diff --git a/docs/_toc.yml b/docs/_toc.yml index 2dd0f99e1..1776e7634 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -14,14 +14,17 @@ parts: - file: intro_concepts - file: wind_data_user - file: floris_models + - file: input_reference_main + - file: turbine_models + sections: + - file: operation_models_user + - file: floating_wind_turbine + - file: multidimensional_wind_turbine + - file: input_reference_turbine + - file: turbine_library - file: advanced_concepts - file: heterogeneous_map - - file: floating_wind_turbine - - file: turbine_interaction - - file: operation_models_user - file: layout_optimization - - file: input_reference_main - - file: input_reference_turbine - file: examples - caption: Theory and Background diff --git a/docs/api_docs.md b/docs/api_docs.md index 2fb249f72..34c87bba6 100644 --- a/docs/api_docs.md +++ b/docs/api_docs.md @@ -1,11 +1,10 @@ # API Documentation -FLORIS is divided into two primary packages. -{py:mod}`floris.simulation` is the core code that models the wind turbines -and wind farms. It is low-level code that generally is not accessed -by typical users. {py:mod}`floris.tools` is the set of analysis routines -that define, drive, and post process a simulation. This is where -more users will interface with the software. +FLORIS is primarily divided into the {py:mod}`floris` package, which contains the user-level API, +and {py:mod}`floris.core` is the core code that models the wind turbines and wind farms. +Additionally, the {py:mod}`turbine_library` package contains turbine models that ship with FLORIS; +and the {py:mod}`optimization` package contains high-level optimization routines that accept and +work on instantiated `FlorisModel` objects. ```{eval-rst} .. autosummary:: diff --git a/docs/floating_wind_turbine.md b/docs/floating_wind_turbine.md index c4dabe90e..37458d156 100644 --- a/docs/floating_wind_turbine.md +++ b/docs/floating_wind_turbine.md @@ -3,22 +3,14 @@ The FLORIS wind turbine description includes a definition of the performance curves (`power` and `thrust_coefficient`) as a function of wind speed, and this lookup table is used -directly in -the calculation of power production for a steady-state atmospheric condition +directly in the calculation of power production for a steady-state atmospheric condition (wind speed and wind direction). The power curve definition typically assumes a -fixed-bottom wind turbine with no active or controllable tilt. However, floating -wind turbines have additional rotational degrees of freedom including pitch which +fixed-bottom wind turbine with a fixed shaft tilt. However, floating +wind turbines have an additional rotational degrees of freedom in the platform pitch, which adds a tilt angle to the rotor. As the turbine tilts, its performance is affected -similar to a yawed condition. The turbine is no longer operating on its defined -performance curve, and corrections must be included to accurately predict the power -production. +because the turbine is no longer operating on its defined performance curve. -Support for modeling this impact on a floating wind turbine were added in -[PR#518](https://github.com/NREL/floris/pull/518/files) and allow for correcting the -user-supplied performance curve for the average tilt. This is accomplished by including -an additional input, `floating_tilt_table`, in the turbine definition which sets the -steady tilt angle of the turbine based on wind speed. An interpolation is created and -the tilt angle is computed for each turbine based on effective velocity. Taking into -account the turbine rotor's built-in tilt, the absolute tilt change can then be used -to correct the power and thrust coefficient. -This tilt angle is then used directly in the selected wake models. +FLORIS allows the user to correct for the tilt angle of the turbine as a function of wind speed. +This is accomplished by including an additional input, `floating_tilt_table`, in the turbine definition that sets the steady tilt angle of the turbine based on wind speed. An interpolation is created and the tilt angle is computed for each turbine based on its rotor effective velocity. Taking into account the turbine rotor's built-in tilt, the absolute tilt is used to compute the power and thrust coefficient. To enable the use of the `floating_tilt_table`, the `correct_cp_ct_for_tilt` input on the turbine definition should be set to `True`. + +The tilt angle is then used directly in the selected wake models to compute wake effects of tilted turbines. diff --git a/docs/input_reference_main.md b/docs/input_reference_main.md index 9054ec8bf..19ab2d95d 100644 --- a/docs/input_reference_main.md +++ b/docs/input_reference_main.md @@ -1,6 +1,6 @@ # Main Input File Reference -In additional to reinitializing {py:class}`FlorisInterface`, users can configure FLORIS +In addition to calling the `set()` method on {py:class}`FlorisModel`, users can configure FLORIS with an input file. The file must be YAML format with either "yaml" or "yml" extension. The below definitions guide a user to the top, mid, and lower level parameterizations. A few reference input files are available in the diff --git a/docs/multidimensional_wind_turbine.ipynb b/docs/multidimensional_wind_turbine.ipynb new file mode 100644 index 000000000..47a106d01 --- /dev/null +++ b/docs/multidimensional_wind_turbine.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ab10767e", + "metadata": {}, + "source": [ + "# Multidimensional Wind Turbine\n", + "\n", + "Many external factors can affect the power and thrust curves of wind turbines. FLORIS supports the\n", + "ability to define \"multidimensional\" wind turbines, where the power and thrust curves are defined\n", + "as a function of external parameters. To enable this functionality, rather than defining `power`\n", + "and `thrust_coefficient` as a function of `wind_speed` on the `power_thrust_table`, users should\n", + "instead provide a path to a data file (described below) as `power_thrust_data_file`. Additionally,\n", + "users must set the `multi_dimensional_cp_ct` option on the turbine definition to `True`.\n", + "\n", + "The power thrust data file should be a CSV file with the following columns:\n", + "(``, ``, ..., `ws`, `power`,\n", + "`thrust_coefficient`). The external parameters can be any relevant factors that affect the turbine\n", + "performance, and the values to be used will be specified at run time or in the FLORIS input file.\n", + "For example, the external parameters could be air density, wave height, etc. The `ws` column should\n", + "contain the wind speed values for specification of the power and thrust coefficient (stored in the\n", + "`power` and `thrust_coefficient` columns, respectively). The wind speed, power, and thrust\n", + "coefficient values should be defined for each combination of the external parameters.\n", + "\n", + "The user can then specify the values of the external parameters either on the FLORIS input file\n", + "or using the `multidim_conditions` argument of `FlorisModel.set()`. The power and thrust coefficient\n", + "are determined based on the specified conditions using a nearest-neighbor approach.\n", + "\n", + "The following code snippet shows an example of a multidimensional wind turbine definition and its\n", + "corresponding power thrust data file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc97a774", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from floris import FlorisModel, TimeSeries\n", + "\n", + "n_wind_speeds = 100\n", + "wind_speeds = np.linspace(0.1, 30, n_wind_speeds)\n", + "\n", + "fmodel = FlorisModel(\"defaults\") # Defaults to NREL 5MW turbine\n", + "fmodel.set(\n", + " wind_data=TimeSeries(\n", + " wind_directions=np.zeros(n_wind_speeds),\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=0.06\n", + " ),\n", + " layout_x=[0],\n", + " layout_y=[0],\n", + " wind_shear=0.0,\n", + " turbine_type=[\"iea_15MW_floating_multi_dim_cp_ct\"],\n", + " reference_wind_height=-1,\n", + ")\n", + "\n", + "# Now, we need to specify what external parameters to run the model for\n", + "fmodel.set(multidim_conditions={\"Tp\": 2, \"Hs\": 1})\n", + "fmodel.run()\n", + "\n", + "powers = fmodel.get_turbine_powers()\n", + "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n", + "\n", + "fig, ax = plt.subplots(2, 1, sharex=True)\n", + "ax[0].plot(wind_speeds, powers, label=\"First condition\")\n", + "ax[0].grid()\n", + "ax[0].set_ylabel(\"Power [kW]\")\n", + "ax[1].plot(wind_speeds, thrust_coefficients)\n", + "ax[1].grid()\n", + "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n", + "ax[1].set_xlabel(\"Wind speed [m/s]\")\n", + "ax[1].set_xlim([0, 30])\n", + "\n", + "# Set a second multidimensional condition and rerun\n", + "fmodel.set(multidim_conditions={\"Tp\": 4, \"Hs\": 5})\n", + "fmodel.run()\n", + "\n", + "powers = fmodel.get_turbine_powers()\n", + "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n", + "ax[0].plot(wind_speeds, powers, label=\"Second condition\")\n", + "ax[0].legend(loc=\"upper left\")\n", + "ax[1].plot(wind_speeds, thrust_coefficients)" + ] + }, + { + "cell_type": "markdown", + "id": "98fd51f6", + "metadata": {}, + "source": [ + "Note that this example is not meant to be represntative of a real turbine, but rather to illustrate\n", + "the functionality. At this time, FLORIS only support a single external condition combination at a\n", + "time, but multiple combinations can be run sequentially as is shown above." + ] + }, + { + "cell_type": "markdown", + "id": "8c21f432", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "floris", + "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": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/references.bib b/docs/references.bib index f193de835..d8e9705c8 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -334,3 +334,29 @@ @article{HeckJohlasHowland2023_yawed_adm title = {Modelling the induction, thrust and power of a yaw-misaligned actuator disk}, journal = {Journal of Fluid Mechanics}, } + +@techreport{jonkman_NREL5MW_2009, + title = {Definition of a 5-{MW} Reference Wind Turbine for Offshore System Development}, + institution = {National Renewable Energy Laboratory}, + url = {http://www.osti.gov/servlets/purl/947422-nhrlni/}, + number = {NREL/TP-500-38060}, + author = {Jonkman, J. and Butterfield, S. and Musial, W. and Scott, G.}, + year = {2009}, + doi = {10.2172/947422}, +} + +@article{kainz_IEA10MW_2024, + title = {{IEA}-{Wind} 740-{10MW} Reference Offshore Wind Plants}, + author = {Kainz, Samuel and Quick, Julian and Souza de Alencar, Mauricio and Sanchez Perez-Moreno, Sebastian and Dykes, Katherine and Bay, Christopher and Zaaijer, Michiel B. and Bortolotti, Pietro}, + year = {2004}, + institution = {International Energy Agency}, +} + +@techreport{gaertner_IEA15MW_2020, + title = {{IEA} {Wind} {TCP} {Task} 37: {Definition} of the {IEA} 15-{Megawatt} Offshore Reference Wind Turbine}, + url = {https://research-hub.nrel.gov/en/publications/iea-wind-tcp-task-37-definition-of-the-iea-15-megawatt-offshore-r}, + number = {NREL/TP-5000-75698}, + institution = {International Energy Agency}, + author = {Gaertner, Evan and Sethuraman, Latha and Anderson, Benjamin and Barter, Garrett and Abbas, Nikhar and Bortolotti, Pietro and Scott, George and Feil, Roland and Shields, Matthew and Rinker, Jennifer and Zahle, Frederik and Meng, Fanzhong and Skrzypinski, Witold and Bredmose, Henrik and Dykes, Katherine and Allen, Christopher and Viselli, Anthony}, + year = {2020}, +} diff --git a/docs/turbine_interaction.ipynb b/docs/turbine_interaction.ipynb deleted file mode 100644 index bbc74fb0a..000000000 --- a/docs/turbine_interaction.ipynb +++ /dev/null @@ -1,437 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "ac224ce9-bd4f-4f5c-88b7-f0e9e49ee498", - "metadata": {}, - "source": [ - "# Turbine Library Interface\n", - "\n", - "FLORIS allows users to select from a set of pre-defined turbines as well as load an external\n", - "library of turbines. This reference demonstrates how to load, compare, and interact with the\n", - "basic turbine properties prior to wake modeling.\n", - "\n", - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "cf882c57-7d16-4f65-a6bb-cbc7e9603ac0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from floris.turbine_library import TurbineInterface, TurbineLibrary" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "71788b47-6641-4080-bb3f-eb799d969e0b", - "metadata": {}, - "source": [ - "## Interacting With A Single Turbine\n", - "\n", - "There are a few different ways that a ``TurbineInterface`` object can be created as demonstrated\n", - "below. For convenience, we'll only consider the object created from the internal library\n", - "(option 3).\n", - "\n", - "- Option 1: Load from a `Turbine` object:\n", - " `ti = TurbineInterface(turbine_obj)`\n", - "- Option 2: Load from a turbine configuration dictionary:\n", - " `ti = TurbineInterface.from_turbine_dict(turbine_dict)`\n", - "- Option 3a: Load a file from the internal turbine library:\n", - " `ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")`\n", - "- Option 3b: Load a file from the internal turbine library:\n", - " `ti = TurbineInterface.from_internal_library(\"iea_15MW.yaml\")`\n", - "- Option 4: Load a file from an external turbine library:\n", - " `ti = TurbineInterface.from_library(\"path/to/user/library\", \"iea_15MW.yaml\")`\n", - "- Option 5: Load a file from anywhere:\n", - " `ti = TurbineInterface.from_yaml(\"path/to/turbine.yaml\")`\n", - "\n", - "### Single Dimensional Turbine" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2275840e-48a3-41d2-ace9-fad05da0dc02", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f2576e8a-47ee-48b5-8707-aca0dc76929c", - "metadata": {}, - "source": [ - "#### Plot the core attributes\n", - "\n", - "For `TurbineInterface`, the core functionality is the power and thrust computation and plotting." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b9a5f00a-0ead-4759-b911-3a1161e55791", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ti.plot_thrust_coefficient_curve()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b24cd983-a22b-4a77-87f1-1c43c6e44842", - "metadata": {}, - "source": [ - "### Interacting With A Multi-Dimensional Turbine" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "91eee045-9019-40e0-9dc4-c95d2737bb3c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ti_md = TurbineInterface.from_library(\"internal\", \"iea_15MW_multi_dim_cp_ct.yaml\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "62b901ba-67c9-40ea-b770-453e62ae50ed", - "metadata": {}, - "source": [ - "#### Plot the core attributes\n", - "\n", - "In this example, we'll demonstrate how the usage for a multi-dimensional turbine is exactly the same, and how to produce cleaner figures." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f6e9f40c-4900-465a-882b-86ea86030f9b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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MgIODA+bMmSM61rt3byiVSoSEhGjUllwux5EjR0r8UWVpaYmHDx/i3LlzZbapVCrrTSL4yqi3M7Zqk19//RUKhQIAykxWHxQUhMOHD0OhUODXX3/FW2+9pashEhERERHpVExMDIDCWU4lsbCwEG4nJSUhNDQUP/zwA54/fy6ql5qaWn2DVOHk5IQRI0bg559/RkFBQYnJ8CdMmIDVq1fj0qVL2LlzJwIDA0sNNFhaWqJjx46i4FXXrl2FQJ6Xl5fomJGREXr06FHhcbds2VJtDA0bNoSdnZ1aGVC4ZK+muX//PgCgbdu2onIbGxtYWVlp1EZ1vg6xsbFo3rw5GjVqVG5d1ecAFK4gyszMREJCgkY5lirSn6ZUJ1c0bNhQFFSuCicnJ7XX3tnZGUBhMKsqeaUyMjIwdOhQvHz5EhEREWq5typKJpNpnHx+7ty5OHr0KHr06AEnJycMGDAAEyZMgLe3d5XGUN8wsFULFH0DAwC9evUqtV7xY5GRkQxsEREREVGdVfTFb1hYWIkXtQYG/1zqjB07FlFRUZg9ezZcXV1hbm4OhUKBQYMGCe3oip2dHXJzc5GRkSEKvhXx8PBAmzZtMGvWLNy9excTJkwosz0fHx+sX78eKSkpQn6tIl5eXti8eTPy8vIQEREBNze3Su32V9pulKWVF186V1pQTtOZRTVJVV6H+qBZs2ai+1u2bNFbTi5N5ebmYtSoUbh8+TIOHz5coY0VtMHFxQW3bt3CgQMH8Pvvv2Pv3r345ptvMH/+fISGhup0LLUZA1taMmXKFNy6dQuJiYmwsLCAk5MT+vXrh//7v/9DixYtqtT29evXARRGvMuKRDdr1gwWFhZIS0tT28mBiKi2uPY4FX/dT0aBFpIKk/bk5eXh5pPCi5Nnf8aXmMSY6jaeA7WDvWEWzI0kMJVIkZieAwBQKJU48r5fldpVKpXIzs6BVAI0tjSDYSkX8hVhZWpUpce3adMGANCkSZMyZ0ckJyfj2LFjCA0Nxfz584XyohlfuhYXFwe5XF7mrJDx48dj4cKFcHFxgaura5nt+fj4YN26dTh69CguXLiA2bNnC8e8vLyQlZWFgwcPIi4uDqNHj9bW09CYlZWVkMC+uKIZVGXR5pK41q1bAyh83x0dHYXyhISEGjHDrE2bNjh8+DCSkpLKnUVV0rl7+/ZtmJqaapzbSdP+KvIeqC6169ixo8aPLc+dO3egVCpF47l9+zYAVDrXtUKhwOTJk3Hs2DH8+OOPZU4iqU5mZmYYN24cxo0bJwTaPv/8c/z3v/+tVCC6PmJgS0uKr3d98eIFXrx4gT///BNffvklvvrqK0yfPr3SbRftctiyZcty69rZ2eHatWuIj4+vdD+lefLkiXA7JycHWVlZFe6Daq/s7OwSb1P9oYtz4ODVZ5jz8zUwplVT/f8L2Xv6uRikmoDnQE33sY8V7BvJIZMZ4HGKlv9WMzCEAkByZj6cbIwhk1Y16KCs0M6IRTOrFAoFFAoF+vfvDwsLCyxatAi9evVSC7YmJCTAxsZGuBAuelyRlStXFo5CqazyrK2SHl/Uf3GXLl3Cr7/+ikGDBqk9rvg43nzzTUilUvTo0UP0vEt6HkUztFasWIG8vDz07NlTON6qVSs0a9YMy5YtE+pW9rkWf1zR7dJeu+Lljo6OSE1NxcWLF9GlSxcAhdcV+/btU2tX9bFFy9iSkpJKHbemz6dPnz4wNDTE6tWr0a9fP+G8KOk8KO21Lq0/TV6H8owcORJff/01QkJC8NVXX6m1UzygEx0djfPnz6Nbt24AgPj4ePzyyy8YOHAgJBKJRn1q2p+ZmRmSk5NLfP9VlbQsWJOxlPV6F3n8+LEoeXxaWhq+//57uLq6okmTJsLjinYULQp8l+Xdd9/F7t27sW7dOrz66qtamb1ZNEuvrLaKnxcvXryAtbW1cMzAwAAuLi44dOgQcnJyYGRU+AXAzZs3YWpqilatWmk8Fk1e17LGp3q9n5OTo3EbusbAVhU5Ojpi1KhR8PT0FNZWx8XFYe/evdizZw+ys7Px9ttvQyKRVHppYNE2sZqs9TUzMwNQuEtMRamuDS/Ln3/+KdqGmOqXU6dO6XsIpGfVcQ68yAaWXpZBoax5CUuJiOgfeQUKJKS8hKmOrySKvlRJT08Xdmf74osv8Pbbb6Nr164YNWoUGjdujIcPHyI8PBweHh5Yvnw5gMKAzvLly5Geno5mzZrh+PHjwoyhnJycCu32FhkZiejoaACFCadfvnwpzATz9PQUcuO89tprkMvl6NGjB2xsbHDr1i1s27YNJiYm+Pjjj0V9KhQK5OfnC2VWVlZ4//33AUAoK3r+GRkZosdaWlqiRYsWiI6ORqtWrWBubi467u7ujl9//RUSiQRdunSp8M52+fn5KCgoEF1fpKenq425uNzcXKE8ICAAH374IUaOHInp06cjMzMTmzdvRps2bXDp0iW11yEvL08oa9OmDWQyGZYsWYJnz57ByMgIfn5+sLGxQX5+vuj1KY+xsTHeffddrFy5EoMHD0b//v1x5coVHD16FNbW1qJ+MzMzhX+LyopeB9X+NH0dylO0y/2aNWtw48YN9OvXDwqFAtHR0fDx8RFdS7q4uGDQoEGYPn06jIyMsGnTJgDAf/7zH63316VLFxw7dgxLly6Fra0tWrduje7du2vUh6YyMjIAFJ7jJY1foVDAyckJ06ZNQ2RkJJo0aYLt27fj2bNnWLNmjegxffv2BQBcvny5zD7XrVuHdevWwd3dHRKJBN99953o+NChQ4Vr64iICAwbNgxz587Fhx9+WGa7eXl5AMo+L4ufF/3790eTJk3g4eGBJk2a4Pbt2/juu+8wYMAAKJVKoV7Hjh3h7e2NAwcOlNk/AGzYsAFpaWnCxJT9+/cLGxn861//EnLAlSQ/Px9ZWVnIysrCzZs3RccSExPL7VtfGNiqgpEjR+KNN95Qm57p7u6OcePG4cCBAxg1ahTy8vLw/vvvY/jw4ZVKalf0S6woWlsWY+PCLZM5m4qIahOFEtgZK0NOAYNaRES1QU2ZWfvaa6+hWbNm+Oqrr7BmzRrk5uaiWbNm8PT0xOuvvy7U++677zB37lxs3LgRSqUSffr0wU8//QQXF5cK93n69GksXbpUVPb5558DKEwEXRTYGjJkCH766Sd88803ePnyJRo3boyhQ4di7ty5oqVw2tCzZ0/s3bu3xMTwHh4e+PXXX+Hs7KzVROGaatSoEcLCwvDxxx9jwYIFaN26NebPn4+4uDhcunSpzMc2bdoUK1aswMqVKzFz5kwUFBTgt99+03i5napPPvkEcrkcW7ZsEXKO7d27F+PGjatUe9r29ddfo2PHjti+fTvmz58PCwsLuLq6inZcBABvb2+4u7tj2bJlePjwIdq1a4dvvvmmwvmhNOnv888/x6xZs/D5558jKysL48eP13pgSxOOjo5YunQp5s+fjzt37qB169bYvHmzEMiqqCtXrgAAzp07V+KOhJcuXVKbNNK0adNKjr50QUFBwudERkYGmjdvjrfeegv/+c9/Kt3m2rVrRSu4fvvtN/z2228ACvMNlhXYqq0kyvqW0a4U9+7dg4ODAwDgjTfewNatW7XS7sKFCzFv3jzh9scff1zhNszMzJCZmQkPDw+cOXOmzLoeHh44e/YszM3NhZlemtJkKWLRL8srV65oNL2T6o7s7Gxhlo6fnx/Xe9dD1XkObI1+gKXhd0RlbWzMYGNetfwrpD0KhQIpKYU5SCwtrSCVSvU8ItI1ngO1w+i2BmhqboSGZnKYNyk/jYWmsvMKRLkPm1nIYc3P6HpFoVAIF/jm5ub8DNATmUyGGTNmYM2aNTrtV5/vv6OjIzp27CgEZ3Rt7ty5+OGHH3D79m1hIkldFRsbi7y8PMhkMrVlj7GxsejcuTOAwuWvmqRK0hXO2Kpmb731FubPnw+lUomTJ09WKrDVoEEDZGZmarS8sGgaZ2W2KK3IiWlsbKy1rVup9pHL5Xz/6zltngMxz15i5R9xorIWlibY/443GsiZnLqmyMrKQnh4OABgwAA3fgbUQzwHaoeYmBjk5+fDwECGNjZV27K+uLsJ6XiZky/cV0rAwEY9JpVK+f7rkUQi0evrr4/3X5/P+cSJE5g3b169+b1X9FqrPt+aHNRjYKuaNWnSBNbW1khMTMSjR48q1UbLli3x7NmzcmdUARCmHFYkXxYRkb7k5ivw/o8XkZv/T0JLiQT4cuwrDGoREdUgqqk36tqaj4KCAiQkJJRZx9zcvFJfHtdUSUlJyM3NLfW4TCar9JI/XUtISEBBQUGpx42MjPSyDBPQ/blVH8/l6lbSUkWqWRjY0oGqblPboUMH/PXXX0hNTcXTp09LzdP15MkTIblcZfIFEBHp2to/YnD1kTi55lRvB/R0tC7lEUREpA+qGyDWtWwm8fHxQlqS0ixYsAAhISG6GZAOjBo1CidPniz1eOvWrXHv3j3dDagK3N3dhc0AStKrVy/RLva6pOtzqz6ey0QMbFWzhIQEYfeA5s2bV6oNHx8fhIWFAQBOnjxZanLD4r+YipJWEhHVVBceJOPrE+LdVds2Mcd/BrbT04iIiKg0qt/T1pTk8dpia2uLI0eOlFlH2wnf9e3LL79EcnJyqcdr07KrHTt2lLl5lpWVlQ5HI6btc6u8oHJdPJdrS4CV9IeBrWq2YcMG4cOnV69elWpj+PDh+L//+z8oFAps2bKl1MBWUcJ7qVSK4cOHV6ovIiJdyMotwAc/XhIlIjaQSrBynCvkhjI9joyIiEoigepSxLoV2ZLL5ejXr5++h6FTbm5u+h6C1tTkL/V1fW7Vx3OZiBn/KunevXu4cOFCmXUOHDiATz/9FEDhNx5TpkwpsV7v3r0hkUggkUhKjEbb2toK2xUfPnwYe/bsUavz008/4fDhwwCASZMmlbpckYioJlhy6AbiEjNEZe/1bYtOLere9sNERHWB6lLEujZji4iIaq96O2MrIiICd+78s7V80XJBALhz544w+6lIUFCQ6P69e/fg7+8PT09PDBs2DK+88gqaNGkCAIiLi8OePXuwZ88e4dusL774Ai1atKj0eD///HP8/vvvSEhIwPjx43H+/HkMHToUQGEA7csvvwQA2NjYYOHChZXuh4ioup2OScC2aHEejFfsLPF/vdvoaURERFSeup48noiIaq96G9jauHEjtm3bVuKxyMhIREZGispUA1tFoqOjER0dXWo/pqamWLlyJd56661KjxUo3OXwt99+w6uvvoqnT59i6dKlWLp0qaiOra0t9u/fj5YtW1apLyKi6pKamYfZP10WlckNpVgx9hUYyDiJmIioplLNsaUEI1tERFQz1NvAVlW5ublh+/btiI6Oxvnz5/HkyRMkJiYiPz8fVlZW6NixI/r27Ytp06YJM7mqysPDA1euXMGqVauwf/9+Ydmig4MDRowYgVmzZsHamjuJEVHNFfLbNTxNyxaV/XewC9rYcMtpIqKajEsRiYiopqq3ga2tW7eqLTesiAYNGuD1118Xcl9VRUW2nm3cuDE+++wzfPbZZ1Xul4hIl/535Qn2XXgkKvNt2xiTerbW04iIiEhT6ksRGdkiIqKages+iIio2j1Py8bH+66IyhrIDbBsTBdIVacBEBFRjaP6Sc0ZW0REVFMwsEVERNVKqVTiw5+vIDkzT1T+2YhOaNbQRE+jIiKiipByxhYREdVQDGwREVG1+uFcPP64+VxUFtDZFiNcm+tpREREVFGqyeP1MWNr69atkEgkQp5Zqnvs7e1L3bRLVe/evdG7d+9q6ffEiROQSCQVShlDRPrDwBYREVWbBy8y8dmB66IymwbGWPhqZ7V8LUREVHOpzdiqx7si7t69GxMnTkTbtm0hkUhKDa4UBUdK+jlz5oyorr29PSQSCfr161diW999953w2PPnzwMAli1bBolEggsXLojqKpVKWFlZQSKR4O7du6Jj2dnZMDY2xoQJEyr57HXr+vXrCAkJYTBTjxYtWoT9+/drtc2goKBS/28U/9