diff --git a/.gitignore b/.gitignore index dec9aca..ae76840 100644 --- a/.gitignore +++ b/.gitignore @@ -59,3 +59,4 @@ notebooks/SnowEx_ASO_MODIS_Snow/download *.shp *.shx +notebooks/icesat2_webinar_demo/data diff --git a/notebooks/icesat2_webinar_demo/README.md b/notebooks/icesat2_webinar_demo/README.md new file mode 100644 index 0000000..cf0c9aa --- /dev/null +++ b/notebooks/icesat2_webinar_demo/README.md @@ -0,0 +1,43 @@ +# Laser Altimetry Applications for a Changing World: Working with ICESat-2 Sea Ice Data + +## Overview +This directory contains a notebook demonstrating how to work with ICESat-2 Sea Ice Data using `earthaccess`, `xarray` and `matplotlib`. + +The demo was presented at the NASA Earthdata Webinar held on Wednesday, 6 August, 2025. + +## Learning Outcomes + +1. How to search for ICESat-2 data sets (collections) using `earthaccess`. +2. How to search for data files using a time range and spatial extent. +3. How to open an ATL07 and ATL10 using `xarray` +4. How to make a simple plot of ATL07 data using `matplotlib` + +## Setup + +You will need at least version 2024.10.1 of `xarray` and version 0.14.0 of `earthaccess` for this tutorial. We recommend creating a virtual environment using the `environment.yml` file in the environment folder using `mamba` or `conda`. + +``` +mamba env update -f environment/environment.yml +``` +or +``` +conda env update -f environment/environment.yml +``` + +This will create an virtual environment called `nsidc-tutorial-icesat2-apps`. + +To activate the environment. + +``` +mamba activate nsidc-tutorial-icesat2-apps +``` +or +``` +conda activate nsidc-tutorial-icesat2-apps +``` + +You can now launch Jupyter Lab and navigate to `working_with_icesat2_sea_ice_data.ipynb`. + +## Bugs + + \ No newline at end of file diff --git a/notebooks/icesat2_webinar_demo/environment/environment.yml b/notebooks/icesat2_webinar_demo/environment/environment.yml new file mode 100644 index 0000000..6938c54 --- /dev/null +++ b/notebooks/icesat2_webinar_demo/environment/environment.yml @@ -0,0 +1,33 @@ +name: nsidc-tutorial-icesat2-apps +channels: +- conda-forge +dependencies: +- python=3.12 + +- jupyterlab +#- jupyter_contrib_nbextensions + +- earthaccess + +- xarray +- rioxarray +- dask +- bottleneck +- h5py +- netcdf4 +- h5netcdf +- libgdal-hdf4 +- python-dateutil + +- geopandas + +- matplotlib +- cartopy + +# For development +- nbdime + +platforms: +- linux-64 +- osx-64 +- win-64 diff --git a/notebooks/icesat2_webinar_demo/working_with_icesat2_sea_ice_data.ipynb b/notebooks/icesat2_webinar_demo/working_with_icesat2_sea_ice_data.ipynb new file mode 100644 index 0000000..bc9f977 --- /dev/null +++ b/notebooks/icesat2_webinar_demo/working_with_icesat2_sea_ice_data.ipynb @@ -0,0 +1,14087 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b693cb9e-7692-4d65-9688-8ec7b1636e3f", + "metadata": {}, + "source": [ + "# Working with ICESat-2 Sea Ice Products\n", + "\n", + "## Overview\n", + "\n", + "In this notebook, we demonstrate searching for and accessing ICESat-2 data using the Python `earthaccess` package, and reading and visualizing the data using `xarray` and `pandas`. We also use `matplotlib` and `cartopy` to produce a map of search results. \n", + "\n", + "`earthaccess` is a community developed open source Python package to streamline programmatic search and access for NASA data archives. Users can find data sets and data granules, and either download or \"stream\" NASA data in as little as three \"lines of code\", regardless of whether users are working in the cloud or on a local machine. The `earthaccess` package handles authentication for NASA Earthdata Login and the AWS hosted NASA Earthdata cloud. All you need is an Earthdata Login.\n", + "\n", + "`xarray` has become the go to Python package for Earth Data Science. With v2024.10.0, `xarray` can be used to read and work with data stored hiearchical file structures like the HDF5 file format used for ICESat-2, using the [`DataTree`](https://xarray.dev/blog/datatree) structure. We use [`xarray.DataTree`](https://docs.xarray.dev/en/stable/generated/xarray.DataTree.html#xarray.DataTree) to read and explore ICESat-2 files.\n", + "\n", + "Although `xarray` could be used to work with the ICESat-2 data, the nested-group structure can be a little cumbersome. So we create a `pandas.DataFrame` object for a subset of data to make plotting easier.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "cebf284d-e1ca-4602-8bf1-d55718675710", + "metadata": {}, + "source": [ + "## Import libraries\n", + "\n", + "As with all Python, we import the libraries we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6c2dc3a3-b0a9-4fcb-a1ba-d75a9dc2c8d0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/apbarret/mambaforge/envs/nsidc-tutorials-dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "earthaccess: 0.14.0\n", + "xarray: 2025.7.1\n", + "cartopy: 0.24.0\n" + ] + } + ], + "source": [ + "# Search for data\n", + "import earthaccess\n", + "\n", + "# Read and work with data\n", + "import xarray as xr\n", + "import pandas as pd\n", + "\n", + "# To plot results\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import ListedColormap, BoundaryNorm\n", + "from matplotlib.lines import Line2D\n", + "\n", + "# To plot map of results\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "# Check package versions: you may want to update if you have older versions\n", + "# See README.md\n", + "print(f\"earthaccess: {earthaccess.__version__}\")\n", + "print(f\"xarray: {xr.__version__}\")\n", + "print(f\"cartopy: {cartopy.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "id": "79c94fa1-0b8f-476d-96eb-e5a70cd0ac81", + "metadata": {}, + "source": [ + "## Authenticate\n", + "\n", + "Although you do not need an Earthdata login to search for NASA data, you do need one to access that data. It is better just to login at the start of a workflow so you don't forget.\n", + "\n", + "You will need an Earthdata login. If you don't have one, you can register for one, for free, [here](https://urs.earthdata.nasa.gov/users/new).\n", + "\n", + "`earthaccess` will prompt for your Earthdata login username and password. You can also set up a `.netrc` file or environment variables. `earthaccess` will search for these alternatives before prompting for a username and login. See the `earthaccess` [documentation](https://earthaccess.readthedocs.io/en/latest/user_guide/authenticate/) to lean how to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "293c6a1e-b7a5-4c60-beab-e34de3ca7399", + "metadata": {}, + "outputs": [], + "source": [ + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "bbf8a2fe-cf35-4d9d-87d7-655a2121e7dd", + "metadata": {}, + "source": [ + "## Search for ICESat-2 Related Datasets\n", + "\n", + "Before we search for data, we want to know what ICESat-2 datasets and what versions of these datasets are available. We will also need to know the `short-name` or `concept-id` of the ICESat-2 dataset we want to use.\n", + "\n", + "The `short-name` can be found on the dataset landing pages for products or we can search for it.\n", + "\n", + "To search for datasets (or Collections as NASA calls them), we use the `search_datasets` method. This allows searches by keywords, platform, time range, spatial extent, version, and whether data are hosted in the cloud or still archived at a NASA DAAC.\n", + "\n", + "Here, we will do a simple search using `platform` for ICESat-2 data. The `platform` and `keyword` searches are not case sensitive. We'll add `downloadable=True` and `cloud_hosted=True` to further refine the search." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "63022c99-7ff9-41ef-8ac0-774f9481fc10", + "metadata": {}, + "outputs": [], + "source": [ + "results = earthaccess.search_datasets(\n", + " platform=\"icesat-2\",\n", + " downloadable=True,\n", + " cloud_hosted=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cecd5fd1-ba0f-4c05-a821-f8e4a07132e0", + "metadata": {}, + "source": [ + "`search_datasets` returns a Python List of data collections. We can find how many datasets were found by getting the length of that list using `len`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4ef29d21-ad9e-4dfd-8a16-c5055c002f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(results)" + ] + }, + { + "cell_type": "markdown", + "id": "70a2bd3a-4de3-478d-8f93-3cdb526ec528", + "metadata": {}, + "source": [ + "There are 47 datasets. Because `results` is a list, we can access any element of that list by giving an index. Here, we'll access the first element (`0`). Just change the index to see a different dataset. \n", + "\n", + "Each data collection has a `summary` method that returns a Python dictionary containing `short-name`, `concept-id`, and `version`, along with information about the file type and links to get the data. The file links are used by earthaccess, so we don't need to worry about these too much." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ecac52e9-9753-46bb-b748-d49750d2548b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'short-name': 'ATL03',\n", + " 'concept-id': 'C2596864127-NSIDC_CPRD',\n", + " 'version': '006',\n", + " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF5', 'FormatDescription': 'HTTPS'}]\",\n", + " 'get-data': ['https://search.earthdata.nasa.gov/search/granules?p=C2596864127-NSIDC_CPRD',\n", + " 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C2596864127-NSIDC_CPRD',\n", + " 'https://nsidc.org/data/data-access-tool/ATL03/versions/6/'],\n", + " 'cloud-info': {'Region': 'us-west-2',\n", + " 'S3BucketAndObjectPrefixNames': ['nsidc-cumulus-prod-protected/ATLAS/ATL03/006',\n", + " 'nsidc-cumulus-prod-public/ATLAS/ATL03/006'],\n", + " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials',\n", + " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME'}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[0].summary()" + ] + }, + { + "cell_type": "markdown", + "id": "34ab719f-024e-46ed-9045-afa1aa7d5d25", + "metadata": {}, + "source": [ + "We also want to be able to see all the other datasets available. Because there are a lot of datasets, we'll just get the `short-name` and `version`.\n", + "\n", + "We'll use a Python _list comprehension_, which is like a for-loop, to extract the information we want. We use the `sorted` function to sort the list into alphabetical order using the `short-name` (the first element of the _tuple_) as a key." