diff --git a/.github/workflows/CI_rosco-compile.yml b/.github/workflows/CI_rosco-compile.yml index 26d1fdd4..bc89b4d7 100644 --- a/.github/workflows/CI_rosco-compile.yml +++ b/.github/workflows/CI_rosco-compile.yml @@ -3,80 +3,152 @@ name: CI_rosco-compile # We run CI on push commits on all branches on: [push, pull_request] -# Specify FORTRAN compiler, used to be "gfortran-10" -env: - FORTRAN_COMPILER: gfortran # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: - build: - name: Build (${{ matrix.os }}) - runs-on: ${{ matrix.os }} - strategy: - fail-fast: true - matrix: - os: ["ubuntu-latest", "windows-latest" , "macOS-latest"] - python-version: ["3.9"] - - steps: - - name: Checkout repository - uses: actions/checkout@v3 - - - name: Install conda/mamba - uses: conda-incubator/setup-miniconda@v2 - # https://github.com/marketplace/actions/setup-miniconda - with: - # To use mamba, uncomment here, comment out the miniforge line - mamba-version: "*" - # miniforge-version: "latest" - auto-update-conda: true - python-version: ${{ matrix.python-version }} - environment-file: environment.yml - activate-environment: test - auto-activate-base: false - miniforge-variant: Mambaforge - - # Install ROSCO toolbox - - name: Install ROSCO toolbox - shell: bash -l {0} - run: | - python setup.py install + build_pip: + name: Pip Build (${{ matrix.os }}) - ${{ matrix.python-version }} + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash -l {0} + + strategy: + fail-fast: false #true + matrix: + os: ["ubuntu-latest", "macOS-latest", "windows-latest"] + python-version: ["3.9", "3.10", "3.11"] + + steps: + - name: Setup GNU Fortran + # if: false == contains( matrix.os, 'windows') + uses: awvwgk/setup-fortran@v1 #modflowpy/install-intelfortran-action@v1 # + + - name: checkout repository + uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + id: cp + with: + python-version: ${{ matrix.python-version }} + update-environment: true + + #- name: Setup tmate session + # if: contains( matrix.os, 'windows') + # uses: mxschmitt/action-tmate@v3 + + # Install ZeroMQ- should be done via Conda + #- name: Install ZeroMQ + # run: | + # sudo apt-get update + # sudo apt-get install libzmq3-dev + + - name: Pip Install ROSCO + run: | + '${{ steps.cp.outputs.python-path }}' -m pip install -e . - # Re-generate registry - - name: Generate Registry - shell: bash -l {0} - run: python ROSCO/rosco_registry/write_registry.py + # Re-generate registry + - name: Generate Registry + run: | + '${{ steps.cp.outputs.python-path }}' rosco/controller/rosco_registry/write_registry.py + + - name: Test run + # skipping pip test on windows for now until wisdem pypi is ready + if: false == contains( matrix.os, 'windows') + run: | + cd Examples + '${{ steps.cp.outputs.python-path }}' 01_turbine_model.py + + + build_and_test_conda: + name: Conda Build (${{ matrix.os }}) - ${{ matrix.python-version }} + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash -el {0} + + strategy: + fail-fast: false #true + matrix: + os: ["ubuntu-latest", "macOS-latest", "windows-latest"] + python-version: ["3.9", "3.10", "3.11"] + + steps: + - name: checkout repository + uses: actions/checkout@v4 + + - uses: conda-incubator/setup-miniconda@v2 + # https://github.com/marketplace/actions/setup-miniconda + with: + #mamba-version: "*" + miniforge-version: "latest" + auto-update-conda: true + python-version: ${{ matrix.python-version }} + environment-file: environment.yml + activate-environment: test + auto-activate-base: false + + - name: Add dependencies windows specific + if: contains( matrix.os, 'windows') + run: | + conda install -y m2w64-toolchain libpython + gfortran --version + + - name: Add dependencies mac specific + if: contains( matrix.os, 'mac') + run: | + conda install -y compilers + gfortran --version + + # Install + - name: Debug + run: | + conda list + printenv + + - name: Conda Install ROSCO + run: | + python -m pip install -e . + + - name: Install OpenFAST + run: | + conda install openfast==3.5.2 + + #- name: Check OpenFAST + # # if: contains( matrix.os, 'windows') + # run: | + # openfast -v + # which openfast + + - name: Generate Registry + run: | + python rosco/controller/rosco_registry/write_registry.py - - name: Add dependencies windows - if: true == contains( matrix.os, 'windows') - run: | - conda install -y m2w64-toolchain - - - name: Add dependencies windows - if: true == contains( matrix.os, 'mac') - shell: bash -l {0} - run: | - conda install -y gfortran - - - name: Setup Workspace - run: | - cmake -E make_directory ${{runner.workspace}}/ROSCO/ROSCO/build - - - name: Configure and Build - unix - if: false == contains( matrix.os, 'windows') - shell: bash -l {0} - working-directory: "${{runner.workspace}}/ROSCO/ROSCO/build" - run: | - cmake \ - -DCMAKE_INSTALL_PREFIX:PATH=${{runner.workspace}}/ROSCO/ROSCO/install \ - -DCMAKE_Fortran_COMPILER:STRING=${{env.FORTRAN_COMPILER}} \ - .. - cmake --build . --target install - - - name: Configure and Build - windows - if: true == contains( matrix.os, 'windows') - #shell: bash #-l {0} - working-directory: "${{runner.workspace}}/ROSCO/ROSCO/build" - run: | - cmake -G "MinGW Makefiles" -DCMAKE_INSTALL_PREFIX="${{runner.workspace}}/ROSCO/ROSCO/build" .. - cmake --build . --target install + - name: Fix example file extensions + run: | + python Examples/Test_Cases/update_libdiscon_extension.py + + #- name: Setup tmate session + # uses: mxschmitt/action-tmate@v3 + # with: + # detached: true + # if: contains( matrix.os, 'windows') + + - name: Run ROSCO testing + run: | + cd rosco/test + python ROSCO_testing.py + + - name: Run regression testing + if: contains( matrix.os, 'ubuntu') + run: | + cd rosco/test + pytest . + + - name: Test walkthrough notebook + if: contains( matrix.os, 'ubuntu') + run: | + cd Examples + treon ROSCO_walkthrough.ipynb + diff --git a/.github/workflows/CI_rosco-pytools.yml b/.github/workflows/CI_rosco-pytools.yml deleted file mode 100644 index 1b28dad2..00000000 --- a/.github/workflows/CI_rosco-pytools.yml +++ /dev/null @@ -1,226 +0,0 @@ -name: CI_rosco-pytools - -# We run CI on push commits on all branches -on: [push, pull_request] - -# Specify FORTRAN compiler, used to be "gfortran-10" -env: - FC: gfortran - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - build: - name: Build (${{ matrix.os }}) - runs-on: ${{ matrix.os }} - strategy: - fail-fast: true - matrix: - os: ["ubuntu-latest", "windows-latest", "macOS-latest"] - python-version: ["3.9"] - defaults: - run: - shell: bash -l {0} - - steps: - - name: Checkout repository and submodules - uses: actions/checkout@v3 - with: - submodules: recursive - - - name: Install conda/mamba - uses: conda-incubator/setup-miniconda@v2 - # https://github.com/marketplace/actions/setup-miniconda - with: - # To use mamba, uncomment here, comment out the miniforge line - mamba-version: "*" - # miniforge-version: "latest" - auto-update-conda: true - python-version: ${{ matrix.python-version }} - environment-file: environment.yml - activate-environment: test - auto-activate-base: false - miniforge-variant: Mambaforge - - - # Install dependencies of ROSCO toolbox - - name: Add dependencies windows specific - if: true == contains( matrix.os, 'windows') - run: | - conda install -y m2w64-toolchain libpython - env: - FC: gfortran - - - - name: Add dependencies macOS specific - if: true == contains( matrix.os, 'macOS') - run: | - conda install compilers - - # Install ROSCO toolbox - - name: Install ROSCO toolbox on Windows - if: true == contains( matrix.os, 'windows') - env: - FC: gfortran - run: | - python setup.py develop --compile-rosco - - - name: Install ROSCO toolbox - if: false == contains( matrix.os, 'windows') - run: | - python setup.py develop --compile-rosco - - - run_examples: - name: Run Examples - runs-on: ${{ matrix.os }} - strategy: - fail-fast: true - matrix: - os: ["ubuntu-latest"] #, "macOS-latest"] - python-version: ["3.9"] - defaults: - run: - shell: bash -l {0} - - steps: - - name: Checkout repository and submodules - uses: actions/checkout@v3 - with: - submodules: recursive - - - name: Install conda/mamba - uses: conda-incubator/setup-miniconda@v2 - # https://github.com/marketplace/actions/setup-miniconda - with: - # To use mamba, uncomment here, comment out the miniforge line - mamba-version: "*" - # miniforge-version: "latest" - auto-update-conda: true - python-version: ${{ matrix.python-version }} - environment-file: environment.yml - activate-environment: test - auto-activate-base: false - miniforge-variant: Mambaforge - - # setup cmake - - name: Setup Workspace - run: cmake -E make_directory ${{runner.workspace}}/ROSCO/ROSCO/build - - # Install dependencies of ROSCO toolbox - - name: Add dependencies ubuntu specific - run: | - conda install -y wisdem - - - name: Add pyFAST dependency - run: | - git clone http://github.com/OpenFAST/python-toolbox - cd python-toolbox - python -m pip install -e . - - # Install ZeroMQ - - name: Install ZeroMQ - run: | - sudo apt-get update - sudo apt-get install libzmq3-dev - - # Install ROSCO toolbox - - name: Install ROSCO toolbox - run: | - python setup.py develop - - - name: Configure and Build ROSCO - unix - working-directory: ${{runner.workspace}}/ROSCO/ROSCO/build - run: | - cmake \ - -DCMAKE_INSTALL_PREFIX:PATH=${{runner.workspace}}/ROSCO/ROSCO/install \ - -DZMQ_CLIENT=ON \ - -DCMAKE_Fortran_COMPILER:STRING=${{env.FORTRAN_COMPILER}} \ - .. - cmake --build . --target install - - # Install OpenFAST - - name: Install OpenFAST - run: | - conda install openfast==3.5 - - # Run examples - - name: Run examples - run: | - cd Examples - python test_examples.py - - # Test walkthrough notebook - - name: Test walkthrough notebook - run: | - cd Examples - treon ROSCO_walkthrough.ipynb - - # # Archive artifacts - # - name: Archive production artifacts - # if: success() || failure() - # uses: actions/upload-artifact@v3 - # with: - # name: ROSCO-artifacts - # path: | - # ${{runner.workspace}} - - run_testing: - name: Run Testing - runs-on: ${{ matrix.os }} - strategy: - fail-fast: true - matrix: - os: ["ubuntu-latest"] #, "macOS-latest"] - python-version: ["3.9"] - defaults: - run: - shell: bash -l {0} - - steps: - - name: Checkout repository and submodules - uses: actions/checkout@v3 - with: - submodules: recursive - - - name: Install conda/mamba - uses: conda-incubator/setup-miniconda@v2 - # https://github.com/marketplace/actions/setup-miniconda - with: - # To use mamba, uncomment here, comment out the miniforge line - mamba-version: "*" - # miniforge-version: "latest" - auto-update-conda: true - python-version: ${{ matrix.python-version }} - environment-file: environment.yml - activate-environment: test - auto-activate-base: false - miniforge-variant: Mambaforge - - - # Install dependencies of ROSCO toolbox - - name: Add dependencies - run: | - conda install -y wisdem - - - # Install ROSCO toolbox - - name: Install ROSCO toolbox - run: | - python setup.py install --compile-rosco - - # Install OpenFAST - - name: Install OpenFAST - run: | - conda install openfast==3.5 - - # Run ROSCO Testing - - name: Run ROSCO testing - run: | - cd ROSCO_testing - python ROSCO_testing.py - - # Regression testing - - name: Run regression testing - run: | - cd ROSCO_testing - python test_checkpoint.py diff --git a/.github/workflows/Publish_ROSCO.yml b/.github/workflows/Publish_ROSCO.yml new file mode 100644 index 00000000..c6d7ba46 --- /dev/null +++ b/.github/workflows/Publish_ROSCO.yml @@ -0,0 +1,72 @@ +name: Build and upload to PyPI +# https://github.com/pypa/cibuildwheel/blob/main/examples/github-deploy.yml + +# Build on every branch push, tag push, and pull request change: +#on: [push, pull_request] +# Alternatively, to publish when a (published) GitHub Release is created, use the following: +on: + release: + types: + - published + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + + steps: + - name: Setup GNU Fortran + uses: awvwgk/setup-fortran@v1 + + - name: Checkout + uses: actions/checkout@v4 + + - name: Build wheels + uses: pypa/cibuildwheel@v2.16.2 + + - uses: actions/upload-artifact@v4 + with: + name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} + path: ./wheelhouse/*.whl + + build_sdist: + name: Build source distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Build sdist + run: pipx run build --sdist + + - uses: actions/upload-artifact@v4 + with: + name: cibw-sdist + path: dist/*.tar.gz + + upload_pypi: + needs: [build_wheels, build_sdist] + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + # upload to PyPI on every tag starting with 'v' + #if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') + # alternatively, to publish when a GitHub Release is created, use the following rule: + if: github.event_name == 'release' && github.event.action == 'published' + steps: + - uses: actions/download-artifact@v4 + with: + # unpacks all CIBW artifacts into dist/ + pattern: cibw-* + path: dist + merge-multiple: true + + - name: Upload to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + #with: + # user: __token__ + # password: ${{ secrets.pypi_password }} + # # To test: repository_url: https://test.pypi.org/legacy/ diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 66971b94..061090c2 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,10 +9,10 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.8" - # nodejs: "18" - # rust: "1.64" - # golang: "1.19" + python: "mambaforge-22.9" + +conda: + environment: environment.yml # Build documentation in the docs/ directory with Sphinx sphinx: diff --git a/Examples/01_turbine_model.py b/Examples/01_turbine_model.py index dbf181b9..ab1870d7 100644 --- a/Examples/01_turbine_model.py +++ b/Examples/01_turbine_model.py @@ -15,14 +15,14 @@ # Python Modules import os # ROSCO Modules -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.inputs.validation import load_rosco_yaml import matplotlib.pyplot as plt # Load yaml file this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_dir = os.path.join(this_dir,'../Tune_Cases') +tune_dir = os.path.join(this_dir,'Tune_Cases') parameter_filename = os.path.join(tune_dir,'NREL5MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] diff --git a/Examples/02_ccblade.py b/Examples/02_ccblade.py index 11afd6a2..581603d7 100644 --- a/Examples/02_ccblade.py +++ b/Examples/02_ccblade.py @@ -12,9 +12,9 @@ # Python modules import os # ROSCO toolbox modules -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.utilities import write_rotor_performance -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.utilities import write_rotor_performance +from rosco.toolbox.inputs.validation import load_rosco_yaml # Initialize parameter dictionaries turbine_params = {} control_params = {} @@ -26,7 +26,7 @@ # Load yaml file this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_dir = os.path.join(this_dir,'../Tune_Cases') +tune_dir = os.path.join(this_dir,'Tune_Cases') parameter_filename = os.path.join(tune_dir,'NREL5MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] @@ -37,7 +37,7 @@ turbine = ROSCO_turbine.Turbine(turbine_params) turbine.load_from_fast( path_params['FAST_InputFile'], - os.path.join(this_dir,path_params['FAST_directory']), + os.path.join(tune_dir,path_params['FAST_directory']), rot_source='cc-blade', txt_filename=None) diff --git a/Examples/03_tune_controller.py b/Examples/03_tune_controller.py index 74e77415..9d426e50 100644 --- a/Examples/03_tune_controller.py +++ b/Examples/03_tune_controller.py @@ -14,15 +14,15 @@ import matplotlib.pyplot as plt import os # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.utilities import write_DISCON -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.utilities import write_DISCON +from rosco.toolbox.inputs.validation import load_rosco_yaml # Load yaml file this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_dir = os.path.join(this_dir,'../Tune_Cases') +tune_dir = os.path.join(this_dir,'Tune_Cases') parameter_filename = os.path.join(tune_dir,'NREL5MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] diff --git a/Examples/04_simple_sim.py b/Examples/04_simple_sim.py index 097af355..a19a2ccb 100644 --- a/Examples/04_simple_sim.py +++ b/Examples/04_simple_sim.py @@ -17,19 +17,20 @@ # Python modules import matplotlib.pyplot as plt import numpy as np -import os, platform +import os # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import sim as ROSCO_sim -from ROSCO_toolbox import control_interface as ROSCO_ci -from ROSCO_toolbox.utilities import write_DISCON -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco import discon_lib_path as lib_name +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import sim as ROSCO_sim +from rosco.toolbox import control_interface as ROSCO_ci +from rosco.toolbox.utilities import write_DISCON +from rosco.toolbox.inputs.validation import load_rosco_yaml # Load yaml file this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_dir = os.path.join(this_dir,'../Tune_Cases') +tune_dir = os.path.join(this_dir,'Tune_Cases') parameter_filename = os.path.join(tune_dir,'NREL5MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] @@ -37,17 +38,11 @@ controller_params = inps['controller_params'] # Specify controller dynamic library path and name -this_dir = os.path.dirname(os.path.abspath(__file__)) -example_out_dir = os.path.join(this_dir,'examples_out') -if not os.path.isdir(example_out_dir): - os.makedirs(example_out_dir) - -if platform.system() == 'Windows': - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.dll') -elif platform.system() == 'Darwin': - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib') -else: - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.so') + +#directories +rosco_dir = os.path.dirname(this_dir) +example_out_dir = os.path.join(this_dir,'examples_out') +os.makedirs(example_out_dir,exist_ok=True) # # Load turbine model from saved pickle turbine = ROSCO_turbine.Turbine diff --git a/Examples/05_openfast_sim.py b/Examples/05_openfast_sim.py index 58934387..3a45d40d 100644 --- a/Examples/05_openfast_sim.py +++ b/Examples/05_openfast_sim.py @@ -11,22 +11,23 @@ Note - you will need to have a compiled controller in ROSCO/build/ ''' # Python Modules -import yaml +#import yaml import os -import numpy as np -import matplotlib.pyplot as plt +#import numpy as np +#import matplotlib.pyplot as plt # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.utilities import write_DISCON, run_openfast -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.utilities import write_DISCON, run_openfast +from rosco.toolbox.inputs.validation import load_rosco_yaml this_dir = os.path.dirname(os.path.abspath(__file__)) +tune_dir = os.path.join(this_dir,'Tune_Cases') example_out_dir = os.path.join(this_dir,'examples_out') # Load yaml file -parameter_filename = os.path.join(os.path.dirname(this_dir), 'Tune_Cases', 'IEA15MW_MultiOmega.yaml') +parameter_filename = os.path.join(tune_dir, 'IEA15MW_MultiOmega.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] turbine_params = inps['turbine_params'] @@ -39,9 +40,9 @@ # Load turbine data from OpenFAST and rotor performance text file turbine.load_from_fast( path_params['FAST_InputFile'], - os.path.join(this_dir,path_params['FAST_directory']), + os.path.join(tune_dir,path_params['FAST_directory']), rot_source='txt', - txt_filename=os.path.join(this_dir,path_params['rotor_performance_filename']) + txt_filename=os.path.join(tune_dir,path_params['rotor_performance_filename']) ) # Tune controller @@ -56,7 +57,7 @@ # If you run the `fastcall` from the command line where you run this script, it should run OpenFAST fastcall = 'openfast' run_openfast( - os.path.join(this_dir,path_params['FAST_directory']), + os.path.join(tune_dir,path_params['FAST_directory']), fastcall=fastcall, fastfile=path_params['FAST_InputFile'], chdir=True diff --git a/Examples/06_peak_shaving.py b/Examples/06_peak_shaving.py index ca3e90cd..9bfa5228 100644 --- a/Examples/06_peak_shaving.py +++ b/Examples/06_peak_shaving.py @@ -14,13 +14,13 @@ import matplotlib.pyplot as plt import os # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.inputs.validation import load_rosco_yaml this_dir = os.path.dirname(__file__) -tune_dir = os.path.join(this_dir,'../Tune_Cases') +tune_dir = os.path.join(this_dir,'Tune_Cases') example_out_dir = os.path.join(this_dir,'examples_out') if not os.path.isdir(example_out_dir): os.makedirs(example_out_dir) diff --git a/Examples/07_openfast_outputs.py b/Examples/07_openfast_outputs.py index d50eab99..ed19bc63 100644 --- a/Examples/07_openfast_outputs.py +++ b/Examples/07_openfast_outputs.py @@ -8,14 +8,14 @@ - Trim the time series - Plot some available channels -Note: need to run openfast model in '../Test_Cases/5MW_Land_DLL_WTurb/' to plot +Note: need to run openfast model in 'Test_Cases/5MW_Land_DLL_WTurb/' to plot ''' # Python Modules -import numpy as np +#import numpy as np import matplotlib.pyplot as plt # ROSCO toolbox modules -from ROSCO_toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.ofTools.fast_io import output_processing import os this_dir = os.path.dirname(os.path.abspath(__file__)) @@ -24,7 +24,7 @@ os.makedirs(example_out_dir) # Define openfast output filenames -filenames = ["../Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.outb"] +filenames = ["Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.outb"] # ---- Note: Could load and plot multiple cases, textfiles, and binaries... # filenames = ["../Test_Cases/NREL-5MW/NREL-5MW.outb", # "../Test_Cases/NREL-5MW/NREL-5MW_ex8.outb"] diff --git a/Examples/08_run_turbsim.py b/Examples/08_run_turbsim.py index 488b07ea..bbacb93e 100644 --- a/Examples/08_run_turbsim.py +++ b/Examples/08_run_turbsim.py @@ -8,13 +8,13 @@ ''' # ROSCO toolbox modules -from ROSCO_toolbox.utilities import run_openfast +from rosco.toolbox.utilities import run_openfast import os this_dir = os.path.dirname(os.path.abspath(__file__)) # Define openfast output filenames -wind_directory = os.path.join(this_dir,'../Test_Cases/Wind/') +wind_directory = os.path.join(this_dir,'Test_Cases/Wind/') turbsim_infile = '90m_12mps_twr.inp' run_openfast(wind_directory, fastcall='turbsim', diff --git a/Examples/09_distributed_aero.py b/Examples/09_distributed_aero.py index 234fd6de..810de3f2 100644 --- a/Examples/09_distributed_aero.py +++ b/Examples/09_distributed_aero.py @@ -18,15 +18,16 @@ # Python Modules import os # ROSCO Modules -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox.inputs.validation import load_rosco_yaml this_dir = os.path.dirname(os.path.abspath(__file__)) +tune_dir = os.path.join(this_dir,'Tune_Cases') # Load yaml file -parameter_filename = os.path.join(os.path.dirname(this_dir),'Tune_Cases/BAR.yaml') +parameter_filename = os.path.join(tune_dir,'BAR.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] turbine_params = inps['turbine_params'] @@ -36,7 +37,7 @@ turbine = ROSCO_turbine.Turbine(turbine_params) turbine.load_from_fast( path_params['FAST_InputFile'], - os.path.join(this_dir,path_params['FAST_directory']) + os.path.join(tune_dir,path_params['FAST_directory']) ) # Tune controller diff --git a/Examples/10_linear_params.py b/Examples/10_linear_params.py index 36e995a3..dc09aba6 100644 --- a/Examples/10_linear_params.py +++ b/Examples/10_linear_params.py @@ -12,23 +12,27 @@ # Python Modules import os # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.inputs.validation import load_rosco_yaml import numpy as np +this_dir = os.path.dirname(os.path.abspath(__file__)) +tune_dir = os.path.join(this_dir,'Tune_Cases') + # Load yaml file -parameter_filename = os.path.join( os.path.dirname( os.path.dirname( os.path.realpath(__file__) )), - 'Tune_Cases', 'IEA15MW.yaml') +parameter_filename = os.path.join( tune_dir, 'IEA15MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] turbine_params = inps['turbine_params'] controller_params = inps['controller_params'] # Linear file output -this_dir = os.path.dirname(os.path.abspath(__file__)) + + +os.path.join(this_dir,path_params['FAST_directory']) example_out_dir = os.path.join(this_dir,'examples_out') if not os.path.isdir(example_out_dir): os.makedirs(example_out_dir) @@ -40,10 +44,9 @@ controller = ROSCO_controller.Controller(controller_params) # Load turbine data from OpenFAST and rotor performance text file -tune_dir = os.path.join(this_dir,'../Tune_Cases') turbine.load_from_fast( path_params['FAST_InputFile'], - os.path.join(this_dir,path_params['FAST_directory']), + os.path.join(tune_dir,path_params['FAST_directory']), rot_source='txt', txt_filename=os.path.join(tune_dir,path_params['rotor_performance_filename']) ) diff --git a/Examples/11_robust_tuning.py b/Examples/11_robust_tuning.py index 4b07629c..171968ae 100644 --- a/Examples/11_robust_tuning.py +++ b/Examples/11_robust_tuning.py @@ -3,7 +3,7 @@ Controller tuning to satisfy a robustness criteria ------------------------------------- NOTE: This example necessitates the mbc3 through either pyFAST or WEIS -pyFAST is the easiest to install by cloning https://github.com/OpenFAST/python-toolbox and +pyFAST is the easiest to install by cloning https://github.com/OpenFAST/openfast_toolbox and running `python setup.py develop` from your conda environment In this example: @@ -18,17 +18,17 @@ import os import numpy as np import matplotlib.pyplot as plt -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.linear.robust_scheduling import rsched_driver, load_linturb -from ROSCO_toolbox.linear.lin_vis import lin_plotting -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import controller as ROSCO_controller +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.linear.robust_scheduling import rsched_driver, load_linturb +from rosco.toolbox.linear.lin_vis import lin_plotting +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import controller as ROSCO_controller def run_example(): # Shorthand directories this_dir = os.path.dirname(os.path.abspath(__file__)) - tune_dir = os.path.join(this_dir, '../Tune_Cases') - test_dir = os.path.join(this_dir, '../Test_Cases') + tune_dir = os.path.join(this_dir, 'Tune_Cases') + test_dir = os.path.join(this_dir, 'Test_Cases') # ROSCO options parameter_filename = os.path.join(tune_dir, 'IEA15MW_robust.yaml') @@ -81,7 +81,7 @@ def run_example(): options['path_options'] = path_options options['opt_options'] = opt_options - options['linturb_options']['linfile_path'] = os.path.join(this_dir, options['linturb_options']['linfile_path']) + options['linturb_options']['linfile_path'] = os.path.join(tune_dir, options['linturb_options']['linfile_path']) # Run robust scheduling sd = rsched_driver(options) diff --git a/Examples/12_tune_ipc.py b/Examples/12_tune_ipc.py index e435becf..6bb5b9e8 100644 --- a/Examples/12_tune_ipc.py +++ b/Examples/12_tune_ipc.py @@ -10,13 +10,13 @@ ''' # Python Modules -import os, platform +import os import matplotlib.pyplot as plt # ROSCO toolbox modules -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl def main(): this_dir = os.path.dirname(os.path.abspath(__file__)) @@ -26,12 +26,11 @@ def main(): run_dir = os.path.join(example_out_dir, example_name) # Load yaml file (Open Loop Case) - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/NREL2p8.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/NREL2p8.yaml') case_inputs = {} case_inputs[('ServoDyn','Ptch_Cntrl')] = {'vals': [1], 'group': 0} case_inputs[('DISCON_in','IPC_SatMode')] = {'vals': [0,1,2,3], 'group': 1} - # simulation set up diff --git a/Examples/14_open_loop_control.py b/Examples/14_open_loop_control.py index 50626d84..862b6306 100644 --- a/Examples/14_open_loop_control.py +++ b/Examples/14_open_loop_control.py @@ -11,23 +11,26 @@ ''' # Python Modules -import yaml, os, platform +import os import numpy as np import matplotlib.pyplot as plt # ROSCO toolbox modules -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import utilities as ROSCO_utilities -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.ofTools.case_gen.CaseLibrary import set_channels -from ROSCO_toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper, runFAST_pywrapper_batch -from ROSCO_toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General +from rosco import discon_lib_path +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import utilities as ROSCO_utilities +from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.ofTools.case_gen.CaseLibrary import set_channels +from rosco.toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper, runFAST_pywrapper_batch +from rosco.toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General this_dir = os.path.dirname(os.path.abspath(__file__)) +tune_dir = os.path.join(this_dir,'Tune_Cases') + rosco_dir = os.path.dirname(this_dir) example_out_dir = os.path.join(this_dir,'examples_out') example_out_dir = os.path.join(this_dir,'examples_out') @@ -35,7 +38,7 @@ os.makedirs(example_out_dir) # Load yaml file (Open Loop Case) -parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW_OL.yaml') +parameter_filename = os.path.join(tune_dir, 'IEA15MW_OL.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] @@ -75,9 +78,9 @@ # Load turbine data from OpenFAST and rotor performance text file turbine.load_from_fast(path_params['FAST_InputFile'], \ - os.path.join(this_dir,path_params['FAST_directory']), \ + os.path.join(tune_dir,path_params['FAST_directory']), \ rot_source='txt',\ - txt_filename=os.path.join(this_dir,path_params['rotor_performance_filename'])) + txt_filename=os.path.join(tune_dir,path_params['rotor_performance_filename'])) # Tune controller controller.tune_controller(turbine) @@ -87,23 +90,14 @@ ROSCO_utilities.write_DISCON(turbine,controller,param_file=param_file, txt_filename=path_params['rotor_performance_filename']) ### Run OpenFAST using aeroelasticse tools - -# Set rosco_dll -if platform.system() == 'Windows': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dll') -elif platform.system() == 'Darwin': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dylib') -else: - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.so') - case_inputs = {} -case_inputs[('ServoDyn','DLL_FileName')] = {'vals': [rosco_dll], 'group': 0} +case_inputs[('ServoDyn','DLL_FileName')] = {'vals': [discon_lib_path], 'group': 0} # Apply all discon variables as case inputs discon_vt = ROSCO_utilities.DISCON_dict( turbine, controller, -txt_filename=os.path.join(this_dir,path_params['FAST_directory'],path_params['rotor_performance_filename']) +txt_filename=os.path.join(tune_dir,path_params['FAST_directory'],path_params['rotor_performance_filename']) ) for discon_input in discon_vt: case_inputs[('DISCON_in',discon_input)] = {'vals': [discon_vt[discon_input]], 'group': 0} @@ -124,7 +118,7 @@ # Run FAST cases fastBatch = runFAST_pywrapper_batch() -fastBatch.FAST_directory = os.path.realpath(os.path.join(rosco_dir,'Tune_Cases',path_params['FAST_directory'])) +fastBatch.FAST_directory = os.path.realpath(os.path.join(tune_dir,path_params['FAST_directory'])) fastBatch.FAST_InputFile = path_params['FAST_InputFile'] fastBatch.channels = channels fastBatch.FAST_runDirectory = run_dir @@ -150,7 +144,7 @@ tt = fo['Time'] valid_ind = tt > 2 # first few timesteps can differ, depending on OpenFAST solve config -# Computer errors +# Compute errors nacelle_yaw_diff = fo['NacYaw'][valid_ind] - np.degrees(np.interp(tt[valid_ind],olc.ol_timeseries['time'],olc.ol_timeseries['nacelle_yaw'])) bld_pitch_diff = fo['BldPitch1'][valid_ind] - np.degrees(np.interp(tt[valid_ind],olc.ol_timeseries['time'],olc.ol_timeseries['blade_pitch'])) gen_tq_diff = fo['GenTq'][valid_ind] - np.interp(tt[valid_ind],olc.ol_timeseries['time'],olc.ol_timeseries['generator_torque'])/1e3 diff --git a/Examples/15_pass_through.py b/Examples/15_pass_through.py index f33fd3f7..c0b7916d 100644 --- a/Examples/15_pass_through.py +++ b/Examples/15_pass_through.py @@ -9,8 +9,8 @@ ''' import os -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl #directories @@ -24,7 +24,7 @@ def main(): # Simulation config r = run_FAST_ROSCO() - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/NREL5MW_PassThrough.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/NREL5MW_PassThrough.yaml') run_dir = os.path.join(example_out_dir,'15_PassThrough') os.makedirs(run_dir,exist_ok=True) diff --git a/Examples/16_external_dll.py b/Examples/16_external_dll.py index e876b2c3..cb79ab07 100644 --- a/Examples/16_external_dll.py +++ b/Examples/16_external_dll.py @@ -8,8 +8,9 @@ ''' import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco import discon_lib_path as lib_name +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl import shutil @@ -19,13 +20,6 @@ example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) - def main(): @@ -36,7 +30,7 @@ def main(): # Ensure external control paths are okay - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW_ExtInterface.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW_ExtInterface.yaml') run_dir = os.path.join(example_out_dir,'16_ExtInterface') os.makedirs(run_dir,exist_ok=True) @@ -44,7 +38,7 @@ def main(): controller_params = {} controller_params['DISCON'] = {} controller_params['DISCON']['DLL_FileName'] = copy_lib - controller_params['DISCON']['DLL_InFile'] = os.path.join(rosco_dir,'Test_Cases/NREL-5MW/DISCON.IN') + controller_params['DISCON']['DLL_InFile'] = os.path.join(this_dir,'Test_Cases/NREL-5MW/DISCON.IN') controller_params['DISCON']['DLL_ProcName'] = 'DISCON' # simulation set up diff --git a/Examples/17a_zeromq_simple.py b/Examples/17a_zeromq_simple.py index 34b5586c..2ccae2be 100644 --- a/Examples/17a_zeromq_simple.py +++ b/Examples/17a_zeromq_simple.py @@ -8,26 +8,30 @@ ''' -import platform import os import matplotlib.pyplot as plt -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.utilities import write_DISCON -from ROSCO_toolbox import control_interface as ROSCO_ci -from ROSCO_toolbox.control_interface import wfc_zmq_server -from ROSCO_toolbox import sim as ROSCO_sim -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox.ofTools.fast_io import output_processing +from rosco import discon_lib_path +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.utilities import write_DISCON +from rosco.toolbox import control_interface as ROSCO_ci +from rosco.toolbox.control_interface import wfc_zmq_server +from rosco.toolbox import sim as ROSCO_sim +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox.ofTools.fast_io import output_processing import numpy as np import multiprocessing as mp -this_dir = os.path.dirname(os.path.abspath(__file__)) -example_out_dir = os.path.join(this_dir, "examples_out") TIME_CHECK = 30 DESIRED_YAW_OFFSET = 20 DESIRED_PITCH_OFFSET = np.deg2rad(2) * np.sin(0.1 * TIME_CHECK) + np.deg2rad(2) +#directories +this_dir = os.path.dirname(os.path.abspath(__file__)) +rosco_dir = os.path.dirname(this_dir) +example_out_dir = os.path.join(this_dir,'examples_out') +os.makedirs(example_out_dir,exist_ok=True) + def run_zmq(logfile=None): # Start the server at the following address network_address = "tcp://*:5555" @@ -63,7 +67,7 @@ def wfc_controller(id,current_time,measurements): def sim_rosco(): # Load yaml file this_dir = os.path.dirname(os.path.abspath(__file__)) - tune_dir = os.path.join(this_dir, '../Tune_Cases') + tune_dir = os.path.join(this_dir, 'Tune_Cases') parameter_filename = os.path.join(tune_dir, 'NREL5MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] @@ -75,19 +79,6 @@ def sim_rosco(): controller_params['ZMQ_Mode'] = 1 controller_params['ZMQ_UpdatePeriod'] = 0.025 - # Specify controller dynamic library path and name - this_dir = os.path.dirname(os.path.abspath(__file__)) - example_out_dir = os.path.join(this_dir, 'examples_out') - if not os.path.isdir(example_out_dir): - os.makedirs(example_out_dir) - - if platform.system() == 'Windows': - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.dll') - elif platform.system() == 'Darwin': - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib') - else: - lib_name = os.path.join(this_dir, '../ROSCO/build/libdiscon.so') - # # Load turbine model from saved pickle turbine = ROSCO_turbine.Turbine turbine = turbine.load(os.path.join(example_out_dir, '01_NREL5MW_saved.p')) @@ -119,7 +110,7 @@ def sim_rosco(): # Load controller library controller_int = ROSCO_ci.ControllerInterface( - lib_name, + discon_lib_path, param_filename=param_filename, sim_name=os.path.join(sim_dir,'sim-zmq') ) @@ -172,4 +163,4 @@ def sim_rosco(): p2 = mp.Process(target=sim_rosco) p2.start() p1.join() - p2.join() \ No newline at end of file + p2.join() diff --git a/Examples/17b_zeromq_multi_openfast.py b/Examples/17b_zeromq_multi_openfast.py index e619114a..0de4d9a7 100644 --- a/Examples/17b_zeromq_multi_openfast.py +++ b/Examples/17b_zeromq_multi_openfast.py @@ -2,10 +2,10 @@ import numpy as np import multiprocessing as mp import matplotlib.pyplot as plt -from ROSCO_toolbox.control_interface import wfc_zmq_server -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.control_interface import wfc_zmq_server +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.fast_io import output_processing this_dir = os.path.dirname(os.path.abspath(__file__)) diff --git a/Examples/18_pitch_offsets.py b/Examples/18_pitch_offsets.py index 1a13752b..74f42201 100644 --- a/Examples/18_pitch_offsets.py +++ b/Examples/18_pitch_offsets.py @@ -7,10 +7,10 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.fast_io import output_processing import numpy as np @@ -20,18 +20,11 @@ example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) - def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW.yaml') run_dir = os.path.join(example_out_dir,'18_PitchFaults') os.makedirs(run_dir,exist_ok=True) @@ -84,4 +77,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/19_update_discon_version.py b/Examples/19_update_discon_version.py index 342cd4d2..e4cd2960 100644 --- a/Examples/19_update_discon_version.py +++ b/Examples/19_update_discon_version.py @@ -5,7 +5,7 @@ ''' import os -from ROSCO_toolbox.tune import update_discon_version +from rosco.toolbox.tune import update_discon_version this_dir = os.path.dirname(os.path.abspath(__file__)) rosco_dir = os.path.dirname(this_dir) @@ -16,7 +16,7 @@ def main(): old_discon_filename = os.path.join(this_dir,'example_inputs','DISCON_v2.2.0.IN') # An IEA-15MW input # Tuning yaml can be anything, does not have to correspond to old discon - tuning_yaml = os.path.join(rosco_dir,'Tune_Cases','NREL5MW.yaml') # dummy for now + tuning_yaml = os.path.join(this_dir,'Tune_Cases','NREL5MW.yaml') # dummy for now update_discon_version( old_discon_filename, tuning_yaml, diff --git a/Examples/20_active_wake_control.py b/Examples/20_active_wake_control.py index c67555d8..aa936a9f 100644 --- a/Examples/20_active_wake_control.py +++ b/Examples/20_active_wake_control.py @@ -153,39 +153,34 @@ [2] - Frederik, Joeri A., et al. "The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms." Wind Energy 23.8 (2020): 1739-1751. ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.utilities import read_DISCON, DISCON_dict -import numpy as np +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +#from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.utilities import read_DISCON #, DISCON_dict +#import numpy as np # Choose your implementation method AWC_Mode = 1 # 1 for SNL implementation, 2 for Coleman Transformation implementation + #directories this_dir = os.path.dirname(os.path.abspath(__file__)) rosco_dir = os.path.dirname(this_dir) example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/NREL2p8.yaml') # will be dummy and overwritten with SNL DISCON params + parameter_filename = os.path.join(this_dir,'Tune_Cases/NREL2p8.yaml') # will be dummy and overwritten with SNL DISCON params run_dir = os.path.join(example_out_dir,'20_active_wake_control/all_cases') os.makedirs(run_dir,exist_ok=True) # Read all DISCON inputs - rosco_vt = read_DISCON(os.path.join(rosco_dir,'Test_Cases','NREL_2p8_127/NREL-2p8-127_DISCON.IN')) + rosco_vt = read_DISCON(os.path.join(this_dir,'Test_Cases','NREL_2p8_127/NREL-2p8-127_DISCON.IN')) # Apply all discon variables as case inputs control_base_case = {} diff --git a/Examples/21_optional_inputs.py b/Examples/21_optional_inputs.py index 8a69b0ac..17b4f90b 100644 --- a/Examples/21_optional_inputs.py +++ b/Examples/21_optional_inputs.py @@ -4,30 +4,23 @@ to the current version ''' -import os, platform -from ROSCO_toolbox import control_interface as ROSCO_ci -from ROSCO_toolbox import sim as ROSCO_sim -from ROSCO_toolbox import turbine as ROSCO_turbine -import numpy as np - - +import os +from rosco import discon_lib_path +from rosco.toolbox import control_interface as ROSCO_ci +#from rosco.toolbox import sim as ROSCO_sim +from rosco.toolbox import turbine as ROSCO_turbine +#import numpy as np + +#directories this_dir = os.path.dirname(os.path.abspath(__file__)) rosco_dir = os.path.dirname(this_dir) - example_out_dir = os.path.join(this_dir,'examples_out') example_in_dir = os.path.join(this_dir,'example_inputs') +os.makedirs(example_out_dir,exist_ok=True) def main(): - # Set up rosco_dll - if platform.system() == 'Windows': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dll') - elif platform.system() == 'Darwin': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dylib') - else: - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.so') - # Load turbine model from saved pickle turbine = ROSCO_turbine.Turbine turbine = turbine.load(os.path.join(example_out_dir,'01_NREL5MW_saved.p')) @@ -40,7 +33,9 @@ def main(): avi_fail = [] for param_filename in param_filenames: - controller_int = ROSCO_ci.ControllerInterface(rosco_dll,param_filename=param_filename,sim_name='sim1') + controller_int = ROSCO_ci.ControllerInterface(discon_lib_path, + param_filename=param_filename, + sim_name='sim1') controller_int.kill_discon() avi_fail.append(controller_int.aviFAIL.value) @@ -48,4 +43,4 @@ def main(): assert avi_fail == [0,-1], "Unexpected pass/fail" if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/Examples/22_cable_control.py b/Examples/22_cable_control.py index 7956d44a..ce9a85b2 100644 --- a/Examples/22_cable_control.py +++ b/Examples/22_cable_control.py @@ -7,15 +7,15 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.fast_io import output_processing import numpy as np -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.inputs.validation import load_rosco_yaml import matplotlib.pyplot as plt -from ROSCO_toolbox.controller import OpenLoopControl +from rosco.toolbox.controller import OpenLoopControl ''' ROSCO currently supports user-defined hooks for cable control actuation, if CC_Mode = 1. @@ -38,7 +38,7 @@ def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW_cable.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW_cable.yaml') run_dir = os.path.join(example_out_dir,'22_cable_control') os.makedirs(run_dir,exist_ok=True) @@ -49,7 +49,7 @@ def main(): # Change inputs programatically, read first reader = InputReader_OpenFAST() reader.FAST_InputFile = path_params['FAST_InputFile'] - reader.FAST_directory = os.path.join(rosco_dir,'Tune_Cases',path_params['FAST_directory']) + reader.FAST_directory = os.path.join(this_dir,'Tune_Cases',path_params['FAST_directory']) reader.execute() # Set control line mapping (ChannelID -> Line(s)) @@ -149,4 +149,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/23_structural_control.py b/Examples/23_structural_control.py index a10a406b..63022907 100644 --- a/Examples/23_structural_control.py +++ b/Examples/23_structural_control.py @@ -7,13 +7,13 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -import numpy as np -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.controller import OpenLoopControl +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +#import numpy as np +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.controller import OpenLoopControl ''' @@ -35,18 +35,11 @@ example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) - def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW_ballast.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW_ballast.yaml') run_dir = os.path.join(example_out_dir,'23_structural_control') os.makedirs(run_dir,exist_ok=True) @@ -57,7 +50,7 @@ def main(): # Change inputs programatically, read first reader = InputReader_OpenFAST() reader.FAST_InputFile = path_params['FAST_InputFile'] - reader.FAST_directory = os.path.join(rosco_dir,'Tune_Cases',path_params['FAST_directory']) + reader.FAST_directory = os.path.join(this_dir,'Tune_Cases',path_params['FAST_directory']) # reader.FAST_directory = '/Users/dzalkind/Tools/ROSCO1/Test_Cases/ptfm_control_archive/IEA-15-240-RWT-UMaineSemi_ballast' reader.execute() @@ -121,4 +114,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/24_floating_feedback.py b/Examples/24_floating_feedback.py index d46378ce..7da30c70 100644 --- a/Examples/24_floating_feedback.py +++ b/Examples/24_floating_feedback.py @@ -12,17 +12,17 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl import numpy as np -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.controller import OpenLoopControl -from ROSCO_toolbox.tune import yaml_to_objs -from ROSCO_toolbox.utilities import write_DISCON, read_DISCON -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox.ofTools.fast_io import output_processing +#from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +#from rosco.toolbox.inputs.validation import load_rosco_yaml +#from rosco.toolbox.controller import OpenLoopControl +from rosco.toolbox.tune import yaml_to_objs +from rosco.toolbox.utilities import write_DISCON, read_DISCON +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox.ofTools.fast_io import output_processing import matplotlib.pyplot as plt @@ -33,18 +33,10 @@ example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) - - def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW.yaml') run_dir = os.path.join(example_out_dir,'24_floating_feedback') os.makedirs(run_dir,exist_ok=True) @@ -162,4 +154,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/25_rotor_position_control.py b/Examples/25_rotor_position_control.py index 2bbf494b..fe1abef8 100644 --- a/Examples/25_rotor_position_control.py +++ b/Examples/25_rotor_position_control.py @@ -7,33 +7,28 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.controller import OpenLoopControl +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.controller import OpenLoopControl import numpy as np -import pandas as pd +#import pandas as pd import matplotlib.pyplot as plt + #directories this_dir = os.path.dirname(os.path.abspath(__file__)) -rosco_dir = os.path.dirname(this_dir) +rosco_dir = os.path.dirname(this_dir) example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) def main(): # Set up paths - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/NREL2p8.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/NREL2p8.yaml') run_dir = os.path.join(example_out_dir,'25_rotor_position_control') os.makedirs(run_dir,exist_ok=True) @@ -117,4 +112,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/26_marine_hydro.py b/Examples/26_marine_hydro.py index 0398747f..6773482b 100644 --- a/Examples/26_marine_hydro.py +++ b/Examples/26_marine_hydro.py @@ -6,15 +6,14 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing -import numpy as np -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -import matplotlib.pyplot as plt -from ROSCO_toolbox.controller import OpenLoopControl +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +#from rosco.toolbox.ofTools.fast_io import output_processing +#from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +#from rosco.toolbox.inputs.validation import load_rosco_yaml +#import matplotlib.pyplot as plt +#from rosco.toolbox.controller import OpenLoopControl ''' Run MHK turbine in OpenFAST with ROSCO torque controller @@ -32,7 +31,7 @@ def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/RM1_MHK.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/RM1_MHK.yaml') run_dir = os.path.join(example_out_dir,'26_MHK/0_baseline') os.makedirs(run_dir,exist_ok=True) @@ -83,4 +82,4 @@ def main(): if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/27_power_ref_control.py b/Examples/27_power_ref_control.py index 31c08665..ebeba3b6 100644 --- a/Examples/27_power_ref_control.py +++ b/Examples/27_power_ref_control.py @@ -8,12 +8,13 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.tune import yaml_to_objs +import os +from rosco import discon_lib_path +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +from rosco.toolbox.tune import yaml_to_objs import numpy as np -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +#from rosco.toolbox.inputs.validation import load_rosco_yaml import matplotlib.pyplot as plt ''' @@ -29,19 +30,13 @@ rosco_dir = os.path.dirname(this_dir) example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) - -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) +lib_name = discon_lib_path def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW.yaml') run_dir = os.path.join(example_out_dir,'27_PRC_0') os.makedirs(run_dir,exist_ok=True) @@ -105,4 +100,4 @@ def main(): r.run_FAST() if __name__=="__main__": - main() \ No newline at end of file + main() diff --git a/Examples/28_tower_resonance.py b/Examples/28_tower_resonance.py index 54b76442..912e4081 100644 --- a/Examples/28_tower_resonance.py +++ b/Examples/28_tower_resonance.py @@ -7,12 +7,12 @@ ''' -import os, platform -from ROSCO_toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +import os +from rosco.toolbox.ofTools.case_gen.run_FAST import run_FAST_ROSCO +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl +#from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.inputs.validation import load_rosco_yaml import numpy as np @@ -25,18 +25,11 @@ example_out_dir = os.path.join(this_dir,'examples_out') os.makedirs(example_out_dir,exist_ok=True) -if platform.system() == 'Windows': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dll')) -elif platform.system() == 'Darwin': - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib')) -else: - lib_name = os.path.realpath(os.path.join(this_dir, '../ROSCO/build/libdiscon.so')) - def main(): # Input yaml and output directory - parameter_filename = os.path.join(rosco_dir,'Tune_Cases/IEA15MW.yaml') + parameter_filename = os.path.join(this_dir,'Tune_Cases/IEA15MW.yaml') run_dir = os.path.join(example_out_dir,'28_TRA_Ramp_0') os.makedirs(run_dir,exist_ok=True) @@ -47,7 +40,7 @@ def main(): reader = InputReader_OpenFAST() reader.FAST_InputFile = path_params['FAST_InputFile'] - reader.FAST_directory = os.path.join(rosco_dir,'Tune_Cases',path_params['FAST_directory']) + reader.FAST_directory = os.path.join(this_dir,'Tune_Cases',path_params['FAST_directory']) # reader.FAST_directory = '/Users/dzalkind/Tools/ROSCO1/Test_Cases/ptfm_control_archive/IEA-15-240-RWT-UMaineSemi_ballast' reader.execute() diff --git a/Examples/ROSCO_walkthrough.ipynb b/Examples/ROSCO_walkthrough.ipynb index 9a129e20..6988320a 100644 --- a/Examples/ROSCO_walkthrough.ipynb +++ b/Examples/ROSCO_walkthrough.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -35,7 +34,7 @@ "output_type": "stream", "text": [ "WARNING: Be sure to pip install simpy and marmot-agents for offshore BOS runs\n", - "Using ofTools in ROSCO_toolbox...\n" + "Using ofTools in rosco.toolbox...\n" ] } ], @@ -48,20 +47,20 @@ "import numpy as np\n", "\n", "# ROSCO Modules\n", - "from ROSCO_toolbox import turbine as ROSCO_turbine\n", - "from ROSCO_toolbox import utilities as ROSCO_utilities\n", - "from ROSCO_toolbox import sim as ROSCO_sim\n", - "from ROSCO_toolbox import utilities as ROSCO_utilities\n", - "from ROSCO_toolbox import controller as ROSCO_controller\n", - "from ROSCO_toolbox import control_interface as ROSCO_ci\n", - "from ROSCO_toolbox.ofTools.fast_io.output_processing import output_processing\n", + "from rosco import discon_lib_path as lib_name\n", + "from rosco.toolbox import turbine as ROSCO_turbine\n", + "from rosco.toolbox import utilities as ROSCO_utilities\n", + "from rosco.toolbox import sim as ROSCO_sim\n", + "from rosco.toolbox import utilities as ROSCO_utilities\n", + "from rosco.toolbox import controller as ROSCO_controller\n", + "from rosco.toolbox import control_interface as ROSCO_ci\n", + "from rosco.toolbox.ofTools.fast_io.output_processing import output_processing\n", "\n", - "from ROSCO_toolbox.inputs.validation import load_rosco_yaml\n", + "from rosco.toolbox.inputs.validation import load_rosco_yaml\n", "\n" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -94,7 +93,7 @@ "outputs": [], "source": [ "# Load yaml file \n", - "parameter_filename = '../Tune_Cases/NREL5MW.yaml'\n", + "parameter_filename = 'Tune_Cases/NREL5MW.yaml'\n", "inps = load_rosco_yaml(parameter_filename)\n", "path_params = inps['path_params']\n", "turbine_params = inps['turbine_params']\n", @@ -121,6 +120,7 @@ " 'max_torque_rate': 1500000.0,\n", " 'rated_power': 5000000.0,\n", " 'rated_rotor_speed': 1.26711,\n", + " 'reynolds_ref': 0,\n", " 'rotor_inertia': 38677040.613,\n", " 'v_max': 25.0,\n", " 'v_min': 3.0,\n", @@ -133,7 +133,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -154,7 +153,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -168,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "slideshow": { "slide_type": "subslide" @@ -183,8 +181,8 @@ "Loading wind turbine data for NREL's ROSCO tuning and simulation processeses\n", "-----------------------------------------------------------------------------\n", "Loading FAST model: NREL-5MW.fst \n", - "Loading rotor performace data from text file: /Users/dzalkind/Tools/ROSCO3/Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt\n", - "Loading rotor performace data from text file: ../Tune_Cases/../Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt\n" + "Loading rotor performace data from text file: /Users/dzalkind/Tools/ROSCO1/Examples/Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt\n", + "Loading rotor performace data from text file: Tune_Cases/../Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt\n" ] } ], @@ -193,13 +191,12 @@ "turbine = ROSCO_turbine.Turbine(turbine_params)\n", "turbine.load_from_fast(\n", " path_params['FAST_InputFile'],\n", - " os.path.join('../Tune_Cases/',path_params['FAST_directory']),\n", - " rot_source='txt',txt_filename=os.path.join('../Tune_Cases/',path_params['rotor_performance_filename'])\n", + " os.path.join('Tune_Cases/',path_params['FAST_directory']),\n", + " rot_source='txt',txt_filename=os.path.join('Tune_Cases',path_params['rotor_performance_filename'])\n", " )\n" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -212,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "slideshow": { "slide_type": "subslide" @@ -236,7 +233,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -253,7 +250,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -267,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "slideshow": { "slide_type": "subslide" @@ -291,7 +287,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -306,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "slideshow": { "slide_type": "subslide" @@ -315,7 +310,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -341,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "slideshow": { "slide_type": "slide" @@ -362,12 +357,11 @@ "ROSCO_utilities.write_DISCON(\n", " turbine,controller,\n", " param_file=param_file, \n", - " txt_filename=os.path.join('../Tune_Cases/',path_params['rotor_performance_filename'])\n", + " txt_filename=os.path.join('Tune_Cases/',path_params['rotor_performance_filename'])\n", ")" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -384,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "slideshow": { "slide_type": "subslide" @@ -402,7 +396,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -429,7 +423,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -443,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "slideshow": { "slide_type": "subslide" @@ -456,74 +449,77 @@ "text": [ " \n", "------------------------------------------------------------------------------\n", - "Running ROSCO-v2.6.0\n", + "Running ROSCO-v2.8.0\n", "A wind turbine controller framework for public use in the scientific field \n", "Developed in collaboration: National Renewable Energy Laboratory \n", " Delft University of Technology, The Netherlands \n", "------------------------------------------------------------------------------\n", + " ROSCO Warning: Did not find correct size F_NotchFreqs in input file. Using default value of [ 0.0000000000000000 ]\n", + " ROSCO Warning: Did not find correct size F_NotchBetaNum in input file. Using default value of [ 0.0000000000000000 ]\n", + " ROSCO Warning: Did not find correct size F_NotchBetaDen in input file. Using default value of [ 0.0000000000000000 ]\n", "Generator speed: 9.5 RPM, Pitch angle: 0.0 deg, Power: 0.0 kW, Est. wind Speed: 10.0 m/s\n", - "Generator speed: 673.4 RPM, Pitch angle: 0.1 deg, Power: 340.8 kW, Est. wind Speed: 4.6 m/s\n", - "Generator speed: 751.3 RPM, Pitch angle: 0.1 deg, Power: 853.4 kW, Est. wind Speed: 7.1 m/s\n", - "Generator speed: 800.7 RPM, Pitch angle: 0.1 deg, Power: 1072.2 kW, Est. wind Speed: 6.8 m/s\n", - "Generator speed: 781.4 RPM, Pitch angle: 0.1 deg, Power: 1190.5 kW, Est. wind Speed: 6.8 m/s\n", - "Generator speed: 765.6 RPM, Pitch angle: 0.1 deg, Power: 1094.1 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 767.7 RPM, Pitch angle: 0.1 deg, Power: 1073.3 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 770.1 RPM, Pitch angle: 0.1 deg, Power: 1084.5 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 770.2 RPM, Pitch angle: 0.1 deg, Power: 1088.2 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 770.1 RPM, Pitch angle: 0.1 deg, Power: 1087.4 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 770.2 RPM, Pitch angle: 0.1 deg, Power: 1088.7 kW, Est. wind Speed: 6.9 m/s\n", - "Generator speed: 838.4 RPM, Pitch angle: 0.1 deg, Power: 1408.9 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 882.6 RPM, Pitch angle: 0.1 deg, Power: 1542.6 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 882.8 RPM, Pitch angle: 0.1 deg, Power: 1674.7 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 873.5 RPM, Pitch angle: 0.1 deg, Power: 1638.1 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 872.7 RPM, Pitch angle: 0.1 deg, Power: 1616.3 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 874.2 RPM, Pitch angle: 0.1 deg, Power: 1617.5 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 875.0 RPM, Pitch angle: 0.1 deg, Power: 1620.4 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 875.4 RPM, Pitch angle: 0.1 deg, Power: 1621.0 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 875.8 RPM, Pitch angle: 0.1 deg, Power: 1620.9 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 876.3 RPM, Pitch angle: 0.1 deg, Power: 1623.7 kW, Est. wind Speed: 7.8 m/s\n", - "Generator speed: 953.2 RPM, Pitch angle: 0.1 deg, Power: 2054.2 kW, Est. wind Speed: 8.8 m/s\n", - "Generator speed: 992.5 RPM, Pitch angle: 0.1 deg, Power: 2266.6 kW, Est. wind Speed: 8.7 m/s\n", - "Generator speed: 987.9 RPM, Pitch angle: 0.1 deg, Power: 2362.9 kW, Est. wind Speed: 8.7 m/s\n", - "Generator speed: 980.6 RPM, Pitch angle: 0.1 deg, Power: 2320.8 kW, Est. wind Speed: 8.7 m/s\n", - "Generator speed: 980.3 RPM, Pitch angle: 0.1 deg, Power: 2302.8 kW, Est. wind Speed: 8.7 m/s\n", - "Generator speed: 981.8 RPM, Pitch angle: 0.1 deg, Power: 2304.0 kW, Est. wind Speed: 8.7 m/s\n", - "Generator speed: 982.7 RPM, Pitch angle: 0.1 deg, Power: 2306.8 kW, Est. wind Speed: 8.8 m/s\n", - "Generator speed: 983.3 RPM, Pitch angle: 0.1 deg, Power: 2307.5 kW, Est. wind Speed: 8.8 m/s\n", - "Generator speed: 983.8 RPM, Pitch angle: 0.1 deg, Power: 2307.5 kW, Est. wind Speed: 8.8 m/s\n", - "Generator speed: 984.4 RPM, Pitch angle: 0.1 deg, Power: 2311.6 kW, Est. wind Speed: 8.8 m/s\n", - "Generator speed: 1069.7 RPM, Pitch angle: 0.1 deg, Power: 2876.5 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1102.9 RPM, Pitch angle: 0.1 deg, Power: 3163.7 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1095.1 RPM, Pitch angle: 0.1 deg, Power: 3221.5 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1088.9 RPM, Pitch angle: 0.1 deg, Power: 3176.5 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1089.1 RPM, Pitch angle: 0.1 deg, Power: 3160.3 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1090.5 RPM, Pitch angle: 0.1 deg, Power: 3162.0 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1091.5 RPM, Pitch angle: 0.1 deg, Power: 3164.7 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1092.1 RPM, Pitch angle: 0.1 deg, Power: 3165.4 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1092.7 RPM, Pitch angle: 0.1 deg, Power: 3165.5 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1093.4 RPM, Pitch angle: 0.1 deg, Power: 3171.1 kW, Est. wind Speed: 9.7 m/s\n", - "Generator speed: 1183.2 RPM, Pitch angle: 2.4 deg, Power: 4016.0 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1175.0 RPM, Pitch angle: 1.6 deg, Power: 4018.1 kW, Est. wind Speed: 10.6 m/s\n", - "Generator speed: 1172.9 RPM, Pitch angle: 1.5 deg, Power: 4066.9 kW, Est. wind Speed: 10.6 m/s\n", - "Generator speed: 1173.1 RPM, Pitch angle: 1.5 deg, Power: 4089.5 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.6 RPM, Pitch angle: 1.5 deg, Power: 4104.5 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4097.0 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4096.7 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.6 RPM, Pitch angle: 1.6 deg, Power: 4095.1 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.6 RPM, Pitch angle: 1.6 deg, Power: 4093.6 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4099.1 kW, Est. wind Speed: 10.7 m/s\n", - "Generator speed: 1174.1 RPM, Pitch angle: 2.6 deg, Power: 4772.0 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1174.7 RPM, Pitch angle: 3.1 deg, Power: 4862.6 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1174.0 RPM, Pitch angle: 3.1 deg, Power: 4886.5 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 3.0 deg, Power: 4885.5 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 3.0 deg, Power: 4882.0 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.5 RPM, Pitch angle: 3.0 deg, Power: 4879.1 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.5 RPM, Pitch angle: 3.0 deg, Power: 4875.4 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.5 RPM, Pitch angle: 3.1 deg, Power: 4872.5 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.5 RPM, Pitch angle: 3.1 deg, Power: 4870.1 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 3.1 deg, Power: 4876.1 kW, Est. wind Speed: 11.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5001.4 kW, Est. wind Speed: 12.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", + "Generator speed: 563.0 RPM, Pitch angle: 0.1 deg, Power: 446.6 kW, Est. wind Speed: 4.3 m/s\n", + "Generator speed: 698.8 RPM, Pitch angle: 0.1 deg, Power: 800.8 kW, Est. wind Speed: 7.0 m/s\n", + "Generator speed: 752.0 RPM, Pitch angle: 0.1 deg, Power: 987.8 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 768.6 RPM, Pitch angle: 0.1 deg, Power: 1061.2 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 772.3 RPM, Pitch angle: 0.1 deg, Power: 1083.9 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 772.4 RPM, Pitch angle: 0.1 deg, Power: 1088.9 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 772.0 RPM, Pitch angle: 0.1 deg, Power: 1089.0 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 771.6 RPM, Pitch angle: 0.1 deg, Power: 1088.3 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 771.4 RPM, Pitch angle: 0.1 deg, Power: 1087.8 kW, Est. wind Speed: 6.8 m/s\n", + "Generator speed: 771.5 RPM, Pitch angle: 0.1 deg, Power: 1089.4 kW, Est. wind Speed: 6.9 m/s\n", + "Generator speed: 848.5 RPM, Pitch angle: 0.1 deg, Power: 1442.1 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 875.6 RPM, Pitch angle: 0.1 deg, Power: 1571.4 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 882.3 RPM, Pitch angle: 0.1 deg, Power: 1614.7 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 882.9 RPM, Pitch angle: 0.1 deg, Power: 1624.9 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 882.4 RPM, Pitch angle: 0.1 deg, Power: 1625.6 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 881.9 RPM, Pitch angle: 0.1 deg, Power: 1624.5 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 881.6 RPM, Pitch angle: 0.1 deg, Power: 1623.7 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 881.5 RPM, Pitch angle: 0.1 deg, Power: 1623.3 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 881.5 RPM, Pitch angle: 0.1 deg, Power: 1623.1 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 881.6 RPM, Pitch angle: 0.1 deg, Power: 1625.9 kW, Est. wind Speed: 7.8 m/s\n", + "Generator speed: 965.3 RPM, Pitch angle: 0.1 deg, Power: 2107.8 kW, Est. wind Speed: 8.8 m/s\n", + "Generator speed: 990.5 RPM, Pitch angle: 0.1 deg, Power: 2266.5 kW, Est. wind Speed: 8.7 m/s\n", + "Generator speed: 994.7 RPM, Pitch angle: 0.1 deg, Power: 2310.9 kW, Est. wind Speed: 8.7 m/s\n", + "Generator speed: 993.9 RPM, Pitch angle: 0.1 deg, Power: 2317.3 kW, Est. wind Speed: 8.7 m/s\n", + "Generator speed: 992.7 RPM, Pitch angle: 0.1 deg, Power: 2315.2 kW, Est. wind Speed: 8.7 m/s\n", + "Generator speed: 992.1 RPM, Pitch angle: 0.1 deg, Power: 2312.9 kW, Est. wind Speed: 8.7 m/s\n", + "Generator speed: 991.8 RPM, Pitch angle: 0.1 deg, Power: 2311.6 kW, Est. wind Speed: 8.8 m/s\n", + "Generator speed: 991.7 RPM, Pitch angle: 0.1 deg, Power: 2311.1 kW, Est. wind Speed: 8.8 m/s\n", + "Generator speed: 991.7 RPM, Pitch angle: 0.1 deg, Power: 2311.0 kW, Est. wind Speed: 8.8 m/s\n", + "Generator speed: 991.8 RPM, Pitch angle: 0.1 deg, Power: 2314.9 kW, Est. wind Speed: 8.8 m/s\n", + "Generator speed: 1081.7 RPM, Pitch angle: 0.1 deg, Power: 2947.8 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1104.7 RPM, Pitch angle: 0.1 deg, Power: 3135.8 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1106.5 RPM, Pitch angle: 0.1 deg, Power: 3179.3 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1104.5 RPM, Pitch angle: 0.1 deg, Power: 3180.8 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1102.9 RPM, Pitch angle: 0.1 deg, Power: 3175.7 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1102.2 RPM, Pitch angle: 0.1 deg, Power: 3172.2 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1101.9 RPM, Pitch angle: 0.1 deg, Power: 3170.6 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1101.9 RPM, Pitch angle: 0.1 deg, Power: 3170.1 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1101.9 RPM, Pitch angle: 0.1 deg, Power: 3170.0 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1102.0 RPM, Pitch angle: 0.1 deg, Power: 3175.4 kW, Est. wind Speed: 9.7 m/s\n", + "Generator speed: 1181.3 RPM, Pitch angle: 3.0 deg, Power: 4314.8 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1176.8 RPM, Pitch angle: 2.0 deg, Power: 4097.9 kW, Est. wind Speed: 10.6 m/s\n", + "Generator speed: 1173.5 RPM, Pitch angle: 1.5 deg, Power: 4069.2 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.1 RPM, Pitch angle: 1.5 deg, Power: 4087.6 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.5 RPM, Pitch angle: 1.5 deg, Power: 4101.7 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4096.4 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4096.0 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.6 RPM, Pitch angle: 1.6 deg, Power: 4094.2 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.6 RPM, Pitch angle: 1.6 deg, Power: 4092.7 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 1.6 deg, Power: 4098.1 kW, Est. wind Speed: 10.7 m/s\n", + "Generator speed: 1173.4 RPM, Pitch angle: 2.6 deg, Power: 4766.7 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1174.2 RPM, Pitch angle: 3.0 deg, Power: 4852.4 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1174.0 RPM, Pitch angle: 3.0 deg, Power: 4879.3 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.8 RPM, Pitch angle: 3.0 deg, Power: 4884.4 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 3.0 deg, Power: 4882.7 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.6 RPM, Pitch angle: 3.0 deg, Power: 4880.2 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.5 RPM, Pitch angle: 3.0 deg, Power: 4876.4 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.5 RPM, Pitch angle: 3.0 deg, Power: 4873.5 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.5 RPM, Pitch angle: 3.1 deg, Power: 4871.0 kW, Est. wind Speed: 11.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 3.1 deg, Power: 4877.0 kW, Est. wind Speed: 11.7 m/s\n", + "Generator speed: 1173.8 RPM, Pitch angle: 5.9 deg, Power: 5001.4 kW, Est. wind Speed: 12.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.1 kW, Est. wind Speed: 12.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", @@ -532,41 +528,41 @@ "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 5.9 deg, Power: 5000.0 kW, Est. wind Speed: 12.6 m/s\n", "Generator speed: 1173.8 RPM, Pitch angle: 5.9 deg, Power: 5008.7 kW, Est. wind Speed: 12.6 m/s\n", - "Generator speed: 1173.9 RPM, Pitch angle: 8.1 deg, Power: 5000.9 kW, Est. wind Speed: 13.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", + "Generator speed: 1174.0 RPM, Pitch angle: 8.1 deg, Power: 5001.9 kW, Est. wind Speed: 13.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.1 kW, Est. wind Speed: 13.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.7 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 8.1 deg, Power: 5000.0 kW, Est. wind Speed: 13.7 m/s\n", "Generator speed: 1173.9 RPM, Pitch angle: 8.1 deg, Power: 5009.0 kW, Est. wind Speed: 13.7 m/s\n", - "Generator speed: 1173.9 RPM, Pitch angle: 10.0 deg, Power: 5001.1 kW, Est. wind Speed: 14.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", + "Generator speed: 1173.9 RPM, Pitch angle: 10.0 deg, Power: 5001.9 kW, Est. wind Speed: 14.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.1 kW, Est. wind Speed: 14.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.6 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.7 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 10.0 deg, Power: 5000.0 kW, Est. wind Speed: 14.7 m/s\n", "Generator speed: 1173.9 RPM, Pitch angle: 10.0 deg, Power: 5009.4 kW, Est. wind Speed: 14.7 m/s\n", - "Generator speed: 1173.8 RPM, Pitch angle: 11.6 deg, Power: 5001.6 kW, Est. wind Speed: 15.