E0yFkZN2/exJw5c+Dq6ooGDRqgWbNmGDJkiPD/q7KCgoJgbl76pkgSiQTvvvtulfooS0U+d+qSeps8noiIqleBQokPfrqIzNwCUfnS0Z3RyMxIT6MiIqLKUP0uQh8rESdNmoTAwEAYGxvrvvNi1q1bh7/++gvu7u548eJFufWDg4Ph7u4uKnNyclKrJ5fLcfz4cTx9+hS2traiYzt27IBcLkd29j87C/v4+AAAIiIi0LVrV6H82rVrSElJgYGBASIjI+Hg4CAcO3fuHHJzc4XH1nTXr19HaGgoevfuDXt7e9Gx8PDwauvXz88PWVlZMDLi3yuLFi3CmDFjMHz4cK21OX36dFEQ9+7du5g/fz7eeust+Pr6CuVt2rTRWp+qNm7ciE2bNmH06NGYMWMGUlNT8e2336Jnz574/fffSw0y1xaafu7UFQxsERFRtdh/4RHO3UsWlY3vYYc+7ZvqaURERFRZ6snjdR/ZkslkkMlkOu9XVVhYGFq0aAGpVIpOnTqVW9/X1xdjxowpt563tzfOnTuH3bt347333hPKHz58iNOnT2PkyJHYu3evUN69e3fI5XJERERg5syZQnlkZCSsra3RvXt3REREYOLEicKxiIgIAKg1ga2yVGfQSSqVQi6XV1v79Z2npyc8PT2F++fPn8f8+fPh6ekpOl+r0/jx4xESEiKaXfXmm2/CxcUFISEhtT6wpennTl3BpYhERFQtfr/2VHS/VSNTfDKkg55GQ0REVaG6fFwfM7ZKy7F16NAh+Pr6wszMDA0aNMCQIUNw7do1UZ3Lly8jKCgIjo6OkMvlsLW1xZtvvqnRjCtVdnZ2kEordhn18uVL5Ofnl1lHLpdj1KhR2Llzp6h8165dsLKywsCBA0XlRkZGcHd3R2RkpKg8MjISnp6e8Pb2LvGYpaWlRgG5Ir1790anTp1w+fJlDBkyBM2bN4ezszP27NkDADh58iQ8PDxgYmKCdu3a4ejRo6LHBwUFqc22AoCQkJAy0xJs3boVr732GgDA399fWE5VlPeqMjm2lEolFi5ciJYtW8LU1BT+/v5q5wpQco6t4q9Dr169YGpqCicnJ41fB00oFAqsWrUKnTt3hlwuh42NDQYNGiRaHle0lG3Hjh1o164d5HI53NzccOrUKa33J5FIkJGRgW3btkEmk8HKygozZsyocD9VZW9vj6FDhyI8PByurq6Qy+Xo0KEDfv75Z7W6sbGxiI2NLbdNNzc3tSWD1tbW8PX1xY0bN0TlmZmZuHnzJhITE6v2REqxZs0adOzYEaamprCyskL37t3VPgdu3ryJBw8eVKhdTT536goGtoiISOsUCiXO3UsSlQX3bQszY04UJiLSOYUCyEis0o80KxGyrBfCjyQzEcr0hKq1q1BU+amFhYVhyJAhMDc3x9KlSzFv3jxcv34dPj4+ogDYkSNHEBcXhylTpmDNmjUIDAzEDz/8gICAgGrf4XHKlCmwsLCAXC6Hv79/mTl8JkyYgLNnz4ouzHfu3IkxY8bA0NBQrb6Pjw8ePXokeq6RkZHw8vKCl5eXsCwRKAzqREVFwdPTs8KBueTkZAwfPhxubm4ICQmBsbExAgMDsXv3bgQGBiIgIABLlixBRkYGxowZg5cvX1ao/ZL4+fkhODgYAPDRRx8hLCwMYWFhcHFxqXSb8+fPx7x58/DKK69g+fLlcHR0xIABA5CRkaHR45OTkzF06FB4eHhg2bJlWn8dpk6dilmzZsHOzg5Lly7Fhx9+CLlcrpYb6eTJk5g1axYmTpyITz/9FC9evMCgQYNw9epVrfYXFhYGY2Nj+Pr6Ytu2bVi/fn215r0qS0xMDMaNG4fBgwdj8eLFMDAwwGuvvYYjR46I6vXt2xd9+/atdD9Pnz5F48aNRWVnz56Fi4sL1q5dq3E7iYmJJf6o+u677xAcHIwOHTrgq6++QmhoKFxdXfHnn3+K6rm4uGDy5Mka91+Rz526gFcYRESkdbefv0RKZp6ozLONtZ5GQ0RUz2UlAcurlqvGFEBH7YzmH7NjAbPG5dcrRXp6OoKDgzFt2jRs2LBBKH/jjTfQrl07LFq0SCifMWMGPvjgA9Hje/bsifHjxyMiIkKU10dbjIyMMHr0aAQEBKBx48a4fv06vvjiC/j6+iIqKkqUF6tInz59YGtri127duGTTz7BjRs3cPHiRaxatQpxcXFq9Yvn2bK3t8fTp08RFxcHb29vdOvWDVKpFFFRUQgICMD169eRnJxcqWWIjx8/xvbt2zFkyBAAwLBhw9ChQwdMmDABUVFR8PDwAFB48T1w4EDs3bu3ygEQR0dH+Pr6YvXq1ejfv3+Vd0BMSEjAsmXLMGTIEPz222/CbLGPP/4YixYt0qiNx48fY+fOnRg/fjwAoH///mjfvr1WXofjx49j69atCA4OxqpVq4TyDz74QC34evXqVZw/fx5ubm4AgMDAQLRr1w7z588vcRZTZfubOHEi3n77bTg6OmLixIlIS0vTqO3qcPv2bezduxejRo0CUBiUa9++PebOnYv+/ftrpY/Tp08jOjoan3zySZXaycjIgI2NjUZ1Dx48iI4dO+Knn36qUp9FKvO5UxcwsEVERFr3Z5x4tlZLKxO0sDTR02iIiKguOnLkCFJSUjB+/HjRTAiZTAYPDw8cP35cKDMx+ed3UHZ2NtLT09GzZ08AwN9//10tga2iWVNFhg8fjjFjxqBLly7473//i99//13tMTKZDGPHjhUCWzt27ICdnR18fX1LDGx5eXlBKpUKubQiIyNhaGgId3d3mJiYoEuXLoiMjERAQICwLLEygS1zc3MEBgYKM5DatWsHS0tLtGjRQgjmABBulzRWfTt69Chyc3Mxc+ZM0RLIWbNmaRzYKnodimjzddi7dy8kEgkWLFigdkx1yaanp6cQ1AKAVq1aYcSIEfjtt99QUFCgUS66ivRXEzRv3hwjR44U7ltYWGDy5MlYunSpaMOFyu6g+fz5c0yYMAEODg6YM2eO6Fjv3r0rNLNTLpfjt99+K/GYahDO0tISDx8+xLlz59SSvRenaf+V+dypCxjYIiIirTt7VxzY6uHQSE8jISKiuiomJgZA4SynklhYWAi3k5KSEBoaih9++AHPnz8X1UtNTa2+QapwcnLCiBEj8PPPP5cagJgwYQJWr16NS5cuYefOnQgMDCw10GBpaYmOHTsKQavIyEh07dpVCOR5eXmJjhkZGaFHjx4VHnfLli3VxtCwYUPY2dmplQGFS/Zqmvv37wMA2rZtKyq3sbGBlZWVRm1U5+sQGxuL5s2bo1Gj8v9mUn0OAODs7IzMzEwkJCSo7apZ1f409fSpOL9qw4YNRUHlqnByclJ77Z2dnQEUBrM0ec6lycjIwNChQ/Hy5UtERESo5d6qKJlMpnHy+blz5+Lo0aPo0aMHnJycMGDAAEyYMAHe3t5VGkNxmnzu1HYMbBERkVYplUr8eVecjLenA5chEhGRdin+f46usLCwEi9qDQz+udQZO3YsoqKiMHv2bLi6usLc3BwKhQKDBg0S2tEVOzs75ObmIiMjQxR8K+Lh4YE2bdpg1qxZuHv3LiZMmFBmez4+Pli/fj1SUlKE/FpFvLy8sHnzZuTl5SEiIgJubm6V2u2vtAvh0sqLzy4pLShXUFBQ4XHoW1Veh/qgWbNmovtbtmzRW04uTeXm5mLUqFG4fPkyDh8+XKGNFbTBxcUFt27dwoEDB/D7779j7969+OabbzB//nyEhoZqrZ/yPndqOwa2iIhIq2ITMpCYnisq83DkjC0iIr0xaVSYz6oK8gsKcOtZuqjMycYcxoZV+ObfpGq/G9q0Kcwb1qRJkzJnRyQnJ+PYsWMIDQ3F/PnzhfKiGV+6FhcXB7lcXuaskPHjx2PhwoVwcXGBq6trme35+Phg3bp1OHr0KC5cuIDZs2cLx7y8vJCVlYWDBw8iLi4Oo0eP1tbT0JiVlZWQwL64ohlUZdHmkrjWrVsDKHzfHR0dhfKEhIQaMcOsTZs2OHz4MJKSksqdRVXSuXv79m2YmppqnNtJ0/4q8h6oJnLv2FF7mfnu3LkDpVIpGs/t27cBoMRdNzWhUCgwefJkHDt2DD/++CN69eqljaFWmJmZGcaNG4dx48YJgbbPP/8c//3vfysViC6JJp87tRl3RSQiIq1SXYbY1MIYrRqZ6mk0REQEqbQwSXsVfiRmjVFgYi36UVSxTVRwZz5VAwcOhIWFBRYtWoS8vDy14wkJCQD+mU2jOnvmq6++qlL/5Snqv7hLly7h119/xYABA8rcmXDatGlYsGABvvzyy3L7KcqZtWLFCuTl5YlmbNnb26NZs2ZYtmyZqK4utWnTBqmpqbh8+bJQ9uTJE+zbt6/cx5qZmQFAiYGxiurXrx8MDQ2xZs0a0blQ3eeBpkaPHg2lUlniLB3Vczc6Ohp///23cD8+Ph6//PILBgwYoPEyM037MzMz0/j179evn+hHdQZXVTx+/Fh0zqSlpeH777+Hq6uraMZmbGysaFfRssycORO7d+/GN998IySl17UXL8SrHIyMjNChQwcolUrR59rNmzfx4MGDcturyudObcYZW0REpFWqyxA9HKxrZBJSIiLSnEQigQRA8ctrfS+zsrCwwLp16zBp0iR069YNgYGBsLGxwYMHD3Dw4EF4e3tj7dq1sLCwgJ+fH5YtW4a8vDy0aNEC4eHhuHv3bqX6PXXqFE6dOgWg8CIyIyMDCxcuBAD4+fnBz88PADBu3DiYmJjAy8sLTZo0wfXr17FhwwaYmppiyZIlZfbRunVrhISEaDSeVq1awc7ODtHR0bC3t0fz5s1Fx728vIRE4drM26OpwMBAzJ07FyNHjkRwcDAyMzOxbt06ODs7i4IzJXF1dYVMJsPSpUuRmpoKY2Nj9OnTB02aNKnwOGxsbPCf//wHixcvxtChQxEQEIALFy7g0KFDaNy48rtzaou/vz8mTZqE1atXIyYmRlgme/r0afj7++Pdd98V6nbq1AkDBw5EcHAwjI2N8c033wBAhZauadqfm5sbjh49ipUrV8LS0hKtW7cuNa9ddXJ2dsbUqVNx7tw5NG3aFJs3b8azZ8+wZcsWUb2+ffsCKD+J/FdffYVvvvkGnp6eMDU1xfbt20XHR44cKQRWT5w4AX9/fyxYsEDj/5eaGjBgAGxtbeHt7Y2mTZvixo0bWLt2LYYMGYIGDRoI9VxcXNCrVy+cOHGizPaq8rlTmzGwRUREWqNUKtV2RGTieCKiukEiAYrHsmpC+qAJEyagefPmWLJkCZYvX46cnBy0aNECvr6+mDJlilBv586dmDlzJr7++msolUoMGDAAhw4dUgsCaeKPP/5QCyDMmzcPALBgwQIhsPXqq69ix44dWLFiBdLS0mBjY4NRo0ZhwYIFcHJyqsKzVufj44Ndu3aJZmsV8fb2xt69e9G+fXtYW+s+56W1tTX27duHf//735gzZw4cHBywePFixMTElBvYsrW1xfr167F48WJMnToVBQUFOH78eKUCWwCwcOFCyOVyrF+/HsePH4eHhwfCw8MxZMiQSrWnbVu2bEGXLl2wadMmzJ49Gw0bNkT37t3V3tdevXrB09MToaGhePDgATp06ICtW7eiS5cuWu9vxYoVeOuttzBv3jxkZWVh/PjxeglstW3bFmvWrMHs2bNx69YtODg4YPfu3Rg4cGCl2rt48SKAwtlv0dHRasfv3r0rBLbS0wuXYWtzBlqR6dOnC58T6enpaNmyJYKDg/HJJ59Uqj1dfu7UJBKlvr9qoVrj4cOHwo4ft2/fLnE3Dqq7srKyEB4eDqDwmwVt7XBCtYcm58CDF5nwW35cVHb0335watJArS7VLvwMIJ4DtUNMTAzy8/NhYGCg1b/VFAoFbjxJQ0GxKweHxmZoIDfUWh9UsykUCqSlpQEonC1XV5c01XQSiQTvvPMO1q5dq9N+9fn+29vbo1OnTjhw4IDO+ixuzpw52LVrF+7cuQNjY2O9jEFXyvodEhMTI+xEGR8fj5YtW+pjiCXipxEREWnNGZVliNZmRmhjUzeTVBIR1Teqi8r59TgR1QfHjx/HvHnz6nxQqzbjUkQiItIa1cTxPRwaMb8WEVEdIVFJsqWoQ5GtgoKCEpMuF2dubl6ndhRLSkpCbm5uqcdlMpnGO+zpW0JCAgoKCko9bmRkVO5Og9VF1+dWfTyXq9u5c+f0PQQqBwNbRESkNeqJ45lfi4iorqjLM7bi4+Ph4OBQZp3qSBytT6NGjcLJkydLPd66detyE3DXFO7u7rh//36pxzVJul1ddH1u1cdzmYiBLSIi0orHKVmIT8oSlXk46j5JLRERVQ/VwJYCdSeyZWtriyNHjpRZx9HRUUej0Y0vv/wSycnJpR6vTXn0duzYgaysrFKPW1lZ6XA0Yto+t8pLkV0Xz+XaEmAl/WFgi4iItEJ1GWJDE0O0a8qk8UREdYXqyvK6NGNLLpejX79++h6GTrm5uel7CFrj7e2t7yGUStfnVn08l4mYPJ6IiLRCdRmiu30jSKXMr0VEVFdxc3UiIqoJGNgiIiKt+FNlxhbzaxER1S1qSxEZ1yIiohqAgS0iIqqy5y+zEZeQISrzcGRgi4ioLqnLSxGJiKj2YmCLiIiqTDW/lrmxATo0s9DTaIiIqDqo74rIyBYREekfA1tERFRlqoEtt9ZWMJDxVwwRUV2iOmNLoZ9hEBERifCqg4iIquzPOJX8WlyGSERU56jN2GKSLSIiqgEY2CIioipJysjFrWcvRWUeDtZ6Gg0REVUXteTxOu5/69atkEgkuHfvno57Jl2xt7dHUFCQRnV79+6N3r17V0u/J06cgEQiwYkTJ7TSPhFVLwa2iIioSs7dE8/WkhtK0blFQz2NhoiIqot68vj6OWNr9+7dmDhxItq2bQuJRFJqcKUoOFLSz5kzZ0R17e3tIZFI0K9fvxLb+u6774THnj9/HgCwbNkySCQSXLhwQVRXqVTCysoKEokEd+/eFR3Lzs6GsbExJkyYUMlnr1vXr19HSEgIg5l6tGjRIuzfv1+rbQYFBZX6f6P4j6ZBTm3YsWMHJBIJzM3Nq9RO79690alTpxKP3bt3DxKJBF988UWV+ijLkydP8OGHH8Lf3x8NGjSoNwFaA30PgIiIajfVZYhura1gZMDvTYiI6hr15PG67X/SpEkIDAyEsbGxbjtWsW7dOvz1119wd3fHixcvyq0fHBwMd3d3UZmTk5NaPblcjuPHj+Pp06ewtbUVHduxYwfkcjmys7OFMh8fHwBAREQEunbtKpRfu3YNKSkpMDAwQGRkJBwcHIRj586dQ25urvDYmu769esIDQ1F7969YW9vLzoWHh5ebf36+fkhKysLRkZG1dZHbbFo0SKMGTMGw4cP11qb06dPFwVx7969i/nz5+Ott96Cr6+vUN6mTRut9VmW9PR0zJkzB2ZmZjrprzrdunULS5cuRdu2bdG5c2dER0fre0g6wcAWERFVydl74j/qe9hzGSIRUV2kthRRx5EtmUwGmUym0z5LEhYWhhYtWkAqlZY6M6M4X19fjBkzptx63t7eOHfuHHbv3o333ntPKH/48CFOnz6NkSNHYu/evUJ59+7dIZfLERERgZkzZwrlkZGRsLa2Rvfu3REREYGJEycKxyIiIgCg1gS2ylKdQSepVAq5XF5t7dd3np6e8PT0FO6fP38e8+fPh6enp+h81ZWFCxeiQYMG8Pf31/rsNF1zc3PDixcv0KhRI+zZswevvfaavoekE/xKnYiIKi0tOw/XH6eJypg4noioblJfiqjb/kvLsXXo0CH4+vrCzMwMDRo0wJAhQ3Dt2jVRncuXLyMoKAiOjo6Qy+WwtbXFm2++qdGMK1V2dnaQSit2GfXy5Uvk5+eXWUcul2PUqFHYuXOnqHzXrl2wsrLCwIEDReVGRkZwd3dHZGSkqDwyMhKenp7w9vYu8ZilpaVGAbkiRUurLl++jCFDhqB58+ZwdnbGnj17AAAnT56Eh4cHTExM0K5dOxw9elT0+KCgILXZVgAQEhICiepJVczWrVuFi3J/f39heVrRsqrK5NhSKpVYuHAhWrZsCVNTU/j7+6udK0DJObaKvw69evWCqakpnJycNH4dNKFQKLBq1Sp07twZcrkcNjY2GDRokLD8FAAkEgneffdd7NixA+3atYNcLoebmxtOnTql9f4kEgkyMjKwbds2yGQyWFlZYcaMGRXup6rs7e0xdOhQhIeHw9XVFXK5HB06dMDPP/+sVjc2NhaxsbEatx0TE4OVK1dixYoVMDAoed5Pamoqbt68idTU1Eo/h9Lk5eUhNDQUbdu2hVwuh7W1NXx8fHDkyBFRnZs3b+LJkyflttegQQM0alT//hZnYIuIiCrt/L0kFN8Uy0gmhaudpd7GQ0RE6hRKBZKyk6r8k5qTjLTcf36Sc6rWnkJZ9fTzYWFhGDJkCMzNzbF06VLMmzcP169fh4+PjygAduTIEcTFxWHKlClYs2YNAgMD8cMPPyAgIKDac4VNmTIFFhYWkMvl8Pf3FwUpVE2YMAFnz54VXZjv3LkTY8aMgaGhoVp9Hx8fPHr0SPRcIyMj4eXlBS8vL2FZIlAY1ImKioKnp2eFA3PJyckYPnw43NzcEBISAmNjYwQGBmL37t0IDAxEQEAAlixZgoyMDIwZMwYvX74sv9Fy+Pn5ITg4GADw0UcfISwsDGFhYXBxcal0m/Pnz8e8efPwyiuvYPny5XB0dMSAAQOQkZGh0eOTk5MxdOhQeHh4YNmyZVp/HaZOnYpZs2bBzs4OS5cuxYcffgi5XK6Wk+3kyZOYNWsWJk6ciE8//RQvXrzAoEGDcPXqVa32FxYWBmNjY/j6+mLbtm1Yv369TvNeFRcTE4Nx48Zh8ODBWLx4MQwMDPDaa6+JAkAA0LdvX/Tt21fjdmfNmgV/f38EBASUWmffvn1wcXHBvn37NGqzoKAAiYmJaj/JyclqdUNCQhAaGgp/f3+sXbsWH3/8MVq1aoW///5bqPPo0SO4uLjgv//9r8bPq77hUkQiIqq0P++K82u52llCbqj/ZSJERPSPlJwU9NrdS9/DUHNy3Ek0kld+ZkF6ejqCg4Mxbdo0bNiwQSh/44030K5dOyxatEgonzFjBj744APR43v27Inx48cjIiJClNdHW4yMjDB69GgEBASgcePGuH79Or744gv4+voiKipKlBerSJ8+fWBra4tdu3bhk08+wY0bN3Dx4kWsWrUKcXFxavWL59myt7fH06dPERcXB29vb3Tr1g1SqRRRUVEICAjA9evXkZycXKlliI8fP8b27dsxZMgQAMCwYcPQoUMHTJgwAVFRUfDw8AAAuLi4YODAgdi7d2+VAyCOjo7w9fXF6tWr0b9//yrvgJiQkIBly5ZhyJAh+O2334TZYh9//DEWLVqkURuPHz/Gzp07MX78eABA//790b59e628DsePH8fWrVsRHByMVatWCeUffPCBWvD16tWrOH/+PNzc3AAAgYGBaNeuHebPn1/iLKbK9jdx4kS8/fbbcHR0xMSJE5GWllZac9Xu9u3b2Lt3L0aNGgWgMCjXvn17zJ07F/37969UmwcPHkR4eDguXbqkzaHi5s2bsLGx0XgMAQEBos8wqjjO2CIiokpTTRzPZYhERKQrR44cQUpKCsaPHy+aFSGTyeDh4YHjx48LdU1MTITb2dnZSExMRM+ePQFANDNCm7y8vLBnzx68+eabGD58OD788EOcOXMGEomk1JkXMpkMY8eOxa5duwAUJo23s7MrNfDm5eUFqVQq5M6KjIyEoaEh3N3dYW5uji5dugjLEYv+rUxgy9zcHIGBgcL9du3awdLSEi4uLkIwB4Bwu6QgnL4dPXoUubm5mDlzpmgJ5KxZszRuozpfh71790IikWDBggVqx1SXbHp6egpBLQBo1aoVRowYgcOHD6OgoEDr/dUEzZs3x8iRI4X7FhYWmDx5Mi5cuICnT58K5ffu3dNoF83c3Fy8//77ePvtt9GhQ4cy6wYFBUGpVGocpLS3t8eRI0fUfrZv365W19LSEteuXUNMTEyZ7SmVSmzdulWj/usjztgiIqJKycjJx9VH4lwDHg5MHE9ERLpRdCHYp0+fEo9bWFgIt5OSkhAaGooffvgBz58/F9Wrjrw5pXFycsKIESPw888/o6CgoMRk+BMmTMDq1atx6dIl7Ny5E4GBgaUGGiwtLdGxY0dR8Kpr165CIM/Ly0t0zMjICD169KjwuFu2bKk2hoYNG8LOzk6tDECJS6707f79+wCAtm3bisptbGxgZWWlURvV+TrExsaiefPmGuVHUn0OAODs7IzMzEwkJCSo7apZ1f40VTzABBS+DsWDylXh5OSk9to7OzsDKAxmafKci1u5ciUSExMRGhqqlfEVZ2ZmJtr1sUhJAbdPP/0UI0aMgLOzMzp16oRBgwZh0qRJ6NKli9bHVZcxsEVERJXy94Nk5BdLsGUglaBba0v9DYiIiOoVhaIwR1dYWFiJF7XFE0GPHTsWUVFRmD17NlxdXWFubg6FQoFBgwYJ7eiKnZ0dcnNzkZGRIQq+FfHw8ECbNm0wa9Ys3L17FxMmTCizPR8fH6xfvx4pKSlCfq0iXl5e2Lx5M/Ly8hAREQE3N7dK7fZX2m6UpZUXXzpXWlBO05lFNUlVXof6oFmzZqL7W7Zs0VtOrrKkpqZi4cKFmDFjBtLS0oQllunp6VAqlbh37x5MTU3RpEmTah+Ln58fYmNj8csvvyA8PBwbN27EypUrsX79ekybNq3a+68rGNgiIqJKUV2G2LllQ5ga8dcKEVFNY2lsiZPjTlapDYVCgRcpL5GQ/U+ZVCJB+2bqgZmKjKsq2rRpAwBo0qRJibMjiiQnJ+PYsWMIDQ3F/PnzhfKylv5Up7i4OMjlcpibm5daZ/z48Vi4cCFcXFzg6upaZns+Pj5Yt24djh49igsXLmD27NnCMS8vL2RlZeHgwYOIi4vD6NGjtfU0NGZlZSUksC+uaAZVWbS5JK5169YACt93R0dHoTwhIaFGzDBr06YNDh8+jKSkpHJnUZV07t6+fRumpqYa53bStL+KvAeqidw7duyo8WPLc+fOHSiVStF4bt++DQAl7rpZluTkZKSnp2PZsmVYtmyZ2nEHBweMGDEC+/fvr8qQNdaoUSNMmTIFU6ZMQXp6Ovz8/BASEsLAVgXwCoSIiCrlrEri+B4OzK9FRFQTSSXSKiVpBwoDWwpjA+QUm9wkgQSN5A2rOLrKGzhwICwsLLBo0SL4+/ur7RqYkJAAGxsbYTaN6uyZr776qlrHV9R/cZcuXcKvv/6KwYMHl7kz4bRp04RcYeUpypm1YsUK5OXliWZs2dvbo1mzZsLFe2Xya1VVmzZtkJqaisuXLwvLq548eaLRDnNmZmYAUGJgrKL69esHQ0NDrFmzBgMGDBACJNV9Hmhq9OjR+PrrrxEaGipK5g5ALaATHR2Nv//+G926dQMAxMfH45dffsGgQYNKnT1W2f7MzMw0fv3LCjBX1ePHj7Fv3z4heXxaWhq+//57uLq6imZsFu0oWhT4LkmTJk1KPP9Wr16N6Oho7Nq1S232WXV58eIFrK3/SeVhbm4OJycnxMfHC2V5eXmIjY1Fw4YNdTau2oaBLSIiqrDsvAJcjE8RlfVkfi0iojpNdeKGEkq1C25dsrCwwLp16zBp0iR069YNgYGBsLGxwYMHD3Dw4EF4e3tj7dq1sLCwgJ+fH5YtW4a8vDy0aNEC4eHhuHv3bqX6PXXqFE6dOgWgMHiVkZGBhQsXAihcVuTn5wcAGDduHExMTODl5YUmTZrg+vXr2LBhA0xNTbFkyZIy+2jdujVCQkI0Gk+rVq1gZ2eH6Oho2Nvbo3nz5qLjXl5eQqJwb2/vCj7bqgsMDMTcuXMxcuRIBAcHIzMzE+vWrYOzs3O5iftdXV0hk8mwdOlSpKamwtjYGH369KnUEjEbGxv85z//weLFizF06FAEBATgwoULOHToEBo3blzZp6c1/v7+mDRpElavXo2YmBhhmezp06fh7++Pd999V6jbqVMnDBw4EMHBwTA2NsY333wDABXKF6Vpf25ubjh69ChWrlwJS0tLtG7dutS8dtXJ2dkZU6dOxblz59C0aVNs3rwZz549w5YtW0T1+vbtC6DkfFZFTE1N8eqrr6qV79+/H2fPnlU7tnXrVkyZMqVallZ26NABvXv3hpubGxo1aoTz589jz549ovf70aNHcHFxwRtvvKFRAvmiz6Nr164BKFyuXbTBxCeffKLV8dcUDGwREVGFXX6UhtyCf762l0oAN3vNEq8SEVHtVFL4SqEEZHrcQG3ChAlo3rw5lixZguXLlyMnJwctWrSAr68vpkyZItTbuXMnZs6cia+//hpKpRIDBgzAoUOH1IJAmvjjjz/UAgjz5s0DACxYsEAIbL366qvYsWMHVqxYgbS0NNjY2GDUqFFYsGABnJycqvCs1fn4+GDXrl2i2VpFvL29sXfvXrRv3140M0RXrK2tsW/fPvz73//GnDlz4ODggMWLFyMmJqbcwJatrS3Wr1+PxYsXY+rUqSgoKMDx48crnfto4cKFkMvlWL9+PY4fPw4PDw+Eh4djyJAhlWpP27Zs2YIuXbpg06ZNmD17Nho2bIju3burva+9evWCp6cnQkND8eDBA3To0AFbt26tcMJxTfpbsWIF3nrrLcybNw9ZWVkYP368XgJbbdu2xZo1azB79mzcunULDg4O2L17NwYOHFjtfaenpwNQzyGmDcHBwfj1118RHh6OnJwctG7dGgsXLhQtKa6oos+jIps3bxZu19XAlkRZ3zLaUaU9fPhQ2PHj9u3bJe7GQXVXVlYWwsPDAQADBgzQ2g4nVHsUPwdijNti7Yl/vunu1MICB2aWvBU51Q38DCCeA7VDTEwM8vPzYWBgoNW/1RQKBZJT0vAoU1zeoZkFDGSlL6mjukOhUAhJti0sLMpcSknVRyKR4J133sHatWt12q8+3397e3t06tQJBw4c0FmfxY0dOxb37t3D2bNn9dK/LpX1OyQmJkbYiTI+Ph4tW7bUxxBLxBlbRERUYefvp4jue3AZIhFR3VfCzCx+RU5EdZlSqcSJEyewfft2fQ+FysDAFhERVUi+ArgYnyoqY+J4IqK6r8SliKgbka2CggIkJCSUWcfc3LzMnQxrm6SkJOTm5pZ6XCaTabzDnr4lJCSgoKCg1ONGRkbl7jRYXXR9btXHc7k6SSQSPH/+XN/DoHIwsEVERBUSnwFk5ytEZT3sGdgiIqrrSgps1ZUZW/Hx8XBwcCizzoIFCzRO6F4bjBo1CidPniz1eOvWrctMwF2TuLu74/79+6Ue79WrF06cOKG7ARWj63OrPp7LRAxsERFRhdxJE1/atLdtACszIz2NhoiIdEUiKQxuFY9lKepIZMvW1hZHjhwps46jo6OORqMbX375JZKTk0s9Xpvy6O3YsQNZWVmlHrey0t8GN9o+t8pLkV0Xz+XaEmAl/am3ga3nz5/j7NmzOHv2LM6dO4dz587hxYsXAKDxNpqZmZn4/fffceTIEZw/fx537txBeno6LCws4OzsjIEDB+Ltt9+Gra1tlcdrb29f5rcQRWrTNytEVDvFqgS2uAyRiKj+kEjEs7TqSFwLcrkc/fr10/cwdMrNzU3fQ9Aab29vfQ+hVLo+t+rjuUxUbwNbTZs2rdLjL1++DG9vb2Hrz+KSkpJw5swZnDlzBitXrsSGDRswbty4KvVHRFQTFCiBOJXAFhPHExHVH6rLEbnBOhER6Vu9DWwV16pVK7Rv317YwloTaWlpQlDL29sbQ4cORffu3WFtbY2EhAT8/PPP+O6775CWlobXX38dFhYWGDx4cJXHOmLECCxcuLDU40ZGXA5ERNXnYQaQo+CMLSKi+ko1sKVgXIuIiPSs3ga25s+fD3d3d7i7u6Np06a4d+9euUn2ipNKpRg7diwWLFiADh06qB0fMGAABg8ejJEjR6KgoAAzZ85ETEwMJJKS0m5qztLSEp06dapSG0RElaW6DNHRxgw2DYz1NBoiItI1iUqSLc7YIiIifau3ga3Q0NAqPd7LywteXl5l1hkxYgRGjRqFvXv3IjY2FhcuXEC3bt2q1C8RkT6pBra4DJGIqH5Rm7Gll1EQERH9Q6rvAdR1/v7+wu3Y2Fg9joSIqGoUSmUJgS0uQyQiqs84Y4uIiPSNga1qlpOTI9yWyWR6HAkRUdXcfpaOrAKVwJYjA1tERPWJalYNxrWIiEjfGNiqZidPnhRuu7i4VLm9U6dOwdXVFQ0aNICpqSkcHBwwbtw47N+/n9+YEVG1Onc/RXS/VSNTNGtoop/BEBGRXjB5PBER1TT1NseWLly6dAkHDx4EAHTu3Fkrga27d++K7t+7dw/37t3Djz/+CG9vb+zevRstWrSoVNsPHz4s8/iTJ0+E2zk5OcjKyqpUP1Q7ZWdnl3ib6o8/416I7ru1suDnQD3CzwDiOVA7KBQK4ctOhUJ7GbCK2lILbCkUWu2nLFu3bsXUqVMRGxsLe3t7nfRJ/yj+PlfXe+7o6IhevXphy5Yt5dbt06cPAOCPP/7Qer8nTpxA3759cezYMfTu3bvK7dcFunj/qWZQKpVQKBRqf+cXX41W0zCwVU1ycnIwbdo0FBQUAAA+//zzKrVnZGSE4cOHY8CAAejUqRMaNmyIlJQUREdHY926dYiPj0dkZCT69++P6OhoNGzYsMJ92NnZaVz3zz//ZM6weuzUqVP6HgLpmFIJ/BknQ/FLGpOXjxAeXnZAnOomfgYQz4Gay9raGiYmJpBIJEhLS9N6+6pLEbOzc5Cm1M3FTlFANT09vVqem6Z+/vln/P777/jrr78QFxcHb29vHDhwQK1eREQEhg0bVmIb4eHhcHd3F+536dIF8fHx6NWrF/bv369Wf9u2bZg1axaAwkBO165dsWrVKoSEhODkyZPo0qWLUFepVMLBwQGpqam4ePEiWrduLRzLzs5G69atMWzYMGzcuLGSr0Dhe1AdFAoF8vLyhPf35s2b2L9/PyZMmIBWrVqJ6ubn5wOAVs4F1X4zMzOFf/V5rtUEX375Jdq3b48hQ4YIZVV9/2fMmIFdu3aVW2/8+PH45ptvqtSXpn788UdMnz4dZmZm5U74KMvQoUPx4sULREdHqx178OABXnnlFXz66aeYOXNmVYZbqp07d+Kdd94p8djNmzfRtGnTMh+fn5+PrKwsZGVl4ebNm6JjiYmJWhuntjGwVU3effddnD9/HgDwxhtvlPpLTVNnz56FpaWlWnnv3r3x7rvvYsyYMQgPD8eNGzcQGhqKFStWVKk/IqLinmUB6fniqxknC64/ISKqb1RnbOnyN8G4ceMwatQoGBsb67BXdZs3b8alS5fQtWtXJCUllVt/+vTp6Nq1q6jM0dFRrZ5cLsfp06fx7NkztYvPn376CXK5XDRbsmfPngCAM2fOiAJbN27cQGpqKgwMDHDmzBlRYOvvv/9Gbm6u8Nia7tatW1i6dCl8fHzUAls///xztfXr5eWFJ0+ewMjIqNr6qC1WrlyJ4cOHiwJbVRUUFIRevXoJ9x88eIBFixbhjTfegKenp1Du4OCgtT7Lkp6ejpCQEJiZmemkP1346KOP1P7PVGbyS23BwFY1WLx4sfANiLu7O77++usqt1lSUKtIgwYN8OOPP8LR0RFJSUnYsGEDlixZUuEP4vj4+DKPP3nyBD169AAAeHh4oE2bNhVqn2q37Oxs4Rt6Pz8/yOVyPY+IdGnXuYfApdvC/aYNjBA4zB8S1a/uqc7iZwDxHKgdHjx4gIKCAhgYGMDCwkJr7SoUCqSnp6sFtgyNjGBhUb/OhR07dqBFixaQSqXo0qVLqa+1qakpgMIlc2PGjCmzTalUCm9vb5w7dw6HDh1CcHCwcOzhw4eIjo7Gq6++ip9//hlmZmawsLBAr169IJfLcf78efznP/8R6l+6dAnW1tZwc3PD33//jX/961/CsYsXLwIA+vXrV+Hzo+gcAABzc3NIpdpP1yyVSmFoaCiMzcSkMJenqampVs/n8volMUNDQ5ibm2vt/e/Xr5/o/vnz57Fo0SL4+fkhKCioKkOtlMWLF8PCwgL+/v745ZdfqnQeGBgYQCaTldiGubk5gMIgdnWda0W/m0eMGIHu3btX+PEJCQkwMTGBubk53NzcRMdq8ootBra07Ntvv8VHH30EAGjfvj3+97//6STy27BhQwQGBuKbb75BRkYGzp8/Dy8vrwq10bJlS43rGhsbC79oqP6Ry+V8/+uZs/fF0/B72FsJf7BT/cPPAOI5UHNJpVIh/011BB7UdkWspn5KsnXrVkyZMgV3794V5dg6dOgQFi1ahL///htSqRR+fn5YtmwZOnbsKNS5fPkyVqxYgVOnTuHx48ewtLREQEAAli9fDmtr6wqNo/gMqCIlvQZFZVKpFBkZGTAxMYGBQemXX3K5HKNGjcKuXbuEZYcAsHv3blhZWWHQoEH4+eefIZVKIZVKIZfL4e7ujqioKFH/0dHR8PT0RI8ePfDjjz+KjkVFRcHS0hJdunTR+H3r3bs3EhMTsX37drzzzju4cOECmjdvjiVLlmDMmDE4efIk5syZg8uXL6NVq1b4+uuvRYGLoKAgnDhxAvfu3RO1GxISgtDQULUNsCQSCaRSqfB+A0Dfvn2F48ePH0fv3r2F3FcnTpzQ6HkAhcs0P//8c6xfvx5JSUnw8PDA2rVrRf0Wtenv7y/0Vfx12LlzJ2bOnIlz585V6HXQhEKhwJo1a7Bx40bExMSgQYMGcHNzw8KFC4UghUQiwTvvvANPT098+umnuH//Pjp27IiVK1fCz89Pq/0VfYH5/fff4/vvvwdQuDxw+/btWv1/X/z/Sknt2tvbo1OnTggODsacOXNw8+ZNODo6YuHChRg1apSoblHwRdNJGDExMfjqq6+wb98+/Pjjj6LxFElNTcWTJ0/QrFkzjWc9lfWZUPxcy8vLw6JFi7B9+3bEx8fDzMwMLi4uWLBgAfr37y/UiY2NRcOGDdGsWTON+i363DE1NYVMJtNozEWKxqf6e17fs2XLwl0RtWjXrl2YMWMGgMJfeEeOHEHjxo111n+HDh2E248ePdJZv0RUtxUolIhWSRzv5dhIT6MhIqKKUioUyE9KqvJPQXIykCL+USQlV7o9pRYSUIeFhWHIkCEwNzfH0qVLMW/ePFy/fh0+Pj6iQMqRI0cQFxeHKVOmYM2aNQgMDMQPP/yAgICAat9ZfMqUKbCwsIBcLoe/v7+QrqQkEyZMwNmzZ0UzI3bu3IkxY8bA0NBQrb6Pjw8ePXokeq6RkZHw8vKCl5cXrl27hpSUFACFQZ2oqCh4enpWOCiRnJyM4cOHw83NDSEhITA2NkZgYCB2796NwMBABAQEYMmSJcjIyMCYMWPw8uXLCrVfEj8/P2Hm2kcffYSwsDCEhYVVaUOu+fPnY968eXjllVewfPlyODo6YsCAAcjIyNDo8cnJyRg6dCg8PDywbNkyrb8OU6dOxaxZs2BnZ4elS5fiww8/hFwux5kzZ0T1Tp48iVmzZmHixIn49NNP8eLFCwwaNAhXr17Van9hYWEwNjaGr68vtm3bhvXr1+tlRhVQGIAaN24cBg8ejMWLF8PAwACvvfYajhw5IqrXt29fUSC0PLNmzYK/vz8CAgJKrbNv3z64uLhg3759GrVZUFCAxMREtZ/k5GS1ukUBXn9/f6xduxYff/wxWrVqhb///luo8+jRI7i4uOC///2vxs/L398fFhYWMDU1xfDhwxETE6PxY2sjztjSkl9//RWTJ0+GQqFAs2bNcOzYsQrNgNIGLgkioupw7XEqUrPyRGU9Ha30NBoiIqqogpQUxHh5a6294uGVDACVvVxqGxUJg0aV/6IkPT0dwcHBmDZtGjZs2CCUv/HGG2jXrh0WLVoklM+YMQMffPCB6PE9e/bE+PHjERERAV9f30qPozRGRkYYPXo0AgIC0LhxY1y/fh1ffPEFfH19ERUVpZZ3Cyhctmhra4tdu3bhk08+wY0bN3Dx4kWsWrUKcXFxavV9fHwAFCaqt7e3x9OnT4WE9t26dYNUKkVUVBQCAgJw/fp1JCcnC4+piMePH2P79u1CnqVhw4ahQ4cOmDBhAqKiouDh4QEAcHFxwcCBA7F3794qB0AcHR3h6+uL1atXo3///lXenTAhIQHLli3DkCFD8NtvvwnXTh9//DEWLVqkURuPHz/Gzp07MX78eABA//790b59e628DsePH8fWrVsRHByMVatWCeUffPCBWvD16tWrOH/+vLBULDAwEO3atcP8+fM1zj2mSX8TJ07E22+/DUdHR0ycOFGvifRv376NvXv3CjO0pk6divbt22Pu3LnCzKaKOnjwIMLDw3Hp0iVtDhU3b96EjY2NxmMICAgQfYZVhampKYKCgoTA1l9//YUVK1bAy8sLf//9d4U2jKtNOGNLC44dO4axY8ciPz8f1tbWOHLkiF7yT12/fl243bx5c533T0R1U8Qd8Q4oTeRK2NazfCpERFTzHDlyBCkpKRg/frxoVoRMJoOHhweOHz8u1C2+pCY7OxuJiYlCAvXiMyO0ycvLC3v27MGbb76J4cOH48MPP8SZM2cgkUhKnXkhk8kwduxYYce4HTt2wM7OrtTAm5eXF6RSKSIiIgAUztYyNDSEu7s7zM3N0aVLF0RGRgrHAFQqsGVubo7AwEDhfrt27WBpaQkXFxchmANAuF1SEE7fjh49itzcXMycOVM0IaD4ss/yVOfrsHfvXkgkEixYsEDtmOoEBk9PT1H+o1atWmHEiBE4fPgwCgoKtN5fTdC8eXOMHDlSuG9hYYHJkyfjwoULePr0qVB+7949tWWvJcnNzcX777+Pt99+W7TyqSRBQUFQKpUaBynt7e1x5MgRtZ/t27er1bW0tMS1a9fKnFFlb28PpVKJrVu3ltv32LFjsWXLFkyePBmvvvoqPvvsMxw+fBgvXrzA559/rtH4ayPO2KqiqKgojBgxAjk5OWjYsCEOHz4sWs+vK6mpqfjhhx8AFEZpK5MojoioJJEqga12DbkbIhER6V/RhWCfPn1KPF48OXNSUhJCQ0Pxww8/4Pnz56J6qamp1TdIFU5OThgxYgR+/vlnFBQUlJj7ZsKECVi9ejUuXbqEnTt3IjAwsNRAg6WlJTp27CgKXnXt2lUI5Hl5eYmOGRkZCZtBVUTLli3VxtCwYUO12R9F+YdKWnKlb/fv3wcAtG3bVlRuY2MDKyvNZqJX5+sQGxuL5s2bo5EGsxhVnwMAODs7IzMzEwkJCbC1tdVqf5oqHmACCl8HbeVjdHJyUnvtnZ2dARQGszR5zsWtXLkSiYmJCA0N1cr4ijMzMysxv1pJAbdPP/0UI0aMgLOzMzp16oRBgwZh0qRJop1Oq8rHxwceHh44evSo1tqsaRjYqoKLFy9iyJAhyMjIgJmZGQ4ePKi2c4AmevfujZMnTwKAWjJMAPj999/Rq1evUj8U0tPTMXbsWLx4UZgDZ+rUqTU6sRsR1R7ZeQU4d0/8R5mzJQNbRESkf0VJ8sPCwkq8qC2eqH3s2LGIiorC7Nmz4erqCnNzcygUCgwaNEhoR1fs7OyQm5uLjIyMEndGK9p9fNasWbh79y4mTJhQZns+Pj5Yv349UlJShPxaRby8vLB582bk5eUhIiICbm5uldrRtLTk06WVF186V1pQTtOZRTVJVV6H+kA1sfmWLVv0lpOrLKmpqVi4cCFmzJiBtLQ0YYlleno6lEol7t27B1NTUzRp0qTax+Ln54fY2Fj88ssvCA8Px8aNG7Fy5UqsX78e06ZN01o/dnZ2uHXrltbaq2nqbWArIiICd+7cEe4nJv4zI+HOnTtq0/xU/0PGxsZi4MCBQjLGhQsXomHDhmUm7GvSpEml/nMsWbIEr7/+OkaNGgUfHx+0adMG5ubmSE1NRVRUFNavX48HDx4AKJwOGxISUuE+iIhKcv5eMnLz//mDXwIlnCzq1x9pRES1nczSEm2jIqvUhkKhwMuXL5GRB6Tk/lNuYiSDQ2PzSo+rKopSfzRp0qTM3eeSk5Nx7NgxhIaGYv78+UK5vpIpx8XFQS6Xw9y89Ndt/PjxWLhwIVxcXODq6lpmez4+Pli3bh2OHj2KCxcuYPbs2cIxLy8vZGVl4eDBg4iLi8Po0aO19TQ0ZmVlJVwzFVc0g6os2lwSV7SbZUxMDBwdHYXyhISEGjHDrE2bNjh8+DCSkpLKnUVV0rl7+/ZtmJqaapzbSdP+KvIeqCZy1+ZKpjt37kCpVIrGc/v2bQBQmxhSnuTkZKSnp2PZsmVYtmyZ2nEHBweMGDEC+/fvr8qQNdaoUSNMmTIFU6ZMQXp6Ovz8/BASEqLVwFZcXJzG50ZtVG8DWxs3bsS2bdtKPBYZGSlM2S2iGtg6ffq0aBrz+++/X26fCxYsqHTQKSkpCRs3bsTGjRtLrdOrVy/s2LFDq9NJiah+U82v1cocMK23vzmIiGoniVRapSTtQGFgS2ZgAFkegJx/ypWGMhg0alC1AVbSwIEDYWFhgUWLFsHf319t18CEhATY2NgIs2lUZ8989dVX1Tq+ov6Lu3TpEn799VcMHjy4zJ0Jp02bJuQKK09RzqwVK1YgLy9PNGPL3t4ezZo1Ey7eK5Nfq6ratGmD1NRUXL58WVhe9eTJE412mDMzMwOAEgNjFdWvXz8YGhpizZo1GDBggBAgqe7zQFOjR4/G119/jdDQUFEydwBqAZ3o6Gj8/fff6NatGwAgPj4ev/zyCwYNGlTq7LHK9mdmZqbx619WgLmqHj9+jH379gnJ49PS0vD999/D1dVVNGOzaEfRsnJeN2nSpMTzb/Xq1YiOjsauXbvUZp9VlxcvXsDa2lq4b25uDicnJ8THxwtleXl5iI2NRcOGDcsdV0mfO//73//w119/CbuM1kW8PKkFvvjiCxw7dgzR0dG4desWEhMTkZKSAlNTUzRv3hweHh4YP3686AOaiEgbmF+LiIiKU/1TU58rrSwsLLBu3TpMmjQJ3bp1Q2BgIGxsbPDgwQMcPHgQ3t7eWLt2LSwsLODn54dly5YhLy8PLVq0QHh4OO7evVupfk+dOoVTp04BKLyIzMjIwMKFCwEULivy8/MDAIwbNw4mJibw8vJCkyZNcP36dWzYsAGmpqZYsmRJmX20bt1a4y/EW7VqBTs7O0RHR8Pe3l5tEykvLy8hUbi3t/Z2x9RUYGAg5s6di5EjRyI4OBiZmZlYt24dnJ2dy03c7+rqCplMhqVLlyI1NRXGxsbo06dPpVbB2NjY4D//+Q8WL16MoUOHIiAgABcuXMChQ4fQuHHjyj49rfH398ekSZOwevVqxMTECMtkT58+DX9/f7z77rtC3U6dOmHgwIEIDg6GsbExvvnmGwCoUL4oTftzc3PD0aNHsXLlSlhaWqJ169al5rWrTs7Ozpg6dSrOnTuHpk2bYvPmzXj27Bm2bNkiqte3b18AJeezKmJqaopXX31VrXz//v04e/as2rGtW7diypQp1bK0skOHDujduzfc3NzQqFEjnD9/Hnv27BG9348ePYKLiwveeOONchPIe3l5oWvXrujevTsaNmyIv//+G5s3b4adnR0++ugjrY69Jqm3ga2tW7dqtKtAaYKCgrR2Up84caLM4927d2cyeCLSuZTMXFx9LE6o68zAFhFRvab6Faq+cwhNmDABzZs3x5IlS7B8+XLk5OSgRYsW8PX1xZQpU4R6O3fuxMyZM/H1119DqVRiwIABOHToUKV2Ev/jjz/UAgjz5s0DULhCoyiw9eqrr2LHjh1YsWIF0tLSYGNjg1GjRmHBggVwcnKqwrNW5+Pjg127dolmaxXx9vbG3r170b59e9HMEF2xtrbGvn378O9//xtz5syBg4MDFi9ejJiYmHIDW7a2tli/fj0WL16MqVOnoqCgAMePH6907qOFCxdCLpdj/fr1OH78ODw8PBAeHo4hQ4ZUqj1t27JlC7p06YJNmzZh9uzZaNiwIbp37672vvbq1Quenp4IDQ3FgwcP0KFDB2zdurXCCcc16W/FihV46623MG/ePGRlZWH8+PF6CWy1bdsWa9aswezZs3Hr1i04ODhg9+7dGDhwYLX3nZ6eDkA9h5g2BAcH49dff0V4eDhycnLQunVrLFy4ULSkuCLGjRuHgwcPIjw8HJmZmWjWrBn+9a9/YcGCBWjatKmWR19zSJT6/m1EtcbDhw+FHT9u375d4m4cVHdlZWUhPDwcADBgwACt7XBCNdehK0/wfzv++YNTbiDF5265MJDyHKiP+BlAPAdqh5iYGOTn58PAwECrf6spFAqkpaUhOx94nv1PuYFUig7N1ROgU91TdA4AhbPlylpKSdVHIpHgnXfewdq1a3Xarz7ff3t7e3Tq1AkHDhzQWZ/FjR07Fvfu3cPZs2f10r8ulfU7JCYmRtiJMj4+Hi1bttTHEEtUb2dsERFR2VTza7m1toSB9HkptYmIqD5QX4rI78iJqO5SKpU4ceIEtm/fru+hUBkY2CIiohKp5tfydLACMhjYIiKqz1SXIipKrFX7FBQUICEhocw65ubmZe5kWNskJSUhNze31OMymazW7KKWkJCAgoKCUo8bGRnpbYMtXZ9b9fFcrk4SiUS0aRzVTAxsERGRmvikTNx7kSkq83RshIdX9DQgIiKqEUrKsaW6Y1ttFB8fDwcHhzLrVGWH85po1KhROHnyZKnHW7duXWYC7prE3d0d9+/fL/V4r169ys1rXF10fW7Vx3OZiIEtIiJSExUrnq1lZWqI9rbmDGwREdV3JcSvlEr1JYq1ja2tLY4cOVJmHUdHRx2NRje+/PJLJCcnl3q8NuXR27FjB7Kysko9bmVlpcPRiGn73Cpv+W9dPJdrS4CV9IeBLSIiUhNx54XovpdTY0hr+1ULERFVWUm/CRRKJaQlHqk95HI5+vXrp+9h6JSbm5u+h6A13t7e+h5CqXR9btXHc5mIW1kQEZGIQqFElEp+LR+nxnoaDRER1SQlha+YPp6IiPSJgS0iIhK5+fQlXmSIk8kysEVEVLtU126FJU3e5c6IRESkTwxsERGRiGp+LbtGJrBrZKqn0RARUUUYGBRmGikoKKiWgFPJSxG13g0REelYQUEB8vPzARTuilqbMLBFREQiEVyGSERUaxkZGQm3ExMTy6hZORIJIFEJb3HGFhFR7VZQUICnT58K983MzPQ4mopj8ngiIhLk5ivwZ1ySqMybgS0iolqjUaNGSEtLA1AY2EpOToZES5t/FH2Tn58vzqt1P0MGmbR2J48nzRSdAwkJCXoeCekD3/+6q+i9LdKwYUM9jaRyGNgiIiLBhQfJyMorEJV5tWFgi4iotjAxMYGlpSVSUlIAFH4Lrw1KpRJZWVkAgNRcCQqKRbak5kYwNqxdy1ao4oqfAyYmJloLmFLtwPe//mjevDnkcrm+h1EhDGwREZEgUmUZYsfmFmhkZlRKbSIiqomaNm0KExMTpKSkIC8vTyttKhQK4aL2YboBcvIVwrEGpsYwM+BlRV1X/BwwNzeHVMqsNvUJ3/+6TSaTwczMDA0bNqx1QS2AgS0iIiqG+bWIiGo/qVQKS0tLWFpaaq3NrKws3Lx5EwCw8VYDPEjKEo6tn+iInm1ttdYX1UzFzwE3NzeYmJjoeUSkS3z/qSZjmJWIiAAAadl5uPQwVVTG/FpERKTK2EB8CZGTr53ljkRERJXBwBYREQEA/oxLQkGxPduNZFK42zfS44iIiKgmUg1s5RZblkhERKRrDGwREREA9fxabq2tYGLEZMBERCSmPmOLgS0iItIfBraIiAiAemDL28laTyMhIqKazIiBLSIiqkEY2CIiIjxLy0bM83RRGfNrERFRSZhji4iIahIGtoiISG22VgO5ATq3aKin0RARUU2mFtjK44wtIiLSHwa2iIgIESqBLU9HaxjI+CuCiIjUcSkiERHVJLxqISKq55RKpdqMLZ+2XIZIREQlM5ZxKSIREdUcDGwREdVzsQnpeJaWIypjfi0iIiqNsYF4x1zO2CIiIn1iYIuIqJ6LiBHP1mrWUA7HxmZ6Gg0REdV0zLFFREQ1CQNbRET1XMSdF6L73k6NIZFI9DQaIiKq6dRzbHEpIhER6Q8DW0RE9Vh+gQJ/xqkGtqz1NBoiIqoN1GZscSkiERHpEQNbRET12OVHqXiZky8q827D/FpERFQ6BraIiKgmYWCLiKgei1TJr+Xc1BxNLOR6Gg0REdUGaksR87gUkYiI9IeBLSKieizijjiwxd0QiYioPJyxRURENQkDW0RE9VRmbj7+fpAsKvNhYIuIiMqhnjyegS0iItIfBraIiOqps3eTkFegFO7LpBJ4ODJxPBERlU11xlYud0UkIiI9YmCLiKieilRZhtjVzhLmxgZ6Gg0REdUWnLFFREQ1ic6vYBISEhAXF4enT58iIyMDhoaGsLS0RKtWreDk5ASZTKbrIRER1UsRd16I7jO/FhERaYI5toiIqCap9sBWRkYGfvnlFxw6dAgnT57Eo0ePSq1rbGyMrl27YsCAARg5ciS6dOlS3cMjIqqXEtNzcONJmqiMgS0iItKEWmCLuyISEZEeVVtg68KFC1izZg1++uknZGZmAgCUSmWZj8nOzkZ0dDTOnDmDTz/9FB07dsQ777yDSZMmwdTUtLqGSkRU70TFimdrmRrJ4GpnqZ/BEBFRrcIZW0REVJNoPbB14cIFzJs3D4cOHQLwTzDL1tYWPXr0gJubG5o0aYJGjRrBysoKWVlZSEpKQnJyMm7fvo1z587h8uXLyMvLw9WrVzFjxgzMmzcPc+bMwcyZM2FsbKztIRMR1TtRKvm1PBwaqeVMISIiKklJObaUSiUkEomeRkRERPWZVgNbU6ZMQVhYGBSKwm9tunXrhtdffx2jR49Gq1atNG4nNzcXp06dwo4dO7Bv3z4kJiZi7ty5+Oabb/D999/Dx8dHm8MmIqpXlEolTseIA1tchkhERJpSnbEFALkFChgbMFcuERHpnla/nt+2bRsMDAzwr3/9Czdv3sT58+fx/vvvVyioBQBGRkbo168ftmzZgmfPnuH7779Hu3btcO/ePfzxxx/aHDIRUb1z/0UmHqVkicp82jKwRUREmikpsMXliEREpC9anbE1Y8YMzJ07F3Z2dlpr09jYGBMnTsTrr7+On376CQUFTE5JRFQVJ249F91vbG6Edk0b6Gk0RERU25QY2MpTAHI9DIaIiOo9rQa21q5dq83mRCQSCcaOHVtt7RMR1Rd/3EoQ3fdztmFeFCIi0lhJORlz8vnlMxER6QczBRMR1SOZufk4EyfeEbFP+yZ6Gg0REdVGXIpIREQ1CQNbRET1SOSdF8gtdvEhk0rg29ZGjyMiIqLaxkAqhUwqnumbk8fAFhER6YdWlyICQPPmzeHn5wdfX1/4+fmhc+fO2u6CiIgq6Y+b4vxa3VtboaGJoZ5GQ0REtZWxgRSZuf8sP+RSRCIi0hetB7aePn2Kn376CT/99BMAwNLSEj4+PvDz84Ofnx/c3NwglXKiGBGRrimVShxXCWz1deEyRCIiqjj1wBZnbBERkX5oPbDVunVr3L9/X7ifnJyMAwcO4MCBAwAAMzMzeHp6CrO6evbsCSMjI20Pg4iIVFx/koanadmiMubXIiKiylBNIM/AFhER6YvWA1t3797Fo0ePcPr0aeHn2rVrUCqVAID09HQcPXoUR48eBQAYGRnB3d1dmNHl7e0NMzMzbQ+LiKjeU52tZdfIBG1szPU0GiIiqs2MDWSi+7kMbBERkZ5oPbAFAC1atEBgYCACAwMBFM7aioyMFAJdf/31F/Ly8gAAOTk5iIyMRGRkJBYvXgyZTAZXV1ch0OXr6wsrK6vqGCYRUb2iml+rT7smkEgkpdQmIiIqnerOiMyxRURE+qKTZFdWVlYYOnQoli5diqioKKSmpuKPP/5AaGgo+vXrB1NTUyiVSiiVSuTn5+P8+fNYuXIlRo4ciSZNqmeZzPPnz3HgwAHMnz8fgwcPRuPGjSGRSCCRSBAUFFTh9g4dOoSRI0eiZcuWMDY2RsuWLTFy5EgcOnRIq+POzMzEsmXL4O7ujkaNGsHMzAzt27fHBx98IFoCSkRUXFJGLi7Ep4jK/LkMkYiIKsnYUCWwxV0RiYhIT6plxlZ55HI5evfujd69ewMACgoK8PfffwszuiIiIvDixQsAgEJRPb8kmzZtqpV2FAoF3nrrLWzatElU/ujRIzx69Aj79+/HtGnT8O2331Y5af6dO3cQEBCAmJgYUfmtW7dw69YtbNy4ETt27MDQoUOr1A8R1T0nbj3H/18RDgAwMZShp6O1/gZERES1mupSRObYIiIifakR2xMqFArk5eUhLy8Pubm5yM/P1+nymFatWmHAgAGVeuzHH38sBLW6du2KXbt24ezZs9i1axe6du0KANi4cSM++eSTKo3x5cuXGDJkiBDU+te//oVjx44hKioKn3/+OczNzZGWloZx48bh4sWLVeqLiOoe1WWI3k6NITeUlVKbiIiobFyKSERENYVeZmxlZGQgKipKmKF19uxZZGcX7tSlLDaloH379vD19a2WMcyfPx/u7u5wd3dH06ZNce/ePTg4OFSojdu3b+OLL74AAHTv3h2nTp2CiYkJAMDd3R3Dhw9Hr169cP78eSxfvhxvvvkmnJycKjXe5cuX4/bt2wCAZcuWYfbs2cIxT09P9O7dG7169UJmZiZmzZqFEydOVKofIqp78gsUOHU7QVTG3RCJiKgq1ANbnLFFRET6oZPA1osXL0S7JF68eBEFBYXf6hQFsoyMjNCtWzf4+PjAx8cH3t7esLauvmUyoaGhVW7jq6++Qn5+PgBgzZo1QlCriKmpKdasWQNPT0/k5+dj5cqV+PrrryvcT15eHlavXg0AcHFxwQcffKBWx8vLC1OnTsW3336LkydP4ty5c3B3d6/EsyKiuuav+8lIy84Xlfm3t9HTaIiIqC5QW4rIHFtERKQn1RLYio+Px+nTp3Hq1CmcPn0aN2/eFI4VBbIsLCzg5eUlBLJ69OgBuVxeHcOpFkqlEr/88guAwpllPXv2LLFez5490a5dO9y6dQu//PIL1q5dW+FllsePH0dqaioA4I033ig1V1dQUBC+/fZbAMC+ffsY2CIiAMAft8TLEDs0s0Czhial1CYiIiqfWvJ4LkUkIiI90Xpgy97eHvHx8QDEywpbtGghBLF8fHzQpUuXWr3N/N27d/H48WMAQK9evcqs26tXL9y6dQuPHj2q1JLHiIgIUVul6d69O0xNTZGZmYnIyMgK9UFEdddxlfxaXIZIRERVxaWIRERUU2g9sPXgwYPChg0MMGbMGAwdOhTe3t5o3bq1trvSq+vXrwu327dvX2bd4sdv3LhR4cCWpn0ZGBjAyckJly9fxo0bNyrUBxHVTfFJmbj9LF1U5s/AFhERVZH6roicsUVERPpRLUsRJRIJCgoK8OOPP+LatWvw9fWFr68vvL290bJly+roUucePnwo3C7vOdnZ2Qm3i2azVaYvMzMzWFpaltvX5cuXkZCQgJycHBgbG1e4n9I8efJEuJ2Tk4OsrCyN26bar2iDB9XbVLMdvvJIdN/K1BDtGhtX6v8vz4H6je8/8Ryo31TffxnEM7Qys/P4t2Edx8+A+o3vP+Xk5Oh7CKXSemBr5cqViIiIQEREBJ49e4YrV67g6tWrWLduHQCgVatWwnJEX19fdOjQQdtD0ImXL18Kt83Nzcusa2ZmJtxOT08vo2bZfZXXT0l9VSSwVTwAV54///wTsbGxGtenuuXUqVP6HgJpaO8NKYB/lou0Mc3BsaNHqtwuz4H6je8/8Ryo306dOoVH8eLfL/fiHyE8vOJf4FLtxM+A+o3vf/2UmJio7yGUSuuBrffeew/vvfceAOD27dvCToinTp3CvXv3cP/+fdy/fx87d+4EAFhZWcHLywu+vr7w8fFB9+7dYWhoqO1haV3xKLWRkVGZdYsHl6oyS6K8frTRFxHVHbkFwJ1UcS7DjlbKUmoTERFpzlAq/n2Sz18vRESkJ9WyFLGIs7MznJ2dMXXqVADAo0ePRIGu69evIykpCQcOHMDBgwcBFAZmevToIczq8vLygoWFRXUOs1KK7+CYm5tbZt3iU/ZMTCq+E1lRX+X1U9W+ylsm+eTJE/To0QMA4OHhgTZt2lSofardsrOzhW9n/Pz8atUupvXV8duJyDt7Wbgvk0gwY1RvWMgr9+UBz4H6je8/8Ryo31Tf//i/nuF/8f/M3m/YyAYDBryir+GRDvAzoH7j+081ecVWtQa2VLVo0QKBgYEIDAwEACQnJyMiIgKnTp3CqVOncOHCBeE/zOnTpwEAMplMo4COrjVo0EC4Xd7ywoyMDOG2JssJS+tLk2WMVemrIvnPjI2NKxWko7pBLpfz/a8FIuJSRPfd7K3Q1Eo7XxTwHKjf+P4Tz4H6TS6Xw8xEnO4iX1G5L3CpduJnQP3G979+qkiaI12Tll+l+lhZWWHYsGFYvnw5/vzzT6SkpGD9+vVwdHSEUqmEUqlEQUHN3GGleBCovKTrxWdCVSSPlWpfGRkZSElJ0agvGxubGn3iEVH1UiqVOH7zuaisD3dDJCIiLVHdFTG3QFFKTSIiouql0xlbJbl27ZowQ+v06dN4/PgxgMKdFZXKmrtYv3jS+5s3b5ZZt/hxFxeXSvW1d+9eoa2ePXuWWC8/P1+YHliZfoio7rj59CWepIp3rOnLwBYREWmJsYH4+/Gc/Jr5ZTQREdV9Og1sFRQU4K+//hJybEVGRiI5OVk4rhrIatWqFfz8/HQ5RI05ODigefPmePz4MU6ePFlm3aK1yC1atIC9vX2F+/Lx8RFunzx5stTA1vnz54WliN7e3hXuh4jqjj9UZmu1tDKBU5OKL4UmIiIqibGhSmArjzO2iIhIP6o1sJWdnY3o6GghkPXnn38iMzNTOK4ayGrXrh38/Pzg5+cHX19ftGrVqjqHVyUSiQQjRozAunXrcPPmTZw5c6bEgNOZM2eEGVsjRoyARCJRq1Oe3r17o2HDhkhNTcW2bdswZ86cEtvZunWrcHvkyJEV7oeI6o6SliFW5vOHiIioJKpLEXPyGdgiIiL90HqOrQMHDmDu3Lnw8vKCpaUl+vXrh9DQUBw/fhwZGRlC7iyJRAJXV1cEBwdjz549ePbsGW7cuIFvv/0Wr7/+eo0OahWZNWsWZLLCX+ozZ85EVlaW6HhWVhZmzpwJADAwMMCsWbNKbCcoKAgSiQQSiQQnTpxQO25kZITg4GAAwI0bN/DFF1+o1YmOjsamTZsAAL169YK7u3tlnxYR1XLJGbn4+0GyqMyfyxCJiEiLuBSRiIhqCq3P2Bo+fLgwK6D4jCwjIyN0794dvr6+8PPzg7e3NywstLM7V2VERETgzp07wv3ExETh9p07d0Szn4DC4JMqZ2dnzJ49G0uWLMH58+fh7e2NuXPnok2bNoiNjcXSpUtx4cIFAMDs2bPRtm3bSo939uzZ2L17N27fvo05c+bgzp07CAwMhImJCY4fP45FixYhPz8fJiYm+OqrryrdDxHVfidvJ0BRbEKs3FAKT0dr/Q2IiIjqHPXAFmdsERGRflTLUkSlUglTU1N4enoKgayePXtCLpdXR3eVsnHjRmzbtq3EY5GRkYiMjBSVlRTYAoDPP/8cz58/x+bNm3HhwgUEBgaq1Zk6dSoWLlxYpfE2aNAABw8eREBAAGJiYrBhwwZs2LBBVMfCwgI7duyAq6trlfoiotpNNb+Wj1NjyA1lpdQmIiKqOGOV3yvMsUVERPqi9cDWsmXL4OvrCzc3NxgY6H3TxWonlUqxadMmjB49Ghs2bMC5c+eQmJiIxo0bw93dHdOnT8fgwYO10peTkxMuXLiAr7/+Gj/99BPu3LmD3Nxc2NnZISAgAO+99x5at26tlb6IqHbKL1Dg5O0EURmXIRIRkbaVtBSxKN0IERGRLmk98vSf//xH201Wi61bt6otN6yKgIAABAQEVPtYzMzMMGfOHMyZM6dSfRFR3XYhPgWpWXmiMv92DGwREZF2qQa2FEogX6GEoYyBLSIi0i2tJ48nIiL9OXZDvAyxvW0DNLc00dNoiIiorlJdiggwzxYREekHA1tERHXIcZX8Wn24DJGIiKqB6owtAMjJ486IRESke1pfitinTx+ttieRSHDs2DGttklEVBc9SsnCrWcvRWV9XRjYIiIi7SsxsMUZW0REpAdaD2ydOHFCSBpZ1QSSTEBJRKQ51d0QrUwN4WpnpafREBFRXWZswKWIRERUM1TbtoVyuRxNmnCmABGRrqguQ+zlbAOZlF8OEBGR9hnKJJBIAKXyn7KcfC5FJCIi3au2wFZ2djaaNWuGyZMnY9y4cbCy4qwBIqLqkpVbgMg7iaIyf+bXIiKiaiKRSGAkk4pmaeXkccYWERHpntaTx3/22WdwdnaGUqnEmTNn8M4776BZs2YYPXo09u/fj7y8vPIbISKiComOSxRdXMikEvRyttHjiIiIqK5TzbOVW8DAFhER6Z7WA1sff/wxbty4gbNnz+Ldd99F48aNkZubi3379mH06NFo1qwZ3nnnHURHR2u7ayKieks1v5ZbKytYmhrpaTRERFQfGBuK82xxxhYREemD1gNbRbp3747Vq1fj8ePH+PXXXzFmzBgYGxsjKSkJ69evh4+PD5ydnfHZZ5/h7t271TUMIqI6T6lU4vjNBFEZlyESEVF1U52xxRxbRESkD9UW2Coik8kwdOhQ/Pjjj3j69Cm+++47+Pr6AgDu3LmDkJAQODk5wdfXF9999x1SUlKqe0hERHXK7WfpeJSSJSrrw8AWERFVM/XAFmdsERGR7lV7YKs4CwsLTJ06FSdOnMDdu3fx6aefwsnJCUqlElFRUXj77bfh4OCgyyEREdV6qssQW1iawLmpuZ5GQ0RE9YWxgcpSRM7YIiIiPdBpYKu4Vq1a4ZNPPsGtW7ewZs0aGBsbQ6lUIicnR19DIiKqlf64+Ux0v0/7JpBIJHoaDRER1RfGhioztphji4iI9MBAXx3Hx8dj+/btCAsLw61bt4RyIyMmOyYi0lRyRi7+up8sKuMyRCIi0gUuRSQioppAp4GttLQ0/PTTT9i+fTtOnz4NpVIJpVIJAPD09MSkSZMwbtw4XQ6JiKhWO3L9GRTKf+7LDaXwbGOtvwEREVG9waWIRERUE1R7YKugoACHDh1CWFgYfvvtN+Tk5AjBLEdHR0ycOBGTJk1CmzZtqnsoRER1zv+uPhHd92/XBHKV7deJiIiqg9qMLS5FJCIiPai2wNa5c+cQFhaG3bt3IzExEUDhlvSWlpYYO3YsJk2aBG9v7+rqnoiozkvNzEPknURR2eDOzfQ0GiIiqm+MDVVnbDGwRUREuqf1wNbChQuxY8cO3L59G0BhMMvQ0BCDBw/GpEmTMGzYMObRIiLSgqM3niGv4J91iEYGUubXIiIinVHPscWliEREpHtaD2zNnz8fEokESqUSHh4emDx5MsaNG4dGjRppuysionrtf1fEyxB7OdvA3Fhve4IQEVE9w+TxRERUE1TbFZCJiQmePXuG5cuXY/ny5ZVuRyKRIDY2VosjIyKq/dKy83A6RrwMMaCzrZ5GQ0RE9ZFa8njm2CIiIj2otsBWVlYW7t27V+V2JBJJ1QdDRFTH/HHjOXIL/rmAMJJJ0delqR5HRERE9Y2xIZciEhGR/mk9sOXn58dgFBFRNVNdhujbtjEs5IZ6Gg0REdVHRjIuRSQiIv3TemDrxIkT2m6SiIiKSc/Jx4nbCaIy7oZIRES6pj5ji4EtIiLSPWn5VYiIqCb54+Zz5Ba7eDCQStCfyxCJiEjHVHNs5TKwRUREesDAFhFRLXNIZRmit1NjNDTlMkQiItIt9V0RmWOLiIh0j4EtIqJaJDM3H8dvPReVDeEyRCIi0gP1wBZnbBERke5pNbD15MmT8itV0dOnT6u9DyKimurErQRkF9tOXSaVoH8HLkMkIiLdMzYUL0XMyWNgi4iIdE+rga02bdogODgYjx490mazAIAff/wRXbp0wYYNG7TeNhFRbaG6G6JXG2tYmRnpaTRERFSfcSkiERHVBFoNbOXn5+Prr7+Gk5MT3njjDYSHh0OhqPw3N/Hx8Vi2bBlcXFwwfvx4XL16FUZGvIAjovopO68Af9wUL0Mc3InLEImISD+4FJGIiGoCA202dvXqVbz//vs4dOgQtm/fju3bt6NJkyYYMWIEevbsCXd3d3To0AESiaTExycmJuLcuXM4e/Ysjh07hqioKCiVSiiVSrRo0QKhoaEICgrS5pCJiGqNE7cSkJn7z7fhUgkwoCOXIRIRkX6o7orIwBYREemDVgNbzs7OOHjwIKKiorBw4UIcPnwYz549w3fffYfvvvsOAGBkZARra2tYWVnBysoKWVlZSEpKQnJyMlJTU4W2lEolAKBly5aYOXMmZs6cCblcrs3hEhHVKoeuipch9nS0RmNzYz2NhoiI6jtjQ5UZW3lcikhERLqn1cBWES8vL/zvf//D7du3sXnzZvz000+4e/cuACAnJwePHz/G48ePIZFIhABWccbGxhg4cCD+9a9/YfDgwZBKuXkjEdVv2XkFOHZDZRkid0MkIiI94lJEIiKqCaolsFXE2dkZS5YswZIlS/DgwQOcPn0aUVFRePjwIRISEpCUlAS5XA4bGxvY2Nigc+fO8PX1RY8ePZhLi4iomIiYRKTn5Av3JRJgIJchEhGRHqkuRcxXKJFfoICBjF9KExGR7lRrYKu4Vq1a4fXXX8frr7+uqy6JiOoM1d0Q3e0boUkDLs8mIiL9UZ2xBQC5DGwREZGO8bcOEVENl5NfgCM3nonKAjrZ6mk0REREhVRzbAFATh6XIxIRkW4xsEVEVMNF3XmBl9n5ojLm1yIiIn1TXYoIMM8WERHpHgNbREQ1nOoyxO6trdDUgssQiYhIv0paipiTz50RiYhItxjYIiKqwfIKFAi/Ll6GyNlaRERUExiVkEuLM7aIiEjXGNgiIqrBomJfIDUrT1Q2iPm1iIioBpBKJWrBrVwGtoiISMcY2CIiqsEOqSxDdLWzRAtLEz2NhoiISEx1OSKXIhIRka4xsEVEVEPlFyhw+NpTUdkQLkMkIqIaRHVnRO6KSEREusbAFhFRDfXn3SQkZ3IZIhER1VyqOyMyxxYREekaA1tERDXUQZVliF1aNoRdI1M9jYaIiEgdlyISEZG+MbBFRFQDFSiUOHxVvAxxcCcuQyQioprFSC2wxRlbRESkWzoJbPXp0wd9+vTBli1bdNEdEVGtd/ZuEl5k5IrKAjpzGSIREdUsxoYqSxGZY4uIiHRMJ4Gt06dP4+TJk7C3t9dFd0REtd6hq+JliB2bW6C1tZmeRkNERFQyLkUkIiJ900lgq0mTJgAAS0tLXXRHRFSrKRRKHFJZhhjA3RCJiKgGUg9sccYWERHplk4CW6+88goA4Pbt27rojoioVjt/PxkJL3NEZYO5GyIREdVA3BWRiIj0TSeBrWnTpkGpVGL9+vW66E5nevfuDYlEUqGfEydOVLifkJCQam2fiGqW/6nshtjetgEcbcz1NBoiIqLSGRuqzNjK41JEIiLSLZ0EtkaNGoWJEyfi5MmTePPNN5GRkaGLbmscqVSKtm3b6nsYRFSDKRRK/M7dEImIqJbgUkQiItI3A1108v3336Nv3764fPkytm3bhl9++QXDhg1Dly5dYGVlBZlMVubjJ0+erIthVtiWLVvKDdJdv34d48aNAwD07dsXLVq0qFKfV65cKfO4g4NDldonIv26EJ+Cp2nZorIhXbgMkYiIaiYuRSQiIn3TSWArKCgIEolEuJ+cnIywsDCNHiuRSGpsYEuTIFLx56mN59GpU6cqt0FENdchlWWIbZuYw6lJAz2NhoiIqGzcFZGIiPRNJ4EtAFAqlWXer4sUCgV27NgBADA3N8eoUaP0PCIiqslK2g1xMHdDJCKiGkwtsJXHGVtERKRbOgls3b17Vxfd1DjHjh3Do0ePAABjxoyBqampnkdERDXZuXtJeJSSJSrjbohERFSTqQW2ChjYIiIi3dJJYKt169a66KbG+f7774XbNXU5JRHVHPsuPBLdd25qjva2XIZIREQ1l7GhSo4tztgiIiId09lSxPomPT0d+/btA1AY2Ovdu7dW2h0wYAAuXryIlJQUWFpaokOHDhg0aBCmT58OKysrrfRBRLqXnVeAgyr5tUZ2bSnKT1iv5OcASl4c1Sh5WZAqcoXb/AuiHuI5UL/9//dfKREHsphji4iI9I1/klSTvXv3CjsmTpw4UWsXp0eOHBFuJyQk4OTJkzh58iSWLl2KrVu3YsSIEZVu++HDh2Uef/Lkn4vunJwcZGVllVGb6prs7OwSb5N2HL7+HC+z84X7EgAD2zeqUf/PdHEOSJ5chOHxEEjj/4QEdT8XY21iAmBY0Z1LehwI6Q3Pgfqt6P3PlxghHxORNWAhIJFCohQHsrJy82vU7y7SHv4tWL/x/aecnBx9D6FUOg9sxcTE4Pvvv0d0dDSePn2KrKwsHD58GE5OTkKdq1ev4sGDBzAzM0OvXr10PUSt0PYyxM6dO+PVV19Fjx490Lx5c+Tl5eHWrVvYsWMHwsPDkZKSgtGjR+O3337D4MGDK9WHnZ2dxnX//PNPxMbGVqofqv1OnTql7yHUOZtuSgH88623k4UCV/48hSv6G1KZtH4OKBVo8/wwOjz5EVIlv+0nIqqpDJS5MLi4GaczmiHJvB3uPJcA+GcWV8KLZISHh+tvgKQT/FuwfuP7Xz8lJibqewil0llgS6FQYM6cOVi1ahUUCoWwK6JEIkFubq6o7oMHDzB06FAYGBjg7t27aNGiha6GqRUPHz7EiRMnAAA9e/aEs7NzldqbNWsWQkJC1Mo9PDwwefJkfPvtt3j77bdRUFCAadOmITY2FnK5vEp9EpHuZOQB11PEszq729Sf2UrGeSnodn8Dmry8qu+hEBGRhiyyHiLJvB0MVBYlMMUWERHpms4CW9OnT8fmzZuhVCrRokULeHp6Ys+ePSXWDQgIgIODA+7du4c9e/bgvffe09UwtWL79u1QKAp/q7/xxhtVbs/S0rLM49OnT8e5c+ewadMmPH78GHv37sXrr79e4X7i4+PLPP7kyRP06NEDQGFQrU2bNhXug2qv7Oxs4dsZPz8/Bk+1aNe5hyhQ3hbuGxtI8f4YPzSQ16zV4tVxDkjjjsPo4KeQZNbcb4CIiEhdB8cWcPYcANx4jrA7/3wxYWRihgEDeupxZFRd+Ldg/cb3n2ryii2dXDUdO3YMmzZtgkQiwUcffYTQ0FDIZDJIpdJSH/Paa69h2bJl+OOPP2pdYCssLAwAYGxsjHHjxumkz+nTp2PTpk0AgJMnT1YqsNWyZUuN6xobG8PExKTCfVDdIJfL+f5r0cFrCaL7/To0RROrmr0bYpXPgfxc4FgoEL22hIMSwOd9oNukwttUI2Tn5OD06dMAAF9fX8iNjfU8ItI1ngP1W8GhDyGL+V24b1iQAUMTE1iYin8X5BUo+TdCPcC/Bes3vv/1k3EN/r2vk8DWhg0bABTOxFq4cKFGjymaGXTt2rVqG1d1OH/+PK5fvw4AGDp0qM52KuzQoYNw+9GjRzrpk4iq7v6LDPx1P1lUNqpr7Vp+XWEvYoE9bwJPLqofM7cFRm0AHGtnfsW6TJmVhUzjW4W3LVsD/IO23uE5UL8pzW3FBdlpALgrIhER6Z9OAlvR0dGQSCSYOnWqxo8pmj309OnT6hpWtSieNF4byxA1pa1dF4lIt/ZfeCy638jMCH7ONnoaTTVTKoFLPwAHPwDyMtSPOw8CRnwDmFnrfmxERFQmpbyhuCA7FQBgbKgS2GKSLSIi0jGdBLaeP38OALC3t9f4MYaGhgCA/Pz86hhStcjLy8MPP/wAALCxsan07oSVUTRLDACaN2+us36JqPKUSiX2XXgoKhvWpRkMZaUv0661stOAg/8GrvykfkxmBAxYCPR4C2CQnoioZjK2EN8vCmwZyETFOfkMbBERkW7pJLBlZmaGlJQUJCQklF/5/3v4sPBir1GjRtU1LK07dOiQ8BwnTJgAAwPdJX7+9ttvhdu9enEJD1FtcDE+BfdeZIrKXq2LyxAf/gXsfRNIvqd+rLEzMGYzYNtZ58MiIiLNKY1LmbGlshQxt0ABhUIJqZRfVBARkW7oZFqAo6MjAPGsovIcOnQIANCxY8dqGVN1KL4McfLkyRo9ZuvWrZBIJJBIJAgJCVE7fuXKFdy5c6fMNjZs2ICNGzcCAGxtbTFy5EjNB01EerPvgjgfnkNjM7jaWepnMNUlai2weUDJQa1uk4G3TjCoRURUCyjlms3YAgqDW0RERLqikylFAwYMwF9//YWvv/4aM2fOLHM3RKAwAFYU8AkICNDFEKssOTkZBw4cAAB06tQJ3bp100q7f/31F6ZNmwZ/f38MHjwYnTt3hrW1NfLz83Hz5k3s2LED4eHhAACZTIYNGzbAzMxMK30TUfXJK1Dgt0vi/FqvuraoW/nyruwBwj9WLzduCAxfBXRkEJ6IqNYobSmiofrf9Tl5CsgN1QNeRERE1UEnga3g4GCsXr0asbGxePvtt/HNN9+UukzvyJEjmDJlCrKzs2FtbY1//etfuhhile3evRs5OTkANJ+tpamCggIcPXoUR48eLbWOtbU1Nm3ahGHDhmm1byKqHidvJSA5M09U9mrXOpQfryAf+OMz9XI7D2D0RsCyle7HRERElaYsJbBlVEJeyMKdEQ11MCoiIiIdBbaaNm2K9evXY/Lkydi0aRMOHz6MIUOGCMdXrVoFpVKJyMhI3Lx5E0qlElKpFFu3boW5ubkuhlhlYWFhAApnTb3++utaazcgIACbNm1CdHQ0Lly4gGfPnuHFixdQKpVo1KgRXnnlFQwaNAhBQUGwsLAov0EiqhH2XRQvQ3RrbYXW1nVotuXVPerLD33+Dfh/DMh0l3+QiIi0RHVXxIIcIC8bxobqn+lMIE9ERLqks6uL119/HYaGhpg+fTri4+Px7bffCktuivJDKZVKAIC5uTm2bdsmCn7VdJGRkZV6XFBQEIKCgko93qRJE7z55pt48803KzkyIqpp0rLzcOT6M1FZnUoarygATi0XlzXvBvSdz10PiYhqKbUZWwCQkwYjk8bqxQxsERGRDul0T/mxY8fizp07CA0NhZubG2QyGZRKpfDTsWNH/Pe//8WdO3eYAJ2I6qzfrzxFbrE/+g1lEvy/9u48LKqy/QP498wMMOwIiIo7KOK+4q5opua+lZYtWprWa2Zlli2a9Xuz1d7KstJcKsu1NNdyA1xRUTR3FHADVEBlh9nO7w9i5DAM68wcBr6f6+Jy5jnPOefGORyGe57nfoa3rSdjRBZ2bhOQWmTRi9A3mNQiIrJnxSW2ctOgUiqgKrICYv5URCIiItuw+XwQHx8fzJs3D/PmzYPBYMDdu3eh1+vh7e0NBwfOxSei6u+P6JuS5/1a+KGWq6NM0ViYwQDs/1zaVrctEPSIPPEQEZFlqJygExyhEjUP2owrIyqg0zxIZnHEFhER2ZKshU4UCgV8fU2HLxMRVVcJ93MQGXdX0ja2Ok1DvLgNSL4gbevL0VpERNWBTukCla5wYus+AMDJQYmswoktLRNbRERkOzaZipidnW2L0xARVXl/Fika765WoX+wn0zRWJgoAvs/lbb5tQKCh8sTDxERWZRW5SJtKDRiqzBORSQiIluyyYitWrVqoUuXLujbty/69euH3r17w9W1Gq3+RURUBqIoYtNJaWJreLt6UDsoZYrIwmL+Am6dkbb1mQ0obFrOkYiIrESrcJY2mE1sccQWERHZjk0SW1qtFpGRkYiMjMSnn34KpVKJTp06ITQ01Jjocnd3t0UoRESyOZeYjst3MiVtoztUk2mIoghEFBmt5dMcaM2FQIiIqgutssgH07npAAAnlfQDGia2iIjIlmzyMfrChQsxePBguLm5QRRF6HQ6HDt2DJ9//jmGDx8OHx8fdO3aFXPmzMH27duRnp5ui7CIiGxqc7R0tFZ9L2eENPGWKRoLu7IXSDwpbev7OqCoJqPRiIgIWqWZqYgORUZsaTkVkYiIbMcmI7bmzp2LuXPnQq/X4+TJkwgPD0dERAQOHjyI9PR06HQ6REVF4cSJE/jiiy+gUCjQvn1744iuESNG2CJMIiKr0ekN+PN0oqRtdEd/KBTVoKh6cbW1ajUF2jwqTzxERGQVZa+xxRFbRERkOzZdFVGpVCIkJAQhISGYM2cODAYDoqOjERERgfDwcBw8eBD379+HXq9HdHQ0oqOj8dVXX0Gn09kyTCIiizscm4rkjDxJ25jqshpi/H7gxlFpW5/XAKWsC+8SEZGFmR2xxamIREQkI1kr+ioUCnTu3BmvvfYatmzZgsuXL2PevHnw9PQEkF9oWRRFOUMkIrKIotMQ29b3RDO/alJbcP9n0ueejYB2j8sTCxERWY35xBZXRSQiIvnI+nH6/fv3sX//foSHhyM8PBz//POPSTKrcePGMkZIRFR52Rod/jp3S9I2urqM1rp2GLh6QNrW+xVA5ShLOEREZD1lr7HFEVtERGQ7Nk1smUtkATD+26RJE2NtrX79+jGxRUR2b9e528jWPPj0WqkQMLK9v4wRWVDRlRDd/YGOT8kTCxERWRWnIhIRUVVkk8TWa6+9ZnZEVtOmTSWJrEaNGtkiJCIim/mjyDTE3s18UdvdSaZoLOhmFBAXJm3rNQtQVYPvjYiITOjMJLYclZyKSERE8rFJYuvLL7+EIAgQRRFNmzY1JrH69euHhg0b2iIEIiJZ3MnIxcHLyZK2sZ2qyTTEoqO1XP2AzpPkiYWIiKzOZMRWXjoA06mIGo7YIiIiG7Jp8XhBEODq6mr8cnFxKX0nIiI7tuVUIgyF1sBwcVRiYKs68gVkIcKt08Dlv6WNvV4GHJzlCYiIiKzOJLGlzQZ0mmKKxzOxRUREtmOTEVvPPPMM9u/fj6tXr+Ls2bM4d+4cvv32WwiCgFatWhlHb4WGhsLHx8cWIRER2cTmU9JpiI+0qQsXR1nX7bAIh8NfShtcfIAuz8kSCxER2YZJYgsA8tJZY4uIiGRlk7+uVq1aBQC4fv06IiIiEB4ejoiICMTFxTHRRUTV1uXbGTibkC5pG1MNVkP0yLkO5eWd0sYeMwBHV3kCIiIimyg2sZWbZjpiS8saW0REZDs2HTbQqFEjPP3003j66acBADdv3kRERAQiIiIQFhaG2NhYk0RX69atcfr0aVuGSURkEZuKFI33c3dCz0BfmaKxnKBbW6QNai8g5HlZYiEiItsxKByhFxygFLUPGnPvw8nBS9KPI7aIiMiWbFpjq6gGDRrgySefxNKlS3H58mXcvHkT8+bNg4eHB0RRhMFgwNmzZ+UMkYioQnR6A34/eVPSNqqDP5QKQaaILMMtNwH+949LG7v/B1B7yBMQERHZlMmordy0YqYicsQWERHZjuyFXmJiYhAeHm6cnnjr1i0AMK6iSERkj/ZevIPb6XmStjEdG8gUjeUE3doKAYXuzU4eQLfp8gVEREQ2pVM6A7q0Bw3FTUXkiC0iIrIhmye2zCWyAEgSWc2aNTPW2SIisje/Hr0ued6hoRda+dv3qCbhbhwa3Dsibew6DXD2kiUeIiKyPdMRW+lwcihaY4uJLSIish2bJLaWLl1aaiIrKCjImMjq168f6tWrZ4vQiIgs7lpqFvbHJEvanureWKZoLEd15CvpaC0H1/yi8UREVGMUOxXRg1MRiYhIPjZJbL3wwgsmUwuDg4Mliaw6derYIhQiIqv77Zh0tJaHWoXh7ew8WX/vKpTnNkrbuk4FXLzliYeIiGRRbGLLm1MRiYhIPjabitiyZUtjIis0NBR+fn62OjURkc3k6fTYECUtGv9o54ZQOyjN7GEnwj+GID74BF5UOUPoMVPGgIiISA5apau0odji8UxsERGR7dgksXXnzh34+tr/EvdERKX56+wt3M3SSNomdmskUzQWcvs8cHqtpEnf4Rmo3GrLFBAREcml2BFbJjW2OBWRiIhsR1F6l8pjUouIaoqiReO7B3ijmZ+bTNFYyL7/AwrV1tIp1NBytBYRUY1UbGKLqyISEZGMbJLYMken0yE5ORnJycnQ6XRyhkJEVGkxtzNwLP6upM3ui8bfOAZc2iFpuuL3CODCDyyIiGqi4hNbplMRC9fWJSIisiabJ7YuXLiAmTNnomXLllCr1ahbty7q1q0LtVqNli1b4uWXX8b58+dtHRYRUaX9VmS0lq+bIwa1qitTNBYgisCeBZKmPJU7Yv2GyBMPERHJTqt0ljbkpcNRZfonhUbPUVtERGQbNk1svfXWW2jXrh2WLFmCS5cuwWDI/zRHFEUYDAZcunQJ3377Ldq3b4+3337blqEREVVKtkaH309Ki8aP79Kw2Df7duPKXuDaIUlTTJ0R0BX9o4aIiGqM4ovHm/6u43REIiKyFZutijhz5kwsWbLEOCy5ZcuW6NatG+rWzR/NcOvWLRw7dgznz5+HXq/HJ598gqysLHz11Ve2CpGIqMK2nU5CRu6DKdWCADzR1Y6LxhsMwN4F0ib3+rjq+5A88RARUZVQlhpbAKBhYouIiGzEJomtQ4cO4dtvv4UgCGjVqhWWLl2Knj17Ftv3yJEjeOGFF3DmzBl88803mDBhgtm+RERVxa9Hr0mehwbVRkNvFzO97cD5TcCtM5ImXe/XYbjlKFNARERUFeiKJrY0mXBSmtbT4ogtIiKyFZvMkfnhhx8AAE2bNsWhQ4dKTFT16NED+/fvR0BAAADg+++/t0WIREQVduZmGk7fTJO0PdXNjovG67XAvv9K23xbQN/mMXniISKiKkOrMv3QxkmXadKWp9XbIhwiIiLbJLYOHDgAQRAwd+5ceHp6ltrf09MTb775JkRRxIEDB2wQIRFRxRUdreXvqUb/YD+ZorGA6F+Au3HStofeBRQ2m71ORERVlMlURAAqTToUgrSNI7aIiMhWbJLYunXrFgCgY8eOZd6nU6dOAIDbt29bJSYiIktIz9Xiz1OJkrbHuzaCsug7fHuhyQbCP5G2+XcCWo6QJx4iIqpS9IIjRIWDpE3IS4OTSilpY2KLiIhsxSaJLbVaDQDIysoq8z4FfZ2cnKwSExGRJWyOTkBOoekWSoWACSENZYyoko4tBTJvSdseXpBfDZ+IiEgQACd3aVtuGpwcpH9WcCoiERHZik0SW02bNgUAbN26tcz7FPQtqLVFRFTViKKIXyOvS9oGtqyDOh5qmSKqpJx7wMEvpG0B/YGAUHniISKiKklUFyktkptusjIiR2wREZGt2CSxNXToUIiiiMWLF2Pv3r2l9g8LC8PixYshCAKGDh1qgwiJiMov6to9XLqdIWl7qrsdF40/9DWQKy2CjwHz5YmFiIiqLicP6fNcTkUkIiL52CSx9corr8DDwwNarRZDhgzBSy+9hJMnT8JgePALz2Aw4OTJk3jppZfwyCOPQKPRwMPDA6+88ootQiQiKrdfI6VF45v4uKBnoI9M0VRSxi0g8jtpW6tRQP1O8sRDRERVluhUdMRWWjEjtjgVkYiIbMMmS1z5+vpi/fr1GDlyJDQaDb777jt89913cHR0hLe3NwRBQGpqKjQaDYD86T2Ojo7YsGEDfHzs9I9EIqrW7mZpsOOMtBbVxG6NoLDXovH7PwN0OQ+eC0rgoXnyxUNERFWXupgRWyY1tjhii4iIbMMmI7YAYNCgQYiMjESXLl0giiJEUUReXh6SkpKQmJiIvLw8Y3uXLl1w9OhRPPzww7YKj4ioXDaeuAGN/sGbdkelAo92ttOi8XfjgBOrpG0dnwR8m8sSDhERVW3Fj9jiVEQiIpKHTUZsFejQoQOOHTuG48ePY8+ePTh79izu3r0LAPD29kabNm3w8MMPIyQkxJZhERGVi8Eg4tej0qLxQ9vWhbero0wRVVLYQsCge/Bc6QSEzpUvHiIiqtLE4kZscSoiERHJxKaJrQIhISFMXhGR3ToUm4JrqdmSNrstGn/rDHBmg7St6/OAZ3154