{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "*This notebook was created by Sergey Tomin (sergey.tomin@desy.de). Source and license info is on [GitHub](https://github.com/ocelot-collab/ocelot). April 2020.\n", "Introduced a few little examples with new features, June 2023, Sergey*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# An Introduction to Ocelot\n", "\n", "Ocelot is a multiphysics simulation toolkit designed for studying Free Electron Lasers (FEL) and storage ring-based light sources. Implemented in Python, Ocelot caters to researchers seeking the flexibility provided by high-level languages like Matlab and Python. Its core principle revolves around scripting beam physics simulations in Python, utilizing Ocelot's modules and extensive collection of Python libraries.\n", "\n", "Users developing high-level control applications can accelerate development by using physics models from Ocelot and Python graphics libraries such as [PyQt](http://pyqt.sourceforge.net/Docs/PyQt5/) and [PyQtGraph](http://www.pyqtgraph.org/) to create a GUI. \n", "\n", "Developing machine learning (ML) applications for accelerators can also benefit from using Ocelot, as many popular ML frameworks are written in Python. Ocelot provides a seamless connection between physics and ML methods, making it easier to integrate physical accelerator simulators with machine learning algorithms.\n", "\n", "> πŸ”— Visit the project website: [https://www.ocelot-collab.com](https://www.ocelot-collab.com)\n", "\n", "## Requirements\n", "- Python 3.9+\n", "- [`numpy`](https://numpy.org/) version 1.8 or higher\n", "- [`scipy`](https://scipy.org/) version 0.15 or higher\n", "- [`matplotlib`](https://matplotlib.org/) version 1.5 or higher\n", "- [`h5py`](https://www.h5py.org/) version 3.10 or higher\n", "\n", "**Orbit Correction module is required**\n", "- [`pandas`](https://pandas.pydata.org/)\n", "\n", "**Optional**, but highly recommended for speeding up calculations\n", "- [`numexpr`](https://numexpr.readthedocs.io/en/latest/user_guide.html) (version 2.6.1 or higher)\n", "- [`pyfftw`](https://pyfftw.readthedocs.io/en/latest/) (version 0.10 or higher)\n", "- [`numba`](https://numba.pydata.org/)\n", "\n", "\n", "## Installation\n", "\n", "\n", "### 1. Install via Anaconda Cloud\n", "\n", "The easiest way to install OCELOT is through Anaconda Cloud. Use the following command:\n", "\n", "```bash\n", "$ conda install -c ocelot-collab ocelot\n", "```\n", "\n", "### 2. Install from GitHub (for advanced users)\n", "If you’re comfortable with Git and Python, you can clone OCELOT from GitHub:\n", "```\n", "$ git clone https://github.com/ocelot-collab/ocelot.git\n", "```\n", "Alternatively, you can download the latest release as a [zip file](https://github.com/ocelot-collab/ocelot/archive/refs/heads/master.zip).\n", "\n", "To install OCELOT from source:\n", "```\n", "$ python setup.py install\n", "```\n", "\n", "### 3. Install by Setting the Python Path\n", "\n", "If you'd like to manually install OCELOT, follow these steps:\n", "\n", "1. Download the [ZIP file](https://github.com/ocelot-collab/ocelot/archive/master.zip) from GitHub.\n", "2. Unzip the file `ocelot-master.zip` to your working directory, e.g., `/your_working_dir/`.\n", "3. Add the directory `../your_working_dir/ocelot-master` to your `PYTHONPATH`:\n", "\n", " - **Windows:**\n", " 1. Go to **Control Panel** β†’ **System and Security** β†’ **System** β†’ **Advanced System Settings** β†’ **Environment Variables**.\n", " 2. Under **User variables**, add `../your_working_dir/ocelot-master/` to `PYTHONPATH`. If `PYTHONPATH` does not exist, create it.\n", "\n", " **Variable name:** `PYTHONPATH`\n", "\n", " **Variable value:** `../your_working_dir/ocelot-master/`\n", "\n", " - **Linux/macOS:**\n", "\n", " ```bash\n", " $ export PYTHONPATH=/your_working_dir/ocelot-master:$PYTHONPATH\n", " ```\n", "\n", "## Ocelot main modules:\n", "\n", "\n", "* **Charged particle beam dynamics module (CPBD)**\n", " - optics\n", " - tracking\n", " - matching\n", " - collective effects (description can be found [here](http://vrws.de/ipac2017/papers/wepab031.pdf) and [here](https://journals.aps.org/prab/abstract/10.1103/PhysRevAccelBeams.22.024401))\n", " - Space Charge (3D Laplace solver)\n", " - CSR (Coherent Synchrotron Radiation) (1D model with arbitrary number of dipoles).\n", " - Wakefields (Taylor expansion up to second order for arbitrary geometry).\n", " - MOGA (Multi Objective Genetics Algorithm) [ref](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/thpmb034.pdf).\n", "* **Native module for spontaneous radiation calculation** (some details can be found [here](http://accelconf.web.cern.ch/AccelConf/ipac2019/papers/wepts017.pdf) and [here](http://scripts.iucr.org/cgi-bin/paper?S1600577519002509))\n", "* **FEL calculations: interface to GENESIS and pre/post-processing**\n", "* **Modules for online beam control and online optimization of accelerator performances.** [ref1](http://accelconf.web.cern.ch/accelconf/IPAC2014/papers/mopro086.pdf), [ref2](https://jacowfs.jlab.org/conf/y15/ipac15/prepress/TUPWA037.PDF), [ref3](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/wepoy036.pdf), [ref4](https://arxiv.org/pdf/1704.02335.pdf).\n", " - This module is being developed in collaboration with SLAC. The module has been migrated to a separate [repository](https://github.com/ocelot-collab/optimizer) (in [ocelot-collab](https://github.com/ocelot-collab) organization) for ease of collaborative development.\n", "\n", "Ocelot extensively uses Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python and give you access to various mathematical and optimization techniques and algorithms. To produce high quality figures Python's [matplotlib](http://matplotlib.org/index.html) library is used.\n", "\n", "It is an open source project and it is being developed by physicists from [The European XFEL](http://www.xfel.eu/), [DESY](http://www.desy.de/) (Germany), [NRC Kurchatov Institute](http://www.nrcki.ru/) (Russia).\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Image\n", "# Image(filename='gui_example.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorials\n", "\n", "\n", "The current tutorials are available at [https://www.ocelot-collab.com/docs/tutorial/intro](https://www.ocelot-collab.com/docs/tutorial/intro). \n", "They are based on Jupyter notebooks, which can be accessed via links provided on each tutorial page or directly on GitHub in the [`ocelot/demos/ipython_tutorials`](https://github.com/ocelot-collab/ocelot/tree/master/demos/ipython_tutorials) folder.\n", "\n", "## Beam dynamics\n", "\n", "Before starting with the tutorials, we recommend looking at [**Ocelot for Students**](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/for_students) β€” a beginner-friendly example aimed at students and newcomers to accelerator physics. This tutorial keeps things simple and interactive to help build intuition about how magnetic elements work.\n", "\n", "---\n", "* [Tutorial N1. Linear optics. Double Bend Achromat](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/dba)\n", " - Linear optics. Double Bend Achromat (DBA). Simple example of usage OCELOT functions to get periodic solution for a storage ring cell. \n", "* [Tutorial N2. Tracking.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/tracking)\n", " - Linear optics of the European XFEL Injector. \n", " - Tracking. First and second order. \n", " - Artificial beam matching - BeamTransform\n", "* [Tutorial N3. Space Charge.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/space_charge)\n", " - Tracking through RF cavities with SC effects and RF focusing.\n", "* [Tutorial N4. Wakefields.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/wake)\n", " - Tracking through corrugated structure (energy chirper) with Wakefields\n", "* [Tutorial N5. CSR.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/CSR)\n", " - Tracking trough bunch compressor with CSR effect.\n", "* [Tutorial N6. RF Coupler Kick.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/coupler_kick)\n", " - Coupler Kick. Example of RF coupler kick influence on trajectory and optics.\n", "* [Tutorial N7. Lattice design.](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/lattice_design)\n", " - Lattice design, twiss matching, twiss backtracking \n", "* [Tutorial N8. Physics process addition. Laser heater](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/laser_heater)\n", " - Theory of Laser Heater, implementation of new Physics Process, track particles w/o laser heater effect. \n", "* [Tutorial N9. Simple accelerator based THz source](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/thz_source)\n", " - A simple accelerator with the electron beam formation system and an undulator to generate THz radiation. \n", "* [Tutorial N10. Corrugated Structure](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/CorrugatedStructures)\n", " - In this tutorial, a few examples for tracking with parallel-plate corrugated structures are shown. The wakefields model are based on analytical wakefield formulas for flat corrugated structures.\n", "* [Tutorial N11. Optics for High Time Resolution Measurements with TDS](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/slotted_foil)\n", " - An additional example demonstrating how to modify the beamline optics using Ocelot.\n", " \n", "## Photon field simulation \n", "\n", "* [PFS tutorial N1. Synchrotron radiation module](https://www.ocelot-collab.com/docs/tutorial/tutorial-photons/pfs_1_synchrotron_radiation)\n", " - Simple examples how to calculate synchrotron radiation with OCELOT Synchrotron Radiation Module.\n", "* [PFS tutorial N2. Coherent radiation module and RadiationField object](https://www.ocelot-collab.com/docs/tutorial/tutorial-photons/pfs_2_radiation_field)\n", "* [PFS tutorial N3. Reflection from imperfect highly polished mirror](https://www.ocelot-collab.com/docs/tutorial/tutorial-photons/pfs_3_imperfect_mirror)\n", "* [PFS tutorial N4. Converting synchrotron radiation Screen object to RadiationField object for viewing and propagation](./tutorial-photons/pfs_4_synchrotron_radiation_visualization.md)\n", "* [PFS tutorial N5: SASE estimation and imitation](https://www.ocelot-collab.com/docs/tutorial/tutorial-photons/pfs_5_SASE_Estimator_and_Imitator)\n", "* [PFS tutorial N6: Spectral Filtering](https://www.ocelot-collab.com/docs/tutorial/tutorial-photons/pfs_6_spectral_filtering)\n", "\n", "## Appendixes\n", "* [Undulator matching](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/undulator_matching)\n", " - brief theory and example in OCELOT\n", "* [Some useful OCELOT functions](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/small_useful_features)\n", " \n", " A collection of small but handy features in Ocelot:\n", " - Aperture\n", " - Losses along accelerator lattice\n", " - RK tracking\n", " - Dump the beam distribution at a specific location of the lattice\n", " - Energy jitter. Or simulation of the jitter in the RF parameters.\n", " - Get Twiss parameters from the beam slice\n", " - Transfer Maps in Ocelot. Global assignment and for specific elements\n", "* [Example of an accelerator section optimization](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/accelerator_optim)\n", " - A simple demo of accelerator section optimization with a standard scipy numerical optimization method. \n", "* [Optics Design for High Time Resolution with TDS](https://www.ocelot-collab.com/docs/tutorial/tutorial-beam-dynamics/optics_design) \n", " An example demonstrating how to perform optics matching in Ocelot to improve time resolution when using a Transverse Deflecting Structure (TDS)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking your installation\n", "\n", "You can run the following code to check the versions of the packages on your system:\n", "\n", "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IPython: 8.20.0\n", "numpy: 1.26.3\n", "scipy: 1.11.4\n", "matplotlib: 3.8.2\n", "initializing ocelot...\n", "ocelot: 24.03.0\n" ] } ], "source": [ "import IPython\n", "print('IPython:', IPython.__version__)\n", "\n", "import numpy\n", "print('numpy:', numpy.__version__)\n", "\n", "import scipy\n", "print('scipy:', scipy.__version__)\n", "\n", "import matplotlib\n", "print('matplotlib:', matplotlib.__version__)\n", "\n", "import ocelot\n", "print('ocelot:', ocelot.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Tutorial N1. Double Bend Achromat.\n", "\n", "We designed a simple lattice to demonstrate the basic concepts and syntax of the optics functions calculation. \n", "Also, we chose DBA to demonstrate the periodic solution for the optical functions calculation. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "# the output of plotting commands is displayed inline within frontends, \n", "# directly below the code cell that produced it\n", "%matplotlib inline\n", "\n", "# import from Ocelot main modules and functions\n", "from ocelot import *\n", "\n", "# import from Ocelot graphical modules\n", "from ocelot.gui.accelerator import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating lattice\n", "Ocelot has following elements: Drift, Quadrupole, Sextupole, Octupole, Bend, SBend, RBend, Edge, Multipole, Hcor, Vcor, Solenoid, Cavity, Monitor, Marker, Undulator. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# defining of the drifts\n", "D1 = Drift(l=2.)\n", "D2 = Drift(l=0.6)\n", "D3 = Drift(l=0.3)\n", "D4 = Drift(l=0.7)\n", "D5 = Drift(l=0.9)\n", "D6 = Drift(l=0.2)\n", "\n", "# defining of the quads\n", "Q1 = Quadrupole(l=0.4, k1=-1.3)\n", "Q2 = Quadrupole(l=0.8, k1=1.4)\n", "Q3 = Quadrupole(l=0.4, k1=-1.7)\n", "Q4 = Quadrupole(l=0.5, k1=1.3)\n", "\n", "# defining of the bending magnet\n", "B = Bend(l=2.7, k1=-.06, angle=2*pi/16., e1=pi/16., e2=pi/16.)\n", "\n", "# defining of the sextupoles\n", "SF = Sextupole(l=0.01, k2=1.5) #random value\n", "SD = Sextupole(l=0.01, k2=-1.5) #random value\n", "\n", "# cell creating\n", "cell = (D1, Q1, D2, Q2, D3, Q3, D4, B, D5, SD, D5, SF, D6, Q4, D6,\n", " SF, D5, SD, D5, B, D4, Q3, D3, Q2, D2, Q1, D1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " )" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*hint: to see a simple description of the function put cursor inside () and press **Shift-Tab** or you can type sign **?** before function. To extend dialog window press **+** *\n", "Also, one can get more info about element just using ```print(element)```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bend(l=2.70000, angle=3.926991e-01, e1=1.963495e-01, e2=1.963495e-01, eid=\"ID_90868229_\")\n" ] } ], "source": [ "# all infro about an element can be seen with \n", "print(B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell is a list of the simple objects which contain a physical information of lattice elements such as length, strength, voltage and so on. In order to create a transport map for every element and bind it with lattice object we have to create new Ocelot object - MagneticLattice() which makes these things automatically. \n", "\n", "```MagneticLattice(sequence, start=None, stop=None, method={\"global\": TransferMap})```: \n", "* sequence - list of the elements,\n", "\n", "other parameters we will consider in tutorial N2. \n", "\n", "Note, in the current version of OCELOT, transfer map belongs to element. See example" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([[ 1.10581521, 0.4140116 , 0. , 0. , 0. ,\n", " 0. ],\n", " [ 0.53821508, 1.10581521, 0. , 0. , 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0.89779021, 0.38627683, 0. ,\n", " 0. ],\n", " [ 0. , 0. , -0.50215988, 0.89779021, 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. ,\n", " 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ,\n", " 1. ]])]\n" ] } ], "source": [ "# R matrix can be printed for any particular element.