From 1be42f24e9f6116514d88827794292ec1f3a4151 Mon Sep 17 00:00:00 2001 From: zmaalick Date: Mon, 9 Nov 2020 09:34:52 +0000 Subject: [PATCH 1/8] add License --- LICENSE | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..5694bee --- /dev/null +++ b/LICENSE @@ -0,0 +1,17 @@ +British Crown Copyright 2020, Met Office + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS +BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE +GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. From 776d113727bbc2fdb9747bb4ba34f22000b5c540 Mon Sep 17 00:00:00 2001 From: zmaalick Date: Mon, 9 Nov 2020 09:39:55 +0000 Subject: [PATCH 2/8] Update README --- LICENCE | 29 +++++++++++++++++++++++++++++ LICENSE | 17 ----------------- README.md | 4 ++-- 3 files changed, 31 insertions(+), 19 deletions(-) create mode 100644 LICENCE delete mode 100644 LICENSE diff --git a/LICENCE b/LICENCE new file mode 100644 index 0000000..9dfda08 --- /dev/null +++ b/LICENCE @@ -0,0 +1,29 @@ +BSD 3-Clause Licence + +Copyright (c) 2020, Met Office +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 5694bee..0000000 --- a/LICENSE +++ /dev/null @@ -1,17 +0,0 @@ -British Crown Copyright 2020, Met Office - -Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer - in the documentation and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, -THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS -BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE -GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT -LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 9a5a8ec..ef01659 100644 --- a/README.md +++ b/README.md @@ -46,14 +46,14 @@ Three additional worksheets are available for use by workshop instructors: * `worksheet6example.ipynb`: Example code for Worksheet 6. ## Data -The data used in the worksheets is currently only available within the Met Office. See the `data/README` for further details. +The data used in the worksheets is currently only available within the Met Office. See the `data/README` for further details. ## Contributing Information on how to contribute can be found in the [Contributing guide](CONTRIBUTING.md). Please also consult the `CONTRIBUTING.ipynb` for information on formatting the worksheets in Jupyter Notebooks. **Note** that we do not currently make use of Jupyter Lab as it doesn't currently support the types of html formatting we use in Jupyter Notebooks. ## Licence -PyPRECIS is **not** currently licenced for use outside of the Met Office. +PyPRECIS is under BSD 3-clause licence for use outside of the Met Office.
Met Office
From 0337526c74829333f68eaa97d84162cb304b0381 Mon Sep 17 00:00:00 2001 From: Hamish Steptoe Date: Mon, 9 Nov 2020 10:30:58 +0000 Subject: [PATCH 3/8] Minor rewording --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ef01659..cbc0ac5 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ Information on how to contribute can be found in the [Contributing guide](CONTRI Please also consult the `CONTRIBUTING.ipynb` for information on formatting the worksheets in Jupyter Notebooks. **Note** that we do not currently make use of Jupyter Lab as it doesn't currently support the types of html formatting we use in Jupyter Notebooks. ## Licence -PyPRECIS is under BSD 3-clause licence for use outside of the Met Office. +PyPRECIS is licenced under BSD 3-clause licence for use outside of the Met Office.
Met Office
From 39aa1c7c277be20f339112261aae7d18b97762db Mon Sep 17 00:00:00 2001 From: Hamish Steptoe Date: Mon, 9 Nov 2020 10:35:32 +0000 Subject: [PATCH 4/8] Update (c) date --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cbc0ac5..52ddebe 100644 --- a/README.md +++ b/README.md @@ -57,5 +57,5 @@ PyPRECIS is licenced under BSD 3-clause licence for use outside of the Met Offic
Met Office
-© British Crown Copyright 2018 - 2019, Met Office +© British Crown Copyright 2018 - 2020, Met Office
From 64ebfd2edc3f5253ff529f277e72c6311607b8d5 Mon Sep 17 00:00:00 2001 From: zmaalick Date: Thu, 3 Dec 2020 10:09:54 +0000 Subject: [PATCH 5/8] ADD CSSP tutorial notebooks --- CSSP_20CRDS_Tutorials/Introduction.ipynb | 329 + CSSP_20CRDS_Tutorials/cssp_utils.py | 20 + .../images/global_airtemp_cp.png | Bin 0 -> 150651 bytes .../images/global_airtemp_ts.png | Bin 0 -> 6278 bytes CSSP_20CRDS_Tutorials/images/region.PNG | Bin 0 -> 101249 bytes .../tutorial_1_data_access.ipynb | 7900 +++++++++++++++++ .../tutorial_2_data_preparation.ipynb | 5963 +++++++++++++ .../tutorial_3_basic_analysis.ipynb | 4208 +++++++++ .../tutorial_4_advance_analysis.ipynb | 709 ++ .../xarray_iris_coord_system.py | 85 + 10 files changed, 19214 insertions(+) create mode 100644 CSSP_20CRDS_Tutorials/Introduction.ipynb create mode 100644 CSSP_20CRDS_Tutorials/cssp_utils.py create mode 100644 CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png create mode 100644 CSSP_20CRDS_Tutorials/images/global_airtemp_ts.png create mode 100644 CSSP_20CRDS_Tutorials/images/region.PNG create mode 100644 CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb create mode 100644 CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb create mode 100644 CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb create mode 100644 CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb create mode 100644 CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py diff --git a/CSSP_20CRDS_Tutorials/Introduction.ipynb b/CSSP_20CRDS_Tutorials/Introduction.ipynb new file mode 100644 index 0000000..0b22663 --- /dev/null +++ b/CSSP_20CRDS_Tutorials/Introduction.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CSSP 20CR dataset - Tutorials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents:\n", + "1. [Introduction](#introduction)\n", + "2. [Description of datasets](#dataset)\n", + "3. [Learning objectives](#objectives)\n", + "4. [Jupyter notebook](#notebook)\n", + "5. [Data format and python libraries](#libs)\n", + "6. [Instructions to create an environment](#env)\n", + "7. [Resources](#resources)\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This short course is an introductory set of tutorials on accessing a large (~3Tb) dataset hosted on a cloud server. By putting the data and the computer resources in the same place, users no longer have to spend time downloading data, finding local storage for and manging the software needed to analyse the data. These notebooks explain how to use this cloud based platform to analyse the 20CR-DS dataset.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Learning objectives\n", + "\n", + "The high level learning objectives for these tutorials are:\n", + "- To access and explore variables of interest\n", + "- To convert data into different formats (xarrays and iris) \n", + "- To prepare data for analysis\n", + "- To carry out basic analyses\n", + "- To carry out advanced analysis\n", + "- To visualise the results \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Description of the tutorial dataset\n", + "\n", + "A climate reanalysis gives a numerical description of the recent climate, produced by combining models with observations. The Twentieth Century Reanalysis Project (20CR-V2c) is a global reanalysis carried out by the National Oceanic and Atmospheric Administration (NOAA). The outputs from this dataset include temperature, pressure, winds, moisture, solar radiation and clouds, from the surface to the top of the atmosphere as far back as the mid-1800s. More information are available from [climate-reanalysis](https://www.ecmwf.int/en/research/climate-reanalysis) and [20CR-V2c](https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.html).\n", + "\n", + "At the UK Met Office we have increased the resolution of the 20CR-V2c reanalysis dataset using a process known as dynamical downscaling and it now covers the whole of China for the period 1851 to 2010 at a horizontal resolution of 25 km [(Amato et al., 2019)](https://doi.org/10.1175/JAMC-D-19-0083.1). (https://zenodo.org/record/2558135#.XJj2uaD7RWE). This work was funded through the Climate Science for Service Partnership China (CSSP-China) project.\n", + "\n", + "The Climate Science for Service Partnership China (CSSP China) is a scientific research project that is building the basis for services to support climate and weather resilient economic development and social welfare through strong, strategic partnerships harnessing UK scientific expertise. Through CSSP China (supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) UK-China Research Innovation Partnership Fund) we are developing a strongly bilateral partnership between the Met Office, the China Meteorological Administration (CMA), the Institute of Atmospheric Physics (IAP) at the Chinese Academy of Sciences, and other key institutes within China and the UK. See the [CSSP-China](https://www.metoffice.gov.uk/research/approach/collaboration/newton/cssp-china/index) for more information.\n", + "\n", + "The dataset used in these tutorials include monthly, daily, 6 hourly, 3 hourly and hourly frequencies for the historical period of 1851-2010. The details of variables and frequencies can be found in [supplementary material](variableslist.pdf). \n", + "\n", + "The data is residing at **/data/share/cssp-data/ZARRSTORE/**\n", + "\n", + "\n", + "
\n", + " \"Trulli\"\n", + "
Figure: Downscaled domain of 20CR datasets (Amato et al., 2019)
\n", + "
\n", + "\n", + "\n", + "The area of interests are devided into seven subregions, shown in figure, are considered for analysis [Burke and Stott (2017)](https://journals.ametsoc.org/jcli/article/30/14/5205/97096/Impact-of-Anthropogenic-Climate-Change-on-the-East). The coordinates of these seven regions are: \n", + "\n", + "\n", + "\n", + "North Central (NC): 104°–113°E, 32°–39°N\n", + "\n", + "North East Coast (NEC): 113°–122°E, 32°–39°N\n", + "\n", + "North East (NE): 113°–131°E, 39°–44°N\n", + "\n", + "Tibetan Plateau (TP): 77°–104°E, 26°–36°N\n", + "\n", + "South Central (SC): 104°–113°E, 26°–32°N\n", + "\n", + "South East Coast (SEC): 113°–122°E, 26°–32°N\n", + "\n", + "South East (SE): 107°–120°E, 21°–26°N" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Jupyter notebook\n", + "Jupyter is an open source platform that contains a suite of tools including Jupyter Notebook: A browser-based application that allows you to create and share documents (i.e. Jupyter Notebook files such as this notbook you are reading right now!). These notebooks can contain simple text content and live code, equations, visualizations and narrative text. It is an Integrated Development Environment (IDE) that allows you to write code, navigate files on the system, inspect variables and more. The Jupyter Notebook file format (.ipynb ) allows you to combine descriptive text, code blocks and code output in a single file. You can then share the notebook itself with anyone who might want to run it and also convert the notebook to a PDF or HTML format that can be viewed like a report.\n", + "\n", + "##### How to run Jupyter Notebook\n", + "A Jupyter Notebook file (.ipynb) has three main parts, which are highlighted in the image below:\n", + "\n", + "- Menu bar\n", + "- Toolbar\n", + "- Cells\n", + "\n", + "Cells can be specified to store documentation text such as Markdown or programming code such as Python. Text written using the Markdown syntax can be rendered in a cell that is of the cell type Markdown. You can run code (e.g. Python) using the Code as cell type write you code and then either click on the run the selected cell button on top or use the Shift+Enter keyboard combination. When you run the code in a Code cell, the code output displayed below.\n", + "\n", + "**Example:** click on the cell below and press Shift+Enter (or Ctrl+Enter), It will print the output below the cell. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CSSP 20CR dataset\n" + ] + } + ], + "source": [ + "print('CSSP 20CR dataset')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also execute shell commands from the cell. For Example cell below list down the contents of ZARR dataset directory" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[38;5;27mdaily\u001b[0m/ \u001b[48;5;10;38;5;21mmonthly\u001b[0m/\r\n" + ] + } + ], + "source": [ + "ls /data/users/zmaalick/cssp/data/ZARRSTORE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that **Shift+Enter** moves the cursor to next cell and by running the script with **Ctrl+Enter** the cursor stays in the same cell\n", + "\n", + "An important component of a Jupyter Notebook is its Kernel. A kernal runs your code in a specific programming language. Jupyter Notebook supports over 40 different languages. In this tutorials, we will use the Python kernel within the Jupyter Notebook IDE.\n", + "\n", + "To learn more about Jupyter Notebooks use the introductory free online course availabe from [Here](https://www.earthdatascience.org/courses/intro-to-earth-data-science/open-reproducible-science/jupyter-python/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Data format and python libraries\n", + "\n", + "##### ZARR\n", + "The data used in our tutorials have been converted from the [Met Office's PP file format](https://help.ceda.ac.uk/article/4424-pp-binary-forma) to Zarr. Zarr is a [specification](https://zarr.readthedocs.io/en/stable/spec.html) for how to store gridded data in a key-value interface (such as Amazon S3 object store), where each chunk of data is a separate value with a corresponding key indicating its position in the full dataset. This has advantage over NetCDF format as it allows for a highly parallel data access where many CPUs can simultaneously read different parts of the same dataset. Zarr is also a [Python library](https://zarr.readthedocs.io/en/stable/api.html) implementation of this specification that allows you to read and write data in a Zarr store.\n", + "\n", + "##### Iris\n", + "In order to explore and analyse our dataset in these tutorials we make use of a Python library called Iris. Iris is a key tool in the [SciTools](https://scitools.org.uk/) project which is a collaborative effort to produce and maintain python-based open-source tooks for Earth scientists. Iris is a useful toolkit as it supports read/write access to a range of data formats, including (CF-)netCDF, GRIB, and PP; fundamental data manipulation operations, such as arithmetic, interpolation, and statistics; and a range of integrated plotting options. See [latest Iris documentation](https://scitools.org.uk/iris/docs/latest/) for more information.\n", + "\n", + "##### CATNIP\n", + "At Met Office we have also developed a python library called CATNIP (Climate Analysis Tools: Now In Python). This library is a collection of routines to make frequently used climate data analysis and visualisation tasks in Iris easier and quicker to perform. We will make use of some of CATNIP's routines in these tutorials. See [CATNIP documentaion](https://metoffice.github.io/CATNIP/#) for more information.\n", + "\n", + "##### CONDA\n", + "For these tutorials we have used the [CONDA](https://docs.conda.io/en/latest/) package managment system to install the packages for our development environment. Next section contains the instructions you need to create a conda enviroment and install these packages." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. How to create an environment\n", + "Run the following highlighted commands in your terminal to create the required conda environment and install the packages.\n", + "\n", + "- Ensure conda setup (needs doing only once) \n", + " - *`conda init bash`*\n", + " \n", + " \n", + "- Create a new environment named: cssp-shared \n", + " - *`conda env create -f /data/share/cssp-shared/environment.yml`* \n", + " \n", + " \n", + "- Activate the environment. This will make all the required Python libraries available for you to use.\n", + " - *`conda activate cssp`*\n", + "\n", + "\n", + "- Install your conda environment as an ipykerel (make sure your env is activated first, the kernel name will be cssp). This makes your Python environment available in the notebooks we will use for these tutorials.\n", + " - *`python -m ipykernel install --name cssp --display-name cssp --user`*\n", + " \n", + "**Initiate ipykernel in notebook**\n", + "\n", + "In order to initiate the \"cssp-shared\" kernel created above, follow the following steps:\n", + "\n", + "1. Open the notebook\n", + "2. Click on to \"No Kernel\"\n", + "3. Drop down box will appear to select the kernel. Select \"cssp-shared\" and click \"ok\"\n", + "4. \"cssp-shared\" will appear in the right corner instead of \"no kernel\". This means that the kernel has initiated and the notebook is now ready to use.\n", + "\n", + "\n", + "**Useful commands**\n", + "\n", + "- List the packages to see if all are installed \n", + " - *`conda list`*\n", + "\n", + "\n", + "- See the available environments \n", + " - *`conda env list`*\n", + "\n", + "\n", + "- To deactivate the active environment \n", + " - *`conda deactivate`*\n", + "\n", + "\n", + "- To remove an environment completely\n", + " - *`conda remove --name env_name --all`*\n", + "\n", + "\n", + "- To list jupyter kernels\n", + " - *`jupyter kernelspec list`*\n", + "\n", + "\n", + "- To uninstall a kernel \n", + " - *`jupyter kernelspec uninstall [unwanted-kernel]`*\n", + "\n", + "\n", + "- To rename an env \n", + " - *`conda create --name new_name --copy --clone old_name`*\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources\n", + "\n", + "The following are the links you can follow for further information for the pacakges that we have installed and use in these tutorials.\n", + "\n", + "- [python](https://docs.python.org/3/library/)\n", + "- [zarr](https://zarr.readthedocs.io/en/stable/)\n", + "- [iris](https://scitools.org.uk/iris/docs/latest/)\n", + "- [numpy](https://numpy.org/)\n", + "- [matplotlib](https://matplotlib.org/)\n", + "- [xarray](http://xarray.pydata.org/en/stable/)\n", + "- [jupyterlab](https://jupyterlab.readthedocs.io/en/stable/)" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CSSP_20CRDS_Tutorials/cssp_utils.py b/CSSP_20CRDS_Tutorials/cssp_utils.py new file mode 100644 index 0000000..c409f5f --- /dev/null +++ b/CSSP_20CRDS_Tutorials/cssp_utils.py @@ -0,0 +1,20 @@ +import iris +import warnings +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + +import numpy as np +import os +import xarray as xr + + + + +def zarr_reader(path, freq): + + ZARR = os.path.join(path,freq) + ds = xr.open_zarr(ZARR) + for xvars in ds: + ds[xvars].attrs['iris_coord_system'] = '{"grid_north_pole_latitude": 51.81999969482422, "grid_north_pole_longitude": 289.8299865722656, "ellipsoid": {"semi_major_axis": 6371229.0}, "coord_system_name": "rotated_latitude_longitude"}' + + return ds diff --git a/CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png b/CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png new file mode 100644 index 0000000000000000000000000000000000000000..019c78315da3d919a0e1cf2e4dd49bd12bd96570 GIT binary patch literal 150651 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b/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb new file mode 100644 index 0000000..246486e --- /dev/null +++ b/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb @@ -0,0 +1,7900 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Tutorial 1: Accessing and exploring CSSP China 20CR datasets\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives:\n", + "\n", + "1. How to load data into Xarrays format\n", + "2. How to convert the data xarrays into iris cube format\n", + "3. How to perform basic cube operations " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Use Xarray to access monthly data](#access_zarr) \n", + "2. [Retrieve single (or list of) variables](#get_vars)\n", + "3. [Convert datasets to iris cube](#to_iris)\n", + "4. [Explore cube attributes and coordinates](#explore_iris)\n", + "5. [Exercises](#exercise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "

\n", + "Prerequisites
\n", + "- Basic programming skills in python
\n", + "- Familiarity with python libraries Numpy and Matplotlib
\n", + "- Basic understanding of climate data
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Use Xarray to acess monthly data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Import libraries.\n", + "Import the necessary libraries. Current datasets are in zarr format, we need zarr and xarray libraries to access the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import zarr\n", + "from xarray_iris_coord_system import XarrayIrisCoordSystem as xics\n", + "xi = xics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Read monthly data\n", + "A Dataset consists of coordinates and data variables. Let's use the xarray's **open_zarr()** method to read all our zarr data into a dataset object and display it's metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:                                    (grid_latitude: 203, grid_latitude_1: 202, grid_longitude: 270, grid_longitude_1: 270, pressure: 17, time: 1920)\n",
+       "Coordinates:\n",
+       "  * grid_latitude                              (grid_latitude) float32 21.119...\n",
+       "  * grid_latitude_1                            (grid_latitude_1) float32 21.0...\n",
+       "  * grid_longitude                             (grid_longitude) float32 325.2...\n",
+       "  * grid_longitude_1                           (grid_longitude_1) float32 325...\n",
+       "  * pressure                                   (pressure) float32 10.0 ... 10...\n",
+       "  * time                                       (time) datetime64[ns] 1851-01-...\n",
+       "Data variables:\n",
+       "    air_pressure_at_sea_level                  (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    air_temperature_at_pressure_mean           (time, pressure, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 1, 203, 270), meta=np.ndarray>\n",
+       "    air_temperature_maximum                    (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    air_temperature_mean                       (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    air_temperature_minimum                    (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    cloud_area_fraction                        (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    geopotential_height                        (time, pressure, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 1, 203, 270), meta=np.ndarray>\n",
+       "    lagrangian_tendency_of_air_pressure        (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array<chunksize=(200, 1, 202, 270), meta=np.ndarray>\n",
+       "    precipitation_flux                         (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    relative_humidity_at_pressure_mean         (time, pressure, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 1, 203, 270), meta=np.ndarray>\n",
+       "    relative_humidity_mean                     (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    specific_humidity                          (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    surface_air_pressure                       (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    surface_downwelling_longwave_flux_in_air   (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    surface_downwelling_shortwave_flux_in_air  (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    surface_temperature                        (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    x_wind_at_pressure_mean                    (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array<chunksize=(200, 1, 202, 270), meta=np.ndarray>\n",
+       "    x_wind_mean                                (time, grid_latitude_1, grid_longitude_1) float32 dask.array<chunksize=(200, 202, 270), meta=np.ndarray>\n",
+       "    y_wind_at_pressure_mean                    (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array<chunksize=(200, 1, 202, 270), meta=np.ndarray>\n",
+       "    y_wind_mean                                (time, grid_latitude_1, grid_longitude_1) float32 dask.array<chunksize=(200, 202, 270), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (grid_latitude: 203, grid_latitude_1: 202, grid_longitude: 270, grid_longitude_1: 270, pressure: 17, time: 1920)\n", + "Coordinates:\n", + " * grid_latitude (grid_latitude) float32 21.119...\n", + " * grid_latitude_1 (grid_latitude_1) float32 21.0...\n", + " * grid_longitude (grid_longitude) float32 325.2...\n", + " * grid_longitude_1 (grid_longitude_1) float32 325...\n", + " * pressure (pressure) float32 10.0 ... 10...\n", + " * time (time) datetime64[ns] 1851-01-...\n", + "Data variables:\n", + " air_pressure_at_sea_level (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_at_pressure_mean (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_maximum (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_mean (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_minimum (time, grid_latitude, grid_longitude) float32 dask.array\n", + " cloud_area_fraction (time, grid_latitude, grid_longitude) float32 dask.array\n", + " geopotential_height (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " lagrangian_tendency_of_air_pressure (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " precipitation_flux (time, grid_latitude, grid_longitude) float32 dask.array\n", + " relative_humidity_at_pressure_mean (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " relative_humidity_mean (time, grid_latitude, grid_longitude) float32 dask.array\n", + " specific_humidity (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_air_pressure (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_downwelling_longwave_flux_in_air (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_downwelling_shortwave_flux_in_air (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_temperature (time, grid_latitude, grid_longitude) float32 dask.array\n", + " x_wind_at_pressure_mean (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " x_wind_mean (time, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " y_wind_at_pressure_mean (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " y_wind_mean (time, grid_latitude_1, grid_longitude_1) float32 dask.array" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the open_zarr() method to read in the whole dataset metadata\n", + "zarr_read = '/data/users/zmaalick/cssp/data/ZARRSTORE/monthly'\n", + "dataset = xr.open_zarr(zarr_read)\n", + "# print out the metadata\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: The dataset lists down coordinates and data varaibles. By clicking on \"page\" and \"cylinder\" icon at the end of each row, you can see details of attributes and size respectively.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We can also access and print list of all the variables in our dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data variables:\n", + " air_pressure_at_sea_level (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_at_pressure_mean (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_maximum (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_mean (time, grid_latitude, grid_longitude) float32 dask.array\n", + " air_temperature_minimum (time, grid_latitude, grid_longitude) float32 dask.array\n", + " cloud_area_fraction (time, grid_latitude, grid_longitude) float32 dask.array\n", + " geopotential_height (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " lagrangian_tendency_of_air_pressure (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " precipitation_flux (time, grid_latitude, grid_longitude) float32 dask.array\n", + " relative_humidity_at_pressure_mean (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " relative_humidity_mean (time, grid_latitude, grid_longitude) float32 dask.array\n", + " specific_humidity (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_air_pressure (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_downwelling_longwave_flux_in_air (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_downwelling_shortwave_flux_in_air (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_temperature (time, grid_latitude, grid_longitude) float32 dask.array\n", + " x_wind_at_pressure_mean (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " x_wind_mean (time, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " y_wind_at_pressure_mean (time, pressure, grid_latitude_1, grid_longitude_1) float32 dask.array\n", + " y_wind_mean (time, grid_latitude_1, grid_longitude_1) float32 dask.array" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# display all the variables in our dataset\n", + "dataset.data_vars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Retrieve single (or list of) variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Read mean air temperature at 2 m \n", + "Access and print just a single variable i.e minumum air temperature at 2m\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: The DataArrays in our dataset can be accessed either as attributes or indexed by name\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'air_temperature_mean' (time: 1920, grid_latitude: 203, grid_longitude: 270)>\n",
+       "dask.array<zarr, shape=(1920, 203, 270), dtype=float32, chunksize=(200, 203, 270), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * grid_latitude   (grid_latitude) float32 21.119999 20.9 ... -23.1 -23.320002\n",
+       "  * grid_longitude  (grid_longitude) float32 325.24002 325.46002 ... 384.42\n",
+       "  * time            (time) datetime64[ns] 1851-01-16T12:00:00 ... 2010-12-16T...\n",
+       "Attributes:\n",
+       "    Height:             1.5 m\n",
+       "    cell_methods:       time: mean (interval: 1 hour)\n",
+       "    iris_coord_system:  {"grid_north_pole_latitude": 51.81999969482422, "grid...\n",
+       "    source:             Data from Met Office Unified Model\n",
+       "    standard_name:      air_temperature\n",
+       "    units:              K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * grid_latitude (grid_latitude) float32 21.119999 20.9 ... -23.1 -23.320002\n", + " * grid_longitude (grid_longitude) float32 325.24002 325.46002 ... 384.42\n", + " * time (time) datetime64[ns] 1851-01-16T12:00:00 ... 2010-12-16T...\n", + "Attributes:\n", + " Height: 1.5 m\n", + " cell_methods: time: mean (interval: 1 hour)\n", + " iris_coord_system: {\"grid_north_pole_latitude\": 51.81999969482422, \"grid...\n", + " source: Data from Met Office Unified Model\n", + " standard_name: air_temperature\n", + " units: K" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access the variable by indexing it with its name\n", + "t2m_mean = dataset['air_temperature_mean']\n", + "# print the metadata\n", + "t2m_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'air_temperature_mean' (time: 1920, grid_latitude: 203, grid_longitude: 270)>\n",
+       "dask.array<zarr, shape=(1920, 203, 270), dtype=float32, chunksize=(200, 203, 270), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * grid_latitude   (grid_latitude) float32 21.119999 20.9 ... -23.1 -23.320002\n",
+       "  * grid_longitude  (grid_longitude) float32 325.24002 325.46002 ... 384.42\n",
+       "  * time            (time) datetime64[ns] 1851-01-16T12:00:00 ... 2010-12-16T...\n",
+       "Attributes:\n",
+       "    Height:             1.5 m\n",
+       "    cell_methods:       time: mean (interval: 1 hour)\n",
+       "    iris_coord_system:  {"grid_north_pole_latitude": 51.81999969482422, "grid...\n",
+       "    source:             Data from Met Office Unified Model\n",
+       "    standard_name:      air_temperature\n",
+       "    units:              K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * grid_latitude (grid_latitude) float32 21.119999 20.9 ... -23.1 -23.320002\n", + " * grid_longitude (grid_longitude) float32 325.24002 325.46002 ... 384.42\n", + " * time (time) datetime64[ns] 1851-01-16T12:00:00 ... 2010-12-16T...\n", + "Attributes:\n", + " Height: 1.5 m\n", + " cell_methods: time: mean (interval: 1 hour)\n", + " iris_coord_system: {\"grid_north_pole_latitude\": 51.81999969482422, \"grid...\n", + " source: Data from Met Office Unified Model\n", + " standard_name: air_temperature\n", + " units: K" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access the variable like an attribute\n", + "t2m_mean = dataset.air_temperature_mean\n", + "# print the metadata\n", + "t2m_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Read list of variables \n", + "We can also create a smaller dataset containing a subset of our variables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:                             (grid_latitude: 203, grid_longitude: 270, pressure: 17, time: 1920)\n",
+       "Coordinates:\n",
+       "  * grid_latitude                       (grid_latitude) float32 21.119999 ......\n",
+       "  * grid_longitude                      (grid_longitude) float32 325.24002 .....\n",
+       "  * pressure                            (pressure) float32 10.0 20.0 ... 1000.0\n",
+       "  * time                                (time) datetime64[ns] 1851-01-16T12:0...\n",
+       "Data variables:\n",
+       "    relative_humidity_mean              (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    relative_humidity_at_pressure_mean  (time, pressure, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 1, 203, 270), meta=np.ndarray>\n",
+       "    specific_humidity                   (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>\n",
+       "    surface_temperature                 (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(200, 203, 270), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (grid_latitude: 203, grid_longitude: 270, pressure: 17, time: 1920)\n", + "Coordinates:\n", + " * grid_latitude (grid_latitude) float32 21.119999 ......\n", + " * grid_longitude (grid_longitude) float32 325.24002 .....\n", + " * pressure (pressure) float32 10.0 20.0 ... 1000.0\n", + " * time (time) datetime64[ns] 1851-01-16T12:0...\n", + "Data variables:\n", + " relative_humidity_mean (time, grid_latitude, grid_longitude) float32 dask.array\n", + " relative_humidity_at_pressure_mean (time, pressure, grid_latitude, grid_longitude) float32 dask.array\n", + " specific_humidity (time, grid_latitude, grid_longitude) float32 dask.array\n", + " surface_temperature (time, grid_latitude, grid_longitude) float32 dask.array" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# creating a list containing a subset of our variables \n", + "varlist = ['relative_humidity_mean',\n", + " 'relative_humidity_at_pressure_mean',\n", + " 'specific_humidity',\n", + " 'surface_temperature'\n", + " ]\n", + "\n", + "# extracting the list of variables from dataset\n", + "mini_ds = dataset[varlist]\n", + "\n", + "# print the metadata\n", + "mini_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Access \"cloud_area_fraction\" using both index and attribute method in the cell below and save it in varaible named **caf**
    • \n", + "
  • Create a dataset **pres_ds** containing all the pressure variables, (hint: use for loop)
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve \"cloud_area_fraction\"\n", + "# write your code here ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve all the pressure variables\n", + "# write your code here ... " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Convert datasets to iris cube" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Convert a variable to an Iris Cube\n", + "We now convert the minimum air temperature variable that we accessed in section 2.1 into iris cube. This can be done simply using the method **DataArray.to_iris()**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tiris_coord_system{\"grid_north_pole_latitude\": 51.81999969482422, \"grid_north_pole_longitude\":...
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use the method to_iris() to convert the xarray data array into an iris cube\n", + "cube_t2m_mean = t2m_mean.to_iris()\n", + "cube_t2m_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3.2 Convert whole Dataset to an Iris Cubelist\n", + "Instead of converting all variables one by one into iris cube one by one, we can convert the whole dataset (or a subset of dataset) into an iris cubelist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: This is not as simple as done for single variable above but it is straightforward with the dataset.apply() method, obviousely will take a bit longer to complete!\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# first import the Iris library\n", + "import iris" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Shape192017203270
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
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\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
\tukmo__process_flags['Minimum value of field during time period', 'Time mean field']
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Cloud Area Fraction (unknown)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Geopotential Height (m)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
\ttimex---
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Attributes
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Cell methods
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Lagrangian Tendency Of Air Pressure (Pa s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
\ttimex---
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Attributes
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Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Relative Humidity (%)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
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Relative Humidity (%)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Specific Humidity (unknown)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
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Surface Air Pressure (Pa)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Downwelling Longwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
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Surface Downwelling Shortwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Surface Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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X Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
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X Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
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Attributes
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Y Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
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Attributes
\tsourceData from Met Office Unified Model
Cell methods
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Y Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight10 m
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Cell methods
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\n", + "
\n", + " \n", + " " + ], + "text/plain": [ + "[,\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + "]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create an empty list to hold the iris cubes\n", + "cubelist = iris.cube.CubeList([])\n", + "\n", + "# use the DataSet.apply() to convert the dataset to Iris Cublelist\n", + "dataset.apply(lambda da: cubelist.append(xi.to_iris(da)))\n", + "\n", + "# print out the cubelist\n", + "cubelist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + " Note: By clicking on any variable above, you can see its dimension coordinates and matadata\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • convert caf variable into iris cube **caf_cube**
    • \n", + "
  • create a cube list containing pressure variables only
    • \n", + "
  • Can you note the difference between cube and cubelist?
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## convert clf into iris cube\n", + "# write your code here ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## convert pressure dataset into iris cube list\n", + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Explore cube attributes and coordinates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Accessing cube from cubelist\n", + "Now that we have our variables in cubelist we can extract any varaible using the variable name. For instance the following code indices for **precipitation_flux** variable." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
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Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets load and print the Precipitation Flux variable\n", + "precipitation_cube = cubelist.extract_strict('precipitation_flux')\n", + "precipitation_cube" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: We can see that we have time, grid_latitude and grig_longitude dimensions, and a cell method of mean: time (1 hour) which means that the cube contains monthly mean Precipitation Flux data.\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Cube attributes\n", + "We can explore the cube information further" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1920, 203, 270)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can print its shape\n", + "precipitation_cube.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can print its dimensions\n", + "precipitation_cube.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "masked_array(\n", + " data=[[[4.99799580e-06, 5.67242569e-06, 9.93618505e-06, ...,\n", + " 3.51872586e-05, 3.64446023e-05, 3.65267260e-05],\n", + " [7.71708164e-06, 5.31700425e-06, 2.88864294e-06, ...,\n", + " 3.51242197e-05, 3.70937632e-05, 3.76386233e-05],\n", + " [7.39437746e-06, 5.20270896e-06, 6.18928016e-06, ...,\n", + " 3.54716030e-05, 3.69266272e-05, 3.78089135e-05],\n", + " ...,\n", + " [3.37646583e-07, 2.87457539e-07, 7.34115531e-07, ...,\n", + " 5.29619720e-05, 5.78387517e-05, 6.20657665e-05],\n", + " [5.67623715e-07, 8.19112756e-07, 1.02674448e-06, ...,\n", + " 5.43356400e-05, 6.03865810e-05, 6.44247120e-05],\n", + " [7.46674004e-07, 1.26674774e-06, 1.51617223e-06, ...,\n", + " 6.10008210e-05, 6.57050405e-05, 7.09281958e-05]],\n", + "\n", + " [[3.49186735e-06, 3.78277741e-06, 5.44517843e-06, ...,\n", + " 8.84533256e-06, 1.06529178e-05, 1.31943580e-05],\n", + " [4.86015415e-06, 3.71758165e-06, 2.25562121e-06, ...,\n", + " 8.91099717e-06, 1.10624023e-05, 1.42126064e-05],\n", + " [5.03610363e-06, 3.74791375e-06, 4.03492641e-06, ...,\n", + " 9.67099641e-06, 1.17356367e-05, 1.53057481e-05],\n", + " ...,\n", + " [1.66773007e-05, 1.40159764e-05, 2.37669483e-05, ...,\n", + " 6.03099652e-05, 5.87690956e-05, 5.43384194e-05],\n", + " [1.91337294e-05, 2.94188194e-05, 2.18362602e-05, ...,\n", + " 5.83833571e-05, 5.62339483e-05, 5.35694235e-05],\n", + " [2.11876850e-05, 1.87064961e-05, 1.66910177e-05, ...,\n", + " 5.81365384e-05, 5.66045346e-05, 5.42040252e-05]],\n", + "\n", + " [[8.21209505e-06, 9.05385423e-06, 1.25120514e-05, ...,\n", + " 1.80668085e-05, 1.97204572e-05, 2.19713820e-05],\n", + " [8.93632296e-06, 7.91138154e-06, 5.92999504e-06, ...,\n", + " 1.63157183e-05, 1.86429897e-05, 2.15853070e-05],\n", + " [7.50231584e-06, 6.58987483e-06, 7.79188849e-06, ...,\n", + " 1.52469656e-05, 1.73812386e-05, 2.08092279e-05],\n", + " ...,\n", + " [4.37470362e-06, 4.50783136e-06, 2.78540097e-06, ...,\n", + " 4.41773009e-05, 4.27932064e-05, 4.06583058e-05],\n", + " [3.75568879e-06, 2.56533008e-06, 1.69237364e-06, ...,\n", + " 4.46013728e-05, 4.46237536e-05, 4.06672953e-05],\n", + " [2.11548854e-06, 1.35489199e-06, 1.44331057e-06, ...,\n", + " 4.85784985e-05, 4.80574599e-05, 4.33935020e-05]],\n", + "\n", + " ...,\n", + "\n", + " [[9.41120197e-06, 9.37018649e-06, 1.06297612e-05, ...,\n", + " 6.62888269e-05, 6.54502655e-05, 6.44727261e-05],\n", + " [9.24649248e-06, 8.01284750e-06, 6.00129215e-06, ...,\n", + " 5.57908170e-05, 5.62452733e-05, 5.64085349e-05],\n", + " [8.64687536e-06, 7.87786303e-06, 7.90833201e-06, ...,\n", + " 5.08622106e-05, 5.12987208e-05, 5.07501609e-05],\n", + " ...,\n", + " [2.52308655e-05, 3.44577456e-05, 3.90460518e-05, ...,\n", + " 1.11543472e-04, 1.20366007e-04, 1.22503727e-04],\n", + " [5.06539254e-05, 4.42032688e-05, 3.49181319e-05, ...,\n", + " 1.10978726e-04, 1.18344295e-04, 1.24885715e-04],\n", + " [4.14023852e-05, 2.82325509e-05, 2.20560869e-05, ...,\n", + " 1.24235958e-04, 1.29434455e-04, 1.31021501e-04]],\n", + "\n", + " [[8.52230096e-06, 1.00732350e-05, 1.37223014e-05, ...,\n", + " 5.73967518e-05, 6.37718986e-05, 6.69352667e-05],\n", + " [9.43512714e-06, 8.38329106e-06, 6.62838875e-06, ...,\n", + " 5.50696022e-05, 5.99383602e-05, 6.23102023e-05],\n", + " [9.06634159e-06, 7.54635357e-06, 7.91282400e-06, ...,\n", + " 5.37563974e-05, 5.68727228e-05, 5.83085930e-05],\n", + " ...,\n", + " [4.69457736e-05, 5.00799906e-05, 6.20738065e-05, ...,\n", + " 1.19872668e-04, 1.24431011e-04, 1.24335769e-04],\n", + " [1.04135805e-04, 6.43078529e-05, 5.91652024e-05, ...,\n", + " 1.16323368e-04, 1.24370636e-04, 1.26204017e-04],\n", + " [1.99892645e-04, 8.47755364e-05, 5.46534357e-05, ...,\n", + " 1.28680418e-04, 1.29988839e-04, 1.30226181e-04]],\n", + "\n", + " [[9.06969831e-07, 1.30259411e-06, 2.03753143e-06, ...,\n", + " 9.03951805e-05, 8.86623457e-05, 9.09506707e-05],\n", + " [9.94005291e-07, 1.08165113e-06, 8.96699532e-07, ...,\n", + " 9.33972915e-05, 9.11435709e-05, 9.23855405e-05],\n", + " [8.43721921e-07, 8.97011944e-07, 1.12586929e-06, ...,\n", + " 9.62585473e-05, 9.30722235e-05, 9.42686456e-05],\n", + " ...,\n", + " [1.58778112e-05, 2.10962608e-05, 3.07982773e-05, ...,\n", + " 1.13055998e-04, 1.09826993e-04, 9.55409560e-05],\n", + " [2.88774991e-05, 3.07871196e-05, 3.29684080e-05, ...,\n", + " 1.11024296e-04, 1.08048800e-04, 9.81074554e-05],\n", + " [3.75478667e-05, 3.50705741e-05, 4.17393967e-05, ...,\n", + " 1.20427205e-04, 1.11447967e-04, 9.59054378e-05]]],\n", + " mask=False,\n", + " fill_value=1e+20,\n", + " dtype=float32)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can print all of the data values (takes a bit of time as it is a large dataset!)\n", + "precipitation_cube.data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum value: 0.0041655204\n", + "Minimum value: 0.0\n", + "Mean value: 3.6445937e-05\n" + ] + } + ], + "source": [ + "# We can also print the maximum, minimum and mean value in data\n", + "print('Maximum value: ', precipitation_cube.data.max())\n", + "print('Minimum value: ', precipitation_cube.data.min())\n", + "print('Mean value: ', precipitation_cube.data.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'precipitation_flux'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can print cube's name\n", + "precipitation_cube.name()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unit('kg m-2 s-1')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can print the unit of data\n", + "precipitation_cube.units" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 'Data from Met Office Unified Model'}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can also print cube's general attributes\n", + "precipitation_cube.attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Rename the cube\n", + "Rename the precipitation_flux cube" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: The name, standard_name, long_name and to an extent var_name are all attributes to describe the phenomenon that the cube represents.\n", + " \n", + "standard_name is restricted to be a CF standard name (see the [CF standard name table](http://cfconventions.org/standard-names.html)). \n", + "\n", + "If there is not a suitable CF standard name, cube.standard_name is set to None and the long_name is used instead. \n", + "long_name is less restrictive and can be set to be any string.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "precipitation_flux\n", + "None\n", + "precipitation_flux\n", + "precipitation_flux\n" + ] + } + ], + "source": [ + "print(precipitation_cube.standard_name)\n", + "print(precipitation_cube.long_name)\n", + "print(precipitation_cube.var_name)\n", + "print(precipitation_cube.name())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# changing the cube name to 'pflx' using \"rename\" method\n", + "precipitation_cube.rename(\"pflx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "pflx\n", + "None\n", + "pflx\n" + ] + } + ], + "source": [ + "print(precipitation_cube.standard_name)\n", + "print(precipitation_cube.long_name)\n", + "print(precipitation_cube.var_name)\n", + "print(precipitation_cube.name())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that standar_name and var_name does not change to non CF standard name and while trying to do so, they are changes to None and long_name is renamed instead.\n", + "\n", + "We can also rename the specific name of the cube. Suppose if we only want to change standard_name." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation_cube.standard_name = 'precipitation_flux'" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "precipitation_flux\n", + "pflx\n", + "None\n", + "precipitation_flux\n" + ] + } + ], + "source": [ + "print(precipitation_cube.standard_name)\n", + "print(precipitation_cube.long_name)\n", + "print(precipitation_cube.var_name)\n", + "print(precipitation_cube.name())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can change lond_name, var_name, and name without using rename method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Change the cube units\n", + "Change precipitation_cube units from kg m-2 s-1 to kg m-2 day-1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: The units attribute on a cube tells us the units of the numbers held in the data array. To convert to 'kg m-2 day-1', we could just multiply the raw data by 86400 seconds, but a clearer way is to use the convert_units() method with the name of the units we want to convert the data into. It will automatically update the data array.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kg m-2 s-1\n", + "0.0041655204\n" + ] + } + ], + "source": [ + "# inspect the current unit and maximum data value\n", + "print(precipitation_cube.units)\n", + "print(precipitation_cube.data.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# convert the units to 'mm day-1' using convert_units method\n", + "precipitation_cube.convert_units('kg m-2 day-1')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kg m-2 day-1\n", + "359.90097\n" + ] + } + ], + "source": [ + "# inspect the current unit and maximum data value after the conversion \n", + "print(precipitation_cube.units)\n", + "print(precipitation_cube.data.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Add or remove the attributes\n", + "In section 4.2 we see how to access the cube attributes. In this section we will try to add or remove the attributes \n", + "\n", + "Let's try to add new attribute to the precipitation_flux. \n", + "We want to keep the information of original units of the cube. Best way is to add this information in the attribute.\n", + "Define the new attribute as a key value pair and we can add the attribute using **update** method." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# defining new attribute\n", + "new_attr = {'original_units':'kg m-2 s-1'}" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 'Data from Met Office Unified Model'}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# List the attibutes\n", + "precipitation_cube.attributes" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 'Data from Met Office Unified Model',\n", + " 'original_units': 'kg m-2 s-1'}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add new attribute using .update() method\n", + "precipitation_cube.attributes.update(new_attr)\n", + "\n", + "# now printing the attributes list to see if new attribute has updated\n", + "precipitation_cube.attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, we got 'original_units' in attributes list. \n", + "\n", + "We can also delete any specific attribute. For example, in our precipitation_cube attributes list, we do not need 'source' and we can think of deleting it. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'original_units': 'kg m-2 s-1'}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del precipitation_cube.attributes['source']\n", + "precipitation_cube.attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Accessing cube coordinates\n", + "Access cube's coordinates and explore coordinates attribute" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: \n", + "
    \n", + "
  • Cubes need coordinate information to help us describe the underlying phenomenon. Typically a cube's coordinates are accessed with the coords or coord methods. The latter must return exactly one coordinate for the given parameter filters, where the former returns a list of matching coordinates.
    • \n", + "
  • The coordinate interface is very similar to that of a cube. The attributes that exist on both cubes and coordinates are: standard_name, long_name, var_name, units, attributes and shape.
    • \n", + "
  • Coordinate does not have data, instead it has points and bounds (bounds may be None), so we can access the actual point data
    • \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['time', 'grid_latitude', 'grid_longitude']\n" + ] + } + ], + "source": [ + "# let's print out all cube's coordinates\n", + "print([coord.name() for coord in precipitation_cube.coords()])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DimCoord(array([ 21.119999 , 20.9 , 20.679998 , 20.46 ,\n", + " 20.24 , 20.019999 , 19.8 , 19.58 ,\n", + " 19.359999 , 19.14 , 18.919998 , 18.699999 ,\n", + " 18.48 , 18.259998 , 18.039999 , 17.82 ,\n", + " 17.599998 , 17.38 , 17.16 , 16.939999 ,\n", + " 16.72 , 16.5 , 16.279999 , 16.06 ,\n", + " 15.839999 , 15.619999 , 15.4 , 15.179999 ,\n", + " 14.959999 , 14.739999 , 14.5199995 , 14.299999 ,\n", + " 14.079999 , 13.86 , 13.639999 , 13.419999 ,\n", + " 13.199999 , 12.98 , 12.759999 , 12.539999 ,\n", + " 12.32 , 12.099999 , 11.879999 , 11.659999 ,\n", + " 11.44 , 11.219999 , 10.999999 , 10.779999 ,\n", + " 10.559999 , 10.339999 , 10.119999 , 9.9 ,\n", + " 9.679999 , 9.459999 , 9.239999 , 9.0199995 ,\n", + " 8.799999 , 8.579999 , 8.36 , 8.139999 ,\n", + " 7.919999 , 7.699999 , 7.4799995 , 7.2599993 ,\n", + " 7.039999 , 6.8199997 , 6.5999985 , 6.379999 ,\n", + " 6.16 , 5.9399986 , 5.7199993 , 5.5 ,\n", + " 5.279999 , 5.0599995 , 4.84 , 4.619999 ,\n", + " 4.3999996 , 4.1799984 , 3.959999 , 3.7399998 ,\n", + " 3.5199986 , 3.2999992 , 3.08 , 2.8599987 ,\n", + " 2.6399994 , 2.42 , 2.1999989 , 1.9799995 ,\n", + " 1.7600002 , 1.539999 , 1.3199997 , 1.0999985 ,\n", + " 0.87999916, 0.65999985, 0.43999863, 0.21999931,\n", + " 0. , -0.22000122, -0.44000053, -0.65999985,\n", + " -0.88000107, -1.1000004 , -1.3200016 , -1.5400009 ,\n", + " -1.7600002 , -1.9800014 , -2.2000008 , -2.42 ,\n", + " -2.6400013 , -2.8600006 , -3.08 , -3.3000011 ,\n", + " -3.5200005 , -3.7399998 , -3.960001 , -4.1800003 ,\n", + " -4.4000015 , -4.620001 , -4.84 , -5.0600014 ,\n", + " -5.2800007 , -5.5 , -5.720001 , -5.9400005 ,\n", + " -6.16 , -6.380001 , -6.6000004 , -6.8200016 ,\n", + " -7.040001 , -7.26 , -7.4800014 , -7.700001 ,\n", + " -7.92 , -8.140001 , -8.360001 , -8.58 ,\n", + " -8.800001 , -9.02 , -9.24 , -9.460001 ,\n", + " -9.680002 , -9.9 , -10.120001 , -10.340002 ,\n", + " -10.559999 , -10.780001 , -11.000002 , -11.219999 ,\n", + " -11.440001 , -11.660002 , -11.879999 , -12.1 ,\n", + " -12.320002 , -12.539999 , -12.76 , -12.980001 ,\n", + " -13.199999 , -13.42 , -13.640001 , -13.8600025 ,\n", + " -14.08 , -14.300001 , -14.520002 , -14.74 ,\n", + " -14.960001 , -15.180002 , -15.4 , -15.620001 ,\n", + " -15.840002 , -16.06 , -16.28 , -16.500002 ,\n", + " -16.72 , -16.94 , -17.160002 , -17.38 ,\n", + " -17.6 , -17.820002 , -18.039999 , -18.26 ,\n", + " -18.480001 , -18.699999 , -18.92 , -19.140001 ,\n", + " -19.359999 , -19.58 , -19.800001 , -20.020002 ,\n", + " -20.24 , -20.460001 , -20.680002 , -20.9 ,\n", + " -21.12 , -21.340002 , -21.56 , -21.78 ,\n", + " -22.000002 , -22.22 , -22.44 , -22.660002 ,\n", + " -22.88 , -23.1 , -23.320002 ], dtype=float32), standard_name='grid_latitude', units=Unit('degrees'), var_name='grid_latitude', coord_system=RotatedGeogCS(51.81999969482422, 289.8299865722656, ellipsoid=GeogCS(6371229.0)))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's access the 'grid_latitude' coordinate and print out the last 10 values\n", + "grid_latitude = precipitation_cube.coord('grid_latitude')\n", + "grid_latitude" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21.119999\n", + "-23.320002\n" + ] + } + ], + "source": [ + "# print the maximum and minimum value of 'grid_latitude' coordinate\n", + "print(grid_latitude.points.max())\n", + "print(grid_latitude.points.min())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Inspect attributes \n", + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Inspect coordinates\n", + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Excercise\n", + "\n", + "In this exercise we will explore the variables and attributes of monthly data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 1: Load hourly data\n", + "Load monthly data into xarrays and display all varaibles\n", + "\n", + "Path to monthly datasets: **'/data/cssp-data/ZARRSTORE/monthly'**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2: Convert to iris cublist\n", + "Convert the dataset into iris cublist and display the cubelist\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3: Extract variable\n", + "Extract x_wind variable from cubelist and display the cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 4: Explore cube attributes \n", + "Using the Iris cube in previous excercise explore its attributes as follow:\n", + "- print out the dimensions\n", + "- print out its shape\n", + "- print out its coordinates\n", + "- print out the maximum and minimum values of latitude and longitude\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 5: Change units and add the original units to attributes list \n", + "\n", + "- change the units of x_wind to km/hr\n", + "- add the original units to the attributes list\n", + "- print out the attributes to see if new attribtue has added successfully\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Summary
\n", + " In this session we learned how:
\n", + "
    \n", + "
  • to load data from a zarr database into xarray dataset and explore its metadata.
    • \n", + "
  • to convert xarray dataset into iris cube and explore its metadata
    • \n", + "
  • to further explore iris cube's attributes thruogh simple operations
    • \n", + "
\n", + "\n", + "
\n", + "\n", + "\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": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb b/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb new file mode 100644 index 0000000..20cb8c2 --- /dev/null +++ b/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb @@ -0,0 +1,5963 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Tutorial 2: Data Preparation and visualisation\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives:\n", + "\n", + "In this session we will learn: \n", + "1. How to perform further cube operations\n", + "2. How to prepare data for analysis\n", + "4. How to visualise data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Constraint and cube extraction](#extract)\n", + "2. [Basic cube calculations](#calc)\n", + "3. [Time series and spatial plots](#plots)\n", + "4. [Saving the cube](#save)\n", + "5. [Exercises](#exercise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Prerequisites
\n", + "- Basic programming skills in python
\n", + "- Familiarity with python libraries Iris, Numpy and Matplotlib
\n", + "- Basic understanding of climate data
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load monthly data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the necessary libraries. Current datasets are in zarr format, we need zarr and xarray libraries to access the data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import zarr\n", + "import iris\n", + "import os\n", + "from cssp_utils import zarr_reader\n", + "from xarray_iris_coord_system import XarrayIrisCoordSystem as xics\n", + "xi = xics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Dataset consists of coordinates and data variables. Let's use the xarray to read all our zarr data into a dataset and display it's metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "path = '/data/users/zmaalick/cssp/data/ZARRSTORE'\n", + "freq = 'monthly'\n", + "\n", + "ds = zarr_reader(path, freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert dataset into iris cubelist" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Air Pressure At Sea Level (Pa)timegrid_latitudegrid_longitude
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Air Temperature (K)timegrid_latitudegrid_longitude
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
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Shape1920203270
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Shape1920203270
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Shape192017203270
Dimension coordinates
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Lagrangian Tendency Of Air Pressure (Pa s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
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Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape1920203270
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Shape192017203270
Dimension coordinates
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Relative Humidity (%)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Specific Humidity (unknown)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Air Pressure (Pa)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Downwelling Longwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Downwelling Shortwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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X Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
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X Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
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Y Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
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Y Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
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\n", + "
\n", + " \n", + " " + ], + "text/plain": [ + "[,\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + "]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create an empty list to hold the iris cubes\n", + "cubelist = iris.cube.CubeList([])\n", + "# use the DataSet.apply() to convert the dataset to Iris Cublelist\n", + "ds.apply(lambda da: cubelist.append(xi.to_iris(da)))\n", + "# print out the cubelist\n", + "cubelist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cubelist printed above holds all of the data from the Zarr file in a list. To see more detail on each of the cubes in the list click on it. That shows a table with information about the name and units of the cube, its shape and coordinates.\n", + "\n", + "We will see in the next section how to obtain a single cube for use in our analysis and visualisation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Constraint and cube extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Indexing the cube\n", + "**AIM:** Extract the ***cloud_area_fraction*** data and index it by a subset of latitudes and longitudes values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: Cubes can be indexed in a similar manner to that of NumPy arrays. The result of indexing a cube is always a copy of the cube\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Cloud Area Fraction (unknown)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# extract the variale from cubelist\n", + "caf = cubelist.extract_strict('cloud_area_fraction')\n", + "caf" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Cloud Area Fraction (unknown)timegrid_latitudegrid_longitude
Shape19201010
Dimension coordinates
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# subsetting the lat lon values by indexing the first 10 values\n", + "subset_caf = caf[..., :10, :10]\n", + "subset_caf" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Cloud Area Fraction (unknown)grid_latitudegrid_longitude
Shape5050
Dimension coordinates
\tgrid_latitudex-
\tgrid_longitude-x
Scalar coordinates
\ttime1851-11-16 00:00:00
Attributes
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# subseting the cube with 50th to 99th lat lon values at time index 10\n", + "subset_caf = caf[10, 50:100, 50:100]\n", + "subset_caf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note:The extract above returns a 2 dimensional cube with lat lon at a single time. Note that time is now a scalar (a single time: 1851-11-16 00:00:00)\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Cloud Area Fraction (unknown)timegrid_latitudegrid_longitude
Shape10203270
Dimension coordinates
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Attributes
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting first 10 elements from time dimension\n", + "subset_caf = caf[:10]\n", + "subset_caf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Time constraint\n", + "**AIM:** Use constraint and extract methods to subet a cube or cubelist.\n", + "\n", + "The monthly data ranges from 1850 to 2000. In some cases we might not need all the time series and we might only be interested in 50 years 1950 - 2000.\n", + "In such cases, we can extract cube creating a time constraint. \n", + "Let's extract \"air_pressure_at_sea_level\" cube, extract the cube containing data from 1950 to 2000 using time constraint." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: We've already seen above the extract_strict method to extract specific cube from cubelist. We can also apply constraints to a single cube (or a CubeList) using the respective constraint and extract methods.\n", + " \n", + "Iris's constraint mechanism provides a powerful way to filter a subset of data from a larger collection.The Constraint constructor takes arbitrary keywords to constrain coordinate values.\n", + " \n", + "extract_strict returns a single cube while extract methods returns a cubelist. If you use extract_strict and more or less than 1 cube matches then it is an error. \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Extracting air pressure at sea level cube from cublist \n", + "air_pres = cubelist.extract_strict('air_pressure_at_sea_level')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Extracting from year 1950 to 2000\n", + "start_time = 1950\n", + "end_time = 2000\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "subcube = air_pres.extract(time_constraint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check it got the right cube, we can print start data and end date of subcube" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start time: 1950-01-16 12:00:00\n", + "End time: 2000-12-16 12:00:00\n" + ] + } + ], + "source": [ + "tcoord = subcube.coord('time')\n", + "units = tcoord.units\n", + "tdata = [units.num2date(point) for point in tcoord.points]\n", + "print('Start time: ',tdata[0])\n", + "print('End time: ',tdata[-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: It is common to want to build a constraint for time.\n", + "This can be achieved by comparing cells containing datetimes\n", + "\n", + "There are a few different approaches for producing time constraints in Iris. We focus here on one approach for constraining on time in Iris.\n", + "\n", + "This approach allows us to access individual components of cell datetime objects and run comparisons on those.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to constraining years, we can also constrain months and days\n", + "\n", + "Consider a case where we want to get only a few months, like March, April and May, from our subcube" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Pressure At Sea Level (Pa)timegrid_latitudegrid_longitude
Shape153203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (4 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# extracting month june, july and august from the list of years\n", + "month_constraint = iris.Constraint(time=lambda cell: cell.point.month in (3,4,5))\n", + "subcube.extract(month_constraint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Extract region.\n", + "\n", + "Note: The original model data is on a rotated pole grid system and we need to the extract_rot_method() in order to get the data on to a regular grid system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets try to extract Shangai region using **extract_rot_cube**.\n", + "\n", + "**extract_rot_cube** takes the latitude and longitude of the region of interest and returns a smaller cube with the extracted region of rotated pole coordinates. \n", + "\n", + "First define the lat lon on Shanghai region:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Pressure At Sea Level (Pa)timegrid_latitudegrid_longitude
Shape19201621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tlatitude-xx
\tlongitude-xx