iEioqqvTDW2OGKLiIhsw6qJre3bt+Ovv/7CtWvXoNfr4e/vj379+mH8+PFwcHCw5qmJiKzm10jpaK0WddzRuXEtmaKppL3/J33u5AH0mS1PLEREZBdEJ3dpQ1666VRE1tgiIiIbsUpi6/bt2xg9ejSOHTtmsm3FihWYP38+Nm/ejLZt21rj9EREVnM7PRe7L9yWtD3ZvREEwQ6Lxl87DFz+W9rW82XAxVueeIiIyD6oTUdsOXpxKiIREcnD4sXj9Xo9Ro4ciaNHjxqLwRf9io+Px+DBg5GSkmLp0xMRWdW64zegN4jG584OSozuaIfT9kQR2PO+tM21NtD9RXniISIiuyE6lV5jS8OpiEREZCMWT2ytX78ex48fhyAIaNasGZYvX44zZ87g4sWL2LBhA7p37w4gf1TXokWLLH16IiKr0ekNWHNMOg1xVAd/eKjtcGr15V3AjUhpW985gJObPPEQEZH9KGbElpMDa2wREZE8rJLYAoAmTZrg2LFjePbZZ9G6dWsEBQVh3LhxOHDgAEJDQyGKIjZs2FDK0YiIqo6wS8lISsuVtD3ZzQ6Lxhv0wN4PpG1ejYDOk2UJh4iI7IvJiK28dKiVRZo4FZGIiGzE4omt6OhoCIKA2bNnw8vLy2S7UqnE++/nT3+Jj49HRkaGpUOwKUEQyvTVr18/i5xvzZo1GDRoEOrWrQu1Wo3GjRvjqaeewpEjRyxyfCIy79ej1yTP2zfwRNsGnmZ6V2HRq4HbZ6Vt/d4GVE7yxENERHZFLLoqIgB3IUfynCO2iIjIViye2EpOTgYAdOnSxWyfwttYZ6tscnJyMGzYMEycOBG7d+/G7du3kZeXh+vXr+PXX39F7969jQlDIrK8G3ezERGTLGmzy9FaOfeBvUXuFbVbAu3GyxIOERHZoaJTEQF4IFvynKsiEhGRrVh8VcScnBwIggA3N/N1WlxcXIyPc3NzzfazJy+++CL+85//mN3u6upaqeM/99xz2LFjBwCgf//+mDVrFvz9/XHmzBksXLgQsbGxWLBgAerVq4dp06ZV6lxEZOrXo9chPqgZD3e1CsPb15MvoIoK/wjITpW2Df4QUCiL709ERFSUgwsgKAHxwXRDt6KJLU5FJCIiG7F4Yqu8xMJ/KdoxPz8/tGnTxirH3rdvH9auXQsAGDFiBDZt2gSlMv+P0JCQEIwcORKdO3fG9evX8eabb+Kxxx5DrVq1rBILUU2Ukas1mYY4rlMDuDjKfgstn9vngWPLpG3Bw4FmA+SJh4iI7JMgAGoPIOeesclNzATw4INcTkUkIiJbsfhURLK8zz//HACgUqmwZMkSY1KrgK+vLz755BMAwP379/Hjjz/aPEai6mzNsevIyNUZnwsC8HQPO5uGKIrAzjckn65D6QQM+q98MRERkf0qMh3RVcySPGdii4iIbMVqww2WLFkCPz8/i/SbP3++pcKyOxkZGdi7dy8A4OGHH0aDBg2K7Td27Fh4eHggPT0dmzZtwpw5c2wZJlG1pdEZsPxgvKRtUKs6CKxtfrp1lXT+T+DqAWlbr1mAd1N54iEiIvtWJLHlYsgE8OA9PaciEhGRrVgtsfXdd9+VuF0QhDL1A2p2Yuv48ePQaDQAgNDQULP9HB0d0b17d+zatQvHjx+HVquFg4ODrcIkqrY2n0rA7fQ8Sdv00ECZoqkgTTaw611pm0cDoPer8sRDRET2r0hiy9mQKXnO4vFERGQrVpmKKIqixb7sxYYNG9CqVSu4uLjA3d0dzZs3x6RJkxAWFlap454/f974ODg4uMS+Bdt1Oh0uX75cqfMSEWAwiPghIlbS1rWpNzo1srMadoe+BNJuSNsG/xdwdCm2OxERUamKJLbUuiKJLU5FJCIiG7H4iK3KJnLsVeEEFABcuXIFV65cwc8//4zRo0dj1apV8PQ0XRq5NDdv3jQ+NjcNsUDDhg2Nj2/cuIFWrVpV+FzFSUpKMj7Oy8tDTk5OuY5P9q3wCqbVZTXT0uy9mIzYZGnNkOd6NLCra1+4fw1OB7+EUKhN36gXNE0HA+X8PmriNUAP8PUnXgM1W9HX30HlJvlDwkFzX9pfq7Or35dUOt4Daja+/pSXl1d6J5lYPLFV0nS56sjFxQUjR47EgAEDEBwcDDc3NyQnJyMiIgLff/89UlNTsXnzZowaNQq7d+8u9/TAjIwM42M3t5Jr+ri6PliJJjMzs4SexSucGCvN0aNHERsbW3pHqpb2798vdwg28eVZJVAoJVTPWURu3Ensije/T1UTEvcV/PUPfgkZoECEy1Bk7N5dqePWlGuAisfXn3gN1Gz79+9H61v30KxQW/rtq5I+mTl52LVrl03jItvhPaBm4+tfM6WkpMgdgll2tlZ91ZOQkAAvLy+T9oEDB2LmzJkYMmQIoqOjERERge+++w4vv/xyuY5fOBvu6OhYYl8nJyfjY35CRlQ5selAfIYgaRtQ3wBBMLNDFVQ7/Sz8005I2q7WHoAM57InsYmIiIqjVUqnszsbpO89ORORiIhshYmtSiouqVWgTp062LhxI4KDg6HVarF48eJyJ7bUarXxcUEReXMKDw10dnYu13mA/OmLJUlKSkLXrl0BAN26dUNgoJ0V0KZKyc3NNX4607dvX8m1WR29uOY0gFTj83qeTnjj8R5wUFqlNKHl6bVwWvl/kibR2Rv1Ji5GPbVXhQ5Z064BkuLrT7wGarair7/r2evArT+M2/3cVYV/bUInChg0aJCtwyQr4j2gZuPrT1V5xhYTW1YWEBCAgQMHYseOHbhy5QoSExPh7+9f5v3d3d2Nj0ubXpiV9aAWUGnTFotTWg2vwpycnCqUPKPqQa1WV+vXP+Z2BsJjUiVtz/cJhIebq5k9qqAjK4BU6SISwsPvwblWPYscvrpfA1Qyvv7Ea6BmU6vVcHT3lbQ5FikebxABB0cnqOzlAyEqF94Daja+/jVT4RliVQ1/09hA4SLuCQkJ5dq3cLKptOLuhUdcladeFhFJ/RARJ3nu6eyACSF29DOVeQcI/1jaVq890PFpeeIhIqLqp8iqiEpNukkXroxIRES2wMSWDQiVKMpTOCl28eLFEvsWbFepVGjevHmFz0lUkyXez8Gfp6QJ6Ek9GsPVyY4GuO55H8gr8gfGkM8AhVKeeIiIqPopkthSFP29Aya2iIjINpjYsoHz588bH5dnGiIAhISEGIvGR0REmO2n0WgQGRlp3Ke8qy8SUb7lB+OhM4jG52oHBSb1bCJfQOV18wRwarW0rd3jQKNu8sRDRETVU5HElpCXDgHSRFaeTm/LiIiIqIZiYsvK4uPjsXv3bgBAYGAg6tevX6793d3dMWDAAADAnj17zE5H/OOPP5Cenv9J2ZgxYyoRMVHNlZatxZpj1yVt47s0hI9b1Z1PLmEwADvnSNsc3YCB78sTDxERVV9FE1sQ4YZcSVueliO2iIjI+pjYqoStW7dCp9OZ3X779m2MGzfOuJrhf/7zH5M+q1atgiAIEAQBCxYsKPY4r7/+OgBAp9NhxowZ0Ouln36lpKTgzTffBJC/SuPUqVMr8u0Q1Xi/RF5FtubBz5dCAKb2DpAxonI6/RuQcELaFvoG4F5XnniIiKj6KpLYAgB3ZEuecyoiERHZgh0Vjal6Zs6cCa1Wi3HjxqFHjx5o0qQJnJ2dkZKSgvDwcPzwww9ISUkBAPTu3RszZsyo0HkeeughPP7441i7di22bNmCgQMH4pVXXoG/vz/OnDmDDz/8ENev548y+eSTT1CrVi2LfY9ENUWuVo+Vh65K2oa180cjHxd5Aiqv3DRgzwJpm08zoNuLsoRDRETVnKMbAAHAg+n7vqocJBb6zJdTEYmIyBaY2KqkxMRELF68GIsXLzbbZ9y4cfjxxx8rtTzmihUrkJ6ejh07diAsLAxhYWGS7QqFAvPmzcO0adMqfA6immzDiZtIzdJI2qb3taPRWhGfAlnJ0rZHPgFUjvLEQ0RE1ZtCAag98j9Y+ZePKgeQJLY4YouIiKyPia1K+OmnnxAREYEjR44gLi4OKSkpSE9Ph5ubGxo2bIiePXti0qRJ6NGjR6XP5ezsjO3bt+O3337DqlWrcPr0ady/fx916tRBnz598NJLL1nkPEQ1kd4gYtn+OElbn+a+aFPfdJpFlZR8CTj6vbStxVCg+cPyxENERDWD2lOS2PJWssYWERHZHhNblRAaGorQ0NBKHWPy5MmYPHlymftPnDgREydOrNQ5iUhq59kkXL8rrQvyQmigTNGUkygCO98ADIU+Ilc6AoM/lC8mIiKqGYrU2aqlKFpji1MRiYjI+lg8nohqNFEU8X1ErKStbX1P9Az0kSmicjq9BogLl7b1nAl429E0SiIisk9qL8lTL5PEFkdsERGR9TGxRUQ12uHYVJxNSJe0vRAaCEEQZIqoHNKTgL/mStvc/YE+s+WJh4iIapYiI7Y8hRzJc47YIiIiW2Bii4hqtKKjtRr7uOCRNnVliqYcRBHY/pqktgkAYPj/AEdXeWIiIqKapUhiy0MoMmKLNbaIiMgGmNgiohrrbEIaDlxOkbQ93ycASoUdjNY6+ztwaYe0re14oMUj8sRDREQ1j0liK0vynFMRiYjIFpjYIqIaq+hoLV83RzzauYFM0ZRDZjKwY460zbU2MOQTeeIhIqKayclD8tRNZPF4IiKyPSa2iKhGup6ajR1nkiRtz/ZqCrWDUqaIymHnHCDnrrRt2CLAxVueeIiIqGYqMmLLTZSO2NJwxBYREdkAE1tEVCMtOxAHg/jguaujEk91ayxfQGV1fgtwbpO0rdWo/C8iIiJbKpLYchUzJc85FZGIiGyBiS0iqnFSMvOwPuqGpO2Jro3g6eIgU0RllH0X2F5kxUNnb2Do5/LEQ0RENVuRxJaLgTW2iIjI9pjYIqIaZ9mBOMmbbQelgCl9msoYURn9NRfIuiNtG/Ip4OYnTzxERFSzmSS2iozY0rLGFhERWR8TW0RUo9xKy8WqQ1clbaM61Ec9T2d5AiqrS38B/6yTtgUNAdo+Kk88RERERRJban0WgAfz/Dlii4iIbIGJLSKqUb7ed1nyRlupEDCjfzMZIyqDnPvAtlekbU6ewPD/AYIgR0REREQmiS0F9HBBnvE5E1tERGQLTGwRUY0Rn5KFdceltbUmhDREU19XmSIqo13vAhnSFRzxyELAo5488RAREQGA2sOkyQMP6mzl6TgVkYiIrI+JLSKqMb7YHQN9oaUQnVQKzBrQXMaIyiB2HxD9i7QtcADQ4Ul54iEiIirgVExiS8g2Ps7TcsQWERFZHxNbRFQjnE1Iw9bTiZK2yb2aoI6HWqaIyiAvA9jysrTN0Q0Y8RWnIBIRkfwUSpPklnTEFhNbRERkfUxsEVGN8PmuS5Ln7moVXgwNlCmaMtqzAEiTTp3EwA8Ar4ayhENERGSiSJ0tyYgtTkUkIiIbYGKLiKq9yLhUhF9KlrS9EBoILxdHmSIqg6sHgeM/Stua9AE6PytPPERERMUpmthC4cQWR2wREZH1MbFFRNWaKIr49K+LkjZfNyc826uJPAGVhSYb+PMlaZuDCzByMaDgbZuIiKoQkxFbhaYissYWERHZAP9CIqJqbe+FOzh5/b6kbdaAZnBxVMkTUFns+y9wL17aNuA9wLupPPEQERGZUySx5Y4c42NORSQiIltgYouIqi29QcRnf0trazXydsGEkEYyRVQGN44BkUukbQ27A12nyRMPERFRSUoascWpiEREZANMbBFRtfXnqQRcup0haXttYBAcVVX01peXCWx6AYD4oE2lBkZ9wymIRERUNZmsisgaW0REZFtVeC4OEVHFaXQGfLE7RtIWXNcdI9v7yxRRGex8E7gbK23r/zbg21yeeGRyNe0qYu7FwAD+QVSVaDQanNGcAQAINwQ4OlbhxRfIKngN1GwFr79aUKOPrg+c4Zy/oYQRWxomtoiIyAaY2CKiamnNseu4eS9H0vbGIy2gUAgyRVSKs78Dp1ZL2+p3AbrPkCceG0vNScXO+J3YGrcV51PPyx0OlWLdkXVyh0Ay4zVQsx3YcwBrhq+Bi4NLKasissYWERFZHxNbRFTtZOXpsHjfZUlbl8a10L+Fn0wRleLeNWDrK9I2R3dg3I+AsvrepnN0OQi/EY6tsVtxOPEw9CL/ACIisgdx6XHYn7AfjzR5pJgRWw8SW1q9CL1BhLKqfqhERETVQvX9i4mIaqwVB+ORkqmRtL05JBiCUAXfWOt1wO9Tgbx0afvwL6rlKogG0YDjt45ja+xW7Lm+B1narNJ3IiKiKudq2tX8ByYjtqT3dY3OAGdHpY2iIiKimoiJLSKqVu5labB0f5yk7aFgP4Q08ZYpolJEfALcPCZta/c40G68PPFYyZV7V7A1biu2x23H7ezbpfb3VnvDW11FX7MaymAwIDMzEwDg5uYGBRc0qHF4DdRsydnJSNOkGZ8nZSXlPyiS2HIXpGUA8nR6JraIiMiqmNgiomrlu4hYZOTpJG2vD2ohUzSluHoIOPC5tK1WE2DY58V2tzeiKGJH/A78dO4nXLh7odT+zipnDGg0ACMCRqBbvW5QKviHUFWSk5ODXbt2AQAGDRoEZ2dnmSMiW+M1ULN9f/J7fHvmW+PzhMyE/AfFjtgSAeSPkubKiEREZG1MbBFRtZGUloNVh69K2kZ18Ecrf4/id5BT9l3gj+cBsdAbfoUKGLcCcHKXLy4LEUURi6MXY9mZZSX2UwgKdKvbDSMCR2BAowH5hYiJiKjKqedaT/I8MTMx/4Fa+jvWUdBDDQ1y4QQAyNMysUVERNbFxBYRVRtf770sWVpcpRDw2sAgGSMyQxSBrS8D6QnS9v7vAA06yxOThX1/+vsSk1pBtYIwImAEhgYMhZ9LFS3qT0RERkUTW0lZSTCIBijUXiZ9PZD9ILHFlRGJiMjKmNgiomohNjkT66NuStoe79oQjX1cZYqoBCdWARe2Stua9gV6vSJHNBa37J9lWHJ6iUl7befaGBYwDMMDhqOFdxWdHkpERMWq5yJNbOkMOiRnJ6OO2sekr4eQhTtiLQCcikhERNbHxBYRVQtf7IqB3iAan6sdFHj5oeYyRmTGnYvAX29J25y9gTFLgWpQiHnl2ZX4Ovprk/a3u72N8UHjWTeLiMhO+ah9oIIKOjyoY5mYlYg6rnUARzdAk2ls90C28TFHbBERkbXZ/19RRFTjnbmZhu1nkiRtz/VqCj8PtUwRmaHNBX6fAuikK0Zh9BLAo17x+9iRX87/gi9OfGHSPq/7PDwR/ASTWkREdkwQBHgpvCRtZgvIC1nGx6yxRURE1sbEFhHZvU//vih57qFWYXrfQJmiKcGe94DbZ6VtIc8DLYbIE48Frbm4Bp8e/9SkfW7XuRjfYrwMERERkaUVTWw9KCBfdGXEwiO2mNgiIiLrYmKLiOza/phkHLicIml7sV8zeLo4yBSRGTF/A0e/l7b5tQIG/Z888VjQhpgNWHh0oUn7611ex5Mtn5QhIiIisoYyJ7YETkUkIiLbYWKLiOxWrlaP+X9KR0D5uTthcs8m8gRkTsYtYPOL0jaVGhi3HHBwlicmC9l0eRM+OPKBSfsrnV7BpNaTZIiIiIispZailuQ5R2wREVFVwMQWEdmt7yNicTU1W9L22sAgODtWoVpOBgOw6QUgO1XaPui/QJ1W8sRkIVtjt+K9w++ZtL/U4SVMaTtFhoiIiMiaTEZsZf2b2HLykLRLRmyxxhYREVkZE1tEZJeupmRhSXispK1TIy+M79JQpojMOPINEBcmbWsxDAiZKk88FrL7+m68e+hdiBAl7dPbTcf09tNlioqIiKyp6IitpMwkGERDMSO2ChWP51REIiKyMpXcARARlZcoipj351loCk1vUCoE/Hd0WygUgoyRFZFwEthbZJqeez1g5GJAqEJxltM5zTmsP7o+/4+ZQqa0mYIZHWbIFBUREVlb0RFbGoMGqTmpqF1ijS2O2CIiIuviiC0isjvbzySZFIyf3LMJWvl7mNlDBlkpwPpnAIO2UKMAjF0KuPrIFlZlXdBewLrsddCL0k/gJ7WahFmdZkGw44QdERGVzE1wgxLS6f4JmQmljNhiYouIiKyLiS0isisZuVp8sPW8pK2uhxqvDgySKaJi6DT5Sa20G9L23q8CTfvKE5MFHEo6hLVZa2GA9I+UJ1s+idldZjOpRURUzSkERfErI3LEFhERyYiJLSKyK1/sjsGdjDxJ2/wRreDmVIVmVv81F7h2SNrWsDvQ/2154rGAfdf34Y1Db0AP6UitCS0m4M2QN5nUIiKqIYotIF8kseUuWRWRNbaIiMi6mNgiIrtxNiENPx2+KmkLDaqNIW3qyhNQcaJWAFHLpW0e9YEJvwBKB3liqqRtcdvwWvhr0EqmVQLjmo/D293eZlKLiKgGKfeILa6KSEREVsbEFhHZBYNBxLubz8JQaBE+J5UCH4xqXXUSK9cOAzvmSNtUauDxXwE3P3liqqT1l9bj7QNvm9TUGtZkGOb3mA+FwF8jREQ1SdGVEYtNbIFTEYmIyHaq0NwdIiLz1hy/jlM37kvaZvRvhsY+rvIEVNT9G8C6pwGDTto+6lvAv6M8MVXS8jPL8eXJL03aOzt2xrtd3mVSi4ioBio6Yqu44vFOghZO0CAPjpyKSEREVsfEFhFVeSmZefhk50VJW4CvK6aHBsgUURGabGDtRCBbulIjer0CtH1UlpAqQxRFLI5ejGVnlpls6+XUC4+oH4FSoSxmTyIiqu6KJraSspIgOnmg6NhpD2QjGY4csUVERFbHxBYRVXkLd1xAeq50JNT/jW4DJ1UVSK6IIvDnDODWP9L25oOAAfPliakSDKIBHx/7GGsurjHZNq31NDS82bDqTP0kIiKbKzoVMU+fh1RRB98i/TyELCSLXqyxRUREVsd5JERUpUXGpeKPkwmStpHt/dGrWdG30DI5+AVw7g9pm09zYNyPgJ2NatIZdJh3aF6xSa03Qt7A1NZTmdQiIqrh3AV3KAXp77fE3GTAwUXSVlBni1MRiYjI2pjYIqIqS6Mz4N3NZyVt7k4qvDu8pUwRFXHpL2Dv/0nbnDyBJ9aY1Bup6jR6DeZEzMGW2C2SdgEC3u/5Pp5u9bRMkRERUVWiEBSo6yJdjTgxy7SAvLtQkNjiiC0iIrIuJrYqKSoqCh988AEGDRqEBg0awMnJCW5ubggKCsKzzz6LgwcPWuQ8CxYsgCAIZfoKDw+3yDmJ5Lb8YDyu3MmUtL0+uAX83NUyRVRI8iXg96kACi3TCAF4dDng21yuqCokW5uNmftmYs/1PZJ2laDCp6GfYmzzsTJFRkREVVE913qS5yWtjMjEFhERWRtrbFVC3759ceDAAZN2jUaDy5cv4/Lly1i1ahWeeeYZLFu2DI6OjjJESWSfbt7Lxtd7L0va2tb3xFPdG8sUUSE594A1TwCaDGn7wwuA5gNlCamiMjQZmLF3BqLvREvanZRO+KLfF+jboK9MkRERUVVVpsRWwYgtLaciEhGRdTGxVQmJiYkAAH9/fzz22GPo06cPGjVqBL1ejyNHjmDRokVISEjAzz//DK1Wi99++80i5z1z5kyJ25s2bWqR8xDJacGW88gp9GZYEIAPx7SBUiFzjSeDHtg4BbgbK21v+xjQa5Y8MVXQ3dy7eGH3C7hw94Kk3UXlgm8GfIOQuiEyRUZERFVZPRdpYishMwFw8pC0eSALQH5ZASIiImtiYqsSgoODsXDhQowbNw5KpbSIZvfu3fH000+jV69eiImJwZo1a/DCCy+gb9/Kj35o06ZNpY9BVJXtPn8bey7clrQ91a0x2jXwkiegwva8B8TulbbVaw+MXJyffbMTt7Nu4/ndzyM+LV7S7unkie8GfIe2tdvKFBkREVV1RUdsJWUmAWofSZsHa2wREZGNsMZWJWzbtg3jx483SWoV8PX1xaJFi4zPN27caKvQiOxWtkaHBVvOSdp83Zzw+uAWMkVUyOl1wOHF0jZXP+Dx3wAHZ3liqoDL9y7j6Z1PmyS1fJ19sXLwSia1iIioRCZTEbMSIZoZscXEFhERWRsTW1bWv39/4+PY2NgSehIRAHy99woS7udI2t4d1hKezg4yRfSvG8eBLTOlbQoHYMIvgGcDeWKqgIMJB/H0zqeRlJUkafd39cdPj/yE5rXsq/A9ERHZnr+Lv+R5ji4H9xylH/A8GLHFGltERGRdTGxZWV5envGxuZFdRJTvzM00/HggTtLWM9AHozr4m9nDRu5cAH59FNDnSduHLQIadZcnpgpYe3EtZuydgSxtlqS9iUcT/DTkJzTyaCRTZEREZE98nX2hFKTvaxOV0un47lwVkYiIbISJLSuLiIgwPm7ZsqVFjjlo0CD4+fnB0dERfn5+6NevHz7++GPcu3fPIscnkkO2RodZa6OhM4jGNgelgA9GtYEgZ+2qe1eBX8YAufel7V2nAZ0nyRFRuekNenxy7BN8ePRDGETpHxjtarfDqkdWoa5rXZmiIyIie6NSqEx+byQK0t8vBSO2NDoDRFEEERGRtbB4vBUZDAZ8/PHHxufjx4+3yHF3795tfJycnIyIiAhERETgk08+wapVqzBq1KgKHffmzZslbk9KejB1KS8vDzk5OSX0puomNze32MeWsmDrRcSlSEcSPd+rMeq7K+W71jLvwOnXUVBkSKft6Zv2h6bvPMAOfgaytFl4N/JdHEo6ZLJtYMOBmBcyD2qoy/R/bO1rgKo2vv7Ea6BmK/r613Wum78a4r+u66S/Rzz+HbEFAGmZWXBSceaCveM9oGbj60+FZ6NVNYLIj1CsZtGiRXj99dcBAGPHjsXvv/9e4WMtWLAAf/zxB0aPHo2uXbvC398fWq0Wly5dwq+//opdu3YByJ/uuHXrVgwZMqTc5yjPqJgff/wRvr6+5T4HUXHO3BXw4yXpG97GbiJmtdZDKdO4UpUuC70vL4Rn7g1Je6prcxxp9gb0Cid5AiuH+4b7WJ25GrcMt0y29XPqh4fUD0EhcOAuERGV3+/ZvyNaE2183g/NsDh+n/H5bdEL3fKWAAA+CtHBhR+nExHZtZSUFEydOhUAcOPGDTRoUHXqDPNXjJVERERg7ty5AAA/Pz989913lTreK6+8ggULFpi0d+vWDc888wx++OEHvPDCC9Dr9Zg6dSpiY2OhVqsrdU4iW0jTAGtipckVR4WIp5vJl9RSGvLQPe4Lk6RWmrohjga8ZhdJrQRdAlZnrUaGmCFpV0KJ0S6j0dGxo0yRERFRdeAleEmepwjST/ILj9himS0iIrImJras4Ny5cxgzZgx0Oh3UajU2bNgAPz+/Sh3Ty8urxO3Tp0/H8ePHsXz5ciQmJuL333/Hk08+Wa5z3Lhxo8TtSUlJ6Nq1K4D8hFpgYGC5jk/2LTc3F/v37wcA9O3b1yKJU4MoYtqvp5Gluytpnz+8JcZ1lKlgvF4Dxz8mQ5l1WdJs8GoCxye3oL9b5X6WbSHsZhhWHF2BPFH6R4anoyc+7fUpOtauWFLLGtcA2Q++/sRroGYr+vprkjQIOx5m3J7jIu3vLGjgCC00cECP3n1Q30u6aiLZH94Daja+/hQbGyt3CGYxsWVh8fHxGDRoEO7duwelUom1a9eib9++Njn39OnTsXz5cgD5I8bKm9gqz1BCJycnODvzDUpNpVarLfL6rzgYj0Ox0qTWI63r4skeAfIUjDfogT9mAHH7pO1udaGY9CecazW2fUzlIIoiVp1bhf+d+B9ESGeZN/Fogm8HfGuxlQ8tdQ2QfeLrT7wGaja1Wo0m3k0kbUl5KRABFP7t7Y5spMITUDryeqlmeA+o2fj610xOTlV31goTWxaUmJiIhx9+GImJiRAEAStWrKhwIfeKaNWqlfFxQkJCCT2J5HfxVjo+/uuipK2OhxM+GttWnqSWKAI75gBni9TCU3sBT28CajWxfUzloDVo8WHkh/j9smktv651u+KLfl/A08lThsiIiKg68neTjqzO1uUgTaGAl+HBvEMPIRupoifydHpbh0dERDUIE1sWkpKSgoEDByIuLg4AsHjxYjzzzDM2jUGWZABRBeRq9Zi15hQ0RYpuLHqsA2q5OsoT1L7/AlHLpW0OrsCTG4E6rYrfp4pIy0vD7IjZOJp01GTbmGZjMK/7PDgoHWSIjIiIqis/Fz8oBAUM4oPf5YlOLvDKyTQ+d/+3zlYei2wREZEVcTksC0hLS8PgwYNx/vx5AMDHH3+MGTNm2DyOgvMDgL+/TPWJiMrg078u4dJtaVHzqb2bondzmVbaPPwNcOBzaZvCAXh8NdAwRJ6Yyuji3Yt4YvsTxSa1Xun0Ct7v+T6TWkREZHEOCgfUcakjaUt0dpc89xD+TWxpmdgiIiLr4YitSsrOzsawYcNw8uRJAMA777yDN998U5ZYfvjhB+Pj0NBQWWIgKs3+mGSsOBQvaQuu6445j7SQJ6Do1cCud6RtggIY9yMQ+JA8MZWBKIr4/fLv+OjoR9AYNJJtaqUaC/ssxMDGA2WKjoiIagJ/N38kZSUZnyc4SWvueCALADgVkYiIrIojtipBo9FgzJgxOHToEABg1qxZ+O9//1vu46xatQqCIEAQBCxYsMBk+5kzZ3DlypUSj7F06VL8+OOPAIC6detizJgx5Y6DyNruZmkwe8NpSZuTSoGvn+gIJ5XS9gFd2AZsmWnaPvxLoPVoW0dTZtnabLxz8B28f+R9k6SWr7MvVj6ykkktIiKyOn9X6QyBRAfpCGHjiC1ORSQiIiviiK1KeOKJJ7Br1y4AwEMPPYQpU6bg7NmzZvs7OjoiKCio3Oc5ceIEpk6div79+2PIkCFo27YtfHx8oNPpcPHiRfz666/GOJRKJZYuXQpXV9eKfVNEViKKIt78/R8kZ+RJ2t8e2hJBddzN7GVFcRHAxmcBscib7YffBzpPsn08ZRR7Pxazw2cjNs10ud12vu2wqN8i1HWtK0NkRERU0xQtIJ+olH5m/mDEFhNbRERkPUxsVcIff/xhfLxv3z60a9euxP6NGzfG1atXK3QuvV6PPXv2YM+ePWb7+Pj4YPny5RgxYkSFzkFkTWuP38Du87clbf1a1MYzPRrbPphrh4G1EwG9dLQTer0C9H7F9vGU0ba4bfjgyAfI0eWYbHuq5VN4rfNrrKdFREQ2U9+tvuR5oiBNYBWM2Cq6WAwREZElMbFlB4YOHYrly5fjyJEjiI6Oxu3bt5GamgpRFOHt7Y327dvjkUceweTJk+Hh4SF3uEQm4pIz8cHW85I2H1dHfPpoO9uv5nl5D7DuKaBocqjTJODhBbaNpYzy9Hn45Ngn2BCzwWSbm4MbPuj1AaceEhGRzZmM2IL0AyMP46qIrLFFRETWw8RWJYiiaJHjTJ48GZMnTza73c/PD8899xyee+45i5yPyJa0egNeWXcKOVrpm9pPH20HP3e1bYM5txn4fSpg0ErbW40Ghv8PsHWSrQxupN/A7IjZuHD3gsm2YO9gLApdhEYejWSIjIiIarqiNbYyRT3SFQI8DPnvkd25KiIREdkAE1tEZFVf7onBPzfTJG1PdW+EAS3rmNnDSqJ/Bba8ZFpTq8UwYOxSQCFD8fpS7L22F/MOzUOGNsNk26NBj+LNkDehVtk4OUhERPSvuq51IUCAiAcf9iaqVPDQ5H+A9GDEFhNbRERkPUxsEZHVHI1LxZJwaZHzwNqueGdoK9sGEvk98Nebpu3tJgCjvgWqWF0qrUGL/534H345/4vJNmeVM+Z1n4cRgaylR0RE8nJQOsDPxQ+3sx/U0ExQqRBckNgSCorHcyoiERFZDxNbRGQVd9JzMWvtKRSeseugFPDV4x3h7Gij0VGiCBz4HNj3X9NtXaYAQz8HFArTbTJKykzCnP1zcDr5tMm2AM8AfNHvCwR6BcoQGRERkan6bvUlia1E1YM/Lzhii4iIbIGJLSKyuFytHs//cgK30nMl7bMHtUCb+p62CUIUgd3zgcNfm27r9Up+ofgqVFNLFEVsjduKj45+hExtpsn2YQHDML/7fLg4uMgQnW0YNBpoYmORe+kSdElJFqtjSJah0+rgfeUKAOD+1WvIdOBbiJqG10DNVvD6691coe/SBaifvyJiPbd6wJ0H/RJVDz688mCNLSIisgG+IyEiixJFEW9s/Aenb9yXtPcI8MHzfQJsE4RBD2yfDZxYabptwHygz2zbxFFG93Lv4YMjH2DP9T0m2xwVjpjbbS4ebf6o7VeQtBJRFKFLTkbepRjkXbqI3EsxyLt4EXnx8YBOJ3d4VALff/+9v3u3rHGQfHgN1GwFr3/ikUgEbtsGpZurSQF56YgtTkUkIiLrY2KLiCzqm31XsOV0oqStobczvn2yE5QKGyRm9Fpg84vAmQ2m24Z8BnSbZv0YymH/zf2Yf2g+UnNTTbY1cGuARf0WoZWPjWuSWZCo1yPv0iVj8io35hLyLl6C/t49uUMjIqIK0t+6hbQ/N8P7ySdR362+ZFvhxJarkAcVdJyKSEREVsXEFhFZzM4zSVi0O0bS5uakwvJJIfB2dbR+ANpcYOOzwKUd0nZBkV8kvsNE68dQRtnabHwW9Rk2xmwsdvvIwJGY23Uu3B3dbRyZZYhaLdL+/BMp330PbUKC3OEQEZGFZR+PgveTT8LfTTpiK0El/fPCDTlMbBERkVUxsUVEFnE2IQ2vrj8laVMIwOKJHRFUxwbJmbxMYO0TQPx+abvCAXh0OdBqlPVjKKNTd07h7YNv40bGDZNttZxq4b0e72FA4wEyRFZ5ok6HtG3bkLLkO2ivX6/QMRwaNIBjYAAUjjZIhlKZ6fV63L6dX0inTh0/KJU2WgSCqgxeAzWb5tZt5J05Y3yeffw4RFE0SWxlKBXIEAS4/1sn0UPIRp6WUxGJiMh6mNgiokq7k56LqT9FIbdIcdi3h7ZE/xZ+1g8g5x7w62PAzePSdpUz8PhqoNnD1o+hDLR6Lb47/R2Wn10Og2j66XXfBn3xfs/34evsW8zeVZtoMCB9x06kfPstNPHxZdpHcHGBunlzOAUHw6lFENTBwXBq3hxKd/scpVbd5eTk4NSuXQCAtoMGwdnZWeaIyNZ4DdRs6TExSBj54EMifWoqNHFxqNekoUnfRJUKLbRaAPl1tjhii4iIrImJLSKqlFytHs//HGWyAuLjIQ0xpXdT6wdw/zrw2+PAnXPSdicPYOJ6oHEP68dQBlfuXcHbB9/GhbsXTLY5q5zxRsgbGNd8nN0ViBcNBmTs3oOUbxYj7/IVs/0c/P3h1Kol1EEt4BTcAuoWLeDQsCEEhcKG0RIRUUWpGjSA1tMTDmlpxrbs48dRKzAQfs5+uJPzYGnERIdCiS0hm8XjiYjIqpjYIqIKE0URczb+g9M30yTt3Zp644NRbayfpLl6CFj/NJBdpPC6szfw9CbAv4N1z18GBtGAX87/gq9Pfg2NQWOyvUPtDljYeyEaeph+4l2ViaKIjH37kLz4G+RdME3WFVC3b4faL78M15497S5pR0REDwiCgJyApnCIPmVsyz52DLUefxz+bv7SxJbqwTRVD2TjNkdsERGRFTGxRUQVtnjfFWwtsgJiI28XfPdUZziqrDwS5/hyYOcbgEEnbXevBzy9GfALtu75y+BG+g0sOLIAx24dM9mmUqgwo8MMPNv6WSgVdlSnRhThEhODpJ9/gebcObPd1K1awfflmXALDWVCi4iomsgOCIBHocRW1r91tuq51cOp5AfthVdG9BCycF3LxBYREVkPE1tEVCHb/0nCF0VWQHR3UmH5pC7WXQFRp8lPaJ1YabrNpznw5AbA2wZTIEug0Wuw/Oxy/PjPj8WO0mrm1Qwf9fkIwd7yJ9/KShRF5ERGouF338P52jWYflf5nIKC4DvzJbg//DATWkRE1UxOQIDkuT45BZr4q6jvVl/SLklsIRsaPRNbRERkPUxsEVG5nUtMx+wNpyRtCgH4emJHNLfmCoiZycD6Z4Drh023NR8EjPsRUHta7/xlEJkUiQ8jP8TV9Ksm2wQIeKbVM5jZaSaclE62D64CRL0eGXv2InXpUuSeOwdzpaIdAwJQe+ZLcB88mHWziIiqKa2PD3QeHlClpxvbso8fh39H6cqICUVGbLHGFhERWRMTW0RULmka4MO1/5isgPjOsFbWXQEx6R9g7UQg7Ybptt6vAg/NA2Sc0peSk4JPj3+KnfE7i91ez7UePuz9IULqhtg4sooRNRqkbd2K1B+Xl7jKoUPjRqg9YwY8hg2DoLSjKZVERFR+goDsgKbwOHXa2JR97Bjq9x4r6ZZUqMaWO3KQx6mIRERkRUxsEVGZafTAsotK3MmSTkR7omtDPNerifVOfG4TsOlFQJcjbVepgVHfAm0ftd65S6E36LHu0josjl6MTG2myXaFoMDE4ImY0WEG3BzdZIiwfAxZWbi3YQPurlwF3e3bZvup/P1Re8Z/4DlqFAQVf5UQEdUUOQEB0sTW8eOo6/ofSZ/7SiWyBAGuovjviC0mtoiIyHr41wgRlYkoivgtVoEbWdK6Sd2aeuP9kVZaAdFgAMIXAvs/M93mUR94/FfAv6Plz1tG51LO4YPID3A+9Xyx29v5tsO8HvPsopaW/v593F39K+798gv0aWlm+2l8fHAvtC+6zp0LFw8PG0ZIRERVQXaROlu6O3dQN1Vr0i9RpUJzrRYeyOZURCIisiomtoioVKIo4st9cYhOldZOauzjgu+ttQJibjqwaTpwaYfptobdgQm/AG5WnPpYgnRNOhafXIx1l9ZBhGiy3d3RHa92fhXjmo+DQqja9aa0t2/j7spVuLd+PcTsbLP9nIKD4T5pEg6LBkCphODgYMMoiYioqtD6+kLh4wNDaqqxTXfyH/g6+yIlJ8XYlqRS5ie2hGzkaQ0QRZGLihARkVUwsUVEJRJFEZ/+fQlLD16TtBesgFjLGisg3o0D1jwBJF803dbpGWDo54DK9sXXRVHEjvgd+Oz4Z0jNTS22z8jAkXit82vwcfaxcXTlkxcbi9SVK5H25xZAa/pJewGXLl3gM+15uPbpg9zcXGDXLhtGSUREVY4gQN25M7IL/T7IPnYc/v38JYmtggLyHsiCKAJavQhHFRNbRERkeUxsEZFZoihi4Y4LWHZAWjxcIQCLJ3ZEMz8rrIB4ZS+w8Tkg9760XVACQz4BQqYCMnzieyH1AhZFLcLRW0eL3R7gGYB3u79bpYvDi3o9MsPDcXf1amQfiSyxr1u/fvCZNg0uneSb6klERFWTSWLr+HH4D+uMf1L+MbYlFiS2hPzRwHk6vXVGeBMRUY3HxBYRFUsURXyw7TxWHroqaRcg4v3hLdHP0isganOBff8HHPnGdJuzNzD+J6BpX8ueswxuZNzAN9HfYEd8MVMiAaiVakxvPx2TWk2Cg7JqTs/T37+P+7//jnu/rYE2IcF8R6USHkOHwmfqVKhbBNkuQCIisivqLp0lz3W3biEwR/phV4JDwYitgsSWAVb4OIyIiIiJLSIyZTCIeG/LOfwSKZ1+KEDEE4EGPNrJ37InvH0e+H0qcOec6Ta/1sATvwG1mlj2nKW4m3sXS/9ZinWX1kFn0BXbJ7RBKOZ2nYsG7g1sGltZ5V66hHurVyNt6zaIublm+wmOjvB6dBy8n3sOjg2q5vdCRERVh0PTplD6+EBfqM5WYFwOUGjx3ySVEgDgLuRAAQNXRiQiIqthYouIJAwGEe9sPos1x65L2hUCMDHQgJDapsXSK3Ey4NgPwO73AH2e6fbg4cCYHwAnN9NtVpKtzcZP53/CqrOrkK0rvph6HZc6eKvbW3io4UNVrhCuqNMhY89e3F39C3KiTpTYV1mrFrzGj4f3009B5etrowiJiMjeCYIAl5AQZPz1l7HN9+IdoMuDPgVTEQHADdnI03JlRCIisg4mtojISG8Q8dYf/2B91E1Ju1Ih4JPRLeGQ9I+ZPSsgPQnY/CIQF2a6TekEDHwf6DodUNimHofWoMXvMb/j+9Pfmy0M7+bghiltp+DJlk/CWeVsk7jKSnf3Lu6vX497a9dBd+tWiX3VrVuj1lNPwWPoECicbF+En4iI7J9LSBdJYkt95ooksXVXqUS2IMBFFPNXRuSILSIishImtogIQH5Sa86G0/gjWlqDSaUQ8NXjHfFQcy/sslRi6/wWYOvLQM49021+rYFxy4A6rS1zrlKIooi/r/2NxScX43rG9WL7OCgc8ETwE3i+7fPwUnvZJK6yEPV6ZB0+grRNfyBj9x6IJaxuCJUKHoMHo9ZTT8K5Q4cqN9KMiIjsi2vXrtKGW8mofV+JZK8Hv1+SVEoEanXwBBNbRERkPUxsERF0egNeW38aW04nStodlAIWP9EJj7Spi5ycnMqfKC8D+GsuEL26+O09XgIemgc4qCt/rjI4lnQMX5z4AudSi6ntBUCAgBGBIzCjwwz4u1m4rlglaK5dw/0/NiHtzz9LHZ2lrO2LWuMnwGvCeDj4WbjgPxER1ViOzZpBWasW9PcefEjVNckF270evF9IVKkQqNXBQ8jiVEQiIrIaJraIajit3oBX1p7C9jNJknZHpQJLnuyEh1vVscyJbhwH/ngeuBdvus3dHxjzHRDQzzLnKoEoioi6HYVl/yzDkaQjZvv1bdAXL3d8GS28W1g9prIwZGUh/a+/cX/TH6XWzgIA5/bt86cbDh4EwdHRBhESEVFNYqyztWuXsa39TQdsbylNbAH5KyNyxBYREVkLE1tENZhGZ8DMNSfx97nbknZHlQI/PN0Z/VtYYISPXgfs/yz/Syzm09pWo4DhXwIu3pU/VwkMogHhN8Kx/Mxy/JNifkplO992eKXzKwipG2LVeMpCFEXknDiB+39sQvpff0HMLr6YfQHBwQEeQ4ei1lNPwbltGxtFSURENVXRxFZgvHR0d0JBYkvIgoaJLSIishImtohqqDydHjN+jcaeC9KklpNKgWXPdEHfoNqVP0nKFWDzC8DN46bbHN2BoZ8B7R8HrFjvSWvQYmf8Tqw4swKxabFm+zXxaIJZnWZhQKMBstef0t66hbTNf+L+pj+gvVZ83a/C1K1awXPsWHgMGwpVrVo2iJCIiAhwKVJnyz01B75pSqR45v8eTVIp89uRwxFbRERkNUxsEdVAuVo9Xlx9AmGXkiXtagcFlk8KQa9mvpU7gSYLOLAIOLwY0GtMtzfsBoxdCtRqUrnzlCBHl4NNlzfhp3M/ITEr0Wy/2s618WKHFzGm2RioFPLdEnX37iHj711I37ED2cePA6JYYn+llxc8Ro6A19ixUAcH2yhKIiKiB5yaN4PS0xP6tDRjW6vrIva3zU9sJRYasZWnY40tIiKyDia2iGqYpLQcvLD6JE7fuC9pd3FUYsXkEHQP8Kn4wUURuLAF+OttIP2m6XZBCfR7C+j9KqC0zu0nXZOOdRfXYfWF1bibe9dsv/pu9TG59WSMbjYaapVtitUXpc/MQubePUjbsQNZhw4DOl3JOygUcOvTB55jx8K9fz/WziIiIlkJCgWcQ7ogc89eY1t+Yiv/cQJrbBERkQ0wsUVUgxy/ehcvrj6JlMw8SburoxKrnuuKkCaVqHOVcgXYOQeI3Vf8du9AYOwyoEHnip+jpNPnpODn8z9j/aX1yNJmme3XzKsZprSdgkeaPCLLCC1Dbi4yI/YjfccOZIaHQ8zLK3Ufx6ZN4TVuLDxGjuTKhkREVKW4du1qktgqkKpSIlcQ4CFkI5OrIhIRkZUwsUVUA4iiiF+PXseCLeegM0inuLk7qbDqua7o3LiCtZk0WcD+z/OnHRq0ptsVKqD7f4DQNwEnt4qdowTnUs5h3aV12B63HRpDMdMe/9W+dntMbTsVfRv0hUJQWDyOkohaLbKOHEH69h3I2LMHhizzibcCCldXeAwdCs+xY+DcoYPsdb+IiIiK4xIiXWyl7n3AJ11EqseDOlseuiykcsQWERFZCRNbRNVcnk6P9/48h7XHb5hsa+zjgqVPd0GLuu7lP3Bp0w4BoGlfYOjnQO0W5T9+CbK12fjr6l9Yd2kdzqeeL7FvL/9emNJ2CrrU6WLT5JAhLw/ZR48iY89eZOzaBf39+6XuIzg5wa1/f3gMHQK30FAonJysHygREVElOLVoAYWnJwxF6mwdaPOgzpaHwKmIRERkPUxsEVVjt9Nz8cLqE4i+ft9kW9+g2lj8eEd4ujiU+7hC6hVg3zwgLqz4Du7+wOAPgdZjLLri4ZV7V7A+Zj22xm5FpjbTfHwQMKjJIExpMwUtfVpa7Pyl0d27h8yICGTuC0PmwYMQs7NL30mlgluvXvAYPgxu/R+C0s3V+oESERFZiKBQwKVzZ2Tue1CKID+xlf84QaVCG2SzeDwREVkNE1tE1dSJa/fwwuoTSM4wreH0Yr9AvD6oBZSK8iWdlPo8BN3+E06n/zY/7bDHDKDvGxabdqjRa7Dn2h6sj1mPE7dPlNhXpVBhVOAoPNvmWTT2aGyR85ca3/XryNi7D5l79yL75EnAUIZPpAUBLl27wmPYULgPHAhVrQpOAyUiIqoCXLqGSBJbLQvV2UpUKdFTyEKeliO2iIjIOpjYIqqG1hy7jvl/noVWL62n5eygxGePtcPwdv7lO6BOA+Wp1RhwYSGctWZWGgzoBwz5DKgdVLGgi7iRcQMbYzZi85XNJa5uCAB1Xevi0eaPYmzzsajtUtsi5zdHNBiQe+YMMvbuQ8a+vdBciS3zvur27eA5bBjcBz8ChzosAk9ERNVD0Tpb/veAWhki7rkLSFSp4M5VEYmIyIqY2CKqRjQ6AxZsPYffjl432dbQ2xlLn+6ClvU8yn5AbS4Q/Qtw8Es4mquj5VE/f9phq9GVnnaYo8tBxI0IbI7djMMJhyFCNNtXgIDe9XtjQosJ6F2/N5QKZaXOXRLd3bvIOnIEWYcPI3P/fuiTU8q2oyDAuWNHuD/UH+6DB8OxYUOrxUhERCQXdXAwFO7uMGRkGNtaXRdxqHVBYisHGm0xI72JiIgsgIktomriTkYu/rP6JKKu3TPZ1qe5L75+vCNquTqW7WDaHODET8ChL4GMpOL7KBz+nXY4p1LTDnUGHSKTIrEjbgf2Xt+