS0sYELlEWBdukFUjwgbKstXoTk+dZ18RixsHJMc4VmXCEt++f8N0fPEXGgSkD3PmOS0TS/howbT7SDiNGIj4OWumFpAoNklJASberiX46CtTt0ghDN+KHcQ/xyTS+buJ8lHcyRrIASl8/2cAQt/hx0JF8UQ9jEWG5WPDeu+8x+i0iwvnrcyReZ58jy7a7JPjEZ09uqGpL70fs2HE5nHFwfIc+tbTrltC2BJ8In77gl94+ZXUR+DQ5Pvrdz7SayY1o0YgOMckEvs2cWBIhIDTLmvkCPnjvlKc/2HJ4OOfZi5fcWR4Rh5ZNbzi/uqCez5FS6PyAdZaURvqhJYRSPaZSoO9aJPlcvcbMj+i1Fueo7JwYFUI1Vn6Mk5oIKWNUth0kYAGXVE0zBVpjog6CcYp778/3CClmM8Gpn2e6owkRhXVNho/6zjOblVxfXNJU9zma32Oz3TBrSsWSXKSpagpX7gSaU4VNCoo9MQkjMpSUoeGhj7x8dsF6+4WS0NM874k7A0Qiq6urvOc0wU153RpBuZapGpI/eRuGd4HXwH8nIj8P/BbwXwD3bwXyF8D9/Pc3gKe3nv8sf+/Hgr6I/A3gbwDUdcPp6b2cySneao3sVBRmR5ak7DCnTVXGOMX+86lrrfrqYHJwZ7rFOsPTh0C77SF6hr6na3vazVaVLmFEgLqumTp9deSUJamrLyEGhnbLFFCM3g0uLi60/C8iZWUZep8rjEhKPa9ffK4BRdCSMGeH1pgsOzWYCcLKtgGXl5daQjrtJj65e5e6cZACP/r4R4DQ9wPNbL5j+CccfeIgtGbQrCYljylr9cAJKlU3ZlqAkMIeh5/m5O4XlxqKdatzfvsf/yYGy1tfesDVsCZISRg9y8WSqpgxdAp93KyucKUjGaHrB9abG6zt6K4Cq00Hqx4JAV9o1UVUe1zvR0ZBrZpDyde+8Q0+f72hr0ot9betzjFtR2LKnkwSppQ+B+QpwOhF3wWaHcyja7CqSvreMwnndgZWSYd2K0mbSN5nOazVCWj/Eo/JDiRq6zhi4OrmnLJeIDjAESVR4Dl2M8Qt2WzWPP/8FVuzAlEocRharq9XLGpHs5jTtRsIkfff/IDRJ77+i3+a1atLXv7eU1wN67Cla5eESruAk02YDNPFnCnapuTh22+zWfUZohw4e/kKDNh5w5sPHhL7nq/8wodcXrVs2xsePbiDjANHB8cczg9ZrzY4CZydXbNczjk7f8Xx8QmrPuSgr3xOiGGvSkG3twZmUeM4W+DKPOe5ID93ug8KT6agHvO7DJddbQaSsNbiEFzMWLpo0JcwUjioq/IWJze9gq7t9WajRo0hEXxkvV6zXs9ZHjpmswUiFkykqRWeE5Tct26CdqbX3MOH6dY6NMZxcbFGgiGFEZn8tri1JvPP31xf62wPmRFj2l1HdhFnep89z/aTHn+coO+APwX8zZTSr4vI30GhnFsfIiWZjq1/yUdK6e8Cfxfg6PhOamYHGdHIZXH+zYyYDO9kHMYqUSc5B5uSAmMUu0tBzbz6YaDdbhnHgXbbEkaPzV25zqqZUsylu7OWsihyMNZz2pDLwpTxsykDMfr/U9IKRVLk8ECz/77f6vBuya+F4pUh20Qn0dmyE0YXclCN9GhWY7AIFxevd7N9+06z+b7r6PsEKWCdw4+KeZMSq5sVzuWsRcBm7iLEkVljGdtIURrW2zVim3yQBHWwTPvFak0+dJlUPHpdRSzGRubLOSf3DjhcLGkOC37wz55y996beiAmw/Pn57SblpOTJQdHSy6vLlUSK9APPU+fXrBYzjhZHtD7BGNkFTp8BGN1cEgYEtu+Z7PpaJqC41nJ1QF0lGxWax4sF1xcXhD9ALt5Xrv8DWssPoxMKKiuJ9jX4yDJQlDL35C9cqLXHoYpg9TQriMTreIg+nq7zSbTjypElCsNISckzpJMyA1pmjWsr254/PZdzq861jdb3vuZd1nfnGGpWTSOwjnu3zslJe3gDb0nlhUheXzUz1NXJcEPfP70Kdv1Fj/29L1gDuaY2jHfjFoRnB6z/c5LbWwkczMIISYOjg6oT5bEuqFwgu9G4tlrmtqxXbfcfHHD/aO7XJ4Hzl5vKKWk36xZbX6Emx3zw49e8Nb777JetWxvtiwPF1xen3N0fLCbD+FDNjMMQe0ect6qsxz02kXdRIg4EA9kLD4XbaqUym4GUwY9xYgJioyAUbfT6bDQhN6QjFURyAQbiRLY+SwHJhUcWXnmqWeOy8tr6rqhqhySBGccJmnFa53FmkkdlnF5M+Hyur9NNnSMKeIwnL16qbEE5Y12k1EmHkB3P+PYsd1uOVrM94djIh8KE4Gr7xkjOxXjH/X44wT9Z8CzlNKv53//r2jQfznBNiLyEHiV///nwONbz38zf++PfIhIJsL0AkxkZcoZa0hCFqZik8rMvA9K/EZP12sLdgiBvm2JGY/Or76bKh8yBOInBU2Ge3YZoEylVr4pomqYfcabsm3D3n3QWmH0A9t2hUEblIJPGOuUJK0qyrrBWLi5vsL3IY+6m0HmGkIKuWfA5/5XGH1AkmUcR5wr2LadktPOcnR4irUOVzRYZ0hpRERHQ1pTYDDYzFsczA2X3nPvXsXzL7xinvngIWl2KztY6McX0pT5gwYuL57vffL7WOMY/UBdLXBlzcnDR0QjvH90qmoXCWzWF6yfP9X1bSqsWMaQOD+/ZrPRQy7EkRhHDKpBr62lb1turm8QMcyayPOPP6M4OOXGDzjfc292wPc+uSF4HVG3N7tSOMr7sLuG+fbnR8ra7ZRlIHrvrTWEMQd8/bFbT8oNZ85gc3W5J+f2ck39/v5ZSjxHrUJzk5wRIflI9JG279hstnz3298GEzCp4Gd/5kvYuuSDD9+nb7d8+1vfw5mS4D1NUyOoRXbfexIBidpg9ORHn3Dv4WOuuo5VTDQCX/zWD3ktEXuoiiQxJs9BjogxFLYi9YFZpYKHs4uNdp0HGPuBdZf4p9/+Id5YUgjUtfDs7AnvPH4L267wbQdjzxefnRNHy0YCx0dH+DHDZCngQ2Dsh9xgpBCNyJQwZYuP/Pcwhkx+koPoVM3nBGniTyYEL8NruiUNKfzz+LZCeoYUdU1YO21uNPDfwsenzzaOnkYKhn5kvdpQuIrCGZ1Zm7QBi1vYur7xvht3+rKvKoUYIl27YbGYc91tmPJ2yQfQTup56/mqo8hVZoJbvb3TT/7JwjsppRci8lREvpxS+n3gLwC/m//8x8Dfzl//Xn7K3wf+cxH5n1AC9/on4fnTY4dHy4TCZiQ2QkxezZa6Dt939F3H0A/Zf0JHDnofM8ampb2YTOSigVWK6RJINtnaX7j9V73YgkymnbBH8Egm6kg5TMaNIUmknjdcri6wSbJcz/DWe29lFYonDIm221DaAmMTQwyst9tdWTd9FSDmQygltQFOweNi4PDwkKIoqGtHVavFs1iDyYHLOYMfhRg0y7DWksZsyRBHbq7gaFmyutkypEpthdNUbqfd32/d9/3izhmGtcIwdhgsQ0zMDwy9b/nu9z7i9N5dYtDGmX4Y6EdPN1hqG3EOxGlzXfSBttvkAOyxTon8EKCqKtp2Q1U7nHOU85rvffR9qmqGmS9YHCwIw4xxGPJn0qCsFrZ7d0thD3WRUuZIcqDJviXeq01EssoBGGvgVtVz+xoAudlt+nfawWn6c/ukYPdnqgzT/voaY7i5XjGf11xfXCLBQhSsSwxdR1OpG6sta+rFAclHttstVVVpB2dK1FVNzIGu63rKuiKlgaq0bNprisWc+TBQDAMrWxNDRIzyGRFIIXD2xQuwhmY+JxbC5esbTHRYIxSLkhftyMndOxgf6TdbNpuRxli+893PMFXBnftvMA4d88WC86sNLkYOD5dcXccMS0Sd7ZB7NRR5+3HLgNvNfGQuROOZyXt+Iugn5C7t4sPum/lQTmhOmPhD9rUoqXw7Kb793gntkdDqV6Xe1hRcXa2pqpqjY4eIy+9vMuQy7ZW9DHT3mubH8faYxQ0vzi64Xq9y8pp2YXzXgBoTp3dPOTg4wI+eGNwtuFZ2iMaUpE4J6096/HHVO38T+B+zcudHwF9H86X/RUT+U+Az4K/mn/3fULnmx6hk86//C19dRGVkMdB3I+Ognh1du6Vt2x0I673XQyFf08muWERwduosdfmrsvgxZ7NMwVwEiRkeyqViEnYHwYSts1s4ianPaTpzzVTX50dTz2mqhm6reH9ZFVxdnjH6ET+OENiVZYhQl1X29jc6UISpbLTqzV0UWFdQlA1VVVGWxe60d05tooNHh6wkVUuMU6IqQR0mYyBEz/nlwN3TJYULxPWK6rgCazi/vmJkAdmhFKkwSQPhPtjnSy8BJZYNPo4IgTAIKXhWqyv6IXLvjbdIL14yXL1kPLlH5wy2rLh59ZI0jrjCYeuSYbvKGVrK2uY//lBYAAAgAElEQVRMxKOfo5nPqOpK1VFFpVAZkbBZ8fCdR1ysLujzjOIxJhaLQ6q64Pzs+cT8KJbqUj6qzf5eR50PS2Fzby7qnx6y/+TkvpkScYIA0iRBTTuSlz9ApGmqETGuQCiIotWfi1MwUGgiADerSx4dvcPBck5VN5RlqQoqazGuVIw6OX7+53+G69UN3/7md0kp4IqSmES9pHLfhg8j1jv6batQksDTdqBuLOWmY+jzwZd9q4TcspE8r558RkqODz78Kv/BX/yzrC+3/OqvfAtXOE5Pjzl4+ICFKxj6jnbTsllv8RtP22948OYjXnzxivc+vMP565ZUaBNfNAbGgAQYu4GEJzFOQEx+7DHwiXCX3T7MTU7G6j1JEdmR7yZLMyEahYUAxHiVWDuXbSCy864oV6bGcVmGm/weOiYHzJSJYu+xzuCHiCk1IdluNjR1iamz3XFMmCJzETkGRGQXnyYyf4ppIsLQderhY5LaOIXdeaVMUkpknR1VVYGNxGGqegIkvxN2OGO0P8iAsQL/AkT9jxX0U0rfAn7xD/lff+EP+dkE/Gf/b15/HAY++fhjhqFnanaI44jLNyjko3V/0t/KzP+5bH2PkxkyHj+x7KJhW4NO3MMbiJK/t6qNP/ia0+Si2ye7sZYUdezgwwcP80QoUF3ylKbkOiE3Tk01pmanuevO2izHtCQztWWLOgwahQgm18skBWPSQ7LIRKERQaISqMZmZ1Kf6LGk2HB+ERC2PDheYorI1eUN9+8cc3Z5w6atEGdABiZLhtuwjl7t/bZdHizZrNcUZWS7ablZjXz9z/15wuo1b3//d7nT9fzD52vCz33A0YMHzI8OuHj2GfW8gc7Sydl+08t0lCneGuLAbH5IUVQUhapb1EhP+MbP/wJf+vAtnjx9wo+6L6CFNBqKRUm36YjZmVUrOW2WCWEghJzREyCFfODvM7PkdF01TZP7Q9ADKdrbINGPrSu90NNGB20Cs8zmx4zB8OjOPeaLGRvf86Pf/bYGG4xCRR6KGDg9nLNoGqxzzBZLouwhzZRU8mudwZV6WPkQGEZPWWqwSjHislvlerXGupIUHefrjtYl7MJCtLdrD6aMySRDip7HHzwGl/jhjz7FpoK33jxi6z3JJPzFOV/7s3+GUArbdmCz7ljfrIljy+GyoTuYYaxlCIb+ZsPj6CC1hNwbF4ZhlyzcrnxuJxTTp9IMezJ+U+gjRm0mI0NA3neUVaUHF2aXbbtCq0LQZCyZ3OSVDxBN/BS+E3JyZ1BMnpxI5jVvouDHQFnoa6/Xa4rC0jSNwi079c/t4P4HqpZba8saS9f1illETwlUYndkdZpSuZSwBOa2xsQ6S78dMeis33EcQQzBOJyUGQp3P1aZ/2GPn+qO3BgCse2x6IxRYsBYtSEWySZGOxZ7H4z2Uqwsv9yVQuq4WRbFDjZJKMZqrLl1AAgSc/ae5Zsicku7rTdSb1Lc/XuSgWnmZxCpQELmBPLz0mRNm3YzTWFq3SaPf7Naok2HSkoQh4yLGx0uARnC0TI4+ATe49NIyPLInC9lLXSC1iDGkvJgByeWxAGfvbrh4Z2a1eC4eLZmsShpamEYzY7L0IC/V0rsr4Rer7t373JyfEwMHj/AmAoe3r/P9gz8mw94sr2hufs+rp6x2qwYRo+pG4yrqIrEwcFCM7uor2eMwWY5blVVlPVsNwDG2gLBsTw45MOf/xJNU/PB4mscnpxSlQWX59esrtf0/Zb19h59ny0ghoG+7xj9yDBoc1qIWe/s80D6NKkjRhKw3XjCOFIUpWrtbzW/KJGYcpaoVyKFTMKnpHl+chhT8/DxG7je41aBuqywqdRegmTp+sByucSUcwqEfvSM25brmzWHR0fs7DFSJCaPs46f/4Wv8tF3vs8wBPquoyjmTL0Gy8WCGIX5fM6b77xNFHj15Bmy7bjp16zbPLdZBMSS4XX6BPcePuDBGw/YrDtu1gP9puXO6ZxZ27HqOm7age98+zu8/eE7DOPAsplxdHAHgI8/+pTtpmPwcPrGCRJrXjy/VKjFOjWwCzEnWGm3tqaE53bgn7JjY4q87rTzGetIZdgFWu8tMcbsraUH327utPdYI+raGfYmZWRIRozCNuY2TIBmzCklXFEQ/FTlaiettY5xHOn7jrbbspjPUeXOJDKBSQQhtw6CGGKGCjW57NqWMSb6YAnRURod0bi+aTE2qBS1Kfjz/9YvI1IgI3hvGHpPSI5mccBBM6OqLK4wOOu0cTOR1Wt/9OOnOugjgFPvDYl5kZgdpqLBeZfhx1032qQwIcMjUyC2ObAaOxGuWs7tF9st8sXKnqydXjftQ90UnKzNPv2oxlcHZRtIgckMSRfBVClY1avnslpvlsnNUwprGMkEWwiMaYIjJHeGqlxTRPDes9rkSVvJZvI1YVJu4U8pe6YoBFFYwZiwOxRD6IlJsNZxceVVNlobCmdZtYEkVrN8o/jvhH1oR6puLOeswloJXGGJtqSpCspyxnB9hjs54vXiFwl+pNhuGV68xg9bhnHAlTUpGWazmkVxb4e1TsS6K/TaNE0DQTM059Re1lLwpffe5c7pEf22xRmoK8fRwQFHB4dEMUQLyIB4Nf2KQUhBq6UUR2LUhrFx1AHx/ejp+4GuG+i7QRvKxoFx6BkHQwh+h9fr1Ch2uPO+ALrtu5JHbiaL2XhSKbR1wkrkzXfewjlh2dSINTx8+xEpRZ4/+xxXJK6HSzCR6+tzlku1QJ5el6T8kRFHih2r1YqiLimszQdDIkqg7dd87zvfZRgDh0dHzOcNlRPcLCISssZJO7XxQpKaf/cv/tv83u98xKsvrji603D88BjXzEm1cP7DjymKyGefPuHJ02cUxnL//h1kVlOUc16+eMXJ3WOauqQuSpCGmzayXa/Zbrb0N1v6bkMKIzF5tXFOUaWjkgi5s17Yz7nQ9aYXN0y7Pic/qtJJhNFjyj2+rYmSISXPOIxsuk4r8Jy8WUR5r7xwzZQAFkXuSDfqj2kMUhSE0WsHbuoVJRBD2w5c3FyznC0UGozaGCbZ4kNsJIrNcJJMCyWfCB5jLVE8H7z3kLt3Dzk8quhbxz/4+/+UIJ5A5Gsfvsnh0QHrTWLTQnN4wKPjY2bzJc+fXfLpDz/D2BZCot0Ytv1IkkRZlT8xrP50B33YEahi93JNpgw55aJKYGpqAXDO7oP9BOmIyWy8Yl7TwtkRJikh3CJA8rDuCd/fQUC7H9BTe1cF5Js7EXmqKjA5E0xMQ7+NsRijmfzOsXIiAbMgMISQvWk0qIoYrLgd0dj3fa5eEqUVTGH10DDanm6I+4NE9hr7GAzeQ9+O+OhxTj+3s44URvo+UDcmD1Qv0AscdsTZ5C6ph4bB2smiedpokaaosK7S9/GG1es1Rhz90NJubuhjS8RTWFQ+2/bMTheUVaUTksoS9XBXy426rggh0G175vN5Vgh53n78Bqe24pv/x69z+eoMd7VhGFpmtsBGYXQG5hWmqCmM44Nf/AaLN+aE4HOZMiOG/bVRzb4O2YCQg4Z+P4hknmiAoGsmiuyCvuQsfF/eT0FfqyNNViQf3IHEBFt44pi43GyZVSUmJrh3lxefv8LaghBHQoxstx1N02SZoR7qIoZxTNx79A5t2rLt1pwsjwD1lHKlWohXlQE/sj17xSiic3xFq+jA5D1osMC9RzN+8PEnPD+/ou86NitPuSi4fHFOez0SYk9hCsqyZrseKOczXl+0xLOesr7EmMC9+/d2ViWvX11B7YDIoipx84BNDcsx4aMhRuXpUlxgrFbAKSZCzuwnPfp+f5L3nF73EILCNHGaSpVNySQR8YRgCR6KfICY0ulkuKT7eyJonU07CM9aTSBnTUGIatSYTEnwDt8JplLH3q7rMDdwVpyzmDccHMywZZmhSSEGhzGJaLzCt1F2E8fGkDi6c8gv3T9FLPgwIDGwaGbMjxquN2uMwLvvPWIIgaJe8P6bj9VUMMBn3/+cf/KPPuLo0PH4jYKztuPqpuf69TkkwZrqJ8bUn+qgL0zty5OMKm+eaUiFkXyTwOaGLD0QdMj4nknPMM+Exd9i7Cf8f1+OTf9j3xk32TCwk+ZpgJCs09ebnDf3RCSJQiPWOWxhd58l+KBwUh5oMnngDEOvk5Ci4vFFWWKsxY+evm93MI91Nv8Oavo1jUP0XuWOfpygJeUtokzBSLTRzGiUUpkZxKRzbiFR1I4UEmOslOAWNWCIIeKcoyiEhLqSaiORUY/87BhYFg0xJfpxwMfIP/vWb3L3wTtI7n/ozi94+fwJEtVvvRTD9uIKZg4zLyEPuYgpUtc1zrmspgFXlIixuYHMUQ2ej//BP2SwuQmv82wZscEgQcA6AgmbRvpB+N5nLX/+v/wP6d1WIblo9h27abKYjkCpFtOTnTRwdbXi6qJlvqioGpMPDdnRM5HpGk8NRNnELmU//mR59sMv+PzT5xgbdTgIBaWDqjJsrKerS6qqohBDWQhdXzKOgIV2q5xWUboMq+khFHzE9y1vvfcO7dUNF5cvcaR8IKust57V2GAYfSAgBAn66QIcHx3yxruPGY3l977zI8JQ8Nnvfc6QtthaWK06bm6e0dSWvovEFBgjmLYmeaFoEl/+8Gs8/f4L0thRLUq++53v8I1f/iXCEPjskye8//6bFK9v2K42tGMPKeKMykIPDg7o8muOYcSPHu+9Wi+HcCvoq7af3YGqOL91TknslHX8IjgpiEk7pWfVnMIWuJDog6cd1