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.6 m/s\n", - "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.6 m/s\n", + "Generator speed: 1173.8 RPM, Pitch angle: 11.6 deg, Power: 5001.7 kW, Est. wind Speed: 15.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.1 kW, Est. wind Speed: 15.6 m/s\n", + "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", "Generator speed: 1173.7 RPM, Pitch angle: 11.6 deg, Power: 5000.0 kW, Est. wind Speed: 15.7 m/s\n", - "Shutting down ../ROSCO/build/libdiscon.dylib\n" + "Shutting down ../lib/libdiscon.dylib\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -576,16 +572,6 @@ } ], "source": [ - "# Specify controller dynamic library path and name\n", - "\n", - "if platform.system() == 'Windows':\n", - " ext = 'dll'\n", - "elif platform.system() == 'Darwin':\n", - " ext = 'dylib'\n", - "else:\n", - " ext = 'so'\n", - " \n", - "lib_name = (f'../ROSCO/build/libdiscon.{ext}')\n", "\n", "# Load the simulator and controller interface\n", "controller_int = ROSCO_ci.ControllerInterface(lib_name,param_filename=param_file)\n", @@ -607,7 +593,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -619,7 +604,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -638,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { "slideshow": { "slide_type": "subslide" @@ -647,7 +631,7 @@ "outputs": [ { "data": { - "image/png": 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", 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2hRBCCCFE3TG7Z3HFihXo9XrGjx9PQEAAzz//vGmOxdo6dOgQ/fv3B+CHH37A29ubuLg4Vq5cyTvvvHNdbQohhBBCiLpndrH473//m40bNxITE8NDDz3EV199RUhICIMHD+arr76q1WouhYWFODo6ArBx40ZuvfVW1Go1ffr0IS4urvbvQgghhBBC1ItaT50TEBDAvHnzOH/+PBs3bsTf359p06bh6+vL9OnTzWojODiYX375hYSEBDZs2MCIESMASEtLw8nJqbYhCSGEEEKIevKP1oYeOnQoq1atYuXKlajVaj766COzznvhhReYPXs2LVu2pHfv3kRERAAVvYxXm35HCCGEEEI0rFpPnXNJbGwsX3zxBStWrCAxMZHBgwfzwAMPmHXubbfdxk033URycjKdO3c2bR86dCi33HLL9YYkhBBCCCHqWK2KxeLiYr7//nu++OILtm/fjr+/P/feey/33XcfLVu2vOb5fn5+TJw4kQkTJjB06FB8fHwq7e/Vq1etghdCCCGEEPXL7MvQ06ZNw8fHh4ceeghPT0/WrVtHbGwsCxYsMKtQBFi9ejU6nY7HHnsMDw8Pbr/9dr788kuysrKuN34hhBBCCFGPzC4W9+zZw4IFC7hw4QLffvstI0eORKVS1erFBg0axFtvvcWZM2fYvXs33bp14/3338fX15dBgwaxZMkSzp07V+s3IYQQQggh6ofZxWJUVBSPP/44bm5udfLCYWFhzJ07lz179hAXF8ddd93F5s2bCQ8Pp2PHjqxbt65OXqc627dvZ/z48fj5+aFSqfjll18q7VcUhfnz5+Pn54ednR2DBg3i+PHj12z3xx9/pEOHDmi1Wjp06MDPP/9cT+9ACCGEEKJh/KPR0FdKSEjg/vvvv65zL13eXrNmDRkZGbz00ktotdq6Cq2KgoICOnfuzHvvvVft/kWLFrF48WLee+899u/fj4+PD8OHDycvL6/GNnfv3s2dd97JlClTOHLkCFOmTOGOO+5g79699fU2hBBCCCHq3XWPhv67rKwsVqxYweeff272OWlpaaSlpWE0Gittr+8R0aNHj2b06NHV7lMUhaVLl/Lss89y6623AhWr13h7e7N69Woefvjhas9bunQpw4cPZ+7cuQDMnTuXbdu2sXTpUr7++uv6eSO1oCgKhaVllJRDYWkZGqV2txCIumUwSC4sheTCMkgeLIfkwjJcyoOiKI0divnF4q+//nrV/bVZ+u/gwYNMnTqVkydPVvlLUKlUlJeXm91WXYuJiSElJcU0UTiAVqtl4MCB7Nq1q8Zicffu3TzxxBOVto0cOZKlS5fW+FolJSWUlJSYnuv1egAMBgMGg+EfvIuqCkvL6PzSZsCaOfs212nb4npJLiyH5MIySB4sh+TCMlgzZEgJzrUcI2Iuc2sNs4vFm2++GZVKddUK19wBL/fddx+hoaF89tlneHt713qgTH1KSUkBwNvbu9L2S+tXX+286s651F51Xn31VRYsWFBl+8aNG9HpdLUJ+5pKyqEOO5KFEEII0QA2b96M1qp+2i4sLDTrOLOrB19fX95//31uvvnmavcfPnyY7t27m9VWTEwMP/30E8HBwea+fIP7ewGrKMo1i9ranjN37lxmzZpleq7X6wkICGDEiBF1vuyhoigMGVLC5s2bGTJkCBqNFI6NyWAok1xYCMmFZZA8WA7JhWW4lIexI4dhY2NTL69x6YrmtZj9r6B79+4cOnSoxmLxWr2OVxo6dChHjhyxyGLx0kThKSkp+Pr6mranpaVV6Tn8+3l/70W81jlarbbagTwajQaNRlPb0K/JWaVCawXO9rb10r4wn8FgkFxYCMmFZZA8WA7JhWW4lAcbG5t6y4O57ZpdLD711FMUFBTUuD84OJgtW7aY1dann37K1KlTOXbsGB07dqwS7IQJE8wNq861atUKHx8fIiMjTetUl5aWsm3bNl5//fUaz4uIiCAyMrLSfYsbN26kb9++9R6zEEIIIUR9MbtY7N+//1X329vbM3DgQLPa2rVrFzt37uS3336rsq8hBrjk5+dz9uxZ0/OYmBgOHz6Mm5sbgYGBzJw5k4ULFxISEkJISAgLFy5Ep9MxefJk0zn33HMP/v7+vPrqqwA8/vjjDBgwgNdff52JEyfyv//9j02bNrFz5856fS9CCCGEEPWpzuZZrI3HHnuMKVOmkJycjNForPRoiJHQBw4coGvXrqaew1mzZtG1a1deeOEFAObMmcPMmTOZPn06PXr0ICkpiY0bN+Lo6GhqIz4+nuTkZNPzvn378s033/DFF1/QqVMnli9fzrfffkvv3r3r/f0IIYQQQtQXs3oWL803aI6ffvrpmsdkZmbyxBNPXPV+vvo0aNCga47qnj9/PvPnz6/xmK1bt1bZdtttt3HbbbfVQYRCCCGEEJbBrJ5FZ2dn08PJyYk//viDAwcOmPYfPHiQP/74A2dnZ7Ne9NZbbzX7/kYhhBBCCNF4zOpZ/OKLL0zfP/3009xxxx0sW7YMK6uKiX/Ky8uZPn262dO9hIaGMnfuXHbu3El4eHiVAS6PPfaYufELIYQQQoh6VOsJlD7//HN27txpKhQBrKysmDVrFn379uWNN964ZhuffvopDg4ObNu2jW3btlXap1KppFgUQgghhLAQtS4Wy8rKOHnyJG3btq20/eTJk1XWeK5JTExMbV9WCCGEEEI0gloXi/fddx/3338/Z8+epU+fPgDs2bOH1157jfvuu6/OAxRCCCGEEI2n1sXim2++iY+PD0uWLDFNHePr68ucOXN48sknazzvtdde47HHHjNrzeO9e/eSkZHB2LFjaxueEEIIIYSoQ7UuFtVqNXPmzGHOnDmmNQXNGdhy4sQJAgMDuf3225kwYQI9evTA09MTqLi0feLECXbu3MmqVatITk5m5cqVtQ1NCCGEEELUsX+0Qri5o58BVq5cSVRUFO+//z533XUXubm5WFlZodVqKSwsBKBr165MmzaNqVOnVrtmshBCCCGEaFi1LhZTU1OZPXs2f/zxB2lpaVUmt77aCiydOnXio48+YtmyZURFRREbG0tRUREeHh506dIFDw+P2r8DIYQQQghRb2pdLN57773Ex8fz/PPP4+vri0qlqvWLqlQqOnfuTOfOnWt9rhBCCCGEaDi1LhZ37tzJjh076NKlSz2EI4QQQgghLIlZy/1dKSAg4KrrKgshhBBCiBtHrYvFpUuX8swzzxAbG1sP4QghhBBCCEtS68vQd955J4WFhbRp0wadTldlXeesrKw6C04IIYQQQjSuWheLS5curYcwhBBCCCGEJap1sTh16tT6iEMIIYQQQlggs4pFvV5vmoD70qotNanNRN1CCCGEEMKymVUsurq6kpycjJeXFy4uLtXOragoCiqV6qqTcgshhBBCiKbFrGJx8+bNuLm5mb6/nom4hRBCCCFE02NWsThw4EDOnj1LcHAwgwYNqueQhBBCCCGEpTB7nsXQ0FACAgK45557+OKLL2SeRSGEEEKIZsDs0dDbtm1j27ZtbN26lUcffZTi4mICAwMZMmQIgwcPZvDgwfj7+9dnrEIIIYQQooGZXSz279+f/v3789xzz2EwGNi9ezdbt25l69atfP3115SUlBAcHEx0dHR9xiuEEEIIIRpQredZBNBoNAwYMICePXsSERHBhg0b+OSTTzh79mxdxyeEEEIIIRpRrYrF4uJidu3axZYtW9i6dSv79++nVatWDBw4kA8//JCBAwfWV5xCCCGEEKIRmF0sDhw4kP3799OmTRsGDBjAjBkzGDhwIN7e3vUZnxBCCCGEaERmF4u7du3C19eXwYMHM2jQIAYMGICHh0d9xiaEEEIIIRqZ2VPn5OTk8PHHH6PT6Xj99dfx9/cnPDycRx99lB9++IH09PT6jFMIIYQQQjQCs3sW7e3tGTVqFKNGjQIgLy+PnTt3smXLFhYtWsRdd91FSEgIx44dq7dghRBCCCFEwzK7Z/Hv7O3tcXNzw83NDVdXV6ytrTl58mRdxtao8vLymDlzJkFBQdjZ2dG3b1/2799f4/Fbt25FpVJVeZw6daoBoxZCCCGEqFtm9ywajUYOHDjA1q1b2bJlC3/++ScFBQX4+/szePBg3n//fQYPHlyfsTaoBx98kGPHjvHll1/i5+fHqlWrGDZsGCdOnLjq5OPR0dE4OTmZnnt6ejZEuEIIIYQQ9cLsYtHFxYWCggJ8fX0ZNGgQixcvZvDgwbRp06Y+42sURUVF/Pjjj/zvf/9jwIABAMyfP59ffvmFDz/8kJdffrnGc728vHBxcWmgSIUQQggh6pfZxeIbb7zB4MGDCQ0Nrc94LEJZWRnl5eXY2tpW2m5nZ8fOnTuvem7Xrl0pLi6mQ4cOPPfcc1ftbS0pKaGkpMT0XK/XA2AwGDAYDP/gHVTvUpv10baoHcmF5ZBcWAbJg+WQXFiGhsiDuW2rFEVR6i2KJqxv377Y2NiwevVqvL29+frrr7nnnnsICQmpdknD6Ohotm/fTvfu3SkpKeHLL79k2bJlbN261dQ7+Xfz589nwYIFVbavXr0anU5X5+9JCCGEEOKSwsJCJk+eTG5ubqVb6P5OisUanDt3jvvvv5/t27djZWVFt27dCA0N5dChQ5w4ccKsNsaPH49KpeLXX3+tdn91PYsBAQFkZGRcNWnXy2AwEBkZyfDhw9FoNHXevjCf5MJySC4sg+TBckguLEND5EGv1+Ph4XHNYvG61oZuDtq0acO2bdsoKChAr9fj6+vLnXfeSatWrcxuo0+fPqxatarG/VqtFq1WW2W7RqOp1x/Q+m5fmE9yYTkkF5ZB8mA5JBeWoT7zYG67Uixeg729Pfb29mRnZ7NhwwYWLVpk9rl//fUXvr6+Zh9/qZP30r2Ldc1gMFBYWIher5cPgEYmubAckgvLIHmwHJILy9AQebhUb1zrIrMUizXYsGEDiqLQtm1bzp49y1NPPUXbtm257777AJg7dy5JSUmsXLkSgKVLl9KyZUvCwsIoLS1l1apV/Pjjj/z4449mv2ZeXh4AAQEBdf+GhBBCCCGqkZeXh7Ozc437pVisQW5uLnPnziUxMRE3NzcmTZrEK6+8Yqruk5OTiY+PNx1fWlrK7NmzSUpKws7OjrCwMNatW8eYMWPMfk0/Pz8SEhJwdHREpVLV+Xu6dE9kQkJCvdwTKcwnubAckgvLIHmwHJILy9AQeVAUhby8PPz8/K56nAxwaUb0ej3Ozs7XvJFV1D/JheWQXFgGyYPlkFxYBkvKw3Uv9yeEEEIIIW58UiwKIYQQQogaSbHYjGi1WubNm1ftdD2iYUkuLIfkwjJIHiyH5MIyWFIe5J5FIYQQQghRI+lZFEIIIYQQNZJiUQghhBBC1EiKRSGEEEIIUSMpFoUQQgghRI2kWBRCCCGEEDWSYlEIIYQQQtRI1oa2IEajkQsXLtTb2tBCCCGEEJdcuTa0Wl1z/6EUixbkwoULBAQENHYYQgghhGhGEhISaNGiRY37pVi0II6OjkBF0upj0XCDwcDGjRsZMWIEGo2mztsX5pNcWA7JhWWQPFgOyYVlaIg86PV6AgICTPVHTaRYtCCXLj07OTnVW7Go0+lwcnKSD4A6Vm5UyMwvIafIQH5JGfnFZRSUlFFcVm46RoUKlQrsbayx00CWoiO33BpfR3vsbKwaMfrmTX4uLIPkwXJILixDQ+bhWre+SbEohJlyiwycT8/nfHoB59Lzic0s4EJOMan6YtLySig31nblTGveiNoJgKtOg5+LHX4udgS46mjr40BbHydCvR3Q2ciPqRBCiMYj/wsJUY2cwlKOJuVWPBJziUrMJSmn6KrnqFXgbKfBwdYaB60GB60VtprKPYblRoWCkjL0RQYy9QWUKFaUlBnJLjSQXWjg+AV9peNVKghy09Et0JUeLd3o1cqVNp4OMgBKCCFEg5FiUQggM7+EvTFZ7Dmfye5zmZxJy6/2OG8nLa09HGjjZU8rDwf8XezwcbbFx8kWDwcbrK3Mm43KYDCwfv16Ro8eQVG5igs5RSTnFpGUU0xsRgHRKXmcSskjI7+E2MxCYjML+emvJKCiF7JvGw8Gt/NiUFtPPBy0dfb3IIQQQvydFIuiWSorN3IwLpvNp9LYGp1OdGpelWOC3HWE+zvTqYUz4f4uhPk74WRbt/eNqFQqnO00ONtpaO9b9T7VzPwSjl3QczA2i32xWRxOyCG70MC6o8msO5qMSgWdWrgwpqMP4zv74ediV6fxCSGEEFIsimYjt9DA1tNp/HEyja3RaeiLyyrtb+vtSEQbd/q0dqNXK3fc7G0aKdLL3B20DAz1ZGCoJwClZUaOJuWwLTqdzdFpHEvScyQhhyMJObz62yl6tnRlQmc/xnXyw9UC4hdCCNH0SbEobmj6YgORx1NZE3WBnWcyKLtiEIqrTsPgtl4MbudF3zbuuDeBy7k21mq6B7nRPciNWSPakqovJvJEKr8eucD+2Cz2x2azPzabl9adZGy4L3f3CaRboKvc4yiEEOK6SbEobjhFpeVEnkxlzZELbItOp7TcaNoX6u3A0PbeDGvvRZcAV6zUTbuI8nay5e4+QdzdJ4jk3CLWRSXz819JHL+g5+e/kvj5ryTa+Thyd58gbuveosqAGyGEEOJapFgUNwRFUTgUn833BxJZG5VMfsnlS8zBXg6M7+THuM6+tPF0aMQo65evsx0P9m/NAze1Iioxl6/2xvHrkQucSsnjuV+OsSTyNPf2bcmUiCBcdHKJWgghhHmkWBRNWqq+mB8PJfLDwUTOpxeYtge42TGhsx/jO/vR1rt5rbWtUqnoHOBC5wAXnh3TgR8OJfL5zhiScop4K/I0H247x797BfLwwNZ4Odo2drhCCCEsnBSLoskpNypsPpXGV3vj2H46nUu3IdpprBgT7svtPVrQq6Ub6iZ+ibkuOOs0PHBTK+6JCGJdVDLLtp3jVEoen+2M4au9cdzXrxWPDGiDs05WaRBCCFE9KRZFk5GeV8J3BxJYvTe+0gTZPVu6cnv3AMZ08sVBK/+kq6OxUnNzV38mdvFj6+l03t50hsMJOXy49Ryr9sTx8IDW3NevFfby9yeEEOJv5H8GYdEUReFgXDZf7olj/dFkDOUV3YiuOg139AjgX70CaeVh38hRNh0qlYrBbb0YFOrJppNpvLkhmujUPN7ceJov98Tx9Kh23NzFX3plhRBCmEixKCxSQUkZ/zt8gS/3xHEy+fISeF0CXJjSJ4ixnXxlZO8/oFKpGN7Bm6HtvFgTdYG3Np4mPquQWd8dYeXuOOaN70DXQNfGDlMIIYQFkGJRWJSzafms2hPHjwcTybs4ollrrWZiFz+m9GlJeAvnRo7wxqJWq5jYxZ9RHX34fGcs722uuDx9ywe7uLWbP8+Oad8k5p8UQghRf+qsWDxy5AjdunWjvLy8rpoUzURZuZFNJ1P5ck8cf57NNG1v6a4zzQ8oU73UL621Ff8Z1IZJ3fx5Y0M03x9M5KdDSWw5lcazYzswqZt/sxpRLoQQ4rI67VlUFOXaBwlxUVpeMd/uS2D1vniSc4sBUKtgSDtvpkQE0T/YQ+6da2BeTra8cXtn7uoTxDM/RnEqJY/Z3x/hp0OJLLwlnJZyf6gQQjQ7ZheLt95661X35+bmSs+DuCZFUTgQl83K3XH8fuzygBU3exvu7BnAXb0DaeGqa+QoRZcAF9bMuInPdsawJPI0u85lMnLpdh4fFsK0/q2xtlI3dohCCCEaiNnF4po1axg+fDje3t7V7pfLz+JqLg1YWbk7llMpeabt3QJdmBIRxJhwX7TWMmDFkmis1DwysA2jO/rw3C/H2HEmg0W/RxN5IpXFd3SRUehCCNFMmF0stm/fnkmTJvHAAw9Uu//w4cOsXbu2zgITTZ+iKEQl5vLdgQR+PXzBNGDFVqNmYmd/pkQE0dFfBqxYuiB3e1be34sfDyWx4Nfj/BWfw5i3d/DfMe24u0+QXFEQQogbnNnFYvfu3Tl06FCNxaJWqyUwMLDOAhNNV2Z+Cb8cvsD3BxIq9SJeGrBye/cAWTGkiVGpVNzWvQURbdx56vsj7DqXyfP/O87GE6ksuq0Tvs52jR2iEEKIemJ2sbhs2bKrXmpu3749MTExdRKUaHoKSsr441Qaa49cYEt0muleRK21mtEdfbijRwB9WrvLgJUmzt/FjlUP9Gbl7lhe/e0UO85kMHLJdhbeGs64Tn6NHZ4QQoh6YHaxqNXKXGuistwiAzvOpLMuKpnNp9IoKTOa9nVu4cztPQIY39kPZzvpRbyRqNUq7u3XiptCPHnyu8McSczl0dV/8efZDF4YF4adjdx7KoQQN5LrmjrHaDRy9uxZ0tLSMBqNlfYNGDCgTgJrbNu3b+eNN97g4MGDJCcn8/PPP3PzzTdf9Zxt27Yxa9Ysjh8/jp+fH3PmzOGRRx5pmIAbQGmZkZPJenaezWBbdDoH47MpN16eLqmlu45xnfwY39mPtj6OjRipaAjBXg78+J++LN10hve3nuXrfQkciM3m3cldaefj1NjhCSGEqCO1Lhb37NnD5MmTiYuLqzKvokqlMmtUdEJCArGxsRQWFuLp6UlYWJjF9VwWFBTQuXNn7rvvPiZNmnTN42NiYhgzZgwPPfQQq1at4s8//2T69Ol4enqadb6l0RcbOJ9ewPn0fKJT8jgUn01UYm6l3kOANp72DO/gw7hOvoT5Oclgh2bG2krN7JFt6dvGnce/PcyZtHwmvvcnz4/rwF29A+XfgxBC3ABqXSw+8sgj9OjRg3Xr1uHr62v2fwZxcXEsW7aMr7/+moSEhEqFpo2NDf3792fatGlMmjQJtbrx53AbPXo0o0ePNvv4ZcuWERgYyNKlS4GKezgPHDjAm2++WWOxWFJSQklJiem5Xl+xBrLBYMBgMFx/8DX45a9EfjirZvP3URiBsnKFMqNCuVHBUG4kt8hAVkEpWYUGCkurL/pd7DR0C3RhQKgHA0M8aOF6eWBDWVlZncd8o7qU3/rIc2PoGeTMmul9ePqn42w7k1Ex1c7pNF65Oczib0O40XLRVEkeLIfkwjI0RB7MbVul1HLZFXt7e44cOUJwcLDZ5zz++ON88cUXjBgxggkTJtCrVy/8/f2xs7MjKyuLY8eOsWPHDr7++musra354osv6NmzZ23Cqlcqleqal6EHDBhA165defvtt03bfv75Z+644w4KCwvRaKr+hzl//nwWLFhQZfvq1avR6ep+YuqfYtRsSzG/EHfSKHjZKXjbQZCDQktHBS9bkM4iUROjAtuSVayJV1OuqHDTKkwNKael3JUghBAWp7CwkMmTJ5Obm4uTU823D9W6Z7F3796cPXu2VsWijY0N586dw9PTs8o+Ly8vhgwZwpAhQ5g3bx7r168nLi7OoopFc6SkpFSZsNzb25uysjIyMjLw9fWtcs7cuXOZNWuW6blerycgIIARI0ZcNWnXy/5UKo47DtE2NBStxhprtQortQprtQprKxXOthrc7G1ws7fB3cEGB22drgYprmAwGIiMjGT48OHV/iLRlI0D7knKZeZ3UcRnFfHeSQ1zR7Xl7t4BFnlZ+kbORVMiebAckgvL0BB5uHRF81rMqgaioqJM38+YMYMnn3ySlJQUwsPDq7yBTp06VTn/jTfeMCsYgDFjxph9rKX5+3+Elzpta/oPUqvVVnuvpkajqZd/GAPbeVNwXmHMwDbyAWAh6ivXja1bSw/WPdafp3+MYv3RFF5cd4qDCbm8PqmTxf4ScqPmoqmRPFgOyYVlqM88mNuuWZ/aXbp0QaVSVbrP8P777zd9f2mfuQNcbkQ+Pj6kpKRU2paWloa1tTXu7u6NFJUQjcfRVsP7k7vxxZ+xLFx/knVRyZxM1vPhXd1ltLwQQlxFWbmRnWcz2ZSkwhK60MwqFutysu2uXbtW29OmUqmwtbUlODiYe++9l8GDB9fZazaEiIgI1qxZU2nbxo0b6dGjh/xmJpotlUrF/Te1onOAC4+uPsT59AImvr+TV24OZ1L3Fo0dnhBCWAxFUTiSmMsvfyWxNiqZjPwSVKh5Wl9MC/fGrSPMKhaDgoLq7AVHjRrFhx9+SHh4OL169UJRFA4cOEBUVBT33nsvJ06cYNiwYfz0009MnDixzl63tvLz8zl79qzpeUxMDIcPH8bNzY3AwEDmzp1LUlISK1euBCpGib/33nvMmjWLhx56iN27d/PZZ5/x9ddfN9ZbEMJidA9yZd1j/Xn8m7/YcSaDJ78/woG4LOaND8NWI5N4CyGar7Np+fx6OIn/HblAXGahabuLnYYwp5JK8xk3llrfPPTqq6/i7e1d6TI0wOeff056ejpPP/30Vc/PyMjgySef5Pnnn6+0/eWXXyYuLo6NGzcyb948XnrppUYtFg8cOFCpd/PSQJSpU6eyfPlykpOTiY+PN+1v1aoV69ev54knnuD999/Hz8+Pd955p0nOsShEfXCzt2H5fb14d/MZ3v7jDF/vS+BIQi4f3t2NIHf7xg5PCCEaTHJuEWuPJPPL4SSOX7g8yMROY8WIMG8mdvGjd5ALmzb+jp+L3VVaahi1LhY/+ugjVq9eXWV7WFgY//rXv65ZLH733XccPHiwyvZ//etfdO/enU8++YR///vfLF68uLah1alBgwZVmXT8SsuXL6+ybeDAgRw6dKgeoxKiabNSq5g5LJTuQa48/s1hTiTrGffuTt68vTMjw3waOzwhhKg3WQWl/H4shV+PJLE3JotLJYa1WsXAUE8mdPFjeAdvdDYVpZklzXNZ62IxJSWl2mlgPD09SU5Ovub5tra27Nq1q8rUO7t27cLW1haoWE7Q0lZ0EULUnf4hnqx77CYeXf0XB+OyefjLg0wb0JqnRrZFY9X4k/ILIURdyCs2sPF4KmuiLrDzTAZlV1xS7tXSjYld/RjT0RdXe5tGjPLaal0sBgQE8Oeff9KqVatK2//880/8/Pyuef6MGTN45JFHOHjwID179kSlUrFv3z4+/fRT/vvf/wKwYcMGunbtWtvQhBBNiK+zHd9M68Prv53i050xfLz9PIfjc3h3cle8nWwbOzwhhLguRaXlbD6Vxq9HktgSnU7pFcvkdvR3YlwnP8Z39sPfAi4vm6vWxeKDDz7IzJkzMRgMDBkyBIA//viDOXPm8OSTT17z/Oeee45WrVrx3nvv8eWXXwLQtm1bPvnkEyZPngxUDBb5z3/+U9vQhBBNjMZKzXPjOtA9yJWnfohiX2wWY9/ZwTv/7krfNh6NHZ4QQpilpKycHaczWBN1gcgTqZWWzG3jac+Ezv6M6+xLG0+HRozy+tW6WJwzZw5ZWVlMnz6d0tJSoOLS8tNPP80zzzxjVht33XUXd911V4377eyaTrUthPjnRof70s7Xif+sOsiplDzu/nQvT45oy38GtkGttrxVX4QQoqzcyJ7zWfx6JInfj6WgLy4z7Qtws2P8xR7Edj6OFrl6VW3UulhUqVS8/vrrPP/885w8eRI7OztCQkJqdY9hTk4OP/zwA+fPn2f27Nm4ublx6NAhvL298ff3r21IQogbQCsPe36e3o/n/3eMHw4m8saGaA7GZbP4js646Cz7fh4hRPNgNCocjM9mzZELrD+aTEZ+qWmfl6OWcZ38mNDFj84tnJt8gXilWheL999/P2+//TaOjo6V1m8uKChgxowZfP7551c9PyoqimHDhuHs7ExsbCwPPvggbm5u/Pzzz8TFxZnmLRRCND92Nla8eXtnerZ05fn/HWfzqTTGvrOTD+/uRqcWLo0dnhCiGTIaFf5KyGZdVAq/HUsmObfYtM9Vp2FMuC/jO/vRs6UbVjfolZBaDztcsWIFRUVFVbYXFRWZVejNmjWLe++9lzNnzphGPwOMHj2a7du31zYcIcQN6M6egfz0n74EuulIyinitg93s2pP3FWnsxJCiLpiNCocjMtiwZrj9H1tM5M+3M3nf8aQnFuMo9aaSd1asPy+nux7dhiv3BJOn9buN2yhCLXoWdTr9SiKgqIo5OXlVSr0ysvLWb9+PV5eXtdsZ//+/Xz00UdVtvv7+1dZW1kI0Xx19HdmzYybmP39ESJPpPLcL8c4EJvFwlvDTfOQCSFEXTEaFQ7FZ7PuaDK/HU0hRX+5B9FBa82w9l6M7eRH/xCPZrfylNmfuC4uLqhUKlQqFaGhoVX2q1QqFixYcM12bG1t0ev1VbZHR0fj6elpbjhCiGbA2U7Dx1O688mO87z+ezS/HL7A8Qt6Pry7O8FeTXNUoRDCclwqENdGJfP7scoFoqPWmmEdvBkT7tssC8QrmV0sbtmyBUVRGDJkCD/++CNubm6mfTY2NgQFBZk1z+LEiRN58cUX+e6774CKIjM+Pp5nnnlGlsYTQlShUqmYNqANXQJceXT1Ic6k5TPhvZ28NqkTEzpf+zNHCCGudGmQyrqrFIhjw33pH+qB1rr5FohXMrtYHDhwIAAxMTEEBgZe9yifN998kzFjxuDl5UVRUREDBw4kJSWFiIgIXnnlletqUwhx4+vVyo21j93E418fZvf5TB77+i8Oxmbx37Ht5QNdCHFVhnIj+2Ky2HA8hQ3HU0jVl5j2OWqtGX6pB1EKxGqZVSxGRUXRsWNH1Go1ubm5HD16tMZjO3XqdNW2nJyc2LlzJ5s3b+bQoUMYjUa6devGsGHDahe5EKLZ8XK05csHerFk02ne33KOFbvjOJyYy/uTu9LCVdfY4QkhLEhRaTnbz6Sz4XgKf5xMI7fo8lrLlwrEsZ18uSlECsRrMatY7NKlCykpKXh5edGlSxdUKlW1oxJVKhXl5eXVtFDVkCFDTCvACCGEuayt1Dw1sh3dg1x54tsjHEnIYdy7O1l6ZxcGtb32IDshxI0rt8jA5lOpbDiWyrbT6RQZLtck7vY2DGvvzciO3vQLlgKxNswqFmNiYkyDT2JiYmr9Iu+8847Zxz722GO1bl8I0fwMaefN2hk38X+rDxGVmMt9y/fzyMA2zBoeisaq1rOCCSGaqDR9MRtPpLLheAq7z2VSZrzcmeXvYsfIMB9GhnnT4waeB7G+mVUsBgUFAZCXl8fp06cxGAz06tULDw/z1m5dsmRJpefp6ekUFhbi4uICVKzootPp8PLykmJRCGG2ADcd3z8SwUtrT7BqTzwfbj3HnvOZvPOvrgS4yWVpIW5EiqJwLj2fTSfT2Hg8hb8ScrjyYmeot8PFAtGHMD+nG2ollcZi9gCXqKgoRo8eTUpKCoqi4OTkxA8//GDWvYZX9kauXr2aDz74gM8++4y2bdsCFdPmPPTQQzz88MPX8RaEEM2Z1tqKl28Op28bD57+MYq/4nMY8/YOXp0UzrhOMlpaiBtBaZmR/bFZbDqZyuZTacRlFlba3zXQxVQgtvKwb6Qob1xmF4vPPPMMgYGBfP/999ja2rJgwQIeffRRTp06VasXfP755/nhhx9MhSJA27ZtWbJkCbfddht33XVXrdoTQgiAMeG+hPs78/g3f3EoPodHV//FzjMZzBsfhp2N3JskRFOTVVDKllNpbD6VxvbT6eSVlJn22VipiWjjzrD2Xgzv4IOPs+1VWhL/lNnF4oEDB1i/fj09evQA4PPPP8fLy4v8/HwcHMyfHDc5ORmDwVBle3l5OampqWa3I4QQfxfgpuPbhyNYuuk0H2w9xzf7EzgQl817k7vSzsepscMTQlyFoiicTs3nj1Op/HEyjUPx2ZUuL3s4aBnSzpOh7b25KdgDe62s5NRQzP6bzsjIIDAw0PTc3d0dnU5Henp6rYrFoUOH8tBDD/HZZ5/RvXt3VCoVBw4c4OGHH5bpc4QQ/5jm4mjpfm08mPntYc6m5TPhvT95fmx77u4TJPcvCWFBig3l7I3JYvPJVP44lUZidlGl/R18nRja3ouh7b3p5O+MWgaoNAqzi0WVSlVpTWhFUUzbrly+z8np6r+9f/7550ydOpVevXqh0WgAKCsrY+TIkXz66afX8x6EEKKKvsEe/PZ4f2Z/f4Qt0ek8/7/jbI1O59VJ4Xg5yiUrIRpDxeCUAradTmf76XT2nM+kpMxo2m9jraZfG3eGtvdmSDsv/FzsGjFacYnZxaKiKFXWhFYUha5du5q+N2eeRU9PT9avX8+ZM2c4efIkiqLQvn37atebFkKIf8LdQctnU3vy+Z8xLPo9mj9OpTFq6Q5evTWckWE+jR2eEM1CXrGBP89mmgrEpJzKvYc+TrYMaltxeblfsDs6G7m8bGlqtTZ0XQoJCSEkJKRO2xRCiL9Tq1U82L81N4V4MPObw5xKyePhLw9ye/cWvDC+A7Yy9kWIOmU0KpxI1rPtdDrbotM5FJ9dae5DGys1vVq5MTDUkwGhnoR6O8jtIRau1mtDX4/XXnuNxx57DJ3u2vOe7d27l4yMDMaOHXvdryeEEH/XzseJ/z3ajyWRZ/ho+zm+P5jI7vOZvH5rWGOHJkSTl5RTxJ9nM9h1NoMdZzLILCittL+1hz0DQj0ZGOpJ79Zu0nvYxJiVrSvvSbyW6u5ZPHHiBIGBgdx+++1MmDCBHj16mFaEKSsr48SJE+zcuZNVq1aRnJzMypUrzX69+vTBBx/wxhtvkJycTFhYGEuXLqV///7VHrt161YGDx5cZfvJkydp165dfYcqhDCD1tqKZ0a3Y0g7L2Z9d5jE7CLu/vwAQ3zVDC0zcvE2aiHENWQVlLL7XCZ/nqsoEGP/Nu+hvY0VfYM9KgrEEE8C3WWS/KbMrGLRxcXF7C7i6u5ZXLlyJVFRUbz//vvcdddd5ObmYmVlhVarpbCw4h9Y165dmTZtGlOnTkWr1dbiLdSPb7/9lpkzZ/LBBx/Qr18/PvroI0aPHm0qfGsSHR1dqWC+VBQLISxHr1Zu/PZ4f15ae4LvDiTyxwU1t3y4mzdu70KXAJfGDk8Ii1NYWsa+mCz+PJvBn2czOZmirzStjZVaRacWzvRr40G/YA+6B7liYy3Lbt4ozCoWr7xfMTY2lmeeeYZ7772XiIgIAHbv3s2KFSt49dVXa2yjU6dOfPTRRyxbtoyoqChiY2MpKirCw8ODLl26mL10YENZvHgxDzzwAA8++CAAS5cuZcOGDXz44YdXfZ9eXl6mZQyFEJbL0VbDots6MzjUg6e++4szaQXc+sGfPNS/NU8MD8VWIzcziuar3AgH4rLZG5vDrrOZ/JWQjaFcqXRMW29H+ga706+NB71au+FkK13zNyqzisUr71d88cUXWbx4Mf/+979N2yZMmEB4eDgff/wxU6dOvWpbKpWKzp0707lz5+sMuf6VlpZy8OBBnnnmmUrbR4wYwa5du656bteuXSkuLqZDhw4899xz1V6avqSkpISSkhLT80uX+w0GQ7UTl/9Tl9qsj7ZF7UguLMfAYFfmdilnV7E/646l8tH282w4nsLCm8Po2dK1scNrNuRnonGVlhk5dkHPvpgs9sZksS/GitK9+ysd4+9iS0RrdyJauxHR2g1Px8pXASV3dashfibMbVulKIpy7cMu0+l0HDlypMpI5tOnT9OlSxfTZeWm7MKFC/j7+/Pnn3/St29f0/aFCxeyYsUKoqOjq5wTHR3N9u3b6d69OyUlJXz55ZcsW7aMrVu3MmDAgGpfZ/78+SxYsKDK9tWrV5s1GEgIUbeOZan47ryaXEPFbTf9fYyMDzSilU5GcYMxGCEuH87pVZzVq4jNU1FqrHy7mb21Qqjz5Ye7FmTQ8o2lsLCQyZMnk5ube9V5sms9HCkgIIBly5bx1ltvVdr+0UcfERAQUPtILdjf79O8NJdkddq2bVtpveuIiAgSEhJ48803aywW586dy6xZs0zP9Xo9AQEBjBgx4pqTm18Pg8FAZGQkw4cPN02ILhqH5MJyXJmLMRoNjxQZeG3Dab4/mMSOFDXni3UsGN+egaFy/3F9kp+J+lVUWs7hxBz2xWSzLzabw4m5lF4xGTaAq05Dz5audA9wpjz5JPdMHIbWxqaRIhYN8TNh7gDmWheLS5YsYdKkSWzYsIE+ffoAsGfPHs6dO8ePP/5Y2+YskoeHB1ZWVqSkpFTanpaWhre3t9nt9OnTh1WrVtW4X6vVVjuYR6PR1OuHZX23L8wnubAcl3LhrtHwxu1dmNDFn2d+PEpSThEPfvkXo8J8eGF8B1lRop7Jz0TdKCgpq7jn8Hwm+2KyOJKYU+WeQw8HLb1bu9GnlRu9W7sT7OmAWq3CYDCwfv1JtDY2kgsLUJ8/E+a2W+ticcyYMZw+fZoPP/yQU6dOoSgKEydO5JFHHrlhehZtbGzo3r07kZGR3HLLLabtkZGRTJw40ex2/vrrL3x9fesjRCFEPesf4snGJwawdNNpPv8zlt+Pp7D9TDozh4VwX79WaKxkpKewHPpiAwdis9h7Pos9MVkcS8ql3Fi5OPRxsqV3azd6t3Knd2s3WnvYy2TYwizXNStmQEAACxcurOtYLMqsWbOYMmUKPXr0ICIigo8//pj4+HgeeeQRoOISclJSkmlOyKVLl9KyZUvCwsIoLS1l1apV/PjjjzdMb6sQzZG91ppnx3ZgUvcWPPfzMQ7EZbNw/Sl+OJjISxM70ru1e2OHKJqpVH0xB2Kz2R+bxYG4LE5c0PO32pAWrnamwrBPK3cC3OykOBTXxaxiMSoqyuwGO3XqdN3BWJI777yTzMxMXnzxRZKTk+nYsSPr168nKCgIgOTkZOLj403Hl5aWMnv2bJKSkrCzsyMsLIx169YxZsyYxnoLQog60s7Hie8ejuDHQ4m8+tspTqfmc+fHexgT7sPTo9oR5G7f2CGKG5jRqHAuPZ/9sdkciM1if1wWCVlFVY5r6a4zFYe9W7vjL7dMiDpiVrHYpUsXVCoV1xo4rVKpqp2Uu6maPn0606dPr3bf8uXLKz2fM2cOc+bMaYCohBCNQa1WcXuPAIZ38OaNDdF8vS+e9UdTiDyRyr19W/LokBCc7eT+LvHPlZSVczQx11QcHozPJqew8hQnahW093WiZ0s3erR0pWdLN7ydbBspYnGjM6tYjImJqe84hBCiSXDR2fDKLeHcE9GSV9afZPvpdD7ZEcMPBxN5bGgI/+4VKBN6i1rJKSzlYFw2+2OzORiXxZFqRirbaazoGuhCj5Zu9AhypWugC44yCbZoIGYVi5cuvQohhKjQ1seRlff3Ymt0GgvXn+R0aj4L1pzg4+3n+b/BwdzRI0CWOxNVKIpCYnbRxXsNK3oOT6fmVznOw8GGHkGXew07+DnJoCrRaGo9wCUzMxN394qbuhMSEvjkk08oKipiwoQJ9O/fv84DFEIISzaorRc3BXvw3YFE3t18huTcYp775Rgfbj3HY0ODubVbC/lPvhkrNpRzLCmXQ/HZHIrL4VB8Nml5JVWOa+1pT88risMgd50MRhEWw+xi8ejRo4wfP56EhARCQkL45ptvGDVqFAUFBajVapYsWcIPP/zAzTffXI/hCiGE5bG2UjO5dyC3dvPnm33xvL/1HEk5RTz941He3XyWB25qxZ09A9DZXNcEFKKJUBSFpJwiDsXncCgum7/iszmRrK8yv6HGSkVHf+eK+w2DXOke5Iq7Q9U5d4WwFGZ/cs2ZM4fw8HBWrVrFqlWrGDduHGPGjOHTTz8FYMaMGbz22mtSLAohmi1bjRX39mvFnT0D+WpvHB9uPUdidhEL1pxg6aYzTOkTxNS+LausqSuapmJDOUeTcjkUl82h+Gz+is+pttfQw0FLt0AXugW50i3QlXB/Z+xs5L5W0XSYXSzu37+fzZs306lTJ7p06cLHH3/M9OnTUasrLq/MmDHDtKKLEEI0Z3Y2VjzYvzV39Q7ih0OJfLrjPHGZhby35Swf7zjPxM5+3NUniM4tnOVSYxNRfnH6miMJOUQl5hKVmMPxC3rK/ja5obVaRQc/J7oFVgxC6RboSgtXmd9QNG1mF4tZWVn4+PgA4ODggL29PW5ubqb9rq6u5OXl1X2EQgjRRNnZWDGlTxCTewWy8XgKH20/z+GEHL4/mMj3BxPp4OvEXX0CmdjFHwetXKK2FIqikJBVxJHEHKIScziSmMvxpFwKSqtODSe9hqI5qNWn099/M5LflIQQ4tqs1CpGh/syqqMPh+Kz+WpPPGuPJnMiWc+zPx/j5bUnGd7Bm5u7+tE/xFMGxDSgS/cZnrig52hSLkcSczmamEP23+Y1BNDZWNHRz5lOLZzpFOBC1wAX6TUUzUKtisV7770XrbbiXpvi4mIeeeQR7O0rVi4oKal6n4YQQojLVCoV3YPc6B7kxvPjOvDjoURW74vnfHoBvx65wK9HLuCq0zAm3Jdxnfzo2dIVaykc64yh3Mi59HyOJ+k5kaznxIWKr7lFVQtDjZWK9r5OFYVhCxc6t3Ah2MsBK7UUhqL5MbtYnDp1aqXnd999d5Vj7rnnnn8ekRBCNAOu9jY82L81D9zUiiOJufzvcBJrjiSTkV/CV3vj+WpvPE621gxq68XQ9l4MDPXERWfT2GE3CYqikJFfypm0PE6n5FUUhsl6TqfkU1purHK8tVpFiLcjYX5OdL5YHLbzdURrLZeThYBaFItffPFFfcYhhBDNkkqlokuAC10CXHh2THv2nM/if4eT2HQylexCg6nH0UpdcVzvVhXr/vYIcsW+md/nqCgKaXklnE7N40xqPmfS8jmblseZtPwqy+Nd4qi1pr2fEx18nejg50SYnxPBXg5SGApxFc37k0YIISyItZWam0I8uCnEg3KjwuGEbDadTGPzyTSiU/M4GJfNwbhsPth6Dit1xVx93QNdCW/hRLi/M608brzLpIqikFlQSlxmIXGZBcRlFhKfVUhsZgFn0/LJKy6r9jyVCoLcdAR7OdLB15EOfk508HUmwE3uMRSitqRYFEIIC2Slvnx/49Oj2pGQVcju85nsPZ/FnvOZJOUUcSQhhyMJOaZzdDZWdPB1oq2PI609HWjtYU9rT3tauOostogsKzeSnl9CQmY+hzNVpO6KIz2/lMTsIlOBWN0o5EvUKmjpbk+ItwMhXo6EeDsQ7OVAG08HWaNbiDoixaIQQjQBAW46Atx03NEjAIDE7EL2xWQRlZjLsaRcjl/QU1haXrHecFx2pXNtrNT4utji7WSLr7MtPk62+Djb4uVoi4tOg5OtBmc7DU521jjaaq6rsFQUhZIyI4Wl5RSUlFFYWk5+iYGsAgNZBSVkFRjILiwlM7+04mtBKam5xaTlFXN5qkIrOB1dpW2VCnydbAl019HS3Z5Adx1Bbva08bKnlYe9XEIWop5JsSiEEE1QC1cdLVx13NqtBVAxaXRMRj7HkvScScsjJqOA8+kFxGQUUFJmvNhLV2hW21prNTbWarTWajRWFd9bq1UoChgVhXJFwWis+L7MqFBcWk5BaRl/m5/abNZqFV6OWrTGItoH+eDnosPPxY4gdx1B7hXvU3oJhWg8UiwKIcQNwEqtItjLkWAvx0rbjUaFC7lFXMgpJkVfTEpuESm5JaToi0jTl6AvNqAvKiO3yECRoeJyb0mZkZIyI9e7zIKtRo29jTX2Wmtc7W1w02lws9fiZq/B1d4Gd3sbXHU2pp5OdwctxvIy1q9fz5gxndFoNP/wb0MIUZekWBRCiBuYWq0y9UJeS2mZEX2xgWJDOaVlRkrLjRjKFErLyzGUK6hVKqzUFSO4rVQq1CoVajXobKyxt7FCp7XGTmN1XZexjTXfliiEaGRSLAohhADAxlqNh4O2scMQQlgYKRYtiKJU3PCj1+vrpX2DwUBhYSF6vV4u8zQyyYXlkFxYBsmD5ZBcWIaGyMOleuNS/VETKRYtSF5exR1CAQEBjRyJEEIIIZqLvLw8nJ2da9yvUq5VTooGYzQauXDhAo6OjvUyaaxerycgIICEhAScnJzqvH1hPsmF5ZBcWAbJg+WQXFiGhsiDoijk5eXh5+eHWl3zOvTSs2hB1Go1LVq0qPfXcXJykg8ACyG5sBySC8sgebAckgvLUN95uFqP4iU1l5FCCCGEEKLZk2JRCCGEEELUSIrFZkSr1TJv3jy0Wpkao7FJLiyH5MIySB4sh+TCMlhSHmSAixBCCCGEqJH0LAohhBBCiBpJsSiEEEIIIWokxaIQQgghhKiRFItCCCGEEKJGUiwKIYQQQogayQouFqS+l/sTQgghhLhElvtrgi5cuEBAQEBjhyGEEEKIZiQhIeGqyw1LsWhBHB0dAept0XCDwcDGjRsZMWIEGo2mztsX5pNcWA7JhWWQPFgOyYVlaIg86PV6AgICTPVHTaRYtCCXLj3X16LhBoMBnU6Hk5OTfAA0MsmF5ZBcWAbJg+WQXFiGhszDtW59k2JRCCGEEMIClJYZScwuJD6rkNj0PHbEqRltAQvtSbEohBBCCNEAFEUhp9BAfFYhcVmFJGQVEp9ZSFxWAQlZRVzILaJybagmq9CAj41NY4UMSLEohBBCCFFnDOVGLuQUEZ9V0UMYn1lY6fu8krKrnq+zsSLQTUcLF1vKclNBehaFEEIIIZqW3CIDCVmFxF1ZCGYVEJ9VyIWcYsqNVy/wvJ20BLrpCHSzJ9BNR5C7jgA3HYFuOjwcbFCpVBgMBtavX4+7g7aB3lXNmnSxuH37dt544w0OHjxIcnIyP//8MzfffLNpv6IoLFiwgI8//pjs7Gx69+7N+++/T1hYmOmYkpISZs+ezddff01RURFDhw7lgw8+qDSEPDs7m8cee4xff/0VgAkTJvDuu+/i4uJiOiY+Pp7/+7//Y/PmzdjZ2TF58mTefPNNbBq561gIIYQQtaMoCtmFBmIzC4jNKCA2s/Di1wLiMgvJLTJc9XyttZoANx1BbpeLwCD3iq8BbjpsNVYN9E7qRpMuFgsKCujcuTP33XcfkyZNqrJ/0aJFLF68mOXLlxMaGsrLL7/M8OHDiY6ONg0TnzlzJmvWrOGbb77B3d2dJ598knHjxnHw4EGsrCqSOXnyZBITE/n9998BmDZtGlOmTGHNmjUAlJeXM3bsWDw9Pdm5cyeZmZlMnToVRVF49913G+hvQwghhBDmulpBGJtRgL746peLPRxsLvYOXny4X+4l9HTQolbfOItrNOlicfTo0YwePbrafYqisHTpUp599lluvfVWAFasWIG3tzerV6/m4YcfJjc3l88++4wvv/ySYcOGAbBq1SoCAgLYtGkTI0eO5OTJk/z+++/s2bOH3r17A/DJJ58QERFBdHQ0bdu2ZePGjZw4cYKEhAT8/PwAeOutt7j33nt55ZVX6mUaHCGEEEJcXU0FYVxmATFmFIS+zrYEueto5WFPkLs9Ld3tTT2E9tomXULVyg37TmNiYkhJSWHEiBGmbVqtloEDB7Jr1y4efvhhDh48iMFgqHSMn58fHTt2ZNeuXYwcOZLdu3fj7OxsKhQB+vTpg7OzM7t27aJt27bs3r2bjh07mgpFgJEjR1JSUsLBgwcZPHhwtTGWlJRQUlJieq7X64GKuZUMhqt3cV+PS23WR9uidiQXlkNyYRkkD5ajKeaiqLScmMwCzqcXcD6jgJiMi9PPZBZesyD0cdIS5K6j5cUisOXF7wNcddjZ1HS5WKn3v5+GyIO5bd+wxWJKSgoA3t7elbZ7e3sTFxdnOsbGxgZXV9cqx1w6PyUlBS8vryrte3l5VTrm76/j6uqKjY2N6ZjqvPrqqyxYsKDK9o0bN6LT6a71Fq9bZGRkvbUtakdyYTkkF5ZB8mA5LC0XigK5pZBarCKtCNKKVKRe/JpdevVLvi42Cp62Ch62XP5qp+ChBRurMqCg4sA8KMuDs7Fwtt7fkXnqMw+FhYVmHXfDFouX/H1WckVRrjlT+d+Pqe746znm7+bOncusWbNMzy8tuzNixIh6W8ElMjKS4cOHy6z8jUxyYTkkF5ZB8mA5GjsXxYZyYjMLTb2El3oKYzIKKCgtr/E8V52G1h72tPa0N/UOXruH0HI1RB4uXdG8lhu2WPTx8QEqev18fX1N29PS0ky9gD4+PpSWlpKdnV2pdzEtLY2+ffuajklNTa3Sfnp6eqV29u7dW2l/dnY2BoOhSo/jlbRaLVpt1SHxGo2mXn9A67t9YT7JheWQXFgGyYPlqM9cKIpCZkEpZ1LzOZde8TifXsC59HyScv4+MfVlVmoVQW46Wns60MbTnjaeDrTxsqe1hwOu9jfm7CP1mQdz271hi8VWrVrh4+NDZGQkXbt2BaC0tJRt27bx+uuvA9C9e3c0Gg2RkZHccccdACQnJ3Ps2DEWLVoEQEREBLm5uezbt49evXoBsHfvXnJzc00FZUREBK+88grJycmmwnTjxo1otVq6d+/eoO9bCCGEsBSKopCRX8qZtDzOpOZzJi2P06n5nEnNI7uw5vvlnGytaePlUFEMejrQ+mJhGOimw8Za3YDvQEATLxbz8/M5e/byXQUxMTEcPnwYNzc3AgMDmTlzJgsXLiQkJISQkBAWLlyITqdj8uTJADg7O/PAAw/w5JNP4u7ujpubG7NnzyY8PNw0Orp9+/aMGjWKhx56iI8++giomDpn3LhxtG3bFoARI0bQoUMHpkyZwhtvvEFWVhazZ8/moYcekpHQQgghbnimojA1jzNp+Zy++PVqRaFKBQGuOoK9HGjtYW8qDlt72uNub3PNW8ZEw2nSxeKBAwcqjTS+dP/f1KlTWb58OXPmzKGoqIjp06ebJuXeuHGjaY5FgCVLlmBtbc0dd9xhmpR7+fLlpjkWAb766isee+wx06jpCRMm8N5775n2W1lZsW7dOqZPn06/fv0qTcothBBC3CiqLQov9hherSgMdNMR4uVIiLcDod4OhHg50sbToUneS9gcNelicdCgQShXWTNRpVIxf/585s+fX+Mxtra2vPvuu1edPNvNzY1Vq1ZdNZbAwEDWrl17zZiFEEKIpqCotJwTKQWcStFzMjmP6JQ8TqXor1oUBrnpCPZyrCgIpSi8YTTpYlEIIYQQ/4zRqBCfVcipi8XgiQu5/HXeipl7/qh2oMmlojDE25EQLwdCvSt6DNt4OjS5ZeyEeaRYFEIIIZqJ7IJSTqXkEZ2i51RKHidT8jiTmkdhlSlpKu4X9HDQ0t7XkbbejrTzdaKdjyPBXlIUNjdSLDYjOYUG1sWrGWIol6kphBDiBlZuVIjNLOD4BT3HL+RyKrmi1zBVX1Lt8VprNaHejrTzcSTEy56cuBPcM34IPq4ODRy5sERSLDYjC9aeZGOSmokf7GHpv7rQqYVLY4ckhBDiHyo2lHMmNZ/jF3IvF4cp1fUWVghws6OdjxPtfRxp6+NEO19HWrrbY6Wu6E00GAyszzmOu0PVeYBF89TgxWJ0dDRff/01O3bsIDY2lsLCQjw9PenatSsjR45k0qRJ1U5ULf65CZ192X4qmfMZBdzywS7+b3AwM4YEo7GSOauEEKIpyC0ycDJZbyoKT1zQczYtnzJj1ZsL7TRWtPN1JMzPifa+TrTzcaKtjyMOWuknErXTYP9i/vrrL+bMmcOOHTvo27cvvXr14uabb8bOzo6srCyOHTvGs88+y4wZM5gzZw4zZ86UorGODW7ryTOdy/mzuAXrjqXwzh9n2HwqlcV3dCHU2/HaDQghhGgwOYWlHE3KJSoxl6OJuRxPziUhq6jaY111GsL8nAnzc6KDnxNhfk608nAw9RYK8U80WLF4880389RTT/Htt9/i5uZW43G7d+9myZIlvPXWW/z3v/9tqPCaDXsNLJ3YiZHhvjz/yzGOJekZ9+5OZo8I5YGbWssHixBCNIL8kjKOJeUSlZhTURwm5RKXWVjtsf4udoT5ORHm52wqDH2dbWUSa1FvGqxYPHPmDDY21163MSIigoiICEpLSxsgquZrQmc/erdy4+kfo9ganc7C9afYdCKNN2/vTKC7rrHDE0KIG1ZRaTknki/3GB5JzOF8RkG109S0dNcR3sKFcH8nOl4sDl10N+YayMJyNVixaE6h+E+OF7Xn7WTLF/f25Jv9Cby89gT7YrMY9fZ2nh3bnsm9AuW3VCGE+IeMRoVz6fn8FZ/DXwnZ/BWfw5m0fMqrucfQ38WOcH9nwls407mFC+H+zjjrZOYK0fhqXSxGRUWZfWynTp1q3Ldv3z62bt1KWloaRqOx0r7FixfXNixxnVQqFf/uFchNwR48+f0R9sVk8ezPx9h4PJXXJ3XCx9m2sUMUQogmI6ewlMMJORyKz+Gv+GwOJ+SQV1xW5TgPBy2dW1wuDDv6O+PpKPfpC8tU62KxS5cu1+xxUhQFlUpFeXn1w/YXLlzIc889R9u2bfH29q7UnvRmNY4ANx3fPNSHz/+MYdGGaLadTmfk0u28ODGMCZ39JC9CCPE35UaF6JQ8U4/hofhszqcXVDnOVqOmUwsXugW60iXAhc4Bzvg4yT2GoumodbH4008/MXv2bJ566ikiIiKAikEpb731FosWLaJr167XbOPtt9/m888/59577611wKL+qNUqHuzfmoGhnsz67ghHk3J5/JvDbDyeyks3d8TNXm4NEEI0X/klZfwVn83+2GwOxGZxOCGn2rkMW3nY0zXAha6BLnQNdKWtj6NMUSaatFoXiwsXLuSdd95hzJgxpm2dOnUiICCA559/noMHD16zDbVaTb9+/Wr70qKBhHg78tP0vry/5Szvbj7LuqPJ7I3J4vVJ4Qxt793Y4QkhRINIzyvhQGwW+2KzOBCbzYlkfZV7DR201nQxFYYudAlwlV+sxQ2n1sXi0aNHadWqVZXtrVq14sSJE2a18cQTT/D++++zdOnS2r68aCAaKzUzh4UytJ03s747zJm0fB5YcYA7erTg+XEdcLSVm66FEDcORVGIzSxkf2wW+2OyOBCXTUxG1UvKLVzt6NnSjR4tXekR5Eawl8xlKG58tS4W27dvz8svv8xnn32GrW3F4IeSkhJefvll2rdvb1Ybs2fPZuzYsbRp04YOHTpUWaf4p59+qm1Yop6Et3BmzYybeGtjNJ/ujOG7A4n8eTaTN27vRN82Ho0dnhBCXBdFUTibls/u85nsOZ/JvphsMvIrr5usUkFbb0d6tnSjZys3erZ0xdfZrpEiFqLx1LpYXLZsGePHjycgIIDOnTsDcOTIEVQqFWvXrjWrjRkzZrBlyxYGDx6Mu7u73ORr4Ww1Vjw7tgPD2nvz5PdHSMwuYvIne7mvX0ueHtUOW41VY4cohBBXpSgKMRkF7D6fye5zmew5n1WlOLSxUtM5wJkeLd3o1dKNboGuMnWNEFxHsdirVy9iYmJYtWoVp06dQlEU7rzzTiZPnoy9vb1ZbaxcuZIff/yRsWPH1jpg0Xh6t3bn95kDeGXdSb7eF88Xf8ay7XQ6i+/oQpcAl8YOTwghTBRFIT6rkN3nMk29h6n6ysWh1lpNj5auRLR2p1crdzq1cJZffoWohtnF4v3338/bb7+No6MjOp2OadOmXfeLurm50aZNm+s+XzQeB601r94azogO3jz9YxTn0wuY9OEupg9qw4whIdhYy4g/IUTjSMy+ojg8l8mF3OJK+22s1HQNdCGijTsRrd3pEuiC1lqKQyGuxexiccWKFbz22ms4Ojr+4xedP38+8+bN44svvkCnk6XlmqLB7bzY+MQAnv/fcdYcucC7m8+y+VQai+/oQluff/5vRAghriWroJSdZzP480wGu85nkJBVVGm/xkpFlwAXIlq706e1O92CXKXnUIjrYHaxqFS3aOV1eueddzh37hze3t60bNmyygCXQ4cO1dlrifrjorPh3X93ZWSYN8/9cozjF/SMf3cnT44I5cH+rWWEoBCiTpWUlXMwNpsdZzPYcSad4xf0ldZTtlKr6NTCmYjW7kS0cad7kCs6mwZb1VaIG1atforqaiDKzTffXCftCMswrpMfvVq68cxPR9l8Ko1XfzvFppOpvHl7Z4LczbuPVQgh/k5RFE6n5rPjTDo7zmSwNyaTYkPl5WHb+TjSP8SDvsEe9GzphoNWikMh6lqtfqpCQ0OvWTBmZWVds5158+bV5mVFE+DlZMtnU3vw3YEEXlxzgv2x2Yx+ewf/HdOeu3oHyoh3IYRZ0vNK2BuXyo4zGew8k0FaXuVBKZ6OWvoHe3BTiAc3BXvg5STr1wtR32pVLC5YsABnZ+c6DWD69Om8+OKLeHjInH1NnUql4s6egfRt48Hs74+wNyaL5345xsYTqSya1AkfZ/lQF0JUVlpm5EBcFn+cSOG3I1Zc2L2t0n5bjZperdwZEFJRILb1dpRfPoVoYLUqFv/1r3/h5eVVpwGsWrWK2bNnS7F4Awlw0/H1Q334Ylcsi34/xfbT6YxYso0XJ3ZkYhc/+aAXoplLzi1ia3Q6W6PT+PNsJvklZRf3VHw2hPk50T/Ek/4hHnSXQSlCNDqzi8X6+g++LgfOCMuhVqt44KZWDAz1YNZ3R4hKzGXmt4fZcDyFl2/uiLuDtrFDFEI0EEO5kUNx2Wy5WCCeSsmrtN/d3oYBIe44FiQyfdJQfFzkXmchLEmjjIYWzUewlyM//qcvH249xzt/nOG3Yynsj83i1Vs7MbyDd2OHJ4SoJ6n6YrZFp7MlOo2dZzLIM/UeViyj1yXAhcFtvRjU1pOOfs6Ul5exfn0C7vY2jRi1EKI6ZheLRqPx2gddh7y8vGsfJJo0jZWax4aGMKSdF098e5gzafk8tPIAt3dvwQvjO+BoK8tpCdHUGY0KhxNz+ONkKltOpXMiWV9pv5u9DQNDPRnU1pP+IZ64/a0oLC9vyGiFELVR6+U2CgoKeP755+nbty/BwcG0bt260sPSzJ8/H5VKVenh4+Nj2q8oCvPnz8fPzw87OzsGDRrE8ePHK7VRUlLCjBkz8PDwwN7engkTJpCYmFjpmOzsbKZMmYKzszPOzs5MmTKFnJychniLTUZHf2fWzLiJaQNao1LB9wcTGbV0B7vOZjR2aEKI61BYWsaG4yk89f0Rei3cxK0f7OL9Lec4kaxHpYLOAS7MHBbCL//Xj/3PDmPJnV2Y2MW/SqEohLBstZ6Q6sEHH2Tbtm1MmTIFX1/fWt3LqFarr3m8SqWirKzsqsfUVlhYGJs2bTI9t7K6fLP0okWLWLx4McuXLyc0NJSXX36Z4cOHEx0dbVqtZubMmaxZs4ZvvvkGd3d3nnzyScaNG8fBgwdNbU2ePJnExER+//13AKZNm8aUKVNYs2ZNnb6Xps5WY8V/x7RnWHtvZn9/hPisQiZ/upd7+7bk6VHtsLORG9mFsGTJuUX8cTKNTSdT2XUuk9Kyy1edHLXWDGzrydD2XgwI8ZR7k4W4QdS6WPztt99Yt24d/fr1q/WL/fzzzzXu27VrF++++2693BtpbW1dqTfxEkVRWLp0Kc8++yy33norULGsobe3N6tXr+bhhx8mNzeXzz77jC+//JJhw4YBFSO4AwIC2LRpEyNHjuTkyZP8/vvv7Nmzh969ewPwySefEBERQXR0NG3btq3z99TU9Wrlxm+P9+eV9SdZvTee5bti2X46nbfu6EzXQNfGDk8IcZGiKBxL0rPpZCqbTqZy/ELly8sBbnYMa+/NsPbe9GzpJuvDC3EDqnWx6Orqipub23W92MSJE6tsO3XqFHPnzmXNmjXcddddvPTSS9fV9tWcOXMGPz8/tFotvXv3ZuHChbRu3ZqYmBhSUlIYMWKE6VitVsvAgQPZtWsXDz/8MAcPHsRgMFQ6xs/Pj44dO7Jr1y5GjhzJ7t27cXZ2NhWKAH369MHZ2Zldu3bVWCyWlJRQUnJ5wlm9vuJD2GAwYDAY6vqvwdRmfbR9PWzUsGBcO4a29eC/Px/nfEYBkz7cxcMDWvHooDY39H86lpaL5kxyUVWxoZzd57PYHJ3OllPppF4xMbZKBV0DXBjS1pMhbT0J9rK/fMVIKcdguL6bDyUPlkNyYRkaIg/mtl3rYvGll17ihRdeYMWKFeh0uloHdsmFCxeYN28eK1asYOTIkRw+fJiOHTted3s16d27NytXriQ0NJTU1FRefvll+vbty/Hjx0lJSQHA27vyqFxvb2/i4uIASElJwcbGBldX1yrHXDo/JSWl2vknvby8TMdU59VXX2XBggVVtm/cuPEf/d1eS2RkZL21fb0ebws/xqo5mKHmw20x/Lr/PHcHl+N3g8+gYYm5aK6aey4Ky+B4toqoLBWnclSUGi/fMmSjVmjnotDRVaGDq4KjJgPyMzhz8CRn6jiO5p4HSyK5sAz1mYfCwkKzjqt1sfjWW29x7tw5vL29admyJRpN5ZGshw4duur5ubm5LFy4kHfffZcuXbrwxx9/0L9//9qGYbbRo0ebvg8PDyciIoI2bdqwYsUK+vTpA1SdQ1JRlGveW/n3Y6o7/lrtzJ07l1mzZpme6/V6AgICGDFiBE5OTld/Y9fBYDAQGRnJ8OHDq+TNEtwO/HYshXlrTpJUaGDxcQ2PDwnmwZtaYqW+sSbytvRcNCfNORcp+mL+OJnGxpNp7IvJpsx4+TYgHyctQ9p5MrSdF71buqKt54mxm3MeLI3kwjI0RB4uXdG8lloXizfffHNtTzFZtGgRr7/+Oj4+Pnz99dfVXpaub/b29oSHh3PmzBnTe0lJScHX19d0TFpamqm30cfHh9LSUrKzsyv1LqalpdG3b1/TMampqVVeKz09vUqv5ZW0Wi1abdUbwDUaTb3+gNZ3+//EhK4B9An2ZO6PR/njVBpvRp5hy+kM3rq9My09brxuRkvORXPTXHJxPj2fDcdT2XA8hcMJOZX2hXo7MDLMh5FhPoT5OTXKakvNJQ9NgeTCMtRnHsxtt9bF4rx582odzCXPPPMMdnZ2BAcHs2LFClasWFHtcT/99NN1v8a1lJSUcPLkSfr370+rVq3w8fEhMjKSrl27AlBaWsq2bdt4/fXXAejevTsajYbIyEjuuOMOAJKTkzl27BiLFi0CICIigtzcXPbt20evXr0A2Lt3L7m5uaaCUpjPy9GWT6f24PsDiby49gQH47IZ/fYO/jumHXf3CZLlAoWohUsDVDYcT2HD8RTOpOVX2t8t0IURFwvEVjfgL2RCiH+u1sUiQE5ODj/88APnzp3jqaeews3NjUOHDuHt7Y2/v3+N591zzz0N/h/97NmzGT9+PIGBgaSlpfHyyy+j1+uZOnUqKpWKmTNnsnDhQkJCQggJCWHhwoXodDomT54MgLOzMw888ABPPvkk7u7uuLm5MXv2bMLDw02jo9u3b8+oUaN46KGH+Oijj4CKqXPGjRsnI6Gvk0ql4o6eAfQNdmf290fYcz6L5/93nI0nUll0Wyd8ne0aO0QhLFZZuZH9sdlsOJ5C5IlUknKKTPus1Soi2rgzMsyHER288XKybcRIhRBNQa2LxaioKIYNG4azszOxsbE89NBDuLm58fPPPxMXF8fKlStrPHf58uX/JNbrkpiYyL///W8yMjLw9PSkT58+7Nmzh6CgIADmzJlDUVER06dPJzs7m969e7Nx40bTHIsAS5YswdramjvuuIOioiKGDh3K8uXLK83X+NVXX/HYY4+ZRk1PmDCB9957r2Hf7A2ohauO1Q/2YfmuWF7//RQ7zmQwYsl2XpwYxs1d/KWXUYiLysqN7IvJYu3RZDYcSyGzoNS0z05jxaC2nowM82FwOy+c7eTSohDCfLUuFmfNmsW9997LokWLKhVUo0ePNvXGXcsff/zB0KFDq9333nvv8eijj9Y2rBp98803V92vUqmYP38+8+fPr/EYW1tb3n33Xd59990aj3Fzc2PVqlXXG6a4CrVaxf03tWJAqCdPfn+EIwk5PPHtETYcS+WVWzrKxL+i2So3KuyNyWRdVDK//61AdNFpGNbem5FhPvQP8cC2ngeoCCFuXLUuFvfv32+61Holf3//q04Tc6VJkyYRGRlJz549K21funQpL7zwQp0Wi+LGEezlwI+PRPDh1nO8/ccZfj+ewoG4LBbeEs6IsKqTrgtxIyo3KuyLyWL90WR+O5ZCRv7lORBddBpGhfkwtpMvfVq7o7G6cecqFUI0nFoXi7a2ttUOtY6OjsbT09OsNpYsWcKYMWPYtm0bHTp0AODNN9/kpZdeYt26dbUNSTQj1lZqZgwNYXA7L5787gjRqXlM+/Igk7q1YN6EDjjZyuU1ceMpNyociM1i3dFk1h+tXCA6210uECPaSIEohKh7tS4WJ06cyIsvvsh3330HVFzGjY+P55lnnmHSpElmtXHfffeRmZnJiBEj2LlzJ99++y0LFy7kt99+k9HDwiwd/Z35dUY/Fkee5uPt5/nxUCK7z2Xwxu2d6Rfs0djhCfGPGY0KB+KyWRd1gd+OpZCWV7lAHBnmzZhwX/oFe0iBKISoV7UuFt98803GjBmDl5cXRUVFDBw4kJSUFCIiInjllVfMbmf27NlkZmbSo0cPysvL2bhxY6Xl8oS4Fq21FXNHt2d4e2+e/P4IcZmF3PXpXqZGBPHM6PbY2cg9WqJpMRoVDsZnsy4qmd+OJZOqv1wgOtlaMzLMhzGdfOnXxuOGXg5TCGFZal0sOjk5sXPnTjZv3syhQ4cwGo1069bNNI1MTd55550q23x9fdHpdAwYMIC9e/eyd+9eAB577LHahiWasR4t3Vj/WH9e/e0kq/bEs2J3HNvPZPDWHZ3pFuh67QaEaERGo8JfCdmsjUrmt6MppOiLTfscba0Z0cGHcZ0qehClQBRCNIbrmmcRYMiQIQwZMsTs45csWVLtdisrK/7880/+/PNPoOKythSLorbstda8fHM4wzv4MOeHI8RkFHDbh7t4ZGAbZg4Llf9khUWpKBBzTD2IyblXFIhaa4aHeZsKRK219JALIRqX2cViUVERf/zxB+PGjQMq1jUuKbl8icTKyoqXXnoJW9vqJ3iNiYn5h6EKcW0DQz3ZOHMg8349xi+HL/DB1nNsiU5n8R2dae9b9+ttC2EuRakoENdHJbP+aDIXrigQHbTWjOhQcQ9i/1ApEIUQlsXsYnHlypWsXbvWVCy+9957hIWFYWdXsZLGqVOn8PPz44knnqifSIUwk7NOw9J/dWVkmA///fkoJ5P1THhvJ08MD2Va/9ZYy2AA0UAUReFwQg7rL45ivnIlFXsbK4Z38GZsJz+ZB1EIYdHMLha/+uqrKoXg6tWrad26NQCrVq3i/fffr7FYfO2115gxYwb29tdee3Tv3r1kZGQwduxYc8MToorR4b70aOnG3J+OsulkKot+j2bTiVTeuqOLrIEr6o2iKEQl5rLuaDLropKrFIjDOngzNtyXAaGeUiAKIZoEs4vF06dPExoaanpua2uLWn25h6ZXr1783//9X43nnzhxgqCgIG6//XYmTJhAjx49TPMylpWVceLECXbu3MmqVatITk6+6rKBQpjL01HLJ/d054eDiby45gSH4nMY8/YO5o5px929g1CrZblA8c8pisLRpFzWRSWz7mgyidmXC0SdjRXD2ldcYh7UVgpEIUTTY3axmJubi7X15cPT09Mr7TcajZXuYfy7lStXEhUVxfvvv89dd91Fbm4uVlZWaLVaCgsLAejatSvTpk1j6tSpaLWyhJuoGyqVitt7BNA32IOnvj/CrnOZvPC/42w8nsqi2zrh52LX2CGKJkhRFI4l6St6EI9eICHrcoFop7FiaHsvxnXyZVBbLykQhRBNmtnFYosWLTh27Bht27atdn9UVBQtWrS4ahudOnXio48+YtmyZURFRREbG0tRUREeHh506dIFDw+ZTFnUH38XO1Y90JuVu2N59bdT7Dybwcil25k/Poxbu/mjUkkvo7g6RVE4fkFvusQcn1Vo2mensWJIey/GhVcUiDLPpxDiRmF2sThmzBheeOEFxo4dW2XEc1FREQsWLDD7HkOVSkXnzp3p3Llz7aIV4h9Sq1Xc268V/UM9efK7IxxOyOHJ74+w4XgKC28Nx8NBerRFZYqicCJZz7qLo5hjMy8XiLYaNUPaeTE23I/B7TzR2Vz3bGRCCGGxzP5k++9//8t3331H27ZtefTRRwkNDUWlUnHq1Cnee+89ysrK+O9//1ufsQpRZ9p4OvDDIxF8tP08SzedZuOJVA7GZfPKLeGM6ujT2OGJRqYoCieT81h/tOIexJiMAtM+rfXFArGTL0PaeUmBKIS44Zn9Keft7c2uXbv4z3/+wzPPPIOiKEBFL+Hw4cP54IMP8Pb2rrdAhahr1lZq/m9wMIPaejLr2yNEp+bxyKqD3NrNn3njw3C20zR2iKIBVdyDmMv6o8n8diylSoE4uO3lAtFeKwWiEKL5qNUnXqtWrfj999/Jysri7NmzAAQHB+Pm5lYvwQnREML8nPl1Rj+WRJ7h4+3n+OlQErvPZbLotk70D/Fs7PBEPbo0SGVNnJq3lu4k/opBKjbWagaFejK2ky9D23vjIAWiEKKZuq5PPzc3N3r16lXXsQjRaLTWVjwzuh3DO3gx67sjxGUWMuWzfdwTEcQzo9vJpcYbiGmam6MVazFXDFJRA0WmHsQxF3sQpUAUQoh/sDa0EDei7kFu/PZ4f15df4ov98Sxcncc20+n89YdXege5NrY4YnrpCgKRxJzL66kUnkeRFuNmnaOZdw3rAvDwnzlErMQQvyNfCoK8Tc6G2teurkjwzt4M+eHKGIzC7l92S4eHtiGmcNCZN3eJqLcqHAoPpsNx1L47VjlpfYuTXMzpqMvN7VxYeumjYwJ90GjkY9EIYT4O/lkFKIGA0I92fDEABb8epyf/kriw63n2HIqjcV3dKGDn1NjhyeqUVpmZNe5DDYcTyXyRAoZ+aWmfTobq4vT3FSeB9FgMDRWuEII0SRIsSjEVTjbaVh8ZxdGhHnz35+PcSolj4nv72TmsFAeHtAaayv1tRsR9aqgpIyt0elsOJ7CllNp5JWUmfY52VoztL03I8O8ZSUVIYS4TlIsCmGGUR196R7kxn9/PkrkiVTe2BDNppOpvHV7Z1p7OjR2eM1OVkEpm06msvF4CtvPZFBaZjTt83TUMqKDN6M6+tCntTsaKeiFEOIfkWJRCDN5Omr5eEp3fjqUxPxfj/NXfA5j3tnB3NHtmdInCLValgusTxdyith4PIUNx1PZG5OJUbm8L8hdx8gwH0aG+dA1wEVyIYQQdUiKRSFqQaVSMal7C/q0cWfOD0f482wm8349zsYTKSy6rTP+LnaNHeINw2hUOJKYw+ZTaWw6mcbJZH2l/e19nRgV5sPIjt609XaUtb2FEKKeSLEoxHXwd7Hjy/t78+WeOF797SR/ns1k1JLtzJsQxqRu/lK4XKeCkjJ2nMngj5OpbIlOqzRARa2C7kGujAzzYUQHHwLddY0YqRBCNB9SLApxndRqFVP7tqR/iAdPfn+Ev+JzmP39ETYcT+HVW8PxcNA2dohNQmJ2IX+cTOOPU2nsOZdJafnl+w8dtNYMDPVkaHsvBrX1ws3ephEjFUKI5kmKxTr2wQcf8MYbb5CcnExYWBhLly6lf//+jR2WqEetPR34/uEIPtp+nqWbThN5IpWDcdksvKUjozr6NnZ4FqfYUM6+mCx2nEln++kMolPzKu0PdNMxtL0Xw9p707OlGzbWMkBFCCEakxSLdejbb79l5syZfPDBB/Tr14+PPvqI0aNHc+LECQIDAxs7PFGPrK3U/N/gYAa39WLWd4c5lZLHI6sOcUtXf+ZPCMPZTtPYITYaRVE4nZrP9tPpbD+Tzr6YLEquGL2sVkGPIDeGtPdiWHsv2ng6yGV8IYSwIFIs1qHFixfzwAMP8OCDDwKwdOlSNmzYwIcffsirr77ayNGJhtDBz4n/PdqPtzedYdm2c/z8VxK7z2Wy6LZODAj1bOzw6p2iVAxRziooZefZDHacyWDHmXRS9SWVjvNxsqV/iAcDQj25KdgDV7m8LIQQFkuKxTpSWlrKwYMHeeaZZyptHzFiBLt27ar2nJKSEkpKLv8nqtdXjPY0GAz1sqrEpTZlxYr6pQaeGNqGQSHuPPXjMeKyCrnn83242Gm41GGmKGAwWDHv8BbqqxPtYt2GgnLF95W3X/xz8Xjliu8rb6eG7Ve2dzW2GjW9WrpyU7AHNwW7E+xpX6n3sDH/TcrPhWWQPFgOyYVlaIg8mNu2FIt1JCMjg/Lycry9vStt9/b2JiUlpdpzXn31VRYsWFBl+8aNG9Hp6m+kZ2RkZL21LSr7v2BYE69mR4qanKK//1CqoOzG/TD20ym0c1Fo56zQ2qkMjToVclI5cwDONHZw1ZCfC8sgebAckgvLUJ95KCwsNOs4KRbr2N/vtVIUpcb7r+bOncusWbNMz/V6PQEBAYwYMQInp7pfe9hgMBAZGcnw4cPRaJrvPXQN7RYgLa+E3CuKxbKyMnbv2kVE375YW9fPj6GKy/8eK76/uP3SV1QVO/6+H9UV31f+N13ddtXftqNSYWOlxtG2aXy8yM+FZZA8WA7JhWVoiDxcuqJ5LU3j07wJ8PDwwMrKqkovYlpaWpXexku0Wi1abdXpVTQaTb3+gNZ3+6IqfzcN/lc8NxgMxOigvZ+L5MJCyM+FZZA8WA7JhWWozzyY264Ui3XExsaG7t27ExkZyS233GLaHhkZycSJE81q49K9YeZW+rVlMBgoLCxEr9fLB0Ajk1xYDsmFZZA8WA7JhWVoiDxcqjeUa9x8LsViHZo1axZTpkyhR48eRERE8PHHHxMfH88jjzxi1vl5eRXzzQUEBNRnmEIIIYQQJnl5eTg7O9e4X4rFOnTnnXeSmZnJiy++SHJyMh07dmT9+vUEBQWZdb6fnx8JCQk4OtbPOreX7olMSEiol3sihfkkF5ZDcmEZJA+WQ3JhGRoiD4qikJeXh5+f31WPUynX6nsUNwy9Xo+zszO5ubnyAdDIJBeWQ3JhGSQPlkNyYRksKQ+yjpYQQgghhKiRFItCCCGEEKJGUiw2I1qtlnnz5lU7XY9oWJILyyG5sAySB8shubAMlpQHuWdRCCGEEELUSHoWhRBCCCFEjaRYFEIIIYQQNZJiUQghhBBC1EiKRSGEEEIIUSMpFoUQQgghRI1kuT8LYjQauXDhQr0t9yeEEEIIccmVy/2p1TX3H0qxaEEuXLhAQEBAY4chhBBCiGYkISGBFi1a1LhfikUL4ujoCFBvi4YbDAY2btzIiBEj0Gg0dd6+MJ/kwnJILiyD5MFySC4sQ0PkQa/XExAQYKo/aiLFogW5dOnZycmp3opFnU6Hk5OTfAA0MsmF5ZBcWAbJg+WQXFiGhszDtW59k2JRCCGEEPXCaFQoKTNSZCin+OLDqIBaBWqVCrVKhUoFarUKq4vfm8oWFaguPru03UqtQqVSYXXF8VbqiqPKjAplRoXycgWD0UhZuYKh3FixzWjEUK5UbDMaubx43eXXVKlUF79W7FEUuHSUoijUtNzdlWVWuVGpeJ2/vb6h3Ei58fLrX7mvrLwiNqNSEb/RqFBuVCgtKycmRs2g0jKcG7lol2JRCCGEaCYUpaJ4qyjcLn4tK6eo9OLzsnIKikrZn64i70AiBiOXjzM9Ko4rNpRTdHFfyRXbK9oqp7jMSGmZsbHfchOnpthgxLmRo5BiUQghhGhiFEUhr6SMrPxSMvJLyDB9vfjIKyWzoITMglIKS8pNxV2xwdzizQrOnqjTmG2s1Gg1aqzUKoxGBaMCRkW5+ODiNuWK3rx/9nrW6ooeSI2VGmsrFdZqNRorFdZWFb2SysXXMCqK6bUu9SBe6mm8RKW63OOouqIv8cr+RkWpeE1rKzXWV7yuRl3xnq2tLm6rJiYrtQprtQr1FV9VisL5c+fQWjf+LIdSLAohhBCNrNyokFNYSkZ+KZn5JaTnl5B5RQGYmV9KVmEpuYUGcooM5BYZKDf+s2rKWq3CVmN18aE2fdVaqcnPzaKFrzd2Ntam7XamY63QWlccb1fN+bZXHGdrffm5lfqfTQl3qZgsVxSMRkzfKxe/t7pYmFUUYaomPwWdwWBgveEM9trGL9UaPwIhhBDiBmA0KmQVlpKqLyan0EBhaXnFvXql5RSWllFQWo6+2IC+qAx9sYHcQoOpVzCroITrqf3sNFZ4ONrg4aA1PTwdbPBw1OJur8XdwQYHrXVFEWhdUbTZ2VQUcdZW1fdYGQwG1q9fz5gxXS1qgItarUKNSgqXRiB/50IIIUQ1FEVBX1xGTmEp2YUGsi/27GVffJ5TWEp6Xgmp+mJS9SWk5RVjKP9nvX2uOg3uDlo8HGxwd9Di6aDF3b6i+HPV2eCq0+Cis8FFp8HZToOtxqqO3q0QNZNiUQghRLNTbCgnKaeIpOwiErOLuJBTRKq+mLS8EtLySkjPKya7sPaXelUqKnr07G2ws6m4TKuzscLWxgp7GyucbDU42WlwsrXGye5yYejhoMXN3gZNDb19QjQmKRaFEEI0aYqikJ5XQnxWIfFZhWQXGigoKaOgtIyCkjLyi8vQF5eRW2RAX2Qg++LlX3PpbKxwsavo0XO1v/hVp8FVZ4O7vQ0+zrZ4Odni42SLp6NWCj5xw5FiUQghhEVTFIXcIgNJOUVcyCkmKbuQ+Kyii8VhAfFZhbUY5XuZvY0VLVx1tHC1w8/FDh/nimLPy1GLl6Mt7g42cqlXCKRYFEIIYQGyC0s5k6ti9b4EMgoMJOcWk5JbTHJuEcm5xRSWll/1fLUK/FzsCHTT4eGgxV5rjb2NFfZaaxy01jjbaXCyq7j062ynwc/ZDhedpsmPmBWiIUixKIQQosGUlJVzPr2A06l5nEzO41SKnlPJeaToiwErOHGyxnM9HGzwc7HDz9mOQHcdAW46gtx0BLrp8He1k8u/QtQTKRaFEELUCUO5kbS8ElJyi0jTV8wVmJ5X8UjLKyE2s4C4zMIaB424axXCgzxp4abD19kOHydbfJxt8XOxw9fZVi4HC9FIpFgUQghRK4ZyI2fT8jlxQc/JZD0nkvWcScsnI7/ErFU3nGytCfV2pJ2vI+18nGjv60grNzt2bN7ImDHdLGpuPyGEmcXir7/+anaDEyZMuO5ghBBCWI6SsnJiMgo4m5bPubQCzqbnX/w+n9Ly6geU2Fip8XLS4u1ki5ejFk/HirkCPR21+LnY0dbHES9HbZV7BQ0GQ0O8JSHEdTCrWLz55psrPVepVChX/Pp45Q99efnVb0K+UbVs2ZK4uLgq26dPn87777/fCBEJIYR5yo0K59LzOZmsJzolj9OpeZxNyyc+q7DGVUUctda093Oig68THfycaOfjiL+LHa46G9T/cFk3IYRlMatYNBov/wa5adMmnn76aRYuXEhERAQqlYpdu3bx3HPPsXDhwnoL1NLt37+/UqF87Ngxhg8fzu23396IUQkhRFWKonA+o4BdZzP482wmu89nkltUfc+eo601wV4OtPF0INjLgWBPB0K9HQlws5ORxEI0E7W+Z3HmzJksW7aMm266ybRt5MiR6HQ6pk2bxsmTNY9ku5F5enpWev7aa6/Rpk0bBg4c2EgRCSFEBUVRiMkoYF9MFvtisth1LvPi6OPLdDZWtPNxpJ2vE229HQnxqigOPau5ZCyEaF5qXSyeO3cOZ2fnKtudnZ2JjY2ti5iavNLSUlatWsWsWbOu+iFbUlJCScnlVQT0ej1Qce9Ofdy/c6lNuTeo8UkuLMeNkgujUSGzoJQLucVcyCniQm4xMRkFnEuveGQXVn5/GisV3QJdiGjtTt/WboT7O2FdzdQzZWVlDRL/jZKHG4HkwjI0RB7MbVulKOaMXbtswIABaDQaVq1aha+vLwApKSlMmTKF0tJStm3bVvtobzDfffcdkydPJj4+Hj8/vxqPmz9/PgsWLKiyffXq1eh0uvoMUQjRhOUZILFARWJBxdcLBSoyS6BcqfmXU2uVQpADtHFSaOOk0NpRwUZmohGiWSssLGTy5Mnk5ubi5ORU43G1LhbPnj3LLbfcQnR0NIGBgQDEx8cTGhrKL7/8QnBw8D+L/AYwcuRIbGxsWLNmzVWPq65nMSAggIyMjKsm7XoZDAYiIyMZPny4TE3RyCQXlsOSc1FQUsb5jALOXhyJfCYtnxPJeaTqq1/XWK0Cbydb/Jwr5ids6a6jtYc9rT3sCfGyR2vB8xRach6aG8mFZWiIPOj1ejw8PK5ZLNb6MnRwcDBRUVFERkZy6tQpFEWhQ4cODBs2TO5rAeLi4ti0aRM//fTTNY/VarVotdoq2zUaTb3+gNZ3+8J8kgvL0Zi5MJQbOZ9ewInkXE4mV4xGPpOaT1JOUbXHq1TQyt2eMH9nwvycCPNzorWnA96O2movJTcl8jNhOSQXlqE+82Buu9c1KbdKpWLEiBEMGDAArVZufr7SF198gZeXF2PHjm3sUIQQFiyv2MCW6HR+P5bM1uj0Gtc+9nCwIdjLgRAvR0K8Hejg60Q7XycctLKmghCiYdT608ZoNPLKK6+wbNkyUlNTOX36NK1bt+b555+nZcuWPPDAA/URZ5NgNBr54osvmDp1KtbW8kEuhKgsp7CUyBOp/H4shR1nMipNbO2gtaa9ryPtfZ0I9XYk1NuRYC8H3OxtGjFiIYS4jmLx5ZdfZsWKFSxatIiHHnrItD08PJwlS5aYVSwaDAZSUlIoLCzE09MTNze32oZhkTZt2kR8fDz3339/Y4cihLAQ8ZmFbIlOI/JEKrvPZ1ZaF7m1hz2jOvowqqMPHf2cZTJrIYRFqnWxuHLlSj7++GOGDh3KI488YtreqVMnTp06VeN5+fn5fPXVV3z99dfs27ev0sCOFi1aMGLECKZNm0bPnj1rG5LFGDFiBLUcLySEuEEUlpZxLq2g4n7DtHzOpuURnZpHQlbl+w7b+TgyqqMPozv6EurtILfxCCEsXq2LxaSkpGpHPBuNxhrn61myZAmvvPIKLVu2ZMKECTzzzDP4+/tjZ2dHVlYWx44dY8eOHQwfPpw+ffrw7rvvEhISUvt3I4QQDSC3yMDRxFyOJOZwJCGHE8l6ErOrH4xirVbRo6Urg9t6MSLMh1Ye9g0crRBC/DO1LhbDwsLYsWMHQUFBlbZ///33dO3atdpzdu3axZYtWwgPD692f69evbj//vtZtmwZn332Gdu2bZNiUQhhEfKKDZxOzSMqMZcjCTlEJeZyPqOg2mMvDUYJNa2A4kiYvxNOtjKiVAjRdNW6WJw3bx5TpkwhKSkJo9HITz/9RHR0NCtXrmTt2rXVnvP999+b1bZWq2X69Om1DUkIIf4xQ7mRsxl6olMqLh9Hp1Q8apq+JtBNR+cAFzq3cKajvzOh3o4yGEUIcUOqdbE4fvx4vv32WxYuXIhKpeKFF16gW7durFmzhuHDh9dHjEIIUefKyo0cis9hw7ELbDpqxVP7N1NaZqz2WC9HLeH+znRq4ULngIqvUhgKIZqL65rfZeTIkYwcOfK6XrC4uJh3332XLVu2kJaWhtFY+cP50KFD19WuEEKYIyGrkK/2xvPdgQSyCkovblUBRhxtrWnv40SojwNtvR1p6+NEqLcDLjopDIUQzdd1FYs5OTn88MMPnD9/ntmzZ+Pm5sahQ4fw9vbG39//qufef//9REZGctttt9GrVy8ZCSiEqHeKorDnfBaf/xnDHydTuTR7jYtOw6AQDxwLEpk6biCtvZxk+hohhPibWheLUVFRDBs2DGdnZ2JjY3nwwQdxc3Pj559/Ji4ujpUrV171/HXr1rF+/Xr69et33UELIYQ5DOVG1kUl8+nO8xxL0pu29w/x4J6Ilgxu64liLGf9+gSC3HVSKAohRDVqXSzOmjWLe++9l0WLFuHo6GjaPnr0aCZPnnzN8/39/SudJ4QQdUlRFI4l6fnf4SR+PXKBtLyKOV1tNWomdWvBff1aEezlYDreYKx+mT0hhBAVal0s7t+/n48++qjKdn9/f1JSUq55/ltvvcXTTz/NsmXLqky/I4QQtVVsKCcqMZcDcVkcisvmYFw22YWX53z1cNAyNSKIu/oEyaAUIYS4DrUuFm1tbdHr9VW2R0dH4+npec3ze/ToQXFxMa1bt0an06HRVJ5/LCsrq7YhCSGakbxiAwdis9l9PpO9MVkcT8qlzFh55SRbjZqh7byZ2MWPgW090VpbNVK0QgjR9NW6WJw4cSIvvvgi3333HQAqlYr4+HieeeYZJk2adM3z//3vf5OUlMTChQvx9vaWAS5CiKvKLyljf2wWe89nsft8JseSciutrwzg6ailR5Ar3S8+wvycsbFWN1LEQghxY6l1sfjmm28yZswYvLy8KCoqYuDAgaSkpBAREcErr7xyzfN37drF7t276dy583UFLIS4ceUWVayWEp2Sx+nUPI4k5HDsgr5KcRjkrqNPK3f6tHGjR5AbLVzt5BdPIYSoJ7UuFp2cnNi5cyebN2/m0KFDGI1GunXrxrBhw8w6v127dhQVVb8ighCi+Sg2lLM3Jos95zM5laznVEoeybnF1R4b6Kajdys3+rR2p08bd/xd7Bo4WiGEaL6ua55FgCFDhjBkyJBan/faa6/x5JNP8sorrxAeHl7lnkUnJ6frDUkIYeEUReFAXDZf741n/bFkig1VV0zxd7Ej1NuBUB9H2vs40bOVmxSHQgjRiK6rWPzjjz9YsmQJJ0+eRKVS0a5dO2bOnGlW7+KoUaMAGDp0aKXtiqKgUqkoL5dpLIS4Ee06l8HSyDPsi708iM3X2Zabgj3oFOBCOx9HQr0dcbbTXKUVIYQQDa3WxeJ7773HE088wW233cbjjz8OwJ49exgzZgyLFy/m0Ucfver5W7Zsub5IhRBN0rH/b+/O46Ks9geOf4YBhkUUWQdEFFIEMQXFTFFBr2JmmvemJalpll6vFgotLi2a5tYtM7OrmWjXzFuWVnpzLQEl3DJX0HLBFQlxYZFtYJ7fH/yYmgsoKDAjfN+v1/wx5znPeb4zh5n5cp7nnOdyFvO2nOCn09cAsLa04K9BzXjqoeYEN3eUaw2FEMLMVTtZnDdvHu+//75RUhgVFUVoaChz5sy5Y7IYFhZW/SiFEPedi9fzeHf7r3x3OA0Aa7UFwx5qzoTwVmib2Jg4OiGEEFVV7WQxOzvbcCr5zyIiIpgyZUqV2rhx4waxsbGG09gBAQE8++yzODk5VTccIYSZSUnLJjYxlY1HLqMrKZ3FPDjIk5ci2tDcyc7E0QkhhKiuai9ENmjQIL755pty5d999x0DBw684/4JCQm0bNmSxYsXc+PGDa5fv87ixYvx8fEhISGhuuEIIczAhWt5fLLrLH/71088ung363+5hK5EoXsrF/77YncWDQuWRFEIIe5T1R5ZDAgIYM6cOcTHx9O1a1eg9JrFn376iZdeeonFixcb6kZFRZXbf+LEiTz11FMsXboUtbr0rgolJSVMmDCBiRMncvz48bt9LUKIOnL5Zj57zlxj79nSx6UbfyyHpbZQ0b+dlue6+xDs3dSEUQohhKgJ1U4WY2Njadq0KSkpKaSkpBjKHR0diY2NNTxXqVQVJotnzpxh/fr1hkQRQK1WExMTw+rVq6sbjlm5fPkyU6ZMYcuWLeTn5+Pn50dsbCydOnUydWhC3JO0m/nsPXutNEFMvcbF68ZrpaotVHTxceKRdlr6BWpxbyzXJAohRH1R7WQxNTX1ng7YsWNHTpw4QZs2bYzKT5w4QVBQ0D21bUo3btwgNDSUXr16sWXLFtzc3Dhz5gyOjo6mDk2IKivRK2TmFnLxeh7Jadkcu5zFgXPXOX8tz6ie2kLFg82a0PUBZx72dSakRVPsNXe9bKsQQggzds/f7sXFxRQUFNCoUaMq1Y+KimLSpEmcPn2ahx9+GCg9jf3RRx8xf/58jh49aqjbvn37ew2vzixYsIDmzZuzatUqQ1nLli1NF5AQt3Ett5A9Z69x7HIWl27kc+lGPulZ+VzNKeR/7qwHgIUKHvRy5GFfJ7r6OhPS0olGkhwKIUSDUOVv+82bN3Pt2jVGjhxpKJszZw6zZ8+muLiY3r178+WXX9K06e2vUYqMjATg1VdfrXCbSqW6Lxfo3rhxI/369WPo0KEkJCTQrFkzJkyYwNixYyvdp7CwkMLCQsPz7OxsAHQ6HTqdrsZjLGuzNtoW1WOKvtDrFX48eZXP91/kpzPXKq2ntlDh5qChjXsjAj0b08GrCSEtmuJg8+evC6Xe/B3J58I8SD+YD+kL81AX/VDVtlWKolQwjlBe7969eeKJJ5g4cSIASUlJ9OjRg1mzZhEQEMBrr71G//79Wbhw4W3bOX/+fJUCA2jRokWV65qajU3pNVoxMTEMHTqU/fv3M3nyZD7++GOeeeaZCveZOXMmb731VrnytWvXYmcnM0dFzUm7BV+eVXMu948FsD3tFHwdFFxsFJw00FSj0MQaHKxKRxKFEELUb3l5eTz99NNkZWXd9nbLVU4W3dzc2LZtG8HBwUBpUpSSksLWrVuB0pHHSZMmcerUqUrb0Ol0jBs3jjfeeANfX9/qvB6zZ21tTUhICElJSYayqKgoDhw4wJ49eyrcp6KRxebNm5OZmVkr98jW6XTs2LGDvn37lrsnt6hbddUXeUXFLIk7y8qk85ToFew1aoY/1JzIzs3xair3Wwb5XJgL6QfzIX1hHuqiH7Kzs3Fxcbljsljl09A5OTk4OzsbnicmJjJkyBDD88DAQNLS0m7bhpWVFd988w1vvPFGVQ973/Dw8KBt27ZGZQEBAaxfv77SfTQaDRqNply5lZVVrX5Aa7t9UXW12RdxJzN4/dvjXL5ZOnP5kUAtMwa1xaOJJIkVkc+FeZB+MB/SF+ahNvuhqu1WeVFuT09PTpw4AUBubi5HjhwhNDTUsP3atWtVOnX617/+lW+//baqh71vhIaG8uuvvxqV/fbbb/fVqXRRP6RnFTDx81949tMDXL6ZTzNHW1Y8E8KykZ0kURRCCFFtVR5ZHDJkCJMnT2b69Ols3rwZrVZrmM0M8PPPP5dbDqcirVq1Yvbs2SQlJdGpUyfs7e2Ntle0NuP9IDo6mm7dujF37lyefPJJ9u/fz/Lly1m+fLmpQxMNRIGuhBW7z/JR3BnydSWoLVSMCW3J5D5+sqyNEEKIu1blX5AZM2aQlpZGVFQUWq2WNWvWGC2s/Z///KdKt/tbsWIFjo6OHDx4kIMHDxptq2wh7/tB586d+eabb5g2bRqzZs3Cx8eHRYsWMXz4cFOHJuq53MJivjxwkdjdZ0nLKgCgo7cjswe3I9CziYmjE0IIcb+rcrJoZ2fHZ599Vun2uLi4KrVzr4t6m7PHHnuMxx57zNRhiAYgr6iYuJNX+f5YGjtPZlCg0wOgbWzDtEf9GdTBE5VKpjQLIYS4d9U+N9W7d282bNhQ7s4k2dnZDB48mJ07d9ZUbEKIPynQlRD/awb/PXqFH09kkK/7Yx3SB1zteb6HL38NboaNlfo2rQghhBDVU+1kMT4+nqKionLlBQUF7N69u8J95s+fT1RUVJUmwOzbt4/MzEwGDBhQ3dCEqHduFRaz+1Qmm49d4YcTv5NX9EeC2NzJlgEPevJYew8CPRvLSKIQQohaUeVk8c+34UtJSSE9Pd3wvKSkhK1bt9KsWbMK901JScHb25uhQ4cyaNAgQkJCcHV1BUpvF5iSkkJiYiJr1qzhypUrrF69+m5fjxD3jWI93MzTUajXkV9UQt7/PzJyCjiTkcv+c9c5eP4GupI/lkJt5mjLY+09GNDegwebNZEEUQghRK2rcrIYFBSESqVCpVLRu3fvctttbW358MMPK9x39erVHD16lI8++ojhw4eTlZWFWq1Go9GQl5cHQHBwMOPGjWPUqFEVrj0oxP3udEYuG4+kse/sNX77PYcbeZaw787X+jZ3suWRQC0D2nvSwUsSRCGEEHWrysliamoqiqLg6+vL/v37DSODUHr3Ejc3N6PZ0f+rffv2fPzxxyxbtoyjR49y7tw58vPzcXFxISgoCBcXl3t7JUKYqeS0LOZvOcnuU5kVbrdWW2CnUWNnpcbWWk1TO2secG1EO68m9GjlQgtnO0kQhRBCmEyVk8WyxaX1ev09HVClUtGhQwc6dOhwT+0IYe4KdCXM33KSf+85h6KApYWKnn6uPBKoxc/NjhM/J/L4gEews5GRdCGEEObrrlbqPXPmDIsWLeLEiROoVCoCAgKYNGkSDzzwQE3HJ8R96fjlLCZ/eZjTGbkADOrgySv92tDcqXSSl06n47wlWKmrfBMlIYQQwiSqnSxu27aNQYMGERQURGhoKIqikJSURGBgIJs2baJv3761EacQ94XiEj3Ld5/l/R2/oStRcHXQ8M8h7Qlv42bq0IQQQoi7Uu1kcerUqURHRzN//vxy5VOmTJFkUTRYZ6/m8tJXRzh04SYAEW3dmf9Ee5zsrU0bmBBCCHEPqp0snjhxgnXr1pUrHzNmDIsWLaqJmIS4r2Tl6fgo/jSf/nSOohI9DhpL3hzYliGdvGRiihBCiPtetZNFV1dXDh8+TOvWrY3KDx8+jJubnGoTDcfpjFxW7znH1wcvGRbL7unnyvy/PYino62JoxNCCCFqRrWTxbFjxzJu3DjOnj1Lt27dUKlUJCYmsmDBAl566aXaiFEIs/F7dgH/PXqFTUfSOHzxpqHcX+vAlP7+hPu5ymiiEEKIeqXayeIbb7yBg4MD7733HtOmTQPA09OTmTNnEhUVVeMBClFTFEUhp7AYa7VFle+frCgKqZm3SPjtKtuS09mXeh3l/2+oYqGC3v7uPBvakm4POEuSKIQQol6qdrKoUqmIjo4mOjqanJwcABwcHAC4fPlypbf8E8IUcguL2fDLJbYcS+fwxZvk60pPF7s0ssbP3YG2Ho1p16wJvq72NLG1QleicC23kJPpORy7nMX+1OtcuJ5n1GanFk0Z2N6DR9t74OZgY4qXJYQQQtSZu1pnsUxZkpiens6cOXNYsWIF+fn5NRKYEPdCV6Ln873nef+HU2Tl68ptz8wtIjP3Gklnrt2xLSu1iod8nAj3c6P/g1q8mtrVRshCCCGEWapysnjz5k0mTpzI9u3bsbKyYurUqbzwwgvMnDmTd999l8DAQFauXFmbsQpRJb+m5zDpi0OcTC8d+fZ1sefpLt6E+bni1dQOnV7PucxbnLySQ3JaFsfTsrl8I5+sfB2WatX/327PngebNaFDc0ce9nXGXnNP/1cJIYQQ960q/wJOnz6dXbt2MWrUKLZu3Up0dDRbt26loKCALVu2EBYWVptxCnFHiqLw2d7zvP39CYqK9TjZW/NyRBue6twctcUf1xPaoqa9lyPtvRyB5iaLVwghhLgfVDlZ/P7771m1ahV9+vRhwoQJtGrVCj8/P1lbUZiFG7eKeOXro/xw4ncAerVx5d2hHXBuJPddFkIIIe5FlZPFtLQ02rZtC4Cvry82NjY8//zztRaYEFWVdDqTmHVHSM8uwFptwdT+/jwb2lJmJwshhBA1oMrJol6vx8rKyvBcrVZjb29fK0GJ+5der3A9r4jiEgWXRtZYqi1q7VhFxXre2/4ry3efRVHA19WeDyODCfRsUmvHFEIIIRqaKieLiqIwevRoNJrS03oFBQWMHz++XMK4YcOGmo3wPjFz5kzeeustozJ3d3fS09NNFFHtKyrWczYzl5NXcjiRns3Ri1kcu5xFbmExAJYWKjq3dOKJTl48HuSJVQ0ljnq9wqajaXzwwynOZt4CIPIhb954LAA7a5mIIoQQQtSkKv+yjho1yuj5iBEjajyY+11gYCA//PCD4blaXbWFn+8XuhI9iacz2XY8naOXsjiVkYOuRKmwrtpCRbFeYc/Za+w5e40PfvyNqN6t+VtHL6PJJtWhKArbktN5f8cpfv29dKazk701c//6II+009716xJCCCFE5aqcLK5atao246gXLC0t0WrNO2m5dKv6+xToSliz9zzLEs6SmVtotK2RxhJ/rQNttA609ypdaqaVayMsVCrOX89j87ErrExM5eL1fF75+igrdqcypX8berVxq/I1hYqisPNkBgt3/EZyWjYADjaWjO3hy7OhLXGwsbpDC0IIIYS4W3LOrgadOnUKT09PNBoNXbp0Ye7cufj6+lZav7CwkMLCP5Kv7OzSREin06HTlV9I+l4tSzjDe0ctsWl2jme6tbxj/fyiEr48eIlPdp8jI6c0Tmd7a/q3c6ebrzP+Ho3wcrQtl/Qp+hJKAK8m1ozr3oKRD3mxZv8FliWk8uvvOYz59GceatmUV/v50cGr8usLFUUh8cw1PvjxDEcuZQFgb61mVNcWjAltQRPb0iSxNt6r2lYW8/0Ye30jfWEepB/Mh/SFeaiLfqhq2ypFUSo+jyiqZcuWLeTl5eHn58fvv//O22+/zcmTJ0lOTsbZ2bnCfSq6zhFg7dq12NnV/F1CNp634Me00usGn2hZQk+Pirv+ZiEcyFQRf8WCXF1pIuikUYhopuchV4W7vfQwrxh2XLZg1xUVxUppu/5N9HTXKgQ4Klj+f7vFejh2vfT453JL61lZKPTUKvT21NNIBhKFEEKIe5aXl8fTTz9NVlYWjRs3rrSeJIu15NatWzzwwAO8+uqrxMTEVFinopHF5s2bk5mZedtOu1tFRUVMXLGT+CulWdnjHTwY270lzZracuVmAXtTr/Pjyasknb1G2V+Fl6MNf+/py9+CPbG2rJkJKmk381m08wzfHk4zHMfOWo2viz0qFZzOyCVfpwfA2tKCpzt78feePrjUozUTdTodO3bsoG/fvkarDIi6J31hHqQfzIf0hXmoi37Izs7GxcXljsminIauJfb29jz44IOcOnWq0joajcYwu/zPrKysau0PY3ALPSHt2vDeD6f47sgVvjtypcJ6nVs25anO3jU6i7lMC1cr3n8qmOg+bViz7zwbfrlMZm4hx///ekQANwcNkQ95M/xhb9wcbGr0+OakNvtaVI/0hXmQfjAf0hfmoTb7oartSrJYSwoLCzlx4gQ9evQwdShGVCr4e08furZy4aO4M/x0OpPCYj121mqCmjvSvbULAx70oIVz7a+h6e1sx/RHA5j6iD+nr+Zy4VoeKhV4NbXDz72RLKothBBCmAFJFmvIyy+/zMCBA/H29iYjI4O3336b7OzscksOmYtOLZxYOdoJRVG4VVSCvbXaZMmZhYUKP3cH/NwdTHJ8IYQQQlROksUacunSJSIjI8nMzMTV1ZWHH36YvXv30qJFC1OHdlsqlYpGGvkzEEIIIUTFJEuoIV988cU9t1E216hsCZ2aptPpyMvLIzs7W65DMTHpC/MhfWEepB/Mh/SFeaiLfijLN+4011mSRTOSk1N6V5LmzZubOBIhhBBCNBQ5OTk0aVL5useydI4Z0ev1pKWl4eDgUCvXD5YtzXPx4sVaWZpHVJ30hfmQvjAP0g/mQ/rCPNRFPyiKQk5ODp6enlhYVL7yiYwsmhELCwu8vLxq/TiNGzeWLwAzIX1hPqQvzIP0g/mQvjAPtd0PtxtRLFOzC+gJIYQQQoh6RZJFIYQQQghRKUkWGxCNRsOMGTMqvGuMqFvSF+ZD+sI8SD+YD+kL82BO/SATXIQQQgghRKVkZFEIIYQQQlRKkkUhhBBCCFEpSRaFEEIIIUSlJFkUQgghhBCVkmSxAfnXv/6Fj48PNjY2dOrUid27d5s6pAZn3rx5dO7cGQcHB9zc3Bg8eDC//vqrqcNq8ObNm4dKpWLy5MmmDqVBunz5MiNGjMDZ2Rk7OzuCgoI4ePCgqcNqUIqLi3n99dfx8fHB1tYWX19fZs2ahV6vN3Vo9d6uXbsYOHAgnp6eqFQqvv32W6PtiqIwc+ZMPD09sbW1JTw8nOTk5DqNUZLFBuLLL79k8uTJvPbaaxw6dIgePXrQv39/Lly4YOrQGpSEhAQmTpzI3r172bFjB8XFxURERHDr1i1Th9ZgHThwgOXLl9O+fXtTh9Ig3bhxg9DQUKysrNiyZQspKSm89957ODo6mjq0BmXBggUsW7aMJUuWcOLECd555x3++c9/8uGHH5o6tHrv1q1bdOjQgSVLllS4/Z133mHhwoUsWbKEAwcOoNVq6du3Lzk5OXUWoyyd00B06dKFjh07snTpUkNZQEAAgwcPZt68eSaMrGG7evUqbm5uJCQk0LNnT1OH0+Dk5ubSsWNH/vWvf/H2228TFBTEokWLTB1WgzJ16lR++uknOdNhYo899hju7u7ExsYayp544gns7Oz47LPPTBhZw6JSqfjmm28YPHgwUDqq6OnpyeTJk5kyZQoAhYWFuLu7s2DBAv7+97/XSVwystgAFBUVcfDgQSIiIozKIyIiSEpKMlFUAiArKwsAJycnE0fSME2cOJEBAwbQp08fU4fSYG3cuJGQkBCGDh2Km5sbwcHBfPLJJ6YOq8Hp3r07P/74I7/99hsAR44cITExkUcffdTEkTVsqamppKenG/1+azQawsLC6vT327LOjiRMJjMzk5KSEtzd3Y3K3d3dSU9PN1FUQlEUYmJi6N69O+3atTN1OA3OF198wS+//MKBAwdMHUqDdvbsWZYuXUpMTAzTp09n//79REVFodFoeOaZZ0wdXoMxZcoUsrKy8Pf3R61WU1JSwpw5c4iMjDR1aA1a2W90Rb/f58+fr7M4JFlsQFQqldFzRVHKlYm688ILL3D06FESExNNHUqDc/HiRSZNmsT27duxsbExdTgNml6vJyQkhLlz5wIQHBxMcnIyS5culWSxDn355ZesWbOGtWvXEhgYyOHDh5k8eTKenp6MGjXK1OE1eKb+/ZZksQFwcXFBrVaXG0XMyMgo99+KqBsvvvgiGzduZNeuXXh5eZk6nAbn4MGDZGRk0KlTJ0NZSUkJu3btYsmSJRQWFqJWq00YYcPh4eFB27ZtjcoCAgJYv369iSJqmF555RWmTp3KsGHDAHjwwQc5f/488+bNk2TRhLRaLVA6wujh4WEor+vfb7lmsQGwtramU6dO7Nixw6h8x44ddOvWzURRNUyKovDCCy+wYcMGdu7ciY+Pj6lDapD+8pe/cOzYMQ4fPmx4hISEMHz4cA4fPiyJYh0KDQ0tt3zUb7/9RosWLUwUUcOUl5eHhYVxSqBWq2XpHBPz8fFBq9Ua/X4XFRWRkJBQp7/fMrLYQMTExDBy5EhCQkLo2rUry5cv58KFC4wfP97UoTUoEydOZO3atXz33Xc4ODgYRnubNGmCra2tiaNrOBwcHMpdJ2pvb4+zs7NcP1rHoqOj6datG3PnzuXJJ59k//79LF++nOXLl5s6tAZl4MCBzJkzB29vbwIDAzl06BALFy5kzJgxpg6t3svNzeX06dOG56mpqRw+fBgnJye8vb2ZPHkyc+fOpXXr1rRu3Zq5c+diZ2fH008/XXdBKqLB+Oijj5QWLVoo1tbWSseOHZWEhARTh9TgABU+Vq1aZerQGrywsDBl0qRJpg6jQdq0aZPSrl07RaPRKP7+/sry5ctNHVKDk52drUyaNEnx9vZWbGxsFF9fX+W1115TCgsLTR1avRcXF1fh78KoUaMURVEUvV6vzJgxQ9FqtYpGo1F69uypHDt2rE5jlHUWhRBCCCFEpeSaRSGEEEIIUSlJFoUQQgghRKUkWRRCCCGEEJWSZFEIIYQQQlRKkkUhhBBCCFEpWWdRiHqopKQEnU5n6jCEELXEyspKFo8XdUaSRSHqEUVRSE9P5+bNm6YORQhRyxwdHdFqtXV6j2DRMEmyKEQ9UpYourm5YWdnJz8iQtRDiqKQl5dHRkYGgNE9g4WoDZIsClFPlJSUGBJFZ2dnU4cjhKhFZbcHzcjIwM3NTU5Ji1olE1yEqCfKrlG0s7MzcSRCiLpQ9lmX65NFbZNkUYh6Rk49C9EwyGdd1BVJFoUQQgghRKUkWRRCCFEvjB49msGDB5s6jBoTHh7O5MmTTR2GEJIsCiFM788/8qNHj0alUpV7PPLII+X2mzt3Lmq1mvnz51fpOBs2bKBfv364uLigUqk4fPhwuTrh4eHljj1s2LDbtnvkyBEiIyNp3rw5tra2BAQE8MEHH1Ra//Tp0zg4OODo6FhuW0JCAp06dcLGxgZfX1+WLVt2x9f15/fM0tISb29v/vGPf3Djxg0ATp48iUqlYt++fUb7denSBY1GQ15enqGsqKgIOzs7li9fXmE//PkxevTocrHMnDmToKCgcuU3b95EpVIRHx8PwLlz5wzxXr582ajulStXsLS0RKVSce7cuTu+/jIffPABn376aZXrV0VJSQnz5s3D398fW1tbnJycePjhh1m1alWNHqciGzZsYPbs2bV+HCHuRJJFIYTZeeSRR7hy5YrR4z//+U+5eqtWreLVV19l5cqVVWr31q1bhIaG3jG5HDt2rNGxP/7449vWP3jwIK6urqxZs4bk5GRee+01pk2bxpIlS8rV1el0REZG0qNHj3LbUlNTefTRR+nRoweHDh1i+vTpREVFsX79+ju+trL37Ny5c6xYsYJNmzYxYcIEAPz9/fHw8CAuLs5QPzc3l0OHDuHm5kZSUpKhfN++feTn59OrVy+j92DRokU0btzYqOx2CXFVeXp6snr1aqOyf//73zRr1qzabTVp0qTCBPxezJw5k0WLFjF79mxSUlKIi4tj7NixhkS8Njk5OeHg4FDrxxHiTiRZFEKYHY1Gg1arNXo0bdrUqE5CQgL5+fnMmjWLW7dusWvXrju2O3LkSN5880369Olz23p2dnZGx27SpMlt648ZM4bFixcTFhaGr68vI0aM4Nlnn2XDhg3l6r7++uv4+/vz5JNPltu2bNkyvL29WbRoEQEBATz//POMGTOGd999946vrew98/LyIiIigqeeeort27cbtoeHhxtG9QB2796Nn58fgwYNMiqPj4+nWbNmtG7dutx7oFKpqvW+VMWoUaPKjdJ9+umnjBo1yqispKSE5557Dh8fH2xtbWnTpk25ZPV/T0OHh4cTFRXFq6++ipOTE1qtlpkzZxrtk5WVxbhx43Bzc6Nx48b07t2bI0eOGLaXJd1Dhw7Fx8eHDh068NxzzxETE2OooygK77zzDr6+vtja2tKhQwe+/vprw/b4+HhUKhXbtm0jODgYW1tbevfuTUZGBlu2bCEgIIDGjRsTGRlpNMorp6GFuZBkUYh6TFEU8oqKTfJQFKVWX1tsbCyRkZFYWVkRGRlJbGxsjbX9+eef4+LiQmBgIC+//DI5OTnVbiMrKwsnJyejsp07d/LVV1/x0UcfVbjPnj17iIiIMCrr168fP//8c7WWRzl79ixbt27FysrKUNarVy8SExMpLi4GIC4ujvDwcMLCwoxGHOPi4ujVq1eVj3WvBg0axI0bN0hMTAQgMTGR69evM3DgQKN6er0eLy8v1q1bR0pKCm+++SbTp09n3bp1t23/3//+N/b29uzbt4933nmHWbNmsWPHDqD08zFgwADS09PZvHkzBw8epGPHjvzlL3/h+vXrAGi1Wnbu3MnVq1crPcbrr7/OqlWrWLp0KcnJyURHRzNixAgSEhKM6s2cOZMlS5aQlJTExYsXefLJJ1m0aBFr167l+++/Z8eOHXz44YfVfg+FqG2yKLcQ9Vi+roS2b24zybFTZvXDzvruvmL++9//0qhRI6OyKVOm8MYbbwCQnZ3N+vXrDadPR4wYQWhoKB9++CGNGze+p7iHDx+Oj48PWq2W48ePM23aNI4cOWJIMKpiz549rFu3ju+//95Qdu3aNUaPHs2aNWsqjTE9PR13d3ejMnd3d4qLi8nMzLztnTrK3rOSkhIKCgoAWLhwoWF7eHg4t27d4sCBA3Tt2pX4+HheeeUVevbsyciRI8nLy8PS0pK9e/dWePq8tlhZWTFixAhWrlxJ9+7dWblyJSNGjDBKdMvqvfXWW4bnPj4+JCUlsW7dugpHacu0b9+eGTNmANC6dWuWLFnCjz/+SN++fYmLi+PYsWNkZGSg0WgAePfdd/n222/5+uuvGTduHAsXLmTIkCFotVoCAwPp1q0bjz/+OP379wdKL21YuHAhO3fupGvXrgD4+vqSmJjIxx9/TFhYmCGWt99+m9DQUACee+45pk2bxpkzZ/D19QVgyJAhxMXFMWXKlHt9W4WoUTKyKIQwO7169eLw4cNGj4kTJxq2r127Fl9fXzp06ABAUFAQvr6+fPHFF0DpyGCjRo0Mj927d1f52GPHjqVPnz60a9eOYcOG8fXXX/PDDz/wyy+/ANC/f39Du4GBgeX2T05O5vHHH+fNN9+kb9++Ru0+/fTT9OzZ87bH/9+188pGaFUqFbt37zZ6XZ9//nm592zfvn28+OKL9OvXjxdffNGwvXXr1nh5eREfH092djaHDh0iLCwMd3d3fHx8+Omnn9i7dy/5+fn07t27Su/Vn2MZP358lfapyHPPPcdXX31Feno6X331FWPGjKmw3rJlywgJCcHV1ZVGjRrxySefcOHChdu23b59e6PnHh4ehtvkHTx4kNzcXJydnY1eS2pqKmfOnAGgbdu2HD9+nL179/Lss8/y+++/M3DgQJ5//nkAUlJSKCgooG/fvkZtrF692tBGRbG4u7tjZ2dnSBTLyspiE8KcyMiiEPWYrZWalFn9THbsu2Vvb0+rVq0q3b5y5UqSk5OxtPzjK0yv1xMbG8u4ceMYNGgQXbp0MWy7m8kSZTp27IiVlRWnTp2iY8eOrFixgvz8fIByo18pKSn07t2bsWPH8vrrrxtt27lzJxs3bjRcf6goCnq9HktLS5YvX86YMWPQarWkp6cb7ZeRkYGlpSXOzs40adLEaAb3n0ch//yeLV68mF69evHWW28ZzaYNDw8nLi6O9u3b07p1a9zc3AAMp6I1Gg0tWrSgZcuWVXpv/hxL2Whp48aNycrKKlf35s2bABVe59iuXTv8/f2JjIwkICCAdu3alZupvm7dOqKjo3nvvffo2rUrDg4O/POf/yw3w/t//W8fqVQq9Ho9UPo34+HhYXTNZpk/T5SxsLCgc+fOdO7cmejoaNasWcPIkSN57bXXDG19//335f7OykYrK4pFpVLdNjYhzIkki0LUYyqV6q5PBZurY8eO8fPPPxMfH290TeDNmzfp2bMnx48fp127djU2izQ5ORmdTmc4BVxZ4pmcnEzv3r0ZNWoUc+bMKbd9z549lJSUGJ5/9913LFiwgKSkJEObXbt2ZdOmTUb7bd++nZCQEKysrLCysrptEv1nM2bMoH///vzjH//A09MTKB19jIqKom3btoSHhxvqhoWFsWTJEjQaTZVHFYEKY/H39+fSpUukp6ej1WoN5QcOHMDCwqLS+MeMGcOECRNYunRphdt3795Nt27dDDO8gXIjd9XVsWNH0tPTsbS0rHKCDKWjjVB6Crpt27ZoNBouXLhgdMpZiPqkfv2KCCHqhcLCwnIjbJaWlri4uBAbG8tDDz1U4encrl27Ehsby/vvv19hu9evX+fChQukpaUB8OuvvwIYZveeOXOGzz//nEcffRQXFxdSUlJ46aWXCA4ONlxrVpHk5GR69epFREQEMTExhtjVajWurq4ABAQEGO3z888/Y2FhQbt27Qxl48ePZ8mSJcTExDB27Fj27NlDbGxshcsG3Ul4eDiBgYHMnTvXcA1ir169uHXrFitXruSTTz4x1A0LC2P06NGo1epKTwFXVUREBAEBAQwbNow5c+bg6enJ0aNHefnllxk/fnylSfzYsWMZOnRopUvftGrVitWrV7Nt2zZ8fHz47LPPOHDgAD4+Pncda58+fejatSuDBw9mwYIFtGnThrS0NDZv3szgwYMJCQlhyJAhhIaG0q1bN7RaLampqUybNg0/Pz/8/f2xtLTk5ZdfJjo6Gr1eT/fu3cnOziYpKYlGjRqVm9UtxP1IrlkUQpidrVu34uHhYfTo3r07RUVFrFmzhieeeKLC/Z544gnWrFlDUVFRhds3btxIcHAwAwYMAGDYsGEEBwcbFr62trbmxx9/pF+/frRp04aoqCgiIiL44YcfUKsrP63+1VdfcfXqVT7//HOjmDt37lyt1+3j48PmzZuJj48nKCiI2bNns3jx4kpf753ExMTwySefcPHiRUP7LVq0ICcnx2gUrFmzZnh7e1NQUHDPM6EtLS3Zvn07vr6+DB8+nMDAQKZOncrzzz9vNOGmov1cXFyMLi34s/Hjx/O3v/2Np556ii5dunDt2jWjUca7oVKp2Lx5Mz179mTMmDH4+fkxbNgwzp07ZzjF369fPzZt2sTAgQPx8/Nj1KhR+Pv7s337dkOss2fP5s0332TevHkEBAQY9rmXRFYIc6JSant9CyFEnSgoKCA1NRUfHx9sbGxMHY4QopbJZ17UFRlZFEIIIYQQlZJkUQghhBBCVEqSRSGEEEIIUSlJFoUQQgghRKUkWRSinpE5a0I0DPJZF3VFkkUh6omyu0Hk5eWZOBIhRF0o+6z/751ghKhpsii3EPWEWq3G0dHRcG9ZOzu7cvcZFkLc/xRFIS8vj4yMDBwdHW+7BqgQNUHWWRSiHlEUhfT0dMN9eIUQ9ZejoyNarVb+KRS1TpJFIeqhkpISdDqdqcMQQtQSKysrGVEUdUaSRSGEEEIIUSmZ4CKEEEIIISolyaIQQgghhKiUJItCCCGEEKJSkiwKIYQQQohKSbIohBBCCCEqJcmiEEIIIYSolCSLQgghhBCiUv8HucQncloT8+0AAAAASUVORK5CYII=", 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", 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rJL6wPuJPrOsk3Gv3y6VzjqWoqAhZWVlYtWpV6Jjb7caaNWswZcqUHixZW2aHB+sONKPcytnW8cLm8mJPrQU/twr4Ync9Vu6sxY+lzdhXZ4HT4+vp4hEREXVJv2xZtFqt2L9/f+hxaWkptm7dipSUFBQUFGDu3Ll4+OGHMWjQIAwaNAgPP/wwdDodrr766h4sdVsJaumvAae3hwsSRcfq6o83fr+IffVW/FjahO9Lm7GxrBl15uAkJzmwe2ub8wUBKEzVY1i2ARMHpODk4hQMzTJCLusdn5eIiPqvfhkWN27ciLPOOiv0ODjecPbs2Xj11Vcxb948OBwOzJkzBy0tLTj55JOxcuXKmM5sjpRBI1Wdo5c3WFW22PHaujJ8+XM9DjbaIAAYkKrHqNxETCxMxinFqRiUkdDjIdLj82NXtRkby1vwY2kTfixtRovd0+68JK0SOsGNzNQkiBBgcnjQaHXB4vSitNGG0kYblv8kTaIyahSYPDAVp5ak4dSSNBSn6Xv8cxIRER2tX4bFqVOnhpa36YggCFi0aBEWLVrUfYWKUDAsOntpWBRFEUu/K8Njn/8Mt9d/+DgQClUfbasGAKToVZg4IBlDswwYmJGAwlQ9spM0SNOrIYtBy5zH50dZow1766zYXtWKLeWt2F7VCqfH3+Y8rVKOCQOSMakoBZOKUjAs2widAli+fDlmzjw5NBZEFEU0WF3YW2vFtspW/BhoiTQ7vVixsw4rdtYBALITNTi1JA2nlaRhSkkqMgyaqH82IiKiSPXLsNgXGDRSEHH5pC7R3sTvF3HPhzvw5o+HAAAnF6XgxlMLMTovCQCwr96KbRWBUFXejGabGyt31WHlrro211HKBWQaNchO1CArUYucRA2yEjXITtQiO1FzzEApiiJa7B5UtzpQ1mTDvjor9tVbsK/OitJGG7wdfE8TtUqML0jCpKJUTCpKwajcRKgUbYf9ejztWxsFQUCGQYMMgwanDUrD788CvD4/fqoyYd2BJny7rxGbyltQY3Li3U2VeHdTJQBgcGaC1Oo4MA2j8xMZHomIqEcwLPZSwZZFEQJsbh86mIwdtx74dBfe/PEQBAG478LhmD2lsE33a06SFmcOTgcAuL1+/FTVii2HWrG/3op99VZUtthRb3HB4xNR2eJAZYsDQEuH7yUTAKNWCaNGCZ1KDrfPD5fHjyabq11L4ZH0KjlKMg0YlmXA+AHJGF+QjOI0fdRaMhVyGcYVJGNcQTJ+f1YJHG4fNpY349v9jfhufyN2Vpuxt86KvXVWvPJdGQAgLUGN4TlGDMs2YGiWAYMzDRiYngCNUh6VMhEREXWEYbGXUitkUMoFeHwirC4vUuJnOOUxvbOxIhR+nrpyLGaNyz3m+SqFDBMGpGDCgJQ2xz0+PxosLtSYHKgxOVHT6kSNyYlaswPVrU7UmpyotzjhF4FWuwetHYwvBKQAlp+ixeAMAwZlJqAkIwGDMw3ITtR06/hBrUqO0wel4/RBUkhusbmx/mATvt3fiB8ONuFgow2NVhfW7m3A2r0NodfJZQIKU3UYEgiPQzINGJJlwIBUPSfPEBFRVDAs9lKCICBBrUCL3QOLs+MgFG9KG2249387AABzzx103KB4LEq5DDlJWuQkaTs9x+vzo9nmhtnpgcnhhd3thUoug1opR5JWiewkDdSK+GyVS9arMHNUNmaOktb6tLulZXl21Zixu8aMvbVW7KmzwOTw4ECDDQcaDk+cAaQ/JgZlJmB0XhLG5idhXH4SBqYnxGSMJxER9W0Mi72YQRMMi/G/fo7PL+Kud7bB6fFjysBU/PHsQTF/T4VchgyjBhnG3j/WT6dShLqtg0RRRL3FhT21FulWZ8HewM3p8WNHlRk7qsxY9oM0NtSgVmB0fiLG5SdjQqHUtZ6o7dsL8Do9PtSbnGh0AgcabBBkcqgUMuhVcujUCuhVcs5AJyI6DobFXiw4btHiiv+w+M7GCmwsb4FeJcfjl49mC1cUCII0wSfTqMEZgTGegBTMK5rt2FVjxtaKVmw9JM3mtri8+G5/E77b3xR4PTAoIwETBqRg4oBkTCxMRkGKLq7Dk8PtQ5PNhSarG802NxqtLjTb3GiyuQPHDj9utrlhdweXC1AAW75rdz21QobcQAt1TpIGRWkJGJwpDUXITdLy3ykRERgWezWDOhAW47xl0ez04IkVewAAt08bjLxkXQ+XqG+TywQUpulRmKYPdWN7fX7sqbNga0UrNpe3YlN5M8qa7KFJNMGZ6WkJakwYkISJA1IwKi8R+Sk6ZBrUUMjD3+zJ5xfh8Phgd3vhcPtgd/vg8PhC9+1uL5ye4H3puMMTvO9td37wvtnpOSL8hU8hEyCHHxq1EgqZDG6vHza3F34RcHn9ONhow8FGW7vX6VRyDAqMYR2abcTwwC1R17dbY4mIjsaw2IsFl88xx3lY/PsX+9Bkc6M4XY/rJxf2dHH6JYVchhE5iRiRk4hrTh4AAGiwuLCpvAWbD7VgY1kzdlSZ0Wh1tVn7EZDCZ3qCGjqVHBql1I3r84vw+Pzw+Pzw+kW4vf5Q4Dty3cxYUClkSNWrkJqgQopejTS9Cil6FVISVEjVS8dS9IH7CSpoZCI+++wzzJx5Xpu1L50eaZJUVasD1a3SrPr9DVbsq7PgYIMNdrcP2ypN2FZpavP+uUlaDMs2Yni2ITA73Yj8ZB1bIYmoz2JY7MWC483Mjvid4FLRbMer68oAAAsvHN5uXULqOekGNc4fmYXzR2YBkMb37agyYWN5CzaWtWBvnQU1Jgc8PhG1ZmfE1xcEaeFynUoOrUoOnVIBjUoO3ZHHVHJolXJoVQroAo81geelcxShaySoFUhNUCFBrYioq7yztS+1KjkKUnUoSG3f0u31+VHWZMe+Ogt+rrVgd40Zu2rMqGxxoKpVun2x+3CgTlArMDRLCo/Ds40YnZeEwZkJEbXIEhHFK4bFXixRK1WfKY7D4jNf7oPXL+K0kjRMHZLR08WhY9Ao5ZhYmIKJhSnAmdIxn19Eg8WFeosTTo/Ueuj2+qGQCVDKZVDIpa9KuXBE0JOCn1ohi+vxj8eikMtQkiEtpTQj0JUPSD9rPwdmpEsz06WJRVaXVwrZ5YfX+9Qq5RiVm4ixBUkYk5eEsQVJyOnmJZmIiKKBYbEXSwq0LJoc8dkNXd5kw3ubqwBIYxWp95HLBGQFdsYhqTX/5OJUnFycGjrm8flxsMEWCpA7qkz4qdIEi8uLH8ua8WNZc+jctAQ1Jg5IxpSSVEwZmIqB6T2/7zkR0fEwLPZixlBYjM+Wxb+v3g+fX8TUIemYMCD5+C8g6oWUchmGZEmLoQfXDvX7RRxstGLLoVZsq2zFtgoTdtdIY0I/31mLz3dKa2JmGNQ4tSQNZw/NwNQh6aFxyERE8YRhsRcLtiy2xmFYrGp14MOtgVbFc9mqSP2LTCagJMOAkgwDrpiYD0AaE7qz2oTvDzZj3YFGbChrQb3FhQ+2VOGDLVVQygWcUpyK6cMzce7wTGQndr7gPBFRd2JY7MWCS3iYOtnKrie9vr4MPr+IycWpGJOf1NPFIepxGqU8tHXl788qgdPjw+ZDLVizpwGrdtXhYKMN3+xrxDf7GnHv/3ZiZK4RZw3JwNQhGRibn9Qntm8URREtdg+qm63Y3SrAsbkKZpcPVpcPVqcXVpcHNpcPFpcXHq8fPlGEzy/d/KJ0U8hkUMllUAW2PFUpZDBqlEjUKpGkk74m6lTIMKiRnSitQ8r904lODMNiLxYasxhn2/3Z3V68Gdg15DenFfVwaYjik0Ypx5SBaZgyMA3zZw7DgQYrVu2qw6pdddh8qCW0A88zX+5Hkk6J0welY1x+EkblJaI4TY9kneqYy/WIogi3zw+3V7r5RUCtlEGjkEMpF6I6VjK4tWa9xYWGwK3e4kS9xYU6s/S13iwdd/uCSyvJgd07o1aGY0nSKZFllMbe5iZpUZiqR0GqDgNSdShI0UGn4n+FRMfCn5BezBins6Hf21QJs9OLAak6nD2UM6CJwjEwPQEDz0zArWcORIPFhTV7G/DVnnqs3duAVrsHH2+rxsfbqkPnK2QCUvSqUIujKEqTbdxeP1yBr52RCYBaIYdGKYNWKYcmsISRRnnE18CMdn+gdc/rF+H3i7C5fbA6pW1GLU4vrC7pFolknRIauFGSk4bUBDUMGiX0agUS1NISSXq1AiqFDHKZAIVMgEwQoJALECDAG1jj0+31w+3zw+X1w+zwwOTwwGSXvrbYpeBaY3LA6fGj1e5Bq92Dn2stHZYnw6DGgFQdBqTqMTgzAYMyDRiSaUA2Z68TAWBY7NWCLYtOjx9Ojy8uulpEUcRr68sBADdOKeRCxURdkG5Q4/IJebh8Qh68Pj+2VLTiu/2NgdZGE2rNTnj90t7gXeEXIe2M4/GhBdH5Y1MmSLO90w1qZBiCXzXINKqRHviaYdQgPUENQfRh+fLlmDlzQmih9FgQRRFmhxc1ZgdqTU7UmpyoaLGjvMmOQ812lDXaYHZ6pZZPiwsbylravN6gVmBQZgKGZBkwPNuIkbmJGJZtjIvftUTdiWGxF0tQKyCDCD8EmByeuPgF9lOVCfvrrVArZLhsQl5PF4eo11PIZTipMAUnFaaEjrm9/tC+2KJ4+FylQgiN51Mr5IGv0hg/QZC2N3R5/XB5fNIfmV4fnJ7DWy4G//CU7kuP5TJAJgiQy6SbVimHQaOEQaMI3KT7yTpV2OMqPZ7It23sCkEQkKhTIlGnxNAsY4fntNrdKG+yo7zZjtIGG/bWW0K7+FhcXmw+1IrNh1pD58tlAgZlJGBkbiJG5SZiZG4ihmcboVX1/O9folhhWOzFBEGAVgHYvFJXdKax59fCez+wruJ5I7K4DAhRjKgUMuQkaZGTFNmMaU2gixla/mwGJelUSNKp2k3Ec3v9KG20YW+dBXtqLdhRbcKOKhMarW78XCvt7PPupkoAUqtqSSBAjsxJDGwHyX3Eqe9gWOzldIGw2BoHM6I9Pj8+CoypunR8bg+Xhoio61SKw+tnXjRGOiaK0taXP1VKwXFHtRk/VZnQYHFhb50Ve+usoT+YAWkf8eD+4UMyDRiUmYDCVD23PaVeh2Gxl9MFarDV7u7ZggD4sbQZzTY3UvUqnFaS1tPFISKKKkEQkJ2oRXaiFtNHZIWO1wUC5E9VJuyqMWNXtTm0h3hVqwOrdh3eR1whE1CYpsegjATpFgiRRWl6qBXsyqb4xLDYy+kUIgAhLhbmDv5CPGdYBhRy/uVMRP1DplGDzOEanDs8M3TMZPcE9g+Xbvvqrdhfb4XV5cX+wP3PjriGXCZgQKoOJekJKE5PQHGaHsXpehSnJyBZp+x1s7L9fjE0W93l9YWWcHId9dXt88HlCcxs90gz+JUKAQqZLLTvvFIug14tR4JaiQSNAglq6dYX1h7tLRgWe7lgy6K5h8OiKIr4YrcUFqcNzzrO2UREfVuiTonJA1MxeeDhfcRFUUSNyYl99Vbsq7Ngf70V++qt2FtngcXpxcEGGw422ADUtb2WVonidD2K0vQYmC61Qhan61GYqu/yxEanx4fyJjsONFixr9aMDQdl+OytbWi2e2B1etus0ekTRYiiCL+I0Fe/KELs5Ks/cE6saZVyJGgUMAYmWCXrVUgJftUrkaxTIUUvPU7Vq5CaoIZeJe91wTseMCz2coe7oXs2LO6ps6CyxQG1QsYuaCKiDgiCEJqYdObg9NBxUZSWQdpXZ8X+egtKG2042CgFx2qTAyaHB1sOtWLLEbOypesBOYlaqQUyTWqFLEzTI0mrhFYlh0wAzE4vzA4PKprtONhoQ1ng2hXN9qMCnQyoaxtSo/e5AZU8MDNfIa3fKd0/8qs8NJbT4/PD4/PD65PW1HR5/bC7faE1PYNriAaXf2qwuADYwiqLWiFDWoIaqQlSgJTuq5GWoAock55LT1AjWa+Ckr1kALoYFvfu3Yuvv/4a9fX18PvbLvy6cOHCqBSMwhMKi46eHbO4dm8DAODUkjQuIUFEFAFBEKSubKMGpw1q+8e20+NDWZMUHEsbbTjQYA20QFphdnpD4yK/2dcY8fsaNAqppTJVC3tDFSaNHY4MoxYGjSIU4NSBxdEFQVpCSSZI5Q3dR+A5mfRYJggQIHWrq5VyqAJdydFszXN5fbAFtoi0uKSF2FvtHjTb3GixudFsD371oNXuRpPVjRa7G3a3Dy6vP/Q9C0eSThlqlUxLCITLQKCUAqZaCp0GNQxqRZ9ttYw4LP7rX//C7373O6SlpSErK6vNN0YQBIbFbiaNWez5lsXvDzYDAKYc0eVCREQnRqOUY2iWsd06kaIootnmllohG4ItkVaUN9lhdXlhd3vhF6VAmKBWIC9Zh6I0HQoDXdnF6XppgXRBgMfjwfLlFZh5SkFMF0mPFinEypGiV0X0OrvbiyarG41WF5qsbjTZXGi0ug8fs7kC991otrngFxHa/edAw/FbLlVyGVITpKWY9Co5dIFdiXQqReixXiU9VitlkAsCZDIB8sA6prIjdiyC34edLQJGNNtRkpnY1W9V1EQcFh988EE89NBD+POf/xyL8lCE9HHQDe3zi9hQKoXFk4sYFomIYk0QBKlVK0GNiUcs2E6d06kU0KUokJ+iO+65fr+IVocHTVYXGoLh0upCk80dCJjB+9JzVpc0zrPG5ESNyRmlEsthyKvFbb0xLLa0tOCKK66IRVmoC4JhsdnWc93Qu6rNsLi8MKgVGJ7T8S4JREREvYUssPd6il6FQZmG457v9PjQZJNCZIvdA4fbC5vLB7vbC2vg6+HH0rjL4L7rPhHw+f3w+UX4/YBPFOHx+dDSYkKGUd0Nn/b4Ig6LV1xxBVauXIlbb701FuWhCOlD3dA9FxZ/KG0CAJxUlMKlDIiIqN/RKOXITdIiN8JdlTojDQ1Yjpnj4mODi7DC4t///vfQ/ZKSEtx77734/vvvMWrUqHbjG/74xz9Gt4R0TPrAt7+lB7uhN5a1AAAmFbErhIiIqK8JKyw+9dRTbR4nJCRgzZo1WLNmTZvjgiAwLHazYDe0w+OD0+Pr8ppbJ2J7ZSsAYOxRe6sSERFR7xdWWCwtLY11OaiLNHJp+yivX0SL3Y3sxOg0gYerweJCtckJQQBGcLwiERFRnxPxapMOR+drE9XU1JxQYShygiCtAwUALbbu74reUWUCABSn6WHQxP+SC0RERBSZiMPiuHHjsHnz5nbH3333XYwePToqhaLIJGkDYbEHJrlsr5TC4pi8pG5/byIiIoq9iMPitGnTMGXKFDz66KMQRRFWqxU33HADZs+ezQW5e0ioZbFHwmIrAGBUXs+vA0VERETRF/HSOc888wwuuOAC3Hjjjfj0009RXV0No9GIDRs2YPjw4WFfp6KiAmVlZbDb7UhPT8eIESOgVsfHekK9TbJOWsW+J2ZE76iWWhZH5TIsEhER9UVd2iF7+vTpuPTSS/Hdd9+hoqICjz76aFhBsby8HPPnz0dhYSEKCwtx5plnYsaMGZg4cSISExMxbdo0vPPOO+32m+5uXq8Xf/nLX1BUVAStVovi4mLcf//9PV6uziSHxix2b8uiye5BndkFABiSdfxFS4mIiKj3iTgsHjhwAJMnT8Ynn3yCFStWYN68ebjkkkswb948eDydt2z96U9/wqhRo7Bv3z7cf//92LlzJ0wmE9xuN2pra7F8+XKcdtppuPfeezF69Ghs2LDhhD7YiXjsscfw/PPP4x//+Ad2796Nxx9/HE888QSeeeaZHivTsfRUN/T+BgsAIDtRw8ktREREfVTE3dBjx47FBRdcgBUrViApKQnTpk3DzJkzcf3112PVqlXYsmVLh69TqVQ4cOAA0tPT2z2XkZGBs88+G2effTbuu+8+LF++HOXl5TjppJMi/0RRsH79elxyySW44IILAACFhYV48803sXHjxh4pz/GEuqG7uWVxX50VAFCSkdCt70tERETdJ+Kw+Nxzz+G6665rc2zKlCnYsmUL5s6d2+nrnnjiibDfY+bMmZEWK6pOO+00PP/889i7dy8GDx6Mbdu24dtvv8WSJUs6PN/lcsHlcoUem81mANJ2PcdqbT1RwWsb1FIDcbPNFdP3O9qeWulzDkzTdev7xrPg94Hfj/jBOok/rJP4wvqIP91VJ+FeXxBFUYxpSTrgcDggiiJ0Oh0AaSzjBx98gGHDhuG8887r7uK0I4oiFixYgMceewxyuRw+nw8PPfQQ5s+f3+H5ixYtwuLFi9sdX7ZsWegzxtJPzQJe2iNHgV7E/432xfz9gv65S4afTTL8stiHKZnd/s+IiIiIToDdbsfVV18Nk8kEo7HzjTUiDosfffRRxxcSBGg0GpSUlKCoqOiY1whOkLn11lvR2tqKoUOHQqlUorGxEU8++SR+97vfRVKkqHvrrbdw11134YknnsCIESOwdetWzJ07F08++SRmz57d7vyOWhbz8/PR2Nh4zG/+ifJ4PFi1ahXShk7CNa9sRl6yFl/dcXrM3u9opz+xBrVmF9666SRMGJDcbe8bz4J1Mm3atHb7plPPYJ3EH9ZJfGF9xJ/uqhOz2Yy0tLTjhsWIu6FnzZoFQRBwdMYMHhMEAaeddho+/PBDJCd3HCA2b94c2m/63XffRWZmJrZs2YL33nsPCxcu7PGweNddd+Huu+/GVVddBQAYNWoUysvL8cgjj3QYFtVqdYfL/iiVym75wUs3Slv8tdjc3faDbnF6UBuYCT0sJ5m/YI7SXXVP4WOdxB/WSXxhfcSfWNdJuNeOeDb0qlWrcNJJJ2HVqlUwmUwwmUxYtWoVJk2ahE8++QRr165FU1MT7rzzzk6vYbfbYTBIS62sXLkSl156KWQyGU455RSUl5dHWqSos9vtkMnafmvkcnncLp2TliAFVZvbB7vb2y3vub9emtySYVAjUcdfLkRERH1VxC2Lf/rTn/Diiy9iypQpoWPnnHMONBoNbr75ZuzcuRNLlizBr3/9606vUVJSgg8//BC/+MUvsGLFCtx+++0AgPr6+ph224broosuwkMPPYSCggKMGDECW7ZswZNPPnnMz9STEtRyqBUyuLx+NFrcKEiNuFojti8QFgdlciY0ERFRX9aldRY7CnRGoxEHDx4EAAwaNAiNjY2dXmPhwoW48847UVhYiJNPPhmTJ08GILUyjhs3LtIiRd0zzzyDyy+/HHPmzMGwYcNw55134pZbbsEDDzzQ00XrkCAISDdIrYsNVtdxzo6OYMvioAwuxk1ERNSXRdwENWHCBNx11114/fXXQ2smNjQ0YN68eaF1Efft24e8vLxOr3H55ZfjtNNOQ01NDcaMGRM6fs455+AXv/hFpEWKOoPBgCVLlnS6VE48SktQo7LFgQZL94TFfXXSgtxcY5GIiKhvizgsvvzyy7jkkkuQl5eH/Px8CIKAQ4cOobi4GP/73/8AAFarFffee2+71+bk5OCSSy7BxRdfjHPOOQdZWVltnp80aVIXPwZ1d8vi3sCC3IMz2bJIRETUl0UcFocMGYLdu3djxYoV2Lt3L0RRxNChQzFt2rTQpJBZs2Z1+Nply5bh448/xh//+EfU1dXhvPPOw8UXX4wLLrgAKSkpJ/RB+rtgWGzshpZFm8uLqlYHAGAQWxaJiIj6tIjDYllZGQoLC3H++efj/PPPj+i1U6dOxdSpU/G3v/0NO3fuxEcffYRnn30WN910EyZPnhxqdRw4cGCkxer3gjOiu6Nl8UCDNfCeKiTrVTF/PyIiIuo5EU9wKS4uxmmnnYYXXngBzc3NXX7jESNGYP78+fj+++9RXl6Oa665Bl9++SVGjRqFkSNH4tNPP+3ytfuj7mxZ5J7QRERE/UfEYXHjxo2YPHkyHnzwwdAYxHfeeafNDiaRysrKwm9/+1t8/PHHaGxsxAMPPNDhItfUufQEqYWvO1oW93EmNBERUb8RcTf0+PHjMX78eDz++OP4+uuvsWzZMtxyyy246aabcNlll2Hp0qVhX6u+vh719fXtFruOhxnRvU2oZbEbwuL+emkm9GCusUhERNTndXn1ZkEQcNZZZ+Gss87C7373O/zmN7/Ba6+9FlZY3LRpE2bPno3du3d3uG2gz+frarH6rdCYRYsrtO1irOwNdUOzZZGIiKiv63JYrKiowJtvvolly5bhp59+wuTJk/GPf/wjrNfeeOONGDx4MF5++WVkZmbGNNj0F8Gw6PT4YXP7kKCOzS4uDrcPFS12ANy9hYiIqD+IOFG8+OKL+M9//oPvvvsOQ4YMwTXXXIMPP/wQhYWFYV+jtLQU77//PkpKSiJ9e+qEXq2ATiWH3e1DvdmJhPTYBLkDDVaIIpCsUyKVM6GJiIj6vIgnuDzwwAOYNGkSNm7ciJ07d2LBggWhoNjQ0BDWNc455xxs27Yt0rem48gyagAAtWZnzN7jyG3+2CJMRETU90Xcsnjo0KE2IUEURXz22Wd46aWX8Omnn4Y1K/qll17C7NmzsWPHDowcORJKpbLN8xdffHGkxSIAWYkaHGy0odYUu7C4LzC5pYRd0ERERP1CxGExGBQPHjyIpUuX4rXXXoPVasUFF1yAt956K6xrrFu3Dt9++y0+++yzDq/PCS5dk52oBQDUxDAsBie3cOcWIiKi/iGibmin04k33ngDU6dOxfDhw7Ft2zbU1NTgm2++wRtvvBH2kjd//OMfcd1116GmpgZ+v7/NjUGx63KSpG7o6sBWfLGwt05qWRzCPaGJiIj6hbDD4pw5c5CTk4Nnn30WV1xxBaqqqvDxxx9DEITQntDhampqwu23347MzMyIC0ydi3XLot3txaFmaSb0kCyGRSIiov4g7G7oF198EX/+859x9913w2A4saBw6aWX4quvvuIe0FGWHeOWxX110kzotAQVUhO4ww4REVF/EHZYfP311/HKK68gOzsbF1xwAa677jqcf/75XXrTwYMHY/78+fj2228xatSodhNc/vjHP3bpuv1dToxbFvfUBXduYasiERFRfxF2WLz66qtx9dVXo6ysDK+88gp+//vfw263w+/3Y9euXRg+fHjYb/rSSy8hISEBa9aswZo1a9o8JwgCw2IXBVsWTQ4P7G4vdKroLsy9tzYwXpFd0ERERP1GxOssFhYWYvHixSgrK8O///1vXHbZZbj22muRl5cXdsgrLS3t9Hbw4MGIPwRJjBplaOeW6tboty7u4eQWIiKififssLhkyRI0NTWFHguCgPPPPx///e9/UV1djTvvvLNdKyF1v+xEqXWxxhT9cYt7Ai2Lg9mySERE1G+EHRYXL16M3NxcXHnllVi5ciVEUQw9l5KSgrlz5x5zV5ZHH30Udrs9rPf64Ycf8Omnn4ZbNDpCdlJg3GKUWxZbbG7UW6QF1zlmkYiIqP8IOyzW1tbi5ZdfRnNzM2bMmIEBAwbgvvvuQ2lpaViv37VrFwoKCvC73/0On332WZutAb1eL7Zv347nnnsOU6ZMwVVXXQWj0Rj5pyHkBsJiZUt4wTxcwfUV85K1oa5uIiIi6vvCDotqtRrXXHMNvvjiCxw4cAA33ngjXn/9dQwaNAjnnnsu3nzzzWNu9ff666/jyy+/hN/vxzXXXIOsrCyoVCoYDAao1WqMGzcOS5cuxQ033ICff/4Zp59+elQ+YH8zIFUHAChvjm5Y5HhFIiKi/iniCS7A4UkupaWl+Pzzz5GZmYmbbroJOTk5x3zd6NGj8cILL6CpqQmbN2/GO++8g3/9619YsWIF6urqsHHjRtx8881Qq7mGX1cVBsNiU5TDIscrEhER9Usn3J8ok8kgCAJEUYTf7w/rNYIgYMyYMRgzZsyJvj0dpSBFDwAob7JF9brc5o+IiKh/6lLLYnl5ORYvXoyioiJMnz4d1dXV+Ne//oWamppol48iVBBoWWyxe2B2eqJyTVEUQy2LXGORiIiofwm7ZdHpdOK9997D0qVLsWbNGmRnZ2P27Nn49a9/jeLi4liWkSKQoFYgLUGFRqsbh5rsGJmbeMLXrDU7YXZ6IZcJKE7XR6GURERE1FuEHRazsrLgdDpx4YUX4uOPP8Z5550HmaxLDZMUYwUpOjRa3SiPUljcXWMGABSn6aFWyE/4ekRERNR7hB0WFy5ciOuvvx5paWmxLA9FwYBUPTYfakV5c3TGLe6qlsLiiBwuZ0RERNTfhN00eMcddyAtLQ3FxcVtdnIJam1tZXd0nChIkcYtHorSjOidgbA4nGGRiIio34m4H7msrAw+n6/dcZfLhaqqqqgUik5MUZo0rvBgQ3RaFneGWhZPvEubiIiIepewu6E/+uij0P0VK1YgMfFwcPD5fFi9ejUKCwujWjjqmpKMBADA/gbrCV/L7PTgUGCB7+HZbFkkIiLqb8IOi7NmzQIgrZE4e/bsNs8plUoUFhbib3/7W1QLR10zMD0BggA029xosrqQmtD1Rc53B1oVcxI1SNarolVEIiIi6iXCDovBBbeLioqwYcMGTnSJY1qVHHnJWlQ0O7C/3npCYfHweEV2QRMREfVHEY9ZLC0t7TQocsxi/ChJl7qi99WfWFf0rhrOhCYiIurPorJQYm1tLW677TaUlJRE43IUBYMC2/LtP8GwyJnQRERE/VvYYbG1tRXXXHMN0tPTkZOTg7///e/w+/1YuHAhiouL8f3332Pp0qWxLCtFIDTJ5QTCosvrw/56aZs/tiwSERH1T2GHxQULFmDt2rWYPXs2UlJScPvtt+PCCy/Et99+i88++wwbNmzAr371q1iWtVtVVVXh2muvRWpqKnQ6HcaOHYtNmzb1dLHCFgyLe+ssXb7GnloLPD4RSTolcpO00SoaERER9SJhT3D59NNP8corr+Dcc8/FnDlzUFJSgsGDB2PJkiUxLF7PaGlpwamnnoqzzjoLn332GTIyMnDgwAEkJSX1dNHCNjjQDV1vcaHZ5kZKF2Yyb6toBQCMyUuCIAjRLB4RERH1EmGHxerqagwfPhwAUFxcDI1Gg5tuuilmBetJjz32GPLz8/HKK6+Ejh1rDUmXywWXyxV6bDZL4/w8Hg88Hk/Myhm8dkfvoZYBA1J0KG+2Y9uhZpxWkhrx9TcfagEAjMwxxPRz9CXHqhPqGayT+MM6iS+sj/jTXXUS7vUFURTFcE6Uy+Wora1Feno6AMBgMGD79u0oKirqeinj1PDhw3HeeeehsrISa9asQW5uLubMmYPf/va3HZ6/aNEiLF68uN3xZcuWQafTxbq4nXp1rwxbmmS4uMCHc3LDquY2HtkqR61DwG+H+jAyOfLXExERUfyy2+24+uqrYTKZYDR2Pjch7LAok8kwY8YMqNXSmn0ff/wxzj77bOj1+jbnvf/++ydQ7Pig0WgASPthX3HFFfjxxx8xd+5cvPDCC7j++uvbnd9Ry2J+fj4aGxuP+c0/UR6PB6tWrcK0adOgVCrbPf/C2lL8ddU+XDAqC0uuHB3RtS1OLyY8/CVEEVj/5zORdgJrNfYnx6sT6n6sk/jDOokvrI/40111YjabkZaWdtywGHY39NG7tlx77bVdL12c8/v9mDhxIh5++GEAwLhx47Bz507885//7DAsqtXqUIg+klKp7JYfvM7eZ1R+MgBgd60l4nL8fMgEUQRyk7TITk6ISjn7k+6qewof6yT+sE7iC+sj/sS6TsK9dthh8cjxe31ddnZ2aHxm0LBhw/Dee+/1UIm6JrjcTWmjDTaXF3p12NWN7ZUmAMCYfO7cQkRE1J9FZVHuvubUU0/Fnj172hzbu3cvBgwY0EMl6pq0BDUyjWqI4uHFtcMVnAk9Oi8p+gUjIiKiXoNhsQO33347vv/+ezz88MPYv38/li1bhhdffBG///3ve7poERsX6IreEpjZHK4jl80hIiKi/othsQMnnXQSPvjgA7z55psYOXIkHnjgASxZsgTXXHNNTxctYuMHJAEANpWHHxZrTA5Um5yQCcCoPHZDExER9WfhD2LrZy688EJceOGFPV2MEzZhgNSyuPlQC0RRDGtx7R9LmwFI+0EnRDDOkYiIiPoetiz2cSNyEqGUC2i0ulHR7AjrNRvKpLB4UmFKLItGREREvQDDYh+nUcoxMlfqSt5Y3hzWazaUSl3WkxgWiYiI+j2GxX5gYqAr+oeDxw+LrXY39tRZpNcxLBIREfV7DIv9wKklaQCAb/c34ngb9vwQGK9YnKZHuoG7thAREfV3DIv9wKSiFKjkMlS1OlDaaDvmuWv3NgAATh+U1h1FIyIiojjHsNgP6FSK0BI63+1v7PQ8URSxJhAWzxic3h1FIyIiojjHsNhPnD5ICn9r9nYeFg822lDZ4oBKLsMpxandVTQiIiKKYwyL/cTUIVJY/GZfA6wub4fnfL1HalU8qSg5on2kiYiIqO9iWOwnhmcbUZSmh8vrx+rddR2e8/G2agDAOUMzu7NoREREFMcYFvsJQRBwwahsAMDyn2raPV/aaMPWilbIZQIuGpPT3cUjIiKiOMWw2I/MDITFr/Y0oMXmbvPch1uqAACnlaRxyRwiIiIKYVjsR4ZlGzAixwi314+3N1aEjru8Pry9QXp86fjcnioeERERxSGGxX5EEATcMKUQAPCvtQdhcXoAAG9vqECt2YlMoxrnj8zqwRISERFRvGFY7GdmjctFUZoeTTY3FnywA1sOteCJz/cAAP5wVgnUCnkPl5CIiIjiCcNiP6OUy/DopaMgE6TZz794bh0sLi9OKkzG1ScP6OniERERUZxhWOyHTi5OxXPXjEdagjSR5eyhGXjxuomQy4QeLhkRERHFG6683E+dPzIb04ZnweX1QafiPwMiIiLqGFsW+zG5TGBQJCIiomNiWCQiIiKiTjEsEhEREVGn2AcZA6IoAgDMZnNM38fj8cBut8NsNkOpVMb0vSg8rJP4wzqJP6yT+ML6iD/dVSfBnBLMLZ1hWIwBi8UCAMjPz+/hkhAREREdm8ViQWJiYqfPC+Lx4iRFzO/3o7q6GgaDAYIQu+VozGYz8vPzUVFRAaPRGLP3ofCxTuIP6yT+sE7iC+sj/nRXnYiiCIvFgpycHMhknY9MZMtiDMhkMuTl5XXb+xmNRv6AxxnWSfxhncQf1kl8YX3En+6ok2O1KAZxggsRERERdYphkYiIiIg6xbDYi6nVatx3331Qq9U9XRQKYJ3EH9ZJ/GGdxBfWR/yJtzrhBBciIiIi6hRbFomIiIioUwyLRERERNQphkUiIiIi6hTDIhERERF1imGRiIiIiDrFHVxioLu2+yMiIiLqKm7314Oqq6uRn5/f08UgIiIiOq6KiopjblPMsBgDBoMBAGK+AbjH48HKlSsxffp0KJXKmL0PhY91En9YJ/GHdRJfWB/xp7vqxGw2Iz8/P5RbOsOwGAPBrudYbwDu8Xig0+lgNBr5Ax4nWCfxh3USf1gn8YX1EX+6u06ON2SOE1yIiIiIqFMMi/1Yq92Nl745iHX7G3u6KERERBSn2A3dT9ndXvziuXUobbQBAB6/bDSuPImTcoiIiKgttiz2Uy+sORgKigDwwKe7YHF6erBEREREFI8YFvshr8+PtzYcAgAs+eVYFKfrYXF68b+t1T1cMiIiIoo3DIv90I9lzagzu5CsU2LGqCxcPakAAPC/rVU9XDIiIiKKNwyL/dDXexoAAGcPzYRaIcf5I7MAAJvKW9Bic/dk0YiIiCjOMCz2Q2v3SmFx6pB0AEBesg5DMg3wi8B3BzgzmoiIiA5jWOxnbC4v9tZZAAAnF6WEjp9cLN3fXN7aE8UiIiKiONUvw+LatWtx0UUXIScnB4Ig4MMPPww95/F48Oc//xmjRo2CXq9HTk4Orr/+elRX943JH9srTfCLQE6iBhlGTej4+IJkAMDmQy09VTQiIiKKQ/0yLNpsNowZMwb/+Mc/2j1nt9uxefNm3Hvvvdi8eTPef/997N27FxdffHEPlDT6tla0AgDGFiS1OR4MizurTXB6fN1cKiIiIopX/XJR7hkzZmDGjBkdPpeYmIhVq1a1OfbMM89g0qRJOHToEAoKCrqjiDGztUJqORybn9TmeH6KFmkJKjRa3dhZbcKEASkdvJqIiIj6m34ZFiNlMpkgCAKSkpI6fN7lcsHlcoUem81mAFKXtscTu4Wug9eO5D22HmoFAIzMNrR73di8RHzxcwM2lDZhdI4hauXsT7pSJxRbrJP4wzqJL6yP+NNddRLu9QVRFMWYliTOCYKADz74ALNmzerweafTidNOOw1Dhw7FG2+80eE5ixYtwuLFi9sdX7ZsGXQ6XTSLe0JMbmDhJgUEiHhskg9qedvnV1UJ+OSQHONT/Zg92N8zhSQiIqJuYbfbcfXVV8NkMsFoNHZ6HlsWj8Hj8eCqq66C3+/Hc8891+l58+fPxx133BF6bDabkZ+fj+nTpx/zmx+N8q1atQrTpk2DUqk87vnf7m8CNm1CYaoev7jotHbPa/c04JM3tsAqN2LmzCmxKHKfF2mdUOyxTuIP6yS+sD7iT3fVSbAn9HgYFjvh8Xhw5ZVXorS0FF9++eUxQ59arYZarW53XKlUdssPXrjvc6DRDgAYkmXs8PwRedIkl4ONNoiCHCpFv5z/FBXdVfcUPtZJ/GGdxBfWR/yJdZ2Ee22mgQ4Eg+K+ffvwxRdfIDU1taeLFBX76qwAgMGZCR0+n5OogUGjgNcv4mCjtTuLRkRERHGqX7YsWq1W7N+/P/S4tLQUW7duRUpKCnJycnD55Zdj8+bN+OSTT+Dz+VBbWwsASElJgUql6qlin7A9gcW4B2d1PHlFEAQMyTRgY3kL9tRaMDQrdl3oRERE1Dv0y5bFjRs3Yty4cRg3bhwA4I477sC4ceOwcOFCVFZW4qOPPkJlZSXGjh2L7Ozs0G3dunU9XPKuE0UR+wJhcUhm5zOdhwSC5O4aS7eUi4iIiOJbv2xZnDp1Ko41CbwvThCvanXA5vZBKRdQmKbv9LyhgbC4pza8Qa9ERETUt/XLlsX+KDhesTgtAUp559U+ONDquLeOYxaJiIiIYbHfONAghb+BGZ23KkrPS5Nfqk0ObvtHREREDIv9xcFGGwCpZfFYUvUqGDUKiCJQGngNERER9V8Mi/1EaYMU/IqOMV4RkGZEF6dLgfJgA8MiERFRf8ew2E8EWwmL0o8dFgFgYCgsctwiERFRf8ew2A/YXF7Ump0AgOLjtCwCQHEgUB5kNzQREVG/x7DYD5Q1SaEvRa9Cku74i4oPDITFA2xZJCIi6vcYFvuBUBd0GK2KANqMWeyLa04SERFR+Prlotz9TbiTW4IGpOogEwCry4sGiwsZRk0si0dERNSriaIIu9sHi9MLi9MD81Ffg8etTi/cPhF+vwi/KMInBu8DwaYZAYAo+lFdLYN6dz3OH53bkx8NAMNivxBpy6JaIUdesg6Hmu040GBjWCQion5HFEVYAo0mDRYXGq2u9vetLjRa3Gi0uuD1R7snToap9VacH+WrdgXDYj9weI3F8MIiIE1yOdRsx8FGKyYPTI1V0YiIiLqV3e1tF/4arO4Og6Db64/o2nKZAINGId3UShi1Chg0Shg0ChgDX5VyGeQyAYIAyAUBMkGATCZILYqQQqrf78euXbvi5v9fhsU+ThTF0BI44SybEzQwPQFf72ngWotERL2Ex+dHndmJeosLLTY3gg1dOpUc6QY1Mg0aGLUKCILQswWNAafHhyZbB4GvXSugCzZ3ZLuTGdQKpBvUSEtQI92gDtxXHXFfuiXplNAq5VH5/no8Hixv3YkxeYknfK1oYFjs45ptbpidXgBAYWpkLYsA11okIopHNpcXu2vM2Fltxs5qE3ZWm7G3zgKP79hdoXqVHLnJWuQl65CXrEVukhbZRhXKLUB1qwMJWj9UChnUCjlEiHB5/XB7/XB5/XB5fHB5/XAe8dXp8cPl9cHl8cPlk1rhBEBqLRMAQZA2e5AJAuQyBL4KkAe/yqRWteDj4POCADjcPthcXtjcPtjdXthcPpidHjRZXWi2udFkc6PJ6kazzQ2ryxvR90+jlElhL0HdQRBUt3lOo5R3tZr6DIbFPi64bE5Ooiaif/DBbQG51iIRUc9qsroCoVAKhruqzShtsqGjxSpUcikEpSaoIJcJEEUpWNZbXDA5PLC5fdhbZ8XeuqMbAhR4csc33fJ5YkUll4Va/DoMf0fc16ui0wLYXzAs9nFljXYAQGEE4xWBw2stVjTb4fL6oFbwLysiolgzOz3YUWnCtkoTtlW0YntlK6pNzg7PzTSqMSInESNyjIFbIvKStZ2GIKfHh+pWBypbpFtVqx2VLQ5UNNtxoKYFdr+s05ZJlUIGtVwGtVIOtUIGjVIGjVIOTeixHCq5DIIA+EURoghphq8ozfr1i9Jxn1+6HXnfJ4rw+QG///DsYBGAVimHXi2HVqWAXiWHTiWNBUzVq5CaoEaKXoW0hMP3jZq+2cUeDxgW+7jyZiksDkjVRfS6dIMaCWoFrC4vypvsGJxpiEXxiIj6LafHh101ZmyvaMX2ShO2VrZ2Ok68KE2P4UeEwhE5RqQlqCN6P41SjuL0hNBaukEejwfLly/HzJnnQS5XwO3zw+XxQ5ABaoUsEAIZwvozhsU+riIQFvNTIguLgiCgOF2P7ZUmHGywMiwSEZ0Ar8+PffVWbK9sxbZKE7ZXtuLnGkuHy63kJWsxJi8Jo/MSMTovCSNzjTBolN1STplMgEYm5zg9aoNhsY8rD4xZHJASWTc0IC21s73SxHGLREQRcnp82FbRih9Lm/FjWTM2lbfA3sEs3LQEFUYHgmEwIKZG2GJIFGsMi33coS52QwNtt/0jIqLO2VxebD7Ugh9Lm/FDaTO2VrS2W6MvQa3AqNxEjM4/HAxzkzofY0gULxgW+zCry4tGqxsAUNClsMjlc4iIOmKye7ChTGo1/KG0GTuqTPAd1aWcblBjUlEKTi5KwUmFKRiSaYBMxmBIvQ/DYh92qElqVUzWKWHswngXLp9DRCRpsLikcFjajO8PNmFPnaXd0jW5SVqcXCyFw0lFqShM1bHVkPoEhsU+7FCzFPIKIliM+0iFaVJrZKvdg2abGyl6VdTKRkQUr0RRREWzAxvLm7Eh0HLY0XCc4nR9IBhKLYd5yZH34BD1BlELi5s3b8bChQvxySefROuSdILKAy2LBRHOhA7SqRTISdSg2uREaaMVKfqUaBaPiCgueH1+/FxrwYayZmwsa8GGsmbUW1xtzhEEYEimAScXpeDk4lScVJiCdAMnolD/EFFYXLVqFVauXAmlUombbroJxcXF+Pnnn3H33Xfj448/xrRp02JVTuqC0OSWLoZFQJrkUm1y4kCDDRMGMCwSUe8miiIqWxz4qcok3SpN2FrR2m67OKVcwMjcRJxUmIJJhSmYWJiMJB17V6h/Cjssvvbaa7jxxhuRkpKC5uZmvPTSS3jyyScxZ84cXHbZZdi2bRtGjhwZy7JShIJhsSuTW4KK0/X4dn8jZ0QTUa/j8flR1mjD3jordlabQgGx1e5pd65BrcCEwmScVJiCiQOSMSY/iWsNEgWEHRafeuopPPzww7j77rvx3//+F1dddRWeeuopbNmyBQMHDoxlGamLgt3QJ9SymMYZ0UQUv/x+EQ1WFypbpK3rShtt2Fdnxb56C0obbR1uX6eUCxiSZcCo3MPrGw7JMkDOmcpEHQo7LB44cAC//OUvAQCXX3455HI5nnzyyV4ZFNeuXYsnnngCmzZtQk1NDT744APMmjUr9Lwoili8eDFefPFFtLS04OSTT8azzz6LESNG9FyhI+Tx+VHV6gAADOjiBBcAKErnjGgi6n6iKMLk8KDB4kKD1YVGqxuNFhcardKtxuSU9jduccDt83d6Hb1KjpJMA4ZmGjA6PxGjchMxJMvA/e6JIhB2WLTZbNDrpdAhk8mg0WiQn58fs4LFks1mw5gxY3DjjTfisssua/f8448/jieffBKvvvoqBg8ejAcffBDTpk3Dnj17YDD0jm3vqlsd8PlFqBUyZJzAIOxgy2J5kw0+v8i/vIn6OZ9fhMXpgcPjg9vrh9vrhyt088HjE+H1+eHx+aX7/sBXnxg45ofL48WuSgF7V++Hze2HyeGB2eGRvjqlr802d4etgh2RywRkJ2qQl6xFQYoOgzIMKMlMwOBMA3ISNVy+hugERTTBZcWKFUhMTAQA+P1+rF69Gjt27GhzzsUXXxy90sXIjBkzMGPGjA6fE0URS5YswT333INLL70UgDReMzMzE8uWLcMtt9zS7jUulwsu1+GZc2azGYC0ObvH035sTLQEr93Rexyol8qQn6yFz+eFr/0uU2HJ0CugVsjg8vpR2mA+oS7t/uBYdUI9g3USPqfHh331VhxqdqDa5EB1qxNVrQ7UW1ww2T0wOb2wOL3Hv1BY5EDFweOeZdQokJagQlqCGmkJKqQmqJGmVyHTqEZeshZ5yVpkGtRQyGUdvt7rjVZ5+y7+jMSf7qqTcK8viOLRy4p2TCbr+AexzcUEAb6uppIeIghCm27ogwcPYuDAgdi8eTPGjRsXOu+SSy5BUlISXnvttXbXWLRoERYvXtzu+LJly6DT9Uy4+rZWwDulcoxI9uPmoZ130YTj0W1y1NgF3DzUhxHJ4f2lT0Txyy8CLS6g2i6g2h74ahPQ4AREhNcKJxdEKARAIQvcBEApA+RC4CaTzgk9bnP88E2rAHQKEVq5dF+rAHRyEQlKwKCUrk1EsWG323H11VfDZDLBaDR2el7YLYt+/4kFjt6itrYWAJCZmdnmeGZmJsrLyzt8zfz583HHHXeEHpvNZuTn52P69OnH/OafKI/Hg1WrVmHatGlQKtvu0LL98z1AaTkmDi3EzJlDT+h9PjNvQ83OOqQVDcfMKQNO6Fp93bHqhHpGf64Tm8uLsiY7ypvsKG2yo7zJhtImO/bVW2FzdfyHfbJOiYHpeuQkapGbpEF2kgZZRg2SdEokapRI1Cpg1Cqh7KQlLxz9uU7iEesj/nRXnQR7Qo+HO7h04ugxLqIodjruRa1WQ61uPy5QqVR2yw9eR+9T2eoEABSlJZxwGQZmJAA761DW7OAvkjB1V91T+PpqnTg9PhxqtuNggw1lTTaUNdpwsFH6evTC0kdSyWUoyUjA0GwDhmUZMTTbgCFZBqQnqLttjF9frZPeivURf2JdJ+FeO+ywuH//fphMJkyYMCF0bPXq1XjwwQdhs9kwa9YsLFiw4LjX2bNnD95880188803KCsrg91uR3p6OsaNG4fzzjsPl112WYfBq7tkZWUBkFoYs7OzQ8fr6+vbtTbGs4pmaSb0iayxGBTcI7qUay0S9SifX8SOKhPWHWjCrhozdteYcbDBCv8xRoek6FUoTNWhME2P4jQ9CtP0GJxpQFGa/oRaB4mo/wg7LN51110YOXJkKCyWlpbioosuwumnn47Ro0fjkUcegU6nw9y5czt8/ZYtWzBv3jx88803mDJlCiZNmoRZs2ZBq9WiubkZO3bswD333IPbbrsN8+bNw9y5c3skNBYVFSErKwurVq0KjVl0u91Ys2YNHnvssW4vT1cFl83JTYpCWEwPrLXYyLUWibqb2+vH6t11+GR7Db7d3wiTo+MFpQvT9CgKhMFgKCxK1SNRx5YiIjoxYYfFjRs3Yt68eaHH//nPfzB48GCsWLECADB69Gg888wznYbFWbNm4a677sLbb7+NlJTOt41bv349nnrqKfztb38Lq6WyK6xWK/bv3x96XFpaiq1btyIlJQUFBQWYO3cuHn74YQwaNAiDBg3Cww8/DJ1Oh6uvvjom5Yk2q8sb+g8lJ0lzwtcLtizWmV2wurxIUHP0AlGs7ao2478bK/C/rVVoOWLHEYNagckDUzG2IAnDsowYlm1EprH7uo6JqP8J+3/9xsZG5OXlhR5/9dVXuOiii0KPp06div/7v//r9PX79u2DSnX8fTUnT56MyZMnw+12h1u0iG3cuBFnnXVW6HFwcsrs2bPx6quvYt68eXA4HJgzZ05oUe6VK1f2qjUWAWnJCYPmxFsVEnVKpOpVaLK5Udpgw6i8xBO+JhG112Jz439bq/DOpkrsrD488DzTqMYvxuVh2vAMjMlL6nSZGCKiWAg7LKakpKCmpgb5+fnw+/3YuHEjbr/99tDzbrcbx1qFp7OgWFlZiZycnHZL84QTLLtq6tSpxyyrIAhYtGgRFi1aFLMyxFJVS6ALOjl6y/YUp+vRZHPjYKOVYZEoinx+EWv3NeDdjZVYtasutBuJUi5g2vBMXDExH6eXpDEgElGPCTssnnnmmXjggQfw3HPP4Z133oHf72/TOrdr1y4UFhZGXIDhw4dj69atKC4ujvi11LHK0HhFbdSuWZyWgA1lLTjISS5EUVHaaMM7Gyvw/uYq1JqdoePDs424cmIeLhmbi2R97P5oJiIKV9hh8cEHH8T06dNRWFgImUyGv//976Ht/wDg3//+N84+++yICxDmmuAUgWA3dF5yFMNiaJILwyJRV1ldXizfXoN3NlVgQ1lL6HiSTolZY3NxxcQ8jMhhyz0RxZeww2JxcTF2796NXbt2IT09HTk5OW2eX7x4cZsxjdRzgt3Q0ZjcElScLk1yOdjAGdFEkRBFET+WNuOdTZVY/lMN7G5pMWyZAJwxOB1XTszHOcMyoFbIe7ikREQdiygsbtiwAWPGjOnw+c6OH8+CBQuOOTuaIhfNZXOCitKklsXSRtsxFygnIkmNyYH3NlXi3U2VKGuyh44XpelxxcQ8XDouD1mJ0fuDjogoVsIOi2VlZTHZ93n+/PlRv2Z/d3iCS/S6oQtSdJDLBNjdPtSanchOjN61ifoKp8eHVbvq8M6mSnyzrwHBUTZ6lRwXjs7BFRPzMGFAMv/YIqJepdsWzHv00Ufxxz/+ETrd8Vu7fvjhBzQ2NuKCCy7ohpL1LR6fH3UWabB8NLuhVQoZBqTqcLDBhgP1NoZFogBRFLGlohUfbK7CR9uq2yyafXJRCq6YmI+Zo7KgU3F9UiLqnSL67bVr1y7U1tYe85zRo0d3+tqCggJcccUVuPjiizFx4kSkp6cDALxeL3bt2oVvv/0Wb7zxBmpqavD6669HUjQKqDU5IYpSuEvTR3cHnJL0BBxssGFfvQWnDUqL6rWJeptDTXZ8sKUKH26tQukRE79yEjW4bEIeLp+QhwGp+mNcgYiod4goLJ5zzjkdzl4WBCE0jq2zrurXX38d27dvx7PPPotrrrkGJpMJcrkcarUadrs0nmfcuHG4+eabMXv27B7dH7o3q2w5vGyOTBbdrq5BmQlYuasO++o5yYX6H1EUsb/eilW767BqVx22HGoNPadVynH+yCz8YlwuTi1JgzzKP3tERD0porD4ww8/hFoDu2L06NF44YUX8Pzzz2P79u0oKyuDw+FAWloaxo4di7Q0tladqOCyOdHsgg4qyZBmRO9nWKR+wunxYXN5C1b/XI8vdteh/IiJKoIAnDowDZeOz8V5I7Kg5zaYRNRHRfTbraCgABkZGSf8poIgYMyYMV2eQU2dq4rBgtxBgzKk7Q4ZFqmv8vlF/FRlwnf7G7HuQCM2lrXA5fWHnlcpZDh1YCrOHZ6Jc4dlItPI2cxE1PfxT+E+JjQTOorL5gQFF+ZutrnRZHUhNYFDBah3s7q82FbRis3lLdh8qAWbyltgdnrbnJNhUOP0QemYNjwDpw9KZwsiEfU7EW33F8v9mik6Qi2LUVw2J0inUiAvWYvKFgf211sZFqlX8fulMYfbKlqx+VALNh9qxZ5aM/xHDcM2aBQ4pTgVp5Wk4dSSVAxMT+BSN0TUr4UdFr/66isAhxfnTk1NbfN8a2srxo8fj4MHD0a3hBSRWI5ZBIBBGQlSWGyw4uTi1OO/gKgHiKKIqlYHtleasKW8GV/vlOGezV/B6vK2Ozc3SYtxBUkYX5CMCQOSMSLHCIVc1gOlJiKKTxH3p3S2OLfL5UJVVVVUCkVdE/wPEgDyYtANDUiTXL7a04B9dRy3SPGj0erC9spWbKswYXtlK7ZXmtBkcx9xhgyAFxqlDCNzEkPhcPyAZI47JCI6jrDD4kcffRS6v2LFCiQmHt7s3ufzYfXq1SgsLIxq4SgyjVY3XF4/BAEx20YsOMnlAPeIph5idnrwU6UJ2ysPB8PgH0lHUsgEDM02YGSOEWgqxzUzTsOwnCS2GhIRRSjssDhr1iwA0kzm2bNnt3lOqVSisLAQf/vb36JaOIpMsAs6w6CGShGb/xAHBpbPYcsixZIoijA7vDjYaMX++iNuDdY2y9cECQIwMD0Bo/MSMSYvCaPzEjEs2wiNUg6Px4Ply8swNMvAoEhE1AVhh0W/X1o+oqioCBs2bOCaiHEolsvmBAXXWqw1O2FxemDQKGP2XtQ3iaIIs9OLerMTtWYn6swu1JmdqG51oLrVgapWB6pbnR2OLwzKS9aGQuHovCSMzDXy3yIRUYxEPGaxtLS00+eqqqqQm5t7QgWirgstm5Mcm/GKAJCoVSLDoEa9xYX99VaMK0iO2XtR7+b0+LCz2oS9dVYcqLfiYKMNpY021JqccHg63unpaBkGNQZlJqAkPQElGQkYmJGAoVlGpOi5MgMRUXeJyoJhtbW1eOihh/DSSy/B4Wg/doi6R3e0LALStn8Mi3Q0p8eH7w82Ye3eRmwqb8bOajO8R69Lc4RErRKZRjUyjRpkGjXITtQgN0mL3GQtcpK0yE3SQqOUd+MnICKijoQdFltbW/H73/8eK1euhFKpxN13340//OEPWLRoEf76179ixIgRWLp0aSzLSsdxOCzGdnbnoAwDvtvfxJ1cCM02Nz7bUYPVu+ux7kAjnB5/m+fTDWoMzzZiYHoCitP1KE7XIzdJi0yjhkGQiKiXCDssLliwAGvXrsXs2bPx+eef4/bbb8fnn38Op9OJzz77DGeeeWYsy0lhONwNHduWxeC4xT11lpi+D8Unq8uLlTtr8dG2any7r7FN62F2ogZTh6TjlOJUjC9IRl6ylgtaExH1cmGHxU8//RSvvPIKzj33XMyZMwclJSUYPHgwlixZEsPiUSQOtyzGbswiAAzLNgIAdteYY/o+FD9cXh++3tOAj7ZW44vddW32Sx6Za8SMkdk4e2gGhmYZGA6JiPqYsMNidXU1hg8fDkDaxUWj0eCmm26KWcEoMlaXFyaHB0Dsdm8JkgIBUGd2cY/oPszr8+P7g834aFsVPttRC8sReyYXp+lx0ZgcXDw2BwPTE3qwlEREFGsRLZ2jVB5emkIul0Ov18ekUBS54BqLRo0i5kuI6NUKFKbqUdpow+4aC04bxLDYV/j9IjYfasHH26rx6U81aLQe3gUly6jBRWOyccnYXIzIMbIFkYionwg7LIqiiBtuuAFqtRQMnE4nbr311naB8f33349uCSks3bFszpGGZxtR2mjDrhoTThvENTd7M6fHhx9Lm7FmbwM+31HbZjeUJJ0SM0Zm45KxOZhUmAKZjAGRiKi/CTssHr1ry7XXXhv1wsQLr9eLRYsW4T//+Q9qa2uRnZ2NG264AX/5y18gk8XnDhCV3bRsTtCwbAM+/akGu6o5brG3EUURBxqsWLO3EWv3NuD7g01txiAmqBWYPjwTF43NwWklaVBy1xMion4t7LD4yiuvxLIcceWxxx7D888/j9deew0jRozAxo0bceONNyIxMRF/+tOferp4HarupmVzgobnBCe5cEZ0b2B2evDdvkas3deANXsaUG1ytnk+06jGGYPScc6wDEwdksFlbYiIKCQqi3L3NevXr8cll1yCCy64AABQWFiIN998Exs3buzwfJfLBZfLFXpsNkutbR6PBx6PJ2blDF7b4/GgoskGAMgyqmP6nkElaVJ39/4GK6x2J9QMFwDa1klPK2204as9DfhqTwM2lre2WeJGpZBh4oAknDEoDaeXpGJQRsIRYxD98By1XmJvFk91QhLWSXxhfcSf7qqTcK8viKLY+RYL/dSjjz6K559/HitXrsTgwYOxbds2TJ8+HUuWLMGvfvWrducvWrQIixcvbnd82bJl0Om6Zwzhkh1ylFoE3DDYh3Gpsa9SUQTu2SiHzSvgzlFe5HNCbFyweICNDQJ+bJCh2t52fGGGRsSwJBFDk0SUGEWomO+JiPo1u92Oq6++GiaTCUajsdPzGBY7IIoiFixYgMceewxyuRw+nw8PPfQQ5s+f3+H5HbUs5ufno7Gx8Zjf/BPl8XiwatUqTJs2DWcvWYdaswvv3DwJY/OTYvaeR5r9ykasO9iMh2eNwBUTuCc40LZOjlw9INa2HGrF0nXl+GJ3fagFUSkXcFJhMs4ako6zBqdjQGr3/OESb3qqTqhzrJP4wvqIP91VJ2azGWlpaccNi+yG7sDbb7+NN954A8uWLcOIESOwdetWzJ07Fzk5Oe0m+gCAWq0OzRI/klKp7J4fPJkc9RYprA5IN3TbD/uI3ESsO9iM3bVW/oI5SnfUvSiKWLGzDi+sPYAth1pDx8fkJ+Hy8bm4aEwOknSqmJahN+m2n0cKG+skvrA+4k+s6yTcazMsduCuu+7C3XffjauuugoAMGrUKJSXl+ORRx7pMCz2tFqzE35RGoeWpu++NQ/HFSQDKMXG8pZue0+SQuLXexvwt5V7sKNKGh+rksswa1wOfn1aEYZmxa41m4iI+h+GxQ7Y7fZ2S+TI5XL4/fE56L+6VZrZmpuk7dZ18CYOSAYA7Kk1w+z0wBjjxcAJWH+gCX9buScU0PUqOW44tRCzpxQiw9A9M+GJiKh/YVjswEUXXYSHHnoIBQUFGDFiBLZs2YInn3wSv/71r3u6aB0KhsVYb/N3tAyjBgUpOhxqtmPLoVacOTi9W9+/P9lRZcJjn/+Mb/Y1ApBaka8/ZQB+N3Ugt1skIqKYYljswDPPPIN7770Xc+bMQX19PXJycnDLLbdg4cKFPV20DlV184LcR5o4IBmHmu3YVNbMsBgD5U02/HXlXny8rRoAoJAJuGpSPv5w1iBkJbIlkYiIYo9hsQMGgwFLlizBkiVLerooYQkusJyb1P2zXScWpuD9LVX4say529+7L2u0uvDM6n34zw+HQrObLxmbg/+bNgQF/XRWMxER9QyGxT6gp7qhAeCU4hQAwKbyFlhdXiSo+U/qRFS3OvCvbw7irR8r4PD4AABnDE7HvPOGYGRuYg+XjoiI+iP+z94HhLb6S+7+buiiND0GpOpQ3mTHt/sacf7IrG4vQ28niiK2VZrwn+/L8eHWKnh8UkvimLxE/Pn8oZhSktbDJSQiov6MYbGXE8XD3dB5PdANLQgCzhqSgVfXleHrPfUMixHYX2/Fql11+N/WKvxce3iP7VOKUzBnaglOH5R2xBZ8REREPYNhsZezeACX1w9BQI9NeDh7qBQWv9pTD1EUGXCOIooiWu0eVLTY8XOtBZvKWvBDaRPKmuyhc9QKGWaOysa1pwzAhMCSRERERPGAYbGXa3FLXzMMaqgUsmOfHCMnF6cgQa1AndmFjeUtOKkwpUfK0ZNcXh8qmu04WG/B2hoB2z7bg8pWJypaHKhstsPi8rZ7jVIuYPLANEwfnomLRucgUcd1KomIKP4wLPZyLS6pFa8nls0JUivkmDkqC//dWIl3N1b2+bBocXqw5VArNpY146cqEw422lDRbIc/tMu6HCgrb/e6dIMaRWl6TBiQjIkDkjGpKAUGLmRORERxjmGxl2uWtoRGbnLPLqdy2fg8/HdjJT79qQaLLh4BrUreo+WJthqTA59ur8GnP9VgW0XrEcHwsAS1AnnJWihdJpw0rAgD0hKQn6JFQYoOeck6aJR963tCRET9A8NiLxdsWeyJZXOOdFJhSmg3l4+2VeGXJxX0aHmioc7sxPKfavDp9pp2+1/np2hx0oAUjCtIQkmGAQPT9Ug3qOH1erF8+XLMnDEkppu/ExERdReGxV6uJdCymNeD3dAAIJMJuPaUAjy8/Ge8sPYgLp+QD3k37lMdLZUtdqzYWYcVO2qxobwZ4hEtiCcVJuOCUdk4b2QWshN79vtNRETUXRgWe7nm4JjFHlhj8WhXnzwA//hyPw422LBiZy1mjsru6SIdk8fnR1WLA7tqzNhY1oLvDzZhV425zTkTBkgBceaobG6vR0RE/RLDYi8XbFnM6eGWRUAas3fDlEL8/cv9+OuKPTh3WGbMZ2ibnR6UNdpQ1mSHye6G2emF2eGBw+OD0+ODw+OHM3Df5fHD6ZXu21w+1Jqd8B01+FAmSF3q543Iwvkjs+Li+0pERNSTGBZ7MavLC7uv52dDH+m3ZxRj2Y8VONhowyvfleKWMwdG9fpOjw9f/lyPL3+ux/cHm1DZ4jih62mUMhSnJUgzlAuTcVpJGlIT1FEqLRERUe/HsNiL1QT2hDZqFHGzBItBo8Sfzx+Cu97djqe+2ItzhmWiJCPhhK9b2WLHS9+U4r1Nle3WLEw3qFGYqkOqXg2jVvpe6FRyaJTBmwwaxRH3A8dzk7TIMKgh64VjK4mIiLoLw2IvVmWSWtXirav0svF5+N/Wany7vxFz396Cd2+d0uVlYyxOD/62ci/+/X15qMs4N0mLC0Zn49SSNIwrSIIxToIyERFRX8Sw2ItVB1oWc+Js4oVMJuCvV4zB+U+vxY4qM+a9ux1PXzU2om0ARVHE5ztqsejjnagzSwMzTytJw61nDsSUgalsDSQiIuomDIu9WDAs5vbwGosdyUrU4Lmrx+P6pT/io23VSNIpseiiEWGFvIpmO+77aCe+/LkeAFCYqsODs0bhtEFpsS42ERERHYVhsRerao3PbuigKSVpeOyy0bjz3W14fX05mm1uPHbZaOjVHf+zc3p8ePnbUvzjy/1weHxQygXceuZA/P6sEu5+QkRE1EMYFnuxalN8dkMf6bIJeRAEYN672/HJ9hpsq2zFndOH4LwRWaEAaLJ78NH2aryw5kBodvOkohQ8/IuRKMkw9GTxiYiI+j2GxV6sOtSyGL9hEQAuHZ+HghQdbntzCyqaHfjTW1uhU8lRkKKD2+dHWaMttNdyllGDeecPwS/G5UY0xpGIiIhig2Gxl/L4/Ki3SBM/4mWNxWOZWJiCL+44Ey99U4q3NhxCjcmJn2stoecHZybglycV4FeT8qFT8Z8lERFRvOD/yr1UrckJvwgoBBGpelVPFycserUCfzp3EG47uwQHGqyobHVArZAWxeZWekRERPGJYbGXCo7tS1Kj1y0jI5MJGJRpwKBMjkckIiKKd7HduJdiJjheMUUtHudMIiIioq5jWOylzhicjpevH4/puQyLREREFDvshu6l0g1qnDEoDdZ9DItEREQUO2xZJCIiIqJOMSwSERERUafYDR0Doih1DZvN5pi+j8fjgd1uh9lshlKpjOl7UXhYJ/GHdRJ/WCfxhfURf7qrToI5JZhbOsOwGAMWi7TYdH5+fg+XhIiIiOjYLBYLEhMTO31eEI8XJylifr8f1dXVMBgMMd2yzmw2Iz8/HxUVFTAajTF7Hwof6yT+sE7iD+skvrA+4k931YkoirBYLMjJyYFM1vnIRLYsxoBMJkNeXl63vZ/RaOQPeJxhncQf1kn8YZ3EF9ZH/OmOOjlWi2IQJ7gQERERUacYFomIiIioUwyLvZharcZ9990HtVrd00WhANZJ/GGdxB/WSXxhfcSfeKsTTnAhIiIiok6xZZGIiIiIOsWwSERERESdYlgkIiIiok4xLBIRERFRpxgWiYiIiKhT3MElBrpruz8iIiKiruJ2fz2ouroa+fn5PV0MIiIiouOqqKg45jbFDIsxYDAYACDmG4B7PB6sXLkS06dPh1KpjNn7UPhYJ/GHdRJ/WCfxhfURf7qrTsxmM/Lz80O5pTMMizEQ7HqO9QbgHo8HOp0ORqORP+BxgnUSf1gn8Yd1El9YH/Gnu+vkeEPmOMGFiIiIiDrFsNjPeH1+fH+wCSaHp6eLQkRERL0Au6H7EafHh2te+gGbyluQpFPi3VsnoyTj2OMUiIiIqH9jy2I/8sKag9hU3gIAaLV7cN9HO3u4RERERBTvGBb7CafHh1fWlQIA7jpvCGQC8N3+JhxqsvdwyYiIiCieMSz2Eyt21qLV7kFesha3njkQUwamAQA+3l7dwyUjIiKieNarw+LatWtx0UUXIScnB4Ig4MMPPzzua9asWYMJEyZAo9GguLgYzz//fLtz3nvvPQwfPhxqtRrDhw/HBx98EIPSd6+VO+sAALPG5kIuEzB9RCYAYP2Bpp4sFhEREcW5Xh0WbTYbxowZg3/84x9hnV9aWoqZM2fi9NNPx5YtW7BgwQL88Y9/xHvvvRc6Z/369fjlL3+J6667Dtu2bcN1112HK6+8Ej/88EOsPkbMubw+fL2nHgAwbbgUEk8uSgUAbCpvgdvr77GyERERUXzr1bOhZ8yYgRkzZoR9/vPPP4+CggIsWbIEADBs2DBs3LgRf/3rX3HZZZcBAJYsWYJp06Zh/vz5AID58+djzZo1WLJkCd58882of4busO5AE2xuHzKNaozKTQQADMpIQLJOiRa7B7trzBiTn9SzhSQiIqK41KvDYqTWr1+P6dOntzl23nnn4eWXX4bH44FSqcT69etx++23tzsnGDA74nK54HK5Qo/NZjMAaQV2jyd26xkGr3289/hiZy0A4Kwh6fD5vPD5pOMjcoz4dn8Ttle0YHiWPmbl7E/CrRPqPqyT+MM6iS+sj/jTXXUS7vX7VVisra1FZmZmm2OZmZnwer1obGxEdnZ2p+fU1tZ2et1HHnkEixcvbnd85cqV0Ol00Sn8MaxateqYz6/+SQ5AgM5UjuXLy0LHNXYZABk++2EHjA3bY1rG/uZ4dULdj3USf1gn8YX1EX9iXSd2e3grovSrsAi03/9QFMV2xzs651j7Js6fPx933HFH6HFwY+7p06fHfG/oVatWYdq0aZ3uHdlsc6N2/dcAgJsvPQepelXoOWFHLb54ezusyiTMnHlKzMrZn4RTJ9S9WCfxh3USX1gf8ae76iTYE3o8/SosZmVltWshrK+vh0KhQGpq6jHPObq18UhqtRpqtbrdcaVS2S0/eMd6n61V0mznkowEZCW17WoempMEAChttEOhUBx3I3EKX3fVPYWPdRJ/WCfxhfURf2JdJ+Feu1fPho7U5MmT2zXprly5EhMnTgx9wzo7Z8qUKd1WzmjaUNoMAJhUlNLuuYIUHQQBsDi9aLK5u7toRERE1Av06rBotVqxdetWbN26FYC0NM7WrVtx6NAhAFL38PXXXx86/9Zbb0V5eTnuuOMO7N69G0uXLsXLL7+MO++8M3TOn/70J6xcuRKPPfYYfv75Zzz22GP44osvMHfu3O78aFHzY5kUFk/uICxqlHLkJmkBAAcbbN1aLiIiIuodenVY3LhxI8aNG4dx48YBAO644w6MGzcOCxcuBADU1NSEgiMAFBUVYfny5fj6668xduxYPPDAA/j73/8eWjYHAKZMmYK33noLr7zyCkaPHo1XX30Vb7/9Nk4++eTu/XBR4PL6sLtGGo8wviC5w3OK0xMAAAcbrN1WLiIiIuo9evWYxalTp4YmqHTk1VdfbXfszDPPxObNm4953csvvxyXX375iRavx+2ptcDjE5GkUyIvWdvhOcVpeqzd24DSRrYsEhERUXu9umWRju2nKhMAYFRuYqeTV4rTpUkvB9gNTURERB1gWOzDdgTC4sjAri0dKUqTwmJpI7uhiYiIqD2GxT7syJbFzgTHLB5qtsPr4x7RRERE1BbDYh/l8vqwp9YC4NhhMduogUoug8cnosbk7K7iERERUS/BsNhHHai3weMTYdQoOp3cAgAymYDsJA0AoLrV0V3FIyIiol6CYbGP2h9YCmdwpuG4O7ME11qsYlgkIiKiozAs9lH766Qu6EGZCcc9NxQWWxgWiYiIqC2GxT5qX73UsjgwPYywmMyWRSIiIuoYw2IfFQyLgzINxz2X3dBERETUGYbFPsjj86MssCPLoIwIWhbZDU1ERERHYVjsg8qbbPD6RehVcmQnao57/pEti8faPpGIiIj6H4bFPmhfndQFXRLGTGgAyE7UQhAAl9ePRqs71sUjIiKiXoRhsQ86GOiCLg5s5Xc8KoUMGQY1AK61SERERG0xLPZBh5rsAIABqbqwXxPsimZYJCIioiMxLPZB5c1Sy2JhangtiwCQFRjbWGvmln9ERER0GMNiH1QeaFksiKBlMdPIsEhERETtMSz2MU6PDzUmKfANSIk8LNabXTEpFxEREfVODIt9TEWz1KpoUCuQoleF/bqsYMuiiS2LREREdBjDYh9zZBd0OMvmBAVbFuvYDU1ERERHUHTlRXv27MEzzzyD3bt3QxAEDB06FLfddhuGDBkS7fJRhMqbI58JDQCZRmnpHIZFIiIiOlLELYvvvvsuRo4ciU2bNmHMmDEYPXo0Nm/ejJEjR+Kdd96JRRkpAuVN0kzoARHMhAYOz4a2uX2wOD1RLxcRERH1ThG3LM6bNw/z58/H/fff3+b4fffdhz//+c+44oorolY4ilxlYH/n/OTIWhZ1KgUMGgUsTi/qzC4YNMpYFI+IiIh6mYhbFmtra3H99de3O37ttdeitrY2KoWirgsuqp2TdPw9oY/GcYtERER0tIjD4tSpU/HNN9+0O/7tt9/i9NNPj0qhIvXcc8+hqKgIGo0GEyZM6LB8QTfccAMEQWh3GzFiROicV199tcNznM74D1HBZXNyAjuyRIIzoomIiOhoEXdDX3zxxfjzn/+MTZs24ZRTTgEAfP/993jnnXewePFifPTRR23OjbW3334bc+fOxXPPPYdTTz0VL7zwAmbMmIFdu3ahoKCg3flPP/00Hn300dBjr9eLMWPGtOs+NxqN2LNnT5tjGk3krXXdyebywuSQxhtmJ55Ay6KFYZGIiIgkEYfFOXPmAJBa85577rkOnwMAQRDg8/lOsHjH9+STT+I3v/kNbrrpJgDAkiVLsGLFCvzzn//EI4880u78xMREJCYmhh5/+OGHaGlpwY033tjmPEEQkJWVFdvCR1mNSeqCNmgUXRpzmJUYmBHNlkUiIiIKiDgs+v3+WJSjS9xuNzZt2oS77767zfHp06dj3bp1YV3j5ZdfxrnnnosBAwa0OW61WjFgwAD4fD6MHTsWDzzwAMaNG9fhNVwuF1yuwzufmM1mAIDH44HHE7uZxcFrB78earICALKNmi69b5peCpjVrY6YlrsvO7pOqOexTuIP6yS+sD7iT3fVSbjX79I6i/GisbERPp8PmZmZbY5nZmaGNdmmpqYGn332GZYtW9bm+NChQ/Hqq69i1KhRMJvNePrpp3Hqqadi27ZtGDRoULvrPPLII1i8eHG74ytXroROF9ms5K5YtWoVAGB9nQBADrnbjOXLl0d8ncpm6fV7K+q69Ho6LFgnFD9YJ/GHdRJfWB/xJ9Z1Yrfbwzov7LD45Zdf4g9/+AO+//57GI3GNs+ZTCZMmTIF//znP3HGGWdEVtIoOHqnElEUw9q95NVXX0VSUhJmzZrV5vgpp5wSGo8JAKeeeirGjx+PZ555Bn//+9/bXWf+/Pm44447Qo/NZjPy8/Mxffr0dt+raPJ4PFi1ahWmTZsGpVKJfav3AwcPYsygAsycOTzi6+VVmvDynh/gkmkwc+aZMShx33d0nVDPY53EH9ZJfGF9xJ/uqpNgT+jxhB0WlyxZgt/+9rcdhp/ExETccssteOqpp7o1LKalpUEul7drRayvr2/X2ng0URSxdOlSXHfddVCpjr2Hskwmw0knnYR9+/Z1+LxarYZarW53XKlUdssPXvB96ixuAEBesq5L75ubkgAAaLC6IZMrIJeFv10gtdVddU/hY53EH9ZJfGF9xJ9Y10m41w576Zxt27bh/PPP7/T56dOnY9OmTeFeLipUKhUmTJjQrpl21apVmDJlyjFfu2bNGuzfvx+/+c1vjvs+oihi69atyM7OPqHyxlpw2ZzsxMiXzQGAtAQVZALg84tosrmO/wIiIiLq88JuWayrqztmAlUoFGhoaIhKoSJxxx134LrrrsPEiRMxefJkvPjiizh06BBuvfVWAFIXcVVVFV5//fU2r3v55Zdx8sknY+TIke2uuXjxYpxyyikYNGgQzGYz/v73v2Pr1q149tlnu+UzdVVwQe7sLizIDQAKuQzpBjXqzC7UmVzIMMT3UkFEREQUe2GHxdzcXPz0008oKSnp8Pnt27f3SMvbL3/5SzQ1NeH+++9HTU0NRo4cieXLl4dmN9fU1ODQoUNtXmMymfDee+/h6aef7vCara2tuPnmm1FbW4vExESMGzcOa9euxaRJk2L+eU5EcOeVrrYsAtJai3VmF+rMToxC4vFfQERERH1a2GFx5syZWLhwIWbMmNFucWqHw4H77rsPF154YdQLGI45c+a0WePxSK+++mq7Y4mJicecAfTUU0/hqaeeilbxuoXN5YXNLa1rmWFoP34yXNLC3CbUcss/IiIiQgRh8S9/+Qvef/99DB48GH/4wx8wZMgQCIKA3bt349lnn4XP58M999wTy7LSMTRYpDGGOpUcenXXV0TKNAYW5mZYJCIiIkQQFjMzM7Fu3Tr87ne/w/z58yGKIgBp2ZrzzjsPzz333HFnIAPAnj178Oabb+Kbb75BWVkZ7HY70tPTMW7cOJx33nm47LLLOpxZTMfWYJXCYvoJtCoCh/eHZlgkIiIiIMJFuQcMGIDly5ejpaUF+/fvhyiKGDRoEJKTk4/72i1btmDevHn45ptvMGXKFEyaNAmzZs2CVqtFc3MzduzYgXvuuQe33XYb5s2bh7lz5zI0RiDYspiecGLfs4xAWKw1czY0ERERdXEHl+TkZJx00kkRvWbWrFm466678PbbbyMlJaXT89avX4+nnnoKf/vb37BgwYKuFK9fCobFDGN0Whbr2bJIRERECDMsXnrppWFf8P333+/w+L59+467+DUATJ48GZMnT4bb7Q77PSl6LYuZoZZFhkUiIiIKc1HuxMTE0M1oNGL16tXYuHFj6PlNmzZh9erVSEzsfKmVzoJiZWUl/H5/2OdTx+otUrg70TGLwQkurXYPnB7fCZeLiIiIerewWhZfeeWV0P0///nPuPLKK/H8889DLpcDAHw+H+bMmdOlfZCHDx+OrVu3ori4OOLX0mGhlsUTDIuJWiXUChlcXj/qzS4UpOqiUTwiIiLqpcLe7i9o6dKluPPOO0NBEQDkcjnuuOMOLF26NOICBGdV04mJ1mxoQRBCXdF1FnZFExER9XcRh0Wv14vdu3e3O7579+4Ou5Ope4QmuERhi77gJJdaE8MiERFRfxfxbOgbb7wRv/71r7F//36ccsopAIDvv/8ejz76KG688caIC7BgwYJjzo6m4/P7RTRapQlBJ9qyCByeUc21FomIiCjisPjXv/4VWVlZeOqpp1BTUwMAyM7Oxrx58/B///d/ERdg/vz5Eb+G2mpxeODzixAEIEV/4hODuDA3ERERBUXcDS2TyTBv3jxUVVWhtbUVra2tqKqqwrx589qMYzzao48+esz9mI/0ww8/4NNPP420aP1WY6ALOlmnglIecZW2k8mFuYmIiCjghJKF0WgMewb0rl27UFBQgN/97nf47LPP0NDQEHrO6/Vi+/bteO655zBlyhRcddVVXZpZ3V8126Uu6NQotCoCQGYiWxaJiIhIElY39Lhx4yAIQlgX3Lx5c4fHX3/9dWzfvh3PPvssrrnmGphMJsjlcqjV6lCL47hx43DzzTdj9uzZ3OovAs02DwAgOVph0cAxi0RERCQJKyzOmjUrKm82evRovPDCC3j++eexfft2lJWVweFwIC0tDWPHjkVaWlpU3qe/aYlyy2LWES2LoiiG/YcCERER9T1hhcX77rsvqm8qCALGjBmDMWPGRPW6/VWzTQqL0ZjcAhwes+j0+GF2eJGoU0blukRERNT7hD1mcenSpXC5OOEhHgW7oaMVFjVKOZIDAbHa5IjKNYmIiKh3Cjss/va3v4XJZAo9zsnJQVlZWSzKRBEKdkNHKywCQE6SFgBQw7BIRETUr4UdFo/els9isXDHljgR7W5o4HBYrGrlJBciIqL+7MQX5aMe12KPbjc0AOQGwmJ1K1sWiYiI+rOww6IgCG1mxR79mHpObFoWpUkuDItERET9W9jb/YmiiMGDB4cCotVqxbhx4yCTtc2bzc3N0S0hHZMoxqZlMTuRLYtEREQUQVh85ZVXYlkO6iKHD/D6pfGksRizWM0xi0RERP1a2GFx9uzZsSzHCXnuuefwxBNPoKamBiNGjMCSJUtw+umnd3ju119/jbPOOqvd8d27d2Po0KGhx++99x7uvfdeHDhwAAMHDsRDDz2EX/ziFzH7DF1llRoVkaBWQK3ofG/uSAXHLNaanfD5RchlHHJARETUH0U8waW4uBhNTU3tjre2tqK4uDgqhYrE22+/jblz5+Kee+7Bli1bcPrpp2PGjBk4dOjQMV+3Z88e1NTUhG6DBg0KPbd+/Xr88pe/xHXXXYdt27bhuuuuw5VXXokffvgh1h8nYjav9DVZH92Fs9MNaihkAnx+EfUWti4SERH1V2G3LAaVlZXB5/O1O+5yuVBZWRmVQkXiySefxG9+8xvcdNNNAIAlS5ZgxYoV+Oc//4lHHnmk09dlZGQgKSmpw+eWLFmCadOmYf78+QCA+fPnY82aNViyZAnefPPNqH+GE2H1SC1+Kfro7qUtlwnIStSgssWB6lZHaAwjEZ04p8eHGpMTjVYXTHYPTA7p1urwwOzwwOPzwy8Cfr8IlUIGo1YBg0aJtAQ1itJ0KEzVI0Wv4iRDIuoWYYfFjz76KHR/xYoVSExMDD32+XxYvXo1ioqKolu643C73di0aRPuvvvuNsenT5+OdevWHfO148aNg9PpxPDhw/GXv/ylTdf0+vXrcfvtt7c5/7zzzsOSJUs6vJbL5Wqzu43ZbAYAeDweeDyeSD5SRDweT6gbOkmriPp7ZQfCYnmjFaNzDFG9dl8VrINY1jtFprvrxOXxodbiQq3JiRqTU/pqdqLW5JIem52hSWknIlGrwKCMBAzOTMDgjAQMzjRgcGYCErXxvz0nf07iC+sj/nRXnYR7/bDD4qxZswBIS+YcPX5RqVSisLAQf/vb38IvYRQ0NjbC5/MhMzOzzfHMzEzU1tZ2+Jrs7Gy8+OKLmDBhAlwuF/7973/jnHPOwddff40zzjgDAFBbWxvRNR955BEsXry43fGVK1dCp9N15aOFzeaVWhYcLfVYvnx5VK8t2GQAZFj9/VbIK7dE9dp93apVq3q6CHSUrtaJKAIuvzQ+2OoBLB4h8BWweo+47xFgdkvHwqGSiTAqAZ0C0ClEaBXSfa0CUAoiBAEQAHj9Ahw+aTKbyQ00OAS0ugGTw4uN5a3YWN7a5rpGpQiDEtAqRGjlgFIGKGSBr4J0X/oqHnFf+qpVAClqEalqQBNxv1Pk4v3nxCcCTi/gDHz/nV7A4ROkx4HjHr8Anyid6z/iqzz4fZVJ9Rm8rw7UhTpQNyqZCJUcUMkCt8B9uQB0d8NxvNdHfxTrOrHb7WGdF/avg+BuLUVFRdiwYQPS0tK6VrIYOLorRhTFTrtnhgwZgiFDhoQeT548GRUVFfjrX/8aCouRXnP+/Pm44447Qo/NZjPy8/Mxffp0GI3GiD9PuDweDz7812oAwKjBRZh5/pDjvCIypV8fxI+r90Odlo+ZM0dG9dp9lcfjwapVqzBt2jQolfHTwuPzi7C7fXB7fRCPc65KLoNaIYNKIesT3ZzHqxNRFNFodaOyxYHKVgeqWhyobHWiKnC/xuyE0xPZblUapQzZRg2yEqWbdF+N7NB9DYwaRZe/v06PD6WNduytt2JfnRV76izYV29FVasTZo8AsweQombXJWoVyE3SoiBFh/xk6WtBivQ1O1FzQpPeuvvnxOnxodXhgckudfW37fb3hp4LHgt+tbnaD7nqLnKZAI1SBp1SDp1KERiKoIBRo4RRc8R9rQJpCWrkJmmQk6jp0vCEeP291Z91V50Ee0KPJ+K/HUtLSzt9rqqqCrm5uZFessvS0tIgl8vbtfjV19e3axk8llNOOQVvvPFG6HFWVlZE11Sr1VCr248ZVCqVMf/BswYmuKQZtFF/r6L0BABAZYuTv0Ai1B11fySPz4+KZjvKm+wobbShrMmGsiY7yhptqDU74fZ2bWtOtUIKjhqlHDqVHBqlHFqVdF+rlIeOa5VyaFRy6JQKaFXS+Sq5DEq5DEqFDCq5AJUi8Dhw0yrlSNQpkaRVQqeSxyyYiqIIuxfY2+BAtdmEimY7DjXbUdHiQGWLHVUtDrjC+P5olXKkJqiQmqBGml6FFH3gfkLb+zmJWiTplDEN2kqlEqMLNBhdkNLmuMXpwcEGWyjwmB0eOD0+uH1+uL1H3I547Arc3D4/WmxuVLbY0WL3wOTwwuSwYFeNpf37ywXkJetQkKLDgFQdcpK0SNWrkGZQIz1BjbQENVITVFDKjz2HMpyfE4/PD4vTC4vTE/h65H3pq9XlhbmD48FQGE79HotaIYPhiJBmCIQ0g1oJrUoOhUyAXC5AKZNBIRcgFwR4/WLge+sLfZ+dHh+cHh8cHh/sbh8cbumx3S0dc7h9oaXQfH4RNpcvEFjdYZdVo5RhUIYBw7INGJZtlG5ZRiTqjv/7qLt/b3UnUZTqI/jvw+rywur0wuXzw+P1w+sX4fH54fFJX70+PyAIECC18MoC92WBJv/gY7lMgFwmQCEToJDLpH8LgcdymSD9e5DJAs8Hj8vaPK+QySCXCVAHfkce+YdYrOsk3GtHpaOhtrYWDz30EF566SU4HN23iLNKpcKECROwatWqNsvarFq1CpdccknY19myZQuys7NDjydPnoxVq1a1Gbe4cuVKTJkyJToFjyJbYLhBahTXWAwakKoHAJQ326J+bYqMKIpotXtQ2eJAVasdlS0OHGq2hwJhVasDPv/x2gw7dmSmOWoL+FCQMDu9J1D641PIBCRqlUjUKmEM3jQKGLXKNq0pikD4CP4C9/pF2F0+2NxeONzSV7vLhxa7G002N5qsbjTZXHB6FMCG7zt9f5kgLUSfm6xFXrIW+ck65CVrkZesQ26SFmkGFXSqbuiXPUEGjRJj8pNO+DpWlxdVLQ7pD5BmOw412QJf7ahoscPjE1HaaENp47F/Nxg0CiSoFdCrFdCrpBYyrUoOUfSjvl6GD5s3QybIDgcpbzBASY9tbm/ErbqdkcsEJAX+jQX/SEnSqZCoVSIp8Fg6rgo9n6hVwqBRQqXovp1xPT5/KDg6AiHS5vKGwq/ZKf0RYHZ6YQ6E4VqzE1UtDtRbXHB6/PipyoSfqkxtrpuTqMHQbCOGZhmQl6xDil6FJJ0SCpkAv8+HMguw5VArZHK5NLlKFOEXRYih+1Jo1gfqUK+W6tOgVkAWw6XVgiHP5pL+KLC6vLC5fLC6Dv/xYHUd8YeDU/rDoaPnPb6u/Y7sbnKZAJVcgOCXoy6pHDefWdLTRQo/LLa2tuL3v/89Vq5cCaVSibvvvht/+MMfsGjRIvz1r3/FiBEjsHTp0liWtUN33HEHrrvuOkycOBGTJ0/Giy++iEOHDuHWW28FIHURV1VV4fXXXwcgzXQuLCzEiBEj4Ha78cYbb+C9997De++9F7rmn/70J5xxxhl47LHHcMkll+B///sfvvjiC3z77bfd/vmOJzgbOjkWYTFFGm9ZZ3bB4fZBq4reOo7UliiKqDU7sbfOiv31VtSaHKgzu1BndqI+MFnC4Tl2l5hOJceAVD2K0nTS11Q9BqTqkJusDf2CVx+na1kURXj9IpweX+g/cJfXH2oBCf0ndsRXe/A5tw92jw/OwPHgX+hurz/wF7sfbp8Yum9z+WB2eOD2SX/VN9mkgBcrqXoVClJ1ge5UXSgU5qfokJWoOW4rWH+SoFZgSJYBQ7LaT2zz+aV/q+VNNhxqksJkncmJBqsLTVY3Gq0uNNnc8PnF0H/WHZNhZ0tj2GXSKuWBVj2pZS90X60MHUsIdc9Kj4N/gCTplEhQd73bvzsFW96Nmshbk1xeH6paHNhTa8GuGjN215ixu8aCqlYHqk1OVJuc+PLn+k5ercBTO37sUpkT1IoO6kb6qlbIIEAIjcGVyQR4j/jdEGzlDrbC2toEQi9sLm+otTVaDGoFEgJ/yGiUcijkQuD7Ln1VyKT7AEKBWYT0+zEYnkUgtGKB1++Hzy/97vT5RXh90rGjH4fO8UlfvX5/hwHW5xfh8IsABHh80flj6USFHRYXLFiAtWvXYvbs2fj8889x++234/PPP4fT6cRnn32GM888M5bl7NQvf/lLNDU14f7770dNTQ1GjhyJ5cuXY8CAAQCAmpqaNmsuut1u3HnnnaiqqoJWq8WIESPw6aefYubMmaFzpkyZgrfeegt/+ctfcO+992LgwIF4++23cfLJJ3f75zue4DqL0dy9JShJJ7XmmJ1eHGq2d/gfB0VGFEXUW1zYW2fB3jor9tVZsDcw3qzz/1QPSzeokZesRW6S1OoVXEalKE2PdIP6hP8zFAQh9AuzO2pbFEU4PX60OtxSl2FgTFmwq8js8MLs9ITu+wK/rBEYeSkTBOjVgZYOlRxalQI6lRzJOiVS9WqkJKiQqJZh03df4xcXTe+zXWzdSS4TkJsk/RucMrDjc/x+Ea0OD1rsbtgC//HbXN5AS6EPPp8P27f/hNGjR0Eul0OlkIYlqJVyaBTSUAeNUmrFMmiklkmG+eNTK+QoTk9AcXoCZow63Ftmcniwt86Cn2vM+LnWgjqzC002F0wOD0QR8Pn9sNns0Ot1kMtkoW5XWbC7NdDl6vJKfxzaXF7Yj+gyD7b41Zg6KViUaJVy6NXBfxPy0B8KCYHeh2BL9tF/OCQccV6CKrYtoV3h94vw+IN/WItwe/2wOV344suvccn47hvadyxhh8VPP/0Ur7zyCs4991zMmTMHJSUlGDx4cKfLyXSnOXPmYM6cOR0+9+qrr7Z5PG/ePMybN++417z88stx+eWXR6N4MWWNYTe0IAgYkKrHT1UmlDfZGBYj4PX5UdFqRWmjDQcbbDjYKE1E2Ftn6bRLVy4TUJiqw6AMA/JTtMg0apBh1CDToEaGUYPsRA00yr7VuisIArQqObQqbczW8vR4PNjRt75tcU8mE5ASGNfZEY/HA33ddsyckMcA3w0StUqcVJiCkwpTOnze4/Fg+fLlmDnz9Ijqw+nxdTqe1Bz46vb5Ay1zUvOcCOl3nTI4kU4uC41nVitkR4RBBRLUUjiUhjEo+uxOYjKZALVM3mYXNo9OjixdbP5v74qww2J1dTWGDx8OQNrFRaPRhBbCpp7h8vjg8seuGxoAClJ1+KnKhEPN4U2v729EUURVqwM7q83YVW3GrmoTtpXK8X8/rO6060QuEzAgVYfBGdK6eCWB9fGK0vRR3bKRiCiWNIFJbumG6G4KQfEnoqVzjvyLQy6XQ6/Xx6RQFJ7GwPgupVyAMUaLohWmSuMWy5o4ycXr8+NAgw07q03YVW2WAmKNGSbH0YuaCgBEaJQyFKUloDhdj4FpegwMLJxcnM5QSEREvUfYCUMURdxwww2hJWKcTiduvfXWdoHx/fffj24JqVP1ZmnXmIwojFXrTHGatHzOgfr4CouiKKKyxYGfay2oaLZLA7hbHTA7PXC4paVClHKpi0OjlEtLmxyxvElqggrJOhVS9Wok66XB7wDg8YlotbtRb3GhqtWB/fXSuMJ99Vbsq7d2uASNQiZgUKYBw7ONGJKpR0vZLlw1cyryUw1xNzaGiIgoUmGHxaN3bbn22mujXhiKTL3lcFiMlUGZUljcV99+rbXudqjJji9/rsOavQ3YVN4S1eVcBKH9sjEd0avkGJ5jxPBsI0bkJGJ4jhGDMhNCLYUejwfLW3ciJ0nLoEhERH1C2GHxlVdeiWU5qAuCYTGW40UGBhbmbrS60Wxzx2TW9fFsPtSCp1btxTf72i6zoZQLKMkwoChNWgsvJ0mLFL0qtCB0cEkGp8eH5sCyLI1WV+CzuNBi8wTW4PO3CYoyAUhLUCMrUYOB6QkoyUjAoIwEDMo0YECKjiGQiIj6lfhfZZY6JIoiDgYWxM2MYVjUqxXIS9aissWBfXUWnFycGrP3OprT48MDn+zCf36Qlj6SCcCkohRMHZKB00rSMDjTEJXFch1uHyxOT2A1fRkS1H131h0REVGkGBZ7qZ9rLXjjhwr8f3v3HhZVtf8P/D3AMIAChggDKjh8RUFIuaaACphg5rH8ncwkNRTFY1qKHEtNj9eEo5WiUiqGmqGmpif1RCklFwkvSGAKZWUYHoXwzk25zf79wWGO0zCKCuwZeb+eZ55H1l577c/eS5yPa++1NtC6I4sA4GzTsSFZLK1os2TxPzer8HrS96q3EIz27oZZzzqj+38XCm9JDUu3cMIJERFRU5gs6qnjF66r/tza6x/2sjVH6vmr+OWPtnlu8dfSCry6+QRKy6vxlJkU68I8Mci5S5scm4iIiNQxWdRTEQMVCHB6Coe+yUBwL+tWPVZPm8ZJLhWtehwAOF9SjnEfn8C1ihr0tjVH4kQfdHuq5UcTiYiIqHmYLOoxpy4d4GwptPr7TnvZNoxcni8phyC03vFKbt9F+JZTuFZRgz52Fkia0l+UCTVERET0P3zZJj1Qb7k5DA0kuF5Zg+Lbd1vlGFU1dZiyPRslZXfR06YjdkUOYKJIRESkA5gs0gOZSA1Vo4uNE05aklIpIOqzPJy7XAarDsbYEu4LSzO+L5aIiEgXMFmkZnm6qwUA4Ox/Wj5ZXPn1TzhS8AeMjQyw+TVvOHTmM4pERES6gskiNcvTXS0BtPzI4menirAp4zcAwHuj+8Lb0apF2yciIqLHw2SRmuXpbp0AAOcu34bQnPfiNcN3v17Dwi/OAQCihjrjRY+uLdIuERERtRwmi9QsLnJzGP13ksvlW3ceu71fSyswLSkHdUoBL3rYY9azzi0QJREREbU0JovULCZSQ7jZNzy3mH3xxmO1dbW8GhHbslF+tw7ejk9h5Ut9W335HyIiIno0TBap2Rpf9Xeq8NGTxYrqOkzadgpFN6rgYGWGhAneMJHyVXtERES6iskiNdszPRomn5z87dGSxZo6JaZ9moNzl8vQuYMxPol4Bp07tu57rYmIiOjxMFmkZvPtYQWJBPjtWiVKyx5ucW6lUsCcvWeQ+es1mBkbYuskXyisO7RSpERERNRSmCxSs1maSeEib3hu8cRD3oqOSf4RB89cgZGBBBvGe6Pvf2dXExERkW5jskgPZWDPhucW086XNnufhIwL+DizEADw3st9EdirS6vERkRERC2PySI9lCEutgCAtPNXUa988HqLyWeLEZP8EwDgnedd8P88u7VqfERERNSynohk8aOPPoJCoYCJiQm8vb1x7NgxrXX379+PkJAQdOnSBRYWFvDz88Phw4fV6mzbtg0SiUTjc/fuwz2n9yTy6fEULEyMcKOyBrlFN+9b98ylW5i9Ow8AMNG/ByIHObVBhERERNSS9D5Z3L17N6KiorBgwQLk5uZi0KBBGD58OIqKipqsn5GRgZCQECQnJyMnJwfBwcEYOXIkcnNz1epZWFiguLhY7WNiYtIWp6TTpIYGCHaxAQCk/PiH1npXbt3BlO2nUV2nRHDvLvjHX/pwLUUiIiI9pPfJ4urVqzF58mRMmTIFrq6uiIuLQ/fu3bFhw4Ym68fFxeHtt9+Gr68vnJ2dERMTA2dnZxw6dEitnkQigVwuV/tQg2FuDdfiX99fRm29UmP73dp6TP30NK6WV8NFbo71r3rB0ICJIhERkT4yEjuAx1FTU4OcnBzMmzdPrTw0NBRZWVnNakOpVKK8vBxWVlZq5RUVFXB0dER9fT08PDywfPlyeHp6NtlGdXU1qqurVT+XlZUBAGpra1FbW/swp/RQGttuzWM0JbCnFaw7GqO0vBqHz17BMDdb1TZBEDB33zmcu1yGp8yk2DjOAzIDoc1jFItYfULasU90D/tEt7A/dE9b9Ulz29frZPHatWuor6+Hra2tWrmtrS1KSkqa1cYHH3yAyspKjBkzRlXm4uKCbdu24emnn0ZZWRnWrl2LgIAAnDlzBs7Omu8wjo2NxdKlSzXKjxw5AjMzs4c8q4eXkpLS6sf4M09LA6RUGGBtci7qf//f6GJasQQHLhrCAALG9biLM1mpONPm0YlPjD6h+2Of6B72iW5hf+ie1u6TqqqqZtXT62Sx0Z+fhRMEoVnPx+3atQtLlizBgQMHYGNjoyofMGAABgwYoPo5ICAAXl5eWL9+PdatW6fRzvz58xEdHa36uaysDN27d0doaCgsLCwe5ZSapba2FikpKQgJCYFUKm214zSl3607SF2TiV/KDCBz8sKzLjb4Iu8KDpw4BwCYN9wFk/wd2zQmXSBmn1DT2Ce6h32iW9gfuqet+qTxTuiD6HWyaG1tDUNDQ41RxNLSUo3Rxj/bvXs3Jk+ejL1792Lo0KH3rWtgYABfX1/88ssvTW6XyWSQyTRfWyeVStvkF6+tjnOvHl2kmBTQA5uPFSJ671kMcOqM1POlEAQg7BkHRA7+v3Y9oUWMPqH7Y5/oHvaJbmF/6J7W7pPmtq3XE1yMjY3h7e2tMUybkpICf39/rfvt2rULEydOxM6dOzFixIgHHkcQBOTl5cHOzu6xY36SzBnWGwN7WqOqph5Hf2pIFCcF9MCKUe7tOlEkIiJ6kuj1yCIAREdHY8KECfDx8YGfnx8SEhJQVFSEadOmAWi4RXz58mVs374dQEOi+Nprr2Ht2rUYMGCAalTS1NQUlpaWAIClS5diwIABcHZ2RllZGdatW4e8vDx8+OGH4pykjpIZGeKTiGeQfLYYRTeq4Pd/neHl8JTYYREREVEL0vtk8ZVXXsH169exbNkyFBcXw93dHcnJyXB0bHherri4WG3NxU2bNqGurg4zZszAjBkzVOXh4eHYtm0bAODWrVuYOnUqSkpKYGlpCU9PT2RkZOCZZ55p03PTB4YGEozsZy92GERERNRK9D5ZBIDp06dj+vTpTW5rTAAbpaWlPbC9NWvWYM2aNS0QGREREZF+0+tnFomIiIiodT0RI4u6RhAEAM2fkv6oamtrUVVVhbKyMs5g0xHsE93DPtE97BPdwv7QPW3VJ415SmPeog2TxVZQXl4OAOjevbvIkRARERHdX3l5uWqSb1MkwoPSSXpoSqUSV65cgbm5easuIdO4+PelS5dadfFvaj72ie5hn+ge9oluYX/onrbqE0EQUF5eDnt7exgYaH8ykSOLrcDAwADdunVrs+NZWFjwF1zHsE90D/tE97BPdAv7Q/e0RZ/cb0SxESe4EBEREZFWTBaJiIiISCsmi3pMJpNh8eLFTb6XmsTBPtE97BPdwz7RLewP3aNrfcIJLkRERESkFUcWiYiIiEgrJotEREREpBWTRSIiIiLSiskiEREREWnFZFFPffTRR1AoFDAxMYG3tzeOHTsmdkjtVmxsLHx9fWFubg4bGxuMGjUK58+fFzssukdsbCwkEgmioqLEDqVdu3z5MsaPH4/OnTvDzMwMHh4eyMnJETusdquurg4LFy6EQqGAqakpnJycsGzZMiiVSrFDazcyMjIwcuRI2NvbQyKR4IsvvlDbLggClixZAnt7e5iamiIoKAj5+fltHieTRT20e/duREVFYcGCBcjNzcWgQYMwfPhwFBUViR1au5Seno4ZM2bgxIkTSElJQV1dHUJDQ1FZWSl2aAQgOzsbCQkJ6Nu3r9ihtGs3b95EQEAApFIpvvrqKxQUFOCDDz5Ap06dxA6t3Vq5ciU2btyI+Ph4/Pjjj1i1ahXee+89rF+/XuzQ2o3Kykr069cP8fHxTW5ftWoVVq9ejfj4eGRnZ0MulyMkJATl5eVtGieXztFD/fv3h5eXFzZs2KAqc3V1xahRoxAbGytiZAQAV69ehY2NDdLT0zF48GCxw2nXKioq4OXlhY8++gjvvvsuPDw8EBcXJ3ZY7dK8efPw3Xff8S6IDvnLX/4CW1tbJCYmqspeeuklmJmZ4dNPPxUxsvZJIpHgX//6F0aNGgWgYVTR3t4eUVFRmDt3LgCguroatra2WLlyJf72t7+1WWwcWdQzNTU1yMnJQWhoqFp5aGgosrKyRIqK7nX79m0AgJWVlciR0IwZMzBixAgMHTpU7FDavYMHD8LHxwcvv/wybGxs4Onpic2bN4sdVrs2cOBAfPvtt/j5558BAGfOnEFmZiaef/55kSMjACgsLERJSYna971MJkNgYGCbf98btenR6LFdu3YN9fX1sLW1VSu3tbVFSUmJSFFRI0EQEB0djYEDB8Ld3V3scNq1zz77DN9//z2ys7PFDoUA/Pbbb9iwYQOio6Pxzjvv4NSpU5g5cyZkMhlee+01scNrl+bOnYvbt2/DxcUFhoaGqK+vx4oVKxAWFiZ2aASovtOb+r7//fff2zQWJot6SiKRqP0sCIJGGbW9N954Az/88AMyMzPFDqVdu3TpEmbNmoUjR47AxMRE7HAIgFKphI+PD2JiYgAAnp6eyM/Px4YNG5gsimT37t1ISkrCzp074ebmhry8PERFRcHe3h7h4eFih0f/pQvf90wW9Yy1tTUMDQ01RhFLS0s1/vdBbevNN9/EwYMHkZGRgW7duokdTruWk5OD0tJSeHt7q8rq6+uRkZGB+Ph4VFdXw9DQUMQI2x87Ozv06dNHrczV1RX79u0TKSJ66623MG/ePIwdOxYA8PTTT+P3339HbGwsk0UdIJfLATSMMNrZ2anKxfi+5zOLesbY2Bje3t5ISUlRK09JSYG/v79IUbVvgiDgjTfewP79+3H06FEoFAqxQ2r3nn32WZw9exZ5eXmqj4+PD8aNG4e8vDwmiiIICAjQWFLq559/hqOjo0gRUVVVFQwM1NMAQ0NDLp2jIxQKBeRyudr3fU1NDdLT09v8+54ji3ooOjoaEyZMgI+PD/z8/JCQkICioiJMmzZN7NDapRkzZmDnzp04cOAAzM3NVaO+lpaWMDU1FTm69snc3FzjmdEOHTqgc+fOfJZUJLNnz4a/vz9iYmIwZswYnDp1CgkJCUhISBA7tHZr5MiRWLFiBRwcHODm5obc3FysXr0aERERYofWblRUVODXX39V/VxYWIi8vDxYWVnBwcEBUVFRiImJgbOzM5ydnRETEwMzMzO8+uqrbRuoQHrpww8/FBwdHQVjY2PBy8tLSE9PFzukdgtAk5+tW7eKHRrdIzAwUJg1a5bYYbRrhw4dEtzd3QWZTCa4uLgICQkJYofUrpWVlQmzZs0SHBwcBBMTE8HJyUlYsGCBUF1dLXZo7UZqamqT3x/h4eGCIAiCUqkUFi9eLMjlckEmkwmDBw8Wzp492+Zxcp1FIiIiItKKzywSERERkVZMFomIiIhIKyaLRERERKQVk0UiIiIi0orJIhERERFpxXUWifSUUqlETU2N2GEQUSuSSqVcRJ5Ex2SRSA/V1NSgsLCQb1ogagc6deoEuVze5u8DJmrEZJFIzwiCgOLiYhgaGqJ79+4ar+sioieDIAioqqpCaWkpAKi9H5ioLTFZJNIzdXV1qKqqgr29PczMzMQOh4haUeMrQ0tLS2FjY8Nb0iQKDkkQ6Zn6+noAgLGxsciREFFbaPxPYW1trciRUHvFZJFIT/H5JaL2gb/rJDYmi0RERESkFZNFIiLSGxMnTsSoUaPEDqPFBAUFISoqSuwwiO6LySIRtYl7v+QnTpwIiUSi8Xnuuec09ouJiYGhoSH++c9/Nus4+/fvx7Bhw2BtbQ2JRIK8vDyNOkFBQRrHHjt27H3bPXPmDMLCwtC9e3eYmprC1dUVa9eu1Vr/119/hbm5OTp16qSxLT09Hd7e3jAxMYGTkxM2btz4wPO695oZGRnBwcEBr7/+Om7evAkA+OmnnyCRSHDy5Em1/fr37w+ZTIaqqipVWU1NDczMzJCQkNBkP9z7mThxokYsS5YsgYeHh0b5rVu3IJFIkJaWBgC4ePGiKt7Lly+r1S0uLoaRkREkEgkuXrz4wPNvtHbtWmzbtq3Z9Zujvr4esbGxcHFxgampKaysrDBgwABs3bq1RY/TlP3792P58uWtfhyix8FkkYhE8dxzz6G4uFjts2vXLo16W7duxdtvv40tW7Y0q93KykoEBAQ8MLmMjIxUO/amTZvuWz8nJwddunRBUlIS8vPzsWDBAsyfPx/x8fEadWtraxEWFoZBgwZpbCssLMTzzz+PQYMGITc3F++88w5mzpyJffv2PfDcGq/ZxYsX8fHHH+PQoUOYPn06AMDFxQV2dnZITU1V1a+oqEBubi5sbGyQlZWlKj958iTu3LmD4OBgtWsQFxcHCwsLtbL7JcTNZW9vj+3bt6uVffLJJ+jatetDt2VpadlkAv44lixZgri4OCxfvhwFBQVITU1FZGSkKhFvTVZWVjA3N2/14xA9DiaLRCQKmUwGuVyu9nnqqafU6qSnp+POnTtYtmwZKisrkZGR8cB2J0yYgEWLFmHo0KH3rWdmZqZ2bEtLy/vWj4iIwLp16xAYGAgnJyeMHz8ekyZNwv79+zXqLly4EC4uLhgzZozGto0bN8LBwQFxcXFwdXXFlClTEBERgffff/+B59Z4zbp164bQ0FC88sorOHLkiGp7UFCQalQPAI4dO4ZevXrhhRdeUCtPS0tD165d4ezsrHENJBLJQ12X5ggPD9cYpdu2bRvCw8PVyurr6zF58mQoFAqYmpqid+/eGsnqn29DBwUFYebMmXj77bdhZWUFuVyOJUuWqO1z+/ZtTJ06FTY2NrCwsMCQIUNw5swZ1fbGpPvll1+GQqFAv379MHnyZERHR6vqCIKAVatWwcnJCaampujXrx8+//xz1fa0tDRIJBIcPnwYnp6eMDU1xZAhQ1BaWoqvvvoKrq6usLCwQFhYmNooL29Dkz5gskik5wRBQFVNnSgfQRBa9dwSExMRFhYGqVSKsLAwJCYmtljbO3bsgLW1Ndzc3DBnzhyUl5c/dBu3b9+GlZWVWtnRo0exd+9efPjhh03uc/z4cYSGhqqVDRs2DKdPn36opVF+++03fP3115BKpaqy4OBgZGZmoq6uDgCQmpqKoKAgBAYGqo04pqamIjg4uNnHelwvvPACbt68iczMTABAZmYmbty4gZEjR6rVUyqV6NatG/bs2YOCggIsWrQI77zzDvbs2XPf9j/55BN06NABJ0+exKpVq7Bs2TKkpKQAaPj9GDFiBEpKSpCcnIycnBx4eXnh2WefxY0bNwAAcrkcR48exdWrV7UeY+HChdi6dSs2bNiA/Px8zJ49G+PHj0d6erpavSVLliA+Ph5ZWVm4dOkSxowZg7i4OOzcuRNffvklUlJSsH79+oe+hkRi4qLcRHruTm09+iw6LMqxC5YNg5nxo/0z8u9//xsdO3ZUK5s7dy7+8Y9/AADKysqwb98+1e3T8ePHIyAgAOvXr4eFhcVjxT1u3DgoFArI5XKcO3cO8+fPx5kzZ1QJRnMcP34ce/bswZdffqkqu379OiZOnIikpCStMZaUlMDW1latzNbWFnV1dbh27dp939LReM3q6+tx9+5dAMDq1atV24OCglBZWYns7Gz4+fkhLS0Nb731FgYPHowJEyagqqoKRkZGOHHiRJO3z1uLVCrF+PHjsWXLFgwcOBBbtmzB+PHj1RLdxnpLly5V/axQKJCVlYU9e/Y0OUrbqG/fvli8eDEAwNnZGfHx8fj2228REhKC1NRUnD17FqWlpZDJZACA999/H1988QU+//xzTJ06FatXr8bo0aMhl8vh5uYGf39/vPjiixg+fDiAhkcbVq9ejaNHj8LPzw8A4OTkhMzMTGzatAmBgYGqWN59910EBAQAACZPnoz58+fjwoULcHJyAgCMHj0aqampmDt37uNeVqI2w5FFIhJFcHAw8vLy1D4zZsxQbd+5cyecnJzQr18/AICHhwecnJzw2WefAWgYGezYsaPqc+zYsWYfOzIyEkOHDoW7uzvGjh2Lzz//HN988w2+//57AMDw4cNV7bq5uWnsn5+fjxdffBGLFi1CSEiIWruvvvoqBg8efN/j/3ndvMYRWolEgmPHjqmd144dOzSu2cmTJ/Hmm29i2LBhePPNN1XbnZ2d0a1bN6SlpaGsrAy5ubkIDAyEra0tFAoFvvvuO5w4cQJ37tzBkCFDmnWt7o1l2rRpzdqnKZMnT8bevXtRUlKCvXv3IiIiosl6GzduhI+PD7p06YKOHTti8+bNKCoqum/bffv2VfvZzs5O9Yq8nJwcVFRUoHPnzmrnUlhYiAsXLgAA+vTpg3PnzuHEiROYNGkS/vjjD4wcORJTpkwBABQUFODu3bsICQlRa2P79u2qNpqKxdbWFmZmZqpEsbGsMTYifcGRRSI9Zyo1RMGyYaId+1F16NABPXv21Lp9y5YtyM/Ph5HR//6ZUiqVSExMxNSpU/HCCy+gf//+qm2PMlmikZeXF6RSKX755Rd4eXnh448/xp07dwBAY/SroKAAQ4YMQWRkJBYuXKi27ejRozh48KDq+UNBEKBUKmFkZISEhARERERALpejpKREbb/S0lIYGRmhc+fOsLS0VJvBfe8o5L3XbN26dQgODsbSpUvVZtMGBQUhNTUVffv2hbOzM2xsbABAdStaJpPB0dERPXr0aNa1uTeWxtFSCwsL3L59W6PurVu3AKDJ5xzd3d3h4uKCsLAwuLq6wt3dXWOm+p49ezB79mx88MEH8PPzg7m5Od577z2NGd5/9uc+kkgkUCqVABr+ztjZ2ak9s9no3okyBgYG8PX1ha+vL2bPno2kpCRMmDABCxYsULX15Zdfavw9axytbCoWiURy39iI9AWTRSI9J5FIHvlWsK46e/YsTp8+jbS0NLVnAm/duoXBgwfj3LlzcHd3b7FZpPn5+aitrVXdAtaWeObn52PIkCEIDw/HihUrNLYfP35c9TpGADhw4ABWrlyJrKwsVZt+fn44dOiQ2n5HjhyBj48PpFIppFLpfZPoey1evBjDhw/H66+/Dnt7ewANo48zZ85Enz59EBQUpKobGBiI+Ph4yGSyZo8qAmgyFhcXF/znP/9BSUkJ5HK5qjw7OxsGBgZa44+IiMD06dOxYcOGJrcfO3YM/v7+qhneADRG7h6Wl5cXSkpKYGRk1OwEGWgYbQQabkH36dMHMpkMRUVFarecidqLJ+sbhoj0RnV1tcYIm5GREaytrZGYmIhnnnmmydu5fn5+SExMxJo1a5ps98aNGygqKsKVK1cAAOfPnwcA1ezeCxcuYMeOHXj++edhbW2NgoIC/P3vf4enp6fqWbOm5OfnIzg4GKGhoYiOjlbFbmhoiC5dugAAXF1d1fY5ffo0DAwM4O7uriqbNm0a4uPjER0djcjISBw/fhyJiYlNLhv0IEFBQXBzc0NMTIzqGcTg4GBUVlZiy5Yt2Lx5s6puYGAgJk6cCENDQ623gJsrNDQUrq6uGDt2LFasWAF7e3v88MMPmDNnDqZNm6Y1iY+MjMTLL7+sdembnj17Yvv27Th8+DAUCgU+/fRTZGdnQ6FQPHKsQ4cOhZ+fH0aNGoWVK1eid+/euHLlCpKTkzFq1Cj4+Phg9OjRCAgIgL+/P+RyOQoLCzF//nz06tULLi4uMDIywpw5czB79mwolUoMHDgQZWVlyMrKQseOHTVmdRM9afjMIhGJ4uuvv4adnZ3aZ+DAgaipqUFSUhJeeumlJvd76aWXkJSUhJqamia3Hzx4EJ6enhgxYgQAYOzYsfD09FQtfG1sbIxvv/0Ww4YNQ+/evTFz5kyEhobim2++gaGh9tvqe/fuxdWrV7Fjxw61mH19fR/qvBUKBZKTk5GWlgYPDw8sX74c69at03q+DxIdHY3Nmzfj0qVLqvYdHR1RXl6uNgrWtWtXODg44O7du489E9rIyAhHjhyBk5MTxo0bBzc3N8ybNw9TpkxRm3DT1H7W1tZqjxbca9q0afjrX/+KV155Bf3798f169fVRhkfhUQiQXJyMgYPHoyIiAj06tULY8eOxcWLF1W3+IcNG4ZDhw5h5MiR6NWrF8LDw+Hi4oIjR46oYl2+fDkWLVqE2NhYuLq6qvZ5nESWSF9IhNZe+4KIWtTdu3dRWFgIhUIBExMTscMholbG33kSG0cWiYiIiEgrJotEREREpBWTRSIiIiLSiskiEREREWnFZJFIT3FuGlH7wN91EhuTRSI907i8i7alY4joyVJVVQVA8001RG2Fi3IT6RkjIyOYmZnh6tWrkEqlMDDg//mInkSCIKCqqgqlpaXo1KnTfdcBJWpNXGeRSA/V1NSgsLCQ75glagc6deoEuVwOiUQidijUTjFZJNJTSqWSt6KJnnBSqZQjiiQ6JotEREREpBUfdiIiIiIirZgsEhEREZFWTBaJiIiISCsmi0RERESkFZNFIiIiItKKySIRERERacVkkYiIiIi0+v9DBGAn7K9g4QAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -669,16 +653,16 @@ "data": { "text/plain": [ "([
,
],\n", - " [array([,\n", - " ,\n", - " ,\n", - " ], dtype=object),\n", - " array([,\n", - " ,\n", - " ], dtype=object)])" + " [array([,\n", + " ,\n", + " ,\n", + " ], dtype=object),\n", + " array([,\n", + " , ],\n", + " dtype=object)])" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -688,7 +672,7 @@ "\n", "# Define openfast output filenames, please fill in your own .outb\n", "# filenames = [\"../Test_Cases/5MW_Step/5MW_Step.outb\"]\n", - "filenames = ['../Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.outb']\n", + "filenames = ['Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.outb']\n", "# Load output info and data\n", "fast_out = op.load_fast_out(filenames)\n", "\n", @@ -702,7 +686,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -716,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "slideshow": { "slide_type": "subslide" @@ -741,7 +724,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -756,28 +738,14 @@ "\n", "I would finally like to note that there has been a lot of work from a lot of very smart (and really great) people that has gone into all of this, some of which are acknowledged on the [ROSCO toolbox](www.github.com/nrel/ROSCO_toolbox) and [ROSCO](www.github.com/nrel/ROSCO_toolbox) github pages. " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "rosco-env3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "rosco-env3" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -789,7 +757,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/Test_Cases/5MW_Land_Simulink/5MW_Land_Simulink.fst b/Examples/Test_Cases/5MW_Land_Simulink/5MW_Land_Simulink.fst similarity index 100% rename from Test_Cases/5MW_Land_Simulink/5MW_Land_Simulink.fst rename to Examples/Test_Cases/5MW_Land_Simulink/5MW_Land_Simulink.fst diff --git a/Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat b/Examples/Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat similarity index 97% rename from Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat rename to Examples/Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat index 27bb021c..3c235956 100644 --- a/Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat +++ b/Examples/Test_Cases/5MW_Land_Simulink/NRELOffshrBsline5MW_Onshore_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed using TimGen ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"../../ROSCO/build/libdiscon.dylib" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"../../../lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "../NREL-5MW/DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] "default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/BAR_10/Airfoils/AF00_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF00_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF00_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF00_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF01_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF01_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF01_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF01_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF02_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF02_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF02_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF02_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF03_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF03_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF03_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF03_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF04_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF04_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF04_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF04_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF05_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF05_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF05_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF05_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF06_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF06_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF06_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF06_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF07_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF07_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF07_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF07_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF08_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF08_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF08_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF08_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF09_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF09_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF09_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF09_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF10_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF10_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF10_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF10_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF11_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF11_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF11_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF11_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF12_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF12_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF12_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF12_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF13_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF13_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF13_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF13_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF14_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF14_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF14_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF14_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF15_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF15_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF15_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF15_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF16_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF16_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF16_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF16_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF17_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF17_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF17_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF17_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF18_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF18_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF18_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF18_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF19_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF19_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF19_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF19_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF20_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF20_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF20_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF20_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF21_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF21_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF21_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF21_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF22_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF22_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF22_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF22_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF23_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF23_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF23_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF23_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF24_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF24_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF24_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF24_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF25_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF25_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF25_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF25_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF26_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF26_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF26_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF26_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF27_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF27_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF27_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF27_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF28_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF28_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF28_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF28_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/AF29_Coords.txt b/Examples/Test_Cases/BAR_10/Airfoils/AF29_Coords.txt similarity index 100% rename from Test_Cases/BAR_10/Airfoils/AF29_Coords.txt rename to Examples/Test_Cases/BAR_10/Airfoils/AF29_Coords.txt diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_00.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_00.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_00.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_00.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_01.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_01.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_01.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_01.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_02.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_02.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_02.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_02.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_03.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_03.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_03.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_03.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_04.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_04.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_04.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_04.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_05.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_05.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_05.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_05.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_06.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_06.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_06.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_06.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_07.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_07.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_07.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_07.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_08.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_08.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_08.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_08.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_09.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_09.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_09.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_09.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_10.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_10.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_10.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_10.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_11.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_11.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_11.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_11.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_12.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_12.dat similarity index 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Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_15.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_16.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_16.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_16.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_16.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_17.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_17.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_17.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_17.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_18.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_18.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_18.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_18.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_19.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_19.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_19.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_19.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_20.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_20.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_20.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_20.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_21.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_21.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_21.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_21.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_22.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_22.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_22.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_22.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_23.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_23.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_23.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_23.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_24.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_24.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_24.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_24.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_25.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_25.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_25.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_25.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_26.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_26.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_26.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_26.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_27.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_27.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_27.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_27.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_28.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_28.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_28.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_28.dat diff --git a/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_29.dat b/Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_29.dat similarity index 100% rename from Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_29.dat rename to Examples/Test_Cases/BAR_10/Airfoils/BAR_10_AeroDyn15_Polar_29.dat diff --git a/Test_Cases/BAR_10/BAR_10.fst b/Examples/Test_Cases/BAR_10/BAR_10.fst similarity index 100% rename from Test_Cases/BAR_10/BAR_10.fst rename to Examples/Test_Cases/BAR_10/BAR_10.fst diff --git a/Test_Cases/BAR_10/BAR_10_AeroDyn15.dat b/Examples/Test_Cases/BAR_10/BAR_10_AeroDyn15.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_AeroDyn15.dat rename to Examples/Test_Cases/BAR_10/BAR_10_AeroDyn15.dat diff --git a/Test_Cases/BAR_10/BAR_10_AeroDyn15_blade.dat b/Examples/Test_Cases/BAR_10/BAR_10_AeroDyn15_blade.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_AeroDyn15_blade.dat rename to Examples/Test_Cases/BAR_10/BAR_10_AeroDyn15_blade.dat diff --git a/Test_Cases/BAR_10/BAR_10_BeamDyn.dat b/Examples/Test_Cases/BAR_10/BAR_10_BeamDyn.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_BeamDyn.dat rename to Examples/Test_Cases/BAR_10/BAR_10_BeamDyn.dat diff --git a/Test_Cases/BAR_10/BAR_10_BeamDyn_Blade.dat b/Examples/Test_Cases/BAR_10/BAR_10_BeamDyn_Blade.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_BeamDyn_Blade.dat rename to Examples/Test_Cases/BAR_10/BAR_10_BeamDyn_Blade.dat diff --git a/Test_Cases/BAR_10/BAR_10_DISCON.IN b/Examples/Test_Cases/BAR_10/BAR_10_DISCON.IN similarity index 99% rename from Test_Cases/BAR_10/BAR_10_DISCON.IN rename to Examples/Test_Cases/BAR_10/BAR_10_DISCON.IN index 8dc17b7d..ebbc56f5 100644 --- a/Test_Cases/BAR_10/BAR_10_DISCON.IN +++ b/Examples/Test_Cases/BAR_10/BAR_10_DISCON.IN @@ -1,9 +1,10 @@ ! Controller parameter input file for the BAR_10 wind turbine -! - File written using ROSCO version 2.8.0 controller tuning logic on 12/21/23 +! - File written using ROSCO version 2.8.0 controller tuning logic on 01/05/24 !------- SIMULATION CONTROL ------------------------------------------------------------ 1 ! LoggingLevel - {0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)} 0 ! DT_Out - {Time step to output .dbg* files, or 0 to match sampling period of OpenFAST} +1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) 0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) !------- CONTROLLER FLAGS ------------------------------------------------- diff --git a/Test_Cases/BAR_10/BAR_10_ElastoDyn.dat b/Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_ElastoDyn.dat rename to Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn.dat diff --git a/Test_Cases/BAR_10/BAR_10_ElastoDyn_blade.dat b/Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn_blade.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_ElastoDyn_blade.dat rename to Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn_blade.dat diff --git a/Test_Cases/BAR_10/BAR_10_ElastoDyn_tower.dat b/Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn_tower.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_ElastoDyn_tower.dat rename to Examples/Test_Cases/BAR_10/BAR_10_ElastoDyn_tower.dat diff --git a/Test_Cases/BAR_10/BAR_10_InflowFile.dat b/Examples/Test_Cases/BAR_10/BAR_10_InflowFile.dat similarity index 100% rename from Test_Cases/BAR_10/BAR_10_InflowFile.dat rename to Examples/Test_Cases/BAR_10/BAR_10_InflowFile.dat diff --git a/Test_Cases/BAR_10/BAR_10_ServoDyn.dat b/Examples/Test_Cases/BAR_10/BAR_10_ServoDyn.dat similarity index 98% rename from Test_Cases/BAR_10/BAR_10_ServoDyn.dat rename to Examples/Test_Cases/BAR_10/BAR_10_ServoDyn.dat index b13a1212..d095227b 100644 --- a/Test_Cases/BAR_10/BAR_10_ServoDyn.dat +++ b/Examples/Test_Cases/BAR_10/BAR_10_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed usin ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"../../ROSCO/install/lib/libdiscon.dylib" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"../../../lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "BAR_10_DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] "default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/BAR_10/Cp_Ct_Cq.BAR_10.txt b/Examples/Test_Cases/BAR_10/Cp_Ct_Cq.BAR_10.txt similarity index 100% rename from Test_Cases/BAR_10/Cp_Ct_Cq.BAR_10.txt rename to Examples/Test_Cases/BAR_10/Cp_Ct_Cq.BAR_10.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_00_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_01_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_02_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_03_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_04_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_05_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_06_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_07_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_08_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_09_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_10_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_11_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_12_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_13_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_14_Coords.txt rename to 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a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16_Coords.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16_Coords.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_16_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_17.dat 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Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_49_Coords.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Airfoils/IEA-15-240-RWT_AeroDyn15_Polar_49_Coords.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/Cp_Ct_Cq.IEA15MW.txt b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Cp_Ct_Cq.IEA15MW.txt similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/Cp_Ct_Cq.IEA15MW.txt rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/Cp_Ct_Cq.IEA15MW.txt diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN similarity index 99% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN index 1ec21c5c..cd58ccf8 100644 --- a/Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN +++ b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/DISCON-UMaineSemi.IN @@ -1,9 +1,10 @@ ! Controller parameter input file for the IEA-15-240-RWT-UMaineSemi wind turbine -! - File written using ROSCO version 2.8.0 controller tuning logic on 12/21/23 +! - File written using ROSCO version 2.8.0 controller tuning logic on 01/05/24 !------- SIMULATION CONTROL ------------------------------------------------------------ 2 ! LoggingLevel - {0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)} 0 ! DT_Out - {Time step to output .dbg* files, or 0 to match sampling period of OpenFAST} +1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) 0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) !------- CONTROLLER FLAGS ------------------------------------------------- diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.1 b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.1 similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.1 rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.1 diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12d b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12d similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12d rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12d diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12s b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12s similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12s rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.12s diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.3 b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.3 similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.3 rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.3 diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.hst b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.hst similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.hst rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.hst diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ss b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ss similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ss rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ss diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ssexctn b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ssexctn similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ssexctn rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi.ssexctn diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.fst b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.fst similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.fst rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi.fst diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn_tower.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn_tower.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn_tower.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ElastoDyn_tower.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_HydroDyn.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_HydroDyn.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_HydroDyn.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_HydroDyn.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_MoorDyn.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_MoorDyn.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_MoorDyn.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_MoorDyn.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat similarity index 98% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat index 51a09d4e..7ea5ee55 100644 --- a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat +++ b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT-UMaineSemi_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed usin ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"../../ROSCO/install/lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"../../../lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "DISCON-UMaineSemi.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] "default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15_blade.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15_blade.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15_blade.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_AeroDyn15_blade.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn_blade.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn_blade.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn_blade.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_BeamDyn_blade.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_ElastoDyn_blade.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_ElastoDyn_blade.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_ElastoDyn_blade.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_ElastoDyn_blade.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_InflowFile.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_InflowFile.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_InflowFile.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/IEA-15-240-RWT_InflowFile.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col1.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col1.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col1.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col1.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col2.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col2.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col2.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col2.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col3.dat b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col3.dat similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col3.dat rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/StC-Force-Col3.dat diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.1.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.1.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.1.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.1.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.10.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.10.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.10.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.10.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.11.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.11.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.11.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.11.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.12.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.12.lin similarity index 100% rename from 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rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.4.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.4.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.5.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.5.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.5.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.5.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.6.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.6.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.6.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.6.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.7.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.7.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.7.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.7.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.8.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.8.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.8.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.8.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.9.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.9.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.9.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_00.9.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.1.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.1.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.1.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.1.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.10.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.10.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.10.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.10.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.11.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.11.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.11.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.11.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_01.12.lin 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Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.8.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.8.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.9.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.9.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.9.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_18.9.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.1.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.1.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.1.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.1.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.10.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.10.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.10.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.10.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.11.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.11.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.11.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.11.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.12.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.12.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.12.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.12.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.2.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.2.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.2.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.2.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.3.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.3.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.3.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.3.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.4.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.4.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.4.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.4.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.5.lin 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a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.8.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.8.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.8.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.8.