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (4 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load extract_rot_cube from catnip\n", + "from catnip.preparation import extract_rot_cube\n", + "ext_cube = extract_rot_cube(air_pres, min_lat, min_lon, max_lat, max_lon)\n", + "ext_cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we can see that the min/max boundaries now changed\n", + "print('latitude: [', ext_cube.coord('grid_latitude').points.min(), ', ', ext_cube.coord('grid_latitude').points.max(), ']')\n", + "print('longitude: [', ext_cube.coord('grid_longitude').points.min(), ', ', ext_cube.coord('grid_longitude').points.max(), ']')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Constraint on cell methods and attributes\n", + "\n", + "In our cubelist, we can see that we have four cubes named air_temperature: Minimum, Maximum and two Means (one with pressure level).\n", + "Let's try to extract air temperaute and see what we get:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Air Temperature (K)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
\ttimex---
\tpressure-x--
\tgrid_latitude--x-
\tgrid_longitude---x
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (4 hour)
\n", + "

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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
\tukmo__process_flags['Maximum value of field during time period', 'Time mean field']
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + "

\n", + "
\n", + " \n", + "\n", + "\n", + "
\n", + "

\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
\tukmo__process_flags['Minimum value of field during time period', 'Time mean field']
\n", + "

\n", + "
\n", + " \n", + " " + ], + "text/plain": [ + "[,\n", + ",\n", + ",\n", + "]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "air_temp = cubelist.extract('air_temperature')\n", + "air_temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to get only one cube i.e. the time mean at the surface and not on the pressure levels, we need to constrain using the cell method. A [cell_method](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch07s03.html) is a piece of metadata which describes additional characteristics of a field. Let try to create a constraint and use it to extract the desired cube." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# contrain for the cube that does not have 'pressure' in its coordinate list\n", + "cube_cons_surf = iris.Constraint(cube_func=lambda c: 'pressure' not in [coord.name() for coord in c.coords()])\n", + "# also constrain to be only mean temperature \n", + "cube_cons_mean = iris.Constraint(cube_func=lambda c: (len(c.cell_methods) > 0) and (c.cell_methods[0].method == 'mean'))\n", + "# \n", + "air_temp_mean = air_temp.extract_strict(cube_cons_surf & cube_cons_mean)\n", + "air_temp_mean " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we got desired cube. Now, if we look into minimum and maximum cubes, that does not contains cell method, instead, information lies in there respective attributes. \n", + "\n", + "We can extract, for example minimum cube, by constraining the attribues:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
\tukmo__process_flags['Minimum value of field during time period', 'Time mean field']
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_cons = iris.Constraint(cube_func=lambda c: ('ukmo__process_flags' in c.attributes) and (c.attributes['ukmo__process_flags'][0].split(' ')[0] == 'Minimum'))\n", + "air_temp_min = air_temp.extract_strict(min_cons)\n", + "air_temp_min " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Extract from cubelist relative humidity cube: year: 1900-2000, months: May-September Cell method: Mean (4 hours) \n", + "\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract relative humidity cube\n", + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Basic Calculations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Calculating mean, max, min\n", + "In this section we will use **iris.analysis** method to calculate basic mean, min and max values\n", + "\n", + "Let extract surface_temerature and calculate mean over the whole region. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# extract surface_temerature\n", + "sft = cubelist.extract_strict('surface_temperature')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the **collapsed** and **analysis** methods over grid_latitude and grid_longitude, we can get the timeseries of mean over the whole domain." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Surface Temperature (K)time
Shape1920
Dimension coordinates
\ttimex
Scalar coordinates
\tgrid_latitude-1.1000013 degrees, bound=(-23.430002, 21.23) degrees
\tgrid_longitude354.83002 degrees, bound=(325.13, 384.53003) degrees
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\tmeangrid_longitude, grid_latitude
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import iris.analysis.cartography\n", + "\n", + "#Since grid_latitude and grid_longitude were both point coordinates we must guess bound positions for them in order to calculate the area of the grid boxes\n", + "sft.coord('grid_latitude').guess_bounds()\n", + "sft.coord('grid_longitude').guess_bounds()\n", + "\n", + "grid_areas = iris.analysis.cartography.area_weights(sft)\n", + "\n", + "# calculating mean using area_weights method\n", + "sft_mean = sft.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MEAN, weights=grid_areas)\n", + "sft_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: The above cube has reduced to only one dimension i.e. \"time\"\n", + "\n", + " \n", + "
iris.analysis provides a range of statistical methods, see [iris.analysis dcumentation](https://scitools.org.uk/iris/docs/v1.9.0/html/iris/iris/analysis.html)\n", + " \n", + "
Collapse method can be applied to one, more or all the dimensions.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Basic arithmetic operations\n", + "\n", + "Basic arithmetic operations like addition, subtraction, multiplication, square root, power etc. can be performed on iris cube.\n", + "\n", + "Let's calculate 10m windspeed using **x_wind** and **y_wind** cubes.\n", + "\n", + "In our cubelist, we have two variables with same cell method. We can constraint using coordinates information.\n", + "\n", + "To calcuate 10m windspeed we need data which is not on pressure levels." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# extract x_wind and y_wind\n", + "xcons = iris.Constraint(cube_func=lambda c: c.standard_name == 'x_wind' and ('pressure' not in [coord.name() for coord in c.coords()]))\n", + "ycons = iris.Constraint(cube_func=lambda c: c.standard_name == 'y_wind' and ('pressure' not in [coord.name() for coord in c.coords()]))\n", + "\n", + "u = cubelist.extract_strict(xcons)\n", + "v = cubelist.extract_strict(ycons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets create a windspeed cube by coping the u cube" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "windspeed = u.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calcuate windspeed:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
X Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight10 m
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "windspeed.data = np.sqrt(u.data**2 + v.data**2)\n", + "windspeed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that cube name is \"x_wind\", that is becuase we copy the u_cube. We can rename it to \"windspeed\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Wind Speed (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight10 m
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "windspeed.rename(\"wind speed\")\n", + "windspeed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: While performing arithmetic calculation, keep into consideration the units, name and other metadata information. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Time series and spatial plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Time series plots\n", + "Using iris quick plot to create time series plots. Let's load the necessary libraries first.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# we first need to load libraries for plotting \n", + "import iris.plot as iplt\n", + "import iris.quickplot as qplt\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the timeseries of mean surface temeprature over Shanghai region from 1950 - 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Surface Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# loading mean air temperature \n", + "sft = cubelist.extract_strict('surface_temperature')\n", + "sft.coord_system()\n", + "sft" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Surface Temperature (K)timegrid_latitudegrid_longitude
Shape19201621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tlatitude-xx
\tlongitude-xx
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shanghai region coordinates \n", + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0\n", + "# load extract_rot_cube from catnip\n", + "from catnip.preparation import extract_rot_cube\n", + "sft_shangai = extract_rot_cube(sft, min_lat, min_lon, max_lat, max_lon)\n", + "sft_shangai" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Surface Temperature (K)timegrid_latitudegrid_longitude
Shape6121621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tlatitude-xx
\tlongitude-xx
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now constrain over time\n", + "start_time = 1950\n", + "end_time = 2000\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "sft_tim = sft_shangai.extract(time_constraint)\n", + "sft_tim" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/cube.py:3218: UserWarning: Collapsing spatial coordinate 'grid_latitude' without weighting\n", + " warnings.warn(msg.format(coord.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n", + " warnings.warn(msg.format(self.name()))\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Surface Temperature (K)time
Shape612
Dimension coordinates
\ttimex
Scalar coordinates
\tgrid_latitude-7.1500006 degrees, bound=(-8.910001, -5.3900003) degrees
\tgrid_longitude369.24 degrees, bound=(366.93, 371.55) degrees
\tlatitude30.474947548228023 degrees, bound=(28.58510876660847, 32.364786329847576) degrees
\tlongitude120.53306838375417 degrees, bound=(117.79606740066862, 123.27006936683973) degrees
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\tmeangrid_latitude, grid_longitude
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# collapse the longitude and latitude and calculate mean over the time period\n", + "timeseries = sft_tim.collapsed(['grid_latitude','grid_longitude'], iris.analysis.MEAN)\n", + "timeseries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have got the time series. Now we can plot the timeseries using [https://scitools.org.uk/iris/docs/latest/iris/iris/quickplot.html?highlight=quickplot](**iris quickplot**)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the timeseries \n", + "qplt.plot(timeseries)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the timeseries using a standard matplotlib function [what does this add?]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# ploting with matplotlib \n", + "plt.plot(timeseries.data)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: iris.quickplot adds extra automatic labelling: axes are labelled with a coordinate name and units, and the plot title is taken from the cube name. On the other hand matplotlib.plot needs to add labels and title manually. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Contour plots\n", + "Using iris quick plot to create contour plots\n", + "\n", + "Let's plot the avarage surface temperature from 1900 to 2000 over Shangai region.\n", + "\n", + "We can collapse 'time' dimension os sft_tim cube to get the spatial mean " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'time'.\n", + " warnings.warn(msg.format(self.name()))\n" + ] + } + ], + "source": [ + "spatial_mean = sft_tim.collapsed(['time'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have got the spatial mean, Now we can plot the contour using iris quickplot contourf method" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the surface temperature contour at the first timestep \n", + "qplt.contourf(spatial_mean)\n", + "# add some coastlines for context\n", + "plt.gca().coastlines()\n", + "# set the figure size\n", + "plt.gcf().set_size_inches(8,12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: iris.quickplot also adds the colorbar\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Plot time series of maximum air temperature from 1900 to 2000 of only summer season (June, July and August)
    • \n", + "
  • Plot contour plot of the maximum air temperature from 1900 to 2000 of only summer season (June, July and August)
    • \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# time series plot\n", + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# contour plot\n", + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Saving the cube" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Save the cube in zarr store\n", + "We can save our cube in zarr store to be used later. \n", + "\n", + "For this purpose, we first need to convert cube into xarray and then save it into zarr store.\n", + "\n", + "Let's save 'spatial_mean' cube from the above section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# converting cube back to xarray\n", + "sft_mean = xr.DataArray.from_iris(spatial_mean)\n", + "\n", + "# rename the xarray\n", + "sft_mean.rename('surface_temperature_mean')\n", + "\n", + "# checking the chunk size of the xarray\n", + "sft_mean.chunks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# convert the xarray into dataset\n", + "sft_mean_ds = sft_mean.to_dataset()\n", + "\n", + "# store the dataset to specfied path as zarr data store\n", + "sft_mean_ds.to_zarr('/data/users/zmaalick/cssp/data/minizarr/monthly/surface_temperature_mean', consolidated=True, mode='w')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this exercise we will analyse the mean precipitation rate from 1950 - 2010 over the Shangai region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 1: Load monthly data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ... " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 2: Extract precipitation_flux" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 3: calculate mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 4: Plot timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 5: Spatial plot over Shangai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Summary
\n", + " In this session we learned how:
\n", + "
    \n", + "
  • to prepre sube for analysis
    • \n", + "
  • to perform basic arithmatic operation
    • \n", + "
  • to plot timeseries and contours
    • \n", + "
  • to save data in zarr format
    • \n", + "
\n", + "\n", + "
\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": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb b/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb new file mode 100644 index 0000000..23d2aca --- /dev/null +++ b/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb @@ -0,0 +1,4208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Tutorial 3: Basic data analysis\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives:\n", + "\n", + "In this session we will learn: \n", + "1. to calculate and visualise annual and monthly means\n", + "2. to calculate and visualise seasonal means\n", + "3. to calculate mean differences (anomalies)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Calculate annual and monthly mean](#annual)\n", + "2. [Calculate seasonal means](#season)\n", + "3. [Calculating differences (anomalies)](#percent)\n", + "4. [Exercises](#exercise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Prerequisites
\n", + "- Basic programming skills in python
\n", + "- Familiarity with python libraries Iris, Numpy and Matplotlib
\n", + "- Basic understanding of climate data
\n", + "- Tutorials 1 and 2\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load libraries and monthly data\n", + "Import the necessary libraries. Current datasets are in zarr format, we need zarr and xarray libraries to access the data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import zarr\n", + "import iris\n", + "import os\n", + "from cssp_utils import zarr_reader\n", + "from catnip.preparation import extract_rot_cube, add_bounds\n", + "from xarray_iris_coord_system import XarrayIrisCoordSystem as xics\n", + "xi = xics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Dataset consists of coordinates and data variables. Let's use the xarray to read all our zarr data into a xarray dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "path = '/data/users/zmaalick/cssp/data/ZARRSTORE'\n", + "freq = 'monthly'\n", + "\n", + "ds = zarr_reader(path, freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the dataset into iris cubelist." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Air Pressure At Sea Level (Pa)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Air Temperature (K)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
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Air Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Cloud Area Fraction (unknown)timegrid_latitudegrid_longitude
Shape1920203270
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Geopotential Height (m)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
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Lagrangian Tendency Of Air Pressure (Pa s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
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Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Relative Humidity (%)timepressuregrid_latitudegrid_longitude
Shape192017203270
Dimension coordinates
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Relative Humidity (%)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Specific Humidity (unknown)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Air Pressure (Pa)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Surface Downwelling Longwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
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Surface Downwelling Shortwave Flux In Air (W m-2)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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Attributes
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Surface Temperature (K)timegrid_latitudegrid_longitude
Shape1920203270
Dimension coordinates
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X Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
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Attributes
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X Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
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Attributes
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Y Wind (m s-1)timepressuregrid_latitudegrid_longitude
Shape192017202270
Dimension coordinates
\ttimex---
\tpressure-x--
\tgrid_latitude--x-
\tgrid_longitude---x
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
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Y Wind (m s-1)timegrid_latitudegrid_longitude
Shape1920202270
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Attributes
\tHeight10 m
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
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\n", + "
\n", + " \n", + " " + ], + "text/plain": [ + "[,\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + ",\n", + "]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create an empty list to hold the iris cubes\n", + "cubelist = iris.cube.CubeList([])\n", + "\n", + "# use the DataSet.apply() to convert the dataset to Iris Cublelist\n", + "ds.apply(lambda da: cubelist.append(xi.to_iris(da)))\n", + "\n", + "# print out the cubelist.\n", + "cubelist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Calculating annual and monthly mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Calculating annual cycle\n", + "\n", + "Here we calculate annual mean, maximum and minimum air_temperature over the Shanghai region from 1981 to 2010. \n", + "\n", + "We will first need to extract the required variables, extract the Shanghai region and constrain by time period. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# extract air_temperature\n", + "air_temp = cubelist.extract('air_temperature')\n", + "\n", + "# extracting maximum air temperature\n", + "cons = iris.Constraint(cube_func=lambda c: ('ukmo__process_flags' in c.attributes) and (c.attributes['ukmo__process_flags'][0].split(' ')[0] == 'Maximum'))\n", + "air_temp_max = air_temp.extract_strict(cons)\n", + "\n", + "# extracting mainimum air temperature\n", + "cons = iris.Constraint(cube_func=lambda c: ('ukmo__process_flags' in c.attributes) and (c.attributes['ukmo__process_flags'][0].split(' ')[0] == 'Minimum'))\n", + "air_temp_min = air_temp.extract_strict(cons)\n", + "\n", + "# extracting mean air temperature\n", + "cons = iris.Constraint(cube_func=lambda c: (len(c.cell_methods) > 0) and (c.cell_methods[0].method == 'mean') and c.cell_methods[0].intervals[0] == '1 hour')\n", + "air_temp_mean = air_temp.extract_strict(cons)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# defining Shangai region coords\n", + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0\n", + "\n", + "\n", + "# extract data for the the Shanghai region using extract_rot_cube() function\n", + "max_cube = extract_rot_cube(air_temp_max, min_lat, min_lon, max_lat, max_lon)\n", + "min_cube = extract_rot_cube(air_temp_min, min_lat, min_lon, max_lat, max_lon)\n", + "mean_cube = extract_rot_cube(air_temp_mean, min_lat, min_lon, max_lat, max_lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# define start and end year for our time constraint\n", + "start_time = 1981\n", + "end_time = 2010\n", + "\n", + "# define the time constraint\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "\n", + "# laod the data into cubes applying the time constraint\n", + "max_cube = max_cube.extract(time_constraint)\n", + "min_cube = min_cube.extract(time_constraint)\n", + "mean_cube = mean_cube.extract(time_constraint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note:The CATNIP library function preparation.add_time_coord_cats adds a range of numeric coordinate categorisations to the cube. for more details see the documentation\n", + "\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have got required cubes. Now we can add categorical coordinates to such as *year* to the time dimension in our cubes using the CATNIP **preparation.add_time_coord_cats** function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# load CATNIP's add_time_coord_cats method\n", + "from catnip.preparation import add_time_coord_cats\n", + "\n", + "# Add other dimension coordinates\n", + "max_cube = add_time_coord_cats(max_cube)\n", + "min_cube = add_time_coord_cats(min_cube)\n", + "mean_cube = add_time_coord_cats(mean_cube)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the *max_cube* and inspect the categorical coordinates that have been added to the time coordinate of our cube." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Air Temperature (K)timegrid_latitudegrid_longitude
Shape3601621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tday_of_monthx--
\tday_of_yearx--
\tmonthx--
\tmonth_numberx--
\tseasonx--
\tseason_numberx--
\tyearx--
\tlatitude-xx
\tlongitude-xx
Attributes
\tHeight1.5 m
\tsourceData from Met Office Unified Model
\tukmo__process_flags['Maximum value of field during time period', 'Time mean field']
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# printing the max_cube. Note the addtional coordinates under the Auxiliary coordinates\n", + "max_cube" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that **add_time_coord_cats** has added a few auxiliary coordinates including the *year* coordinate to the *time* dimension.\n", + "\n", + "Now we can calculate maximum, minimum and mean values over the *year* coordinate using **aggregated_by** method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate yearly max, min and mean values\n", + "yearly_max = max_cube.aggregated_by(['year'], iris.analysis.MAX)\n", + "yearly_min = min_cube.aggregated_by(['year'], iris.analysis.MIN)\n", + "yearly_mean = mean_cube.aggregated_by(['year'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_longitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_longitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/cube.py:3218: UserWarning: Collapsing spatial coordinate 'grid_latitude' without weighting\n", + " warnings.warn(msg.format(coord.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1410: UserWarning: Collapsing a non-contiguous coordinate. Metadata may not be fully descriptive for 'grid_longitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'latitude'.\n", + " warnings.warn(msg.format(self.name()))\n", + "/home/h01/zmaalick/miniconda3/envs/csspenv/lib/python3.7/site-packages/iris/coords.py:1406: UserWarning: Collapsing a multi-dimensional coordinate. Metadata may not be fully descriptive for 'longitude'.\n", + " warnings.warn(msg.format(self.name()))\n" + ] + } + ], + "source": [ + "# Collapse longitude and latitude to get a timeseries\n", + "yearly_max = yearly_max.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MAX)\n", + "yearly_min = yearly_min.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MIN)\n", + "yearly_mean = yearly_mean.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the *year* coordinate of max cube to see if we have the correct years for our constraint time period." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994\n", + " 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008\n", + " 2009 2010]\n" + ] + } + ], + "source": [ + "print(yearly_max.coord('year').points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Calculating monthly mean\n", + "\n", + "We can calculate monthly means for *precipitation_flux* over the Shangai region from 1981 to 2010 (30 years). " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# extract the precipitation_flux data into an iris cube from the cubelist\n", + "pflx = cubelist.extract_strict('precipitation_flux')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape19201621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tlatitude-xx
\tlongitude-xx
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0\n", + "\n", + "# extract data for the the Shanghai region using extract_rot_cube() function\n", + "pflx_ext = extract_rot_cube(pflx, min_lat, min_lon, max_lat, max_lon)\n", + "pflx_ext" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extracting time constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
Precipitation Flux (kg m-2 s-1)timegrid_latitudegrid_longitude
Shape3601621
Dimension coordinates
\ttimex--
\tgrid_latitude-x-
\tgrid_longitude--x
Auxiliary coordinates
\tlatitude-xx
\tlongitude-xx
Attributes
\tsourceData from Met Office Unified Model
Cell methods
\tmeantime (1 hour)
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "start_time = 1981\n", + "end_time = 2010\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "subcube = pflx_ext.extract(time_constraint)\n", + "subcube" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove auxiliry corrds\n", + "subcube.remove_coord(\"latitude\")\n", + "subcube.remove_coord(\"longitude\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we can use the **add_time_coord_cats** method to add categorical coordinates such as *month* to the *time* dimension in our cube" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "subcube = add_time_coord_cats(subcube)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate monthly mean values\n", + "monthly_mean = subcube.aggregated_by(['month_number'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_latitude bounds added\n", + "grid_longitude bounds added\n" + ] + } + ], + "source": [ + "monthly_mean = add_bounds(monthly_mean, ['grid_latitude','grid_longitude'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can calculate the area weight using **iris.analysis.cartography.area_weights**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import iris.analysis.cartography\n", + "grid_areas = iris.analysis.cartography.area_weights(monthly_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate area averaged monthly mean rainfall\n", + "monthly_mean = monthly_mean.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MEAN, weights=grid_areas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Visualising yearly and monthly means\n", + "\n", + "Let's now visualise yearly mean, max, min data for the air temperature and monthly mean data for the precipitation_flux." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# we first need to load libraries for plotting \n", + "import iris.plot as iplt\n", + "import iris.quickplot as qplt\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize yearly max, min and mean data for *air_temperature* " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the timeseries \n", + "ax1 = qplt.plot(yearly_max, label = 'Max Temp')\n", + "ax2 = qplt.plot(yearly_min, label = 'Min Temp')\n", + "ax3 = qplt.plot(yearly_mean, label = 'Mean Temp')\n", + "plt.legend(bbox_to_anchor=(1.18, 0.78))\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's Visualise monthly precipitation mean over the thirty years (1980-2010)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "qplt.plot(monthly_mean.coord('month_number'), monthly_mean,color='seagreen')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate and visualise the monthly mean over Tibatan region from 1981 to 2010. Create the monthly mean with month names
    • \n", + "
  • Coordinates of Tibatan region: Latitude = [26 36], Longitude = [77 104]
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# time series plot\n", + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculating seasonal means" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Calculating seasonal means: djf, mam, jja and son\n", + "\n", + "Calculate mean wind speed and wind direction from 1981 to 2010 for different seasons over the entire domain.\n", + "\n", + "First we need to calcuate wind speed and wind direction. In previous tutorial, we calculated the wind speed using hard coded simple arithmatic operations. In this tutorial, we will use catnip's **windspeed** and **wind_direction** methods." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# define constraints for x_wind and y_wind data\n", + "xcons = iris.Constraint(cube_func=lambda c: c.standard_name == 'x_wind' and ('pressure' not in [coord.name() for coord in c.coords()]))\n", + "ycons = iris.Constraint(cube_func=lambda c: c.standard_name == 'y_wind' and ('pressure' not in [coord.name() for coord in c.coords()]))\n", + "\n", + "# apply the constraint and load the x_wind and y_wind data\n", + "u = cubelist.extract_strict(xcons)\n", + "v = cubelist.extract_strict(ycons)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# time constraint\n", + "start_time = 1981\n", + "end_time = 2010\n", + "cons = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "u = u.extract(time_constraint)\n", + "v = v.extract(time_constraint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now import and use catnip's *windspeed* and *wind_direction* methods" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# import catnip methods\n", + "from catnip.analysis import windspeed\n", + "from catnip.analysis import wind_direction" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data is on rotated coord system, un-rotating . . .\n" + ] + } + ], + "source": [ + "# calculate windspeed and wind direction\n", + "wind_speed_cube = windspeed(u,v)\n", + "wind_direction_cube = wind_direction(u,v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add coordinates and extract different seasons" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "wind_speed_cube = add_time_coord_cats(wind_speed_cube)\n", + "wind_direction_cube = add_time_coord_cats(wind_direction_cube)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the windspeed data for the all four season \n", + "wndspd_djf = wind_speed_cube.extract(iris.Constraint(season='djf'))\n", + "wndspd_mam = wind_speed_cube.extract(iris.Constraint(season='mam'))\n", + "wndspd_jja = wind_speed_cube.extract(iris.Constraint(season='jja'))\n", + "wndspd_son = wind_speed_cube.extract(iris.Constraint(season='son'))\n", + "\n", + "# Extract the wind direction data for the all four season \n", + "wnddir_djf = wind_direction_cube.extract(iris.Constraint(season='djf'))\n", + "wnddir_mam = wind_direction_cube.extract(iris.Constraint(season='mam'))\n", + "wnddir_jja = wind_direction_cube.extract(iris.Constraint(season='jja'))\n", + "wnddir_son = wind_direction_cube.extract(iris.Constraint(season='son'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate seasonal means" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate the windspeed mean over the seasons\n", + "wspd_djf_mean = wndspd_djf.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wspd_mam_mean = wndspd_mam.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wspd_jja_mean = wndspd_jja.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wspd_son_mean = wndspd_son.aggregated_by(['season'], iris.analysis.MEAN)\n", + "\n", + "# calculate the wind direction mean over the seasons\n", + "wndir_djf_mean = wnddir_djf.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wndir_mam_mean = wnddir_mam.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wndir_jja_mean = wnddir_jja.aggregated_by(['season'], iris.analysis.MEAN)\n", + "wndir_son_mean = wnddir_son.aggregated_by(['season'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now visualise seasonal means" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# we first need to load libraries for plotting \n", + "import iris.plot as iplt\n", + "import iris.quickplot as qplt\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lxttlbKwwI43plc6+CXPMMcccOUSoMWstXDgb6NlyyNGIoqja1cs4INjrAODqZc+yyH9gSqFPdiiBHIwRSeq3VSuRXVrG1v34bysR2cYC2c0NEH7MntmfVmTrC2TLjZNpagJIUuTdHdRB/8Tg+Cy4UJMuPT4mMI0KZq1V/P9wtMfW1qe5eeNbn2h/Xw54xy4X9L0D0iuLb/dhvCNgo4D4+grSOhjEUAmxb6axTUps7fGBh9WS+MYKcn+AXaph84BLDmKiO7vwpcXAOBOmEqKSDJdZzGIV1R1hVhvYQBVBqwCSq0uIcUa42UYdDJGBQo4z5DhD98eMc66yUBIbKOKX1mYa9exilURAsNVFdEa4ZhmZN2dmSzX0/oCkUfa0hlJAuNUhXqgUtyK9tED4+jamFJBeaKEPBmSrdQSg2rM0HsBnsccprhQgB7HnTisJxuKk8IurgyHpWgORZIgkI1up45REHwwBUUw0YpSgemOyfOHmgMGGpvYww5YE4zVIW/6T5NYvkPbbLH7k6/yPM5A9sA3IBiHZ4HjFY4455pjjJNhQI5OMrFYCKZHdoQ9qpUSOE8xKE31358TfiiSnXSQGkWQ4rRB7h+ICcpxCmiG6I0RmyFZbM9Qyu9ZC9kbYehk7lZmlUcFttjHrLUgNVCNsOUK2+zhjj/cdWQhubx8eV6hxSeYTHfbxCUORZOi94/0nx3az3jo8t1Dy+U//I65e+w0EQeX0H32Z4h0TIMtRAsaBEujNDiL9Moq+zgkrBdTf2oyaVRKb0weKv1Uj4hfWkHsDhLGIzBzyZrWA2KcUXaTJWpUTm8beTZg0zAl8ZcMKgbnQOsxI5ANlcNtTIMxSjbiswfqGPQYxphoiBwnRGzvYUCHGGWaxSrbenNmXA8xCFVuLIH8HxMEAmhWy5RqlL24iBzG2GmG1Qo1S6Iyg6bMYJtKYUoBpVXClgHSjBYDa6aHbwzwLnR+2zPnTeYCfXln0gXJnhMsXYCr/jWmUfSBdDnz58WHbfz6Ii7/JUeonEwABgwuatCaJG5LRskJY/L7GD9j62/+cjT/xuxmXJZW8Whj0BVnj6SpHo9deJ1xfR9XefX0Ic8wxx1NAS9IN38hsFmuonS52wc9VIs4w6y1spYTsj3C5WoQT+Cro2AfFthQcjlnkQSk+CZStNME5ZHfoA9RAkS0sgpQz2WirFU4KxCiGhUMFCEdO25gKmmVmUJvt48FxoDCNCmLo51ExjLFKIsRxYaAzcZReIQWmVsLWK4j8XNyUrMard3+aTKRcvvrrnknCK8tiOt07LC68eKry0rsJ74gAWW91CXZm5eBEcLQtbeqzcYqIdEEyn+Othw01duMwuAvv7pEtVnHViOj1HcQ4Jbm4gK2EBJ0R5IOQCyTpSoMwD7DeyXDCB5AmD2IdHGYNTvi+TAzqUYe4FmFLeQbUWmQ/Jrm67FVDHrRx1YhsuU54fx89yPnKR55lF2gIgMz4hSMgJ4HtOIVKiIr9NS1tdohTkzeKCJ9JKR9SZ6SxBAeej2cWq6SrDVTe5OemmjZtOUTtDwgetQ851ZFGDBNEkhE+amOqkVfA2B8gAkW2Uie4v0+61sRWQmzDlyuHq4q0JhGZY7iucBrUCERsePDf/wBXfv/XI69dwx0ITDJGCEEgI0bHCqGPh9ncZ/Ov/Y8Irbn4Z/80werKE29jjvPD3+cKTitUe1DQtQBEqEgbVd9g1Bkcn7DneNtgowAZp4//4rsAIlAkFxZx5QhT9Ukjc+Gw4mwbZdRuF3uzil2oISbjSikgvbqG3O+h97q4SqmYmwA/3mpFdnm5UBiaNM25OEWMElz1eJLK1su48iGlQo4T5E4HYQ75Z9I55IO9IuC15bDgI2crTWwlQkrPX5ZCIHt5xe8UuulE9eIsZGsL2KbPDLvUeJUNHGKvx57a49GdX+AjX/PHkdYHzUkyIAjKiLM04s7A3Xs/y+07/4oLGx/j5Zd+61Nv552CtzVAltahHh6gTyj9utSgdvuFSsMEqjsivN8GLOlqg2y5fuy3c7z1MJUInA8gs2YZHacFlzorB+h8EDL1MmaximkPUcNTmhXeITALVdILrXN/P2uVCbZ7yP3hzOIhW6z6lfxCFbfXR3d8RsPUy+jOCLnVxa43MaFAJr6KIozzCib9GFv119EGinSjhS0H6N0+JHmmPjWEm53D/TXKM7xzfW//8LtCgJKF4os0luCNHa9oEipEYjCtCnbJv3fpxQXMQgUbapwUXj0jD4jilTouUJhG2ZcxF2s4YLSqSFoKNbTUHmZ0rwekJXxz3tZtXJZR/qavYfggILl/h7s//MMkbV9avPDH/wjRyy8+0X0a37kFQO3ySxz8059g9T/5fU/0+znOB1sOyS4sQnA4bWTVkperMhanFdlqC0LtS9JxOhM8z/H2wQUKs9pE3ts9/JtWOC1nMqjPDEKcGtg9LaZVI9IrKz6JYC0zQsNT+xepQcYpNgogD5FNNe8XqUSw7VD7vZntyt0u9tLyMflNMYzRj/axC7UiIC8+SzJcoLENH4iKNEPf2QZHvu/8e9udmQY9MUqwpZBsYwFhHdHdHU+pOCfO1b+cZghrUQ/3cVph1hdwCNxSg/tv/BQrH/gGSmGDtD/goH2Lz/7aDwCgVMQ3fcP/5dzHMsHtO/+aKGqyt/8KBwdvsLj4whNv452EtzxADh61SdebhLd3kYPjUifTCDc7uO2uV1VoVdCbHd8olEPtD+YB8jsFubakrZewpWCG4+ymJlRy5Q2zWH0+AXI+MJtahBzGvqz/lJhulnwc1G4fvdvHCeF5v3mArHd6uEAzySNk1RLBuE+w2ys62IL+mIwmKvEjXlIGPXCUXt3GVsMZ+UKzWEX2x+gzaCumNSvuLIdeFcJUQuxKHTmIEeMUu1glfH0bkRji9Wahnz2BGCW4coitRF7aLQqwSiJHKbYUFKoc8cU6auywkWBwSWO0QGdQfZgxuKBxShAMYFgbsv2DP0LrW7+F0YMGW3/77zH4lU8B8NJv+RO88k/+Gvopsr/Vj3+UdR2y+Xe/HwA7HiNL8+a+Zw2RZIi8vCz3e8jBGBcGZJeWC5qRPOij7/Z8Jm6OdwxcoHGVaEb+zAUKs9byQfMTav4+DqZZ8XJpzpGtNpG90enqDefd5moT1R7gpDycU04IjmWaIXsjr341jCGX0iRQuDxx4EqhN9UQeH7yVtv/+ASur0gy9IM9bLOKWW3N7ssY1O1tsNY3UpdD5E4XnM9y2ytT49mUGpeNAmy9jN7tEt3f9fr8b3I94aQgu7CEyAyyM8AFCrXbhd0uLgowFw+btDeHr9DefY2vfM9vwvUH/OzP/VeUohZal7hx/VvY3v7sUx3DN379X+SV136Mzc1fYXfvC+/6APmtzX87h2qP0Dt91GOC4wkmGshimPisGeBCRXJxgeSl9ed7vF9GkP0YkT7B6hXPT52ojdhyUKg52HqJ5OrhyyiSKY5XThd4Wuk+FyiSSwuIafUFJTDlkKxZZnzdN8GlG00v9/NmcM4BSxpLsNnBhYr4Peu+iS3/zFZLMwLuRbbGuGKxIKbK0CaAaC8h3O75t/PIdRJAuNk97MCuTgXxSpItVAojmOI0Qk1yacFTOfIGPJEZr56RGD9RJBl6qztzrOrgUCbJXF4gubpEcmOF+MU14hdWi4Baj73TY1YSWKC0a6jfSZEGStuG8laGEYZH3/c/UX7pJWof/WqshfjOPcKLFwB8cNxqIRuzC15nLYOf+DnGP/6LyO3Ts5Hlj34QVfe/3f67P4AzhiByhAsjdD1Gqi8vVZxnAVsKSa+ukm0sYJbqmBc2Cm66SA1yEKMO+uh7u6iH++j7uz7QmAfHzw2mXia95MdWWythK+fTCDcTbuw0lUtKXCkkW22e/KOngM2zq7ZVLehbdqHmg/PH4LFzghCk19fIrq2eOjTLNEO9sYmrRGQXFovkAICtVzz/GD+0Z1dWyG6uz1xDMYqR+bjsAHHQJ7i1WVRIcA6Zj5FinCDv7uBKAdnNdVw5Qu72UN0haEmy1pppprel0FdZLi2RXV/D5Ukkl5pT55qgHJ6cIT/p8liH3m5j62Wyq6u4asn3zQAyTglefYjc7dLp3OHVT/5DPvjSd1NNygwGvnoXRU2ybMwrr/4Yi4vHq3jjcYfXXv/ntNu3cO7kBZXWETeufTMAm1u/wvbO0wXa7xS8JRlk6Rz0xui8qSvYfrKmrfDuPmJKWiy5vHiiru8cTw9bO78ZgwCi17cRoxTy0n48pft7FHKUYiohwrpC31fvn65Reea+jcXUIrJyiOrlAZNxqFGCMxqbB6B6p3f4+QmwoUImp0/kLlS41jlt9ibqHg6ckmQr9SKAzVbr/vl3DpEaso0m6o1dP8DkbtMus8j2kNGVGtFeSvTaNsLllIoLLdKKIBjmPOT20HOQ8Z9P5IJMvUR2cQGrjw+myQtrxfirhOfnZXkQnbzvgpeRwy+SJgEQAgbvW0KNHDp2GCFIGpJg5I5ZcscNSThwhB1L1HNFFsYEMLyoKe1ktO9/DqEUi7/ttyINhHczNr7xtxBXY6IXbpBsbqHqNUQuU7f9d3+Q9MEjpBFIERBGNbZ+5se58M2/jfKv/yhZdDijCMC+sYMOKyx+5VcxbD9i5wf+Pht/5LdRbvmslbAx44OIdG46cm6IOEGME+zCcetZ26wgxgku1KhR7DN1czwfCLz6wTD26gnlKG/mrXkay2OuvS2HuFoJtdWeyZBO5tCjesBvBi7UMAD16AA50e439sxFk61Mqn1nL2KdVrMB/knfiTM/dmqFq5UxU9VLc2Qh4HLpt8mI6fCZZSMENhQEjzroHR+r2FKIXayBEFjllXuC+3uQGbKV6mFGe7FGJgRuqYY7kgJ09TJZLiknhjFqq41pVf14LgW2EuGiANkbonLjsFGzgq35Bmm13/P6y7mMnd7uIKY45bYckm0sgpLIgz5qq+2/Ww69ioaSuErErU//KDdvfBut1jUASqUWL73wm2k0LlEqLbC981k21j8KwGCwza9+5vuplJfp9x6wXL7OK49+BKckH/nKP0wUHWngt4Z+7z4AH3z/7+azn/thlIpYOiHgfjfguQbIwljfgHfw5po1BBSNSsC5JE/meH6QB0MfHENxX6PbuyRrjSJrO1XJI726hFUSdTBETTiJT2v+Yh3lV7dPLAmKJCua/07itYtqhBunYCy2XkLuDbxbW6CwebBs6iXkMCa50Dp3E6gVAluLUP0Y/aiDXWsUkniTzyd0BQeMX1jBCUGw3c2l0oBSgB5b9H5uiVoKSG4so5RCDw+fdzlKcFqSbLSwtYjSFzfJWpXCdlwa6wfbqWOfeVsOhoQ7PZLLXrFicpxilMwukhzI2KHyooIA9MgiDVgFNhDosd9yMLCFUYg0FvZ8eY9mmeqDDBU7KkaTbe1x8H0/iFCa3qufpbS0zmDzNlf/8l/AZQm9n/sFwt/52333s8nQMuLyB76NFXEZJRWDG9t86l/+T7QePqDx1V+NfXkJoTXJ527z8H/5m0ihWCm/gPr4b+SVH/vvaf/rX6b8PR/0pyMd0dIYGRmSnfKxyWuO4xAO9FabzDpcJYS8LC33e+jdrtdTXV/A7XbRu+9upZp3MkSgyTYWCF/fLHoIspsbuECdHRwHCpd56TK1eVC4xrlAIVJzqIEuBGapjtrrnb6tc2KywJ5uBgzu7pzu8Amnc5WP8JjFhCpx1v5rpZze5xfqIk6P8YmPwiqFiwJEnJIt1RFCIFKLmgTHlQiM8XHHJJoexZAZbLWEW6ohkwzrnOccL/mgMcRxtE5eHH/m+dEuzXAOspcv+uz0QR85GGPLoTcf2euTWeebAHNKm4hTnJKkF5eQgzHBQc9L1MWJl7drVorAXu33fOAdalwYIMcJSgY8ePgJut27DEe7DPpbhGGNg/brvPc9v4PRaI/9g1dZWX4fYVhnPD7g6pVfz3uDj1HeuAbArVs/xSd/6X/k2uVfz8L6e4gi38j4yqv/hO2tT9PQKyyOm3zw/d/LZ37tB/n4x/4UYXh8of1Ox/MNkBNzTJ3iWSB62PYZyzejBTzHU0MfnJD9TQzhvQPScUa21piVwckzy3a5VmRls4sLYPZQg6fgpT0lXy5ulnFLVaK7+9hyiKmkiNRgSgEqD5DT1TrhvfSYyPtJEIDIJdeylTqqHxPs9XHtAcl7NmaD1Hxwk70xcpSCtQUHO7nQwpYCVAIi/1u6UMFJibGuSJp4ZYAR6VoD2yx7iot12NXDVbzojBDGHpPmK46jVSEN1FSmeDIJHQ8Yg9HsdKTSw59M23VLg9f1VBLaw6JhMNbLBENwlYgNeZPgq343vVIP1x/ynq/9ZsRSi1/5xb9G/5c+wd6P/CQAC9/5m5BRyOgLr2DHY9Y/eK3YT7W0yld9zZ/k3p2f4d4P/S0QUP+Gr0XokNqlF/iKl34vAKO25PJ/8N288aPfx+p3vg9VP1TECWopUjni7SrnMKL8ssbkqdA7eQOoll4jNq9gyPbAl5W/RJQR3lYEymf5ToBLM7CO9OJSQYEqXDlP+s2kD6OR84Az46kUeYnfVUuIwRiRl/izy56Wpg56b1rqy1VLZIDuj7BSItOsCI5lqLH5v20p9DSvzBRVMKdlQTez5RBXCr1hBuDK0akVyqMwi3X0wz0fIFtXNM8dhXQOeWfbS6Clxm8/z6qr3rhI8mQbC/kPpK8GHvSL5tNsfQEQqM0DhFazShr395DlEBbqMwkTANeokDnnpTNbvsdECOFd+Yzz86cA0gy93cH2x1PSmwNUe0D24gWolUgqIeE9n83W+z2SVhVb0shhgkgNqjPEKYFZqKMe7PEh9XH2qruMarC48AJrXKRnD/i1ez/OF1/5x2xtfwprMxYXXqA/eOTPpd+lJjcw1uEizQv1j7Og1rj14Jf54u0fZ3HhRdYvfJRe/xEfXP021rgInTFLNFlb/RB37v4ML77wHee6f+8kvCNk3p4YcUZwZ4/4xlzO6SjEOEVKgTnnYPI46JImS8xM1l6ewVXWuz1Mo1Q0Q0zDkZfJyPWVq9HTBchPABsor9VsHcFun/iyH+xsOUTUDHq3VwSlSAgedZCJQfTHuPpjKBbO+QEeoBwUAYUwrlCmKKAV0jmC7a4PkCcQzLg+mY0W7PYLHvHEiU6NU9TDtp/gWrlsT71EfGWxmDjkOPUB9PrpbkoWZq2tA4dKwJXPzrJMQ7jDYLmAmmiDCpyAdL2J3u2jemNsNSS9tsxS/TrBtYCwZ4m2DS6B8sYVRq/cLzZz98/9RS790T+BHedOj87OSAWVXJkXXv4t3Ax/Kw/FLe7/4j+h9NEPMNi+y8HNTRbUOuW2o3Z9g9Kly+z91OdZ/W0fmD3UckZpvc94u4rN5pnko3CBwmws4iqRf3d2uz5QyewMX17Aob3uHE8NB2QXllB73ZNVPxyo7hCz0pxJPEhjvQX8Edhq5AOt/ggx4dO2qoVEJfhy/8y2+iOcPbJMlp5K4M5bsVUSyiG2HJIsNyBOCaZkzZLFOnrzID/fRfTmASIzmOWGX2RNEh9akq0vIAeH18JcWDjfMeCb40RmEXGKrZ08hos4RT7aR45T5HiyAAyKypKoRJjFuk8e5PQJ2R8RbLdxk2pjo4IIlKe7LNURU+O4yAxmqYF+sAedIe7G+rHst21WYWqKcIC5soLtDiHNINConY7n+yfZsRyGSzJ/32olkguLnvIhQPbHZJdXkAd9RGpwWkK9jA0DCLw04/qohAnrmJUm9EZUZYnx+IAo9HPHg4efIEn6RMrPNfu7r3Bz8eXDxTKwHF1hObpCZhPuJ6/w2c/+IC+8+B28sfkrrFUvAqA6Q66ufIxPfP5vcuP6t6DUu4sa+5alYL3ZwLPrLJfDBDWlaDGHzzCGd/YIXtkieHDwTGR2+mXLwQua/oXDlz++ujzbHAakSzVvO+wgurP3WD6ZtI7w4Dj3zVSjMwO802ADhTnJREWKgs4hkoxgs4vTAhdIEL6xQWQWUy8xemkdmQfL4XYPKxzuBDlu2Y/Beicl06wghgnOOCgHXuIqUujdfrEt8AOyFYLkxspsMOogvL3rS5544fpsvYHa7SNzrWL9qEP42jbCOpKbh5rJVklso4xwjuBhm/C1bZyWvpP6nNJN6jHrE3GEGiWd81mU07bXHuAibxyi8651OUgIXtvGhOC0wOr8fmSOWuUCw8/8GgBLlz5E4+YH6fyznzrc/1EdzUnmO7a8/mN/g/H2Q8L1dZof+zib25/0n1lHtGNovvhhBp957cTjlJGhstFHhfOGsqMwuSarwz9jLg/Mso0FsvUFTKPiF8BzHfpnAgEED/fObJKeUQKaoDPw8+pCbcY3QOSBpYx95tkBjE9vipfWEpxgXmGrJbITNH9Pw7Sds0wywnuz1ArZ9+OZ0BLZH6Hy83WBxlSiYs7IlhsQBYjxJHEhsZPzT7OiMfw0iH0fF2SXlgsN45nP4xR9b8cv6qd04OUwRm23/blIiWlVcYEieP0R4a0t9P29Iji2pQBzYbE4X1ctYQONNAZ9bxf9ms+8mhVPOxQnlKuO9nJAnkSqljylQqtDPf3MQGpmgmy13fZUjf7Yzy+lAIxFbx4g93rYxTpmrYVdamDDANEfQ2owq97kSncGqFtbUCshqhXK0QL7B69SKi1QLi/Ral7j/tYvAbAUXjz1eh+km3yh/W9wWC6sfxWjuM3AtovPqyNNtbJCp3P31G28U/HWZZCFwIaa0+0/nhzhdod4ufbl5Hh8JpwQZNeXkTs9zzMbpZ47mENkhuBBm3S98Vhe1gR65Py9q4oiQ2qVJLm+QvCw7ZvtRN6MlhmfMayE/mUsBycO7NI5glu7vnv3CNQ4LXR/nwQyNSeWG2U8O+nIQewrD3lnsFMSG2lknHrzi/x7YpQSPuxg1ps+82It4Z09kusrRQOFrUWIcUp0a4f4hVXGN1eL/YS3dwketolf8H9z0aGlc3Jtmei17eJ40/UmVkvkICa8n2dVpjhnk4A6a1UgMygEojMkOBj67moouOCmVUHtDwgftnGRZvzi2rHzd4GayVoDBPcPkMPYN3qsN4r7JsYJen9Itt5A7/aL5koRKNJaRHrRZ3acBjkyRUUgurM3m6GKM3TXUN7M0PFEZwquhh/EXX3E0sp7+fQv/y0A1i5+lNX3fCf15uVj93Ny3EII3vvR382rr/xTOv/7T7PyHb+F3X/wI7Dx7QCo1CEzgc3MrIDqNLSlcmHAaKf0ZW9tbVpVbKNCcN8rUpAZ7KKn6VgBojFlo9uqYgA5itF3TrbvneMJkZrTWfECOEmRp1UlW6j58UkI1L6nMx7VxxVAcGcHQkV6Y+NYJlM+2MOd0EQnxgnox8/YjpymYaz/Twj0/d1jfUdFdjyz6O2ODwbLIQjQg9gvyGolbKuGjNPDJmtrEd2hlxFdbc5kwklSCAPfxJ9Z9K0tsI7swqJvaJzav9rpYFo1XBSQvnDh8O8P91HdISLUmNWmd5zbaiPbg6l7Mnt9hLGonQ62FCKHnjYnncMlaUE01g92DxMMB33StYWiKimMQd7fw11cKqqqEoe8tX0mZWn6GZGjBI5I56VXVkAK1HYHukOvWBSnyHFSUKNc3i+QVUv+s90uZqXJy4vfRFfdJauF3Hr0b7h961/yvsY3ktgxVyqzVbhpLIYbXC6/jwfjV9ja/jRLCy+yN7pPtdLy13ecAgLr3n3JiLcsQBbWHTYkPStYoDd+y+2X38kwocZcPLkc5bQiubqEGCWEd/dIryw9tukhaeQqCRLSmiDsO8+Xw2d79f4AnFelmMj0qO4YOUywlZDkQuvYICs3u6drYhpLsN3DRhrhHOIMpYmngcC7w8lurpNpPBfYBopge5YvH+wNCPYGmGpIcmXJN64N4xnzEFcKMAtVX8qaXnQI4ekuvbEPSEsBrhSgximmFJAuVgm2uj7zvlwjvLfvDUQkJFcWsRN6h3O5O92B5/ZOGYKAH4tlILH5sdhaRPk1L9tzUhbfnsatVoJstYFpHU7EMsmI7h34rMQgBucK9yaXGv8+C4FZb2BTgTpDncZKQVZXCEHR3AdeFujl1f8AU1K894PfTaN2mVJj2WflH6NDvbj+ft77NR/mc9//XxLGEXFuNgIQxNDceJlHP/2/MXwoqVw8eRnthKO0OmK8C1nvyzdInvAaIacJTVMpUoMcpxjnZrLGthz5heaczP1c4aLgxIZhOyX/ZcvhqcknUy/n9zDxQePUttR2Bzk4udFPpKaobKHlqY32tlHBVSLCW1uw1cZZez6JzLzRTO31vA6wALPcQKQZwf1d3FSmOHi477XX82MXgzEiyQqFFWcc6kGuKnFpeUZWTu71fPZayoISMQ0Xagg1yZUVyCzBgx1IMu/Wt9rClQKC21u4KETEqacdpQa115u55ke3O1N9yyzB5gFmnGBrJVw5IrvqkydynODCANcfHQbHkcY5zm5uxGfj3WQ/Uvr/sgwxitGnNHAKKUgvLKIfHXhO+zjFScHC6kusZMsM0y7N1m9kJbqKEmeHiE5JRFjl/eLXsxJd49a9f8dK4yX62dbhdzLDcniZvb0vsLz08pnbe6fhLeUgi6dsrjpzm3PNzSeGTDJUd4zd7JBN82QB2Rmh8kyi1RI5FTCN1jThMDvkIzdKnr5gPbe2GCC0Yvzi6mE5PIcJIa1JSjRxJY0LNKX7+4cv+BRsLSJdaxLe3UP1n62ElN4foLd7h6U6waEk0VEIAdbL2snEoN7YJV2uEV9uIlO/LdUeHqOFiDjDtMoz5huTjPv4xor/vBaRrTeQw9g71ClJfH0ZVwqQvXEu7xSiH3V80BJp3JGMuA0U4yuLRK/vkKzUUQdDiDOvFX5pkfMi3WgV/5bWoe/v++s+udfGklxsIQcJun240NX7g+OSfdMSJvgmRNOqoDIQ4+Mzp94fQKvC6oWvKGhBMs6lATl9rlUptO4alIrofeZTOCBRCaEJwTpaB2Xql15m9wf+JWt/+NuIFk5/jspLY4aJxMTvzraMJ4Vp+LKvHE7dY4BAkS3WZ6TdXKhJr68hBrlWerPiG7AO+vPg+C2AGKdeJeGMvhJRK3nCpPVUGDGMC9UKlMRGASJJsPjMsMtL97I7yE009KkUD9OsYNYW0Pd3j6lmiFBj1luIUQJ5MPc44s2koOOU9IF7nvm2CzVcKfSmHO7IMSmJveSbCWW7j95sFxnoybmQG2Uc1VyW3SH2wuKsq11O35j8Nr2y4q3Tt9v+PJQkvrKCHIxRD3s4pWAYn3luLj/OU+McY31Qvd/DLNZx+fnJ/R62VfNKE1Jg1hd8Y2Gc+kXHWfucnjutJbh99vdFqEg2lnz/zNRx6v0+yfU1spUmlR2o6PPRHIWx/t4DWoYMxrs0WCQ1s8/JZXGTn9v6B2ysf5RG49K5tv1OwLt+Ngh3esTNyrEu0TlyHMn6ALhGGVSHYK+PWahCoNAPDtDj1OtIAjAgW6jApcMA2WqBs4cCWVYI0tU6wWa30PQFIDMEmx1Mq+KzgFLinEMawXhRedoGVQgESb9y6I6YB9sAQXdMtt4ku7qE/uLmiUH000IOkyI4dkpiqyGqe7JmspMCVw5Q+wm27GkSQXdMmja9WUp3BKEqKBt6p4dZqpK+uOqzGgdDnJZFc54rB7hy4JtDkozw9h5iGJOuNdC7PYLtbt5gkmG1RBlXaIhOB8emFpGuNjyVZnJPtMQpQbLePGbRfhRaSbITBnKZGqLbuxAfnyxdoLFVoDtClALcaU6IzmcpnPUZ52yxghwkhDsx5ojCiWoPkeP0sCs/78BXvbG35j7l+CfbF7HjvR/+XXzx8z9KUG3woPcZrle8hmcwdLz88m/lUz/71+n+wsusfPuFU2dvJxzR4pjho7Ou27tUXvIIZ9OFuui2F3hOJsJXBaiWTj5LIZC9EaozgO22N1x4AlvcOd4kekNY8kGLCUXhujmBFYJssYHe7WJrZaR1kAfIsjvyqjxB4O/jfh+zseCrPystSDNsvUx4e4uJAxxQUOBEmoEUmIXqsQDZZQbZGT6ZzF++6nVKIkZxQQmx5cgrW/RGRePeJEA29TJWe1k7lbveCSicHf3BUFQ3p5FdX0UjcMbCKEG1+4hRQnrd08+Ec9AeIJ2XhTOLNcQgRj/cP5f7n2lUQAlkdzSjsOTwQb+thBCFqO22p5k4jknqyc4AtB/L5UEfp9VMQ9yzQrKx5Ku0m0eaa40luL9LemmZNNQEWwdPLM27EGzwQu1jfL77syg0mU3R0i9KSqrK++rfyK997of56o/9KZQ6f1P424l3v05aYt71IvXySClc9sYED9u+UetNZN2lsUSvbSOPZNmtEGS1yFMjdnuEr237DGacYRYqjF9eJ7m0QLZQodS2NN/IzRbc8fjCLNZO1DTWB0OfBR2nRalMGG8mMV7wg1h3Q5Gu1DH1EsnlBZKpbLZLDWq754+19GxfphkDEetLTOqUcr4wFjXh3Y69CL3Lu7sBb3s6df9MvYSTEisEerND+OAg7yRWZItV4usrqM7IN9FZhxrESOeDdow7pKeUNK4WnSiw7wSe5qEkwWYHvdcHrXDVCFsrHQuOT+qlOik4htw0IA+OjwZK4WabcKfnM7WPoT9MromtlRAI5Dgl2Okhj0gEOilILrT8oqE78lJ2+bNzVjPg9DVvli6w9OFvQLbqPPzUv2AwOMyilNMylz7+W9n+/h+i88v3T9rU4blHBnFKECwDQ/nCu7MpOMmsd8JrVclWmpjrh7x0R257Gwa404LjCSa6uQ7In9853hrog0Exjmelk1d5E2e24M520WwG+MWPFIfv0zRNplnxBhhRgClHOCGILy2TrhyOxXKY+MD4JD6ydd498QnmKRcGpJeXwVpkd4p2GSg/Tl5cRLYHqPFUcDp51kZJEVC7UjiTVRfGFjzbaQgE4o1N5FYblPASn9fXfEDaG6LaA/RuF9EbeXpGGCCz7NzW2ML6LKow1s8LUpBeXcWsL2DWWrh6BRfqwmXQnKSOZF2x4JSjxGtHv0lr7pOgH+4TnMAPB0+pQQhEmj1VQkoIwZXKB2hG61RLy/xa96dnaDIbpZu01Cr//pPfh7Xvjsr/uz6DbOqlYmB4N2LSUIWSuFB73ug0SX+7S9Yo47TELtW8e9p5t73V9c1RD9okV5d8w1qoPJ8tHyOneeGOPPASouCiBvcPSC8tUN7KEEfeGQGovf5MVnkathpBqJHjFJFLz5X2DeMlBRJMWZIsaoQ6tKU2vXERwAa7PX8cz7FbXjivyZwZS3T3ZMmqyd5VSWNHKSJOvUFHo4yM0xlXR5HTV+Qo8ecRKMxCBbOYa11aS7TVPRaIHHP9k5KsWfEZaCULCoiNtNe73O55lYg48wHrpYVTg5snETOZ3ElTi0guL6I6o8J8RYwzCBRCCB+UnwEZKGxqSFcbqIMBYa6H7o7Ypk7k7CbOjE5JP+E4YJRCJUT2x75DWytMcFxiLq4JRpvblF68Qfpok1/++b/KRz72R2m2rgBwOXyZ5Bu/i4f/wz8m+r//EUprJw97wslTr6EsZajo3UknUOXQyzm9SbilOm4wPjEImeP5wJYCbLOK2m4jdruwvkDUP/k5FPnC9ihvVeCQwwSXZsjuEDkcY63DTdxppcRq4MqKD2iE8Eo0+73iXuuHe2SrrWdyTq5WwlUir3M87eI3CYLDAGntzCJYjhPfJjflqCszgxiMcVNKG07NzhUCkFttRJLhGhXvQliOim3qRweHzp9TmsZPgqPSfKZe9tSPI1Knrl4mk8JztlPjs93jFGLfvI11yM4AOYifG3X0LKUUrEPtdlAn+RycE6YaMdjt8MEP/h5+5ZN/A60CXq5+HVr6a/Gh2m/gE8N/xq3b/5KbN771qffzVuH5ZpDFoY5r1iwzfs8G6ZSpwUnffyKEiuzK4rkLn+pgSPCwTenzj7xU1zsARaAx4fIc7WC1Dt0eEuz2Cb+4SfTK1rlumnCOIFdbUL0x4e1dwle30Lf3kJ0R+hRKQfCwjZoaYF2gkNZRalui7uzALPpjgq3uqbct3O5BZgjf2PbScw/bKAPlHVMMdOM1zXBVMVhTjBck6ZXFQq7N5dfFPUf6jAiUD+RPyYg6AdlChXSlzmg152U5QAr0/gAZZ2SLFaz2OsTRgzbBdrcIeJOV+kyAH9zeO1G949h+tcSVA8xCBRf6RVG2WCW57K1Eddtnek0tIrm2PNNcd/YJn00UcAsV4suLvilRSW+5jX8Oxi+tMXp5nSyX8zsL0xnm4EG7yFi4yvFqgMoMcpQilEDivFVsJcSGCjFMCB60EZlX6yiehakJZLAG7U9/gsVvfZnLf/57WP0938tnfvX7Odh/3X8hdVwtfZj6+gts/Z2fIWmffK/HnYDTBiGXPEv9nbcWUj2b98dKSXZlFXEOZYM5ng1cKcS2qmQXl3OHOE4085DOeaOPye+mx0zjEFlGcnMd9XDfN9/1Z51GJ81vk2ZAB5hLy4h87CGziN5xd9InhvRVR31/twiO7YQzPMlCZ4e6+7YSkq0vFEoPM1bZmSmyrDLxiQvTLOGED36D1x6hX3mAOujjyiF26TD2kIOxV9qY2p4YxhAo5DkVno6fm5f9tKstNK6Q2wMfzJNmnlucK3pZ4bPZtlXFlUJcJcIu1k81NnlecIH20nrOvangGAGP1EPK5UVazWt8w6/7C2SVkF86+DGM83GNEIIPr/4mHj76JXq9B8/oDJ4fnmsG2ZUCkvddQBiHyctz9ozSrA31MVmus5As189tByxHCcGDAwQ+4LG1yD+01p3boed5IFuuET5on+u7gjw7MFl9ngHZHs6USSaNbmoQo07rWsZnlN0oxdxYRuSueMHdfdxq3a9yp2BrJUxusXzi9kYJ4e29YkBXB0NEasiuLGJT0LEjiwTxwpSGpwUnlrwGcKj8wJaWoPMMBucT4PLnUu4dHxiElp7P26pAZind8pJWpl4ivbKIGGcE9/f9MQPh/f2ZUmO2UMG0yujtLtlqwwv7nzMzIDLrZdWmjkukhtJtXx5zpYBkvXE4aeaQvTGu5kulKs6Qe/0Z1Q3tfEbZTL02MncDnEj40Syj4pTwzp6nMOEpL3KYILd7M016p0Hn9yvYmV1AidTMSP9J6w678XN3PlcOia9617Do7h7psn/2BBxKxE0FaVlFYPoDqu9ZQciMxd/4EjL8XXzhh3+Er/m6P4McZehUcfnD38Hn/vFfofvJj1J5+SJBIyEoWUwGWT8g7Z3unmhjhbDvfkbam4YUGC2P0bbmeE4Q5NxWL7snD/ozDZQFusOZzL4ItLeZnoxHoecfTygALtBIIbAT+ptzjFuKqG0QFQkji9WK9OIy8tG+p98EGt5skCwkwZ3tGXWiScOciHShA+xKPoucXV4Ba4ugcZp2kF1exlVLSGtR93ZzEWF/zSZShWa16bWglxuIcYLabAOg0sxzkqeRz6lWiCfO1ZmFGnateWg08sYmOjNkV1aQvRFyv5cv/CPMcoNg88CfVyn0GWUpkN1hISHqwBu1PGcak8NLPLrSodHVSd8517E4MPu71GobAARBmfe873fw2U/9XW4PPs3Nmu8NKY8kL179Fr7wyv/GV33lHzuudf8OwvMNkMlXpPrwsovx6QHwkwTHkDebnQHVHqJ3er5EnR3qTIrUEDw4QLWH/qa2KpgLzRnZnOcJ6RwMYq/5WC95tYdzcrhMvXQumsVZ1/nxv00pv7KFM5bkfRe8Lu4piwhbCs5UmXCR9pys1Qa2USJ41CF4bZv05gr1exmZhtGqIqv4a58sKqKOxTYP761pVnC7fcTo2Zd2RWZRB0N093Dgn1ieDm+s4AJF6fVtb1uaU1Syy4uIfowLNXGuMxxu9YtrnrV8YGyrkaerLNXQmx2C9vB0qaRAFTSKdK2B6ozQW11cqEgXa6Alwf0D0JJ0rUF2ipX0tGqGetjGlaY4eoB6dQunFeb6MiLOCO/sIZOMrFVGjVIfAAWaYBjPLLBMs4wth96A5gkwaX4UWmKU9AtSDUlNUrnVI3zQJr20cGi1KyG+vIDeHxDsefc2VzvD9ltAloyQpagY3YN6SvObrrL/DyWD/ha1uh+wF9qaC1/7m9n6wR+h8n/+z4jjCuetIzkEce9dz0h7UxCAvrfzeIqFyHuDj/x5IhE4x/khxmlOMcoTS6fwpWyziplSrjClAFepF06HLk6Lsn220vQuiZ2BD6TLITKDUtuQRYLBukaPHJVHqeenX8ub2QYnVx2fCMYijqythHVefi08TMA4KTG5vjOpIV4LifbTwmzE1krYWom4qah+egenFObaMtL6py67uIQYxrOLCeXNoeQoObGKprc7ZJmFJ+DHilCRNqrHDEnSa2uILEMM4pmGPDmMEfd2ijKe7I9PdE8U8JZw/M16C6c1+v7u8eBYSdyEV33OY0nNGF0+nIOEEFy69vW88cpPcBMfIGMdl+LLPJSf4v6DX+Typa97FqfyXPCWjvjSOqK9/jPpBXehPlW5Qo4SgoedU0n2RwM63R6iO0NMvUS2UEXv9ckWqwU/8omRmTMF1uVOz2vuaukbep+gweGsDPzM92oRPIYneiYy/2I4IU4NjsEHwGdNfJNrLXIB+fRCi+DBAcHrO8Q3VtBGEnYtWUVilWPchHIo0Ee6tJPrK4Svbxc8u2cFlxrCI0GfyKy//2V/3qZeKjSS44sLMEr89Z1udlmqYXPjDnOxBUIQ3tpBJxnOcnwBNOGB5wPPjMycdZhmGVEKyC62fPlzol9ai3yWdwoys8i9vndrss67PC3VSK4vz+4yz+omud02kS4mXN0+XCDoUTr7jiqBrZcI7+49UYCTXFrwFJlS4Hna+fvqJ2OfIZ/IPZlSgEoNWCi9vjNzvUScwimNmi4QhC7yihn9BFHP+YWBQzeaJHEfJmsJ47hc/hAHpV+h9w/+BY3v+jZM9fznY7rv3l6HZwHRGSBOqT5NI1tp4sKA4OE+WFsYSbhKhOyNvOLB26yAcVq27J0EW4kQE2mxPIEwMW+ZQJEnovBB8iRAlp0hYprfKyjGK9kbeXWFE0r50vmgJqsIkoacodXprfYzvW4iUKQLNW9QtDyrbiONgThDJCmuFGJCr6IyGX/MQg0bCKKdBDWMyZYbnoo2qUJFQaFLL51DnafpzdhjyhGT8xWhIllpojrDIqDN1hewzYrnDu/1kP0hyjhsks1cIxdozErDV/UO+seUQN5O6K32iYtZwC9mnnB7oSzRi3dn/hZFTWI38nFCoEnXWujNA9678Rv55df+V5bDS5RXrzztKTxXvKW5bdEenot/ea5tJRml17ePKUCogyHRGzvn7kAt4Hy2K7qzhzaW8O6+z66dsLp7LB7D0ZOTTGhmn6hb1CmBXTzfjG7rvkyFFGTNpwj0pSgqVgDqYOADlSMwC1XGL60zvrlCNnVsrhSQbDQLZYrwYZsgb/ZKLy4Q31zxvFNjiTqW6v0UkfkdmghGS4cNU1ZCFgjSx0iXPTNoSXKpiUwd4f02Ya5i4ZTAVkKCzY4vEaaeomPCXPvAOZLLC75q4hxqkOBSe2J1wAbKN9wdgcPfu2ylTnppAZerYZRe2fS/U9JTNfb6hcB/eGeXYKeH3hugD4ZEp1mw59llOaVSkd5YIbnYOpWy48oB45urmHrpiSo8Dr+w0O0h4f0DSm/sFGogE4vV7PKC50qGCjNtYHL0ek2/4plFTWXRnAQlQnS9zuDu4cSjI0tw8xLb4zdmggOdwPtf/O20P/vvGf3LT6CeoGJssnd6SPV84ZqVGUtjs1gjvb5WaMnacki23MAu1nG1EsnVFd94TG5IsdfDViLSG+uk19dIr6xgWk+wQnmWeIt41LYSHap/PCHEMJ7qED7chuxM0cEe7ePygEse9HFCeDe1cHZBaVZbh+6YRxYncmqbMnGUNv04b8MjOvaNCtkLG5iTKB5PARMG2MW6t4QuhQjnfIViGBfVXOdg3FJUHoyL5jk/tkRkZUlwf4dsrYVdbhDXJfaE2+r646dThJCC7Noq6ZUV4mtrXn5zKh6QB330vV3C1x+hdzpexvNIcAy+MU493Ec/2HtHBcfArDwrPlnxZlAuLxHH3SN/WyTLxvTWNMn1NZ9FTw3NjuI99a/lk1/4AeLdR29qv88Lb22A/BTBsVDiRBkx8F27cufwZkjnvNuY8130Rztazws7Sv2g3hv7IDkvTQVPoCBRHNMJGRdbCrCBwpxVOp5Culpn/NIa4/dsHMseAojo5NuYLlZxzpFeWsDmdsUIgXjMS+AEJOte53ey0JCjFNnLecy7fcJbuz5gcs4bPUQB6YUWyXqTdKXO+MYyJneJM7l1tN4fEN7e9Y0AQhBsddC5M1zUNdTvJughjFY042VN/6Jm3JL0rgQMV5XXxX2TL/B5kCzXEFaAtei9AS6zZK0KyfUV9E4POUoJNjt+4pMCkfmALbm5cqho8bgytHOeG3dkAZNeaM1UCYL7BwS7fWyoCe7vI5PMVyC2ujghkKPkGPXEnjL5WylIXlzzmqiTvwX+usbXl0kutIhfWCW+uVJQM5LVBnKYFPzr80IApVe3cHg+nxinhxbdSYbsjLClkPjmKjYKzq5STL93Oi+95khDvxgRVpB1p54NZVn6j76S3dc/wbC/S6YdVP12IlXnw1/5B9n++Z8k/fnPo8yXd+B7XjiEl/8KFdlywwddUYC5vIQLFObS8mypOQqKZjEZp+jdLrI7xKWZdyobJycuut8SPC+VgKN9Gq0qpvZ0lUiBzwpnK80ZQ5YJjxbwusH5NfRucQJXicjWW0XkY6uRpxpIgY0C/9kU3DhB7R7SAMKBJdrLjjVl2+UGTivsM1rUyHGCzLOUartDcGsLOYjRj/Z9w12jDFFA2DfozYMikMvWmoAg6KYzi3YnOVFpSR/RHD6KSR+KLYeHzY0CsisrRfOcGHj95emRQsbpcaOdUyB451csgJn+kKeBGiW4bPadFkJy5fI38MW7/9w3KJYj3+jr4GL0MlerH+RTX/h7mOwdtnjgLQyQpXNP5YhmrZuRezmKaeK4yA6zdSazRbnlSTHzIEdB0UGbnjPAl8YWfC914HnQ0zBrDZKX1kguL3qO7mMQ7g98QHZKQ6JJLWlFkNTyrK+EpJZnm/NsZnp9BVsLwTnsaZSOkvaqCJcWMQsVslbZKxCQBymTgaTqy+Xha1uQWfR+32ftU4NZrpGtNYpgGiBbPMw4qH5M+KDt1ThGKao9RIwS9FaX0t0erddSag8ygp4lq0lGaxobCVzZ34P4vRskV87vEPckcCJ3fVuue456Py7UNkyrjC0FiFFKstHEWesVWgKBshDc3Uc/7BDezstLJzwrol4ivraErYbI1BQLjAnS5RpmsVpkWAF03hQj48zrJe8PsOWQ5OICrhKeqMZiz8hYTVcFZn4Tasxi1S/eyiHp5UWyVgVbjbyKxNNw2gNvbT6hQokkQ/bGlF7b9rKFIYfPfyXAVMIT3wffK+C81uiRTJDTUNk0VNavMvz8635hk6N6tU7j634d9x78HOMVTXf58LNKdZn3ffB38+Cn/j7m1mxJcI7TYRsVkhsbM4GwVYrs+tqxxevEIS19YYNsrcXEbCS4vU3wYA+9fToN7nnjeQUrNk69EUtOhXBRgFtuHHMVPRdCjVtrYZfqmLWWzyjnmBy/ubziy/xJSnZlFbPcQFmbB8kLXiVhfaH4nbm6gjshYJ9ehIrukFLbIJNT7NmjgGyl4SkgT5M0moxPxiI2D6A/Qu33Dp34UoNpVTEXvPyn3u55Gka1RLbSIFuuY0oSaQVmsYZrVIgbEqvFMQrYpLJ3FKZZIdtY8JWE6Squ87/J1hc973uy+HiMi967GTYKME1Pt5HjN/c+NtQK/dEWaTrbyH3l8jcwGGzRP7iPq5XIprSgr1c/wkKwzhc++w9ndJPfCXjuAbKMU4L7B5Re2XqqwfAkc4ppOHX4grqp0pFwJ2vznnu/WnpZrWtLT8yZFp0R4e09r2c4ZTRRHCe5rI6SJDdXH5tJdplFdcfeRvgESAs2EAwuaIbriuGaYrCuMSHEN1eRCKwUxNdWikyyqYQzmpKmUWL0whrJtWXfICcE2aVFH2zlDkOTCdCVQ7LLC7484xzZch0xTolu7yKsRbWHRPf2Ce/uAWAbpWNlxgnnVjgIH7SRvTHh/hCVGHTsqG1mqOlxS+RUi1Bg1el6tW8GWcvrFcvOCH0w9A1p+SJrItuWXl7ELNVIr694W1Acoj9Gd0eo9tBnf51DnaC6EbfK2Fpphooi3KHig3dY6uPyxdV0OU5MBnEHtlnGNsuo1BCcIMsj4wz5Ji2ATRT45jkpcPm7cC4oSbpcw4aa+PIier9fZHlEZr3DYj3C1iKcA31vn+i1baxWJDdWSE/Yjz4YUvr8Iz95aTW7gBg4wq5l+cWvpfszP8v4YOo5k47Wb/ogO5ufRh/EngozNZ+3lq6y+rFv4eGP/q84M1dleDM4qm8t8G5htlXzWceFGrYc+IrHmzA/eqdDOLD1Mma1SbbaxEUBViuyKyuISZVEikNpszOQXlk5VGnK5wvAN0/l33GA6I9R7aHX3m1WkK88RIwSn32+uoqdygoevU9+R2a2SntCBWq4ohmsaQhyObilBtnl5adqbLdTiSvZGx2jfJiGl0ozgUDudtHbHU+rWG1ilxooC3rok2FmtYVT3kG0tH/yIt4eyeq7cojZWPSNjfVyESfIkXdYFUoghjH67g6yN8obtJ/NQs4BIpDFv8+TIHuuCBTZxaVTk29PCi0D1uovc//BL878XUrF2sqH2dz+FODfkekA7X2Lv55R2ubhw3/3TI7jWeG5Bsgis0Sv76CfIff4KKYpDM+ygSteqfty91Nw1eQo8Zq4t3ZRvTEyNQSP2ujt7jF3MCsFybVl0qOqBMJzOKcHrvBRm+De/onBT9SxVB9mebOboNS2JFXpX8Cpa5SuNcjWGiQ3VohfXCvoD2KUztJBRM5RvbbsyyKlYIan6qQ3NnGhJluq+QE7zih97pFXW8BzusUw8QHSGVxrOU59wJwZgkkG1oJuHz4zRkPnRkBSF8gkI76+/NTcvpOQNctkqw2ccwR5xn96cVZMMlP3Q9rctCIvJbuSz6zore5h0JtDBKowtDkp65s1y1654VGnqIpE9w5mqyBKYldqSEBvd4ne2IHEYMtBMWFmLS8ZFL72ZJSIs+ACRbZUe6y0IEBWi8jWm8Qvrnpb7u2p6onwFY50vUlak0R32ujuCBeqQ2fCSJOuNWa1sMk73a2DQBF8cbPYpI79Qri5cIWg0mDrB/8Nhfg6UHupSrC+Su/OFyjvGOK6f2aydMyruz9L9+EXiB/cZ/v7/948SH6GcEB2c91bzQPSWsQwOdSz/RKG2u8hMjPTUGejgCQ3azELNbKprK4rBXCEYiRCfexaTZrOjlIkkKJIXggpsfXyYTCOl1E7yyBCDsYzlBN3NHjP55usphg1cyk0jV/Ix+m5xoXZE5mq+pLL2E12VS9jcxt0mTn0vqdQuloJp5VX0piMy/n1GWwERAcGecopumldYyUxa63D/Z+UMTUO1Rkgxwl2oYba6Xin0yOw1Sds2tUSc32N+No6pl7G3Fwnvb5eZG/fShT+FMsN1EEf1X4T+sdHcCP6ELfv/KsZR1OA9Y2vZHPvM7jB0Dde5pWMrtnls+m/I7UxX3z1H7N/8PozO5Y3i+cbIKfmXPycxyKXtjrpRVSDuOAIP8vV2OOcbOQZGWp5Qjla7w0ItnvoO3sn/iZba5BNqWZkC1WSq0vEL697t0DwpiGdEdEbu4UddelzD4tMY9h3NN9Iqd/PKO8aSgd+YLP1UnGjXSUqeLJWK5LrKySXFryxSN5EN16UdK7q2d6olbqXdGsP0Y86XmMzM6i9vm9am752U/8OdnvozY63R16ski6dkIkMFPGVRdLFKqZRRuQDqJpSspAOSgeGctuRLVQZXygTT+n7vhmkSzXSy4uI1FB+dcvbQOP5ucnFBZwSxTEBXqN5CjZQJFcWkeOU8O4eOucMT5dV49VGwR+3tYh0rTHDkXelANMqk643sVp6vu6RRWW6WMVqhdzuEmz3ikVncnGBbKVWbNu0yqfKQT3V9bm+7N+tcyxI5CQbJMSxTKEw1vOftSJqZ6iDAcklb0ri8JJHqjPyDYpXvCHKzHOVlztdcMJxCEHj8nsx3T7CzH5e+6qPsLX3Ga+cMXaMhnv8u1/4bxn0H1H9po9T+9hHGf7qZ7j9f/qzDD//hSe5NHOcAb+IzoOT/T6uFJLeXIe3SE7zecEB6eXlWW78kS+o+36cn3l+62XftDiMPX0iz2xmy03SC4uYxXoRLJl8rJChPDZLH6VIuFq5MMGwgLm4VIw1oj9GbbYLK2R5MNXAmxlIMh9AqjOCXCmLRbuKPbVMZr4KaDYWSC8unf7bE+AkM0Ymk7K+q0aYi4cVW2Hs4bOSGoLXH6Hv7SKPjCsyPlttwS7WMblih6mXDyXzALPSJNuYpexNaCNOS0ScFjS3o5CPkb2bphw5IFtpYaPAB+mrzaL6bTbOR7V8lhAOkALZGaL6T9CpfA7UZItQlDFmlotcra4SqSqD1z+DfriHjFNuD36VXz74p5RrK9y4/i0AfOpX/2f+1U//+WO/fzvw3J30ngVsoMhW6iTrjRM/n+zmKNf3KNwJE7yTJ6sjBAdDgnv7xxoLpbHorQ7R5zfRd/c9NeCNHcJbO8hh7Cd5c/pqXWTmVD1fO0W1mDRqWSVJri6RvO+CP87Q21NGd/bQ+wPvOHaOcp1oD4umMHuE0mFaFZKX1klyTV8VO2wkcdFUM1RVYIUj3Op6hz7tg8dgq+ub9I7AlQLGN1aQ4wzVj0kvLZBdaJFu+CY+N/1spAY5Tsk2moyvNOlvaLpXNcP1w0GjtJlR3rM44xiuK+IFiW2UGb//wvHs+xPAlQPMujfxKN/bnxGwl6nxyhSXFgm2uoUG8ElucJRDHJ5j7UJFcm2JbCoL6uqH19w0K5hGCZxvhhzfWEHv9tAHQ8LNDqXXdgjuHfgFoTpejpNHnx8hCHIKiEgNpPbplEtOgZsI5yePz7DKUVq4MdlmZZYKIwUmd9IM7h9gVuozeteqOy4y9E4IxJHm3EnwnV46zL4ldUnnWoCT0LjyXoaf/Sz2SDm09NJlBl3fJa3Hjoe7n6T58lew/t3fS+VDH2Dl93wPl/9vfwGAre/7W489xzmeDnKcoLY7nGgF9w7C47LcAlC73RmL+aOQ48RnJ6dVC4Qgu7g01ZCaK6vsdX1GbbVZKIK4UoDY7zGOYNSaPR4TniJvut9D39kmfGMT/WAPMU483/PaKq4aEdzeQm+1D1UwtIJQYxdqmIuHQeJRnv9oWZO08qbdpi54vq4SYZvVY+/p4yAsx00nSgHm0rJfKDvns7YHgyKwlXGKWW1hXrp4rFG9tP/4cUnYiZPn7NznKof66bZawjSrReJBJIbg7k5BeXtiFG6AEdlLF7w5WWeA6I8Jb20SvLFZjGnurAXKc4Jp1XxlYSrGmW6WFlqRXl72Qf0TYjW6xubWJ4/9vVG9SDfdRfbHiCTj1uCTfPT693L1yjeytvohfsM3/dd86AO/F3Dcvfdvnuq8niWeb4D8jJJYcpQSPGx7j/MjpShbCTFaIQfxY5sAzQm6xsI6gpNksTKLbZSKLIHa6xPe2SP8wiOCnb5XOOiOiO7soYYJapAQvbFLdHsPd0pzA/jscun17WNZSJgN4MPNTiExJ5zDSkG23mT00rpvEit+c74Xy7QqfsXIycoaDA/F03Uef6X5QOzwgYhPnXh5M5lm2GaZ8YtrJy48TKhxlbAI1KLNLnqr662Z1xreMnnqZ8F2j/D1HfTYEnYspjS7zWRZk5YFwzVF0lR+jo29G1K21piVCTsvIs+TdXgu7DQNqGhmq+Z8WUGRWT7a6SsdBK9tF3zd5MaKz/Z3c9H+PPNbfH+cUrq955+97S5ymJBcXCgWakLA+GVv62xyNYlstVHYSYskw4aq4NbJ/hjGGUQavdNDdYbY1ZMXk08L2Rs/1szG1CKyxWqxqJSd4cwaOV2u4ZREb/dI15tkU8cohwl6f3D4vu32j9Gy1N4A2R/PXH8Tgo0EpT1DdfEiweoqg1fuzfyufH2ReLBPmsUIAaGs4EwKUw19utXk6v/rv2H1D/2BJ7ksc5wXjbJfQB70gdOVid4JOFo9dIBpVlFTgYIcJYUqy0lw4KlXeUAm+mPY73nVjzxjKZx/n8QooWAVS/8vu9zELdYRmUNmsxNpWjk+3so4RW13fHArPI/XlUJke4C+v4u+vV0EQk4p5N4s3U/d2YHcTEs/2EPmyg9ZJPx4m8NE4tj+bRgg3gTdzZUCsim+tXywh9rreem0/Xxu1grXqp7offC4J0kmGaI/9g2LR7SfpbEEufaxTFNcJZqtDAiv6/00dL5J1dEs1HBSorY66EcHBPd3/fyVGfTtLeREXzp864JkoaWnAk3GWCW9m+oUHzxrVHCBQiRPnsm9Wv0gu3tfPPb3+uIVuuawKTqQJVwyRZMVguXl9/Kxj/4fWV390BPv91njXVPr0vsD9F6f8c0VH0jkb0W6WofUEOb0gLOQLVYPuZpL1YLneCryoEtvdwkfdVC98TNzgoq2Osf4yGKap5tZwtt7hK/vUPrCo5mA2izVSK4t+Zf2HPNMWhYMVxVIn0kOb+8d07QNHraLF3riqJssKxze6c4GAunwVIiVOuHrO17u7d4+pl4ivr5MtlQtAkvVHXn3uDyb6FJDsNPzvFrnfPb35ipZHrgjJSZX3QhGjvJ2RjSVGcgC6F/WlPcMla0MG4i87O8HlSynbphzGqmYWsToxgqA1+jtx9hqWGyn6BIPFKrjBeqzhVMa1fpjRGqIb64SX/elVzlK/CAYatKN5kx1I3xwUExWQgjCzQ7R3f3DhVpqkEmG2u2jBnn5cdpZsF4i3WgVOsrBIz/AJ4tVbxRSjU410XkayFFSmKk4cfo1zpZrZCv1wyB36pl1pQCz2vCNettdr0wxjVD5d3KphkwN4QnVIIHvaJ9B/gql+QIubC0xejC74C2tGILVFbpyF+cgUlXMYIA8khiSYUj1gx94zNWY42lgw6BwNcsW69jyUyxonxF8wFvxgc851CVcKcCstxhveBpEenHpmMX7UQiAqUysMOYYD9jk9BPTqnJIEnAznOIgdqiRQ+SaxHHT20FP70fudtH3dnzp/oUNkuvruFwlwDYrZBeXSK+vFQki/WAXvdPFDWKEtYg4Ibu6Alohcv60avt3SKcQ9A1qfDg3xSt6xh0XwOSL1iflmNtqhLm6WjT7qe22X/DnknQCvB7xpSdvlp9A7Pe8ZN5JxiibB7jUYGslXGa9xNz0wtzhr8XU3CyeRD420sW9OKyOcaglbR1yFGOurBDf2MDUn13l7yxM+y+IUJNcXS2UV/yxSsxCFb3ZfqqYp6waxHH7mCpFrbZOj25xL0NZJk2OV6Dr9YtUKytPvuNnjHdNgAx4q14lfRPQ9RVvv7vZpXRnD5KM+MrpXB4RKF+2ChREmnSteWaAbGpRkfWbKBicBhU92aAgcstddSSon8lcSIFtlHzQatyxBYCtlYgvLaAOhpRe20IkWWGtfdQ8JRg5SnvepYyJysLWoX60GKck15cgydDOoWKHjh1ZKOjcCHAK6vdS1G7flwnzJio5SlDDxLsaViMftFWjQvMx2J0dWCb7it7YIbi3j8uVEuL3XWD8vg0fIOcobyVUdgx6MOXkZAUyg6htad1KoSSIGzlHL68syMfpD+OrDsmVJaSxPpObUxackqQbreKtMM0yIjWE+bVyC36AVQcDgvv76Jx77ZT0GpqR9rJOjqI5icwgxocKINK5Yn+mHJJc8I07Kj9+VQowpYDwjV2CLR/4EmlMpFF7fUgN2XrTl+vGKU6JIrOsuiME3rzlWUJ2Rof30R1ZyOVwpcBPMoFCHQyIXt9G9WOElthKQHItL5/u9bGBZHy5QVLNM0b9sTc4WG8i9weU3tg5OVsdKkRm0blmq6poSh1LeSsjbShcJNE2IOscF0OV5YBR6AMWpSNMFuPeVaPflwAmvSITZ8VnDFsKfeB6RtDrBJiNBcxy0yvznENRQ45Twtc3EaPY0yDq5aJByyzUSC8szlLG8E15bko9wTWruKXZqo5ba/mEwlTZf6I8oScNoxayqqSzrslKAj20SEA92kftdNC3t7wzYWY9XUJJZGeAenSAerTv+3OET0Agcl5yPj2Em/sEr216XeU8QLX1Mk5Jv3gAsI7KVkZ5Kz1MGmhRVBcnMFdXSW6sY5bPT3ez9bKXqZtkjg/6qDxjnC03MCvNvKl3oeANq6dY+NtqyVO2TmzIy2Vhl+qYRb+4l0d6HI7SLJ9EucNEh8/5JEB31VIRIDs8DxpyvvjE9OUt4iS7UBNfXoZAeam94mAt4eubT21sIvEJC+eOUFRVSOaSIluuZUj69q2VH4t31xQRZ0XW1VZC4osLyHGKGKcIIbDV6FQjiSwfrKyWxKsNkGK2RCa8BW/yvgskL62R5hM6znmO6xklluwc3MxpWOewQqAOhjMNBzNcn9xIIl1vMH5x7USera2VkKFCjDNKr20T3j8g2OoSvraFTLJDagQg83HShBrTqswG45HGBRrVHuH2BuCgtJNzowJB0LXorT7hVpfw/oEP1KTwDZJ5QDR9Xx4HOUrRnRHh7V1karAcZ+Ooti/P17e87BtAMC3jY2FclcQtibOO4KHPcB7jth1BtlAhvrbsnRhv7c5Y3qquN4bBgoo0Ik6Jbu+AdcSXF7HGS9iFjzro9ohgt4/cH3ge8+JhdkLg72XWquCcQ45TTB5cW/A8csAsV4uMQZYPwuN6ieTygl9k5KeS5cGETLLi/IKHbWSc+YXeJLssJfFLa8c45m8W0812gqlGvClk0/uU0rtKCRhfWya+sVpIG2VLNZKbq57HmTjkwYDw9p5X/tjpET7qnKh44wSMry6TvLBa0GkOlvw1KrUtumdwQHnhIr1PnMB9+5r3ce8X/xHjcRcVlTBZjA2eh1jgHEeh7+2ib2+h8mYnkWQFJ/SZQgpfEs5jqBPvrvPUn+j2Fqo79HJsK83HUz4yg95qE732kOjWJnrPL5ptJcI1KsgjznUizQjvbBN84T6yP/K0iSNzk9Ve2cYd2beLAri7W4ypKnG4QDBe0KjUoe7vojpD1F4PMZUQEKPEN7I9OkB1BqjO0EuVdYeoae7xZD+Z9QoVcYoYxsjdLgSa7IUN3JFsq8qgvJl6WcrYESSzV9cBhPp4ZegUmFbV87En23+0j95q+20FCtuq+etWLc0ci3kKiUBXL5NdXSkqGDPHsVj3AXQ5wpVD74w6CZqP9lBM8ASKXG6K1jlp/hNx6k1dAJObr+h7u+j7u0XAnr1FjawTsyn1cP9k6uV5t3Pk2RZCUtUttrY/M/P3SnkJJUM+3/9FPzdWqqSld24Y+s49stMwxTO29dIhH9c6gs2T5VjgsGs2W6p5DdnMzHCWs1bFN09J4fmz+Ka/6LVthHVnOrg9aQlCWIdMc77X3f3DY57Wa3S5qcbDNiIzmKUqqj08lh0uSiXTf0+8j71TjsGGIi0LbElg8++k6w1PNwkFcVN6TWbyQChfCAQDR9DNVRLqErtQJl2pY6shwaMOem+AyCxpoBCpQeWOeGah4mXHtPTGD2fMO6ofE726iZxy0wom92m14e9r6qjfTWncTona1meMc4Q9S+O+bwKUw9nMcXxlkax1pFylJLYSorojynd2TwzECr1j65CxQeTBsYs05df9ImTauEb1xyDEiVlbu9bwpgGtCgiB7Iy8lrXLjTlqJfS2n2gLlQwh0Pkk47TXFJ4oUmSrDVykkaMEnWti24UKlENspMk2ml4941nj2Orl+LtgK6E3AumOMbVSYZltSwHSWvQrW750GihcqFCJl8gL9gc+WG4PT+4FyGEaZZ+hl8JPZEB515DTONEjhx5aSpVFxAluUOu/42NUP/4RPv3Zv4ONAmwyxr7NEqRfLnCBmq3sWHem7OPTQg5j1IF3vnRSFLKKE14v5Au8YVxkjl0pxC7Vya6snIsX7TKLm5KtFGmGGIyxmcHUy2R5wC2sg9jbDutHBzjnyMp5pnSKa2pzrvBR2Hq5GJeDkSVsZ4R9P2dg3TElEFeNvBmLc5jFOtlqngFODfrhPuqgfzIbT0rscgN9d8c7HSZpsd+jCAaW+t2Eyv0EpDhGNRD90clZWjy/1tbLntpyYREzJXOnttuoTj7mCcjWF7w03SgprMgLesoJx3aerLIrhSdeZ1crkV1ehsz4BYexhRufbVZP5FYLPJXksZbbAphKerl8nhOpQW93sKHGLtUJNg+Qg/GsS99bYaAjPHddP9wvFq9Pgplp4YQ5oRouIsUR/wOp+YqP/EH24nvcGX8WnSmMObtC/3biXTdF6J0e6UT2DM/HVb0xqh+faqQBoOOUFA4bnaa+60pBUd4PHrWRvTEu1KjYd3iGua7v84AaxMg3dkiuLJ5I5RCJIbq16wdv63ClHmmr6h/ucXqijJ4LNThHsDskXNaIzNG9HKDGltqmAa2wrQrDBUlcF4R9i97uo/cHmCTDNMtkZUntkWE8cozWNKNxSCkPPMQoofS619lVeXCvOyMvQi+Fp64450XYrSW6PSVtJ6U3KEkPS4hqt4+9uOCzIdtduLLkV/KRQKXejZBKRO96hNWisECdyMDZWomsVUa3/Uvu8muQXlrE6Q56t+8lw4wlnDgD5odjSwHx1SXCR22vojA5zNRnJMfXVkBJote2i2s9GciEljONZkdhAoWolXxWJV/ACec3kNxYIby7V3CMJ9DtASLfd7rexFbCQ/WMfBBSO3kgKfyJWC2JJwokuVnJWdbNJ0F2RrhG+cQFzbTQftYok63UKd3dg9TgpPDBQBSgOt4sJX5xDVrl4nj13X2vUZ5PFtK4oqyabLQQ1h06EJ4AF2nMRvPY34OR81J5xpuFhH3LqNoi228f34ZwrP/+r+X2K2+wPXqNtN/BScu7MUfwboNZX/Dj6Xan4IKKJEV1z5dtPAqHf/fOCrKF9QY+TgjMSsM3Ve90j38vf9dsKSRbrHu6wmQfJ21YCkyrhuqPcEmGCzRilPhsaE6VCF57eHisMpc7NBaZeh3wQQ3K+yC1LMruaUmQNBTVbR8IFk6F+Tte3jPFcWVXPDdTJhlYiwi1t18XwttAC+EtvY+cgxNHkjkCnLXo7ZzKpSU2z4QL52YC5cG6prKZITKHDQS9dU3pICPsHY6JerNNtt5CtoeH0m35MU1rDwNIa3GDGNXuF5lLWw4xay0fyFpHdtFLPer7u4j+GPPihWNZeABxZxsuvzm+qr6/e4yep7bakFlEoEiXG+hHB/7fS3VspVTwtE+CijQmzhB7PZgsBvLEm8DfC7tQQ+73jqmGvGV4kwU02ygje2NvrnJCVTEsLzCO28f+HgQVPvD+7+Xf/8r3sdF8P+Nx580dyHPEuy5AVsMEXt8hu7FcTLIqf8DOWkeaqRdLDr21cbpUwy7XsNM2m3FGtljzWbxpVQMJ6YUF9G7/XDzXJ4GAPFN8RlZlEpyNM8LNUx4oLXGZRSYZ0d19bKiotioIIWjeShktKto3A6K2pbJjkLElua7pDzWNcRn3qOOzsb0xIvDZ13DoGDk3o9kr8oBJVUKyYeKve2YLg43icHb6uECRXFrwg6CD9EITLARbnSILihB+IBKC9PIicUMSdS2DC5rKlkHtWkwkyKqS2v2U4MEB6cWF4to5IL20iKmP0J0hjDOfabUO3c9X5nnGKFlvYlplZJwR3dr19IRAYcthESDbUoCIfTkxeNQhW6nPUDdsqJCJIVnw1szC2NOpPVOBnSuHRHf3SBcquHJ4rPJgGqXiGLK1Q9WK9Opy8R3Z8659foMQ3NnzOsWTY2uWT80AnQa11y/OM1s7EvBnFrNcI8Erq5hmGVcOiC8tEDxsk1xa8IulOMOWA0zV3xcz3awjha/cBMqXdIcJ5Nk9VwkhM16ru3+8CTbNj0l2R948YSpYjxuS4ZqieTtDpg5nwVQULs0QVuYB8CF02dL6to/R//FPoSsNks+/gf7KF57oWs3xdLCLdU8dcM4HCqHGLNYKzul54cAHo/UyYjAmeLBfGFmcBOFcUbo/CbI/8lbNAPUytAf581j2/QTVEuGD3aJSZxZqmJUmZrWJSDJvljTVWCUygws0ZqmC3mof6gfv9bBrLYS1lPfBKuhe0lQ2U/TYkTQUSUtS6gnUaGqsyYPak97o6WqRGMZeem695RsiJ3xXIRBaItoD1CCeMQSxYYDZWPCNWONkZlGt7u+RXlnCKgGBIKsqTMmgx4644XWRg27eX1EOYTBGZAa9OXuthXM+ex2nqDTDaOWtsPMmcSe8wZKzDntlpZCUVA/3vPKHlL6qqdWJwbFMs0If/U1hett5RVDt9z1V7soKYphXFi8uYUsh0thTzTVMKcBKiSSb6aVwYUB2Yx39xiYIznwuwWs2y/7omQkDPGtMU1Sz5QZynBTKW06AiEKsPVnytlJZpl6/CJUyu7uf4+aNb0WId16y4l0XIIMPiN1mB7vR8n/QGqZFpcu5h/pUUDNNTRCJL33Z1fqxly655oORoxI+wlJkkm0pIF1veo7XGVnrJ8GZwfF5kc30QiMTQ3hnDzVKsdYRNMuMX1jA5nzQUscx2ndkdcFgLUD2Wp7b2hujcu6UTBzlHcNoRVHqWUTiipXncLnm6SKDuLgOTknS9QYizjCtiqeopIb04gIizii9soULFPHNVZySBLt91MEA3R6AhfjqIoEqk1QFpiRx0mCWa7gQROrQA1eU2YQS0IvRg9irJ4QK2fUBcXh7DxVnZLUQs9ogvLvvg+nQawvr7S6mUSJbqvkAcfswuyTHqW9C7I39YmpKOcHUIpIri/48lPTcxEcd0suzYvMnwVZCxi+vFwGsqUYzlCBTiSCzqGGCnTQs5trV4Bv8Sg/bhxJ/wlcgsjzb4zMTTxYcBw/bhY616o+PBcjhgwPS9QZmuUaWZLjcqtxWI5IX13LbdEBwrLNfjBJcOSS9MtWBLuWx7zmtSK4uETw4mH2ftHcOBC8DaHKXvgmSpkRlIFO/9e5lzaPP/jTVr/wIcU8TNo9nZgQOoTSX3/vN3PmRH+Yivx/9/suYd3CjyJcK3BHnMbPaAnLpt/wBcaXAB5UnZKSAQoaMUoCtlEhurHlmRHeE7I+RxuDG6ZnJkmnI0RRlLwrIrq1CZ4DNG+qEsVgzNa5Ou4kedb8bJQQPdn1m+4hUnDroeyrURNfX5HbJ+XmXDjKyesTwQkjpYUKQB8ly8hUhvPqP8nPRJGgSwxg58JlyOYyRb2zhyj4bbmtlz8sGKEc465B3t4sEj4xTbJyRXl1BdoeQG5DIvR5iOEY43xQ9WNWeuz3p05WC0r5BCFFcD1ct+Ub41GAr0bHmLjmM/ZyUZ+whV7C4tDwzZsk49cFx7HtozGLNZ9NPkRqzgUY8qaPdSdtZX0C8vul7P0KNWW35SseEliEEplkpmgXFfu/E6q2tRBAF3pBFCjhK98oMLgrILi4R3t4+c3H3NLSHtwO2FGKXG6hbhw6n44WQR69+io9+xR869XcOx8LCDfq9h3zxUz/Mi5e/FbW8fOr33w68KwNkAN0fM3llXDI7IMbLNc8ZvX248vcZQR9ImFaFbJRgT3EbE4A6I0ssxyl6p4tMMj9pL9W8rNlTBsvZUhW1P3hzK8VQEa+3vEqF8oOizK2LkXjlg3KIHoMbW8YtibBQfWQZXtDEKwobNag1y8j8OMYLkvJeRvneGCerZAEECZCXzyZB5zSmaQzBwQBTDdFbXS/3lhmckEUHe7beBC3Rm91iohCJwSkYbmiifUPQtyAF8YIiGPiJKsv1gp3xGeKJHbPqHfK4VF62C/oxcuCz3CKQRdNEes0PzLI7Inw0m5G3kUZMb2uqBGYutHyQF2kvkYR4Ihck303tVS3Mah3dGRbmJC70gWJ0a9dnWCrhDC9SHBxatjsB2WKNYM8HF0JA8MYO6ZXFJ5NamspwyFGK2utjlvz1lZlB9saUxinxi2ukR5wLHbkqh4Ojzn3SWvSjDsmNlXNX8sxyzdNpIk1ybQmrFepgiFiokF5sFddCRBIXW4ShkLwykSDTCYNPfpJLf/G/IOuUCKsZ6CMqKmGIGfS5WPsA6lrMrR/+2zSuv5/V7/ztJKvMA+U3AdkdIoYxbn3h3FYgZrWFW2nixom3SZ5Iew1jr8xz0PP0nKP7shY6Ax+0RAEs1IomLDlK0Jv7cMLvjsJlFpmZQrHIagV5cCw7A/RO5zB4hyKInD5nz3cuo3Y7Be3DNSq4vR7ZpWUvF9ofozcPkL1R4RZX2c3A5rrGziETiw0l440QdT9B5hSytC5xAkq7CVIrn1VNM9DKZ6mPZFB9oL7n3xchvPJTfn1EtQTTc1uucmGbhz0Uchj74BqIFxQmElQ2U2TiPB0lFOhOflHy6yaNxaYGVyv5zO9jkC03DmkkgJDCB/C73eJ+mwuLh9JsR5ogZ873GSSXbKDRUQBxWlS/XCk8TEbUyoU1sgB0TmE5dixxWoyF6cbiTHUBfBUxu7jktYWfR6PqWwUlSS8sovb7yDj1/POp922r+wVaretUKmunb0KFpL02XxH+Bj43+rf8/K/9D3z48n9E48YH34ozOBfeeTnt8yI2SOu8Y9GRJ1XgcKWAdDpTZUFNZQltFMxwTmd+P0qOrQ4dYFsVz68VoAYJIrU+e7rXR/ZjskbZZygnvxGehvA4qL0B8kjmz0uHPYGDTWJ8o4O1frAl10u+uYJVClsvHUqPAaNlxXBDM7xw6OqUNhRpTWHzxgQTCuTBEDlMKe0ZZOJIaqKwJT0pSxNMn69xyGHqJdCsA+sD6GnZnGy5Pht4SeEzFQ4qewbZG5GUIWkonzkZHZYCZT8m2O35bL+x2OX6TAke8gkw31+8XJ/Kvvru3dKR4Bg8L/HYiyEElDQm33e2UscsVHBTPOTz3i05TLxEmxBe8g9w5QBbLxE86vgMzwmZ4HDPl6NFIBFKovf6/rm0liA3rJFPuMiapo44wFUP718wWQCd0bXtRJ6hKgXerCdvMLRSHrsXj4ONApL3XyB+cQ0baM+ljFNffi2HRfDUWZc4JahtZoQ9S1YWdK4qtj/7s9SuvxdVq2GNYLRbQhwhVZffc5N4sEd3cJ/1ix/la97znzK8/wbdT/0ylQcC9e5I2rwzkWS+7Lzfe/x3p2CFwJWjQsXH4aXPbKtamGoAoKWX43rpAvGNDdIXL8w0QRXbK4ek19fJ1he8dNcJ75Ktl0mvrWJe2Jgx8ZmG3uvN8JxdvXzsu04rXC7lNa1IJPd6eUOgb+J19TLmxjokGXIz72mxfmE33AgYXgixudaxFTBaC4omOJtrDsutDvrBvv9toP2i/yx6gXV+AZxk6K024a2tGSkvB3CCeRaZKQI74bz7ZDC0KCmIWyqv7E0t3AH5cL8438dl7105nAmOAR8cb7eRU1lT0R97mcwpS2cRpzPKHeAl2p4FPLc4wq41PTXkwZ5fiB1Ff3xigzf4+W3SYCeOVMqktQSvPCS4v/tYesU7Hc5Y36jYqkJmCO4f9hlloeTOo1/g0vJXnrmN9bWPcG/zF9FRyHs/+D18sPXNfObRj5MkT0a7ep54WwJk9TiDjvOiP0b0j5dRHb4cpY4MHsH+sHh43UIFu1A5KQ6ZHRRzJYHkyiLxxRamFs2Q20WuNiFTA0qQTjVtmVaFwaWFx7rwCCiUEZzwpff46tK5y+Xpco2sWcaVAoLXdwg/94jg1W1fqi8FCCUpvb5D8KidZz2h/jBDWgqpq7BjEJnD5YPxaElitcA0ypjVOnIQezqF4VCmbKIiUjrMoCZHVUScg0DOltF6Y+Q4xUo/4AWP2lPXW+WZQQvWC90PLmqssEQH2UymMtjq+KDbOa8bLQVpbvSRLVYx5RDTKJGsN4mvLhWZUZHbIUe3TlayOFEqzjnSvJQnkizX/D3i6rh3vhfbaenvC+DGqZfjW2+i9gbo9hBTLxVUBn+8idcizlfo1jicc4XedPm1bVRv7OXmgvNnj6VzM9lxF6g8azsgemNnSl1Fn2o84oDk+jI21DnPPC8NC0FysXXuY5nATgLuHNla85AfmBkYJV7T2zgmaUqZOJyG9u1PU/sPf13xWzMKGO1NjTUOEAEL3/GtvP7GTwGgVhZ534e/l0c//Y/4/N/8S4x/+leQZ0mvzHE6JqoL7b7X330GsKUQs1QnvbZK8sIFzErzXDq0PqFR9TJul5fJbq6TvXShkFV0WnkFizOqLUfHATVOcnfIqUVlJfKW8DA7b1QjhFaeY51Lelklsc0KsjM8lHAzfuwFkFN9HlkoiMsCqyCtK4Tz25SDsaeYkDfAPYmxRJzOzF2uEvmF6LETF54vjn+39CiXHsMRtxR6YBBTxiEiTpGTIPaMpKgLFNlyw5uSHIHsDmf46EIr33BoHfr+LurBnpdCu7VV8IGnz+NZwDUq3tEvDFATZYcTqD7iDEWr4juBKnqkir9ttYuGNlsvY/KA/HnDCX89nyUEoPZ73nnwyHDZDfvIKGJx7T1nbmNl+X0IrdkavIHearMaXeHC4of5+V/8f/LvfvmvvyMC5bc8QE7Wm/QvLTyTbanuyAcF1WjG3Uv1x778PzqyujaW0t19ZK5D7IQv6xzthlZTJSIb+cyeyCyl13eOleOnoQ+GM5+L1OC0YnxtmaxVngl6ToKpRcQvrZFcX/Y2zWsN0uVaoZ94+oXw2TqnJVJ5LqpIMsI3dohe3UaMU58VdkDqz1UPHZUHhzxtNXYEU6YcJhRkFYGpa2R3ROnWLnp/hB7l105L0ssLmGaF+MYq6WodUy9ha9Hs8QqB7I6LLC547rDMea9oVZh8ZItVXL1EWhE4heffCt8gGR1Ywq1BIfEFh3xbG2qOip+aaoStBF4neLnmr01/jOyMCO/ueRrIYzSTj0IPxsju6NTuX7v0GNmfaQj/fCCEpyEEyjdfBso39uULGWkdwU4PF6rCFltYhzBTB5FZCCTJ1aVz7146h767P/PsS2spfWGT8EF7Vi7xMZfJ5rrEdqWObVWQ+Kx2UpfnplechplMlZI4JQn7blYSyYAcWtKtLYKrF2Z+n/VCxrsl0m5A/3YDFVrqX32FUXcLgM7gEXvbn+PilV9HEFS4/89/ENt+sgzoHDmaFd8onBj0ra3DoOlNwqw0T5ToOi8mgaCVEnNxiezFjXMZWmRHzExcatCP9gm++AC1fXweMHmw44BsbYFkI28kjj0FUEsBCzVvNjIZu5yjvJuh+6aQ4Sz2X5WYSGI1ZGWJa9V8sDPNm67lTblPolqTv1OT5sSjMmlmveVtsoGkqRA5xz+peapHZdfMqmOcUxfY1