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19b2366c-a0e6-4dec-aa4b-3024501d9f4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ATL02', '007'),\n", + " ('ATL02', '006'),\n", + " ('ATL03', '006'),\n", + " ('ATL03', '007'),\n", + " ('ATL04', '007'),\n", + " ('ATL04', '006'),\n", + " ('ATL06', '006'),\n", + " ('ATL06', '007'),\n", + " ('ATL07', '006'),\n", + " ('ATL07', '007'),\n", + " ('ATL07QL', '007'),\n", + " ('ATL08', '006'),\n", + " ('ATL08', '007'),\n", + " ('ATL08QL', '007'),\n", + " ('ATL08QL', '006'),\n", + " ('ATL09', '006'),\n", + " ('ATL09', '007'),\n", + " ('ATL09QL', '007'),\n", + " ('ATL10', '006'),\n", + " ('ATL10', '007'),\n", + " ('ATL10QL', '007'),\n", + " ('ATL11', '006'),\n", + " ('ATL12', '006'),\n", + " ('ATL12', '007'),\n", + " ('ATL13', '006'),\n", + " ('ATL13', '007'),\n", + " ('ATL13QL', '007'),\n", + " ('ATL14', '004'),\n", + " ('ATL15', '004'),\n", + " ('ATL16', '005'),\n", + " ('ATL17', '005'),\n", + " ('ATL19', '003'),\n", + " ('ATL20', '004'),\n", + " ('ATL21', '003'),\n", + " ('ATL22', '003'),\n", + " ('ATL23', '001'),\n", + " ('ATL24', '001'),\n", + " ('Boreal_AGB_Density_ICESat2_2186', '1'),\n", + " ('CMS_Global_Forest_AGC_2180', '1'),\n", + " ('GEDI_ICESAT2_Global_Veg_Height_2294', '1'),\n", + " ('IS2ATBABD', '1'),\n", + " ('IS2CHM', '1'),\n", + " ('IS2GZANT', '1'),\n", + " ('IS2MPDDA', '3'),\n", + " ('IS2SITDAT4', '001'),\n", + " ('IS2SITMOGR4', '3'),\n", + " ('NSIDC-0782', '1')]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(\n", + " [(r.summary()[\"short-name\"], r.summary()[\"version\"]) for r in results], \n", + " key=lambda x: x[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ebe4a069-5ca2-45ab-bd8b-bc5140fc6ed9", + "metadata": {}, + "source": [ + "The datasets with `short-names` that start with ATL are the standard ICESat-2 products. Some of these `short-names` have `QL` at the end. These are quick-look products. Also, most products have two versions. This is because the two most recent versions are archived." + ] + }, + { + "cell_type": "markdown", + "id": "b1452be2-1a0c-4b23-a35a-654817d75875", + "metadata": {}, + "source": [ + "## Search for ATL07 data granules\n", + "\n", + "Now that we know the product short_name, we can search for data. Here, I am interested in granules that were collected during the validation campaign. I know there was an underflight of ICESat-2 over sea ice on 26 July 2022, so we will search for ATL07 data for that date.\n", + "\n", + "To search for data, we use `earthaccess.search_data`. There are many ways to construct a search. Some examples are below.\n", + "\n", + "Currently, processing of ATL07 and ATL10 have been halted because of some issues with input data, so only version 006 is available." + ] + }, + { + "cell_type": "markdown", + "id": "56c86d4d-5617-4d65-8176-e4cde9c8abbf", + "metadata": {}, + "source": [ + "### By temporal range\n", + "\n", + "Searching using the `temporal` filter with `short-name` and `version` will return all data granules within the time range specified. \n", + "\n", + "The `temporal` keyword expects a tuple with two date-like variables. These can be strings following the format `YYYY-MM-DD` or `datetime` objects. Because we only want one day of data, the dates are the same.\n", + "\n", + "As with the datasets `results`, `search_data` returns a Python List so we can find the number of granules returned using `len`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0a9a6d1e-04b3-4b6c-b924-418dce9e4b22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "granules = earthaccess.search_data(\n", + " short_name=\"ATL07\",\n", + " temporal=(\"2022-07-26\",\"2022-07-26\"),\n", + " version=\"006\",\n", + ")\n", + "\n", + "len(granules)" + ] + }, + { + "cell_type": "markdown", + "id": "f8f91810-8db0-41e3-b808-5c73c084a129", + "metadata": {}, + "source": [ + "In a Jupyter notebook, we can get a rendering of information about a single granule, including some thumbnails of the location and data just by running a code-cell with one granule result." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "11440ca7-8648-43b9-b21b-b10541b30a01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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We need to know a little about the structure of the granule results. Here, we print the list of granule file names." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b60179aa-9902-4fb7-a655-81b16d7dc9ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ATL07-02_20220725225403_05201601_006_02.h5',\n", + " 'ATL07-01_20220726002820_05211601_006_02.h5',\n", + " 'ATL07-02_20220726002820_05211601_006_02.h5',\n", + " 'ATL07-01_20220726020237_05221601_006_02.h5',\n", + " 'ATL07-02_20220726020237_05221601_006_02.h5',\n", + " 'ATL07-01_20220726033654_05231601_006_02.h5',\n", + " 'ATL07-02_20220726033654_05231601_006_02.h5',\n", + " 'ATL07-01_20220726051112_05241601_006_02.h5',\n", + " 'ATL07-02_20220726051112_05241601_006_02.h5',\n", + " 'ATL07-01_20220726064529_05251601_006_02.h5',\n", + " 'ATL07-02_20220726064529_05251601_006_02.h5',\n", + " 'ATL07-01_20220726081946_05261601_006_02.h5',\n", + " 'ATL07-02_20220726081946_05261601_006_02.h5',\n", + " 'ATL07-01_20220726095404_05271601_006_02.h5',\n", + " 'ATL07-02_20220726095404_05271601_006_02.h5',\n", + " 'ATL07-01_20220726112821_05281601_006_02.h5',\n", + " 'ATL07-02_20220726112821_05281601_006_02.h5',\n", + " 'ATL07-01_20220726130238_05291601_006_02.h5',\n", + " 'ATL07-02_20220726130238_05291601_006_02.h5',\n", + " 'ATL07-01_20220726143655_05301601_006_02.h5',\n", + " 'ATL07-02_20220726143655_05301601_006_02.h5',\n", + " 'ATL07-01_20220726161113_05311601_006_02.h5',\n", + " 'ATL07-02_20220726161113_05311601_006_02.h5',\n", + " 'ATL07-01_20220726174530_05321601_006_02.h5',\n", + " 'ATL07-02_20220726174530_05321601_006_02.h5',\n", + " 'ATL07-01_20220726191947_05331601_006_02.h5',\n", + " 'ATL07-02_20220726191947_05331601_006_02.h5',\n", + " 'ATL07-01_20220726205405_05341601_006_02.h5',\n", + " 'ATL07-02_20220726205405_05341601_006_02.h5',\n", + " 'ATL07-01_20220726222822_05351601_006_02.h5',\n", + " 'ATL07-02_20220726222822_05351601_006_02.h5']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[g['umm']['GranuleUR'] for g in granules]" + ] + }, + { + "cell_type": "markdown", + "id": "b636ffce-c447-4785-a263-1cb2f062813a", + "metadata": {}, + "source": [ + "### Search By `bounding_box`\n", + "\n", + "We can further refine the search by adding a `bounding_box`. The coordinates of the bounding box are latitudes and longitudes in WGS84. The `bounding_box` is a tuple with `(min_lon, min_lat, max_lon, max_lat)`.\n", + "\n", + "Here, we search for ATL07 files in the Arctic." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "24396504-3930-4d27-9a96-f2bf0abd3772", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "granules = earthaccess.search_data(\n", + " short_name=\"ATL07\",\n", + " temporal=(\"2022-07-26\",\"2022-07-26\"),\n", + " version=\"006\",\n", + " bounding_box=(-180., 60., 180., 90.), # To restrict to N.Hem. only\n", + ")\n", + "len(granules)" + ] + }, + { + "cell_type": "markdown", + "id": "f7278212-8dd7-45bc-a0d2-66abd157fd27", + "metadata": {}, + "source": [ + "### Search By Polygon\n", + "\n", + "Searching by bounding-box does not always make sense in the Arctic, where meridians are converging. Defining a polygon might be more useful. \n", + "\n", + "The polygon argument is a Python List of longitude, latitude pairs, with the last pair of points matching the first point. For example:\n", + "\n", + "```\n", + "[(lon0,lat0), (lon1,lat1), (lon2,lat2), (lon3,lat3), (lon0,lat0)]\n", + "```\n", + "\n", + "The points have to be in counter-clockwise order. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a78e3efa-f652-47cd-bda8-98e8d447080e", + "metadata": {}, + "outputs": [], + "source": [ + "latp = [84, 85, 86.5, 85, 84]\n", + "lonp = [-80, -100, -100, -60, -80]\n", + "poly = [(x,y) for x, y in zip(lonp[::-1],latp[::-1])]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f606fe55-0edf-4859-bb99-e32569eabfe2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "granules = earthaccess.search_data(\n", + " short_name=\"ATL07\",\n", + " temporal=(\"2022-07-26\",\"2022-07-26\"),\n", + " version=\"006\",\n", + " polygon=poly,\n", + ")\n", + "len(granules)" + ] + }, + { + "cell_type": "markdown", + "id": "2900d86e-7673-4107-8c53-882a5cdce0dd", + "metadata": {}, + "source": [ + "This returns 4 granules." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ecbf4359-6a9a-4172-93ff-45cfb1a67bc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ATL07-01_20220726002820_05211601_006_02.h5',\n", + " 'ATL07-01_20220726020237_05221601_006_02.h5',\n", + " 'ATL07-01_20220726161113_05311601_006_02.h5',\n", + " 'ATL07-01_20220726174530_05321601_006_02.h5']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[g['umm']['GranuleUR'] for g in granules]" + ] + }, + { + "cell_type": "markdown", + "id": "c4774a48-c113-4b32-a435-42fa5c89c5d3", + "metadata": {}, + "source": [ + "## Search for a particular RGT\n", + "\n", + "ICESat-2 data are often referenced by Reference Ground Tracks. Reference Grounds Tracks (RGT) are the imaginary line traced on the surface of the Earth as ICESat-2 passes overhead. There are 1387 RGT. Each RGT is followed once in every 91-day orbit cycle. RGT in different cycles are distinguished by a two digit cycle number. This information is in the file metadata and also encoded in the file name.\n", + "\n", + "`ATL07-[HH]_[yyyymmdd][hhmmss]_[ttttccss]_[vvv_rr].h5`\n", + "\n", + "where `tttt` is the four-digit RGT and `cc` is the cycle number.\n", + "\n", + "Below, we filter the granules to get RGT `0531` by spliting the filename on `_` and then looking for the third group of characters (index 2) that starts with `0531`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b4ef8e3c-1eeb-432a-939f-6e07459d63ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Collection: {'EntryTitle': 'ATLAS/ICESat-2 L3A Sea Ice Height V006'}\n", + " Spatial coverage: {'HorizontalSpatialDomain': {'Orbit': {'AscendingCrossing': 81.6295895841921, 'StartLatitude': 27.0, 'StartDirection': 'A', 'EndLatitude': 27.0, 'EndDirection': 'D'}}}\n", + " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2022-07-26T16:28:32.700Z', 'EndingDateTime': '2022-07-26T16:39:56.875Z'}}\n", + " Size(MB): 107.49841690063477\n", + " Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL07/006/2022/07/26/ATL07-01_20220726161113_05311601_006_02.h5']]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = [g for g in granules if g[\"umm\"]['GranuleUR'].split('_')[2].startswith('0531')]\n", + "g" + ] + }, + { + "cell_type": "markdown", + "id": "7a792527-54c4-4899-9b41-184d07c6ff47", + "metadata": {}, + "source": [ + "### Search by Granule File Name\n", + "\n", + "We can also search for a particular granule. We still need to provide `short_name` or `concept_id` becase CMR does not allow searching across collections." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "18313ec2-1a1b-424e-b9a9-50bac6d63582", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Collection: {'EntryTitle': 'ATLAS/ICESat-2 L3A Sea Ice Height V006'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Orbit': {'AscendingCrossing': 81.6295895841921, 'StartLatitude': 27.0, 'StartDirection': 'A', 'EndLatitude': 27.0, 'EndDirection': 'D'}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2022-07-26T16:28:32.700Z', 'EndingDateTime': '2022-07-26T16:39:56.875Z'}}\n", + "Size(MB): 107.49841690063477\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ATLAS/ATL07/006/2022/07/26/ATL07-01_20220726161113_05311601_006_02.h5']]\n" + ] + } + ], + "source": [ + "granules = earthaccess.search_data(\n", + " short_name=\"ATL07\",\n", + " granule_ur=\"ATL07-01_20220726161113_05311601_006_02.h5\",\n", + ")\n", + "print(granules)" + ] + }, + { + "cell_type": "markdown", + "id": "c67c9c70-9532-42f4-ae0e-d489c1c4dfb7", + "metadata": {}, + "source": [ + "## Download the data\n", + "\n", + "We can either download data to our local machine or stream data directly into memory. Streaming data works well in the cloud.\n", + "\n", + "Below we download data. To stream data, we use the `earthaccess.open` method.\n", + "\n", + "```\n", + "files = earthaccess.open(granules)\n", + "```\n", + "\n", + "For `earthaccess.download`, files are downloaded to our current working directory or the directory specified in `local_path`. A list of the paths to these local files is returned.\n", + "\n", + "If we use `earthaccess.open`, `files` is a list of file-like objects." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "dde9d3eb-da94-4222-abc1-a2f953839d84", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "QUEUEING TASKS | : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 812.38it/s]\n", + "PROCESSING TASKS | : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 7796.10it/s]\n", + "COLLECTING RESULTS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 9446.63it/s]\n" + ] + } + ], + "source": [ + "files = earthaccess.download(granules, local_path=\"./data\")" + ] + }, + { + "cell_type": "markdown", + "id": "f38f6a33-de8c-4abc-a750-6eaa633f918d", + "metadata": {}, + "source": [ + "## Read datafile using `xarray`\n", + "\n", + "We'll use `xarray.open_datatree` to open the file. Whether we use `earthaccess.download` or `earthaccess.open`, the list of file paths or file-like objects in `files` can be passed to `xarray` file readers. Currently, `xarray.open_datatree` will only open a single file, so we have to index `files`. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "240ff452-c0eb-4b76-acd4-d9335163e403", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'data/ATL07-01_20220726161113_05311601_006_02.h5'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files[0]" + ] + }, + { + "cell_type": "markdown", + "id": "a328e17b-5e75-4961-bc73-cab0be4d59cf", + "metadata": {}, + "source": [ + "`decode_timedelta=True` is set so that we don't get a warning." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "25355435-0f98-47d2-9ba9-a8be77f40778", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DatasetView> Size: 0B\n",
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+       "    short_name:                         ATL07\n",
+       "    level:                              L3A\n",
+       "    title:                              SET_BY_META\n",
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+       "    references:                         http://nsidc.org/data/icesat2/data.html\n",
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<xarray.DataArray 'sc_orient' (sc_orient_time: 1)> Size: 1B\n",
+       "[1 values with dtype=int8]\n",
+       "Coordinates:\n",
+       "  * sc_orient_time  (sc_orient_time) datetime64[ns] 8B 2022-07-25T23:30:00\n",
+       "Attributes:\n",
+       "    long_name:      Spacecraft Orientation\n",
+       "    units:          1\n",
+       "    source:         POD/PPD\n",
+       "    valid_min:      0\n",
+       "    valid_max:      2\n",
+       "    contentType:    referenceInformation\n",
+       "    description:    This parameter tracks the spacecraft orientation between ...\n",
+       "    flag_meanings:  backward forward transition\n",
+       "    flag_values:    [0 1 2]
" + ], + "text/plain": [ + " Size: 1B\n", + "[1 values with dtype=int8]\n", + "Coordinates:\n", + " * sc_orient_time (sc_orient_time) datetime64[ns] 8B 2022-07-25T23:30:00\n", + "Attributes:\n", + " long_name: Spacecraft Orientation\n", + " units: 1\n", + " source: POD/PPD\n", + " valid_min: 0\n", + " valid_max: 2\n", + " contentType: referenceInformation\n", + " description: This parameter tracks the spacecraft orientation between ...\n", + " flag_meanings: backward forward transition\n", + " flag_values: [0 1 2]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dt[\"orbit_info\"][\"sc_orient\"]" + ] + }, + { + "cell_type": "markdown", + "id": "69231947-8c32-4c62-991c-317e8b383c88", + "metadata": {}, + "source": [ + "`sc_orient` is `1`, so the spacecraft is in the forward orientation. Left beams are weak and right beams are strong." + ] + }, + { + "cell_type": "markdown", + "id": "10259343-78a6-4ba4-80ac-99ad27a50923", + "metadata": {}, + "source": [ + "We will work with the first strong beam \"GT1R\".\n", + "\n", + "The datatree structure is a little cumbersome, and for this demonstration we only want a few variables, so we will load the data into a `pandas.DataFrame`.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c6d8ee24-3cef-4926-821f-25634cf7818e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.....................
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90446 rows × 6 columns

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" + ], + "text/plain": [ + " distance height latitude ssh_flag surface_type quality\n", + "0 7.378386e+06 NaN 66.172199 0 1 0\n", + "1 7.379108e+06 NaN 66.178631 0 1 0\n", + "2 7.379192e+06 NaN 66.179382 0 1 0\n", + "3 7.379263e+06 NaN 66.180009 0 1 0\n", + "4 7.379278e+06 NaN 66.180148 0 1 0\n", + "... ... ... ... ... ... ...\n", + "90441 1.223160e+07 NaN 70.277541 0 1 0\n", + "90442 1.223179e+07 NaN 70.275878 0 1 0\n", + "90443 1.223203e+07 NaN 70.273740 0 1 0\n", + "90444 1.223227e+07 NaN 70.271634 0 1 0\n", + "90445 1.223251e+07 NaN 70.269436 0 1 0\n", + "\n", + "[90446 rows x 6 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " {\n", + " \"distance\": dt[\"gt1r\"][\"sea_ice_segments\"][\"seg_dist_x\"].values,\n", + " \"height\": dt[\"gt1r\"][\"sea_ice_segments\"][\"heights\"][\"height_segment_height\"].values,\n", + " \"latitude\": dt[\"gt1r\"][\"sea_ice_segments\"][\"latitude\"].values,\n", + " \"ssh_flag\": dt[\"gt1r\"][\"sea_ice_segments\"][\"heights\"][\"height_segment_ssh_flag\"].values,\n", + " \"surface_type\": dt[\"gt1r\"][\"sea_ice_segments\"][\"heights\"][\"height_segment_type\"].values,\n", + " \"quality\": dt[\"gt1r\"][\"sea_ice_segments\"][\"heights\"][\"height_segment_quality\"].values,\n", + " }\n", + ")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "a82e8314-2424-4416-9950-636f8cbc0aae", + "metadata": {}, + "source": [ + "## Plot the data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "28def223-3c4f-46d9-8b45-006a5de6c15e", + "metadata": {}, + "outputs": [], + "source": [ + "# 0 - cloud\n", + "# 1 - snow/ice\n", + "# 2 - 5 Specular Lead\n", + "# 6 - 9 Dark Lead\n", + "surface = [\"Cloud\", \"Snow/Ice\", \"Specular Lead\", \"Dark Lead\"]\n", + "colors = [\"grey\", \"darkorange\", \"cyan\", \"darkblue\"]\n", + "bounds = [-0.5, .5, 1.5, 5.5, 9.5]\n", + "surface_type_cmap = ListedColormap(colors)\n", + "surface_type_norm = BoundaryNorm(boundaries=bounds, ncolors=4)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dfa97406-846a-49d7-b30d-6ff35418b522", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0/snjy8+UKFGC1q1b88knn7B//36Sk5OpVKkSw4YN46WXXsrRa1SoUIG5c+fy3HPP0aRJE0qXLs2DDz7IK6+84rJd586d2bRpkz14KlWqFPXr1+fEiRPUq1cPgEuXLrFw4UI++eSTLN/z1Vdfxc/Pj9dee40TJ04QFRXF8OHDAQgODmbZsmW88MIL9O/fn/j4eCpUqEDXrl0JDfVs/tm1wDCd+x5VOnFxcYSFhREbG6tfMKWUckNCQgIHDx6kWrVqGc5Ky5HoDVK0c//s9Gv51bhFeqaugbX8pk+fziuvvMKOHTu83RSvyuo7VdDHb+2hUkopVXREtpQK6PHHpc5UYpzM5qt8Y4HkTBUWJUqU4P333/d2M5QTDaiUUkoVPSEVoMFgb7fCa7p37+7tJqg0NCldKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3FRkAqoxY8bQqlUrQkJCCA8Pp1+/fuzevTvb5y1dupQWLVoQFBRE9erVGT9+fAG0VimlVH46DkwCPrP+PO7NxihFEQqoli5dymOPPcaaNWtYsGABKSkpdO/ePcs1lA4ePEjv3r1p3749mzdv5qWXXuKJJ55g2rRpBdhypZRSnrIB6A9UBe4HnrT+rGq9f4O3GlZILFmyBMMwuHDhgreb4jGGYTBz5kxvNyNbRSag+uuvvxgyZAgNGjSgSZMmfPfddxw5coSNGzdm+pzx48dTuXJlxo4dS7169Rg6dCgPPPAAH374YabPSUxMJC4uzuWilFLK+2YA7aw/U9I8luL0+Mx8eO+YmBgefvhhKleuTGBgIJGRkfTo0YPVq1fnw7sVTm+88QZNmzb1djMKrSITUKUVGxsLQOnSpTPdZvXq1emKn/Xo0YMNGzaQnJyc4XPGjBlDWFiY/VKpUiXPNVoppVSebAAGAlms4gfWx+/C8z1VAwYM4N9//2Xy5Mns2bOH2bNn06lTJ86dO+fhd/K+pKQkbzehSCqSAZVpmowcOZJ27drRsGHDTLeLjo4mIiLC5b6IiAhSUlI4c+ZMhs8ZNWoUsbGx9svRo0c92nallFK59y7ZB1M2icAYD773hQsXWLFiBe+//z6dO3emSpUqXHfddYwaNYqbbrrJvp1hGHz55Zf06tWLYsWKUa1aNX777TeX1zp+/Dh33nknpUqVokyZMvTt25dDhw65bDNx4kQaNGhAYGAgUVFRjBgxAoBDhw5hGAZbtmxxaZthGCxZsiTDtp89e5aBAwdSsWJFgoODadSoET/99JPLNp06dWLEiBGMHDmSsmXL0q1btzz9nrL7bOvXr6dbt26ULVuWsLAwOnbsyKZNm1xeY+/evXTo0IGgoCDq16/PggUL8tQWbyiSAdWIESPYunVrui9FRgzDcLltWws67f02gYGBhIaGulyUUkp5z3Fgdi6fMxvPJaqXKFGCEiVKMHPmTBITsw7rXn31VXtv1r333svAgQPZuXMnAJcvX6Zz586UKFGCZcuWsWLFCkqUKEHPnj3tvUJffvkljz32GA899BD//fcfs2fPpmbNmnlue0JCAi1atODPP/9k27ZtPPTQQwwaNIi1a9e6bDd58mT8/PxYuXIlX331Va7fJyefLT4+nsGDB7N8+XLWrFlDrVq16N27N/Hx8QBYLBb69++Pr68va9asYfz48bzwwgt5/uwFzixiRowYYVasWNE8cOBAttu2b9/efOKJJ1zumz59uunn52cmJSXl6P1iY2NNwIyNjc1Te5VSSokrV66YO3bsMK9cuZKr531nmiZ5uEzyVMNN0/z999/NUqVKmUFBQWbbtm3NUaNGmf/++6/LNoA5fPhwl/tat25tPvLII6ZpmuaECRPMOnXqmBaLxf54YmKiWaxYMXP+/PmmaZpm+fLlzZdffjnDNhw8eNAEzM2bN9vvO3/+vAmYixcvNk3TNBcvXmwC5vnz5zP9LL179zafeeYZ++2OHTuaTZs2zfZ38Prrr5tNmjTJ8LGcfLa0UlJSzJCQEPOPP/4wTdM058+fb/r6+ppHjx61bzNv3jwTMGfMmJHha2T1nSro43eR6aEyTZMRI0Ywffp0Fi1aRLVq1bJ9Tps2bdJ1F/7999+0bNkSf3///GqqUkopD8rr1CBPTikaMGAAJ06cYPbs2fTo0YMlS5bQvHlzJk2a5LJdmzZt0t229VBt3LiRffv2ERISYu/1Kl26NAkJCezfv5+YmBhOnDhB165dPdbu1NRU3nnnHRo3bkyZMmUoUaIEf//9N0eOHHHZrmXLlm69T3afDSSxf/jw4dSuXduep3zx4kV7W3bu3EnlypWpWLGi/XXT/j4LMz9vNyCnHnvsMaZOncqsWbMICQkhOjoagLCwMIoVKwZI/tPx48f5/vvvARg+fDiff/45I0eOZNiwYaxevZoJEybkaKhQKaVU4ZDXxAtPJ2wEBQXRrVs3unXrxmuvvcbQoUN5/fXXGTJkSJbPs6WYWCwWWrRowZQpU9JtU65cOXx8su7jsD1uWlNXgEwnWNl89NFHfPLJJ4wdO5ZGjRpRvHhxnnrqqXSJ58WLF8/ydbKT3WcDGDJkCKdPn2bs2LFUqVKFwMBA2rRpY2+L8+eyySw9pzAqMj1UX375JbGxsXTq1ImoqCj75ZdffrFvc/LkSZeou1q1asydO5clS5bQtGlT3nrrLT777DMGDBjgjY+glFIqD7qR+7N/P+DGfGiLs/r166erhbhmzZp0t+vWrQtA8+bN2bt3L+Hh4dSsWdPlEhYWRkhICFWrVmXhwoUZvp8tMDl58qT9PucE9YwsX76cvn37cu+999KkSROqV6/O3r17c/tRs5XdZ7O15YknnqB37972pHvnCWL169fnyJEjnDhxwn5fUSpLUWR6qDKKXNNK2/UKZDiLQCmlVNFRAeiD1JnKqVusz/OEs2fPcvvtt/PAAw/QuHFjQkJC2LBhAx988AF9+/Z12fa3336jZcuWtGvXjilTprBu3TomTJgAwD333MP//vc/+vbty5tvvknFihU5cuQI06dP57nnnqNixYq88cYbDB8+nPDwcHr16kV8fDwrV67k8ccfp1ixYlx//fW89957VK1alTNnzvDKK69k2faaNWsybdo0Vq1aRalSpfj444+Jjo6mXr16efpdXLlyJV0QV6JEiRx9tpo1a/LDDz/QsmVL4uLieO655+wjTAA33ngjderU4b777uOjjz4iLi6Ol19+OU/t9IYi00OllFLq2vUSEJjDbYOAUR587xIlStC6dWs++eQTOnToQMOGDXn11VcZNmwYn3/+ucu2o0eP5ueff6Zx48ZMnjyZKVOmUL9+fQCCg4NZtmwZlStXpn///tSrV48HHniAK1eu2GeUDx48mLFjxzJu3DgaNGjAzTff7NKjNHHiRJKTk2nZsiVPPvkkb7/9dpZtf/XVV2nevDk9evSgU6dOREZG0q9fvzz/Lvbs2UOzZs1cLkOHDs3RZ5s4cSLnz5+nWbNmDBo0iCeeeILw8HD7a/v4+DBjxgwSExO57rrrGDp0KO+8806e21rQDDMnXT/XsLi4OMLCwoiNjdUSCkop5YaEhAQOHjxItWrVCAoKyvXzZyJFO7MqXBAI/Az0y0sD3WQYBjNmzHArYFG5k9V3qqCP39pDpZRSqkjoB6xA1uxLm6/iZ71/Bd4JppQqMjlUSimlVEtgGlK08x+kNEIokoDuqZwppfJCAyqllFJFTgVgsLcbkYZm0FzbdMhPKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3KSz/JRSShU5x4/Hs2DBYeLikggNDaBbtypUqBDi7Wapa5gGVEoppYqMDRuieffdtfzxx35SUiz2+/38fOjTpwYvvdSali0jvdjCvBsyZAgXLlxg5syZ3m5Klt544w1mzpyZ7cLM1xod8lNKKVUkzJixl3btfmLGjL0uwRRASorF/vjMmXszeYW8GzJkCIZhYBgG/v7+RERE0K1bNyZOnIjFYsn+BfKJYRiFPgC7VmhApZRSqtDbsCGagQP/JDExNcvtEhNTueuuP9mwIdrjbejZsycnT57k0KFDzJs3j86dO/Pkk09y8803k5KSkufXTU1N9WpQpjxDAyqllFKF3rvvrs02mLJJTExlzJi1Hm9DYGAgkZGRVKhQgebNm/PSSy8xa9Ys5s2bx6RJk+zbffzxxzRq1IjixYtTqVIlHn30US5evGh/fNKkSZQsWZI///yT+vXrExgYyOHDh9O938aNGwkPD+edd97Jc5u/++476tWrR1BQEHXr1mXcuHEuj7/wwgvUrl2b4OBgqlevzquvvkpycrLLNu+99x4RERGEhITw4IMPkpCQkOf2XM00oFJKKVWoHT8ez+zZ+3L1nNmz93P8eHw+tcihS5cuNGnShOnTp9vv8/Hx4bPPPmPbtm1MnjyZRYsW8fzzz7s87/Lly4wZM4Zvv/2W7du3Ex4e7vL4kiVL6Nq1K6NHj+bll1/OU9u++eYbXn75Zd555x127tzJu+++y6uvvsrkyZPt24SEhDBp0iR27NjBp59+yjfffMMnn3xif/zXX3/l9ddf55133mHDhg1ERUWlC8qU0KR0pZRShdqCBYdJTc3dOnkpKRb++ecwgwc3zKdWOdStW5etW7fabz/11FP269WqVeOtt97ikUcecQlEkpOTGTduHE2aNEn3erNmzWLQoEF89dVXDBw4MM/teuutt/joo4/o37+/vS07duzgq6++YvBgWQnxlVdesW9ftWpVnnnmGX755Rd7ADh27FgeeOABhg4dCsDbb7/NP//8o71UGdCASimlVKEWF5dUoM/LLdM0MQzDfnvx4sW8++677Nixg7i4OFJSUkhISODSpUsUL14cgICAABo3bpzutdauXcuff/7Jb7/9xq233prnNp0+fZqjR4/y4IMPMmzYMPv9KSkphIWF2W///vvvjB07ln379nHx4kVSUlIIDQ21P75z506GDx/u8tpt2rRh8eLFeW7b1UqH/JRSShVqoaEBBfq83Nq5cyfVqlUD4PDhw/Tu3ZuGDRsybdo0Nm7cyBdffAHgkptUrFgxlyDMpkaNGtStW5eJEyeSlJT3gNCW5P7NN9+wZcsW+2Xbtm2sWbMGgDVr1nDXXXfRq1cv/vzzTzZv3szLL7/s1vteyzSgUkopVah161YFP7/cHa78/Hy48cYq+dQih0WLFvHff/8xYMAAADZs2EBKSgofffQR119/PbVr1+bEiRM5fr2yZcuyaNEi9u/fz5133pkuQTynIiIiqFChAgcOHKBmzZouF1vwt3LlSqpUqcLLL79My5YtqVWrVrrk+Hr16tkDMJu0t5XQIT+llFKFWoUKIfTpU4MZM3JeX+qWW2p4vHJ6YmIi0dHRpKamcurUKf766y/GjBnDzTffzH333QdID1NKSgr/93//R58+fVi5ciXjx4/P1fuEh4ezaNEiOnfuzMCBA/n555/x88v8cH3w4MF0RTZr1qzJG2+8wRNPPEFoaCi9evUiMTGRDRs2cP78eUaOHEnNmjU5cuQIP//8M61atWLOnDnMmDHD5XWefPJJBg8eTMuWLWnXrh1Tpkxh+/btVK9ePVef6VqgPVRKKaUKvZdeak1goG+Otg0K8mPUqNYeb8Nff/1FVFQUVatWpWfPnixevJjPPvuMWbNm4esrbWvatCkff/wx77//Pg0bNmTKlCmMGTMm1+8VGRlp7/265557SE3NvGTEyJEjadasmctlw4YNDB06lG+//ZZJkybRqFEjOnbsyKRJk+w9VH379uXpp59mxIgRNG3alFWrVvHqq6+6vPadd97Ja6+9xgsvvECLFi04fPgwjzzySK4/z7XAME0zd1MnrjFxcXGEhYURGxvrkqinlFIqdxISEjh48CDVqlUjKCgo18+fOXMvd92VdXHPwEBffv75Zvr1q+VOU1URkdV3qqCP39pDpZRSqkjo168WK1YMpH//Wulyqvz8fOjfXx7XYEp5g+ZQKaWUKjJatoxk2rS+HD8ezz//HCYuLonQ0ABuvLGKx3OmlMoNDaiUUkoVORUqhBRI0U6lckqH/JRSSiml3KQBlVJKqQKlc6GUpxSm75IGVEoppQqEv78/IAsDK+UJtu+S7bvlTZpDpZRSqkD4+vpSsmRJYmJiAAgODs5w+RWlsmOaJpcvXyYmJoaSJUva64B5kwZUSimlCkxkZCSAPahSyh0lS5a0f6e8TQMqpZRSBcYwDKKioggPD8/zOnVKgQzzFYaeKRsNqJRSShU4X1/fQnUwVMpdmpSulFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUclORCqiWLVtGnz59KF++PIZhMHPmzCy3X7JkCYZhpLvs2rWrYBqslFJKqWtCkZrld+nSJZo0acL999/PgAEDcvy83bt3Exoaar9drly5/GieUkoppa5RRSqg6tWrF7169cr188LDwylZsmSOtk1MTCQxMdF+Oy4uLtfvp5RSSqlrS5Ea8surZs2aERUVRdeuXVm8eHGW244ZM4awsDD7pVKlSgXUSqWUUkoVVVd1QBUVFcXXX3/NtGnTmD59OnXq1KFr164sW7Ys0+eMGjWK2NhY++Xo0aMF2GKllFJKFUVFasgvt+rUqUOdOnXst9u0acPRo0f58MMP6dChQ4bPCQwMJDAwsKCaqJRSSqmrwFXdQ5WR66+/nr1793q7GUoppZS6ilxzAdXmzZuJiorydjOUUkopdRUpUkN+Fy9eZN++ffbbBw8eZMuWLZQuXZrKlSszatQojh8/zvfffw/A2LFjqVq1Kg0aNCApKYkff/yRadOmMW3aNG99BKWUUkpdhYpUQLVhwwY6d+5svz1y5EgABg8ezKRJkzh58iRHjhyxP56UlMSzzz7L8ePHKVasGA0aNGDOnDn07t27wNuulFJKqauXYZqm6e1GFGZxcXGEhYURGxvrUhxUKaWUUoVXQR+/r7kcKqWUUkopT9OASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppZRSbtKASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppZRSbtKASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppZRSbtKASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppVRBu7AfLp70diuUB2lApZRSShWk5S/BhJrwdUXY/au3W6M8RAOqouDyabgU7e1WKKWU8oSNH8lP0wIbx3q1KcpzNKAq7LZPhi8jYXwUbPo/b7dGKaWUu0rXB8MXMKBcY2+3RnmIn7cboLKx9l3AYr3+DjR/3KvNUUop5aYB82DTpxAQCs2f8nZrlIcUqR6qZcuW0adPH8qXL49hGMycOTPb5yxdupQWLVoQFBRE9erVGT9+fP431JNK1gTDR85mStfxdmuUUkq5q3gktB8DrUeBfzFvt0Z5SJEKqC5dukSTJk34/PPPc7T9wYMH6d27N+3bt2fz5s289NJLPPHEE0ybNi2fW+ohcUfg5FoZZw8Mg56TvN0ipVRhZZpw8C/YOwMsKd5ujcrOzqnwXT2YeQtcOZf1tqYJ276DhSPgxJqCaZ/KtSI15NerVy969eqV4+3Hjx9P5cqVGTt2LAD16tVjw4YNfPjhhwwYMCDD5yQmJpKYmGi/HRcX51ab3bJ3GiSclesJ5+DcLgir5r32KKUKr1Wvw5q35Hq9e6D3j95tj8pcwnmYdx+YqXB+r6RzdPoo8+13/QzzHwAM+Hc83LMOIpoXWHNVzhSpHqrcWr16Nd27d3e5r0ePHmzYsIHk5OQMnzNmzBjCwsLsl0qVKhVEUzNWtpH8NHzBxx9K1fZeW5RShdve6Y7r+2Z5rx0qe6ZFLjapSVlvf26H7YkShP09NN+apvLuqg6ooqOjiYiIcLkvIiKClJQUzpw5k+FzRo0aRWxsrP1y9OjRgmhqxqrcCH1nQvMn4Y7FULKG99qilCrcavZ1XK9+s/faobJXrAx0/RyKhUPkddD6pay3rzcIl8N1ohdHTlSmitSQX14YhuFy2zTNDO+3CQwMJDAwMN/blWM1+7ruKJVSKiM3vA1RbSDlCtTs5+3WqOw0fVQuOVG6NvSYCAuGgWFAh/fyt20qT67qgCoyMpLoaNeCmDExMfj5+VGmTBkvtUoppfKBYUAN7Zm6ajUcDHXvBAzwK0Qn/cruqh7ya9OmDQsWLHC57++//6Zly5b4+/t7qVVKKaVULuz+DX5sBX8PA0s2+VbKa4pUQHXx4kW2bNnCli1bACmLsGXLFo4cOQJI/tN9991n33748OEcPnyYkSNHsnPnTiZOnMiECRN49tlnvdF8pZRSKncun4E5A+HUBtg1Fda97+0WqUwUqSG/DRs20LlzZ/vtkSNHAjB48GAmTZrEyZMn7cEVQLVq1Zg7dy5PP/00X3zxBeXLl+ezzz7LtGSCUkoVeZZUazHgjPNEVRFjSZKZfQAYkHzJq81RmTNMW5a2ylBcXBxhYWHExsYSGhrq7eYopVTmljwLGz+RGcG3L4DQKt5ukfKEtWOkVlWp2tDvDwip4O0WFQkFffzWgCobGlAppYqE2IPwbXW5bvhC8yeg08febZNSXlTQx+8ilUOllFIqE/4hUgAYQ4aITFMuSqkCoQGVUoXFrp9hwcNwaL63W6KKouCycMt0KFMfMGDTWFiYwzpHSim3aUClVGFwaIHM5PlvAkzvDWd3ertFqigqEWX97lh7pv4dDykJXm2SyoJpyiw+S2r226pCTwMqpQqD87vlp5kqa3xd2O/d9qiiaccP2IMpgBIVwVeLQBZKqUnwezf4shxMagCXT3u7RcpNGlApVRjUvh1CKsv1ck2hchevNkcVUeWaIAGVAT6BMOAvLZ9QWB1dCkcWyvXze6TGVEbO74VpPeHXrnB6a8G1T+VanupQVa9enfXr16dbvuXChQs0b96cAwcOeKRxSl0zikfAA3sg/giEVQOfIlUiThUWDYbId+fMNmgw2JpPpQqlkAqALdg1HSdUac1/EE6skm3mDIQh2wuogSq38rTXPnToEKmp6cd8ExMTOX78uNuNUuqa5BcIpWp5uxWqKDMMqD/I261QOVGmPtzyu0xGqdgh8wWtk+IkDQATkuILsoUql3IVUM2ePdt+ff78+YSFhdlvp6amsnDhQqpWreqxximlVKF1KRpm3yZDMm1eg2aPebtFqqip1V8uWen8Kfx5J1hSoPHDMPdeiGgBzZ+Uiviq0MhVYU8fH/njGYZB2qf5+/tTtWpVPvroI26++epZ8VwLeyqlMrToSdjyhXVZEAMeOQXB5bzdKnW1ij8G31QDM0Vu178Pek32bpsKuYI+fueqh8pisQCyRt769espW7ZsvjRKKaUKPR9/x3XDkOrkSuWXSycdwRTIjM4bx4N/Me+1SbnIUw7VwYMHPd0OpZQqWlq/BBf2wrndcP0rUKy0t1ukrmbhzSVxPf6I3PYLAh8N4guTPE8lWrhwIQsXLiQmJsbec2UzceJEtxum1DUn/jjMuQtiD8ENb0HDId5ukcpKsdLQb5a3W6GuFT6+cO8GmP8AXDoF7d4G3wBvt0o5yVNANXr0aN58801atmxJVFQUhtY5Ucp9a96EE6slJ+fvoVB7AASEeLtVSqnCIrgc9JsN53aCnw71FTZ5CqjGjx/PpEmTGDRIp+cq5TEuOTgGjho1SillteIlWPceYECPCdDwfm+3SFnlac5lUlISbdu29XRblLq2tXldKqSXrAm9voeAEt5ukVKqsNn0mfWKCZs/92pTlKs8BVRDhw5l6tRMyuQrpXLv6BJY9hxU7QEP7IZ6A73dIqVUYRTe1NqbbUg9KlVo5HjIb+TIkfbrFouFr7/+mn/++YfGjRvj7+/vsu3HH3/suRYqdbW7dAqm9ZDCfTt+kLypxg95u1WFl2nCrp9kXbP690LZht5ukVIFp99sqX/mXxyaajHZwiTHAdXmzZtdbjdt2hSAbdu2udyvCepK5dKlaFl5HuTMM1bLkmRp5xSYN0h+V/+Og2GHIaiUt1ulVMEoVkYq86tCJ8cB1eLFi/OzHUpdu8o1gpq3wr4ZMoun0VBvt6hwi9kiwZSZKmubxR3RgEop5XW6pL1S3mb4wC3T4PIpCCqttWUux8DhhRDRHErXSf94/Xth63hIvgQV2kHZBgXfRqWUSiNPAdWtt96a4dCeYRgEBQVRs2ZN7r77burUyWBnqHLuwFzYNRXK3wBNhsvyFurqZBhQPNLbrfC+K+dgciMJqjCgVE3o8n+SrG9Tqg7cOhd8/CDqOvmplFJelqdZfmFhYSxatIhNmzbZA6vNmzezaNEiUlJS+OWXX2jSpAkrV670aGOvKef3wsw+kny78FHYO93bLVIq/53aaA2mAEz5P5h9G5jW1RiunIVJ9eHXjjDvXkiM9VpTlVLKWZ4CqsjISO6++24OHDjAtGnTmD59Ovv37+fee++lRo0a7Ny5k8GDB/PCCy94ur3XjvijchAxLYChicrq2hDeDAJLut5nSZaZfQAH5kDcIbkeexAOzi3I1inlWclXYMmzMPMWOKJ5ykVdngKqCRMm8NRTT+Hj43i6j48Pjz/+OF9//TWGYTBixIh0MwBVLlRoB5U6y/XQKlDvHu+2R6mCEFwWbnjb9b76gx2LwJZtABiSd4YBZeoXdAuV8pz178OmT2D/nzDjJplkoYqsPCUfpKSksGvXLmrXru1y/65du0hNTQUgKChISyi4wzcAbl8oU+qDy2meiLp2VGiHPWgyU6FcY7CkSlAV0QL6z4VDf0G1XlrYUBVtl6KRJaYskHIFki7mbP3OK2dhz28QVs01v1B5VZ6O0oMGDeLBBx/kpZdeolWrVhiGwbp163j33Xe57777AFi6dCkNGujsG7cYBpSI8nYrlCpY4U0kaFr/ARxdDItGwIlVcNMUebxaT7koVdS1eBoO/AEXT0LLZ9Lv71OTYdXrcPpfmZhUo4+cXPx0A5zfLdt0nwCNHij4tqt0DNO0JSfkXGpqKu+99x6ff/45p06dAiAiIoLHH3+cF154AV9fX44cOYKPjw8VK1b0eKMLUlxcHGFhYcTGxhIaGurt5ih17filIxxbZr1hwMgU61CfUlcR0yKFff2C0j+2cSwsGQmYYPjBQ0fk/q/Ky0/DB+rc5TjZUC4K+vidpx4qX19fXn75ZV5++WXi4uIA0jW2cuXK7rdOKXXtqmgLqAyIaq3BlLo6GT4ZB1MgM15tQ99mCiSeh9J1IaoNnFwtkzVqD8jb+6YmwdZvICkWGj8sFdiVW9xOzNFeG6VUvmj7hhT2vHIWGgx2fez4KphzF6QkQI+JUONmrzRRqXzV5BHJlbqwDxo9BKXrSSrI7QvhyD8QUlmGyPNi6XOw+f/k9fbPhrvXeLbt16AcB1TNmzdn4cKFlCpVimbNmmWZcL5p0yaPNE4pdQ0zfNLPbl35Kmz+wrrszEXAAgsehhrHvdLEQispHv4dDz7+0vvgX8zbLVJ5EVoJHthjHRIMdNzvX0zyqdwRvQ4wpZfrlB6zPSHHAVXfvn0JDJQ/aL9+/fKrPUoplbHT/8Ea55IK1pmA/sW91qRC68+74OBfgPVg2ft7b7dI5ZVhuAZTntL4YTi5xnFduS1PSenXEq8lpe+dKfVJyjaCDv+DhLPgXwKCShZcG5QqTM7ugkn1HLdL1YbgcOj8qaz7pxzGlYMrZ+R6yZrw4F7vtkcVTuf3Sk9veNOrcmmzIpGUDnDhwgV+//139u/fz3PPPUfp0qXZtGkTERERVKhQwZNtvPZcjoE/bpNhjWPLZcrs8RVSm6rfH1C1u7dbqFTBK1MXOn8G/46DyOug21eZJ/Ne65o8CmvelOtNH/NuW1ThVaqWt1twVclTD9XWrVu58cYbCQsL49ChQ+zevZvq1avz6quvcvjwYb7//urpXvZKD9WFAzChhvWGD2BdxwxD6u/01+U2lMrS+b0w/0FIipMerEodvd2ignd6qxQE1mry6hpV0MfvPM1DHjlyJEOGDGHv3r0EBTnOEHv16sWyZcuyeKbKkZLVoeVzYPjKLKcSFeU6yJRZpTJiWmDtezCzL+z+zdut8a6F1mKgp7fCn3d6uzXeUa6xBlNKFaA8DfmtX7+er776Kt39FSpUIDo62u1GKaDjB9DhfRnXvrAfNn4i+SKtnvd2y1R+2jsT/h4K/sHQ5zepv5RTu36CFaPk+v4/JP+uzDUagFtSAFMulhRvt0Ypzzu6BHb8AJGtJKn8KsyBKmry1EMVFBRkL+jpbPfu3ZQrV87tRmVl3LhxVKtWjaCgIFq0aMHy5csz3XbJkiUYhpHusmvXrnxto8fY/kFK1oCun0Ob1zRn5Gq34CGZgBB/HJY8nbvnXjyJrAsGYMKVGE+3rujo/In05oZUgl6Tvd0apTwr/hj83h22T4Z/HpGTKeV1eQqo+vbty5tvvklycjIAhmFw5MgRXnzxRQYMyGPV1hz45ZdfeOqpp3j55ZfZvHkz7du3p1evXhw5ciTL5+3evZuTJ0/aL7VqaSKe8qCjS+G7evB9Mxlicod/cSkFYBgyqzM3GgyGMtZZcBU7Q1Rb99pSlJVrDEO2y1Id1W/Kn/dIuii9BJdP58/rK5WZ+KNgSZaJS4aP5Awqr8tTQPXhhx9y+vRpwsPDuXLlCh07dqRmzZqUKFGCd955x9NttPv444958MEHGTp0KPXq1WPs2LFUqlSJL7/8MsvnhYeHExkZab/4+vpmum1iYiJxcXEuF684u0OmiavCb959cG43nNkqZ4vu6POrBEKVu8KN43P33OByjnoyxxbDylfca4vKXGIcfN8Yfu0ME2rB+X3ebpG6lkS2kn0ESCpI2pUElFfkKYcqNDSUFStWsHjxYjZu3IjFYqF58+bceOONnm6fXVJSEhs3buTFF190ub979+6sWrUqy+c2a9aMhIQE6tevzyuvvELnzp0z3XbMmDGMHj3aI23Os7XvwoqX5XrHj6DlSO+2R0HcYVj3nvQaXf8KBIbJ/SmJkHzJc+8T2QoGZj6Mna2tTrmNW8dDh/fcb5NK78RKiD0o15NiYf8saPmMd9ukrh0+fnDbAog/AsGR+VP4U+VanutQLVy4kIULFxITE4PFYmHXrl1MnToVgIkTJ3qsgTZnzpwhNTWViIgIl/sjIiIyTYSPiori66+/pkWLFiQmJvLDDz/QtWtXlixZQocOHTJ8zqhRoxg50hHAxMXFUalSJc99kJzY/H+u1zWg8r4ZN8PZnYAJF084Vnefd5/kPAEElYEbs+4tzVd7fpe2AWBAZC4S2lXulGkAvkGQmii3I6/zbnvUtccwILSKt1uhnOQpoBo9ejRvvvkmLVu2JCoqKst1/Twt7XuZppnp+9epU4c6derYb7dp04ajR4/y4YcfZhpQBQYG2pfY8ZqIVnBwjqyxpDvqwiH2oOQrgCxUanN4geN6UGnJ3cmOacqB2JMTDJLiYc7dkleBAeXbyCxBlT9CK8tisvtnQ4UboGJ7b7dIKeVleQqoxo8fz6RJkxg0aJCn25OpsmXL4uvrm643KiYmJl2vVVauv/56fvzxR083z7NumgJbxkntqaZu5uQoz2g7GpY+K9d9/CDhPASVgrp3yiK0AHUHZv86SfHwezc4uRaq9oB+s6UCvrtMiyPgM3ygbEMIzOdCdpZURwJ9QdgzTWY+BofDzb/I7FdvCm8iF6WuVqYFVr8Jx5ZB/fug4RBvt6hQy1NSelJSEm3bFuwMooCAAFq0aMGCBQtc7l+wYEGu2rJ582aioqI83TzPCgiB616AVs/qwq+FRcMHwbCef5xcC2usky+6fgED5sMdi6HN69m/zu5f5fkAh+bLxRMCw6DrOChWDiJaQOt8Tkhf+SqMDZCK/hf259/7XDkHi56Uqudz75XZTTFbYNmL2T5VKeWmnVNg9Wg4uhjm3w9ntnm7RYVannqohg4dytSpU3n11Vc93Z4sjRw5kkGDBtGyZUvatGnD119/zZEjRxg+fDgg+U/Hjx+3L30zduxYqlatSoMGDUhKSuLHH39k2rRpTJs2rUDb7TbThEVPwPZJMrzQ53cIyOWUeuUeS4qjBwgg5Yr8NHxyvraiacLxla73BYd7pn0ATR6WS367eBLWvC3X447Aho/gxnH5814LHoZ9M+R358wnT+eCSilnllRZK7ZEeSgemf7xK2eR2nbW/7+EcwXZuiInxwGVc6K2xWLh66+/5p9//qFx48b4+/u7bPvxxx97roVO7rzzTs6ePcubb77JyZMnadiwIXPnzqVKFUnMO3nypEtNqqSkJJ599lmOHz9OsWLFaNCgAXPmzKF379750r58c2I1bPlcrh/6G7Z9B80f926brjXBZaVY5Ko3ZKip9SjHY6YJ696HXVOgSjfo8D/wyaA0x5GFsP07x23DD2b1g56Ti9aC1/7B4BsIqUmACcXK5t97xR5wBLJ+wVKos3gEdPgg/95TqWuBacr+58Cf4BMAt/2dfs3LBoNh11SIXi8pDeVv8EpTi4ocL46cVakBlxc0DBYtWuRWowoTryyOnNapzfBjc8ftbt9A46HeaYtK7/hK+Lmd43bvqVAvg3yq3b9mvK5ccDg8cir/2pcfDs2H9R/KWpMdPpAgKz/smwV/3gGpKdDlU2g2In/eR6lrTfxx+LqiXDd8oN69ma8qYEnN+CSxkCvo43eOe6gWL16cn+1QWYloBp0+gW0ToWIHLeJW2NiG/zK7bVOjL1TtBYfmpXmgCK7BVbWHXPJbzb7w6FnppbLV/sqrZOvfxb+Y++1SqqgrVlaG+S6flv+viOaZb1sEgylvyHEP1bWqUPRQqcLNtMDfD8Pun6FyF5mBllVJhPN7ZR2uuMPSO9XoQTi+CspfDze8JbMIlWft+R3m3iPDHD0mQv17vd0ipbzvwn45UQ+rDg3vl56qq0hBH781oMqGBlQqX53fCxPrYE/67PYVNH7Iq026qpzZLuVHZt4CF6zrnZWoCA8f9W67CkpSvOS7eaI0h8ofyZdkNp1/iJRhucqCGm8q6OO3/uWU8qbYQ9iDKcPHOqumCEi6CLP6w9eVZZZfYbTmbZjcECZZF4w2fOUSUtG77Sooa96G/wuDceWkB1QVTrP6y2zWuXfD8pe83RrlBg2olCoIqUkwsx98EgAz+8oagGvHwLTuSA6VAeWaQqNh7r/XmW3w1xCp1ZR82f3Xy8imz2T9uvijUvD03J78eR93OC/hlJoIDYZA/UEyJHu1S0mEla8DpgS/a8fAsRVwdEn6EhSeYlry7/t2NTu21HH9yELvtUO5TZM1lCoI+2ZJAAKyXMn+WbD6Dcfj5W9wb1FkG0sq/NbV2tNlgiUJOnm4jMmh+VLsz7Q43VkIMwcir4MDcwETyreFHt96u0UFx9dfSn1cOSO3Yw/AL9blcZoM9/yakxcOwC+d4OJRqH4z9J2picw5VedO2CG1E6l3j3fbotyiAZVSBSHtDLXAMAitaq0ybkLp2p55n9QkmbWDCRjWIUUPW/aiBGogw5Tt35PyCYXNTVNlCScfP2hyjS3hZPjAgL9h/fuQGAsH5zoe+/craPEMlKrpuffb/H9w8ZhcP/AnzBskv3+VvR4Tpec0IASidEHzokyH/JTKjq3C+dGleR8uqdIN2r0rvSbt3oEq3aH/PJnh1/JZ6Dw2/XvmhX8xaG3Nw/AvDi2fydvrZKV4hCMfqUwDaPWc59/DE2xLOLV8Jv/qZBVm4U0kqEm3fJUJez28WkTxKFx6KQ/M8ezrX818fKHKjRpMXQU0oCpI5/dKbsuiJyHhgrdbc/WxpKYZhvKQ1aOlcOevnWBpJgFK/DH4vil8WhzWvpv+ccOQ6ur3rJWAxzCgZHWZ1dfhfTn422z5EsYWg/FR6ZeqyYl2b8OQHdDxo/xZC7LHRKmaXLOfLINUGJ3fB9smQexBb7fE+yp3SX9fRAvPvkeLp1xfs0o3z76+UkWAlk3IhkenXU6oZd3Bm1D3Huj9vUfamCcpCXBkEYRWgbINvNcOT9nxA/w9DHyDoO/0jA8iefVtDclBASmG9+jp9NssehK2fOFYJuXh47I+VnbiDsPWbyC0sizAbKbC2GDA+jqGLwxcBVHX5by9ibHwXV24FC1DP3csgYrtc/78grL/D8ktq9JNpotnxdZLaKZKcVsji2Ko5/fC902kwKp/cRi8DcKqerTpRYppwsF58vuzJEKlLlA9i+W3kq/AxeMQVi13eVCWVFkNIDVRAm6/QPfbrpQbCm2ldOUB8cesB1wD4g55rx2mBX7tAidXS1v6zYIafbzXHk9Y9LjsyFOTYPmLcM86z7125a7w3wHH9YwEhOAof+AngV12LCnwc3u4eEK+FwnnZfjPPxiS42Ub0wJbv85dQHVmmwRTNof/LnwB1anNMtvRMGDbBKnYnHYdMZvUJJh9Gxz4Q263GAmdsijVcGyZo1p98iU4sfLaDqgMQwKorIIom7ijMLU1XDoJUddLMJ7TwMjHN+Mll5S6RuiQX0Fq9w7gI1W0r3/Fe+24eMIaTCE72z2FdNgmp9a+J70yNsERnn39rl9Az0nQ/VtZyDgj170Ade+FUrWh7RsQVCr7102MlbIDZirgA6c2yUHplunWaunWVd7L1Mtde8s2guK23jEDqhTAEjG5FWtNxrcN0Z7fm/m2Cx5yBFMAO3/M+rUrdpSFlAH8S0CFdllvfzXbNxt+uxGWPCOlFLKza6ojGD+5RoJRpVSOaA9VQWo5UpKQfQK8u55Y8UgIqyEHNdMClTp5ry2e4FxvyK+Y5CV5kq9/9usnBoRIoHx+D6x8xZozlU2RvmJloPbtsOc3CaQaPSj3V70RBm2WnqmSNaHpY7lrb2Ao3LdFyhuUawLlGuXu+QWhak8o2xjObJXPWOvWzLc9kmYd0cx6CW1K1YTB/8HxFTI8GFrF/fYWRZdOwewBYKbI8H5wBDR/Ala9LkPYzZ+CCje4Pqd0XcCUoWYMGfZT165Vb8DmLyCypdRvC9TVQrKiOVTZuGqXnrl8Gnb/AiVrQLVe3m6Ne2b2k3wcgFr94ZbfvNOO/wuVpT4AytSHIduzf45pgZh/ZeZcTnKuChPThOj1EgzmJcnZkgJxRyCkkgStmVn5Gqx5S65X7Sk1jmzDUMmXwMdfl1bJyPm9MNGpHEe5ZhDZAv6bABhyUvdIjOsMSNOUfMQTq6DuXUX/ZEvl3ZltMNl6Mmb4wA3vQOsXvdumXNIcKlUwgstBsxHeboVn9P5REsINH2j6qPfaUamLo3hnle45e47hAxHNMn7s0N9SObn6zYUvBwpgxcuwboxcb/dO9j1yafn4yUzH7LQdLYGUaZEeFVtC+saxMpTlXwz6/QGVO+fu/a92JWtC9T6O4dLTm+UCgCnBaPJF14DKMKDBfXLJjZ1TJRCr2B6ue1HXoytKrpyVnLky9V3/bj5OJzmm6dmTlgv7Zf3CMvWh1oCsJ5kUIdpDlY2rtodKeV5KghxY/IpBnTvcqxR9Yg381Na6ozFg8FbZ+WQmNVkqsPsFQbXeBbOD+jICLsfI9bDqMHR/zp+bcB62T5ZZk/Xuzv0B2DTh02IyEQFDEtrvcBoaTDgvfwe/HEwOuJrtmwWz+mX8mH8I1B4g9dFKROX9Pc7ugElOM4V7T722ktPP7pTA3sdX6smVrOHtFuXcyXXwa2dIuSz7jVv/dN13bPoMNn8uk2K6feOZVJXt30v5INsknp6Tcx/A55D2UClVWJkmbP4MDv8jZ1UNh7g+7hcEjR7wzHud2YokbVt3Ome2Zx1Qzb9fzvgArhsF7TOoheUJCedl0d2UyxDeAg7Nk/uTLsowcnC5nL3O790kCR9T8nnavJa7dhiGFJOMPwIYUKKS47HlL0nPWUAItHoB9s2U3KAbx7nW+7oWVL9ZqsTvmyXDsgf+xH4gS46H7ZNkeO/+XXkPwm1BNQAGXI7OdNOr0pyBcOY/wJD/j4ErvN2inNs20XpSglTTjz3gGhA2f0IunmKa8M8juBSBjV6XbwFVQdN+WXX1SDgvxRyPeWBNvIwcnAuLn5KD0vz7YXwFmH4TXHI6oJzaKNtsm+TeIrTV+8jkAZDE4OwKJdpyyEACiPyyYDhs+lRqZzkXzUw4C5vG5uw1UpPl92Tbqeb179V/DtS5S9am6/Kp3Jd8Gda9J9eTLsLKV+HUBpm9tu79vL1PUebjK4Hk8ONw62y4dwNEtHTd5vwex0E1Lyq0lxMMgHKNof7VcXDMscQLMhxtWiDxvLdbkzvlGsssY8MXAks59jnOTAts+AjmDfbMvtXPaYgZH6h7t/uvmdaOH+HLKPipYGf4ag+VujpYUmSI7NwuuX3TT5JU60kXT7revnQCDp6AH1vAQ4fhyjn4uYMcnMxUyRGqf2/e3qtElBSkXD4KkuLkc5W/PvPta9ziKCeQ1Yw5d13Y5yheevE49tIOmDmrvQWSgF7nTpkUAbKOWV6UqQ83TUnz2oEyjJhw1hrQ2oJaQ/KFrnURzaUEyK+d4Yq1QG39Qe4Njfr4wi2/y5C3b+BVkw+TY13+D+YOkt9Dp7Hebk3uNBkuuVLndkvvekYrK2z9GpY+K8Pyu3+Bh47mvCc6LcOQwstLnwF8oMtnuauxlxMbP4MlT8r1xFOefe1saA5VNjSHqoiIOwLfWKfHGz5ykOg5ybPvkRgHv3WVHo+0HjkD8YcluAI542v5LHR4L+/vt/ZdWGEtweBXDIZHQ0CJjLe1pMiwTn7nUO2dAX/eKe/X+RMILAkbPpTyDN2+yvlSN5ZUqXEUVMbzlfpPb5U2FS8vv7cN/5P6YP1mQ0hFz75XUXZ8JfzzKCTFQof/QZ3bvd2iost2GL0ag8mlz8PGjx0nUoP/g7INvdumzKQkyPJfSH27uAQIe4UCO35rQJUNe0B14TyhYSW93RyVGUsq/NDcmnuErDFXe4Dn38c0Zbhr5SuOM3yQmWitX5IiiseWQkAY3L0q67yn7Cx4WKa423ZkDx2DkArutd8TEuPAkix1tEBmi/07Xq43GZ4/6wcqz5tzj/Q4mKnSs/R4fNblK9S1wbRIPmb8UWhwv/Ts/txOcuVqWsvSFNZZnJYU+KK0vXxNnG8UYU+f1ICqsLAHVPuWE1rjGq64XBQkxcuaZWHVpRBdftg7A2b3R9IPnRZi9i8uByTTIsNzIZXcL4J3+j8Zmkk4C40egu4eLljqKdN7y+8dpCfogd3ebY/KmTn3wu6frQFVEDwepwFVUZOaLLmMYVU9V9Zg/f9g2fMSNIVWhQf2SKCScE5yrAp7L9zRpbD6DShenriW7xAWWU1n+RU6xfI4ZqwKTkCIlCvIT2e2kS6YAkmGTk2UITdPDWGVawTDT0iPUHBZz7xmds5sk0TugFDo8IEUHM3OkYWO6+f3WHfwBVhhOzFWKoJHr5c6ZO3eLfw7/cKg/bsyS/LSSejwvgZTRU3CBZh6PZzfLTXH7lmXsyWvshO9DjDk5DD2gORwBpVyr7RGQdn9O+ybAbVuhyYPw8VLBfr2hbTfrhAqCl8mlf/q3e3YaZWs4yh+1+Lp/Kl55BuQ92Dq3G5ZciQ1OefPmXGL1LPaOQUW5mDJmxNrZPFiZwU95LflS1meJilOZvid3lqw719UhVaGu5bBg3tlhQFVtBycK8EUyGQR55m+7qh/n+OEpMYtngnSCsKeafDn7TKjd9FjMCcfZg9mQwMqdfUwTVj3Afx4Hawa7V7ZgsyUrAHDDkpi5v3b4NHTMuul00eefy937JsF39WXJPrpvXP+u7hy2jEF/HIOZsgkxaa/7+jS3LXVHUkXYetXuPQY+gYW3PtfrfZMkwkRZ3KwfJLyjlK1AcORz1Sqdpab51iNPtB3NkS2lhPG+GOeeV2A3b/C15VlH33hgOdeFyBmk+vt/bM9+/o5oAGVunocWwrLX4BT62UM3VNnbGkFhMgsFx8/CAwrnDPHdv2EvWTAkX/gypmcPa/zWFm8OyAUbng7++0r3whVndaCNHzh5Orctjbv9k6HuEOO2w0flGGKbZMk2FK5t/s3+OM2KY76UxspB6Jy5sx2OZlJLoChpsiW0G8WNBoGt8zIuqxKbi1+Qvaj+2ZaC3F6gCVFalnFH5XgZ0Uul6rKTt2BrsvlBJWG8QW7PqrmUKmrR2Kc6+2kuIy3uxZUaGet82RIr1pQadfHL0VLxfeI5q4zERs9CA0Gy1lvTmby+PjKTn1qa4jZLAFV7XzOY3N25azrbf9gmHGTXP/vGxi4suDacrU4tVH+jmaqTPSIPQDFSmf/vGvdofkwrTdggbKNYdBGOenKTzX6yMXTEs9LLzWGTIpx197psPotsDilBxhuLM2VkbINZSb0tokQd1DqZ+Ui28ETtIdKXT2q9YLat8tZSrWboPZt3m6R9zR9TEpHdPxQggrndQWvnIPvm8C8QTC5iSRzO/Pxy920aF9/GLgKbl8o+TiePFPOyuUYKXyKNd+j/hBJirc5sQpS3KgAfq2qO9AxbBrRUqppq+ztneHIPTqz1fNDWgWpyxfgV1x64OsPyTpl4OA8Wdfzq4pwLINldxIuwJ93wekt1iANmYnd3o0afZkpHg6tX3TtNS9AGlCpou3iCcfQjq8/9J4C178iJQuiMyjAea0wDKnD1XIkBIe7PrZzimP9NTMVDv0t1y+fgb/uh1m3Qsy/Gb+uack4yd0vCCp3kUTnghJ/HCyJgClnuyWiXJOrq3QHP82nypUz26WnceBq62VlzqbjXzwpM12vZZW7OGrGhVSG0Cruv+a692FiXVnyKTeTS9xVb6CckCZegH8ehiVPZ77tguGyjufFE7Do8fSPW5Kkbp2zktUhtFL6bT2l5i1SuqaA6ZCfKroWPQGb/w/wkWGt7l/L6umr3pCAYt8sKTsQGObtluYv05T8sZQEqNrd0bu0/w9ZVzCoDPT+UaZDR6+X1eNtDB+ocqNcX/ykdZjQlGD04aOu7xO9QRLcE85Bx//JzEZvKtcYqvaQoZagUtBgCJSuDWUaSsBYzTtnqUWKaZGK/NHrZU2+FS/JwS+ojCyYnJNgasEjsHW85BbevhAiW+V/uwuL1GRYOAKOLoaG98vnP7dbTmbcDeaj18PyF+X6+d3ye230oPttzoljy2HHZMftrV9LfmVG/Is7euYymuGbkgCVusjvyJbXWbWnJ1ubnuEDLUbCXwW7j9KAylssqbDndzBTJOdEa8Bk7HKMHMgjW7muH5UYZw2mACxwYS/Mvh3q3om9hkrKZUnG9nZAZZqw5m3pGq89AFo+49nXX/MWrHpdrjccCj2+kfece4/kwMQdlmKkZzOYsXX9axDVWq5fOmXtkjfl92aarvWc1rxtXSPPAkufk2FF2wHXkgIbx8rfockjEN7Us58xIz6+0H+efL7gCPAvJvdXaJv/73212DZJ6o5huE7iSDgLMRslYM3K5dMSTIH0UG3+P+j1fX61tvDZ8T3897VcX/GS1IJq6qEkbpdyJEb68iT5KTbNcGVYjYy3O79Xar/t+kmKw974pevjpgV+7SRLg2FKGYamj0rvcX5r8RQE1YRX8iHHLBM65Octi5+EOXfB3Hth/v3ebk3hFH8MvqsnScbf1ZMuZRv/4PRDWZYkOcg714Na90HBtDWtPb9L0b35D8r1Va/J7Lelz3q+rMCun53e99f0j5umFG9MKyAUGg9z3L5htASfPn7Q6eP0xTHtv28fWcPPOeF24ydSXfm/CfBLp4Ib/jEMqRJtC6ZU7lw6ae3RtC4kbevdDCoD4S2yf35AKASWsiaxWzI/8F6t0k7N92TQU74tNH9K/tdq9JEe2IJSoy+UrifXi0fBbX+l3+bAPPiurgzzXYyGW/+UHmJnKQlS6NdMxZ7rWLVHwRXerdShYN7HSgOqnDrntJyGaaY/YBycB790lAOodR2hLNmW6gAZsnCWdDF/p3xbUqTg45lt+fcenrDxExleAjljPvyP47GLJ6FSJ/CzLhbsGwTdv4Uy9VzzeHY7BRsF5fIZScI8uRa2T4KdU10fT7zg2fer5tR9bjvzMwy46SepoBxW1fF7tDMkgd+5YG2FG+Cxs/DE5fRn2fv/kCTbsg3l/frPdU1cP79HbpupUpsq3fupQqnRg/IdAekpv+9f6WEavDVnBWX9AuGORTIz9IY3ofWo/GnnvtlychR3NPttC4olFfbPcdwOqSxBkKcYhixAPuK8zKQtyJOGoJLyHRh2ROrslUizhujl0/DnHY4k87gDkg6Qln+wDIWCBN2NH87XZnubDvnl1Iyb4amTMhTya2c4u0MOSDf9JENLs26Vs5PjK6BYWVnKISt175LcBZBu0H8eldcp09A6cwnoNVkqc9skXZQDV5n67lXl/uMOKc8P0OsHqH+vXLekwqkNMnwSVlXyA9aNkbHvWgOk96egzizijsgQko3hC5HXOW7P7GOtiG0dk09NcMxkq9pD1tMDqNLNM+1JO/yVldQER3IqhiRfVu0hAWGNvlAtgx1PVu97cJ58N2r0zXhouMP/IOp6ORusc6fj/uo3yeWv+6VWk23nZ5NRPobhA75pzrMunoSZtwLWz1SqDkRd57pNk+GSf5UUL591+yTJk8ivNRU9JTVJegxLlPfckkFFSfFIyZVKuezIf8nt7yG8KfSY4PGm2e34AebdBxiwaSw