LbF3Jdam81d4Y13wcxgWNQ323+hU+b0kMubnIPnECWYcPI+vwEeRduFDmfQW1Gq69esH9oYfg1i8UKh8fq8RIRERUVQhKZX6drfBwY1t+Yit/KqJCEAGN+dqYRERElcHEFlE1EH09v57W7XTTelrT+gbgjcEtoFIqSj+QJguIWgEc+hrIumO2m75xXyiHL6rwtENRFHE6+TS2x23Hrmu7Sp1qCAAhdUMwvsV4DGg4AA7K8he8LzEegwF5Fy/+m8g6jOwTJyHmmf5fmqP08YFb/35wf2gAXHt0h8LZ2aLxERERVXUuXbuaJLYAIFmlggaAMi9dnsCIiKjaY2KLyI5pdAZ8Fx6Lb8OuQKOX1q5QOyjwybh2GNWhDKOa8jKAY8uAI98A2almu6W4tsCleqPRaewrcHZxKXe8V+5dwY74HdgRvwMJmQml9nd3dMeowFF4rMVjCPAMKPf5zBFFEdqEBGQfPYqsQ4eRFRkJ/d3Sk2uFOQYG5o/Keqg/nNu3h6AoQ+KQiIiomipaZ6v+XcArU8R9NwFJKhVUWia2iIjIOpjYIrJTJ67dxdzfz+DyHdOh/fW9nLH0mc5o7e9Z8kFy04CjS4HIb4Ec0ymMRk37Iq/7Kzh08d9zlaOWVlJmEnZe3YkdcTtw6d6lUvsrBAW61e2GYQHDMKjJIDirKj/6SdTrkRcTg+wTJ5Fz8gSyo05Ad8f8iLTiCC4ucA0JgWuvnnDr2xeOTZpUOi4iIqLqQt0yGAo3NxgyH7wvaXldxJFWAhJUKqi0GSXsTUREVHFMbBHZmYxcLT77+xJ+ibwGsZja6j0CfPDtk53gXVI9rbQE4MTK/KRWXpr5foEDgNA3gEbdYcjJAS7uKjU+g2jAhbsXEHEjAuE3wnHhbtnqU7X1bYthAcMwuMlg+Dr7lmkfszHk5iL3zBlknziRn8yKjpa80S4ThQLqtm3g2rMn3Hr2zB+V5VjGGmVEREQ1jKBSwblzJ2RF7De2tb4u4kgrINFBCUcmtoiIyEqY2CKyI7vP38a8zWdxKz3XZJtSIWBa3wDMHhhUfD0tgx64sgeIWglc/hsQS1h2O+gRoO8bQIPOZYorR5eDo0lHEX4jHPtv7kdyTnKZ9mvi0QTDAoZhaNOhaOTRqEz7FEeXmoqc0//kj8Y6cRI5Z88CFVh9yaFRI7j27AHXnj3h2q0blJ6ljHgjIiIiI9eQEEliq6DOVqJKBUcti8cTEZF1MLFFZAfuZOTi/S3nsf1M8SsUtvb3wCfj2qFN/WISMemJwMlfgJM/A+k3Sz5R8HCg7+uAf8dSY7qVdQv7b+5HxM0IHE06ijx92Yqt+zn7YUjTIRgaMBQtvVtCKMe0RgDQ37+PnHPnkHvmLHLPnUXO2XPQJZlZubEUSm9vuISE5CeyevWEY4MGFToOERER5ReQL6xBKuCZJSJRpUKjHI7YIiIi62Bii6gKE0UR66Nu4MPtF5CeqzPZrnZQ4LWBQXiuV1PpKC2DHojdlz86K+YvQNSXcBYBaDUK6DsHqNvGbC+dQYcbuhuI0cbgl12/4NL90utlFXB3dMegxoMwtOlQdK7TGUqFskz76TMzkXvuPHLPnkXO2TPIPXsO2hs3ynzeohwaNYJLp05w6dIZzp06w7Fpk3In1oiIiKh46latoHBxgSE729jW8rqIxKZKBOk5YouIiKyDiS2iKio+JQtv/fEPIuOKX62vdzNfLBzTFo18Cq1OmJ4ERK/OH52Vdr3kEzi6AW0fA7pNB/xammzW6rU4l3oOUbejEHUrCtF3opGt+/eNahkGZzX2aIzQBqEIbRCKjnU6wkHhYLavKIrQ3b6NvJgY5MXEIPdSDHLPnoUmPr70E5mjUMApuAVcOneBS+dOcO7UCQ5+fhU/HhEREZUov85WZ2QdOGBsa31dxNbmKjgzsUVERFbCxJYFXbt2DV9//TW2b9+OGzduwMnJCYGBgRg/fjxmzJgBFxeX0g9SBjt37sTSpUtx/PhxJCcno3bt2ggJCcG0adMwZMgQi5yD5KPVG7B0fxy+2nsZGp1pHSwvFwfMG9YKYzvVzx9tpM0F4sLyE1qXdpYyOgtAvQ5Al2eBNuMAJ3djc54+D/8k/4Oo21E4cfsETt85jVy9aS0vc5SCEh39OuYnsxqGoqln02L76TMzkRdz+d8k1iXkxsQg7/IVGNJKKGJfBoKLC5xbt4Zzl85w6dQZzh07QOnmVqljEhERUfm4hIRIElstr4tYoVTCwcCpiEREZB1MbFnI1q1b8dRTTyE9Pd3Ylp2djaioKERFReHHH3/E9u3b0axZswqfw2AwYNq0aVi+fLmkPSEhAQkJCdi8eTOmTp2KH374AQpFMcXDqUrTG0Rs+ycRi/ddwZU7xX+qOaqDP+YNbwVfBw1w7g/gwjbg8i5AU8qnoA6uQNtH8xNa/9bPytZm45+kSETdikLU7SicST4DjUFTrpjdHdzRu35vhDYMRe/6veHp9KDGlz4zC5qrV6GJj0fe5cvG0VjaxMRynaM4gqMj1C1bQt2mDdRt2sC5TWs4BgRAUJZtiiMRERFZh2vXEBReQqZRCuCWA2iVlfsAi4iIyBwmtiwgOjoaEyZMQE5ODtzc3PDWW2+hf//+yMnJwdq1a7Fs2TLExMRg2LBhiIqKgru7e+kHLcY777xjTGp17NgRb7zxBgIDAxEbG4tPP/0U0dHR+PHHH1G7dm0sXLjQkt8iWZFWb8CfpxKxJOwK4lKyiu1T38sZnwz1R2/dcWDLx0BsGFCWYu112wFdnkVOyxG4lJWAc6nncD5uI86nnkdcWhwMJa2MaEZtRW0MbDYQDzd9GO1rtYaYeBua+KvQhP+BpH8TWZqrV6FLLtvKiKVycIA6KOjfJFZrOLdpA6dmzSA4mJ/aSERERPJQt24NwcUFYqE6W62ui8jxKP49DhERUWUxsWUBs2bNQk5ODlQqFXbt2oUePXoYtz300ENo3rw53njjDcTExGDRokVYsGBBuc8RExODzz//HADQpUsX7N+/H87OzgCAkJAQjBw5EqGhoYiKisJnn32G5557rlKjw8j6NDoDfj95E0vCr+DG3Zxi+9QXUvFus3gMUhyDctNhoAyJqBxHV1wKfhjn6rXGeX06zidsRdz5ryqUxAKAli4B6K0IQmuNH3SnkuB2NwP1D8RDf20eYm/eBPSlTH0sB4WrK5yaN4dTUBCcglvAuW1bOAUFQeHkZLFzEBERkfUIDg5w6dgRWYcOGdtaXReR1SEPOr1ButgNERGRBTCxVUnHjh3DgX/rCEyZMkWS1Cowe/ZsrFy5EhcuXMBXX32Fd955Bw7lHG3y5ZdfQqfLXxVv8eLFxqRWARcXFyxevBg9evSATqfD//73P3z77bcV/K7ImnK1emyIuoHvwmORmCatYSXAgGDhBvopTmO0+iRa6GMAM4sAGgAkqZSIc3BAnIMDLteqj3POzojT3IMhIxrIiC5TPM65IvzSAN90EX73gRYabzTJcYXvfQPUKRkw3IsBECPZp/g0XDkolXBs2gTqoCA4BbXIT2QFBcGhvj9XKSQiIrJzLiEhJomtE120yNMxsUVERJbHxFYlbd682fj42WefLbaPQqHAM888g7feegv3799HWFgYBg0aVOZziKKIP//8EwAQHByM7t27F9uve/fuaNGiBS5duoQ///wT33zzDZMEVUiORo/fjl3HDxGxuJORP41QCT1aC1fRVXER3RQX0FVxEZ7Cv0P3/x0IpQVwzcEBcQ4qxDvmJ7HiHRxw1UGFHEkttWwg78Gwf5VORK1MwCsT8M7Mf1yr4N8MoFaWCJ90wNVkRmPKv1/5ybNKEQQ41K8Px6ZNoW4RZExgOQYEQOHoWNmjExERURXk0rWr5HnjZOCATo88nQGuHIRNREQWxsRWJR08eBAA4Orqis6dO5vtFxoaanx86NChciW24uPjkfhvwe3CxzF3nkuXLiEhIQFXr15F06bFr0xHtpOVp8Mvkdfw44E4pGVmo50Qi0eV+YmszooYuAm5yBUEJKmUOKdSIVHlipsqlTGBdcNBBb0gAKII5zzAIwdwTwNaZotwzzHAIxvwyBHhkQXU+jeJ5ZWZ389WlN7ecGzSJP+raf6/Tk2awKFRI04jJCIiqmGc27SG3lEJpeZBuQKX20CeVgeAH2wREZFlMbFVSRcuXAAANGvWDCqV+f/O4OBgk33K6vz588UepyznYWLLugwGEfeyNUjJ1CA5Iw8pGbm4l5aG9PR7yEhPR3ZmGlLvJCBQewZvOF2EV60E3BVEpOhVCNersEXviSydN/RaBVzy8kdPueSKcM0F2uUAvbMB9xx9fvIqG1BVeghVxSlq1UK2qyu0tWqhQbeucG3ePD+R1bgxlF5e8gVGREREVYrg6AhNcCM4/xNvbPNOUkKTnQF4ucgYGRERVUdMbFVCbm4uUlLyp2w1aNCgxL61atWCq6srsrKycOOGmaJJZty8edP4uLTzNGzY0Pi4MucpTuHjbZo6BLXUNXRVOoMIiCJgMEAQAUCEAEAwiPnPRcBTBDxFAYIICCLgoAEcNQA0atTRAXUeHAxlmfCnQcHkQCsRBCi9vaH084OqTh0o6/z7r59ffpufHzSCgJNHjwIAXLp1Q1bBSKzk5Pwvqvby8vKM97zY2Fg4cTRejcLXn3gN1Gzlff1TmzaB6sSDGp3+/wC7nu1nxQjJFkQx/9/VS+bKGwjJgq9/zXYvV2t8XFD/u6pgYqsSMjIyjI/d3NxK7V+Q2MrMzLTaeVxdXY2Py3uewkmx0ry5P7ZcxyY7wJeUiIiIrOms3AEQEZElJCcno0mTJnKHYcRlSSohN/fBinaOZSiEXfDJVk5O+Yoflec8hT89K+95iIiIiIiIiIhKcvv2bblDkOCIrUpQq9XGxxqNptT+eXn5y885Oztb7TwF56jIeUqbuhgfH4++ffsCAA4fPlyuEV5k/5KSktD131WOjh07hnr16skcEdkar4Gaja8/8Rqo2fj6E6+Bmo2vP924cQM9e/YEUHrtb1tjYqsS3N3djY/LMu0vKysLQNmmLVb0PAXnqMh5SqvfVVjDhg3L1Z+ql3r16vH1r+F4DdRsfP2J10DNxtefeA3UbHz9qfDgm6qAUxErQa1Ww8fHB0Dphdfv3btnTDqVd6RT4ZtGeQq8c0QVEREREREREVVnTGxVUqtWrQAAV65cKXFlgIsXLxoft2zZskLnKHocS5+HiIiIiIiIiMieMLFVSb179waQPwXwxIkTZvtFREQYH/fq1atc52jatCn8/f1NjlOc/fv3AwDq169fpVYpICIiIiIiIiKyNCa2Kmn06NHGxytXriy2j8FgwM8//wwA8PLyQv/+/ct1DkEQMGrUKAD5I7IiIyOL7RcZGWkcsTVq1CgIglCu8xARERERERER2RMmtiqpa9eu6NOnDwBg+fLlOHLkiEmfRYsW4cKFCwCAWbNmwcHBQbI9PDwcgiBAEARMnjy52PO88sorUCqVAICZM2ciJydHsj0nJwczZ84EAKhUKrzyyiuV+baIiIiIiIiIiKo8JrYs4KuvvoKzszN0Oh0GDRqEjz76CJGRkQgLC8P06dPxxhtvAACCgoIwe/bsCp0jKCgIc+bMAQBERUWhV69eWLduHaKiorBu3Tr06tULUVFRAIA5c+agefPmlvnmiIiIiIiIiIiqKJXcAVQHHTt2xLp16/DUU08hPT0db7/9tkmfoKAgbN++He7u7hU+z4cffog7d+5gxYoViI6OxuOPP27SZ8qUKfjvf/9b4XMQEREREREREdkLQRRFUe4gqotr167hq6++wvbt23Hz5k04OjqiWbNmeOyxx/DSSy/BxcWl2P3Cw8ONdbcmTZqEVatWlXieHTt2YOnSpTh+/DhSUlLg6+uLkJAQTJ8+HUOGDLH0t0VEREREREREVCUxsUVERERERERERHaJNbaIiIiIiIiIiMguMbFFRERERERERER2iYktIiIiIiIiIiKyS0xsERERERERERGRXWJii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrvExBYREREREREREdklJraIiIiIiIiIiMguMbFFZXLt2jXMnj0bwcHBcHV1hbe3N0JCQvDZZ58hOztb7vDICgRBKNNXv3795A6VKuDOnTvYtm0b5s+fjyFDhsDX19f4mk6ePLncx9u5cyfGjBmDBg0awMnJCQ0aNMCYMWOwc+dOywdPFmGJa2DVqlVlvlesWrXKqt8PlU9UVBQ++OADDBo0yPhz6+bmhqCgIDz77LM4ePBguY7He4D9scQ1wHuAfUpPT8fatWsxe/ZshIaGolmzZvD09ISjoyP8/PzQr18/fPrpp0hNTS3T8Q4fPoynnnoKjRs3hlqtRt26dTF48GCsWbPGyt8JVZQlroHw8PAy//wvWLDAdt8cVdqbb74pef3Cw8NL3Uf29wEiUSm2bNkienh4iACK/QoKChIvX74sd5hkYeZe76JfoaGhcodKFVDSazpp0qQyH0ev14tTpkwp8XhTp04V9Xq99b4ZqhBLXAMrV64s871i5cqVVv1+qOz69OlTptfsmWeeEfPy8ko8Fu8B9slS1wDvAfZp9+7dZXrNfH19xb/++qvEY7333nuiQqEwe4xhw4aJOTk5NvrOqKwscQ2EhYWV+ef/vffes+03SBUWHR0tqlQqyesXFhZmtn9VeR+gAlEJoqOjMWHCBOTk5MDNzQ1vvfUW+vfvj5ycHKxduxbLli1DTEwMhg0bhqioKLi7u8sdMlnYiy++iP/85z9mt7u6utowGrKGRo0aITg4GLt27Sr3vu+88w6WL18OAOjYsSPeeOMNBAYGIjY2Fp9++imio6Px448/onbt2li4cKGlQycLqcw1UODvv/+Gv7+/2e0NGjSo8LHJshITEwEA/v7+eOyxx9CnTx80atQIer0eR44cwaJFi5CQkICff/4ZWq0Wv/32m9lj8R5gnyx5DRTgPcC+NGzYEP3790fnzp3RsGFD1KtXDwaDATdv3sTGjRvxxx9/ICUlBSNHjsSxY8fQvn17k2P88MMPeP/99wEAgYGBePvtt9G2bVskJibiq6++QlhYGLZv347nnnuuTNcQ2ZYlroECK1asQEhIiNntfn5+1vgWyMIMBgOmTZsGnU4HPz8/3Llzp9R9qsz7AKumzcjuFXyip1KpxMOHD5ts//TTT5mJr6b4ulZv8+fPF7du3SreunVLFEVRjI+PL/donUuXLhk/0enSpYuYnZ0t2Z6VlSV26dLFeA/hyM6qxRLXQOHRGvHx8dYLlixq2LBh4rp160SdTlfs9uTkZDEoKMj42kZERBTbj/cA+2Wpa4D3APtk7nUvbNOmTcbXdsyYMSbbU1NTRU9PTxGA2KhRIzE5OdnkHCNGjCjTiA+yPUtcA4VHbPH1rR7+97//iQDE4OBg8a233ir19a1K7wNYY4vMOnbsGA4cOAAAmDJlCnr06GHSZ/bs2WjZsiUA4KuvvoJWq7VpjERUMe+//z6GDx+OOnXqVPgYX375JXQ6HQBg8eLFcHZ2lmx3cXHB4sWLAQA6nQ7/+9//Kh4wWZwlrgGyT9u2bcP48eOhVCqL3e7r64tFixYZn2/cuLHYfrwH2C9LXQNkn8y97oWNHj0aLVq0AADj3wOF/fjjj0hLSwMAfPLJJ/D19TU5x5IlS4zn+uyzzyobNlmQJa4Bql6uX7+OefPmAQC+//57ODo6lrpPVXofwMQWmbV582bj42effbbYPgqFAs888wwA4P79+wgLC7NFaEQkM1EU8eeffwIAgoOD0b1792L7de/e3fim6M8//4QoijaLkYgqrn///sbHsbGxJtt5D6j+SrsGqPorKDGSm5trsq3g7wQPDw+MHTu22P0bNGiAhx9+GACwd+9eZGRkWCdQspqSrgGqXmbMmIHMzExMmjQJoaGhpfavau8DmNgiswpWw3F1dUXnzp3N9it84R86dMjqcRGR/OLj4401Wkr75VewPSEhAVevXrV2aERkAXl5ecbHxX2yz3tA9VfaNUDV26VLl3Dq1CkA+X+0FqbRaHDs2DEAQI8ePUoc2VHw85+Xl4eoqCjrBEtWUdI1QNXL+vXrsW3bNnh7e+Pzzz8v0z5V7X0AE1tk1oULFwAAzZo1g0plfp2Bwje6gn2o+tiwYQNatWoFFxcXuLu7o3nz5pg0aRJH59Vw58+fNz4u7c0O7xE1w7PPPgt/f384OjrC19cX3bt3x7vvvouEhAS5Q6MKiIiIMD4uKDlQGO8B1V9p10BRvAfYv+zsbFy+fBlffPEFQkNDjVOMXnnlFUm/mJgY6PV6APz5r27Keg0U9c4776Bx48ZwcnJCrVq10LFjR7z66quIiYmxQdRUGffv38esWbMAFD+t2Jyq9j6AiS0qVm5uLlJSUgCUvopNrVq1jCvj3bhxw+qxkW2dP38eFy5cQE5ODjIzM3HlyhX8/PPPeOihhzBmzBhjfQWqWW7evGl8XNo9omHDhsbHvEdUX+Hh4UhKSoJWq0VqaiqOHj2KDz/8EM2aNcMPP/wgd3hUDgaDAR9//LHx+fjx40368B5QvZXlGiiK9wD7tGrVKgiCAEEQ4OrqiqCgIMyePRu3b98GAMydOxcTJ06U7MOf/+qlItdAUYcPH8b169eh0Whw//59nDp1Cl9++SVatmyJBQsWcBp6FfbGG2/g1q1b6NWrF6ZMmVLm/arafcD8MByq0QrPgXdzcyu1v6urK7KyspCZmWnNsMiGXFxcMHLkSAwYMADBwcFwc3NDcnIyIiIi8P333yM1NRWbN2/GqFGjsHv3bjg4OMgdMtlQee4RBYlvALxHVEMBAQEYO3YsevToYXzjEhcXh99//x0bN25Ebm4uXnjhBQiCgGnTpskcLZXF//73P+M0o7FjxxZbjoD3gOqtLNdAAd4DqqcOHTpg6dKlCAkJMdnGn/+aoaRroEC9evUwduxY9O7dGwEBAVCpVLh+/Tq2bduGn3/+GVqtFu+//z40Gg0WLlxow+ipLA4cOIAff/wRKpUK33//PQRBKPO+Ve0+wMQWFatwgcCyrIjg5OQEAMjJybFaTGRbCQkJ8PLyMmkfOHAgZs6ciSFDhiA6OhoRERH47rvv8PLLL9s+SJJNee4RBfcHgPeI6mbMmDGYNGmSyRuhkJAQTJgwAdu2bcPYsWOh1Wrx6quvYuTIkahbt65M0VJZREREYO7cuQAAPz8/fPfdd8X24z2g+irrNQDwHlAdjB49Gl26dAGQ//MZGxuL9evXY9OmTXjiiSfw5ZdfYvjw4ZJ9+PNfvVTkGgDyf86vXbtm8uF2p06dMHr0aEybNg2DBg1CWloaPv74Y0yYMAHt27e3yfdEpdNoNJg2bRpEUcSrr76KNm3alGv/qnYf4FREKpZarTY+1mg0pfYvKDBadIlPsl/FJbUK1KlTBxs3bjT+IitYxpVqjvLcIwoXIOY9onrx9PQs8dO94cOHY/78+QDy63YsX77cVqFRBZw7dw5jxoyBTqeDWq3Ghg0b4OfnV2xf3gOqp/JcAwDvAdWBl5cX2rRpgzZt2iAkJASPP/44/vjjD/z888+Ii4vDqFGjsGrVKsk+/PmvXipyDQD5o3BKmrHRtWtXfPPNNwDyV9AreExVw8KFC3Hx4kU0atQI7733Xrn3r2r3ASa2qFgFS7sCZRsumJWVBaBs0xapeggICMDAgQMBAFeuXDGuikE1Q3nuEQX3B4D3iJpo2rRpxj98CxejpqolPj4egwYNwr1796BUKrF27Vr07dvXbH/eA6qf8l4DZcV7gH16+umn8dhjj8FgMOCll17C3bt3jdv4818zlHQNlNXjjz8ODw8PAPz5r0ouXryIjz76CED+AIXCUwXLqqrdB5jYomKp1Wr4+PgAkBaGK869e/eMF2vhwnBU/bVq1cr4mKse1SyFi0SWdo8oXCSS94iax8/Pz/j7hPeJqikxMREPP/wwEhMTIQgCVqxYgVGjRpW4D+8B1UtFroGy4j3AfhVcA1lZWfjrr7+M7fz5rznMXQNlpVKpEBQUBIA//1XJ//73P2g0GgQEBCA7Oxtr1641+Tp79qyx/759+4ztBX/3V7X7AGtskVmtWrXCgQMHcOXKFeh0OqhUxV8uFy9eND4uy3LQVH2Up8AgVS+Fk5qF7wHF4T2CeK+oulJSUjBw4EDExcUByP/k9plnnil1P94Dqo+KXgPlwXuAfapdu7bx8bVr14yPg4KCoFQqodfr+fNfzZm7BsqDP/9VT8HUwLi4ODzxxBOl9v+///s/4+P4+Hi4urpWufcBHLFFZvXu3RtAfob+xIkTZvsVHlbaq1cvq8dFVcf58+eNj/39/WWMhGytadOmxte8tKHl+/fvBwDUr18fTZo0sXZoVMUkJycjJSUFAO8TVU1aWhoGDx5svJd//PHHmDFjRpn25T2geqjMNVBWvAfYr8IjbApPH3J0dETXrl0BAEeOHCmxvk7B/cHJyclYoJzsh7lroKx0Oh1iYmIA8Oe/uqlq7wOY2CKzRo8ebXy8cuXKYvsYDAb8/PPPAPILD/bv398WoVEVEB8fj927dwMAAgMDUb9+fZkjIlsSBME4PP3ixYuIjIwstl9kZKTxU5pRo0bxU7saaOnSpRBFEQAQGhoqczRUIDs7G8OGDcPJkycBAO+88w7efPPNMu/Pe4D9q+w1UFa8B9ivDRs2GB+3bdtWsq3g74T09HT88ccfxe5/8+ZN7NmzBwAwYMAASU0esg8lXQNlsW7dOqSlpQHgz39VsmrVKoiiWOJX4YLyYWFhxvaCxFSVex8gEpWgT58+IgBRpVKJhw8fNtn+6aefigBEAOJ7771n+wDJKrZs2SJqtVqz22/duiV27NjR+NovWrTIhtGRNcTHxxtfz0mTJpVpn0uXLolKpVIEIHbp0kXMzs6WbM/Ozha7dOlivIfExMRYIXKylPJeA/Hx8eLJkydL7LN161bR0dFRBCA6OzuLN2/etFC0VBl5eXnioEGDjK/3rFmzKnQc3gPslyWuAd4D7NfKlSvFnJycEvt88cUXxuujadOmok6nk2xPTU0VPT09RQBi48aNxZSUFMl2nU4njhgxwniMsLAwS38bVAmVvQbu3r1b6mt69OhR0cvLSwQgCoIgRkVFWSJ0spH33nuv1J/fqvQ+gDW2qERfffUVevXqhZycHAwaNAhvv/02+vfvj5ycHKxduxZLly4FkD/Xfvbs2TJHS5Yyc+ZMaLVajBs3Dj169ECTJk3g7OyMlJQUhIeH44cffjBOK+jdu7fFpy2Q9R08eBBXrlwxPi94PYH8VS6LLus8efJkk2MEBQVhzpw5+PjjjxEVFYVevXrhzTffRGBgIGJjY/HJJ58gOjoaADBnzhw0b97cKt8LVUxlr4GrV6+if//+6NGjB0aMGIH27dvDz88PQH7Nho0bN2Ljxo3GkRqff/45R3ZWEU888QR27doFAHjooYcwZcoUSZHYohwdHY3FfwvjPcB+WeIa4D3Afi1YsACzZ8/GuHHj0Lt3bwQGBsLNzQ0ZGRk4c+YMfv31Vxw6dAhA/mu/dOlSKJVKyTG8vb3xySef4IUXXsC1a9fQrVs3vPPOO2jbti0SExPx5ZdfIiwsDED+9davXz9bf5tUgspeA2lpaejfvz/atWuH0aNHo3PnzqhXrx6USiWuX7+Obdu24ZdffjFOU3399dfRuXNnWb5Xsp4q9T7Aaikzqja2bNkienh4GDO2Rb+CgoLEy5cvyx0mWVDjxo3Nvt6Fv8aNGyfeu3dP7nCpAiZNmlSm17jgyxy9Xi8+99xzJe47ZcoUUa/X2/C7o7Ko7DUQFhZWpv1cXFzEH374QYbvkMwpz+uOf0djmMN7gH2yxDXAe4D9Kuv7vAYNGoi7du0q8Vjz588XBUEwe4yhQ4eWOjKIbK+y10DhUd4lfSmVSnHBggWiwWCQ4bukyijLiC1RrDrvAzhii0o1YsQI/PPPP/jqq6+wfft23Lx5E46OjmjWrBkee+wxvPTSS3BxcZE7TLKgn376CREREThy5Aji4uKQkpKC9PR0uLm5oWHDhujZsycmTZqEHj16yB0qyUyhUGD58uUYN24cli5diuPHjyMlJQW+vr4ICQnB9OnTMWTIELnDJCvo3LkzVq9ejSNHjiAqKgpJSUlISUmBTqdDrVq10Lp1awwYMABTp041juKg6of3gJqL9wD79ffff2P79u04dOgQrly5gtu3byM1NRXOzs7w8/NDhw4dMHz4cIwfP77U9/jvv/8+Bg8ejG+//RYHDhzA7du34eXlhfbt2+PZZ58t04prZHuVvQb8/f2xYcMGHDlyBMeOHUNCQgJSUlKQm5sLT09PtGjRAv369cPUqVO5aEg1V1XeBwii+O/4YCIiIiIiIiIiIjvCVRGJiIiIiIiIiMguMbFFRERERERERER2iYktIiIiIiIiIiKyS0xsERERERERERGRXWJii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrvExBYREREREREREdklJraIiIiIiIiIiMguMbFFRERERERERER2iYktIiIiIiIiIiKyS0xsERERERERERGRXWJii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrvExBYREREREREREdklJraIiIjI7ixYsACCIEAQBLlDwdWrV42xrFq1Su5wapxVq1YZ//+vXr1a6eOtWLECgiCgbdu2EEWx8gFWURs2bIAgCAgKCoJWq5U7HCIiogpjYouIiIgqRa/Xw8PDA4IgoFOnTiX2FUURPj4+xkTEihUrSuz/008/Gft+9913lgy7Srp58yYWLFiAPn36oHbt2nBwcICzszMaNGiAvn37YtasWdi4cSPS0tLkDrVayszMxNtvvw0AmD9/fpVInBY2aNAgCIKAWbNmVfpY48aNQ6tWrXD58mUsXrzYAtERERHJg4ktIiIiqhSlUomePXsCAE6fPo309HSzfc+dO4e7d+8anx84cKDEYxfe3rdv30pGWrUtW7YMLVq0wPvvv4+DBw8iJSUFOp0Oubm5SEhIwIEDB/D111/jsccew/Tp0+UOt1r6+uuvcfv2bbRq1QqPPvqo3OFIZGRkICIiAgAwYsSISh9PoVDgnXfeAQB8/PHHyMrKqvQxiYiI5MDEFhEREVVaQdLJYDDg8OHDZvsVJKqUSqXkeWn9fX190apVK2P7ggULIIpitZkqtmbNGkybNg3Z2dlQq9V48cUXsXnzZkRFReH48eP4888/MW/ePHTs2FHuUKutnJwcfPHFFwCAV199tcqN1vr777+h0Wjg4eGB0NBQixxzwoQJqF+/PpKTk/HDDz9Y5JhERES2xsQWERERVVrh0VT79+83269g22OPPQYAiI2NRWJiYrF979y5g5iYGABA7969q1yiwVL0ej1ee+01AIC7uzuOHj2KJUuWG8g3vgAAE0ZJREFUYNSoUejcuTO6dOmCkSNH4oMPPsDJkydx/vx5jB07Vuaoq5/Vq1cjNTUVTk5OVW60FgBs3boVADB48GA4ODhY5JhKpRITJkwAAHzzzTcwGAwWOS4REZEtMbFFRERElRYSEgK1Wg2g5FFYBdseffRRBAYGlti/pkxDPHr0KG7dugUAmD59Otq1a1di/5YtW2L8+PG2CK1GWb58OQBg2LBh8PLykjeYIgwGA3bs2AEAGD58uEWP/eSTTwIA4uPjERYWZtFjExER2QITW0RERFRpTk5O6Nq1KwDg+PHjyMvLM+kTHx+PhIQEAPkjsHr37g2gYomt0lZFbNKkCQRBwOTJkwEAly5dwvPPP48mTZrAyckJderUwZgxYxAZGVnq96bX67FkyRJ069YNHh4e8PT0RKdOnfD5558X+32W1/Xr142PmzVrVuHjFLc644YNG/Dwww/Dz88Pzs7OCA4OxltvvYX79++X6ZhhYWGYNGkSAgIC4OLiAg8PD7Rt2xZz5swxO9LO0se4d+8e5s6di+DgYDg7O8PPzw8PP/wwNmzYUKbzl8W1a9dw9OhRAPlF1c0JDw83/h+Hh4dDFEUsX74cvXv3ho+PDzw8PNC1a1f88ssvkv00Gg2+//57dO/eHd7e3nB3d0evXr2wfv36MsUXGRmJlJQUKBQKDB061GT7iRMnMGXKFAQFBcHV1RVqtRoNGzZE586dMWPGDGzZssXstN1OnTqhadOmAPKnxBIREdkdkYiIiMgC3n33XRGACECMiIgw2b5q1SoRgNi8eXNRFEVx2bJlIgCxbdu2xR6vU6dOIgDRw8ND1Ol0km3vvfee8VzFady4sQhAnDRpkvjHH3+ILi4uxv6Fv5RKpbh27Vqz31NGRobYp0+fYvcFIHbq1Ek8efKk8fnKlSvL+L/1wO+//27cf9asWeXev0B8fLwkjueee85s3P7+/uKFCxfMHisnJ0d8/PHHze4PQHR1dRW3bNli1WOcP39e9Pf3N7v/s88+K65cudL4PD4+vkL/dwXXJgAxNjbWbL+wsDBjv127dokjRowwG9vLL78siqIo3r17V+zbt6/Zfh9++GGp8c2dO1cEIPbq1ctk2xdffCEqFIoS/58BiBkZGWaPX/A61a9fvwz/W0RERFULR2wRERGRRRQeVVXcKKyCtoKRWgX/nj17Fvfu3ZP0zcjIwOnTpwEAPXv2NBabL68zZ85g4sSJqFOnDr755htERkbiyJEjWLBgAdRqNfR6PaZNm4bk5ORi93/qqaeMcXft2hVr1qxBVFQUtm/fjsceewwnT56s9AqFhQvC//DDD9i3b1+ljgcAS5YswYoVKyQx79ixwziFMTExEYMHD0ZGRobJvqIo4tFHH8XatWsB5K/A98svv+DQoUM4cuQIvvrqKzRq1AhZWVl49NFHERUVZZVjpKenY/DgwcZRXRMmTMCOHTsQFRWF3377DV26dMHKlSuxZMmSSv9/FbzGPj4+CAgIKNM+8+bNw9atW/Hkk09i+/btOHHiBNasWYMWLVoAyF9hcc+ePZg8eTIOHz6MF198Ebt27cKJEyewfPly+Pv7AwDmz5+Pc+fOlXiugvpaRVdD/Oeff/D666/DYDCgadOmWLRoEfbu3Yvo6Gjs378fy5Ytw8SJE+Hq6lri8QtGWyYkJODKlStl+v6JiIiqDLkza0RERFQ9ZGRkiCqVSgQgDh482GR7UFCQCEBcsWKFsc3X11cEIG7dulXS96+//jKONFm4cKHJsco6YguA2LlzZzEtLc2kz+rVq419vvjiC5Pt27ZtM24fOnSoqNVqTfq8//77klExFRmxJYqiOHz4cMlxQkJCxPnz54s7duwQk5OTy3SMwiO2Sor5gw8+MPaZM2eOyfalS5eKAEQHBwdx586dxZ7r7t27YuvWrc2OIrLEMV5//fUSrwGNRiMOGjRI8j1XdMRWy5YtRQDigAEDSuxXeMQWAPHLL7806ZOUlCS6u7uLAMTatWuLgiCImzZtMul3+vRp40irgtFdxYmLizOe7+zZs5Jt8+bNM458u3Xrltlj3L9/X9Tr9Wa3R0REGM9R0ghGIiKiqogjtoiIiMgi3NzcjKOPDh8+DL1eb9xWdIXDAr169QJgOsLLkoXjV6xYAQ8PD5P2iRMnGkfNFDfCrGAkkJOTE5YtWwaVSmXS591330WbNm0qFR8ArFy5EiEhIcbnx48fxwcffIChQ4eidu3aaNGiBWbOnImTJ0+W6XglxfzOO+8YY16+fDk0Go1xmyiK+OSTTwAAL7/8Mh555JFij1+rVi189tlnAIBDhw7h8uXLFj2GRqMxFnNv164d5s6da7K/g4MDli9fbpEVAm/evAkA8PPzK/M+3bp1w6xZs0za69atizFjxgAAkpOTMX78eIwePdqkX7t27UqtMwcA27ZtAwA0bdoUrVu3lmwrWHQgKCgIderUMXsMT09PKBTm3/YX/r7j4uLM9iMiIqqKmNgiIiIiiylIQmVkZODUqVPG9v379wMA6tSpg+bNmxvbC/6wL9heoOAPfbVaLUn4lFfbtm3NrjIoCIIxEVf0j3m9Xo/w8HAAwKBBg4wJsKIUCgUmTZpU4fgK+Pr64tChQ1i6dCk6depksj0mJgbffPMNOnfujKeffhpZWVklHq+sMd+9e1eSLDt//jxiY2MB5K9cWZLCCccjR45Y9BgnTpwwTk+dNGmS2UUCGjRogEGDBpV4jtLk5eUZp2TWqlWrzPs9/vjjZre1b9++XP1KSiaZm4YIAPXq1QOQ/39+7NixkgMugbe3t/FxQbKMiIjIXjCxRURERBbTp08f4+PCo1CK1tcq2v/EiRPIyckBkD9ap+CP9G7dusHR0bHC8QQHB5e4veAP+qK1pmJjY5GdnQ0ApSbWCuoTVZaDgwOef/55nDhxAgkJCVi7di1ef/119OnTRzIqafXq1Rg5cqRkRFxR5Yn5zJkzxseFa1316NHDuAJgcV9ubm7GvoWTIZY4RuGYrP3/f/fuXePj8iS2goKCzG7z8vIqV7/iap0VtEdERAAoPrH1xBNPwMHBAXl5eejVqxdGjBiB77//HmfPnjW7CmJxCn/fpSVNiYiIqhomtoiIiMhi+vTpYxxdU5bEVqdOneDi4gKtVovIyEgA+dPwcnNzAVR+GqKLi0uJ2wumZxVNEhVOdpQ2Pa2kKWAV5e/vjwkTJuCzzz7D/v37cevWLbz11lvGePft24c1a9aY3b88MRf+Xu/cuVOheAuSgJY6hi3//9VqtfFxQXK1LEq6tgpP+ytLP4PBUOz2v//+GxqNBu7u7ggNDTXZHhwcjDVr1qBWrVrQ6XTYtm0bXnzxRbRt2xZ+fn54+umnS5zmWKDw922JqZ1ERES2ZFp4gYiIiKiCvL290bp1a5w9e9b4B3V6erpxhcOiiS0HBwd07doV4eHh2L9/P/r372/R+lqWYG4anC15e3tj4cKFEEURH3/8MQBgw4YNeOqpp4rtX9GYCyf4tm7diiZNmpRpv8LJJ0scozBr//97eXlBpVJBp9NJEmpVQUF9rcGDB5tNOI0bNw4PP/ww1q1bh7///hsHDhxAcnIyUlJSsHr1aqxevRqTJk3CihUrzNbZKvx9Fx5tRkREZA+Y2CIiIiKL6tu3L86ePYvk5GRcvHgR8fHxMBgMkuLyhfXu3Rvh4eHGhFZBvS0HBwf06NHDprEXKDw16/bt2yX2LW27JT3//PPGxNaVK1fM9itPzIXrK/n4+Bgfe3l5VagwviWOUfT/v6TpfJX9/xcEAb6+vrh165axrldVYDAYsGPHDgDFT0MszNPTE9OmTcO0adMAABcuXMCff/6JxYsXIzExET/99BM6duxYbLF7AJLvu1GjRhb6DoiIiGyDUxGJiIjIoorW2SpIWHXv3h1KpdKkf8EorsjISOTl5eHw4cMA8qcpurq62iBiU4GBgXB2dgaQPzWyJKVtt6TCBeFLGslUnpgLJ54KJx4PHTpUkRAtcoy2bdsaH9vi/7/gfAUrd1YFkZGRSE5OhkKhwNChQ8u1b8uWLTF37lxERkYaf4bWr19vtn/h77voyotERERVHRNbREREZFGFpw/u37/fOAKr6DTEAj169IBSqURWVhZWrVqFtLQ0k+PYmkqlQr9+/QAAu3btQlJSUrH9DAYDfvrpp0qdqzxFvgsXZg8ICDDbr6wx16pVS7IKY6dOndCgQQMAwNKlS421zsrDEsfo3LmzcdTWL7/8Yvb/KCEhAbt27Sr38YsqSMZeunTJbCF3WytYDbFHjx7w9fWt0DEaNmxoHO2WkpJitl9BctDBwaHYVTmJiIiqMia2iIiIyKL8/f0RGBgIAAgLCzMmYwqP5CrMw8PDOGLm008/NbbLXV/rxRdfBADk5eVh+vTpxa5C+NFHH0lW8KuInTt3Yvz48YiOji6x3927d/Hyyy8bn48aNcps35Ji/vjjj40xP/fcc3BycjJuUygUePvttwEAcXFxeOaZZ5CXl2f2POnp6fjmm28kbZY4hpOTE5599lkAwKlTp/DZZ5+Z7KfT6fD8889Do9GYPXZZFVybBoNBkjyUU0Fia/jw4Wb7bN68Gffv3ze7/caNG7h48SIAoGnTpmb7FaxC2qNHD8lKlURERPaANbaIiIjI4vr06YPY2FgkJCQAyB8B1b17d7P9e/fujVOnTiEuLg5AfnLE3AgvWxkxYgRGjBiBrVu3YuvWrejVqxdeffVVNG/eHHfu3MGqVauwbt06dOnSpVLJEIPBgA0bNmDDhg1o3749hg0bhpCQENSrVw+Ojo64c+cODh48iKVLlxpXHOzcuTMmTZpk9phdunQpNuaffvoJa9euBQA0aNAA8+bNM9n3hRdewO7du7Fp0yZs2LABJ0+exPTp09G1a1d4enoiPT0dFy9eRHh4OLZs2QK1Wo2XXnrJ4seYP38+1q9fj5s3b+LNN9/EqVOn8Mwzz8DPzw8xMTH44osvcPz48Ur//wNAz549Ubt2bSQnJ2Pv3r3o379/pY5XWVevXsW5c+cAlFxf68svv8STTz6JYcOG4aGHHkLLli3h6emJe/fuISoqCosXLzauePjCCy8Ue4yMjAzjiK0xY8ZY+DshIiKyAZGIiIjIwlasWCECMH6FhISU2H/t2rWS/u3bty+x/3vvvWfsW5zGjRuLAMRJkyaVeJxJkyaJAMTGjRsXuz09PV3s1auXJLbCXx07dhRPnDhhfL5y5coSz1ecgwcPiq6urmbPUfRr4MCBYkpKislx4uPjJXFMnjzZ7DHq1asnnjt3zmxMGo1GfPHFF0VBEEqNp2nTplY7xtmzZ8W6deua3W/y5MniypUrjc/j4+PL/f9fYPbs2SIAMSAgwGyfsLAw47nCwsLM9itrTOau46+//rrE/5cCoaGhpf7fKhQK8f/+7//MHmPVqlUiAFGlUolJSUklno+IiKgq4lREIiIisrii0whLG31VdJqi3NMQC7i7uyM8PByLFy9GSEgI3Nzc4O7ujg4dOuCjjz7C4cOHJasKVkSvXr2QnJyMLVu24LXXXkNoaCj8/f3h5OQElUoFb29vdOrUCdOnT0dYWBh27dolWXnQnJUrV+K3335Dv3794OPjAycnJwQFBeGNN97AuXPn0KpVK7P7Ojg4YMmSJTh9+jRmzpyJtm3bwtPTE0qlEp6enujQoQOmTJmCjRs34sKFC1Y7RuvWrXHu3Dm88cYbaN68OZycnODr64v+/fvjt99+w8qVK8v2n1wGzz//PID86ZORkZEWO25FFExDLG01xDVr1mDp0qWYOHEiOnTogLp160KlUsHNzQ2tW7fGiy++iOjoaLz77rtmj/Hbb78ByB+tVbduXct9E0RERDYiiGI5KpYSERERUZV09epVYx2llStXYvLkyfIGZIeGDh2KnTt3YurUqVi2bJksMWRkZMDX1xcajQa7du3CwIEDrXaua9euITAwEHq9HkeOHClxujAREVFVxRFbRERERETIXwxAoVDg559/xo0bN2SJYdeuXdBoNHB3d0doaKhVz7Vw4ULo9Xo88sgjTGoREZHdYmKLiIiIiAhA+/btMXHiRGg0Gnz00UeyxODu7o733nsPixcvhqOjo9XOc+PGDaxatQpKpVKyGikREZG94aqIRERERET/WrhwIQIDA6FWqyGKIgRBsOn5Bw0ahEGDBln9PDdu3MBbb72FgIAAtG3b1urnIyIishbW2CIiIiKqBlhji4iIiGoiTkUkIiIiIiIiIiK7xBFbRERERERERERklzhii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrvExBYREREREREREdklJraIiIiIiIiIiMguMbFFRERERERERER2iYktIiIiIiIiIiKyS0xsERERERERERGRXWJii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrvExBYREREREREREdklJraIiIiIiIiIiMguMbFFRERERERERER2iYktIiIiIiIiIiKyS0xsERERERERERGRXWJii4iIiIiIiIiI7BITW0REREREREREZJeY2CIiIiIiIiIiIrv0/5jBicKdK+iGAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ti_md.plot_power_curve(\n", - " fig_kwargs={\"figsize\": (6, 3)}, # The legend is a bit wider, so we'll need to change the dimensions\n", - " legend_kwargs={\"fontsize\": 6}, # The labels are quite long, so let's shrink the font\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b61286ae-ec6f-46e8-a863-0384b2e87855", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ti_md.plot_thrust_coefficient_curve(\n", - " legend_kwargs={\"fontsize\": 6}, # The labels are quite long, so let's shrink the font\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "edf8e153-b756-4b0d-aba8-e82a39f18c89", - "metadata": {}, - "source": [ - "## Compare multiple turbines\n", - "\n", - "The other class provided by the turbine library is the `TurbineLibrary` object, which allows users\n", - "to simultaneously load internal and external turbine library configurations and compare them.