\n", "print(Q1.R(energy=0))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# or you can directly get transfer maps \n", "Q2.tms" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length of the cell: 20.34 m\n", "[[ 0.68401288 0.38454837 0. 0. 0. 0.05268746]\n", " [-1.38376969 0.68401288 0. 0. 0. 0.23072876]\n", " [ 0. 0. 0.81775255 -0.29733817 0. 0. ]\n", " [ 0. 0. 1.11415489 0.81775255 0. 0. ]\n", " [ 0.23072876 0.05268746 0. 0. 1. 0.02228572]\n", " [ 0. 0. 0. 0. 0. 1. ]]\n" ] } ], "source": [ "lat = MagneticLattice(cell)\n", "\n", "# to see total lenth of the lattice \n", "print(\"length of the cell: \", lat.totalLen, \"m\")\n", "\n", "# or, for example, you can get R matrix for whole lattice\n", "\n", "B, R, T = lat.transfer_maps(energy=0)\n", "print(R)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optical function calculation\n", "Uses: \n", "* twiss() function and,\n", "* Twiss() object contains twiss parameters and other information at one certain position (s) of lattice\n", "\n", "To calculate twiss parameters you have to run **twiss(lattice, tws0=None, nPoints=None)** function. If you want to get a periodic solution leave tws0 by default. \n", "\n", "You can change the number of points over the cell, If nPoints=None, then twiss parameters are calculated at the end of each element.\n", "twiss() function returns list of Twiss() objects.\n", "\n", "##### You will see the Twiss object contains more information than just twiss parameters. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length = 1000\n", "emit_x = 0.0\n", "emit_y = 0.0\n", "beta_x = 0.5271613695963895\n", "beta_y = 0.5165977895295946\n", "alpha_x = -4.440892098500626e-16\n", "alpha_y = 6.661338147750939e-15\n", "gamma_x = 1.8969523521149319\n", "gamma_y = 1.9357419258618653\n", "Dx = 0.16673927708143915\n", "Dy = 0.0\n", "Dxp = 4.440892098500626e-16\n", "Dyp = 0.0\n", "mux = 7.100731992120578\n", "muy = 5.669884351617213\n", "nu_x = 1.1301165961167512\n", "nu_y = 0.9023901213192655\n", "E = 0.0\n", "s = 20.34\n", "\n" ] } ], "source": [ "tws = twiss(lat, nPoints=1000)\n", "\n", "# to see twiss paraments at the begining of the cell, uncomment next line\n", "# print(tws[0])\n", "print(\"length = \", len(tws))\n", "# to see twiss paraments at the end of the cell, uncomment next line\n", "print(tws[-1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot optical functions.\n", "plot_opt_func(lat, tws, top_plot = [\"Dx\", \"Dy\"], legend=False, font_size=10)\n", "plt.show()\n", "\n", "# you also can use standard matplotlib functions for plotting\n", "#s = [tw.s for tw in tws]\n", "#bx = [tw.beta_x for tw in tws]\n", "#plt.plot(s, bx)\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# you can play with quadrupole strength and try to make achromat\n", "Q4.k1 = 1.18\n", "\n", "# to make achromat uncomment next line\n", "# Q4.k1 = 1.18543769836\n", "# To use matching function, please see ocelot/demos/ebeam/dba.py \n", "\n", "# updating transfer maps after changing element parameters. \n", "#lat.update_transfer_maps() - not needed anymore\n", "\n", "# recalculate twiss parameters. Argument nPoints is None by default - Twiss is calculating at the end of each element. \n", "# If you want smooth twiss functions you can set number of points. \n", "tws=twiss(lat, nPoints=1000)\n", "\n", "plot_opt_func(lat, tws, legend=False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# More about periodic solution for the Twiss function\n", "In some cases, one needs to quickly find a periodic solution. Here is a simple example with Cavity element:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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NG+Hm5obOnTsna+/bty+6d++Oo0ePonbt2q98rK+vrz4xBAAHBwf06NEDEyZMwJ07d1CgQAHcuXMHx48fx4wZM/SJIQDUrl0bpUqVwsaNG5kcEhERkU0wOjlcvnx5ZsaRbmfOnEHp0qWTJW2AzJFUv/+q5PDMmTOoV6/eS+3qY8+ePYsCBQro51qq7S+ee/DgwVRjjI2NTTb0HBYWBgBo9PleOLhkT/WxlLr4+Hg4buK8T1PgtTQNXkfT4bU0DV5H07CF65gQE2nUeekqZWNJQkJCULx48Zfa1R1aQkJCUn1saru7qI9V//uqc1N7DQCYMWMGpk6d+lL7g/BY2MWlPORNaRDNgusmw2tpGryOpsNraRq8jqZh5dcxycg1ElafHAKpz2183bzHtDz2Vee+7jXGjx+fbCeZsLAwFCpUCHlzOMPBxSXVx1Lq4uPj4ejoqHUYNoHX0jR4HU2H19I0eB1NwxauY0JMIm4ZcZ7VJ4e5c+dOsefu8ePHAFLf49nYx+bOnRtAyr2Qjx8/fu0+0s7Oziku5Nk9piHc3d1TfSyl7vTp0yhfvrzWYdgEXkvT4HU0HV5L0+B1NA1buI5hYWHw+Pj151l9Eezy5cvj/PnzSEhISNZ++vRpAEC5cuVSfax6XmqPVf/7qnNTew0iIiIia2L1yWH79u0RERGBDRs2JGtfuXIlvL29UaNGjVQfe+HChWQlaxISErBq1SrUqFED3t7eAIACBQqgevXqWLVqFRITDfMNjhw5gosXL6JDhw4mfldERERE2rD65DAwMBBNmzbF4MGD8e2332LPnj0YOHAgtm3bhlmzZulrHL7zzjtwcHDAjRs39I/t168fypYti86dO2PNmjXYuXMn3nrrLVy8eBGfffZZstf57LPPcOHCBXTu3Bk7d+7EmjVr8NZbb6FcuXLo27evWd8zERERUWax+uQQAH755Rf07NkTkyZNQosWLXD06FGsXbsWb7/9tv6cxMREJCYmQlEUfZuzszN27dqFgIAADB8+HG3atMG9e/ewdetWNGjQINlrNGzYEFu2bMG9e/fQpk0bDB8+HAEBAdi1a1eaCoMTERERWTKd8ny2ZAKNGjWCt7c3JkyYgDJlypjyqW1GWFgYPDw8EBoaygUpGWQLE4QtBa+lafA6mg6vpWnwOpqGLVxHY/MPk/cc7t27F2vWrEGFChXQs2dPUz89EREREWUikyeHSUlJCA8Px++//4433njD1E9PRERERJkoU+ocZs+eHS1btkTLli0z4+mJiIiIKJOkq+fwo48+MnUcRERERGQB0pUcfvbZZxg5cuQrv3/z5s10B0RERERE2klXcvjLL79g8eLFGDBgQLLSMOHh4fjggw/g5+dnsgCJiIiIyHzSNeewdevW2LJlC9q2bYvIyEgsX74cy5Ytw5QpU/D48WP069fP1HESERERkRmke0FKw4YN9QWk8+bNi4iICLz55puYOXMmfH19TRkjEREREZlJupPDkydPYsKECYiMjAQA1K1bF+vXr9dvV0dERERE1iddcw67d++OatWq4dy5c1i2bBn++usvnDlzBu3atUNsbKypYyQiIiIiM0lXcrhp0yZMnjwZly9fRp8+fVCnTh3s3r0bx48fR4sWLRAREWHqOImIiIjIDNI1rHz58mXkz58/WZu/vz/27duHJk2aoFGjRjh27JhJAiQiIiIi80lXz+GLiaHK19cXf/31F548eZKhoIiIiIhIG2lKDsPCwjBixAgUKlQIDg4OcHd3R7Vq1TB16lQ8ePAAAFC0aFH89ddfmRIsEREREWUuo5PDuLg4BAQE4Msvv4ROp0PDhg1RsWJF3L59G1OnTkXx4sWxdOlSAK/uWSQiIiIiy2b0nMNFixbh9OnTWLFiBXr16pXse8ePH8eHH36IQYMGISkpCQMHDjR5oERERESU+YzuOdywYQPeeeedlxJDAKhWrRq2b9+OAQMGYMSIEQgODjZpkERERERkHkYnh2fPnkWzZs1SPWfBggXInz8/vv322wwHRkRERETmZ3RyGBoainz58qV6joODA/r06YM9e/ZkODAiIiIiMj+jk8PExEQ4OLx+imLVqlVx4cKFDAVFRERERNpIUymb+/fvv/YcT09P1jkkIiIislJp2iGlffv2cHNzg7+/PypVqoRKlSrB398fZcuW1fcq2tnZcX9lIiIiIitldHL4xx9/ICgoCEFBQThx4oS+0LVOp4OjoyPKli2LSpUqIUeOHJkWLBERERFlLqOTw8DAQAQGBuqPHz9+rE8U1aRx+fLlUBQFOp0uU4IlIiIiosyVpmHl5+XKlQtNmjRBkyZN9G1hYWE4ceIETp48aZLgiIiIiMi80p0cpsTd3R0BAQEICAgw5dMSERERkZmkabUyEREREdk2JodEREREpMfkkIiIiIj0mBwSERERkR6TQyIiIiLSY3JIRERERHpMDomIiIhIj8khEREREekxOSQiIiIiPSaHRERERKTH5JCIiIiI9JgcEhEREZEek0MiIiIi0mNySERERER6TA6JiIiISI/JIRERERHpMTkkIiIiIj0mh0RERESkx+SQiIiIiPRsIjmMiIjAiBEj4O3tDRcXF/j7++PHH380+vEPHjxAnz594OXlhWzZsqFWrVrYtWvXS+fFxsZi9uzZKFeuHLJnz458+fIhMDAQhw4dMuXbISIiItKMTSSHHTp0wMqVKzF58mRs3boV1apVQ7du3bBmzZrXPjY2NhaNGzfGrl27MH/+fPz222/Ily8fWrRogX379iU7d8CAAfjggw/Qrl07bNq0CV9//TUePnyIBg0a4NixY5n19oiIiIjMxkHrADJqy5Yt2LFjB9asWYNu3boBAAICAnDjxg28//776NKlC+zt7V/5+O+++w5nzpzBoUOHUKtWLf3jK1asiLFjx+Lo0aMAJIlcs2YNunfvjunTp+sfX6dOHXh7e2P16tWoXr16Jr5TIiIiosxn9T2HGzduhJubGzp37pysvW/fvrh7964+uUvt8b6+vvrEEAAcHBzQo0cPHDt2DHfu3AEA2NnZwc7ODh4eHske7+7uDjs7O7i4uJjoHRERERFpx+qTwzNnzqB06dJwcEjeCVqhQgX991/3ePXclB5/9uxZAICjoyOGDBmClStX4tdff0VYWBiuX7+OAQMGwMPDAwMGDHjla8TGxiIsLCzZFxEREZElsvph5ZCQEBQvXvyl9ly5cum//7rHq+e+7vFz586Fh4cHOnbsiKSkJABA4cKFsXv3bvj4+LzyNWbMmIGpU6e+1H727Fm4ubmlGh+lLiwsDKdPn9Y6DJvAa2kavI6mw2tpGryOpmEL1zEiIsKo8ywqOdy7dy8CAgKMOvfkyZPw9/cHAOh0uleel9r3jDnn+e998skn+PzzzzFlyhTUq1cPYWFhWLBgAZo2bYo///wTlSpVSvE5xo8fj1GjRumPw8LCUKhQIZQtWxbu7u6vjY9e7fTp0yhfvrzWYdgEXkvT4HU0HV5L0+B1NA1buI7GjlxaVHLo6+uLb7/91qhzCxcuDADInTt3ir2Djx8/BoAUewWfZ+zjz58/j0mTJmHWrFkYM2aM/rzAwECUKVMGo0aNwp49e1J8DWdnZzg7OxvxroiIiIi0ZVHJ4RtvvIH+/fun6THly5fH2rVrkZCQkGzeodr1W65cudc+PqVu4hcf/++//0JRFFSrVi3ZeY6OjqhYseJLZW+IiIiIrJHVL0hp3749IiIisGHDhmTtK1euhLe3N2rUqPHax1+4cCHZquaEhASsWrUKNWrUgLe3NwDo/3vkyJFkj4+NjUVQUBAKFixoirdDREREpCmL6jlMj8DAQDRt2hSDBw9GWFgYfHx8sHbtWmzbtg2rVq1KVuPwnXfewcqVK3H16lUUKVIEANCvXz98/fXX6Ny5M2bOnIm8efNi4cKFuHjxInbu3Kl/bN26dVGtWjVMmTIFUVFRqF+/PkJDQ/HVV1/h2rVr+OGHH8z+3omIiIhMzeqTQwD45ZdfMHHiREyaNAmPHz+Gn58f1q5di65duyY7LzExEYmJiVAURd/m7OyMXbt2YezYsRg+fDiioqLg7++PrVu3okGDBvrz7OzssGPHDsyePRvr1q3D559/Djc3N5QpUwZbtmxBYGCg2d4vERERUWbRKc9nSmQWYWFh8PDwQGhoKFcrZ5AtrB6zFLyWpsHraDq8lqbB62gatnAdjc0/rH7OIRERERGZDpNDIiIiItJjckhEREREekwOiYiIiEiPySERERER6dlEKRtrk5iYCAC4ffs2VytnUHBwMDw9PbUOwybwWpoGr6Pp8FqaBq+jadjCdVT3VlbzkFdhcqiBc+fOAQDKli2rcSRERESU1Vy5cuWl7YCfx+RQA+pWe7du3WLPYQadPXuWSbaJ8FqaBq+j6fBamgavo2nYwnUMCwtDoUKF4OPjk+p5TA41oG7p5+7uzuQwg9zc3HgNTYTX0jR4HU2H19I0eB1Nw5au4/NbC6eEC1KIiIiISI/JIRERERHpMTnUgLOzs9YhEBEREaWIyaEGmBwSERGRpWJySERERER6XK1s4RRFgaIoWodhsRRFQVJSEgBAp9NBp9NpHBEREZFlunvXuPOYHGpo61bgrbeAV+UzsbGxSEhIMG9QVsbe3h7R0dH6Y51OBycnJ9jb2zNRJCIiAhAZCcyeDcyaZdz5TA411LUr8M03wOefA1WrJv+emhg6OTnBzs6Oic4rODo6wtXVVd/DmpiYiNjYWDg4OHBuJxERZWmJicD33wMTJwL37hn/OCaHGtu3D6hWTRLFTz8FihWToVI1MXR0dNQ6RIum0+lgZ2eYOuvg4AA7OzvEx8dDURQm1URElCXt3g2MHg3884+hzd5eEsbX4YIUDRUvbvj/H38EfH2BUaOAkBCZY/h80kPGs7Oz41xNIiLKki5eBN58E2jcOHli2K4dcOyYcc/B7ENDx44BCxYAXl5yHB8PzJ0LlCsHrFsHxMWx1ys92FtIRERZzaNHwPDhkkNs2mRor1QJ2LMH2LgReM2WynpMDjXk6AgMHQpcvSrzAVxdpf3pU+C774Du3WXRyrPFuERERETJxMYCc+ZI4rdgAaCuY/X2BlasAP7+G2jYMG3PyTmHFsDdHZg+HXj3XWDyZGDNGmm/fx/48ENg1SpgxAiZm0hENiQpSe7cUVGAnZ2ULlC/MnL84vfy5QPc3LR+t0RkQooCbNgAjBsH/PefoT1bNmkbPRrInj19z83k0IIULCg9hu+9ByxfDhw5Iu0XLkjiOGgQMGDAq0vfEJEVefQIaNECOHEi81/LzQ348kugb9/Mfy0iynSxsUC3bjJUrNLpgD59pLPJ2ztjz89hZQtUrpz8486bB/j5GdoXLwYWLZJPC0RkxR48ABo1Mk9iCAAREUC/fsC335rn9Ygo08TGAh07Jk8MAwKAoCBg2bKMJ4YAew4tWrVqwA8/yLDy/PnStnSpzCcYNsz6exCjoqIwe/ZsrF27FtevX0f27NnRrFkzLFy4EJ6enlqHR5Q57t+XZYTnzsmxtzfQo4d86lO/kpKMP37ducHBwM6d8loDB8r3Bw3S7v0TUbrFxEhiuGWLHGfLJnlC+/amzQmYHFo4OzugVy/AxQX47DNpW7FCEsQRI6w3QVQUBd26dcP+/fsxadIk+Pv749SpUxg5ciRKlCiB6dOnax0ikendvSs9hhcvynHBgrKM0NglhOmhKMDYsVJtH5A5KklJwODBmfeaRGRyMTGSBG7bJsfZskmS2KCB6V+LyaGVeOstwMEB+OQTOV61ShLEMWOsM0Hct28ffv/9d6xbtw6dOnUCAAQEBGDGjBmIjIzUODqiTHD7tiSGly/LceHCkhg+X/A0M+h0smeWnZ1h76whQyRBHDo0c1+biEwiOlrqFP75pxxnzy6JYf36mfN6nHNoRTp0ACZNMiSDP/4IzJxpnaVu1q1bB09PT7Rv317ftn//fgQHB6NRo0YaRkaUCW7elI/3amJYrJhsj5TZiaFKp5ObxfjxhrZhw2SRChFZtKgoKWqtJoZubtJ7mFmJIcCeQ6vz8cfA9etAWJgcnz4t9/wcOcwfS/78UoUjPQ4dOoQaNWoAAO7evYvt27dj7NixaNy4MVq3bm3CKIk0dv26zBa/fl2OS5SQHsNChcwbh04nQw92doYhiPfek0+XI0aYNxYiMoqaGO7aJcdqYlinTua+LpNDK3P/PhASkrwtPt6QLFqDmJgYnDlzBm3btsWnn36KSZMmAQAKFy6MFStWcIcTsh1Xr8pQ8s2bclyypCSGBQpoE49OJ58w7ezkvwAwcqQkiKNGaRMTEaUoMhJo00ZuGYB0Am3bBtSunfmvzeTQyuTPb/j/2FggNNRw7OIiBbW1iCUtgoKCkJCQgOrVq8PPzw81atTAkSNHMG/ePNSvXx+nTp2CGwv2krW7fFl6DO/ckWM/P2D3buCNN7SNS6cDpk0D7O2BKVOkbfRoSRDHjNE0NCISkZFA69bA3r1y7O4ObN8O1KxpntdncmhlXhzG3bULmDDBsF1O8+Zy33ew4H/ZY892/q5evTq8vLxQvHhxNGvWDKVKlUK3bt1w5MgRNGnSBEOHDkVMTAy+++47JCUloX379ihSpAi+5DwpsnQXL0pieO+eHJcpI4lhvnzaxvW8yZMlUZw8WY7ffx9ITJStFYhIMxERQKtWwP79cuzhIfMNq1c3XwxckGLlGjeWEjdqMrh9u+zTHB+vbVypOXbsGIoXLw4vL68Uv+/9rILn+PHj8fPPP+PmzZv44IMPkJiYiLlz55ozVKI0c756VRafqIlh+fLy8d+SEkPVpElScV/1wQfAjBnaxUOUxUVEAC1bJk8Md+wwb2IIsOfQJjRsKCXM3n9fksKdO6UDYMYMwNFR6+heduzYMbzxwtCaoihYunQpypUrhzJlygAAChYsiF69euHNN98EABw4cAD29vZmj5fIaGfOoHi/fsCTJ3Ls7y939ld8ELIIEyfKHMQJE+R4wgS5gXz4obZxEWUx4eFAYCBw8KAc58wpt4+qVc0fC3sOAYSHh2Ps2LFo1qwZ8uTJA51OhynqXJwXBAUFoUmTJnBzc0POnDnRoUMH/Pf8jtcaqVcPmDsXcHKS4z17pO5tXJy2cb3o8ePHuHr1Kg4ePIj+/ftj165d2LRpEzp06IADBw5gyZIlyc739/fHv//+i2+++YbzEMmy/fsv0LAhHNTEsEoVmfdhyYmhavx4KXug+ugjmZ9CRGYRFiZbrauJoaen3D60SAwBJocAgJCQECxZsgSxsbFo167dK8+7cOECGjZsiLi4OPz8889YtmwZLl26hHr16uHhw4fmC/gVatWS/ZhdXOR4/36ZXx4bq2lYyajzDXv16oVt27YhMDAQw4YNg4ODA44ePYpatWolO3fq1Kno3LkzVq5cqVXIRK8XFCSrktVSAtWrSxd+rlzaxpUW48YBs2cbjidPNixYIaJMExoq6wUOHZLjXLkkMaxcWbuYOKwMoEiRInjy5Al0Oh0ePXqEpUuXpnjepEmT4OzsjM2bN8P92bLgKlWqoGTJkvj888/xmbq/nYZq1JB9mN97T7baOXhQKlTMmWNIGrV07NgxODo6YvHixXBJJaCbN2+iU6dOWLVqFXx9feHr64vx48ejSJEiZoyWyAjHjwPNmgFPnwIAIitWRPY//5TJQtZmzBgZYh49Wo6nTpVVzFOnWudWTEQWTk0Mjx6VYzUx9PfXNCz2HAKATqd7bW29hIQEbN68GR07dtQnhoAklgEBAdi4cWNmh2m0qlWBBQtk30UAOHJEatxGR2saFgBJDsuXL59qYhgeHo7WrVtj8uTJaNiwId544w306NGD+y2T5TlyBGjSRJ8Yom5dXF+0yDoTQ9WoUTJHRfXxxzLMrCjaxURkg54+lc+VamKYO7cUNdA6MQSYHBrt6tWriI6ORoUKFV76XoUKFXDlyhXExMSk+NjY2FiEhYUl+8pslSpJgpg9uxwfPw78739SbV1Lx48fR/XXLLvKkSMHTp06hXfeeUfftnDhQnz77beZHR6R8Q4elDu7+vvcoAGwdSuS1F86azZiRPKt9T75RBaqMEEkMoknT4CmTYFnM63g5SWJYcWK2sal4rCykUKezSXKlcIcoly5ckFRFDx58uSlVbgAMGPGDEydOvWl9rNnz6a4yEJRFNjb28PR0TFDu4VkywaMH++CGTO8ERlpj6AgYMCAaIwbdxeurtpsyHzgwAEAwGV1j9kMioyMfOm5FEVBfHw8EhMTudtKGoSFheH06dNah2EVsv39N4oOHQr7Z93xEdWr4/pnn0G5ds12rmPDhsg1YQIKfPqpHM+ciYfBwbg/YoTZhpht5lpqjNfRNEx1HcPC7DBwYDGcOyfDe56eCVi8+D/odLHI7H+miIgIo85jcphGqSUbr/re+PHjMeq5ranCwsJQqFAhlC1bNtkQtSopKQnR0dFwdXWFnV3GOndLlgSKFgWGDJEOjgsXXPHFFyXw1Vfa7MdsapcvX0bJkiWTtZny+mUlp0+fRvny5bUOw/Lt2QMMG2aYp9GsGdx+/RXlXF0B2Nh1LF8eKFhQbiAA8ixfjjy5cwOzZpklQbSpa6khXkfTMMV1fPwY6NMHOHdOjvPmBXbvdkDZsqUyHqARjB255F9OI+XOnRuAoQfxeY8fP4ZOp0POnDlTfKyzszPc3d2TfZlT6dLAN99IzSQAOH3akCwSURrs3ClbF6jzMwIDgd9+A54lhjZp8GBg0SLD8eefy4IVDjETpUlIiGxcERQkx/nyyWfNsmW1jSslTA6NVKJECbi6uqbYpXz69Gn4+PikushCa35+cn9XE8Rz5+Ser86jJ6LX2L4daNPG0GPYujWwcaNllAHIbIMGAc/XIJ07Fxg5kgkikZEePZLE8J9/5Dh/ftk46dmeDxaHyaGRHBwc0KZNG/zyyy8IDw/Xt9+8eRN79uxBhw4dNIzOOCVLyv39WScoLlwA3n3XsJkDEb3Cli3Am29KfSgAaNcO2LABcHbWNCyzGjAA+O47w3CyWjOLCSJRqh4+lDKo//4rx2+8IYmhn5+mYaWKyeEzW7duxfr167Fp0yYAwLlz57B+/XqsX78eUc+GkKZOnYqoqCi0bt0aW7duxcaNG9GqVSt4eXlhtFoXzMKVKAEsXmzYtOHyZUkQHz/WNi4ii/X775IMqtsNdewI/PyzYTuirKRfP2DZMkOC+NVXMv+SCSJRih48kMRQHXT09pbE0NdX07Bei8nhM4MHD0bnzp3Rr18/AMC6devQuXNndO7cGQ8ePAAA+Pn5Ye/evXB0dESnTp3Qp08f+Pj4YP/+/ciTJ4+W4adJsWLAt9/KRFgAuHIFGDhQPt0Q0XM2bpRkMD5ejrt0AdautcxNy82lTx9gxQpDgrhwITB0qBTLJiI9NTE8c0aOCxSQxLCUedaeZAhXKz9z/fp1o86rUqUKdu7cmbnBmEHhwjLE/O67wP37wLVrMq1o0SJD0kiUpa1bB3TrBiQmyvHbb0tS5MDbJnr1kp1UeveWpPCbb+Q6ffONtBNlccHBkhiqq5ILFpTFJz4+2sZlLP4WZ2GFCkkPore3HN+4IdOK7t/XNi4iza1dmzwx7N0bWLmSieHzevQAfvjBkAwuWSKfMNmDSFnc/ftAQIAhMSxUSHoMrSUxBJgcZnne3nJPL1hQjm/fliHmu3e1jYtIM6tWSeKjJobvvCPz7OzttY3LEnXvDqxebUgQly6VT5hMECmLuncPaNgQOH9ejgsXlsSwRAkto0o7JoeEN96QBLFwYTm+c0fu77dvaxsXkdmtWCFDpmpyo5Zw4VDpq3XtKj2tavK8bJkk1GpyTZRF3L0rieHFi3JcpIgkhsWLaxlV+vCORwCkGOeSJbKbCiDd4gMHArduaRoWkfksXSqrcdWVt0OHcg6dsd56C/jxR0OCuGKFXEsmiJRF3LkjieGlS3JctKgkhsWKaRhUBvCuR3p58kiCqH7KCQ6WHkQj1+qkWVRUFKZOnQo/Pz+4uLggd+7c6NatG56w8CKZ26JF8sOuJobvvSdlWrg3t/E6dQJ++skwL/P772VlMxNEsnG3b0tiePmyHBcrJomh2tlijZgcUjK5c0uCqE6cffhQRtb++8+0r6MoCrp164Z58+Zh0KBB2Lp1KyZNmoSffvoJc+bMMe2LEaVmwQLZLkg1erTsAMLEMO3UGpBqgrhqlQzTJyRoGxdRJrl1SxLDK1fkuHhxSQyLFNEyqoxjckgv8fSUjhS1SOejRzLErP7wm8K+ffvw+++/49tvv8XIkSMREBCA9957D3nz5kVkZKTpXogoNXPnAsOHG44/+ACYPZuJYUa0bw+sX2+oBblmDdCzJxNEsjk3b0piePWqHJcoIYmhOn/fmjE5pBR5esp0q9Kl5fjJE+lBVOdTZNS6devg6emJ9u3b69v279+P4OBgNGrUyDQvQpSa2bOBUaMMxx9+CHz6KRNDU2jbVrYXVHeR+fFHWdmsFhMnsnI3bkhiqI6qlSwJ7NsnZWtsAZNDeiUPD0kQy5WT46dPpWi2ukQ/Iw4dOoQaNWoAAO7evYvly5ejY8eOaNy4MVq3bp3xFyBKzaefAmPHGo6nTgU+/piJoSm1aQP88oshQVSLijNBJCt3/TrQoIFsHgHIjid79sgOKLaCySGlKkcOmZJVoYIch4bK9KyzZ9P/nDExMThz5gxq1KiBTz/9FAUKFEC/fv2QLVs2rFixAjr+gabMNG0aMHGi4fiTT4BJk7SLx5a1agX8+ivg7CzHGzbIFoTqPtVEVubaNUkMb9yQY19f20sMAW6fZ32qVjX7FiY5AHynSM+h+qFfVxmI8smPbGf/TvPzBQUFISEhAdWrV4efnx9q1KiBI0eOYN68eahfvz5OnToFNzc3k74HIigKMHmy9BCqPvsseQ8imV5gIPDbbzLUHBsr+1V36SIrm9VeRSIrcPu2IwYNMpR48/MDdu+WWsG2hsmhtbl/XwoqmZkdgFwvtD24Alz6B/D3T9tzHTt2DABQvXp1eHl5oXjx4mjWrBlKlSqFbt264ciRI6hatSpy586Np0+fIkeOHACA9evXY968eThw4EBG3w5lNYoivYUzZhjavvgCGDlSu5iykubNgd9/lwQxJkZ6Ezt3lpXNaq8ikQW7ehXo27c4goPluHRpSQzz59c2rszC5NDaaPyTqCjA01AZFQpxyI9hw4D584EqVYx/jmPHjqF48eLw8vJK8fve3t7ImTMnihcvjn///Rd169ZFYmIiJk2ahG+++cZE74SyDEWR3sHPPze0ffll8lXKlPmaNQM2bQLefBOIjpZksVMnWdnMBJEs2JUrsvgkOFh6usuUkcQwXz5t48pMTA6tzd9pH8Y1JR0A1xjgozHA4cMAooH//Q+YNw+oVs245zh27BjeeKEfXlEULF26FOXKlUOZMmUAAFWqVME///yDunXr4vvvv0ehQoXQoEEDk74fsnGKIiuS580ztC1cmLyuIZlPkybA5s1A69aSIG7eDHToIHMRXVy0jo7oJZcvS2J4964cly0riWHevJqGlem4IIXSzMUFmDMHqFtXjmNiZEOJw4df/9jHjx/j6tWrOHjwIPr3749du3Zh06ZN6NChAw4cOIAlS5boz1WTw9jYWEydOhWffPJJJr0jskmKYvjkolqyhImh1ho1ArZsAbJlk+MtW6Q2YkyMtnERveDiRVl8oiaGJUtGY88e208MASaHlE7OzlImrmFDOY6NlQ6a100HVOcb9urVC9u2bUNgYCCGDRsGBwcHHD16FLVq1dKfqyaHixYtQpUqVVC1atVMejdkc5KSgCFDZKk9ICVqli2TLfJIew0bAlu3Atmzy/G2bTIfMTpa07CIVBcuyI/pvXtyXKECsHTpNeTJo2lYZsPkkNLNyQmYOVM6AgCZhzhmDLB//6sfc+zYMTg6OmLx4sW4ffs24uLicOPGDaxbtw4VK1ZMdm7lypVx7tw5zJw5Ex8/v8KUKDVJSVKxfdEiObazA1auBPr21TYuSq5+/eQJ4p9/SoIYFaVtXJTlnT8viaFaGMTfX4aSPT2zzj7hTA4pQxwdpZ5w06ZyHB8PvP++1H1KybFjx1C+fHm4GDG/KGfOnChQoACaNm2qn4dIlKrEROCdd4ClS+XYzg744QfZvo0sT716wPbtgFq6ascOWbDCBJE0cu6cuvhEjitVAnbuBHLn1jQss2NySBnm6AhMnw60aCHHCQmyRe3OnS+fe/z4cVSvXt2o542IiEBERASmTp1qwmjJZiUkAH36ACtWyLG9PbB2rWzbRparTh1JEJ+VrMKuXbJghXusk5mdOSOJ4YMHcly5ctZMDAEmh2QiDg6y8YS6811CAjBhgtzznxccHGx0OZqPPvoIXbt2RbFixUwcLdmchATpHVy1So4dHKTI8ltvaRsXGad2bRlWdneX4z17gFatoGMPIpnJ6dMyRerhQzmuUkUSw1wvFvjNIpgcksnY28suZG3bynFiIvDhh7IYMS3++ecfeHh44OzZs5g+fbrpAyXbEh8ve/b++KMcOzpK7byOHbWNi9KmZk0ZVvbwkON9+1B06FAgIkLbuMjmnTqVPDGsVk0SQ09PbePSEpNDMil7e0kI1b/LSUmSMP7+u/HP4e/vj9DQUPz555/Irk5WJ0pJXJxsxbZ+vRw7OQG//GL4hELWpXr1ZAmi24kTsv1eeLjGgZGt+ucfSQwfPZLjGjWkEztnTi2j0h6TQzI5Oztg/HjDiJ6iAFOnypaqRCYTGys7bKg/WM7OsoevOreBrJPabaP+dT5wQCY0h4VpGhbZnpMngcaNgZAQOa5ZU6ZCZfXEEGBySJlEp5Mdy55fCzB9OrBunXYxkQ2JiZGdNTZtkmMXF/l/dVUUWbeqVYFdu5CgzkE8dEj+bUNDtY2LbEZQkCSGjx/Lca1akhiqsxqyOiaHlGl0OimM3auXoW3mTMPUMKJ0iY6WYWN1Mmu2bMAffxjqKZFtqFwZ17791rAi4PBhoHlzJoiUYSdOSGL45Ikcqwvm1c8ixOSQMplOJzuYPV9/ePZsw6JSojSJigLatJFJQYAUUN661VCJnWxKTOnSUn1YrSVy9CjQrBnw9KmmcZH1On5ctvhWf4Tq1pVbiFpJiQSTQ8p0Oh0wdGjyncvmzjWUoyMySkQE0KqV1MED5G6+fbvstEG2q2JFSRC9vOT42DHpJVa7fYiMpP7oqIlhvXpMDF+FySGZhU4HvPuufKm++sqwkQVRqsLDgZYtgb175djdXXoP69TRNCwykwoVpPahurHt339L9486YYzoNY4elcRQnZXQoIHMTFE356HkmBxaMEVRtA7B5AYMAIYNMxx/8w2weLGsaDYVW7xuWVpYmCxG+OsvOc6ZU1az1qypaVhkZuXKSYKYN68cBwVJgqguNSV6hcOHJTFUF7w3bCjTlJkYvhqTQwuk0+kAAElJSRpHkjn69gVGjDAcL1kiSaKpcrqkpCTodDr9dSQr9vSp3NUPHZJjT08ZVq5WTdOwSCNly0qCmC+fHKu1SNQidUQvOHhQpqmqpTIbNZLEkCV0U+egdQD0Mp1OBwcHB8TFxQEA7OzsbC7Reftt2chi/nw5/uEHKZg9eLAMQRtLURQkJSVBURQoioLExEQkJCTAwcHB5q5ZlvP4sdzVT5yQ49y5JTGsWFHbuEhbZcrI9IKAAOD+feDffyVB3LXLMC+RCFIiMzDQsMlOkyZSCjVbNm3jsgZMDi2Us7MzAOgTRFvUpo389+uv5b8//SQ7oQ0caPxzxMfHIzo6Wn+s0+ng7OwMBwf+aFu1kBC5k//zjxznySN//MuX1zQsshB+foYE8d49w/5nu3YZ5iVSlvbXX5IYRkbKcdOmkhi6umobl7XgX1AL5uzsDCcnJ5ueQ/fWW9LdP3y4HB8/Dty8CXz+uXE9iImJiXB99tvOoWQb8fChJIanTslxvnyyWrVMGW3jIsvi6wvs2ycJ4p07wOnT8v+7dxvmJVKWtG+fFDZQE8PmzWUjJSaGxmNyaOGyQsIzYIDsydy/v8w7nDdPNsD4+mvZii81Op0Odq87iaxHcLAMEZ49K8dvvCF/7P38tI2LLFPJkoYexNu35edGTRDVeYmUpezdK4lhVJQct2ghiaGLi6ZhWR3+VSWL0K+f1D1U87xFi4BBg2QeImUR9+7JMkI1MSxQQLoAmBhSanx8JCMoVEiOz52Tn6N797SMijSwe7dUvFITw5YtmRimF5NDshi9esnCFDVBXLpUksbERG3jIjO4c0f+oF+4IMeFCkliWLKkpmGRlShRQhLEwoXl+MIFw3xEyhJ27QJat5bdNQHpPfzlFyaG6cXkkCxK9+6y97K9vRyvXAn07g0kJGgbF2WiW7ekIu2lS3JctKgkhiVKaBoWWZnixeXnpkgROb54UT5w3LmjaViU+XbsSJ4YtmkDbNgAPFvXSenA5JAsTufOwM8/A+qC49WrgZ49mSDapBs3JDG8elWOixeXHqBixTQNi6yU+sGiaFE5vnRJEsTbtzUMijLT9u2SDMbEyHHbtsD69UwMM4rJIVmkDh3kk5+joxz/+CPQtauUuiEbce2aJIbXrsmxj0/ynh+i9ChSRH6O1A8YV65IgnjrlqZhkelt2ybJYGysHLdvLx0LTk7axmULLGq1clBQULoeV6ZMGbhwYoHNefNNmUzcoQMQFyfJ4ltvST1E/vJbuStXpC6d+ge7VCnZ+cLbW9u4yDYULmwoc3P1qnw1bCg/Y+q8RLJqW7ZIMqiWAu7YEVi71tChQBljUclh1apV01W25fjx46hcuXImRERaa9UK+P13oF07GTb49Ve5CXDYwIpduiSJoToXrHRpWWaYP7+2cZFtKVTIUObmyhXgv/8MCSJ7p63aH38YOg0AoFMnYM0aJoamZFHJIQBMnDgRJYyciJ6YmIgBAwZkckSktebNgc2bZV5JdLT8f7t2shKNrMyFC5IYqqtIy5WTZYYsWkyZoWBBSRAbNZIPJdeuGRJEdV4iWZVNm6SDQJ1i9NZbwKpVTAxNzeKSw9atW6N69epGnZuYmIj+/ftnckRkCRo3lmGE1q2l6r0612T6dNsuEG5Tzp6Vf8jgYDmuUAHYuZPbnVHmKlBAksFGjWQF8/XrMteVC5+szm+/yYJFNTHs2lXKn3G3VNOzqAUpGzduhK+vr9Hn29vbY+PGjfDx8cnEqMhSNGwoSaGbmxzv2AEMG1ZUv0USWbBTp2R4T00MK1WSoWQmhmQO3t6SIKoF1W/eTL5Knizexo0yfKwmht26MTHMTBaVHLZt2xYeHh5pfoy7u3smRfSykydPol27dvD29ka2bNng5+eHadOmIUotyU6Zqm5d4M8/gRw55Pj4cTe0bCn7M5OF+ucf6bV5+FCOq1aVoeTcuTUNi7KYN96Q3kJ1j+5bt+QT55UrWkZFRvjlFxk+VsuZvf028P33TAwzk0Ulh5bu3LlzqF27Nq5fv4558+Zh8+bN6Nq1K6ZNm4Zu3bppHV6WUauWjEaqnyP275f9M8PCtI2LUnDihCSGISFyXKOGdPl6emobF2VN+fJJj3XZsnJ8+7YkiJcvaxoWvdr69ckTw549ZXMEJoaZy6Iv76+//orVq1fjxo0biFErXD6j0+nw77//mjWeNWvWICYmBhs2bNAvmmnUqBHu3buHJUuW4MmTJ/DkHz2zqF5dOp8aNUpAWJgDDh0CmjWTYeecObWOjgAAx47JP0poqBzXrg1s3QqYsaef6CVqgtikCXD6tGHrxt27gTRMa6LMt26dDB+rW6j27g18951hBy3KPBbbczh79mx06NAB+/fvh6OjI3Lnzp3sK1euXGaPyfHZcqgXh75z5swJOzs7OLH4nllVqQIsXXpNPzp59CjQtCnw+LG2cRGAw4flH0NNDOvVk8ydiSFZgrx55dNlhQpyfPeuzIlV9/Ymzf30U/LEsG9fJobmZLHJ4cKFC9GvXz/cvXsXBw8exJ49e176MrfevXsjZ86cGDx4MP777z+Eh4dj8+bNWLx4MYYOHYrs2bObPaaszs8vBnv2GNY1/P23LIhVRzFJAwcOSI+hOs4fECA9hupEUSJLkCePJIj+/nJ87570IJ4/r2VUBClm3b27ITF85x1g6VImhuZksclhSEgIunfvDnsL+mkoWrQoDh8+jDNnzqBEiRJwd3dHmzZt0Lt3b8yfP/+Vj4uNjUVYWFiyLzKd8uVlnnm+fHL84voHMqN9+2QCaESEHDdpIoUp+cGJLJGXl0xgrlRJjoODJUE8d07TsLKy1auBHj2ApCQ57t8fWLIEsLPYbMU26RRFUbQOIiWBgYFo3bo1hg4dqnUoetevX0fTpk2RL18+jBw5Enny5MHRo0cxffp0dOrUCd99912Kj5syZQqmTp36UvuhQ4fgptZloXQJCwvTr1a/ds0Z/fsXw8OHMvxfokTMs2HnBC1DtBrPX8v0yH70KIoOHw67Z/ODw2vXxo1586Bksa0tM3odycBc19I+NBTFBg6E67NewwRPT/y3dCliS5bM9Nc2B2v5mdy0KSc++qggkpKkfm2nTiH48MO7FpMYWst1TE1ERARq166N0NDQ1N+LYqEuXLiglC5dWvntt9+U2NhYrcNRFEVRunTpouTNm1eJiIhI1r5s2TIFgLJ3794UHxcTE6OEhobqv27duqUAUEJDQ80Rtk07depUsuNLlxSlQAFFAeTLz09R7t7VKDgr8+K1TJPt2xXFxcVw4Vu1UpToaNMFZ0UydB0pGbNey8ePFaVqVcPPsJeXotjIv6U1/EyuWKEoOp3h8g8erCiJiVpHlZw1XMfXCQ0NNSr/sJB8/GU+Pj5o0qQJ2rdvj2zZssHd3T3ZV1rrIZrCP//8gzJlyrw0t7BatWoAgDNnzqT4OGdn55fip8xRsqSMbBYuLMcXLsgokbqNL2WCrVuBN9+Uza8B+f8NG4As1mNIVs7TU8osPbuf49EjmS9r5qoYWdGKFbLgRB3HHDoU+PprDiVryWJL2YwdOxYLFiyAv78/SpcubRErgb29vXHmzBlEREQkGw4+fPgwAKBgwYJahUbPKVFCEsSAANkp69Il2Qxh925D0kgmsnmzbHQaFyfHHTrIbHIL+H0lSrOcOSVBbN5cyh+EhMgE5ucXrpBJLVsm8wrVxHD4cGD+fEDHnVE1ZbHJ4YoVKzBu3DjMmDFD61D0RowYgXbt2qFp06YYOXIkvLy8cOTIEcyYMQNlypRBYGCg1iHSM0WLSoLYqJHskHX1qiSIe/bI98gEfv1VqtOq+1l17iyzyZ+VfCKySh4esg1TixZSkunxY7mR7NwJVK6sdXQ2ZelSYMAAw/F77wFz5zIxtAQW22mbmJiIpk2bah1GMm+++SZ27doFd3d3vPfee2jdujVWrlyJQYMGYf/+/RbRu0kGhQtLgqjOKb9+ndupmsyGDZIMPr/R6Zo1TAzJNri7S13O2rXl+MkTWXl/4oS2cdmQJUuSJ4YjRjAxtCQWmxw2a9YMR44c0TqMlwQEBGD79u24d+8eoqKicPHiRXz++efIzX1iLVKBApIg+vnJ8c2bcr/XoEymbVAU4IsvgC5dku9n9cMP3M+KbIuaINatK8dqgnj8uLZx2YDFi4FBgwzHo0bJbYWJoeWw2OTwo48+wqpVqzB//nxcuXIFjx8/fumLyBhvvCF1EMuUkeMHD+QeP3u2YZ4LGSE8XIaRR482VKft0wdYvpzVack25cghC67q1ZPjp09l+OGHHzQNy1olJADjxgHvvmtoGzMG+PxzJoaWxmKTw4oVK+LChQsYNWoUfH19kSdPnpe+iIyVLx/w118yzxyQAqtjxwKdOhk28qBUnDsnqzjXrze0TZzI/azI9rm5AVu2SFIIANHRQK9ewJAhQGystrFZkeBg2VFz1ixD29ixcszE0PJY7DjQpEmToONPDJlQrlzAH38A06bJFwD88gtw5oz8t2xZbeOzWD/9JPtXRUbKsYeH9Jy0aaNtXETm4uYmQ8z/+x/w7bfS9s03QFAQsG4dUKiQtvFZuIMHZYryvXty7OAgvYX/+x8TQ0tlscnhlClTtA6BbJC9PTB1qnSC9ewpo0SXLgE1akgnWJcuWkdoQeLj5aP9vHmGtooVZTFKiRKahUWkCRcXWUVRs6ah1/DoUVnB/OOPsqk7JaMowJdfytCxOkXZ2xv4+WegTh1tY6PUWeywMlFmat0a+PtvyXUA6RTr2hUYOdKwADdLu3tXCkU+nxj27g0cOsTEkLK2fv3k90CtifXoEdCsGTBjhmFDYEJEBNC9u6xCVhPDhg2ls5WJoeWzqOSwQoUKr9xlJCVJSUmoUKECzj/bD5MoLUqUkHt8796GtnnzpKSZOvyRJe3bJ70hBw/KsZMTsGiRLDzJlk3b2IgsQeXKUtamZUs5TkoCJkwA2reX4Ygs7sIFGY358UdD29ixUl88Xz7t4iLjWVRyeObMGURHRxt9vqIoaX4M0fOyZZOcZ9EiQ4m+Awfk3n/ggLaxmZ2iAHPmyPBYcLC0FSokK3kGDeLkIKLn5coFbNok81TU343ff5c5K6dOaRubhtavl0tw7pwc58ghc7o/+4zVrqyJxf1TtWvXDs7Ozkafz0UrlFE6neQ+/v6yevn2beD+fRlVnT1bqvbb/I9ZeDgKjxkjH+1VTZrIVnheXtrFRWTJ7OyASZOA6tWBt9+W3VSuXJF5iYsXy8TmLCI+Hhg/Xj5fqsqVkynKpUppFxelj0Ulh72fH99LAy/+8SITqFFD5sN07Sr7MCckyBzEI0dkm6fnttO2LefOAR06wOPiRUPbxInSI8IyNUSv16KFDDN36iT/VcvdHD4s236kocPDGt27J4v5/vrL0Na9u6zfyZ5du7go/SwqOVy+fLnWIVAWlycPsH078NFHwMyZ0vbTT8Dp0zI04uurbXwmxzI1RKZRtKjMRRk+XD5NAlmi3M1ff0lt/Pv35djRUfLhIUOywIiLDbOoOYdElsDBQRYebtwoO2gBhhrQv/yibWwmEx8vywi7dtUnhtG+vtLrwcSQKH1cXKQO4nffGXoL1XI3u3ZpG5uJqTtpBgQYEsMCBYD9+4GhQ5kYWjsmh0Sv0K6dbKOqFscODwc6dpTtn9TSDFZJLVMzf76hrXdvXP3+e5apITIFGy93Ex4uw8jP76TZqJF0ktasqW1sZBpMDolSUaqUfPDv1s3QNmuW3OcfPNAurnRLpUyN4uqqbWxEtsRGy92cOyfrb9atM7SNHw/8+SeQN692cZFpMTkkeo3s2YHVq6XSv1qKYc8eufcfPqxtbEZTFNmv6sUyNQcOsEwNUWaxsXI3P/0kieGFC3Ls7g78+ivw6adcu2ZrmBwSGUGnk3nme/cCb7whbXfuAA0aAF9/LbmXxQoLk41N33/fMAbUtKmMAVWrpm1sRLZOLXezZYski4Ch3M0PP2gbm5FSmKKMChWkY7RtW01Do0zC5JAoDerUkZyqfn05jo8Hhg2TqhVRUdrGlqKzZ+Wj/oYNhrYPPwS2bmX9QiJzUsvdVKkix2q5G3WfZguV0hTlnj1l1MTHR7u4KHNZTXJYv359HMhyW1aQJcqfH9i5UyZjq1atAmrVkg4Bi/HTT1K8Ua1f6OEhQ1off8wxICItqOVu+vc3tH3zjQxB3LqlWViv8uIUZUdHCXflSu6kaeusJjn83//+h0GDBiEwMBAnT57UOhzK4hwdZQrfzz8biryeOgVUrSpTjDQVF/fyGFDFiixTQ2QJrKDcTWpTlN99l1OUswKrSQ47deqE06dP46233kKHDh3QuXNnXFBnxRJppHNnKXfj5yfHoaHAm2/KyK06vc+sXlGmBocPs0wNkSWx0HI3YWGy0UtKU5SrV9csLDIzq0kOAcDOzg59+/bFxYsXUa9ePTRu3Bh9+/bFzZs3tQ6NsrDSpYFjx+SGqvrkEyAwUO73ZqOOAR06JMfPlakBy9QQWR613E1goBxrXO7mzBkZ/Xi+2D+nKGdNVpMcRkZG4tChQ1i4cCGGDRuGH374AU+fPsXBgwdRq1YtjB49GtHR0VqHSVlUjhwyxDxnjmE6344dMvf8+PFMfnGWqSGyXrlyAZs3A1OmaFruZs0amaJ8+bIc58wpU2Q4RTlrsprk0MPDA3379sVff/2FUqVKYdasWbh79y4uXbqEixcvQlEUdOjQQeswKQvT6YBRo2TakFoM9uZNoG5dmWKUKeVuWKaGyPrZ2QGTJ0u5G09PaVPL3axalakvHRcnZbrefttQccHfXzo0W7fO1JcmC+agdQDGCgkJgYeHR4rfc3NzwxdffIGcOXOaNyiiFDRoILnZW2/JCG9cHDBwIHDkCLBggQlHeM+elf381NXIgIwBTZnCj/pE1qhFC7l5dOok2Vl0tNSNOXQImDvXsIDFRG7flvvU88X8+/aV2q2ciZK1WVXP4evs27fPDJEQvV6BArKLyvDhhrZly6RO4rVrJniBH3+U2eFqYsgxICLbYKZyN7t3J9/lyckJWLJEFlEzMSSrSQ6NUbFiRa1DINJzcpIt91avNtQEO3lS5iFu3ZrOJ42LA957TzZ7VseA1DI1HAMisg2ZWO5GUYDPPpPZJw8fSluRIlLLcMAATlEmYVPJIZEl6t5dhpTV3QSePAFatQKmTUtjxQq1TM2XXxra1DI1xYubNGYisgAmLncTGioLoT/4wPDw5s3ls2XVqqYLm6yfVSeH/fr1w7Jly5CoSUE5IuOVLw/8/bdhH1JFkfnnbdoAjx8b8QR79wKVKiUvU7N4McvUENk6E5W7UYv0//abHOt0cg/64w8gd27Th03WzaqTQ0VRsHbtWg4nk1Xw8JD6YTNmyOJEQBYnVq0qw80pUhRg9mygSRPgwQNpK1xY5iQNHMgxIKKsIIPlbjZvzomaNQ3be3p6SlLItWv0KladHC5fvhw7duzAv//+q3UoREaxs5MhnT//NBSVvXYNqF0bWLHihZPVrQrGjjWUqWnWTHoRWKaGKGtJR7mb2FhgyBBgwoRCUMsAv9gRSZQSq0kOR4wYAeUVheLs+dGHrEzjxsm3o4qJkRIS774rN3ScPSsJ4ItbFWzZwq0KiLIytdxN5cpyrJa7GTLk2c1D3LwJ1K8vC51V/fvLwpNixcwcM1kdq0kOr1+/jrZt2yJKXaEJ4NatWxg0aJCGURGlX6FCwP79khCqFi8GPi6zFknVqgOXLkljzpwypMQyNUQEyAKVgweBd94xtD1X7mbnTskdjx2Tbzk5JeG772QBtIuLJhGTlbGa5HDjxo3w8/ND3bp1cfToUQwePBjlypWDo6Oj1qERpZuzs9zTly8HcjjHYT7+h+n/dYdd9AtbFbRqpWmcRGRhXFyApUvl67lyN5F+lTGz6S6EhEhT0aLA999fRb9+mkVKVshqkkOdTofhw4dDURTUrl0bT548wcWLF7FgwQKtQyPKsD5N7+CuXwD+h6/0bSvQB7PaHUJSUZapIaJXeOcd4NAhJBYuCgDIHvUI29EMH2AGWgUm4cQJoEyZGG1jJKtjNclh//79Ua5cOdStWxeLFi3CwYMHcVndIZzImu3dC1SuDLd/pUxNvJ0TBmIx+mIZxk1xTWvFCiLKYv6xq4xquhPYAlllYo8kzMAEbHJoj1x2T7UNjqyS1SSHLi4uOHfuHL766isMGDAAGzZsQI8ePbDipSWeRFbiFWVq7A8dQKFpA6F7VrJCrVhx+rSGsRKRRVq5EqhVCzh5IxdaYzNmuk6B8uzeodskNw8Xdf4ykZGsJjlcsGABChQooD+uXr06/vrrL8ydO1fDqIjSKZUyNXY1quGjj16uWFGjhmzFR0QUEwMMGgT06SP/DwBVqtqh2/nJ0L1w8yjRo8cry90QpcSiksPQ0FBMmjQJo0ePxpYtW/TtUVFR2L17Ny5cuJDs/MKFC+PgwYPmDpMoY1IqU6Nmg8+VqWnRQtaiPF+xokcPYPhw2WKZiLKmGzeAevWAJUsMbYMGSW38IkXwUrkbu5gYKXczdGiycjdEr2JRyeHgwYPxySefYM2aNWjdujWaN2+Ou3fvonz58mjatCnKli0Lf39/HFPX5wNwc3PTMGKiNFq7VoobvlimZtq0FMvUFCsmFSueX2m4YAHQsCFw545ZIiYiC7J9u+R8f/8txy4uUkB/0SLDomUAKZe7WbhQX+6GKDUWlRz++eefmDdvHu7du4e9e/fi5MmTqFOnDp4+fYo1a9Zg9erV8PLyQsOGDXHmzBmtwyUyXlwc8L//Ad27A1FpK1Pj4gJ9jTInJ2k7fFj+QOzdm6lRE5GFSEqSz5CBgYb92IsXl3tB796veNCzcje3p0xJVu4GlSsDu3aZI2yyUhaVHEZGRqJChQoAgPr16+Ozzz7DjRs3MHLkSHTp0gVdu3bFzp070bx5c3z88ccaR0tkpDt3gIAA4CtDmRr07QscOiR3dyP17y/DRoULy/GDB7KW5fPPZW0LEdmmx4+B1q1l9zz1d71NG/ls6e//+sc/6dBBehGLFpWGR49kjvOMGZJ1Er3AopLDokWLJtsnuUWLFgCAmjVrJjvvnXfeSTa0TGSx9uyRT+mHpEwNnJxkotB33wGurml+umrV5A9Cs2ZynJgIvP8+0LkzEB5uwriJyCIEBQFVqgBbt8qxnR3wySfAr7/KrBSjVakiN49nf1eRlARMmADWyqKUWFRy2LVrV0ydOhUbN26EoijIly8fpk2bBh8fn2TnZcuWDffu3dMoSiIjKAowa9ZLZWpw8CAwYADwrNREenh5ydqVDz80tG3YIFMZz5/PYNxEZDG++w6oXRu4fl2OvbxkzuGECZIkplmuXMAffwBTphjuQWqtrFOnTBQ12QKLSg7Hjh2LunXromPHjsiTJw/atGmDpKQkXLhwAY/VSRYA1q5di7x582oYKVEq1DI148YZhmyelalB1aomeQl7e9lq+fffAQ8PabtwQe7xP/9skpcgIo3ExMg0kv79DYuLq1eXXsQmTTL45HZ2Mj79Yq2smjVZ7ob0LCo5dHV1xW+//Ybdu3ejS5cuuHfvHqZPn46WLVsiT548KFmyJOrXr49ly5ahcePGiGM9D7I0Z85IAviaMjWm0qaNrFp8NlUXkZFAly7A6NFAfLzJX46IMtm1a0CdOtJrqBoyBNi/HyhUyIQv9EK5G0RHs9wN6VlUcqhq2LAhvv76awQFBSE8PBwHDx7EF198gerVq+PevXtQFAUrV66Eu7s7qlWrhmHDhmkdMhGwZo1Uqla3dXxNmRpT8fGRFYs9ehjavvgCaNwYuH8/016WiExs61aZGhgUJMeursAPPwBff/1CmRpTYbkbegWLTA6f5+zsjFq1auG9997D6tWrcfnyZYSEhGDr1q2YOHEi8uXLhw0bNpg1pgMHDqBly5bw9PSEq6srSpYsydXTWZlapubtt9NcpsZUsmUDvv9e/og4OkrbX39Jp8CBA2YJgYjSKTFRRnpbtQKePJE2Hx/gyJHkH/oyxbNyN1i6lOVuSM/ik8OUeHp6onnz5vjoo4+wefNmsy5OWbNmDRo0aAAPDw98//332LJlC8aNGweFtUSypjt3pCJ1BsvUmIJOZxh+UneavHdPqujMn89yN0SWKCREksJp0wy/o+3aJZ8uYhbvvMNyN6TnoHUA1uTOnTsYOHAgBg0ahIULF+rbAwICNIyKNLNnD9C1q2E1spOTbF/Sv3+GViNnVM2aMizVtauEmJAAjBghvRDffgtwUyEiy/D337J27cYNObazAz79VLZc1+QWopa7efttYNs2Q7mbI0eAlSvTWDuHrJlV9hxqZenSpYiMjMS4ceO0DoW0lFKZmiJFTFKmxlTy5gX+/FMWTKt+/FESR3XnPiLShqJIudM6dQyJYZ48wI4d8jur6S1ELXczeTLL3WRhTA7TYP/+/ciVKxcuXLgAf39/ODg4IG/evHj33XcRFhb2ysfFxsYiLCws2RdZqdBQoGPH5GVqmjc3aZkaU3FwAGbOlBqIOXJI29mzEubGjdrGRpRVRUfLXumDBsl0ZQCoVQs4eRJo1Ejb2PTs7KQW4h9/sNxNFqVTOFnOaH5+frhx4wYcHR0xfvx41KpVC8ePH8fkyZNRuXJl/PXXX9Cl8JFvypQpmDp16kvthw4dghvH+DIkLCwM7u7uZnkt58uXUWTUKDirH/UBBA8ahAfvvpupq5FN4do1J4waVQRXr7ro2/r1e4Bhw4Lh8GxyiTmvpS3jdTQdW7uWt287YtSoIrhwwbA7UvfujzB69H04Omben+KMXEfH27dRZPRouD5XYT+kSxfcGzsWirr6LYuwhZ/HiIgI1K5dG6Ghoam/F4WMVrJkSQWAMmPGjGTt8+bNUwAoO3bsSPFxMTExSmhoqP7r1q1bCgAlNDTUHGHbtFOnTpnnhVavVpRs2RRFRoQUJWdORfnjD/O8tomEhytKly6GtwAoSkCAogQHy/fNdi1tHK+j6djStdy0SW4b6u9etmyKsmaNeV47w9cxOlpR3nkn+c2jRg1FuXnTNAFaCVv4eQwNDTUq/+Cwchrkzp0bANC8efNk7YGBgQCAILU41QucnZ3h7u6e7IusRFwcMHx4ymVqWrbUNLS0cnMD1q4F5s2DvrdQ3fr5yBFNQyOyWYmJstVlmzaGLYxLlZJqMd26aRqa8VjuJsthcpgGFV5RV0B5NjJvl67NLsli3b4tZWoWLDC0aVSmxlR0OuC994Ddu4H8+aXtzh2gfn3gp59ysdwNkQk9egQEBgKffGJo69ABOH4cKFdOu7jSjeVusgxmM2nQsWNHAMDWrVuTtW/ZsgUAULNmTbPHRJlE7VI7fFiOnZ2lDsyyZbJtgZWrV0/K3dSrJ8fx8cAnnxRAnz6GDlIiSr9jx+QWsmOHHNvbA7NnA+vXA1Y9eKSWu2nRQo7Vcjft2xu6RsnqMTlMg2bNmqFNmzaYNm0apk+fjp07d2LmzJmYMGECWrdujbp162odImXU82VqHj6UtiJFZJuR/v21jc3E3nhDRoRGjjS0ff+9rJy8elW7uIismaIA33wD1K1r2IEuXz75XRszxiIqXWUcy93YPCaHafTTTz9hxIgRWLJkCQIDA/HNN99g5MiRWL9+vdahUUaFhsqYjxWUqTEVR0fZh/mnnwBX10QAcm+vUkW2hSYi40VFAb17y05F8fHSVqeO9NI3aKBtbCbHcjc2jclhGrm6umLmzJm4efMm4uPjcePGDXz66adwzpRd0clsTp+WT72//mpomzRJbnzPFiLZsrfeAtasuQpfXzkODZUJ9B99JBPqiSh1al70ww+GthEjZIaKt7dmYWW+wED5AF25shxHRwM9ewJDhxoKOZLVYXJItHq13NUvX5ZjT09JCqdOtfj6haZUokQsjh2TzlPV9OmyKDskRLu4iCzdb79Jb/vp03KcPbvsSDR3rvTO27xixWShyjvvGNoWLpSVburYOlkVJoeUdallanr0MKzCqFTJKsvUmIq7u0yYnzVLRo0A2YavShXZB5aIDBISgPHjgXbtAHXjKz8/WYzSpYumoZmfWu7m229Z7sYGMDmkrCmlMjX9+smn32LFNAvLEuh0wPvvAzt3yn6vgOz/WqeO3PuJSLZVb95ctqhUde4siWGZMtrFpbn+/VnuxgYwOaSsZ/fulMvUfPedTZSpMZWAAJlIr1ZoiosDBgyQe39MjLaxEWnpyBG5hezeLcf29oaFXeo+5lkay91YPSaHlHUoCvDZZ0DTpjZfpsZUChYE9u0Dhg0ztH33nfQiXr+uWVhEmlAUGWyoX1+KxwNSTH7PHikJZRNlakyF5W6sGpNDyhr+/ltqSXzwgWFoo0ULmy5TYypOTsBXX8kqTLVjNShIpmd+9ZWhZAeRLYuIkOnJw4cbfubr1wdOnjQUk6cXpFbu5ocfwC2ZLBeTQ7Jtt25JWYVq1YC//pI2nU4+zW7enCXK1JhKjx4ynFaihBw/fQr873+yDdhvv/E+T7YpMVHm2pYqBaxZY2gfPVrm5arbUFIqUip306sX0LixfNIki8PkkGxTeLjsdl+qVPKCrCVLAtu2yafZLFSmxlQqVJBO2B49DG2XLslqTXWOIpEtUBRgyxagYkWZa3vvnrS7uQHr1gGff55FytSYSkrlbvbskfmJPXrIqjeyGEwOybYkJABLlkgS+MknhpUTuXIB8+cDZ87IyjlKt5w5ZUTo6FHZIky1b5+M0PfuLYvBiazVyZMyNblVK+DsWUN727byvU6dtIvNqqnlbtavB4oXN7SvXg34+gJjx3LBioVgcki2Y/t2mQg3aBAQHCxtjo7AqFEyz+V//5MJdGQS1asD+/cDGzYYhpoVRfZnLlVKdleJiNA2RqK0uHVLPtxUqZK8NJ/6s/7rr4CPj2bh2Y6OHYHz54F58+SDOwDExgKzZ8vNZP587q6iMSaHZP3OnpU5LS1aSM+gSr0BzZljmAxNJqXTyY4q587JbhDqZY6Olt1VfHyko4Bb8JElCw2VYtalSsmHG3X+bLFistPJkSNcdGJyTk7Ae+8BV69KYVW1cPbjx7LvYJkyMn7PycyaYHJI1is4GN7TpslEuG3bDO3q4pP16w1dWpSpnJzkfn7livxXnYsVHCzztSpVkp1WiCxJfLyUpvHxkWLW6iwUT0/5THn+vOx0whI1mShnTtmS6eJF4O23De1Xr8qm77Vry1xFMismh2R9oqOBTz8FfHyQe/16Q2mawoVl7sqRI8knw5HZ5MolPYjnziXfo/n0adlNIjAw+RwuIi0oCvDLL0DZslKa5tEjaXdyklXIV6/KbBS1M4vMoEgRWTx4/LjsXqVS7+cdOsjqNzILJodkPZKSDBOXJ040TGjLkUOSxQsXgO7dDZsCk2Z8fGQu4v79yctIbtsmHb3vvmuYFkpkTuoQcceOwOXLhvauXeUW8vnnnIWiqapVZeuZzZuB0qUN7Rs3GrJ5dRMDyjT8K0rW4a+/pHBqjx4yaxwA7OwQ0rmz3OHHj+fWdxaoXj1Z1bxqFVCokLQlJQGLF0sC+emn0hFMlNnUUcpatZKPUtavL/shr12b5bdVtxw6nSwVP3VKbhb58kl7QoJhHsCMGbx5ZCImh2TZrlyRj/j168twgyowEDh1Cnc/+shw4yCLZGcnU4kuXpRk0M1N2iMipAPY11eSR3V2AJEphYTIPNjSpWV9g8rXV4q3790r05TJAjk4AAMHyt+ByZOBbNmkPSxM9mouVQpYuZI3j0zA5JAs05MnMumnTBmZHKQqV05K1mzZIkMMZDVcXaWD98oVqTakjv6rm9jUqGHYxIYoo2JikldGUbe8y5sXWLhQ5sG++SYXm1gFNzfZuODyZaB/f8PN4/ZtoE8fqT20c6eWEdocJodkWeLi5E5eooSsbFDv6PnySXHrf/5hEWsrly8fsGiRjBgFBhra//5bOog7dEg+F4woLdSpyX5+UlM5NFTaXV1l06QrV4DBg7m7iVXy9ga+/VZuHi1bGtr/+UeqlgcGStZPGcbkkCyDohgmHI8YIT2HgOGOfvmy1EThlnc2o2xZ6QDevh0oX97QvnGjdBiPGCElz4iMtWePFKx+fjc2nQ7o21duIR9/LOvXyMqVLQv88YdUKq9UydC+bRvg7y+9i3fvahaeLWBySNr7+28pXdChg3ysV/XsKRPVeEe3ac2ayZZk334L5M8vbQkJ0oHs4yMdyNwsgVJz7hzQpg3QqBFw4oShvXlz6VRatgwoUECz8CizNGokfz++/z75irfvvpMtVCdNAsLDtY3RSjE5JO2ok82qVZOaJ6oGDV7+hSebZm8vH/YvX5Zt99SF589PPd2wgZslUHL370tZpPLlpfKJqkIF6ZFWSyeRDbOzM3QkzJwJuLtLe1SUdCz4+Mg8loQEbeO0MkwOyfzCw2WouFQpWaaqKllSxhT37JEJxpTluLkB06ZJrdtevQztV68CnToZyo5Q1hYZKT8nPj5S6URdrFqgALB8ORAUxKnJWY6rKzBunNws/vc/WekMAA8eyCTT8uWB33/nJ0wjMTkk80lMlLHDkiWBTz5JvlfVvHmyL3K7dlw+SChYUCpUnDiRfLOEAwdkVfPbbxvmlFHWkZhoGDGcPFmSREA+VEyfLh8q+vTh1OQszctL5qScPy+fKFUXLgBt2wIBATIyRalickjm8eefMlF44EDD1hiOjsDIkTLP8L33ZO8qoudUriybJfz2m3Q0q9askTp148dLyTOybYoCbN1qWGtw756029sDQ4ZIZ9HEiYYyeETw8ZHClgcPSuVz1b59MpWpe3fg+nXNwrN0TA4pc509K+UFmjeXnkFVx44yi/yLL2RDXqJX0OmkHt2ZM8BXXwG5c0t7bKxMMeKUItumVq9q2TL5LaRtWzn++mupXUiUotq1JUFcv15uFqq1a+UT5pgxhuoYpMfkkDJHcLDMFK9QQWaFq9TFJy/+ohK9hqMjMGyYdDSPGWPoaH74UKYUVawopXE4pcg23LoFTJxYEJUrJ69vXK2adP78+qvUMiR6LZ1OOiTOngW+/NLwCTMuDpgzx1BXNzZW2zgtCJNDMq3oaNnzsmTJ5DPFCxWSxSfqrvdE6ZQzp+x8cf687JWrOndOtmNt1kxq5JJ1Cg017Iy2aZOnPtkvWlQ6e44ckYVJRGnm5AQMHy7zEMaNA5ydpV0ti1C6NPDTT/yECSaHZCpJSTIRzM9P7uxqbSk3N9lQ9+JFWUVgxx85Mo3ixeU+fvAgULOmoX3nzpfnppHli48HFiyQAYUZM5KvV5szR9YTdO3KWwiZgIeHzEm5dEnK4KiLIK9dkx+ymjWz/F6e/DWjjDtwQH6Z3n4buHlT2uzsZAPdK1dk1YBauI7IxGrXBg4dAn78UXqXAPngr65qnTbNsKqVLM/zmyMNHw48eiTtTk5Ar14PceWKdOqonTxEJlO4sNTTPXECaNzY0H7smHRPt2snHRtZEJNDSj+1+Fy9esDx44b2Fi2Af/+VVQL58mkXH2UZOh3QpYsMNc+aZaiDGxkpJU9KlZLSOOosB7IM6iyTF/fT7tpVegrHjLnP9WqU+SpVAnbskEnLZcsa2n/7TY6HDpV6iVkIk0NKuydPgNGjZX7Ghg2G9nLlZFuCrVvl/4nMzMUFeP996bAeOtRQ7+7uXal/V7Wq1FgnbV29KvNFa9WSaQGqevWAo0dlbmGxYtrFR1mQTieVNf75R+rxvvGGtCcmAgsXAj4+yLNkiey8kgUwOSTjxcUZNrz94guZJARI7+CSJYaaE0Qay5NH5q+dOSN77qpOnpTtWN98U3qmyLxCQqS0aenSUoJO5esrq4/37QOqV9csPCLZWUXdy3PqVCB7dmkPD0f+BQtkGGL5ckkabRiTQ3o9RZE7d7lywIgRwOPH0u7iItvgXb4MDBjAbQnI4vj5yY5Zu3bJIhXVpk3y4zxsmGGOG2WemBhZYV6ihGyGpH6uzJNHOmVOn5a6hdwciSxG9uzApEkyDDFokGEl1J07QL9+UqH/zz+1jTETMTmk1J04IdsNtW+ffFJQz56y0uvjj4EcObSLj8gIjRrJjlnLlwPe3tKWmCgFlH18JHFRV8eS6TxfxGDsWClTA8j6tIkT5e/u4MFSw5LIIuXPL/PnT59G2PM1lE6dks0dmje3ydpZTA4pZbdvA716ySStffsM7fXry+KT77+X2oVEVsLeXuYdXroko0XqVmuhoZK4sMSZae3b9/I+2Dod0Lev/BtMn25YOERk8cqUwY0FC2TScpUqhnZ1a9i+feXvpo1gckjJRUQAH30k8yp++MHQ7uMj9Sb27pWEkchKqaNFly/L6JA6lHn9uqySrVMHOHxY0xCt2vnzMqezYUPprVU1ayZzPpctAwoW1Cw8ooxp2FBK3axeDRQpIm2KAqxYIX83J060iQ3fmRySSEwEli6VwnDTp8tOJ4BUoJ03T7YdateOk4LIZnh7Sy3EkyeBJk0M7YcPS+3ELl2kJi4ZJzhYhojLl5c5naoKFaSIwfbtssUhkdWzswO6d5dVbbNnS1FtQP5ufvqpdKYsXGiYXGuFmByS1HeqVEkWldy/L22OjrKs8MoV4L33DBvZEtmYihVlZOiPP2RoWfXzzzJX7v33gadPNQvP4kVGytRjHx+ZmqUu4vT2ljmeQUEsYkA2ysVFNnq/elUWa6qTZx8+lFpa5crJYk4rnKvC5DArO3cOaNlS7tynTxvaO3SQ733xBViBlrICnU5+FU6dkg/8efJIe1wc8PnnkvgsWGDVHQEml5goPa+lSskwfUSEtLu5yeDD5csyx5NFDMjm5c4NzJ378obvly7JYs4GDaSApxVhcpgVPXgg4z8VKkjBalXVqsD+/VLY2sdHu/iINOLgIL8aly8DH3xg2LItJES2ditfXkrjWGFHgMkoCrBtmww29O8vBcYBSQKHDJFOlIkTDQt+iLKMEiVkVduRI0Dduob2v/6SLWa7dgX++0+7+NKAyWFWEhMjm42/OP5TqBCwapV8sqlXT9sYiSyAhwcwY4Zsq9q9u6H94kWpx9eokQyXZjVqnfvAwOSDDW3bSsHxr78G8ubVLDwiy1CjhnS0bNwo8/hVP/0kc1VGjTLUC7ZQTA6zAkWR/ah8fYHx44HwcGl3cwM++UT+4r39tqHIJxEBkMWIq1fL56Y6dQzt6qL9Pn2kJq6tu31b3mvlysDOnYb2atWkZM2vv8rfPCJ6RqeTRZxnz8qcFC8vaY+PlyHoEiVkzoqFFlhlNmDrDh6U7uzu3YGbN6XNzk4qvl+5AkyYIBVpieiVqleXkaH16+WeDshnrpUrpWPg+Tl3tiQsTG4RJUvKe1WH04sWlc+bR45I6VMiegVHR1mccvWq/DK5uEj706ey2q10afllSkrSNMwXMTm0VVevAp07y7yHY8cM7S1aAP/+K8PK+fJpFx+RldHpgI4dpSPgiy+AnDmlPTpaVuuWLCkLNGxhy9X4eBkiLlFChtfVzg1PT2DOHKng0bUrBxuIjObuLiN1ly4BvXsnL7DavbsMRT+/4YTG+KudAUuXLoVOp4Obm5vWoRg8eQKMHi2fRtavN7SXKyezyLdulf8nonRxdk5e5cnBQdrv35cFGl26+GDHDm1jTC9FkWlSL+477eQk06SuXJH/qgt1iCiNChWSgtknTwJNmxra//5bCmy/+aasetYYk8N0unPnDsaMGQNvdaNWrcXHA19+KYtNvvjCUHMjb15gyRL5QWzeXNsYiWxI7txSH/7cOalWobp0yRXNmklpnHPnNAsvzY4elSHiDh2kc0PVpYv8rZozh5WtiExGLbC6bZuUQVBt2iTHgwdLZXmNMDlMp3fffRf169dH0+czfy0oCvDbb0DZstKNoa6AcnEx7Gw/YIChe4OITKpkSeCXX2RE6PktV7dulWpRgwdL9ShL9d9/kgDWrAkcOGBor1dPEsYffwSKF9cuPiKb1ry5dN58951UjgdkbsqiRdLZ8/HHUmnezJgcpsOqVauwb98+LFy4UNtAgoKAgABZEXX5sqG9Rw/DzvY5cmgWHlFWUr++TO/99NNb+r2Dn7/Hz5hh2JXSEjx+LEPEfn6yG4yqVClZfbxvnyzEIaJMZm8vG71fuiTJoDpVLSJCVruVKmX2Cc1MDtPowYMHGDFiBGbOnImCRu4eHxsbi7CwsGRfGXL7tkxorVo1+QTWevWA48eBH36QeQ1EZFZ2dkDr1k9x6ZLMPVfv8eHhslDRzw9Ys0bbhYkxMVJBo0QJqaihzkDJk0cWoZw5I3ULuY06kZllzw58+KGM+A0ebNhe6O5dmdDs7y9DEmaowq9TlKxc6z/tOnXqhHv37uHAgQPQ6XTo06cP1q9fj4hU6lhMmTIFU6dOfan90KFDaVrMYhcVBa/ly5Fn5UrYPVcbKbZwYdwfORJhjRpluTt6WFgY3N3dtQ7DJvBamsbz1zEkxAFff50Xv/ySC0lJht/NcuWiMGbMPVSuHGW2uJKSgG3bPPDll/lx965hr3Rn5yT06vUIffs+hJubZZXT4M+kafA6moa5r6PztWvIN28ePPbsSdYeXrMm7o8ahZh0FBeNiIhA7dq1ERoamvp7Ucho69evV5ycnJSzZ8/q23r37q1kz5491cfFxMQooaGh+q9bt24pAJTQ0FDjXjghQVGWLlWU/PkVRT4zyJenp6LMnasosbEZeFfW7dSpU1qHYDN4LU0jpet4+rSitGiR/NcXUJQOHRTl8uXMj2nvXkWpWjX5a+t0itKnj6LcupX5r59e/Jk0DV5H09DsOu7bpyjVqr38C9yrl6LcvJmmpwoNDTUq/+AqBSNFRERg6NChGD58OLy9vfH06VMAQFxcHADg6dOncHR0RPbs2V96rLOzM5zTUvshOho4cUJmgx85Ahw6ZNjAFDAU1fzoIy4fJLIC5crJaNCff0qlqTNnpP2XX2R+nzqAoNO9/JXRdkUBbtxIHk+zZsCsWbJgkogsXP36kgv8/LPscnb9uvxif/+9tDVoIBOEq1eXbYtMUMOYyaGRHj16hODgYMyZMwdz5sx56fuenp5o27Ytfv3117Q9saLI/IIjRwzJ4L//AgkJKZ/foQPw2Wcyw52IrEqzZrI/8bJl8tkuOFiGezM6DdlY5csDs2ezqhWR1bGzk8rz7dvL5ODp06WucUwMsH27fKmKFDEki9Wry76XaazHzOTQSPnz58eeF8b9AWDmzJnYt28ftm7dCi9170RjdeokK45DQlI/z81NdjqZMEEWnRCR1bK3l+pSXbvKwpDffgNiYw3jRcCLg8+ptxvzvXz5ZGVyr16GOe5EZIWcneWXuU8fKYHw/fcv18q6cUO+1q2TYzs7KXdXvXrymoqpYHJoJBcXFzRs2PCl9hUrVsDe3j7F771WStso6HRAmTJSdEz9Kl2ad3QiG5MjBzB1qnwREaVJrlwyDDBrFnDzptTRUr/+/huIem6xW1IScPq0fBmJyaHWvLySJ4LVqskejERERESp0elkGLlIEaBzZ2lLSJBtjZ5PGE+fTlOdRCaHGbRixQqsWLEifQ8+eVJmhGex8jNERESUSRwcZPi4fHngnXekLSpKco79+2WK2muwCLaWihdnYkhERESZK1s2oE4dqXRiBCaHRERERKTH5JCIiIiI9JgcEhEREZEek0MiIiIi0mNySERERER6TA6JiIiISI91DjWQ+KwQ5e3bt+HOgtcZEhwcDE9PT63DsAm8lqbB62g6vJamwetoGrZwHcOebeSe+JqC2EwONXDu3DkAQNmyZTWOhIiIiLKaK1euoFq1aq/8PpNDDRQsWBAAcOvWLfYcZtDZs2eZZJsIr6Vp8DqaDq+lafA6moYtXMewsDAUKlQIPj4+qZ7H5FAD9vb2AAB3d3cmhxnk5ubGa2givJamwetoOryWpsHraBq2dB3VPORVuCCFiIiIiPSYHBIRERGRHpNDDTg7O2sdAhEREVGKmBxqgMkhERERWSouSCGiFMXHx7+2Fha9TFEUxMTEaB2GTbDGa+no6Pjayf5Elo7JoZb++QdwczPuXC8voHDhTA2HCJBSB48ePUJsbKzWoVglRVFw7do1rcOwCdZ4LXU6HTw8PJA/f37odDqtwyFbd/Mm8OiR8edHRBh1GpNDLTVoYPy5Li7AxYtMEClThYWF4c6dO3Bzc4OXlxccHR35By6NoqOj4erqqnUYNsHarqWiKIiMjMTDhw/h6uqKnDlzah0S2bKbNwFfXyATeteZHFqLmBj5dMDkkDLRo0eP4ObmhoIFCzIpTKekpCS4uLhoHYZNsMZr6erqitjYWDx48AAeHh78PaLM8+hRpiSGABekENEziqIgNjaWf9CIMsjd3R2JiYmcs0tWi8khESXj6OiodQhEVs3BQQblEhISNI6EKH2YHBJRMuw1JMoY/g6RteOcQyIyTlpXxWUGrtrPdGFJYYhJ0rZ8jIudC9ztbGMPWyJrxOSQiF4vE1fFpQlX7WeqsKQw/PB0JRJ0SZrG4aDYoWfO3nDgnygiTfA3j4heLxNXxaWJja3a37t3LwICArBnzx40bNhQ63AQkxSDBF0Smv/7A3JFBGsSw2O3fNhesSdikmLgBiPrwBKRSTE5JCKiZHJFBCNv2G2twyAijXBBChERERHpMTkkoizljz/+gL+/P5ydnVGsWDF8/vnnmDJlin6F6fXr16HT6bBixYqXHqvT6TBlyhT98ZUrV9C3b1+ULFkS2bJlQ4ECBdCpUyecPn36pcdeuHABLVq0QLZs2eDl5YV3330X4eHhmfU2s7QLFy6gW7duyJcvH5ydnVG4cGH06tULsbGxePjwIYYMGYIyZcrAzc0NefPmRaNGjfDXX3/pHx8fH4+8efOiZ8+eLz3306dP4erqilGjRpnzLRGZFYeViSjL2LVrF9q2bYtatWrhxx9/RGJiImbNmoXg4PTNr7t79y5y586NmTNnIk+ePHj8+DGWLVuGGjVq4OTJk/D19QUABAcHo0GDBnB0dMTChQuRL18+rF69GsOGDTPl2yMA//77L+rWrQsvLy9MmzYNJUuWxL179/D7778jLi4Ojx8/BgBMnjwZ+fPnR0REBDZu3IiGDRti165daNiwIRwdHdGjRw8sWrQIX3/9NdzdDSun165di5iYGPTt21ert0iU6ZgcUpZjCaU6LFGcEgc7xQ7xSjzsFfvk31QSYAulsSdOnIh8+fJhx44d+m3ZmjdvjqJFi6br+erXr4/69evrjxMTExEQEIBq1aph8eLF+OKLLwAAc+fOxcOHD3Hy5ElUrFgRABAYGIhmzZrh5s2bGXtTlMyoUaPg4OCAY8eOIU+ePPr2t99+GwDg6+uLhQsX6tsTExPRvHlzXL9+HV9++aV+YVDfvn0xd+5c/PTTTxgwYID+/OUrlqNylcrwK+eHeCU+xRjilXgkKokISQiBY4L2vzmxSiweJDzQOoxkWK7IsjE5pCzFUkp1WCKfBB94OXghNDEUUYlRyb7nkBiK3BrFZSqRkZE4fvw4hgwZkmy/3hw5cqBNmzZYuXJlmp8zISEBs2bNwqpVq3DlyhXExxuShfPnz+v/f8+ePShbtqw+MVR1794dO3bsSMe7oZRERUVh3759eOedd5Ilhi9atGgRlixZgnPnziE2Nlbf7ufnp///8uXLo0qVKli+fLk+OTxz7gyOHzuOGV/OwOPEx698/vjEeEQqkTgUeQjR8dEmeGcZUzShKA6GH9Q6jGTUckVMEC0Tk0PKUiyhVIelupltAlx0TvCIcoFLwgs7PES/+g+htXjy5AmSkpKQP3/+l76XUpsxRo0aha+//hrjxo1DgwYN4Onpibi4OAwbNgzR0YakICQkBMWKFTPZ61LKnjx5gsTERBQsWPCV53zxxRcYPXo03n33XXz88cfw8vKCvb09Pvroo2QJPQD069cPQ4cOxYULF+Dn54fly5fD2dkZvdo2SPX+EROn4GlsGFoFLYNj4h2Tvb/0uukyErXuztU6DL3nyxUxObRMTA4pS2Kpjpfdd46FfVIiHBPj4Zj4wjcTUx4+syaenp7Q6XS4f//+S997vk3tVXy+RwmQBO9Fq1atQq9evfDpp5/q26KiovDo0SPkzJlT35Y7d+7Xvi5lXK5cuWBvb4/bt1/9u71q1So0bNgQ33zzTbL2lBYHdevWDaNGjcKKFSvwySefYM2qNWjRtgXyuGeHY2LcK18jMRGwT0pE7sj7cInT/j4T7BDN+x2lCVcrE1GWkD17dlSvXh2//PILYp4r6B0eHo5Nmzbpj/PlywcXFxecOnUq2eN/++23l55Tp9PB2dk5Wdu2bdtw507y3qKAgACcPXsW//77b7L2NWvWpPv90MtcXV3RoEEDrFu3Do9esdVjSv9mp06dwuHDh18619PTE+3atcP333+PzZs34/79++jWp1umxE5kSdhzSERZxscff4wWLVqgadOmGD16NBITE/HZZ58he/bs+lWsOp0OPXr0wLJly1CiRAlUrFgRx44dSzGRa926