pjoKUR2kGuKGvR3fNIOLUhAJPhI9LoHRNTGpCxUdu2sYdO2nJweMfqBvt1oEmAcdV0ya2YUxpFQqweFjnIiiDZ+pWAhORCDc4mH90+4/H6PFFp5WXcILPnkh6+Jo6EoLRfnax69cxeRlu26p2wK7syEx2++rVJSSfzgkz86rv50B33RDN05h3WTdn5v/4udSL+U7Q0iU4OUtUWuhGVfFUx3Nj//NvwxVQDTG4vsWXzJ3EGKBjIvMGXaIhNnoP3B04zZSTZljFHvcACBKhNufd8yDvv5vdYZnK0oKsGPA8PQ4kc94KrSEEIuNwX17UgRHyNjiNjkSJncLYzALRsJlbmp748kICTKUsBEisJQ1gXjEBmiELwlYSFNfIdFSBSFnS4FU6NLCIGxVxJtVukgGZOJ6MIJLkUq71mWiYPjQ4pa+Oj8GalrEQub83PScklRKLzUDzFXNapWEDE733/nHMlGRu8JIfH+u2/SfnHOvCiYmYhJMLqEFAVvpobZV99lW1ku1jcsFwtcN3Dxj58yfPwc95UTUrJqOZHSHv/MnAUpkWwiJCHpcFXKsmGzveDegzsMw1qrG9ivl7T7D5OlAJDXVqBre/7ZP/y/oQ8UmStISa21F3iujUKBoxPmrqQ+ndHcv0vft7jKYpJhtdpwcHQICAFt6kokXr18Qbtp+dJXvkqznPHy6ecY05FSpO87losFXhyxruhbrRCNBQrYjC1fvP6CZAzWBarGcX19AwSaZU1pJR+62vlaGKPqueEKayzjGn7tV36d5XLBcllx8eqCoqz56Ld+m8IV3Lt7gAkD7yTL9TZylTzNVcsbr1uWpufeoxMu3p7x/PiQi1mkaz3joA1GqjILOTHxkD1yJoTVh0SIRr2XUiT5gE86ES0BRbHkYtVia6cwX+EYNiuOmhlh7DNnlrQyTlNTZdL3AuV4onpF1bUmPN024KoaY1Skse16LAXnL884Pj5iPl+wWM7BkKHXtTZKSqKyDfP5TKHf/HskgSgeiEh0RJ84uXuXu61w7+E9Nv2M9dpDWqGd2uD9SHM0ozmqSOlAj8CkB2T0SpSPffcT4+pPd9A3hsWiuaXmgAnQ3+nY8/ck//z0r710bv/32+3QU6WQbpFI08NkIm7SyWaMBz0c9sTt9Eebo5x6BTm3Kzd3bH2alC+RIWlnsGSsWlUoavfQti3bzE1YW6hVAhNXOGWS5HZtISUlr0LQrsWULFhL8Hl6kJFsFSAYZ7I5laoQClvSbjuue0vMxgzGZAWE1grKcIjkzsQ8izVqVYOB+eEc56xK2ya8O4BISRgTjx89IMWORWPZDhs2q3MWy4r5fM7Z+QWz40MqY3BlScpt82UukdWkK7tchkAMQlEU3Ll7h8paNldrghOkEKwHn4SFc9CNuN/4fe62gcWyYOUsmyIyayJPf+U3+NpX/zKD9AT2HYwpQTJ7e4aYJusLvYajGTg9PeLi+pzlrMnX/g+SZRMheWuVJoNYw/d/89sclxXFewu1rxaLq2aM59fw5DMktSxMgbWOwo6MF2d0JJrDJd0wEq0hGbX1ODg40MoioFLTFLi+ueTbv/NNvvaVr1AVJb21pCyzjTGyXM5pW1U7heDJc7AYQ+T8bAOiM6Zfnn3BoplnK+yIMVAU2sg1jluGQbvXq6oi+sjVzTXN4SHl3BLCQNOUDMPA0EW8GOqm5PJ6xfB7L9k+v6T80+9SfOk+pw+Fd45nvPHhY85ngY9/84esfUU3CN5HYhxJyRCjIRCJ0ea1F8EHJGoHq58GpoBKjK1gIsRuZPPyc6Bgfb0m5JkLDsfFZktRF9S22KEBBrVOUcI1ERm1USs5qkqoqsSsqYk+EBNsNz0ClBcdb/SexlSchyvW65rXz28oKsvhcc1ysaBuKhKB0HrabsN61TN4qKpaq9myICVL22v38J1Hb3H85jv048jQe4YeYhgIaUNEIVURMNapk4AIKUqeYZywxQxnZz8xrv5UB30Nsvo1p+rAvuNxh6feCvCTUidm8ifmKkCmwbeJnKHHLJe0P/YctWje+9BPplkT7q4DxKe2b32EGPF9r4t1+hy5k1YxADV/Eixia4qi1gogDAxDyB2RgnVCSE75npB7AyY1QCZP9f2nyWDgSqFwpbp+eohxRMXkitdXM1GFRx/1ILGW1VZlmEaaTJHoZ9SqKGGtyQGfPOJQCU9VIFgKV1DPK81Ovfqwx+w1nGKi61pCCHz9F77Mb/7WN3ny6TNuVmtC39FUlu12g5AImy3LB3eJBJxYSufywBvFrJ0zO8M4HAx+5K33HvH02z/g5GLgpDR4Z5iPgVUAmTmSidy4gCkM62NDf1oTcMh6ZN1esPjeDzB3Gx2fhcpYE6reKm2Ru2W1c7k0eSOlEWsCZVFQVi5nn5qI6PS1/PcpDc2VoQPa82teff+H2JjoPtkgHoaupalq6igcJkuXBOcMRsDEQIpw/cUFd+/cISXPuusQK0iC1c2KZt6o74xTWIQE3arn9z/6Ls1shisM/aDX7fzijIPlUq0PJGAKAawe7ibtDurea2PgrC7xPuF9rnyz3chkiZ2iTr6y1lHOS2aNxQ8Dp/dOGLZbxgDbvkNEWH/yhDCMLG+2EDuq12eUxvEt66m+ep/F1+8R2oE/dfiQ/mDG8ckBN6tLko2UVU3Xgk8DXTsQU6QqayRYwpjYdh2db2m3Ha9enuFjYrQ9Llm61Zo09MQxUZUL+jwa1blSM+s8WzvkSVtNoQekHz0xeQxbCJ7rqw11VVIUEMJIYx2FlJh7DW1c42LNcVuCAxHParuipKDrIPiRF88vMM5QFJZZU1OUwtHREc1swdj3bNqe568uOTx6iLglF9c3nF++pG87UgoUhcWhA1JEtJ9jHAb8OLK6WZFE3XatVT+g2aKhKgvE3oKp/5DHT3XQl91/RRUG0/cmfOdWBj9Z1ooIO8sT0RI7xqBj3khZ2hUIE+EqBmPszr54ehhjSEExdGOzIx97u2FM2h0WxAy9GKfYeQ6EhWu0LC4KYkwMvacPI8PYs/PvR9vBFYrS0ttkpt9n2ZhzDlepumiz2dKtQ4YYJs8ehXGCVxzeFuVUI0AcEVuAsRgmyWW2mZbJT1/yoST5wFHLgpQtKkKGs1xRUBYlMUZWV+sddhyjHlopqqYlAj6OfPrJE1yKhPYaF3VOQD8M9L36qpSlfo7CFbnJKBHGyYtl393qvU45ck4x7WpZ8N0nH3NfCo4WDdXDU2ZFhYu5KvnqAx7+7M9RvnGXwQouanCWiI4b3PacnV2y3WyJKeCKCj8OpKGj3w6EcUY3GrrNFdvRM8aBKJHKWCqxlE1FtEKg5+23H7B6cUXXjdiywM0Ms3mDNY6hh7NXL5k/OoSkLqi+G5BXA24cMaVjXQpbSmofsT5SRmj9SArQh47ZfMGma3eQQIiBfttRNwXvvv+YbtPuJpLpZKXsjDrtiRQ4PztnGMJOkABQFAqbBh8IPsMcMe1M+sYw5n4TkKgKJ4VDc3VrhLp03Ltzl8uLG1KKHJ0ec/PkBQbD5eUl6gdlcQ9rTB3g2Qs2zy959xe/xFsnd1n/zif87rc/47fOL9k2BWMI9GMgOMPR8ZIwVpSlJmVlWbHdwuB13sM4DqzaG/phzdhtcyObQjQpJqxYTATjXjOiVWNVVcxmMy4vN7smphgChrw+skX58cLw1uM7nN7V2QYnpwu1Mn/tVVBwOqOnYzxZ88Wz10DgsDmgdlbJ2hiJQe2i16sNKSUq11GUhuurLc7dYKqKo5NTDg6P+fzlNev1FXVVU88aTaaGAR8hGJWJIgpzmcKyqCpmdUk1axh8T4qRrut49XKlFs/2T8575/+Xh3bSaQs2kBG7mAdxZLIyRsTkGbETu4+ejsVEtqa4k0fGpMCJudVIY63NQ4ezXDC3X096X1DFw/TiAjmbj5SFpW4sXTvStiPzeYGxhteXV4SQdsMzjJloZV1wthSKwuFctgswqq6JSZCoME677fFhy1Q9qPqn0gYVo6omTEKi5/DIIiayWikMs1wsmZWW6/UWH6ZMfq8w2l3jJBm2yo0rpB/7OcnXVSdxtT/mw7P389bfUTKRbozh1atzwqDSyETAWj04rCnoOg1SNzdbnC2IbLINc8TZfXYzQVtFaXnj0RIzRr7xr/4CP/vVd/jub3yH57/7hKsfPWPTQX8649/5S/8md77xNsM4cPHsJVU146a7pmkcpWswpsRve8pqhnUVEDk6mukdTeoi+vplx+vzC07unmLKGlLPEAbKWQPtSBctN23Jm4eWsX2Jt4FH7z3i4OSIN+4fU1cVMSaePH/Bo3dPcPErJPZ+PN2q5aPf+G1e/+BTZlSIUcVJipFBhHI0VDHgN2uqusY6JeJT1HU6jAPWQVkV2LnChJP0tOu6XVaud4Q9qZ8rZoUUdTiRtYaiMIw+crBY6Ov3PSaaHXchmVeByXbCs1jMERwvX1wgBGaze3jj6AdI0dM0BT4Kvh84jwN3lyVuk7isBg5qz7eevuRiGHjSdmxjTxg2xCSUriCZkc3Fmhgs6zTtSeFmtWUIkiXCiWgCYnUUL3n28y/9ma8TvGUctiybOb4buTi7plzOsC5yc22ZP3zI0yefsFlfgYxYk6iqkmY2YzFv6PuW3/mdV4xpUIkzhjFV2FFwElQ8kRJpNfCVR4/oomV7PVCczHQyV26onFUl87n2TwyjJ3jPEA2bccvR4QFPz65pN4GidIjr6ELHojjiwZv3tSPbWmKGoGNIGF9w/vKc1eUFRwdLnr14zmw+y06gQl1ZlvMC5/5kbRj+hB+Ss/n0Y4FGEriy2EM/lslkQwPWLVKNTJiI1c65ED0hym4IQ4hBMfyYctZvSKOate3KhVxaiRhMikgKhKRStBBG/AhtOzJBKm3X69jDqswQilrESm51h9zCHyPj4BkGnXepJlja2i0kHaoAqFY+kSxoB6nZyU0R3QTGNlyttogk6mqOMZa269isBrJTlfq872a5TgR0ViJNvIVkKGlKCbO6abIZmK5xDHmK0fRju65E7e6PKZJEMKXDJ1VTWecoiwrEslxW2YclZknq3qzOh2yMZ4QYA82swhrh8OBAA3Ny1PeO+df/8p/D/aVIf7Xh49/7lLc+/JDFyYwuBjwwppFuc8bWb1ltR6rygJOjU81y81uEIECpB3I0uLLALUbuz+dsbi4p7Ui4CRwuFoSZJQjMZEbRCGVtePz4yzkxiczmDWVlcz+FcHmxwgfNtG1REkLCuQI/drz1s1+iOp7zrV/7bcZxYIgBKwYvgjSG5f0HLO/fIwVDkTM9kurYHRY/9DgLZVllbFoH/wQ/4sdpwIYni4qBoOZwGXpSuGOSDufkJgaGMaidQNJBOlm1TFmWOysOER0cQ4CL1+e4yhIRPv/0BZvVmqaxuYKzVKYhJPVQ6p2h8sJQNPzqt75F4QypnLNKG5wDI5bR98SRvF577ds1KfvkDOA9iMm9NrkS19FvRBHuP7hPt0nM/BJpA0PluP/eAamwJAtnlWHjhQcPjvn82Q3GgmBQ78WOTesZx5CFCQ5JavfhJEIRCFiS0f3pXCTdq9h2I4z5FmWoT3ZqQJ1ZUBQWYwu2fcQdn/LqosNKyarvOFnM+ODt96jKBT4IfoiEmAiZbzIiVMaw3mxZX62YN46b7TVlU3F0epIrOkMcoO9aum77E6PqT3XQ32XsZh9cjOxhkRDiHm+fGjtkn6mG3NAzjemDqRKYGiaADGlM+H2IasMguMygZslYGBFR/Xb0gnUdVeVwjdNRcLEkhJg33TRqr1dw/hb5nExUzNxkJVJ+f5UiTc6B+rmCxGwzq939Uy9AJOn4yFGJKOsKxASsLSgK7RUf/agTr8wtl9KsNJpkrdPmnziSXfYvWlHtDpVdM5cahBTOIYXDj/5WRTDxDxr8gw+EOJLSSFEU9F0HxBzkQZxWLtYmrXQmqSvsCGSRwHxZ0cxqhiGyXB5gnIBFFTgxEaylOjngja88xpWG4dbErOXhDJJwmA41x41Cyoe8s4kwKbqSwnbJ5AlXPnB51XJ21WNdouyE9dMnPHj8kG4cSLImltDUDyA6QHHiupnp5xfHk2fPCaOSps1ct9kwKKRWlkKykXtvv8G/dnjE7/zGNylry8HJkuXRkmZ+AMBqtaUdR8qiZBqYTfYiSrZg6AfKotxnhcEQfaDrwk5hNvEO83nNwcF8Z3YWIdlwiBcAACAASURBVBPwekAVhUVsgbEFEkcmi5GpyjAm7brbp700DD22sPTe8/rVOavVDfN5RYzDjtcIuVruUqRoCvqLa6xsefCgwInjYg1ujKRg6PNIVJ3CmaHPTKordOi16SnDVJKlyEkXDSSdhXB2ds42Oo4OF/gisO0GZvM5m82Gw3sHHMTIzaW6jEpWxJGN2XSiV9hV+drrQ4YbRe91rtrECkESToRglNMJaU/pT4mQEYfvhU07IEWN7z3rbsvRUck3vvxz6mTqhTBO1aaOK00m2zYjSBAuzy9BVEF0dfaaB2+8SZiUQEkwLtEsappl/RPj6k910Bdylpn17DHrqSc9/d5eQZg09SHE3MQUcuv11Hl7u5MPJke76T0gaWdtytwAk9QxkZLncFYwa2pW6xZfWGZ1ydB7NquBKBNxqoqaZAykIuPlmhWwC4gFIel4tRQSSMCanG1YqyZRVjF2suxTyzVP3wWGAZACV8DJ0RHj6LlpdSi6zQF4wl/1MSmQ9htJAyCaTSWFy6bruWuE2ZHX7HDeKfh777Px1d5t8vZzp3/nC01VVdRVlQNs9qAX3bDWTT5C+mmN7G3Fy8pydHKE95E09NRVdcvSmr2nEUJROrx4bKoyHJirvJSbfPRD5feJFA78iGr/vaeqapBAMombtufy5ooHD04Y+p7tOLB4eMj1pqesSmIfKKNBFoZgVNZqTVJIMEbW6w2vX58jLlEmKJuaIIZYsevq1uCZqBY1X/mlr7Gcz7RxKkWST/hhxBjDMAyZ68kB7tZ9cM6xXq85PDzMw4ESRVnoTIWg1z7lwT9N3eCsISWrlVQeKK/qJe0zaJqGsm4yv5XlxnmeMeytzfWAUZhHypKDxZLL1YrZYU3sh0zs7207prXi/x/q3uVnlyRb7/qtiMh8r99l7127Lruqq6uPj92X4wNIIBuJGTBATJiAbCEhhEAeIvEXmAETJCQmSCBLHgATCywkLDEACcwAsLB8bJ1jn3vTru5TVfu+v+t7ycyIWAxWRGa+X9WpBreQilR37W9/+33zEhmxYq1nPetZjWNxTHDwdG7Nbb+DZsEgihSnrVYe1zamtQqCQmrIuY7F6RwtMx0fAsvVipvbHjkmXFQ2YUm662m9Z393h2+WJnE+2pcH81drTiSfzmfMkFd2XmBKLw7DgMPIDRUgsLoez2EfuT10bC/PiZoZuiO/8ZOfsF6t0WjOiMONkvGCGXy8s/akOfDm+TXDsWN7vubN1RVnZ+eEprLqqkc8Ocrfdnynjb7ZdlO6y8VI1fJr53IpWCreeXIjZGIeqeB8GJO9Fb7QrKRSkVvZCbUwS0YjEc2L1FwMi+fqfsf1jfXnzHQc9xX/FqQw8SvzpRGTMjDeuU1brQu9LDDnPEH8jDbqSMlK0fs+kotQWk7WqMQVTDU0JrubovD27R2+CVZRW/TBVSulqQzh2IqOMUIyMTcpGkB1wueTXEhNpNokLBtnlZSo1pRp4x1/VowOKda0RbOgLoznGD9fzjsXrxsPJ6w3Cx5dWvONYci0TUtoGvM4C72zGrWoZnxTzsbZHqUvZhNp+ouFzDjaBoZkPQpUbDHEpPTqaC6e0PeO6Nestp5Hy5YrItG1dN7hFgsGNU0V7x2LtsGpySz/yRd/gg+UxVh1cxx+timKOLIzmezsi3hg4YxnkuVrHEZg0FnxkEBtCm4JziW7+wPbsy0utGy2HuQVMSabl2q5guubHVfXk6EcGWtFtE9dw+V6jUQx5tLCj/P0sO9wvkQD5fsxWn/ifn9nTosKQzL1SzOcMkUbRc1TgxCWjv2XX/HxX/in6d2Wq1d7uiETFoEhFp2dCjuKiQIJRTXWsEcobLy5k4FCTBZJbraeRlokKYNmUpcgGSzWkEhEXAhWMBUzoXHj+hSdoNfq8Z9AnED2GZ88WZRDPxCTY+h7XOzBNYX+mQHHftfT9ZnNo3N2+cAHn3zEJ0+/T46KljocqaEBxQkrcFYaDCXojgO3N7c0LTiv7A8HPv30Y9sgrZenkT9kNJrfenynjb5m6yNZG5XXgopJp8N6jR6PR3IRITMp2gnSoBo2KZoYUtosFqtQG2hkzda2TITgPeKF7bKlDT2ORB8bIJBLwUZt6GARgozQiGZBe09WYYiRmBLZ2f0qWCFVMbK4mkwVtJT+O7HG4U616PtkxAVCaKg8fQRC2yLOmWBYrBovJTfhYAaOjSG+lMlrXmZRL8w6yVOMUhFulJSufU2rBMZoNJi8H8YrletKxYJLlDbbGGpERP3sg2+LCKvVku12VTjfPSIN6+3ScOLGoyrWG9eLNdfO1n5u6DskuJlhnakeTrOq/Nc29GUIZHHEvkdDi6J8+vSc1LW4lOlSIjdWi3D+pKWLyjosGfpE8AknipBYLFeknHjx4hVd1020YlHLV2QlOq2IoW2y2SHqWbhlSTyVBHupOtcx8TpFsiYayMx4G85+c3PFk/ceIQJ/7kefsd8frcAvDmMhV50fOedahD06CG+u9nz48Se8fXGF7vcEF8qGG4kxEXB0RZnNiudMbbLrjD1yeXFJjAOWQ8ykHIvnXokAmX0rtE82vPm/nhOvO773G7+OXHek40DYLghhgfeOodujGotDUuaKWlS3WCxmHaYY5Uxqfurv/p2/z2Z9QZsCThLSOFvjRW9LvEKjvLt6g7g4g2IK+6w6QeXe65SpkHGzaViEJeldNP2eZkW+umdIAwFFB5NpdhK4O+zJqmzPt/Qa+clP/jzL9doYaq6wAouh14oEjFFCWT8SuH73ihSPnF8seP76BU+eXBrkW1pruloaPnuWbzu+00YfUdpF1bQxO51ish19qNVzat2i2ulBa2GVKCaGBNYHtHiwguUAECWXRt9O4PzSio1u7+7pjpmgHZdPWvbHjut3HYOGsutXuKgsvFA9IOum4+quLaGwiizjr2VjrpxrVzS9FwvH5aMFw5DZHywpllKkbeDiYsvQKzd3O1BLBmUSMUUkTx45NZLRCbJSVVPfxIxFnQtaJVyLB+lDzV/I6NXUiW/eZGHTOBOTSynRd1bLcOLpY3kDb4oVs4BjkrSw61aobfaqxdGEQNO2rFYtzjl2u0OJLjKXl2dYUZHQNEs2mzXn52sW7ZJh6Hn16kvubyPLhePQZYZYvS2ZnJ8atmuGEhGQlcViaRFkipZwFsh+QXJKaBRdmfhcI4lHWrSgXK39UBDP/V1Htzvw9u1V0SOyWpCYBt7dXgPW0cra7Rnm6rBCJC8ZxI+FgBWygnnHMp3BLDbvTF3SDN6ibXnz+orHjy/wvmVT4Mj63DmnUula9YaKqmipi7jfNby93tNutqQ3b+gHo8m2bYvg6fsDikVcThzd0Bm8I8J+f8/ZmbFU1DmyRiuaqgbUVoM1Enq0Jp0tub47cvVb/4D7oaXbBvZXr9Ci9xSKIKG1HazRjVGuLUr1ZROj5CjKnNfAFz9/DrxErPSE7Aw2cUjpKWHr0PtACAlN1SESrCOeDdnx2JV5O0WxznsaWnJO9Azo1cDbf/Az7vrI2Q8eoU7wSch4rq/3+MYR1oHweMGPfv3HkBtywmol1JUCMHvHWZSYLfEuak5l4wJ31wcOd/esWtMq6vuBD59tCgGhrp1xlQGnzLxvOr7bRl+hP3R0/cAQS1ITKeqLk4JdTlrwMPvaiNOXENl7zxAH6vrRWdhZqy9ReHe1NyllFVQ8u0G4fz6AeHxzRnAl1HNiLwwMm65hVfG2tVIzpSw2FDJ4NWw+lQkExssfYuT1m54YFScNUgrG9po4HO7MQwmlwtY6r5gkBbMJW57BJo09IyKIqxBNQTjmUI6U+6/eby4Yao6YYqhNpCFNHluee+2lE1m99gTbgJMpMpigoNm9jm+qMEmcsFwuUJSu7zh2JaElQhcj64sFPigpRppFS58cb68GfEj4kFhuLjgceh49eZ+zVBLZ2VgjKaeZ02ALy6p8EzFmYlLicDyJEJaLQN3YswQrokuhhP2mEZPVzpNGjrypps7F14JvefLo/dFIZZVS2GWRXhwyoWkN7gtujEC1NOV2RQIjDXl87zVPYWM55ao2wXN/f8/Z2TneB1RtHnnnCkHB6jJiTGjBzaVVgoB3dzx79hEvnj+nHwYen31AzJHj/kDrHetNS98nhr6nCYG+GzCVBIsaj8cjbdtaZFujSK1FfwpYA5Q+J2QdCJcXhKxcJGiOR6TdMEQ9yTHkGVU7j2OqpeLc5n5lmdnctVxETAkhj2Nk894Xp6Y4IEVbp67bqvNTHR7nmNZOdaCGzM31lX0nCzkMvEoZt27YBI9L1vf66nqPbx3JZ55+/DE/+MFnpE7IGhBv584qBNfQHY68e/0WgEeXG3zbkEq/YFW4ff0OMqwuFnz51XOePHnMKBRZ7McYDcEI4X7b8Z02+jlnDn1v4VLrCc7gj9oMfcQmgWo8aohU24dVxUpz7kvIpvPkpht5y1XXJ48el2HuOEgOPBbmawKhaNQzjbPmRBDzdsWbfrYLDSqetsm0TSRI4HAQ7ncJCaYmuVh4VqtAHBz9YH10zRtrxsSliiXn6uTMteXcN4RytWdr3ZTAqoapz+wm72Wc6PXLYlCCSM09TFx5KN3GxtOeGvw5xioyiVoxSybPvSeAEBrbTFI0ho9AaEMphiv3jZocQLnuEI0CGkuuR7Fk48tXsO86010vfV/NGw9lLDIilmwTnwhB8C0sam5hzH0oUiCtlDOZwGF/a3klB3hH49sRncojNVJxuSbHa5XyBIMpk4xGVkWyYbbb7QopVa85GaY99MPJWIqYXPaE7cu4Ruo1RIxaeX19zcXFpTG5yr9Zktm+s1i0ZSO0+wzlPT//4uc0fkHOma++fMWzZ8948sFTnj9/gSbBhcjCt+C99WoWR86Fs1/efxzSGIloNo+2OiNZM31vEiROE8EpWYWUPE32eM04HMmVinhAfPGl1JHUaNVWTCajYJ6Nq22AKSbOzteQZ5F4sREGaw7E2FkWbjSQpjI62o86qUt0Nc5rAZFaq+OQdk3UqUo/DnBzc4dvHSpHfvQbP8GFFcdDpvUB5yxidDjikHh99ZY3L1+hqefxkxWbIGyWZ7zbH1FZcvXuhj4NuEWmywNDTpxdnBcV0m9Y92NU9DVM8+T4Tht9RCyBKVLw72I86kON/MYC2VQvSYW+6xGZjIbtBXmEdyxKUHLsiWqfgcJ39oILxo7w3hO8J4ijkQpf+HHjiTEyaPHCVemHQjtTIUdhiKZTE6ShGyJ3Q8eQHOLbslMbJnnc9/RdNvhGc9E8sUmdU2JIg+ULqBNZHrz4WeK1yPhWLH807swkLJhw4fr9CZUvoCJSPK1ZzwG1TSMEX1g9+WQTru9tdPDHPETZLB5c24kwDANtYyH9crWaPgTWCg7Fl4pfcZ77+x0XF++R1Rqo23pUyEcuzxtiHkjZIsCYbM5Y5fI0JsW6l7EpUAQTrGLtB20+mQ57sBxCTayad1CeuVZAU8hi9jev842wjCe5fESRrBy7I8vlhqwJ5yIaMrEfoDgsqlaxvL/f04aGGPMYsVZmW86p6E6Zh922LXd3d2zPzmhCKM6sUlv7KYp4awdp78kjzvPRB+/x1S9eElOi9S23VzfcvL7iB5/9GvvdkbfvnpNbBXUMyebaYrHg6dP3OByqyJfNPecdbbMc1T5VE21wXJxvWC4bzlYrsnZ0MSLNYFBlZ4J6QjtCtFV0TVETVxNL2Fb83TbyCeYUUeLQc3N9N845k+x2fPb9j+j2mesrZxLapTVH1VHSMfydraoyXijWxtEFE+MTz3q1oW0arADWs98N7HeJ7vaaR083vH77ij//43+GL1/tODtbs94GukPi+VcvuL2+Q4DgM02jHI97vnpxz2/+ZMtaHX0n3L67gWFgex548fI1j957XPO2Ngdn6/+XGfr58d02+jAaFLJMr6N6OWqwDgqScsGq1ZKgCin1hKa3JHBYsli0+KAsgic4C5v3xwOIhfJ14MwpDlZJG4X+mOg0QsojHdKauRhenIq+vGn2m1cSmtI42Uc0J3a7e7w24IMVHYkVRKWc2R8VL421Jyx84a7vTSFz9JD9zGOs9Qmlq1cdF6269mbkalLQhqyMWZ3ADyZM9frQuhDmNNRTGqcUmIQCHegscqiLJo+nq5j6A3E8oPHWmOT8bEu7aOj7vvQtnrxlJFuytAi9qcv0xwNyObA+C3THSN9Z0czmcsvnv/hD2tYjBHIymYfQBIILpjPuXAndLSnppcFURT1ZTEZCi/NQPcmUzSBTNsOa1KNEQQ4dDQfi636C0YIVFS1eqppESLZBzg2wFggeicU468CQEvvD0aCSIZIQvDNp7ApZeG/5ICnVl1YtXt+1QwJc3Vyz3W4LLl8hvuIOZoOZyKU7W2i438UREhz6PXmZ2V5c8Ic//Smb1Zpf+8EPeX1/xe3V6zKHrGZjt7unaZbEwjYx3SSDUlQyLghpcLTLLRLW3NwcuXq3R9WkObLA2aMNftlYnWU2/F8kWBK6QJJV4jyXdzLmJkoEP2Tb4MUJ2/M0eun1ePPumifbRzQtCHGCNYt+f4lHijNiSdb6oF48kqxyPZyd82s//k1efnXP7vYGjR1X7w40bcPbm7ecXayIx0i8PnD/9iWffPIxb64GfvbHX3K4P5KSyUsvWodvEstGWS3XhGbBP/7qmsXykqvXN+QYwSdiFo5dxwcXK1JSPFYkOGVLqq2kOCTfaErH4ztv9Efvsrxse4m1+CRz4klVD6sY8M12wdP3zklp4N3bI7e3HYTAZgmLRrm5uWU3OJwPY37Ae+tA5SWB+uIBGvVSfUaC8e91gqrx2Tzb1aplu3Ycj0cOxWtZLj3bjWez2nJ/3XN1m4wqNi84c9UvL8k2rR6oGbqawKshfCmwLQaxDpSMEFY9Ksb+TV7AHL+cf35kCHEaKlavvM4wzWXTUSnhdT45b909RkNfi690uhYFV/bBszvsxzzHNGkL44rA7n5guzkzYTKUnO95cv4RnTP+d8pwvm756IMnpmSZDROP2hsVNipxiPTJtP5TH0l5KEUx0f49ixWUYdXVVgVm867vo8EBWoqkRly4RkV64nGW25/eY42wslEQVZVm2fLo/QtksERu3Qx1iBx2uxJo1PM5S5wiDGR8gaQk1sFyNZVgNr2M2/3dgc3GlW5KNTIGLVGEiLMagqi8ev6Ss+15Sfoeub4+0nVH3nv/fXZ3Hb/9u7/LR8+e8ej8MffX7/A+WP+E7oiI5Tu8lIpqsarv6ij44DkcDqzWG5r1lmHIxBjQ1EDK9Dc9lxdrhng0Y+ZKFzChaMmU/g05j3pTuWzMCFbIVOo9VDOr1cqcnxLp1v7St7s9i02goRnncu0zLQJZh/IemTZJNbaN4rg4Pyep53i/w3Hgxz/6lNT3/OEf/oKrd9f0w5GUGpys+ODiMd3NLc3mnDcv7rh6fQNJwR3wZw20DcvNkvP1ir7PuOWSplmTe8/NzQ059yw2gTdv33H53rnRxZOSZ+vjwaqera0//fjOG/3ahaomH1VLNeEMp6uQg9Rm5wXC2R96fv5FV7xeMzxyTFwfIoji/ZK20RGOsHaD5oml3GC7qKIk072JIFhz5SxVrC2U7wvHY2S3j2UCeXJS7u87bu8SojsciyKeVP2uyaCP+QEdXx1mSKD+ZmSjZKvk1ZrGqji86uw8I+B8Mp7lrJNhZoJadHJRq3NeNpeZJ69zWKj8eeJaTJBR9aAq1KPjvmxefC7289h1xQM3qixqeLEpPDbknNnvD9ze3PHpZx+BF37vZ/+Yv/fX/xbrqwYdkun1iNCuHL51NJs16/ffQ31Ls2zwrWfZKIvlArynXbQsFyva1rG6wDw51+JCwjUJ1YCoRQOaPPt9h8rU+UpmUGKdlzY3lbE5epo2gHGEtEhhYBDd0PVIkuKxmkLp7m5HTnmk+SrOKpmL0RPnRlJDbd9nFyz3BMUzsPd9d7sjhI71WZXcnajKzmVEBhyZ5aq1mphBClc8cjjsePnVcy7fe5+L7ZaXz7/EkRBcye8oz559zJvX70BLHJSq82LXd2MkLNxcv+Xy4oLlsuE4CN0ghLZl6Dt+/vwlTeNY+ToXHepnmDoVpy+ePhgdDos8x6SuaullUG2IiS4qisfRH4YJ6hWBMTdgC8QXCJfi8Ss1Cey430X2+44hXvHk/XOeffiIr754iya4v9+Dy2QSx37gf/+7/8jyLGefs7l8Qo6J3f2dFVa6cxbLR7x8fseXwzUpCd//tQWX7625entVXqVtQPf7Ax99/L4BdKWdX0U1RiesOh2ns+0bj++20a+eDjpSL80QluINnbLYtQhGR7XN6vxaxWQdj+zMu6nGUvMEkWjRfHEOnO/NAw/gg8O7BY1zRr0kk0rrwb6vNLDiIWspxhkhDY931rNSRNBSzu6KJwRmCKvY22iIndHHqkyqQUrVgBYNFCZDrBWyKc/5EL4Rc2PGf5PxMzNop/6uIisyfb+8jrEs3VHvxTyvEfoZvds5c6JGYnJyP4qyXC1NhVQzmnJpI2q6MabxbxHEMBwY+hX73T1ZE//z//i/0h5AugHpFTTQZmF4k0hJuG1aDr8pyGKJOkEbR+Nh6ZakHivaccJxOAI9Ej3psDbYrkkMRIKPeKw/c8wgLtTiZZoGnGTAWzeyandxZMlkSfiK+MzGUZKit3vboFNCY3nnLuICLD9asbzYkF1msWzxTeCw7+n7wQgCIpATIh6cWFLTla1BbK7WTciouEIIyuFwpH97O/La27YtnnACyeQkHA8R7zK+5AaSJnKEYzxw9folbdPy5IMPOBwOXF/tcUDTNizXW7y/sapUgZgTMlKCq3MRySIcj4k7jWyWLY8vzmkvz/C+IQehj0948+aK3d0dw9CbkFgpuoRsssgFvhDxpieqSu2NnXTu1dcxF9Aqu2Jdge2dWPGZmdA0Gn1VEG/FacvWqshj9lxcbNkuz/jiT16SFO72VzxtlnzwaMUXfxTRlIixY7Fd0Dae3X6PDwvEO/phoHv9ku12Q2hhfbFlSMLbN7d4dfh2SVLl3dsb7m969BDRIdN4YX97x2rTMnHspLzr0vNjDu/OIrlvO77bRr8cWrB71SmBBuZ1q5qHXsUWKp49wRZxMmqFwkmRXrUWgJkmeGtg4TaFBgZJhWHoSSkTD5nIkb7sssOQccFD6TJVBa/sXidPt3bPyjrp10hNHjEZx1ygCLBFG8K0GdQQFTiZzA+PanTnFM4TWGcO48zHtnidduTRQIUQrE+vKx6jyIPFxAzKcGU/qRuyVXJOfUbMzXfeYKDgPOvNphTcudL70+iELlnVc01S1mhIS1jgneePfv+n9DeRsPbcnQuanAlzSSrOQSbknvzF7xGxiE2cEGVAosNlh4qiwfIRDiuQ8s6jXgltg28bFq2S1ZOGlt2xB/HWoU3Au7qReWo/AhtbB0UfKLjGckB1vorA7p7V7/yMJ4cBddNcic5xu0787iU8/fH3ePbkKYfdnrvdHs2mI0VRldXsxkUPjHTRmJSmsdDKfJ/i8Qq4RUCiwV5VhnksanJ57Pu6392hRYp4hP9ypNvfszi/4OXLrzg/P8d7ISblow8/wDUN7WpLF29xMql4nnrodb5lg9WyEIcdjQzsushiu+Fss2Xx9BHDexdjdXmVhk8Fsz8eDwydyUFHMrk027F+udOaMqaOsaFqyGNS4MaMy0lKpFPabWqmCS2bzSMuHl2yu99z7Hq0cfzoR3+Oj95/Sn+vfP5/vYZwtJ4HL694+dUrXr9+Q0w9JqkS0CGVblyRqJnQtpyfLVm0jvOLp3S5J0YYukh7vkJFOb9c8ukn7/OHv/1TzheXHI57tmctt28PPPszH1k+48Hina/zXwbpzI/vtNFXLDGTYiSEntXSgy7oOy30xchqIWyXAVWjd2ZHEYdqDEt0hs2KeJx4UoZhMI8y58zxGNmnAYiIWBWg1H66BblYrxZsNktud3uO3QDemxJhuUdzfaXk7myChxBGr1d0wjblpN0i44bkfNHqF6bQDVeilhJK12jlAW4/f+nz5NVJuDdGH3YepW40I19lhGFEIOWID1Umwf7vZjmCnCbKasVwCrfINrVcWD0F4mkWjQnUlWbqVS7X7i3RtN4aaiM41cK6StRevqZubb1Kf/+PP0cddCliBTzl2XUaz0EyOSRE+xF9cjmgTkmu1DIkgWR5gwO3IJgcgoESSGlDh4K1PKiyBXXMCofzZHMtPpkw5jtmExohsXrc87kORDe9F1+23k4afvHHP+fJ5pKutw1jGJJBwc7RJ5Oo9r6hcaEI2U1R3hBNh4lCP65QiFAZXZN2TM7WolIR2kXgzZvXDH1X4LpKUxTUFbnx1pH7yPH+Fi8mLrZYrHj06H3evLzBieN43CMCvhTylXC0rpTSayEzJOXQGSliGCLXzztSviJpY20P6zrAl9qEhEokBCvgOhwOKJWdV1hqyIj1n0x7SaM4o9G3JyE1611rI/MX/7m/yKurOw67gfv7gfXZhh//Uz/kfHtGztCnRJaAqHK463l+1/G3b/4+t/c9OQ1AIriWoYvEJKgMqHdIKzx670PSIKScWEpLR8/2bEsXB3yIuCT87A9eQG/Fl1l7hlgUe3G8u7phe/aY1psKbyKP6xX4hjX/px/faaOPQhwG2tbz9MkFyMC7d0eGJFbE5JxhtDLQHyNDVMMARUjHVLjhlTIYJtliNfaDMTACzgXmSbhUuhfVAby9O3B7d8CFYCH+CKEUtow4XFO0xzOj9jgYq8L5qoRkvTXHx3vwohRF0+R3jwGdGOd9rv5Xfz8Vk0ybyfycMjP0UDy3Gmow2mvmKpcn3yvvQaTUCFR2g/djkRtz2YeKNQsokeA96+2S9XpR2E2mpUR5nlpVamyneYP5GlmYZzt0HS7Am9srrm7u8QJGqJmC3lSM1BRJ1S5i9aFKaz01YzKG/WLRocyz8xR40JkXxlmBkAAAIABJREFU6MZK4jq+lBxLKgZexw2zXt9+nkFdZYz2LSCTVIiq0s+uG2PkzbvXrFdrck5FH3/C3HKpSrc+0DrWQKiUaLBExHU8nUwCZvVeqkxyzoIENX3+1tEfE1lngn2qJhfhAne3O/M4RVi0S5x48qDs9x2L5ZL7u4kcEJO1KJzIAiUCDZ4+ZlI6su/DCM1sNhu2Z1vevTsQk0FPSZO1A4wF7pNM7g4nAoK5PJsTN+ZKkGkzFbTcs20KNqUsMiqsbXJSPvnoA1599YIhe15f33P54RN+9Od+3eQ9OqFtHHe7G/qYcAIpdhyHgZu7axSD/7QBCZ44RJbbxhraZ+GDJ2dcnC+4vVau3t2yaBztaslut+ft21sWi4a71YFls2TTLDge73BtYn9/y+HuwO//9u+yvVzx6NEWJw2UGpFRVXjmV/z/3ujXSXo89HzxRYdIANegLhdKoOPqtjNLi9H0JBmE4sQXKqG9cHPYHOIypW14sVPFZyvGy5gDzuRMS5jMKE+cqU1WjDo3FUGllAoO78YFBYxhZjWQtcrvNDE1hflSmnEbg2fSAkGmpK+rVM3xPFOIP/HpC610jtXPYsN6rvl9jrRMZMLfAa3wldQaACaDL9VPOk0s5ZxYrho2mw2haRAcQ4zkYSClaPUPIUzPrxR2inn5c8aSory73fFRctxf74gxmva7ZmM0lM8/ZCSJmHphfW7x842WUrWMGYRx4cyNt6MyxEwy152M0xgUFPZOZTE9BNIs2ivUulKNK2ow4sPxp+RF3r59x+Z7G0PDY1XFLFLWpU+qjvoshblyMicqLFgzR6dQob2jzBCzNQUReO/pY4TM/f1ArkVVqrgCZaVRrkA4HI40zYaPP/4eL1+/PZlP88hz7qSoFvmU5IymmtKoPZNdx+XjJxx3V+yP/eikVOkFI9flsgFbkl+wHJlieH5miky/xlgrtTW5rAvzUww23p4tWTULjjFxu+949skzPvnsGf2QkaQ0TnCS2Z4t+fGf/5Tf/50/ZOgcWQLNIhDTEekjTuHu/tZYaLFhfXlOfzxytjnjeNPT3UaG+z2dz4T+SNMuWCwaa2c6HGCbOFt4brsdy2bJ7vpAQ4TesXu3Rz+KhO2Cm/0ORQsV11p7zov0vomtNz9+JaMvIv8B8O9hS/0fAv8O8BHwN4AnwG8B/5aq9iKyAP4r4J8F3gJ/SVU//39wjdI8WEcDYInYil2XNoWz6sdiBsdFZqZfgaEwc3SsdC3q7SBC6gZEo/W89B4Va/ghuRSDzJezSKk8LGxeKfCM1sWlY/tAkZERPPPA3Tdg9Dpdo3iUlkYo9ykySgY7qfyf4lfONg5XE9UAs4rCMQE88wamBan4os9TPe0RujqJGqqEQwVymKIGtTsCYb1e0zawbI19M/SmU45MIllzKKqPA5W4espCULyH3X7H//G//Rbb7RJXciCq1tbP9p44eni298i4gam54uWZmM2HGpHkk3eRTzadEuUUofRae1URfBuCyaidLrjTDWj+b3VYa29hu26h5Ip1nDrsrUtZ1x0IvqnBIi4Fc3QEizKLetoUfUybPapkKZpPgPh59XnV01dCKBpHTy45f3ROisOoNJk1Fi68ktXkflPMZB148eI567MNN8c7fMyWNNfxCccxqO/U6uIyEm2zTTkTQqA7dvze7/4Rsae0DLVTZDc57lkKM08N5hM8qThDWafVWR2q6piZU8iYhJcCvSFCipH3Hp2z6wf2LvLBZ5/y9L1n7O6sAr5tE34ViWoJ++99/32eXLSkbs+ruxu+/PI519eR1bpl0XjLE2VHTHC461CF3//9L7m82CLSoF7YPNrStA19D20IhEXEi9B6x+Gwp9eEHCOpFdJRrb/ybsfv/fY/4ge/8UPudnvef/oRLtdsUoHr1KKa/8+Mvoh8DPz7wE9U9SAi/w3wl4F/FfhPVfVviMh/Afy7wH9e/rxS1V8Xkb8M/MfAX/pl11E1bfwTTxjTnDdxJwvri0q7LfBUPM9C1QLrArRohLPzBW3r6I6Rwz5zlEDjMpsWHm3P2K5WXB92XN8duN/FE2Nde1XWpOzUdMSUOYExiSxYy7RKOqoeSJWHmBvbOXtmXCDV25xdZ/SkC8ygqjgpzVhGO6mjpRonQjkmw3a6EKunaLkHw2PFT9K4tUduNajlDJPRp3icDhZLa/p+2B043Nvmtlq1oJmmCeSU6PoenLAJay4vL3n+/PlJxDMfkwozpdgTo3B9czBBtzGnUJLhKVXzjgn8Ts/8dbxTprEtz1Ojszn8MffUyzSY3tn015O5Wu9nus7pux7HS+RrZ5ggIrv+fn+gHyJdN+BWVVKhQE45IZIRHwodWSY5EM1lXpRtVBl721KgwKgZvENjNgOek7XudCYZbKquRV4Yo2/mbD0Acs4M3UA3DDz9cMP9YY8ERxyplnb4Wde+qcjQRjXPmho554iDEvuexaB2TyXYdBnU25t1CskJ2cF6u2ZIwNCRNZYou77XSrCohXR13ufRmdJsNTjeBVKGdrng137wAxbLC+7vh9Jv2uxJTpGbuzsWrZJyR05HVAeerAPv/+gzDl3PixdveftuR0ymv7RplyyWC5x31gY0R+7vB46HxO2h4+n7l6CBlI7kbI2GfF5wPB7xC89xd8dnf+YHvH3xltvnb0ix56NnH3C4u+XZhx+ZfStRtkNImXHu/TKE51eFdwKwEpEBWAPPgX8R+DfLv/+XwH+IGf1/rfwM8DeB/0xERH/ZtoSMC3/ES6snnaqkMCV8s/JvXxJPhtuXRRRNkfPu/kBK1kzC+UAQQdVxfd9zfXtvTQ1cwIXW2BUYhxvm3q+OHunIvih9dOvnLHnGKMHs3FRRm2dGFDkN8cc/y7+dFpeUzxflyVoQ5VxN+Er9wHSdWRQwbSJ6cu8VQjLuuZBI4PO4GdTEq9YbqIM+u3e7D+FsuyaEwMX51oywKF3XTQY9JjbrNe3Zhu1ixetXryZm09eMft34QDXS9QM594UhMo1NShlS5vHFOSEEjjHRx2FcADFGM14Fzvn6jDuNfuqYPPTOq5muRl9mG8r0OT15hjp3H07zh5+HyTcGa8rx+vVrYoLlcmmGfbaJVA2kXOQIfCib/7jpl2uUnhG5aAlNVL8S7YgVU/n6zC6UFoLmMFkfByUlT8I2mpQS0gpDisZ4aeHd/tb6xjKxvLJGtGogjQ6DjvpR9f1aUx6l1cwHWVjESqUUi1CyWn4gKccAbzeeru859D2+bJ4mWFc1q6zOQ4rwYbWCVcFFUZwomhOr1Yrb+x0390eWT55yTsswgGbTxc9JEfXs73csL1Z097d0fWeRhpiEwrJx/PpnH/HhR4nXt3e8/94H9H1ivx+IKZcKXM8HH5g6aMxwfXXH3W1H1g6RzHIFsRU0ZpbSMByUr37xinTcQdfxZLXi5s1rNv4xu+t3rM4uUN+g6qyq2pWcE/J1b+TB8U9s9FX1SxH5T4BfAAfgf8LgnGtVrRbwC+Dj8vPHwJ+U70YRucEgoDfz84rIXwH+CkDTtKj2oBbie+dYhIbWB+N2eyUW7yMlSzjlLAzJvAUlIcVgjk03itqec5NHlUtILLJG2mJ8XGnAjJ3HGvHWBSpjS79x8UwjM3qLFcKAyUB552YqnwXC+ZqMQU2qmjddC00mj7Ni0A6rUmSEkGwDsCSYrem6gCjG3+FKXsEiCBmlG8QV/FcY8xg1l+GcJapTxffzqXHOObI9O0fV0w8Z7wXnknUIU/Bls1lsNuQY+fST7/HixQtSSjQ+oIUrXQ1rhd5UEykn4tDjiHhfn6ZutsawCaHl3e09Thx98UqDOJarhuADx976Mmgt9hthmXlh2txI6FjzAbVBfDHJRUKjVoU/mL8jZFM307lBGo12yUWkFEdMuiauq2NjCW+x63lGZhhikUwVpVNNpshJMLaIl9JMhuI4TPkeESE4a9ijRCSAqLeEcVb7rkxwmB3JCufK5uXFEbPVUXQ9vHp+bUWKGINqiMnWZ3GobN7XAsuyWrLWOmZUE2jigPJ5A8tlwDcNxER/7HE54XIGL7jgic7TDT2hOjlqMgnzjdUeuRbL1V8WZ4rKybeG6EPfM/SRP/it3+GzH/5ZHj3+1Fg9UqvDle4Q6cKRbn/k5u7eCvzawFKETWhQrA7k/UePCT5w9vSCDxZLY/upQ7vEoesZkkJObLZbu/Ws+CAMXeTtm2ukWRD7RDfA/tUN6+2C5QfnNBrp7nZIitzdvWPIicvHH2IEvLL2x0nItx6/CrzzCPPefwBcA/8t8K/8k56vHqr614C/BnB+fqbPPlpbJWdyxCESh8jhuGe/z/R4EgHBFyNdkjRVz5xiHmYe5IQtFtnXmbc64vzVYZ5hwOWZJyw6T71C52qmE7vDjEKFZsbrjs2lrYjH7qVCGdXkzX8/izDqGM1+rmqY9bsiOhapnMAvIqNnOk96ziUEvMxaGRZDZ0ay3P8sOpjG0J55s12x3S5LCYU1ro5F3tiJaeSkFHn2g8d8/9NPeHt/y4uXL2jFDE5lyeQMKevIgFJss1etBr54tkXXP6VYIMCBy4tzhmGgP0SUhPeeJtiG33U6qo86b1LDMabZ5m4a6/X91jGy/rPWUOT66trmVx0/e/oTr74aNilVnarKer3i7u7+JGqzRLZtBl3XmcFP1ky+NuWx9y7lfECJamr9x0n0UKLCWpVev19zRw/zOM5lc4DUun6l0v5wSNYSclxDs3PhnOW3sCY2aODF87fkWAQCZ+yxrrP+uXa9wvzKSl2VLihUCG0Gv6Wc8IsV+75jOHaGEZV8A9iz+PErZd24AuoVwz/fjM1BeQCtlblrYnCRpnF4r/S98rM//pzf+M1zVtszi3K80B0GDvs7fnGzpz8ccF4Ii9q2caaJVSKJGDNDH21u6YLjoScOicPeGs5452gWHrdoSKoM8cjd7sDF+WN8UJDA+RA5HA7c391z38GgjqY94+rdnuV2yfnlkprXQV1xaMps+SXgya8C7/zLwD9W1ddlcP874F8ALkUkFG//E+DL8vkvge8BX4hIAC6whO6fegwx89XLHajgCBayeYe6hRkJkZLJd+XfSgPnYiTMmStFGmOxyHT+ug7GxcFk9GsSU8Zy9sljr17fuEFQ4JGSNBrhmZJ8Ne0QMHDVjWG2c2GEhaYQ34y+nd9NnlG959HbtE9XZ8ePlbu53LdMn5ep2ncM/7Gisliby6MkmZ4raWU3VW0gYzg5N6suKxvjdrtivV6AFI8OpTsexnFzAutNy6ef/hkuHl9w3O/56R/8gRWy5OpFUzjWlMRkcXK1UFURKyTLZgBirjLDdpMxWkTw/gfvse2O9H2PAZ22MS+XrTG6gOPhSIyRpmlYLtsSZdipcmbCt4MvsFyFSmoYncfNp75XW2gO5+3dtm1LitazuTt2xfhOSeK2bQnBF7w3j93LvPfj+7brVhmSySmZRwxTNFZJBSWym7OfRgx0ijKs2Kf0yQXEebwvWvXJqJJT7wSLulwpw1bAuO6ZRQvZhVnkN8kdW5SnJ4l8o1Q6VmvPo+0K7QdCaDhmpesHNust+0PP9d0dF5szKxBsbA47TXinxbhP7141k7KS+jRee9yIqcl8vraOnPekFFk069KasYfc8bM//iN+8NmfZegjx2PPdrMAjewOB+PJe6sOFppRAiOrFfWtlmf4Zol4XyDoSHAQxWonHKlMMgdp4OJ8S3doWT5dcna+oesi++MAvcMtPBeXZ2RXaKspsyWjGmkX6xGmrZCzzOsrvuX4VYz+L4B/XkTWGLzzLwF/D/jbwL+OMXj+beC/L5//W+Xvf6f8+//yy/B8ESE0S1vo0ZHygPMdFsgsIGEJLZfxkvEEcrTqRhEL5dFp4lectkobmPOaCljD+NmKi9XNQGf3M/9zDm/UcL7KPtgCM5ZELNWmtngLj7li6OVGKkggKtPCokIdoN4SWZJl7A86efiVFfHAo6kbWBFBqQY9m+s181atfZ7oZChcuS/z8Ox8vjCdTL3TJHxXqxXLRWOqimQEtX6hZW9KKdEDT5884dH773F/fcs//J1/RNsYZmp0+cIwKpvP9L4qjRFUlIwlayt17zSOnbqKtT4QFpaHqGqjzrdlw060y5amdGmKpS/BfI6oOmI8IFJrLky+2NgucRz7aferuQL7ftOYmFdXio9q8t6JH4v2KmTU933h1tvpctmUR4iwatyUcTiN0mx8zfMvMheq+KZB0bEgyZrvRJwEvC81KVSvOE6VvarGMyeCijGGsnmeUpVtnDFNcsqmXKrCYh2M5olDGVAZVXAwvN2ezTVlQxIlpZ6r645l23K2WnIePLENpU1k5sPVExbtitdvXtlqX1hltTXHKdpMTE5YCFJkMcB5N8PvGRu7jJkCsRW/DIEB5dB1490GlxkOO1599XNTVyWQ+sZqScS6gnn1EB373RHvlaZd4QWWZxua5QbVUIpHTSo9NI52qWw2LV3X0XWd0biT4vDW6vLpI1Qcvmk5axrOy9TOWem7xH7f0bvBROZS4NWbo73r4BBnDYe8t2efnMxvPn4VTP//FJG/Cfx9IAL/AINl/gfgb4jIf1R+99fLV/468F+LyE+BdxjT55dcRNAh4wM8+bDl6dMnHI4Db9/uuL87slqv2G5WLBaBvuu5u9tzHAZyfSyzgqP6XsXOq/drn3FjyDn+auZBzcPih0nG6ZgUCyleWe0SNTcQc7YJs4Wrs2sWiO7Enp14fnlilZwkgAvWWxzS040JY/mMf59HKtQerDYylO8/1Ou3aGDaDMEScNfX1yZOpcbLP9usgFm9VtPw9P2n+Mbz+S++5PVXX6GqVhlZISS1vqtz+GNiLBVpZZFyXwoPcxyAasJ7iEMmlgUXU4SspFSZJ2k876TSOukd2butm7UZ8K7rZp7yfB6Mf7P/qUExOUF3PG2AUlNcxkCZNu2srnDgLUo4jUIrtj6xrk49++kzYwRbKLlZM6Hx5RnM0bBiOn0wZqfl+76onGZNDP1As27HF2nB16wxDKbtc3N9w6/98Pv8/Is/tk4BOuWvHhIUKsPLNJUsAo7ieH19g08mdOa8dZyKg3DV35N1YBgS/dARfDPmPabNd9oIa5bAxsaNzxSaArVGi85s7jmGshmllIzzLlbBL+IY4j3rzYqcEv0woL3N2Tm5oQmOq6sDu/3Ao8szkAONX+J8MOcISuNzGb8XQiiJeStK7LvMe0+eslg2xKRjcR2Urn4Ci6WwWIbSicHyFf0hcjx2dN2R4TiQUmeQpnNjlPenHb8Se0dV/yrwVx/8+mfAX/iGzx6Bf+P/7TXaRUBQdncDV29ekFOLeI/IgpQiV9fXlpSSooQXwhhyVaM/eTvVsy4LTQQZyZ5Tf0yYDJt95mvPMv4p1KpcRm+sYuDGILCII5eEnX1kTuWbLeCTizAmcAVGbZ4aqtbbyxWGGfex6Zx1A3Al6pmHxPXPUJrNz0nMY2phdlSobDrMm/Nj5tc41H3pA2Ca+5knn3yCiHGQn794TU6J5XI5jZ9MRiIEPxkVrXiytdALPozcfIr65ZyKJ856EKSkDFGJKaGaRliqPs/o0aNT1bXU/IeM705HHaISNs834vJzhV7qADlTnbC/Oil/mX1RJrhES39UES0UzPlYzzSOZJobX68l8CXy0lHCw+7X3rcP1hxlbLRy4rTI+Fko+Y7yHME1JDKH45HlYkEVOLQ57cfoZdEa/NHtMnE34HUg5jjOk3GdAM7LOGdrfUAaYh1IehWUBAOQy1ouEZQU2eghA5oskqjzujxPSqmuJMQlnJsiIO8dbeORVTCjyIrjPnEcesjK8WCN7J+8916ZK9ZGs++DUVRjLu1Jbd7ElBiGnt57vG8YIuzu3hL8NReXt7z/7CmXjx5R1VG1BGE5C9bGXGkWC+sy5gMh2OdSnuSxJwHJMi6YiB9lgw+tsGkWbM6W5XOmJDB2CvyW4ztdkWu7sDFRc7IBk0CpajWB14xaNf0oc5g47Rk5QSiT9z5BI6oyYcg6GRuV2YIYbd1pRWP1xKzBS8X+zSiPsgip9v0snoebbmtaEvaLycSfjkG9twq1VLqe+Y4PvKnRqEzfq+efeDwzD69ugGXXmCCOmXemTJWRdVMoi9WJJUWXrWO7XiLOW6MH73n28fs8/fgTEOXq7RsOx3taH068tOoB1Qionrt611LgqfPtlpurK9u4s1C1ZWyPMGrubh8R4lScJnGUZpjPA8oGPSoSV/nSZHK7zsvYc9mim1MSwBxTt5/9eL/MuOnzeVg/bxuwjvfgisVPD+dUidzGKlimfMB0L0Zc8OXazpeEqULOjtQNLFauJPZd6QAms/sps1VkphRr33clf5ZSYrEI1MRope+imcWi4fb2np/99GeQI6oTS6iOU41qfbm3yfNxowR1dRikzuO5VPdsrkmRvKi/rzz1cTxOIotaa2EbanaW95KmNqDJSMoGaSnEOHB99Y7L9x6Te1uLdZ6aeKBiiqVlyWeIQyLFRA6Btm3ILvHu7TVvXl+zOd/w9MMPuLx8zH7XcewPrM5WhEWD6AKkoY+R5cJ6EuTsShtNIzOMrSYllncPqn6ySUxJ8KxlXJzNr0Vo+bbju230AaRoipfuVnMjp0kxDZM6iSdFvXGiVNqkTM5rbSxsP08UtTl0U+WCKy46GaTpqJMtYobQz1QT58wdu874U/128cQrbPENj/6AyvmQgVHPM7+XKfFWxsh+GH9XjUZt74hOqWKRufEtIWkxUg8hBsS8bxFh1QrLtilCX5D6yIfPnnHx+AkkIcXM81+8ZFFXzGzsp3NaUs1enY7v07lqeBoeP7nk+uqaNMj4bOMGJWA6ONNm5pxBBjJ75zX5NacxjjNjxm6SGXulVnB9k4LpSdQ3Rm+c/Fz//SGTa8pZTJuxfWA+504N/tzw27lyYT7ZGITGlSC2VoVHBG9Nb2ZYb+0rbK/SnzCCRtIDJScz9LSl3+4IKWWlXXggcezuqB3H5hXGzK42be612blglbLTs07jJaMxp9bIVCejCKVVB2UU0zuBNE7X6rSmjTCkqqOKZyyUbRGh63u6rmMRFqMaqS/zIFc7MHPK6vvouo6+P9K0gUVoaZqW7jjw+eefE/wLXr+6Zb9P/PA3P+OHv/F9nDbsdwfWiwWZ0/vU0RucFWViG8DkKApZy0YkZvxPgvhfcnynjb5QPA7n0KJmWAs73MzAarYOO2PnnFy8tKJOWYuXkIcLt9qIWXvB4qtXPM3uQcaJZt/T0wEu8rWKSeDay5uUNHHTuTRXLRhhbKc33ke9wMzjK2OQHxiN0ThpvUlmhmJ2byfQj/1CZLZpaGHLaE1E2/VqZOS9wwVXFnQa6YtJjUPeNA3ebL2xe8TE0774ky/58ouXtpGoJdtDcKUmwiIjC/MNh9RaKl+McPXS6nvKWdluzwm+sfiujLF4b8ydmM1DR3GlF2zOBQooBiSPInpKzpViOOHAE8wUxvcCOkoXVMNom8YpP/+h918/N/+zHvP3aI5FGg2mFAOZLbZiuVrhQ6WSlu+gI7RR37vYj7bJlQpW78G5ULqcFSeiOEhSoo25Q2HzLI+QX86J4B1xGMCrUTCljq9t4NvNmt3+1pwzNb36cU6Xl6dZUXFI7aTlxxCLWlDmpNQBoLiSI3OuGdeBOGdihCLlPecxUrL8SIlEiuNRqRBjHCtY8VjN69RoX63SPidjbHXHPeuLtvTN9iNTz86jEKccoTmk5Zmzo+9g6A442eNLApeF46MPH7PvOq7v3vDl5y0eePzee7RFRz8nqzeiRDkWdWUQZx5/3QC1t6dRG0sUspqIZFaDgB7mab7p+E4bfcRojWaoKwVLRw+Yk7DOdm+YeXeIiTvhDW8tk8m+ql9foGUDOLHn4k50feYeRsVaTzy3WWHO6BUxeU4wGeDqYZRHASbPpX6/joMylxSYmkGfhjCnBsauPTNenHqf43PrdP05R11ErK4gREIbyMkj+KLFbuXracg0xSjVMRCBpvGlU1ZCvJb3U5OnkKK1C7arQpVg5gGENsFNFs8vFiucGKdcxs3OerJWzX2w3+fsysK0TSSULkwp2sbjQyDGYZxHIYTRk67vbq4rP8E58sCznJyJh978yTjPPvuQhTP2gJAJ+/Y+sFiuaBcmFiZY20E3pw9XyEYVkhKcA+fMmNTEuK/zxiponRPQWizmTqI9ZBKZK4EgwRu1uF0EJHgkGWdeEd5//wlffnXk7vZuLHQ8dWLsvK5smiL23m3t+JmsShjHwout8dHgm8Wuy3yMvusYTPmfSRBxippqDqyMkdp3mqahH5IRHCTjXSiKvafS0/aeikJngRRTqmvI7FLN5ajG0RtPaSBnIwK0q8hyuWa53vDu+TV9d+Tl83csN2suLh9xcXFBu2jQ4jylZBPbiSO5VApPBcpGW2nd5QGnv2sotRDffny3jb5y4vE9ZAWcLKyyECcACNstXZGKzRP1cn6eaSEzm1hFcrZMusqPziVxN2f/TPCJ/U3c9PeJVjb9XFUFEaaEM1C5xbYYZuwQGZ31mRc/FWXoTKv/W3d5mTNO5p7/5HlSzlFZLiJC0wSLsFIpjkrRIBdNBFeL4kynvKpmjjo25Z5TNO38VATXyCAlbK8S19XQPHw3D9+zc0Z9bFtLTloqJRCjgjMaZk1KJhKZNBbE2OvUspHbJupkqlit76xpwsm1a1Q13wDqn9UwVBhgbswfvpP697ngXO0rUEWzKr0TKBTPhjAWVwmoGc9UYKqgYknunFGxc2tZC3XtpNTTNA1NaHBSKY1THsK5yvqaNjqrFC7R9SBkdRwOBxarBa0PZE3WG2HR8umnz+j6I/vdnqHvy9rTMTqeQzMWaWacL7IprsJKVSZYoSaVmaIqxSQUVZXQ2BqpEVKdb/UdlKkMGF2zPtccCnLeceyibYBlXoTg8GFBSpT3UGtupnDZ1mFZN6MDWYUV6xrU9QlwAAAgAElEQVSz9xVTZnN+wdXNHamPhKFntx8QElk7hmHPYXfPm5fPWW/XnJ2fsVmvCU2DE2vxmtVshm2zflwf9RpGcTabp1K5+t9+fKeNvo5eX2F51MUmc9M+ftgM6ujZ2uQe3QPlZDHWw2HnGwPB0eMtIXE5t2rGjYqcMENf7PJaJ+rcuBYvpxoM++fpOg/vA6jyvXaqPIXjRT7CVqeFdlrkF8bkzgMvvqopWjCUyyKWcWyrEZwbJosoZNQbijGdbLyjEZRS3JUzqbVr2OJ7YLCdUlt+2wJz1HZIoqdQXdUHF7Em4LUqN8Zom7aWph9Vc8ibVDZQGq8waa07wUsguFA2sTReKwQpHZVmRWsYxGDyEXLyDNUx8N6P0ZnOx4Jpo/smLL8a4Boh6GgA7ZhLTCNG6ZwcmDkBoUBuQSBa9yfTFlNccKSYLDEJtN7RBE/MCedhs1kTREo0rISa9AU0p1I45ZAi1Nb6dpwbeEs0DnGg63pca3K+tUjNOc9qtWaz3pTkZ5UZMXkIRcgaTyPInABnUZdQorQp8WvryHBarxalhlLIV99YlZN20VndiUxrViQXSC6garCVIQCV7VMKOJ0aBKbQLhtycgx9pmlMAkS1FrLV2gM/bk4m7FauVyBcFSWnaA3MEXb393z0/lNur6558fo1mYxz4PqWtm1BBmIUhv6ew/0dm80ZZ5dbNhdrfAMuLhiitROq/xUt8F+NxrIADUrkl/v533GjX48KvbhiNaucgDkqs4UJ40Sov6MCPTJpzJwkjU6xnAc/1u9P+YJyB6Ou/AkUwXQ/p4u/7sA1EVS3h9NLz71J++XkxdfJdeqx2w9WLPXNid4pinn4rNPTjcaqfC4Xb+shU2keUlvTclisF8XDquJnxngIoaqJcloRPUui69jIY3puqQtXShKdyrByJeSf3W9pwdh3Hc41RfohQvGeR7igtMgbhkn4zTs/XtPkHIz1FZrGNiqZ8Pbpfcy9u1lUVWQjHiZZ53+OvRBmxn4ujzA36oxG395ZzSWMwncFKskpjWPinJDG+gU7f9s0tNIy/lKgaRx9obZWz1hCIIRA3w+gRUK5bFC1Y5tz1tlsyIk4RCQE1qsWJZOTI+dYeOgNfT8UhsxQoAnANWPVsZJpXGs0TIllDsSvefcnLR2R0UmgrPFhGMAlFgvrY4yzaFyzkqMSNeJDoHYU8FIrqpX9fkDEKqjRad45mepsbD2WdSMKopNTIWKRdllFcw+7SnGAJw4Dr16+sOZBOiCixASkzNB3HA6wWCxYrtb0CofdgTdv3rC5OOODZx9xvlngF9DHHnUlos2MkHEtyKtOa93Uvu34zht97w1CsEnlS6Vd8aCr8XgAa8ybh3zNyDOHC+oLm3m/oydf8RuZet3O5GHHzHqesV9K/PFwyLVIutoZUzFcHtRPnxaYdulqjitOXqOVU4NzsrE8gG7mRlrJU8hcdgBxc6bAxPIZI6VvuEaFpcRJKUZSdvs9i+UFVUW05l6qlnmFMeoY4pypYoIVDEExBp4Jr6y4qi/QmLfqyBM65MQbd07LuZSmaajJ0DgM5Rzm9bXtYvTSYkqWIM1WZJRTsorIEIAExdhXL7y+bx9qA53ZvFNGg1YNecXntVz7YR7ga59lJtfhKrzjpmboTLBQnROL5cISeAWaaduWvjPu+wg9iSOnTNs2NK0xpELjC6GgbBZFz6dtm5IrMVmTWswIMvbVVZTQBFJMqDYsFgtiBOeakpQPeN+SYiK0pvzZ99F0iEoC1QTWbJ5Z+0PKZpPGzT6reelz71+TmqS6lAK00GAd0CzsrU3QnRN8A6FEK2PPiYd2gNpzuNoRWx8iMPQDi+UCk3iw5LaUKEzLmqmnlbnDJdO6yTptICn19tzUehmrnyBDfzhyOBwI7YL16v9u7+1ibcuy86BvzDnX2nuf+1N1q7rdFG3HbiedEEdIuLESPyQREqj9I4gDkSIjJHdCJAsJJCKEoJEliOAlAcFDFJTICCs2CklAEKUfQEkTAXlySOx0bCdOp9uNo/6r6q6un3vvOWevteacg4dvjDnn3vdWVao7fesUfWbp1jln7b3Xnmv+jDnGN8b4xj2kEPDw9dfwjW+8hruHu/iulz6I5154HmFiOHEpChRGFFHZKx1JMCvg7dqNFvpuNlGL5yC6htfwM1s8vkBDcG55bjgPt+MlP6V5hZPTeXoEsUUDNPE7HBqnGhzN4bGsIqAtSUtFMC4vA59hNUZs4r2aFoySwaEbd+bp+Gbru/QcgDNYxl8HlRJeI9gHD1l0a6dWj4t2aKrHrbO7Hhapxu7oSSIKwWSLTXFx5wCTbwiWqFWKwTClNtgqSEAVHpLBNC53NsYpQap23B/UfrZSAEO1ZrsfYZgABLLq5FKN/lfgUT2lUgiEmJjApBUohE5YOUkQLUJp1Jw1V8QKiCSrutYtJYeaSiGcEyQwP8TnzyJ+nFuJS4xFRZJMEImIgdXDXCh4EXFVtRhtruOpRQlZ5Jo6nCgGhRhdMei4VTWrKAD7w4x13dpBV0HhsF0X3J/uArbGmS3KxKVgVk/bP6rtAKKVEjFNAcAGqeZEB3B5dY11W3Dnzh0whp3JTCKCaZ4s41iwmwKWZUOMgpgs8sf2ocNutZCR1YAbqIpVwLM+AIBVyfJ61ClZUECtkFlQdYUnqrUkOUirh+0Z2CKCNAmCHE254T5LcUbeyO+Tc8Ze9hCx+hLC0oxBqhXSMd9RUIQGR9FfJNEEvzLWn74si2JyBQ6EXn1NQhV5W3GtbwKJpJHzPGNbHuJLX3iEr35xxv7ePdy/fx/3n3sO+xShMeGNNx7juF5jt5uw39MfMZRQemq70UK/992cb2dYaTNrqNoCYKJCrT26oQ3AwH55ogUPGrJWJZe8yVnXwhyeGE33Xp2+34sHx9NQtW41NAPChElLTALgERotPv8pQzIePicRMxgchzp81vsntEMcNw9uWYwHx8jP36AxDKyPTCMPkWM97yJSnGmxuPCTinmeLYzWNJ5iER2Tx1zDuFBgfEKu3Z9CY6LaKoQVM5kb5BBYBJyx/QkxurCKJ3Ps1oxbGkEEuXR8uR/kXVsupSAmryGgDT/NuaAqwwK3Ulo4sOPgWkuDYHzM+TymHFgavod/iq0hOuTCyTyMffd7ONyBNh+CYDBCCJH8SfYM7otx5eC5555r5H7+3c7c7FCKf38IYagP0YVZNF6XlBJyLgiBTKrLsuHicDiBY+iTCY36IaUJta6oFpkyTRNDQlMiOR4CprRDTAkxxRadVUvnRhrQVps3WoqTj3MwS84UGrdaXLGpNTdFKa9HjmXoWdW+BoBTq94QLv4cNiUVzSd9Pymk9rtTS6srgsO+6pFGbqwUrLWy5KsIyrpikoQ57aAVuDq+gcuvv4FXDzOe+9CLePRmwW9+4WWsyxHzLuB3/vO/Fc+/cBc1v71Yv9lC3+S605r74LWzQDqGVU1TtBegTWDYAGuPlYdp8iPmDxim3lgCuzD119rkol+Hv48KdnuPyPAOf434BRzjdScrLY1wciC0z2k7zxqINArqdhi1Q80gK7iz0e5Z1Qsd8n3BwkNt4bbEmQZZ1PY9YYpIaY/dbkLOR9x//i7u3DlgmhQP39zw+quPMU8JMUnLCi2FsEPNudH0+mZ0rSsgUoBXpQPs5GAV4xqn09orOgEuqIi7qwSDPQgFkXwrtciOIPQBeWZrzplCN/Q++WKLFkNKobM2p60kVj/SIJjixClY3Yx3uMqhIHR/BnoyFexwjykiaWR/4D6KYGvHLLkG6djcxFOIzIWqiFgUTLDqct03Mk2phUjy/StUmVfB9Ue/i4fR+nr1HdHi3UXs3gW5FEwGd/nhFgITjbQoDheHtg/VKlf53PA7D0Yb0sMhRYD9/oC8VWjiIPgc5rzxMAv0GWWjf46RtNWANp4Z5u9wHhhtp+SrqX2OSw1tPGLkukSAh9cRGQDa2LfkLKf/gLggGg7oHrjQD3rb50NSY9HKXDRXRgwW7hxTfkgAXhSl5oIcMq6WI0Qi9tMOMUWUqwXrFxfM8x6/63e+hEeXj/HyV1/DF7/4JXzgQz+APDISPKXdbKGPngHXhOnwGuDClP+rloTiAgsiKOpauOPQhq/YBIxNw/AN2oXBaA34vciJYad/+xxPcxfsGIS3DP3tp7sTbdhnjSlRPeGqnUkM2MLZZJ76KXqSESS1A0h1gyBCPfLIHq1mq1UbA2ISYo6Fgo8hiyw1P88TJNnBJ0CQhOvH17h6eMVxUEWcKez5vE4n3bHwVoDcxoJO1v66SI9SAVzLUoQpGX02nysO2cSiirw6/uwFTQRkj+xOYS3M0o2WyTlNEx2gBoFNxqFfPXrEaq6mmbHpKUYcjxnbyjDG63qEw2QxROR8jYs7F+Z3Yty2WCgrs+MZz+7aazLaBYcMSq12r9EBzDEIAILBm1VrOwzcouH3JYh4dqwdDNRsWvy4C11VNG08Z4etQhPQJZfBekUTUFOMqLavtm1rkVWMdIqI8QLH44p5p8a22rVgAA0q4mFLzd4PDH9PmqL1LyMZrcV+f8C6GvV4CIjgIQsMNCfacyS8kAsFsNMvWwKVwVT+2TjxPYJqXF5yoqgFCVjXDYdDaAt3UNKfsv98f6vBcMagNISBu1IF+1Nrly0nyqJ06wHVlbCC6+MREgS73Q6QginOePPR1/HBl57Hh7/vt+HVr1/hK1/8Gh48eIC3azda6HvI2Lk3etTez0GQJmfttO7Qi5/G2oTnSZy8nkar+LVRsx4nZJzkapuk1p5c5X0coaSTybR3NU3e7tYPHTTcfBwPF5DjvXn/xFjvk75rH4eTcUHT1i4uLrDbBUyzYprpVLu+PmJdGWqpymIjjmlPlo6vMEEUIjQGVBMYY3EZ35jVYI8RlqI1RSEZLauWPzsEUfzIHwaJ82REbAWY5oPBPn1sa819TFPs9W5dQzbTq3q6gyq0CtJEgi46agMECXmr2LZihxIdqVOaWL9AFfv9HsnGreHhpoXG2MM1gwKzVR9r2qmqwRzdPzMGJETLkK1qETBSrfhKatYtNWvtpQFtuGghW9gjimn/cxtj9w35ule1+TchEycWd8mGAWmtCEraZq4dL0dKX9vu4oDL4xHP378zFApif06icAx+8ut+YIfA/AhgalaaiOBwCEZ/zSXg8BvnuedINOjL1nbj3VEdDoWeeDjN0SyCCZDS1o9bW+4bG/feKC/6HjvdU4wgtn08hOc6efpJNFiAVf0reELGyZMJgR6evCyXmNIeV9cPMUnEq19+HSk9xIMXX0CMM1575WW8XbvRQr83M/dVT0xYIgPdAuhgjXHtEDehlqk0o8VMT+iTQpgCoH8r56Z2zR19UUH6PQqMJEk7tt4wdpwdS61f7DHvZYcSegJZ4xhR75mb/tI6Om6sOpqfPg7iFsRohpqzcEpM0qkLjkfFw4fMIJymmdp98PBLRtmEwGiYAjo1c81AEKtmFRDFnImVulWSgFIy47oTSb9IqhWAREw/54I0TcTlG5TFw4Hl/Gy8PLoCpFJgTDkQwsyQOjMj+qEIy+UgKVa1ocnLRv56CSyoogYJBBjXCuc3l9IENrM3KeByKcZbbiGUTtRn0RtO+0vlIWC2aBlST7dzvM2FO9W96pSvFwCkiBjmzef/XEA0OuRKB2NVc/JapI/W0mCLbdtowcWIo9UtTtE59jkGEgNy3pAXCs3D4UCHe85Yl9WyYWFC32Abg6emOOHho0vcvbiACEtrNotm8C+MAi3n3MbZn82v0cruIcweSQXgRKiPh8DYCG+ldki2vYuKi4sdneSV8w8V47KxuPzQdo59D0M+R0y+Q5E9V8YWKiORgpu3tLZEGKPvEJNCzZFu2cQWburNLf+GarSxECxXK+5/1wN845VvYFseYZoi3njtMS4uDidRbk9rN17oqxV0jg5ZtIy0M3SmbTqY7eQnNgWsWbyEZYaFx++wn8OAUzgTMjlPuedmrO2PoL45h+xYv8eAE7pQcqz0XLMTRFBsSDtAeG5FdEZCO/k9k7D630NJPO2LT4xXJ0Q6C6nVZtYeFcFRgP1hj93+AruUUCwwuVpxjhgT1OOZES2blpgWOXqAmguK9SUGRksUCyHb73eowk0QEJAioaM0RcZQD4dR3gjReMx/K34h0jjxmb1KLqK7d+/C8WEAjX8liGLZVpRtg24MC4yRjkIY14prh6VmhGi86zGgNs797kwthRp1itOpAAkjZQXazxhJcLYsmeGktlEpHBVpOncimyN/gBdcOvjh5GvFwzadJ6rhwCD1AvtD7Hue5xbKR/zfOHxiQkym/cYALYWhk9EoCuYZpRDKWZbV/CKC3X6PUmrbk24RBTuweX4mvPnwMe7cuaAPyDjo/VAcncV9fMsgQNEOE1cCzve6w1u9EDo/N1ahG8npgNDohtkXJuG9+OIDvPLy1y1M1/xNcIiG3EO1ZsQ4o+ZikbThiZBIVbR+nPgA0bV0Qj4sIxpNyNOK4pqm3+t0XYiGJq9c3jQloCjeePXr+MAHPoBivoN5TkZOuMfbtRsv9NnkZDAAE9T250n8s6n2jIDhSlTHe8dkJ3TTz02w88aFqYOwHOLZbRGOwvvEJBt7P7x2/plzXFC7et+Fgv3PBUsbg+qagC3U2kPFbDCGplBkOhLjhDuHHebdDiFGxBRZLKTS1qi1tE2ZS0Gpm7E3UqtzEiq3uJIYaVWw+OqqkBQR7EDSjXMQdnOLijjlN3EoDowBL+Rbv9jtGv6bzAHJPqkdKhWQ1OcRrtEu0Agc9geUQLgmpYQiQIq0wkqtkBTh5S5qrVbirkdUOCw0Qn/unByfocN00rU4hTmUN7jjn8JI2prrc35q2o8asWvKyQ7kEyghsBaER1ZReHb/gJiV5utbQ8BmdAe73Y6atT2Ph3j2vhA7dujLx2WeBdtGKJOZ0or9Yd+eK8SAXdzh6uoS83zfIonyyZ5zoT1SRZwKabS14ePuFcL6gddhFT8w9vt9E+5+rx6CGtuh6RFsL/2zL+CNN97EdVmQ5sl4+itQiyXo8eDVYT+KaLPWxmizkzk93XhPzLUf8KMCcQ4VcX9bWZgzBcE3ds4ZX//615EmFoC5uLPDvXt34HTMb9XeB0Lf4IszytamNUgfaI6HsfA1LMwGTegUY7wsWlGSc60fMHPd06otIgTDouVhYpEXdsCojhEE7Idotz4cdDkV+ICbhn6wjFbiaC4aOABH9M/kxBNWCs87bd+RjA3z4uKAabboEhYnwLJk5kD44rbbVHSHnChNVNe8AzzmuGe8tkQ1G8+qdAxT0AK5CfgB4hj+jomYOq8F5LwCqE0wiEEsWulEpRD00MFqGC2AlCBGTxxSIqwUI0peTTgrYrI+ll5EGyBEkQsPR2q12niFPNySGzZaVjYhJygThqI7cQWoMMbKYV11QrIhSkm7AuIQkUd3eTRZ3bIJLA/19dh6aX6KEHv+RnTGUYnIRQ3+YyH5R8drhDhjnneIMWFdF5RK52yQCJgW6nQQrNOjrRgNLUBmUmtVcsoL4RFSnwDTtMMbDx/iwfPPnViZnuXqzzzPySJ6QtP6FQZbieBwuMD19bUdcFyZo4Y/7t1Rkeh+j+7UPx6PYJKeUSBA8dHf8RH8+hd+A1krEhTBUAJq7eYvyNVRYjjRmaWK0K/iSt6JjDAZgBFR4Lzz86HF+xMOqv1wgRo+q4jBXle3pvTkEGJy2QaIYNkUd8MFNAyy4Cntxgv9FskROgwg6N52F6QcvAEvbCYahSWgBl0AnnbtzT8zavvqk+kTNWjPIuEkXrfWvrj8fq2qUxPY1MA86am/zyS0hTNSjJxOWod6ulAerYqTJnKy+AR04KXoUEpCQMTxeI1aFlTTkJi92ftQhXijWnRJLbWNQY+4YWe8Yk+pBRVqG4oma1VW9JHW55Mna6Y9NXhqaU6LwLfQoepYaBFOwmTazam1AKzbRjKw5FqkwUgGqkfDirfl2mrZnsZKN9wc2rJFGTZ4ThVh82aDECKFtK8xZ7YkO0DoQhPdgdrXoJ7QNp/i+E5S1q0J1W4NNArvIK3eLl9PlhBnkIMCUiuSCLFsEWzrglWBw36H/bTDljPpi+0A8SzczkVEdtQQInLeuuVpwoiHEtcBAOSiePONh3jh+fvIpQ7Cz5+bxIaM+YeNdUCuBdngPF0WzPMO2WrjnlvG51aTz9PoK/DXDofDyWEBCKYE/K6P/jZ84fNfQN4ywmREa7Wi1AyRPXekmgbvNSdsq4UAZscO5njbv09szSfljO93n2dedAXX+xkgpiyMSYMeLUbM1WjkJb7Pk7PagemkYR6DOyQlDcLPNyJLFJrmqUOa6aBGn0NFp9o+IxL8bB8x1bbhG90yO9q1Vt+sXYjQiYOmAZ9j+RTObL6ITzh4njY0Z4u/PeLwHo9smKaJ2lIUqLLAQ1kLQjQMvm5tUzOKYsLmiSwhAKVDGmP0QYOyGrQREMSS2QrT6H3jtaxGF4pDZIM/y2jaT9MECHHlhIighJqmEIA4ncSsyyDIpshIiZI3w4bt/iKQiTQe0e5/DieEiVFEkpjsFaTHmo+b1Z+1VlonKqfRJOLJTYGaXjA/h49XGMbiHOtufRkVkEGT9Tk9Xxsu5BwSyqWgoGCeut/ELUBG3Vj+CwTX19eI6cC5UkVdqTnO84weztphqyejZ4x+uBaIFX+vtSKVBBTF5cMr3Hv+PnLJjfLBP8/QUdfc69naqtjWXs95hGJ9j7QiJ7WzlPoYPX78+AlB63vvJBktAXfv3MUbrz0cvqNH1pFdN9AxLRE9jLyT6KFa1FeQE1n/5D5V1wGbUjY+j33KZFWEc+vwIO5rqOe0mPwrBZoVdavQ97XQBxqtsRlTUBQLybONY1iwmIabS4G6YwgwnJv3GoVrExawvaCWvCRiMdx9oyhG7e4Uc+U8jlZH77uHtAFdWIpIm3Sz4eCp2acTDzhdq0ND3kS6LeCZnI5nQ1h8mwdRRUwTKgp2+wlzEqTIRKh0QfOWBxHDKhXSanpOQVEEWNeVqf7o5q4LvHHz+/jXQghNAJScUWBEYW66u+Vgz0lirj5HjrcS2ijGeUKIIUpCXjPSLnFh+96w71Cfm9oF67b1tUJhmLGVSgd3MYqIEFjgZSNdsMMkBT07VVUb5bJn/wJ6ImxV1crvVUSDlRSK7OOjVESKkv5A2/pilUUXbN2R2TVYVcOYAeCsQhyF3RAeKIJkTKWKYgyTk42La+0VKQZAAvYpIm/Abp5JZTAni8pykjwhAR3Y946lMxY+F3NWB7FIJsPRJ+qc1+sGfXyFw2EHd5Aydt/4YwaFSSQAHp0FgQZFKVt7Tt9LzTrMGfv93rJ6PUKJ++hwOGBbV1tzPfx1PFg4BQHrVlHFsnarWS9KZlo1KzsA2LYVITDiyeP/RUgvgdpxft/BvnNVHaNBl7oi7d42oCbgLSN+MBjU4WuIQUsRueT2HaTUroBUHJcFb9dutNAXQc+clI5lwWJcOC6WTacYIntOU9gbBFTHnFS26pu4NkkMCFoiTNUnnUvn2rqf1m2Sh0XVn6VDP124dzN3zElo95bOKeQOW16vjQ2wMQQ2WIjx79CC3TxjmglNkdckYl1YfScaP0iPImA/cq5AFezv7DDvFNM+4PHVim0J2E8zdGMcfK0bRmoLVW1wDvzJRlPWhN65hkqB7AdAHIT1ht2OjrlqQpwJSrEJ8RO6AvtOVuFKBhEp5pnOyFIKinHGhCAoVhWqZmqo1XhUnHJ5nLfOcsntsq7kp3ct7wSuIxeC0UxYxIUQ45cgSDG2DN4RJlCgxbefKAhDc1TE8x7ovKVGPM8z94D2dZRSAoSZt4RS6vCTWuK6rkjzzqiW89n6NIEjQxKZ8fWP+yBvFVtl4RzPmPV7LMtiUURWyCYlQieloGfbuiWjba7PrcARxhkPRc87eFq5R4e7WvhzlQYz+j1FhJXADE0Uj9d3/4Cq1R/oa3RdVpRQMe0GawwgTi/u2/M1Yfd1a0U956IriCcyyZROPXtFxpvZBfrYOiV1rYrr6yPeqd1ooa8YiqgEpykQBEmO2APAIEA9esIWf1VY9eQW2QKcwjUahKXRRIaC431heN3Xc83rZPylb2A3385xexew57HKfi81x3DHl08tFOjpAnBT259txAGh3ICHw46FTkSQotjhyJKHHpI34tRaKjV1FRx2F0i7hMOdFyH1dZSJTsGyFVSVFtPuz+A4vCce+didO24dohu1UlX6AdZ1beMRY8TxuCJvGbUWzLsJy7LhsL9rGpLh5obzngoHAEotX8nF1uLEAVhkF8vcpckSe5If/OgC08fe1hXHKiKEZDBFeALuKln7PBpMUKBQYyY9qbkc7KAe5tZDSU/2gWnbLvSDYfqiXDe1Asuywik4HP5Z1xXR4AZXiDzSRyRgWzNinJDXCk0VMMoLETqqt5zJpmkEdbXWRmt+sp5hyU6hNnI+p8KgFUWY7/HlJe7eucCdO3eQtw259Nh8h4l4IPb7j/PKNRXaYTuOjzdfRx7tU2ppNC725pP3e/DFlrc2Fw4HwoW37WeuExKh5VKxLiumeTpR6Njvcf5GSMerb4VW+wNn4Z92o8EH1sW/B420/WTovYcZ56Xi1eNr7wDu3HChD7h2oxA18iQARc8dOmrm/oizA5BgRRIs8cLgkmB4vd0agBVjaFiPv+CwiwtZL6Y9OmKG92oP5XTNmV/FA0pbNrAfKD3Ouj+OZ1Hi9LVmBaBV31IrKwd7miCsCKQA4j4h7GaIHXZznLCuSxPQ2UsGBmYmVmUI47xnAszrj6+hjxT1a28iDAeubyYXioQ4ejidj0YtFUGsEHol5k54wgjfglo6hTJczpOsVG0REzaAKOJkOnMI0NCZUwk1mJajilwydCtgEoxgmrhJci7Ytq1piPR9VWRV1GAQTrFavbWPecPCNTQFQ5XhcCEG5j+bBenQDwDZfr0AACAASURBVMMQ3cnqkVsVwRyk1WogNWcuurBo2btngpUrFE12qIqRd1kSkGvgWhqFhqrnC9Av45YBcWjuGQmVFlsVBJ2wLdmmgOGrITpPvYWw1kroZdDCOe/Oe9UPRx6GwDyzP8tCDv9XX30Tx+MRL7z4gq25jFqFOZDK6lXF+2wW0xgm6+vXNfhmkdj+GMNDvQWwxKJTcZxb1OTfz5yZwv1KuJO+khQSBAF5A4DSSAdFJ+SVSVgxjYlap74uV84AcM8aPHcSDKLa32MHjYjprCGY5QE47XZUW5GqEPtuKcyiP09SO283XOgrN5kAUE+tFjgHcHOMYJDV1lpolGCYjEGTt88Td3Xzq30YcAeJEZWNzjZumqfYZoKThUgkzg8OX2gjB04X5F2rH+4N10p6YlfDB1uf3RGYGFpXBfMcMIWAvKwIcUapFcu6AHD64h6bLYF+EECR162Z4yx4TkExRnG4YNq2jGT8OnGaoDD6gRb3rQZX8CBy6IJmrQnqBloWoPS0mHVbmUiF3CKBlrUgxhnAUE4Q0s3uFPuIW0blljdMISBGNKekj3kQJ2HjWgnBE7B6zHrzVWhs0UDOLClhOkmIcwFdGsZv9MCmfaO6FefQZF84oxAd4ZWm5WmvS1BN4JVSmxbt2uWY1NSE/nDvkrNtHfaJ2niFClDqhl66sJdMTBIbTOfa++hQrbViXVfsdvPJ8/j4TdPEko+J/dntdti2jIdvXGF/2NH3UwpQu38HbkXoKfWBf98YkTMejLB7uaXmY0mF7nzP9Qgszzs5eR398PJC8V6kJ0bSZLsCxMOh2vi5c540H+eNcqcLjtHq8+/vSIGrlD2qDu7LqRGMPBPzI1Q4T9P5oXfebrjQByDOJzNE4aALRrfEnvhYm1QvWmDv5W8D3O/TDYxZtr5YADcpvQ9n1kT7ALt2bto9CQsJoGgc2t6bJhzaodYtDd7JwUFpGZKAaQ4CiBQgVBZZTjNyXlBKxnFbseXNvrdaglLHziuA3X42cjFmnHrsujswxwgJmEWhAjBbUnHcVhufDfs4kbgriIUxAqoFaYqNYbEJf3RhGYyHRK04BoRZu4zE4venOMGrFXX4zX0v2jYgAmPkQ5qogaogxgmqPWtTFagFBh0JHF5bV1I1xEhO82CWVynSSNWiJUW1CI9AzLsqDw1XMlzQiIjRULiQ4py60HXBM27Y8eeIV/vPEyvAYvtPVqTqydoDLEtaqcWmFOEsnP49YXDCQoznZt3aHuPhGJpgcs1+t5sJt7kAM3jHzd6SazuU+TwTHj2+wpsP38AHPvgA80Qe++bLiAEx9JwDh4fcWeuRV22XDd/b5rYO+R3DGI4HB8enmn/V4OAz3xr3KAv4kM000Jk77xoURwc5D8QYBF5IJQQLbHCFUs93M+8vo3AXMa4Ol1vD63YY9YRMt65MVkhtStfbtZsv9E8Wsw3ecGoDgA7CGDjXwqXXzjWohpQGhA5UQ9PyiekFYoDBKW4pGFS9L4O14BPpn62EcSjcTzHvpsGZQGi3a4/jMAKG06lbMTzg7ZQ/SwEIQbDbJ1xcXGDbNoQ0M9KlFmiomHYU5HktmHfJtNZqWlrGtS6AKlJM2FZi1a32rGk005SwbcUEJZ8h64ppjrhrFalKLpji1A7LbM46CFBcSBifiEf5QATbumK321HTU2DTjIvdjK0waWzdNiBYpSGRVpN225gp7AKQjlRzImZaVUvdmFimQK6lrSEJlREXRU3wWhihBky7gwkDw6unnnzmRWGmlNBqwTq8OCgQtMiYNEUZaRBVtVGotcFA3QLpVqnj8ueHwWgRnGexwr/rTOC7A1Wkk3tVIwYj9XCBIAIxIISCi/2eUBmA/d1dc/Z7PgS5q/jPU1hEjEqgDrQPIh1Wq8yKL6UCUjDPEcc14vJqwyoFx+MR9+7f5eG/ZVTj7y+1w0luafg4ePPDot0ffXx8HHyczkOhRZjhraKI0KaMOQULakHJLqI9TBI4Ltdcs4mVyWoprcQmmrXWc4P6DBmK4Fa7YwFacbJ4RmE+zG2oIPwEWrOunLr88Wd6u/Yk98BZE5GfE5GvicivDddeEJFPi8jn7OcDuy4i8qdF5PMi8isi8rHhM5+w939ORD7xTt/bPgfGWkMjmJT1pJe+2bNwGOdU8HeTza8FLnKrIgTg1KTHaXjmWzVXeJo2Jd3BNGr3Y/OwsWFcmvTu1scTg9D6NAA/Rlks2O1m7HY7pJRaotXl5WXT0j32/s6dO2BiDTfwbrfD4c4B+4sd9hc7pEnIC4PSNCWHdmjKprb55t2Eizt77HaJoWqbIkjCkjeQuQYtwuUcyoBZKj5W0zS1UDuIFwGhdVKq88iQhsH74gJodAb7PXoBEM71umQcrxccrxes64p13XB1tWBZrgB0zWhZVhNw9HnUIlCN2DaLJw8JRRWw6J9aPQtYjGc+wEv21YomeHwdjVj0iFH7T8/6bb6HQUMd3+vP7K+PQnk8BEY6ie4klxZH38Jm7X25ZqT9DJkS5t3OIoK0wTT7/b59fpqmBgdxwUZi88p9ReXA+X1iz1pFj7q5e3EXV4+ukVXw/IPnmeVrPhOncPbv37atzb1Tc4yBCCfb5SlsmH6fnHO7dz9QT/NjRudx3jY7BMgqSw4ojvGyLC14weeByhTHg+uT0Mt4T1oCDK/kT0U4k8R8JtfkO7zrh4nvhXFv9cPmW8f0/zyAPwPgF4ZrnwTwN1T1T4rIJ+3v/xjAjwH4qP37PQD+LIDfIyIvAPjPAPwQKOJ+SUQ+paqvv/1Xu1qrbSKd5c9fxYB9nThC4BmktqlCh1ZC08I7SRcAklvZ+VtKq6zZtG7i8R3aEfFDJjbTyy2CJ07bdgi0C2i0yW5FQgeBb9oUxvdo+0kzkE6vXBV3E8PiqlZcPrqkAPHN5u8XhxSITa7r0gjhHEN1DZMHLbFKCKtUCcpAKxCQtwxIRdXYnG0Mb+P3bTlbzdqeyKMpQWNA3irmQEpmluOb2nxQQFit1OL4nTtJ7dAQQUxTM4yTl0YUQQij8KvQSRDnGTM6vLLb7amJhwBR5gQ4f4wPOMeOVh4FLcfCYSBq+0BICZsdwBjWFDcl59aLj/j69A3MeHWPhDmlGh5hBuewbz6JcBo5NIYMuyB0zLnWeiLoWz9gOLpRLijE+Ovp66JgD0N1K3Rcv1QEqxAlISCXHmRQqsF4htHnXFl9zKJ/ggQLn1bcv3cPD68ucXV1BOqGGAKee/4+RDyb+TRpb2TafFqWdAjJnp2+lc7YiTYm4UzC0llM+KtbSdystXIt1pOMYrT5WNcV8zSbQmTO9eylOgMQmeFbiwdUmwbfZJXNhPrf54iF9vm1PmkdqTyM00IIQ6o8SQF93t5R01fVvwngtbPLPwHg5+33nwfwB4frv6BsvwjgeRF5CcCPAPi0qr5mgv7TAH70nb7bvp8/QZNL7CfU+TD8Pwz/OnwjljDCF2gSQdA2Wdd4aB62jdYMB8uGk9gNLelaHHHQ4WRWgZw566R3x94Lc6jV1ie+hbHdAm0wAMGMHkrHG/KHL+DD4cIuM048iGBnWrbjoTGSr9y1w4uLA+Z5wv5wwG6/x+GCoXRuMagWi2BQIJAILUTihs5LRIslQmrFnCJSEOR1RTQsNE0TSu2l+EopCBBsxxURwWLZA4JM2DZaFD6XGgIUAaUA+90e7vwNtGk5ZyZQ6QvgWGllPL3z/qMGhDCjjg7g4HzwE0QZpRKjYL/fD/H3gpQCJKCNYS0WJifj4chiLroVi2whs2Y05k5fD6Sy6CUWATSHt2pXZM7hwBOMHD2XgetQhvdagl37ybXnsepOROZCM1i1raJoJQpTDBBVLNdHrOvaNGkeynNTaFJKmMzBTuUgNAtGIiG4otR4FRHrxsQ/DUBIoZEhhgCoFNy9ILQXUkTOildffQ2vfuN1uD42JgG6I3WaJvO9jJQsfSzdmtiZxTLWgxitrCAeoy/mH+sHIhAYVpprO7w7BIdmiazbilw2SATSFBjJ0yLkAlleU7eKSqUFqV4A2v715ziVff45wpt8exAK7xYW4s7tJ2CCJ9s3i+l/SFW/ar+/DOBD9vuHAXxxeN+X7NpbXX+iichPA/hpAJjmHa8BgNWu5VnpgxEb7KGQpimhwSDnZp90s+gM/3qqwxU2qHYwVy+3xL+44OHOMH3iu558OLTv4tvjYB1oN+O89yda3HCLSrzx7uGAe/fuGK5NSG9dlqahCai9xMjv6QIg4+pqa9DNPE+GqXsSUsTdexeo2bSf0Nk116NvNDUBCdQBxpns+WQY0zGiyQ+qUgrmlLCtGblUpCTNZE/TZKZ0x2Z9fFwrHmGj8f58zfh+xByTQ1jfWMzFtVQ33z1h6Jz9cDTNWeGJmmyDT05w9f7MLiA8nHLUNFUZ0x9CtLKQpzDE+Xo9Xwv+mo9ljHQw9kPhNAy0h9h6OK2FccK5bxih5E51kqo5qRuhu91MCuItM3KrFofgalOkvG/TNEHUMpxLRDboo+QVIQKTsYJms0Yu9glXVwVxH1A21om9vLrC88/dOcHxXXsXkZM1622xbNRpOi3G0uBDDM7/6rWP86DJe2x+h1uXZWG1KuCp6yLG2BP2EhCNybVW+q5qBUKsiElaRJICENVzEXUij06v9zlv4eXD+0SYB7Ju20m+0dPat+zIVVWVsbzTt36/nwXwswBwceeePvkArs8bro9qQtmdp11gj4KXG20I93Imzpa0xYxMFwQCT57otkOHWsxxYpu6UTagwze+gBxW8QHyvjpkwMQqNO3EuYXI6uoHGlp8bhDyyzz33HPc0HnDthF22UpBmhJ0Kw1uyaqN3rZAG4PkLiXo9XUT+CEEqyDJ79kyIS5GYTC4cl1WzPNkJQdpaVU3M01YwDZmMcdxO8g83NPM7Skl5Kp00No4llKtnm2BqqDminnawYviFIMQrM4F1Mr3pRQMX/bY7WGzl2xKgLT58gxNCmN0zLYqRDI8+crnBFZCUUD4IMbYuOdFLBegUBBNwcv0offFmRmFEUMFQEgTtDDxzJOSkjF7dvpfXzuGgxuGXh0OkOF5lNnHtVbUUhvclYFmRRkK0GCwaHvFAqEJhQCQGAjziEEU6pmkFZXB6li2jbQLAogw2kuL875Qg06TWbSqSMx7BYIwa9W+1Ctw5ZIx73fYHWaUNePx48fYtozj9Yb9fg/VrflJovtPKquaObtniIJpntqh4AfomOl97vdoSXtaSY8BT2CDaWNKXB/mo4KHV3a5wAxkmN9kInWIPSeKfYfyGs/e4XAfMu2fhHaaVDw9kGwNCDxhjwyi15fXOF4vTxwY5+2bFfqviMhLqvpVg2++Zte/DOB7hvd9t137MoB/6ez6//WO36JoYVTtaANGUL0dlDw0Bx/5manE93R8vG9o6Z+XHqvvUTi+v5oDEt4dvrHayTseNK7JizDxQ0/nD8CA0Q2N3/eUPgsx1hAj9rsZh4Mlhwgw73ctOsK1qSm6BaHNDFYodvs9PFOUmZQ7RGF4HKkYQnOc7vd7MD2FbIPzPONwOHCp29gy4dkAKOlhc7VWJg9ZCKWYa7dpvSaYIMHGGQAq5nkiY6dpQyVvCDunNXANq1Jo2OGcUkJM7NO2UiMsJTeWywDiso0m2DRnN6dVgbxtIJNvgEhplqB4dSg4a2bBumyYd7ExkgLU2FKjhnAFAwYfOkslF14IQFaDKCsLc/gyGNP3qXyYE7RWePEajm1fP2qL1/0DKUVs69ZhIvTs2BHWyM6eqUb1IdrcYGo5Bcu6ktdmo/a8rqalpwlbyUhq0VQBiBJRBBBhIpOqrZs0A4lU3GHucf7FsG3X3hHoc7i6vEYxKut1XfH664/w4EHC4SKeRDk5A26aEoKI8Qyd+jvGvIKnRf640/xRvT7Z503oG36oqliXBeuyoBY0amaVYuymPJCOx2vkvMP9e3fsMFSLAAyoOlqoI+lctyK9b09o+XBYZ8jQtUO/VsWyLDwk1437KZzKkPP2jpj+W7RPAfiE/f4JAH91uP5TwvbDAN40GOivAfi4iDwQRvp83K69Y1MzsxoiciYpG1TSVGkTxoMX/mk42WieNUenRV4A4YnPqG1Uf29QhZbaMl7ZM33i/m4qAmhVrrx/PVa4fwYnE9ujk6Ty3/2791oUhcMaV1dXOB6P1OYNsnEBMVYT8gXtOPsYKVJrxRwTa7mmCZoLphARJWCe56Z1et+naWrc8TFyBapGbKooZvJHO6wEPXqluskdhGGTEUhzwDQni9wwgV9YOKWvAce6A5x7ByCMs64rlmPnHEmG29ZqJRqrNuoCN/k9MimbOZyNcgDgYyp0SEo7DZP0zzdISTuE5SR7LkBGZYBQSLHi4eUkgslhBqDDGIAgbwqtATEOXP4SIIWZyWoCDWDEy7aR8piO+pX5A4Nj+Hg80oF/VonJNcmGkUdndcwsui5M/kFh9qqWgmVZWgRLy39oNOOkxL6+XlEyr3nUDesveFFzRys7VELNXrHf7aFV8I1XX8fxeoWqNt+EZ4Wz5npBjLzHmLPgc+dx/f5srvVzfmuLt3eF5nwfjy2XFcfjFS6vHuPqasHl4yMePbrC5eMrlKy4fHyJ1157zfayQpFZkCWRulzVDxYGhpw7lUd54BAwYeDob2gHBddhxtXVlVk3pKAP3yqfvoj8RVBL/4CIfAmMwvmTAP4nEfljAP4xgD9sb//fAPw4gM8DuALwR61zr4nIfwHgb9v7/nNVPXcOv9X3o8WhAk3Lblq4wSXVOKUpu+VESPWJE4hEqzjFv8fvGX8yaqS/I4TQT9CqqBqsiIY0M/vcidIti9OsO2rJY+Yc3xdMC/ASgK7NO9vfuhxRqsWsN9FUsa4LppQwRWaZlpyxLgu1JYsycKGUEuGHxo0SJxQtCElIcyDsx7IecWG1TmNKxD1VMUVWcCoo