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.9.lin b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.9.lin similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.9.lin rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/linearizations/lin_19.9.lin diff --git a/Test_Cases/IEA-15-240-RWT-UMaineSemi/ss_ops.yaml b/Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/ss_ops.yaml similarity index 100% rename from Test_Cases/IEA-15-240-RWT-UMaineSemi/ss_ops.yaml rename to Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi/ss_ops.yaml diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0240.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0240.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0240.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0240.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0240_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0240_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0240_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0240_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0247.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0247.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0247.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0247.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0247_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0247_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0247_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0247_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0259.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0259.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0259.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0259.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0259_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0259_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0259_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0259_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0276.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0276.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0276.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0276.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0276_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0276_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0276_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0276_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0329.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0329.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0329.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0329.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0329_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0329_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0329_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0329_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0444.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0444.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0444.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0444.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0444_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0444_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0444_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0444_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0629.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0629.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0629.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0629.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0629_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0629_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0629_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0629_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0864.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0864.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0864.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0864.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_0864_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0864_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_0864_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_0864_coords.txt diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_1000.dat b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_1000.dat similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_1000.dat rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_1000.dat diff --git a/Test_Cases/MHK_RM1/Airfoils/NACA6_1000_coords.txt b/Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_1000_coords.txt similarity index 100% rename from Test_Cases/MHK_RM1/Airfoils/NACA6_1000_coords.txt rename to Examples/Test_Cases/MHK_RM1/Airfoils/NACA6_1000_coords.txt diff --git a/Test_Cases/MHK_RM1/MHK_RM1_AeroDyn15_Blade.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_AeroDyn15_Blade.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_AeroDyn15_Blade.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_AeroDyn15_Blade.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Cp_Ct_Cq.txt b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Cp_Ct_Cq.txt similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Cp_Ct_Cq.txt rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Cp_Ct_Cq.txt diff --git a/Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN b/Examples/Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN similarity index 99% rename from Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN index 34d3d4a3..322213ff 100644 --- a/Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN +++ b/Examples/Test_Cases/MHK_RM1/MHK_RM1_DISCON.IN @@ -1,9 +1,10 @@ ! Controller parameter input file for the MHK_RM1_Floating wind turbine -! - File written using ROSCO version 2.8.0 controller tuning logic on 12/21/23 +! - File written using ROSCO version 2.8.0 controller tuning logic on 01/05/24 !------- SIMULATION CONTROL ------------------------------------------------------------ 2 ! LoggingLevel - {0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)} 0 ! DT_Out - {Time step to output .dbg* files, or 0 to match sampling period of OpenFAST} +1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) 0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) !------- CONTROLLER FLAGS ------------------------------------------------- diff --git a/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Blade.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Blade.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Blade.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Blade.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Tower.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Tower.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Tower.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_ElastoDyn_Tower.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating.1 b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.1 similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating.1 rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.1 diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating.3 b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.3 similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating.3 rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.3 diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating.fst b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.fst similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating.fst rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.fst diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating.hst b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.hst similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating.hst rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating.hst diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating_AeroDyn15.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_AeroDyn15.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating_AeroDyn15.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_AeroDyn15.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating_ElastoDyn.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_ElastoDyn.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating_ElastoDyn.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_ElastoDyn.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating_HydroDyn.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_HydroDyn.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating_HydroDyn.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_HydroDyn.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating_InflowWind.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_InflowWind.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating_InflowWind.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_InflowWind.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_Floating_MoorDyn.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_MoorDyn.dat similarity index 100% rename from Test_Cases/MHK_RM1/MHK_RM1_Floating_MoorDyn.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_Floating_MoorDyn.dat diff --git a/Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat b/Examples/Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat similarity index 97% rename from Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat rename to Examples/Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat index 1e9b8ff8..c41f6a55 100644 --- a/Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat +++ b/Examples/Test_Cases/MHK_RM1/MHK_RM1_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed using TimGen ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"../../ROSCO/install/lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"../../../lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "MHK_RM1_DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] "default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/NREL-5MW/AeroData/Cylinder1.dat b/Examples/Test_Cases/NREL-5MW/AeroData/Cylinder1.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/Cylinder1.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/Cylinder1.dat diff --git a/Test_Cases/NREL-5MW/AeroData/Cylinder2.dat b/Examples/Test_Cases/NREL-5MW/AeroData/Cylinder2.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/Cylinder2.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/Cylinder2.dat diff --git a/Test_Cases/NREL-5MW/AeroData/DU21_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/DU21_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/DU21_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/DU21_A17.dat diff --git a/Test_Cases/NREL-5MW/AeroData/DU25_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/DU25_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/DU25_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/DU25_A17.dat diff --git a/Test_Cases/NREL-5MW/AeroData/DU30_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/DU30_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/DU30_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/DU30_A17.dat diff --git a/Test_Cases/NREL-5MW/AeroData/DU35_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/DU35_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/DU35_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/DU35_A17.dat diff --git a/Test_Cases/NREL-5MW/AeroData/DU40_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/DU40_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/DU40_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/DU40_A17.dat diff --git a/Test_Cases/NREL-5MW/AeroData/NACA64_A17.dat b/Examples/Test_Cases/NREL-5MW/AeroData/NACA64_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/AeroData/NACA64_A17.dat rename to Examples/Test_Cases/NREL-5MW/AeroData/NACA64_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/Cylinder1.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder1.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/Cylinder1.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder1.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/Cylinder1_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder1_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/Cylinder1_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder1_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/Cylinder2.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder2.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/Cylinder2.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder2.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/Cylinder2_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder2_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/Cylinder2_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/Cylinder2_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/DU21_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/DU21_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU21_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU21_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/DU21_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/DU21_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU21_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU21_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/DU25_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/DU25_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU25_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU25_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/DU25_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/DU25_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU25_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU25_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/DU30_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/DU30_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU30_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU30_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/DU30_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/DU30_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU30_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU30_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/DU35_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/DU35_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU35_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU35_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/DU35_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/DU35_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU35_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU35_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/DU40_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/DU40_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU40_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU40_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/DU40_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/DU40_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/DU40_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/DU40_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Airfoils/NACA64_A17.dat b/Examples/Test_Cases/NREL-5MW/Airfoils/NACA64_A17.dat similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/NACA64_A17.dat rename to Examples/Test_Cases/NREL-5MW/Airfoils/NACA64_A17.dat diff --git a/Test_Cases/NREL-5MW/Airfoils/NACA64_A17_coords.txt b/Examples/Test_Cases/NREL-5MW/Airfoils/NACA64_A17_coords.txt similarity index 100% rename from Test_Cases/NREL-5MW/Airfoils/NACA64_A17_coords.txt rename to Examples/Test_Cases/NREL-5MW/Airfoils/NACA64_A17_coords.txt diff --git a/Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt b/Examples/Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt similarity index 100% rename from Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt rename to Examples/Test_Cases/NREL-5MW/Cp_Ct_Cq.NREL5MW.txt diff --git a/Test_Cases/NREL-5MW/DISCON.IN b/Examples/Test_Cases/NREL-5MW/DISCON.IN similarity index 99% rename from Test_Cases/NREL-5MW/DISCON.IN rename to Examples/Test_Cases/NREL-5MW/DISCON.IN index ee3b7b70..0d8024d1 100644 --- a/Test_Cases/NREL-5MW/DISCON.IN +++ b/Examples/Test_Cases/NREL-5MW/DISCON.IN @@ -1,9 +1,10 @@ ! Controller parameter input file for the NREL-5MW wind turbine -! - File written using ROSCO version 2.8.0 controller tuning logic on 12/21/23 +! - File written using ROSCO version 2.8.0 controller tuning logic on 01/05/24 !------- SIMULATION CONTROL ------------------------------------------------------------ 1 ! LoggingLevel - {0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)} 0 ! DT_Out - {Time step to output .dbg* files, or 0 to match sampling period of OpenFAST} +1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) 0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) !------- CONTROLLER FLAGS ------------------------------------------------- diff --git a/Test_Cases/NREL-5MW/IceDyn_Input.dat b/Examples/Test_Cases/NREL-5MW/IceDyn_Input.dat similarity index 100% rename from Test_Cases/NREL-5MW/IceDyn_Input.dat rename to Examples/Test_Cases/NREL-5MW/IceDyn_Input.dat diff --git a/Test_Cases/NREL-5MW/IceFloe_IEC_Crushing.dat b/Examples/Test_Cases/NREL-5MW/IceFloe_IEC_Crushing.dat similarity index 100% rename from Test_Cases/NREL-5MW/IceFloe_IEC_Crushing.dat rename to Examples/Test_Cases/NREL-5MW/IceFloe_IEC_Crushing.dat diff --git a/Test_Cases/NREL-5MW/NREL-5MW.fst b/Examples/Test_Cases/NREL-5MW/NREL-5MW.fst similarity index 100% rename from Test_Cases/NREL-5MW/NREL-5MW.fst rename to Examples/Test_Cases/NREL-5MW/NREL-5MW.fst diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_AeroDyn_blade.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_AeroDyn_blade.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_AeroDyn_blade.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_AeroDyn_blade.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn_Blade.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn_Blade.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn_Blade.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_BeamDyn_Blade.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Blade.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Blade.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Blade.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Blade.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_InflowWind.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_InflowWind.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_InflowWind.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_InflowWind.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Monopile_IEC_Crushing.inp b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Monopile_IEC_Crushing.inp similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Monopile_IEC_Crushing.inp rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Monopile_IEC_Crushing.inp diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_AeroDyn15.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn_Tower.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn_Tower.dat similarity index 100% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn_Tower.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ElastoDyn_Tower.dat diff --git a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat similarity index 97% rename from Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat rename to Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat index 86351f2d..41627843 100644 --- a/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat +++ b/Examples/Test_Cases/NREL-5MW/NRELOffshrBsline5MW_Onshore_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed using TimGen ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"../../ROSCO/install/lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"../../../lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] "default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/NREL-5MW/README.md b/Examples/Test_Cases/NREL-5MW/README.md similarity index 100% rename from Test_Cases/NREL-5MW/README.md rename to Examples/Test_Cases/NREL-5MW/README.md diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF00_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF00_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF00_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF00_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF01_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF01_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF01_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF01_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF02_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF02_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF02_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF02_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF03_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF03_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF03_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF03_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF04_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF04_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF04_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF04_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF05_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF05_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF05_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF05_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF06_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF06_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF06_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF06_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF07_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF07_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF07_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF07_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF08_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF08_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF08_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF08_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF09_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF09_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF09_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF09_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF10_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF10_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF10_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF10_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF11_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF11_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF11_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF11_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF12_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF12_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF12_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF12_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF13_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF13_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF13_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF13_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF14_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF14_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF14_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF14_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF15_Coords.txt b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF15_Coords.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF15_Coords.txt rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF15_Coords.txt diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AF16_Coords.txt 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a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_23.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_23.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_23.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_23.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_24.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_24.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_24.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_24.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_25.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_25.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_25.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_25.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_26.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_26.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_26.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_26.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_27.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_27.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_27.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_27.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_28.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_28.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_28.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_28.dat diff --git a/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_29.dat b/Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_29.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_29.dat rename to Examples/Test_Cases/NREL_2p8_127/Airfoils/NREL-2p8-127_AeroDyn15_Polar_29.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2.8-127.xlsx b/Examples/Test_Cases/NREL_2p8_127/NREL-2.8-127.xlsx similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2.8-127.xlsx rename to Examples/Test_Cases/NREL_2p8_127/NREL-2.8-127.xlsx diff --git a/Test_Cases/NREL_2p8_127/NREL-2.8-127_pitch-speed.csv b/Examples/Test_Cases/NREL_2p8_127/NREL-2.8-127_pitch-speed.csv similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2.8-127_pitch-speed.csv rename to Examples/Test_Cases/NREL_2p8_127/NREL-2.8-127_pitch-speed.csv diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127.fst b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127.fst similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127.fst rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127.fst diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15_blade.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15_blade.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15_blade.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_AeroDyn15_blade.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_Cp_Ct_Cq.txt b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_Cp_Ct_Cq.txt similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_Cp_Ct_Cq.txt rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_Cp_Ct_Cq.txt diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN similarity index 99% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN index acc3b083..31d0ed79 100644 --- a/Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN +++ b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_DISCON.IN @@ -1,9 +1,10 @@ ! Controller parameter input file for the NREL-2p8-127 wind turbine -! - File written using ROSCO version 2.8.0 controller tuning logic on 12/21/23 +! - File written using ROSCO version 2.8.0 controller tuning logic on 01/05/24 !------- SIMULATION CONTROL ------------------------------------------------------------ 2 ! LoggingLevel - {0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)} 0 ! DT_Out - {Time step to output .dbg* files, or 0 to match sampling period of OpenFAST} +1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) 0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) !------- CONTROLLER FLAGS ------------------------------------------------- diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_blade.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_blade.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_blade.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_blade.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_tower.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_tower.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_tower.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ElastoDyn_tower.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_InflowFile.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_InflowFile.dat similarity index 100% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_InflowFile.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_InflowFile.dat diff --git a/Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat similarity index 98% rename from Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat rename to Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat index bdc2d74d..078bd87f 100644 --- a/Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat +++ b/Examples/Test_Cases/NREL_2p8_127/NREL-2p8-127_ServoDyn.dat @@ -74,7 +74,7 @@ True GenTiStp - Method to stop the generator {T: timed usin ---------------------- CABLE CONTROL ------------------------------------------- 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) ---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] -"/pscratch/ndeveld/awc/ROSCO_B/ROSCO/build/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"/pscratch/ndeveld/awc/ROSCO_B/lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] "NREL-2p8-127_DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] "DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] default DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] diff --git a/Test_Cases/Wind/90m_12mps_twr.inp b/Examples/Test_Cases/Wind/90m_12mps_twr.inp similarity index 100% rename from Test_Cases/Wind/90m_12mps_twr.inp rename to Examples/Test_Cases/Wind/90m_12mps_twr.inp diff --git a/Test_Cases/Wind/NoShr_3-15_50s.wnd b/Examples/Test_Cases/Wind/NoShr_3-15_50s.wnd similarity index 100% rename from Test_Cases/Wind/NoShr_3-15_50s.wnd rename to Examples/Test_Cases/Wind/NoShr_3-15_50s.wnd diff --git a/Examples/Test_Cases/update_libdiscon_extension.py b/Examples/Test_Cases/update_libdiscon_extension.py new file mode 100644 index 00000000..f11d18a0 --- /dev/null +++ b/Examples/Test_Cases/update_libdiscon_extension.py @@ -0,0 +1,21 @@ +import glob +import os +from rosco import discon_lib_path + +if __name__ == "__main__": + this_dir = os.path.dirname(os.path.abspath(__file__)) + servo_list = glob.glob(os.path.join(this_dir, '*/*Servo*.dat')) + + for ifile in servo_list: + # Read in current ServoDyn file + with open(ifile, "r") as f: + lines = f.readlines() + + # Write correction + with open(ifile, "w") as f: + for line in lines: + if line.find("DLL_FileName") >= 0: + f.write(f"\"{discon_lib_path}\" DLL_FileName - Name/location of the dynamic library (.dll [Windows] or .so [Linux]) in the Bladed-DLL format (-) [used only with Bladed Interface]\n") + else: + f.write(line) + diff --git a/Test_Cases/update_rosco_discons.py b/Examples/Test_Cases/update_rosco_discons.py similarity index 93% rename from Test_Cases/update_rosco_discons.py rename to Examples/Test_Cases/update_rosco_discons.py index b2a724fb..07cdc5e5 100644 --- a/Test_Cases/update_rosco_discons.py +++ b/Examples/Test_Cases/update_rosco_discons.py @@ -3,7 +3,7 @@ ''' import os -from ROSCO_toolbox.ofTools.fast_io.update_discons import update_discons +from rosco.toolbox.ofTools.fast_io.update_discons import update_discons if __name__=="__main__": diff --git a/Tune_Cases/BAR.yaml b/Examples/Tune_Cases/BAR.yaml similarity index 100% rename from Tune_Cases/BAR.yaml rename to Examples/Tune_Cases/BAR.yaml diff --git a/Tune_Cases/IEA15MW.yaml b/Examples/Tune_Cases/IEA15MW.yaml similarity index 100% rename from Tune_Cases/IEA15MW.yaml rename to Examples/Tune_Cases/IEA15MW.yaml diff --git a/Tune_Cases/IEA15MW_ExtInterface.yaml b/Examples/Tune_Cases/IEA15MW_ExtInterface.yaml similarity index 97% rename from Tune_Cases/IEA15MW_ExtInterface.yaml rename to Examples/Tune_Cases/IEA15MW_ExtInterface.yaml index 0f65812d..32780386 100644 --- a/Tune_Cases/IEA15MW_ExtInterface.yaml +++ b/Examples/Tune_Cases/IEA15MW_ExtInterface.yaml @@ -54,6 +54,6 @@ controller_params: ps_percent: 0.8 # Percent peak shaving [%, <= 1 ], {default = 80%} DISCON: - DLL_FileName: /Users/dzalkind/Tools/ROSCO/ROSCO/build/libdiscon_copy.dylib + DLL_FileName: /Users/dzalkind/Tools/ROSCO/lib/libdiscon_copy.dylib DLL_InFile: /Users/dzalkind/Tools/ROSCO/Test_Cases/NREL-5MW/DISCON.IN - DLL_ProcName: DISCON \ No newline at end of file + DLL_ProcName: DISCON diff --git a/Tune_Cases/IEA15MW_FOCAL.yaml b/Examples/Tune_Cases/IEA15MW_FOCAL.yaml similarity index 100% rename from Tune_Cases/IEA15MW_FOCAL.yaml rename to Examples/Tune_Cases/IEA15MW_FOCAL.yaml diff --git a/Tune_Cases/IEA15MW_MultiOmega.yaml b/Examples/Tune_Cases/IEA15MW_MultiOmega.yaml similarity index 100% rename from Tune_Cases/IEA15MW_MultiOmega.yaml rename to Examples/Tune_Cases/IEA15MW_MultiOmega.yaml diff --git a/Tune_Cases/IEA15MW_OL.yaml b/Examples/Tune_Cases/IEA15MW_OL.yaml similarity index 100% rename from Tune_Cases/IEA15MW_OL.yaml rename to Examples/Tune_Cases/IEA15MW_OL.yaml diff --git a/Tune_Cases/IEA15MW_PRC.yaml b/Examples/Tune_Cases/IEA15MW_PRC.yaml similarity index 100% rename from Tune_Cases/IEA15MW_PRC.yaml rename to Examples/Tune_Cases/IEA15MW_PRC.yaml diff --git a/Tune_Cases/IEA15MW_ballast.yaml b/Examples/Tune_Cases/IEA15MW_ballast.yaml similarity index 100% rename from Tune_Cases/IEA15MW_ballast.yaml rename to Examples/Tune_Cases/IEA15MW_ballast.yaml diff --git a/Tune_Cases/IEA15MW_cable.yaml b/Examples/Tune_Cases/IEA15MW_cable.yaml similarity index 100% rename from Tune_Cases/IEA15MW_cable.yaml rename to Examples/Tune_Cases/IEA15MW_cable.yaml diff --git a/Tune_Cases/IEA15MW_robust.yaml b/Examples/Tune_Cases/IEA15MW_robust.yaml similarity index 100% rename from Tune_Cases/IEA15MW_robust.yaml rename to Examples/Tune_Cases/IEA15MW_robust.yaml diff --git a/Tune_Cases/NREL2p8.yaml b/Examples/Tune_Cases/NREL2p8.yaml similarity index 100% rename from Tune_Cases/NREL2p8.yaml rename to Examples/Tune_Cases/NREL2p8.yaml diff --git a/Tune_Cases/NREL5MW.yaml b/Examples/Tune_Cases/NREL5MW.yaml similarity index 100% rename from Tune_Cases/NREL5MW.yaml rename to Examples/Tune_Cases/NREL5MW.yaml diff --git a/Tune_Cases/NREL5MW_PassThrough.yaml b/Examples/Tune_Cases/NREL5MW_PassThrough.yaml similarity index 100% rename from Tune_Cases/NREL5MW_PassThrough.yaml rename to Examples/Tune_Cases/NREL5MW_PassThrough.yaml diff --git a/Tune_Cases/README.md b/Examples/Tune_Cases/README.md similarity index 100% rename from Tune_Cases/README.md rename to Examples/Tune_Cases/README.md diff --git a/Tune_Cases/RM1_MHK.yaml b/Examples/Tune_Cases/RM1_MHK.yaml similarity index 100% rename from Tune_Cases/RM1_MHK.yaml rename to Examples/Tune_Cases/RM1_MHK.yaml diff --git a/Matlab_Toolbox/Utilities/ReadWrite_FAST.m b/Matlab_Toolbox/Utilities/ReadWrite_FAST.m index 02c160f3..025ab0ce 100644 --- a/Matlab_Toolbox/Utilities/ReadWrite_FAST.m +++ b/Matlab_Toolbox/Utilities/ReadWrite_FAST.m @@ -43,8 +43,10 @@ SD_dllP = ROSCO2Matlab(SD_dllFile,2); PerfFileName = GetFASTPar(SD_dllP,'PerfFileName'); + % Cp Surface -Cx = Pre_LoadRotPerf(fullfile(fast.FAST_directory,PerfFileName(2:end-1))); +discon_dir = fileparts(SD_dllFile); +Cx = Pre_LoadRotPerf(fullfile(discon_dir,PerfFileName(2:end-1))); %% Write Outputs diff --git a/docs/conf.py b/docs/conf.py index fb94dae9..952f6936 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,8 +23,8 @@ author = 'Nikhar J. Abbas, Daniel S. Zalkind' # The full version, including alpha/beta/rc tags -import ROSCO_toolbox -release = str(ROSCO_toolbox.__version__) +import rosco.toolbox +release = str(rosco.toolbox.__version__) from unittest import mock class MockModule(mock.Mock): @@ -52,6 +52,7 @@ def __getattr__(cls, name): "sphinx.ext.intersphinx", "sphinx.ext.graphviz", "sphinx.ext.autosectionlabel", + "sphinx_rtd_theme", # "sphinxcontrib.bibtex", ] @@ -84,7 +85,7 @@ def __getattr__(cls, name): # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = "furo" +html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, diff --git a/docs/index.rst b/docs/index.rst index 1e60107e..e763859e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,54 +3,39 @@ ROSCO Documentation :Version: |release| :Date: |today| -NREL's Reference OpenSource Controller (ROSCO) tool-set for wind turbine applications designed to ease controller implementation for the wind turbine researcher. The purpose of these documents is to provide information for the use of the tool-set. +NREL's "Reference Open Source Controller" (ROSCO) is a reference controller framework that facilitates design and implementation of wind turbine and wind farm controllers for fixed and floating offshore wind turbines. -:numref:`fig-RT` shows the general workflow for the ROSCO tool-chain. with OpenFAST +ROSCO frameworks includes a large set of available controllers and advanced functionalities that can be combined in a modular fashion based on the intended application and can be easily adapted to a wide variety wind turbines. +For example, ROSCO can be used to design turbine yaw controller along with an individual blade pitch controller with floating platform feedback for an offshore turbine while simulating a pitch actuator fault and running a user-defined torque controller. -.. _fig-RT: -.. figure:: /source/figures/ROSCO_toolbox.svg - :align: center - :width: 80% +ROSCO provides a single framework for designing controllers for onshore and offshore turbines of varying sizes. +It can be used to run representative dynamic simulations using OpenFAST. +This helps researchers perform 'apples-to-apples' comparison of controller capabilities across turbines. +Control engineers can also design their own controllers and compare them with reference controller design using ROSCO for existing and new turbines. +ROSCO has been used to provide reference controllers for many recent reference turbines including the `IEA 3.4-MW `_ , `IEA 10-MW `_ , `IEA 15-MW `_ and the upcoming `IEA 22-MW `_ turbines. - ROSCO toolchain general workflow +The ROSCO framework also includes a python based toolbox that primarily enables tuning the controllers. +The tuning process is extemely simple where only a tuning parameters need to be provided. +It is not necessary to run aeroelastic simulations or provide linearized state-space models to tune the controller to tune the controllers. +The toolbox has other capabilities like simple 1-DOF turbine simulations for quick controller capability verifications, linear model analysis, and parsing of input and output files. +Source code for ROSCO toolset can be found in this `github repository `_ and it can be installed following the instructions provided in :ref:`install`. -**ROSCO Toolbox** -The python-based toolbox primarily used for tuning the controller and writing the DISCON.IN. +**Documentation Directory** -* Generic tuning of NREL's ROSCO controller -* Simple 1-DOF turbine simulations for quick controller capability verifications -* Parsing of OpenFAST input and output files -* Linear model analysis capability - -**ROSCO Controller** -The controller implementation itself. This is compiled to :code:`libdiscon.*` file, reads the DISCON.IN file, and interfaces with OpenFAST using the Bladed-style interface. - -* Fortran based -* Follows Bladed-style control interface -* Modular - -Standard Use ------------- - -For the standard use case in OpenFAST (or similar), ROSCO will need to be compiled. This is made possible via the instructions found in :ref:`install`. Once the controller is compiled, the turbine model needs to point to the compiled binary. In OpenFAST, this is ensured by changing the :code:`DLL_FileName` parameter in the ServoDyn input file. - -Additionally, an additional input file is needed for the ROSCO controller. Though the controller only needs to be compiled once, each individual turbine/controller tuning requires an input file. This input file is generically dubbed "DISCON.IN''. In OpenFAST, the :code:`DLL_InFile` parameter should be set to point to the desired input file. The ROSCO toolbox is used to automatically generate the input file. These instructions are provided in the instructions for :ref:`standard_use`. - -Technical Documentation ------------------------ -A publication highlighting much of the theory behind the controller tuning and implementation methods can be found at: -https://wes.copernicus.org/preprints/wes-2021-19/ - -Survey ------- -Please help us better understand the ROSCO user-base and how we can improve ROSCO through this brief survey: - -.. raw:: html - - +.. toctree:: + :maxdepth: 3 + :numbered: - + source/standard_use.rst + source/install.rst + source/examples.rst + source/rosco.rst + source/rosco_toolbox.rst + source/api_change.rst + source/toolbox_input.rst + source/how_to_contribute_code.rst + source/ROSCO_instructions_for_Bladed.rst License ------- @@ -67,15 +52,3 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. - -.. toctree:: - :maxdepth: 3 - :numbered: - - source/install.rst - source/standard_use.rst - source/rosco_toolbox.rst - source/rosco.rst - source/api_change.rst - source/toolbox_input.rst - source/ROSCO_instructions_for_Bladed.rst diff --git a/docs/requirements.txt b/docs/requirements.txt index 6ff6c653..5b42fa1e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,4 +1,4 @@ # File: docs/requirements.txt sphinx>4.0.0 -furo \ No newline at end of file +sphinx_rtd_theme diff --git a/docs/source/api_change.rst b/docs/source/api_change.rst index 5bb95cdd..0127cd07 100644 --- a/docs/source/api_change.rst +++ b/docs/source/api_change.rst @@ -9,18 +9,25 @@ The changes are tabulated according to the line number, and flag name. The line number corresponds to the resulting line number after all changes are implemented. Thus, be sure to implement each in order so that subsequent line numbers are correct. -2.8.0 to develop +2.8.0 to 2.9.0 ------------------------------- +**Flag to use exteneded Bladed Interface** + +* Set `Ext_Interface` to 1 to use the extened bladed interface with OpenFAST v3.5.0 and greater + **Gain scheduling of floating feedback** * The floating feedback gain can be scheduled on the low pass filtered wind speed signal. Note that Fl_Kp can now be an array. + +**Rotor position tracking** + +* Control the azimuth position of the rotor with `OL_Mode` of 2 using a PID torque controller with gains defined by `RP_Gains`. * Control all three blade pitch inputs in open loop -* Rotor position control of azimuth position with PID (RP_Gains) control inputs **New torque control mode settings** -* VS_ControlMode determines how the generator speed set point is determined: using the WSE or (P/K)^(1/3) -* VS_ConstPower determines whether constant power is used +* VS_ControlMode determines how the generator speed set point is determined: using the WSE (mode 2) or (P/K)^(1/3) (mode 3). The power signal in mode 3 is filtered using `VS_PwrFiltF`. +* VS_ConstPower determines whether constant power is used (0 is constant torque, 1 is constant power) **Multiple notch filters** @@ -35,7 +42,7 @@ Thus, be sure to implement each in order so that subsequent line numbers are cor * Each turbine is assigned a `ZMQ_ID` by the controller, which is tracked by a farm-level controller -**Tower resonance avoidance +**Tower resonance avoidance** * When `TRA_Mode` is 1, change the torque control generator speed setpoint to avoid TRA_ExclSpeed +/- TRA_ExclBand. * The set point is changed at a slow rate `TRA_RateLimit` to avoid generator power spikes. `VS_RefSpd`/100 is recommended. @@ -56,32 +63,33 @@ New in ROSCO develop ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Line Input Name Example Value ====== ======================= =============================================================================================================================================================================================================================================================== -13 VS_ConstPower 0 ! VS_ConstPower - Do constant power torque control, where above rated torque varies, 0 for constant torque} -17 PRC_Mode 0 ! PRC_Mode - Power reference tracking mode{0: use standard rotor speed set points, 1: use PRC rotor speed setpoints} -37 F_NumNotchFilts 1 ! F_NumNotchFilts - Number of notch filters placed on sensors -38 F_NotchFreqs 3.3550 ! F_NotchFreqs - Natural frequency of the notch filters. Array with length F_NumNotchFilts -39 F_NotchBetaNum 0.0000 ! F_NotchBetaNum - Damping value of numerator (determines the width of notch). Array with length F_NumNotchFilts, [-] -40 F_NotchBetaDen 0.2500 ! F_NotchBetaDen - Damping value of denominator (determines the depth of notch). Array with length F_NumNotchFilts, [-] -41 F_GenSpdNotch_N 0 ! F_GenSpdNotch_N - Number of notch filters on generator speed -42 F_GenSpdNotch_Ind 0 ! F_GenSpdNotch_Ind - Indices of notch filters on generator speed -43 F_TwrTopNotch_N 1 ! F_TwrTopNotch_N - Number of notch filters on tower top acceleration signal -44 F_TwrTopNotch_Ind 1 ! F_TwrTopNotch_Ind - Indices of notch filters on tower top acceleration signal -91 VS_PwrFiltF 0.3140 ! VS_PwrFiltF - Low pass filter on power used to determine generator speed set point. Only used in VS_ControlMode = 3. -97 PRC_Section !------- POWER REFERENCE TRACKING -------------------------------------- -98 PRC_n 2 ! PRC_n - Number of elements in PRC_WindSpeeds and PRC_GenSpeeds array -99 PRC_LPF_Freq 0.07854 ! PRC_LPF_Freq - Frequency of the low pass filter on the wind speed estimate used to set PRC_GenSpeeds [rad/s] -100 PRC_WindSpeeds 3.0000 25.0000 ! PRC_WindSpeeds - Array of wind speeds used in rotor speed vs. wind speed lookup table [m/s] -101 PRC_GenSpeeds 0.7917 0.7917 ! PRC_GenSpeeds - Array of generator speeds corresponding to PRC_WindSpeeds [rad/s] -102 Empty Line -127 TRA_ExclSpeed 0.00000 ! TRA_ExclSpeed - Rotor speed for exclusion [LSS, rad/s] -128 TRA_ExclBand 0.00000 ! TRA_ExclBand - Size of the rotor frequency exclusion band [LSS, rad/s]. Torque controller reference will be TRA_ExclSpeed +/- TRA_ExlBand/2 -129 TRA_RateLimit 0.00000e+00 ! TRA_RateLimit - Rate limit of change in rotor speed reference [LSS, rad/s]. Suggested to be VS_RefSpd/100. -144 Fl_n 1 ! Fl_n - Number of Fl_Kp gains in gain scheduling, optional with default of 1 -146 Fl_U 0.0000 ! Fl_U - Wind speeds for scheduling Fl_Kp, optional if Fl_Kp is single value [m/s] -160 Ind_Azimuth 0 ! Ind_Azimuth - The column in OL_Filename that contains the desired azimuth position in rad (used if OL_Mode = 2) -161 RP_Gains 0.0000 0.0000 0.0000 0.0000 ! RP_Gains - PID gains and Tf of derivative for rotor position control (used if OL_Mode = 2) -185 ZMQ_ID 0 ! ZMQ_ID - Integer identifier of turbine -====== ================= ====================================================================================================================================================================================================== +7 Ext_Interface 1 ! Ext_Interface - (0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.) +14 VS_ConstPower 0 ! VS_ConstPower - Do constant power torque control, where above rated torque varies, 0 for constant torque} +18 PRC_Mode 0 ! PRC_Mode - Power reference tracking mode{0: use standard rotor speed set points, 1: use PRC rotor speed setpoints} +38 F_NumNotchFilts 1 ! F_NumNotchFilts - Number of notch filters placed on sensors +39 F_NotchFreqs 3.3550 ! F_NotchFreqs - Natural frequency of the notch filters. Array with length F_NumNotchFilts +40 F_NotchBetaNum 0.0000 ! F_NotchBetaNum - Damping value of numerator (determines the width of notch). Array with length F_NumNotchFilts, [-] +41 F_NotchBetaDen 0.2500 ! F_NotchBetaDen - Damping value of denominator (determines the depth of notch). Array with length F_NumNotchFilts, [-] +42 F_GenSpdNotch_N 0 ! F_GenSpdNotch_N - Number of notch filters on generator speed +43 F_GenSpdNotch_Ind 0 ! F_GenSpdNotch_Ind - Indices of notch filters on generator speed +44 F_TwrTopNotch_N 1 ! F_TwrTopNotch_N - Number of notch filters on tower top acceleration signal +45 F_TwrTopNotch_Ind 1 ! F_TwrTopNotch_Ind - Indices of notch filters on tower top acceleration signal +92 VS_PwrFiltF 0.3140 ! VS_PwrFiltF - Low pass filter on power used to determine generator speed set point. Only used in VS_ControlMode = 3. +98 PRC_Section !------- POWER REFERENCE TRACKING -------------------------------------- +99 PRC_n 2 ! PRC_n - Number of elements in PRC_WindSpeeds and PRC_GenSpeeds array +100 PRC_LPF_Freq 0.07854 ! PRC_LPF_Freq - Frequency of the low pass filter on the wind speed estimate used to set PRC_GenSpeeds [rad/s] +101 PRC_WindSpeeds 3.0000 25.0000 ! PRC_WindSpeeds - Array of wind speeds used in rotor speed vs. wind speed lookup table [m/s] +102 PRC_GenSpeeds 0.7917 0.7917 ! PRC_GenSpeeds - Array of generator speeds corresponding to PRC_WindSpeeds [rad/s] +103 Empty Line +128 TRA_ExclSpeed 0.00000 ! TRA_ExclSpeed - Rotor speed for exclusion [LSS, rad/s] +129 TRA_ExclBand 0.00000 ! TRA_ExclBand - Size of the rotor frequency exclusion band [LSS, rad/s]. Torque controller reference will be TRA_ExclSpeed +/- TRA_ExlBand/2 +130 TRA_RateLimit 0.00000e+00 ! TRA_RateLimit - Rate limit of change in rotor speed reference [LSS, rad/s]. Suggested to be VS_RefSpd/100. +145 Fl_n 1 ! Fl_n - Number of Fl_Kp gains in gain scheduling, optional with default of 1 +147 Fl_U 0.0000 ! Fl_U - Wind speeds for scheduling Fl_Kp, optional if Fl_Kp is single value [m/s] +161 Ind_Azimuth 0 ! Ind_Azimuth - The column in OL_Filename that contains the desired azimuth position in rad (used if OL_Mode = 2) +162 RP_Gains 0.0000 0.0000 0.0000 0.0000 ! RP_Gains - PID gains and Tf of derivative for rotor position control (used if OL_Mode = 2) +186 ZMQ_ID 0 ! ZMQ_ID - Integer identifier of turbine +====== ======================= =============================================================================================================================================================================================================================================================== ====== ================= ====================================================================================================================================================================================================== Changed in ROSCO develop diff --git a/docs/source/examples.rst b/docs/source/examples.rst new file mode 100644 index 00000000..5302c1c0 --- /dev/null +++ b/docs/source/examples.rst @@ -0,0 +1,100 @@ + +.. toctree:: + +.. _examplepage: + +ROSCO Examples +============== +Methods for reading turbine models, generating the control parameters of a :code:`DISCON.IN`: file, and running aeroelastic simulations to test controllers +Reading Turbine Models +---------------------- +Control parameters depend on the turbine model. +The rosco.toolbox uses OpenFAST inputs and an additional :code:`.yaml` formatted file to set up a :code:`turbine` object in python. +Several OpenFAST inputs are located in `Test_Cases/ `_. +The controller tuning :code:`.yaml` are located in `Tune_Cases/ `_. +A detailed description of the ROSCO control inputs and tuning :code:`.yaml` are provided in :ref:`discon_in` and :ref:`rt_tuning_yaml`, respectively. + +* :code:`01_turbine_model.py` loads an OpenFAST turbine model and displays a summary of its information + +ROSCO requires the power and thrust coefficients for tuning control inputs and running the extended Kalman filter wind speed estimator. + +* :code:`02_ccblade.py` runs cc-blade, a blade element momentum solver from WISDEM, to generate a :math:`C_p` surface. + +The :code:`Cp_Cq_Ct.txt` (or similar) file contains the rotor performance tables that are necessary to run the ROSCO controller. +This file can be located wherever you desire, just be sure to point to it properly with the :code:`PerfFileName` parameter in :code:`DISCON.IN`. + + +Tuning Controllers and Generating DISCON.IN +------------------------------------------- +The ROSCO :code:`turbine` object, which contains turbine information required for controller tuning, along with control parameters in the tuning yaml and the :math:`C_p` surface are used to generate control parameters and :code:`DISCON.IN` files. +To tune the PI gains of the torque control, set :code:`omega_vs` and :code:`zeta_vs` in the yaml. +Similarly, set :code:`omega_pc` and :code:`zeta_pc` to tune the PI pitch controller; gain scheduling is automatically handled using turbine information. +Generally :code:`omega_*` increases the responsiveness of the controller, reducing generator speed variations, but an also increases loading on the turbine. +:code:`zeta_*` changes the damping of the controller and is generally less important of a tuning parameter, but could also help with loading. +The default parameters in `Tune_Cases/ `_ are known to work well with the turbines in this repository. + +* :code:`03_tune_controller.py` loads a turbine and tunes the PI control gains +* :code:`04_simple_sim.py` tunes a controller and runs a simple simualtion (not using OpenFAST) +* :code:`05_openfast_sim.py` loads a turbine, tunes a controller, and runs an OpenFAST simulation + +Each of these examples generates a :code:`DISCON.IN` file, which is an input to libdiscon.*. +When running the controller in OpenFAST, :code:`DISCON.IN` must be appropriately named using the :code:`DLL_FileName` parameter in ServoDyn. + +OpenFAST can be installed from `source `_ or in a conda environment using: + +.. code-block:: bash + + conda install -c conda-forge openfast + +ROSCO can implement peak shaving (or thrust clipping) by changing the minimum pitch angle based on the estimated wind speed: + +* :code:`06_peak_shaving.py` loads a turbine and tunes a controller with peak shaving. + +By setting the :code:`ps_percent` value in the tuning yaml, the minimum pitch versus wind speed table changes and is updated in the :code:`DISCON.IN` file. + +ROSCO also contains a method for distributed aerodynamic control (e.g., via trailing edge flaps): + +* :code:`09_distributed_aero.py` tunes a controller for distributed aerodynamic control + +The ROSCO toolbox also contains methods for working with OpenFAST linear models +* :code:`10_linear_params.py` exports a file of the parameters used for the simplified linear models used to tune ROSCO +* :code:`11_robust_tuning.py` shows how linear models generated using OpenFAST can be used to tune controllers with robust stability properties. +* :code:`12_tune_ipc.py` shows the tuning procedure for IPC + +Running OpenFAST Simulations +---------------------------- + +To run an aeroelastic simulation with ROSCO, the ROSCO input (:code:`DISCON.IN`) must point to a properly formatted :code:`Cp_Cq_Ct.txt` file using the :code:`PerfFileName` parameter. +If called from OpenFAST, the main OpenFAST input points to the ServoDyn input, which points to the :code:`DISCON.IN` file and the :code:`libdiscon.*` dynamic library. + +For example in `Test_Cases/NREL-5MW`: + +* :code:`NREL-5MW.fst` has :code:`"NRELOffshrBsline5MW_Onshore_ServoDyn.dat"` as the :code:`ServoFile` input +* :code:`NRELOffshrBsline5MW_Onshore_ServoDyn.dat` has :code:`"../../ROSCO/build/libdiscon.dylib"` as the :code:`DLL_FileName` input and :code:`"DISCON.IN"` as the :code:`DLL_InFile` input. + Note that these file paths are relative to the path of the main fast input (:code:`NREL-5MW.fst`) +* :code:`DISCON.IN` has :code:`"Cp_Ct_Cq.NREL5MW.txt"` as the :code:`PerfFileName` input + +The rosco.toolbox has methods for running OpenFAST (and other) binary executables using system calls, as well as post-processing tools in `ofTools/ `_. + +Several example scripts are set up to quickly simulate ROSCO with OpenFAST: + +* :code:`05_openfast_sim.py` loads a turbine, tunes a controller, and runs an OpenFAST simulation +* :code:`07_openfast_outputs.py` loads the OpenFAST output files and plots the results +* :code:`08_run_turbsim.py` runs TurbSim, for generating turbulent wind inputs +* :code:`14_open_loop_control.py` runs an OpenFAST simulation with ROSCO providing open loop control inputs + + +Testing ROSCO +------------- + +The rosco.toolbox also contains tools for testing ROSCO in IEC design load cases (DLCs), located in `ROSCO_testing/ `_. +The script :code:`run_Testing.py` allows the user to set up their own set of tests. +By setting :code:`testtype`, the user can run a variety of tests: + +* :code:`lite`, which runs DLC 1.1 simulations at 5 wind speed from cut-in to cut-out, in 330 second simulations +* :code:`heavy`, which runs DLC 1.3 from cut-in to cut-out in 2 m/s steps and 2 seeds for each, in 630 seconds, as well as DLC 1.4 simulations +* :code:`binary-comp`, where the user can compare :code:`libdiscon.*` dynamic libraries (compiled ROSCO source code), with either a lite or heavy set of simulations +* :code:`discon-comp`, where the user can compare :code:`DISCON.IN` controller tunings (and the complied ROSCO source is constant) + +Setting the :code:`turbine2test` allows the user to test either the IEA-15MW with the UMaine floating semisubmersible or the NREL-5MW reference onshore turbine. + diff --git a/docs/source/figures/ROSCOFramework.svg b/docs/source/figures/ROSCOFramework.svg new file mode 100644 index 00000000..9ef35b0c --- /dev/null +++ b/docs/source/figures/ROSCOFramework.svg @@ -0,0 +1,4 @@ + + + +
Tuning.yaml
Tuning.yaml
ROSCO
Toolbox
ROSCO...
OpenFAST
Model
OpenFAST...
DISCON.IN
DISCON.IN
ROSCO
Controller
ROSCO...
OpenFAST
OpenFASTText is not SVG - cannot display \ No newline at end of file diff --git a/docs/source/figures/ROSCO_toolbox.svg b/docs/source/figures/ROSCO_toolbox.svg index 8bbc5587..2ba1daf5 100644 --- a/docs/source/figures/ROSCO_toolbox.svg +++ b/docs/source/figures/ROSCO_toolbox.svg @@ -1,13 +1,5 @@ image/svg+xmlROSCOControllerROSCO`_. +If any example fails, this information is passed on to GitHub and a red X will be shown next to the commit. +Otherwise, if all tests pass, a green check mark appears to signify the code changes are valid. + +The examples that are covered are shown in :code:`ROSCO/rosco/test/test_examples.py`. +If you add an example to ROSCO, make sure to add a call to it in the :code:`run_examples.py` script as well. + + +Pull requests +------------- +Once you have added or modified code, submit a pull request via the GitHub interface. +This will automatically go through all of the tests in the repo to make sure everything is functioning properly. +The main developers of ROSCO will then merge in the request or provide feedback on how to improve the contribution. + diff --git a/docs/source/install.rst b/docs/source/install.rst index 023da03c..40734441 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -2,11 +2,44 @@ .. _install: -Installing the ROSCO tools -=========================== -As a reminder, the ROSCO toolbox is a python-based tool used to write the DISCON.IN file, which is read by the ROSCO controller (a compiled binary file). If you only wish to run the controller, you *do not* need to install the ROSCO toolbox. +Installing ROSCO toolset +======================== +ROSCO toolsets can be utilized either to run an existing controller or to design and tune a controller from scratch. +We recommend using the instructions provided in the :ref:`full_rosco` to install the full ROSCO toolset. +This allows for full use of the provided functionalities including the controller and toolbox to facilitate controller tuning. +However, if only the ROSCO binary is needed (to run an existing controller, for example), then users should follow the instructions provided in :ref:`rosco_controller` -Depending on what is needed, a user can choose to use just the ROSCO controller or to use both the ROSCO controller and the toolbox. Both the controller and the toolbox should be installed if one wishes to leverage the full ROSCO tool-chain. :numref:`rosco_table` provides an overview of the primary methods available for :ref:`rosco_controller`. Additionally, :numref:`roscotoolbox_table` provides an overview of the primary methods available to acquire the ROSCO toolbox. Finally, if you wish to install and use both the controller and toolbox, the section about :ref:`full_rosco` provides the best methods of doing so. +.. _full_rosco: + +Complete ROSCO Installation +--------------------------- +Steps for the installation of the complete rosco toolset are: + +1. Create a conda environment for ROSCO + +.. code-block:: bash + + conda config --add channels conda-forge # (Enable Conda-forge Channel For Conda Package Manager) + conda create -y --name rosco-env python=3.10 # (Create a new environment named "rosco-env" that contains Python 3.8) + conda activate rosco-env # (Activate your "rosco-env" environment) + +2. Clone and Install the ROSCO toolbox with ROSCO controller + +.. code-block:: bash + + git clone https://github.com/NREL/ROSCO.git + cd ROSCO + pip install -e . + +This step creates the rosco controller binary (:code:`libdiscon.so` (Linux), :code:`libdiscon.dylib` (Mac), or :code:`libdisscon.dll` (Windows)`) in the directory :code:`ROSCO/rosco/lib` and installs the python toolbox in the conda environment in the develop mode. + + + +.. _rosco_controller: + +Installing only the ROSCO controller +------------------------------------ +:numref:`rosco_table` provides an overview of the primary methods available for installing only the ROSCO controller binary. .. _rosco_table: .. list-table:: Methods for Installing the ROSCO Controller @@ -16,34 +49,18 @@ Depending on what is needed, a user can choose to use just the ROSCO controller * - Method - Use Case * - :ref:`rosco_direct_download` - - Best for users who simply want to use a released version of the controller without working through the compilation procedures. + - Best for users who simply want to use a released version of the controller binary without working through the compilation procedures. * - :ref:`rosco_anaconda_download` - - Best for users who just want to use the controller but prefer to download using the Anaconda package man age Full ROSCO Installation. - * - :ref:`full_rosco` - - Best for users who wish to both use the controller and leverage the tools in the ROSCO toolbox + - Best for users who just want to use the controller binary but prefer to download using the Anaconda package manager. * - :ref:`cmake_compile` - - Best for users who need to re-compile the source code often, plan to use non-released versions of ROSCO (including modified source code), or who simply want to compile the controller themselves so they have the full code available locally. This is necessary for users who wish to use the :ref:`zmq_build`. - -.. _roscotoolbox_table: -.. list-table:: Methods for Installing the ROSCO Toolbox - :widths: 30 70 - :header-rows: 1 - - * - Method - - Use Case - * - :ref:`roscotoolbox_anaconda_download` - - Best for users who simply want to use the primary ROSCO toolbox functions - * - :ref:`full_rosco` - - (Recommended) Best for users who wish to both use the primary ROSCO toolbox functions, as well run and use the many example and testing scripts available. This process can be done with or without compiling ROSCO. - -For many of the methods used to install both ROSCO and the ROSCO toolbox, both Anaconda_ and CMake_ are necessary. Anaconda is a popular package manager used to distribute software packages of various types. Anaconda is used to download requisite packages and distribute pre-compiled versions of the ROSCO tools. CMake is a build configuration system that creates files as input to a build tool like GNU Make, Visual Studio, or Ninja. CMake does not compile code or run compilers directly, but rather creates the environment needed for another tool to run compilers and create binaries. CMake is used to ease the processes of compiling the ROSCO controller locally. For more information on CMake, please see `understanding CMake `_ in the OpenFAST documentation. + - Best for users who need to re-compile the source code often, plan to use non-released versions of ROSCO (including modified source code), or who simply want to compile the controller themselves so they have the full code available locally. - -.. _rosco_controller: - -Installing the ROSCO controller --------------------------------- -The standard ROSCO controller is based in Fortran and must be compiled; the source code can be found at: https://github.com/NREL/ROSCO/ROSCO. +Anaconda is a popular package manager used to distribute software packages of various types. +Anaconda is used to download requisite packages and distribute pre-compiled versions of the ROSCO tools. +CMake is a build configuration system that creates files as input to a build tool like GNU Make, Visual Studio, or Ninja. +CMake does not compile code or run compilers directly, but rather creates the environment needed for another tool to run compilers and create binaries. +CMake is used to ease the processes of compiling the ROSCO controller locally. +For more information on CMake, please see `understanding CMake `_ in the OpenFAST documentation. .. _rosco_direct_download: @@ -53,15 +70,15 @@ The most recent tagged version releases of the controller are `available for dow .. _rosco_anaconda_download: -Anaconda Download - ROSCO -.......................... +Anaconda Download +................. Using the popular package manager, Anaconda_, the tagged 64-bit versions of ROSCO are available through the conda-forge channel. In order to download the most recently compiled version release, from an anaconda powershell (Windows) or terminal (Mac/Linux) window, create a new anaconda virtual environment: .. code-block:: bash conda config --add channels conda-forge - conda create -y --name rosco-env python=3.9 + conda create -y --name rosco-env python=3.10 conda activate rosco-env navigate to your desired folder to save the compiled binary using: @@ -114,108 +131,16 @@ Once the CMake and the required compilers are downloaded, the following code can git clone https://github.com/NREL/ROSCO.git # Compile ROSCO - cd ROSCO/ROSCO + cd ROSCO/rosco/controller mkdir build cd build cmake .. # Mac/linux only cmake .. -G "MinGW Makefiles" # Windows only make install -This will generate a file called :code:`libdiscon.so` (Linux), :code:`libdiscon.dylib` (Mac), or :code:`libdisscon.dll` (Windows) in the :code:`/ROSCO/install/lib` directory. - -.. _zmq_build: - -ZeroMQ Interface -..................... -There is an option to interface ROSCO with external inputs using `ZeroMQ `_. Currently, only externally commanded yaw offset inputs are supported, though this could easily be expanded if the need arises. - -To use the ZeroMQ interface, the software must be downloaded following the `ZeroMQ download instructions `_. Using CMake, ROSCO can then be compiled to enable this interface by using the :code:`ZMQ_CLIENT:ON` flag with the :code:`cmake` command in :ref:`cmake_compile`: - -.. code-block:: bash - - cmake -DZMQ_CLIENT:ON .. - -.. _rosco_toolbox_install: - -Installing the ROSCO toolbox ----------------------------- -The ROSCO toolbox is based in python and contains all relevant ROSCO tools; the source code can be found at: https://github.com/NREL/ROSCO/. In addition to tuning procedures, the ROSCO toolbox also contains example scripts, a Simulink Model of ROSCO, OpenFAST pre-and post-processing functions, linearized systems analysis tools, and a testing suite. - -.. _roscotoolbox_anaconda_download: - -Anaconda Download - ROSCO Toolbox -.................................. -If one wishes to simply use the modules provided in the ROSCO toolbox through scripts of their own, the ROSCO toolbox can be installed via the conda-forge channel of Anaconda. They can then be accessed using the standard methods of loading modules in python, e.g: - -.. code-block:: python - - from ROSCO_toolbox import controller as ROSCO_controller - from ROSCO_toolbox import turbine as ROSCO_turbine - -Note that the install procedures for the ROSCO toolbox are the same as in :ref:`rosco_anaconda_download`, but do not involve moving the controller binary file. -In order to download the most recently compiled version release, from an anaconda powershell (Windows) or terminal (Mac/Linux) window, create a new anaconda virtual environment: - -.. code-block:: bash - - conda config --add channels conda-forge - conda create -y --name rosco-env python=3.8 - conda activate rosco-env - -navigate to your desired folder to save the compiled binary using: - -.. code-block:: bash - - cd - -and download the controller: - -.. code-block:: bash - - conda install -y ROSCO - - - -.. _full_rosco: - -Full ROSCO Installation ------------------------ - -We recommend using the full ROSCO tool-chain. This allows for full use of the provided functions along with the developed python packages and controller code, - -Please follow the following steps to install the ROSCO tool-chain. You should do step 2 *or* 3. If you simply want to install the ROSCO toolbox without the controller, do step 3. If you would like to install the ROSCO toolbox and compile the controller simultaneously, do step 2. - -1. Create a conda environment for ROSCO - -.. code-block:: bash - - conda config --add channels conda-forge # (Enable Conda-forge Channel For Conda Package Manager) - conda create -y --name rosco-env python=3.8 # (Create a new environment named "rosco-env" that contains Python 3.8) - conda activate rosco-env # (Activate your "rosco-env" environment) - -2. Clone and Install the ROSCO toolbox with ROSCO controller - -.. code-block:: bash - - git clone https://github.com/NREL/ROSCO.git - cd ROSCO - conda install compilers # (Mac/Linux only) - conda install m2w64-toolchain libpython # (Windows only) - conda env config vars set FC=gfortran # Sometimes needed for Windows - conda install -y wisdem>3.7 - python setup.py install --compile-rosco - -3. Clone and Install the ROSCO toolbox without ROSCO controller - -.. code-block:: bash - - git clone https://github.com/NREL/ROSCO.git - cd ROSCO - python setup.py install +This will generate a file called :code:`libdiscon.so` (Linux), :code:`libdiscon.dylib` (Mac), or :code:`libdisscon.dll` (Windows). -Getting Started ----------------- -Please see :ref:`standard_use` for several example scripts using ROSCO and the ROSCO_toolbox. .. _Anaconda: https://www.anaconda.com/ diff --git a/docs/source/rosco.rst b/docs/source/rosco.rst index cb6e75a9..167e7fff 100644 --- a/docs/source/rosco.rst +++ b/docs/source/rosco.rst @@ -2,8 +2,8 @@ .. _rosco: -ROSCO Controller Structure -========================== +ROSCO Structure: Controller +=========================== Here, we give an overview of the structure of the ROSCO controller and how the code is implemented. ----- @@ -14,12 +14,15 @@ The primary functions of the ROSCO toolbox are separated into several files. The * :code:`DISCON.f90` is the primary driver function. * :code:`ReadSetParameters.f90` primarily handles file I/O and the Bladed Interface. -* :code:`ROSCO_Types.f90` allocates variables in memory. +* :code:`ROSCO_Types.f90` allocates variables in memory; it is procedurally generated from :code:`rosco_registry` * :code:`Constants.f90` establishes some global constants. * :code:`Controllers.f90` contains the primary controller algorithms (e.g. blade pitch control) * :code:`ControllerBlocks.f90` contains additional control features that are not necessarily primary controllers (e.g. wind speed estimator) * :code:`Filters.f90` contains the various filter implementations. * :code:`Functions.f90` contains various functions used in the controller. +* :code:`ExtControl.f90` contains subroutines for calling external dynamic libraries +* :code:`ROSCO_Helpers.f90` contains subroutines for file I/O and other helpful routines, borrowed heavily from NWTC.IO in OpenFAST +* :code:`ROSCO_IO.f90` is procedurally generated using the :code:`rosco_registry` for writing debug and checkpoint files .. _discon_in: diff --git a/docs/source/rosco_toolbox.rst b/docs/source/rosco_toolbox.rst index a61b3131..637c0f8f 100644 --- a/docs/source/rosco_toolbox.rst +++ b/docs/source/rosco_toolbox.rst @@ -2,7 +2,7 @@ .. _rosco_toolbox_main: -ROSCO Toolbox Structure +ROSCO Structure: Toolbox ======================== Here, we give an overview of the structure of the ROSCO toolbox and how the code is implemented. @@ -25,28 +25,30 @@ The source code for the ROSCO toolbox generic tuning implementations lives here. Examples ......... -A number of examples are included to showcase the numerous capabilities of the ROSCO toolbox; they are described in the :ref:`standard_use`. - -Matlab_Toolbox -............... -A simulink implementation of the ROSCO controller is included in the Matlab Toolbox. Some requisite MATLAB utility scripts are also included. +A number of examples are included to showcase the numerous capabilities of the ROSCO toolbox; they are described in the :ref:`examplepage`. ROSCO_testing ............. Testing scripts for the ROSCO toolbox are held here and showcased with :code:`run_testing.py`. These can be used to compare different controller tunings or different controllers all together. -Test_Cases -.......... +Examples/Test_Cases +................... Example OpenFAST models consistent with the latest release of OpenFAST are provided here for simple testing and simulation cases. -Tune_Cases -.......... +Examples/Tune_Cases +................... Some example tuning scripts and tuning input files are provided here. The code found in :code:`tune_ROSCO.py` can be modified by the user to easily enable tuning of their own wind turbine model. The ROSCO Toolbox Tuning File ------------------------------ A yaml_ formatted input file is used for the standard ROSCO toolbox tuning process. This file contains the necessary inputs for the ROSCO toolbox to load an OpenFAST input file deck and tune the ROSCO controller. It can be found here: :ref:`rt_tuning_yaml`. - + +Matlab_Toolbox +............... +A simulink implementation of the ROSCO controller is included in the Matlab Toolbox. Some requisite MATLAB utility scripts are also included. +These scripts are not maintained by NREL as of ROSCO v2.5.0. +The Simulink controller there should not be used and referenenced as "ROSCO" because it has never been validated against the official ROSCO dynamic library and has drifted away from the official implementation. +We will leave the implementation in place to be used only as a learning tool. .. _OpenFAST: https://github.com/openfast/openfast .. _yaml: https://yaml.org/ diff --git a/docs/source/standard_use.rst b/docs/source/standard_use.rst index a23310dc..08cc8caa 100644 --- a/docs/source/standard_use.rst +++ b/docs/source/standard_use.rst @@ -4,99 +4,30 @@ Standard ROSCO Workflow ======================= -This page outlines methods for reading turbine models, generating the control parameters of a :code:`DISCON.IN`: file, and running aeroelastic simulations to test controllers. -A set of `example scripts `_ demonstrate the functionality of the ROSCO toolbox and controller. -Reading Turbine Models ----------------------- -Control parameters depend on the turbine model. -The ROSCO_toolbox uses OpenFAST inputs and an additional :code:`.yaml` formatted file to set up a :code:`turbine` object in python. -Several OpenFAST inputs are located in `Test_Cases/ `_. -The controller tuning :code:`.yaml` are located in `Tune_Cases/ `_. -A detailed description of the ROSCO control inputs and tuning :code:`.yaml` are provided in :ref:`discon_in` and :ref:`rt_tuning_yaml`, respectively. +.. _fig-RT: +.. figure:: /source/figures/ROSCOFramework.svg + :align: center + :width: 90% -* :code:`01_turbine_model.py` loads an OpenFAST turbine model and displays a summary of its information + ROSCO toolchain general workflow -ROSCO requires the power and thrust coefficients for tuning control inputs and running the extended Kalman filter wind speed estimator. +:numref:`fig-RT` shows the general workflow for the ROSCO tool-chain with OpenFAST. +For the standard use case in OpenFAST (or similar), ROSCO controller needs to be compiled. +The controller is a fortran based module that follows the bladed-style control interface. +Compiling the controller ouputs a dynamic-link library (or equivalent) called :code:`libdiscon.dll` for windows, :code:`libdiscon.so` for linux and, :code:`libdiscon.dylib` for mac-os. +Instructions for the compilation are provided in :ref:`install`. +Once the controller is compiled the turbine simulation tool must point to the compiled library. +In OpenFAST, this is ensured by changing the :code:`DLL_FileName` parameter in the ServoDyn input file. +This step enables communication between the ROSCO controller and OpenFAST. -* :code:`02_ccblade.py` runs cc-blade, a blade element momentum solver from WISDEM, to generate a :math:`C_p` surface. - -The :code:`Cp_Cq_Ct.txt` (or similar) file contains the rotor performance tables that are necessary to run the ROSCO controller. -This file can be located wherever you desire, just be sure to point to it properly with the :code:`PerfFileName` parameter in :code:`DISCON.IN`. - - -Tuning Controllers and Generating DISCON.IN -------------------------------------------- -The ROSCO :code:`turbine` object, which contains turbine information required for controller tuning, along with control parameters in the tuning yaml and the :math:`C_p` surface are used to generate control parameters and :code:`DISCON.IN` files. -To tune the PI gains of the torque control, set :code:`omega_vs` and :code:`zeta_vs` in the yaml. -Similarly, set :code:`omega_pc` and :code:`zeta_pc` to tune the PI pitch controller; gain scheduling is automatically handled using turbine information. -Generally :code:`omega_*` increases the responsiveness of the controller, reducing generator speed variations, but an also increases loading on the turbine. -:code:`zeta_*` changes the damping of the controller and is generally less important of a tuning parameter, but could also help with loading. -The default parameters in `Tune_Cases/ `_ are known to work well with the turbines in this repository. - -* :code:`03_tune_controller.py` loads a turbine and tunes the PI control gains -* :code:`04_simple_sim.py` tunes a controller and runs a simple simualtion (not using OpenFAST) -* :code:`05_openfast_sim.py` loads a turbine, tunes a controller, and runs an OpenFAST simulation - -Each of these examples generates a :code:`DISCON.IN` file, which is an input to libdiscon.*. -When running the controller in OpenFAST, :code:`DISCON.IN` must be appropriately named using the :code:`DLL_FileName` parameter in ServoDyn. - -OpenFAST can be installed from `source `_ or in a conda environment using: - -.. code-block:: bash - - conda install -c conda-forge openfast - -ROSCO can implement peak shaving (or thrust clipping) by changing the minimum pitch angle based on the estimated wind speed: - -* :code:`06_peak_shaving.py` loads a turbine and tunes a controller with peak shaving. - -By setting the :code:`ps_percent` value in the tuning yaml, the minimum pitch versus wind speed table changes and is updated in the :code:`DISCON.IN` file. - -ROSCO also contains a method for distributed aerodynamic control (e.g., via trailing edge flaps): - -* :code:`09_distributed_aero.py` tunes a controller for distributed aerodynamic control - -The ROSCO toolbox also contains methods for working with OpenFAST linear models -* :code:`10_linear_params.py` exports a file of the parameters used for the simplified linear models used to tune ROSCO -* :code:`11_robust_tuning.py` shows how linear models generated using OpenFAST can be used to tune controllers with robust stability properties. -* :code:`12_tune_ipc.py` shows the tuning procedure for IPC - -Running OpenFAST Simulations ----------------------------- - -To run an aeroelastic simulation with ROSCO, the ROSCO input (:code:`DISCON.IN`) must point to a properly formatted :code:`Cp_Cq_Ct.txt` file using the :code:`PerfFileName` parameter. -If called from OpenFAST, the main OpenFAST input points to the ServoDyn input, which points to the :code:`DISCON.IN` file and the :code:`libdiscon.*` dynamic library. - -For example in `Test_Cases/NREL-5MW`: - -* :code:`NREL-5MW.fst` has :code:`"NRELOffshrBsline5MW_Onshore_ServoDyn.dat"` as the :code:`ServoFile` input -* :code:`NRELOffshrBsline5MW_Onshore_ServoDyn.dat` has :code:`"../../ROSCO/build/libdiscon.dylib"` as the :code:`DLL_FileName` input and :code:`"DISCON.IN"` as the :code:`DLL_InFile` input. - Note that these file paths are relative to the path of the main fast input (:code:`NREL-5MW.fst`) -* :code:`DISCON.IN` has :code:`"Cp_Ct_Cq.NREL5MW.txt"` as the :code:`PerfFileName` input - -The ROSCO_toolbox has methods for running OpenFAST (and other) binary executables using system calls, as well as post-processing tools in `ofTools/ `_. - -Several example scripts are set up to quickly simulate ROSCO with OpenFAST: - -* :code:`05_openfast_sim.py` loads a turbine, tunes a controller, and runs an OpenFAST simulation -* :code:`07_openfast_outputs.py` loads the OpenFAST output files and plots the results -* :code:`08_run_turbsim.py` runs TurbSim, for generating turbulent wind inputs -* :code:`14_open_loop_control.py` runs an OpenFAST simulation with ROSCO providing open loop control inputs - - -Testing ROSCO -------------- - -The ROSCO_toolbox also contains tools for testing ROSCO in IEC design load cases (DLCs), located in `ROSCO_testing/ `_. -The script :code:`run_Testing.py` allows the user to set up their own set of tests. -By setting :code:`testtype`, the user can run a variety of tests: - -* :code:`lite`, which runs DLC 1.1 simulations at 5 wind speed from cut-in to cut-out, in 330 second simulations -* :code:`heavy`, which runs DLC 1.3 from cut-in to cut-out in 2 m/s steps and 2 seeds for each, in 630 seconds, as well as DLC 1.4 simulations -* :code:`binary-comp`, where the user can compare :code:`libdiscon.*` dynamic libraries (compiled ROSCO source code), with either a lite or heavy set of simulations -* :code:`discon-comp`, where the user can compare :code:`DISCON.IN` controller tunings (and the complied ROSCO source is constant) - -Setting the :code:`turbine2test` allows the user to test either the IEA-15MW with the UMaine floating semisubmersible or the NREL-5MW reference onshore turbine. +The compiled ROSCO controller library requires an input file (generally called :code:`DISCON.IN`). +It stores several flags and parameters needed by the controller and is read by the compiled dynamic-link library. +Several different :code:`DISCON.IN` files, for varous turbines and controller tunings, can use the same dynamic-link library. +In OpenFAST, the :code:`DLL_InFile` parameter in the ServoDyn input file determines the desired input file. +The ROSCO toolbox is used to tune the ROSCO controller and generate a :code:`DISCON.IN` input file. +To tune the controller, ROSCO toolbox needs the OpenFAST model of the turbine and some user inputs in the form of a :code:`tuning.yaml` file. +The functionality of ROSCO toolset can be best understood by following the set of included example scripts in :ref:`examplepage`. +ROSCO toolset can be installed using the instructions provided in :ref:`install`. diff --git a/environment.yml b/environment.yml index d90d5165..c44181ad 100644 --- a/environment.yml +++ b/environment.yml @@ -3,16 +3,19 @@ channels: - defaults dependencies: + - cmake + - cmake-build-extension + - control + - make - matplotlib + - ninja - numpy - - pytest - - scipy - - pyYAML - pandas - - wisdem - - cmake - - pyparsing==2.4.7 - - control + - pyYAML + - pyparsing + - pytest - pyzmq - - mpi4py + - scipy + - sphinx_rtd_theme - treon + - wisdem diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..ce80267c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,139 @@ +[build-system] +requires = ["setuptools", "cmake", "numpy", "wheel", "pyzmq", "cmake-build-extension"] +build-backend = "setuptools.build_meta" + +[project] +name = "rosco" +version = "2.9.0" +description = "A reference open source controller toolset for wind turbine applications." +readme = "README.md" +requires-python = ">=3.9" +license = {text = "Apache-2.0"} +keywords = ["wind", "turbine", "control", "", ""] +authors = [ + {name = "National Wind Technology Center, NREL", email = "daniel.zalkind@nrel.gov" } +] +maintainers = [ + {name = "Daniel Zalkind", email = "daniel.zalkind@nrel.gov" } +] +classifiers = [ # Optional + # How mature is this project? Common values are + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + "Development Status :: 4 - Beta", + + # Indicate who your project is intended for + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering", + + "License :: OSI Approved :: Apache Software License", + + # Specify the Python versions you support here. In particular, ensure + # that you indicate you support Python 3. These classifiers are *not* + # checked by "pip install". See instead "python_requires" below. + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Fortran", +] + +dependencies = [ + "control", + "numpy", + "matplotlib", + "scipy", + "pandas", + "pyparsing", + "pyYAML", + "pyzmq", + "treon", + "wisdem", + "ruamel.yaml", +] + +# List additional groups of dependencies here (e.g. development +# dependencies). Users will be able to install these using the "extras" +# syntax, for example: +# +# $ pip install sampleproject[dev] +# +# Similar to `dependencies` above, these must be valid existing +# projects. +[project.optional-dependencies] # Optional +dev = ["cmake"] +test = ["pytest"] + +# List URLs that are relevant to your project +# +# This field corresponds to the "Project-URL" and "Home-Page" metadata fields: +# https://packaging.python.org/specifications/core-metadata/#project-url-multiple-use +# https://packaging.python.org/specifications/core-metadata/#home-page-optional +# +# Examples listed include a pattern for specifying where the package tracks +# issues, where the source is hosted, where to say thanks to the package +# maintainers, and where to support the project financially. The key is +# what's used to render the link text on PyPI. +[project.urls] # Optional +"Homepage" = "https://github.com/NREL/ROSCO" +"Documentation" = "https://rosco.readthedocs.io" + +# This is configuration specific to the `setuptools` build backend. +# If you are using a different build backend, you will need to change this. +[tool.setuptools] +zip-safe = false +include-package-data = true + +#[tool.setuptools.packages] +#find = {} + +[tool.setuptools.packages.find] +#where = ["rosco"] +exclude = ["Matlab_Toolbox"] +namespaces = true + +[tool.setuptools.package-data] +# If there are data files included in your packages that need to be +# installed, specify them here. +"*" = ["*.yaml", "*.xlsx", "*.txt", "*.so", "*.lib", "*.pyd", "*.pdb", "*.dylib", "*.dll", "*.exe"] + +[tool.black] +line-length = 120 +target-version = ['py311'] +include = '\.pyi?$' +exclude = ''' +/( + \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist +)/ +''' + +[tool.isort] +# https://github.com/PyCQA/isort +multi_line_output = "3" +include_trailing_comma = true +force_grid_wrap = false +use_parentheses = true +line_length = "120" +sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"] +known_first_party = ["wisdem"] +length_sort = "1" +profile = "black" +skip_glob = ['__init__.py'] +atomic = true +#lines_after_imports = 2 +#lines_between_types = 1 +#src_paths=isort,test + +[tool.cibuildwheel] +skip = ["cp36-*", "cp37-*", "cp38-*", "*-win32"] +build-frontend = "build" diff --git a/rosco/__init__.py b/rosco/__init__.py new file mode 100644 index 00000000..c0bd0537 --- /dev/null +++ b/rosco/__init__.py @@ -0,0 +1,32 @@ +import platform +import os +import sysconfig + +if platform.system() == "Windows": + lib_ext = ".dll" +elif platform.system() == "Darwin": + lib_ext = ".dylib" +else: + lib_ext = ".so" + +rosco_dir = os.path.dirname( os.path.abspath(__file__) ) +libname = "libdiscon" + +lib_path = [os.path.join(rosco_dir, "lib", libname+lib_ext), # pip installs (regular and editable) + os.path.join(os.path.dirname( os.path.dirname( rosco_dir )), "local", "lib", libname+lib_ext), # WEIS library + os.path.join(os.path.dirname( sysconfig.get_path('stdlib') ), libname+lib_ext), # conda installs + os.path.join(os.path.dirname( sysconfig.get_path('stdlib') ), "lib", libname+lib_ext), # conda installs + os.path.join(os.path.dirname( sysconfig.get_path('stdlib') ), "Library", "lib", libname+lib_ext), # conda installs + os.path.join( sysconfig.get_path('platlib'), "rosco", "lib", libname+lib_ext), # system-wide pip installs + os.path.join( sysconfig.get_config_var("userbase"), "lib", "python", "site-packages", "rosco", "lib", libname+lib_ext), # system wide local + ] + +discon_lib_path = None +for p in lib_path: + if os.path.exists(p): + discon_lib_path = str(p) + break + +if discon_lib_path is None: + raise Exception(f"Cannot find {libname+lib_ext} in {lib_path}") + diff --git a/ROSCO/CMakeLists.txt b/rosco/controller/CMakeLists.txt similarity index 87% rename from ROSCO/CMakeLists.txt rename to rosco/controller/CMakeLists.txt index b450a30f..0d290851 100644 --- a/ROSCO/CMakeLists.txt +++ b/rosco/controller/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.6) -project(ROSCO VERSION 2.8.0 LANGUAGES Fortran C) +project(ROSCO VERSION 2.9.0 LANGUAGES Fortran C) set(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/ftnmods") @@ -26,13 +26,6 @@ set(CMAKE_Fortran_FLAGS "${CMAKE_Fortran_FLAGS} -libs:static -free -static -fpp endif() endif() -# Enable ZMQ_Client if compiler flag is set -option(ZMQ_CLIENT "Enable use of ZeroMQ client" off) -if(ZMQ_Client) - set(CMAKE_Fortran_FLAGS "${CMAKE_Fortran_FLAGS} -lzmq") -endif() - - set(SOURCES src/Constants.f90 src/ControllerBlocks.f90 @@ -69,11 +62,11 @@ else (NWTC_SYS_FILE) message(FATAL_ERROR "Cannot determine system file used with NWTC_Library") endif (NWTC_SYS_FILE) -# Library -if(ZMQ_CLIENT) +# ZMQ Library +find_package(PkgConfig) +pkg_check_modules(PC_ZeroMQ libzmq) +if(PC_ZeroMQ_FOUND) # Find ZeroMQ installation - find_package(PkgConfig) - pkg_check_modules(PC_ZeroMQ libzmq) find_path(ZeroMQ_INCLUDE_DIR NAMES zmq.h PATHS ${PC_ZeroMQ_INCLUDE_DIRS} @@ -84,20 +77,21 @@ if(ZMQ_CLIENT) ) include_directories(${ZeroMQ_INCLUDE_DIR}) + set(CMAKE_Fortran_FLAGS "${CMAKE_Fortran_FLAGS} -L${PC_ZeroMQ_LIBRARY_DIRS} -lzmq") # Compile C-based ZeroMQ client as object library add_compile_options(-I${ZeroMQ_INCLUDE_DIR} -l${ZeroMQ_LIBRARY} -fPIC) - add_library(zmq_client OBJECT ../src/zmq_client.c) + add_library(zmq_client OBJECT src/zmq_client.c) # Add definition add_definitions(-DZMQ_CLIENT="TRUE") - set(SOURCES ${SOURCES} + set(SOURCES ${SOURCES} $) endif() add_library(discon SHARED ${SOURCES}) -if(ZMQ_CLIENT) +if(PC_ZeroMQ_FOUND) target_include_directories(discon PUBLIC ${ZeroMQ_INCLUDE_DIR}) target_link_libraries(discon PUBLIC ${ZeroMQ_LIBRARY}) endif() @@ -114,5 +108,5 @@ set(linuxDefault ${CMAKE_INSTALL_PREFIX} STREQUAL "/usr/local") set(windowsDefault ${CMAKE_INSTALL_PREFIX} STREQUAL "C:\\Program Files (x86)") if(${linuxDefault} OR ${windowsDefault}) message("TRUE") - set(CMAKE_INSTALL_PREFIX "${CMAKE_SOURCE_DIR}/install") -endif() \ No newline at end of file + set(CMAKE_INSTALL_PREFIX "${CMAKE_SOURCE_DIR}/../../") +endif() diff --git a/ROSCO/rosco_registry/__init__.py b/rosco/controller/__init__.py similarity index 100% rename from ROSCO/rosco_registry/__init__.py rename to rosco/controller/__init__.py diff --git a/ROSCO_toolbox/inputs/__init__.py b/rosco/controller/rosco_registry/__init__.py similarity index 100% rename from ROSCO_toolbox/inputs/__init__.py rename to rosco/controller/rosco_registry/__init__.py diff --git a/ROSCO/rosco_registry/rosco_types.yaml b/rosco/controller/rosco_registry/rosco_types.yaml similarity index 99% rename from ROSCO/rosco_registry/rosco_types.yaml rename to rosco/controller/rosco_registry/rosco_types.yaml index a853772f..6f888f81 100644 --- a/ROSCO/rosco_registry/rosco_types.yaml +++ b/rosco/controller/rosco_registry/rosco_types.yaml @@ -83,6 +83,9 @@ ControlParameters: Echo: <<: *integer description: 0 - no Echo, 1 - Echo input data to .echo + Ext_Interface: + <<: *integer + description: 0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0. DT_Out: <<: *real diff --git a/ROSCO/rosco_registry/wfc_interface.yaml b/rosco/controller/rosco_registry/wfc_interface.yaml similarity index 100% rename from ROSCO/rosco_registry/wfc_interface.yaml rename to rosco/controller/rosco_registry/wfc_interface.yaml diff --git a/ROSCO/rosco_registry/write_registry.py b/rosco/controller/rosco_registry/write_registry.py similarity index 96% rename from ROSCO/rosco_registry/write_registry.py rename to rosco/controller/rosco_registry/write_registry.py index eef08e8c..1c07fc98 100644 --- a/ROSCO/rosco_registry/write_registry.py +++ b/rosco/controller/rosco_registry/write_registry.py @@ -1,7 +1,7 @@ import yaml -import ROSCO_toolbox +import rosco.toolbox import os -from ROSCO_toolbox.ofTools.util.FileTools import load_yaml +from rosco.toolbox.ofTools.util.FileTools import load_yaml def generate(yfile): ''' @@ -26,7 +26,7 @@ def write_types(yfile): file = open(registry_fname, 'w') # Header file.write('! ROSCO Registry\n') - file.write('! This file is automatically generated by write_registry.py using ROSCO v{}\n'.format(ROSCO_toolbox.__version__)) + file.write('! This file is automatically generated by write_registry.py using ROSCO v{}\n'.format(rosco.toolbox.__version__)) file.write('! For any modification to the registry, please edit the rosco_types.yaml accordingly\n \n') file.write('MODULE ROSCO_Types\n') file.write('USE, INTRINSIC :: ISO_C_Binding\n') @@ -62,7 +62,7 @@ def write_roscoio(yfile): registry_fname = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),'src','ROSCO_IO.f90') file = open(registry_fname, 'w') file.write('! ROSCO IO\n') - file.write('! This file is automatically generated by write_registry.py using ROSCO v{}\n'.format(ROSCO_toolbox.__version__)) + file.write('! This file is automatically generated by write_registry.py using ROSCO v{}\n'.format(rosco.toolbox.__version__)) file.write('! For any modification to the registry, please edit the rosco_types.yaml accordingly\n \n') file.write('MODULE ROSCO_IO\n') file.write(' USE, INTRINSIC :: ISO_C_Binding\n') @@ -95,7 +95,8 @@ def write_roscoio(yfile): file.write(" OPEN(unit=Un, FILE=TRIM(InFile), STATUS='UNKNOWN', FORM='UNFORMATTED' , ACCESS='STREAM', IOSTAT=ErrStat, ACTION='WRITE' )\n") file.write("\n") file.write(" IF ( ErrStat /= 0 ) THEN\n") - file.write(" ErrMsg = 'Cannot open file "'//TRIM( InFile )//'". Another program may have locked it for writing.'\n") + file.write(" ErrVar%aviFAIL = 1\n") + file.write(" ErrVar%ErrMsg = 'ROSCO_IO: Cannot open checkpoint file '//TRIM(InFile)//' for writing'\n") file.write("\n") file.write(" ELSE\n") @@ -112,6 +113,10 @@ def write_roscoio(yfile): for var in reg['ObjectInstances']: file.write(' WRITE( Un, IOSTAT=ErrStat) objInst%{}\n'.format(var)) file.write(' Close ( Un )\n') + file.write(" IF ( ErrStat /= 0 ) THEN\n") + file.write(" ErrVar%aviFAIL = 1\n") + file.write(" ErrVar%ErrMsg = 'ROSCO_IO: Error writing checkpoint file.'\n") + file.write(" ENDIF\n") file.write(' ENDIF\n') file.write('END SUBROUTINE WriteRestartFile\n') file.write('\n \n') @@ -141,7 +146,8 @@ def write_roscoio(yfile): file.write(" OPEN(unit=Un, FILE=TRIM(InFile), STATUS='UNKNOWN', FORM='UNFORMATTED' , ACCESS='STREAM', IOSTAT=ErrStat, ACTION='READ' )\n") file.write("\n") file.write(" IF ( ErrStat /= 0 ) THEN\n") - file.write(" ErrMsg = 'Cannot open file "'//TRIM( InFile )//'". Another program may have locked it for writing.'\n") + file.write(" ErrVar%aviFAIL = 1\n") + file.write(" ErrVar%ErrMsg = 'ROSCO_IO: Cannot open checkpoint file '//TRIM(InFile)//' for reading'\n") file.write("\n") file.write(" ELSE\n") @@ -160,6 +166,10 @@ def write_roscoio(yfile): for var in reg['ObjectInstances']: file.write(' READ( Un, IOSTAT=ErrStat) objInst%{}\n'.format(var)) file.write(' Close ( Un )\n') + file.write(" IF ( ErrStat /= 0 ) THEN\n") + file.write(" ErrVar%aviFAIL = 1\n") + file.write(" ErrVar%ErrMsg = 'ROSCO_IO: Error reading checkpoint file.'\n") + file.write(" ENDIF\n") file.write(' ENDIF\n') file.write(' ! Read Parameter files\n') file.write(' CALL ReadControlParameterFileSub(CntrPar, LocalVar, LocalVar%ACC_INFILE, LocalVar%ACC_INFILE_SIZE, RootName, ErrVar)\n') diff --git a/ROSCO/rosco_registry/write_wfc_interface.py b/rosco/controller/rosco_registry/write_wfc_interface.py similarity index 99% rename from ROSCO/rosco_registry/write_wfc_interface.py rename to rosco/controller/rosco_registry/write_wfc_interface.py index 67790aa2..06895db8 100644 --- a/ROSCO/rosco_registry/write_wfc_interface.py +++ b/rosco/controller/rosco_registry/write_wfc_interface.py @@ -1,6 +1,6 @@ -import ROSCO_toolbox +import rosco.toolbox import os -from ROSCO_toolbox.ofTools.util.FileTools import load_yaml +from rosco.toolbox.ofTools.util.FileTools import load_yaml import re this_dir = os.path.dirname(__file__) diff --git a/ROSCO/src/Constants.f90 b/rosco/controller/src/Constants.f90 similarity index 97% rename from ROSCO/src/Constants.f90 rename to rosco/controller/src/Constants.f90 index b85eefaa..56298879 100644 --- a/ROSCO/src/Constants.f90 +++ b/rosco/controller/src/Constants.f90 @@ -14,7 +14,7 @@ MODULE Constants USE, INTRINSIC :: ISO_C_Binding - Character(*), PARAMETER :: rosco_version = 'v2.8.0' ! ROSCO version + Character(*), PARAMETER :: rosco_version = 'v2.9.0' ! ROSCO version INTEGER, PARAMETER :: DbKi = C_DOUBLE !< Default kind for double floating-point numbers INTEGER, PARAMETER :: ReKi = C_FLOAT !< Default kind for single floating-point numbers INTEGER, PARAMETER :: IntKi = C_INT !< Default kind for integer numbers diff --git a/ROSCO/src/ControllerBlocks.f90 b/rosco/controller/src/ControllerBlocks.f90 similarity index 100% rename from ROSCO/src/ControllerBlocks.f90 rename to rosco/controller/src/ControllerBlocks.f90 diff --git a/ROSCO/src/Controllers.f90 b/rosco/controller/src/Controllers.f90 similarity index 100% rename from ROSCO/src/Controllers.f90 rename to rosco/controller/src/Controllers.f90 diff --git a/ROSCO/src/DISCON.F90 b/rosco/controller/src/DISCON.F90 similarity index 100% rename from ROSCO/src/DISCON.F90 rename to rosco/controller/src/DISCON.F90 diff --git a/ROSCO/src/ExtControl.f90 b/rosco/controller/src/ExtControl.f90 similarity index 100% rename from ROSCO/src/ExtControl.f90 rename to rosco/controller/src/ExtControl.f90 diff --git a/ROSCO/src/Filters.f90 b/rosco/controller/src/Filters.f90 similarity index 100% rename from ROSCO/src/Filters.f90 rename to rosco/controller/src/Filters.f90 diff --git a/ROSCO/src/Functions.f90 b/rosco/controller/src/Functions.f90 similarity index 100% rename from ROSCO/src/Functions.f90 rename to rosco/controller/src/Functions.f90 diff --git a/ROSCO/src/ROSCO_Helpers.f90 b/rosco/controller/src/ROSCO_Helpers.f90 similarity index 100% rename from ROSCO/src/ROSCO_Helpers.f90 rename to rosco/controller/src/ROSCO_Helpers.f90 diff --git a/ROSCO/src/ROSCO_IO.f90 b/rosco/controller/src/ROSCO_IO.f90 similarity index 98% rename from ROSCO/src/ROSCO_IO.f90 rename to rosco/controller/src/ROSCO_IO.f90 index a5e9dc24..a4f44283 100644 --- a/ROSCO/src/ROSCO_IO.f90 +++ b/rosco/controller/src/ROSCO_IO.f90 @@ -1,5 +1,5 @@ ! ROSCO IO -! This file is automatically generated by write_registry.py using ROSCO v2.8.0 +! This file is automatically generated by write_registry.py using ROSCO v2.9.0 ! For any modification to the registry, please edit the rosco_types.yaml accordingly MODULE ROSCO_IO @@ -32,7 +32,8 @@ SUBROUTINE WriteRestartFile(LocalVar, CntrPar, ErrVar, objInst, RootName, size_a OPEN(unit=Un, FILE=TRIM(InFile), STATUS='UNKNOWN', FORM='UNFORMATTED' , ACCESS='STREAM', IOSTAT=ErrStat, ACTION='WRITE' ) IF ( ErrStat /= 0 ) THEN - ErrMsg = 'Cannot open file //TRIM( InFile )//. Another program may have locked it for writing.' + ErrVar%aviFAIL = 1 + ErrVar%ErrMsg = 'ROSCO_IO: Cannot open checkpoint file '//TRIM(InFile)//' for writing' ELSE WRITE( Un, IOSTAT=ErrStat) LocalVar%iStatus @@ -302,6 +303,10 @@ SUBROUTINE WriteRestartFile(LocalVar, CntrPar, ErrVar, objInst, RootName, size_a WRITE( Un, IOSTAT=ErrStat) objInst%instPI WRITE( Un, IOSTAT=ErrStat) objInst%instRL Close ( Un ) + IF ( ErrStat /= 0 ) THEN + ErrVar%aviFAIL = 1 + ErrVar%ErrMsg = 'ROSCO_IO: Error writing checkpoint file.' + ENDIF ENDIF END SUBROUTINE WriteRestartFile @@ -329,7 +334,8 @@ SUBROUTINE ReadRestartFile(avrSWAP, LocalVar, CntrPar, objInst, PerfData, RootNa OPEN(unit=Un, FILE=TRIM(InFile), STATUS='UNKNOWN', FORM='UNFORMATTED' , ACCESS='STREAM', IOSTAT=ErrStat, ACTION='READ' ) IF ( ErrStat /= 0 ) THEN - ErrMsg = 'Cannot open file //TRIM( InFile )//. Another program may have locked it for writing.' + ErrVar%aviFAIL = 1 + ErrVar%ErrMsg = 'ROSCO_IO: Cannot open checkpoint file '//TRIM(InFile)//' for reading' ELSE READ( Un, IOSTAT=ErrStat) LocalVar%iStatus @@ -600,6 +606,10 @@ SUBROUTINE ReadRestartFile(avrSWAP, LocalVar, CntrPar, objInst, PerfData, RootNa READ( Un, IOSTAT=ErrStat) objInst%instPI READ( Un, IOSTAT=ErrStat) objInst%instRL Close ( Un ) + IF ( ErrStat /= 0 ) THEN + ErrVar%aviFAIL = 1 + ErrVar%ErrMsg = 'ROSCO_IO: Error reading checkpoint file.' + ENDIF ENDIF ! Read Parameter files CALL ReadControlParameterFileSub(CntrPar, LocalVar, LocalVar%ACC_INFILE, LocalVar%ACC_INFILE_SIZE, RootName, ErrVar) diff --git a/ROSCO/src/ROSCO_Types.f90 b/rosco/controller/src/ROSCO_Types.f90 similarity index 99% rename from ROSCO/src/ROSCO_Types.f90 rename to rosco/controller/src/ROSCO_Types.f90 index 6b8d00dd..64385192 100644 --- a/ROSCO/src/ROSCO_Types.f90 +++ b/rosco/controller/src/ROSCO_Types.f90 @@ -1,5 +1,5 @@ ! ROSCO Registry -! This file is automatically generated by write_registry.py using ROSCO v2.8.0 +! This file is automatically generated by write_registry.py using ROSCO v2.9.0 ! For any modification to the registry, please edit the rosco_types.yaml accordingly MODULE ROSCO_Types @@ -11,6 +11,7 @@ MODULE ROSCO_Types INTEGER(IntKi) :: ZMQ_ID ! 0000 - 9999, Identifier of the rosco, used for zeromq interface only INTEGER(IntKi) :: LoggingLevel ! 0 - write no debug files, 1 - write standard output .dbg-file, 2 - write standard output .dbg-file and complete avrSWAP-array .dbg2-file INTEGER(IntKi) :: Echo ! 0 - no Echo, 1 - Echo input data to .echo + INTEGER(IntKi) :: Ext_Interface ! 0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0. REAL(DbKi) :: DT_Out ! Output time step INTEGER(IntKi) :: n_DT_Out ! output every this many steps INTEGER(IntKi) :: n_DT_ZMQ ! Send measurements to ZMQ after this many time steps diff --git a/ROSCO/src/ReadSetParameters.f90 b/rosco/controller/src/ReadSetParameters.f90 similarity index 95% rename from ROSCO/src/ReadSetParameters.f90 rename to rosco/controller/src/ReadSetParameters.f90 index 96bde566..131b6581 100644 --- a/ROSCO/src/ReadSetParameters.f90 +++ b/rosco/controller/src/ReadSetParameters.f90 @@ -55,25 +55,29 @@ SUBROUTINE ReadAvrSWAP(avrSWAP, LocalVar, CntrPar) LocalVar%Azimuth = avrSWAP(60) LocalVar%NumBl = NINT(avrSWAP(61)) - ! Platform signals - LocalVar%PtfmTDX = avrSWAP(1001) - LocalVar%PtfmTDY = avrSWAP(1002) - LocalVar%PtfmTDZ = avrSWAP(1003) - LocalVar%PtfmRDX = avrSWAP(1004) - LocalVar%PtfmRDY = avrSWAP(1005) - LocalVar%PtfmRDZ = avrSWAP(1006) - LocalVar%PtfmTVX = avrSWAP(1007) - LocalVar%PtfmTVY = avrSWAP(1008) - LocalVar%PtfmTVZ = avrSWAP(1009) - LocalVar%PtfmRVX = avrSWAP(1010) - LocalVar%PtfmRVY = avrSWAP(1011) - LocalVar%PtfmRVZ = avrSWAP(1012) - LocalVar%PtfmTAX = avrSWAP(1013) - LocalVar%PtfmTAY = avrSWAP(1014) - LocalVar%PtfmTAZ = avrSWAP(1015) - LocalVar%PtfmRAX = avrSWAP(1016) - LocalVar%PtfmRAY = avrSWAP(1017) - LocalVar%PtfmRAZ = avrSWAP(1018) + if (CntrPar%Ext_Interface > 0) THEN ! Use extended bladed interface + + ! Platform signals + LocalVar%PtfmTDX = avrSWAP(1001) + LocalVar%PtfmTDY = avrSWAP(1002) + LocalVar%PtfmTDZ = avrSWAP(1003) + LocalVar%PtfmRDX = avrSWAP(1004) + LocalVar%PtfmRDY = avrSWAP(1005) + LocalVar%PtfmRDZ = avrSWAP(1006) + LocalVar%PtfmTVX = avrSWAP(1007) + LocalVar%PtfmTVY = avrSWAP(1008) + LocalVar%PtfmTVZ = avrSWAP(1009) + LocalVar%PtfmRVX = avrSWAP(1010) + LocalVar%PtfmRVY = avrSWAP(1011) + LocalVar%PtfmRVZ = avrSWAP(1012) + LocalVar%PtfmTAX = avrSWAP(1013) + LocalVar%PtfmTAY = avrSWAP(1014) + LocalVar%PtfmTAZ = avrSWAP(1015) + LocalVar%PtfmRAX = avrSWAP(1016) + LocalVar%PtfmRAY = avrSWAP(1017) + LocalVar%PtfmRAZ = avrSWAP(1018) + + ENDIF ! GenTemp = avrSWAP(1026) @@ -331,6 +335,7 @@ SUBROUTINE ReadControlParameterFileSub(CntrPar, LocalVar, accINFILE, accINFILE_s !----------------------- Simulation Control -------------------------- CALL ParseInput(FileLines,'LoggingLevel', CntrPar%LoggingLevel, accINFILE(1), ErrVar, .TRUE., UnEc=UnEc) CALL ParseInput(FileLines,'DT_Out', CntrPar%DT_Out, accINFILE(1), ErrVar, .TRUE., UnEc=UnEc) + CALL ParseInput(FileLines,'Ext_Interface', CntrPar%Ext_Interface, accINFILE(1), ErrVar, .TRUE., UnEc=UnEc) IF (ErrVar%aviFAIL < 0) RETURN !----------------- CONTROLLER FLAGS --------------------- @@ -362,7 +367,7 @@ SUBROUTINE ReadControlParameterFileSub(CntrPar, LocalVar, accINFILE, accINFILE_s !----------------- FILTER CONSTANTS --------------------- CALL ParseInput(FileLines, 'F_LPFCornerFreq', CntrPar%F_LPFCornerFreq, accINFILE(1), ErrVar, .FALSE., UnEc) CALL ParseInput(FileLines, 'F_LPFDamping', CntrPar%F_LPFDamping, accINFILE(1), ErrVar, CntrPar%F_LPFType == 1, UnEc) - CALL ParseInput(FileLines, 'F_NumNotchFilts', CntrPar%F_NumNotchFilts, accINFILE(1), ErrVar, .FALSE., UnEc) + CALL ParseInput(FileLines, 'F_NumNotchFilts', CntrPar%F_NumNotchFilts, accINFILE(1), ErrVar, .TRUE., UnEc) CALL ParseAry( FileLines, 'F_NotchFreqs', CntrPar%F_NotchFreqs, CntrPar%F_NumNotchFilts, accINFILE(1), ErrVar, CntrPar%F_NumNotchFilts == 0, UnEc) CALL ParseAry( FileLines, 'F_NotchBetaNum', CntrPar%F_NotchBetaNum, CntrPar%F_NumNotchFilts, accINFILE(1), ErrVar, CntrPar%F_NumNotchFilts == 0, UnEc) CALL ParseAry( FileLines, 'F_NotchBetaDen', CntrPar%F_NotchBetaDen, CntrPar%F_NumNotchFilts, accINFILE(1), ErrVar, CntrPar%F_NumNotchFilts == 0, UnEc) @@ -741,8 +746,10 @@ SUBROUTINE ReadCpFile(CntrPar,PerfData, ErrVar) INTEGER(IntKi) :: i ! iteration index INTEGER(IntKi) :: CurLine - CHARACTER(*), PARAMETER :: RoutineName = 'ReadCpFile' - REAL(DbKi), DIMENSION(:), ALLOCATABLE :: TmpPerf + INTEGER :: IOS + CHARACTER(256) :: IOM + CHARACTER(*), PARAMETER :: RoutineName = 'ReadCpFile' + REAL(DbKi), DIMENSION(:), ALLOCATABLE :: TmpPerf CurLine = 1 CALL GetNewUnit(UnPerfParameters, ErrVar) @@ -765,7 +772,13 @@ SUBROUTINE ReadCpFile(CntrPar,PerfData, ErrVar) CALL ReadEmptyLine(UnPerfParameters,CurLine) ALLOCATE(PerfData%Cp_mat(CntrPar%PerfTableSize(2),CntrPar%PerfTableSize(1))) DO i = 1,CntrPar%PerfTableSize(2) - READ(UnPerfParameters, *) PerfData%Cp_mat(i,:) ! Read Cp table + READ(UnPerfParameters, *,IOSTAT=IOS,IOMSG=IOM) PerfData%Cp_mat(i,:) ! Read Cp table + IF (IOS > 0) THEN + ErrVar%aviFAIL = -1 + ErrVar%ErrMsg = "Error reading "//TRIM(CntrPar%PerfFileName)//" Cp table, IOMSG="//IOM//"Please check formatting and size of matrices in that file." + CLOSE(UnPerfParameters) + RETURN + ENDIF END DO CALL ReadEmptyLine(UnPerfParameters,CurLine) CALL ReadEmptyLine(UnPerfParameters,CurLine) @@ -773,7 +786,13 @@ SUBROUTINE ReadCpFile(CntrPar,PerfData, ErrVar) CALL ReadEmptyLine(UnPerfParameters,CurLine) ALLOCATE(PerfData%Ct_mat(CntrPar%PerfTableSize(2),CntrPar%PerfTableSize(1))) DO i = 1,CntrPar%PerfTableSize(2) - READ(UnPerfParameters, *) PerfData%Ct_mat(i,:) ! Read Ct table + READ(UnPerfParameters, *,IOSTAT=IOS,IOMSG=IOM) PerfData%Ct_mat(i,:) ! Read Ct table + IF (IOS > 0) THEN + ErrVar%aviFAIL = -1 + ErrVar%ErrMsg = "Error reading "//TRIM(CntrPar%PerfFileName)//" Cp table, IOMSG="//IOM//"Please check formatting and size of matrices in that file." + CLOSE(UnPerfParameters) + RETURN + ENDIF END DO CALL ReadEmptyLine(UnPerfParameters,CurLine) CALL ReadEmptyLine(UnPerfParameters,CurLine) @@ -781,7 +800,13 @@ SUBROUTINE ReadCpFile(CntrPar,PerfData, ErrVar) CALL ReadEmptyLine(UnPerfParameters,CurLine) ALLOCATE(PerfData%Cq_mat(CntrPar%PerfTableSize(2),CntrPar%PerfTableSize(1))) DO i = 1,CntrPar%PerfTableSize(2) - READ(UnPerfParameters, *) PerfData%Cq_mat(i,:) ! Read Cq table + READ(UnPerfParameters, *,IOSTAT=IOS,IOMSG=IOM) PerfData%Cq_mat(i,:) ! Read Cq table + IF (IOS > 0) THEN + ErrVar%aviFAIL = -1 + ErrVar%ErrMsg = "Error reading "//TRIM(CntrPar%PerfFileName)//" Cp table IOMSG="//IOM//"Please check formatting and size of matrices in that file." + CLOSE(UnPerfParameters) + RETURN + ENDIF END DO ! Close file @@ -1401,6 +1426,13 @@ SUBROUTINE CheckInputs(LocalVar, CntrPar, avrSWAP, ErrVar, size_avcMSG) END IF IF (CntrPar%CC_Mode > 0) THEN + + ! Extended avrSWAP must be used + IF (CntrPar%Ext_Interface == 0) THEN + ErrVar%aviFAIL = -1 + ErrVar%ErrMsg = 'The OpenFAST extended bladed interface must be used with Ext_Interface > 0 in the DISCON' + ENDIF + IF (CntrPar%CC_ActTau .LE. 0) THEN ErrVar%aviFAIL = -1 ErrVar%ErrMsg = 'CC_ActTau must be greater than 0.' @@ -1415,6 +1447,14 @@ SUBROUTINE CheckInputs(LocalVar, CntrPar, avrSWAP, ErrVar, size_avcMSG) END IF IF (CntrPar%StC_Mode > 0) THEN + + ! Extended avrSWAP must be used + IF (CntrPar%Ext_Interface == 0) THEN + ErrVar%aviFAIL = -1 + ErrVar%ErrMsg = 'The OpenFAST extended bladed interface must be used with Ext_Interface > 0 in the DISCON' + ENDIF + + ! Check indices DO I = 1,CntrPar%StC_Group_N IF (CntrPar%StC_GroupIndex(I) < 2801) THEN ErrVar%aviFAIL = -1 diff --git a/ROSCO/src/SysFiles/SysGnuLinux.f90 b/rosco/controller/src/SysFiles/SysGnuLinux.f90 similarity index 100% rename from ROSCO/src/SysFiles/SysGnuLinux.f90 rename to rosco/controller/src/SysFiles/SysGnuLinux.f90 diff --git a/ROSCO/src/SysFiles/SysGnuWin.f90 b/rosco/controller/src/SysFiles/SysGnuWin.f90 similarity index 100% rename from ROSCO/src/SysFiles/SysGnuWin.f90 rename to rosco/controller/src/SysFiles/SysGnuWin.f90 diff --git a/ROSCO/src/SysFiles/SysIFL.f90 b/rosco/controller/src/SysFiles/SysIFL.f90 similarity index 100% rename from ROSCO/src/SysFiles/SysIFL.f90 rename to rosco/controller/src/SysFiles/SysIFL.f90 diff --git a/ROSCO/src/SysFiles/SysIVF.f90 b/rosco/controller/src/SysFiles/SysIVF.f90 similarity index 100% rename from ROSCO/src/SysFiles/SysIVF.f90 rename to rosco/controller/src/SysFiles/SysIVF.f90 diff --git a/ROSCO/src/ZeroMQInterface.f90 b/rosco/controller/src/ZeroMQInterface.f90 similarity index 100% rename from ROSCO/src/ZeroMQInterface.f90 rename to rosco/controller/src/ZeroMQInterface.f90 diff --git a/ROSCO/src/zmq_client.c b/rosco/controller/src/zmq_client.c similarity index 95% rename from ROSCO/src/zmq_client.c rename to rosco/controller/src/zmq_client.c index 5df4b656..d0aed4a5 100644 --- a/ROSCO/src/zmq_client.c +++ b/rosco/controller/src/zmq_client.c @@ -5,16 +5,14 @@ #include -void delete_blank_spaces_in_string(char *s) -{ +void delete_blank_spaces_in_string(char *s) { + int i,k=0; - for(i=0;s[i];i++) - { + for(i=0;s[i];i++) { s[i]=s[i+k]; - if(s[i]==" "|| s[i]=="\t") - { - k++; - i--; + if(s[i]==" "|| s[i]=="\t") { + k++; + i--; } } } @@ -24,8 +22,7 @@ int zmq_client ( char *zmq_address, double measurements[17], double setpoints[5] -) -{ +) { int num_measurements = 17; // Number of setpoints and measurements, respectively, and float precision (character length) int char_buffer_size_single = 20; // Char buffer for a single measurement int char_buffer_size_array = (num_measurements * (char_buffer_size_single + 1)); // Char buffer for full messages to and from ROSCO diff --git a/ROSCO_toolbox/linear/__init__.py b/rosco/lib/__init__.py similarity index 100% rename from ROSCO_toolbox/linear/__init__.py rename to rosco/lib/__init__.py diff --git a/ROSCO_testing/ROSCO_testing.py b/rosco/test/ROSCO_testing.py similarity index 95% rename from ROSCO_testing/ROSCO_testing.py rename to rosco/test/ROSCO_testing.py index 7ddfea70..8b4f61d3 100644 --- a/ROSCO_testing/ROSCO_testing.py +++ b/rosco/test/ROSCO_testing.py @@ -13,17 +13,20 @@ ''' import numpy as np -import os, platform -import glob +import os +import platform import multiprocessing as mp -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC -from ROSCO_toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper_batch -from matplotlib.backends.backend_pdf import FigureCanvasPdf, PdfPages -from ROSCO_toolbox.ofTools.fast_io import output_processing +from rosco import discon_lib_path +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC +from rosco.toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper_batch +from matplotlib.backends.backend_pdf import PdfPages #, FigureCanvasPdf +from rosco.toolbox.ofTools.fast_io import output_processing import matplotlib.pyplot as plt +this_dir = os.path.dirname(os.path.realpath(__file__)) +rosco_root = os.path.dirname( os.path.dirname( this_dir ) ) class ROSCO_testing(): @@ -35,17 +38,14 @@ def __init__(self, **kwargs): # Setup simulation parameters - self.runDir = os.path.join(os.path.dirname( os.path.realpath(__file__) ), 'testing' ) # directory to run simulations in + self.runDir = os.path.join(this_dir, 'testing' ) # directory to run simulations in self.wind_dir = None self.namebase = 'ROtest' # root name for output simulations self.FAST_exe = 'openfast_single' # name of openfast executable (may need full path) self.Turbsim_exe = 'turbsim_single' # name of turbsim executable self.FAST_ver = 'OpenFAST' # Fast version # Path to ROSCO controller - default to ROSCO Toolbox submodule - try: - self.rosco_path = glob.glob(os.path.join(os.path.dirname(os.path.realpath(__file__)),'../ROSCO/build/libdiscon.*'))[0] - except: - print('No compiled ROSCO version found, please provide ROSCO_testing.rosco_path.') + self.rosco_path = discon_lib_path self.debug_level = 2 # debug level. 0 - no outputs, 1 - minimal outputs, 2 - all outputs self.overwrite = False # overwrite existing files? self.cores = 4 # number of cores to use @@ -58,7 +58,7 @@ def __init__(self, **kwargs): # - Default to NREL 5MW self.Turbine_Class = 'I' self.Turbulence_Class = 'A' - self.FAST_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../Test_Cases/NREL-5MW') + self.FAST_directory = os.path.join(rosco_root, 'Examples', 'Test_Cases','NREL-5MW') self.FAST_InputFile = 'NREL-5MW.fst' # Desired output channesl @@ -544,9 +544,6 @@ def print_results(self,outfiles): if __name__=='__main__': rt = ROSCO_testing() - this_dir = os.path.dirname(__file__) - - ## =================== INITIALIZATION =================== # Setup simulation parameters rt.namebase = 'IEA-15MW' # Base name for FAST files @@ -554,11 +551,13 @@ def print_results(self,outfiles): rt.Turbsim_exe = 'turbsim' # Turbsim executable path # path to compiled ROSCO controller if platform.system() == 'Windows': - rt.rosco_path = os.path.join(this_dir, '../ROSCO/build/libdiscon.dll') + sfx = 'dll' elif platform.system() == 'Darwin': - rt.rosco_path = os.path.join(this_dir, '../ROSCO/build/libdiscon.dylib') + sfx = 'dylib' else: - rt.rosco_path = os.path.join(this_dir, '../ROSCO/build/libdiscon.so') + sfx = 'so' + rt.rosco_path = discon_lib_path + rt.debug_level = 2 # debug level. 0 - no outputs, 1 - minimal outputs, 2 - all outputs rt.overwrite = True # overwite fast sims? rt.cores = 1 # number of cores if multiprocessings @@ -569,7 +568,7 @@ def print_results(self,outfiles): # Setup turbine rt.Turbine_Class = 'I' rt.Turbulence_Class = 'B' - rt.FAST_directory = os.path.join(this_dir, '../Test_Cases/IEA-15-240-RWT-UMaineSemi') + rt.FAST_directory = os.path.join(rosco_root, 'Examples','Test_Cases','IEA-15-240-RWT-UMaineSemi') rt.FAST_InputFile = 'IEA-15-240-RWT-UMaineSemi.fst' # Additional inputs diff --git a/ROSCO_testing/__init__.py b/rosco/test/__init__.py similarity index 100% rename from ROSCO_testing/__init__.py rename to rosco/test/__init__.py diff --git a/ROSCO_testing/run_Testing.py b/rosco/test/run_Testing.py similarity index 92% rename from ROSCO_testing/run_Testing.py rename to rosco/test/run_Testing.py index f10c0d01..1a375bb1 100644 --- a/ROSCO_testing/run_Testing.py +++ b/rosco/test/run_Testing.py @@ -5,7 +5,7 @@ import os import glob import ROSCO_testing -import importlib +from rosco import discon_lib_path os.system("taskset -p 0xffffffffffff %d" % os.getpid()) @@ -52,12 +52,12 @@ def run_testing(turbine2test, testtype, rosco_binaries=[], discon_files=[], **kw if turbine2test == 'NREL-5MW': rt.Turbine_Class = 'I' rt.Turbulence_Class = 'A' - rt.FAST_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../Test_Cases/NREL-5MW') + rt.FAST_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../Examples/Test_Cases/NREL-5MW') rt.FAST_InputFile = 'NREL-5MW.fst' elif turbine2test == 'IEA-15MW': rt.Turbine_Class = 'I' rt.Turbulence_Class = 'B' - rt.FAST_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../Test_Cases/IEA-15-240-RWT-UMaineSemi') + rt.FAST_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../Examples/Test_Cases/IEA-15-240-RWT-UMaineSemi') rt.FAST_InputFile = 'IEA-15-240-RWT-UMaineSemi.fst' else: raise ValueError('{} is not an available turbine to test!'.format(turbine2test)) @@ -106,8 +106,8 @@ def run_testing(turbine2test, testtype, rosco_binaries=[], discon_files=[], **kw testtype = 'heavy' # lite, heavy, binary-comp, discon-comp # Only fill one of these if comparing controllers - rosco_binaries = [glob.glob(os.path.join(this_dir,'../ROSCO/build/libdiscon.*'))[0]] # Differently named libdiscons to compare discon_files = [] # Differently named DISCON.IN files to compare # Run testing - run_testing(turbine2test, testtype, rosco_binaries=rosco_binaries, discon_files=discon_files, **rt_kwargs) + run_testing(turbine2test, testtype, rosco_binaries=discon_lib_path, + discon_files=discon_files, **rt_kwargs) diff --git a/ROSCO_testing/submit.sh b/rosco/test/submit.sh similarity index 100% rename from ROSCO_testing/submit.sh rename to rosco/test/submit.sh diff --git a/ROSCO_testing/test_checkpoint.py b/rosco/test/test_checkpoint.py similarity index 77% rename from ROSCO_testing/test_checkpoint.py rename to rosco/test/test_checkpoint.py index e1d15f03..78c65db8 100644 --- a/ROSCO_testing/test_checkpoint.py +++ b/rosco/test/test_checkpoint.py @@ -11,44 +11,36 @@ import numpy as np # Python Modules -import os -import platform from shutil import copyfile # ROSCO toolbox modules -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.ofTools.fast_io import output_processing -from ROSCO_toolbox.ofTools.case_gen.CaseLibrary import set_channels -from ROSCO_toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General -from ROSCO_toolbox.ofTools.fast_io.FAST_writer import InputReader_OpenFAST, InputWriter_OpenFAST -from ROSCO_toolbox.utilities import run_openfast +from rosco import discon_lib_path +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.ofTools.case_gen.CaseLibrary import set_channels +from rosco.toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General +from rosco.toolbox.ofTools.fast_io.FAST_writer import InputReader_OpenFAST, InputWriter_OpenFAST +from rosco.toolbox.utilities import run_openfast class RegressionTesting(unittest.TestCase): def test_restart(self): this_dir = os.path.dirname(os.path.abspath(__file__)) - rosco_dir = os.path.dirname(this_dir) + rosco_dir = os.path.dirname( os.path.dirname(this_dir) ) test_out_dir = os.path.join(this_dir, 'test_out') # Load yaml file (Open Loop Case) - parameter_filename = os.path.join(rosco_dir, 'Tune_Cases/IEA15MW.yaml') + tune_directory = os.path.join(rosco_dir,'Examples','Tune_Cases') + parameter_filename = os.path.join(tune_directory, 'IEA15MW.yaml') inps = load_rosco_yaml(parameter_filename) path_params = inps['path_params'] turbine_params = inps['turbine_params'] controller_params = inps['controller_params'] - # Set rosco_dll - if platform.system() == 'Windows': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dll') - elif platform.system() == 'Darwin': - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.dylib') - else: - rosco_dll = os.path.join(rosco_dir, 'ROSCO/build/libdiscon.so') - case_inputs = {} case_inputs[('Fst', 'TMax')] = {'vals': [3.], 'group': 0} - case_inputs[('ServoDyn', 'DLL_FileName')] = {'vals': [rosco_dll], 'group': 0} + case_inputs[('ServoDyn', 'DLL_FileName')] = {'vals': [discon_lib_path], 'group': 0} case_inputs[('Fst', 'ChkptTime')] = {'vals': [1.], 'group': 1} case_inputs[('Fst', 'OutFileFmt')] = {'vals': [2], 'group': 1} case_inputs[('Fst', 'DT')] = {'vals': [0.025], 'group': 0} @@ -67,7 +59,8 @@ def test_restart(self): reader = InputReader_OpenFAST() writer = InputWriter_OpenFAST() reader.FAST_InputFile = path_params['FAST_InputFile'] - reader.FAST_directory = os.path.realpath(os.path.join( rosco_dir, 'Tune_Cases', path_params['FAST_directory'])) + reader.FAST_directory = os.path.realpath(os.path.join( tune_directory, path_params['FAST_directory'])) + reader.execute() writer.fst_vt = reader.fst_vt writer.FAST_runDirectory = test_out_dir @@ -115,6 +108,7 @@ def test_restart(self): fig, ax = op.plot_fast_out(cases=cases, showplot=False) plt.show() + print(fastout[1]['GenPwr'], fastout[0]['GenPwr']) self.check_relative_error(fastout[1]['GenPwr'], fastout[0]['GenPwr'], 1e-3) def check_relative_error(self, meas, real, tol): diff --git a/Examples/test_examples.py b/rosco/test/test_examples.py similarity index 86% rename from Examples/test_examples.py rename to rosco/test/test_examples.py index b0c92174..2aa44cc0 100644 --- a/Examples/test_examples.py +++ b/rosco/test/test_examples.py @@ -36,8 +36,10 @@ ] def execute_script(fscript): - examples_dir = os.path.dirname(os.path.realpath(__file__)) - test_case_dir = os.path.realpath(os.path.join(os.path.dirname(os.path.realpath(__file__)),'../Test_Cases')) + this_dir = os.path.dirname(os.path.abspath(__file__)) + rosco_dir = os.path.dirname( os.path.dirname(this_dir) ) + examples_dir = os.path.join(rosco_dir,'Examples') + test_case_dir = os.path.join(examples_dir,'Test_Cases') # Go to location due to relative path use for airfoil files print("\n\n") diff --git a/ROSCO_toolbox/__init__.py b/rosco/toolbox/__init__.py similarity index 63% rename from ROSCO_toolbox/__init__.py rename to rosco/toolbox/__init__.py index e200b2d2..3aa708bc 100644 --- a/ROSCO_toolbox/__init__.py +++ b/rosco/toolbox/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -"""Top-level package for ROSCO_toolbox Repo.""" +"""Top-level package for ROSCO Repo.""" __author__ = """Nikhar J. Abbas and Daniel S. Zalkind""" __email__ = 'daniel.zalkind@nrel.gov' -__version__ = '2.8.0' +__version__ = '2.9.0' diff --git a/ROSCO_toolbox/control_interface.py b/rosco/toolbox/control_interface.py similarity index 99% rename from ROSCO_toolbox/control_interface.py rename to rosco/toolbox/control_interface.py index c62c442f..1a92decf 100644 --- a/ROSCO_toolbox/control_interface.py +++ b/rosco/toolbox/control_interface.py @@ -9,22 +9,24 @@ # CONDITIONS OF ANY KIND, either express or implied. See the License for the # specific language governing permissions and limitations under the License. +import ctypes from ctypes import ( byref, cdll, POINTER, c_float, c_char_p, - c_double, + #c_double, create_string_buffer, c_int32, c_void_p, ) import numpy as np -import platform, ctypes, os +import platform +import os import zmq import logging -from ROSCO_toolbox.ofTools.util.FileTools import load_yaml +from rosco.toolbox.ofTools.util.FileTools import load_yaml @@ -337,7 +339,7 @@ class wfc_zmq_server: # Read the interface file to obtain the structure of measurements and setpoints interface_file = os.path.realpath( os.path.join( - os.path.dirname(__file__), "../ROSCO/rosco_registry/wfc_interface.yaml" + os.path.dirname(__file__), "../controller/rosco_registry/wfc_interface.yaml" ) ) wfc_interface = load_yaml(interface_file) diff --git a/ROSCO_toolbox/controller.py b/rosco/toolbox/controller.py similarity index 99% rename from ROSCO_toolbox/controller.py rename to rosco/toolbox/controller.py index 82a726a0..9b78cce5 100644 --- a/ROSCO_toolbox/controller.py +++ b/rosco/toolbox/controller.py @@ -13,7 +13,7 @@ import os import datetime from scipy import interpolate, integrate -from ROSCO_toolbox.utilities import list_check +from rosco.toolbox.utilities import list_check from scipy import optimize # Some useful constants @@ -106,14 +106,14 @@ def __init__(self, controller_params): self.flp_tau = controller_params['flp_tau'] except: raise Exception( - 'ROSCO_toolbox:controller: flp_kp_norm and flp_tau must be set if Flp_Mode > 0') + 'rosco.toolbox:controller: flp_kp_norm and flp_tau must be set if Flp_Mode > 0') if self.Fl_Mode > 0: try: self.twr_freq = controller_params['twr_freq'] self.ptfm_freq = controller_params['ptfm_freq'] except: - raise Exception('ROSCO_toolbox:controller: twr_freq and ptfm_freq must be set if Fl_Mode > 0') + raise Exception('rosco.toolbox:controller: twr_freq and ptfm_freq must be set if Fl_Mode > 0') # Kp_float direct setting if 'Kp_float' in controller_params: diff --git a/ROSCO_toolbox/inputs/.gitignore b/rosco/toolbox/inputs/.gitignore similarity index 100% rename from ROSCO_toolbox/inputs/.gitignore rename to rosco/toolbox/inputs/.gitignore diff --git a/ROSCO_toolbox/ofTools/__init__.py b/rosco/toolbox/inputs/__init__.py similarity index 100% rename from ROSCO_toolbox/ofTools/__init__.py rename to rosco/toolbox/inputs/__init__.py diff --git a/ROSCO_toolbox/inputs/schema2rst.py b/rosco/toolbox/inputs/schema2rst.py similarity index 100% rename from ROSCO_toolbox/inputs/schema2rst.py rename to rosco/toolbox/inputs/schema2rst.py diff --git a/ROSCO_toolbox/inputs/toolbox_schema.yaml b/rosco/toolbox/inputs/toolbox_schema.yaml similarity index 99% rename from ROSCO_toolbox/inputs/toolbox_schema.yaml rename to rosco/toolbox/inputs/toolbox_schema.yaml index 2d90620a..44573ae1 100644 --- a/ROSCO_toolbox/inputs/toolbox_schema.yaml +++ b/rosco/toolbox/inputs/toolbox_schema.yaml @@ -558,6 +558,12 @@ properties: type: number description: Time step to output .dbg* files, or 0 to match sampling period of OpenFAST default: 0 + Ext_Interface: + type: number + description: 0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0. + minimum: 0 + maximum: 1 + default: 1 F_LPFType: type: number description: 1- first-order low-pass filter, 2- second-order low-pass filter (currently filters generator speed and pitch control signals diff --git a/ROSCO_toolbox/inputs/validation.py b/rosco/toolbox/inputs/validation.py similarity index 100% rename from ROSCO_toolbox/inputs/validation.py rename to rosco/toolbox/inputs/validation.py diff --git a/ROSCO_toolbox/ofTools/case_gen/__init__.py b/rosco/toolbox/linear/__init__.py similarity index 100% rename from ROSCO_toolbox/ofTools/case_gen/__init__.py rename to rosco/toolbox/linear/__init__.py diff --git a/rosco/toolbox/linear/getMats.py b/rosco/toolbox/linear/getMats.py new file mode 100644 index 00000000..7682b368 --- /dev/null +++ b/rosco/toolbox/linear/getMats.py @@ -0,0 +1,603 @@ +############################################################# +# Extract OpenFAST Matrices from linearization files # +# Authors: Srinivasa B. Ramisetti # +# Created: 01-July-2020 # +# E-mail: ramisettisrinivas@yahoo.com # +# Web: http://ramisetti.github.io # +############################################################# +#!/usr/bin/env python + +import os +from itertools import islice +import numpy as np +import re + +def _isbool(str): + flag=0 + if str=='T' or str=='True': + flag=1 + return flag + + +def findBladeTriplets(rotFrame, Desc, verbose=True): + + # Find the number of, and indices for, triplets in the rotating frame: + chkStr = [r'[Bb]lade \d', r'[Bb]lade [Rr]oot \d', r'BD_\d', r'[Bb]\d', r'[Bb]lade\d', r'PitchBearing\d', r'\d'] + + origDesc = Desc; + + # hack for ElastoDyn state names (remove unnecessary text in parenthesis) + for i in range(len(rotFrame)): + if Desc[i] is None: + raise Exception('Description not defined, likely a bug.') + ix = Desc[i].find('(internal DOF index = ') + if ix>0: + ix2 = Desc[i].find(')') + Desc[i] = Desc[i][:ix]+Desc[i][ix2+1:] + + + NTriplets = 0; # first initialize to zero + Triplets = []; + for i in range(len(rotFrame)): # loop through inputs/outputs/states + if rotFrame[i] == 'T': # this is in the rotating frame + Tmp = -1*np.ones(3) + foundTriplet = False + foundBladeNumber = False + for chk in chkStr: + BldNoCol = re.search(chk,Desc[i]) + if BldNoCol!=None: + foundBladeNumber = True + + Bldstr=BldNoCol.group() + # create another regular expression to find the + # exact match on a different blade: + strng = re.split(Bldstr,Desc[i],1) #this should return the strings before and after the match + + + FirstStr = strng[0] + Bldstr[:len(Bldstr)-1] + '.' + checkThisStr = FirstStr + strng[1] + + #we need to get rid of the special characters that + #may exist in Desc{}: + checkThisStr=checkThisStr.replace(')',r'\)').replace('(', r'\(').replace('^',r'\^') + FirstStr = FirstStr. replace(')',r'\)').replace('(', r'\(').replace('^',r'\^') + + k = int(Bldstr[len(Bldstr)-1]) + Tmp[k-1] = int(i) + break + + #print(Tmp,j) + + # find the other match values + if foundBladeNumber: + for j in range((i+1),len(rotFrame)): # loop through all remaining control inputs + #print(i, j) + if rotFrame[j]: # this is in the rotating frame + BldNoCol = re.search(checkThisStr, Desc[j]) + if BldNoCol!=None: + Num = re.search(FirstStr,Desc[j]).group(); + k = int(Num[len(Num)-1]); + Tmp[k-1] = int(j); # save the indices for the remaining blades + #TmpTmp=Tmp+1 + #print(BldNoCol.group(),i,j,k) + if ( (Tmp>-1).all() ): # true if all the elements of Tmp are nonzero; thus, we found a triplet of rotating indices + foundTriplet = True; + + Triplets.append(Tmp); # these are the indices for control input triplets in the rotating frame + + NTriplets = NTriplets + 1; # this is the number of control input triplets in the rotating frame + + # we'll set rotFrame to false so that we don't have to check the found channels again; also allows us to throw error if we have a rotating channel that doesn't have a unique match + for idx in Tmp: + id=int(idx) + rotFrame[id] = 0; + + break; + + if foundTriplet==False: + if verbose: + print('Rotating channel "', i, Desc[i], '" does not form a unique blade triplet. Blade(s) not found: ', np.array(np.where(Tmp == -1))+1 ) + else: + if verbose: + print( 'Could not find blade number in rotating channel "', Desc[i], '".') + #print(NTriplets) + #print(Triplets) + + return Triplets, NTriplets + +def reOrderByIdx_1D(arry,Indi): + tmp=[None]*len(arry) + j=0 + for i in Indi: + tmp[i]=arry[j] + j=j+1 + return tmp + +def reOrderByIdx_2D(arry,Indi,Indj): + #tmp=[[None]*arry.shape[0]]*arry.shape[1] + tmp=np.empty((arry.shape[0], arry.shape[1])) + #print(arry.shape, len(Indi), len(Indj)) + if arry.shape[0]!= len(Indi): + Indi=np.arange(0,arry.shape[0]) + if arry.shape[1]!= len(Indj): + Indj=np.arange(0,arry.shape[1]) + + p=0 + q=0 + for i in Indi: + q=0 + for j in Indj: + tmp[i][j]=arry[p][q] + q=q+1 + p=p+1 + return tmp + +def getStateOrderingIndx(matData): + + #StateOrderingIndx={} + StateOrderingIndx = np.arange(0,matData['NumStates']) + lastModName = ''; + lastModOrd = 0; + mod_nDOFs = 0; # number of DOFs in each module + sum_nDOFs2 = 0; # running total of second-order DOFs + sum_nDOFs1 = 0; # running total of first-order DOFs + indx_start = 0; # starting index of the modules + + for i in range(0,matData['NumStates']): + + tmp=(matData['DescStates'][i]); # name of the module whose states we are looking at + modName = tmp.split(' ')[0] + ModOrd = matData['StateDerivOrder'][i] + + if lastModName!=modName or lastModOrd!= ModOrd: + # this is the start of a new set of DOFs, so we'll set the + # indices for the last matrix + if lastModOrd == 2: + mod_nDOFs = int(mod_nDOFs/2); + #print('mmmm ', mod_nDOFs, indx_start, matData['ndof2'], i) + StateOrderingIndx[ indx_start :(indx_start+mod_nDOFs)] = sum_nDOFs2 + np.arange(0,mod_nDOFs); # q2 starts at 1 + StateOrderingIndx[ (indx_start+mod_nDOFs):(i)] = sum_nDOFs2 + matData['ndof2'] + np.arange(0,mod_nDOFs); # q2_dot starts at matData.ndof2 + 1 + + sum_nDOFs2 = sum_nDOFs2 + mod_nDOFs; + else: + if indx_start < mod_nDOFs: + StateOrderingIndx[indx_start:(indx_start+mod_nDOFs)] = sum_nDOFs1 + matData['NumStates2'] + np.arange(0,mod_nDOFs); # q1 starts at matData.NumStates2 + 1 + + sum_nDOFs1 = sum_nDOFs1 + mod_nDOFs; + + # reset for a new module (or new 1st-order states in the same module) + mod_nDOFs = 0; + + indx_start = i; #start of this module + lastModName = modName; + lastModOrd = matData['StateDerivOrder'][i]; + + mod_nDOFs = mod_nDOFs+1; + + + # repeat for the last module found: + if lastModOrd == 2: + mod_nDOFs = int(mod_nDOFs/2); + #print(mod_nDOFs,indx_start) + StateOrderingIndx[indx_start:(indx_start+mod_nDOFs)] = sum_nDOFs2 + np.arange(0,mod_nDOFs); # q2 starts at 1 + StateOrderingIndx[(indx_start+mod_nDOFs):matData['NumStates']] = sum_nDOFs2 + matData['ndof2'] + np.arange(0,mod_nDOFs); # q2_dot starts at matData.ndof2 + 1 + else: + StateOrderingIndx[indx_start:(indx_start+mod_nDOFs)] = sum_nDOFs1 + matData['NumStates2'] + np.arange(0,mod_nDOFs); # q1 starts at matData.NumStates2 + 1 + + #print(StateOrderingIndx) + return StateOrderingIndx + +def readOP(fid, n, defaultDerivOrder=2): + OP=[] + Var = {'RotatingFrame': [], 'DerivativeOrder': [], 'Description': []} + colNames=fid.readline().strip() + dummy= fid.readline().strip() + bHasDeriv= colNames.find('Derivative Order')>=0 + for i, line in enumerate(fid): + sp=line.strip().split() + if sp[1].find(',')>=0: + # Most likely this OP has three values (e.g. orientation angles) + # For now we discard the two other values + OP.append(float(sp[1][:-1])) + iRot=4 + else: + OP.append(float(sp[1])) + iRot=2 + Var['RotatingFrame'].append(sp[iRot]) + if bHasDeriv: + Var['DerivativeOrder'].append(int(sp[iRot+1])) + Var['Description'].append(' '.join(sp[iRot+2:]).strip()) + else: + Var['DerivativeOrder'].append(defaultDerivOrder) + Var['Description'].append(' '.join(sp[iRot+1:]).strip()) + if i>=n-1: + break + + tmp = dict() + tmp['x_op'] = OP + tmp['x_rotFrame'] = Var['RotatingFrame'] + tmp['x_DerivOrder'] = Var['DerivativeOrder'] + tmp['x_desc'] = Var['Description'] + + return tmp + + + +def readFASTMatrix(f): + name="" + m=0 + tmp=[] + for line in islice(f,1): + # copy matrix name to tmp_name + tmp_name=line.strip().split(':')[0] + # get matrix dimensions if matrix exists + if tmp_name != "": + m=int(line.strip().split(':')[1].split('x')[0]) + n=int(line.strip().split(':')[1].split('x')[1]) + + # copy matrix into tmp list + if m!=0: + name=tmp_name + for line in islice(f,m): + tmp.append([float(num) for num in line.split()]) + tmp=np.array(tmp) + return name,tmp + +def ReadFASTLinear(filename): + + def extractVal(lines, key): + for l in lines: + if l.find(key)>=0: + return l.split(key)[1].split()[0] + return None + + def readToMarker(fid, marker, nMax): + lines=[] + for i, line in enumerate(fid): + if i>nMax: + raise BrokenFormatError('`{}` not found in file'.format(marker)) + if line.find(marker)>=0: + break + lines.append(line.strip()) + return lines, line + + with open(filename) as f: + info = {} + data = {} + SetOfMatrices = 1 + info['name'] = os.path.splitext(os.path.basename(filename))[0] + + # --- + header, lastLine=readToMarker(f, 'Jacobians included', 30) + header.append(lastLine) + data['t'] = float(extractVal(header,'Simulation time:' )) + data['n_x'] = int(extractVal(header,'Number of continuous states:')) + data['n_xd'] = int(extractVal(header,'Number of discrete states:' )) + data['n_z'] = int(extractVal(header,'Number of constraint states:')) + data['n_u'] = int(extractVal(header,'Number of inputs:' )) + data['n_y'] = int(extractVal(header,'Number of outputs:' )) + bJac = extractVal(header,'Jacobians included in this file?') + if bJac: + SetOfMatrices = 2 + try: + data['Azimuth'] = float(extractVal(header,'Azimuth:')) + except: + data['Azimuth'] = None + try: + data['RotSpeed'] = float(extractVal(header,'Rotor Speed:')) # rad/s + except: + data['RotSpeed'] = np.nan + try: + data['WindSpeed'] = float(extractVal(header,'Wind Speed:')) + except: + data['WindSpeed'] = np.nan + + # --- Old method for reading + # header = [f.readline() for _ in range(17)] + # info['fast_version'] = header[1].strip() + # info['modules'] = header[2].strip() + # info['description'] = header[4].strip() + # ln=7; + # data = np.array([line.split() for line in f.readlines()]).astype(np.float) + # data['t']=np.float(header[ln].split(':')[1].strip().split(' ')[0]) + # data['RotSpeed']=np.float(header[ln+1].split(':')[1].strip().split(' ')[0]) + # data['Azimuth']=np.float(header[ln+2].split(':')[1].strip().split(' ')[0]) + # data['WindSpeed']=np.float(header[ln+3].split(':')[1].strip().split(' ')[0]) + # data['n_x']=np.float(header[ln+4].split(':')[1].strip().split(' ')[0]) + # data['n_xd']=np.float(header[ln+5].split(':')[1].strip().split(' ')[0]) + # data['n_z']=np.float(header[ln+6].split(':')[1].strip().split(' ')[0]) + # data['n_u']=np.float(header[ln+7].split(':')[1].strip().split(' ')[0]) + # data['n_y']=np.float(header[ln+8].split(':')[1].strip().split(' ')[0]) + # if header[ln+9].split('?')[1].strip()=='Yes': + # SetOfMatrices=2 + + data['Azimuth']=np.mod(data['Azimuth'],2.0*np.pi) + try: + # skip next three lines + for line in islice(f,2): + pass + if data['n_x'] > 0: + temp = readOP(f, data['n_x']) + data['x_op']=temp['x_op'] + data['x_rotFrame']=temp['x_rotFrame'] + data['x_DerivOrder']=temp['x_DerivOrder'] + data['x_desc']=temp['x_desc'] + + # skip next three lines + for line in islice(f,2): + pass + + temp = readOP(f, data['n_x'], defaultDerivOrder=2) + data['xdot_op']=temp['x_op'] + data['xdot_desc']=temp['x_desc'] + + #(number of second-order states) + data['n_x2'] = sum(1 for i in data['x_DerivOrder'] if i == 2) + else: + data['n_x2'] = 0; + + + if data['n_xd'] > 0: + # skip next three lines + for line in islice(f,2): + pass + temp = readOP(f, data['n_xd'], defaultDerivOrder=2) + data['xd_op']=temp['x_op'] + data['xd_desc']=temp['x_desc'] + if data['n_z'] > 0: + # skip next three lines + for line in islice(f,2): + pass + temp = readOP(f, data['n_z'], defaultDerivOrder=0) + data['z_op']=temp['x_op'] + data['z_desc']=temp['x_desc'] + if data['n_u'] > 0: + # skip next three lines + for line in islice(f,2): + pass + temp = readOP(f, data['n_u'], defaultDerivOrder=0) + data['u_op']=temp['x_op'] + data['u_desc']=temp['x_desc'] + data['u_rotFrame']=temp['x_rotFrame'] + if data['n_y'] > 0: + # skip next three lines + for line in islice(f,2): + pass + temp = readOP(f, data['n_y'], defaultDerivOrder=0) + data['y_op']=temp['x_op'] + data['y_desc']=temp['x_desc'] + data['y_rotFrame']=temp['x_rotFrame'] + + # skip next one line + for line in islice(f,4): + pass + + mat_names=[] + while True: + name,mat=readFASTMatrix(f) + if not name: + break; + mat_names.append(name) + data[name]=mat + + return data, info + + except (ValueError, AssertionError): + raise + + +def get_Mats(FileNames, verbose=True, removeTwrAzimuth=False): + NAzimStep = len(FileNames) + data = [None]*NAzimStep + # --- Read all files + for iFile, filename in enumerate(FileNames): + data[iFile],_= ReadFASTLinear(FileNames[iFile]); + Azimuth = np.array([d['Azimuth'] for d in data])*180/np.pi + # --- Sort by azimuth, not required, but filenames are not sorted, nor are the lin times azimuth + ISort = np.argsort(Azimuth) + Azimuth = Azimuth[ISort] + data = [data[i] for i in ISort] + FileNames = [FileNames[i] for i in ISort] + if removeTwrAzimuth: + IDiscard = [i for i,a in enumerate(Azimuth) if np.any(np.abs(np.array([60,180,300])-a)<4) ] + n=len(FileNames[0]); sfmt='{:'+str(n+2)+'s}' + for i in IDiscard: + print(' discard: '+sfmt.format(FileNames[i]) + ' (psi={:5.1f})'.format(Azimuth[i])) + data = [d for i,d in enumerate(data) if i not in IDiscard] + NAzimStep= len(data) + + + matData={} + matData['NAzimStep']= NAzimStep; + + # --- Using last azimuth to initial some of the "matData" variables + # Input data from linearization files + matData['NumStates'] = int(data[NAzimStep-1]['n_x']); + matData['NumStates2'] = int(data[NAzimStep-1]['n_x2']); + + #return data, matData + matData['ndof1'] = int(matData['NumStates'] - matData['NumStates2']); # number of first-order states = number of first-order DOFs + matData['ndof2'] = int(data[NAzimStep-1]['n_x2'] / 2); # half the number of second-order states = number of second-order DOFs + #% matData['ndof'] = matData['ndof2'] + matData['ndof1']; #half the number of second-order states plus the number of first-order states (i.e., states that aren't derivatives) + + matData['NumInputs'] = int(data[NAzimStep-1]['n_u']); + matData['NumOutputs'] = int(data[NAzimStep-1]['n_y']); + + # allocate space for these variables + matData['Azimuth'] = np.zeros(NAzimStep); + matData['Omega'] = np.zeros(NAzimStep); + matData['OmegaDot'] = np.zeros(NAzimStep); + + matData['WindSpeed'] = np.zeros(NAzimStep)*np.nan; + + if matData['NumStates'] > 0: + matData['DescStates'] = data[NAzimStep-1]['x_desc']; + matData['StateDerivOrder'] = data[NAzimStep-1]['x_DerivOrder']; + matData['xdop'] = np.zeros((matData['NumStates'], NAzimStep)) + matData['xop'] = np.zeros((matData['NumStates'], NAzimStep)) + matData['A'] = np.zeros((matData['NumStates'], matData['NumStates'], NAzimStep)) + + if matData['NumInputs'] > 0: + matData['DescCntrlInpt'] = data[NAzimStep-1]['u_desc']; + matData['u_op'] = np.zeros((matData['NumInputs'],NAzimStep)) + if matData['NumStates']>0: + matData['B'] = np.zeros((matData['NumStates'], matData['NumInputs'],NAzimStep)) + + if matData['NumOutputs'] > 0: + matData['DescOutput'] = data[NAzimStep-1]['y_desc'] + matData['y_op'] = np.zeros((matData['NumOutputs'],NAzimStep)) + + if matData['NumStates'] > 0: + matData['C'] = np.zeros((matData['NumOutputs'], matData['NumStates'], NAzimStep)) + if matData['NumInputs'] > 0: + matData['D'] = np.zeros((matData['NumOutputs'], matData['NumInputs'], NAzimStep)) + + # Reorder state matrices so that they follow the {q2, q2_dot, q1} + # format that is assumed in the MBC3 equations. + if matData['NumStates'] > 0: + # keep StateOrderingIndx for applying inverse of MBC3 later + # (to visualize mode shapes) + matData['StateOrderingIndx'] = getStateOrderingIndx(matData) + + sortedIndx=matData['StateOrderingIndx'] + #print(sortedIndx) + x_rotFrame= reOrderByIdx_1D(data[NAzimStep-1]['x_rotFrame'],sortedIndx) + matData['DescStates'] = reOrderByIdx_1D(data[NAzimStep-1]['x_desc'],sortedIndx) + matData['StateDerivOrder'] = reOrderByIdx_1D(data[NAzimStep-1]['x_DerivOrder'],sortedIndx) + + # --- Store file data into matData + for iFile in np.arange(0,NAzimStep): + matData['Omega'][iFile] = data[iFile]['RotSpeed'] ; + matData['Azimuth'][iFile] = data[iFile]['Azimuth']*180/np.pi; + + matData['WindSpeed'][iFile] = data[iFile]['WindSpeed'] + + if 'A' in data[iFile]: + matData['A'][:,:,iFile]=reOrderByIdx_2D(data[iFile]['A'],sortedIndx,sortedIndx) + #print('size of matData[A] for file ', iFile, ' is :', matData['A'].shape) + if 'B' in data[iFile]: + matData['B'][:,:,iFile]=reOrderByIdx_2D(data[iFile]['B'],sortedIndx,np.arange(0,matData['NumStates'])) + #print('size of matData[B] for file ', iFile, ' is :', matData['B'].shape) + if 'C' in data[iFile]: + matData['C'][:,:,iFile]=reOrderByIdx_2D(data[iFile]['C'],np.arange(0,matData['NumStates']),sortedIndx) + #print('size of matData[C] for file ', iFile, ' is :', matData['C'].shape) + if 'D' in data[iFile]: + matData['D'][:,:,iFile]=reOrderByIdx_2D(data[iFile]['D'],sortedIndx,sortedIndx) + #print('size of matData[D] for file ', iFile, ' is :', matData['D'].shape) + + if 'x_op' in data[iFile]: + matData['xop'][:,iFile] = reOrderByIdx_1D(data[iFile]['x_op'],sortedIndx) + #print('matData[xop] for file ', iFile,' is :',(matData['xop'][:,iFile])) + if 'xdot_op' in data[iFile]: + matData['xdop'][:,iFile] = reOrderByIdx_1D(data[iFile]['xdot_op'],sortedIndx) + #print('matData[xdop] for file ', iFile,' is :',(matData['xdop'][:,iFile])) + + if 'u_op' in data[iFile]: + matData['u_op'][:,iFile] = data[iFile]['u_op'] + + if 'y_op' in data[iFile]: + matData['y_op'][:,iFile] = data[iFile]['y_op'] + + + # Find the azimuth-averaged linearized 1st order state matrices: + if 'A' in matData: + matData['Avgxdop'] = np.mean(matData['xdop'],axis=1) + matData['Avgxop'] = np.mean(matData['xop'], axis=1) + matData['AvgA'] = np.mean(matData['A'],axis=2) + + #print(matData['AvgA']) + if 'B' in matData: + matData['AvgB'] = np.mean(matData['B'],axis=2) + + if 'C' in matData: + matData['AvgC'] = np.mean(matData['C'],axis=2) + + if 'D' in matData: + matData['AvgD'] = np.mean(matData['D'],axis=2) + + + foundED = True; + for i in range(matData['ndof2']): + # find the starting index of the string 'DOF_GeAz' + if (matData['DescStates'][i].find('DOF_GeAz') != -1): + matData['Omega'] = matData['xdop'][i,:] + matData['OmegaDot'] = matData['xdop'][i+matData['ndof2'],:] + foundED = True + break + + for i in range(matData['ndof2']): + # find the starting index of the string 'DOF_DrTr' + if (matData['DescStates'][i].find('DOF_DrTr') != -1): + matData['Omega'] = matData['Omega'] + matData['xdop'][i,:] #This always comes after DOF_GeAz so let's just add it here (it won't get written over later). + matData['OmegaDot'] = matData['OmegaDot'] + matData['xdop'][i+matData['ndof2'],:] + foundED = True + break + + if not foundED: + for i in range(matData['ndof2']): + # find the starting index of the string 'Gearbox_Rot' + if (matData['DescStates'][i].find('MBD Gearbox_Rot') != -1): + matData['Omega'] = matData['xdop'][i,:] + matData['OmegaDot'] = matData['xdop'][i+matData['ndof2'],:] + break + + #print("\n".join(matData['DescStates'])) + #exit() + # ----------- Find multi-blade coordinate (MBC) transformation indices ---- + + # Find the indices for, state triplets in the rotating frame + # (note that we avoid the "first time derivative" states) + if matData['ndof2'] > 0: + matData['RotTripletIndicesStates2'], matData['n_RotTripletStates2'] = findBladeTriplets(x_rotFrame[0:matData['ndof2']],matData['DescStates'][0:matData['ndof2']], verbose=verbose) + else: + matData['RotTripletIndicesStates2'] = []; + matData['n_RotTripletStates2'] = 0; + + if matData['ndof1'] > 0: + matData['RotTripletIndicesStates1'], matData['n_RotTripletStates1'] = findBladeTriplets( x_rotFrame[matData['NumStates2']:] ,matData['DescStates'][matData['NumStates2']:] , verbose=verbose); + else: + matData['RotTripletIndicesStates1'] = []; + matData['n_RotTripletStates1'] = 0; + + # Find the indices for control input triplets in the rotating frame: + if matData['NumInputs'] > 0: + matData['RotTripletIndicesCntrlInpt'], matData['n_RotTripletInputs'] = findBladeTriplets(data[0]['u_rotFrame'],matData['DescCntrlInpt'], verbose=verbose ); + else: + matData['RotTripletIndicesCntrlInpt'] = []; + matData['n_RotTripletInputs'] = 0; + + # Find the indices for output measurement triplets in the rotating frame: + if (matData['NumOutputs'] > 0 ): + matData['RotTripletIndicesOutput'], matData['n_RotTripletOutputs'] = findBladeTriplets(data[0]['y_rotFrame'],matData['DescOutput'], verbose=verbose ); + else: + matData['RotTripletIndicesOutput'] = []; + matData['n_RotTripletOutputs'] = 0; + + return matData, data + +#matData,FAST_linData=get_Mats(FileNames) + +#print("\n".join(matData['DescStates'])) + +# print(info) +# print(data['t']) +# print(data['RotSpeed']) +# print(data['Azimuth']) +# print(data['WindSpeed']) +# print(data['n_x']) +# print(data['n_xd']) +# print(data['n_z']) +# print(data['n_u']) +# print(data['n_y']) +# print(data['n_x2']) +# print(data['x_op']) +# print(data['x_desc']) +# print(data['x_rotFrame']) +# print(data['xdot_op']) +# print(data['xdot_desc']) +# print(data['u_op']) +# print(data['u_desc']) +# print(data['u_rotFrame']) +# print(data['y_op']) +# print(data['y_desc']) +# print(data['y_rotFrame']) diff --git a/ROSCO_toolbox/linear/lin_util.py b/rosco/toolbox/linear/lin_util.py similarity index 100% rename from ROSCO_toolbox/linear/lin_util.py rename to rosco/toolbox/linear/lin_util.py diff --git a/ROSCO_toolbox/linear/lin_vis.py b/rosco/toolbox/linear/lin_vis.py similarity index 97% rename from ROSCO_toolbox/linear/lin_vis.py rename to rosco/toolbox/linear/lin_vis.py index 35d50714..56694e09 100644 --- a/ROSCO_toolbox/linear/lin_vis.py +++ b/rosco/toolbox/linear/lin_vis.py @@ -1,7 +1,7 @@ ''' Visualization helpers for linear models ''' -from ROSCO_toolbox.linear.lin_util import add_pcomp, pc_openloop +from rosco.toolbox.linear.lin_util import add_pcomp, pc_openloop import numpy as np import matplotlib.pyplot as plt import scipy as sp diff --git a/ROSCO_toolbox/linear/linear_models.py b/rosco/toolbox/linear/linear_models.py similarity index 98% rename from ROSCO_toolbox/linear/linear_models.py rename to rosco/toolbox/linear/linear_models.py index f096ce56..f2004a7a 100644 --- a/ROSCO_toolbox/linear/linear_models.py +++ b/rosco/toolbox/linear/linear_models.py @@ -11,15 +11,9 @@ import control as co import matplotlib.pyplot as plt import multiprocessing as mp -from itertools import chain from scipy.io import loadmat -try: - import pyFAST.linearization.mbc as mbc -except ImportError: - import weis.control.mbc.mbc3 as mbc -except ImportError: - raise ImportError('Unable to load mbc3 from pyFAST or WEIS') +import rosco.toolbox.linear.mbc3 as mbc class LinearTurbineModel(object): @@ -462,7 +456,7 @@ def solve(self, disturbance, Plot=False, open_loop=True, controller={}, reduce_s OutList = [out_name.split()[1][:-1] for out_name in P_op.OutputName] OutData_arr = y.T - # Turn OutData into dict like in ROSCO_toolbox + # Turn OutData into dict like in rosco.toolbox OutData = {} for i, out_chan in enumerate(OutList): OutData[out_chan] = OutData_arr[:, i] @@ -710,7 +704,7 @@ def run_mbc3(fnames): Helper function to run mbc3 ''' print('Loading linearizations from:', ''.join(fnames[0].split('.')[:-2])) - MBC, matData = mbc.fx_mbc3(fnames, verbose=False) + MBC, matData, _ = mbc.fx_mbc3(fnames, verbose=False) return MBC, matData diff --git a/rosco/toolbox/linear/mbc3.py b/rosco/toolbox/linear/mbc3.py new file mode 100644 index 00000000..e80956e8 --- /dev/null +++ b/rosco/toolbox/linear/mbc3.py @@ -0,0 +1,1071 @@ +############################################################# +# Perform MBC3 Analysis and plot Campbell Diagram # +############################################################# + +import rosco.toolbox.linear.getMats as gm +import numpy as np +import scipy.linalg as scp +import re +import os +import sys +# import plotCampbellData as pCD + +def getScaleFactors(DescStates, TowerLen, BladeLen): + + ScalingFactor = np.ones(len(DescStates)) + + # look at the state description strings for tower and blade + # translational dofs: + for i in range(len(ScalingFactor)): + + # look for blade dofs: + if DescStates[i].find('blade')!=-1 or DescStates[i].find('Blade')!=-1: + if DescStates[i].find('rotational')==-1: # make sure this isn't a rotational dof from BeamDyn + ScalingFactor[i] = 1.0/BladeLen; + #print(DescStates[i]) + + # look for tower translational dofs: + if DescStates[i].find('tower')!=-1 or DescStates[i].find('Tower')!=-1: + ScalingFactor[i] = 1.0/TowerLen; + + # look for blade dofs: + elif DescStates[i].find('blade')!=-1 or DescStates[i].find('Blade')!=-1: + if DescStates[i].find('rotational')==-1: # make sure this isn't a rotational dof from BeamDyn + ScalingFactor[i] = 1.0/BladeLen; + + return ScalingFactor + + +def IdentifyModes(CampbellData): + """ + Attempts to perform an identification of the modes. + For now, the method is based on the energy content of the modes, and the state descriptions where the energy is maximum + + Original contribution by: Srinivasa B. Ramisett, ramisettisrinivas@yahoo.com, http://ramisetti.github.io + """ + + # --- Looking at states descriptions (of first run, first mode), to see if we are offshore. + # NOTE: DescStates is likely the same for all modes + DescStates = CampbellData[0]['Modes'][0]['DescStates'] + hasHeave = any(['heave' in s.lower() for s in DescStates]) + hasSurge = any(['surge' in s.lower() for s in DescStates]) + hasSway = any(['sway' in s.lower() for s in DescStates]) + hasYaw = any(['platform yaw' in s.lower() for s in DescStates]) + hasRoll = any(['platform roll tilt' in s.lower() for s in DescStates]) + hasPitch = any(['platform pitch tilt' in s.lower() for s in DescStates]) + hasEdge1Col = any(['1st edgewise bending-mode dof of blade collective' in s.lower() for s in DescStates]) + + # --- Setting up a list of modes with + modesDesc = [] + modesDesc.append( ['Generator DOF (not shown)' , 'ED Variable speed generator DOF, rad'] ) + # Platform DOFs + if hasSurge: + modesDesc.append( ['Platform surge', 'ED Platform horizontal surge translation DOF, m'] ) + if hasSway: + modesDesc.append( ['Platform sway', 'ED Platform horizontal sway translation DOF, m'] ) + if hasHeave: + modesDesc.append( ['Platform heave', 'ED Platform vertical heave translation DOF, m'] ) + if hasRoll: + modesDesc.append( ['Platform roll', 'ED Platform roll tilt rotation DOF, rad'] ) + if hasPitch: + modesDesc.append( ['Platform pitch', 'ED Platform pitch tilt rotation DOF, rad'] ) + if hasYaw: + modesDesc.append( ['Platform yaw', 'ED Platform yaw rotation DOF, rad'] ) + + modesDesc.append( ['1st Tower FA' , 'ED 1st tower fore-aft bending mode DOF, m'] ) + modesDesc.append( ['1st Tower SS' , 'ED 1st tower side-to-side bending mode DOF, m'] ) + modesDesc.append( ['1st Blade Flap (Regressive)' , 'ED 1st flapwise bending-mode DOF of blade (sine|cosine), m', r'Blade (sine|cosine) finite element node \d rotational displacement in Y, rad'] ) + modesDesc.append( ['1st Blade Flap (Collective)' , 'ED 1st flapwise bending-mode DOF of blade collective, m', r'Blade collective finite element node \d rotational displacement in Y, rad'] ) + modesDesc.append( ['1st Blade Flap (Progressive)' , 'ED 1st flapwise bending-mode DOF of blade (sine|cosine), m'] ) # , ... # 'Blade (sine|cosine) finite element node \d rotational displacement in Y, rad'] + modesDesc.append( ['1st Blade Edge (Regressive)' , 'ED 1st edgewise bending-mode DOF of blade (sine|cosine), m', r'Blade (sine|cosine) finite element node \d rotational displacement in X, rad'] ) + if hasEdge1Col: + modesDesc.append(['1st Blade Edge (Collective)', 'ED 1st edgewise bending-mode DOF of blade collective, m'] ) # , ... # 'Blade (sine|cosine) finite element node \d rotational displacement in Y, rad'] + modesDesc.append( ['1st Blade Edge (Progressive)' , 'ED 1st edgewise bending-mode DOF of blade (sine|cosine), m'] ) + modesDesc.append( ['1st Drivetrain Torsion' , 'ED Drivetrain rotational-flexibility DOF, rad'] ) + modesDesc.append( ['2nd Tower FA' , 'ED 2nd tower fore-aft bending mode DOF, m'] ) + modesDesc.append( ['2nd Tower SS' , 'ED 2nd tower side-to-side bending mode DOF, m'] ) + modesDesc.append( ['2nd Blade Flap (Regressive)' , 'ED 2nd flapwise bending-mode DOF of blade (sine|cosine), m'] ) + modesDesc.append( ['2nd Blade Flap (Collective)' , 'ED 2nd flapwise bending-mode DOF of blade collective, m', r'Blade collective finite element node \d rotational displacement in Y, rad'] ) + modesDesc.append( ['2nd Blade Flap (Progressive)' , 'ED 2nd flapwise bending-mode DOF of blade (sine|cosine), m'] ) + modesDesc.append( ['Nacelle Yaw (not shown)' , 'ED Nacelle yaw DOF, rad'] ) + + + nModes = int(len(modesDesc)) + nRuns = int(len(CampbellData)) + modeID_table=np.zeros((nModes,nRuns)).astype(int) + + + + def doesDescriptionMatch(description, listOfModePatterns): + """ loop through all mode desrption """ + for iModePattern, modeIDdescList in enumerate(listOfModePatterns): + modeIDName = modeIDdescList[0] + patternList = modeIDdescList[1:] # list of patterns for a given mode + found, pattern = doesDescriptionMatchPatternList(description, patternList) + if found: + return True, iModePattern, pattern + return False, -1, '' + + def doesDescriptionMatchPatternList(description, patternList): + """ loop through all patterns to find a match """ + for pattern in patternList: + # Looking for targetDesc into description + if re.search(pattern ,description, re.IGNORECASE)!=None: + return True, pattern + return False, '' + + + verbose=False + Levels=[1,2,3] + + + # --- Loop on operating points/Runs + for i in range(nRuns): + Modes = CampbellData[i]['Modes'] + nModes = len(Modes) + # Array of logical, False for Modes that are not identified + modesIdentified = [False] * nModes + modesSkipped = [False] * nModes + #verbose=verbose and i==0 # only display for first mode for now.. + #verbose=i==1 + + # --- Give an index to each mode so that we can easily identify them + for im, mode in enumerate(Modes): + mode['index']=im + + # --- Skip modes based on simple criteria + for im, mode in enumerate(Modes): + if mode['NaturalFreq_Hz'] < 1e-5 or mode['DampingRatio'] > 0.98: + modesSkipped[im]=True + + + + modesDescLocal = modesDesc.copy() + + # --- Level 1 - Find well-defined modes (modes which have only one Max) + if 1 in Levels: + for im, mode in enumerate(Modes): + if modesIdentified[im] or modesSkipped[im]: + continue # skip this mode since it has already been identified + stateMax=np.argwhere((mode['StateHasMaxAtThisMode']==1)).flatten() + if len(stateMax)==1: + description = mode['DescStates'][stateMax[0]] + if description.startswith('AD'): + # We skipp the pure "AD" modes + modesSkipped[im] = True + continue + found, modeID, patternMatched = doesDescriptionMatch(description, modesDescLocal) + if found and modeID_table[modeID,i]==0: + modesDescLocal[modeID] = [None,] # we empty this mode patternlist so that it cannot be matched again + modesIdentified[im] = True + modeID_table[modeID,i] = im+1 # Using Matlab Indexing + if verbose: + print('L1 Mode {} identified using pattern: {}'.format(im+1,patternMatched)) + print(' String was: ', description) + #else: + # print('>>> Cannot identify mode with description {}. Update MBC3 script.'.format(description)) + + # --- Level 2 - Find modes with several max - Looping on mode pattern to respect expected frequency order + if 2 in Levels: + for modeID, modeIDdescList in enumerate(modesDescLocal): + modeIDName = modeIDdescList[0] + patternList = modeIDdescList[1:] + # Skip modes already identified above + if modeID_table[modeID,i]>0: + continue + if verbose: + print('------------------------- LEVEL 2 - LOOKING FOR MODE ',modeIDName) + + found = False; + # --- Loop on all non-identified modes in increasing order + im = 0; + for im, mode in enumerate(Modes): + if modesIdentified[im] or modesSkipped[im]: + continue # move to next mode + # List of component descriptions where this mode has maximum values + stateMax=np.argwhere((mode['StateHasMaxAtThisMode']==1)).flatten() + descriptions = np.array(mode['DescStates'])[stateMax] + descriptions = descriptions[:7] # we keep only the first 7 descriptions + descriptionsED = [d for d in descriptions if d.startswith('ED')] + descriptionsBD = [d for d in descriptions if d.startswith('BD')] + descriptionsAD = [d for d in descriptions if d.startswith('AD')] + descriptionsMisc = [d for d in descriptions if d not in descriptionsED+descriptionsBD+descriptionsAD] + descriptions = descriptionsED+descriptionsBD+descriptionsMisc # NOTE: we skipp AD descriptions + j = 0; + for description in descriptions: + found, pattern = doesDescriptionMatchPatternList(description, patternList) + if found: + if verbose: + print('L2 Mode {} identified using pattern {}'.format(im+1,pattern)) + modeID_table[modeID,i] = im+1 # Using Matlab Indexing + modesDescLocal[modeID] = [None,] # we empty this mode patternlist so that it cannot be matched again + modesIdentified[im] = True; + break + if found: + break + if verbose: + print('>> modeIDTable',modeID_table[:,i]) + # We disqualify modes that had max and that didn't match anything: + for im, mode in enumerate(Modes): + if modesIdentified[im] or modesSkipped[im]: + continue # move to next mode + stateMax=np.argwhere((mode['StateHasMaxAtThisMode']==1)).flatten() + if len(stateMax)>=1: + modesSkipped[im]=True + shortdescr = CampbellData[i]['ShortModeDescr'][im] + if shortdescr.find('ED')>=0: + print('>>>> short', CampbellData[i]['ShortModeDescr'][im]) + print('>>>> Problem in IndentifyModes. ED DOF found in level 2') + # import pdb; pdb.set_trace() + + if 3 in Levels: + # --- Level 3 - Try our best for modes with no max + # Loop on modes to be identified + for modeID, modeIDdescList in enumerate(modesDescLocal): + modeIDName = modeIDdescList[0] + patternList = modeIDdescList[1:] + + # Skip modes already identified above + if modeID_table[modeID,i]>0: + continue + if verbose: + print('------------------------- LEVEL 3 - LOOKING FOR MODE ',modeIDName) + + found = False; + # --- Loop on all non-identified modes in increasing order + im = 0; + while not found and im < nModes: # Loop on modes + mode = Modes[im] + if modesIdentified[im] or modesSkipped[im]: + pass + else: + # --- Otherwise, use as mode descriptions the other ones. Seems weird + stateMax=np.argwhere((mode['StateHasMaxAtThisMode']==0)).flatten() + descriptions = np.array(mode['DescStates'])[stateMax] + ADcounts = np.sum([s.startswith('AD') for s in descriptions[:5]]) + + descriptions2= np.array(mode['DescStates'])[mode['StateHasMaxAtThisMode']] + if len(descriptions2) == 0: + noMax=True + descriptions3 = mode['DescStates'][:5] + else: + noMax=False +# import pdb; pdb.set_trace() + if ADcounts<5: + descriptions=[d for d in descriptions if not d.startswith('AD')] +# descriptionsED = [d for d in descriptions if d.startswith('ED')] +# descriptionsBD = [d for d in descriptions if d.startswith('BD')] +# descriptionsAD = [d for d in descriptions if d.startswith('AD')] +# descriptionsMisc = [d for d in descriptions if d not in descriptionsED+descriptionsBD+descriptionsAD] +# descriptions = descriptionsED+descriptionsBD+descriptionsMisc # NOTE: we skipp AD descriptions + descriptions = descriptions[:5] # we keep only the first 7 descriptions + if verbose: + print('>>> Mode',mode['index'], modesIdentified[im], modesSkipped[im]) + print('>>>> descr', [replaceModeDescription(s) for s in descriptions]) + print('>>>> short', CampbellData[i]['ShortModeDescr'][im]) + else: + descriptions=[] +# #descriptions = descriptions[:7] # we keep only the first 7 descriptions + + j = 0; + while not found and j < len(descriptions): + j = j + 1; + if not found: + for targetDesc in patternList: + # Looking for targetDesc into list of descriptions + if re.search(targetDesc ,descriptions[j-1],re.IGNORECASE)!=None: + modeID_table[modeID,i] = im+1 # Using Matlab Indexing + if verbose: + print('L3 Mode {} identified as {}'.format(im+1,targetDesc)) + print(' String was: ', descriptions[j-1]) + modesIdentified[im] = True; + found = True; + break; + im=im+1; # increment counter + if verbose: + print('>> modeIDTable',modeID_table[:,i]) + + if verbose: + print('---------- Summary') + for j in np.arange(len(modeID_table)): + print('{:32s} {:d}'.format(modesDesc[j][0],modeID_table[j,i])) + print('---------- ') + + + return modeID_table,modesDesc + +def IdentifiedModesDict(CampbellData,modeID_table,modesDesc): + """ + To be called with the results of IdentifyModes. + Create a list of dictionaries to more easily interprete the result + """ + nOP = modeID_table.shape[1] + modesInfoPerOP=[] + for iOP in np.arange(nOP): + modesInfo={} + for i in np.arange(len(modesDesc)): + desc = modesDesc[i][0] + ID = int(modeID_table[i,iOP]) + if ID==0: + freq=np.nan + damp=np.nan + cont='' + else: + freq = np.around(CampbellData[iOP]['Modes'][ID-1]['NaturalFreq_Hz'],5) + damp = np.around(CampbellData[iOP]['Modes'][ID-1]['DampingRatio'],5) + cont = CampbellData[iOP]['ShortModeDescr'][ID-1] + modesInfo[desc]={'ID':ID,'f0':freq,'zeta':damp,'cont':cont} + modesInfoPerOP.append(modesInfo) + return modesInfoPerOP + +def campbell_diagram_data(mbc_data, BladeLen, TowerLen): + """ + + Original contribution by: Srinivasa B. Ramisett, ramisettisrinivas@yahoo.com, http://ramisetti.github.io + """ + + CampbellData={} + usePercent = False; + # + # mbc_data.eigSol = eiganalysis(mbc_data.AvgA); + ndof = mbc_data['ndof2'] + mbc_data['ndof1']; #size(mbc_data.AvgA,1)/2; # number of translational states + nModes = len(mbc_data['eigSol']['Evals']) + #print(nModes) + DescStates = PrettyStateDescriptions(mbc_data['DescStates'], mbc_data['ndof2'], mbc_data['performedTransformation']); + + ## store indices of max mode for state and to order natural frequencies + #StatesMaxMode_vals = np.amax(mbc_data['eigSol']['MagnitudeModes'],axis=1); # find which mode has the maximum value for each state (max of each row before scaling) + StatesMaxMode = np.argmax(mbc_data['eigSol']['MagnitudeModes'],axis=1); # find which mode has the maximum value for each state (max of each row before scaling) + SortedFreqIndx = np.argsort((mbc_data['eigSol']['NaturalFreqs_Hz']).flatten(),kind="heapsort"); + #print(SortedFreqIndx) + + + if BladeLen!=0 or TowerLen!=0: + ## get the scaling factors for the mode rows + ScalingFactor = getScaleFactors(DescStates, TowerLen, BladeLen); + + ## scale the magnitude of the modes by ScalingFactor (for consistent units) + # and then scale the columns so that their maximum is 1 + + ModesMagnitude = np.matmul(np.diag(ScalingFactor), mbc_data['eigSol']['MagnitudeModes']); # scale the rows + #print(ModesMagnitude) + + CampbellData['ScalingFactor'] = ScalingFactor; + else: + ModesMagnitude = mbc_data['eigSol']['MagnitudeModes']; + + if usePercent: + scaleCol = np.sum( ModesMagnitude )/100; # find the sum of the column, and multiply by 100 (divide here) to get a percentage + else: + scaleCol = np.amax(ModesMagnitude,axis=0); #find the maximum value in the column, so the first element has value of 1 + + ModesMagnitude = np.matmul(ModesMagnitude,np.diag(1./scaleCol)) # scale the columns + + # --- Summary data (array for all modes) + CampbellData['NaturalFreq_Hz'] = mbc_data['eigSol']['NaturalFreqs_Hz'][SortedFreqIndx] + CampbellData['DampingRatio'] = mbc_data['eigSol']['DampRatios'][SortedFreqIndx] + CampbellData['RotSpeed_rpm'] = mbc_data['RotSpeed_rpm'] + if 'WindSpeed' in mbc_data: + CampbellData['WindSpeed'] = mbc_data['WindSpeed'] + + #print(ModesMagnitude) + + # --- Storing data per mode + CampbellData['Modes']=[] + + for i in range(nModes): + CData={} + CData['NaturalFreq_Hz'] = mbc_data['eigSol']['NaturalFreqs_Hz'][SortedFreqIndx[i]][0] + CData['DampedFreq_Hz'] = mbc_data['eigSol']['DampedFreqs_Hz'][SortedFreqIndx[i]][0]; + CData['DampingRatio'] = mbc_data['eigSol']['DampRatios'][SortedFreqIndx[i]][0]; + + + #print(np.argsort(ModesMagnitude[:,SortedFreqIndx[0]])[::-1]) + # sort indices in descending order + sort_state = np.argsort( ModesMagnitude[:,SortedFreqIndx[i]])[::-1]; + + #print(type(sort_state)) + CData['DescStates']=[DescStates[i] for i in sort_state] + CData['MagnitudePhase']=ModesMagnitude[sort_state,SortedFreqIndx[i]]; + Phase = mbc_data['eigSol']['PhaseModes_deg'][sort_state,SortedFreqIndx[i]]; + # if the phase is more than +/- 90 degrees different than the first + # one (whose value == 1 or is the largest %), we'll stick a negative value on the magnitude: + Phase = np.mod(Phase, 360); + + CData['PhaseDiff'] = np.mod( Phase - Phase[0], 360); # difference in range [0, 360) + PhaseIndx = CData['PhaseDiff'] > 180; + CData['PhaseDiff'][PhaseIndx] = CData['PhaseDiff'][PhaseIndx] - 360; # move to range (-180, 180] + + if ~usePercent: + PhaseIndx = CData['PhaseDiff'] > 90; + CData['MagnitudePhase'][PhaseIndx] = -1*CData['MagnitudePhase'][PhaseIndx]; + CData['PhaseDiff'][PhaseIndx] = CData['PhaseDiff'][PhaseIndx] - 180; + + PhaseIndx = CData['PhaseDiff'] <= -90; + CData['MagnitudePhase'][PhaseIndx] = -1*CData['MagnitudePhase'][PhaseIndx]; + CData['PhaseDiff'][PhaseIndx] = CData['PhaseDiff'][PhaseIndx] + 180; + + #print(CData['MagnitudePhase']) + #print(CData['PhaseDiff']) + + CData['StateHasMaxAtThisMode'] = np.ones(ndof, dtype=bool); + ix = (StatesMaxMode == SortedFreqIndx[i]); + tmp=ix[sort_state] + CData['StateHasMaxAtThisMode']=tmp + + #print(CData['StateHasMaxAtThisMode']) + #print(CData['NaturalFreq_Hz']) + CampbellData['Modes'].append(CData) + + #print(CampbellData[0]['MagnitudePhase']) + # Adding short description to summary + CampbellData['ShortModeDescr'] =[extractShortModeDescription(CampbellData['Modes'][i]) for i in range(nModes)] + + + CampbellData['nColsPerMode'] = 5; + CampbellData['ModesTable'] = {} + + for i in range(nModes): + colStart = i*CampbellData['nColsPerMode']; + CampbellData['ModesTable'][1, colStart+1 ] = 'Mode number:'; + CampbellData['ModesTable'][1, colStart+2 ] = i; + + CampbellData['ModesTable'][2, colStart+1 ] = 'Natural (undamped) frequency (Hz):'; + CampbellData['ModesTable'][2, colStart+2 ] = CampbellData['Modes'][i]['NaturalFreq_Hz'] + + CampbellData['ModesTable'][3, colStart+1 ] = 'Damped frequency (Hz):'; + CampbellData['ModesTable'][3, colStart+2 ] = CampbellData['Modes'][i]['DampedFreq_Hz'] + + CampbellData['ModesTable'][4, colStart+1 ] = 'Damping ratio (-):'; + CampbellData['ModesTable'][4, colStart+2 ] = CampbellData['Modes'][i]['DampingRatio'] + + CampbellData['ModesTable'][5, colStart+1 ] = 'Mode ' + str(i) + ' state description'; + CampbellData['ModesTable'][5, colStart+2 ] = 'State has max at mode ' + str(i); + if usePercent: + CampbellData['ModesTable'][5, colStart+3 ] = 'Mode ' + str(i) + ' contribution (%)'; + else: + CampbellData['ModesTable'][5, colStart+3 ] = 'Mode ' + str(i) + ' signed magnitude'; + + CampbellData['ModesTable'][5, colStart+4 ] = 'Mode ' + str(i) + ' phase (deg)'; + + # need to cross check these 4 lines + CampbellData['ModesTable'][6,colStart+1] = CampbellData['Modes'][i]['DescStates']; + CampbellData['ModesTable'][6,colStart+2] = CampbellData['Modes'][i]['StateHasMaxAtThisMode']; + CampbellData['ModesTable'][6,colStart+3] = CampbellData['Modes'][i]['MagnitudePhase']; + CampbellData['ModesTable'][6,colStart+4] = CampbellData['Modes'][i]['PhaseDiff']; + + #print(CampbellData['ModesTable']) + return CampbellData + + + +def extractShortModeDescription(mode): + """ + Look at which modes have max, append these description, perform shortening substitution + The description starts with noMax if no maximum exsits in this mode + The ElastoDyn modes are placed first + """ + descriptions = np.array(mode['DescStates'])[mode['StateHasMaxAtThisMode']] + if len(descriptions) == 0: + noMax=True + descriptions = mode['DescStates'][:5] + else: + noMax=False + sED = [s for s in descriptions if s.startswith('ED')] + sBD = [s for s in descriptions if s.startswith('BD')] + sMisc = [s for s in descriptions if s not in sED+sBD] + sAll = [replaceModeDescription(s) for s in sED+sBD+sMisc] + shortdescr = ' - '.join(sAll) + if noMax: + shortdescr = 'NoMax - ' + shortdescr + return shortdescr + + +def replaceModeDescription(s): + """ Perform substitutions to make the mode description shorter""" + s = s.replace('Blade','Bld') + s = s.replace('blade','Bld') + s = s.replace('First time derivative of','d/dt of') + s = s.replace('fore-aft bending mode DOF, m','FA') + s = s.replace('side-to-side bending mode DOF, m','SS') + s = s.replace('bending-mode DOF of Bld ','') + s = s.replace(' rotational-flexibility DOF, rad','-rot') + s = s.replace('rotational displacement in ','rot') + s = s.replace('translational displacement in ','trans') + s = s.replace('Platform horizontal surge translation DOF','Platform surge') + s = s.replace('Platform vertical heave translation DOF','Platform heave') + s = s.replace('Platform pitch tilt rotation DOF','Platform pitch') + s = s.replace(', rad','') + s = s.replace(', m','') + s = s.replace('finite element node ','N') + s = s.replace('cosine','cos') + s = s.replace('sine','sin') + s = s.replace('flapwise','FLAP') + s = s.replace('edgewise','EDGE') + s = s.replace('collective','coll.') + s = s.replace('rotZ','TORS-ROT') + s = s.replace('transX','FLAP-DISP') + s = s.replace('transY','EDGE-DISP') + s = s.replace('rotX','EDGE') + s = s.replace('rotY','FLAP') + return s + + +def PrettyStateDescriptions(DescStates, ndof2, performedTransformation): + idx=np.array(list(range(0,ndof2))+list(range(ndof2*2+1,len(DescStates)))) + tmpDS = [DescStates[i] for i in idx] + + if performedTransformation: + key_vals=[['BD_1','Blade collective'],['BD_2','Blade cosine'],['BD_3','Blade sine'],['blade 1','blade collective'], ['blade 2','blade cosine'], ['blade 3','Blade sine '], + ['PitchBearing1','Pitch bearing collective '], ['PitchBearing2','Pitch bearing cosine '], ['PitchBearing3','Pitch bearing sine '], + ['at Blade', 'at blade'], + ['of Blade', 'of blade'], + ] + # Replace Substrings from String List + sub = dict(key_vals) + for key, val in sub.items(): + for idx, ele in enumerate(tmpDS): + if key in ele: + tmpDS[idx] = ele.replace(key, val) + + StateDesc=tmpDS + else: + StateDesc = tmpDS + + for i in range(len( StateDesc )): + First = re.split(r'\(',StateDesc[i],2) + Last = re.split(r'\)',StateDesc[i],2) + + if len(First)>0 and len(Last)>0 and len(First[0]) != len(StateDesc[i]) and len( Last[-1] ) != len(StateDesc[i]): + StateDesc[i] = (First[0]).strip() + Last[-1]; + #print(StateDesc[i]) + + return StateDesc + + +def get_tt_inverse(sin_col, cos_col): + + c1 = cos_col[0]; + c2 = cos_col[1]; + c3 = cos_col[2]; + + s1 = sin_col[0]; + s2 = sin_col[1]; + s3 = sin_col[2]; + + + ttv = [ [c2*s3 - s2*c3, c3*s1 - s3*c1, c1*s2 - s1*c2], + [ s2 - s3 , s3 - s1, s1 - s2 ], + [ c3 - c2 , c1 - c3, c2 - c1 ] ] + ttv = ttv/(1.5*np.sqrt(3)); + + return ttv + +def get_new_seq(rot_triplet,ntot): +# rot_triplet is size n x 3 + #print('rot_triplet ', len(rot_triplet)) + rot_triplet=np.array(rot_triplet,dtype=int) + if (rot_triplet.size==0): + nRotTriplets=0 + nb=0 + #return np.array([]),0,0; + else: + nRotTriplets,nb = rot_triplet.shape; + + #print(nRotTriplets,nb,rot_triplet.flatten()) + + if (nb != 3 and nRotTriplets != 0 and ntot!= 0): + print('**ERROR: the number of column vectors in the rotating triplet must equal 3, the num of blades'); + new_seq = np.range(1,ntot) + else: + non_rotating = np.ones(ntot,dtype=int); + #print(non_rotating) + non_rotating[rot_triplet.flatten()] = 0; # if they are rotating, set them false; + a=np.array(np.nonzero(non_rotating)).flatten() + b=(rot_triplet.reshape(nRotTriplets*nb, 1)).flatten() + new_seq = np.concatenate((a,b)); + + #print(new_seq) + return new_seq,nRotTriplets,nb + +def eiganalysis(A, ndof2, ndof1): + # this line has to be the first line for this function + # to get the number of function arguments + nargin=len(locals()) + + mbc={} + m, ns = A.shape; + if(m!=ns): + print('**ERROR: the state-space matrix is not a square matrix.'); + + if nargin == 1: + ndof1 = 0; + ndof2 = ns/2; + + if np.mod(ns,2) != 0: + print('**ERROR: the input matrix is not of even order.'); + elif nargin == 2: + ndof1 = ns - 2*ndof2; + if ndof1 < 0: + print('**ERROR: ndof2 must be no larger than half the dimension of the state-space matrix.'); + else: + if ns != 2*ndof2 + ndof1: + print('**ERROR: the dimension of the state-space matrix must equal 2*ndof2 + ndof1.'); + + ndof = ndof2 + ndof1; + + origEvals, origEigenVects = np.linalg.eig(A); #,'nobalance' + # errorInSolution = norm(A * mbc.EigenVects - mbc.EigenVects* diag(mbc.EigenVals) ) + # these eigenvalues aren't sorted, so we just take the ones with + # positive imaginary parts to get the pairs for modes with damping < 1: + positiveImagEvals = np.argwhere( origEvals.imag > 0.0); + + mbc['Evals'] = origEvals[positiveImagEvals]; + row=np.array(list(range(0,ndof2))+list(range(ndof2*2+1,ns))) + col=positiveImagEvals + + mbc['EigenVects']=origEigenVects[row,col].transpose(); # save q2 and q1, throw away q2_dot + # print('GGGGGG ', row.shape,col.shape) + # with open('AAAA', "a") as f: + # np.savetxt(f,mbc['EigenVects'].imag,fmt='%5.4f') + # f.write('\n') + + EigenVects_save=origEigenVects[:,positiveImagEvals]; # save these for VTK visualization; + + #print('EigenVects_save shape ',EigenVects_save[:,:,0].shape, origEigenVects.shape) + + real_Evals = mbc['Evals'].real; + imag_Evals = mbc['Evals'].imag; + + mbc['NaturalFrequencies'] = np.sqrt( real_Evals**2 + imag_Evals**2 ); + mbc['DampRatios'] = -real_Evals/mbc['NaturalFrequencies'] + mbc['DampedFrequencies'] = imag_Evals; + + mbc['NumRigidBodyModes'] = ndof - len(positiveImagEvals); + + mbc['NaturalFreqs_Hz'] = mbc['NaturalFrequencies']/(2.0*np.pi) + mbc['DampedFreqs_Hz'] = mbc['DampedFrequencies']/(2.0*np.pi); + mbc['MagnitudeModes'] = np.abs(mbc['EigenVects']); + mbc['PhaseModes_deg'] = np.angle(mbc['EigenVects'])*180.0/np.pi; + + # print(real_Evals[0:10]) + # print(imag_Evals[0:10]) + # print(mbc['NaturalFrequencies'][0:10]) + # print(mbc['DampRatios'][0:10]) + # print(mbc['NaturalFreqs_Hz'][0:10]) + # print(mbc['DampedFreqs_Hz'][0:10]) + # print(mbc['MagnitudeModes'].shape) + # print(mbc['PhaseModes_deg'][0,0]) + + # with open('MagModes.txt', "a") as f: + # np.savetxt(f,mbc['MagnitudeModes'],fmt='%g') + # f.write('\n') + + # with open('PhsModes.txt', "a") as f: + # np.savetxt(f,mbc['PhaseModes_deg'],fmt='%g') + # f.write('\n') + + # with open('NFreqs_Hz.txt', "a") as f: + # np.savetxt(f,mbc['NaturalFreqs_Hz'],fmt='%g') + # f.write('\n') + # with open('DampRatios.txt', "a") as f: + # np.savetxt(f,mbc['DampRatios'],fmt='%g') + # f.write('\n') + # with open('DFreqs_Hz.txt', "a") as f: + # np.savetxt(f,mbc['DampedFreqs_Hz'],fmt='%g') + # f.write('\n') + + return mbc,EigenVects_save[:,:,0] + +def fx_mbc3(FileNames, verbose=True, removeTwrAzimuth=False): + """ + Original contribution by: Srinivasa B. Ramisett, ramisettisrinivas@yahoo.com, http://ramisetti.github.io + """ + MBC={} + matData, FAST_linData = gm.get_Mats(FileNames, verbose=verbose, removeTwrAzimuth=removeTwrAzimuth) + + # print('matData[Omega] ', matData['Omega']) + # print('matData[OmegaDot] ', matData['OmegaDot']) + + MBC['DescStates'] = matData['DescStates'] # save this in the MBC type for possible campbell_diagram processing later + MBC['ndof2'] = matData['ndof2'] + MBC['ndof1'] = matData['ndof1'] + MBC['RotSpeed_rpm'] = np.mean(matData['Omega'])*(30/np.pi); #rad/s to rpm + MBC['WindSpeed'] = np.mean(matData['WindSpeed']) # NOTE: might be NaN for old files + + # print('RotSpeed_rpm ',MBC['RotSpeed_rpm']) + # print('ndof1 ', MBC['ndof1']) + # print('ndof2 ', MBC['ndof2']) + # print(matData['RotTripletIndicesStates2']) + + # nb = 3; % number of blades required for MBC3 + # ---------- Multi-Blade-Coordinate transformation ------------------------------------------- + new_seq_dof2, dummy, nb = get_new_seq(matData['RotTripletIndicesStates2'],matData['ndof2']); # these are the first ndof2 states (not "first time derivative" states); these values are used to calculate matrix transformations + new_seq_dof1, dummy, nb2 = get_new_seq(matData['RotTripletIndicesStates1'],matData['ndof1']); # these are the first-order ndof1 states; these values are used to calculate matrix transformations + + # print('new_seq_dof2 ', new_seq_dof2) + # print('new_seq_dof1 ', new_seq_dof1) + # print('dummy ', dummy, ' nb ', nb) + #nb = max(nb,nb2); + #if (nb==0): + # print('*** fx_mbc3: no states were found, so assuming turbine has 3 blades. ***') + # nb = 3 + + + new_seq_states=np.concatenate((new_seq_dof2, new_seq_dof2+matData['ndof2'])) + if new_seq_dof1.size!=0: + new_seq_states=np.concatenate((new_seq_states,new_seq_dof1+matData['NumStates2'])) + + #new_seq_states = [new_seq_dof2; new_seq_dof2+matData['ndof2']; new_seq_dof1+matData['NumStates2']]; # combine the second-order states, including "first time derivatives", with first-order states (assumes ordering of displacements and velocities in state matrices); these values are used to calculate matrix transformations + # second-order NonRotating q2, second-order Rotating q2, + # second-order NonRotating q2_dot, second-order Rotating q2_dot, + # first-order NonRotating q1, first-order Rotating q1 + + + if nb == 3: + MBC['performedTransformation'] = True; + + if matData['n_RotTripletStates2'] + matData['n_RotTripletStates1'] < 1: + print('*** There are no rotating states. MBC transformation, therefore, cannot be performed.'); + # perhaps just warn and perform eigenanalysis anyway? + elif (matData['n_RotTripletStates2']*nb > matData['ndof2']): + print('**ERROR: the rotating second-order dof exceeds the total num of second-order dof'); + elif (matData['n_RotTripletStates1']*nb > matData['ndof1']): + print('**ERROR: the rotating first-order dof exceeds the total num of first-order dof'); + + new_seq_inp,dummy,dummy = get_new_seq(matData['RotTripletIndicesCntrlInpt'],matData['NumInputs']); + new_seq_out,dummy,dummy = get_new_seq(matData['RotTripletIndicesOutput'],matData['NumOutputs']); + + n_FixFrameStates2 = matData['ndof2'] - matData['n_RotTripletStates2']*nb; # fixed-frame second-order dof + n_FixFrameStates1 = matData['ndof1'] - matData['n_RotTripletStates1']*nb; # fixed-frame first-order dof + n_FixFrameInputs = matData['NumInputs'] - matData['n_RotTripletInputs']*nb; # fixed-frame control inputs + n_FixFrameOutputs = matData['NumOutputs'] - matData['n_RotTripletOutputs']*nb; # fixed-frame outputs + + #print(n_FixFrameOutputs,n_FixFrameInputs, n_FixFrameStates1, n_FixFrameStates2) + + if ( len(matData['Omega']) != matData['NAzimStep']): + print('**ERROR: the size of Omega vector must equal matData.NAzimStep, the num of azimuth steps') + if ( len(matData['OmegaDot']) != matData['NAzimStep']): + print('**ERROR: the size of OmegaDot vector must equal matData.NAzimStep, the num of azimuth steps'); + + + nLin = matData['A'].shape[-1] + MBC['A'] = np.zeros(matData['A'].shape) + MBC['B'] = np.zeros((len(new_seq_states),len(new_seq_inp),matData['NAzimStep'])) + if 'C' in matData.keys(): + MBC['C']=np.zeros(matData['C'].shape) + else: + MBC['C']=np.zeros((0,0,nLin)) + if 'D' in matData.keys(): + MBC['D']=np.zeros(matData['D'].shape) + else: + MBC['D']=np.zeros((0,0,nLin)) + + # print('new_seq_inp ',new_seq_inp) + # print('new_seq_out ',new_seq_out) + # print('new_seq_states ', new_seq_states) + + # begin azimuth loop + for iaz in reversed(range(matData['NAzimStep'])): + #(loop backwards so we don't reallocate memory each time [i.e. variables with iaz index aren't getting larger each time]) + + temp=np.arange(nb) + # compute azimuth positions of blades: + az = matData['Azimuth'][iaz]*np.pi/180.0 + 2*np.pi/nb* temp ; # Eq. 1, azimuth in radians + + # get rotor speed squared + OmegaSquared = matData['Omega'][iaz]**2; + + #print(OmegaSquared) + + # compute transformation matrices + cos_col = np.cos(az); + sin_col = np.sin(az); + + tt=np.column_stack((np.ones(3),cos_col,sin_col)) # Eq. 9, t_tilde + ttv = get_tt_inverse(sin_col, cos_col); # inverse of tt (computed analytically in function below) + tt2 = np.column_stack((np.zeros(3), -sin_col, cos_col)) # Eq. 16 a, t_tilde_2 + tt3 = np.column_stack((np.zeros(3), -cos_col, -sin_col)) # Eq. 16 b, t_tilde_3 + + #--- + T1 = np.eye(n_FixFrameStates2); # Eq. 11 for second-order states only + #print('B ',T1, n_FixFrameStates2, matData['n_RotTripletStates2']) + for ii in range(matData['n_RotTripletStates2']): + T1 = scp.block_diag(T1,tt) + + T1v = np.eye(n_FixFrameStates2); # inverse of T1 + for ii in range(matData['n_RotTripletStates2']): + T1v = scp.block_diag(T1v, ttv); + + T2 = np.zeros([n_FixFrameStates2,n_FixFrameStates2]); # Eq. 14 for second-order states only + for ii in range(matData['n_RotTripletStates2']): + T2 = scp.block_diag(T2, tt2); + + #print('T1, T1v, T2 ',T1.shape, T1v.shape, T2.shape) + #--- + T1q = np.eye(n_FixFrameStates1); # Eq. 11 for first-order states (eq. 8 in MBC3 Update document) + for ii in range(matData['n_RotTripletStates1']): + T1q = scp.block_diag(T1q, tt); + + T1qv = np.eye(n_FixFrameStates1); # inverse of T1q + for ii in range(matData['n_RotTripletStates1']): + T1qv = scp.block_diag(T1qv, ttv); + + T2q = np.zeros([n_FixFrameStates1,n_FixFrameStates1]); # Eq. 14 for first-order states (eq. 9 in MBC3 Update document) + for ii in range(matData['n_RotTripletStates1']): + T2q = scp.block_diag(T2q, tt2); + + #print('T1q, T1qv, T2q ',T1q.shape, T1qv.shape, T2q.shape) + # T1qc = np.eye(matData.NumHDInputs); # inverse of T1q + + #--- + T3 = np.zeros([n_FixFrameStates2,n_FixFrameStates2]); # Eq. 15 + for ii in range(matData['n_RotTripletStates2']): + T3 = scp.block_diag(T3, tt3); + + #--- + T1c = np.eye(n_FixFrameInputs); # Eq. 21 + for ii in range(matData['n_RotTripletInputs']): + T1c = scp.block_diag(T1c, tt) + + T1ov = np.eye(n_FixFrameOutputs); # inverse of Tlo (Eq. 23) + for ii in range(matData['n_RotTripletOutputs']): + T1ov = scp.block_diag(T1ov, ttv); + + #print('T3, T1c, T1ov ',T3.shape, T1c.shape, T1ov.shape, matData['A'].shape) + # mbc transformation of first-order matrices + # if ( MBC.EqnsOrder == 1 ) # activate later + + #print('Before ',T1c) + + if 'A' in matData: + # Eq. 29 + L1=np.concatenate((T1, np.zeros([matData['ndof2'],matData['ndof2']]), np.zeros([matData['ndof2'], matData['ndof1']])), axis=1) + L2=np.concatenate((matData['Omega'][iaz]*T2,T1,np.zeros([matData['ndof2'], matData['ndof1']])), axis=1) + L3=np.concatenate((np.zeros([matData['ndof1'], matData['ndof2']]),np.zeros([matData['ndof1'], matData['ndof2']]), T1q), axis=1) + L=np.matmul(matData['A'][new_seq_states[:,None],new_seq_states,iaz],np.concatenate((L1,L2,L3),axis=0)) + + R1=np.concatenate((matData['Omega'][iaz]*T2, np.zeros([matData['ndof2'],matData['ndof2']]), np.zeros([matData['ndof2'], matData['ndof1']])), axis=1) + R2=np.concatenate((OmegaSquared*T3 + matData['OmegaDot'][iaz]*T2, 2*matData['Omega'][iaz]*T2, np.zeros([matData['ndof2'], matData['ndof1']])),axis=1) + R3=np.concatenate((np.zeros([matData['ndof1'], matData['ndof2']]), np.zeros([matData['ndof1'], matData['ndof2']]), matData['Omega'][iaz]*T2q), axis=1) + + R=np.concatenate((R1,R2,R3),axis=0) + + MBC['A'][new_seq_states[:,None],new_seq_states,iaz]=np.matmul(scp.block_diag(T1v, T1v, T1qv),(L-R)) + + # ffname='AAA'+str(iaz)+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['A'][:,:,iaz],fmt='%5.4f') + # f.write('\n') + + if 'B' in matData: + # Eq. 30 + MBC['B'][new_seq_states[:,None],new_seq_inp,iaz]=np.matmul(np.matmul(scp.block_diag(T1v, T1v, T1qv), matData['B'][new_seq_states[:,None],new_seq_inp,iaz]),T1c) + + # ffname='BBB'+str(iaz)+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['B'][:,:,iaz],fmt='%5.4f') + # f.write('\n') + + if 'C' in matData: + # Eq. 31 + + L1=np.concatenate((T1, np.zeros([matData['ndof2'],matData['ndof2']]), np.zeros([matData['ndof2'], matData['ndof1']])),axis=1) + L2=np.concatenate((matData['Omega'][iaz]*T2, T1, np.zeros([matData['ndof2'], matData['ndof1']])), axis=1) + L3=np.concatenate((np.zeros([matData['ndof1'], matData['ndof2']]), np.zeros([matData['ndof1'], matData['ndof2']]), T1q), axis=1) + + MBC['C'][new_seq_out[:,None], new_seq_states,iaz]=np.matmul(np.matmul(T1ov,matData['C'][new_seq_out[:,None],new_seq_states,iaz]),np.concatenate((L1,L2,L3),axis=0)) + + # ffname='CCC'+str(iaz)+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['C'][:,:,iaz],fmt='%5.4f') + # f.write('\n') + + if 'D' in matData: + # Eq. 32 + MBC['D'][new_seq_out[:,None],new_seq_inp,iaz] = np.matmul(np.matmul(T1ov,matData['D'][new_seq_out[:,None],new_seq_inp,iaz]), T1c) + + # ffname='DDD'+str(iaz)+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['D'][:,:,iaz],fmt='%5.4f') + # f.write('\n') + + # end # end of azimuth loop + else: + print(' fx_mbc3 WARNING: Number of blades is ', str(nb), ' not 3. MBC transformation was not performed.') + MBC['performedTransformation'] = False; + + # initialize matrices + if 'A' in matData: + MBC['A'] = matData['A'] # initalize matrix + if 'B' in matData: + MBC['B'] = matData['B'] # initalize matrix + if 'C' in matData: + MBC['C'] = matData['C'] # initalize matrix + if 'D' in matData: + MBC['D'] = matData['D'] # initalize matrix + + # ------------- Eigensolution and Azimuth Averages ------------------------- + if 'A' in MBC: + MBC['AvgA'] = np.mean(MBC['A'],axis=2); # azimuth-average of azimuth-dependent MBC.A matrices + MBC['eigSol'], EigenVects_save = eiganalysis(MBC['AvgA'],matData['ndof2'], matData['ndof1']); + + # ffname='AAA_avg'+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['AvgA'],fmt='%5.4f') + # f.write('\n') + + + # save eigenvectors (doing inverse of MBC3) for VTK visualization in FAST + # if nargout > 3 or nargin > 1: + # [VTK] = GetDataForVTK(MBC, matData, nb, EigenVects_save); + # if nargin > 1 + # WriteDataForVTK(VTK, ModeVizFileName) + + if 'B' in MBC: + MBC['AvgB'] = np.mean(MBC['B'],axis=2); # azimuth-average of azimuth-dependent MBC.B matrices + # ffname='BBB_avg'+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['AvgB'],fmt='%5.4f') + # f.write('\n') + + if 'C' in MBC: + MBC['AvgC'] = np.mean(MBC['C'],axis=2); # azimuth-average of azimuth-dependent MBC.C matrices + # ffname='CCC_avg'+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['AvgC'],fmt='%5.4f') + # f.write('\n') + + if 'D' in MBC: + MBC['AvgD'] = np.mean(MBC['D'],axis=2); # azimuth-average of azimuth-dependent MBC.D matrices + # ffname='DDD_avg'+'.txt' + # with open(ffname, "a") as f: + # np.savetxt(f,MBC['AvgD'],fmt='%5.4f') + # f.write('\n') + + return MBC, matData, FAST_linData + + +def runMBC(FileNames, NLinTimes=None, removeTwrAzimuth=False): + ''' + FileNames are .fst files + ''' + CampbellData={} + HubRad=None;TipRad=None; + BladeLen=None; TowerHt=None + dataFound=False; + for i in range(len(FileNames)): + basename=os.path.splitext(os.path.basename(FileNames[i]))[0] + dirname = os.path.dirname(FileNames[i]) + with open(FileNames[i]) as f: + datafile = f.readlines() + + if dataFound==False: + for line in datafile: + if 'EDFile' in line: + EDFile=line.split()[0].replace('"','') + with open(os.path.join(dirname,EDFile)) as edfile: + for edline in edfile: + if 'HubRad' in edline: + HubRad=float(edline.split()[0]) + elif 'TipRad' in edline: + TipRad=float(edline.split()[0]) + elif 'TowerHt' in edline: + TowerHt=float(edline.split()[0]) + + if((TipRad!=None and HubRad!=None) or TowerHt!=None): + BladeLen=HubRad-TipRad + dataFound=True + if(TowerHt==None or BladeLen==None): + print('TowerHt and BladeLen are not available!'); + sys.exit() + + if NLinTimes==None: + found=False + for line in datafile: + if found==False and 'NLinTimes' in line: + NLinTimes=int(line.split()[0]) + found=True + if NLinTimes<1: + print('NLinTimes should be greater than 0!') + sys.exit() + + linFileNames=[os.path.join(dirname,f'{basename}.{x:d}.lin') for x in range(1,NLinTimes+1)] + + print('Processing ', FileNames[i], ' file!') + MBC_data,getMatData,FAST_linData=fx_mbc3(linFileNames, removeTwrAzimuth=removeTwrAzimuth) + print('Multi-Blade Coordinate transformation completed!'); + print(' '); + CampbellData[i]=campbell_diagram_data(MBC_data,BladeLen,TowerHt) + + return CampbellData + + + +if __name__=='__main__': + pass + + # FileNames=['5MW_Land_ModeShapes-1.fst', '5MW_Land_ModeShapes-2.fst', '5MW_Land_ModeShapes-3.fst', '5MW_Land_ModeShapes-6.fst', '5MW_Land_ModeShapes-7.fst']; + #FileNames=['5MW_Land_BD_Linear-1.fst', '5MW_Land_BD_Linear-2.fst', '5MW_Land_BD_Linear-3.fst', '5MW_Land_BD_Linear-6.fst', '5MW_Land_BD_Linear-7.fst']; + + #FileNames=['5MW_Land_BD_Linear-1.fst']; + + #FileNames=['DLC-1.1/5MW_Land_BD_Linear-7.1.lin', 'DLC-1.1/5MW_Land_BD_Linear-7.2.lin'] + #FileNames=['/Users/sramiset/Desktop/OpenFAST/5MW_Land_BD_Linear/5MW_Land_BD_Linear-1.1.lin','/Users/sramiset/Desktop/OpenFAST/5MW_Land_BD_Linear/5MW_Land_BD_Linear-1.2.lin'] + # CampbellData=runMBC(FileNames) + # print('Preparing campbell diagram data!'); + # # TO DO read x-axis for wind speed or rotor speed from csv file + # #op_csv=pd.read_csv('input.csv', sep=',') + # OP=[2,4,6,8,10] + + # modeID_table,modesDesc=IdentifyModes(CampbellData) + + # #print(modesDesc) + + # nModes=modeID_table.shape[0] + # nRuns=modeID_table.shape[1] + # cols=[item[0] for item in list(modesDesc.values())] + # #cols.append('1P');cols.append('3P');cols.append('6P') + # #cols.append('9P');cols.append('12P') + # frequency=pd.DataFrame(np.nan, index=np.arange(nRuns), columns=cols) + # dampratio=pd.DataFrame(np.nan, index=np.arange(nRuns), columns=cols) + # FreqPlotData=np.zeros((nRuns,nModes)) + # DampPlotData=np.zeros((nRuns,nModes)) + # for i in range(nRuns): + # for modeID in range(len(modesDesc)): # list of modes we want to identify + # idx=int(modeID_table[modeID,i]) + # FreqPlotData[i,modeID]=CampbellData[i]['Modes'][idx]['NaturalFreq_Hz'] + # DampPlotData[i,modeID]=CampbellData[i]['Modes'][idx]['DampingRatio'] + # #print(i,modeID,modesDesc[modeID][0],FreqPlotData[i,modeID]) + # frequency.iloc[i,:]=FreqPlotData[i,:] + # dampratio.iloc[i,:]=DampPlotData[i,:] + + # for i in range(len(OP)): + # # for 15 DOF + # frequency.index.values[i]=OP[i] + # dampratio.index.values[i]=OP[i] + + # # import openpyxl + # # xfile = openpyxl.load_workbook('/Users/sramiset/Desktop/OpenFAST/mbc3_py/CampbellDiagram_Template.xlsx') + + # pCD.plotCampbellData(OP,frequency,dampratio) + + # frequency['1P']=np.nan + # frequency['3P']=np.nan + # frequency['6P']=np.nan + # frequency['9P']=np.nan + # frequency['12P']=np.nan + + # print(nRuns) + # for i in range(nRuns): + # # for 1P,3P,6P,9P,and 12P harmonics + # tmp=OP[i]/60.0 + # print(i,tmp) + # LZ=15 + # frequency.iloc[i,LZ]=tmp + # frequency.iloc[i,LZ+1]=3*tmp + # frequency.iloc[i,LZ+2]=6*tmp + # frequency.iloc[i,LZ+3]=9*tmp + # frequency.iloc[i,LZ+4]=12*tmp + # print(frequency) + # frequency.transpose().to_excel(r'CampbellData.xlsx') diff --git a/ROSCO_toolbox/linear/robust_scheduling.py b/rosco/toolbox/linear/robust_scheduling.py similarity index 98% rename from ROSCO_toolbox/linear/robust_scheduling.py rename to rosco/toolbox/linear/robust_scheduling.py index 7f70e2de..a6db2409 100644 --- a/ROSCO_toolbox/linear/robust_scheduling.py +++ b/rosco/toolbox/linear/robust_scheduling.py @@ -10,12 +10,12 @@ import matplotlib.pyplot as plt import pandas as pd import multiprocessing as mp -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.linear.linear_models import LinearTurbineModel -from ROSCO_toolbox.linear.lin_util import add_pcomp, smargin -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.utilities import list_check +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.linear.linear_models import LinearTurbineModel +from rosco.toolbox.linear.lin_util import add_pcomp, smargin +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.utilities import list_check class RobustScheduling(om.ExplicitComponent): diff --git a/ROSCO_toolbox/ofTools/README.md b/rosco/toolbox/ofTools/README.md similarity index 100% rename from ROSCO_toolbox/ofTools/README.md rename to rosco/toolbox/ofTools/README.md diff --git a/ROSCO_toolbox/ofTools/fast_io/__init__.py b/rosco/toolbox/ofTools/__init__.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/__init__.py rename to rosco/toolbox/ofTools/__init__.py diff --git a/ROSCO_toolbox/ofTools/case_gen/CaseGen_General.py b/rosco/toolbox/ofTools/case_gen/CaseGen_General.py similarity index 99% rename from ROSCO_toolbox/ofTools/case_gen/CaseGen_General.py rename to rosco/toolbox/ofTools/case_gen/CaseGen_General.py index 2637a21d..040924fd 100644 --- a/ROSCO_toolbox/ofTools/case_gen/CaseGen_General.py +++ b/rosco/toolbox/ofTools/case_gen/CaseGen_General.py @@ -1,6 +1,6 @@ import os, itertools import numpy as np -from ROSCO_toolbox.ofTools.util.FileTools import save_yaml +from rosco.toolbox.ofTools.util.FileTools import save_yaml def save_case_matrix_direct(case_list, dir_matrix): ### assumes all elements of the list are dict for that case that has the same keys! diff --git a/ROSCO_toolbox/ofTools/case_gen/CaseGen_IEC.py b/rosco/toolbox/ofTools/case_gen/CaseGen_IEC.py similarity index 99% rename from ROSCO_toolbox/ofTools/case_gen/CaseGen_IEC.py rename to rosco/toolbox/ofTools/case_gen/CaseGen_IEC.py index 2fd3cf3b..d3552970 100644 --- a/ROSCO_toolbox/ofTools/case_gen/CaseGen_IEC.py +++ b/rosco/toolbox/ofTools/case_gen/CaseGen_IEC.py @@ -2,8 +2,8 @@ import os, sys, copy, itertools import multiprocessing as mp -from ROSCO_toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General, save_case_matrix, save_case_matrix_yaml -from ROSCO_toolbox.ofTools.fast_io.pyIECWind import pyIECWind_extreme, pyIECWind_turb +from rosco.toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General, save_case_matrix, save_case_matrix_yaml +from rosco.toolbox.ofTools.fast_io.pyIECWind import pyIECWind_extreme, pyIECWind_turb try: from mpi4py import MPI diff --git a/ROSCO_toolbox/ofTools/case_gen/CaseLibrary.py b/rosco/toolbox/ofTools/case_gen/CaseLibrary.py similarity index 97% rename from ROSCO_toolbox/ofTools/case_gen/CaseLibrary.py rename to rosco/toolbox/ofTools/case_gen/CaseLibrary.py index bc22598b..f006ac21 100644 --- a/ROSCO_toolbox/ofTools/case_gen/CaseLibrary.py +++ b/rosco/toolbox/ofTools/case_gen/CaseLibrary.py @@ -1,20 +1,20 @@ import os, yaml import numpy as np -from ROSCO_toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General -from ROSCO_toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC -from ROSCO_toolbox.ofTools.case_gen.HH_WindFile import HH_StepFile, HH_WindFile +from rosco.toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General +from rosco.toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC +from rosco.toolbox.ofTools.case_gen.HH_WindFile import HH_StepFile, HH_WindFile # ROSCO -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox import utilities as ROSCO_utilities +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox import utilities as ROSCO_utilities -from ROSCO_toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.inputs.validation import load_rosco_yaml # Globals this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_case_dir = os.path.realpath(os.path.join(this_dir,'../../../Tune_Cases')) +tune_case_dir = os.path.realpath(os.path.join(this_dir,'../../../../Examples/Tune_Cases')) def find_max_group(case_inputs): max_group = 0 @@ -507,7 +507,7 @@ def sweep_ps_percent(start_group, **control_sweep_opts): turbine_params = inps['turbine_params'] controller_params = inps['controller_params'] - # make default controller, turbine objects for ROSCO_toolbox + # make default controller, turbine objects for rosco.toolbox turbine = ROSCO_turbine.Turbine(turbine_params) turbine.load_from_fast( path_params['FAST_InputFile'],path_params['FAST_directory']) @@ -559,7 +559,7 @@ def sweep_yaml_input(start_group, **control_sweep_opts): path_params = inps['path_params'] turbine_params = inps['turbine_params'] - # make default controller, turbine objects for ROSCO_toolbox + # make default controller, turbine objects for rosco.toolbox turbine = ROSCO_turbine.Turbine(turbine_params) yaml_dir = os.path.dirname(control_param_yaml) turbine.load_from_fast( path_params['FAST_InputFile'],os.path.join(yaml_dir,path_params['FAST_directory'])) @@ -609,7 +609,7 @@ def check_inputs(control_sweep_opts,required_inputs): # turbine_params = inps['turbine_params'] # controller_params = inps['controller_params'] -# # make default controller, turbine objects for ROSCO_toolbox +# # make default controller, turbine objects for rosco.toolbox # turbine = ROSCO_turbine.Turbine(turbine_params) # turbine.load_from_fast( path_params['FAST_InputFile'],path_params['FAST_directory']) diff --git a/ROSCO_toolbox/ofTools/case_gen/HH_WindFile.py b/rosco/toolbox/ofTools/case_gen/HH_WindFile.py similarity index 100% rename from ROSCO_toolbox/ofTools/case_gen/HH_WindFile.py rename to rosco/toolbox/ofTools/case_gen/HH_WindFile.py diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/__init__.py b/rosco/toolbox/ofTools/case_gen/__init__.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/__init__.py rename to rosco/toolbox/ofTools/case_gen/__init__.py diff --git a/ROSCO_toolbox/ofTools/case_gen/runFAST_pywrapper.py b/rosco/toolbox/ofTools/case_gen/runFAST_pywrapper.py similarity index 98% rename from ROSCO_toolbox/ofTools/case_gen/runFAST_pywrapper.py rename to rosco/toolbox/ofTools/case_gen/runFAST_pywrapper.py index 886d496b..e4ece622 100644 --- a/ROSCO_toolbox/ofTools/case_gen/runFAST_pywrapper.py +++ b/rosco/toolbox/ofTools/case_gen/runFAST_pywrapper.py @@ -8,9 +8,9 @@ import os, platform import multiprocessing as mp -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox.ofTools.fast_io.FAST_writer import InputWriter_OpenFAST -from ROSCO_toolbox.ofTools.fast_io.FAST_wrapper import FAST_wrapper +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.ofTools.fast_io.FAST_writer import InputWriter_OpenFAST +from rosco.toolbox.ofTools.fast_io.FAST_wrapper import FAST_wrapper # TODO: import weis and use library, import pCrunch and re-enable post-processing features available here diff --git a/ROSCO_toolbox/ofTools/case_gen/run_FAST.py b/rosco/toolbox/ofTools/case_gen/run_FAST.py similarity index 88% rename from ROSCO_toolbox/ofTools/case_gen/run_FAST.py rename to rosco/toolbox/ofTools/case_gen/run_FAST.py index 97c6b053..49061f5b 100644 --- a/ROSCO_toolbox/ofTools/case_gen/run_FAST.py +++ b/rosco/toolbox/ofTools/case_gen/run_FAST.py @@ -6,30 +6,32 @@ Otherwise, the directories can be defined as attributes of the run_FAST_ROSCO """ +import sys +import os +import platform +#import pickle +import collections.abc +import numpy as np +from rosco import discon_lib_path +from rosco.toolbox import utilities as ROSCO_utilities +from rosco.toolbox.inputs.validation import load_rosco_yaml + +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine try: from weis.aeroelasticse.runFAST_pywrapper import runFAST_pywrapper_batch in_weis = True -except: - from ROSCO_toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper_batch +except Exception: + from rosco.toolbox.ofTools.case_gen.runFAST_pywrapper import runFAST_pywrapper_batch in_weis = False -from ROSCO_toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC -from ROSCO_toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General -from ROSCO_toolbox.ofTools.case_gen import CaseLibrary as cl +#from rosco.toolbox.ofTools.case_gen.CaseGen_IEC import CaseGen_IEC +from rosco.toolbox.ofTools.case_gen.CaseGen_General import CaseGen_General +from rosco.toolbox.ofTools.case_gen import CaseLibrary as cl from wisdem.commonse.mpi_tools import MPI -import sys, os, platform, pickle -import collections.abc -import numpy as np -from ROSCO_toolbox import utilities as ROSCO_utilities -from ROSCO_toolbox.inputs.validation import load_rosco_yaml - -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine # Globals this_dir = os.path.dirname(os.path.abspath(__file__)) -tune_case_dir = os.path.realpath(os.path.join(this_dir,'../../../Tune_Cases')) -rosco_dir = os.path.realpath(os.path.join(this_dir,'../../..')) # https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth def update_deep(d, u): @@ -67,10 +69,11 @@ def run_FAST(self): # handle directories, set defaults if not self.rosco_dir: - self.rosco_dir = os.path.realpath(os.path.join(this_dir,'../../..')) + # Top ROSCO directory of whole repo + self.rosco_dir = os.path.realpath(os.path.join(this_dir,'../../../..')) if not self.tune_case_dir: - self.tune_case_dir = os.path.realpath(os.path.join(self.rosco_dir,'Tune_Cases')) + self.tune_case_dir = os.path.realpath(os.path.join(self.rosco_dir,'Examples/Tune_Cases')) if not self.save_dir: self.save_dir = os.path.join(self.rosco_dir,'outputs') @@ -133,23 +136,8 @@ def run_FAST(self): case_inputs = self.wind_case_fcn(**self.wind_case_opts) case_inputs.update(control_base_case) - # Set up dll: - # OS platform - if platform.system() == 'Windows': - dll_ext = '.dll' - elif platform.system() == 'Darwin': - dll_ext = '.dylib' - else: - dll_ext = '.so' - - # lib dir - if not in_weis: # in ROSCO - dll_dir = os.path.join(self.rosco_dir, 'ROSCO/build/') - else: - dll_dir = os.path.join(self.rosco_dir,'../local/lib/') - if not self.rosco_dll: - self.rosco_dll = os.path.join(dll_dir,'libdiscon'+dll_ext) + self.rosco_dll = discon_lib_path case_inputs[('ServoDyn','DLL_FileName')] = {'vals': [self.rosco_dll], 'group': 0} diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_post.py b/rosco/toolbox/ofTools/fast_io/FAST_post.py similarity index 63% rename from ROSCO_toolbox/ofTools/fast_io/FAST_post.py rename to rosco/toolbox/ofTools/fast_io/FAST_post.py index adee1d5d..51295b88 100644 --- a/ROSCO_toolbox/ofTools/fast_io/FAST_post.py +++ b/rosco/toolbox/ofTools/fast_io/FAST_post.py @@ -1,13 +1,13 @@ from __future__ import print_function -from ROSCO_toolbox.ofTools.fast_io.output_processing import output_processing -import ROSCO_toolbox +from rosco.toolbox.ofTools.fast_io.output_processing import output_processing +import rosco.toolbox def FAST_IO_timeseries(fname): # interface to FAST_IO data load try: - test = ROSCO_toolbox.__file__ + test = rosco.toolbox.__file__ except: - print('WARNING: ROSCO_toolbox required for wisdem.aeroelasticse.FAST_post.FAST_IO_timeseries') + print('WARNING: rosco.toolbox required for wisdem.aeroelasticse.FAST_post.FAST_IO_timeseries') fast_out = output_processing.output_processing() fast_data = fast_out.load_fast_out(fname, verbose=True)[0] diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_reader.py b/rosco/toolbox/ofTools/fast_io/FAST_reader.py similarity index 98% rename from ROSCO_toolbox/ofTools/fast_io/FAST_reader.py rename to rosco/toolbox/ofTools/fast_io/FAST_reader.py index 68aa594d..9ff6fc75 100644 --- a/ROSCO_toolbox/ofTools/fast_io/FAST_reader.py +++ b/rosco/toolbox/ofTools/fast_io/FAST_reader.py @@ -4,9 +4,9 @@ from functools import reduce import operator -from ROSCO_toolbox.ofTools.fast_io.FAST_vars_out import FstOutput -from ROSCO_toolbox.utilities import read_DISCON, load_from_txt -from ROSCO_toolbox import turbine as ROSCO_turbine +from rosco.toolbox.ofTools.fast_io.FAST_vars_out import FstOutput +from rosco.toolbox.utilities import read_DISCON, load_from_txt +from rosco.toolbox import turbine as ROSCO_turbine ROSCO = True def readline_filterComments(f): @@ -1680,7 +1680,7 @@ def read_StC(self,filename): return StC_vt def read_DISCON_in(self): - # Read the Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/ROSCO_toolbox + # Read the Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/rosco.toolbox discon_in_file = os.path.normpath(os.path.join(self.FAST_directory, self.fst_vt['ServoDyn']['DLL_InFile'])) diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_vars.py b/rosco/toolbox/ofTools/fast_io/FAST_vars.py similarity index 96% rename from ROSCO_toolbox/ofTools/fast_io/FAST_vars.py rename to rosco/toolbox/ofTools/fast_io/FAST_vars.py index a1b605df..8e5e89d1 100644 --- a/ROSCO_toolbox/ofTools/fast_io/FAST_vars.py +++ b/rosco/toolbox/ofTools/fast_io/FAST_vars.py @@ -1,6 +1,6 @@ from numpy import zeros, array import numpy as np -from ROSCO_toolbox.ofTools.fast_io.FAST_vars_out import FstOutput, Fst7Output +from rosco.toolbox.ofTools.fast_io.FAST_vars_out import FstOutput, Fst7Output # This variable tree contains all parameters required to create a FAST model # for FAST versions 7 and 8. @@ -619,7 +619,7 @@ ServoDyn['OutFmt'] = '' ServoDyn['TStart'] = 0.0 -# Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/ROSCO_toolbox +# Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/rosco.toolbox DISCON_in = {} DISCON_in['LoggingLevel'] = 0 DISCON_in['F_LPFType'] = 0 diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_vars_out.py b/rosco/toolbox/ofTools/fast_io/FAST_vars_out.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/FAST_vars_out.py rename to rosco/toolbox/ofTools/fast_io/FAST_vars_out.py diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_wrapper.py b/rosco/toolbox/ofTools/fast_io/FAST_wrapper.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/FAST_wrapper.py rename to rosco/toolbox/ofTools/fast_io/FAST_wrapper.py diff --git a/ROSCO_toolbox/ofTools/fast_io/FAST_writer.py b/rosco/toolbox/ofTools/fast_io/FAST_writer.py similarity index 98% rename from ROSCO_toolbox/ofTools/fast_io/FAST_writer.py rename to rosco/toolbox/ofTools/fast_io/FAST_writer.py index 28e63321..758cbfde 100644 --- a/ROSCO_toolbox/ofTools/fast_io/FAST_writer.py +++ b/rosco/toolbox/ofTools/fast_io/FAST_writer.py @@ -6,9 +6,9 @@ import numpy as np from functools import reduce -from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST +from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST -from ROSCO_toolbox import utilities as ROSCO_utilities +from rosco.toolbox.utilities import write_rotor_performance, write_DISCON ROSCO = True @@ -1384,7 +1384,7 @@ def write_ServoDyn(self): f.close() def write_DISCON_in(self): - # Generate Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/ROSCO_toolbox + # Generate Bladed style Interface controller input file, intended for ROSCO https://github.com/NREL/rosco.toolbox # Fill controller and turbine objects for ROSCO # - controller diff --git a/ROSCO_toolbox/ofTools/util/__init__.py b/rosco/toolbox/ofTools/fast_io/__init__.py similarity index 100% rename from ROSCO_toolbox/ofTools/util/__init__.py rename to rosco/toolbox/ofTools/fast_io/__init__.py diff --git a/ROSCO_toolbox/ofTools/fast_io/file.py b/rosco/toolbox/ofTools/fast_io/file.