NVasWAE/Pz9UqFABJ06cwKxZs14a1hwxYgSWLVuGVq1aYfr06frVyhcuXDDL+06rx275rPa1v/jiC9StWxc1atTABx98AB8fHwQHB+P333/H4sWL0bp1a3z88ceYPHkyGjRogIsXL2LatGkoVqwYEhISXnq+fv364aeffsKwYcNQsGBB1G9cH4h6mKEYiSwdk0Mier3cOQFnJyD21UNpZuHiAnh5pfvhTZs2xa+//ooPP/wQXbp0Qf78+TFkyBBER0dj6tSp+vPmzJkDAJg1axYiIiLQqFEjbN68+aVVzfPnz4ejoyNmzJiBiIgIVK5cGWvXrsX06dOTnZc/f37s27cP7733HgYPHoxs2bKhffv2WLBgAdq2bZvu92NqLnYucFDssL3iy/X9zMlBsYOLncvrT0yBmsxPnjwZ48ePR3h4OPLnz49GjRrByckJEydORFRUFL777jvMmjULZcqUwaJFi7Bx40bs3bv3pedr0qQJChUqhFu3buGDCR/Azo4DbmT7mBwS0esVzA8c3QCEPAUAxNs7Isw1F9ztPeCoM+NtxMsrw/sqt2nTBm3atEnW9nxhawBwd3fHt99++9JjFUVJdpwzZ04sXbo0WVtUVFSKSUbp0qXx559/vvY5teRu546eOXtrXupJLXMShajXn5yC0qVL4+eff37l92fPno3Zs2cna3tVkm5nZ6cvNxSvxKe6SpnIVjA5JCLjFMwvXwBg74QEt3yAfS5Ap30dNzIddzt3riAlyuLYP05EREREekwOiSjLmzJlikUN7xIRaYnJIRERERHpMTkkoucoYP8ZUcYoAKAo8kVkhbggxUrcA3Dvha2dSFaGBgUFGX1+hFMEUCATA7JmSfFAUgLiEwFXI05PjE9EdGw04qBxeRsLk5SUhMjISKPPdwKQ2Ut64gBY4x43ab2WmS3JLsmof6yEJABKAhwSn2Z2SFYr9H4oTl0+hVwOubQOxWhp/XuT2VzPn0fpTHpuJodWYrG9Pab26KF1GBZn+vTp+PDDD40+v2CFghizd0wmRmS9dEocnKMvIjQqP3K46KDTpX5+REgE7t6/a57grEiBAgVe2iHlVZwAlNPpMr2H6REAa/yXSsu1NAcnVyfk8319ke6waAX28Y9gn/TylnwkDq04hMmzJmsdRpqk9e9NZisE4KqDAxxTKN6eUUwOtbRvH+DmZtSpgxIT8aa9fSYHZH2ioqJw4sQJo8+PcIrASZzMxIism9eT9bjjWgq38QY8sungaA+klCPG2ytwcXNBruK5YMfZKckoioLixYsbda5dbCzizJD8uAEoXqAA8MK2cZYuLdfSHBQ7BdEx0YiJU5D44v7jkOHkyFggLPQp3ghZCR0nabxS7T61MabjGKvrOUzL3xtzeJyYiHxpyQ0iIoAGDV57GpNDLfn7A+7G1RN749kXJXf69GmUL1/e6PMfJDzAyXAmh6/iHnUUuPspHkV3xB1XP8DOASmlh4l29oh0jkF2XXbY6/ih5XlxcXFwcnIy9mTgFXsAm5yjI2BsXBYiTdfSDBKVREQqkXgaGwb7pBSyQyjQJUYh55Mt8IjYb/b4rIlHfg9UKFkBeR3yah2K0dL698YihYUZdRqTQyJKxj3qKNyjjiLe3hOJdjmQ0vhySPb8OFS5H1plb4XcDrk1iNJyXbp0CcWKFTPu5LNngXffzdyAVBs2AL6+5nktE0nTtTSDkIQQHIo8hFZBy5A78v7LJygKHBNDYJ9kOfMkidKDySERpcgx8QkcE5+k/D2XREQ7RcPRxREuDunbA9dW6XQ6uLgYeU10OuDGjcwN6PnXMjYuC5Gma2kGjgmOiI6PhmPiHbjE3dY6HKJMw8lCRERERKTH5JCIiIiI9JgcEhEREZEek0MiIiIi0mNySERERER6TA6JiIiISI/JIRERERHpsc4hZUmP3V6/P2pWE+vgigfuBY06l9ePsjJr+/lPy++2OVjb9cuKmBxSluJi5wIHxQ7bK/bUOhSLU/RaURwsNsbo8x0UO7jYWU6BYqLMZq33j7T+bpsD7x+WjckhZSnudu7ombM3YpJitA7F4tx0uIlaOWoZfb6LnQvc7YzbG5zIFljr/SOtv9vmwPuHZWNySFmOu507b0opCNYFI69DXq3DILJo1nj/4O82pRUXpBARERGRHpNDIiIiItJjckhEREREekwOiYiIiEiPySERERER6TE5JCIiIiI9lrLRgKIoAICwsDCNI7F+ERERvI4mwmtpGmm6jhERmRvMi69lZf++/Jk0DV5H07CF66jGr+Yhr8LkUAMhISEAgEKFCmkcCRFlGQ0aaB0BEVmI8PBweHh4vPL7TA41kCtXLgDAzZs3U/3HodSFhYWhUKFCuHXrFtzdrasoraXhtTQNXkfT4bU0DV5H07CV66goCsLDw+Ht7Z3qeUwONWBnJ1M9PTw8rPqHzFK4u7vzOpoIr6Vp8DqaDq+lafA6moYtXEdjOqW4IIWIiIiI9JgcEhEREZEek0MNODs7Y/LkyXB2dtY6FKvG62g6vJamwetoOryWpsHraBpZ7TrqlNetZyYiIiKiLIM9h0RERESkx+SQiIiIiPSYHBIRERGRHpNDM4qIiMCIESPg7e0NFxcX+Pv748cff9Q6LKsTHh6OsWPHolmzZsiTJw90Oh2mTJmidVhWZ/fu3ejXrx/8/PyQPXt2FChQAG3btsWJEye0Ds2q/PPPP2jVqhUKFy4MV1dX5MqVC7Vq1cKqVau0Ds3qLV26FDqdDm5ublqHYlX27t0LnU6X4teRI0e0Ds/qHDhwAC1btoSnpydcXV1RsmRJfPzxx1qHlalYBNuMOnTogOPHj2PmzJkoVaoU1qxZg27duiEpKQndu3fXOjyrERISgiVLlqBixYpo164dli5dqnVIVumbb75BSEgI3nvvPZQpUwYPHz7EnDlzULNmTWzfvh2NGjXSOkSr8PTpUxQqVAjdunVDgQIFEBkZidWrV6Nnz564fv06PvzwQ61DtEp37tzBmDFj4O3tjdDQUK3DsUqffvopAgICkrWVK1dOo2is05o1a9CzZ0+89dZb+P777+Hm5oarV6/i7t27WoeWqbha2Uy2bNmCVq1a6RNCVbNmzXD27FncvHkT9vb2GkZoPdQfWZ1Oh0ePHiFPnjyYPHkyew/T6MGDB8ibN2+ytoiICPj4+KBcuXLYuXOnRpHZhpo1a+Lu3bu4efOm1qFYpTZt2kCn0yFXrlxYv349IiIitA7JauzduxcBAQFYt24dOnXqpHU4VuvOnTvw9fVFr169sHDhQq3DMSsOK5vJxo0b4ebmhs6dOydr79u3L+7evYujR49qFJn1UYdHKGNeTAwBwM3NDWXKlMGtW7c0iMi2eHl5wcGBgzPpsWrVKuzbty/L/UEmy7J06VJERkZi3LhxWodidkwOzeTMmTMoXbr0S38sKlSooP8+kdZCQ0MRFBSEsmXLah2K1UlKSkJCQgIePnyIhQsXYvv27Vnyj0pGPXjwACNGjMDMmTNRsGBBrcOxakOHDoWDgwPc3d3RvHlzHDhwQOuQrMr+/fuRK1cuXLhwAf7+/nBwcEDevHnx7rvvIiwsTOvwMhWTQzMJCQlBrly5XmpX20JCQswdEtFLhg4disjISEycOFHrUKzOkCFD4OjoiLx582LkyJH48ssvMWjQIK3DsjpDhgyBr68vBg8erHUoVsvDwwPvvfceFi9ejD179mD+/Pm4desWGjZsiO3bt2sdntW4c+cOoqKi0LlzZ3Tp0gU7d+7E+++/j++//x4tW7aELc/K45iHGaU2FMphUtLaRx99hNWrV+Orr75ClSpVtA7H6kyYMAH9+/fHgwcPsGnTJgwbNgyRkZEYM2aM1qFZjQ0bNmDTpk04efIk74kZUKlSJVSqVEl/XK9ePbRv3x7ly5fH2LFj0bx5cw2jsx5JSUmIiYnB5MmT8cEHHwAAGjZsCCcnJ4wYMQK7du1CkyZNNI4yc7Dn0Exy586dYu/g48ePASDFXkUic5k6dSqmT5+OTz75BMOGDdM6HKtUuHBhVK1aFS1btsQ333yDgQMHYvz48Xj48KHWoVmFiIgIDB06FMOHD4e3tzeePn2Kp0+fIi4uDoCsCo+MjNQ4SuuVM2dOtG7dGqdOnUJ0dLTW4ViF3LlzA8BLyXRgYCAAICgoyOwxmQuTQzMpX748zp8/j4SEhGTtp0+fBsDyAqSdqVOnYsqUKZgyZQomTJigdTg2o3r16khISMB///2ndShW4dGjRwgODsacOXPg6emp/1q7di0iIyPh6emJt99+W+swrdrzlR7o9dQ1AS9Sr6Odne2mULb7zixM+/btERERgQ0bNiRrX7lyJby9vVGjRg2NIqOs7OOPP8aUKVPw4YcfYvLkyVqHY1P27NkDOzs7FC9eXOtQrEL+/PmxZ8+el76aN28OFxcX7NmzB9OnT9c6TKv15MkTbN68Gf7+/nBxcdE6HKvQsWNHAMDWrVuTtW/ZsgWAlKuyVZxzaCaBgYFo2rQpBg8ejLCwMPj4+GDt2rXYtm0bVq1axRqHabR161ZERkYiPDwcAHDu3DmsX78eANCyZUtky5ZNy/Cswpw5czBp0iS0aNECrVq1emnnBFu+8ZnSwIED4e7ujurVqyNfvnx49OgR1q1bh59++gnvv/8+8uTJo3WIVsHFxQUNGzZ8qX3FihWwt7dP8XuUsu7du+unOXh5eeHy5cuYM2cOgoODsWLFCq3DsxrNmjVDmzZtMG3aNCQlJaFmzZr4+++/MXXqVLRu3Rp169bVOsTMo5DZhIeHK//73/+U/PnzK05OTkqFChWUtWvXah2WVSpSpIgCIMWva9euaR2eVWjQoMErryFvDcZbtmyZUq9ePcXLy0txcHBQcubMqTRo0ED54YcftA7NJvTu3VvJnj271mFYlRkzZij+/v6Kh4eHYm9vr+TJk0dp3769cuzYMa1DszpRUVHKuHHjlEKFCikODg5K4cKFlfHjxysxMTFah5apuEMKEREREelxziERERER6TE5JCIiIiI9JodEREREpMfkkIiIiIj0mBwSERERkR6TQyIiIiLSY3JIRERERHpMDomIiIhIj8khEZEF6tOnD3Q6HXQ6HcqVK2fS5x4xYoT+ud3c3Ez63ERk/ZgcEhFZqPz58+Pw4cNYs2aNSZ935MiROHz4MFq2bGnS5yUi2+CgdQBERJQyZ2dn1KxZ0+TPW6RIERQpUgR58uQx+XMTkfVjzyERUSZ6+PAhBg4ciEKFCsHZ2Rl58uRBnTp1sHPnznQ/p06nw7Bhw7B8+XL4+vrC1dUVVatWxZEjR6AoCmbPno1ixYrBzc0NjRo1wpUrV0z4jojI1rHnkIgoE/Xs2RNBQUH45JNPUKpUKTx9+hRBQUEICQnJ0PNu3rwZJ0+exMyZM6HT6TBu3Di0atUKvXv3xn///YcFCxYgNDQUo0aNQseOHfHPP/9Ap9OZ6F0RkS1jckhElIkOHjyI/v37Y8CAAfq2tm3bZvh5Y2Nj8eeffyJ79uwApDexXbt22LNnD4KCgvSJ4MOHDzFixAicOXMG5cuXz/DrEpHt47AyEVEmql69OlasWIHp06fjyJEjiI+PN8nzBgQE6BNDAChdujQAIDAwMFkPodp+48YNk7wuEdk+JodERJnop59+Qu/evbF06VLUqlULuXLlQq9evXD//v0MPW+uXLmSHTs5OaXaHhMTk6HXI6Ksg8khEVEm8vLywrx583D9+nXcuHEDM2bMwC+//II+ffpoHRoRUYqYHBIRmUnhwoUxbNgwNG3aFEFBQVqHQ0SUIi5IISLKJKGhoQgICED37t3h5+eHHDly4Pjx49i2bRs6dOigdXhERClickhElElcXFxQo0YN/PDDD7h+/Tri4+NRuHBhjBs3DmPHjtU6PCKiFDE5JCLKJM7Ozvjmm28y9BwJCQnQ6XSwt7fXtymK8tJ5RYsWTbG9YcOGL7UnJSUhKSkpxfOJiDjnkIjIQt24cQOOjo6oWLGiSZ931KhRcHR0xPfff2/S5yUi26BT+NGRiMjiXL9+HY8ePQIAuLq6omzZsiZ77lu3biE4OBgAYG9vj0qVKpnsuYnI+jE5JCIiIiI9DisTERERkR6TQyIiIiLSY3JIRERERHpMDomIiIhIj8khEREREekxOSQiIiIiPSaHRERERKTH5JCIiIiI9P4PAewU5X2bfdoAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "final Twiss: emit_x = 0.0\n", "emit_y = 0.0\n", "beta_x = 10.788391405898434\n", "beta_y = 3.804736768160358\n", "alpha_x = -0.009168816187736392\n", "alpha_y = -0.005202303371245248\n", "gamma_x = 0.09270001704271681\n", "gamma_y = 0.26283738531638107\n", "Dx = 0.0\n", "Dy = 0.0\n", "Dxp = 0.0\n", "Dyp = 0.0\n", "mux = 1.0722774502509878\n", "muy = 1.0719541872890197\n", "nu_x = 0.1706582565734186\n", "nu_y = 0.17060680767510286\n", "E = 0.6969615506024416\n", "s = 6.6\n", "\n" ] } ], "source": [ "d = Drift(l=1)\n", "qf_h = Quadrupole(l=0.3/2, k1=1)\n", "qd = Quadrupole(l=0.3, k1=-1)\n", "c = Cavity(l=1, v=0.1, phi=10)\n", "\n", "fodo_cell = (qf_h, d, c, d, qd, d,c,d,qf_h)\n", "lat = MagneticLattice(fodo_cell)\n", "\n", "tws0 = Twiss(E=0.5) # E = 0.5 GeV. Initial energy is required for the focusing effect caclulation in the Cavities \n", "tws = twiss(lat, tws0)\n", "plot_opt_func(lat, tws)\n", "plt.show()\n", "print(\"final Twiss:\", tws[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### another way to get periodic solution is " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "emit_x = 0.0\n", "emit_y = 0.0\n", "beta_x = 10.788391405898436\n", "beta_y = 3.804736768160356\n", "alpha_x = -0.00916881618773698\n", "alpha_y = -0.005202303371245172\n", "gamma_x = 0.0927000170427168\n", "gamma_y = 0.26283738531638123\n", "Dx = 0.0\n", "Dy = 0.0\n", "Dxp = 0.0\n", "Dyp = 0.0\n", "mux = 0.0\n", "muy = 0.0\n", "nu_x = 0.0\n", "nu_y = 0.0\n", "E = 0.5\n", "s = 0.0\n", "\n" ] } ], "source": [ "tws_p = lat.periodic_twiss(tws=tws0)\n", "print(tws_p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.15" }, "name": "" }, "nbformat": 4, "nbformat_minor": 4 }