nwSh2HinUJzbrdMUvTmwWzmWYIcjFG7HQQC1RshBmO/QFp8NhljAHECz1f0R54KM8U5njmPcoiZoqScBN8sfxh/yL0eMpeys6UAs9z0MnFvkeW6OUdQf17IiVNqb+QdHI9g0H1Erbbx2LFfCMnCwk261RgnBXfjz1EdBSyFlxgMtnjt9Z94Zsf8tHjLOch2qfrMonLdj0nXmyTXl8FY33DUOZsL7FKDvrVb6CEj/Ms3MXKQqUHtH3anqkGMuvUEAtpTZTbVj4lu75JcWSS9tEgKyO4I1R6h4/QYRy5bqSPiDLU/wK42vGVw1XOcy1/c5DQEW10fPJwgoi6TDKdyFYFKONOtG22PqIsyNrSEPUuCJG5KgrElalvShmKwrmnt5tzPqc3HV5YQw4Rgp0dyaRGzXMcIH7RmzqEetL3Zh3MILXOKhQPpg1c1SjyVQkuSmyu5CoIfjEwoULk5SFqT6DFUt2yRAS4u9WIVOUxQ4xS92fHZ5zTDljTCOYK9gTciCfWZGrvTEIEiXmuguqMZCs5ERi582CZ+af3E304GhCzyx3/a0KcGsQ/o6yWSGyteKzi3ZU6bZZwU6K0eplXGlr3rH4Arg9j2PLkZSEF8dfnMbNhR6Fu7hURfAXPKMcfepXGiqnEarBC+AbYfw2K1kON7VjgrEMiGfUQYIqPjGZmsFzF502wqkZUSJhnjBHzxCz+KqFVRUQW9vAydR7R/6Rdpfes3P9Nj/3KA1YqsVfNSacait9sk109+V54EYjA+1uD3ZmDV+d4Tu1BD9kbH3wkHcr+HXW3OBAHmwiK2VUXf3fFGTOXIG4lUSp6CMIpxF5f8GNwbQd2/6844qjsZQwe2ojD54aU1hRo71NiR1hVJ38LG4ozJkysFZFdXcNo7oYlRcqaGbrbcwC7W0He2oRL5a2ssTJlxTBYjQguysiDM1wJZVVG/lyCOJFVFpI9LyJ0AddD3zYrTfxP463GU0ph4XWIAVYnIjPVVgcWaTwCJ8+U3pJbYMzjKeqdDejQLHwWYxVrRRHoSbKOM6pysYuFPwKHu7WJvbiAGY/ROx7srCgEC5EEf3Rmc2oz6LCHcY6g4T4ikWfXPWqhPVPQaMaBUap1rW1qXSdMB7caY127/MrXyGpXGCvpgh82tT/LCzW8nDJ8g8fSM8ZYHyLIz8jbOJY0cv8mHI/MNYU5J5CAmubaMsO7x1tDDBDXcJ2bRr2inRkAxiJ+prIocJkRf2IRKSLpUxTTK2HqJVIhjXfuyO/ZNW5kl0QqmAkJTi1D92GdLnZvJyAJk1XAmoJvo09pS4K1Sp6yqTaNEcmUJWysRdrKi0ULgsxbtG2Ex+lgNWaOEyg1Aiu1XQqLPPSRbrSMyQ3B3H7NUxbQqWCGwlxYwyzWC+/sklxZBCPR2FzVOPZk/LzEGd/ZI15ozmeFSe1I7FzgB1UfZ4S3SAjL/W9OqeJWJ/UERAI9fWCXbaBW8WdkdP9GCLF6tY1oVTC2iPNz2GdZIFZrKZq3xWOrLeElR3k4p3etgaiVsTinKIlG4ArpAHXJ3hwnhvX2/6Egzb/U6JQ8ne2OCrS7JpQVcfLxDP12uYUsB6mBQmJI8DmapejxAPgPhow5xo3wq1WICF2ps89llKwJjSSfNRoAJvMEIUGiKWg07O5+i/PLjJdtsKlGVMkJKxqMO8ajNi7/rPyW+WcWKjO6f/s/p/cIn5gHy06JRhv0+TEl5TXBUW/e80JsHmBvrx0rWzx0TiTTyxe+k4VurfLA03uAhD+Bts+ppSkv1ouw/0Sx24xDRHaJvbxfvb3plBTUpsVuobGcgMwarmqzqx+R4SRclbBMJ3/w3dYguCrzE214Pk5uDyJ0uwlpcFGCrEeH9PUgynxHNub8uDHC5JB3ab1H0R7ipJkSHo3Y3QRrPnAh6BnXClG0DjahXnk7X2nGsgjt9bmaxTtIo+wTYa48Q/bGndMQp9uLjK2ajmiBqn/75UYqDGKeIcYxZaaK22sf1m53D5Q2kj0VqPIe5FGIrEdnGgqcU3t977kYghT79c4Da63m3xVyjWvbHxQLJRpqtnVd4ceM7z7WtcnmRdvsWo/EBjcZlPvTB3wvAysGH+OSv/s/EcffLK0AO7x+QtSo+K2Ys0cP2cSrEE2ASDNtIE97d85q7vcc3CgBEj9q5SHyAbVWKRjyx1T132eg88F3ZCeEwAdUBZ31DypGBIcg5rA4/mUSvb4OD5Mqil8G6d+B5USc890cbDicvhxinxJcXCR8c5A53apbqMZV1THOnJ6zzGeWmovrAc5VNveSld/LGK5GblASbXQK8hfN0hlEogS0FJFeWiqa69PJioTwinENkBjlM/MSZn3f/kv9u/X7GYE1R2TbFgsVKSCuCqDtNpJv6dz5xSXIqnJzNep8HE0UJfTDEWefVlfLzlYOYbKK9LQTmFBWUynaGGhrU3gAzZRGuY4eIM9Ll+iFvEV9VQICtRwQPR9jJxAXI/pjw7h7CQXRr59hzbSohNt/HSW5+p8E0K4xDjeqOj+lXn/wDi9rsYI8oWZyEiSZ03JSEHXuud/E0iDt7hMIvKNILC/QvhrTuZjjriBclWSTor0P7f/m3XPyT38Vh2HwybKJxVlB5//u4u/8JpA5J7RgXVBFolr77PyZ9+OhNHPGXN2wYkF1YQN3fy+3SD4Pi/z97fxZs2bbmd2G/0cxm9c3ucmefJ885t6tGhaoRVSokJCwQXaGQgABjmjAYW3SycYQdhCMcPGBM2IYIDC84COMIYXhAElZhgVBRQkhCbVVJVbrd6bLPnbtbzV7tnHM0fhhzrb3WbjL3zsxz7r2YL+LGzbPXWrOfY3zj+/7N2yTH5AYvJT43FxLy3kecTdxlb4QwDtkfgYfiRidUhh/tIwqD6dTRh0OiL/ZOMbcnMwolg9rBVuvcPnynTpFEYcx8fhQGqTRGjGe4WoqQYaDJ6pL4xFLUVIAfzBwuEqjMkfYvnpNkaSuthlN8oilub2IrCjUJGsGm20COpvgVYpxr1YIc6gqierVKb1LB5GaEnjqSgcVU5KX7B65tTATlNHZJIufSCHNvGzyooxPEopLvPcK5ZbfxTREPLj9m6f2aox8EfghJqYN/QdVVvij1ma/A9RAAg0nQAC8XSkLKlQnq3UJqhVvVta7EuE4d/bK3fk1jTbHbJXp5fC2b7MtC+NN50bZrUNiArR/NOGycwJGg0354pW112h/w/e//52w0H2Ltaae+03lIo37zB66N/APZux5MST87QI0z5nc31ow63jZkZoL5w+fnk4hLwziEcahxhn4W2MGeMyYH7ztsIJ/JWXGuCrwIAehXJ4jcYpsp8eMjZGEp7nSYf/Nm0GouiXJeBOvoy0IA8bMetl1F5DZAUF6dED3vg3Uh0S3JIV4KPJ6076i+sqSHhnlXkddFqEQ86y31LaNXQ4qVKqcaTNegKaI3IXrWI/l0H3lBhcALEfDZX99dOrUJQrU4GofvV47sWjVfOs617VcVMTCW9NMDku8HIfZ8u4VL9ZWfL5Fq9NEIMc0xnSrzr90ge7BJsdkIbo3Fyr4PL5afApAGoud9bLd2nvWe6OXgKgH9ahhgMFqBEJhbHSgccjhDTHPiJ73lNfCxPkeIMHc33rqy5ivXc9zTvcnVTXJkUEohebtjW0LKY42a5MhpgZcCF7HEauZ1ico81s4wJ1Nk8xu8LjlehDeK9i/9vRx+/lcwxZxZfLpAaP7872DjD/2Btzrm/zFCuHoFeyeY16gXx0tCmjw+QT89XLOWf1OooyF2t7Mkjb3PEASbY/3JC2Rp+iCzHH0wDC3k8r2TJ9NgfUxo2S/fGVd2gspHTr5Gt3ahzOHrKbZVw2y3wXv0y164Hj6M5aYSnmm3kN8lFDt8VZE1JVlLIaxDPzlAOh+Oq7DBWto5xCwnfvQqcHCyHF9Pce0a5s7Wmvyjr6ecA1StjCN67mk+L6gdBXOQS5Pzd6jqL87twu3OC6InB8RP9oPJSqeO2WqW6hriylKWAi51qrtw3NQKX0nwaYy9e55M6GMddIxvdDhr941W2G5jXft4AVc5mSJGM5ySmI3L5+vrRNGorOly240mrlE5x1mypenNdTk3rw3rggnPXj+Q5MtrORw9Z6P7MeKKz0UUVbl/7+/k8Yv/jixfL9T8zE//i9Tr7w7Repf4waXnzhPtn1D57ADpHKZdgejtDsdVIuYf7WA61beuVukVQL3ZuHol7ssKQZCMUYMpMrcknx2QfnGEzAyunpB9tIOtJ0GGZ//1FUBBWJQIKSi2GpDosEj5dD9Y/JaEr2jsUJkn6VlMVZAMHKYumd4MBECzUVtWWIvdNnajvlSwED6oSqj+JJADn/XRw3LVv6LiIZPXD2zSsnTtk1dZ7AqBl2KNpElhg0pGM0XOzaULkfUdS2a3u1CeR/LFEcKESoXITHDIWmGZLyUkVmLeCecphzPUNF+rHq+GKy+B8z5gsO90lwQ7T9BxDZNijNmqB83kRkp2f5Psw+0lDtcLlpJ8bxv+mu+cfAN8CcL9G93SCOsR2dsNyt6Dzs2SAGi6tfKdgKy8zpVjS9Jz2PEYVavhvUAIj7ioxXImdKtJ55f+Plw2Q3TePGFJ5Uk2348p0P8/hKul2M0mcjxHf/EqqAIcniCnGfrxAfHjffSL46UlMYTnXu311rZjb268E0FvNaTzyOEUdTBEPz8i/nwP1Rsj/GkSrPb65383zYgeHyCyIhAQX5xKWtmNBub2ZnDeuyJ5zO52cO16CaXqhuTFOoQLiai0oMtimqlKXCKxEuZbESYVeCXDsUzn4HxQQ+g2TmFqJri8xQdDZH+M7I9Rez1UedwXEdMuCpH7JZztslh6CbxH+Mtij2Je8nQk0K4F8xYhlhrQV97eFavNlx3HatjtNubBDr6WYu5urjvwOYdtVbHl/rwUS6iNV/K0MDK5BqfpdSEFvkzSbaOCr6cgBEX7NH+x7Vp4D0fTNX7Uu4QX4CMdVFRqCSI3wRfCeYpiQhRdL3+6devn0FEFpd7Pe/4+4wdvFFLeNDXO8Pbt2rEyM4jcBD1dIdC9CaZVCQnm+IoP48wsTRJsu4odzt6IZf4ywwsw2010b0x+s010eBKqA8/7QVZLBdWC6HlIRCHAHHys0f3peYyU8wHfGitMPUWbICem+1PUIIjdx7JCNHUIBzY9xcu6GEY3Fa2TlORFv4Qb5KE9toIjF9YTvRyEtuR2I7if1ZJlN8kDx3ck0UxQe2Euvdd5UwYpL2C+ESoq8fjigdqW+F5XidcwtXp/hLhAw9JW44uxt84R7Q3DMUlJsVNfOvNFR2O8OHVZBM4RBgFU5pH9CfGLAcVO882VayEwJZt+VTN4ddtmu4np1JaDq1eS7OEW6WcHS4zbVWSvLgtXT5f49qv94AoJr/UkfUt0zXlA5MGadTGh6c8PKXZbZHe7+PI+Kw9FS5H23WmlTSm8McTdGXEz3FuXK4qJxo7SS/X4G7/jZ6n/9N+G0K8fBlWloLo9w19XZPr/z8NtNjFaEh2P1vGp3sO8QM6LQIKLFN4HNSF3QaVYzjLk4Ql4H7oeW61rd01kb4Q6GK6rL6x+Ps2Qjw+utC0BoARmo4lrh3fVLCQg5zmyP17Tb9aJwhZuKeW5dlxphPxsD2Edxa0uSlRhlqHHmqJxfvywdYXtW1QSoQYTzK0NcA51gWGIL/WP1/Z3dBISqbeIs4Q3Saggm3Khf50QWgUb7YtkNlf+7QWYG90lBEaeTLE3u1dKyCMBxZf4yro0DnCbo9JfwQVb7bAAqgXifPndBXxF9kZLV8EojTBX7chdFN6fVuBXuCFuo4k1FhDYjQZikl3b0v11IfwpXt1XEjiZBc6BUAghL7WXviyk1Pzcz/wRvH9/sNb3FV9JBdm0Ktjm6UvpUx00UldWX8K4tyfHOU/yrEf0YrCWGOb3Nq5lVhDvDYkfBdkS0/3BVpGFB9WbkN3ZCIno4j0wlvh7e8SPjxCzguJOl/xmO6zqVHB4czKQ+hatP6EVZqOGU4r4xQB9PA4ck04N062FJLs3QbpAgBJSkPaCU1llr2DecWRdEWxgjSM6KnVO1Qp+DSh2mmQPtih2W9hSgF5npxVcASgvKOqS8S0dTEfOxKwrQ+URmG0r5htqTerrbLhujeJuF7tVJ7/dwTZSbCMNKhkL3eB6TL7bwsXqUojAwrYZCETKUuFkQaZw9XRZzbwsoqlHH46wzQrmkuoxXLEyTmgNiml+Du/mlKSoLixP372q+abzWo1Vcslq67KoCGabaokAjsd+KQ911dBH46XFu8qKkCw3UkQjPSWNepBZmKTnXcXoQcz0xRek928Ha+mysi8TS9LNqN0eoeLLKyeTX/1LTP7Un0f2zYX6xyq2VHam/2Ny/Jbh2vU1bW6hJGazSXF7A7PTxnbrS6lND4i8QL3sIcYr5NppHhLYWY7qjVFPD6/UJVgNPZy8VfHFNipLTP36iYUOn356QPRkfwkjobDnEtUis+EcpWB8K2K2qU/tlq0LShdaocpFgECQDtal1AQBYmZ0wPZTiUN1/tkR+ukRWIurpbgVHLFbQKjkqXupPjpZ2mCrSFJUJXlDnpNkFFLg9AInDLNNzezMeO3wmMXq86oQGCWxjQrZ3c0r4YjtThu/orBx1rHzdeGfHl35u28dZxYbwrrQ1awmFy72xCwPsMgHO8ybr1cFelOok1mopgN6tl6NsDsdfKyJPtsjen70VioWSyL5RbGYR8v74QHbqjEcPqHZuH3tfRkz4/uf/klOTp5d+7dfZnw1CfJOE9NdqYo1UmynRnane2Ww/RvDefRguqyE6eEsJJBvkKY6G2qSo/oTXC1+s/7wew5VT9ZkrKQSoATx8/6phWnJZlPjjORFaAe6ThVKKbPkaQ9pfRh0F/rCiQ7mEytYX0pHO7NZD9JrjRR9cALOk9XD9JO1JMmwpDALcBVFsVmn2GoEvea7GxRlco6SIaGJNXYjaDebherBCpGgshdUGExdMrkVYSpiKdsDgcjW3Dv9fv2Fea1cmCf8WCqJbVfJ722E/91qYzZqZHe7ZPe3sN0aPtIXE0IqJZkQ8JUI315hcpfPgK1fLYkUPihEvGtIYwPM45Ly5wKDt0gor7391QrMNaxa1TgLJFjviZ+dapcKAsTEq3KSeItwlXh5j+TBCJQM5grzgvjREXhP9dBQ27PYVFC0FeAx2RjdPU+MAvDKUbkxWUuosidPcbMZ81//Lof/5S8z+43f5Nn/7d+i+JXfRJ1pGulmdhVY849OfAVKEKt7kN6HRV4ZTitcp46vV3CdIKVl7m2DLIljHtTJNEzqL0u4RaOynrzNcvTjw6BX+4aESZSfX4fR7wkLYg+gVbCL1hJiRXFvG68k3nvU8SiQjGcFlBVy36hgS3WFxXVQi/FvQRr2fmkK4tMYe2sDe38bkRuiJwfh89yjpqFo5Am6umJqcTG4SGKb1UDklQK728He3MDc2sDcaC/vsaulFHc2KR5sh2uclHNLmfAb75nuaGbbEbPtCB+d3jmjCERBKQK0zHvS0dn3+lRd5kqFKK0o7m7hWlX0y94bxy6fRssK/SJcq3a1Z7hU8fjS4wIH3NeFr6cIIZDzIsi8vUuUNuEQZPHEGd1xObmaWMFlIZxfwxLbFejGsmNQDwtI16hg2zWyfEySXjwWnw3vPb3+5wB87/t/nMHgEb/17f83v/W3/mPm88E7HPn7i68GYhEHYpJppqjRnOh4WlbkYrKHW+j9IdHh+3dNEdZh26XsUH718n1yMGLaqGDSCPU+1SxKv/nLYny7A1qh94ZEx2Oc9edbUCswFFFCS1R/ipQiJHOFRRSWqLTV9pRazmdxT5HCJ5rkaQ/hSvzfYIbXCp3UMBXBdEehZo7kGASCeUsiTStoPDoP3uFjhauFFn10NAYPZrcVEpu7p6oVy91OPY2nBdMNFYTwI4GeeRY4/2hyen2qB+W5CxFYs5ddu3kRYByrMIVOjcWVE4B+3j9/DcpwkcY1UrKv3QhGByuDgu/UyKrxlRdydqN2rYrsZeGdp9ht4d7QDr2unXP4kYdJBguDj24NYyx40FdIuJNXQ+YPtgKEaf8Es9NEzzyNZwbhIHrWo7iGE+QibKeKmBUk39sLkCACbtRVYky7Gp7rSZApGd2JAuylCePv/C26/8DvuXzDyiGrBjsN1+rlv/3vEt+9Q+1rX6d195v8tq/9Ewx6n/P9P/snqHz+Hbp/4JdwO+G627kmrhv8+9R+/AHGrCvRU0c0e//nI+c58ugkGITEmmK7HZRgVr4jsgL97BC3211W2FyscXGEMDa4lJULLDma4m07uDRW4rWOhZjnRM+PA2a/li5JgWvHM5igDoeYu1t4e/Vx3FdizO1NZGGQe/2AFe7UUYNJOCat1mylz/2+JGl5QO31UcMJxf1tfBpT2yuWn62G0wpZS5CTjOjpIWazSdRoEw8M49sRtiZBePQUfCqDbOiD7XBNnF+aXxFpfKIDDG6e4zaby33ZWorKxohpjq8kCONpvCiYboVSsVx0fIRAFR5VOvwJF/D+r3tiPEESz79msW0XLoK90ZUSt4tMOq4cUmK7X4E82FlYy5tIz40Kfq+PPoO1f+tQYokTV73REuoDvJHI6GqhaLawRT8XZ55x220Eib1ZDlKiCO+uub2BHE6ZTPbx3lGrnic3XhTT6SF/42/+h3zw4O9mPh/wtbu/n/b2Rzx59t/x137t3+drH/2DbG392JUJf19GfLkVZBEwn57AGM3vbpB/fAMkJI+OSL/zEjnOvrSqRnR4Agjy6zicAd5Y4uc99Lvgg86ErSdMv3aDYuVYznZ0F5AAs1UPAPhKjDDucredWAUN6HFG1q4y+3D73HeLW52QxKZnXtwiEP8Wvva2ESAFwWwlR1sC4L+hkF6SbXhm2+FxST49IP7uHvH3XpE8Pl5W7T0QDSbET46XlRsA5TwqP8Ucq5kPVte5RxUeL+HkQbRMetDr5zDbkGSvaQT4NHptoqj3BkucdrHVID8jVaZOZuGlt47oi/W2nJPiWl0Oc4FD10L54zrhY419DczHRwF6Ynbb1942JbliuS3vMZt1zE7jao3rzBA/OaLYba3h9BfufiI3pN97hd4/ufah+UqEV5L8bheXRviyhWg366eTjwdfjlx5NqQ4OCB58DXM9PLJSUang/3Nf/VfoTg4YPb4C2Yn+zx79Vdodx/y07/jXyKewJN/599i8sd+FQYTzDhm8rxG1kvJ+l++LeyXHTaVTG/GzDbeX3dMek/07Aj9+AA5noeBIDNEL3qo3nkCsZyHJFkenQTS3IvjgEeezNcmZeGBsip2GdFKUFbKjk+fNTkYEz3aD+5speLDWVmt1UqfqyZrz/2i4usiHXSMS0MEUVhUb4ws4QzLY4jUmtHG8jj646WZxOsULpbHUQ+VcleJkUcnJP2CaOYRBagcKF1S5w2YN2Uwanp2RPT0EPXJC9TL46BqUY7pYr7OtbDbAdKyOh7J3FN7WZAen0ITR7c1WVthYxGAvKvbiC6fq92bsPzHo6AO8qbrkEThnrwrOfPLVKMiFODOJaFvKGg4KUOlv3RZfOdjWMWEn0lo/QU24rbboLi1gflwF3NnE7vVQl7xOkXPDjG73dBdqcSIJweI4RRfSbCbTQ6Pvs3O9o9dWZqtWt3i3p2/gy8e/Wk6nQ/5zqM/iXv0lIetn+Mnf/yf5IvHv8Jv/I3/B0dH38P792s6ddX4UivIPonIPzhdTQjARQoba5TJg/LBi34Q0/4SQk4LKp/uLzX7rhqmVUWNSochIa6Fezobtp5gNutBkFwKXLdGHgWcrRrP8TYkJ/HLwXJyiF6dYBspxd1A9JhXY9IvjtagCh7Ib3WC816Wo/ZyOBytk6gihW1Vli6B8aPDYCEc61B9XhxjIw2Y25eDkOTECpF71Dxob5qqQBYCmZd4o1gtHYBcrClutYn2BkFjWUv0YIb67it8eV8X+8l3W6cENAfpsUVmoYcotMDEMPg4RjloPCkQucclMO9K2k9e/4zISYac5uewv3KSrZmx6OGU7P7mmlquANTRmOJ251JIA4CNy4nqGiGNI358TPbNm+9su7waPlIBNnPBbON0YFCr/BIJJe/Rj45ASVRuzjk6LsI20kuJqnJW4LVcU/awiQA8tllBHo2Jjke47ca1CVWuEiOnOdmH20GE/kyr1K+Mv5O/8FdpPPgG2kfM9xVRMyftZucqvt6cHkNy9w73/o1/PRzz4YAn/86/y07nm8RJg69/4w8yvrPHo5d/kSf/xr9J5cYdanceIpIYM3/HlugPKIrCYlIR5Lp6hqyp0O+xgqyeHl4speXc5YYIxp2Sm14TPtLoF8eYrRZq+BomfklUFZM5+tVg7aOz8AqvFeb2BnqvF5w/b7TRL/vLhNIXFjGalZJi5W+kCGYPt7qAACVDAnwwCCoWqzrK3iP2+mvERHU8CkWPFS3is+HaNXwS4Ssx0eevEIMJfrOJzjxWCaQnENtcUHNZ7ZgJv7LQB4SWmPs75/ex1UJ/+nL9+niWhGyrwcWS+YaEDcBD7WWOnnucAFMJ1eWLwkcK8R547b6W4KopovjyXebeJbySS9tlCF1LF7+5oxeeK4/f63+p6K2zRSPbrC7NZJZKIeM5fiWvsI1K8Fq4IERptW7ubAV0zTxfYqCd9Oy9+g2+8fU/eOXjE0Lw8OHfw717fydaJwgh+WT4l/nmnX+EZnKHn/3pf5n9g9/k0ZP/hu998ifo1u/RjW4xESOi6N3w21eNLzdBXpDEoHTAMchJjprmmFYFPZxdTYLrKvuC0kUMvJS4bhUxnBP3xufaPmu2zIJz2bMeTssqlcAnGvkORiYiM7hqjJxkuEaKTTTFZiu0sOblYOk97mhE9GpInkQI6zDN08FZCIE350kbyaMzJITFeUoRiHvNSiD4laconMdVY8xmg+RJkPyxtRhEaIu7NOA/VX+K2WpQfx7uV+VQkLV1GJhn+ZrigSgsrhJhNhvEz/vYWJHfbBPvn1z48ot5Eba/2yI5cUy3FVnnjG6jhCKGOIciLc0mFgP4JUmqPjhBTnLcRn3p9ianecCungkfa8xmfc1+WriA8SjK6nLWlggT1DNEVuCT6NrJMYBTArvAM77HcK0KrBBfi6rAVCXpkcXGIpgLXJIgu9Lu9E0qLcXNFv6VWFbfV8NDaPPePXWziicBKuOaKRyNA5FpdSVyxVgsDJV12DNwFZMIZjsKtGdSyzj6jf+OD/+RP4I8BrsjKE4S3Dwi7s5QlXKCNQo7Xa9GLZQr9O4mtQdf4+jgO9y883MA1Ou7/HjzH2b+tX+Q4dHnDJ5/F+8dler7qfp81WEl5E2Fnhv0zKNn7y/xEKPZpTqz7yP0sxKHv9MOZNVLEuRFhfkq3RqpBD6JKFZssO3NLv54FLCVziNLAw95dILdbuFKVQrpHOqzPXw1we52KeqVJYlWnExRoxkiLy7USNe9EaZ9Xht9EQJQB8OQfBiLOpliN5u4RKCmjsqRYXInRi6moxViltCS/EYHqin60X5w8LxgPyIvLu3YjncjXPVM9U+ASSV6brEV+Vo+iNtuIcfnjazeZPd8Nnykg9LGOxSmvqpYGIrZdg27c43xYZK99+TYnq0Y19LQUS4XiHbjou7myhygJHa7dWmCDCEpVvt93E4He6OzVOfY3/+b1ESDbvPetec6rcMYf/fOL/LfP/+38KMRIumiZgU3G99g97f/FNPpMYP+Zxw++00aqovxX02x4ktNkEVuiJ8co2b5OSLQWSLPYjXm3wLW4IH8TjckDSuRfxCT3WkQv5oQH40RmcG0KqjhDFId2hMXvbjlHV60sd8mhBLIWMOsIHl0hJwV2GpMcW8DkyrGu5Jk4Kg/meMrMdnHNxDzgvjT/YDl2m0hJhk+jdAvB29uS6URxU4zDKz9Ka4aL49dHo2x3WqQSUrjYFZRhm1Xsc0K6Sf7mE4Vc6OF8D54h5RVCj33xCOwsSTbiIlfxchJvqyOI+XyJZRSkndrZI0UdXCC6dbW3BJ9Gi0r2mKS4WU1OPedOGxLYRcnuiCFV8VaYioLQMB4V1M5NEvbYVVO+kGXs9xIrAMuW0CxUQ/46kGo6pgbLWy3RuXREb6wwUGJU0WHvCapHBSISf52ON9FCIHZvpo+6mUhJ9mFuObVqlXekORthTSeZODwbximzJ0N1KevlhrYF0UwqqlcmCDbVuUCeTkRHoVqsqw+u9ysVbquE/rzA9xHO2GhXR5m3pSYisREnpPHf43k/j3kbhezMjfZXDJ7VUNVC2RksePktXNtevMO08/21//oPKmL0Te+yc7WN5d6Uc+/92fe6lx+kBF5EWyMrxAiK4Ik2jzHKYXbbr0WCyrKzthZ+T9fibGtWihYVBKclui9/nLydUmARanhFK/EEp5zbvuulP17QxtY7/XxWiGmc0SsggnGJXbAPjPI41HA50aC0a6m9tKj4s7auegXxwEaYQzm5gYKkE8OA3lpPEc93g+kw0ghCxsWClpS7AYZsuhVb5374jzqyQH+/s6FZC4vBL4So/ph4W6bQdc/6Rnm3TBmx8cGvxkk4OJqGiQOrcVVkqVNtPlgBy8EYpohKvHaOCGG0wtb7x5wVXnhqKFK1Zi8pYimr0mQI42rJOcWKZcmx1pS3NpAHY8QuTlVoFpoB68ctwLetZR2QS3sWiFPghyqr1eQ1iFe9VHTLJDUzlhSvzFqaSicXSQ5+hbhBbjWelU1wHWSAF0Cold97K2NMwuncI2FVuS3Ntbk4DxAHIVKvvehsHSzG0xqxvNgN13eoxd7f4X793/XUpf5bSKKKiRRnXzvOZV2C5dGgXcioFrdoFrd4Hblm8HIJ9E8fvI333pfV40vN0G2/tIq1TndVetwzqFjjb2Kz/nqflhPuEUk8IXHK4G34Do1Zp0aWIs6maOHM/LNBl4rkme9S6sSwSDi7RJkbz1mXpR2oSGLk/MgXVU5tMxbmrwt4dnpA+XTCJFqio0GXkni5/3Akr7C/qS1yEmGcJ5itx3wmuWIYDfrYTtzg6+lAQpRbtTVUxAw/3AbWSaJXghs4dYIa+mRYXIzYnIrArlN3Dfr8mONNFRfygWOixTuVshailaVeDZcO08AfTgi3kxJBg4995w0QzKrHMRlTuZigVwRqxcefCwoGhKVKSrHFnU8ZiF2Gz/tYUvba6clxdduLG2shffQP115+lhjIoUUYHfWmbf1Fyvdjnck3b1Lgi0nGWowfTPxr7yfRUWQDN5ctHVahgmtHeOFWFbTPQRoTklM1EcXm9DYM4tRgCKF6Y6i/sqS39tAHY+JX/TJH26/cWIS0zzIBq7AKYqbnfD8LX4sBaYdnrncTun/F7/C7h/+x5lvL498/RinEZY3X3tVr5EVjy5EUxU1gZ95oplFjd6TwP9XHOoaGrUiUkESKjNIDPJlj/yD17hZTeYXamPbWnBwO90wYXIezUDKsPB/Fro7drN1Tq937ZgKE+xsEZeSZhbWwAF6tokXguTZ4bLC56oJrlUN8nHTbPmceeco6oKspakers87rppQ7LTwSVh4OuehUQnzTVYEKEZhUSvrR29cSBCtO7X1LaXNfCUOEIpLVmtKiiBrFutwPco2diJj5lsCmwiSE0uxobEVyfRWjE92kWemy8WYrQ6GwUzi7tapHGc1QQ5PoR9OgtdhjPVAPDAU7VMZOuUhmnt8LDCXuGIuKsSCoL5z1fCla525nSDmOfrxAXareaE8mrVu3ZTjmiGzAvX8GPNg+62TOHU4xDWr2HoF9fRg2SXwaXRtQrInKHK8jwTZV2KKu1sXdgbcbgdeBgdcMctRn+8hkxi0xHYbuGYlKH5sNgP5dGGUI0WoEGuFfnqIr8a43e4Sc+0alXDOxvJq7zdwzrGx/Y13PpcorpP5CdX9AdzagMzgnx+Hf8Pyvc2/CoUSfpBOemdCEJIf85YSUauSMdaFlz0ZnrK1XQyznTBAFTea+HYVV0+YfWM32DefIW4tj+k1FbY3xVniuyhJcOpkRm3fEo38WjVX5gaR25IlKvCV6OptmCLoE+vehPTZMbI/XWKAgWBNLQUyNyHR2Glim8GqUh+NEfhlpSH+4pD4RR+9fxKc06wjHnvan+Skx5bJrYj51hl8U6Qo2tWSNLOeSPhuNWCT+9N1WSYliaYhOQaWurnJvllOutHEL62nRVkRX1ovl09vdHy66lWTjPh5P1g4l+oWy8lBiKWxyPK421XyndY5RzoBiGp8qRveVxUu1rhS81iezJAr1Vy10t1Q5TW8qsYyQP7hNsVuG3OjRbFZx6cR+Z0u84fbIYE5GqEPL06Q5QUtZOUCfnFaEsDsRn2ph/2m0P0J0fPTTokaTnGr0nrGkcceW96m/me/RuXje7R+dpu4M0Oqt39PKx98yPD4iwurzHrmkcajhrNLr8X/kMJJib23fbpQyU3Q+V1JqhYhnUcfDC7czlks/3IsbFZwtUCKE/Mc26nj2jVsqxqk3hoVxNlqsXEkzw7BXK1wInsj5Dwnu7sdnO62Wpg7m7h6JUgHNqtLfLEndKUEBDxlEbgZaq+HmMzxscLGEnKDmGa4zeY5ZRlXiTE3OhT3tzG3N0IiMpgsBx4hReCf1NNAPNNq7d1dbmeB5+zUMTttfDUN1V4Hzcc50TjIvom5Q80cpqoY30soKue3JQCZF8G58OnBkufj6pWQCDkHAmY7EaYiwUHlVUF6bNfmLTkK6jbCQDy+eG4uFj+Y5ZdW7S8KV10hCqcx5u5WcMu7KK6ZHC+SdTkYBxfHR/uAR7xlcixLS28f6YAxXxDqt1uh+v1WW30/4eLoUtiM0yoQSsXp3CnnOXI8J3p6iNcKe6OzrCq7RT4Sa1yzunzvzZ0tXKRRzociVbuGFMHV8emr/54PH/49VybnvS463Ycc6eMAVRrPA0R0NEOMyvGnfI71NVTJ3iV+aBLkRbyVhqqSFLut5csp7fl2isp8kKopMbiOUB0a39SMb2pOvtlkfn/jVCtyJd7oiHbVKJ39kqc90pdTqnsFqn9WwN6jexMq334ZWNSlkoG74LjgvBIGALkledGn+u090k/3kdbhtCK/3V0atpjNBsWdgKsTuUUtDCecD5rBUhAdjtDH42XCKwlyP9JBtqXXGM5ChIQov3FaiZUnM3AeJyX53Y1QnltZMQvrgiNfGdHAouZuDeeWHlmimaeoCKalxTCFBxtchKSxS/vr1YiOxsSPjtbUNOC8A57t1s5BcxbhxHk7AkFIDL6yiBS2E54BkVuiV2Ul3nn8SncmGbhrG3OsftvcaDH/cBvXqiByEyrXo/ml5j3RRQlT5tHGr40qZutqyhjCOtQsR5TPmjqjgCFkcHcEMKlj/Bt/mdpv/1nsTBO3c6p3Tkh3Juh6jkoLVLUgas2JuzN0I+d186KbTlH6YvZ5NPfIHKL+9IeeNPS+wgsRZNMWuumzAr3XW7MoloVBPj+6VEPbX0JW8ouu2jTD19OAixQCu9sl//g25tYG+Y32uWfG5wbi6OLxbiUEYXGlXvbCu7PVwpX7EGVlV55MkUcnCB90XtMjSzKwwewi0gGmNpqjxnPSz/ZJ9kr1hXJu8s1qqEincTBoSoMhhxAiJFGxXjt+X6pf6Je9pR63vYB749XpPOM69QAtKRNDYVcWIwJqewV6GqTX5rvxOeUfAJ+UZkLTHHEY3qeiJjG3NwJPJ5KYEnMsgHjisKnArWxKz8v76zyVo7JiemYqWpD2zurwvi58GuG317t2vnr9Tt1Fj4Oc50Tfe47+bA/9ahDIkmmEvbd9Vszk6lF2Rn2s19pMrlMPC4hrdr2BoPjyHkLml8NSJSCHkwvx3F7Jc4vR5cJ4XoRkVJRz3oLAWl5wbyz0RoyGzzA2p9168D5OBWNmqHoTs9kMWP5ZvhwvgLDwBPz8y+M9rMYP3mr6PYTTEjWcBXmoG/VTXOpojij90fXBiOh4FCrIW43gliYD9q2oSXwkyLeDn7lwHnU0Cm1n59eJhCs6nVeKC/B5AOmTHk7LoOxxMie/t4GLNdlum+TFAKEltlsl+v5+cN7ZbhLvDdZkXeB8lfpsiMwQf7KP6VYxZ2AEy0PMiiWTVU4y5DijuN1BzgryexvL1bI6HOEiRdzT5E3JaFtRf16QdRXJsSHqTzDbpxUAkRtEVmDrMVRjso/WWdWisERHJ2T1LZCCoqOWVWF1PEYfT8g+Dr8p6jLIDpURTX1g4r9mJVncaF679fWm0M96AaJzq71MXJefHY4wW413xrpdFm6zTl49xYOu4uMFEE1dqSTxbuGr8dJWWxqLGGWIeXHqwNiuogbBzvys7qceWrKWCiPzNV4T26ygTuah9ShZStwtEOVeSUxD4fGMpi8wgwnpR1+nGDp01YAAXTXh3xeE6GbMjhPMOGJ9WvVk2YCk2j73G6dAWEp4UkGxWYfnVz+nH7XQTw9xNzqha6EV3N1CPTltJUeHA9xggrB2KSV2aZyRZRPzHNLQwVMHw6UWrt7rYe9srSeU9Qp2u4U+GK6pzVxLLrEaL99DKQTO+2Di0ajgZjn66SH0RvDBDWp7Q2RvDJUYd2cTpxVmt41+0YPcnKpiDKfYvAhJ990t8B799BA1nqEms2DWcPY4YhUS31mOnGT4lz3szY23ggvY7TYAeuoQJW/DVYNl87SjSHsGU5HE/QIfKcy9TeT+EDUYIybB3CdvSHAR0cxfyFMwlTOdtAW8bSFUD8xbKlSaz/xWja9mWuQJFUn/DnVXaS3qsz2EVhQ3OohI4xZj0RnSqKun2NvndbKvFYuOiHU4KUMXwVrwPhRhrnkqMi/WFpzvFLM8dDtqKcJYyM1yseEAHuxgx7OlE6WrJmFhWjtPHl+FA4pZjqunISmdF1BJwrmnQUjAJxF7z3+dGzs/iZTvp4g4nw9pte7jW1WYF+jDUBByjWqAUS260/FXY+L2A02QvZbnEj5gbVC8SsjMIA9G5Lfay+RY5iYklLnFDmbhBXehGhI976PFAHmnE9peyjK6rZcyZllLIZPg/pd+8mpNJqjo1kAKopUKl2lWsBu1YDNcT9GDKXJeBNLbRp1ob3jOpMKlEdmH20Hu6Di0bm0MvlOjyExYlRYuKFp0qsTPeueuyZuE2U8vqMOlcdkiUsiTGdHekPzBJmowRc7CsUJIWtXJDNtIQyXRh2MVuUHvn+CBmoBks8rodsTofoRNBMJ4kscZaW8PLwW2WaG40SphKjDbUHgF1Z5bCpt7JVHjHGkd87rCxAJd3oNoIVlXPgyr0kJ5TVB7GdparhqTfesm8nBEdBCuY7ETEuOzCez7iAWeXA1nywnDlm6NajjDbtZD0n5Jxf9dYqEcAQQjgKzANOMwYR1ZnBJEk/crmei0gvLZIJLISU5xu4Pp1gKm/UyCXDkO0ojztiTtXf1YbLtK7nzA5FVjbKknvXjmx7c0piqZ7ThO/sxfovk7fydCSuxcIpzEy9fvy0tHujWjSA3Z0SmZRdcLoq/dY/SfviQrxiRRHdmf4jpVpA0kURnFuERTbP1g7ee/7PBaoR7vI6sJ9vZmmAzvbhM9ehXG6dwir9DadLV1m11pbMAba0mx1Q7kMQicBevQn7/CaxkwjwvzkGYVdzLF3N0K5OW93hJP/MbzSCPM3YB7l3mBfNkPTnWLzysx9uENxNEJiIB7t3c20c+PYDCBTh3TqeILVxoqRcRPDkIHsDcORCYJLk2wO21MGoP3qINhINglwfhEjmYUmy18s4rMChjN1m2TCUot4ukh0jnMRvMUt10YOEOm80DWVaS94KAWTR3R44zJbkTRVJimwjtPlDlEAZ4S05xG6L0+7ugEudlluhsRDy2uVrbVV3azKtuoLKeGMiuFhqR/PjkW3r8RXuGSKBRjkjd3At4Yw2koDhWW6NlRuNeNCuZGe70yqxW2U3+n5Fh4T9Qfh6koK/CNSnBXLMOLssNxAfnx3LbGc/TB4Fxn83WxxDhrhatXEFmxpi8uCJVVZxxqrxcgSyvVeCcENKrYRhUf63PuhGvHt6Lvrfd6mBtdinvhHdSP9jG3N4IroxB473m592v8wt/+v7/yubwpup0POXr+N7jTC91tl8aYWxsBcppEuHYNNZhw4Pbe2z5fFz/QBPmi5BiuvRgLUSZlqjcBKUj2T5YDqhydWi4KLZl/sIXePwmteecQBALE5Jam+VlGcpAz30lBgWlVcLstmBVEL/rYrQZiNCe/1QYh0EcjzN1uIIfUEtQwJMcAepJR3GiRP9gM+NFZgT4alTigguTzA2wtwWw30XtDlBL4dlCSgNDKt7HCtKt4rULSuBKrybFINP4SPVvhCGRESstkIZCFJfn8cNk21Cdz8D30JDgzxc9DQi4PR7idJj7WZD92K6wcc4MaFTSewuhuhJCC+Y2IaLqFMh4xzoieHIfKk/fYVoWoUmd0R+NiQe15SG4XahZIQTz2mJ4lawcmdf61G3jviV4NsJWYhGp4SYDZtiZ+dFrBckLgtpuhjVrYc1rIANHzHrZbX+J53zZsp0oRa9QkwyV6KS+lhlPEvAgD2ZeQHAPYiOUCEBFUVvKKZL6hKCoCWxHUD78c+IfqTzCtKpSJqy8NgADkyTxIuy2+O3cU9etVyKQQ2G5YZOqXA4qS4AkhMVjY4Oa9I8a/9m22/5l/MuwrNW9MjlcjahS4PEOlhvnTI9RGDZ95oqRGNumRtOu4eoJTAaql5gFzlz/c5vjwO9c6px+18NstxDhg/1x/HHCG/fGl4/S5kCJo/d44vXfSuYB1tC4Y8TwPxDyXRpg7WwgpkM8O8Tc6S1iGjQRCq0CSHs2w3RrFSahkrdX+Y31ha9vFgQin9gfI4QTXqp2bU5xWsDjOZhW13w9V3VIJQJpQrECIoBXrHDJW+MUCwYWKtnh8AGmEtA5fWFwtCcmTdUTj2dK4ySURJBHCe+TxCcJ57FYL3x+jSjc2vd/HzvMAOTmjimBjgfCepHdmkeCg9rJgsqMxtcAzmezGVPeKU2e8hSrH8Yj0RQUb18jbp2OUyv2y46OnDi+haKhzAExpLU4pVl83oQTe+lCQ+HAXZx3Ri+O1DoOIFPlWWCjo50fI8Rz9ooe5s8nbhsgNZrMJnVpQ/xBimcT7RONEitnthuv/Dl1E2Rshh9PTxdkZroq0JWb7impXcjS7MhzD7HahnuBWoDdilqP3+7g0DrAHHVwefbeBKB3xXOfyBHiRHMvjE3y3cWGH1QNuoxGs1Cdz7GYD9WqAr8Tolz1sSfp89uwvUK1sEMfvVjgwJiPLhqRpBx1VmGTH+OppHujr6dIDwldiGEx4Mvz1d9rnVeOHB2KhFfmNJvHL4Wk7YwFnUAJbic8rX0AgUdRTdG9C8vnBssKxmiYsWm22WwvmHLEO+FvvST47wGzUqe2pUEFWgviLHkX7BsKBKSdqoVVIrmKJayQgZUnaWcepypOwehWxQghB5Xt7kGh84TCb9VNIhAwMYXk0JuqfCuD7g1FQYSiJY5Of3EWWpiKvi8uS47MhMkN+p4uaZCHZTSNsNUb3J+jBDCJJfruDnObo3oTocIRwLihjEKq+ydMe+Qeb6OMpdVFluqsxdcHJPU3rC4NopJhv7GLLQSP57ABfiUlrIZnLuqG6aDs1bCeA/QUePXNkXYXXIJ8HC2Ov5OnktrAfNhcngWargT64wLlrmqEHM/TJnOxOF9e4GG96lTBbDXAOfTwm/fwwVPGVgMwu96V6U2Re4NLSorp8nv1G/e0xcKwkx4SBwgPJ2JMbMFWJnjjEJdrH7xr6cITILWbnPIkmejWgqG3jFnhJD+41jlsXhdwbIJXEbDWWjlBCCGZtwWwrKLIUNU/vV3+Fxi/+ApUP7xO158St6ytLJBtzRr/xBY//j/8xQmtUUmWj/RGVzdtggEgtyY7SlS3hWNDrfXLtff0ohdMq2BGPZqiDAfrw5HQsfkPYRoX83gbShQW5dA75/BhhbaiunQlRKvpMd1KSaBtpQEuBcaW7JmBudpHTDOfAdRu4bgN1dIIczUIlq11BjTJkblAve0gd9O3lKHR3Fi52QEj0pxlohd9qLbXSF+E3W9jt9pKoLAqDfnpIcXsTOZ5hbm/iaykyN8hX/SXcQwDMi9PF4jQPEnKtKsX9nYBrXt2PEEuDFLvZxG82cbMswC8ICZBdWWBAgPrMtjTV/eLi4pGH2oFlfFMGiFVFMu+opbSf69QxpTuf7k9IuinTFb3j2XbE3AcFi7RnkcZTNErJTQEKgfV+LVFb3nfvl3m0kxKkxN7ZRD47RuQFdqOJ3zjlISxw1nIyR+bmFBZxxRDzHK/k8hrJeY7sTwI/YEHinuaYO5uIWRZI5krgG1W896F7UdhQlX1DZVmMZ+iD0OI3Wy18JYLq+twh9gfYdg1fuSJ++hp1A1FPsGeuuZiX1uIlBtc2qktioyzt1MXkvLnSucOY5tiN83/3C33mUuhADcfI0RRXTcM19x4xyzExPHryq/zcz/yRq5/QJfHd7/8xjg6/A0JQq25z7+HvxYkyQZ/nxF+8whc26JF3G2Ra0P+1Z++836vED02CvMDAzj7aDpi/wuLa1eC0N7pYSggA47CNNGBnX5MkLtrxujcJUIjNYOdcPNzCO0ifT6CZBkLZRzu4SJA3JLW9sp0koLjTReQOvTfEbjawG3Xcsr0+RY6zpWZsttUME01mYB4qptH+CS6NAoM4t6eaz2VCYBspIjPo/hQ9nFF0qvidJi6WZLdaqGm+Rmp7m5DO4ysR2dd3YTwPMi8EtYT41RAKh5gXQf+2FyYYfTxBzgtsLcFVY/KHW3itiI96xMdj9LRL/6OU0n8iLEZWMHa2mRI/OsQmN8gbEj0JL19RF6STUj5JhKqNmrlQvUkDM9duN2FehOpsiY9K+utVFCFFkBqa5KHSWeoOS+dhPCdeJM3Ok7zokz3cfifipd4foUqyoTcuJFVlJCtW1XJaACWpQEB2DctzOZzhajGmrpcqHxeG80Q9g9nWqOx6yXE0y7HWnWPlXxSBqHEx7jR/uB1kuLTAphL85YsYJ4N0n54HbL8Yz1HDGWqcLZVkFol2nsK8TI4Biopj9t3vc+uP/F1UdifI5O2ZzOmtHVS1xv3f9T9lRz5AOUHWkkTHjiKfonSMlPr03A2MXf+t9/fDHCHJyxHTDDWZh5ZxEpct8fzNeGMpcDeD9u9inPZShoTlElMSIQTxs0NwG0HjWCusIsDgCJvyjQquloaiwiKRUhLXquKrSUiOtQpkuTQKOsiZCXKeKyTSBbFnEf5kClstfKuKFyJo7J7FBEcaV02InoYumzChOuxiDTc6yC9eXXwtvA8J8HBCcQHuVfoAGxOAfnaIvbuFubOFdA6r5JrBj5jMoZYiLdRfvOEeOE/9eU5Wl8x3ItwZLoJLY+ztTSShSqwLj1lZxHrBEmK4uBJ6YsE6/MkMViT7xHgOpfnRRc0bpxRut4MwdmkkIaZZIEeuLFr0iyPMrc1rJcliPA9445ZGWot+et5GHCB6crD+hxV9XwCG4MazgE0WIiTeZ/S+xSqkp3ax9bVrVNccF98UdrMVFglK4pUketm79LvO+SAAvRpn2MarkB2/3cblNsAtuq9XX7K3N85hwGVWnBJBncPc3UK/6gft5wWhUgTztEH/E5qNW6Tpxbymq4aNBc2th0zGr/jZn/mXUbNSitZ7fH9EXkyJfQUhBPpgiM0N4xZEUZUsG77Tvq8SPzQJsiDge2hX11QFirtdmBeo48kyKVkNHwV74/zeRiARzQvk5BLt4EVlKjfE392juNstdYA96bMePtEU9zawsSaeQtYKbWs980tmsijJCsmn+2HArCdQjXGRRkRmWfVW/cnyBXOVCESospiNOsmjQ2Qpe75w9bP1hPxOl/jJEeSExOd4QtSfMvtoh6wbMftwEznMqTzvXcoef21IgbcO1ZsENYcVCS67WcfMC9Rwiu5PEIOQ6NtGippkyElIzj0l5lYJRMlITz8/oN68iZehHUm6IjujJG6jXlb/fRicy0RutqWJRjmi/K6eexovDHIwRQ2m+EaKUzK48s0L8geb+FgTj/3ymfGEgcRlhsrT40D8e97HR4p4MD2PWzSO+MkxppHgthoXmF1c4TK+BWPZbF5NzWER0cEJ2b0N5h1Jfe/1ieAiMb7MOQ/CYkEUBu88FBbdn6JGczQw/3jnHJb4bDitLj1vryT6YETRbJG3JdU9e6EMjw1QTdTcoY4nxPsny4TK1uIlMQ8AY8k6yRpWsXj1ChHFxHdayOS8isZ1wkVt4mqHtFeguwKnYKzHfP7dX6a/9x3wjlr3Do1bH1GLNqioJiJ5Nz3sH7aQ3iNfHIek49yHArvRwAqIHwf8rdcqkIDOhO2EYsMqT8AD5vYmsjCoMy13X25fFJbo8UEg6H58i3FXUTk2QVEn1oHUd6bS6+rBHEMI0M+PAvwj0dhqcF4T1gX5sAsKKkIrTLOCj6MwnguBFmBegx9dQNDkeI54cgR3Q0KXP9gh2utdvngoLNGTA2yzGirWiwR8BSYipzk8Ow4SdFIiRCDJyQXZT15s3PG6SKaOOVy4qF6MlwLAeEQkiIbBedNWZAm1EIjcU9kv0DMXKokrxQR1MECM55jazqXQBekc8mAY7LlLV0I5nJ5/xjKD/uJVwIQ3q9CoXOoyuIxI4cpCiZ/m1yICnzvOSYb8bA+hJM46io9urn++2rW95FyvkxwDyNE0GHd4f04K8dx3j0fnugmrpkt2oxE6lGU4IfCdWiCXviG8EMjCIHrjMChbh55mIRfp1LFbrfDOC4Hr1Jf3ZUEG7PU/o9v96BpnfnEoA53abZ67AiUV+vkr7IMdDo6+yxeDP8ts3kephG79Hu3qLSqvFHG+RZ5/NZKbPzQJMpwyKK0GrwUeT9HQCK9JajF2o4bqT9HTDDEvdQi3GvhKsHL2Sgb2ppRruONF6H6YVL1WCCnQByOyZkrWUkSdKro/Jf78kKJbxW01qb/way0R4aGoKtzNNiiJOgnaqMJ5ihtNzHYT166iXw6WcmlFp7p8uFRvEpLjWRGwTErgrF+y9JefleFijcwNlc8PEHaT6e2UBEEmN4LOr/PnBujFsHjR6+y9R+iQcBa5xe40EHOzxOWa2x2KG03ip71QLSwtq9VwFqrL5Xb1YBoSl4VCh/WB7Z870sfBwrq40QzXoJ4GglO3jkskRV3iXxUknx1RrezgI4GJBdG0THptmJTUOIOnx+QPthAmCPLLcYbtrmDnXg2D8x8QPzleuvUt3PIum1zEvCCaF/j+lPmH22906Xof4dpXG0jFLA+koXbAXNtEvPaeQiDSRGNHPHHkdRHa1SsTpADi7768VPFETjLkwQhzu3PpNbPtKurlAGUs9oLr5RoJwgXd4DABh7+bVCyPJWsrVOap/toear6ebJ/TS3b+FEMJ+AjG+0+ItjZQ6bvJrblc0f9jfwO8Z74V8d0nf4ruB7+Np//VH6dy9yEP/rH/Az4VzB4/YvL4M4a938Ie98mOD99pvz9sIfYHF0pNCYBpFiqA1YT8w93wnkuBHE4Q0zwoFqwkoWKWL/H4q+EiDZ06emFAUG5/odEuJAE6ZUPpOK8rUlNFcvH761dIRPb+DvrxfjA1ycaniedohu028JOVOSDWFHe3wljsfcAMA3ZeLGEQcjhB5AbbqSP747UqdDjHjOjRK7K72xSdBBFtEX22d3l30/lQvR5OMLc3QzX8jBKHnMzRL45xN7s4J0iGpwuQ1espplkocLzG1RACtMnjUfkbMscoQNfSnsEpwfhuzOxmjJWexrMiaB5bR/T5K+zmwqE1FKDMbudUwabsGshecCYEUE8Pl/OSGs0QaRSKRJ6lzN/6dc3Rsxz2B6FaWcrmoWRpJhIvz9u3VirZb+mZsBbW4a1DnMUWF2ZNtu7s528TYjI/VUS5Qqh5fs490CcRrp4iJllIYs/uQ8kr46FFb7x0bYTT98136mEbxi9dIymvuxzPMdWE4clT7t39XVc+l8vCFhmffP+XuXv7F3j89M8jfEZtOOa7j36Zb339D9Hpfsxs3qPf/4Lh+BkH4oTpi79EWttkNj548w7eMX7gCbIHRBphS9e20HqXzLsqEHNcqNhmXYWca9S8gswd0aAgHhl8JQ6SN4+Oli+eXbi6lVXWxapZzgu8YEl287WgrDDvKpLbnZCYncyJDsfQm4RfCchutpdQhLwliYcOsdMMGFljifeGyN4ENhuhunD/lHww78ggSTYJGrYLRYxiox7c8l4OAAI0Y+XB9oBPdWAGG0fy2QHEN8laCi9jXHUrJKvPe6jBDJFoihU1hYsqG8KzvCbR8XhNtqtYJEdCBBjF3S7RQdD+9AtJlURj0ghXT0Il98kxKBHsigsbqsWJDpJcJZkrfnIcKiXW4SqK+ETjYonZqBHNPCd3gytUemSpHIfhIC6NP9QkJ3oWquVmo47t1pDWIY9GmJ1WuJaFRU7zC/Hpr33uKlEwNrH+nK7n60Iai17BHK5FuehZ4JFXQ0zyc3jEiyI6GAXiZkk0FB7GdzR66pfX5/zJBOc/D0xuBnOZ9qNiaY8sKAfOS7oO+mCELGwgpyWlPfeZJNh2a9jBFEZzuEAdxNVTlAE1OGspf3qlVO4RjqXhy9r3nA9VlVJHllijJwGTDjBvOU7+6J9j+5/+JcQ7mIIA5Mdjjv6/f5IP/9E/wud/7N+n9TM/x/d/+d+j9pM/TvOf+PsRGmSloPutO2wlt4IclhcIBL/5D/3r77TvH6a4DDIDIGJ9qiihgyGAyn0g+XSCoZPMitKq3ENRLKXdpPf4SQb1JLRxL1CfWC7WFo9LXiBNTNZRSwmzs0+J7I0CLEIt1BcUxd0tZH+C7dQQ04zoYIiLI+x2C1GYpZSWWTFDEOMZaq+PUgp7N8Ag1H4fVbpsyuMRblVtQsmAxfbgcxsIfXqToqIQ7RqqN7508Qrh8ui9HraWhmt29rxGM+SnLzEf3VyzhV5+Ppwi5vnSSrioySD1Fh7LJWQDQoEBv6I+AcEBT6wvmuXEYlqKeUcRjSzSEyAuiODGWYT3ES3Rh+tye6o3orizhdrrB1WHeoqYzAOx6+hkbe5xSYS5fyrvKa3DT7OgxDCYIIzFNoKBCyXRzpfGKojy/1cS4TW+z+jdukir4c7qdp+sV7zdWQjOW8RZqM/bxKIzox/tL/8mrAtVcMBXEuyKvrSY5Uu44rm4tOKigjmHc0vjm0XYjQaD4SOszdnceHfnvKfP/gJaJWhd4fnLv0wl7fLZt/8sP/kT/3RIjnc0PtmlpW9SP3O8v/Yf/qvvvP83xQ88QXa1UnNVCERhSB4fkVpH5e4GLlIIIG9IJrua+ZbAaYGwkmiskTnEI4eaO8y3tomP5ujRPEiqrVY3VncoZdk6CJO+LEKLerqtwG9Q+eygJJCUAz+QvBwwb6R4IagenA72845kvhFR6WrS/YWQ9RQfqQC/KJPv+SZU94APtkg+OwjJ+mCK7VTJb7UDnvdpj/xOFx9rkuc9xKxYVskX4aVguqVIeyER8UBxu4u9FVzwRG6IHx2t6za/JhbXJVRcPfZ2F6dkwPBKSVFiQnVvQrHTXFOHSD4/ACGY39sg+eKQ2ufHZLc75B9sBdvgxT4Ku1zpR/tjqmloU08fNkj7Dj3z2AqYqsAfgyrlpCAQMBeYblfYoHBwNFraLnspiF8OLk1YvQDXCNbaLHDO4wxZWIrNOraWII1DGotX8sLJaTWk8ySPjpawDa8l2f3NIPE0nC0JbOnnByHRK12HAKK9QdjHJaYky+uVG0S6UiWfe/K2Ihm8uWoqCJA1K8Ao0OU85YCiXV3aSZ87r/J8dPl5sdPENc5XiX2iy/cCMI7o4GT5jKyFschpgW2la8RCWUJBigdbRF+sd0vEvGABYheTjOm9KkVH4ZRHWsHJX/8rQcLqxsfkhwIdO0T8dhhkXY9BCKJaC+8scneTjX/4H6Lze3+MZGOEjNahAuEAL9aN/VEO4S9eMNlGJZBjPn0ZVBlubiClWL4fs64ibycIkyKLYCCxCu/x4zmyd4LPqviNxrrm70V+3oA6PCGpxphawnwrQpqCaFoeX24g1kHDNSuwu93TfcXRUvHBJRF5NUVk5fizCgdZqQD6RhWDIHpxjPr8FTpWa3rqgrJSXVbJTaeOL/HIAF5rspZivqFQ0wauXUcNJ6jj17R9jVtWpF01Oa/pXC4QL7QLblWX6hrAmpTj0lVUhIWMKjztL8L5Sy2xxmGqEmE9ceaX02Ll2KCnjtlOtKZoATDvauKTvMSji1BB9n55fiIzRM+OAjHOeViocDw5XJLHFiGzAvWyF6rAi4V3GuG1xJmgp2132uFFW0jMrShSXBbiZHrOsfVdwp0xLVnbv5bvpNkM4TrIaxipAK+dj1b1iuX+IFzX7VYorK1U2dX+ALvVXOu8LLdxCazOTzMQAr3Xp7jZXbOMd9bwxaM/w80bP/1etI+1TknSFs4ZhJBsbHyd3d2fZqP7ESYVFPWvRu/40uP7Qe3YA2aniT4cUfn0AJvokOiUk3X6/VegJU5JxK0O0TRm2pUUnYAFrr4qcLFk1hVkmwqvKuhxhfTIkx45qp/1gqXnYLqWMIqytZY/3MalEao3oeZThh+nSOPRgzrx8xUyjgCMQ4zmQW5kpfqV9h1pv6xSl9UT16zgBSFRNRaKDUYfVpjejFCZRzdShHPLRcEiTLeGjxU+1czvbix1lhe4T0GosqV9e07rdvEi+ViTf7yDfjk4hZOU8j3F7Q6isOiTGczOt7n0YAaij7vVOUcEKHZby2NVx+NAqLrTDRjC/ZOwrVlB+ukBxVYds9MifnSE1zLgw3db+GZljRgny3yvcmipHp7eH6clpl0JmN1YUflkP2hFn8zgZIZPNcXdQL81N1ok3391qT6q8KBOLh6U4lcnoTKx2h5NVJkoh+O2neqSwCaB6IsDWCGCCuOCkomSgdDowey2sJUY146QuQl/lwLhPMmLHlm6vaYRey6kCLgyH4ZkPfcBkn7FN1VMHXHhz2EQ38RqhtOKohpML1b68CDsqVqH7k0wrQr5VspsS9F6FhYF6mReqh+cbmO2IZEFwdxAiGB88/QY26riKlHp2ufxkQ7vQFeBAGUFk4NnHP43f4qbf+RfRJS4zPlBlcruJPhbXzMGf+7XqXz9A57+1/8xd37xDzL4td/C+Iz+n/qv2f4DP8fWH/r5a2/zRzHMRhO9IAktAKoQlB6UCrCmSUb8ZB9vHEoIzG6HChX0zJO3VMCTl8nxvKPC3zKNmtSIZ6Ue70p1znZqgSeyUIGQAu88cpqRfLqHqd5idiMOcpGDIDHnNpslZtNfiIFexILMt5j4XbOKa9fRez30i2OyBztMbsWkR4a4JPoKgNxiNptBL/hVP7zXez28CkAPX02CtnE1Ce/nVpO8KQGBiBQ6EpioGXDKZbK4qvHv6mlIMicZeI/baKCfHAansAWpsZa+kQ/hYsFsQ4PzVI/sWndG+FOteHUwwG40mbUVoEj7wdCCwyGUZiP40lhoYMhaimRoyboaZTzJYakx36rhFq116/CljBgEvpCPFMX9LfT+IKhSXOJupk6mcHJ5tTf+7LymrReByCnKyrIXIiiPaBU6A28ijl4xbLuGHEwuha64Srx0c7vO8nh1HShKGM2lUJy3iFXpRWEs8mSKq8RrmOgFr0sYd+Gxu3YdN5ysFSoAov0B+YMdXKIRbr0s8PmzXyHSVe7c+YV3PocsG1IUU46OvkOVOpsb3+Dw8LcYTw6Ioxof/OQvoXjwzvt5l/iBWU2bzXpo3ZcC42o0XyY6pl0hu7tBvlNin8ZzRO5ofDam9UlG42lBMnRUH02ovTBEJ2CqkN83jH7WcPC3SwY/tYGtJ3itmH+8Q377FOweMHAeNZgSvxwQ9WZUDywuEth2NVTKAFtLKLabIdGrJVfC9ngpUCezIKOWGSrfOyA5NuRNT9aQ+FSXrmEzdInrVUcB7pB8ehDwy0pS3O4sbaEXIYdT0r5DFSVu9My+83rA89oyubG1hOzDHYgVcjTHbNSYP9zGlGoKxU4T20wRUdiSuwBDqI7GqKPxUrvRNSvISUb6yT7Jo6NzBij6cIw6HmO2GhS3O+T3N7EbdeTxGH0QklIxy4lHbnkvzu1znJWYGIlZrbhKKErrYjnOypf/LZUMCnt+wMosYlqgJqUayeoKfpojz1T0XaLD4mHhD388DhXqWYHvVLE7zVJftNyPA3WBDN1q5Lc64RpPc5ibZaJ7qa6wFoweRAwfRIxua0xNLq/tWpXqGgUQfQkZT2QGsYCP6JCoikqEi4N6hSnzINsJVryrmOfkxJNMTuswrpGSfWM3POcbdfL7gU1uYwEW9MSR9Cyz0SGPf/k/5Pbv/kNUKlvL7dlCMntZx2XXqzC4ec7ef/BfsfH7vol+uMPx9/8q9/7efwrpJW40ZvDn/4etdbwWtTRIZjUqFB/fori/HaAy/fGpjFSrSv7BLvlHNzG3N1GHJ+jH+ySPjqk/z0LyRcDSJn2LmjmylmLw9YjjbyWc3I+xzQSnVXDw2mytVa786jsYaaKJJxpZTCzw7Xpo27/slQmGC7bJVzRZcJ36KcEsM0HPFphuayirzK6srPlEB/Keh2K7RVGaE9gbbXw1wSuFubsVNI6lpLZvqBwW4MA/P0L2x8GdDHCNSpB4K6vWap6HBKZTD8l+2S01tzdx9RSXxhQ3u+eO/2wU1WANbeqKyd34UilFt9UCJUnGjuRkNZE6v5hU80CajkdhPKzuFWvwjOU2lQxQitWqY2GJXhxfuzJ6lRCeQBAsSpnArAgLlfeYHAOhOqrVeUe5biPM+0mArMkryh0uf79IjsdzotXk+DqazK/5rshyZFFafy8EBMp7Y0oFEw/YreZrtf+9vqDykptSElEjB+OAzZ/MefrsL3B8/H2+8fU/iBDvnjoeHX+Px09+lZ/8uf8l+yffo6Ia3L3zi0ipOBk9YzY6evNGvuT4yivIXoQqgj4KuC2XaMjt2qCnT0KSIQobmL2dGqKwJM/68GJI0a0ijCsTXEly2GFyu8LooSLfFJBCtimpvVRlayNHljgaryTFThOBX+J/RWZIhg5dLnJNMyU6HCMnGWazzvzru+G45gV+Xiyd0y4L26pSZBY5z8ludynqivjYU+k59GiORwTimXHh2IqC2TdvIPOgr7vIIsSZFsiq7IzqT6A/wd3uog9GeCmIqIdr2qyQ3Q9VVjmak++2iZ8eo6c5+VYDs9MICXweNJFzIVDOL3WLgdK1SSLrDYQL900VoAh60D7WQaljZWRxicZVIqJXQ/L7m+EeF5ZiK7RZo/0T9NGY/HYHv5L3illB0Yoo6jJofRpH8vkhxW4rLCaOJ6UAfyfAbqY58eMjfKTeaLX9TrGyYFjgE1dDZobK907lngQQPwskxQVmzTSrS9IggB5nvG5492kUFhDPemQPNgOpbc8w3VHYRJyTcps1w6JITxzx2FE9tOiZQ2RFSFIpKx/XcG66rNKhjcFnOdZ5XDXBbNZxUiIzT9K3SwgFQuDLQXnelqQDhyw8XoEsjQUAvJRYmzMdH9Bo3V7uRxAw1ScbOY/+xH/Azs/+PtoPfxI78JjG6bFZI5jt1Yg3ZkSNq02aMo358N/5Z0nubLHzCzN+7R/9i4xffcF8csTOP/ePMf4r3+HJ/+VPo1PDrX/p77v6NfsRDKckrmTui6y40HhDlThPn8b4SGPubhE/PQjas5OMYreLdw797AhRTahNm0TDmMntBK8FKMH0ZoywNxDlwLY6jrk0CqS5zITkB0iPTPgtgBDIBR40UpidztLcYEF/eF2IahxkvJRE1FPqewU2EvhmDQZT7FYzLNprKV5K5t+8SdFUJH2La1UQq6YYhQmWu0oiSYgXcmy5BWGCJm0SBeUBJTHtOro3KqUgLV4LbCTQmae4v4OczoMJSRIFfd4keq3kmV1JiK0M7qRqHhad6ciF90oKnIOiIYlnfi0xs1tNZCxxKwQ+ZUN3ShpoPMsRBsY3ozXYjLAO5jnm9iaGQFaU49AlOlsg+TLDJ8Gd8L1G2RU5ty/ANWunjnXXGT/LkP1xkE1bwGAIyexFOPSL9v/afTofFCh22thOPTgCl11CvTJHuHbtXEd4EWJhWqIVGMuo6JGoKrFMQ+fmRjtwkqTg4PBv8ez5X+S3/9Q/TxS9Pv+5atzY+duot++QtLfZvvvbGZ68pBh/Tmf3m2w5ycn+pxz857/G7td/N5Wvf/297PO68ZUnyMKXOMvyv2VmQEtsJUFkBtuuEB2MkFl4iYvNemj9Ok9+sx0qvkdjis068x+7hZjlSOOo9h1uT4GGvAM2hflmRLRfIX4RIBPFVh0f6SBjNi+WD+6SfLDAbO60yNMI1Z8u5ViEEOjPD/CRemOCDGAbCfpohO5PqIkKalKgRkF9Q1BixmKNnOWYjRoohU9DlSX97h75w61AmOK0++lW9usqMdHhiALQvdCKVOM5xf1Se7OaEH26XzrL1ckebBE/7xG/HGCmYZ/xiwEiM5gbLeyZVebStUmIZXtf5h79coCtp9jNOnqahwljJYrdNuZWBzGan8JEpjnZB5vBrnI4C/evkZ6K8s9zaOqgcNFTmE4VV4mxjVBpF5QVHkKiajs1sm/dxBNws/rwJBBW0uCgdbbS+9YxL5bWzvqKVQth/dK2G4IyCJwqa5y9zheGL+2yywEvOXHEU4fVAlPKDkLA5qvcU39WoKeliUpuiL84DIQ8HRaDtlPDX8EGdbn7zCDnxVJOafl3KaFwMM2gni4dH6OZJ5pdXMnPm5LKwOEiGN6NqBxakhNPbuf8pV/511m8hH/H7/s/IYRE5p5ZVzKOxzz5M3+U5r1vsPHjAfIg/TLdPz0mL8iOqrhiTtK92mRdeRgWvFuVoAyw/+lfpP7bPsKNeti9Qya/+RKAm3/494cW7//AQxqLerSPKCdKCGOTKhn+ajiFEj/rY4334dkiN+gnBxQPdym+diskggKiGURTj60IkEHrFCWQx6ESJWZZMNgxDp/G2Bud0KJ/XsLStIIVTLPd7S5Jbr6EPAnvsc6vTf4L98PVcAj8xzcxkUC5AHXSWYBNFLc38WlMsWJDnXU0LhGkx6FoIwRL/Kl6cYzICszd7bV9+Lh01NQyFD5GsyDusNkk32wGjd1KBASMsJAi8FQqCex2UIcnRC+OsWkM90+3XVQEpipJRg6Ze1TmKEph3OjEkvYM0+0IW5WYuicaGmabmmjsKJqKovDU9k7JukR6LTmGMKZXXxTkjdPOk1eExfjiHixIc4vzrST4aY6vJLidDuJ4hDoaruChSy7Be4QUQCkx9zbypq8JOQgQOHsBRhe7cAsSF5qkXLi9eQ7zPHR4zybzWgX5wjckyF6Aubv9ese9OELMyvEuiS6H0J1JjsU0C5V4QA0n+ErM/uATfuPV/weAm+nH/ET770L1R9jqBlZJjo6/x/c/+2V+8sf/KdK0/dpjv04oFdGq3mQeC3y9wvjlHtPZMR/+7D/HF3/m/4mWCePJK6qPNmjtfEje+eoRwT8QDPKFigM+kBTOEtMQwWBDFgazkIKKFG67ESoIkcZXgoOQqQqSoSBvO/K2Z76tEa5LMmgiMxsULwgMbH+jhU8jZG8MahUbaxFSYlvVUJ6QAu8AFRKwc3JUlOSo/RNcI8VVY6T36JdBsSLaP0HOCtRgnRUbBt4SMzbNsb5cYTZS3DDGu0C881JArMnvdNeqC76R4k7KFzDWYIKSgy0rweJkhjCO7OOdpZal14GMogenmDA1zZGPjph/6ybKe9y8CIO990GP0zhESYoT8wI1zSkWEI5IoVYSR5kZ1GCK3ajjm5UAa3Ge6NUQNZwFct1wFmARzodJ0wRNXuE80pbQjxWb4QUu1scKXW4HKcL9Icj82a1GOZHPcM2UdKWqCyXTW6t14fc3hG2maxVkK+U5zfbLwm2fus0F1nEnaHgrEcxmpjmkOiScF4SvxucXLAZQMLqrSXohEcnakubjYrmwk9aRPD46VaswQbsWgPjqUAQBRKWhCoCY5uGYYo1aJM+vMxfRgnk9yLsJD9468qbGa4E0nrwhefT8r7Ka7H57778gqXSIKk2ypyMOf+1X6X7rd3Dj5//e5Xfy+uUTbjFMERLi9tUrWs9nG9z8n//dnPzmE3RDcvif/mkAZKWKm02ZfPcZ9R+7d+Xt/bCGe1OZVcogY7Wwc33NV0VuSmxuGYkOcBvrkN4HPXgBKnPYimTJA3SEMahRwd5oo58elQo5YUzLNivo2i3k2ihZ/rRVxfVGuBV8pXd+nfwHSCfAGtT+AH+zi0MEFYtYQ2k/vbp1X4kD7r1MggVQHYSxSRUG9cUrfDVdWiK7m12w/pycnevUQoIiRNhmEarhC2ksn8YUFUneUlT3Crz0SHzg2LRq2GYVGwtcLPAqEJOE9cE6WkDehvSgID5xIAzCepKJwztP7aBgcifBJAKzHSGNR+Uea8FEgklXUds3IGDWUVQGZ3gXWjDfDGPDIkGu7ZslRyRIy6lQhV+91qMZnEww9RuBiFmS+OyNDl6JUtbv/SbI7zM59lLiKxGqP8Z21w2cZKnf7KsJfpoFycDV47iYZxqwxs9eDwu4yhVx7XogNV4AeVx+pxIjB2OCqN/VYBsLTfKF3rNt1/BK8L1P/ywAtajLy/kn1MYd9CQiKbZ4Mv0t5vM+P/atf5xm8/brNv/WkZw4Oh/+FOMXn3Fj60OO/uqvMp+d8sBevPzLfHz8DxBPHfOuxlS+uqLFD1zFAggM3zJplsX6qivqh8TSW4/uT7H1BHOrHYTVJzlRf4K52camMmjo5o7kWGCqHh8LZjsaIQTJMAwCHpjdawbdXRGqsmpmwHuiF4PTdnhp+JHd2wjVklm+TBiglE8pWbnR4yPUOMMfjYIlc7IouUp8HCAJejxfl9lyLG2xxaxA9oOsnCgMxd2NYLoBFPc2giKGZM21yEeK4u4GstSEdmkUZOx8qDl7GSTHhJY4KYme9daNVlwpNRerJbzFLhYECoRWeCmR0ymiEgTDfSUm++hUsuciHcqoP8HXk9BqLCveujdZkthcJaK4u7GEIKiD4Eqnpjk+0ecSr2KnSbFZh1gjx3NkVhA97+OaFZSSmJJEIFyAzGS1bYqNOtECquLBa4Vrpujjq8vsFLe7ayYVZrN+5XaiP6OZ6YHso51APPGe5MkxZrO+pgpylVBZ0JvONgJ2v/rKEplTpSzZn6yx8W0jXZLtxKjECSoR1DsyE1zHLgkxK8B79KsT9PGY/Bu7mM16qPSNM9i8/NgnG5K8XcJffKjKZR1JemxRmWd0R9P7lb/G7X/2X0B/4w6D//rPMD88IveG7Nt/DRkn3P9n/tfE7S5GgtMem3jceXW5tcj7CVHVXEPdQtD9/T9F++/6cb79B/9NonaXYtDDFwUoxeEf/w3iux+R1ENS86MatrAXOoUtwkmBubeNODpBlYYOIjeX64gv3vtEY24F7LgcTNBHJxS3uvhGdSlJmLc1XgRpstifvtuuW8c3q8sukk1lkABchRAZu5T/sne31hPzlX8vzAvwHowL7f8v9hHVZCmtpQGZRJhSX1fO83PmJXazudTyxThQ693Cc1Jgi9+WNsNiNMPHGpdEqKxYdv7Ui2PcTgOVVkCHxa78Yj9Ysj+8gUtC8lzUwphtErBJIFh7DTaCrKFpPjakJzZwGZ4e4hON3elQfZGT1yV65tClyVQydZzcjJbtUZuIUIETJtwbKTAxzG/EWFWaDZW69nKlep9XwxyqppLa3im523XrqL0+cjgJRD4lsTttXKsaKr2XaBQvOqJvE1E1Jp/l14bViUhhqkmo4Jbzo2tWQpI/ydZIpHgfoCpCrJEUl9uaZcHVtbOeVIvRbGkhLlZgZGth7BLqeVn4NMaVqiwXnosQeO9xtcAVECezNTe914aUuHolyML1x9Cs0hs/RkUpv+un/gVcovhbv/VHmdVgPtnn5OA32N39GX7yx/+p96JYcek5GU8609z9/f8zjr7zlzn64s8BEEd18mKMEJL+4Au64iG1lwVFLcwvX0X8cCTIF4SMFM7aUAUr/+bT6BRCAFBPyKsxQoDVgsaTAmnARRHxMAygehY0Nb0WCOOZtyTTXUk88DS+mJb6yQKzUUMPpthGiq0nxHvDpeOTnGREL/rkD7cDNifRUFYf5NH41CZXQPxiEFzBWhXE8Rg5L5ZY57OxOljow/FSsULOikCeMy4Q3MZZUL6pxjghiJ8e4yoxZqtBsjcE55DzMCCpV0PcrQ6iEiGMI/7sgGKniT6b3GmF3Wkunaq8gPH9GFeROBmgMNWXBTFV5MksYGdX1CwEZWUxUlDYU3bz3ATJuvubuCiQAxdVYNOtUdxsl5JBY+xGHVPKhKlygXA2wsQpkTawwfMPEqKnx6HC/fkhqlXBbNSWhBUxzbE3muhBwC3bWhKw3tckdojxHL/aLUijknB3hd/m5pzmsRegsgL94vLJ401hE0EycMw3ghZ3epgRf35I9o3dsIBZIXVkDzbXKhC6dEYsNuqY7WaoNn9/79LzEQT4SlRqUjPOcK0KtlkNyjAXQDAW4eLTZwoB012NmjkqRxaTCKaPP8GbguSDe4i6484f/gUQHmElxVySH1dwVjK/dgVKkA1i0u3r4RQFMdWvf4Nucp9j89e5/fHvJfrt3+KT/+T/ysG/9ys0/8DvoXJHENXfHznoqwxpHdHjA3wa4WoprlE5lyy7SMNuFzXPITPlYu712y12u6eKEZtNikjhG+Vk7T3pUUHe1ggPeUsRjx1FCRFyrVpQE2op0hdj0iI672a3osW9SI7FNLSuXbMSChBJtIQPqf1+SQRsog+GqPy0cCAAnxWoM+3txRgcnvcTrPfYrRauEuM+3H3jtRVZETDEo1kgY3HqDuwPBtjtNsIGHXt90sJ16sjDITI3oZDiPWpiqE4NPtWMbkdkXUG+bXG1AO0jA3eoySYRpuKovsqD21kzxZfW1JUvhqjeCPfgRlB7KDy1V8USQ63KvMxLgRcwvR1hIrG8xzYRTG7oAMlY5U2OHVCACMm6KNfUrlkN9stlNVodDrH31qEnQGjxrxDc3jY5Bph2GoiWWzOdeVOIWJPf31l3ZHQeOZygSvtpURjU4RB5MsXc317qbF8UPo7Wro8nSLjpF8eB/1MPcrDqAqy0gHXpwbOhJPbOeXvy5e9HM+Q8x2618PU0QDQHk3XN7teEUxJ2O+hH+wEHfXzCi/lfZuvGj6PqDRTwU3/b/+JK23rfkZ5Y8qbilnrISxVx/97v4ej4u/z0b//DDIdP+N73/wQPP/h97Gz/BNHEnVPy+rLihzZBnu80gxrEikyXaVXWHp48BQgubLUjS1mwwlQ8KgflBDYVDD6MiCae2svQahJeYqoe20jIP9oJkmyzoKUrckO0F/YpgPR5n/mNFsI40u/vg3MUm/Xg4CYEUfmS+UgFzd3jCWqSoyavXynaasCsLc5v1cZXjeaosuKnFzJqABLym51ATitx2d6sC+rr/hRXjbH1tDwfS/zsggHFWPTeYAlnED5IcE1qApeAygR5UxGPDa5ZwTVS9NEY26ws8cC2WcHc7iD7E+K9YbgOBNJj+sUB84c7+EiFKjwBRyxzQ/S0hyhsgGIAxc32mp6uOhqHxLuRQFFWmnsT9HHY/8KIxac6SKh5kHMbFi7WBzWERhrw4qVihzQWSvy0a1bQhydEJ/NLJeICXOM0QY6fHr82OS62G0RHY7zzp7AGSuLeZ/vIwq0J3l8n9zNJwC5ObmpqL01oo2Y+mDlEapkYry4CVH9CUUJjpHXIWY7QElvCP5ySuCR+bUVDlxhyCM+ng+BmOZgGEuFHO+dOY94OTPuz4WLBy+Q5j3/1j2KzKTu/55dwNUmU5stMzCuHrjmi6phsEGPHCdZcb0q10wj87Fozce/PPSF79ITdn/n7ePgLvwucZ5JpfuIX/zBPPv8VXv2f/z1u/G//pcAV+BEMXXY0xLxAzQvU8Qhzs3vhxGrTGJIYu9kIhNPCYLZb6P74nOGHOjwJ8ANjIVJBfzUrnel8qIAuzCxk4RnfirCpDEoJ0zBuxUOLr1+sDS6tQz49xHXqUInxWqKOR8jJHNUfh/G+nmJ22qiXvSAn5oP1NYTx6KKFsasm2I0GIgqScKI0Nole9VDHI8QkC7ji7VawWPc+nJeSQYaw3I4A9LMjzK2NC/GiqjfGpTGuUQnnMpwGU5EySRJZgIMs7ISF8dRfFpg0omgKRF3gpIcK+MiBk9iqZHw3IRpr0p5F5h4xz4n2B+E6PzvElYnqGqHX+aUs3ORGhI0EMnPUXxZMd6KgjlGRTG5E6IldGrUICK56F4RTarka8HF0Kl9pT+XtXDVBHwxBCoqbGxAp4uellvzC9e0K0DcRa3w9Rb66enIcjlEuk2NZGOSrfngmVsZiOZpBYbB3tpbJcfTFK2y7huue6ZQt3HoXx1Ue23J/F7glXhY+1hS3N0meH+Jzi+nULzUkEUB0OMQJoHTQsze7gRxbLtKuGj6J+H7/L/Ji/G2sN3z941+68m+/tHAQTSyff/Jf0Wzc4f6938X93Z9HFJZ05ydROuWzz/8Uk8k+Hzz4n3xlh/UDS5BdooNs1MrfVrUjVW9Cfm+D2BwvoQHR4QmuGi+NIpJxMPzwQuAIZhPR1NP5nuHkgUZOPPHIMd3VFA3ByQcRLhKomaPx3AR1hliTfe0GEKTTVh35AHxhSZ4tvM09thIHCS7nkYMpFA7bTIOma6nxeZVYgzu8JlaPxSlF9Ly/4prkyO9tkH5xsJa8qdEc26lhthtEK7JiLo3C6rZMivQ0XyoqqP6EyolEzyrMNkJ7vHZoobDELwehbSkltp4g5zku0hR3OiDEMrm3jRRbS4hfDfGFQw4mQc+4rDRGT3tBVSQzFDunON2zIbMC/SpU8G2rQnGni+3W8M0K3tqAdRSCYre9/I1PozX4RxicV3zqtSIv9ZMhkAmL3aD5G78annOZ0+MMQ1khcH5pYw2nVSfbTEv2pAs24xt1fHYGowlL0qDQElOJw+LnGgnc+LZGSIGTAXdc2y+JVEIgcru0+V21b1azgoIA6RClnbBJo3Unqjdk6WuYw8EUUUtw1ZhitxXcI4/G2M31VqNNQ6cm7Vlm2yuYeSVQ3TbFeMDu/+qfJ/nahwDEtfOJhReeuJNBO6cYa4pBBXfFRNl7ESzB9NXx5uO//l1u3/1FqrWw8BKFobovELT4sYd/iG8nf5LBf/SfsfnP/hNX3uYPe1x251dNOFAiFA/nOfmdTeLH++vjTFFgfVgQisEkJMiLiVoKJAI1scw3FemBW2Lus+0IMzIgBOnRa4hIo2kYLy5JigSh0xNPXy2PS53MTslLF523AHdrA69On34vBNRTfKQRJl8Srfxoiqym4b/LhMoLoJJg6+lSZlI/OQhFAyXXukMujVEnU8ytjZDkE6qA0V5viQNWg0mAZZSJmMw9tecGLyJmU4Gte4QVxD2BdJDuF+ipQ1q7TOa8UrhGBdupo58fhWPV8txNFtZTNE8X7y6RjB8ka98xpZSc04ZK7+rvkLl5yhtxrRq+muJaVYT3uOEUu9FYEiyLu1swy6FRCZ3AvT5Yd948ZXX7JcZbvgYWdmGsXoPhxeYirppgt7fXCW25QQ2n5xPkC8IJgUoiyArU/gChrjZWCeuCMks1RUQWv7kyJ5amMYu5Th4OwzHFisUV8GmMfbgbsPWX4KLPhpQCe7MLeRs7tvz8z//v0O9JleJdQ+SW/YO/ye/8+X8t/HdhkYMJtpayufE1mo3b/NW//n+n1bzLxsbXvpJj+lLRzv41D4pcJMcrD+UqCF9NcyqfHSCnebAu7tawaUz89BjVC7gy0ZsgS0MMAehpeEKEh2jkqb3IiQeGBXxw0Ya3FclsUwWGdQQmFYhZvpZ8Xnrcs4CXVf1JULfwPiT6PiRb1wopKDbqS93iN359BcoAgeyYPD5ar2xGivxWJ7Tu+lPyW21mX99l/tE2XgqKO51g3AFBG3SSET3rEb8YkDztUfv2Pq3fGtD4ZBwwz5HCxTpg8bwn/fyQ5LNDkifHof0HoUpb3ka7UcPWw6AbHY6JVuAlxd0u2Uc75Dda6IMTor0B+tUQOTrtEqjBNKiHAEJJVElE9ICLVfCiX8g8DaYBD7u4Pt4z21TYiHC8pdb16+6pbVfJd1oXfBAc9oDg5FWGaVeZf/0G2YNN5ChDjOdLmAhwrrW2Kl84v79Bfm8DV3m9nNNqeMDrkByrwlPrrSfyxc02chK0YcWKUH+xGYgz0aMjorKDIHOzNoHba2hy2k6N9ItDVD+QMG0tXsNkF5XQutUzjyw80eT0vBWCLMl59Z3/FqE18cP7CCFQ1eL1eGHhiRoFtVtjotYccUXwobgmXrjxcz/Bi5d/iYkqlRpK7VMAnGf3F/4B8tGA+X/5V6613R+WsGfaul4rfBqj9vvox/vL51x6jxxOkccjKAy+bI2rkxnRk8NzHRSfW2SJu1QnM9Reb/0LMuhjq7knOilIBmE/VgVsct5SZJ3z7WxpLeplj+g1znTBCbUeqparx+Xca5MoX00vrdKZe9uYrdaSeyB80HderTYKH2Ae+mCIKnWV7Y025v4OdrOJVwIX6wCBu7eFuVu6xHqPEuAbFcwHN9YW7+JMFycZO1qPchpPBfVHkupTQeUYai9zoqkjenpI9Oke8aNX6GdHyNEUs9vBVxPMbofo0T5ieH4uqh6cvy6OsgjgQY9X+Au162E89cFw+W85ni019b0QmAc7+GYVUWarLtLBxEUInJSYWxtBY/ru1hLCdy4WXbJL3B+Xx92uLQtoEJLf5SbOqkesGGwhJVqcFgXsdmutEygXRidSIM8QnsVotty2gDdrDy4P1iGdw97oYM5AK9RhuJ6LuU4uVGRyi9rroUqDHydFqIBfpTCXG/zRCaPpC44Ov8PO1k+8N8m29xHJCHY2fpwn3/0vA/mwluJunRa14rjGt37yH+M73/vPyPOLXWHfd3ypFWQf69B6fp05wusEuBcJoXWB5FWGPh5juzXcZj3Y6NYFRU3itSAaO+KRIx57hAtEt+TQhGpWLJbyQVlHYdoKB4jc0ZpFmI06cW+8RnQ6G4sXKD4YlUoULAfka8vaOB8k595BDmd1UWG6NeRoTvRqSLHbxjVS4r0hrh+w1fn9zTBpbdSDjvFgeq5iLnJDXLbW7XGM2WlidoL1tKvExPtDVCWGcUb0xWHQJi5Z4mo0Jykxz2ajTrI3WEt+F2E36+jjMWoYlDYKJXGNFOkc0ctB0FlONMVOk/hJLyhjlAkvQAzkgD4chY5CPUHMcvSzHhV2lgOfGs+JXp3gj8bku621gXMtGpf83frwhqy0AGVWgFY4rbCNNEAPFvqTj4+Rsxy33VwObE6IQNrzoaIEUNzbxC3a3t4jRvM1OMfp/l15f8PvooE9lWxafKVTPa1u+ZKXmEa4akz6xeGy8r3o2CTf3aP42g1spILG9AWdDC8FZrO+9t7GR+HfcjLHdqoUu22ig1MIRt6UCO9Qc4/bXC9nGOV4+ef/c8yoz+6//IeRUYTAU7miLJuXjqSbkbYL5sMIN4mxxcVJjqoWePn6SfRsdH7xJr3/YodB/py6/vhcJUZbze7P//28+G//+LW2+8MSVkl8GiONCZAsY4m/CEovthu0rMU8D2Yc5bNuC7M20V+Gm5eTDLtFwE56H2BgVYm0HjULbX3tgnqJnHv01GFqKvDBnA/ubZk/tZUGnJTo3FwKf4IyeR1Or4fnjzXu1uWGHB6CIkOnBqMZAY9HUKQQIkAiRtM1FQhRXgPfrgcjkAV5a2Eb7U7/0xoHSgbTlLvbodjy9GANa70IPfc0nmZkTRWk3oZBZ1xae1ppzQwyM8jJHKMk1FJcowp7/Ys7VBf8LT0yyMIH3eSBxZTWvqvJ8ptC4kOHcRGFRR8MgqnKSqinR2FsqofOm7AOklNohqsm+ES/tgvrz0BZ7EZjzcHQNatlMls6NRan39fzPNxjERZDQivE4Ql+USV+cgjtGjSruG5jrXosj0dhcaMERrq1xEm+pmPxpvCZgeoFi8ThNHAFSgKofbADo2nocsxy9NEJ7mY3nE+Jx19eEw3qojVirMnrgr/x1/4j7t3429m9/XNvfdxfRgjg3jf+bv7mX/sP+Kh8WD1l7a0/xrdrtGv36HYecnD0W1/JMX3pEAuz3cQrGZKuM4mgiNS5QdCXtryvC5EbpLE4rUKrOxFLVmNRl8y3wcnQ7o1HLrCDAWfdmoyQFVA0PE4J0iNJIupkiSZ5cvzmE7MX2zdeN96nVqTIArZK9cMg7qpxgIJMc+Q0R8wKzN0wQSxaTZfVEBcPZvLoCKQk3w7mIqaRknVqJOMMUVjiT/ZDQpsZXCNF5AY1mFHc7WL7EfJkjj44CYoNK9UBn0ZktzvBoKR0u1J7QRpP5gZvw2RiPtjEOX864QB5+W+vVYC7QJBySqLA8i334UryjpgXJI+OyO90guX1mSqFv6zitCAGreLVVqAWZrux5uRnO1VUmagXdzeWx5F/tAMrv3MrKhfeB6zlZftfEN6ANdOCi45TltqyLlLog5NTWIiW5Pc3iF4MglrMaA7dWmihXxAiUpjNBvpohJQCb/wSghKNM6x1uDQi320HtYM4SLiZqiDpOeovDKqA6p5hfkMzOHrM9G99m7v/yv8GtstJRwDR9VwQvXQknQw6GS5TFBONm2uEVUE6q5KTdvJrv5feZsw+/ZRJ52N84+G5hCUZOmq3HmLnV1dB+WEKHUcU97dPcborIQeT00S4sKhqgpmVGq5XSD7FPA+TeasazGnSmKIW3N5kHkhmXgmINEVF4CoSvMf607Fnthvh9wMRLJ6EpPpNlUK4PGm/MKQIznVvsHOGkKDTuhhv7rebiMMT1Hi2fCfkaAbPjrB3NpZycerJIdI6zIPtsD1ZInpzE8YqglSXv7mxJicJpaJGGiQ+pQlSibpcQPhLOAN6rx8IeDe7mIc3LiSaqcyTHhmcDt1dPXVLfLGeBulIUXik9aTDlar5m9r35Wei/KdbQD2KgE1fbmeaIafZkhwXLp7EfHhjeV8uaxIJY8PzeOZz2woSe/plL8At02BiJSaBXyJGs6XShpUSicVut8P3AEqTEDkLx+YalQvHD7vTDvDEwsOZ6cJVU2Rv/HYExAueYTGeI6xDv+xhHu6Ga6oktEuYTrlIEMMAazorF5p1NNXDi+e0z77402xtfou7D373OY3kH4YYzfeDBfXxIWmm8LudACVMYxjPEY0K29s/wbNXX0037yu5QnajTvb1XYrNetDbXPxdLwaOlbgsYVz5mvAgVsh7lSOLLlmNQobkGIJA/bi03wWQ9vwjbGOPiz1ZWyH7E6JXw3Pf+VEJNckxN1rkDzaDh/yZSrg+maFfDsJ5Ho2DnfIl4VNNvkhmnCN+NUSN5gjrcWmEixXZxztkDzbJb7XJvrFLfm8jqCdEKlTHp/mywnJWg7jYaQbWbjmAyhISAqEqLjODmBUBfzXOiJ73g43zaL5MlG0zRWaG5PMDhID83sba4OZblWBNXd52fTgm/vZLoufrmEZ5GR58ofCxKtu20l5bVkPKsN0atpGiTuZL3C+UhLj6JVVqKbCdy8lfeWMFM3iJtSyAOh7jygnbJfo0cY8U2d2N4IJ2qxOIrmV7d5UAuxo+M0R7A3BB/3ntM+NQewMgJNJRSc5UmWfeUYhILO2xkxNH/WnB6Nf/Ols/8XdQHzWXcCfvBcX46sSSsyETS9LNqNyckN45oXJrRNLN8G9hrejmOdH2Jge/+WcZTl6cnislSWnoiDJIOhew9H8EYuEK67p1bGO9UyGcRw0nyGFI/rNacOQS1l044cvogmrncTlmuqDIuoBSuFgGMlgeignznTiQjDgdztXMBXvnhmK2HVEkwGQO18WaroRPI4o7mxQf3MBXY3wSYe5sXhnW9LpwSmFvdMg/vIm5t71UipGTOfqL/SWu36dRqDz31lvBwljwfomT9o3KWisfYLalmdyKme5GzLcCsVGWY5GoxOf0nyG8i7aUW7tUhcFDMrQkfUvlwKyR7wRhXq29zKm/CARw4TzkRShWvO6aCIHt1k8x3dUkWG2vztlcUoxxDkqXUmks/hITjSDLd36cls4Fy3QI1VYpcFpRLDDfgCxheu7+NuZrt06r/KsxzUIlu33JwmgFqnE2g/b1dEm0vG6IfP18pXVEr3qB7FdYxPCCRXmjApEOHg4XhNOCWff8M2BtwcHhb/Hwg9+H7I+vBlr+ikNM5kRRjb/x2X8SlEYeBQiYr8So3gjZH1OrbjObXaGI+R7iy8UgC5ZVKicF5kaL+Uc7S9KWnBVrFT+4+CXyMmA/l6Elvp7ggZN7mvmGovHSBEb0Kokk8+BhtuPIWwv80+neZOZJjkBPBMoEY4xrkwB+iMLHGtdIgrxZq4KPVVDc6J6+9Ko3IX4d5KUMmdlgFX1m8FalNqmtp6GKW0uY7SZL/3e0wuw0Ub1JqGZWY/IHW8Fis7BEz/vEj49QZQK5GLzk0fkVuDqZBeLk4Ql6OCN5fET8anB6vguVhlmB6J9WxqTzS2iH2Wli29WgbtKuhgHzzCCsF4YrZ/FvC+hCPV1OSsVrklkAc6cbrvdVHPOuEKoINs4ApqnOLSjlNCf+4pB4b4htVcgebOIW7nlSkO22lnhHFymKO11cGiGMvRD+AiWev9QfX2h1r8okRSXmeTHAinlBcmJDgSxdOb5IwCRj8P1fZ7f1EwgEcmVMz48qmMkPXkhHt2o8/Lf/FVp/5+/mxfFvLP++qIgJID4qcOZqxNof1vBpjL21EQwCEn3huCtPpq8dA00cnauw+cIiC3Pq0OWD09tiMZQcG1wU9NUX+1LlZ3rqSI8N1b0CYTzKvmbB+rpzSyLMBzcwX7tFcX8HX0sDxO/uNsWDneBa954j4Iy3MdutYOygJL6wJS65Q3FnE99ax3j6ahK6ZGcxwlKQtRST3WiNSAcLXGu5T6UwFyR42d0t3E572ZK/9JhV6LKudqZWY7U1L8Zh/F2rNC6S5dysJVh2ux1+s+BgxBq0QhYmaHBDMJW5IBYwCHF0cnkV1nn0/uDcn711OCUxN7vYu1unH3TqoRMkJW47cEycEOe6h8vt1FLs/e1LP39TuEq8lry4enp5e3Z1iOxPgsRcbxT+9/xoafDkzn55sW2tKO5tIbIidIDKby0WEMmJJe9ofLL+28Nv/zm6epfYRaFo9VZn+uXGjZ3fxu/8+X8N43MGJedePT0Ewruj9wcIZ8mmg6/keL7U2cklgv7DCGE99ecGnXmckmT3uqSfBka0SaNAdltVCVhhA7tqjIsVelTifLQku7uBi3VZrXA4HbBetVcWF4mlzFRWrqJkDtmGxytP3oF0D6JpYFAXDYmLoHJog8PZj3CI3JB+fohNNHJu8FqSfbBF/OQYW41RuQmY1pVqrohUUDdQC0C1CDCJSXDmmz/YRO+fnGJVM4M+GOFqpwlg5QxxTI7mCGMpNuu4MjkXQPz5walKyTgLutO1BH00XsPCLmA20dEYjtZXyQtIgitZ6IsEJhpMA8RjVhDvDxG5pdhqYDvVZaU8ejXE1mLMRh0xyxFJhDwahUq1ErhaeposA0wyKJPr7MEmFHZpvHFZOClwK6S9d41o4tHGkXUUVsN4R1Lbs8tEP/7i8JRY0q6e4qzb1aUhyCJkYUPFxgcpvesMkML5ZWJNYal8so/VMkBOvMenmzSfGkT5aJlUMNtSVH59iLCedCaZe6j0JeN6qT7jBfODKlErC9CIt6j+vq+Imjnph1uMf+2TCz8fn7wkH/Yu/OxHLeyNDpYAr4iOT9ZgbmcXj2djgfU0m02Ec6G17EH0Rtg7XVTuEbmnemiwJ5bxzYjZpoZkRTWCAG8DyDY00ciWJOtgbS17b17Anw2z2VxqMn+V4QF/Bq8KIRGdfVAnHjl0KWlXVIPjazpkveKoBZPdCHNJ0io9OPxpNatdw59JJuV4fq4yKkazUKHsNII2MiBt6OxcJcS8OE+0X1SvL7jW0jrUi2PsvS1cJUGeTJdksuKDG7g4unDxJbIiJIcXVUtXv3eR9fKCaHdGstAJQfHhjaDpfQUJtMtMdK4aqj8+hZpECnNrI0j77fUD0XN1XyVpE8Abi7qAjKqGE1yiz5lOLbehFa5VRb/qYyfzkCCPZph7W0QkpMfBDdFDiaO3FPmESCSQFQEW95aLgS87hBDUajuMTZ/G7W+inoUE2XbqyOGEJy/+e27f/Dmev/hLX/qxfOkjSjyyVHoOmXmEFjjv0fvTU1meeU7RDUoO+mAUbrR1+EpEvtVYkpesdciDEXargalI9CALK/FqBbtYKUlwSgSb02QF5ykD4M0mIOegp1BUHVknGIbEkUBasPc2iD55hb/E0tJVojUM6g9lGIcyOfmtDtHBCeknr8B6so93MIVFH5ygZqGF5tKI4uHW2qo5r4vAOh9mJE9750h8OI8+OCH71s1LD8G1KrjWeivXE2Tg9EqlV8wN+qy1OKe4bE+oAOMc+nC8rGjG391DxGqNTClmBekn++v7a1UCBKDcnoBSo7q39r38TgcxN0SH6wNVdDTGtwJm2aVRMAv5AYTIfZgkBRQNie05dOZhfiqT6OH88UUKt9ISl/1JIEYW9q2w72J+6qQVqoahm2B2Q4VG5oGkNbqt8TJAQuKoxnbtQ/b3foPGj/1eKARRT5BvLPYvKIYpbhqj2/MfqBmH6Q9I09Y510oAOx5R2brJ+NmnP5iD+xLCtWsUlZhooUsrRIAQXVZBFmDubCIm86V7mo90qOwZu+Y8CaALaLwomOxGl0qIi8KRNxVpz5D2DHJm3rxwUwLTaaB7Y3AOrwSiEr9nY+N3C2GDjNq0rlAO5NjiqhKrBaaliY4K4rHDx5LZDY2JQid1vkkgkyuBxVM58JDB7HZM7Xk5bmsFW010fxK08J1DjmahDX04xNzZCs5upXmF709w97cvJAO+9hymc6itFwTk0Qlynp8j4AH4Itw79ewIpeQprK7c72UybnKWL/XYXT1dJ/y9JnwarUMfzn6OOGfY9N5i0V5a7uz0P5xSIALUw9/ZDJblK8n9Yux19aCZfNlYLDODfHaEiBT5TvucXrjd7eLaNfSTw1J2tLrsICQDuzy8eUcRjeBu9cf4bw//X3xUlajm20FCvqrI5gMq1MICwQQnUP3sCJwnz8fc2PltP/oJspp7anvlS6IEvQeaeOSojxL00Qgfa2YfbTHbiUiGjmgrVCW8sdhKhKlJotIf3iYSV07EUX9O9PgYoYIu71IQ3UHrcYFXMPzwdMBUmaDwHluFaAAeT3bbUjQl9b2SMKFC5bJII/T4shf5hzw5XglXjclvd0ieBG/46Hmf7MEW9oMtpAcxmCKMXSbHHphtK0wiaL4wuFpCdqdD9LyP6dTQh6PTNmys1lafen8YKtaLQVEGow67Au2QzhONs2tNYoKgVAEeoSSmlqBPZsGQRQh0fjmGWgDpkyMoXl8tMTtNvFJEh/2QYK4wqMW8QD89Jr93fjL4SsNDfGSYb2niYZkcExYitp+gxv8/8v47SLItv+/EPsdckz7LV7Xvfv6NeW8cxgAYGA4wMAuCFiSWoFmCZkVqyaCWISkk/bURXO2GVhEKLikupZUEEBDBJUEQhAcH4GAMzAzGm2e7X/vyJiv9Necc/XFuZmWW6+qefm9mqF/ExJvOysx77s17z/md3+9rEvKl+lQyfFzkCzXyxTpimBHf3Dq7HFERxyUuybnmgUEAgPXQpmRWoQfeoCSqzpGlPeq3crqXA8IWmKrARBMt2kxitsrk+znhbIIqvXVQJ5NIhHLIckziknFynFW8rjoOSqUZkv23Bvf2VoaNArKrS54lPlvzttE7bQ9zKMiwtl4au9W5KJiuyI0IZpbxfTkKZx0yhXgrp79yguuiFvSXIWgrylsGEQakTyx7w6b7O8dyUmwYeOe++RqiqBC+5clxIOg3Fa6i/NqBr/SK3Lf9CSS2eByNBDMBmzAKkksBesj0Q6Uhr0xteRkuCCr3HXkg6M+pMfnKztVJ57xurjTF5kaIA/yxkphzs35OLoWPSMgSU85vYzvlEwoFLvZmLk4V5ipS+PPLDcHN9SPY3cNh4+DMybEI1LFJ+lsRDu8AOcLaA956uoDNTDqMOiCvllDHdEXOvBHIDMH6HvmV0G+OJv9WikAJbBx6feNDkZUlWUUSbWcEMkLpmDTrU9Jnk5b9ZoVWMW5jC5nPYGdr3oK7XkbtdSkFdfqDt6ab95b1pJxxSOfd2bLZGErnkO0B4XoHIeo4J+jPSWygUIkmbnm5NoCkLhHWEXaLxKAckV2aI7yzQ+n1DdILsx5nmRnvJBdI4oZksOBPL+gKnIRkwZHXfZKME9hQjEkjvRVN7V5+FIf6bRqq1SNfapA8vYwoEn5R7HodkC+UMZEg6HmWtABKm9M6y3qnh8wtokiOnRReKWHCblqvtdA7vjXmwIukG4dI83GCLK0jeGPrVNmmk2K8u9aQXZjBdHx1WvaSaThEES7UpBeaRPf2TpXrO/hAQQCBY7UkZSc5Uiz4ZkSp4xjOu/GGcRzWefOD+WOIJxMhu0OvCW2c7yA8ZHJ8UrhjknJZSNFFuwaRW8q2zL29V5GZI9zOMUua0gYMLoI5BKswqWawrlFxTjQ7REYPf8881PhTxWC9QtgcgpAY7a9nb1lhYknjZoa1hrWtL33bqlg8KGxmcM0q/aWA8m6Onatj5x78OQCKqqA7BnMv9/s4LQmIKW1kZDWFiyVmMlcTfm7Oq5pOVVO7kyKdQt/cGCfHQTkkm4BgyWHmzYri8KEcxB5XOKA/r8gPyXMZAQRAMJ2M2hAQIIu6iw1gsAQqYWzdjABXkhyeaVzxksodpb3jnwU7QcobJUmuHE1BWs4chROi79Zl4/URQO14WceT5nEBkFvsbI18puq7wa2ex42eYRBnNdkCyBYaR5LFtyoEjEUBRmHj0DtHBhqz3Jz6m1uoY7Pcq508auQWub6HPW5T4DiRmOkkRHvGP4vOUZY12uuvUbn6gdOMYb/5oRT5ShMzf5D0u5kK3a27tHq3mQnfmgT/LUuQBVC9lzFsSnTqvDNYvYRAHLRju16STRVaxbI9gEoEQtJf0gibE/SdN7fYavsdauqJX8nTS949CEd032vpZg1FHgqiPYNqQ14TmBiSJXBWErZ9NXmw7Aj3oXoP8kuzyDe2T7Xg/RmJVQABAABJREFU/XaIYKeHK4XeJrp5VAxcepu1qYnr8NbAVrzr2+h1YR0uNVAq5NOcQ+/0sLG3eDb1Ei7WhPdbY7ycbA8Ii98jLfDAj0LCIbeUXlnHBhK32xu7AR6J1GOkXWoe3KoF9G73iEzOZAg8U/qwmsNbHlmB4+9PrzTZhRlkZ3gquUSv76N3umdapB4mnIBwdY/02gJJTSGN128N+5as67XIN/de4bXtT/DcuR8CIO44hktABuEaDM8dv4Cboaa/WkWVM0pzyUM5451t8JD1ArLdElhBulti8KXbVBav0D3vlW+iHX/MzbUvsr95naf+0j/k1f/PP3q84/gWCBdpryBRU+zNScr3c+Iz4lTdbI2sUL4IrCVP8wM8p7XIdoKpxJSGEHZ9AjSY9yYh4J+vaBe/0VOC7qWQyu3E22MXG9zs8HzhHPrWpicdNivYE5QHHjXSqvTJvASRO4Q9kB+zClxFcYwgkh8akJeLpBj/jOQ1b6Kj0uINoQDhMEfW+MPyCBBtF2th3yLysz3ASgjMCQoFanUHEMdWG4ExJMFR6LZPOgNemMccIucdHr2tl3DNCiNzFNk9e1L4MKWpYGufzDkP9fkmhMqOXoN8Zbao1E+fiRUCe36O6MbaUVnbI+8+OUR3iBhmHhIVKPJLC4jdLgiBHKaYNIMwICvLsa542POcE9Hp86XWb+OsYbE/i9hsQUGs/FYLa3Pa7bvUnrs4/XoY8LX2x1lYeRuXLn8Pd+584k0fy1vKalBDR2W9YMU7hxMCV2BVhXGoDFRqPfnoXgsxSMlmK4RBk7TqCQ5h1xDe2UE4LxWWN8sHbmVCeN3lUCNDTeV+Tve8F6JXxjFsacyywxRQHpkJbAimAomG/SuaeM/CUp341vabcg3ciOh0As75kUPgWcJJgcy3jvDOLqYckl5bOPbt6tC6MyJBjMgu+XwNG2qvEpHmvmLrGFeQrfBanaZZRgwyXKAO2ktJTnB312NegXy+iq1E2JkK8Uurj3aO1iITe6oElMATAM8aIreIk/bSAtLlxjc1ORZS4EaVtP7RSdmF2jsZnvR58Gokb8LYkqsLiCzHSsFgWeMcNG9myNRvho2zvLL5O7x37k/S1OfoAxiHbhuyukIPBNE6JMvuZOvjfkCvr9H1lHg2xZ0oBn16CCcwiSRPFDZR2CSYsq8WQHZvnbmLb/OkX7yjGUC/t0Xj2tuIamctq36Lh/AkSp3g5bykRCUOLTzsbDCvccqcWLGcDK9O4J/5DMbycA6vhysKI6g8P5CNK+3mpA1FHvnE00agtyx5VWGFlzmTZgW10fLkp5NOY5ii1lMv7VU/WgR41DCxHBO9HyZcIBjMgzmB73Xw+umJrgDiTUeQiLEqk6kr+kDQs1OmKsceJ8mOJdEBuHJ8vPYuIAr76tHozEJ9yn3OavVAHLM5VzwjuUHd2zlWmu1xhMsMcrf7TUuQjwtXO+YezM34mplCyQkKjsyFOWwcEtxYO1uFHdD3tz0mNzeI3S4uDrzudbEhcRzIgQY4zFYHN1Nlc3iDoe3xvks/iQgjbyjzLRqDwS5hUCUOyuOVWQDy/g7dfI+3n38/Ur41qes3RWNJ7fcxjbKvEBdSMCOsm3SO8PVNP8kqieoOsXsDarZIpPcHB7v5OU8UQYqplrFplpGA3htSyyI6VwKcFkzdhc6TivKy///hnsCUYRAoShuPISHSkmy26lvbExWQ5Oo8sjskLOx/zxqmFGJmyrjQS9dMalAJ7clYDg9nEO0Baq/nCWn9FDXMMGcgmMleisiNN/UowtZLY6Kk22wTbHYKObwMGwWYWuwxqIH28ntakddLqCSb0lkWaQ6VyMuFSXGy3vU3I6Qgb5an3BoBhJRjScI3IxxgJ1yP1H4fUy+NYT5WeRxs1H7Ia5UbT3QtsJlitHE6y5gCiRICW1TvZBxgj2l9Zst1XDkkiyP6i/7e00OHFRDf2CJtxrxy91doRMs0wiXyietY2rNjKaugJ2AThosnn6NDkLUjTM8T+aJa9kDFC+EE+VCSD7yZiE0V7jCL7PBnlPLXzjmiPe8KCFCpLnPv9ueYf99HTv38t0vksWAwpylv5qgUlBJeH3cf2IDW1ZBkVmPLknDPHEnI8kigEnd00yUl7hCpy0mJwhdERl2OLC5MIawnTmN9UjqqpuUVRR4bxGLj1ASZ4v16vUVWL3HMiE6N7rmA8lY+hgSNorTrtYqzpn5AKusHkEe+amxqPJa2tRw49MB3N0dhAVtXXupxLTs9ST6cHKfebc/OVE+stoudDnpr38O1ZmvYWsmvKw8hjyd6Q0Q58tr17b5PDh/zXG9DjTk/510fT1B4eBwhukMv7/kNhtra94olQng4UG8ESYqwBelOxCGcsWM96ScQ7HXI5upQjnyxsXi+ov1Ch3yrjd7p0Nm9xytbv8ez534Qe3HpGz6nNzuUVLgsRd7fOYCU5AY5SKlUltjdu87y0otvyVje8tKYwGvxgse4hje3iO7uEd1v+dZ7YS4hAuWltRBEd3cJ73jChm4PsbEmPdcEKRHO45sPh9zYJ7q5jd7qUl7NPZbKgW77/5bvCWQmME2BTED1HcNFi5POEw2+gXN0JV+1zRdr3iJ59LooKoIPKSnjgHy57u21q7GXviuF4/+a4GDXb4uKbnp1gXxUnT9B7/bIcerxlHzb5LgBXHEu1lhUIb+WX5jB1mKvXFGJIFBkl2ZJnlryxy+0L0dVaTlIxxXRI8d5Eye848KUvXapiQOy5Qb2kE6nM5bo9g7BagvZffy6kTbwjlngjVKC1da0NrHj0dQmnJsqUp1F29PhVVqSKwtkBSELKeifb06RTgDyeol83m+iBgseq1u/k1NdzdG7PWQ/5frN38Ttt3lX84f8BmSpPv68KiyHRxF0BNH2g8dojSDdKdG/VyNtRZCpg/N0Apcq0v2QwUaZ3p06g/Uq2X6MSfSxyXH3c1+g9+Wvjs/fSYuxOeG+pbx5wAJfXH4nrten9doXHzjGb4fQA0ftXoZKHVlJ0KtOX5vyRo4C8ljSXwnI4+m/52X5wJVDDNKxbJe4u4V6fW28p89LAjW0BCmEXYja4LSYer6SakEEPot0mzrdfTWrSNLqoQFrgYk9dO/w+WEdpR1DZTU99ZnPS9C5AIMlQVY7sQ91ajh5dNzqlAaYA094nDANGm20wc/V/QXNYG6i0hvq480xJsLOVjGFZrPa6RDc2kTd3jrznCdbPYK72+jXV5GtLna2RvbkCtnKw6kluAfcV9Ja5F7XP6/HGKY8jhC9Ifr+9mOZ78UwRRYybnZi8zjF3ZiArDxgDz8VLrfojRb61fueFHkoZD8htymfXftFnjr3/cxffc/02Dr9KRLmNyucs7z8yi+R537ts85hhcWWI69gdWvDF97OzfLU/Ie5fv03MOatgcC+5RVkB6SXfBvGBfKgqGss4RtbPgkMNfmVOc/ATXOc8NVNL1lmSZ9bwUrp9XaNHSduwNj6Nl9qIKxD7/UwcxXqN1Ksgs5lzRBQqSAvOYz2ONx0yVB+psNA1ElWNeKZZc/0PwvRa3RsLUmWG9NJ8UwZsdXBZcYnt0JApEmeP4do9YmKv536vUB0d5fkwuzJjmwTb3YF6W5kKiG7Q2Q5RESBdy88JmSa+1ZlIZsjANH2kmCuFmNDjcsteaNEdmEGMVK/mEi+8sgvVCrzv3N2cZYMkPuDsalHeHvn5IlHSlxJTWliv5lhGiVcP0XtDwjv7HiNzsMVD+vQuz30bg8XaYZPPb4duMo8+QZAbrY9FnpCZ1lan8w8bLhAe7IQkKV9enqXYdTh9uqn+c6lv3gsvCdf9AoXerONGlU58NXgUYhAkc6UsUWHQQwzVKKw2h1U4fpD7vdeYqv9Mh9a/EmkDkiOSbLjXUN3oo0d7gusdmTNB5+fyQVmLybdAyGcd8808DAoRmctWz/3LwG49H/+bxh+9mvk23vMPfkU3aYkbrtx0iWk4upTP8z1z//2mb//2yX0wOH09D0W9i1iPaO/7H8zDznx73EK4uOgFwW5axROioPW8sUF5NoeZDki0JR2DBbDYFFja0db9lkZ0ook7gqyhTp6uzPV6p8MWwrJz42wn8eEFCSLAUaCTjNk6u/9NPYKCyaS9M6HhHs5pZadevbVwKE7BqsFNpZHkheVg0JivoFyiphgwUkg3HIE/aPvUxbEwOBG6hjGjSvuJhJkVUnQtZiosKc+gYQrW90xNEEkGSLLkVmO1d4h0C42Ea0ewlps9Xjb5WOjgNJgHXq9hUlzbCX2RiNn+Xg5QvYTHoigyi2q1fPY7quPvxrqIWn7Pi/pDuEhqsjSOugPx5Vh2Rt6qbakTT5bpUsbaza4Z69T7S9zcfhB70kw6UvwqLeSsQfwl9wglCTptnip/Qnmmk+ydPX9Rz7iamX/TA7SsSPkNyO2t19hbf1zgOPpp/4kr379F7kQPefXMeF9GURnQLC1z3xap6kWWVv/wlsytrc0QZa5Qa3uY5olMM6TABhg6jE20OidLrYUkBVGC7LI9PLZKmamTHRjC9MoY6UkWG1h5qtj+1BpLPr+Hqo9JG+WyS7MkK00x8fun9NkJeg9aSE/aPGB72jLvqK7VkPOGHbeEVC9rbB6kfKrG2fGC6ezlankGHz1Lrkyj1prkV842E1bKWC2wnCmjNjpEe52T0/Gc0t0a9sbXaw0imt3TLiDNCE/P+MhFr0UdbPAVEeavBL5aq1jrFcc3d1FGIsNNNIYX5UfOcmt7WPmKshOgnQOM8xwpRCR5kR3d7FaeXvj2cpBpbg7JFjbJ1uqT2ki580ywV7/CA7OVCLSK3MgBGqnS1hYGL+ZoTpDj69mArd8SiVKJDlymB1J9r6hKA6n2kOyJ446OclH2OCLNIfckofw+T/8xwRhBZMnDIbb/P7eL/LC7A9TzYrfJNSYQCFy4zHjgxRTCXGB9gtWarwmazkkPz+L1RKR5KhegpmtEO9bkoaENCft7vKFV/9nIhfz3rkfJ6o2Sc7PHHu9dM+hHFNkp2hH4AJH/hCwQucE7hGukcsPICfpS6+z+1u/wYvv+dtI5U1z8gj0BL+oOf8E2df+EzAKkYLBjE+mdAGTCA8po9hQeBxw4pWEJi2J06oibJujC/lhNYmRHFya4cIAc6iS2L0UkMx5orTIHU4LrAaZe516JwXtiwH1rIyrlghvrh87P9p6Gdkdere2MMDOTRt2JPUD1Yz++YBgx5tfpLPTS186ozEN0Ps5Qc+hUouTAqsEemDJhYeBTIbIoLzq6M8zJtxJB7JXTMKRINcHrwNTqhCToRKId70sHviEWHWMr7RnQHKA4XYKsAfzvE4cOvFasWGgsYcqq7JwvbRSYptVbNNXk9XanjeGsQ6ZW+SdLbKV2SPX8FFC7XZRJ1ghHxfiBI3kE9/v8Jum+PHqX4vtti/QCAjWd8nOzSFKIVYIDyHJjd9cAHJ1B5FbL0FWDr3ZST/FVmPMfJ1gfW88tvXNr/HK679MWTVopxuw82XS/W2eLR9NXM8SthQeaEfXSlOKHvruNrfVDW7sfIzF2ed56rk/dfL5OtB3Nr2m8mPE8T9MDJMWAK3WTd546VcI84Cnmu/FZPm4ABfcP5DYXK49w+ru8YZOjzve1ARZ5NYz7IuqmHDe1le3B5iaFwS3oSY9N+PxREoQ3m/hdnukxcKaz9e8ZnKsGT6zPMYdyW6C7CWYRsnvKotqcj5bwTRK6I02dqmOpVDQuJ/TOaeJ1iVB31/0keuX1ZDMQ9DXZNpBA/bf5sjqAdIs+ipasctLzzVRrf4UrhiAQGEWjp9YTKQxV47XbLRCwHyV4XwV0U28e1GkvVLH9tEJRvVS1BtbJBfnHujq5jiGJZvk6Ek86oQ5hpPCLzSHZhwBYyk3gOjGFi4OSBdriEGGIkN1QO90Sa/MYysRweo+Ms0J1vZ9Qq0krlkmX25gZyveAW5i4+FCn6SpnR7qjJCQbzSckv6cHyLBiu7uYKLAb1KCo4+PyMyx0mcPivTq/LEQE5EZZHeIOWRxLfsJatdrWavMkMcBZqXhrW7jABcoXvnSz9Ocvcqz7/gLvrUdOu5+9bf40vqv8F1zfwGhJcMnF8cuW+CJVuNjZAZ9d5fsyhxJXXkiV+KI3tjCNErejS1x6L5j0Nni61/+OVYuvZ+ry98JlYjhKdAOAYiBhYkqskBQ3hT0LvrOzpsZMgzRC/PYXh+3M2B24VmqosGgKXFakFUkejDhMKcV5XPX6Nz8+ps6rjcz0qoknQ8wCmxg0OvH49KTmkRmjur96aqtzA3x7R6uWRnrxh/H5he9IWKQ+rZ+kVi4SkyAY2Q1owaOoJh6Rm5tJvItZpWNiheC/uUIkTmq3SbhxCI5isP2w3mjPJUsTCo/GAlm4eTNrZFgZjTJIVSAOY2wZxzlDU/QM9qhUzG2hR5VK5zkwCo6nDhHd9BdF0DYytFDh868G+FJIYxPekebadkbEmy0DswotPRa0eXIu6J2Bx7mNF8fJ8cAZmUGt1hHru4iet6wQm+2yGql4w578nicQ7WPKXs/zHdIeVCFPmMEtzZxcUB2fs5zYCa/D08YPbxZGP89yRCDFAQ+4e34a6TbxTrngNwSFDbHItTYSRK7EKiRzGg/QU3snWV36O/54ifs6B6vXv9V3vH8X2Ju7gkskKY9PveZf0yFGhfLzz/UeQstkYN0TOC2h8xS7u19iTe6n+W97/47lKtHCfpT31VQs/TqLqafYJdn3nJJ04X553n9+q/xxLWPcuf67/J89bsQQTAmYJqnziG29scbrlmxyEv7v/eWjO3NrSBbS3h3l3yhhpmv4g4B0YfPLCOHOaVX1iDSWOsQ1RC6KeH1TUy9RHZxBjtXxeKmQPnZEwu4zKD2B+i9HrYUkjyzxCgdDO7u4iKNiDR6p0d2YQaUJ+8kTYeJHXZiwyQRRPeglAvIHd1rgmQOBrMa5BLBnR1vj7zdHXvHT0VmEO3hWJXjYcMBrhpBAaEY4Xr1+j6q569bXi+hukOEdURrLYbVpfEOK7y9M06wZD8d47NEbkguzhKttR5cCT8L3lWCiUPUMPMqGaUQW/FYaMrhODnMrsx5aIWShKst/1pusAs1nLEMn1ryRMq7u8iieqvX2sdqG79ZobvJsYzuUyMxqKSQn1puoPYH2GqELYWI3MOEsqeXpypFEhC7PS+3l3m777ElNCD6KaqXjMmRardHUFwzUUBmXKixlQjZTdCb3shhMv3UBTwkO9cEIdjbepV+f5v3fPC/Ai3ontPkMVTvXqC7dt2PvxxhpUAZC93hGIOYJh2sNcSl5lgBJexYRJIjkoxsoUq+WB13YOI9w93tL1JffILLT/6Js2MxD9vYAs5CaU3QvwD2MevSCQfK+EXFhHDh//i/QwhB62d/iUY0V1TSCigBMJyRRC2LcpBnjrzbeqzjeatitFCnDYUpckc9PIEHgHfh6p1XmFCMJTehUDEoWs7ScKyWtugnfsEuRwghENai1vfIn1jBru4hyyG2WcWF4qAKXdwGNvBKD6rgL8vUEnQtyawmnyuh2iXUA7RkxXYbJmycw64FkTFY/Ma7Psr4CrOpKkwgUD3jr6OArK4QgUAPDUHb4JTHWfsTc56EHgryWCJyhx7a8fmndW+qE3YsMjc4XRCu93uQGVw5QiQZervt54FQ+4JQsRy63E4nNdb/DnqyKltAH8ROx/NUFhrYagmrFPbiAtJaXD9BPKSutBymBKu7U05xjxQPmRyPQgwz1HYHYQwY64sDWnt5OeewVw5gGBIQ93eKvx0UjtJKjNpun2qz7tIDh0d9b+eBPKLJqev61ie5VnqB2dlr47kx1mUqqoESp1/v/WyLup5DiIPcZ+T066zDNCu+mztMx2N6rfOHvG/2x6m1BLbqsJxcqGBivVWtHnKQYlZmTu5QvwkRx02+73v+EdbmvPLyv6WsGx4mkuU4FaICRd6sjBPkxPRQKiTP3/xc4c2tIBuP4ws22t4Wc6KiJDtDVGeIqcYQeHtTCdjcICKNSLysmG1FR6pnAFZJ5DDDLNSwizUcnpUsrNetzOIVgqHfaZsZv/CXNw1WKcyCwJQP7mCBg9BgIo2TDmmhtCEYnHO0n1CkW5JqME90e8+bLYw+qCXZTBmZ5Kj2kOjuLtwXuEhjQo0rBdhG+YEOZyeFLYWkVxfQWx30Rnu6Mpka1EabfLnhJ8csR1hJdm5m7NBjmmWv8gEkgSJca03hex3eXc1FuqhUJkjrlQikdeSVyKtkGOsn5jhA73Q9mdI6XDkgu7Zw7I7ThnqMVU2UJLy3izAOOcgIb+8wfHoJ5yC9XMgCCUH8ytojXadHCVMJvWrHo35B7q+Jag8INtqIQPndvJZH2qhqtYXe7ZHUYkgN0e0dkredO6gA9VP/mxXXSw4zb8suJaLQc47u7zG4tohq9Y52L0bHafXJZyvkIdy68btcuvq9CKnpnNPkJYHIDXeu/y5PV96NrYSYiz6RMFLgKhHD/g7OWb7wh/8jxmS860N/hzicIYyqXl3muleXGT7jZYVkgV3Ly5IgqtFpr/pkoSQwoSdd6cQhB0f1BbKyII+Ov/oihXATr5f8DYbuehKgTr0yR+/edaoXn2R4DvKywG13ab/0JZ7+jv8KV4p8OxtIG16PPdrOkVsdzKwm/TZ10jO5pbsSYLUg2snRwwOFjqkIBL3lwEu/GXdE3QE4UeZrtJGjHI1lpgCy2QhdO4caOtxSw+PsraW0npHMabIJDLIaesKbSjwO1wUSE/oxJLMalc7h9rrezW1yY6vVmGwkWz1wDjdhJBF2LEGS0p/X5KVHJ3apTu7x13tmqjMngHDPJ4giN4jusCCE97BSgnOYy4uQuAM4S5J5LK1zBNXYyx9ut33xRYDQ6lhuihikYw14d0JOmc9VsY2KV5IQApRE7veQvQSX+fnEJ3kBplaG2aof58NgjgG510VttL7pJkpqf0J9aKIIN8LVCmtxUuJ6Q3RngNMKM1tFbRVYY2OLHORs3BdhLKJ3ti7nTrnD3uY675z9AWxviKiWEMbSuv11enmLpfjqkc9kdohxOfvZFl9s/SZXyi9yvvQMVT0zlSgDuChAtgdeWKBIaqUOSe3AV7JvbcK5uTEUdTJkmqP3ph3+RJKhbm0imhXcUvNMBO+zhDQWekPPTVCStLtH3tql2jiHWWwihGDtjU8zE54jlIW4QKGpnmVmiry+ndxleeEd3L736ccyttPiTU2QR2oQMtLk5RA9SMcTiwDC+y1MJZpyKpORxggxfo84pcIXrLbI56pjx7bSrqG/4O2SkxlFuG8orxtEUa3TA0fzjZxuX9HPJOkiWOlQtQxdykj3KzjlW1ildVB9GK5ANiNIZzTx/DyVdc/U1zs9sqvzWC0JJvGy1iEGGXqQwf4At96GUuBxvzMVzIRSgkwyD6lQEleND2Tadnse61qNMM0y+ULNQ1NafU8c0pJkpYktBf7zpYDkiQVAIITAxlWcg2HTV8CSpkSXIvLaImp/iNof+JaU4EDSTTBO0E4LY6zH62pJdsUnxyKWJIFPelTqvJa08iQYcuctkTtlT6pMc4R1lK5vQW4wxTnKfvr4taFPCwfiG5Ag0mmOu7/niRkwXsxGeGuZ+YVSZMartmjvEkmgsLHGDTIoBYhh7rH09QO4jKnFfkKQE73Z1KC3O8jeCexdAdlSg06yzkuf+9dUasssLr+TpCnJSz572f693yYMKswtPE96ZX78zKVpl1vXf4e1e58hrM4w+9R7EUHAF//gnwIwO/8sswvP8GT8hHcbzHKU8+TF7HyIGlp2t14GBIM4Z3hxGssmHaiuRaV+w2xC4eWqTomg662oz0LaOy6U9RrLauAn+HzY5+5v/zyd26/w5F/836Cb5zGdLls/+/Msn38PcclvFtIiaXdaEK/mBFsd9F4f04iRQYxJ3xr4z+MOPbBEm/ZE8hbAsCr9xsYWuGDJFPxIlBQuMUe0zAQOfWOD7Nqyn8OKxNqGAhMI0sWA6v0UywGJSOWW8iZkXUuyoDFajPH2WQWfIAvIq/4zeUXRX4ZSWMM2K6jNFmqvBwLSy4uIxFcyhXWo/T7s97GVCDNbw1ViROoor2a4SJKHYJU3qEJ6E4+xdKgGV1Jj3LJyIIZef700YZ4iAKz1GP3cEN7fBiHIl2dwUVHhHWaISoy9cKCfPU6spfSJdBwgeolP6pVEZEVyrAQ29gmrMAZhPaRFWusT7jBAdgYHG08lccZ6KbdGBVPWOF0b/93WywS3Nz2sYHQOwww93Mdt7cPKzLGawjI3iN1O0S0oMCFKIpLs0Qyf3sJwxa5N39r0mN0iAXZxgJ2tofe6UEDYHsbF70zHdpY3el/k1uaXeb75YZTTqHt+g53aIS/v/BbP1j+EEtMp2E5yj6+3P0nftGgEizxT+xD3+i9xq/8lAhGzFF/lYvntNALf2dN7XVxuMbMeOpMkbdKsi63GOKF8hf2NdVQp9G5/Jd/lFYOUoPjs4RD4arLoDsjm6g9UQDktpLWItb2pe3Unuc8f7/17SqrO96i/hllw7G29ys21T/EdMz9+MI7OAOrlgpd0kGMZlyH1N46TP0u8uQlyoMgXaqjOkHyhRhZq3+rZ7iCGvkKsesnBDlQJktkq4WrLM4YdXmMX78gmMoOLNLZo89k4GJtbgJePqt31qhdJU5KVfXKgB87bVTtH1HFU1w06cfSGisEFEMozHkQKcUvQu+IYrLixdI6JoX8RhiuC4bym8UqJsFnGKYncHxzRz50MATDICAYZbHchVMVEOG0WIrTEBMorKYyStlafvJeQnWuSVyJEbJHdhPTKHPtPlTxO8NYQ4aB9JUT3HeXN3BPslCRuWa95unTwM9duiSl9R7XVwSzUMNovEvKYTpkQAlf0aV0p9AtXozzGrqbCYbUgKwuGM74ymFckeuhorBqcsZiZMqozGMMpRhUf1U0eytjjccVJVdizhssteu8o7k6MzmujjW4d/F1M4nyXGl6beJARbHVIL8+NyY3+O6wXlD9UQTrN8GMwG3Fj55NsrH6BJ5/7kywuv9MnD4Xd+vbv/jqt21/j7e/8S+S1OS8NZQ13b32Suzd/j+aVd/L0X/7fE88s+vNzDtmos/7pX6Vx/lne+NpvsHDpp2hQQbcGfndfQHrCrmNx5V3cvfl7fOHX/xHnP/qTNK69fTw2K8DWJA+7BMU7AleCPHq4TYwygvJ9MQWoXvu9X6Jz+xXKlUVmzSJ797dZ++WfZbZ6hWvXvMtfVhYMFhVB13opssSMr3mgSpjk29NqWmtJ3CoqrJys1Rv2LE4ZolaOiSRWC+REQt2tC/RAErUtsjAlcEJ4/eGikGGlV18BUKmjsuEroqNJ3gHZTIg0Dj1wBH1LcDejt6QZzkyTpw9HXlF0KgqdOcLyLGHZQ45coCAokc/WfHW5CNnzPBUCRT5bw81UcIklfOB0k/vE9JgNtADEXhe12cJVS5hGGVeJyS4verOZZkRvJaB+KwZjMCuz485pHvsKvcwcKlGUg+XxNRGjHybLj+BpTwrbT5D7PVwp8rrFae6rptZ5NYvM+ZwWr3QhZqsE948STQWesEeop3SPZauH3tp/eBjat0gIa/0mJc1RkxCQ4jc1pQiZ9f05PsbYzzZ5uf0ppFB8aO7PU1ITvgLO8Pvb/4rl+EmWoifGr/fzfV7ufJpWus4T1fdxqfw2pPCbw8XoCm90v0A330MKxSvtT/P+uT/tP1hAP1RviJ2vEwRlFuaf52trv0G5vMCLMx+llGgYdR72Dsb5oFl1JCPn9nuY83PHcm5OCzlIvF71RJ6T2SGf2/sVAC6X34mRgrX7n+PGG7/Fi/M/Qk3O+VyoFCEHCWy0PJZ+4hYMZYl21j58uDcl3nQVi3yxRrDTIX5ji2ymDBaCnYJ8puT0w2fcGK/qwgJmsdOFne44iXJA8uSiN3AQTOlfqp0uwjlEP0O1YmyBqRzMSYbzmqziiDcdtXs58Z6vaDmpGUQhKhxgGpCNH56j52I19C/BcD5k9uuW8rabgj3kc5UpMtuxkRpEURWUgcRmBZ4ot8hjdnN6z1dC8uUGwNgtRxhIZgUmKlFZMxBITGTJy9JvBiyUt8yUXKlOHXoCU5jUJJGtepJFCnq9DVqOq8pimOHiALnX8y55lQgbaZJnV2CQ+gqThaBriO/0iGYr9Fc0SVP6SrZ2uJ0u4WqL5LkV0stzRLd2Hw5vdtqK/jiisMBW+4PHkqjni17v1wUKU4kw9RjVHviNTxEjcqV0bmqDBx4mEd7f47g4KTl2zvHVO7+MjEq890N/nzDyv19aEZ54lCasvfJJrjz5A5TqS15qMeny0pd/HiEUT//Y3yW4eGH6WEKw+O7vY/Hd34cAane/Qrc0pJFWxhvC5NKBXe3KhfexcuF9tPfv8rWP/zw7X/40K9/9JynNnzvrpTv2jEsb0Lv4cHjkaB0OZ+Pp1gYAF69+GHP7Lje//DNcvvJ9nLv8nUghsNLRvRCg+47K/RwUyL5vR9tyiApjgrBGkn8bKllM3DinPUoydZS2/QZdJvkYnjWKsONd3MQgQd3bwczVcLO1se62zA2mrCH1kBp1exMXBd4ooQgTCwZLgXeL28g8Rtg6SpsZWSU6wio+QjIG8kCQnwtJZzSlzQyZe1Md6mXYPmbhzIwn823t46IAGwbFhtThShGuHPl5cqft+QHlCFmJcEjoDT38SQhEmiH7Q29sUi9jzs1ipdeFtnOasO3ndWH8wM1cfQpWmDQUyZwAK4j2Babt16C0LhnO6eLahyCPKou4UJBrge5PqFmUI8wEQWss1SXF1OeziiSrSYKhX9RMveTnuol5eAS7yJeaXhnEOdRGiyOM7TcxVCkkqcSo7mCqoiu0wkSB77QWijhikKLTzOOOj5OtXJnB1Uo463CRxgXaG4/t9718GGDOzeKWGoidzkMpbpwWnWyXz+3+Gs/UPsD50nNjOdRR7KVrJLbPk9X3jf+2ObzF19of52LpbbzQ+EG0LOy+hScHxo1zPL90ERdo+q11Pn/nXx05rkhSsBaVWN7x9p/COcvNW7/L79//OS6VnueJ8nuOVKvPGmKYoW9u+HvjjM6FXkt658gGs286OBwCwcXyc1zf+gSb2V3ec+knKC9dgdtbZM0qdr6O6CcEd7aQh8yCqnqWe/ufe6Rzedh40xNkIYRvFeeWYKsAWV+cRThHuDq9c7OR9phL5ydoUw6RveQA8B5qTByMK7eqNfAqFkW4OCAs5MxUe0BSL2G1HOPcnILBskAlksqWJRhQMKDwZgIC0lmQThy7IMcbXv4pfKbNfjPC/WFA+a7AlANUP/PVFC2n1BlOClMOMUKgsuOTMlMKMc0SwXZ3SqOwu6LBQe1+TrYNvfMBnSvFpFGRdCoHi1pQ+LCPonIvO6jkOEdeEZRvdAg3OweamtUIUS8hlRi775nGgbubHGYEqy3EMCN5bsW3sbRX8BBAZdMStR3tS4LhvCBZiAlWwWh85T9cQt/Z8Tv6U66TA5JrC7hyiBxmRHd24RslghyOSJNcmR+bq0Svb3o96G8gRC+BOJgyxnBxQHRzGzkiMBVhIz023BhFsDWNCTtLbCd3SIdt3vW+/wIpDzZsI1mq7p1XcDanu3+PG6/8GpeufR9f/Mw/ZfH8u5j73h/GlB9gH5sldNr3qL/rGYzteu5ALR47LE5GvXGRD3z4f8ut7he4+cv/nKd+8r8mqNSP+dYzRvZweGTdATU8upWYXXqeYXuLu9c/TuzKXLr0Yebe+z3YrkUOncemCkF1vSDjGCD2TpF5wWG49uRHefkrv/Do5/LtEnIEcJuOkXubK0VkT53zqgeFsoSwFrHRQp33cALR7iMH6REMoy2ImRboL2nCfgqW8SYFDrpYYgxsPggn/Hs9e1/RuyQp30tRvQysxWk17uIcCeumMLxQVFfn616ztnh95N43mhOtVt7BNLPYUoRtVMaSWNJCtNEfOwjK3FFdTUEJspmItKEQxlHazL0By9B61Q7nE+bSVo5wnug6mdQ6Af3lABN4qJpT+DnYuHFVv7yRk5ckwxlF2LXE27m39i40odVmCzNTw0YKG3inNVsKMcszuM4QvXZos2csenWXPM2x83VYbBxRCXkzI2mUvQzdTAV9Z9srQzUruEJXGOt8QU0JaFY86iM3BLc3jnQd5H4f06iAhOzq8sHriVf1sHi1BqsULDY96fkx2GJf736WJ6rvYeXc+xCto8WyG73Po4Tmi63f4snq+xiaDq92/oh3NX+YmdCP01Zi7FwVV46PVHl39taYCy8c+V5bjpH7fdRGCztbRfQTniq9l5X3vJfXX/sVXup+infUvu/RT8w632XIjL83HhB6be9Y+GJdzxHKEs45Xmp/io3hG3zgA//Q81xG91q97KFE/QQbBUfIk3PhBUL30qOfy0PEm54gO2B4ac5jsgrinXAO0yyTSEF0Z9cTuqrR2Iwhna8R3t9D9VNMLSZbqiO18iYXzhHe2fWwjdnKWDMZmJ7TlcBKwf5ljStII2HbM6fLrRHgzDH3cs6OVfSvROPP6z2H7guG56crV8JAaUfS2y1hM83+s4LBYonqYki0lVC6uXskOfbueYUd9kTL/HCLX2hJHmqcEshhzvDKDP1LMZXVKkjozyuitiXas/RWNHloKN3eB9mke8knsmnVESQHreXuxWCMB1TWS3JFr294XPNchfpXW4SbnalLp7oJ6nVfbROBx8K5YU6+WEMYe1AhV/LAJGQEvwBsLNBDR2nbMECRLIXot50jbSicAj2QpNcWEM6h77fQrT42UJ5caaYrGiI3xXcGZPX4WNk7f43FkW7CWSJrHsBEZC9BpjnZUh0nBMFeD3FGe+bJ0K0+Zu4QZqsYl97okF6NUMZirPMLQJF8BastsqX6yWXiU2IvXWV++W1TyTH4iiBAaekSs1fexdatL7J07t3cuv4xmueeY+6j/9lY1eC4EBIcjsH2KkIobt/4Ha7OfYDyQJJenDnxczoTXJ75DtLn26z+5r/i2g/9NKb6aERV8HjkvOzIzwA7073jL2CltkwzPkczWMbkCefmXqQfCYKO8+ocpQCRuylZMMcEiRRYXHnh/z8S5BMirUnykkQljmjfIHDIQlUiX5nFTfArxljXYm5Iq76rNVjQSAsWR9CdwDPnjubNDKsDpBX+2lcc4aFH3oRgcOihI9jLSWY0SUMRG4cc5g/sTuULDd9yH7n8wRQsYzJEpLFA8uQSaUMTtc0RCNpwVhGZELXbQczVMM7L1vXOh1jhierltQMFhHj/6PgOV4vBw93K69kBV2Ly8TGM5xSVGkTuGM5p0qshQc9S3shACMxi8+D7ikPklxd9d69Rxq3vHmtMobfb5ICdr2OyfKq6amZrqHbv2OKGU9Irbkxiox8mik2HXm8hnCMb2QwXEdzZnKosO0AoOdUWsXEAgUZ2Btj9Pq4xzYfI5wqYSWcAtRKi7Tc3+coMZDl6c/9IV++s4ZxjN13ludnvwVYi1DEJ8oXSs2gRspncRPB+Xun8Ae+d+THqwbw3YlqeOWLXPhnt3n2GpsMd8xqX1NPj18UgQRpbOBUXv9cwo1Ke5fm3/UX++I//MRvJTZaio6TAs8boWbH9BDNThdrJpE5zfha1sQ/DaSK8iyPqeoHZ8BxD2+X5+ncTJxIbeUgG+Oso2oOTn0sheOfVP8/vffG/e+RzOWu8uSoWxeQoMoMcZgeYxft7ZIOMfLEGUiKzHNG2OCVJzzenlBZEmnvcsRAF3rc7lj2z5YPKqhhmY7KcKYfe7c1B7U5Gf1mTF5VVD0+YSHotVDYc2Ywg6AgGKw6nQSUC3bKkE3mAKlwN5HaAbUBehbzqSGcUalgmfqpEeTUn2ugRbnfJ6yWGlxqYkpf2iTY9wUf1ElQn8ddHCtLFOqYekpUlJvLseZE7T64CMJDVJGmzqITj6FzUqKyBnniWs7rD9iFqiSniI0CwazwhJdKeeLTVeeAk5jIDXjOdYHOisinFuL2upPDJHuA0tC9pwo6ltGlAwHBe070ksKURXjVDJQ4nBPn5Jro39HjbYyJe22dYjTzD+hQb6sPWymeNYKeLna1glTe/cJEew0vMfBXZKZRJzph421JAOmFOA6DSHL1aML1HOOT9AVLJMaHPT2qeZf6gpNw5S2YTQlUityl93efe8BXetvxXj7w36jmGDoJqk8YL72P31hfZWP0CUXWWJ37yH5yaHNvI0TvnCNpQ6ixgsgH3bn2K+flnKZsYOcimpOq0kpjMeCa4dSglubbyfXzp1v+d3U99jPnv+kGy2qMrCJS3Jb3YYYLTfwt1wuXrtO7SUPM8VXmf707EZaK2RfUM4e0dbLRE9f70h0U/RTjHfr7FcLDH3OJzjzz+b2ZkmS04GMcnY2eNsGcRlrF8mZtwSXONEo6CRNYdemysALPU9J8tzEYqaxnKOJKaImkqsq4lKNwiZeYob1vyknetE+aUGUoI0qIzmNUVeV0RbUoIziPbfdRe1xseHTYXmanijMW1+9iZCnK/fywR3JYjzCVPQla5o7Sbc9zuVQ0doiCt2cz4blrgDUJUDtXVbLxRHQ8dkGu73mDiFALUOHm17lRsTNjOCXYTb7SS5Oj1ffJry6QVSV6RxNs58QTB0DnnZdFOeZT0dhuTZJjFhvcZaPcxM1XMQh3VOr5QIYz1xKpHDJsbCASiMxjDI08L3+mZvjAyycgWGuSLDcRhbeRhiquVsbWBlzMrR+i1XaRWpItN0OpUUYDDkYQWqQKk0KS2x1rvNUqlGeTTT3p8fhwcIf8tRtfo5DuQwJdav8X50rPUg3lcFJBfmsep0wsJTeZ4Ofsy7e0tLi4+OVa1EMZNETBHEWy3YWWW557/Cb721Z+nrBrU9OyR9z1MyH6C7HuivqmWsMtNDusU2VKEvbKINAax1Ua1e9gwJF1p0L67xfP1D1PWvhJtekO/OSo6P2KYYsteOlUO0/F9ap1hfXiDejBP9y1SFHpzSXqjtnya+7ZsOUCUA1w/I9jpEuwPxjv+UeU1esNDJEw1wlRjbD1GZIbo1rZnDE+EbA8xzTIizZGDlHymQjhoTbXJVc6Uv3u0d5SFHe9aZr9iUblAGD1mU/tW7cEskgeOIBWUtgX9yEEzQ5VzUkrkNUjnof20JtxtUl5tUN7wSWl/UYEUmFgUtq1lXxkwjrhlkcYn6nrg6C9KXHCQ4PaXFJV1Q+V+Tu9SULT9hMfMOY8zBVB9ixqAiX2yWF7NSZqKylpO52pI2PMnnV6ZR3SHROttr0jwKGEdquXd8MxEm13koI0grStfbd80pHNeTmsUvUsBtdsZIvVJcnp1gfD2DhSJYT5T9soPXS9JpNbb2HNNTL3kk/RHVJ04NnKLvrtDPlsl2OlxeMXwWOGzH89GGlcOySOBHlqCe3sEvQSbW9Knl6aldiY0gB0gtcQ9IDlOTJ8/2vo3JKbHfOUKO4O7BFGFC1c/TL1x8egHjCPazHnjxm+z9bnfBaC0cIGrf+pvo0unY8myEiD9s7P+8V9h6dx7eOZtfwYhFcnc0VTB7vWI7uz68yxpBFDOJM9+51/l6x//56SvBCy89/unMJnj83eW9T/4dZpPv5vSwvljx+MslFe9PrJRJ/8mRoM8hFqSuWXrzhd5X+NHvcTQ1XlE6DdyaEX+pCdYHdYGloMUi+WLn/9nOGd45u1/7tRr9q0aLhQMC4MMlaVe4u0Y+MIoZJojdju4cjTtrmUL2NbofRM6u2K/jyhHqDtb3qK4FHnYwn4fK4R31cPL/gkpiPYNJhLkFUUwOLjvKzsGa3Osgt6lqEiUnYclCEibIFPQSaE0ohRDY8gisEsBkBFRxtTLPhHNjceY7vcwtZLvGEmFqJcwi03sTBWhFAxT1KaHjgklfDEBX7gtlu1jr9UIdmJWZskjgQ28eYowvnvopK8oi9wiukNcrYTc2vdY2O4QtdvxmsTfiIuZFMg0J7zXQmRmTGzP6oq8LCntGKSFQUMhM0+MdEphnjqHSzJkq3fE6MNWIuQgRd5YH5+63O8hBsnjnYMnQq+3oKiCyjTncNnEnJ9D3d85XnFiRKh0ENzfJr+4MCaVyd2Ol9TTivzSgnd1zIyv+jtfCAqOMaE5MaTgTnSXV2//FkrHlMtzdDqrlMsLPPe2nxjPcWZpBn17c/wx6wy/s/n/HP/7SuVFrpRf8NX+83Me7nFK5EmX6zt/wLvP/zkWzOIDC1yqFJIPUvTtTWZX5nniyR/hczd+nfc1/zOq6vgOYD/f52bvS7yt8T0PvAwut7goOJIcT4ZVCpZnPA/BWlo3Pk9Z1SnruudvSeHdW4F8eYZgvYVsD7C1srcqn0j6Vwev8rX271HVs8yIJx84vscRb4nVtKlG6A2fBOaBQo0wACdhxcArWNRjb2e8tn+szajqDDAjRyEpvA31nn8Qgnt7Y5MDPXBj0p3uWwbzinjbTOlYRvsWlCRIBEZD0LHYQwWvbBHsniNsQbQJw7ogqKeYfoAZegwzGpJF0ENJXlM4Z8cKBsP5o5c7b0LlTorKvPi+yhx5IA4kuBoKJyDoF9V44SsATgvaVwNs6CENlbs5puTNIEzgpbRcIMhjL2MkJzYFrhaTVGNEe4Da7XnDjMmINKYg6OjMHKvHqff6qL0+2aXZcZJsIuGxckA6o7CKQhP44AEyEgZNSXnTwydMqEmuLaBve9vObLlBsNEGEm+rPVdBFI58WIeOA/JhhqrFmEdw3DuMEVfdFNnbw1YjVGeI2utjZspI69B3dsabqePIQqOOyOj7xkoUAj/W/YFPfkM1lRzbmfLB5tE51L29Y4kmh+N+/yVmSxe58tQPstp9iasrf5641Dz1M3HLUgo9wfPc9/xpZp59Hyo63YERGG8SrXbs3vsa7/3g30fIUQfj6LZB9r0rlSskGsPXNnA46k8v8/SP/Je8/h/+Z0w64Py7fwRzWIvWwXB7DffUA65BDuW7gv6SO5ZEC5DP4iuSE2LU23/8ScrxLNVgjnS5gY0Dwlvbnvy6UMPI6V82GGZkcYCbrdAb7uCcQamIucWHc7z6VgmHN7kwuAOs5knkK+fQd71zqGv1IDfY2Wlsy0j6S7UPWsh6bQ8bh16dB0+Skm3v9Cj3ulNEvWFVYLUkahmGc9NzojMFwS8HObSYkqS8ntE/FxaqGZDXBKbr9eqHmTfIsYFPnIeLAWnDogeW0o7xWshLzXElexRmxVfRXKD9vVyO4MI86sa6X/CL62MmML0nhhakJcFwThN0DEHXolKLDYrPFUYhplk4ypYjL1EnhS8GrO3hyhE6UByWnrahJI/ABD75JlbInqG0nZPHXm886jpPSGt1saVw7JbnFKjEgXEMq56oroZ2nNRbJRHlCLG5fwx35qA45Kol8ka5cO98PGS240L2E2QcYISH6MgkGyf7ADbQuCtL0PUav7o3xGbGkyUrEcGdbd/BEl7ybhQu9AQ9MUgQhXY7Acidh+d7AOTVmDfufIp3vfjTZFmfJO3y4jv/+hGImy0VFtQFnFIKRSQrVHSDa5V3Mx9dAjyU8Tid4sOx2/LV0wWWEWdgrmccrFnBdpvzV19ECMFnXv8V3tv8ERr6qMve0PZI7YNdEU2jPKU1fqbIUq5vfIInKu8BwIWBfwY6A9TmPmaxQd4sWtbOoQ5BLLrOIwTmw4sEUfPsx/0G4i1JkBECIYXf+RW7P1kOMf2TjRpUL8Hdb6H6yYlFPOGA9hAaXm4H60iuLfjjjYxHgPKOIZlVHk+5qLGhwGqvmyzTwrZzlBB2DGZGEbYNIhHYSDFc9LALIx1mDkwFVE9AohmsVhHj7FOgBvjKbt2RVx1orwEbtyTiGA6AdY72lYDqvRxhj7c1zeqKrMDFpzXvAhjugSuBHkC469UqdJexSP+wkPfqXQhQha6pKFyacGADSC5XiOsl3GbbQy4c5M2ydx0sIgWCe3tTkmXj6w/orc44QR4sKEZdUVeM+7jI6wq3aehc0lQ2DQw9LpncEr2xNVZ8MDMVZC8l3NjHGYcLFb3lOtG9PbqXZglvbj+0XJupxUfl2ZTAlgJUZ0iwuoethKiN9oFyijhQVZk6j5nKGFcPjDGYeuhASfKZslchKYino+Rx9F8GKXq9jeodT9Q8HOuDGzx56SPoxSUuLZ7dReNK/TuY+dsfmMKIPihG+rf7X/4sNh3y2U/895y78iHOX/gQb7z668wuPEsy3KfbWePKkz9AM6iD9Lh0lZlxF0dvtpkJ6jz70f8Vr/zmP6Ga1Jh78btIZifsgKXk6o//rbMNzEBpVZDVIZs/qm6Rh47+OQj2PNkrL8Pmy5/iHc//FClNbIH5tnGA3mhj5quevDX0kBHpHOr6JubJRdZ2v8aN137dH9YkvPLavzvz9ftWCp1D9VbRgj+hSaHvbaPS3BfhTCF76TzZyc3WDnTqt9snYgNtvQTCG0iYpeY4sR5xA0QssUNLXlE46YlpTsBgvsD3HoIiyNxhc483Dlsebxz0IImgf06gLMT3/eY/7BZKFgZsJMkjyQB/DtGuob+kQQriVo4aTOPMR2uQ1Qr79DmCG+ue4AUPTo6B7pLGBIJ4zxAVFfbaWk4+SlABpCCrKWTqkJXYt48LswmhJWp11yd3lxcg9Elh0lDHFlVoeJfD0UYzbzhK91Py83Pj8aZViQkEpa0cEwuS2WJu6h1KrIz1eOQ48LbKo+EWY3NSetUHpbzJy6HOg6mVQMlj8baPEjkH5Ey5vou9tIgYpohAe7IkQLWEqZYwxhJcX/OVSKW8HJhW5MvNqU6ViwKv3ewgXNshm2/4TsEjjrEtvNxarXbhiErF4bDV0pRe9Pcs/BQCOfU5m+bINH9gkvzSq7+EtRm/ff+f8MG5P0cr3aCTbzMbnmdj+AYlVeOp2vvHahUiOygCuswgNvZYXnkRKTVfeO1XeX/zT1FW05vf2fAcs+EpykORJl9sYk/BSZ8Ue907KBGwHPvqr+wNxw+f2u1gq/HBBkZ4VRYxSDEu52v7H2dt+DoACUNWN/7goY//KPHooMCHCL1VtMYnKjVJLcaVJmwWlSRdaUxJC5mlGumF2Sms8eFQRRVRDFLf9heC6PUNRJof7LGkQDoPTbChoLKak9YV+1dDhk2J6yVjpmR50xDtGdK6orRrUX2PrZ0ME0M65x3nbKowQ38eYSaxJZ9Am7pA94uqeQ26Fy2DFUteOphcpIGwbYn2PQ7PBg9u6Ouh/87hkiNt+HcnC4reisJVJDL3Qv9pfQJnPbRgvR01xpJHgv6cYrDgCYz5Yp30ktfGzQ8TzIB8vkreOArIT67Ne4vlYtMT7dspDGhQ4B3VwE615YyC3jmNKUn65wNM6O8LoaV37SveF97bI1xtYaIAW418q1X7ijojreCHCIcn4x0OkdspjHV4ewe9f4ClE44pmbbx68aOcfbgccSTYeb9tRR4vWn/XQ611yO6sUl8Y+vMyTHAwLSJg8aZ3z85/vL2w7VFRxrg6do6pac9GWR99Qt89aWfJwlTbt3+j9x54z+yu/UyN17+VcxcFRtrVKuPCRS2FJBcnff8gwyauxHPfudf5+71/0j++s0p7PxDnw+CsC2o3BHoY9ZlE8Fw2dG/4EhnHSZPEEGAnamM3fvy5QamWfKOlNtdgpG0nnVYDLbf5/rLv8Izb/8Jll74CACtzdcffdDfxLDOd5ROSo4BZJZ7O90sJ78wT35thfzcLPbyIadMJf3ieExiILvDMZRCWouwFn1zw7vDAUngu0xBx2CVr4iWt3PSuqR3ISSPpr+vvG3QRbUz2jWoQyYnRkIydzBnysnpQAjSpiaZ0YUWvpfA7J4L6V6J6J4PaF8N6V4MpldBIcivLOIeAvIQdC2VtYxo38CI5Jk7dDIxXuuIWmZcvcUcDNYpiTk3i5mve4OGQk1BWDfV+Zs694kuTB4K+kte3SmPRfGdgngnJ+xa32Fczz2krz39hU5J7EwVV4owx5yzsBa12yG4vek3RhPzXb4y46EBs49uJHE45DAbb6jkwMuLBbe30NfXCG+uo+9ujdvuQvlEWq+3vLV2ZzD+t5qQBpMjqT7ApQa9uovafbTqMcBguEcUNx+YHANT+vbgq8iHPycA8QAtZmsNcdSg2fAku1d6f8Trvc/gtOIr+7/DTnqPW/0vs5eue+IieAWQCRla1R6gtvZZlpe4eOm7+WL7P+BOsmM8bgzVmPzK0iMlxwCZTcdVdhlpn+tNPCJqcx/VG6K3296hMQrIbcrm8BZ9erzwjr9GvX6Rtf6rXLj4wUcaw8PGW1NBLhJjq9VBZWmjPbWDs4HCNMokoSa8v+fNEtpD8uUGSaPk9Y1zi9poe3KQgOC+r2zqYYpLct/SmCkjck+8Mc0S2YVZyB3VOxmDWUVWkyQNiSy6sIMljUpLVL60hpmpYFbqlDYNvfOatCGRiRu/99RTtBDcd2glyMsQ9EBmkrzvGCw7EL6alZcd4S5EewKrIJlTviXdsYRdR9L3EznAcN6i+wLdPzi4TAXxtsAqRzLncDhMIDATD4KwgnCiyGPKnvSSz3nNY504ssyPKakJ4l2HrcUkTy8RrO8jM0M2Wx0rLKh9T2oQsfbV+cwUIuIdVC9B7/c94SFUhPsNsoqkezFADyxZTY7xg5MxImzlGtpXApq3Mkgd2XKD8N7e9LVNvCmFKRXa15klvr55LKFC1mPy3CD70zg1WwrI56pHvnsynJakF2fHUoEPisP26Ycx8mLC9S7Y7KDaA8Qwf+TKxUrpaW6ufoKnLv6lMdzhrBF0z3YfH47mR74fl+dUNgNaX/osnbuvceXHfhqEwgx7bHz8V9i79WWGgz2CUI+xg/l81etmVyJUq49tlGiYGc6/64e5cfNjXHvXE5yE63xQrP/hb5K2d7n00b9EvCHoX3FTcCiV4Ls1kSAPHLUn3sb9O7/Ppe/6syR1SeOWn4PkwBMKnZZjN05ze5U/Wv95BvfbCKFw1YjmlfcTnTuHmG9w++f+x0ca8zczbOgNKqr30hMTrnFXAxBaYQOFC/zzP+p+AN6pbaaKzHLk2t4UDln2Exj9uztEFO5l8uYGebNCSNGZShyQ01sOqN1PiVqGZEbTvxBRvjMkfnWD/PIiSEV5K2c4o4j3DLJr0SVF6nxXR2VQOgE6qto5tiDxmUgic+cr0lp4cmqRRJS2syOcFPcwbWPwifFDhm1UkMOWP56UfuN/iLAXdixBNyGZUSQzpy/V5c0M4Q5w9IfHJFJLvJNzKn5YSmwcIM/ATXGBOjAVOQ4eNoLWHQPlsUVl8LSn38YBFHb240hyZJIj+5vesVBJ/x1JRnBny+daSiKyHNHzrq02CrBzNdjcf2zY6UW7zCvd/8De3hvMzFw79b2nOVdOhuwOcCO46HF/l4q3ve0nKZfm6Q+2ef36r3P1wg9y8cKHuJp26e+u8saN3+JW70s0zj/vyXlSTG9knUPtdMjn61y88J1srX+F9eQmK/ETxx5zMmwlxlyYn9osG5PxmT/+v/HcM3/2gdcBYH72Gb6e/AIdu0tp6VkvSzdhEy4nZPbUfp/Vwat8Zf93EUJRLs9Tq63w9uf/cza3vsrKyvt4442PPfCY32i8JQlydn4GW4mmkpPDt4EcZpReWZt6LdjuInLrdXitN+XIzjc9vnO3d8DyHXoFAlkkTNlKk/CeT7JFP0VoCWiqazntUkhekYgiQUQIsookvTKP3ukxaHpsnA0FSSBROah9sM2DcamiuDiJg7QShhdAtyBs+SpXMutVMMI9n9gOl/yA01mHqYFIHDKDuCXpXgoorft2mA9HVgdTA30X33vCY6idBGKJ045k3hFvn55oOIDAa/2OwkR+gZATVQ4XajDeKju8v4dp9cgvzJIv1ad0fbGWYG0fNYIqGIfqDHHNkq/WW0DAYFGjexaZO9LwND0xSEuCKHWeDX9/b0p+yAXefVB2kwKfzPHM8zhgcHEWjKP02trUwicHGWrzYNdgS4FPkCaHkVui22dLjsetxtECECiyJ70LnchyXDANyXBaklxZoHR945EttZ+uf5Av7v4Gn/vk/5XG/BOEcZ1+b4vm7BOcu/j+Ez/nnEMI4WETZ3ziRxALVfctuDyCxeAjLL7vI+P3yGqDCz/2lyn9zhyvv/TLvHvpT41Jt5NJxui+Ew7mLryTu1/4VdjYg4uPxqYebq+SdryG63DBJ8cCkENw1tL/2GdZ/9SvcuHDfxbikO4bL7P40b/OsKmmlCrS8zO4UuA3XkVsuDuUZldItntE5Vn2kzXu/ew/451/7/9C/6qEn3ukIX9zwzmq909OjgVMEXb1vW1M1VeJxTBFpoZsuTklP2UDjb0wh763M5Ukj7+zO8TUy7hyBP0Et1D3klpFlTLsWrKao7cUUFnLSGY0zoEpa/IL8yAESVUQth3JjJe4DPqWLFVUVh2DWQ8vOwmKOdK+B0jmTr7pnRIgHVnJOwdGHd/tOkDgHh8mFKj9BAqN+rTik7XgMIThmND3d8byeCO+grAOJ4/O48JBvGtQQ8dwOZja4I5cCyW+eq7vbGEb5WkzBy3YvxQQdCzlrdPbNmap6Qnvb6xDoHxb/oT3iswQvrGOLYfkK7OYmepU1Tafq2Nnq+jCZngy5DFqC4fjcJLulPTXqVAsUht7cAypTSYZaInT0tsbdwZQOPg+rggywQuzP8hXXvoFarXzlMsLCKFIkn2uXP5eKpUD+JsYTii9FPPwcSEcXgb2FJhFrboy/u+7X/wb49fDsEq4/DQvNFb43Of/Gbv3vsxSfBW13Z6WPtWSbHkWV41R7T7L59/L6urXWOH0BNlFAebi3JHnIcv6DId7DJPWkc/ksSBvt7n+8r9nv3OHd1/5C9zf/Dz1cIn42jO+A7zfO2oWNzqmc9xKvs7K/Avs7b1BPuzx2uu/hlIhzz37Z08d7+OMt6aCTLFIbnXGagXg8aCyl5yqX6tb/Wn86wnOaqMWeHhzm+TqPHl3iGoNiHrbPqkOvcFGsJPTf0YRdECaQrN3y2BjTb7SIGp5rcugY0mrEqsdYVeQNQ/GqLuCoAuD8w4zgf4w2mHmIW8Iwq2Dv3mpuEPtwcBBsS7b2FFaFfSXJ38OQbjroRy9c47KPa/3aAMxVuUIWoJkweG2vTPNKcR00rJAKU/2MJEgq0l05gh6Dr3VGcubTaorqF6KfHUdEQfkkfZSaw7v717gqvK5CqbqlUZGpi0qcVRWc/qLivJ6jsoLhY5l72YYHKNVO4JZWCGw1RjVG45/Z1sOcVp6ebz2UWKejQNspNH7A6Ibmz4ROFwVEmCbZdRmB1ONTnbNO2vueuhC56UQqyTBvV3ypYY3wmn3x7+6yC3Rjc1HTo4BtAx579yP00rX6PR3GfS7zDYucvvmJ4hLM8zOH+hiOufotm5z/eu/yiBtsbjyAivBR+B8AxcKsnSADOMTJ2ydTacIpuK7H5PdDABnDCKO2N1+lZZ4ldnGNcQwQ/aSKRm4UVQGAeWVK7SG96jzaAnylR/7aQDyksMpr1oR7Fj2P/Yf2Xn5D4jjWZZX3s3d//ivqDUu8cSLf4aZ4ALRbW/D3q4N6d96jaW5d/pziAXO+Zb23b0vsPRdP0r6OUP3/nVqb38Xzzz9LIMVgZFvDnv/zQ4hJGlDEXcsR1hg+F/ZrMx4cX+ANEcWuPqR6UZwd9tj2KU8kFXMTq5IqnZ/bPGs+wkI6UnV9fLBM5FashlNVhQFyhsZIneYSoDKHGHbKxoEHYswjmDgCrKh79DZR5fWHsdgKWAyfdNpihr409NKkp8g+yUzN06OAdKmtyg/U4x046XAXJifMg8SSTaGqUyGt+RO6c3rcYdxtOEZFRPyS9OkK4fnntTuZUeSmzwS0xCQ0fELLVpOSY4nQ/ZTgnvbZOfmvBGUVgQ3N9C7HegOPK76McThgoiwgD2okttQY+tlj2Wt+EKNSrsIrbzBxWOOebHCh2d+km1zj36/T6YNlcoSX/7qz/GB7/gHSOk7w3K/TzoTc7f7NW7e/wRlWedq5UWW4mtIobxhFymq1jgTUe+0SMkRKuBG93M0gyUipiEzQgpcOUKt7aH2e8wvX+WNwW/DKQgZoRX5xfljlSriuMH3f+9/e+T14f4mr3/pt9hr3+Ri6Xm6TvGFN36BmfAcLzz9F5G9FLXdxpUjtoM9ot0BdTU39R37QZuMlEtXv4+17S+zvPRunnryR3BvobMjvIUJMkA2UyZYb/sJuVn2GpQPe74PmINkP0VvdcguzGLOH21ZSOtwylMzpIHSeu6hBk1JadeOcXqlbUNpNcGFks6lkCj2iWk640B6GEP5riCZsaSzfpJyxaFM4BicO3sDOS85XCgQqV8MTOSx0lFLIDOvtTxOFgOBKKR6QBLcg9GR0rInBpa3JIehRf2l0U/tZZawEG0Zgs3OeBFUmRknvqoUYAbZuLqkT2i76dYAtT+A3OJC5SdJIGwbXz22INsDIhORNA/MRYQFmVjCrkUNHWrgxiSi9PIcwoHa6aA32qjdHk7LE628bSUkW2ni9P6BjfmhEA6CbY87kw9J7DtLmKbfHLg4wGmJXm/jChtxUwpRg/ShMdPHhRCCmegcM3gihctgON9h4/7npxLkzbUv8cpX/xfe0fwIM/XzXB98lS/8xn9LtX6OcmWR9fufo9I8z6C3zfyLH2b5gz88faDUoex0UpguQHCX8b1l84yb/+5/wnX8b/Ly3u/xwZVnfefmhHPNNUQzSwxbm9QBoYWf9B7y0tjAoQcCtd6je/c69774e2ijeOH5v0J53jtNPXPxo+P7kQJ7OZyR3Pjcv6V9/avs/UAPKb2Va//Wdfr3btHfW0O+eImFd/wVlrYGMFv10Kujuf63TSgtSWY02QyEWxlR+5hJtJgDnPAWyXau5q1e7x50VB7WOEfudLCXY/KnVnBSeBLZROihI3M+0SuvpgQDR1qVSONwmYeGqNQdVD4dBHs5Zk6j+5BPdPBU6sab7FFMEvDOGoPlkPL9DJnaE5Nj4Mi6VV3LT65OHApzYR6beStve4gEeDg5Fp0BstPHNqu4ckRlI6d7McRMrNxGUOCsJ/DBsaC/GFC/k06RH+V2GxcHKKZlYGSSIbfbj6RjLJKc8OYGSEm+WHAkrHt0GdFHCJnmyAJWYBbqnpO014X8bIn+o4SWIcvyAFqQXzzH3Xu/z377NjPNa4i9LjjHF1Z/idb+LT409xdIbI9Xe3/E1zq/x0LjGYZ5m1bnDnPZM+xcf5XveO/fo1pdPvmgJ0SrdYuvfPVfEIkS7XyPnfQe50oTBZNCrSW8sQoF7COSZZyzpHZAKI+RBdKS7OL8mZQqxGDIcHOV9f2XuNv6IlfKL/DO+Z8ikBHP1Dxe2DQrmPkZ5O1NBNCvwee//DMEusQTix+mrOpkZOz179DqrFJvXKJSWeT97/v7RFETrd/6SfgtS5BlezC2ms4uzGDjgPgYZYTHEcF2BztbxR5nLmEh2hUIJwj2vbpA54kQI7wEkrCMFxDf9nDU7uToniSrK9RQoCeKj0FfkM46og1BsuTGSTI8jIIuDGct8brAVOVUGy3oiaMV10CM7nGklYxobVnNYY+5h2zokCkUmBKsdajUV8zMfBUnBbKfEN7eHbc78iI5NtUIneTHSr0BYKz3uo+DKUJAeGsbUyshcoPe7pJenqO2qmhfDrDKL8Rh2xK3JhahifXXCcjna4jUoAvymzuJUe4A66YwTMfGqOJyxnab0IVc0BneLgcZtl4in62CEATd4cTfHn9CPh4jkPXbxI2Dauywv8Od13+HZ5rfxaWZd2DSnHeG38nbL3836+4egyhj6X1/C2MyXn/9V48laggEsmsxE8gaox3DujeiAejdv0Fv7SbnLn6QF2d/CJ0KzELNd4lOSBZsIJBBiMkSsrJjuGhBgXQCkXnYke4LdNeP4XA459j9+mfY+fInEakh7e9TrV/g4sUPsVR75sCWFg6S44kwkWCwdQ+AzY/9ewBUNab+3S9Q/7EXmJ35fsSShjTCLpQZPooDzbdYjFUagGQ+IOocVQZy5RjTMLiZCjb2lVHxkFjc444rW72xpBpAoCVZ0UUJepasb8lqitJ2jg29VJowDpV5bkXt3nSSFbW9UkQaCwZLB8lkeS2ldynCCrxttTtNnfXkMBL65wNKqxkqsZhC11iGkswVUAgtDtQpRnGGOWU0LgeQG1ygkWmG2Ol4pYjZ6lTCLHc6qO19X3ypFiRp6yitpwznNXl88N7hYkh5PQUhsDgGy+G0bFtmkPs975K3UEdUDyXIq7tHLH0fOmzRNn/MYUuhv1bt/oPNraxDGourxl7L2rmHL8I9YtgsJcv6lGJ/v8vukDvBbXr9Lb7r6t+kOgiAOear1+iXLVv7r6FKl3hm6U/QC4fs7L6K1o9GgLtz79PEssKV0jspqfpRJQrncFJOYaKlBSk1xh1DQNeS7ML8lMze4UiSDjdv/Q6t3Rvk2QCJZD66zHfM/jjVY8xIxCBB39seG5p0020q5UV6/U1eWf1tAGZnnmJm5gmePP8ClYrfKExCVt7qeEsSZJXkBHd2vUrCYg2npXcoe7PCAoMUakdvtqjncGuG4bImrwjyWBeOYoLBkodhyDwj6LuxLJYAyjsWt2MZzkmSOa95rIbWt2Yzn8iKNd+GtvXT27HSCZxwU2tUXoHBZQh2HbpzfHIwCge4kfJD30DBaPbQEHGkepw0HOG+QKejqopA5pbBgiIYMBZYd8YipCSvR6hhhrWO9PIc2TAnurONyI6vqLiC3DFKYKWxvo0ycjyMA6hEiMxRv54yWFSeeLKokSafcvdSuz30bpd8toqZrWBWGogk8xjiY3SPRYE1C5NsfDwnQGp1clIPiFCTNkuFQopAJBlBqz/l4miU9BbYZ6gMqd6QnLp3fowDjJaoYiMlAFMJMTOVU0mCjxoizacm1te+/Iss6ctcrbyLvBDeB5CpZTm6RLo0y1D0ScQQJxxBpXHs90YtSV63U/epmQHR9lXk0tIlSosXWL37h8zMV1lZfjeZkoQ7Xcwx8AqAaOCozV3kxm/+vwgXF4l+8D1+fyMchEAIec2haxCvTTwHwt+jax/7Nww37vHkUz9C3mmzcP4FpPKT+OFfSeIVHCaJKlHHosIy6lqTaCngmbcHfOfffg4jAv5g7SqJVUCXrBuQ7Zawp7m5fRuGHM1Zh14XaVbIfU24kz6GZEf1hjhjxzKa+UTSJoDKekbnQiFrVlc4LXBajDf6aVWOXfgA/yPnjrDrkHlKshBA7hA5yIHFluVDtWG1lOSH7KmNhN55T2p0Esjw1WQlMJHERAKVPGTLwzlsksEo4SgSFbm5P1ZYYKeNaVZxiw0soPe645t6Ug1BJY7K/QwbSoZNiakqjIbOhWm1pxGEQuAxu7YUYWaq2Lk6h8Ocm8W1+6idji8MPCIUTCYPVwxwgJ2tYWarKClx1kI/QfQTRG6w1ZKHagUalPD60aeEAES7j21WsDjsfAN9SCHCgTdm+gbgbscee6+HQKCUv3l7rsMrd36TD7z7f011zTD6MUWWU9mHCk/ijKWzt0MQ+HUnDGsnff2pcX7lO/jy9s/wtezjfGTxbxxVysjNuFM8CrXXpaHn+cTWz/EnFn+aQPpxC6185fiU5Dgb7PO5z/9TlivP8FzlQyj0OCl3oiD9Hto0yiSfgtjqbk6adVmYfxu5Sbh25SM0Gpce6fzfrHhLrKYpGKtpIa0Uv7L+prU9RhFstsmNnSKmAeAg3rcgc6+JLJ0X2RjdOwJ6FwNU31LeNlOamYFziB2LGmb0LgS+peXEOBHSA4EeCGzb0buIb1Ef4/oVrYIpM2VjDQWGeRF0GeINnxwMFjwLI94QxybNZqKKEO8IpkrYHIxLZowXRicY49h6C454x0AlIn1iAQTYOPQi48aCELhSQHJpjvjGlv/CQzhwNUj9//op2WIN1R6OR2rKIflCDZdkUAq9dvLAkcx4RYXeOY3dzIn3fCIXrrag+G8KmNkK2cU59MY+Tsoj5A0TKLBunBwDCCUxWh4LabCxRlhH8uTCdHuzGmHmqshBit7soDrD46XdpMAdUy2aqlJYR75YR93eHl+nfK7qdWI39hFK4dKj7PlHjYvxc3zhxm+wde9L5PkQrOXq/J/w45poE2ezFfLlGnduf5q7Nz/h5cyy5ET3OpF5PeHJ+9RIR1LDb7jiMhc/+lPc+5V/waZYY2G54R0vhxnppTlPOgkOVSEzxzl7jbXmOYZ371Bpv4fsmPw8L/tNY9AD8ES8dGuLvetf4kPv/QfIRgMZJlg1PYnn2YCt9a8ipCSUFXZ3X2P54rtpVC9gpCAYOK79mb/Dnc/+a3ovXef8f/eXkXoXieFSfZfXWx7HGVQzwrJhuB9geyEmkzxcT+hbJ/LMjBVMopbBKbBKINODKqta3/Ob42tLWKW8HGH7MXT4cou6uw2XFshiicwOVfSMpbSVe535mkKmlqBjyWc1RniMsIly4h1PnFZFVw88REPfnUjIRpb3xT+Djpki6x0XeiMhXziaCDgBSVVS2vVziDT4KpyAeO8RHlwhDpJj8BXO0evjF33SQn9IXiszWelQm/uYi/NjiBr4DW9508JmTvdccMSAx5QVSIOzzhMfTwkXBdgoQAF5vTxlwf1QUJWHvDRmsTHWyzYAUnnb4ZHknHPou9tekeHQ4yfUtCHI+PV+gsqNh/ENjuGaaIVREvmYE+SgNeDy3Hv5w8/8D1TKC3R7G1y48CGqgwjc0cR+aHp8ufUxBrZNYnpU9Ixfs6KH79zMzT3NtUvfz/r9z7OfbTIXXXjgZ0SS8Uz1Q2wNb5PYPoGMzpQcA9x7/RPMBxd4rnSUHO5KXr2ot3GL3cFtanqevtmnn7d5uvZ+pPDnN+vmefEdf40vffVnOX/u/d9yyTG82QnyMEcOM2yzTBIoX0m0zhPm0ofDsz1syEFGeG+PRMnCMtiHCT0et7yWM5iz5A2B7jkkgmh3ZCjiE8j2BYlKrN+xbxjY6cJcjWgI2b4hmVE45TyGWDh0qDBDi1U+uQi3PBEjax44f8UbAj0UKCNIm94eOmwJhisH8Iy8DC7w36GGgmTRkQ29VJfTkFUcJoZwzyfk43CCUV16MpkOugUBLhjBSA7+ljYVaVOhU4fel+ihI8iAzHnmcBFCCLKlOq4eY7QivLuLTA0iz33iLSWynxLdmtZdUv0Uddu/NnxigfaTZfLq9ESeNBVx2+GMm3IeCldbZLnBKS+DlM9VUd3pxFUOMmykx6oUTgryRvlYLLKNvTbvSOJJph4H6JRE9BLvaBQH3r0vy5HDo/doslQfLxzhbm9chTClUVtaEr22Tt4oM7y2SPzGNlhLvNoiHWY4B8PLswR39x7a5OSkaIbLfM/SX6GdbqJl5NtbgSJrltHbXV/BrsfslFrc+ON/iRCC93zw75HpnPZ8Rnn58onfHe4KTDztXJfPQtgDcohnFrn0oz/F6//6H9NrrRL3REEOMt5843CCjJcsmr/wTgZhikoERylEPrKGv9/TuqN8D9Z/+99wbeYDyLqvgB0mAW6sfZkbr/4q1aVr5MMug84WkSojjGNeKexzKwyrkvU//k2Gd+9w/u/+KHfac8zGA5rRgGowvZg6aYlmEpjxkASbPgZW2DchRGqp3UroXI5ImgoTacrrGaOzEfjnF2NRd3egGqO6g2+IUDp1/GGKWN8je9sCeSwpTZp1aD/n9Bc01rlCG954+FeRuKZNjYl95VY4Qe1mwpHtigRTnf59ZP7gDY3MmVKsmPz/eVUxtH5TIfB6w4N5PQX7MIFA5e6R907m/Bx2r4va7yGMxZS8G6HsDr2MmsMrwziH6AxO1GdW6VF3SQckVUHUPiOkrFiTj3PLs+VoSq3EFeoDo5XkoavOgSK9sjSei0WSIQLloSeZOfBMyAwuUKj96c2a04p8oYFeO9qJloPUV+u1RKQ56bUl9P0DCMlx1dTHFc8G7+Xy8tvp6SHly8voWhN9Z3O6C+dy7va/zo3e57lce4HLz36U7f5t6rULyF6K2B/gavGBjN4Z48q1j5Dv73Gr/xVmwnNIMb3OCq1wh867GswSqxoSb96TXZg7liQ6Gb3eBvdaX+I7537ihDf0+frqr7Gb3mcpvsat3lcoqzp72RoXys9R1b7i0p0VfP3lf02zcYXz5973UOf6VsWbDrEQg8y338sh0fVNTKNEer5JeGfnzBqB30jIXjKVIKvUt6jyikTmEG95jU9vunF4AfQl16ypsHuWfN7vdJ1xVNYNVgvyqle0yCuO7kJO7Y7AaXChV7kI9r3zVzLrGfdBMfeIDGq3vbuPsxAUihUAQRtk5ieIYAAJkMw7kkNFgEHsCPe8KoR0ArLpxFhrQT6xSMj85DpAHgrywn0v6Fiqq9PJoY0Dv8EBVG5IrxytSEjnYJgje0OC7e6RBVZkfuHLy35TEe9I4m1Daefgoc0uzxG+vDY+i5HDH4DsJmTLDaLb00m4TPLxhCqsO5GoJ4cZers7lqwL7uz6RSFQYwLSg6olwljyRf95kVv0bg9bCshWGsheQrDaIltuoHe6IMBUQ1R7iCvMSJIr8wSr+48tOR6FEpqZaAJ3llv/GwBb6R0+//K/J4obXHnyB1g6926EkmSXAsrR6bUhgaC8KehfdGPYkJGOwZwbdzXC+WVmL7ydl176N3z3zE/4LsHaPtlJMm5aMNADVFQlGIgTcb6mBKbkEAZ2Pv9J7HDA+ee+a6qKNopue5XXX/5lTDZg/kMf4fq//Se4LMVFOYsrL5K7KjYQ7MttOq9+lQv/p39IdE6QuoTPblxkLu4zyE82JEKAjN6cRfXNDjtStxEHBhNO+1RQAHKzNeYeiGGKfhCW/xFCDRKCTk7SjOieC6ncT9FDh7MFVjl31O6k9Bc1vUsh4pCrntXCd7MKQlow0dnzeu5q6i5yEvLDlubHhLAOVWjPe+hCStJUJLMaGwiSOe3d+PbNuPo9IhPbAPoXQ+TQUl59NGc2B7hCW3paN+aE8e73cLXylOkWQNw2mEhMdRQB0vmAaJAeq15iioS/upX7gsh8Ddr9I8Urga/KAr6yqJWHRGy0xkm1y+2ZNZShgPMp6efbLPeGMmf65GhMDluNyS4vgnPIzgC118VGAcJ6Eykbh+QrM+iN1rH4ahsFj4a7LoVYKT2cxBWV7IlrVspjSnkMwyFsrftzLDqPX2n9DqvD11goXeU97/qblGsrOGCu9Iw3QRkZR+12cKUQszKDDU9PWCfj6jt+jE98+r9hfXh9mqQnBaZeOrL5yeslss0Ezi+TR5UpiNVxYUzGS1/5BZ6svI9YHchfOFWYfDnL69mX2c3XcQIunPsAd65/jVppibpboKKagN9w3d75I+bmnuWpJ3/kzOf3VsebniAH2x1Uq4etRBPJzaO72Jw1TDn07fKdLi7SmJkDbcjypl/obCBIaxKcI697wl3QsWMTCz1wqNSRxNC5EhBu5cQ9B6ljOCfJKxI1sNRftyQzCl3xbHyZHahaZDWIdyHePZi4lPM45IwDHPJIsWK47Kac+5LCLU/1gEhg9PTCkNW9rjLWUb4vUKmfZvLYMaxZ4q2HN0uM9k5OBGRnSHBvD/fU4hF2qxUCSgG25Nt1hxPZcKtDpR6TVYIxA10P7ehUENZipfC61fkEUbIINUiRd05wBijaq9lCDdMoIZJ8rFoxct1zscZM6Dkn13yyOikj+KBJWm91CpvYZEweHLWjont7kBnC+3seyz7IjixkwWprbJbzVsVmfgeA93zw7xGE/jnoz6uxq9wDIwe9A2ZCRSqvQmId0ZZPoqP6HN3VGwD+eZvUzT4UWQi99dssvf+jkHOqgYl0IF/aZeszH+O9b/9buJkqSgpM8XsPetu89tq/p7N7l1J5niE77L38OVyWoufnsO0upcWLZE6yfyWg89pdKheeIOqXyNqWqJniBOwMK8cP4D+FCCT9QkNX5Q5nHWrokIMUtbE3dsJ8s2KUiEQ3toAFeucjkoYirTrykqB2KyHoijFJ2kpBfTUlq8oxEc+7aPqbpH8uLDpeOTKHZEFjtCAoKs9pUyMsR5LF40INHZXtnO6CJt7Jve7wnsEpQdrwUl0j++isqjCxpHs5QvUtLvaEaluS5GV54JJ3OEb39gOy38N/FrlBtLxWrG2UvZFHo+KhgYGgvJGNVZdE6qiu5fTnFbbix22kf676M4ry5tE5R6UOlXjCY5g5HILs3Cz61ubJ+sfGkl31pKnDV3cyOXZa4sIA2U9QcYA5DI1bao6LES7Q5Bfn0RMJ9wMjt+jVXfILc6jVXVShvuFKIc5YVDbAViKv031CEvywybEIFOliE1cr+R2nsaAVOEdwaxNxyvflzSom6bO6/hqN0nne8R1/YwpeI6xFHHJVFYMUfXuLfKGBbZ5xfhKSkq6T2kNqJNYdSyTtpTsEQYWgXHtg5Rjg7uu/S4kyl8pvn3rdacXd8B7X7/0u2imUk2hR4uXbv+EPH0gabgkhBLYUkl+Yp/O5Ozxx5QfOdl7fpHhTE2QXKkSWoxKvqftWIvhcKcA556EW91tkqSFfqpPUJGldUt7OKe16W+n+oiLc84970DtIkPOKJC9DuG9IG4reRUUPUMnBje0CSbyaY0OJuuftU1UCtVuCtOwwsSOPClF7AAluYCndzXHnDjQt/bEF6cCbh4xCpYLMOaJdbxXbv8yUa5jT3jksXhdTFWKnfIXjka6dPqWWUdhBy60uduUoeHQET7a1GBdNm2WIQUawtk+pOUteYAOHs4qgl6Pv7qL7KSbURyrPphb79m8/9QmzFEcf9kCR12L0dgc5zEgvzZKVAsLXfGXClgLSc35SVjtd9HbXV30fdpJ0EN3dmcLaCWM96WaEeT6ER566Pm9hcryXrJHaAa1sg2fe/ufHyTGBIJt5OLhA2POa25OR1cGWQQwt977y2yzMPkdeL5FfmsVZi95sj6vtAEnSptteZbtzg6y3T+WCF6gXCXAMeVs6EC/vcvcX/h9cufi9xPPLXiLSOvI84fb1j7G+/iUaV9+B3XqD+eZTRJc+QLu9CsDiT/9Vdv7lL3Jn97NcbX6AsGPpr9+mtHgRaQTOStKOJqi/dXJU34zQ2idwykDlXoYwDrm17wlZh94rtMTEXjUACtLVIP2GlABEQYKTg5TojW2sXqRzOSB5cUB9pkvyCw3i/aIQkDoyASIQhEM31ii2kUQmFqc8ic93vKYnOFNR45JDKCTpGWx0TaEHXF2dvgfU0EJDIS243FfaTbGhdMYhM0c6gXYYrARkbUO8b6Zk1fwHznihDoXTCjc/vdEUxlC+k/rE7LDKiHVFIlxUdZVgMKe8ZvBJx5BibCsvBqmvCp8yJlsv+fftdnzVNPB4dUqhJ2AV85vILSJPcFHA4NIiarOFahUFhUqErZeRxqKuryKUxAb67MlxEbI3JHhjfUpS0kWBV1bq+jvnG1bmGH1vKSQ/P4fTCplm6Ps7kFvSSwsQ6iknysNhneFe56uExAQy5oX3/PQ09hxPdDdXFj0XYHIzYSxqfQ8Xam+684Bo791kkLeZjw7heYUYF2ucc7TzTfp5m5t7X2V5+V1nSo7Xrn+ae5uf44NzfxYVamxx3dv5Fi8NPkeadmiWzrHfvcsztQ+R2B53el9npnGNa098lK9+6We4mr9AadDA9vt0ky2qs996uOPJeHMTZCnHLTR4CKD/YwjZGcKE9a+v/AXosES/pHyb1jokUN3wFQMXCZLGoX2xgNKWwVQkeYlCLu0grIL2tRCZOOo3M9pPhSC9lFrQ9cQCG0BvzuON8xroDlitPe6PI4fDVCFL3FiVQg0KeISE8h1IZr3L3sHJiSMuWbrvJ8ijB3BebuiUSbN3TjO0oFuG8tZ0NdmWQpInFk74JNjcogXkSpI+sYi+s4ONA4K9vp+44pC4Zck2BbaiyMuSrCTQWkJmUJkhn61gajHCWMJ1z/Kemk+sQ1Qj0qrH6AXbXTD+c/mIlGmdx78GCjKDqca4UKN2e97GPDNE93YfjSh36DOyOyTYOGBKm3rssWSn6Kh+o2EjjS2H6L3jiVS5TfnM9i8CoHWJRvMAZ5yUxMOftgFtBfkhdRajHTICFZa48vyPkpdmEL0EV4k8OdM5+t0NdFDms5/+H5BBSPOZd/Hkn/97SOWnHzUAcyhBVkYQ3jfc/be/wMrSu7hw6TtJdc7XPv+ziMwwHOxRrS7zwnv/Jt3dO+w6x9WV78Y2yyxGsLX5VTqf+gMW/vJPsvnPf4bB0hYz1ffQvvl1rr7zOzGRP4+8H/wnnyCPGO1WUUiU5eid47t46fLMlFSeAaS10Op53KaxqEH6UBySyQRG9hPi61vkeoHhVU2/FFNDICS4NCfcd0RdC7k7qDAWn3VS+OQ+kJjjLIwLNSJhHGI/hVmNBAInSE7I8IfnQ0RiiTfzsQlT2LVjeIaR0LsWIRzkxXSth9Ybr2z7a5DMe43ZrK7I6wrVMwQ9O1bnyWOBzDy++hsOpRCZmeKHnBTCOEqb+eFcbCrKO8YbTIGXaTsEr3FxSL7U9PjgzgC5uU8w0aZ3mSF9cgW0Qrb76NVdEAIzW8WWosJcRuAWGrhWzxcr6mVPpFvfQymFzQ0yfzRYz2G9dbXX9eoJI8LjY4h8eWZcwZVJhr69OS58qJ22dx88pQtzt/8SL298CoCl2jMnSrnZOMReWULu9/x5jL4zUIg4ONM+K0/6zIbnx1CGUQjncLt79PMBm8lNrnf/mHLQ5PzFD7B84Tse+L39+9e5sfpx3j/7pymFddYq+7x649coiwr72RaXmu/m0uIP8Qd3/wVXKi+M4R3nZl7g43f+J5yzXLn2J/jDN36J5y/8KP29m1QqS48sa/dWxZsLsRCQLTcINvbfMi3CUcjUTN1QAlCdIbZeonEjJdju4SpRQRyE2j0/2WVlQffiIRJZQ6I6Fn2KLqiNBIN5hXNQvp+RVXyLLugJEI686qvBahvSpsNGYtweM6EbQyMATABm2RG0fLV6VBnOYofu42ET1pI1i/crRx4K9IQKWl5mbBc8GXkEJnZjLduTwkhgRsHW0S+xpVNwSlqSF5OHlWKMVZb91Iu4z5QRUlC7b2hfFhBLTCwwK01sJfKEjPYQVw7HDGXhOFKFscMMEWq/WFiLcL7FZ8shsj0kuL1NtlAnuTpPdGcX3R2Sz1fR2x3EaON0TJZ4nAX1A0PKAwx0KfDnetoO5DGENBZ3inGDDHxFIC7N8p4P/f0pkXVzVmjF4TjhGU52t0BAuTKPy4yv2tRikv4+L335X9Ju3UZIjbM51qTUrjxPUG2w9+rnwTpm3vleTMVhwmLRMYLuv/s425/5j1TjRS4+8b04qei37pB090j6O1y4/GHu3f4keadFN93hqWd/nGy+TFaT9IIuJhlQevopgqUFlv7rv8P2z/0r2p/8X5j70Pejrq2QFdAllwQPSdP/9g0HWOl8ZTIKplvCyps8uEP6uIBXe5k9kJ8yFJCo7gC11T61tXxcqO6Q2tfW0dkSvXNl4n1Df1aBCIl3DeQjojEE+4as4fHFLhBEWzmZBUq+Im6OmZKdEuR1/4doM8MpgZzTx24KjQBi31U0scRpQdidrpgb4QcjHcRrKcJCXlGEPYPIC8WLws7a4avdavdg3lQ5j3X9OxUnekjWUIwGdVLYA5ifKCqUthJj6yX0dhusxUnhreNnquhWFysELgq80gVe6xqtkPs+ITX1EmbBu4nK/T4uNNhyhLuy6OEb3SHUSpBbjHh8j954nP3Ef+djsJcWgcI2K4h+guwM0N3BWMXIhRrVfrCxSqR80ebpKz/EpSsffmBxwjYq2EbFc3rgiNnZabHfX6URLB6ReVsdvM7L7U+SuQMYRzvd5L0XP0CeD3nj5u9w+dKHiaLpjoXJh3zlCz9De7DKc7UPU9FNnHFsrH2BSJZopRtU9Aw39z7DWvtrBDJkJX7Kj7sSs8prBEGFcmmeRv0iUTzDjZsfQ8mA5575M2c+r29WvOkYZFsOxwzptzqmoF9CIIxF9FN0HKB3emRKYuohOEeY+y65HjiqdzP6Fw5874cLmrO4zCazCuVA9yzCCqz2lYO0qSivHtywasDYLhq8U57a9X+XCQds5PEJFJOeAhsVkIqdwmWv7jCRr8B5lQv/IZVwpKoMBTY6OFtGYATeLnrHjnU7J0MGEnucNvIh3K0YpGTnmoiC5NdZVsTb5gAjWFQwXL2EXvNueHq7c+qiInJL0EumksRwbX/6PpMCFyislqhugtrrk16cPZCrOyacVsDDLfiuOA5AWosJNt8EjP1hH/Hcok6qukjBTX2DWuMSL77vb451gkdhSo+2JNkTilY28/Cpzv49ao0LmNkq27uv8vXP/L/H71k6924a197O9t6rlJd9W+3ub/9/iWYWmXnuvZTuQl71ZFa9B1t/9Lu8811/nUp1gfubn6ez+QZR3CQbtokrC/S66zzb+DCEAbMvfDc6iElUTi4061/9OPMvfBe1F1/ECUv9uZzS/+E/J931D1YykTFYCzZRyPjbk4D3MKGHFjXwCZG5NA+9xHfStETE4UMtxA68eUW1hNjrEux0jjDkTwuR5ATdnOZN/28beRiIzNyU019pO0cYRzLrl6qsonBFUmytg2O7ZGCKeTAvS7KKoryaMpzT2Oj4m9iGXiUDIUgrx78naOWeHFjMb/15XZgdGcKeZbAUkEceC60mYBbiDGoajyPE0Kv48IiWxSNZVluNsfUyttVDDlLCmxueMFaJcWmOq5YwSzPeACnN0dvtMc5chBoz75V+9N0txDAjX5nBESECjVAS1R2QzdUwy80De/NjB3RoznvgCfhsWwiv5mRnqqgTOiVnDWctojMg2GxBZrwRzUModlhnuNH/As889eOcP//+h+rcPczzCGBtjlQBnWyH3KZoGWIwvNr+fe70vzZ+37vf/l/Q7m8glX+Q9vZucO/+HzA39/SRBHl342VcnvG9T/5dkkGLl3d/HwG0k1V6psXF0ttYG77Gu2d+BIdlqfEkZpCS2RTCEjde/13e8+7/kiDwc+/83DPMzz3zUOf1zYw3V+bNWEp3d47VKnwrw++iHao99LihawtkKw3Pfm5KdN9i2wmiFOKAoO8IWl7GbRRGOEwZVH9aKQJAKYEpEkgjoL+sxwc+Dv8lDCgnDvQ69w++L973i0TWdAdt5+JBkSkMFxylLZCJIGx7ndj+ZYcpe1k2T7UQRxQrnHQIK5AJRHtnf/CSGd82rNzNxu5RVsFwTpGVBY07dlyJNaGv3kxangKI1Hgb6uLfYduQNCcWoeIPcn8wVqAQzldzxeAEdriU9J9YoPT6JhYwM+WpxNRUQsxsBb2+j+r6XXOwvs/w+ZVTJYmOMyN5UMjMINfbAOg3IzkGbKiO1WU+HCJQ3FDXuX33U7zwHX/rSHLsAHsG8tKRUIWZx6H4/7H338GyZdl5H/jb5pyTPvN681z59t0AGo1Gg2gCBEHQi6JGhpREBkWGpJGoEDmSJjgRDCmkiJEmhoqQJjRDaoYaiZqQKJKiF2cEBggKJAjXDbAN0AbVVfX8u96lP2ab+WOfzJt5zXP13quq7voiKurdtPucPGfvtdf61vcVB4ds/ef/JcnaJl/5lf8Hn/vR/4ChPeJbZXCs6210tcHCT/4UUWuRK4TmDm9DMFVZ2gjjJqjB6KHHe0/SWOKrX/7zxO1l4vYSnWsfZfdbvwBKUL/xOoOb36Rz5UdJXn2dtAnv/NrfYu/eV1BSY0zKq//Cv4vIQ6Um7yZcpA8+gTUS+aRe1x8gWOfQuae+e3r9OKVOtWZ5eoXniQpD0a4jdk+CPNxjzPdiVv7POVTqcDoYycy9bjCGzmm3vJ3p2fDRJb/pzEcUpfRb0VAXB9MXfO54/WI+5oRbPJXjrCtMPVAtkq6lclAwuBLjynE978KEUgI3SAPlzLrAI33K4BgImyUp0Ed9XG80dTyDsmGs/Fv1xxB1T4PXRGOuLBLf3CVfaSNyQ7R9FDLEZTYUglvf5NqQ3dGc8+rFA3qyq1LY00Y3oeQTVzYuhPVED04bw+3aAqYSE93evZQz7bVEGIfzji8f/11qrTU2X4CM2c1b/4C79wKV42f3/t/8zrV/iy8d/S16xWlC6JWXf4r20qt0ll+fPmZtOE8TB8BZNKMVTvJtvrzzN0nTIzaXvh/RHzOwR2gRk8gqdb3AcnINDxzHY75+76+S2iHsAUJQr11OyXy/4/kGyLnFX+K+9qIhlMBbD1qG0osURLcOqEcKl0T4akx855BipYmvxXP0BKc8+YKnaAfTjcqWnwbJHs+45on7p1PhpMkPOKdNCSEYmO0fkbNuXQbiniAan1GsoKRbFJ7xqqd+r/wkK5ADjyggXXEUDUgOOR1PKacx0T4WiFNL54nUxiNgFQyvRzTvFIg8HHvWCbJI/U2NHgcL1qyjiIYOPT7DW27Pn4R44EnXTydHOZEgmlnAiuUGZr1NtH2COhyGktHshOkc6mgUYmspptrJlH+blSayn6IPZnho1QikxFTjpwqEz6JYbmCWGyQ3D6bNd89rQRSPyFi4KHATe5sJt3715/jMD/2b1OrnJyZXEZcqRjwM/pKZQi8t0vihzzH4tV8DYIu32frS3wNARjEf+SN/BiEkMpovC9s8nP/q6rXpY3nTIweW7b/1P5D29oirHa5+8Q+i2h0Gd7/DG3/k/4irK2791b9Aa+EG8SuvgRDc+/LfIR+d8Ft+239Imp3w7bf+FnG7Q1aySopu8tDfxT/NCfkAwRaO2v18LnD0AInAyDANKB946V74cmMvkJl77OvZSQEbCzg6iIN+4Dg/LMDJDdGdfcwra3ghqN7pU5kE7MYGJ7VagksiKidBYi1dDzxML2C8CvEJ6JnGfxsFQ4+LkhJF6+HB2KNCMemDPKgH0jN9KrYioAu6ALwnOTJBKq6jaNzPp1S6Zw07zlHdUbBgdh672MCudp768zzgtELmBt+qBc75JW6kIitOKQzGTrnAwtiQtTWh38RtLITHhylyeDrnumr8WA6ljz12rea1ja0rtaSDmtWz+6KHj9lVY1ynjt4+5u74G4hKhY9/4g+fozw8D1zZ/Dx7+79Bmp6wsvRRvmL+0TQ4fuWl386VK18gis7raA9HewDE8Xz2uNe7z2/85n+PRNGQHT75sT9I7+QuScXxkYUfobss+eWv/Vd8fvEPAmBcxj99+3/ijcYPc6X1Cd723yK7wCDlg4QXYjX9foAvM7yql1J7a5einpC/vorXCtUb41pV8htLQCj/yUISdy04yBcUlYPQsW+qHtMURGWiUCCIH5I0fFj8KcwpJeLccwXoYkaVoswM1w7kucncq0C5EB70EKLsNDttNahyI22jsPhN+c4XDK6yH6SO0lU9L24uYXAtorJVoMee2o4lHjhOXosxswo0j9kXIUcO25RIC1E5f802OkRHQ8xSAy8lxZVO0DA+nL/Z4p3TxrhZC8vsxhKuniD7p45+pl3FrjRDYP20GsRShKa/8rvkKCfa6T2RMoVtJLha/MQ0jIc1/Hkgf3kZkRnu3P8ZVjc+Q72xeuFri8rTTdSXWacLIah/9vsYfPlXAXjwC3+H5sc/w43f9q/grUXFF3de62qdqz/5h2i/+inG+w84ePNX6PyrfxD3G2+Sdw/5LT/xH/H2b/497v3MX8F7C1KiK3Xu/+O/ic8zVPsqHoeQipOjm3zs038IlVRwr1zl1R/4U+RNPyOiKB4eAL3g/ogXDSEFRUOCCzrIriax0SPOCaALT2WvQKWPf4I8Ar/coqjGRDMZw3Ovq8TYq0tTe3rfqgU75OV24Eg3q+jvbCHaNYrNBeKhwx2FRjpTEeiRJF2GxoPTz1TPqddSeqjdz8vkhD7nzjfZ4JsJvc2BOrHooXumwbEA5PZR4A03Krh2Hbu+gGvVUEd9bLuO2u9iV9pP9fnq7v50LnsUNUHOSpJZH1QdYKqC4QFzden0DizpdbZZxbVq+GYVufMQesWTYobW52oJdrmF2j15tsExBIm38pq+SEfZ1St4KXHe8dbgy3z2B/73LyQ4BqhWF6lVV0nTE/YPfxMhFJ/51B+j1bqG1glCXFw5vHrlh2k01tE65u13/j7LSx+l03mJ27f/N16ufz/rldf4lcO/wck37+A9ZG7Ij7z0r/HLX/tvARiaLgvxBgNzRFU1uVr7GBjHjZd/bM498oOI75kAeRYmUqiTEa4SYZcb+MrpjyicDzquFYHKPHHXYtoqZEiMgygEmkqVNIl3kSC/LDiejmUmaSooXfLsBUHtQcgkQ5CTm13O1GyQHYGYmbBl5qacvOTIUjkOTScC0KOC4fVojndqNAyuR8Qnlvpu2QR5Zji2Li9s7Ds35kNL0ZTogUWUC4yfbYJ0HpkV+IoOFqgnj26GmB7nZLKsJ9Myp21XcZUIdTh4Kj68jxXZq6tBEH2Uk9w+ACHwSuJihe3U0HvnZbPOQo1z5OAC+9N3AQEkdw7pLgn2dr7OZ37wX7/0tU/LP/YPScBFqyu0f/InSF55ifG336T1o18gXVSBtOwcMgc9FKh0np60+PEfwivP4JvfYXj/ber/+NfZ/un/mVff+L0oqflU/FsYXv8+3NVlfvkf/afs/OL/j+qV6yz8zp/iwf/zz5OlXYb1nMz04aUNegsRtpzRTPMJgrrv8gyy1pLx6sULlQfkJfbpJhIMrsTooQUfzDyioUOl/pHXua9XMNdXiO4fBGe02c9dbeMXm+G7nUfePwAfdJn9YjMoNJTlflevkC5parsFyUkYx3hVkxH6OF5Ef2V0YvAChi8lFzYFxoMJ9SL0ioyWNbU9gyo8WVuRdJ8NfccDrlOaOUyoCkd91FEfYRzxIA2GEGWALI8HiNKJFCFgoc6kJiCdQ97dR2QG+9JqMNh4yozurEHI5LeYBMnTsU82QvUE36wGjvLwMougJ4ddaiKPB6HBrVNHHfSemcTb9Ds6dbySyN4IV69gNhdJbu9Or29fJlDUfpfbxTdptq7QaKw/0zE8Cpsbn2Vl5RNkWRfwLC6+dmlgPEGStFhb/TQAO7tfpVpdZOvuL9E/ucunl79ALKt8ceVfYWx7dIt9fqP7D/navb/Jxsr3UYla3Nv/BldrH2Vr/CadKByvq1c+8MExfA8GyLaekL+0hOqO0dtdfDWaWtY6FayX44HDNBSicFSOHa2bOcM1RXwkGV/xmAUoFjzKQGUn8IEvhIThpiM5EujR+deIwqNTP0fJmMXZ+eoypUVZPMYSITzZoqe6O7PTnmlYKZqSohWc7eKuI11WlzZl5R1FpRcafrQFM7NomEQwXpLokZ93uwKwjmi7S3F1IZQlgWh4+hrbqZE7jzAOdTwk2ukiC4utJw/VyJSJJl2oI3ODOhqS3D8mfWUF508X8vjuESJWc9J/T4TCgXXowwHCuqCrfHUBfTzENiuY1RZRL4XcUKw0UUdDpPeYZgWcRw+zsMGyjw4uLoNtVpCji+XjRGYY3ruJ1lWEvPy2drWns0v2D5ljo8VFFn9/cEOqf+LjYawzS5+tQdHxKCsQYxc2aRJcBbyDpc/8KEW3y4P/5X/kU5/9EywsvYq+d4RMC5rEZFR4/WP/LA8efIn0/l3il65RWVzny//kP6e6do3Wq5+iWJlR6aict9196LEVT8HJ/gDBzCiqmEpo6HUqcHh9EloA9Aiql/SumvrpNZN3QoY0OjIkPfvQ7LtLIoorS+i7+9NyvBcCN6OI4QfjOQtjuXWEv7EaZNNeD86QNhHYWKDKKSD0MCiyBciWQtNzdLHa4RQ6lowjF153wZgTpcjsxXODaSiKjr6QmpQcGWTupskOmYW1w+8ZBFDUQyXyae955T02N9Ngw0/sh2eSCVP6lQzW1RAoDXr3ZCoHJ6wL18FSWUY3dhrU+nKh8VI+1ThnpdZcsxpUjoRAjkITqG9WEWVGWh8NcP1x2Aw9A5WJCfTeCViPeakR1DMO+/hI4SM9d329G8j+GDHKsGsdzGIzGFuZ09/WNYJGtMwN++ltmos3sLZAqRcXKK6ufupdvf/zP/jv8itf+i9oqkW+uPwvo2WgximhaehFJIqPNn8Lv9n/RVZ0k+WFj3Br6+f5md2/SCJrfLr9O7CtGm7zEhfVDxie78og33+ZGTnMiO8dIYcZwoWADe9RhwPkqECPHTYS2Ch0QA82gsRQY8vSvG3QA2boC57RVc941eEvUN4xiccllxt2+EhgZjJ6iXq64EWmbiqRdRmKWlDAkJdky1wUVDeKWnh+tmkFwCYeN8OJHncC1cNecAWly5rRtQjOZMiFkuG8D7LQMW498XB+3HaxjqvHwYwkDdIicpjPZTfEGQ1QZx0CP6VUiMyQ3Dqg9s4+Ita4RIeMRmGxjZlAqhbjqhf8OLGiWG6QX1sgu7FE9vIy+cc3glh7pFBHQ1wtQRiLWahjVpqQG3zJy1MnI6SxoUmlWQmWy/rd32pmoUb28vKlzS2LfoWa7vC1L/3X3L/9T8jS3tzzPhaY93BLbFWQOyw6wWTExoByfOPP/xkOfuPnuf7KTzAa7vHmN/4GWVOdpqMKx+b1H+azP/zv4IqM4ZvfYvOP/xtUVq+Qnxyw9OkfnX6HSzzZEyZt3DhGPAYX/4MKpyFdhPEKjNcEeVtgGgKbnCodmpogv9z8cA5Whnt8uBFdrLU++92VGPPaJsXmIma1jXl5FTFMUdtHQQf3sBds7AXYVg27FDLLWIcqA6ho4BhtxsH1VAl06qkcGOITKBqCdDmwxZyCy0R/x8qRdbhcqvCS4BjARpfz9lXuMRVB/2qEqUmq+yHbjBKoRFHdN08dHEvnke/snNMmnoVfbAalniTCvLIRMncEfV4oTTnKzOdss6Mo3UNdozKVjfNPOUfNbthdNUbkBrV3gt46Qj84RN8/QB2Uc1FupvQMMfN98pI5zcugqWw2FjDLLWynjlnr4GZMM1yjgnMes9YJn1XSPERh54Jj26njmk+wc54dx+R72jVEuVkRJ0OEL6Xg6hXsSgtfTfBSsL7+/fR6d/inX/1/cXT0NtY+e/v2Z43Dw+/wT37pP6WwYz7S/ALf6P0j7mdvzsVxNd3mpfpn+L7O7+T29j+htfIyP/CJP47zhnbrGo2Pfgq7ufhdw1p7vkYhz/PDnxKCwEOe/p0b8KHJTA0yZKyxEVT3DNmqpmgpui1F3LNUtw3125L+R8Q06PUE849B06FGoMZiqjGcl1qr+iH9YLM0i9zYaVBwtkT1MEQDf2np3FQ8OhXoTNC4J3hks37pCCRHDtc4ncDSZY8eQ1LK0dmmgh0bBPZjgWkq5MjipcA0ggVr2hRUjk+PwgP566tQWPTYE/XNxVqVZ20xrQt2q80E2c9w7kzzkHFEO/PBoMTjvA9p+Jlsi6tGU1ULrxX5tQXiu0dzTXv5SisEzpE6J8hvF+q4eoLIDJV39ilWmpiVJpU7+1NZulm1CdVLw2SevnsyohzlOCWDYcIFz1d1k881fzfHVzNuHv0Kd27+HPXGOq999PfRaG0yXH66DRg8no6rzgX5127R/fY/RW0s0fqpH3/o622vT//NN6d/33nnZ0kqbbK0iylG/EDyE0h/yjMa+h6qUqN24zX8Qo2NP/t/ID6CeCgx0mMaHtsSF6ptPAzOQXqckCy++8bN9yO0VhTNR4dp2YLAKU80DIo5j3qHqUoGN2KiI0Ol5y7VnXVSQKt2ykctgyRhQp3BrraRvRF2NuvkPdZYRKRITqBoK8arEWOgsl+QHFlMTSK8DC6lPlQ5nA/VgygXUz1lOJXHfNawiSBvaLwKlDydeap7BVhPb1lQ230Xq6AUIahrX24z7AGz2kFkxbTBThz0kKM82C6vLQS3vHZJD/AeukPkUcjo+tIqWXaHqGeQaVV73bnr5rJ1zHvw7rSaZm2Yp8VZqUAhAqe3EiOURQzGiHE+fZ2MNT4z4TXNKiLNgy7zBXCtGqIwyP7j0/Wm723XsWXD4RTNKtY6/HJrKsnm2xpbi9mIrnBVvsZ9/w7v3PoZxuMDNjc+x8sv/eQLyShbW7B9/0v0x7tcufLDtJpXHvr6o6O32Nv/BlJqnDN8u/dPOCl22UnfJiJmLXl57vV9c8TG+g/igc7Ka/zEj/9nwPsz5ns3eK4Z5GfZpfrMMdm9Ok+03Q2TyGIdRjnCQOXYIUenO+O8pRhei6hvW6oPID4SRN35SdfWIF/ymJrHi5A9hjMqFQ/BJIkljZ9O7v4xLrl8SV2aAZvYVgsD4jE2sa50tju7OrrklIfq8TgRzFEqJ47anqV1M6e+bantGar7hvY7+YUNil5KfBIRDwMFY24yLf9wzQqudpqSF5TZ0xvLFMuNxwrWrFa4ShRc8yb8OA/R/mkXoe6Nie8ekW92MK0qtp5QrLWwrQp6r0/y1i4YixrnJG/vEd0PTSU+1tPmDznKEGmBuMxcRIpn1igSHQxIbh3MBeCzp0IoiW0kNOnwiY/+i3zhx/8sK+uf4ptf+x+wkbuUyvM4EI+I77337P9Pf4Ptv/2X8Yd9jn76p6el28vQ+8Vf4uB//Kvc+D//x8SdYCiTpaHx8mDvW7x98ivhhWUG4+DB1+m8/v00BvXpNZAvwuCaY3wlqMw8aXA8QdGNKXoPMWD4AONRv8MsipZgtDGfTX7Yu50IJhmDGzFF7fGuL9+oUrwWsp0C0HtdRBrse2UvZDa9VoFWMMwQ3hMfzzThrkQUHUV9qyA5DI1wgtCk52JIVwT9K4SMcQkb8VzIyqYiqe8UoY+iPFFxP2zgpX13X+kAt/wYaf1GBXXUR9/dR9/cQfVG+GpMcXV56gHgS/k3dXMXvXPaSIeUIdO7cxIoYI/AZZneCc4drwx9GpxJNIgyKTX927lpZUxoGXSXl5q4egV90CO+tUt0/wB1MkT1RpAbzGqb9NpyCHqtQ46yoOhx0bji0hznKediv9w895iLNHalPadX7MvHAez6AhurP8DnPvtv8/nP/SkGg52pDNvzRJqe8KUv/5ccHbzJ0dFb7O5+/ZHv+dqv/yWOjt7ih3/o3wfgpNidPvfV458md6fJAw9sjX+T61e+8MzH/n7D86VYvI/jY6dO1SD08Yh4u0vy5g6+WkoJAVHfoTx47RlecWQdweBGROuWpbrrqRxIqvcF8syGNFsPJeSJLbXRjxHktk+77p0W08zy5Q7vp5PRw6rDahKcuzJoVw8fi1WhEc+cEcuPT0J2fHZMnrKsWf7hIshbkspRWLQqx+cnXBmHzzXx9G1hnAcDxGwj3pkJVQ9S8H6uoXIWrhZjW5XpxkcNsmmmGAKdIn1l5RzVQfVT4vtHFNcXyV9eDnQJJSk22nglSd7cRd/aR6ZFoGcUFpEWqNLi2baqD1WYcNXznc7PBLEK9I/X17DNCq4WkV5bIH9pmeLaIj7RSAdXrn+BOGlx9ODbPOKnfyj0I3jurj/g5Ku/jKzVGOzd5trv/lce2r2d3bk71UIW9Rqb//q/OX0uaa2iVMKt/lfD71VuloreEc1oFZkLdO/Cj30XEGSHVdKDyncd3cIWDvUYx6RST9z1VA488cz5fZyzYSWMN6InagJ1GwvT+1kQOJ5q64jorS3UpOegXQMhSHouqAoR5ox0STNe1TS2ijlDpNl/m5bAVMs5RgvcUyq4PAzCeVTuiXs2WFUTrKWB0Nz4IuAJKdmZeajYWECeDIne2iK6tROyxGkOxYTIrTGvrGOXm0/kOJetdR66rPtKhFnrYFbbgYvsPao7mpsjbbt+4Zolxzm2VSO7vkqxvoBdaOBaF1AiIoW5toyvJaf0EyVCL9Elc3GxEOb1h/WyPOyYJkHvE2GyyQOSpM1rr/0eHmx9Ceee73WxvfMVsrxPPz+kWl3k+rUfvfS1zll2d7+OUgnLyx+lUmnz+mu/F4BW8ypVFTZoO+k7p2/yjtQOqLnzm4bvNnzPNelNcJHpgjAuGFPUE5z0JCcOaQoGmxphIVvyiF0YbWrcTAB7NlPuhCdbPv1bmeCuZ+qSaOACz/cMP/tJ12SZe1w886YLNI2lh+pWwfBKBDLIwF1kP30ZZi2w60eKwjuyhbB4CicYrSpsImjfzJG5R0iJmtmgT5J5Ijf4WJdcPUV9x087vwFUYYl2e+RvrE0fK9ZaFOstKncOIbeI1FB7c+fCxo782gK2HfQdxTgnubU/p4XqEo1ZbYYS4gVZEjXMie4foUvbUJvEqKwAfGi4Ox6Rr9TJNmrUf/MA1Q/NJ8VqE2E98f3L5Yr04fBinvOTQAqKlSbR4QCvJbaWYNdauHITMZEnPP/lYfdy9cZv4d47P8dHP/Yp7PJT3vIGlDmvzT2BajVZ/df+KFEW0Wm/RrEePdSPcOu/+K8A2Pz3/1Rw2ry6QOOzn0Ucjejf+nZ4rvoRso3ONEOT5wMalcCjTLqSov3sNdZNP2E0jtGdMXHDBGWCDziUE+hjh128fJJRFmoH4kLHzMeFB8arEfXtoJf+KDit8C+toe4fIIYZvp5gl9vIkwH0RtiNFr0bGoSl/sBSOTS4isQkZSBaVwwqcq6BVFqBdKWAioDxqkBYj1ecMzB6FogGDi9Dvwoi8L2LlqJ5K3+23yeZEsYjJShmficvBeblNYg06uZOsHvvp+jjUC3zmUHf3Z/jMptOHXJDfPPgyYbRH1+6YbLNKnZjMaxthcF1GugHB3OScD7SwbnxklOjeqOQIb4EHshfWcN7R+U7O0CpPbzQQO2cBBOTi8Z9MgyGKuMnp5GYpcck5z8Cjfoa9doq2ztf4cpzNA65dvVHiHSVzsIr1GvnbadnMRhs881v/zVqtVXeeP2fAWB5+ePs7X+Tbvc2AEpEtKPTgCZ3KVokSKXezznQZ4Lv2QD5MqiTEeP1Coffr9B9z8pXHa17BiFCkJMuOcTxvBh9bVeQLnmKS+4jL8oJFDCNi5P28cn5bLFNPOoShYy54HjyJTPQQ4fXgSM3C+GDesa5918AmZcC+UNHddcgLQw2NKYs1dtq+H+6qKnuW6QL5cW57xvn0yyRcoASjDcj4lsFlPJucqeLXajhZsp3k/fYJEKVyhMXud+5SCHGBdXdHbAe7x0+0kFvuBIRPzhBZobk9uG5986dr5nstZopw0V7fbwS+EpE7dsH06y08H5ey/gSsp1MC0RakF1fRBSWaK+HeNwgJNbYWGGWG6FBZKmOk09e9Fle+yTvvPm/0rv9LZLlTz/x+yeQA4/tXP58/fvCZ6f+0Tzg1T/xxzBHx8TXrk4fW/mjf5j9P/+XAEgqi6w3P0a01yNvVYLLl/fIcoMnimCcY5NnP0VbI7AHNYpjj6rlqJohqtoPbLCspERncFFooAqIup54fLHU25PCasHgakyyVxAPHr2B8QTlA7++MJV5tGudQKuqSMynUj7xqVu8+c3r1P5+Qu1+jqkIxisa05Dn+zusp74lGC96TG2iLBGUTfRz8CzIFzXZ6mkTn4tC8sPUZTAKefCMqkczp9K/vYOqxtj1U06sj8N8aSoxshij97unzzUq5wwz1EGP4pV1bKeOuoSzexFUd4SI1DnpPghccrXfRQ/G+MLiEn0uOy0K867sn91SEznMUTPudnKcI8dHAPhIhYTVzFphm1Vkf/6cPC7sQj1oHz8jXL/+Rb7z1t9jbfXTaH2xRvy7hdYVrl59PPpDo7HOjes/xsrKJ6eBdLWywPd/5o/zj37+P6KmF3GuoKXP6+qL3HzXB8jf3fpGTwF9PKJ6Z0DtLqiRJG8I9NAijKe6Z0j2IOp5nHTkrfLycIJkX6AvMclIVz2T+vZlF9RFVAp5wYomJCFqfQiSY0vzvkGlnvRMxtCLh0t2eTxFI/CoBYJkz9B4YALlw3sa93N0Mf/92aJi8Eo8VeTwEQw2ggqBLyV/4DSj7ASk9dPjVcMsTKYXoLixRLHUuPA5CDbP0cEgyLdZh5CS7KXlaXD8buEiFT6vEdzYJvJuxUpj7rc07fMORRMIINrpEe1eEhwriavFmIUa+WaH7NUV0k9sMn5jjfylZVwjdKY/TXAMIITko5/6F7n9lb+N7D19I048fLwyx+PwgOuf/iTtH//iuexG+5//vSx87rcSVxq0PvmD5K+thj7L7S4RCfmMMod6TFOap4WzgqKfkO7W6d9ukd57NpmkFw0hKLWoy6bhsSfqeyr7nupWaMp7lpJbTkC6Fj3WJhxCA5TXKvCI97vo+wfIgx5J31L5iubWb1xheNwkWwxqNFHqaWwVJMeW2oMLrOitp7oP8Ulo9o17pT78c1jtzipceBWy26P16NQh9Gk/OxaMVjU2nsyfHjEYY6+vIIcpavfk/HuuLGHfuIKZcJeVCJry3RFmpYVZaU+b9tAKu9oO5kdPgIuCY4Do3gHqeDB9XhR2TtLv3cI1q9jlVuAqV2PsQoPi6un8OHmNS+Z7CdRDst4Pg69EuLWFR7/wCbC0+Aat5lXu3vv5Z/q5TwspNa++8jvPNfFJqfnkJ/5lhFa81vjBuXlaiQiHxZrn0PX6PsOHAfIEM5SHZOuE5V8+YuHtgnjkQUo67xRUjh2LNw3NB4bmXUvcFZhqmAQFgsYtqG6FBr4J91crga3A6CUYveRJlx2jTfdYma+Lgmbn/SPNSZJeeMFldAohBdVdQ9y1qOx0HJEXaCuIBiIocmhBtqxBTlyqBEhJbctMg93advi3iQJ3uagJMCFj3r8RQTRzDKXuMzDVQQYoVpq45ukkhxRkrVCy9ICvPYKiEClMuxqk2Vae3YRsazFeK+K7RyTf2Q0qJ+OCaLsb5JRmrhl18nAhVpmbuaYUYkWx0iR/Y43xxzbIXlmhuLIQZO6qcciYPkN0Fl+h2brK0S/97FPf9DI9va6fF1S1Suv7P0uanVAIw298+b/j4Kv/G8VGC7G8wLhyOiknA/FYDZvPCsZ8sLnJ1QNo3YPqHlSOgnbw8zoiD2SPsHc+h/4IeTLEVRPsYhNhHO0vH1P9a5LON0xoXiYYm0gL9R1DdDSidj+bOw4JqNSRdKFxL9hRewXZomC8NNM3IYL83fOCnKGZCCXI2uqJpE9V7qnuB6nL8ZLCRgK134M0D0Fyfwz5aYZ68slOCtxyq+Tk+iCR9sp60FA3luL1TVyjgjzsoXZOLreTriUUV5awnctVNOZenxtcLaG4thze4/ycvfS7gYs1sj8OzXp39pCjDHU8QG8dIgan36F7o2ejeyzAbjwfubJXXvkpHjz4EqPRk9FbXiScs3TaL9NpXmdkuxxm9/nS4d8htQPEYgchNUXn+WTA30/4MECe4EwGJTocUP/WLnpvEAKbCfm//F889EQDS77gKZoeWXha9wytdyyN247aOx5hwJa6alZ4rAqd9rYaGvn8U1BThRcPbdybf/Hlj+vUU9+xNG8X6HIiFz1DshsmZD3yWBOa+oYrasrjK5oSlXqqOwY8wVlrHE6KB8ZXIpyC6r7FJILepiJvCEYrKjy+XYRS+QwF3C41pl3WQdINRuvqnI4yAKVSw/RQIsn41RWKa4u4eoI6GVN9c4foIdljW7tArWC2eW/ybylAiSAIX0+mjwvrEGkRGvnOnGoXz2fChZaIMxkaoSXZq6uYtRY2fnEsp9c+/gfYuvmL+N0nLzVOIAfPLyK1ozF3/8P/BDpVKsvr/Pqv/XccnrzFntzmrW/9XXbe/HnixVOeOgbUcyibfzfj3WSKxRNSvvO2Iu08QZDcrIXgbbmFlAJ17wB9PKByc5/Or27TvBcCHzdzDHr7mPheF9W3KC3CimY99a0CPbAkJwbhZpIFIvy7qEFRgaIZ9KDfLdT4dB6cYFbCs9BBO9pHj5675Yy2tCiLj6YqGa1GmJdX8UloGjPXlhEz5h5nf1l3dZnipVVE2WDmk+jUVQ9Q+72H8n2dc4Ge8ZhZYF+JMNdXgspPHjL7cvDuAmRVzo+TPh+v5JROEgbpscst3ETL2bipLKS6pCr5OLCLTdxzcoKrVha4cePHuHnrZ57L5z8LvHPz7/MLv/Sfsnnt89wbf5s3+7/McbHFUb7Fl+/8ZazLiaLLK7vfLfgwQH4YCku80w07dSHQB/0Q3PVT4rd2qewb4qOgEZy3PC4SJH1PfdfSfttQuwuYUlXhDEfYas/omqeoe1wc3v+wbuLL5N7OTsoA48VAnpdm/j2SEMg5Af0bEYOrGqehshuiVVtXRGNH856hdc8gXaBk5B1F2pGkS4qsXVpT9xzCB45zbccEfjGhm73/SozKPI17BS6WDK9EZIsKUxHokaW+ZageXbLaKgmFp3Hf4CcW1M3K9OiLpXpQalhrBQkhB/GDE+LbByR3Dqf84YctQ8V6m2K1GRzwlupkLy9jKhGqnuAFpC+vBPWJQYZZaZK+toarx7hy4fIVjV2ohbJweQdNRe9FoGX4WFMsNxh9ZJ3RR9bJri1OedXvlfphkrSIK214sP/0zl7j55dFFUqy+Ad+H/LKIqJeZTTaA2B/7xscHHyL1U//GO3X5jnU+jFpHx/iGeApLtxsSTNa0Y+VOT336TNKNnKcE93cQR4P5sYhAN0bkpwYCusYrIfA10mo7xqSrqW6kxN3QRhPchTek7cgXymbP5fEu06lx12LHs3Paa56Ov7IQHW3wGeP3mXkZ+MyB5Uji61KsraeNt/6JMJbNyfjp2bOs1cSX4nDWkDgCE9l45w/Z7h0FiIt0PcPp5rJFyEErCEQlYVFbR+R3NlHjJ5MTs0DTAJaGWgqrhqTljxrUVjsQp1iMyj0TIJ816njlpr4SoQXoTo6WWrNY8jWXQQRa1xp1/280G5dZ/g+ziAvL32U7/++fx0d1yncmEKFHebt7Bt0x1t84Yf+9EOb/75b8GGAPAMvLs4ueqWC1mIvJb59SPzgGNesoApBNJLIAsbrcPSGwiowVcHwmiYaQ/VB4AnI3vnFxQlPuu4ZXvNkS/7iX0N6xiuO0dVLFqcLrtGiKRmtq7kSn/TQuJmj+w7hQA8cRV2SLShkEfSIrYasLdEl7UL3LMIL0mXH4JoiW1DYeuBl+3JNyTrhe1RvIsHkKaqe0aZGpR6ZO4qGx0WebEGBVkHB4oLNgE0EveuaoiqIRqcayd56ZBmAymEWKB0rTdJXV3EVjeqnc5JuE1yWQRDOYzs1zEKNYq09zfoObixhW1V8ok8NQmINkUIOcxCSYrVJ+vIKKIkcZNOKQl7ypGVmKNbbpG+sYdbbpxN5u0r+6gq2WQHrkIfvTeqz3ljj7a/9LY5+7h9gxk8+hkfJvb0byCSh/RM/Hv691sEWp9knazKWfvDHz03K8v1vUPVdg0e55l2GoqUYbugnKlc7IbBLZzKXzqP2u6i9bnBkOx7gncMrRZR79DjwjdNlweBaRFEV9K/FpGsRqoD69mkWXGVMucMmguJd9mHZijjHuXYz2WJvfUgo1B+95KoLFEAmwXe2rMk6iqIuw6YjiZh1D3Td04xw2gyKHwD+DO1A9UaXSqKJWGHWgzKQHKaXNvF5SgONdm0anKruCH/W7OMRsJ06dqVN/vI6rhpjWzV8LQncYjlJSkTY1Q4iK40+pAhc6k49aNUP03B+I425uhxUm6yjuLYcqn9PgKJTf+7NZ0nSJstO+Oa3/hr7+998Iq3yF4GFhVdZ6LyMVhWiqM44C02QvXSHK5ufp1K9RDnpuwwvrr6b6BBRPQNHsecF4UFdsPPV+33yl5cpXlkh/s4utlXFrLdxUQiqa1vBqnV81eO1prbjsXFwVqrsO7IlcanCxQQqK3e/Drz04VxZGG/6aQkwb3niAcx2hdhq4EHr0fzkbFpq2twBIIeOvK0ompLkyJJ0LUOtqe1b+ldPFy/TVHAYJs5o6Mg6Eq/A1AgSbgqGVzR64IOUUjNkiG1doocOWXjyjsLG0H81xkmCfrEAU5eYmkCPLp4MTEVgq5J0UxLdzMPGwnviu4dTBQs1zOGtXaRzZK+skL28gt7rE+2dF8ZNOzXU0RB5hmOnjoboYQbWUSw1sJ0qapjBg+NTJychcLV4qqxRXD1t1oi2TjArDcxGG9upEu33EaMc20gCT3mU49rnV1wnBPmNJWQ/PefQ96LwsU//IY4O3mJ3659y8nf/e17+Q3/yyT4g92FlfM7Jg4V/7vez/Pv/GfzdYx789f8Pa7/t93P04Ddo/fDncRFIA7J4PioWH+LZw1QktiKmPQiPg4s0a12zhl3rBOe33RPM1SV8I9xrUgriQZjH8wUJKg4VM0KGN2/KacBVOQanPbZUurAJ6HfByc4755dSNTidd1wiGK1HRD1LNHx4ZtNUSgWMrXkFDD20mLoiXSozth4qOzlRTnBI9X6quCCAuGenHH1fmw8S7Xon8IPPJCp8JaK4toxXCjqhke+s6oTQkmKxiToaoCaOfNWI9OoK8e3dCznNrl65lI88CcCLSoSPNKo7xHsQ1XhKCZoonPhGheL1zbBRHqaovS7m+gr5S6vI3jjYTOcGl0RB3aI7wlxbQfZG6K2jh5z18vgBZpr+nhcqlQ4//EP/HvsH3+Std34a5wxra5957t/7pIiiKl/8LX8W7x1b279Gt3uHTucV+oNtmo2N93p4zx3Pf5WOFflmh/Hra4xfWyO7vvieBQdPCzXMws7Ye1wlQh8NqXx7GzUO6hZ5OzS2VR8IsmUYr0q8FpiaxFQlouChM68ygvqOnDbfpUuevO2xNeb4cdmKp389KExAkIEbXPPknYuy06cybABeQ94Jf2eLiuH1GFuRISidyWqYRFDUQ4krHgQucnVXUjkReDy9a47hy57h1Rl+XVPiZAiA8xnOoZNBUk7lTDnH45XL92RRmSUxGrJSSk7dPz51qRMz/zMOkU+6pWcW0hm+b7TbOxcco2XQOy6zJ8I5fCUiv76E6o6RwzwszNahRjnx7QP0bneOS6dORuidHtHdI/TJiPz6EsXVBUSpra0PB3Pan2fhmhX8RTzo54wiH+KcZWnlI7zx8T/I+GiL3Z/92xSDx+ckCwTaPefouISNPO7VDhv/pz9Fb3CHnZ/+6xQdsHUo2pAte8x3v1b9dwWkh+gJFc/8Qj1UcAjulR6Q3SH6zh7RQS8oWtw/RN8vS9XGIz3UdixxLzTjTWbG/IIGueoRU2pY0RJk5R5YP6PScdw/nXu8D4G7i8XUuGQytvGyJmsF46rxsma0EWErci6LKQhNifUH+dQwpYhhuBHTvR6FpuYZeHhoA6tXClsP1DWz3sFsLOJqCebGKsxsTOxKey6T74FitYNr15El8c8uNCjWF4Ps1yUNf4/TrCcKG4xByvyQPBlOne/kICW6vUt0cweMC9fCKAvNevtdopu7oCS+U0fvnkwl7VRvFFRRjoLkjdDylA539vtjhb2xioufD/d4Au8dWdYjiupc2fw816/9KLdu/0O2tn4V/6gO/PcIQkiubP4QH//Yv8A3v/VXuH3n597rIb0QPNcMsq9EjN9YBwKHLH5wjHgfZ5AfhvjeMegu3jiya4shQ+gcybEhXyhtPAtB/UEpSSw9OIGPBNUDGMYed8nGNN4F1Xele53HNkNCwJyRCRKAHjDV/hSERj8bARFc5MwQSUnhHLYyPynYMo4clNljG3lkEQKgwdUIZUMGpHLiGNYkyaGlqAmSY0G27MmXQuCrRwIhuVRZw0diancNXNx4BxRVga2IkOlpK4qGpHJUoLpBn7hYaSCMQx+PELkhf3kZV4uJ7h2hu+NAk5Biais9By0xzQr6eDSnj+kSjeql6O7W3MvlMJt+jhpkiMxglpqowwGuGiOcR5fj8iLw8GQ/mwbjIlb4C8p66nCAX6jjnqCT/VkgHR/x9rf/Hof7wYDjxqs/yer6p3n1o7+XQX+Ld/76/503/sifQerHXBgs8IQCBU8LZQVulJPduXv5a5wg2g/NVzYJmre+Nldo+RDvMeJDg39CExKPoHhlHdEdoraPpzkGMc7DnNWqhfupmkBWoDKFqUnqhwaZ+aDAcwFsBNlCoOdUtj3jdYFTEA9DT0d8bDCb734DO2vPrvJgl50tavrX4zAv+rBMGAnCSlwcqnwAuvAX5lS8FFQPTKjMNRVFHWQuSJc1LhLokZsmGh6FoCmcoo4GmM2lQE0QAqpn5q4ZyoRd6+BaNeRBD28crlMPutWDFP3gAKEU3tpL80G+EoeExhl6h6tE6J3j+fNnXeCcT/7OTNBzHowRuUGUQfckw632u3NjnUAO0mkGuug0EIVFdc/TRoq1heD+95zgvefo+G2+/ut/afrYD3zfv0GzscmVzc/zYPvLZHmPl1/67c9tDO8G3rtpAB9Hj6ds8kHH8w2Qy7tEdsck2yeP5fX+XsDV4yBJM35EisM4hJLT8rkoLPWbI0ytjWko4pNgNzq1aY4gr3hUBiSCi1SQox5UDhyVI8egHtropAcrPX7mXhVAsiOIZpqSRBYCCKs8Rvtz/FA9dFR3c8zL8aVOfRNeoZp5rxThM21bUTRDebJyYLEbinggyZbDcaQbHpl5XAzRMSTHEqEgbXoqfYG/IJlgdNBIrh65OYm5vCXxSlDfMpiKYLQOlR3PZE+h9wfTphKh5JRXZpYa6EGG1xLXrFwYINtKRHElpIf08SlHzyw1kOMcfTzCVSPMQp146wRRuKBcMtFvdg590Cc6GJC+sRaahyYZaM+8YUipm3xRKKC6Y+iOKV5efuYybg/Dl37+zwFw5fqPsL/7G9x552e5887PAiBVjLM54/371DdefrwPfEFDlx6Gf+Pn2P6l/y/Nj3ya5ud/6MLXRXtM7ws9Lgcooah5TMNj6+9r1/v3HDJzuOT5VvXi9Onmfg/4dj04uM1Uccxy67ThjGB8UTEWFzUo6pJK1+JiQXFGas5rwWg9cJVlHoJYh0eNw9/VA/NErqYC0AUUF+wtx5sRta1iqoccD4ITqbR+qp882cR5JabBMUB0GKJrH8FoSRMNHXH/NPitnVh6TRWoeeU8m7cVRVshtkoXv0Tijb/UmMhXYswbV5C7J0S3d7GLzaCLfPZ17TqU3OYJ/3fq0jdpPK4luFYN1R1BJKG4+Pe2rSo+0kQzRh8QJNXErd3ALGxWQwCcFfhGBWY40DI3iN2T0Azdqs1lu8Ul2WtgSp3z9QqkOVxQNHvele1e7940OH7t1d/D2+/8r3zla39x+ryUEXnWf18GyAL4hV/8v5CbIY36Oqurn3qvh/RC8Nw5yOpwQLT99LJSLwL5WhthLMndR3OUsI5o+4Rio4NPIkSsaTywDG5IirokGrupDXRe8WSrl6hPGIEYe2QerKp16tEmBJBi6GGmdCy9oLIFKp2fuZWQkFkUk8DgFMJDY88iCqgclKYh6xHmIUlCp3xwNJ2ZZ7yEaBiyGZVjRz5TyvOcUkDyRYhGgZecL3lcEqgZELrHvRLIwpOcWNJVzbCmaN3Kp01uk0BdAFEvZFJsQ2OrETIzgXNmHWhJvh4mcZkWuFpMsVgn2u8j0wLTrobJUEq8FCF4LgNakYXFb8rLizWubI6QuUH109B4Ui93Jt7jpcBrFcxIgGinO5/9kAKpBG6yIBgbTEtmOruDBa2ieGUlNGM8RnCsACEE5hk0b/zWn/rP8N4hpebGq7+dnQe/xta9L5GOj3A2lCKdefzKjn9BnQvCCKr1FVRcYfHV76f9sc+QnjhcS2BlaHpKduc3jVM4iAaB+oSCvOEx7VAp+RDzkMbjnrOk6ZNKxJ2F21hE3tqZJln0yRBXWie79QXc5iIIQW3fYEUw2ageGHwcqnh5taRQJKG7WDhPVM6Zra1TF0GnBTp1lxljTqEsxAelTr7zDDcjzAylTWYOG4m5W10WnubdHOFgeD2ZVvEuwuBGhBp5hD611XbSUCnpFSL3wXvqzG3rgWwpQt/PgxpGJEJT9BlEAowP069b7wQOSHHxHOBqyTQpMKE1ECkQKliTO4ccpNj1BdQgRRQOGSmckvi0CA1z5fkVxiLOJjGUxCcRMtb43CCzAmEdrpbgKzEQAmQfKURRZqf900nIicLg2jWMdchxNkeFi+4fYBvVoE40CahrCbI/xi02QqXiXaDdvs6PffE/QUqNEIJ26zo7u19le+crOFfgXIGxEu/9+04hwgMLep0+h/zA9/8baP38edrvBzzXpU7klvh9HhwDxPePcdVHl5cnvUn6cEjUS8kX6tjVJgJoPjD01xVFTdK6XTDc1HglMEZg9Rm5NS+oboEoJE760Bi3Y4kODWZNT79MD4JmcnQcgmNlIdk3OBUWHC+ghjpnMy2Axv0CUXZDV44CZyvZKzBXzh+nMiAyh4nL5ic8piqxSdArHmwoopYI2Ypy5YiPBPmiJzkSFAshMLbRzEI4c8jSeKwsmxDLNcRq6F/V1O8ZbEVgGxJZStZVjxyq8MQnjmKzExrmEo3wHikETgjU8Yho64T04xvBWYkQUBbXgvK/GOXooyF4j0oLCiEo1tuo/phofzA9UZP3Yj0yN+SvrISge6VJtN/Ha4VtV5Flllj15idl06zgo9MAGg/qeIgrtZqjW/sgBMWrq4FaIc5ucsIaA6ClwDiPTIvp+9yrq3MW3E8DISSi1KOL4jrXXv4xNq//CG9/+++yv/Pr1Ddepbr8mA0XOmh6vwh4DfWPfZJPfOw/Cw8UUDmU+EOPjwXKiEea5gBgIe4K4i7YSmh2dY1H22F/r2CWfqWlxFr3RFnUx/uSd/d2pyRmtYM8HiAzgzcWkZYB1+097GvrYcpx4Z4aLiuEh/qDImx6r0QUDUlRCb95sSCIRwR7+plGtXxBET0I1a2JAsRZKAONB3mIMEvEJ3YaICvjqT8oGK/o6Rw8waQXQ58Y7NL88qs8xHsFTkPelEgTNtZSCdKqx8aarCOp7RvUyF/6G9mJcoW8fGNSlMecLmtsRVKXC1Mb9+l4to4CrzjRYMMaYpdbEGtcLUGeDFHHA2Sag3W4Vg0hQkBfVBPs5iJYh767j8hCUCzSArvQQI+zU77yJAkw6THJTdB63ljASYE/jhBZgaslIUNdQlyiwvEw6MMehRC4hUbQ2z7ooQ/KBm/j5hQ7XL2CKhv7xGCMv7ocMtDvAkqdrr/t9nXa7etsrH+Wb/3mX8fanM3Nz7/vguMJPvkDfzTwv5+TRfb7Ec83QH6KC/i9gMwN8oKO6bPwiZ42YvnCEu31cInGt6tgPM0ty3ghTJKN+wXDDY2NJOMrhMBJgTdQfQCiEPg4ZJI9MFoJqhMej61CfALJ0UxGwkP9XjFHS0gX5FxwHEuJsY7o2J5TishbQcf4LOITS+3A4iZueWWGs39FkbdkkJfzYbzV/SCqL3wYXzQUyCLI2GXLfo5zVzmZyTSXC7CVYEteYLboyAuByANv0IvQhe6BoiGov3WCSAvMWitYfsYCp0OWWnbHxNtdbDVIHAVZtpBhlmkOSpLcPZxmm0RmUN4HZ7xZlRIpoOTd2nqMeWkZV05Otl1F7/cRhQnSbIUlOj4vqi9H+WnGmVIjeemUn+UjhRrmJG/tBu76meY8W4SseLR9gjocYj+xiTwZlWVRj35wTP7S8rnvfbdQKuIjn/znuf7KT/DrX/lvGT64eU5j+CIU1RcXVDrh8YlAnOl3FAhE/nS0CZUKqqmAA4+pCEzN4+rnN7HfCxBAcmAoFvQ0fo23MvAw2ni2jUouEsjs3Z1j16qFICzNEeMct9AI2cbjQUiFTqYcD/VDS9qU06RG60EBOibuQrpUqv9EHn0mKDQVyXBN4y4JjgVQ2y7mgmMIQfEEchw04ifNdGcxax89eW90YqfVR5yn+mCEryfkDcd4IyLvlFU2r0iXJclhyC6fHcdkjADSgrpEd9mpQEMyVYlLJMMrMY27+dxnuOUWFAZ5MsQrgV1dwLVrp6/xPmxQ+mPQEtkLEm9eMA2IZXeIzIq59/hmlSKJ0Ld25m5iJ4Lpia8l2Gunc7FLIlRWBHOma8vEO8cXNgP6mUz1pchMoHdISXF9GbfcwkQKdTSYjjn8KBKz3iF+Z6ccN0TbR5iVDrSq07E9C7RaV/n85/402zv/lNu3/zeuX/0RlHrxTdyPgo8jeP8N67nigyUn8R5DlsGxK0XJkQJZGNTREHU4CBPbYVC28MJT37ZUtxzJHtTug6hlOBeaKoqGZ3DNMXjJky45smVJuioYb3jiY3EuOK49KFADMyeSf7YJRRwWVLfNhU0aUerP6XRW9g313TChDa5GOB2CSXkyorlliQ898REIbREtw+iKJ1v2eAmuEmS2AKIugcPnQOeCZD8EMBPMjkYUk1JmCKrzpsBpj8djYkgXJemiwqy1KF5axpdNEyr3RKPQ3hztdME5fKSQ/RRXi0k/soGvaJK394nf3sdW53nXviyl+aX6aTe7EDgpMEt1ipngGEJTqW1XQUrU0ZBis4NpzO+cXawoNjvok1Crza90gqnIjKSSXSy/r7BEWyenj+uglz1x57O1JNhtC4Hr1KZjlIMMvdNF7/ae2pUqvnuIvISqEccNOp1X2P/Vf/hYn2UaLzaQNM+rkucEeiSoHEhqdwTNO5LKnkD3g9bzRcll+czTqu8t1DDIPTbu51QODFHfolJH9hClmYfhYWdnkjh4FvCVOEiQEYInu75wvvHVeOK+ZbQiGayGjXf1wFDbKahuX5LOFgKhBKahwpx1wZCTA4PMz8yvWpB21PSasY2y0e6SDYEwfo7zXd0uSLo2UPMkZAu65MoWxAOH7loq+0DhcIklb3tMXdDf8JgL9JtFyGkQDd2cY6mPBcN1ja0KpAvZ7LjUr7dRCJgnNtyeUjmkXsEtNLCrHVyzQvT29rRxzjerp6pBxqG3jnCViOK1DXyiid7egtzgZrKuk6SZizW2XSYSSjqai3VQxLi+Mp2Lhfche1yJQqBtHfnm4pyJDICrxI8Ojufe4NDbx0jncO06xY3VoLs8gXWomQbB6TFuH6HffEDy9jZq++jSefVJIYSg03kZYzP29r/xTD7zQ7x7vDgd5O8iTBvBvCfaOdXftcOM/PoS41VNtqiQuaN1y1DfDwv9zkYUyKUaVB64Ck54XAeKWak25Yn7TLPD1W1DNPTTyUikBV7L4IbkmFqamoqgqEuqB5az+TWZe/TIYWoz5dRSk1QaaN0uQuBbjRCVCDws3DTYbcFwUZEvQN4G0/LoZkHWianulNk8JXAVT7roqe5A3Lt8qZxYrXpNmJBjUCbQObyA1KsglK8FYpATPzghv9LBV2OcgvGKQh42SbZOUKMM1RtTXF3A1pKpQopZbmBWmsjeeMorr2x3SZuVYEDQSFC9FB8r5CDDxfpc05xdqGMX6rhRTnxzHx8piutLyNv7yFEBWobnZ7LHIjXo3S5m7bTRxbZrZElEfHMfM5NZVoa57I9rV8nbVUQpJVgsNYK+MhAdDEJ5M6/OT+IPgfT+dJHJDPrWQeBAz7xmPDrk67/6F6k2V1j9/O945Ge6xGNqj3zZM0XQOX4BgamBqC+I+uX1SahoEIRlcNY/vsX7+xRq7EJA5IM7ZnJSBorGk3QtTsFwM8JdojRz6ecaT227QOYeWxGkixpXVo0EQOqmQduzOoOPyuB54PjVmMFrHq89xTcFnbcL1MkAYRuMNxQ2gbjncHEY62jVYyPQWalOo6G+e8qBktZfmBV2MnCecR5UMFEyD9F8rgwc+cLp36YqUaVkpXSQdC15XaJljHRQ2zNUu47GXU/eCGZNAsgc5M3T/hPlyiqdgKIhiQengbytCsarEVYLBnWFSh3JkZlbD8IxQlGTIcniPeqgN3XS80JgFxr4hWCK5Coxpl1HH/TwSiKsQ1hHdGc/0CQAt9rBSYE8GaB3ToLBS2+Ea9XwK204GeJKK2yR5bgr89UynxvMy8FeXt/aRW8fYW6sYjYW0PdDo5+IFXa1jXhw+NCqtY8UFKcKGyIr0G9vI7TCFybc5/UKeI8YZUHdY70Txn32s4wNdI9hiq1XQmVjnCPHeVDCWGzgpEQai88NvpYgStMVt7l47vq9d/+XuH3n51hZ/gSLC69degwf4sXiwwD5GUL1UuI7hzixSDRw6PRUqic+SFn8pzW6HwMTB2OPZF+QrZyfRG0CJjmVc8ubEmFCOVDkPtA8RER1xzC8GkqhybElW1DoQeh2nkXelMGJ6cxXjTcj4psF3pWBvvfYtdZcsKitoPXAwAMYripOPi4QHY+pQ7rqqeyF4N+LoE+brXgq+2c40TLw4aYNJcrjK2GDULRBHYLMwutQAlsV9K9HNG5B9tpqKNkpEc7ByBOVPDFRNsZF944RK03sUh19OETv97ELNVyrGgLNwwG+sIjeGNq1IBU0yvGizBSedWcjlOu88/hqFKgZSQRSkK93woYgCeddeD9dHKLDAfnG+S5wV4nIPrbxUOUKMc7xlYj4nX3MSgOfaGyzgq1GRPsD8msLQSP0EqjuKFwXzqMHWeD6NRLMciNUBQB7MMAuh8Vtf/cbvPWtv8PVz/wUrS/86CPpCh5PuvTiaQhnG1NfFAQlv9md/v1BRzR006ApGrq5I/ICxpvxuSrT46C2Y6aunSr11LceoQb0nCAPeqjuCOkdthKzIJdQJiJrS6QX9G/E1FWd+DCneVOTrsppcBzGHhzxvPRU9mF0VTBa8tSC4zlOCYYvJ4iRRaeepNxsyDwkKtyM0+B4M6Z5Jz9tMCBkZ/Nm2Jio9FR6M1vWqCysFxQWORwTu9NKlwDIHNoLkh0DwjBeUKgljWkIxmtB8Sc6KLCrYV4ar0WYmqVyYMg7imxhfqm3FUnRUnPuiGlLUTmxpxVIIbArbdxKC2EcXqsQuJfXjto+RvXKuXii6lNYzMYi6rCHyA1yMA7UmE4DN0iRgzRQMlo1nJIQq1BxK+x5g5EiuOZN5loiBXm4tlytgqsn2JV2mJuFwDWrl7r+AdilVpCIm63EOY+foVfKYTBxcosN1NEg0AoIiQ6RFnN0ESDwlrujOW60HKb4gx5RrPC5DcmN9U4wMhllyLe3MUtN3GITawu+89b/wvHJLT732T9JpdK5dPwf4sXjvQ2QKxH5ciMYKzxKYu19BFfRoZv2Avkc1U+pvbWPUxJXiTAbE8UFQ/3QoX+95D62NTZ+eMAxUcMompKiNM6oHFgqoho4USNfNnKEDIbTPrj7ASISMKOj3H01CjJBA0fRCJ9llCerQjQEs1rKZoigCWqTYPfcW5NUD4KNa+3EkW0pRjqBGEwTxtqT7AuqW4J8yQfHQOeoHJ4uPONFR9EGLCgv8MJP5Y1cW1CkPigRzMT1Jha4qkCNg1oBJmw2kq5FGIdZrKMHKeQhIyCMpdjs4GKNsG6qe2k22rhmQnLvaGos4qUge2UFvdfD1ZLTUl8Jsd8nfbkVNihGkL+yMn3O12I8wSzEt6tTd7zk9n5wQaxczN18lKybzAy2GpNvdoi3TxCpIXtlGeox2UrzwvdL61DbXfBBl9nW4qkTpKtGuGpMcvtUTsnFikFvizwf8OY3/gZvfO4PIz75ymNxeYtmaBh9kdC9IIP4IZ4NZnmvZ6+mdFFhyudl7kCKx8ok64FFXsJznYVrKEZtSXJsH1un90nhlpqhzH7URw5SKt/ZRh+2MM0E14hJF3SovFViGruGSldgI0G+EpHVIO+UnNdYYKtlE5ya17OwEmhpTMNTtBXxcUm7m1GwUKXjXV4VYfPsPHHfIW0IwEcbEbWdguGNBBdaOMhbCpkbbCdCx6d0jawdsr15U5K3FO07OcIG6+nkBHw1JCjSFfA6mmu0K5oKpy//HYvGfE9KsaRJhm5Kg5vAE5R8iqoIG6tRhigMqj8qFxtxSv1LdOApNyowCIHwBObqMrIw0DuVXLLNGq5ZJb6zh1lpBz37yXk8Hsw5/dnNxdAEKEK2XoxzxCBFDFPcUgu7voAsDGJCp4PAUz8eMKkL+SSCi6hqsz+zdVM+sr6/D4DrNPBKEt/bv9Ap8NzHCYHPLQgR3B7rlUDDLD/f7+3T9Xvsn7xFmh7z6U/+q8Txh65H7ze8kABZJBo8czs1CLtN2U/Pu529zyEyi7iEeyQiBWmBIjjwCWPxWqGPhvjFOkkPql1H3nUMNjSV9SFFN8aO5wMrWwVXZs/U2E1d8dJlRb6kkAaM9iETURd4BMKAjyX96xpblUR9R33LEA8dY69xSuDLOVEA8ZEN1A3CAhkNHKoIZTZZNvlVDixqHBpHROpp3AOEpmhDsQy6BeOKJzqC6gPB6GqQ0+KEaed65UBSPQ76yPmSI+pk0E9wVuC8J10H+eB8tjBtS+pjG7SKZ9RQhHUUG22459B5mGx1P8WMC+xSY3p8UM55zoP1U51MWU7Q+mCA1GPSqwtTXWUIXdR6FLJCQkCkJLlxRJnBJMFYZZaW4Wox6cc2g1PWU5qA6P0+6mSEbVXJX14hfmcPdTTEVOPLZeGGGfpkpnFQnJaxZVrM6YJab/hHv/yfABDFDbRO+M6v/hXEr8GVT/wU7U/8IN/56b9AvLDC5hf/AHFrcfpeH0Nxukd47lAp6IEg7n7ws7bvJ7ia4pwuGOHen7VKjvouUC5iiaSkcXkYLyqK5qmRRXQU5pZHIa9L0rVw34w2JFHfUts3z16cWgjsahvXqKD2u4hxjt49Qe+CWWyg087pS4Hk7X18LUGPmqSfipnMGl5CulQ268aBFjYbNDrvMQ0QVmDi6Bxf3dTDORqvnc7pThsqx5a45xhtRKRLGifKQPfYoEvVjNF6BC5QOapHlrgbEgCylGsRxiOGGZGLqT8oEFaTLQqKpsDVIN4/la9DzLupCsKGxiQySN81QrWydhyqZQ4Yrkc07l/cAStckEmL7u9Pkxmm1E3We+X8LMP3OSXhTOIBCHSK0plPeA/DDKkD9UEO0hBQi5BQ8FrNaxunQclCOoe+vReqZQc9bEn5ENZRXFlGjIL5iRxlyO5ouhZEh338ZRSMmeMVEILsSGE7DdR+F9kdBk72xAxq5q1RNaYY53Mfh/dlgF5HGIu+vTt1fN1Nb/HVk5+GHahEbbyEr3ztvyGKanz6U3+UQX+Lt975aa5d/RGuX/vi+1bV4nsBLyRAdrnBe5CJhux0gvbWTR3JPgiQkcIIEayIL4Er5l2EZo9P7XRxmx2sCJJplUMorksqayOGt9uoEfhE4JTHNoDjwCurbxt6r8TYxKOyUmA+KqkYFRhvepp3JdiS6lBOikVTMl5V1PZCmaeoi1Aq9p7KvqVyfDpZREOHumCfEo3LmUMK8rpgcFXhYoiPwDQ9GR5VteQrcvq9XgQaSTQWgZcYC5z1eFV+lj81EaluCbIFj9OBnq2KIKIvCAob6qBPvNefyyQAxHcOUbN2zsYhRzm2Fof33t5HDnJErHCTSa3UttTWQRlw+8KS3DpAaIlVkuKVFYorC8iJPrOHvLChEXO7i19pYNbaU6tovdtFjnLyG8v4d+GQJwsHmUENMrJmheLlZaJbB9i0uDArLY0NzYqzj6WG7I01krtHiLRAzBjzZHZELGssVa6xM3qLVmOTj776h5HVKl//9l+md+/b5CeHpEc7jHbu8PE/8R+HcyZhtO5fiCSaSqFyIJDZhwvC84DVwbFyek+XmAR0EPR9K71Ax5pVQBAELuzYelwkqO2ZR2sbCxhuzOsDQ8hscmznKlzPEr6W4CM9tRsG0EcDxGCMTWJcu4ZrVEN5PpKowlPZD06mNgmVu7gL+XKYa9OWp1oWYrSUGDym5okGBN5qHHjIToPKw39nkS1qhPWhv6I8B8GAyUzXC516GvdzbCSm5iKCMJ8qC7WDkJF0tYSipRiuaqSDyqHHNMNYx6sgdwjmVOXp1ZmntpXjJZhYktclRQOyRUAI8tQTDSHq2Wml8vwBFGgXAtnZSp866iNm570LGuXkYT8kipII16kjRhm+GuZpleaB/0ugJsi3gqupSyLsjdW5OdVXYoR1qHsHU4OQ4toywtjQMNcb4RaboWGvnOuFOx3sZTbYl8HLQLUQzk3d+rwOms3TLLXgfHAMIMBcW0H2x+jt47mnUtunrjooETE2PTabH+f15S+y2/0WX/3KX6TR3CDPe7xz8+9TSTqsrT1aXehDPB+8mAxymdZy/rQx2DaSYBV8ODxnO/l+RREp8peWkWlOfPPgQlbiw5Z2dTREFJbspSBqnxx6xg+qFN0EXFC6sBVPek1gY09RBzEUjFbLJoby/pY+BG7Zkp+WvW3kUXbm23XgjWYLClMN+sN5B1TmifpMJ+Dwo4gLJ3XpPRiHixQ2htGaDs10Oiwik4a7qJ0hxxqTJyQHgmIZiqYn6kHzVsFoXZF3JMKDsjIcL+FaECZo1AoTJujqoaX3cowaOuL9lHjn4hq7GmTnHptk9UUvRQ7CAfncghTYaoRKi3ICLzNFicIjSl3V0j3POHwsT8+1c+h7x8h+WlI5wrWqjkfEu72ptWnlW1vYVpXi+iJPCpUbmJnI1X6fYrND9voassxETI+xrGbKwwEyt+RXOojCEu31A7VECoqlOvGDk7nvqOkWP7HxJwD4dOcngy7yCXBi+OxH/hg3D36B48O3QEpWfuC3lQOB4boLVubPEQKIDgXxyTzXd7h1i2RhBV1tPN8BfA8h3YzJck/9Xo6QgvGCnOMdx/vFhUHOBNHAkbfUY2V/fSznguNJFVuWlKnnCV9PcIWZC5J9bpH5GPpjxEIDt9rGJhKVOeq7BhtJbF0gi6DuIJwiXROYhsAMPToFW96nE14yhHgxXQ4ybHoQgmk3o0FsE9A5pCsRamap8zIoS8xqJavMz0l5Tr/DOhTgyqZoL8En4FNCxrWU5/SxJ2sLmnsCO/MjhbkLikUJJeUiPoGi4clboEchIRH37bnfVhCCXDnKKF7bQJTcWmBuEw7gSzm22U29OuhOs+x5s4I8GeKcD1U7LRHGIWONm0k8+UQjpJhTQJKDMXrvZCrfaTt1fBIRbR1Oud7qsM+7U42fOe6sQBqLXWkjhynCWMyVJeTJCAjrj1tooo76597r4yhUJ7aOzt0qN+qf5kb90+E3I9AxGBmuRG/Qai/yi4f/MwBraz9AkrR4r2FtwUn3FkuLb7zXQ3nheGEcZOE5zbxqiW3XcLUYl0TE948ulFV6v0GN8uCit9m51E7Tq1Jd4gIKhiBwlKOtHmajHYTstyROh8aQ1m2DTQTCKUabULQ88QmYkjMsTZjYkoOJULwmXfQUHR94YsIFWoMSDK77UktZTJtBVB4mbadDt3r89sW8bznMkKMcvdcDIcg+vokygs5vjrBa0P9IjbwNToZjNCNNVC8whzFqGFQ18iWwtaC/XN23yMwjC4U9gvHLpROadmQLEpULotRT27VIF0q8LgLfSIILE4AU+LOTMSC1xGuFifXU9MNXdFCqKINor1XgJI/ycsEM43Y6vEfkBuGD+oVL9ByPL751gBgXwayjsPhYI3sp8fZxaOIqtTcnPOingTiZ11dWx0PsRhtXmqLMITNBqL+Uv/NSYlfreBkaBdEK36piRnm4FqVAd8dT/W4gBMcSXBwh04J86w4Pdn4JgIWPfo7lz/wopubJV0LW8XlCOUGyPU+v8d7R/eovcvcX/jabP/bPsfyZH32+g/geggOIBaM1ja+qc25u0UMoE7YiSNeCEkI0dI/mEpdzoLJQ2c1RY894RaPH5xuGnzVcu45v13GHfaLucErv8wDVOBhcDFL8q+uoRONzS+d2Tl6T2IqgchTmLJxivC4p6qFp+uywk6PSuKnQ5GUlzMFUL34SOFd3ywB7JkFbNANlRY8cwnhU5kh68+dUWovcPkYMUlynDusLSOepvXmIyhYZXo0paoAQqKHHC4GrCLLIoTNACkwiGK5F1LcLqscW4UGnEhsJkgH0r8B40aOHitpemc31Hj/KoF4JTWabi4jSHtophTjr/CLBJzGuEp/LErlmFTXhHftA05DDFNuo4CoxcpBilJwmz0SisZtLc58h+mN0adrhAdepY1fa4TEbkjwP29g9LcRhH9Y62IUGsjsK53k8k5xxF98DrlFBDFP8Q9aEi6gTd0a/AUA7WmV56Q06nZfe1fjfLfL0hF/71b9Aagf82Bf/4/elPvPzxHvTpGcc8YNQdjALNexiHXU4/ED0ievjEXJcTBUUzuKh5ijTFErQ5dSpp3K7QBUebz2qnyLaVdpvG2yisRVovZ2SL2jG66c78mgYsgyd7xRkbcl4RYISNG8X6Az6VxUyO3Wtm459JLCJp+hAddvNTShynKNORoi0QA1zTD3GLNTx9RjvPXKQ4T0oqUmODEVdBa7yGAwJMnLEm2OGsopKQ+kpXfaokSTuOap7BZUjS+/lCDECGpBsDEhdHXmgEAWo1BDf3MepdbqvJUFuqFkJGV4pUGY+zW0XauSdWuACC6aNeT7WmNXmNECWuUGWv4vqjfE2BLSzFA2f6KA/fObnEpPmUSVJX10hvn1IlPZO31ON0SejYGZS0jsed5oOmZkB8fFoPkvsQRwNYel85tSXTSy+WcFrSXLvCDOoUVxZmL7GKYmb/buekNw6wCzVUccjhPMUi43w+J1DvnEcNJBrrXU6r38fviYZb7wASoUTVB/Ma2YDPPjHf4ujXw8B+9KnfuS5j+N7EaZxPs+mC3/pHBz4wzGunFPk4wQjZZWqfj+bmgjV9h/f0vzdwgN+qUm+1EBuHSH745CIKbPKpbkkwzr4pqJ6aIlHDtF3yO0j6scJetSiaMZEPU/zVj6nCCE9JD2LsFA9cYy6KriiAq3fzEJTpI8Zr3PppKAcVI4MoqSuzEJmBdH9g0ANkAK71ERYh7yzF7KTWeitcFqgh4HyYesCW3GMNgXJMSQTk7ianNJravtBgm34SoJ14KUn23RUjxR521PZTlG3dhFAcX0FagmyP5qqP5xN/ngIJiL10pJantEobtWnAbI6COoWKIkYjFGlHNwk0+8B2zjfDaz3Tqb/NtdX8LEmurc/ta121QQ5PN985wFfjecqCU8CdTJEjjPsYmiU1rf3zj1/9vvMlSWIFNH9gzC2RgXXqKB3Ty68DryWYByp7XN//G2W42sMXJe11feWWuGc4Rd+5c8B8MbSb/2eC47hvVaxIAScHzTIs17yj4sJJ6yXYpsVKgRFBOE9ydt7eODgB5u4RCCtxxuQvZTGvkXadqmt7JGpJ7m5h7SOOIlIrnTov5qAEMjeiOpWTLaQBI7ZDKK+w+ogqda8N79Q6f3+1EbZViPyl5bDDtf7wPcdZOSbHWw9wUtB3Ac9DhPh2DlyqlRWh1Q2R6S7oTnD1iBbFoz7ktp+2Rhz7HCJZqQtQjlUqyB6W9G5a8F5XCMExh5PURPB0a7UMp4gv7pAtN9HH4/OXz+RwiZ6np8MUxqPPpyf0EQkyRcb2JXmfJCqxLSUB+E3j+8eBZrG5OfUCpTAPAW1QqZFcPvL7YVrZ7zbJWtVL7aZtg7vPPnLK+gHx+efZ74pO0jaBR3QiZi+TAvsUh2vJevXfpA7d3+eUW8HpCNde6JDeWokO5wLjgFcFn6767/rjyLOLLYf4vkhOpqfE0xV4JQgGjhsVUyDY2VBXqLxOwcPUd/OOWy+F/AI7OYSFpBHfaLjIPvoVLi3kuMzfSPvbGM7dYavdcB4pA30tsD3DQeTdTS6axB5UDwQtZjGnqPoGYZXYrwSxCNP3AvcZq+ZVOXL7zRkC5rKVhb4woAsaSfSOcTOMbI3nt7DE6kzfXc/mG+8tAqyVPWRemqKFH4XSbFcgFAIBHEZJGfrMdG9fKoIROqgKsGBj0JDYtJ3kES4xQaynwYzLO9RW8fhTKZ54BMDaBUMPAYpeud0HhKxwiqFq1dwi01k73SOngSUopR8m15FUmI7NfxSc/q7AFAYiPSczbQcpoijYhocA5Dn0/NkV9thbhYiUNK0Irm1M6WFPClEZs7xiIGgn3wmQ+wbFXyjQvzO9nT9kIMUu9TCNqqo/vmeK1evIMY5cValHa1xkN97qnE+LyiVsPnab32vh/Ge4D0PkL9b4JoJsn+eF3sRRFZQubmPq8dkL6/ghSB/eRmyAlMN2ZnW25bRpia93qS6n1G93SPuN0IQPEinsng6t3D/mEayjLQQ7faI7xu8Wmd4vUJ1ryBbj7ASTFMirUeOPcmtg9CVqyVylM9xetW4IL5/jFlpIow7fc57hHXU72SYToX+y1FwqDqSmJ4jNTWSKyNUrcAPY+L9wI3OFzTSGISFYrGc/JwABDIKiiAi91MqhHBgGmDGEt9IzhsMWEd2Y4nkrd1z1Byn5IX85LPwsaJYbOCX6heaDvTXFPUHDi/A1YKesGskYbOQmbCJ2OygDwaoJ8xOSOuo3Dl8eNOIoyzfXRAgKwkqvGRWgm5SZVTHQ7yUwQIdMCtNzHK4dqKDQXCEygy5UqRvrHHy9X+AyUcsfOKH4aMbL8R2WfdBjc+f99HuPVY++xOs//DvIm4vXfDOD/G8MFtxMlXBcDNkjMZnNkxqYB+r2jdp7Hs/wS02yRabyMEYeTKE3CAmTm46qCeIayv4SoRNJNVuQWXXUd0viG7u4p2lYteIBm7Kr9Z3909Vc66v0PBi2ozXeHuILGp4GfjFeZl9zjsa4UD1cvTWcTCaMBbZHQaK3plxy3FOfGcfjMXVE5yUiH5KJdVI4xmX+se1XXCxZ4hGIDAtiHvh86yE0ZJCZh6vxbSpWviQRTZVprtqu9rBrpb/lh4pBb5ZQ9/emwa1XgjM5hL67j4yned6+6VaUB96e/tSGgKE6pZdbCLqCe6iqyo6DVE8gVtuF5uBH1xmtF09wS63kXf2gvX4gwNIYmyziltslu999vXpYqWF2uvOVY1dpaS++dBUOD0v1gXnwQsCZDlIwTpSO6Rb7JJETW689OPPfLxPipMH3+YLn/v30HENGb1gh6j3CT4MkJ8FKprs2hLVN3eeqOFQDvMgRXQcqA1ylLOsLNTqKOOJvjXG1zR6pxd42yLwyaSPmTU91oMMtztAjvOpHXbjNw/R+QpyXKBTz2hdY2qSqOeoPyiwtTg0dhH0cc8Gobo7Ro9y8rXTJgE5yrFLDfKVSlj8tgx5U1I0BdpKdN+RH1fQ9QLT80R9SYRApo7k2GIrcqrFqkZgxxqVOLz0xG/tIkTYrau1GgiNSyRehcyCPhnhYkV+dRFfi0PD4wVx3ONm9/ONYJ16EbJ20J32sSD7xJW5DG/+0rzTk1tpBm3NJ4A4Hl0aHIuyWaXYaJ8K5D8mJlVvuxAy+FoKXGFRez1cpPHVaJrV8IWl9tYu+WKdl1//XRzufYvGGx+D9RfTFJJcIuG2/5Wfo/vW1/jkv/3nXsg4PsQpspUIW7FEfUu2dnk59ZzV8gzyhqRoSOoH9rk34r0buEYVd6aULw2Q5cj+CBe3aL49wNcSWndzRJYj8rL5TCu0hWFbEfcsbqk5VTlQR30orealc8jbe1RrV4LjqAsc76IhMTVJbStDWo/r1FH7XXAeu7GAGGao3gWV1UmWOCuC42bpqhn1HfgifG5dofLgrmfqHovAxtB6J8drEQyjgPHq6dKvRwLXU3gFXnGa8c8NRCELTTXGri8EesQ4C9bTpbyavCAAliclZfIhwfHkd/Alz/mhr1tp49cXpskMX0sori5BrKdmHkLLstkaGOeImeC6WO2g71/cWP+0kL0RZmPxlEpRjYOsW1qEZsasQJaUDH3Q4zKezSTAruk2y/E1DvJ7XL3yhWc40ieH956vvfNX2Bh+lo999H/3no7lvcSHAfIzgE8N0fbJU6lxqO4YfXRa9q+/fYKth8lRjAvMShM9KXWNc9xHVi8k98c7XYjV1NVNjgtq394B58g32tikico81QcZle/sBuvL8r3ystJTYYnvHyOUxDmPHmbYfooWMS5WCDz1bUO3EeMFyEJQZIoiU6dyJQTZOVMJckreQ2XswcFwq0pyfYirBD6xKhvL9MAQnXi8EthEoMqOaJFb9MkIiw9yOzxcNeRhEMkFl74UZA0B1gcqy2NItXrrkE/YnCce8qk+N3NqGY+LJFJkZ4JuYxyV2wenPOqz31VYRGbIRBepY+KXb/DkV/CTQ7nLpdw2f+s/S+uVTyD1h1PTs4K/oGFYGzBnTrHntHHsYTB1Rdy7MN9H3lZ4LfDWPIec3fOFMBZ9rwx2agnR/UOKa8uove7UQU0Zh3UOR2h0RYpp7wOAGqT4o37IXJ4MkUB8az9U6yoxeqGBl47KoUUOc/SDw8DHnVCfTobIy2REJ/e3cUGJoxKFDKsQxEOPLOxUsk+PwGtw2gcjkaVAOchbkupemNiUg3ivQKQyULnEmflOlRlmB/ZKqObYhQZ06iAIcm21BBepc2N+aC/O7OuKx6swyFhjz/De/cwGRxCUPgQlzWKtg5/dqBz2nvn1KIfZKc0j1pirS6iDPup4UGaSy/EKgUgfr8qoZcx6++PPeKRPDiEE3/fpf404/t5WEHquBL9+cUBuPzg6x08LwdNzqaOjwbnH1DBHDXOk80FKDMB5ZGqI73fL0tn5RgZTC9J5UzgXblEhiAae+lZO7dYB0nOeo/sQOOeBIIGW3DlEH6fo1KFSFybK8nXxCCq7gmRPTDlxADb29F5RDK5F6JGjeq8g7jpwkO7WkJnArjTJbyyRv7KCb1Ro38ypbxlk6nHtCmYhqJ6okxFykOGaFfI31qaT+BOj3GRMmhMBhmuS8bKiaCvat4rH67aLNfYhFtAX4ZHlPiVxjeTCp0RaQClJp2c0QrMLFtX43tGlwfEEtlWhWltCKI2LXpDc4kP0b6N6i4WPfPbFjON7BCc7d3jwa//r9O/KfkH9TkZlv0BmF+ufPwymJhm8kmCr89exrYQNbWWv+ECoEp2Dlpi1NubK4rR8r+8fzNkLe2OJb+0iR1lp1OHPqdeo/S5Yhy75tnKcIwcprl4JjnhtFWYAIeb6AiavfZxEi945Ir65S3R3H8YZwjtUdjqzxAOobxG0mgXYmsJWJemSon8jpmgp4oOCeFhmXAWokZ9ahocDkSG4G2VBztJ5ZH8ckivOT1VKzLWVqVnHE2P26/a6c1SNWdhHnBMP04AYGc6rzHLU7gn6nZ2nbtJ7FCYbAVuvgAN1EtZzmebI0tiECzaotlPHLp13zqurBfxjLTzPH4uLr9NobLzXw3hP8VzTNB7HUX6f9errz/NrPth4wpgkOhwCAh+fz/LIrJiT84LQbOarUVBM6GfwFI0K065lERrkXLNKdO8Y1Rsz/sQG0bHFVgQ+lsH6Go+XAiHBu2BqIiTI1FE7nmhYOl7+5BZbO8skJ9W575LdMdX7x7haTLFUD4ohzofAsRoxSXU4JQNHuB6jc/tIIXgZnZqGOIL+cHLnECcFtlPDVCTtmwWDa/qcPull8AAXZaMv+v5BhqtofO1y6oStx8Eeu3JxiVsdj/DVCBdrrABKubewkOWnBibbXVT/AktVJTCNCj5SuHrC19/6awzTA2w2YnjvLeqbzz84/dAZ6sVj5+v/kI3v+x0oHRH3HGhB3A+yYsM1DReoWjwMTkC6GFF/cKo+MF7SVI7sOSOSDwo8ApqBa+mcD+oH3VEIEGdfV1ji7SNQy0H28UyyQXjQW4dTMwsAs7kIcdCAqxyWjp7HA8xLa4EHnQfpszlVBK3w1l642VBK45yBtCC6exAULpZbRCcmOCZKgbeOuBvmSBUJbOFLnrkgOTTEgzAXD6968o2c2gN17rvkYR+93w3HHetAsdASu9oJx0oZnKpAh/PVeLq5eCyUm3y1e4I6HuAHY9wr6+df9xhzhmvXg0TpOCd+Z+fxx/AMILICvX00F/CbtU4I1Efnk1E+1kE2rsTAHPOlo7+NUhXS4Qkf9y7IcX6I9xTPNUCuqtaHwTGBG2VqMTK38523T4no8HzWGZg27s3BeuJ39hFSYGvxlIIxgS+F2mfhYarvO/d4pBGZobq7B7nBC4jvHSPdQohZS8UBG0H/WsTomqO6I4mGApl6GjunAaxbNvzI2i1+7rjCQIdFSXbHJPdOhdXlKCcZze/8i+UGcpShTiTRdpdis43t1ODOIeoRAXJRiaavEblB7fXwxmEXwvdHw7Js7MGKZ39zqO6I+F6KKb/Pi1Klo8xgZzeWLuVFQwjoVW9MvljHnwnK1SAlunNE+sYa+nh46TXilMQu1HGNhFF2yNHBm+AdV/6DP0109cozOtKHw0WTX/jDQPlF4fv/2P8VqUJDlwDyqiBvanTqcE8YHE8x+/MpQXJsH62N/AHBhBfr2vWQeDjqo7Ii0Byswxc2NKzVkwvL57NBs1lfwNfDfT1rzW3XOmVwXIA+1XGHwGedyroNUkR/PHe683YNddjDri/gakmY060Ljn+Hp/OgqQh6NyJGVUgKiEZQ3S2IZ34nt+xor/eRZr7/QKT5NDgWiZ7aNIuJTF4ast3CedRBj+L1jWCTPd57aBbcq2AahXPB8TPNkWXm1VUvrpw9Fh7icPtIlDr3T4tzQbAU+GpC9ODgwtdPrbnL7743fpPCpTTaV/mhT/ypD4Pj9wmea4BsfcF+eoeVyo3n+TXvbyjJ6LVVKHlq0d0jdO/F0k5Cs4S/2H3uAq7r5PXnHs8N0V5oRnHViOylZeL7x1S+vQ1A9tJyUHrYG9LKEkytMv08aYI2Mh7yumC0oPiF/Vc52GsTVSX9q5qGqFKM6oE+MDyf7Xaxwqy1mNjJqYMBIjOBC2YcVCK894jMUKy1cImmst2dZpZnM6p6t4ca5UFftGxErO6VrniHNmiMPmO4eoI+HgUlifIcTgJdL4Dq5ZllAcTv7OOdI7p/HE6BkuAcdrFOdO84aDv3xhdnjkvI3BLfPmB3LeVrX/lvaH7ms1Q/+0nia1eROpRNnX2+gasTUDTK8m+JvH/M4P7bLH7sc8/1u78XoXTE9lf/AWuf+CKVYXlP2tAbYKtPtxArC/Wd0w25sJ5o9AHLHAsYLypU7olTfyn1xyURbCxOrTFkmiN6I9xyC+8J2cqHNaPNNNuKwqC2jgIlIdao3ZMLy/9ynCPvH176kaHpK0g3Eil8JIne3KIUq8feWMXFGj0w1HbEVOUiHI+AMnk5WlTYQnKy12I1FzhdNisCVIKRl69E2I1FhHNEb4e5Hu/xZZVLZEVIaliH6o5C9TCKgqmR99hmFTUYn1JyZ4JnOUxDA6QnaMlvnOq3XwbpHPL+IaIw2KVWMFCBS+kZjwNbryC7Fzd+PxWcJ7q7+1gV4q8M/yF73W9x7eqPcu3qj6D1u9gkPAPs7f0G9foa9frqezqO9wOea4CcuzHfOP6H/LaNP/7Q19lqHOyOn4AX+4GB9wgb7JkB7EabaJw/sS/880Kx3JgGbE8CZRxIQbHWwizWw3Eaizoc4Do1pJQ07lt6rwiirqf5wJC1AgfOJB61mLHTbWN6Cb7pMRUBaJp0QgbXe6Kb+2EBsB41zJC5pfrWLibR5DeWKK4uhABTSbLXyoU/MyRv72KbFXwlIjOOeOvk/PjLzLTp1HDl5mUSFj5OidgkAlX4qZ3s4+Bs1hctsfWESIkQYNw+DBxseT5AneZcPXOych4o1trTsevjUXADfAj32DUSjO+h4iqr/+q/iIslUSel0snxQH4Sk58kPM8Mb7bqkTmoXDC49xY3//Z/DfBhgPwcYE3Bztd/liW1QdL6GADRyKFHDlN7ugA5OjHva5WKx4FJRLDNloKxD+ZLtZ1izknzIrhKDGVwKA97c8HxlIYwA711iNMzjWzWIcen2u6qnpBVg87v4zatTRqUZW+ErSVI64KyRT1BDjP0nb3Ac11pU+k68rbHJYLKvqFyYhmtaFTqGFxX+Aj0vsY0BYOGpr6Vo9LAhDU3VlAHPWRvhGvVAjVgnKO2j4P18mob00rgxgrEEXaljV1ph+N6cIjsj7EbC/jjeJqNPotJksY2q49k30pAPjiaZmzFOINOHdkbPRm14wzOGn48DVw1xteSqaLJXHCsBGahiRym5zZEZtTnpRs/wSsv/+S7HsO7gbU59+7/Ejdv/QzXr32R11793e/peN4PeK4Bcita5UdW/6VHvk6Nc1wcNFlxPtzgJ6OHLvIfGDhP9fYB2cvL2FjjIlU6sh0g0vdYIzRS6CcMjr2WuCRCDTNUd0y008W2qshRPpVYy5SkWKtTOfGMTzyVkncsnMfFgrzj0RWLHQe3QFP3xF0wdcngiqaxZXBOkL98GizKwqJ2e+iTETq3uP0BdjmI2ateiswM0vtQZvNQuXMQMqyPOMcufrxbYJbfCzxVhlno+VK2Kg1jxh9ZD2Ye9SS4Al6ColM7R53wiSa5uT/9W6ZFaA6JFGapEdRNzn2QZfvBr7L8g78NFyuSpTFRK58uTvFChogc+X71ueiHQrm4l+vEJDhe/8LveS7f9UygYbzwwaQPNJau8rnP/slTTr0WWMFcI+2TIl1U4Pw5a+QPEnTqad8ryGoCaSB6Cgtst9SiiKOQCU3zeZnJMigi1sijwYW0A1eNya8sgfPo/uM3ek9+OnUyDE5tE/3dyRxjHfKoT77ZxlQllSPDeC2aUiuE86QrmqIBtuGIxxrwwQG1raiX86YYZaijAS6JcK3atLI4kaHTO8eQrOJ1mQ1Pc6R1wUCjfG18c/eRcm9eAI9q9PMeef9gjr7iJ1XK59SE97hw9WRKFzn3XBIFjnKZ9Z9F7sYcpff45LWHJxFfBA6PvsPNWz8DwObGD73Ho3l/4MVpKcWKfKWFTxQu1gjjqN4+wE/cZnJLfO8ImVm8u1hC6IMKX1jiO4dkr68F/wetyK8vkby1d2GH6wtD8XiC/7MQxqFcWAREbhDGzcnUAUTHw6ltc5QGO1Ybl4vQiWVwTZBoj24VmH6MLxTSiGBtWpd0X4uRqSPuuSktIxkJ3NUFXD0menCCGmbYlSaqn045vBPYRjD2EKkhOujPOeKdhW+eL2cJKfDOo6TAe4/zzAXHF0EJgX3Ub3mB4kb04AQ5yjGbnQuDY3UyKhVMCtTx+SyHyC6W0xJazSualPAVTfrKMtlvGuTVJlErI2qdn9SjRrnZ2X8+AvFOQNGC4a9+BYDGtTdY+cHf/ly+66mhoah6TM1j6+8uoHwv4R3T4DhvSrKVKPj0PAXiE4MeByqOfowm1vc9jCfpvbvj8M0qthkajdX20dTxDeuR4xzTquJXWsh75/moKjfBOjq/RFNSyUCnKucwoeSUCzyByAy+5O6qmcYvX02QBcTOkddlcEDMPXlTUjk0FE2FjwSu5jA1h4sEsmBqcgJBSs28vjn9224soO8fIpTAVBJEVhDd2kU6Nx3j7GG4ejI1t3roOawG85PLoPa7ZSPjTJU51rhmBbV9hF1pI0fpuSb1FwUxzC63aW9UQmXgAtpi3koQ+/I9p1UAfOvbfx2Az33236FW+9CkCV5kgGx92EU6hToZI7MCZ+YD4UnG+AO6Dj0UvvScn2JGJseVqgS6+wGRxHMX2zZPIIc5sjfGtaq4isAmElOV2BiE8SAExUgTNQqEdlCoYIU8+XgBriqnTlUAmYfo2FLVdXyriiuDzWnGRElMq1I2tWTo/X5oMnxIcGzr8YVmHN55ZD8lun+Mq0SYpToyLYI7Xem8p05G2HoCkUIdDoh2e7DZwXVqlyahfH6+IjKVCHSe4tq8XbU0jvj+eYvTs++fwLSrqEGGsC7YAuvT82cbSTgG4zhO7zM+3mb5c/8SlcXs0vFGjQI7zjGDh28OnhYu9uS9wLNc++Hf9d6rW2goEo+telxNYKPvggAQ0DO3QNZRTx8cHxuqR+8Patj7FXZjER9p1EHQ3ZXDlOR+cWllyJfNdRc96ysR9upy0Ia/u48Y5+eCYwjcWycbUytNEWt8boKFMYAFr4JVuJcwXin7HgCBQJ0oUB6rQ4BsmorceKKBQ5RqQRO4akLx+uZpI3VvhN4KjdWiEuHSouREa8QoQw4vn19m4eoXNydL71H3DxAX0S+NCeoX3RF2uTVtIn8vcO73K/tkIDRju04jmMjMwHvPzb1fYGP9B1/MIB8BVya+Go0LVES+R/ECA2RHvH0xB+m7GkqSb7SDBM7swzPUhvzaIj5S6HH+VDJsALYWowoDxTMseU40fODUx3j26Us6lc1iHerBItpGgnQ9wktwKnyezBz5QRUcuFSjxhCXc4cWAnORbqTwmNWgIdq5WUzHMuH7mVaF4soC6nCA6qWoYR4k4JoVvJZEgxR/5ty46uWBnxznwYZ7nJPcnZmcDwek1xan+s/yZES03Q1NdPePKXKDWb3Yje7s5O2lwJd8YT3OmQ2fhfeo3Se7X+Qkm6wkZr0991yx3kYkmjQb8O0v/99Y+SP/PM3XJf4RJOrqYsZgED0XqoVrClStRrKwSnVl89FveDeQHqEFRnicCiYKXvlgLxwBicDK2evuuyM4BtAzG6Vo6Mjip+Mdx98lChXPEx5w6238UhNx1Efv9/CPmtMnDnAzD7lagru2PHWOs/VKWB8AEWn8DFdZ9MckN3fw5Zw4McyY1Wf2JaVmcCUCIUjLpj2Zg+4rPOFeAHBa4PRD7ncl8Nafa+Z2pV60XWmDc6gJT3jyfBKhvLv4fCgxNR6ZQ3d0cXAMQXe4OwqOglKi0vx9c9d6H86Pr8S4du2Ul8wpT/1u8W26xS4/8Oq/9a5Mr54V6rVVNtY/+8FQ0HhByZQXblflazGyKDVrpSB9eZlouzttmvqugpZkmx3cBUYS0TANTRDtKr7kwZpKjM6fLots21WKZgW900MP0gvLOU8CoSSO0yDYS3GpusUsPOBqMU4Fu2YAE5VP+DAJV44FaSLIDkP5PjkKs3flwFI5suRNiakJXCSmTURCgElCBnqwrmhsh5KkWaxPNX3PjUUr8qsLoavbOpLv7IRKRolZSTVtHWYmU2JWW5jVFupoON/kV1hUP8WuNlE7PfTRAKEk4zfWAt0hulwyy9eSuU2HkBLvQjOMbcyMZb8fMtJPiAn/0VUiXDVG789OypKD3tscsE31kx9n5Xe/BPLRAY9XDplYbPbspworPbXf9yPUf+ILpLEA7SH1qDS4MgoD2onQDHYm7enxSClAgZPhsyZWuU6X/9YgIoFT4ERpdnP5kT7z43s/Qo0dPFoo4EI8xuXyPY+8rUiXgtNgVG1jO/Wwcc0KRGGRw/HcRtkuNLBrncDf7Y1wnTqyP8a1a9PgWABydEpRsIlGFKfUKgFzTd+TOXvaLCjFVMrPxRI1dlP1kuTIk3XARwKERzpo3M4uVHMo6hI9dBgdaGiq8FBLQqZ6mE7fI7ujoEwx+2YtsZuLmCRC7XXns6kSfBwFFYmDHmatc6r88RCXUi/AbC6VDoj7U4rH+wEC8ElEcXXpnL51f0HgnODWW1/n45/6Q4xfD03W1Z080JfeI3z+h/70e/bdTwKfCEarlys+PUu80ADZi9BoJMf5tKwcPzjBq/d67/TsYOsJZrGO8D5QAS5QJJDdcchmaom9erpaiXfBR463u2SRori+SOE98d0jVD99qp2pWahhmxWSu6ed1o9jfWzrMbZdQ/ZTiuUaw3WFSh22IkGEDHLe8VQOJdW7HlMXIEGlAlkK6Asg6TmSHthYcPyR0NRX2RPU7znyRUXRFIwKRW3f4tsVRqsVohyQYJYaQdKun4ZsdVrg6glOSbKPbSKcL/l+fppBloMMtdPFvBZkbURuQre29+ea4mw9xqy10Ht99OEg8JVjBUria/FDwywXKVwlOm0+NRZpoFhpBj1qH4Ll6OjpO6ptIwm/3Vu709Ko9ZZ//A/+LACq2WTlX/7dwWf2cSGf36RtpYcGTAPUGtjazN/l/4X3lFs0fNlM9HhB7fdG4Pu4mFgRPymUBWk/QGfT2Km05rNE2lEUixo5stR3zpfzk67Facg7mrytgBg9tFROLCr1ONqIkyFinCMHY/xycFOzax38cgunJK4SEz04pKjE+CRCnAyRZQLJy0fzeSdQxmIIUnYmEojC4yOBrcqpnJvKobYH0/sscxcGxzYRjNY00chRmzluF2vcteXQIG0sdEf4RgU8qMPe6ViNQ987wLyyhl1t41dboWm7DOJ9JcbVYvSbW+jtI8yNVWR/jD68PFHgOoFWonZP5tc4KULQfkY7+lnjrKfA3HOCEBz3x+i9k+nj90ff5htf/zkAlIqJbryEkeHsjzZjohNDtes+8AoxzwtFVZBuxE9NE3tSvJgAOdHkK018pEhuzTcqTLt+lSC7uhiC5vvH7ypYfC/hKhGu/XDr4UlTWbHUmGYJpPPvOouuj0e4zKC7Y7yxpK+u4GNNtHXySH6z0BJnHK6RUFxZCAFkrJCXlAcvMhgRhUMNUkyjwngpyCfVH+SMVyOKpsTNJHklguYtg6lJTCPw44q6IB6e/u7jZYXMwdShaINATaWpTEPi9y1ZQzBeO72MlYN4oU3SbZ6TYHMQqCKV+d2nq0bYGWvnaOsklAP7Y8SZ4zcrpT2o9xRXAuf4ce9VWdgLlVmiMtPrDwfYWvKuBOu9lKjuGB9riuuL8OAEmRV0Fl/l5OgdVKvOwo9efbIPtc8+0HhSBI76B3NOeK8xmUpNTVC0n/y3VB5qWwXefoDO/wWJiXeLrK3IlsJc4+oKH9vzbpsCqocWWfjQQyEDXWGwGaHHnvquwXfq0Kljz6TynSxpBs0qrpbgJ1lUO5MdvqyCN0uHm6Cs2OVthTSe+lZO/0aY52b1jnXhSfYKirok72jSRUXleH43NF7SoXekrsha7px6iRMCIg3Lp/Qyc3U5KFqMstCgXEtO+0wQ5Vx8SnPziMC7Xmkj0vPOdOfgPdHOBT0apSX2c4UUlwbHEOQ4o7sHiMJMVSwEsBCfUsnWP/0TmPbp2uUJGyvThvjQEHefvIn+uxlZW5Et6xe6CjzfAFmU1sRl45I6eYiMjfUkdy4XRn8c+FAlek/hGo/uRhVpgWlXp8GW9B599/Ch7kOPA9VPpyYRAqjcOiRfa1Jc6aDTIgi3z0IKiuUmeq8HJQfOVSL+/+z9d5xkyX3diX4j4pr0pnx1tTfT44AxGMwM/MCQAAlDA4KiF0W3lCjDFR8/kt7b1ertSitHUYbioxONuKKDSIIgCZIgCG8GA4z33T3tu7p8VfrMayLi/XGzsirLdbWr7h7M+XzmMzNZmTdvZt4bceIX53cOkca7WNmUHENSUTaes+LtCcl/hzHRYLa3pWdXyRa0l1T+spOJ16jXseiKoX7EI3IstYMKfxG8RuJgoQILpUTsGwxaVBtEN8RCe4KwKNftPmgJ7RGH9nDiUSxDi4yTIIPUokZt4Bwowhin0koqzfkU0WgB/9TcxtdSt+qsh/M9C7rlKr0wdsMdg977rNLSGd9JKtyrvj8Rapxw83vEpNx+G6kN4NTaxANZ4l0lAOLRAqIVctu9P8YTn/o3DLzndTjF7JbHWA0bKnR4C2jSXsOmiGNNc7dH7F/edCsseIsxKrRJ3Ps2YQWJRv967HiHMbIV9MIhNsUWjghXgqAgCYb6p8vmiJtUh+urKn4W5GyF9ByYQhbhKqyURFlJa8ShMe6QuxhtTPxEQhAB4m5ghrAWp7q1/ZvxXWzaW+fla7HoZb25En2NgmERRGTJzmictkGFFifU6KJDUHaIcwqnrvGbBhFavJqm3ZVlBMMuKgxxOpeebI2SkN+6YNRDtzDgXFxMkujs1ruf18K7+IqxDQnjsqe1DFbG7KxT5I0HfpCvn/5/GLrrLRsfWkBnyCHOSbzFxDXmG5ooS0FzWBFfaeLnVeD6EmRLrzlNdsINAxu2gi6kkO0QEZlE25pPbZkStuPkWAjicgbjO7gzNXQ+tWVUMHR9aiON7ZZ1ZDPAv1hZT16vEMnk1I2JNgZvpka7kKazu4yz0EzcQ1wHXcpgPZWk0HVfGxfT6GKa9Cv9UaEy7aLb0bqbVG4Q7Skcic34SUMegIDcrKaaEsQ5wEu2GoWFIC2Q3bANqRMbizgniHOS9liXeGrIXID2uCXOJMESy2iN9V++TseSvRgjlhdKFmy89eCSxD8vYXynp2N25hubXkui0oLBHGqhgV6lH/eOz2BdRXhgqP/4rRBRayfpgKsXiJbLrnJdihxDosmOx4soKYiNpR7P0xlVTB37a9yRMqV33Y0rLbuySzQin4XO1kSjM7/19fwabn5owWWTY0jGkuWKqQBUQyekV4L1JVaAbBtklxzKyOLVDVvJvR2RtAFczlAtsch2hE67yEoDudjA5lNYdf0nTEtCVsINKu/aF2jfIS6BPxvhdmOkZSdKgiy68iwLqIEcQpdpjblEabl5JHeX2Mu0h4k16vz8JSOURRhtWM2M8xls97QNIGOLvxATDDrEvsV1wa1pOkMObkMnRKypIafQrkAPOIQDye9uVzXtWSDOSJzO5gUUJQV2qdHrPUEIbDGzpc+7M7WAbIfoYgY9PoBcauDMVDZ9/vI11Duio7bULG96HCk2r8xfISygR4o4C3XQBm0NC7kaTr7Mcy/9Gfte934cf2sLzTgliXd5OJElNRuhtrEgebUhTgs6o94N28S87hKL1IUl4pSLCiKEEJe1TSrrK8L/xDLnxibt6ZyfpJh1txqtteh8ClVpIYwlGl9xDpCxBgTGkahIJ8bpocamXYIDQ5iUi2iFSdX8Gt6cYnlAWoaxpE8nDQzLg4BsR5icjymkUF1nEZ31iMaK+Gfn11WyzSpybJVAFzPrvI+XEeV8rCMw1pKdjHtVBqduUG1BLCTNcYFNJROBWV3oWSUsssISpyDKW7yqQESgMwYqCqNsQqjXwK1pnPkmNp9GWUus5JbkWDYD3MlKkka3qwRS4MzVN5ejCHokWgRxYiHX3SE0GXdlR6Daxqm2UO2oTy4hpFh19VvsNa5yGVcRjRWT0JFIQynDsac/SqM2CcDghx+hPOxx/8gZUk6ItYKn5nYx39nYoD9c8q9Lc95r2GFcg/KThQ0rOEKA0zaImB5R3vI4MxXIZ3o7MZeCANSZWUQnwgwXMeUcNrtz5Lg97BAVtn4vLaE15uI0NJl5jd47jKm1knE41qhGB7XYwPUcCt17TUEvujohsUma5jJMbJLGvdbmc55UMnGssGxIDG05R5yWCG3JTsUIC6maJhh0kiZYbWnudjGeJC6oxNJtzbWy/LtbQPkSExhiH8hJZGiJ8gq3oRG6P2jFLjVxppawjkwI4vjApuRYWou4uIhohZh8GjOe2F2KzT67ksQjRUwxm2if621I+xhH4p2Zuexikx4toRbq6xIQrwYCEplHdy6NTYcnT/5u7+/l/a/f9rFiV9Da5ZHpJhx+Q8ARtAYUUf7Gyvuuu8TCatOLkO79tBtYhm348rXyrmu8yrtcWJW4DqBkolstprG+g3UU8WAWHIXsRLhTFWTXZkx4Tt8NKxxJ7DnJDdSJrik53hRhv5bJCrCeQlVayGZANJJPHBsWGshLJM8JY9dFZfakLVKiR4tgIDcZ47a6CwlApwVeRSAjgc4kZ+M0DSbbrZgo6OQSBwMVCHAE7V3dbmydxBLrbjEzGLK4ta5LQUwivSBxvDCZxJjeVltQ3ro6akUSlW1LmV5D21Y+mtZ2K+R0tcjd6Gi12EQ1AqzvIts1VG3jXQ676rcWqxYs1wLheDGJsjYGm/V719W9b/xfONV8nEr9JCzNcX/xFKluNUgIy33DF3lhcZSLzWLf8Wwkiao33rz+NdxcUBacxRinY3BiLruZSNbbmNw2t9xZLkDaRFJRziXWiO4OLNqkoD10eRN0nFO0fEn2QoQtrFQHzWAh0aB2d1NFvY1YqMH+UQCEWREQG5W4RaQWQuQGwUCrYTaoGi/LvXQhjfYVUU6SnotRQZeoecm9n15InGJM9/+tsX0R9xs1d3c8gxcku3o4IvFTlitOQyq2pGe6lc58isgfSSQn3ZjqzbA8T+pDY5jVv+0mKae6lMV0ZWJGCOgeW1aaG9qkWiG27mm61N+3gJNyidekJ4qUj10TKe2rDG8a+ghzhQZnzn2OVjBHnqENjrgxjID2mEfubHALdcpeAaSgU5BEA86ONeJteTo34k3NFlZYNyOWV75OtY2wJFIK30VV2rhT1SRmuRHgvzKLf3IO2ez6VVrWrWZtbFCtMOlKvkGEX5cySaf0xQrR7nLPt3c7W/hY+hvNPEVwcJhwV4lgb7n32y6TY0gqp8syCq+2ctXrzMrl1y4agiFLMNx9XQROd37QaRKvWikIyhaThfYuS2fE0t5lCQa7HdjGgqtAgL4EORZBjM146MFcnxG+3IIgC0DUuhZ6voOotfFOz+MsNUFJ3JkVcmzSLnEpA97G17owdl2T45XeF8KR6MEcshmiKu1EjiQT7XZ7d5rCu9+OO+DRfPoUv/mhP+t/rbDcOTALWNCS2nMNGscdWhdz2KuIjms+8xyLf/YX2PgGx6m/hmtqsJq6GJKq6GRn6Ao67fVgAXGJVMq1sGkfPZDbMV4Q5iSNvd72yLEjiDLJvyGp9jXHnXUzq/VdTNdVQ9VaPWmW7tqrWZs0ITV2e0lzXzu87J/NpDyiw+PE4wOYXYNIIchORniNlXFmmQSrcM1lsUbu1Rmi7zMIwO06s6mYZOtgzWu0I2iMu93xR2JTHjbtoUdLm5+0tVBro4eLfeRYGpMEo2z0mjDui66WYYyaqyax12uIblIM2oBoS4HJpdAD3d2zzRqjhejro1mLaM0iReczBBOD6/slBWQnjrB795uI4w7H//q/sXjqqQ2PKS2kJtvw0nnSF0NU9z7TKlnghDmJ9bZ/dbx87GMsLB7f9vNvCBxBp6xo7PcIBm8Ocgw7oEHeCDKIicsZ1FLrlhCfr11dro02vpVgAT2YA0fSOTKaEEqSbS63sb3Uo9WICmmEseiBFUKqjO25cwBgLLnJmMoRD2HBdseb1fxLtQUq0y9OTM8Kmvttn/tFOLC6CptUkeMS2KpFte3G3dwbQLZDtCP74p+lsV1pzCbwHWzaTTqYw7hngxccGML4Dqmzi4h2CFISDeUxGY9ICvxTc1sS7977X6F7hY0NstZOEqVWLQzqexyWOuc583O/hA1ClCsZe93AutcLLNIYJn/3WRb/5JOodJbBt7yb9PseuqLzAQjOnaf6N58h94b78HaNX/FxXsPVQ15DuzPtiW01Z22GSzbXbQL3zCwYS7x7ELtJBdq5kDgkxbu3X5lbDesJWkNOX4LnVggLEJYT94WOhfwFwCTa0eaIQ3Zm4/hoPTHYk0TolIAAwoIiLCpUYBIHiXwaPZBHLdWxFmQ3TGTL88/4oA22lE16XGKbVPlXwW2ZFevNLeC0kl251ZLI7czVUgqEI7a9eBLGJraaaz9LJ0J0NlgkdB8QrQCbSyfk+NT0igTQkUlBS0pkECEsiKC/8GNdRbx/tEd8pTFJCl+0fow2uRQml8KZq/anskowTn+TugVMLo2sr+c1erTM8YUvcO6ZL/YeU+76/g4JOKeXeOGp36Fen6RcOsjBw+8jNTqGjBPtdzDg0AbcuiazpCHa+ru+OPV1FhaP85Y3/ZMtn3cjoFOCIK/QBcXN42K9gutKkLfyN75VyPGrCkoSjRUxvpOszpd9gLXBPbPQZza/XTjzDVho0LxnAoDU2SWsozCj69PkpLYYR/QZ1feO0xY4F9awWyMQHduLyo3XzK3eksB4lqiYhIi0Rx2iQfDmYvy62ZIo69L6LT8jBcH+QZyZxNVDadPbAdBZj3DfEK4AmgEm6xOXMjiVFs5snfDAEMGuEqrRwZmp4Z9PyLN1ZOK3fLm6uK7WWV1Cd58seLI9sh51XVSCnCBwY2af+Cw2CPmWf34/dx2J2H3fMJOxZXVu1/mFNMf+8f+DaAvueN/fR03XePYvf409d+7H2Tt6yXO1xtB47OvkHnwA0dWGDnzwWxn44Lde1md+DdcHq5P0rhbhoEuUM+SmI3ZqRjNj5YTstMM+MuVMLmCVRLa68qau566ot7HbdU7oQqcFrW36q2ofgmLymh4EWLNCIuOsojkK2QWdkMVVY5GFnkezVzdYB8K8xF+ISVUTezUrROIXnEsh52vEjkIEUZ8jwlqoxTrGc2iPpkhVNrHnFFySHAO4rSuzVTRAuyhJL2xvLrFKYofWzxU24xPftgtRaSHCCKzFKoUt51aIbRgjV3kgWyCeGOr3X159TN+BKMb6bq9JHrrV7tEizsXFdbI3WW+vs4yznkM8XsYqiddduCEleu8QVkqcNVa2Op+moiqcO/9FhHI49J6/Q6o0hp8rrTvH9nMvcOzJ32d09B7uOfARjp/8c44f+zj3p38cALcjCAeSzxrlFbWsIjcZIrt2g63WPJ2gwkD5cO+Y73rk/97wu79RMJ4gzCa6d71VYuNNgOtLkD0naWLbwHni5v5abk5EpgMILjRfYF/uHqS4vMpQMJGk+slGEiASpQWpC3W8meoVT3aCxP1Cxsl/RxPlDX/bMC8xjsBpGrIXYxr7PfSGYThi3f+KmF439lqk5iWpRYtdJtEOxOMOnSFL4WyU6OW2+1laIarSItw72HvMvbCU7BgkjdioY9Oo2BDcPUG8u4wwBlXr4F5YxKls0Ny3jYCVeCC7runxUsR4GbqUSaz6qu1ER9hd9AQDiqXKC4SnT/H+/8/r+EffXWEkHwJLRNblXFjimfY+KnGGL/3uDI7Mc/hb/w6Fi5pOSuGkcngDw1teFlZrovkFFv/sE7Sfe4HsG+7vEeRXI8SN9pC8CWAkkJZoVyQ2jDsAC8R7h3FOTvWIpjSmj7j0NVhdplRJpwTtLcix8SBKg3HBpgRaQVStYGptwvlZ8nfdQ9fZrg9xVlFLJbrj9EKMV9/4bgozEr+ie6RWdEJEs4MZLCSR03uHAXDOzW0Z/GR9F0pZ/OoW9pw22cI3Apxm0lx3qSbEy4Ey187ezyISz+guhDaoyQV0OZeMu1NLyfwzWlr5Uqxd1wxoUi44inisjFqsoxYbqLkaeryMNAaWmkkgySaSR5NL9ZIQIbnWvIuLBHuHiYcKyIU68d5h8F1EJ+xbDMVjZUwpy+ln/zvlPa9j6M43Udh9dMP3cZcijr3wF9w1/j6Ghu/Ena+SMim88sjK724sqmOIu4scKxNpS1SvEoR1Hn/iF0mlyrzhm/8JUV7hV3TPXeVGwnqCMJ2Q4vgy5CE3Gte92yHcN4gzX8edvvzo3NfQjxO1xzjXfBaAicwdeGp7VZJ4MItaaLKcX26VTOI5WyZJbbvE/bPVoAyJ/d7yBKGmq+ixpOFLVtuYQgqEwAmXdcJJZG3+VIj1Bc1dztY3jEkqxzIE1QKnIVBhYnRvvOTMlBbIhkH7kL0Y40QWlFhpE98GVKWFO1UlOJhMRrLWxlls9pxTlDZEsUma65xuDDcQjpdwRZVo9wAm38KdqfWFiwjoWyBKV2LWeMpu6Q++BuF4EWehgQw11lPEEyUQguC2sWRyANplSc3MsvDoXzN2R4F/+Hc0I+5Kw4grIg75c0xGZY7P5Zn/2GMcPfphihc0zkwV1QgxUUBqymAzivbExhPH1C/8EsHpM6RvP0r6ztuR3s7Ef94IKM+QGr51pVXXEqpjkDtEjpdhhcAq1bMzW61667unfAeb2qbGWQra5SQcYx2UIMhZdE6g1/zZWsvp//B/AiCUIn/XPUkzIf3jpDKQuRgjwlX7NRtIwNxG0rDrnJ5NxtFulViHGr3KD1nEmnhiAHdykY2gi0newKXWcaqpE596C5m5GFM1BDlBWL46OiCB7MUQcQ2vDaE1amoJYp2QVEuiG9bd79R3MOUkPdU9NY1YJZGzrnM88twAAQAASURBVIPJeKhqC4jwTk0nJFhJTDmLaHYSz+VL5A9sWI2ONM5stZfW58wsAaLPdcSmPUwpy8Wpx1mqnuLA/T+wKTkGqJ1+HhsEjIajiIuLWCDlFakEtb7rSoQWVqszWh2+/Oi/AcDzCtx25IPEaYlOSVpjEqXBqcakGuaScoxrBQuYlCBKS3Tu1iLFq3FdCbIIYlSlRVzOJlvWtc6G3rmvYXu4vfhWGtEii+EFHLn9RhdVaSEche16NNu0B9bin1vc0rVhGVtd2jbtoouJXEGEMe58A5PxsIU0shXiVFqE+wZRncRsfnl1n0wEFndJo1yBXzcEOUkw2F/NUG2BiC3puZVtQddXRE0NzW7zZEWTmV3DhjeoBniOItxEYyxbYTJQdp0phLGoxqoqrhQJ6SaRFIiut6eINaraRpgF4oEswYEh0ifnNtULLk/k1pGJxi/W6GI6iV7fBpyl1goBlwIijWoEiQbcldQLmmBEsXTqSYLTk/yLjz/EqDu57jjWChbiDF/41VPksxOMT2cJi8mkgkmObz2J2qKQXXz3OwkvTlF85G1I/9XpeCElOMUOfjHEfoNUkFUMbiXGbVuksVjZdQsQFqXZsUl2Lcy+YaxMrBvV+bne48FwCWeuiog0Oru9ooHxBO0xl9hdP7pFWQgH6e+jWAUhBPv+wT/h7C/8W1RuRR6wWnsrLWQmI2S4ZhzY4KtTxuKcm+vXygqBqjaxWR9TyGCNxWRTif5ayfWkTkko99s1CtF13pGiz0EnXdFEWbXiXx0aUosQZ2XiY60tUdnZ8LvZCqque+RYtMNkwZ7y1jXzSSkwWzSoi1aQNNY5CqsUJptC1lorxF8mn9U0A0yh+3tLgR7IIastcBWmkMHk0zjnVq4TaxI3FDtSSiK752t936PJpzFpD9kKthfnrVcWPnJVCq7JpjAC9GgBazTHT/wZg0cfpLTv7k0PJWsBp178c16XfwemkCYaK+GfncdokLJ/TnQ6pq/qH+cd9u97F/n8BPnb7kb7sucsAklznx5wiMqJX/fqps1rBQsYXxKnBHFKYLOKDZxYbzlcX4LcjY22JLZiGIPOeFcdqfxqgLWW0LTx1dZm4avRKSgWL14AuCx5hdCWuOQhjUV3Byvn/NKWoSvbhe5u6atI41xYApJO6c6AJLvo9KXHuXVDUOo/71R1RSuc6WjStaSa0RlOIiWdNohG/50WBSsk11+ISc/pXnV8K2xGjlW1hdP9LmS1lVRiimm4WOkR7SjrY6VEF1JdScUS0Z6BpErQfcw6kihfxrqqX1rhSMLxIt7kUkKKjU0mD2uT5L7xEjKI+wbZzSC7KYw4EozFugqbcpKq1vxjnP/zjyUTiDW8/2du4/7CenJsrOSzjaOcWCgx/YnP8Ma9P4iqaWS1hWwFzEYXyAzuJhiXiC2abbKvu4vs6+665DnfilCuQeVC/EKEleZV7ay0jF6lqWr6FpgCUDfBN2CWQzSqTWQnShqrjMHm02jfxTk32/Mp3wo6lfjKLksqdNhBSIV0XKIMdIYuPZbUn30SgPzr7185rrQsj26ypdeT4zWIMklgiJxMPID70C2Rq5kKZFMYJdEDOUQnWiF1q+xSTcpbR+jFQp1wooDxBH511e8ZWGTU36MhgPzkiq7crYcYX9ApXTrBTFrw5yLcmu5VaG230LBRGNJm5FgAotpCzVaID42txFJnU8hGO0k6zaWx3chqs2eo72uwjkLEGp1P92zl9GAe2woRnRBTzGKKq+z3hgpEaQ+RcqHewRQzic59ob7l512G9RyikWIi9QhjsEkoVZhzeOLk71B/ZRIQOKkMI3e/fctj1V9+ioxTYtCfQCMQUiGAhfZZRgbf0PdcuSbePBxJM/rmbwFrCbKb/1ZGQHvUJcob/EqMuoqEvh4hTgvitMRk5E3ZZHe12JEUAAFJ1cuRdHaVsFuFMXyDoBJO8dj8H/Ge8Z/EkZfemo6G85yufqX7f4KwlMLtaEQnwlqL2AZBXF7RyVobp3Ztvn+32sZpBmua0JJYU11KY1a5W3gti7O2U3jNWClCS2rRojoR7d0uBCufSwBuzaBzEi3BMZCZN4lFzyaemZtBrNaqxYZlEbNaSgiyEYJgoow3uYRVEjOShIDEE2VUawan2sZMGLSUxBNlZG0KnUsq9EbbPpcn6yl0MUMUxDiz9eR+0BbrO8R7BpLI732D+MemN9fB+U7PCUOPFTGu6g1uJuPTrE9x/qsfA6VAa+7+9v38s5+yG64bnu1McCEa4NE/mic7vI+cLGLcEG+ygjCWheZJyre9HuPSC0J5tUNKEF6MSsU4mRjpd6voN/i8dhKpqRBnh6UTVwJZbWEFRLsHkwqhEBjfxThO3w/mWkssRN9vqH1Ja5ebkGMlCPKWl3/t3yNdj0M/+U/7HHM2g7WWxc9/CoC4Vlk5trQs35Ve7RJ0QSY+wu7xJqq+8VgsHIlZLRdxHeTC0sr/L48VghW7MkAYA80gqZDuKqwfY93EG9lZWy9YdcoCUIElOxMTtA3hsNtHgFILcdJs7QvcjkWGXTeKZVegKwhBSpoXJfH+kb4QJSFFojtOe9iwS8K7xFuShKqgJDbtJzrsVWOezaXRW/hu22wqkaV4ThLM0uhgsilUs3NJ6QWATXmYYgab8hCuQp2b45Uzf029MYnjZYjDFoff+xOkCps7q7gNzeSZRzmSvjeRfxQzyXkEIQuNM9w1/EN9z9+IAq+uGF8KcUYSZzyUBtlMosbd0CbSja3gCMJUlxDnFPqGmATvLHY2Jis2uBcrhHsHXvUE2QJSCYzeeJWWd5Mb5tNTv8J7J/4+sYk2JMqtuEo9WqQ0/BbC2WRl6zgpZtxpUoUSTzz6C+xK387rB75py/MRxvaqrOoakWMAq826gcSdq5FOu8mgtfw8mfwjtxmG6LYsei6mPbJyiapOkghlBTT2uxhXYD2B2OZl3As0AehEyYBLYnvXGcwh6p2+yFZTSKFrKbAr1SujJOFIIYnsXvVYNFHGFJOBONw7gH9hKan2AKIdIcOYeDiPbEe9yn00mEv8irvHiMsZnIWNwwHCvQPIdoR7sdKbhCxJ3K1R4OTGKb75LUQXj/F3/+Rd3OlPklNn1x1nSWd5vjPBy4uDTH7s8xze863odJ54wsGZreMsNIhcSyqTftV10koJUhmsMgjXIB2NdA2Ob5Py6avs814u1M3PjQHQ5RyUsuC72GIWNbkAnpO4PLQ6vYWqvbCAyPrYgWRxG+YkwUhCjoMixMVERlF84E0sfOYvqV04TmbfQQCEs35Mabz0HP7YLpzSQJJsZDStUyfQrSb1F59l9k//J6/7W/8b6WwZZ7Mo6S46RYmM7KZJeTblEu8d6Y0Py1jrVS88h2jvMHaVlZ+ot1HTFUwpi9uxsMaWr50TpJa2X+/zawad0piuL7QAvHrS4NdbUKUklqvvQbDZ9dZn1lGQSyOWGsgoxpDGpv1Ezt0JYXkR4aqkWa57jjQ7Gx5P1Ns400vocg4zmE/mxeXEu92Dyc7E2VnkRgRZCOKhApSyPf98m/bA95LfaqjA4czbmWmdYPyB9zF8x5u3/LxKQ3jiNFHQYDizm3i0hM2lURcX0C4IIXGV11+dvUb3qVagC4qooGiTLDZkZCGyK6mOAowS4MtvCEK8FjueI6taIe7Mq79hz7oKrSRSdwc0JdFpF+O7aAdOz3wZgOHBO5kebPD0s7/Jww/8NK0zL7HLv42/mvwF3jry/Xxp9ndIOQUekm+lNHCQmYtPEMdtjj3/hwgh8f0CDb1wyfPRmZXSiNu8fL/jtVi2ONsIqhmSenma4M7xFc3xZXY3WyBOS1TbYFISKyBOCcK8xK0bUjMxjT0uzSFFakETpwUI8Jsbr4RlM8C9WCE4ktiW2TVRtxZ6Gu3eaxpBbyFnFps9r2c9kF3X/6fLK1t3NuUS7BnAvbiEipKI8dTpeTq7y4T7BhGxToiu33/7ReMl9FAO52K1T/5iRdKhrn0Xm0/1zP6DvKS1KznG/OnHqT3+GN/yi9+MwHJXanrdd2Cs5EuN27BIzn1tCTqaEbuLxCpEIFshOp8iaNTJlksb/i7XAkJYpAIhLVZYLN1Kt7ArPqfSgkgeFzJpvU8eW+UmsZbQLj9/+R9J77hWmG94ArwV3Jq+dAVpEzhSEO9g6NGyhZuaraCHi7hnZxHLVdhV3Xt6z1Cvea495BAVFVEawsFkUYkDncY8S499ITneRIkzv/jvEMph9Nv+FtJzCefnmPnY7zPywe9i+o9+h90/8lO45UHcgUGi+VmEozj18/8n7sgownGpT53EO/LAll7A1hMEZUX+XJhodTeA3jWwjhwDCNs/iEaFDMZRSbNY2sM4ClPMYgsZrBDIarLgXk6eA/CXLs/O05KQpPRsRHvE7X2f6QWNiC3agfaAIndxewO8M7mAddTWASIbwKzRWFtYIccbnDMbkGNIrp9YycTCrdbG5FKJTCefBmtRU0vIMEa4ap31aTxUSEj16uOlV+bWKOfxwrm/xs8NMHjbg5f8TE4t5uyFR9lTvg9cp7djINsh9VEff6G4TrpwrVxC1sIAxhVwmdrzVzN2nCBD0mgUDeVw5xs34u13BDLSEGm01ZzSzzNdOYY2MUJAGDYwXeKcG9zH6fOfA+CxJ/4L1hqG7nw9TEJVVoDET3ph7mUW51fScOK4ze797+DCmc9jnAzVcJaiN7L5CXW9UFUQYaOrvMMciS5vTpCBZHARIqmi+s46652tINoh0YBPnBHkz0a0xpzeFlJzl4PbMOQmY5yOJcpLovzK0rYD+HMx6cX+z+jM1TF+UuFQCw1UvZOk3Y0WNz0PtWqXw71YweT8jVOZNoD1HcIDw8hWiHexArHGnaoSHhjqRpPn1pF0AOM6xHsHkKfme5Pn6uetTv0LixKrLOcf/zMqX/wcb/3593PHG7NMuIuk1pTqrRV8uXmYiknTCH2mPn+MgSMP4M63UDN1wvEiqh1iPUUUtXHSOa60nVY5FtwY6RqEY5COQSiL4wDK7Giz2y1SFL2h8Crxtn1r1yHWMLUIe4av7UltA7LeRo+U0BODuOfmsJHGriKCvc2iLjmuMMvMZ/+SzsxkUi6zFtNsYVodkJJOZ5poYR5neJDzv/afyRy6DZUrYMKgJ6lY+PRfIr75g+hmMnepgTzx0hKZN9xO5eMXmHnxi4wcegAbr+wcLjfLLSPILK/c2NDX2KY9zCaOMNp3UV2plSVZjMt2iDO5kEgt8pnk3+MDCCyi0VlH6LY7EksSL2SjBG5T49YN4QBoB6KcIs4q0lMhbtuSnouxjqA1qLBphQgNXkXjrqmki04IQbSO7F4vCEBOLYIQCSFfdnLK+H1FI0jmHef8HKLbEBnvHkQu1HHma90CShq75rtcjU6nytef+K/46SKH3/8TSLX1XKEMpCqaufnnuWPvjyE6BmeuSjwxCNoQV6u47gY9SrHtWfW9huuLG0KQbzYIJROpwDWGtZbHG3+J8H1ue9134ThpwOK6GcKgzhOP/hdOHf+LVc9PzuHlxpcAeG76EwAEQZXnn/yt3vNKA4epLL5CNjfKngOPsLRwnEfn/oD3TfyDTc/FmW8k4RiNjbf0TNpFBPE6o3RIfI5VLYnZRgo6ewe3NKxPXqRBG9zJCriyz1v4UrBpD2++g9PxMa5A6K7WrPv3KCcJcwK3polT/ZewBTrDDsLGfduI4f4VDZgtZ6EZ9KXOqWoLIoNTaREPZJFBhKq0VqybUu62yfFqmIxH5/D6hUtfM5ExfZo9IwTBoWHUdDVpOtlV7n+xgPlchcWLJ6k8+gztF4/zpv/3W3noXcnneV2qvzEvtB5faR7gfDSItYLnF8bAv4DtBIlWOjZ455eSRVQ7ImhXkMX15v2bw6LSMU42wstorNr4XnqNrN6cuCqfVEcRj5Yv/bzrAN0NmDCuQ3RgFDFX6wVwQNJE1ClJopyi1rjAmT/7VYofeISBv/1O8oWAlBuT2ZXjwv98nNO/8SVmf/63AIjnkh054xhazzwOQDg/C0D77Ckmf/uXMUEyjupGUqFN338nthJT/fznWHj2MXL5e3vnsSaIFa9lCEsW0QjXpbwB6E0qn0ASIR3MIIIIPZGkCsqLXds3242xlgI9ahOy1w6xw8UruveSq0LgGJBdTXVqNqS5K1mwWwFh2cFtR8jIJoWM5SaxtCROy3WLL5vyiA+Obf6m1iKCONldu1RRZblKb7pNpd3fXmid6K9l0sgsIp2kF/alu5pEM726Ai0S4mxcB9P9DVS1ic6nsaOlXlT4WszPv0wnqHL8xMcBuOPDP4PKbE6kl+HPJE2RjpPGBm0gm1S152tY3yWcmyIlN06elG2DuQzd8Wu4MtwwguzcRNXj60GOAZpem2Znnocf/mcI0X8xu16Wt3/z/00Y1GnULnL+zBcASy6/izhuMzB0lH2H3o2UDk88+l8Y3/0gu/a+maC9hDswwlI8SX7PPYzNWS6cyeLHW3eX2OUK8iYEWbY3J7zC2J52N86nsCkHb3Jp0+dDYqnjn5zD+g4yiPBOzhGP5DH5zQf/vtdnPBwLOrTkLsbU9zh9A0JzYmu9W3vYQYURbnP91GCkWEfYl63qrKdWUumG84hI41Ra6Mz16VYTYbwh8bZAPLZS3Zbd9D6A2oTD5Itfpvr1rzBw/24Of8/tvOm7kjjnw94MQ06iVQ+MxyvhEC92JujY5Ps61yhSi1KgJTIyfb6hxIbqgEZUU0R3+1yqhCyVxckHidNDlxS/RoJvPVxtfPTlNsheK6yWDRgpYbQEMgneiHKyR9aMDwvHniH/jocpfOtb2T1WQa3SGO/9vofY870P0jq7wPxXTjL3pZN444NY6eCN3od1U5Q+/F4qH/8b6p/8EkP/6AdAW2Q6hel0UPks3r5d5N67j+bXnyCrVu5bV6wL0kOGluzFCGdpfb+ByXgbJsuJehthDKaYJR4fQFYamK7UROj+6r8eLiI6QS8t1Vzl77O6ZuK0LTI0yTa/m5Dgxm4X7EpCn2ob3JYhKirCkoPTsdtfhAmBTW1Ty7wsYZGSvq7orjWczaY2J9lS9pFjEetkZ29NTHl8cKxv91Mai5ycR5dz2FwaYzTPPv/bZAYmKO1/PYNHHsDNbr4ruQyvulJdFzbxVV5mY858IkGt6XkywxP91aEu/EpMfJ3mpJsdFgh2aE1+fUe2DTRUy/hG2B3QuTRCynXkeDU8P8/A8FEGhtcYiIvEq1MrePMH/iVO7IArEOUJwpKiwDgh4DUiGvUpjN3aIkznU8haB3kF1m6r9bBOI0CcXtgyOll4ks6BEUSkMd2bWIYxzuQSYqaWxIv6DtGu0vrXWovxJSLsz/nwK4Y4I5GhxboCu8kFpCJQLY2MLO42ehFlEOFM14hLGUwxjS2ke75B1lNYz8HW230a40tBOHJTH+S12G5Vepkcd0qSM89+nMrXv8hbf/37eOgNK7/DmFPh7tQFnm7vYSoqs6CzrG4RjY3D6cpwt7vPIazN9N2I8WCWyfpXyd55F5mJBlHNI66lMGsMLZUf4+RDvFzc1RC/hlsZzhVqj282aF/Q3uWtNBMpQZixhAMC+yKQVpSKTZSzfuwSQpDdP0R2/xD7vu+h3uPGCM5dHAQE5e95P6UPv3fDQBxVk9haB2tMYu/WHS70UhNnsY5J+4lDw1CBYMAhNdVGVdcTZD1S2vR+UlNLiUbV2r7KsxkqIlqzKw3IGR9ZS+Rv8QZkezWk1iilWFseWSsLWQ2vbpKwmMjSHnWJ0137ve62v04J/IolezYJg1prS3a9YQG2cK7Y8DWOZCNWslYaKOYqyU5rJoU2MZ/7wj8nN7qfox/YfPd2LZyGJjXffw22dZ2Ms/JbaRsz1T7OXbt/DC0TOUbfMdoWZ1Wi3jcKhBS0By1xZmcY5HWPmrZpF7FFdfLViriUZonTiMuMgwbAFdR3r06Y23xFHWckMxefxFMbb8UsQzUDZKN29QsTbVCbNJYswyISf95Vka/Gc4gODOOeW0DVOtAMEEFMNFHqkUQRxbjnFwkPDK9b+fszbdwlhZAS40jaw4qwqHDrGq+e2A1hIbVoLuszysUWqp4E2HSK6W4CkINsRSAFppghLKY3bJjZDCa+vHPYLjpFSb0UsvT1L7LnRx7hwfv7K0dLOsfHa/fRbzKXwFrBM3NjhFYS1jxKD76dc5/511T3PUix6RMX0tSKmqmXvsbEh38aIQReKcQvRkQthdESqQxuyrxWLX4VQRmQV1M9vgmgU4JOccWvN04lYR86l4xF2glpXTxNcc8RivnLc/BpBy7LxEkIgVhDjmUkkFWJaikax15Ad5o06tMMlA4BYNIeKoxR3YAsPZBPGk1XNX9ZkTR6mWK2r6ppPJG4CthE/6rHy+Al/vDxxMoOmEl7iIE8aqGeWFR6Du5y0MVmLLcLsdCAahMxMYhdrcmtd5BxjCmt1wr7cwFEGqEk2YtJ87RxBW7dIBxB5AmcwPTCoG4NbG/ENsUcppDFSsHUxScAOPDOH7rEq1bgNDXZ2X5yfHj0EU7OPs6gP9F77EzzafKpMYrOKGKTWpRqfmMRZONBZziJ1t4pXNdv14okalo43xg/oiXZIgv2DnDRmeTU8b/iyB3fdtnHaefFtqMZTXeJo23XUkwK5Ebft7HrPHavZOhartxGI/l+f9Gcv7KVF+rEOL0LGcbIMO4O7Ct7I6oZ4J2exz8+nQRnCImw4J+aS5ocV0E1Q9LHZ0m/OEXm2Ay5yZDSqZDslMZrWNILhsxCkirnn5hBLfZXZ6Rdb7cnWwFut/tdrKr4RkN5rEjM9y1cFjmGSw+1QafGk4/+V+rVC5c8lvEElSMui4clZ4OnOPPl38HfPci3/OTeFTeH5eNaZ0NyDHBsaZiFIIvQkmgphRf7DN3xJp69+KcYa9EjeU4d/ysG3/Z2Mm/0en54VlicbIxXCHGy8ab64tdwa0I29S29mxenBc0Jj7jo0ClBc6+gPSqIcwIUhKWAyd//Ddys4vC3Hb7s41drmzeTyZbAnXJQrYSYOyb5dxStyAeFsUkCp+g22yqJCizkVqRmAkE8VuoLsQgKkvpuj8Zer+cqYIrZxG1GSnBU3watGS4Sj5WT5jwhwCThNmIT3SwkY6KstRDaIOv9Ddci7WE9F7nUL4UUgKo0cScXcE/P4Jyfw2uZJIEPILa4LYPQIGot1Fx10/e/GTA98zRPPf3r236+TbmETszxE3/O+YuPsuehb8PbhqQCwGkbsjPxuol3LHcbtWiOyfYxAALd4nTrGQ7f9+EtJ2n1Ktn52Q6CIrTG18e+X29c17eTQYRVkmC0gDdZuZ5vddWwSqx4/20DlXCayegVigMHGR69G5SkaSrMzj7L/PMv0Gkvccc938vA0G2XfS7av4xqZVay58AjNGZPAxBnfXTOx5taGZgsEO4ZwD8z31fNv9yJ0SqJdRUijJP4cGNxKy2IDdFYManEztQQnoN7sYLQhuDAMGqmhqq2CY+MJomKq7BMhL3T89iUi875uHMN/BMzBHsGeprleDCX+GgvNBBBjH98hngg29Ppylq7px2GhPzqgSyylZDwpMGQRC8Zxkn0driKhHurmuQKaYJDI/02bNauq2pfKZ578jdp1qdoNefJF3f3/a1dTCpgC8e/hpMvsXTmJVpPXyRu1VBph4E3H+bNHzpI3tu+VOZsvcS5RrIwaS/5WCtw24KxOx9h+ulP8ZXg9zly5AdZmj/G67/nXTx08ASPTh3g+tTBX8PNBHmDYqOvBaKMpDEimD33GPWFswx+8/sQ2RzWs9SffpLg3Dmajz1NeleJ+/7jR5CXqcVtd1zCaGOCKWOBu+iwehTNjxwkUxinUNi78rxqE+t7EMaYrhtNnJaJJtd3EqmatahKCz2SjGXGlwTDSaVaO4JOXpJaXFVxrrchl0LMVKGUAS+xXrOlLCKIEkvIUg6bcvub0EhcJJYfs80g0d52nXFEo500/RkD1iKrLVS1SeS7kPGSnUGS5kg9VMA9NolsBohXJmG41HOmULPVhHBHeiUK+iZEsznDiy99FEga5FdLIZOGeUG7s8jc3Avkc+OcOvNppHKoNy6SG9zL0OvfxsDhN2xy9H6o2JKdXk+OZaxxGwG35R/mueqnqYTTFP0xBrL7sHuGCTqG1Ca2fJfDV25VWFfQHgJ9g+TW15ePG1CzdeKRPHEnSjxlt6nN3GlczsU2ryd5fO6PAThfeYqR9imMDqksnSaOWtzx+u9hePR1CHkF8gopeobs20HsCtyRYdx60nkto5goX0TXOqhuzLN0ZOLDnPW3bMa7FMSqUBD/9DzBkRHikQLpE9OoahsRxeisT7h3gPSJ2UQrF2v0aAFV6yCaAcbZeMBUrRBWx60ai39ugbiYASWSKOguoY1LGeLRPGqhiWiFCGv7LNmMq4i6lWpnvrGizTNAp2uRFPYPOnaNTnxdo8haLRpXLjGIo+RcTx3/BMXyPlLpMi2/w9mzn2f2k5/FHx1H+D7GxAgJoz/0CIOjlnsedBnw2+sqx1thqZPh2FLXRSNWxHUX1QERCzJth3e8+19x8cQXeOnZ30WbiOywIOtGjGVrXGxurzLyGm5dbPdS2kqTulOwniDIyV5ATpRTHPv0r9I6lVTeWuePkz60C+KY+uPHkJ7DA7/2w6Qnruw6rtU37zuQS2qdz5bnpJGui+OsSBVs2kNVmslOVK+pLvkijecig7j7t25FWQraI07f2GJU//vYbvVZtAMUFj1SSl5qLer0DPH+UZBiJSRDm8TpwXWSxLdGJzlGd6dRaINaaqIqTeKxAZzZSm+ct4AzvYTQhni4gMlncM/OQqQTmYercM7PI+cqSYOdkqhmGxtrzGAePbz+u+9dS9aiZiuI2CDaAXq42Nd4eUXoHns7S3u76oJ++difcMft34kWlnZU4clHfwE/UyZoL1EaPcq5i19m+NAbSe/az97hfbjp7dvUCUjI8UZe4fU2NjbszdzNeOoIT1T/kqVwnkJ6vHuSW5z/DkoNbgSiDIRDN9bO7roXrJ35OqaUJhovEY2XcGZquHNbZ53HpQyq2kKXMlhH4VZbPXK00+jZfAHGWqbVJC/P/Q0AhdI+yoOHSaXLKOUztvtBCqU9G3sXbhOdgkBf5gXhHz3CuSc/Qfu+D+AvBrjnFnuzmQVsbJCNANSVS12EklhjehOqDGNkvZNsIUYGd76Otd3ADCWJcx56rIhoRdi0QzyU7Y1c1pWI7XgxW9Z5LZu0S7S7jDNdJR4rIqtt/PNJ5dj4DvFQHm9yCdGOcOfqSfhF1kM1t9ZNRwOXNzCvHbfi1PadAO5/+Kd4/Mv/ieK+u/jal/8D+x78Ts48mlQycg8+QGr/IEPf/jBeGqwylLw2D4xeQIrLW9wYK3lhYYzlKzioJnpK0b2V2oMK7UL60HvQf/w4WMtIOnEn2V+ovEaQvwHgtrd3ze40ORatALTphYIAtIYc4nRCkJupFrPHvtgjx4NvOUz53j24xTTSd3F/4D4Kd+1K5AZXgCBwaQcbl61ULJDtNY2rBmQIhfHDzM29SLmUJPKZYhbdDhPC2g2UWA7qWG4AM6Vs8jcJzWG1Tl5nsgrm1gtRbSqRQfSeJwTKc3AuzIM2mHIuCcxxJMauqo4uyztSHibjIZcLExbU1CImm0I2kx0qAdCVyznTFexcFdFlocKRGM/BZH30YAH39ExvrtQjRUwph6i3sPkMYlWqnVhs4Cw1iPcMoa+BRaB1BK0hRXo2JhhwsArSM3ESHiXFpg4audwYtx35EBcXn2GheZonnvlVSvn9nD33OaTjkdlzkIkD95IfO3hV5+dWNSLY+BxMOUfkKmSzgxQ5Doy+l6ee+01SuSHilEBtIZkPc69S6aoUdMqWKHfjFwDXnSALC85MbcVWa4NVlEm5mKyXxOx6imiihC5nep37eiSPe2ahVxHdCQhXEaddrBCYrE+nucgzZ/8QoRwO3/kh8odfR1xIrMdEaEnNxbitq5xFpCAcvvyfxC0NkSmMMdM+xeih+5JDxQZ1fLrna+yfmb+qU4vyPvFwAdGJkJ2EfK6WNNBdtVtHgTHIeodooozTCaEV4FRaCAHtYppwoox/5tLpfxtBtCNkMyAe7Xqg5lOYjIvsRIQTZdyZakJehUA2AkzOJ5wok3llZlNnCStI3CuuENoTNMcdiqe3R2BdP483MEzm0FHs8a/2yPGe//0fUHj9ANI3QKIhHM9UuWtwFnkF8Uln6yVaetUEGnabmLLQGTSIEtQOS+RIwOvueA/xk8+zf7QFCHJuh5wb0oi+Ma2ENoLawYCTnYCybDpx32jYNSEOVkCUlkRZmDn9KPOf/guKDx/lgd/4UbJ7S9f8/ZeqWxQ56jIhiavgLibXxtChB3jpE/+VQwffi1LJvWczPnaVz7kgGTNk2sNogxkrgyNojrsb9p5olWitnVWLGbkcfEGymBDtAJv2iXYPIlsBznQFaQy20UmqwV35gwDUubkk0th3MRNDyNMzoDU6l8amPcQaPTJKoLOpxAtfW5bLA865OeLhInpXEmyB60AUJ42C3Xhvm0++R9EOewTZlHPXLCgkzEs6gw7+YowwoPMKLSxml4tOS5yW2dJirjhyiFPnP8PInW/l4pN/RXXpDLmxgxx819++rCrxVnCaWxf3bC6NzqVBQjpbZnfqA/jFYZCCOCexlfV9AlFG9ppSX00wntjxRrytcN0JsgX0qlAEU0xjFxp9P7iMNWExk9iQmUTruTpIwQrR8/G1aZc452N9F7XQvKSjwmbQGQ/ZiTYMxgDo7Cr1tr3mZ57n+Ct/wvjt72D4vnegCw59BYSUoD3h4p6MNt5G2Saaw1eedz70hndw7st/zvDY65DSwToSa1Y1pnmKYFcJd6qK3MKibS1Myk28gOsBqjFHPJRDlzN9uwDKd9DdY4pIgwGpk/eORwqoxWavCc47t7ihvdt2oRyJf3oe4Ui05xCNFjApD9mKcGdryCBO9NYnZ7GuIt5TTjwvldxU3qPL2ctuxOt7vQt2G79bqznHS8/+Ppk9B2kvTtHJJgs+d9c4TiFN6YFSX45o2W9x9+DMZUkqlmEtXGyU+h5TmQjdSW75uGxR2ZB0ucPe0hK3l8F7551Eq+7MXdkKxytbpDN+A2HAb3LnwBxP3+gTuYYQrZu7Qc+SuPQEAwoRWwI/5PzX/oTWiZd53c9/L6XDJQa9Fnl3gUA7THWuzY5HELh0ws0Xhk67n5goK3p+6+7QKPnSHi5MPsq+vW9PPkchgw1jpDEYKQkKkqDskA+7RFFAc8TZtDFbxRa1ptIvz81hdw8mcdLNTuJgUc6hR0uIxW5jXRgnv++q8r9YqCV9GRfmiQ+MYpQkOjye2GsuV9sH8thGG7nURDQ7CNdB7xpE6PmkspxKYu9Vo4MzW4VqEyK9MvetGUtFK8Bcwm7uSuE2DJgYr2nolBVagtMwpOdiwpLaVGN/+sxnaLcXaOs6TipHHKwsCobveMs1I8eQ9DdtqpVwBUFaEOcUurs7Mjryzt6ftS8Jiwq/2t11AMLCikb91QILRHkIB24ud6Tra/OmBOFto31G5SbjEe4bxFlo9EIrbGzwzy4k20JrtJ+iG9UrO0l1Tmc84uECznwd2emSY0cSZ3yc2vYsfEzaTbSrWzzHWWzScENOnfgklcWTHHnkh3FuP7jOL3IZWkJrQJKZvzIpSFCQhKUrXxFmj96N+9yjfPFT/xtH7vwO9kw8uCINcRXh/iGs5xCPdFPTNoCQAq1kv4OEsXRuGyV1chYRamQzJB7KE5czOEvJoKJXEe6k4SO5xNV8g3g4j1y1iFGtEHV67oo/p+mSXBsbZBziTlYQJnksHs4nCwPfJQ7ymIEsRkqkNut9mwXEhTR6INufasf6ho2tYIGwrLCOIChI/IbtmyjmZ15AOj7xWI4LL/0ljdok0gyz52d/Bjk6wKFH3oA/1EJl1i9aDhcXrogcA3Rin2bcfy95xRAvq7EGhKtJORF3DMwwnG6SFhFvyp7gM407e8/fm69yrjZIx7z6KhXbRclrc6C4wHB6vWftrQ5xY1RrWyKZKCVhycGs8jufbx7j4h/+CanDYzz06z+Ek3O5vTDNnkwFgGO1a7eQqzU3DzNSsVgfoLOmCr/nrd/BM3/ybzh56q94x9v+v0kl2XMwWLQv6Ay7RBmo73Hxmoa44KBX3WLLQQh+rfsbhatiq6XAGgtRvCLRyPiohTpyqYFsdpLwIQH4LtaCWmr0Krqy1d2FjTS2HUI2lcjwhEBEGjWzhNk9hMmlMbk0amoR2ehAFCPDCOk5LHNOq5M2XqsNTtpDL+/wrunnsenL2IUSSfVQxRZrkka5rWQywoLXNERpQTDg9I4hDX2NjcZozp77HLsn3kyleprTZ/4G18sxfv97GDz6ENLx2PPw5TtObQfhsIuVEV4AYIllQnx1RvZI8VYIhhzijERoi8movmvlVQEl6AzsnLfx5eC6+yCvTfERxmLyKcJ8CvdiBYwlHszinV/EZlKJO0LfGUp0MY11FaoV4iw0kwSiVWOSEQLrb3DVqCTwYb1gtL8/35JobLGGsOBT8xosNU5y9iufY2z3G7nrQz9DPHZpjWo4qHDaBm+D9LYtX5eXtMev7qcQQjDyjm+l+vsvc+LFj1FbPM196m3JH6VALTSJx4voYgZ7YWnD5hy7gdBQhjEiiNFpDydsJ13LnYhorIhT6/SaOXrnYQwoiXVl7286l+qRaQCpJGZ1+pMSWL3ehm0bHxq5yk7OuqpHTuORlYqFVZJwTxlZ7STbhEoS7B1YR4wBFueP89wTv8E9b/wJSgOX1p4FJUmUTch0a9yB6ZhYGE49+zHioEX9zEtYo5Guj8xlGP9Hf5/Uwf1IZVHZgFQp3NA6zZeakt9a9/h2EW3W2eBoFJaJXIXbygsoofFFzDfln+d8OND3VCkMRwemeWZ+F98Y0T4Jsk7ESKbGrmydrLtzsq6dht2mleROoT2giEoORiQNOh1RpzU/SfX5r9O5eI6RH3ovh983gZDJNn9HO5xrlpkPsiyE16jiZwWd9uappDbY4DtbM2w6g8NkBiZoLU7y+S/+H9x7z48yUD4ECIJiMk8FZXBDReCtX4gLwGmtbCapVVVQ07Wr1PtXFgQ2m0IP5hHLhYhwlVtCyu2TM5hcOiHJlvWNx45MEugAJQXaWOxYGV1rJ8FJkUYDCmB1UEnap1PO4TbnVj5A34Ev4zqzK84qTz7936jVzvPOd/xfmz9frFw3y9BZRVAw+G1LozrNiy//IaWRI1w48zlOn/k0jpchP3aYI9/yvyDk9dfxagl62OXyI7oSLO+kvBqhfQhGxBXvnF9v7HhGqKy2MOVsUinobrWLMEaXMn2kRtbamEI6qXoOd1e/2iBn6whrsTKJpLRSJF2wUYzOp1DtpDIc+w7R7jLu+aXEIWH1OSxXSH2HcDCHKWfoBDVOvvznLLz0MunMILniLo6++ydwj+wl3ub9bYHWbhc7FePXtqHtE0mjVGfw2iwJ0yO78YtDBNV5ZqafhgOPJM2NxuIuNDDlDCblogvpxFFkgw+w1n8YJXvdzkhJPJBJvm9tkm7kNYdw6x3sdA1r7Er6nBTJcbqEOZYSm3WIxgpYz0Fog3th/e+0GWTGRbcirCMTS6JuxWL5GMG+wb5Ia0sSJa2LGeIgQnhObztRGAvGoKVlYeZFXnz2dwF4+aU/YnDsTgaHbiM/coivf/Jfc/SuDzMwdBSEICwmxFjnV9nDxTHzdorzn/ofGAz5Nz9MpugQTk0RTc9glgKG3jGEEA2s1EnD4iafsZxqXpWrXN4LKXodquHK9+AIy2imyqHiEiln5bt+KHMSieGZzt51xxn2agxfmGdu9+t5dZJkS8ENyXttin6HwVSbtHNlsq1bDtfItvBqEeZWoqGjNCwsvMDsx/8M3Wzg7hsne8dejvxv30RhwLByxwjONIe2OuwVoR046M1iOmFd5C/Q9Snuv5MPvueHef6j/wqAOFq10O0+TXUgTFu8TfrVnVXrMtVZedPlwoZdQ+x6bhHG4JybS3ZcF+qQ9iC7MgaYco446yMnFxEpt98tQ4ieVlm3Q3AVRhtELpW4IA0VejHIyUlKlJJQb/f73gcRRmusurJ5LQxqnDn7WarVMwA8++LvkEuPsnviYarVs5w+82nuu/fHcN0MQXe3YTUsUM91uLD4Ime/9j8ZPPIAgRsxcOh+Fk8+SRy2GLv3XTtCjq8lgvpiEuCUu/rGxpsBURaCIcHNnMW64wRZr60QA0jZ84hcht3A4NwoiRnfvs5MVVobky4piIZy6JECQVBn6tRnmDz7FXbtfZjDb/xuwtF0ryp4uS0slqSSGGU12QWD2MjMWwmCnCAcdIivsZRo5OH3cf6T/wOAL839AXdPfIj00Dj+mQXUUhMzXkKPFzcmyBsg2FNOSKyxGE8Rj+RBSvwTGze92a5XMUD69DxGyr7BE0B2IjpHRpHtEDVXT6Q229RuG9+hvXcQd7aOsyYMZPm3Fmst2lafn78yKVhrqM6e5MQrf06rvUCqOMzgnQ9ROHIvOmxTP/0iz3311ykeuZcoqPP8k79FJj/G0e/7f6H95ProLM0y+8xnqb70JCiFUyqSfdP9DH/4HTi5CMexdM5PE8wukto9CI7e1nAwlLq6LX0hDA+OnmO2nSU2kowTUfI76yQbg6rOuFPhL+r3buh7fP6JOT7z9z7PR74wysuVsas6pxsNiSHnRuS9NnkvoND950oaIF8NcKvb70W4XtApQWfYwShYap1h9sufIZifZuCHv53BB3YzPNBCyKRpdSfQ6lxCDrABd9ZOkvIlV001fn4ARLIr+fyLv8eupZMcve1DZBc1dVfg1SStcfCaYsuxT1rwLiONTjY6yE6UyC7mk+TUeLwMXfs00exgsinMgdENX+8KiIwF30VYi1xqYjIe5NKYoQJR2kNWmqh6m7icI7bgBDXkqoqy6EQ4J6bQRydWdM2XQBS1mZp+nDNnP4vFki/tZfzud1E+cj/1qVe4+MznOHvu82QywzSb03zxy/+S249+J6Xhh4FkLK9NHufsF/8g6WGKAtIDu7j92/8hqbG9qAis0QwceSNxq0p+/Mi2v9ObBWe/9FGk43L4m370Rp/KVWFZQhQVbo4F+lbYcYK8Eawjsfl+3ZfNXH33vKz1b2rYlEPDCZjRp6lNT9E8MU3QqTA0ehd3v+PvIY6M07xGhDUqKCoFhRNaRCexR7NKYH2ZNHVdm7fpQfmSONRkdx3oPdZoTPHVY7/CPUsfYNw9gFNtE4+XMI7qq+huiWU9mbXIToR/doFggyjojWBjQ7inhHdhsfv5JdFIHne6iqq2kka/Tb4I68i+dLtlyCAm9fJ0Mk9t0PtgfQdjDN2NwE0hFxo89uIvEwR19t73QQoPPoh1+m+H3J4jOMPDqIEiY+/5ds5/7DeJGlW0L7n4hY9TOfEUBk32dXcy8dPfS/bwAP5YEYsBsbIASR0cJnVw+JLf16pPwUjmyuUVyxDCMpppbPp3F8PDmZN8vnmUutl4W3nk9hIf/Lk3c+q/fZ4DP/IeTtcGN3zezQQlLFknJOMGZN2QnBuQdyPSTnjFmu5XG2Ro8Lbo7t8uLGB8gZHgBmx7oWuFZZrzLFw8RvvlaVrzF5DpNPl3PsToez/C+EQLx9n82r1e6HQ21x8DCWPd6HVlQXqmXyZWPngvS6eeJje6n4tTX+Pi1Nd4+1v/OenFNDrtoTpglWXt+kxqsCr5bv3ZCC4jzMUUMsSxxpRyid1bEGIKK44cstZCZzf/jGa6gnQVppTDSpGEl4QRcqmBKecSOUc2BdNLOHO1DY8hXIXO+NveoahUz/HU079KPj/Bne/+KdyJsWRx0UV6YJz0wDjNufOkS6OU2kUe/9LPI4TEpARP/PrPkBs/SBi3yL/xIfKvuw8nX0Sl0xigbSEzY5GBojhx+cFdNwsm3vAtNGbPUj3/EsU9d9zo07kySEFn8ObUG2+Em4IgXy9EewcwYYztxOBITk9/mQsnv0B5z91kDt5GefDtpId2IdMe8XUSvseeAO/6q+pb2ZhoFPIXytz99/89No45/4nfpnb+JcKgBi6J9rprEq9T7rZs8/yLFcJYowtpVCNAtCLUUhPZiZCug4m2rkIJu6IBF9rgTlURgDu7yd6iqyDSG5Lj3jElGNch2jeYNIqEGtzEk5MtolUrS6dpN+aSCoOOaLUS67vcww/3tiwtFp2BKG8x2Qy5u94NQPDyDJ2Fae78sX9B9dQLzD/9eWQqxfg//NsMvnkMK5eN9a+edAz4bRx5fat7AstbssdY1Dmm49Kmz0sXfVJFjyd++zjv+F/vY76dpR5dgkTsCCxZNyalQjJOSMaJyLghBS/Ck+HNoh64KZGejXDr16Yi29jtYrq7KdJAdjJChgbrCSI/abKzSiAji9dK3rPhtHn+yd9E25js6+8h8+CDFA99CDWebB2ns20cZ+c7CNsdlyi+xLb7JjOmTkFnBNILoldUOPjI96Pf8d00nQav/Pq/xeoIEKhupVlGrGv4E0D2fIA0JD008eUv6HoWaykP2QoQraBnsWY9NymMbOKJv2wd1zufdohzLtEW21J2pTFwrIyIddLAt/q90x7x7qEtPfettVyY/Aq+VyDWAdPTT2GtIXvgdtzduzZ8TX78MPnxJCp86m/+iIldDzI2eh9Pfe43k/Ms5tj7XT+B9NYX1qyAzrAge5GkwfEWRXZkH6c//7sMHnnjrUmQlaA1klgc3iq4+QiytaiFJnro2jRdaM9J4oWByXNf5vAHfxJvz57e33du8+76QmiBdS1RyuK0FHiK8be+n84fT3K68RRjqSP4TqZHPHUpvT1faW36YquFtbjd/w+KKazv4k1u7IoB4E5X+6oqYvV/bDRWrdVAr0G4q4QuZXpWQrLRwVlKbOTiwdyK7nkDzF58mqkLj+G4adKFZItRun5Pi2ZSls4QaL8nwlh53+Yibr6McDxmHvsrkArT6VC8v9wjx9cKE7nKNT3eWjgY3pY9xm5viS81L73VuOeBYX766x9GCMve/BIvLI5f1/PbCBkVU/SblPwORb9D3g0R36DSiKuFCixRXiI0uK2r+w5VaFnefDAykZeJ0KIz67vzA5uQlaVzJzCuYO9P/iw2ZYlG+1lis5ki5UXksjvbIFlrbMMLfYsdxjgtaE6AqoPT3UAKyx7IAhNv+hAXH/1TTp/5NIcPfQvSdB0q1jb41TRyeQi8AnK8GnqkiBkp9jXdCW1wzs8T79/a9SOJyHYh7aGHC4mX86pVpwX07iHMfK1PlyzbIc7kAnrPUN/zlxGUFGFWcOLznwAEfrpIZnA3VCE1sLHsY90xWhXKxYM0mzNUzr+YvG8uuyE57n0XCiLf9n6XWxV3fufPItXNR9suCUfQGk3kSLcSbqrTXe7CvRpy3GkvcfyFj+G4acZ3P0g2P0oUNKhWzmKxyKGBSx/kFoRXBWEEbkcktj3Wcuz3fq739xern+O+wW9dqZS6269qR4M5nHo7qdRCzzvanW9gLlEd37QSfBljv3AkWghkpPFmasTNIImX7kR9CYvu5BKq1gZr0V0Lt97bWUMUNkkP7CJsVRj/1u9hTzqHcpPZPSxYwuHN2wX82w9gPx7RvPAK42/+AKc//iuM/eQPojLXVkSecyLGtpBFXC18EfPO3EsMO0kFvyAvPWMIIVDd66Xo7UwDW1rFlFNNyn6LoVQH/xulcW4HEBQVcUbitM2VE2Qp6JQkcb7//teOgK7J/9yprzN/7Kvkdh9heP+DSCEIOzVmzz+BNz6OyRrMwAZ9DAjmlwo0WyFD5QZqB6rJYejQvpT+GDDCLkuLN/k7mIIgWmX7W3/maaa/9EcAnL/wJfbsfjMi9JCx7CseeFVNan7rnSPlK3Rw6e9DtAJs2uuRVN0ds/XI9np4Vkc+225Fei0sSbz1WshWgDw1QzRWWom7BnCSvptWbR7p+qQHxpBeigPv/xH21Orb8h52Gprxkft55dRfMrHrIUqjt1OZPc6uD3w3l+zuuHWLxz3ciuTYuoL2KLekPd1N9W3btMflZJouzB2jUjmJiSPGdz9IOj3Ic8/9D/KH7sDJFjjx9J8StWq4qTzewAj7v/0ncNJXmfV+00LgrpL0CiEoHLiL2ukXAKiEU8tPS5D1kyrsNracdCmNyfsbpt95jiSUEtGJtuVxYFedwnZhjO1NumizYYOhSbuYlNuzk5ONAFvO9JpEFuZeptWe4+D3/zTK69fbBgOG8FKNwdkU2aN3Mv2VT3Dwwz9F8d6HWPiTTzL0vp9AXEXIyFocKs1eN51sSkR8U/5FSmqlAfDO1DST0QDzenuL0ngz+7hrgKLXYSRdZyTTfFXbq91oRIVkpkoCDK4MYVYQlJPpQ4cd5l/5GkFjEdfNMnrvO1lsnGby659g9IMfpnnyOC/96c8jXQ+VL5A+dIjid76HuLw10WsHHlNzZcaGKjjudZQcWcn8UoHtjkzGMYhw+w4I2YOJ7lVKB2NigqCGEw3hrFInOIElNR9f8gyaGUht59ZIedfVpUR0wqRZr7ZBr4QQ2ChGBFEfQe7kJQY4/+ifMHbPuym9593EGUEDSHk52KIvWQWGzKxGhobUwBE6L/4eM7PPcPjuD/HMo7/M4le+QPGtb9v09dL2O4O8hp2BdQXtMW5aG7dL4aYiyMC2b+ozZz/L1OTjDNz9EHG9whNf+c8oP0PxyOsZedO3IIRg8J63XueTvXnRvHgapMRx08RRm/FMMkiLOJmUjBCJDnkb1mrexco6l5FlRIFGlzK4kb5k05+FbZPy1RDG9qrXq6GzXhJeEmmi3WWslCvhJaVMr3oSRS2mLnyN7N7b1pHjsGgvTY67SL/lXpa+9kUufOWPMW73fK7hHFRwO1s21V0NHAzvyr3cR44BHBHznvxLfLW5nzPRpRsJ5ztbRPBeESyjmTqHCkvkvCt1Cn0NWyGKEmJhVnnuKgOppSsnnVGxm8jYaXLsU7+CMzxEes9+5p/4KlPP/g3C9Rj9O9+P+8ZDDHAHQ4vfgYgEuqgx6e3f/7EWTM+XGBms4nnbi3K/HOjYYXYhTxhtv7xl5KVagPtRefIx0oO7aS9cACCVKmNj2xfs6VYuTY4B/Nr2qun2Gi7alyEA6m1sPo1crCe7dd33Wp1Ia3IpdDm3bpfSOIL6xVdozZ9n/C3v72vU6gwJoiKk50CsaUp0AktuKsZ2td1KuWSzo7z08h+yN/X+RCant76mnOoGeQiv4frC6VaOb1FyDDcjQd4Gzp/5PNOzT3Hou/8BbraA0THewAj+wBj5vbdul+q1RFhfpHbyOXJjh2nOnmE+OI+1FtmO0F1CaD0F2zBLkO0I2d54chJ6xdbtUhBw+eTYU4SFNE6jg+j0T+h6IIvxXfyTs8hqJ/k8AF7igW2NZmH+GKeP/yXZvbcx+o739x/cgXBw++eT3r2Poe/6LtonXwEN+Ycf4uy//guGf+D9pEcV0r+6reBdueqln3SFuCd9lkFn48ZIV0S8LXeCkU6NJ9oHWQ4grk01ee5jp3ngh47i51yMVVxsXDsPzqyKuHtoiqJ/iwsDb3aEhtSCpjXebaYDMhcj5GXYh62G8SWxLzBxyLFP/hLp229n6Fs+QDwa47/tNhpfeYrCN70ZUUoWUwaLGbzyeyPWgpm5EsVinULu2iyirJFU6inq9cyG9oZbYhMni81Q+dqX0fUaQ0cfYv7YYyxVTjEwcF//IbepN76R6Yc2ildS7VZ5CNuUR7RrALXUSBL9Gh0II+JdK4437U6VyRPPMnXsS+x/5/cij+xi7UfRLrRHIX1hpe4ggMxs1CPHy7jndT/I2bnHqC2dpTBxlHh6ntqjj5J/+OF1yXtKg1/fIu75FkDlzHPoKGDwyAM3+lS2BSEFzZFbU1axGrccQW63Fjh3+vMc/v6fwc0mQi+pHIbuffsNPrObC6Xb7ke3m+T2HiU3Y3nyU/+OyHTwWu6quOwb101qUi4m5eBUuuRIJjrXvtjqlEvn4HBSDZmTuJ1+W6HESihJbHRna6sCTZLPderEJ1maP87wGx6heO+b1p1DlLJslQmgjCC6MEfz6WdpnzhBuDCLcFx02EG3GzSffBqVzXPmZ3+OXf/r36f8hiy4Vz6DpdT12UaWWI76s5d83tHUDGWnzWcbd9AO4finLvDVX32R3fcPs+/hUY5Xrl3sdNHr8MDIBZS8gTP+Nwg8R/VV+NyFGBlceXNe2K38TT/7WdzhEYa+6QPEgzHGsaiJQYofec9Vn/NaaAuLlTzNZoZCvkk2HVz+8GUhCF3qLZ92K702EXnbEPry3njvj/9DGk8+yZ6D7ySsLvLysT/mkd33snpZuJnMWmiTjH+XKZe4LnTQdbBuQhnEat/jdoCstTDZFGqpCcYggxjZCjC+i9YhTzz9S2THD3D4O38Kc9sIm32FWoHxQXXlEG5V97IEZueep9GYYnrmaYSQxHEHJ5OncvZ5APzzQzSPvcDY3/7RHkkWgD9nb+lO/E5tngtf/3OC2vwtQZAt0B60aPfWcavYDLccQV6ce5nSwdfj5V8daTLXC0KI3qLBFsH7QoHY0biduDd4quDGBQWEu8uoSlK+tgLCfYNYJUm9skLkosFcb6vQqa9MJzrj9Yzp/dNzyOWmld6epaXTrjA9+Th3fPfPIgY3bkyxa65+aUE2QXUEMgDZtrzyC/8GoRx2P/hBBl9/O0HOoAZWKiNCSS4++mfUv/YEubsewb0KglwLfUaug8QiLWLUNktPI06N2/wp/uIxl8///DP8g698ByKV5q9+7Syv/Plnue0XfvKqz0cJy33DF18jxzsEa2wvqlYCqcaVs4U4LQgHkhtn6eyzjHz392IyFpPamepcECnmFgssKUs61SHlh6Q8g1LLKXvdLgcriLUkjCVh5BCGDmHkEV8muV0LaQUqWu/QsRXc0gDld72HpoHBN7wN9aiFyOJoiBUoy6Zex3YLu7Qtz3N6CVPKYlOXbjyUUiR9HpcBPVrCVluIMEI0A5zZ/t0vK8DkE1eQc1NfITt+kIPv+kHiFLQv8RNob4Uge41kjKhUzvD8C7/L+NgbuOP2D+N5eZR08fwinaIlGHARQvLM7/xzzOIiajAZo70F2zvWrYpTn/4thFTc/yM/hzWa5/7gX7H7wQ8wcOj+G31qGyIscsv4HF8KtxxBbnbm8PbtvM3UrQytoDR0iNnOWfb7dyXexLAu4W4zmJSbRJdeQ/in5xHL0dODOYzv9L1HPJBFlzM4MzW8SgvbtX+LSxmiiRL+yTlSJ2c3dMmIUy4Lsy9S3nXHpuQY+rcr3SqklyS2+5hpNbjw57+DVB73PfT3yOXHIALTEjQGnd7WUfPiaSovPkHhPe9gy3L0NjDVLHGouHjNm/Sa1qWqMxTVpfU0F6ISz7V3M3Sfy7v+1dupk+fl6V2c/KvP4OSvjf54OF3HU9deT/oaNkZsDFE3jtebi67YPsy4gva413PJ6VTm8IbH0MWdX+jEWlBvpqk3t2HNdg0hqnJTB4tLwUpI7z3IuU/+DnHcgcCFjFwXFLIpwrhnWXrJ8wxj3PPzxIfHt0yzk9VmUv3ds9J/INohpF3sFiV6m/LQXfItmh3cCwt9DfbCgqw0MUMFZqefZfcjHwHA2cYCRa/i9CqGxcUTvPDSRxnIHeD2274dIVd2sSxgsx5SSS58/c+x1vTkH17F4u181sw1x+Fv/nHC+gJCCMJWnahVJVXc2qbvRiFOQVh6dZBjuAUJchi3yPjXulHo1Y/88AGqZ45jcq/HCIEzU9tekh5bNHyIpMorW+G2mv16UJJ4KGniULU27nwDd75/JHMWm4ggRjWDvmqNVTIJ+sh4SdS0qwhHC8hGgFNpYdMueqJM/fkLpHbt3vI0vJbALCTVCqcteu/TOX2K05/8LUZ33c/r3/VDfQOyDCyZCxGtvS5RHHD+U7/D0Hd+G5k33oe8ytG4rR1m29nr0qj32cbtfHP+RTJyYw1nbB2OBSP8zfztfOnFCTrnl7Cd/cycH0emXI782x8FJak/cRKZdsneufeKzyXrvmbZtpMQKYUR4C/E+LUrrx5rT7BsYmKiAKEU0nWI3W+MxY7sCJza1XUcyXSadHaIZnMWd9k5prPNBcZ2yXGjkzhIaINtBbBFch6FDCa90rgsF2qouRp4Cj1QwKZchOtgupVsWW1ic+m+yrbNpognBnBmKn0+9sJatI5oNmbIDE4kD0YWZSR6Cx23zUjsQqIMP3H8z5mdeYG77vweBsqH+p8HtIcd4oykcvZ5Kmee4/bv/6fExQL+wq1NjiXgT4WYZpVOfQp3bABrLV62xH0//G/BGuZe+grlg/fi3Cx8SAnC4Vtb670WtxRBDjpVqnMnGRp7/6Wf/Br6YBSQSYEjkY0Osr19krIp+RUQjxbwzi6saOQuYdNngfDAECaV+AevjQPve98NgkxsqmstNV5ExJp4OI/p+n06lRZxLkW1NsnC3Mvc/tZv2fpcNPiVNQ0dFha//jn2H34Pu/e/GWvtuvQlp2Px5mOmznwRb9duMm+8D7A4vrnqoWGqWbguBLluUvxJ9T52uYuMOVUyMsQgqOs0M3GBmajAQpDlE784x/wffRRVKOB6WS7+wjS5199D5o67kJ7H1G98FJTkzt/4aWR+43jqS0GbW7it+RaEUjKRVlwFOYbuGNLF/Imvk96zD+ttreN/tUB2BN68e9X3t1XJRpMQK5Vjp3Ppo4pOCLHG5i5dMbe5FNGRXUhrNwzrWA0jRB/xVpXE5UaEGme6GwDlSKJdg9hMcr87r0wl8dXdwokpZDC5NGEujdQaMVdDVluYlMep03/N4MgdSGfFL17VDbq4+XlpaYlyIBdaXJz8Om950z9FplIrqYOOIMhKwoLEeBJrDOcf+1P2veW7SHtF7JRF3Dj14FXDCS1iusqLX/89qvXz5HPjdF6qIRyH0T0PUCjvY3bxRaZf/iKN2bMceMf33uhTBqA9YG9px4qNcEsR5HOnPsvAbQ/gly5tR/UaVmAci8hkCcI64dEBMHbLKOftHxhSx2cQkU4qCtZyKXVAPJRLJBvW4pxdQDUuUyCWTQZpIQTh3kRnJrXBm0sa+KwjWVw4wcDgEbx8eW2S6yWhqpo47JBKD2KMwdqNe4Gsheqp5yl/+/uS13kGew2kEQvtHNqo66LP1QjOR4Ocjwb7Hq+HKV5eGmaukmfhY/+TAx/+u7gHJhh+PKaSb7B44nHqf/kFwrjB6P3vYebpz6IRXOlY2IyvbbjKa9ga1lpkU1+2g0zfMYC4kEwX1mgmH/8E+/7uP0ZnbuHup21AALIqcarqmtTFjAMqkyUIa3jd32Nbx70CT+OtpBW9w2qDuriAdR1UO9g4yTQ2uOfnMJkU1pFYz0HWmgib7C6uDhUxSsFYGTtaAiGYe/4F7rjzI0hW+uT8ukDnxZZV5LgsUVNtHD9LuCuHjCyppeTcLMn36DYMMtJUwmnAUpi4DfRWwpCbE44G2hoVWLy2RQaG2bmTmHaTtz30T3ErHWwnZDFbZWbuWU6fewbHSTEydBfW85MF1w0u2oa5V4/ueDVuKYIcxg0yu1+zcbtcWAleeZCgXUGEGpPzN0xAuhKI5QFVb26WZNIuxAapNbqYVEDUxcqW5Fj5zoqjhZKABW2xQaLDM50IulVoUWv3LOBEbBifeICvf+k/MFarQPbykhOdjkVIidEBEsGGNWEJda9GUJnBu+1gcoq5ayMb0AguNPPsy1euyfG2QjPyeaUyyEwrT2fBJzwVQmzJlMYJZbLNN1DPMTD2CIw9QlCQzJ9/hsb+I6hUitZUGq8Q4GQvbxnSCHdWN/qNjlibbVUpt4KQAuN1U9nCDkI5uMMjxLlbuFS3BQQgmhK3piC6dhO/cARefoB2VCXdbZyMyw5RYHDbW8gOfBf8jReWFogzcstUREcK4jULJBlr1Pk5RBADlyhUWJDNZLdPF5JKMbEB14ENUlmXyfnExMNcuPAohw8dISx2n6ct3pylPbp1FRlfok2EVpCZWyXdiC3phS5Ztobq5DMU99yx9fnvMJQF0TbI0CB0l8B2C0jCJN7+ynQ9nzf42ZrNGUpqBGUEGIPTChhppRjhQfR4Gj0xyFNP/hrlfQ+ivRscguIIoh0OKFbOzpDxW6YgXl06w/zU868mecuOQYWCxoUT5Et7MLmkAquvsDt6M2x1uepcCpP1wazIJsQWDhpWCtrduHHrO7SPjNLZN4TxnV5jn02tTBa6nCWcKBGXMlhX4fl5RifewPTX//qyP4f2BPnCBPXaJCaKwVpif+XTtXOGl2Y+xcu//+8pvvc9OBmJW+rgFa+drvZsdQh9jezU1sJawVw7yxOzu/ny1H5mmkXaU1l0JYV8YpJCYTdOKFBhkoIE4IlkISRjiyNcRBDTnspgOg7BbAZ7GSELkGitY3NLrc1vacjI4levstJrLO5SjDWa2Re/iAnaCA/MZXoC3+yQsUBVFP6kh7vgXFNyDGADTX36JO6B/Wg/GYO1hNYujzi1xXtdol9kq6AMudTAzNXW/6Ha2nIc3gyq1u45Bokgwj1xEe/YBZwL8+ueu3viYSqV00RnzqBWFaedDnjVra8dNVQGLOrsHFiIfdE3/bfaCzz+5C8xO/MsI3e85bI/x7WG0pBaiCmcD8mdCshORaQXNKmKxq9q/JrBqxvcpsFpW0Swuf1cY/E8JW8MWWsj10gNl8O+lPSQzeiGu3S0S5brGLC6ITr5nel7uGUI8rNP/DoAqcGxG3wmtx6ieoWZ5z/Hrr2rvIAvQZBjExGZa3PnOXN1VKWFzqcwuW7DiLP5+wtjEc2AaDBHuH8IHInNeARHRtGljRsSdDlLtLuMHki2+/Ydejf1udMsfOlTWLN9uUI4oMgduJOpi4/z7MmPcoETNGWDMCs413iGpz/3H5l74Uvs/9f/mN0//EbSE3X88rUdoTpG8cz8GPYaiTutFSx2Mry8OMIXJw/x1NxuFjpZTMehdSGHbTpkzwvs5CzpzBDpRYPTltT2OEmX+GQF/+QsqmPIOYME0xdJvWyQFiyCoHppK6m1aISvySx2Cm5X/nS1SDUMjZnTTD31KQC09+qQV0grkA2JN+PgXnRxaqrnZnOtUfvCF3DTedK71je5RtmNx8QgqG2ZjCdI0uY2/Ju1iFhju5ZrcmFVWFA+tWH194pgSXpbFhIiHvuC2Bco5XH7bd/Oc8/+D+ITZ/te4lXA2SIjSBclg3c8xONP/hLHFj9HY+k8RkfEccDxE3/KY1/7j6QKI9z1kX9GqjR6bT7HFcKtaXJnA/zKim/zlUJaS7s1R1YVUYt1rO9gVu0eqFgjrCWTGiA8cxa3fuPsMrUHcXZn2bFQYPM7M/bcMmWcO17/vbzw1G/j5ja37XoNG2Pp619kcPgO8oWJ3mM666PqmzfIvVJ/jDONp3jX2I/hqavbEhdANJQjHlv57TZTilkB0UR5UyK8rfdrR7jpDK9/w4/w0rO/T236JPu+/Uf7GkU2gwWcuw5x29F/xuwrX2Xq+Bc4PT2JNzxCtLTAyA99kPw94/jjDqzLgrp2mO/k+NrMXu4amLnsCGZjJZUgxVKQphKkqQQZ9BqyHVY9osUUwkqyFwUyhqi6SCpdwm1aZMcQlgRWgtMKITZ45yuwZ5j8wD4u/P5/Y+DHfwAxlscGlz/JtmKXEq8l6N1KMNaSGz2IkAprNOYW1h9LI6AlkG2Jagt2JDSp3mHuuS9x8N0/hBDryXBcdGDJrNOKf/nRf0O5dIj77v3Ry35LKwR6eGXcNYP5lf/2XMJD46jJBVT90vei8ByiUhakQIQa60hEEKGqK/aRzlwtkb9NDGIcCIqKYe5C65Dnvvbf2Bd8gOI9DyXHA9Lz0Brtt3ZbhpaW4nd8gMwb7qf21Ud56dgfE7fquF6WdGGEI+/7cXJjh9cl5+00VGzJLMTXbHfbWmjFddIqCULTpRyiE/a8/22kEQt1Jva/lccf/0XUU2kG3v5N1+bNLxNhaeffM07rHRMS3DIEGWtJFUeR3haWNa9hHeJ2g7ljj/LGN//j/j9cYkwxNtl6e2bpkzww+G1XPgi5CmsspiuZWIYuZ1D1NtFYEZP1SZ1bgDBxpbgacgxg0wkRTqXL3PvGn+D5p3+b6S/+Gbve+Z3rnqtTlihrsV2OZyUIV6A9j9zRt1H67oeBNp0Tp8kcGcUb2TlLnWqY4ivT+yh5LYbSTQpeQMaJ8ZRGCgMIYqNox4pm7NAIU1TCFLUgvWl8rjCS1lwK3Uq+I38GiMCfjykU93Lh5OfZV34j2UwW6zpIY7GdCAG4Sy3CXUWOPPC3eOVrf8DMf/8tRv7RjyNTlz+MtF5r1Lt1IKA17BDnFQLI7jlC+q6jOxYOci0gLIhAIDoS1REQCsQOt3NVnvwq2ZG9ZIb2bPh3I5LwIrFKrWW7VGCpcpK5uRcYHr7rmp+XmRhEnJtDtjbZCZMCXcphRgrrChui3u4jyACy3kZNLcL4AF43lGZs7D7y+Qkef/KXODoxQXqoa8FpLJkZQXvIEqc3/j3cvbsY2fNdqJohnJmFxRq50QN99ps3Em5FX9ukvk5IKbOL6c4r7Bq8F5v1cWeW+p7i1Fukh8Z444Ef4Kuv/AYi5VN+cIfThB1BfAPaSYy7c+POjhFkaZKtHr1NL8fVMCbm5PG/YOI933XDV4u3GiY//VFGx+/F2pgobOJ6iQRBrtGeNaMl2rrOUGov1loa8RJHR9/FsZnPYNCoVZeKUAK7zZzWcDCHHsyu68I2+RS6lMFZbBKmPYwF4Uj0SOGKP6vxoFNQ+E2D6ja8CKlYnDuG157tJ8jCJoPyhm9nEVj84TZuLlm1p0YOXPF5XS0qYYZKePXEPG46hAsZTNes31sEtykS7VzF4Awd5fhzf4gzV0cPZpAxgEA4sqc5dGoBopRmz7u/m6d+93/HxjHuFWx3tfVrBPlWQXtAEeUTMlKdfJmwscDIIw9fx/2Tq4e0AoKEFMuOREaCHRdKrkI0P8vCl/+GI+/9CVoLF8iuIckCSM1EiNAyO/scxeI+fL9ARS6i/AxDB+5nduH5HkFWU4uYcq4/Kc8RBDmJX9n8l5HWIk/PIIxBHxzDyCQV0OweRJ6cXqd1tikPvXcIIzeWf9h8mnj3IO5sBRuuvK+qtrBKYUZW7xpatA4wM0swtMqj3ljSs0k8cZzb+DfSwqLzkO6M4Pg3V0iG17527Fgu1lHzNQYKh2jUFlGxJjYWnU31LPgACGKEtaQn9rMvegf1qROU2VmCHKa6qZU7jR0UBl/3t5LWohYauCdnEZUr21K9cPZL+IMjZPcfvcZn9+pGvDRHa/IM+w6+m8e+8O84c/JvgKSa4qyRV5xrPs/jCx/HWsvpxhMEusno0bfjyhSh7v/dtkuOddZPKsebLGr08t+6UdPhgaGr2joxShAMKmp7XZqjqufTWRo8TNhYImp2G1WEpT22GTlO4JaDHjm+1SGMpDObpjOb7ZFjtwL+UnL7+3MxGHAcH4EkbtVBG0yXw0bFDDrjYaVARjFIaPshKp0jvVdd0ffUea2CfPNDJOQ47KbwWas59/WPM/jBD2KHb672FaUFsilxFhXetINz3sGddXGqDjKQN5QcS6DxxccYOvIQUaPCyx//TxjdX6BQDd2rtj7/4u9x8vRf09F1jj/3Pxm/591kJw71jcOmnIM1MdKRC51BB7bo8DdCYAbyyYJ3qYloBUkTn5TEe4YQ3kpV1mR94v0jm5Lj3vNyaaK9I4g1emZnsd6TBQB4brLInz3+FZzmehKfWgC3tvkMoMKkue9mgjJcteZ4GdJanIU6wlhygUdb1xI5RSfErpLG9NDsoBUYo0nnd16Dra/MBv/qsYPKrus6yglLz6jc+k7PQeFysFQ9yflzX2Tsnd9xrU/vVY/6M0/jeXke+8K/A8D3i4gowj0z1+d32YyW6OiEPH566leZ7Zzh3sPfi/A8RrOHOdV4gvnOOUJzeaOTdRVskbBnfJfg8Ag27WFTbl8jwpXAaVty5yO8iiYsKeq7FAi454EfY9+Rd3Phk7+LiWO0D/GWBVl7TV0pbiTipkPzQo64uTKZekuQWkhufadl8LoOB8ZEWAxSSND0zPbj8SLhwWHCI6OYchZhoPrKs2TuuB03c2U2X+FGosPXcF1grtD/uFNShOVlcmw4+fhHcXeN4r39COYGGq8KQIYCVZM4cw6pSQ9n0sVdcFANhQjljssntoJsWJZe+hqdygwnP/1bALRnzuA0dZJuuBRjfEmrLJmMTwAwPf0kX3305ynsPsrI3W+nsOsItco5FhaOs7R0Cpvy1hUT3O6taNQW0dKVJqaUxRSzqMU67sUFnJkKanoJk/KIDowRj5aIhwroPdsvWBhHJWEiqx6zAqxZYTOel+ftb/0/aLfmqT/7OE6jnyQLILUE/oJlQ3OUm1DRI9rXcB9lod6r4Ee6gxTJvSfCGLuRi4lN3Evm5p5nYGTni4f2Bgl0ZbBz9/b1/Ygiuab1YA49mLvk01fDWsPp03/D1IWvsfdbfgi/NHR9zvFVDOWmaDVmknx64PSJv+JQaz/TrVfIOmUkimO1L1MJpyn549w1+j4GiodwBocwhURcNHzgIZ569tc533yONwx+kOHU/jVvImCTirJTaSGMWQn0aAY4MzXiA0PbMrG/Ergti9vSSA2dQUV9r0tuOmbv/nfReP73OP3H/z/2fduPIHdlN53kBSCMwKqbcETeJoQVtOdTxI1+IuotrlSOlYH89MoA36jPkPFKuG6KqOsyYh2BiLvfg5IYKbAWKqeeZfD734pwr2w534zVdQtEeQ39iMyV/Ua6a28Y1Oc5+eXfhZTLyM/+7d7OzE5C2uWmOoHTUdhVH+lmv0utL3CzRarnX+w99vJf/hLvfMe/5MQrf8Gho9/K2XNfYPHkkwSNJYaOPszQ0YdIlUZRblJUUl6awp47eOa53wLgkbf/X8i1GtzIotqmz/JNVpuIdogeKyf/32hjSlnsaBHRWLFrU5UmJpdOXIbKlzdXL8OkPcRADrWYJIEKRyErTai10aMlINmluvuu7+OZ536bPZ0qgw+9G53p/xxeA7wOdPIWmxFYJRCRxa1e0WldV8joGuUJAG612buWa2aesjee/C2Kscvjcfe5KAn5NO3WDNrGuAcP7mRhNcENGAcAnI5C75DA67oS5M0uncYuB7dh8DeJPrXW8OKLf0CHJke+92dec664QpTe/DbCxRkUHudPfw6A47Wvcq7+NGPpI9TieSZGH+Dorr+FW15ZgKz+VfJjh+FZcGVqPTkm2SEQW0xR8e4VB3FntoZqhYiTc0R7BjD+9bv8UvOaOC+JU4L6Xpf8Wbjrdd/HqTOf4uRHf5H9f+9nCEc20dUhaM2nyIy0r0k63k7Dhor2bAYd9X8+b2mFHAOkpmNYNcC7XoZAtzGAdsF4gvp+F3cxJr2gcV+ZoXN0lIqZI5ifonR3+SrOUjDfSV+XWO3X0A8rV02s230Nyb3drsxw/JO/TOntj5D78JuwW1QnrwdkIJB1ieqsyCRutTtSuzDx43+XC7/5K2RL4ywc+zqp/DCPPvZzdDoVvPEJZl/6Mgff9UNkBsYh46OVRa5RJJb23snSqafYv+9dSKmQYQztoC/JLjMTIVb7DS/WsZHuEWQ9lOjKjJTofLpP1yocedXfrR4pYVMeztQSRHqlgc8Y9HgyF+Tzu3jjG/4uzzz73xGPKwbf9C5id811FVtSS8CS5Wb+xcW1YqWNDra7q6uLGaQoEtS635212GyaaM8QItY4U0sIJdBhwOz5p8mVJjCZG9CwaG6MBtkaEJ2ded+dL5LLZMD265tfWacvfI62qXHg239yW9Zcr2FjGAFj7/su8uciCqW9hEGDdmMG6jAXnmPfoXcxvv/tWzY+CiGYyN+Ns0mww1aR1cJTaCV6MZhy1VPdM/MER6+Tp7UQCGtxaoZ4SCVG/GVJdsZy6MB7qSyeYvZjf8jgd3wIPbKxK4puuTQvODj5Dn4+xqpbw9IqqruEC+l1HspOHfzFFXLsdCxe9x6cufgkOmwxNnQfvkwz3zhFvj6MtB5agR52kBr0viLHXvpDluZOMPDWRyBf4momrzO1AUbSTcQtuAi5lSD15U1jcUrQGfMITcDJT/53Bt/9PjLvewNmB3dUZEvg1BK5xKsBMpNl70/9Y0yjTvr527DzVaaf+QwAkyc+x5H3/hhy3zidjOhqOwX+osVbZVtc2vc6lJNibOy+5IFGG2e2SuS7vWa91RsyMowhiPsa+fr+2+0f020Yr9M1XwlMIUMEuLNVrDFY302a9nw30T+TyP3uvOMjPPX0r5NKl8jc9wb0reOp1YO9RpencBXaxhyrf5WR7D0Ml27n+ZmPcij3BlS9gx622GwKGm2QgsX2FM89/j9wUwUO3PWt1+YkLvecb2CIpmjvzLiw85ekgdyFzb/ZdmuBqVNf5sj3/cxr5PgaIHahPqYYiu/sPTYycT++X8DzNxD+b4C8P8R8/VTfYzrn96Kiw91lnLn6OmcMrVRfRnxcSOGEMcG+Qdz5OlcCnRJ0SpLMgkliOtfAAq0RSXZGowIDJCvrqKSwixoRwT2v/2GOnfoE537xP7Pnx34Ks2fjLUUTC8KlNOGSRaU0Khvh5yLszZgeZiFYSBHV1+v8VShIrwm58pZWZtILZ75I2K4xdfJRXJmiGs1QaoaoZhqTk0gLrVGHc1/7GGRc9vyLnyW9PyblxrgqohFdWbdGNUzz/OIodw/MvEaSryOMI5Juky3WeFFGon2BjC3hsIsWMP3UZ/B376b04MME6Z3R5MuOwFlSiOjGEGNlBUJbbGS7kcCA6S4wupeo7RZ5rOpaQjrbXybKXJ7Mw29Aaci/9a10Js+R3nMAk3eIV90DTjuRGqyGUIpUqkRzaZJMehA7kMdUm0k/SXedb9QKSRbVpDoswwjZ/e/V1WZyKUyzg3VUEiiizTWr1dpChjCfxjk3i2wn144zW8U0OsR7h5O3z43z+tf9IC+89FFG61OMve396yvJNznMNTpf47u04wrnWs8yc/okQ/4eItshNG1SsULW25hiFptLU9ub5ckn/oKjR7+D0oHXJffrNTmLy4O8gT3saod0yDdkzbbZR7PW8Ozjv46TzuHmSjt5Sq9qxFlJa0SRmU1uo9WBIdvBSPYgZ5ee7HtsmRwb30EX03izawivgHgw2/eQHs6ju+lN0Xjp8j4EyZZ/c7ebVDVziuz5CLUmQUoAmZnkc8oIUnMxYVFhPEFjzCEzE+OQ5s7bPszxV/6Mc7/8n9jz/T+JvH0YvSnxFeiOg+44xEtpnEIHvxTeNPILYQWt2XTP27jvb0B6RqwjR15n5dzT2WGKxb2U2jmCsMHe/D3YrEd2TlPNSYKcxe0IgtYihUfeSe5IjOfGPDx+Ck8avjazh2p4ZYaYU80ircjnrsEZcu5N1qL+aoHlkp3fTstgXEV7JLmG2pUZpp/5GyZ+4MeJMtffmF9GArWkkJ2dI8bKCETboDqgInAi0ddUtj0kTsXCFUSOxXigU2BSWwnPQCug5OGVDnfJTfJsacFdtOvIMYAQkoHyEToLF5DZQ5hiFnOgfxfOOAK5rEE2K/92ppaSd+g2e5mBPMZ3MV2yel0gBKaQRbYrQPcyLPXPCcXiPh7Y/bd4/PzvE36qzu53fQRzBb7qNwo2rbDEVy00kMaQVlkc4XFX4e0sRdPcXXgnKdUt3qy6mKKoCQjyR+6mPXDjiohOADeqlV3u0AL6Jtu/EsRxh9Toxkbqr+HKEZQV4SYek5eCZRNTfc8h3D+YZMWHK9Vjm3IJ9g6uC/yw3b9dKbTTnVhI/t0aczachJbP1Aks6UVD4WKM6hi0K1BdHaMQgqNHPsSuA2/h1K/8W2b+86+TOWtQ0dbfkTEQVlK0LmYQ5ua4fdqrgj/Wwqn2Bw8sYzW3P3LHtzE39yLZI69j38F3YQ9OYHwXYZLfLBiwNHdprDWogmZvaYEHRs7jK40Qll3Zq+ueqYYpvjK1l2fmx6mHrwUBXWu424gsTxpT199NTqkM1zHWVRqBs6BwppzrTo4FoDrgL1lyU5A5b0jPJ5VaFYC9QrcPARBZ3Db4VcjMQO6cJT1jcasWtY1Km7TgVS3ZyfWV42Uou1LJVtNLqLkqhv7q9erIaZPxkzE37REPFYgPjyfEuHRlTXhXAlPOEXe9kAUglxrINbHnGTI89OBPU69d4Mk/+Ocsnnhix87vaqFlshi6Wtggxk3lObrrPZxqP8vR/JsYTx9Z+Xu3Sa9TUmhpEEIR+ze22i6DpNH7RsDuUG3qplqqCSFIDY5RPHj3jT6VVyU6Yy7eqfCyfQTr0SK+yq7/gzbIZohJucTFNDgSnU8lndDXAW7b4jYMUS4ZLOKUICzITZs9lyECS/5sjHWBNeqefeNvZbh8lFeOf4Kzv/fLHPjI36MzCvEl5hAdOrQuZsmMN2+oPjms+H0Wbmvh1zYmHUFa4HclKq6XpTRwiIXOebzdb0R1v6Ooe1hpoH32JFpqCm86zO3ll/uOlXGvxV6bYKZVYKZVIO+GDKbrDKZalP1ONzXwNVwp1KUa6xxBu6SIiiuNPkIKVC6PmhhEO9d+NpJWIKoSp3H9PYpVCE7D4rVWO+5c5xnWJp69TgeoWIQURJ7FuGAcErZoEzmEDJJq3KXOS8SWVmuOgtqH8Bys5yCCCJlyNzQSsvk00e271/9hh90H7KpwMNkOUaeniYo5TD7N/5+9/46TJLvuO9HvveHSZ3nXvmd6MMAMgCE8QJCgJ2hEIy5FkaIkSqJWFPk+Wr0nrux+uPtWWolarfREaldPhlqRkmhE8hEUvSdoARAeA2Bse1PeZaWNiHvv++NGZmVWZVVXdVd1V/fc7+dT3VWZkZE3bkaeOHHuOb9D6KNOjeMJwRc891fZqt3i+T/8cYImlN/81gc6znulU/UotO8zIVdrSFKm8xd4IflN4uky4UqjF/EXUYABwi3FK9f+gHNnvwgZBQ9euaIPAXibBjX6aKXFHIYT5SA3kzVaG4uUL7zh7hs7Do3yoDUiya8d7mvlyaDX8rRH1l0tuLVO59IUyZmx4S8+SgwUb6fEVUk87oM2eOnBLnQCEAlIX6L7CguFEBTzUzzzhj/LR/7gn7HxwicY5W3ELUM8ueuoB1CJpLlQpDDzkJzkxCNZ3zv/VxoxNHoM0Jn0iZoJZPM3MvYEi2svUn73e6jcShGx2c6vUyDXFCaOMe0OtYUmtz+xzFNfeRov8PZVMbkXtpKQrWSca7VxJIZSEFMIOuT9hMhLibyUQCp8aZB0e80YpAApTPaj9nSsbfShv2LfStdJYRCPoTOeDtNQ7RII6qfC3spMl6WrH6Xy5rdijvhrLbVA1CRBfVCq7TjwG4ZoyzqgloeXEmW0sc7y/WQRJQYpA1CKtGq1jP1bK+hyAar332nzOPBvLCM7g0bIxApveRMwmPFKpoQE+ds1iu088cT7uPz8r/DMuUuIkXvvrPqgSEoealPhte/9/BJRgDAQtA2l/BQrep7JufMEt1cx2qCzgkqpAGPodLZAQO3Oy0g/ojR17oiO5nDk6gJVtZH0x5GH7iB3NXqFkNy68yeMPv02pPfQh/XYEk/45OrJobr/JCImlNtRYRP6tJ+YJFisYQIPc58NPg6DAKJNTbR59+wnacwuveWtCUFxYfe2fpDnzW/7bj79B/+B0rnXEVJGGGhP7T9PKpY054vkZxrgP1jnqr0RYu6a/TZcikf5UJ/xKN1RoA1Ts2/m6pXforF+G86fJthQpN2lQwnhW19H4cbT3Pk3v8TPrd9h9eV1cpWQC++dxZfHd9waQS2JqN1TIeD27EgMUggrYWdgr0oIibaNyAQEUpH3O4xELaYLDYpBZ+hrTjrKF5hQ7PrOJ3lBZzLoOcdGK0Tk05EJay9+lNPf/b3o6Ii6hCWZXFvTRoyPy1UVgFc35GoC8Xg0wuwhUkOatgn8PCJLadOlPMY74d7JsPC2FOhKAak1RkrE6haybT+ws/IStbEa8x/6Zc589bc/Es5XeyqgeCvZzvs+JNr37Mprojh77ou5eu13mHjr95KcnRxo6gXwuqe+kT/52P+J98IUVz/yswC89a/88/s+hnvBaEO0BK2Z4/tOP0we3qknYL75An/0wX/IH/7O/8Lnbv0CC6/8IeNves9DG9JrAS2sY3SYk1nkciy1r7KeLKIDDx164EmSuRHSyYMpYTxIZCchvLpC8PIiwhhkrYW3YTUl97vLL1XmmDv9Tm7+4o+i4g7BlsA/gEyvSiSt+RImfrBalLq1/42JFoZ4nwBMUpRsnfFRkcDzQqZm3kjtymfRAtZaN1i58UnSTgMk6BAmv+IbaL18C3nB1gjEjRRjBLU4x8nUKhWY7EchSYxAmcz73QONJDaSWEsaacBKu8Srm5P80fx5PrU89+CGfoQYbXppVQabmtSYDWjOhahAYIzh1T/8L3ziR/8Oz//sD3Ltgz9OODmDfHL8nt9TGoFsC7wNj3AhIJgP8OresaZT+A1D8Q7kVxmqcPOoI7RB6ZhPLv8iYr1uszRGiphjSmm7XwQgWjH4kuTsJMmTs6hqEZW1TQ4vLyBvLCPqLeRWs/c640mePPPlbKxdYf0jf3DSCqWGkgaCxpR3X7LA+tQ4BB5za6O0Gst0OjVMLuTG5ifY2rqN1tZRFqMVnnnDn+HqR36WqGK/o+ZBJeUOwetAtHz3UM2jyMMJ1QaCW+oVrn/iA5x9/3cSjc+y9rkPMfPuryEaOcaqWgcAaV7SmvQoLB9MHKZ64U3wyk+x1r7BaDBtBeA3mruK8E4K/mIN2YqJXzeDEYJgvYmqWpWF3Mb+0c7zF7+C5IX/xqs/+S+49Of+R3IbPvXS3SOkKhW0ForkpprI3PELRAoj0OruJqkzaTCesbnIQz7uNCeonQ/wW5rS2Lt46Sf/OfU7l/FLZTZf/BQiihj/5m+g/O53IvIB+YuXMMUCr/+Wp5h7bpJPLs+x3CiQ1hp4pRwy2NukqK02XvlkXswPwlLr5N0MHgRf2RWDzmyIDsVA3qJKOtx84Tdortziyf/pB2ndusLWy5+h/E3vQ/ed99IISLA5/Fr07od6xZ4GhBKQCnwl0Yl5YO2evTbkNkyWSvH4OcZdVMFjbOJ1bG5eJzVtZLODLuxeWUkKkqCtD11rcuQkKbqUQ4+VMVGAaMe2VX0r7hWEynaCvLWKkYJ0ooIpRph8hATe/Ka/xKc/8x9J0hbjX/RVD/dYDkBa9GjMCopLaS917TBoIdCnJ5Bac8p7O3/0oR/k9a/7Fl559Zfx/TyeF/LWt3wvZmKUauUsAOee+1PUm0u9XgYq6aDTBD9X3LO/gVYpGI3071/zukvQBLFo6EyJ4y4peKA8cAdZmZjPvfBzNFZucub9307u0lMETcHMu77mQQ/lkUV1Wix/4oMU5y5SPndvPdg7Y95As4j9EEJSyk8xEW3nOYn2yV2/TKsFglihu0uP2vSKHXZiAOGLnkETQvLUG76ZT33i31O7+jlGX/fmA7+vVoLWQoHcdBMvf7xOss2OPlgno3gMkjGNbGN/EoFQmXOT7SItCMTMLE/8z/+Y+Z/+MbauvUT+qUtEFy/QfPElyu9+J7oAhWefZe3Xf5Xpf/VX+GzaZvHFOtf/yY+h2x1Mp0PxuTcy+z1/CrN5h+jMBDK0Rnjlt64z/69+jMk/93VM/5kv2Lc5jeNoURhap8Jd/lJj+QaXP/ifyJ2/wJm//H3IICD3xicJ334RkQjEqkCkAi+VmEMIrdpT6vg/X09ZSbSgefdtj4v60nXWr3ySube8Hy883ps/FQqKZy9RXvwsocyj17aGOsjtcY849SjOP1wbbQJbgCdqTcI7q7tSBQDwJfG5KfA9W0yAlTyT8+uUynne/Kbv4uOf/LdMv/lLSSsnvy9CmpfUz4RESwlB497uULSUPHHpa5mYfiOfef7H8LyQS09+Hdeu/Q7NxgKjK2WE9Bkfex1qYYlT7/5SlDGs/Mlvc+uF30ZKHyEEF5/5Bkqvf5Zmc5XC+La06+Vf/w+01xd48v3fTX78cJKv++G3wZuH9qTtv/A4cKwOchT6nDu7vUyXJIoPfvgn8T3DO77nB1gYSdBtGNU+y5/6fYSQzD37dopjLoq8Hx/76X/L0qc/xFPv+3ouvuPd97QPYwxLpkbRM0yUi/v6WUYbLldO0/SWePbSG9HKML/eYHS0SOEERgSXbq9j7mwwEnjkRgq0ixFxJyG+vc7o03MDY752wxZBnD87uJy8OP8sG7deIff2t+EHkrHSwXR+tTHMr/hE023mxgrH5iisNJv4pYDxoHLgd9BAkipSpUiVzorVTO/CFHgegS/hL/8Zln7m50g3mzQ+/BFOf+f3EmzCxGyJtblnqec/xNX/+adpfv5VAKa/9Os585avQKaKl3/1P3H7B3+a5isvIqKIaG6O6PQco6dPU7lwntWf/U1Ove0C59781HFMy7Hz/MMewD0QFULOzowO3JR0Oh1+9Sd+hPPf8h1ces8XAvbcvfPKy6x++uNUz5xj5rm3EpWiE3cz02zHrN3cotLxGSsXiKoPZyF0bWmZT//6j5DGTd7xld9CrlQ91vczxrC4dYqX2uv4pwW+EYhiSHVie2Vjfb0B+QAtodTyGXtIxXv1tQYb15bwG21Koc/IuYmh59Hm6hYbt1dBCEzoU700i/A9WlGA73ucmjjN518sk965xfSZN1IoHF3U87jQ2rBZaNFabeKvp5wZrxIE95J+N06lCq+88ofcuv37zM09xZufewdGw+LSFl/4hd/Br/3aP+Xyy7/Se8W3fssPUiiOsLZ2k9/8rX9F6ebHWVl5mUJpilxU4dSZd0CjSakwyY3f/yne/73/29EdOLYgeHm1zmYuZfrcCOE9HffBePnY9ryNOM7cFSHEx45t5w6Hw/HgWTHGvP9hD+IwODvscDgeQ47dFh+rg+xwOBwOh8PhcDxqPAoFog6Hw+FwOBwOxwPDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How//fl48mTtnYt0+qrE8MNLQACC98CGP5HAkXgKADB6tceskRocgwkdr3ADC2PNb5oKHPJLDodsJxaBDkL+vr/gDJ2mlVL0W+bx42EO5Jz7/fPLrxpj3P+j3nYzOnlhbrEyKiXw8eXK//4+CbU1FDIA4oWM0SYz2DTI6meMD8HULOLn2XLcTin5MkPMe9lD2JBc3AE60jf7cEdnh+7p6xrrNe6a+7X7H8MBZOmMd5HL19EMeyeG4PboIQGHqzEMeyeFoLt2kedoQnXm05hvAUy8CkH9y9iGP5HC0Xp3nXTPXmH7D2MMeyqFY/PwaX1v5DG9448m9yO7Hc+duTTyM9411m/dMfOvDeOu7spkskZyfplI+9bCHsid3qksAFCdOrm1dDm4BkJs7mWNs37lJPJMQnT+5dn68+VkAyk9NP+SRDGfr5UXeOX6NU89UH/ZQ9uTpax8B4Nk3ncybDICnz84fiR12KRYOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH0IY8y9v1iIXwMmjm44DofD8UizYox5/4N+U2eLHQ6Ho8eR2OH7cpAdDofD4XA4HI7HDZdi4XA4HA6Hw+Fw9OEcZIfD4XA4HA6Ho48DOchCiPcLIV4SQrwqhPi7Q54XQogfzp7/jBDiLUc/1MMjhPi/hRBLQojP7vH8lwghNoUQn8p+fuBBj3EYQoicEOJPhBCfFkJ8Tgjx/x6yzYmccwAhhCeE+KQQ4peGPHdS53xECPGzQogXhRAvCCHeveP5EznfQojX9c3lp4QQNSHE39yxzUmd8/9BCPHZ7Bz/m0OePzFzPsyWCCHGhBC/KYR4Jft/dI/XXhNCPJ/N/cfuYwwn3g6fdJv7qNjWk25DT7K9fFRs4km0fyfBzt3D+P5Zdh5+RgjxASHEyB6v3dd+DsUYs+8P4AGXgYtACHwaeMOObb4W+FVAAO8CPnK3/T6IH+CLgbcAn93j+S8Bfulhj3PIuARQyn4PgI8A73oU5jwb2/8L+Ilhc3uC5/zHgO/Ofg+BkUdlvvvG6AELwLmTPufAs8BngQLgA78FXDqpcz7MlgD/O/B3s9//LvBP93jtNWDiCD7bE2+HT7rNfVRs60m3oY+KvTypNvGk2r+HbefucXxfBfjZ7/902PgOYj+H/RwkgvwO4FVjzBVjTAz8FPCNO7b5RuA/GcuHgREhxOwB9n2sGGN+H1h72OM4LNk81rM/g+xnZzXliZxzIcRp4OuAH3nYYzkoQogK9ov3HwCMMbExZmPHZidyvnfw5cBlY8z1hz2QA/B64MPGmKYxJgV+D/jmHducmDnfw5Z8I9ZRIPv/m45xCI+EHT7pNvdRsK0n3YY+YvbypNrEE2n/ToCd25dh4zPG/EY2hwAfBk4PeelB7OcuDuIgnwJu9v19K3vssNucVN6dLbf9qhDimYc9mC7ZEtungCXgN40xH9mxyUmd838J/G1A77PNSZvzi8Ay8B+zZc0fEUIUd2xzUue7nz8L/OQez520Of8s8MVCiHEhRAEbLTmzY5uTPufTxph5gOz/qT22M8BvCCE+LoT47+/xvR4nO/xQz8VHwLb+S062DX2U7OVJtYmPkv17kHbufvnL2Kj7Tu5pLg/iIIshj+284z7INieRT2CXXt4M/Cvg5x/ucLYxxihjzHPYu6F3CCGe3bHJiZtzIcTXA0vGmI/vs9lJnHMfu2zz/zXGfAHQwC4l9XPi5rsfIUQIfAPwM0OePnFzbox5Absc9pvAr2GXvNIdm53oOT8EX2iMeQvwNcD3CSG++B728bjY4Yd+Lp5k2/qI2NBHwl6eZJv4mNq/o7Bz94wQ4h9g5/DHhz095LG7zuVBHORbDN7ZnAbu3MM2Jw5jTK273GaM+RUgEEKcKLH9bOnqg8BO0euTOOdfCHyDEOIadgnjy4QQ/6V/gxM657eAW32RpJ/FXgB2bnPS5rufrwE+YYxZ3PnECZ1zjDH/wRjzFmPMF2OXzV7ZsclJn/PF7pJn9v/SsI2MMXey/5eAD2CX+w7LY2GHT9K5eEJt66NgQx8Ve3mibeIjZP8epJ27J4QQfxH4euDPGWOGOb73NJcHcZA/ClwSQlzI7sj+LPALO7b5BeAvZFWX7wI2uyH5k4wQYkYIIbLf34Gdj9WHOyoQQkx2KzGFEHngK4AXd2x24ubcGPP3jDGnjTHnsefJ7xhjvrN/m5M458aYBeCmEOJ12UNfDnx+x2Ynbr538O3ssZR4Euc8G8tU9v9Z4E+ze/wnfc5/AfiL2e9/EfhvOzcQQhSFEOXu79iCkqEKD3fhsbDDD/tcPOm29VGwoY+QvTzRNvERsn8P0s4dGiHE+4G/A3yDMaa5x2YHsZ+7MQerHPxa4GVsFeA/yB77HuB7st8F8H9lzz8PvO0g+z3uH+wJNw8k2DuIv7Jj3P8P4HPY5Y0PA+952GPOxvUm4JPAZ7An2Q88KnPedwxfQlYl/IjM+XPAx7I5/3lg9FGZb2wl9CpQ7XvsUZjzP8BeWD8NfPmQcZ+YOd/DlowDv42N/Pw2MJZtOwf8Svb7xez4Pp19Bv/gPsZw4u3wSbe5j5JtPck29KTby0fBJp5E+3cS7Nw9jO9VbH7xp7Kff7NzfNnfu+zn3X5cq2mHw+FwOBwOh6MP10nP4XA4HA6Hw+HowznIDofD4XA4HA5HH85BdjgcDofD4XA4+nAOssPhcDgcDofD0YdzkB0Oh8PhcDgcjj6cg+x4TSCEeFoI8SEhREcI8f0PezwOh8PxWsPZYcejhP+wB+BwPCDWgL8BfNNDHofD4XC8VnF22PHI4CLIjhOFEOK8EOJFIcSPCCE+K4T4cSHEVwgh/kgI8UrW9Wjna54RQvyJEOJTQojPCCEu7dzGGLNkjPkoVmDc4XA4HHvg7LDD4Rxkx8nkSeCHsF2vnga+A3gv8P3A3x+y/fcAP2SMeQ54G7bDjsPhcDjuHWeHHa9pXIqF4yRy1RjzPIAQ4nPAbxtjjBDieeD8kO0/BPwDIcRp4OeMMa88uKE6HA7HY4mzw47XNC6C7DiJdPp+131/a4bc1BljfgL4BqAF/LoQ4suEEN+XLfV9Sggxd+wjdjgcjscLZ4cdr2lcBNnxyCOEuAhcMcb8cPb7m4wx/xL4vx7uyBwOh+O1gbPDjscN5yA7Hge+DfhOIUQCLAD/684NhBAzwMeACqCFEH8TeIMxpvYgB+pwOByPKc4OOx4rhDHmYY/B4XA4HA6Hw+E4MbgcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OBwOh8PhcDj6cA6yw+FwOBwOh8PRh3OQHQ6Hw+FwOByOPpyD7HA4HA6Hw+Fw9OEcZIfD4XA4HA6How/nIDscDofD4XA4HH04B9nhcDgcDofD4ejDOcgOh8PhcDgcDkcfzkF2OHYghPhzQojf6Pu7LoS4+DDH5HA4HA6H48HhHGTHkSCEeK8Q4o+FEJtCiDUhxB8JId7+sMd1LxhjftwY81V9f5eMMVce5pgcDodjGI+T7XU4ThL+wx6A49FHCFEBfgn468BPAyHwRUDnYY7L4XA4Hmec7XU4jg8XQXYcBU8BGGN+0hijjDEtY8xvGGM+091ACPGXhRAvCCHWhRC/LoQ41/fcDwkhbgohakKIjwshvqjvuXcIIT6WPbcohPgXfc99gxDic0KIDSHEB4UQr+977poQ4vuFEJ/JIiv/VQiRO8jBCCG+Swjxh31/GyHEk9nvXyeE+GQ2nptCiP/lHufM4XA47pfHzfY+KYT4vex1K0KI/9r33HuEEB/NnvuoEOI9fc99UAjxD7Po+ZYQ4jeEEBP3MqEORxfnIDuOgpcBJYT4MSHE1wghRvufFEJ8E/D3gT8NTAJ/APxk3yYfBZ4DxoCfAH6mz6D+EPBDxpgK8AQ2SoIQ4qlsH38z2+evAL8ohAj79vtngPcDF4A3Ad/VN6YNIcR77+FYG8BfAEaArwP+enZ8DofD8aB53GzvPwR+AxgFTgP/KnvNGPDLwA8D48C/AH5ZCDHe99rvAP4SMIWNpH//Hu/hcBwI5yA77htjTA14L2CAfw8sCyF+QQgxnW3y14B/Yox5wRiTAv8YeK4byTDG/BdjzKoxJjXG/HMgAl6XvTYBnhRCTBhj6saYD2ePfxvwy8aY3zTGJMD/AeSBXlQB+GFjzB1jzBrwi9gLQXfMI8aYP+SQGGM+aIx53hijsyjNTwLvO+x+HA6H4355DG1vApwD5owx7b7tvg54xRjzn7Ox/iTwIvCn+l77H40xLxtjWlhn/jkcjvvAOciOIyEzwN9ljDkNPAvMAf8ye/oc8ENZ5GADWAMEcApACPG3siXAzez5KtBdHvsr2GXEF7Nlta/PHp8Drve9vwZudveZsdD3exMo3e9xCiHeKYT4XSHEshBiE/ievrE6HA7HA+Uxs71/Oxvfn2QpHH952HtmXD+i93Q4huIcZMeRY4x5EfhRrLEGazz/WhY56P7kjTF/nOW8/R3sktyoMWYE2MQaSYwxrxhjvh27bPZPgZ8VQhSBO1jjD4AQQgBngNvHfHg/AfwCcMYYUwX+TXesDofD8TB51G2vMWbBGPNXjTFz2Oj3v87qPwbeM+PsUbynw7EXzkF23DdCiKezSMTp7O8zwLcD3SW5fwP8PSHEM9nzVSHEt2bPlYEUWAZ8IcQPAJW+fX+nEGIyi1JsZA8r7BLa1wkhvlwIEQB/C1u5/cfHeKjd8a4ZY9pCiHdg894cDofjgfO42V4hxLd2jwVYx6aOKGye81NCiO8QQvhCiG8D3oBV8HA4jgXnIDuOgi3gncBHhBANrHH+LNZwYoz5ADYC8VNCiFr23Ndkr/114FexxSbXgTY26tHl/cDnhBB1bNHIn81y014CvhNbxLGCzUX7U8aY+CADFrb5xxfdfctdfC/wvwohtoAfICtccTgcjofA42Z7354dSx27Uvc/GGOuGmNWga/PjmsVm4rx9caYlYO8p8NxLwhjzMMeg8Nxosjy3r7TGPNlQgiJjWCcM8bceMhDczgcDofD8QBwEWSHYzfPAFez35/FRlYW9t7c4XA4HA7H44TrpOdw9CGE+HngEvCtQohvAf4d8HcOunzocDgcDofj0celWDgcDofD4XA4HH24FAuHw+FwOBwOh6OPY02xGB97yiRJ8zjf4mRgDGgNwsrhiiS14jQCjO+B5+37chEnoLNIvhSYMDjmAZ8Uskl6zWIgVfZXmd2raoPoPnZQpLDnntJHO7wHQEyH1MTko1F73Cfte6AUItn+PGrp8q8bY97/EEd0aM6MPWVq9c2HPYz96dpKKew5IAVGHt42iEQhlLamBTBSILJzykQ+RgpkK9l+z+y7I9JH77vzyCNAhz6ykwJgfDnw2R3F/k3gYTyJ0AYRp5gowGSnlVAaup+7L+12Bnst1+bw54QAI7bPt70wgQQhEXE68Fr75PbfOmftnzCA0na8ffs2gbTfmex1IlGYwENojUi1Pd/7h2eAJLXXGl9iAOOB9uybdzaX8EsVKOUYhkxAnICviUwMQkG9dvvYbfGxOshJ0uTtb/u+43yLY6Fro+/59RiENmgpD/Rdl7Um3p01BKBGS6jpkXt/c8cjg2y08W/uVimSkY+Olb3xOgC6lEONlAjm1x45J/nlzsdJSyFPPvE1+DeXkY0OupRDz42h5cNf4AquLiI6Se/vX1v4149c18R20uA9U9/2sIdxODxJ56lptHdv54A0BqEMWmvC66uk4yXUWBFPacIX5o94sI57wQCqmsffbB3tjiMfoTQm1SAF7SeniK6vQpyi8yG6EKKreXQ+JLizgb/WQBVC4ouT+Es1gqWtgRsrnQ9IRwqE8/vfZKpiaPe5Ut93u/jUCOFKHTrpwONGWCc2HS+ixoroKEC2Y6Kb6wPbGrKbvci3MZaJErIZEyzWBvbXOT+BjnwIBgN0crOFv1YnvjBpb0S1IckbPvqb/4Rzf/qvol4/jhkSmwjXIVp7+DYZIKwpPvaj/+Ox2+KTcbQnjAHnWGnE1uG+wBqBOqBzDKArBdKzkyRnJh5751i2Y0Sr87CHcSzIldrdN+pDF3OoamHX4/FICR0dPHoq622CWyuPnHOscgHzrVeYnHgG2YqRjQ6qkic9PXEinGOwUS3HQ0BpvL4LvjhkrYwWAuVLTOjTuTSNGisCNsIHIAKJKkWocg6TOwErFa9BBBy9c+xJWk9OEWefN9oQXl2BTkp8Zgw8SbBSR9Y7iFaMt9awY8lsp56q0Dk7TjpZ7u1SlXKo8RKqPBhZVcVo8K0b8V2dY4Dw9sYu5xisc5xMlEhmR2wkONVE11Z3O9Khh6rmkbU23lab6MYa4Wo9G1O4vb9OQrC4idjx+vD2OrovSpwWBKvNa/j5IvniJKXbEq89ZOAnqFwtruy/Kn9UOBWLuyCSFJGqgXNDag31NqZSOLJzxhSiu2/0GOBdW7KR8rEyaqr6sIdzZMgkxVvdgrHSgZw7YQxGG/TsGLKTINrbUUrZaCPUIdMsHjUk3PCuEgQFKpUzsNVC5wLM7NjDHtkAupxHNh7PG7qTjrfewPgSVcoR3lqn89T0fe9TS0Hn4iS6EA48LrfaBEtbyNagWI3JBahCCErj11r7OglGQDJdhUDibbTwtoZ5GRZVivDqw88rEUiSYg48m2YSrDe3UwEcQzEyW/7PUhVM38qDTBSd8+MYKdGhj54qk46XCBY2EUA6UsDbaOJtta0TXMlB24PsBk1mtjmZG0HV2/grdWQnJZmtYtYa+JmTbTe+9+VnIyCdqiC32oQ3Vm3G5pCPXcYKubS1/YDSNhpfiojPjRNeW8VrdHoRb+N7pDP2WisMpGNFewMgoT7nERcEN3/ql5h6U9a7RUHhtiCuQloF44NoG6Kt116wwDnId8HkQkyu764sVXhXFhDakPgevEYc26NAdP8xINe20KNFTPDonoKi1sREAUQBcmEDYQzy1irm7OTdb5yaHaTS6EoBdXaS4MqCXRLERoQfZ4Qv6SRtrl//XZ4586cQ2mAqBdQR3nAeFWakBGt1iHdHfBzHizAQLG0RZM6A1AZ9D3nJO9npHAPoco5OOYest/E2moiOQo0XUSPbKzx6pU64sPcyu6rkURMl+3u1QHBrHX9jdw2OEWLfm6723Ci6L1pp8iHBjbXXdLXGXnTTDeLTo+QuL5OMFEAIvB3z62208GqtXtqEbCVZfrIgmauCgODWOur1s/YFfU6u14ztDUqi0FGAzofITopINclUGW+tYT+bwKP11DSilRDdXt8Vue2NOUul2Ikw4C/V8Osd+3y2jfAlOtV3//yFzadPp8p4V7ePv/+mzwhIZ6qoUFA/5aNDweaLH0e0Es4UnqNuyHK0BeEmhL3T/bV59r32bgnuE+N7pLNjpBMV5xwfEmNM72ZDVx9d51jUW/g3lwnurOGt1hCtGNmwTq1sdvCvLSLr+y8dmkLUW+bSUqKPaS688GTNsS5EdJ6Y5SXvM4xNPk313LPgZQUjD3twQzBAMvn4rHQ80uwRcT1KdClHcnqM+InJAecYQE+U9k3H2FmclZwa2bUsD3b1qN9B0rkA2Z8nuuM9VCVPOl05xFEM5yR+vw6L8KW9wfElqhjZ1YBcgMkFGE+gixFCa2StjarkevMvUjXw+Xhbbfy1BkIZwhtrJFMV0rG+m6FCSDw3ggg8u3qwskV4ex2ZpKQzFZCCaH7DFsd1P7tEEdzZwIQ+8exIb1/JVJm0kkcHHjryhzrHXYKVem81UYZ2v2kU2HzjXZMhegWH3WMKbq3vql3Rxd3nYKcqyS+nxO0ad/7oF5j72m+ncTE3sD+HiyDfE6acx5TzD3sYJw4ZJ8iFDXQxQo8PGnRhDN61JWRW8OQ1WpikfGyO4XEhV2p4K7Xe/bRXa+E1By/cop3g31oFKUmnq+hqEZkqkAItpU2vyO72e685Jj3yNE7R1SLeZuPuGz8ARLPDnZd+j7XNy7ztLd97ZPt98aUPUK2eY3bmLUe2zy6mnEeXco99ZP+k46/V0Y0OQb2NloJkpoouPrgghQF06OH1pUP14zXaiGaMKYRgDP5KHW/HOWMANVrAX2+ChHiqaiPVi7Ve/qpcb2CmKgMObTpZxngSb7OF104wStvHAg/Ziq3igja7IqddVDmHKkYDEXAdeEhtHqnahc5kGTVeGngsKdi0rM65CUw+wNtsIbCqDp2Lk9bhlBLvlUVrgwOvp5yhIx+v3iG8tWZzlPtQY0XalTzB5SV7Y7TZQsQKCsZGmDsp4e116FO58deb+LU2uq92QUcBZrKM0Ybo6u6ibLDnVTpWwttqo/MB/nrTFmpj63ZQu68P6WhhML0D8DeayOx6lFbz9ngnSrtem19WdKKU67/4Hxl50zspzJwbls1xaDrrS9z+vQ9w5iu+jaA0cgR7fLg8Wt6J40QjlzaRzQ4iSXsOslQK43kYITChD11FgFTjX1lAVYvomVEr+NZOEKK7bLZPpAYD9Q6iHdu76PHyA4mOSGPwhxXi9eUHqrEycn2rJxck6210KY93ZQGMQYyWkLUWxvesUkMW4dUcz3LOsICAEeCHPmqPJcDjJI3g1Vu/z9ve9X0Egb3JXF7+LJ31ZcLSKGNTT+P7w2WG9mJ19WXuzH+UxcVPgzHMzr71yMet58bxrrpu4w8Tr97p5e1K7M3pvTjIItX3XHyZnB0nrXesA7zWQG61EZGP8iReOyF3ZRmT89GeN9RZVdU8yalR1FQFfInObpL1dAXVTvDqHYKlLfytNvETUwNOixor9ooNB/bJ9mPhjVW8WttGKpPU5uVKQXpmzDp1fQ6yGimgpNilfnBSUZXcLue4H5OlzuhSDjyJbCV4iULlQ7zM1qWVPMmpEcKrK3jN2DrKwhbY5a4sEz85PZDGo31J59I03mYTmShMvU3at7IwNI9caWTfTYcwBi0EXr3Vy2fujRkrRQdZHUujg2x0Buy2GOIcA3h7FDjKzLHWpQg1as8NAcg4RWXXGwHcWfkUfphn9m1WKc0kHbZ+9/fw8yXM+SmKZ54cuv/9uPLf/h1JbY1X/+sPc+nb/5/4hfLdX3SCcQ6y4/5JUvC9Xt6WyAokpDF4lxcgDGwl8MyIzdPtRlVMphkNiEaboCt55gnic9P2TrirL600IlH4rTY6VgMGRGmN2rEM3ovSHiF307cEWzk84K0bq7/Zfa23ZqNEIlXIa4uk56ZsXttICbaayEYHLx+gU41OlF2+S+6vYG9X9NiA2sPoHhc6F6Amq9xY/ziVyhnC0gStzTWuXPkNVlZfYiyYZfnOVZ7L/xXGRp841L6DoICUPn6QZ37xk8fjIEtBMjMKN4981457pLsULQDR6OzrLHtKo7qFW/dhFgxgSvZ94nIOmTk/AN5qnXB+E+NJ0rGiLeZuJYO2qmpvCvUO6S0tBPH5CaIrS8hmgmgliPUGjO52iPdDVfLWQc6HeImyIzb2R/sS4cterYPxJWqkQLBaP94iwKzu5H5QpYj47Dhyq40J/eEpBxn2+pK94VYbxkuYTFte5wMQAl3O2dxittX4RayQq3X05KBTJ5sx4fwGeIJ0ogyBR+f8ONHNtaGR3X5MzsdU7Gfubex2aEU2JtFJ8VcbvccOgtgR+TeeTT8RW23UVBndd+4YBuv92nnF8sc/xPQ7vxIhJKuf/TCdhRusvvQnhFGZzic3edPf+BcHHMk2lQtvYPOFT5C269RvXWbkqecOvY+ThMtBdgxFGoN3Zw1Zu3ujF9mKrZPcTRPoJMjVGlobdCkP7Rh/pYastVDVTG4JSKeqpGcme9HjHsoQXlvCX1jHX9rEX9zAX6nhbTYwO5xjsE6nt7COXK8jurnAd9YQd8kDPgiiFeMtrFtdzY27pynIRhtTjLZzuRK19xKmNgS3V62RHCnagtDQp3Vqgvj0BGqqSjp28Dvwg16DBGw3KDmi/ZrQ33c7U4hY6dzg2vXf5alL34DfMXzow/+MxaVPo1Sb4thppPQpl2YPPK4ulcpp3vuev0+attnYuMLm5o1D7+MgDMvlczw8ZDMmmN8genGeYJ/iueDWOuEL8wS31oBBhYP7RffdhOuxIjrw6FyYRFfztJ+Yov3MHMnUwVe4VF9dy14RwmH0+kxkjSO8zeZ2gxQhtm+y+4ynUBo8aZfij4hucERlecKQpT7cZ6yiW1h5t89O1lqEWR6ujnxk5gSzsxajfx76Phy5Q3nEi1PChQ3QkBZz1rEWwmrP53cXe+4knqz0ItJen82VfYWiXq3dS/m4H3QxJJ0soSs50r4UHX9+E3+xZldwAeVrXn7xA0TlUSoX30hz6Ra3f+en2bz+eYzRjE+9npELb7qnMZx635/m9Fd9O0alLH/id9Hpo13c7CLIjuGsbuHVmlBrYta2UKU8VAq9lIB+dKWAjBNEt1rWgL9cQ6UaXS3YAolmB1lvoebG0cUINVrG9HfsGSnCct9FTh8iomEMXua8Gt8jOTOBbMd4zQ7JDL188f5oz15IYxDza5h8hB4tIVod5GbDSq8dMJKrS3mk0oh2YnOu99FHNnGKWK9jRku2Sjof4t9ZQ8QpyZmJA79ntzJaVQq2SOU+82V1Ierlst3t2maAdLKKV2v2NMO9ch7VbIMy6GJEp73F5278V9707HdSKIwD8LqnvgkpPK7f+D1uLHyI8bHXEQR2+bLT2ULrlCRt8uJLP4fQcKryLDOX3ov0dqff+H4OKX0K+XGe/9yP8wXPfTfFwuR9zYHjZCMMNuomId5DHlA2Oj0lCX+jhVCrJOfG7yugKQ1o0f1926bItQZCa/ylGnrUOstWVaBiV7/Wm/jrTZJKfs/374+Mes34rjZLAP6dDWQzJh0t4NXs914Y2yUwPjOGLkVW2SFOMUmfXT2KugdPDERRk1KEt9UmnSgjlmqIVNt0D//+VsK6jq4ZokDSTzcnN63kSaarvb914KEqOUzk2xqRPfSKvVbcW32UnYTo2kpvznQ5Am3QcYLIBXvmogMQ+WjAZDcfohlj2tlqgi9pzlTJ3VjdHbmXAtNfxOkJCH2UEPhxikk1XuSTChDtLG1kvEg6XkLW2shae6CQVG628DebtlhRa9BwfeXDJOurPPumv4hZUhi/xNwXfj1KKzY+9zHu3PgwF7/5r9v5NobOxjLSD2m88lnmP/JrVM+8ntG3vIfC3IXhhz5q7W7oFbj1mz/Bma/+TsQJ0bU/LM5BdgxnrNRz7EQ7wW9bR0+HPvrcJHpn++yt9u7I7nodb33bEHmpJvUk6ZlBx0UArN9dYP0giFQRXFvsGRj/9iqmEGF8D1lrIioF9NzYrguU3Gggt5o2R0xZnet4tIQeLYHvoUt52+lth07q0DG044Hua7K9Q1u1T+bHSNFLuzD5EH9xo7ck6S9tooc0EhlGOjeOf3sVfEk6WiWs31+3sF4L04NsW4hsIVucILda6GKO+NQ4crOBP7+OmqyytHaNauUM5fIplIpZXnqetRufItUJ73rj96AKAf2u+Ec//n8Sx1vkgyrnJt5JIQ65vvYpVj9+mWfe8heQ/vaFUmvF9Zd/HWFgYuIN5LwSn/r0/83b3/q9hOGjnQPnuDvpSBG9h6KQt0Nmzdtqk9ZavWXvwyA7CeH1VYSBzulRwvkNRDslmSwj20lP9zhY2oLVBp1z473Ip5qu4q9brV1xeYl0dHg+sRkpWP3drOhObLZgZG8bINe3dXj7O73pyKdzcRJ/rWHlzHIBZDnR6UgBf6O5HW0esvR/UHTgI9W2res2/ohur2GUQQDRleV9nWOdD7bbf++B30m5u+XdjjB77ZQk8AbUP+Kz44h2QnRl2abjaI3XiJGBh+6Oz2SFlJ4hf2215xwbQGdFbyYX2ADEHmkpyUSJdKaKaCcYYyDVhDfXtp3eVJO7smz32+3YJ8jOl8h299to2nGMFEhmqjYtZHmLYGWL+oVJ8ITdZytBrjbwhECNZ8pQibI6yrFCVXJ0zk9sO80S1q5+ktPn3gvlCH1nia07n2Jj6XnG3vE+Xvddf4+008YL7fepuXCdyz/zw4RRmXxhgme/4C9Q27zJ9V/8UU5/0TdTfsNzA8ceLy+w/ke/A8DTl76Zlz7/M8z/3geY+9JvOcCnd/JwDrJjKN022TudXhFnvdx3bj9eJvUlcrNhe94H/m7R/SFRYblSw1+vH7iSesCY7cBkzwsh0EmKMNn4O9u5il6tCZ0ETylUpQjC5g2ns2MY30OsbPZ63ot6G5Eq/IV127LzLnfBupizKRaFCJqdXRcFVc4jt1qoySreRiObS5sP5y9uDG7XtNJxXfm4u85LO7aFGOt1SBRqvGzl55r3Jo3l7ZFao4s5G+Xui4rLZgdRb9njAWSrbT/XtTom8PHm15kIp7le/13+4A//EQCjlQvMRRd5ceuPaZpNXnr+V1jfuMyXvu9/QwjB1MSzxEmdt+S/1GoQBzAxeorPbP4WH/rjf8ZM5WnGzryR0fGnWFz8FMubrzAXPcVZniTX9mhOvolXXv1VnnnDn7mn43c8QmhN9Ooi6WR5d2fKIdFX2YzRmYMsay0bYT1AhCu4vWFVDIDw2krP4QmyVsUDTSKUJrq+SlrOoaYraCAdsfnBspUQtjZIOwlJnxwYZLnIk+Wesyub8S65uYExrQ1P+xKd1NoxpW2HylyAGi3SCf0sV9v0opvt188iG52BYzooO4vOemTOMdhGHfvSzYnua+9ssGlbMrMpdwt2e1KgtEGdGrE6wu2E8OoyJgpI5kYGtk3mRlAjBVsbcnmZeLpi0zLIosRC4LfSnv1OJkt49Q5ys4UJfbyN5sC1TZVzg01hpETW24TXVjGFkPj0KJ1L03Z1cr05oCQitLFSktNVjCfJvbyASDXxbBWdD8ldWUa0EpLTo5jQQ0uJbHSQrZh4bgQ8adtjL9cJ1psk5Qh/q92bf9lO7A1bzkeFPqoYMTv5Ji4///OoT/8Mvh8xPfFGJiafYe2FjxONzXD5Z36Yiefex9wXfyOF6bMI6fH6N307I2MXAaiOnqc6ep4X/ugnWP30H1F84ilG3vAOglKVW7/xUxSjSS694ZvI5Ud45s1/no9+6F9Seeo5SqcOV1tyEnAO8hEia028lZrN6xorYcoHi/6dRETmcO0iF+y55KerRXR1u8Wnf22xZ+BE6JOctZFjIazBE6nCX60dqoBjL+dYl3Kks7aVKAzKygmlB9pbm0oelmt4a9vdiIJbqySnxkjOTeEtbuBtNAjm12wedT48UOQYZTsu6lIe0ezgbe6MXmXpB0ub23ObasyOi7OstQ5dR+StboHvIZTC27IqGeYImirsxPjS6hYHPmqibB17bWwqiBC2UKScx99s2iKg81MYKcmt1Hjf2HdgjCEp+YhqFf/OGre5zqs3fpv1jcsA3HjxN5g58w5u3fmQfcOZL+q9txQeXzDz9WxuzbPUucoLL/wcUnqcOfdejDBcmv2SrKrccLH0Bfzh4o/QbK1SyI8f+Tw4Tg7ehv2+BLfWMblgoE27mq0iOwmysf397c/39NebsNYgPj+x73vITrKdz8r2CpAIJK2z45h8SHh1Ga/vfVAaf6OJv9nCYHY5n/5qA1XODbT9lXGK32c3/GaHvWKrshkj9oi8Cmw+rdBmINLZLWRMTu9ISfHkoZ3joWPKB6SpvqtTnFbzyE6KbCekUxUrl9bnBZvQs/rQcYoIPNrn9u+wKRY2CTopyZkx0krORusbMSJWA/NncgEq9PFW6oQrW6B0zzk2oUcnk3rrX3nQxVwvTSZ3ZXlgPkXgIbrpbJkCk79cw0SBLfxrxraByXiRdKoCEyXSOMXbaJJMVRAYTLWAyoo2k4kywXoDf3kLXQjR+QCvGaM3W/ZmJ1G2OBAbXZb1ji2yBFAaNVnG5EN0OUfu1aXtG7Z2itdO8WptngwvcebLv4hEJfi1FtHtLTZmIu588qPMf/ADAHRuXKP+yueJlxcwWrF0/WM9BxlgvF7i3U//NRbUddZuvszLH/+nFM88iV8aoZibYe7MuwCb+nbhya/kzp/8JqVvdg7yaxoj6EXRgttrpBMpeuL+Bd4fFDJVeK/OI32JRkA+hB2OoWgnyJXa3Y9LCtTcGLqdIDcbVtYsW9IzBrwl64QehT6bCDzUqfHBaJHSyP52zf2FGEOcXdHqWCf53CRGCtR4GX+zgTRm1xwMQwY+dAXea03EPkVwYo/fAXQ+tB2YhuVgewIdBr2bAON7NqKx3rBqIP3vqRTSSNRoEW99MMpkQh9diHp52wA69DG5ENnqIBKF8AQmyy3UxWi785c2tqCwWsBIiWrFeBsN/NUa2hjU+Wl04JMCxgPZXbmcqKA7NgUjbChoWSM/4k3STOyNii9CLi/+HiXVV31tDCL7XA2gijkq7QkqwQSjwRyv1v+EuFHD93PM+3c4LSdAG8JGytzEc8zPf4wnLn71np+F49Gn+x0SBqKb63apOnM4tBB0LkxaB3erjbfWQBdt2oNMldUpNuCvbFmFgj3w9shZTUo5TFas5e2laGDMnje84UKN9pPbDrJ/c30w3aCd4mmNGhLhFnvYJZnz0e2UaHkLnWrU+P5KGMIYwhurux7XoY+P6enxHoTWZBldzhFeX92zlTZkBYKZjVPlaECtR+Z8FNvd8NLAQ2cNpgIpSJvxroYtqpSDcrfP9PbjJlF4q/WePJystwlvrA1XJEoU/mqDdLJMOlVBF0KC2xsEi5ukY8Vdah+qGJKcn7Arl80YE3rIzTbhwuaOonNtVxe6bzM3QjpbHaqypCZKthujNiAFcqtNdH21J8UnPIlRGnxpI+xLW6BtSo2JfHtsibISntqQVvMIbQYj3NqgAc8LEJ71V6JoBKmhQIUmsLV2ncrnP0+jvmTnsb2dhiONsfn0wOzsE4w9/TpGRi/y0md/lgvf9Ne5+Ss/xtnzX4jJNCCmpt/M5Zd+hXhzlbD6aAUrHs3M6ROKKRdIJyqokSKqWrDyWodQC3jYiNUtm/6aakSqSEd3a07qfIjZUc3va403v4ZcqQ04pSYXokeKpLNjeFstgmuLyI06cr1u827vsWf9TtJSHqMN3vwa0eV5oquL+K/O75kjtlcBm2jHBFcX8dbqVrf5LuNTlTwqW6rVSV9UanFj31ayu9632e51FRTJHs4xoEZKqMkqaqyMrmRLhK0YoXZXCguDvUnYGnKscdqLZve217q3JAugwr4LUH8HqlYHacBIaSMkrQ66ENn0mXzUa/wiANEtnG91rFHu17bODnHUTLCw+TlK/igFr0LBq/LC2u8CUPRGes5xd58yk6wzxvDR9f/GejJPThSYnX0bS6ufQ1UKveMvJ2UazeWhc+l4TGknhC8t7Fr611FAOlGm89RMzxH2r6/1nKlgoUb+lUX85S2k0nh9KV9yq4233kTnA+LZaq9zmoFeEwYvTqGdYCKfZLqCyQ2PPaVjRXSfcyfaCcGdje0Ngt2XZNMergQg2wk69G0BVp+vlXQDEVl7Yj/Tbd4L/9Z6L3WkH12KSA7ZidPLIp13k8T06h1krKy6hRy0tZ1yDl3N92TMvHhbISltJ0SvLvUcUD9zHE0hRKQ291ZuNu1nlEWhg8VNG2k3BhP5e2pgC0OvPbTcbOGvNhCJsikxtzd6RXHbB2HTEL3NFrLWxltp4K8PT3nx1psD56RoxnasO/wDudkimN8gWNgkuLlGlEW3u5jueZlq8i8v4NesHRdKQ6qRypBOlnvBD9lJBuphuvuQ2XwKbW/ewsUalfwsK8ufYzpnI73trVU2Vl8FYDa8aJvKMCh3GsxvUl+8ykuf/RnAkJ+YJlcYY/3mC8hsbFL6FEpTdNaXhs7NScZFkI+Y/sjqo+Iai2YH0erg1fq+3FIOLdTS5XwvYtJ7bKOxvSy4UiN9ag7dF/Ho5tGKdoK/sHH04+8k+PNryHo7u97tloI78L6yFQB/pWaPXxibopEPUELauSLTzjwiKWEZp5gwsML+++iR+lstzHodo/eOSO1kWCRbwK6cb5FqRLrtTItO0ismHIi4pxpqTRgp9rr/pWcn7SrBjotpNyfaX9xAl3K9ue1nMjrLF45/G5FX4HeW/iOjwSzPlr+EgjdCKHcXUonUSlOp0RJfyX/P7daLvLLwQd7y1u/hytXfxDxdxWw0rNFXPok6muJPx8nHhB50Hb3MCfJW6+hStKvxkMAqFgzQSW2krts4w5ek5Vyv8Cyt5FHjJdJaGy9RtkFItl+RadgmEyXUaBE9ViT3ymJPc7hLOlbECz3kQl9hW1ZIl44Xh6887fFlV1MVUl9ihCC8stzT9d21j05KdHmZZLqC7mt9LQD/5lrv+Hbib7Z6jZsOir/Zwqu1DmwbjbFOVs8eSYFfaw862KkmvL4KUpCcGbMd7wSgTU/1QwLh4iainWKkoHlpGqRAdBJyrywRXVsBY2g/NUNycZLg8rJt/BH5PadYZzc3/sIm/krd2q/QIwlttz2dC+zqWtdxr7UpvLSw6zMehuykhK8ukU5XUCMF/OUsvc9sy0TLeruXPtGbHyn2tvX9qR6pxktjdCEgHSkQrtYxWiOH3FyJTF1FGtNLz/A3WzyX/1KS6N0sti+z2L5MqZPjzTPfjUaR80qk8xvoU6OYvhQlAUyaGd7+3r/FlZd/ldU//C1GRp9gc+MaU2qaOAsgBX4B1Xj0bLFzkO+Xrhj7I9YyuYtcreEvD5EhC23lswg8TF8+mbe8aVUdKn351aMlUuyX1OQCW0iw2QCl0aOlgaX8YzmGeyxEuxtGCoQRmCggPjOBrDXx+6TPxNb96yxD1zndcQyesHnafRcK083nPpJ33c1AUaY2No+uMyTHMbtAaCGgmEMmtp01SZo5yoGt9F6r42d53l4nQe+xmlIO7LLbV0//9YGI8Z7j9CUEPlL4nC08y/XmZ9BJiyRpoD2Jl6UGTYSneX7ld2g0FikWpw87HY5HCB36dC5MECzVrLRgV6Gh3iFY3qLz9OyAo3eg1u6ptjnKGf5GE5GoniNKnCI7NoobZAGCbiRWe5LOTJXg9vqAsxjdWN1VcwA2EupvNHvOWj/eehOTD3c5qv3NRkzkQ9dB3rEPHfnonG+j4P0OsjH7pkGgtC2WA4JiRHrAVbHDBA7S06O9HGDIbO6wOah30KFno+L5gPDmGgZBctHmjov1JqKdIjxJOpK39ZJsz4UJpJWulAItBLoYIjZaJJNl+/6BR3xunCD7HACQgk4lb5uutGKrtsFgg46DOMe9eQG7/8Ua6VSFdLKETBTBlWXSidJ2cCTwiMdL6GKIjFOCW+sHnlPZTMhfXd53XGkpZ1MsNlsDOey+DPFlyPnSF3Cu+NwuW+ytN0mmK7uUYfxGh9LcNKfPvZcrL/0K5bkvoNWct8WEnQQdBUxNv4nbn/oQY0+/De0d1xXs6HEpFveD0ngbdeQROUoPGgG2qHAYiYJUk4wP5hoLA/6dNfwbyz1nSguBHiujpqp26X+riT9vm3zI1S2bZiIFMtgdkT7JyDi1bbM9idxs4i+s3/1FR4Uy1km9x5a494LJBaQTFcjSJ+Qw5xircNFbogP8q4tEV+YJLy/gz68T3FjGv76Ev7wdGUqytJBhpFO2gvsgzjHYi54/v0Z6ahwTBRS8ET7/uZ/BkyHN5kpP8ksKj0vFd/DZz/w46c4bEMdjharmwZMkM1XSuWrfE3bpeVfXMSEgPJw9Ep3USqp1nWsD0bVVCi8v9qJ5/dFbNVKwhVn9+4iVLUwbKxKfHt1+nN2ObRd/rYHsc0paY7ttghpwfAefSyfLpBPlwaJdkSlmnBsjnqkSz1bZCzVSYOvM6KHn6yD010cIT+65gmaA+PwEwhiiayu2jXQr7uWGd+1kUs2TzI7YiGymCY0n0bmAZKaKSRXh9VWriQ09nfm0nMNbb/ScPwPEM1X81TrRzTWMtnm3e6p27HuQElWKennIAgiWauRfuEP08iJeM7Y3KsLeILTPjaMmSjY3/M7m4Vcq98oZ9yTxqRFb3GlsWspeHQmH2WKBLWr1a9srjTr0UYFH9OoiOb9Es7nM8uLz1Dsr6FKO4NoqUmkmp5/FJCnrv/0bxxbgOQ6cg3w/eBI1WUUP6Xb2KJwEQqm9v3xKE91cxuQCzJCzRMbp3sVrfV8uuVlHKI0q5Ymr+xeLnFS8rZZ1jrtzdYzfGtWnzypg35SLo0a2EyvfdpcmLaKTIG/boh7bDtxg+o2yMbsvJFJgcsNF/mWzM1DEciA8iSlGpONl3jL2dYznzhBGZW7f+QimL3/6bOEZSlR49cVfQOtHu6vTUaKLIZ0np4ifmiaZqdy1AcNJJ1jeIv/yArLeGUwNE4Jkpore0YHNAJ1To/fd4Y1EDayw7Uwz2kv9RhUj69TfQ9CgPe7tskG6kh9wkvsJbq8TXl/ZXo1KVM+W6UKEmihhxku94l92zFXQaIMQxGO7a1Lul/7c56Sat3Kaw8gFmNC3Ocd9r+kVw+2YR7nRIry9ji7naJ8fx9toUXh5geiVxZ4zqqJtKVJ/rUGwXLeP50ObonVnY1utZB8nNR0v2hbWfXT/judGaL1+lvj8BGn2+fTOuT4zKxJlNbK1Iby+imx0iK6tHlj+9CAYT6IqeZv3fm3ZrrpcmiYdose9F8FibaCuygSevVDFisq6xzu/6O/QbKxQry9QCxtWdeOlBfxah2e+4M9z+9Xfo/Xii0d2TMeNc5CPAblex3/xlk1HyHhYDrNMUkR7t5GWWiNv7a5c7sckCv/6EmLHd1SNlUiemBnIRZa1Jv7NZYJriwR3tvOo1NRolruVDl+uPyHoKBhwTvffePtX48k9mxTcC+JeIhQPAdFoW0M5JK94GMHSJv7SxtDnZP3gXQq7pGNlmydfKZA8OUth5gJRWObmrT/i8vqHB5zxN1S+mNbWIjdv/D4A+tFayLhv4kSRTJa3m1ZUcsQXJtGZ5FU6UaZ9cZL2U9OkI0fXdviBk2qi2+sDKVdqJG9VAYagi9GuCO/9sjPP3uzhAPtZc6B0D6cWsijm6VEbadYGmaVD+E2DGeI36b1aHxswUYDfim3B7ZAxJT7E0xVUMaL9xKSNeHZfnthGF4e+iT0AO+XgOhcnh0eq2wmkCpkMzq+/2cJbqW+nm2TRfTWSJx0r2nbkC7Xt4nNhz//2hUnUVLnXebCLMENy0++Cv9EaVB4JbbtxVc4NFGt3pTeT2RGMFLbFd3ZDYEKvd67IrNHHURWxdxFxSnR1xa4cN2Kktik0ydwIKlN2OchnLIoR7SemrARdo9OTNvQaHYKwQC4/yuT0s3zkM/+aWNhzLljZIh9WePpN386V3/3P6NW1u7zLyeDRTJw94ejREma0aAsQriwgtAYDydzYLgWI40RuNvAX1q1Y+nmbgynjFDm/hpekB8qf6nV8y8SLdbWImhrZvV07QTY6GAm6kOspRfh3VhGehxb7iMqfAGQn6TUU6UeXclZKba2vwCBrCGCA9NS4LUI8ojxoeUCH8+A7hHRq9MjTQ4QBb61OenYSf379rjc/5ggjIcBgm3JjWFu/zOTEM5yaeycvvvwBktENnpFvQwhJ5BV4pvw+PnLjA4yOXaJSOUN71CNav/dizkcJWQhJpyukgFR6VzQVbDGbbMR4tUczXayHNoRXVtDF0Nq9vtSBbjHUwOY75MLuF3+9iS5G28099nByvEaMv1jbd74FVrNdS9FzmOJSRPn2bhsht9oEa8OLoAQ2h5dcYLVxgfjiRC9PG0DlJWpsu7tfem4c79UlyNI+vHrn+O23MVn0evjyvtBmaP52sLAJgVWUUJN2NVfW2gTLW9AthsOmocRnx6ys33INf+tgNlsH3v66zv05yVgtY6QgPj1q9Z0z0umy7XYX+qjRgl3dADxtUJljmlYL+JtN20L9GOh+huloYaCQPp0o4zUO5pSbjlVr0YVw4MZA5UPiuE6jvsjFr/2rpL8X8weLP86bR7+SCU7jX1th4uLrOH3uvVz/xR/lma/4PuKpkDQ8uVbYRZCPCYMAIVCjJZvPo/SeupXHgWzHvbQA0U7wsuidMQbZig9VXAC2oEMAXrODaHaQSuEpjewkeMubeBvWOAs9KKMmDNmdvzpYYcxJI1W7l7m0wYt8hBTIWvNIl8GOHM1958jvXDbuIhstG6l9CEUXA9+ltU2WVz7HxLm3MDX3Jt7zrr9NPV7h5eZHARu5KRYmeXrsi3nx5Z9Ha0VSlKSF14b58/oc4mHOsax3COc38bMGNWLINo8S3QiZv9YgvLGGbHQI5jeIPn9nsNkHVv/4qOmPGvr7tFAOVup3TaESG81eHjWAvzS8ZkR00j1lLbuk+cBKQ8Yp/o21AXss25q4JFBhJl8nBJ2LkwOrhMed7tVtEz0sxctIgQm8XV0FVSkiPjNGcHPd3lD4HtIYon7pvAy/FRPc3iB3ZZlguX7g1br9NO17SEE6UiC5NIUaKxLc2SD30gJgm46EV1cIr63alA5t8NYaeGsNpNI959jrJARLte3c6WOsP9mVk186xCpoqu2K347zQZcilheep3r+GcxkiVP/3Xfx1Lv+HJ9e/3U2/RpCG4JrK5y9+GVIz2flhQ9TuppQWEiRJ9Q1eLQt4UlGaeRmAz1aIrk4gxovw/jeQvRHhdQab2kD/9bKQLjEW6vj31i+70iiygW2S9wr83iv3MG/umi7uB3xctBJwIt8W+m9oyOezockndRWAtfbx67Scb8ctF31nuxxgRCxFaPfpQ+6B0aAyC4GB839VKOloTJT/vqWzXVWCm+9gTGaZKpApyDw/RzPPvMd3Gx+lsRL0Vkl+qz/BIEJuX3zj/ESg4pObuTiQRLscLqOOtr/MPG22kRXV6ymrTYDzqu3VBvsfHcMmPuMuvprDavYkA+IT43uSgnovc9o4a7fqV76hy9BQPTSQi8H10tscKM96rHxZEBaEGhPEl+cIJmu0Lk4SXxqdJ+9HwFDbsxU3rbwbl+ctEWS/RHhUmQL9+LURmWLEUaAXNgcGrQwqe1seNg0trsWyUnonB0jOT3ak/1DaXQhRFXyhLfWbSpCMya4tY633iCc3yS8s0H44jzBzTWCm2uEV1ZspD6LVh/X91B4cqCbJIA/5IZiGOlYETyBV2vtUkDRlTwCgYyss22A4LnXMXfpfVxJnieZreLVO/ibLS694Zu5/upvEbdrRJua4s3kRDrJzkE+LrTudQbSQthivgNW6d8rst7Cf3XBpgMMuduXzc79RbGz8ctOgrjfqKEUQw3iSaIzViY5M7nrcV3MIT2J8D30IYX0H0X2FP034C1u3LWob+Al3aiV5+2pr9r/uK7kEcOaLnRS23Dm+jJp2sL3Ikp3UryOfXUYlsjnJ9gsp7blbr2N9H2ezX8h1258EH17mVz98XEE74ejXtkRvjzW6Nd90ed0HFVE1ET+oC3rl/Adv7/CNtlO8OodK8cVeDZFYAjak3CXdJGuM5yMFIjPT6CKkY1qKttQJGgaog0NnqBxOsCEVhItzfLX1WiBeKZ6FM1Ph4+v0SG8vjqwuim0Jp6u0JmObBpf17GTgmRuBJMqK5s2XiS+MGHbe/elJxxlCo0OPERfIxeTC4hnqrSfnMYEPv5SjeDOBsH8hu1053v4mc6wkQKdC0inKgNpdMLYPGo/a7DSz1Hp7O86jqwQsYtsJwNyhnthgGS6QlrO4y/WBuXu8iE68onjOoHYrmPQAopnn6K+NY8pRqhSRLCwSbkwxeyZd/DqC/8NYwx+y5BbOHlF1CfUij06eEsbePNr+DetELl/Y9kWEgT+A28z7R/SWTkoJrDFBRiDt9WyS/Z7tVU9IKqct87nMRR+HAW6EKLLBeSO6LAMffyVGlICcYo4Jg3mRwVvc3j0fGeuoOnmFnarwtO9838HHjfsKhLtvffSJiJOCWUeKTza7Y2egwwQRRXasmk/y6kRVLVA0R/h/NR7ePHTP4lqv7Y/uy7xufEjvVlNqnnifdo2Hyc69FD7KHL0R87MEciW6cCjfXGSzunR3nvLRqd3Dqez1V1tke+J7spL6BPs8VklWfqBt8f7db9H/lYHIQTpmTGS2arVV+82v+gYgrpGC2iXd39D1USJdPp4rmtevWOVHPoeM4FHOlbEr6uBrqDJqM3lzV1ZgVywHc30PUSmIKHKOToXJo5kbKoQoss50kwhx2CjxmqkgLfRInd1mWBpC3+tgb+a/Ww0ka0E40uS2SrJlC2CfNirrSLVpGNF2/Sk+2B2Sol9bmxtd1Rt03S6D4b284lPjQBQqszRWbhFuLG96hjlKnRaGxiVEp8bx/gewdVlzp//UlrNFe7c+GO7XU3jnzAf+VgdZB1AY9qnMe3TqXqPhvbZIZBa463V8TabyEbbOksCeMCFLqLRtmoCh1QCOPD+E3XX1qGHxdtsElxb3DYWUuxp2I8NuUeXKN8jPTWOaLR36UTHWfvtNAwxhehxO6WPDLHjRk0ofW+RSmNsgeiw9+j+LwSekQib+U9StGZtfPwpbs9/jHRmFF3OQ6pJJypc1E9RMmWe/+SPOuk3QPse8VSfQ+sNjwAPk3vctQ2QjhT21NA+blS1QLqHagXsUChI7j+YIBNF7toKwfwmceZwCm2gk/QcuiQbj/GlVS44DNm5343WSaXxPnurp0Mu+5o96JECyWiB+sXJQcm3rIlKkjm2opPg39mwRW0jBYQ25F5asLrvQPFOitfRyD2+GmqyfE/ydPdCMlUhqXhE19cJuh3/pERV88h6BxGndM6ND0iVqewGIh0r7sq1PQzCl8RzI7QvThKfHcPbbOI1thtF5a4sk3tx3qYo7bMaIVJNeHuD6MYquSvLeAeI1u65ryGtyO8Fb7NFeGWZ8OVFTOQTz4xgBHSm99bDJufbehQpbDR5vET70gzJ3EgvUi+lj8FQXFTkVrLGVmNVcoVRVl/9OAhBOl5CthLy1zd49rm/wM1rv8/inU/abZsnq//wsTrIxhOkJY+05NGesI7yCUwzuWe0lKRzY6hK3nYhSzVqZhS9T65x4Mmhsmv3igD8W6v4t/eXbDsJmMCzjSiGoKpF2uMPNuKO3n3PZgQkMyOgNMHieu/5bgFJt9hRNtvH1sHvccTc40qBSBRG9emeDml/DhDJAu3VeQD8pr1Yzc68jSRusLzyOUScIJRCT1RQUyM8U3wffiJ49cVfuKdxPW6o8RKds2PEp0fpPD1D6+nZnkMFQODRuTh1V8cona4g9O68/QeF7F+GH8aw1LP71IAWrcQ6l0pj8iGd180AWQtlrBIFwqYqqbEiDFFi2HO4owV7w9GyKkEIgVDGBi1aMeHtdaJry1br+NUlgq02QmniUyO9SHo8UUIXI/RY0RbgBR7+WoP8K4vIetumIGljpdSAYKFG6UZCuLl3047ksI7+Ieku55tCSLSW4G9sB506T0xgCiHB/IZNDVne2u4Q2Pf5hrfXD+WMGk9u+ye+pHVhAjVSwBRCwlvriJ2rpqk+XIBEZJ/n1L2vrpgjuKkDIJCo0YI9b+PUKphU8pho7++3SHUWZCjReXLKro5ojewLCEZRhU7L1jnlVjV+bEhLklPv/iZevvNbaJ2iRgsk0xVEO2HsVsKbX/+dXH7pl6ht3NxztfBh8UBTLNKiR2vq0czZ9NMUmd2NilbcK1zSlQJqbpz0yVl0tYB3ew0Su/QuN3bL7qh1Wywnj+gCYgBdzj+0iM1eiCEXUpEo24hiCP5WC391j65+DxA1NYIpRAR31gd73Xc/ewNqvDxUbsixN90VCONJdBRgBKTTI3d1FvzF9V6uqBFg9igYnIjOsbjyvH2v7qKE9Dh3+r3cufNRjOf1Gojo8TL67BTPzLyf1fVXmV/4xBEc4aOPruRRI4VerUQ6WabzhjniS1O0z4xhcgGd06N75henowXSyXK24vQgR76Nt9W2zQxg+IqlxKoIbDQJ1uuYyKdxdvxQzRL66dcL9pe3801NFJDMjQCgpSAZK2Vaxm3aFyf27GDWRecDkpkK6dwIyelR0moeb7OFzrKUdOhj8iHt18+iowBvs0VyagSTavJXlpGd1OYrBx7R8hYyVWhPkpwZ3XbQOinhtVWCrJtduGyLrf21Ov5GZ2D6GtMezSmPNJedGzNVWq+f3bM5yf3SOTdOOl1GNmNyry4NjEVnhXC6YG9G0skyMjsn/eWt7XSLVO/ZiVTn/F2fuVDbDm+SRTnDm1av937tvSpGdJ6eJTk12lMFSsZLxKdGhl4rjxtvrYm/tEU6VuzNp9eKbXOSPTCpJrq6bPW2tenJ8pm+c6AQjaN1ytbmLQSQX7I3q8XTF8kXJ1m//hnArkKoUoRJFNVGnktv+GY+/+n/QtIeLlX4sHjgV/mk7EEw/L4rKUjao95AdbmBk5GnemcdeXMFsAnp7Ihkdbs3iTixOTrteGi0QlcKJE+dQleHt929F9TcGOnk3ksjR631uRdGWPH95NQ4nSdmSebGSM5MkE5UBrtbDXttqkAbVLVgl2knhxSDeBI1cnzd+Ew+RFeL+LdX943yy436fS3dvVYxhYjk4ozNEzRk6id3mcdux69ibl8N8bOFZ1isvUSrNShAPzH1DLWtm3R0AzW1/R0xxRycP83Tb/gWXnnll3a9zmHRUqCioNdpTxcjOmfGhtrkrgSXHikcS1viQzNsuVJD/uUFwlvrtuGPFOBL1NzIgKTZQek6F2AbJURXl3tFWLrPeU5nq6hShL/awIQeSeakmlxg0y52zKfxPdseOvs7mRtBVfLIdmJzQbvpSkJgQqtJq0s5G/FMFOHCJsIYVOhhEoW3ZNUfVDmPv1LvdXoT0KsnEa2EYGEToQzhcq3nLEohML6gM+rRmrSfqw7t3PWrghwVqpxDl3MoD6Lb67uK12R2s52cGkUHtsGGzq61vbkPPXQ5t32ztINksmI7Ge5BsFgjvLVu53yrjT9EKjOt5g+8Iq6LYU9iUY0U6JwZs+fEaJH2zD5pDfeI6Y5vD9cpOTVCOl6yKUFAeH3VdjXUZqCAb9dxRIF1igshsmZXH/pT4ITvc/rcF3Ht8m9hjMFrGDwDaVEy+vq3sbD2fG98qnsDWYoYm3gdoxOXuP7xD2wXcp8AHriDLA0wJAiUFCTN2YDOmE/jVNhzopOyZPNcYHOYHzCi2UF0NX0FBzKgarKKyYeYsTI8wCI9PV6GPRQVjku/0oQ+6UQFNV4mnaiQXpgmPTOJKVvDYyoF64iE/p5SYQPjjFNkpsUqW53dASCljy2tQY0USU5PINfrdzX6u5baHHdHCpLZUUQnsTnzHFBfNEO04215piHRt1DmmQrPc/mlXx543PNCJieeYXHx08g4xZtfQyrN4uKn+cif/H94/nM/QarafOgj/wfLy587Ucb5pKKLEZ3zEwORZAOQOdEGjqUtcTpa2DMieLgd9dnDVoLUBk2WNiDBO0TKhb+jOYdsxIQ3h0tpprMjCG3wVxuoct62/s0FJGfG6GTR3t5+1I7vhifRpQidD2k/MYkWwqpSKG1zQrMARDphZRE9pcm9tNiTsfNrLUg14Y1VZCclmSgPdZ5UOYcqhDZtZMOucmpjyK8pMIZo3Y5LpjaUrYsRyeTRFWQmU2Xic+PWabtd6zUq6Uf0Oas7yxPSiZL1aoxdrRpWO6MLIaaa73Xd2xMBqponvLMx/GZLiH1TLHrd6WBAfxtA9znnuponHTm6gBlkBXWJQudD5JAItbfWIJ2p2AlsxXhbbZA2mJacsXJ+MvB23bj5tRbRK4vbx71zXqRg9vTbWVt+iY21y9k47EaVNzzH+sZVVGLPKxX6tCqSF1d+lw998B+xeOeTbFz+DC/95x8krp2MgMWDd5BjM7SKU/V1U5Ga3sSrUFgdxAmf1tgDdpKV7l3E1XgFNT1y15fo0ZJtgctAR+JjRWCjmns1rBCp6unPHhR5gAiQjgL0WBkTBYhUITebyFoTqfWA8ZFrWwfO1epGRvqbjUCWwyp2t3I9CtKJCmpmFLnZwFvZvPsLHIdGFyIQgmBhO6+7P/p2N0Tfd3Gv71UlmIB09/kxOfkMq2sv491ZxdtssvLSh7l89Td56qlv5Iu+8H/i7W/9PgCe/9yP85nP/ufDHNZrFl0I6Zwe610f08nyoIzlMSz6GU/ecy77XghArtZ7Cj3paJHWIdIthklxiVY8tKGHjnzS0QLeRhOkQFVsK2KJ7djZet0MyYwNqmjfsznLwe7W6CYfIrUhvLyMv96wOcq1FsGdDcIbawhAxX0OtgQpBflXFvHqHVQxQpcj0iE3MbLeQaSaZLxkncje4O1/ftfP08Z2izs3TjpVOZLaorSa77X+Dm6uES4NNnHxsgCQ15ePbHYEK3QxIq0WkInC7zr4/YEjTxKfH8dgu3HuWzRpILy+YptiDKG7/71Qo/Y8MvmANIuWdtl5FqvZo5fP85oxuhAy7Irpb7a2nT9h2163Xj9H54nJXtFnEvq0nprZFZCQSYowBl3Noyu7V/U8P6JQnMDobN6y76zvRYyMXqD2wid7kf7nl3+NNO3wti/7fp793h9k5gu/nnhjmRd/9B/RXls8imm4Lx64g6wjQVySpHlBUpB0qh7NSZ+kbIciDRTuxJBmeqaN7cthPOoTlx7ckE05j8mW9E3hEJ1mHjDerRX8hY19O7ppKQ7cnAEgrgxeJHQ+3HZohECNlayKQyfBv7OGt9HAW92yv19dtFJwcbe6uoi4H71g30ONlXu96o8SNV5GT1SQa1v4S5vHpj35mkcbK4XYf4Nzj9JiexVhnco/zXrzFq32YASvWJym0ViETMFgq7HA7PizjI0+gRCCcvkUX/Yl/5gnLr6f1dUX72lMr0V0KaJzcZJ0pDAg/SWAYIdzcxQEaw1kfPRV7v5Sjdzn7uA1YpviNcTO6MDr2c90pLBv3qjBdk/rRwAedlm5l7crBDJReLe2z1ddsQ5bMlnGW6qhQkGaH9J6eb1ho3OxwhRC0rkRvPVGz/Hof0UyVqJTzfeuD7ocWZm3mQrxbHXgexis1hFpiq7mejc8SUHQqUgQgubUkOMW+8uDHQSTD0jPWI3n4M6G1QXeQXOmQjJdQW61e2kWw1J9eiumUhKfHh0oilP5oJdPbADV32AlkLtC0vfTSKbb1CSeqqBDHyn6uiy24sG8ak/SefYUnadne5JpR4G/3hy6gqwKYe/zNdkqBkKAEIRZd0kTeFbpaccUCyHvKvN65sL7uHX9j4DtGzzjC4rlGbbiFfwFG4jaqt3mwqWvwitWENJj6q1fxrPf978DsP7ix+75uI+KB14xp4HW9N6RI9HRAwUeXtsQrad0Ru1Q46kAP06Q8YOJz550f0mmajsNZB8Om2bRX0xnRJY64kn85U3rrPbdMKhy3haB5EK71GeMjSSvadTpcfRIiU61iH9t6d6KCdPt4j7jyz4DyH2F6VW1gJqoIBpt/OWHXyD4OLMzNUaNFI+8A2EgI6rRDPWtefK57a5fuWgEgKbfpqjy5L0yq/WlXa8/d/aLOXf2i490TI87phCS7EhJEICq5PC32kPrMIwApDh8qtIx6cd2b4pVMUIXwoFubV263c1sqlCVxJN4K3XChd0rTsncCLo8GFARtRZogx4poLOcUz+r/vdqLVJGMYDcaNpIeT4kzYf4bTO04FGkCuMJRKerbmEblni+h9Ia2knvdV6jM+D0Bws1/IUa8dkx1HgJtdnC62/BrSG8ukr8+llUIGmPeb3PKilK4pIgrA9+FibwEWls284f8nPVhZDk/AQG61T6a0PsQuRjyjnSvMZba2SpJUNSBzabyHqbZLqCLufQuYDc5WU7xsgnmapYOdTAQ7Ziohtr2xf5PRQidOBtf/6HQHZSTD7YVtiotYmurxLPVFETJYIbazYlpi81yfgSRouYteb9NfXKMIHVMhU7ggrp2TFkrYUuDNZTeRvNXgTZRD7+0tauTqlGafybq8QXtptodbsZdhmdeIorL/+q3T5zsJUH0dxZVtb/2OYvNzrk8mO0W+tU1EjvtdLzedPf+Bf3fexHwYkrxVc5Sf1sOBApzq0pgq0s1UFA81SADo92qa1L4Amrp9pOHrqg90EwnWTf5cyjWJJUk1XrEEcB6cwoCPDm1whfvoN/bRE9NUJybgo1PWIVAiYqpKcnSM9PYXwPUW/j31xBVwuo+8xNHHD078M5TqdHULNjiGaH4Pbq3fPRHEeCCDyS0+O7GrAcFZHMkSSD+xZCkMuN0p4KIfTwZYiKH6xW+WsJjS2g6pwZ3vVNVQuo0uHUD/QDqPTXhRCMIdhv6VwbwhurkNp6iHhIcV9wZwNvo4Xss1Um9HfnT8ttvd6u9RHtrH17FgmWCuKydUoBkiyarMp52k/PYnyP6OoK4c11ZJzSODtG++IU8bkJjLQOVzw3sqtIWgD+ah1/eQudC2g/MWVl4boNSQLZu4kp30opLeteK+DO+O7PIr44QTxTpXVp+lBBJVWKiC9OoqWwXeiyojqd8wcUMjpZGkd4aw0T+bvOh15qXq2NGrFqKt3i9G5BtSqE+BtNoqsryHqb6Ob6ga7xO51jVTz4anLar/LRfV12SRZKEywOSelLFHH/iowvD7X6249sp8NX3FJtr83K9K59UmurZJLh1VoEO2XyfGnrDwB/fnvsZscKcRAUSOKmLdTri4nJ0xPEyRbplFW78f0cado5sZHIE6m5poWNMicVTX5FIWNN0NBWAQNQEpqnQgq3jzaSLI2BF+/gG006Nw65ANHqIDop+hjVE+4HU8yhzk7i3V6zaQ+lPHJtqxedE9qQzo6iS3n8+bVdub1ddCGyChw7Ij6qnEePlZHaIBY3dnVOk22Nd20RE9icOS0FqlzoaUELwF/ZRCQpJhfgrT9kGRcpSCcq6NESstbEn18fdI6FcM7yMaKkvGtxy/3gm5B2Y2X3436Ozfo81Yk34W+FKH2yZBEfS/YodjM5314Qhyyj78VBCjp1PsAIMRANPQyyneBttoYWhvXjNWLyryxglLHR4tEC+ZcWei2SBVYFgeUtOqdG0dU8JhdgGFw5jceLhPODDpLJByTliOjKMvGlabQnMRKaswF6KSW3rvGWagRLW6STJWS77zzW4G1afXZvo2kj41r3IqjDjsNrxFaffqpCfHHSOm231klnqzb+kCpkM0ZX8uQWUtp79DLQQsBEaaCL4N1Iq3nUaRs5D26uDaRVmMDvRb1VKUKXc4TXV/EacU+fWxqDf2MNL1GYRNF+3YxVtNiRDpmM5G2Xu41mzxHbKWemKjmbf30Ah3mvc9GArfXpSlrCdvQYm+bYuTCx7SgbQzq7rWAhE4V/cw1dDEmnq6hyDm+rTVKMEKm6r3SPnUSvLtlugK+f7dnj8MrKwLkvW7ttpJC2WDQtThC8uoSKi7ucYwAhPKQX0Gmv421Nkmb1YzLK0+nUaFUEQZTH80OU6qBPXKjWckKHZUnzkvqZgPbYtv5iFyWhOesjvCO81Cpti4H0dt96udHAW1gfqml8UtD5iOTJWfSYVbJQU1VUdduh91dqBDeX93SOAUSSDl0O9bZaRK/O4718e++2wsou6Zk4RbQT/OVNvMUN5EoN/9qSjcYrg7dW35btyodHXnBzN0wuIL0wbR3+JMVfWN/tDA9xjg0cS/7zaxHZSWzE/j7Re0Qgx8JZVlZe2PX4E0+8n2vXf5fF1qv4IiDVR3excQxnrw6IxvcO9d3XgXeg2gDZSu7ZOYZMQ7kvH3hflOnlFRshUMOKmrUhurmGt9awzlPfqqdoJ6ixYk+mrEs6WbZyeZlNBcg1DChDmhPWgV3ayhpk1HdFB8Pb6/jrzUNF5ESiyL84T3RluVd41yts871eq+yopqm8ElO5Pvie3Y9ZANGdjQO9ZzJZJjkzhhaC4Pb6rpxjb6ttUymAdKJMsLCJrNuIfTpZRm61Ca8sW/WFdoJQGtmOSSdKuwrH1FTFthXPBVZfecTmeXt9jp2q5HdF+Ps1rvvpzvnOpkUCBpzjZKZqNYP7Maa38NlVJZGJIryxSvTyAl4z7jmmOrvBlO1k8EboiEj6CgPFVntbJWgfTJwijUELQefStP0eDPnOCSGojJxlbeVlgr46sqA6xuwT7+FTf/JvURsbeF6ESju7ClFPCicygtyPgV7+8U6UL+gUBOHW0UT8tO+hqgW8ze38HzU7hpodvlR40hCNNv6dNbvcJAQm8FCTVfw7a5goAE/t0u9V5bztprNPbrBJ1fCoQD4kLedtkUei8Ortnn7w3SLFckd+lZACL/BJh4xDRD4gMJ0EPwp2bSNDj6RcQDTb23e9nkAVcphSzubHedIafSGQcUpwcxlzwBQaAcfWxvtxQvoSBOi7dXs6gtSlvdRMfBHie7ud53Jplje/8S/yyU//B948+tWkutMz9I7jwWD1X+WOyJfxpI0uzw9ZXiZrtOHJ3tK2TBQikOjkkJ3L7oFD73+zha8NtBKbnrDz3M6H+PObpGMF4kAQpNApSaIsmpxOlKzklxR4gUR1FN5mC2GsLjHFCBJD9XpiC62zZh+ineLv4dToXLDLoRKB1UPeD9mMEc0YU4wQzRiR89FSDkQIIyAGVAhS2RSMbkzBW6rZboI79puOFjChbyOvxo5PjRaQ7QR/qYZX2w7cGLY/g+7/0Y1V0Ia0mkcXQ8JrK9vNQPpeR247RWTnc6qUs8V97aQnuab6bEhwe33XTdhOabad+0yqeYLVrDmMn+X6dluC+xJ/rY4ZK6L7xmT6CiK7ih3e4uAcyHoH2U5Q40XCtcb+nSGHMew8HIK/1iBY2qLz9MzBapRCj6ScH7CZxpeIPbTsAz+P50UELYOnbVATYOIrvhb1qx1evPZLRLkR0rS9Z1vzh82JjiAfBC8+2uVwNTtGOlmxzT5OaCthA73OQQOE/vZdrNKIVCO7jmrW+UaNFEmnqhgBIvRRp8ZtI4+DRnSyZh3J3BjxuSn0WBldLaInKiTnp0jPT6FGS6jR0lC92j2PSRuSPZz0NBeRZstmaSfZlY+llT1euSM/WVcL6GIOkQsg9JGbTfwby3hXFrYvFlKgqoXdd/qOQ6OURg0ztMfg1ezlIBejMRrx8ChguTzH3OzbWOAmbV0nbR9PHrRjm+Tc+C59dlEI0aHfkzTb9ZpTIwNNNgBM1zmWwnbsGy3YqOBDJlzcJFisIaSkc3Z8W8u4m8vbSRDGILc6+A2D0SATg/EgzQvUeAnje7RGBGsz3Wom+x3q11iWCfgde61TVasa0nlyapfd0pE/VAVhmHOsQ29XLm/3OhDeWsP0KYZ0c5/NjVXSnKA+51ubm2S50vU23mqd1sXJXZ0WVSln9Ymx0Wpvs0X+lUWiV5cIWsl2jdx0hfYb5noR6+2B2i10KYe/2tjlHEMWrd3nGqYmbFMMnQuGnjdD5fqGOJkiy+nWpWjg8xHaILTGCIhnq3Qmy1ZxZYem/rBeCqqSG3CcBdgGHFLSmR7+HdmXAwYgZCe1mtBCoEcLw7siegJdCEjHirSemhlICQEg8GzwrX+/2cdQKE3RbC6DgWhp214rCRNf+XXUNm9g0DS27uB1DPnFtJfnflJ4pB1kv6nxOkc/o3q8QvLEDHD8EyRyBzfynapHc8qncTZk/YxvpXf6sHlbfRcjY5CtGCMFstlBV4uomVHr1Jbz6FTb9tnlPMmlOVR5tyZkV85IVQukExWS2THUzCimMlzYXOdC1PQIanqE+MIMyamxA8t57WXeZLPday4xdEulbc51/0XAGILbawSX5/FfvoP30m38hXVkczBHLp0asccTnPjFlBOPMMMvNDulk47TBjbSNaJg70LQkeoFWmqL8cJ5br76e1az23FsaCnpXJoimSqjiiHpSB6V2YN0omxlxiLfdrPLGpCoagE9UULnfPQQZ8YEHulkmfbFKdLswi6Ce7fUXj4gyVrfHv4As/+Vtl3oug93O6tqm4oRzm8SLG8RXV4mXG6hPGhOe70LjDGQFiAuWqcZXyJjZeti9sAA6YhNDUgmSrYVuy8Jssi88aSd82q+1x0tnq32uuiBwHhi4LhlJ7XNRGKFjnwQkEai10EvHSuSFAS5NY1sxARXVqzcXNamWSSKeEdnuOjOBtHlZbzVhk2daHQgVhhP0njdjG1vHvmk46Wh+b2y+9kqPRDISaYqvXbRZiRvI+57OIc69NGVPLqcIzlvpQkNtjHKYdqMJ+Ucaqxoa27630obMFb7WI2XEKm2qTN3Oaf8hU2r4tEnRQfbcnA6FxyJvTS+7OVug3XK0/EiyUwFYbLmPmfHdnUuTibKdC5O9dqmg1UK2fe9slXcRn2RMLTvGW1q/P74VxhSGD9NoTjF6tKLtJqr5DY01asJfuvkeMmPtFdwrB3NhMAUouMtrhRQm5AII8mvKLzOPhdrCe0x29lGGPASg/b7mqus1tDlQi/FweQCa3UThRor4202B3LdTCmPqLUQy5vomVF7vMUc9HUpSqdH0CNZ62WlwTPIlRpyaR1VzPeKIbyNBkJpdDHCjJZsK0pAewLKBRIguH34zjjd5TaRDKZ4iL6LhhopItoJclhraGN2LdfZC3EOk4+gnEd7Em95E6+2/5fecQ90Cx53XLQE9oIlj6Hpy8ubH+HC9Dv2fF7rBE8GPDX7Xj525ceJP7rBuQtfRjg1e+Rjea0hhYBaC1PODdhNLQR6angkTI2XaI3vvqFRUYB6chrRSshd7pPk04ZgacsWqY0WSE5ZOb/EGORGC9mOrbzcIfSSE20wniQ+P4HcbNk2y+3DnZsCm1fcy+n0JcQgCyEJdrm+q9Agbm8gdBUv3g5IFNY1qiBpzPmI2ylybsQ6ugaQNgWt2xTDQC+NTsSq5zTqfNgr5DJYJwiRpWXMVAlvreFvNDNFiwQZpyTjJdLpCsHtdbzNlm3BneGZTJZ10sNvZu9dsvsMt5Rte50oRJKiqgXCO5v4y1vEZ8YGUiVQNhCj8iFeN7XOl8SzIwCkU1bjXnRScjdWd312cTHC32ghk3Q77SHnk06VkY1OJgsn8Gotm9d7F5UJAySnR0lnqlaX3Ze7pOWEJ21tzY7XqdECIknxdqo7dLcJPDum5S3is2P7pnBJbfDX7fv6q4MpiV69Y/ezWj+SBThViGzudr2N14hJx0voYmRvhppxryAUT9K+MEEwv4HxPPSOLonSGPylLUwlv+exmVxAvXaH2sYNnn72W7ePaUv1ivUATJKQL4xz4amv5tMf/XecvfhlzJ5+B+VbCVvnAtJjUio7DI+0g5yUPcyGQhxxmsUDw0BhOaUxF1A/5VO8k+C39zgWDUFDE24qvI4Z+NLIzSbecg2Z6WrqXEB6fhoAb2mjpyHsLW3ibTRQZyfRlQKp0gPyP/25ZiYX2K6ASqOLOfzFDWA7iuvF9V15xl47htWtgX2o2TF0uYCqtA/thB7k6yHiFA4aBfQk6YUZawgyZJI+fGWNxxFfkk5WrUrIEO7FOTbC3tjJreHqB/V0jZbeYvzi3g5yPj9Obes20Uied4//d1xpfYqPv/SfeHv+e/DL1T1f57g7YmWLcH7TRqYy5+e+yQd2BSpzVrrKALoYDixFGCFQowUUBdS0wbu13tMZvhuykxIubNKJfHQ1T6eSI3p5cc8OanfDYJtDeCtb1oHPnKbo9jrEivjcOCYXWPsmoD7j4XUMYV2TFj3qp32gsN00JC8wEsp/cgc1VrSOXeac+I0OIlbIlUEbJrBNIrp4G01UNU+wUge2Q3mmEFht5zNjqImY3I01m47hS6u6UJEIZSisKGStRTqep1OV5FZTgoVNK6cW+HjrVpvYBB7hzbWhttvrqzuJZ6u9dssmSw3szk//PCanR3uFk956s5f2oLJrlQg8exoIW/RmDiHBZnxJ7qUV4jOjNi0k3T7H4tnqwM2CCDw6cyPoQoS/WNvz2hSs1HvFfmZYykIfcqXe04wetvLmL9QG5mz3m3mQ5XUPINh+LAtSyKzDqKoW6LYIl624l/scXlshzqTxTC4Y0DjuRwtB/OTUdgGuMXbedqTqLNz5ONNzb8Hztz+PoKnp9DnI+eIEW8tXeWLsXeSfneDqK79Gu7XOxafeT7SUkp4+eKfV4+KRTrEAaFcefh7a/eB1DJVbCcGWxtvLOc7wW3qXcww21zZ93Sn0SBFdiGwkOEmR63XU1AjpnL2jVxMVdC5EZk6LHi1h+tIqTCHqqQPITCIouLZoJYruoVOSaCf41xbxljeHLp3pnblmd9vfEDkZ2ewczNnyJOlYedA5xuDdXn0k9K4fNdLJkT1VDO4VYYB9lFgu1z/O2el3IYc0EOhSLs9RDEe5yav4YxNcevJrGRu/xOev/Dxr65dRysm/3QvWibOfd1BrD6zy3A8GUJGPCX3Sap74/IRtbzxRJu2LbolWTHBnA5GkaClIzo4Rv36WzrlxdOCRTJaszu8+RDfXkI2OLTq7j6JcAUTXVvDrHfLXVpDtBF2M6DwxBb7sRZIB8ARpXtKe9GnO+MjYUFhUJMW+nFRjrxNog+q2Rs6+WgfVh5aJIlipb6crdPfd55DqfEic7V9lKWdhTZNbzwIQxtCc8DASSFJrU6fKtmHK7Q3AOuXeXdQQVCVnHbW++QqurfQaVPQ/Ht7abk3fnxPczb3WgWfzjz1pVzABEjVUWWEoxkAUDJ4bvkRuWTtjsHndzYuT6GKIv7CJv7JPl0ilEZ0UEflD7V+/6JYqRXSenEJNlHdtB0AgB9tk9+NLOk9O0X56dpdzqvuCXmklZ28eK7aTYrC42Vt18fu6XYpWQnRjbVfu/zCMEHg96RIBgbf9N5DEDRZvf5xTZ98z8LqdXZCnn34vt+c/Qpw0GR17gufOfAuLdz7BnZsfob18667jeBA88g6yyj/yhwCJwW/ru4ZMw61uoQqo/uUHpbNE+xLp2UnUpI2E6VG7dKkrBdLT4+ixsnVU+2R5dqK6r8FGZ0WiCG4sD61ylQcxzga81a1dndSAA8nKAOgosF36Dhl11IXINiUYLRFfnOlpM/fe/876gcfgOBz+4gbBncOn1dyVfW6q1tUSI08+d9ddnH/yq7gz/3GQEn9hg2fEWyFJ+dznf4oXX/r/HeFgHw3kVrvX+vVe8WLT68hlEkVwY3gU8V7QxYj2xQkrC7bHBdzkQ1QpwvjbF2vlSXQ5R/zUNOl0lfjipM3V3fONjHWMpBhonX0veN1irE5KdGUZb6OJiVNMqq2c3O0sOpkaRq4l+EkWSdQ2X7M3eRLqsz7aF6ixIrnLy0SvLm7LZWaOnQq9AwUcdirM+JvN3lv5S1tZhJleoZ0w9FY1VbVAUpIUb8fkLq8gUo2/UBtIb1OVnC3mGt1RoyIgPjVC+4kp4rPjvYel1lbLeEjh3X6oYmQjo+tN1MSQmoODyr8KgQm9njMrAg+hdE92LpkbofPkFF6tRf6VJYKV+oEkB9NhRd+Jwn95sdfVzhRCTC7An98Yug+v1t4z+KMKodXI9iTJjhzq/sYm/mYLVc2TTpTxVxsYKXtFijslEVUxGrjp3AthDP5L89uKWFojb1lbLzopW7XbFMuzRLnt75AJBcnIjoLQp+aozj7NvLqKt9ki1/F55rk/z/XLv83zH/r3tK69ctexHDePdIoFWEdR5cRdo68nnc6IR1zxKN22DptMUsR6Az1SwIQ7q3p3qHcMK4LLIgCy1oRKAV3KIxptRLODmrF3kLQ6kN9RLV7MkU5WbeenXGjzl9d23zHrQkR8ahzRaBOsbB4q56/LQS+gspPcUy541yk3gIgTm07SNYSdZJfUnOP+MYAXBeh7aSl+APb6zLRRdNItisEY3OVUzOdGieMtdLXAteUPc6v2GaJ8FWM0i0ufYW727YyOPnEMoz9heAKJzfG/X51VqWzqQ3xq1OazbrUxt9ZITluJTNGKIQruSfvcq7XQI/mByNgwdCWPt9bAX61jTo/2ooz9kbx0pko6U7U5w60YWe/grzd6kdSugkJXa/dedZVV/3mqDeGt9V4hFEL0HFshBaaTkr9p2LoQIbJzV/vC2i0N+RWFDq3THqw1bHe0rCmT8T1UJYcO/G3JMWyuLNrgb7X27Diqc4HND15vokYL6JE8ZnkLUwhIh3RBFEDpTkqwqUhOj9najx0thq1KRZOg3hm02QbrUK83EIupzZ9W2n6fD2PcMwmz3LVl4pkRe5w7CTwMdw/eSG0QxmT1NbaILZ0o2YYk9Q468vFX6wTLNYzcTvNR5ZwtDN/n3BiqshN4Vju4uw0QXF+1Ws6HpH9FVk2WMaGHrLV7jr0OPauiEXqks1Wbz7xkU0NyV1fonBkjreQG0nDSYTcaQzBCoEs5e+4CSEmaFfHJeptWY4VCaWrgNY1xOfRjjkpjJJ0Gccnn0y/8V8Syj+dHxJ0a1377x3n6L/0AQj68IOhjEH6F1vRw/cPjJM0J4vLwD30/pC+Hdo0pLqaU+qRQjNJ4a1t4hyhuU6FA77jl8ebXYbOBt7hOcNNWHJOlVZj88GiMHi/jL20iV2roPVpD62rBSr5NFKm/cY7OpZmhEjZHxf18ugJbFextNvE2GngbjYfiHA+V5tu5zSPekEQAKnnwopb1dI1cUMVTd59j38+Tpm1E5HFz6zOcu/DlnDv3Zbz+6W8BYGn5s8c93BOBUQalDGqsSJy1jz38TszAKozO+b3vqtfX/EG2EqKXFg6tGiLJcoQP2MDDRL7d/trqvjbDYJfp08ky7b6IZ7i0ZRUEUoXfdx6rSm6XysVhVS8EEC7WrNO1sIncbGG0QQQeQSIYfTmmfNu+Z5oTNGY9jGejuM0Zn/aETzJZxniyJ2mmRwsQp3jrDeKZqlUFyY4tOTNm0zq6cpsic+660eHE6uL3VvdaCe03zNK5MLmnZFrQMBD66FKEGiugi6Et5MreQ7YSwtsbQ2XlgsUa/pqVafO22rbg7iAX0L6xqEwi0C5xskshZyfdldahVqF73kpJ0m1A0ozt5+9LRKLQxShTcpgk6YuumrtImMpmbBWi9kBgI7334hwD6D7VJUMW3T8zRufCRC8VCSAtRD3d8W4DFdGM8TaapHMjtqslNnqs75Iz3U8yNzKohyyEvdnopGzFS0SF7Rss4wnSPVJh/aBAEjfY3LgGQjB76T1c/JLvZOTJ50gaNZL6xoHHdBw8Fg6y8qEx5T3Qdt6dUZ/WVICKDue6qdR26tuJjA0k20dgciHJ606hzk3ZPNta865OYmvSpzEXEpclnRGPpCBJT49jygWMZ09QXQjRUuxqDLLLzhiNqRYQ9eGFLibKnGFlsjwkn+bT070+7Q+Lk+xgDtUI3rnNY5APfZB2rUeJ8T2u61eYmXruQNt7XkA+P8b65k2UThgfe4rxsUtMTrwBgWR+/uPbG0tBUpTEZYk+AVXVR4ng/iWqvVprICdT5ENUMYuOGnpOghm13eG8O9upHAd678zREq2kJ30l2WcloRgRv36W+InJQ10PeqkbcUp4bYXo6gomS0fQ+YAk08Y3vtx2jO9BRUl0Urx6x+bq9q1w9e/JACYQJBWPuChJirYhh98ypNOVAR1fA+B7SGNsNDfwiM+M9ZwdHQV0umoRlTzxuXE6l6atBnIxJB0rosq5bZWNw9QNSGm15oUgHj24VNpBUZUc7UtTqL7UEeN7NiUiH6AO8J5GgLfWIHhlcddzcrOFKoYDNwP+Yg0T+MTTVYS2863GrTJTNzXC22rvqWIBfepL+2xjx3bv3769lDp0MSI5M2Zz9it5ZHad18IWYnaR9Q5GCOILk8SnR0nPjQ/dXxeBLfTs/j4MIwTtyYiVm59mavbNvcebE3sHEovjp9lcv4ZWMWFYIv+Wt+I9cYa5L/3TAHQ2VvYd13HzWDjIAGnRoz1h+8QnBcnmeesoHhtZEcphHeTDXJSUlxW+tDq2bfPl+Z4w+67hADoQ6EDQmgpoj/u0pwNap4skeYmeqKDGSqiJql0a66ZNpMqevPU2BtOLQKczY7YDXaNDOlndHaFPslQQBWHd5kanBUlnqkB8YYrk9DjppDXm6dSDUwdQ2hzOyD9ADqTKMaQTlWMIfV9toxMWai9w6vy7D/zyU6feyatXf50gKJAk2woA737X9/MFz303YAvP6udCmjMBramArTOumcxORNb5rafbCqTnJ3rpDd681QbWQiBDL1vFsT/+zbuvjsnsomxzQbL0qPUm4eXlPTudKW+f4qbufo3BX9wk/9IC4fVVooXtwjlhGOheZqQgyLSBVSVvZcx8OdRJ9w7RHIlUE8xvDGgdaw9E37ntxQavY3+6ucBaCvwlO16pNbLeAWM7o0U31mxKWl+gQJcim06RRY61J0kuTROfHSedqVq1jNAjHb+7w7kzdUB0be0Bte4Pg86HyLZtH64jP7tx8BCJ6jWOAvbtAut3DP5q3Rap7dx/MSLdGdDxpJ2vQoDxZG91xF+s4W9sO7zDbLkIPJumkckW7pe2ZODgedJDuFtKlL+8RXJqxObVZwGL/pUe2YptJ1FPWh3qu6zAi9iqlpAogivLe253q/15RiaeIB+NANCpSuKRvYNW0aWL4EnWVl8hjhvITLrWz5e48A1/lcLM2X3Hddw88jnI/cRVzzqsmXakOsaIj8yivUnVJ6zFdmk5FEfa2U92c+DHK+jxitXrvbqImRvDlAa/8AIoLCS0Z0JUdj5qCZ0xn3gMindimBqxT8QpJpdFeRpt1HgREXhoX5CWPKIN1ZPL0XNjNueo2bGR7ECiY5tnlpb78r80lD61gC7l0aMlkhE7F3q80mvb/SAQSqNGS4+0dNvJdO9PGH3B+CRpI6RHOjtCuHyw9I5Tc+9kfv7jNJvLA8oVudwIudwISUHSmj0akf7HGV3JwVINf6VO3LUZQhBfnCB6ZRF/o4mIU5LzE5hUIww92a7uku9+mMCj/cTkQP6yyQfgCRvRu4syRRdpDGKtYdsppxo/TnoRYi9R+37OXiNGBx5GYHWEs3xVVS0gt9pEizVrUz1Ju5wj6BzM9vh9KShmvITxJGnRI2zY1ryGbQe5Pfb/Z+9PY2RZ0/w+7PcuEZF7Zu2nzn7X3ma6Z1/MGXMsUqQg0hSH9lCWTciwKYg2bMIwbdiwYcNfrA80IIGGKRgGbcAyd1GwCVEckpI4nBnOUJph9/Swp6eXu5699qrcMyPiXfzhjYzKrMqqU3WWe283+w/MdHedqozIyIw3nvd5/ouif09TOXboekK0P8CtNWCS4asRTqtyXB/tD4h6E7LVerBQK+zX1CglJ+wzzoZ2O61giR/1WczzjTEWRhm0q2XDaOnfFOP36yLa65efS/rGOtH+IEQbnynS9eGwdGY4C3UyClSJzfPis2Xfv1lSnI8103c2qby/T36jXfoVXwRfiZi8tQFCIL3HWIftnC/K5/EykzZ1PMJekB4oJ2Ea4JUkv79e/o4+mLNgjfWlHs3lazmHQ+BijenUiPb7lz6g0rRHtRks4mxVML5xeYlpE8mtP/JLvPd3/q/opI6Zq6Wb97/w3PN73fi+KZBNFUwFKidhJxsNLdWj6wvHph2FTt1z01xmCX42FmRtRdKzpB1F5cggX9whaCnEJAsq7Y023nlkd4SrxiVtYgY99VR3cka3o3Mju/HNGGFB9wyVE3BxA7XfDcVksd5I44n6xckXN89sDOQjhYs1thKhfIpbwl/WucHvdbGDCVqE8aqrVxCj6ymUXxbye7g4/kxjFvzxaZ6CFPgzD5bMTYl17fKgnbOvIyRf+tK/xcOHv0q9vrXwby6WTH9QHF8JrhKVwjMAG8Pwpqa+YxFvbaJ2e6E4Ox6WdoolR7k/Jf7ooBiV+6Ujc9sJm3A5nCKnJkTfdmpMv3DzWucZPTwKndYC19aOzDi13hMVhUZG8F5OmxX0oyDs0oULhE8ibEWjhyneLpajPlak99ZQJ+NgvdafEk1ybKdGrOqB1z1xJD3HaEuhp4FekTclec1jNprhOJMckRpsLQEpFvmsqSHeOeNMIkIDQYxS/JKO6rWhVVlkXmYD6gs+71XhVRDiCV9Y+saFuFqEoln4YpNUYPYdOQs5mBLt9oMA8YpTxYXJg1YhAbJTxTcT9JMTpLHknTq+XUX2xkT7A3AeP8eHdkLgbi8v2F8VBISQk2XOJdU4bOi0LG0A5XBaeh5D4VCV5udios/CybAhEdMc265S+fAg+EZfAJuOqcTtUHNsXq28rGzf4/Yf/u/h8k+2TrgKPnMF8oy/c12oTJC2PXkNonHgHr0I4oFleCem8TQvu8RLf2/kcMcGW5FIE36vdmguVAw/D/Pe3ueQG5j5Pm538Esc4eTxANepo1JIDnKy9Qg390sOQIFb1VR6DmGC8btrVvGRRh0Pca0avnaBjdJGG/n+M0RuyN7ehjPKUgGlMEPNWbo9zxPT1SuISfpKeas/6MC+HrhYI4w9tfcpICK1VJTzIvBalQptr+S5Y50tjgGsz3HeEg+ud/PVqmt8/nO/eO7n07Z80dv4exZinIU0zGsIdWZwc8WWyiDpOqZrCp15XNHZix8dLf1bNc5KN4C0Gi+1KpPdMcmcSM92x4HGccWiRzq/UBy/FOY2iXKc4hOFr8Zk99bQh8NQPAtB9lYQulnvcc4TPzpGjdIQbbxWBw+2VUUfDUPxnVuEsWEzcTKmrlcRDiYbES7y6LFHAllHEfUdiZYkj67p4e6LQIjiM3E6UCTEJc+5C2EscmpOudvNykKgyzzkNdeG+YRcW4sDP1bJkJDoPPlaY6EoXsbHlbklfngUaBQvEVpji6660+qckNWth5CU5PFxoC2O0uem+C28tpIQqxdygAJOmxWFC0d5XkD27hb4IrLaOuI5z2PbSDB315Y6yohpjupPkIMU82a4x/T+IGgxbnZwiUb2pws+1vNwWYZXjrwpMZWrP4lXv/hTePuKO4uvAJ9egSxZWky+aHEjrKe+O/eDigIWRzE2Esj8fNDGwmlZqByZ5y8aHionlgVPqWs+VWVmEL0RIs0R4yk+iQNF4P4mbq4A9a1FKoMAyA3C+XIH6Dv1smhN+o5kmJE2JdmaXiiUPUWRISX529uIInjBbbSD2hXwWiCKol+M0xAgoiRaAM6jdrvYm2dsgAaTa7dlRCVCjKdX8pV8FRBa4ZcElvwAV4O8gOv3qopjWLQv8rG+Ej1nYI5Yrdy59vfImJSvfu0/BOCdt/8Yq6vvIoRATxz593gA0UXIa4JoPHehpMDFIIlfWce80nXQPV0MRWaQc92rfLOFbVcRsYLcEj86QkxNCCAo+Mk+UqHzV0ugWcELys9XjTLkRwdkNzuIJMJewp8UuTlHJ7gOzk0s5iYo+mQcbLIkpG9vhVS5RBPt9IIQepoHV41xhhynmHa1FEqJ1FD5aH/hWDKzxI+PSd/cQNrA34wHDpsI4qHDKxjflJi6wDYS5HHgxPpKhE30AmUDgIoOzZQ5SoKc5AgTdA5eCga3NEnXoscenV78Dcirgmhuqiq0ws2FmTghyDabZZKimBrUmcmhF8E+Txh3Ltp5KSSYe2sL4U4Arn2FTVyxbpg7K691EiSKDaXwEO33SS9IoLsQVxBuXwR1Mg5c/+K5PK8T8kIQPT1Z4E2Xh1xvXMw5TjRykKImGbY7hnaI1nb1JNjipQZxCd883X3K3S/8JHnj6vecBNLf+Rbf/o3/J9t3f5p77/whWO2QrWvMpxym94mI9EwS/m8e3r14l/dKWPLa6jnF8QzxwCFfgkB/4SlNMsRggnp2TPzxHvrhXgjRGE4RDlyzitnq4KW4VGcmpxnRh7vI7uki4896BTpP0rMk++eLmnRlTgXdqKDwyL0usjsKXp214G84XVWlLyiETiFK4jrnR6G+USl5zVeCEqQ31z7Rbu8PiuPvLVzVim9ojqjKq3l4Anz7u/9fTk4+Ym//Gyid8Nabf5j3v/Of8ntf/X+Rj7vEQ0dtN/++m0R4YLSt8Wrxp/3b+oUsK89CEDifere3ICIS05z0rc3SGkvkBp8EDqSLNdmbmyHkI1aok1Hp8qAL4ZxTEs6sLWKSk3x4QPytZ1S/9YzqB/vhuKMUlVtUd0z80QHJBwcw82u9KmJFdnslnNOsOL5MxORAFo4FrlUlux8cAeQoJfn2DsnDI4QHWwQ6iCy8fwqLLRcpxMx6zXmip130sy5Oh/RU4UKxrKYOmYKLBbZTw6zUQvhGEaKS3l9boB7k9QrTtzeD9ddcmIQohN4q8zSe5qQdRdq5vBSIzlAOPZwTQ8+EjbZewazW8VqiasX5KEl2JyQgXiaqs7W4bNSYTn2hOM7Wm+QbzbBpeh4Kr+nneWdfBZe+wpxtoRhlVxNiF98plZmXSnFVJ6Oi4zsl/vhwQewJLE2vDX+4+I6UgOhZN9gOClF+1+P9AdUP9lCjLPC4pcA1kgvDS5wzTMfHJNU27gr1k8sz3v+b/wEitzz+xj/gjXf+NZRM+Oqv/yUOfu/XaXyckRzbT3Udfq0dZF/o5bIOuEQQ9TzJoOAXwfWJYJdAEqzSXCxCI3fycsXQslHuy72gJ3pycM4ayCURIssLXlV86k08W5dPhmUiXomicHCNCq6uyGKIRg6ZhesqrINphq9XiIcOnWeYWJCtRThFSQkpcdBH9cf44QTXqZMUo+qsqagcn17HvFVHHfYR4zQs6NEp0V8QCP2zV7atKjI1Fy6Gs+vrC56ZUBKpJTb95D10f4DvXXjv2J1+yI+9+2ev/Df7+9/g8PDbrKy8hbM5K/Fdfm71T/Ph8Kv89lf/Mu/c+yNs3P0Jmo8ck47GtD5ZC8nXhljgtWB4N6K6Z9BjDw46H+cvlbYujUPtdNHDtByx+8Mh+Z0VbKtK/LQbYnVvrVD56AB9EoR75m7oDnoRHuaVbz9bsMCU40JpLwQ2VujljpOhyJjmRNMcDs/rD6K9/uJDVooQzztLNGOxn2Jjje3UsJ3aaRfueRdorjiZTfVEZstiyIsgkhI7vZBsttHEJBF6apBnRIJqkiEnGZW9BmhN1oKsJdFKIK3ANCW2FVIDo8NhiMdWwfrM1BKiIrI57k9wm01cPQkxzFoS7Q+Qg7Tke6sMarsGOzcOVzWNHV9/HdYFVWbGObbtKtY4NDkIcLWI+NERanTxxldlFldcS3fGEcSuXsFlo+AqukZCfgXf+avA+kuckeYaU3a1fqV1ohSbXuKTfBUIX0RxGxvuu/40CCZnxzljeeqB/N4qrrrYlvXOo45HyFFK2q5imxV8JFHdyWnK4mCKdR5zbw1VpG6elaQcH36XevMGSdLCTT3mOR+XGQ+YHDzhm3/t36OSdHDO8Lkv/DFu3vlpvv2Nv8Xx4Xu8/fn/Ns3+JuMtja1ec6P7CvBaC2QXweS2KF0Vso7AxZ7qxS4h14YAkkND0rfgA8F/vKGozhV2F7A5Lscr+iSktYiDfhApzBXHXoowQs5t+U1TBz3M3dMEGmUdaq+LbdUCX6mAqyWw0SJfrTLaDBdXWE9kPcKCenKImGa41SZ+rQmpJE498WxhOrPYu1qwARL1MC7SUmKsO38NWlXEYR992IfDwhYpUpjVZiiwMxNiWtdbuNUmLjPoj3aXW+J4kINTb+d8vYWvxkSPDl5qV/0DfP9jnpvcNweoqEK1s33lv//5P/B/5IOP/gHVZIVIV/n2o7/Hjyd/iHeaP816cpfvPPlnfPT4V7l146fYuP0jtGqd4EsbfW/2lHNjMYnAVMP5m1gwuBPRfpAjUw82rJEvctep1BB/fHBuVCyA+FkPM8rK6F45Od0sq1GG/mAfU40C1Wx6UVfKgwpiONWfvBAd6+w4P19vYNYaVN/fA+fJ7q0Rf3x4WgxMc2YlXL7ZIr/RpvLhfkjbWyYWkRI3V7wp62AwJToz3jYbTcQ0x601kMaiL9JnxAqRWeKdPvmdVVTmyRsCXzyts0agPFT656kKC+Ls3KIfH5NvtlCjtPTxjSYZZu53o4lfEKUf3hJUjyTJ8XXJKeE14qcnKOfx1iNUUdQYR/X9/aUc5QUYi1urg3FluuG1zsA4pAj2qGeDq+Rwilfy+oFWl4xz7Ww6UY3IizS5K7/s5HJ9zlUgpzm2Fgef5jNUt7N8/ny7jW0uEWfO7t2ixhDW4SoxWk5Pn8XGoZ6ehLj3gtOt9gfYdrV0Njna/zZrm8F5Ih4GHcJliNtrvPtn/rfs/MbfY/NH/yBP/uHfolW/yer2F/mRn/qf8OTBP+Xrv/V/p71yn+1bP0l77S10JcHL18w+mMNr5yDbs9foFdc+lb2ceFjY9UiBs4767pkCcO6/yyzHW3dhityrhMSjPtg9tbiZW1ztRhtfiYgeH0BRi8pxhhhO8Y1CGawkZqNdjpnKTkcS4ZIQUhL3LUnflYJCkZvyRlFHA+iPyd+6gUdcWHj6ojD2gHp6hG3X8MuUwcsiH3OLHE2DkBDIt1fxs6S+WGPub4bI00cH5xZbdTIKdjPVGBqVYND+7i3EYEL0dLmo5wf4/oHQEv8iHDwpSur/h8OvcWv7J6/351Lx7tt/HACbTvit3/m/cdTuszZusBJv8zOrf4qTfIcnh9/mwbN/ioorKBWT55cb/39WYRQM7oeHpXSFfWTqzhWbL/LMiR4fX8yjtIsF3NnRrM8t6hL+uige+tHjY7L767hG5YWTxxaOqwNVbPr2ZqBfSBkEc/3QovbGBf5wJSJ+ckz2xgb5ZisUbd6HQI35t1nReO9RvTH6aLTUn1l4wLpS9BU96+JTc+6ae4IgTGUWlVlywmQ0Hjgqh5b+mzFqQnAeEpTRy+XfNhevkRqmqOFiR8rntnx/5flR+Ojr8JqTDc1kPXSXk/7V7lFTT0JHPDXlY945UPUYN8qeXxzPzi9SuHrlNOHvOlAXC2z1cbB8S9/avOA3XgyuWSmTGK8D/ZwgkatCOBcSEs/QgPxKDfb6wWWjEpXfvYW/BfTTk2BTW3Sc1WASHGNmrwOgJXqULmysvFblMbOsy+H+t/jJd/5oeI0r2t1WVrd4408E3/m7P/0n+fhr/5DV7S8ipeLum7/Arbs/y97O13n84Nf4/X/x12m2bjIZH6P19cXEL4JPTKSnR56kD7JYO9I2JL3L/+b5r2nL4lh2R8i9k9AJmSt+hbF4rXC313BSBi77YApCBpEIAj+eIhrVV65c9xTKkvIbFf6reWMLl0SBC/jGDZjm+EYFMU4h1kg8UgiMB9aaeDzyeIjuT3Bb7dJi7dzCZcN7zd+8QfJgL3R0c4vc72OvENYhBsUDolHFaojGDqwtOUtOK2ynjprnPscac3sdmRv8NC+L4/LfKzFqv1fuDdxaMxTuhM8mv7m6KEIEfLOKr0QLxuY/wPcfbCVGDi8veJa52sw2gCPT48Ts8YU7/6MXPgeVVNnc/GGO+h+ypr8SXl8IVuObrMY38d7TlceYGx2SpMM/+6//4gsf69OCBKK+JZr4sGZcYaGT1iH6E3znYossVRRaSyHAVuMgtmtWkGlOdDJeKKaf61hkHclH+/jo1T6m1HCKW62T1zWTDYUeO/Ar2EEVIlWMwINgcFYozFwToiXhJmqcod5fFNzNiwqRgnylDnPR1HKckd9ZQXUnCwWtKF4PgEkWBI55RHLsURNL3LVkHYVVUD0aLjQ97FqdfKtF9Ts7oekiCm1KpJDGYpPoVDgXhdAWF2t8NUYAg9saF4vwyAqXgLwuSBb3AxfC3GiHDdFcbWQ7VbJOnWS0JBFNSvL1erBKm792SuKaScnRLn+9P1lwTLkuvJIv1aC7yOUyv7f2Qi/rX5ECzNUSzFwMdvlzKTErNfTRCHcB3UQMpqjC4WXmYDPjgCMFQgvIHBhHvtVaeJ+22BRkdcHHT36LGzd+jDgJ55E2rv/mGu98kelv/C2ybEAch9dROuHmnZ/h5p2fwdqc/ne/TrvWwgvLb4z/6rWPcV281gJZAFHPEw1BmmIx1IJJx2NqEE8EothpCKD+KEM6z7SjyDrPSUICKt2iU7rXXQiGELOdp5J458II7/1n+DsbISY0aSNPhqiHXYSUpU2KbVbxm+0FB4mXgQfMRgc5nIQRiHH4ROOTKMRA1yS1Q0jXYkxVEg8U0dghjgfYSOObVUxdkjzsBloDIJ8eY97cWn6ORSEru8NQHM9+fDwIopj25aQgX4mwlVBI66lHTw0otUhRUXLxwVa4abhIQ/EgmzU1yob5/Koy63bEOqirlVw6tTztul+wKv0A3/N4XnEMFxdQtlXjo6P/ihs3fwKlXk7qXPFVJuNn0Dpz7Ehim3XaaQVbXVkMSfgeQuQljZ3rjav1k2PUIMUfDMje2jznJACXi16zmyvlAxQgSwRms4XeH6AGU2wzwUea+Fk3PMQv6KYJN8dFvQZnc5lf9gyqP8U/PsbfWUWPHKPbCmEcFSoh1IIwZo6enmA2W6jDIXa1hupNzztFLEG+0cSs1kk+Pgjpe42kDKAQ4wyqUWnz5oW8sCvuE43qTdBR0anTitqxI+so0u0IPelQtS7Yx1Vj7I12EKdV4jCN9CEmO7u7hnQOEMjv7IR7SohTq67ZRsdrajsWlXkG98I9tdAJvGAt9ipEYXtRcLvnqBFynOP18vdn6zFmswVClLx0T9DiUAg4Z4gfHWEusBa7KuQkD53WF4TaG+Dq8Tkrtxd9Opk7q8jvLKcgXhUesDdaF/673Wii+6EIXmpDV2wITWGdFz3rokYZPtFkd1dxSUT84QEqN7hikyjGGX4uoCc3Ew4/+uf8yE/8uSDuX5NM16+/VgoviJMm7uFTxK03F44BoFTE+vaXiJ92F8SorxOv1cVCZhAPIV0TjO4Kxnclg1tg6gKEYLThma5QZtGnHYWwUD2yxL2LF18J1J5mqKlH4FH908XVA3atSX53g/ztbex60TmNwk458Ng8rl3H14J9kKtXwDpUd4R67xnx+09fWdjE5I0203c3sTfXMHc3sFsr4BxpRzFdleA91Qc9Gh8MiHcGCOvIN5sMPtciXVVEI4e70SJ/5yZmpRHSi44vP7dZh80Lge3UyW+vPbc4BkKBO9etmfkYioNeOC6BGmI/fwvz1g3yW2uYt7bP+Sk6D/rpEepJ6BrMVMeCIDq0rRr5vc3SXF3ud9GPT8eAMs1LAY1t1Zb6NX4W4TkvLPl+xSfFAZvBVWLc3HRikO5z0H+f+3d/4aVfu9bcop+fF0aYagW73gIPYvj8wuizCvkC989MyCsyi3raPaeQBxas285CnPE01WkQOpmtFtN3Nsk3m0Q73ZD2td3BF93CZd8r1Z+ijobo/GrCMa8l+ZmRt69opm9vYtpVXKQwWy3U0ZDK3hSb+GDrVxTHAOQ2TMfWG9j1RnAKeHqCj57/yIwOBlS+uxuK42pMdufUEtPXYtThMAR2CAHNZDl1jeAKEe31UYNTK0yRe6KBw3rP4I2E/lc2mXzxFtkbG2WnP7txOi1UwxQ5mOK0wml5WiDNO8RECleNqO8Eup4ruPbSQ2Vw+rnnS5q3TsFkLZy/3h+c8/SVmSFaIpwEyg2PbVfJN8L4P7/VWeqFDeDPFLdnE/Uugsgt8ccHgf/9AlSIGfTxsDxnvd8P7xeQV/TjPgunFfIlhYQi0Zc29JxWTN7awEUqeIlbF5xmDgaBVlOJmH7xJmYrFNmz6UW23cElERiHr0ZM39osQ0fine7CMZ59/R+xfuOHqdenaHZjAAEAAElEQVQ2GG2rFyqOIWyGa41NBuk+8ZPjcs1ZsPos1rJX4U5yFbzWAtlLGN8S2EpY+Owc6U0iqB5ApXvaJcpbitGmBimoHhqqe/m5ExRAspujilx6cXT6pUUJ7O21wO+tJXghSmshMoN6dkz05Ij4o13iR/uI0RRhXODQUpzjjRW8lKi9LvrxIbIoDPXTI/SjA+RwcqUdn4sFJhEkPbtgkWMSQfftCnlTnAojnEf2x0Q7J0Qf7yInhsqJCd6XQO9WzOBWXHKTz4YnnDv2ZgtXS0IX1rpzsdTPRVIseLNrnOYLQgWHCB3jRgW1cxJcLTj9HMUkQw4miOE0UC+a1bK4EVrit1dC51iF6yyPgxJbTINNjtw5HWMKa79nRHuCU6uj73d8Ehz+ebhWNXDVCzwd/D5bd38SHb18Ilhj5S5D1z0/xcgM+vFBiFgfh+/nvyzI59Twuj8heX9vwQdbHY+Iji7eqPsLHmBiGhx7hJDkW22yd7ZACqZvb5F+6WZ4WC9xK4h3ephIX2mzLIxDH42C7/CNFtmtFbzzVD7YR/cmIewmM9iVOiKKiAYER4/54z09CQXqbg9yWybkifxqXezZWapJRvzRIepoWD7w3VqwQBPTPPgHbzUXLdEIYQ75RhNf0STPukTPuujdHniPnjraH+fosWeypcnOeM6eFWslT09KXrS5s4IXonw/5d9oVfogq6kD56numHLCCyz6Z8/+bu5HV7VlnEEV1yN4XtfJ1xvn0hRFasA6su3Ouc/+oomOoCiqirCQ5P09RG7J7qwuJuVdA2ISIsppJIG3uz9AFSJM9fTkhZ9RlyUQXgmpCZTRJXCymOLv9kMdk1uEcUTPukR7feQSj2Rb1EszGqU+CTkNM0cM6f2C2DZVOfv73+DenZ8Pf998icLVQ6N1i4EMpgbqaTdoEOYSIUt91SvQIlwFr9fmTV88fpDGo87cT05BuqqQ1ocu8tDhlSl3JMpCspctFJxRb3QqCIgj3JliUBedYEEgn0MQKZCf99cTPtARZv8mRlP88RA22+B9+aD0xQuWi4OWuChQJ+yNlfAz6xnfjomGlmjo0KknAnx3SlwV+FhSezQkenCIa9ewG61wfsaRfLSH6dRgpYmPFNXDHGGCdc30i7eQFtR+N3Cr6xUoRCfldUdg7m4gBhOEc2XYx1XgJaR1QSU9NYc3Wx3ypkalbjFGWwjEJAXXgNEU/ew48NnSHLsW/C7lfg93aw23vYLXCt+pl50ObxzSONASs9JEJBEOcM0aclpYyQynRdyoxgtxYVjFD/DJwi8ZuV8XdqWB1wp1EDjqRIp8o40cTMJGdP7h2x+XmzTnHbu9b/FjX/rzr8QjczTapV7fwm51QmEXa+LHB8hZQawkbquzMF35fsT8CNauN8jnBWmZJXl0TN6qokbpUiHaPHy8+KAsp/NKhHV0lIVx9VyB4IRAxJLsZgcEqKPRwucrjLtWIaJGKSbRoCC/vYI6GhGNQ+GUPDjCx4p8pU7n96uo3CFiXVJJTKdGPOmhj0ao41H5XfRaYlbP82YvPY9JhppkuOMR+f11XKRIP799Kk2pJ+RKYjJDNA7rm0gNNBLSW6sh2WyUoscZrhoTJVXUKCceSPKmZLKliSYm0BwICX8z0Vg0mOKNI37WJX17E5so0jfXzwk01V4Ps1V0notuhyqmCLKwEXOFfV3WlESD4HAhXEhOBFBX7PDPMCt2BECaY+Y63zOUDa4zdZd0jvjjQ1ykyLfaYSpcdJ7FcEr84Kh0A0nvrb1QQuTCuVZjsi9ul91aoQR+1uGc5gjxAhZkzpF16sg0hLeoUfpChbbqTZa+v5n8yVUi/CQ8P+OHR7hGBdtISsrEPGaFsJ5k5AS3LTXKkNMcV4lK7/IZjg6/TW39NpVqmJK4l1mQvWc02GV1/V3y2gpeibCxLNYaMc3RRcw79Ws6kbwgXm+BfMm/Gb1cqOcVJf+4UlAt4klQXPszQR/qoBe4trHGdOpwRiAmhxPETJQwp/ZdBteoIHJ7zrdX90fBlP3mKvJkhOyPQ5E2/1LGIU2GmTv+LJFvshUBBp1acudQ05zagUJECvX4OBTuvTFuo425t4F+chQs3/oTxEnYxUUU5uub7VJYaNv1EMsrQodXPzvG3N/Ez5mA+2Y1LJjd0ZULZKQgaypwYGqSaJJjqxph/blF1QPmzga+GiONxdUrgT92c/V0ISk2JU5K2Oos/L3QMoh2vA8d5pmn8rz5evH/8ttryFGK3Ote7X38AK8VcvBylAOz2catBiGGyHJUb4w3Nvhs2/MOC/PBE0/th9Sb29RV56XOYQaFwtkU26mHsTeLAkLbroeJyfc5omfd4Jm70cRGCrMRNujRbi8Up6kJUcrPgY/VOSut6MMD7God26kRPzgKTgcC8pthUzJ7wBvnkEC+3UFkthT46e7kwoCCZRCRwlajspCdvr0ZUuweHaPz8N0VmSXe6xPt9bErNWR/Qn5/PdAibjTKiF7XqITRc/G9PLs58IkuvGiX0FCiQG3wkzz4wg+mMOeXK50nenYSpoLidLMgc4vc7aNF4L0K6wJftBKhJo74o4MQMECD8ZZieFORnFjyhqQxqYGWeCkRqUHa4NwkBlMmb9bR1YR4uHiu+mQc6GzViOlqoJtYBVFuib+7i6snZG+EqGVTFURzX4MZT9m5642kvXHo3R5mq1Xed1eFPBwiJjlqEoIyIHTds9srp/69Bd3jZYtjKDQ4c1SG9I2NUqAa1quCg30N6IMhrhKRrwd6SfW9PZh9x6XEViOUsae0AiHCJqDYPFLRoanXm4TUyTP84tkaaguaEARalGtdfD1mvt2kIWzHtqqYSY5XEu09Rgiyt4MLiIgku+/9U26++XPhb16BDaaUCmumoRgeTBDGlQJZr4rvtLWM177PXCzmIYzHa0HWEUjjiQpTBGkpO5R5Q1HrOrz1MCfkK19jNEUdDbDNCu5WyBXPavLU1SI3RPu9eQMJhATbqIVFZr+HkKLkNrqVBjhH/N6z8PuVKEQ5Z4b40QFmpYFr1vD1BPn4sBz3LIyzzgR6RGPH1HqqJ0XBJ2UZ+uEBu9VB7ByHL3Ka4+oVbL2CnKS4RhV5Mjwd1Y2mqI8LKkg9IbuxgqtIVB7G3WajjWD5LnZZ8t2FkOC1IF3TSGCyHlK2arv5gon/DLMHodMKzsRPazx+NMUbey7sZOFWsp5o5xhzbzMU9OfEW6LMlP8BPhuY//ye60ZwBq4Wl8UxgGvVUL1xGL1foQg6mD7gxs2vXOOIFyDNIYlYGdYx2ZTRaI9G40ZxUh7brCKcL8VMaXr1ruH3ImyrQnQwhOMR2Re2sUpi21XkcIoeXP3+yzdbp8WCh+jpcXB6GGeYwbQoBsGu1JGTHH3cDSlwzQpaCCYrguTE4mKFa1cxKmxQov0+QorSCcNVouADW41Rc+N926yQ3V5BpCYUtsYhEh26abUYP83C9KugQ4myiy3CexRFWt1mi7Se4GpxoDicjAOHc7hoP2ZaVeQkK38+fz7pWh2z3kSdjImehlAHWVDWXKyR+33keLb5O6+7Ed6jhileCtI7qyCh8tEBMtFUHp2gJgaZtxjeihjdihBA3o6JTlKEdIjc4iMZNjr7feKNCoM7EdnQ0dh3ZVGf3VlF+EDpk8Xz1qvQ9heRDAmFJ2PsSo3q0en0dV4Mmd9ohU3Hfu/KwQPR4TCIw5fZil6CpVZ6uQ2CsllYjZbYs6K0l8C8RtFVIph1rI0LTR15NXpB9PQEc7MTglt2e2TtavHcnuN7bzSWOlPMYDeaJQ9XHQ3DullPSsOBi3BZcQws0CfiZ10m72yR314JG8DDIWw0y9pnKEYMek/ZWv9hAKbNlyuQvRJs3vgKD9/7L3i7fR8I93J+u5jKRwq7Wkfu9Xny7Ldf6lhXxadSICfHMC2sCNP1oIA9K3SNRkVxXOCsgFakOWajTX6jFSKRE4nVoKcZKnOop0cLTg7C+RBgsd4qX9BHuuT1kpnwAbSqqP4Eu70KUqA/3AXj0Ad9/GCKWW2QrzVRw+mpW8YMgwkiicKoTggEgtpOfrH/cKuGrcYwShHVOBQa1gb+zYwaUonxabbQsRajlOTD3fA/lMS2a7iN9oV2TNeBCM+H8DABsnawQSppLddwlfB7vZLLZJzDrZ2qbYX32GqCTjN8ZgPHqzfCt+vljrm0SvIevXfKET1rNfcDvH74WOMjXfL1F/6tlpy/Fy57rbOxwXN8Va/VxRGpUvAs+5BBesA7a5+78vGWIc8nPPrWLxNvbGPyIcZnSHm6HHqtsJvtMJ3Z7+KTmL30917qmJ91mK02UX8KqUHsDxAbzSBG6l5tWmAbSfAVblcRqSHa6YIQC3zBmQtEdrODXa2HtUaK0Hg4HJJtNciaCj3xJOMMfzxk+mYHOYlDXHWsiJ51gdB91vt9zEaLyneeIZUk3WhhV2oIIag8OfVoFidjZEXjI8X0jQ3QkvjRcXDUqESokzE+UshBijoeI+6sIDJbduXMSh3bqKC7I9Rg8bu+0FWPFNn9NVRvEkbfxebKrtSQg2noaO8Pgij93a3Lr2c9DvddrHH1GDxUPjzEC8/4/ibVD/aIDgbYRkKTkJDoBIxvR7QMYQq40SR+1g3XwXkqD/uYSofJlmZQkTQfhrjj+e5j9diRN4NYz8Wa6f0N9F6f+OkJqRKl1ZqNwsR39uye/Ty37vSaSMjXmiHo5Xi0VLAnXkC3YVbruEJ0L1MTrOHqSaApFs9bs1oHD3q3F9YVa5GTHJ9o7FYbd13x6jSHWC/ocQDMeuPKojGV2+DYkRrye2vInR7yeBSSDjkViV5mcaq647ChmLlvzfyNjUUfDU+pMpdATLJzUx7VHZeCuHyzSbQ/INrvl5uX+YLdx4L3f+OvcuvuzyKExMeC9AXFeYNH7zH+4NtEnTWmT99HyqhMd9PTDDOchg1lPcE1EuwgZvfJ11/oWNfFJ14gz3hLeujxOkRQZ01Q04InPHalSAzngkBi5wRhDObGSik4c6tNvIbBvQhbEegJyMwhUofcO1lqwq66I9xaEy/EQkdTOFcS/u3NNUS2F8JEhMJVY1y7juiNsHc3MBWJSj1+tRm4k5MM16gE3uTJEDnJQiG3vYpvVIgPhvg4OucPPIOLNMxb2rnAI6NICzO1BHtrNXDQdk/Ov4B1qONhoGkkUen3vAwSj7+gy1zCeJJDs6BE1WPHZE2R9Oz5mOolEGmO2j3BbK8icoMcTpHD6UKB7ISAW2tY54k/3Anjy+EU2nW8D1Qa16gGsd/sdYv//F612/pehsjMhd3d6xv6n7kzlYSCbnNRcexjTX5/i8ff+Ie8/e4fI0kutja6Cnr9hzzsfY2t5EeoVDp8+Yf+DLXaevnvdnsleIgPJuBBngzopw9e6pifZQjCWN8JAbFGjdJLRXhnYTpV8tunE6ToYHCu0zqDB8QkReQVbFXhC+6pjzQq97QeFaFD99cRgynS+vJhLqFMLLOJgIJ/6uOIdKOBrSXIQRBfzzsqxEVRDeBrEdN765jVeihuqnGwZJvkyOI9i48McpJjtlq41TqmGeOVwLWr6GddoiIA5dz0JLdUPjogvbVyLhrZVzT05673YBroBdahTsahyy0ETPOw/tUrmM0mIjMk7+0hlADrsWv1IB60nnyjCZFC9zKaH3uG2xpbE4xXJbVDh1utkzYrBdd/gDockBw3yTsKkwgmHUH12J+zxZN5aCrJ7hg1mJLfWUE88uiDIaZZCQ0gB+e4UIBbrUFRIKd3Av9XDqdE/eUbreh4hK/F17Jgc60qrp6UnFRhz6cKXsgTH4ZuePrG+nNT9eQ0R3bHmBvtMkJ84d9H6aWd3rPwUpxGTReR5/HBAPb7Cx7hKs1ZViLL4ZT4yQn5JMfeWPQlluMMfTgM9oHPeV/z3uLSBcrOfJrlzKFDnYxR3THZ7cXp8Ik+YTze5ys/GUI+RuvqhfnHx7/zT8kO92is36O9cp/NL3yZbCiId3r43AW9AEFHYDs1xncapB99MtO8T7zSiE48OgVdrp2nH7Fy0NjLT8czUiKnk1IsE+2cYG7LMijDJBLhBWoCUglcLBnfTojaG8RDF/5ulBJ1h7jcYu6uI4ZTVHeIa9VCtxKCa8VucGOwG21EkQ7npUBOsqDOFeBGU5L9Ka4SQ6uG3WiH3ar3YWTsPdFHe4jcoHaPkSokhXlCQIarV57LBbZrLaInh+U4Ux0P0IMx+RmKwvk/dEFAuNeF7dWlvyIfH4LzmHunSUKTdV0qT3XXUDmxxF1D2lHoqSPuWnTqsZFAGLCxIGspqkfmQpK5T6JwjNyEEJFphnp8uJASWJ5Tf1y+V5nm+P645JuqMzxXEakQn7q/SFz3kcauNlB73Vci2voBrg+7UkeMs6UiSh9rXBKVmx01GC8E1zityLdXiR6fhgm4WrJQeIvMIL7zgH7/MfWf+bfhJUXMqyvv0GjcZK39FtsrPwS1GMYpWWTZ3f06WlfodN6geiPcS2IwwXzwAslen1HIYYo+HIRxdG4XHs6uEpHeXwvd3+6YeKdHvtUiOhqVdo+LLwb2TNfKx+pC6o2PFNJ49Hd3sZ0adrOJizWuXYHMBk7leiNwDjs1bCJOHRSGKSJW+FiTPAxCZV+LSd9YR6QmuE8M00vHzIxzot0e+a0VRG5JPtxfMG7PN5uY9SbJB/tBpLjXJ6oFP+HxjZgo6SCtRfamS9+fSA2Vh0fkq/UwilcijP+PRwtLZrTbCxu/Wyvkt1ZOL6exxA+O0Pt9XDXCNSuIRJfWl/ooFIKuGmE2Q3GmD4eogyFNs0Lvrai8Xp5T4ZXZbAYa4bMT9MY6DknlJDQ/vIDa4elnG41cCOLa6eFqwZc432pR+WAf8fEh+f214oM///6dkrhYIbPgQKSOhqGgv6i3Yh3xw6PgvXtrJRzvCpDj7MXXe+fD8+Q5vybGGfpkvFRACJz3FX7eYZUkfXMDIQRylIVUw2XHnZrTTvESREdDZG7I7q6dvnarSnZvjWh/QHZvbenflShoRdJYokfH56LeBYVdoAxOYGffZ//pd+lsvYsQEiSY5ouLtm/+8B/mO7/8l/npN/4XRM+6mLiBXxMcDZ5y3P2Qll5nTW6hj4bogwHZrQilY6x9/evxJ14gyyXbIj06dXoQziO6Y3ynFpLo5nlv1qEeHkBBlYgmHuXBCnAFHcMUARwuNlSOY6jEZEkUHBbiiOjxYeANaxXENwpsIhGrTaTz+EYF887N03G+cYHu4CF6EuKPFSPYOSl/RwAkUfDwzA2uCMHwxiEihbm/tdRofxnUyfmd0awoNNuBVyePBxcuDKo3Dlznrc7i7vIkWKnZM4W2qQpccWp2VYcOsQ88ZGE5tf4poqxl5nGRIG3KhSS/mWfyzBoOQlHjIx02FOstfCUqnTHk8aDgoRYLeSEqUc/OJ1XNX4dlELn5QXH8KcNHGhG7wOs9A5EZ1Hz3ObdI7xcoQWcpM8soGwNzQC1eoTZ9eZN4azPStEc72SoLo4MPfotv9X+N9q3P443nw6//50ipqVXXSeImafb9wUGWgynJo6MLCxY5zam+vxe6oGsN8qJ4nLy1Qfyse85iKV9vnlvfzGYLt95AHo3Qe/3Fe1MH//eZADd6eET6zhbTjiQ58ej9PmhZFgeVE4fXlE4+M7hWlehpF3km+jnfaOBjTfy0W/7MA3a1jltrYLUsC2i920NYR/rGOvogcDnNehN9MFgQBcpxTuWDfbDr5I2I3o9uUn2WUnlvb/m6M08zmDuHeQgPlWfd4DE77+ahFenNDrobeMuulpyKt7QMoi4XurlRYYU1S8izKzVkpokmvoyTFpMsOHqsN0Oh5zw2kUTD8LyNhpbJ5mIpkPQcejcIZtVgSvzxqeWpHGfE390jfWcr2NXlFjlMA7WF8HnKonufLEkevAgiNSQfHWCbFcyNVumacSFeIkDKtqvPpUXEj46C1/cr8uEXQPTxAWp0NTu86GBwvkAuNjtCS8zcpqqEVuds/i6C7E2InxyfGwLYehKSL/fCpibfbgd+8xzGzx7Qad0Pvx+9gIPH7HRTz+TRY9qtu1CIXV2a8fvf/o8Z9J+yduOLHPa+zr/oP6VW36BCnUq+SfYJ6UE+8QJZzX12auyCT/DYIdIM0R2j+uOgCj3qYyN9arVECAuQ0ww1mAQusfPIgcW2zn/R5z0TfaNCfm8jdKGKL4+cZFhCR3S6ptBjjS9EZgsf9pyjwlkICAutDYVBSQWYW1jztVb58BDT7Bz/UqY5ojcOo5dacuq6sQy1JLhXOIe+hIOrTobISYrdDkk4eF/a3bkzaUTNp3kohh1YBSoHnCeeZjCjUwhB2gobD5k6RO5x8UK+HrYiEdbD1COlwDmPr592i2ddDK9DNaKOBvg4wnUa5Fqhnx0vfNbXQjXGVePQTaom5fX3olAYT3Nkmv0guvo1Ijoe4i5JVpuH0OocX962aoHOIETgC56xjBJasTN5QLt996XP1TnLN7/9N7mx+WUeHn+Vve98g7W1z3EyeJ/P/+E/R3LrTjim92T7e6T9I7LhCay2GH17/zmv/tmHPhpebjEEYD2iKHLmR8jZvTXkNEfv9dGTDGcc0f4AOUrJ3tgAAlVDjFJ8u4rZaBJ1x6H7mShI7YIQSJ2MC9qdI69rXCSQfrtcS+U0x0UKObVB2zG3rrt6Qv7OJvrRMV5L9GBauOKEiUYmBMluD2cc2Z2Vcu0T44zoYIBLNGarRR4p5CRD96d456i+vxe66mdhHMkH+/h3NhndjuBGjD6ohQjtF4TPLZUP9jCVqOTRutU6vhoh9g3SOOSMmiAl2Zsb2IJiFvjJKogRi+eGKDYzYphS+fiQ9J0tXDXGxBp1NAx81eIa5k2Jf2aJ+xnpSpPhLU1yYsvu83yhpc48l1ysITPIcfg85SQLxfnR8MoF4EVQg+BikL27dakzhDAXP5svP4DEFjSdiyCNRfan5Nvt01TXl4ToT691bZYVui6JyLda6MPh8qZbZq4sppWTbBlDJghz93Py7Q5qOA08/bXTxtpETenuv8+dO8G9wr1485j0owc8+O4/4t0v/iK/9Rv/PgCVwSoOxxf/B/97bF1zw4DvTxj1d7D9HpPuPrfe+EWe/vr/78UPfEV8ogWy8qJUyEJQCuupQ3qP+ngfpMA1a4hpGNXKM1+QWTqTSHP04wPc7XXiocVWguo5yjxGC7K2otK1KO/x3uOkRCQR6uF+SM7r1JHdMfrxAf7OOgjB6GZE3A/eywJgNMUnUShoe8sXQFdLMLfXUAd91MmQ/NZaCBc47IcOiZb4OQcJn0RIPC6zEGvkcIJ6elR+Sc1WB7faQB0PQYpg6zbJUb1ipGYdeVsib60EzrOSp52FMxDTHP3xHl6KYKFTJgB1sbUKIsvxmx2cVqUZvJp/qTmusYlhuq7RY0fSt0zWNHlVknRDV0cAydCV9bKb47JpJTHWIccZznniYtdu7qyXmwXfqGLvbxE9OVwQVi5ASZSW2DlBh0si0AqzvRK8oAkPiGCFU/C8it+1gLQOBhPUYbCt8pEui+hZR8lrFbxwpQj86UkKLhT4PtJlKMoPEOBqCXKa4c3yUeEy+CUPnJJ6433hF7Voy5jlU3aG3+Gnv/i/fOlzzrIhvf5jfujdP81v/tb/hS/9ib/A7nu/yb0v/emyOAbwQhBt3SDaulH+bOfbv/rSx/80Ib1HPY8zXlz75GjAtKJxZ2hR1GMmb68hTeiCSucXHsrRoyPkJMftD0LHd3bPpktcGmb/JQ2Nksm6QhhF5dgR7fSIjoalWNdLyN7cXEhac0KQ3VsLojfpiZ90gwezCFQRZx1UI8x6jWk7JJNGVocO7+EwxA+v1oh3++CKtT+3oTM6c8tIwkRQ+HAe+nCEuldhuiZJtlu4VhV9MirtxoSS+Gs47ngz54zRm8Bh0R3LLKZdxVeiIKRbqyPnup5ld7GWkEERtpRTf2agEmHW6lD4UUsZOJ0zy65q1CavS3yssbEiGrrggmE9emKDfdglwR/CWJKPD8LnIgpOdW8S+LSXvVc52wDbsvCcFyP6WAXf3iViuHlI40gOBi/cuXyeQE+MwjrvC+H+q8B1G0AXRaWbjSbq5LRBJjITNo8UNIs5frE6HOLWG8uv0wVTbX04DGE7x0OyO2tzLi8Beye/T3PlDs3WrfAyL7F/6D75Fps3foTJ5Jj2yj3Wt7/C7v7XufVHfhFbC+/DaGC1SmX1TQBmFdUnUSC/1iS9c0gXFw2VBn6uFQJze538rW3cSn0pGR4Wd1RylCJ2jtFjR+NxTjy0qIMR0dhR38kRxiOeHKE+3gs34nASBHRJhN1awdeSIHx7fETtWYatSPJGcTmGk0CrcG6BzD4fjmDbNdyd9UBn2GzjKnGZGDeLS/Xel50wFQdvSZfZ0w6zVvhaJXQ/I4VfaZRBJ7ZZxXUaIWCjmgR6QjtmdEOTrirMOzfJL+AaQ1i0bLMagkS8C2O6SAVO1WEf1Z8Eu6MlcAry+qkh+kydGvcs0kDtxAYOsg0qZgBtPRQPXjHfvd85RkyDwf1CmMmZTrqLNfkldnS2U2dyd3MhTS2k7LmFEZh6fED8/g7xg71ARZlmKGODn6QQuM7p98trGbxFtSpdGnwS4atx+L84WuxgCHE9P7N/GSC4duy0WLIwy9yUse9eq4WHg/eeb/T/MVubP0SSXF0QcxGSpIkUktQMqdc3efCrf5Pp8Q4Oh3dX64J/z0IIXHI5x9PFIbHO54EbGj86CjZnB4Ngq+Y9tiJxxTLtpChjaG0E03trpLdXMGt1/HNKmNm/6sMByX6OTj3TDY2LBapwTHGNCqZTQ7hgabX0bUmw1RBbbVfqIUUs0Zi1RpjujXNMLTg9ZB1Nfn8dT+iM6v0Bpp5g2lVQRXd1oxkCSwhWU36uKPexwmmB1wJX17hmhezuWsmddd6X1+P8iYJ9Hsc2s5DZ8Gy8vYLZaAaxoFaYucJbzd13dqWO7dSJDgYkjwJtJNvunE5qZh3m3OIrGpVDpeuwdcHwToQee/TIkRzZck13cbDGoxKVnNUZZG6RUoT0u3YNfEGneF5X11P43M/bMglEZpDjDNmforoT1MkY2QsBKSozpXUbhGd/8uDgQsrdc2FdKUK77DzL07uG//ZFkKP0ym4w5XFh4X0vvuDp56EPBmWmAhCmD5kh/uiAeLd3ocOQa1eXVoAyM7iKRkzDZzLfqR6PDnj2tV/m7r0/eHqeU3+aCnxNVFdvMBw8o9m8xeH+t3n80T8Bqcgnnw2Xqk+WYqFnBmJFneFheDuitm8QVJDDCfrZyVJag5cijI+kIHtjK3RId06Q332KSDT5rfWlUZRyNEUcD/EzDs1swSj+Q46m6FFOcqJCBr3z+FoFUxSqai8IwnykyO9tondPgm9ye64zLELENQQahbm1hjrsB2/MRwe4+5t0tyKaTy0iN0FhaizeOsydmXK+4PhO8yCsWGuWP83vhdGlrSi8CscTgDpc5N7NW7C51WYQERIaux4gN8FOLbehMFwSQe2B8Y0IpwTC5KQbESYJFyvd0Mi9EPMdzyYBxRplRylqNMVWYvLoVIQph1N8Jca1QrdDHvbDQ3ptSaHTqiG6Q3x2fuFTgwmuXsHVK2WkqTAOYbIwzi1cQpS1eOdg6tDTM2I+KRArjeCpzJlo1Bl39oL7UuT2ytyuf5kwu5auEqGMC51krbBaLoR7zMMsUViHe7SCK6gW84E9H42+RiqnfOmdf+Olz9c5y6/9+v+JO3d+jt/53b/CO2//MSJdYzja5aNf+f+QtNb5oV/63730cT6rcED+5jr0pyQ73dOCphZD4S07/7kJT9kZBWCvT75SQ200y0QtMc7w1QgbCQZ3IxARwiREI0fcdyBEEPgR3C70MC2P64u0M92bIAdTbP0Wg3sCG0P+xgaM0rKDqrvjkvax7I1FI1+u79IWTjmbLcThkOp39xF2g94X64zXJPHAIaQgX2sE2692FZwn+nAfby3x0ZDpvbXT+GMtEWnwgndJRDTx+EigU18OO/I7q8SPjjHVCHOzQ/TxIbq4PzyAFKWva+XbO/hEL9BNzsKu1c9bd85vLo/HyFjhajH6cBg4z86HrvvJiPEPbyOcKDw7PS7R5PfXFjb9auJJug7poPlksRDMt1qo6gSz1Q5izSeLLkou1kU3c3xlL3Th/cJ0ErjQ7eQs0jurqOE0+FG/JPTBANepXshz9nO+2dFu/9SL90WOtdsjOhwGwWUjufL7BcJ9cqahIDjVddhYhxS+wRS7acJ3SglIw3/30zOhZnNwscY2Kov3d4H8Rgd9MAhe2LNTySd863f/Ovff+ldpr9xfOJ/mk5zhliJfQne9CMMnH/DRb/51Vjbe5f1v/V2+/BN/lnRywu6z3+G7f/0vcutf+SXWfuhnr/x6rwOfaIFsdaBVyCz0FbKWQmUemXnUQQ95PLjww5wlvAgpIArdPpsb9H4viOGmWVmElcfb6mA7ddAKF0tkLSkLWdcMC6ItOoqVE8tkXS90I6NHB+Xuy6swcjO311kGX4y+fHEOzjpgGERxzoXxh4SoO8K1fPjSz8ZTHtKmImspKnGdqBZDfP7GrfQslYEFB+Kwf86T1lUiRGrCDn2Ow5lXBDorjtmsXuqXnLUVthJuyNHtxULGasH4Zkx8ZFBpEFXOPi9fS7CFQ8dM2De7LguWMoNJiOaeL5CtK+3yTK2CypZUqZkhenQQXrOwwJtBnQwxzWDWv6y4nkG4wH3+AV4eIlJhL2YsrohYN1EYRVONQvfx453lgQFqcRGdOVtg/bmQmEfj3+fJ5Dv8+Jf/HeQVjfgvh6dSXWFj/QtsbvwQ3/jmX+OnfvR/xje++VdpNLbpvPPjeOcw6Yio+vLd6s8Czi6pTghoV0kFxLs9vJRk223iR8fnaG3LEJ2M4WSMWakFJwglid/fI7+5QlPC4KbG1MAminxFI/xK2BQ3Elyjgt/vlxZccu5+lbFCp57KscNUJG4YnAaS9/bw1WLqc0FnNmsIZL649gCn60SsUP0p8UkSaBzOh2ZIoomenIQ1yHlsbnGdWvBx3uuXLgEmCc4WsjdB5pbqoaVSuD5E7++RFRHS07c2Tq97PS47t+gQoS0zgxxMwXuy26skHx+E4rUWFyLqenBFqifPDc+wRZy0HKWo49ECJcnHGp2f8kNdsxKig4UIG5pEE+30yG91iAaLN6mMVZh05sFRxK01se0q9ngUglcAkkBTSb6zg3w1FN3L32urgmtXn08PWoJlxXtoMA1xy4RuEKa6lQg1mC42Uq4Jvd8vvZ9dJQp2e9cpkNX5Z7WY5qWZAZstXDXCa4krivppFWJbwTUrmJV6ee+cex2W85yBEC3eSIoOncVlU37vm3+VzuqbbN/56fO/76CxY0lHnnRTY6+wVLs8pbJ+izf+O3+OZ3//b3O0/22UTjg5+oDWyn2aq3cx0zFSx0j96Vi7fuJHNYknLr5vlSND5dgSPdi7sNt0Ft6DtBanQuGV315fsA6TvRGuXcdGApV78k4MUhCNHCQRYpQixyl2q7PQBYZAIZhBOH9aHFci7P2tcKOl+QIFxFQFTgviYpGZ3YyuVcO1amR1iYsFrceFt+XN1bCDlzBZ1eRNhcgc03VJNILx7YTqniIeXTBamfuxB8wbW8GyJjeoZ2HMEsaBAoxluhGTrmqU8ciJo7q/6A+tnh0Hxwslyepywf9YZq4Q480dXhCiSKVGTR2NZzlOi5CA6H24oec6BHa9teDJ6O5tIA4WO99ScMpzPDNS8lLg6pXQvcltCBQ4U0TJcUr84Q42icLC/QLG8z/A1eEaFczNNeTxAH3YD3qBB/uhw397DYdAHXTBhQeNa1RBiuAb7nzQGODpDp4xHh/Q7z/mC82fQ3VHQahUC4b/x9kzPhj+Nj/xlX+XuL18Y3pdSKn52Z/+XwHBycKYMd3hI1ZX3+HG5lf41tf+Dk++9p8B8ON/9t9/Jcf8tGHTfOa7vwDXqpI2KsQf7FP58ODarzsLPAg+xDpQMZ4IkgdVpm90GNyPcQJGtzQqO7XJmtGkvJZkN1dQ/Qm6O4apCRzpicNUC4qXC1oOMcmxjWRpJ8/GMN7SeAnth2ZB5+JlUNhnWy3MWo2476mcGCjoZT5Sp/QB58je3iRvx8GNpyiavQxFZfLxIXgfuuVFyEk4SYHsTXDNSkhJu9XBJVFw8lhrED84RBSdYrPWIPlgPzhYfLRfJtmpYRrS054T8uBUWH+F81SPCo50PVyX+PEx+UYz2Cqe8RSOHp+gRmnwRK7FqMMhujvGbbexZ4qw3qageihIjmxwb3pwiG1Xyd4M8coiVjghCZHHQ6KT0TmbsFcNWzwfXkRond9ZQR2OznGqo2GK4WK9qtloED8+eT4lZgmk8+hHh6jh6TG9koEffw34wnFrATOjgdn0bqO54Ki1ECN+CZdb7fVx1RiL5+j4O/SzfVaT26xX7iK8x6w3gmjae977zt8nqbR46/N//NJY8KTvSIY5006gZl7mjdx640u03vgSBlC3b2CeHtLvPeLuH/rvc/TP/wnf+U/+A8Cz/XN/go0f+4VLr9PrwmstkKUB6Vm4SHquOZj0T4U9LolCep1z6GdHFypUvZKoD3aRjQrmVkFrGKelv7ArrNsGtyPqTzOSR118LQnj22YV/egArxX5/RXSFU3t0CKzIm2p2IHL4wEoGcYL1mM22qeR1UVx7GR4b3hwkSiz2IU/HfOlLclkXaNTT3JiGW1pKifhyz1ZD4ptL8FXJPEQajsZ0dSfe5LNv7/yOjQquNyU5yP7QQ1uNtohDEUG+5V0RZOcGLKOxscCU5c4BXE/CFJM4dyRNSXp5uJO82xxrHJPbTcHBOObEbYiGW5H+IpEzPHLG89Oxzpnz9tJCVudhfemnhxCsWGxG23kJMVX4qCytW7RD/mi4je32LsbWK3Qjw7Crl8r8tsF3eVcfPUPUEIIbCN4dIvcIiYp0vuw4VlyvW27jhinRN1FX1c5miIe7uPX2+hxEZjjC1HsyfD0/hqn9D/8Nl99/NfKv/3cnR9FUbiO9Id8PP4GH42+zg998d+k0r48cexFIWVEHLf47nt/l7fe+KOMJ4fEcYssCxs4m01Q8asR6HyakMahHx4teKPq3V5wTGhWkHOUNq8l+VarDGF43jhbjTPMWp18u1OugQAqBZwnXffIVFB/CsmDLmqSkd5dK5O69MmI7N5aiLneCbZS0oCenBZ/6ZsbxO/vLWpQ+hNcK+g9JmuK+o4ha0nIHepwhF2rI3KLixQ+0aAV0oDa76MmeRCwAXhI39oMwrxYgZLoqSd6cBginhPN9J0tZKzC2q5Ct30erh6Xo201zlDv75fddack6e3VYF9ViRCTnOz+OpX3d8viuPxMDoa4Shy4ofMooox9rJEWagc2CI4FZSiUqyekn7ux1PVB7w+Qk/A5yaNwbXQ/0CJcagK9poDwnmhYRIGvNU7dC7wn/vAAmeZMv7BN9OQ4pJoVv6OmOfJwGII1XoNbkBpMsat1lL2onL0AcRDZ2XYNMcmoPD4uA2R8bpGXeA3bdo1UCLhGgIne7xfXYEmwkhQXTkAuxDIOckEXnfGTL5sI+znuuPAevdMrg3aEddhGwvt7/5gnx/8cgH5+yOrKW0TPurhqxMh0+ei9f0CWDvjyT/zZ4Hv8PDhP5diTDAPtwtSe/zeVzZs8/Op/iZSaW+1bPB0dkVTapNMuo6cffn8WyMJB/RmkdY+tC2wEk1sCmULl2DO6FZMcGvz9TYRx4QE7Si+1bxGZCf7k8+4Q1RjZH6MOwrgwv9HBNGImm5poWC+9A30tCR3XWOO1wFYlg5uC6qEJwSIWxGCCOuhh7m1itkIG+bxd2QyyOEU99aipRT47xq63IImQR6EgE506uhujMo9IImp7BmksPrc0TybBtaMakzcC/zkae2R3iFcqCP5iQV4RxCI5Z8fiKzGuEOnph/unvFxrwVpE7hE6AgFZU6GmjtpejiieMb37MS4GUysSkQiOFNILnPJM1wUy99T2KIvd5HDWnfHUdnIGNzWuoGPo1BP3LNM1RVaXxMMrWs0MJwjn0bsn2HGKa9fJ39oOm4zMEH28u/Dez9p/zUM/O8beWDn1LzU2BJH8gDt8IUSsye+sh0THs7AO/fjwnPo6ehr8wJc9qsQ0D24kxf+W04z4o91yofdSgFY82v9t6rVNVrY+z/7T3+FXnvw/qMomia6T2wlV3eLHvvw/pt65/Qrf7ZlzFYIf+5F/h9HogG9+62+UxvON+jbD0Q69936X1U+ZA/cqoJQMXrYPjzCdGq5dxW21ygeraVfL8AlhXPDWrURLPa3PIVLkm60g4JtkwRKuEuGEoPOx4ShWpBuevCqQNzvM7l6z2QocyWJtnW7XsK0qgrCWiKlHHo/wkcI1K/gkIr9z2j2exRoLH0a7ANE4hHdEuz3cag0fB/qETA3xTo/sRouoPwnio8LLOXlwGk6DkmRbLVwjCTxpwFaiMPnTivTuKj7RiGYC2ZzF5Sy22HtsEYc7X9jo42HgOCsZurcn48VJYKLIttrQSJamoPqKxFuBHKallkYeDFD96UJc9aw4Pusx7jYagW8ba/CeaKeHHOe4RoKfK46jJ8eBj/q5G6eCdAFZXRANCdQ4XRRn/WkQeicaV0uwlQhbdPeTjw6Q4+W0BB9d3ae3PP9qFNIR05zreh+nt1ZOHY2qMY6gS/NK4qoR8U6PtFm5MKfAXcPBQqYmbCojjb1I2HdJMXsWHhY+n/LnUgRq5tSg0jxMTq8AMc4WGkXmZocsG7Nz/A3anfusb32JD7/79/n1p/8RSkZUpiv0ug+5fe8PcPvez6P09TrpIvM0HxtGNxRZ+/KNQf3eu9z/o/9DsqN9vvl3/j0AavUQaNb/+PdhMoXq1Tcqrwqvn2JhPEkP6HnyKuTrElP1jG8K1ACyekTlSFLbM2GX16zht9rIp8fIcVpadpUFYKTIb67hqnGIx5xk6IPeKVdmu4W0jsZjE2gVs1jiRCCOJsjRFLvVIR44pM1IG6oswMQkw9dizNs3ixjIwOmReMRBH7feOt2hFwl6qCBG8s0q0cd7+EqENBaBQO6cEO0U16GI8jx7e5jtFQRzxX6nEXhjMnSppxsR01WPTj3VQ4PMi9eY69b4uRGM6o6Q/QleCWS9Ql10kAhkGigjIjO4Tp3abk7ekKipxNQE422Bq3hc1ZauBDIXYCS1Q4gGlmhgIDfBgcIEtxA9sXghy/dV2zW4+BqLwHoLMZzgM4vqjUNkdtHtx/ulPo3LoJMIM8kQT48WHk7q+Opxud8rEJEK/PUr+g5fhJB4uHFhNLnIDfKSDcmVMfd5COex6ZT97CE/8zN/AdVosdn5IsKH3+kNnqBVzK0bP4G/4LxeJSqVDknSIorqrLbf5OD422T5kDu3foa93/s1bmz8CGarigOyl0u3/tShBsHTNM9bmPVTfnW+3cFVY9TJCDXKkLEKXP7n3XuxYnpnNfjKNish9netEWKIjUWjaD6yCK8xNUmlZzEVgZo6ZBrWexcJVHdKnAYrTk8QzyYPj7EVHaZIQmBnaYy9SeicLilcpA9cXldPiN/fx2w0sa0KcpwipznJg6Pyd5euUNYRPesuOFaYOf/X2TF9trj5d1IgrSN+cBjcJyIV+MQ3wuRR5Jbo8Qn5/TVcEp1LCCV3wRavGgfq3ZrEVASmInAxCCvQU0/zsQi2esZiV+uYrdMvpOxN0CcjdGrwucVXI7I3N3BClPHhEDyw9fEI20gwZ9LWXLOC6k1Qh8PT1/YQFSP76RtrZWMof2cT/fgYdTQK076TEXatgdNgOjXicRasMpVcdJt4AU/hfLNF8vAIoRV5pxbSDa+KM4Lg9O0ikW2vHygKRd1wafLiJcgagmTgiB4eBZrMZhNrHPr4/LopJ/lCgaxqMS63FwdgAfGjY7L7gV6mdnuBObnVJt/uED/rIvcH2DsXu1nNw9UTsjfWgxbnoI/t1Dk6/Dad1bf4oR/9t/HeUamukFRXSKddBr0n3H/7X6XRunn9CzOH2m6oKZ4n4Ku/+Q7RwFKrb+CtYzzap7P2NgLB4Fd/nVtf+lcYb6oylOyTwOulWOQeZYI4DyCagNpzoRgT4FqCvAWgcHWFMD5wWo3A3d0IdiNaIk9GZYHshQgLZXeE69QRscZ2GoGbal2ZjlPpFuMHRSm+qw81FDe77I/RtoIen96wcjgJvGMhEGke3COURD46Qo5T1MkQj8e1G4hpFm52rSC3mDe20Ef9wJGSlMmlJZzH15JA1I90QeGQcKY7XXKYHcWimGMScEogi/GS6I4Qoyn2xkoQN86J9Tweu9nG1RL00yOSDw5wK02EKfyZjcOOU9heIepnEEdMVhV5QzPpOGxrbtxqBO4kLBy2rkhXNVFfBGqJ8UQHQ9R+F/fuzbLIEoDKrr4IOinJtlbQjw9PwwFG03MCxOchz/JS3ftZh4/0pZ1wCOJNH0dIuxgDDOCco0wie0GISGHurC8tjmWao3aOwzled6R5Cbz3TOyAB+ZbtDv3iOMmZJ7OXPhHu3M//O4rO+rVziuJG9yU91htrrDxw79A5Sjjm9mEr/3nf5G3vvKL1L78FUzj+8PjT+/2cZ36QnqbLTrL8lvPcEqS3V2h8vEB+KLDvIRqIbxHDVOcD2r/9O3NsHkjRFlbLbGbdaQVIAR5VTDa1jSe5uijFJtEeHyZzGe2WiAEeq8PxiIyUXz/PMpmoTt3NEQA+SgLxcJcBLB+fIytJ2T314I93bMuthZfK1BCsMhz1ScjxMEAW0+gFSwI5z10Z2l1jLOSZ6y74VqpvT5mqxWEWfmU5KND8pVaSGeb/X2sMI1KGL3PijQP2YpkdNuDBDXyVHdEcIHKg68/WjBLgFSZIX58TMH4C687ydEfH4YkvWmOXa0HKoEPvsP5vTWU88ijIW6tjlMybFiSKEwPzlwToOx6i9Tg8KFwS02I+N7pka3U8EaWceSmVcU1EuInJyEWe72BqyXBPzmzECu8lM/lFYvckr65jlPy2r60wthycyDHGeo41BIzFy0A8ZxEvUtROISoYRo0QWsN9MkSkTkhwVJPsvIzGt1oQ6KpvL93oT2eHKZEj47KYxArzFYbt1qH/X6IZV9Nrxx57SMdYqS1Y5Ie8PDD/5K3Pv/HARBCsr71QwC0N++ynn7plSzEAqjvWMaepZ1k5cDnHmk8tayCySb8ROePM6gesv65n2Moh3zjq3+F7vFHfP7Lv4Te7pCtvQrB9vPxeikWFhqPUtKmJF/RWC2QGcRHnmztNJ4waxe2YJEgrUuSQiw3sxGawRMKIP1gL9ildeqhi9qowDTHJRomGW4loXdbUdstiuRYkHQtItLYG2EM5CONEKJMHnJCYNdbRN99GtYdAXrnBGXmdnhFB1idzHcl87BTHqdB0AHBZeLMtTA3V8+5bFwId2pPqLLgFrHAM6pXkHtd5MP9cwWhXW+XxzH3NgOfdDhdcG9Q/TFqkpFvthFaUTmGvCaxWpJbgWs6XOQRGahJMeoRYKoSlXrk1CPHKdF+N1zLg/4Cr/i68PUK9u4G0c7xC3tbXrXT/FnArEN/WbklALvaxCpJ9ORw8d9etmiVgvzuxoVRq3K/98pTB32i2R1/wL84/GWajVt8/nO/+Epf/2Xw8NGvAYKN+D4qVuRRHZlP+Erlv8nR9k/xL373b/DmagPuvP1pn+orgQDk4QA3V1xCaD6Y9WaYdsUaF2uUceSbLVyiQ5jG/O/njmivT77ZxNVC2IMYTqEeGgE4H7o9KlA3hncC5ctLUXJbVV4UucXo32y3MRtNRGoWop7xLHQOo8IP2Scau1JHnYzC+HhqAiWjKDbVBaP+q2K2MVCDKez2AEp+sRylxI+OMCt1zEYz0C/mOPv6YBCsvWYuAkU4yQKcx6zWF7rW1b5nsu6J+uDq4RklrUfkxX0/K9CL/+mcx3Rq2HaV5GHokmc32sSHw9K6DxWiu81GE4pkRJvlRNMMmyYlD9lXooWaKH54hFmr4xqVsHnwQUMgJzn2Rqv0fs43miGQC/DNKpmU+JUaTgrs0Qjz5nr5DMve2IDcBG9+Ap9c9aeo3njpOh4dj5i+HcbtcnBB46SwOHUVvegUMc7KAlkdj8rNywJeIko6HvkgvqtGMCn41xcUuwLwc/8mvA8bkyRCmYudLRZs2DIb0iUrEdlGk3inR/ysy/Sdq+s0PPCND/82vZMHbN38cdY2vrDw78ObmrwpkV6hRg5hPU4JVOapdR3k13/+CKC+a9FjT9ZRkAgoaJlxz2ETwbQj+ObX/yPuv/WHqUX3aR1v4PYH1N9c56d+/n/Dgw/+C37va/9vfvSn/6dUjz8Zf4nXfxRfKBv7GdOOIl3TJEPQmWd6Q+KEL3mxMFc8pOFLIDylyrjsMGYGs9E63TF7jxpNcKoOAnp3NP3Pw7Sj2P5qHlLerEPtHJc2bb4aI/CoD3dDu3ezDbkNNnIujPbF3G7vUjgfxH8X/LMtHC1eBOrJYfASlhIRK2ykcTdWMPc3kb0RYpJdakPjqwm2muAjHagos3F3boieHgWXgXubtHYMQkakQ8l0Q4H0CCeoDDzaeOpPsiBmLCgWC04YJ8Ng97W98sJjcVdLyN7YQr//7Huq2H0RyKt0uT2onWOIdJlWN2/j9DIw663lnOPZoRN9oR+01wqvJPIq/NQZlMTe3WTFraGO/zE//EN/hkrlcrX+JwlrMxr1G8ikAplB5AYxCePXjtriC5/7U3zn1/86d7/4Fz7tU30hLEvkinuT8ICuxgsiJduqBGs0AR6Bzy1qMMG0a6HgOvPwN50aZjU4M+R3Vko3Gl+JEN4jrCereWpjj/CWdFVhqyII6kR4aEIoXlRvEpTzniCYu8JmOd4fMOnUiHZ7gQ7hoPLe3stcrudCn4yDD+04C4X74RCRW2wlQp8RtarBNIjEWiHsRI4yov4k+IX7UDBFOz2yN05dWmTuWHnPMzmWZC2FsNAsrpOYZCQfhc4+lQjTrmI6NfLbK0FYW9BjXCPB9ichIAmCIweLDUFfjUsbu2UQ3iMnGfGTjPRzN5BSYClCSebMRDxgNk8pO64awZy1WP7G+oJ40EWq1AVBoK64VhW3Wid+cHBepD5Hy1DzFA0pQsrhJMfVYtQoxXZq2EYF+cF+eEbNW9/NjlkILUVmgt2gdXAFC0khBWkN4pRzRaJDoIDoycmFtmpnoXqT0Pm9ZoEePz4O/txrDfLchkTIcVZuVi59DwKEkmzf/mmUSvj8D//S4i9EgrxZCD8FuMbp89wA2aoiPrbUTtw5P+urINSC5zcQOvXEfYc1Gc3Ve+TVZvBGn2Qk39kl/fw2b7zzRxkP9/jwO/8Z73zxT1772C+C11og+zO1UqUb4kRl7hm8kVDdCfHEswJZetDT4kZOorJQcu06rntKs3CViPR2Owjk9rr4OMJutMhaiqgfEtPkCJLe3AeoJKboHsujAcJY7EYbu9FGPztG75xc/makwGy0oRN8KuXOSRnzLNP8wm6gbddKMd2LwNxeDyPv3ROYZKhp6Fjb7VXsZgcImwlZdEycVuR1SdpWNHZO3SRcp45pVJC7JyVR33bqIWkPwHqaT3K4pdETiY8B79FDR2MnRwymJQ1iRgMpoWWIvp5k5Pc3z9vSXBFhE7Dcpm3mF/3c1zh7bt9LkJL5kBwBkBvyO+thMT/TSX4RuGYVt3q5v69faYR49WUK6jMcwqt8Li7W9AbP+J1v/BVqlfVXkob3KnH3zs/z27/9lxjceJfazTfwcYRr1RHW4lpV1sUX2Bo+5OQf/PKnfaovhMz7c/eFz20hzBthu2PMzQ5inBHv9sA48s0msxmf6k3Cz5d8HYR1oANdLHpwRPbuVlkMWSUC9/gIkqMMkginYbIRHjtR/7QAdo0K6Wo9nMODw+few17JEE4zSkM0rvXo3uSFpysu1mHjNxu/PwdqdNqUcLFGDaalSwuEdci2quQ3O8hBiupPcZEOXdetFmqni1mph/CNuQMmH+4jJzleCtS9tfOj8ySkfcppDtOcaJqj9/rkNzvY1TrZRov46QmVjw8XtRjjDLffx2xeTKQ/W0Dr3R7COLLbK3ghkE+72MIBYR7Se+Lv7pK/s4VVEtmbYJsJPpJIGyYGqhs2FW6jeaHrgq3FmE4dfXxJitrcCabF9SlDTLTE1hKind5pqtycI8isQM42mthOLUxSRinuKrxoYzE1zehWRN611PfmHFXGafkZCu+vTLNWx6NAOyn8lpdhWWy5SA3R0y6+VcVtNvFHoVE270ZCEQa2AO/xueW7+7/Czjd/ZXmRmXuSI0t6AYXBA+mqIl9R1J7mIZznFSGeCN783L/O+9/6u/zUnX8LX1gqymke0jArEZ//8r/JV3/zL3Gj9+SVHfcyvFYVjIsFprp4M6jMB5668YgckiNDcmKQmaPxJENPPaJbKKonGfrBPmKaBYcICL58cUgykqMp6niI6o4QDpKuRTpoPM5pPoDGs8VCyzSK/YAUqJMh+sEepHkIuHiOutSstXArQYDipMTeWsPc28StNQNvK9aY9RZmewVfCVHF+Z117Pbq+c6yLvhkV72OSUR+b5P89hr5zdVzBbdPIqxWIdTOWKKRw0uYrix+yZ1WmNvrmM12GKHeWCkT6DxgZRh5CEBmIHOC5Y8Li8DsjM+eeb61grm7gb238cLFMQR/6/niOETeFse84n34vVgceyC/vU729nYIljkDfdC7tn/mOYgg0nA3L9+sSQHEGrO5vMMrrFvoHs8+F9uqLUSxL2A84dvf/Nu889a/zk/+1J+/mlXQJ4g4rtNs3WbUoXRV8JFCjKaBMmAsdzo/Svejb3y6J/qCkIkmfXPjdNR/BmqYkry3F4qMWcKdkqeWbaNseeALoKY5wnvyOysLxbEnrP964tFTdyp4nmuG1A/n9A6JDvqSeoK58Xw1pLAOVfjAzqgXIrPLN3VXgMwM6orF8bK/XSiOtSS/vYLuT4ienOCaCbYWEx0NqXywD5OM/NYKvhbjGglm9fSez+6skt3sIJwn+fiQ5L3dQDcZp8GRYzDFbCyuEQKIn3WJHx6dUqPOrJdegFlZ9P0/i/k/UZkJAjwtcfWCfrHEokwIEN0xwjh80cDSR0OS9/fLzxpCMRjtD0je3wsCuYumYc+bks13k3tB7Ojb1eDjv9XC12LsvC3b3LTOr9Yxq/XT90Oge/hZIuQl/vlqmJ4e+8yXxNUSsjc3INEhrENfbX0TBF70ZR3kbKMR+O9L/pZxhpMSs9FYSGR0GlR/Say1EJwMH3H43m/zs/+t/wM37/zM0mPWDi2Nx3nwAr8ATsD4ZnS1Cfs1sLr+LtPJMXowDWtBwZtPPthH7/bQusLWzR9jb+93X/GRl+O1UyxC/POZy+ig9SQjq8lQgAF65BCZD0bx+0FJLE+GyGmGG05x6y3sagO31go7/eMBrl3DvnsT+ewY/dEu5s462UoMhUAumoZFOm0pKiem/MDlLAAk1titNjYRxI+76MOLU9ZEtjhS9hBGPAV9Yv7WOhtAMo+sIck2woOqupOhLvkSnsWyaOjFX/DoJ0fY+5vUn+XY6gXWNavNsotoY4FNBPmKxkTnb1RfrImuXQ+iseEkpOHNQ4pgoRcH+5mzaVbLoPa6oRic45mL3e7isZMIu9EubcWuAhfrq1EYPiuIdfDn3jkKD1kfigXXqKKKZEk5mBBNXiBBSkrsWjMEtdRiriLrkw8PQjDOZodcqSA8XULhkbHGzT98Yo1P1ULC4QxPs/fRrTYbb/4UXP9tvHZMp12Ojr+L85aNfAM9MZj7W+SzTbl11HyduNpmki158HwPQPfGl8Yay0qEm+a4WCEzi8guj1Z3sQYRrK2ix8dkd9fQu71AkdCKrCUZbxfcz4kDKRjd1uT1sCbFh+aUU0tYT0fbiqTnkM0qnOE7fy9BGFfGMuteSA4tN5WpIfn4EFePye6vh43YbBLpPfEH+7gkIr27GuxIi42D7GWBPzvHoZ1PFPWVwgot0WS3V7DtKtGzLqo/CVob44JlqRQLjg1iGgKYzgaLqKcnYbUwDr03IL+9gtloIodpKDBnGyFP6aIU7/bJtoPXryz0OrNP2FeDkJHMEh0M8AcDqESBKjBH8VFLtA8La8p8I6v4r04I0ne3yg26XW+QW0t0METv9gLnmbDHc0s64OX1TC4uh2ynhvKB5zwv7F/4ncJG77mUibkLI3J7ud7DOPIbLcTTbvis5v4p2emS1jZxG03i7+7i93qYrTZegF0NNYgcTsP0ohLCbT5675d563N/nDg+34iZRzT2RA9zTFUwXVGYpjxXDL8OJuTu06+RpQN2+9/lRv42cjgN/HMP8aMj7GaL1fXP8f57/+lrOPp5vPYCeXwjQjmIjk0pvgPAQTx0KDwOgUg9Mjeop6FQUDsnwfeQ4KXqsxx5PAzc5GYt2IAphYNgFTaaYpoRauJQFtTjHI9ntBXhhaA6V2PZm6uID3Zw7TrCQXQwQS/bcc1Bpjmvwk3X1CS2WJ/G2zGV/RwbC1wsEMajUk+c+hciwhPp4PNM8GmWF6XxAT4W5Ilgunn5LtA2FRzbUADFGqxdKJDNeqvsusncn6PVXPi6hahPdke4ahxoJGcKb19Lru1mITODqifY0WewEjuDkn4zThdElK6a4OqV8mee0KXQ43RB5PE8COdQ3SGWRrDxOuyF+2ercyEH2d3ooB4fhnFoMQVR+71QZBsXnFC8J725itrtlh7JwljM7XX0s8NzhdjO6LvcePcPoMxns7+fJC2++Plf4unT32J353e4U/tiiGtPDaKa4JTENSrEUYPvxfJYKkm+3cF2ashRGjquZ3sWs4e0koAtRXAXvubc5kgVnbeZo4QHspZEGI9wUC0imc2sOO5aqsdnxsZA5cShM3+abPcKISqadL2JjxT6YHC9uN/rQgpMu4ocppj1Rkjdm+QLgkE5ypDHwRptoaCSAtupnrOxc+0aYre/KGIu6Fi2Hp8WgToErQDBeu9kHJyXaiE6+eyk1FeiBYEghAbSPIVEpTk5YVoUPT0pviOe/GYHVwthM74aQlCSx8dMPnfj3HHyrTYisyWVQABMc+JnXdJaHBob03xpsejm1jxTjdDF78x3Vs9Or8xWG5Ha4GLxqmA8zYcGdcFL+lgF2tIwDddoSbPAK4ltVUrxZ3Q4vHTqER2PAoVo7meuGoU1NrOI/gS7UiffbJXe0yoHmSj80Rh9OAjTCi2ZDA+YDg/Z3P7yld+ynngaE4OPBdOGwDQVRAJSR23fvvKJ7eb2j6CjKt/87j9iPbmHllEILaknyNxS+WCP2u02k8H1kz9fBJ+IFNBKsOsaaXzwJp6DK5hucjgh2uuWC4BMc1wlDl9w60ArXKMSOsiJRuYWsd/FbnbC71ViZOZLn0aVB95d9cCEn3nKRLoQ9SwRWY6nit7vLoxilsFfhTogwCYCvAiCidmPc18+kJK+xTRV4AR6UFOHR2JjgWlrMgFTD3LqUJlH5h6dhv9+0ZhzhmlHYSsCXfC8ESGpzyaCeOTKbrXXgsHtuBRElqgIstijJyEkBDh/zDMjMLd2yicVngXB5VXgZoEvSwphfdh/oZGpGaWfXaqFUiFMoF07DaCpJZg3b8Bggj4eoEZTxHCCbdeDOX+zGmg71qEf7V+cJlgITOchcos67OFWGohpHsJ4Jvv4W2sLKYcizYPQsl7Bv3kDxiHNEClL5xcA+mNIc6IH+4spet1ReCie+bhSO6afH/CV5heu/d34pCCE5MaNHwUhONj7PW52fhKnFbI/xmkdwjDImQz2P+1TfSHIolhx1ThslLqTC9POLusyz0NoiY0LD/kiIMEU7ggCaD4xOAW+UL8DpUBA+OAfm9dlyeVU3mMt4EA/fY4epHgpqdXVvcCtB+uQxpYF/euCkIL81srCz7I3N1De40eB5y2mOfH+gHTm+EHohE4/v730NT2hOFRzBfKM3jT/bBLzb00tittnRasARG8SKCrOY5uVhfS+eGexey8mOdGzbtC65PY06niYlU4UthKhJ8HBQZ2MQ+pdd0y8P8DGIUxmaafUedTBAHdrBV24hJyFAJTzWCmwm60wua0nzw3xyO+uvvLngE49Noaz1YCcZKdWiEoyeXcLNZwS7/XL5D6gcDWJYfa7z3u+LclOENMcESl8bkn2+mS5RQ6D17ed1IPwNnPIZoWsmAxI5zj+5m9yq/J5ZGbxyfU2oSLzVI89HF//eXwdVGur3Lr7sxw8+TrdbIf1yl3UOCPKTHi0ZJbu0++QJC0m45fX5DwPn4xXRoHxjQiVefTIhsjlom6UxqKeHJ2OYwC32ggd3ocH4aEtJebOOnlDEg8cLhYhEUd48oYiHjhUaoLHMBApibEOMUdhEJMMX41Rh/3g8SplGfjhtQpxoI1K6FouELIkbuNy1b0XMLoVYZd88VQe/J0xHjX1JIeG6bomOjFIC/HIEY8cSIuNIatJTEeTnbn/tQFSG/ylM4/Kgg0cPhw/Wws8P1M/X8xnHYiGlrhvyRrqtDjWgknbQ1ViVHiCmRbUdgUYj48EPhaI2UNutYHNcvQkwyl1OXd7Lnb7LMoACq3wab6cMzVbPLQMxV92tYfhZ7Y4Jozq7JLvkos1rDXJY0X09BjfrmG3V8B7RNENd0pi4+jCB3y+vVr6dc9D+OCIYW6tIfMiSOAsX9h59NOj8F2vJsHlpV4p612ZG3xmSo9TWLzOglP7w3nhnpYxSkT0ew9pb7575ev0aUAKhRO+3BCEgIcD7O01RuNdtK6Qpd+7o/8Z8tsriHGGyA1xd3Ita0XbSBCpIb/VKR0romfdIDgqCmRcGDNLy4KPdmXfMNnS2JoMa5YqxHwxyKMU0akgcrNoa7UMWjJ9e4tot7fctusspMBUIqK9Xpka+jrhjUMOpudoC1YIaCSkb2+in5ygu2PU4QB3O+gCPEBNYoQPPuhSBAem4pwviinWwxRrLE6rxbWvGpFvNKEIUJlBOE/85Pj0Hu2O8YcRNtHI1JR+zgvHWCKci3pjfByS8fScyCze78PxEKZhnVKZATEIxfUSqO4YNQndY6EEfonQ0g+m0K7iIoXb7ix9nWV4HTSAwe2QihvPCdTU0ahsTtgk5BzYdo20npC8t1f+m5pkL31SwoObuZIYF5w4Csj+NISoceYw1tNQHT4Y/TZvpDm6NwmuLy8YkPK6IXWEjQS2kaCGKda48rt91P2Am3d/lg+/8/de+3l8ogUyFJzXWGPqirhriAYOL8Ct1HFxBEkURvlaIbIcu9GCQriUNySTzYisGbz56qJTcGgl9G1p+yZzAx8doiJd8IoEdrONcI7owX4YM9eS0FVLQ4KR3ewQfbwXXCMO+6F7SeDBms02/rI4Ry2YrKqlxTGAjQTD2zFR16BSh9OE4JCzv+48agrVqYW+Y9JRmJbCFd8MozkngpOATMNrLeMIzS+YeUuTN1X5ix4Yb1MY35/+tVUwXoPaHlgBo+0oFPgzk/qba1goVe5LWOaQBwN5lAzj+flz9h710e6VhXeuEofi7mhQfi7XgRcg5PKR1zlIntupfxmo47CYLSuSoRCJaRkEQ48PEcYi0jx4Im+2l3J8ofjeT1LsRviu6r3uoqBlMAGO8RstfON8ZKeYpGGqkltUPob+GN8f4+9t4BHIJ0dXs3YThI1ncZ5KaG5W36U7ekKbz3aBbGyK1rUQFmQt8qQYbfYnWJETJQ0YfW92kefhKhHMupZrTfTjI+QoC1ZhkxCmcO7elBLvHNmdVURuF8byYpovdtTmgzScK60fk64jXwMXCeonFqcEXguGtzWt1CMtCAdmtY5tJOijUSnEm4etRKDlOVGV0BKT6PD9g6IwTELUshDI4ZT4wdGr20BriS3oAbPpzUysJ9zFi4gHstsrRTDK6c+na4rp+uL6no8c9Scm+KKvNZDjfMHWU6jgX884g1bgHZsbLcQ4FJyuFp8r1CHQbhY8eSd56ABfB6kpudYLMO68F/Al1g7CAzN3JimDWPsM9MmIrP0cDc4nBJV7puuKeDSnwZgrNN3c885ptRi0Y/3S7/R1IS4QM14UQOW1ZKv6Dt/s/ippYmmc5EQ7PfLbK0t//9OGMSm6WSvXjgWKic2ey6F+VfhkKBbFWqryEFnsGgobCyabEeNNjzCeZDVCIMhWNdJ5dNeSjINjRYmZhW9DUDkK/yNrKfKWwiaBFlDbN4jeGDITHBq8R3aH+EihuqPywW0LaoCPNXatBVqVThl+vYX1obM8s1JbBlMNAkDXUM+tqawCu7Z4udNVjY0FwhGM4C0IE0zhpYO4b/ESXPNieocDXHKGVxYJph2PTQRRP3g6Ow22CRR8b1yxE71gA2krBKcN47FaMLwVEx3nJENf7oZnw5+lt2qky27+OeT2Wl7HcjhF5ha33iKvV4IHaXdY+lTbGyt4KYieHS/9+xlf/Wxn9dzvNSoLWfWvHAJIIkSaI6ZZoDDM/7P3yN1u8XBxCzxP3R9jN9vYYlM38zWdf2l1PEROcvKbK7haHKJU56AGE/xoimhWcSuN8vjqoLfAgS5fc5KhHh5g7m6UeoDnwnOuiK9EDbrTqwstPy1Euood9lDHg4Xvga8lNNQNBt1Hn+LZvR44LclvrYSxeKcWEvWSwPvztRjbSLDFKDt+fBw6TnMFsExzVGaCt+0ZqO6Y6FmX7PM3yhCJym7O8KZmtKWpHlqMDgWzr0hE6nGJLoVUvrec8S0zE2Kx56YZvhKRvhHS1i58r41KsAW7SoFS0WRrDXykQzDGKEN4hxcSHylcNcKv1i+0LFs84aKFUI5joPdGhJ5oooFDmdAxtksidPO6JK8L4pHHxZr0zXX0/gCZ5sj+FNOskN/qAKD3++jjURmmMevYpvfXym6/APSjo2tpGV4FrsL5zrZaxDu902po7hmhhumVvX5fN5ITy/SMDZrbaMLRMEzg5iLAIdj9yVmH/HWdlITp/Q3iC+hJoj8Nj5+kRXbwDD1p4Y0LfPclDZNPG5Gu4EYDtD2/BtTjNQb9T8bm7RMpkL0kfOlzyJuK2m6OSh2DOzH9L3hsxVF9qKj0ZFG0Ccy6Zuqh8TRDpR6nYLoeKATRMNiRTdZUme9tEkHjKBQUbr0Fa83QUTYOn0Sogs/qmtXgCrBzgnn7Bk6EvHhGU1zRqfZc3OGbIWuGbvbLwjRePjIxa4KpEhL9VIj2nhWv2cqSW/IKWebaioUugFVgNyJcbKkeXp/DJ0TgQloXFnqz3kJ3Rwsj+8vgi46MrwYLPdeuIW1Qx88eUnYU0pjOQo7SS51FIHTVXmlxLIA4wikZBI7VGN+uXegkoQ56gSOvFXa1EWz15jh73tgQpd6skjerwSapN0KfDBf482KSEn+0e+71vQgpi764LxaO3Ts/Pp1NH+QkQ/bHL7ywO+8Ypsek5gqj8E8ZWTZENVqY2+uonePyuzTtH/C1j/8G7Zuf5+TJNz/ls7w+XLGhlcPp0oeh3h9gVutMViV67BErNbJ2tSw2hfdEj45Lm7j50Af9tIu3nmx7TouQGaInJ0Gh7zyiN4HCYiwaeVpPDINbmnRFkpy44hin5yOA5Ds7FyeSZZb4abf8315Afm/t0uJ4BtupInITkvDGGa4SkTzrlrxa26xgb7TDmHx2/RpXi/G9CGlDMNnQRMOgJXE6dM7z5mkow2XQMzbabg/bqobOMxA9OQ4OEEIQPT0pu5RmrYFdb6AOh6BE+ZlL74k+Orw0WOrTxCyJ9qLmiRxOPxMFcjT0nOXqeB3WeVuLFqLIAVyzQtqsoA8GC2mQrxQO1Cglu3tq46l3esEyUQjkOGNserhsit3ZxVdDrRPtD0g/gwVylo+QrSrTd7fCRrugkTwcfoPHw9/hczf/u5/IeXwiBbI+s3mc3IhKOoTqg2l7xl80ZPsR0VgiMk/cAyQMbkVUDw1J39F4mDFd16QrCnsvLFpq6oiLAJL5G8tEAjnO0Qd9zK01aFbRD/cRg0k4tpJhAdYK9eQQ12kwXlNUj67Ic32BFJlXDU/gFmftV78v9eI8RUMQJgAvAnEyDOKywr3CrbfI15rIg35JO7gIrp5A5fzCePaB6OsVKLondqWOyAxiOMFX4mI64BG5PW9TBxeKlp6HIBaSuChE8/pWDWrJ1TpLczi7IbMEK7z5rrc47CE6DXxSLMIrDUyzivpgB1+NsWtN1NFgLlAnDoLW4RR7Zx235BpKdzoOFbHGRhphXRiNFzx9tdc9954Fi98Pu9JAjNNzNIzf7/8qE8Z86ct/5lrX45OCGE5QhwPsWpPjkw/Y2PhSsJfsnQpudoffZmX9XTb+5J/m5C//rz/dE34BuGlO/OAQNUyDBdicrRZQjln1xNN/U1E5kgtBCF4IsnvnE9cEQXwmvccJgT4YlNzb/PYKIrMkDw5R3QlupR7We+dhAvUdw/B2FCZo1geNgxRk1RDfm7+xTvLg6Er8aNuqYi/g555FoMoVIq/CBSFba4QgFCVDgXHNe3cp5rrG8dCRrsgQsXtF6JHD1gNvTha3lMgt0W4vhFEIgRpnQbQGZbc1226jD4fY9QY+DpHLdqXoHD84Qk4ybCO8d9mfBk7sZwR6r3/hRtxHcilV5NOAAOLBmQIZQhri2e/OnA7nMtvEV4Forx98mCtxsKPrjfGRwq7UsEcH/NcH/wlvNX+Crcpb5d/IcRYaSEu8lj8t5NmIyfSYzsoW4sERPjp9zj8Y/i4/8lP/Lo3WLfgXr/9cXmuBfFYQYZJCMJQFo/joMKf2zDN+LOn9sMCsWmQuIRLYiqf+JASLmKrExRIvg03azJWicpiTHOfBhqqe4Np1pLGIgx6iluBbNeyttTDOzi353U3U4wPsZudUwZ+ZsI41KgWH+GpfYj0JQjkbf7KSMA+ISJDHnrwlsM/ZUAsJpmrxkcdFHjHj2DqBMBANNX7JW7bSM12HeACq2ODEx+ZKHsfL4DqNc4ufF+JyXrAUmNUmfr11JV2Da9XIzkR6y+JhApR0Gdkbofd6C6l15xBrzEoDnAtBNLkNXPlWHb9aD1z5koX9imDdgmjCbnXA2lLAqE5GqJNRiC6/WQTQKFkWq75RxU1POYp2rYFvVIPQdUlxDITQm9VQ3Lrb67h5jrsPyv/o4z1mT2BfhNbgHHKcogtai+qdilRmGJpj9qYf8XNf+PPIT4gzdlXISYra75Uez344JUv7NLoe7bunv2gdDLrorSZ567Ms/7wYWoQR9TxdYh4yt8ijIbIa0coTxrcTTDWEfJS/M5yGTvP99dMOmffIwRQfKaLjEfmNNkl3HETPhS2kWQ3JaOKjA/L769CbIGoxmuBqZKsSIT1WQbYicRHEI4tNItLN1oUj4xnytQZm+/Jp3+KbFefEbm69ge+Og8vLYPpcd4Sl0II8ClZ2piExBe1NpwV94hrPCeWg8dRgqgJlPDaWZDVB1lgjGnvUKCP+MNhcicEU1hqB8tENQj7hfYhnVhJ1Mg5UKykQ03xhg6SE+EwVyAJQscZk5tyzIr27dk7L8rrP5Xlr+1m5yjKaUfzgkPz+YtT2q0SYzktsM8E2C3vAgu/tI0W82yONFB9mv8dm5Q3uNb5y7jX04ZDsM1QgZ+mAimygHKE+m/uKGnJ0dPk0+FXitRbIMvO0H2ZMmxKbCNRE4BKBlyLkfCcS6R3RVNB+TzG45ZEecJAceJKupXJkSVcU6apGeKgc5ERZwdf1oYvoawlehS+nenIIqcHXK3hRFDBJFIQbSmJuri2EU6ijPq5dA61o7C52v6QgGLO3q7gzt6wA9MgGc/BPAKYKeQNcVRSiveU3nFBgKhavwGsPdY+ba62f/W9eG6Kj8B6kh8qOJ10V2AqYCqGTPzuHjiYzPgSuWI+pS/TY4SOBScJnLLPwuS07u2ULjq/FsGTEb1ca+I1WEAW+BJZ1cl27Tt6sEX28i23WAvd4MAFB8NomLHZupQG5xVeTYE+VRC99PpeiKI6lsTCc4guu8FmHD9UfB0HLnP3azOrJtapBQe4cPgmG/vbexqWHLSPLh5MQSduoQDVG9Eao4+ECp9hLgRingac73y0+Uxz380O+1v37vHv7jyA3z3cfPy0IQO53kcdDvHd4PFIoZG9EnCvyyQAqi9drq/ImX338D2g//YVP5ZxfFjOKhRqmVN/fC6l68w9zKdDdMVmtQ2VnjHIS6U7vG+kcSZGyZ5wDGf42enhUckt9YW1mW1Vsq4rqjtGHwyIoKnSq9KOj0IkWIljBPcgZ3dCYumC0rbE1QePJnPCpU4XnFMjXnvwYC4V7RtqSJH0Xun/b7dBlv0KBLKRg2hLkVYFPJD4W2AvqH5NcvzCa0QyisSerC0Y3Vcn7lpmn/kzAvTX0yQg1mGLXGsVmOMElEdPi/H09Yfqlm6jDIdF+H9esLEwPLgvG+LQw2Qie7dF+f8HNRB+PMLfi1+JKcQ65JXpyEjrCl8AVbi0XQbmwmTGDKb5VvdCF5GUQmpAOu1Ivu8BymOJqMSoPxgWPv/2POBx9l59c+zeWn+doWk6BPi1Yk6F02ADpuEbulusPbtTf5sn7v8LbX/5Tn8h5vf47xPhg6TaDJAjbtCBrBw6x9MHcvPWQ4NEnoHZoiMcOLwTJyOEii564crQhuyNsp44QovTSlblBTPOwwIzTwC0uCuWyKC7+UxN6xa5RKUcgyvpgxVO8lnp4ENKHujH+7sa5XWBl4DDNglN25m3bKAjd4iGvxGtmuhLEd4vw+JrHKo8aSASCdDXHV69+QDU6Lfjk2KNyqB9AWvPEYxa2yFZS8q6FDzSMc1ZuPkR+XxWuXcfUK8hnR6XRue3UyyCR1wUvBbZWwa82QlLVsyNAlIW9nGaoZ8ehG+s95s56WRzL40EQ/l0kQnwJzNMq8kqEr8Sl52V57lAK7HxRpKpxiu+NAo+66IzLwQS31gwbRe8Zjw+o1zfPHVNOMxil6KNeMV1wOCnQu90yIljEGnITOMlPLvef9N7x+/1f497dX+DGvf/GS1+TVwUBqMeHZfjMbx79x9RVhx9d+ddwPmeYH1NZaZ27X1vVLTbiL3D8G7/yyZ/0K4CYvz9zi97pFgluFrPZLP2Ro4MBIjVhzYt0cGloVkKXckbD6U5gvYEcTBeEV8L5kNo1CB7LcpSWnFJbj1GjDDVMiT8+xK7Uw9rRqoIEmRHoBISJYVYXIAXxwJU2TxdBjVJUdxw0Cf1xoB9VLtaG+EYFM4sWnvucXT0h++LNK9ErXATjrdfz6BRAVKyfTsNoW6NST3PX4GQo6tOORKenHrflec05LUkty4ANux44yfOQzhPtf3YsC81aHX00Cn6+wxR5xupPn4zRo5S8VcWtNxYnXa8Y0cEgdN0vsSkFzhXHwvvQnJs5xMiQLBsdj0hbVVyxIUntGIEgVq/OlUOOs0CbAZIHh5iVGt44+tkBjwbf4Gc3/jRV3Vz+x47gCHPJffM6kWVD/qt/8n/mJ/7AX6De2GTUfUwsayBD40fM2bu+W/9p/uneX+Pm6Oc/kXP75LeQ7rSAEiYEYUQjh60I8oZaEIDJB/vY9RaiWaW2PydEouhknXlpH2nMVgfVH4exeGYwlThEUxeuAWlLEvcdfucYNc2xc0lwbprDbIwzmASboHoS0qce7JFvdBYtsnJP42FGXhWMb86Nf7RgeiM4RNiKp3Jw2u+droBtCgQCOXRUuwL/nPz5Gc/Vnnlyu8STrxtwEI0U3oGcSLx0CzMil5z+nfAgvcRPPXqgEGl4MEUDH84Fj3c+FPYsvAzKeNTQYloaNQp+zMIGlwx8GPdIc30fYqcV/u4mdjRFyPO2cK8N3sFggnQOcVYQpBVEChvXTsNqZn+mFVLKV+oGJ41F7p4gh9PT633QCwEhZ78fUqCO+uE77ouzsA69s9htU8MJz8xHnHQ/JEv7HB2/x0/++P+cZvPm6UulOfrBonWZa9fwkSK/tVY6wSAE6tnRcr/qOexPH/Dx6HfRSZ2bd37mha7F60Aojg9KZw/rc4bmOPxffsx3h/+M1fgmLbl5jvbj4oi763+Qf/47/+GncOYvD3n2QZ79/9n78yDJkvy+E/u4+zviPvLOyrq7qrune6avGcx9ADMAiAGIgwQJcnmA5BIUuVxS2tXSZBKpXWlXJpOZJJpWopbiLq8VwQWXF7AkCBAAF8AAA3AGc/b03V3VdWRV5Z2RcUe8w931h7+IjMirsrqremZIfc3Gpjoj4sV7L97z9/Offw/tYoktiH5MfGEWGadj2zTVi7AqdW4NBx6awlrnn7zecjqF0B93cYPVhrOfGk6M1Z4kOVPHNrrO4qo9gN0u8UqN4YwkLUhKqwn9c75bVcwaJ2lR4g8T0vOziNs7yN7xVIDg7h5kdmMm8IgeX5x6XeUVeuCeORbnXGSBpCjQgURoCPsWc0q2gYgs4a4mmn24RZo3tJTWU0TsJhZYF7jiZSFRCigM9eGUigxKG/QobtkcXPM8gF50bCiMLgSYSg4xTF3C7H2eTw8Fmfexd4xzCeBiqne62J0uyfmZI+lC7xYySlCNHtKXqNZgquMurHXuH8d1gqMUf7017jyLYYwQAhml6DTixvpvkvb3WN97mZI3w0fn/zCefDjPulHxPaJXpMM+q52vc6v7TZ6sfur44niEb6Mf8vb6SwC8/PW/ywee/1O88fI/4/n657HCaXvURIHsyxxnK+9n/e5X35N9e/QFsjYIKY7k4OQmOo1Cc8gXWKQatdvBdgdOER0lpEt199DOuKZiECObXfSy42Taesn5G8cJNvAzw3UgTjEFn/6Ch/E0hYaP3+oj7+2OO5aThZkNPPRcBVMr4l9bc52XtV3S5Rn3/RPwBxY1MOi8O4CoaMc8vbQgGCw7r2JTFOOYabDoskCXQHXA7+9zfQ9CSFe4HoSKJXZHIRLBqE7yugq6Bzh2gcEqXNRvMv07qAj8tsXvg8WiLIiBcTGx2pJvukCXJC/x0kxMc0oh4/j7PRD3KZwtuG7/A235nUNiXdhJb4gQExeep0iX6keq/WWrh2wPnPDhANf5KAhAtJ3loLB2rGob3wtSgFKQpPit3rhLPDpPohfBEZZUwlgw+4lWx6Hdvse1O7/EpUufQ+fnyOdnefGlv8+Vxz7P8tILblu7E10kKbGexLuz47rmV85MHbfsR46HPePCB1Rj2jZvfXCNNztf4uLSJ1i68gmkfHRdngeF3G5N2d7JiSpjL9kgNkOer33+SJ9pESeU9iSBXyRNvxvDpvehKzni5Rr+Zgs5SLCeIndjIrY1VESPLThx2CB2dITsnFhcY8K/s4fFkp6tI4bpuEA+0ps1C81IsnCHdFjCBh7RjGKw6Jwd/IELTxrMewxnlKOHSeiueJRuJZhL84Q3t08skkewuelHmg4Ee+ck3lCQ39F4Q8fp7c86rvAIQ8Bva8KmQQ2O85rZR2FHE3YNgxl1OieK2OLvaXROIFPwBhMexAYXXxzvf68AVCdGbbYwtSLWl/hrLXQ1T7pQdsEUvoR8gPYVwYjuIgHfI54vu2hxa/FWG1glpzxvTTlHdG6GcKt9KJkzWamP6Rc6Lrugi0eMUwW+ZBBAuN4iSg22nDuS+/tOoRo9kIJotkyw3iTK+xB4GJyLCIkhyZwiBCM6nBtXVGuA7EWoRg9dyRGu7oGxxJWAG2/9CoPBLnPLT3G+Ps+guc7vbP0cH5v/w4Tq9JxaK9xvlyxUUJ0h/mYbG7qUYUauTlbz8vq/xtOS52Y+z0y4cuI2dSk8tcj1USCXd9dlsbzMzVf+FY+XP8pMuOI68P7he2umdJk3G7/7nuzboy+Qlbxv0WOVIKqpQzHUeq6C2mm7JeARPIls9Zzf6jB2heyIixklbnmwmBv7J8vOEJGmTsDXizBextOZTAQbxKjtNqYYTsX/gkBYiykX0AtV1FYT794u+swM5kCB5Hf1uEBW8XTnVQfHizSMAFMRJJlnvEqFS2JKINcWkFowlmAH0gLjlDIrgECgrEtPkhH7qgGdDbSjBKZAjj2NReq6ETa7H/Jb2Y1uILeZ4PeP7ose+fdUu0CXiWM9CvKAK5xRuJStbycaXWwy4kprhKdI6iWYKR3PxcoFiPU9hKcwicb6CmkMdhA7x4wkxRRzCE9hjcXbbCKGp2tLPeyJQWpivrH3Szzx+I+ysLQvzKjXH+Ota7/I8tILLnVvsiNsDCIeZbU7v1vZG6I2m+PlaFMvoeerh5wt1gfXeKPzuzy78hOUHnv/Qz6adwepzSGnFCEk7yt/EnDd5Kq/gBTuprBKkq7MorZayGGMSA2yN6RYXqY/ePTxpo8MUpDMlV0HTEpklB4qjnTgO45wlBDedMdqhDsnwlr89RbpYmWcmhfuHBGcIkAoifEk1jqesCYrMCwuhrgvwFgK6+77w5ZhMA9JWSITS24nJaoq4pJgsOBRjesUrm9NdTOPpF8cKNJlbMntaOKqonPuBOoFzlM/HlH+hsZN6o0lv2eQR4iT1dBSWktJC4L+io+WbpzMb6QYX4BwIVEAYddMx0Dvn6rxvS+thX7snHZ6EWrP2SsabbFSIOMUud3Bb/YgmVjRCjw3AQc35mcBHnFqXPBLZ4gNFIm1SIBEYwIPU80zLIaonY4LCGoP3HM1+7fJ+ZhyDpMl7H0nwSaaYK0JnmLw5BKQnT9wtIi+ewhPhnZIbZA7XWzgwrJkN8KUQvRM0Ym5I7eKIkb0E2MJr7nre5wOqqRLLZSC4NrWkemAwVoTs60g0aQzRe5xg73dazz3kb8wFXDxyhf+Jvf6b3C5/EF3DXjy8ErmAUSPLWCVRHWH+I0eeJK0WnCdYyUR2vD13V9ECY9nZn4QKTwIfbRytCWsRRrruMvCkuYDdOY7/u1CtX6J2YWnuPq+H+flL/231GofATK7wpkiXnfo6qAMFX+ebvewlemjwKN1scC6YrYQYk7gaw7rLugj3zpAIci6wXKv65LBAP/29qHPp0o6n9vQP5x4J4WzchAC8jlm3ohdklvTCcN0teAK7KwoSkMBErzBqLAXLvIX0MszmGrRLX0XQsePcdccQduQFg1pQeINIL9hiWbFOCTlNLBA6lnwnEjO5iC/5l7z++5/hz4hHFNA2cxDMgsbwdjMgF6gCxLjCbekGhlkajG+SyD0O5p80yDjk29MCdDsOYFWlIC1JBcdn9W7vQVCYLJ0K5vzXUy4J1064gTEMEbmMrHFaNB5jyFgyi9Z+Irk0tIh/8qpzwxjTC5AlPOOH62kS/bbbk29TzUPuzl8O7Daf4V66cJUcQzQ7a4zU7+CNAZvwy1L64obYFVmf7c1vElcVCwm83jrjanq3WbHrfa6SF9isgd1N91jOXeVWrjEd9ajNOuSH/GTXCg+A8BXG/+SlfyT++/XxvHPM0FXulBDF332fu/6e7XLjwbGkru1Q7xYcQ+dCcQrNfx7TXQ1j39vz/GSs8JIWrDaMHx8EeGrqQlkfH4Gb6frqBgW/I0WyXINXXcNBBkl+HddJ83rDhHDFFMv4MVuzOwvKvI7ehw7DY4zHXQMQdugtOvukveIrywQ3NoZR86Pi+MsxW70N2+7My7gBZBvGHJ7ht6yd6purxFg8vvvS6qK/LqzGj0KXt9SXE1Ic4KgZVCxo6fI3S4idfQtK0CfqTm9Q6LxV3dRicakhuRsHVPOEbyxfuR4eEiImEzvh5jwQbc5n/hsHdka4G930OUc8XIVW8kjU014YwcS7f4WeNi8T7pUBXDhWNpglCRY3XUpfXn/O644noJ2kwlvu3NosmRyPtGVfc2FyM6JxVF/SA30IwYzRTfxmIhsNjkf66sx5WP8u2hDcH0LXQqPjc4G5wyjCwHxcoXbv/MbPP70H5xOf9MJ/bTJ1YorBgUc8v221vJG64tcLn+IULn7KXd9ekIaXZxz9LrtDulSFaMknWSXj8z/JLKQJ16sfEcGgUzC83O8//mfZtDfJU77FD1Xb6nOwE1mUotQEputZO10r1OtX6S5++jH40fbQY6SMS/SAgQeeqY8FtWNcDB4QjY64Klxl9bWS7DbhtQcmXamtppOtJR1j0cQ3aHr+M5N829snGCEwNYK6PmqC2jIPudF1omcUme5JQ50wG0hJL2wgFFAavDX9tBnZhFAcT1B5wXDGQ+Q5NcsSQnS+iS14vQwJxRa3tDidTUitfjNCIFAJKnrnB8nYLDZhGWYYPMhZqaAOHiPJylyt+O8cLOBXi/PINYbqIPnfa0BqRl3SaUnSVfm8O5so1p9d94Wqti6E8KJYYJ3ewu9VHcTmodUR8ooAU9i1OmWieR2ayoZLi3mTiyOAUf1ESITIcWuY3Nwti8hvrCAd293KgXPBh7pUh0xjJ212OkP7R1hO1rl5uBbPP/8z0z9fTBocPful/ieF/4jvNXtccCIavdJTMTbvW9ifMmt5tegCRu7LzPnn+VM/gkC6e4Ptd1GKIH1PaLlOv6qm7CmNqKVbCO7Q/xbW05VfZ9wlvcCAvAy7/NO0uDt3td4qvIpAuloUrvRXfq6xVLmDWqKOdd1EgK53cKEAaZcIN3ZRIjvHMrIO4axLq3sAPz1FnjSBXwA7E3PxgVuNU7Plqb+Fr615caVQBFdWcTmPESUIqPUpeKFPtFjC85WMbNKDG7tkFbylLu58cN75NyQFC2pBX0pwIss5dWE8q0EYUAHHtH5WWf9NnCpZMKTDB9bgGFCuN50PNXNNkGjR1IMMXMldM5HWCitpeicYFCX2LwCxXiiPtoHc8TNKSwkJXlkgSyHCd5mm7ScQ824Vcrg5s7U5F8XQ9IVVxyPRJIiSsfaAv/eHsnZOvHjSzBMkIME1eo7DdgwRRcDdDmP0AYbetis03gw5tnknUBx9D+w4/RAAP9OY0zLEsbibbQQApL58r64MXt26FoBr9FDPWLv3ncNy3ilYwrSxXmDSxg0OR8/o4TpeoF0oULu5rabbKVmzIEeQc8WnTjsCE60SA1e8z5UK0+SBoLrb/wiflCgWr809fLqtS9Q8urUgiVM6BGv1BGpRvZituNVOt17dJqr7PRusJXcplRd4WzlWeYHdeQEJVDt9RCxHgeoaKOJTZ/GTIo8f1iQ/Z0Aay1rd75MmvS58Njnxn+79dovc774zP5KnqfctTtRHAN06FCZufDdXyDb0CddqLpGZ5Sg2v2p2e5x1iK2EKK2Wi71TkqstehqEbXbwZTyhwpk63uuczzRPRbG4K03XOraTFYgS+ie8fE7kjA8s59omQ/RvsDb7TnVfj6AwF2Ek7dN6CuiUXxnTWGVhxCzUwWPGliK91w3IZrxEEiCrgVfYKR1y5WSMc8O3PKNHf0S1nWBVQT+gWA3v6tRQ2ezpoYa0ewhjBsECZSz+jpwPkfFoF6ecTGYhZzrBof+4eIYwPdIz9YxgUT1ndgvLku83BxeO0a0eniDyPGdhjG2kHP8aGNJMx63CHwYxm4pfq+L2GphCwHpQg1TKeCt75GmBjN7H+HAfSCjBLXecIlzniJdqmFK9xduWCEwgYcSAptoZD8is9Y+FumKsyqzs2VEs+es1g7CgL+6fZjHmqSIOEHtHG+Efxz8UkhyiphWgNQkvNX9MlvDmzz1+B+kVFoevzYYNHj19X/MyspHKfY8GLoBXtuUu4M3uN77KlL5nJn/Hr7nsb+EEJKvfO3/xc7wNm92vkTRq/Hx2Z9yg7O2CKaFi720SWwG3Bu8yRn7OGoYQz44tILwnqM7wCaanWiVb+z9awyax0sfIZB5tqPbvNT8dZ6pfT9SKUwuRPaGY5cLAFMInaVeJYd4lBZ/jxBJopGlEHPCdeR47RP0hULgnCkm0xyL7iEsohR8iZWS+FwdYS3BnT38taZzOYlSZ6lZy6OreUwhRFcKLtQiW3Kz2rgibauNmS1hkBTvJDTe77nxb2hIc5LOWZ/y3ZTR3WlyPsPHFlCtPsG9PZLQwwSZoHZbjQU9NtGO19rsQ84jKeUwM0XAo7SuOdbvXrlVPyMdN9hLmVreHe2/bA/d6l0pRFfzBPeamPaA+MIsw6eW8NdaeM0B6UyRJFvCFgKsr4jPzzoXiRvbpHMlrKfwttvYXozJ+6QLZdIFNzZ62x28rQ6mnBsn6AFTfHrrSUw+cIFAF51AbFiXhLqEnOg2y2Qiun4ilCO4swfSeUqbSt6Njf3TjTnfsTBZQuRSBW+rMz3uWvc7GISjbG+2UJMFryddPPRpIsmPgM377MwlvPHKz5IvzvPUs39iyklm+943uHPnd/j4/B9xkxJt8DdadKqaG1u/ydbGS8zMP8HshWd5YumnuXntV1m78yV2d97AWMOT1U9ysfSc29VRAZ81eJLYTQJa3VVy7XlKlTN8J8Fay1uv/nM27n2NcuUsFx77HNZo3nz5nzHsbPHM7L59m9ceYEo50kpunBIJLpreeI+6zZTtwyPdumW/OLUW2Rtisw6E6EeouzvIXOAss6x1iv18gNztIPvOOSI9O+8u5vmq4wpv7B36GjFMUFvNsZ8rgJWS5NKiW05fa6Bny1AIyDVSvJ6eFmZJgUzd/o3ipj1c8T7ZxI0S7ai8cYowirimEKkl3zg82HpDi7eWEBclSU2hkUgyqsIpIbPv9lopYccg44xDNppY5HznAOF7DBY8co0UlYLc2MMUc9hyHj1fJapJ4pIi10gJ8DDZUk+SFy54RWcCHOV4dToviasKr6exoUR7Ai+y5IRAz+cwSjieVt8gNUShwAQCNbSoxEKa0SzmytiZkos+7rvfzsyUodXH2+2QZBZrxx6/1th+7FwkrHXdcV8h233X+d/t7Mcxpxp1dxcWa+Pf8DiYuQpmrvKOqAAm9N13LNYQ7T6q0ZmKhD5S5GXd9WhzgesCxcmpqSVxNzpVUb0Xr/Ot5q9RL57nIx/8S6hiZer1t679S0rBHFeC58b0olayxUvt30CFeT7wgT9JtXphaiD/xMf/Klub3+J8epmv3Ps5duM7zIcX3IvajikaAE9VPs3G8G1W+y+zNbzBE+VPkNvOY1a+vR7Io8n0jd43MGjO5Z8iryrc6H6DW/1v8UL9R6iHjsM4Wgkxob/v8Vx1q1hhWCFNvjsFegbonZshd2NnauXkJIhE7/NaM1jrunHedgeEGFMpVKbnsIEizRVdKp02eLs9VKNHfHneRblnfrAmkBgE/kDjb3WwjR7x2Rk8QieQM4Jwz9BflqR5QVxwDQOr3DiEddS4QS2PSMx4ZqsreUcJURI70YhhmOIPnfuBLQToci6LzRaQ86Ytw7RFHTCKkNo4B4Nsu/5Ga+zRG5+fQdcK2HIO794epIbBcgByBsQe6QS/U/vQPeOhhpbipkZXcm4pP+cTl+bHDgQjKHBBK1udscPI+DcthkSX5pC9GFPLYzzlrL4yNwK/axBKYZXCKKeBGa+tZsX/9AbB3+7CdvdQgup7BZUPMKk+VXrifSEFNu8jspWGg68BJGdr+BttVLM/ncJbyTu60PbJCa9HwdEifpe11Ve4/PgPs3jmhakxNU2GvPbKP+Fj8z9FwatiE41NNbfaX+PGza+xuPw8H//sf47v72ucrjz5+1lYfhatY3KNhJdXf2FcII+g+s7uMK9C3v/Cn6Z99w1e+vrf5cJj38/y2e9Byu8Mz+tBf5eNe19DSI8r7/txomGL11/6H1Gp4CNLfwSVGYqb0EMBwb09p7eaQCGos9W7857s76M9a5MHJoTjrGbL4CIfkJ6bx7u7g9eP3ODje65YzWavIkoRvSG2Vhx7v8ojZrYCUI0uVkrM3ERh4Clo91GdAaYQOq/PZoJ3Z5v0sWX6dYkJBKVNt/TnVKAS0R0g7u0iZsowEQFsPEhii397m1xUBVG+b+R00DMEPTNOwLPCxWBHcx7aE6ihweu74lfgBGzCgD90gjqLM/ImSjClHFYbrJSktQAZ5MafU5FFWOd2oRfdcp7Izo1IQabuwTIyxwec4tUX6KzAHfEARwNkWtx/TKShoHtuX/AQVdx3hg1DuKezQAB3LqyQGCUxEqQVJGfn8O/sOH/UkbjLGNTqtktvO8JiRnQHqLu7J0SPeociowXgbTZJrd2fmD0iWHCx0qUc3s3N8dKlrpfchM8YJ7Rp90E7sUx63gVQyFQ7PneUoAbRYarGgWM68u+e49XbRJOYId9s/Srvu/j7mT3/3KH3drrr7Dbe4v0rP4pod0F43Evf5o3WF7ly5UdYWnx2esKYIQxKnF/5GOrODgYzpiWYnAvembwXC16Vy6UXuFB8hre7X+NLu/+UC8NnuTj3I1P+rO81RgWhyEqeUJW4O3idjfhtPjr3hynI0lhrIIxxq14XF5BZTKvNtBNCSMr1C7R2vvt4yEo4b1SRnH5KeJBfaYHw9u6+iMhavO02ul5A7LiulYg16dkKurvvkSwsBG9vu8jzKCV+Yokk59Fb9imvgqwXENoQzQZ4caah0HaqkzBY8JzoDScmLq8myMhiESRlD3/gGge6XsBrdEmtxc/5zrZzAgIQ/Xjstw7uQRxdXUQag1pv4XcjZwFXzWPyAV6z74TeR5wjXc5hS46+YJQkPj/rzkvmUJGs1KeKTUHWRBhaF1I5V4Y43X/PgWaBzoRX0VPLY0vJyXdMxmUDmNL+v1XiVgWsFAzmlGtcZJ+Ozs/hbbdRx7iCCJjidY9xMDruIcHiKA2DxQpIibfZxtvpIKTjQOtSznFRpcCqLH47StzzpB8faVenCwHpfBkJpN39YtPb21/FNoWQ6PI8YhCTe3tf25TWCni73VO5phzEvf7r7Pbf5sOf+M/wgsNOR6s3vwBAXlXcyngy5OX2bzBQQ1746F+iUDwcTiKkolq/CEA7fms8Dk/BZqsCiWaptsBKUGbp8vu5tvXb3Ln5Bd7//J/6jugmy2wVzpqUfMfw8us/S33uKleqH0Xu9ACLLoYkZ2qEN7bdPXvgMqwzx7WN98aT/hFPKw4MKxNCPSOcQ4SQAnTmvRp4LpxgcguZIEw2e6jmtK3UQYyWygHkXtcZdmcPZ6/ZQ1cLmMBDP+Y6RoNFDx2CCZw3sirlxrJiUy64bQTemEtpfIEyYOpFvPUmuXxuyj9Q7nbch+slN7hYOxYnqkEErRTrKYJCiDcwjtZxUB2dOju7UVElu0PHLfY9rJKYSoHBok9SVnhdTXFzpALff6AZzy1BOD61JX+3TWGQkC7XSYXAjorwnhk7h1igf8Z3sd6+G2CP7SQogQkscVWg84q0KKjcTsZuFen5OegNx04VJvRJLswjdjrI3v7DRg5i1I0Nx3P2PexSbcwjFvpkH0+1sYepFLCFEJGkbrvZA9FrdEgL4bHxyg8TRkrSy0uw10VE6VTAiS7lM953f0o8ajwFo4ncxh6q2dtXSR/3PYXQ2T1l7hLkA3eNbLVYa12nPnvlyOIYII5dJ+SVe7/IdVniA0s/zJvtf8vzz/0MpdLSiccnmj2GnR1iEVPJLYGA9MICaqcNR0xWlfB4vPxRLhaf5Uu7/4zK28vUn/ow9zfOejQYKduXc1dIzICbvW8gUTy9/MPkbMlRmqwdd++schOPgzoJgPc99Qf58m//X9/T/X8Y8Dx1rOetyfuZKv7o5eSROMb6ygWr5zy0p5x7UDmHv9pwXrmAavZJztQwoT8dIkJWsMwUsb5CDS357RTjCdKVOhhD74xPblsjjHPkkXEmuotdo8D4bsJvJAzLkkKkERYG8wpvNXUrTVnxpLQh5XSrdTJO8TZaeN1oHDIF4O/sP2t0IUBqA1GKqeTQWUCHrheJy3IcXuUOVuD37FjAagIxbmKI2FLccINiIARxph13RYBFbbWR/dh1fZcq45RWI1wTQ6X2WGbIkb/dIMYWQ0wgEBrSvI+0rpBORQV1FHd3BCWdlWQGixuDTgpteSewQLJUnQoy0ZUc6YwTeU+uMMpBvE/5MU6Abo/x71WDBJkJDs1Kffx3rx+7IJLUYDyJLwVJPkBXcpl9Z57gbuOQF/ppsdp7iffVPkXg54+cS/S7zjLvNzb+Dh+ofz/bbGDKBZ59/08j1f0bCbu9m8zlLhz6uwXIsiFG/OiZTp4Xzv8h7qVv89pL/4gPffwvIx+S7/I7RS4/Q6VwBpGkfP2Nf4AQksvPfgajFGLL3XOmGLhiX5sx334S5aTE5Su/j7de+/lHvr/ftr67TFLU5t6486aa/bEpfXJ2Fn/DLVfJZnfchbsfRoIPkc00xTBxDzpPIqIEsdmE5RmMFAjjTNj7ix46L/B2DLaW3aS+54Qdcer4sonGzFXwBplrhO85VXC7j9puoReqmFoJ2ekjhwl2e5/flZydhXyIWmsgkn2zekLPCYDmymNLOnDpZyTOMkzE2nWNc4GLwwZ0ThCXJZYDCYXgxFPaIjXIzOrNSoGplaDmBmK/n0WrHhjYBRA0NWleopL9vwGogRkHuqR5QVzzyO+4bndcgeGcxO8oilva8exCfzwZUlstTCFw3OCZknN/aOxzwqw2oA1qmEDsfK5NPsRUi5hW/8gVg9G+2ZKz8zPlPHqmjNrYc04MqcG7ve2+t5CDauGRJS+JjH892bH2BJhmzz1w8wHpTGnqN4ascGv23OQHMJXClLPGJEw5P+ZAT0E5AWVjb4ty+eyx+1ivXeaF5/4cL7/6cyzOPsPrrS9y5bHP37c4BqBWJEqLJDs9fvXe/5uPzP0klbVw7HhxHHyZ533nfj+vrv5LPrp6BnX+3P2/6xHAGIsIPETkkVdlnqv9EIaUkplBz5RcLPcEZD9Cbew5P/UDne+CV3sP9/zRw2YCLYRwnFPjxouRn3G8XEUkLpxBJpr4bH0/OMFavK3OuDiOV+r4my03IRy4zpvFebbKbuRoCJ4aT7qD7v5sUHYjqrckwoJuWvyOQUVu0pLfTPH6ls6VAKstNhCYifjm0t3UjQVZEWvKOUQ3mhLJTiH0XGd11CG308Xw/k6BzgUIY0gvzblK1thDYt7hjMRvaWS8n6CW5gWds4ryHacXObTpQYy8sU3eU0QX5xDGENzcQUxOYtoDbM4jrRVI58qHGimyMxwXEBRDV0QHAqEtKjaucKznITEUNrPJyHINkQkl7xszfWAFQeAcQkygkPFDoEBksKHnusMTCG/tkiyUpwShMtEEb2/fd5ptPYk11h3fMIGJDrsYusaTAESrD7Ml5BvrqPkKyflZt1rb6Lk03/uIto9CrAf00iaFpUvHWoVeed+Ps7T8PK++9HOYcpHGxm0+/MJfOVVxDGDTmBudr3Gn9wqfXfqzUyt/R7mNeFsdzpx9gkblTd5+9Ze4+oE/8MDH9bAhlcdj+Q8SyAJ5Vcbb6BBPUJHkIEGOVqCOWF0VwNLc07zFv8sF8r1dx6sSwnWThXs4WcAWc6QzZdR2y3XVRkb1WZdNV48uJmw2446KgrAlUJvO3cL4HkLHmPkqSUGiYoOINUHH4mcDj0iy/x/G2NDH29gb+y9P8krTQOCV84itFtL00dUitpgtPZfyrgMi3L7qQojNRGPJY8sIa5HbbVRvgB0JfoYJTBRPpl7KIpwPdG+zNDNvaKm9nS39GEcJGVMilDs/MttdsdMeOzzoxbpzLcicOaa6HhniYyyQdF6i8876KNhLye2keH0XJoKXiVZSt4820VMrBbLVc64k9+mQuh8uxbu9ja7kMcuugDmuQAbw1hrZPxTJxQXMyizy5ub+qkMvQvYi7HYLHls60WrwnULd2HRG8mfd0pg0Bm5t4WVLp+m5uXFxLIYxcreDPjODvLs7JQY7rjgGVyCP0Ghcp9O9x/LSB9neeZVeb4u95ts8fvVHj/38MGpx996XSJIetzf+LefOfoLFhWdPdXxGSpLA/XBL+SsURNkVxyN/w2MggIVhncbC83xz7Rd4fvFnUOHx4Spax0jpHUn1eFcIfdJ6ia3dW8yF5yl4ExSsRB/p8aqaPWyzh724gAl9rr/9y/T621y98iMPd9++zRAWwtXG1N+sJxGxy+y0oe+WtbOldW+96TidxuD1orHdGrhOtC6GqGbfTXZxYqX4bJ38tY2x04Cu5BwVYQKmkh+PDcUt7RwFtMHreURVid/XqI4mrrlCyhT2xzxpoHPWw+sbcg0wZ2que7zbRXUjZJIihu5ejC/PYwoBMtUEb25gc74rhgLliqdsH4yviC/OHS4ipXDPoomuZWU1deco52OUW2UcdfMmg68mY4iDtaY73kQT3tx2q41HOBaJYYq/0QZtpwR6woK/1nTFgwCEQOd9gsSJSWygiB+bo3FO4Q0kpbUUFVsI5NgtxHqKZLZEtrdOoH7wWA/sk8n5hy3n3iVklBKuNkjrBdIzNUQvBiy2MN3ptL5yq2aDk2kPIjWYUkh8YXZMWZGdId5WBzWIx7xvhHC+yLFGDuJ9W8Js+0eG3kwgNTH3+q8TqiJlb5aNwXV2olVWas/AmePdIzqtVW5c+xUAXl39Fzz34b+A8sJj338QtuUmc4+VPzw1Vh5LxQP8u3s8vvKDfP21v8vda1/g7NXvPX771mJ0gvIeTac5jrp0+5vUSyt4Mqt7+i5HYEThcffi4fPfS5u81vxNZsNzmLXaI9m/g3hPC2SRuR/YUt4Fdwyb6FrR8TUnfWmjBDNTxlSL+Pd23FJR6GOlRBdC543cj8YWbSPRkb+xR3rBx+94ThCYD5yJeqIxpTzWU+PACzFwISNCuB+pt+TU04WMimQKOUTPCaTUXtct5+ed9VCak9jHllDWopWkv+ARdA0+lWkO9AFYIdALVTRVdCCIK044JwykOXeJe0O7X0hKQX9WYSZCRkYDsDc0qKHFhIIkLylsp2O6hJEwmPfwyzWCVopa34MocXSTOEE2e4cdJCSkxZOLE+0LBgv7xXzQTMnvameTp6TzofY9rMo4z0KgF2vOw/qUS1Yi8FDtAbK/gV6onk4wkmpXeC7W0JU83nY2iAceRrnVA6sNPAIqrJ6rjEUfstlzARpSoCsF7Gx5ilqhdjtuEmfdasdkgXwspMBmkaqt1m1ee+OfUq2c58aNf8PMzBUqlfM8/+yfxfePd+9YXf1ttrZfAeCJx3+ClTMffqBjvP72L/P+yz/J2f5EhO8JxfEkroYf5LWZhG+88vd59gM/TRAc5oYbo/nS7/11Hr/6oyzMP9yQkXShStxusNu/zfvnP42eLWOFwNtpY3M+enkGb233kDOOAFdA+4I7d11q02DwsYe6b9+JGHUHBc6OLT43w/CpLIlLG3JvbTiO8AGEt3bcuNUaoIuBi7MeZGEjEzoN1R5ORSKDeyCOuq9YsuJMUFpL6C+6R9RksJCWkJTEuAtd2NG0L/iYUFNc1xmntTTuQMpBjLfVGXcFjaeIHl9yRdcIWbEqUtfh8HY6JBNL8/sHsL/fRmWc6ezf3XMeXt+S39KogZlyCbIWVD92qW+jKG4hjtYfSEF0bgbZi1zBd7BYFJBcXcRK9yzw15vOX7qaR3Yi9Nk6SUFivawrYS3WWtpnfbyeoLjpdixddvoa1exjRW9aqFYrYD055Q0sh4kLQUofXgd5BG+vj9ceOncTXOczvrA/kbJA9Ng8aquN3+xPTc4OQnUjvK026aI7Pm+3i8oKX5no8QRNZSsHesTdtvaQGPI4vNb6LSLdZ5C2SMyQpfKTLCw/x+LjHz/2M8ZoXvvWz7l98gt86BP/CWF4fL1wEN32Gju9G3z/8p9/oIhqAZR3Yp774M/wza/9bbTUnL/82Snx4Ai726/zxsv/hE9+7v946u0/CDbufZWF/GU86aMLASiJ6gwRxhKdnSVc3UUm2lG77PTEcStdZTe6i7aaSnT1kezfQbynBbLNT8yUKgXYbGJDD1MuYH1XyKpWD//WlrP3ODtHmnn5HRySk8tLyM4A2ZyY+aYGudbAXlhwhaonAUF6ZWl8omWrh6kWseU8cVEitVsGK26kU99hZsuY2bKbUcbJVAy1F1mSkiIREHQNQccwnM2K79PUDcLxhK3aP7DRUpzxIS4rtC+wBTX2T/a7mjCjVOhQkObl2EUj6GiGM8p5nPYNMtIUNt2me2cCxNIiYVNnXQQfM5ulDDZ7zvM19MFA0NLEtZMvCYtL9BMGRM0jv2ccD7g7cB3tfOgeDNqMvazjXIB/Y+NULNSR+tyU825Solz08f0+q5pdzEwJM1tBJ9pNjqrFrGNiEcI9TGQ/ctZdDwl2IlFRRI7SYxeqRy6xpSuzqPWGiy4/ZYFp8uGYv7u2/nVWLn+SmQ9/lnPtNuVBwS1F3wcrZz6M5+W4cP57Tyykj0IUtWl37iIqL9BPWxS8KrEZEMi8G8DgyIF2BBUlPFX4ONf8F/nmi3+X7/nQf4SU0+dfSsUTj/8E9dqlB9q3U8FTXL/2r7hQeAZfhthGx+kQZsvO03mYTCV1mpyPnquitlt4azvcKd3NXhGEherD37/3AIk2Lj0v0aT1wpRl0iR0KUROuKYIILjTIEmr6NmSE04dURyD69xFF2fxNjuO72xGnG6ByfmktQJBo+dWsdLpLuy4OJ6E51432QqVGhqKdw3DJR/tQW/FJ93TFLYy68ueIS5LijsGkgOdz3wwVWwB08UxTvRq8gHpXAnZixH6/kXgqGgf1iWDOUW4Zyg0XcFbWT1QaEnB8FzGeX98keDGFulMacpubX+HLUgxDvA4CpNUj2S5tp/IlzUvk4IAC4XtFB0Keku+Ezoe6IGIKAVrSVbqU77KXnc49gbW+YDkbB3V6k8VzA8bVhviSy74wl9vOVeOicmBBdKFCulCBdmPUY0eXrPv6Bi1AuHtXYyvHM1iglKna4XxdT0pCPe3O6SzRUw5h7fdcVSbUzRyUhOzNbjBpxd/GiU80nIAF5ePT2DNIKXi0tUfIl+YZW7x6QdeLdva+BbWk+wEu8wnC4DFWoMqFkkqIQJ59PWEsz0sbRue+8hf4OWv/T2AsQfxJKq1C7zvmT/6QPt1LKQgKjkjAKzFNFvcvflFPjrjrNzUMIFsJT1Y20NPZFhY7aigKlCY1NCZ87h3/S0A6tXLDOXDXck4Dt8+749R160zxNRK486rHESu3T7J1z0KWRyqLefRmQek7Ed4xsCdbdJzzjFAYBFNJ5KSg2hsASYDhd/TU8JhOYwdJcMDlYLY67rOdSbSU1ECux3sUh2Z2PESmt93ThQwTnTeRxbvaYQAIejPuTjTEeUgKSmUBq+ZEjY1Isms1kI5FS4SNDU6cOK7sKX3KRLWunOV0UusEIiR8h4I9/RYKAIZBaA9wFQLyL0OtrKAyvY56Bni2tGnWweueE/zzs0jv+V+m2FFOou8XGGabzfJ+Q08bCmHmOjSBcWQ+ASfSbXXdddCqp21mxCYrHiWgcIc7CBYp1C31Yno8dFLQmDjFBHFePcapPPVd+3BfBQmBXqiM0B2BpiZknNfaXSQbRcyokLP2RkdU2xMYjIqtbF3jafe/33gS8RsjUFsKd25v9K6XF6hXF55R8e0sfkiAC/f+5fUc+e4mHuabzZ/haKq0dNNAB6f+TSXg+M7v6of8Xj4DGm1z7de+oc898yfRsjpAmV+7n3vaP/uB2ktO9EqT81/CnD3nIpSbNJ1q0MHudRSumt1p803G79Cu9XOXrC8+M2/90j28VFD5HyGjy/irTVRzeni2HoC4/uoQTwWYOlKDjmK3YV96kH+Pl0rIUjO1vHXm+NtyV7M8Ikl8BXDSo7cjW1Hw5qgLwghsNbiSUGaGrck3uoTr9RRQ+vocF03wR1MXDZRXSFjS9g0lLY0cU5gkweTg45i760QJOdm3N9C/4E4qElBktvV5BvZmJzqI8Oa/J5zJbK+ch7Lu93j9/U+kcMHcXAkSQvSPWMQyMTpUmxs3QQCkHmFGbrfwUinq0l7Ed5e/xCVQg5i/Ht7qGGCyXnI4X7xn1azldndkwX094MqBOis6AUQiSa41yC6MIdsD7EzBcyED7kpBNjQw2v2XehK4LmaINGIVO9z5WH/OsZ18VWzj64ViB5zFrLB7R1U9/SOFXvxGhV/gUDls3Co5UMOJMfh/OXvPfX3TMJay/b6S0TDFi/e/mc8+8Kf4c7NL9LYu47n5dE6QiD51MIfJ+8d3ZWW3SGhV+fZ9/80X3/xv6NQmGF++fmp9/hBkdn5J4/8/APtbyDonvVI/f3z0r17i2rlPEU/ez4buy8ENaA606t46UKZJPAY7m3ylVf+Dmnixq61ziuI6OE1uU7Ce+h8b90y/+i/siUPFU/PBPT5edL5CubC/H23qGsl0pUZzEzZLXUDJkqdvdZaI1P5t8d+raYQji9kHWsEkOTlmHtrsy6jSDMbLyXx1hqoLE5Ye66wlXd3UPG0AENIwbCmiA+e0YxjDc53OKmoQ9YlWkE069E7H9Bfdt2UwkaCmnjPYMlnsOiTFpyYRQxi5G7bFZ1HDMbejQ28O9sEvZSgs+8IYbKEPBElzndaynFBLyNL0EyPHbSHM5AWBX7Pcf8k4A2coGZUgE8hK2hFdzjmJY5fOoUJuxgm7kGRasyERZU+woIqna+cLDwJvH3Cv3/4fD0olBSI7hB54NEkGx3UvV28zSbCmP0lopFcHeejfZri+OC+KhVgmntOzQ54vUefciWE5NLF76dQmKcZrfHN5q/wgaf+KEvnvofFeRfVfKf9onuvr5z93BGQUcJT4UcQScram7/5rvYpilpE0ek6WTbV5IIqW2J96u9THEPp3BrArTC0Xv097vZeYye5w1Of/Yu8/6f+GiDIVxf5boTKnDlEnB7WAaR2WtAmID47Q3x5nnS2iPXlfje3kidZrJDW8hAcvofEMEHt9aYCFgQQrDUJVndRuz0wBm8n++2ycXG0EhFnA15wJ0volIL8juOJiyjBSg4l3Q0WPYazChFbwrYhKUvXPT0l5BG30IMKtPINTW5voqA9QRAsYJygNyVAksL58Y3eZx7cRSE0+5OD8r2E8moCsXWd5M2Mgpd113sFS1wUWcHuxs1kpU6yWJkK8xrts+rHbiw7MG7JYbLvGX4KyKJzjBj/9kAyV6J7aY7o4qxL4CznSJaqmMAnfGuTYKNF+MYG+Wub5K9t7l8/GR3Caw0Ibmy754UUiCjd51Rbi67mxw05jCW4u0dwawe11yd/bfOBimMATwREpptRADgdVe7dwhpK1RUee/LHENLjW9/4+3T7m3zoE/8ptZnLFEtLGJuyEx3vDyws5N/coHx3yDPP/yneev0XiU85jh6HTvse1h6IyFaC7ll/qjgG8GuztDt3ifVgHO5zEsRGg/U3fovVzreo1S/y/J/8v3Dpo3+EaNhi5dM//q72+7R4DzvIYmqWpfbcjNMm2nV2M/qF8RTMnpKXM3mCD4xpKvM/ttY6nnPW/RhxWgWAL+gv+/jtrCMb+liPsV2ZrRRgt4Pc7bigh8BziXSp3l/SGmNUCDnPYOsJ/IEFLL155+9sCvfn+OK78I7i3ZhgOxlzfkfJMWpUiCap844u5vZv/gmYmZLrqt/dxVycEA34Hnq+gnd769BnhIX8ribfNvRriqSyf36N57oRNvs3gOxq/MhOnwdrUYAWAuGrjCO3e/qC8Cgc+Oihh7xwwk5O4MYJQLb7ztWjcrxY7NS7lGpkp4/N+2Nvb7Xe2BfbBR7pXMVFoIN7uGTX/2lDQoB9MSdw6cJneeuVn+dJ/ZPUZi6CFqRphFL+wxe3ZTh/7pPuuy9+ln5/h35/m7mJbu+F7Y/y4us/y9rgLZbVk47Hd8y25DDlavghXtr7Dc7Yz51IzTgJL738D+n2Nvi+z/yfjnzdGM3erReZFUvIIEe+vECX9vj1g5Z6Virn3qANnWSHl5q/RqIHBEGZ6O5Nmt07fOqT/wXDxZCv/vO/+o72+duORB/peytgalnZWlDtAbpWwCzX0MXcuOMGkM67lRdt3Mqc7EdjW6lgs511Fqa/Y9QZGo0mqhu5rl9u//FjA0HrgkftWuJEZmfrGE8gNQxmJYXbBhG4psRIrwFO6zCclehQUNhM6S8qpAbv5hEBEaPvwjlNuPH5wWGl04z4/WyiOrAITyCEwCT3L2y9zbajo4GL0TWG+Gwd1R669D9c8eyvNbOQrPKUZZhR7vutEoRdkxVpEfLmDn4hIL48j4xBYhG+ZFCCoGPIT7geWSHQwehs7EPPl1032FeuwXHgFI39sTMRn4zScaE6tZ1CQDpbwt/pTFkMDuuZ1V8vBm3RM0XSpSrSuCfzqAM8wug3TKt5RKxRvQh/Y2SHNxG5nHlbq0Q7hkpnSPiWsxCNL82DLyHa367qRu/Ysq4WLBPKEq80f53HKx8jtyYZXPJIpXkgwd2DQEjF08/9CQDOXvg425uvUCqfIV+Y4enn/yQ6jdm49zWuv/0FFnKXCNUxzzhrSRarFMpF5haeYnPtG5y79Jl3tE/DQZNvfOlvcPmJH+bcxU9jA8GwIomqcpwMLG7tYHd3KC49RpBUSNMBUR7MuRmCO40TO7Rvtf8tq72XoCUIwjK99RuwUOHpP/9/RoUPRhV8p3hvKRbB/tL/ZMiD2OtB/l1eWPnQLdtN3qwjnqeU0BsiQh/v7i7pxQWIU9LQO5SEJ1KckFDJTHnsBlpvu+XEH4+fcel1o/eP3rvXI2eKICGqStKCxDY13tAtY+rc6QsYNTTOYL9jgIRo3h93TpKiRA0lXlBEK5zl2q5GxhMPOcDWSphaCXGEHZcQAlvMTRVfU0gshe2UpJdx/oR7KKiBdQK8bLyTqcXGGY8o65qIYeL+Vi06ukd3gDWP2AVXStRWC7TBFHNHcsFEu+/Uykf42wLj1LCTkv0mYaSE5Znxfytjpp0o4hS510UvzyA6A7xsFeKBMfEQWFx8Fgu8+cYvMBjuEQQl4rhLuXSG55/7syh1eAm8211nOGxRrz+GOqWV0HEoFOYoFKaN7EvzF3nf3g9yY+t3OZN//L7bqBWWEQNFp3mbSmZ+/6C4ePGzJMm06t515lJ29q5z+8av0+rdYyZYQSmfvu3xgaXPwNC9Uc9VkLsdzFLdUV8mVjfumLdZWv4gOzuvMRg20IMhzfU3UWe+H98+GqvAR40k0ai93onvkYGHidOx6t3b7hBdXYScC+RRjR56Zv/eMVJAveBEpM2Bu7+NdfSMfjzujgpPYlNzaFIS3G0QX5rDBBKRWIZlSdhyK13x2fq+C0Tq6Ga24COEoLCR0r2wPx6W7qRYAZ2LPp2MGqEV9M54lLb1IT4yTC3mHIvg9i7pufrUsv742L2MV6m1K3CB9orC69uxyPskqF40LjzjM1V00a1sehP0F9Ua7F+Xez3ixYrz8VfObUjnBVaK/ZVM45L+dDU/1bzRGIZ1H2EgbE08IzyQRzROLZBmdqlKmyNtttxJOIaLPvq9Q8/pi0J/qkD2drJUQAt4kmShjOzHhHcah6zlpjc83WEfJRlO7bt0okdTCjF53yUDxtolP0YPb7VNCMHzsz/MtfaX+O3Nn0UgEKsKbRMuX/19rFz61NT7pbXQ6rHdeJP83DL58vI7bg6MML84TWlTXsDKhY+zt/Eam4O3OV/6wJGfSxYqLv2yNWDxzPNcf/1fvuMCOQhLXLjyOSrnnqC3qIirbqXK7xh0p03jja+y/uYXSJI+Z5c/ym7jTS4ufgLv8mX81V0nCD7caQQgNQkbw+tcvfIjXLv+y3hejub6m8hahfLFR0PHOwrfFg6ytfaAO8G76C5ObCE9N4+6t4uYsIIRgGp0UI3OOLJQJim2H0HJxTOLxKBDiRrx15QcJxuZmZJb7hsVUNogG11sIXT/GxUwYYD/1hr6yhL5XWB3/4Ys3UswHvTOhZj71MlhIyW359TzJhAEHUPQi0gD53Opc5LhrOcUyhl6gaB0N55WTWfHbsv5Q9egBfREQuBx8PsGdTehd9ZHdIGjaGYH/H1t3sWFe6vb6JkS3t1d10VN0iNjmB8GbC5wXYlmD3FrE3PpsL+v9RUmF2AO8JMBiBJko4PNh9hjCugRjnM3M1IiMluskVWQnqsgtHae3u8QMvSnsgGWFp9lafFZjEkZRi3CoMJrr/8T7q19ZdztBYjjLm9d+0X2mjcoFhZ4482f59y5TzI3+ySFwhxr61+jUj6LlB6eFxKG70yA1m7fZbX5DTrpDi+3foMPVD974vtFaiibCtHGXXiHBfKIrzzyksZaeju3+dbWL+N7ec4FT/LU7MdZG7xJ2ZtjIXeRYPQ8tbB392WuRd/kg4/9RWQWPmSlYFBTbFy/xgtXfwbPC2l17rK09Bxnll8Yf/a7ETbVY+5xWsljA4W3M81/7Z+rk7u1O/aJ1Zl/rAk8yKzJ2OkgLJhEI72MXHSggJosXHQ5R3y2Tu7WAY9fnNcpg4RoJk+uaUAI8vciJ67KRLQqwf19Ox2LeVRkKa0m9FacWE+luDCkzZTBgud8gA0kZUnHE45mcOiEuK7vSTA5D3WviTk3c+g1FVu3kpeN/XFVYpRAxacb39K5kmvCBB6mvC9M0sVwfP4mu6jEmuDOHnq3R3J53p2v5oH9LeeIHl88FJwhE6jeSBBSIHoR/k6X+MIsOhTjldKDSBYr5K5vHivIPAnDMzX89ZZzpThCDDrFX67kQQiCm9v3vbe8xskTPBN6pDNF/K2Oo1RkBXw6U0RttU/87DuBL0Oeqn0v76t+htRGJCZGCskXr/8PLJz94FRUdPvlr/L61q/jyxzRjT754jyL5z/I3OIHSJI+u1uvMr/0LMNBg3Jl5ZA+4zSw1nDn1hdpNG+wy3VK/gwz4RG6E23IX9vEAsXLSwwHTaw1D7QCaQGdF8SlkPoTP4LBIhuG/LYGbdh+6YusXvufWchd4sP1H6cR3SPuDni68hlKjz1HeG0ToQ1pKeSlxq9S95a4lHt6vMJq4oh1e5tCeZGF8x9ic+dl5q5+lNLHPvrA5+Xd4ttSIMt2f1y0CTh+lvqAMJ7CXFhAbbVQjcPcmlEHQ93eIr24iIpBJc7WR2Wdhv68R9DRY36xXqgd3v9EH7qfbc7HVPOOX3bEtWZ8cd/iGCDNS/q+ICk7rrLX0vh9gz80bp/aTt0Z1RXRTNaRT5313MjmDaY7JOrGBtJa0tmKS83aaTuXjkrh2Bnc+FhjQ24jGXOjJ6HLHjT0ERQIi+hHeImjoojhg3G8joIVrhCWR/lgWruvWk5SV4wrNcUltPnQrRwchdDZfd0PstFB7bTdNZFqvO4AA+iluhN3Xph3sdMZ1E5n3zv1HUKbacX/eF+kRyHvlPmFwhzN5o2pAvnuvS+xtf0yn/rEf47v52m1brOx+U2+8eLfJkn6eF4OKT3iuMPS0gs89eQfeuB9Gw6bfOvl/56LpRfoDDbv/4EMJW+GXrzL/VUGR8NaS6dzF2+3z17jGo34Hs10i6tnPsf55Pz4fVX/6G+43v0q7WSLL3zxv8BiWJz/AO3WGslWn3xuhlyuzqWLhxXe363wcj7x1UX8mzt43QHxQhU9W5zyvvW298dLXc6RjCzAGr39DmDmjywAe4oxW3WGqPaAeL5MuNo4lIrl7XbJZYV4ficTtonptomAcXDRSPymhpb8euJEQAF4Q7IiO0Vo8IeG1qWANO8oa0d1kadwxBiYLlYR/dONW2HLTHVn74dJAdnU32dLxIAphoS3DqfcqX6M3uuh60dP4v3bu+hq3r0+EsG3h6jukGS5ioy1s9QaxOR2xWH6BKDuNByNxlOgJyroCV/ktFZAtQdHewULgZ4tIjdOUZRmbh0oebIoMUtzPKnnKqOUYL0FvnKuFYmG9dZ9C+t3CyEEvsjhSzfRkUjazdWx0E3sdnh169eo+os8N/N5LJbV3kvsXv8ab7368xiryfs1bl3/N6TpkOc/+h9TqT54qNKdm7/NztpLPFn9JK+3fhsljl4tHIXiCE/iB0Wk8omjDmHuhAaJL4hDV5vonMDk5b4WoDfAvH2PQWdI+94bbO2+Sk4U+fj8H6HguW2WfbfqOLw872hVSoCGVq7L1vbLbPEyN4Iv4fl5cvk67eYqnhdy5tzH8Pw8z3zvf0z3vP8oUs7vi/e8QBaDCLW+hymGpGdmXSH7kArkEfRCFVLtEvgmYJVAL9SQG3ukOYVMrBMkTHRBc3vpsTPr8faXp7uQnhSkxrowjqMgYbgwvfwtDfh7KfGMN+5sK+3in72BweQl2hMkVUVSVQhtyW8m+APX5c7tuVjW4ayiuJk4asgxkOA4iFtNRtG6tjtwVntHjXFZWMroYeX1DSqxjiM9eR4URBU1FXMNIFq9zEf26J2aXG7Vs2VElOz70AYKnQ+Rrf7UgDgSJh55fP1oHCii52vuIXp311nF3acjfFqIJB0H14xEnxYn7hK9yBXIFpLlGbz1Pff+Vs91rku5Qz67p0W4uk1azGEXa8faCEVRm9mZJ8b/vb7+ddbWv8b3fOgvE6ocBqhWL1CtXuDqlR8FLFK6W/+ll//BuNB+UAwGuyRJn2t7v8On5/7EdAjHCfCkTzPaIU2HeF7u/h+YQH+wy2uv/1OiQZMoaXO28BTLuSs8l/99SEJOk8Xb1x0WF55lc+tbAPizCzzx/s9RlfMofcJj+JHyhB4dpBCOc1rNo/oxwcY03SdZKJMuVEhwVCEtJaIf42+2juQtP9B3DxKSMzXS2SK6kh8HhgB47SG6MySZyeG14kPhEIdO94T4ze9byndS0oJwThepQWgnYJYx+J0sRON+xTEc2yA4uD+PAqrVB+uKZm+rjb/VOdb7PZkroavH6yfiy4cnhEIbvEYPr9nbT0rM+ftJhgoMTqwoEo3XGqBag6nvt8pRIYJ1d9142T4fBdmPUBkn3UqB9eSxyXsjSsnwiSWEEMhUY6MUb7ONGsZY3yNerrrxU2dpg/cLKkm0s4crhuicD7465I7wqGCsJjFDZupX3PnbbvGtV/8BFX+BZ2d+CCEEAsHF0nNcLD2HtikSNynsLof87tf+7+Tyx9QQ90G3s0ant8aar/mhlb98/33NhJmel6PdusNcqcawJl1NpK2rlUKBLkrS4PDVKC20vvJl7n7jl9xxGcHZ/JM8W/tBasGSy1WoFdGlEBFrvL0e/nYHGbuEYICotU2ldoHhoEEcdUjiLhev/ABPvv+nCMIS+IJBVRLPqG9LcQzvcYEsscj1PWwpB92hS1nrDBFxgq0WHqo/rVmuo3rDqe6d0Ba11XLWbb6AoUb2Isxk1PN9imNPTAt5LZCelLojnbWbPnCmjYS4prDC0SqClh4n4AEUb8fonMgEKc4z2T/geev1Dcw6z2QvPX4f0vPz2CTFhj5qo4kaRJjyEQOt57oKY2HZ6BgVFFdjCCWJ5wJF0rLjYQ/nPOKqonw3ZnwVl53H9VGDqMkHpEt1/I09xCBGDBPE0HGDTb0EQrilx0oB/97usVy3EYQSLk44e5vf6KAzvvFJoS0ylPTzjowoU+s6T7E94NE3cQ58D311BboDV9DvdjCz5Smqin9nZ6pbbvOBO/dA+PYGNtUPnEZlE+2oI/2I5Pz8kZHZQVBmt/EW1ep5knTAzVu/zhNXf4xyafnQwCKlwvR7rF7/VWKVsNe8wczM/bnDR6FWu8zKmQ9zb+0r3Ene4AnvdAEk8+EF3tz5EuEbv8KV9//Eqb9PaMMrL/4Dlmef4XL+cUwhHAcAAEdyGG3mgT2J8+VnuLX7IjP1q1z50E8h5mp4PY1snzwMv5P42e8EpNl50bMldC86xN/0d3uOTpbzsVkaXnBv76FQSnQphESTLNccN8mb7haKQYzx8lhPkuazYnfcpj56m2qni9AaOV/BG2ST+O02Sbnm7M2A0toRA7mAYU2iIjsW2L3XEFKMXSyA8TGqne7YY1gAshhiDjj96Eqe4NYO0lqMkqT1IqZ6slhJV3KYLYWwlmSxjCnnpnUWen/BU/sKXQwwhRCv1R8XMkIb/K32furkMadO5APH+x39t7HjbRyEzfkksyVMyfGvLZlLlKfGhf7kJWCUJDpbJ7e6e2JICIAYJE70JwXpXOmwfVjdNYa85mEKyLuBQOKJgN2v/yb1whl2ejfpJQ2+b+k/PJLCoKRHozTgXu9V2HK/ie+9M/HZ40/9AbY3XqKdbNNP2/dtVuhs8jcz/wSvvfgP+Uj9f8vwseMTAA8i/dY11l78FT60/IeopE64K4RAl0KimSJ24jqTnSEySrHDZGosnhlWuWUM1hpWnv88M5/8HH4iiCNLpEAXDnpEvfd4b5P0ttousz5bLpFb+50M0RuOuWcPAwpILi7g390d28uJnAfDFL1cxxsYvLs7mNKDXZB2rYHXHbqb15Po2Qq2kucoGZoNBd2V4JA10fj1THSQlBXewCAnbeNw4SFT3sIHjzGxVG/FxHnJSU8z4yko+mhrMednSNP9DoWwFtHoggBTdUtzSohxH84XE3VHZAgi55es25r+GXdsxndpfuMkPyUxhaOT4mScuMH6wgKy3d+Pi5ZinDo3CjBJzs4RbDSwEwOiVBIzih4PfWz223qFgLQfO1eUzGf1WN9WAe05dUg4OdmlP/I8SuG67tk+TnYzZKMzVRzrch6zMjO+LkZWVqcpji0uqEb0ozGlxMYp6tYWdrGGLU9fsxcvfB83b/06r7z6j/C8PMvLH2Ju7ilEp4+dmAh51tJae5tX136ZfFDHaEM+N8viwjP33aejIITg6pXfTyiK7G2/Cae8lUreDFdLHyGOTz9RAFjf/hZozWX9OEKI6eIYMNawMbzGrfh1hFLkkoDN6AZPlj/BxeIzIAQq73PevI+W12YwbJBTJUx331fcBoLUz/j+gcDmlfPK7Zvv2g4yYr/YSGeKhwVO2uBvPnyeJrgoa5Pzia4suBWpSdpTzsfMFAkG1qWaavaHsZN6DlGCt9cn9hRkaXnpbAnjS9K8xChHm5OJReYyv99sm8O6C3Ty+4/eIvEo2AMTflvNY4cp4b1pdZ/uRYcut/DGNqacQ3SGzhFECuJyOOZnH/2FFpE4MZSMUlRzD1MMjg0hiS/Njz83Wo4HNzaKjHIhSiE6SvcdLcBNsAYxcmJMPg6m4BNdnDt5v5m+BGRrgNfqYzN7VoQr8o5a4fD2epjAQyUaeaeBruTwBonz38Z5c5OlEJpy7qF1mIUQfM/cT3Ct/WVudr5GyZ/huZnPHyqOLZDMl7jTf5lbb/8my+c+wvrdr3Dhse+/L/84qkjiukQkELY0fs+dJc/P8+FP/hXeevXn2c3tUUhPLpCD9gCvG3H1/A+wu/X6lI3q/eA3E66//CtcKjxHVVeyGZ0greSRgxgZpQyDDrdufoG93WsUS8sM21v0Btt8ZulPESq3qhuUa1xY+QFef/WfYCt5hJCkAXBEx/rbhfc2SW+2hEnSKQeLR4VUCPA90uU66vaWu4FTi50pYUp5RGeAKeSckAqQRQ/dO94DWPQjZLs/vfQvhSuM1hpQCNFnZqY6fEKDMOz7G40+ZkH2NTan0MoVmMO6R2ndDSzadx3jk4rjMQwE/fsvQNjYIKTjEXkDAyMBqRBQCPYfXkKgrR3TKyJhkXb/rIw6IGpokX2NKbqDi2c8/H48HtXMUg11e+sQV9Fq6zpI1mIqBRLfI1hvoJpdbJa8J7sD1GaTdLnuOqCeixy0vkdayuFtZw/zrFAWQHqgWBLauE7LEbSEYc0Vx7ndlKBr3G+EdR3wkQLvwOfEMEb0I8yMmy2n5+cREw97r7H/MNH1Enqx5q4rY9w5fhAeci5Az1fxVies+JREz5SmEh3H3+2FXL3yw1y98sNTf7fZyojWCd3uGmvrX2Vn900eu/z7WF764LtWUoPjQq+sfJg761/mdu9lLhSPVk+DuzRkFlVrPIHInd5uLx12uf7WL/HB+ueP3e9vNX+Nrmly+YkfYWvrZTa3XmSm8hhJPYcO8qjOgKjf48uNn6d+/jkufO6P0S8Hbok71Jj80cuJ4LoZ360w2iB3uui5EqYQjjuBNqs0xFGq04cImWhU9v1JJT8uvEYhE2IQY0oBXmwxviCVLq30EHwXBKKXqshhgi2GYxchAs99tiBoX/AJuobClqZTBVEaFcWW6t301F0p4Utiz75jO7jTwAiByHkkixVklKDLeUSSjq02ZD9GxBobeog4HXtMm7yP7EbkXt8gvjiLKR7dXBKegpyPGCZjPq4axOjFCklekhYEYcuOBXnSWsRuz8U5HwMda6IrC+RubO9HM2f2jiazQD0RiUGY/aAtcDZ1JvQRSep8ubV12zSuwFeN3vR2PUV8cQ6110ckKf5e3z1XMh9klRqnD5ktks4U0e2hE5qCa9IB5Dx3rh8iqsEiH5r78WNf76dtWl6T69d/D+XleO7Df55CaYFLV3/wvtuOy5L+clay5ZwQNdzT5LfcOc8X51hc+SCvvfoLLC7+aRdkMgnJeJXXJhqBRmgwJiWeP31zsv3iV7DRkPOzz2I8D2EM8XINPIlq9RlGTX7vpb/PzPzjPPGBn+KbX/5vmF96hn7UYLLLsCbv8dar/5qzP/BH31NnigfBe1sgK0W6XMfvDqa9YAXYE7hV7wYmF2AeX0G2eqitFjrrGItUo+crICw2tewtSkr3xLHRvWq7hYhT0jMzeDttTD4YC7vUVhPV6KJub2EeW97/UGoprCf0z/hTqXi5zQS/Z7BK01vwph6+FtCZx6WKdVa83QeWYwu70cvCuoI96DrLJePvCxPtQYu9kZpUQVzxyO25FL9hXaFDSXEjQcWWcM913pKiJCkr4pIcd+KM7xEv1h1NYmrTwnGG13axxRzpyizJ+Xm81W28rRZstVzsuLX4WXfZlHKki3WEEjDpUDKp9B4V5rnARQfLzG7iwPmIqoq0IClsJFOiRsAFy7T72Jw/PifSWsTGHrLVxxZzmJkyAuu619nHZLM39mDW1cI4UU+uNUBJzEJ1SuRyP1hPOlu6fuw6JfUydq58pOXUicg68uvrX+Ot67/I2ZWP8dEP/68fOG76fvALVS5d+hxv3fjVEwtkhHDLqL6iT0KlfLrgDWstr771zzhTfB9V//Bn+mmL2/2XaMXbDG0HT+XY3HoRgGZ3lac/8McwkUvNa0Wb+GGZM5/8UawvkRa0gLj63WnhdipY8Hc6rpOc6HFBLJTE+OqQw8RD/mqii7OEb29jfUU6Xx47aMh+7O7R1GAlyK0u/SsVohlF7e3D+6RF9oxXkihbDla7XXTWRc51TOaLrMZeyTajskWzitqtBBGd3nKyuSJRMXiD4xsnDwNWCPRc6TB7PhMfi9jR40zOJ1jdJV2sYAoBshMR3G3gbbZJz89gJ+xHx5sA4sUK4e0D43B7iC0WGMwqjDIUtjM6RWtwiKN+EKMAJCvFofMy6VRkszFYWLB5n2il7oJj3t5CdqMpsaLsx8jOcKprfRJsot02ZooIbVDdGJnGJLMlx6Vu9kkXyphiSO6tzczBfwJSkFYLj2zl5Dh8aesfk9ghTz/3J5hdePr0TQopGC4dLteiusJFirvfb2nlg9x+81e53XuJq5WPTL03LefxWq45qUsh6UyRpKBI0wF6uXqq1DhzZ4Nbr/9rPlT/MfA9TCFAV/KOupOk3JttcuvGr4OwbK2/yNyiex4M+rvML32AwHMOLroUstl6nUuP/z5yl576tlMpjsN7UiCLOEWt7YJSmGI4VRwLXxEv1MbdrocNOYih52KohbGoe7ukV8+4gcfYcfhFfjs9tjiWuIS/EadGl/JuuT2DHu2/MYeoczIylG7HRFlanw6E47oCGOviro3z+gQ3vxrHSD/IcXYGrhOzWJv6/iTvrOFyEybxwjIujk/cpnZiQOPBcEZlRbshzUtUrF2XJ7L4PUPadsW8xT2UpHEWcyZ/wHnCWmdxlA+RnQGy0cHMlIkvLeLd3UH2okOzetkdorw2eqm+T1FQMguKsZBZBznKSwmxvkd6bv7IgjIuS0rr6dEWRkqOo8gFwMi/OEuWstYim11MLXtPqvFubWEKAQQKEzg3DIFF3mugOgNH84izjtAphwErBCLVmFIOM1fBTHDCxSA6PKG5D7Z3X6dYXOTqlR95ZIEim9svUQrmTnyPsBYxjIlzkt3GNc6f+9SJ7x9h497XGQz3ePaxn4AJjqO1llv9F7nR/SZlb5bYDhBC8vIr/xAQrJz5CBub38RajRy4679td6nUzjvXAWEIWprhrPfvdoGc+dmORFZjpAb5kAXSByHyHjbng4DwToO0VhhPFi0gopTg3h62vAzWjTnBAS64DQSMrNXI7qJQICKLni1hPRApLk0vtqjIhYlYXLyzKYHBPjCnWkWW4vopOqKPCP7dPVRW0AjABIr47AxpJUClrotsCgGqGyGvbZEsVcYOFxPNQmcld2CCHt5rYKp58jt6TPUDsFm403HHbD1JdHEOb7c75W981PuSxSqmFLgAlWxl1QLxxTnsgY53Ol92wtBTFsgCCO/tES9VCbY74/wD55SSFfuxJtw67AYCjh/tvcfFcT9tk9ghV5/6A8wd8DG+H4ZVMdVkm0Q0485tflvT720zTNrUK8uH3zjhhmSKIaaSZ3fjJQpzZ5He/UtB2U24/YV/xqXKh5xDUJSOOenDIOH1l/4RSdzDGotJEpTwee3Fn0WpgOryVaK9bZLlsnO2OT9L93fvcf7Jz7nFgu8cVsUU3psCuTNwYiycIMstb/iYQoidqxwfWPFuvxdQq1tTBblU0nEKs9ASmxmQj4pS0Rkc4njS7rtUvTEODyEnuiUYe8jpwQL9BY+0pNwsW/NAXcZDX1EpjPmxgOuGNnv4s2X8wbvj28kUihsnL0VNxW5PPN+s7011fW3goZdqjq7SG+JttTDdIfrcHGZlFnltzQkFy24SIuIUOYhRbbeUJnoR6UJ1LOjDWvw37zk7OSGR3Yj0wsI4PvUgClvHFMcHj/kIq0AxjN2Dp+K42tZT6MeWMEKgJ64Jud1GZTQiOYgJbm/vJ2fdD0pgSzlMtTimc5BtWd3ZRvQi9NUzGCURnQGq1UPXitgDXHprLW++9T8xHDYZ9Hd435N/6JEVxwCt9ipXah879nVrLbf06+wm9+jsbjA39z6KxfuLQjbWv8nbq/+GFy78FP52l27aRCLppDu80vpNSt4MF4rPcr37e3zy4p8lP/Tpznt85bW/zZnlDyEEvPLqz/Fs/nvJU2DXbLFQeYGgtV/46Nx36Oj8kOA/BCrNO4XNElRtPkD047EwSpdzxOdnSMoSteucTEwxIGilU9dpVJP0Fz1kaqm87UKbBguKqK7wOwa/Z+gvKkp3Uye8UxCkAtU0dM57VFZTgnZ8qOAzAURlRX73+LGxuPHtK47BOVukixXwPWSrj7/RIndjG53zUakeix11lp5Hpr+Qu138vb5LJLw45zQhoTdd0BpH/1KRwGRzQ2Etwe3dQ8csCwHaGMQwJZ0r4a+3XNgJHAqAATC+wuQDvJ0OiVed8noGpuggoh+P6SS5A13u+yI1BHf3pv9m2Z8I3efHe69+216yx7f2fo2CVyUf1Dlz7iP3/9AkJMSzJ5dq0YwiqkvibzYBKHr1Q+9JFiuYYsCwt8fNu7/M4FaHbned8z/y0/fdBTXU3PwXf5tcrLhY/QBpOaCpt8n7NW42f5u7r36Z85e/lzs3f5tabplPnvnzpCbinneXu80XmX3+07zxC/81N6tf43L4LElrlyTuo68sH6vR+k7Ae1Igm9kyzLjW+lEq/EcFC9iMbzxGnKJ2ushBhF6acUt92y30bNklsrV6LklIuIHDZsKsUf9PNnuozT3MpUVnD2ezzsQDKtwFUNzWxD2DTCyDBY+4KAg6+6NNUpAgmKICSE+gU7dMKBsdKOYw4RHddyXdeX+XiGr7y3ZB304l9p0GthiifYXa64BxqwnBjY0pxwHZjzCNrnOFqBSQvSHJ8sz4nI4oLCP+ndp2NAxbziMit/xpQ5/kohMCHTVojzCO6j4CstVzQkVA14uI3hAZJW62vVQfW+OozSakGr0y6ziEgHd9Az1TGhe1VgrH/4vT0xfHgC7kxvswtW9rDeRocqAkcq+Lt9kE3DlNDhTIxiSsrX8VgJUzH6FSOXvqfXhQGJMipcf5xY9hYoMp5cfJgQPd4VbvW+SCCm82fweA5575M8zMXAU41qRe65hG4xrXb/xrXpj/MWqdkKEY8G93/wkCQSiKzIcXuVR8gdviLer+FYKVC8jbW5SiHEIIrr/9yzz37H/IzVu/zlc2/wX13Dnag3WeHNkwZRDvbv74HQ/7iDnGJ0EMEkSUksyWCPuN8d9VZ4i/0UKYynhVSPZjVHtAurgvIAvbhrhsnDtF1kn2uwYdCIprTm3cX1Dj6OfukkfQ0gRdxgKmae6qoDvvkk6FgVxTH//7v9vTluhsleudwSqJSLRLyKsVMLUCstFDdYZujMsKZBml+Hca4+XzEcTAcc/NYoX03AzerR2sp1wsc6gwUmZuHllnXghs6I0TzmzqlsIHF93KkNrtHlqFENaFdCSLFfytLJVSCJLzJ/vKi0zMZco5wjc3MDn/XfnFj+Bvtsa/21FBJY8CQgmnrTkGjfge7WSLdrLFU8/+sQfe/rAi0ae5jIRgONxjafkF/PoCZNdDWs2jSwG7t75BxzZpbL9Bt7tBUK7zxJ/5q6gwjzUGcUyjMt3YovHVL2G7XZ6d+0mS83Pc3P63rN78Law11OqXuXr1h6maGrt+jQv5DyCExC+WmVt4jjfv/gqdwSbPffwv8trXf5Z2bpUk7jG/9Az5PUvv0bBrHwreMw6yEWLKx/IQBGObseOstt4JdL10WBToScwZ5/1q2U+VE9ZCkiKNxSjhjLFDQX53Ihgk1aRn5/a9k0+TW3ocjB37UZbvHF6u8o8Q35l0n0Mn+5FzAgk90jOzU4XySFRoPTVtd2Ysojc83CU/Bn5b0z0bYH2BLluKdx7MF9VUi4hBjNqd6MYeYccluwPMbBlbKyFafdRmE7tYxUiJVa5I14s1ZLOHjBL8e7sIX42VybpSGPONR8XxIUulU+wr4KgemW+xrhbQS/XxtuVuB7XXJVnZ9w621oLWyM7AFciJRs9VMHWXJIgx6IUqwdouNjn5IXBwJUJ0BtjAQ/aGmHzgONC9IWqziSnn0ZUC4giBTpK4h8Pjz/0Us5c/iO0bOI3o8x2g01nDUzmoldFCQJxireVm70Xe6n4JgKKdx/cLJEl/XBB/+Sv/D3K5Os8986cPbfNb3/r7WCG4svx91OIa2qYMdYd8YY5eb4Pa/GW2dl9lbfdNluee4+nSpwhubGAt7IgNPC/P08/+MaIZnzMzP0Tu3jmGvV2eXfg8gT1wjt9BYth3E7QnSebLyF7kiqP3GMHqLjY4/Kjxd3t4zT5CW6Q2Yy4x4GghWdpceV2zd0mQKvDBRTpnYjBrHRUi7FrnvVqUWCUIusmR3eE4L9B5SfVWcqgw1oHAeIwt4Kxyk6c0J04lmE4KLh476NpMdPbuHq+2ECCbfYI7DSc0my+jZ4rjyG/ZGSJ7jst7kEubVvPoan7cvdWBh358iSALILHHWKUJA9GC+57wxjaqH6N2u45Gli3Rm9BDV/N4Wx3XqIlSwtX9yY/NueOW7QFyEKPrxfHv72130KUQmw/QOT+bJNlD16UJPRdq9QDXa1rNky5WCG/uuOK+lndF8gFrwYeNk4pjgNTECCH56Gf+dwThgzWtrBLE86e/jrqNVQr52njylMyV6M8oXvnG/5dO6y5gyRfmsSYlHXZRYZ7B9j2u/aO/zpU/8p9QWDw/tb347l2u/8u/yXx4kQ/Uf5B4qYrxBMP2NrX6ZdqtVTp7t+k0bqOEx8XS88xceI4408y8+eI/ZP4Dn6Z84QkSDU/U/hytN7+Jkj5zlz/I4AGO7duBR7p3xsv4Y0JghOOc6UBgAjlWsFolIJSkE7XzyOUh6BhXJL6L55cthOiZ8ni5PF2qja2v+jMKK6G4kxXAQhA/voTMalWZWgbzHvmWGRftJ3nrHoSUzqP3neJgYIevJMnI4gxnyePd28NrdPHv7pBcXMRkg5jsDVHNnuuiz5YRrb6jYRiD7A7Q9yuQPQGpRRqorCd0zvinSgLEWjchMXY8cfB32vf9CeUgdsVg9hnV6mGiGEp5vCwOWO20pyYj2vfcQ7QQHtkt7y4oR6k4YmyUAFGCjRJEnCLiBGEsplyYCvWQ7b7rShdceInsDR01KDt/MkmRaw2EBZ0Vt2ah6jju3SGyN0TXS443fB+ilQg8bPFAcIavIPRJrp7Z/1vgoZfr44L+4LlNkgFvvPkLnD//aSovfIRYgM4ZSmsPX4xlTMpu4y08P89L1/8x73v8Jwl3Wnyl8S/YS9bG71tafoHz5z5Ft7tOqeSOpd/fJghKR2530N/lyuO/Hz9RfGn9n9NOtvBUjkS7yW6/t80zyz9GLbdM0NcMh11ux6+gqnU2dt7kwpXvR+QL49WXuYWnwII3NIeyREz+32H+MeAFHul8ifAh+76eFjJKxxzREWzOJ1qpOeux7TbGl0yZdis5jk222dg7nFN4d9yKkUws2ofeiofXt1hraZ3PQpdOGHPTvMAbmCO7xkLbKWcamb2nv6jIb2kX0hSnRxb7bkdhMO8R9BIXVvEQgkZ0rXBk8p7sDvHXW4gkdeK8bL9t6EGUOvFanOLf2Rv/3XoS1Y2cQGvu6PsO6xwmVKPnusGWQ11jGaWISVcJKRztox9jCj7xmRreRgt/p+t0IQvumSkHMd5mG39XEl2eByVRxyTdiShFqgfrPHmtAX4vwqYubXa0AmnN6YWZDxt70Ro3et/ggx//Xz1wcQzQn5PHco8PvXfrLipfYvXV36Q667GSe4L+9ipf+tb/ADjHIWNSnvq+P0+0FJBqN/mI247aYo9Y6TSNJmG+zplnfoi7m69y7+V/DkASu99teeV7aG28xSeX/gS66IwLGo236ay+TKG0QHP3bd7/Q3/cOYoo0OcqlFc+g7DQ+S4Ydh9pgWw9QfvcEYOEcnJkm7gLV0jhhBYGSK3z1i0q0qJCaQi3j3AceLA92f/XBDdVJpbhvEc7L5EaimvTXQU1tFTvJu+4o/1uimPgUGBHMhl6ApBYzEIN249hGDuOaiHnir1+tB/lvdd19mBSgFSYg7HKR+nHJo85sZTvJWgsOhCo2DoP4CQFX03vpxCY0HcCvO4QOeKcH4QQLhXKWlR74Arrdn9MUdBlZ8s16RsstMEUc4jUFbCmWkAo6b7fWCdgmygwcw3NsRE8rR7e+t6hP+taCV0roppuAHDuHwarzVhsKOPUdeGLOWi5UBKkxI4K1mylROR8VxzPVxGpxqYnr+Un9cMPrYPXALhr2FaPv3XffOsX8Pw8ix/7/JjfpfOS3tkAr6MJBgZxAtXkQRDHPW7d/g08L0+/v40dDLi++zvj4njlzEfI5Wa4cN4J8srlFQDS7DfM5w4vxQ4GuwTkePW1/5Fcrs7VC59jdvZxNvZep9vbYGbmcV5/9R9TK5YI0pRO0uDrzV+iNvcY/dYt2q1Vzj7x/agEZNbdOY5aE5fkoRCff9eQpprg7t60b60EnQ9dQQVOOOspV0jlfOcY04vxG13E8OFaYUHmQGNdAah2u4h+4pbZM3rUpBxDAKV7Kb0lj/6yQmhH/Rq9p7QWO2eeUKIMlNaPuc88QVSVY+rFQUi9f71MQgfOdhOAieJY5pTzZM8Ez97AIhNHuUvn3z29zQPshPjMZOJdlAQl3cQenOtEdp9HF2b3C/jAQwwzDUc/GncVTc7HlI5OsDSBh7fZHgd4YC0q9NAHJziec08Q2jh3ksUK6uYOab2Ev90Zx5gnCxWsEEhrCe7uud1MDQwSbCWHVPLI2HIB04lc94EohtgoGW9LwHgfjozEfg/Q94Z8ffOXeeKZP0SxdDrHnknEJUFcO30V2bn1GpuvfgGAl3d/jfpnPsqXfutvABDmqvjFGhd/4s+SFEtIYPRkSbpNAPxSdWp70jpXk15nnZe+8rep1M7z3If/AiBYv/Nl8oU5up01auESUkhILDdv/RZr977MzJn38/ab/wopPUR5eoXztAX/dwLek0eDBXQOkgKYwr4oAISzgJmY3nldyO3uNwq1gv6Sj9c35Hf1A3NgAWy5gO1FiCjB226hCyFGCHIdgyBlsOA5MUkoULFTPAtfOq/GA24PowLxJPjWknYHiHx4pPXOcfCUID1iUMjGqTFkqpF3dpywzVOki1XUdhvRj1BZQWmVRC/V8Db28Bod4iv7HUh70P5MuE6+Vq6QOJK/m1o3Ibej5UdnVu7d2oKcj64WoZzPHrI+JvQxcxXkThu10z48gx8Fg2iT2YS4DoPR7uFmCyEc4Zc96XAh+rErwKMEmfkUp1fPjM/3Sb/TIRGfFKTzVWwxxHhyXCADpAtV8D2SegnRG+Jt7OFvNIgfOzNedtSl3KHf2XgKRpZv96FW2Jw/dtB4N9jdfZNma5Vnf///BlOaLq7TUJCGHkNcJzVoaryeeVfdlVyuyrMf+NO89Mo/AOD3XvlbJGmflTMfRSmfK499/sjPCeEGgfm5p4F9HvNw2ORr3/hbLPkXiXSXK+e/n060y42X/w7vf/o/oFI9z1e++v/kwtxHCaTr4r/R+R3On/8kZy98iv6MoHnvDarewrgDeBx0ThAtPBr3nO8oZBemruTQhRBbDLD54P7jUuhj6wW81cbDj+tNDaoXkRYCkuUawdtbRI/NY4oBaDCJwd9soefLmMAjGFi81YTUF87aSri5r9/RyMx+XfWN8zU/prBKlWvCTDr6HIdJGXbpboof7f8d3L3UOSspblqCZJ/rnDtB9HcSJKCkIJko5sSdBv4Erzg+UwMp8Ha7TuR4ZQExiJ1tXqxJztZccTwa331FupzRB+OU8OY2IjF4u11MOXfIO1klGpU59kwWp/qIAlZO8oXjdNwJVo3eOMDHFIMxHcRREZ0+xBRCqLiktYeVTjlccPaBo+t0pEM5yZHjUcAqgSmGqPaQ11u/zfLZDzO38PSDbycUDM882Ni08KHPEe1t0XzzG/i5Mr/9W/8V5eo5csU6tec+Rv7KE0d+Tij3PX6piklThFIIIdj58m+w++aXWcxfYSgGnFv+KNde+wWCoMz7nv0PuPbav2D97lf47JmfAWA4aHBn+3f48NN/Du/MCucf+yx7ub3vqoL4IB4txULCYBZ0QWA9i84ZTGgROevU+omFROB3FCTuMk5LgiS2BNMGAqQFSbcgya8nR3JzT9yPfIC5ME9wfQMxTFC3t9Hn5hDGUPzWLvLpJfpnAmdTNowh9OnVJWpoCNvZ4Je5W4wszk6CvbOD33cj6ig04igIaxGtPrZWwCLQ3SHe+h7p5aXp7cUa4cl9q7CW4+GKe7sk513ykV6oYqXEX28gBrEzzq8WsK2e+2+yRnFGgbCBt9+dNJAUBUnB2cEdVViqzSbWWsxiDeMJJArr57HFENmL8IZNFy+tpEuRK+Zcd7mYQzR7LvIX9oUY2oxFZuPzkepxV1cc020V2QAulBNUjo6LOMUcUaQeBWktxMnU4Gk95R4+d3embenACWV8z9FVijl0KY/Ifl9bzDkXiYXayV86PJ5LZ3FWge8W1lrurv0eFx//fkz9ZApNmpOkSxKVgt9MCXvmHa+UpHqItYYwrBJFLS5e/ByXL37uxM8o5fPpT/4f8LyQ1Tu/w/W3f5nv/fR/xb2136NeOs/T4WdYyF/mxvpX8bw8Jo5or1/j9bu/DMDtnS9z+dyzBLGgk+7yeGabVGhYCvmjHwSTSIqS4ZJ/7ALDv0vwfEV8fvb+bzwCRgjiC7PI7hBvq/NQOcyyG+EZiw0U0lPkrm/Tf+YMCIESoFoD0moBXfQI1jvYehE/lXiJppnZZvodg/ZBJlDe0EcWx8YDhHPEyO9ovFMEf0wWVQeDQrQPOi8orqcEB7rRpwp3OngeAP+tTdKFMmR0CmEMMkqwnnArq8aiWn3iS/PYUg612XIdd08hO5FrUmSf9Xa6rgjOPGpt6EGsHe0r8EnLIf6tXZQnSfM+6flZDKDu7Y1jnKdSKo8Sz02sRgjLWCA4+bm0vs/1N0B0ZWFqfFZ7/fvGRh8HXQxBinFBLOIU2RnuT2Dmy/gZR/q9hFUS6ysGukujfYOPf+hPPPhGpKB7xkM/4M4LqYjbjgueDF0BdfFH/yxqpnLiODfz9EepPf6Cs8j8m3+F5U/9OLXHPsDuG1/l6hM/xtJmgWvtf8vN1/815XCWjdabbK2/xNodpy95rfVFnqv9IM2gTbm8gnfGrRLKmSrh2ZOtP7/T8WgpFgEMlw02b7A5e6B4sRACoSUpWPy7+7OltCbxIoM8MBZbIFry8VfjB36Yi0E8dhMQw5jw7XVX2OR9/K0eQcEtEYzijo0n8CavKs8NyF7vmMINULe20CszjqMaeM5TsjzNHxP9CO/OtpvpS+n2a7uJXqxjKgVMvYT/9jq6WtznO/sKsddFbTan1feDGP/GOiJxaW3pxQWSs3OEtzadtVqcusS3RtdRGHbaLkmrcpjTFnTMyf7L1uI1e87yLvAxgedEc/GB5VdtUM3eVAfWSoFerCOGMWrvZJ9LoV2Qib2P20kyW3ZWccPEOXYUwjEH+MTtY5G3tw5FPos4PZJyYcp5RCHcDwUZRMhml3Qk8gy8TMTHFE1FGpfqNOrPjkSKk8JCADzpBH0PIWa91bpFr7fF42f+KKctY7QHes5jOOfSy1Rf4/WN87g+5S22MP9+yh/+z8jlqsRxnzA8XSfc89wx12uXuXrlR3nl1X/EsLfLc7UfAGA+OMd8cA5rLb+a/n94/e4vc2bpgzzxxB/gN3/rf8/m4DoysYS5Grmwdrqd9QT9GUVS/i4gwD0kPIwFZlPKEZdyeNsdvK32sS4xDwLVi/btwnC3kOzGTjeiJOZ9y1ghSMoSrxnsL6OlFmHAKEualwznJUJbyqvTY1FcEvg9S1LYTyCzHeM8sB8E2kx5yKoEVPPobbyj85JqdDWP9RXhmxtIrbFZsnm8UsPkA8LrW8he7NJBPYlZqUOqyV3bcuEcUpCSadzny1ghCDZah2LFo0tzmGKI1xxghwkq0S6FNB+g3qmLxAF70lH3VjX72Gp+P800SiH0HG2tPSDYbh+zwfsgUCSLFRd6k2hElDiBupLEK3WsL8m9nUV3TxpCvweQsUbu9nhl+BVWLn4SqR58haq7rI5N9LwfLv7oz4AxWCxCKkSucP/DzwmiZR9vYFn57B9GCcX1f/Rfs7D4DLW5x7DdBk+ITwBwt/c6d/WrvP7Sz/HMJ/4icafB9df+J/aqMasbLzG/8qzbpicYLH/3r849Yg6yRdfvP0M00mJ9g0iy5Wpp6S0LlBGotiGc0AhoAb05RWHjAdONRm4To/vYWFQWD6xafVR3iCnlnYVY6FMUs1NPllE4gzqOjjeIsL5y6VQnCPlsIUQvz6Ay4Vp6bg6100a2+pBol9gHeLttdBYwYZbrmHoJESVThSeASAwi58MwIbi3QzJbRfseMtF4G3uY8/OY2bKjX+x2EP0IrSQicgIMKvlMROl4YsB4QBPGjD2qbSHEDBPnRTyMUcN4PDCZWtGJ2Brdsf/v1D4ai7fewOI4boeK6oPvtxzqLh+E2nLnyXqS5NLS1APsJMjN1qHi+PDGJclSHVHKHYqqlht7zg7ngMgxyUvCgcVk51Dd2XEF8jnX4U8vLzkOtZLu+DuZYGqmfGQc9jtBkvSJozYbb32RM/5nSeZzp7MHyjCiYER191yRQ4McGry+wR8eXzALISkU3IQhlzu9iHWEXm+La9d/iXxY5WPVP4A0ilGsk8m48z/An6MRr/H1jV/i7OXPcOH89/La6q8DcOH8994/kcoTDEuSZMb7jvbdfBRIMJgQtBLEZem6ktoStM3YseG0SOfLmNAjvNt4qIXH6CfJ394lni1hZ0rYrCkR7hm3UtUdYnIgPEV+M6W/7BHPqPEWhrOS3O4+ZSiuKpKSpbihiWadfiIpS6KKJGw/wM6fcmx5pzCewmRR01IbMLhnSaLx11vEF2ZJlqoIa6fHCk+RLFUI7jWdEC1Kx5zycViGJxAITEaTCO/tkZTzTkOCc5wwWXy90AebWKc9gP1PRRdmMaUQ2Y8JVnfxb+4QL1fxtzuo9hBdCpHDZMyHPjU86XQgSpL6HuGdXURGW0vrBXccoYcp+KgsSjq+NIdqD/B2e5icTzpXItxsTzco3iGSudKJoSZJ1KWztUt99iq1mcun3m53WZGU3vn15uUezDMtKVmiBYsVoJoJ0fYGOy9/kctP/DDLK9+DGKRTz8uVwpMs5R/jm9Fvcv3lX+ADn/gLiDf+Fb/32n+L5+V5/OmfBCnonPEe6NnznYpHWiB7nqZe7aKkwSJodfKk6dFfmS5p1BbIaP/i0NKiawIrLbmJ5l5aVAxn7Ykm7wdhCyHp0gzeeuPI11Wrj2q5osV4ynnuTnQO01DgRZmQ7IgOrM2H6JUJA/Te0KVFHeEWYSqFqW2kxZwTvGUxvN5aAyyottsfOYxJzsyg56tOQNOPEambOaME8bl5gtub2FhPHZ9INHKtgVmZHXdf5CBG3tra35mNPde9qZeQ/aHjhS3U8LaaiESTXljA+ArV6IwH1fFxlPPjyYDoR6jO4ETOlxMWplOuIu8Uo+8QqUFttxx/N07va19nFqp4vcHY5kjPlDD5EC+LEgeyzn/+6IeFUiTztUOv+X2DWN2G8/OuW584UZ7o9B0HXpuxKt8EHsw+eCF5P8zOPsnTT/0R7q79Httf+BvML7yfyhPP460s3f/DB2AAk5OQk8Q1UBZUWxP0NOo+S9TGuLCH0wSTJEmf1974JwBcCJ7mpeb/zE68ykdnfpJasLhPdxE+c8F5PJWjWeqx8sHPU1i5zLC7w9nZF47dvg0Ew7Ikrf77VxiPIANF6+JhsWdSUZRWkwemBZhKnmhlhuBO4+EvYWtLsNWBrQ429IjOzUAuW9XLhGUiSgk3DXG9OI6UBhjOeei8Ib/jUj3TvKCQCfYm3Sni8tEFshXuf/LdFP6pQecl6h0YxpjQJ3pyCXoxphSSu741LnoP8oXH+1wvYrY7ztJtwlZOdSNQkuH5GcQg2XeiiDX+7n5hp7PiWGqDid99pLa320VstJxzCU4nMurmCk+5/cKFm4gs2AQgWSgjO9E0tQNI5sukM8XDftKJE56qXoS310f2IqKri8469V4TgODmzv5zYuhEoHGtgL/97p494NIJTWc4Ps6DeKryaXbEJq996+eozTzGzNxV5peeRR3XUZbiXRfHB2GtxRqNVEfXXEnZMlxw977fhL0v/Do7L38RgErxDL/7G/8l1fplnr/6x8jdc9ePEAIlAs4ufZhXr/88w4slPvL7/hr33vgtSpUz5KtzdJY90vy/G4PtIy2QpbRUywP2WgVanZOXv42wmLmUcN3n4JPM5A7bLCQ1j7BjkA+gxhfD6HT73Y+Qq9ukcxVsVgCOYqjNEQWYJwTpATP+kbPC4X1IIE0RxRykGtHsIaLEWYL5CnME91ZECcHdHSfWO8jZMrjiV2ZKtwy6nMeWcnjre45aMVdB10tHUhwEjP8uALGxNy4WvZsbmNA/VBwDeO0+yVzZCRyVRM+W0TNldzxpVqwfOAXCZgEnp4CuFJCd/n2XLVWzh2z1sIFPelyBnKSOe10tYmKNLYTYXICedcX6uDgWkM5W3ARHCheJ3Ru6jrbIVOVLNXcs2iB9hc5+Z71UdyK+7da+Y0UmBvTu7qCX6o7CM5oMPeSEMykV8/NPMzf3Php7b9NovMVrv/7fsPT+72XhhZM5wfeDFqCririqUKkl3E4PaQGGwxY3b/06GxvfIJ+f4SMf/k9P7Ow2Gtd56/q/YnHhOTa3XuSNzu+MX/ty45/zmfk/STC75GLUtWEnvkMYVCgvPYba1izlr2DzV44saGwgGNT+/aJSHAd5zG9gBXQu+BTX0geOtzfVPOng5C7au4WIUsLrW5hiQHxpnqQg0IEgbHmI1FBcS+hcDMb2k9I6bnlS3OcnBz1LXBSM+jICsGq/4TH1ffYEisRYrHCfnbb2HRXHIxgpYeRdXAjwIufLPznqJwUx7vwbIFmq4m+00eW8E6pFWTjLYkbdygek/fhQkAiAHCTI9oDceuuhUHFGBfAIuhA4h4tGb+r7DxbCshuRnKujrm+OVyYsgBQEdxqYcs511Qfu2WJzPvGFWVR74BxaYo1q9pG9eGwpOEVHxF1Px2lbHhThzaMjrEcoejWK1Fg8f5U1c5vtzVe4ee3XePZDP0OhdCBB1Hcd1/QhJnoOXnqFtW/8Cr32Gmc+8weYe/ZTU69HNUs8uy8uXf/v/x5+qc7cc59h58Xf4sVv/B0AWns3eOv1n+eZyv7zI1mpc+/Wb3DmmR/A+oLuxZC52ufQgaBZlA/lOvpOwXuiLxwOj7aUmYSMBf6Od6g4BvBbh0+5JUuaewCYeglxnIflEfAaHWQv43DlVZZsJ/Ck2J+ZRgl2q3n4w0oemaIkBhH+3V28N++h3t5A7XaQ3aEbuBM9HqCtr0jnq/tFVKKPNHcfDSKHvj5OMKU8ppRD7bbBWuwpU53EJAXCcoiSIEf84FSjbm8jmz2sr9BzFWQmnDOVwrGtZP+UBvqj4ljXis4l4+B+BgpdK5Iu1kjPzmEuzB+/Md9zvsGJ65KIfoRqdPCvrSH3uuh6iXSpTnJ5CVvOo3baeLe3Ca6t4d3ZgThFWEgvLWACH4HFu7WJHu4LIFWrh3dnZzyZSOcqYyGkSDRi5K/sew+9OJ46L0IyO3OVq1d+hA9/6H/J+su/QdJ/h3y/I6Azflky0SXQOuEbL/53JGmfhYVnsg7yyce423iTfn+Lq1d+mPPnPnXo9W/u/Qp6tuwmM4HHjtpmceEZipuZJ6053O2zQFRVdM8F///i+JSIZt/ZeTKLlUdKP7BAulBGDRLUThdvYIlqit6SwiqJTKByM6Z0J6H+dkLlrRg1cT2YvHQd4YlhM9jTVFbTQ8Xx5Hf25xXWF4dfOM2T/4gxVr5DpwZ9pkZ0cRZ94JllD57ycg6ZuVR47QEiSomXquhSSLC6i2r2Sc7WD/k325xPcraO1+idmnagTuHtPHmaZN95H6uJ4lj6ytHUJt6o+jHhjW1M6JPMl4nP1hk+uYQu5VDDBH+zTXB3D3+3i9caIDuugTEVY32f82zz/qHC/FHC5nwCG3IpusSzT/1xzl74JDev/5up9+icoH3Bf6jFcevGK9z80j+mPHuBXG1xygJLKBgsmnFxDKB60Ln5Gq1rL7L08cOuQ2ud12hE9/bf3+yx13ib2fPPAa5ki+rKhfQ8tKP4zsAjLZDTVJGkEnsCWU2mgmDDw9/wp+gV4IqOcNfiH+Nvf5/chcPvD3ziherpP2As3p0dZLOH0Yb+gkdUVaTaGZFLLGqtcWQy3BjZPgbZzSsm3AxO2n1hQcQJVoCeLZPO7T+MhMw8hAPl+Lr3drHDBJk5LQAQpaiNPdLFuttWlGBzwQOfM3C2PHbiQTjqcutyHlMvOd60NshhjLfVwl9vZBHL7v3S96YONj1mWeqocwBuYmOPWGK0CPRiLXs957ovJ0HJcVd/PMEZfY9wFnJqr4fa2EMM4inXDVMIGD62gPQ9xCDGGtCXFhFZoqK6veU43qPvkvtcdOf9qR0/21p3DbxH8b9hWGF+/v00Xv/yQ92uE8wGmExMYq0mSXq023fxghzPP/+/OPHz19/+Fdrtu5w/9ymCoMRjl38IgFxuhtB3HrKJifA2msh+BNYyZECYqx5b3OAJ+ss+wznv3wt3itPC3OdaeydjAmRaheDRTUKcxkBhrcXfbKF6MYXtlKSi6J71sEog0iz5LksYnQwF0p7rkA/r++NCbkJcd1RnXfiCaEYRFY84KUIg/Qd/ZL5TP3wjxD6tZCI0Y5S+On4foMs5kjM19wcJeq6Eag1Q7SHBvSb5NzamGh9pJU90fga13XmgojE6qckiBfFSlfjpM0RPnSE+W4fQQ/Xj8bhoQo/+hVnSIwptkToamlUSEaWo9tDR9g6cv3i+SHRpDu/eHv5qYxwqo1oDVPd4O0LZi+EReHq7nRf71ZR01JDhlQWiKwtET5/B5HyWz32Y5u41omGLuOicKtoX/IfO1TVxRBr1aPfvMfc9n2V21D2W0F+2pBMa6rTVYvWv/9+oPfkhLvzwn0J6AZf/4F8EoDJ3CU/lEAiGuju1faMTvNz9RfHf7Xi0Nm9GsLk1c+ysQmmBt+2NLd4Owt+zBCes4PmDd/AYLOWd2fsBoZjJ+eNOqSyERLNliFP83TbeVgtTzlNcs/SXA4KW3i+eAu9QPPAkrAWVpNi3N5ALVbdc7EnSYs51FYfx0UbmqUa1+tjAw5TyrrBq97HaoH0PPV9Frm475XemBNcj6XO2OdkZoM/MuLAKpRBp7ApdKRAPYK+j6yVHY4hizMRnVXeIVsq5cXQG+8V/ovFvb2FKOUScokcTioOGzqeE3O0cuTRmkxQhxOms3aLERVh3Dy8zYvcFm0fBhj7puXmMAnFtHd9Y0vPzmFF3mIxSkz1oLKDn9oMCRDNLnRrG4+Jbn5k5MgTkUeD8uU/x9W/+Leaf/T6k9/CUxVpCfyUgvxHjDXJ88uN/jbTiM5wPMG0NjeOvsbX1r5KmA973vj8E/P/Y+/Ng2bb8rg/8rGHvnIczD3d6Y81VojSiAUkINYPASIZm6sYOwO7GNGA6uh3dYBwEjoBwE+DoaGjTYTvaYWyCyQYDAtwCDUhoKqkkqlSqqlf1hjvfM5+ch733GvqPtTNP5jl5pvvufe9V1f1GKFTvnsyde+/cudZ3/db39/0Gbds3f/qPc/feT9BqvUVR1dgqvD7dwREW3LCP3jhnNyoS9G/EXxeNIc8a5oLfuh57yntPTxqslDzLWy60xJmTRjsXK4Yf3kC3x8T3DvFqnaqD0ZpiuC6p7Ni5vgcxtFA/OSNbENhC+G/tCJK83CfYpeZsxdeGv89KLWwcbOSECJ7Al60nLurDeBp4ggRxwr/i+0eYm0vT1FSEILuzEorcBz3ILHq3g+6Gsc6LcF9n7dpcOQ67Xr3xhcmDp6EWyDQmSLeb2HopT+Dz2HoRryTx4xbZRp3ocRuvJdFOB3WO7aXqJ2dkGnP3QgqyzTCPRq28Z6gY4SN1xrHjNOKd9uUXeBWcdu0oRqQ3mvhSjHQeL8TcRrjPF2JaF9m8+e08vPvTbL7ye8hqz6c+ufSRb6Hx6ieQ0XxRabTqMKfqTGKQkRztEddWqN58DYDyxm1e+uE/zr1/8l+jVIxEsVy4MX1PWhLoqHhpxf7rAc89KMQskEwIQHYkUU/jz+G4ygsKvfN/uDrzqKfwnPSAaZTRsxYzWmJurhLf2wPjMNZhKzGiUsCOU1RnSHx3D1WI8NF6sIuTEqdUaIA7c3ICP6loAH5CxkX4YWVrjWkohGz1Q4TxOQOGSA3R/f3puQPINCN+cDAl+R5wK7VAJGffrAJxtRvNoI19dBQqrZFCpYsjPqf3SZxUcFWrjxgmpDdCSlN0d2+6QJg0201cJYQOyUjCOky9jK+VkMc9fCHC98dnXDiuAtUdTg3m5fDEFsot16b3RHiPeniIL0Ther1HeA+9Eao1mKvcXxWuUsSu1BClOEhrDgfh/q03pxV12RmcfOYoDy4p6BBZnWSI4x66f+LPOWkE9ZE+nXj83FApr7HUfJWdz/xzbnz3jzzTY1sJg+2YuGUodAWFgScepJcShO/8jv8EaxOKxeb035qNO5RLq7Rab5G5hJvlj869xyvBeX32gxX1ghyfg0ULcAEU9w2F1rsLivGxguv/pM8/ngcxY83lSjG9OzG1JxrVH4ewi5tLqFHEaCuEnQy2NYVWkN2UDy3dmpqriqvEYQvyZG0+qRwvqoY6qDw2RDO7FKYkkdoTDecji6WSIUnvFK58P08HNi16iQqLQ2FAAS6zeCmI3tone219SpInZ5ut1YgOeifacCVCYJGaX8io7oh0tRr8gveuLr+66Gy9kqGxMJ+XXFGTvLoegpdaw0DIB08vcfAC0tvLRAd94t38nCMVCHle3HKxQhi3uOjk8tFDiXPDZC6DrRTIthpEO+0whteLuPpJ34u9hDRu3/qN/NvP/Fcs7X0Hsnbrqc7hKjhNjtOGxywId6zoNT787/1ZotpJoqmMYmq3PwRAqbJG0RYoqpOys+oMQ67BU97DryW85xkn0guiPY3unE+OIfeZvOD+q+67oBf18vy2oglEbzLYyCSj8MYT4q88nhIajEVYRzRwQTfcPUf3AWA8qtUneutJcFaoFMle2QhSgHIhpMQRnC70XvtccnwuPFNybFbruGYFVy4gZgZ8s72MubWKfnKM6A6Rg3FebR5PAzbOQIqwXbdUnXPZEMZiXt5AZCG2dJFTxKRw7YRA5uehnxwjeyPcSh1fLYVK+BUg8gH9zAfMVJHdcg271pj7u12qIvsjxChFPTlGf/UJeqd1LXJsZjyJvZLh+8onMd+sYLeWT54Ta/NAFB0WB9aGmO3EEL2zg767FwaTU5OoB+xzto46jQ+//sMcvv0rjDsHz/zYHkiWNP07MYNNjS9cfm1RVJojxxN86PXfxUc/8vtoNG9TKi0xdgOsN7hSTLm5zWCwt/DzbeUFOz4P0gn0zG6bslB7kFF8l+RYen+5ZeJ1kducTVD+yi7Lv3qMTG2QWXmInnSIDodUdmxYCFtP/6YmrQqcFJSfZEQzTYc2fx6tCg4Wcx+3YBMnHnjETFG90Ftsh9dbE5fKU1TrgnniAnLsCcEmgw2NKYRdMvnwGH3QI7u5hCvHRG8fzDeiCbBLFZIPbQbnB8CWC5jNBq4YYasFbDnGxWqacHeu37wgD30qIq4gK3EFTXz/KJDjggZJ2GFzHjk27z5gRoJtlPFS4mONaZbJNuokN5fI1k+Yn4/0Ga316ct6N8TO1oqhYvzyWvge6leb0yYolZe58+oP8vgz/yzEkr8HyKqeZHXxZ8VDQWFpHann75kQgk/+yb+KUgU2b34rzjt69TD/6mIdvCexz69B94OC93SWlkOB3lOI5OKPVWOIL1jU6tRT7Dw9QXaRxtXnZRHquD9n2TLVpk7+O1bIcYps9SHWqJ1jRJKR1iTDDU0yk5kuj3vovXb4jMmPNdeqTkJEgJAyd8XGufOgD7uo9oDo4eFUC+2LEa5exhdjhFZEBx1sozL1FT1v4WEbFagWUa3+ycIAMBtNxChBHXTxxWju7dmt1UBoyasL1pPcWMGVYuxGE/XkmOiNR8hhgq8UpkEs58FHmuSVDdJXNk/OF7CrDezN1emEJAbz22kega+VyF7dwpdizI2VQF5PwdVKZDdXzkZN59CH3ZMqdaxR+53QULnXnko99MMDord3UPf2EeMgWxGtPiKzU5eTi5w3BKeiWt8DxHGFl25/Pw9++u+HqPHnAEewYBzciKba5OvAe8/Dhz9Ho36bVvsu+/YJ/3r/b/HF0S+QjXqMRsdodfY7FfLCtfQ3PCItqT0yFA8t5T1D/Z3sUqu+hcd5eBx2ZQDZHYXwitEzJsin4YLtW+mt/dD8ah0yNeijQbAQ+/IOlbcHREPP4EZE76UIaaB6f0zjnZT63Yxoppgy2Nb0bmiShsQUBd3bV3hWZ3+qs5Xp1IeUvgsgUjO3sD/z91E2HW9moQQU2o7qE4O0nrQuyW4tY1aq6MN+aLqLJGqm+iv6J7trZruJK2hkbxw8gLebiMQgk4zslfVgRdlPFroTTQ6SNcukd1YYv7p+6Xa6TAzJK2skH9tm9PoGo9c3Q4NldzSdH94NstUauj2k+M4BhQfH6PaQaK9L4e7h3CJEDZJnv2jL4WOFX373ututm99GmvTo/covPYOzOgcRpHXPaMsx3lj8W9epwJ/zaCatfdpvfZ7m2is8OvgV3jK/xs99+W9yLI5p3ZYYM8KuXG9x8LWI94Qgy6GgsBMRHUaI9BJybKF8eP5WjnJQ2c3e9Yzor+ikAMG3duogYR0idzRQBx1kGpKanALR6qPv7qH3O/lnnJBB9SQQamHnt38mdmBXdZiYg5Jkd9ZJX96Ymr1Dfmu8R/RHYXDOLPH9vQvN2V0xChVZHzRTs8luer89dcSI7u0je6Pg0AAgJd7NxI46hxhnmDvr4T6RFyMeHiC7Q+zKgn2eGYjMoPbaofHPuLCN1ayE2OxYY26uYraXcS+tn3uMSeDJouqI7I2IHh0hssW6Sw+YzSVsrRTkIM5hbq2GZsDlKnK/HcJWbqwEMp5Xxd1SlezmKm61fubZcqcWFQBctAPxnHDr5neR9lokrd3n+jlOwLh5/ec5TXu89c7/ShSVeenO9/OFvX8OQDJu8XOHf5+j46+ytLTAdP8bQAv3biCEAAelI0uh7a6lOZ1AtYJNl2wPiZ60KTw4ntppvScwDt0Z5fHJRWRmiZ+0Ec5TetSitGeQk96LzFN464DCgzbFNw+pPDrxep5UD4ebmv7tCJRgcDMCffIMiYuep5lbVzx2ZyzdkvqpRnPniB8s9t6H4KwwGWtlPhc4dfIVicQgU0/cc6QVgS9ozGo16I63muiD3rSwM+uV7IFsu0l6eznIELwnu7NCemsZ7x22WsSVIuQFld2oNQiFDSlx3pNtzHu3m2YJ0wh+8bagQ2Emd3jSrQHFt/aDJ/HTWqspgcnjs1VriFeCdLtJeqN5wlxiRfLa+rRiPnv99gqOG1eFLcekr23gnsFYI6XmlQ/9Dg6++otzrivPAlnZM7rt6d12JGsec1FuyAUS1f3P/gSPfuLvs/bJ72c8anP/OJD5J/ohv/Kv/0satz9+Rsbx9Yjnq0G2oA8VanhFezEgPvBcJM6MDzK4YGsishbbGyHS4JXo6+WFXNov17DWodr90JhxAeaS3xplfN4pK/pjosRTOjCMlzWuXCDaawe9rRSo3RbgcfUysjvEl0NVdXrccTrt6MhurRE/PDipAouLq5BA2I7MDLo7PAlUINiy6UeHc84TC9v6Z5vm8rS7aVPUbFXDgb67h8w1MQIgDrICfX8fu1xFDBJkkoVBslJE5GRYDceQmDwdr4WLImQkcdn5I8Okei1KES41yP4Iu1IHFaruczZCzkE/BJzIUYJqDwGHWWksbsi7AJPmGlcrhfO2LsRIE7TG8riPTDLMZhNXjKf/5paruEYl+E4D5tYa6skxcpjgChHmzjrSOsRee5o0KIYJLF0tkvlZQQiJkhHm0Q4sbz3XzzI1FZr0rhEJXyjU+e7v/HNEUYn9/S9gbHgGj9JHRFGZT3z8D1Gtnj1v/0Jd8VyhD3tTnare703DHd4PzI5zk1Q2jCPe61Mq1uhvKwYbivhJZSpHEEpS3jUMbkXIYSCbpiqnu1EmgrQIcT/4I9uSoNBe/NyagsDpIMOYRbA09MT9+XHNrteJ3jlAjtK5IoYcJGfCP5xWKCmmrg0RYHTeVJ1/XPBAzt+gJK5amDqJSO+RBz3MeiCy6WoRmXpk6sK/bzaACJEYim/unluEsuUYX9Do7pjCvSN8FOKh3anKrNls4LUiuwXqeEDxjR1cKUIlBq6YlOcB2ywHz/lYoY+HOCWDXK0cY1ar6PYQaT3jl1YRzhM/OArzWaRIbiwhrJs2D7qCDtXx1KKGKaZZRvfHIcClHnafZG987Uhws918JuR4AqljknEH+aSPvfls5oFkyZMuX/3C5AU/4+3v/RFu/ObfS+/wgPHoiCiuYknYffzLRMU6L/3If/ANsWv3XAmysOLK5BhA9T36kiyP+IJoVGkdvLWLnt1C3m9j62X8an3OBszDtGJ6nVQ3ryR+qYrNTNBDAXHfIU2GbcSk28tEh91AkvY7yO4okKdidMa1QORVRO+BSGFLMSob4QoRvhTPNbTZZgVfKgQ9rXOhAW+UED1ZXJ3wSuIaFVT3ApI4uU9KhEXA7EJACGy1iBglmNtrwXKuN0INE0hNiMaGqaewzIL+1txaQ3aH6L02Zq1OtrFE9OQoDJgu6LuvumjOKiXUqIvAo1o97Hpz5o8WvMfFGpVk6KP571AfdM7omD1BR+ylRKQhnMVXirhSjDrs5k2GCqRApSYMtHk8NMbhSzHZemOqIffFOPzfrFzj8RFsNDG31wgGh2FQdVohbiyj397FZxY9Tt+zJr1ZvPzSb+HNz/8o8ss/xvZ3/C6WXvrkc/kcD4xrkmLreldZKIQdho98+N9lb//X6Pd3aXfu0my8xMryhxe+JzvtWfsCc7DW4eW8Bdp1IAfpiTPO+0iOYb5ooDojspVqiHBvlIn7nuKhI1lVdD/cpFIvonJSGqcec2CwBUHUcyjLXFNnVpHEfYuXF9veZVXJeFkiHxn0jExlIlvyav4+OyVDwtspTPoaZGbD1r0PJNs6P+3nsK1BSBHMryFZVpiSoHho0WOPHqpgpeY8KIEXAtUZYdbrSOvwqHAgJYNn9eSzCxqzWSc66M85W4QTEpg7KzglsSsZhXuHIbEV5oI+TL0UvKj7Y/Bgl8qgBIWdDn4BOXYFvTB1TkUSuiN8pEjWVoNcIr//vhhN5yjTLOHLMcJ5zEoglGa5gshsyAxYLhMdD0KD3uz3tVEnu7mEdH5KcKXzqJ02+hx9uC9GpBv10MTpPWKY4orPzv0HoF6/SWPpZT73T/4S1a1X2f7tf5CofPHu6kVImtcjx3DxJrwqhJ3ReGObre/53aTHexx96TOYbMRLv+MPf0OQY3gPXCyuA3UJOdaOC7cHxV5rqpGbwrhg4dUZYtYaZyzZ/Fo9xAFf1Sj90RF2uQrWI6yZDmZ67IPZd70cLNykxK41UIeh8mLvrE8HxbMHlUT39hCT6nG1iCsGguxqJTAWt7kUHsrGTPMcHn/QDW4Sp9wh3HoTr9Wcfd0UUmArRYg1vhDhSjHx27tBSyvCPUP44HE8TIJTQ7kQZAZCBLuyvfbJ4fLjCxTqqDs9F33QJbuzHhKQrlBR8FJM5SdmtY6rl9H5/TutlRPGgsgbA0vxQqsp7+YHyxA3voTM0wu9Etgby3gpQUn0XhtvLbLVxydZ0ArvtvB5WIUrhQFM7bRCgEUhwm02575X2yhDrp0+3QLlEaQbS0Q7x/jMIr0P9/M9xMb6p1hf+ySHh1/iq7/wT2je/hhCPp8SbLqsKQwc4hpplxM0GndoNO7gvePg8EtIGfFLn/3rfMe3/Zkzr30RCHIJZqwfnwrvkWf3dSGNDZ7sSuKVwMXB51gZz2BToWyRQseG5uNRhlaapBmITuVBymBLY4vht2qaipGB4pGdI742FqjU42NBUhFkFQFC0LuhqT2cCRyRMFhTSAulw8vnEp9v/7uZ72ZKsoHxqqLsynPyofK+ZbQS0izHawJlobiTTavZHkjvBFclnxqi0Yx3vRCI1Ez7X8xqLewa7vdwlRhhQ8NltlYLc6GSuGJEcnM5NN/NPAOuFJHdbCL7CYX7R3gtST6yhW2UMa3hhTZtc/dAgM3cNOVOpgazXCHaCY5IfpDi8uKDGGfIcTYNj0EI5CAhfnjM+PUNfKRxkQrz8sxcHj9uYW6dNFYDOClwN5aQqQmLP4LtnVmuBHlcMZofl0vPTqoxgVQRH/3UH8CahDe+8A/o/OrPs/o9v+2pjmXKfi7448q4wrAphGDtm78fgNVv/QFsmrD/mR8j6x6z8qnvvv5nfo3hA0WQbRl8/wIrmfHFJOvCH6Z16N0WdpyekE3CwGGqRVTral5FcpjMNVX4cgGfp7xFIxd+WJMfo5K4XHO7iBz71QZZMQ6NX7luGZg2l7lChFlkIzd5PyKvgnv0MDmxkyM0sflGBXdrFTdIQsVVAEpBtYBT878Ou1RF9oZBZiDArDRC6pzzIenvuIerFHG5f68vnfj+Tj8zyVDJqdS9Vg+u2MgjZmZyXy6cGNtrOdUui9ylwuUOFnIYwiTO3pv5apMrx9i1OvKoO7X4s7US3nlkq4tIzFRyMvtdyN4oWOVtL+OLESKzyM5g6svsqkX8UhUxGCNSM7XvOw++Wgx2db3ryT/eLYxJePT4F4jjKhvrn2Jl5SNUdz/Lr/2dv8jN7/hhVl7/1mf+mR4YrmrKT7KndksQQrK+9gneufuvFjpYZCWBuWai5jcavA9ET2dcW38cPzi6MuF5z+FPqppeCnofC3Ko0rFDJZb+LY1XgTT7YmjeU4mn92qMSBxq7HExeBl27NOapNSyc3K04bqi+siQVASjdU3Uc1TfTknrEls6iavu3gpkW489XECQZVHhxjN/12qajCccmJJgtKyQxuMn5Ng68B6FovrEgITerZC+NrgZYY4t5YO8uDJpAF9E6k5JBERqEUoGsiwE0ZMWZrWKHKQn82O1gG2WptVWH2vSG0uIsSF+3MJLQXJnBe896mgw9eQ/89Gnqsde5Fpve7LAUO1h6BuJJGQueDTnxFwNU9Rb+2Hx8OFNiFRoADQuNItmNuiVpUAen8zlqp8gv7xD8qENsB5fipCAOOyT3l5B5HPYJJDlvUCv84jD/S+xtPo6zaWXufXy9/PFz/0PdB6/wa0f+veJa0tXP5iG9OwGxZXgrlkULzRDUm337hfx8IIgv9cwJYGLPeqcvgGVXJDIZ12QHlwC1R4g0wx7a226SgzSh6cz85TtAdTLOCHQb+1g1xsh0jiH1ypUK+fcGzxkLqx462X03ZmJP1LBcNxPywrBKaF0gSBeCEythDzqBcJejEI0MgQiXL9IqR9g1+rIVj+4qa3Wp8TeZjbofpUgemcP9dXHmPUmdr2JGowvbdQ5LfEwG00QLCS1s9+ffnBiYWRrpeD4kRmix0e4OMKtNRDjFPUg2JbZRhmRmqlGca5yXIwwt9eRnQFq1v861qiDzpxjBxC21maqEEFiYoi+8hhELiuZumj40FjYHoJz+O4Qs9FcGAKidoPlnBxniEi9Z2lvX33zn/Lo8UmS3ltv/QvW1j6OtRmbr3wX937m71Lbfp240njmn21KkrQuKXSf7mqTpEu7c49u99GZv7lYkGy+N2ErX8swwtO7oynvWQqdqxNkAZeGL3xQoI/6xO0abhIKknhq9zO6L0V4LSi0bKju7lu6ryl8RaJST+OtlMG2JqtKqrs5OTYW8gZfFwl8IQSOAMTdcJxia/55lhnYIpiiwMdi8a6JEhzdUUQ9gSkG673ykQvhJQAC+jc0OKjtzZDo2eqnCtrR6iND7+UIE3nGS5JiZ+Y45+B007LdqKNGKYX7R8HSrRz83l11fq4RM2N8utXAF6PgR20c2XotlwMOiXY7QZ6wVqPwcLH0L9uog3FER/O9P+lGg3gnL0xIMe0HkaeKML4UUXhwBNZN59SJ7Ee3F0smhAohKfGDY5LX1hHtIfFuB9cdkt1ZnasuP0+kSY9f/tn/EmPCb+rBOz/Jxva3kCZdmsuvYUTGo5/4B7zyI3/8ysccrTjsU56+L4rQ7HjFodnuPGHQDVyltHbjkld/feADRZDjzvnkGLjQN9CPrl7lEMMUdf8Av1rDV0uhqY6nSz+SoxR1d49sOWjh1EF3jiADC6zNxNSkXuLnU+IyG6KMrUMmGeqwG7wdLyLIBD21XWvgVKimqAX3yueWEme0iJkJzhRrjXATZgi1avXhsItrlIMlXWbRR12yQvRU3cmqPTir8Ys12fYKWIs67CJHaWh6mVhKtQf51pkBn3d7Oxc8lusl9DA9S3JnrxsgNei9zty/u2opaLoneuo8xSu7s47K7fOINb5cCK4a5JPMDHmWgwRmqiZilBLd38e8tIGbfO+TQADrpnIUn1eiXXO+4ixTA+M0NHUqid9cetcyDGNTatVtPvmJP8ze/ud5+50fY2f3VwBot98BIclGvedCkAHStYhonF46gS/Cg4c/y8NHP8vmxqf59G/4PwB5h35VkqxHLMgheoFT8JKcHF9vkeLJdZ6j7FpxxO8HhIfKV/ZJXw1OA2KYosoxxWOLLUg6r8UUWhang3dxec8QD/LgiEkRMwmVWqlPFq9q7DCRCGNq4imMFqtVZuempCwopJ6sJoknfsy5v3NpX6CHnv5LEVmk6FQly2+b0JjnoX4vw2qB0aBMro6JRRizjZ82VgnriVoWu6oCsb4VUX/7FJlcoDsX49BIDeAiRfraOtHdQ+QwReWyMp+/DhHmLvPSKurNPcgshQdHwev9lOTNNssgIH7YQl+QSuoKGrdcIGoPp/pnoSV2uYI46OGNwyyVg+Sin2CWKiehJzC1FXSRCg2BvSss4Kyj8PZBkL4piS/FuFgjEkv85h7py2uhue85w9oUqSLu3Pkebr30vfzSz/5V9p78yvTvQkiKGzevfrzifGz0deGEJ6tA1Lt8EFVjz6/9T38NgK3v+d0sffTZ7zh+EPGBIMgCiI898SW9cuICPrYwOeeiY41TokdHuEohWIfdWkVEGp+FBjSVZggPViuEMXOr6NPwqUHvtsm2l/GnqrVOEUjd6YHKORiluEqRbHs5+BjnkJ3B1ERejFJcs4LsDsM2vnUhOrpSnJMWujxxSVpgkCCsw5/yARaexaN7pLHLNYbrweC9vGdOuqZvrKD32ycEVEtMs0r04OkCJ0Q+CLtSHIhwpDDlAuqwg9cKu95EPDma05EJn6fpxZrs5irRk2P0/X1kYsjurGOiESpv0nPF+EzwihhnqINO6I5W8sTizdq5JpVJc4kcjKdNiLYQIdv9k8r0VbTqHqJHh5hyIchsck9qv7VEtlILnsqjNFTRW31stRSekWGQaczFmKYG7qyd0TNfBx/7yP92+r/v3P4+tja/hcdPPsODh/8Ga1PwDjO8eprWdeGA0UZE5XF27S3+2+vfgUSwffM3EjeWGTYUriixLxrzrozIimuT4wmy7Wao5L25P23W+qBCJBb96Jj09spU41s6ciAd3VJMsnRSQdUDBxmMNoJsYdyUeBmIbvTOHllu6SUtSAO13RBpPXGYUCK4OkyKH9HAkSyH449XFIWuOyHHMK0CTyrPlQcZw1sRIvXTYwKkVUmyJLEFkJlAWo8tSPTIU9kxyMSh97sh/GOm7rLo17CoKVP1xphihN7v4koxrlYkubNC8Z2D4DbUH+OrRYR1xA/DvXTlmHS1Giq8QiDTDFMtoNJAYmVpjBymmI067HVRgxRbibHNQHSRoXlQJkGypw968+OuUiBEKD4Yhz4a5P8u511LyvE0cERmFnl4PhE/cy8AjEP2x7hqkeRDG2GHZK9L/OYerhJjbq+cqSbr/S5Ra0DWKOcuIE+PUnmF7/z+Pz/979/4fX+Ofm+Hr37xHzEeHWOyEaPdByGl7rKERTzJ6rs6HQCyZYh6VykPej7+G/4wo6RN5dPfd+n5fb3gfSHIeuDxBYGdZGhcgRxLILooWrpWCivEK9rLTI87SJCdIa5ZCdwx1thKcc5hQB520cnlBMJXSwzXNNJ4SkcZ8skxolmZeuXOfe5RD9keYF7bmgaGTBoFZ6uhcpgg3z7xrZ3IDfAeedCZOjtIG46JDu4VV6EhTgVtYjTyyM6AassFh4ZCjIugvxWjxhq1XSZup0SP24HYX5BUdOHnVYrYpSpq5whzczXIFKpFGKZEj8ICQbUHoYO7UUZ2QjyprZWw6w2EVvkk5UK1B/BanmiVyRcepyAAu7WM7A3RO62TfzdunojHOngsF2O8VuhHh0Er3D+/SuHz9805gBAqxJPvUXWGZNvLQVtejIPt23EvaJ0Tg0ryh18JsttryPZg2ugoRmG3492S5FnEcZWXX/otLC29xt0HP8nYD6huLvAXfoYwsWCwqYOH+TV+0sbDBAAA0JJJREFUonF9hVvbvxPhYdRU06aqF7g69LvcQvaAU+IqPT3vO1Q/FBFMMQQ41R6bIFm4l9K/oTGlcC+GWxHVBxnlXMpQbM+MJ8YhD3u49Tp65FHj+YBzmWRE7xwipGD8oQ1Eb4z2RaoPgz9/lHLpMx6NPCJ12FgwbkpU4lGZp9h2FHfGuAhEFDG4GWGB8QokTYUeKCrVJtHQ46LQqBcdG+IFwS+LKsi2FoomEys4UxCMlyO82kDNOD1M9L3qeADWEe12cJEie3UNG6lQQHnSRh8PUPePQpqd89NdRVeOsUsVsI7iV/cQdlJ8SMKiy/spET6p2pxcg1mpYDabeDzRo1bw4D41xp5ebM/Ooeeh+KhFulzFrVZwUmI26ghjUZ0R+uHxtKndlSLsWp1oP4zN0WEfH+tpAuGzgBCSWv0Gn/6OP8GTh7/Iw3s/w9Knv+dK5NPUwBbeffOs1Z6kKSi0L3ldUVL9yKcoNBXmG4Qcw/tAkIWH4mHuwBAJTOTRV8hMkAN7YfXJCYFZrs01WF0VqjM4424xd+zVOkaHrSwxSEI1d9Fxdo6JSquozCN326j+GNkfk726hewOMRs1ZP4bd2sN/Gp92rxnSgVUdvGNsMs17Fpu1yPEtFFtAjlKphUNMUzCivwCMqtmtuxcrYTsj4keHWHWG4haGZV5srpiWAG1qahWdOiYzsxTSVLkKAFjkdYTPTggu7UKUp4krgqBWQmaNl8JVQynVfBmPuqFRopqKVQ4ctKqHx+FgVOAKEbnNgTKzOAaFWx/PPUidqUYUS4gnAsepc0waEoIeuz8cXMFHYj9bmveP1OK0GRjL6Gu1hE9aZG+sjGtJLnlGhx153R43nl8HJ25sWKUInZasLV8tRt9RTQbd/gNn/gj/NrDf8jO53+cm9/2u57p8U/DlCSDGzHlvezKzhaCMMlL44n67gVBfs6QQuBOuVbIUToXKCFzKdIHEg4K7xzAdoNKGiGGhuhJG7PZoBSVSBoeFwVbN1cUqLEnKwm8Cpr2uOMYf2x7eri5KnCO6HErED4L+lGL7NYyAqZx1FJJ3BVKFOVdi7IgTsnhbC0m3u2hWi086/ReLWCLnnQJ8J7hpqK0C2iJtqEpcRFmybHWEmPcVF4x/fc0EO1kWVI8nq/qZms1zHotWKstV7FrVUgMUskQJlWaLWGLUBkWgmytglmtIQcJ+qA3JccQihIIgdlqovtJ6GGZzIGFCOUhW68FNyDCzuvk2RPGkW3UgxzklLTCC+akb+fBG0e038X1x6SvrAW51o0lshuhMS563EL1ww6sWSdo0XPSHx31w9aB9fhaMfQQPQNIqbl553tYXf8Ev/JLf4Pma5+isHR+EJZQkD2D6vEEdhlEF/wlP+nR+gdCcPCe4rnONgKIup64Hf6vcOQpHvjp/C8yTzS8mGjpkaN4kFE+vsLDv1wLOfDXPc9RetIUdw5csxII1vZysH0R4kxXsOqNKN47QjoYfmiF/kdXwz14Zxd51MXlaU1inMIomdOWuu1lzO21kDd/3g2ZaHTzBrFZZwwtBfbGSnC1gJD4d8mW6GzksJASXy+TvbqFqJYQhMTCuGUoHliirkdNSE2kp5HKl0GUYszWUtBcO4/Mz0kkGXqvDamZyiO895A3Naqd47Ddl99rOUgCeSTorSf3SI7zbXsPblYGc7pqlssu7I0VfKRCcmEhwuNDUt9y7cQn2zlEfxxcOwrBucJrhV2dX5C4cgG7Up+bAM7cYyGw9TJYS/zWk1CVzu+BeWUTWy9Np1LhIb63hzzlqOLKBWRnSHRvD3ncO/OsimHy1NHVh60vM2w9ob/zzlO9/7owsaB3K2a4rklrIe7XFiS2GBqXJv/nYzG9L9HQgQdTeUGOnzdOk2MgJNbNvuaDSo5ziMRQvHtE6Sv7JMsxrd+4ha0X0J2M+hfbxMdhQT2uhedpvBzi0UdrIV0vaUjGS+HZPI34/hFyeDKu6gWuDe4Kv0UbC/TYnyHHHvBKkK3XSF9aRXpB/UFG+TGUH0DUEcEOVUts7MmUx5bmzzOtipmDheOb874zD1Hf4bSYIwM+CvIGdTRAP2mHMc55CvdO5G92qTIN31CdEdFBD9MoY2slCvcOie8eYpfKpFuN6Xiscs98D6S5n7GdjNUekpvLU3IMuRVcs0y2WceLQJbTG0vYynxzrlmpnrh+LIArRXPJenKYIhcU3LIbSyR3Vsg2GyAEyc3myficGOKHLeInbdSkofAZwZiEnUe/hBkPGe4+uPC1o2WHlVcrMFwFVnjSyrM73tcTnuuSQKZQbF3+urn3+OANKT3Eh9m1ut89YFbq6HPCMy587zCBytWsXuxaA/k494YsRGFbf0Ye4QoRya0l7FadKNXogw6iO0J3EijEYBzCOfwMx/QEazPKBfxyDXnYRWQmpNINkzBY5Vv2/riP+/ANpLH41AR/39SAVig8bpjir0Jgc0KolcTMDOpT4u6hfGQRnQG+WkLKYOHgENjtJdQ76bmrdqGDtthuNPNwk7M/QNkbEfdGYZWeeyDr3VZoApmQe+fJbq4gIg0iyF2Ec3OWcNPPnPkMs1TFxxr95DhoKLvDafOkXa5N7ZBEZoM/aL6BIABx2MO8uonXCnXQDXKI3RbZSxtwEAZGrxVmrYHevfgBd7k8RHWHQWfcHyP6Y0Szgt1cQqw3UYMkpPY1yshhgt1cCsEl1pLdWAGtiB8d4scZetwJuuVSIWwNGoNPw4TDq5tT31CcQwhxrve2956Hj36Ohzu/wPa3/xCNWx+78DqeJTzBu/gy/2IByMQjVGhaeoFnCwXIR8foUYYpx9MqGuS2XKlBDhLEFW0aP3DI/ZGzhsS1HTIKEry4ZUhXY7KGxB9aCt2QrAeBuA43NRrB2Hkqj7NpZRhCeh/WoVMTehaMQ3ZGuMbVCgYT+HPWenq/i1itYQsi+McDIvXUH5ws/rOyYLzkSW4JXAOEUSzdDTusHhhuaMAQd2z4senziSMEeZ7VBE/p3OnCl2LE0QB9PMh/iMG+dPTRTUQ+b0jnpkOwSCdhIkPU8SCMziLs0vlYkypJ/KgF1qN32kHbvFzB77bxkyTAcYqQM6O6c8jjYdA2ExLwVG+MGoxD+MdX9nCVAq5awCxXkKMsjKULkLy0GvIGnrRRrSHZRh17DqF2tSLC+xDyVS/hynEIjsoXGaZZxq5erTsuEmCSE//pRUiTPl/83P9IVF/itd/3pylt3jn3tbbgyern/vmpYUr+Ss1632h4X6ed0xSn+jANVUot8MY/leLS1cu4znBaZb0q5DAJ4RlX+YxyAV7awKdZkAJ0h3OJdtF+h+avWNKbTXxBhcCLYUp8/5DkzipUiws34MQoAURonjjlfywygxgkwfJtYk921MM2ythy4cSWaBCCPVy5cOVsAJNkoXKbV1lnLcqGa5qSqiAsiMdHoBXpa8uk9Yiq20Ad9eaSCH0xCo4UOemFcG+xbqEsQ2hJcmsVfdAJDRSlGHN7jeitndBtvFQNA60Avdc5k5g3d6yZmFaRGtxqHWssar8TjObzYA7frOCFQEuBKcVzLiPyoIM67uGXKmHbMPeGdqXCXPy2qxRR+20Q4kLtm4/1Wf9RgtbargRrPawLSYprDfxgPH2PlxpfjIkfHMynU2V2KsfxM8dklEIUIsAZJGE7NdKIcYoozJvff/FLf4/RuMWHftsfJ15eO/eevp/wnNhrvcCzh9xpo9u5DWMhmo7Hsj8mvnf0jBTv7x9kYmj88mMqS5WQqNZQeFFFH4+oPoxImhIkCOOnhRkAnUH1bkL39Zjxuia6d7JAsCtVzEqVzHn0kxaqPQqa1VME2RRDhfg86LFHRBJrHb07EbVHBpV5fKSRnRGiEojlxJvdi+DTXDxIiVPQQ814SZPegjQVdFNF/YlFWE9l19Df0jgtptpqCN9tWpeh0JD50NPjwi4tyOkcAoSQkNfmt/k9kDQlhY4Lu11v7yOSMO5N5RzWk90IISK6M0K1h5j1eqgCjzOi4z4qD+fwhIRR2yyjWoNQAZ7Z+VOtITI1034gX4iwUuKrRbySmLXalDwDmNUqapSc6L+lIFuphEjv/Lh2q4Fdq50rj9B73SAd0YHQp7mpxOhDG8h+gsgsduV8ciyMQ6QZrlxA73VRhz1krIOjkRQI50hvn8ztw8EBn/+l/4aV176V1R/4ndPFxyJ4PMn5yot3Bf9sgwK/bvC+EuTZ4UN5TrbwZ8mxFiRVSaHvwFyN8rntZdS9vSun4wHo3ijogq/4ehepqVWbr5eDtrV7oiHWx/2FdjeFu/uhqri1FBLcZuCFCLKIBSbvtqgRscZHVYQJ8ZdeCFjNV9etPtl6DVkpzrlX6IeH4bNmPTBnLNQA0qXw61CFQqjYwTQwJKtIoqEkamehUt4aEXUbmKKESGLXG+BOkvxsowKxDt7OkcJuhs+2K7Wgc24NUJ28S1lAtrkcpA6Tiq7zQV9dKeK2QkVLeFD78+RYlWLsTIezJ5ciTLTJwzF+MMYt18L9yOxJNXWc3+MHBygRpCmQF0omDXlKBg10FpK4fKUwrRZ7KVCdAWa1jl2t43da02s6DZGaMymAkPtBRypfFAGZJb67FxYSkQZrEC58f6cbACffoa2XEGk2tT6iVgr3T4rpDoIcpahHhwgE3U3NO49/GmdTWu273Ln1vfTf/HWWP/3dEMckvWNUXEIXrlcNe4GvLchBEqpieQBItlLFbJ3Ih6ZVQ1jsevM1BOEhOh4QHQ/wBR0aej1w1KcQa7Lby9i6nrML9DrYCAofLNZmvWKTpmS0qoj6jkJ5BTW0c6Qmqwj0wNG/EVF/mCFnDHWEcXgtGa4pygeWcQniPpT3DZ5Q/R29UiUe+KklYlYKCbO2KBivKuKeRvjw9dQf5DHVCmxZ0n5FUHliiAaBJA9uRKgsI8qT9lwsMBVBVpL4SKAcyIGjfGgudTlxGobrmuoTQ/ToGJE5hPG4YoQrRnMexK5SCPKLm35uB8tsNnDlmHjGI9nVSnghiB+3yW4t4a2bEnXdHp7siAFmq3ES0d0ZoQ96YafRedKXVnG1Iun2EtFRH+8h26yfCQBxQkzn7UXwBT115EheWyd63AIbJCi+UZrvQZmBOuoHpw4bNNbJjSWigx5IwfjVNfRBn+igF+ZVa9nd+zWODr7MaHBIY+VlfKzoP36L2q0P4UxG2mtRaKzOP1t1sPHz+UEK9bW+HH4++MBsXFoRtp1sJFAeTCQQ1pMsh85jpwylo6sRXqck2a3VQNKu+Dz51AQpQePpulTt9jK+FKOPupc6acjeKPgOby6RblZPKg3FmGStSFpVlI4NKjk5eWlDNTerhx+3SmLidpG4Hz7LLVWRmcdrwHLSYFYuwChILmzelBJS3PyJu4YPHdJeCcq7GTrxyP4IXy1RfZKhBhnR3T1wHrtcQwhB+SCQtqwiYXMJX4zQu+1QBa6VcEsV1EEX/dbOieY81xSb1TqqM8CuNvD5AObzZD9hg1zFbjSnzRtkdqpThkCsR1tLRPcPpvpfkR9/+hrjUA8PYbWOW63DTJV4kjIVtMjhJyC7w+CakWTYWmmqR/a31wh+oDEql1cI50OVf7mGyO33sjvr6Af7ZwZQ1R0ih2N8McIu10Lz3zjFV4tIa4On8wTW4crBdlC2++j9zrk6crNWD41+3qMOOsjjPn6YTBdHAo96fDxN7BvZHj//K/8jAHFUxTnDUetNVCfi6MHnuPUbfw9f/Jd/HYBb3/V7Wf/ody383Bf42oerBHkOxkJR4zZPqnBynCG746/56vEizFl1phaVWsSb+8h2leFKEx8LpBVYAYPtk6kxqUsKeSW20PUka4K0ESKflY8QA0f50OIk9Lc1pSOL14LBWiCU5ISWzCBSyCoVTM9N5YMTAgtB4tFbUhTaluKxO2n8G3jq9zNUa4jqjbGNElqWWPqyZXArIit7xqseU9BUdiylI8d45MjKkmiQy/9ST2XH4oWld0tjSxJbk4ysopK7eQgh5npTpudVEMQdG4hp+yT8yTVKwUO4ffJafdgn225Od6zU8SAs2iMVSOOkf8R79HEfmYVQq8Jul7RRCoWJQoRZqc65DOknbbIbS6juaEqyvZSYtTBny94YWysGT+anhXVEh32izoh0rUZ2Z5X4zT2ig8GFlWNgakEX/JZDE6MYZUjrg1uGCxZ2b3/2f+Jh+3MAFEvLHO2/wVZc48kbf5e1j3w3mUrZ+5UfB+Djf+L/gYpiUGCeYWPeGbxo71iIDwxBhrBCNZXFq7usqYmHDrXAzmYRXBxhG5VpZfMqUO3BmZCP68AtVTHNCvLx0Ukl8gLo3VZoHttcmu4KOSWwZcnYK8q7BjEjNykfGfqRCANbQTDaiLAFQ7HngitAHBYVE5LmYZqIJ8i39fpj9OMjzPaJI0Lcd8Sd4Fc5IaUT8qzSQKjtWiNUygsSG8vQOEXu/9lQRKoaLMk6Q+K3duau09ZK+I0mbnYLL698i/4IvdcOWuBIkby0HuKwCX7QE9kFUk6T9oSH+P7+nAPEIghAH3YxtdJJaMfseW0sBYKZV4A9YNYa03sGuS7cWPTjw9CBTV6pHiZEX318Qv53js+tLvhCjLu5ghMixFR7iO7thxM8tZiSxqAOu8E7Oeov9F02m0u4ZgXRDxOVXW/iizHysDuVCamHh3Oxr7pU59PLvxNx5ya7u79KvX6Lrc1vBgR37/04X/7x/w8AxeYmD3/+H77vBLlwbLAlObXleoFnC68V6Ye3AD8nvVGH/a8bcmzrxQuTAL2WJK+uIYcpS28b+tsKU1XIxFM8sgy3NU57Rqvh34IkwRMfGUZrYeq0AqhK+tWT53TcUBSOLIVji40Fo3WFzDzl3RgBFI8tw3VF7YEJ454Px/VaUtkDOKevw4JrlnHNMqNlyXBNUmx5dC80EaM9yQ0QVhENPPUdO911lf0xrlLAR5L+bT3nI27rCr9nw87hAnIsrKP0VrA5deX5wCp9fHZ+nW2W04c9ot0Ti1SzUkFlNlRapQB/ktboMzsXCpJ8aAM7o93Ntpuooz7xJPBJS7LtBnKUEd09RA0SfCnClmIQYDcaU9u6q2I63maW6LjPeLlC9sra1Bd/EdTxIOQpLFfQrUHQpXeDS0b81j7R3QOSD23i8mu5E3+c7dVX6fpj+oUxa9ufpilWuHPnN/GFz/8dep3QpCfjEgef/XE2v/OHGK268Kw9J5hp09/Xy6//2eADRZDPI8cQyN5oI6b2MMVfQoym71mrQ2dw5SqyGKXIdv9Mutl14ISAYnwlggyg2wNMKYacmBfaFlsSmIqi86rClIJerPLEEPcd4tS1J03NcAv0EEwRXOzRg+DF6ZRADSwq8RTb4Yfvq0XMh2+cvSWnXB+kdYjDbpAIDMaY22sh4S0VyEmqX67FcDo0XclmZdpI6MpBs2s3m/P301hUq48rRPh6GVEqTG10bK65048OAwmslkKTXGrC9uhsmtcVnwEAn5lQQZ5pQgEQvRF6/4Scm9trc1t6QKjkHnSnGmrXrGDXm8Qz5BiY82I+DbtSC/IZOEmgOq/TPc2/p2KEj/QcQXalOByrWkKOEvSjo/DZuQZ6MnnJ9uCEHEuJrRbRvSEr25/ENeo0G/NNIK+8/L8hy4Y8fvIZxu1dPvkH/8K51/JeIOpa4qOUL3/lH7H8qe+gfPv19/V8vl4RyMPJUywIUrOvF7hSfCFBFsZRvHsAmSOpFqg+hmQ56HILPYdpW9KmwpQ93Zc1ygiky8OfCO4PNgJXkDggq3i8hLgn6N+WDG5I9HiGiFYVUcsGSURJMl6WlI4vn54moRgCj9kMMoPiToKXBbKqxEUiWM51IWt6TFWEc8wt7BAQke8sOTAKVAboQPCtBFcQ0x1L2R1DvYjzHtkbgxD4QkT8pE26QP53Gro9xK7V8EqG6vEMbK2IHKTIowFmvYarFUN4SbN0UplWkuTW0pRQisTgpSDOLdgmSLcCOS7cP5xKYMQoQ+eSM68Vbq3GdTDrSiLGBnU8wC5XcKvnH0cOU7yWZNtN7FIZvdMBFRqks43GVM7oVqrY3ohqfxkPLLEdtN4r66gHR5RszKe+5Y/xcz/5FwGobL3E+rf+AOM1964S864EEaLS1YKfy/hol52f/afc+V1/DKk+UJTxueNr6mqtCjGece+KVWSlMKv1QHCuCL3XxsiQfvbUWKlhIjW12RHDZOq9O4dcC6yPemSNMiAQnQHlrMhou0BWV+iRR2ae8apmvH4y2EpAtwympnAmDIJq7JEJCE5CWFxZEXdPRZBecvoC0Pf3ITWhorrRmJMz2JUadq3BeElRPLYU25burRhsCTlqIIdJCALx/oyLgtAqNLgd9zD1Mk5JZClGDNPg7jFMYZggtMQTBmav8sr2qbhbV4iCpCCUPsJALiWyP5pLYBK5rRCnNd/FKLiEZBa7WpvKLWahDrogwKw3oF7GaRWa8whSDyll0M1dAHXUxZRWQ+DIea8Vk+qMwFWLoQkzyU4s9AW4myt4pZDWoXbbJ9eRWVBBDy4GY/TBjAWRc6HBc9JtPhgH2Ubu+Qzw5Tf+4TR6+vXf8kefW+T0VWFbx/z8L/y/SbM+q6Nve1/P5RsJntChfxLg8LUNvXf5uD9pMovvHWEaJaQrh94Q66jsBhu23isKnXnUMOzS+VyvmeW2g4VWGF+cFPjcMSLu5k4Vs9pmFaQKWV5tHq9pEIbSUb74jnLiOgPZHVF41MJ7T3prGS8E6rBPvNtBd2oMX2/S31bYiqfQkdiGI1vx9G4qGvcM42WFKwnqDw0iCZXqSbR1f1tjc6u70bKkupPfi0fHECu8g/SlldAUni/UXTE6G8ilBKYedul8QYcdv7zgkt1YovjweNpkLJJQNZ+QRp/rgdWMbMPHak47rA97qN54uoM3sWvT+70gUXNgK/G0+e/kuz2/aLEIqjU449iiuqNLw0HMWnXa6O1KMekrJ03Prn5yHR7IXlrDdMf4UkR8/4gslzeZ1Sp2OOTnfvIvA1Be2ubmD/4BsrUCWf29aQQwZY8az8/X7Tc+y4N/+XcAcueobyy8rwTZybMRzJchq6qF5u3nfsZKHTdMr+5q4UPgB0qG5LOngBNiWhEGEEsV5Nu7Z7fLvcfHoXFEPzgk22gS77SwlQKlaA2nBcKHhgsXC0arGl+U4IIFXtx3+GNLVoKsEbRvMvOMlhXpUvhqvfXoBbKUSSNeVj6RS0wgc39iCCRSciIHBlDHPVyjMrWFmXQwj9cipG1Mj3euxVi5gO8Ow+IhzQfMvEFR77fBeZx1CCGDXrdcmGsqnF6D83jvwBE0ldYhvA//ewaqO8QtVc+4SRBp7OYSpyGtm0aO2tU6SDGV3qi9NqoVtgGFB+cuz7eTg4TC3V1cumDrNNY4KUIjn3EIY6cx3h5QhQibZMg4IpvotPfbkGT4vJEQQYg5VzK8N3fy8FriaiVU7qssR2nYFSAE00yaQcvlNdZWP8ZgeAD6/V8zf/mX/zZp1mdj/dPUN14lvfwtL/AMIFODGF+PVHyQcZ3pXA5T4mEKBz1srBGJIXltnfKxIKtLVOqpPgnV3/6tCFMQ6MRTPDBTnXDpCIYbClOSNN/O8Ao6r8b4KMgjvA/yiuKxJasIxqsafL47uq6IBg6V+yILAjmOH7fDeFgt4Ool9EGPaK8LWuKLEYW+Y5hKhBAIC3IApgqjOyBUhMj35btbmsZDE0Iu8vFBj0KSn4+CQwY5QXaVQiCk5NXbWCMmleBFkgXnwXqEdWEXdpzhtcQXovD+GfvNeK8LzhN1R9h6cbrzJgBihakWsesn1VolBXajHkJFJh+XpwFGMwsgVw2VaR9rbKOIK8ZQu9r8LUcZ0U57Lgxn+vmDBJmaaTV7EfwC+d65rwV8TpptrTgdq10pxsiM5sprlJobHO5+gahRZ7D83nXJ2oYIWvJ8PrdpMiXHH/6R/xihvhayNJ8t3t/Z8JwRbJEd2AS2LHGxmHb5XgX25gry7Z0rx1ALD3rnGPPyBu6aD4UsKlLviAxTKYhHYF/awBtL/PgInw8KrlbCbC4RvfUEMUxOPHXjYLlU2cnwWOTxCCEFlTRUmU/O0yOPe8QHXbJXNiAP0ih2g/OCjQRxz56Q20kVcZSi9lrYO+uosYPM5J29YqrHnUA/PAx+omJm/eghenSIf2UTCHGlKvV46Rmua+qPUtSTTiCWq/OmjdOKaF6t1Y8OsUtVfKTRe228DIb10cPDIH2wDp07Pcx940IgMoNqXT6hi1GK2u8Ex40LXxg+xCcZlAshVaxeOtFE44Mt2wRSBBndFeQePs01foVoOgHZcozdWgbn0Pf3T7yfJ+8BjM9dRVKDenwUfKVNsIXzhSgEmuSVdDFMgiylP8o1jQpXLs4Q5HAfXTkOVZ4cB4dfotvNzenF+6f5VR7i/YyX7nw/v/5rf5sbH/vN2Mb7T9i/ESCA+M29c3X03zAwDmVSsrUqtqRQBqqPDcoLpPeI/QHVtIypKqLEI1OH6I7wsUJ3x6hemWQ9SJ2EhdJORv+WCo40RjK4HREfBdeI8bKndOzw3hN3HWrsUZnFO0fh4THMLFZ0YuD+0TRBLtlq4mtFMJ7GQ0vn5TD2l44k6diTrHhGG57yE4HOQCaOcV1QbJ18wWrsUSVPGoOpn7h1mDsrmM4oFC4m1dqjMO5Fj1rTedRDcPvxHt29ojRnQpYTQ/HtA5I7K8HLuKDxM6FJE9gkzE2mVpzqneUwndNKe5im+mWb9TBmXxEyNRTuHZ6/s+eBJLswkfap4T3RQY+kXgquRN7QPnqL9tFbAIxW3Zy7yvOGlZ60CnE3b66MCxSWNogbKxRuv/TencgHCO/r7CNPF9Qk9LdCJ215jzMpQxCe1+FmRGnPoJKrEV4vBGZzKQSIXBBXPQfjUA+P4M7aXBPLRXCxoLOtQITbGnUtpa5FJD5UJHN3DdoDvJLQKOecTODqRVR3FBYHwzHqyTFqcGpL/qiLLRWCj2WSwTibkla938HcDG2uYmgod8a5vZmBSGNKYVGhTHCokMYi3tpBNiohBntrKZDUweiExBImThfrIDnxHpRCtvtBo5y/xitB5UkGStC7FTFuaspHEdHOMda54Dsca2adrd1ydSqJ8JHC10pkuTWZenyEXa0T3dsL57FgYeMjdaHu9zTUcQ9XLeDLp6oKEpKqJBq46fPocy2vM3mISCn3mEagivE0lc/lWuyr7k4IrcheWg/peiuWyWJHjNKchM9fj4ATizfvTyKy6yX0boLIJpZ0Knw33uNqJYSxyGES9M47J5ZK3nt8uYDbaM59zsb6p+h2H/DK9/9hatvvn95XtwyPvvBjDJMW3/LH/lqYNN63s/nGQmhQrRMd98E4vAAR6/D8fQORZq8ltlxA98aYUYX4YQtXjjHrddRuB308INrpkL60gqsWgxfyjM5ZJhm+fLLFXvvcHnq4hvACKQW97bC7JzND6SDsesnEIH1E9PA4BLQ4d6aY4zM7lTlAGP9kd4TXCi8KxG2LrSosEHcEXnrS5RAAUX1g0KMQsT2LSXql1z4EUJQE0cCHAmLu6+xFeJ2XArNcmTbReUIl1yxXUO0hujNPkIUUOHeFLINKAXNRpTdSYY7REhep0Ax3cwlhHeqNXVwlxqxUceUYoSXRfjcQzisiuruYHHslSW8sIby7cmrsdWGXK+j2cOrPr6MSOipjsiGv/pm/gHl6v4CnP6eih25wMnnjv/9L3PyB30ftzkfe+xP5gOB990GeFjcjQVLzeC1yEgyVJ2JhM5aNBMPtiNr9xQlti+CqJcx689Lks1mIcYp6cAA3V6db7hfB6hMXCICsrsjqCuVClbG8Fzoj/GaDtCKJBw794AhfjjFby+COgjduYuY8lYGQAT8TEAGhCWHS4CYHY6SxOK0CQeqOcMU42JCNM5SWkBjkOBipp69vT0M+zEYTXykS3d+fklGfHwfC1vxE02sbZcyt1bn7LvJOaWE9pX1DVpH4RhnbGaCO+/j+GLzHbC5Nbcj0w8PgxGAszGh/pXOo3gjZHyE82KUq4KdVUB8p7FIVvT+js70iZGuAPU2QHeenNSqJL813bZv1RggSkQI1ysIC5IqwxWgqOxFCIPKGRr3XvvB9PlJTeY4vxbhGBdfqh4CCcgFzYyVUoE/5LbuCxtxYRe+3keMsfM8LBvvV1Y/y5lv/jLhQQVxxMfg84CKB1iX27/8UN9wf+oZrCHm/YdZr2LUqar83TSiTmaFw7wiuqef8WoVXMjQEd0cUv7IXQn2GKWqQzFlK6oM+aaWAHmVz6wc1SInvHZJtBGtJOcqIj0bBesx5ag/MdKdKdkf5oj2mcO8A0U9JXl1Dt4YL3SFmUXjcJr2zjItDMFBlz5L1Hf2bgQUXehJbdHgZ5BPRyE71zWKY4ssxMvNUnxiSugRCtPas5RzWIWSwBrVLZcxGA2Esuh10uVnunR0/ODqjS/ZXIccQ+koqhQtfIgCzXsesn+xGeiVJPr49d+9No4RYJGO7ABPFiIsVIrXYZikkmo4zXK0wry18l5DDBNlLsBv1wH2MQxiHetTC3VpG6wKr6x9j9/FnEZv1S4/3PCBmpkIz7NF559dfEOTnBe8urvCaSuialELQ34ZZ+YCTkJY88dmsjenfR8sKYT1ZVVI8sme0tGdQL0Oucb0qxChF39/H3Fm/lCSLc45rJSAF/ZsxyoVz90CUd8z6zSWEELhSAd+sBB3XTGpbOMgCDe6s1taDOOzCRjP44o5SZHdwZsvUlQu4coHo/j7Ch/ulj3u43mjOJcJsNtGPzqZpqc4QNU4xW8vBts1YNCcylGjoiIbBf5Pbazg8E5Xu1BLNWERmp5Hg6rA7rX5PKsoTQqh7o7kmOJHZpyLH4WDXkw8I70MD3Mz2mi8XMHmF2VpHdHfvjOb5PLiZhELhLyfG0/fVSqjjPma1HiQUj4+Q4yzEed9YQYySeXIscru6ZhWMwS5VMcWTxpnT6PUeE0dV6huvva8V26ymqH/fD/Dp3/R9L8jxM0SWJXgfNP2XwQsxl07mIk1yY4nC3YOvy0qy1BI3Q+xkYpAHwXN9duyTp5u3Bgnxw+OFYVSqn2DWPOpJGwFEj1vEO52QhndrOSS7ke8E7XeJW8PpcYpvH1zpvEWSUXhrj/HLa2TNQpA5DDw6AxMBBso7Eo8nqwvYB68FSV1QEDGj1dBgLS3osc19cE81VE8CpbzEbAQy7EoxtEfo4wGyn2BWq5jlKtFVJRanr2OcnU+QJ1uUEPpVMjsX2Xz6cZS9MbZxveZ6W4xQqUXmxFp3R+hBGopRrQHZjaXp9/VuIID4/nGofHeGpK9vQDnGC1CdESYPVRlnXda/7QfxpdmLf+9gGmAHHjUSfOI/+i/ONLZ/o+G5Xv3o+Am9nbfO/bvPXYaedpGWNhTJssbF8kqRtE4Ksq3l6+uJUkN0bw85vrhdSCchsvQi2JmseReHFLekHmFKIl/O+hClWVws/Le10hkvylmovfa02rtITyhGCbrVD/KEzAXSpeSUkHuCPjhaQI6nSAzq/n5wc5AS2R0iTlVS9aRRD4EpS3p3YpJGvl0mJdmrm2Q3V6fRttOmQCB7aeMkpdCcdDxP4AVnEpIuhJJkN1ZwG2cb8i6CF2LuWZFHPdRuC9nqo3ZbFO5dnRwLLWHGX5ncBtAXI8xGk/TVTWxz8Z6ayhMZfTFGPz6aSi2sEohxSvT4aObF4Rn3pZj4rR3id/aIHh4S53KV0xiNjimXVkmzPr2Du1e6lucJIcQLcvyM0e3s8jP/8j/FubwhapiiWuf7up6GK8ckL69h3k0AwwcU7rSUQYrg1HAFXGghl1lUni4nPGBdkALkx47fOaD01j7Rfg+fu+yI685LDkoPjik86qB7QW5XfZSh8iFJ+tx5QMogldAwWtO0X4tI63LaIJ9UBUlNELctetZ1Q4a+k+hJG3XYB+vmE/NKUagsbzVI15+y4jmzaI+etOcLQ7PDvhBz5HgRstfW5xZ3F0Hkc4o9LcdwuRSN0C/inwE5hlySkuu5bbOMFwJHkKlkN0MOQvv4HRpbH2H/l38cyyXFvucED4y2PeM1x/iOZPgqjF72DG96xquOrOrf78619xTP9VJVoURUusCHUAAOnFvclmcqnFtBXnisKyBoXYvoeweISwjv3Psyi7q/Dyv1M41nJy+C4m7GcOtqA+xoO0aMHbYQNLx+6cTs0N5eC1Zrp7Y25fh8WckkFMXr4C90mrzZWgmdZfi8+cOVCyEUI9bIJ8fIWGGERKSL09tmISKNOu4HKUQaZBLZeiM0rnVHuLU6lCJ85ogTj9xJkQODNDZYlQ2T4Cm8Wg9kWElkZvBaIffbcEFMuPDMpebJSCGkCA0dC2CrxYXSguvCL1dxmUEOU1R7cK31vY30vJbdOWyjHFw0jEUd9pDnxFVPoPdac04oMjHIh4cn5wfYrWV8tYR+cDANVgGmUeOiP0b2hvjNJYx3/MJn/hoAhWLjSjKiF/jaxJ1Xf3BaQdad4Xz0/BXgyjG+HBMNkoVV068XCOdDU9a7RPSodWZK0ntdZD8JjWS1InK/O32NWani6kUKdw9PH+pC+MwS7/Www4z0zgoi9dTeTrEFgU49SVUy3FQMtjXlHRMCS3Yyor6bNtiNVhXjLc/S2yFXRAoRPJBTQ/zg6MTZZL/DLG/TnRF6kOAiBdZj6iVso0ThcevCXVpbL+IKUYhensRK7wR9t48UtlI4P9LZe2zepHwa7qrVTutQ+z3MVmParGwrBVSSgvGMX1tHKnnl3qOLII0NkqVSFKrlzO9GpLdXADja/3W++G//NgCl1z/ExLb0/YKpgc+/BCeAAtgCZA3PGI9KBboLcV+cl2vzdYHnSpB1uU6xuXHu300ZVAoyYe6B0AOPLYupIftVIK6x4PKIEJ5wDYIM+bb4YRc/GGNvrMwlw00QDR2l3YzxZnQpgXIAxTzSWM5vqTgpMS9tBOI646G8KFltAi8FwnmEcfOLbxW8el2jTFIqoB8dBgnGOEXttNDDcSBXmUXgrtTJ7o1FKjENtiAzwfnj1logYALs1lLYJbMeZYPmTh11p1IUI4J22tVKQRvuXPBPnnhuluKTppVZKIErxNNqg7tkwpZJhn0GA44XImjG2lddtZ0gOGm0cevNUJ1Yqk7nGr3TOiupWXSMS67Tl2JctYQ87J45nisVIDXoJ0fYlTqpTbj/4KfRuoQxIz71B/78wuf5vYByEB9k6BRGKwpTfkHUnyWUirjzym8OBNl7ZHdM9vL1c2s9YAp6rlns6xHPwslj0UgjCNIMdfeA5M4q6e0VCvfD7k/UGeKuGC51Gi5vWC68sz+VcWUbdWRqKHYdTpcZryikg8a9LEw83iNGafCCj/Nen0nYxnEfmuWQqjgOCau2HM+FdNh6MVRfjUMmGbo1DKl2g+RSCaMaZcFvWAKVGOlC5LQvxbjV6oX333lCMNJT3an8+pREH/WDfVtBT69HtB3ShzCpZ1W/lccD9PGAdKsxnX9Ub0zxzb3gYrTdpDN4wt1HPw3A0m/9QZq/87c/o0+/PpQRFB+DMDDe9Oc2CtrYY1chWwHV9RQ7Et79uvIDh+c6EyWtPd740b+x8G+mFLpjkxXBcAOiXnC1kImndAjlJ57q/tUIjYTL9censVwlfWXjqbyOxShFXyC5iAeO0k52qdxiFmn97FfhhMDcWMFsL19p+83PDByzd87l20nxXht11D2RLIjgETw1cfcnW08Ljw/TGFHh/JlxUDgfZBdaoTrDM2mCLg8Ygdxrc6cVbNWKMcz4IbubKyDDltoimxtXKmBurU79J72S+EifO2iKcUbhnV3UfuddW52r4x5inOFqpWtV4QRBKiHa/TA5JRmiO0Ttt6f2a+fBFeN8V+DkYH7mf9tmJSQXGoe+t4c+zP1BpcA2KpiNJq5aJLq7Gwb/lRqDwT73H/w0r3/z7+c3/Ht/+f0jxxaqj1L23vg3/PxP/SXi7tc3+Xo/YG3Gz/yrP0975w2Kdw+RmT3f1uoSmBtLEH/j+aE+UzgoPDia8/ENRPPpGiFdpYArRchhhs4JWfHtA+K3D4gfHFM8suixZ9yUJxVgHUh1VhQIISntndh4ukZuO7bVwORygGy7iVk5xZa8x9WLmPU6ToRdKl+MMCsVstXqtMprlivz/R+ZDc+gkIHwtkKvTLpZv7Rq66UA44IcY5CEyOprwgOUIlRvHFw5ZAhnMssVvHPIa+ipVWuAOuqjdzsI55HGUvzKDno39Mm4tRrpR7ewK1XMjL+zSAyyn4B1vPPmjzE8eMit/+zP0fih33bt63lWUF5QeuT40t/4T2l96ZeJ2leQrQpP1oD+bcdow+EK741uerzy3khQnq/EIi6y8tq3LPybzfuVbBTCQootoO0x+b9LA1cVqcuxu5YvMuTbMbHMDcafYuVuHPrhYSAfC1L3oqFDP0oZNRSmfvmK96Lzd/Uyaa2EenR4EiG86BinBtiJcGVSsfWZnepZbbOCXWugHxyc8d89FyLo2XC5u4UQeDyucnIP5SgNequlKmqvjcm9kqfXslTFeI9briH6Y3ylgLAOu7V0Mml7D0qiOkPscg3f7s83QLpgaYbM9ciVQhjwxxlitnIaKYSUuCQLg3KrF0zmS0+nKxO5lZrXErO1jNptnXUbuQzGoe+d9TyewEcKMjtH5H0xgkwiTLjHtl4OGnLrMOsNfCFGHXaQwwQxOaySQeMd6+Cz/OQ4BJvkRLjVvsvK+scofeKT76tzRXyU8cXP/3329j8HnKSTvcCzQxSXqFZvsHQsET7FRWq60L0uXKRIXlpFHfVRw/RM8tgLXBHWI224d6ZRQiQZMnc1uC5UfzxNpJtgYoGZbjcRQlB5YhhsaYrtvBgiBa5RZnBTk9Y95d2T9/pcquCEgKUyqj2k8M4B6fbSNGVRdceo7ph0o45Zq0KsEdbhtcKWw5julERmNthaluKph/PJPXDEd/dRqcuDuS4fl6Vx6IfHiMwi+2OyG0uXumAsglNy2lrutURoiV2q5MFdV1uk68Me0e7JIkemBpxHZI7oeIAvRVApYvPFgW2U4ElrukjJbjRxWjHqH3DrD/yH6LUgt9AFiyxn2LHCjebtUZ8n7K/v8MW/9TdwWYJNRpjK1TmVJwTUmGpInIzbAjXiOaTveUZrHvMemXw8V4JcqK+y9tHvWvy3DhT6MK57ognH8KAv32k+A1OUjJcUhYFDXJMov6ttdxuIh+sMcDdWzmigROopHxj8gUFogYkgrStMdZ4wK+MpXZIO6IUI8oVWP6TNzRxgQoS9FNiNpeDbKEKle5LKdgZKXo8ckxPEGV1zurlE9PjojB+xrxRDoEVOVrUUGOumMhq3HFbSvlpEdoao3WPMnfWpBZx8fDyjQfZzpByCq0N0d28qOVALVvw+1pitJRASL/Nu6ViHavU1IXojVGeAzAwkBpPrhmXvmuSYMHFddM8XySgm2vLpf3eGuEIUzsM54vt7Z3xTzUoNrwTRm0/mdxPyLu/j1ldZ2fz4+0qOAeygPyXH3/T9/zFZ7UV18lmjXF7hW9f/XVRvjC9G03jbp8HUF32rSUbQ2s42br3A9eBKITgoHiRXDrI6DS9EIMgLFisu1iAE0nqqjw1SBEmaLSgEoVDlCh6nF48DsjtGqOCDjBIILed86eOjXjh+Ys7VT0dHiyVpXoCPIhiNQqrcBZgIEEVnGLyPn7YpcNGxU0t894D0Zu5YcQX/Yek9asb72VYKiFEWKuMAzhM/bIUCzsdvhGZ077HVIiq1IehEK0ajI5Jhi8InPoTFU1gdEdVOvkdhJUkvwvQKOPP8xmrdg96XvozLEoprN6h933eRNZ/uWLYMo7JHZQLd8RT6Av8MNgZ9DKP1oIV+r/Ce9iO6GLIiRGOQKWB9qBxfEzLzuOjkYVHGk9aDo4UeOio716hqPINnTg4S1N090vXFPrMCwHi0AT0ycGDJigJbEAgHhZ67up/zUhUjBfowNDi4WKEGCRiLcB69c4w41litz7g/zEId9a59nT4fpQSE6m21GAIFTjlYeBVu6iRww++1EaUYYo2+fxDkETlRla0+dnMJX4wR/RGyN8JsLRHd3cXHEa5cRB7PD7DyqHepe4RIDdH9sDhwhQjz8vla+AuPM0yCHZsL35GPNa5RDuekJMIzZ0N3KZ6ycnfmMEmGPLX4ccUYH+tQ1ZYShAjBeDOnp3ZaWC0x2ZhSZeWZnMu7wcHjzwFw8+O/Ff3qnff3ZL5O4Z1HJRmuFJG+uv7U+k3pHNE7gQT5SCGNhfGLCvK7gRxlxKP2uztIHri0CMWdDsmra0EeJ0DudMhuNtDtBFuLEE4gk+CodBJKcPJ+s9XA5H7HAONX1ogfHiNGGbZWxMeKePfpbDeFD7ZqyCDhOA9ynBE9OCa7s4JdqQYpQx6uAYTKtZKBtO50sMvlOUvNRfCnGpLF2BC/fUC20cCuVs951wn0/aO5Zjs5SLD1Eq4u0UcDXKxDRTmXLXohcFJOm/ImaEcjdK2OrYsz5DicpyNuJhQaKUk3ImsV8f7ZE2VhYffn/zkAN//4nyJbe/eFChtNdMoe1YVCVyKu1/YFgCtA0nDY2ntvfPeeEWQXwWArfLEZUNz36KezTpwjx4WWoXgcbHJMSVy7wcI1KthResZv97rwmSV6fBQql5Ot7XM/1BMNZyrn14RrVEgbJ8tcl2SoJ8fTyqRPzFPr2S7C3L11OVHN/81VQ668HCbY9WYIKDEWt1zDC4EcJMHU3jqiBwfYZhW73sAtV6eWbb5awlaKqL12SJ27tYo66s1XQIsxZnuZ6O7upd+1bVQQ1uKq13ewUI+PENZhbq2SvbY11Q2jVdh1WK5ilqrIwy7q+GqLDV+KsWt11HAMFpwO6YoLG/SUnFZ/kGJavb4IIjOIJLf4Sw00K2Sby+idFkJLTKOCX6ryzv1/hS6UKX7qE++7te1AhHu3/Ilvf5/P5OsYqYXUIkryqb9vdTwg3uuc+LG/IMYfGMg0uANN/ztS08ZlMUpRhz3Mao2sKlHVIioLsi3hJcoJRK4S8LFAJJ60GirGcdedqR+5WJO8uo6YIahykJ4JKbouTktEptdiHIV3DkJF9t4hEqYuKul2E1eOie8fhXPqBn9m1RmRvLZ+7jEhl0OcgvCgjwfnEmSZZKiDPj5WuGKEWakGLXQuiZvEbXslSF5epfTWXnCKmpgP7HXxscIuhbk7TXrc/8zfZ/lH/h0KK2fJ8dz9EZ64kaKLlvFeBWefLUl2tXC8wsomfjXmWVJRJ8A1IGs41BjUUKCS0BAoPXjH1O7XSbDK4zS42OPKAhu9f7PUe0aQ7cyCLpBZnpogz6LYO0k3ikbhRmZlicw8akFU9cJz21wKmtenqKqehjDuXXfZzkEL0mLoyooHiwm8n1RIU0N0fw+xIFRk4aGLEeZdDGzTZjCYa8iT7QGuWUFOgjBWaoj7+4hihFmtoyek0ljcZjN4KTsPrR6qP0aMUrKbqyDliW5NS7KNJXylgH54ePlCKNZB1/yUcPXylAyLNEMe9cB53NZy+LuYVFyu+IzVy7itJRCC7JUtvPdBi3yaHCuJbVTwa/MNK9I69N3dEANciILc5dQgP0n6cpUidq0eqt87LYT3mOUavhTT3X2L3b3P8Ru+6/+EuYYJvB5Y4p4jSn0gSUqQxYK0KjHVp682ND78aeK1TaLq039XL3AxonxHZ7IF7C4gDudCsDCs6AVOQQqymUjm9wwzO5Czrj6mWcasBMIXDdyJr2+++1V5nDG4EeEkJBVBMfHEfc9F41qoRk9IX+dychwpkhtLUIlDEeWwF2QXk+lsQrTzOWTW494pgS1F+FijOyP8zHVGOx1Mo4gvRRTuHiASg6mXMGvVC8kxhJTRObu17SbCWPwFUg/ZHs7Lic75joX1lN7aD9Z3M415tl486aWJBF+5968ofPQ1ln/H60TVq83DsmCJmmOSo2cbf22VZ+X3/15KL72MexY2Lud9TjGPswauNne+v2POe0aQzanvUz6jAoT3zDc0aRita4QQ1O4lV76/rhQH0b4STz0RiFKEtTwT/0SvBUlNYpY0k8WibRtKrQvkGLHGvraN74+QoxTVGZ7brW62lrCjFHXJ4BaVY7LhJfsiUuAijTQGrEcfdrHWYW+E7aTQ7FFFJAa3WoOcWKvuMDRFRJrosDPnrxrtHodO6lIBIUVYvR/1EAfdqU+z0ApnbSDLsSbbaCJ7I1RvCKlB7bZwm0tP9RObk8pEGh9pmGluksc9dHtwadS0F+Bq5RBbnT8XHpB5x/MclMTcWl24PeiUJH1tGzlOw/ZdZ3g2iU+IvPmyjrCOeLeF9x4ZKw52v8D95ItUymvcuPUb8bevZvOlDJT2UtT41F10nijzRANH2ndXsjVchMrqLSqrt57inS9wVYiZ5+68NMXL4K8TzPONDOc/UJpsOUiYqKzkzHBjY4FKPXroabyZ4mMxdTuSo5TowTGuUsA2S8FpqFac/r61dbhBApVCcNBoj3CVGK8VwjjscgUE6P1e0L1nFjlOMdVCsOjcaOBWqojuGOHc1A3IS0nxnQNMHmMtrUc/OkaOMky+QzkLESnMVpP4/tF0x9TVCvjS5X0mdnsJlZhpk6kcpaHh7/T9AximiMwQXRL/Pf8BDtMoTX2WITTLp8Mu7zz4eeQr27Tufp5X/tT/hah6PYFuXDUkR5e/7rqof/d3PvuDfo3jPSHIrgC2MqMZtlAYBAeEd4vhhkaOHbaug/1VboHlgawkr2z/5qslzFodXw0NYE+DrFIKBO3xEX5z6amDF3wsGNyIQ0T1DNKmBgylo/N/UE4IqJWxtTJuvYFoDUKl9xRRlu3BXFCKjDQ2M8H1QorpSj0bpsFCLMmmFUrIvShnCK2vFKBrma5Icu9iMUrwxeDPS8md0ROr9gCcw7lTvbrGYdYa+GYFbyzxo8MztnrO5Z7NUpBtLeFLBWyliF+tQ3d4ofXbdeCFCMfMIccp6qBzpcWX8GEhgFbYjWb+bz7oh099J7Z+uXbOFWMkoVFv/hyD77SrlZDtAeqwg1OhESdLUj6/96MAtNvv8KHf8h9ir/DL12NHZc+E9IALEA8ctmVIl76BIpa+huCsw5VisrUq7ik18PIDRPo+6DjtRDGRjD6XwpzIG0POObZQIbHNRhBnJ5kO6tQUIlI/dXVQrSEys9OKqa0VSWcqq2KvS+F4gK0WSF9aJfnwvFPRBOmdFeQwQQ7SM+mETitYPpEJinE2tYIT4yzsmN4/nBLYOUu8yfFXq8h+ghqmmEYpyCau6GjhpGD86jqqNSR63EK3hiAEZrs530D/pI0+HmCWK6TrddQgCSmJxl0amGNXq4hxdpKKW47ZP3yLnQe/AA9ALzWo3I6eCQ96geeD5++pJGG8IuYegfjQz22VvBuYoiRtaqwEK+b6keatwa4At1xDXte2awa61QuVzt4I/fYTZP/pNCSjJXWGHE9ga1cnIR4Rmvpe3jhjoSNH6dyAbfEIKadR0NNjKIHZbGJur0Hh5LOtUsFDWklcnqo324Xt8lRA2RvlA174MHHKwUEYi6uXMTdXcLWw4haRwjbK+EYZ9eAAOUxCgMgpXffk+xVSIFsDRB6o4rQK2udnkJ53GgKuJvE4DWOn50dqcEtVshvL0+/FA375nOaQU0Epvj8+kwIppED0x0Tv7IbKsvXTbUZVLPDpT/4RANY//r1U73zk0tON+pbKzuXkeIJi56xe8QU+GDBA8uoa7nSs7jWgrlg9s7Ui6VaD9EaTbKOOK10tVfQ8fF1QBxF8dq9avb/WNcvgXiTP6XkxjXKQiTnws/Hi1iOtQ3WGiGGK3u9OCyBms0G61cDUS2TrNczt5blj2vVacN+4QqXWlQuYtdqcbOLMJXhP4Z0Dop3Q7Ce8nzYCngstsc0yOn8u7XKF9PYK9hpx3dGTNl4JspuhcqyPB+jHJ64BApC5xM8LEZriixHpdpPkKk4wzs81ZTsh2Lz1bax+8nsAePkv/IFpWt11kPReFCLeKzz3Oz1cF9iZMVJloJ8uMOhKkB5U3xL1Hfr0tvAlUDtP4Ws7i1lphgsJaeaOxsVXnyR8LC7UdFoFnVdiikeWQmd+BSunEdECcdBBpAb30nogjLfWEL1RCPgoF1DHPVSrP03Zm9iLBceNGUu1egWRWXy5gGlWUfsdECCdJb2zFt7TGyGfHJ9cA8Bk1Ww9sjNAJBmuWcFvNCEnihPLIJVrlk0uyTAQ9NRv7yK8x0YaX4hIX94gevMxp2OOnHHI7hDZHWLXGvjl6lTO8KwhnA+2R9e0rfFKIpIMXyvhC9E05MTWyvjdVnDHiM75Oc4sWMQwCdXrGbhyATlMZiQruZdy7pmdrTUoqhgpNeXmxoXWbspCfJgR96/XsCqsRw4d9kUK3gcOunx9a8PTyLaaCGuDk4tWQYrm8srlhAQoiZv9LOfwWhI9bj/14ukDteiSInjmXtEndwLhfJAakEv5RhdL1q51zTY46yTNEtH+2R6aiXxB2iA/mMAUBLrrKD5sYZfKwQmiM8IuV3CRxK9UsTOGC7NmOE4rklfXr3OWCyF8Xl3P3YEm/SZBG3yJBlOK0Dg96cMox3iTz2PGXklmoTsjdGfE6CObiN1OeP9MI7RwHrNSRViLyBzx41Zw0Ogn2OZJ9sHEZvXkjbn0MzVzhSkfC5JqjFTr8AWINhtcFy5RmM4LudN7hedKkJ0661mnhs+vJlB5HLSSiwYY0RtdWFGUg/G7I8eLYB1qp4W7c/XBZFy7Qqe5ENh4/ipFZlD39s9UzcXDQ/zt3OanVpr6MfpSjG9xaaKWavUDkdYSl1eIdazJEKEqOs6InhyDUngbunnD5wikdchxOq0a2/VGIOQSfCHG3l6DJEM/PkL2RtgZeYHwHpd3+04SmbwQ2GoZPUrwWXAucSs17HINORijnxyjDzr4ww4+jiBSmO3lOYK58HYOkzCgXoFUOynIbq8jjnrT6xLOhSr5BLHGFaKp5tMXIvxSdeH36smbRM+DCdGnE8jOYM5H2WsZHFOGCT6OyO6sIQbj8J0Abw5+ie7bfQ5ab/DSnR9g49a3sUg1LYCobSi17JkFyFWhMs811w0v8B7gWXhdu/rVJmWVGNROG5U5fJo9H1nB+wEJ6VYDH2viu4dXIrG2WkCkduqYIAg7dxDGsklqqb9ev+/MOYVFikwN8mBxg7mPctlCHjEujMWs10lrEj0QmNUq5hRRW9TkPTskyEjisosHCTnOUPs9fDFvhhOQbYb7h/dEux1wnmy7ifPginra7OcihfT+Yl/o1BLfO8IuV5B73UCMY43ojtFHPbKXVueKJNI5GKRQDa4S0rjp3Ccmiz7jQpjH5JqlgBlHC3cUEe90UMMUNUxDo19i5nugAKEk2WZjjkQP9JBf//n/luLaDdpvfJYP/9d/mlpdcau+R0WnPBk02B1eXJXO+hHZYfmqjrAv8AzwfCvIp0YRZaHYfTba40VQ6TmZM85Pq3eLIDODnqmAPkucDtG4/A1Xm8yyumLkPMWjnJRGGvvKJvRHeCmR/UD4xTBB3d/HVYuIcYYepfi8QpzdXEE/aQH+UjmKN2EL3TYrJMUYvdsivrePnUR1T8hxIZo256knR/OSCp9rpF/dwqmc9BVj7FojEMzMIBIT/JULEXa9eeY87PYyznvkXhtXL+PLBWSrHyKuAQTYjSbqoIvNHTLOgxiMA3ktX8953GkFG/PnJtsDVKuPK2j81vIzadQE5sgxgNtaxk8IdauP3u8EHbcAu1pD9keonRY+UkHDfdzg4N5nARiPWwufL+WhtJOiRu/udylm5BhFL0mE+/rYIn+BSyGHCbI7JmqdNAY/z+pv2MV5/k+XL2iy9Rq+Xpr+pm2zhG5fLp9T/bP2jZN7ImY86n0xwml1NmnuEqTrdVRvhBqkC8m1LcchKTbJiN/eDzILKTDr9bD7qNUZcrwIoxVFlhdu6vcyeiuC4mFo4D0P6qgPSmDrJfTxgPT1jRPvYSHItppAaFaO39mfq8pn203kMCE6uNgJRI7SaV+Kz4OgXL1IOrOYE8OUeKc9daxwsSZ5fR05E14iUoONQ7+GWzkrc5PjDL3fRXbnv59FVqrJhzZC8/gprX8pjRkf7TA+2gGgOtrnu7YlUoTrXi0N2Rj2eON4k8Sd3UFOWgWy9ovK8XuN91TMIvs+eN49Jwy3Yyq72VndZB6reR78OAsyg+dxUtZNjcKvhAvCPU4jbWqEh+Jx+JE5rSCXQaiZ+Gw5Sk8qFzPv14+PzlR4XDHGF/SZJrAJQqpbrke0LpBwraak2+ZVamAuFc4u16ZEdEqOJ59ZL0O+ePaLZAaZQaRmmrTnhZirurqlKr4Uh9fk8gXZHeEuiZSeHO9dwTrEKMU1K7jmFSKY3gXEMMGXC9NnSeTkWcSK7MYqrhAhBuMgM1mq4iW8XP8t3LjxXdy7/1MkSZvjJ79O/bVPTY+pHJQfp9eOal94fvkiq3hkiI4TZFEyvPXsdeAv8MFD9OTE7suLfPv7GX/GLCl+L8gxQLZSxZ6aO9x6Hd8ZPbPq+JUkBQsQ7XUuPAeXN9bJ9hCRz7uTCqm66OO0YFwVFIYeLIxXQuiQSsIcWT60iEvqPvZGcA/yzmOWygsb1iUQ3T04I1nxBX2pz7YXArNWReeyksmCQwgQnREiC8FZrhSdFGkiNU2R1L2TBY5ITQgq0WftWfVuB33YP+sHXYwW2tuJzAb/+tP/Xoj4pj/51+g2+tz/63+F0c/+W8bf9FHKSydz1Ea5TzV6yC/u3MEiEF7gMkk2Uu+KHLskQRbew/i5ryM8X8HgqaPLZ59dMQdTEAzWzuH85+k7CZIA85GbmDtrJx2nzxDq8RU9WbTANq63ZjELXi+Pe5dKJ2BxV7VI0hNv3sLV7oU3FqTErtTw9TLyKDR8mBnC6K94rIWI9KVkVgwTZKs/PWd7e+3C16snR2ea3yDouGVnMOcxLOBMU5ye0V1yqhFJEKQQ6qATjvWMcLrSPYmgNpUSrhAhOwOih4fozgD16IDojcf4wzbd3kOOjr/C/sEXefOn/9b0HFXiKD05IcfGJLz19v+Pg8MvhSjUa2IS4CMzz1fe/Kf8m3/5n5H0noMf0Qt84JC+tk76+jq2HCMuIsfF6FLJ03l4r0jxBGa5EizLTsHGOhCq91lufx459gTPXb3fJb5/FKr6OXRvjLxkbhg1JaMNTe+mBuFZumuo38+oP877VC6Zx8Uw7FJ6ACkwG6FKLUcp8d2DaSVWWDet7PqZMTR+eIzO3Y5cfE4/jveY1Rrpx7dJXl+fLmK8B90aEu8EYuuqRZKP3WD8iRuMPryJrxYpvnOAGJ9cRHTQQwhxhhxL79FHZ8kxhKqyaZTOBIKJU1XltCoYbCo6L0f0ZYfR0Ru4/ogv/c9f5Y3/9f6Z45Z1gsfT/swe9//qT9L7FUt6/O6KDPf/b3+enb/537yrY3yj4rlWkE8/cKYichPyABsF0vwsdWqmLC/cfnNq3g9y9lx9qYB7aSMk4xx0npkmWfbHyHYf17w4wjKpSK4bkGMl8x0UgNleRu8eT6sGV8Ekac2VYkRi0K0eJFevath6CbdSR9/bC1tWxmE3mmTFOJD15+AoMYdKcdozBCf/X05s/2YeB+E9drWxcKJ2cRRkDbNOHgCn7Nf8XhtRL4eKdV4RF/nCQvZGQb5RKQYt9HOC2VqaVtalcyfBLakJUe5acWAf8IVf/3tEUZliscnLr/0g9beTM4N+t/eYN7/6o2gjONj9PA+KP8M3fdMfReurVx4mEePjjYiVj3wHYz1GxS8qyN8I8IAtRPiNOuru4fkvTLOn1ri/l0hvLZ2pHM/CLlVIKwWi+0dnSNH7DbtcQbWGwWJy0hxYK6KSDFOMz8Qsn0bp2OIUpHWJUwKVeFQGUkuuIprysTojDYPghCKsh3I+Jk5kfUoyvrNC6c19mCHNAPKchkgBqPYwLGAKoZoruyPMeh1zexl3GORuwge3DqzDxhq11z3jjhFipvcxuWbYRwq3VMHlUhBhLKo3njuvyXljZ85PSnylQG68B0BWkaQNhS16nvzo32P05TcBuPHNq7z6fTfOXNdur8zOP/o8R//gp2jc+RgP/+pfYeW3/RDV3/o9i2/2FdD4wR9A1S6Pz36Bs3hPJRa2CMMtgTAelUBaAwTEHYh6l2vWbHTJ1lCOpDRPxAGIBMMlhdOC6pOLD+IiBdvL+KUK0c4x/ppdy4sQnBoufkht8Sk3JScd5Tl8vUxWLyOTDP3kaGFE8WR1P9kWz5ZruOWQ+uOrYKpF9MODixslZo9XiEL3biEKljh5Jf66+t6nhStECyvevj8O+24z5+G9h4vsgC6ocEljEa0+oj9GrDXmpgthQge1q5eDW8fzxiTABELjZjFGSYOLggXfMB7x5X/7TwD41Cf+fRqN2wsPMxge8LnP/3e8tvzd3Bav4rzjx/f/WzIzuBJBHo1bfPaz/xXf/Dv+LNQqOAGFV17hlVdeeWaX+gJfG7iULH4NkGNXii8kxxPYWONfXqPw1d3zw5uuAFHQwcnhEl/dq8I2SuDcVCctAFspkN5ZmXudyCxoeVb+56CyZynv2bk52Xh3taL5AnIMIIcp6a3lqbOGlQIXa4QxCBOCNfQpO0FPKN7MzkO2UgARbNpQInxXqQ0uHt5jG+UgN3OOwhu7093UdLOB6p/VeQdr02DjRu6qMSG5drkSClyD5Ey15fS5JjeaeO9Re13MxnzD3c6XfoLRl9+kcaPC3/iJT/Jq4ZBj84ADU2PHNDm2VVKr+fH/51fo/soTXv7d/0fWh5s8cSscfOmNKxPk1r/4MexgwOrv+z3Tf1v+d37oSu99gbN4zw31bAzEAjMz/iTL4JSn2D7/fR7wiitJtWxRwoxNlY8F/ZsxTuQG6bNLvAvgSgWyW2vIvOlJD5OnH8SuEOns1MUEufPwy3QefpmDL/8c3/S//8/RxUC4jQK94PCuEGFe2kDsd1Ct+YYHXy3itUJ2hkEjfYowTt6rHxyELt/TqW+zxwJ8MQ7a4K2lqY+yGKWL7Xasu9gT1PsrNytOkuwkLLRJE5kF7/HlAiIziM4Qt3pxt/Ai5LJK5G4L2R+T3Vydf4S8D1rq9wCTOzPVeo/DlqbbXsbO3Lfjna9QiGt893f+WZQ6v5L9+PEvcGP7O3hJfhzGGYkbEkcVCtVlFlpe5Oh0H3Jw8Ovs7P4qmRmiiC56+Qt8nUM6T7x7NtDhaw2ufPGuj/eeN7/0jxmPjtFRiU9t/a6FFmtXRbJcQVj3ro4xB3d2PI/3uyTFUMSYWI/J/hjVG5PdXlk4HZ52Z1i083pVSOeC40Nm8cWI+P4RXgiS19eJdjsU7x1iJj7dMoy1tlHGboUihH7SRncC4bf1IkiJ6iehGtwILivpZgNEqJqr1uBM9VkNEsxqNfgtSxGsNz2kt5fPBJlM4TzRTgeZhMKDsJNwKjBLFdTRAEGI9HaNEtL7Ezs9KTB1RVKxjL7yBb7zz3wz/8mfMLxaegRAJR5zK24xcjv8L51v4Y0nVQ5/7PN86Pf+nynHyxTaBpcllNdvXnp/Oz/105hOl+5P/TTFD73+FN/QCyzCB8Zx2jQEfgAiO/tTDSu8q/sny3S+TDGuSVz+a7cKhquauG9Ro8sbSVykcbme1TuPvr+3sCJ7GU6HXCxEfPH63Iz7HHz558LxZgiPjcW5ns8h5rmC6gzmqhyyP8ZVimSvbqLP0Ug7rche2cQTdNSqt7hzW0QKXwzNEPr+Pub2Gr4Yox8f4mpl7Ept3qrMOdQ7u7hGBbu+oIv6MnI8S7AjHbS+Rz1kLWjCfCEKmnMRZA+uXkIai7q3jy/FVypiyc4AmV+vXa3jc4mF21rGH/dOoneNRR31wFp8tXQ+SU7NtGotxmmoVpxTabn03HZbyPZgWrWf2iNVi5ibIUJ6Z/ff8sZX/iEffv2HLyTH/f4ue/tf4Nu/9U9jux41zijKKsYmuHGCvKCC/OU3/meGwwNee/WHqFTXMdcIsXmBrz/Io/5CXf/XGi4LwBBCsPv4l/HeIYTCftP/jqg9mutbuA50aziVJz0TOD9tXrOloAmX44zCvUO8FCSvb+AjhY8UqjtGvbVH8tJqaPI+B9c5OzlKEWODjxUi1vjUTHXH+niATU1IpFOSjCDPU0eDk4qsAyEh22qAkkjnsY0SqjMK/R3j7MR7e2aucGvVEEAmBGathj7sEc0s2GRvjFmpkG030Ud9kjsr0zF4tmYmE4PsjoJ3fzEmvblE8e0D5GzTeTEmW68je2OE8WQ3muEcJruVkaC3rjBFz72/+//CHh7wm3/bLV4tPThzvz4/ukXmFZ/7m59nefUjbOzW8DJDv3NI1VToHj6mclcw3vTYBYo12+9z/I9/FKRk+ff9Hiqf+Pg1vq0XuAgfmBnNA6N1KB4Q9JMzEHAtPe3pZsDT/53VFVldEXUt5YNrDGoCspur6Hd2g75UiClpdKUY8oFoEUzjYocDWxTYS/jS8mvfyvE7n6P76A3kDOkxJUmhu/gGTT1xT20B+khibubBHDdWziVrk3e5ldpCguyVRGQW2R7gaqWw9B/m8dKFCNXqBxujmev31iGsQx33cEtVhAT19i6+XMBtL+Mua+KZqT4LYxHDoKkV5xB4u7WEOO6D89gLv4cTy3fXqMyd8wROSVibIfUzEdIXYmaB5C+Jk74Mfr2ByW371OHJBOB6fR7f+wqyWObw8EtUK5vcuPEdFx7rnbv/ipdu/CbKHYdtVhGDMcngGC1jlLr4PL/tW/4kzhnsWo10Rb/wQP4Gh7wkAONrAkrimpfvBH3H9/7f+cWf/i+oVNfxQLpWI55JYbsOzpsznhbRfndKaM1KBWEs8W7uRd8sB2/1SJ1EYo8N+nGb9M4KqjVEtYeYzfqVkvImEAT5mTzoTauqi6B646mdnXeW4lv7cMpLGAAH8eMW2Uad+K39uT4llzsWQXCjsBv1UMTZ76H3uvhiRPbyKma1hj7qY5qhQq+PB8QPjkPhxHrKb+8zvhnSTOXxANssI1JD/M7BjO3pCLOgUVMNU4pf3QUh8M7NeY0nVcFRfED3wTuYscTs7fHb/9wn+ZGP7Zw5zlvJGm+mm/zSFysc/+sv8D1Lf4jSV3YZv7yKGqb0ZZe4uYK7oEddVavc/sv/OSKOkc+x5+UbER8YggxgNYw3BeXHzKfSXRenOvCjked08Vk5KLevmYi00wrb2GsNXLWE0BI9TvGZxW40EZFGvvnk7BulgFMxwsYk7O7+KjdvficASe3yauLg4AHdR28AAqnVNK7bVBUcWzhVfRejlOjx4ULdn8gc+skxYpySvbp1+bUPznp6AtN4UrXXButwtTLEmtGapjKsBSJ80EG2BtibK3it8MU42L7FOkxIAthcQu0co776BFkp4G6uLvQSltZNLYNEf4x+dNa038cas9EEKVAHwVHDrdRy67NTASuEiizWYbeXr1cqMRbZHeKqpYs1ze8WaQYzA5+TEqolfLWEX60jjCXtd/jsG/89w73QIFUpr/Oxj/7+Cw87HB3R6dzn09H3orMBst1HONgdv8Vq6eVLAyZEqUDWKJPVn64S/gJfP5CjdBrL+7WGo/FDtIxpxBuYWvFK3t2/9tn/DoCVjU8AYJfK+KPenDvC+4VZf16zVMJLiHYDadbHA1R7QPrKOrZZDrIL6yAnw7ZZQrWHxG8f4CpxkF9c0tQnOyMKT9oLnZPMUhkfa/Re/vnlmGyYnvhBX7Abq8YZaawx63WivVAI8ASJiChGcDxEjA36UYvs5hJmrRaCUwbJia2cdUQzISrZeh0fKwqP2/jMUXh4HKrnS5XQQ1OMSD+6hXrcRrdDk74+HmDrRVwxQh8PpgsLoSTj19aReSPkBK29r/LVn/3/TsvSn/i9r/HDPxwTi/mF0P10mV8cvkY/jfny3/sqK7c+TWlQxluHVxIvBQdHX2Tje/8Qo62Ln0pVfb4Wo9+o+EARZAArgh55qnc6Lbi8BNJDlMy/WKYOlThs4eSHXtjL5gil05fb0Lmc5E6b2QBTL6OOeiEhbmabzAMi0pAZXLlwhuz1B7t89a0fpVhconHnY1ciGftf/DfhfwhBa2VIeVzCPdzHO0tNrHI6g8+XYmwhRkiBSExoIpu9L70R7gpewAKIOv0LvwIBUxcFH2sKbYsvFzEvFRGjJCTtaRW6lPsjZKuHubWGenCATA3Z9jJuqYY67uGtQ93dw28uzTX5qYNOkDNEGrNcDdHV5RgxnK9cidSgd1vBG9MEQj1pxMB5ZKuPsA673rg8ye4iaDV9Fp4r4ohIS7KZRhXhPVgXFhxaYYqK4fgQpYrc2P52Go071GrbFx728eNfZHv90yinSDeXgyaxM6CXHbNcvHGxVF8Khlsx9gM3grzAe43ocSu4JrzfJ/KU+OWjfwzAD3z0/4rPt8ovgvee4WAPgG7r/vTfduxDNv06UjzDBWNBB+J5xWbp01DDDFeKST6+jRimRPdD+lx0/whbL+FjFUJQpAhhGMPgme/qxdDsl9ngFjEToCGtQ+13EYlBpnZazXXlCBdpdG883bHUrWFIDczf299sIKyjcP8K9o+ppfDOATiPK0a4coxZqeALGg8kr65SuH8Uzm+thito3EYdO7Nbmm0tUdjt4HPy7ksRrlIgqRTQD49R/QTZHs4FpjghYKuBMJZsvY4+7qPbI1R3jNeSbCMQdhspvJRnGjqHabi2xnd/jK11y2/6kx/mE/Uvzb3mfrrMvxl8iONxmV++t0HnJ/8XvvnDfxQGeZOlkjgs41GL+OU7T+GS/QLPAh/I6S1pBKmFANIKmDKUD7iUJAtCItiiimn1ccZoWZE2NTr1RMP5F41WNZXdixnyoq3xacRwfzzvlFCKyW6vIZ1faJJeaob46V/79f+BT3/rX+H4q7/E6oe+ffr3/v59Kqs3efiZf8K4vc+Hfsd/RFQKZEwWYvb/xT9Elsp0PvNz1G9+hNr3/gdU9/wZezvfrKB2Fm/9ecAt0gCfvsZRcmUnD1/QuFoJfTRA1Mvhq7AeP1mcKIlIDcJD9Ohg+l3J3hC31kAMx5g768jjfvBMnT32pIqaN+YhBG69iXxwcEZCMhtS4pwHa9GPj/DFCLu1fKVr+SAhOzVBeiHmZDGFQo3NjW/GecNrr/72S49nTMLO7q/ybd/yp7DHDnXUQxqDKEQYadAqvvDnNmrKF+T4BYCgIdWd4deEQ8UZSPjInd/JG/f/OTvRE0q9FO8d9cYtAJzN6Pd3qVTW+dmf+It88pv/CMtrH0apGGtTOu17PLz7MxzsfYFe5yHVtT9IPb7Yg/06sEoG55ynfH/x/hHZSjUEWwxTpPOkS8HfWQ4S4r0uHPUxpXjaBJduN4N9mnEIcRI4MoUQJzIKAdlGHVcvhejlfjI9zvTls6mBsULvXd0bftZaTYxDZLUrRvjcqcJqFfpKCmEwkuMsBH/VijglsUtlzPEAlct/4t0O6UurOCVJX1oN83O+qyilCHMFTP8O4Dsz/TOAWa3iCnou+TBpSNKmwuOJX/oo6is/zsd+z2t81w+EezepHluv+LXxDX59fIPMxnzucJvdf/YmleZNlg/DeejOCNUbM5YGpQu4JY/Qjv9/e/8dHkl23mfD9zkVOgdkDICJO3Fz5nKX3F2KpBjEINKiSNGUZMl+ZVmSbcmWP0ufLNmWP0d9ry3rta1gZYsirUBRlEwxc3cZNnFz3tnJM8ip0bHSOe8f1QAaQCMOZgaze+7rmmsAdHX36eqqU796zvP8Hh2albrLzSW9xK23RLsaYVrQ6NWkJiBREwRZTbWfuL3lGrdSVjXCXq1dro4bGAC4k0tfJEoKwrRsyUDdGDKMkC0d6+ICMRvhhaiObCzgLIFSEXPeCJ4boVyJF5a48MQXAbjmHT/C+Ivf4MJjf03YqDDy9Fe45Yf+Ha/81a9x5Ht+kokXv8WuW96J1pr80FEmXv4WqtGgevo1kkf2A1CdOIPvhPguOMvScPValmW2JGixRhNJSTUnsQJNoqoWIux6M9ELpbFKVeR0hSibiq3kMoklVkILTUNUM5+s7sdRCKXivNqx2YWormj4sZWZJVGFNMF8RKV506GSLsHhQUQUNyuhVMWZLi8R9Ikz47HY80PCrssQ8aXZ0SlsLpVdqjdRGuEHRLbmqWd/H98vc8ft/3BDTx0bf4ZicT/pVAciGFvIgwxzaRrUSIhMXJHd5hyOEpKgw6hjQ4zKJfEO9iGnq227ju0kfOkx61bjbm2OxUzlLKdPfAWArt5jfPMrvwRAoWM/nd2HCcMG5049yMDuuwBNNh+vymTzA5RmTpNIFTlz+uv09F1PuXSO85zkWrZPIFu1i8zrDlWcntD0yldJB2d4Jk5vE03bMj/C9uuxXWik4zSMuTqy6tM43LeQPmZbkjBSKCkIe3M442WCnnyc2jBPxsUfLMbewnP1Fa5PyVMTCC9CuzZSirhFNGClXTzbwpqrr3r8CMCeisW1Z1uQSxLs7168tgQR9onFfGV/V4GoK0vYlyMKIpypCqIe4L46hj/Ugcoll7SEVqtY9cnl/QCEiG8I8osVc16vTSShPH6SU3/y6/R/7B7uvD/LvA3Ql8vXUbSqDAcd1LSL1pKnJnbheS5zX3uEQz1vQ7Tah2tNtUvilIroQIC9k8+q1y+X9CqnpEI5ChlsvuVQmBKErsb2NJkJgZfVaJs1bd7cVQrVAGp9NkFakpgOcVpEdJCWeD32pooAF5i/M5YSCunYi3ZPL7rhQzN1YW7uHM+/+qcIx2k2TdBY6Qxudx9eeYoTX/m9hZe78Pj/icdUjy3Zpo5/B4CRp75MbeoC5bFT6ChCZjOouTKpoR5qj4NMpRh5+qsc3vX2FUMUTstd57xX2fzwWyKQGqh2WYTJuLI4yGky5/24sUo2iUonFjvsrYEIIqzpCsK1CaTAUYqwvrg/AMin0DOxMI56CnHluxDg2EQDnVgTJfRUOc4blgIhF7sczUfx7TPjqEwy3kaIhWYdFLMEhQzWuYmF1AvdXALUxDcxhNGWHSTW/fxagx9ijZeQ1QbBUBdkt69Zhq0UqtKIbfqUpl6f5uFn/ztCCO59yy8h5cZO6UplhI7CfsRMZUmRkF2tE/g13GyStneMtqDe71w60W+4KlGuDbkkYqa2oS6e7ZBJhyiIFuoathOtNa/5T3Gm9CTpTG/cOU1DNrcL183h+2Wef/IPAUhneinNnCKZ6sT34rSx4XOPAPDwA/+WvsHbKTVTK6x8nmh4HGv3LjgHZyce48DA9STF9ueEKsfCgi1ZjUZJF1nzY9/hRoBVqy0ISZV2CTszqLSLc2EmLjyzLfzdHWBLbCkIlSb0QrDja3nYm0f15NA0/YgjRbi7c8E1CWJXicTpSWjJyxZePHaVsGkMFkk03SE82yLY00lUaSArHm6phg7i4yDsysSWbq35ys3r2hIPZ0vSONIfR8obwUKRoWo6DuliGjlRiVMNN+FbHeWSWJX42qcihQwVyl7UNGFKEArN6EtfZ+LLf03fD9zLO37qILZctFadjLJMRs00FQ3PT/Uy66VoTCbwRobpHNoNLOZKR/kUvpjCSqSwPEFg+i1dES55GEilNHKLCTS2amZBRppEae1tpQa7tsrEKgVB1sKKIDGzOLk0Oiy8zngXuLNtKmnXQTk2YXcenU4sRGqVJRfEYBT5PP38H7DnLR+hcORGvG5BOB889X0Sj3yD8ovP4g2fW/K6L/7l/w1AafRV0gcOUzv5KuWxk9AUgek330zly9+i5959JO74J5T+8IuMPvMVDu5+B8vzUFTSjS3YGgFRLhVb8DRTFFrdIryOWBzPE7mCICXAij2rJd2omUp8MyDAmZxbc6JWfhhHNysN7PESUX8HqpiJ/YKFQA1145wYgXRiyYh1Pg3pBNbJMWTDJxrsWtBprdupfBpZrsf+y9mlS4BKCNSeXogU9vAU0guIUonYkzmMVnTFi9+4+errrHosaMYwQnhBLNjniwbrPtbUHOj45iMY7EJnU8sbHW4ZOTWHmJhDphOMlF/l1Mg3qFTiyui77/q5DYtjgFSyk3pjGtJLj5fI8/HCCvbe/UuiKwDYgmqfbVIrDG2RdT92qNni8/1M3OXNnihva3dVgAvhq4x5p7j97p8mkVyaVrZr6A7OnX6IWnUCgFp1HICp8RcJw3hJrn/wdkYvxAGLamUU0CQ7+vCrs0g3SfHme+hJXsPTX/svTPrnGEocXXM8UdrdVHRYy9i6LLQE7rnNu2VYzSioPVcnzMeFe6IREmUTC6kEFuDvXxr9dkZm4/zn3Z0L4ngeJQTSD5E1L3Yz8kO0a8ffnYhtQht7u0i8Nh530GsdT7lB4nSc8kYQYZfrqKkKOuEQ9uVR2WQsrkUsUIOePKmX47lOJ532vsVSgLRWRHdbx6t6268gSsA+O0Wwu3NF4xTVlSWaraOybuzlXKpBMyfbzwhefO5T1B8dxx8ZJrm/j/f+4wN0Jitt3iW+zLw008dIrYA/m0DXXRL5ThrlcdLEYw47MwT9eeqnX8Lt7CYy9XdXjEubYqHFun3b10KLpQEs5QjCpMbywVoWzJTh6p7GkRM/ogGVFFgNjXIFfufix3crW5MwazWdqNpVpJOgePgm6v2CqGXWl65L571vp/Pet6OCAG/kPMrzcDo6iaoVZCKJtbeXyKoz9Wd/RfZNN5G4JTYA11rT/4l34mYjnHKG7JHrSAQJ/JyFU1l5gVKug9UImrlgzbQJSy5UJ7feKCyMD3BqGhGGYFtxlLYztxCE9gsZKNeR1QbW7MqcMp1OYA1PE+7ri1shz1YR1QZojTVTjQsalUaE0UIk2xqbRbt2nJ6ypzv2Xj4/GVuzNVtVi2aFr+rIxtu1IKoNtGMtOD4ISxLu7kYg1o94bjAdaGGrZnHcks+cchd8iJc8Z6KESDro3NYbiQitEY2AcKgLz40498JjJBNFDh96P7aVJJHYXPMTzy/jOBl0IQ1T5QX/2ml/mGyqB5FZOlblSur9NqFjlvoM7Qm7snGx3hZbL88vn18K5pwq3YUbVohjiNMljt34MYK0oMQUYdhAE4ElsBJphG3hdPaQPnsbpeNP0/+W92Mlm22Jm6ldQgjShX5yhd2kWf9c1K4NqwhkmXTw0y5OubEQhBBK4w7PLhGputlhbkNnZItAtecW8/BkECGVjjvAnZuGYpqomMYenyMqpuPivOYKk/TChVzfeZRr4x3sWzr+Ug2rVEe7Firloh0LEa08JmQjgEYQrxIqjTsSR8HCQopgdydRysWq+yTOzVA/2LsQnAiziZb3qqMKK8WwaARxqmMhhT1eJurJruwY2Eo5jlwLHeuOVjQQXNMTX0OCCDm/IukKRtWp+Jj4ux8ge2M/x/Y36EzW2r5FpCyenepnop5FeRb+bAKrBkF1jlSw+JminhxIyXTtNNZ9h3B21QhrDrrhvB5sxq8qLm0sSIFV2/pStp/SJHxAQL0LwgzMSxS7FucozxM5YtUc4mg+amtBbdAlfc6n3mMvEU1yDQWl7diGLTmz8aUt7Qq8jAQpqPewRBwvRzoOqT37F//Q3UPQFRFlFJCg68e/b8n2QggCnaRcVaBh7C//hES+h+xY+1D9fPRAlusgBSqTRFYbsV9kRi4Rx6JZqKAAYQviuEKMfX4ynvC6Yt9Jcink+GxzHzUN18MI4VixMG3mZ+uEs+AVbJ8aXbAhUq69pIBRZZNYk3Ox+E266Gv6kWMl7AtTRD0FdFcOgijOS55P2fBDRBDGvtSZJDIIESPTqGwKa7pMONC52I75ChH1rLwobwZRqaMzyTiarjWPP/IreN4s9731X63rVbwaxeJ+Tpz4PIfCI0uWG09Xn2Gg56aF3zXgFyy8btukVRjWxBktLbEY2yhCigXLykuCLdEIhFg91c8rSGr9Nhb9tLtiaSC79wjZvUeW/F1Yi1tXZi9QLp0j7LyJ9Zi3EFtCwiayJfWhTnAsIqVxT44vtY5rdbLZRG2IStj4Q52456eXfEfCC0mcGMfry+Pv7VqI3Ie9scjXAM2osHNmCv9gz8Jqqaz7yFKdqL+wZJUvKqaJMgkSpyY3dNMjln/38786EuosWLXNX9tbC/faiWOIi8Xnm4mIMAI/ih1BVts/uSSNa1d3/VkYoWOhnLgl9VS2xKlP/QZdH347PR+8iZQVcmjgZNvnzzTSvDDVTy1yEEriTaSRWpKahELXAUYnnmZ/9tb4vRyLem2K2YkT7Lvrgzi5ACcXoBo29ZE067c3M2wXO3qxNCxIZKAIUxBmlh4UYXrporsGSAjwVk60umVeVEB1l4NelvSuVRxVjlyB29AQNl/HEVSHXCKhSZbV4t9XQ0Ijb9EoaM498HmsQn5BoG+UsBihMutPfkpJJAKEJKjNrb6O32LvFuVSsYuDH6ITNl5fM9rqCqodmigZF0M6VfBTAre8+Hmjwa4Vp2bUlccenSEqNrv1EefIiSDuLLdizHt6sE6OoSOF9EPsc81ltL4OkAJZ97HPTqB2xz7IUV8RnXCwRmewpstxBBmIeguojixyroY9GZvDK9fBqtSbhYK12NZtXriX48jWVtpMXwrC0MOynDUv2iiFqHkL+1FrTaMxSxBUufPOfwypBPhbExbdXUc5c+qrTFRO0Js8QDjYxfiZx6npMgPX3B2/nyuo9dhLUm8MhlVZx7tepZzYZrFFoCnXwt/fjXt6al1xrRI2YXcWe6q68eYaEiYKNUZeepKDRz/QftjJeHXkYvEbcb7yjD9Cb2r/OluzJG/M392xwi5MSUGYT+E01mhBvaYX4yLSC0memWxvF+eFJM5Ox4JSaaJ0fNMtQ0WUdokGi6hmW2hruorVdKmwGkH8ffohYTFN4sJsHOGWAl0P2qxk2gvR17Ww5+pE5Ua8ItHswGe1RL2tqoc9UV5aHLgcIRbabQcDxVU301oThR62s77d6cJzXEG5B8Zeeoz0dUfZ9fG7ABjIziKWBcJCZfPyTDfD1TwgEFpQG0sRBZLkGFi+YP+B7+aF7/wuezM3YTkOSmtefeHP6b7jfmQmBc0uDjIZYhd8gtLq3U0N28uOvvIpoWn0CMLsyjsmq815Fq7SrjMxp8if80lOBMhA45ZWRoLr3TaV3S71PofywKKi9ZOCSBK3cl4mjpWK0FrHOc4ZSbXPprw/wYwe5cRDnyRwQgZ/+Mc2/oFdTdATEOU3HhkQCtAK2109iz/sKRAMdYNjYVUbWBemEHWPMCWJBCCh2hW7eQAEhTga73csRg1FzUMHURwpaKnqFXUPlXBQXTmipviMCumFyQnAFgJZim2BtGWhWtaJZM1D1P045SOVIOrKIWse8uzEQvWwKmYIrukn6srFrheAPV7CPj0eR5tzqbjwZK62EA3VQDjQiWj4uCdHcS5Mxz7NYbSRhItLyvT0cR765r9mcuqVtTeUEs9VVKvjTE8f56mnf5uHH/0Vir2H4Lo9VIacOE98Cwgh2HfgnTw1+wXmgjF02uV07WkOHfkgQcbFz0kqQ64Rx4YNs9xnfTmyHqwQwcKPYsuw5t+DvjzRclux+ed7Ie6F2bbiWOmVc2ZkKc5nJ3jmpU9y8OgH6Bu4ecU2YVpQ2bM9qyPzLd2TVnadLZu0vKlYpZ5D9ebxB4txRLQF0Uy1iNIuYcdSYb3qZ2kRx1GLNdp8nu/83GnVfFQuSZRNYM/WcF8bRzYFamzjmYzT9ZRGORbakrHAjhR4ISwTx5rYUWIj4niexPAsWoj4pgqIltWZzLeuBnDOTW+5Sc2zT/wO3/rav9rQtpXGOBMdJY6XH+ap3/3nTD78FXq/9xaEG38XWWcx71Nryem5Dr55YT/D1QLza9u18RRRw8aqgVMVOKWIXH4AYTs8OPaHNPCpzF2gHpTo+/FbSHUvtaVK5F8H3SqvIi5fBFlqwqzCnpNs1EzNrmqUFecNL0e0iRSv9bKWEiTmFIm5+ABTriBo6V4XZlocHVqiy1bT6syutERho4BXT/41Ixcep+vQHey55/uIQp+ZU09x7lufwc4WyF1/I31vfTtWOkmYjFBJjUjoOGNBg46IHWCUQEuNTEC0Vp7HKmgJ/R/+ON53nl91G1WMs/yjcjK2YKvUUX1F/KIFlqDWA1HLSn3kxN7TzlhLfpvW8f4t1+Oiu/19KEuiuvJYJ0djAe06sVn7rs74eeU6Opci0hp7qkzo2JB2VxTg6HQCUfPiAjdAujZKa6xTYzDQicqnEUoj/JCor4hqBNgTs0gvQFbqhL1FnEp9yetGvQV0NhVHqFsmZvf0ODqMYheIhINKJVYU+V1qnn42di4plc7Q031s4e+NRolKdQTXyfLK8c+RTOSZLZ1BShvLTdG7/3YOXP+jkEzFNzYIvH4X55y//spGG7q7jrBn6C08OfIF9r84RaU2RrHzGqq9K1dYDIZ12WCahLYllpCoIC6MdiYr6KSN35Uj6kjHrggbpOSP8cLsA8wF49x6zSfIZfqJpOaJV36fen2KQsd+9h961wpxrAGvQ9Lo3b7UoezgIQodB8i6m/dZXy0/VgHkU7jjS6PIqpl7bDUCqC/Ob1EuiUo5OONx8yq1StGkiKLFFA29sk24M11daK6BF2LP1rDOz8SiOGEvNAeJCilU2sWeaZ93C83L8lo7uV0UPGg2CbEluBbBrkI83zfTK1QzD9kKIuxSPXas2KSD59TEy8xOvQZAvTZNKr34vU1PHsfq7mBq5AUmTj9BsqufufMvI1yH5JGDDP7sj9Dxpl2IxKJuOFnqIVAWtdBhtFqgsaya2ZtMEtUcpIbUVCyqE6V4H9/ypp/gpUf/gG9f+