8uL9w9EgaPrJY+cXjgFmwPS/5KfmKFPUNrZJ54viBP2XRZGfHlsgxI+0+qvsEaPGMlG7J64ojR5dIp0atW9OPTBQiGlDl1OUY6dE5OA/OWg/We36D5k9KhdpU2/Rsn5z1QNzwtiTqmSnw71eOrmPDR+4DyW2wBVQXT8APzeWLXrIW3Lshb4vvpiQ4gimQGV+2gOrPO2HvNMCAG96SITdbfsTRJRLUeCo/IDuXonFUvzag0VAoU9fx+IUD2IMpG9sBoeNHjgCj7kD32nHhAEzrKQHJ9a9Cm1ezf05IRWg7WpZBKVUbWr0gO968WPWa5C4B1LkLbv4p/TY+vlmvYVh3oByUHE+Q3r3ybTLe3rTAhf1yVuofLGvl2YIpkO999HrX5OOIFvDwCTmhmHGzzBxc/ab0eJSpl8MPW8BME6bfJIVPAbqOK7jvd2FiGHlfLigxFlaPlqnxrUdBqVo5e96eaTI5IrwJtH8/6wTu4ysddbEunYSLx3L+PvnJMGDAfFj/HgSVhbave7tF7ruwH6a2kRUTqnSXnmjnkis2ZRpgH8Kz7Yejrs/4hM8w8n6yYkmR2l5HFsjtde/JCUAhnb2rQ365sWq0nNE5H+iLlZNk6XbvSP2jsOrQ6vnsX8swoEpXyVM4tlx2FmaqHMwMX7k4LzC7b6ZjKZALe2XILi98A6FcU+z/DBWtY8wJF6zBFIAJK1+BQ/Ncn3s6k6n0npSaDDFbZBiiRj+5L6SSlEJw1uY16xUfOZNq84ZjuMvHV3pqyjZwraKdFxv+JwmalmQJbmzlBrLT8hl49CzctyXvwRTI393mwJ95e42q3eGB3VKXqulj0PwJuPmXjLe1pMCvXWFibfi6iuxgS9eBih1dt7t8Ov1zA0pI4G+mAqb8zo4vz1ubC0LCOUcwBTJb6+Ra77WnKFr8FGz6VJKzp+VwVuXFEzJcdGwJbPrMaXJJJurehX1/Fdla9rGFRZm60HMSdPpQZjkWddu+cwzXH/5bSmiAnJz+N0FOwC2p0kPdfw40e0L2vd2+gtv+9nx7dv/iCKZA9udxhz3/Ph5S5Hqoxo0bx//+9z9OnjxJgwYNGDt2LO3bt890+6VLlzJy5Ei2b99O+fLlef755xk+fHju39jwAV8/qD9Iei32/CqBSfEIebz1S1CxMxyYLQtVlsxBcua2STD/AVx6Wlo+K1VqMaCN0xlP2UbWdljPFko79dbk6nMYMrV35w8ytGcbIgoIkWDxUrTTezZ1fHkNX8dYeH5JSYSf28uSB4Gl4O410P0bGc9PW3G41bPQ4D5ZJiWoZPrXWvgo/GvtXevxHTQckrc2BZZE/j6GnH35ZjPUmhQv6+dFr5c23/wb1OqXt/cGqNnPketW/ea8v07JGnKpPSDr7U5tlAMdQOI52P69JKvf+gf81g2i10o7MhtKLVNP/ia2isjr/wcNH8j/itF5EVRKgnH7DtqUis62WY8qe7EHrUUfLXDxaM6Gxte84xh+NjKoxG1aXPPzKnWCB/fIe5W/IeMeE+UZpWrJCZHhK/+ztmW15twD+6bL9ZNroctnUpYkr6VJLClSbuPAbKh5K3T9IuO/qyXF9XZoVdeOhkKmEO7lMvfLL7/w1FNPMW7cOG644Qa++uorevXqxY4dO6hcOX1BwYMHD9K7d2+GDRvGjz/+yMqVK3n00UcpV64cAwZkc2BJyzcAAkrK9Qt74dweGdM9sw26fCq9KL92lC/juvfgzuVQsV3mr3duj8z4Sjts1WCI69CWTcX2MoviyGKo2TfjbXKqWGkZqnRpzy7ptr58Wj6Dfwno8J4kmp7ZKvkBzmUL8kP0egmmQBKbd//s1BOVgazG0rd+67j+3zd5D6havyzLm5zfA9e9kP0w64aPHZXHLSlSR8adgKrtmxDVxpFDlRd7Z8rOsFIX199D3GEJIMKbyUEL5HvsGyhtN1MdgXtACNyzRnKOMqtNlJIo5Rmcl5e4sE+GjkMqSd5haGWZSXjhAKRckXxAb+WdGD5w2yL4vpH8fjGlFpOSv//qt+TEyydA/g8aDEq/XeuXpL5ZSgK0fSv7v2ViLPw7znHbPwSaWYtBJpyX4fXoDXKy1GOC/I1Sk+RSoV3O6mKpvKt/n6xDeGqTHIuKR8r9Rxc6tjm8IMOnZij5EmyxllJo+ohjaHnfLNhm3Udv/UrySmvekv75dQdK+sDBeVIOpfdPmZcYWve+9LBVaA9dP/fKsGCRCqg+/vhjHnzwQYYOHQrA2LFjmT9/Pl9++SVjxoxJt/348eOpXLkyY8eOBaBevXps2LCBDz/8MPcBVUqC1Bup3ttagdt6hnV2m/RI1LjFtRpszObMA6qD8yTPJG2ScLkmMrySGVuSsaeseRu2fCHd6Jdj4NwOaVNwhOS+2Hrfwgto6YmS1aUHyJIsB/OyjeHUZlmoNSlOapzkdDmZyBYS2JgWGdvPq4ASUjA0p5yDCZBkZ3cYhnzn0jq7Q4ZGq3TLOtA9vVWCHMOQPKoS5WUIMOEC/NDC0TvQf66cbZYoD7cvkgNpREvrcIuTrA5oV0679nCC5FoUj5SJHMeXQ0CY9C6utOaiXfcitE//v1tgSlWHIdtluLtcUxmGVzIUt+ZNx+2/7pPAqavT8FxKghR89A2CevdBq+eyf13fIAnOky4BppTtsP2PbJvo2LdunySL/papB1Nay0lsyVpwz1rpWVT5wzCkTlRade+Gf62BUb170z8es0X+ZmUby0iGLbCeN1hO2jBkKai+1l6utOv4Zdbr6BsAN03Jvt0rXoW11lzT87sholnGnyOfFZmAKikpiY0bN/Liiy+63N+9e3dWrVqV4XNWr15N9+6uBcR69OjBhAkTSE5Oxt8/faSbmJhIYqJj/a+4uDQL7K57X5JxL+x1vX//bKnJkhQns/xq9sv8w+zOIH/F8JEovaDO1k//5zio7Z/tVKbfkFwwWzAF1i59k1yt75YXJcrDXculSFzkddKz82tXxwy1+Q/mPKC6da70TAWWlCGngtL8aTi8UNatKtcEbs6Hsg0n18JP7WTyQvEouH9n5sVL9/8BmI61uC7sl5/ndzuCKcMHji2THqaYjTIMbCvQZ1pg/lAJxsrfALfOzjxXpEQF+Q4fnCs7zOtelNk9Z7Y5cqmSL7rmzGz6LH8DqtQk+d4cWiCB54V90tt7y3QJlkFmtHq62GpRF3sQyVty6kHf8rmkIdjKKcy6FQ5Z6xP995UU1a3cWdaKxIRiZdK/rm8AdP5McnHKNoC2bzgeCyqNIz8VGco/MMexr72wV27Xz+CArvJX189lprdfUPrSEAnn4ed2EmCbqXIMs6WHRK/DXuMsep3jOTVvkfyr/bOlE2HHDzKZq949eWvfxo9dbxdkEVQnRSagOnPmDKmpqURERLjcHxERQXR0dIbPiY6OznD7lJQUzpw5Q1RU+imcY8aMYfTo0Zk35ORquWQkKU6Kod29OvMDXEqiJFXaVvIuVk6GVUrXke7zguISHJnWg6QBAcWhy/9JonzZhhB7SAprJpyFjh9Ds8fyt12RLV2n2vsXl3bhA365mCpdrLQM0RW04LIyNJafDv7l6N28dFLODit1TL/dlbMy086mWFkp9QFyJlmqtrV+lC8ElobZt8r3YuNYGHpQPsvxFdJzALK8zfbJ0GxExu2KOyzDsLVuk2UfVoyS0iAlKkrPhK2cRGBJGVo2DBluzE+7foKdP8r1K9ZJHYcXSBJ144esqxVY5PeiqxU4NH1Efm/2GcuGBKC2IZv4445gysbHX/JC/35QjqHdxrsWjgVJc7Ad/Cp3ldl+4c2gXCMZbjq/V2b1NbxfhoNt9fhss/xKpSkcqTzj4gnwC844HxWsS1Rl0nsbf0yG9kD+Ts71DWv2c5xANXXabxg+kipT+zb4pQPgIx0NYdUzn32cleLlIXY/YEJwpMwK94IiE1DZGGl6cEzTTHdfdttndL/NqFGjGDlypP12XFwclSrlYpHFczvl4LFmjCTchTeFdu9KN3XCBZhynZxpFSsnB6ZGw3JXmyP5EuDjfh2Wsg2gwwcy5BcQJnlSIEnVC4ZJva2gshBhLdVgWmDJU3IQKsgDT9f/gwVJknvR8aOCe9/CrHxb7D0HxcrJmnYZuXLWaQjSR87+bL0L/sWk9MaxZXISsG2itWSHRXqRZt0qS9W4DDGb0gubmdn9rbXBkNplth6wi9Ycqvij8hrn90hPh+Ersw7zVSb7hoAQ+Ocxx7Ihh/+W2VqF1eUzcsJWsoBmuJWpD8Oj4dIJmdwRe0iW8rDtd/yLSwBlS3Oo2EkKw/51nyPYX/2ma0BlmrK/sVn5qvSyGr6yCHNUa2j/rms7oq6DvrNkxnG13unrn3nb2V2SKlG5a/ZLXKUmS3kdW89oYbHqDSl94eMvPbc1cjn5pUx9+fxHFkqZFVtF92PLYLP17x1YCppkUNTTPgvbViD0cN4Cqlv/kM8QUEKOt176HReZgKps2bL4+vqm642KiYlJ1wtlExkZmeH2fn5+lCmTQXc0EBgYSGBgBsls/sFADpbTKNNQzuxWWnubTq6Bo8tgyDapiG7rvr5yWs7ccxNMbZsEfw+THZZfMcCUvAXnbvPcaPWcXCamqRN05Yz8TDhjrfpu7SEKCM1+ho1pyvTamE0yI9L2zxF7UBJQs6q+vHOqrIcVUlkKpoZUkFlYAzJY9uBalXwFFj6GfeZhzX5yZpmRUrXkrH/H91A8PH3wEhDiCJjq3ClFE5MvydDdiZXyHjt/lOGwY8ulxEZWXfJxRxwFTRMuOD1gQvwR120bDJGhtvxWd6AU7tw/S3rG/IrJBIu6d8MKp1IcB62rFVhSJBANDi88RRoPzIVZ/SR4af6UVM8uCH6BMqMqoyLFgWEyVLv7V8lRbP+e/L5K1bGu24bMEJtYVw6SbV6XnlR7vT4c9fZMiyQeZza7suYtchI37z4J+m/6CSp38ehHzZNjy+DXLvKdL1lLKotnVnD55DqY1kNODNu+kfVkm4Jkmo4C05YUKROT24DKx1dKJpzdKX9zW2B52CmRPfG89FxVTDPpo0ZfSY04/S+EN8/7TOYydTOu0VfAikwdqoCAAFq0aMGCBa4zDBYsWEDbthmX+2/Tpk267f/++29atmyZYf5UlpyXmmn4oFR/rXFL+lL6Hf4nOw5n53bILKLSdQDDMdyW24KHK16y7oQskHJJXnP1aPeXkHGeLednPfM0fKStbUdLcdEqN8osw+zyqFa/JT1c/34JP90gw1OLn4Zvq8NX5V2X3HGWfBn+GiI73xOrHIv9Kldn/nPK3zMlT2zRExlvaxhSbf/Rs7KEREbFEE2L7OTLNpRhvoGrrEPPTrkzdQdKMnDH/2UdULd7x1peJMA18MpoqHb1G9l8UA/x9ZfikQlnIe6gzB5t+Yz0jjgvp1HnDhn2mFATxkfKbLO0U7bdcfmMVMpPOJ/75276zNGWTZ9KT4cnmSbs+gVWvg7n92W/7anNEtQvfVbyYvyLO3quev0gdc6ajZD9yoW9MtS7YhTsSZM7GmQ9uTJ8sl/RYOEICXQvn878+17Q9s3G/n9yYa+cdGybBMdWyOLtztZ/ID2MmFLPMPmK6+PJV6R3NyWhABruxDAkaDZ85Xqey/H4yKiHcy9d9d6O40WJihI4pRUYCoM2SW/ovesLX+9dLhWZHiqAkSNHMmjQIFq2bEmbNm34+uuvOXLkiL2u1KhRozh+/Djffy+rnQ8fPpzPP/+ckSNHMmzYMFavXs2ECRP46ac8RLK+gUCiBBstnpYvTyNrsnOdO2Hv79LtXaULzEuTNFntJtnphDeFAfNgx4+S2J7b2WclKsKlU7gkbgJYLBlunmPdxkvibsoVST708Zfkz8qdZTp91TQ7O0uKTKsNqeTaw2ZJlaVq7Ex5XdvCvJYUOThEtZEZIUGl5cCb0UHaMCR/wjdALpdPy/TtwNDC03PgDaXrSC6UrRcRJA+o25eZB7vFSmd8/5Wzsv7k2e2yTEy/2dKjGNFCenUO/Cm9hVkN8zlr8rAEXz6+8n1v86rkxCx7Ac7+57rt8ZU5e83cOr5SZtjW6OtYNiN6nWM4M/6oDGee2mDNyakj5UFq3CITTuKta8Ud/hv+eURKTdS9y73vXPwx+L6pBHVpZ9DmRNkG0h7DV+rweLqm147v5WTG8JEhuYcOu1ZONy2SNHwpWv7v0y7AveMHx1BdcFlZWPv4KqkpZ9tX2X7/NlW6Qc/Jks8W0VwC+qz4l7D+DYzCU0CzchfYaE1DCA6XHrTL1hERw0dOum1lSuzBuyEpFmd3yN/VL0iC7Skt5WSyZA24Z33uZjKeWCNDZ9VvgjPb5Ttc/SbrqEoODJgPGz6U97zuxey3z6mo1jD4P+l9qtw185Izho/j/8E0JZcq7oiUzrCVbcirmAIoRu2kSAVUd955J2fPnuXNN9/k5MmTNGzYkLlz51KlShUATp48yZEjjqGFatWqMXfuXJ5++mm++OILypcvz2effZb7kgkgs8/OrZHk37Rl9Ct3lgvAibWuheoqdYW+Tku9xB2RYZSdP8oXv+P/ct6GW36TaajHlrrev2MyhLuRX1SmHtydZqZkZrkKpkUKPB5bIkHm7YukPgjIASrt7ApLipwBxR2Wf5Yy9WF6T+kCx5SegsunZAio5ySp0B5SRXZQ/xcmSfLVb4Fd1qmzPgFSG8sbCefecmK1JFdHtZYA9N6N8Ht3makHUoDz6FLHdzCndk6RHTtIgvGxpdIT6RsgCZ6piXLmPW+QFFnNCeedZokomNLKcbZeuq5jjcX6Q3LX1pw4tACmWWf1rh4ND+yVJNv690kPDBYJHE+sdAxNnt8t5UMCS8lMRNtkEUz4byL8960MV7gzBfvQfMc+4fIpWeWgXi6WRGr3ruTKXYmR+nGePqGIXu9I+k44KwsQO9e5W/cerHiZdLP+bDJKVt7v1HsDUvT42BJ5j0pdoP88Cbwb3Of6vPhjsOhx+c50/EDWkdw0VgKuYmUkmOySTWX1nEi6KMPAJSo4arDlVvXeUm/wzH/yf7Tlc8djpgWWvyS9+/88JhX5gyMlJy3pvARQYdVh0GY48IejuOyF/dKrn9Pvx+5fZckwAP9QSLb+r0VeJ/+zOfmuhFWVE+n8UKZuzmomnt8rx8NTm2DZc4Ahve/378p7IdeEC3KsKUCGacvSVhmKi4sjLCyM2N0LCV0z0nHmEZHJ7KSf2sOJFdYbBty5xLG8C8D3zWRKPchZ4BNpFpjMzu895GzVWVAZeOxMxttnJyVRzqpCKuWsLMK5PfCdrVaWIWfvN011PL70eTnbAclZ6TVZqrxv+lSCpOZPwWfFyXDHPHCV9JBYUmBskNMCwxm4e232Carxx2UtwuAIWby3MFbrzk78MZhQQ3r/zFS4ZRrU6g/bf5AEYJug0tDr+9zVKdszTWp82RK3B//nOFn4toZ16jwyVPjA7ty3PfYQfOtU1Tg4Snowzvwnf5N71kqOnKeseFkO/raeENv3CeSAdfGEFOOdf382L+QUOBg+Unen1+S8t+vwIvi9m7ymjx/ct9W9wrw2nipncmK11AlLTZTimXcscT2IzbhFDvoZqX6zJDKnnahyaL4Mm6ZVsqZ8zzLLNZpxi5TdwJQhwSu2pZ4MWV/0+pdz+eEyYJrwUxvHMkPdvk4/GzEzKYkydOdc+805kHdm+EowvDyLk7+mj0mu6dQ2EvyYpkwW8Q2Q/WV2CwHPHQS7pqavaQjwyOmsc1adXTwhayuWrAkNBhdsNfoDc2DmLfIZgspaTz6s/3+Pnsm4/EZOnN1B3PgGhL0CsbGxhIbmsKfdDUUmh8rr/nlEDgSn/5VpwRk5v8+x9hFIjSDnYAqsiZfWPKrw5rlvR0aJm3k9wzqzA74sB99UlbpGWdXuOLZMepPijzoqxmPKcJOtttG/X0lSIyY0eABGnIMafeQMqPMnsnjq+T1kOfMKZEcUXM6Rx+WTwc7XNoyYGdOUg8TaMZJzseoNx2NHFsnwzvm9mT690Ig9KH8XM1V+H2e2y/317nbkoICsv/XnXRnvWDNTq78Mz9TsK/WynHteO39qzaUzZHg6L+ddoVWkaKzN5ZPyPwQyhLszBwX7cqNSF+y7tLDqrrMfQ6tIcHV8Bem+f8ERae5z+qymCfXuxWIx+fjjDdx77xwWLUqTYJ+VlERrAGeR123zetbB1MG/YPmo7NcUPL9PAu1PAiVv0R3l28DQA3DbP7Joe3Kak7wG92H/vZbvCGHWZbWKR8kSJBnN+q3aA26Zmf7+C/skqM1M4gX5nZsW12FtzDS33ZAU5/r7Xfd+zv5vzu6AryrCl+Hw1/2O/4nM1pYzU2U43UWa796BObJP7z9HKsb3/0tOCiY3gq8ry6L0WanWK+O2l6gox6lPg2W/kFXe3YUDsm7nujHynOWjsn5PkKG0Jc/KcG9G+4bLMRKopySmf8zGtMjKEvOHOl4j4Qz2/79KXay1yfKodF0ZaixAGlDllGmxXszMEwcXPGxdwgLAgC6fy5f1yjnHNp0/leDihreg36zct6PN69DtG1mSpNMnUoCx1w+5fx2AOXdaZ/EhtbVOZFJf69Rm+KWTDI383g3qOVXPtiRLEjnA9u8c9+/8PuPX2jcT1xwwH6mJdOOX0q1/KRqmXg+J8dKz1WyE9PJV6iIHeJDkxio9sv5stuEq20K9tkD30N9S2X75KPixpeQvFGZRrR25doGlZMjPkiKBjm2o1cb2/cwpw5Aew1YvpM9hsSTJxbRI4HNkYYYvke3rt3s7zX2+yG7HIsNYpim9b+46utS6+kCKDOsN2uSaB2STUWHYOgPTV2628SsGGz/iuy9X8MwzS/jpp1307Pk7J09ag46URDnZiD+W8fPjjjhmOBo+WS/senQpTO8lCcw/t5d9R0YsqRKkxR6Sz7vqtYwXq84NH3/p8fy1E0ys41rxvvZt0kNZ63Y4sVQ+Q4cPpGcnOIMcl5QE+c5cOiH1zZwFlcl8ndNDf0t+YGCo5Ew515wyfKVsgycEhErunE3sfplJmZ3NX1jXWUVyQG1/y9q3ZdL77SMBQc1b5XM1fEDqIxUrh33mtK0OW7Ve0HksRDSFPb/JfZZkGXLOSr274c5l0GMSXDdK/h4V2svJ+v7Zkhe7+xfrEGwmdvzomHEJ6euLpXXlnBTy3DRW8sZsJ0amKYHbnHvgy0j4qS1MauAY5k9r7buw9Blr3lkG+60Gg90b3jZ84Jbf8/78PNCAKqe6fG5NTDcl7+LwP+m3SbBWCAbZARz6S84ivyrvmELqFyh5EK1fyv0SCqnJ0lO27j253fxJGcrKa00q50APMh9+idmM4wtvykwWW3ATEAIVrUUl7eugWWeLzB8KW79xPcgfWZTmxS2SD9VEJhaw/kNZoDflkvQGtnoeyreGOxZK1zjI/RuyyT3zC5LZmIDUYLpX2nF8pdzGlDPVzP7Z3XX5DPzYCj4JgEVP5q2HB6Tr/67lsjzKsEOSi/FpMHxRRmqY1R8kO+sSFaD3D7nvql/wsAx/TGogr/tbd8lfSXvSsPu3vLW/clfpBavUWeqItXwOe0C94mUYHyXDuxs+zvJlsrXlc8eU/EN/kWEv6PGVMlusmLX30zZUVud2eGCXDOVX6uL63JTLcPgfDq6Yg6+vgcVikpxs4eTJS/L/+PMNktg/oWbGvUp7nXbopkV6BdO6cg42fuI4eJoWOZja8tvSWvaCtafN+p3yDcpd0duMHP5bhn1A8rzSzsYNreL4LGYKLHseZvaRA6vzbMjUZElrmHuvLFCeegX779Pwk5OjjGZyndkG03rJgT/xgpQDubDP8bxb53hueNgw0pctcE4vSL4k9aXSBvphVbEv3OwX7Og9CSopE5IMH9nvV+ggvXgGkuoQGAqPnpa1Cbt/DUN2SLma1i9Cj4mu7xFY0tFjalrkf9GlBEkGKraHhoNlYkCPCfJ7SjtEm9HJxcm10nt/JU0wnna5qbTij0gvpm0R5dNb5ff1TRX5X941Fft3M3Y/TGokkxTSSnsCH1bTMSph+LkWeM6r/F7dIw0NqHLq7A7HDtu0OKpHO6vgtHafmeIoGpia5FrQLq92/iiJerH75az0ZB4qcsdska7kT0tA5Rsd9xcLlzOjjA781dLkQqQmyA6//mAYvF0OUP9NkBpcbV6TM6VzO+UsbsFDklA9fyh8XQ2OL7O/zOqo6+ly51JeL1aO2J0/yY7M31pfCxxT8G32OB3Ud+dgSZfu30gCd6WOMPdu+L6xHNhtrxlShZTSjYmJuYTHUwn/HSe1uCzJsPkzx1BXXvj4STJ/QAkpQWFJllIHGz6UvKlHT8PDxzI+WGdnm1OvYsoVWQT13/Fy1l3GaQjwv6/TlwPJCcOQWbF3LIKWIyU/wtYbdOW0dTHuFElETUmQGUvbvpMZiLlRqg722lyBJTOuzbVgOMQesNaAqwAtn5UaZxXayoF80Qjp1QwsaX2CY/f4YLfTRETIQalv3xo0bRou3/FTG2UDS4r8f/7WDT72k2EWS4qj99YmbR0e05Qe0yUj5fk+1u9mqVryvbWkSEL97t/kAH90SfplNm7+xf3p5iec9yWGrGvo7PjKNAcna5AUs8m1J+3sdjjvdJKSckUO0LX6w+0LMp/Nd34fYHEENjudek3MFM8vilznTjnhKlZOTkxs9Y8uHIBvqsGketJL6JwG0fwpmRBT9264/R/XCRg3/ywnC62eg34z5TtmWod5TzmlgYDkNXV4X8qMpK1M7htgXYrJuj868AeMK5P18PjZnRIYLXsRZvZhgo8fFuvzTZDeqqppevQvnpRRhy3j5GSkRj/sf9M907IeIizb0HHy7FdMTurWvy85q2lnoIPc53xiYdMkzUSPco0cv7M6t8s+z/4Zd8DsATIpK+16oYVIEczS9ZKzO11vh1VLv03VHvLlNHxlRxlcTsb9TUv2Naf2zZKz1DINoOOHGfc6pa2Lk5c6OStfgYvHpU17foYe38nwwZUYOeuMaJG+aF6J8jIdfleachPHl0sQNLG2BFk2wZGOthk+Egjtm+nyVBO4ZcB8btnzO6NtOWlR18sU3gsH5B+o1XOuSZlVussitiDDOtkxDDkwH10st89ss541JwAGx45e4IYqYzlyJpjevasxa9at+Pl56BwjMMwpOHVzqndKImybIDv3oDJwSdrv9pRigKhW1oOpta2mKb17vgGO4WCbTZ9BzwxOJHKjzp2Ok5Fi4ZIzYfrIMMyh+VLAEiSoGXow86Uw0rruRTlpSbwglx3fO8qa2NgPyqb8Lm3FKBPjJB8Ei/TS1Ltbhub3/yG9QSXKU+2WVzg0qDJnzyYQEREsKy2EVpVeisQLEgicWCPlGECGWUKryoxVmxp90vcUHPzLMUkFZJj1xq/k/2f9/yTfyFamoMlwa6+VU/Af0SL3hRgz4nyyUjwi/aSbxU86gh2/4tKDjCEzOUMrO7YLqyYFfJOt3x3/EjIrL7vE4io3yr4vXc4R8l0o2yi3nyhrPr7QI4PhtJ1THDMyT66W2ci2Re59/eH6V9I/B+TzdXjPcbv+fdZZf4bU5bLZ8DFs+EhK6Nw0NePq6ml7VUyLVJ3PqKhuapL0kCacAzOV32oN4GhoFUzrxAoDJHhLO3QWd9ixzzZ8ZHjW8JG/ccwmGYXJLPj18ZMTpDPb5W8fVMr6vXYKpvxDpXfXTJH2V2if/nVq3CQzcQ/Nl2PO706J/UfTzGSf2c+6tIz1/7XfDOlhS02WtIhdU6WWYYPBOS8XkQ80oMqpOrfB/u/kCxJaBa5/Pf02NW6W2irHV8iBI7SKrGNUorycpdskX5J/1NJ1ZYd0+QzMvk1e+9hyuS+jf9z6gyTP4OhiSRQNKiPF+MKbyULCORFYEvv4vX8Jx9CdTeIF2YlPv0kCr/ZjJHehdAYBYWKcBGPOwRTImLhtinxwhEzhTRtQlaxJfEAIvQ/OwYKBD6b0uJmpma8u3nsK7L5FDvg5XSQ5bSX6c7bA2GTShpYcOysJ73PnHmTLyn9pmfC5HMzajnavkneTRyRZP3qDdVi2hOz88nKm/c9wWUMPpBc0ornsxDKqYJ1bt86VZNRD/0hQXeVGR7HatPllKVfSPz+3qnSVqdCxB+WMdEYfCSiKV5BVAGwSL8CSp6Hnd5m9kquEs4515wwfCfbTBlTNnoD5Q+T65VMSMNW8Rf4H/AIdw5yBJeX32+A+l2n9/kBkZHEJui4ckGT9e9bJiYZfsOSDOFtv/fuEVZdab+d2wWehErD0nyc/Z2fQq/jfN47AzPnguvt3a9Kuk1Mb5SDd6tmc/Z4yE9nKmkdkyvBVWv7FrT2LpvxfdPyffJ7ad7jO2AsMk/y1jR8BhvRW52SWVkAJKSFwdBnM7gfJF7H4BmNc9xxGgyE5n63mrjL1rcN6vtaLD3xXXwKQtqNz9nve9JmjhELLZ6GR9YTxwgHHd+RQtKyZ2dbpOHJkkSR5l20sQYJtCNnwldl3GUmMdRmy+7HBEAxLCr6YpBi+pPgGEOTc02wT0QKi2sLJVfJ9rzvQsXix4etYyzUzPn5SMNemyo2uozARzSUH7uJx2RfWyGT2camacgHJI/vvG+v1ND1q9h4/Qya3rHlHOgdAauXFH5HH9v8BA3KQD5dPNKDKqfLXw4N75SBZvi34ZXJgTLMTpsunro8nX4YfWsgZgF+wTB0PCHF0bxuGNRcrA35BUosKJO/i22rWsWwL9PkdamdQXytmiwRIVXvI2f71r8pnsKRYkyBbSHR/aD5U7yNd3/MflEJxZioseYbURkPZW+8e6qx+E8OevOgjO/eTaYdmrGdCvb6X3B4T+ScJbybj7KlyUPa5sI8vYg/yT7XeDNg7XZ4T2cppuCWjzx+Yvm5Ndso2lKUqlr2QbvmTqqXOYzF98PEBwzBocOQZiLGeGZ3ZAYPWy/XUZNmB5CZB0jdAZkAdmAsz+8rf17+ETOUvl7uz7ZSTa3mz7Rv8V6Yhny59lsqXT0lwEBwuPaeHF0gem/MOLqfO/Cd5a7azy0sn5fcM0PIpx7IUfsHuH7RtbDvRuKOOwOFcBj0TuUm0Dqkk/5e2IbY6d6bfJm2BU1tPhH8x6DsT1rwlvUptR2f+Pqc2WmfEJsj/zsDVcvJzalPmz4k9IMnpi54ETLgQD7+0lwKOaU9GMBy/E3CdwZU2mLJtH53NjMCcuGkqbLEWh82o5lbP76Q6uiVV9mnlGssBMCOlasokk5zYMk5656vcKL/74lEwZDufXTnNc+HNKWMYzAWa2rZPTfbcWqLRGyR1Iqi05PkFh8vQ5A1vwYZPZGj9j9sca5kue14Cy/UfSJB805SMe4k3Oe3zd3wv9bTAdf9hmnJSkXxFvn+7foY5TrWnev4g+Xw7p0hg0jOTsh3B5WQxcuuQ2uES5fk3ojl33PwL159YTbNzu+k8pZUsNt32TUcb1o6RYMo3SBLaa/aB0//B9omy31/4mKR6OI/EnNku/19Vbkw/QlP+Bvk92o5dx5Y46iWGVsk8oHJ24zjrjHVTAnWQ4cddP8l3bc80OQa2e9f15Mu+XzflRMrZgWwS7D1MA6rcCKvq/vpjpzY4CjKmJMiXpO3rcia37n3Zof07Xr6EzZ/M/HViD1iXMkDOKKLXpw+oDs2XRE9MWWvq7rUyiyj2oKOQn1+QTNl1Zu+GNrD4BdPLL5gFYdVIxnR8YfyLWRdqNrHPZkk4a+3i/9Qx1X5ibWmraZFEQxsffx5MusiDVXtK3ZuLx6S6dU6ClitnJd8l/risi5Xdul5175LkeOeAqs5d3NMwhDO1K7NuTzD33deAYkc/xx5YnPlPKtAvGiHL6JSsJd3cIRWzb5+NaUoFalsQmnxR8qm6f5Pz1wC+6fIZb1XuyrPr/0fFuEOAKe0qXQ9m3CQHZR9/6RnIruJ0Wnun49JVf2i+BP3+wZLjUe8eOYiVqe/5RbF9/bHP+EvLx8+1Vzc7ho8UmT22TP53StdOv03VnjLstv9PCUDr3OH0WHe5ZGfXz44FgU9tlGHkiGZyRt52tAQll0+RbtZSYqxjSAUkD2TVG67FTiH983Ki3iAZnp1zjwRXzR7PfGgqMwEhcN3zmT9euo7kDXnS0aXWdSlxqa23pc80nqrdHxM4BbwJTE++LL3mx5bIyWHfWY7APy9MU5LqL8dgTwC/aarsfw7Ot87mMx1DlzaLHpe/Ydwh+LmD7OtCq7qmdES0lBmYBq6lccKqQefP5CTl8ikpyHx6iwTW/6QJYg/95ShmHH9E9qGZ9dKVcOyTdluXjvmtzh2EJcby9Kax8sCat2Vx6fJtZELVamvPWGoCzL0HHr8gIyn2/0fTtUf69H/wYwv57geESGK9874wuBzcsdS6jS3vzPpdPv1vxu12tv17Gc0p10Rmrx+YI/v5BbbAyZRcwVoDZLg2opX8TtKyTVyyv24Oe7g9RJPS81vSRemZMS1yYAoqK0EHBmCRni+QGRr175XtUq7A4pHp14NyVq6xY8qtbyA7GwymDdAEWA5w5bz1n9T6pb6wF76KchRrxJQDS9wRmN4bZvWXREWQM7S6A6FCO/beNp8F0oXDRPusOdL0AFgc9UOS4+UMzmKBXzrITB3bWbbz1Nzi5eGHJvB1RZYmXuCbgDCSvq4EX5SVGZGZTaU/u1PG2nf9IkOrM/vmbG2zGn0c14uFQ8/JGD2+5qk37mDq1Jvp2bOa6zpWlkT4PEyCKZDx+9xOLNg+Of0MmpDKGW+bhZgqMnkgwJKM6Rxwxmx29HA4l6/IjYodnedvyg7NecZYmfrS8+XpYArk7L7nROldcrm/PDx8MuMK3FnxC5RlkjIKpkA+Q7/Z8HQy3Lk445lP2Ym8zmVG2OktnzPSNHkGONPmNXjkpKx56ax4pEyXTxsgbvtOgilbInpeXP+qDFtu+VIOQpdjpF6cu+t7FoTLMenuSvbxo3OVG+3fSQtQDCR38tgSufPQfM4e+pv95Cn8tEtJjKPnrX9SdvgJHql9J/8BsYDp45PJK5uuswEv7JV956QG1kr8Vj0nyslI2zclWd1Z88etSxlZD72n/7WmITj9X/sGuNZQA2uQngmn5PheTiUPhpxc6zrX9fS/MmNwdpoT7+R4GfHYOw37yU39wa5pHseWOk4kkuIdw4PO1r7t2MaZrbhuZuIOw1+DpbNh2wT4qgLMvhUWDEX+Dta/xaWTjlnMvSZDqFMvWVg1uG1h+orvOc3B9BANqPLTud0ylXRSPZjaVmbXTW4gCXo3vCkJ2M6zL+xrphlycEib3+TMN0CGG+5aAUMPMLxMPdYA2y0pxE+7CcaVTh/Bu6z0bpGlSiY3kinS+2ZI/SeQ/JEWz0BqEuXn3ktY8mV8TZOHu41nxj3rJAem2eOZty0xVs48j6/IfBtr79ruUrXpWv0mui8agV/iBcyEs/DH7fCJP0xubF270OpyjJQiiNmE/R8t5XLOkvOdF329EpN+vUWQMzhnzgUOTUv2VYvTvWeaIqa1b8/Tkjm3ARgGnzcbwT+Vb+RMUBnG2xatDrLmpwSEZL/AbAbMWv246+GT3HrLdJ7o+gUr71ji+aVNstJgsEyMsPVeBpWG+7bkb86MO1Wg69wua3Na3V33Lj4DPgXsKcPl2+DYtfpAzf5y0OvwgQzV+tpyjqw5Ic2fgog0Vf9DKkOp+mQqsJS8lq1yuI81v8kms7pahUmNPtYyFQ4X/UtwIc2ab+3AWrtJ/F2lG1E1bqIm8CDwGhAG1ATSTB3KnGHwwKDNzK/Wi7PB4Yyv1ZfGwAv/fkXsqc1YcnVoNK2lAqz8i0tJhOtfznj9ugrtrIGZIWkRYdXgph9lAkOpujBwDTR9xFHEuWrP9LP0nLV6DureA6Xr8tPlGH4ApgNtY9IMQ//zqJS5SEpzol61p/SC2fNLDfk/dN4PVO4KPtYeQf9QGV5P6/xeHDO0/RxB46bPpGMhM8mX09yWXkGnUEpKFtW9R0ZFDs6X4K1kdcd7xB60rvOa5u/W6KHM3zcf6JBffto+2VFDJHqt4499aJ4kdaZdE/CGN61raR2WM8/s6kv5BUpvQvxRTgSWBN8A2h9bTu9DOUjK8w2SqdHO/1wXj8vPpHipr5NymRBg5ZRWfHfzr9Qv24B+ka1km/1/pn1FYfhK7tCaMWkeSDO0U6o2nNrA7tL1SPXx40JAGBUMH3xMMGzF8878JwnZtrUQz+2xzi5yUrFTzupwlazuenvP746aMjbB5VheoR3Fki/TMu3OyPCTZSJyyl7zyrpLKNdEcrnycDC3hdVxgWH0vG0+ADcBw0HqU51YLUOsIRVy/doxwK8lIqHWrfgAF4Abcv0q7klePRo/M1VCz9Qk12U9CiOnoY7dpeqQaj3w2Ot/B5WSxapXvi7fO1vQYxgy+88lb8o6GaP5E1JY09abm3jedYg6rIac/dvu6/qFfb03E9jV5BGqRW8gKHqdfE+zm1VcGPgFpQveSzUeTingvNN9XUACihKV4OJR/q/ZCFKsJyrOAzpxQHPgaeANIODcblgyklWRrdjX5FFuKR5OSaft96XpySyRFM8XCx/FNzerDdiUz8V/Te0B0H+uDKPVuUNOhqr1gocOuW539xpJC8lsmR6bgBISkAGBgP1UMbyZtdSJUx902lmUYdWgz2/yvfPxt/YwmenLvJSuC5EtpBc8OV6G1p2HzEGCQFtuk4+fo7fKkpL1SW/purJaw75Z9ruSffw4ElKZatZOgdTAUvj5+EpvYNriuIav/I5Cq6Z/7cgWmb9vPtAeqvxUuh4uQYTtAO4bkPFBwz9Een5u+il97aeMrHlbxqyntOL+NW9jYFLvzPbsnwcyzh1U2rVb1zZz7nKMU8V3aHB2Bx+eWM0DOPW3VOnmelYdUhmGHYYnL8vrOM+w8i8O3b91PLt4FPT5hSutXyK0+k3UtqQy8OafWF65G8m2IqE2+2Y6/oEiWoBfmno7jXN4BpI2vyw4PN3ZzBXDl5795xGYkpC+w7/rF7mboZd8yaXmFiGV89wzUgtwztwKAuwTvotHyAzPPARTAKWAcMAX+aZ6eHJ6ts4AfwWXI9XwIdXwISGv63YVpLqO5OFX17yFryWFN1a9wcpfuzpmYzZ+SIb/Bq605qZYZbS80/FlkhPXwinpP20PkyVFZsF1nyA5h06L5w4Eelw+zQ+BoexvMjzr3uPCJnqD6+0KbXnZ6WZvoA7IvvPiUbaWbUS8fwj2kgBpJAD/nFzHa+s+wvyuLuNCK3ND2zcYXDycUqZJ5OUzvHZuL6ZpknaedqrhS4qPny2DKOfDib7FcpfvBxJAXfd89jm52QVTWenyf9DyGZeh7QTfANfPFXtQZo0HhrqWG0gzgYfEC04pBaZUSd/9m2u6hfN3OzVB9nmBJSXPLKuhN8OQFBHbX7R0A2bcvpjHu/wfl/2Lk+QbwIxOH3HmyKL0wVTxCtIx0GhYxr2BBUx7qPKTdR2hmGLlWBPeCiO4DC3NVKIaD0s/fHRijdQTsSTJgb7/3Ky7eQFz67f8XOdObji2nBfWvUvvfTNpejaHuRNNHpUerkGbZew8qIxjqmpYdajeFw5YzxgqdZX8Lmd+gXDPGkg4L1/ktEsvNHtMAqCEcxJ8+fpLLs7pf6Fab+KKR9Dkhjc5BJQFfixTn+tu+4tAIOmPO/Db87uUUgCZCQPSE1XjZjnLx5Qu6IxmNmYktIocjFa/Id3sfdIXmrtc61ZKXDlLrQuO4cHUqjfj23927ofB/IvL9OczW+V2pU65e74TA1iHnHmnAO8AHqhABUAAsAL4GqgGPOyh182pO4AdXb/gI99AwhJj2d/uXbKYilE4VOooycYxmxj237fUMgLotHWcfFuPLpLvfWaTAxren74wJ8gBq9VzUL2X5A9u/Mi1WnmNm2WWYppSECuAuYlxnJ5Ym0BLkrQh9hDc6IFCwgWhwWCZqAGyT6zchWeA64F4wD6I7ePL/uZPcV37MST6BmAAzYC0cyuf3PAJnywdSWxAKMdCKvJkZ8eMuy5HFhKWeIH3atyC/+rRPJOaRPs2r7Pcmtx+xT+YO27+ldGrXifRJ4C6mIT5+Mtw44HZjjdxrrMFMnM58XzuUwLym3+wjIS0eR3+eYQ3w6oxuu3r3L7zF36edw/2FSNsFegbDrWWu8BplQmrwDDJp7RV7z9hXaqs9h3Qx5o/ZqsVZjsZ6D8354uAn9mKLYRdUK4Rd1prf5UccQHDNHnV1583z+1mi7XEjn1vnBwPMSchZqN0BGRWcqeAGKbHS0RfXeLi4ggLC8vbatWWFKYtfJzp5dswtf691lk+Jt8aBg+m3fan9nDCKeeozl1w809pt3IxZcs4/vEN4rvMFms2fOV1djl9yWreCrVuTT8bIi3TlITy4Ih8ifzfAZznIY1GcoXqAfdePsPjM2+m8emtLG3+JL3aOw0fxh+X2lcXT0qNLE8UNXQy6PhKfvhZ/plTMbBc/yr+N2QxjT4rCedl2nOJ8vJ7L8jcpCIiEpnJZfM3TgfRwiw1CXb/Cr4BvHzxOG8tecZxAnDn0vSLotsknIefbnDKV7Gq2su1fk7CeVld4NRGSWJuPybd9+ciUAnouuc3fv/DafilVG1Ze68oME2p/n4pWmZwZVaOBphkWrjfqVf5OuREA+BGYCWwaWJtvmtwP++seJnptQdwZx9HwdI3V7zKa+3e4oOlz/HMho8wDR9+bPksQ5wLcjqZB/QEqQdlyy8FmVV38ZjjdtVeMlO6EP9/XzRNwpMvM3tWX7ocWcTBso2oUaae5HXaTkpN6/Czb4CcFKR15SzsnCqFSeOtPUX+xeEJp/yoA3OknlbNvpn/D2Rk02dSPBZ46L6tfONUWsYXOdH7Cui3+ze++XsYJZPiMBo+IEnsOHoTjcHbXFJp3Dp+54H2UOUnHz9Gd/mMHhs/dgwvGQYTIX1AlXYoIKt6TFb/Nn6IoH+/si24kV7dgVLJ1lYiAaQKe9p8ogwcMwyGlKrFKeADIJOKM3mWdmByNPA6MBSYG1yWqXfLUhh10753SAW47W/yyysRLdkW3oyGMZtJCQghMLt1rbISVEoWd1aZ6g/YKhZVQQ6MRYJvANS/l/3AuITz3LpzCi1PbeSPOnfRJ6t8mqBSMGQb7PwZ5jlVvk5bST+oFPSdlmUTTiA5b9vLNMSCI3/jcr178V6t6FwyDJkckwPL0wzRO88zsxV0WBd5PUEpifhhcsOJVRiWVEzrUPup4HAwTZpY84p8zVTqHl2S4XuVxSmwj2otw2erR8sJZq/vpZf83C6ZcFD/3kIdTAEEGgZ37pvOjdZFzmuc2YrZ9g2MWrc6NjKMrGfkBZWWha0jmjsCqhq3uG5T/Sa55FbzJ2SSgmnSoWR1nAvLpALVrT8XVu1Oi2GH+C8ghOI+vmxOuUyzXT85jn/n96TPTS5AmkOVz+r4+lMx/iiGU6JjhgN5DQaneeLt2b72vT5+fN/wflZFXk8qBpd8g2Q8H2QmYc/vZAZRVGuZQXjDWzkKpgBeAJYggc8dyJfZk5zn09nyd0Byg5wHFzPpe8s3dfwCaXj3GrhnHYFDDxaN5N4izFaJCuAwsMeLbcmL74ALQaVode8G/J5OZtzNOZh4YPjI4r+OOzLOrcqCBbD1q+wqU48uty/i+3r38nD3Cfi0eTVXr1VU5KRO/5Bek6xlD6DCxROUcJqp+1WT4ZS5HMO4po9hsQ5LNYxqja0vxvmvdgYZcrRrNkLWzByyTQKKDu9Dv1myKLE7s0YLiD9Q0jYj2MqS23zFde/DjJtYdekkn7V5gwW3TMPs9b3nGhlWDUpWpwOuPT0VgCeA9du/5+wXZdg/rgzFd/wAwOiOH3PEWnZle5n6WKp495RMh/yy4W6XYQIwOCmesnt+Y3dES24vVZuH/ILS9yhZUmDhCFlapt7dEvzk4KznWcA66s0wJBcG03T7jOk2YCYSSAUhOxdPd2f+gUxzXgTMd7p/BjIUFIDM2lFXry+BR5GDWRBwHJkCX1T8CjhXZPsDyPEg9MrXYc2b0jvV/y9ZpDmHFgEZVemaADyQwf1Xgx1AS7IPrHwtKcxb+hzNjyxiWof/8XA1KdjqB7RB6vRFXjxJWGIsT5WuwwOGwb9Ij5etP7kdsIxMev6LqEWmyeL1H9D14DySavWnu/MagzkxtQ0n4w5RbehBUnz8SPXx40ecyoV4yHTAOTP2PeQEn2+qYcYdAsAIqQwPHeYJ4NvkK9SIO4hfyZpsTjNxqKCH/DSgykZB/0FyIxnoBKwCSiA7BE/1p+xFDhSngI9xPWh42ltILRmbv8ikF09ddSzAOORgOZSiF0CbyAnNP8iBJZvMxPQSLkjpk1xW/V6LJG47a4vkEV3NpgG3k34GXgBg6+MLQ/Zbtt9oPPL9qo30eM9wet4PuPaIr0GGUnthLSh6lbmCnOSXysuT/xzIutj9tL5HBlt9TAsvGz686cH2gZRyaYD0EgYDm5EaYzt+u5G6R5dgAKkVOxBwxyISgc+Ac8DjQPk0r6UBVSFTmAMq552qD/ASEpwUJSZQBkfdmfrANq6uM0Ol8kM55KBjEwRc5ur/39kJnASikRO/EUhA9T+kFtVLSCmQjOxF8jJtKQZvAlfnAGk+OPQ3KdN702PAfBZV6UrZpHjWBIRQIx/eKgY5OWiJTLxYB/S5dIpX17yFj2mS3OZVnsxoHcU0NCld5VhV5CwqCRmay+UqboWCiZwxgQSFVbn6DwhKeUJ1XAOqkMw2vMrUI+Oe+Jz0lJTCEUz54FSMVWUvvDl+ZioLfu/GwbBqRKUmETzsUL7kkIUDTunylAHOFI/gcevSMt9m9KRCQJPSi7AIZJx/BPA9kjxe1Pgg02FLIsFU2vrqSqmMTUZyfcoiQ6Uz0JOR7JQFhlivByH5eyqHgmUdWh9MasQeIPjiMTi9tUDeugbwCzIU+waOv2Fho0N+2SjMQ35KKaVyxwQOAaUpWhMgCoVvqkPcQcftR2IK9VJRBX381h4qpZRS1wwDWRVAg6k86Pa1rANr+EgF9kIcTHmD5lAppZRSKntVb4QnL1kXU9bwIS39jSillFIqZwyfdAvLK6G/FaWUUkopN2lApZRSSinlJg2olFJKKaXcpAGVUkoppZSbNKBSSimllHKTBlRKKaWUUm7SgEoppZRSyk0aUCmllFJKuUkDKqWUUkopN2lApZRSSinlJg2olFJKKaXcpAGVUkoppZSbikxAdf78eQYNGkRYWBhhYWEMGjSICxcuZPmcIUOGYBiGy+X6668vmAYrpZRS6prh5+0G5NTdd9/NsWPH+OuvvwB46KGHGDRoEH/88UeWz+vZsyffffed/XZAQEC+tlMppZRS154iEVDt3LmTv/76izVr1tC6dWsAvvnmG9q0acPu3bupU6dOps8NDAwkMjKyoJqqlFJKqWtQkRjyW716NWFhYfZgCuD6668nLCyMVatWZfncJUuWEB4eTu3atRk2bBgxMTFZbp+YmEhcXJzLRSmllFIqK0UioIqOjiY8PDzd/eHh4URHR2f6vF69ejFlyhQWLVrERx99xPr16+nSpQuJiYmZPmfMmDH2PK2wsDAqVarkkc+glFJKqauXVwOqN954I13SeNrLhg0bADAMI93zTdPM8H6bO++8k5tuuomGDRvSp08f5s2bx549e5gzZ06mzxk1ahSxsbH2y9GjR93/oEoppZS6qnk1h2rEiBHcddddWW5TtWpVtm7dyqlTp9I9dvr0aSIiInL8flFRUVSpUoW9e/dmuk1gYCCBgYE5fk2llFJKKa8GVGXLlqVs2bLZbtemTRtiY2NZt24d1113HQBr164lNjaWtm3b5vj9zp49y9GjR4mKispzm5VSSiml0ioSOVT16tWjZ8+eDBs2jDVr1rBmzRqGDRvGzTff7DLDr27dusyYMQOAixcv8uyzz7J69WoOHTrEkiVL6NOnD2XLluXWW2/11kdRSiml1FWoSARUAFOmTKFRo0Z0796d7t2707hxY3744QeXbXbv3k1sbCwAvr6+/Pfff/Tt25fatWszePBgateuzerVqwkJCfHGR1BKKaXUVcowTdP0diMKs7i4OMLCwoiNjSU0NNTbzVFKKaVUDhT08bvI9FAppZRSShVWGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3KQBlVJKKaWUmzSgUkoppZRykwZUSimllFJu0oBKKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3KQBlVJKKaWUmzSgUkoppZRykwZUSimllFJu0oBKKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3KQBlVJKKaWUmzSgUkoppZRykwZUSimllFJu0oBKKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3KQBlVJKKaWUmzSgUkoppZRykwZUSimllFJu0oBKKaWUUspNGlAppZRSSrlJAyqllFJKKTdpQKWUUkop5SYNqJRSSiml3FRkAqp33nmHtm3bEhwcTMmSJXP0HNM0eeONNyhfvjzFihWjU6dObN++PX8bqpRSSqlrTpEJqJKSkrj99tt55JFHcvycDz74gI8//pjPP/+c9evXExkZSbdu3YiPj8/HliqllFLqWlNkAqrRo0fz9NNP06hRoxxtb5omY8eO5eWXX6Z///40bNiQyZMnc/nyZaZOnZrPrVVKKaXUtaTIBFS5dfDgQaKjo+nevbv9vsDAQDp27MiqVasyfV5iYiJxcXEuF6WUUkqprFy1AVV0dDQAERERLvdHRETYH8vImDFjCAsLs18qVaqUr+1USimlVNHn1YDqjTfewDCMLC8bNmxw6z0Mw3C5bZpmuvucjRo1itjYWPvl6NGjbr2/Ukoppa5+ft588xEjRnDXXXdluU3VqlXz9NqRkZGA9FRFRUXZ74+JiUnXa+UsMDCQwMDAPL2nUkoppa5NXg2oypYtS9myZfPltatVq0ZkZCQLFiygWbNmgMwUXLp0Ke+//36+vKdSSimlrk1FJofqyJEjbNmyhSNHjpCamsqWLVvYsmULFy9etG9Tt25dZsyYAchQ31NPPcW7777LjBkz2LZtG0OGDCE4OJi7777bWx9DKaWUUlchr/ZQ5cZrr73G5MmT7bdtvU6LFy+mU6dOAOzevZvY2Fj7Ns8//zxXrlzh0Ucf5fz587Ru3Zq///6bkJCQAm27Ukoppa5uhmmaprcbUZjFxcURFhZGbGwsoaGh3m6OUkoppXKgoI/fRWbITymllFKqsNKASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppZRSbtKASimllFLKTRpQKaWUUkq5SQMqpZRSSik3aUCllFJKKeUmDaiUUkoppdykAZVSSimllJs0oFJKKaWUcpMGVEoppZRSbvLzdgMKO9M0AYiLi/NyS5RSSimVU7bjtu04nt80oMrG2bNnAahUqZKXW6KUUkqp3Dp79ixhYWH5/j4aUGWjdOnSABw5cqRA/iAqc3FxcVSqVImjR48SGhrq7eZc0/RvUbjo36Pw0L9F4REbG0vlypXtx/H8pgFVNnx8JM0sLCxM/zkKidDQUP1bFBL6tyhc9O9ReOjfovCwHcfz/X0K5F2UUkoppa5iGlAppZRSSrlJA6psBAYG8vrrrxMYGOjtplzz9G9ReOjfonDRv0fhoX+LwqOg/xaGWVDzCZVSSimlrlLaQ6XU/7d3/2E13v8fwJ9HnX6odlKpU5Ki/KzjWifqGNWUypBwGdbOGJZGaIyZLlfNtWsyk2obxprYXMtFuMziclglV4kdNacioYR1hFXzY2qr1/cPl/u7o8MlJ5V9Xo/ruq/L/X6/7vu87/frOqfXuX8cjDHGmIG4oGKMMcYYMxAXVIwxxhhjBuKCijHGGGPMQFxQPcXGjRvh5uYGMzMzyOVy5OXldfaQXnrHjx/HhAkT4OTkBJFIhP379+v0ExESEhLg5OQEc3NzBAYGorS0VCemsbERCxcuhJ2dHSwsLBAeHo5r167pxNTV1UGpVEIikUAikUCpVKK+vv4FH93LZc2aNRg2bBisrKxgb2+PiIgIlJeX68RwPjrGpk2bIJPJhB+DVCgUOHTokNDPeeg8a9asgUgkQmxsrNDG+egYCQkJEIlEOotUKhX6u1weiOmVkZFBYrGYtm7dSmVlZbR48WKysLCgK1eudPbQXmpZWVkUFxdHmZmZBID27dun05+YmEhWVlaUmZlJGo2Gpk2bRo6OjvTnn38KMdHR0dSrVy9SqVR05swZev3112no0KH0zz//CDFhYWHk6elJ+fn5lJ+fT56enjR+/PiOOsyXQmhoKG3bto1KSkqouLiYxo0bRy4uLnT37l0hhvPRMQ4cOEA///wzlZeXU3l5Oa1cuZLEYjGVlJQQEeehs5w6dYpcXV1JJpPR4sWLhXbOR8eIj4+nIUOGUE1NjbDU1tYK/V0tD1xQPcHw4cMpOjpap23gwIG0YsWKThrRf8/jBVVLSwtJpVJKTEwU2h48eEASiYQ2b95MRET19fUkFospIyNDiLl+/Tp169aNDh8+TEREZWVlBIBOnjwpxBQUFBAAOn/+/As+qpdXbW0tAaDc3Fwi4nx0th49etC3337Leegkd+7cIQ8PD1KpVBQQECAUVJyPjhMfH09Dhw7V29cV88CX/PRoamqCWq1GSEiITntISAjy8/M7aVT/fZWVldBqtTrzbmpqioCAAGHe1Wo1/v77b50YJycneHp6CjEFBQWQSCTw9fUVYvz8/CCRSDh/T9HQ0ADg//9DcM5H52hubkZGRgbu3bsHhULBeegkCxYswLhx4xAcHKzTzvnoWBUVFXBycoKbmxumT5+Oy5cvA+iaeeD/HFmPW7duobm5GQ4ODjrtDg4O0Gq1nTSq/75Hc6tv3q9cuSLEmJiYoEePHq1iHm2v1Wphb2/fav/29vacvycgIixZsgQjR46Ep6cnAM5HR9NoNFAoFHjw4AEsLS2xb98+DB48WPhQ5zx0nIyMDJw5cwanT59u1cfvi47j6+uLHTt2oH///rhx4wY+/fRTjBgxAqWlpV0yD1xQPYVIJNJZJ6JWbaz9Pc+8Px6jL57z92QxMTE4e/YsTpw40aqP89ExBgwYgOLiYtTX1yMzMxMzZ85Ebm6u0M956BhXr17F4sWLceTIEZiZmT0xjvPx4o0dO1b4t5eXFxQKBfr164ft27fDz88PQNfKA1/y08POzg5GRkatqtPa2tpW1TBrP4+e3njavEulUjQ1NaGuru6pMTdu3Gi1/5s3b3L+9Fi4cCEOHDiA7OxsODs7C+2cj45lYmICd3d3+Pj4YM2aNRg6dChSUlI4Dx1MrVajtrYWcrkcxsbGMDY2Rm5uLlJTU2FsbCzMFeej41lYWMDLywsVFRVd8n3BBZUeJiYmkMvlUKlUOu0qlQojRozopFH997m5uUEqlerMe1NTE3Jzc4V5l8vlEIvFOjE1NTUoKSkRYhQKBRoaGnDq1CkhprCwEA0NDZy/fyEixMTEYO/evfjll1/g5uam08/56FxEhMbGRs5DBwsKCoJGo0FxcbGw+Pj4IDIyEsXFxejbty/no5M0Njbi3LlzcHR07Jrvizbdwv4/5NHPJqSlpVFZWRnFxsaShYUFVVVVdfbQXmp37tyhoqIiKioqIgCUlJRERUVFws9RJCYmkkQiob1795JGo6EZM2bofQzW2dmZjh49SmfOnKHRo0frfQxWJpNRQUEBFRQUkJeXFz+O/Jj333+fJBIJ5eTk6DyWfP/+fSGG89ExPv74Yzp+/DhVVlbS2bNnaeXKldStWzc6cuQIEXEeOtu/n/Ij4nx0lKVLl1JOTg5dvnyZTp48SePHjycrKyvh73BXywMXVE/x9ddfU58+fcjExIS8vb2Fx8nZ88vOziYArZaZM2cS0cNHYePj40kqlZKpqSn5+/uTRqPR2cdff/1FMTExZGNjQ+bm5jR+/Hiqrq7Wibl9+zZFRkaSlZUVWVlZUWRkJNXV1XXQUb4c9OUBAG3btk2I4Xx0jNmzZwufNT179qSgoCChmCLiPHS2xwsqzkfHePS7UmKxmJycnGjy5MlUWloq9He1PIiIiNp41o0xxhhjjP0L30PFGGOMMWYgLqgYY4wxxgzEBRVjjDHGmIG4oGKMMcYYMxAXVIwxxhhjBuKCijHGGGPMQFxQMcYYY4wZiAsqxhhjjOm1ZcsWBAYG4pVXXoFIJEJ9ff0zbbdx40a4ubnBzMwMcrkceXl5T4ydN28eRCIRkpOT2zS2WbNmQSQStVqGDBnSpv20Fy6oGGNdXmBgIGJjYwEArq6ubf7gZYw9WWBgINLT0/X23b9/H2FhYVi5cuUz72/Xrl2IjY1FXFwcioqKMGrUKIwdOxbV1dWtYvfv34/CwkI4OTm1edwpKSmoqakRlqtXr8LGxgZTp05t877aAxdUjLGXyunTpxEVFfVMsVx8MWaY2NhYrFixAn5+fs+8TVJSEubMmYO5c+di0KBBSE5ORu/evbFp0yaduOvXryMmJgY7d+6EWCxutZ+GhgZERUXB3t4er7zyCkaPHo3ffvtN6JdIJJBKpcLy66+/oq6uDu++++7zH7ABuKBijL1Uevbsie7du3f2MBhjejQ1NUGtViMkJESnPSQkBPn5+cJ6S0sLlEolli1bpvcSHRFh3Lhx0Gq1yMrKglqthre3N4KCgvDHH3/ofe20tDQEBwejT58+7XtQz4gLKsZYl3Lv3j288847sLS0hKOjI9avX6/T//hZp4SEBLi4uMDU1BROTk5YtGgRgIeXMa5cuYIPPvhAuLcCAG7fvo0ZM2bA2dkZ3bt3h5eXF3788Ued1wgMDMSiRYuwfPly2NjYQCqVIiEhQSemvr4eUVFRcHBwgJmZGTw9PXHw4EGhPz8/H/7+/jA3N0fv3r2xaNEi3Lt3rx1nirGu59atW2huboaDg4NOu4ODA7RarbC+du1aGBsbC+/Xx2VnZ0Oj0WD37t3w8fGBh4cHvvjiC1hbW2PPnj2t4mtqanDo0CHMnTu3fQ+oDbigYox1KcuWLUN2djb27duHI0eOICcnB2q1Wm/snj17sGHDBnzzzTeoqKjA/v374eXlBQDYu3cvnJ2dsXr1auEeCwB48OAB5HI5Dh48iJKSEkRFRUGpVKKwsFBn39u3b4eFhQUKCwvx+eefY/Xq1VCpVAAefrseO3Ys8vPz8cMPP6CsrAyJiYkwMjICAGg0GoSGhmLy5Mk4e/Ysdu3ahRMnTiAmJuZFTRtjz+yzzz6DpaWlsOTl5SE6OrpVmyEefYF5hIiENrVajZSUFKSnp7eKe0StVuPu3buwtbXVGVdlZSUuXbrUKj49PR3W1taIiIgwaNyGMO60V2aMscfcvXsXaWlp2LFjB8aMGQPgYWHj7OysN766uhpSqRTBwcEQi8VwcXHB8OHDAQA2NjYwMjKClZUVpFKpsE2vXr3w4YcfCusLFy7E4cOHsXv3bvj6+grtMpkM8fHxAAAPDw989dVXOHbsGMaMGYOjR4/i1KlTOHfuHPr37w8A6Nu3r7DtunXr8NZbbwk30nt4eCA1NRUBAQHYtGkTzMzM2mG2GHs+0dHRePPNN4X1yMhITJkyBZMnTxbaevXq9Vz7trOzg5GRkc7ZKACora0Vzlrl5eWhtrYWLi4uQn9zczOWLl2K5ORkVFVVoaWlBY6OjsjJyWn1GtbW1jrrRITvvvsOSqUSJiYmzzXu9sAFFWOsy7h06RKampqgUCiENhsbGwwYMEBv/NSpU5GcnIy+ffsiLCwMb7zxBiZMmABj4yd/tDU3NyMxMRG7du3C9evX0djYiMbGRlhYWOjEyWQynXVHR0fU1tYCAIqLi+Hs7CwUU49Tq9W4ePEidu7cKbQREVpaWlBZWYlBgwY9fSIYe4FsbGxgY2MjrJubm8Pe3h7u7u4G79vExARyuRwqlQqTJk0S2lUqFSZOnAgAUCqVCA4O1tkuNDQUSqVSuKHc29sbWq0WxsbGcHV1fepr5ubm4uLFi5gzZ47B4zcEF1SMsS6DiNoU37t3b5SXl0OlUuHo0aOYP38+1q1bh9zcXL1PDQHA+vXrsWHDBiQnJ8PLywsWFhaIjY1FU1OTTtzj24tEIrS0tAB4+AfoaVpaWjBv3jy994f8+1s5Y12dVquFVqvFxYsXATy8nG1lZQUXFxehKAsKCsKkSZOES9pLliyBUqmEj48PFAoFtmzZgurqakRHRwMAbG1tYWtrq/M6YrEYUqlU+PIUHBwMhUKBiIgIrF27FgMGDMDvv/+OrKwsREREwMfHR9g2LS0Nvr6+8PT0fOHz8TRcUDHGugx3d3eIxWKcPHlSKDzq6upw4cIFBAQE6N3G3Nwc4eHhCA8Px4IFCzBw4EBoNBp4e3vDxMQEzc3NOvF5eXmYOHEi3n77bQAPi5+Kioo2nTWSyWS4du0aLly4oPcslbe3N0pLS9vlGz9jnWnz5s345JNPhHV/f38AwLZt2zBr1iwAD88s37p1S4iZNm0abt++Ldy/6OnpiaysrDY9fScSiZCVlYW4uDjMnj0bN2/ehFQqhb+/v84N7w0NDcjMzERKSoqBR2o4LqgYY12GpaUl5syZg2XLlsHW1hYODg6Ii4tDt276n59JT09Hc3MzfH190b17d3z//fcwNzcXPrhdXV1x/PhxTJ8+HaamprCzs4O7uzsyMzORn5+PHj16ICkpCVqttk0FVUBAAPz9/TFlyhQkJSXB3d0d58+fh0gkQlhYGD766CP4+flhwYIFeO+992BhYYFz585BpVLhyy+/bJe5Yqy96LtP6ZGEhIRWT7g+rqqqqlXb/PnzMX/+/Gceg759WFlZITU1FampqU/cTiKR4P79+8/8Oi8SP+XHGOtS1q1bB39/f4SHhyM4OBgjR46EXC7XG2ttbY2tW7fitddeg0wmw7Fjx/DTTz8JlxNWr16Nqqoq9OvXDz179gQArFq1Ct7e3ggNDUVgYCCkUulzPRmUmZmJYcOGYcaMGRg8eDCWL18unA2TyWTIzc1FRUUFRo0ahVdffRWrVq2Co6Pj800KY6zLE1Fbb1pgjDHGGGM6+AwVY4wxxpiBuKBijDHGGDMQF1SMMcYYYwbigooxxhhjzEBcUDHGGGOMGYgLKsYYY4wxA3FBxRhjjDFmIC6oGGOMMcYMxAUVY4wxxpiBuKBijDHGGDMQF1SMMcYYYwb6P1lUVWp9BtE4AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = df.plot.scatter(x=\"distance\", y=\"height\", c=\"surface_type\", \n", + " s=3, cmap=surface_type_cmap, norm=surface_type_norm,\n", + " colorbar=False, #)\n", + " xlim=(1.04e7,1.0405e7))\n", + "\n", + "handles = [Line2D([0], [0], linestyle='none', marker='o',\n", + " markerfacecolor=col, markeredgecolor='none',\n", + " markersize=10, label=sfc) for sfc, col in zip(surface, colors)]\n", + "m.legend(handles=handles)" + ] + }, + { + "cell_type": "markdown", + "id": "ec815ec9-eeac-4259-97a2-412b0cb82f64", + "metadata": {}, + "source": [ + "## Plot ICESat-2 Tracks\n", + "\n", + "It is always helpful to see where the data are located. We plot GT1R track on a map, with the `Polygon` used in `search_data`. This time we use latitude and longitude directly from the `DataTree` object." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e9e2b175-5da8-48ca-a295-29d537c55f0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define NSIDC WGS84 North Polar Stereographic projection\n", + "map_projection = ccrs.Stereographic(\n", + " central_latitude=90., \n", + " central_longitude=-45,\n", + " true_scale_latitude=70.,\n", + ")\n", + "extent = [-1000000., 1000000., -2200000., 240000.]\n", + "\n", + "fig = plt.figure(figsize=(7,7))\n", + "ax = fig.add_subplot(projection=map_projection)\n", + "\n", + "ax.set_extent(extent, map_projection)\n", + "\n", + "ax.add_feature(cfeature.OCEAN)\n", + "ax.add_feature(cfeature.LAND)\n", + "\n", + "# Plot polygon\n", + "ax.plot(lonp, latp, transform=ccrs.PlateCarree())\n", + "\n", + "ax.plot(\n", + " dt[\"gt1r\"][\"sea_ice_segments\"][\"longitude\"][::100],\n", + " dt[\"gt1r\"][\"sea_ice_segments\"][\"latitude\"][::100],\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", + "ax.gridlines(draw_labels=True, x_inline=False, y_inline=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8040291-0ca3-4210-a7c4-c39e1a5823df", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}