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/file.py rename to rosco/toolbox/ofTools/fast_io/file.py diff --git a/ROSCO_toolbox/ofTools/fast_io/output_processing.py b/rosco/toolbox/ofTools/fast_io/output_processing.py similarity index 99% rename from ROSCO_toolbox/ofTools/fast_io/output_processing.py rename to rosco/toolbox/ofTools/fast_io/output_processing.py index ef3684b1..bafe84bf 100644 --- a/ROSCO_toolbox/ofTools/fast_io/output_processing.py +++ b/rosco/toolbox/ofTools/fast_io/output_processing.py @@ -17,7 +17,7 @@ import struct import multiprocessing as mp -from ROSCO_toolbox.ofTools.util import spectral +from rosco.toolbox.ofTools.util import spectral class output_processing(): ''' diff --git a/ROSCO_toolbox/ofTools/fast_io/plot_FAST.ipynb b/rosco/toolbox/ofTools/fast_io/plot_FAST.ipynb similarity index 99% rename from ROSCO_toolbox/ofTools/fast_io/plot_FAST.ipynb rename to rosco/toolbox/ofTools/fast_io/plot_FAST.ipynb index 52fe840c..edf7715d 100644 --- a/ROSCO_toolbox/ofTools/fast_io/plot_FAST.ipynb +++ b/rosco/toolbox/ofTools/fast_io/plot_FAST.ipynb @@ -28,9 +28,9 @@ "# from weis.aeroelasticse.Util import FileTools\n", "\n", "# Instantiate fast_IO\n", - "from ROSCO_toolbox.ofTools.fast_io import output_processing\n", + "from rosco.toolbox.ofTools.fast_io import output_processing\n", "\n", - "from ROSCO_toolbox.ofTools.util import spectral\n", + "from rosco.toolbox.ofTools.util import spectral\n", "\n", "\n", "import pandas as pd\n", @@ -71,7 +71,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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mecEPAJjt/JiMfxSz/q7TqCnbEeYIhRDi8+tsb2HFn79D3H2FLGh4BoAVyefQ/P0tzL3uaVIy8sIc4ch1RCWQSql/KKUalFKb+5UlKaXeUUrt8v+buJ9ry5RSm5RS65VSq4cvaiFGj8joWBZcejs9N9SwfOyPAJjZvZysx+ax8c6TqC7ZFuYIhRDiwDramlnxp28Rd18h85v+B8DytAtpu3oH8695guT0nDBHOPIdUQkk8Bhw+l5lNwLvaa3HAe/5v9+fJVrrmVrr4iGKT4ijQmRUDAsuuZXen9ezYuKNeLVies9qsp9YwJbbj6O6ZEu4QxRCiH10tDWz8v6Lib9/DPObXwJgecY36LiuhAVX/ZWElIwwR3jkOKISSK31R0DLXsXnAI/7Hz8OnDucMQlxNLNFRDL/op/ju7melVNvoVebmdK7kewnFrHttoVU7d584CcRQogh1tHWzKr7LiT+/jHMa3sdgOU5l+H8cTkLrvwL8QnJYY7wyLPfBFIpNdixhCGJ5otJ11rXAvj/TdvPeRp4Wym1Ril1xbBFJ8RRwGqLYN4F12P+ZQOrZ99Jj7Yyyb2VnCePYcfv5kkiKYQIi872FlbfewHx949hbvtbACzLv5Ken1Wz4PJ7iYlLCG+ARzCltR74gFJrge9rrVfsVX45cJPWeswwxDdQXAXAq1rrqf7v27TWCf2Ot2qt9xkHqZTK0lrXKKXSgHeAa/wtmgPd4wrgCoD09PQ5Tz/99OF/IXtxOBzExsYO+X1GOqmH0VEHPp+Xzt2fcUr1n4hWLgB2qgJ2Tv4xsakHNyh9NNTDoZI6MEg9GKQeDr4OXN2dxK19kEXuZYGy1xIuIXLqOZgttqEMcVgM18/CkiVL1uxv2N9gCeSxwIPASuAGIB/4C1AF/EhrHZatKQZIIHcAi7XWtUqpTGCp1nrQPYWUUrcCDq31Hw50v+LiYr169dDPuVm6dCmLFy8e8vuMdFIPo6sOtM/HhvefoejjHxGnugEoNRWgz3+UMVPnD3rtaKqHL0rqwCD1YJB6OHAdtDXVUfLPy5nt/DhQtnzc9cz92s2YLZZhiHB4DNfPglJqvwnkfruptdafALOBemAP8DJwi9b6q+FKHvfjZeDb/sffBl7a+wSlVIxSKq7vMXAqIH1qQgwxZTIx8+SLift1HZtPeoJW4ij0lTHm+VMp/80Udq79MNwhCiFGgeb6KtbffToJf54QSB5XTPo53NrOgm/cMqqSx5HiQJNovgpcDDwE1AJfU0olDXlU+6GUegpYBkxQSlUppS4D7gROUUrtAk7xf49SKksp9br/0nTgE6XUBowW1de01m8O/ysQ4ug19bhzSLy1is2nPEkTCeT7qhj/8pep+vVEti5/E+3zhTtEIcQRpqG6lA13nkzyQ1OY2WV0V6+YdBPc2s78rw22KIs4VPtNyZVS7wLdwMla61Kl1M3A1cAqpdRdWutHhyvIPlrri/dz6KQBzq0BzvQ/LgFmDGFoQoiDNPWYL8ExX2LLZ6+T/PY15OhaePNr1LyVRstJ9zBl0dko0xG1QIQQYpjVlu+g6T9XMs21NjBzdsWkm5j31Z8yX94/hsVgbboPaq1f6PtGG4Ml/6SUeg64Bxj2BFIIMXpMWXQmLDqT7SveJu7Nq8nW9WS9+03q302m/oS70TpsnR1CiBHK2VLNltuPZ0rvBjL9ZSum/JJ5X7leEsdhtt8Esn/yuFd5HfCNIYtICHFUmTj/VJi/k+2r3iXm9WvI1TWkf3gZDTqB9d47mHHSRdIiKcRRrnzHerqev4qz3MYmBR5tYu20XzL3/OskcQyTwbqwN2GsnbjPIYwGyelDFpUQ4qgzce7JMHcbO1a/T+Tr15DvqyLt0+/T8ukNlC34LbNO/ZYkkkIcZcq2rcb13x8wwbMdAJe2smH6L5h73rXMk/eDsBqsC/vsYYtCCCH8JhSfCMVbePmpR5i26y8U+spIWv5D2pffxO55v2X26d+RRFKIUa5k8wo8L17NeM9OAJw6kq2zfkVn/EROPHGfaQ8iDAZbxqe878tfNM7/uIF9txMUQojDKj5zAoW/2sDu815nt7kIO07mrLyerl9nsvqVR2TWthCj0O4Nn7Lnt7MY8/ypjPfspINoVs++i6hf1TD33B9gMpnDHaLwO+DCSEqp72LsypIEFAE5wMMMMPPZf74JY8ZzFsYs7i1a6/rDFbAQ4ugydsYxMGMtpVtW4H7BaJEoXvMzelbfzKbZv6H4S1dKi6QQR7hd6z7C8srVjPUZbVatxFEy79fMOu1Sis2SNI5EB7Oy5g+AecAKAK31Lv92gCGUUkUYO9acDOwCGoFIYLxSqgt4BHhcay3NBkKIz61wynyYsory7Wvpfv4qJnq2MXfdz3Gv/QXrZ/2G4i9fJYmkEEcYY8zztYzzVQLQRAKVi25jxkkXM0cSxxHtYBJIl9a6VykFgFLKwsCTa36HseD49/Re+yP6E86vA98EHj+kiIUQR7X8ibPhF8up3L2JzmeuZLJ7M3PX34xv3S9YNfM3zDvv2nCHKIQ4gO0r3yH2jauZoOsAaCCJ2uPvYvriC0iRD4JHhINJID9USt0ERCmlTgGuAl7Z+6RBFvlGa90A3P9FgxRCiL3ljp0GN39KTel2Wp+6gim9G5i34Zew4ZesnPZr5n3lunCHKITYy9blb5L05tVMpBGAOlJpPPEeph77JdIkcTyi7Pd/SynVt3n2jRjd0ZuA7wGvA78Y5Lqv9tt3+hdKqf8ppWYfvpCFECIoq3AiU276iPrL17Ixcg4A8zbdArfaWfnf+8IcnRACYOuyN2i8tYDJb36NDBqpUhlsPf0ZMm7dzbTjz5HhJ0egwVog/6qUigWeAp7WWv/1IJ/zl1rr55RSxwKnAX/A6Nqef2ihCiHE/qXnFJF+4/s01VVQ/fjlzOhewbxNt8KmW1kx+RfM/cr1mGRMlRDDRvt8bP7kJXLev5bJdABQYcqm5+y/MH724vAGJw7ZYMv4zMJYC9ILPK+UWq+UukEplX+A5/T6/z0LeEhr/RJgOyzRCiHEAaRk5DHjhrdpuWor66IXATB/6+8w/TaJ5U/djsfdG+YIhRjdtM/H+veepvM3OUx7/1IS6aDMlMeer7xF3q+2SvI4SgzaZqy13qG1/rXWejLwbSABeF8p9ekgl1UrpR4BLgReV0pFHOg+QghxuCWlZTPrZ2/Q/sM9rI09HoAFO+7Cclsqy5+8BVdPV5gjFGJ08Xm9rHn9n3T9OpOZH3+PeJzsMY+h4usfUvCrTRRNWxDuEMVhdFCJnX9txzQgHYgB/+jXgV0IvAWcrrVuw1g/8qeHFqYQQnwx9sQUZv/kFdqv3c2auCUALNh9PxF3ZrLssRvpdnaGOUIhjmxej4dVL/0F128ymLPyOmJUDzst46n4+ocU/XIdeeNnhjtEMQQGnYWtlDoOuBg4F9gMPA38SGvdPsC5q4FPgTeA17XWPQBa61qg9vCGLYQQn489KZU5P36R9pZGdj12JcUd77Kw7CH4/UMsz7mMqRf+ktj4xHCHKcQRw93rYt0rf2Hmxt8xV3lAwXbrZGIufJjx42aEOzwxxAabhV0J3AlsA2ZprU/VWv9joOTRbwHwArAYY+mf15VSP1RKjT9cwSql/qGUalBKbe5XlqSUekcptcv/74B/AZRSpyuldiildiulbjxcMQkhjiz2pFSKr/8v7dfuZlXCGQAsqPo7sfcWsOyRH9De2hTmCIUY2XpdPax4+g64LZN5m27FpjxssU2n8pJPmHjzMnIleTwqDNaFfazW+hit9Z8OZitCrbVHa71Ua32j1no+cBnQCfxOKbVOKfWXwxDvY8Dpe5XdCLyntR4HvOf/PoRSygw8CJwBTAYuVkpNPgzxCCGOUPakVOZe9zTt1+5mZeLZACysfRL7H4tY/uDltDXVhTlCIUaWnm4ny5+8Fdsd6czffidW5WVTxGyqv/UZU2762FibVRw1BpuFXb6/Y0qpRw/0xFrrWn+L5YXAHODfXyzEkOf8CGjZq/gcgrvbPI7R3b63ecBurXWJ1roXoyv+nEONRwhx5LMnpTLvh/+m/Yd7WJFyPgALGp8j4c8TWPGnb9HSUB3mCIUIr55uJ8sev4nIu7JYsNtYW3V91AJqvr2CaT//gOwxU8IcoQiH/Y6BVEol7e8QcOYg1xUDNwP5/Z9faz39C8Z4IOn+cZZorWsH2qcbyAYq+31fhaxLKYTox56Ywvyr/0lH2x/Y+p+fs6DhGeY3v4TvwZdZNet3zDrrCixWWZFMHD20z8eG958h55MbWUgbAGtjjiPzwnuYmT8hvMGJsFN7bVsdPKCUFyjHSBj7aP/32VrrAd9JlVI7MGZdbwJ8gQsHadH8XAErVQC8qrWe6v++TWud0O94q9Y6ca9rvgqcprW+3P/9N4F5Wutr9nOPK4ArANLT0+c8/fTThyP0QTkcDmJjY4f8PiOd1IPUQZ9w14O7x4ne+BRTulaQTQNVpLE27SvEjD8Rs2V4Eslw18FIIfVgGK568Pm8dJYsZ2L1c0zQpTRpOzttk2mf+n9E2Qdqoxk+8rNgGK56WLJkyRqtdfFAxwabhV0CnKS1rtj7gH+Czf40aq1f/pwxHop6pVSmv/UxE2gY4JwqILff9zlAzf6eUGv9KPAoQHFxsV68ePFhDHdgS5cuZTjuM9JJPUgd9BkR9XD6Wfi8Xta+8y/sq+7nyw0PUdvwPBVTrmTWl6/GFhE5pLcfEXUwAkg9GIa6HnxeL+vffoKEVfczxldGjUpjxZRfMevLP2DREP+sHyz5WTCMhHoYLIG8H0gE9kkggbsHue4WpdTfMCa0uPoKtdb/+yIBHoSXMRY5v9P/70sDnLMKGKeUKgSqgYuArw9RPEKIUcRkNjP79EvRp36Lde/+h9gV9zF/y2+p3/IXyiZdwYwvX0NkVEy4wxTiC/N6PKx765+krPkjs32VVKkMVs74LbPO+h5ZtohwhydGqP0mkFrrBwc59qdBnvM7wETASrALWwOHnEAqpZ7CWCYoRSlVBdyCkTg+q5S6DCPZ/ar/3Czgb1rrM7XWHqXU1RgLnJuBf2ittxxqPEKIo4cymZh16iXok7/OhqXPEvXZPczfdgeN2x5m/fjLmXnudURGS9eaOHJ43L2sf+PvpK3/E8W+aipVFqtm3s6ss75Ljoz3FQcw2CSa8we7cJAWxRla6yGZy6+1vng/h04a4Nwa+k320Vq/Drw+FHEJIY4eymRixokXoRdfyMaPXsD26R9YsPP3NN39V9aP/Q7Tz/0R0bH2cIcpxH65e12se+1RsjY+SLGupdyUy+o5dzPrjMvItQy6v4gQAYP9pHzJ/28asAh43//9EmAp+29RXK6Umqy13npYIhRCiBFImUxMX/wVWPwVNn/8EqaPf8+C3ffR8oe/s3HMpUw993rZ2UaMKL2uHta/+hA5mx9inq6n1FTAmrn3Muu0S8k3m8MdnjjCDNaF/R0ApdSrwOS+pXL8E1X2270NHAt8WylVijEGUhlPN2TL+AghRFhNPe4cOO4ctnz2OvrDu1hQ8gBt9/6DZQXfZOp5PyPOvr9V0YQYeq6eLta//CD5Wx9hHo3sMY9h3bwHmHHyJRRK4ii+oINpqy7oSx796oHBtifce6cYIYQ4KkxZdCYsOpPtK96m94M7WVj2EB33PcGyvG8w+bwbsCemhDtEcRTp6Xay4aUHKNj+V+bTzC7zWOoW/IYZJ12EMg22EZ0QB3YwCeRSpdRbwFMYk2EuAj7Y+ySlVKzW2nGAHWxitdaOLxytEEIcASbOPxXmn8qO1e/T894dLKx4lM77/8Xy3K8z6bwbsCenhztEMYr1dDlY/+L9FO38G/NpZYdlAnWL7mT64gskcRSHzQETSK311f4JNcf5ix7VWr8wwKkvKaXWYyyjs0Zr7QRQSo3BGDd5IfBX4PnDEbgQQox0E4pPhOIT2bXuI5zv3M6Cqr/jfODfLMu+kInn3URiama4QxSjSJejnY0v3su43f9kAe1ss06m7th7mXrcuZI4isPuoKZb+WdcD7oMj9b6JKXUmcD3gGOUUomAB9gBvAZ8W2tdd4jxCiHEEWfcrONh1vHs2fgZ7W/dzsKaJ+j68zMsz7yAsef+nJSM3AM/iRD74ehoZdMLf2Bi6RMsoIMttmnUHv8zpiw6WxJHMWQOmED6Wx/vwpiNrQhOionf+1xZKkcIIfavaPoimP4qpVtW0PrGbcyr/Q+uh55jecb5jD3nJlKy8sMdojiCdLQ1s+WFu5lU/iQLcbA5Yia1i29kysIzwh2aOAocTAvk3cCXtNbbhjoYIYQ4GhROmU/hlJcp37aGxjduZ27dM3ge+S8r0s6h4JybSM8pCneIYgRrb2lk64t3M6Xi3yzEycbIYupOvJGp804Jd2jiKHIwCWS9JI9CCHH45U+aQ/6k/1Kxcz31r93GnIb/4fvri6xIOZv8c34R7vDECONydrDsbz9iauVTLFTdbIiaR+RJP2d68YnhDk0chQ4mgVytlHoGeJHh2dtaCCGOKnnjZ5I3/jmqdm+m5tXbmN30Cvrvr0DkYmryM8gqnBjuEEUYtTRUs+PFOzm2+lliVA/rYhYRe+pNzJh53IEvFmKIHEwCGQ90Aaf2Kzsse1sLIYQIyhk7lZzrnqKmdDtVr/yORc2vY3psISsTTyfr7JvJGTs13CGKYdRUV8nuF+9geu3zLFQullvnkfrlXzNr+qJwhybEQS3j853hCEQIIYQhq3AiWdc+yesvPkNy5evManoV87/eYFXCKaSfdTN542eGO0QxhJpqytn94m3MqH+BebhZF3cCSWf+gp4GpzERS4gRYL8JpFLqZ1rru5VSf8JocQyhtb52SCMTQoijXHRCOvPPfZyG6lJKX/wdMxtewvLvd1gdfyKpZ95M/qQ54Q5RHEb1VXsoe/E2Zja+zFw8rLOfROqZNzNn4mwAyhuWhjdAIfoZrAWyb+LM6uEIRAghxMDSsgtJ+8Hfaar5Fbtfup0Zdf8j4umTWBt3PIln3EzhlPnhDlEcgtryHVS8fDuzml4lGR/rEk4h4+ybKR43I9yhCbFfgyWQRUqpucC/tdae4QpICCHEwFKy8kn5/iM01f2CDf6xcdHPncra14/DftpN0r15hKku2Ub1K79jVssbpADrEk8j+8u/YO6YKeEOTYgDGiyBzAH+CExUSm0EPgM+BZZprVuGIzghhBD7SsnIJeXKv9DScDMbXryLadXPEvu/M1j/5kJiTrnJ2PlGjFhVuzdT++rvmNX6Fqko1iafRe6XbmaezLYXR5D97nGktf6J1noRkAHcBLQA/wdsVkptHab4DppS6odKqc1KqS1KqesGOL5YKdWulFrv//pVGMIUQojDJiktm4VXPID32g0sz7mMIud6xr30JTbceTI7Vr8f7vDEXip2rmf1vReQ+a9jmdH6DmtSz6Xl8pXMv/ZfslSTOOIczDI+URhL+dj9XzXApqEM6vNSSk0FvgvMA3qBN5VSr2mtd+116sda67OHPUAhhBhC9uR0Flx+L+2tN7HshbuZUvEk8a+ex8Z352BbciMT55964CcRQ6Zs22qaXr+d2R3vk4aFVWkXMObcm5mfXRju0IT4wgabhf0oMAXoBFZgdGHfq7VuHabYPo9JwHKtdReAUupD4DyMbRiFEOKoYE9MYeH/3U1n+40se+FuJpX9i4Q3vsqW92bA4huZsujMcId4VCnZvIK2N3/HzM6PScfKyoyLGHveTSzIyAt3aEIcMqX1Piv0GAeUehNIATZjJI/LgM16fxeEkVJqEvASsBDoBt4DVmutr+l3zmLgv0AVRivqT7TWW/bzfFcAVwCkp6fPefrpp4cyfAAcDgexsbFDfp+RTupB6qCP1MOh14Hb1UXvttdY0PoyyaqDjaZJlOZ/jfjcGSjTfkcwjThH2s9CZ90uUnc/wwLPKpw6gk9jT8c36TwiYxMP6XmPtHoYClIHhuGqhyVLlqzRWhcPdGy/CSSAUkphtEIu8n9NxRgLuUxrfcsQxPqFKaUuA34AOICtQLfW+kf9jscDPq21Qyl1JvBHrfW4Az1vcXGxXr166FcyWrp0KYsXLx7y+4x0Ug9SB32kHg5fHXQ52tn44n2M3f1PUmhju3Uyvcf8hGnHn3dEJJJHys/CrnUf4XzndmZ2LcOho9iUfSETz/s5iamZh+X5j5R6GEpSB4bhqgel1H4TyEHfObRhM/A68AbGLOwi4IeHPcpDpLX+u9Z6ttb6eIwkd9dexzu01g7/49cBq1IqJQyhCiHEsIqOtbPgkluJ/dkWlk/4GcnuWqYv/T923T6f9e89jfb5wh3iEW376vfYcOfJjHvpS4zp2sCy3MvxXruBhVc8cNiSRyFGmsHGQF6L0ep4DODGv4QP8A9G2CQaAKVUmta6QSmVB5yP0Z3d/3gGUK+11kqpeRjJc3MYQhVCiLCIjI5lwcU309N9HSte/hMF2x5l/MffY9dnv8cx/0fMPPnrR0SL5EixbcVbeN6/k2mutbQTw7K87zH5vJ+xMFHaJsToN9gs7ALgeeBHWuva4QnnkPxXKZWMkez+QGvdqpS6EkBr/TBwAfB9pZQHY5zkRSNxPKcQQgy1yKgY5n/tRnpd17Hi5T+Tt+Vhxn32A/as+APtc69j5infxGQ2hzvMEUn7fGxd9gZ8eBdTejfQShzLC37AlPN+wkJ7UrjDE2LY7DeB1FpfP5yBHCqt9XEDlD3c7/GfgT8Pa1BCCDGC2SIimf/Vn+A+5xpWvvIQ2Zv/QtHyH1K68l5a5lzLzNMuxWw5mNXeRj/t87H5k1cwf3w3U9ybaSGe5WOuZdp5P2ZBXEK4wxNi2Mk7gxBCHOWstgjmfeU6PF++ilWvPUrGhgeZs+rHlK+5j8aZ1zDrzMuP2kRS+3xs+vB/2D79A9M822gigeXjrmf6OdexINYe7vCECJuj8x1BCCHEPixWG3PPvRrPWVew+o1/kLr+TxSvvYHKdX+kbsYPmHXWFVistnCHOSy0z8eGD54letk9TPfspIEklk/4GTPP+SELomUZGSEkgRRCCBHCYrVR/OUr8Z55OWveeoykNQ8wd/3NVG94gJppP2Dm2VditUWEO8whoX0+1r/7H+JW3MtM7x7qSGHFpJuY8eWrWRAVE+7whBgxJIEUQggxILPFwpyzLsd3+ndY+86/SFh5H3M3/orajX+iYsqVzPry1dgiIsMd5mHh83pZ//YTJKy6n1m+MmpJZcWUXzHryz8gY5S8RiEOJ0kghRBCDMpkNjP79EvRp36Ldf7Wuflbfkv9lr9QNukKZnz5GiKP0NY5r8fDurf+ScqaB5jtq6BapbNq+m+YefaVZI7SVlYhDgdJIIUQQhwUZTIx69RL0Cd/nQ1LnyXqs3uYv+0OGrY9wvoJlzPznB8SeYSMD/S4e1nfN87TV0WlymLVzNuZddZ3yT5KxnkKcSgkgRRCCPG5KJOJGSdehF58IRv9M5QX7LibprsfZf24/2P6OdcRPUJnKLt7Xax77VEyN/6FYl1DuSmH1XPuZtYZl5F7lM40F+KLkN8WIYQQX4gymZi+5AL0Ceezyb9G4oJd99Lyh7+xccylTDvvx8SMkDUS3b0u1vnXupyn6yk15bOm+B5mnnYp+ZI4CvG5yW+NEEKIQ6JMJqYdfw4cfw5bPnsdlt7JgpIHaL3nn2wq+BZTzvsJcWHapaXX1cM6/24782hkj7mQdfMeYMbJl1Aou+0I8YVJAimEEOKwmbLoTFh0ZmCf6AVlD9Jx32Msy7uEyef9DPsw7RPd0+1kg3+/7/k0s8s8lnXzb5X9voU4TCSBFEIIcdhNmn8azD+N7avfw/XuHSyseITO+59gee7XmXTeDdiT04fkvj1dDta/eD9FO//GfFrZaRlP3aLbmb74QkkchTiMJIEUQggxZCYWnwTFJ7Fr3Uc437mdBVV/x/HAf1iWfSETz/s5iamZh+U+XY52Nr54H2N3/5MFtLHdOpnaY+5h2vHnSeIoxBCQBFIIIcSQGzfreJh1PLs3fErn27ezsOZxuv78NMszL2DceTeRnJ7zhZ7X0dHKphfvZULJYyygg622adQd9wBTjvmSJI5CDCFJIIUQQgybsTOOgRmvUbplBa1v3MaCun/T/ZfnWJ5xPmPPu5mUjLyDep7O9hY2v3A3k8r+xUIcbI6YSe0JNxhjMIUQQ04SSCGEEMOucMp8Cqe8TPm2NTS+fhtz657B/dB/WZ52LmPOvZm07MIBr2tvbWLrC3cxpeLfLMTJxsg51C25kanzTx3mVyDE0W3UJJBKqR8C3wUU8Fet9f17HVfAH4EzgS7gUq312uGOUwghRFD+pDnkT/ofFTvXU//abcxteB7voy+wIuVs8s/9JRm5YwFob65n6wt3MrXyKRaqbjZEzSPypJ8zvfjEML8CIY5OoyKBVEpNxUge5wG9wJtKqde01rv6nXYGMM7/NR94yP+vEEKIMMsbP5O88c9RtXszNa/expyml/H97RXWxJ+AyeXF8sEKFqoe1sUsIvbUm5gx87hwhyzEUW1UJJDAJGC51roLQCn1IXAecHe/c84BntBaa2C5UipBKZWpta4d/nCFEEIMJGfsVHKue4qa0u1UvfxbZra8gU15WRt7HPGn/pxZM44Jd4hCCEAZ+dSRTSk1CXgJWAh0A+8Bq7XW1/Q751XgTq31J/7v3wNu0FqvHuD5rgCuAEhPT5/z9NNPD/lrcDgcxMbGDvl9RjqpB6mDPlIPUgcA3R1NODvaSMkZG+5Qwk5+HqQO+gxXPSxZsmSN1rp4oGOjogVSa71NKXUX8A7gADYAnr1OUwNdup/nexR4FKC4uFgvXrz48AW7H0uXLmU47jPSST1IHfSRepA66CP1YJB6kDroMxLqYdQskqW1/rvWerbW+nigBdi11ylVQG6/73OAmuGKTwghhBBitBg1CaRSKs3/bx5wPvDUXqe8DHxLGRYA7TL+UQghhBDi8xsVYyABlFIfA8mAG7hea/2eUupKAK31w/5lfP4MnI6xjM93Bhr/OMDzNgLlQxd5QArQNAz3GemkHqQO+kg9SB30kXowSD1IHfQZrnrI11qnDnRg1CSQRzql1Or9DVQ9mkg9SB30kXqQOugj9WCQepA66DMS6mHUdGELIYQQQojhIQmkEEIIIYT4XCSBHDkeDXcAI4TUg9RBH6kHqYM+Ug8GqQepgz5hrwcZAymEEEIIIT4XaYEUQgghhBCfiySQQgghhBDic5EEUgghhBBCfC6SQAohhBBCiM/FEu4ARrqUlBRdUFAw5PdxOp3ExMQM+X1GOqkHqYM+Ug9SB32kHgxSD1IHfYarHtasWdO0v51oJIE8gIKCAlavPuCOh4ds6dKlLF68eMjvM9JJPUgd9JF6kDroI/VgkHqQOugzXPWglNrvVs7ShS2EEEIIIT4XSSCFEEIIIcTnIgnkKLF0RwMFN75GQ0dPuEMRQgghxCgnCeQo8cpb7/Ci7ZfsKN3vcAUhhBBCiMNCEshR4lLnX5lp2oO5dm24QxFCCCHEKCcJ5CgR43MC0NXjCnMkQgghhBjtJIEcJXzK+K/UzuYwRyKEEEKI0U4SyFHCo/3/lV1N4Q1ECCGEEKOeJJCjhPK5ATD3tIQ5EiGEEEKMdpJAjhLRPgcAEb1t4Q1ECCGEEKOeJJCjRKw2Esgod1t4AxFCCCHEqCcJ5CigtSZSG7Ovoz1t4Q1GCCGEEKOeJJCjQE+vFxvGGMgo/3I+QgghhBBDRRLIUaCzuxuz0gBE+rrCHI0QQgghRjtJIEcBhzOYNEbRHcZIhBBCCHE0kARyFOjuNhJIJ9HE6G60zxfmiIQQQggxmkkCOQq4XUaro8Nsx6w0rh4ZBymEEEKIoSMJ5Cjg6e0BoNtqB6Crsy2M0QghhBBitJMEchTw+hNIly0JgB5HRzjDEUIIIcQoJwnkKNDXAumJTASgx9keznCEEEIIMcpJAjkK+DxGAqmjkgHo7WoLYzRCCCGEGO0kgRwFvL3GLjQqJgWA3i7pwhZCCCHE0JEEchTQbqMF0hJnJJDe7s5whiOEEEKIUU4SyFFAe4wWyIj4NAC8PZJACiGEEGLoSAI5Cvj8CWR0YrrxvSSQQgghhBhCkkCOAto/iSY2IdX43iUJpBBCCCGGzqhMIJVS/1BKNSilNvcrS1JKvaOU2uX/NzGcMR5O2m20QEZFx+LUEdDrCHNEQgghhBjNRmUCCTwGnL5X2Y3Ae1rrccB7/u9HB38XtrJE0qWiMLklgRRCCCHE0BmVCaTW+iOgZa/ic4DH/Y8fB84dzpiGlNdIILHY6FFRmN2yF7YQQgghho7SWoc7hiGhlCoAXtVaT/V/36a1Tuh3vFVrPWA3tlLqCuAKgPT09DlPP/30kMfrcDiIjY39Qtc2r3yKr3Q9zYfHP0/GRz+l05KA49hbD2+Aw+RQ6mG0kDowSD1IHfSRejBIPUgd9BmueliyZMkarXXxQMcsQ373I5DW+lHgUYDi4mK9ePHiIb/n0qVL+aL3eWfLC9AFJyw5mc3L4ohRboqHIeahcCj1MFpIHRikHqQO+kg9GKQepA76jIR6GJVd2PtRr5TKBPD/2xDmeA4bk89FLxZQil5LNBG+rnCHJIQQQohR7AslkEqpTYc7kGHwMvBt/+NvAy+FMZbDSnl76cUGgMcSQ4RXEkghhBBCDJ39dmErpc7f3yEgY2jCOTyUUk8Bi4EUpVQVcAtwJ/CsUuoyoAL4avgiPLxM3l48ygqA1xJDlO4Oc0RCCCGEGM0GGwP5DPBvYKBZNpFDE87hobW+eD+HThrWQIaJyecKJJA+ayxRHDiB9Po0CjCZ1CHf3+nyYDWbsFlCG7R73F4a2nvIS4nZ55qKukayUpKwWMyh17hctLW1kJCQtM81TU1NpKSk7FPe3dWFyWwiImLfH8telwtbRMQ+5drnRSkTqEN//UIIIcTRZrAu7I3AH7TW39n7C2gbnvDEwTD7enErowsbWyxR9KK97v2e39DZw4m3vcJPn127z7HP9jTxt49L8PlCPzd8tqWEh35zJcs2bg8pb253sPyOM3nxgev2ea53H7yWyD9NZs/uHSHlG1e8T/pDE/n4HzeElHu9PnKX3UTv/bPpdrSHHFvxyt9I+XMRq1/+S0h5T08PTb+fQ8Xdi/B5vSHHlr/4F7g9i82fvBp6TbeTqt9NY/U95+0T8+qXHqT51nz2bF2z1zVdbLttEZ89/IN9r3njMcp/M5W68u17XeNk851LWP6Pn+5zzaZ3/sWG206gsbYipNzd6yLy09v55Km79rlGCCHE0Wvj2mW8+cYLjJTVcwZLIK8DOvZzbN+/vCJsTL5gFzYRxrT+Hsf+/uvgzY+W8Zrnu3xly9WUNwUXHXd7fWx9/DqOeeccPtu8K+Satjdv4/u+p+h65/aQ8k0fv8hJrODCjsdpbgrOS+p0Ojm77UnSVBtVnzwZco1j+WNEKA8zap5G+3yB8j07NjGF3aTRyvZPXwy5JmrzUwBEb/5PSPme1W+Tq2sY591DxdblIceSN/8Tm/LQtfKJkPKtn7xErq+aYscH1FeXhl6z8VGSaaNx6SOh13z4PJPcW1hU9ySOjtaQY5kr7yDfV0nZOw+HlG//5EWm9qxlQcWjOJ2hi7snffY7ZrjXs/vNB0PKNy99jgXuFRy743Z6u0PX81z+1B3U/XostSWbEUIIMfr4fJqXPlxJeWP7Xge8FL10LqevuJS1W3eGJ7i97DeB1Fp/rLWu2M+x1UMXkvi8LL5evCajBVJFxgHQ5Wjd7/kZ2x4jVvWwyLyV3es+DJRv2lnC5aZXmGSqoLtf0tXr8TGz4wMApnV+hLdf66SjfGPgcenadwOPa3YEW/Ds9StD7p/UafzwJ9FJU13wR6xp6weBxz3loS2AKb3GefmuXSEtjY0lGwKP67YtCzzudnlIc1cDkNO5PuS5OiqCCVjN5o8DjzvbW8jzlAOQ3BzaOttTuzX4OtcF4+xoayRb1wEQ37Aq9Jrdweeu2Bh87HE0B66JrVsRek1VsD5LNn0SPKA1Y3c8RIZupOz9f4Zc43H3svzBy1jzamjSK4QQYmRq6OjhpfXV+7QmrvvkNc5+/1RW/fWakPLOsrXEqB7j2jUjYw7wAWdhK6VSlVI3KaUe9e8x/Q+l1D/2c+5CpdSDSqmNSqlGpVSFUup1pdQPlFL2wx++ALD43Hj8CSTRxhhBV3vjgOdqrSlyrKIiaqLxfelHgWMtu4LJTEJ9MBmrrK4iSzXTbE4hjVZqq4KtdtEdu+hSUcY9q4PJT0e1kXDtNheR3r07JIY0TxUVphwAarYHk0tVvxmnjqTUlE9067ZAeU93F+m+RhpIIkb1UFMaPKaad+Mkik4dhakhmBiWVVViV07aVRxZuh5nZ/DTnG4pwUkUXq1wVQUT0OodazArTaXKItdTFjIMwNJWSrc26thZGXydlVuNpLFKZZDTWxrSohrfupkalWbUR/m6QHnTbiM5rdVJZLv2QL83EGtbCV5tjMtsL1sfKG9vriMF4zXEN4Ymqts++i8LGp9n5qob8HpChy60NVSz+vV/4t2re18IIcTQW1/ZRlXrviujvP2PW8j637m8v3ZbSLlt6/OYleZE13u0OV2B8o6dwQYFa+PIWAjnYJbxeQmwA+8Cr/X7CqGUegO4HHgLYx/qTGAy8AuMSTcvKaW+fHjCFv2ZtRufP4E0xaYC4O6oH/DcmqoyiqimKe8M6syZxDYHkyFdYyQ2mxNPYULvVnxeIxlq3mMkP1V55xrf7zYSGJ9Pk95TSm3sVKpVOtEtwV8ET8NufFrRlHkCmTTR5TS61NuaG0iik+qMEwHoqQ2OG4zqrKDGlE5zTBGpPeWB8rry7ZiVpixlsXH/fi2I8V3l1NtyqbLmE9tZEihvqzRiqUw5HoDa0i2BYwndFdRFjqHWlIG1NZjcdtYajytSlxCp3DRX7wkci+sqpzRiIq3EY2oJXuOsNx7XpB5PPE4a66sDx5J6a6mJm4GDKHRTcEhAa43RArvVfgJJdNDeXNvvPhVsMk+mS0egm4L3qSszEvJGEslwlYV8au3yt3SalaZ0S2iL5s7Hvk/xyutY92po9zpAl7MDj3v/Y2WFEEIcmMfrY0tN+z7llc1OXn74F/z2L/8Iec929bq4qPVR5pp24lj7fMg1iW3G37ck5WDPruDfLVf9Tjp0FFstk0h2hDbKhMvBJJDRWusbtNbPaq3/2/c1wHnf1FpfprV+WWtdo7X2aK0dWuu1Wut7tNaLgc8Ob/gCwKp78fon0VjijATS4xi4BbJhw9sAxE06mYb4qeT37Aj8YNtbt1BjzqYrawHxqov6KiPpcdUYSWb6ccYymq4q49NPbXsXY6imN3kCTRF5xHVVBmNqK6HOlIYlc6pxX3+rYV2Z8QsRVbiATh0FbcFEMdFVTbM5HXd8Iem+Bty9RnN9iz8ZjJx0KgBddcFEMc1dRWdMPp3R+SS7qgLl3fVG7NZJZwDQWWU8h9enyfTW0hVbQEtEFvE9wYTP01yCTyuiJiwBoKkyOM4k3V2NIzafOmsOsY6yQLm3qRSPNhExwUiIG8uMX35XTxdpuhm3vYBac+g1vY178GgTtrEnGPVYHpxklO6pptWWSa0lm+iOYEtvZ41xTmniMSTTTntLU+BYXPNG2jFmurfsCXb9+7w+pjr940L3vE9/jrYmXL+fwsZ7zkIIIcSBOV0enC7PPuV/fG8XFz/wFq9trA0p37riTX5l/Rd39d5OVUuwFbJ89zYsymigSWjoN8TL6yGjp4QN5ikAtJQFWxpVWzmVOg1PYhFZvhp6veGfSHMwCeSrSqkzD3SS1rrpcJwjPj+L7sVnNhJImz0dAF/nwAmkqewjWnUs+VMW4E6fSaZqpra6HK01+a4dNMZPJjZvJgCNe4wWSVvTNlqIJ6NwGnUkY2kxkpma0u1EqV5sGVPoicsn3VMT6MK1d5XTHJFDbPYkINil3VlltDgm5k6m3pJFlMMY2+j1eMj01dERkYk5dSxmpakrM8511RnJYMGsE+kiAt1aBkB7eweZuglPQhGehALSaaanq9N4oc178KLImG386HoajU9s9c0tZKgWfIlj6I7JJcVTF6zH9nIaVRKpY6YD4KgzWiDbW5tJogOdWEhnTAFpvf0S5Y4KGkyppBQadebwty7WV+zEpDTWlELaovNI6ZfcmlrLqSWVgvHTjOevNl5fe3M9CThwRmXRFpVHkit4H0/DbrxaYRp3ivF/Ux5shU3prWan/Vh6tBXdEGwFbqwrI1oZXSAZnaETb7YtfZpEOpjds4Kmfi2tANuWvcHmO06gpmQrQghxtPH69k3OPF4fZ//pE8584GPcXl/Isei1j7Ax8ruUfPpcSLltzzsAJCgnuzcHe4c6qo2/bW2mRHJ79wRXPemoxoKX6tTjAPA1Bhsxop2VNFgyMSePIV210eboOfQXeoj2m0AqpTqVUh3ADzGSyG6lVEe/8kGv2+urUin1glJqzFC8iKOdFTfan0AmJSbh0hY8ncaM6KdWVnDNU+todfaC1mS1rmRbxAxsVgsxY+YC0LhjGQ21FaTTgjttBhljZwLgqjaSjgTHbmpthaAUdbYCEhxGwuGoMo7b86ehkscQp7ppajCSyExPNV1xhaTlGwlkb4ORJHkajUQos2Ai7ZFZJLiMFsCG6hJsyoMrOoP47PEAtFYav2Sm1hLaiCU+KZ0GcwYR/qSzrnwbJqWxpI3HljbOKPMnnVGdZTSY0rEnJFNPEpY2o9WyqcJIsCLSxuKz55NIJ51tzQDEdlXRbMsiI2cMvdqMt9loAaz3d3/b0sfjTSwkhbbATOy47kpabFmk5xbRq834/N3Orf6kMDZjHK74QtJ8jXh7jfU5o7sqabJlkp43waibJiO2Ov99vLFZ9NoLyfA14O41EkBreyn1pjQSC/zJrb9FstvpII0WPAmF1JszsHYEk85Gf7f3NsskMn31uF3B9UHNNcF5cBUbghOpAPQHtzHVtZ6ytx5gbzs+eYHtq97dp1wIIUaDNeUtTLnlTZ5fUxVSvqGqjZktbzKv7XW21ARTIJfHy8ldbwIwsfGtkGtiHaV4/WmWuzo43r63wUgMa9JPIJd66tuMho++IVGejJm0qzgiO/y9bT4fib21OKJyiUovMp6jI7S1MxwGm4Udp7WO9/9r0lpH9fs+fpDnvBf4KZAN5AA/Af4KPA0MOPlGHBqrduMzGYtlp8RF0kI82tmEw+Xh3Zf/zflbf8j/nnscd+MuUr0NNKfOByBr0gI82oS7YjX1242uzuiCYpKSU6ghFVvTNrTPR7ango64sQB0xBWR5akEnzfQ2pWUP43odCOBqy/fTnNDFbGqG51URGJCAnU6CXOr8Ytgay+h3pSGLTIKV1w+Gd56tM9Lc6WREPliM0nNnwxAd53xSxbjKKfBkm3cPyqHhJ4a47G/Wzo+ZyLxWUYy1upv4UzqqaQ1MteIyZJNrNNIOjv9LYT2nAnYUo3PMw0VxjXJ7loc0blYrVYaTGlYO43u9Y4af6tpzsRAolrvT85SPbV0xeRgsdqoM2cQ0VEGQE+DkWSn5E5AJRdhUpom/2tM7q3BGZ2LLTqOZhIwt5f7YzOOm+OzsKSOxaJ8ge7t+O4KmiJySC+YiFcrvP4W1b61J62pRbRHZmHv1yXv9D9fc/YSzErTWBEcb2pv38EW03h8WtHbb4a5z+ujwGXUUWpz6GILLbVlTHj3Uia+9hV6ukKXJQLo6ZXxlEKII8PfPynlO/9cSa8ntDXxudVV9Lh9/GtZWUh52Z7t3Gd7iN9bH2XrjuB7aW11JeNMxvvuDO9m2p29gWMprkp2xC2iBxsRTcHxjObWUjqJwlqwCKvyUlNmvFe3VRvv6/bMsTTZcrH3DQvrrMWGG09CPgnZxt8g7Rh4nsNwOphZ2Of1n0GtlEpQSp07yCWna60f0Vp3aq07tNaPAmdqrZ8BEg89ZLE3W78WyEirmSaVRISzlo827OA+8x9ZYt7AN0tvwPXYefi0InbmuQDEx9kpMeUT27QBV8UafFqROdFILmsiCkl07qa1voJYutGpRoLmTZlIJL046ncT3baLBpWCKTqB5FzjeGfNDhrLjYQkOnMCSinqrdnEOo0kKb7LSIQATEkF2JSHptoyuvzd1Jb4TJJSMujQMSh/0pnSW0VHTB4A7rhcMnx19Lq9uP1JVHrBJNLHGGNGXPW78Hi8ZPlq6Y4rAKAjOo/UXuPTpM9/TUr+JOKzjJnondU76OkyWvJ8duM+LRFZxHUbbwruBuOajIJJ2HOMa9qqdtLZ3kISHXjt+f5rcknoNhJVX0sZ3dpGckYu0ZnBFtVeRysJdAavsWUR4/Tfx986G5mYSVxfQly5He3zke6poSs2n9joaGpVKpb2Un8cxhtZXNYEumPySPfWBYYR6OY9uLQV+2Sj27u1IpgoJvVW0xJTRJ0pFWt7cExpfa3R7d2lI8hzl4VMstmzIrgge+m60DGVq954HPNt6ax571mEEGIkcHt9/Px/G/lgR0NIucvj5bevbuWDHY18tDN0uFd32SrWRVzBnPrn8PTrqo6sCK5YElX6TuBxq3+Mfol9AWmqjYoq429Au7OHbF2HK7HIaMRwBMf7xzjKqDNnk5Bn/N3qqDLem50Nxvj49NwiHLEFZHiMZX4c/pZJW0ohiVnG35OIruDwq3A5mDGQt2itA9OLtNZtGHtL749PKXWhUsrk/7qw37Hwj/ocZbw+7U8gg9v11VtzSOwux7H8ceJVN81ffYnNjCG2q4qXfMcwe+rkwLnVMZPJ7tpKfMMqSkx5JCYaWwh2xo8ny1NF4y5jxnVMtnFNZJYxKaZ5zwaSu0tpiDQSobS8Cfi0wtO4B4e/ZTA5z7imIzqfVFdVsGs7tgCAqHSjVbOpYge6uYRebSEyPgVlMvnHR5bT7XSQQRMeu9FaaE4ZQ4xyUVNTgamtjBbiiY5Lwp6QTAvxmFpLqK+rIk51o5L9Tf32QhLowNvViqWjjBbsWKMTSCswutc9DbuoqzBa3Swpxn26Y3JJ8xgtnda2EupIITI6lkz/Ne6GXTRUGEmvLdV4HT1xBWR4a9E+H5GdFdSbM1AmEyn+enDV7aShYof/GuM+XTE5pLhr0FpjbSul3pSKxWIjrSDYCtvaVEs8XZBkvJ5Ga3ZgwpKr0WjpzMifCIl5xKpuWpqMN5aIjlJqzRmkFRr/Zz31xmv0dXeQqNvx2AtpjsjF7iwL/Dw0+VtWd9iPIUK5qa0MzvbzNPabSV4Suk5mzPq/Y1VerKv/xt5WvvAnVvznt/uUCyHE4VDR3MWjH+3ZZ2zi21vqeWplJZc/Htqbsr22k0Q6mKZKWFXeEijXWnNax/MkKgffVq9T1hzczMHevIF2FUeniiWuJdia2FPn7z0bb0zYbKs0jtVX7sSmvJhTxtERnUNSb7B3KNlVRVtUXuBvpMffSEFLGTU6mbyUeHyJY8hQLTS2ttDqn9AalzkOFZNCNxFEuQae5zCcDiaBHOicwfbQ/gbwTaABqPc/vkQpFQVc/bkjFIPq9fiIwA0WW6CsI6aQJG8jxzY/T2XMVJKnLObt2Q9zVe+1vJBzA/Yoa+DcrtQZxGknE7rWUh4/J1Cu0yZjwYt36ysApI2ZARCYYNJVtYEcTyWOeKM53WyLpM6UTkT7HnyNO3FpKxl5xjF3whgS6KClfDOxqjuQCCXlGq15jtqd2DrLqTVlYDYbP1rt0bmkuKoCs7at/q7jGH9XeWPFDqKc1TRbMwMxN1iyiXGU0+wf5xiZYZxr8Sd4zZXbiXdW0GAzusPtdjt1JGNuK6Gt2kiuYjONc30JBdhx0tHWSFy/VtOYuAQaScTcVkJ7jfFLHZ9lXENyEdHKRVNtOfaeatojsgDIyMigRcdCSwmt/vvYs8YF7pNBE80dDuzdFTTbjPskpmTSSRSqZQ8N/qQuKsO4T2d0Hmm9Vcb6ka1ldBCNPTmdyFSjXvtmjyf2VNIamUdqSirNOh7VYrRaNvrHllpSxtAdm0eapy4wE99Z638zHHcaAK0VwTfKiPZSKlUWjSRiaw62Znp9mqweI5HN6d4ashZmY20F8zb8gvk7/zDgpJz6yl1on6xRKYQ4sMqWLtpcvn3Kf/nSZm5/fTsvra8JKV9VZiSHXp+m2RFcU3FLTQdP2W7jlYhf4CvvN7ml28M0bbyv55saqKkI9s4kdpdRayugLno8WT39doJp3k2vNpM790sAuOqM99fOauO9NDpjPJ74ArJ0A12uXny9PaT5GnHFF2KKSaJDxQV6gWyOKurNGUTbLFj7/taVbaO7fqe/ZXIcKIUrJptx1iMjgVytlLpXKVWklBqjlLoPWLO/k7XWJVrrL2mtU7TWqf7Hu7XW3VrrT/Z3nfhiet1ebHjQlshAmc4wWpyyVDO+Wd8C4PunzmDCid/izq/NDbneMv7kwOPOvJMCj2PzZwIwue4lanUy6ZnGeMLczDSqdArxpW8SpXrxpU8LXNMYVUBqdymRHSVUm7MwW4xksC+Bq1n1IgBR/q7j9Bz/xJPmEuzdVbREZAeey20fQ5qvkeaS9QCBruNU/8QTZ91uknprcETlBOOPySOlt5pufxKUmGu0Fsb6u4PbKreT6q7GEZ0XuKbekkOcs5yeBuMXODXX6B6ITPOPjyzfQbqnGmdMfvB12nKIdZbT22QkTWl5Rmx9XdWN5VtJ99bRE2fUmdVsosacTZSjjC7/fTLyJ/rvU4RZaWrLd5LuqabL3+2uTCbqzNlEOcoDYyOT/K/HnVBIHE48jiaiHJU0mI0kOt6flHbW7cbr8ZDlrcUVX4DJpKizZBHtX0qoxZ9gxmeNQyXmE6+ctDQbb0a6aTcubSV79umA0Wrax95dSUtkDvW2POKcwe6YiupKEpSDelM6SXTS3G93ofL1wa7u6vXBbh+ANe8+Q/rfi1n55K8QQog+A+313N7t5sR7lnLzJ93BWct+6yvbAPhsd+hCL9UtDr5pfpuFpi1sr+sMlDtrtjHRZPTiTGgOTgqsbmgk19RIZbqxLJu7PLjETqanks7YQpwJkxjjq8DZbSSkUR1l1JoyiUgppAcb5majNbHXv5RcUv4kzCljiFBuqitKaK7aiVlpSPF/4LflEN9lvGfaXTV0RBp/BxOy/UOsanZASynVOoW8VGM0YcIZv8BVdMbBVeYQOpgE8hqgF3gGeBboBq7a38lKqfFKqfeUUpv9309XSv3icAQr9uVyuzApjbIEu7CTpp3Gbl8WWygi7wRj7UZ7lJUfnjyOrISokOsXzJzO9b1Xcpf7IiYtCq7zPmFqMfU6AYBd0TMwmYzdUSIsZirN+WS7jF+SxDHBVssu+ziyvVVkdO+hNSqYcMX5E8bIUuMXNbXASHBtNpsxoaa9jAxvNV2xwcTOkmIkVux+D4DMIiNRtWeOwYfC3bCTDN2Ixx68xptQSDrNULeRXm0mzZ8MpuZNxKcV3ZUbSKMFb2JwMYCOmHxjfGRrGV06gqTUbH/M/nGLO5eTgAOfv9UUwBGTR5q7GtXX+pdkrL2ZkudfsmjXp8SoHnRCQeCa1shcknoq0S1ltBNDYrJxjd0/47xx2yfE04VOHhuMLTqPZFcV7sbdeLQpkHRaUo1EsbliG0muatr9SXRanvFc7qYS6qv2YFMeTCnG87VH5QbWyezyv7GlF0wiyp8o15UbiWJkZ5nR7Z2Rh0MbLaBAYPhBd1wBzth80tzBT/rN5UaLb41/cfj60uCSQX2fwgG8taG7J5jWPg5AVtmL7G3da3+lbe1zIa2ZQojRb2VpC7N/+w5vbAqdZbymvAW3V+N0w476YDLY0eOmvdsYq72tX5IIkN/wPr+1PsYT1jsprWsOlNtrjSWp2yKymeZaH+j6bq/wv0dNPhcA1Wy8f3W0GBtgeBKLUOmTiVRuavwrfiT2VNAUkQsmk3+8v9HTY24rwakjSUzNIbZvHHzVjsCYySh/C6MzroBMTxXeHgeJvlbc/oaH1MAQq91EdpZTa84iymY24pv6FVqTZn/uuj3cDiaBPFNrfaPWutj/dRMw2OrDfwV+DrgBtNYbgYsOPVQxkL6lWZQ52IV9wqQclp/+KtYr3kVZI/d3KWAklpdceSMzLr6VCZnByfX26Aiei/s2DTqB+smXhVzTaR8feFwwsd8PcdoUbMpLum7EkTIzUJxdNAmvVozr3oBTR5CRG0ySmiLyGOtYRTSuwPhDgLhso9VwXPsnxhaGcQnG67RG0WxKJqvpU6zKizk1GIs1zXje7KZPqTFnYrYYXfWZyQnUkkxytdEaZksPXtObMAY7naS2rafOnIkyGb8SfUvsWEuNBDYyY0LgGm9iEcm0Y2/bGmj9A0jLKTImrVQtNa5JD77OnvgCUnxNJHTupMGShVJGQt7X4hnhv09U5sTANW7/Uj6RrTupN6VhtRkfEuL8b0btlVtJ9zXQG2ck0TFxCTRjx9xeQbN/wkxM5oTA/VN1M7rXCS2ltOo40lLSSPQnsG3+7vjEnkraInMxmU3UmIPrdDbVVRCtXKjkInyJhSTSQUeb8WnfWWsknwnTjG7vztrgWElLWwmtKoHt5gnEdwTLAVK6jTfaLG81DkdwWYxuZyezVv2EczueZPva0CWGAHq6nfuUCSGOLB9sb+D3b23fp7XxmVWVtHa5+c/KipDy9ZXBnV621QbfL6paupmu9vD36AdxNJQHWie11kxzGomiVXlRVcHWxPjOnThVDJV55zBOVdHQZCSX7lpjyE7C2IXUqTRi2o0P0H0bRFjTxhOXZzSAtJdvAp+XDG8NzsCEzQJjeBEQ01lGtTnbGAfvn2Tqqt9FT1/LpL/BQScVkalaqNxhdOyakgsBiIi200Qi1vYSEl3VdPTrbRspDiaB/PlBlvWJ1lqv3Kts36XbxWHhdhmLifZPFE0mxSWLihiflXRQzzE7L5HTp2buU37aN3/KX+e9wZmnnh5Sbpp3GY06nudiLyEqMtjymTwj2KSeMHlJ4HFaQnxg7+vSiEmYzObAMUdaMXEYK/THjpkfvMY/icSOk5rIYCIG0BBVxBRttIDGF8wKlNv9Tf45upbW6MJAucVsTMrJ8Rjdrkljgklv37I8Rb07aIkdFyiPsyfRRAIzu4w3oIyiYFd9X2I40bOd1rhgMmqxWKg1pzPJbbwJZY4Lts6a/BN6JvZuCbmP1Z5FN5HM7TZGd6TmTwkc61tQfabzM+qjgi2gqXnj8WqFe89HRitjUkHgWJMlg2hnFV3+bvy0win++xsxt1XvJNJRSb0lE5NJBVote5tKjcXcvXX0xBt11xKZS2KP0c3TNw4zOmM8EWnGNfWl/jGNzXvwakXe7FPxaBPelrJAPHb/8kMd0bkk9QZbLT29LjJ89ZSZcjErTf2u4KScsk3BkS7tm96kv9KtqzDdmceyv/8YIcTIt6Gyjbau3pAyrTXfeWwVD36wh7UVbSHH1lUaa+xuqekISS5Lm5xkJ0RhUaEtkFWtXfzC+iQn+T7lZtNjNHQaXcutXW4m6z3UJBbjwUxy3aeBazJ6SqiPKsKUMQ2T0rSWGbutWZp30K1txGYW0RBZGPiQ2+Vfyi0+Z3JgPoCnbiuOhjIicONLNN6fPQlFZOt62jodJLsqaPEvJRefMYYerJiad6Gad9GiY8nMMMbIR/obNJrWGvMNYrKCjQgNthyyHFuI0w7c9mCv3kgx2ELiZyil/gRkK6Ue6Pf1GIMnhE1KqSL8M66VUhcA4V/xcpTy+Benpl8X9uEyLj2Om8+aTExE6JypE+cX89m5yzjm8j+Enl+Yz5Nxl/F8xLlMLV4ccmx3upGENhR8KaQ8buIJgcdF048JPE5MTsehje72rsz5Idd0JM8IPC6YGEwgMwqDyVdXeuhYz167kRR1axu5Y4LnFUwIPpen33hOgIoYY8JQC/Fk+BdEB0jICf6C+9KnhlzTGmG8YbQTQ1pOMOnLGhM8z5TR7z4mE3siJ2NTXlqIJzM/mJD2tcKalMaVHJw5n5kUTzWpFDR+YJyXE3w9nVHZRqLWtAunjiQlw2idjPZPKGqt3E6iq4bOKKOr3hqThINoVHsF9VV7iFDuQLd3T1w+ab568LoDk2uS8yaT6P803e7vno7oKKXRnIY5IoYGUyq2Dv9SRj5Nhn/5IXd8Hqm+Rrxu4w9JXcVOrMpLZZrxQcNRHdxBp3OP8fmzU0dhbQqdeFO/9FFsysO0iif36d6uLtnC6lcekW5vIUaI7XUdnPPgp3z3idBZ0CVNwV6ElaXBWdAer4/y5i5iIyy0OHtp7Dfxpba1i5tNj3NvxKPUNAcTyNaa3cwzGe9FJ5rWUtNg9IzU1NdTpGpwZi2izDaebIfRPd3T62GMrwJH/Dhi8433f7d/u974jl1UWvJQJjOO+CJyvFVorxtv407c2kx67nhi4xOpJQVb6w6ay/s2mTDeX63p47EoH3V7NpLma8AZ52/IMJmpMecQ27mH6M5SKk3ZRFqNhpQk/0zsvh6ypLzg+7kjtoBcr/EhPrJfz9lIMVgLZA2wGujBmDTT9/UycNog1/0AeASYqJSqBq4Dvn84ghX78vr3izZZD38CuT8mk+KcWTn7jKdUSvH1H93D+Tc8htkc+qM171u38fqiZ1h0wQ9DyqfOO5k306/gw1n3ERkZbEVVJhMbpt3INlVE4cmXh1yTffy36dIRfJj6dSy24DWx9iQ2RBTj0SbyFpwXco3Nvyf2xvgTMFmCCXHOmMnUkwxA/oLzQ65xFywGoDxudqBrGyB77Axc2ugez50fek131gIASmKLQ64pmhxsjUybE5pEOzIWAlAWOyvkmsyiYHIbN3Fx4LHFbKLBmk2Mv+U2e9K8wLHe+HzSfI3EdOymxpITeL5kf3dJV/VW0nwNePo+zSpFi9VotWzxz17v6/YmaQwWfHTW7sbXuItebSE9d2xwKSP/mpqJPZWBT9ottizie4wunMbmZtJUG77EIizJhZiVpqHKuKZvpnzcZKPVsu+5AHxNu2ghns3WqaR2B2dBAsS1GX8oYlUPDZWhXeKeJy+keM3P2PLpKwghho/Wmo6efTcS+HCHMTlvVVkrPe7gagvba4MJ4I66YHd0Q6cLr0+zsMh4T65sCe6eldm6ijO7XuTLLCW7/oNAeWSV0bLYeMwt2JSXrpJlADhKV2NSGlteMc1xEyjwlIDWNFSXEK+68KROIi13PA4dianReD9Kd5XRFGUkfd7k8UQoN601u4lo202FysAeFw1Ara2QJGcJXbVGy6Q910j64nOM98aerW9gQuNNCvaeNUcVkuYqJ7mngqaIYGtisn8SZqFnD5U6lTHZqYFjEZnBhoPEiccNUPPhNdhONBu01o8DRVrrx/t9/U9r3TrIdSVa65OBVGCi1vpYrXXZ4Q9dAHjcfQlk1AHOHB4mkwpMuOnPHh3BmaeeTqTNGlJusZg5/fu/54Rz/m+fa4654Dom3bKWzOyCkPK8sVNx/6SMY658cJ9rxl37AjXf+ozccdNDymedfBGbTv8vk7/3z5ByZTLT+6032HrKk4GJOn1mn3stq2f+jrxLQu9ji4ik8pznWXv8P8gaMzn0mgt+yopxPyb3G6HbANqiYthy0hMsn/tH8seF3mf6BT9jed4VZH/19yHl9sQUVhRexaf2s5k0P3QYgSfBGC9apTKxJyQH75M2DovyMdW1nvaYYDd+Vno6jTqeyMqPsCgf1pRg66gzOoek3trAm2GKf/hA3yfe5srtRHSUUWvOxGyxEBkdSz3JWNpK8Xp9ZHlrAou2O2LyyfAY653V+5dgiswYR0yGcb/mKmO8ZN8uQ7kTZlGnUrG0lQbiiXVW0mjNotOWQbq3Hq83+IcnpbeKamXs9167e32gvLm+inyfkbg6Nr8RUlfuXhfLH/gW6996HCHE4ffYZ2XM/PXbrC5rCSnf2m+s4o5+E1xq2ozEcFq2ndLmrkB5bXs3E1UFv275GZeZXw+c5/VpZvcsw6OsdKh4ju98PXCNvWUTTqKJW3ApPq2C27RWG8NiksYvwJUyhTi66KzfjaPC2FLQmjmVqAgre1Qece070N2tpOpmnP4x/hGZRlLYVraRRGcpddZg0tcZX0SmpxLVuI02HUNmltHT07fmrr3CWHHClh4cO9+dMI5M3UCCr5WuxGC5ioijxmQMIdtum0qEJTjEK33BhfRoKx/7pjF9XPD9fKTY73qOSqlntdYXAuuUUvvMqddaT9/r/Ov38zx95997aKGKgXj6WiCHoAt7JOv7JLi36Jh48or23WlTKcW0BScPcAXkjpkAYybsU261Wik+95oBrxk7e/GA5ZFRMcz/xsDL0kw57pyBY45NYMH//X7AY/O/fceA5UmLvoXzpdeomvp9+g+tTp90DKwzHvtyg13/UTYzO01ZzOhaDwSXaQLw2vPJb1tBTe02HDqK1AzjjbJvGSRn7U4Se4zxPH1voU22HOKcFTTWVZGhutH+T9qexDHYW5w4WhsC3d72nEmYoo1NqJx1xqB01bKHTh1FUmoW623Z2LuCA+aTe6upjp9Jry2DiC431dXlZOeNoaerk3SaWZ56IdkNzwaWawJoKNtCXxod0xbcZgxg8/v/YUHLS3g/exnfyZeEjMEVQhy6F9fX4NPw37XVFBcEx97XtHWTHGOj2dlLWbOTGbkJAFS3dRMbYWF6jp1XNtT0O7+Hmyz/JqttEzdb1vJU3UUwI4uGzh4Wqc00JM9lpyuZhR1v4HR0EhMbR3bXVsojJzA5LokSlY292UgQY5s3Ukk6uYnpmHLmwA5o27kcT61/u8A8I4WpjRhDUfendJZvIB7Qqcb7XkK+8UG/t2o9+Z4a1iYcH4jTkzyBiCY32fVL2UMWM2L8O8HFJdFAMoU923BpC4n5wcYCVXAMVD0SfNzP+tRzyKp/mLrc0PnJGblj+ez8j7HH2bGYD2bKyvAaLKK+vsazgS8N8LW3OP9XMUaXdbb/60pg8gDni8OgrwvbbDu6Esij3fjZi4m8pY4Few0JyBk7nXKT0Z2cU7xXV7l/O0iPNlEwKdilbksdQ7RykdG6mlprDib/G1VWVjYdOgrduMO/pmTwE7AzNo80T3VgtnffkhRW/5qfTWVbAvt1p+ZPIjWzgF5tQbeWGec7yqmzGDMUHTH5pLqrQWt6e7pJ10247QUQa7Q09u0hXltqdDNZ8ubRRizmtj3B11ZtJI1bbNPIcoV2e7t2GVuQmZWmak/oUkKrXnmUHb+dQ13lHoQQg6tu6+bTvdZaBKhvN/4Oba1pDymvaeth/pgklDImwQTLu/lp5Iv8bMdFjHNtCSzD09ZQybGmzbinXYxJaTLKXgagoaaCCaYqurOPoSF+GhHKQ8v2T9C9XRR4ymiyG4laWeREsru2gtZkOLZRajNaE+35M+jRVryVq4lq2kSFL5W09AwAWuPGEevrxLXlNQBsuca4+pyMVMp9aWSVvYgZHz39Wg37dmSL87RQEjk50FAGsDN6JgCb9BjG9+uOHl98Mht9hazzjSV/8oKQepp98a3cNfVVTjvvm/vU7aIZk5g+JmuA/43wGyyB9Cql7gcexEgCW7XW5X1fe5+stf611vrXQAowW2v9Y631j4E5wMibfz5K+NzGIGPzCOnCFsPHPMBQAWUyEfu91yn/xsdkFU4MOWbKKQZgt3U8kVExgfK4QiOZHK+qaOs3qzwuykaVyiS1YRk25cGcFnwD1YmFJNFBZ4mxi0OifyB43xigzpodWNvLaFDJWCNjMVssxlJE/gk2Ka4q2qON9kxvQgFxdNHb0UhD5U5MSmNOHkOE3XiD7/S3Wrb5t8i050yk3pxJlKMyEI+naTdubaYj6ziSacfZHuxKi3aU49VGXTXuDt0DIWft75ng3U3p2w/tt56FEIaLH13ON/62gvJ+W/y5PF7qO40Eckd9Z2AZHa9PU9/RQ0FyDGlxEVS1BsczWpq3803XU9h7qrjb+ijVLUY3dnzlB5iUxnrM1Ww2T2ZCk9EV7N1tjHk0jV1MT9IUPNqEZ8+HOMrXYlVeXOkzAWiyT8Pua4fqtaR462mMM96X8lLsbNKFRDWsI7ljKzvMYwOTWNypRld1wrb/UKcTScv0Tzy0WdhgnkK8y2gh7c1ZGIg/qTA4Pr0pNXSS5/rsb1DqS+fFmAuC6zYC6QkxvFD8JE9O/itTcxJCrslIiOKGC44jLW7wZfdGmsESyCcAJ/AnIBZ4YJBz+8vDWHi8Ty9Q8EWCEwfmc/e1QB5ZP3hi6CSn55G/1xhQgFnn/pBlud8l5oI/h5Snji0OPNb5x4Yca4nIJd1rvIH2rX8GEOEfHxlX8T692kJqrtECmZY73ljKp3EXCV1lNNqCnx1bI7KI76nG1dNFuq8Bd4LRohlotazYRmu1MTEmJqMIS3y6sb96szE+0lVnHMsonIIjMotEV3Bxh4j2MupM6Vj9Y44aKoKzt5N7q9gYewxereitDZa3traQqRuM19GwKuR1u1zdbL3tGJb/8ZJ96lGIo5HD5aHCn+gt3RHcRq+2rQetYUaOnR63jyan0ajR5HAR4evisq3f4T++G3C0BH9f53W8jcZEzfxfUGSqxblzKQDZzZ/RqJIhfQob7EvIcZdC4w7iqpbSpONJHjuXhNgYNuoxxNR8Stf29/FphSnfSO5c6cYSbd5lxrh1Z4rxPmiPtrLVNJ709g0k9dZSFRX8MGzLnUu3tmH1OFjnG0tBSnB41OZ4Y+JKlU4hJy84bjw/I5nre6/kP54TUWNDh0bNW7SEkz33M2nxvstf3/Llqdxz0eyQFssj2WAJZIbW+mat9Vta62uAff8iDexfwEql1K1KqVuAFYCMXh8i2tPXAild2GJwkZGRLLzsD+RO3Gs7y8gYluVdwVbbdKYsCX3TU/0m24yZFhy3k5BjvAFPca2n1pyJ1WpMjkqKj6GaNKxtJWR6quiMKQhc0x2TQ6qnlrqy7ZiVDiSOcf6tJttrdtBdb3R7p+RNRJltNJuSsbYbrZbm1hKaSCDOnkRvfC5pvgZ8/gk2Cd0VNEfmBXY96vDvOd7T3UWGrwFX4gQaVQrW9mDnSelOozu7g1iyXXtClv/Z9tELTHZvZkHrKzTWVx98JQsxSm2pDnZPb+83c7qvZXHBmOSQ72vauvmW+R2SO7ZS5NnNhY1/AqCn183Jvk+pTFpI5MLv0qmjsO/8L3g9TOxazbboYlCK6sxT8KFg83/Jal7GMmYQHxVBnA1WMZXk9i1E7X6NTbqQ9AxjWbKInBk0ajvmLf+lV5sxZweXeSuJDQ7bqU0KdiHnpyfyL69xrzcjTifaFpwa0p5zMtf1XsW3em9kUmZcoDzaZmFz6pnc5LmcU6aGdrDOK0xi629O4xvzR966jYfbYAmkUkolKqWSlFJJgHmv7wektb4N+A7QCrQB39FaDzwTQByyYAukdGGLL27h//2eyTd9TIw99Fc7edbZAKyPXoQtItjKnVEQXBezPi64bplSinprNukdG4jHCSnBLnGdkE8CDhq2G4uzx+cY3UtpeROMhdEbdkNLKV06gtR0Yxxna0QWcf5lgWKc5TRajT8UKjEfm/LQXFeOz+sl01tDT1wBqf7tHt0NRmtlfbk/WU0bR7Mtg5juYDLY6d9BpzR1MYl00twYbCHxlC8PPK7eGFwyBGDbmg/ZctsxlG5ZcaBqFWLUqPWPc0yJjWBXvSNQXtXahcLHuepDjjdtCCSQDc3NfNfyKp25J/Jx5qWc6PkEXbeZlm0fkaOaaCz4Eol2O+8wn/z6d6D0Q2K1k6rkRQAkpOexyjcBPryLGE8bW6KND75KKXbEzceMl/j27Xzim0pestFqmJMcw4te44Puu745ZKalBOKsTzuWNy0n8rj3NCw5wcSyKDWGOzwXs6DnzzRnhC6Vs2hcCi/6jqU1Kp/k2NBGmif+bz5PX7GAgpQY9tZ/JvVoNlgCaSd0/cd4YK3/8eq9T1ZKxfY91lqv1Vr/0f+1bqBzxOGhPcZoAat0YYshMLH4JEq/9gHjfvBcSHlkdByN/s+RuvD4kGM98YWk+IytweLygrMQbanG0kOmPcae6H3LJqUlxFFDKubWUqydFdSbMwITeVyxuaR66vD6NGnuKjpjjE/1UWlGy2hT1U4aa8uIUr2olCIS7XbqdBKWtjLAWDgdIDZrAs7oXJLddYF4fI1GkmkZa+zh3VgRnNUd27qVEoxktbsqdOKN6707mOLeTON7BzuqR4gjX5N/Ue95OdEh4xmrWrq4x/oIk1bcwBO2uzDtehuAxM2Pk6QcsPgGysZdSoeOwv3+HZg3/BuHjoQJZ6KUYnnsyUT4uuDJ83HqCDpyjM0F8pKiedUbbCmsSgqOQWxLKWaZZT5Os53XrKcSH+lflzcxmj94LuThxJ9wg/sKchKD3dG5ybFc6bicW9zfDkn60uIjSY6NpIFE5uQnhrzm06Zk8I35efzp4n33nc6wRwZaXY9Wg60DWaC1HqO1Lhzga8wAl7yklLpHKXW8Uirwv6OUGqOUukwp9RZw+gDXDSulVJlSapNSar1Sap9E+Ijj78K2SAIphkjhpNnExOz72a/h9EdZk3kxM88I3Ss9OivYOpk7PbjTkN3fVT2t4+NAVzQYa4fWW7KIcZZj76mmI7LfjMPEfNJppaS8nBTa8CYa4yYTs43ub2fdHhrL+7ZZnIBSigb/cwH01ButjBmFU/Da80ijhZ4uo/Ukor2UJpWEvWAmAO21wcXME7orqYsaR71KwdIanKHt82nyuox9cTPaAp+NA9a+/ndWvPaPgStSiCNYY0cPN9qe4c9lZ3GK8xVcHmP4SGzVh5xv/hgWXMV2Cjlp281Qt4mp5Y/zoZ5J7Jj5JKdm8E/vGdh2vkp6yX95yXsMGalG62BzynzWW4wRck94TyUl2UjKcpOieda7mD3jL+en6nrsKcHtdnOTovmu+3quynwaa3JwdYhMeyQeUwR31s7GoaLJ7ZdAjk8PdkFPz7GHvLbLjxtDUoyNL80I3dI30mrmtvOmcey4FMS+BtvKcPZgX3ufr7U+CXgP+B6wRSnVoZRqBp4EMoBva62fH6oX8jkt0VrP1FoXH/jUEc5jdCtYIiSBFMNryvxTmPO9h4nY62cv/7iLaSSR1QmnEx+fECyfNAenjsSmPFTGhG4B2RGVS4arjBxvFd0Jwb3Co9KKMClN6cpXAYjMMJLQtBwjgXQ3l+GsMZLEFH/3dXt0Him9Rre3ai2hjVjsyelYkwsAqK/0rwPXXUFTRC7pecb93E3+xcy9blJ8Dbji8miw5pDQFRw3WVlbSxKddBFJnq86kIwCtLc0Mnvl9cxf9SMaa8o+X2UKMcKl1i7lStNLmPBxq+UxWja+BVpzSt1faTCnw8m/5v7Em8Dng4ePxeZx8p/ob6GUIjshir97zqDTPoHmqELu815Aht1438hJiuYKz0/YePJT3O35Gtn+Hc5yk6JxYePFlCt4rrs4tDUxKRqHy8v6age5ScFyi9lEVoLxvIXJMSGzoJdMTAMgLsLC+LRgMglw5QlFrLr5ZMbuVS4Gt9+FxIF7/P9GYqztuAFQGJNpVgDH7n2B1vp14PW9y8XQ0V5pgRQjS2p6Nr03bmOOzRZSbrHa2BI1jRk9q+jNCx1r5EsaQ6TDBQosWcH5ekk5/l0hSo19YhP9+8RGRsXQSBLm9kp8rk56tDWQVPbGF5DY+Tq6p50YRzn1lmwSgNgM43hb9U7yxs8gw1NDeeISrFHxtGDH1GYkih11JcTjQyUX0eVuJbv1w0A8jeVbyQdKEo9haut71JZuoXCKsYxH2eo36Fvco3zt26RmXRHyGttbmohPSArZrlKII4LWLGl4jHpTOhVffRP7U2dT8PbVUHoSRZ5dPJ31cy6y2DCnjuWa7t/xtzEfcW/VJBzxxu9rdmIUHcTw3Nxn2F7XgXlnI1b/MJWcxGgaeix86pmAZntgPGN8pJXEaCuf7Wn2nxcc59+XTLZ3u8lLCt1UYnZeIpUt3ft0R6fERvD0FQuIjbAMuFvaQMuiicHtN4HUWi8BUEo9DVyhtd7k/34q8JPhCW9IaOBt/+46j2itH937BKXUFcAVAOnp6SxdunTIg3I4HF/oPs7GegCWrViFMtsOcPbI90XrYTQZzXXgmHA5NRUTsCXPDnmNTh38I9DkiWPp0qU4HA629bg4BpjVvQwvij3VzZTVG9elmFKJ6Cyj1xFFlcqg6uOPAah3G8+19LVnmeSqYKdtCrVLl9Ld0c5EoGLLciq7ojhbdbDKF8/SpUvJMKUS2VnO0qVL6alYzelAQ5eJWF8M8+ngnbffxGqLpH7LRxQD1dFTmdr6Hhs/fZvyRmM8WPumzwIJZNvWpSyNDU4gaitdxZfKbuO1pG8TOyN0n/ZB62sU/yx8HlIPhnDVQ2LLOmb07uAvkd8ltbSMh93X8YL7DmybnuVN71yWq1lkLF2K7uzlg/YM3k+9lP9s6WaqtZ2lS5eitcZmghWbdlLl8BGjCLyO9joPAK+t2oVZwY51y9nlX+Ym0eplTbmxc3Jz2TaWtu7E4XDQ4Qgux2Vur2Lp0uDY5llRXjbZTUyLaBqwrnqApbuGpp6G00j4nRisBbLPxL7kEUBrvVkpNXPoQhpyx2ita5RSacA7SqntWuuP+p/gTyofBSguLtaLFy8e8qCWLl3KF7nPsl0vQwcsOfEUGAVrS33RehhNRn8d7Ls+WvfcYir/8Di1UWM5/Vzj+NKlSznmhLNoXPEzUmmizJzPSSefFrhm9bp8stvX4lVWGmLHB+psZaQJ3vwDubFeMmihJGMyixYvRvt89Ky5klRLF3EZRldVzpSFTF28mI3rC0hr28TsE05gw/+MNSHnLj6Tls1WWPM04/LSKZg4i6U7XsanFYu+8n144I+kR/aywH/fz1Y/SKNKpkPFkUozM/r9Hy5f+yBmpZnY8TFjF//xoGtq9P8sHBypB0NY6kFr+Oed1JFMzdiL+O5ps7nh426enP0UX8lo4prnvNwxewqL5+RQHVXO66WbGTdjPh1vf8DsCQUsXmwMO8lZuxQVG0e3s5NJefEsXmyMhEupbufP6z9hR5uPvOQYTlwSfH3/q11HyYYaTAouOnMxkVYzS5cu5awTTuBvOz6kpq2by750PAnRoY0nlw68a+yoMhJ+Jw6mL2WbUupvSqnFSqkTlFJ/BbYNdWBDRWtd4/+3AXgBmBfeiA6Rz4VLW0dF8iiOXlExsWT+Ygtzf/py6AGlKIs31m+rT1kYcsidNJ5MmsjRtbgSg+Mmk3ONP1ieHcZsUKt/0XNlMlFvziDCUYnTv/Vhgn/nHJ2QT6ZupKWzC09zKT3aSnZOIbFpxnzBtlpjIk1ERxmNphTikjKN7RQ7gnt4J3aX0xSRS2tkHsk9wV1yAPI71wNQ4CmlyxG63VtPTzetTfWfo7aEGEZln0DFMh5yn01SfBxWs4mM+Eh2OyPZFb8AN5ZA93Jf1/KK0hZ82hjf2Cc7IYrqtm5q2rsD4xSNa4xr3V4dMp4R4JixxoSavKTowM4xYCzl8+z3FvLWdfsmj2L4HEwC+R1gC8be2NcBW/1lRxylVIxSKq7vMXAqsDm8UR0a5e3FpazhDkOIQ2YxmwbcoSHn/N+xKuMiJnzlVyHl8eOD4yhjxwWHZGenpVCnEyloMbq0k/ODE3ba/Lvh+Jp24dWKtHwjgbSlFmJRPmoq9mDtKKfWlIHNaiYlx1guyNlQBkBidwUtkcYalQ3mDCKdxrqS2ucj01OFM7aQnvgC0r11aK/RNefraiVTN7DVNNa4x57Qt5yd952F+c+zaG+UBcvFCPTR3Xhj0njau4SUOGMtxOzEKKpbu6lqNXam6UsCc/3/frbH2C+7//jEnMRoNlW30+P2hZTbo6yB8Ycz95odfdb0LM6ansldX9l3H5OU2Ajyk/ddg1EMnwMmkFrrHq31fVrr8/xf92mte4YjuCGQDnyilNoArARe01q/GeaYDonJ68KNJJBi9MrMH8/cKx8hIS07pHzsrONpJJF2Yiiac1KgPNJqps6cRSS99GoLOWODf3x6YnJI89Rha9tDnSkNW6TxB8+eaUywaa3eSXx3NW0RxlJCSRn5eLQJX1slPq+PTG8NXXEFAHREZJLg306xqbGWBOVEJ4/FnJiHVXlprjNaIRsrjNbO+nRjSaP2qmAHTmtNCdNda4jHyfZPXjhsdSbEYVGxAko/onHaFbiwkepfTLuvNbGytQuzSZERb7QoZvlnUH+225j40r9FcUpWfODxhIzgY6UUX5lt/G6fOCk95PaxERYe/Pps5h/l6y2OVIMt47NJKbVxf1/DGeThorUu0VrP8H9N8e+ac0QzeXslgRRHpYjIaLhiKT2Xf0x0THzIMVe8sTZciW0ctsjgHzGdWECc6ia7cyPNkcGtxlJyjSWAuhtKSPPU4orLA0CZrcZ2ip1VNDbWYldOdJLRKtkTm0Oarx58PprKthgxZYwnOq0AgIZqo9u7pdJYoDx+qrEMbm9DcAR/yebgjjemyuBjIUaEZX+GyAR25V0IQGq/Fsi69h72NDjJSYzC4p9RHWk1kx4fQV1HD1ZzMLEEY3Z0n/7JJMBvz53Ku9efwMzchCF+QeJwGmwSzdnDFoX4wky+XtzShS2OUqlZBQOW24+9jJZXP6J7zvdCymPzpsMuSKeZkqQvBcptSbl4MGGtWU2M6sHUb3HidlsGMd21NJVvJR2ISDfGW+qEfCIa3Dhba3D4t0VMzJ0U2B3KUV8CQE+Dse5kzoQ5NL+TgKm9KvDcjhqjNbLcnE9MZ2lIrB53L851z1CSEsmYqQsQYlh11sP212DhVdR3G+MPU/wtkGPTYvH4NO9vb2BhUWjrYG5iNPUdLopSY0OWxpmUGccVx48hNzGKmIjQ1CPCYmZsmmxUd6QZbBmf8r3LlFIpQLPWWg9pVOKgmXy9eCSBFCLExOITobiSpL3Ki2aeYGx3AMRNWBI8YLbQak5lZtenoCAqO7gFozMml+zmZZTVGF3RibnGHt42/8LkzVW78DSX4tWK9Nxxgf3pXc3+CTYtJTTqBNKSk9htSSOyuybw3NbWPbQST0P8VApbPw2Jdd2b/+Cs9v9Q8sIymLr+0CpEiM9r28ugvTDzEpq2GesN97VATs0yxir2en0UpYYmfrPyElhd3srcgtDfPqUUN505CTF6DNaFvUAptVQp9T+l1Cyl1GaMCSf1Sqmwb0koDMrnwasOZjUmIURUXCKfjf0xaxNOY9Kis0KONcZOMPbuBXInBRdn8CQWkaZa8VWtpVebScszZnnH+cdNdtTuwdpeToNKwRYRSWRsAu3EYmo3xkBGOSposGShlMIZlUFCb3DGdXxXOQ22XNwJRaTQhrOjJXDMvNvYM7zAU4ajo3UIakOIQZR9AvE5kDqBxk4XUVZzoOVwTL+kcXZ+Qshl3zuhiG8vzOeqJUXDGa0Ig8Em0fwZuB14CngfuFxrnQEcD9wxDLEdNXbt3Ip96Q2svPerrH3rSXxe70Ffa/K5JYEU4nNYdMmvmH3ds5gtoS33nRlGN3GFyiIhJSNQbk0zlgHKb/6IWnMmNv8OO5n+LRB7GkqI7a6mxRbcw7vVkkZklzHBJqm3hs7oHAA8sdmk+Rrx+PcRznBX0hlbgC3FGI/ZUBXcdzuu03hsUpqq7SsP06sX4iBoDeWfQf4iUIomhyvQ+gjGri3XnzKeKVnxLJ6QFnJpSmwEvz5nKpn2qL2fVYwygyWQFq3121rr54A6rfVyAK319uEJ7ehRs301s9jOvI63mb3sB2y/41jKdm05qGvN2o1XurCFOGSTzriStfEn0bb49pDylCJjweNMmmiMmRAoT7DbacIObeWkuGtwxuQEjjkiM0nsrcHt6iLF14LHbiSIpoRcolQv9fU1tLc1k0I73sQi4tILAGirNcZBuj0esr3VrLEaa2C2Vcjbrhg+nsZd4GwwEkigsdNFSmzoeovXnjSO1649jtgIacA4Wg2WQPr6Pe7e65iMgTyMLMpYL672a2+yZubvyPWUkfzkSax/9+kDXmt0YUsCKcShik9IZvb1/2P6CaFbDWYXTaEDY705d05wMXOlFM2WDBLbt5BCG77k4GLmPfH5ZPnqqC7djklpLCnGguRRqQUANFeX0FBmbMdmTRtParbRHd7dZAw9r6ssIVq5qLfPpFeboTl077XNy95k+V+uoLdn77dmIQ6N1poHH3vceJx/DAANnaEtkELA4AnkDKVUh1KqE5juf9z3/bRBrhOfU9+Cw5aIaOacew3d/7eUeksW0z6+kuX/+R3a59vvtWbtxmeST4BCDBWlFDvm3MKGyGImn3ZZyLGOmAKKvEarYUze7OA1yUVEKjdV640ZOwlZRnKZkGkkkp31e+isMZLChOzxJKbl4NZmfK3GuMmWCqMHwmvPo9aUQURn6JzGlLd+wIKGZ9j83pOH++WKo9zm6g5yO9fTqOPZ0J0KQENHD+n9luQRAgZJILXWZq11vNY6Tmtt8T/u+16avA4j7XUDBMZkpeVNIOtHH7AxZhELdv6e1X+6hJ4ux4DXmrUHn7RACjGk5n7pe8y48T3sCaEzS315xwQe500JLrUT5V/qJ7rcSCAzCoyZ28k5xnhKT3MZvY3GMj9peeNRZgtNpmQsDmM3mm7/jG9rQi7ttkzieoIztxuq9pCBsdOHZ/cHh+9FCgFsq+1gnmk7K30TWV3eSo/bS0ePhzRpgRR7OZitDMUQ60sgLf0G9UfH2pnx41dYnn0pc1tfo/4P83nqiYe5580tvLu1Hq017W0tZHqqpQVSiDApOuZ8mrGzJnYx9uTgLhrJecZyJbO7l9FIYmAXHVtsEp1EY26vwNReRjN2YuONBZY7bOnE9BgTb2jZQ5eOICImka6YbFI8wZnbjbvXAeDSVhI6Q7u2hThUTdW7yVFNbLVOY3N1O42dxhI+aXHSAilCSeYxEvS1QFpDBymbzGYWfPePbPzwBFI+/DkXl9yAc08EdZ8l0WBykU4LKPBEpoQjaiGOeikZufTcsI1ZttA/rmk5Y2nGTjLt1EWNI7XvgFI0WTKJ7qrChpsmSyZ9yzB3R2eT1rIarTWRjkrqzJkokwlffC4JLZ30ONqIjE2gu9bo3l4fv5hpHR+hfT6USdoCxOERWWPsiOTMnM/mmg4aOo11TVPjpQVShJJ3nREg2IVtG/D49BPOJ+vmzfC1fxM1/1JUxlTKExawouBKtp74T2Z996HhDFcI0U9kVAwmszmkTJlMlNiNLu2u/BNDjnVE5ZDaW0WGq4yOmIJAuc+eSzrNdDi6SXRV0x5lzOo2JxkzuJv9WyOamnbRrOPRmbOIVi5ammqH6qWJo1B2+1qcplji86ZT0uigoqULQLqwxT6kBXIk8E+isdoG+QU1W2HS2Zgmnc0YYMzwRCaE+IImfOvPrP3kWWadcUVIuSNpMjM6PwRgd/rUQLk1pRBzmaauYhcF3jpqY48FIDrNWJC5o3YP2RPmEOsoocaaR2TaGNhp7IST7O8iF+JQTXJtojxuOpOyEvBpeHuLMXwiLyn6AFeKo420QI4EvtBJNEKII198chqzz7k6sPB4oHzM3MDjuMLg49gM42Nh/dYPiVBuVLLxfUK2kUB2NxqzvZN7q+mMzsPeN6O7dvfQvQhxVNEdteRRS33iHCZnxQPwxuY60uIiiIuUv08ilCSQI4E/gcQ8cBe2EGL0GDf3NOpJpkalMX7uyYHyvhnatlJj5nZ0hjGTOy0jhx5tRbdV0OtsJ1m34U0oIDXXON/dHFzip73bzan3fcgf3toxXC9HjCLduz8BoCN9HrmJ0YFFwsekxoQzLDFCSQI5Aij/GEhM8glPiNEuMjoW61WfEH3tCqyW4Cii+LRCOoliQddSADLHFwMQYbVQq9KwdVZSV24s7xORVkRsfCKtxGHuqAg8x2sbqklsWMUjH2ynx33wW6IKAeAu+RinjkBlzsBkUiwsMqZ4zS1IOsCV4mgkCeRI4HPj1QpkJqUQR4WktCwSEvf6o2wyUW4zWhUbSSQlIzdwqMWaYey3XWm0LNqzjS0VG83pRDqrA+fFbHqCZyJ+y08sz7CrfuC1Y4UA+OWLmzn/L5/i8QY3qrBWLWeNbzxJcUaL4/WnjOecmVl8c2F+uMIUI5hkLCOBz41H5jMJcdTrzjEWJi9NOjak3BmdRZK7nu4GY7xjZqGxzmR7RCYJruAs7DEN7wJwimkN22o7hiNkMYJprdlc3Y7PF7r7sNPl4V/Ly1lb0caykmajsKuFqLadrPJNICXOGE41KTOeP140S9aAFAOSBHIEUF4PHswHPlEIMapNu+BmPh1/I4UX3h5S7onPw04nkQ0baSOO+ARj7deemGxSfQ2gNdrnY0yv0UI5xlRHU23psMcvRpYnl5dz9p8+4d8rQrfCXFvRGni8sardeFD2CQrNMt9kkmNkyR5xYJJAjgDK58atpAVSiKNdZHQsx3z956Rm5IWUx6T9P3v3HV/nWR7+/3M9Zx/tLVnDkrcdz9hxduIkJCGQkAQChBZI2aVQWrqA0pb+2tLC99svLXRQwgyrIcwESAJZyiKe8d5DtiVZ1t5nn+f+/fEcyZItybIt6UjW9X5FL51zP+s6lx350n0/9/04M66X9r5CvXf+YLudU4WfGJGuJprqj5IhEerm3AOAq3n31AWu0uqFgy0caek9p/23+5wleJ7YeWpY+8HTzr4+t8XRltStDsdqiVkBdrGA/Ayd0KnOTwvIacAyCZLaA6mUGkXhXGfI2k+cUN6SwXZPQTXgrAV5+uguAJJLnAIy2HXuYw5PtPcTH3LP24BYwh51yLujP8bJ9tCI2+o7Qpzujoy6ra0vOuK2pu4wzT0jH9fZH+N4W/+I2yLx5Khx2rZhV0PXOcO1Aw6c7iEcG3li0dHWPrrD8RG31bX10xWKjbjtSEsvPZGRj9vT2E1/NHFOuzGGbSc6Rvxz6I8meOVwG8ac+xn2N/VwuPPc+A+e7uV9397C739j07DjjDG8fsLpadx7qmdYXho6w2T63KyrzuNoa6qArHuRuoxVZAWDuCwZ8TMpNZQWkNOA2HEdwlZKjWrusqsHX/uW3jn4OpjqmexrPkakaR8AxUtvpNtdSEHo2LBz/O5oGzf/31r++cn955z///vlXu768svUHmwZ1m6M4Z1fe43b/+3FwUfaDeiJxLnnP1/hvv96lVhieDHU1hflzn9/ibf/z2vnFEO9kThv/sor3PXll88p6GzbcP9/v8qd//4STd3hc+L88x/v5K4vv8zmuo5ztn3r1Tre8p+v8t+1566L+crhNt747y/zFz/eec62Iy293PlvL/HQtzafs+1ke4g7/u1FHvr2lhGO6+POf3+Z94+wbXNdB3f/xyv81U92nbPth5tP8ravvsa/P3vonG1/84s9vPubm3hy9+lh7Unb8K6vb+TzmyLUdwwv5l8+3ApAc0+UuiGFd2tflP5YkivmZBOKJanvPHNcfUeIirwAC4oyOdraj+k6Ce1H2OVdRWGmDl+r8dECchoQO0FSJ9EopUbhdrvZcf1/s7nkQVbe8JbB9vw5znB2tO04rvbDdJFJdmEZnZnzqUieHDbD9mevO7O1n9hxalhRZ9uGX+1yJuIMDHkOONzSx+GWPqIJm+f3Dy8uf3ekja5QnNM9EbaeGF7QvXiwlVAsSV1bP9vru4Yfd7Sdjv4YHf0xtp3oHLZt76kejreHiCZsnjvrepF4kl+n4vzx1vpzcvTL1DDt4ztOnbPtV7uctqf3nj6n2H1q92kStmFHfRctZ/WKPrO/mXjSsLO+i2Otw2e1/3bfaZK2YeuJTk51DS92n9ztxPnr3U3nXO/pPU5xePbnS9qGZ/c3D557qD2N3XSFnJ7OV4+0Ddu2/WTX4Ou9p870zg4Umjcvcp7EfmJIL3JDZ5iKvCCV+UH6ogkiu34BwItcpcPXaty0gJwGLJMgrgWkUmoMq2//fdZ/9GtYQ9aOLCkqoM1kI10nyOo7RpOnCkSI5C5kgZyitfdMYTNQXLT3x2gcUvDUtfcPDt9uPT68ENx6/EyBt+dU97Btm+uGbGs8e9uZ8+w8q4Acum1TXfuwbTvqz5xz47GztznncVvC1rMKz/5ogl2N3YjAkda+c4aON6WumbQNuxvPiud4xzn7nYn1TAw7G4Yft2XIvmd//qEF9d4hebNtM1g0HzjdO2zY/GhrH72RROqY4cP0Q689tGAEON7ez3XzC3BZMnhvIzD4DOvrFzgTrgZ6II0x1HeGqMwPUJ4bcOLa+ziULGdftIiCTC0g1fhoATkNWHacpOgQtlLqwvjcLpqlGF9fAyWxk/Rk1ABgFy0lKFE6Gpxh0mgiyeHmXq6qzgOc4dcBx1qdYc9r5xVQ19Y/rNfyWGsffo/F+up89p1V1Bxr62NZWTZlOf5zCp6jrX2sr86nMNN7znFHW/u4Yk42C4szOXB6+MSPQ819ZPncvGFpMYeah287nIr5bVdWcLy9f9hC6XVt/RjjbDOGYfdJRhNJTrT388DaCoBzrnm0pY83ryzDEs655tHWfm5bUozXbY3w+fu5dUkxIsMLPmMMx1qdbUPjBmjujRCKJdmwuGgwv4PnS/053LK4iGOtfed8voDHxaI8i2NtZ44xxnCyPcSC4kzKcwOcGDK8fbLd+SXhyqo8PC6hvsN53xmKE4olqc4WFoZ38En3Twg2b6O54g5ae6PaA6nGTQvIaUAn0SilLlaPv4zivoMU0I1d4Dz+0FuxGoBYww7AGb5M2IY7rygFzhQrzjbn9a1LioknzbDeyePt/VQXZLCwJHPY/XUAx9v6qSnKYGlZ9rCer4Hj5qW27T89vPCqa+unpjCD+UWZZyZwpBxq7mVhSSbzizM53hY6p5gNeFxct6AAYxgWz8B57l5ZBsC+IQXkyfYQtoHrFxQQ8Lg42nLmuFAswanuCEtKsqjKDw7LS9JOFWclmalYz2yLJWzqO0JcMSebyrzgsM/R0hslFEty48JCPC4Zds6BmG9LFZcj/jksLcE20DDknsUT7SHmFgQpzbCGfe6uUJzeaIKq/CCV+YFh90fWd4YozfYT8Loozw3Q1tYMB58m+Zu/4Wfev+M9tTex4MkH+WPXz9lkL+HuVxfQG02wqCQLpcZDx02nAS0glVIXy86uIjfyMgBZNWsByKlaScy4cDc7kzgGCos1VXlk+d3DerHq2vrJCXi4cm4u4BQ1cwsyBrctLHaKq85QnJ5InGy/h3jSpr4zzN0r55CwDS8fbiWetPG4LLrDcdr6YtQUZpDpc/O9jSdI2gaXJUQTSeo7Qty7upxE0ubZ/c2Dx4HTM3r7shLmF2USS9o0dIapLswYjKumMIOFxVmD75eWZQ++FoFr5hWQ7XcP62UcKPzmF2UyryhjWLF3vM3JS03RuQXtqa4wsaTNvMIMTrSFODJk28kOpyitKcygKj9IfeeQWwLazlxvbkHGsF7GgevduLAItyXD/hyOt/eTn+FlWeoznewIsSD1WY+39bO4NIusWISXGmKDfw4DPY5zCzKozAsO3kMJ0NHWzFuDu+CpZ/hO9DdUHamDo4Zc3JxgHm0rPkjRFRv4v3tziHpy+LuqXFZX5lKZH0Sp8Zh1BaSIvBH4MuACvmGM+UKaQ3IKSF0HUil1EXxVq6HlBwBUr7oJgIKcLPabSjI79gBnCsjK/ABzC4KDw5ng9G5VF2ZQle8UagP3ziVtw8mOELcvK6UqVVQ4vW451HeESNqG6sIM3JYQTxrq2vpZVJI1uARPdWEG+Rleogmb4+39zC/KpD5VeM0rzCBhGxK2oaEzTE1hBt3hOO39sVTvZKpobOsbLCDr2vpZWZFDzWBBOWT4t62firwAfo+LJWf1iA4UdDWFGdQUZpxZODt1foB5hZnUFGbw6lFnCR0R4djA5yjIoKqgnxcOtmDbBsuSYeesKggOTowBBj9/TWEG1QUZg/kEp0j0ui2q8oPMyQ0M+3M43haiusDpSXRy7WxLJG3qO0PcubwU6XLun2zoCLNsjmfw3FX5QeZnJ2kObSLy6xdIHK3lG+37sMTAtgAmsIx/C1/JiazVBKrXc9XCcq5cU45YwqcWj/S3Sqnzm1VVi4i4gP8CbgcagC0i8oQxZl8643LZCb0HUil1Ua7Y8CAHdn6bjuKruS4rFwDLEva5l3Jfz3MQ7aO+M4zfY1GU6aMyLzjsXr/j7f2snZtHYaYXn9saHDo91RUmnjTUFAYHe6UGCsjj7QNFUpCg1/ln5MDpXqeATG2bV5hBJO4MQR863cv8oszBIduawgyiqdnJ9R0hagozBovcuQVDr+cUUXHb0NAZ4r415QS8LkqyfcMKs6MtfcwrzARgaWkWP329cbAQrGvroyjLR5bfw9xUsZdI2rhdFnWtA8Wuc81I3Ka1L0pxlp+6VIFaU5hBZX6QaMKmpTdKaY5/WJFYlR+koz9GbyROlt9DXXs/XpfFnNwAFXkBNh5rH4zleFs/VflBLEuoyAucNUzdzzXzCijK9OH3WIP5aOqOEE8a5uYHCUWc9Rkbu8Isy7NxHX6az7qfYcEvvsii07v5kNcmutnNbnsRr5m3seL6e7j99rsox8MHYglyg3p/o5o4s6qABNYDR4wxxwBE5FHgXiCtBeQVib00SUk6Q1BKzVCBzGyWfPa1c9pfz9rA27ufYu8jf0JeZyF/lAGypYF746d5taONjY9uBOC23tPcFClANm/hj4KH6fhdlNe6S+kMxXjI1cFVzfspiwT4A9cBDj7+PAV78unqCPE+VzeLjh8l4HbxQfd+Qi++zJb9mURb+viAq5fqw846lB9076fnhZfYvDuDnvYQH3T1sOjoYeK24YOuw+z/6TN4KnJo7Y3yIVc3q+v3UNTl58Ou/bQ8/Wu21BUSam7nQ1aS2zvK4JVs/th7goYdYbbFShAMt7e3styfAy+/wv39XWQkTrPxuy/icVksOtnJap8LXtrBnR3dIKd59du1ZAc8FDb18ulgjOBr+7ixrY+PuxrZ+sizlOb4yWzt48+8EYpe382N7SE+4Wpk6yO/pSzHT05rH3/pj5K7eTe3NfcQcTex6VvPUJjppbqph88GbVwvbuctnZ3kJVvZ8u3f4ve4uPZEB/dkeuH5l3l/rJl9TT3s+u7PwcDv9beyrjcfeT6Pv/GfoGtjnN0dRfSE4/ylu5PrT7xMuKuFv3f3UvXjv8GYOt6MIer24PJdTf+1f8Z/HiuBinWsW1DOB6rzyQl4APACXrcWj2piyUgr3l+uROQB4I3GmA+m3r8HuNoY8/Gz9vsw8GGAkpKStY8++uikxrWh9l4Aajc8PqnXmQn6+vrIzMxMdxhppTlwaB4uLQff2R3hgZZ/427XxgmOSl0o2wx5souAIBgYXI/T4Gy3BARIIgz9p9ngLF8EhojtZqc9n81mKfXBKyioXMK1lRlT9VHSTn8uOKYqD7fccss2Y8y6kbbNth7IkZ7PdE4FbYx5GHgYYN26dWbDhg2TGlRPxW/ZfeQkk32dmaC2tnbW50Fz4NA8XFoObrrJ0BO+hUjvCbzBbCzrzG0y3eEYydRPPrfLItvvAYSkMXSHYrgtC8tl4XNbgxNcIgmb3kgcSf0YzfR78HucbbGEoSdyZu3FrIAHn3tgm01fNImIYFlC0GvhcTmxROJJwnEbr9vC67ZwW+KcX4RYwqYnEsclwsZNm7jlpuvxu53jDM4jFge4XBa5gTM9bL3RBPGkcQoyEbIDnsG4e6JxovEzP/bzgh7cqXj6Ywki8SSWWIhAps+N22UBQk80TjJpcLkEt2Xhd7uwUo/8648lU8cJIpDt9wxu6+yPkUg9RlAECjK8iAgYQ3cojp0a3na7JPXnAGIbukKxwSIy4HXh9bmpra3lmutvZIVtuMbrcs4zy+jPBcd0yMNsKyAbgMoh7yuAcx9bMMWyF1xNvOHcx3YppdTFsiwhN8MHGYvO2ZYzSseFC8gfZZsf8GePvM0LFI6y+ot3rHP6nPOOeJwXClMTggN+P/7gmQsIUOAdfbZwlmfUTWSPMZSbEXCTERjluMDoj/jL8LnJ8I38z2neKOsqisio21yWjPpIQb9H75dX08NsWwdyC7BQRGpExAs8CDyR5piUUkoppWaUWdUDaYxJiMjHgd/g/LL9LWPM3jSHpZRSSik1o8yqSTQXQ0RagRNTcKlCoG0KrjPdaR40BwM0D5qDAZoHh+ZBczBgqvIw1xhTNNIGLSCnCRHZOtpMp9lE86A5GKB50BwM0Dw4NA+agwHTIQ+z7R5IpZRSSil1ibSAVEoppZRSF0QLyOnj4XQHME1oHjQHAzQPmoMBmgeH5kFzMCDtedB7IJVSSiml1AXRHkillFJKKXVBtIBUSimllFIXRAtIpZRSSil1QbSAVEoppZRSF2RWPcrwYhQWFprq6upJv05/fz8ZGRmTfp3pTvOgORigedAcDNA8ODQPmoMBU5WHbdu2tY32JBotIM+jurqarVu3Tvp1amtr2bBhw6RfZ7rTPGgOBmgeNAcDNA8OzYPmYMBU5UFERn2Usw5hK6WUUkqpC6IFpFJKKaWUuiA6hK2UUmp8jCHRcYLT+16h8/guNsWqeaxzEXlZGdy/ppy7V80h06f/rCg1G+j/6Uoppc6VjBM+fZCWw6/Tf3IHntbdFPcdIMf0UAFUACuAd1o5/CZ6E1/7+Y3806+reeuV5fzBddXMK8pM8wdQSk0mLSCVUmq2MoZ4dxPt9QfpPnWIWPNhXJ1HyO6royTeQIAEc4G4cXGUCrYFryFctIqMeeuZt3g1lV2byNr1I9528Gke8P2So4GVfGXLBu7cuI6bl5TzkZvnsW5uHiKS7k+qlJpgWkAqpdRlKB4N09nSSE/rScJtJ0l0ncL0nsbVfxp/uJmsWAsFyVZ8xCkFSoGEsWigmAZPJfvzrsEuXELm3NWUL1zFoqI8llhnFYJl98DSe5C+VtjxfeZv/TZfDn+FzwcK+e7xW/no/luorJrLR26ax+3LSnGdfby6YJF4kl31XRw7tIvI0Vcp7tpJJiH63PlEg8WYzDl48qvIKJ1HYVk1VYVZ5AQ8WsSrCacFpFJKjZOxbQDCoV5s28a2bYwxYAzGTmKMwRinHQzYBtvYGOPsl4zHSURD2HYC2zYYO4Gxne12MkEyHiOZ2m6SCbpPHGdb6CjJaAgTj2CSMUjGMdFeSESQRBRXrAdXMoI33o03GSIz2U223UOmhCkGiofEHzVu2sml01XACd8CDgZvws6uwFM4j6yyRZRWL6IqN5vqCy30Movghk/CdZ+Aw78lc/PD/NHRx/hI4Gc8134d//ODW/li/mo+cOM83nZlBQGva6L+SC57zT0RXj96mlMHNhI78hLR5/+JK+UQ66UbgH4rk15XLtmR1wmGQ9AOpBZeiRkX9aaYndYcugJVRHIX4i5ZQl71SuZVzKEqP4ilRb26SFpAKjVO8ViEZCIxWCQ434d/YQwY2ykKMGe+D24zJJNxYpGwcw47tf/AvsbQe/oIR3Z6cAoQG9sYwJCIRUjGwphkEmMSmKSNseMkYxHsRBTspFPgmKTz2thgJzCxEBh7xC8xBkwSSYSRwXaD4MQFBjF26r3TJhisZBQxSZx/elLnIXUcOOfCONtT5xs4h7OPwWNHz7QP7DtwPIYa26b+RRmyfWAbqbjOHCNDjgPwkMBFIhWLs58M2Y8h72XIcXDuvgJYMvTaELzEv0vjtQagceRtUeMhhps+ySRm+QlbmUTc2fQEqzjpy8MO5OPKKsGTV06goILs4rnkF5Yyx+dhzmQFbLlg8V3OV+shXFu+we07fsgdvpc4GpnHN355C1/9zc289dolvPuauZRk+ycrkhnJGMPJjhCvHzpO+76XCTRtYlFsD7fKMXzi/H3uDJQTLr2Z3kU3kbXwBjIKF5NhpRZUifZhehoJtRynu+kwkZY6rM5jLOo9QUF4F95wHJqAHdBgCqllLq0Zi4gVLSdYs5758xextCwLn1sLfHV+Yow5/16z2Lp164wuJD5+xraJRsPEY1FikRDhvm6S8SiJeIx4uNfpfUnEsZNxEtE+TDyKScYxyQQmEaWjuZG83GywE2AnkGQciYecIsckEXvgexx3MoJTvNiISWIZG8F57bGjWCRTxUOq6BkofrCxjI2PKIKNlSpQrCGFjrPf0Nc2Lpn5/68kzcAndrIy8DoiviHtFiDY4pRS9pCSyhZnW0Lc2OI+qwQTkCFlmQwp68T5B25wXxGSlg/EGmxzqrOB80EkGsXvDwyei9S5AEwqNoZcc5AIxnJjLO/w4waPGWiyhpzLGrJpyL6p4yUV82Cby4u4PEP2swZfi2Wd2S+VL0m9FhEsXwZiucByIyKIy42I5Xy5XHgCWVguD5bLxf4Dh1ixciUeXwCfPxO314fL4yWYkY3LPUN+/4/2wq4fYbZ8E2nZR0QC/CJxDT+2b6X8iht473XVrD3PfZKXy8/HkTR2hdmy7ygd+14go+k1lsf3sFROYokhgZv27KVI1TXkL72JzQ1Jrrvz/ou7kJ2ErpOEGvfQeXwniVN7CHQeoDByAgunZ73OLuEVs5qThdcTWHAzaxeWs3Zu3rSaWX/O34XUL7YDX8lkgmQyiR0LYewkmCS2bZzRAtvG2ElsO0kskSSZtDGJGCQiqV/ikzi/O9sYnF/wTeqX6ngySSKZBNsGbKx42Lk0znbDmU4C5xjnF4LUCTHGEEsknX0wqW/OuVyJKKTO43ykgeMMBgZ/QT/TDk0RD/d88G8nPd8iss0Ys26kbdPnb4VKm0g4RFf7aSJ9XYS6WolH+oj3dWBH+0mGOiEeQiI9WIl+JB7Gk+jDZUfxJ/rwmCgeEyVownhMHB8x/JLkkvoVnJEZ4saFjUVI/CRxXtu4SIrzPWb5naIn9d6IhS0WtuUl6s7CiAsjrlSBYTnFSuofdCMWtjuAERcyuF0Giwpn34H3qW0uD7i8TiEAqf2HFhoD361h70XOFE0DhYTlDTqFhgjCmeICEerrG6maOzd1rDX4XVxuPH6n+LBcbsRyY7ncuN0ePIFMLJcLl8uNWBaW5cKy3IjLRTAjC7fbg1gWLmCkvoXp+GCw2tparr9Mi4bxauhMUL10xJ/dM4cvC676ILLuA1C/Gf+27/COvT/jwcQLHDtYzk/23sBXCu7gtmvWcd/qcnKCnnRHPKm6w3E2HzxJ067n8NW/yvLodt6SKhhj4qOjcCUd8x8gf8nNuCuvosR7pr871lp78Re2XJBfQzC/huCKe860x0KY5j10HXqVjMMv8GBLLZ7O3xDb7GLvphqeMFVEg+Vk5uSRG/QS9MjgCIMZMophTGrkwSRxJ8NYxinexE5iJxN4kmHEjiND2rETeO0IFolUR4DTASDGxmuiuE0Ca/BXXecX/zUmSag2iYd46l+F4b/Yj/Yz7nKz2bUWmPwCcixaQF6GGo/tpeFXXyCr9xhJy0PclUHMXwguD2LHsRIh/JFWsuOt5CU7yJYQpec5Z8j4CEuAiPgIW5kkLB9hTw59Lj+2y0fCk4Vx+cDtw/iyELcPLA+uQDbi8eFye7E8Aad3xe3B5fbi8Qfx+jOx3G7cHi8ul4dtO3dzy4ZbsSwLT2pYxjf5KZtWemprWTPLCyd1GRKBqquh6mqsu74Ae37G3O0/4K8afwQ9P2LL04v4ylPXEV7wJm5bv5qbFhXhcc38Z10kbcPOk60cfv1FzNEXmN+7lQ1yBI8kieOhNX8V7QseoOCK2/BWrqPUPcU/8bxBpHI9eZXr4bZPQjwCJ16Bw7XMO7aRJZ3bCESfh5bxnzJmXCRwk8QikSrzYuIhhjPSkRTnF34jLqLiJykeTKoTwFgusFwkxUPC8qY6AqxUL7+LcCRKMCubhOUfbHNGHVyAhcvlwnK5SFpejOUZ/KV94EvEAsvCZTn7Ii5sd+DML+rWWaMGliDiwrIEj9s9+Eu/cfvBcjogRM50HlgiYDn7DB15AMHvdWENdhZYqX4GAbcfXO7BYwRxrsuZczn/OSMcAnRu3jwZfxsuiBaQl5l9rz3F3Kf/gHxs6ryL8NgRsuJt5IZ2YGETx0McLz3uPDoCNZwOXoPJKMbKKMAVzMGbmY8nkEMgOx9/MIuMnAKCGdkE3e4pue/L7T44c4bmlFIXx58D696Ha937oP0o7P4JK3Y8xlVd34G677D96AK+5lpHcv6drLnqBhL2zLp95HRXmO3bt9C77xmKWn/HOrOXKyWMjdCStYTW6g9StOqNeGquZY4nkO5wh/P4YcEb8C54A96BtngYon3YCKGEQRAsy+UUOZaFZTmFmZUqqLwuD95JmvWtIxMOn9d7/p0mmf5LfRk5uutVqp5+H+2uArx/8AuWVS0add/z9TgqpdSUKJgPGz6F/+a/gpb9JPb8nHl7nmJN56Nw5FFOH87jd2YF3z64ibzFN7Jk6XIWlmRPqyWBIvEk2/cdpHnnbwjUv8Ly2HbuknYA2j1ltJffg6y8k8wlt1IazE9ztBfBEwBPAAvQ5eHVAC0gLyPdT/5/FGDhfd8TlFYuSHc4Sik1fiJQsgx3yTJybvss9DYTO/A0ZueT3NH4MpnNL0Hzv9D1YgbbmEtbxkLskhXkzr+KxSuuoihn6u7kjSdtDhw5QuPO57FOvEJN33aulQYA+iST5uKraVp8G6Vr7qKgYB4FUxaZUlNHC8jLSF68mbqMVazR4lEpNdNlleC96iHKrnqI2hee4+YlhXQc/B09x7ZS1raPlaGn8dc9DnXQ/4yP7e6FdOavwV99NZUrb6KiomrCFs/u7u3n6L7NdB7ahKdpG5X9u1khTawAwvhpzFnF0ZoHKb/yjWRWXkmmNRumcajZTgvIy0iW3UOTe1m6w1BKqYklLqRsFQVlqyjYkGqzk0SbD3Fq/+/oO7qJvNbtrGj9Ae7W78EWOE0BpwILCecuxlOyiIzSheQWzyWnqBSvPwvbGJLJJEk7gZ1IkEwmCHW10dN6kr7mY8Rbj+LpPEJ+/1Gq7AauTK3D2CXZnM5Zwf6q36Ns5W3kzl/PAtflPXNcqZFoAXkZ8ZgYtksX5lVKzQKWC1/ZUmrKlsKtHwDAjvZzfM+rtB96DTm9i8Leg8w5tRl3kz3sUNvI4OLwQ5091NwshbQFathbcDPBqjWUL7+e3NKF5OpjAZXSAvJy4iGBcad/ZpZSSqWD5cugeu0dVK+9Y7AtHovQcOIAnY1HiLQ3kOxvx53oS60R60ot6u68JpCDP6+cnLIaSqqWUBLMoiSNn0ep6UwLyMuI18SctRiVUkoB4PH6qVi4moqFq9MdilKXlZm/SqtyJBO4xXYWJFVKKaWUmkRaQF4m7HjYeTHVTzFQSiml1KxzWRaQIlIpIi+IyH4R2Ssif5JqzxeRZ0TkcOp7XrpjnSix6EABqT2QSimllJpcl2UBCSSAPzfGLAWuAT4mIsuATwPPGWMWAs+l3l8WYhGngBSPFpBKKaWUmlyXZQFpjGkyxryeet0L7AfKgXuBR1K7PQLcl5YAJ0E8EgLA0iFspZRSSk0yMWZmPaT+QolINfASsBw4aYzJHbKt0xhzzjC2iHwY+DBASUnJ2kcffXTS4+zr6yMz8+KfMhptO86de/6En5T+GYVLbp7AyKbWpebhcqA5cGgeNAcDNA8OzYPmYMBU5eGWW27ZZoxZN9K2y3oZHxHJBH4K/Kkxpme8j7UyxjwMPAywbt06s2HDhkmLcUBtbS2Xcp36Pa/CHqiqXsD6KYh3slxqHi4HmgOH5kFzMEDz4NA8aA4GTIc8XJZD2AAi4sEpHn9gjPlZqrlZRMpS28uAlnTFN9HiqUk0llfvgVRKKaXU5LosC0hxuhq/Cew3xnxpyKYngIdSrx8CHp/q2CZLIhYBwO0NpDkSpZRSSl3uLtch7OuB9wC7RWRHqu2vgS8Aj4nIB4CTwNvTE97ES8acHkiX9kAqpZRSapJdlgWkMeYVYLQbHm+bylimSjK1kLj2QCqllFJqsl2WQ9izUTI1hO3RAlIppZRSk0wLyMuEHU8VkH4tIJVSSik1ubSAvEzY8SgAHp8WkEoppZSaXFpAXiZMqgfS6wumORKllFJKXe4mfBKNiKwDbgTmAGFgD/CsMaZjoq+lzjCJVAEZ0B5IpZRSSk2uCeuBFJE/EJHXgc8AAeAgzkLdNwDPiMgjIlI1UddTZ4lHsI3g8+qzsJVSSik1uSayBzIDuN4YEx5po4isBhbirL+oJloiQhQPfrcr3ZEopZRS6jI3YQWkMea/zrN9x0RdS51LklFieAiM83nfSimllFIXazLugawB/hioHnp+Y8xbJvpaaohkjJh40h2FUkoppWaB8xaQIvJLwIy2fYTC8Bc4z6H+JWBfSnBq/KxklBjedIehlFJKqVlgPD2Qx4BS4Pup9+8CjgO/GWX/iDHmK5cemroQkogS1x5IpZRSSk2B8RSQa4wxNw15/0sReckY89ej7P9lEfkc8FsgOtBojHn9EuJU5+Gyo8RFeyCVUkopNfnGU0AWicg8Y8wxGLzHsWiM/VcA7wFu5cwQtkm9V5PEsqMktIBUSiml1BQYTwH5SaBWRI6l3lcDHx5j//uBecaY2CXGpi6A246RsLSAVEoppdTkG7WAFJFrjDEbjTFPi8hCYElq0wFjTHS044CdQC7OIuJqirjsGFErM91hKKWUUmoWGKsH8r+BKwFSBePOcZ6zBDggIlsYfg+kLuMziVx2jKRbn0KjlFJKqck34etAAp+bhHOq8/CYGEmXDmErpZRSavKNVUDOE5EnRts4Wo+iMebFgdcicrcx5leXEJ8aJ4+JY1vaA6mUUkqpyTdWAdkK/L9LPP8/AFpATgGPiWG7tIBUSiml1OQbq4DsHdqbeJH0wcxTxEsMowWkUkoppaaANca24xNw/o9MwDnUOHhNHHQSjVJKKaWmwKg9kMaYtw68FpHrcNZ/dA/Z/t2h+4vI0KfVnNNujHnpEmNVozEGn8S1B1IppZRSU+K8s7BF5HvAfGAHkEw1G+C7Z+36lyMcboBVQAXguugo1ZiS8aiTXLc/3aEopZRSahYYzzI+64Blxhgz1k7GmHuGvheRG4DPAk3Axy86QnVesUiYACAe7YFUSiml1OQbTwG5ByjFKQTPS0RuA/4Wp/fxn40xz1x8eGo8opF+AqA9kEoppZSaEuMpIAuBfSKymTGeLCMib8bpcewGPmuMeXUiA1Wji0XDAFgeLSCVUkopNfnGU0D+/TjP9UugAWgHPiUyfAUffZTh5IlHQwBYOgtbKaWUUlPgvAWkMeZFEZkLLDTGPCsiQUaeEHPLhEenxiUejQBgebUHUimllFKTb6x1IAEQkQ8BPwG+lmoqB35x9n7GmBdTC4/3Dbwe0pY9gTGfl4h8S0RaRGTPkLZ8EXlGRA6nvudNZUyTKZEawnZ5A2mORCmllFKzwXkLSOBjwPVAD4Ax5jBQPMb+XxeRFQNvRORdwN9cSpAX4TvAG89q+zTwnDFmIfBc6v20UH9kN6Gu0xd9fCKWKiA9WkAqpZRSavKNp4CMGmNiA29ExI0zw3o0DwCPiMjSVO/lHwF3XFqYFya1aHnHWc33Ao+kXj8C3DeVMY0l8cPfJ3/PNy/6+GTMGcJ26RC2UkoppabAeCbRvCgifw0EROR2nILwl6PtbIw5JiIP4gxz1wN3GGPCExHsJSoxxjQBGGOaRGSsXtQ0GHOZzTEl40563T7tgVRKKaXU5JPzrA+OiFjAB3B6EQX4DfCNsxcWF5HdDK+CinGW9IkCGGNWTlzY5yci1cCvjDHLU++7jDG5Q7Z3GmNGvA9SRD4MfBigpKRk7aOPPjqpsVbUfpxWVwnRG//2oo7vOfQibzn1JZ5Y9hWyi+dOcHRTq6+vj8zMzHSHkVaaA4fmQXMwQPPg0DxoDgZMVR5uueWWbcaYdSNtG88sbBv4euprLHdfRGxTqVlEylK9j2VAy2g7GmMeBh4GWLdundmwYcOkBnbsJReWwMVeZ1v3PjgFa9auo3L+FRMb3BSrra296DxcLjQHDs2D5mCA5sGhedAcDJgOeRj1HkgRuVdEPjbk/SYROZb6evsIh7QbY06M9pU6Rzp/bXgCeCj1+iHg8TTGMow5a83MC2UnnCFsjw5hK6WUUmoKjDWJ5q9wiq4BPuAqYAPwhyPs/7iI/D8RuUlEMgYaRWSeiHxARH7DuTOjJ4WI/C/wGrBYRBpE5APAF4DbReQwcHvq/bRxKSWkiTuTaLxaQCqllFJqCow1hO01xtQPef+KMaYdaB9aIA4wxtwmIm8CPgJcn1pnMQEcBH4NPGSMufi1ai6AMeZdo2y6bSquf+EEznMv6lhM3Jkk7/UHJyogpZRSSqlRjVVADptgYoz5+JC3RSMdYIx5EnhyAuKaVQzCpczCNgmnB9Ln1x5IpZRSSk2+sYawN6XWcRxGRD4CbJ68kGanS7oLMhEhYSzcbs9EhaOUUkopNaqxeiA/CfxCRH4PeD3VthbnXsj7JjkudQEkESGKl4xLnIyjlFJKKTUeoxaQxpgW4DoRuRUYWBvm18aY56ckslnkUoewJRklKh7OuTFVKaWUUmoSjGcdyOcBLRonkwhyCZNorESEGL4JDEgppZRSanTjeRa2mmQGuaR7ICUZJS7eCYtHKaWUUmosWkBeBlx2lJgWkEoppZSaIlpAThsXP4TtSkZJWFpAKqWUUmpqaAE5DThD2JdQQNoxktoDqZRSSqkpogXktHBpT6Jx2xESLv8ExqOUUkopNTotIC8DbhMjqUPYSimllJoiWkBOA5c6C9tjx0hqD6RSSimlpogWkNOBXNpC4h4Tw7Z0HUillFJKTQ0tIKcBA5c0icZLDNutBaRSSimlpoYWkJcBn4mBDmErpZRSaopoATktXModkOAljtEeSKWUUkpNES0gp4FLWQcymYjjkSS4AxMclVJKKaXUyLSAnA4uYRJNNNLvvPBoD6RSSimlpoYWkNPExQ5ix8Ih53jtgVRKKaXUFNECchq4+PnXEI2mCkivTqJRSiml1NTQAnJauPh7IGOhPgAsj/ZAKqWUUmpqaAE5LVzCPZBhp4B0B7ImMB6llFJKqdFpATlNyEWOY8f6ewDwaAGplFJKqSmiBeQ0YOTi14FMRHoB8AazJyocpZRSSqkxaQE5bVxcF2Q8VUD6tAdSKaWUUlNEC8hp4FIWErdT90D6MrQHUimllFJTQwvIaeJiB7GTUaeADGgBqZRSSqkpogXktHDx90CaWKqAzMyZqGCUUkoppcakBeQ0YC7hUYZE+4kZF16fLiSulFJKqakx6wpIEXmjiBwUkSMi8ul0x+O4+HsgXZF2ukWHr5VSSik1dWZVASkiLuC/gLuAZcC7RGRZeqO6NN5oBz2uvHSHoZRSSqlZxJ3uAKbYeuCIMeYYgIg8CtwL7EtrVAgukvR2d2DbNhiDsZPYdtJ5b9sYDLadxNg2tm1jbBuwyYo20+/RAlIppZRSU2e2FZDlQP2Q9w3A1WmKZVDS8rDIHId/q7mo4zdnvWliA1JKKaWUGoMYc5GTN2YgEXk7cKcx5oOp9+8B1htj/vis/T4MfBigpKRk7aOPPjqpcfU2H8Nu2IzL7QEExMIgIJJ6P1KblXpt4ZmzkkB24aTGOFX6+vrIzMxMdxhppTlwaB40BwM0Dw7Ng+ZgwFTl4ZZbbtlmjFk30rbZ1gPZAFQOeV8BnDp7J2PMw8DDAOvWrTMbNmyY5LA2UFs7j8m/zvRXW1s76/OgOXBoHjQHAzQPDs2D5mDAdMjDrJpEA2wBFopIjYh4gQeBJ9Ick1JKKaXUjDKreiCNMQkR+TjwG8AFfMsYszfNYSmllFJKzSizqoAEMMY8CTyZ7jiUUkoppWaqWTWJ5mKISCtwYgouVQi0TcF1pjvNg+ZggOZBczBA8+DQPGgOBkxVHuYaY4pG2qAF5DQhIltHm+k0m2geNAcDNA+agwGaB4fmQXMwYDrkYbZNolFKKaWUUpdIC0illFJKKXVBtICcPh5OdwDThOZBczBA86A5GKB5cGgeNAcD0p4HvQdSKaWUUkpdEO2BVEoppZRSF0QLSKWUUkopdUG0gFRKKaWUUhdk1j2J5kIVFhaa6urqSb9Of38/GRkZk36d6U7zoDkYoHnQHAzQPDg0D5qDAVOVh23btrWNtpC4FpDnUV1dzdatWyf9OrW1tWzYsGHSrzPdaR40BwM0D5qDAZoHh+ZBczBgqvIgIqM+iU+HsJVSSiml1AXRAlIppZRSSl0QLSAvA33dHRz54o3s+K93pzsUpZRSSs0CWkBeBvb//AssCO9idesvObrr1XSHo5RSSqnLnBaQl4Fg4yuDr9te/FoaI1FKKaXUbKAF5GWgMlZHnVQBUNP+cpqjUUoppdTlTgvIGS7c10O2hGjJXc0u/1UU00F/b1e6w1JKKaXUZUwLyBnu5IHUGpVlq4guuQ+AXT/7P+kLSCmllFKXvWlfQIrIt0SkRUT2DGn7vyJyQER2icjPRSR3yLbPiMgRETkoIncOaV8rIrtT274iIjLFH2VSdB/fDkBOzWpWvvH9ACw/9q10hqSUUkqpy9xMeBLNd4D/BL47pO0Z4DPGmISIfBH4DPApEVkGPAhcAcwBnhWRRcaYJPBV4MPARuBJ4I3AU1P2KSZLywEAKhavxecPsjOwnlXhzWz84T8y/8Z3cuz575B16hVKo8dxk6DJU0lf9kI8V7yZxdfegy+QmeYPoJRSSqmZZtoXkMaYl0Sk+qy23w55uxF4IPX6XuBRY0wUqBORI8B6ETkOZBtjXgMQke8C93EZFJC+3uMAZGbnAVD9oR/S/pW1XHPoX+HQv1IEhIyPY575JMVLUbyRxe0H4KVfwkt/yDFXDe3Zy7ALl5BZtYqSecvJKarA4/Wl70MppZRSalqb9gXkOLwf+FHqdTlOQTmgIdUWT70+u33GKwofp17mUJl6n5NfRO8nd/C7J76CCXeRteRWll9/N8tdZ+5WaG1u5EjtD3A1bmFO7y6u6vw1dP4aDgPPgW2EZiufDs8cQv5iEjnzsPIqCBbPI7ukmvKaZVjuy+GvjlJKqREZA7t/AoeeJnJqD6FQH/GEIWHbhPHRJCVYlkXA48KI0OUuIubOItvvxrKEpHjo8ZWBy0t+ppdEdhVJdwaZfg+Fudkks5x/gguzfGT7PWn+sOpiiDEm3TGcV6oH8lfGmOVntX8WWAe81RhjROS/gNeMMd9Pbf8mznD1SeBfjDFvSLXfCPyVMeaeUa73YZzhbkpKStY++uijk/PBhujr6yMz88KGk41tc8tL97PJvY7wDX970ddOJOKEOk6R7DqBO9RCZriRrEQ7hclWykwLPkmcc0zI+GiWQvqtTDrdJcRdQUL+EowniB0swvJn4c4owHJ78QWyxh3LxeThcqM5cGgeNAcDNA+OqcpD2anfsuDIN3HZEQAO2hUcNXNIipscd4IKacVlkiQMGGMoopMc+i74OlHjdEQkcXHKFBLGiwBuC7olm47U9Aa3BV6X0G4V0mf7cLlceCwIeoQeVx4h68y/MZkeIeCBHk8Jccs/2O5zQX5AMOIi5sk+J5agW/C6Zs7UiKn6u3DLLbdsM8asG2nbjO1GEpGHgLuB28yZKrgBBjvjACqAU6n2ihHaR2SMeRh4GGDdunVmw4YNExf4KGpra7nQ67SfPgkvgSldccHHXoieni5aju+ju+EAsa5T0NuMp7ceX7yb3Nhp5sROkU8vhEc+vt/4iYqXHiuHPk8BtriIBMqwPUGMy4+VX41YLsTlobvXMGfOckAom78Cj8frnEQs/IHgpH3G6eRi/i5cjjQPmoMBmgfHpOeh6yT86D3QtIMoXv5P/F18K3kX77h6Hu+/oYb5RRdQsPS1Qn8r3eE43Z1tuPsaSdqGtt4o/v56XIkQiaShN5ogK3IKbzJEAAjHkriSvcxPnGY+TRgDyaQhL9GFj/iEfMyw8dKP/5x2S8DGooV8+jj33xuPy8LjdkbzbFy0uYqJyrm3e4kImT437iEFaVI8dPrmYMvIZZcAOUEPfrdr1Lhtl5feYBWI0NXdwd13332+jzqpZmQBKSJvBD4F3GyMCQ3Z9ATwQxH5Es4kmoXAZmNMUkR6ReQaYBPwXuA/pjruidZ06HUKAG/ZFZN6nezsXLJXXgcrrxt1n1B/D/F4nKYjO0lE+gk1H8UkY9gddVjxEK5YL8FIMy47TlG8nkBkPx6TICAxaDrrZEdHvkYbuUTkzP/0Pe4Cot68wfe2uIhlzAFvxvADLQ/uwmpc7nP/R/dmF5NdNPrdDG5fJnPmLhh1u1JKXRY2fhWe/jQA30/cxt8nHuJt62p4/e6lZF3MEHNmEWQWkQPkVJ9prhxt//OxbYj18fIrr3DjDTcQiiew41GsjjP/YMSTNk1dEVyxHrz9Z/5hsYGO/iiJpCEj0oQn3jvs1Enb0B9NYBtDVqKdYKL7nPIxaRtiiSTYSQRDUbKZ+bEDI4dqDGZIh6ybJEGiF/vJR7TZdSVOKZM+076AFJH/BTYAhSLSAHwOZ9a1D3gmtRrPRmPMHxpj9orIY8A+IAF8LDUDG+CjODO6AziTZ2b8BJqeum0AFCy4Ks2RQDDDGRLIWXfrBR3X3d5MOOT8z9zdfJwDu7ZRXV1DrPs0id7Wwf1cvY1Y0e7B995YN1mxVgLh/sG2QruZrL5RukFPXFBY54iZ0X8rHBDHQ4urhOQov2GeT5+ngLi/ACscZsuO/xm2LenJgNyqwfeWP4dA0dzB9+JyU1y9Ass1fGWunPwSnRCllBqZMbDrMRLP/RPunpN0mEw+GPsLAvOv5Zn7VlBTmHH+c0wVywJ/Nkl3EPzZBAf6EvLLhu2WM8rhNZMa3DiEuyA2+jB/LGHTEYqNul2MwdV9Akk4/8aF605PdIQXbNoXkMaYd43Q/M0x9v888PkR2rcCy889YubytO0DoHzesjRHcvFyCkrIKSgBoLRyAU19blZN8BBNR/NJutubz2lPxiL0nDqMbZ97jycAxpBsr8OOhUbePoQYG2/vSazkxf2W6U/0UBKpQyJ1GAwSPTP04SNCHr3QOsYJxtDJuff7dFgFRNzntgMkLS+hzAoYoxA27gBW4TyE898zJG4vWXMWYbku7MdNT+MBTh7Oo7h8/gUdd24Agj8wjf4hVCrdQh2Y1/6L5GtfxZ3oxw38T+Ieni75EJ9+83KumVeQ7ggvP4Fc52sUXqD0vGlfOPjKdNdeekyXaNoXkGp0BaEjtJFL4cB9gmpE+SVV5JdUjbxx9Y1TG8w4jHSfU7ivh0TSKXSNMZw+ugs7ceZ+oP7WEyRCXcOOMZEepKcRGD5RzhNpxx9tRwY758+wTJKS2Am8kX2jxuc1MfwSP/fWg7Fsv4B9UxaBszLABOgkm365uHtoDRbdniLi7vHf/2XEIhYsBd/IRbrxZuAtrGbosxzEEnLKl+DxnblNI5kYvUdCqQtiDBx6muhrD+M7/jwCdJpsHkm8nbpF7+cjty3jDyty0x2lmkG0gJzByhONHPEtpTDdgahJF8gcXohkr70lTZE4utpOEwmPb9Zl56mjxPq7z7/jWY7XHaUkkMBcYhElfadxRzou+nh/tI1gvAsS4/8MuXYHuX2vjLjNklRBf+z856kCYi87t0/0S5B2VzG2uOjzl5HwZJHMrsDKKCBYupDCucsoLp+PS5fYUgOMgZOvEdv2faw9P8VtR/ABLyVX8FPP3VRefT8fvLGG3KB2QqgLpz9pZqhIuB+/xOnLmpfuUNQslFtYOu59SysvbhJSZ20t11ymM29P1R0gFhlegPe1nyLSdea+pmR/J131+8nOzsaK9+PrPwXGJjd6ijl9+yg2bVidqWJ0SIdxg5TRGpxPOH8pgeqrmb/+TrKzRu4JVZehWAgO/Jr+vU/iOfI03mQIL3DYLufnyetpWfIe3nbdFXx5vg5Tq0ujBeQMVX9wBwsBKVqc7lCUUhdoTs2Sce1XW1vLtaMU0fFYlFAkxOm6ffQ21xFp2oe78yi5vUe4ou81vP2vQP3X4WU4QhX1mSuIlqwhc8mtrFm5igyf/vi/LPQ0Qd1LhOs2ETtSS06fMys5A9htV/O8WUtzzdu4du0a/mx5Ke6zJtopdbH0J8gM1X5kCwuB7Jor0x2KUioNPF4fHq+PBauuB64/Z3vLiQM0bv8t7hMvUdyzm1v6fg19v4aj/0Tfr/y87F5FR+E6Amse4Po1K7WgnO5sm0CoEbPzUXrrthKr30525x68qcW+A0CbXUStuY59GeuRJXdz4/IaPjG/gNRqJUpNKP2JMUNJkzMroeqKa9IciVJqOiqeu4TiuUuATzgNiSid+56nedez+Jq2cmP/JmjeBE//F6eeyufFwDX0Lryfq296E9UXsmC0mnjRPmjcSvjk6/Qc3467dR8F/Ue4OrU5G2g12fzOXsxh1zy681Yi825i+bwK7lxUxL2e8y87ptSl0gJyhsruOUzEeAhm5qY7FKXUTOD2kbfyLvJW3uW8TyYIH3mJU689Rk7ji7wp8iTsfpK+XX5+7b6OziW/xy233015biC9cV/u+tvgxKt0HtlMtH4HmZ37yEw4k74Cqa+DdgWvmOs45anCLluDZ+7VzKuYw5Vz89iQoRNgVHpoATlDFcUbaXKVp39xVKXUzORyE1h8K/MXpxb/76qnsfabuA/8gjdHnoe9z9O6J5sf+2/HvvoPue+mtfjGeMyaGodwF3bdy3Qe3kjs5FZyOvcQtJ3JVHlArwmw18zlmOsa+nKXYCquorD6ClZWFnB3YQYvv/SiPtJRTRtaQM5AxrYppIuTwZVaQCqlJkZuJeX3/T3w99DdwOlnvkJw/495e/Sn8NJPee2FZeyp+n1uvue9LCrVWd3nZdvQup/+A8/RdehVslq2kh1vwwIKgG4T5HW7hpO+hfQXrsRVcyOL5s1jTVUuV+v9qGoG0L+lM1Bb0wmKgFiuPqNZKTUJcioofeD/AP+H5LGXOP2bf+Pa5ue5tvGztH31i3w3435K7/pL7lhx0U82vvwYA42v07XzCUJHX6Oo83U8Jk4GzozonfY8DrvX0l14Je75G1i8cCHrKnO5Qe9XVDOUFpAzUP2eVygCfFU6A1spNblc826i/KM3QbiLjme/RNbrX+O9oUfgp4/wo1+8Geumv+CBm9bMzpm+rQfp3fFzevc/z5yOTQDkAkHj4hV7OfUZK0hU38Scpddx7cJSVgU9aQ1XqYmkBeQMFK3fAcCcZdemNxCl1OwRyCX/nn+Au/8/olu/S/+z/4d3Rn8NL/ya519YR8+VH+PuN993ea8zGOsnsfsXtOx8kvyGF/Db/WQBPuPiJXsFJ3LWEZ13B8tWrefmmgIsaxYW1WrWGLWAFJE/G+tAY8yXJj4cNR7ezkMAFM/Rp9AopaaYCL6rHsJ31UMkDz9P06//mVu7tsDr76Nh219yuPx+Vt7/5xQUlaU70onRdoS+Ld8nvO9pinr34wbm4AxJ7/WvoXfunSxdt4FrFxRx0+VcPCt1lrF6ILNS3xcDVwFPpN7fA7w0mUGpsWVGW2gnhwJLf1gppdLHtfBWKv70VkxXPQd+/i9UnfgZt5z6OvzX1znkXYa99F4W3/kRJJiX7lDHL5mAYy/QvvlH+OqeJTPRSSaA8fMr+xqaSm8hc9W93LlmPqt0CR01i41aQBpj/j8AEfktcKUxpjf1/u+BH09JdGpEZfGTnPAvRp9kqpSaDiS3kqXv+2/gv9n/0k/o2/gdrgq9DDv3wc5/odG/EFl4O2XXvAMpWwXT7ZffcCf2rh/Tsf0J8k+/goWhADhql/Fz91voWXAvK6+6mTcvKJyd93oqNYLx3ANZBcSGvI8B1ZMSjTqvSDhEtoQIZ+jsR6XU9LP0pgfgpgfoCUV45dffxXPoSa4ObyR793/D7v8mJj46i64if+lNeBa+AcqvhKkuyoyBE78juveXRPY9TU5/HRZQCGy0l7Ij4wbsK97GHeuX857irPOdTalZaTwF5PeAzSLyc8AA9wPfndSo1KiO73mNJYBduCjdoSil1Kiyg37e9PYPAx+msSvMEy/Wktz/Kxb0b+f6lleg5RV48Z8B6MlZgn/uOrxVa6HiKihcDO4JHB5OJuDU6ySP1tJ7oJbc068C4AO6TC7P2jdwvOg28lbfzf1rq7lGh6aVOq/zFpDGmM+LyFPAjamm9xljtk9uWGo0Xcd3AFC49Ob0BqKUUuNUnhvg3ffeBffeRX80wU/3nObI7o346l9hQWwfqzqPkd39fdj1/cFjQplzcZUtx1u6lKJ2oLUMssvBmzF6j2U8DJEeaN4DrQeIndpLpH472V37AHABAePhJXsF2z2r6Z//ZtasXM19V5Ti0hnTSl2Q8S7jEwR6jDHfFpEiEakxxtRNZmBqZKbrJABzFq5McyRKKXXhMnxu3ra2AtY+ADxAZ3+Mlw638p2604RPbCWzfTcLzEmW9JxkYe8zyOFfcwXAvn8FIO4KEPflAyAiGGMAsBIh/LHOYdfyAqfsEmrNtRxwLaS//AbmLlnHG5aVcVNBcOo+tFKXofMWkCLyOWAdzmzsbwMe4PvA9ZMbmhpJoOMAfSZAZmZuukNRSqlLlpfh5d7V5dy7uhxYizGGhs4w2+u7+M3pHpoaT5Kof52iRBP5poPSZOfwu/KH6DUBmqSIaLCMWP5isucsYmllEetr8nlLTmBKP5dSl7vx9EDeD6wBXgcwxpwSkfPeVSwiGUDEGJO8tBDVUHPCh+i08pxlJZRS6jIjIlTmB6nMD8KqOcASamu9bNiwgZ5InJaeCAC9kQSNXWEq8oJk+pzHARZn+8n269NelJoK4ykgY8YYIyIGBgvDc4iIBTwI/D7OupFRwCcircCTwMPGmMMTE/bsZGybYjrY5VuHzsFWSs022X7PsAJxTdUMWl9SqcvMeBbjekxEvgbkisiHgGeBr4+w3wvAfOAzQKkxptIYU4wz+WYj8AURefcExT0rNTc6t52G8pakORKllFJKzWbjmYX9ryJyO9CDcx/k3xljnhlh1zcYY+IjHN8B/BT4qYjo2MIlaD22k1LAV6PPwFZKKaVU+ox3FvYhwBhjnhWRoIhkDTyZZsDQ4lFE8oDKoec3xrw+UoGpxq+vfjcAhfNWpTkSpZRSSs1m45mF/SHgw0A+zhB1OfA/wG2j7P+PwB8AR3EWHif1/dZLD3d2s9qdW0jLqnQRcaWUUkqlz3jugfwYzpI9PQCpiTDFY+z/DmC+MWaDMeaW1NdFF48i8i0RaRGRPUPa8kXkGRE5nPqeN2TbZ0TkiIgcFJE7h7SvFZHdqW1fkRn4QNOc3iOEjA+315fuUJRSSik1i42ngIwaYwZX3RIRN2d6FkeyB8i9xLiG+g7wxrPaPg08Z4xZCDyXeo+ILMOZCX5F6pj/FhFX6piv4vSkLkx9nX3Oaa8yfoxGT1W6w1BKKaXULDeeAvJFEflrIJCaTPNj4Jdj7P8vwHYR+Y2IPDHwdbEBGmNeAjrOar4XeCT1+hHgviHtjxpjoqkn5RwB1otIGZBtjHnNOI8t+O6QY2aESDhEhkRpz1yc7lCUUkopNcuNZxLNp4EPALuBj+Cs6fiNMfZ/BPhian/7UgMcRYkxpgnAGNMkIgND6uU4SwYNaEi1xVOvz26fMVoajlAFuEqWpjsUpZRSSs1y41nGxxaRR4BNOEPXB83Aw0dH1maM+cpEBXiBRrqv0YzRPvJJRD6MM9xNSUkJtbW1ExLcWPr6+sa8TtexzVQBpxM5UxJPupwvD7OB5sChedAcDNA8ODQPmoMB0yEP45mF/WacWddHcQqxGhH5iDHmqVEO2SYi/wI8gfM0GsBZxmcC4h3QLCJlqd7HMqAl1d4Awx7SUgGcSrVXjNA+ImPMw8DDAOvWrTMbNmyYwNBHVltby1jX2XjqVQCuu+M+Ckou3+fQnC8Ps4HmwKF50BwM0Dw4NA+agwHTIQ/jGcL+f8AtxpgjACIyH/g1MFoBuSb1/ZohbRO9jM8TwEPAF1LfHx/S/kMR+RIwB2eyzGZjTFJEekXkGpye1PcC/zGB8Uw66ThCrwmQXzSjRt6VUkopdRkaTwHZMlA8phzjTI/fOYwxt1xyVEOIyP8CG4BCEWkAPodTOD4mIh8ATgJvT117r4g8BuwDEsDHjDHJ1Kk+ijOjO4BT/I5WAE9LGT1HOeWpYrE1nnlPSimllFKTZzwF5F4ReRJ4DKcn8e3AFhF5K4Ax5mdDdxaRApwi74bU/q8A/2CMab+YAI0x7xpl04gLmRtjPg98foT2rcDyi4lhOiiNneBY7nXpDkMppZRSalzL+PiBZuBmnJ7AVpyn0twD3D3C/o+m9nkb8EDq9Y8mINZZq7urnUK6sPMXpDsUpZRSSqlxzcJ+3wWeM98Y849D3v+TiNx3gedQQzQf20sO4CvVRxgqpZRSKv1G7YEUkQ+JyMLUa0k9UrBbRHaJyJrRjgNeEJEHRcRKfb0DZ9KNukg9pw4AkFexJM2RKKWUUkqNPYT9J8Dx1Ot3AauAecCfAWOt8/gR4Ic4S/hEcYa0/yw1C7rnUgOejeKnD5I0Qtm8ZekORSmllFJqzAIyYYyJp17fDXzXGNNujHkWyBjpABER4ApjjGWM8aS+LGNMVuore4LjnxW8nYc4ZZXh84+YdqWUUkqpKTVWAWmLSJmI+HFmPD87ZFtgpANST6j5+QTGp4CCcB3tgep0h6GUUkopBYxdQP4dsBVnGPsJY8xeABG5GWctyNFsFJGrJizCWc5OJilLniaSNTfdoSillFJKAWPMwjbG/EpE5gJZxpjOIZu2Au8c45y3AB8RkRNAP87jD40xZuVEBDzbtDYdp0TiSMG8dIeilFJKKQWcZxkfY0wC6BSR64Dqs/b/7iiH3TUxoSmA04dfpwTIqlyR7lCUUkoppYBxrAMpIt8D5gM7gIHHAhrOKiBFJNMY02eMOTHGuTKNMX0XH+7sE0ot4TNnwer0BqKUUkoplTKeRxmuA5alJsiM5XER2QE8DmwzxvQDiMg8nGHtdwBfB35y8eHOPqbtCN1kkltYmu5QlFJKKaWA8RWQe4BSoGmsnYwxt4nIm3DWgbxeRPKABHAQZyHxh4wxpy8x3lknq7+OZk8FOSLpDkUppZRSChhfAVkI7BORzTgLgwNgjHnL2TsaY54Enpy48FRxrIH6nHXpDkMppZRSatB4Csi/n+wg1Mi62popoZ26gsXpDkUppZRSatB5C0hjzIup5XwWGmOeFZEg4Jr80FTDgS3kAhnVYz16XCmllFJqao21kDgAIvIhnIkvX0s1lQO/mMSYVEpf/S4AyhauTXMkSimllFJnnLeABD4GXA/0ABhjDgPFkxmUSmk/TA9BCkoq0x2JUkoppdSg8RSQUWNMbOCNiLhx1oFUkyzQV0+LqwyxxvPHpJRSSik1NcZTmbwoIn8NBETkduDHwC8nNyxljKEscpSujJp0h6KUUkopNcx4CshPA63Abpw1Hp8E/mYyg1LQ1nSSYjpIlukEGqWUUkpNL+OZhW3jPEHm65Mfjhpw6uAWioCsuVemOxSllFJKqWFG7YEUkXtF5GND3m8SkWOpr7dPTXizV7jpIAAl81emORKllFJKqeHGGsL+K+CJIe99wFXABuAPJzEmBdBZR8j4yC+ak+5IlFJKKaWGGauA9Bpj6oe8f8UY026MOQlkTHJcs15u525OeufrDGyllFJKTTtjVSd5Q98YYz4+5G3R5ISjAJLxGPPiR+gsWJ3uUJRSSimlzjFWAbkp9RSaYUTkI8DmyQtJtRzfi1cSULIi3aEopZRSSp1jrFnYnwR+ISK/B7yealuLcy/kfZMc16zWevIAZUBOxZJ0h6KUUkopdY5ReyCNMS3GmOuAfwSOp77+wRhzrTGmeWrCG5uIfFJE9orIHhH5XxHxi0i+iDwjIodT3/OG7P8ZETkiIgdF5M50xj6WUOM+AMpqlqU5EqWUUkqpc41nHcjngeenIJYLIiLlwCeAZcaYsIg8BjwILAOeM8Z8QUQ+jbMQ+qdEZFlq+xXAHOBZEVlkjEmm6SOMKtC0kRNWBXMLS9MdilJKKaXUOWb6FF83ziMW3UAQOAXcCzyS2v4IZ4bb7wUeNcZEjTF1wBFg/dSGe37GtqkMH6AlW9d/VEoppdT0JMaYdMdw0UTkT4DPA2Hgt8aY3xeRLmNM7pB9Oo0xeSLyn8BGY8z3U+3fBJ4yxvxkhPN+GPgwQElJydpHH3100j9LX18fmZmZRPu7uXPLe/l13kNkrHrrpF93uhnIw2ymOXBoHjQHAzQPDs2D5mDAVOXhlltu2WaMWTfStvMOYU9XqXsb7wVqgC7gxyLy7rEOGaFtxOrZGPMw8DDAunXrzIYNGy4p1vGora1lw4YNHHz9RQDKl61n9RRcd7oZyMNspjlwaB40BwM0Dw7Ng+ZgwHTIw0wewn4DUGeMaTXGxIGfAdcBzSJSBpD63pLavwGoHHJ8Bc6Q97TSefBVAIoXjFjwK6WUUkql3UwuIE8C14hIUEQEuA3Yj/P4xYdS+zwEPJ56/QTwoIj4RKQGWMg0XM/SfWorpylkTo0u4aOUUkqp6WnGDmEbYzaJyE9w1qhMANtxhp0zgcdE5AM4RebbU/vvTc3U3pfa/2PTcQZ2bug4Lf4adP61UkoppaarGVtAAhhjPgd87qzmKE5v5Ej7fx5n0s20ZCeTlCfq2Vmgw9dKKaWUmr5m8hD2Zefwob0EJIa3bGm6Q1FKKaWUGpUWkNPIyV0vA1Cz6sY0R6KUUkopNTotIKcRX9MWQvjJm7s63aEopZRSSo1KC8hppLR3Nyf8S8A1o29NVUoppdRlTgvIaSLa382CxFG6i65KdyhKKaWUUmPSAnKaiDTtwRJD7oo70h2KUkoppdSYtICcJjy9DQBULNElfJRSSik1vWkBOU1khhtoIZ/M7Px0h6KUUkopNSYtIKeJkng9Lb656Q5DKaWUUuq8tICcBrrbm1lgH6e3aG26Q1FKKaWUOi8tIKeB/b/9Ji4xFFz5lnSHopRSSil1XlpATgPVB79FtwmyYNUN6Q5FKaWUUuq8dMXqNLOTSU6u+BhNPUnudbnSHY5SSiml1HlpD2SaWS4X69/2SXJqdPkepZRSSs0MWkAqpZRSSqkLogWkUkoppZS6IFpAKqWUUkqpCyLGmHTHMK2JSCtwYgouVQi0TcF1pjvNg+ZggOZBczBA8+DQPGgOBkxVHuYaY4pG2qAF5DQhIluNMbN+Jo3mQXMwQPOgORigeXBoHjQHA6ZDHnQIWymllFJKXRAtIJVSSiml1AXRAnL6eDjdAUwTmgfNwQDNg+ZggObBoXnQHAxIex70HkillFJKKXVBtAdSKaWUUkpdEC0glVJKKaXUBdECUimllFJKXRB3ugOY7goLC011dfWkX6e/v5+MjIxJv850p3nQHAzQPGgOBmgeHJoHzcGAqcrDtm3b2kZbSFwLyPOorq5m69atk36d2tpaNmzYMOnXme40D5qDAZoHzcEAzYND86A5GDBVeRCRUZ/Ep0PYSimllFLqgmgBqZRSSimlLogWkDPdt98EX16d7iiUUkopNYvoPZAzWTwMJ15NdxRKKaVmgkgPeDOg7TCIQEcdRHvB2FB1NeRVpztCNYNoATmD9X/nbehcNKWUuswlExDtoaBtC7y21ykE7Ti4AxBqA38O9LeBLxM6jzudC3YSkjEwBlr3gzcTuk4AAozwBLrsCvjjreAJTPGHUzOVFpAzlTFkNJ7pfbTjUSyPL40BKaWUumThLmjaCb1NcOxF6G91RpriIVYA7BnYMVUIuv2QiIA3CxJhyCgGfza4PODygUnCwjshHoKV73T2yasBTxCySiCjCLt+C9avPwm7HoO1D6Xrk6sZRgvIGap934sUAPV2EZVWK5FwH0EtIJVSZ9v/S2g54PRSFS+B0hXgywFLb4FPq/526GmAhi3QeQJO73L+nPpbnaIPIJAHOZWw9C0wZw276jupfsMHiLsyeGZ/C+vKM6it62NBvpe+pEUsnqQo209DZ5i5+UFeONjCdQsKeXJXEysqc9hZ301hppe9O3voicTpCsXpj56mvb+YX3mryXnq/+Ge/3bKcoPpzY2aEbSAnKEKfnwvAIcqH6Cy8atEwiGC2QVpjkopNa00bIMfvfvc9kA+VK6H7HJY/lbInuP0SolMfYyzQVc9tB6E9sPQsBV6GuHka2e2u3yQVQoL3wDBQqi5EZNRzLcP+VhTU8xTe05z7GA/2+taaN+25YIu/fWX6wD48baGMfYSHuWN/FPif3jnF/+br/3tJ8gNei/ig6rZRAvIGajnxA6ygXaTRWZ+KTRCIhZNd1hKqekkEYPHPwZA5O3/y5ZoFavMAbKjp6F+E3TWOUOkW7/p7J9d4dxDV7keltzj9FRml6XxA8xAtg29p+D4K9DXDAeehPYjzn2KA7LLIbMYbvwLKFtFi6eccP4SWnqjvHqkjVjC5vGfnKIn3ERvNAEcGXaJNVW5HDzdy3uuncu+Uz1cXZPPweY+AOYXZXC4pY/11fk8f6CFB6+q5Jl9zdy1oozdDV0sLs3GYIjGba6cm0dDZ4irqvPZ39TDypIN8C//w498/8i//+4t/OkbFk1d3tSMpAXkDLR94wvcDOy+9t/JiLQAWkAqpYaIheCfneLvA7E/57nvGeAEECAvuIj5RVdyz6o5FLj6uCO7Aemsw9OwETqOwevfdb5cXihdCfnznHvnciuhcJH2Ug6Ih6HnlHN/YkcdNG6F46+eGX4GKFgAS+92ivOaG+l15fKz4z6qizL5zqt1JI4bXj58Gjg97NS5QQ+heJIlpVmsr8kn4HXxe+ureOrFjXzk/uuQcfwZPHRdNQB3rXD+Hty+rOScfWoKnWmYa6ryUheeC10n+NnGg/zRhgV43XqbgxqdFpAzUO6pl2g12Vx1893sf/4HACTi4TRHpZSaLmI//zhe4Pnkap6z11KVH+TGhYX89PUG4knD1hOdbD3RObi/y5rLm1dcg5UL77nGYp6/m5yjv8RqPwyHnobdjzk7lq6AwsWw8HaoudnpSbNcafmMUyqZgHAnNGyGo89DdwMceRbshLNdXE7P4rUfg+w59JWuw184n29u7aAjFGfnwS4Ov9RLe3/7sNO6LacQvHlREd3hOJ+5awnb67t425UVeF0WWX43lnWmWFyS7xpX8XjR7v0veORuloW28p5vFvKjj1w7eddSM54WkDOMiUdY1f08m7LfwNUB/+DMa+2BVEoBRA6/iH//T3kkcTufS/wBr33mVspynKVZ/uKOxXjdFpvrOqhr66epO8wz+5oJxZI8sfMUAL/Y4ZxnTs4buXLuuyhfmuDji/twte4jePiXTo/bnp+cueC696d6KR+EjMKZ30Np29B1HLobYf8T0HXSuV8x0u1s92VDRhGs/QMoX0uTp4Jj3qV0huN86ZlDFGb42Hy8BWgZdtqFxZn0ROL88a0Lae6JcMcVpVTkBcjwuinN8Q/ud/W8NN7LXnUtxp/DzYmdfKZuPZF4Er9nFvyCoC6KFpAzTP2/3UYVkG2ce15c7lQBGdcCUqnZbv93/5Slx74NwIHlf8G+t64j6D3zYz4vw5kYccuSYm5JtX32zcvojyZ4fMcpls3J5u8e34OIsO9UN7/a1QTA1zYC1HDX8n9m/hVB7s0+yFy7Ac+L/4zs/bnTO/fbv3GWlFl2H5RcARXrYO51U/fhL5Yxzv2K278HrYegcRt0HHW2eYJQMB/m3QJzr6PfX8KTkZWUF2Tz9ZePkThtePlwG7B58HTHWvtZVJJJTsBDdUEG775mLk3dEe68ooSkbXC7pvGwsMuN1NzMW09s5jMdhhcOtAwOgSt1Ni0gZ5hT3rlUhfZQ9pGfAuDyOgVkMhZJZ1hKqTTb99pTLEsVjz9c/V3+5b5rxn1shs/N711dBcATH78BgN0N3bT3RznW2s9PtjUQiiV4ao9zr95/4gFqqCn8Ie+/vpo5kSNs8OzF1X4Ydv8Edj3qnNiX48wuXvF2yKlwisr8eekb9g51OPcr1r0Iv/sPZ2mjzroz23PnDg5F21nl/KqzAl92Ib/Y3sj257roCMWIJfYP7u51W+QEPDx0XTUNHSE+efsijrf3c1V1/rCeu1WVzne3awb0zi68Hd/+J7g2o4lf7WrSAlKNSgvIGcSOhqjq3Mguz3JWZmUCYHmcoQ9beyCVmpW6O9to/c87WJY8SgfZ+P58N7+XlXvJ511RkQPAhsXw/htqiMSTPLWniZrCTP7sRztwWUJ9Z4i/fXwvABnexVTmr+G9b/hLFufaLGz7Ldk9R+HUdnjhn4acWaB4KSx5MwQLoPoGyCx1Xk/E2pTJuDPc3LwHTu9xJru88E9QdR2c/N3wfe3UhJcb/4LtmTezLVZBOJbkFy81Eo4lOdV9AmfyEVTlB0kkbf789kV0huLcvqyEJaVZwJmeXYDK/Bm+huKiu0AsPly4l48eqKA/miDDp6WCOpf+rZhBjr72OAulne1zP87KVJsrdQ9kUgtIpWYVY9vseOZ7rHntE+QAx60qPO9+lPwJKB5H4ve4uH9NBQDP/8UGADYda+dIax/HWvv55it1HDjdy1//wnlUitddxdKy5WT738an31MGvadYFN6OJ9zuTMx56V8Z9ki9/HlQsd55OkrVdRDMdybtnP1oPWOg9zR4g1C/xVlTsa/ZeXpLuAtOve48deVs/a0AJFa9m6bSW3nhtA+7+Aoeee0E5nXD8fZewOldLMz00RWKcc+qOVwzLx+Py+LtayvojyXJvNyLqcwiqLya9b2biMRv59n9zdy7ujzdUalp6DL/P+Hy0nV0M0kjXHfPBwbb3KkeSC0glZo9erraafrPu1iTOAjAa+Xv49oP/fuUx3H1vAKunleAMYY/ecNC3Jbwr785RNK2+e2+ZnbWd+GyhDcfdtZBLMhYwJKyNSwqv58331lCuKWO63xHcYXa4PBvnB7CnlPw6pedC7gDkFHEarJhv0B/i1NA9recG0z+fGdyy/K3QelK7P2/Ily6lp7l7+XX248TyajgextP0LktTixhpw7ah99jkR/0sqQ0i8+8aSmHm3t5cH0VXpd1zjI2l33xOGDRG8l49nOsyOrjV7uatIBUI5ol/zdcHvKaXuKQZzFLc3MG29ypeyDthBaQSl3uers7OPjND7Gu51mygS25d1F4+ye59oqr0xqXiJDt9wDwd/csA+CTty+ioz9GZyjG9147QabfzQ83neTVI+28eqSdb796HIBMXx75GSXcuuQWSqv9FLjC3FvRS7K7icCpTRDuxHVsC/jnOD2SxoaylZCM0ZO1ALt4OV0mwMsnIlTkBfn+xhO4e4SNJ6rpPhCH2n2pKA/hdVlcM7+A091hPnfPFRxr7WPD4mIq8gKDn+PmRUVTnb7pZ8Eb4NnP8cE5x/nLg9n0ROKDf75KDdACcobobjvFgsQRXp37sWHtAz2QRgtIpS5Lxhia29o49a33cmX4d6wD9npXEgqUsu4TP0Sm6TOtc4PewcfhrZ2bD8Anbl2I3+vi6T2n2Xq8g0yfh2+9WocIfOd3xweP/UsAfFy/4H4EITf7AcpLKzne1o/f42LjC+3kBb0cau7FNvvOubbPbZG0DW+9spz6jhCfuG0hsYRNZX6QRSVZg/tdv6BwEjMwg5VcARnF3OjaQyy5nGf2NvO2tRXpjkpNM1pAzhCNh14nB8icv35Yu8eXmkSjBaRSl5We3m7a6/dx7B//lPl2HaXAEdd8wu4cln/quWlbOI6lONv5efWOdZW8Y50zNfmv3rgYn9vix9sa8LosGrvCPLm7CY/L4tUj7VgCtgGOHUPEGcGek+Onrq2f0mw/Ny8u4sDpXj5683wONfdyw8IiqguCxBL24PXUBRKBeTeTd6yWipyH+NWuU1pAqnNMqwJSRFzAVqDRGHO3iOQDPwKqgePAO4wxnal9PwN8AEgCnzDG/CbVvhb4DhAAngT+xBhjRMQHfBdYC7QD7zTGHJ+yD3eJ+k7uBqB0/qph7QND2EbvgVRqxovHoux/5ReEjv6ONQ0/4G0Sp41cduTdQSdZ3PIn30p3iBNuYLmbgYIS4GO3LMAYw5bjnVQXBvnBU69QXrOYO5eXsv1kJ9fMK8AY8Lhk2LqKd1xROuXxX7YWvRHZ/WP+aPEp/m53jO5wnJyADmOrM6ZVAQn8Cc40uOzU+08DzxljviAin069/5SILAMeBK4A5gDPisgiY0wS+CrwYWAjTgH5RuApnGKz0xizQEQeBL4IvHPqPtolOr2LTrIoLps7rNmTKiBJxtMQlFJqIhza9gKdWx6joHUTK5POItY7g1dzKnc9197/UVYXz75JDCLC+hpn6HtNsZsNVzkF5obFxekMa/ZY8mbw53Bn7Bn+2n6QFw60cN+a2ff3UI1u2oyBiEgF8GbgG0Oa7wUeSb1+BLhvSPujxpioMaYOOAKsF5EyINsY85oxxuD0ON43wrl+Atwmk/pQ0YljbJuaro3UZaw5Z9jK63du/tZ7IJWaOVoajrH7hcfY+qUHaPv7uSz65X2saXoMnx1m65VfoPUP97LqU78lsPgN5M7C4lFNA54ArHgH+Sd/wxX5Nj/cfDLdEalpZjr1QP478FdA1pC2EmNME4AxpklEBn71LMfpYRzQkGqLp16f3T5wTH3qXAkR6QYKgLaJ/RgT7+S+15hLB0fn337ONk/qUYZoAanUtBXp76blxEEaX/o2md2HWRx6nWJJEjcutufcxqG8+ax4619RmZNP5flPp9TUuPI9yJav85nyXbx792oON/eysCTr/MepWWFaFJAicjfQYozZJiIbxnPICG1mjPaxjhkpng/jDINTUlJCbW3tOEK6NH19faNeJ/7696gwQpunasR9rjVuerrapiTOyTZWHmYLzYFjJuchHLeJtBwhs/k1ssONrI1toUpsSo2LE1LB7/w301XxBnw5ZQSynWHabdt3nXOemZyDiaR5cKQjD2sz57Gi8THcsoov/vR3vHuZb0qvfzb9u+CYDnmYFgUkcD3wFhF5E+AHskXk+0CziJSleh/LgIHVYxtg2C/qFcCpVHvFCO1Dj2kQETeQA3SMFIwx5mHgYYB169aZDRs2XPonPI/a2lpGu86ejf/AMVcNd99z34jb+17wkBP0c/UUxDnZxsrDbKE5cMyUPPT3dtHacITT259COo5R0LmTuYnjeCRJwlh0kcXWwrdAxXrKV25g4fwrWDjOc8+UHEw2zYMjLXkI/hE8+Rf84eI+HqnL498/cENaF1TXvwuO6ZCHaVFAGmM+A3wGINUD+RfGmHeLyP8FHgK+kPr+eOqQJ4AfisiXcCbRLAQ2G2OSItIrItcAm4D3Av8x5JiHgNeAB4DnU/dJTmvGtimLHOVo9vpR94mJG7FjUxiVUrOTnUhQt28zPaePETv0HN7Qaa7o30y1JKgGusmg3Spid+Xv48otZ+4tH6CwoAhdbVDNWCsegN98lvdbT/Kf0Xfy89cbeM+11emOSk0D06KAHMMXgMdE5APASeDtAMaYvSLyGLAPSAAfS83ABvgoZ5bxeSr1BfBN4HsicgSn5/HBqfoQl+LgjldZQheH5t406j4JPEhSC0ilJlr94Z20H99L+OTr+Nv3UhQ+xnxzGoCQ8dHqKmJn3u3I/FvIrVrOglXXk3Oecyo1owTy4LqPk//y/+NPixfy5ee83Lq0hPLcwPmPVZe1aVdAGmNqgdrU63bgtlH2+zzw+RHatwLLR2iPkCpAZ5LmA79jCVC2+g2j7pMQj/ZAKnWRjG3TdvokXS31tO95DnpPk9+2lYJkC5V0D94rc8KqpMtbyqnFHyO7fAnlS9YxNyefuWOeXanLwIa/hhOv8Ymmr9IYj/PVb+zl79f04RYgoxhyymHhHeDSdSJnk2lXQKrhvH2NxI2Lyrmj3zUVFw+i60AqNSZj2/R0d9B0ZAfddduxO4+T3b6T7Hg7leYURTj3wkSMh0Z3JccKbuJwTjX5y28np7iCuRXz0/0RlEoPlxve9g2sR+7h/3Z8GfqAl8FgIdgA2OKmO38l3us/Skb5cihZlt6Y1aTTAnKa8/Q10moVMMc9+h9VEjcu7YFUCoBkIkFnWxMndzxHvLsZq2k7gXAT5dFj5NEzbIj5kHsRbYFqGksfwFs0j5LF11A+bynzAS0XlRoipxw+tgkat/HEjkb+dUuExngWufSx1jrEGusI97e9Qt4THwKgPWMBntxyAjVX48mdA/k1gEDZSmdYXM145y0gReQ5Y8xt52tTkyMYPk2Xp4Q5Y+yTsLxYtvZAqtkjGgnR1dZE6/G9RLtbiLUcxte2j8zoaRbED1MoZnDiSg8ZnPLM5VjWWuKla3BnlVB55e3k5JewKJiZ1s+h1Izi8kDVNbylCu64K8mexm4CXheLSh6kuSfCidZOntm9GTnxOyo7N1LSe4wljS8OO4UtbuL+fOyscvyVq5FgAWTPgaxSKF8HgVxwp3epIDU+oxaQIuIHgkChiORxZh3FbBiznlETxNg2JfF6juVcO+Z+SXFjGS0g1eUlkYhRf2Q38UiIlt3PYULtZLTuICPeQXminhKJUzJk/3qZQ787l01zfh/JKiO7Zi3FNVeQk1/CEq/+g6TURPJ7XKyrzh98X5EXpCIvyNWL7seY+zjc0sex1j5+sP8k4e42QvW76I8brrIOUBjvZnF/A5XNPyNPenGlhsEH9OUtI+EvIKOoCk9mAcxZDTmVkFmClYyAMTAzHiR3WRurB/IjwJ/iFIuvD2nvAf5rEmNSKXt3bmY53Rytvn7M/ZLixT3CEHZ//S6Ih8moWa//s6lpJxoJYVkuDm56mkjXaeKnD0AiTEb3IYojJ7jRdON7xfnFaF7qmJNWOR2+SnbmrUFKl+PNKSWrdB6BrDwqqxen78MopQaJCItKslhUksUbl5cBYMybCMeTnOoK09EfZ19LL79q7aeju5f+rlaC3YfJ7D/BHGlnWdsJ8qSROad2kSv9eEgMnvsmILSlBClagiurCG/ZsjMTeQL5ULoCLFeaPvnsMmoBaYz5MvBlEfljY8x/jLafmjztx3cCUL38ujH3S1oefIn+YW2hzibkm7cTJMLexR/ninedM2FdqUljbKdH4dTxg3Q0HCLSUU+i4yQS6yOnfQfBZA+VyQYMsFyc5VhtI8Rx0yG51OesZU/cRfZ8Z/3TqqvupqC0iiqvj6p0fSil1EUTEYJeNwuKnUchrq/JH7bdGEN3OE5nKM6h5l4akobHT3TS3R/C07IbK9yJq6+BLLuPlcljlIQaqZLXKdj3k2HnSYiHuCebeEYZBHJxl69Gcirx5ZViZZZAbiV4M8CXrYXmJRrPJJqHRCQJ/NAY03W+nUVkHXAjTs9lGNgDPGuMGfGpL2p00nYY2wiFVUvH3M+2PLjOGsI+/PJPWUWEk6aImoPfINr/l/gycicxWjXbdHe00nhoG9GeNqKn9oHlwt+0mUC8i9JEPW6TpFwigw+jB0ga4ZB3KR3+Kpqyb8KIC1/1egprVpBTVE52TgFllkUZzpMW1usTJ5SaFUSE3KCX3KCXmsIMAN68siy1dR0AoViCl19+maKFqzna2s/m3ghd3V3Euproaz9FQaKZ7J5D5MZ7KQ13ki+NLG98FUvOfWZIyJVDT+5S4r5cMgorSfhyKaxahlgWUnOTM9FHR+7GNJ4C8kHgfcBWEdkKfBv47dlPcRGRPwA+AdQB24CDOI8lvAH4lIjsAf7WGHNy4sK/vAV6jtFiFVHqyxhzP9vy4j6rgEye2EiXyaTlDf9B1XMPsvPZR1h1759MZrjqMmEnk/R2d9CwfzMuj5fOAy8hfc14wi34Yp3kx06Ra3cTIMkySQw7tkFK6fSWcSjjRmxPBia3kuwF1xLILqB07hKMsVmaqUttK6UuXNDrxucSrqzK48qqkWdy90UTdIfjNHWFaQ3H+capdgJ2L6fqj+JP9OJtP0AikaA6foTy1hYKOYq/8QWCEoUtZ86TxKLPU0DInYudUUzSX4DkVeObswxvVgG5xVVOkZlZPGsLzfMWkMaYI8BnReRvgbuBbwG2iHwL+PKQnsUM4HpjTHik84jIapxl1rSAHAdjDKV9+2j2z6P0PPvarnMLyPyu3RzzL2XNdXdy7PlKMvb8ALSAnPU6Whrp7Wimt7WeaG8bsdajSLgLd6iFgr5DZNi9FJpOcsQMW+6mzwTolUy6PUU0BxZQnzEH4/IRXHwr/pwiSuYuIZGIU140hwrLStvnU0rNbpk+N5k+9+CTcm5bOjDV7uph+/VE4nT1x+kOx3m9tQ/CnZw+cRAvMayGzeTQj9XbSE4sRHF/A9lykDn1v8TaPbw3M4lFv5VJT6AKwSZSuAIw+CqvxOWyyKpaTUbAh+RVgz/nsio2x7UOpIisxOmFfBPwU+AHOD2LzwOrAYwxY06sMcbsuIQ4Z53jx49SY5ponfv7593XWB7cZkhPkDEUJU/TmHcVLpdFffXbubnuSzQd3ErZ4nWTGLVKp9P1R4iGeuk6dYRw/S6IdOHqP4031k1RpI6EeKiyG8k/67iYcRERH8eCq2jzZHM0swwJ5OErXoCIRdnSqympmE8mUDbShZVSaobJ9nvI9jtPzllRkQOUw3UDD7F7FwC2bbAs4UhLHxEMz5+sJ9HbTKj9FLGeFrzhVhJ9reQl28nubcRLksV9vyCBi+wTjw27XgILg0Wnq4Cwv5iIlYHJn0/CWGRUrcK2bYoXXw3JOJmVyxHLPe2f7DOedSC3AV04z5L+tDEmmtq0SUTOmR4sIjXAHwPVQ89vjHnLBMQ7azTtfpEaoPiKm8+7r+3y4uFMD2Sot4MMIpjsCgAW3fFBov/zFZpe+NqsLCCNbSOWNfg9EY/h9niJRkJ4PD5isQiW5cIYm0QsiscXoL+nk4zsPLrbT5OZU0B3+2kCWXmEetrx+ILEIqHBXyTj0TBef5BIbyeBzFz6utsJZOXT39WCPzOHaKgXjy9IPBoCBLFcJONRPP4g0VAP/owcQl2t+LPyifR20NfSTHd7M8GsXMKhPrpa6gn3dNDf3kiyv4NE9ykk0kVm1wFs8VAd2YcBSnEmUg08Wi9phFYpIGRlcjq4GMskaCy6B0/xAnxZhWQWVpKVX0JeYRmZIqx26Q3lSik1wLKcH/ILijNT35cBIz9hJ5F0Jg4ebO7F74JNB7Yjbj+hY5tIiJtg605iiSRF0RO4+/opoY7Cnu24sPGd/J5zkt+lzoULN0kaXeUYy0OXbw4SyCHqLcBXUEnS8hJz10zqZx+P8fRAvt0Yc2ykDcaYt47Q/AucYvOXcNbiTmrczMnXiOBhzpKrz7+z5cM7ZAi7reEoVYA333mKb1lZOa9l3sSK078k0tuJP2vke0eifR14/RnEe9vx+ANEQv1gbMDQ3dJIJNSDHY/izcihu7ONfuPH6qkny4qSCPcg/hxioR76kh7saD+WZTlD63YcEZBYP8btwwp3YjxBXOF2kt4svOFmku4MXLEevDGbXZv/GYNg2QlcJo7BwpvsI2H5CCa6ibqCBBM9RKwgWXY3MfHhs8MkxI3bJBAMgsFPlBgeMk2IfgmQY/roEz9ZhIkYD14S2AheDJYYbCP4Ujdbe1M5KUp9D6S+j+fuvZLz7zKmZQD7nNsNPDgLr54tZHw0u8qwxeJQ1jUkvVnY2eV48ipxB3OZf9WdZGblUqrDyUopNencLudn7RVznH8l5pdscDZcf82I+0cTSVp6okisn7pjB/Ba0HlsG263F6tpOwnLR37PPuLGzdy+Q0ivTT49+Bqd0UZjLYW775/0zzWWsRYS/7Mhr8/Zboz50iiHRowxX7n00Ga38s7N1HmXstRz/gWQjcs7bJ2snpbjAGQUVw+2Zdz8p2Q++TzbH/9X1rzbWdInGe7m6O8ex7/xSxTHG/CnejEHiqcAZwx9DVBxgZ8HIGrc+CRBt8kgSIQuySZgInRLNm7iRMVPvrGxkx6SuAFD1MrAY2KE3Nl4kmHafZV4k/30BUtwJUN0uudimTjG8g0udW+7fJBMkPRmYyX6Mb4crHgfSX8eVqwP25eDJMIYTxDsJIgFLi/YcfBmIrF+COZhIr1Yqe8SyMXE+7F8mdiJOOLyIC43JpnA8mWQjIZwBXOxQ124M/NIhntwZ+SRCPfg8mVhElEstxdEsJNxXN4gyUgv7mAuiVA3nqxC4v0deDLyOH5wDyU5Xky4C1xepygMZJNRVElGThH5pZUEAxmk//dPpZRSF8PndlGZHwSCVJSmuiquuXHEfRNJG9tAb08nRzt6MH0tHN+7nVVTF+6IxuqB/FdgB/AUEOXMk2jO58si8jngt6njADDGvD76IWqoSH8PlYmTbCp7//gOcHnxSmJwiDbS3gjgzBJLWXHVTWx57hquOPIN9v88m2QsxIL9/80iYhyjgq1FD2ACedh2AuMOYts2WC4sjx+TjOMtqMYTyALLRayvk5yCYrIkguRU0mEyyczMItbbRmZ2HgV+QyCYRSRhA4LHn0EsFiEjM5tINEqO34+dtClK/cY29GFytbW1bJjlS7d0ShHXzPIcKKWUcgz0bhbkF1CQXwDU0No14nzlKTVWAXklzhI+b8ZZlud/gefOXr5nBCuA9wC3cmYI26Teq3E4uvkprhBDcP7YC4gPcjt9hvFYFK8/QLK/HYCcwjPzt0WEwnd8hcbvPcDSnf8MwHbXSkJX/RGrbrqPecGz+xjHb+7gq+FPuBy6+JDX5/Sk+v1+ACyXDq0qpZRSM9VYT6LZgdMD+WkRuQ5nWtJ/iMinjDFPjHHO+4F5xphzn62nxsXa+g3Cxsviq980rv1lsICM4PUHMOEOwsZLRkbmsP1q5i8m8tfb2XX0CIlwL1esWIPPM66J+EoppZRSg87bDSQiRcAanJ7FBqDlPIfsBHIvObJZxCTjLHz5j9jzilOX+8PN1PmXEggGx3cCl9O7F49GnLfhTnolc8R7V/1eNyuXLuHKK6/S4lEppZRSF2WsSTTvA96J8zSZnwDvMMacr3gEZxLqARHZwvB7IHUZn1Hs+dkXWZFspPzZ93Ag+ARL7BNszF4/7uPFnSogY04B6Y510ufKpnhSolVKKaXUbDdWF9Q3gd04T465E7hjaI/WGAXh5yYsulmiv61h8PWSJ5y02pnne/7MGTLkHkgAf7ybsFsfF6eUUkqpyTFWAXnLxZzQGPPiwGsRudsY86uLOc9skpRz/xhcgdxxH2+llvpJxJ0eyGCih/aM+RMSm1JKKaXU2caaRPPi2W0ikgdUGmN2jfP8/wBoAXk+c66E098bfLvHfyXr7v/EuA+3UkPYidQQdpbp4bQ3d0JDVEoppZQaMJ5JNLUiki0i+TgTZL4tIqMtIn7O4ZcU3SwR9A1/hJzc+je4PN5R9j7XYA9kLArGkG36SPhyJzJEpZRSSqlB41mML8cY0wO8Ffi2MWYt8IZxnv8jFx3ZLGLs4U98LJ6/+oKOd6UKSDseIRnpwS02thaQSimllJok41nHxS0iZcA7gM+OtpOI3DRWuzHmpYuKcBYw5kwBGTZecnJGflb1aCyPszh3Mhahv7ebbED8WRMZolJKKaXUoPEUkP8A/AZ4xRizRUTmAYdH2O8vR2gzwCqcRye7RtiuGF5AWhi87gt7SstAD2QiESXc10U24NICUimllFKT5LwFpDHmx8CPh7w/BrxthP3uGfpeRG7A6bFsAj5+yZFezoYMYSew8F3g4W6v0wNp4lEifd1OWyB7oqJTSimllBpmrIXE/TgLiXcCv8TpYbwJOAr8ozGmbZTjbgP+Fqf38Z+NMc9MdNCXnSE9kOYi5h25vM5zrJOxCNF+p4D0BLWAVEoppdTkGGus9LvAHcD7gVpgLvCfQC/wnbN3FpE3i8jvgL8APmuMueVCikcRqRSRF0Rkv4jsFZE/SbXni8gzInI49T1vyDGfEZEjInJQRO4c0r5WRHantn1FUiugi4hPRH6Uat8kItXjjW8yDZ1E4yF5wcd7B3ogExFi4R6nLagLiSullFJqcow1hL3MGLNcRNxAgzHm5lT70yKyc4T9f4nzrOx24FNnP4d5HI8yTAB/box5XUSygG0i8gzwB8BzxpgviMingU+nzr8MeBC4ApgDPCsii4wxSeCrwIeBjcCTwBuBp4APAJ3GmAUi8iDwRZxe1rQaeg+kT+IXfLzH5zwz28SjJEK9AAQytQdSKaWUUpNjrAIyBmCMSYjIqbO2jdRNdlFPrhlgjGnCuV8SY0yviOwHyoF7gQ2p3R7B6Q39VKr9UWNMFKgTkSPAehE5DmQbY14DEJHvAvfhFJD3An+fOtdPgP8UETHGmEuJ/VKJsc+/0xg8PqcH0k5ESIadcwUyL2wmt1JKKaXUeI1VQFaIyFdwFgMfeE3qffnZOw88uUZE1hpjtg3dJiL3nL3/WFJDy2uATUBJqrjEGNMkIsWp3cpxehgHNKTa4qnXZ7cPHFOfOldCRLqBAmDY/Zwi8mGcHkxKSkqora29kPAvWE/Tmfp8s+86Qhd4vVg0wh1Ae/MpBjp+d+zeh//wsYkLcor09fVNer6nO82BQ/OgORigeXBoHjQHA6ZDHsYqIIcuy7P1rG1nvx/q6yLykDFmN4CIvAv4U5wh7vMSkUzgp8CfGmN6zh4KH7rrCG1mjPaxjhneYMzDwMMA69atMxs2bDhP1JdmU+s2aIXmdz7FVYuuQlyeCzo+Ho/Da5CfnYmYOMkO4fbb78DlurDlgKaD2tpaJjvf053mwKF50BwM0Dw4NA+agwHTIQ9jPQv7ERFxAV8wxoy0xuNoHgB+IiK/D9wAvBdnMs55iYgHp3j8gTHmZ6nmZhEpS/U+lgEtqfYGoHLI4RXAqVR7xQjtQ49pSN3bmQN0XMBnmxypIWxXXsUFF48AbrebuHFBMorEQ/QTIHsGFo9KKaWUmhnGrDJSE1LWXsgJU+tEPohTCD4A3GGM6T7fcamZ0t8E9htjhj5r+wngodTrh4DHh7Q/mJpZXQMsBDanhrt7ReSa1Dnfe9YxA+d6AHg+3fc/AoMFpMjFFX0iQgw3kowi8X7C4p/I6JRSSimlhhnPk2i2i8gTOIuJ9w80DukhBEBEdjN8ODgf5+kzm0QEY8zK81zneuA9wG4R2ZFq+2vgC8BjIvIB4CTw9tT194rIY8A+nBncH0sVvAAfxVlqKIAzeeapVPs3ge+lJtx04BS66ZcqIC3r4h/WExMvJKK4EiGiEpioyJRSSimlzjGeAjIfZ2meW4e0GeBnZ+1396UEYox5hZHvUQS4bZRjPg98foT2rcDyEdojpArQ6cQMFpAXP+wcx4OVTBWQlvZAKqWUUmryjKeA/Giq8DqfdmNM31g7iEjm+faZlQaW8bmEHsiEeJBkDHcyTNzSHkillFJKTZ7xdHntEZFX///27jo+7vp+4PjrfXdxbbxJGqm7ewu0FCgMKTIYsCEbg7HBNjYmMGUKM7YfDHfYgOHuEqDUvU011aSRRhqXs8/vj++luWukFm3ez8cjj9x9vnKfe9+1ffejInKXiHxFRNrb4uR1EfmHiJwqIhHNhSIyWESuE5H3sRb0VofrjBZICcLmdRLsbcBl1wRSKaWUUl3niC2Qvl1bMoBTsLqp7xeRSmPMxMPOWyAiXwG+A8zxbTnoBrYBbwPXGGOKO/sNnBQ6IYF0S7AvgWykWhNIpZRSSnWhIyaQIpKONcHlFGACkAssbutcY8w7WFsHqmPhPfFJNC4Jxu5pIsQ04nGEd1bNlFJKKaVaOZoxkPuAlcCfjTE3dnF9+qXmSTRyAi2QHlsQduMi1DTg1QRSKaWUUl2o3YzFt9A2WFsKPg1cKSJLReRp35I6qpOIbynKE2mB9NhCcHibCDNNeIM0gVRKKaVU1+moBXIFMNkYs15EdgI7sbqxvwGcirWmouoEzctXnsgYSI8tmGBvIyHiAk0glVJKKdWFjmYM5CogBFgCfAGcaozZ29UV61c6oQXSawsmwlsDgARHHOFspZRSSqnj11ECmSQiPwb+B3iby4BLfDvL3N3+peqYHNrKsL111I/Maw8mxtSAgIRoAqmUUkqprtNRAmkHIml/dxjVWYwXr5ET6sL22kIIFjcAtpDIzqqZUkoppVQrHSWQRcaY33dbTfoz48WLHNWq7u2yB7c81ARSKaWUUl2oo5xFWx67iy+BPKFb+CWQQRHtbRaklFJKKXXiOkogF3RbLfo748WcWPsjBIUeehisCaRSSimlulC7WYsxpqI7K9KvdUILJMFRhx6GRcae2L2UUkoppTpwgs1eqlN0QgIpodGHHodFxJ5ghZRSSiml2qcJZC8gxpxwF7Y9vKXbOjw69gRrpJRSSinVPk0ge4NOaIEMCm9pgQwPj+rgTKWUUkqpE6MJZK/gxZxwAhl76LHNrh+rUkoppbqOZhq9QSe0QIZEDOikyiillFJKdUwTyF5AOmEZn7Co2M6pjFJKKaXUEWgC2RsYc8ItkDHxKQDsJ7EzaqSUUkop1a6OtjJU3aUTurAjomL4bMLfSBk+vZMqpZRSSinVNk0gewExHoyceGPwaRfd0Am1UUoppZTqmHZh9wKCF49+FEoppZTqIzRr6QXE68GDvaeroZRSSil1VLQLuxeYWv1hT1dBKaWUUuqo9bsWSBE5W0S2iUieiNzW0/VRSimllOpr+lUCKSJ24D7gHGA0cIWIjO7ZWimllFJK9S39KoEEpgN5xphdxhgn8DywqIfrpJRSSinVp/S3BDINyPd7XuAr61G7bVksd0zr6WoopZRSSh0VMcb0dB26jYhcCiw0xnzb9/wqYLox5vuHnXcDcANAcnLylOeff75L65WeczOl9mSaTvl1l75OX1BbW0tkZGRPV6NHaQwsGgeNQTONg0XjoDFo1l1xmD9//mpjzNS2jvW3WdgFwCC/5+lA4eEnGWMeBh4GmDp1qpk3b16XVmr353ZEhK5+nb4gJyen38dBY2DROGgMmmkcLBoHjUGz3hCH/taFvRIYJiLZIhIMXA680cN1AvpPK7BSSiml+r5+1QJpjHGLyM3A+4AdeNwYk9vD1fI5sb2wlVJKKaW6S79KIAGMMe8A7/R0PZRSSiml+qp+l0D2RsUD51PW0N9GEyillFKqr9KspReYdf3/ETVel6NUSimlVN+gCaRSSimllDommkAqpZRSSqljogmkUkoppZQ6Jv1qJ5rjISKlwN5ueKkEoKwbXqe30zhoDJppHDQGzTQOFo2DxqBZd8Uh0xiT2NYBTSB7CRFZ1d52Qf2JxkFj0EzjoDFopnGwaBw0Bs16Qxy0C1sppZRSSh0TTSCVUkoppdQx0QSy93i4pyvQS2gcNAbNNA4ag2YaB4vGQWPQrMfjoGMglVJKKaXUMdEWSKWUUkopdUw0gVRKKaWUUsdEE0illFJKKXVMHD1dgd4uISHBZGVldfnr1NXVERER0eWv09tpHDQGzTQOGoNmGgeLxkFj0Ky74rB69eqy9hYS1wTyCLKysli1alWXv05OTg7z5s3r8tfp7TQOGoNmGgeNQTONg0XjoDFo1l1xEJF2d+LTLmyllFJKKXVMNIFUSimllFLHRLuwT0LG62XnxiWUb1uCt6HyGK6UYzj16P7vcQx3BBHqystZlv/JEc87hpse9Wt39j0Ncgy3bTmxtqyMZYWLOzj1aG969O9JjvaeXRF7pM1Taw4cYPmBFcd5T06KOFWXFLOifN2hex7tXU1nf05ixx4agdiDWn4codQnjsPmCGVIUiQJkSHH8JrqRBlj2LJjO0UrXsPeWI4Yg+D1++1F8C8zAWU248FmXBhsGLEBgldsfs9tfs8Fgw23LeQY/14L1PF17R/0P1JdWckXu94/yhc8rkNH+BPRQT2PNy7HcbCqKRR6uCtfE8iTyIp/f5OU8uWEmEaGUs7Qnq7Q8ars6Qr0AhU9XYFeoqynK9ALHOjpCrSvwCTwF9flfGSfy82nD+P6UwYT7NCOra7i8RpWbtjMgZUvkVrwLhO8WxgtrTcD8RjBiw0DGGx4rTSy1WMnDgSw4fUdMdh8Z9l8d7D5Ek47XuxtvFaPqO7pCvS8FfbJwG96tA6aQJ5EppS+il0MayJPZW/2AtKnnENccvpRtZoc9Y5Ex7Bz0bHscmSMF4AlXy5h9pzZnXTPozu3K+4J5uhDddiJy5YvY+aMme2c6z3KW3ZFnI7utY/t9duP06qVq5g6bWrAuUfNe5Tv6Rju2RNxWrN6DZOnTEbM0df02F7/6M7zup24m+rxuJ0YtxOvx4WjtpjkjQ9y78F/sz30M37wwdd5be1o/nzxOKZlxR11HVTHGl0eVq5ZzcHVrzCo5CMmkIdNDIVBGWzJ+g7pc64gZtAYq1dIbCCCHbB3dkWMAY+zs+96zD77/HNOO/XULrt/R38mO/rz0tEfpQ7v2eF17R+rWdxBL1U30QTyJFJsS6IoahxTf/xyT1fluDlCwgiPjOnpavSokLAoYuLaXDWhXwmN2klCyqCerkaPCt9ZQMqgXtyXMO8qWPMUwz/+Pe+E/pKX6s7mugcv5PwZo/j5OSOJDg3q6Rr2SfVONytWLqdmzUsMK/uYU3wTYfc4Mtk5/CbS5lxOavo4UruzUiLg6PlhCsYW1KX1OP6u7e5ld/T8ny1NIE8iQcaF1xbc09VQSvUXNjtM/RaMvhDbJ3/g0lVP8JXIJdyx6jLO2nwGdywaz9ljU3q6ln1Co8vDylXLqFz1IiPKPmKe5AOwN2IsxVB/XwAAdXBJREFUO0fcxqDZl7Mnd68uYaN6DU0gTyJBuDF2TSCVUt0sPA7O+ycy+Woi3/4Jf9//EFu9n3Lbf7/BK6PmcMcFY0iNDevpWvY6Hq9h9fr1HFj6HENK3uMU2QPAnohx7B59DRlzryQzNs3vinaX5FOq22kCeRIJNk6Mvee7GJRS/VTqJLjuQ1j/HCM+uoPXPL/hzbw5XH33FVy6YDbfnJOtk2yArbv2suuz/5Cy702mmy0A7AsfSd6oX5A19wqy4jJ6uIZKHZkmkCeRIFyaQCqlepbNBpO+jow6HxbfzXlL7+dszwqe+vAMvrbiSn54wUzmjUjq6Vp2u9LKGtZ98gLhW15kqnMlI8VNUdAgtg/7AZmnXU1G8rCerqJSx0QTyJOE8XoIFg84tAtbKdULhEbDGXcgU79FUM5fuG7df7my7lMeeeYcXsi6ih+cN42RKdE9Xcsu5XJ7WLXsU+pXPMOkqo84U2qplFh2Zn6NtNO+ycDBUxl4vIsHKtXDNIE8STidjYQAoi2QSqneJDYDLrwPmf19Qj79Ez/c8io1Be/x1L8X8t9R3+T6s6eTER/e07XsVHv27mbnR4+Rmf86s9iHEwc740+jccZVpE49n1i7/tOr+j79Fp8knI1WAtkblllQSqlWkkZi/9ozULyRkE/u4ubtr9Gw/R1e2jqP4lHXctnCeWTGR/R0LY9bfX0daz9+geCNzzGpaSVZ4mVXyEg2j/ktw06/hlGR8T1dRaU6lSaQJwlnU4P1QBNIpVRvljKO4Cv/C6XbIOdurtj8Eo7tH/DJlkm8k3E5p5x9GWMH9Y2FyI3Xy7Z1X1Dx5VOMKvuAOVJDuQxgY8Y3yFjwbQZnTejpKirVZTSBPEm4nI0ASJAmkEqpPiBxBGGXPgQ1v6N28UNMX/0Ep+//OQWP/o3/RZ1D8inXMmfqZILsvW/W9oH8Hez+9EmS97zOSG8+TuNgc/QcSqZdzYg5i4i39/wiz0p1NU0gTxLuJiuBtOkYSKVUXxKVQuQ5v4Uzb6d+/WvIFw/xtcpn4N1nWP3eGMqyzqcuZlRP15Lasnzycp4lbPvrjHDmkgRsdYxi5ahfM+LMa5gYq7tHqf5FE8iThNulLZBKqT7MEUz4lMsIn3IZ7rLd7P30cdK2vcyU3XfhNjY25P6d2tS5xI06lezxcwgJ7/otT2sKtrB3yYuE7nqPoY25TAR2ySAWD/oOGaddw8ihY7q8Dkr1VppAniTcvi5suyaQSqk+zpGQzZBL/wDm95TtWMmGt+4jq2494/c+AHsfwPuuUOBI42D0SGTgOOKGTCNl+DRsUSe2vqT7YD6FG3Ko3fYpcSVLSPEUMRbYTiafplxH4ozLGDNxBoN16R2lNIE8WTR3YduDQnu4Jkop1UlESBg+HdvkegbPm8fB0iJ2rfuM2t0rCS/PJbViPWkHP4LN1ukHbQM4GD4Y14AhBCVkE5EwiPCYJGyhkXg9LrxuFx63G6/bicdZT315Aa7SXdgP7iSudjsDvAfJAGpMGLlBY9mQ+XUGTlvEmNHjGG7TpFEpf5pAniQ8vi5sW7C2QCqlTk4DEgcy5czLgcsBcHu8bM/Pp3DLSurz1xFasYXEmt1k1LxFTH79Ud2z1oSylxTWhk6mIWEckUNnMXLSXGbGRnbhO1Gq79ME8iTRnEA6gsN6uCZKKdU9HHYbw7MyGZ6VCXwVAGMMxVUNbC8uoapkL41Vpdg9TYjdgdiDrB+HA5sjhIj4NFIHDmJkXDhjtIVRqWOiCeRJwutqArQLWynVv4kIA2PDGRibDSOze7o6Sp20et8CW+q4tLRAagKplFJKqa6lCeRJorkFUhNIpZRSSnU1TSBPEsZtJZBBIZpAKqWUUqpraQJ5kmhOILUFUimllFJdTRPIk0RzAhkcorOwlVJKKdW1en0CKSKDRORTEdkiIrki8kNfeZyIfCgiO3y/B/hdc7uI5InINhFZ6Fc+RUQ2+o7dI3LybCfQkkBqC6RSSimlulavTyABN3CrMWYUMBO4SURGA7cBHxtjhgEf+57jO3Y5MAY4G7hfROy+ez0A3AAM8/2c3Z1vpCtJcwKp60AqpZRSqov1+gTSGFNkjFnje1wDbAHSgEXAU77TngIu9D1eBDxvjGkyxuwG8oDpIjIQiDbGLDXGGOBpv2v6Po8Tp3Fgs/f6j1QppZRSfVyfyjZEJAuYBCwHko0xRWAlmUCS77Q0IN/vsgJfWZrv8eHlJwd3E06CeroWSimllOoH+sxONCISCbwM3GKMqe5g+GJbB0wH5W291g1YXd0kJyeTk5NzzPU9VrW1tSf0Ot6agzhxdEtdu9KJxuFkoDGwaBw0Bs00DhaNg8agWW+IQ59IIEUkCCt5/K8x5hVfcYmIDDTGFPm6pw/4yguAQX6XpwOFvvL0NspbMcY8DDwMMHXqVDNv3rzOeivtysnJ4UReZ8W6h3A3BJ3QPXqDE43DyUBjYNE4aAyaaRwsGgeNQbPeEIde34Xtmyn9GLDFGHO336E3gGt8j68BXvcrv1xEQkQkG2uyzApfN3eNiMz03fNqv2v6PJvXiUu0C1sppZRSXa8vtEDOAa4CNorIOl/ZL4C7gBdE5DpgH3ApgDEmV0ReADZjzeC+yRjj8V33XeBJIAx41/dzUrB5nbg1gVRKKaVUN+gwgfQtf/MDY8w/u6k+rRhjFtP2+EWABe1c8yfgT22UrwLGdl7teg+b14Vbgnu6GkoppZTqBzrswva13C3qprqoE6AtkEoppZTqLkfThf2liPwb+B9Q11zYvDaj6h3sXidum7ZAKqWUUqrrHU0COdv3+/d+ZQY4vfOro46Xw+ukyRHZ09VQSimlVD9wxATSGDO/OyqiTozDuGjQMZBKKaWU6gZHTCBFZCewDPgC+NwYs7mDc0OB84BTgFSgAdgEvG2Mye2UGqs2OYwLj03HQCqllFKq6x3NOpCjgYeAeODvIrJLRF49/CQRuQP4EpiFtdXgQ8ALWEvp3CUiH4rI+M6quArkME6MXVsglVJKKdX1jmYMpAdw+X57gRJadn3xt9IYc0c797hbRJKAjOOppDqyIOPCq5NolFJKKdUNjiaBrAY2AncDjxhjyts5b7+IiDGmzf2ljTEHaDvxVJ0gCLe2QCqllFKqWxxNAnkFMBf4HvBtEVmCNRby48POexTIFpE1WF3ZS4BlxpjqzqywaluQcWHsIT1dDaWUUkr1A0ccA2mMed0Y81PgO8A7wLXAW22cNxUYhLUDjBP4AbBDRNaLyP2dWWnVWjCaQCqllFKqexzNLOyXgYlAHtZM7KuxJsm0YoypB3JEZKXvnDm+88/upPqqNhivl2Bxg0MTSKWUUkp1vaPpwr4TWOvb1rBdInIl1qLjE4EmoDmJnGuMKT7BeqoOOJ2NhAA4dAykUkoppbpeuwmkiAwD/g4MATaKyE+MMfs7uNfDwFbgQawxkts7taaqXc7GBkIA0S5spZRSSnWDjsZAPo411vESYA1w7xHuFQPcAIQCd4jIahF5S0R+KSK67WEXcjU1WA+0C1sppZRS3aCjLuwoY8wjvsd/882ubpevi3uN7+ffIpIMfBX4EdY+2vZOqK9qg8vVCIAEaQKplFJKqa7XUQIZKiKTAPE9D/N/bowJSCh9u8zM9vsJBpZitVx+2cn1Vn5cjVYCadMubKWUUkp1g44SyCKsxcObFfs9N8Dh3dJPYiWK7wK/Nsbs7aQ6qiNwawukUkoppbpRuwmkMWb+sdzIGDMZQESmHJ48isj5xpg3j6+K6kjcTiuBtAeH9nBNlFJKKdUfHM0yPojIbCDL/3xjzNPtnP6IiFxjjNnou/YK4BZAE8gu0pxA2oI0gVRKKaVU1zuahcSfwVrKZx3QvBakAdpLIL8KvCQiX8faAvFq4KwTrqlql8fZBIBdu7CVUkop1Q2OpgVyKjDaGGOO5obGmF0icjnwGpAPnGWMaTj+Kqoj8fjGQDq0C1sppZRS3eBoEshNQArWpJp2ichGrJbJZnFYS/csFxGMMeOPu5aqQ15fAmnXLmyllFJKdYOjSSATgM0isgJri0IAjDEXHHbeeZ1ZMXX0vC7rY9EWSKWUUkp1h6NJIO84ynuVG2NqOzpBRCKPdI46dl63dmErpZRSqvt0tJUhAMaYz4A9QJDv8Uqs3WYO97qI/ENEThWRiOZCERksIteJyPvA2Z1Ub+WnuQUyKEQTSKWUUkp1vSMmkCJyPfAS8JCvKA1rgkwAY8wC4GPgO0CuiFSJSDnwH6wxlNcYY17qpHorP8bd3IUd1sM1UUoppVR/cMQEErgJmANUAxhjdgBJbZ1ojHnHGPN1Y0yWMSbGGBNvjJltjPmTMab4eCooIo+LyAER2eRXFiciH4rIDt/vAX7HbheRPBHZJiIL/cqniMhG37F7REQOf62+qjmBDA7VBFIppZRSXe9oEsgmY4yz+YmIOAicbd3VnqR11/dtwMfGmGFYrZ63+eo2GrgcGOO75n4RsfuueQC4ARjm+zlputMPJZA6BlIppZRS3eBoEsjPROQXQJiInAm8SDfuKmOM+RyoOKx4EfCU7/FTwIV+5c8bY5qMMbuBPGC6iAwEoo0xS33rWT7td03f57bye00glVJKKdUd5Ejrg4uIDbgOazcZAd4HHj3ahcU7g4hkAW8ZY8b6nlcaY2L9jh80xgwQkX8Dy4wx//GVPwa8izUJ6C5jzBm+8lOAnxtj2lx6SERuwGqtJDk5ecrzzz/fVW/tkNraWiIjI4/r2qaVTzCv9i2Wzn+5k2vV/U4kDicLjYFF46AxaKZxsGgcNAbNuisO8+fPX22MmdrWsSMu42OM8QKP+H56u7bGNZoOyttkjHkYeBhg6tSpZt68eZ1SuY7k5ORwvK+zbMtzuGqDjvv63uRE4nCy0BhYNA4ag2YaB4vGQWPQrDfEod0ubBFZJCI3+T1fLiK7fD+Xdk/12lXi65bG9/uAr7wAGOR3XjpQ6CtPb6P8pCDuJlxyNEt6KqWUUkqduI7GQP4MeMPveQgwDZgH3NiFdToabwDX+B5fA7zuV365iISISDbWZJkVxpgioEZEZvpmX1/td02fJ54mXAT1dDWUUkop1U901GwVbIzJ93u+2BhTDpT7LxTe1UTkOaykNUFECoDfAncBL4jIdcA+4FIAY0yuiLwAbAbcwE3GGI/vVt/FmtEdhjUu8t3ueg9dzeZ14pTgnq6GUkoppfqJjhLIAf5PjDE3+z1N7JrqtGaMuaKdQwvaOf9PwJ/aKF8FjO3EqvUaNo8TlyaQSimllOomHXVhL/ftQhNARL4DrOi6KqljZfM6cWsCqZRSSqlu0lEL5I+A10TkSlr2vp6CNRbywi6ulzoGdm8THpuOgVRKKaVU92g3gTTGHABmi8jpWDu7ALxtjPmkW2qmjprD68QtIT1dDaWUUkr1E0ezDuQngCaNXWjZf39HVbULjnNNJ7tx4bSHd26llFJKKaXaoYsH9gIpO18CW8pxXx/kdVJr0xZIpZRSSnWPo9kLW3Uxg40ONsY5Iodx4rVrAqmUUkqp7qEJZC9g7bV4/AlkkHHhteksbKWUUkp1D00gewEj0uZm3UcrCCfGoS2QSimllOoemkD2CgLm+Fsgg9EubKWUUkp1H00gewGDcCJjIEOMC+zaha2UUkqp7qEJZC9gOP4ubOP1EixucIR2ap2UUkoppdqjCWRvIILgPa5Lnc4G64FDWyCVUkop1T00gewFTqQFsqnRSiBFWyCVUkop1U00gew1jm8MpKupuQVSE0illFJKdQ9NIHsBg+2414FsTiBtQToLWymllFLdQxPIXsCcwDI+zsZ6AGxB2gKplFJKqe6hCWRvIHLcLZDNCaQ9JLwza6SUUkop1S5NIHuBE9nK0NlQC4AjNKITa6SUUkop1T5NIHuF49/I0NVQA0BQaGRnVUYppZRSqkOaQPYCRo5/Eo2n0WqBDAqN6swqKaWUUkq1SxPIXuBEurA9TXUAhIRrC6RSSimluocmkL3C8U+i8foSyNBwbYFUSimlVPfQBLJXEI53IXHjbG6B1ARSKaWUUt1DE8hewIggx5c/YpzWMj5hEZpAKqWUUqp7aALZC5gTaIEUZy1O48ARFNy5lVJKKaWUake/SyBF5GwR2SYieSJyW0/XxyLYjjOBtDUepEq09VEppZRS3adfJZAiYgfuA84BRgNXiMjonq2V1YV9vC2QQc5KamwxnVshpZRSSqkO9KsEEpgO5BljdhljnMDzwKIerhMnMgs7zHmQ+qDYzq2OUkoppVQHHD1dgW6WBuT7PS8AZvRQXQ4xCJneAnb8YQrQsiak9dsgxvj2qjGHyqzjMNhTxMawU3qg1koppZTqr8SY45z+2weJyKXAQmPMt33PrwKmG2O+f9h5NwA3ACQnJ095/vnnu7ReVdtySCv5GMTmm1DTkkIigemk9duXTop1bmn62cRkTuzSOnaX2tpaIiP796LoGgOLxkFj0EzjYNE4aAyadVcc5s+fv9oYM7WtY/2tBbIAGOT3PB0oPPwkY8zDwMMAU6dONfPmzevaWs2bR07OPLr8dfqAnJycfh8HjYFF46AxaKZxsGgcNAbNekMc+tsYyJXAMBHJFpFg4HLgjR6uk1JKKaVUn9KvWiCNMW4RuRl4H7ADjxtjcnu4WkoppZRSfUq/SiABjDHvAO/0dD2UUkoppfqqfjWJ5niISCmwtxteKgEo64bX6e00DhqDZhoHjUEzjYNF46AxaNZdccg0xiS2dUATyF5CRFa1N9OpP9E4aAyaaRw0Bs00DhaNg8agWW+IQ3+bRKOUUkoppU6QJpBKKaWUUuqYaALZezzc0xXoJTQOGoNmGgeNQTONg0XjoDFo1uNx0DGQSimllFLqmGgLpFJKKaWUOiaaQCqllFJKqWOiCaRSSimllDom/W4nmmOVkJBgsrKyuvx16urqiIiI6PLX6e00DhqDZhoHjUEzjYNF46AxaNZdcVi9enVZewuJawJ5BFlZWaxatarLXycnJ4d58+Z1+ev0dhoHjUEzjYPGoJnGwaJx0Bg06644iEi7O/FpF7ZSSimllDommkAqpZRSSqljogmkUkopdbLyuKGt9Z7rK+CTP0Ltge6vkzopaAKplFJK9XYeF4kHvoCmmtbH1v8PXr8ZnHWB5XVl8K9x8OzXWl/z9o/h87/BJ3/omvqqk54mkEoppVRvcWAr1JW3Ls+5kzGb/w7v3hZY7qyH126Etc/A6qcCj61/HmoKYcf7ULi2pdzjhu0fWI+3vtN2C6VSR6AJpFJK9Rdf3gMvXtu6pUp1L68HXI2ty/cugftnwDOLWid1m16xfm98EdxNLeU7PgDjtR5vfzfwmn1LISTaerz7i5byA7ngqoOsU6C+DA7uPrH3o/olTSCVUqo/qCmBD38Nua/C2v+2Pr75Ddi3vPvr1d+4GuCeSXDvZGisDjzW3IJYvBH2LWspr7OSvKroEeBpgsJ1Lcf2LYWgcJj6LShYbSWnYCWg+cth5LkQmxHYApm/wvo97Trr94EtnfoWVf/QJxJIEfmRiOSKyCYReU5EQkUkTkQ+FJEdvt8D/M6/XUTyRGSbiCz0K58iIht9x+4REemZd6SUUt1s92ctj3e8H3hsz2J44Sp4/Ky2x9ipY/fxH+DRM6C2NLB8y5tQuReq98OG/wUeK9kE6dOsx3u/bCnfv9r6lXau77yNLccK10HKeEidZLUqVvqW7Tu4B+pKrfsljoKyHYGvExYHQ063nvsfU+oo9foEUkTSgB8AU40xYwE7cDlwG/CxMWYY8LHvOSIy2nd8DHA2cL+I2H23ewC4ARjm+zm7G99KlzDG4PJ4e7oaSqnermwHiB0mfh3yVwZ2ka57tuXxtvcCr6vYbbWYrXy0e+rZlxzcA5/+ufWQgOoi+OLvULASlj8YeGzPYggbADGDYPfnLeVeL5TnwaAZED80sMWwYCWIna3hU/HagvBW+K3tXLYNZ/wIntgWBICn1JcMlucBsMuWSU55DO6yHdZrAFTsxh2bzfdezqOcGPJ3buqEYKj+ptcnkD4OIExEHEA4UAgsAppHDD8FXOh7vAh43hjTZIzZDeQB00VkIBBtjFlqjDHA037X9FnLHvgO8vv4nq6GUqq3K9sOAzIhbTI0VUF1Ycuxog1Wa1RwpNXt6e+926FiF7z/q9bj9oyxloE5mSdhGGN1/x/O64HnvwGf/QWW3Bt4bOtb1u+o1NatveV5kDjSShQL/HY5q8oHdyNNsUMoCs7EU7q95VjhWupih/PL5bDbncCuvFyrvK4cGg7yUK6de9ZbRdtyfYln+U4Arny1jA9KonB4Gikp3AWAs3QXHxSF8X5uCfneBErz844nMqqf6/VbGRpj9ovI34F9QAPwgTHmAxFJNsYU+c4pEpEk3yVpgN/gEQp8ZS7f48PLWxGRG7BaKklOTiYnJ6cT31Hbamtrj+t15h34Hwh89PFHOOy9/uM8ouONw8lEY2DROHRuDKbuXUehieORDw7yN2D9xy9yMG4iGC+nlG6jMGgwUWGZyLYvWBthvabN42Tujo9oCk0hrLGYtW8/RlXsmEP3HLTvVYbsepK8Id+iYNCiTqlnW3ryuzBo3ysM2fUUuaN/RmnSnEPl0VVbmOzrSm5Y9gTLmXno2LDtH5PkiGB/3Clk7n2RxR+9i8cRBsDswk2UxU9nX2koM2sK+eKjd/A4wokrX8N44McfVDKmKYLrHTtZ/MlHGJuDyfm5fFGfSXSQocQkM6B0Fzk5OVYdgNzGeG6clkjdhlD2bFvHgZwcUjZ/TpoJIyk2hunxqbAXPv7gHdKyRjG3toB93qn8ZmYIns2JxDbs5ZNPP8XWB0Z16d8Llt4Qh16fcfjGNi4CsoFK4EUR+UZHl7RRZjoob11ozMPAwwBTp0413bHf5HHva5lj/Zo2YQwxCQM7s0o9Qvc51Rg00zicQAzWPQvrn4NLn4LwOPB6cX1WRI5rDJ+7UyAU0gcEMWHePDi4Fz5zUpU0lSqnl1F1K1pes3gjfOEibMFP4e1bmZTghLm+Y8bAv38CwNDiNxn69X+Aza9Tq7rQmrAz8Uqry/YEdPl3YVeOteTNgt9CtN/fo7Wl8PklAIypeA8u+2XLsS/WWL9P/Slhn/+NeVNGQlSKVbbn75AymoHTzkf2/o9TRiVB2hRr8e6cKtYyjNfzQ5gZDDOGJxKcMQ3Xl5tgI6x1pjMhsZaggx6GZqaQlj0K92el7Jfp/GJmBJF7h5O6/y0yZs3FvrEA1sLs6TO5+vwzKNiSTJypZOa8eexedxd7TTKP33g6YdXZ8PAfSAw3nDo+C/nckJA5mq9esIDNFa+RunsNoRNnEBHi4MlP1vEt+7tEn/FzCArtupgfJ/17wdIb4tAXurDPAHYbY0qNMS7gFWA2UOLrlsb3u3k5/QJgkN/16Vhd3gW+x4eXnxQa6nXgu1IKK+l57buw+3PqvrTG31Uf2E2QaSImfRQv/fQimoyDwj3brPN9Eyh+t9TFe/tDsdeVWGsLgtXtDTQMnIY3Ki1wtm7JJijfYXXF1pVC6WEzed//he/nV23Xs61lbLpSU601ZvFwbie8fL2VcOfcGXhs16fgdcPoRVC8IXB9xrIdEJkCQ8+wnvvPjC7Po8A+iEXPW/8sVeVvPlQO8PK+cMJTRwOQu97qxs7fsYFKE8EdXzuF02dZE2mK9+2A6kIcxk106lBiQoSg+GxipJ69+/dTtX8HXiMkZwwHoD48lRhnEcYYwusKKA1KJSEyhIikbAC85XuoKbLqEJo8FIDg+EzCxElxYT735+xk0vJbiV5+t/XZKdWBvpBA7gNmiki4b9b0AmAL8AZwje+ca4DXfY/fAC4XkRARycaaLLPC191dIyIzffe52u+aPsnjbPkLuLG+tgdropTqNXwze6tNOIWr3wZg/w5rgNyIsZNJj4ugmARrzB2wY7M1w3fW9JmMGj0OgPJC30SMsjwMwimP7mNJVRx1hX5JYvO6gmf+3vrtP3bSWdcyGWfD/1pPMln+MPwpBZbe1xnvuIWroe01Lo2Bx8+2JgPtWRx4bO9iqDsA4fHWWovNy+AAFK0HRyhMu956XrCy5Vj5Dpyxg/nO+9bfvbX7feMSG6uhpohnd4ZQG56Oy9jZkWvFuDLfOmfshKn87frz8BqhrsQaq2gr38FuUjljdApJg4YBUFO8i5piK+GLSbESvuhU6/eBfdtoLN1FEXEMGWi18Hqj0xloSimtaSDOVURduK/NxBFCuS2e4NoCKn2fbXTKEAAikrIAqCrZw77CQk6zbwDAuctv3Uil2tDrE0hjzHLgJWANsBGrzg8DdwFnisgO4Ezfc4wxucALwGbgPeAmY0zz3wjfBR7FmlizEzhs1dXezbga8fwungOPWdtSHTyQf+hYkyaQSimgbtWzrPcO5kXPaQxq2EZNfSO1+63Eb+CQcYgIB4OTCau3OmBK92yikkh+cP5M0gdbrWIH9m73HdtIvjeBrOR4dpOGrXxHy4SZsu2YsAG8WpaG2xEOpdtaKrHjA3A3wJwfgtcVOFnE47YmnmDgs79aSZ+//Wvgwbmw6vG23+Dm1wNb+5o11VrX/XMsVO0PPFa80Vr6xnhbT3jZ/TnYHHD6r8FZAyW5LcfKtmPih/BqsTVR0V3sO2YM3rIdvFcSzfs7myg1MezcYk1ecfsmv1RGZPHuj0+nxJZ0qOVz77b1OI2di+bPJigknHJbHPaqfQDE1u2hNCQTm02ITsrEgw13xV4O7rcSvsiBVuKYmDECgJrCPOxVeygwSWTGRwAQkpBNrNSxP28DQbjxxGQceitVIanENBXSVLqbJhNESnoWAHEDrdbJhtJ9RJRa/9FY5h1NcMV2aKxq+zNQij6QQAIYY35rjBlpjBlrjLnKN8O63BizwBgzzPe7wu/8PxljhhhjRhhj3vUrX+W7xxBjzM2+2dh9xvpPXsBu3CTlv0djZQkHS1oSSFej7iyhVH/nKsoloiKXT0PmM23GXELFxa4dWzBl26k2ESQlWy1S9WGpDHBZM4sja3ZRGpKBw2EnOdOXnPhavWoKNlPoGMQz180gKXMkYaYBT22ZdY/CLWxqSuZHL2xgizOJg/v8Eq99yyEoAubcYj3f75dAFq23dj8Zdxk0VloJo7+PfmslfB/8BhoqA49tfQdeuBoemW+NJ/S38QWri7ihovXSObmvWksYjb3Eajn1uFqOFa6FlHFUp8311XV1y7Gy7WxxDeRHr++m2Axg1xZfXevKsDVWsr4hkedvmElJSCahlVbMdvsSyXPmnUJUaBBVIalENlgJrb1iBwW2gWQnxQBQGTKQiPoCaKwi1ltBQ7TVKog9iAp7Ao6aAhoO7MJjhKR0X5dzwmAA3OW7iKzfT0VwKkF265/y2FTrWNXmTwEIih986K00RaaR6CnBe3APBSaBjPhIAELirCSz6eA+Uus2YxBKhlwKwJ5t61CqPX0igVQW07w0BLAvdwm1ZS2Tyj1NmkAq1d9tefvfOI2dyV/5NimDre7oin2bCK/eTVFQOuKb5OKOziDeHMTTVE+qK5+aSCvRSEpOp86EYMp3Y7weUlwFeOOHEhZsJ9w3jq4032oRayrZxi6TyoPfmMJ+xyDcB/wWoy7dSkPsEH74xl6qgxJxlfi1Tu71dSGfak3AYd/SlmMNlVaCl32a1Rq4KyfwDa56zPptvIFrV4K1t3PcYBg8H/I+Djy282PInA2jzrcW2y5a33KsLI/y0EzmPphHvQmhZJfVhYurEXNwLx8ciOYbMzMoCc5EfGNCK3zJ8vDRk5k5OJ7ayGxSXPlgDDX7N+MydsaPnQSAMyqdBHcJbo+XAfVWK2OzpshBJLiLqSvcCoAtcdihY7WhqUQ37scc3EMR8WQkWkknIVFU22KJqt5BrKecpqiWVsbYFOtzdORbi5BHDRzS8j5jMxhIOcFVezhgTyY0yLc8ckQCToJoKN3LeNlJTWQ28+dZC4y/84XuTKTapwlkHxJXvZVcm9VCcHDPepoqWhJIr+5tq1S/s/L1+1nx8r8wXi9uZxPpBW+xLnw2p04aTUKWlUC6ireS5MynJjL70HW2uCwA9mxaSoJU4YmzWrdsdhsl9hRCavMp2b+bcGnCnmhN0Ij1jb0r37+DqoOlDPAeJCFrLGePTcGeOIx4T8mh7ujG4i28WxzNOxuL2NiYRPlev4WqizdBzCCW1STQEDkIU+J3bN9SwMDcH4EjLHA7P7cT9nwJ078DcUMCd2rxeiF/mZUkZs6GA5uh4WDLsdLt1MeN4oeLrYVHXAW+tRKbaqG6gP/khRAVFsJuM5CyPdbSPM7SHQiGxpgh/Pb8MdTHDGGgOx/j9ZK/fR0AEydZk11M/HBiqOVgaSHBB3dSIMnERIUD4IjLJEGqKCguIdlTRN1hn0MKFeRtshK1qPTRh465YrJIM8XYq/ZSYksmLNh+6Fh1+CDGO6062ONb7iexVjI5snE9XiMMzBx+6FhIQjYO8ZLt3kVNmN8KdiJUBSUR7Sxhoi0P98DJh8ZHVhftZGOBdmOrtmkC2Vd4XKR6CiiLn0IpA6BkM1T5JZBN9T1YOaVUd9u+Jodpa29n+sbfkrvkLbav/oQ4qnFM+CoAtog4DkosEaVrSaICry9JBAhPthKEg+utSTahA0ceOlYZkkZsYwHFO62WuOhB1rqPKb6JHQ1leyjKs45FplkJjy1hODYM1fu34qyrJLS+mIPh2az85RnUR2UTWbPn0NhJz4GtbHKmcPnDy1hSlUBNvl/Xd8EqEDs1yVNpShoXuBtL0XprXGXWHMiYZSWXzaOQyvOg4SAV8VO4b3s0YFrGMlblg7uBB3IdvL5bqDWh5PkmDnl9M9BLQzN55XuzqY8ZQnzDHgCK86xWylnTZ1ldxAnDiKSBipJ8PKU7aDRBZGRb/6EPSxtlXbNrA7H1uzkQ3NIqGOlLxg6s/wAHHrzxLa2MEcmDsYnBs+NjXMbOwKxRh44FJQ4hWSpJa9xJZaj/AiLgjskiSSp992/5XIlMxi1BJEg1xcSRmRR76FBMast57ij/hUqgMXwgU23bSZRqwgfPgJAovKFxDJJSluwsQ6m2aALZR1Tu30YQHkziSA6EDWFAbR5BtfupMyEAeLQFUql+w+Vswv72LRwkCpexU7/qOaq2fIrXCEOmn3vovNKwIcxqWgJASEpLkhiXbiU+cUXWVnpxGS2LgzdFDSLZU0ydb+JN6hCrJTMhMYlqE445uI+qAmtZmoTssQBE+hKo0j2byN9uJV7jJk4nNjwYkzCMSOrw1BwArxfPgW0sq0ngJ2cN50BIBqHVuw/NfDalW6kKz2DO35fw0r4omoo3tySJvpbKx3fF8Nf1QdZYx1rf6m2+JYRuyXHzn51Wy19zV7TxTe5ZXZfISzfOZr8jHe8Bq8t4n6+u8+fMITk6FOeAYaSYUhrrqqgttN5jmu/9R6RZMSreuYGQqp0U2FIJCw0GICnbOqd230aS3YXURrWMPUzyLbHj2vYhAGF+yXr8IOtzGFm9lN0mhazkmEPHYtKsY+HSRFN0SysjQHBSS9d0clZLqyU2G5Xh1rmF9paxkQADUlsS16DkEQH3Iy6bgWKNKQ3NtFpVbQMyGBZSwaq9B1GqLZpA9hHlu9YBEJE+Flf8CDI8+4io30+hw/qfpHFqC6RSJ6O6mkrWfvAfmhpb/oyvfvp2hnh2s3vWnWyKnEVa1Wqiipez25FNTFziofMaBgzDJlYCNiCzJUlMGWiNdRzs2oHL2EnJbElqzIDBhImTsP1LqCWcmESr9UtEKLGnEFqbj7tkG06/6wYOtu7dULiVir1WF3DK0AkAhKRYyUrZno1UFOYRbJpIGTKBm08fRlzmOIJx0VRqbbFXuXcjS6oTGDkwmrKwTEJc1VbiCVC2HaeE8ofFNWxyWot9V+6zksrmZHevpPLP679CrQll/3ar9XLPVuv3wtNOZWpWHFURg0lqsvaSri3cgscIw0ZZdQ1OsRLhop2boHQHBSaBzIEJACT7xpTW7t/MgIa9lIW0tOKlpA+h3oQQlf8pQbgDWxl9739I5Ze++7R8DpG+luBwaaIoKIMQR0s39YD0liTPnuKXJAKxmRMOPR6SlRFwrCne+kz2hgwPKLfFtrRipo89JeBY8vDpfk/G+iqQSZa9jHX5lfSx+aaqm2gC2Uc0FG7Ga4Sk7PGEpY8nVFyM9O7gYITvf7rOho5voJTqkzY/ej2TltzE+oestQjXvP8MMwseY2XsOUxeeBVNabNIMyWMda6jLG5KwLWOlJZkpbklDSA02EGxzdo1pciegj0ouOWYb4HpsXXLKAoaBH7b21WFpjOgsYDQql0U21OxO4KseyclsN8kQPkOXMVbcBoHadlW0hPnS1wP7ss9tB5l5khrgkmYrwu8dM8mPM5GohrycccN5383zGTiJGtrwAO+lsTSXevZ5knhhlOH8otrLgRg3zZrVnTBjnUUmAQeuu5UZg5JoMCeTmiVNSu6rnALFSaSr546EQDXgGEkmgoaaw5iK9tBAUmkJcQeVtdNRNTsoigo41ArXtLATGpNGPYDm0jyFFMf3dIKaLPbKXSkM7LGau2NSBvb8iFExFNjiyJFKig3UWSk+o0/jBqICyuGNX73A5CklqQxedTsgGPhY8+lIGwEX2b/ICDpBIiddxP7wkYx5YKbAsqxB1F+xv+xa+JPGTV8WMCh4OxZ1oOM2WC36kPMIOLcByitaaS4upsXfVd9giaQfYStbBv7SSQtKZ60YZMPlXsTRuIydsStLZBK9WUNdTV43O6Asqo9a5hW9QEAEyvepaq8hOgV/2SPLYMJ330SgIGTzj50vmTNCbg+ZcKZeI2w3DH1ULJ36N5hVotUWURgd2ZMmtVyFSxuaiKyAo41RmWQ7CkmqXEXB8NbjtltQnHQICJqdhNauYNCR9qh1xuUNZwGE4yzZDs1Bda4xIzhVgKZmGklWjX5uRTvzsWBl4Ts8YgI8ZlWa+DBguYdc7ZzICSTny4cQXb2EKpN+KGu6ODKPEqCBjEyJdq6JjybxEarlTG0Ko98+yAiQqwJNCEDrfsW7lxPZO1uioMzsNusJDltyFjcxoareEuriUdis1EYNIjsisU48EKC39hDoMovVslDJwQcOxhmHdvnyCTY4ffPrs1OUaiVODrSJgVcgyOYVbPu47VBP2P88KGHHQsh/ecrmHPNHzhcxOCZZPx8GVmjprQ6Fj/3WgZf2MbOQCnj4Jq34LKnW8qiBuLwNhFNHevzdSKNak0TyD4iunYn+4MycNhtRGb4tSQMHEkjwYhbWyCV6qvKCvfS+LdR7P/zeBrqrG1Jy4rzmbLnAfIllc1n/49g8bD59b8z1LOT4sEXExxi7VOcMWIy62xj2OtNYuRplwbcNz5jFFsv/ZTs777Q6jXdw74CQPDorwSUp/nN3PWkjA+8KG4wQeJhkCnCOSAwqamJzCLZuY+Uxl1URLS0psVGhLJPUgk6mIetbBsVxBATn2zVPT2NUhONKdt+aPZzrC+pHJQ1DJex03ggD3HVk+gtRZJG4rDbCAlyUGBPJ6wqD4wh2dmyFBGAc8BQEk05rvpKEhv3UBnRciwuy7p/1b5NJLsKAmZFh4aGUWhLIbF0KWE04Y0LbKmrjsgm3rfkcERqYLeya4B1bpmJJiMlMeBYfbz1mvsiD0sSAe95/+LDhKuYdublrY5NXfgNLrzul9hs0upYp8s+BSL96u3b1zvNXsn6gsquf33V52gC2UcMcBZTF+4bcxMUxsq483AZO2njTqFRQpDDd3NQSvU6dTWVbF3+AcbrDSjf+9wPGUANGd79bHjnIaoOluF8aAHxppK6s/+PUdPPooR4Zu17GICMOVcculZsNib8IoeBv9hAdGRkq9ccPXYSSfHxrcqnLfoeNd9eytizvx1QHhoayvogqwUtefxZAcei/CZihKSODTjmjRtKOI2kUoo7fmTAsYrQDGIb9hJdu5sDfusgRoQ42GcbRFj1LpqKrGE6aUOtpDUmIowiScRWuYemCmvThJCUllnKB8OzSWjcR0P5PsJpxPiNO2wey1i8MYcYUxMwAz01ezROYydkz6eE4AwYrwhQEZbF4CarZTNsYOA4Qo/fuSmDA5PrEN/kmCJJCpi8ApD0ldv5LPYiRiy6lcNljZ3FmTf/m/iY1p9dj4pOBWBaXBPr8yt7ti6qV9IEsg9w11cSQQMS0zJ2ZsINj1B67RISUzJoIgSbtkAq1at5PR723XMOI9+9lGXP/PpQ+fpPX2RKzacszfgOe23pROS9yZbnfkGSt5QPh/2WkTPOQmw29iTOB2CvLZ3U7MAETRzBBIeGHVN9xGaz1h2U1q1bA699im2nP0rG6OkB5UPHt3SRp48/LeBY2MCW5C4sbVzAscaYwSS5i8l07woYOwhQHppJQsMegip2UChJREdFHzpWGZJGRH0B7korgUzMbknanHHDiDcVFOdaC5OHp7bEZECGldxWr30VaOm2BggNCWG/LZWhFZ/5rms5BtAY25JsxmcFJomhvvvUmlAyBga2Mg6bvYjl4adRcdqfOFxcSian3fIkIwdntzrWa/laICfENrA+vxKv1zeRRifUKB9NIPuA4nxrhmJYQstsu+DQ8EP/iLglCPE6e6RuSqlATY31bLjrDJY+9P2A8vUf/YdRLmtpmOG7n8HtcrLm/WcYnPN99trSmXzlHRQOPJOxTeuYXvICG6JOITq9pZUvdsbX8RjhwPjvdfl7SErLZsSpl7Yqj4iJZ03mt8hN/SpxaYEtdwlZLXVNGBI4BtCWOBy7GCKkCXtSYKteQ8xgokwNGTXrKAvNDDjWGJVBkquQoNoCXMbOoCEtk4Ka165s2PC6VechLcle+tAxOI2dQSWfABCXGdhaWh6WRTDWdobNs6ubBSW31G9wdmCymz3lLLbbBrN42M+QwxLvyOg4ZvzsDU6bfzYnhShrpvuoyDrqnB6ueWIF5qPfw18y4cCW1uc31Rxajkn1D5pA9gHl+dYf1gGpQ9s87rYFY/NoAqlUd8pdv4KVj/2IxvqagPI1z/2O8Y0rmVX0NHu2WHtAN9TVELf8b+yzpbF62j+Ip4q1bz/MyCW3UhyUTvA1rxASGk7idN8i4GKwTQgcEzdi6um4fl7AtAsPm13bzSZ/85+MueGxVuWDModQamIotSUELAsEEO23w0rU4GkBx2yJ1iSeeFNBfUxgwmaLH0KM1JFSt4UC20DCwkIPHYvPtBLGweWfUm3CSU/POnQsMiyUAlsq0aaaRhNEelZg0trka2UsNTFkZQQmrYMmnUmtCePt2CsDJ7wA0XFJDP/NWs7+Ruuu6JNOUBiExjIyoo4fnD6UdTv2weJ/QmMVLP5X4Ll7l8BdmfDGD3qkqqpnaALZB7j2W4PLk4e2HoAN4JZg7N6m7qySUv2C8XrZ8NmrlJcUBJRXVNcR+uo3mZb/OGteuPNQeX7eRibveYxNIRNxGTtFnz9Bft5GSv8xg0GeAg6e+gdGnXYpDSaYaet+STAuIq/6LwMzfWsFjpvNHtsg9kka4+Zf1qo+oeG9bJycn9BgB1z3EY4bPkZsgUvLpA9taR1MHTUr4Fh8dksLoP2wmcgRg6xj47xbKT+sdXLQ4JE0GQehOMl3ZBB02HI2za2ZefYhRIaFBByLHj4XgI0Rsw7NwG6WlDGShh9uZcH37u34DfcH0anYaov5wYJhnB61D8ELkcmw69PAruzlD4LxwLr/Qm1p4D2qCqB4Y/fWW3WLPpFAikisiLwkIltFZIuIzBKROBH5UER2+H4P8Dv/dhHJE5FtIrLQr3yKiGz0HbtHDu+D6KUcB/MoJJEBA+LaPO6RIOxeVzfXSqmTR2VZMfm+NQr9rXzlX4z/9FpqH1qI19PSPbfh5b8wBCupHLrnObweD8X5edT97wacOEi55mm2hE0io/hDGp//JlHeajbOe4QJ8y4hPDKGrRFWK9yGqLmHkkewxiUm37qU5J+vwu5wdPG77nyJGcMZkJLVujwhno/H383KWQ+0SoJHDGtprRw0YUHAsaRhUw89rk8I7GoOCw3hgM0ah1geE9hFDWDPsMZv5g+Y0erY2FMvZvEp/2HMdQ+0/T7iYq2EuL+LSoHqQhx2GxcnFuI1QuOUG6C2xNoiEqy9xnd/AfFDAQP5y1uud9bDIwvgodOs7XcPp+Mp+7TjSiBF5JudXZEj+D/gPWPMSGACsAW4DfjYGDMM+Nj3HBEZDVwOjAHOBu4Xkeb/mj4A3AAM8/30icEq0bW7KQnOaPe4xx6M3WgXtlJHsmvTcpxNgYsiN9TVUHX/AtL+cxrbVn0SUD5k078AyPQWsHnJWwCU79/J9D0PsiF8Jisn3UkSFeR++QY8tpDBzu1sm/4HElIzaRx6LmmmhGHuHeSN/ykT5reMKQyecxObg8eRcEHrdfzCIqIICQ3vgnffsxZcfB3TFl7Zqjw+Koz3R93J+5k/ITUzcJhOXFIaNcaaHBQx7JRW1+4cYJXZx1zY6tiYi37O+6PuZMrX72h1TGw25i44n+T4tv9TrnyiUqGmGICJtjx2mDT+tNGa5PTs29bWjJRtt7aVnPldjM2BO39Vy/Xb3oHaYqt1cvWTgfde/z/4cxqs/U83vBHVFY63BfJ3nVqLDohINHAq8BiAMcZpjKkEFgFP+U57CrjQ93gR8LwxpskYsxvIA6aLyEAg2hiz1Fj7Mj3td03vZQwp7gJqo9qfveexBePQSTRKAVC4Zxt7t61rVb78xX8w+KWz2HDf1wPK17/+LzK9BdjEUJfzr5byV62xihvnP0G9CaFu3StsXvER7kfORDBEXfRPhp96GU5jZ9wn15Jkysk7579MPdfaMWboqV8DoMaEMfG8GwNec8yccxn9i8VkDJ/Yqe+9r1r4te+x8Ju/blUuIqyeeS//DfsG42ad0+r4pG/9HzkLP2TW/PNaHQsNDWXh175HUpwmicctKsVqbfS4iS5fT3H0WN4ujAJg55a17Cuvh6J11rmZczngGMinXy5m037fwuO7P4fQGMg6BfYtCbz38gfBVQef/DFw8o3XA+/e1nqcpep12k0gRWRDOz8bgeRurONgoBR4QkTWisijIhIBJBtjigB8v5N856cB+X7XF/jK0nyPDy/v1Q6WWGuccdhaZf6MLRiHaacL29Wo3QTqpNNQV8PK1/5NTVVFQHl5SQGhT55B+rPzyM9rGXdVX1vFyNx/ADC1+iOK9+2wyuuqGbr9UXKDx7M8/kJG1iyjoa6GNe89yei8h9kYMolxp13M1qiZjCl7nwHv3IgRG3vPfZbsYaOJGZDAtrCJAKxMuoTRM1s6NeKS0th5yfs0fXclQcGBY/DU0Zt3zqWkzbiU4CB7q2MxkeHMmzW9exba7o+iUqzWw8K1SMNBZsxZwF+umo8nJJYsKeLlNQXUFGzG2Bx8WhbJ+oYkMk0hD+TstK7fuwQyZuFMnY63OJePN+6zyiv3QeEaSBwFNUV4inNbXnP1E7D8Afjot5C/MrA+lftIKvkc3Npg0ht0NMgjGVgIHDysXIAlrU/vMg5gMvB9Y8xyEfk/fN3V7WjrbxLTQXnrG4jcgNXVTXJyMjk5OcdU4eNRW1vb5utU71rOBUCFK7TdetidhmRvU6vj4XX7mLzm55Qkz2PH8O90ep27Qntx6E80Bpba2lreuP82EDvRo84MOCZL/8VpTZ/yxeb38Mz+yaFy58rHOItqENj28p/YOcNq+atb+z/OpY43km/mgpJ/s+q1+wgbfS7eNU9yJpUsGfRTjNfFjPLX+PJfX2OO60t2Moh9I26gPCeHqqjJTK79jEgaeGvIH4isD6LY9xlVZ11JftFwgoZd2Obnll++E7buPO4Y6HdB49Csu+MQX1bBOGD3x4+RDWwpcRPUtJX6oHhGBJXz6493MDpoCcNsiXzv2fX8Mmgg8816Psjdz4fvFXFm+Q52RU/nk1w338bLP59/G2/pSLL3v8FQYGXy5Uwr/S3/eORxxs1ZRJhDmLLqfuxhaQQ3lbHplb9TMf5GbCKI18XUVbcwur6A/Cd3sHPodQF1DXJW4XZEYGz9Y+xqb/gz0VGk3wIijTHrDj8gIjldVaE2FAAFxpjmkbkvYSWQJSIy0BhT5OuePuB3/iC/69OBQl95ehvlrRhjHgYeBpg6daqZN29eJ72V9uXk5NDW66zb9w5eI8w+/xoS4wa0vhBYseERgivcra4vf+12HJ560grfIe2qByAstvMr3snai0N/crLGoHn3FbEFdnzs+OJF6r+4j6gL/sLgsS0THl5/8m4WHbAmOew+7UKyR1sTTwryNpHamAMCM5uWUDd2JLEJKZQV7yP80/dZHX06Nk8T4+uXk3DKfykvKSAk53XWh83ggu/+id2/f5MhNcvxrljNGOcGVsaezQVX3Yzb5aTmj39hjutLdjiGkfWzLxniazmsmzKRjfd+Sm3aXM77xuFLlcwDvk1XOFm/C8dK42Dp9jjsj4JNfybbsxuASQuvgLABUDyK4QVboB6G2YsosGfg8dg4ZcYMgpa9RZy3kge/qODMIKhImcNr26v4dhBkmEK8yRcxtOgBSBjOstivMNz8hVT3PopCs/jWtATI2QWn/ZzNy98nuHwLGzxp3HLGcNj6NtQX4BUHyfs/YOX433LxdN+Y2V058My1mAHZ1H8rh4jI6MD34fWCrU/MGT5qveHPRLsRNcZcZ4xZ3M6x1iOhfURkqoj8SET+JiK/F5HLROS4B6EYY4qBfBFpnqq4ANgMvAFc4yu7Bnjd9/gN4HIRCRGRbKzJMit83dw1IjLTN/v6ar9rei37wZ0UkETCgNgOTgohiNZd2NVbcw49du5b2eq4Up2tobaK9Tkvt9qqr2jvdkp+P4zcv8wPONbYUEfcxz9hgnMtNW//KuCazH0vUW+sBK7ks0db7vXm73DiYN3cBwkSD9u/eBGAnS/+mmBcJC36Pe4R55FEBcv/8xvCHppBiHESfZ41YaU47SxGuTYzxrmB5aN/xdQfPAeAIyiY3IEXU0YsjosfCOh2joiKZdwvPmPWNa13GVHqpBVlbWdI/jJrYfEwXyNGbCYxTUX8+pyhZNtKmD1jFit/eQaZQ631PqcPqGGYbyTZj3KaOBhqTQIdF1LMz1/eQOWuVewJGcHDX+ymLDSLCaElvL6+EIrWA4YdQcP5sDaLkbKPF5Zsx+3xQu5rEDaAJ+NuIdg08r9XX6Ostgk8bnj9+2C8SMVO/vXX37CztNaqpzHw8vWYP6Xw/qtPUlipO7Z1pqNKyUVksoj8QES+LyKT2znnWhFZA9wOhAHbsFoF5wIfishTItL+VOKOfR/4r4hsACYCfwbuAs4UkR3Amb7nGGNygRewksz3gJuMMc0jdL8LPIo1sWYn8O5x1qfbhNQXUxmU2GrXA3/GHkLwYWMgjddLYuMe3sJa76xomyaQqnMYr5dlT97GmvefOeyAYfuDVzIh51usfPWegEP73vgTKZQxtmkdub7ZzAAbP3yGeCrZ5hjB2PqVVBzYD0DukneY6M1lw7Dvsj5sBhkHPsF4vezcuIwplR+ybuBlTDj9axSTSOi211n990XMKH+N1QkXMGjoOIafYs14nrX735Tb4ym+4kOyx1itm6lzrsRl7KyJPI3pX701oEV05o33k3DH3kOtnUr1axGJIL4/H4l+i8PHZiCueq7LqkC8LhxJI4gJC4LYLAD+eno0v5kOXkcY48eM4+9XzoKYDObEHMReX0asp5xn9sRQ63STkD2OIVLI+vxKnnrpFQB+syqEwuDB2MQQ27CXlXnFsO1dXMPP5bEDw/EiTLNt5a31hbD7M6jax5ZT7meHN43ZZg2PLbZaTNnwAmx8AfE0MXTtXfz4+dUt76FwHeaeSWz9yzyufXQxHm/giLbGJicf5BZbyatq0xETSBH5DdYs53ggAWsyy6/aODUCmGOMucQY82djzKPGmH8bY35gjJkC/BOrNfCYGWPWGWOmGmPGG2MuNMYcNMaUG2MWGGOG+X5X+J3/J2PMEGPMCGPMu37lq4wxY33HbvbNxu7VopwHaAxL6fgkezDBuPB/O+UlBURST0T2TCpNBDXFeV1cU9VX5a1fzMHSolbly577MwfuyGb/rtyA8rVv3s/MPQ8weenNAZNYtqx4nwm1VqfFgM0tyWV9bRVjyt5nTeRpOI2dmtz3AXB5vISue5JCWwr28+/GLoa8L17E43bj+OR3lJg4Jl78U5zDziXVHGDZU7eT+PJF1Eg4I7/6G2t/6KT5jG9cyZTaHJZm3sikGx4CICYukbXhs3EbG65FD5M5smWB6syRk2n40Q4m/fi1Vt3pSik/dgeE+LqDU/0WeY/1tQXl+ZbySfB1EMYOAoSwugLCq3ZgSxrJfd+Yxqwh8ZA4nDHBxfzt1CAARkyYyd2XTSAmYxzhrgoSbLXEV20i35vI0mJh9szZAIwOKuKf/3kFnDXcszeL/a5IXFEZTA/bz7ubiiH3FQiJ5q6dg1hvH8NMxw7e31CA2+WCD3+De+BUfuq9iSG2Ijx7l7O3vA6cdfDiNUjFLkY2rCV592t8kGstV4Qx8N7tBN2ZwvJn/8BDn+8KCIm7qog7n3qN//toR6twVTW42FxY3Smh7wuO5m/PK4BpxpjfGmN+C8wEvn74ScaY+4wx7bYP+5LAj4+/qv1PbX09Cd5yTMygjk90hOAQL253Sytkoe8f/eTs0RTbkrFX7evKqqpewrSzF+2W5e+z/L7raGqsb1U+9NVzKXswcBmU/LyNzNz2F5KooODtvx4q93o8pK7916Hn27989dDj+iWPUmvCWDbwKoa4d1JVUQbApvceJVIaCJ3zXXYGjyS+dAUAi994nHHeLVRPuJ4h42ZTRCJRW//HjrvmMsK9leXJXyM0PJIhc63t/WbtfZAD9oFUXfkOsQnWf6oGTL8CgJWx5zDrm38hOKRlq7tRN79I3Q+3M3TCnFbxiI6N1+RRqaMRHGH9zvDbQShusPV7u/WfQRJ8YxEdIVZX98G9ULotsNUyYQT2ijxOH2DtVHPZOWdx0aT0Q+e8elkCC2MLiRg8jTsvHsd5808BsbMw8SAjvdsBeLE4manJdkJSxzAmqIjluysoWfcei80EPttVQ8KYeYSZepIbd7Ft2dtQW8ybkZfwkcvaCWmabSvv5xbD4n9hDu7l9pi/sMM2mKuCcnhzg29KxPKHYNn9uI3wQ8fLvLXCbwH0/avx3jOFn+26lo8+fi+gS9zUlvLavbfys3ufYkleWUAIG5wefvS/dXy8paRVeGub3JRUN7Yq7wuO5m/QPUCo3/MQrO7fNolItojcLSKviMgbzT8nWM9+adf6xQSJh8isKR2eJ0HWx9PU2PJlri23/jDEpWRRHZpGVEOb84VUJ/O2093hcbvbLDfG4HQe25IUe/fuJvezl1qVr8/dRPnvsln+8PcDyqvKSxj0zjXMKH2JdW/eH3As+ANrQYNhnrxD+zYDFHz8MG5jY2vQKAZVtCy6kPvl66RQyspJd1JvQnDvsloci7avZVLlR6xPvpDocedgE8PuNR9SuGcbmZvuY7t9OKNmLKQyeQaDXXkse+BG5q+/lZ1BwxhxzvcQm429yQsY5drMSPcWVk66k6iRZwHWcjjrwmZSTgyR174QsHbiiKmnU/7dXKb+4NlW8QgNjyQmLvGYYquUOszZd8KEK2HI/JayuGxAoGQTRKZYaz02G5AJJRuhpggSW3ZZInE4uBtgxwcQFgeRvpX3fOcMql5DUE0+ccNmcsX0DOxBIRA/lNPjD/LjUdWYyBQe//4ibpwQAkmjiGvKZ4j9AMmmjJzGIYxJjWbWqdbGc1Mcu1j5/n9pIITbNg7ktAkjIHEkp4fv5L2NRXg3vUxF8myeKxmEc/ACRrGLFdvyaTqwEz7+HdujZ/E19x1ESwNjqhezv7IBqgsxz11BjScIlwRxk+N1Fu/wJYoeFzVPXso19U/xZPBfeWHp9pb37XGx6/5L+HHuV3ng+dcCusRdjbW88X8/4Kd/u7/V+MzSqnp+/d8cthS1btHctL+KGmfPd6B2tA7kvSJyD9AE5IrIkyLyBLAJqO3gnq9hJZ33Av/w+1HHqHbvWgBSRrVuQfEnDmuwv9MvgWyotr7YsfGJuKIzSPKUtNs61VOMMTS62q7T9tzVlJaXtyovzN/JFy/9G7c78DqPx8tnj97G+sVvt7pm7UfPs+wfl1FXG/gHsa76IOvuOpOlr/y71TVLn7mDbX+YRmX5gYDyon15FPxuBMuf/mWra5Y8eDMlfxxOuW8cX7P9eRuo+2MGSx77aatrvvz39Rz48xhKywLfa2HBHnb/bizLnwuctFHf2EjDExcy5tPr2Lw0cAiv580fkyBVzCh8murKlvttefvfRIr13QjPa4nP7tzlDPHsYlmy1YpXssHqIKirqWJY0Rvkhk+jcvD5pJoDFOfnYbxegr74GyXEMe6sa8gLG0tyxSpqqio4+Mot1BPKsEt+zeBJ82gwwYQt+xfRT5xGhKnHdt4/EJuNARPOxSFeZpY8x6roMxn4w4+RYGvXlcQ513CQKJaP+TXTFn0voIVw3K1vE/2LHaQMCtypBCA+OV1bE5XqKqMXwUUPgD2opcwR0tKNnTQq8PzYzJa9rxP8Esjmx7s+ta5pHtcfnQ5BEbDOmsxGml+DSdIo7GVbiD24EUmbwui0GBw2gcRRiNfNm6fsBeC2G67hre/PJSRxCITFsSixmNOCNpMXOo4JWSn8ZOEISJ/GWJNHbcEmbBU7+WfBcLITIhg5fSF2PIx0b6XgjT/gNvDt8q8zatIpuENimSbbWL6zDN76Ed6GKi5v+gVFgy/lNPsGVu46YM3wfvvHRJet5UXOIEGqCd394aEhZU05/2BM5acMspXyLe9LbCmqOfT2dv3vdq6s/y//sP2TV1cEDjPb8Nh3+e32i3j25ZcDyuuqyil+7ErKVjx3lB9g1+nob91VwGrgVeAXwKdADvBLOp580miMuccY86kx5rPmn86qcH9iK9tKjQkjbmBWx+c1J5DOlmZwV501Ni00Kh57XBbB4qa82K8b29VgDTBuqAy8mTE0vP8HDr53Z+DuAADOOgo/uIfKgm2t6rBv3ccsff5OGhsDm+I9TfWs+/t5fPLMna2uWXHv1VT/cTCb134ZUJ639E2Gv3g6xfedGzBj1+VyUvfEVzll0y9Z+fajAdd8+b+/clrBA4z48BqcTU2HyksL9zDui+8ys+Z91r9xb8A1m57+MRMbVzBrwy9pbKg7VL5r03Jm7fwnIzzb2fx24DXFz91Muilmxq5/B1yzecXHzC5+hoGmlO3vBCakVS/cRDR1zM5/mMb6lv97bVj8NnPLXySdA2x5K3DSSeF/v0u2yWfa1r8FbL236d1HGMkeAGqXP91Sr/ydTGxYwZZga7/g3WusZNB4vSTteYNtjpEsT7iYIY25uF1Wi2fph/+iyQQx/JJfU0I8joKlAKx/5W8kUEnI/J+QOMZqdchf/R7LnvgZI12b2TX6e4SGRVCfOocs7z6a/jmJkQ3r2TD6VpKS0wgNi2B7+GRGuLdSbYum5lufMXTSqQCMmHI6yxMuZlXUfMbf9B/CI1taLoaMn82AOwqYcWnLuo7N7A6HLsatVG/SnDimHTavdkBWy+PkMS2PE4b7XTu65bHNZrVOVuy0JuwMnBB4XsUuKM8LfJ1k6/rw9U9CUDiOgeOtiaYikDaFqU3LyTYFjJt7Hi/cOIv0AeGQPpVQdxU/jfoAgJTpl/DwVVOwZ0zHIMwN2kZ8/ge83jSZcnsiNy0Yjj1zFrMcWylY9hJsf48H7FfiiR9BxqQzCKOJgztXYlY8DGue5gHvhWyc8Ctc9jBGOzeyu6wO8j7CsfhvvOGZxb7sy5hr28jK3VYXPvvXMGz3f8iXVBKlGvfW9w69veLcL1hQ/RoO8TKr5FlqGluGp+1+9hbO8C5mRkzlUX5QXaejZXyeMsY8BZQDzzQ/9ytvz/+JyG9FZJZv9vbk9mZuq46F1eZT7Eg7YutKcxe2q8lvPEb9QZw4ICic0CRrG8TygpZBv843boVXrqf22WsD7lX25ROELf07A5bdxa7PAmfZ5j39fVKX/JqDj1+Kx9OSXDYVbSbtta8ya+tdLH/29wHXbHr+V0ys/YLTd95FQf7eQ+U7l7/FjIo3SJJKaj76W8sFxmD7yNrSbJx3C1vWtqwkte6thxjmtQY0h2/+X8t7aWxg3HYraQsVFzvWfNryOu/dhw1Dgwkmes8Hh8qrykuYVPoGZcRa5637/NCxA589TJMJolCSiC5oKS8p2MmE+mXsslvx3OV3Td2XD1NjwthnSyOqsKXLNz9vI6OdG9gcNNa6Zv0Xh465lj/KQaLYacsmobBleHBdxX6mNixhtz0Lmxh2bWxJsMO2vsJ+SWFt+GxSq9YeKt/z6ZPYxBBywT9wGTv1edbrbPz8VQZ791A54jIcg+cSLk3s2riEvVvXMOXgu6xNvpi4pDTyoyeRWbOW1e88wcRdD7MxbDojp59F9pgZHCSKaet+yaz8R1gVfQZTL7TWQRw40xqbmEAlG059kNlfa2lhjV10Jytiv4Lra88zMLOlFUJsNmbc/ARTb30tYLyiUqqPmftjSBoDk74RWD5oesvjWL+FVyLiWx6nTw28pnmsZNJoCIlqKfdv3fS/JmEEOEKh4SCkT7Mm+zRLmwJ1viQty2//9HRrZYUznR9D2lRuXnQKw5KjIDQGSRrNDfIasVLHmAVX8dnP5pM+IBzJnE0GxZxa8jT7TTwPNJzJr84bhT3L6hWcVv85zvd/y3L7FP7hvoxr5g7DlTqdGbYt5K35BPPs19hJOq+k3MKgSWcSLQ3WiigH99LwwvWUmmhy5jyD0xZKfPkqq3vb48L29i0UmTj2DVzIbFsua/ZaDUKmfCcjS97m/cgLOTDue+1+NN3laPp9Lgd2iMhfRWTUEc+GccD1WMvqNHdf//34q9j/eD0eVj30XSY0rqQm7Mi7LdqCrJYZ/wRSGiupt0WBCLEDrW6/2hLfbLK6MuwbrebvyPxPqS+2EktvTSmhH/+a1WY4e0wKDcuePHS/kk2fMrTgZUpNNNnevaz5zLeEpjGUPHczNSaMnSaV9Pw3DjXdl+1cx+hdT7LdNgSAPUtf8b1BL7aP76CQRNZEnc6w2lWHuqRde5cw2LObLzO/C0BF7ifWy3i9DMh9ij2SzsqEixjamIvL15K24aNnGEANqybfBUDVNit5cjbWk73vZXJDJrAheRGDm7Ycap3c9uHjBIub4tP/D4DqrVYyWFddwcjS98iNmk1+wqkB1+z64EFsYnB/5Z++17GuqTxYzqjKz9gyYD5FCbMDrin65EG8Rgi78G7fNVZjfEVZCaOrF7M9fgGlCdPIbtp26BrZ+RFeI3jOs1o/K7dY1xzIz2N041ryU8+mKXUG6aaIssK9NNVXM2zX02wKGs/gsTPYFTSMAaWrqK+tIuKLP1IoSUy64CYyJ1tjCqtz/k3Y85f4upx/Y8U361QSqGTKilvYFzSYgVc9Yn237Ha2JVrb8y1LvIwpt7x4qCUwc/gENp7+NDsXPsPEBZcHfCczR05m+i3PkTlS/++o1EkpYwZ8b0nLhJpmmXOsn4V/bummbjb8HLAFwZAFgeUDJ1q/s+YGlvvP/E71+7vE7mhp3fRPEiGwC9y/NdN/Qs+ow/ZOHzQdG14QOyPnLCIh0tfb4Zs4NNG2C8f4r7Lhd+dw+shkiErGPWAo33G8TYhp5D8x13PPFZMZkhhJ2LDTGGErYMySH1HoieWrDb/kxnOmIb73Frv/M5xPXYi3upA7HLfw1VMnURU/kSlmM1v3FtD4/LUk1efxVtqPSJp8PrFSx97N1vj0A6//Bpex45p1C73BERNIY8w3gElYE2eeEJGlInKDiES1c8lFwGBjzGnGmPm+n9M7sc4npfQvf8ZWX3fpspf/xdQia1KAa+I1HV0GgC24dQtksKuaRoe1/EJSutVi1lRhbQW+78v/YcfLvQNuB2DrMmtEwsb3HyPS1FJ6yp8pTzmF7MbN1Dc0YNxNuF+/hSITj/P6xbiNjbptOQDs/uQxMqpX80HqjVQPv4QhJp/9+wswrkZqXvgOtYQT+s3XOEg0tnyri3T7J0+R7dzBtlHfR4bMJ05q2LVlDXjcDN/7LHtIZdIVd1AoKYQVWTN2cz97iaHuHRSN+haOwXOIkEbyNi7D5XKRsPr/2GsbxOSvXM8+WzrhxdY161+7m2TKcc+9leDsOYRLEzs3LqGpsY6srQ+zJWgUY+ZewB7bIMJLrHUyN718F7HUEn76rQQNtq7ZvWkpVRUHGLP3P6wLn83wKfOta4qtaza+dCeR0kDSgpsJ8r3OntylVB3Yx7j9L7Ay6nSyx8xgjy2D8GLrL4Lcl+8kTJwMXPA9QrNmECZOduUu50BJEafUvsum6FMYOulU8iWV0CLrdfa9/ns82Mk46ybiRltdy3tWv8eGx24mnkrcp/0CgIrE6Yx0bab8HzPJdu+meMYvCQ4JJSFlELttWUyttpbeOPDV14hPtjZnGnPWteTZh7DNMYJBP3yPhNSsQ9+lKdffR9E3VzDzpkdatYaPO3URQ2ZdcMTvqFKqnwgKhW++A7Nuan3skkfh5pUQedjktinXwrl3w/xfBJbHDrIS0QsfhNDDdpc5/ddWIjrlsH8jM2ZayeKC3wSO27TZrclA4fHWb39jLrR+T/0WBIW1lPsloMmn3YDdb891xzArralJmc69P7iCr4wbCIBkW8N10qSMT1O+ySM3LGDm4HiITqUmIoubzHMEV+7iRuctnHXepYQF2wkddhqjbPuIfPFrOHa8y53ur3PGxdcROtS6F3s+w/P2T0ne9xYvBZ3HWTP8EuMedFSbRhpjqkXkZawFwm/BShJ/KiL3GGPuPez09UAsLVsLqiNorDrAUNc2WPkrVh7Yy+y9jxw6NuOMS454vcPXhe32GwMZ7q3FGWT9gQuPjKWaCKi2Jnc4N7/FbpPCVd/+ERV/+zfevUuAHxC+/TXybFksXHAGuWY/4SUvsmH9UmwHNjLWtYf3x/+ThemZ7AwZRlzZKqorS4n54nfk2kaw8Bs/p2rHYthxL3vWfczBwi8Z17SV98b+lbMHZbAxcjwZNevYvXUtaYtvY7tkM+vC71JTlAfrfk3ppo9xfnInY00BH437G1mhoWyJnczgii/YvvRtBn32I/JlIJMXfY/a8mJYAZUb3iM3524mmgLWz7yHTIeD4tjJjCz/iM2fvcjIrf9mQ/AkJp5yPuUl+bAcKte/w9ZP/84EKig59f8Qm40DsZMYWf4R6z94hnF7nmR1xFymTD6NssJMWA4H176B88PfM4YGYs+9A4CS2EmMqviINW8/wtSCp9gQNZfx4+YQGZ8Gy6Fi9Su431vLMDwknP9b65oBkxhV/iErX72X6YX/YX30PCaMnkF4TBysgPJVL+N9dyUjaSTxPOt1imImMqzyC5Y//2emlr7B8viLmJ05HHdqFvWvhzB19c8AWJp8BTPnWC2FcVMvhjefZpApZN2pDzHZr3Wwdt4drFv2EDHn/YEhfotlh0fGMPgXKxGRVkliUHBIQDe0Ukodl5BI6+dwQaEw7brW5dB2IgrWrHD/meHNQqPhpuWtywEW/dsa2+8IDiwfPA++vwZi0gPLHcFWeVVBy1JFzeb/AtxNRE2/PrA8bQpm+Nm4Kwv5xg0/D0hiwyd/Db74C8ujF/KPb/+QpGjr3+6oUWfAkr+Q1bCZv9u+ydALbyU7IQKIoDI4haurHoKV8IR7IQnn/4ZgR++YNHjEBFJEzge+BQwBngGmG2MOiEg4sAVrtrW/ZGCriKzEmsENgDFGmynaUVZccGiT7mm+5HFF7FfI/OofSD6K6+2HJZAeryHSW4M7uOUPQ4U9geC6YmuHmsqNrImcS3ZECOvCxpBQuZHq8mKGObfwWfoNDBUhc8J8+BIqtn9J9N732WdLZ8GF1wJQkziFkQUv8uUzv2W+qaH0gueJiQghavQcGl8LJnb9o4x1beTjmEtY+NUbrLplzCF982L2P/dVXOLA/vUXCA0JJjRzFGUygDnbrEk2r0ZewQUXWvsKS9YcBhx8hwHvX8luGYT7a88REhJGSGo2hZLCrD334TVCTsZNnHbmVVYssucQXfEGoz/9NjttWcR87X5EhISUDPIllVn51vI0i4f8iLmnXBBwzYQlN7PdMYzUy61u7YTULIolkZkFj+E0dlaM+QUzfbuZ2LOsayav/AlbgkeR+o0HDl1TIAOZuf8pGk0Qayb+nhkjrP8tOrJmE13+OtPW/4pNIRPIuPpB3zVDyZdU5hQ+Sb0J4c2BP2TRCF83TOYcBlS+w4ytf2FtxCwmfutf1r2CglkTfxbTK95kWcYNzPpWyzjSEVPms6b8PkIjY5k4O7CrZtypF8GpF7X5PbLZ7UfxbVNKqT7KZrd+2hI/pP3yto6FDYAL7mldbrMhVzxPELTqwrfP+xkMHMuMoQsg2G8MePo0qkd/nW0VHq79+p0kRLW0gtaNuozY9ffwhpzOkKvv49ThvWdpsqNpgbwU+Kcx5nP/QmNMvYh8q43zf9spNetH/BcAbzZg7nUkp7desqQtjmDry+bxJZC1jW5iqKMxJPbQOXUhyUQ1lrB3x0ayqCE0y2p9cqZMJmPXchZ//D9OBVImWOPkopIyKZV4QnZ9wASziW3Drsdht/7XEzFsLqH7n2VB+X/ZEjmNUROtAcW24FD2ho1mbMM6qgln/FV3HdqCMX3yQtj8F9KkjM0z/sLoob4ZeSIUJp1GQslrrI+YQ+zky7D7Xmfo7IupW/sHvNgIueZlsrNaWsHKh19G8tZ7WTbkh8y7+neHysecfiW71z2IMYa4mz5kQHzSoWNFY76NLfchCqf+jLnnfftQ+aj5V7B93aM0OKIZcvOrREa1zAwumv07Gpf8iYo5v2LmmS3dHuMXXsv6zS/hsYcy+ub/ERoW0VK3U/6Ae/HvqD7tD8w4rSVZG3/mVazb8jrVwcnM/O6DLZNIRKie+0tWLn2csAU/I6ap5S+WCQuvZvPWF6kNSWLSzf8JmIk8+cbHqDqwh5nprVsHJ5/1jVZlSimlukF7Ww/bHTC6jbY0EaIvu5+2NlBNW3QHpUNP5axhpxAa2rsmHh4xgTTGXN3BsVY7y/gv2SMi5xlj3jr8HBXI6wlcLmfNsB8weeoZR329w5eIeFxWAlnd6CJGamn0W9zVFTGQpLptrFmTQxYwbNI8AGKGzsC2+34Scx+jUYIZNqFlQHJJ9DhmVeWAwKC5Vxwqz5g431rQCYg4NXDR6pCJl8LSdRSNvZERCS3JW+LQKewe+R1q3TbGnf2dgGtGX/VPNi4+lRGnXsayFS17lUYnplL9vZUEh0WSGhUXcM24y39PVd1tzImMCCgPjYgm4/aV2Gw25LD/aU7/6q3w1Vs5fFpSeGQMw3+9irZMOvPrcObXyTqsPDgklAm3fdTmNRNOvxROv7RVeVBoBBN/1vYKWGMWfAMWWElfTk7OofKQ8GhG3972KliO4BBi2kgelVJKnSRsdhLHndnTtWhTuwmkiNQA/kud+6fUxhhz2IjWNv0e0ATyCLyHrbcYM+7sY7o+KDgwgayqa2SQNHAgfMChc2wxqSSWVtGw4wsaCCVh8EQAMsedAh/CKNnLrohJDPZrVg8fPBPW5lAeNJD4zJbZcCGxqZSNvgapLSFjeuD/prLOugmmn88I/+UbfLIv/2urMgBHZBzjzm57/Et0Uuv7ACBCzGHJYzO7I6jNcqWUUkp1jo5aID8GUoBXgOeNMcezmXI77bjK3+EJZHhMwjFdHxRi7eRhfAlkfbW1C4k9vKXVLiQ+A/Jgrmc5pdGjyfC1zoVGx3NQYhhgqogcfmrAfQfPv4aG4k+Imf/zVk3yCZe1MfYDrPMGZB5T/ZVSSinVt3S0kPiFwEKgFHhERD4Tke+JSFx717ThO0c+RRlv4D7JkTHx7ZzZtiBfF7bXZc1ZavRtYxgc2fJRJaVZa3UlSDW2jMCRFmHjFlnnzLoioJzoVMK+8yGO4Uffna6UUkqpk1+Hc8GNMVXGmCeAc4AHsbqkr23rXBE59fAfINTv8QkREbuIrBWRt3zP40TkQxHZ4fs9wO/c20UkT0S2ichCv/IpIrLRd+wekfZGunavw/eojoga0M6ZbWuejOF1Wwmks/YgACF+4wYHDG5ZhHXg2NMCrg89/6/wkx2t9zRVSimllGpDh5NoRGQ2cAVwCrAYuMgY80U7p/+0jTIDTADSgRNdI+SHWMsGNY+9vA342Bhzl4jc5nv+cxEZjbV7zhggFfhIRIYbYzzAA8ANwDLgHeBsOt7Xu1sYjzfg+bEupxIc4pvy7+vCdtVa2x6F+XeFRyZhQmOhsQr7kHmBNwgKC1w8VSmllFKqAx1NotkDVALPYyVdbl/5ZABjzBr/840x5x92/Vzgl0ARcPOJVFJE0oFzgT8BP/YVLwLm+R4/hTUv+Oe+8ueNMU3AbhHJA6b73k+0MWap755PAxfSCxLIw8dAHqvmZXyMx2qB9Nb7EsiowK5w+cFaaKyC4LYnnyillFJKHY2OWiD3YLUgLgTO4rBZ2ECb2xOKyALg175z/myM+bAT6vkv4GeA//aJycaYIgBjTJGINK8Zk4bVwtiswFfm8j0+vLyt93ADVtJMcnJywLIqXaF673bG+D0/5tczhnlAdXkpOTk5HCzaA8DS9VtxBRe3ccHe46pnd6itre3yePd2GgOLxkFj0EzjYNE4aAya9YY4tJtAGmPmAYhIqDGm0f+YiLRazVJEzsVqcawCfmmM+bIzKigi5wEHjDGrRWTe0VzSRpnpoLx1oTEPAw8DTJ061cybdzQve/w25pTD7pbnx/N6TZ8FERMRwinz5vH2pjegFuac/pXWWzb1cjk5Ocf1/k8mGgOLxkFj0EzjYNE4aAya9YY4HM1ONEuAyUdR9iZWq1451ljEgIMnsJXhHOACEfkKEApEi8h/gBIRGehrfRxIy97bBcAgv+vTgUJfeXob5T3OGp55YpwEIR4rz3c4q2gkhNA+ljwqpZRSqm/oaAxkClYXb5iITKKlBS8aCG/jkjZ2NT9xxpjbgdt9dZoH/MQY8w0R+RtwDXCX7/frvkveAJ4VkbuxJtEMA1YYYzwiUiMiM4HlwNW03se7RxhvyySaeltEm8E9EqeEYPPNwg5yVlFnj6J3bXqklFJKqZNFRy2QC7GW7EkH7vYrrwF+cfjJzVsYisgUY8xq/2Micv7h53eCu4AXROQ6YB/Wnt0YY3JF5AVgM9bEn5tMSxPfd4EngTCsyTM9PoEGWpbx2Tn2h6TNa2t78SNzEYTNN4kmxF1Ng/1oNgpSSimllDp2HY2BfAp4SkQuMca8fAz3fERErjHGbAQQkSuAW7C6uE+IMSYH3y7MxphyYEE75/0Ja8b24eWrgLEnWo/O1pxAmhHnEJqQdVz3cNpCsHutLuwwTw3O4KgjXKGUUkopdXw6XEjc52MRuVtEVvl+/iEiMR2c/1WsxHOUiFwPfA9rFrdqR3MCeazrP/pzSwh2XwtkhLcWZ3BHH5FSSiml1PE7mgTyMaxu68t8P9XAE+2dbIzZhbWQ98tYyeRZxpiqE6/qyau5h90uJ5BA2oJxGCdOt5doavGGaAKplFJKqa5xNLOwhxhjLvF7/jsRWXf4SSKykcBlceKwdp9ZLiIYY8afUE1PYsbjG6JpO5EEMgSHu5HqRhcx1FERGts5lVNKKaWUOszRJJANIjLXGLMYQETmAA1tnHdep9asPzHNXdhH83G0zWMLIcRbTXVtLQnSBGHHtp+2UkoppdTROpqM5btYYxpjsJbyqcBaNudw5caY2o5uJCKRRzqnPzo0BtJ2AgmkPYRg00RtVTkA9nBNIJVSSinVNY6YsRhj1gETRCTa97y6nVNf93Vtvw6sNsbUAYjIYKw1Ii8DHgFeOvFqn2R860Da7G1tlnOUt7CHEGycNFRbCWRQZPwRrlBKKaWUOj5HTCB9LY+/BU71Pf8M+P3hE2OMMQt8u8V8B5gjIgOw1mHcBrwNXGOMaWtj5n6vZRb28bdAeu2hBOHEWVsBQEhUXKfUTSmllFLqcEeTsTwObMJqQQS4CmsW9sWHn2iMeQd4p9Nq11+YE+/C9tpDCTFOXL4EMixaWyCVUkop1TU6bRa2On7NLZByAutA4gghBBeeOiuBDI9J6IyqKaWUUkq1cjTrQDaIyNzmJx3MwlbHqXkvbPsJdGEbRygh4sJbbyWQoVHaAqmUUkqprtGZs7DV8TrUhX0iLZCh1j3qSgCQsNgTrZVSSimlVJuOeRY2UA98DdjQhfXqV6QTtjKUoDAAQhoOUEs4kSeSjCqllFJKdaDdLmwRiRaR20Xk3yJyJtZ2hlcDebRMqFGdoGUdyBNJIEMAiGoqocam2xgqpZRSqut0NAbyGWAEsBG4HvgAuBS40BizqBvqBoCIDBKRT0Vki4jkisgPfeVxIvKhiOzw/R7gd83tIpInIttEZKFf+RQR2eg7do+IHP/Ci53IGN8YSMfxj4FsboGM9xyg1hHbGdVSSimllGpTRwnkYGPMtcaYh4ArgKnAeb4u7e7kBm41xowCZgI3icho4DbgY2PMMOBj33N8xy4HxgBnA/eLSHPT3gPADcAw38/Z3flG2tUJYyBtIREApJpSGoJ0FxqllFJKdZ2OEkhX8wNjjAfYbYyp6foqBTLGFBlj1vge1wBbgDRgEfCU77SngAt9jxcBzxtjmowxu7G63KeLyEAg2hiz1BhjgKf9rulZnTALW0KsIao2MbhCdBFxpZRSSnWdjjKWCSLSvG2hAGG+5wIYY0x0+5d2DRHJAiYBy4FkY0wRVmWKRCTJd1oasMzvsgJfmcv3+PDyHieHWiCPZlWldu4R2jLu0ROmCaRSSimluk67CaQxpldN4xWRSOBl4BZjTHUHwxfbOmA6KG/rtW7A6uomOTmZnJycY67vsWisPIjHCF98/vlx36OkqICJvsdlDdLlde4qtbW1fbbunUVjYNE4aAyaaRwsGgeNQbPeEIfj7zPtRiIShJU8/tcY84qvuEREBvpaHwcCB3zlBcAgv8vTgUJfeXob5a0YYx4GHgaYOnWqmTdvXme9lTYt3f4K3mobJ/I6m7dvs3YdB5KzRjGli+vcVXJyck4oDicDjYFF46AxaKZxsGgcNAbNekMcjr/PtJv4Zko/Bmwxxtztd+gNWhY0vwZ43a/8chEJEZFsrMkyK3zd3TUiMtN3z6v9rulZxoP3BD+KoPCWiTMhsSknWiOllFJKqXb1hRbIOcBVwEa/Pbh/AdwFvCAi1wH7sJYYwhiTKyIvAJuxZnDf5JsEBNauOk8CYcC7vp8eJ14PnhNMIGOjW4akhiQNOdEqKaWUUkq1q9cnkMaYxbQ9fhFgQTvX/An4Uxvlq4CxnVe7zmJOuAUyLjKEGhNGlDSQPGhoJ9VLKaWUUqq1Xp9A9gteD952c+SjY7cJ1zh/TrIc5IGI8E6qmFJKKaVUa5pA9gLSCWMgAW697ipMm/PKlVJKKaU6jyaQvYHxnvAYSIA5QxM6oTJKKaWUUh3r9bOw+4POaoFUSimllOoOmrX0Bl5NIJVSSinVd2jW0guI8eIR/SiUUkop1Tdo1tILiHFrC6RSSiml+gzNWnoBMV5NIJVSSinVZ2jW0gvoJBqllFJK9SWatfQGxosHe0/XQimllFLqqGgC2QtoC6RSSiml+hLNWnoBm/Hg1VnYSimllOojNGvpBbQFUimllFJ9iWYtvYDd68ItQT1dDaWUUkqpo9LvEkgROVtEtolInojc1tP1AbAZF27dllwppZRSfUS/SiBFxA7cB5wDjAauEJHRPVsrsBs3HtEEUimllFJ9Q79KIIHpQJ4xZpcxxgk8Dyzq4TphNy5NIJVSSinVZ/S3BDINyPd7XuAr61GVYZlUhaT2dDWUUkoppY6KGGN6ug7dRkQuBRYaY77te34VMN0Y8/3DzrsBuAEgOTl5yvPPP9/ldautrSUyMrLLX6e30zhoDJppHDQGzTQOFo2DxqBZd8Vh/vz5q40xU9s61t/6TQuAQX7P04HCw08yxjwMPAwwdepUM2/evC6vWE5ODt3xOr2dxkFj0EzjoDFopnGwaBw0Bs16Qxz6Wxf2SmCYiGSLSDBwOfBGD9dJKaWUUqpP6VctkMYYt4jcDLwP2IHHjTG5PVwtpZRSSqk+pV+NgTweIlIK7O2Gl0oAyrrhdXo7jYPGoJnGQWPQTONg0ThoDJp1VxwyjTGJbR3QBLKXEJFV7Q1U7U80DhqDZhoHjUEzjYNF46AxaNYb4tDfxkAqpZRSSqkTpAmkUkoppZQ6JppA9h4P93QFegmNg8agmcZBY9BM42DROGgMmvV4HHQMpFJKKaWUOibaAqmUUkoppY6JJpA9TETOFpFtIpInIrf1dH26k4jsEZGNIrJORFb5yuJE5EMR2eH7PaCn69nZRORxETkgIpv8ytp93yJyu+/7sU1EFvZMrTtXOzG4Q0T2+74P60TkK37HTroYAIjIIBH5VES2iEiuiPzQV95vvg8dxKBffR9EJFREVojIel8cfucr70/fhfZi0K++C81ExC4ia0XkLd/z3vVdMMboTw/9YC1mvhMYDAQD64HRPV2vbnz/e4CEw8r+Ctzme3wb8JeermcXvO9TgcnApiO9b2C073sRAmT7vi/2nn4PXRSDO4CftHHuSRkD33sbCEz2PY4Ctvveb7/5PnQQg371fQAEiPQ9DgKWAzP72XehvRj0q++C3/v7MfAs8Jbvea/6LmgLZM+aDuQZY3YZY5zA88CiHq5TT1sEPOV7/BRwYc9VpWsYYz4HKg4rbu99LwKeN8Y0GWN2A3lY35s+rZ0YtOekjAGAMabIGLPG97gG2AKk0Y++Dx3EoD0nXQwAjKXW9zTI92PoX9+F9mLQnpMuBs1EJB04F3jUr7hXfRc0gexZaUC+3/MCOv6L82RjgA9EZLWI3OArSzbGFIH1DwuQ1GO1617tve/+9h25WUQ2+Lq4m7tn+kUMRCQLmITV6tIvvw+HxQD62ffB12W5DjgAfGiM6XffhXZiAP3suwD8C/gZ4PUr61XfBU0ge5a0UdafpsXPMcZMBs4BbhKRU3u6Qr1Qf/qOPAAMASYCRcA/fOUnfQxEJBJ4GbjFGFPd0altlJ0UsWgjBv3u+2CM8RhjJgLpwHQRGdvB6SdlHNqJQb/6LojIecABY8zqo72kjbIuj4MmkD2rABjk9zwdKOyhunQ7Y0yh7/cB4FWsJvcSERkI4Pt9oOdq2K3ae9/95jtijCnx/ePhBR6hpQvmpI6BiARhJU7/Nca84ivuV9+HtmLQX78PAMaYSiAHOJt+9l1o5h+DfvhdmANcICJ7sIa2nS4i/6GXfRc0gexZK4FhIpItIsHA5cAbPVynbiEiESIS1fwYOAvYhPX+r/Gddg3wes/UsNu1977fAC4XkRARyQaGASt6oH5drvkvRp+LsL4PcBLHQEQEeAzYYoy52+9Qv/k+tBeD/vZ9EJFEEYn1PQ4DzgC20r++C23GoL99F4wxtxtj0o0xWVh5wSfGmG/Qy74Ljq5+AdU+Y4xbRG4G3seakf24MSa3h6vVXZKBV61/O3AAzxpj3hORlcALInIdsA+4tAfr2CVE5DlgHpAgIgXAb4G7aON9G2NyReQFYDPgBm4yxnh6pOKdqJ0YzBORiVhdL3uA78DJGwOfOcBVwEbfuC+AX9C/vg/txeCKfvZ9GAg8JSJ2rMadF4wxb4nIUvrPd6G9GDzTz74L7elVfy/oTjRKKaWUUuqYaBe2UkoppZQ6JppAKqWUUkqpY6IJpFJKKaWUOiaaQCqllFJKqWOis7CVUq2sXr06yeFwPAqMRf+jqfo3L7DJ7XZ/e8qUKf1lXVqljkgTSKVUKw6H49GUlJRRiYmJB202my7VoPotr9crpaWlo4uLix8FLujp+ijVW2jLglKqLWMTExOrNXlU/Z3NZjOJiYlVWK3xSikfTSCVUm2xafKolMX3Z0H/vVTKj/6BUEoppZRSx0QTSKVUryQiU66//vr05ue/+c1vkn/84x+nAvz4xz9OTUpKGj9y5MjRQ4YMGfPQQw/FNZ93ySWXZKWlpY0bOXLk6JEjR46eNGnSSIB77rkn/uqrr87wf42amhrbvHnzhmZnZ48ZOnTomO9973tpR6pXU1OTXHzxxVnDhw8fPXjw4DG33357Skfnf//7309LSUkZHx4ePulYY9CbnSyfzymnnDJsxIgRo4cOHTrmyiuvzHC73ccaCqX6JU0glVK9UnBwsHnnnXcGFBUVtTnZ78YbbyzZunXr5tdeey3v1ltvzWxqapLmY3/84x8Ltm7dunnr1q2b165du7Wj17n11ltLdu/enbtp06bNy5cvj3zhhReiOzr/iSeeGOB0Om3bt2/fvH79+i1PP/104rZt24LbO//CCy+sXL58+ZYjvd++5mT5fF5//fWd27Zt27x9+/bc8vLyoMcff3zAkd67UkpnYSuljuCnL60ftL24Jrwz7zk8Jar+b1+dkN/ROXa73Vx99dWlf/7zn5Pvvffe/e2dN27cuKbQ0FBvWVmZPS0t7Ziaj6Kiorznn39+DUBoaKgZP358fX5+fjBYLWUhISHebdu2hZWXlwfdeeed+VdccUWViFBfX29zuVzU1dVJUFCQiY2N9bT3GgsWLKg7ljods9duGsSBzZ36+ZA0up4L7+sXn09cXJwXwOVyicvlEhFp71SllB9tgVRK9Vo//elPD7zyyitx5eXl9vbOWbx4cXhmZmajf3Lyq1/9Kr25i/SCCy7IPprXKisrs3/44Yex55xzTnVzWX5+fsiKFSu2vfnmmztuueWWzPr6ern22msPhoeHe5OSkiZkZ2ePv/nmm4uTk5PbTVBOZifL5zN37txhiYmJEyIiIjzf/OY3Dx5NfZTq77QFUinVoSO1FHaluLg476WXXlp+1113JYWFhXn9jz344IPJTz/9dGJBQUHwyy+/vMP/2B//+MeCY0kEXC4XF1988eAbbrihZPTo0c7m8ksuuaTCbrczbty4pkGDBjWtW7cutLa21maz2UxxcfGGsrIy+5w5c0Z+5Stfqfa/rlsdoaWwK50sn8/ixYt31NfXy0UXXTT4zTffjL7ooouq2ztXKWXRFkilVK92++23lzz77LMJdXV1AX9f3XjjjSV79uzZ9Nhjj+26/vrrs+vr64+77/HKK6/MGjx4cONvfvObgJ1GDu/OFBGeeeaZ+IULF1aFhISYtLQ097Rp02qXLFkScbyv3dedLJ9PeHi4Oe+88ypfffXV2OOtp1L9iSaQSqleLTk52XP++ecffPbZZxPaOn7NNddUjhs3ru6+++6LP577/+AHP0itrq62P/bYY61a8l555ZUBHo+H3NzckPz8/JAJEyY0ZmRkOD/99NNor9dLdXW1bc2aNRHjxo1rPJ7XPhn05c+nqqrKtnfv3iCwWjnfe++9mJEjRzYcTz2V6m80gVRK9Xq//OUviysrK9sdcnPHHXcU3XfffSkejzXUzX+M3ciRI0c3NjYKwEsvvRSfnJw8vvln586dQffee+/AHTt2hI4ZM2b0yJEjR999992HEqGhQ4c2TZ8+fcS555477F//+tfe8PBw87Of/exAXV2dbfjw4WMmTZo06sorryybMWNGu0nHjTfemJ6cnDy+sbHRlpycPL55qZuTSV/9fKqrq23nnnvu0OHDh48eNWrUmISEBNdPf/rT0k4Oj1InJTFGN5tQSgVav379ngkTJpT1dD160iWXXJJ13nnnVemkit6puz+f9evXJ0yYMCGrO15Lqb5AWyCVUkoppdQx0VnYSinVhpdffnnPsZw/fvz4kU6nM+A/5U8//fTu6dOn65i6LqCfj1I9SxNIpZTqBBs2bOhwRxXVs/TzUapzaRe2UqotXq/Xq1tyKAX4/ix4j3iiUv2IJpBKqbZsKi0tjdEkUvV3Xq9XSktLY4BNPV0XpXoT7cJWSrXidru/XVxc/GhxcfFY9D+aqn/zApvcbve3e7oiSvUmuoyPUkoppZQ6JtqyoJRSSimljokmkEoppZRS6phoAqmUUkoppY6JJpBKKaWUUuqYaAKplFJKKaWOyf8DVAMnCe+16cEAAAAASUVORK5CYII=", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] diff --git a/ROSCO_toolbox/ofTools/fast_io/pyIECWind.py b/rosco/toolbox/ofTools/fast_io/pyIECWind.py similarity index 99% rename from ROSCO_toolbox/ofTools/fast_io/pyIECWind.py rename to rosco/toolbox/ofTools/fast_io/pyIECWind.py index 3cc899bb..f254c624 100644 --- a/ROSCO_toolbox/ofTools/fast_io/pyIECWind.py +++ b/rosco/toolbox/ofTools/fast_io/pyIECWind.py @@ -2,9 +2,9 @@ import os, sys # import matplotlib.pyplot as plt -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.turbsim_writer import TurbsimBuilder -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.turbsim_wrapper import Turbsim_wrapper -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.turbsim_vartrees import turbsiminputs +from rosco.toolbox.ofTools.fast_io.turbsim_io.turbsim_writer import TurbsimBuilder +from rosco.toolbox.ofTools.fast_io.turbsim_io.turbsim_wrapper import Turbsim_wrapper +from rosco.toolbox.ofTools.fast_io.turbsim_io.turbsim_vartrees import turbsiminputs # from AeroelasticSE.Turbsim_mdao.pyturbsim_wrapper import pyTurbsim_wrapper diff --git a/rosco/toolbox/ofTools/fast_io/turbsim_io/__init__.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_reader.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_reader.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_reader.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_reader.py diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_vartrees.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_vartrees.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_vartrees.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_vartrees.py diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_wrapper.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_wrapper.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_wrapper.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_wrapper.py diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py similarity index 98% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py index 167ebf5e..4b7c6ecb 100644 --- a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py +++ b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbsim_writer.py @@ -1,6 +1,6 @@ -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.turbsim_vartrees import turbsiminputs -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.turbulence_spectrum import turb_specs -from ROSCO_toolbox.ofTools.fast_io.turbsim_io.wind_profile_writer import write_wind +from rosco.toolbox.ofTools.fast_io.turbsim_io.turbsim_vartrees import turbsiminputs +from rosco.toolbox.ofTools.fast_io.turbsim_io.turbulence_spectrum import turb_specs +from rosco.toolbox.ofTools.fast_io.turbsim_io.wind_profile_writer import write_wind import os import numpy as np import random diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbulence_spectrum.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/turbulence_spectrum.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/turbulence_spectrum.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/turbulence_spectrum.py diff --git a/ROSCO_toolbox/ofTools/fast_io/turbsim_io/wind_profile_writer.py b/rosco/toolbox/ofTools/fast_io/turbsim_io/wind_profile_writer.py similarity index 100% rename from ROSCO_toolbox/ofTools/fast_io/turbsim_io/wind_profile_writer.py rename to rosco/toolbox/ofTools/fast_io/turbsim_io/wind_profile_writer.py diff --git a/ROSCO_toolbox/ofTools/fast_io/update_discons.py b/rosco/toolbox/ofTools/fast_io/update_discons.py similarity index 91% rename from ROSCO_toolbox/ofTools/fast_io/update_discons.py rename to rosco/toolbox/ofTools/fast_io/update_discons.py index 72abb605..a1604b50 100644 --- a/ROSCO_toolbox/ofTools/fast_io/update_discons.py +++ b/rosco/toolbox/ofTools/fast_io/update_discons.py @@ -1,8 +1,8 @@ import os # ROSCO toolbox modules -from ROSCO_toolbox.utilities import write_DISCON -from ROSCO_toolbox.tune import yaml_to_objs +from rosco.toolbox.utilities import write_DISCON +from rosco.toolbox.tune import yaml_to_objs def update_discons(tune_to_test_map): # Update a set of discon files diff --git a/ROSCO_toolbox/ofTools/util/FileTools.py b/rosco/toolbox/ofTools/util/FileTools.py similarity index 100% rename from ROSCO_toolbox/ofTools/util/FileTools.py rename to rosco/toolbox/ofTools/util/FileTools.py diff --git a/rosco/toolbox/ofTools/util/__init__.py b/rosco/toolbox/ofTools/util/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ROSCO_toolbox/ofTools/util/spectral.py b/rosco/toolbox/ofTools/util/spectral.py similarity index 100% rename from ROSCO_toolbox/ofTools/util/spectral.py rename to rosco/toolbox/ofTools/util/spectral.py diff --git a/ROSCO_toolbox/sim.py b/rosco/toolbox/sim.py similarity index 99% rename from ROSCO_toolbox/sim.py rename to rosco/toolbox/sim.py index 39ab71f6..2c845ca7 100644 --- a/ROSCO_toolbox/sim.py +++ b/rosco/toolbox/sim.py @@ -10,7 +10,7 @@ # speROSCO_cific language governing permissions and limitations under the License. import numpy as np -from ROSCO_toolbox import turbine as ROSCO_turbine +from rosco.toolbox import turbine as ROSCO_turbine import matplotlib.pyplot as plt import sys diff --git a/ROSCO_toolbox/tune.py b/rosco/toolbox/tune.py similarity index 95% rename from ROSCO_toolbox/tune.py rename to rosco/toolbox/tune.py index 247067cd..fa121247 100644 --- a/ROSCO_toolbox/tune.py +++ b/rosco/toolbox/tune.py @@ -10,10 +10,10 @@ # specific language governing permissions and limitations under the License. import os -from ROSCO_toolbox import controller as ROSCO_controller -from ROSCO_toolbox import turbine as ROSCO_turbine -from ROSCO_toolbox.inputs.validation import load_rosco_yaml -from ROSCO_toolbox.utilities import read_DISCON, write_DISCON, DISCON_dict +from rosco.toolbox import controller as ROSCO_controller +from rosco.toolbox import turbine as ROSCO_turbine +from rosco.toolbox.inputs.validation import load_rosco_yaml +from rosco.toolbox.utilities import read_DISCON, write_DISCON, DISCON_dict def yaml_to_objs(tuning_yaml): diff --git a/ROSCO_toolbox/turbine.py b/rosco/toolbox/turbine.py similarity index 98% rename from ROSCO_toolbox/turbine.py rename to rosco/toolbox/turbine.py index a900b1f9..73965285 100644 --- a/ROSCO_toolbox/turbine.py +++ b/rosco/toolbox/turbine.py @@ -18,16 +18,16 @@ import matplotlib.pyplot as plt import pandas as pd -from ROSCO_toolbox.utilities import load_from_txt +from rosco.toolbox.utilities import load_from_txt # Load OpenFAST readers try: import weis.aeroelasticse use_weis = True - print('Using weis.aeroelasticse in ROSCO_toolbox...') + print('Using weis.aeroelasticse in rosco.toolbox...') except: use_weis = False - print('Using ofTools in ROSCO_toolbox...') + print('Using ofTools in rosco.toolbox...') # Some useful constants @@ -158,7 +158,7 @@ def load_from_fast( if use_weis: from weis.aeroelasticse.FAST_reader import InputReader_OpenFAST else: - from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST + from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST # Load OpenFAST model using the FAST_reader print('Loading FAST model: %s ' % FAST_InputFile) @@ -351,8 +351,8 @@ def generate_rotperf_fast(self, openfast_path, FAST_runDirectory=None, run_BeamD from weis.aeroelasticse import runFAST_pywrapper, CaseGen_General from weis.aeroelasticse.Util import FileTools else: - from ROSCO_toolbox.ofTools.case_gen import runFAST_pywrapper, CaseGen_General - from ROSCO_toolbox.ofTools.util import FileTools + from rosco.toolbox.ofTools.case_gen import runFAST_pywrapper, CaseGen_General + from rosco.toolbox.ofTools.util import FileTools # Load pCrunch tools from pCrunch import Processing @@ -536,7 +536,7 @@ def load_blade_info(self): if use_weis: from weis.aeroelasticse.FAST_reader import InputReader_OpenFAST else: - from ROSCO_toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST + from rosco.toolbox.ofTools.fast_io.FAST_reader import InputReader_OpenFAST from wisdem.ccblade.ccblade import CCAirfoil, CCBlade # Create CC-Blade Rotor @@ -633,7 +633,7 @@ def __init__(self,performance_table, pitch_initial_rad, TSR_initial): # If there is more than one max pitch angle: if len(self.max_ind[1]) > 1: - print('ROSCO_toolbox Warning: repeated maximum values in a performance table and the last one @ pitch = {} rad. was taken...'.format(self.pitch_opt[-1])) + print('rosco.toolbox Warning: repeated maximum values in a performance table and the last one @ pitch = {} rad. was taken...'.format(self.pitch_opt[-1])) # Find TSR that maximizes Cx at fine pitch # - TSR to satisfy: max( Cx(TSR, \beta_fine) ) = TSR_opt @@ -643,7 +643,7 @@ def __init__(self,performance_table, pitch_initial_rad, TSR_initial): f_performance = interpolate.interp1d(TSR_initial,performance_beta_max,bounds_error='False',kind='quadratic') # interpolate function for Cx(tsr) values performance_fine = f_performance(TSR_fine_ind) # Cx values at fine pitch performance_max_ind = np.where(performance_fine == np.max(performance_fine)) # Find max performance at fine pitch - self.TSR_opt = float(TSR_fine[performance_max_ind[0]]) # TSR to maximize Cx at fine pitch + self.TSR_opt = float(TSR_fine[performance_max_ind[0]][0]) # TSR to maximize Cx at fine pitch def interp_surface(self,pitch,TSR): ''' diff --git a/ROSCO_toolbox/utilities.py b/rosco/toolbox/utilities.py similarity index 99% rename from ROSCO_toolbox/utilities.py rename to rosco/toolbox/utilities.py index fc761a10..15e4d550 100644 --- a/ROSCO_toolbox/utilities.py +++ b/rosco/toolbox/utilities.py @@ -26,11 +26,11 @@ import os import numpy as np import subprocess -import ROSCO_toolbox +import rosco.toolbox from wisdem.inputs import load_yaml from wisdem.inputs import load_yaml -from ROSCO_toolbox.ofTools.util.FileTools import remove_numpy +from rosco.toolbox.ofTools.util.FileTools import remove_numpy # Some useful constants now = datetime.datetime.now() @@ -85,11 +85,12 @@ def write_DISCON(turbine, controller, param_file='DISCON.IN', txt_filename='Cp_C # Should be obvious what's going on here... file = open(param_file,'w') file.write('! Controller parameter input file for the %s wind turbine\n' % turbine.TurbineName) - file.write('! - File written using ROSCO version {} controller tuning logic on {}\n'.format(ROSCO_toolbox.__version__, now.strftime('%m/%d/%y'))) + file.write('! - File written using ROSCO version {} controller tuning logic on {}\n'.format(rosco.toolbox.__version__, now.strftime('%m/%d/%y'))) file.write('\n') file.write('!------- SIMULATION CONTROL ------------------------------------------------------------\n') file.write('{0:<12d} ! LoggingLevel - {{0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)}}\n'.format(int(rosco_vt['LoggingLevel']))) file.write('{} ! DT_Out - {{Time step to output .dbg* files, or 0 to match sampling period of OpenFAST}}\n'.format(rosco_vt['DT_Out'])) + file.write('{:<11d} ! Ext_Interface - ({})\n'.format(int(rosco_vt['Ext_Interface']), input_descriptions['Ext_Interface'])) file.write('{:<11d} ! Echo - ({})\n'.format(int(rosco_vt['Echo']), input_descriptions['Echo'])) file.write('\n') file.write('!------- CONTROLLER FLAGS -------------------------------------------------\n') diff --git a/setup.py b/setup.py index f86cf03c..9f3c886d 100644 --- a/setup.py +++ b/setup.py @@ -1,196 +1,68 @@ -# Copyright 2019 NREL - -# Licensed under the Apache License, Version 2.0 (the "License"); you may not use -# this file except in compliance with the License. You may obtain a copy of the -# License at http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software distributed -# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR -# CONDITIONS OF ANY KIND, either express or implied. See the License for the -# specific language governing permissions and limitations under the License. - -"""The setup script.""" - -# This setup file was made to mimic the setup.py script for https://github.com/NREL/floris/ -# Accessed on January 9, 2020 - -# Note: To use the 'upload' functionality of this file, you must: -# $ pip install twine - -import io import os import sys -from shutil import rmtree, copy -import glob +from pathlib import Path import platform -from setuptools import find_packages, setup, Command, Extension -from setuptools.command.build_ext import build_ext -from setuptools.command.install import install as _install - -from io import open - -# Package meta-data. -NAME = 'rosco' -DESCRIPTION = 'A reference open source controller toolset for wind turbine applications.' -URL = 'https://github.com/NREL/ROSCO' -EMAIL = 'daniel.zalkind@nrel.gov' -AUTHOR = 'NREL, National Wind Technology Center' -REQUIRES_PYTHON = '>=3.8' -VERSION = '2.8.0' - -# These packages are required for all of the code to be executed. -# - Maybe you can get away with older versions... -REQUIRED = [ - 'matplotlib', - 'numpy', - #'pytest', - 'scipy', - 'pyYAML', - #'future', - 'pandas' -] - - -# For the CMake Extensions -this_directory = os.path.abspath(os.path.dirname(__file__)) - -class CMakeExtension(Extension): - - def __init__(self, name, sourcedir='', **kwa): - Extension.__init__(self, name, sources=[], **kwa) - self.sourcedir = os.path.abspath(sourcedir) - -class CMakeBuildExt(build_ext): - - def copy_extensions_to_source(self): - newext = [] - for ext in self.extensions: - if isinstance(ext, CMakeExtension): continue - newext.append( ext ) - self.extensions = newext - super().copy_extensions_to_source() - - def build_extension(self, ext): - if not isinstance(ext, CMakeExtension): - super().build_extension(ext) - - else: - # Ensure that CMake is present and working - try: - self.spawn(['cmake', '--version']) - except OSError: - raise RuntimeError('Cannot find CMake executable') - - # Refresh build directory - localdir = os.path.join(this_directory, 'ROSCO','install') - os.makedirs(localdir, exist_ok=True) - - cmake_args = ['-DBUILD_SHARED_LIBS=OFF'] - cmake_args += ['-DCMAKE_Fortran_FLAGS=-ffree-line-length-0'] - cmake_args += ['-DCMAKE_INSTALL_PREFIX={}'.format(localdir)] - - if platform.system() == 'Windows': - if "gfortran" in os.environ["FC"].lower(): - cmake_args += ['-G', 'MinGW Makefiles'] - elif self.compiler.compiler_type == 'msvc': - cmake_args += ['-DCMAKE_GENERATOR_PLATFORM=x64'] - else: - raise ValueError("Unable to find the system's Fortran compiler.") - - self.build_temp = os.path.join( os.path.dirname( os.path.realpath(__file__) ), 'ROSCO', 'build') - os.makedirs(localdir, exist_ok=True) - # Need fresh build directory for CMake - os.makedirs(self.build_temp, exist_ok=True) - self.spawn(['cmake', '-S', ext.sourcedir, '-B', self.build_temp] + cmake_args) - self.spawn(['cmake', '--build', self.build_temp, '--target', 'install', '--config', 'Release']) +import cmake_build_extension +import setuptools +# Extra options passed to the CI/CD pipeline that uses cibuildwheel +CIBW_CMAKE_OPTIONS = [] +if "CIBUILDWHEEL" in os.environ and os.environ["CIBUILDWHEEL"] == "1": + # The manylinux variant runs in Debian Stretch and it uses lib64 folder + if sys.platform == "linux": + CIBW_CMAKE_OPTIONS += ["-DCMAKE_INSTALL_LIBDIR=lib"] -# All of the extensions -roscoExt = CMakeExtension('rosco','ROSCO') - -# The rest you shouldn't have to touch too much :) -# ------------------------------------------------ -# Except, perhaps the License and Trove Classifiers! -# If you do change the License, remember to change the Trove Classifier for that! - -here = os.path.abspath(os.path.dirname(__file__)) - -# Import the README and use it as the long-description. -try: - with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = '\n' + f.read() -except FileNotFoundError: - long_description = DESCRIPTION - -# Load the package's __version__.py module as a dictionary. -about = {} -if not VERSION: - project_slug = NAME.lower().replace("-", "_").replace(" ", "_") - with open(os.path.join(here, project_slug, '__version__.py')) as f: - exec(f.read(), about) +# Set Cmake options +if "CMAKE_ARGS" in os.environ: + cmake_args = [m for m in os.environ["CMAKE_ARGS"].split()] + user_flag = True else: - about['__version__'] = VERSION - - -class UploadCommand(Command): - """Support setup.py upload.""" - - description = 'Build and publish the package.' - user_options = [] - - @staticmethod - def status(s): - """Prints things in bold.""" - print('\033[1m{0}\033[0m'.format(s)) - - def initialize_options(self): - pass + user_flag = False + cmake_args = [] - def finalize_options(self): - pass +# Always build shared libraries +cmake_args += [f"-DPython3_ROOT_DIR={Path(sys.prefix)}", + "-DCALL_FROM_SETUP_PY:BOOL=ON", + "-DBUILD_SHARED_LIBS=ON"] - def run(self): - try: - self.status('Removing previous builds...') - rmtree(os.path.join(here, 'dist')) - except OSError: - pass - - self.status('Building Source and Wheel (universal) distribution...') - os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable)) - - self.status('Uploading the package to PyPI via Twine...') - os.system('twine upload dist/*') - - self.status('Pushing git tags...') - os.system('git tag v{0}'.format(about['__version__'])) - os.system('git push --tags') +# Append fortran flags +if not user_flag or "-DCMAKE_Fortran_FLAGS" not in os.environ["CMAKE_ARGS"]: + cmake_args += ['-DCMAKE_Fortran_FLAGS=-ffree-line-length-0'] +else: + for im, m in enumerate(cmake_args): + if m.find("-DCMAKE_Fortran_FLAGS") >= 0: + cmake_args[im] += ' -ffree-line-length-0' + +# Set if unset +#if not user_flag or "-DCMAKE_INSTALL_PREFIX" not in os.environ["CMAKE_ARGS"]: +# cmake_args += [f'-DCMAKE_INSTALL_PREFIX={this_directory}/rosco'] + +# Set if unset +#if not user_flag or "-DCMAKE_PREFIX_PATH" not in os.environ["CMAKE_ARGS"]: +# python_root = os.path.dirname( os.path.dirname( sysconfig.get_path('stdlib') ) ) +# cmake_args += [f'-DCMAKE_PREFIX_PATH={python_root}'] + +if platform.system() == 'Windows': + if "FC" not in os.environ: + os.environ["FC"] = "gfortran" + + #if "gfortran" in os.environ["FC"].lower(): + # cmake_args += ['-G', 'MinGW Makefiles'] + #else: + # cmake_args += ['-DCMAKE_GENERATOR_PLATFORM=x64'] - sys.exit() - - - -metadata = dict( - name = NAME, - version = about['__version__'], - description = DESCRIPTION, - long_description = long_description, - long_description_content_type = 'text/markdown', - author = AUTHOR, - author_email = EMAIL, - url = URL, - install_requires = REQUIRED, - python_requires = REQUIRES_PYTHON, - packages = find_packages(exclude=["tests", "*.tests", "*.tests.*", "tests.*"]), - package_data = {'': ['*.yaml']}, - license = 'Apache License, Version 2.0', - cmdclass = {'build_ext': CMakeBuildExt, 'upload': UploadCommand}, - zip_safe = False, +setuptools.setup( + ext_modules=[ + cmake_build_extension.CMakeExtension( + # This could be anything you like, it is used to create build folders + name="rosco", + install_prefix="rosco", + # Selects the folder where the main CMakeLists.txt is stored + source_dir=os.path.join('rosco','controller'), + cmake_configure_options=cmake_args + CIBW_CMAKE_OPTIONS, + ), + ], + cmdclass={'build_ext': cmake_build_extension.BuildExtension}, ) -if "--compile-rosco" in sys.argv: - metadata['ext_modules'] = [roscoExt] - sys.argv.remove("--compile-rosco") - -setup(**metadata)