0MBIhWznbjUOoFIrtCiQN+SSERIL2zjExoOM1kAQwbZuyDFgKxVRAqYY8ebVI9y9d79BGaM5TrZZjg2d0w7HdBhiszJ9OVPIbzm3Ah+ignVZkSIx6hqUGqooy9Od8aEsV0eoOcN3E4XKNO1Qa276RlFloRZTlSUIEM3RIlynLXLELIMQBZ4D0nh8gAZjKBTJtNY6CAw1Ri53Dp5XpvIDJqXEbOu2ziouLlhKr1jfSzbrKRjNtvuWSiY5nGZgVUwhYHdxYPLatiKk2LiEFK7RdkuHlmNFFCX2D/fHKGpd7TM9kQ3oFb/c2X19POLi7h1cXl7i4aNrILDucKNxDsJx9wM3TQ2bD8Igi3Vb23rn2ASz6g2OhOLuvbtI84R12YDJM8MBhaCow7Fd8StaoLk0egpmKnrhHuC4Znzt1dfw4MHzVv+Y6z1GYV5IjdDKyLIYYUqH0nEd0Kx0wKpm+9wWtTwIbWDHtjp0V1lwBU8iHOdN3gn/eS/bxcU9/e3/3A8CTeifxi6LSmNF1NAXuwSvhhQATE2T9caBe5LHHDg92d1UfKpXvZl/HW3yNpphIoLTwim1TeL4zX1hAT1rlX2+f/8+Uoq4d+8CaY7IzJtHzRnbxrhjVGLsxDup9bXNLn5vN5ELLi72uL6+Rs6KZdlwcbhA1YwYAjniCzW3qhVpmtu4BQyCRxhd41p111AtRFOiOVQNz24Zj5aIEgWhkt++qBrzZULeMq6OGw4Xd08O3fPx9c2/bZvx77dTvkV5LMetzb1KT+TxQhMSCG9QUw1mvZjTsRRA6fhM8wwEwW5KplmzViwPltqEyTSNPqVOBYLBqhudsX3NabdOxItodx4fZ6Pk76fKymg9MmJIW4ADy11W04wHbvzSLRRCSOWkX1NiTeqsFZMVkZl3O0gMuLy8bCGLAJC3bJFp0uCdEAJKXdshP1o0HnPvyogfUOdWMmETQpRXl9eoFbi4s8P9+xeAVqQQUZTZxfw+rpPNchJSSqwP4Bi+Oj1EsXFwC1Xw8OEjfOUrX8GjyzfIw5MVXooT5rOg5TzMYTBfjwSj2TAL1p5fJODevXvNcvEqW7UGc/SWk5DUZv2fCYe2TpzTyaRhzhnXV0dcH69tLfa6FS+//PIvqeoP4SntfZCRS4Eork0Er04kzb3qzjSHNCRbKBkEGk4hFA7guNng9rw1Oy29AIb0tPl+D3+f/92TaTq+30/dQeEHEzPssECHlVyL6+AChewHP/hB7Pd7pHkHRYFaxA+FkkdqkWmx5IJyvSImaQ7YcBLb7Vil4M03H6OUim2lsLu8vIYkctCkCuyCa1QBq2ZW35pnLGVDyQVzTFZliSF7Y3gpo1kmVKMX06Jn5FIcP62FBFHaLR0YBONOyQCcMDs6tEIzndYJC3mXbnUBQAhY8gZ3FlYrM5dSJHOn9SUgoKhDJD1ZTCS38MayFUiMQHDTvJgFZkpI4jrxSl1t3XoiYS0nmsG5084jtjx0GDa/rpmGEFvZRCo82vDcHrONNjbTNJEx1eCgeYhkgduIFah5xbRPKJrhpQ+D+TGYn8HDWMuG/W5njl6GQS7L0p7DNVMGJlSrnZCbsBwFvsf9izkzvI/ruoHhlgGdmZXaeM4LUuIavbzcIEmwn3cIhUXmRSkQAYbFTvMMNTgJprBtlmTF8aPFX0s1KuWMu3dn/Jbv/S6s6wPkZcW2rrg+LsTpSyXjaCmYd36wAiFObTyDAvSzKyREUsJDgbqhbECaD9z3hbvCAw/c+vHQ7SCC0mBgk1W9IHeHim2u1221JE00a+udNP2bLfQbmsLFX9RMHHshBECCad3KOOqqynqv7hAx7dKFTjDY3nlGOsVCtxSqmeeRWfoNQhLzADuU8xRAB0wqKS0WuUUA2fEtNZlpa5q8nR2idDpKotmuVbDb3cP9e88jxIASMlSB6+MRdWUqu/rhFxgpMSUzlwe83zWdZdkgwhh+2CGQl0IqgmZ2Z0zTrlkKLmSSRKBUZOQe/qeKedph2wokBUQF1cdoh1hVlG1F3AWsZQMKEFWggVgxRJEFdFBWUOsPdHhtJWC/n0xbRZvHZV2IywZSPMzThAq34hhlAgWqWrnDMBmRmDDix+LY55kWIqOEIoJM0CqGWxOyCGGg34gVcQrIuSLGHfFjC9lVVWpXwEnmKTdeofCuxRK5tGW/sriMRT1Fr8NqYZYCeKEax2lVtRX5CBIwpQmKwjwEy+ocCdckRqhpBdRReianakWaElKaWcC+MGZ+s0NSVY0nycRSiljyhqSEeZynCY250qJnQkCcApbjAogg14rr62tcXFyQh0kVNVsBlcC9U404rqgAGpsPomZCXfv9Dtu2IoviwQeew/Xjh8jbiunuXdS8QlWAYlDWWpCmgBQDstUY2AY/ifuTPAxVgmfg8hCYpz0ECfO0QykZF/dY+lFrbbU4cskdqmvUy4parSC7Rc97gp4a3FjygmShzdQBGInmqqNDUlRSWAsC6v4hHXVMKm5O063uiK8WvC8DnPf0drOF/qB996jZUVMak1hskVo90n4P08YD4Bm0FHqEOZ7GxRGEWqsnJ4l46byu3fm99aSjruF3/LXfW9qT+Bw6zzfnkHZLUNPCCvDwzUs8fOMK05SA1JkDifo4VQLDBXdTxGG/M8y+IOfVQiAni9QIgJD1EhKgRZBmXgt2EjIKhrHVHQrq5mzeMqYUUXNBFeBqvSZ9cjGhkwR126CVGyyXFdMqEJ6yWHLGLs5QpdMpVjCJpVSkSG10yxUxTtZfd6rRcU7GTc5l9CLg9o8JNLYuwlBdabC4YqRvwnH+GGeD+Zx3h59xuKhbadyoAcEKpi+QwSxvqjaMD39YUyLdUnGIwZX8bILeoSaPbPJ1eFIKMAar4SQohZBcCFYP1ZSVYmGjMsBYiAEFCkmxabql9FDbUnigNogFQ/a0PZfYg0ig/yXGyGzv4b19Hyr2h32Ll5/nGcu60ucUrBxltf5ITzarWQ1u8utqHEuKlGZUbFjWKzz/4C6WbYNixTRFOGpbayX3VF6Q8wYyhZIuu9XGFs+CjrRE4FCSrZcYsDOLRkFiuAlA3tYGizkZYM4ZNVdTMnplMjWYa6R1EIOQso3dNEXzhYyCbsg7kWjhu7QovWBSXhUWxtUst82sTtj7ngZZn7ebLfStNXxb0fjpQ7CiJ02D7yecCM4cpQC58D0i+bTakWOmrlUqPLwK7Z7VC7KgwzzDXh9OZDfLYMXIe81K1WqbFKBVwM9WO1jYb7DOZRVmvoK88boEbMe+qalpmjYB4Pq44frqukEEbRP6sDjkpIBEsMITAlK0NG7EXgDDBNduv8NkTJXZwhg1k1wKwYwoqUBRbDUDk0BqRc4VV1dHiATsp4jdPAGRG90rMAkEupHBMaZkByrveXFxYCRNVAsbNUur5T8Zd745jEM0GMLgAvd95ZIxxcBsUtDpvG3VClkoYpwANS13K4bvR9S6IudqpRpBzh2Dj8ggGhCjNHIxtDUUWghvhZIcS9GEfamVpScb5NEpGIIzqwrXEsepfz47A2WwMNLaoZpaCmLqGcIjr46XMCyF3+1rVcx/oVpRpZAhNEUWlvfoG4PQttzDPaHoFCXqBGykZADQDlWx/al2eFwfj5juXFARUiENuGfIB3f0wyx1ZnN3kjYgRdKMvP7aFV767g9BK3B9RcK9ZHzyjx8/xjT7PjYeHKVP4GjRXTGEIbDBqbI9hLLTG0xpanOz27kFxTBUKnMRMnmCGaPOSs7Nkg6RVa2ccXaKCVOglZ+5oA3jN/lh+5g5EYYjiMsIbYJdDRVwhQcAqq2ZfxJoB3ifCH0ubo/57oK2VnX5iZMwK3k64VAvkn56IPQwLTsl1XH3zmPRrYcRj5XWp+7c7R59ZUCRCWdpE+hT3HpAAN8wO5td4d2JC2J4v2PHOJ1gBRCCwQf9AAphDBvs2CrHkZpjzqaF2kIsNUMlYLteEGQFasdl3YqphiXHaMVRgqCsFWX1TUGBtKy1ZaxO04S1VnsmO9BKRqkLQkxIaBG6tAAADLxJREFUaTIM3e8BaKFGq9Y3ETEnW8DxuEAkIaoiTgkwh2pKEZICCioO8w55y41LfrebDN9lNEWw6lWhUONfjitiSNjvd1hXaqui0eL96UdJJp87jTKaj6DWyipZMaAujFAhq6KFinbVFCrFHOEToTEr55dLJj+RKkODlfVxRWhtxRjauocdAq4VjlEqHkWTl4xpnga/AwAxCMfXmFBjNbeKlQnkmM/T1PjjVVn7OMaE/WE2KLAzYSJEmqumnEQh1JdiQkFBmARBK6b9DBUe3jXURnxrXpWGZvR6yoo7hwsEjXjt62/gxRcf4M7dPa63zfI6FPvD1CCslCaGEtvnZ8P5mwYOxXFdcDgcMImPnx+KTvWdB2u3olQgRloCMYBQWwhISFYQJph1LbSgVTDbgeFWEcD1UwUoIpBqLJ1wTn9Gj/nBC1N2atlsfsxacQvMZA1AKnNg3KtPb+8ToQ+4oKymEjbhbJii+ioRtCxXN03dTNeW/XYKFY3OVg8Zc+34HP4BPIGiNEhJ0c3bZm3AnFQNiEMThG8VMeWCjm/3DvVncgfxeXje8LYGN52Hg573v31nDSb4Sj9wAARVBNuCvmBPx8EEZlVsdaXDNhjrkFsVUkz7LdDyJGd8BRo3f9WMLVPjXZbOeV5NSKm4RVAJFwEA6PtY8oqY5SQaRCszLNd1hScoeY1Xh048wUkE2O1pyXkym4d8qvbi52oWhtjnqvs3Is2eCkZUZ8ssFqOu8IgK4rnE9z1KQ62PF4epaW4+t9SK7UlbaUaukTxo3y68nQvf4QXnzXHfr69B3tvpiE8pH/w513XDLu5QSkGRCkTCYwrOR95WXF+XE60ZoCYtA999zswAzwVYF8XFBX01eVmseLwwCs+s0zRNUAtBTCm1uHwmIW3Y7Sc8fLTgS195Bf/MS9/FpDOru3vclhZR5DkBXr6zO/8D1m2FBDQWUT8gfcxGUrZxvfrY+Hv9eowREdFCZ4NvIRSQEsQVhub4Fs8KVzQtvmh7T4D7EhWCaGs0IYTc4CIf32goSIwChgozW/+L+NIT+97bjRf6o/bRoA0XrGE8COyUttNS4IfCiP+foP3wSB4vKUfzSttPexdG+QvfjKFj9yca+/iH9FtguGfPOZC2id0H4O/xmPO+UMRNCtu8/hUdR+RXdFOQiFcX8B4HDZC3JBtbIseyYWf826ynJix0GJuhKYg3R1C7dT4RN3q4cLtw9agoNfNVYSa+SmMGZdo7E5nanIo0HBo+VlCIFKSJhTzcASuRzzGJwNI3+I1mMU1zanNK/nRqd6WqOVSH6bfltqzkNBILaQ3SnfklE+6CAhGK/TwjV4Z0pimhtvBX3jQbUyjzHxakyKACFV9b1NhYtc0gHCVoZHxzNmcMXmC8v7b6BSn2BCWfn7F5vkQp2dZcD99Ue32aZztYArZ1QwnM8QhGrZC8LGPVRlSWc2ZiXBCLQyfEyUidhHVZLTz4AKggSmzrOHgBHPCAy7kXQWmFSiQg5w37/YxSgVde/gYUBS8+eA7JMrA9VNcFaLE+TdOEvGWEmDAnweX1JZxviKnIvu+Y+zHSc5dK3q+WXVursWqikdIBwDQNVkFh6cdsB0kMqTmSYbNKUj7jMgp8ZtZ87kpkVYMJRTAWnJdmtfFgEXPgXty5+44smzdb6BvBmQLd/AROhLBv/mDRDgBa6JOibzRmQFriB1gt20nXPPJDWiYbv3N0wgCGvvjiaHQJXfh761CPHwrS7hntWQZE1har8biomBffP+M7nN9T7TSnFUONTZs2IO0gbDq/XxfpbJ9BLJpHDFYy8Ei6Zql+iLTbeT9c6zFYJKAduAFC0x4wbVj64XNyXHiSyVgRio6zUguFWAEkEdsWC6lW48MJdoA73YEg4Pp6RUzM7C2FzrqYBHnxTMsK1V4pilFNjK6JFq6pUlGVYaSKgQ1zCpikZwRz2xrcZs47t3BEYMW4TbMLlQWNDFKoRTHFCRVA3qptZh7wpapprhHzNNHhbIU4ogjUcHaWOPT1LY2y2Ee52LoOQZAtVJW0BdKyj52PyYUpYJE0iWZBrdoipaoyfwJakYsL84Cgvvb5fSklFtKZSFftVnMU4RKZEpblCMyWnW2QpogwEqZWxrCbJTRq1hAmj+VMGCaK4rCfAIfezF3nmvpIHliLU2sItvUa0xxx2O+xrQXbUiGhWjSfWPSWlwYlNJONjRcGv47+jt0u4vLysgUZlJLRCpajYhd9bBXVggSYLydNRoRglNbGVtotPgVghd1NhoSAltk/UrloEKRpxjzvBrn29Hajk7NE5BGAz77X/XgX7QMAXn2vO/Eu2vupv++nvgLvr/6+n/oK3Pb3n6R9r6p+8Gkv3GxNH/isvkVW2U1sIvJ3bvv77Wnvp74C76/+vp/6Ctz291ttbw/+3Lbbdttu2237/1W7Ffq37bbdttv2HdRuutD/2fe6A++y3fb329feT30F3l/9fT/1Fbjt77fUbrQj97bdttt2227bP9120zX923bbbtttu23/FNuNFfoi8qMi8lkR+byIfPIG9Od7ROT/FJF/ICJ/X0T+fbv+J0TkyyLyGfv348Nn/hPr/2dF5Efegz7/poj8qvXr79i1F0Tk0yLyOfv5wK6LiPxp6++viMjHnnFff8cwhp8RkYci8sdvyviKyM+JyNdE5NeGa+96LEXkE/b+z4nIJ55xf/8rEfmH1qe/IiLP2/XvE5HrYYz/3PCZf9HW0OftmeRp3/dt6Ou7nvdnJTPeor9/eejrb4rIZ+z6ezq2T22e7n2T/oE5TL8B4PsBzAD+HoAfeI/79BKAj9nv9wD8IwA/AOBPAPgPn/L+H7B+7wB8xJ4nPuM+/yaAD5xd+y8BfNJ+/ySAP2W//ziA/x1MGflhAH/rPZ7/lwF8700ZXwC/H8DHAPzaNzuWAF4A8AX7+cB+f/AM+/txAMl+/1NDf79vfN/Zff4fewaxZ/qxZ9TXdzXvz1JmPK2/Z6//1wD+05swtk/7d1M1/d8N4POq+gVVXQH8JQA/8V52SFW/qqq/bL8/AvDrAD78Nh/5CQB/SVUXVf1/wRKSv/vb39N3bD8B4Oft958H8AeH67+gbL8I4Hlh0fv3ov3LAH5DVf/x27znmY6vqv5NAOclPt/tWP4IgE+r6muq+jqATwP40WfVX1X966rq/AK/COC73+4e1uf7qvqLSin1C+jP+G3t69u0t5r3ZyYz3q6/pq3/YQB/8e3u8azG9mntpgr9DwP44vD3l/D2AvaZNhH5PgA/COBv2aV/z0zmn3MTHzfjGRTAXxeRXxKRn7ZrH1IWqweoTX/Ifr8J/fX2kzjdNDd1fN/tWN6EPnv7t0Ht0ttHROTvisj/LSK/z659GDhh7nrW/X03835Txvb3AXhFVT83XLtRY3tThf6NbSJyF8D/AuCPq+pDAH8WwG8F8C8A+Cpo2t2U9ntV9WMAfgzAvysiv3980TSMGxW+JSIzgD8A4H+2Szd5fFu7iWP5Vk1EfgZABvAX7NJXAfwWVf1BAP8BgP9RRO6/V/2z9r6Y96e0fxOnCsuNG9ubKvS/DOB7hr+/2669p01EJlDg/wVV/V8BQFVfUdWi5Kj979Ahhvf8GVT1y/bzawD+ivXtFYdt7OfX7O3veX+t/RiAX1bVV4CbPb5492P5nvdZRP4IgH8VwL9lBxUMKvmG/f5LIDb+261vIwT0zPr7Tcz7TRjbBODfAPCX/dpNHNubKvT/NoCPishHTPP7SQCfei87ZFjdfw/g11X1vxmuj7j3vw7APfqfAvCTIrITkY8A+CjouHlW/b0jIvf8d9CJ92vWL48a+QSAvzr096cs8uSHAbw5QBfPsp1oSjd1fIc+vJux/GsAPi4iDwyu+LhdeyZNRH4UwH8E4A+o6tVw/YNiVdxF5PvBsfyC9fmhiPywrf+fGp7x293XdzvvN0Fm/CsA/qGqNtjmJo7tt91T/M3+AyMg/hF4Mv7MDejP7wXN918B8Bn79+MA/gcAv2rXPwXgpeEzP2P9/yyekWd++O7vByMY/h6Av+9jCOBFAH8DwOcA/B8AXrDrAuC/tf7+KoAfeg/G+A6AbwB4brh2I8YXPIi+CmAD8dc/9s2MJYilf97+/dFn3N/Pg7i3r98/Z+/9Q7ZGPgPglwH8a8N9fggUuL8B4M/AEjqfQV/f9bw/K5nxtP7a9T8P4N85e+97OrZP+3ebkXvbbtttu23fQe2mwju37bbdttt2274N7Vbo37bbdttu23dQuxX6t+223bbb9h3UboX+bbttt+22fQe1W6F/227bbbtt30HtVujfttt2227bd1C7Ffq37bbdttv2HdRuhf5tu2237bZ9B7X/D2FrKp/qPzqcAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "%pylab inline\n", - "\n", - "# image before projection\n", - "ori_image = seq002.camera[camera_name][seq_idx]\n", - "plt.imshow(ori_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.Show projected points on image colorized by distances." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:41.286291Z", - "start_time": "2020-05-14T14:57:40.339882Z" - }, - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.cm as cm\n", - "import numpy as np\n", - "\n", - "# image after projection\n", - "plt.imshow(ori_image)\n", - "distances = np.sqrt(np.sum(np.square(camera_points_3d), axis=-1))\n", - "colors = cm.jet(distances / np.max(distances))\n", - "plt.gca().scatter(projected_points2d[:, 0], projected_points2d[:, 1], color=colors, s=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5.Show projected points on image colorized by semantic segmentation." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-05-14T14:57:42.753978Z", - "start_time": "2020-05-14T14:57:41.803767Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.cm as cm\n", - "import numpy as np\n", - "import random\n", - "\n", - "# image after projection\n", - "plt.imshow(ori_image)\n", - "\n", - "# load semseg\n", - "semseg = seq002.semseg[seq_idx].to_numpy()\n", - "\n", - "# get semseg on image by filting outside points\n", - "semseg_on_image = semseg[inner_indices].flatten()\n", - "\n", - "# random gnerate colors for semseg\n", - "max_seg_id = np.max(semseg_on_image)\n", - "color_maps = [(random.random(), random.random(), random.random()) for _ in range(max_seg_id + 1)]\n", - "colors = np.array([color_maps[seg_id] for seg_id in semseg_on_image])\n", - "\n", - "plt.gca().scatter(projected_points2d[:, 0], projected_points2d[:, 1], color=colors, s=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "file_extension": ".py", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - }, - "mimetype": "text/x-python", - "name": "python", - "npconvert_exporter": "python", - "pygments_lexer": "ipython3", - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "version": 3, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/raw_depth_projection/pandar64_channel_distribution.csv b/tutorials/raw_depth_projection/pandar64_channel_distribution.csv deleted file mode 100644 index 2ce24ea..0000000 --- a/tutorials/raw_depth_projection/pandar64_channel_distribution.csv +++ /dev/null @@ -1,65 +0,0 @@ -channel,horizontal_angle_offset,vertical_angle -1,-1.042,14.87 -2,-1.042,11.02 -3,-1.042,8.047 -4,-1.042,5.045 -5,-1.042,3.028 -6,-1.042,2.016 -7,1.042,1.848 -8,3.125,1.676 -9,5.208,1.51 -10,-5.208,1.339 -11,-3.125,1.172 -12,-1.042,1.001 -13,1.042,0.834 -14,3.125,0.663 -15,5.208,0.496 -16,-5.208,0.325 -17,-3.125,0.157 -18,-1.042,-0.012 -19,1.042,-0.181 -20,3.125,-0.349 -21,5.208,-0.52 -22,-5.208,-0.687 -23,-3.125,-0.857 -24,-1.042,-1.025 -25,1.042,-1.196 -26,3.125,-1.363 -27,5.208,-1.534 -28,-5.208,-1.7 -29,-3.125,-1.872 -30,-1.042,-2.04 -31,1.042,-2.21 -32,3.125,-2.377 -33,5.208,-2.548 -34,-5.208,-2.712 -35,-3.125,-2.885 -36,-1.042,-3.052 -37,1.042,-3.222 -38,3.125,-3.387 -39,5.208,-3.56 -40,-5.208,-3.724 -41,-3.125,-3.896 -42,-1.042,-4.062 -43,1.042,-4.233 -44,3.125,-4.397 -45,5.208,-4.57 -46,-5.208,-4.732 -47,-3.125,-4.904 -48,-1.042,-5.069 -49,1.042,-5.241 -50,3.125,-5.403 -51,5.208,-5.577 -52,-5.208,-5.738 -53,-3.125,-5.91 -54,-1.042,-6.073 -55,-1.042,-7.075 -56,-1.042,-8.071 -57,-1.042,-9.072 -58,-1.042,-9.897 -59,-1.042,-11.044 -60,-1.042,-12.018 -61,-1.042,-12.986 -62,-1.042,-13.942 -63,-1.042,-18.901 -64,-1.042,-24.909 diff --git a/tutorials/raw_depth_projection/raw_depth_projection.ipynb b/tutorials/raw_depth_projection/raw_depth_projection.ipynb deleted file mode 100644 index 47ce1f8..0000000 --- a/tutorials/raw_depth_projection/raw_depth_projection.ipynb +++ /dev/null @@ -1,225 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Raw Depth Projection Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### This tutorial shows how to do a cylindrical depth projection of the 3D LiDAR point clouds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1. Download the raw point cloud data\n", - "You can find the link to download the raw data for the LiDAR point clouds [here](https://github.com/scaleapi/pandaset-devkit/issues/67#issuecomment-674403708).\n", - "In the following we define `/data/pandaset` as the location of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2. Import required python modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import csv\n", - "import gzip\n", - "import pickle\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3. Load the raw sensor data\n", - "The description of the raw sensor data is provided in [Data.Instructions.pdf](https://github.com/scaleapi/pandaset-devkit/files/5078794/PandaSet.Raw.Data.Instructions.pdf).\n", - "The data directly provides the laser and column id for each measured point." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " laser_id column_id elevation azimuth_col azimuth_col_corrected \\\n", - "0 8 37793 1.510 354.850006 360.058014 \n", - "1 14 37793 0.496 354.850006 360.058014 \n", - "2 20 37793 -0.520 354.850006 360.058014 \n", - "3 26 37793 -1.534 354.850006 360.058014 \n", - "4 32 37793 -2.548 354.850006 360.058014 \n", - "... ... ... ... ... ... \n", - "106881 13 39644 0.663 5.050000 8.175000 \n", - "106882 18 39644 -0.181 5.050000 6.092000 \n", - "106883 39 39644 -3.724 5.050000 -0.158000 \n", - "106884 45 39644 -4.732 5.050000 -0.158000 \n", - "106885 51 39644 -5.738 5.050000 -0.158000 \n", - "\n", - " distance intensity \n", - "0 84.695999 39 \n", - "1 103.564003 9 \n", - "2 61.416000 56 \n", - "3 45.952000 3 \n", - "4 14.436000 0 \n", - "... ... ... \n", - "106881 174.084000 10 \n", - "106882 50.796001 156 \n", - "106883 23.176001 2 \n", - "106884 19.440001 1 \n", - "106885 16.832001 2 \n", - "\n", - "[106886 rows x 7 columns]\n" - ] - } - ], - "source": [ - "file = \"/data/pandaset/001/lidar/00.pkl.gz\"\n", - "with gzip.open(file, \"rb\") as fin:\n", - " data = pickle.load(fin)\n", - "\n", - "num_rows = 64 # the number of lasers\n", - "num_columns = int(360 / 0.2) # horizontal field of view / horizontal angular resolution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3. Load the horizontal angle offets\n", - "The column ids of the raw data are those collected at the same motor rotation angle. This is not the same horizontal angle, due to horizontal angle offsets in the mounting position of the lasers.\n", - "With the data provided in the `csv` file, taken from the Pandar64 User Manual, we can correct the column ids to sort the points accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"pandar64_channel_distribution.csv\", \"r\") as fin:\n", - " reader = csv.DictReader(fin)\n", - " horizontal_angle_offset = np.array([float(r[\"horizontal_angle_offset\"]) for r in reader])\n", - "\n", - "column_shift = (num_columns / 360 * horizontal_angle_offset).astype(np.int64)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4. Retrieve the laser and column ids\n", - "The column ids are given in absolute values, therefore we have to substract the smallest value to obtain the indices relative to the current frame. Due to the angle offets, the span from minimum to maximum value is exactly 1852 per frame instead of 1800. This is since the maximum offset in both positive and negative is 26 `>> 2 * 26 = 52`. With the offsets we can compute the correct column ids." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "rows_list = data[\"laser_id\"].values\n", - "\n", - "cols_list = data[\"column_id\"].values\n", - "cols_list -= np.min(cols_list)\n", - "cols_list = np.mod(cols_list + column_shift[rows_list] + num_columns, num_columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5. Create the depth projection\n", - "First, we initialize an empty image. All locations without measurements will have the value `-1`. Then we scatter the point cloud into the image." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(64, 1800)\n", - "[[ -1. -1. -1. ... -1. 16.24 16.304]\n", - " [ -1. -1. -1. ... 42.98 43.068 43.112]\n", - " [101.408 101.288 101.24 ... 101.504 101.456 101.4 ]\n", - " ...\n", - " [ 8.08 8.076 8.064 ... 8.096 8.084 8.072]\n", - " [ -1. -1. -1. ... -1. -1. -1. ]\n", - " [ 1.008 0.964 0.956 ... -1. 1.032 0.908]]\n" - ] - } - ], - "source": [ - "depth_img = np.full((num_rows, num_columns), fill_value=-1, dtype=np.float32)\n", - "depth_img[rows_list, cols_list] = data[\"distance\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 4))\n", - "plt.imshow(depth_img, vmin=0.5, vmax=80)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/requirements.txt b/tutorials/requirements.txt deleted file mode 100644 index cd4e403..0000000 --- a/tutorials/requirements.txt +++ /dev/null @@ -1,64 +0,0 @@ -appnope==0.1.0 -attrs==19.3.0 -backcall==0.1.0 -bleach==3.1.4 -certifi==2019.11.28 -chardet==3.0.4 -cycler==0.10.0 -decorator==4.4.2 -defusedxml==0.6.0 -entrypoints==0.3 -gmplot==1.2.0 -idna==2.9 -importlib-metadata==1.5.2 -ipykernel==5.2.0 -ipython==7.13.0 -ipython-genutils==0.2.0 -ipywidgets==7.5.1 -jedi==0.16.0 -Jinja2==2.11.1 -jsonschema==3.2.0 -jupyter==1.0.0 -jupyter-client==6.1.2 -jupyter-console==6.1.0 -jupyter-core==4.6.3 -kiwisolver==1.2.0 -MarkupSafe==1.1.1 -matplotlib==3.2.1 -mistune==0.8.4 -nbconvert==5.6.1 -nbformat==5.0.4 -notebook==6.0.3 -numpy==1.18.2 -open3d==0.9.0.0 -pandas==1.0.3 -pandaset==0.2.dev0 -pandocfilters==1.4.2 -parso==0.6.2 -pexpect==4.8.0 -pickleshare==0.7.5 -Pillow==7.0.0 -prometheus-client==0.7.1 -prompt-toolkit==3.0.5 -ptyprocess==0.6.0 -Pygments==2.6.1 -pyparsing==2.4.7 -pyrsistent==0.16.0 -python-dateutil==2.8.1 -pytz==2019.3 -pyzmq==19.0.0 -qtconsole==4.7.2 -QtPy==1.9.0 -requests==2.23.0 -Send2Trash==1.5.0 -six==1.14.0 -terminado==0.8.3 -testpath==0.4.4 -tornado==6.0.4 -traitlets==4.3.3 -transforms3d==0.3.1 -urllib3==1.25.8 -wcwidth==0.1.9 -webencodings==0.5.1 -widgetsnbextension==3.5.1 -zipp==3.1.0