\n", - "\n", - "Note that turbine names can overlap between the internal and external turbine libraries in this\n", - "interface, so a little more care is required. This is distinct from how turbine inputs are\n", - "treated through `FlorisInterface` via `floris.simulation.farm` where duplicate turbine names\n", - "will raise an error.\n", - "\n", - "### Loading the libraries\n", - "\n", - "Loading a turbine library is a 2 or more step process depending on how many turbine libraries\n", - "are going to be compared." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "52c74e32-c93c-449e-90f2-4ed5b2bf0f72", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iea_15MW_multi_dim_cp_ct\n", - "nrel_5MW\n", - "iea_10MW\n", - "iea_15MW_floating\n" - ] - } - ], - "source": [ - "# Initialize the turbine library (no definitions required!)\n", - "tl = TurbineLibrary()\n", - "\n", - "# Load the internal library, except the IEA 15MW turbine\n", - "tl.load_internal_library(exclude=[\"iea_15MW.yaml\"])\n", - "for turbine in tl.turbine_map:\n", - " print(turbine)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "533c277e-c181-42a1-b5dd-d76dc5d3bb5e", - "metadata": {}, - "source": [ - "In addition to the `load_internal_library` method, there is a `load_external_library` method with\n", - "the same parameterizations, but with an argument for a new library file path.\n", - "\n", - "We can also override previously ignored or loaded files by rerunning the load method again. Notice\n", - "how we use `which=[\"x_20MW.yaml\"]` to now include the file. This makes it so we only load the one\n", - "turbine configuration, however, the same could be achieved by specifying none of the keyword\n", - "arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "273da4ef-0243-4296-9838-3f8e98615deb", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iea_15MW_multi_dim_cp_ct\n", - "nrel_5MW\n", - "iea_10MW\n", - "iea_15MW_floating\n", - "iea_15MW\n" - ] - } - ], - "source": [ - "tl.load_internal_library(which=[\"iea_15MW.yaml\"])\n", - "for turbine in tl.turbine_map:\n", - " print(turbine)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bac88742-33af-44f3-a35b-e178e60a49d3", - "metadata": {}, - "source": [ - "Notice that the \"iea_15MW\" turbine is now loaded.\n", - "\n", - "### Comparing turbines\n", - "\n", - "There are a number of methods that will plot the varying properties for each turbine against each\n", - "other, but here the primary output will be displayed.\n", - "\n", - "It should be noted that the 15MW turbines are all variations of each other, and so the\n", - "multi-dimensional example is removed in favor the of the floating 15MW turbine to highlight\n", - "a multi-dimensional turbine in relation to the standard 15 MW example." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "14008ddc-35be-4ac7-8371-f17cbf2f9ac3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tl.plot_comparison(\n", - " exclude=[\"iea_15MW_multi_dim_cp_ct\"], # Remove a turbine just for demonstration\n", - " wind_speeds=np.linspace(0, 30, 61), # 0 -> 30 m/s, every 0.5 m/s\n", - " fig_kwargs={\"figsize\": (8, 8)}, # Size the figure appropriately for the docs page\n", - " plot_kwargs={\"linewidth\": 1}, # Ensure the line plots look nice\n", - " legend_kwargs={\"fontsize\": 5}, # Ensure all the legend items fit\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "38f654ff-82f5-4019-8c43-e31c1b783cc1", - "metadata": { - "tags": [] - }, - "source": [ - "Alternatively, these can all be ploted individually with:\n", - "\n", - "- `plot_power_curves()`\n", - "- `plot_Ct_curves()`\n", - "- `plot_rotor_diameters()`\n", - "- `plot_hub_heights()`\n", - "\n", - "For a text based approach, we can access the attributes like the following:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5168be89-64be-482a-8a8a-c6889e64de88", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Turbine | Rotor Diameter (m) | Hub Height (m) | TSR | Air Density (ρ) | Tilt (º)\n", - "-----------------------------------------------------------------------------------------------------\n", - " iea_15MW_multi_dim_cp_ct | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", - " nrel_5MW | 125.88 | 90.0 | 8.0 | 1.225 | 5.000\n", - " iea_10MW | 198.00 | 119.0 | 8.0 | 1.225 | 6.000\n", - " iea_15MW_floating | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n", - " iea_15MW | 242.24 | 150.0 | 8.0 | 1.225 | 6.000\n" - ] - } - ], - "source": [ - "header = f\"\\\n", - "{'Turbine':>25} | \\\n", - "{'Rotor Diameter (m)':>18} | \\\n", - "{'Hub Height (m)':>14} | \\\n", - "{'TSR':>6} | \\\n", - "{'Air Density (ρ)':>15} | \\\n", - "{'Tilt (º)':>8}\\\n", - "\"\n", - "print(header)\n", - "print(\"-\" * len(header))\n", - "for name, t in tl.turbine_map.items():\n", - " print(f\"{name:>25}\", end=\" | \")\n", - " print(f\"{t.turbine.rotor_diameter:>18,.2f}\", end=\" | \")\n", - " print(f\"{t.turbine.hub_height:>14,.1f}\", end=\" | \")\n", - " print(f\"{t.turbine.TSR:>6,.1f}\", end=\" | \")\n", - " if t.turbine.multi_dimensional_cp_ct:\n", - " condition_keys = list(t.turbine.power_thrust_table.keys())\n", - " print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_air_density']:>15,.3f}\", end=\" | \")\n", - " print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_tilt']:>8,.3f}\")\n", - " else:\n", - " print(f\"{t.turbine.power_thrust_table['ref_air_density']:>15,.3f}\", end=\" | \")\n", - " print(f\"{t.turbine.power_thrust_table['ref_tilt']:>8,.3f}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/turbine_library.md b/docs/turbine_library.md new file mode 100644 index 000000000..646125817 --- /dev/null +++ b/docs/turbine_library.md @@ -0,0 +1,57 @@ + +# Turbine Library + +FLORIS includes a library of predefined wind turbine models that can be used to quickly set up +simulations without needing to define the turbine characteristics manually. These include standard +reference wind turbines as well as fictional wind turbine models for the purpose of demonstrating +various features of FLORIS. These turbines are stored in the `floris.turbine_library` module. + +## NREL 5MW reference wind turbine + +FLORIS representation of the NREL 5MW reference wind turbine {cite:t}`jonkman_NREL5MW_2009`. Data +based on https://github.com/NREL/turbine-models/blob/master/Offshore/NREL_5MW_126_RWT_corrected.csv. +Specified as `"nrel_5MW"` in the `turbine_type` field of the FLORIS input dictionary. + +The NREL 5MW turbine is the default turbine model used in most FLORIS examples and tutorials. It is +also the model used if FLORIS is instantiated in the defaults configuration using +`FlorisModel("defaults")`. + + +## IEA 15MW reference wind turbine + +FLORIS representation of the IEA 15MW reference wind turbine {cite:t}`gaertner_IEA15MW_2020`. Data +based on https://github.com/IEAWindTask37/IEA-15-240-RWT/blob/master/Documentation/IEA-15-240-RWT_tabular.xlsx. +Specified as `"iea_15MW"` in the `turbine_type` field of the FLORIS input dictionary. + +The IEA 15MW turbine is used in the following examples: +- examples/examples_control_types/004_helix_active_wake_mixing.py + +## IEA 10MW reference wind turbine + +FLORIS representation of the IEA 10MW reference wind turbine {cite:t}`kainz_IEA10MW_2024`. Data +based on https://github.com/NREL/turbine-models/blob/master/Offshore/IEA_10MW_198_RWT.csv. +Specified as `"iea_10MW"` in the `turbine_type` field of the FLORIS input dictionary. + +The IEA 10MW turbine is used in the following examples: +- examples/examples_turbine/002_multiple_turbine_types.py + +## IEA 15MW multidimensional + +Fictional IEA 15MW turbine model used to demonstrate the use of multidimensional power and thrust +coefficient data. Reads in fictional multidimensional data describing the power and thrust coefficient +relationships on wave period `Tp` and wave height `Hs` from `iea_15MW_multi_dim_Tp_Hs.csv` in the +`turbine_library` folder. Specified as `"iea_15MW_multi_dim"` in the `turbine_type` field of the FLORIS +input dictionary. This data should be treated as fictional and for demonstrative purposes only. + +This fictional turbine model is not currently used in examples. + +## IEA 15MW floating, multidimensional + +The same as the multidimensional IEA 15MW turbine model above, but with an additional fictional +floating platform tilt table. This model is used to demonstrate the floating wind turbine capabilities +in FLORIS. Specified as `"iea_15MW_floating_multi_dim"` in the `turbine_type` field of the FLORIS input +dictionary. This data should be treated as fictional and for demonstrative purposes only. + +This fictional turbine model is used in the following examples: +- examples/examples_multidim/001_multi_dimensional_cp_ct.py +- examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py diff --git a/docs/turbine_models.ipynb b/docs/turbine_models.ipynb new file mode 100644 index 000000000..1c93321e8 --- /dev/null +++ b/docs/turbine_models.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ab10767e", + "metadata": {}, + "source": [ + "# Wind turbine models\n", + "\n", + "FLORIS generally represents wind turbines as actuator disks specified by a power curve and a\n", + "thrust coefficient curve (both specified as a function of wind speed). We can easily investigate the\n", + "power and thrust coefficients of a turbine by running a single-turbine `FlorisModel` using a range\n", + "of wind speeds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc97a774", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from floris import FlorisModel, TimeSeries\n", + "\n", + "n_wind_speeds = 100\n", + "wind_speeds = np.linspace(0.1, 30, n_wind_speeds)\n", + "\n", + "fmodel = FlorisModel(\"defaults\") # Defaults to NREL 5MW turbine\n", + "fmodel.set(\n", + " wind_data=TimeSeries(\n", + " wind_directions=np.zeros(n_wind_speeds),\n", + " wind_speeds=wind_speeds,\n", + " turbulence_intensities=0.06\n", + " ),\n", + " layout_x=[0],\n", + " layout_y=[0],\n", + " wind_shear=0.0\n", + ")\n", + "\n", + "fmodel.run()\n", + "\n", + "powers = fmodel.get_turbine_powers()\n", + "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n", + "\n", + "fig, ax = plt.subplots(2, 1, sharex=True)\n", + "ax[0].plot(wind_speeds, powers)\n", + "ax[0].grid()\n", + "ax[0].set_ylabel(\"Power [kW]\")\n", + "ax[1].plot(wind_speeds, thrust_coefficients)\n", + "ax[1].grid()\n", + "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n", + "ax[1].set_xlabel(\"Wind speed [m/s]\")\n", + "ax[1].set_xlim([0, 30])" + ] + }, + { + "cell_type": "markdown", + "id": "88d2ebce", + "metadata": {}, + "source": [ + "## Prepackaged turbine models\n", + "\n", + "FLORIS ships with three reference wind turbine models: the NREL 5MW turbine {cite:t}`XXNREL5MWXX`, \n", + "the IEA 10 MW turbine {cite:t}`XXXIEA10MWXX`, and the IEA 15 MW turbine {cite:t}`XXIEA15MW`. The\n", + "turbine models used for FLORIS simulations can be changed by specifying the `turbine_type` keyword\n", + "argument to `FlorisModel.set()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c1ec9ba", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(2, 1, sharex=True)\n", + "\n", + "turbine_models = [\"NREL_5MW\", \"IEA_10MW\", \"IEA_15MW\"]\n", + "\n", + "for turbine_model in turbine_models:\n", + " fmodel.set(turbine_type=[turbine_model], reference_wind_height=-1)\n", + "\n", + " fmodel.run()\n", + "\n", + " powers = fmodel.get_turbine_powers()\n", + " thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n", + "\n", + " ax[0].plot(wind_speeds, powers, label=turbine_model)\n", + " ax[1].plot(wind_speeds, thrust_coefficients)\n", + "\n", + "\n", + "ax[0].grid()\n", + "ax[0].set_ylabel(\"Power [kW]\")\n", + "ax[0].legend()\n", + "ax[1].grid()\n", + "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n", + "ax[1].set_xlabel(\"Wind speed [m/s]\")\n", + "ax[1].set_xlim([0, 30])" + ] + }, + { + "cell_type": "markdown", + "id": "bd2c8544", + "metadata": {}, + "source": [ + "## User-defined wind turbine models\n", + "\n", + "Users may also provide their own wind turbine models, provided that they contain the appropriate\n", + "information. To include your own wind turbine model in your main FLORIS input YAML, use the\n", + "`!include` specifier, e.g.\n", + "```\n", + " turbine_type:\n", + " - !include path/to/your/turbine.yaml\n", + "```\n", + "\n", + "You can also `set` the `turbine_type` to your own turbine using the path, e.g.\n", + "```python\n", + "fmodel.set(turbine_type=[\"path/to/your/turbine.yaml\"])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "cdd9ad19", + "metadata": {}, + "source": [ + "The following pages describe in closer detail how wind turbines are implemented in FLORIS and\n", + "provide information on advanced wind turbine operation and modeling.\n", + "\n", + "```{note}\n", + "The `TurbineInterface` and `TurbineLibrary` classes are now deprecated and will be removed in a\n", + "future release. Users should simply use an instantiated `FlorisModel` to investigate wind turbine\n", + "models.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "98fd51f6", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "floris", + "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": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/examples_floating/001_floating_turbine_models.py b/examples/examples_floating/001_floating_turbine_models.py index 75936b09a..900b588fe 100644 --- a/examples/examples_floating/001_floating_turbine_models.py +++ b/examples/examples_floating/001_floating_turbine_models.py @@ -11,9 +11,9 @@ If `correct_cp_ct_for_tilt` is True, then the difference between the current tilt as interpolated from the floating tilt table is used to scale the turbine power and thrust. -If `correct_cp_ct_for_tilt` is False, then it is assumed that the Cp/Ct tables provided -already account for the variation in tilt with wind speed (for example they were computed from -a turbine simulator with tilt degree-of-freedom enabled and the floating platform simulated), +If `correct_cp_ct_for_tilt` is False, then it is assumed that the power/thrust coefficient tables +provided already account for the variation in tilt with wind speed (for example they were computed +from a turbine simulator with tilt degree-of-freedom enabled and the floating platform simulated), and no correction is made. In the example below, three single-turbine simulations are run to show the different behaviors. @@ -21,7 +21,7 @@ fmodel_fixed: Fixed bottom turbine (no tilt variation with wind speed) fmodel_floating: Floating turbine (tilt varies with wind speed) fmodel_floating_defined_floating: Floating turbine (tilt varies with wind speed, but - tilt does not scale cp/ct) + tilt does not scale power/thrust coefficient) """ @@ -89,7 +89,7 @@ tilt_angles_floating_defined_floating, color="m", ls="--", - label="Floating (cp/ct not scaled by tilt)", + label="Floating (power/thrust coefficient not scaled by tilt)", ) ax.grid(True) ax.legend() @@ -104,7 +104,7 @@ power_floating_defined_floating, color="m", ls="--", - label="Floating (cp/ct not scaled by tilt)", + label="Floating (power/thrust coefficient not scaled by tilt)", ) ax.grid(True) ax.legend() @@ -119,7 +119,7 @@ power_floating_defined_floating - power_fixed, color="m", ls="--", - label="Floating (cp/ct not scaled by tilt)", + label="Floating (power/thrust coefficient not scaled by tilt)", ) ax.grid(True) ax.legend() @@ -134,7 +134,7 @@ ct_floating_defined_floating, color="m", ls="--", - label="Floating (cp/ct not scaled by tilt)", + label="Floating (power/thrust coefficient not scaled by tilt)", ) ax.grid(True) ax.legend() diff --git a/examples/examples_multidim/001_multi_dimensional_cp_ct.py b/examples/examples_multidim/001_multi_dimensional_cp_ct.py index b1bf0441b..c86b4e4fc 100644 --- a/examples/examples_multidim/001_multi_dimensional_cp_ct.py +++ b/examples/examples_multidim/001_multi_dimensional_cp_ct.py @@ -1,25 +1,27 @@ -"""Example: Multi-dimensional Cp/Ct data +"""Example: Multi-dimensional power/thrust coefficient data This example creates a FLORIS instance and: 1) Makes a two-turbine layout 2) Demonstrates single ws/wd simulations 3) Demonstrates multiple ws/wd simulations -with the modification of using a turbine definition that has a multi-dimensional Cp/Ct table. +with the modification of using a turbine definition that has a multi-dimensional +power/thrust coefficient table. In the input file `gch_multi_dim_cp_ct.yaml`, the turbine_type points to a turbine definition, iea_15MW_floating_multi_dim_cp_ct.yaml located in the turbine_library, -that supplies a multi-dimensional Cp/Ct data file in the form of a .csv file. This .csv file -contains two additional conditions to define Cp/Ct values for: Tp for wave period, and Hs for wave -height. For every combination of Tp and Hs defined, a Cp/Ct/Wind speed table of values is also -defined. It is required for this .csv file to have the last 3 columns be ws, Cp, and Ct. In order +that supplies a multi-dimensional power/thrust coefficient data file in the form of a .csv file. +This .csv file contains two additional conditions to define power and thrust coefficient values for: +Tp for wave period, and Hs for wave height. For every combination of Tp and Hs defined, a +power/thrust coefficient/Wind speed table of values is also defined. It is required for this .csv +file to have the last 3 columns be ws, power, and thrust coefficient. In order for this table to be used, the flag 'multi_dimensional_cp_ct' must be present and set to true in the turbine definition. With this flag enabled, the solver will down-select to use the interpolant defined at the closest conditions. The user must supply these conditions in the main input file under the 'flow_field' section, e.g.: -NOTE: The multi-dimensional Cp/Ct data used in this example is fictional for the purposes of -facilitating this example. The Cp/Ct values for the different wave conditions are scaled -values of the original Cp/Ct data for the IEA 15MW turbine. +NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the +purposes of facilitating this example. The power/thrust coefficient values for the different wave +conditions are scaled values of the original power/thrust coefficient data for the IEA 15MW turbine. flow_field: multidim_conditions: @@ -30,7 +32,7 @@ and used to select the interpolant at each turbine. Also note in the example below that there is a specific method for computing powers when -using turbines with multi-dimensional Cp/Ct data under FlorisModel, called +using turbines with multi-dimensional power/thrust coefficient data under FlorisModel, called 'get_turbine_powers_multidim'. The normal 'get_turbine_powers' method will not work. """ diff --git a/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py b/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py index 8cf206f07..10db44e46 100644 --- a/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py +++ b/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py @@ -1,9 +1,9 @@ -"""Example: Multi-dimensional Cp/Ct with 2 Hs values +"""Example: Multi-dimensional power/thrust coefficient with 2 Hs values This example follows the previous example but shows the effect of changing the Hs setting. -NOTE: The multi-dimensional Cp/Ct data used in this example is fictional for the purposes of -facilitating this example. The Cp/Ct values for the different wave conditions are scaled -values of the original Cp/Ct data for the IEA 15MW turbine. +NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the +purposes of facilitating this example. The power/thrust coefficient values for the different wave +conditions are scaled values of the original power/thrust coefficient data for the IEA 15MW turbine. """ diff --git a/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py b/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py new file mode 100644 index 000000000..97e48b488 --- /dev/null +++ b/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py @@ -0,0 +1,48 @@ +"""Example: Multi-dimensional power/thrust coefficients with turbulence intensity +This example follows the previous example, but demonstrating how a multidimensional turbine can be +used to model the effect of turbulence intensity on power and thrust coefficient. + +NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the +purposes of facilitating this example and the power values shown should not be taken as +representative of the actual effect of turbulence intensity on power/thrust coefficient. +""" + + +import matplotlib.pyplot as plt +import numpy as np + +from floris import FlorisModel, TimeSeries + + +# Initialize FLORIS with the given input file. +fmodel = FlorisModel("../inputs/gch_multi_dim_cp_ct_TI.yaml") + +# Set both cases to 3 turbine layout +fmodel.set(layout_x=[0.0, 500.0, 1000.0], layout_y=[0.0, 0.0, 0.0]) + +# Use a sweep of wind speeds +wind_speeds = np.arange(5, 20, 0.1) +time_series = TimeSeries( + wind_directions=270.0, wind_speeds=wind_speeds, turbulence_intensities=0.06 +) +fmodel.set(wind_data=time_series) + +# Loop over different turbulence intensities using set() +# When running with TI=0.10, the multidimensional data handler will find the nearest defined +# value of 0.08 and use that data. +fig, axarr = plt.subplots(1, 3, sharex=True, figsize=(12, 4)) +for ti, col in zip([0.06, 0.10], ["k", "r"]): + fmodel.set(multidim_conditions={"TI": ti}) + fmodel.run() + turbine_powers = fmodel.get_turbine_powers() / 1000.0 + + for t_idx in range(3): + ax = axarr[t_idx] + ax.plot(wind_speeds, turbine_powers[:, t_idx], color=col, label="TI={0:.2f}".format(ti)) +for t_idx in range(3): + axarr[t_idx].grid(True) + axarr[t_idx].set_xlabel("Wind Speed (m/s)") + axarr[t_idx].set_title(f"Turbine {t_idx}") +axarr[0].legend() + +plt.show() diff --git a/examples/examples_turbine/001_check_turbine.py b/examples/examples_turbine/001_check_turbine.py index 52a879dab..75698dda2 100644 --- a/examples/examples_turbine/001_check_turbine.py +++ b/examples/examples_turbine/001_check_turbine.py @@ -29,7 +29,7 @@ ) # Get a list of available turbine models provided through FLORIS, and remove -# multi-dimensional Cp/Ct turbine definitions as they require different handling +# multi-dimensional power/thrust coefficient turbine definitions as they require different handling turbines = [ t.stem for t in fmodel.core.farm.internal_turbine_library.iterdir() diff --git a/examples/inputs/gch.yaml b/examples/inputs/gch.yaml index 35f285613..29e0e2de2 100644 --- a/examples/inputs/gch.yaml +++ b/examples/inputs/gch.yaml @@ -1,197 +1,202 @@ ### -# A name for this input file. -# This is not currently only for the user's reference. +# Name for the input file. +# This is not used by FLORIS and is simply for the user's reference. +# String type. name: GCH ### -# A description of the contents of this input file. -# This is not currently only for the user's reference. +# Description of the contents of this input file. +# This is not used by FLORIS and is simply for the user's reference. +# String type. description: Three turbines using Gauss Curl Hybrid model ### -# The earliest version of FLORIS this input file supports. -# This is not currently only for the user's reference. +# The FLORIS version that the file is defined for. +# This is not used by FLORIS and is simply for the user's reference. +# String type. floris_version: v4 ### -# Configure the logging level and where to show the logs. +# Upper-level group of options for configuring logging. logging: ### - # Settings for logging to the console (i.e. terminal). + # Group of settings for logging to the console (i.e. terminal). console: ### - # Can be "true" or "false". + # Flag to enable console logging. Boolean type. enable: true ### - # Set the severity to show output. Messages at this level or higher will be shown. - # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + # Severity to show output in console. Messages at this level or higher will be shown. + # String type. Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". level: WARNING ### - # Settings for logging to a file. + # Group of settings for logging to a file. file: ### - # Can be "true" or "false". + # Flag to enable file logging. Boolean type. enable: false ### - # Set the severity to show output. Messages at this level or higher will be shown. - # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". + # Severity to show output in file. Messages at this level or higher will be shown. + # String type. Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG". level: WARNING ### -# Configure the solver for the type of simulation. +# Upper-level group of options for configuring solution grid. solver: ### - # Select the solver type. - # Can be one of: "turbine_grid", "flow_field_grid", "flow_field_planar_grid". + # Grid type for solving flow values at the turbines. + # String type. Can be one of: "turbine_grid", "turbine_cubature_grid". type: turbine_grid ### - # Options for the turbine type selected above. See the solver documentation for available parameters. + # Number of grid points per turbine for solve. For turbine_grid type solve, represents the number + # of points along each of the two axes. For turbine_cubature_grid type solve, represents the + # total number of points used in the cubature solve. Integer type. turbine_grid_points: 3 ### -# Configure the turbine types and their placement within the wind farm. +# Group for setting the wind farm configuration. farm: ### - # Coordinates for the turbine locations in the x-direction which is typically considered - # to be the streamwise direction (left, right) when the wind is out of the west. - # The order of the coordinates here corresponds to the index of the turbine in the primary - # data structures. + # x-coordinates for the turbine locations, with the x axis corresponding to the + # "west to east" direction. The order of the coordinates corresponds to the index of the + # turbine in the primary data structures. + # List of float type. layout_x: - 0.0 - 630.0 - 1260.0 ### - # Coordinates for the turbine locations in the y-direction which is typically considered - # to be the spanwise direction (up, down) when the wind is out of the west. - # The order of the coordinates here corresponds to the index of the turbine in the primary - # data structures. + # y-coordinates for the turbine locations, with the y axis corresponding to the + # "south to north" direction. The order of the coordinates corresponds to the index of the + # turbine in the primary data structures. + # List of float type. layout_y: - 0.0 - 0.0 - 0.0 ### - # Listing of turbine types for placement at the x and y coordinates given above. + # Listing of turbine types for placement at the x and y coordinates. # The list length must be 1 or the same as ``layout_x`` and ``layout_y``. If it is a # single value, all turbines are of the same type. Otherwise, the turbine type # is mapped to the location at the same index in ``layout_x`` and ``layout_y``. - # The types can be either a name included in the turbine_library or - # a full definition of a wind turbine directly. + # The types can be either a string for a turbine included in the turbine_library, + # a string beginning with !include for a path to the user's turbine; or a full + # definition of a wind turbine (as a nested dictionary). turbine_type: - nrel_5MW ### -# Configure the atmospheric conditions. +# Group for defining the atmospheric conditions. flow_field: ### - # Air density. + # Air density. Float type. air_density: 1.225 ### - # The height to consider the "center" of the vertical wind speed profile + # The height in meters to consider the "center" of the vertical wind speed profile # due to shear. With a shear exponent not 1, the wind speed at this height # will be the value given in ``wind_speeds``. Above and below this height, # the wind speed will change according to the shear profile; see # :py:meth:`.FlowField.initialize_velocity_field`. # For farms consisting of one wind turbine type, use ``reference_wind_height: -1`` # to use the hub height of the wind turbine definition. For multiple wind turbine - # types, the reference wind height must be given explicitly. + # types, the reference wind height must be given explicitly. Float type (or -1). reference_wind_height: -1 ### - # The turbulence intensities to include in the simulation, specified as a decimal. + # Turbulence intensities for the simulation, specified as a decimal value. Type list of floats. turbulence_intensities: - 0.06 ### - # The wind directions to include in the simulation. - # 0 is north and 270 is west. + # Wind directions for the simulation, specified in degrees according to compass directions + # (0 is northerly, 90 is easterly, etc). Type list of floats. wind_directions: - 270.0 ### # The exponent used to model the wind shear profile; see - # :py:meth:`.FlowField.initialize_velocity_field`. + # :py:meth:`.FlowField.initialize_velocity_field`. Float type. wind_shear: 0.12 ### - # The wind speeds to include in the simulation. + # The wind speeds for the simulation, specified in m/s at the ``reference_wind_height``. + # Type list of floats. wind_speeds: - 8.0 ### - # The wind veer as a constant value for all points in the grid. + # The wind veer (in degrees) as a constant value for all points in the grid. Only used in + # certain models. Float type. wind_veer: 0.0 ### - # The conditions that are specified for use with the multi-dimensional Cp/Ct capability. + # Conditions that are specified for use with the multi-dimensional power/thrust capability. # These conditions are external to FLORIS and specified by the user. They are used internally # through a nearest-neighbor selection process to choose the correct Cp/Ct interpolants - # to use. + # to use. Type dictionary of string:float pairs. multidim_conditions: Tp: 2.5 Hs: 3.01 ### -# Configure the wake model. +# Group for defining the wake model parameters. wake: ### - # Select the models to use for the simulation. - # See :py:mod:`~.wake` for a list - # of available models and their descriptions. + # Group for selecting the model elements for the simulation. + # See :py:mod:`~.wake` for a list of available models and their descriptions. model_strings: ### - # Select the wake combination model. + # Wake combination model. String type.. combination_model: sosfs ### - # Select the wake deflection model. + # Wake deflection model. String type. deflection_model: gauss ### - # Select the wake turbulence model. + # Wake turbulence model. String type.. turbulence_model: crespo_hernandez ### - # Select the wake velocity deficit model. + # Wake velocity deficit model. String type. velocity_model: gauss ### - # Can be "true" or "false". + # Flag to include secondary steering effects. Only used in some models. Boolean type. enable_secondary_steering: true ### - # Can be "true" or "false". + # Flag to include yaw added recovery effects. Only used in some models. Boolean type. enable_yaw_added_recovery: true ### - # Can be "true" or "false". + # Flag to include active wake mixing effects. Only used in Empirical Guassian model. Boolean type. enable_active_wake_mixing: false ### - # Can be "true" or "false". + # Flag to compute transverse velocities across turbine rotors. Only used in some models. + # Boolean type. enable_transverse_velocities: true ### - # Configure the parameters for the wake deflection model - # selected above. - # Additional blocks can be provided for - # models that are not enabled, but the enabled model - # must have a corresponding parameter block. + # Parameters for the wake deflection model. See model descriptions and implementations for + # details of each parameter and its use. wake_deflection_parameters: gauss: ad: 0.0 @@ -207,11 +212,8 @@ wake: kd: 0.05 ### - # Configure the parameters for the wake velocity deficit model - # selected above. - # Additional blocks can be provided for - # models that are not enabled, but the enabled model - # must have a corresponding parameter block. + # Parameters for the wake velocity deficit model. See model descriptions and implementations for + # details of each parameter and its use. wake_velocity_parameters: cc: a_s: 0.179367259 @@ -231,11 +233,8 @@ wake: we: 0.05 ### - # Configure the parameters for the wake turbulence model - # selected above. - # Additional blocks can be provided for - # models that are not enabled, but the enabled model - # must have a corresponding parameter block. + # Parameters for the wake turbulence model. See model descriptions and implementations for + # details of each parameter and its use. wake_turbulence_parameters: crespo_hernandez: initial: 0.1 diff --git a/examples/inputs/gch_multi_dim_cp_ct.yaml b/examples/inputs/gch_multi_dim_cp_ct.yaml index d1c788431..b3d1e27ed 100644 --- a/examples/inputs/gch_multi_dim_cp_ct.yaml +++ b/examples/inputs/gch_multi_dim_cp_ct.yaml @@ -1,5 +1,5 @@ -name: GCH multi dimensional Cp/Ct +name: GCH multi dimensional power/thrust coefficient description: Three turbines using GCH model floris_version: v4 diff --git a/examples/inputs/gch_multi_dim_cp_ct_TI.yaml b/examples/inputs/gch_multi_dim_cp_ct_TI.yaml new file mode 100644 index 000000000..23969b476 --- /dev/null +++ b/examples/inputs/gch_multi_dim_cp_ct_TI.yaml @@ -0,0 +1,114 @@ + +name: GCH multi dimensional power/thrust coefficient +description: Three turbines using GCH model +floris_version: v4 + +logging: + console: + enable: true + level: WARNING + file: + enable: false + level: WARNING + +solver: + type: turbine_grid + turbine_grid_points: 3 + +farm: + layout_x: + - 0.0 + - 630.0 + - 1260.0 + layout_y: + - 0.0 + - 0.0 + - 0.0 + turbine_type: + - iea_15MW_multi_dim_TI.yaml + turbine_library_path: ../inputs/turbine_files/ + +flow_field: + multidim_conditions: + TI: 0.06 + air_density: 1.225 + reference_wind_height: -1 # -1 is code for use the hub height + turbulence_intensities: + - 0.06 + wind_directions: + - 270.0 + wind_shear: 0.12 + wind_speeds: + - 8.0 + wind_veer: 0.0 + +wake: + model_strings: + combination_model: sosfs + deflection_model: gauss + turbulence_model: crespo_hernandez + velocity_model: gauss + + enable_secondary_steering: true + enable_yaw_added_recovery: true + enable_transverse_velocities: true + enable_active_wake_mixing: false + + wake_deflection_parameters: + gauss: + ad: 0.0 + alpha: 0.58 + bd: 0.0 + beta: 0.077 + dm: 1.0 + ka: 0.38 + kb: 0.004 + jimenez: + ad: 0.0 + bd: 0.0 + kd: 0.05 + empirical_gauss: + horizontal_deflection_gain_D: 3.0 + vertical_deflection_gain_D: -1 + deflection_rate: 22 + mixing_gain_deflection: 0.0 + yaw_added_mixing_gain: 0.0 + + wake_velocity_parameters: + cc: + a_s: 0.179367259 + b_s: 0.0118889215 + c_s1: 0.0563691592 + c_s2: 0.13290157 + a_f: 3.11 + b_f: -0.68 + c_f: 2.41 + alpha_mod: 1.0 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 + turbopark: + A: 0.04 + sigma_max_rel: 4.0 + empirical_gauss: + wake_expansion_rates: + - 0.023 + - 0.008 + breakpoints_D: + - 10 + sigma_0_D: 0.28 + smoothing_length_D: 2.0 + mixing_gain_velocity: 2.0 + + wake_turbulence_parameters: + crespo_hernandez: + initial: 0.01 + constant: 0.9 + ai: 0.83 + downstream: -0.25 + wake_induced_mixing: + atmospheric_ti_gain: 0.0 diff --git a/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv new file mode 100644 index 000000000..656b8dabd --- /dev/null +++ b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv @@ -0,0 +1,109 @@ +TI,ws,power,thrust_coefficient +0.06,0.0,0.0,0.0 +0.06,2.9,0.0,0.0 +0.06,3.0,42.733312,0.80742173 +0.06,3.54953237,292.585981,0.784655297 +0.06,4.067900771,607.966543,0.781771245 +0.06,4.553906848,981.097693,0.785377072 +0.06,5.006427063,1401.98084,0.788045584 +0.06,5.424415288,1858.67086,0.789922119 +0.06,5.806905228,2337.575997,0.790464625 +0.06,6.153012649,2824.097302,0.789868339 +0.06,6.461937428,3303.06456,0.788727582 +0.06,6.732965398,3759.432328,0.787359348 +0.06,6.965470002,4178.637714,0.785895402 +0.06,7.158913742,4547.19121,0.778275899 +0.06,7.312849418,4855.342682,0.778275899 +0.06,7.426921164,5091.537139,0.778275899 +0.06,7.500865272,5248.453137,0.778275899 +0.06,7.534510799,5320.793207,0.778275899 +0.06,7.541241633,5335.345498,0.778275899 +0.06,7.58833327,5437.90563,0.778275899 +0.06,7.675676842,5631.253025,0.778275899 +0.06,7.803070431,5920.980626,0.778275899 +0.06,7.970219531,6315.115602,0.778275899 +0.06,8.176737731,6824.470067,0.778275899 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+0.08,19.23077353,15000.0,0.093453742 +0.08,20.02994808,15000.0,0.082979637 +0.08,20.8406123,15000.0,0.073986457 +0.08,21.66089211,15000.0,0.066241166 +0.08,22.4888912,15000.0,0.059552107 +0.08,23.32269542,15000.0,0.053756866 +0.08,24.1603772,15000.0,0.048721662 +0.08,25.0,15000.0,0.044334197 +0.08,25.02,0.0,0.0 +0.08,50.0,0.0,0.0 diff --git a/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml new file mode 100644 index 000000000..4737b6f6b --- /dev/null +++ b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml @@ -0,0 +1,13 @@ +turbine_type: 'iea_15MW_floating' +hub_height: 150.0 +rotor_diameter: 242.24 +TSR: 8.0 +multi_dimensional_cp_ct: True +power_thrust_table: + ref_air_density: 1.225 + ref_tilt: 6.0 + cosine_loss_exponent_yaw: 1.88 + cosine_loss_exponent_tilt: 1.88 + # Note that the multidimensional data provided here is fictional and does not represent a real + # wind turbine. + power_thrust_data_file: './iea_15MW_multi_dim_TI.csv' diff --git a/floris/core/farm.py b/floris/core/farm.py index 38235bc80..ada46a846 100644 --- a/floris/core/farm.py +++ b/floris/core/farm.py @@ -154,6 +154,9 @@ def __attrs_post_init__(self) -> None: "Please specify a unique 'turbine_type' for each turbine definition." ) self._turbine_definition_cache[t["turbine_type"]] = t + self._turbine_definition_cache[t["turbine_type"]]["turbine_library_path"] = ( + self.turbine_library_path + ) # If a turbine type is a string, then it is expected in the internal or external # turbine library @@ -186,6 +189,9 @@ def __attrs_post_init__(self) -> None: " external turbine library." ) self._turbine_definition_cache[t] = load_yaml(full_path) + self._turbine_definition_cache[t]["turbine_library_path"] = ( + self.turbine_library_path + ) # Convert any dict entries in the turbine_type list to the type string. Since the # definition is saved above, we can make the whole list consistent now to use it diff --git a/floris/core/turbine/turbine.py b/floris/core/turbine/turbine.py index 039662ef1..0994b5c8f 100644 --- a/floris/core/turbine/turbine.py +++ b/floris/core/turbine/turbine.py @@ -51,23 +51,37 @@ } -def select_multidim_condition( - condition: dict | tuple, - specified_conditions: Iterable[tuple] +def _select_multidim_condition( + condition: dict, + specified_conditions: Iterable[tuple], + condition_keys: list[str] ) -> tuple: """ Convert condition to the type expected by power_thrust_table and select nearest specified condition """ - if type(condition) is tuple: - pass - elif type(condition) is dict: - condition = tuple(condition.values()) + if type(condition) is dict: + # Check valid keys + if set(condition.keys()) != set(condition_keys): + raise ValueError( + f"The provided condition keys {list(condition.keys())} do not match the " + f"expected keys {condition_keys}. A single value should be provided for " + "each dimension of the multidimensional power/thrust_coefficient table." + ) + # Create a tuple of the condition values in the correct order + condition = tuple(condition[k] for k in condition_keys) + elif condition is None: + raise ValueError( + "multidim_condition must be provided if using multidimensional " + "power/thrust_coefficient." + ) else: - raise TypeError("condition should be of type dict or tuple.") + raise TypeError("condition should be of type dict.") # Find the nearest key to the specified conditions. specified_conditions = np.array(specified_conditions) + if specified_conditions.ndim == 1: # Single specified condition + specified_conditions = specified_conditions.reshape(-1, 1) # Find the nearest key to the specified conditions. nearest_condition = np.zeros_like(condition) @@ -76,7 +90,7 @@ def select_multidim_condition( specified_conditions[:, i][np.absolute(specified_conditions[:, i] - c).argmin()] ) - return tuple(nearest_condition) + return tuple(nearest_condition)# if len(specified_conditions) def power( @@ -96,7 +110,7 @@ def power( average_method: str = "cubic-mean", cubature_weights: NDArrayFloat | None = None, correct_cp_ct_for_tilt: bool = False, - multidim_condition: tuple | None = None, # Assuming only one condition at a time? + multidim_condition: dict | None = None, ) -> NDArrayFloat: """Power produced by a turbine adjusted for yaw and tilt. Value given in Watts. @@ -130,7 +144,7 @@ def power( to determine a rotor-average wind speed. Defaults to "cubic-mean". cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to None. - multidim_condition (tuple | None): The condition tuple used to select the appropriate + multidim_condition (dict | None): The condition dictionary used to select the appropriate thrust coefficient relationship for multidimensional power/thrust tables. Defaults to None. @@ -163,9 +177,10 @@ def power( else: # assumed multidimensional, use multidim lookup # Currently, only works for single mutlidim condition. May need to # loop in the case where there are multiple conditions. - multidim_condition = select_multidim_condition( + multidim_condition = _select_multidim_condition( multidim_condition, - list(turbine_power_thrust_tables[turb_type].keys()) + [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"], + turbine_power_thrust_tables[turb_type]["condition_keys"] ) power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition] @@ -210,7 +225,7 @@ def thrust_coefficient( ix_filter: NDArrayFilter | Iterable[int] | None = None, average_method: str = "cubic-mean", cubature_weights: NDArrayFloat | None = None, - multidim_condition: tuple | None = None, # Assuming only one condition at a time? + multidim_condition: dict | None = None, ) -> NDArrayFloat: """Thrust coefficient of a turbine. @@ -246,7 +261,7 @@ def thrust_coefficient( to determine a rotor-average wind speed. Defaults to "cubic-mean". cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to None. - multidim_condition (tuple | None): The condition tuple used to select the appropriate + multidim_condition (dict | None): The condition dictionary used to select the appropriate thrust coefficient relationship for multidimensional power/thrust tables. Defaults to None. @@ -279,9 +294,10 @@ def thrust_coefficient( else: # assumed multidimensional, use multidim lookup # Currently, only works for single mutlidim condition. May need to # loop in the case where there are multiple conditions. - multidim_condition = select_multidim_condition( + multidim_condition = _select_multidim_condition( multidim_condition, - list(turbine_power_thrust_tables[turb_type].keys()) + [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"], + turbine_power_thrust_tables[turb_type]["condition_keys"] ) power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition] @@ -329,7 +345,7 @@ def axial_induction( ix_filter: NDArrayFilter | Iterable[int] | None = None, average_method: str = "cubic-mean", cubature_weights: NDArrayFloat | None = None, - multidim_condition: tuple | None = None, # Assuming only one condition at a time? + multidim_condition: dict | None = None, ) -> NDArrayFloat: """Axial induction factor of the turbine incorporating the thrust coefficient and yaw angle. @@ -361,7 +377,7 @@ def axial_induction( to determine a rotor-average wind speed. Defaults to "cubic-mean". cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to None. - multidim_condition (tuple | None): The condition tuple used to select the appropriate + multidim_condition (dict | None): The condition dictionary used to select the appropriate thrust coefficient relationship for multidimensional power/thrust tables. Defaults to None. @@ -393,10 +409,10 @@ def axial_induction( power_thrust_table = turbine_power_thrust_tables[turb_type] else: # assumed multidimensional, use multidim lookup # Currently, only works for single mutlidim condition. May need to - # loop in the case where there are multiple conditions. - multidim_condition = select_multidim_condition( + multidim_condition = _select_multidim_condition( multidim_condition, - list(turbine_power_thrust_tables[turb_type].keys()) + [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"], + turbine_power_thrust_tables[turb_type]["condition_keys"] ) power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition] @@ -592,6 +608,8 @@ def _initialize_multidim_power_thrust_table(self): for key in df2.index.unique(): # Select the correct ws/Cp/Ct data data = df2.loc[key] + if type(key) is not tuple: + key = (key,) # Build the interpolants power_thrust_table_.update( @@ -606,7 +624,8 @@ def _initialize_multidim_power_thrust_table(self): ) # Add reference information at the lower level - # Set on-object version + # Save names of dimensions and set on-object version + power_thrust_table_.update({"condition_keys": self.condition_keys}) self.power_thrust_table = power_thrust_table_ @power_thrust_table.validator @@ -617,14 +636,16 @@ def check_power_thrust_table(self, instance: attrs.Attribute, value: dict) -> No """ if self.multi_dimensional_cp_ct: - if isinstance(list(value.keys())[0], tuple): - value = list(value.values())[0] # Check the first entry of multidim - elif "power_thrust_data_file" in value.keys(): + if "power_thrust_data_file" in value.keys(): return None else: - raise ValueError( - "power_thrust_data_file must be defined if multi_dimensional_cp_ct is True." - ) + key_types = [type(k) for k in value.keys()] + if key_types[0] in (tuple, float, int): + value = list(value.values())[0] # Check the first entry of multidim + else: + raise ValueError( + "power_thrust_data_file must be defined if multi_dimensional_cp_ct is True." + ) if not {"wind_speed", "power", "thrust_coefficient"} <= set(value.keys()): raise ValueError( diff --git a/floris/floris_model.py b/floris/floris_model.py index 999370cbd..aaa384029 100644 --- a/floris/floris_model.py +++ b/floris/floris_model.py @@ -142,6 +142,7 @@ def _reinitialize( solver_settings: dict | None = None, heterogeneous_inflow_config=None, wind_data: type[WindDataBase] | None = None, + multidim_conditions: dict | None = None, ): """ Instantiate a new Floris object with updated conditions set by arguments. Any parameters @@ -264,6 +265,8 @@ def _reinitialize( flow_field_dict["heterogeneous_inflow_config"] = heterogeneous_inflow_config + if multidim_conditions is not None: + flow_field_dict["multidim_conditions"] = multidim_conditions if solver_settings is not None: @@ -401,6 +404,7 @@ def set( awc_amplitudes: NDArrayFloat | list[float] | list[float, None] | None = None, awc_frequencies: NDArrayFloat | list[float] | list[float, None] | None = None, disable_turbines: NDArrayBool | list[bool] | None = None, + multidim_conditions: dict | None = None, ): """ Set the wind conditions and operation setpoints for the wind farm. @@ -456,6 +460,7 @@ def set( solver_settings=solver_settings, heterogeneous_inflow_config=heterogeneous_inflow_config, wind_data=wind_data, + multidim_conditions=multidim_conditions, ) # If the yaw angles or power setpoints are not the default, set them back to the diff --git a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml index 646a4e86a..b8dc8bc62 100644 --- a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml +++ b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml @@ -8,7 +8,13 @@ power_thrust_table: ref_tilt: 6.0 cosine_loss_exponent_yaw: 1.88 cosine_loss_exponent_tilt: 1.88 + # Note that the multidimensional data provided here is fictional and does not represent a real + # wind turbine. power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv' +# The floating_tilt_table describes the steady state tilt of the floating platform as a function of +# wind speed. This is used to adjust the power and thrust coefficients when correct_cp_ct_for_tilt +# is True. The values specified here should be treated as a fictional example only, and may not +# represent a real floating platform. floating_tilt_table: tilt: - 5.747296314800103 diff --git a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml index b08b348de..9e8f20461 100644 --- a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml +++ b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml @@ -8,4 +8,6 @@ power_thrust_table: ref_tilt: 6.0 cosine_loss_exponent_yaw: 1.88 cosine_loss_exponent_tilt: 1.88 + # Note that the multidimensional data provided here is fictional and does not represent a real + # wind turbine. power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv' diff --git a/floris/turbine_library/nrel_5MW.yaml b/floris/turbine_library/nrel_5MW.yaml index 9a70ba270..ff760ee79 100644 --- a/floris/turbine_library/nrel_5MW.yaml +++ b/floris/turbine_library/nrel_5MW.yaml @@ -6,56 +6,72 @@ ### # An ID for this type of turbine definition. -# This is not currently used, but it will be enabled in the future. This should typically -# match the root name of the file. +# This is used to uniquely identify different turbines in the simulation, so should be different +# for each different turbine definition being used in the same simulation. +# String type. turbine_type: 'nrel_5MW' ### -# Hub height. +# Turbine hub height in meters. Float type. hub_height: 90.0 ### -# Rotor diameter. +# Turbine rotor diameter in meters. Float type. rotor_diameter: 125.88 ### -# Tip speed ratio defined as linear blade tip speed normalized by the incoming wind speed. +# Nominal wind turbine tip-speed ratio for below-rated operation. Only used in some wake models. +# Float type. TSR: 8.0 ### -# Model for power and thrust curve interpretation. +# Model for power and thrust curve interpretation. See floris.core.turbine.operation_models for +# details. String type. operation_model: 'cosine-loss' ### -# Parameters needed to evaluate the power and thrust produced by the turbine. +# Group of parameters needed to evaluate the power and thrust produced by the turbine. power_thrust_table: - ### Power thrust table parameters - # The air density at which the power and thrust_coefficient curves are defined. + ### + # Air density at which the power and thrust_coefficient curves are defined (kg / m^3). Float type. ref_air_density: 1.225 ### - # The tilt angle at which the Cp and Ct curves are defined. This is used to capture - # the effects of a floating platform on a turbine's power and wake. + # Tilt angle at which the power and thrust_coefficient curves are defined (degrees). + # Used to capture the effects of a floating platform on a turbine's power and wake. + # Float type. ref_tilt: 5.0 ### - # Cosine exponent for power loss due to tilt. + # Cosine exponent for power loss due to tilt. Float type. cosine_loss_exponent_tilt: 1.88 ### - # Cosine exponent for power loss due to yaw misalignment. + # Cosine exponent for power loss due to yaw misalignment. Float type. cosine_loss_exponent_yaw: 1.88 ### - # Helix parameters + # Helix parameter a. See documentation for details. Float type. helix_a: 1.802 + ### + # Helix parameter b for power calculation. See documentation for details. Float type. helix_power_b: 4.568e-03 + ### + # Helix parameter c for power calculation. See documentation for details. Float type. helix_power_c: 1.629e-10 + ### + # Helix parameter b for thrust calculation. See documentation for details. Float type. helix_thrust_b: 1.027e-03 + ### + # Helix parameter c for thrust calculation. See documentation for details. Float type. helix_thrust_c: 1.378e-06 - ### Peak shaving parameters - # Fraction of peak thrust by which to reduce + ### + # Fraction of peak thrust by which to reduce (specified as a decimal). Float type. peak_shaving_fraction: 0.2 - # Threshold turbulence intensity above which to apply peak shaving + ### + # Threshold turbulence intensity above which to apply peak shaving (specified as a decimal). + # Float tpe. peak_shaving_TI_threshold: 0.1 - ### Parameters for the 'controller-dependenter-dependent' operation model + ### + # Parameters for the 'controller-dependenter-dependent' operation model. See + # floris.core.turbine.controller_dependent_operation_model and documentation for details. controller_dependent_turbine_parameters: rated_rpm: 12.1 rotor_solidity: 0.05132 @@ -67,8 +83,8 @@ power_thrust_table: cl_alfa: 4.275049 cp_ct_data_file: "demo_cp_ct_surfaces/nrel_5MW_demo_cp_ct_surface.npz" - ### Power thrust table data - # wind speeds for look-up tables of power and thrust_coefficient + ### + # Wind speeds for look-up tables of power and thrust_coefficient. List of float type. wind_speed: - 0.0 - 2.9 @@ -125,7 +141,7 @@ power_thrust_table: - 25.1 - 50.0 ### - # power values (specified in kW) for lookup by wind speed + # Power values (specified in kW) for lookup by wind speed. List of float type. power: - 0.0 - 0.0 @@ -182,7 +198,7 @@ power_thrust_table: - 0.0 - 0.0 ### - # thrust coefficient values (unitless) for lookup by wind speed + # Thrust coefficient values (unitless) for lookup by wind speed. List of float type. thrust_coefficient: - 0.0 - 0.0 @@ -240,14 +256,44 @@ power_thrust_table: - 0.0 ### -# A boolean flag used when the user wants FLORIS to use the user-supplied multi-dimensional -# Cp/Ct information. +# Boolean flag used when the user wants FLORIS to use the user-supplied multi-dimensional +# power/thrust coefficient information information. Boolean type. multi_dimensional_cp_ct: False ### -# The path to the .csv file that contains the multi-dimensional Cp/Ct data. The format of this -# file is such that any external conditions, such as wave height or wave period, that the -# Cp/Ct data is dependent on come first, in column format. The last three columns of the .csv -# file must be ``ws``, ``Cp``, and ``Ct``, in that order. An example of fictional data is given -# in ``floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv``. +# Path to the .csv file that contains the multi-dimensional power/thrust coefficient data. +# The format of this file is such that any external conditions, such as wave height or wave period, +# that the power/thrust data is dependent on come first, in column format. The last three columns +# of the .csv file must be ``ws``, ``power``, and ``thrust_coefficient``, in that order. An example +# of fictional data is given in ``floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv``. +# String type. power_thrust_data_file: '../floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv' + +### +# Group of parameters needed to evaluate the tilt angle of a floating turbine across wind speeds. +floating_tilt_table: + ### + # Wind speeds at which steady tilt angles are defined (m/s). List of float type. + wind_speed: + - 4.0 + - 6.0 + - 8.0 + - 10.0 + - 12.0 + - 14.0 + - 16.0 + ### + # Tilt angle for each wind speed (degrees, positive "tilted back"). List of float type. + tilt: + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 5.0 + +### +# Flag for whether to apply the floating tilt table to correct turbine power and thrust curves. +# Boolean type. +correct_cp_ct_for_tilt: false diff --git a/floris/turbine_library/turbine_previewer.py b/floris/turbine_library/turbine_previewer.py index d8b20064f..7623d4fbe 100644 --- a/floris/turbine_library/turbine_previewer.py +++ b/floris/turbine_library/turbine_previewer.py @@ -25,6 +25,11 @@ INTERNAL_LIBRARY = Path(__file__).parent DEFAULT_WIND_SPEEDS = np.linspace(0, 40, 81) +DEPRECATION_MESSAGE = ( + "The TurbineInterface and TurbineLibrary classes are now deprecated as will be removed in a", + " future FLORIS release." +) + @define(auto_attribs=True) class TurbineInterface: @@ -43,6 +48,7 @@ def from_library(cls, library_path: str | Path, file_name: str): Returns: (TurbineInterface): Creates a new ``TurbineInterface`` object. """ + print(DEPRECATION_MESSAGE) # Use the pre-mapped internal turbine library or validate the user's library if library_path == "internal": library_path = INTERNAL_LIBRARY @@ -64,6 +70,7 @@ def from_yaml(cls, file_path: str | Path): Returns: (TurbineInterface): Creates a new ``TurbineInterface`` object. """ + print(DEPRECATION_MESSAGE) file_path = Path(file_path).resolve() # Add in the library specification if needed, and load from dict @@ -81,6 +88,7 @@ def from_turbine_dict(cls, config_dict: dict): Returns: (`TurbineInterface`): Returns a ``TurbineInterface`` object. """ + print(DEPRECATION_MESSAGE) return cls(turbine=Turbine.from_dict(config_dict)) def power_curve( @@ -351,6 +359,7 @@ def load_internal_library(self, which: list[str] = [], exclude: list[str] = []) exclude (list[str], optional): A list of file names to exclude from loading. Defaults to []. """ + print(DEPRECATION_MESSAGE) include = [el for el in INTERNAL_LIBRARY.iterdir() if el.suffix in (".yaml", ".yml")] which = [INTERNAL_LIBRARY / el for el in which] if which != [] else include exclude = [INTERNAL_LIBRARY / el for el in exclude] @@ -379,6 +388,7 @@ def load_external_library( exclude (list[str], optional): A list of file names to exclude from loading. Defaults to []. """ + print(DEPRECATION_MESSAGE) library_path = Path(library_path).resolve() include = [el for el in library_path.iterdir() if el.suffix in (".yaml", ".yml")] which = [library_path / el for el in which] if which != [] else include diff --git a/tests/data/iea_15MW_multi_dim_TI.csv b/tests/data/iea_15MW_multi_dim_TI.csv new file mode 120000 index 000000000..761a14eae --- /dev/null +++ b/tests/data/iea_15MW_multi_dim_TI.csv @@ -0,0 +1 @@ +../../examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv \ No newline at end of file diff --git a/tests/floris_model_integration_test.py b/tests/floris_model_integration_test.py index d70a4e7af..f55e4b6a9 100644 --- a/tests/floris_model_integration_test.py +++ b/tests/floris_model_integration_test.py @@ -11,6 +11,7 @@ WindRose, ) from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT, POWER_SETPOINT_DISABLED +from tests.conftest import SampleInputs TEST_DATA = Path(__file__).resolve().parent / "data" @@ -938,3 +939,55 @@ def test_sample_ti_at_points(): fmodel.set(turbulence_intensities=[ti_inf + 0.02, ti_inf + 0.02]) ti_sampled_2 = fmodel.sample_ti_at_points(x, y, z) assert np.all(ti_sampled_2 > ti_sampled_1) + +def test_set_multidim(): + fmodel = FlorisModel(configuration=YAML_INPUT) + fmodel.set(turbine_type=[SampleInputs().turbine_multi_dim]) + + # No multidim condition has been set; should raise value error + with pytest.raises(ValueError): + fmodel.run() + + # Set a multidim condition that is not a valid type + with pytest.raises(TypeError): + fmodel.set(multidim_conditions="invalid_type") + fmodel.run() + + # Set an invalid multidim condition (not all dimensions specified) + with pytest.raises(ValueError): + fmodel.set(multidim_conditions={"Hs": 1.0}) + fmodel.run() + + # Set an invalid key (but the correct total number of keys) + with pytest.raises(ValueError): + fmodel.set(multidim_conditions={"invalid_key": 2.0, "Hs": 1.0}) + fmodel.run() + + # Set with an invalid key (as well as other keys being correct) + with pytest.raises(ValueError): + fmodel.set(multidim_conditions={"invalid_key": 2.0, "Hs": 1.0, "Tp": 8.0}) + fmodel.run() + + # Set a valid multidim condition, order of dictionary keys should not matter + fmodel.set(multidim_conditions={"Hs": 1.0, "Tp": 8.0}) + fmodel.run() + powers_1 = fmodel.get_turbine_powers() + fmodel.set(multidim_conditions={"Tp": 8.0, "Hs": 1.0}) + fmodel.run() + powers_2 = fmodel.get_turbine_powers() + assert np.array_equal(powers_1, powers_2) + + # Create a single-dimensional table + turbine = SampleInputs().turbine_multi_dim + turbine["power_thrust_table"]["power_thrust_data_file"] = "iea_15MW_multi_dim_TI.csv" + fmodel.set( + turbine_type=[turbine], + turbine_library_path=Path(__file__).resolve().parent / "data/" + ) + + with pytest.raises(ValueError): + fmodel.set(multidim_conditions={"TI": 0.06, "Tp": 8.0}) + fmodel.run() + + fmodel.set(multidim_conditions={"TI": 0.06}) + fmodel.run() diff --git a/tests/turbine_multi_dim_unit_test.py b/tests/turbine_multi_dim_unit_test.py index 0c11c2564..cc5a82fe1 100644 --- a/tests/turbine_multi_dim_unit_test.py +++ b/tests/turbine_multi_dim_unit_test.py @@ -50,15 +50,15 @@ def test_turbine_init(): turbine_data = SampleInputs().turbine_multi_dim turbine = Turbine.from_dict(turbine_data) - condition = (2, 1) + condition_tuple = (2, 1) assert turbine.rotor_diameter == turbine_data["rotor_diameter"] assert turbine.hub_height == turbine_data["hub_height"] assert ( - turbine.power_thrust_table[condition]["cosine_loss_exponent_yaw"] + turbine.power_thrust_table[condition_tuple]["cosine_loss_exponent_yaw"] == turbine_data["power_thrust_table"]["cosine_loss_exponent_yaw"] ) assert ( - turbine.power_thrust_table[condition]["cosine_loss_exponent_tilt"] + turbine.power_thrust_table[condition_tuple]["cosine_loss_exponent_tilt"] == turbine_data["power_thrust_table"]["cosine_loss_exponent_tilt"] ) @@ -75,7 +75,7 @@ def test_ct(): turbine = Turbine.from_dict(turbine_data) turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type]) turbine_type_map = turbine_type_map[None, :] - condition = (2, 1) + condition = {"Tp":2, "Hs":1} # Single turbine # yaw angle / fCt are (n wind direction, n wind speed, n turbine) @@ -154,7 +154,8 @@ def test_power(): turbine = Turbine.from_dict(turbine_data) turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type]) turbine_type_map = turbine_type_map[None, :] - condition = (2, 1) + condition = {"Tp":2, "Hs":1} + condition_tuple = tuple(condition[k] for k in condition.keys()) # Single turbine wind_speed = 10.0 @@ -164,14 +165,14 @@ def test_power(): air_density=AIR_DENSITY, power_functions={turbine.turbine_type: turbine.power_function}, yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine - tilt_angles=turbine.power_thrust_table[condition]["ref_tilt"] * np.ones((1, 1)), + tilt_angles=turbine.power_thrust_table[condition_tuple]["ref_tilt"] * np.ones((1, 1)), power_setpoints=np.ones((1, 1)) * POWER_SETPOINT_DEFAULT, awc_modes=np.array([["baseline"]*N_TURBINES]*1), awc_amplitudes=np.zeros((1, 1)), tilt_interps={turbine.turbine_type: turbine.tilt_interp}, turbine_type_map=turbine_type_map[:,0], turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table}, - multidim_condition=condition + multidim_condition=condition, ) power_truth = 12424759.67683091 @@ -202,7 +203,7 @@ def test_power(): assert len(p[0]) == len(INDEX_FILTER) power_truth = turbine.power_function( - power_thrust_table=turbine.power_thrust_table[condition], + power_thrust_table=turbine.power_thrust_table[condition_tuple], velocities=velocities, air_density=AIR_DENSITY, yaw_angles=np.zeros((1, N_TURBINES)), @@ -220,7 +221,7 @@ def test_axial_induction(): turbine = Turbine.from_dict(turbine_data) turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type]) turbine_type_map = turbine_type_map[None, :] - condition = (2, 1) + condition = {"Tp":2, "Hs":1} baseline_ai = np.array([[0.26447651]])