F/0WXeS2X2AfYMzlMMEk42WGy5bYTmKaAvOYIbNc9kEshACEbV3jlqNxCyESfDa6BfZ5kZqRS5TC6FWS8LlTlUtEciWI4matjLuzOKEM69Z3XmHDAHHx7/OyIXHAZg6/jhTJ55Aui5aaZyOLvb85E9DRxKVUTSS7QZKswoO5meGzRv3xCihUTlJoBtoRyCWt5tGI2aqqI4suiuLLtdQuRRBNra9a/TRNgVEWywRnPM5v3JyDhFGyOEp1O6e2M/SlojRGbRtofKZxe+4+UVrIMqnEWGEok2ueBBhj81CFCE0+D15tOvgXJjCGpmOX8OxkaUaKuEQdmcRDR9rtoo1MYe0qytEt2gE2GcnEDVvYekQiCPIVpzzKLwA4W7sArmZ43Y99u65nzNnH2R07CkymV76em9ieOQxXjvxBZQKEEKy/+bvRaHIXnMtnde/edXXiiRUBl0Sk0Hc/GaTV/uDB9/L4K47ePzJX0ejCV0V75+L/IyGNx4bPj+URtEi6jIJwn1dC57bUV8e7VhYM1XkGg2KIhQPT/zJwu9PnvgjpHSQlkMY1Nh36F3s2X//Cj/+MC2o99qEie29CfS7bIQUNFJiTTvSFSRsVNfqUWer3IBlEeaFkUeLhddAbHFWiaOpQS6Jdi2c8ZUpGsv364p85mCxOYkARD1YmEOlFzZvaLJYszVUNknQk1vS0lmLxeuHSjq4o2vYUK0y2cRuQQpsiVVpIIIojpYrTdSRRhBb33m7O9Gr5CKvRbHjAG4ij4oCnnn8t7jlrp9ARQFnTz3I6PnHAEh276L3vR+iMnKCXX/r7aQO95Lqq7U92MuBy4vT/W3fy59NEJRjUW/PgvDBDsFqBvrcRI5b7vkJXjvxJc6deYjsDTcgpEa2q11yIjAC+bJw2QSyjsCqbq5gr9EFJCTLzyCp4wYiy//u5S3sIGobTZPL59mWTfwshBlFegxS4wFueXGyCJuRND8tmZs9y9lTD1CdPEvxjrvJ33YHcrAD3ZnASllooVFCN6f+rUrezZO64yhTf/IZyrpEflkFtdAsFKkp10Hv7UVZklqfQ6OLBdu55YgQwryFmlPIig/N19C5FKLmIaoe1thMM5chjlKIIELlm8t9XoDOpuKCuqQDtoWoepBPo4qZpc4XUhDs7o3zicdLcU6xLWPLoDCK3TD29xF157Fmq0RdGaKuPCqTxLkwtXRptymGrbk4oqGFWKiSnkclXMLd3es6V0RJQaNgEaYkyZmIxPLUHNEsMFon93I5B/a/k/GpF+i7/j5eevTPuHDhUebK50jl+9h7/8dwUjncbMeGXy+yodYfRyZkQyFCvVTRa5CRxvJ07AG+7EbS6eqlWNzP5NRLeAOJFVXcBsNGkHpj58HyQEY0UFjSkEZLEadcJZy4uGoZISHng+OcnnuCbH6QvoFb6Om7AdtOYdmrFK06Ai8t8AsW4RbTktYjsqHrmtu5cPJpBpKDCwJRC7Gkm1wrmrhz3Vp7Tied2F6zmZpmJWxCrRFt9k2rh3RUSKGd9gJ5s8hlAj3KJIg6MnGHPi8k7MvHbZvnI9FSNm2jVOy2cTGECufcDMFgkajpsWyPzWHN1vCO9G9JHANYtsuxGz/GiVf+D6l0F4888O/Ide6lPH2GrlvvJXPPbch9fQjbpsAhrFRAuq++6fkxrNr4M7E4llqQnI0DEHo+UNaMa3g5i9S9b0b/wQOIbEjebTBeXxkWF1tYaTZsjR1dpBfnxK48GOyShnY5yFmLSsbCKoWkptYWqEG2edLaAr+ocecgMbVUHENsLae15uzEY4w8/SU67/9uuu7+QXSnhc6ohdFFV7DPqnQdsoeuZ/jMw+QH37XkMSUEZJOxBVo6gU44+DkZdw/MrH6mO6FAo2kUJOlyHKWQCEQ2ie9045waw5ppilxLEvYVkdPlWHxpjX1+iuCa/oXGHqLmYc3V0Gk37pKnNVbdJ+zKoQrxpKdzKaK6jyzH6RL+gT6k0sizE7EjRjGDKFVxXx0luKavfcRqectpWxJ2ZNGpBKLhI+dqoDXCC+MLzyp4BYtGd7ODVLWNOIZmqszGJyutNb4/h22nEQmXsBlmKlcuAHD0Qz+DtDdZ0dmCEqBSa1+MpAZ3KsQtLUaI7IbmwP0/SC+zaGfr798OATilCLumsJs3rqEtCFOCKB93nzK8PggdG7u+OdN7lXKJEkuPOefs9JI2vmExFTd7iBSeE/DCK38KSI7e+DEKHXuXvqAt0BaEVuzlHiUEKi0J3ctz11ccupZTT/wF5T018n4cLFhNHEPsnqAyaxddqaRD40A3yVOT8T7IJgh68iROTaxe2CgFKuXijM9tavzKsVaI4SU0reWsqSpRNh67jhQEEWFnGvdCLJBbhfp8k42gN7dlj+uoI70gjgFUZwbhhyu6fSoZ1+Ws922HoQdaI6VDtTxK+pojMArl6TPkr7+Nwg9/YInysLM+ye7Ni2PtW3gTaeZHJMsarZorJRaUB+JrTJSW+DlFuKuDo/f+EwrZOj2pKV6eaR+RNlweLrtAtm1JuJnWxW2w1sjHjwRERZtERSHb5SkDflYS5CyEFFR6NFYDEtOaxGzLuITATwsqiQYnv/6/8Rsl+n/67+Ps7yVyNdvT+mGbiKDru76bM7/+nykmB+guHMa2l066OpPEz0mUIwhSknCdm+75dBgZ6EVf4ZGZ2DKpp7DQfAQgKmTQUiL9CO0HcSvOlrxeUa4vRHSdqTJeIUO0q3Mhxi6b1nLasWMrs5qHdX4yjvDakmhvb2w/B+iBTpzT49hjJaKeAsKWq7bCFq6FN9QTC2Ip2nont2P+7t6dCZt5fttzxz4x8TzPv/gpHCdDsnsXuYFDdBy4hY79N6G1uihxvFGUgEa3TZi3SEwG2HUdf8+WTSrXt+7zN4NdU6SnQsQypw3H1zg1YDrCy0n8bgdlotZXPeGeTjg3jV2qr79xkyi3dJ6yx0pLxDE0RXQxzczUcV5+8k/o6b+RA0ffG7vwpOKGRjploWyu+HEk02n27L+fl2e+yUHnejqt9tZhfn8BZ7S0UIS25ms2XRuihI1V87GnqljTK9PKltB08pHV9bufLnmv9br0NedaASTOz6AdGdvQSbHgftGOKJ8k7M0jK96m22erlEM03/ij2QVVORZq98pcb7nBy/IT3/5VGvUZbDdN753vINUzRE/uPQR2QO6+Ny8Rx5ajSHU3Ni2OhZLUxzLolifKYFm6T0aiXaj3KHRGkdlVAydDf77O2XLHijxmAB2aqMLl4jIKZE1YiPDTCmfEQVxERmeUAHud8z50BO4ygdzosEjMRviFWBxXezRagjOnyY4sVt5qW1DPwfmxRxj5+pfJ3X47uz7+t9FJC1Ckkj7JRIBjR9iWRgiN0oIoEkSRJAgtgtAiCh38ULB92aur0BDIgSLFu+/lxa9/is6OQxw98mGSLab4GvC6HVTz3HJrAr9jpSk6xFFGpTRONSIxE+FnJU5Fxc4U89XTu3uwT41BGGFNl7Gm42U8nY4veFF3buF91/v0YngakU0u2JmpdAI573M8W0UnnXi5FZDTcYci4Ydx5DqXWoxkL8PvLiC8AHt4CpVLEw2sXTyjBYR+jcnJl+iNbtiyxzCA580hpc3JU1+m0Zghne5ldOwpOnYdo/vme8kPHAbgwNs+seX3uBhCVxAOuFgREGmibYywCSAxuTRK3RYdO8y4DZ9av2OakFzlaCAY6sCueu3txJaTsFHLrLrmWxHPE+ZT1DIRJ575Y+Zmz3L41o+SuPYopczOFAleh6T/2H2c/qtf4DFe4Y7uD9Hh7kKKxfRClXKIurNIL0AlNnBTrDRWxUM5FkF3FmeysqEorEq5aLsOm9PIGydSse2miNNm5iPFy9FJm2BPF8C6bcTDYhoRhFhVn2lvBK8/Q3FwsZAZe3NpmlpratUJEskC3/rqv+TI9R9hfOp5GvUZBt76veQP3ojsLRDkNdnssbav4XbV0ZsMey/YuYVL57QordGzxM2rXPDyirAA0tKkB2pgx8f/ybmVDafmX1d7O3rh/3XFpW817SpUQqPT8f8AwgU2ehMpNH5/gFW2sCrxyREVJaKi1zSXD9NySXe8RjHuFhcU42XdWm/cAjs7BnZFxf7ITpXxyiuUp4apPnsKkU7S989+DGd/L8lEQCZdJZsOmt5qG0QLgtDCDyR+YOP5Dp7nbJvZt1WW2OX4ayy+6R6mvv4FpmeO8+1H/iPfdf+/W9iutWgOgEiTHo0L9LxOEedoK1Au2LPx6BKzscARTQML1bV4MVOWJNzXi655iKSLApzzk4tdtJoG+gIQ2WQsemseYXrlcqLq71j0hhcQaAgH4wlVZZMLrZwBdCYB1UacBx2EsQ9zud42imzN1ZANP06FSG1A7CrNayMPMHLym1TyPnv67yFRj+39yuVhstn+tX2Lm0zPneS5Z/8XUegznyI0Uz6LtGyG3vlxXGfrHfW2m8gCVrFH3ApWBKmRAMvbhFWhr8kMB1QHnYWcf8PViQyidcVxlE0QdmUhk1iyPC7n6lhzdbTWlNQ4E2KcuZlpZk+cYNfeu9hz30eJ+lKbqn+73KTHI1zfYve++zh3+kEen/wLbuh4B4PpRfE1/4nDvvyKbpztmI+oyyCCuY1H5625ehxYqLYrFBexM9EyWlcGN4IGwu6lBXrLiTKJOFCiNN7BXuzJSpz60fL2yrWQfoRKOkR9eTh5lscu/BlMwt09v4jjxukVYdAg8CukMu0FZCtKhZw+/mXOnX5woabjlef/FICuu99B7u1vIUzEonU1rESIldp885vGVIKosVJeRSmoHdAIvehaZdma1K7qgjheC79qb9QsxrANXHKBHPVEKGvZN7qZdTAtsEoWumWkkYjt39bSqUHOoirA8jVR1lrIQVMynqBS04tOGEEazp99lNGnvkT68BGcQ4MU33s9uWsHyeRCCtkZxIoqvw0iNI4T4jiQmb8r0IKGZ9PwHTzfJghcwujihYGVztD99vcw+dW/AaBcHyWXWsxhkqFGtUTppB//sxtApFtruhZ/EBBkJE515c5WtgX59ML2wVA3QilEtYHwAlRnLKjlfMvnho9wV0ZMWo+OYPnJby0VpKqQIcymsE6NYV2YJurMEjk2lozQy4pW5qu5Ie70J2reQoS7ldLcOcreGGPTL1Aaib2JLzzzZS48/WU69t/I3tvez5MP/DeKxf0cPfwh0umlk7PnzVGpjBKENUqlM4xOPMvA7e+heOAWaqULjD3zAJXhkxSGjuwocbwRgtocF77zefa+9aMr3ACWY1ciMhPRhu2+lhBqkuMBlYGtR+0NVxZZ9XDPTK2xAaDi1R+dcuNGPi3ohMNcn83xc1+gWh2ns+cYhe5j9N31HuzdfQRXwb2TOxsXLO87+A5Gh58k8MuU1TS+auDKeFVs3vN4I+IYIOzNYdU8RCNc4j4hLLloxdYGZ7LS3LCNGF4tL3oz4ti1UbZEZRKEkcKebr+SZ8/WiTIJVDNNIuzJoaVYaC2tkw7e/m6c10YYL79CpTTLyGjsIpFIFvn2A/8Wx0lz9IaPUpm7wKnjX+DA4fcysPuuFUWZszOnCRKKucp5ZodfRqmAgx/7GbQLflDj3Kd+E4DUO+7E30AdtFPYfPg9KDsLjhXtUPNRJ+LIcap/Y+J45ivPEJQzpG++bdNjMmyNSx9BXi6OgTAZLeTiSCXQAWumXFg1C90ioywtEMH6Eaowa7Wr5SNywbN9GtUxKmdfZfaZR7G6O+j/xb9PZl8HyWRALu1jO5srcNgwQpNMBiSTi5NRGNrUGjb1hkuj4W44wqyyGl1TCC8Wkm7fYs7bE0/+FnsH38Ke3W/Fshzs2ZCwZ6VAFcsKzQRg+xrb1zSK1orH238kjXYswMJ+9QLhQNfCY1HVA8dCpxJtq7UtGU/gG613U5aE/X0wUwHHItzbS+QFOOcmVkSvdNIh7O+IiwWXXRRKc+c4f+FhpksnyO0+QurYMTq//8NY+SJCSuqvvMz5T/8O0yeeJDdwiNnh4zzy2H/mbff9W+qNadCaCzPPMHr+caLQI9Ozl/zQEY6++R+SLMb5vLJ4hNzuI0gvWtc1YyfSKE0wdfxx9r71o6tuM59S0baQcRNYdY0daJNqcbWScuOGFo0AlXZRaRftOmhbIlIukS2x/BCqfuz+wnzhapnK3DCTY88zOfYCu665m73v+CFUZyJe4WBHVXysiWgugUnLIfDjqOrp0pNcKL/Ind0fIud0x0VuM9UlRWdronTbOXgtcbx0w9UdNJafaZs68yKFkAL3/DRhcY0b/0iRODtN0JMj7Gs2k+rKEtZ87FId3fAYfvVBhqefxbIdcvnd7L/+feR7DpK00qi6x0vPfornnvgd3ESOnv4bOfnq52k0Zjh07IPMTB0nmezgtVNfZPrCcyR7hkj29JO7/U6yN92M7nFQUmMD++/6/6OjaEmL8NWwHIWd3lz0WPsWwdTGgiDSakaOnY3NmzPfOI5V6DEC+TIi9AatebZCcnC3HvjFf4hKaaxIxB172liUyIrEmV6q1S0vLpyr7Vp5yto1TWpi8+OJwgbnT3+T0stPEk5PYfd2kz62l463XEvXLbvIpr0lM4REkbZCkrZHxvJJ2wEpyydtRdhSobWgFlmUgiTTfoYZP0O0HR5ZWlKqJChX0oQbKPOXSmCPWwhfooKA0c9+GukmmHviEQAOHfwedg/dgwZqu10iR5CYCvG6Fz2AJSCriydqalohfUXkirhZyjqHyYJnZhghz08S7utb8th6R9lGtlkPqRRibBY5V4tv0BM2wb6+tib8SkU88NAvAZrDf+9fIPZ0tn3/sFLGSmdwpkKO/+6/Z2//PeweuofHHv+vVGvjZPsPMHj7e8n07CXMSaIEuKU2toJXMbNnX+TEl3+H2/7u/73iMSuC1Ki/bYWMtV57iT/5TuOJ3/mnT2itb7/S49gM3T379PW3/YPL8l4rVqFWoVw6z/mz32Z68mUQkCruIr/7KB3X34nMb7Ljww4icz7ArTbTqqaOM3L+O6TSXZw9+TUA3r7rx3BkAlyL+uH+hYYbrZ1HodkpzpGolIt7ahJrk8V2G2I+gn+Z1uyFLakd3bVgSy8A+8IMF4Yf46Xpr9PZfYRjd/8I9T5niRWfHYEKQgJ8ZCKF+vbTHH/us9x0549hWUkeefDfAtD35vdQPHIr6lAH4QabGa6FU/BIdG68Q5/Qgtpwlshf/5pt2c3I8QbFcVBx8CbSnP/3v0L6umvp/MD3bHhcr1dO/eOfveRz8SWPINtN4SsigUooor42d2TuyhPUnY1zhNsJJ7H5lCCiBLz8xd/C6i8y8JPvY+i2AnaLUXxCVsk7dfJOg4JTJ+eEOMJfN+jnWlB06+zNzKC1YNpPMeFlmfJy1Np14NgIQlHI1SlkG8yWU8zNZdaMoCipCfpCrBkLq+Iw8JEfBCB/0+1M/Pmf0PDiSLgA7FKISEoSpQgZamr98RgTowFumzQKy9/Y5Dm/lbYtdE+h7WMbef7FoKSEXZ3o3iJyeIpooHOJOD51+muMjD6B62RRUiEsGyuVhlXEMYCdjS/WjYlhhJDs3vMWXn7pz6nWxgHY/7YfxE3HURGnGv+DOJc7suNOh0KDFQjkBvflTiPbu5eeY/es+LtdjchMtvcdN7wx2ciRUC6d57knf4/eW9/OwXe9DydbWDd952ohyEncZqCho+sQHV2H0FohhMWZE18hUI1YIPsRwg+xZmvYkxX83Z2oZotta6qykH4QZVysdazzolySKJNYuxlHO9YQxhsprt4suhk5D4spHn3wP5JIdaB1RL0Wp+WkDh6hvG9lilVoAZaN1ZQr45MvMrD3blw3xyMP/nsA8geup+ct76DRD5G9PfORldyc0KhPJjcmjl0Vi2NrYysAqmHjT8TpKfm33I070N4Z5VJjhwIaGhHGek6oZprrwsUfEHGTMeVolAskxZY6BO8ULrlAFi25tXqVY0fUl56Klo4jyGG6/YS7Vu5xmIC6N4OVSODYaUQU/83LK/zSJHt/5v3suTGxcPZnrIBrCyMU3dVbZW4UITRdiRpdiRowTj1MMOGlmfQyzPoZ1qnpb/OCmmK+Ri7jMTmdpe6tnp+pBYSdESqtcGZstA+VF5/BK41zrjRBPjtIX9+NyBBouns4VUVm2EcLEXdh2ybmu+5dDmSpiqh6RP1FLGKRrCyJ2t2zYtsIn0ZjhkZjhr473oWaOEFy996VL9oGFQVoFRFpn0ZjFstK0HP9W3BSS6NdXh6ioqBdSrkVCqyKxqkLhL9dZZqXHjuZYc/dH174XRB7KSdmtzdMrgGV3rnRY8PmUCqiUZ8mle5aUtza8GZwO3vpuvP+Kze4S0SYt2BC0ZovVqtOMDt9HNA8NvkZ3tr7CSzpIIIIWfURSpM4O0VYSMeNPUqL1yKrXYFdC8q18Ac7sNcoktsKl2puckZmiYppfL+M55WwnRT7b/9bvPbYp8kcbO8isZxIRIRBjSCoEkUe6d49dN9xH/XBuLPsdiHbpIeuhl9yCSvr10/EzUYa6A0W+uvAojGeXki5zL91ZaDiUiC1QNQ1lgdWQ2B7otn7bKNHRst2tiBIacK0RmXEtn5Hl5rL4heipSbKKFSq/UER5RWoCLscXxzt2XhJP+ho/2WI5nVZ00zr1IAl8LKayXNPM/65PwUBmcPXkujdRVgtUx89TWJPP4PHkgsJ8hkr4I6uszjy0tRFp2yPPbbHnswMSltMeCnGGnkmG7lNiWXLiujrKTFXTjFTyqyZn6ySGn9XgJyUzD7yjeZfNeeHH6av78YVtm52PW4CshZe0cIpR23TBpS1LJ0giMC5fCJH1H2sudqCz7JlSaJChqh3aRS7pucYnXiO/rvfj8pZ5O54EwV3samKRqMTGuVqRASyJpfkxWd3HyRZ7GNi4gVuvOEHefAb/4pEamnkyyuCX1j9u4nsWDz7RZBaIhoqnoD8+O5cBztfNEsNydFgW2+o5gnTciHn1HB1o1XE80/+PpXyMFI69A3cihACzysxOfkyu+774JUe4iVBCagXBKnpxTm1UZ+hNHM6/jmqUI8qZGVHnFvc7I6HBnt29SCNBlQ2gawHS6zStG3hDs9gzW08FeBSE2VchNLIdpFvDSMXHsdN5Nl70/fgdSdIXXM9N9xx64Zff9c938NLn/pPHDj8Hg4e/QCvvfw5rKE+wm0WXmHDwk2sHwQI5lz86fWDQk7BI9mxcT9loST10QxqGwr4N4LlCawKOHUQHgi2yUoxBKcscMqxWAtTgjCrUdmdH12+LAJZpRRRx+oHmkRgVRa/DBFAkGXVi2WUgIquM/yVP6V66jg68Om6/7txOroY//xn2PVzP47oyVP7xpP403NYe7L03/dmdr91EKslnePawsglE8fLkSKiL1mhL1lBaYvxRpqRRp5JL8tG78ryuTqpZMD4VIFgDbNwDeguTee9b6f6/PN402Nx5yBij2e1SRGiidu/tquiE8u/1sspjsv1pS2rASKFnC6jO7Oxywbx+CeC0yQ7+uh459tWHFfaUQRdEbrl2JCewhlbTJGRART33sBLD/8pxTft4/rrPs7zj/wxXcfejJBNb+j0xicyJTSkBFFLwxapRRzh9zUyjN9TBsT5+5tsZ70lbEGQ0LihQLdpsmOFkB7xL02qiBR4bQpIDTuf6cnjnHzlr+PGC06Kozd+jNHz3yGy4bof/dfUpy5QeuFJpOVgD+3lwH3fRbLr9dshLOiySc0GCykMHV0Hse0kjkxR92dQzdJxEUYbPq8FtPUZlvVgdUeKK8RaUW+VSTB64UkOXvsB3DtuJrkFDSY7O1Eq4JnH/yfX3/X3GKm8yMRDX6TjEx+6iFGvJJhOIm2NnWmvEYQWNKYSazpWxGgSXQ2cvL+pVMLGjLvCR3m7sfxYFLsVgbgsUkhg18GuC/SEjjv6pjU6KwidnXUcw+USyJm1o026ER9s83gdoNeoZPdkjfN/+puk9uxj/0//cxrVCS782n/D7u2m56c+jr2/HwH0fO+dOHZENu2RSCz99jvc6rakVWwFKSL6U2X6U2W8yOVsLc9wrRN/tRyUFhwnZLBvhrGpLPXG6idm6avfZvqhr2Jl4hSAvt4bgDjNJUpKYOPL48k1ltKvRMTTleArELkUwVAXzkQJmv7LKp1A1LzFAhRguPEKZ57+K/q/+2+tFMeuIuyLVhjBq4SOz44wThVz6lDcex3nHv4MTzz1m6jIZ9eR+1Chh+XGKldYF1dqqAQoh9gMehmWkuApLD9OP3I8saYP+KaQgkZRE+QABJ6G1Ggc2Z7Hrisy4+GlyTeWUO23adM0yrDDmRx/kVdf+HOO3PxRkrv3cfbZL/DMY79J5+HbGbr/h1CuJLFrN727dm/9TaRGOc2cfiuew7Sl0RZxzmPrtClASAEtj8VrZHr+YYQWcafQQCP9eAnZrQv0Ni2KRBIaeUFyNn7Pb3/93xCFHiFxlDcp4woyWfVikbzWR3ctIn/19ca12ljvNLzOJC9Nf43Ar5DYfw1bbQgnhKD3zncy/tiXeeKRXyNoVOm677vBj+KioG1CI2iMp7HcECsbYCUiLAuiCKKGTVRObEDAahLddZzc5tVn5F2aYJPVALsicGqXSxS3RyCwG2A3BEwDjsBPaaKMJlolvfZyc+lzkCXoZoMQ6QvQLDQMWdhm2SjWEsdaa4b/9x+Q3n+Q7ne9n6gYYReGGPpvv4hwXaTrIND0ds2RSq1+J7s3Pb31D7WNJCyfQ7lJDmRnOFnp5Fy1c/30C6Ho655jejbDXKW9pUzi2AEAMjfcgKOTnHz0y+weugcRJYkS4pIUYVwu/OaFTAFkU4S2hXV6HAGEfUWEEGgpqVbHOX7ui5TnLrD7I38P59DyfGNN2B2tmhM1L3edaU1jdgJ9YYIbb/hhxsefZXziBUZeeZCRVx5k71u+n67Dd6C3a0mqDZFcGnGu07z7r2vsOkhva9+nBhpdmrAl+q2bhRazZ57nxFd+jxu/+2coBD2XZsayBdU+m3AroSTDFaVaGePV5/+c6+/4EcTNB/Bt6B/8Prre+g7szMUX3kUpjVfQqHT7jp+rs3bxGULHtj0ORGkI0PgarJImNSvR25BaH3TZJGZ9BLBr6E2cP/0QN9z2ozz3xO/y3OxXuK3r/dizdZRrLzhZtCMKNl25culwLfzePM75mU2NKSymOFt/gfMnHyeV6eH6d/80/uDW/OC1VtTOn6DQfZDEW/KUzr5AY2aMs5/8H6S/updd7/4+xI0DRJvIH16PyLeJprcmlZyCtyVxDGAlI1Sza96F//Sf0VHE0M//s02/jtDx9cGqChJVwY7ttBOAGwiYi29w/awmLG5f0eVWuKQCWUAc5WoIZCji4jE0wWCItjTSF+gozpvVjkZswAm+duJVomqF7nd+TyyEml1wrOyip2QxX11THGdtj+5Ee1PzK4UlIg7lJtidLvFCqY9pf32PzM5iFctSzJRWetq4g30M/eovYGcziFHN1KNfI4oCZJCII4ZdFqmpjV8JbEsQBWqFuT8srXjeDru2zaKSLuzthZoHCWfBX/Xl1/6S1J4DXPO+70ftKax4XpTUqFVOPhkKdAhzDz7EqUceQHsNctlBIhWQzw3R030ttdo4vb03cf6Zr2IlUjj7brrEn3TZ+F2IXIFfiNMf7JLGrWzyNVIsEcfQbDU+NcvM6Wfp2XMbqVlB4NQJguqKJikXNf6koN7vmrzjq5TTr32ZPQfehn1gL63db51s8eJeWGoaXZogf3EvsxmU0Khi7CufGr74yFpoQ5gROFXNgcPvZmjvW0gk8xw4/B7GTj68uF13Fnu6ilylQcd6abXSsdBat+0kuiEsEbdv9jbg2KA06JV1EiphExXT2GNzbYVzKVnj1PEHOHr995M+cIT6FpoBqTDg1F/8On5pBlu4OE4aaTn0dB/BS46S79hLsmeQ05/+Da7p/yWiXVd+UpGWJtGxdXs+O+MTlBKUH36U5OFD8coI4I+OYRcLyGT7vOd5twnpiTha7Iurx0h8ngjcksApaVRK4Oc1Ufbya4vLsKgpSEzFuYWxkBI44xbKipe2QBB0hihHYQXrH9SNc6fJHr0eYcXNQ5YLHEtqCrm123EezI7v2J4NScvn1o5znK528Vqlm/XigoVcHaUkpfLKO3KrmIsPqB5Boqufuj9FXsUpF37RJjkVESUF9gY8bEM/jPPl3JWHTJCTOOW4g5QcnSHqX79Fkag2lrpdBCEiiNp2utsIKuVCSzvpmdkTeKrGNbe+C69gtZ0fdBt7wXlkSRJeGGX0q5+l2HOYG27+fpw2XfC00My5JWbHXqWbyyuQW4lsiLoEYQFSE3H75g09r83udmY0ke8x/doT3PuWf4ltJ3jyqd9itnR6SfvyLSOgUbDwu+wdsYxm2Bpzs2e45uj34Be3cVnbhUYfhFeooWJka+oDkD538aLCK1g41RAhJIlkrPbzHfsYl48vbqT11pZ+mmbCoQCkRG5RIGshUI6F1SKQg55VWkeHCvfC7MKvyrXjNJaEjUo5CEvCsuYlGjhz7kF277+Pzr6jzPVuvs5Aa83UA1+kOnKam+74MQrFvQi5eMwN7XtrPLygwfArXycYH4Vdg5t+n+1GuOFFLdPqZg5K/eVX8EfHGPr5f0ZUq3Hh3/8KnR98H4Xvun8hXUIGcbG8DOaP2x0qcDaJQGDVIVWP0zAaOXVZ85UvqUBesMdbdu6KQGK13DBbFYnYQPtpW0iC0gyp3ftW3cZ1/TWPjbzdoCd5sdHjS5ugIATsz06Rtj2enx1ErfNeHYUqvm+vaQOXP3gDE8MvUuhYTDOo9duEGYvCWb9Nj+dFlAUSK87rWzHY+EJgNXTcUEQIhBegE2tPhMIP0S1BcmuyjGj4hEPdyLkaqmvtZgFBWmLXVt8zpbmzFPYcQ0hrdVvA1VIrGlB58HGmvvR5dt/+AQ7k74xzgNtwfPTrlIZfYeCjP7jmeC8XkQ21XZAYa7YQ3ySWD84cFBqd3H/vLyOljag26NODXNNxHdbwNNFA55bHpxKSeo9NmHh9TOBvVLSK8L0KsqODDfQyWv/1iCPGQff2WnVthcjWNDo0yamL+2BhTsYR2pZCvHxhN0FUpxaWSNsF3LG5LTXq8IY6cEbnkGukZ2wEEaoVxX/SC9Bi/eh10J9HKI17fnUXDZVLMnvqDPsOv4tGXmy6ziCslRn74mdozIxxy10/Sb7QPpc9Cn2eePT/IbvvKHJv784ImF7EiSG0wJtKYlVh4IOfQNka4YGcE+RvvJ2eQ/dgXYgjxGt1IX5dEUByWsL05Ysk74iyGLkBc22nrEnMRERTM1hHrweadxdKLMk3ctbpab43M3VRYz3ojnN9YoS6dnnF6+F0sH1LzsvpS1aQxQs8PTvIeoK8p7PChdGOtp38BODkigTBSXSgsasROiGJkjKeBNc52pQbeyK2G4GXt1AJiXLijntRX3FDn011LE0L0Y6FLAW4p8fQWkNHJm78sQpCaZQdCy4tAQV2ANJXaK2ZLp2kc+/dQFzU1n4Q7ffp3F88SO2JZzj07h8j3TVIJdIkp0MSc0un3dlogtFTj7Dvx/8JsnNjn/tyoACvTyBH4iKktVj+uD0ZUP72txktjRDVynR2HKI/e5j92ZvRkeK5s59DlPIcPvqhzeWYymbUuNNEjV8PNBolHDeDdi5eHUcJjdcT/79TCAsgZrmofGQN+GmBW178XEJIEnYOX9VJU9hyFztnvLxEHIedGaJsgsTZrdXWtIZ8NmIZpwFcG+1acXtxLyTqSBNlkyTGSnHBHDCX8Ygin2S6k9omb4q1ijj56V+jq+sYx970t7Ds1QNAp099ldTQPno+/gMExU29zSUj8i2COQcnv7l8nXl3DPnMDLXHnqA+NYLVUaRw9FbS/XvZd//HaYyOcepLf0z/3e8lt+fIJfoEO5fLdVOwIwTyejhzmuRMXDTkT4yR23toMTrtAS0r39Ya3WlSMqQvuckkzWUcciexRURO1Lk9dZZr3CmebQwwEV2a9qg9yQqHc+O8Wu5bcztpReTz1bb5yMBCGF8AmdFme1MBQUqs60wQeyUvpVG0cGqKIB1fIMVFOiqo7jwiUlgzlVjQnxpDCUG0twdlWRCEOKfH0a5N1FfExiVIy4VOgNqKLefsasTEK4/g6zrFfbFzh+UR30gt81wUbSqQpRJ4J88wePO7SHcNYkVgVRRep02UVKTH51vDCs6c+iadt79lR4njeRTgdQhSY2t/L04dgpCFyM7Jz/w6bmDT1XEIp7CHyTNP8NqZL9LpDiJtl5lghMbYq+zd/3aSyZV53e0IU4JGr0Nkv0EiHW8ATrz8V/QN3HJxS8guNDrUtrQF3m60AD+lcSoXd8z6OYlbXp52cPHxzSU5yxLCrizCi/+mXAvpr6/sVcIGIZCNYNNfY9SVQSXjudc71IdoBAu/M1lm3iXpxVf+jH3XvB0h5JoNvtohR+aIfI+DR9+39lhCj+Ez3+aaH/v5HSOO5/Gm0kSeT7Lor9tWWiiJN+cQlhJ4r11g5A9+k97+m+nruJZGo8SFz38SIST5rn1UZy6g/CpTTzz4hhTIl4sdLZDtmiZRWoxyTZ98moHb3o3ju/jNav5Y5CyKAEuufhYOZaYR27x812FVuTdznNe8Hp7xhrgUqRd70jNMetl1C/cK2QaVSpqgzdKO29XHVO3Rhd9j9wIbZYNb2/wyXZCTeF3x4ePOhhvKY14L0QiQc4u2ezqIJxPrxCgMdKKyKaKhLqyRGUg4+FmJaC5dRgmo9QvsmiYlLVQ+ieUmkVazzTkgq4oot/S7sQLJ8k+uPdB1D8tJgtakRgIsT5OcZDHFRECl22Lum68w9J6fuKjPfSkJk6xY4l2BhswI1IuaIKWoTw9z65v+OY4Tn2B75/ppBGVm/GE83aAnu5uT/rNxe+31Lni2oNZpEeSufMGMYfuIQo+ZqeNce/Mn2MDi30qspjAu7Awrp9VQzsWn0kVZGTtmtJwrGbeLcjBF0d0mL2gFieNjC0VcGyXoyyPrwaoFgmu/5+I3pwGdXEypC5MOttIoIZBVF8eN74CcmsLr3NhcYIUgp+rY9vo1KZO1k6T696D3F9iJR1RYcalUXCxXIZMB0o0QtgIEOhKoQBI1bHTDQjdXxfVr43R0HWLo3g+TnImQIezZfz+VC8cp+xN07L2GqYmXSO6+5kp/vNc1l91bSUiIMlFcKg/EufZtmhL4cbHRkiVgrZCWy5LeHsueatvtr9q2UOxOly5q7ADT0crKUQEcSkxwU+L8Rb9+O4SAawtjyPVOfqEp5NtEyKN4uYpmyoKwBPUeG79gtXWl2Aiy5cKxHV3VdNIh6i0i7MUJNBzqRuVSWOen4rzkVILgQH/cGruqcOqaxGSIFcSHU5gW1HoFnYdvJaiXmRs+vvBaKxqaAIQgW9ucB7F7hZsuoM6OkpiJkF6Lf2rzNeYKAWdf+QoimcDp7r3oz34p2Ugxg1aa5DTI50ZxE3kcZSPKcaGrSrkkrQy7UofYl74BKSSpVCdRau0LnZ+VVPa4Rhy/DtFaIYSFlBZqk6sCYVpT26MJdrg4BrYl1qFEnGbRikYht/nSK2BBtK4WPdaAP1AEq3kdCKJ1fZjjDZvjtwRBVxZsid2mack8wWAH9UN9eAd72XvwHZw58VW0VthVjb2B4mF3NiJ/yidpZfEbcyi1egBntn6Bc2e+QeqGYzsqRacdkS8J5hJ4k2kao1kaoxm8iTTBbBLVsGNx7AtS58A/fop8ZgAZ6oXIuxCCnnKO3YWb6Bu4mTCokch2XdkP9TrnsgvkIBsRdkUEgyHB7oD6oI83EBAWQ2jJJU5MLz3YT3/jfzNz6hnsRHqJJ+by3hoJp71YG0jNYrVVSZvjXLB6gdKhxASd1qWxj0tZPrtS6wv8bMYjsSwPW84JSk89Smf3YZBQHnII8rFwkVtt/NDyvI0UWG4EVUgTtrhYiDAi2tVJuLsbtazob34hIFmKsGoKd7IpZDOC+oBk4PZ3ce7hzxL5dcIkRPn2h7o77WBPWiSGXRIjLvaMTaKnn8mx55FjpSXXSG3BTOUcz3z5P1OrjbHrB/7OtnzuS4nehD71Z8bIZfoRSi8u13Yurn9L18axkwReFbu6yk2RI6jucqj3OVymDqmGy0i9Ns23vvaviaJYIEWbyCsNcpr6Lr3j28vOI7bpAA4yi3OP1ygxVT1NV/IiGqdsEQHIioduOk0IpdmQnVOzGYmONNq1YpEcRMgNpNV1dB3ETeQ4e/LrCCA1Gq5535GYisiMRXFNiZ3EFSnGXnsYvazSP1IRJ8Ye4rmHf5PUsSNk3/mW9T/HDscpQfpC3DjDnxwjk91FYkYtSU3xDvcRdTUj8pGNnCqzRlap4SK57CkW8xp1vlLZmpNYFSsuElBxAwunrFcUVtWnhwFI5LuXWlMlF09S144Qsr0I3pOZ3ZbxD4dFzgdFhpz2r7ffmWQ6Wt/DeCvsz05zoV5gvdBGZ2eZkfF4O0sJ/JdOUxs/x423fi/VXntpLugqLzVXvkC1Osau/lvXHZeyQKUlMmg6WayDJk7RWJ6bB6B3dRAmHUSkYus2WGoH14bMRIh2Yv/jRo/CciTuW28lXTrDy1/5DXq/96MkZXvbHx2BVVuqIvP33sN0eY6HH/8vdBYPkMsO0d17lJfOfZ7K2Cnyx26h9yMfu+hGCJeDzbQV92bGSSe70SkXnXRwBISpxZMtCiNyTi/VmXGqlVGy2aVLxEFa4vUbYfx6RrdUrWlApzcWY4mSGq/36hDG88htaqigshaMxfvt1MufZ2/mRpLWxhOvT1eepje5n7S9sZz/VRFgz9XRCZsw6RB2pLFnNtFNVoCs+oT9eaJ8Ci1ACoFap5vfsZt+gKcf/XWCoMb+Q+8mNSKo7VoqPawIkqMBbmXpax07+hFeO/clzo4+TEf3IYodB7ATaV585o8Rts2uj/0g7u3HNv4ZdiBSC9wxcKrxxJmYDKlXJ8nmeuLLc0sWqTVZQXghUW+OYucBxk99h2sHbqSyf+1rpGFrXPYIslW1kJ5AKoE9bWHP2ogwbiSidbxUnpxdeYU98LYfAsAtdKCy8eM6oZp5YjGpVPvq26JTJ2WtU86/CZ6o76MUrdLB7hL2bkxZPkVnbY9ngIQbkMs094WGya99gb7r3kr9mhxhZqliatuhSsC5iUd56eU/Y3rmtbbvoVrmN6/TotZn43VYRK6gsU6eWaPbpt7rLImsLLyuEKjOHFFPAdaxilsYrq/JnvbJjEUkpwSBq9CWoOsDHyJ/x5u58Ie/iTc6vKHXAhCpFF3f92EO/d2fI3vsJiqyxFPP/h52tsDhf/Ef6Pv+H7gqxDGwqTM8Cj2gJXo8PAONxfNGKI2jHQ533ccTT/0m09OLx4afkdR3GXH8eied6WHX0J3ki3uI0mLD37fXfRWkVCzD9rZpZaw5HVbKo0xPH2dP5sYNP1drzculb/DQ2B8S6aWpBsqxNrdPmxv7+7oJdnfGDUI2ghR4uzvxrx0g2NOJdm10wkb4Edpf/3qXSOS55U0/QaM+w4vPfBK7EsarVEByKiJ7NiD3mr9CHAPkhg5x810/wZE7P4Hb3ce5iUd55ZXPsuv9H2XPv/uXV704tmuQOSsWxfFMRKKkiEIPa7wMSsUN0QBZaeBMlLHn6iROT9F3zd1IYfH0134Vu7xT2+Nd3VyR/q7OpI193saqrDxBrbJua30z8fLDdB26g0a3jOsdpCbsXIxmWEJTzLXPixpIzW7TyGMCJA/VDjHbJlJ8Ply/ScbF0LNBF45MOt4XtRdPEs7N0nPsnrauZu0qixsdFiqbJNt/gKef+V2iaOXNhQwhPRKflFFCghQEOYvaoItftFeNTCtX4hfi793v3L4FDAGkJ0PcssapNnPshKD45rvI3fcmxh/6a6L0xlNsLA+cRJbcjbfR9dGPcOCf/Ut6/vYPoK2rqyXyZlr0dh6+g5HRJ9EJB6Fj/z9ralnDACkYTB/l2sK9PP3s7+J5JaKEwOt3rjoBZNgY1myN5MsjWJMVotBnevIVDhz+HuobLLgKU3rH54cux/bFvBHDRSMDjdaac6ceYPfQ3SSszbVZlsIm7RQ5Pvfwkr+rXBKV2VxjJS1iYT1PlEnEDT9W2544b1kVUqhlQQGdsNHOxuZwN5Hj2pv+NtXyCJMzr6ClwJ2OSE1GOKtYiC6QkMgb9lF46/3s/8hPsudf/DzOvddvsv34zsIKBclRQWpEMl8pbtcUqfH4oOsbuJWR0SexZuvo5s2E1RrtDyPsasBN13w/AsHpv/nDy/8h3gBc0qv9qsdvJNr62AkgWV75d29ukslXH6XnvvcQpkVsKt8RLYkedxTLbdMr0lbAQKpNV6CLxNMWD1QP8YrXS1mlmI0yPFXfzdng0ibNd7obWxKr1V0IIyZ+61MMvvkDjL3wIOXhldFglbXwc5KR6Wd4/sVPxZZcmZDZ08/S/d1Nex3RnFAjBc2IQWokaJu/vNBPw2r/7av54kxPEbpiwSZuW1CQHA9JjWosFTcf8HsCZj//VerPv0zYHaGSaydsSQ2JKU16NG7b7NQhNQaZCUFyaqG29HVJIt9NENZRKkKOzmCVasjq0lUZoTQyVPRc+xaEsHDSWep9zs4w5jdsO9ZMFff8DCrtEnVnOXvqAQrFfaR27yNsswLUDr/z6jtpZJto5lZxpyKmxl+kXDrHQP4Gnpr6/Iqc2laemPwrxusnAbjAKdK5PgYP3ocvlgYqhB/SGpcP+vM0rhugcbBN4bAtCXqyRIX0ktxhnXYJ961+zQr78kTFzQl6mqOKli0AlmZO4zVKNFQc5FleP9SOICUo73EWem4IS7Tt/nm1YAWC5LggfWYxpQLiFJPcaLSgitKZHjxVw51aDIhF+dSS10qMzKKLaXbtuQs7ufnvyLA+l1QgL59iLA/Sw5rcBbDaeOs6s3qFJ69WirMPf4bON92H2lMENGF3iMosTjD5bI1spl30WHMkP4rYrAHjBgmRPOcN8cXKMb5SPcKJoOeSvE8rWXtjqSIN38F77BRuuoCuNbjw2F9THlkpkCMB9V6HaTXK+PhznPde4qX/89/JHr0B98BeksU+Zmpn440tCW486wUFq22RhhVBasRf+T3agsgVWJ5Geors+YDEdEitz469mLeJRENjhZAajb2PZcVi1y//I0TCpfb0y6hV2ktLHee+Z4bBXRakF8RpHE4VEuNX38V+o0jbwbaT+H6FaKCLYE8PYXd+xXbKsShVh0lluynvSRE5V3Eox9AWATjDszgjJVTCJtzdSbUyxvC5Rxi65b3UBjeW/hRkNW2Mf3Y0AkiUt+fSaGlIlBVjI08xOPAmnn3xk4w1Tqy52jLhnebF0kOcqj7FKxNf59CxD5LJ7aJUH1kirK2Kh1VdvB6oTAItBGJ5cEJC40APYV+BYKgDtcy5KHJtVHrZ9+lY+INFwp6t+fsLWNItF6DYeYDd++9n9PmvI2bqyHXqVYKUoLbbIWpdqND6qgxS2DVIjQhSZ8EpC1rDh5aC3NlgSTfbVKJAw59DtzSDUfkk2m3ZGX6EqAeU587h7L7yrbVfj1yW9WLpa1LjmvR486QJ9Yo2uFYAiWUmDSr0OfOtPyW0Qor3vw1hQdAXotItrTuzNTqL7Z0jDmYn6E5cGleJK4UUig1lnmmov3yc+sQFzjz0aQBGnvkqkd+IO9U18SszDD/1JaZPPQPA5InH6bjrrXR/8MMgBfnrb+WVlz/DQ9/8N8zOnlp4XqIU4S+L/loRZM96OC03P9KNt9FaL+QmZy/ExvTJmYjMSECt38HPrX8oRq6g3mURZNYwvAs1qdEAey4iOaax5iTJ/kF6/8nfYep3/wzvxNmFTQVgVzXJcU3mXGx1tm7TlEbcuOb1hlOOsE6Mg1Ykyxo5U0GnE6juPCq19OIp6z5jZx6nt+eGqycX27CA9EOcCzPItYqrtEbbFt6xXXiH+pgrj/D8k7/PnlvfR3ioZ0PpNNoFf2e7ILbFLsEKg/St0uxUOjn2PKdOfZW5YByA52a+HD/c/A6UjijpSZ4ofxGARlRmNlXhpjt/nHxxD/niXiJCvjX+ab459knUshZ/OuWgUm58Y3NuZukYFDjnZ5Bzi/UrjtZLLv7egV78wWJst5lJ4F3TS9SxvcXmQggOHH43+cJezn/xk7jja9fTNPrtlWmBSpAcEcirIL/CUgJnFrJnJakRiV1buXJuKcicDRDL7O/mJs+Qc7oQGqymFZ8MIoS/tEg2EhGT4y+RP3zzJf40b0wuj4uFI+K7xfmIoyUIlwWmElNLD5BGaYLjX/qfJPfuZdf3/SgiaeP3Bih73pdW01GskM+2K8zTHM6Nszcz0+axNwb5bJ3qd91E6aEHF/+oFdJ2eOaP/gU9x96CnUgz8uxXSA3soec97yd16ChWaukyTu6eNzP+zb8BIJlcml8dZizSowH1PgctwJoLQYFQCjk2S7SrE+U3LYUiCNOSICtJVTVR88JgNyO+jV4HEfqL4lrEXrpBRoKOc6VVziIS4BNPLMJTpKYipLd0hcCpKpyqohba+BkLXIvkkQN0/ciHGf3t36P3fX+L4tGbSI2D2ELtZnIuPn6vBpm8kcUTS8X523NzZZJ2fmFZL3AsdDaF2tNLVPfjdAsNWgqmT5zlwB1vXZIHKGyB72gQ8VLiehEiwxWiEaDS7oqc0la0EIS9cfTwwplvcebE1xi64/1kb79zQ2+hEtAY0AtuRVcLVihITYttO7cjCV5esu/Quzh9/ItI6aBUwKw/Qi0s8dDYH/L2fT/JIxN/jkZT7DzALcd+glxhCCEWJawQgv7emzk9/A1cmUIsi21FTccfUWkgaysnNcsLCdOLrZrFyUkcpQj3dBI1i6Gjjsy2i+JWpCVRkeLIdR/m+Euf5enHfpMbbvsR3ESbKLUtCN32x6dVF2ROa8KkIEpqIhd0WuyIY00AVhXsssCpwloxyHlxbHkrx+37ZTrsYvNF4/2gXBt/qAN3eBaUJuzLM9cYI5Hvwsnnty1n3rDIJRXI2tax04QnqA0InDmNOwuhq4laloGsBkts3fxqiVf+5n/Q9Y53U7jtLnA1QW+wkL9qS01XZ4lUcmXlZkqGXFsYpjOxCfuaqwilJdHaJQ1A7IdcONaJ+sWfwHIzXPjFXwFp8fT/+heo0Kc2fZ7q1Hn2/IOfwensRFgC3SZ6KtMZkp27EH5EMllcOhZHYDXUggvNvMWbKNeRVW/hfFWuiFtCCxE3JjkzhsymUB2x1ZEINdoV1Adcgrn4WSpr0aYh4AKRBFKS2i5J9vzKlA6A1HiIn7NQaYk9ZZG+8VpSP9TD+d/77xRTexFWbJtkRSAaETJomrLrZkpIbpUxRBorFIQ7ug9lkw1cM2Ql9h1Np7upedMEysORiYXGMtK2CNMJonSCYHqSM2Pfxg+rOLsG4hcQ0CjSvGlYPDbd2ZWrQoYrj1qWy7gWw+ce4fzZb3P0A/8IMbixFDI/pwl6dNui4J2MpQSpEcEa6cFbwuu3Gbz2fnL5QYKgzsvPfoqGqvHQWFxY9eTcF0lnezl208fXXJHp6r2WcyOPcDh/94rt7HKD0F897B1mE6gW1wpZ9+No8/FxRF9+y6kUmyGK4muFtBwOX/d9nHz18xx/6S+59qa/veLz6FBjh6w+xyqBXQO7Nv88TZgWBDlNlL38wQurEYtit7qx4s61xDFak0n3UJo4B5nr0V4IzZubqJjG1xrnwiyTYoxTr36ZzND+plf1VXbCXQVc2ku8BL8vjC3dxi38giTKixUuFbJleUFrxYlv/BGFu+6hcNtdaEcR9kULd4cJJ6Svew5pLT8KNUOpWQ7nJ7elIchOpRJuvEKht7NMeGQQP7TY+/v/AaZC9Lk5Rv7X7xHlHAZ/4KcQh/J4TnyjYc1J7Nmlh4QQgnTvbvLBylxU6SkanYvLYPPRROGH0MxzE4Cflgt5qsqGqDu/dEKcN6KHhQYma9FiC0lkQWXIxapEcWvokloQy4K4MjgzalERFrIhEbsG6bj7bRz/3/+FA2/5GP32Aew2+fAAejKk1m+vsMYDtv0ieqnYSAT51Yd+j/rcOEcOfy+92YOcr7/I/swtyJkKypaEUoJtMTt7mude+iS9vTdw/Xf91EIr70YHBLmVk7NfFPEKwepNtww7mMrcMKdf+zLH3vVTsAFxLCTUuhXhpdda244VCVLDYksrSusRCajuSVDgKFZD0dt/I2FQZ2L0Wc6e+jrFrmvYve/eddOVsqkelA7pTu5d+WAQIWs+USGFcCx0sCwFY5lThU45EEQIpbCmq5dFIAvilDvlx9Zlew68jWce/588/+Tvc/SG78dxM0u2dSdCwl0blSgtgnkKGkUV37BfQs1oBQJrDhIVsamUnNXEcaM+y6MP/QcGc9ezt/tOHqs/wJH8PVjTFVR6sUFZWEzz6rkvMfHCK+w9/A7St9+GutruRq8SLq1Abl6cldQExQh3XBIJDcv0hkouSp7S5AnCoEbnW98OribsXRTHmVSDns7KYhu1Jmkr4Nr8CB2v06hxK5PeJqpVBThugB82d3iXjejqZODmf7qwSdR6rx22P8k6D93G8Ff/jAO7v2vJ3zPjIY1ii2VQQgKq6Q0UIqIo9rYOJSLSYAmigo0uJdBhy/2uvblU+OVyNrIgalrHRTnInvUXbsKcmiJKS5JTUO+N37Hr/nfSmd7Pa3/zP7EPf4j+vpvbvo9ofsbyPmtFNEzYrTJ9c6PXro5TUpREBbqto8t2sRGBHHk1OjsO8sKLn0ZFId25ewCwynWsukd4oB8FvHL8Lzl4+HtIv+nOxQ5ctiBY49oaJjEC+Srl7KkH2HPwbYj9fese6VFS4/VBdDWsqizD8gTp0c2JnM2iBFT22BROBYgAHDfNwJ67GNhz14ZfQ0qb/tQhRuvH2Ze9Of6jiOMLOu2imm4TQS6JPb2s9mbZFBMMdqCFQNb9yxp49GyNHcSXcMdJc9tdP8Xxlz7HU4/9Bjfd/n+RSC4GYhJzijAV4Rc36Nc8TwjJSQmzsVCO8tuXfmEpgSxrnIrAamx+x0kNmfPtI8dR5OFYSaSCx0//MUJIpLCwZuuobG3BTWRu9izj0y9x/fv+KeFgzjgIXUIu6XQmQoE7aqNcjfRXP5giF/wc2FXFic/9Bm53LyIpCHrDZlqFppivUsyvTOrvS85xXWHsdR01nkdrwUi9uKnnhMHGJxe70X5bN1NEqTb7V8cRWq95c6tTzYK87lzcXSlUkHBwqwqn4lMdcIhSkrm9cRRcapC1iHAT7WrXI7KgUZAkZuJElERVERQ1eAIrYqEiOrX/Gq676+9y/DufpjZ9gSFxgHSyEzXQtdQXVIGsK1RrQaIlCLdQSq1dRdgToZot1QPiLko0QNYkVlWy3VerjQjkjl3XMX76Me647SeRkSY75sH8cm2oYLZK1JfH7ujES4Tkq4ow2/SyTq69tPcGOC1fl0yOv8jE6LP03PjWNcWxRuN3aILOqyMnfznOHCSn4HKoDCWgnpekprZ6UgiSVnZpgZ6OI/f+3q7FVbVsYoVA1omll3qVbOYdJy7vHY1TW3qUCGlx6NoPcu7EV3nykf/GwaPvp7PnCJYVpxRkxiNUUhImtzAvzgvlGfCzEGY1Krn549QKBbKqsasCuw4X422QvhC0dfACSKW7cBMFkk6eu3o/QlJmsGT8PbljJepNgZxMFfH9Cqp746lShq1xyV0shB+3khb+2m/ldQrqgxb5m+8gc8MNC+LYEtDTObdCHFtojuVHubE4/IYQxwAjjRy15eaS66A22GvYilaPoOggWnX5T7W0rY4cAQKUlER9RfR8Jzwdd8+LUkuPASVom75wsQQ5i2DeFSPUZC8E2FVFakQvWA8JBenBA9xw/SeYrp3l0ck/p1oewxqeWvF6YllKUJDcWuQ47F0Ux/MooVEpTdgVEQyGqMQ2X6k3MNSeu96OZSepN6ZxMx2E+3qheeFUKRfVmUPX61THz5DafwjVYusWrjFHWwoStcsYnjJsG51dh+IfBjpX38iGxqDGvwrFsaXiRg3JCcnlTJYO0xf3XkpHK1acVMJBtTQwEi3FeABRLklU2Lk+ufZ0jcONa9nVdysnX/0bXnrmU4t2dhpywyErMio3QwRuSZC+IMmdliRHYncJqwZ2ILAjgR0KbF/Ef5sDdzo+PnJnJOkzguSkxK4vtWfbLKmJEKe6+pkipc3Rmz7Kef9Vkp396K7C4oOBworifTI18TId3degLvPNzRuRHdUWLEJRO3mc9P03oKTGsRX9vTNk0ksTwzKWz51dZxhKz16ZgV4BlJacLG/SM0lDsMFesHqN3LvG9AipZJsLpSPwexcFuwaC1MpDKkqKhe55S1ilmcjFYEWQGw5wyy1CM9RkhoO4iciwJj0S2wxqV5LLDXD7bf+AawbfxremPk1Uq8XLji3oZb6ha4nCtdDrLPMpSxP2hUSp7RPJ1kaS8CQUi/soXXgFwgglJeHeXnTKRTQChBcwMfki6Xw/yWIvKtHyHSfbTyFSQ3Jco9t4ZRsuL3otO7dVmJk+QaZnD3a+o+3jYVpT260JrzKPYwC7DOmW9r6XE5Vaw6JyPSxBJZwiZS+tBxHLivMi2yLKNmtVpCQaKG71HS8LIlIEQx3sO/Zu7rjnZ6hWxnn1hb9Y3CDQpEa2p5WyjsCpCZJTkvSIJHVWkDotSJ0RpM6J+G8TksRM8/jYprQbu6ZITK8/r2dy/fh+hUqfSzDYQdjSpEV78WDGhp+ge+/t2zMww5pc2kYhTU0khUB6sc2b1dCIVbxmqydeQRYy2Hv7SSV8BnpncJylR2h/ssRd3WfIOu3s3V6/nKh0UVebu2MMQ5uN3vGKoP12AnDcDJFaqaDrebnC5cHvtpdeAAQ0+pZGvTXQ6ITyENS3ufGgOxWs6mWcnI1IzoRYzY+iEgIvH1+whobexGDqKC/OPYg1OrN0r7ktH1KA2lIUSCA30HxAA6o72rbkp42kWCggc+wGRsovoH0fu7kKEOztRXVmsc9PUSzup16ZoDJ2iqnj3+HpP/pFvLnJtkUwVgDpEZAm93hH4IWbD7+NjHyH4g1vavOIxutU1HdpoqusY4Ndg8x5QWpcXjFLLCWAVezL1iXp4MgUSrdcE6XA37NyEg33dBH05/EO9RI5279Kt52EvYvd+qTlcNMd/xfTk68wO31yYRunqrH9q+t4m0cA2fGNeE+BEJKunqOMnH8MgGigAM3mIKJZq9PddwMjr32DqFHn9F//Lqf+6ncu0cgNlziCPG+CrlEuIEXsWdhcltdRRPn5p5l79gkCq8HMM98k+4430Vmo0NdTQsjFq3tSRlxXGOaG4gjyDZJSMc9ckOJsdY2lzlWo+xufGFcTyFIJovIcUjpxR7nWVIs2aRehI6gNOkRJiRZQ67EJ7aXbCVssuB7ozWWMrImlwK2srghlwycxGS7Y0Wmg0ePQ6LJQjs2xwn1M+xeYnHsNMdVsTy6WFh4Fqa2vyDple0MG90pogsL2hC7aWfe1I7P7IIlUkanhZ9EXphf+HvUUiDqzpJId7L/1w5z8+h8x8crDRF6Nxtwk+C2dvUJITmhSwxqxQzyQLRVHb5xKhF2NsH19OWuSdgYbmOUb9VmGRx5jqnGGUmqO2ekTFA/fsnQjB2oDGr99UHlHYkXNZg3n4mYN0rvy377a4lVXCYGnqlhicdIMurKozEpnIyUFYXcOdYXFsQCs2Rpyro7wQ5xz08jS2g1Ckqki1xx9H8df/CxRuBiYkbWrsxzNKUWIdnZuq7D3mndw4cy30VqhpMQ72EvU7JIIMLT3LbhOlpEHP8vcyecpn3rhUg39Dc+lLdKL2oipluXqiS99jvrwaUQuxfjffAbt+dz0y+8mkV08gWyhGEzNcE1u+g2Ta9yK0pIXZnehtnBZDzZj1LtaKkaoSfUOMfPtP+bM6QfZs/utcctpQK2yvB4mJZXBjV0F7G1sdGjPhGsmQ8qRGXR3nvSopN7vEDZzaf2ijQw0iT393KjezdOTn+e6hENH152EiaVNA4LC1i+wOgKqArLrT5Yqo9BltW7u/lpYWrDRlgdSQ3fnUc5NPUeio4+JU08yMvoEu4fuYWDXHTgCBjJHGM52kCj0UB07jQo8MpOCSOpY+Ac7R3zalYhkKcJqtPn8UuCnBUHBIlzlGH49Ya+jkEN8nnjsv5Hde5jaUw8QVEp0Xv9mLDeJRoMjCJIav0dfUtus7cCKBKKusRoCpz5v27azBn0xt45SOjw1/XnesevvY0t3wSJzJyK1xj05gagvTY+wSnV8u7utsAeQ5QaD4W5mM0M89+Tvc+1NP4CbyC03r7pqSJQ2J+zTmW4cN83Zk19ncM89fPvrv0z/4O10zV5LV89RhBAcvf4jfPvr/4bi4VuZffVJtNZXvKupHYKoK2SgkaFGRIv1O1oKtIxrlpQr0GlJuLMXNoDL1UlvGUFplvEv/wXe6DC7fv7v0XvAwp4bBwGJbDyZ52yPPZkpdiXLiKv1zNgGXit3UYnc9Tdsg9pg/jEA1ir72BXIgTj3OZXpWRDHSLHEfUJJiFI0uwetQ6hJzMSeyG51q3ZpS7EUJJdFj6XWS7qFRXu6EZaF9DXpYX/BTQPA63Zw5xS5a2/jhnMpnjn9p9y57zBRf/fiNoXYceVisCsSP7uxCTNKKeyLEMirpTK13xi6D7+J8rNjPPXy/6KY30sU+czMvEa1Osa1Rz6MDAQdziCBtDn2wZ8h1TUAoaY6/CrV8bPYyQzFPdfhpFd6ZkeBx9Srj9F99M0L/smXAktDYjTAWSvapDRuReNWFGFK4PUs3iy9HrGt1Y+h8dmXOPHi5ygcuYXB+z6EjiLqUxdwBvqpdyqiLICOi5aGBVLHKypRUqOygsi+MnOz1AIZAF7skGT5YPvzOaM7+7u8mMyUwYE7mDpxZqHLnj1VQfXl1+yKeCWQkcI5M7Ugjlu9bgSQGJ7F27vMMYiWDYBre9/BS6Vv8MoLf84Nt/4douzVdzMrNau6VqzFwWPfy4mX/4py6XwcSVYRrzz/Z1x/yw+RL+7BdlJYVoL+6+6j7653I4RABT6l5x4n8uokC33kD1+HamOh6h8/iWrUSd9w3UWbt9hVhTunSNQ1rLlq2OYxR+An4g67Yd5acJjaSVx2gdyYPM+F3/8tsm97E0M/+0F6+3yk5UEmrtgsOjVuKI6StBaXViJtIdDIjSRUvo4IlMPZ2srUipmnh3n2Zz/Fvr9zD3s/sbqPptIbn1BUWmFVVx6hERrdK8l2712w3oHF/PIFLGh0geOJVXOAJbFoi2yBO7fwShse42oIIDG2MvdYjM5iCYj6O5pjtuLUia4cIgTLU03v5thQvtFlYXma3IHr6Km/zHD1RboybyNKgJeH6CIr0CF2dZF1gUpdBmGxiTQHBaiBLEMDHyf17Dc5/fhfkCvuRqkI180uLu8N3sXjT/4PikduZu7CceaGX6V08hm6Bm6kPjbK8BNf4OA7f5TJ44+T67+GdPcQbraD2ece49xTn2Xq1cc59N4fx05sf1W97WnS4wFiE7mKdl1jnfUJchK/07kqfXy3ymtnv8TE8LMMvuOj5HYfbrpSCNShIRoCLF9glzRuWSyx6ZQecQHTVOxzHWY0OiMIne0/pqUWiIZGeiD9WBTbkYg9EoErKYa11ox+43NMPP0gR3/kF3Fz6+eeWBGxsN8ibt8QqTMFZLOZgNAgQgU7KM/Y8kMSpyaXNCtZ8S15Ie6rY/j7V0aSVTaJysbVnwf63sPDD/z/qKZ9QucioxNXAOltbVWto+sabrnrJ3j2O78NgO0kQWtsO94vQkiG9r2ViYe/SMe976RyocToV/6ClFsglelh4pVnmX362+y67rsYH3ma7gO346bzJN0Cxx/8FPXaFEPj303n29+96bHZAdilkGRZb2quXUGgcQNwKxF6PCJKCfysJCzsHLEstlLhvOEXF+I7l+zFDQaD4fIzqbXe/FXlCmLmYYPB8Drkks/Fl1QgGwwGg8FgMBgMVxtXX1KPwWAwGAwGg8FwCTEC2WAwGAwGg8FgaMEIZIPBYDAYDAaDoQUjkA0Gg8FgMBgMhhaMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2GAwGg8FgMBhaMALZYDAYDAaDwWBowQhkg8FgMBgMBoOhBSOQDQaDwWAwGAyGFoxANhgMBoPBYDAYWjAC2WAwGAwGg8FgaMEIZIPBYDAYDAaDoQUjkA0Gg8FgMBgMhhaMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2GAwGg8FgMBhaMALZYDAYDAaDwWBowQhkg8FgMBgMBoOhBSOQDQaDwWAwGAyGFoxANhgMBoPBYDAYWjAC2WAwGAwGg8FgaMEIZIPBYDAYDAaDoQUjkA0Gg8FgMBgMhhaMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2GAwGg8FgMBhaMALZYDAYDAaDwWBowQhkg8FgMBgMBoOhBSOQDQaDwWAwGAyGFoxANhgMBoPBYDAYWjAC2WAwGAwGg8FgaMEIZIPBYDAYDAaDoQUjkA0Gg8FgMBgMhhaMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2GAwGg8FgMBhaMALZYDAYDAaDwWBowQhkg8FgMBgMBoOhBSOQDQaDwWAwGAyGFuyLeXJPYo/2VWO7xrLthG78vyXdKzuQVYiUj3IE0tmZ4wMIhQ+A2MFjjLQHgEzszDEqz8d1ImTSudJDWZVUWAXASVpXeCTtCRoROdkglRJXeihr8sJzwRe11u++3O/bk9y7o+fidkQ6QCdspLUzz9u1CKwAYEfP3e1YmM/dq2vc2veRjtrRc+hqpKIKAE7qouTWZSeohxSs+o6fc9vx4jbNwxf1jfmqwd3dH7nYMVwyJgfjLzafG7zCI2nPXPkClSGHTPfuKz2UVZlwzgOQHNi5Y5xTJwFI7Bu6wiNpj3f6PAO9s+QO913poazKtVOPAjB4XeEKj6Q9F14o8fbMy1x/486+QB7dM9J9Jd7XVw3u7v3olXjrLVPyx/Gu6SFX2Jnn7Vpc6BgDIN27c+fFdky6ZwFI7L669rl37jzpgQqpg7uu9FA2zU2VhwHou7bzCo9kc4y9OM17889y7Q1X180UwM17z2/LPGxSLAwGg8FgMBgMhhaMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2GAwGg8FgMBhaMALZYDAYDAaDwWBowQhkg8FgMBgMBoOhBSOQDQaDwWAwGAyGFoxANhgMBoPBYDAYWjAC2WAwGAwGg8FgaMEIZIPBYDAYDAaDoQUjkA0Gg8FgMBgMhhaMQDYYDAaDwWAwGFoQWuutP1mILwDd2zccg8FguKqZ1Fq/+3K/qZmLDQaDYYFtmYcvSiAbDAaDwWAwGAyvN0yKhcFgMBgMBoPB0IIRyAaDwWAwGAwGQwtGIBsMBoPBYDAYDC1sSCALId4thHhFCPGaEOLn2jwuhBC/1nz8WSHErds/1DXH97tCiHEhxPOrPH6/EKIkhHi6+e+XLvP4kkKIx4QQzwghXhBC/Os221zRfdgcgyWEeEoI8ddtHrui+7A5hqIQ4s+EEC8LIV4SQrx52eNX+jg80rJ/nhZCzAkhfnrZNlf6WPzHQojnm8fhT7d5/LLvw3bnrxCiUwjxZSHE8eb/Has897QQ4rnmvvzOZR7jrzSPxWeFEH8hhCiu8tw1589NjGFHz8OrsdPn59W4Wubt1djp8/lq7PR5vh1Xw9y/FjvxutCOy36t0Fqv+Q+wgBPAAcAFngGuXbbNe4G/AQRwF/Doeq+7nf+Ae4FbgedXefx+4K8v55iWvb8Ass2fHeBR4K6dtA+bY/gnwB+321dXeh82x/AHwN9r/uwCxZ22D1vGYgGjwN6dsh+B64HngTRgA18BDl3pfdju/AX+E/BzzZ9/DviPqzz3NNB9hcb43YDd/Pk/thvjRubPTRxPO3oe3sy+W/b4FZ9bVhnXVTFvrzH+HT2frzHuq2aeX2X8O27uX2e8O/K6sMpYL+u1YiMR5DuB17TWJ7XWPvBp4IPLtvkg8Ic65hGgKITYtYHX3ha01g8B05fr/TZLc79Umr86zX/L7UOu6D4UQgwB3wP89uV6z80ghMgTnxy/A6C19rXWs8s2u6L7cBlvB05orc9cofdvxzHgEa11TWsdAg8CH1q2zWXfh6ucvx8kvlDS/P97L+UY1qPdGLXWX2ruR4BHgKE2T93I/LkRdvw8vBo7fX5ejath3l6NnT6fr8ZVOM+3YyfO/WuxI68L7bjc14qNCORB4FzL7+ebf9vsNleaNzeXyv5GCHHd5X7z5nLX08A48GWt9aPLNrnS+/BXgf8PoNbY5kruwwPABPB7zWXD3xZCZJZtc6X3YSsfAz61ymNXaj8+D9wrhOgSQqSJowK7l22zU/Zhn9Z6BKD5f+8q22ngS0KIJ4QQP3bZRreSHyWOsCxnu/bn62UeXo0rOj+vxlUwb6/Gr7Kz5/PVuNrm+XbsxLl/La6m60I7Ltm1YiMCWazyRpvd5kryJPFyx03A/wN89nIPQGsdaa1vJo4y3SmEuH7ZJldsHwoh3geMa62fWGOzK70PbeKllV/XWt8CVImXU1rZEcehEMIFPgD8aZuHr9h+1Fq/RJwK8GXgC8TL9OGyzXbEPtwE92itbwXeA/ykEOLeyz0AIcQvEO/HT7Z7uM3ftrI/Xw/z8Gpc6bllVXbyvL0aV8l8vhpXzTzfjp0696/F6/S60I5NXys2IpDPs/RuYggY3sI2Vwyt9dz8UpnW+vOAI4S4Il2nmstFDwDLu7xcyX14D/ABIcRp4qXb7xJC/FHrBjtgH54HzrdEcP6MeCJdvs1OOA7fAzyptR5b/sCV3o9a69/RWt+qtb6XeKnq+LJNdso+HJtfwmv+P95uI631cPP/ceAviFMRLhtCiB8G3gf8ba11uwvGdu3Pq34eXo0rfU5shB06b6/G1TCfr8bVNM+3Y8fO/WtxFV0X2nHJrhUbEciPA4eEEPubd0cfAz63bJvPAT/UrHS8CyjNh7x3AkKIfiGEaP58J/HnnrqM798jmhXuQogU8A7g5WWbXbF9qLX+ea31kNZ6H/H3+zWt9Sdat7nS+1BrPQqcE0Icaf7p7cCLyzbbKcfhD7DKEtuV3o9CiN7m/3uAD7cZ507Zh58Dfrj58w8Df7l8AyFERgiRm/+ZuGiurVPCpUAI8W7gnwMf0FrXVtlsI/PnRrjq5+HVuNLnxGrs9Hl7Na6G+Xw1rrJ5vh07du5fi6voutCOS3et0BurHHwv8CpxFfUvNP/248CPN38WwH9vPv4ccPtGXne7/hF/mSNAQHyn83eXje+ngBeIlw4eAe6+zOO7EXgKeLb5pfzSTtuHLWO9n2al7U7ah80x3Ax8p7kfPwt07LR9SFwJPAUUWv62Y/Yj8A3iC84zwNt3wnG4yvnbBXyVOJLxVaCzue0A8Pnmzwean+OZ5j79hcs8xteI8/Kebv77jeVjbP6+Yv7c4hh29Dy8yX23Y86JNcZ91czba3yG+9mh8/kaY76ZHT7PrzLuHT33rzP2HXddWGWcl/VaIZpPNhgMBoPBYDAYDJhOegaDwWAwGAwGwxKMQDYYDAaDwWAwGFowAtlgMBgMBoPBYGjBCGSDwWAwGAwGg6EFI5ANBoPBYDAYDIYWjEA2vCEQQhwVQjwshPCEED97pcdjMBgMbzTMPGy4mrCv9AAMhsvENPCPgO+9wuMwGAyGNypmHjZcNZgIsmFHIYTYJ4R4WQjx20KI54UQnxRCvEMI8S0hxPFmB6Llz7lOCPGYEOJpIcSzQohDy7fRWo9rrR8nNhg3GAwGwyqYedhgMALZsDM5CPxX4k5WR4GPA28Bfhb4/7bZ/seB/6q1vhm4nbjDjsFgMBi2jpmHDW9oTIqFYSdySmv9HIAQ4gXgq1prLYR4DtjXZvuHgV8QQgwBn9FaH798QzUYDIbXJWYeNryhMRFkw07Ea/lZtfyuaHNTp7X+Y+ADQB34ohDiu4QQP9lc6ntaCDFwyUdsMBgMry/MPGx4Q2MiyIarHiHEAeCk1vrXmj/fqLX+VeC/X9mRGQwGwxsDMw8bXm8YgWx4PfBR4BNCiAAYBX55+QZCiH7gO0AeUEKInwau1VrPXc6BGgwGw+sUMw8bXlcIrfWVHoPBYDAYDAaDwbBjMDnIBoPBYDAYDAZDC0YgGwwGg8FgMBgMLRiBbDAYDAaDwWAwtGAEssFgMBgMBoPB0IIRyAaDwWAwGAwGQwtGIBsMBoPBYDAYDC0YgWwwGAwGg8FgMLTw/wI8l/HGZiEWCgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# list of seasonal cubes to loop through\n", + "seasonal_cubes = [wspd_djf_mean, wspd_mam_mean, wspd_jja_mean, wspd_son_mean]\n", + "\n", + "# set a figure big enough to hold the subplots\n", + "plt.figure(figsize=(10, 10))\n", + "\n", + "# loop through the seaonal cube list and plot the data\n", + "for i in range(len(seasonal_cubes)): \n", + " \n", + " plt.subplot(2, 2, i+1)\n", + " # plot the windspeed at the first timestep \n", + " qplt.contourf(seasonal_cubes[i][0,:,:])\n", + " # add some coastlines for context\n", + " plt.gca().coastlines() \n", + " # get the season name from the coordinate\n", + " season = seasonal_cubes[i].coord('season').points[0]\n", + " # add the name as plot's title\n", + " plt.title('Season: '+ season)\n", + " plt.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate and visualise the seasonal mean of surface temperature over Tibatan region from 1981 to 2010.
    • \n", + "
  • Coordinates of Tibatan region: Latitude = [26 36], Longitude = [77 104]
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate seasonal means\n", + "# write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualising seasonal means\n", + "# write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculating differences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 mean surface temperature diffference in winter season (dec, jan, feb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can find the difference of mean surface temperature past 30 years (1851-1880) to present 30 years (1981-2010)\n", + "\n", + "First, we need to extract out desired data" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# extract air_temperature\n", + "sft = cubelist.extract_strict('surface_temperature')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# constraints: two 30 years periods - past and presnet\n", + "cons1 = iris.Constraint(time=lambda cell: 1851 <= cell.point.year <= 1880)\n", + "cons2 = iris.Constraint(time=lambda cell: 1981 <= cell.point.year <= 2010)\n", + "past = sft.extract(cons1)\n", + "present = sft.extract(cons2)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# load catnip's add_time_coord_cats method\n", + "from catnip.preparation import add_time_coord_cats\n", + "\n", + "# Add other dimension coordinates\n", + "past = add_time_coord_cats(past)\n", + "present = add_time_coord_cats(present)\n", + "\n", + "# Extract the winter season \n", + "past_djf = past.extract(iris.Constraint(season='djf'))\n", + "present_djf = present.extract(iris.Constraint(season='djf'))\n", + "\n", + "# extract data for Shanghai region\n", + "past_djf = extract_rot_cube(past_djf, min_lat, min_lon, max_lat, max_lon)\n", + "present_djf = extract_rot_cube(present_djf, min_lat, min_lon, max_lat, max_lon)\n", + "\n", + "# calculate 30 year mean of winter season\n", + "past_djf = past_djf.aggregated_by(['season'], iris.analysis.MEAN)\n", + "present_djf = present_djf.aggregated_by(['season'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now got our cubes for different climatological periods. We now calcuate the difference by subtracting the past data form present using **iris.analysis.math.subtract** method." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "djf_diff = iris.analysis.maths.subtract(present_djf, past_djf)\n", + "djf_diff.rename('surface temperature difference: Winter')\n", + "past_djf.rename('surface temperature past climate: Winter 1851-1880 ')\n", + "present_djf.rename('surface temperature present climate: Winter 1981-2010 ')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_latitude bounds added\n", + "grid_longitude bounds added\n", + "grid_latitude bounds added\n", + "grid_longitude bounds added\n", + "grid_latitude bounds added\n", + "grid_longitude bounds added\n" + ] + } + ], + "source": [ + "# add bounds to the cubes \n", + "past_djf = add_bounds(past_djf, ['grid_latitude','grid_longitude'])\n", + "present_djf = add_bounds(present_djf, ['grid_latitude','grid_longitude'])\n", + "djf_diff = add_bounds(djf_diff, ['grid_latitude','grid_longitude'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Note: iris.analysis.math provides a range of mathematical and statistical operations. See the documentation for more information\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now visualise the difference" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# list of our djf cubes and diff cube to loop through\n", + "seasonal_cubes = [present_djf, past_djf, djf_diff]\n", + "plt.figure(figsize=(15, 10))\n", + "# loop through the seaonal cube list and plot the data\n", + "for i in range(len(seasonal_cubes)):\n", + " plt.subplot(2, 2, i+1)\n", + " # plot the windspeed at the first timestep \n", + " if i==2:\n", + " qplt.pcolormesh(seasonal_cubes[i][0,:,:],cmap=plt.cm.get_cmap('Reds'),vmin=0, vmax=2)\n", + " else:\n", + " qplt.pcolormesh(seasonal_cubes[i][0,:,:],vmin=277.5, vmax=289)\n", + " \n", + " # add some coastlines for context\n", + " plt.gca().coastlines() \n", + " # get the season name from the coordinate\n", + " season = seasonal_cubes[i].coord('season').points[0]\n", + " # add the name as plot's title\n", + " plt.title(seasonal_cubes[i].name())\n", + " \n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Percentage difference in winter precipitaition " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also calculate the percentage difference. \n", + "\n", + "Let's calculate the change in mean precipitation from a past 30 year period (1851-1880) to the most recent 30 years (1981-2010)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# extract precipitation flux\n", + "pflx = cubelist.extract_strict('precipitation_flux')\n", + "\n", + "# extract the time contraints \n", + "cons1 = iris.Constraint(time=lambda cell: 1851 <= cell.point.year <= 1880)\n", + "cons2 = iris.Constraint(time=lambda cell: 1981 <= cell.point.year <= 2010)\n", + "past = pflx.extract(cons1)\n", + "present = pflx.extract(cons2)\n", + "\n", + "# Add other dimension coordinates\n", + "past = add_time_coord_cats(past)\n", + "present = add_time_coord_cats(present)\n", + "\n", + "# Extract the precipitation data for the winter season \n", + "past_djf = past.extract(iris.Constraint(season='djf'))\n", + "present_djf = present.extract(iris.Constraint(season='djf'))\n", + "\n", + "# extract data for Shanghai region\n", + "past_djf = extract_rot_cube(past_djf, min_lat, min_lon, max_lat, max_lon)\n", + "present_djf = extract_rot_cube(present_djf, min_lat, min_lon, max_lat, max_lon)\n", + "\n", + "# calculate the means \n", + "past_djf = past_djf.aggregated_by(['season'], iris.analysis.MEAN)\n", + "present_djf = present_djf.aggregated_by(['season'], iris.analysis.MEAN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now calculate the different using **subtract** function" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "djf_diff = iris.analysis.maths.subtract(present_djf, past_djf)\n", + "djf_diff.rename('precipitation flux difference: Winter')\n", + "past_djf.rename('precipitation flux past climate: Winter 1851-1880 ')\n", + "present_djf.rename('precipitation flux present climate: Winter 1981-2010 ')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid_latitude bounds added\n", + "grid_longitude bounds added\n", + "grid_latitude bounds added\n", + "grid_longitude bounds added\n", + "grid_latitude bounds added\n", + "grid_longitude bounds added\n" + ] + } + ], + "source": [ + "# add bounds to the cubes \n", + "past_djf = add_bounds(past_djf, ['grid_latitude','grid_longitude'])\n", + "present_djf = add_bounds(present_djf, ['grid_latitude','grid_longitude'])\n", + "djf_diff = add_bounds(djf_diff, ['grid_latitude','grid_longitude'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To calcuate the percentage difference, we can use **analysis.maths.multiply** and **iris.analysis.maths.divide** to calculate percentage change" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the percentage change\n", + "pcent_change = iris.analysis.maths.multiply(iris.analysis.maths.divide(djf_diff, past_djf), 100)\n", + "\n", + "# remember to change the title and units to reflect the data processing\n", + "pcent_change.rename('precipitation flux percent difference')\n", + "pcent_change.units = '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# using iris plot for better colour saturation!\n", + "import iris.plot as iplt\n", + "\n", + "# list of our winter cubes and the diff cube to loop through for plotting\n", + "seasonal_cubes = [present_djf, past_djf, pcent_change]\n", + "plt.figure(figsize=(15, 10))\n", + "# loop through the seaonal cube list and plot the data\n", + "for i in range(len(seasonal_cubes)):\n", + " plt.subplot(2, 2, i+1)\n", + " # plot the windspeed at the first timestep \n", + " if i==2:\n", + " qplt.pcolormesh(seasonal_cubes[i][0,:,:],cmap=plt.cm.get_cmap('RdBu'))\n", + " else:\n", + " qplt.pcolormesh(seasonal_cubes[i][0,:,:])\n", + " \n", + " #rcmp = iplt.pcolormesh(seasonal_cubes[i][0,:,:])\n", + " #olorbar_axes = plt.gcf().add_axes()\n", + " #colorbar = plt.colorbar(rcmp, colorbar_axes, orientation='horizontal')\n", + " # add some coastlines for context\n", + " plt.gca().coastlines() \n", + " # get the season name from the coordinate\n", + " season = seasonal_cubes[i].coord('season').points[0]\n", + " # add the name as plot's title\n", + " plt.title(seasonal_cubes[i].name())\n", + " \n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate mean surface temperature differece over Tibatian region from past 30 years (1851-1880) to present 30 years (1981-2010).\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this exercise we will analyse the mean air temperture from past 30 years (1851-1880) to present 30 years (1981-2010), over the Shangai region, in all four seasons. Visualize past, present and difference in a row." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 1: Load monthly data and constraint time and region" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 2: Calculate seasonal mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 3: Visualise the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Summary
\n", + " In this session we learned how:
\n", + "
    \n", + "
  • to calculate yearly and monthly means
    • \n", + "
  • to calculate seasonal means and differences
    • \n", + "
  • to visualize the results
    • \n", + "
\n", + "\n", + "
\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": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb b/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb new file mode 100644 index 0000000..aa54aef --- /dev/null +++ b/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb @@ -0,0 +1,709 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Tutorial 4: Advanced data analysis\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives:\n", + "\n", + "In this session we will learn: \n", + "1. to calculate frequency of wet days\n", + "2. to calculate percentiles\n", + "3. how to calculate some useful climate extremes statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Frequency of wet days](#freq)\n", + "2. [Percentiles](#percent)\n", + "3. [Investigating extremes](#extremes)\n", + "4. [Exercises](#exercise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Prerequisites
\n", + "- Basic programming skills in python
\n", + "- Familiarity with python libraries Iris, Numpy and Matplotlib
\n", + "- Basic understanding of climate data
\n", + "- Tutorial 1 and 2\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load libraries and daily data\n", + "Import the necessary libraries. Current datasets are in zarr format, we need zarr and xarray libraries to access the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import zarr\n", + "import iris\n", + "import os\n", + "from cssp_utils import zarr_reader\n", + "from iris.analysis import Aggregator\n", + "import dask\n", + "dask.config.set(scheduler=dask.get)\n", + "import dask.array as da\n", + "import iris.quickplot as qplt\n", + "import iris.plot as iplt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "from catnip.preparation import extract_rot_cube, add_bounds\n", + "from xarray_iris_coord_system import XarrayIrisCoordSystem as xics\n", + "xi = xics()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Dataset consists of coordinates and data variables. Let's use the xarray to read all our zarr data into a xarray dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = '/data/users/zmaalick/cssp/data/ZARRSTORE'\n", + "freq = 'daily'\n", + "\n", + "ds = zarr_reader(path, freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the dataset into an iris cubelist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from xarray_iris_coord_system import XarrayIrisCoordSystem as xics\n", + "xi = xics()\n", + "# create an empty list to hold the iris cubes\n", + "cubelist = iris.cube.CubeList([])\n", + "\n", + "# use the DataSet.apply() to convert the dataset to Iris Cublelist\n", + "ds.apply(lambda da: cubelist.append(xi.to_iris(da)))\n", + "\n", + "# print out the cubelist.\n", + "cubelist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + " Note: The following sections demonstrate analysis of moderate extremes. The basis of climate extremes analysis is a common set of standard extreme climate indices, defined by the World Climate Research Programme Expert Team on Climate Change Detection and Indices (ETCCDI)\n", + " \n", + "
There are 27 climate extremes indices, nicely summarised by the Climdex website.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Frequency of wet days" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Calculate number of wet days ($\\mathrm{pr} \\geq 1 mm \\;day^{-1}$)\n", + "\n", + "In this section we'll be looking at wet days, a threshold measure giving the count of days when $\\mathrm{pr} \\geq 1 mm \\;day^{-1}$, and R95p, the 95th percentile of precipitation on wet days ($\\mathrm{pr} \\geq 1 mm \\;day^{-1}$) in the 1851-1900 period over the Shangai region." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the 'precipitation_flux' cube\n", + "pflx = cubelist.extract_strict('precipitation_flux')\n", + "# To avoid warnings when collapsing coordinates and also when plotting, add bounds to all coordinates\n", + "pflx = add_bounds(pflx,['time', 'grid_latitude', 'grid_longitude'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Applying the time and region constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define time constraint and extract 1851-1900 period\n", + "start_time = 1851\n", + "end_time = 1900\n", + "\n", + "# define the time constraint\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "\n", + "# laod the data into cubes applying the time constraint\n", + "pflx = pflx.extract(time_constraint)\n", + "\n", + "# extract Shangai region and constain with time\n", + "\n", + "# defining Shangai region coords\n", + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0\n", + "\n", + "# extract data for the the Shanghai region using extract_rot_cube() function\n", + "pflx = extract_rot_cube(pflx, min_lat, min_lon, max_lat, max_lon)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each day: is rainfall >= 1? True/False\n", + "\n", + "Sum over all days to get number of wet days at each grid point and then calcuate the percentage of wet days.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a new aggregator to help count non-zero days\n", + "# (This uses a dask array to reduce memory load)\n", + "count_nonzero = Aggregator('count', None,\n", + " units_func=lambda units: 1,\n", + " lazy_func=da.count_nonzero)\n", + "\n", + "wetdays = pflx.collapsed('time', count_nonzero)\n", + "wetdays.rename('number of wet days (>=1mm/day)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find wet days as a percentage of total days\n", + "total_days = len(pflx.coord('time').points)\n", + "pcent_wetdays = (wetdays / total_days) * 100\n", + "\n", + "# renaming the cube name and units\n", + "pcent_wetdays.rename('percentage of wet days (>=1mm/day)')\n", + "pcent_wetdays.units = '%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can plot the number and percententage of wet days" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12, 6))\n", + "fig.suptitle('Number of wet days (1851-1900)', fontsize=16)\n", + "ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.PlateCarree())\n", + "qplt.pcolormesh(wetdays)\n", + "ax1.coastlines()\n", + "ax1 = fig.add_subplot(1, 2, 2, projection=ccrs.PlateCarree())\n", + "qplt.pcolormesh(pcent_wetdays)\n", + "ax1.coastlines()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate and visualise the percentage difference of wet days from past (1851-1880) to present (1981-2010)\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Percentiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Calculating 95th percentile of precipitation\n", + "\n", + "In this section we will calculate the extreme precipitation i.e. 95th percentile. We have already extracted our cube *Pflx%* so we can use the *iris.analysis.PERCENTILE* method to calculate the percentile." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pflx_pc95 = pflx.collapsed('time', iris.analysis.PERCENTILE, percent=95.)\n", + "pflx_pc95.rename('R95p of daily rainfall')\n", + "pflx_pc95.convert_units('kg m-2 d-1')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12, 6))\n", + "fig.suptitle('Extreme rainfall', fontsize=16)\n", + "qplt.pcolormesh(pflx_pc95)\n", + "plt.gca().coastlines()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate the change in extreme precipitation over south east coast. \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Investigate extremes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Calculate the extreme index TX90P" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the frequency of warm days in the present (extreme index TX90P), i.e. the number of days which exceed the 90th percentile temperatures in the baseline. Then calculate the numbers of days as a percentage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# first extract the air_temperature at 1.5m cubes from the cubelist\n", + "air_temp = cubelist.extract('air_temperature' & iris.AttributeConstraint(Height='1.5 m'))\n", + "\n", + "# constraint for the maximum temperature \n", + "max_temp_cons = iris.Constraint(cube_func=lambda c: (len(c.cell_methods) > 0) and \n", + " (c.cell_methods[0].method == 'maximum'))\n", + "\n", + "# define time constraint and extract 1851-1900 period (the baseline)\n", + "start_time = 1851\n", + "end_time = 1900\n", + "\n", + "# define the time constraint\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "\n", + "# applying the pressure, maximum temperature and time constraints getting a single cube\n", + "max_temp = air_temp.extract_strict(max_temp_cons & time_constraint)\n", + "\n", + "# defining Shangai region coords\n", + "min_lat=29.0\n", + "max_lat=32.0\n", + "min_lon=118.0\n", + "max_lon=123.0\n", + "\n", + "# extract data for the the Shanghai region using extract_rot_cube() function\n", + "max_temp = extract_rot_cube(max_temp, min_lat, min_lon, max_lat, max_lon)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "max_temp_pc90 = max_temp.collapsed('time', iris.analysis.PERCENTILE, percent=90.)\n", + "max_temp_pc90.rename('R90p of daily maximum temperature')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now extract present day\n", + "# extract a single cube of maximum air_temperature at 1.5m cube from the cubelist\n", + "max_temp = cubelist.extract_strict('air_temperature' & iris.AttributeConstraint(Height='1.5 m') &\n", + " max_temp_cons)\n", + "# extract data for the the Shanghai region using extract_rot_cube() function\n", + "max_temp = extract_rot_cube(max_temp, min_lat, min_lon, max_lat, max_lon)\n", + "\n", + "\n", + "start_time = 1981\n", + "end_time = 2010\n", + "\n", + "time_constraint = iris.Constraint(time=lambda cell: start_time <= cell.point.year <= end_time)\n", + "max_temp = max_temp.extract(time_constraint)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to calculate the number of warm days, we do so by counting all the data points that are greater than 90th percentile of the baseline period within the last 30 years." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make new cube to hold the counts\n", + "nwarmdays = max_temp_pc90.copy()\n", + "\n", + "# Use broadcasting to identify all cells where daily temperatures in the future exceed the 95th percentile\n", + "temp_gt_pc90 = np.where(max_temp.data >= max_temp_pc90.data, 1, 0)\n", + "nwarmdays.data = np.ma.sum(temp_gt_pc90, axis=0)\n", + "# the sum above removes the mask - reinstate it with \n", + "nwarmdays.data.mask = max_temp_pc90.data.mask\n", + "nwarmdays.units = '1'\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qplt.pcolormesh(nwarmdays)\n", + "plt.gca().coastlines() \n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate percentage of warmest days by using **iris.analysis.maths**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ndays = max_temp.shape[0]\n", + "# calculating percentage \n", + "nwd_pcent = iris.analysis.maths.divide(iris.analysis.maths.multiply(nwarmdays, 100), ndays)\n", + "nwd_pcent.units=\"%\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ploting the percentage of warm days" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qplt.pcolormesh(nwd_pcent)\n", + "plt.title('Percentage of warm days')\n", + "plt.gca().coastlines() \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task:
    \n", + "
  • Calculate and plot the past (1851-1880) and present (1981-2010) 90th percentile of maximum temperature and the difference between them.\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Enter your code here .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Enter your code here .." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Exercises\n", + "\n", + "In this exercise we will calculate the percentage of total precipitation from 1981-2010 which falls on very wet days (where a very wet day is one on which daily rainfall exceeds the 95th percentile of the baseline) over Shangai region.\n", + "\n", + "Further we also calculate the percentage of very wet days in the past (1851-1880) and see the difference by Ploting the difference of heavy rainfall in the past and present." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 1: calculate the percentage of total precipitation from 1981-2010 on very wet days (=> 95th Percentile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise 2: calculate the percentage of total precipitation from 1951-1880 on very wet days (=> 95th Percentile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3: Calculate the difference " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4: Plot the percentages and difference " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your code here ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Summary
\n", + " In this session we:
\n", + "
    \n", + "
  • learn how to to calculate extreme values and percentages\n", + "
  • calcuate basic extreme value indices \n", + "
\n", + "\n", + "
\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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py b/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py new file mode 100644 index 0000000..f359e98 --- /dev/null +++ b/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py @@ -0,0 +1,85 @@ +import json + +import iris +import iris.coord_systems +import xarray as xr + + +class XarrayIrisCoordSystem(object): + coord_systems_lookup = {'latitude_longitude': iris.coord_systems.GeogCS, + 'rotated_latitude_longitude': iris.coord_systems.RotatedGeogCS, + 'mercator': iris.coord_systems.Mercator} + + def __init__(self): + self._cube = None + self._data_array = None + + self.coord_system_attr_name = "iris_coord_system" + + @property + def cube(self): + return self._cube + + @cube.setter + def cube(self, value): + self._cube = value + + @property + def data_array(self): + return self._data_array + + @data_array.setter + def data_array(self, value): + self._data_array = value + + def _attrs_as_dict(self, attrs): + return {k: v for (k, v) in attrs} + + def _store_coord_system(self): + coord_system = self.cube.coord_system() + attrs = self._attrs_as_dict(coord_system._pretty_attrs()) + # We don't want the default ellipsoid attr, which is a reference to an Iris class. + attrs["ellipsoid"] = self._attrs_as_dict(coord_system.ellipsoid._pretty_attrs()) + attrs["coord_system_name"] = coord_system.grid_mapping_name + return json.dumps(attrs) + + def _build_ellipsoid(self, ellipsoid_kwargs): + return iris.coord_systems.GeogCS(**ellipsoid_kwargs) + + def _build_coord_system(self, coord_system_str): + coord_system_dict = json.loads(coord_system_str) + coord_system_name = coord_system_dict.pop("coord_system_name") + ellipsoid_kwargs = coord_system_dict.pop("ellipsoid") + + ellipsoid = self._build_ellipsoid(ellipsoid_kwargs) + + constructor = self.coord_systems_lookup.get(coord_system_name) + if constructor is not None: + result = constructor(**coord_system_dict, ellipsoid=ellipsoid) + else: + raise ValueError(f"Coord system name {coord_system_name!r} is either not known or supported.") + return result + + def from_iris(self, cube): + self.cube = cube + + data_array = xr.DataArray.from_iris(self.cube) + data_array.attrs[self.coord_system_attr_name] = self._store_coord_system() + + self.data_array = data_array + return self.data_array + + def to_iris(self, data_array): + self.data_array = data_array + coord_system_str = self.data_array.attrs.pop(self.coord_system_attr_name, None) + + self.cube = self.data_array.to_iris() + if coord_system_str is not None: + cube_coord_system = self._build_coord_system(coord_system_str) + for axis in ['X', 'Y']: + try: + self.cube.coord(axis=axis).coord_system = cube_coord_system + except AttributeError: + pass + + return self.cube From c97fbb3d374e56490025cf5e8c1217508e96c048 Mon Sep 17 00:00:00 2001 From: zmaalick Date: Thu, 3 Dec 2020 10:27:21 +0000 Subject: [PATCH 6/8] fix typos in tutorial 3 and 4 --- .../tutorial_1_data_access.ipynb | 2 +- .../tutorial_2_data_preparation.ipynb | 6 ++-- .../tutorial_3_basic_analysis.ipynb | 4 +-- .../tutorial_4_advance_analysis.ipynb | 6 ++-- LICENSE | 29 +++++++++++++++++++ 5 files changed, 38 insertions(+), 9 deletions(-) create mode 100644 LICENSE diff --git a/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb b/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb index 246486e..31afeed 100644 --- a/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb +++ b/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb @@ -7892,7 +7892,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb b/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb index 20cb8c2..193f821 100644 --- a/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb +++ b/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb @@ -5823,14 +5823,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Exercises" + "## 5. Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this exercise we will analyse the mean precipitation rate from 1950 - 2010 over the Shangai region" + "In this exercise we will analyse the mean precipitation rate from 1950 - 2010 over the Shanghai region" ] }, { @@ -5955,7 +5955,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb b/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb index 23d2aca..8c9a464 100644 --- a/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb +++ b/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb @@ -4108,7 +4108,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this exercise we will analyse the mean air temperture from past 30 years (1851-1880) to present 30 years (1981-2010), over the Shangai region, in all four seasons. Visualize past, present and difference in a row." + "In this exercise we will analyse the mean air temperature from past 30 years (1851-1880) to present 30 years (1981-2010), over the Shanghai region, in all four seasons. Visualize past, present and difference in a row." ] }, { @@ -4200,7 +4200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb b/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb index aa54aef..6bcfaee 100644 --- a/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb +++ b/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb @@ -675,10 +675,10 @@ "\n", "
\n", "Summary
\n", - " In this session we:
\n", + " In this session we learned how:
\n", "
    \n", - "
  • learn how to to calculate extreme values and percentages\n", - "
  • calcuate basic extreme value indices \n", + "
  • to calculate extreme values and percentages\n", + "
  • to calcuate basic extreme value indices \n", "
\n", "\n", "
\n" diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..9dfda08 --- /dev/null +++ b/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause Licence + +Copyright (c) 2020, Met Office +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. From 3e3695069a11c4167e4b31e83ec8be6e9fad78eb Mon Sep 17 00:00:00 2001 From: zmaalick Date: Fri, 4 Dec 2020 09:18:15 +0000 Subject: [PATCH 7/8] Update README, Remove LICENSE and move the CSSP directory under notebooks --- LICENSE | 29 ------------------ README.md | 2 ++ .../CSSP_20CRDS_Tutorials}/Introduction.ipynb | 0 .../CSSP_20CRDS_Tutorials}/cssp_utils.py | 0 .../images/global_airtemp_cp.png | Bin .../images/global_airtemp_ts.png | Bin .../CSSP_20CRDS_Tutorials}/images/region.PNG | Bin .../tutorial_1_data_access.ipynb | 0 .../tutorial_2_data_preparation.ipynb | 0 .../tutorial_3_basic_analysis.ipynb | 0 .../tutorial_4_advance_analysis.ipynb | 0 .../xarray_iris_coord_system.py | 0 12 files changed, 2 insertions(+), 29 deletions(-) delete mode 100644 LICENSE rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/Introduction.ipynb (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/cssp_utils.py (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/images/global_airtemp_cp.png (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/images/global_airtemp_ts.png (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/images/region.PNG (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/tutorial_1_data_access.ipynb (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/tutorial_2_data_preparation.ipynb (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/tutorial_3_basic_analysis.ipynb (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/tutorial_4_advance_analysis.ipynb (100%) rename {CSSP_20CRDS_Tutorials => notebooks/CSSP_20CRDS_Tutorials}/xarray_iris_coord_system.py (100%) diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 9dfda08..0000000 --- a/LICENSE +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause Licence - -Copyright (c) 2020, Met Office -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 52ddebe..a3e9187 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,8 @@ Three additional worksheets are available for use by workshop instructors: * `worksheet_solutions.ipyn`: Solutions to worksheet exercices. * `worksheet6example.ipynb`: Example code for Worksheet 6. +Notebooks also contains the tutorials for CSSP 20th Century reanalysis datasets. + ## Data The data used in the worksheets is currently only available within the Met Office. See the `data/README` for further details. diff --git a/CSSP_20CRDS_Tutorials/Introduction.ipynb b/notebooks/CSSP_20CRDS_Tutorials/Introduction.ipynb similarity index 100% rename from CSSP_20CRDS_Tutorials/Introduction.ipynb rename to notebooks/CSSP_20CRDS_Tutorials/Introduction.ipynb diff --git a/CSSP_20CRDS_Tutorials/cssp_utils.py b/notebooks/CSSP_20CRDS_Tutorials/cssp_utils.py similarity index 100% rename from CSSP_20CRDS_Tutorials/cssp_utils.py rename to notebooks/CSSP_20CRDS_Tutorials/cssp_utils.py diff --git a/CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png b/notebooks/CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png similarity index 100% rename from CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png rename to notebooks/CSSP_20CRDS_Tutorials/images/global_airtemp_cp.png diff --git a/CSSP_20CRDS_Tutorials/images/global_airtemp_ts.png b/notebooks/CSSP_20CRDS_Tutorials/images/global_airtemp_ts.png similarity index 100% rename from CSSP_20CRDS_Tutorials/images/global_airtemp_ts.png rename to notebooks/CSSP_20CRDS_Tutorials/images/global_airtemp_ts.png diff --git a/CSSP_20CRDS_Tutorials/images/region.PNG b/notebooks/CSSP_20CRDS_Tutorials/images/region.PNG similarity index 100% rename from CSSP_20CRDS_Tutorials/images/region.PNG rename to notebooks/CSSP_20CRDS_Tutorials/images/region.PNG diff --git a/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb b/notebooks/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb similarity index 100% rename from CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb rename to notebooks/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb diff --git a/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb b/notebooks/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb similarity index 100% rename from CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb rename to notebooks/CSSP_20CRDS_Tutorials/tutorial_2_data_preparation.ipynb diff --git a/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb b/notebooks/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb similarity index 100% rename from CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb rename to notebooks/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb diff --git a/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb b/notebooks/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb similarity index 100% rename from CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb rename to notebooks/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb diff --git a/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py b/notebooks/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py similarity index 100% rename from CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py rename to notebooks/CSSP_20CRDS_Tutorials/xarray_iris_coord_system.py From 439d580413cb175ecb34dbef6d233b0ada8ab08c Mon Sep 17 00:00:00 2001 From: Hamish Steptoe Date: Fri, 4 Dec 2020 09:53:11 +0000 Subject: [PATCH 8/8] Update table of contents --- README.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a3e9187..47c6d17 100644 --- a/README.md +++ b/README.md @@ -39,13 +39,20 @@ Worksheet | Aims [5](notebooks/worksheet1.ipynb) |
  • Have an appreciation for working with daily model data
  • Understand how to calculate some useful climate extremes statistics
  • Be aware of some coding stratagies for dealing with large data sets
    • [6](notebooks/worksheet1.ipynb) | An extended coding exercise designed to allow you to put everything you've learned into practise +Additional tutorials specific to the CSSP 20th Century reanalysis datasets: + +Worksheet | Aims +:----: | ----------- +[CSSP 1](notebooks/CSSP_20CRDS_Tutorials/Introduction.ipynb) |
  • How to use a cloud based platform to analyse the 20CR-DS dataset
  • Settig up a python environment
    • +[CSSP 2](notebooks/CSSP_20CRDS_Tutorials/tutorial_1_data_access.ipynb) |
  • How to load data into Xarrays format
  • How to convert the data xarrays into iris cube format
  • How to perform basic cube operations
    • +[CSSP 3](notebooks/CSSP_20CRDS_Tutorials/tutorial_3_basic_analysis.ipynb) |
  • Calculate and visualise annual and monthly means
  • Calculate and visualise seasonal means
  • Calculate mean differences (anomalies)
    • +[CSSP 4](notebooks/CSSP_20CRDS_Tutorials/tutorial_4_advance_analysis.ipynb) |
  • Calculate frequency of wet days
  • Calculate percentiles
  • Calculate some useful climate extremes statistics
    • + Three additional worksheets are available for use by workshop instructors: * `makedata.ipynb`: Provides scripts for preparing raw model output for use in notebook exercises. * `worksheet_solutions.ipyn`: Solutions to worksheet exercices. -* `worksheet6example.ipynb`: Example code for Worksheet 6. - -Notebooks also contains the tutorials for CSSP 20th Century reanalysis datasets. +* `worksheet6example.ipynb`: Example code for Worksheet 6. ## Data The data used in the worksheets is currently only available within the Met Office. See the `data/README` for further details.
    \n", + " Task:
      \n", + "
    • Inspect the following attributes of caf_cube you created in previous task
      • \n", + "
        \n", + "
      • name (standar_name)
        • \n", + "
      • dimensions (ndim)
        • \n", + "
      • units
        • \n", + "
      • mean of data
        • \n", + "
      \n", + "
    • Print all the coordinates of caf_cube, (hint: use for loop)
      • \n", + "
    • Explore attributes of \"grid_latitude\"
      • \n", + "
        \n", + "
      • name (standar_name)
        • \n", + "
      • shape
        • \n", + "
      • units
        • \n", + "
      \n", + " \n", + " \n", + "